xszheng2020/the_stack_dedup_python_hits_4 · Datasets at Hugging Face

2153d39edc3615558885d89b50e5691b1fe03941

1,128

py

Python

qcloudsdkcdb/CdbMysqlInitRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

qcloudsdkcdb/CdbMysqlInitRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

qcloudsdkcdb/CdbMysqlInitRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from qcloudsdkcore.request import Request class CdbMysqlInitRequest(Request): def __init__(self): super(CdbMysqlInitRequest, self).__init__( 'cdb', 'qcloudcliV1', 'CdbMysqlInit', 'cdb.api.qcloud.com') def get_cdbInstanceId(self): return self.get_params().get('cdbInstanceId') def set_cdbInstanceId(self, cdbInstanceId): self.add_param('cdbInstanceId', cdbInstanceId) def get_charset(self): return self.get_params().get('charset') def set_charset(self, charset): self.add_param('charset', charset) def get_lowerCaseTableNames(self): return self.get_params().get('lowerCaseTableNames') def set_lowerCaseTableNames(self, lowerCaseTableNames): self.add_param('lowerCaseTableNames', lowerCaseTableNames) def get_password(self): return self.get_params().get('password') def set_password(self, password): self.add_param('password', password) def get_port(self): return self.get_params().get('port') def set_port(self, port): self.add_param('port', port)

28.2

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0.679078

125

1,128

5.904

0.24

0.04065

0.094851

0.115176

0.176152

0

0.002205

0.195922

1,128

39

72

28.923077

0.811466

0.018617

0

0.132127

0

1

0.44

false

0.16

0.04

0.2

0.72

0

null

0

null

0

1

0

1

0

1

0

4

0d0b98f301237063e88d3afb295ac70c49a47a13

273

py

Python

src/services/python_api/random_object_app/extensions.py

chanakagithub/ProgrammingChallenge

937decbca6b6a1ff8a0fea0122bb451a8d9c7b46

[ "MIT" ]

null

src/services/python_api/random_object_app/extensions.py

chanakagithub/ProgrammingChallenge

937decbca6b6a1ff8a0fea0122bb451a8d9c7b46

[ "MIT" ]

null

src/services/python_api/random_object_app/extensions.py

chanakagithub/ProgrammingChallenge

937decbca6b6a1ff8a0fea0122bb451a8d9c7b46

[ "MIT" ]

null

from flask_restx import Api api = Api(version='1.0', prefix='/api/v1/', title='Random Object Generator API', description='This is a simple Flask (Python) API. Generate random .txt file with 2MB (2,097,152bytes) in size. Calculate random object count category wise.', )

54.6

161

0.736264

43

273

4.651163

0.790698

0.06

0

0.047009

0.142857

273

5

162

54.6

0.807692

0

1

0.25

0.656934

0

1

0

false

0

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0

0.25

0

null

0

1

0

1

0

null

0

4

0d273bd7ae26ab8d0a4f13dd92062f92e277cd99

105

py

Python

kerberos/RegisterService/apps.py

st12138/kerberos_puf

6035c45f5b0d070879f221d101defb9cab1578b8

[ "MIT" ]

null

kerberos/RegisterService/apps.py

st12138/kerberos_puf

6035c45f5b0d070879f221d101defb9cab1578b8

[ "MIT" ]

null

kerberos/RegisterService/apps.py

st12138/kerberos_puf

6035c45f5b0d070879f221d101defb9cab1578b8

[ "MIT" ]

null

from django.apps import AppConfig class RegisterserviceConfig(AppConfig): name = 'RegisterService'

17.5

39

0.790476

10

105

8.3

0.9

0

0.142857

105

5

40

21

0.922222

0

0.142857

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

0d2896ce7b862d0281614ac405fdb72bae3dc314

164

py

Python

killtracker/constants.py

buahaha/aa-killtracker

c4a13bead53cb8762b47c0eb47a19466ed1ec4c6

[ "MIT" ]

null

killtracker/constants.py

buahaha/aa-killtracker

c4a13bead53cb8762b47c0eb47a19466ed1ec4c6

[ "MIT" ]

null

killtracker/constants.py

buahaha/aa-killtracker

c4a13bead53cb8762b47c0eb47a19466ed1ec4c6

[ "MIT" ]

null

# Eve IDs EVE_CATEGORY_ID_SHIP = 6 EVE_CATEGORY_ID_STRUCTURE = 65 EVE_CATEGORY_ID_FIGHTER = 87 EVE_GROUP_MINING_DRONE = 101 EVE_GROUP_ORBITAL_INFRASTRUCTURE = 1025

23.428571

39

0.853659

27

164

4.62963

0.62963

0.264

0.312

0

0.082192

0.109756

164

6

40

27.333333

0.773973

0.042683

0

1

0

false

0

1

0

null

1

0

1

0

null

0

4

0d355cbe854cc40d5c882b13d4d48177ab71f08d

1,284

py

Python

practicer_flask/user_exercise_stats/api.py

DominikPott/practicer-flask

c8e523095bdd5912dadb7357d16a4e76229a04da

[ "MIT" ]

null

practicer_flask/user_exercise_stats/api.py

DominikPott/practicer-flask

c8e523095bdd5912dadb7357d16a4e76229a04da

[ "MIT" ]

null

practicer_flask/user_exercise_stats/api.py

DominikPott/practicer-flask

c8e523095bdd5912dadb7357d16a4e76229a04da

[ "MIT" ]

null

""""Interface for exercise statistics.""" import datetime import practicer_flask.user_exercise_stats.history_postgres as exercise_history import practicer_flask.user_exercise_stats.streak import practicer_flask.user_exercise_stats.experience import practicer_flask.user_exercise_stats.progress as exercise_progress history_db = exercise_history streak_db = practicer_flask.user_exercise_stats.streak experience_db = practicer_flask.user_exercise_stats.experience def progress(user): progress_data = dict() experience_data = experience(user) for exercise in experience_data.keys(): progress_data[exercise] = exercise_progress.experience_to_progress(experience_data[exercise]) return progress_data def experience(user): return experience_db.experience(user=user) def increase_experience(user, exercise): experience_db.increment_experience(user, exercise) def history(user): return history_db.exercieses(user=user) def add_exercise_to_history(user, exercise): date = datetime.date.today() history_db.add_exercise(user, date, exercise["uuid"]) def streak(user): return streak_db.user_streak(user=user) def increase_streak(user): streak_db.update_streak(user=user) if __name__ == "__main__": print(progress(user=2))

26.75

101

0.798287

166

1,284

5.831325

0.222892

0.11157

0.161157

0.254132

0

0.000883

0.117601

1,284

48

102

26.75

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false

0

0.172414

0.103448

0.551724

0.034483

0

null

0

1

0

null

0

1

0

1

0

4

0d3b37de1c0e02ace0edd37f584d3c101c8888b8

133

py

Python

python/vowpalwabbit/version.py

sisco0/vowpal_wabbit

9c5d47790ba841cabf42b91d87e5454805beec8f

[ "BSD-3-Clause" ]

3

2020-10-23T14:05:57.000Z

2021-02-25T14:30:51.000Z

python/vowpalwabbit/version.py

clabra/vowpal_wabbit

8aa5890610770ca7b13158fc3f545c74a6854a6a

[ "BSD-3-Clause" ]

4

2021-05-27T11:17:29.000Z

2021-06-18T17:41:31.000Z

python/vowpalwabbit/version.py

clabra/vowpal_wabbit

8aa5890610770ca7b13158fc3f545c74a6854a6a

[ "BSD-3-Clause" ]

1

2021-07-18T15:45:51.000Z

# Provides the present version of VowpalWabbit import pkg_resources __version__ = pkg_resources.require('vowpalwabbit')[0].version

22.166667

62

0.819549

16

133

6.4375

0.6875

0.23301

0

0.008403

0.105263

133

5

63

26.6

0.857143

0.330827

0

0.137931

0

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false

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null

1

0

1

0

null

0

1

0

4

0d4fe5b7b315c3102a3945660178faee0faa924f

187

py

Python

core-python-robust-resource-and-error-handling/exception_chaining/implicit_chaining/__init__.py

hassonor/core-python

92672aa72c1474061df5247a2dd4dfd9fab1642a

[ "MIT" ]

1

2022-03-09T20:58:33.000Z

core-python-robust-resource-and-error-handling/exception_chaining/implicit_chaining/__init__.py

hassonor/core-python

92672aa72c1474061df5247a2dd4dfd9fab1642a

[ "MIT" ]

null

core-python-robust-resource-and-error-handling/exception_chaining/implicit_chaining/__init__.py

hassonor/core-python

92672aa72c1474061df5247a2dd4dfd9fab1642a

[ "MIT" ]

null

""" Implicit chaining -> Occurs when an exception is raised incidentally during processing of another. -> The original exception is stored on the __context__ attribute of the second. """

31.166667

80

0.780749

25

187

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187

5

81

37.4

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0

null

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null

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null

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null

true

0

null

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null

0

1

0

1

0

null

0

1

0

4

b49657758f0bfcf7c9c953995219d65763848de9

218

py

Python

server/service/strategy/uniform.py

EtienneTurc/IChooseYou

9ea7767b793ba6ef389f7cc806a02db2e1434c70

[ "MIT" ]

4

2022-01-10T10:04:12.000Z

2022-01-10T18:05:48.000Z

server/service/strategy/uniform.py

EtienneTurc/IChooseYou

9ea7767b793ba6ef389f7cc806a02db2e1434c70

[ "MIT" ]

null

server/service/strategy/uniform.py

EtienneTurc/IChooseYou

9ea7767b793ba6ef389f7cc806a02db2e1434c70

[ "MIT" ]

null

from dataclasses import dataclass from server.service.strategy.base import BaseStrategy @dataclass class UniformStrategy(BaseStrategy): def update(self, **kwargs) -> list[float]: return self.weight_list

21.8

53

0.766055

25

218

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0

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218

9

54

24.222222

0.897297

0

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false

0

0.333333

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null

0

1

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null

0

1

0

4

b4a5f69f9e1ab9579068639d5ccee070c555c2d4

120

py

Python

hwtLib/amba/axis_comp/__init__.py

optical-o/hwtLib

edad621f5ad4cdbea20a5751ff4468979afe2f77

[ "MIT" ]

24

2017-02-23T10:00:50.000Z

2022-01-28T12:20:21.000Z

hwtLib/amba/axis_comp/__init__.py

optical-o/hwtLib

edad621f5ad4cdbea20a5751ff4468979afe2f77

[ "MIT" ]

32

2017-04-28T10:29:34.000Z

2021-04-27T09:16:43.000Z

hwtLib/amba/axis_comp/__init__.py

optical-o/hwtLib

edad621f5ad4cdbea20a5751ff4468979afe2f77

[ "MIT" ]

8

2019-09-19T03:34:36.000Z

2022-01-21T06:56:58.000Z

""" This package is dedicated for a componets and utils which are related to AMBA AXI-stream (AXI4-Stream) interface """

40

112

0.766667

19

120

4.842105

0.947368

0

0.009804

0.15

120

3

113

40

0.892157

0.933333

0

null

0

null

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true

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0

1

0

1

0

null

0

1

0

4

b4bf508e3acbd90804437ff7f0380d4e7a41c44a

184

py

Python

maro/communication/driver/__init__.py

VinayaSathyanarayana/maro

0ba55f36d89c235ef3af04efbac78b3885d8695d

[ "MIT" ]

1

2020-09-30T09:31:05.000Z

maro/communication/driver/__init__.py

VinayaSathyanarayana/maro

0ba55f36d89c235ef3af04efbac78b3885d8695d

[ "MIT" ]

null

maro/communication/driver/__init__.py

VinayaSathyanarayana/maro

0ba55f36d89c235ef3af04efbac78b3885d8695d

[ "MIT" ]

null

# Copyright (c) Microsoft Corporation. # Licensed under the MIT license. from .abs_driver import AbsDriver from .driver_type import DriverType __all__ = ["AbsDriver", "DriverType"]

20.444444

38

0.771739

22

184

6.181818

0.772727

0

0.141304

184

8

39

23

0.860759

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false

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1

0

null

0

1

0

1

0

4

b4e0a6255c297c1277a7c74ba0b80de2bcb9f651

141

py

Python

timestamp_app/apps.py

shahjalalh/timestamp_microservice

780465645409c8bf747dfc1601540a4e9fbe8bfa

[ "MIT" ]

null

timestamp_app/apps.py

shahjalalh/timestamp_microservice

780465645409c8bf747dfc1601540a4e9fbe8bfa

[ "MIT" ]

null

timestamp_app/apps.py

shahjalalh/timestamp_microservice

780465645409c8bf747dfc1601540a4e9fbe8bfa

[ "MIT" ]

null

from __future__ import unicode_literals from django.apps import AppConfig class TimestampAppConfig(AppConfig): name = 'timestamp_app'

17.625

39

0.808511

16

141

6.75

0.8125

0

0.141844

141

7

40

20.142857

0.892562

0

0.092199

0

1

0

false

0

0.5

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

b4eb0c8f3d874189eda314af09f7c577eb85fab9

394

py

Python

models/basemodel.py

Mark-Jung/MKR

53aab473609c13dcc68879139e0fc9c98c07059e

[ "MIT" ]

null

models/basemodel.py

Mark-Jung/MKR

53aab473609c13dcc68879139e0fc9c98c07059e

[ "MIT" ]

null

models/basemodel.py

Mark-Jung/MKR

53aab473609c13dcc68879139e0fc9c98c07059e

[ "MIT" ]

null

from db import db from sqlalchemy import desc class BaseModel(): def save_to_db(self): db.session.add(self) db.session.commit() def delete_from_db(self): db.session.delete(self) db.session.commit() @classmethod def find_by_id(cls, _id): return cls.query.get(_id) @classmethod def get_all(cls): return cls.query.all()

18.761905

33

0.624365

55

394

4.309091

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0.101266

0.219409

0.126582

0

0.269036

394

20

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0.822917

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0.266667

false

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null

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0

null

0

1

0

1

0

4

370e0c2643634470901e737b06f8b4ac159d0eed

107

py

Python

fixtest/__init__.py

kennt/fixtest

e68f06ba1ceb9d50c7c3b67e0293510f17597aef

[ "MIT" ]

16

2015-04-17T12:52:21.000Z

2021-12-13T13:56:43.000Z

fixtest/__init__.py

kennt/fixtest

e68f06ba1ceb9d50c7c3b67e0293510f17597aef

[ "MIT" ]

4

2015-04-17T14:19:45.000Z

2022-02-11T03:40:14.000Z

fixtest/__init__.py

kennt/fixtest

e68f06ba1ceb9d50c7c3b67e0293510f17597aef

[ "MIT" ]

6

2015-04-17T12:54:29.000Z

2020-10-10T06:43:10.000Z

15.285714

37

0.663551

15

107

4.733333

0.933333

0

0.05

0.252336

107

6

38

17.833333

0.8375

0.831776

0

null

0

null

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null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

4

2ea1efc8c2f005d275845ec6008915c9a5d5296b

271

py

Python

medspacy_ssi/anatomical_location.py

abchapman93/medspacy_ssi

1ab42dc0e2f8f4a2fdd263af406182731497d11e

[ "MIT" ]

null

medspacy_ssi/anatomical_location.py

abchapman93/medspacy_ssi

1ab42dc0e2f8f4a2fdd263af406182731497d11e

[ "MIT" ]

null

medspacy_ssi/anatomical_location.py

abchapman93/medspacy_ssi

1ab42dc0e2f8f4a2fdd263af406182731497d11e

[ "MIT" ]

1

2021-03-18T18:18:45.000Z

from spacy.tokens import Span def get_anatomical_location(span): for modifier in span._.modifiers: if modifier.category == "ANATOMY": return modifier.span return None Span.set_extension("anatomical_location", getter=get_anatomical_location)

27.1

73

0.738007

33

271

5.848485

0.636364

0.279793

0.217617

0

0.184502

271

9

74

30.111111

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false

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1

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1

0

null

0

1

0

4

2eadeddffc0c15c3f8d94e439332a623c0d2f663

203

py

Python

backend/src/file_upload/forms.py

pavan168/IncidentManagement

7fbf111922a735d4cbe75969159858d6605a1e0b

[ "MIT" ]

17

2019-01-16T13:10:25.000Z

2021-02-07T02:04:11.000Z

backend/src/file_upload/forms.py

pavan168/IncidentManagement

7fbf111922a735d4cbe75969159858d6605a1e0b

[ "MIT" ]

360

2019-02-13T15:24:44.000Z

2022-02-26T17:42:33.000Z

backend/src/file_upload/forms.py

mohamednizar/request-management

a88a2ce35a7a1a98630ffd14c1a31a5173b662c8

[ "MIT" ]

46

2019-01-16T13:10:25.000Z

2021-06-23T02:44:18.000Z

from django import forms from .models import File class FileForm(forms.ModelForm): class Meta: model = File fields = ('original_name', 'name', 'document', 'incident', 'created_date')

29

82

0.674877

24

203

5.625

0.75

0

0.206897

203

7

82

29

0.838509

0

0.220588

0

1

0

false

0

0.333333

0

0.666667

0

1

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null

0

1

0

null

0

1

0

1

0

4

2c11df158ac5c8c60578d893d3f72f1cb01c6355

671

bzl

Python

source/bazel/deps/mpmc_queue/get.bzl

luxe/CodeLang-compiler

78837d90bdd09c4b5aabbf0586a5d8f8f0c1e76a

[ "MIT" ]

1

2019-01-06T08:45:46.000Z

source/bazel/deps/mpmc_queue/get.bzl

luxe/CodeLang-compiler

78837d90bdd09c4b5aabbf0586a5d8f8f0c1e76a

[ "MIT" ]

264

2015-11-30T08:34:00.000Z

2018-06-26T02:28:41.000Z

source/bazel/deps/mpmc_queue/get.bzl

UniLang/compiler

c338ee92994600af801033a37dfb2f1a0c9ca897

[ "MIT" ]

null

# Do not edit this file directly. # It was auto-generated by: code/programs/reflexivity/reflexive_refresh load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive") load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_file") def mpmcQueue(): http_archive( name = "mpmc_queue", build_file = "//bazel/deps/mpmc_queue:build.BUILD", sha256 = "675004f332c74390c16efea98f30ebc636a2855434bdbfa24eaa703501a6ae0f", strip_prefix = "MPMCQueue-5883e32b07e8a60c22d532d9120ea5c11348aea9", urls = [ "https://github.com/Unilang/MPMCQueue/archive/5883e32b07e8a60c22d532d9120ea5c11348aea9.tar.gz", ], )

39.470588

107

0.718331

69

671

6.811594

0.623188

0.038298

0.059574

0.080851

0.182979

0

0.172291

0.160954

671

16

108

41.9375

0.662522

0.150522

0

1

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0.634921

0.417989

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1

0.083333

true

0

0.083333

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null

0

1

0

1

null

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0

4

257f2d3639dc8cf4cc2953fc9725a537dbfd2963

1,613

py

Python

pyexplorer/formatters.py

dexpota/pyexplorer

5d9b86deb94809c8f23e0faee5c050184d7f9e8f

[ "MIT" ]

null

pyexplorer/formatters.py

dexpota/pyexplorer

5d9b86deb94809c8f23e0faee5c050184d7f9e8f

[ "MIT" ]

null

pyexplorer/formatters.py

dexpota/pyexplorer

5d9b86deb94809c8f23e0faee5c050184d7f9e8f

[ "MIT" ]

null

from __future__ import unicode_literals from . import text from .extract import extract_basic_information from prompt_toolkit import print_formatted_text from prompt_toolkit.styles import Style from prompt_toolkit.formatted_text import FormattedText from termcolor import colored import inspect def module_format(entity): style = Style.from_dict({ "type": "#ff0066", "name": "#44ff44 italic", "docstring": "#cccccc italic" }) type_name, entity_name, entity_docstring = extract_basic_information(entity) if entity_docstring: entity_docstring = inspect.cleandoc(entity_docstring) text_fragments = FormattedText([ ('class:type', text(type_name)), ('', ' '), ('class:name', text(entity_name)), ('', '\n'), ('class:docstring', text(entity_docstring)), ('', u'\n\n') ]) print_formatted_text(text_fragments, style=style) def attribute_format(entity): style = Style.from_dict({ "type": "#ff0066", "name": "#44ff44 italic", "docstring": "#cccccc italic" }) type_name, entity_name, entity_docstring = extract_basic_information(entity) if entity_docstring: entity_docstring = inspect.cleandoc(entity_docstring) else: entity_docstring = "No docstring" text_fragments = FormattedText([ ('class:type', text(type_name)), ('', ' '), ('class:name', text(entity_name)), ('', '\n'), ('class:docstring', text(entity_docstring)), ('', u'\n') ]) print_formatted_text(text_fragments, style=style)

27.810345

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0.647241

172

1,613

5.790698

0.232558

0.165663

0.069277

0.044177

0.708835

0.625502

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0.223187

1,613

58

81

27.810345

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0

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null

0

1

0

1

0

null

0

4

25bb9e07eab4597bdb8a58f1db94b1b3aec95e81

145

py

Python

src/views/__init__.py

madhanbose99/python-microservices

eedff0408d699d6394aa04acacf0866f06d50141

[ "Apache-2.0" ]

2

2020-09-14T11:30:51.000Z

2020-09-24T14:39:52.000Z

src/views/__init__.py

budtmo/myAPI

ac5e6d8484eeb75774cfe12872cdaea6b3a205c9

[ "Apache-2.0" ]

null

src/views/__init__.py

budtmo/myAPI

ac5e6d8484eeb75774cfe12872cdaea6b3a205c9

[ "Apache-2.0" ]

null

""" Root endpoints. """ def root() -> str: """ Root endpoint. :return: project description """ return 'Sample RESTful API'

