xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

c083353bf0265a8bcdde74e6a9ffbe91b66c25cc

553

py

Python

pgdrive/utils/__init__.py

decisionforce/pgdrive

19af5d09a40a68a2a5f8b3ac8b40f109e71c26ee

[ "Apache-2.0" ]

97

2020-12-25T06:02:17.000Z

2022-01-16T06:58:39.000Z

pgdrive/utils/__init__.py

decisionforce/pgdrive

19af5d09a40a68a2a5f8b3ac8b40f109e71c26ee

[ "Apache-2.0" ]

192

2020-12-25T07:58:17.000Z

2021-08-28T10:13:59.000Z

pgdrive/utils/__init__.py

decisionforce/pgdrive

19af5d09a40a68a2a5f8b3ac8b40f109e71c26ee

[ "Apache-2.0" ]

11

2020-12-29T11:23:44.000Z

2021-12-06T23:25:49.000Z

from pgdrive.utils.config import Config, merge_config_with_unknown_keys, merge_config from pgdrive.utils.coordinates_shift import panda_heading, panda_position, pgdrive_heading, pgdrive_position from pgdrive.utils.cutils import import_cutils from pgdrive.utils.math_utils import safe_clip, clip, norm, distance_greater, safe_clip_for_small_array, Vector from pgdrive.utils.random_utils import get_np_random, random_string from pgdrive.utils.utils import is_mac, import_pygame, recursive_equal, setup_logger, merge_dicts, \ concat_step_infos, is_win

69.125

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553

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5

c0884d813c49f4a3f61d2dbcd88f49881dcf8e50

98

py

Python

Project/Python/project/public/__init__.py

renwei-release/dave

773301edd3bee6e7526e0d5587ff8af9f01e288f

[ "MIT" ]

null

Project/Python/project/public/__init__.py

renwei-release/dave

773301edd3bee6e7526e0d5587ff8af9f01e288f

[ "MIT" ]

null

Project/Python/project/public/__init__.py

renwei-release/dave

773301edd3bee6e7526e0d5587ff8af9f01e288f

[ "MIT" ]

null

import ctypes import struct import os from .auto import * from .base import * from .tools import *

16.333333

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98

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0.533333

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0.173469

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20

16.333333

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0

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0

null

0

1

0

1

0

5

c0bf403a44a580f2e583da1f68f06408454bb542

185

py

Python

api/utils/images/__init__.py

BytesToBits/BytesToBits-API

bfa305e4f6ded995da95bbedc79c91ef8ce498fb

[ "MIT" ]

null

api/utils/images/__init__.py

BytesToBits/BytesToBits-API

bfa305e4f6ded995da95bbedc79c91ef8ce498fb

[ "MIT" ]

null

api/utils/images/__init__.py

BytesToBits/BytesToBits-API

bfa305e4f6ded995da95bbedc79c91ef8ce498fb

[ "MIT" ]

null

from .message_faker import make_message as DiscordMessage from .btb_convert import btbify from .transparent import clear_background as make_transparent from .hue_shift import change_hue

46.25

61

0.875676

27

185

5.740741

0.592593

0

0.102703

185

4

62

46.25

0.933735

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true

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null

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1

0

1

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1

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5

238bd2848e42414413b60c512f8ceeed06f77a78

161

py

Python

DigitalSecurityHub/orders/admin.py

vineethsai/DigitalSecurityHub

fb3380e983d71bbd67dde19346fad274f6ed2ba8

[ "MIT" ]

null

DigitalSecurityHub/orders/admin.py

vineethsai/DigitalSecurityHub

fb3380e983d71bbd67dde19346fad274f6ed2ba8

[ "MIT" ]

null

DigitalSecurityHub/orders/admin.py

vineethsai/DigitalSecurityHub

fb3380e983d71bbd67dde19346fad274f6ed2ba8

[ "MIT" ]

null

from django.contrib import admin from orders.models import Order, LineItem # Register your models here. admin.site.register(Order) admin.site.register(LineItem)

26.833333

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0.819876

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161

5.73913

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1

0

1

0

null

0

1

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1

0

5

23e16a756e40261fb9da766729672d1a54852a50

151

py

Python

Curso_Python_3_UDEMY/pacotes/pacote_v4.py

DanilooSilva/Cursos_de_Python

8f167a4c6e16f01601e23b6f107578aa1454472d

[ "MIT" ]

null

Curso_Python_3_UDEMY/pacotes/pacote_v4.py

DanilooSilva/Cursos_de_Python

8f167a4c6e16f01601e23b6f107578aa1454472d

[ "MIT" ]

null

Curso_Python_3_UDEMY/pacotes/pacote_v4.py

DanilooSilva/Cursos_de_Python

8f167a4c6e16f01601e23b6f107578aa1454472d

[ "MIT" ]

null

from pacotes.pacote1.modulo1 import soma from pacotes.pacote2.modulo1 import subtracao print('Soma ', soma(3, 2)) print('Subtração ', subtracao(3, 2))

30.2

45

0.761589

22

151

5.227273

0.545455

0.191304

0

0.059259

0.10596

151

5

46

30.2

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0.098684

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1

0

true

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null

0

1

0

1

0

1

0

5

23ec23a754736bccd13dc932c3cea50ec364d0e2

88

py

Python

perception/encoders/base.py

ramaneswaran/perception

045b85634412355d66b2db6a102a97796c9aa11f

[ "Apache-2.0" ]

1

2021-04-14T10:58:13.000Z

perception/encoders/base.py

shivamsaraswat8/perception

045b85634412355d66b2db6a102a97796c9aa11f

[ "Apache-2.0" ]

null

perception/encoders/base.py

shivamsaraswat8/perception

045b85634412355d66b2db6a102a97796c9aa11f

[ "Apache-2.0" ]

1

2021-04-10T18:02:45.000Z

from abc import ABC class BaseEncoder(ABC): def encode(self, image): pass

14.666667

28

0.647727

12

88

4.75

0.833333

0

0.272727

88

6

29

14.666667

0.890625

0

1

0.25

false

0.25

0

0.75

0

1

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null

0

1

0

null

0

1

0

1

0

1

0

5

f1a0f5b307a6777215d944687feda8d4f1e1e408

55

py

Python

src/ssp/ml/dataset/__init__.py

gyan42/spark-streaming-playground

147ef9cbc31b7aed242663dee36143ebf0e8043f

[ "Apache-2.0" ]

10

2020-03-12T11:51:46.000Z

2022-03-24T04:56:05.000Z

src/ssp/ml/dataset/__init__.py

gyan42/spark-streaming-playground

147ef9cbc31b7aed242663dee36143ebf0e8043f

[ "Apache-2.0" ]

12

2020-04-23T07:28:14.000Z

2022-03-12T00:20:24.000Z

src/ssp/ml/dataset/__init__.py

gyan42/spark-streaming-playground

147ef9cbc31b7aed242663dee36143ebf0e8043f

[ "Apache-2.0" ]

1

2020-04-20T14:48:38.000Z

from ssp.ml.dataset.prepare_dataset import SSPMLDataset

55

0.890909

8

55

6

0.875

0

0.054545

55

1

55

0.923077

0

1

0

true

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1

0

1

0

null

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1

0

null

0

1

0

1

0

1

0

5

f1bacff637bbe8e15fe212a8f1a82ec675d4428c

353

py

Python

test_accounts/admin.py

AllFactors/django-organizations

df079e97f8c88214bcdc0b87d2717a8b5323bc4f

[ "BSD-2-Clause" ]

855

2015-01-06T21:08:34.000Z

2022-03-31T04:24:49.000Z

test_accounts/admin.py

AllFactors/django-organizations

df079e97f8c88214bcdc0b87d2717a8b5323bc4f

[ "BSD-2-Clause" ]

156

2015-02-09T01:51:40.000Z

2022-03-29T22:23:01.000Z

test_accounts/admin.py

AllFactors/django-organizations

df079e97f8c88214bcdc0b87d2717a8b5323bc4f

[ "BSD-2-Clause" ]

186

2015-01-21T06:21:59.000Z

2022-03-29T12:44:24.000Z

from django.contrib import admin from test_accounts.models import Account from test_accounts.models import AccountInvitation from test_accounts.models import AccountOwner from test_accounts.models import AccountUser admin.site.register(Account) admin.site.register(AccountInvitation) admin.site.register(AccountUser) admin.site.register(AccountOwner)

29.416667

50

0.866856

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353

6.711111

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0.211921

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11

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true

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null

0

1

0

1

0

1

0

5

9e3a37d04e9189dac290eaea4e61eb9154f0d4d8

156

py

Python

opennsfw2/_typing.py

bhky/opennsfw2

9da0836d2d59ee85f7dde4332712d361474d799e

[ "MIT" ]

66

2021-11-08T06:42:32.000Z

2022-03-29T16:51:35.000Z

opennsfw2/_typing.py

bhky/opennsfw2

9da0836d2d59ee85f7dde4332712d361474d799e

[ "MIT" ]

2

2021-11-10T09:37:37.000Z

2022-01-26T00:11:37.000Z

opennsfw2/_typing.py

bhky/opennsfw2

9da0836d2d59ee85f7dde4332712d361474d799e

[ "MIT" ]

10

2021-11-08T12:36:20.000Z

2021-12-30T15:33:07.000Z

""" Typing utilities. """ import numpy as np import numpy.typing NDFloat32Array = np.typing.NDArray[np.float32] NDUInt8Array = np.typing.NDArray[np.uint8]

17.333333

46

0.762821

21

156

5.666667

0.52381

0.184874

0.252101

0.285714

0

0.043165

0.108974

156

8

47

19.5

0.81295

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1

0

null

0

1

0

5

9e5077f94bfb14abbaa805fcba9ac206e84a7226

107

py

Python

imouto/__init__.py

bakalab/imouto

01944746d4f7530a741bcb082866e18c48d07f3a

[ "BSD-3-Clause" ]

9

2017-06-18T06:03:00.000Z

2019-05-07T10:06:22.000Z

imouto/__init__.py

bakalab/imouto

01944746d4f7530a741bcb082866e18c48d07f3a

[ "BSD-3-Clause" ]

3

2017-08-05T08:01:42.000Z

2017-12-08T01:58:33.000Z

imouto/__init__.py

bakalab/imouto

01944746d4f7530a741bcb082866e18c48d07f3a

[ "BSD-3-Clause" ]

null

from imouto.request import Request from imouto.response import Response __all__ = ['Request', 'Response']

21.4

36

0.785047

13

107

6.153846

0.461538

0.25

0

0.121495

107

4

37

26.75

0.851064

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0.140187

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false

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0.666667

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0.666667

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1

0

null

0

1

0

1

0

5

9e69e83265ce9c985c18d2c67f7fe86732ac968d

80

py

Python

staticPageServer/serverCode/__init__.py

hydrogen602/simpleServer

d5cb39fd8b196fbc77899038e5fe392d433d2888

[ "MIT" ]

null

staticPageServer/serverCode/__init__.py

hydrogen602/simpleServer

d5cb39fd8b196fbc77899038e5fe392d433d2888

[ "MIT" ]

null

staticPageServer/serverCode/__init__.py

hydrogen602/simpleServer

d5cb39fd8b196fbc77899038e5fe392d433d2888

[ "MIT" ]

null

from .fileLoader import fetch # NOQA from .functionTools import enforce # NOQA

20

41

0.7875

10

80

6.3

0.7

0

0.1625

80

3

42

26.666667

0.940299

0.1125

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true

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null

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1

0

1

0

1

0

5

7b3af7830688de69c4b58ce38f190fa11b1036e4

41

py

Python

tests/__init__.py

sbliven/gem-painting

368b128723240b095bf9af5274dd26a147504f11

[ "BSD-3-Clause" ]

null

tests/__init__.py

sbliven/gem-painting

368b128723240b095bf9af5274dd26a147504f11

[ "BSD-3-Clause" ]

1

2021-11-22T15:23:40.000Z

tests/__init__.py

sbliven/diamond-art

368b128723240b095bf9af5274dd26a147504f11

[ "BSD-3-Clause" ]

null

"""Unit test package for diamond_art."""

20.5

40

0.707317

6

41

4.666667

1

0

0.121951

41

1

41

0.777778

0.829268

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true

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null

0

1

0

5

7b63c9dab171ee5fdeea6d357f5936c74a3691fc

114

py

Python

wlcsim/special/__init__.py

SpakowitzLab/BasicWLC

13edbbc8e8cd36a3586571ff4d80880fc89d30e6

[ "MIT" ]

1

2021-03-16T01:39:18.000Z

wlcsim/special/__init__.py

riscalab/wlcsim

e34877ef6c5dc83c6444380dbe624b371d70faf2

[ "MIT" ]

17

2016-07-08T21:17:40.000Z

2017-01-24T09:05:25.000Z

wlcsim/special/__init__.py

riscalab/wlcsim

e34877ef6c5dc83c6444380dbe624b371d70faf2

[ "MIT" ]

9

2017-02-19T06:28:38.000Z

2021-11-05T22:28:08.000Z

"""Hold modules that act as helpers for generating more complicated simulations.""" from . import homolog_process

28.5

67

0.798246

15

114

6

1

0

0.131579

114

3

68

38

0.909091

0.675439

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true

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null

0

1

0

1

0

1

0

5

7b63f8238a1a8fdcb4df729dd17b9bc9bd9f6719

125

py

Python

verybasic/hello_world.py

nightmarebadger/tutorials-python-basic

a4c49e01bf9c9c5006239c013c81d85603dd96fd

[ "MIT" ]

null

verybasic/hello_world.py

nightmarebadger/tutorials-python-basic

a4c49e01bf9c9c5006239c013c81d85603dd96fd

[ "MIT" ]

null

verybasic/hello_world.py

nightmarebadger/tutorials-python-basic

a4c49e01bf9c9c5006239c013c81d85603dd96fd

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ A program that prints "Hello World!". """ if __name__ == '__main__': print("Hello World!")

13.888889

37

0.568

15

125

4.2

0.866667

0.31746

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0.01

0.2

125

8

38

15.625

0.62

0.48

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0.350877

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true

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null

0

1

0

1

0

5

7b6c4a833a0a09c292e4df972e01eba6a1797a13

117

py

Python

soft/disable_wifi.py

Mirmik/zippo

50097d9b33c165d8f6a8ec65b22db4b1c4e1f61c

[ "MIT" ]

null

soft/disable_wifi.py

Mirmik/zippo

50097d9b33c165d8f6a8ec65b22db4b1c4e1f61c

[ "MIT" ]

null

soft/disable_wifi.py

Mirmik/zippo

50097d9b33c165d8f6a8ec65b22db4b1c4e1f61c

[ "MIT" ]

null

sudo systemctl stop hostapd sudo systemctl stop dnsmasq sudo systemctl stop wpa_supplicant sudo ifconfig wlan0 down

19.5

34

0.854701

17

117

5.823529

0.588235

0.393939

0.515152

0

0.01

0.145299

117

5

35

23.4

0.98

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1

0

null

0

1

0

5

7b7266eec594120271ed819d6beaea12f493db4a

46

py

Python

src/crowdai/predict.py

kristijanbartol/DeepMusicClassifier

f47295c3171e77733be5b80ddcec9790dfc3165b

[ "MIT" ]

64

2017-11-23T09:43:30.000Z

2021-12-22T12:41:53.000Z

src/crowdai/predict.py

kristijanbartol/DeepMusicClassifier

f47295c3171e77733be5b80ddcec9790dfc3165b

[ "MIT" ]

null

src/crowdai/predict.py

kristijanbartol/DeepMusicClassifier

f47295c3171e77733be5b80ddcec9790dfc3165b

[ "MIT" ]

7

2018-04-11T07:29:47.000Z

2020-04-11T21:14:13.000Z

import model model = model.load_best_model()

11.5

31

0.782609

7

46

4.857143

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0.588235

0

0.130435

46

3

32

15.333333

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false

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1

0

1

0

null

0

1

0

5

7ba25e6c18082a60f0bed540d473f37be244e248

48

py

Python

tests/components/flick_electric/__init__.py

domwillcode/home-assistant

f170c80bea70c939c098b5c88320a1c789858958

[ "Apache-2.0" ]

30,023

2016-04-13T10:17:53.000Z

2020-03-02T12:56:31.000Z

tests/components/flick_electric/__init__.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

31,101

2020-03-02T13:00:16.000Z

2022-03-31T23:57:36.000Z

tests/components/flick_electric/__init__.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

11,956

2016-04-13T18:42:31.000Z

2020-03-02T09:32:12.000Z

"""Tests for the Flick Electric integration."""

24

47

0.729167

6

48

5.833333

1

0

0.125

48

1

48

0.833333

0.854167

0

null

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null

true

0

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1

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1

0

null

0

1

0

5

7bc0b7e85e7d1ab860f1d51242fda3ed8fb427cc

57

py

Python

starbucks/__init__.py

movermeyer/starbucks-py

9eee89e2837d9950c27a6350ff891c891f8a07b6

[ "BSD-2-Clause" ]

23

2015-03-02T16:13:27.000Z

2021-09-06T22:09:09.000Z

starbucks/__init__.py

movermeyer/starbucks-py

9eee89e2837d9950c27a6350ff891c891f8a07b6

[ "BSD-2-Clause" ]

2

2015-03-07T05:13:52.000Z

2015-03-07T05:18:38.000Z

starbucks/__init__.py

movermeyer/starbucks-py

9eee89e2837d9950c27a6350ff891c891f8a07b6

[ "BSD-2-Clause" ]

6

2015-03-02T16:14:41.000Z

2020-10-22T17:20:52.000Z

from .starbucks import Starbucks, Card, Beverage, Coupon

28.5

56

0.807018

7

57

6.571429

0.857143

0

0.122807

57

1

57

0.92

0

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true

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1

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null

0

1

0

1

0

1

0

5

c8a29f1d132776013f4907eca0466c583d9edcb3

135

py

Python

featuretools/primitives/premium/__init__.py

oslab-ewha/featuretools

c1d86433f9050bf383e55520a0d42cc63fa16839

[ "BSD-3-Clause" ]

2

2021-07-13T07:40:20.000Z

2021-08-19T04:57:24.000Z

featuretools/primitives/premium/__init__.py

oslab-ewha/featuretools

c1d86433f9050bf383e55520a0d42cc63fa16839

[ "BSD-3-Clause" ]

6

2021-07-19T05:15:38.000Z

2021-08-24T11:34:58.000Z

featuretools/primitives/premium/__init__.py

oslab-ewha/featuretools

c1d86433f9050bf383e55520a0d42cc63fa16839

[ "BSD-3-Clause" ]

2

2021-07-02T00:48:07.000Z

2021-07-02T09:35:49.000Z

# flake8: noqa from .api import * import nltk # nltk.download('stopwords', quiet=True) # nltk.download('vader_lexicon', quiet=True)

15

44

0.718519

18

135

5.333333

0.666667

0.25

0

0.008547

0.133333

135

8

45

16.875

0.811966

0.696296

0

1

0

true

0

1

0

1

0

1

0

null

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0

1

0

null

0

1

0

1

0

5

c8a9c3fd32f3eb12e576a233c543e706a39f439e

2,329

py

Python

src/apps/core/views/PublicationViews.py

crivet/HydroLearn

f4caa868fffa08f1bf4163de94d44a234c8879b2

[ "BSD-3-Clause" ]

null

src/apps/core/views/PublicationViews.py

crivet/HydroLearn

f4caa868fffa08f1bf4163de94d44a234c8879b2

[ "BSD-3-Clause" ]

23

2018-08-09T18:46:20.000Z

2021-06-10T20:21:26.000Z

src/apps/core/views/PublicationViews.py

crivet/HydroLearn

f4caa868fffa08f1bf4163de94d44a234c8879b2

[ "BSD-3-Clause" ]

1

2019-01-28T15:42:39.000Z

from django.http import Http404 from src.apps.core.models.PublicationModels import Publication class PublicationViewMixin(object): ''' mixin to restrict access to publication objects based on module defined 'user_has_access' result if a user does not have access to an object, a 404 error will be raised ''' def get_object(self, queryset=None): object = super(PublicationViewMixin, self).get_object(queryset) # check if the requesting user has access, if not return None if not object.user_has_access(self.request.user): raise Http404 return object class PublicationChildViewMixin(object): ''' mixin to restrict access to publication objects based on module defined 'user_has_access' result if a user does not have access to an object, a 404 error will be raised ''' def get_object(self, queryset=None): object = super(PublicationChildViewMixin, self).get_object(queryset) # check if the requesting user has access, if not return None if not object.get_Publishable_parent().user_has_access(self.request.user): raise Http404 return object class DraftOnlyViewMixin(object): ''' mixin to restrict access of a particular view to Draft versions of publications ''' def get_object(self, queryset=None): object = super(DraftOnlyViewMixin, self).get_object(queryset) accepted_statuses = [Publication.DRAFT_ONLY, Publication.PUBLISHED] # check if this object's publishable parent is the Current Publication if not object.get_Publishable_parent().get_publish_status() in accepted_statuses: raise Http404 return object class PublicOnlyViewMixin(object): ''' mixin to restrict access of a particular view to Published versions of publications ''' def get_object(self, queryset=None): object = super(PublicOnlyViewMixin, self).get_object(queryset) accepted_statuses = [Publication.PUBLICATION_OBJECT, Publication.PAST_PUBLICATION] # check if this object's publishable parent is the Current Publication if not object.get_Publishable_parent().get_publish_status() in accepted_statuses: raise Http404 return object