12.083333

32

0.558621

14

145

5.785714

0.785714

0

0.282759

145

11

33

13.181818

0.778846

0.406897

0

0.305085

0

1

0.5

true

0

1

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0

null

0

null

0

1

0

1

0

4

25bdb1a15b1aec1583b71f52c1319bd5c651c79e

44

py

Python

base/test-show-scope/class-4.py

jpolitz/lambda-py-paper

746ef63fc1123714b4adaf78119028afbea7bd76

[ "Apache-2.0" ]

25

2015-04-16T04:31:49.000Z

2022-03-10T15:53:28.000Z

base/test-show-scope/class-4.py

jpolitz/lambda-py-paper

746ef63fc1123714b4adaf78119028afbea7bd76

[ "Apache-2.0" ]

1

2018-11-21T22:40:02.000Z

2018-11-26T17:53:11.000Z

base/test-show-scope/class-4.py

jpolitz/lambda-py-paper

746ef63fc1123714b4adaf78119028afbea7bd76

[ "Apache-2.0" ]

1

2021-03-26T03:36:19.000Z

x = 8 class C: x = 7 def f(): return x

6.285714

10

0.477273

10

44

2.1

0.8

0

0.074074

0.386364

44

6

11

7.333333

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0

0.2

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0

null

0

1

0

null

0

1

0

4

25ca936b7bd0559180a8c7ff930c08d3c8e25b4e

161

py

Python

python_api/primitive/component/geometry/__init__.py

openNGP/openNGP

085d6e2f94fcdc5c1c15a62027d31b31398842bb

[ "MIT" ]

3

2022-03-04T09:16:20.000Z

2022-03-19T02:57:01.000Z

python_api/primitive/component/geometry/__init__.py

openNGP/openNGP

085d6e2f94fcdc5c1c15a62027d31b31398842bb

[ "MIT" ]

2

2022-03-08T10:54:47.000Z

2022-03-11T08:58:18.000Z

python_api/primitive/component/geometry/__init__.py

openNGP/openNGP

085d6e2f94fcdc5c1c15a62027d31b31398842bb

[ "MIT" ]

null

from .hash_grid import HashGrid from .mock_hash_grid import MockHashGrid from .sigma import Sigma __all__ = [ 'HashGrid', 'MockHashGrid', 'Sigma', ]

17.888889

40

0.714286

19

161

5.684211

0.473684

0.148148

0.259259

0

0.192547

161

9

41

17.888889

0.830769

0

0.154321

0

1

0

false

0

0.375

0

0.375

0

1

0

null

0

1

0

1

0

null

0

1

0

4

25d5fd6f216c0ad7d8ae41f8ef9194bf2121e17d

359

py

Python

application/__init__.py

fontoberta/taktaan

b5780b80b7bcc40064fb66ebb08366c9d4e4b86d

[ "MIT" ]

null

application/__init__.py

fontoberta/taktaan

b5780b80b7bcc40064fb66ebb08366c9d4e4b86d

[ "MIT" ]

null

application/__init__.py

fontoberta/taktaan

b5780b80b7bcc40064fb66ebb08366c9d4e4b86d

[ "MIT" ]

null

from flask import Flask from flask_restful import Api from flask_cors import CORS from application.resources import ContainerList from application.resources import Container def create_app(): app = Flask(__name__) CORS(app) api = Api(app) api.add_resource(ContainerList, '/') api.add_resource(Container, '/<string:cid>/') return app

23.933333

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0.743733

47

359

5.489362

0.404255

0.104651

0.186047

0.232558

0

0.169916

359

14

50

25.642857

0.865772

0

0.041783

0

1

0.083333

false

0

0.416667

0

0.583333

0

null

0

1

0

null

0

1

0

1

0

4

25e60e36c53a1efec4fc5807335e81aff4f07869

103

py

Python

medInformation/apps.py

MyMedicalAssistant/MyMedicalAssistant

e03758109167cef13efed7ee1d450dbd18a1fed7

[ "MIT" ]

null

medInformation/apps.py

MyMedicalAssistant/MyMedicalAssistant

e03758109167cef13efed7ee1d450dbd18a1fed7

[ "MIT" ]

1

2020-08-05T22:58:28.000Z

medInformation/apps.py

MyMedicalAssistant/MyMedicalAssistant

e03758109167cef13efed7ee1d450dbd18a1fed7

[ "MIT" ]

null

from django.apps import AppConfig class MedinformationConfig(AppConfig): name = 'medInformation'

17.166667

38

0.786408

10

103

8.1

0.9

0

0.145631

103

5

39

20.6

0.920455

0

0.135922

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

25f117b5e140ac7c53be4db8ec4c46d34373c3f6

104

py

Python

exercises/chapter_02/exercise_02_04/exercise_02_04.py

HenrikSamuelsson/python-crash-course

0550343d413e4636f402a66041860bc1a319fc8f

[ "MIT" ]

1

2017-04-30T18:05:26.000Z

exercises/chapter_02/exercise_02_04/exercise_02_04.py

HenrikSamuelsson/python-crash-course

0550343d413e4636f402a66041860bc1a319fc8f

[ "MIT" ]

null

exercises/chapter_02/exercise_02_04/exercise_02_04.py

HenrikSamuelsson/python-crash-course

0550343d413e4636f402a66041860bc1a319fc8f

[ "MIT" ]

null

# 2-4 Name Cases name = "henrik samuelsson" print(name.lower()) print(name.upper()) print(name.title())

17.333333

26

0.701923

16

104

4.5625

0.625

0.369863

0

0.021505

0.105769

104

5

27

20.8

0.763441

0.134615

0

0.193182

0

1

0

false

0

0.75

1

0

null

1

0

1

0

null

0

1

0

4

25ff2a4ef2c1d2c09a159d7c940f52a9d2f33ab9

109

py

Python

cogs/utils/typing.py

hrmorley34/wowbot

b702bb793e62f1bf0c9d1278243e22916397c615

[ "MIT" ]

null

cogs/utils/typing.py

hrmorley34/wowbot

b702bb793e62f1bf0c9d1278243e22916397c615

[ "MIT" ]

null

cogs/utils/typing.py

hrmorley34/wowbot

b702bb793e62f1bf0c9d1278243e22916397c615

[ "MIT" ]

null

from typing import TypedDict class ReactionGuild(TypedDict, total=True): channel: int message: int

15.571429

43

0.743119

13

109

6.230769

0.846154

0

0.192661

109

6

44

18.166667

0.920455

0

1

0

true

0

0.25

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

d300e466542d1a5876109bbe6c668b04c6a4f276

51

py

Python

code/arc053_a_01.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

3

2019-08-16T16:55:48.000Z

2021-04-11T10:21:40.000Z

code/arc053_a_01.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

null

code/arc053_a_01.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

null

H,W=map(int,input().split()) print(H*(W-1)+(H-1)*W)

25.5

28

0.568627

13

51

2.230769

0.615385

0.137931

0

0.04

0.019608

51

2

29

25.5

0.54

0

1

0

true

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

4

d327694cb0499d181f0f470de3de7b41c8fa9491

85

py

Python

tests/unittests/000-simple/pogfile.py

peterino2/pmake

99fc3c7b9b552da0d437ef35b26eebe12781744b

[ "MIT" ]

null

tests/unittests/000-simple/pogfile.py

peterino2/pmake

99fc3c7b9b552da0d437ef35b26eebe12781744b

[ "MIT" ]

12

2021-01-22T16:51:16.000Z

2021-02-16T12:58:56.000Z

tests/unittests/000-simple/pogfile.py

peterino2/pmake

99fc3c7b9b552da0d437ef35b26eebe12781744b

[ "MIT" ]

null

@job(desc="job1's desc") def job1(): pass @job("test1") def job2(): pass

7.727273

24

0.541176

13

85

3.538462

0.615385

0

0.0625

0.247059

85

10

25

8.5

0.65625

0

0.333333

0

0.192771

0

1

0.333333

true

0.333333

0

0.333333

0

1

0

null

0

null

0

1

0

4

d37e701ea18218537cd897a1238e1dce78f76030

10,088

py

Python

src/test/test_Sun.py

rezemika/astral

8e7bdaab402558463be33a55ca1415cfb9ef8af4

[ "Apache-2.0" ]

null

src/test/test_Sun.py

rezemika/astral

8e7bdaab402558463be33a55ca1415cfb9ef8af4

[ "Apache-2.0" ]

null

src/test/test_Sun.py

rezemika/astral

8e7bdaab402558463be33a55ca1415cfb9ef8af4

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Test data taken from http://www.timeanddate.com/sun/uk/london import pytest import pytz import datetime from astral import Astral, AstralError, SUN_RISING, SUN_SETTING def float_almost_equal(value1, value2, diff=0.5): return abs(value1 - value2) <= diff def datetime_almost_equal(datetime1, datetime2, seconds=60): dd = datetime1 - datetime2 sd = (dd.days * 24 * 60 * 60) + dd.seconds return abs(sd) <= seconds def test_Astral_Dawn_Civil(): a = Astral() l = a['London'] test_data = { datetime.date(2015, 12, 1): datetime.datetime(2015, 12, 1, 7, 4), datetime.date(2015, 12, 2): datetime.datetime(2015, 12, 2, 7, 6), datetime.date(2015, 12, 3): datetime.datetime(2015, 12, 3, 7, 7), datetime.date(2015, 12, 12): datetime.datetime(2015, 12, 12, 7, 17), datetime.date(2015, 12, 25): datetime.datetime(2015, 12, 25, 7, 25), } for day, dawn in test_data.items(): dawn = pytz.UTC.localize(dawn) dawn_utc = a.dawn_utc(day, l.latitude, l.longitude) assert datetime_almost_equal(dawn, dawn_utc) def test_Astral_Dawn_Nautical(): a = Astral() a.solar_depression = 'nautical' l = a['London'] test_data = { datetime.date(2015, 12, 1): datetime.datetime(2015, 12, 1, 6, 22), datetime.date(2015, 12, 2): datetime.datetime(2015, 12, 2, 6, 23), datetime.date(2015, 12, 3): datetime.datetime(2015, 12, 3, 6, 24), datetime.date(2015, 12, 12): datetime.datetime(2015, 12, 12, 6, 34), datetime.date(2015, 12, 25): datetime.datetime(2015, 12, 25, 6, 42), } for day, dawn in test_data.items(): dawn = pytz.UTC.localize(dawn) dawn_utc = a.dawn_utc(day, l.latitude, l.longitude) assert datetime_almost_equal(dawn, dawn_utc) def test_Astral_Sunrise(): a = Astral() l = a['London'] test_data = { datetime.date(2015, 1, 1): datetime.datetime(2015, 1, 1, 8, 6), datetime.date(2015, 12, 1): datetime.datetime(2015, 12, 1, 7, 43), datetime.date(2015, 12, 2): datetime.datetime(2015, 12, 2, 7, 45), datetime.date(2015, 12, 3): datetime.datetime(2015, 12, 3, 7, 46), datetime.date(2015, 12, 12): datetime.datetime(2015, 12, 12, 7, 57), datetime.date(2015, 12, 25): datetime.datetime(2015, 12, 25, 8, 5), } for day, sunrise in test_data.items(): sunrise = pytz.UTC.localize(sunrise) sunrise_utc = a.sunrise_utc(day, l.latitude, l.longitude) assert datetime_almost_equal(sunrise, sunrise_utc) def test_Astral_Sunset(): a = Astral() l = a['London'] test_data = { datetime.date(2015, 1, 1): datetime.datetime(2015, 1, 1, 16, 2), datetime.date(2015, 12, 1): datetime.datetime(2015, 12, 1, 15, 55), datetime.date(2015, 12, 2): datetime.datetime(2015, 12, 2, 15, 55), datetime.date(2015, 12, 3): datetime.datetime(2015, 12, 3, 15, 54), datetime.date(2015, 12, 12): datetime.datetime(2015, 12, 12, 15, 51), datetime.date(2015, 12, 25): datetime.datetime(2015, 12, 25, 15, 56), } for day, sunset in test_data.items(): sunset = pytz.UTC.localize(sunset) sunset_utc = a.sunset_utc(day, l.latitude, l.longitude) assert datetime_almost_equal(sunset, sunset_utc) def test_Astral_Dusk_Civil(): a = Astral() l = a['London'] test_data = { datetime.date(2015, 12, 1): datetime.datetime(2015, 12, 1, 16, 34), datetime.date(2015, 12, 2): datetime.datetime(2015, 12, 2, 16, 34), datetime.date(2015, 12, 3): datetime.datetime(2015, 12, 3, 16, 33), datetime.date(2015, 12, 12): datetime.datetime(2015, 12, 12, 16, 31), datetime.date(2015, 12, 25): datetime.datetime(2015, 12, 25, 16, 36), } for day, dusk in test_data.items(): dusk = pytz.UTC.localize(dusk) dusk_utc = a.dusk_utc(day, l.latitude, l.longitude) assert datetime_almost_equal(dusk, dusk_utc) def test_Astral_Dusk_Nautical(): a = Astral() a.solar_depression = 'nautical' l = a['London'] test_data = { datetime.date(2015, 12, 1): datetime.datetime(2015, 12, 1, 17, 16), datetime.date(2015, 12, 2): datetime.datetime(2015, 12, 2, 17, 16), datetime.date(2015, 12, 3): datetime.datetime(2015, 12, 3, 17, 16), datetime.date(2015, 12, 12): datetime.datetime(2015, 12, 12, 17, 14), datetime.date(2015, 12, 25): datetime.datetime(2015, 12, 25, 17, 19), } for day, dusk in test_data.items(): dusk = pytz.UTC.localize(dusk) dusk_utc = a.dusk_utc(day, l.latitude, l.longitude) assert datetime_almost_equal(dusk, dusk_utc) def test_Astral_SolarNoon(): a = Astral() l = a['London'] test_data = { datetime.date(2015, 12, 1): datetime.datetime(2015, 12, 1, 11, 49), datetime.date(2015, 12, 2): datetime.datetime(2015, 12, 2, 11, 50), datetime.date(2015, 12, 3): datetime.datetime(2015, 12, 3, 11, 50), datetime.date(2015, 12, 12): datetime.datetime(2015, 12, 12, 11, 54), datetime.date(2015, 12, 25): datetime.datetime(2015, 12, 25, 12, 00), } for day, solar_noon in test_data.items(): solar_noon = pytz.UTC.localize(solar_noon) solar_noon_utc = a.solar_noon_utc(day, l.longitude) assert datetime_almost_equal(solar_noon, solar_noon_utc) def test_Astral_SolarMidnight(): a = Astral() l = a['London'] test_data = { datetime.date(2016, 2, 18): datetime.datetime(2016, 2, 18, 0, 14), datetime.date(2016, 10, 26): datetime.datetime(2016, 10, 25, 23, 44), } for day, solar_midnight in test_data.items(): solar_midnight = pytz.UTC.localize(solar_midnight) solar_midnight_utc = a.solar_midnight_utc(day, l.longitude) assert datetime_almost_equal(solar_midnight, solar_midnight_utc) # Test data from http://www.astroloka.com/rahukaal.aspx?City=Delhi def test_Astral_Rahukaalam(): a = Astral() l = a['New Delhi'] test_data = { datetime.date(2015, 12, 1): (datetime.datetime(2015, 12, 1, 9, 17), datetime.datetime(2015, 12, 1, 10, 35)), datetime.date(2015, 12, 2): (datetime.datetime(2015, 12, 2, 6, 40), datetime.datetime(2015, 12, 2, 7, 58)), } for day, (start, end) in test_data.items(): start = pytz.UTC.localize(start) end = pytz.UTC.localize(end) info = a.rahukaalam_utc(day, l.latitude, l.longitude) start_utc = info[0] end_utc = info[1] assert datetime_almost_equal(start, start_utc) assert datetime_almost_equal(end, end_utc) def test_Astral_SolarElevation(): a = Astral() l = a['London'] test_data = { datetime.datetime(2015, 12, 14, 11, 0, 0): 14, datetime.datetime(2015, 12, 14, 20, 1, 0): -37, } for dt, angle1 in test_data.items(): angle2 = a.solar_elevation(dt, l.latitude, l.longitude) assert float_almost_equal(angle1, angle2) def test_Astral_SolarAzimuth(): a = Astral() l = a['London'] test_data = { datetime.datetime(2015, 12, 14, 11, 0, 0, tzinfo=pytz.UTC): 167, datetime.datetime(2015, 12, 14, 20, 1, 0): 279, } for dt, angle1 in test_data.items(): angle2 = a.solar_azimuth(dt, l.latitude, l.longitude) assert float_almost_equal(angle1, angle2) def test_Astral_TimeAtElevation_SunRising(): a = Astral() l = a['London'] d = datetime.date(2016, 1, 4) dt = a.time_at_elevation_utc(6, SUN_RISING, d, l.latitude, l.longitude) cdt = datetime.datetime(2016, 1, 4, 9, 5, 0, tzinfo=pytz.UTC) # Use error of 5 minutes as website has a rather coarse accuracy assert datetime_almost_equal(dt, cdt, 300) def test_Astral_TimeAtElevation_SunSetting(): a = Astral() l = a['London'] d = datetime.date(2016, 1, 4) dt = a.time_at_elevation_utc(14, SUN_SETTING, d, l.latitude, l.longitude) cdt = datetime.datetime(2016, 1, 4, 13, 20, 0, tzinfo=pytz.UTC) assert datetime_almost_equal(dt, cdt, 300) def test_Astral_TimeAtElevation_GreaterThan90(): a = Astral() l = a['London'] d = datetime.date(2016, 1, 4) dt = a.time_at_elevation_utc(166, SUN_RISING, d, l.latitude, l.longitude) cdt = datetime.datetime(2016, 1, 4, 13, 20, 0, tzinfo=pytz.UTC) assert datetime_almost_equal(dt, cdt, 300) def test_Astral_TimeAtElevation_GreaterThan180(): a = Astral() l = a['London'] d = datetime.date(2015, 12, 1) dt = a.time_at_elevation_utc(186, SUN_RISING, d, l.latitude, l.longitude) cdt = datetime.datetime(2015, 12, 1, 16, 34, tzinfo=pytz.UTC) assert datetime_almost_equal(dt, cdt, 300) def test_Astral_TimeAtElevation_SunRisingBelowHorizon(): a = Astral() l = a['London'] d = datetime.date(2016, 1, 4) dt = a.time_at_elevation_utc(-18, SUN_RISING, d, l.latitude, l.longitude) cdt = datetime.datetime(2016, 1, 4, 6, 0, 0, tzinfo=pytz.UTC) assert datetime_almost_equal(dt, cdt, 300) def test_Astral_TimeAtElevation_BadElevation(): a = Astral() l = a['London'] d = datetime.date(2016, 1, 4) with pytest.raises(AstralError): a.time_at_elevation_utc(20, SUN_RISING, d, l.latitude, l.longitude) def test_Astral_Daylight(): a = Astral() l = a['London'] d = datetime.date(2016, 1, 6) start, end = a.daylight_utc(d, l.latitude, l.longitude) cstart = datetime.datetime(2016, 1, 6, 8, 5, 0, tzinfo=pytz.UTC) cend = datetime.datetime(2016, 1, 6, 16, 7, 0, tzinfo=pytz.UTC) assert datetime_almost_equal(start, cstart, 300) assert datetime_almost_equal(end, cend, 300) def test_Astral_Nighttime(): a = Astral() l = a['London'] d = datetime.date(2016, 1, 6) start, end = a.night_utc(d, l.latitude, l.longitude) cstart = datetime.datetime(2016, 1, 6, 18, 10, 0, tzinfo=pytz.UTC) cend = datetime.datetime(2016, 1, 7, 6, 2, 0, tzinfo=pytz.UTC) assert datetime_almost_equal(start, cstart, 300) assert datetime_almost_equal(end, cend, 300)

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Python

LuoguCodes/AT2826.py

Anguei/OI-Codes

0ef271e9af0619d4c236e314cd6d8708d356536a

[ "MIT" ]

null

LuoguCodes/AT2826.py

Anguei/OI-Codes

0ef271e9af0619d4c236e314cd6d8708d356536a

[ "MIT" ]

null

LuoguCodes/AT2826.py

Anguei/OI-Codes

0ef271e9af0619d4c236e314cd6d8708d356536a

[ "MIT" ]

null

n = int(raw_input()) s = raw_input().split() ans = ';'; for i in s: ans += i print int(ans) % 1000000007

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Python

1.py

ChenJnHui/git_demo

f818b1dd2e36c1210857f2ca82c03a772deb11c8

[ "MIT" ]

null

1.py

ChenJnHui/git_demo

f818b1dd2e36c1210857f2ca82c03a772deb11c8

[ "MIT" ]

null

1.py

ChenJnHui/git_demo

f818b1dd2e36c1210857f2ca82c03a772deb11c8

[ "MIT" ]

null

num1 = 10 num2 = 20 num3 = 454545 num3 = 12012

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6ccbd4b902db61e7486da8fb31c318f3ef0861f8

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py

Python

bnn_mcmc_examples/examples/mlp/noisy_xor/setting2/mcmc/datascanners.py

papamarkou/bnn_mcmc_examples

7bb4ecfb33db4c30a8e61e31f528bda0efb24e3d

[ "MIT" ]

1

2021-09-09T15:55:37.000Z

bnn_mcmc_examples/examples/mlp/noisy_xor/setting2/mcmc/datascanners.py

kushagragpt99/bnn_mcmc_examples

297cdb1e74335860989bebdb4ff6f6322b6adc06

[ "MIT" ]

null

bnn_mcmc_examples/examples/mlp/noisy_xor/setting2/mcmc/datascanners.py

kushagragpt99/bnn_mcmc_examples

297cdb1e74335860989bebdb4ff6f6322b6adc06

[ "MIT" ]

1

2021-10-05T06:38:57.000Z

# %% Import packages from bnn_mcmc_examples.datasets import load_xydataset_from_file from bnn_mcmc_examples.datasets.noisy_xor.data2.constants import test_data_path from bnn_mcmc_examples.examples.mlp.noisy_xor.setting2.constants import dtype # %% Load test dataloader with batch size of 1 test_dataset, test_dataloader = load_xydataset_from_file(test_data_path, dtype=dtype, batch_size=1)

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6cfefdef040b59b0c8d19ee6adbdb916b341a8ae

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py

Python

rejected_article_tracker/tests/Fakes/fake_classifier.py

sagepublishing/rejected_article_tracker_pkg

6b7616b14816406f012980695404bc5bdd7ab93a

[ "MIT" ]

10

2020-12-15T17:28:06.000Z

2022-03-11T21:50:47.000Z

rejected_article_tracker/tests/Fakes/fake_classifier.py

ad48/rejected_article_tracker_pkg

90a5042b730b01b371c0be67e1e915daa322251a

[ "MIT" ]

11

2021-06-15T00:42:35.000Z

2021-08-02T16:15:58.000Z

rejected_article_tracker/tests/Fakes/fake_classifier.py

ad48/rejected_article_tracker_pkg

90a5042b730b01b371c0be67e1e915daa322251a

[ "MIT" ]

2

2020-09-14T14:12:38.000Z

2021-08-02T19:04:14.000Z

def predict_proba(arr): return [ [[], 99.999], ]

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9f06d1e8bdaacf2bd72e0c2c152fe92cfdb654f0

135

py

Python

ai-app/image_app/models.py

duytq99/trafficsigns-detection-hog-svm

fb0f67b78839f166557cc1c2d81aa4d6ef30394b

[ "MIT" ]

null

ai-app/image_app/models.py

duytq99/trafficsigns-detection-hog-svm

fb0f67b78839f166557cc1c2d81aa4d6ef30394b

[ "MIT" ]

null

ai-app/image_app/models.py

duytq99/trafficsigns-detection-hog-svm

fb0f67b78839f166557cc1c2d81aa4d6ef30394b

[ "MIT" ]

null

from django.db import models class MyImage(models.Model): model_pic = models.ImageField(upload_to = '', default = 'none/no-img.jpg')

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py

Python

torchmm/config.py

pytorch-duo/torchmm

0e44f8599d26a29c345e7cc85e2885813346dc0b

[ "MIT" ]

null

torchmm/config.py

pytorch-duo/torchmm

0e44f8599d26a29c345e7cc85e2885813346dc0b

[ "MIT" ]

3

2021-06-08T22:22:40.000Z

2022-03-12T00:47:40.000Z

torchmm/config.py

macabdul9/torchmm

0e44f8599d26a29c345e7cc85e2885813346dc0b

[ "MIT" ]

null

config = { "modalities": }

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4

9f25e4151c4015baedb06b5a35d65bc26010cfef

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py

Python

scripts/interhemisphere/hemisphere_cascades.py

mwinding/connectome_analysis

dbc747290891805863c9481921d8080dc2043d21

[ "MIT" ]

null

scripts/interhemisphere/hemisphere_cascades.py

mwinding/connectome_analysis

dbc747290891805863c9481921d8080dc2043d21

[ "MIT" ]

2

2022-02-10T11:03:49.000Z

2022-02-10T11:04:08.000Z

scripts/interhemisphere/hemisphere_cascades.py

mwinding/connectome_analysis

dbc747290891805863c9481921d8080dc2043d21

[ "MIT" ]

null

#%% from pymaid_creds import url, name, password, token from data_settings import pairs_path, data_date import pymaid rm = pymaid.CatmaidInstance(url, token, name, password) import matplotlib.pyplot as plt import seaborn as sns import numpy as np import pandas as pd import cmasher as cmr from contools import Cascade_Analyzer, Promat, Celltype, Celltype_Analyzer # allows text to be editable in Illustrator plt.rcParams['pdf.fonttype'] = 42 plt.rcParams['ps.fonttype'] = 42 # font settings plt.rcParams['font.size'] = 5 plt.rcParams['font.family'] = 'arial' adj_ad = Promat.pull_adj(type_adj='ad', date=data_date) pairs = Promat.get_pairs(pairs_path=pairs_path) # %% # pull sensory annotations and then pull associated skids order = ['olfactory', 'gustatory-external', 'gustatory-pharyngeal', 'enteric', 'thermo-warm', 'thermo-cold', 'visual', 'noci', 'mechano-Ch', 'mechano-II/III', 'proprio', 'respiratory'] sens = [Celltype(name, Celltype_Analyzer.get_skids_from_meta_annotation(f'mw {name}')) for name in order] input_skids_list = [x.get_skids() for x in sens] input_skids = [val for sublist in input_skids_list for val in sublist] output_names = pymaid.get_annotated('mw brain outputs').name output_skids_list = list(map(pymaid.get_skids_by_annotation, pymaid.get_annotated('mw brain outputs').name)) output_skids = [val for sublist in output_skids_list for val in sublist] # identify contralateral sens neurons and contra-contra neurons to flip their left/right identities neurons_to_flip = list(np.intersect1d(pymaid.get_skids_by_annotation('mw contralateral axon'), pymaid.get_skids_by_annotation('mw contralateral dendrite'))) inputs_to_flip = [skid for skid in pymaid.get_skids_by_annotation('mw contralateral axon') if skid in input_skids] neurons_to_flip = neurons_to_flip + inputs_to_flip # define left and right neurons from a hemispheric propagation perspective, flip left/right identity as appropriate left, right = Promat.get_hemis('mw left', 'mw right', neurons_to_flip=neurons_to_flip) input_skids_left = list(np.intersect1d(input_skids, left)) input_skids_right = list(np.intersect1d(input_skids, right)) # remove bilateral axon input neurons to see how the mixing happens at the interneuron level bilat_axon = pymaid.get_skids_by_annotation('mw bilateral axon') bilat_axon = bilat_axon + [3795424, 11291344] # remove the ambiguous v'td neurons (project to middle of SEZ) input_skids_left = list(np.setdiff1d(input_skids_left, bilat_axon)) input_skids_right = list(np.setdiff1d(input_skids_right, bilat_axon)) input_skids_list = [input_skids_left, input_skids_right] #%% # cascades from left or right hemisphere input neurons # save as pickle to use later because cascades are stochastic; prevents the need to remake plots everytime import pickle p = 0.05 max_hops = 8 n_init = 1000 simultaneous = True adj=adj_ad ''' input_hit_hist_list = Cascade_Analyzer.run_cascades_parallel(source_skids_list=input_skids_list, source_names = ['left_inputs', 'right_inputs'], stop_skids=output_skids, adj=adj_ad, p=p, max_hops=max_hops, n_init=n_init, simultaneous=simultaneous, pairs=pairs, pairwise=True, disable_tqdm=False) pickle.dump(input_hit_hist_list, open(f'data/cascades/left-right-hemisphere-cascades_{n_init}-n_init_{data_date}.p', 'wb')) ''' input_hit_hist_list = pickle.load(open(f'data/cascades/left-right-hemisphere-cascades_{n_init}-n_init_{data_date}.p', 'rb')) # %% # plot heatmaps of number of neurons over-threshold per hop def intersect_stats(hit_hist1, hit_hist2, threshold, hops): intersect_hops = [] total_hops = [] for i in np.arange(0, hops+1): intersect = list(np.logical_and(hit_hist1.loc[:,i]>=threshold, hit_hist2.loc[:,i]>=threshold)) total = list(np.logical_or(hit_hist1.loc[:,i]>=threshold, hit_hist2.loc[:,i]>=threshold)) intersect_hops.append(intersect) total_hops.append(total) intersect_hops = pd.DataFrame(intersect_hops, index=range(0, hops+1), columns = hit_hist1.index).T total_hops = pd.DataFrame(total_hops, index=range(0, hops+1), columns = hit_hist1.index).T percent = [] for i in np.arange(0, hops+1): if(sum(total_hops[i])>0): percent.append(sum(intersect_hops[i])/sum(total_hops[i])) if(sum(total_hops[i])==0): percent.append(0) return(intersect_hops, total_hops, percent) all_inputs_hit_hist_left = input_hit_hist_list[0].skid_hit_hist all_inputs_hit_hist_right = input_hit_hist_list[1].skid_hit_hist threshold = n_init/2 hops = 8 all_inputs_intersect, all_inputs_total, all_inputs_percent = intersect_stats(all_inputs_hit_hist_left, all_inputs_hit_hist_right, threshold, hops) # identify left/right ipsi, bilateral, contralaterals # majority types ipsi = list(np.intersect1d(pymaid.get_skids_by_annotation('mw ipsilateral axon'), pymaid.get_skids_by_annotation('mw ipsilateral dendrite'))) ipsi = ipsi + list(np.intersect1d(pymaid.get_skids_by_annotation('mw contralateral axon'), pymaid.get_skids_by_annotation('mw contralateral dendrite'))) bilateral = list(np.intersect1d(pymaid.get_skids_by_annotation('mw bilateral axon'), pymaid.get_skids_by_annotation('mw ipsilateral dendrite'))) contralateral = list(np.intersect1d(pymaid.get_skids_by_annotation('mw contralateral axon'), pymaid.get_skids_by_annotation('mw ipsilateral dendrite'))) # add ipsilateral sensory to each ipsi = ipsi + input_skids_left + input_skids_right ipsi_left = list(np.intersect1d(ipsi, left)) ipsi_right = list(np.intersect1d(ipsi, right)) bilateral_left = list(np.intersect1d(bilateral, left)) bilateral_right = list(np.intersect1d(bilateral, right)) contra_left = list(np.intersect1d(contralateral, left)) contra_right = list(np.intersect1d(contralateral, right)) ipsi_left = list(np.intersect1d(ipsi_left, all_inputs_hit_hist_left.index)) ipsi_right = list(np.intersect1d(ipsi_right, all_inputs_hit_hist_right.index)) bilateral_left = list(np.intersect1d(bilateral_left, all_inputs_hit_hist_left.index)) bilateral_right = list(np.intersect1d(bilateral_right, all_inputs_hit_hist_right.index)) contra_left = list(np.intersect1d(contra_left, all_inputs_hit_hist_left.index)) contra_right = list(np.intersect1d(contra_right, all_inputs_hit_hist_right.index)) # plot results fig, axs = plt.subplots( 3, 1, figsize=(1, 1.75), sharex=True ) fig.tight_layout(pad=0.05) ax = axs[0] i_left = (all_inputs_hit_hist_left.loc[ipsi_left]>threshold).sum(axis=0) b_left = (all_inputs_hit_hist_left.loc[bilateral_left]>threshold).sum(axis=0) c_left = (all_inputs_hit_hist_left.loc[contra_left]>threshold).sum(axis=0) c_right = (all_inputs_hit_hist_left.loc[contra_right]>threshold).sum(axis=0) b_right = (all_inputs_hit_hist_left.loc[bilateral_right]>threshold).sum(axis=0) i_right = (all_inputs_hit_hist_left.loc[ipsi_right]>threshold).sum(axis=0) data_left = pd.DataFrame([i_left, b_left, c_left, c_right, b_right, i_right], index = ['Ipsi(L)', 'Bilateral(L)', 'Contra(L)', 'Contra(R)', 'Bilateral(R)', 'Ipsi(R)']) sns.heatmap(data_left.iloc[:, 0:5], ax = ax, annot=True, fmt="d", cbar = False) ax.tick_params(left=False, bottom=False) ax = axs[1] i_left = (all_inputs_hit_hist_right.loc[ipsi_left]>threshold).sum(axis=0) b_left = (all_inputs_hit_hist_right.loc[bilateral_left]>threshold).sum(axis=0) c_left = (all_inputs_hit_hist_right.loc[contra_left]>threshold).sum(axis=0) c_rightc_right = (all_inputs_hit_hist_right.loc[contra_right]>threshold).sum(axis=0) b_right = (all_inputs_hit_hist_right.loc[bilateral_right]>threshold).sum(axis=0) i_right = (all_inputs_hit_hist_right.loc[ipsi_right]>threshold).sum(axis=0) data_right = pd.DataFrame([i_left, b_left, c_left, c_right, b_right, i_right], index = ['Ipsi(L)', 'Bilateral(L)', 'Contra(L)', 'Contra(R)', 'Bilateral(R)', 'Ipsi(R)']) sns.heatmap(data_right.iloc[:, 0:5], ax = ax, annot=True, fmt="d", cbar = False) ax.tick_params(left=False, bottom=False) ax = axs[2] i_left = all_inputs_intersect.loc[ipsi_left].sum(axis=0)/all_inputs_total.loc[ipsi_left].sum(axis=0) b_left = all_inputs_intersect.loc[bilateral_left].sum(axis=0)/all_inputs_total.loc[bilateral_left].sum(axis=0) c_left = all_inputs_intersect.loc[contra_left].sum(axis=0)/all_inputs_total.loc[contra_left].sum(axis=0) c_right = all_inputs_intersect.loc[contra_right].sum(axis=0)/all_inputs_total.loc[contra_right].sum(axis=0) b_right = all_inputs_intersect.loc[bilateral_right].sum(axis=0)/all_inputs_total.loc[bilateral_right].sum(axis=0) i_right = all_inputs_intersect.loc[ipsi_right].sum(axis=0)/all_inputs_total.loc[ipsi_right].sum(axis=0) data = pd.DataFrame([i_left, b_left, c_left, c_right, b_right, i_right], index = ['Ipsi(L)', 'Bilateral(L)', 'Contra(L)', 'Contra(R)', 'Bilateral(R)', 'Ipsi(R)']) data = data.fillna(0) sns.heatmap(data.iloc[:, 0:5], ax = ax, annot=True, fmt=".0%", cbar = False, cmap = cmr.lavender) ax.tick_params(left=False, bottom=False) fig.savefig('plots/interhemisphere-summary_intersect-plot.pdf', format='pdf', bbox_inches='tight') # plot results fig, axs = plt.subplots( 3, 1, figsize=(1, 1.75), sharex=True ) fig.tight_layout(pad=0.05) ax = axs[0] i_left = (all_inputs_hit_hist_left.loc[ipsi_left]>threshold).sum(axis=0) b_left = (all_inputs_hit_hist_left.loc[bilateral_left]>threshold).sum(axis=0) c_left = (all_inputs_hit_hist_left.loc[contra_left]>threshold).sum(axis=0) c_right = (all_inputs_hit_hist_left.loc[contra_right]>threshold).sum(axis=0) b_right = (all_inputs_hit_hist_left.loc[bilateral_right]>threshold).sum(axis=0) i_right = (all_inputs_hit_hist_left.loc[ipsi_right]>threshold).sum(axis=0) data_left = pd.DataFrame([i_left, b_left, c_left, c_right, b_right, i_right], index = ['Ipsi(L)', 'Bilateral(L)', 'Contra(L)', 'Contra(R)', 'Bilateral(R)', 'Ipsi(R)']) sns.heatmap(data_left.iloc[:, 0:5], ax = ax, annot=True, fmt="d", cbar = False) ax.tick_params(left=False, bottom=False) ax = axs[1] i_left = (all_inputs_hit_hist_right.loc[ipsi_left]>threshold).sum(axis=0) b_left = (all_inputs_hit_hist_right.loc[bilateral_left]>threshold).sum(axis=0) c_left = (all_inputs_hit_hist_right.loc[contra_left]>threshold).sum(axis=0) c_rightc_right = (all_inputs_hit_hist_right.loc[contra_right]>threshold).sum(axis=0) b_right = (all_inputs_hit_hist_right.loc[bilateral_right]>threshold).sum(axis=0) i_right = (all_inputs_hit_hist_right.loc[ipsi_right]>threshold).sum(axis=0) data_right = pd.DataFrame([i_left, b_left, c_left, c_right, b_right, i_right], index = ['Ipsi(L)', 'Bilateral(L)', 'Contra(L)', 'Contra(R)', 'Bilateral(R)', 'Ipsi(R)']) sns.heatmap(data_right.iloc[:, 0:5], ax = ax, annot=True, fmt="d", cbar = False) ax.tick_params(left=False, bottom=False) ax = axs[2] i_left = all_inputs_intersect.loc[ipsi_left].sum(axis=0) b_left = all_inputs_intersect.loc[bilateral_left].sum(axis=0) c_left = all_inputs_intersect.loc[contra_left].sum(axis=0) c_right = all_inputs_intersect.loc[contra_right].sum(axis=0) b_right = all_inputs_intersect.loc[bilateral_right].sum(axis=0) i_right = all_inputs_intersect.loc[ipsi_right].sum(axis=0) data = pd.DataFrame([i_left, b_left, c_left, c_right, b_right, i_right], index = ['Ipsi(L)', 'Bilateral(L)', 'Contra(L)', 'Contra(R)', 'Bilateral(R)', 'Ipsi(R)']) data = data.fillna(0) sns.heatmap(data.iloc[:, 0:5], ax = ax, annot=True, cbar = False, cmap = cmr.lavender) ax.tick_params(left=False, bottom=False) fig.savefig('plots/interhemisphere-summary_intersect-plot_raw-counts.pdf', format='pdf', bbox_inches='tight') # %% # identify integration center neurons # what types of neurons are they? data_mat = pd.DataFrame(all_inputs_intersect) data_ipsi = data_mat.loc[np.intersect1d(ipsi, data_mat.index), :] data_bilat = data_mat.loc[np.intersect1d(bilateral, data_mat.index), :] data_contra = data_mat.loc[np.intersect1d(contralateral, data_mat.index), :] all_cats = [] for i in range(len(data_mat.columns)): cats_hop = [] cats_hop.append(Celltype(f'hop{i}_ipsi_integrators', list(data_ipsi[data_ipsi.iloc[:, i]].index))) cats_hop.append(Celltype(f'hop{i}_bilateral_integrators', list(data_bilat[data_bilat.iloc[:, i]].index))) cats_hop.append(Celltype(f'hop{i}_contra_integrators', list(data_contra[data_contra.iloc[:, i]].index))) all_cats.append(cats_hop) _, celltypes = Celltype_Analyzer.default_celltypes() all_cat_memberships=[] for i in range(len(all_cats)): all_cats_analyzer = Celltype_Analyzer(all_cats[i]) all_cats_analyzer.set_known_types(celltypes) cats_memberships = all_cats_analyzer.memberships(raw_num=True) #switch to False for percent neurons all_cat_memberships.append(cats_memberships) integrator2hop = [skid for subset in [x.skids for x in all_cats[2]] for skid in subset] integrator3hop = [skid for subset in [x.skids for x in all_cats[3]] for skid in subset] integrator4hop = [skid for subset in [x.skids for x in all_cats[4]] for skid in subset] pymaid.add_annotations(integrator2hop, 'mw interhemispheric integration 2-hop') pymaid.add_annotations(integrator3hop, 'mw interhemispheric integration 3-hop') pymaid.add_annotations(integrator4hop, 'mw interhemispheric integration 4-hop') colors = [x.get_color() for x in celltypes] + ['tab:gray'] fraction_types_names = all_cat_memberships[1].index #plt.bar(x=fraction_types_names,height=[1]*len(colors),color=colors) for i in range(1, 5): plts=[] fig, ax = plt.subplots(figsize=(0.55,.6)) plt1 = plt.bar(all_cat_memberships[i].columns, all_cat_memberships[i].iloc[0, :], color=colors[0]) bottom = all_cat_memberships[i].iloc[0, :] plt.xticks(rotation=45, ha='right') ax.set(ylim=(0,100)) plts.append(plt1) for j in range(1, len(all_cat_memberships[i].iloc[:, 0])): plt_next = plt.bar(all_cat_memberships[i].columns, all_cat_memberships[i].iloc[j, :], bottom = bottom, color = colors[j]) bottom = bottom + all_cat_memberships[i].iloc[j, :] plts.append(plt_next) ax.set(ylim=(0,100)) plt.xticks(rotation=45, ha='right') plt.savefig(f'plots/interhemisphere_integrators_hop{i}.pdf', format='pdf', bbox_inches='tight') # %% # cascades to descendings; L/R bias of descending input pairs = Promat.get_pairs(pairs_path=pairs_path) dVNC = Promat.load_pairs_from_annotation('mw dVNC', pairs) dSEZ = Promat.load_pairs_from_annotation('mw dSEZ', pairs) RGN = Promat.load_pairs_from_annotation('mw RGN', pairs) dVNC_left = list(dVNC.leftid) dVNC_right = list(dVNC.rightid) dSEZ_left = list(dSEZ.leftid) dSEZ_right = list(dSEZ.rightid) RGN_left = list(RGN.leftid) RGN_right = list(RGN.rightid) left_signal = all_inputs_hit_hist_left/n_init left_signal = left_signal.sum(axis=1) right_signal = all_inputs_hit_hist_right/n_init right_signal = -(right_signal.sum(axis=1)) integration = (left_signal + right_signal) integration_df = pd.DataFrame(list(zip(left_signal, right_signal, integration)), index = adj.index) df_left = integration_df.loc[dVNC_left, :] df_left = df_left.append([[np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan]]) df_left = df_left.append(integration_df.loc[dSEZ_left, :]) df_left = df_left.append([[np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan]]) df_left = df_left.append(integration_df.loc[RGN_left, :]) df_right = integration_df.loc[dVNC_right, :] df_right = df_right.append([[np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan]]) df_right = df_right.append(integration_df.loc[dSEZ_right, :]) df_right = df_right.append([[np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan], [np.nan, np.nan, np.nan]]) df_right = df_right.append(integration_df.loc[RGN_right, :]) fig, axs = plt.subplots(1,2, figsize=(1.5,1.5), sharey=True) fig.tight_layout(pad=0.05) ax=axs[0] sns.heatmap(df_left, cmap=cmr.iceburn, ax=ax, cbar=False) ax.tick_params(left=False, bottom=False) ax.set(yticks=([])) ax=axs[1] sns.heatmap(df_right, cmap=cmr.iceburn, ax=ax, cbar=False) ax.tick_params(left=False, bottom=False) ax.set(yticks=([])) fig.savefig('plots/interhemisphere_left-right-visits_brain_outputs.pdf', format='pdf', bbox_inches='tight') fig, ax = plt.subplots(1,1, figsize=(1.5,1.5), sharey=True) sns.heatmap(df_left, cmap=cmr.iceburn, ax=ax) fig.savefig('plots/interhemisphere_left-right-visits_brain_outputs_cbar.pdf', format='pdf', bbox_inches='tight') # %% # lateralization metric to determine how much left/right mixing happens per neuron pairs = Promat.get_pairs(pairs_path=pairs_path) dVNC = Promat.load_pairs_from_annotation('mw dVNC', pairs, return_type='pairs') dSEZ = Promat.load_pairs_from_annotation('mw dSEZ', pairs, return_type='pairs') RGN = Promat.load_pairs_from_annotation('mw RGN', pairs, return_type='pairs') left_signal = all_inputs_hit_hist_left/n_init left_signal = left_signal.sum(axis=1) right_signal = all_inputs_hit_hist_right/n_init right_signal = -(right_signal.sum(axis=1)) integration = (left_signal + right_signal) integration_df = pd.DataFrame(list(zip(left_signal, right_signal, integration)), index = adj.index, columns = ['left_signal', 'right_signal', 'left_right_signal']) left_int = [] right_int = [] left_right_int = [] for i in dVNC.index: leftid = dVNC.loc[i, 'leftid'] rightid = dVNC.loc[i, 'rightid'] int_left = integration_df.loc[leftid, 'left_right_signal'] int_right = integration_df.loc[rightid, 'left_right_signal'] left_int.append(int_left) right_int.append(int_right) left_right_int.append(((int_left)+-(int_right))/2) dVNC['left_integration'] = left_int dVNC['right_integration'] = right_int dVNC['lateralization'] = left_right_int left_int = [] right_int = [] left_right_int = [] for i in dSEZ.index: leftid = dSEZ.loc[i, 'leftid'] rightid = dSEZ.loc[i, 'rightid'] int_left = integration_df.loc[leftid, 'left_right_signal'] int_right = integration_df.loc[rightid, 'left_right_signal'] left_int.append(int_left) right_int.append(int_right) left_right_int.append(((int_left)+-(int_right))/2) dSEZ['left_integration'] = left_int dSEZ['right_integration'] = right_int dSEZ['lateralization'] = left_right_int left_int = [] right_int = [] left_right_int = [] for i in RGN.index: leftid = RGN.loc[i, 'leftid'] rightid = RGN.loc[i, 'rightid'] int_left = integration_df.loc[leftid, 'left_right_signal'] int_right = integration_df.loc[rightid, 'left_right_signal'] left_int.append(int_left) right_int.append(int_right) left_right_int.append(((int_left)+-(int_right))/2) RGN['left_integration'] = left_int RGN['right_integration'] = right_int RGN['lateralization'] = left_right_int s = 2 fig, ax = plt.subplots(1,1,figsize=(2,2)) sns.scatterplot(x=[x for x in range(0, len(dVNC))], y=dVNC.lateralization.sort_values(), color='#A52A2A', ax=ax, s=s) sns.scatterplot(x=[x+len(dVNC) for x in range(0, len(dSEZ))], y=dSEZ.lateralization.sort_values(), color='#C47451', ax=ax, s=s) sns.scatterplot(x=[x+len(dVNC)+len(dSEZ) for x in range(0, len(RGN))], y=RGN.lateralization.sort_values(), color='#9467BD', ax=ax, s=s) ax.set(ylim=(-1,1)) plt.savefig('plots/interhemisphere_signal-lateralization.pdf', format='pdf', bbox_inches='tight') s = 2 fig, ax = plt.subplots(1,1,figsize=(1,2)) sns.scatterplot(x=[x for x in range(0, len(dVNC))], y=dVNC.lateralization.sort_values(), color='#A52A2A', ax=ax, s=s) sns.scatterplot(x=[x for x in range(0, len(dSEZ))], y=dSEZ.lateralization.sort_values(), color='#C47451', ax=ax, s=s) sns.scatterplot(x=[x for x in range(0, len(RGN))], y=RGN.lateralization.sort_values(), color='#9467BD', ax=ax, s=s) ax.set(ylim=(-1,1)) plt.savefig('plots/interhemisphere_signal-lateralization_overlapping.pdf', format='pdf', bbox_inches='tight') # percent >0.25 dVNC_lateralized = dVNC[abs(dVNC.lateralization)>0.25].leftid dSEZ_lateralized = dSEZ[abs(dSEZ.lateralization)>0.25].leftid RGN_lateralized = RGN[abs(RGN.lateralization)>0.25].leftid lateralized = pd.DataFrame([[len(dVNC_lateralized)/len(dVNC), 'lateralized', 'dVNC'], [1-len(dVNC_lateralized)/len(dVNC), 'mixed', 'dVNC'], [len(dSEZ_lateralized)/len(dSEZ), 'lateralized', 'dSEZ'], [1-len(dSEZ_lateralized)/len(dSEZ), 'mixed', 'dSEZ'], [len(RGN_lateralized)/len(RGN), 'lateralized', 'RGN'], [1-len(RGN_lateralized)/len(RGN), 'mixed', 'RGN']], columns = ['fraction', 'lateralization', 'type']) lateralized = pd.DataFrame([[len(dVNC_lateralized)/len(dVNC), len(dSEZ_lateralized)/len(dSEZ), len(RGN_lateralized)/len(RGN)], [1-len(dVNC_lateralized)/len(dVNC), 1-len(dSEZ_lateralized)/len(dSEZ), 1-len(RGN_lateralized)/len(RGN)]], index = ['lateralized', 'mixed'], columns = ['dVNC', 'dSEZ', 'RGN']) fig, ax = plt.subplots(1,1, figsize=(2,2)) ax.bar(x = lateralized.columns, height = lateralized.loc['lateralized', :]) ax.bar(x = lateralized.columns, height = lateralized.loc['mixed', :], bottom = lateralized.loc['lateralized', :]) plt.savefig('plots/interhemisphere_signal-lateralization_summary.pdf', format='pdf', bbox_inches='tight') # %% # lateralization of all brain neurons skids = np.setdiff1d(integration_df.index, input_skids + pymaid.get_skids_by_annotation('mw A1 ascending unknown') + pymaid.get_skids_by_annotation('mw motor')) brain = Promat.load_pairs_from_annotation(annot='', pairList=pairs, return_type='all_pair_ids_bothsides', skids=skids, use_skids=True) left_int = [] right_int = [] left_right_int = [] for i in brain.index: leftid = brain.loc[i, 'leftid'] rightid = brain.loc[i, 'rightid'] int_left = integration_df.loc[leftid, 'left_right_signal'] int_right = integration_df.loc[rightid, 'left_right_signal'] # for paired neurons if(leftid!=rightid): left_int.append(int_left) right_int.append(int_right) left_right_int.append(((int_left)+-(int_right))/2) # for nonpaired neurons if(leftid==rightid): # determine if neuron is left or right neuron to set appropriate signal polarity if(leftid in left): left_int.append(int_left) right_int.append(-int_right) left_right_int.append(((int_left)+-(-int_right))/2) if(leftid in right): left_int.append(-int_left) right_int.append(int_right) left_right_int.append(((-int_left)+-(int_right))/2) # center neuron case if((leftid not in left) & (leftid not in right)): left_int.append(int_left) right_int.append(-int_right) left_right_int.append(((int_left)+-(-int_right))/2) brain['left_integration'] = left_int brain['right_integration'] = right_int brain['lateralization'] = left_right_int threshold = 0.25 # plot all brain lateralization brain_sort_subthres = brain.lateralization.sort_values()[(brain.lateralization.sort_values()<=threshold) & (brain.lateralization.sort_values()>=-threshold)] brain_sort_thres = brain.lateralization.sort_values()[brain.lateralization.sort_values()>threshold] brain_sort_contra_thres = brain.lateralization.sort_values()[brain.lateralization.sort_values()<-threshold] s=6 alpha = 0.25 fig, ax = plt.subplots(1,1,figsize=(1,2)) plt.scatter(x=[x for x in range(0, len(brain_sort_contra_thres))], y=brain_sort_contra_thres, color='none', edgecolor=sns.color_palette()[3], linewidths=0.2, alpha=alpha, s=s) plt.scatter(x=[x for x in range(len(brain_sort_contra_thres), len(brain_sort_contra_thres) + len(brain_sort_subthres))], y=brain_sort_subthres, color='none', edgecolor=sns.color_palette()[0], linewidths=0.2, alpha=alpha, s=s) plt.scatter(x=[x for x in range(len(brain_sort_contra_thres) + len(brain_sort_subthres), len(brain_sort_subthres) + len(brain_sort_thres) + len(brain_sort_contra_thres))], y=brain_sort_thres, color='none', edgecolor=sns.color_palette()[1], linewidths=0.2, alpha=alpha, s=s) ax.set(ylim=(-1.05,1.05)) plt.savefig('plots/interhemisphere_signal-lateralization_whole-brain.pdf', format='pdf', bbox_inches='tight') # identify neurons with >0.25 lateralization threshold = 0.25 brain_lateralized_ipsi_left = list(brain[(brain.lateralization>threshold)].leftid) brain_lateralized_ipsi_right = list(brain[(brain.lateralization>threshold)].rightid) brain_lateralized_ipsi = brain_lateralized_ipsi_left + brain_lateralized_ipsi_right brain_lateralized_ipsi_ct = Celltype('ipsi_lateralized', brain_lateralized_ipsi, color=sns.color_palette()[1]) brain_lateralized_contra_left = list(brain[(brain.lateralization<-threshold)].leftid) brain_lateralized_contra_right = list(brain[(brain.lateralization<-threshold)].rightid) brain_lateralized_contra = brain_lateralized_contra_left + brain_lateralized_contra_right brain_lateralized_contra_ct = Celltype('contra_lateralized', brain_lateralized_contra, color=sns.color_palette()[3]) brain_nonlat_left = list(brain[(brain.lateralization<=threshold) & (brain.lateralization>=-threshold)].leftid) brain_nonlat_right = list(brain[(brain.lateralization<=threshold) & (brain.lateralization>=-threshold)].rightid) brain_nonlateralized = brain_nonlat_left + brain_nonlat_right brain_nonlateralized_ct = Celltype('non_lateralized', brain_nonlateralized, color=sns.color_palette()[0]) pdiff = pymaid.get_skids_by_annotation('mw partially differentiated') _, celltypes = Celltype_Analyzer.default_celltypes(exclude=pdiff) celltype_analyzer = Celltype_Analyzer([celltype for celltype in celltypes if celltype.name not in ['ascendings', 'sensories']]) celltype_analyzer.set_known_types([brain_lateralized_ipsi_ct, brain_lateralized_contra_ct, brain_nonlateralized_ct]) memberships = celltype_analyzer.memberships() celltype_analyzer.plot_memberships('plots/interhemisphere_signal-lateralization_by-celltype.pdf', figsize=(4,2)) print(f'{len(brain_sort_thres)/(len(brain_sort_thres)+len(brain_sort_subthres))*100:.1f}% of neurons are ipsi-lateralized with 8 hops') print(f'{len(brain_sort_subthres)/(len(brain_sort_thres)+len(brain_sort_subthres))*100:.1f}% of neurons are non-lateralized with 8 hops') # %% ######### # repeat lateralization analysis with 5-hop cascades ######### # lateralization metric to determine how much left/right mixing happens per neuron pairs = Promat.get_pairs(pairs_path=pairs_path) dVNC = Promat.load_pairs_from_annotation('mw dVNC', pairs, return_type='pairs') dSEZ = Promat.load_pairs_from_annotation('mw dSEZ', pairs, return_type='pairs') RGN = Promat.load_pairs_from_annotation('mw RGN', pairs, return_type='pairs') left_signal = all_inputs_hit_hist_left.loc[:, [0,1,2,3,4,5]]/n_init left_signal = left_signal.sum(axis=1) right_signal = all_inputs_hit_hist_right.loc[:, [0,1,2,3,4,5]]/n_init right_signal = -(right_signal.sum(axis=1)) integration = (left_signal + right_signal) integration_df = pd.DataFrame(list(zip(left_signal, right_signal, integration)), index = adj.index, columns = ['left_signal', 'right_signal', 'left_right_signal']) # lateralization of all brain neurons skids = np.setdiff1d(integration_df.index, input_skids + pymaid.get_skids_by_annotation('mw A1 ascending unknown') + pymaid.get_skids_by_annotation('mw motor')) brain = Promat.load_pairs_from_annotation(annot='', pairList=pairs, return_type='all_pair_ids_bothsides', skids=skids, use_skids=True) left_int = [] right_int = [] left_right_int = [] for i in brain.index: leftid = brain.loc[i, 'leftid'] rightid = brain.loc[i, 'rightid'] int_left = integration_df.loc[leftid, 'left_right_signal'] int_right = integration_df.loc[rightid, 'left_right_signal'] # for paired neurons if(leftid!=rightid): left_int.append(int_left) right_int.append(int_right) left_right_int.append(((int_left)+-(int_right))/2) # for nonpaired neurons if(leftid==rightid): # determine if neuron is left or right neuron to set appropriate signal polarity if(leftid in left): left_int.append(int_left) right_int.append(-int_right) left_right_int.append(((int_left)+-(-int_right))/2) if(leftid in right): left_int.append(-int_left) right_int.append(int_right) left_right_int.append(((-int_left)+-(int_right))/2) # center neuron case if((leftid not in left) & (leftid not in right)): left_int.append(int_left) right_int.append(-int_right) left_right_int.append(((int_left)+-(-int_right))/2) brain['left_integration'] = left_int brain['right_integration'] = right_int brain['lateralization'] = left_right_int threshold = 0.25 # plot all brain lateralization brain_sort_subthres = brain.lateralization.sort_values()[(brain.lateralization.sort_values()<=threshold) & (brain.lateralization.sort_values()>=-threshold)] brain_sort_thres = brain.lateralization.sort_values()[brain.lateralization.sort_values()>threshold] brain_sort_contra_thres = brain.lateralization.sort_values()[brain.lateralization.sort_values()<-threshold] s=6 alpha = 0.25 fig, ax = plt.subplots(1,1,figsize=(1,2)) plt.scatter(x=[x for x in range(0, len(brain_sort_contra_thres))], y=brain_sort_contra_thres, color='none', edgecolor=sns.color_palette()[3], linewidths=0.2, alpha=alpha, s=s) plt.scatter(x=[x for x in range(len(brain_sort_contra_thres), len(brain_sort_contra_thres) + len(brain_sort_subthres))], y=brain_sort_subthres, color='none', edgecolor=sns.color_palette()[0], linewidths=0.2, alpha=alpha, s=s) plt.scatter(x=[x for x in range(len(brain_sort_contra_thres) + len(brain_sort_subthres), len(brain_sort_subthres) + len(brain_sort_thres) + len(brain_sort_contra_thres))], y=brain_sort_thres, color='none', edgecolor=sns.color_palette()[1], linewidths=0.2, alpha=alpha, s=s) ax.set(ylim=(-1.05,1.05)) plt.savefig('plots/interhemisphere_signal-lateralization_whole-brain_5hops.pdf', format='pdf', bbox_inches='tight') # identify neurons with >0.25 lateralization threshold = 0.25 brain_lateralized_ipsi_left = list(brain[(brain.lateralization>threshold)].leftid) brain_lateralized_ipsi_right = list(brain[(brain.lateralization>threshold)].rightid) brain_lateralized_ipsi = brain_lateralized_ipsi_left + brain_lateralized_ipsi_right brain_lateralized_ipsi_ct = Celltype('ipsi_lateralized', brain_lateralized_ipsi, color=sns.color_palette()[1]) brain_lateralized_contra_left = list(brain[(brain.lateralization<-threshold)].leftid) brain_lateralized_contra_right = list(brain[(brain.lateralization<-threshold)].rightid) brain_lateralized_contra = brain_lateralized_contra_left + brain_lateralized_contra_right brain_lateralized_contra_ct = Celltype('contra_lateralized', brain_lateralized_contra, color=sns.color_palette()[3]) brain_nonlat_left = list(brain[(brain.lateralization<=threshold) & (brain.lateralization>=-threshold)].leftid) brain_nonlat_right = list(brain[(brain.lateralization<=threshold) & (brain.lateralization>=-threshold)].rightid) brain_nonlateralized = brain_nonlat_left + brain_nonlat_right brain_nonlateralized_ct = Celltype('non_lateralized', brain_nonlateralized, color=sns.color_palette()[0]) pdiff = pymaid.get_skids_by_annotation('mw partially differentiated') _, celltypes = Celltype_Analyzer.default_celltypes(exclude=pdiff) celltype_analyzer = Celltype_Analyzer([celltype for celltype in celltypes if celltype.name not in ['ascendings', 'sensories']]) celltype_analyzer.set_known_types([brain_lateralized_ipsi_ct, brain_lateralized_contra_ct, brain_nonlateralized_ct]) memberships_5hops = celltype_analyzer.memberships() celltype_analyzer.plot_memberships('plots/interhemisphere_signal-lateralization_by-celltype_5hops.pdf', figsize=(4,2)) print(f'{len(brain_sort_thres)/(len(brain_sort_thres)+len(brain_sort_subthres))*100:.1f}% of neurons are ipsi-lateralized with 5 hops') print(f'{len(brain_sort_subthres)/(len(brain_sort_thres)+len(brain_sort_subthres))*100:.1f}% of neurons are non-lateralized with 5 hops') # %%