34.761194

91

0.701159

285

2,329

5.614035

0.252632

0.045

0.04875

0.0525

0.789375

0.78625

0.77375

0.71375

0

0.011818

0.237012

2,329

67

92

34.761194

0.888576

0.330614

0

0.5

0

1

0.142857

false

0

0.071429

0

0.5

0

null

0

1

0

null

0

5

c8b5d13ececfd49165f5fe6cbb058db81a662eda

88

py

Python

ontology/logistic_regression/sherlock/listify_circuits_k16_reverse.py

ehbeam/neuro-knowledge-engine

9dc56ade0bbbd8d14f0660774f787c3f46d7e632

[ "MIT" ]

15

2020-07-17T07:10:26.000Z

2022-02-18T05:51:45.000Z

ontology/neural_network/sherlock/listify_circuits_k16_reverse.py

YifeiCAO/neuro-knowledge-engine

9dc56ade0bbbd8d14f0660774f787c3f46d7e632

[ "MIT" ]

2

2022-01-14T09:10:12.000Z

2022-01-28T17:32:42.000Z

ontology/neural_network/sherlock/listify_circuits_k16_reverse.py

YifeiCAO/neuro-knowledge-engine

9dc56ade0bbbd8d14f0660774f787c3f46d7e632

[ "MIT" ]

4

2021-12-22T13:27:32.000Z

2022-02-18T05:51:47.000Z

#!/bin/python import listify_circuits listify_circuits.optimize_circuits(16, 'reverse')

22

49

0.829545

11

88

6.363636

0.727273

0.428571

0

0.024096

0.056818

88

4

49

22

0.819277

0.136364

0

0.092105

0

1

0

true

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

c8c062eb3c8379c179aec8d4a8501d88269ab5f8

50

py

Python

13-protocol-abc/double/double_object.py

SeirousLee/example-code-2e

81ec1669a4b8fd098db44a78a3d551287eec7bc9

[ "MIT" ]

990

2019-03-21T21:17:34.000Z

2022-03-31T00:55:07.000Z

13-protocol-abc/double/double_object.py

Turall/example-code-2e

1702717182cff9a48beb55b2a9f5618e9bd1da18

[ "MIT" ]

17

2019-12-18T18:00:05.000Z

2022-01-12T14:23:47.000Z

13-protocol-abc/double/double_object.py

Turall/example-code-2e

1702717182cff9a48beb55b2a9f5618e9bd1da18

[ "MIT" ]

276

2019-04-06T12:32:00.000Z

2022-03-29T11:50:47.000Z

def double(x: object) -> object: return x * 2

16.666667

32

0.6

8

50

3.75

0.75

0

0.027027

0.26

50

2

33

25

0.783784

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

c8c0794a2f9a2f2e3e30f549b0c91a91964bcf15

151

py

Python

spaced_repetition/gateways/django_gateway/django_project/apps/problem/apps.py

MBlistein/spaced-repetition

c10281d43e928f8d1799076190f962f8e49a405b

[ "MIT" ]

null

spaced_repetition/gateways/django_gateway/django_project/apps/problem/apps.py

MBlistein/spaced-repetition

c10281d43e928f8d1799076190f962f8e49a405b

[ "MIT" ]

null

spaced_repetition/gateways/django_gateway/django_project/apps/problem/apps.py

MBlistein/spaced-repetition

c10281d43e928f8d1799076190f962f8e49a405b

[ "MIT" ]

null

from django.apps import AppConfig class ProblemConfig(AppConfig): name = 'spaced_repetition.gateways.django_gateway.django_project.apps.problem'

25.166667

82

0.821192

18

151

6.722222

0.777778

0

0.099338

151

5

83

30.2

0.889706

0

0.456954

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

1

null

0

1

0

1

0

5

c8ddf6c863862394d55fb5d3c211fbaba720645c

83

py

Python

src/sage/crypto/public_key/all.py

defeo/sage

d8822036a9843bd4d75845024072515ede56bcb9

[ "BSL-1.0" ]

2

2018-06-30T01:37:35.000Z

2018-06-30T01:37:39.000Z

src/sage/crypto/public_key/all.py

boothby/sage

1b1e6f608d1ef8ee664bb19e991efbbc68cbd51f

[ "BSL-1.0" ]

null

src/sage/crypto/public_key/all.py

boothby/sage

1b1e6f608d1ef8ee664bb19e991efbbc68cbd51f

[ "BSL-1.0" ]

null

from __future__ import absolute_import from .blum_goldwasser import BlumGoldwasser

27.666667

43

0.891566

10

83

6.8

0.7

0

0.096386

83

2

44

41.5

0.906667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

c8fa394a588b95fa7b1fbb38d060f1d00788c0cc

64

py

Python

sources/simulators/serial_execution_simulator/__init__.py

M4rukku/impact_of_non_iid_data_in_federated_learning

c818db03699c82e42217d56f8ddd4cc2081c8bb1

[ "MIT" ]

null

sources/simulators/serial_execution_simulator/__init__.py

M4rukku/impact_of_non_iid_data_in_federated_learning

c818db03699c82e42217d56f8ddd4cc2081c8bb1

[ "MIT" ]

null

sources/simulators/serial_execution_simulator/__init__.py

M4rukku/impact_of_non_iid_data_in_federated_learning

c818db03699c82e42217d56f8ddd4cc2081c8bb1

[ "MIT" ]

null

from .serial_execution_simulator import SerialExecutionSimulator

64

0.9375

6

64

9.666667

1

0

0.046875

64

1

64

0.95082

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

cdb528de4b73f90990fa79e3dd1091aaa743293b

82

py

Python

dj_scaffold/conf/app/views.py

vicalloy/dj-scaffold

92e9a991e0699f8b88c16d8a95b23bd5b3cf29e1

[ "BSD-3-Clause" ]

10

2015-04-29T08:24:06.000Z

2021-09-06T14:58:01.000Z

dj_scaffold/conf/app/views.py

vicalloy/dj-scaffold

92e9a991e0699f8b88c16d8a95b23bd5b3cf29e1

[ "BSD-3-Clause" ]

null

dj_scaffold/conf/app/views.py

vicalloy/dj-scaffold

92e9a991e0699f8b88c16d8a95b23bd5b3cf29e1

[ "BSD-3-Clause" ]

3

2015-10-12T04:36:13.000Z

2016-03-24T11:33:11.000Z

#!/usr/bin/env python # -*- coding: UTF-8 -*- from django.shortcuts import render

20.5

35

0.682927

12

82

4.666667

1

0

0.014085

0.134146

82

3

36

27.333333

0.774648

0.512195

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

cdbdabdd3293ab394848c537c3b52781c81be447

29

py

Python

turbosensei/modB.py

FORCaist/turbosensei

d6d800ef9c9e73fd4cd1e130d9480334b27e7d3e

[ "MIT" ]

null

turbosensei/modB.py

FORCaist/turbosensei

d6d800ef9c9e73fd4cd1e130d9480334b27e7d3e

[ "MIT" ]

null

turbosensei/modB.py

FORCaist/turbosensei

d6d800ef9c9e73fd4cd1e130d9480334b27e7d3e

[ "MIT" ]

null

def funcB(x): return x+2

9.666667

14

0.586207

6

29

2.833333

0.833333

0

0.047619

0.275862

29

3

14

9.666667

0.761905

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

cdd76e6ae8fe667ba2cf5fb3e8fc1654f15a1044

22

py

Python

my_script.py

Ramguru94/watchtower

efb45e93850b1c7c0eee7edcfc0f602f9f9979ce

[ "MIT" ]

null

my_script.py

Ramguru94/watchtower

efb45e93850b1c7c0eee7edcfc0f602f9f9979ce

[ "MIT" ]

null

my_script.py

Ramguru94/watchtower

efb45e93850b1c7c0eee7edcfc0f602f9f9979ce

[ "MIT" ]

null

print("y") print("z")

7.333333

10

0.545455

4

22

3

0.75

0

0.090909

22

2

11

0.6

0

0.090909

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

b541c081b66be3e6eff6a14b2027d41398fddb67

923

py

Python

homes_for_sale/querysets.py

Xtuden-com/django-property

6656d469a5d06c103a34c2e68b9f1754413fb3ba

[ "MIT" ]

null

homes_for_sale/querysets.py

Xtuden-com/django-property

6656d469a5d06c103a34c2e68b9f1754413fb3ba

[ "MIT" ]

null

homes_for_sale/querysets.py

Xtuden-com/django-property

6656d469a5d06c103a34c2e68b9f1754413fb3ba

[ "MIT" ]

null

from datetime import datetime from django.contrib.gis.db import models from homes.behaviours import Publishable import pytz class SaleQuerySet(models.query.QuerySet): def published(self): return self.filter(status=Publishable.STATUS_CHOICE_ACTIVE) def unpublished(self): return self.filter(status=Publishable.STATUS_CHOICE_INACTIVE) def unexpired(self): return self.filter(expires_at__isnull=True) | self.filter(expires_at__gt=datetime.utcnow().replace(tzinfo=pytz.utc)) def expired(self): return self.filter(expires_at__lte=datetime.utcnow().replace(tzinfo=pytz.utc)) def new_home(self): return self.filter(new_home=True) def shared_ownership(self): return self.filter(shared_ownership=True) def auction(self): return self.filter(auction=True) def tenure(self, slug): return self.filter(property_tenure__slug=slug)

27.969697

124

0.732394

120

923

5.466667

0.383333

0.137195

0.195122

0.213415

0.35061

0.262195

0.14939

0

0.171181

923

33

125

27.969697

0.857516

0

1

0.380952

false

0

0.190476

0.380952

1

0

null

0

1

0

null

0

1

0

1

0

5

b574ab83dfdd91d5f2f3d89787e2611685ca4722

163

py

Python

src/api/domain/operation/GetDataOperationJobExecutionLogList/GetDataOperationJobExecutionLogListRequest.py

PythonDataIntegrator/pythondataintegrator

6167778c36c2295e36199ac0d4d256a4a0c28d7a

[ "MIT" ]

14

2020-12-19T15:06:13.000Z

2022-01-12T19:52:17.000Z

src/api/domain/operation/GetDataOperationJobExecutionLogList/GetDataOperationJobExecutionLogListRequest.py

PythonDataIntegrator/pythondataintegrator

6167778c36c2295e36199ac0d4d256a4a0c28d7a

[ "MIT" ]

43

2021-01-06T22:05:22.000Z

2022-03-10T10:30:30.000Z

src/api/domain/operation/GetDataOperationJobExecutionLogList/GetDataOperationJobExecutionLogListRequest.py

PythonDataIntegrator/pythondataintegrator

6167778c36c2295e36199ac0d4d256a4a0c28d7a

[ "MIT" ]

4

2020-12-18T23:10:09.000Z

2021-04-02T13:03:12.000Z

from infrastructure.cqrs.decorators.requestclass import requestclass @requestclass class GetDataOperationJobExecutionLogListRequest: ExecutionId: int = None

23.285714

68

0.852761

13

163

10.692308

0.846154

0

0.104294

163

6

69

27.166667

0.952055

0

1

0

true

0

0.25

0

0.75

0

1

0

1

null

0

1

0

null

0

1

0

1

0

5

b5a4e0f879c15315e646c8f4869ec5e8ec64fb44

132

py

Python

tests/test_schema.py

Ssvenkerud/population_generator

7ba62b3222eee3935ee1ed689810d15dcda9c1b0

[ "MIT" ]

null

tests/test_schema.py

Ssvenkerud/population_generator

7ba62b3222eee3935ee1ed689810d15dcda9c1b0

[ "MIT" ]

null

tests/test_schema.py

Ssvenkerud/population_generator

7ba62b3222eee3935ee1ed689810d15dcda9c1b0

[ "MIT" ]

null

import pytest from src.Schema import * def test_schema_blank_init(): schema = Schema() assert isinstance(schema, Schema)

14.666667

37

0.727273

17

132

5.470588

0.647059

0.258065

0

0.189394

132

8

38

16.5

0.869159

0

0.2

1

0.2

false

0

0.4

0

0.6

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

a9236d67efc8d5b685346d8f0ee798ba0e7ee216

4,465

py

Python

apps/sso/access_requests/tests.py

g10f/sso

ba6eb712add388c69d4880f5620a2e4ce42d3fee

[ "BSD-3-Clause" ]

3

2021-05-16T17:06:57.000Z

2021-05-28T17:14:05.000Z

apps/sso/access_requests/tests.py

g10f/sso

ba6eb712add388c69d4880f5620a2e4ce42d3fee

[ "BSD-3-Clause" ]

null

apps/sso/access_requests/tests.py

g10f/sso

ba6eb712add388c69d4880f5620a2e4ce42d3fee

[ "BSD-3-Clause" ]

null

import os from selenium.webdriver.common.by import By from selenium.webdriver.support.select import Select from django.conf import settings from django.contrib.auth import get_user_model from django.urls import reverse from sso.organisations.models import Organisation from sso.tests import SSOSeleniumTests class AccessRequestsSeleniumTests(SSOSeleniumTests): fixtures = ['roles.json', 'test_l10n_data.json', 'app_roles.json', 'test_organisation_data.json', 'test_app_roles.json', 'test_user_data.json'] def test_new_access_request(self): self.login(username='GunnarScherf', password='gsf') # add new access request self.selenium.get('%s%s' % (self.live_server_url, reverse('access_requests:extend_access'))) self.selenium.find_element_by_name("message").send_keys('Hello world.') picture = os.path.abspath(os.path.join(settings.BASE_DIR, 'sso/static/img/face-cool.png')) self.add_picture(picture) self.selenium.find_element_by_tag_name("form").submit() self.wait_page_loaded() url = reverse('access_requests:extend_access_thanks') full_url = self.live_server_url + url self.assertEqual(self.selenium.current_url, full_url) self.logout() # login as organisation admin and accept the request self.login(username='CenterAdmin', password='gsf') list_url = reverse('access_requests:extend_access_list') self.selenium.get('%s%s' % (self.live_server_url, list_url)) elems = self.selenium.find_elements(by=By.XPATH, value="//a[starts-with(@href, '%s')]" % list_url) # should be one element in the list elems[0].click() self.wait_page_loaded() self.selenium.find_element_by_tag_name("form").submit() # check success message self.wait_page_loaded() self.selenium.find_element_by_class_name("alert-success") self.logout() # check if the user got the member profile user = get_user_model().objects.get(username='GunnarScherf') self.assertIn(get_user_model().get_default_role_profile(), user.role_profiles.all()) self.assertNotIn(get_user_model().get_default_guest_profile(), user.role_profiles.all()) def test_new_access_request_for_user_without_organisation(self): # remove all organisations from user user = get_user_model().objects.get(username='GunnarScherf') user.organisations.clear() self.login(username='GunnarScherf', password='gsf') # add new access request self.selenium.get('%s%s?app_id=%s' % (self.live_server_url, reverse('access_requests:extend_access'), 'bc0ee635a536491eb8e7fbe5749e8111')) self.selenium.find_element_by_name("message").send_keys('Hello world.') picture = os.path.abspath(os.path.join(settings.BASE_DIR, 'sso/static/img/face-cool.png')) self.add_picture(picture) Select(self.selenium.find_element_by_name("organisation")).select_by_index(1) self.selenium.find_element_by_tag_name("form").submit() self.wait_page_loaded() url = reverse('access_requests:extend_access_thanks') full_url = self.live_server_url + url self.assertEqual(self.selenium.current_url, full_url) self.logout() # login as organisation admin and accept the request self.login(username='CenterAdmin', password='gsf') list_url = reverse('access_requests:extend_access_list') self.selenium.get('%s%s' % (self.live_server_url, list_url)) elems = self.selenium.find_elements(by=By.XPATH, value="//a[starts-with(@href, '%s')]" % list_url) # should be one element in the list elems[0].click() self.wait_page_loaded() self.selenium.find_element_by_tag_name("form").submit() # check success message self.wait_page_loaded() self.selenium.find_element_by_class_name("alert-success") self.logout() user.refresh_from_db() organisation = Organisation.objects.get(uuid='31664dd38ca4454e916e55fe8b1f0745') self.assertIn(organisation, user.organisations.all()) self.assertEqual(len(user.organisations.all()), 1) self.assertIn(get_user_model().get_default_role_profile(), user.role_profiles.all()) self.assertNotIn(get_user_model().get_default_guest_profile(), user.role_profiles.all())

46.030928

109

0.694513

573

4,465

5.155323

0.226876

0.069059

0.05958

0.070074

0.739336

0.723764

0.713947

0.682803

0

0.013227

0.187234

4,465

96

110

46.510417

0.800772

0.075028

0

0.676471

0

0.16606

0.09444

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a925d4613fa40116b9e05b914aa9e031f1eb8bc1

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py

Python

ImageFetcher/__init__.py

finleyexp/georef_imageregistration

c896ddea1055b9c8b919560643a3cb5f87dcc0f1

[ "Apache-2.0" ]

11

2018-01-26T09:06:28.000Z

2022-01-02T07:32:26.000Z

ImageFetcher/__init__.py

finleyexp/georef_imageregistration

c896ddea1055b9c8b919560643a3cb5f87dcc0f1

[ "Apache-2.0" ]

null

ImageFetcher/__init__.py

finleyexp/georef_imageregistration

c896ddea1055b9c8b919560643a3cb5f87dcc0f1

[ "Apache-2.0" ]

9

2017-07-16T03:14:11.000Z

2021-08-29T01:06:45.000Z

""" ImageFetcher """

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a94437504b6c0ec20447bfe79a350a2b289965e8

22,589

py

Python

research/PromptKGC/utils.py

zjunlp/PromptKG

791bf82390eeadc30876d9f95e8dd26cd05de3dc

[ "MIT" ]

11

2022-02-04T12:32:37.000Z

2022-03-25T11:49:48.000Z

research/PromptKGC/utils.py

zjunlp/PromptKG

791bf82390eeadc30876d9f95e8dd26cd05de3dc

[ "MIT" ]

null

research/PromptKGC/utils.py

zjunlp/PromptKG

791bf82390eeadc30876d9f95e8dd26cd05de3dc

[ "MIT" ]