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Python

kedro/extras/datasets/biosequence/__init__.py

daniel-falk/kedro

19187199339ddc4a757aaaa328f319ec4c1e452a

[ "Apache-2.0" ]

2,047

2022-01-10T15:22:12.000Z

2022-03-31T13:38:56.000Z

kedro/extras/datasets/biosequence/__init__.py

daniel-falk/kedro

19187199339ddc4a757aaaa328f319ec4c1e452a

[ "Apache-2.0" ]

170

2022-01-10T12:44:31.000Z

2022-03-31T17:01:24.000Z

kedro/extras/datasets/biosequence/__init__.py

daniel-falk/kedro

19187199339ddc4a757aaaa328f319ec4c1e452a

[ "Apache-2.0" ]

112

2022-01-10T19:15:24.000Z

2022-03-30T11:20:52.000Z

"""``AbstractDataSet`` implementation to read/write from/to a sequence file.""" __all__ = ["BioSequenceDataSet"] from contextlib import suppress with suppress(ImportError): from .biosequence_dataset import BioSequenceDataSet

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py

Python

moviepy/version.py

justinmeister/moviepy

2f6f1de5d142e076bba031ebccbb89c1ad715a6b

[ "MIT" ]

1

2018-06-29T07:19:08.000Z

moviepy/version.py

justinmeister/moviepy

2f6f1de5d142e076bba031ebccbb89c1ad715a6b

[ "MIT" ]

null

moviepy/version.py

justinmeister/moviepy

2f6f1de5d142e076bba031ebccbb89c1ad715a6b

[ "MIT" ]

null

__version__ = "0.2.1.8"

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9f6542a395393520bc4e19ba9e49c8090ebd4563

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py

Python

alphatrading/trading/__init__.py

LoannData/Q26_AlphaTrading

b8e6983e59f942352150f76541d880143cca4478

[ "MIT" ]

null

alphatrading/trading/__init__.py

LoannData/Q26_AlphaTrading

b8e6983e59f942352150f76541d880143cca4478

[ "MIT" ]

null

alphatrading/trading/__init__.py

LoannData/Q26_AlphaTrading

b8e6983e59f942352150f76541d880143cca4478

[ "MIT" ]

null

"""! # Trading folder **This folder contains all the modules related to live trading** """

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9fa0c8741a05c3c0907c5e48d8c621dea4264854

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py

Python

any_imagefield/models/backends/default.py

edoburu/django-any-imagefield

866bcdad6c87281587abc8a696561b1bee5719a1

[ "Apache-2.0" ]

13

2015-03-24T03:31:26.000Z

2020-04-09T08:06:21.000Z

any_imagefield/models/backends/default.py

edoburu/django-any-imagefield

866bcdad6c87281587abc8a696561b1bee5719a1

[ "Apache-2.0" ]

7

2015-02-13T05:41:54.000Z

2019-09-30T10:17:21.000Z

any_imagefield/models/backends/default.py

edoburu/django-any-imagefield

866bcdad6c87281587abc8a696561b1bee5719a1

[ "Apache-2.0" ]

5

2015-05-15T04:55:27.000Z

2019-09-30T09:43:54.000Z

from django.db import models class AnyFileField(models.FileField): """ The standard Django `~django.forms.FileField` with a `~django.forms.ClearableFileInput` widget. """ pass class AnyImageField(models.ImageField): """ The standard Django `~django.forms.ImageField` with a `~django.forms.ClearableFileInput` widget. """ pass

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4c9593a01284498b53cf1f01dcf0b7ae36feaea1

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py

Python

tests/module/test_base.py

mgorny/pkgcheck

ef3feae583e85bc996a8632958e3961c23049e4b

[ "BSD-3-Clause" ]

null

tests/module/test_base.py

mgorny/pkgcheck

ef3feae583e85bc996a8632958e3961c23049e4b

[ "BSD-3-Clause" ]

null

tests/module/test_base.py

mgorny/pkgcheck

ef3feae583e85bc996a8632958e3961c23049e4b

[ "BSD-3-Clause" ]

null

from pkgcheck import base class TestUtilities(object): def test_convert_check_filter(self): assert base.convert_check_filter('foo')('a.foO.b') assert not base.convert_check_filter('foo')('a.foObaR') assert not base.convert_check_filter('foo.*')('a.fOoBar') assert base.convert_check_filter('foo.*')('fOoBar') assert base.convert_check_filter('foo.bar')('foo.bar.baz') assert base.convert_check_filter('bar.baz')('foo.bar.baz') assert not base.convert_check_filter('baz.spork')('foo.bar.baz') assert not base.convert_check_filter('bar.foo')('foo.bar.baz')

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4c95b87792c8c140ce1276b7e66d6ccbddef614f

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py

Python

nodeconductor/openstack/__init__.py

p-p-m/nodeconductor

bc702302ef65c89793452f0fd6ca9a6bec79782f

[ "Apache-2.0" ]

null

nodeconductor/openstack/__init__.py

p-p-m/nodeconductor

bc702302ef65c89793452f0fd6ca9a6bec79782f

[ "Apache-2.0" ]

null

nodeconductor/openstack/__init__.py

p-p-m/nodeconductor

bc702302ef65c89793452f0fd6ca9a6bec79782f

[ "Apache-2.0" ]

null

default_app_config = 'nodeconductor.openstack.apps.OpenStackConfig'

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0

1

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1

null

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4

4cc497297667a28d581b19eb9d635333e1dd8c9c

172

py

Python

pythonteste/aula13.py

mateusmarinho/python3-cursoemvideo

706d419865532e156fb80b8a873e18cb90d6e0da

[ "MIT" ]

null

pythonteste/aula13.py

mateusmarinho/python3-cursoemvideo

706d419865532e156fb80b8a873e18cb90d6e0da

[ "MIT" ]

null

pythonteste/aula13.py

mateusmarinho/python3-cursoemvideo

706d419865532e156fb80b8a873e18cb90d6e0da

[ "MIT" ]

null

'''for i in range(1, 7): print(i) print('Fim')''' '''for i in range(6, 0, -1): print(i) print('FIM')''' for i in range(0, 7, 2): print(i) print('FIM')

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py

Python

apps/link/tasks.py

DrMartiner/kaptilo_back

df7f716030edbb1a70388fcbb808b0985dabefbf

[ "Apache-2.0" ]

null

apps/link/tasks.py

DrMartiner/kaptilo_back

df7f716030edbb1a70388fcbb808b0985dabefbf

[ "Apache-2.0" ]

null

apps/link/tasks.py

DrMartiner/kaptilo_back

df7f716030edbb1a70388fcbb808b0985dabefbf

[ "Apache-2.0" ]

null

import dramatiq @dramatiq.actor def do_something(): ...