4

2022-02-04T05:08:23.000Z

2022-03-16T02:07:52.000Z

import argparse import csv import logging import os import random import sys import numpy as np import torch import pickle from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset, Dataset from torch.utils.data.distributed import DistributedSampler from tqdm import tqdm, trange from data import convert_examples_to_features from data import KGProcessor # from torch.nn import CrossEntropyLoss, MSELoss # from scipy.stats import pearsonr, spearmanr # from sklearn.metrics import matthews_corrcoef, f1_scoreclass logger = logging.getLogger(__name__) class InputExample(object): """A single training/test example for simple sequence classification.""" def __init__(self, guid, text_a, text_b=None, text_c=None, label=None): """Constructs a InputExample. Args: guid: Unique id for the example. text_a: string. The untokenized text of the first sequence. For single sequence tasks, only this sequence must be specified. text_b: (Optional) string. The untokenized text of the second sequence. Only must be specified for sequence pair tasks. text_c: (Optional) string. The untokenized text of the third sequence. Only must be specified for sequence triple tasks. label: (Optional) string. The label of the example. This should be specified for train and dev examples, but not for test examples. """ self.guid = guid self.text_a = text_a self.text_b = text_b self.text_c = text_c self.label = label class InputFeatures(object): """A single set of features of data.""" def __init__(self, input_ids, input_mask, segment_ids, label_id): self.input_ids = input_ids self.input_mask = input_mask self.segment_ids = segment_ids self.label_id = label_id class DataProcessor(object): """Base class for data converters for sequence classification data sets.""" def get_train_examples(self, data_dir): """Gets a collection of `InputExample`s for the train set.""" raise NotImplementedError() def get_dev_examples(self, data_dir): """Gets a collection of `InputExample`s for the dev set.""" raise NotImplementedError() def get_labels(self, data_dir): """Gets the list of labels for this data set.""" raise NotImplementedError() @classmethod def _read_tsv(cls, input_file, quotechar=None): """Reads a tab separated value file.""" with open(input_file, "r", encoding="utf-8") as f: reader = csv.reader(f, delimiter="\t", quotechar=quotechar) lines = [] for line in reader: if sys.version_info[0] == 2: line = list(unicode(cell, "utf-8") for cell in line) lines.append(line) return lines def _truncate_seq_pair(tokens_a, tokens_b, max_length): """Truncates a sequence pair in place to the maximum length.""" # This is a simple heuristic which will always truncate the longer sequence # one token at a time. This makes more sense than truncating an equal percent # of tokens from each, since if one sequence is very short then each token # that's truncated likely contains more information than a longer sequence. while True: total_length = len(tokens_a) + len(tokens_b) if total_length <= max_length: break if len(tokens_a) > len(tokens_b): tokens_a.pop() else: tokens_b.pop() def _truncate_seq_triple(tokens_a, tokens_b, tokens_c, max_length): """Truncates a sequence triple in place to the maximum length.""" # This is a simple heuristic which will always truncate the longer sequence # one token at a time. This makes more sense than truncating an equal percent # of tokens from each, since if one sequence is very short then each token # that's truncated likely contains more information than a longer sequence. while True: total_length = len(tokens_a) + len(tokens_b) + len(tokens_c) if total_length <= max_length: break if len(tokens_a) > len(tokens_b) and len(tokens_a) > len(tokens_c): tokens_a.pop() elif len(tokens_b) > len(tokens_a) and len(tokens_b) > len(tokens_c): tokens_b.pop() elif len(tokens_c) > len(tokens_a) and len(tokens_c) > len(tokens_b): tokens_c.pop() else: tokens_c.pop() logger = logging.getLogger() # TODO write a dataset for fast test processing class TestDataset(Dataset): def __init__(self, args, test_triples, tokenizer, processor): self.test_triples = test_triples self.tokenizer = tokenizer self.processor = processor self.args = args self.label_list = processor.get_labels(args.data_dir) self.entity_list = processor.get_entities(args.data_dir) def __len__(self): return len(self.test_triples) def __getitem__(self, index): entity_list = self.entity_list all_triples_str_set = set() processor = self.processor args = self.args tokenizer = self.tokenizer label_list = self.label_list test_triple = self.test_triples[index] head = test_triple[0] relation = test_triple[1] tail = test_triple[2] # print(test_triple, head, relation, tail) head_corrupt_list = [test_triple] for corrupt_ent in entity_list: if corrupt_ent != head: tmp_triple = [corrupt_ent, relation, tail] tmp_triple_str = "\t".join(tmp_triple) if tmp_triple_str not in all_triples_str_set: # may be slow head_corrupt_list.append(tmp_triple) tmp_examples = processor._create_examples( head_corrupt_list, "test", args.data_dir ) # print(len(tmp_examples)) tmp_features = convert_examples_to_features( tmp_examples, label_list, args.max_seq_length, tokenizer, args ) all_input_ids = torch.tensor( [f.input_ids for f in tmp_features], dtype=torch.long ) all_input_mask = torch.tensor( [f.input_mask for f in tmp_features], dtype=torch.long ) all_segment_ids = torch.tensor( [f.segment_ids for f in tmp_features], dtype=torch.long ) all_label_ids = torch.tensor( [f.label_id for f in tmp_features], dtype=torch.long ) eval_data = TensorDataset( all_input_ids, all_input_mask, all_segment_ids, all_label_ids ) # Run prediction for temp data eval_sampler = SequentialSampler(eval_data) left_eval_dataloader = DataLoader( eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size, num_workers=16 ) tail_corrupt_list = [test_triple] for corrupt_ent in entity_list: if corrupt_ent != tail: tmp_triple = [head, relation, corrupt_ent] tmp_triple_str = "\t".join(tmp_triple) if tmp_triple_str not in all_triples_str_set: # may be slow tail_corrupt_list.append(tmp_triple) tmp_examples = processor._create_examples( tail_corrupt_list, "test", args.data_dir ) # print(len(tmp_examples)) tmp_features = convert_examples_to_features( tmp_examples, label_list, args.max_seq_length, tokenizer, args ) all_input_ids = torch.tensor( [f.input_ids for f in tmp_features], dtype=torch.long ) all_input_mask = torch.tensor( [f.input_mask for f in tmp_features], dtype=torch.long ) all_segment_ids = torch.tensor( [f.segment_ids for f in tmp_features], dtype=torch.long ) all_label_ids = torch.tensor( [f.label_id for f in tmp_features], dtype=torch.long ) eval_data = TensorDataset( all_input_ids, all_input_mask, all_segment_ids, all_label_ids ) # Run prediction for temp data eval_sampler = SequentialSampler(eval_data) right_eval_dataloader = DataLoader( eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size, num_workers=16 ) return dict(left=left_eval_dataloader, right=right_eval_dataloader) def test_model(args, model, tokenizer, wandb_logger): model.eval() processor = KGProcessor() # get the chunk entities test_triples = processor.get_test_triples(args.data_dir, args.chunk) dataset = TestDataset(args, test_triples,tokenizer=tokenizer, processor=processor) dataloader = DataLoader(dataset, batch_size=1,shuffle=False, num_workers=4, collate_fn=lambda x:x) all_triples_str_set = set() # get all the entities entity_list = processor.get_entities(args.data_dir) label_list = processor.get_labels(args.data_dir) device = torch.device("cuda:0") model = model.to(device) ranks = [] ranks_left = [] ranks_right = [] hits_left = [] hits_right = [] hits = [] top_ten_hit_count = 0 for i in range(10): hits_left.append([]) hits_right.append([]) hits.append([]) pbar = tqdm(total=len(test_triples), desc="Testing...") for batch in dataloader: left_dataloader = batch[0]['left'] right_dataloader = batch[0]['right'] preds = [] for input_ids, input_mask, segment_ids, label_ids in left_dataloader: input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) label_ids = label_ids.to(device) with torch.no_grad(): logits = model( input_ids, token_type_ids=segment_ids, attention_mask=input_mask ) if len(preds) == 0: batch_logits = logits.detach().cpu().numpy() preds.append(batch_logits) else: batch_logits = logits.detach().cpu().numpy() preds[0] = np.append(preds[0], batch_logits, axis=0) preds = preds[0] # get the dimension corresponding to current label 1 # print(preds, preds.shape) rel_values = preds[:, 1] rel_values = torch.tensor(rel_values) # print(rel_values, rel_values.shape) _, argsort1 = torch.sort(rel_values, descending=True) # print(max_values) # print(argsort1) argsort1 = argsort1.cpu().numpy() rank1 = np.where(argsort1 == 0)[0][0] # print("left: ", rank1) ranks.append(rank1 + 1) ranks_left.append(rank1 + 1) if rank1 < 10: top_ten_hit_count += 1 preds = [] for input_ids, input_mask, segment_ids, label_ids in right_dataloader: input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) label_ids = label_ids.to(device) with torch.no_grad(): logits = model( input_ids, token_type_ids=segment_ids, attention_mask=input_mask ) if len(preds) == 0: batch_logits = logits.detach().cpu().numpy() preds.append(batch_logits) else: batch_logits = logits.detach().cpu().numpy() preds[0] = np.append(preds[0], batch_logits, axis=0) preds = preds[0] # get the dimension corresponding to current label 1 rel_values = preds[:, 1] rel_values = torch.tensor(rel_values) _, argsort1 = torch.sort(rel_values, descending=True) argsort1 = argsort1.cpu().numpy() rank2 = np.where(argsort1 == 0)[0][0] ranks.append(rank2 + 1) ranks_right.append(rank2 + 1) # print("right: ", rank2) # print("mean rank until now: ", np.mean(ranks)) if rank2 < 10: top_ten_hit_count += 1 for hits_level in range(10): if rank1 <= hits_level: hits[hits_level].append(1.0) hits_left[hits_level].append(1.0) else: hits[hits_level].append(0.0) hits_left[hits_level].append(0.0) if rank2 <= hits_level: hits[hits_level].append(1.0) hits_right[hits_level].append(1.0) else: hits[hits_level].append(0.0) hits_right[hits_level].append(0.0) pbar.update(1) pbar.set_postfix({"mean rank": np.mean(ranks), "hit@10": top_ten_hit_count * 1.0 / len(ranks) }) if args.chunk: with open(f"chuck{args.chunk}_result_rank.pkl", "wb") as file: pickle.dump(ranks, file) print(f"mean rank: {np.mean(ranks)} \nhits@10: {top_ten_hit_count * 1.0 / len(ranks)}") def _test_model(args, model, tokenizer, wandb_logger): # run link prediction # only use one gpu processor = KGProcessor() # get the chunk entities test_triples = processor.get_test_triples(args.data_dir, args.chunk) dataset = TestDataset(args, test_triples,tokenizer=tokenizer, processor=processor) dataloader = DataLoader(dataset, batch_size=1,shuffle=False, num_workers=2) all_triples_str_set = set() # get all the entities entity_list = processor.get_entities(args.data_dir) label_list = processor.get_labels(args.data_dir) device = torch.device("cuda:0") model = model.to(device) ranks = [] ranks_left = [] ranks_right = [] hits_left = [] hits_right = [] hits = [] top_ten_hit_count = 0 for i in range(10): hits_left.append([]) hits_right.append([]) hits.append([]) pbar = tqdm(total=len(test_triples), desc="Testing...") for test_triple in test_triples: head = test_triple[0] relation = test_triple[1] tail = test_triple[2] # print(test_triple, head, relation, tail) head_corrupt_list = [test_triple] for corrupt_ent in entity_list: if corrupt_ent != head: tmp_triple = [corrupt_ent, relation, tail] tmp_triple_str = "\t".join(tmp_triple) if tmp_triple_str not in all_triples_str_set: # may be slow head_corrupt_list.append(tmp_triple) tmp_examples = processor._create_examples( head_corrupt_list, "test", args.data_dir ) # print(len(tmp_examples)) tmp_features = convert_examples_to_features( tmp_examples, label_list, args.max_seq_length, tokenizer, args ) all_input_ids = torch.tensor( [f.input_ids for f in tmp_features], dtype=torch.long ) all_input_mask = torch.tensor( [f.input_mask for f in tmp_features], dtype=torch.long ) all_segment_ids = torch.tensor( [f.segment_ids for f in tmp_features], dtype=torch.long ) all_label_ids = torch.tensor( [f.label_id for f in tmp_features], dtype=torch.long ) eval_data = TensorDataset( all_input_ids, all_input_mask, all_segment_ids, all_label_ids ) # Run prediction for temp data eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader( eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size, num_workers=16 ) model.eval() preds = [] for input_ids, input_mask, segment_ids, label_ids in eval_dataloader: input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) label_ids = label_ids.to(device) with torch.no_grad(): logits = model( input_ids, token_type_ids=segment_ids, attention_mask=input_mask ) if len(preds) == 0: batch_logits = logits.detach().cpu().numpy() preds.append(batch_logits) else: batch_logits = logits.detach().cpu().numpy() preds[0] = np.append(preds[0], batch_logits, axis=0) preds = preds[0] # get the dimension corresponding to current label 1 # print(preds, preds.shape) rel_values = preds[:, all_label_ids[0]] rel_values = torch.tensor(rel_values) # print(rel_values, rel_values.shape) _, argsort1 = torch.sort(rel_values, descending=True) # print(max_values) # print(argsort1) argsort1 = argsort1.cpu().numpy() rank1 = np.where(argsort1 == 0)[0][0] # print("left: ", rank1) ranks.append(rank1 + 1) ranks_left.append(rank1 + 1) if rank1 < 10: top_ten_hit_count += 1 tail_corrupt_list = [test_triple] for corrupt_ent in entity_list: if corrupt_ent != tail: tmp_triple = [head, relation, corrupt_ent] tmp_triple_str = "\t".join(tmp_triple) if tmp_triple_str not in all_triples_str_set: # may be slow tail_corrupt_list.append(tmp_triple) tmp_examples = processor._create_examples( tail_corrupt_list, "test", args.data_dir ) # print(len(tmp_examples)) tmp_features = convert_examples_to_features( tmp_examples, label_list, args.max_seq_length, tokenizer, args ) all_input_ids = torch.tensor( [f.input_ids for f in tmp_features], dtype=torch.long ) all_input_mask = torch.tensor( [f.input_mask for f in tmp_features], dtype=torch.long ) all_segment_ids = torch.tensor( [f.segment_ids for f in tmp_features], dtype=torch.long ) all_label_ids = torch.tensor( [f.label_id for f in tmp_features], dtype=torch.long ) eval_data = TensorDataset( all_input_ids, all_input_mask, all_segment_ids, all_label_ids ) # Run prediction for temp data eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader( eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size, num_workers=16 ) model.eval() preds = [] for input_ids, input_mask, segment_ids, label_ids in eval_dataloader: input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) label_ids = label_ids.to(device) with torch.no_grad(): logits = model( input_ids, token_type_ids=segment_ids, attention_mask=input_mask ) if len(preds) == 0: batch_logits = logits.detach().cpu().numpy() preds.append(batch_logits) else: batch_logits = logits.detach().cpu().numpy() preds[0] = np.append(preds[0], batch_logits, axis=0) preds = preds[0] # get the dimension corresponding to current label 1 rel_values = preds[:, all_label_ids[0]] rel_values = torch.tensor(rel_values) _, argsort1 = torch.sort(rel_values, descending=True) argsort1 = argsort1.cpu().numpy() rank2 = np.where(argsort1 == 0)[0][0] ranks.append(rank2 + 1) ranks_right.append(rank2 + 1) # print("right: ", rank2) # print("mean rank until now: ", np.mean(ranks)) if rank2 < 10: top_ten_hit_count += 1 # print("hit@10 until now: ", top_ten_hit_count * 1.0 / len(ranks)) pbar.update(1) pbar.set_postfix({"mean rank": np.mean(ranks), "hit@10": top_ten_hit_count * 1.0 / len(ranks) }) # file_prefix = ( # str(args.data_dir[7:]) # + "_" # + str(args.batch_size) # + "_" # + str(args.lr) # + "_" # + str(args.max_seq_length) # + "_" # + str(args.max_epochs) # ) # # file_prefix = str(args.data_dir[7:]) # f = open(file_prefix + "_ranks.txt", "a") # f.write(str(rank1) + "\t" + str(rank2) + "\n") # f.close() # this could be done more elegantly, but here you go for hits_level in range(10): if rank1 <= hits_level: hits[hits_level].append(1.0) hits_left[hits_level].append(1.0) else: hits[hits_level].append(0.0) hits_left[hits_level].append(0.0) if rank2 <= hits_level: hits[hits_level].append(1.0) hits_right[hits_level].append(1.0) else: hits[hits_level].append(0.0) hits_right[hits_level].append(0.0) for i in [0, 2, 9]: logger.info("Hits left @{0}: {1}".format(i + 1, np.mean(hits_left[i]))) logger.info("Hits right @{0}: {1}".format(i + 1, np.mean(hits_right[i]))) logger.info("Hits @{0}: {1}".format(i + 1, np.mean(hits[i]))) wandb_logger.log_metrics({f'hits{i+1}': np.mean(hits[i])}) logger.info("Mean rank left: {0}".format(np.mean(ranks_left))) logger.info("Mean rank right: {0}".format(np.mean(ranks_right))) logger.info("Mean rank: {0}".format(np.mean(ranks))) logger.info( "Mean reciprocal rank left: {0}".format(np.mean(1.0 / np.array(ranks_left))) ) logger.info( "Mean reciprocal rank right: {0}".format(np.mean(1.0 / np.array(ranks_right))) ) logger.info("Mean reciprocal rank: {0}".format(np.mean(1.0 / np.array(ranks)))) wandb_logger.log_metrics({'mrr': np.mean(1.0 / np.array(ranks))}) wandb_logger.log_metrics({'mr': np.mean(ranks)}) if args.chunk: with open(f"chuck{args.chunk}_result_rank.pkl", "wb") as file: pickle.dump(ranks, file) def gather_all_ranks(): ranks = np.array([]) for i in range(10): with open(f"chuck{i}_result_rank.pkl", "rb") as file: ranks = np.concatenate([ranks, pickle.load(file)], axis=0) return ranks.mean(), (ranks<=10).mean()

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a99a82b7c5b854b68f699ed3ce351501204a31bd

7,460

py

Python

project/game/ai/tests/test_riichi.py

MahjongRepository/tenhou-python-bot

e07f480d519930148534e02978ef7687f424924e

[ "MIT" ]

201

2016-06-11T20:04:09.000Z

2021-12-30T06:32:09.000Z

project/game/ai/tests/test_riichi.py

MahjongRepository/tenhou-python-bot

e07f480d519930148534e02978ef7687f424924e

[ "MIT" ]

131

2016-05-21T08:06:44.000Z

2021-03-01T01:01:50.000Z

project/game/ai/tests/test_riichi.py

MahjongRepository/tenhou-python-bot

e07f480d519930148534e02978ef7687f424924e

[ "MIT" ]

68

2016-10-12T16:17:07.000Z

2022-02-26T17:36:17.000Z

from game.table import Table from mahjong.tile import Tile from utils.test_helpers import enemy_called_riichi_helper, string_to_136_array, string_to_136_tile def test_dont_call_riichi_with_yaku_and_central_tanki_wait(): table = _make_table() tiles = string_to_136_array(sou="234567", pin="234567", man="4") table.player.init_hand(tiles) table.player.draw_tile(string_to_136_tile(man="5")) _, with_riichi = table.player.discard_tile() assert with_riichi is False def test_dont_call_riichi_expensive_damaten_with_yaku(): table = _make_table( dora_indicators=[ string_to_136_tile(man="7"), string_to_136_tile(man="5"), string_to_136_tile(sou="1"), ] ) # tanyao pinfu sanshoku dora 4 - this is damaten baiman, let's not riichi it tiles = string_to_136_array(man="67888", sou="678", pin="34678") table.player.init_hand(tiles) table.player.draw_tile(string_to_136_tile(honors="3")) _, with_riichi = table.player.discard_tile() assert with_riichi is False # let's test lots of doras hand, tanyao dora 8, also damaten baiman tiles = string_to_136_array(man="666888", sou="22", pin="34678") table.player.init_hand(tiles) table.player.draw_tile(string_to_136_tile(honors="3")) _, with_riichi = table.player.discard_tile() assert with_riichi is False # chuuren tiles = string_to_136_array(man="1112345678999") table.player.init_hand(tiles) table.player.draw_tile(string_to_136_tile(honors="3")) _, with_riichi = table.player.discard_tile() assert with_riichi is False def test_riichi_expensive_hand_without_yaku_2(): table = _make_table( dora_indicators=[ string_to_136_tile(man="1"), string_to_136_tile(sou="1"), string_to_136_tile(pin="1"), ] ) tiles = string_to_136_array(man="222", sou="22278", pin="22789") table.player.init_hand(tiles) table.player.draw_tile(string_to_136_tile(honors="3")) _, with_riichi = table.player.discard_tile() assert with_riichi is True def test_riichi_tanki_honor_without_yaku(): table = _make_table(dora_indicators=[string_to_136_tile(man="2"), string_to_136_tile(sou="6")]) tiles = string_to_136_array(man="345678", sou="789", pin="123", honors="2") table.player.init_hand(tiles) table.player.draw_tile(string_to_136_tile(honors="3")) _, with_riichi = table.player.discard_tile() assert with_riichi is True def test_riichi_tanki_honor_chiitoitsu(): table = _make_table() tiles = string_to_136_array(man="22336688", sou="99", pin="99", honors="2") table.player.init_hand(tiles) table.player.draw_tile(string_to_136_tile(honors="3")) _, with_riichi = table.player.discard_tile() assert with_riichi is True def test_always_call_daburi(): table = _make_table() table.player.round_step = 0 tiles = string_to_136_array(sou="234567", pin="234567", man="4") table.player.init_hand(tiles) table.player.draw_tile(string_to_136_tile(man="5")) _, with_riichi = table.player.discard_tile() assert with_riichi is True def test_dont_call_karaten_tanki_riichi(): table = _make_table() tiles = string_to_136_array(man="22336688", sou="99", pin="99", honors="2") table.player.init_hand(tiles) for _ in range(0, 3): table.add_discarded_tile(1, string_to_136_tile(honors="2"), False) table.add_discarded_tile(1, string_to_136_tile(honors="3"), False) table.player.draw_tile(string_to_136_tile(honors="3")) _, with_riichi = table.player.discard_tile() assert with_riichi is False def test_dont_call_karaten_ryanmen_riichi(): table = _make_table( dora_indicators=[ string_to_136_tile(man="1"), string_to_136_tile(sou="1"), string_to_136_tile(pin="1"), ] ) tiles = string_to_136_array(man="222", sou="22278", pin="22789") table.player.init_hand(tiles) for _ in range(0, 4): table.add_discarded_tile(1, string_to_136_tile(sou="6"), False) table.add_discarded_tile(1, string_to_136_tile(sou="9"), False) table.player.draw_tile(string_to_136_tile(honors="3")) _, with_riichi = table.player.discard_tile() assert with_riichi is False def test_call_riichi_penchan_with_suji(): table = _make_table( dora_indicators=[ string_to_136_tile(pin="1"), ] ) tiles = string_to_136_array(sou="11223", pin="234567", man="66") table.player.init_hand(tiles) table.player.draw_tile(string_to_136_tile(sou="6")) _, with_riichi = table.player.discard_tile() assert with_riichi is True def test_call_riichi_tanki_with_kabe(): table = _make_table( dora_indicators=[ string_to_136_tile(pin="1"), ] ) for _ in range(0, 3): table.add_discarded_tile(1, string_to_136_tile(honors="1"), False) for _ in range(0, 4): table.add_discarded_tile(1, string_to_136_tile(sou="8"), False) tiles = string_to_136_array(sou="1119", pin="234567", man="666") table.player.init_hand(tiles) table.player.draw_tile(string_to_136_tile(honors="1")) _, with_riichi = table.player.discard_tile() assert with_riichi is True def test_call_riichi_chiitoitsu_with_suji(): table = _make_table( dora_indicators=[ string_to_136_tile(man="1"), ] ) for _ in range(0, 3): table.add_discarded_tile(1, string_to_136_tile(honors="3"), False) tiles = string_to_136_array(man="22336688", sou="9", pin="99", honors="22") table.player.init_hand(tiles) table.player.add_discarded_tile(Tile(string_to_136_tile(sou="6"), True)) table.player.draw_tile(string_to_136_tile(honors="3")) _, with_riichi = table.player.discard_tile() assert with_riichi is True def test_dont_call_riichi_chiitoitsu_bad_wait(): table = _make_table( dora_indicators=[ string_to_136_tile(man="1"), ] ) for _ in range(0, 3): table.add_discarded_tile(1, string_to_136_tile(honors="3"), False) tiles = string_to_136_array(man="22336688", sou="4", pin="99", honors="22") table.player.init_hand(tiles) table.player.draw_tile(string_to_136_tile(honors="3")) _, with_riichi = table.player.discard_tile() assert with_riichi is False def test_dont_call_pinfu_nomi_chasing_riichi(): table = _make_table() enemy_called_riichi_helper(table, 3) tiles = string_to_136_array(man="123", sou="234567", pin="2278") table.player.init_hand(tiles) table.player.draw_tile(string_to_136_tile(honors="3")) # on early stages it is fine to call chasing riichi here _, with_riichi = table.player.discard_tile() assert with_riichi is True table.player.round_step = 9 table.player.draw_tile(string_to_136_tile(honors="3")) # on late stage let's save riichi stick _, with_riichi = table.player.discard_tile() assert with_riichi is False def _make_table(dora_indicators=None) -> Table: table = Table() table.count_of_remaining_tiles = 60 table.player.scores = 25000 # with that we don't have daburi anymore table.player.round_step = 1 # with that we are not dealer anymore table.player.seat = 1 if dora_indicators: for x in dora_indicators: table.add_dora_indicator(x) return table

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py

Python

longboxed/api_v1/decorators.py

timbueno/longboxed

b5969739bbc3226fa1a6e851ee1c7b3f978b0631

[ "MIT" ]

9

2015-06-17T07:39:17.000Z

2022-01-16T21:58:41.000Z

longboxed/api_v1/decorators.py

timbueno/longboxed

b5969739bbc3226fa1a6e851ee1c7b3f978b0631

[ "MIT" ]

21

2015-02-04T01:35:33.000Z

2021-02-18T03:11:29.000Z

longboxed/api_v1/decorators.py

timbueno/longboxed

b5969739bbc3226fa1a6e851ee1c7b3f978b0631

[ "MIT" ]

2

2015-09-02T22:32:07.000Z

2019-05-14T23:29:50.000Z

# -*- coding: utf-8 -*- """ longboxed.api.decorators ~~~~~~~~~~~~~ longboxed api decorators """

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py

Python

day20/src_test.py

arcadecoffee/advent-2015

711ac1061f661c07d511c0b2c77c0b111a22ff44

[ "MIT" ]

null

day20/src_test.py

arcadecoffee/advent-2015

711ac1061f661c07d511c0b2c77c0b111a22ff44

[ "MIT" ]

null

day20/src_test.py

arcadecoffee/advent-2015

711ac1061f661c07d511c0b2c77c0b111a22ff44

[ "MIT" ]

null

import day20.src as src def test_part1(): assert src.part1(src.TEST_INPUT_FILE) == 8 def test_part1_full(): assert src.part1(src.FULL_INPUT_FILE) == 665280 def test_part2(): assert src.part2(src.TEST_INPUT_FILE) == 8 def test_part2_full(): assert src.part2(src.FULL_INPUT_FILE) == 705600

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py

Python

lib/__init__.py

motomizuki/tarsier-output-slack

68fe77fec0bb339b7f13fa680931be76cde207e9

[ "MIT" ]

null

lib/__init__.py

motomizuki/tarsier-output-slack

68fe77fec0bb339b7f13fa680931be76cde207e9

[ "MIT" ]

null

lib/__init__.py

motomizuki/tarsier-output-slack

68fe77fec0bb339b7f13fa680931be76cde207e9

[ "MIT" ]

null

from .tarsier_output_slack import TarsierOutputSlack

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8d8d896d6bc37e69726cae003b5031d695ffd011

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py

Python

bcbio/picard/utils.py

a113n/bcbio-nextgen

1d4afef27ad2e84a4ecb6145ccc5058f2abb4616

[ "MIT" ]

418

2015-01-01T18:21:17.000Z

2018-03-02T07:26:28.000Z

bcbio/picard/utils.py

ahmedelhosseiny/bcbio-nextgen

b5618f3c100a1a5c04bd5c8acad8f96d0587e41c

[ "MIT" ]

1,634

2015-01-04T11:43:43.000Z

2018-03-05T18:06:39.000Z

bcbio/picard/utils.py

ahmedelhosseiny/bcbio-nextgen

b5618f3c100a1a5c04bd5c8acad8f96d0587e41c

[ "MIT" ]

218

2015-01-26T05:58:18.000Z

2018-03-03T05:50:05.000Z

# Placeholder for back compatibility. from bcbio.utils import *

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py

Python

src/modulepaths.py

nicholaschiasson/focus

9c081806eec09d0b894e39b282a553fa292d458b

[ "MIT" ]

null

src/modulepaths.py

nicholaschiasson/focus

9c081806eec09d0b894e39b282a553fa292d458b

[ "MIT" ]

null

src/modulepaths.py

nicholaschiasson/focus

9c081806eec09d0b894e39b282a553fa292d458b

[ "MIT" ]

null

import os import sys sys.path.append(os.path.dirname(__file__) + "/../modules/nai/src")

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py

Python

zcrmsdk/src/com/zoho/crm/api/pipeline/transfer_action_handler.py

zoho/zohocrm-python-sdk-2.1

cde6fcd1c5c8f7a572154ebb2b947ec697c24209

[ "Apache-2.0" ]

null

zcrmsdk/src/com/zoho/crm/api/pipeline/transfer_action_handler.py

zoho/zohocrm-python-sdk-2.1

cde6fcd1c5c8f7a572154ebb2b947ec697c24209

[ "Apache-2.0" ]

null

zcrmsdk/src/com/zoho/crm/api/pipeline/transfer_action_handler.py

zoho/zohocrm-python-sdk-2.1

cde6fcd1c5c8f7a572154ebb2b947ec697c24209

[ "Apache-2.0" ]

null

from abc import ABC, abstractmethod class TransferActionHandler(ABC): def __init__(self): """Creates an instance of TransferActionHandler""" pass

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py

Python

moredata/enricher/api_connector/__init__.py

thomassonobe/more-data

b3d4a8e32f385a69749c8139915e3638fcced37b

[ "BSD-3-Clause" ]

7

2021-02-08T12:09:26.000Z

2022-03-29T15:11:35.000Z

moredata/enricher/api_connector/__init__.py

thomassonobe/more-data

b3d4a8e32f385a69749c8139915e3638fcced37b

[ "BSD-3-Clause" ]

14

2021-06-02T17:24:51.000Z

2022-02-28T13:52:05.000Z

moredata/enricher/api_connector/__init__.py

thomassonobe/more-data

b3d4a8e32f385a69749c8139915e3638fcced37b

[ "BSD-3-Clause" ]

1

2021-10-05T21:12:14.000Z

from .api_connector import ApiConnector

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py

Python

harvester/sharekit/admin.py

surfedushare/search-portal

708a0d05eee13c696ca9abd7e84ab620d3900fbe

[ "MIT" ]

2

2021-08-19T09:40:59.000Z

2021-12-14T11:08:20.000Z

harvester/sharekit/admin.py

surfedushare/search-portal

708a0d05eee13c696ca9abd7e84ab620d3900fbe

[ "MIT" ]

159

2020-05-14T14:17:34.000Z

2022-03-23T10:28:13.000Z

harvester/sharekit/admin.py

nppo/search-portal

aedf21e334f178c049f9d6cf37cafd6efc07bc0d

[ "MIT" ]

1

2021-11-11T13:37:22.000Z

from django.contrib import admin from datagrowth.admin import HttpResourceAdmin from sharekit.models import SharekitMetadataHarvest admin.site.register(SharekitMetadataHarvest, HttpResourceAdmin)

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py

Python

autotest/smoother/freyberg/start_pestpp_slaves.py

hwreeves-USGS/pyemu

6b443601fbb9bcb9e97a8c200a78480c11c51f22

[ "BSD-3-Clause" ]

94

2015-01-09T14:19:47.000Z

2022-03-14T18:42:23.000Z

autotest/smoother/freyberg/start_pestpp_slaves.py

hwreeves-USGS/pyemu

6b443601fbb9bcb9e97a8c200a78480c11c51f22

[ "BSD-3-Clause" ]

184

2020-05-29T14:25:23.000Z

2022-03-29T04:01:42.000Z

autotest/smoother/freyberg/start_pestpp_slaves.py

hwreeves-USGS/pyemu

6b443601fbb9bcb9e97a8c200a78480c11c51f22

[ "BSD-3-Clause" ]

51

2015-01-14T15:55:11.000Z

2021-12-28T17:59:24.000Z

import os import pyemu pyemu.utils.start_workers('template',"pestpp","freyberg.pst",15,worker_root='.',port=4004)

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py

Python

cupy_alias/logic/comparison.py

fixstars/clpy

693485f85397cc110fa45803c36c30c24c297df0

[ "BSD-3-Clause" ]

142

2018-06-07T07:43:10.000Z

2021-10-30T21:06:32.000Z

cupy_alias/logic/comparison.py

fixstars/clpy

693485f85397cc110fa45803c36c30c24c297df0

[ "BSD-3-Clause" ]

282

2018-06-07T08:35:03.000Z

2021-03-31T03:14:32.000Z

cupy_alias/logic/comparison.py

fixstars/clpy

693485f85397cc110fa45803c36c30c24c297df0

[ "BSD-3-Clause" ]

19

2018-06-19T11:07:53.000Z

2021-05-13T20:57:04.000Z

from clpy.logic.comparison import * # NOQA

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py

Python

api/organisations/permissions/__init__.py

mevinbabuc/flagsmith

751bd6cb4a34bd2f80af5a9c547559da9c2fa010

[ "BSD-3-Clause" ]

1,259

2021-06-10T11:24:09.000Z

2022-03-31T10:30:44.000Z

api/organisations/permissions/__init__.py

mevinbabuc/flagsmith

751bd6cb4a34bd2f80af5a9c547559da9c2fa010

[ "BSD-3-Clause" ]

392

2021-06-10T11:12:29.000Z

2022-03-31T10:13:53.000Z

api/organisations/permissions/__init__.py

mevinbabuc/flagsmith

751bd6cb4a34bd2f80af5a9c547559da9c2fa010

[ "BSD-3-Clause" ]

58

2021-06-11T03:18:07.000Z

2022-03-31T14:39:10.000Z

default_app_config = "organisations.permissions.apps.OrganisationPermissionsConfig"

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93f00b7232ccc79b89fae031d0ecb3d5fe4b0bc3

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py

Python

test_Classes/__init__.py

nickmarton/Paxos-Distributed-Calendar

45316c59ee7c300fdd33cdb9f149d19e1ec9c199

[ "MIT" ]

3

2015-11-08T23:26:57.000Z

2018-11-06T19:37:48.000Z

test_Classes/__init__.py

nickmarton/Paxos-Distributed-Calendar

45316c59ee7c300fdd33cdb9f149d19e1ec9c199

[ "MIT" ]

null

test_Classes/__init__.py

nickmarton/Paxos-Distributed-Calendar

45316c59ee7c300fdd33cdb9f149d19e1ec9c199

[ "MIT" ]

null

"""Treate Paxos directory as a package."""

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5

9e118d81ad5eb424d969aa5f7b4a638fcafdad99

127

py

Python

mysite/aiir/admin.py

kztrp/aiir-server

0c49250cee57b8b155b06982721f493cd729ee58

[ "MIT" ]

null

mysite/aiir/admin.py

kztrp/aiir-server

0c49250cee57b8b155b06982721f493cd729ee58

[ "MIT" ]

null

mysite/aiir/admin.py

kztrp/aiir-server

0c49250cee57b8b155b06982721f493cd729ee58

[ "MIT" ]

null

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

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f53c8f4323d6c8865809f6cc0584c26b285ee272

192

py

Python

Contributors/EXAMPLE/Hello.py

The-Arjun-Thakor/HacktoberFest2021

09648b39f6160d421e169fbf68d7e6f1809c3c6c

[ "MIT" ]

3

2021-10-01T06:15:56.000Z

2021-10-05T11:12:44.000Z

Contributors/EXAMPLE/Hello.py

The-Arjun-Thakor/HacktoberFest2021

09648b39f6160d421e169fbf68d7e6f1809c3c6c

[ "MIT" ]

5

2021-10-05T11:08:56.000Z

2021-10-14T05:55:27.000Z

Contributors/EXAMPLE/Hello.py

The-Arjun-Thakor/HacktoberFest2021

09648b39f6160d421e169fbf68d7e6f1809c3c6c

[ "MIT" ]

4

2021-10-01T07:56:03.000Z

2021-10-14T05:35:16.000Z

print("Hello World !") print("I am <YourName>") print("I am From <Place>") print("I am <Your College Name and Year of Study>") print("Email ID : <Your Email>") print("Github : <Your Github>")

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f57323fef7d9721fdaebe1d7d2f042c430a065b0

41

py

Python

jklib/django/utils/__init__.py

Jordan-Kowal/jklib

84dc8ad64b9216926ba9af0ec11f1dbd5d8a53f4

[ "MIT" ]

1

2020-02-28T21:53:51.000Z

jklib/django/utils/__init__.py

Jordan-Kowal/jklib

84dc8ad64b9216926ba9af0ec11f1dbd5d8a53f4

[ "MIT" ]

null

jklib/django/utils/__init__.py

Jordan-Kowal/jklib

84dc8ad64b9216926ba9af0ec11f1dbd5d8a53f4

[ "MIT" ]

null

"""Contains utility and QOL functions"""

20.5

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0

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0

5

f5950dab0a9b00527ec12a6e8345dd080f68b7e5

923

py

Python

swagger_client/api/__init__.py

chbndrhnns/finapi-client

259beda8b05e912c49d2dc4c3ed71205134e5d8a

[ "MIT" ]

2

2019-04-15T05:58:21.000Z

2021-11-15T18:26:37.000Z

swagger_client/api/__init__.py

chbndrhnns/finapi-client

259beda8b05e912c49d2dc4c3ed71205134e5d8a

[ "MIT" ]

1

2021-06-18T09:46:25.000Z

2021-06-18T20:12:41.000Z

swagger_client/api/__init__.py

chbndrhnns/finapi-client

259beda8b05e912c49d2dc4c3ed71205134e5d8a

[ "MIT" ]

2

2019-07-08T13:41:09.000Z

2020-12-07T12:10:04.000Z

from __future__ import absolute_import # flake8: noqa # import apis into api package from swagger_client.api.accounts_api import AccountsApi from swagger_client.api.authorization_api import AuthorizationApi from swagger_client.api.bank_connections_api import BankConnectionsApi from swagger_client.api.banks_api import BanksApi from swagger_client.api.categories_api import CategoriesApi from swagger_client.api.client_configuration_api import ClientConfigurationApi from swagger_client.api.labels_api import LabelsApi from swagger_client.api.mandator_administration_api import MandatorAdministrationApi from swagger_client.api.mocks_and_tests_api import MocksAndTestsApi from swagger_client.api.notification_rules_api import NotificationRulesApi from swagger_client.api.securities_api import SecuritiesApi from swagger_client.api.transactions_api import TransactionsApi from swagger_client.api.users_api import UsersApi

48.578947

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191d31015106b5044484975723e580eb42a050c1

42

py

Python

src/test/resources/python/invalid.py

VolkerHartmann/testAction

5e92ad5e2598633ac401bfaaaa84b567a900d16c

[ "Apache-2.0" ]

null

src/test/resources/python/invalid.py

VolkerHartmann/testAction

5e92ad5e2598633ac401bfaaaa84b567a900d16c

[ "Apache-2.0" ]

2

2020-12-16T14:36:34.000Z

2021-12-13T15:05:03.000Z

src/test/resources/python/invalid.py

VolkerHartmann/testAction

5e92ad5e2598633ac401bfaaaa84b567a900d16c

[ "Apache-2.0" ]

2

2020-12-15T13:09:59.000Z

2020-12-15T13:20:36.000Z

#!/usr/bin/python print "Not allowed"

6

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42

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20

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0

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null

0

1

0

1

0

5

1941cee21dbce7d84a7811e27e22a78643751e0a

49

py

Python

tests/__init__.py

nkaenzig/ml_model_evaluation

0064a223b3a6362b7e281d9241cb9ffe97247bb0

[ "MIT" ]

null

tests/__init__.py

nkaenzig/ml_model_evaluation

0064a223b3a6362b7e281d9241cb9ffe97247bb0

[ "MIT" ]

null

tests/__init__.py

nkaenzig/ml_model_evaluation

0064a223b3a6362b7e281d9241cb9ffe97247bb0

[ "MIT" ]

null

"""Unit test package for ml_model_evaluation."""

24.5

48

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5

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1

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null

0

1

0

5

195629075bfb37adc9480358948ed4cf1c6bf460

8,113

py

Python

models/bert_hie_self_sup_pretrain.py

alibaba/Retrieval-based-Pre-training-for-Machine-Reading-Comprehension

b27dc55446a29a53af7fffdad8628ccb545420da

[ "Apache-2.0" ]

7

2021-06-16T01:40:23.000Z

2021-12-04T02:40:35.000Z

models/bert_hie_self_sup_pretrain.py

SparkJiao/Retrieval-based-Pre-training-for-Machine-Reading-Comprehension

9ccad31bd0bf2216004cf729d1d511fc3e0b77c9

[ "Apache-2.0" ]

1

2021-08-16T09:10:05.000Z

2021-08-25T08:44:44.000Z

models/bert_hie_self_sup_pretrain.py

SparkJiao/Retrieval-based-Pre-training-for-Machine-Reading-Comprehension

9ccad31bd0bf2216004cf729d1d511fc3e0b77c9

[ "Apache-2.0" ]

3

2021-09-13T02:03:37.000Z

2021-10-11T18:48:21.000Z

import torch from torch import nn from torch.nn import functional as F from transformers import BertPreTrainedModel, BertModel from modules import layers class BertSelfSupPretain(BertPreTrainedModel): """ Pre-training BERT backbone or together with LinearSelfAttn """ def __init__(self, config): super().__init__(config) print(f'The model {self.__class__.__name__} is loading...') layers.set_seq_dropout(True) layers.set_my_dropout_prob(config.hidden_dropout_prob) self.bert = BertModel(config) self.sent_self_attn = layers.LinearSelfAttnAllennlp(config.hidden_size) self.project1 = nn.Linear(config.hidden_size, config.hidden_size) self.project2 = nn.Linear(config.hidden_size, config.hidden_size) self.init_weights() def forward(self, input_ids, token_type_ids=None, attention_mask=None, answers=None, p_sentence_spans=None, q_sentence_spans=None): sequence_output = self.bert(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)[0] # mask: 1 for masked value and 0 for true value hidden, mask, sent_mask = split_doc_sen_que(sequence_output, q_sentence_spans, p_sentence_spans) batch, sent_num, seq_len = mask.size() hidden = hidden.view(batch * sent_num, seq_len, -1) mask = mask.view(batch * sent_num, seq_len) alpha = self.sent_self_attn(hidden, mask) hidden = alpha.unsqueeze(1).bmm(hidden).squeeze().reshape(batch, sent_num, -1) assert hidden.size(-1) == sequence_output.size(-1) query = self.project1(hidden) key = self.project2(hidden) scores = query.bmm(key.transpose(1, 2)) output_dict = {"logits": scores} if answers is not None: if sent_num > answers.size(1): scores = scores[:, :answers.size(1)] elif answers.size(1) > sent_num: answers = answers[:, :sent_num] assert answers.size(1) == scores.size(1) scores = scores + sent_mask[:, None, :] * -10000.0 loss = F.cross_entropy(scores.reshape(-1, sent_num), answers.reshape(-1), ignore_index=-1, reduction='sum') / (batch * 1.0) output_dict["loss"] = loss _, pred = scores.max(dim=-1) valid_num = torch.sum(answers != -1) acc = torch.sum(pred == answers).to(dtype=scores.dtype) / (valid_num * 1.0) output_dict["acc"] = acc output_dict["valid_num"] = valid_num return output_dict class BertSelfSupPretainClsQuery(BertPreTrainedModel): """ Pre-training BERT backbone or together with LinearSelfAttn. Use representation of [CLS] as query to make it trained for downstream task. """ model_prefix = 'self_sup_pretrain_cls_query' def __init__(self, config): super().__init__(config) print(f'The model {self.__class__.__name__} is loading...') layers.set_seq_dropout(True) layers.set_my_dropout_prob(config.hidden_dropout_prob) self.bert = BertModel(config) self.cls_w = nn.Linear(config.hidden_size, config.hidden_size) self.project1 = nn.Linear(config.hidden_size, config.hidden_size) self.project2 = nn.Linear(config.hidden_size, config.hidden_size) self.init_weights() def forward(self, input_ids, token_type_ids=None, attention_mask=None, answers=None, p_sentence_spans=None, q_sentence_spans=None): sequence_output = self.bert(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)[0] # mask: 1 for masked value and 0 for true value hidden, mask, sent_mask, cls_h = split_doc_sen_que(sequence_output, q_sentence_spans, p_sentence_spans, sep_cls=True) batch, sent_num, seq_len = mask.size() # hidden = hidden.view(batch * sent_num, seq_len, -1) # hidden = hidden.view(batch, sent_num * seq_len, -1) # mask = mask.view(batch * sent_num, seq_len) hidden = layers.dropout(hidden, p=layers.my_dropout_p, training=self.training) cls_h = self.cls_w(cls_h) # [batch, h] alpha = torch.einsum('bh,bsth->bst', cls_h, hidden) alpha = (alpha + mask * -10000.0).softmax(dim=-1) hidden = torch.einsum('bst,bsth->bsh', alpha, hidden) query = self.project1(hidden) key = self.project2(hidden) scores = query.bmm(key.transpose(1, 2)) output_dict = {"logits": scores} if answers is not None: if sent_num > answers.size(1): scores = scores[:, :answers.size(1)] elif answers.size(1) > sent_num: answers = answers[:, :sent_num] assert answers.size(1) == scores.size(1) scores = scores + sent_mask[:, None, :] * -10000.0 loss = F.cross_entropy(scores.reshape(-1, sent_num), answers.reshape(-1), ignore_index=-1, reduction='sum') / (batch * 1.0) output_dict["loss"] = loss _, pred = scores.max(dim=-1) valid_num = torch.sum(answers != -1) acc = torch.sum(pred == answers).to(dtype=scores.dtype) / (valid_num * 1.0) output_dict["acc"] = acc output_dict["valid_num"] = valid_num return output_dict def split_sentence(hidden_state, sentence_spans): batch, seq_len, h = hidden_state.size() max_sent_len = 0 for b in range(batch): max_sent_len = max(max_sent_len, max(map(lambda x: x[1] - x[0] + 1, sentence_spans[b]))) max_sent_num = max(map(lambda x: len(x), sentence_spans)) output = hidden_state.new_zeros((batch, max_sent_num, max_sent_len, h)) mask = hidden_state.new_ones(batch, max_sent_num, max_sent_len) for b in range(batch): for sent_id, (start, end) in enumerate(sentence_spans[b]): lens = end - start + 1 output[b][sent_id][:lens] = hidden_state[b][start:(end + 1)] mask[b][sent_id][:lens] = hidden_state.new_zeros(lens) return output, mask def split_doc_sen_que(hidden_state, q_sentence_spans, p_sentence_spans, sep_cls=False): # q_hidden, q_mask = split_sentence(hidden_state, q_sentence_spans) # p_hidden, p_mask = split_sentence(hidden_state, p_sentence_spans) # return q_hidden, q_mask, p_hidden, p_mask cls_h = hidden_state[:, 0] batch = hidden_state.size(0) h = hidden_state.size(-1) # print(hidden_state.size()) # print(len(q_sentence_spans)) max_sent_len = 0 for b in range(batch): max_sent_len = max(max_sent_len, max(map(lambda x: x[1] - x[0] + 1, q_sentence_spans[b] + p_sentence_spans[b]))) max_sent_num = max(map(lambda x: len(x[0]) + len(x[1]), zip(q_sentence_spans, p_sentence_spans))) # print(max_sent_len) # print(max_sent_num) output = hidden_state.new_zeros((batch, max_sent_num, max_sent_len, h)) mask = hidden_state.new_ones(batch, max_sent_num, max_sent_len) sent_mask = hidden_state.new_ones(batch, max_sent_num) for b in range(batch): for sent_id, (start, end) in enumerate(q_sentence_spans[b] + p_sentence_spans[b]): if sep_cls and start == 0: assert end >= 1 start += 1 lens = end - start + 1 output[b][sent_id][:lens] = hidden_state[b][start:(end + 1)] mask[b][sent_id][:lens] = hidden_state.new_zeros(lens) sent_mask[b][sent_id] = 0 # print(b, sent_id, lens, start, end) if sep_cls: return output, mask, sent_mask, cls_h return output, mask, sent_mask