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py

Python

tests/unit_tests/startup_scripts/test_startup_scripts.py

kurumuz/datacrunch-python

94b02c68da48b1017c0c837b3b37a97b4b2543a5

[ "MIT" ]

9

2021-01-07T17:56:11.000Z

2022-02-05T01:42:42.000Z

tests/unit_tests/startup_scripts/test_startup_scripts.py

kurumuz/datacrunch-python

94b02c68da48b1017c0c837b3b37a97b4b2543a5

[ "MIT" ]

3

2021-05-26T16:17:33.000Z

2021-12-17T09:25:06.000Z

tests/unit_tests/startup_scripts/test_startup_scripts.py

kurumuz/datacrunch-python

94b02c68da48b1017c0c837b3b37a97b4b2543a5

[ "MIT" ]

3

2021-05-16T00:47:40.000Z

2021-12-17T08:59:16.000Z

import pytest import responses # https://github.com/getsentry/responses from datacrunch.exceptions import APIException from datacrunch.startup_scripts.startup_scripts import StartupScriptsService, StartupScript INVALID_REQUEST = 'invalid_request' INVALID_REQUEST_MESSAGE = 'Your existence is invalid' SCRIPT_ID = 'deadc0de-a5d2-4972-ae4e-d429115d055b' SCRIPT_NAME = 'next episode of _____' SCRIPT_VALUE = 'this was not in the script!' script_ID_2 = 'beefbeef-a5d2-4972-ae4e-d429115d055b' PAYLOAD = [ { 'id': SCRIPT_ID, 'name': SCRIPT_NAME, 'script': SCRIPT_VALUE } ] class TestStartupScripts: @pytest.fixture def startup_script_service(self, http_client): return StartupScriptsService(http_client) @pytest.fixture def endpoint(self, http_client): return http_client._base_url + "/scripts" def test_get_scripts(self, startup_script_service, endpoint): # arrange - add response mock responses.add( responses.GET, endpoint, json=PAYLOAD, status=200 ) # act scripts = startup_script_service.get() # assert assert type(scripts) == list assert len(scripts) == 1 assert type(scripts[0]) == StartupScript assert scripts[0].id == SCRIPT_ID assert scripts[0].name == SCRIPT_NAME assert scripts[0].script == SCRIPT_VALUE assert responses.assert_call_count(endpoint, 1) is True def test_get_script_by_id_successful(self, startup_script_service, endpoint): # arrange - add response mock url = endpoint + '/' + SCRIPT_ID responses.add( responses.GET, url, json=PAYLOAD, status=200 ) # act script = startup_script_service.get_by_id(SCRIPT_ID) # assert assert type(script) == StartupScript assert script.id == SCRIPT_ID assert script.name == SCRIPT_NAME assert script.script == SCRIPT_VALUE assert responses.assert_call_count(url, 1) is True def test_get_script_by_id_failed(self, startup_script_service, endpoint): # arrange - add response mock url = endpoint + '/x' responses.add( responses.GET, url, json={"code": INVALID_REQUEST, "message": INVALID_REQUEST_MESSAGE}, status=400 ) # act with pytest.raises(APIException) as excinfo: startup_script_service.get_by_id('x') # assert assert excinfo.value.code == INVALID_REQUEST assert excinfo.value.message == INVALID_REQUEST_MESSAGE assert responses.assert_call_count(url, 1) is True def test_create_script_successful(self, startup_script_service, endpoint): # arrange - add response mock responses.add( responses.POST, endpoint, body=SCRIPT_ID, status=201 ) # act script = startup_script_service.create(SCRIPT_NAME, SCRIPT_VALUE) # assert assert type(script) == StartupScript assert script.id == SCRIPT_ID assert responses.assert_call_count(endpoint, 1) is True def test_create_script_failed(self, startup_script_service, endpoint): # arrange - add response mock responses.add( responses.POST, endpoint, json={"code": INVALID_REQUEST, "message": INVALID_REQUEST_MESSAGE}, status=400 ) # act with pytest.raises(APIException) as excinfo: startup_script_service.create(SCRIPT_NAME, SCRIPT_VALUE) # assert assert excinfo.value.code == INVALID_REQUEST assert excinfo.value.message == INVALID_REQUEST_MESSAGE assert responses.assert_call_count(endpoint, 1) is True def test_delete_scripts_successful(self, startup_script_service, endpoint): # arrange - add response mock responses.add( responses.DELETE, endpoint, status=200 ) # act result = startup_script_service.delete([SCRIPT_ID, script_ID_2]) # assert assert result is None assert responses.assert_call_count(endpoint, 1) is True def test_delete_scripts_failed(self, startup_script_service, endpoint): # arrange - add response mock responses.add( responses.DELETE, endpoint, json={"code": INVALID_REQUEST, "message": INVALID_REQUEST_MESSAGE}, status=400 ) # act with pytest.raises(APIException) as excinfo: startup_script_service.delete(['x']) # assert assert excinfo.value.code == INVALID_REQUEST assert excinfo.value.message == INVALID_REQUEST_MESSAGE assert responses.assert_call_count(endpoint, 1) is True def test_delete_script_by_id_successful(self, startup_script_service, endpoint): # arrange - add response mock url = endpoint + '/' + SCRIPT_ID responses.add( responses.DELETE, url, status=200 ) # act result = startup_script_service.delete_by_id(SCRIPT_ID) # assert assert result is None assert responses.assert_call_count(url, 1) is True def test_delete_script_by_id_failed(self, startup_script_service, endpoint): # arrange - add response mock url = endpoint + '/x' responses.add( responses.DELETE, url, json={"code": INVALID_REQUEST, "message": INVALID_REQUEST_MESSAGE}, status=400 ) # act with pytest.raises(APIException) as excinfo: startup_script_service.delete_by_id('x') # assert assert excinfo.value.code == INVALID_REQUEST assert excinfo.value.message == INVALID_REQUEST_MESSAGE assert responses.assert_call_count(url, 1) is True

30.654822

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0.630734

660

6,039

5.519697

0.134848

0.073017

0.10431

0.059292

0.757068

0.737304

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null

0

1

0

1

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null

0

4

e22ed17c083e616decc8874689375dbb011a253f

421

py

Python

zcrmsdk/src/com/zoho/crm/api/file/__init__.py

zoho/zohocrm-python-sdk-2.0

3a93eb3b57fed4e08f26bd5b311e101cb2995411

[ "Apache-2.0" ]

null

zcrmsdk/src/com/zoho/crm/api/file/__init__.py

zoho/zohocrm-python-sdk-2.0

3a93eb3b57fed4e08f26bd5b311e101cb2995411

[ "Apache-2.0" ]

null

zcrmsdk/src/com/zoho/crm/api/file/__init__.py

zoho/zohocrm-python-sdk-2.0

3a93eb3b57fed4e08f26bd5b311e101cb2995411

[ "Apache-2.0" ]

null

from .action_wrapper import ActionWrapper from .file_operations import FileOperations, UploadFilesParam, GetFileParam from .api_exception import APIException from .response_handler import ResponseHandler from .action_response import ActionResponse from .success_response import SuccessResponse from .file_body_wrapper import FileBodyWrapper from .body_wrapper import BodyWrapper from .action_handler import ActionHandler

42.1

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421

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76

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null

0

1

0

1

0

1

0

4

e2324c8838838a8fe7d8a0255e2d2eb905ff5a35

817

py

Python

App/AuthServer/utils/RSAHelper.py

anindya/license-pool

2fb62c86c452947dacdfeb02b676e4a045e006d6

[ "Apache-2.0" ]

null

App/AuthServer/utils/RSAHelper.py

anindya/license-pool

2fb62c86c452947dacdfeb02b676e4a045e006d6

[ "Apache-2.0" ]

null

App/AuthServer/utils/RSAHelper.py

anindya/license-pool

2fb62c86c452947dacdfeb02b676e4a045e006d6

[ "Apache-2.0" ]

null

from Crypto.PublicKey import RSA from Crypto.Cipher import PKCS1_OAEP import json import base64 def encryptMessage(message, public_key): pkey = RSA.importKey(public_key) cipher = PKCS1_OAEP.new(pkey) return base64.b64encode(cipher.encrypt((json.dumps(message, default=str)).encode())) def generateKeyPairs(password): key = RSA.generate(2048) return key.publickey().exportKey("OpenSSH").decode('utf-8'), key.export_key(pkcs=8, passphrase=password).decode('utf-8') def decryptMessage(password, message, private_key): key = RSA.import_key(private_key, passphrase=password) cipher = PKCS1_OAEP.new(key) return cipher.decrypt(message) def decryptBase64Message(password, message, private_key): return decryptMessage(password, base64.b64decode(message.encode()), private_key).decode()

38.904762

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105

817

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817

21

125

38.904762

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null

0

null

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1

0

1

0

1

0

4

e26fbba51485dec9f40079a1144db486959c0d9b

32

py

Python

tests/__init__.py

grantmcconnaughey/ci.py

0602fcd9d9caaa5dbea4818198ecc29c9e4b5da8

[ "MIT" ]

2

2020-04-15T12:54:34.000Z

2020-05-07T00:15:02.000Z

tests/__init__.py

grantmcconnaughey/ci.py

0602fcd9d9caaa5dbea4818198ecc29c9e4b5da8

[ "MIT" ]

null

tests/__init__.py

grantmcconnaughey/ci.py

0602fcd9d9caaa5dbea4818198ecc29c9e4b5da8

[ "MIT" ]

1

2020-11-21T19:23:51.000Z

"""Unit test package for ci."""

16

31

0.625

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32

1

32

0.740741

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0

null

0

null

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null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

e278a0adca138cbfbcc972b0d40297fa96db52c7

265

py

Python

bin/PythonDAnCE/generator/deploy_requirement.py

jwillemsen/DAnCE

516ff2b502001e7b42e717af8b3034be6df2544d

[ "DOC" ]

8

2016-07-20T00:56:05.000Z

2020-10-04T12:31:16.000Z

bin/PythonDAnCE/generator/deploy_requirement.py

jwillemsen/DAnCE

516ff2b502001e7b42e717af8b3034be6df2544d

[ "DOC" ]

5

2016-06-20T16:16:23.000Z

2019-06-26T12:18:45.000Z

bin/PythonDAnCE/generator/deploy_requirement.py

jwillemsen/DAnCE

516ff2b502001e7b42e717af8b3034be6df2544d

[ "DOC" ]

12

2016-04-20T10:01:06.000Z

2021-12-24T17:24:04.000Z

from templet import stringfunction @stringfunction def template (requirement_name, requirement_type) : """ <deployRequirement> <name>${requirement_name}</name> <resourceType>${requirement_type}</resourceType> </deployRequirement> """

20.384615

54

0.701887

21

265

8.666667

0.52381

0.164835

0

0.177358

265

12

55

22.083333

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0.475472

0

1

0.333333

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0

0.333333

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0

1

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null

0

null

0

1

0

1

0

1

0

4

e297395c76efe81edb15e842cd65b697575ed3a9

141

py

Python

dashboard/urls.py

pwodyk/CI_MilestoneProject4

0f7402c3b707c3496d14c3aa711c652bf03f781c

[ "CC0-1.0" ]

null

dashboard/urls.py

pwodyk/CI_MilestoneProject4

0f7402c3b707c3496d14c3aa711c652bf03f781c

[ "CC0-1.0" ]

1

2021-06-01T23:53:20.000Z

dashboard/urls.py

pawodyk/CI_MilestoneProject4

0f7402c3b707c3496d14c3aa711c652bf03f781c

[ "CC0-1.0" ]

1

2019-06-28T20:55:47.000Z

from django.conf.urls import url from .views import display_dashboard urlpatterns = [ url(r'^$', display_dashboard, name='dashboard'), ]

23.5

52

0.737589

18

141

5.666667

0.666667

0.313725

0

0.134752

141

6

53

23.5

0.836066

0

0.077465

0

1

0

false

0

0.4

0

0.4

0

1

0

null

1

0

1

0

null

0

1

0

4

e298ba50255864e8867c081157540b09eaef2317

143

py

Python

2.py

zhenyakeg/Dictionaries

0d6f5dc89fe55cc70c056d5b833f162780e9ce90

[ "PSF-2.0" ]

null

2.py

zhenyakeg/Dictionaries

0d6f5dc89fe55cc70c056d5b833f162780e9ce90

[ "PSF-2.0" ]

null

2.py

zhenyakeg/Dictionaries

0d6f5dc89fe55cc70c056d5b833f162780e9ce90

[ "PSF-2.0" ]

null

__author__ = 'student' A = set('0123456789') B = set('02468') C = set('12345') D = set('56789') E = ((A - B)&(C - D)) | ((D-A)&(B-C)) print(E)

17.875

37

0.51049

25

143

2.76

0.52

0.057971

0.086957

0

0.211864

0.174825

143

8

38

17.875

0.372881

0

0.222222

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false

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0.142857

1

0

null

0

1

0

1

0

1

0

null

0

4

2c48504f5a280cf3b200b3aa7f615aa0fd217ba6

208

py

Python

core/admin.py

Guilehm/crawler

5bc0c45842c5224621f8e437ed3ab9d4804ad161

[ "MIT" ]

null

core/admin.py

Guilehm/crawler

5bc0c45842c5224621f8e437ed3ab9d4804ad161

[ "MIT" ]

17

2019-02-03T17:04:13.000Z

2021-06-10T21:17:47.000Z

core/admin.py

Guilehm/crawler

5bc0c45842c5224621f8e437ed3ab9d4804ad161

[ "MIT" ]

null

from django.contrib import admin from core.models import DataFile @admin.register(DataFile) class DataFileAdmin(admin.ModelAdmin): list_display = ('id', 'date_added') list_filter = ('date_added',)

20.8

39

0.745192

26

208

5.807692

0.692308

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0

0.139423

208

9

40

23.111111

0.843575

0

0.105769

0

1

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false

0

0.333333

0

0.833333

0

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null

0

1

0

null

0

1

0

1

0

4

2c4f3677c45cdd7a9b7fca845f095adc089511fa

151

py

Python

academic/apps/publishing/settings.py

phretor/django-academic

864452238056e07056990479396e8446a1bad086

[ "BSD-3-Clause" ]

2

2015-10-16T17:07:03.000Z

2016-06-23T09:54:51.000Z

academic/apps/publishing/settings.py

phretor/django-academic

864452238056e07056990479396e8446a1bad086

[ "BSD-3-Clause" ]

null

academic/apps/publishing/settings.py

phretor/django-academic

864452238056e07056990479396e8446a1bad086

[ "BSD-3-Clause" ]

null

from django.conf import settings PUBLISHING_DEFAULT_DIRECTORY = getattr( settings, 'ACADEMIC_PUBLISHING_DEFAULT_DIRECTORY', 'publishing')

21.571429

44

0.781457

15

151

7.533333

0.666667

0.300885

0.460177

0

0.152318

151

6

45

25.166667

0.882813

0

0.311258

0.245033

0

1

0

false

0

0.2

0

0.2

0

1

0

null

1

0

1

0

null

0

4

2c98f2eddc298cd36143111b403bc4459b0e82b4

23

py

Python

src/michelson_kernel/__init__.py

miracle2k/pytezos

e6b99f00f342d9a05b0c36a9883040961fd6d58e

[ "MIT" ]

98

2019-02-07T16:33:38.000Z

2022-03-31T15:53:41.000Z

src/michelson_kernel/__init__.py

miracle2k/pytezos

e6b99f00f342d9a05b0c36a9883040961fd6d58e

[ "MIT" ]

152

2019-05-20T16:38:56.000Z

2022-03-30T14:24:38.000Z

src/michelson_kernel/__init__.py

miracle2k/pytezos

e6b99f00f342d9a05b0c36a9883040961fd6d58e

[ "MIT" ]

34

2019-07-25T12:03:51.000Z

2021-11-11T22:23:38.000Z

__version__ = '3.2.11'

11.5

22

0.652174

4

23

2.75

1

0

0.2

0.130435

23

1

23

0.35

0

0.26087

0

1

0

false

0

1

0

null

0

1

0

1

0

null

0

4

2cabd6801d5f77acc2f017c7503801f4d3b2bf8b

735

py

Python

clientes/models.py

macs03/autoservicio

5c51af0a991ac40534974d74c3c9aa1b20f02293

[ "MIT" ]

null

clientes/models.py

macs03/autoservicio

5c51af0a991ac40534974d74c3c9aa1b20f02293

[ "MIT" ]

null

clientes/models.py

macs03/autoservicio

5c51af0a991ac40534974d74c3c9aa1b20f02293

[ "MIT" ]

null

from django.db import models from django.forms import ModelForm # Create your models here. class Clientes(models.Model): nombre = models.CharField(max_length=100) apellido = models.CharField(max_length=100) cedula = models.CharField(max_length=100) direccion = models.CharField(max_length=300) telefono = models.CharField(max_length=100) placa = models.CharField(max_length=20) modelo = models.CharField(max_length=100) marca = models.CharField(max_length=100) def __str__(self): return "%s %s - %s" % (self.nombre,self.apellido,self.placa) class ClientesForm(ModelForm): class Meta: model = Clientes fields = ('nombre', 'apellido', 'cedula','direccion','telefono','placa','modelo','marca')

36.75

91

0.727891

95

735

5.505263

0.368421

0.229446

0.275335

0.367113

0.309751

0

0.03645

0.141497

735

20

91

36.75

0.792393

0.032653

0

0.088732

0

null

0

0.117647

null

0

null

1

0

null

0

1

0

4

e2cfe83c2ebe3ff71cb583d63a5bdfbee523dd1f

235

py

Python

src/glom_dict/__init__.py

sanders41/glom-dict

46af6aac1444d1fe90a3a9ff46dec6bd926e098a

[ "MIT" ]

1

2021-09-06T23:34:15.000Z

src/glom_dict/__init__.py

sanders41/glom-dict

46af6aac1444d1fe90a3a9ff46dec6bd926e098a

[ "MIT" ]

null

src/glom_dict/__init__.py

sanders41/glom-dict

46af6aac1444d1fe90a3a9ff46dec6bd926e098a

[ "MIT" ]

1

2021-09-14T12:16:44.000Z

""" glom_dict package. Custom Dictionary with glom get and set methods """ from typing import List from glom import Path, PathAccessError from .__main__ import GlomDict __all__: List[str] = ["GlomDict", "Path", "PathAccessError"]

16.785714

60

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30

235

5.566667

0.666667

0.227545

0

0.153191

235

13

61

18.076923

0.839196

0.285106

0

0.16875

0

1

0

true

0

0.75

0

0.75

0

null

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0

null

0

1

0

1

0

1

0

4

e2db90c942e4507f2d2c8d9069570506e95a9f73

160

py

Python

virtual/lib/python3.6/site-packages/pylint/test/functional/yield_from_outside_func.py

drewheathens/The-Moringa-Tribune

98ee4d63c9df6f1f7497fc6876960a822d914500

[ "MIT" ]