40.363184

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null

0

5

19703e863617688eac91cae73c0d98cc775d3f20

169

py

Python

linky/tools/__init__.py

apizzimenti/LinkY

47f493fd4ed8d61177e25f26e8f9d2f3b2a67607

[ "MIT" ]

1

2017-05-17T17:38:38.000Z

linky/tools/__init__.py

apizzimenti/LinkY

47f493fd4ed8d61177e25f26e8f9d2f3b2a67607

[ "MIT" ]

null

linky/tools/__init__.py

apizzimenti/LinkY

47f493fd4ed8d61177e25f26e8f9d2f3b2a67607

[ "MIT" ]

null

#!/usr/bin/env python3 from .paths import load_package from .paths import load_command from .help import helping __all__ = ["load_package", "load_command", "helping"]

21.125

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5.041667

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0.247934

0.31405

0

0.006757

0.12426

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7

54

24.142857

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false

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null

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1

0

null

0

1

0

5

1997468adb2be470585667dae4fc8e1eb32343bf

84

py

Python

athena/utils/gluonts/__init__.py

NREL/ATHENA-forecast

ddf51ff5dd3bdbab55076ea335668bc569672a02

[ "BSD-3-Clause" ]

1

2021-07-02T09:20:51.000Z

athena/utils/gluonts/__init__.py

NREL/ATHENA-forecast

ddf51ff5dd3bdbab55076ea335668bc569672a02

[ "BSD-3-Clause" ]

null

athena/utils/gluonts/__init__.py

NREL/ATHENA-forecast

ddf51ff5dd3bdbab55076ea335668bc569672a02

[ "BSD-3-Clause" ]

1

2021-09-02T11:34:01.000Z

from . evaluation import evaluate_gluonts from . transform import DataTransformGluon

42

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1

0

5

5fd68527ce40ebc582c7ae4e6d718b54a11875c9

63

py

Python

CovidSonglinePawit/__init__.py

PawitKrai/CovidSonglinePawit

497b2421641ebf942774ad2c82e427feda5caf45

[ "MIT" ]

null

CovidSonglinePawit/__init__.py

PawitKrai/CovidSonglinePawit

497b2421641ebf942774ad2c82e427feda5caf45

[ "MIT" ]

null

CovidSonglinePawit/__init__.py

PawitKrai/CovidSonglinePawit

497b2421641ebf942774ad2c82e427feda5caf45

[ "MIT" ]

null

#__init__.py from CovidSonglinePawit.covidreport import report

31.5

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0

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true

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1

0

1

0

1

0

5

5feba2c104e82f836a366fbe03ce20b98e4a9284

191

py

Python

FAS/forms.py

codeLAlit/FAS

99369198fc85b24fc55f77d33afb43834b6f6e7f

[ "MIT" ]

null

FAS/forms.py

codeLAlit/FAS

99369198fc85b24fc55f77d33afb43834b6f6e7f

[ "MIT" ]

null

FAS/forms.py

codeLAlit/FAS

99369198fc85b24fc55f77d33afb43834b6f6e7f

[ "MIT" ]

null

from django import forms class emp_reg(forms.Form): emp_code=forms.CharField(label="Employee Code", max_length=8) emp_name=forms.CharField(label="Employee Name", max_length=100)

31.833333

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1

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1

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null

0

5

27285689b4d5d21b4f671763486bacadc0af09c0

60

py

Python

axesresearch/settings/__init__.py

kevinmcguinness/axes-research

2d7bffcec128d30ae538b5979148aa90b91df393

[ "Apache-2.0" ]

1

2015-03-31T11:58:35.000Z

axesresearch/settings/__init__.py

kevinmcguinness/axes-research

2d7bffcec128d30ae538b5979148aa90b91df393

[ "Apache-2.0" ]

null

axesresearch/settings/__init__.py

kevinmcguinness/axes-research

2d7bffcec128d30ae538b5979148aa90b91df393

[ "Apache-2.0" ]

null

# By default, use the development settings from dev import *

30

42

0.783333

9

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5.222222

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0.166667

60

2

43

30

0.94

0.666667

0

1

0

true

0

1

0

1

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null

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1

0

null

0

1

0

1

0

1

0

5

27919f24862ce116c3efa55f0282d2d7b7809d29

111

py

Python

pyinstaller/hooks/hook-ms_deisotope.py

mstim/glycresoft

1d305c42c7e6cba60326d8246e4a485596a53513

[ "Apache-2.0" ]

18

2017-09-01T12:26:12.000Z

2022-02-23T02:31:29.000Z

pyinstaller/hooks/hook-ms_deisotope.py

mstim/glycresoft

1d305c42c7e6cba60326d8246e4a485596a53513

[ "Apache-2.0" ]

19

2017-03-12T20:40:36.000Z

2022-03-31T22:50:47.000Z

pyinstaller/hooks/hook-ms_deisotope.py

mstim/glycresoft

1d305c42c7e6cba60326d8246e4a485596a53513

[ "Apache-2.0" ]

14

2016-05-06T02:25:30.000Z

2022-03-31T14:40:06.000Z

from PyInstaller.utils.hooks import collect_submodules hiddenimports = collect_submodules("ms_deisotope._c")

22.2

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null

0

1

0

1

0

5

27e2b1fdee13ad4af42c1ce4038ba5acc06d2c90

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py

Python

pfbayes/common/metric.py

xinshi-chen/MetaParticleFlow

2fb400f7a9ffe03cd654fe78f1d51c405cf6b7df

[ "MIT" ]

12

2019-05-29T02:29:09.000Z

2021-06-15T14:24:35.000Z

pfbayes/common/metric.py

xinshi-chen/MetaParticleFlow

2fb400f7a9ffe03cd654fe78f1d51c405cf6b7df

[ "MIT" ]

null

pfbayes/common/metric.py

xinshi-chen/MetaParticleFlow

2fb400f7a9ffe03cd654fe78f1d51c405cf6b7df

[ "MIT" ]

5

2019-08-11T23:29:26.000Z

2022-03-12T15:58:43.000Z

from __future__ import print_function from __future__ import absolute_import from __future__ import division import numpy as np import sklearn.metrics.pairwise as sk_metric from pfbayes.common.distributions import KDE import torch from pfbayes.common.cmd_args import cmd_args from numpy import linalg as LA import pickle import os def square_mmd_fine(p_samples, q_samples, n_p, n_q, kernel_type): """ n_p: number of samples from true distribution p assume n_p >> n_q """ kernel_dict = { 'gaussian': sk_metric.rbf_kernel, 'laplacian': sk_metric.laplacian_kernel, 'sigmoid': sk_metric.sigmoid_kernel, 'polynomial': sk_metric.polynomial_kernel, 'cosine': sk_metric.cosine_similarity, } kernel = kernel_dict[kernel_type] p_samples = np.array(p_samples) q_samples = np.array(q_samples) k_xi_xj = kernel(p_samples, p_samples) k_yi_yj = kernel(q_samples, q_samples) k_xi_yj = kernel(p_samples, q_samples) off_diag_k_xi_xj = (np.sum(k_xi_xj) - np.sum(np.diag(k_xi_xj))) / n_p / (n_p - 1) off_diag_k_yi_yj = (np.sum(k_yi_yj) - np.sum(np.diag(k_yi_yj))) / n_q / (n_q - 1) sum_k_xi_yj = np.sum(k_xi_yj) * 2 / n_p / n_q return off_diag_k_xi_xj + off_diag_k_yi_yj - sum_k_xi_yj def e_p_log_q(p_samples, q_samples): q_samples = torch.Tensor(q_samples) p_samples = torch.Tensor(p_samples) kde = KDE(q_samples) log_score = kde.log_pdf(p_samples) return torch.mean(log_score) class EvalMetric(object): """ using numpy """ def __init__(self, particles, true_mean, true_cov, dim, num_true_samples=None): self.dim = dim self.particles = np.array(particles).reshape(-1, dim) self.n_particles = self.particles.shape[0] self.true_mean = np.array(true_mean).reshape(dim) self.true_cov = np.array(true_cov).reshape(dim, dim) if num_true_samples is None: self.n_samples = max(5000, 10 * cmd_args.num_particles) else: self.n_samples = num_true_samples def square_mmd(self, kernel_type='gaussian'): p_particles = np.random.multivariate_normal(self.true_mean.astype(np.float64), self.true_cov.astype(np.float64), self.n_samples) return square_mmd_fine(p_particles, self.particles, self.n_samples, self.n_particles, kernel_type) def cross_entropy(self): p_particles = np.random.multivariate_normal(self.true_mean.astype(np.float64), self.true_cov.astype(np.float64), self.n_samples) return -np.array(e_p_log_q(p_particles, self.particles).cpu()) def integral_eval(self, test_function): full_path = os.path.realpath(__file__) path = os.path.dirname(full_path) filename = path+'/test_function/test_function_'+str(cmd_args.gauss_dim)+'.pkl' with open(filename, 'rb') as f: matrix_aa, matrix_a, matrix_b, a, b = pickle.load(f) if test_function == 'x': return self.dist_of_mean() elif test_function == 'xAx': return self.distance_of_xax(matrix_aa) elif test_function == 'quadratic': return self.distance_of_quadratic(matrix_a, a, matrix_b, b) else: print('test function not supported') def dist_of_mean(self, q_samples=None): if q_samples is None: q_samples = self.particles else: q_samples = np.array(q_samples).reshape(-1, self.dim) q_mean = np.mean(q_samples, 0) return LA.norm(q_mean-self.true_mean) def distance_of_xax(self, matrix_a, q_samples=None): """||E_q[x'Ax] - E_p[x'Ax]||""" if q_samples is None: q_samples = self.particles else: q_samples = np.array(q_samples).reshape(-1, self.dim) mean = self.true_mean.reshape(1, self.dim) true_val = np.trace(np.matmul(matrix_a, self.true_cov)) true_val += np.sum(np.dot(mean, np.matmul(matrix_a, mean.T))) est_ax = np.matmul(matrix_a, q_samples.T) est_xax = np.diag(np.matmul(q_samples, est_ax)) est_xax = np.mean(est_xax) return np.abs(true_val - est_xax) def distance_of_quadratic(self, matrix_a, a, matrix_b, b, q_samples=None): """||E_q[(Ax+a)'(Bx+b)] - E_p[~~~]||""" if q_samples is None: q_samples = self.particles else: q_samples = np.array(q_samples).reshape(-1, self.dim) # format matrix_a = np.array(matrix_a) matrix_b = np.array(matrix_b) true_val = np.trace(np.matmul(np.matmul(matrix_a, self.true_cov), matrix_b.T)) true_val += np.dot(matrix_a.dot(self.true_mean) + a, matrix_b.dot(self.true_mean) + b) est_ax_a = np.matmul(q_samples, matrix_a.T) + a est_bx_b = np.matmul(q_samples, matrix_b.T) + b est_val = np.diag(np.matmul(est_ax_a, est_bx_b.T)) est_val = np.mean(est_val) return np.abs(true_val - est_val) def create_metric_dict(num_epoch, len_sequence): metric = dict() metric['mmd'] = dict() metric['mmd']['gaussian'] = np.zeros([num_epoch, len_sequence], dtype=np.float32) metric['mmd']['laplacian'] = np.zeros([num_epoch, len_sequence], dtype=np.float32) metric['mmd']['sigmoid'] = np.zeros([num_epoch, len_sequence], dtype=np.float32) metric['mmd']['polynomial'] = np.zeros([num_epoch, len_sequence], dtype=np.float32) metric['mmd']['cosine'] = np.zeros([num_epoch, len_sequence], dtype=np.float32) metric['cross-entropy'] = np.zeros([num_epoch, len_sequence], dtype=np.float32) metric['integral-eval'] = dict() metric['integral-eval']['x'] = np.zeros([num_epoch, len_sequence], dtype=np.float32) metric['integral-eval']['xAx'] = np.zeros([num_epoch, len_sequence], dtype=np.float32) metric['integral-eval']['quadratic'] = np.zeros([num_epoch, len_sequence], dtype=np.float32) print('evaluate MMD, cross-entropy and discrepancy of integral evaluations') return metric class EvalMetricKbr(object): """ using numpy """ def __init__(self, weights, particles, equal_particles, true_mean, true_cov, dim, num_true_samples=None): self.dim = dim self.particles = np.array(particles).reshape(-1, dim) self.n_particles = self.particles.shape[0] self.true_mean = np.array(true_mean).reshape(dim) self.true_cov = np.array(true_cov).reshape(dim, dim) self.weights = np.array(weights).reshape(-1) self.equal_particles = np.array(equal_particles).reshape(-1, dim) if num_true_samples is None: self.n_samples = max(5000, 10 * cmd_args.num_particles) else: self.n_samples = num_true_samples def square_mmd(self, kernel_type='gaussian'): p_particles = np.random.multivariate_normal(self.true_mean.astype(np.float64), self.true_cov.astype(np.float64), self.n_samples) return square_mmd_fine(p_particles, self.equal_particles, self.n_samples, self.n_particles, kernel_type) def cross_entropy(self): p_particles = np.random.multivariate_normal(self.true_mean.astype(np.float64), self.true_cov.astype(np.float64), self.n_samples) return -np.array(e_p_log_q(p_particles, self.equal_particles).cpu()) def integral_eval(self, test_function): full_path = os.path.realpath(__file__) path = os.path.dirname(full_path) filename = path+'/test_function/test_function.pkl' with open(filename, 'rb') as f: matrix_aa, matrix_a, matrix_b, a, b = pickle.load(f) if test_function == 'x': return self.dist_of_mean() elif test_function == 'xAx': return self.distance_of_xax(matrix_aa) elif test_function == 'quadratic': return self.distance_of_quadratic(matrix_a, a, matrix_b, b) else: print('test function not supported') def dist_of_mean(self): q_mean = np.sum(self.weights.reshape(-1, 1) * self.particles, 0) return LA.norm(q_mean-self.true_mean) def distance_of_xax(self, matrix_a): """||E_q[x'Ax] - E_p[x'Ax]||""" q_samples = self.particles mean = self.true_mean.reshape(1, self.dim) true_val = np.trace(np.matmul(matrix_a, self.true_cov)) true_val += np.sum(np.dot(mean, np.matmul(matrix_a, mean.T))) est_ax = np.matmul(matrix_a, q_samples.T) est_xax = np.diag(np.matmul(q_samples, est_ax)) est_xax = np.sum(self.weights.reshape(-1) * est_xax) return np.abs(true_val - est_xax) def distance_of_quadratic(self, matrix_a, a, matrix_b, b): """||E_q[(Ax+a)'(Bx+b)] - E_p[~~~]||""" q_samples = self.particles # format matrix_a = np.array(matrix_a) matrix_b = np.array(matrix_b) true_val = np.trace(np.matmul(np.matmul(matrix_a, self.true_cov), matrix_b.T)) true_val += np.dot(matrix_a.dot(self.true_mean) + a, matrix_b.dot(self.true_mean) + b) est_ax_a = np.matmul(q_samples, matrix_a.T) + a est_bx_b = np.matmul(q_samples, matrix_b.T) + b est_val = np.diag(np.matmul(est_ax_a, est_bx_b.T)) est_val = np.sum(self.weights.reshape(-1) * est_val) return np.abs(true_val - est_val)

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null

0

1

0

null

0

5

fd8e293c314a1ac55d0177b42161abb290baa0d0

248

py

Python

app/config.py

MattiasZurkovic/CSGO_News

d74555e57037ad9b71cceb2449bf7f81c99aa78f

[ "Apache-2.0" ]

1

2015-07-24T19:30:40.000Z

app/config.py

MattiasZurkovic/CSGO_News

d74555e57037ad9b71cceb2449bf7f81c99aa78f

[ "Apache-2.0" ]

null

app/config.py

MattiasZurkovic/CSGO_News

d74555e57037ad9b71cceb2449bf7f81c99aa78f

[ "Apache-2.0" ]

null

consumer_key='aRahVNAuCVdWy5PGFjMoAIWui' consumer_secret='fABUmGW1uV4pnlgTpwSx8KAxQdbVH6fz2le4dEW4e9wlnxmP2b' access_token_key='2834176217-coE5CGfxIdniddoou1HOBcG3r4KVdVG2UzJQStS' access_token_secret='3tfg6G4clDY42ie6wYekxf77xHGKCZjmWtUzIEqRTHqoW'

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248

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1

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0

1

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1

null

0

5

fda5e1007ee96c32e432e9a0f453fbc39aba1e7f

519

py

Python

codeforces/api/json_objects/__init__.py

ericbrandwein/CodeforcesAPI

12ae641910a3308033584dc518bb2fc0173e56f3

[ "MIT" ]

26

2015-06-21T16:19:44.000Z

2021-11-15T12:32:25.000Z

codeforces/api/json_objects/__init__.py

ericbrandwein/CodeforcesAPI

12ae641910a3308033584dc518bb2fc0173e56f3

[ "MIT" ]

5

2015-03-10T06:00:52.000Z

2020-01-18T12:59:25.000Z

codeforces/api/json_objects/__init__.py

ericbrandwein/CodeforcesAPI

12ae641910a3308033584dc518bb2fc0173e56f3

[ "MIT" ]

12

2015-04-24T17:16:50.000Z

2022-01-04T14:21:25.000Z

from ..json_objects.base_json_object import * from ..json_objects.problem import * from ..json_objects.problem_statistics import * from ..json_objects.contest import * from ..json_objects.user import * from ..json_objects.member import * from ..json_objects.party import * from ..json_objects.submission import * from ..json_objects.rating_change import * from ..json_objects.judge_protocol import * from ..json_objects.hack import * from ..json_objects.problem_result import * from ..json_objects.ranklist_row import *

39.923077

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519

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0.492424

0.636364

0.212121

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0.098266

519

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39.923077

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true

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null

1

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null

0

1

0

1

0

5

fdbda646d2a0f91568026b465be36f6ae4150dbe

440

py

Python

goblin/driver/__init__.py

goblin-ogm/goblin

c5801affb30be690e6cc260010a8414fdea31291

[ "Apache-2.0" ]

11

2020-01-26T14:35:23.000Z

2021-12-01T17:04:19.000Z

goblin/driver/__init__.py

goblin-ogm/goblin

c5801affb30be690e6cc260010a8414fdea31291

[ "Apache-2.0" ]

3

2020-04-21T20:34:23.000Z

2021-05-10T15:31:47.000Z

goblin/driver/__init__.py

goblin-ogm/goblin

c5801affb30be690e6cc260010a8414fdea31291

[ "Apache-2.0" ]

4

2020-04-21T09:50:35.000Z

2022-01-12T22:16:22.000Z

from aiogremlin import Cluster, DriverRemoteConnection, Graph # type: ignore from aiogremlin.driver.client import Client # type: ignore from aiogremlin.driver.connection import Connection # type: ignore from aiogremlin.driver.pool import ConnectionPool # type: ignore from aiogremlin.driver.server import GremlinServer # type: ignore from gremlin_python.driver.serializer import GraphSONMessageSerializer # type: ignore AsyncGraph = Graph

48.888889

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0.834091

51

440

7.176471

0.392157

0.163934

0.191257

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440

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null

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1

0

1

0

5

fde575cefa723a9ef79f6aaff09174faf2e17660

241

py

Python

website/doctype/blog_post/templates/pages/blog.py

hafeez3000/wnframework

1160c108fef8f4956f5e14a072ea43e75230b9eb

[ "MIT" ]

6

2015-08-24T23:10:57.000Z

2019-11-10T06:57:23.000Z

website/doctype/blog_post/templates/pages/blog.py

hafeez3000/wnframework

1160c108fef8f4956f5e14a072ea43e75230b9eb

[ "MIT" ]

null

website/doctype/blog_post/templates/pages/blog.py

hafeez3000/wnframework

1160c108fef8f4956f5e14a072ea43e75230b9eb

[ "MIT" ]

5

2015-01-05T06:59:45.000Z

2020-11-07T15:15:07.000Z

# Copyright (c) 2013, Web Notes Technologies Pvt. Ltd. and Contributors # MIT License. See license.txt from __future__ import unicode_literals import webnotes def get_context(): return webnotes.doc("Blog Settings", "Blog Settings").fields

30.125

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0.784232

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241

5.545455

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0.131148

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0.019048

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8

72

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null

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1

0

5

fdef65358f811d396fef06191094726e506165b0

96

py

Python

tools/archive/archive.py

AlexP11223/WebChangeNotifier

96317685e260af6920cf9cc65f0ceb4822db1154

[ "MIT" ]

3

2018-03-23T22:04:59.000Z

2021-09-07T22:06:22.000Z

tools/archive/archive.py

AlexP11223/WebChangeNotifier

96317685e260af6920cf9cc65f0ceb4822db1154

[ "MIT" ]

2

2018-06-01T14:58:53.000Z

2021-06-01T22:01:03.000Z

tools/archive/archive.py

AlexP11223/WebChangeNotifier

96317685e260af6920cf9cc65f0ceb4822db1154

[ "MIT" ]

1

2018-11-08T18:40:54.000Z

import shutil import sys shutil.make_archive(sys.argv[1], "zip", sys.argv[2], sys.argv[3])

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0

null

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5

a906fc77230a446170944a03a6e61f71ecef168c

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py

Python

Day Pogress - 18~100/Day 03/Tresure-Island Project/tempCodeRunnerFile.py

Abbhiishek/Python

3ad5310ca29469f353f9afa99531f01273eec6bd

[ "MIT" ]

1

2022-02-04T07:04:34.000Z

Day Pogress - 18~100/Day 03/Tresure-Island Project/tempCodeRunnerFile.py

Abbhiishek/Python

3ad5310ca29469f353f9afa99531f01273eec6bd

[ "MIT" ]

12

2022-02-13T12:10:32.000Z

2022-02-17T09:36:49.000Z

Day Pogress - 18~100/Day 03/Tresure-Island Project/tempCodeRunnerFile.py

Abbhiishek/Python

3ad5310ca29469f353f9afa99531f01273eec6bd

[ "MIT" ]

null

elif choice_number == 1: # elif choice_number == 2: # else: # print

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0.7

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0.533333

0

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1

0

1

0

null

0

1

0

5

e301d9b06d7843c17d09041bc21393b5ed964a82

59

py

Python

bumpv/client/vcs/__init__.py

kylie-a/bumpversion

13a150daa02f29e7dd74b5240c54c7929ec176b8

[ "MIT" ]

null

bumpv/client/vcs/__init__.py

kylie-a/bumpversion

13a150daa02f29e7dd74b5240c54c7929ec176b8

[ "MIT" ]

null

bumpv/client/vcs/__init__.py

kylie-a/bumpversion

13a150daa02f29e7dd74b5240c54c7929ec176b8

[ "MIT" ]

1

2019-11-24T15:36:19.000Z

from .vcs import get_vcs, WorkingDirectoryIsDirtyException

29.5

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1

0

1

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5

e33290af77bf94757395650bd581889f234f0eab

97

py

Python

Python_for_Everybody-Coursera/CHAPTER_9/chap_09_02.py

andresdelarosa1887/Public-Projects

db8d8e0c0f5f0f7326346462fcdfe21ce8142a12

[ "Unlicense" ]

1

2020-09-29T17:29:34.000Z

Python_for_Everybody-Coursera/CHAPTER_9/chap_09_02.py

andresdelarosa1887/Public-Projects

db8d8e0c0f5f0f7326346462fcdfe21ce8142a12

[ "Unlicense" ]

null

Python_for_Everybody-Coursera/CHAPTER_9/chap_09_02.py

andresdelarosa1887/Public-Projects

db8d8e0c0f5f0f7326346462fcdfe21ce8142a12

[ "Unlicense" ]

null

ccc= dict() ccc['csev'] = 1 ccc['cwen'] = 1 #print(ccc) ccc['cwen']= ccc['cwen'] + 1 print(ccc)

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null

1

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null

0

5

e34949e322b3087aec4604c60454e9400e0217cf

34

py

Python

server/blueprints/upload/__init__.py

mmaltsev/onti40

4b00e7130e2dece80afd9680b38ebc311c1d60f5

[ "MIT" ]

null

server/blueprints/upload/__init__.py

mmaltsev/onti40

4b00e7130e2dece80afd9680b38ebc311c1d60f5

[ "MIT" ]

null

server/blueprints/upload/__init__.py

mmaltsev/onti40

4b00e7130e2dece80afd9680b38ebc311c1d60f5

[ "MIT" ]

null

from .upload import upload_handler

34

0.882353

5

34

5.8

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1

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0.935484

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null

0

1

0

null

0

1

0

1

0

5

e37419148c5e5670209ba558fd2ad4ae6a214439

34

py

Python

imageserver/src/lib/statistics.py

nicolageorge/ownimageserver

630576ff944215a97476f4e10d88bbae1a97c543

[ "MIT" ]

null

imageserver/src/lib/statistics.py

nicolageorge/ownimageserver

630576ff944215a97476f4e10d88bbae1a97c543

[ "MIT" ]

3

2021-09-08T00:48:49.000Z

2022-03-11T23:41:23.000Z

imageserver/src/lib/statistics.py

nicolageorge/ownimageserver

630576ff944215a97476f4e10d88bbae1a97c543

[ "MIT" ]

null

class Statistics(object): pass

17

25

0.735294

4

34

6.25

1

0

0.176471

34

2

26

17

0.892857

0

1

0

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

5

e374c9e1e50c6126451bff22f1e930b3da41f19f

155

py

Python

pubclouds/skydrive/__init__.py

mk-fg/tahoe-lafs-public-clouds

84e61e1742db08f1868e09a6ec77b762d41f85c2

[ "WTFPL" ]

21

2015-01-23T04:39:54.000Z

2020-04-07T17:39:55.000Z

pubclouds/skydrive/__init__.py

mk-fg/tahoe-lafs-public-clouds

84e61e1742db08f1868e09a6ec77b762d41f85c2

[ "WTFPL" ]

null

pubclouds/skydrive/__init__.py

mk-fg/tahoe-lafs-public-clouds

84e61e1742db08f1868e09a6ec77b762d41f85c2

[ "WTFPL" ]

2

2020-06-29T15:56:51.000Z

2021-08-21T07:28:37.000Z

from allmydata.storage.backends.cloud.skydrive.skydrive_container import configure_skydrive_container configure_container = configure_skydrive_container

31

101

0.903226

17

155

7.882353

0.529412

0.380597

0.38806

0

0.058065

155

4

102

38.75

0.917808

0

1

0

false

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

8b82826ad67019a1ab8a8d94a4ad5f0fd638fb0f

24

py

Python

flask_app/app/models.py

ruteckimikolaj/demo-gatsby-flask-scraper

09490bac49147760a1301012ffa2619e1c690c78

[ "MIT" ]

null

flask_app/app/models.py

ruteckimikolaj/demo-gatsby-flask-scraper

09490bac49147760a1301012ffa2619e1c690c78

[ "MIT" ]

1

2021-03-31T19:32:20.000Z

flask_app/app/models.py

ruteckimikolaj/demo-gatsby-flask-scraper

09490bac49147760a1301012ffa2619e1c690c78

[ "MIT" ]

null

from app import db

6

19

0.625

4

24

3.75

1

0

0.375

24

3

20

8

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

8bb5d5ec27c6744767b1ddcd4771827f1ef9fff5

270

py

Python

opaware/ingests/__init__.py

cyclogenesis-au/opaware

871f2af83a1958c9171f39dc73357d0c0859c9ca

[ "BSD-3-Clause" ]

null

opaware/ingests/__init__.py

cyclogenesis-au/opaware

871f2af83a1958c9171f39dc73357d0c0859c9ca

[ "BSD-3-Clause" ]

null

opaware/ingests/__init__.py

cyclogenesis-au/opaware

871f2af83a1958c9171f39dc73357d0c0859c9ca

[ "BSD-3-Clause" ]

1

2021-02-26T14:49:19.000Z

from .ambient_json import ingest_ambient """ =============== opaware.ingests (opaware.ingests) =============== .. currentmodule:: opaware.ingests This module contains procedures for reading and writing . .. autosummary:: :toctree: generated/ ingest_ambient """

20.769231

57

0.666667

26

270

6.807692

0.730769

0.237288

0

0.133333

270

12

58

22.5

0.75641

0

1

0

true

0

1

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

5

8be421f474cc68333a103cf034943550b886b021

20

py

Python

code/sample_2-2-4.py

KoyanagiHitoshi/AtCoder-Python-Introduction

6d014e333a873f545b4d32d438e57cf428b10b96

[ "MIT" ]

1

2022-03-29T13:50:12.000Z

code/sample_2-2-4.py

KoyanagiHitoshi/AtCoder-Python-Introduction

6d014e333a873f545b4d32d438e57cf428b10b96

[ "MIT" ]

null

code/sample_2-2-4.py

KoyanagiHitoshi/AtCoder-Python-Introduction

6d014e333a873f545b4d32d438e57cf428b10b96

[ "MIT" ]

null

print(True or True)

10

19

0.75

4

20

3.75

0.75

0

0.15

20

1

20

0.882353

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

9a5717f062dd11cb61743b3dce5c1fd756394c10

22

py

Python

request.py

bHodges97/timetable

bca2df1e54a288c27d71f0fa27cced7af45e9b0e

[ "MIT" ]

null

request.py

bHodges97/timetable

bca2df1e54a288c27d71f0fa27cced7af45e9b0e

[ "MIT" ]

null

request.py

bHodges97/timetable

bca2df1e54a288c27d71f0fa27cced7af45e9b0e

[ "MIT" ]

null

import configparser

5.5

19

0.818182

2

22

9

1

0

0.181818

22

3

20

7.333333

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

9a580ca64dd0ea28427d0938dedcab21841f5233

178

py

Python

python2.7libs/hammer_tools/widgets/__init__.py

anvdev/Hammer-Tools

0211ec837da6754e537c98624ecd07c23abab28e

[ "Apache-2.0" ]

19

2019-10-09T13:48:11.000Z

2021-06-14T01:25:23.000Z

python2.7libs/hammer_tools/widgets/__init__.py

anvdev/Hammer-Tools

0211ec837da6754e537c98624ecd07c23abab28e

[ "Apache-2.0" ]

219

2019-10-08T14:44:48.000Z

2021-06-19T06:27:46.000Z

python2.7libs/hammer_tools/widgets/__init__.py

anvdev/Hammer-Tools

0211ec837da6754e537c98624ecd07c23abab28e

[ "Apache-2.0" ]

3

2020-02-14T06:18:06.000Z

2020-11-25T20:47:06.000Z

from .input_field import InputField from .location_field import LocationField from .file_path_field import FilePathField from .slider import Slider from .combobx import ComboBox

29.666667

42

0.859551

24

178

6.208333

0.541667

0.221477

0

0.11236

178

5

43

35.6

0.943038

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

9a67619df30b4390db893b936e710bbe6260154a

75

py

Python

web_scripts/parts.py

celskeggs/hwops

55b847a19ec0d2afa75c613de2ffd6deef5c227f

[ "MIT" ]

1

2021-10-12T04:03:56.000Z

web_scripts/parts.py

celskeggs/hwops

55b847a19ec0d2afa75c613de2ffd6deef5c227f

[ "MIT" ]

1

2019-05-06T21:33:47.000Z

2019-05-06T21:34:52.000Z

web_scripts/parts.py

sipb/hwops

55b847a19ec0d2afa75c613de2ffd6deef5c227f

[ "MIT" ]

null

#!/usr/bin/python2 # -*- coding: utf-8 -*- import main main.print_parts()

12.5

23

0.64

11

75

4.272727

0.909091

0

0.030769

0.133333

75

5

24

15

0.692308

0.52

0

1

0

true

0

0.5

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

7bd1b5637b46cbf260b4cfc0430e95bf6ca2a588

53

py

Python

script_runner/__init__.py

sajjadmaneshi/dws_dev_007_python_q2

b95617041f13de43fbdce398adb0cbbcc6276a1e

[ "Apache-2.0" ]

null

script_runner/__init__.py

sajjadmaneshi/dws_dev_007_python_q2

b95617041f13de43fbdce398adb0cbbcc6276a1e

[ "Apache-2.0" ]

null

script_runner/__init__.py

sajjadmaneshi/dws_dev_007_python_q2

b95617041f13de43fbdce398adb0cbbcc6276a1e

[ "Apache-2.0" ]

null

from script_runner.script_runner import script_runner

53

0.924528

8

53

5.75

0.5

0.782609

0

0.056604

53

1

53

0.92

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

d025446e48f593c45251511938177f50c1f875ba

938

py

Python

tests/test_number_to_word.py

gillespied/ideal-octo-spoon

fbc4d16fcce235c5e99e9f592c24f9c11d66dde3

[ "MIT" ]

null

tests/test_number_to_word.py

gillespied/ideal-octo-spoon

fbc4d16fcce235c5e99e9f592c24f9c11d66dde3

[ "MIT" ]

null

tests/test_number_to_word.py

gillespied/ideal-octo-spoon

fbc4d16fcce235c5e99e9f592c24f9c11d66dde3

[ "MIT" ]

null

import pytest from number_to_word import number_to_word def test_valid_string(): assert number_to_word.number_to_word("111") == 'one hundred eleven' def test_invalid_string(): with pytest.raises(ValueError): number_to_word.number_to_word("not a number") def test_int(): assert number_to_word.number_to_word(111) == 'one hundred eleven' def test_float(): assert number_to_word.number_to_word(111.0) == 'one hundred eleven' def test_decimal_places(): assert number_to_word.number_to_word(.999) == 'zero point nine nine nine' def test_minus_int(): assert number_to_word.number_to_word(-1) == 'minus one' def test_minus_float(): assert number_to_word.number_to_word(-1.01) == 'minus one point zero one' def test_zero(): assert number_to_word.number_to_word(0) == 'zero' def test_bigger_than_max(): with pytest.raises(AssertionError): number_to_word.number_to_word(10**30)

22.878049

77

0.73774

149

938

4.268456

0.261745

0.251572

0.377358

0.254717

0.556604

0.52044

0.444969

0.350629

0.176101

0

0.027743

0.154584

938

40

78

23.45

0.774275

0

0.139659

0

0.363636

1

0.409091

true

0

0.090909

0

0.5

0

null

1

0

null

0

1

0

5

d06aa1ade7088e045d5def36afa74a53ac3c96d3

1,467

py

Python

terrascript/resource/e_breuninger/netbox.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

507

2017-07-26T02:58:38.000Z

2022-01-21T12:35:13.000Z

terrascript/resource/e_breuninger/netbox.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

135

2017-07-20T12:01:59.000Z

2021-10-04T22:25:40.000Z

terrascript/resource/e_breuninger/netbox.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

81

2018-02-20T17:55:28.000Z

2022-01-31T07:08:40.000Z

# terrascript/resource/e-breuninger/netbox.py # Automatically generated by tools/makecode.py (24-Sep-2021 15:22:23 UTC) import terrascript class netbox_available_ip_address(terrascript.Resource): pass class netbox_cluster(terrascript.Resource): pass class netbox_cluster_group(terrascript.Resource): pass class netbox_cluster_type(terrascript.Resource): pass class netbox_device_role(terrascript.Resource): pass class netbox_interface(terrascript.Resource): pass class netbox_ip_address(terrascript.Resource): pass class netbox_platform(terrascript.Resource): pass class netbox_prefix(terrascript.Resource): pass class netbox_primary_ip(terrascript.Resource): pass class netbox_service(terrascript.Resource): pass class netbox_tag(terrascript.Resource): pass class netbox_tenant(terrascript.Resource): pass class netbox_tenant_group(terrascript.Resource): pass class netbox_virtual_machine(terrascript.Resource): pass class netbox_vrf(terrascript.Resource): pass __all__ = [ "netbox_available_ip_address", "netbox_cluster", "netbox_cluster_group", "netbox_cluster_type", "netbox_device_role", "netbox_interface", "netbox_ip_address", "netbox_platform", "netbox_prefix", "netbox_primary_ip", "netbox_service", "netbox_tag", "netbox_tenant", "netbox_tenant_group", "netbox_virtual_machine", "netbox_vrf", ]

16.670455

73

0.746421

168

1,467

6.196429

0.238095

0.310279

0.353506

0.403458

0.548511

0.287224

0.082613

0

0.009796

0.164963

1,467

87

74

16.862069

0.84

0.078391

0

0.313725

1

0

0.195701

0.036323

0

1

0

false

0.313725

0.019608

0

0.333333

0

null

1

0

1

0

null

0

1

0

5

d0800a8682f4d47c2a25ca68191b5ec341b5824e

139

py

Python

pkg/pkg/flow/__init__.py

Restok/networks-course

c1c959b1a73b6bb301a4273bd9c1bb4c0a2fa4ff

[ "MIT" ]

8

2022-01-03T23:54:30.000Z

2022-03-18T11:04:18.000Z

pkg/pkg/flow/__init__.py

Restok/networks-course

c1c959b1a73b6bb301a4273bd9c1bb4c0a2fa4ff

[ "MIT" ]

17

2021-03-03T14:48:54.000Z

2021-09-08T15:52:50.000Z

pkg/pkg/flow/__init__.py

Restok/networks-course

c1c959b1a73b6bb301a4273bd9c1bb4c0a2fa4ff

[ "MIT" ]

16

2022-01-04T17:54:57.000Z

2022-03-29T00:34:14.000Z

from .flow import ( estimate_spring_rank_P, signal_flow, rank_signal_flow, rank_graph_match_flow, calculate_p_upper, )

17.375

27

0.726619

19

139

4.736842

0.631579

0.222222

0.311111

0

0.215827

139

7

28

19.857143

0.825688

0

1

0

true

0

0.142857

0

0.142857

0

1

0

null

1

0

1

0

null

0

1

0

5

d082de69dd1616f8bd7b344e3861a74a5dc47696

1,418

py

Python

wheel5/metrics/pipeline.py

xdralex/pytorch-wheel5

336529e354a45908cf3f8f12cd401a95fb2a5351

[ "MIT" ]

2

2020-06-08T13:10:06.000Z

2020-07-07T05:34:18.000Z

wheel5/metrics/pipeline.py

xdralex/pytorch-wheel5

336529e354a45908cf3f8f12cd401a95fb2a5351

[ "MIT" ]

1

2020-04-29T08:46:14.000Z

wheel5/metrics/pipeline.py

xdralex/pytorch-wheel5

336529e354a45908cf3f8f12cd401a95fb2a5351

[ "MIT" ]

null

import logging from typing import Tuple from torch import Tensor from torch import nn from .functional import exact_match_accuracy, jaccard_accuracy, dice_accuracy class ExactMatchAccuracy(nn.Module): def __init__(self): super(ExactMatchAccuracy, self).__init__() self.logger = logging.getLogger(f'{__name__}') self.debug = self.logger.isEnabledFor(logging.DEBUG) def forward(self, input: Tensor, target: Tensor, name: str = '') -> Tuple[Tensor, Tensor]: return exact_match_accuracy(input, target, name=name, logger=self.logger, debug=self.debug) class JaccardAccuracy(nn.Module): def __init__(self): super(JaccardAccuracy, self).__init__() self.logger = logging.getLogger(f'{__name__}') self.debug = self.logger.isEnabledFor(logging.DEBUG) def forward(self, input: Tensor, target: Tensor, name: str = '') -> Tuple[Tensor, Tensor]: return jaccard_accuracy(input, target, name=name, logger=self.logger, debug=self.debug) class DiceAccuracy(nn.Module): def __init__(self): super(DiceAccuracy, self).__init__() self.logger = logging.getLogger(f'{__name__}') self.debug = self.logger.isEnabledFor(logging.DEBUG) def forward(self, input: Tensor, target: Tensor, name: str = '') -> Tuple[Tensor, Tensor]: return dice_accuracy(input, target, name=name, logger=self.logger, debug=self.debug)

34.585366

99

0.703103

172

1,418

5.540698

0.197674

0.094439

0.034627

0.047219

0.734523

0.658972

0

0.174189

1,418

40

100

35.45

0.813834

0

0.461538

0

0.021157

0

1

0.230769

false

0

0.192308

0.115385

0.653846

0

null

0

1

0

1

0

null

0

1

0

1

0

5

d0b5cb080d8002fd69df8c9dee28cd1dbc316af1

115

py

Python

mysite/learn/views.py

hjjia/python

665b89614f6d12fdbbe2250a4920f568e6cc0181

[ "MIT" ]

null

mysite/learn/views.py

hjjia/python

665b89614f6d12fdbbe2250a4920f568e6cc0181

[ "MIT" ]

null

mysite/learn/views.py

hjjia/python

665b89614f6d12fdbbe2250a4920f568e6cc0181

[ "MIT" ]

null

# -*- coding: utf-8 -*- from django.http import HttpResponse def index(req): return HttpResponse(u'欢迎光临 Django')

19.166667

36

0.713043

16

115

5.125

0.875

0

0.010101

0.13913

115

5

37

23

0.818182

0.182609

0

0.119565

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

d0df5652bb6b3e265635f4610887551434c8b380

56

py

Python

Online-Judges/CodingBat/Python/String-01/01-hello_name.py

shihab4t/Competitive-Programming

e8eec7d4f7d86bfa1c00b7fbbedfd6a1518f19be

[ "Unlicense" ]

3

2021-06-15T01:19:23.000Z

2022-03-16T18:23:53.000Z

Online-Judges/CodingBat/Python/String-01/01-hello_name.py

shihab4t/Competitive-Programming

e8eec7d4f7d86bfa1c00b7fbbedfd6a1518f19be

[ "Unlicense" ]

null

Online-Judges/CodingBat/Python/String-01/01-hello_name.py

shihab4t/Competitive-Programming

e8eec7d4f7d86bfa1c00b7fbbedfd6a1518f19be

[ "Unlicense" ]

null

def hello_name(name): return ("Hello"+" "+name+"!")