463

2015-01-15T08:17:42.000Z

2022-03-28T15:10:20.000Z

virtual/lib/python3.6/site-packages/pylint/test/functional/yield_from_outside_func.py

drewheathens/The-Moringa-Tribune

98ee4d63c9df6f1f7497fc6876960a822d914500

[ "MIT" ]

52

2015-01-06T02:43:59.000Z

2022-03-14T11:15:21.000Z

virtual/lib/python3.6/site-packages/pylint/test/functional/yield_from_outside_func.py

drewheathens/The-Moringa-Tribune

98ee4d63c9df6f1f7497fc6876960a822d914500

[ "MIT" ]

249

2015-01-07T22:49:49.000Z

2022-03-18T02:32:06.000Z

"""This is gramatically correct, but it's still a SyntaxError""" yield from [1, 2] # [yield-outside-function] LAMBDA_WITH_YIELD = lambda: (yield from [1, 2])

32

64

0.70625

25

160

4.44

0.72

0.162162

0.18018

0.198198

0

0.029412

0.15

160

4

65

40

0.786765

0.525

0

1

0

false

0

1

0

null

0

1

0

1

0

null

0

4

e2ecc142d94e8cd686e68136a36dac571c12b607

83

py

Python

main.py

Viole-Grace/Python_Sem_IV

de9dfe114888bfa4acbedbefaca91ffdce667a57

[ "MIT" ]

1

2019-03-09T23:12:08.000Z

main.py

Viole-Grace/Python_Sem_IV

de9dfe114888bfa4acbedbefaca91ffdce667a57

[ "MIT" ]

null

main.py

Viole-Grace/Python_Sem_IV

de9dfe114888bfa4acbedbefaca91ffdce667a57

[ "MIT" ]

null

import stack def input(word): stack.push(word) def remove(word): stack.pop(word)

13.833333

17

0.73494

14

83

4.357143

0.571429

0.295082

0

0.120482

83

5

18

16.6

0.835616

0

1

0.4

false

0

0.2

0

0.6

0

1

0

null

1

0

1

0

null

0

1

0

4

3943a85ac14b433951bde5bf3d036b654624d5d3

731

py

Python

app/config/queries.py

selutin99/moscow-books-recomendation-system

84aaa15a29b57a51bb241a2515e4bbceff5fae25

[ "Apache-2.0" ]

1

2022-01-03T15:25:15.000Z

app/config/queries.py

selutin99/moscow-books-recomendation-system

84aaa15a29b57a51bb241a2515e4bbceff5fae25

[ "Apache-2.0" ]

null

app/config/queries.py

selutin99/moscow-books-recomendation-system

84aaa15a29b57a51bb241a2515e4bbceff5fae25

[ "Apache-2.0" ]

null

class Queries: GET_BOOKS_COUNT = 'SELECT COUNT(*) FROM moscow_books.book;' GET_USER_HISTORY_BOOK_IDS = 'SELECT catalogueRecordID FROM moscow_books.books_issuance WHERE readerID=%s;' GET_BOOK = 'SELECT recId AS id, aut AS author, title FROM moscow_books.book WHERE recId=%s;' GET_BOOK_NEWCOMER = 'SELECT recId AS id, aut AS author, title FROM moscow_books.book LIMIT 10 OFFSET %s;' FIND_BOOK_COEFFICIENT = 'SELECT (place+publ+yea+lan+rubrics+person+serial+material+biblevel)/10 as summary FROM moscow_books.books_converted WHERE recId=%s;' FIND_NEAREST_BOOK = 'SELECT recId, (place+publ+yea+lan+rubrics+person+serial+material+biblevel)/10 as summary FROM moscow_books.books_converted LIMIT 100 OFFSET %s;'

73.1

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731

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1

0

null

0

1

0

4

1a5248489938637fdc7d3a3d5551d3a74ef65f15

23

py

Python

experiments/data/__init__.py

TheoryInPractice/practical-oct

e57119b26ca7e17d91f12a07cca55bf26e0b5aeb

[ "BSD-3-Clause" ]

4

2018-05-08T11:16:17.000Z

2020-03-23T11:53:05.000Z

experiments/data/__init__.py

TheoryInPractice/practical-oct

e57119b26ca7e17d91f12a07cca55bf26e0b5aeb

[ "BSD-3-Clause" ]

2

2020-03-23T14:53:14.000Z

2021-03-27T07:35:14.000Z

experiments/data/__init__.py

TheoryInPractice/practical-oct

e57119b26ca7e17d91f12a07cca55bf26e0b5aeb

[ "BSD-3-Clause" ]

null

"""Experiment data."""

11.5

22

0.608696

2

23

7

1

0

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23

1

23

0.666667

0.695652

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null

0

null

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null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

1a93b82d3664cb06d644e42ebc3e8a1bb861181b

161

py

Python

spree/rest/traversal/__init__.py

spreecode/python-spree-rest

877bd2c5dc8fc7efc6c04675939f5b389e5ffd24

[ "MIT" ]

null

spree/rest/traversal/__init__.py

spreecode/python-spree-rest

877bd2c5dc8fc7efc6c04675939f5b389e5ffd24

[ "MIT" ]

null

spree/rest/traversal/__init__.py

spreecode/python-spree-rest

877bd2c5dc8fc7efc6c04675939f5b389e5ffd24

[ "MIT" ]

null

from .endpoints import ( APIEndpoint, APIEntity, APICollection, APIAction ) from .views import TraversalResourceView from .fields import NodeRef

17.888889

40

0.745342

15

161

8

0.733333

0

0.204969

161

8

41

20.125

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0

1

0

true

0

0.375

0

0.375

0

1

0

null

0

1

0

null

0

1

0

1

0

4

1ace9e8f44f6c40af15e5d82ef8c83d068d86ba4

366

py

Python

inheritance_explorer/_testing.py

chrishavlin/inheritance_explorer

b72699a2c712f216531b84cd725d913a89bed683

[ "MIT" ]

null

inheritance_explorer/_testing.py

chrishavlin/inheritance_explorer

b72699a2c712f216531b84cd725d913a89bed683

[ "MIT" ]

null

inheritance_explorer/_testing.py

chrishavlin/inheritance_explorer

b72699a2c712f216531b84cd725d913a89bed683

[ "MIT" ]

null

class ClassForTesting: def use_this_func(self, a): return a class ClassForTesting2(ClassForTesting): def use_this_func(self, a): b = a * 10 return b class ClassForTesting3(ClassForTesting): pass class ClassForTesting4(ClassForTesting2): def use_this_func(self, a): b = a * 10 c = b + 10 return c

17.428571

41

0.625683

45

366

4.955556

0.355556

0.080717

0.134529

0.188341

0.426009

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0

0.038911

0.297814

366

20

42

18.3

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0

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1

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false

0.071429

0

0.071429

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null

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1

0

null

0

1

0

1

0

1

0

4

46bbcb0b621c9d3507a395d9a301b7cf89520b98

66

py

Python

api/folioman/__init__.py

Kk-ships/folioman

b9bb1782cf6a01ee9e438b5b2a41216036b4cc91

[ "MIT" ]

19

2021-05-10T15:13:28.000Z

2022-03-11T10:22:00.000Z

api/folioman/__init__.py

Kk-ships/folioman

b9bb1782cf6a01ee9e438b5b2a41216036b4cc91

[ "MIT" ]

5

2021-09-06T13:16:54.000Z

2022-02-14T18:16:02.000Z

api/folioman/__init__.py

Kk-ships/folioman

b9bb1782cf6a01ee9e438b5b2a41216036b4cc91

[ "MIT" ]

8

2021-05-29T11:02:26.000Z

2022-01-05T08:57:58.000Z

from taskman import app as celery_app __all__ = ("celery_app",)

13.2

37

0.742424

10

66

4.3

0.7

0.418605

0

0.166667

66

4

38

16.5

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0

1

0

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1

0

null

1

0

1

0

null

0

1

0

4

46d074c9522dc3d488c147a5534aa63a11f70514

283

py

Python

tera/__init__.py

Erik-BM/TERA

263a49e06b2b716eabf33366b9b148a14a4ec717

[ "MIT" ]

1

2020-03-06T11:49:45.000Z

tera/__init__.py

NIVA-Knowledge-Graph/TERA

263a49e06b2b716eabf33366b9b148a14a4ec717

[ "MIT" ]

null

tera/__init__.py

NIVA-Knowledge-Graph/TERA

263a49e06b2b716eabf33366b9b148a14a4ec717

[ "MIT" ]

null

# -*- coding: utf-8 -*- __version__ = '0.2.0' __doc__ = """ TERA : the Toxicological and Risk Assessment Knowledge Graph. The set of APIs enables aggregation, integration and access of several data sources relevant to the toxicological and risk assessment domain. """

35.375

146

0.710247

37

283

5.216216

0.756757

0.165803

0.196891

0.238342

0.341969

0

0.017699

0.201413

283

7

147

40.428571

0.836283

0.074205

0

0.2

0.865385

0

1

0

false

0

null

0

1

0

1

0

1

0

null

0

4

46ffe677179011b418b45730e29bdcf4a90baef3

331

py

Python

payoneer_mobile_api/apis/__init__.py

brainbeanapps/payoneer-mobile-api-python

bd26f3f6219ba6e3df36b86f7c6b6f83abb879c3

[ "MIT" ]

null

payoneer_mobile_api/apis/__init__.py

brainbeanapps/payoneer-mobile-api-python

bd26f3f6219ba6e3df36b86f7c6b6f83abb879c3

[ "MIT" ]

null

payoneer_mobile_api/apis/__init__.py

brainbeanapps/payoneer-mobile-api-python

bd26f3f6219ba6e3df36b86f7c6b6f83abb879c3

[ "MIT" ]

null

from __future__ import absolute_import # import apis into api package from .account_api import AccountApi from .app_api import AppApi from .authentication_api import AuthenticationApi from .balance_api import BalanceApi from .payment_api import PaymentApi from .transaction_api import TransactionApi from .user_api import UserApi

30.090909

49

0.858006

45

331

6.044444

0.488889

0.231618

0

0.117825

331

10

50

33.1

0.931507

0.084592

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

4

202eeaabd4c709dbf224a559a9633929b9d8398d

174

py

Python

tests/context.py

roycoding/thumbs-up-api

27326034fcd912464046c8d301b75ce252873115

[ "MIT" ]

3

2019-04-04T20:50:58.000Z

2019-04-05T13:23:43.000Z

tests/context.py

roycoding/thumbs-up-api

27326034fcd912464046c8d301b75ce252873115

[ "MIT" ]

10

2019-03-27T16:12:34.000Z

2019-04-16T21:03:16.000Z

tests/context.py

roycoding/thumbs-up-api

27326034fcd912464046c8d301b75ce252873115

[ "MIT" ]

2

2019-03-29T04:20:11.000Z

2019-12-14T22:05:39.000Z

#!/usr/bin/env python3 """Context for running tests""" import os import sys sys.path.insert( 0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "src")) )

17.4

76

0.666667

27

174

4.148148

0.703704

0.160714

0

0.013158

0.126437

174

9

77

19.333333

0.723684

0.270115

0

0.041322

0

1

0

true

0

0.4

0

0.4

0

1

0

null

0

1

0

null

0

1

0

1

0

4

20441cfae732225966618fdcce40e1c05245f6cc

225

py

Python

platformer/views/__init__.py

pythonarcade/community-platformer

81e8983358504b9a33249373f646f3ad89c5ab82

[ "MIT" ]

1

2021-08-18T04:04:23.000Z

platformer/views/__init__.py

pythonarcade/community-platformer

81e8983358504b9a33249373f646f3ad89c5ab82

[ "MIT" ]

null

platformer/views/__init__.py

pythonarcade/community-platformer

81e8983358504b9a33249373f646f3ad89c5ab82

[ "MIT" ]

2

2022-01-21T10:19:32.000Z

2022-01-26T18:35:50.000Z

from .view import View from .view_character_select import CharacterSelectView from .view_game import GameView from .view_game_over import GameOverView from .view_pause import PauseView from .view_settings import SettingsView

32.142857

54

0.866667

31

225

6.064516

0.451613

0.255319

0.12766

0

0.106667

225

6

55

37.5

0.935323

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

4

20593624c1280373e48d5ea355d1990189cdf2a5

110

py

Python

back_end/mlh/apps/verifications/constants.py

22014471/malonghui_Django

c9c2a68882450f9327e141333f30fdd73e530c28

[ "MIT" ]

1

2021-01-31T16:57:35.000Z

back_end/mlh/apps/verifications/constants.py

22014471/malonghui_Django

c9c2a68882450f9327e141333f30fdd73e530c28

[ "MIT" ]

null

back_end/mlh/apps/verifications/constants.py

22014471/malonghui_Django

c9c2a68882450f9327e141333f30fdd73e530c28

[ "MIT" ]

null

# 短信验证码有效期，单位秒 SMS_CODE_TIME = 5 * 60 # 发送短信验证码时间，单位秒 SEND_SMS_CODE_TIME = 60 # 短信验证码模板 SMS_CODE_TEMP_ID = 1

13.75

23

0.754545

20

110

3.75

0.65

0.28

0.293333

0

0.065217

0.163636

110

8

24

13.75

0.75

0.309091

0

1

0

false

0

1

0

null

1

0

1

0

null

0

4

647110642fc96dd8dfa62fda1e8569b3698cc96a

119

py

Python

openprocurement/schemas/dgf/exceptions.py

yevheniimoroziuk/openprocurement.schemas.dgf

431b1dec4c885c4634c0ec946401f8f6ccd52993

[ "Apache-2.0" ]

null

openprocurement/schemas/dgf/exceptions.py

yevheniimoroziuk/openprocurement.schemas.dgf

431b1dec4c885c4634c0ec946401f8f6ccd52993

[ "Apache-2.0" ]

4

2019-12-26T17:33:03.000Z

2022-03-21T22:18:06.000Z

openprocurement/schemas/dgf/exceptions.py

yevheniimoroziuk/openprocurement.schemas.dgf

431b1dec4c885c4634c0ec946401f8f6ccd52993

[ "Apache-2.0" ]

7

2017-01-26T17:19:20.000Z

2018-12-04T13:42:55.000Z

# -*- coding: utf-8 -*- class NotFoundSchema(Exception): """ When can't find schema raise exception """ pass

17

50

0.621849

14

119

5.285714

0.928571

0

0.010753

0.218487

119

6

51

19.833333

0.784946

0.521008

0

1

0

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

4

64a2f141a83c62d5ca5aee4f2fd47ef99e73c572

729

py

Python

core/serializers.py

henrylei80/customers-app-api

d8e889219cd2aa86de57b421c02a1ce34769c022

[ "MIT" ]

null

core/serializers.py

henrylei80/customers-app-api

d8e889219cd2aa86de57b421c02a1ce34769c022

[ "MIT" ]

null

core/serializers.py

henrylei80/customers-app-api

d8e889219cd2aa86de57b421c02a1ce34769c022

[ "MIT" ]

null

from rest_framework import serializers from .models import Customer, Profession, DataSheet, Document class CustomerSerializer(serializers.ModelSerializer): class Meta: model = Customer fields =( 'id','name', 'address', 'Professions', 'data_sheet') class ProfessionSerializer(serializers.ModelSerializer): class Meta: model = Profession fields =( 'id', 'description') class DataSheetSerializer(serializers.ModelSerializer): class Meta: model = DataSheet fields =( 'id', 'description', 'historical_data') class DocumentSerializer(serializers.ModelSerializer): class Meta: model = Document fields =( 'id', 'dtype', 'doc_number', 'customer')

24.3

70

0.685871

65

729

7.630769

0.461538

0.209677

0.25

0.282258

0.322581

0

0.207133

729

29

71

25.137931

0.858131

0

0.222222

0

0.137174

0

1

0

false

0

0.111111

0

0.555556

0

null

1

0

null

0

1

0

4

64ab1be95699b2234879b12b9c25e5e202f71bd9

142

py

Python

test/pytools/test_sample.py

terasakisatoshi/jldev_poetry

af81d469ad443f3876d110fc03c9ee1e22e20690

[ "MIT" ]

10

2021-12-13T15:47:30.000Z

2022-01-09T01:01:05.000Z

test/pytools/test_sample.py

terasakisatoshi/jldev_poetry

af81d469ad443f3876d110fc03c9ee1e22e20690

[ "MIT" ]

null

test/pytools/test_sample.py

terasakisatoshi/jldev_poetry

af81d469ad443f3876d110fc03c9ee1e22e20690

[ "MIT" ]

null

from pytools import sample def test_greet(): assert sample.greet() == "Hello World" def test_loop(): assert sample.loop(10) == 55

14.2

42

0.669014

20

142

4.65

0.65

0.150538

0

0.035398

0.204225

142

9

43

15.777778

0.787611

0

0.077465

0

0.4

1

0.4

true

0

0.2

0

0.6

0

1

0

null

0

1

0

null

0

1

0

4

64cc5a54305aece86e5c9f55c4132bfcb3763ef9

35

py

Python

constants.py

the7erm/ipfs-feed-translator

6d21c8e69fd104a9b7a22e98461f50947c92bf6e

[ "MIT" ]

1

2022-02-19T20:42:19.000Z

constants.py

the7erm/ipfs-feed-translator

6d21c8e69fd104a9b7a22e98461f50947c92bf6e

[ "MIT" ]

1

2021-06-01T22:36:34.000Z

constants.py

the7erm/ipfs-feed-translator

6d21c8e69fd104a9b7a22e98461f50947c92bf6e

[ "MIT" ]

null

HTTP_OK = 200 HTTP_PARTIAL = 206

7

18

0.714286

6

35

3.833333

0.833333

0

0.222222

0.228571

35

4

19

8.75

0.62963

0

1

0

false

0

1

0

null

0

1

0

1

0

null

0

4

b3db5b8fe411f73612336ce63a5981b8a153aff5

128

py

Python

database.py

Natwara2014/senior_project2

e2a9f5a16df74180dde4d9a21ac188e41abf8015

[ "Apache-2.0" ]

null

database.py

Natwara2014/senior_project2

e2a9f5a16df74180dde4d9a21ac188e41abf8015

[ "Apache-2.0" ]

null

database.py

Natwara2014/senior_project2

e2a9f5a16df74180dde4d9a21ac188e41abf8015

[ "Apache-2.0" ]

null

import mysql.connector as mysql db = mysql.connect(host = "localhost",user = "root", passwd = "", database = "seniorproject")

42.666667

93

0.695313

15

128

5.933333

0.866667

0

0.148438

128

3

93

42.666667

0.816514

0

0.204724

0

1

0

false

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

4

b3ef39da5b3e93f0573858c1bef261cbe2b0cd8e

6,423

py

Python

tests/server/test_process_minder.py

dskard/mitmmanager

623d5433ded06b44db0b4ab4b7f6318736ebbfb3

[ "MIT" ]

null

tests/server/test_process_minder.py

dskard/mitmmanager

623d5433ded06b44db0b4ab4b7f6318736ebbfb3

[ "MIT" ]

null

tests/server/test_process_minder.py

dskard/mitmmanager

623d5433ded06b44db0b4ab4b7f6318736ebbfb3

[ "MIT" ]

null

import pytest from manageritm.server.process_minder import ProcessMinder from subprocess import STDOUT, TimeoutExpired class TestProcessMinder: @pytest.fixture(scope="function", autouse=True) def setup(self, mocker): def mocked_open_log_files(self): pass # mock the pm._open_log_files function # so we don't create log files when running tests mocker.patch('manageritm.server.process_minder.ProcessMinder._open_log_files', mocked_open_log_files) def test_start(self, mocker): class MockedPopen: def __init__(self,command,**args): self.command = command self.args = args self.pid = 400 def poll(self): # set to 0 so __del__() succeeds self.returncode = 0 return self.returncode mocker.patch('manageritm.server.process_minder.Popen', MockedPopen) command = "my_command" pm = ProcessMinder(command) # call the start() function pm.start() # check that Popen was called with the correct args to start a process assert pm.process.command == command # this isnt true when we save stdout and stderr to log files assert pm.process.args["stdout"] == pm.log assert pm.process.args["stderr"] == STDOUT def test_stop_terminate(self, mocker): class MockedPopen: def __init__(self,command,**args): self.command = command self.args = args self.pid = 400 self.returncode = None def poll(self): # set to 0 so __del__() succeeds self.returncode = 0 return self.returncode def terminate(self): pass def wait(self,s=5): self.returncode = 0 def kill(self): self.returncode = 1 mocker.patch('manageritm.server.process_minder.Popen', MockedPopen) command = "my_command" pm = ProcessMinder(command) pm.start() # setup spies so we can count function calls terminate_spy = mocker.spy(pm.process, 'terminate') wait_spy = mocker.spy(pm.process, 'wait') kill_spy = mocker.spy(pm.process, 'kill') # call the stop() function pm.stop() # check that the terminate() and wait() functions were called, # check that kill() was not called assert pm.process.returncode == 0 assert terminate_spy.call_count == 1 assert wait_spy.call_count == 1 assert kill_spy.call_count == 0 def test_stop_kill(self, mocker): class MockedPopen: def __init__(self,command,**args): self.command = command self.args = args self.pid = 400 self.returncode = None self._raise_exception = True def poll(self): # set to 0 so __del__() succeeds self.returncode = 0 return self.returncode def terminate(self): pass def wait(self,s=5): if self._raise_exception is True: self._raise_exception = False self.returncode = 0 raise TimeoutExpired(cmd=self.command,timeout=s) else: self._raise_exception = True def kill(self): self.returncode = 1 mocker.patch('manageritm.server.process_minder.Popen', MockedPopen) command = "my_command" pm = ProcessMinder(command) pm.start() # setup spies so we can count function calls terminate_spy = mocker.spy(pm.process, 'terminate') wait_spy = mocker.spy(pm.process, 'wait') kill_spy = mocker.spy(pm.process, 'kill') # call the stop() function pm.stop() # check that the terminate(), wait() and kill() functions were called assert pm.process.returncode == 1 assert terminate_spy.call_count == 1 assert wait_spy.call_count == 2 assert kill_spy.call_count == 1 def test_status_process_not_started(self, mocker): command = "my_command" pm = ProcessMinder(command) # don't start the process # call the status() function status = pm.status() # check that the value returned by status is -1 assert status == -1 def test_status_process_running(self, mocker): class MockedPopen: def __init__(self,command,**args): self.command = command self.args = args self.pid = 400 self.returncode = -2 def poll(self): self.returncode = None return self.returncode def terminate(self): pass def wait(self,s=5): self.returncode = 0 def kill(self): self.returncode = 1 mocker.patch('manageritm.server.process_minder.Popen', MockedPopen) command = "my_command" pm = ProcessMinder(command) pm.start() # setup spies so we can count function calls poll_spy = mocker.spy(pm.process, 'poll') # call the stop() function pm.status() # check that the terminate(), wait() and kill() functions were called assert pm.process.returncode is None assert poll_spy.call_count == 1 def test_status_process_exited(self, mocker): class MockedPopen: def __init__(self,command,**args): self.command = command self.args = args self.pid = 400 self.returncode = -2 def poll(self): self.returncode = 0 return self.returncode mocker.patch('manageritm.server.process_minder.Popen', MockedPopen) command = "my_command" pm = ProcessMinder(command) pm.start() # setup spies so we can count function calls poll_spy = mocker.spy(pm.process, 'poll') # call the stop() function status = pm.status() # check that the poll() function is called assert status == 0 assert poll_spy.call_count == 1

30.879808

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0.142259

0.079704

0.027327

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0.74694

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0.029851

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null

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1

0

1

0

null

0

1

0

4

b3f9dd307e6f72a22f48f6f57e55f256543ed9a6

424

py

Python

emailapp/sql_helpers/email_subject.py

manisharmagarg/Email_Management

4241d3e0970558ea8a650b424a3cdb4b5a009149

[ "Apache-2.0" ]

null

emailapp/sql_helpers/email_subject.py

manisharmagarg/Email_Management

4241d3e0970558ea8a650b424a3cdb4b5a009149

[ "Apache-2.0" ]

null

emailapp/sql_helpers/email_subject.py

manisharmagarg/Email_Management

4241d3e0970558ea8a650b424a3cdb4b5a009149

[ "Apache-2.0" ]

null

from .database import Database class EmailSubjectHelper(Database): def __init__(self, *args): super(EmailSubjectHelper, self).__init__(*args) def create_email_subject(self, email_subject, preview_text, user_id): data = {"email_subject": email_subject, "preview_text": preview_text, "user_id": user_id} email_subject_id = self.insert('email_subject', data) return email_subject_id

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null

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1

0

1

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4

b6013a77fc290696e4bee07f4a4e0da88ca91ba3

217

py

Python

Lesson29/RSP/management/commands/update_doctor.py

IslamRaslambekov/HomeWork

1adb97cee4ada46fbcca3fa6c575cf43a4133ef2

[ "MIT" ]

null

Lesson29/RSP/management/commands/update_doctor.py

IslamRaslambekov/HomeWork

1adb97cee4ada46fbcca3fa6c575cf43a4133ef2

[ "MIT" ]

null

Lesson29/RSP/management/commands/update_doctor.py

IslamRaslambekov/HomeWork

1adb97cee4ada46fbcca3fa6c575cf43a4133ef2

[ "MIT" ]

null

from django.core.management.base import BaseCommand from RSP.models import Doctor class Command(BaseCommand): def handle(self, *args, **options): doctor = Doctor.objects.filter(id=1).update(name='Alex')

27.125

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null

0

1

0

1

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4

b60fa838951b89169610f82c4495350643b86665

1,639

py

Python

ci/__main__.py

chigozienri/bmp2sysex

43cf0aaea75b3f08e885c2b89140a1a2d9ef76fd

[ "MIT" ]

1

2022-01-08T17:05:22.000Z

ci/__main__.py

chigozienri/bmp2sysex

43cf0aaea75b3f08e885c2b89140a1a2d9ef76fd

[ "MIT" ]

null

ci/__main__.py

chigozienri/bmp2sysex

43cf0aaea75b3f08e885c2b89140a1a2d9ef76fd

[ "MIT" ]

1

2021-07-22T16:11:46.000Z

import subprocess import click @click.group() def cli(): pass @cli.command(name="test", help="Run the tests") def test(): cmd = [ "python", "-X", "faulthandler", "-m", "unittest", "discover", "--buffer", "-s", "tests", ] cproc = subprocess.run(cmd) rc = cproc.returncode if rc is not None and rc != 0: click.echo(cproc.stdout) click.echo(cproc.stderr) raise click.ClickException("Failed tests") @cli.command(name="flake8", help="Run flake8") def flake8(): """ Run flake8 on the codebase""" cmd = ["python", "-m", "flake8", "."] cproc = subprocess.run(cmd) rc = cproc.returncode if rc is not None and rc != 0: click.echo(cproc.stdout) click.echo(cproc.stderr) raise click.ClickException("Failed flake8") @cli.command(name="isort", help="Run isort") def black(): """ Run isort on the codebase""" cmd = ["python", "-m", "isort", "."] cproc = subprocess.run(cmd) rc = cproc.returncode if rc is not None and rc != 0: click.echo(cproc.stdout) click.echo(cproc.stderr) raise click.ClickException("Failed isort") @cli.command(name="flake8", help="Run flake8") def isort(): """ Run flake8 on the codebase""" cmd = ["python", "-m", "flake8", "."] cproc = subprocess.run(cmd) rc = cproc.returncode if rc is not None and rc != 0: click.echo(cproc.stdout) click.echo(cproc.stderr) raise click.ClickException("Failed flake8") if __name__ == "__main__": cli(prog_name="python -m ci")

23.084507

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0.012458

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1,639

70

52

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0.018868

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0

null

0

1

0

1

0

null

0

4

376cdb0bf6d962bda3134a8033b0edafed7eb83c

773

py

Python

trattoria/__init__.py

fdlm/trattoria

b6779799fe57d1da506802e6d8417e387f2d2a33

[ "MIT" ]

null

trattoria/__init__.py

fdlm/trattoria

b6779799fe57d1da506802e6d8417e387f2d2a33

[ "MIT" ]

null

trattoria/__init__.py

fdlm/trattoria

b6779799fe57d1da506802e6d8417e387f2d2a33

[ "MIT" ]

null

from . import data from . import iterators from . import nets from . import objectives from . import outputs from . import schedules from . import training __version__ = "0.1.dev0" import yaml import numpy as np def _yaml_rep_npfloat(self, val): return self.represent_float(val) def _yaml_rep_npint(self, val): return self.represent_int(val) yaml.add_representer(np.float, _yaml_rep_npfloat) yaml.add_representer(np.float16, _yaml_rep_npfloat) yaml.add_representer(np.float32, _yaml_rep_npfloat) yaml.add_representer(np.float64, _yaml_rep_npfloat) yaml.add_representer(np.int, _yaml_rep_npint) yaml.add_representer(np.int16, _yaml_rep_npint) yaml.add_representer(np.int32, _yaml_rep_npint) yaml.add_representer(np.int64, _yaml_rep_npint) del yaml del np

21.472222

51

0.803364

121

773

4.768595

0.297521

0.121317

0.249567

0.277296

0.492201

0.40208

0

0.021834

0.111255

773

35

52

22.085714

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0.375

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0.541667

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null

0

1

0

null

0

1

0

1

0

4

378c73282accd866454033b066e69bb1a026cfb9

58

py

Python

hacknehs/__init__.py

jeffreyzpan/hacknehs

f9e5cfb425712092db68c3020d8e947cace15cae

[ "MIT" ]

null

hacknehs/__init__.py

jeffreyzpan/hacknehs

f9e5cfb425712092db68c3020d8e947cace15cae

[ "MIT" ]

4

2020-10-02T03:38:31.000Z

2020-11-14T02:20:48.000Z

hacknehs/__init__.py

jeffreyzpan/hacknehs

f9e5cfb425712092db68c3020d8e947cace15cae

[ "MIT" ]

2

2020-10-02T03:27:57.000Z

2020-10-02T03:36:40.000Z

import webbrowser webbrowser.open("http://hacknehs.org")

14.5

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6.428571

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58

3

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19.333333

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1

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null

0

1

0

1

0

4

37baff8332e3bf4a043fc95f1b65cc6e9b5f665d

128

py

Python

models/plot.py

WesBAn/explicit_and_implicit_schemas

b2f6026d527e8a419d580b830c21d363a1b0e6b9

[ "MIT" ]

null

models/plot.py

WesBAn/explicit_and_implicit_schemas

b2f6026d527e8a419d580b830c21d363a1b0e6b9

[ "MIT" ]

null

models/plot.py

WesBAn/explicit_and_implicit_schemas

b2f6026d527e8a419d580b830c21d363a1b0e6b9

[ "MIT" ]

null

import dataclasses import numpy as np @dataclasses.dataclass class PlotData: x: np.array t: np.array u: np.array

11.636364

22

0.695313

19

128

4.684211

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0

0.234375

128

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true

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null

1

0

null

0

1

0

1

0

4

806d85724f5d4c7f7e07749fd60db1a606712430

72

py

Python

shellprofile.py

lambdamusic/dice

37f824a4ae8e11f4d2e95d80e071b39184842a85

[ "MIT" ]

null

shellprofile.py

lambdamusic/dice

37f824a4ae8e11f4d2e95d80e071b39184842a85

[ "MIT" ]

null

shellprofile.py

lambdamusic/dice

37f824a4ae8e11f4d2e95d80e071b39184842a85

[ "MIT" ]

null

# startup file for ipython # $ ipython profile.py -i from dice import *

18

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72

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0.909091

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0.194444

72

4

27

18

0.896552

0.666667

0

1

0

true

0

1

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1

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null

0

1

0

1

0

4

8077f39e713a836e3b0c1b98efe5a7ac3fbb35ff

656

py

Python

cvnets/layers/base_layer.py

KelOdgSmile/ml-cvnets

503ec3b4ec187cfa0ed451d0f61de22f669b0081

[ "AML" ]

1

2021-12-20T09:25:18.000Z

cvnets/layers/base_layer.py

footh/ml-cvnets

d9064fe7e7a2d6a7a9817df936432856a0500a25

[ "AML" ]

null

cvnets/layers/base_layer.py

footh/ml-cvnets

d9064fe7e7a2d6a7a9817df936432856a0500a25

[ "AML" ]

null

# # For licensing see accompanying LICENSE file. # Copyright (C) 2020 Apple Inc. All Rights Reserved. # from torch import nn, Tensor import argparse from typing import Tuple class BaseLayer(nn.Module): def __init__(self, *args, **kwargs): super(BaseLayer, self).__init__() @classmethod def add_arguments(cls, parser: argparse.ArgumentParser): return parser def forward(self, *args, **kwargs) -> Tensor or Tuple[Tensor]: pass def profile_module(self, *args, **kwargs) -> (Tensor, float, float): raise NotImplementedError def __repr__(self): return "{}".format(self.__class__.__name__)

24.296296

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656

26

73

25.230769

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0.2

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0.733333

0

null

0

null

0

1

0

1

0

1

0

4

80795b4f9eada53d0dfc64c8ca491f005a690db1

120

py

Python

collective/computedfield/__init__.py

collective/collective.computedfield

057502953ace3667c76c941d7f1bc666537f2b02

[ "MIT" ]

null

collective/computedfield/__init__.py

collective/collective.computedfield

057502953ace3667c76c941d7f1bc666537f2b02

[ "MIT" ]

null

collective/computedfield/__init__.py

collective/collective.computedfield

057502953ace3667c76c941d7f1bc666537f2b02

[ "MIT" ]

null

from zope.i18nmessageid import MessageFactory ComputedFieldMessageFactory = MessageFactory('collective.computedfield')

30

72

0.875

9

120

11.666667

0.888889

0

0.017857

0.066667

120

3

73

40

0.919643

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0.2

0

1

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false

0

0.5

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0.5

0

1

0

1

null

0

1

0

null

0

1

0

4

8091dd551509c66a4b25430dc1610a2ecf6dbe74

75

py

Python

src/hera_display_games/core/__init__.py

loco-lab/HERA-Display-Games

12d7d21d3478304ef87fe9dd24dad29bdc9f144b

[ "MIT" ]

null

src/hera_display_games/core/__init__.py

loco-lab/HERA-Display-Games

12d7d21d3478304ef87fe9dd24dad29bdc9f144b

[ "MIT" ]

15

2019-12-19T22:56:55.000Z

2020-02-21T16:04:24.000Z

src/hera_display_games/core/__init__.py

loco-lab/hera-display-games

12d7d21d3478304ef87fe9dd24dad29bdc9f144b

[ "MIT" ]

null

""" Core routines for controlling the display board with a controller. """

18.75

66

0.746667

10

75

5.6

1

0

0.16

75

3

67

25

0.888889

0.88

0

null

0

null

0

null

0

null

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null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

4

809bc36356aad6d00cc08f8eb5808a04ec4a1d41

479

py

Python

07_DoublingNumbers/test_DoublingNumbers.py

knishina/python_recursion

20c5c2bed37e7f2edc8838e4e633ff62255b8aa1

[ "MIT" ]

null

07_DoublingNumbers/test_DoublingNumbers.py

knishina/python_recursion

20c5c2bed37e7f2edc8838e4e633ff62255b8aa1

[ "MIT" ]

null

07_DoublingNumbers/test_DoublingNumbers.py

knishina/python_recursion

20c5c2bed37e7f2edc8838e4e633ff62255b8aa1

[ "MIT" ]

null

from DoublingNumbers import rice, rice_r # Test out the rice function. def test_rice_0(): assert (rice(0) == 0) def test_rice_1(): assert (rice(1) == 1) def test_rice_2(): assert (rice(2) == 3) def test_rice_4(): assert (rice(4) == 15) # Test out the rice_r function. def test_ricer_0(): assert (rice_r(0) == 0) def test_ricer_1(): assert (rice_r(1) == 1) def test_ricer_2(): assert (rice_r(2) == 3) def test_ricer_4(): assert (rice_r(4) == 15)

21.772727

40

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479

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479

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null

0

1

0

1

0

1

0

4

80bb5c0a928d6317a5b4df536226ac9e97bf3ee9

344

py

Python

tests/MathTests/euler.py

FabricExile/Kraken

d8c1f5189cb191945e2c18a1369c458d05305afc

[ "BSD-3-Clause" ]

7

2017-12-04T16:57:42.000Z

2021-09-07T07:02:38.000Z

tests/MathTests/euler.py

xtvjxk123456/Kraken

d8c1f5189cb191945e2c18a1369c458d05305afc

[ "BSD-3-Clause" ]

null

tests/MathTests/euler.py

xtvjxk123456/Kraken

d8c1f5189cb191945e2c18a1369c458d05305afc

[ "BSD-3-Clause" ]

6

2017-11-14T06:50:48.000Z

2021-08-21T22:47:29.000Z

import json from kraken.core.maths import * euler = Euler() print "euler:" + str(euler) print "mat33:" + str(euler.toMat33()) euler = Euler(1.0, 0.0, 2.0, 'ZYX'); print "euler:" + str(euler) print "mat33:" + str(euler.toMat33()) print "clone:" + str(euler.clone()) euler = Euler(1.0, 0.0, 2.0, RotationOrder()); print "euler:" + str(euler)

21.5

46

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54

344

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1

0

null

0

1

0

1

0

4

80d07be350d7de5930e10f6c3cbb9b1a5ce32341

196

py

Python

mmdet/models/necks/__init__.py

Lechatelia/own_mmdet

eac5db1d1bee8eafe0ed46fa4bb61ca8605b502f

[ "Apache-2.0" ]

24

2021-10-14T03:28:28.000Z

2022-03-29T09:30:04.000Z

mmdet/models/necks/__init__.py

Lechatelia/own_mmdet

eac5db1d1bee8eafe0ed46fa4bb61ca8605b502f

[ "Apache-2.0" ]

4

2021-12-14T15:04:49.000Z

2022-02-19T09:54:42.000Z

mmdet/models/necks/__init__.py

Lechatelia/own_mmdet

eac5db1d1bee8eafe0ed46fa4bb61ca8605b502f

[ "Apache-2.0" ]

4

2021-10-31T11:23:06.000Z

2021-12-17T06:38:50.000Z

from .bfp import BFP from .fpn import FPN from .fpn_carafe import FPN_CARAFE from .hrfpn import HRFPN from .nas_fpn import NASFPN __all__ = ['FPN', 'BFP', 'HRFPN', 'NASFPN', 'FPN_CARAFE']

24.5

58

0.709184

30

196

4.366667

0.3

0.206107

0

0.178571

196

7

59

28

0.813665

0

0.142857

0

1

0

false

0

0.833333

0

0.833333

0

null

1

0

1

0

null

0

1

0

1

0

4

80f4fe1190385dd557331d0fc5d8c4f51b864228

659

py

Python

investing_algorithm_framework/views/__init__.py

coding-kitties/investing-algorithm-framework

1156acf903345ec5e6787ee8767c68e24c4daffd

[ "Apache-2.0" ]

9

2020-09-14T13:46:32.000Z

2022-02-01T15:40:12.000Z

investing_algorithm_framework/views/__init__.py

coding-kitties/investing-algorithm-framework

1156acf903345ec5e6787ee8767c68e24c4daffd

[ "Apache-2.0" ]

44

2020-12-28T16:22:20.000Z

2022-03-23T22:11:26.000Z

investing_algorithm_framework/views/__init__.py

coding-kitties/investing-algorithm-framework

1156acf903345ec5e6787ee8767c68e24c4daffd

[ "Apache-2.0" ]

2

2020-12-25T06:14:39.000Z

2022-01-19T19:00:20.000Z

from investing_algorithm_framework import current_app from .operational_views import blueprint as operational_views_blueprint from investing_algorithm_framework.views.order_views import blueprint \ as order_views_blueprint from investing_algorithm_framework.views.position_views import blueprint \ as position_views_blueprint from investing_algorithm_framework.views.portfolio_views import blueprint \ as portfolio_views_blueprint app = current_app app.register_blueprint(operational_views_blueprint) app.register_blueprint(order_views_blueprint) app.register_blueprint(position_views_blueprint) app.register_blueprint(portfolio_views_blueprint)

43.933333

75

0.887709

82

659

6.719512

0.182927

0.203267

0.15971

0.225045

0.45735

0.272232

0

0.07739

659

14

76

47.071429

0.90625

0

1

0

false

0

0.384615

0

0.384615

0.846154

0

null

1

0

1

0

null

0

1

0

1

0

4

038a4e2425643785cb2f571d803b9a54b8153fdf

1,284

py

Python

beamit/resources/password.py

ksweta/BeamIt-Server

0678bab9fce6427c5af45c85e24d851ccd5fbdfb

[ "Apache-2.0" ]

null

beamit/resources/password.py

ksweta/BeamIt-Server

0678bab9fce6427c5af45c85e24d851ccd5fbdfb

[ "Apache-2.0" ]

null

beamit/resources/password.py

ksweta/BeamIt-Server

0678bab9fce6427c5af45c85e24d851ccd5fbdfb

[ "Apache-2.0" ]

null

from beamit.resources.base import Resource class PasswordChangeRequest(Resource): MEDIA_TYPE = 'application/vnd.beamit.password.change.request+json' def __init__(self, email, password, new_password): self.email = email self.password = password self.new_password = new_password def __repr__(self): return "<PasswordChangeRequest email: {}, password: {}, new_password: {}>".format( self.email, self.password, self.new_password, ) def to_dict(self): return dict(email=self.email, password=self.password, new_password=self.new_password) @classmethod def from_dict(cls, dct): return cls( email=dct.get("email"), password=dct.get("password"), new_password=dct.get("new_password"), ) class PasswordChangeResponse(Resource): MEDIA_TYPE = 'application/vnd.beamit.password.change.response+json' def __init__(self, user_id): self.user_id = user_id def __repr__(self): return "<PasswordChangeResponse user_id: {}>".format(self.user_id) def to_dict(self): return dict(user_id=self.user_id) @classmethod def from_dict(cls, dct): return cls(user_id=dct.get("user_id"))

26.75

93

0.64486

150

1,284

5.253333

0.226667

0.125635

0.120558

0.087563

0.322335

0.281726

0.22335

0

0.241433

1,284

47

94

27.319149

0.809035

0

0.242424

0

0.183801

0.115265

0

1

0.242424

false

0.424242

0.030303

0.181818

0.575758

0

null

0

null

0

1

0

1

0

1

0

4

03960c4a39534f4210baefe5cb3b5712997531ca

136

py

Python

Tests/magnetorquer.py

Rogozin-high-school/sat_monitoring_system

d73a7c1db26c9e5ac61982609ce155c20d14da51

[ "Apache-2.0" ]

4

2017-11-22T12:34:43.000Z

2018-09-08T19:02:32.000Z

Tests/magnetorquer.py

Rogozin-high-school/sat_monitoring_system

d73a7c1db26c9e5ac61982609ce155c20d14da51

[ "Apache-2.0" ]

43

2017-11-19T16:19:56.000Z

2022-01-12T23:02:54.000Z

Tests/magnetorquer.py

Rogozin-high-school/sat_monitoring_system

d73a7c1db26c9e5ac61982609ce155c20d14da51

[ "Apache-2.0" ]