18.666667

33

0.571429

7

56

4.428571

0.571429

0.580645

0

0.160714

56

2

34

28

0.659574

0

0.125

0

1

0.5

false

0

0.5

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

ef9949a9c29303d2cf0543d9c03378cf43a6ccab

60

py

Python

python/3.module/base/3.use_name.py

dunitian/BaseCode

4855ef4c6dd7c95d7239d2048832d8acfe26e084

[ "Apache-2.0" ]

25

2018-06-13T08:13:44.000Z

2020-11-19T14:02:11.000Z

python/3.module/base/3.use_name.py

dunitian/BaseCode

4855ef4c6dd7c95d7239d2048832d8acfe26e084

[ "Apache-2.0" ]

null

python/3.module/base/3.use_name.py

dunitian/BaseCode

4855ef4c6dd7c95d7239d2048832d8acfe26e084

[ "Apache-2.0" ]

13

2018-06-13T08:13:38.000Z

2022-01-06T06:45:07.000Z

import get_user_infos as user_infos user_infos.get_infos()

15

35

0.85

11

60

4.181818

0.454545

0.586957

0

0.1

60

3

36

20

0.851852

0

1

0

true

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

efe9b09df68e42492b21fa980740e106d0f3b652

48

py

Python

tests/__init__.py

ciresnave/eetools

0752a2dec8f19c647e3b3e4dfd33982101cabc34

[ "MIT" ]

null

tests/__init__.py

ciresnave/eetools

0752a2dec8f19c647e3b3e4dfd33982101cabc34

[ "MIT" ]

null

tests/__init__.py

ciresnave/eetools

0752a2dec8f19c647e3b3e4dfd33982101cabc34

[ "MIT" ]

null

"""Test suite for the eepythontools package."""

24

47

0.729167

6

48

5.833333

1

0

0.125

48

1

48

0.833333

0.854167

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

4bd74bb31bac22aa8ce2ab224526db6abcd3e1ca

24

py

Python

src/ynab_bank_import/__init__.py

zagy/ynab-bank-imports

9c26ef8f124a25af2ed734e6af44190fcfe5c90c

[ "BSD-2-Clause" ]

1

2021-07-07T05:25:49.000Z

src/ynab_bank_import/__init__.py

zagy/ynab-bank-imports

9c26ef8f124a25af2ed734e6af44190fcfe5c90c

[ "BSD-2-Clause" ]

null

src/ynab_bank_import/__init__.py

zagy/ynab-bank-imports

9c26ef8f124a25af2ed734e6af44190fcfe5c90c

[ "BSD-2-Clause" ]

1

2021-03-20T09:42:55.000Z

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py

Python

sig-contributor-experience/surveys/k8s_survey_analysis/plot_utils.py

shubham14bajpai/community

f4c7914af3132a765472090b3a03b024e7aa951e

[ "Apache-2.0" ]

9,963

2016-05-03T08:34:46.000Z

2022-03-31T16:28:24.000Z

sig-contributor-experience/surveys/k8s_survey_analysis/plot_utils.py

shubham14bajpai/community

f4c7914af3132a765472090b3a03b024e7aa951e

[ "Apache-2.0" ]

6,434

2016-05-11T16:11:43.000Z

2022-03-31T23:40:33.000Z

sig-contributor-experience/surveys/k8s_survey_analysis/plot_utils.py

shubham14bajpai/community

f4c7914af3132a765472090b3a03b024e7aa951e

[ "Apache-2.0" ]

5,067

2016-05-04T21:46:00.000Z

2022-03-31T15:00:56.000Z

from textwrap import wrap import math import plotnine as p9 import pandas as pd import textwrap from textwrap import shorten from matplotlib import pyplot as plt from copy import copy from mizani.palettes import brewer_pal from plotnine.scales.scale import scale_discrete # Custom scales for plotnine that reverse the direction of the colors class reverse_scale_color_brewer(p9.scale_color_brewer): def __init__(self, type="seq", palette=1, direction=-1, **kwargs): self.palette = brewer_pal(type, palette, direction) scale_discrete.__init__(self, **kwargs) class reverse_scale_fill_brewer(p9.scale_fill_brewer): def __init__(self, type="seq", palette=1, direction=-1, **kwargs): self.palette = brewer_pal(type, palette, direction) scale_discrete.__init__(self, **kwargs) def split_for_likert(topic_data_long, mid_point): """ Returns the aggregated counts for ratings in the top and bottom halves of the of each category, necssary for making offset bar charts Args: topic_data_long (pandas.Dataframe): A pandas Dataframe storing each respondents ratings for a given topic, in long format mid_point (int): The midpoint to use to split the into two halves, based on ratings Returns: (tuple): Tuple containing: (pandas.DataFrame): Aggregated counts for ratings greater than or equal to the midpoinnt (pandas.DataFrame): Aggregated counts for ratings less than or equal to the midpoinnt """ x = topic_data_long.columns.tolist() x.remove("level_1") top_cutoff = topic_data_long["rating"] >= mid_point bottom_cutoff = topic_data_long["rating"] <= mid_point top_scores = ( topic_data_long[top_cutoff] .groupby(x) .count() .reindex( pd.MultiIndex.from_product( [topic_data_long[y].unique().tolist() for y in x], names=x ), fill_value=0, ) .reset_index() .sort_index(ascending=False) ) # The mid point is in both the top and bottom halves, so divide by two top_scores.loc[top_scores["rating"] == mid_point, "level_1"] = ( top_scores[top_scores["rating"] == mid_point]["level_1"] / 2.0 ) bottom_scores = ( topic_data_long[bottom_cutoff] .groupby(x) .count() .reindex( pd.MultiIndex.from_product( [topic_data_long[y].unique().tolist() for y in x], names=x ), fill_value=0, ) .reset_index() ) # The mid point is in both the top and bottom halves, so divide by two bottom_scores.loc[bottom_scores["rating"] == mid_point, "level_1"] = ( bottom_scores[bottom_scores["rating"] == mid_point]["level_1"] / 2.0 ) return top_scores, bottom_scores def make_long(data, facets, multi_year=False): """Converts a wide dataframe with columns for each topic's rating into a long dataframe Args: data (pandas.DataFrame): A wide dataframe facets (list): List of columns to keep as their own column mulit_year (bool, optional) Defaults to False. If True, add the "year" column to the list of facets Returns: (pandas.DataFrame): Long dataframe """ facets = copy(facets) if multi_year: facets.append("year") long_data = data.set_index(facets, append=True).stack().reset_index() # Rename so Level_0 always has the values of the topic we are interested in long_data = long_data.rename( columns={ "level_0": "level_1", "level_4": "level_0", "level_3": "level_0", "level_2": "level_0", 0: "rating", } ) long_data = long_data.assign( level_0=pd.Categorical(long_data.level_0, ordered=True) ) return long_data def get_data_subset( survey_data, topic, facets=[], exclude_new_contributors=False, include_year=False ): """Get only the relevant columns from the data Args: survey_data (pandas.DataFrame): Raw data read in from Kubernetes Survey topic (str): String that all questions of interest start with facets (list, optional): List of columns use for grouping exclude_new_contributors: (bool, optional) Defaults to False. If True, remove all responses from contributors who have been involved a year or less. include_year: (bool, optional) Defaults to False. If True, include the year column in the output Returns: (pandas.DataFrame): Survey dataframe with only columns relevant to the topics and facets remaining. """ og_cols = [x for x in survey_data.columns if x.startswith(topic)] facets = copy(facets) if include_year: facets.append("year") if facets: if "." in facets: facets.remove(".") cols = og_cols + facets facets.append(".") else: cols = og_cols + facets else: cols = og_cols if exclude_new_contributors: topic_data = survey_data[ survey_data["Contributing_Length"] != "less than one year" ][cols] else: topic_data = survey_data[cols] return topic_data def get_multi_year_data_subset( survey_data, topic, facet_by=[], exclude_new_contributors=False ): """Get appropriate data for multi-year plots and convert it to long form Args: survey_data (pandas.DataFrame): Raw data read in from Kubernetes Survey topic (str): String that all questions of interest start with facet_by (list, optional): List of columns use for grouping exclude_new_contributors (bool, optional) Defaults to False. If True, remove all responses from contributors who have been involved a year or less. Returns: (pandas.DataFrame): Long dataframe """ topic_data = get_data_subset( survey_data, topic, facet_by, exclude_new_contributors, include_year=True ) if facet_by: if "." in facet_by: facet_by.remove(".") topic_data_long = make_long(topic_data, facet_by, multi_year=True) facet_by.append(".") else: topic_data_long = make_long(topic_data, facet_by, multi_year=True) else: topic_data_long = make_long(topic_data, [], multi_year=True) return topic_data_long def get_single_year_data_subset(survey_data, topic, facet_by=[]): """Get appropriate data for single-year plots and convert it to long form Args: survey_data (pandas.DataFrame): Raw data read in from Kubernetes Survey topic (str): String that all questions of interest start with facet_by (list, optional): List of columns use for grouping Returns: (pandas.DataFrame): Long dataframe """ topic_data = get_data_subset(survey_data, topic, facet_by) if facet_by: if "." in facet_by: facet_by.remove(".") topic_data_long = make_long(topic_data, facet_by) facet_by.append(".") else: topic_data_long = make_long(topic_data, facet_by) else: topic_data_long = ( topic_data.unstack().reset_index().rename(columns={0: "rating"}) ) topic_data_long = topic_data_long.assign( level_0=pd.Categorical(topic_data_long.level_0, ordered=True) ) return topic_data_long def make_bar_chart_multi_year( survey_data, topic, facet_by=[], exclude_new_contributors=False ): """Make a barchart showing proportions of respondents listing each column that starts with topic. Bars are colored by which year of the survey they correspond to. If facet_by is not empty, the resulting plot will be faceted into subplots by the variables given. Args: survey_data (pandas.DataFrame): Raw data read in from Kubernetes Survey topic (str): String that all questions of interest start with facet_by (list,optional): List of columns use for grouping exclude_new_contributors (bool, optiona ): Defaults to False. If True, do not include any responses from contributors with less than one year of experience Returns: (plotnine.ggplot): Plot object which can be displayed in a notebook or saved out to a file """ topic_data = get_data_subset( survey_data, topic, facet_by, exclude_new_contributors, include_year=True ) if facet_by: fix = False if "." in facet_by: facet_by.remove(".") fix = True agg = ( topic_data.groupby(facet_by + ["year"]) .sum() .reset_index() .melt(id_vars=facet_by + ["year"]) ) totals = ( topic_data.groupby(facet_by + ["year"]) .count() .reset_index() .melt(id_vars=facet_by + ["year"]) ) percent = agg.merge(totals, on=facet_by + ["year", "variable"]) if fix: facet_by.append(".") else: agg = topic_data.groupby(["year"]).sum().reset_index().melt(id_vars=["year"]) totals = ( topic_data.groupby(["year"]).count().reset_index().melt(id_vars=["year"]) ) percent = agg.merge(totals, on=["year", "variable"]) # This plot is always done proportionally percent = percent.assign(value=percent["value_x"] / percent["value_y"]) percent = percent.assign(variable=pd.Categorical(percent.variable, ordered=True)) br = ( p9.ggplot(percent, p9.aes(x="variable", fill="factor(year)", y="value")) + p9.geom_bar(show_legend=True, position="dodge", stat="identity") + p9.theme( axis_text_x=p9.element_text(angle=45, ha="right"), strip_text_y=p9.element_text(angle=0, ha="left"), ) + p9.scale_x_discrete( limits=sorted(percent["variable"].unique().tolist()), labels=[ shorten( x.replace(topic, "").replace("_", " "), placeholder="...", width=30 ) for x in sorted(percent["variable"].unique().tolist()) ], ) ) # Uncomment to return dataframe instead of plot # return percent if facet_by: br = ( br + p9.facet_grid( facet_by, shrink=False, labeller=lambda x: "\n".join(wrap(x.replace("/", "/ "), 15)), ) + p9.theme( strip_text_x=p9.element_text(wrap=True, va="bottom", margin={"b": -0.5}) ) ) return br def make_single_bar_chart_multi_year(survey_data, column, facet, proportionally=False): """Make a barchart showing the number of respondents responding to a single column. Bars are colored by which year of the survey they correspond to. If facet is not empty, the resulting plot will be faceted into subplots by the variables given. Args: survey_data (pandas.DataFrame): Raw data read in from Kubernetes Survey column (str): Column to plot responses to facet (list,optional): List of columns use for grouping proportionally (bool, optiona ): Defaults to False. If True, the bars heights are determined proportionally to the total number of responses in that facet. Returns: (plotnine.ggplot): Plot object which can be displayed in a notebook or saved out to a file """ cols = [column, facet] show_legend = False topic_data = survey_data[cols + ["year"]] topic_data_long = make_long(topic_data, facet, multi_year=True) if proportionally: proportions = ( topic_data_long[topic_data_long.rating == 1].groupby(facet + ["year"]).sum() / topic_data_long.groupby(facet + ["year"]).sum() ).reset_index() else: proportions = ( topic_data_long[topic_data_long.rating == 1] .groupby(facet + ["year"]) .count() .reset_index() ) x = topic_data_long.columns.tolist() x.remove("level_1") ## Uncomment to return dataframe instead of plot # return proportions return ( p9.ggplot(proportions, p9.aes(x=facet, fill="year", y="level_1")) + p9.geom_bar(show_legend=show_legend, stat="identity") + p9.theme( axis_text_x=p9.element_text(angle=45, ha="right"), strip_text_y=p9.element_text(angle=0, ha="left"), ) + p9.scale_x_discrete( limits=topic_data_long[facet].unique().tolist(), labels=[ x.replace("_", " ") for x in topic_data_long[facet].unique().tolist() ], ) ) def make_likert_chart_multi_year( survey_data, topic, labels, facet_by=[], five_is_high=False, exclude_new_contributors=False, ): """Make an offset stacked barchart showing the number of respondents at each rank or value for all columns in the topic. Each column in the topic is a facet, with the years displayed along the x-axis. Args: survey_data (pandas.DataFrame): Raw data read in from Kubernetes Survey topic (str): String that all questions of interest start with labels (list): List of strings to use as labels, corresponding to the numerical values given by the respondents. facet_by (list,optional): List of columns use for grouping five_is_high (bool, optiona ): Defaults to False. If True, five is considered the highest value in a ranking, otherwise it is taken as the lowest value. exclude_new_contributors (bool, optional): Defaults to False. If True, do not include any responses from contributors with less than one year of experience Returns: (plotnine.ggplot): Offset stacked barchart plot object which can be displayed in a notebook or saved out to a file """ facet_by = copy(facet_by) og_cols = [x for x in survey_data.columns if x.startswith(topic)] show_legend = True topic_data_long = get_multi_year_data_subset( survey_data, topic, facet_by, exclude_new_contributors ) if not five_is_high: topic_data_long = topic_data_long.assign(rating=topic_data_long.rating * -1.0) mid_point = 3 if five_is_high else -3 top_scores, bottom_scores = split_for_likert(topic_data_long, mid_point) if facet_by: fix = False if "." in facet_by: facet_by.remove(".") fix = True # Calculate proportion for each rank top_scores = top_scores.merge( topic_data_long.groupby(facet_by + ["year"]).count().reset_index(), on=facet_by + ["year"], ).rename(columns={"rating_x": "rating", "level_0_x": "level_0"}) top_scores = top_scores.assign( level_1=top_scores.level_1_x / (top_scores.level_1_y / len(og_cols)) ) bottom_scores = bottom_scores.merge( topic_data_long.groupby(facet_by + ["year"]).count().reset_index(), on=facet_by + ["year"], ).rename(columns={"rating_x": "rating", "level_0_x": "level_0"}) bottom_scores = bottom_scores.assign( level_1=bottom_scores.level_1_x * -1 / (bottom_scores.level_1_y / len(og_cols)) ) if fix: facet_by.append(".") else: # Calculate proportion for each rank top_scores = top_scores.merge( topic_data_long.groupby(["year"]).count().reset_index(), on=["year"] ).rename(columns={"rating_x": "rating", "level_0_x": "level_0"}) top_scores = top_scores.assign( level_1=top_scores.level_1_x / (top_scores.level_1_y / len(og_cols)) ) bottom_scores = bottom_scores.merge( topic_data_long.groupby(["year"]).count().reset_index(), on=["year"] ).rename(columns={"rating_x": "rating", "level_0_x": "level_0"}) bottom_scores = bottom_scores.assign( level_1=bottom_scores.level_1_x * -1 / (bottom_scores.level_1_y / len(og_cols)) ) vp = ( p9.ggplot( topic_data_long, p9.aes(x="factor(year)", fill="factor(rating)", color="factor(rating)"), ) + p9.geom_col( data=top_scores, mapping=p9.aes(y="level_1"), show_legend=show_legend, size=0.25, position=p9.position_stack(reverse=True), ) + p9.geom_col( data=bottom_scores, mapping=p9.aes(y="level_1"), show_legend=show_legend, size=0.25, position=p9.position_stack(), ) + p9.geom_hline(yintercept=0, color="white") ) if five_is_high: vp = ( vp + p9.scale_color_brewer( "div", "RdBu", limits=[1, 2, 3, 4, 5], labels=labels ) + p9.scale_fill_brewer("div", "RdBu", limits=[1, 2, 3, 4, 5], labels=labels) + p9.theme( axis_text_x=p9.element_text(angle=45, ha="right"), strip_text_y=p9.element_text(angle=0, ha="left"), ) ) else: vp = ( vp + p9.scale_color_brewer( "div", "RdBu", limits=[-5, -4, -3, -2, -1], labels=labels ) + p9.scale_fill_brewer( "div", "RdBu", limits=[-5, -4, -3, -2, -1], labels=labels ) + p9.theme(strip_text_y=p9.element_text(angle=0, ha="left")) ) if facet_by: facet_by.remove(".") else: facet_by.append(".") vp = ( vp + p9.facet_grid( facet_by + ["level_0"], labeller=lambda x: "\n".join( wrap( x.replace(topic, "").replace("_", " ").replace("/", "/ ").strip(), 15, ) ), ) + p9.theme( strip_text_x=p9.element_text(wrap=True, ma="left"), panel_spacing_x=0.1 ) ) return vp def make_bar_chart(survey_data, topic, facet_by=[], proportional=False): """Make a barchart showing the number of respondents listing each column that starts with topic for a single year. If facet_by is not empty, the resulting plot will be faceted into subplots by the variables given. Args: survey_data (pandas.DataFrame): Raw data read in from Kubernetes Survey topic (str): String that all questions of interest start with facet_by (list,optional): List of columns use for grouping proportional (bool, optiona ): Defaults to False. If True, the bars heights are determined proportionally to the total number of responses in that facet. Returns: (plotnine.ggplot): Plot object which can be displayed in a notebook or saved out to a file """ show_legend = False if facet_by: show_legend = True topic_data_long = get_single_year_data_subset(survey_data, topic, facet_by) x = topic_data_long.columns.tolist() x.remove("level_1") if facet_by: period = False if "." in facet_by: facet_by.remove(".") period = True aggregate_data = ( topic_data_long[topic_data_long.rating == 1] .dropna() .groupby(["level_0"] + facet_by) .count() .reset_index() ) if period: facet_by.append(".") else: aggregate_data = ( topic_data_long[topic_data_long.rating == 1] .dropna() .groupby("level_0") .count() .reset_index() ) if proportional and facet_by: period = False if "." in facet_by: facet_by.remove(".") period = True facet_sums = ( topic_data_long[topic_data_long.rating == 1] .dropna() .groupby(facet_by) .count() .reset_index() ) aggregate_data = aggregate_data.merge(facet_sums, on=facet_by).rename( columns={"level_0_x": "level_0"} ) aggregate_data = aggregate_data.assign( rating=aggregate_data.rating_x / aggregate_data.rating_y ) if period: facet_by.append(".") br = ( p9.ggplot(aggregate_data, p9.aes(x="level_0", fill="level_0", y="rating")) + p9.geom_bar(show_legend=show_legend, stat="identity") + p9.theme( axis_text_x=p9.element_text(angle=45, ha="right"), strip_text_y=p9.element_text(angle=0, ha="left"), ) + p9.scale_x_discrete( limits=topic_data_long["level_0"].unique().tolist(), labels=[ "\n".join( textwrap.wrap(x.replace(topic, "").replace("_", " "), width=35)[0:2] ) for x in topic_data_long["level_0"].unique().tolist() ], ) ) if facet_by: br = ( br + p9.facet_grid( facet_by, shrink=False, labeller=lambda x: "\n".join(wrap(x, 15)) ) + p9.theme( axis_text_x=p9.element_blank(), strip_text_x=p9.element_text( wrap=True, va="bottom", margin={"b": -0.5} ), ) + p9.scale_fill_discrete( limits=topic_data_long["level_0"].unique().tolist(), labels=[ "\n".join( wrap( x.replace(topic, "") .replace("_", " ") .replace("/", "/ ") .strip(), 30, ) ) for x in topic_data_long["level_0"].unique().tolist() ], ) ) return br def make_likert_chart( survey_data, topic, labels, facet_by=[], max_value=5, max_is_high=False, wrap_facets=True, sort_x=False, ): """Make an offset stacked barchart showing the number of respondents at each rank or value for all columns in the topic. Each column in the original data is a tick on the x-axis Args: survey_data (pandas.DataFrame): Raw data read in from Kubernetes Survey topic (str): String that all questions of interest start with labels (list): List of strings to use as labels, corresponding to the numerical values given by the respondents. facet_by (list,optional): List of columns use for grouping max_value (int, optional): Defaults to 5. The maximuum value a respondent can assign. max_is_high (bool, optiona ): Defaults to False. If True, the max_value is considered the highest value in a ranking, otherwise it is taken as the lowest value. wrap_facets (bool, optional): Defaults to True. If True, the facet labels are wrapped sort_x (bool, optional): Defaults to False. If True, the x-axis is sorted by the mean value for each column in the original data Returns: (plotnine.ggplot): Offset stacked barchart plot object which can be displayed in a notebook or saved out to a file """ mid_point = math.ceil(max_value / 2) og_cols = [x for x in survey_data.columns if x.startswith(topic)] show_legend = True topic_data_long = get_single_year_data_subset(survey_data, topic, facet_by) if not max_is_high: topic_data_long = topic_data_long.assign(rating=topic_data_long.rating * -1.0) mid_point = -1 * mid_point top_scores, bottom_scores = split_for_likert(topic_data_long, mid_point) if facet_by: fix = False if "." in facet_by: facet_by.remove(".") fix = True top_scores = top_scores.merge( topic_data_long.groupby(facet_by).count().reset_index(), on=facet_by ).rename(columns={"rating_x": "rating", "level_0_x": "level_0"}) top_scores = top_scores.assign( level_1=top_scores.level_1_x / (top_scores.level_1_y / len(og_cols)) ) bottom_scores = bottom_scores.merge( topic_data_long.groupby(facet_by).count().reset_index(), on=facet_by ).rename(columns={"rating_x": "rating", "level_0_x": "level_0"}) bottom_scores = bottom_scores.assign( level_1=bottom_scores.level_1_x * -1 / (bottom_scores.level_1_y / len(og_cols)) ) if fix: facet_by.append(".") else: bottom_scores = bottom_scores.assign(level_1=bottom_scores.level_1 * -1) if sort_x: x_sort_order = ( topic_data_long.groupby("level_0") .mean() .sort_values("rating") .reset_index()["level_0"] .values.tolist() ) x_sort_order.reverse() else: x_sort_order = topic_data_long["level_0"].unique().tolist() vp = ( p9.ggplot( topic_data_long, p9.aes(x="level_0", fill="factor(rating)", color="factor(rating)"), ) + p9.geom_col( data=top_scores, mapping=p9.aes(y="level_1"), show_legend=show_legend, size=0.25, position=p9.position_stack(reverse=True), ) + p9.geom_col( data=bottom_scores, mapping=p9.aes(y="level_1"), show_legend=show_legend, size=0.25, position=p9.position_stack(), ) + p9.geom_hline(yintercept=0, color="white") + p9.theme( axis_text_x=p9.element_text(angle=45, ha="right"), strip_text_y=p9.element_text(angle=0, ha="left"), ) + p9.scale_x_discrete( limits=x_sort_order, labels=[ "\n".join( textwrap.wrap(x.replace(topic, "").replace("_", " "), width=35)[0:2] ) for x in x_sort_order ], ) ) if max_is_high: vp = ( vp + p9.scale_color_brewer( "div", "RdBu", limits=list(range(1, max_value + 1)), labels=labels ) + p9.scale_fill_brewer( "div", "RdBu", limits=list(range(1, max_value + 1)), labels=labels ) ) else: vp = ( vp + reverse_scale_fill_brewer( "div", "RdBu", limits=list(reversed(range(-max_value, 0))), labels=labels, ) + reverse_scale_color_brewer( "div", "RdBu", limits=list(reversed(range(-max_value, 0))), labels=labels, ) ) if facet_by: if wrap_facets: vp = ( vp + p9.facet_grid(facet_by, labeller=lambda x: "\n".join(wrap(x, 15))) + p9.theme( strip_text_x=p9.element_text( wrap=True, va="bottom", margin={"b": -0.5} ) ) ) else: vp = vp + p9.facet_grid(facet_by, space="free", labeller=lambda x: x) return vp def make_single_likert_chart(survey_data, column, facet, labels, five_is_high=False): """Make an offset stacked barchart showing the number of respondents at each rank or value for a single columns in the original data. Each facet is shown as a tick on the x-axis Args: survey_data (pandas.DataFrame): Raw data read in from Kubernetes Survey topic (str): String that all questions of interest start with labels (list): List of strings to use as labels, corresponding to the numerical values given by the respondents. facet (str): Column used for grouping five_is_high (bool, optionalc): Defaults to False. If True, 5 is considered the highest value in a ranking, otherwise it is taken as the lowest value. Returns: (plotnine.ggplot): Offset stacked barchart plot object which can be displayed in a notebook or saved out to a file """ mid_point = 3 cols = [column, facet] show_legend = True topic_data = survey_data[cols] topic_data_long = make_long(topic_data, facet) if not five_is_high: topic_data_long = topic_data_long.assign(rating=topic_data_long.rating * -1.0) x = topic_data_long.columns.tolist() x.remove("level_1") x.remove("level_0") if not five_is_high: mid_point *= -1 top_cutoff = topic_data_long["rating"] >= mid_point bottom_cutoff = topic_data_long["rating"] <= mid_point top_scores = ( topic_data_long[top_cutoff] .groupby(x) .count() .reset_index() .sort_index(ascending=False) ) top_scores.loc[top_scores["rating"] == mid_point, "level_1"] = ( top_scores[top_scores["rating"] == mid_point]["level_1"] / 2.0 ) top_scores = top_scores.merge( topic_data_long.groupby(facet).count().reset_index(), on=facet ) top_scores = top_scores.assign(level_1=top_scores.level_1_x / top_scores.level_1_y) bottom_scores = topic_data_long[bottom_cutoff].groupby(x).count().reset_index() bottom_scores.loc[bottom_scores["rating"] == mid_point, "level_1"] = ( bottom_scores[bottom_scores["rating"] == mid_point]["level_1"] / 2.0 ) bottom_scores = bottom_scores.merge( topic_data_long.groupby(facet).count().reset_index(), on=facet ) bottom_scores = bottom_scores.assign( level_1=bottom_scores.level_1_x * -1 / bottom_scores.level_1_y ) vp = ( p9.ggplot( topic_data_long, p9.aes(x=facet, fill="factor(rating_x)", color="factor(rating_x)"), ) + p9.geom_col( data=top_scores, mapping=p9.aes(y="level_1"), show_legend=show_legend, size=0.25, position=p9.position_stack(reverse=True), ) + p9.geom_col( data=bottom_scores, mapping=p9.aes(y="level_1"), show_legend=show_legend, size=0.25, ) + p9.geom_hline(yintercept=0, color="white") + p9.theme( axis_text_x=p9.element_text(angle=45, ha="right"), strip_text_y=p9.element_text(angle=0, ha="left"), ) + p9.scale_x_discrete( limits=topic_data_long[facet].unique().tolist(), labels=[ x.replace("_", " ") for x in topic_data_long[facet].unique().tolist() ], ) ) if five_is_high: vp = ( vp + p9.scale_color_brewer( "div", "RdBu", limits=[1, 2, 3, 4, 5], labels=["\n".join(wrap(x, 15)) for x in labels], ) + p9.scale_fill_brewer( "div", "RdBu", limits=[1, 2, 3, 4, 5], labels=["\n".join(wrap(x, 15)) for x in labels], ) ) else: vp = ( vp + reverse_scale_fill_brewer( "div", "RdBu", limits=[-1, -2, -3, -4, -5], labels=["\n".join(wrap(x, 15)) for x in labels], ) + reverse_scale_color_brewer( "div", "RdBu", limits=[-1, -2, -3, -4, -5], labels=["\n".join(wrap(x, 15)) for x in labels], ) ) return vp def make_single_bar_chart( survey_data, column, facet, proportionally=False, facet2=None ): """Make a barchart showing the number of respondents marking a certain column in the original dataset as True. The facet variable values are used as ticks on the x-axis Args: survey_data (pandas.DataFrame): Raw data read in from Kubernetes Survey topic (str): String that all questions of interest start with facet (str): Column use for grouping proportional (bool, optiona ): Defaults to False. If True, the bars heights are determined proportionally to the total number of responses in that facet. facet2 (str, optional): If provided, a second variable to facet against. Returns: (plotnine.ggplot): Plot object which can be displayed in a notebook or saved out to a file """ cols = [column, facet] if facet2: cols.append(facet2) show_legend = False topic_data = survey_data[cols] grouper = [facet, facet2] if facet2 else facet topic_data_long = make_long(topic_data, grouper) if proportionally: proportions = ( topic_data_long[topic_data_long.rating == 1].groupby(grouper).sum() / topic_data_long.groupby(grouper).sum() ).reset_index() else: proportions = ( topic_data_long[topic_data_long.rating == 1] .groupby(grouper) .count() .reset_index() ) x = topic_data_long.columns.tolist() x.remove("level_1") br = ( p9.ggplot(proportions, p9.aes(x=facet, fill=facet, y="level_1")) + p9.geom_bar(show_legend=show_legend, stat="identity") + p9.theme( axis_text_x=p9.element_text(angle=45, ha="right"), strip_text_y=p9.element_text(angle=0, ha="left"), ) + p9.scale_x_discrete( limits=topic_data_long[facet].unique().tolist(), labels=[ x.replace("_", " ") for x in topic_data_long[facet].unique().tolist() ], ) ) if facet2: br = br + p9.facet_grid([facet2, "."]) return br