1

2018-03-08T10:56:56.000Z

from ..Modules.Magnetorquer.hBridge import hBridge a = hBridge(input(), input(), input(), input()) while True: a.SetDirection(input())

27.2

50

0.720588

17

136

5.764706

0.588235

0.306122

0

0.102941

136

5

51

27.2

0.803279

0

1

0

false

0

0.25

0

0.25

0

1

0

null

1

0

1

0

null

0

4

03c35451804250faa0a245b31c19827c17201c05

464

py

Python

agents/agent.py

TomMakkink/transformers-for-rl

9d025f92611e957004030af9ef05a07e320856a7

[ "MIT" ]

1

2022-03-09T20:44:27.000Z

agents/agent.py

TomMakkink/transformers-for-rl

9d025f92611e957004030af9ef05a07e320856a7

[ "MIT" ]

null

agents/agent.py

TomMakkink/transformers-for-rl

9d025f92611e957004030af9ef05a07e320856a7

[ "MIT" ]

null

from abc import ABC, abstractmethod class Agent(ABC): def __init__(self, state_size, action_size, hidden_size, memory, **kwargs): super(Agent, self).__init__() @abstractmethod def optimize_network(self): pass @abstractmethod def act(self, state): pass @abstractmethod def collect_experience(self, state, action, reward, next_state, done): pass @abstractmethod def reset(self): pass

20.173913

79

0.650862

52

464

5.538462

0.5

0.236111

0.21875

0

0.258621

464

22

80

21.090909

0.837209

0

0.5

0

1

0.3125

false

0.25

0.0625

0

0.4375

0

null

1

0

null

0

1

0

1

0

4

ff0d1a36c138438be7941914080a9d945b688c55

94

py

Python

blogapp/apps.py

finebrush/takeatripsFB

85a5be1a2ee68531f04f2601a3f69ddc608d4d27

[ "BSD-3-Clause" ]

1

2021-04-06T15:10:09.000Z

blogapp/apps.py

NitinPSingh/blogprojlive

769685f22218d31b8eb2195d65d9c3c351e02772

[ "MIT" ]

13

2020-02-12T03:05:15.000Z

2022-02-10T14:26:50.000Z

blogapp/apps.py

NitinPSingh/blogprojlive

769685f22218d31b8eb2195d65d9c3c351e02772

[ "MIT" ]

null

from django.apps import AppConfig class BlogappConfig(AppConfig): name = 'blogapp'

15.666667

34

0.712766

10

94

6.7

0.9

0

0.212766

94

5

35

18.8

0.905405

0

0.078652

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

ff100b1084f8f13622982e56a46ab966432a9a18

68

py

Python

main_block/game.py

ramalho/arcade_tutorial

0950e8c1694f5b35cda8a7e268609c8cbd921481

[ "Apache-2.0" ]

7

2017-02-18T06:07:23.000Z

2021-03-11T09:07:28.000Z

docs/main_block/game.py

pauleveritt/arcade_tutorial

eb95c00e806f0cc909fd3c0af60db809f1b1291a

[ "Apache-2.0" ]

3

2021-06-08T18:51:19.000Z

2022-01-13T00:31:40.000Z

arcade_setup/game.py

pauleveritt/visual_debugging_games

cdf766e50f7f47b3638f8abfbf0dbfdd50fcdd25

[ "Apache-2.0" ]

3

2019-02-24T20:10:28.000Z

2020-08-14T18:51:00.000Z

import arcade if __name__ == '__main__': print(arcade.RELEASE)

13.6

26

0.705882

8

68

5

0.875

0

0.176471

68

4

27

17

0.714286

0

0.117647

0

1

0

true

0

0.333333

0

0.333333

1

0

null

0

1

0

null

0

1

0

1

0

4

2075481955f911d8a900c906c0edf0b1268028f8

592

py

Python

fundamentus/fundamentus.py

alardosa/python-for-finance

6ced4191a9a54befe463885f79178750eb71eb89

[ "MIT" ]

23

2017-06-27T21:03:33.000Z

2021-11-23T02:32:57.000Z

fundamentus/fundamentus.py

alardosa/python-for-finance

6ced4191a9a54befe463885f79178750eb71eb89

[ "MIT" ]

null

fundamentus/fundamentus.py

alardosa/python-for-finance

6ced4191a9a54befe463885f79178750eb71eb89

[ "MIT" ]

17

2017-07-12T09:46:37.000Z

2021-09-20T13:11:33.000Z

import requests BASE_URL = "http://fundamentus.com.br/" """ >>> get_stock_url('ITSA3') 'http://fundamentus.com.br/detalhes.php?papel=ITSA3' """ def get_stock_url(stock): return "{}detalhes.php?papel={}".format(BASE_URL, stock) """ >>> get_base_url() "http://fundamentus.com.br/" """ def get_base_url(): return "http://fundamentus.com.br/" def get_stocks(): with open("fundamentus.txt", "r") as fundamentus_file: stocks = fundamentus_file.read().split() return stocks def download_stock_html(stock_url): req = requests.get(stock_url) return req.content

19.733333

60

0.680743

81

592

4.765432

0.37037

0.072539

0.186529

0.207254

0.222798

0

0.003929

0.140203

592

29

61

20.413793

0.75442

0

0.202673

0.051225

0

1

0.307692

false

0

0.076923

0.153846

0.692308

0

null

0

1

0

null

0

1

0

1

0

4

20b15684a1dae7d2bc412f5462f35c791808ad66

10,755

py

Python

avod/builders/avod_corr_layers_builder.py

Guoxs/DODT

f354cda6ef08465018fdeec1a8b4be4002e6a71f

[ "MIT" ]

1

2021-09-01T00:34:17.000Z

avod/builders/avod_corr_layers_builder.py

Guoxs/DODT

f354cda6ef08465018fdeec1a8b4be4002e6a71f

[ "MIT" ]

null

avod/builders/avod_corr_layers_builder.py

Guoxs/DODT

f354cda6ef08465018fdeec1a8b4be4002e6a71f

[ "MIT" ]

null

import tensorflow as tf from tensorflow.contrib import slim from avod.core.avod_fc_layers import avod_fc_layer_utils def build(layers_config, input_rois, is_training): """Builds second stage fully connected layers Args: layers_config: Configuration object bev_rois: List of input corr ROI feature maps box_rep: Box representation (e.g. 'box_3d', 'box_8c', etc.) is_training (bool): Whether the network is training or evaluating Returns: corr_out: correlation feature output """ with tf.variable_scope('corr_predictor'): fc_layers_type = layers_config.WhichOneof('fc_layers') if fc_layers_type == 'basic_fc_layers': corr_layers_config = layers_config.basic_fc_layers corr_out = basic_corr_layers( corr_layers_config=corr_layers_config, input_rois=input_rois, is_training=is_training) elif fc_layers_type == 'fusion_fc_layers': corr_layers_config = layers_config.fusion_fc_layers corr_out = fusion_corr_layers( corr_layers_config=corr_layers_config, input_rois=input_rois, is_training=is_training) else: raise ValueError('Invalid fc layers config') return corr_out def basic_corr_layers(corr_layers_config, input_rois, is_training): num_layers = corr_layers_config.num_layers layer_sizes = corr_layers_config.layer_sizes l2_weight_decay = corr_layers_config.l2_weight_decay keep_prob = corr_layers_config.keep_prob if not num_layers == len(layer_sizes): raise ValueError('num_layers does not match length of layer_sizes') if l2_weight_decay > 0: weights_regularizer = slim.l2_regularizer(l2_weight_decay) else: weights_regularizer = None with slim.arg_scope([slim.fully_connected], weights_regularizer=weights_regularizer): # Flatten fc_drop = slim.flatten(input_rois, scope='corr_flatten') for layer_idx in range(num_layers): fc_name_idx = 6 + layer_idx # Use conv2d instead of fully_connected layers. fc_layer = slim.fully_connected(fc_drop, layer_sizes[layer_idx], scope='corr_fc{}'.format(fc_name_idx)) fc_drop = slim.dropout(fc_layer, keep_prob=keep_prob, is_training=is_training, scope='corr_fc{}_drop'.format(fc_name_idx)) fc_name_idx += 1 # [delta_x, delta_z, delta_theta] corr_out_size = 3 corr_out = slim.fully_connected(fc_drop, corr_out_size, activation_fn=None, scope='off_out') return corr_out def fusion_corr_layers(corr_layers_config, input_rois, is_training): # Parse configs fusion_type = corr_layers_config.fusion_type fusion_method = corr_layers_config.fusion_method num_layers = corr_layers_config.num_layers layer_sizes = corr_layers_config.layer_sizes l2_weight_decay = corr_layers_config.l2_weight_decay keep_prob = corr_layers_config.keep_prob if not len(layer_sizes) == num_layers: raise ValueError('Length of layer_sizes does not match num_layers') if fusion_type == 'early': corr_out = _early_fusion_fc_layers(num_layers=num_layers, layer_sizes=layer_sizes, input_rois=input_rois, l2_weight_decay=l2_weight_decay, keep_prob=keep_prob, is_training=is_training) # elif fusion_type == 'late': # corr_out = _late_fusion_fc_layers(num_layers=num_layers, # layer_sizes=layer_sizes, # input_rois=input_rois, # input_weights=input_weights, # fusion_method=fusion_method, # l2_weight_decay=l2_weight_decay, # keep_prob=keep_prob, # is_training=is_training) # elif fusion_type == 'deep': # corr_out = _deep_fusion_fc_layers(num_layers=num_layers, # layer_sizes=layer_sizes, # input_rois=input_rois, # input_weights=input_weights, # fusion_method=fusion_method, # l2_weight_decay=l2_weight_decay, # keep_prob=keep_prob, # is_training=is_training) else: raise ValueError('Invalid fusion type {}'.format(fusion_type)) return corr_out def _early_fusion_fc_layers(num_layers, layer_sizes, input_rois, l2_weight_decay, keep_prob, is_training): if not num_layers == len(layer_sizes): raise ValueError('num_layers does not match length of layer_sizes') if l2_weight_decay > 0: weights_regularizer = slim.l2_regularizer(l2_weight_decay) else: weights_regularizer = None # Flatten fc_drop = slim.flatten(input_rois) with slim.arg_scope([slim.fully_connected], weights_regularizer=weights_regularizer): for layer_idx in range(num_layers): fc_name_idx = 6 + layer_idx # Use conv2d instead of fully_connected layers. fc_layer = slim.fully_connected(fc_drop, layer_sizes[layer_idx], scope='fc{}'.format(fc_name_idx)) # fc_layer = slim.conv2d(fc_drop, layer_sizes[layer_idx], # [1, 1], scope='fc{}'.format(fc_name_idx)) fc_drop = slim.dropout( fc_layer, keep_prob=keep_prob, is_training=is_training, scope='fc{}_drop'.format(fc_name_idx)) fc_name_idx += 1 # correlation out # [delta_x, delta_z, delta_theta] corr_out_size = 3 corr_out = slim.fully_connected(fc_drop, corr_out_size, activation_fn=None, scope='off_out') return corr_out def _late_fusion_fc_layers(num_layers, layer_sizes, input_rois, input_weights, fusion_method, l2_weight_decay, keep_prob, is_training): if l2_weight_decay > 0: weights_regularizer = slim.l2_regularizer(l2_weight_decay) else: weights_regularizer = None # Build fc layers, one branch per input num_branches = len(input_rois) branch_outputs = [] with slim.arg_scope( [slim.fully_connected], weights_regularizer=weights_regularizer): for branch_idx in range(num_branches): # Branch feature ROIs branch_rois = input_rois[branch_idx] fc_drop = slim.flatten(branch_rois, scope='br{}_flatten'.format(branch_idx)) for layer_idx in range(num_layers): fc_name_idx = 6 + layer_idx # Use conv2d instead of fully_connected layers. fc_layer = slim.fully_connected( fc_drop, layer_sizes[layer_idx], scope='br{}_fc{}'.format(branch_idx, fc_name_idx)) fc_drop = slim.dropout( fc_layer, keep_prob=keep_prob, is_training=is_training, scope='br{}_fc{}_drop'.format(branch_idx, fc_name_idx)) branch_outputs.append(fc_drop) # Feature fusion fused_features = avod_fc_layer_utils.feature_fusion(fusion_method, branch_outputs, input_weights) # correlation out # [delta_x, delta_y, delta_z, delta_theta] corr_out_size = 4 corr_out = slim.fully_connected(fused_features, corr_out_size, activation_fn=None, scope='off_out') return corr_out def _deep_fusion_fc_layers(num_layers, layer_sizes, input_rois, input_weights, fusion_method, l2_weight_decay, keep_prob, is_training): if l2_weight_decay > 0: weights_regularizer = slim.l2_regularizer(l2_weight_decay) else: weights_regularizer = None # Apply fusion fusion_layer = avod_fc_layer_utils.feature_fusion(fusion_method, input_rois, input_weights) fusion_layer = slim.flatten(fusion_layer, scope='flatten') with slim.arg_scope( [slim.fully_connected], weights_regularizer=weights_regularizer): # Build layers for layer_idx in range(num_layers): fc_name_idx = 6 + layer_idx all_branches = [] for branch_idx in range(len(input_rois)): fc_layer = slim.fully_connected( fusion_layer, layer_sizes[layer_idx], scope='br{}_fc{}'.format(branch_idx, fc_name_idx)) fc_drop = slim.dropout( fc_layer, keep_prob=keep_prob, is_training=is_training, scope='br{}_fc{}_drop'.format(branch_idx, fc_name_idx)) all_branches.append(fc_drop) # Apply fusion fusion_layer = avod_fc_layer_utils.feature_fusion(fusion_method, all_branches, input_weights) # correlation out # [delta_x, delta_y, delta_z, delta_theta] corr_out_size = 4 corr_out = slim.fully_connected(fusion_layer, corr_out_size, activation_fn=None, scope='off_out') return corr_out

39.395604

89

0.548303

1,157

10,755

4.671564

0.109767

0.048104

0.050509

0.033303

0.781869

0.758372

0.739315

0.709343

0.686401

0.66198

0

0.007143

0.388192

10,755

273

90

39.395604

0.814286

0.187355

0

0.670659

0

0.044668

0

1

0.035928

false

0

0.017964

0

0.08982

0

null

0

1

0

1

0

null

0

4

20ca02e3a92a2d5570286d46247ba4a8e7abe79e

397

py

Python

main/models.py

swastikanata/sbf-be-3

11944cf3c55481a61ca81ac6d32c5a9ad99a9d6d

[ "Unlicense" ]

null

main/models.py

swastikanata/sbf-be-3

11944cf3c55481a61ca81ac6d32c5a9ad99a9d6d

[ "Unlicense" ]

null

main/models.py

swastikanata/sbf-be-3

11944cf3c55481a61ca81ac6d32c5a9ad99a9d6d

[ "Unlicense" ]

null

from django.db import models # Create your models here. class Film(models.Model): title = models.CharField(max_length=200) poster = models.CharField(max_length=200) trailer = models.CharField(max_length=200) genre = models.CharField(max_length=200) year_released = models.IntegerField() likes = models.IntegerField(default=0) dislikes = models.IntegerField(default=0)

33.083333

46

0.743073

51

397

5.686275

0.509804

0.206897

0.248276

0.331034

0.372414

0

0.041667

0.153652

397

11

47

36.090909

0.821429

0.060453

0

1

0

false

0

0.111111

0

1

0

null

1

0

null

0

1

0

4

20db15ebb649367f8d22812704736e78ec932d89

106

py

Python

spi_translations/apps.py

Swiss-Polar-Institute/rdmo-app

d4f5288fa173726a6ac0cf629f0f6d1c91ee856c

[ "Apache-2.0" ]

null

spi_translations/apps.py

Swiss-Polar-Institute/rdmo-app

d4f5288fa173726a6ac0cf629f0f6d1c91ee856c

[ "Apache-2.0" ]

16

2021-04-15T14:55:37.000Z

2021-11-02T13:10:47.000Z

spi_translations/apps.py

Swiss-Polar-Institute/rdmo-app

d4f5288fa173726a6ac0cf629f0f6d1c91ee856c

[ "Apache-2.0" ]

null

from django.apps import AppConfig class SpiTranslationsConfig(AppConfig): name = 'spi_translations'

17.666667

39

0.792453

11

106

7.545455

0.909091

0

0.141509

106

5

40

21.2

0.912088

0

0.150943

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

20e5040bdf449210358cd55d58442df840115f0a

663

py

Python

tests/descriptor_util_classes/test_descriptor_file_helper.py

brighthive/data-resource-api

a012fc0743f1ce2b72ddacf348c57adf44245cfa

[ "MIT" ]

4

2019-02-14T01:07:54.000Z

2019-11-04T17:28:35.000Z

tests/descriptor_util_classes/test_descriptor_file_helper.py

brighthive/data-resource-api

a012fc0743f1ce2b72ddacf348c57adf44245cfa

[ "MIT" ]

39

2019-05-30T22:08:46.000Z

2022-02-17T02:47:00.000Z

tests/descriptor_util_classes/test_descriptor_file_helper.py

brighthive/data-resource-api

a012fc0743f1ce2b72ddacf348c57adf44245cfa

[ "MIT" ]

1

2020-04-29T18:16:20.000Z

from tests.schemas import frameworks_descriptor import pytest from data_resource_api.app.utils.descriptor import DescriptorsFromDirectory from expects import equal, expect @pytest.mark.skip def test_check_if_path_exists(self): # expect it to raise an error when given a directory that doesnt exist # expect it not to raise an error when given a real directory pass @pytest.mark.skip def test_get_only_json_files(self): # helper = DescriptorsFromDirectory.__class__(fake_self, test_dir) # expect(fake_self.schemas).to(equal(['invalid_json.json', 'valid_json.json'])) pass @pytest.mark.skip def test_returns_correctly(self): pass

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45526aa54676e08896fae2b52581a5567e5fc41e

411

py

Python

codes_/0893_Groups_of_SpecialEquivalent_Strings.py

SaitoTsutomu/leetcode

4656d66ab721a5c7bc59890db9a2331c6823b2bf

[ "MIT" ]

null

codes_/0893_Groups_of_SpecialEquivalent_Strings.py

SaitoTsutomu/leetcode

4656d66ab721a5c7bc59890db9a2331c6823b2bf

[ "MIT" ]

null

codes_/0893_Groups_of_SpecialEquivalent_Strings.py

SaitoTsutomu/leetcode

4656d66ab721a5c7bc59890db9a2331c6823b2bf

[ "MIT" ]

null

# %% [893. Groups of Special-Equivalent Strings](https://leetcode.com/problems/groups-of-special-equivalent-strings/) # 問題：偶数番目同士または奇数番目同士を交換し一致すれば同一グループ。グループ数を返せ # 解法：奇数番目を大文字に変えcollections.Counterを用いる class Solution: def numSpecialEquivGroups(self, A: List[str]) -> int: cc = [collections.Counter(i[::2].upper() + i[1::2]) for i in A] return len(set(tuple(sorted(c.items())) for c in cc))

51.375

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4

457eaade2326266a02980198a8b994793ea921bf

211

py

Python

praw/models/list/redditor.py

NedJunk/praw

dd75d91e5574f1499cbef445dd68eb71445629df

[ "BSD-2-Clause" ]

1

2022-02-04T04:16:05.000Z

praw/models/list/redditor.py

seanwallawalla-forks/praw

849d2edbf26549e3e1b97e72479cdba78c96ddb1

[ "BSD-2-Clause" ]

2

2020-06-27T20:47:08.000Z

2020-07-06T17:25:00.000Z

praw/models/list/redditor.py

seanwallawalla-forks/praw

849d2edbf26549e3e1b97e72479cdba78c96ddb1

[ "BSD-2-Clause" ]

1

2020-07-11T06:28:50.000Z

"""Provide the RedditorList class.""" from .base import BaseList class RedditorList(BaseList): """A list of :class:`.Redditor` objects. Works just like a regular list.""" CHILD_ATTRIBUTE = "children"

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45ae094156e1cd8d5a7c95d9a1789ed7b941a183

17

py

Python

python/testData/intentions/PyConvertCollectionLiteralIntentionTest/convertTupleWithoutClosingParenthesisToList.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/intentions/PyConvertCollectionLiteralIntentionTest/convertTupleWithoutClosingParenthesisToList.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/intentions/PyConvertCollectionLiteralIntentionTest/convertTupleWithoutClosingParenthesisToList.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

xs = (1, <caret>2

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null

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1

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null

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1

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4

45b4916f58c4ce5f3c6ea09fcaa426555e87d317

837

py

Python

board/buildings.py

vigneshRajakumar/Catan

67d3352308daaab6cc5e46325d64d834d443a2a2

[ "MIT" ]

null

board/buildings.py

vigneshRajakumar/Catan

67d3352308daaab6cc5e46325d64d834d443a2a2

[ "MIT" ]

null

board/buildings.py

vigneshRajakumar/Catan

67d3352308daaab6cc5e46325d64d834d443a2a2

[ "MIT" ]

null

import abc class Building(abc.ABC): def __init__(self, owner, name): self.owner = owner self.name = name def description(self): return "%s owned by %s" % (self.name, self.owner) class Abode(Building): def __init__(self, owner, name, victory_points): super.__init__(owner, name) self.victory_points = victory_points class Settlement(Abode): def __init__(self, owner): super.__init__(self, owner, "Settlement", 1) class City(Abode): def __init__(self, owner): super.__init__(self, owner, "City", 2) class Road(Building): def __init__(self, owner): super.__init__(self, owner, "Road") class Point: def __init__(self, abode, position, n1, n2, n3): self.abode = abode self.n1 = n1 self.n2 = n2 self.n3 = n3

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