33.452867

107

0.578334

4,329

34,423

4.376992

0.081081

0.050348

0.057631

0.01425

0.812803

0.773644

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0.720023

0.686194

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0

0.015216

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34,423

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null

0

1

0

1

0

null

0

5

322f1ef60534e5ae1808cafd8bca72607da38b91

392

py

Python

tests/client/mappings/test_resetting.py

symonk/pytest-wiremock

372956418174adafadcb33ad38db121a049f7f2b

[ "MIT" ]

null

tests/client/mappings/test_resetting.py

symonk/pytest-wiremock

372956418174adafadcb33ad38db121a049f7f2b

[ "MIT" ]

7

2022-03-14T08:41:55.000Z

2022-03-28T18:01:22.000Z

tests/client/mappings/test_resetting.py

symonk/pytest-wiremock

372956418174adafadcb33ad38db121a049f7f2b

[ "MIT" ]

null

def test_resetting_removes_created_stubs(connected_client, random_stub) -> None: assert connected_client.stubs.create_stub(random_stub).status_code == 201 assert connected_client.stubs.get_all_stubs().json()["meta"]["total"] == 1 assert connected_client.stubs.reset_stub_mappings().status_code == 200 assert connected_client.stubs.get_all_stubs().json()["meta"]["total"] == 0

65.333333

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0.767857

54

392

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0.266904

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392

5

81

78.4

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0

null

1

0

1

0

null

0

1

0

5

322ff2da22fee1f102983aa091df59ba03d41aef

133

py

Python

main/lib/flaskext/wtf/recaptcha/__init__.py

topless/gae-init-docs

8a727e35b7b7aa8abadf482325d7ca6489a2c2cd

[ "MIT" ]

1

2015-11-05T03:51:44.000Z

main/lib/flaskext/wtf/recaptcha/__init__.py

topless/gae-init-docs

8a727e35b7b7aa8abadf482325d7ca6489a2c2cd

[ "MIT" ]

1

2020-02-25T10:02:30.000Z

main/lib/flaskext/wtf/recaptcha/__init__.py

topless/gae-init-docs

8a727e35b7b7aa8abadf482325d7ca6489a2c2cd

[ "MIT" ]

null

from . import fields from . import validators from . import widgets __all__ = fields.__all__ + validators.__all__ + widgets.__all__

22.166667

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0.781955

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133

5.5

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0

0.150376

133

5

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false

0

0.75

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null

1

0

1

0

null

0

1

0

1

0

5

087036ef938f94834f4f08434dcaacedbc54b508

54

py

Python

sparkplug/helpers/__init__.py

Quva/sparkplug

c6ec310ae1f53067fece6e690d7b10c1eb69516e

[ "Apache-2.0" ]

null

sparkplug/helpers/__init__.py

Quva/sparkplug

c6ec310ae1f53067fece6e690d7b10c1eb69516e

[ "Apache-2.0" ]

null

sparkplug/helpers/__init__.py

Quva/sparkplug

c6ec310ae1f53067fece6e690d7b10c1eb69516e

[ "Apache-2.0" ]

null

from .tag_info import TagInfo from .helpers import *

13.5

29

0.777778

8

54

5.125

0.75

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0.166667

54

3

30

18

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0

1

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1

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null

0

1

0

1

0

1

0

5

08792ed8a338510fe1a55bb95c5925c275c9d886

83

py

Python

pod/__init__.py

TaitoUnited/pod

df8c0f303cd99e9d56bbc323c8e6e4444dccc1a7

[ "MIT" ]

null

pod/__init__.py

TaitoUnited/pod

df8c0f303cd99e9d56bbc323c8e6e4444dccc1a7

[ "MIT" ]

1

2019-06-10T18:27:31.000Z

2019-08-19T12:28:23.000Z

pod/__init__.py

TaitoUnited/pod

df8c0f303cd99e9d56bbc323c8e6e4444dccc1a7

[ "MIT" ]

null

from .fetcher import fetcher # noqa from .application import application # noqa

27.666667

45

0.771084

10

83

6.4

0.5

0

0.180723

83

2

46

41.5

0.941176

0.108434

0

1

0

true

0

1

0

1

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1

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null

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1

0

null

0

1

0

1

0

1

0

5

08bb287c9bd0e638671daa441f3c1ee00ccd815a

454

py

Python

iridium/test/glance_tests/glance_test.py

Toure/Rhea

fda0e4cd7c568943725245393bfe762bc858e917

[ "Apache-2.0" ]

1

2015-08-19T15:55:46.000Z

iridium/test/glance_tests/glance_test.py

Toure/Rhea

fda0e4cd7c568943725245393bfe762bc858e917

[ "Apache-2.0" ]

null

iridium/test/glance_tests/glance_test.py

Toure/Rhea

fda0e4cd7c568943725245393bfe762bc858e917

[ "Apache-2.0" ]

null

__author__ = "Toure Dunnon" __license__ = "Apache License 2.0" __version__ = "0.1" __email__ = "toure@redhat.com" __status__ = "Alpha" def test_create_image(): pass def test_list_image(): pass def test_add_location(): pass def test_delete_image(): pass def test_delete_location(): pass def test_update_location(): pass def test_image_upload(): pass def test_get_info(): # data and get calls here. pass

11.641026

34

0.680617

62

454

4.403226

0.5

0.205128

0.282051

0.175824

0

0.011299

0.220264

454

38

35

11.947368

0.759887

0.052863

0

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false

0.380952

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0

null

1

0

null

0

1

0

1

0

5

08d948640d49fc7c2325d22b0ffe8fe0514749cc

137

py

Python

readpower.py

motivatedsloth/powerpi

2d66b82fd3c74b90f160d6ef0589e6b423925e7b

[ "MIT" ]

null

readpower.py

motivatedsloth/powerpi

2d66b82fd3c74b90f160d6ef0589e6b423925e7b

[ "MIT" ]

null

readpower.py

motivatedsloth/powerpi

2d66b82fd3c74b90f160d6ef0589e6b423925e7b

[ "MIT" ]

null

#! /usr/bin/env python3 from subprocess import call call(['/usr/bin/python3 /home/pi/powerpi/reader/reader.py 2>/dev/null'], shell=True)

34.25

84

0.737226

23

137

4.391304

0.782609

0.118812

0

0.02381

0.080292

137

3

85

45.666667

0.777778

0.160584

0

0.5

0.54386

0.289474

0

1

0

true

0

0.5

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0.5

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1

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null

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1

0

1

0

null

0

1

0

1

0

5

3e99c77ad33dffb9bbf37c3a59391e5945109036

407

py

Python

spatialstats/polyspectra/__init__.py

mjo22/mobstats

7fe5fb8cf96b87845851826cb057828f271891f2

[ "MIT" ]

10

2021-04-09T17:23:50.000Z

2022-03-16T12:12:20.000Z

spatialstats/polyspectra/__init__.py

mjo22/softstats

7fe5fb8cf96b87845851826cb057828f271891f2

[ "MIT" ]

null

spatialstats/polyspectra/__init__.py

mjo22/softstats

7fe5fb8cf96b87845851826cb057828f271891f2

[ "MIT" ]

1

2021-05-04T22:00:51.000Z

""" Calculating spectral correlation functions for vector and scalar fields. .. moduleauthor:: Michael O'Brien <michaelobrien@g.harvard.edu> """ import spatialstats if spatialstats.config.gpu is False: from .powerspectrum import powerspectrum from .bispectrum import bispectrum else: from .cuda_powerspectrum import powerspectrum from .cuda_bispectrum import bispectrum del spatialstats

23.941176

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0.788698

46

407

6.934783

0.652174

0.119122

0.200627

0.225705

0

0.149877

407

16

73

25.4375

0.921965

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true

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null

0

1

0

1

0

1

0

5

3ebe75f77b3312ea32892c2b24e742cc5ceb4697

3,842

py

Python

cropper/migrations/0001_initial.py

ro5k0/django-image-cropper

fd2660d5f6b1941a2052a900276e3ba6faa19c7f

[ "MIT" ]

null

cropper/migrations/0001_initial.py

ro5k0/django-image-cropper

fd2660d5f6b1941a2052a900276e3ba6faa19c7f

[ "MIT" ]

null

cropper/migrations/0001_initial.py

ro5k0/django-image-cropper

fd2660d5f6b1941a2052a900276e3ba6faa19c7f

[ "MIT" ]

null

# encoding: utf-8 import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding model 'Original' db.create_table('cropper_original', ( ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('name', self.gf('django.db.models.fields.CharField')(max_length=255)), ('image', self.gf('django.db.models.fields.files.ImageField')(max_length=100)), ('image_width', self.gf('django.db.models.fields.PositiveIntegerField')(default=0)), ('image_height', self.gf('django.db.models.fields.PositiveIntegerField')(default=0)), )) db.send_create_signal('cropper', ['Original']) # Adding model 'Cropped' db.create_table('cropper_cropped', ( ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('name', self.gf('django.db.models.fields.CharField')(max_length=255)), ('original', self.gf('django.db.models.fields.related.ForeignKey')(related_name='cropped', to=orm['cropper.Original'])), ('image', self.gf('django.db.models.fields.files.ImageField')(max_length=100)), ('x', self.gf('django.db.models.fields.PositiveIntegerField')(default=0)), ('y', self.gf('django.db.models.fields.PositiveIntegerField')(default=0)), ('w', self.gf('django.db.models.fields.PositiveIntegerField')(null=True, blank=True)), ('h', self.gf('django.db.models.fields.PositiveIntegerField')(null=True, blank=True)), ('w_display', self.gf('django.db.models.fields.PositiveIntegerField')(null=True, blank=True)), ('h_display', self.gf('django.db.models.fields.PositiveIntegerField')(null=True, blank=True)), )) db.send_create_signal('cropper', ['Cropped']) def backwards(self, orm): # Deleting model 'Original' db.delete_table('cropper_original') # Deleting model 'Cropped' db.delete_table('cropper_cropped') models = { 'cropper.cropped': { 'Meta': {'object_name': 'Cropped'}, 'h': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}), 'h_display': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.files.ImageField', [], {'max_length': '100'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'original': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'cropped'", 'to': "orm['cropper.Original']"}), 'w': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}), 'w_display': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}), 'x': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}), 'y': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}) }, 'cropper.original': { 'Meta': {'object_name': 'Original'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.files.ImageField', [], {'max_length': '100'}), 'image_height': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}), 'image_width': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}) } } complete_apps = ['cropper']

54.112676

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0.195471

3,842

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0

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0

null

0

1

0

1

0

null

0

5

3ed039cb340d354c71730fb8253768b8ad5d6b1b

191

py

Python

DPGAnalysis/SiStripTools/python/filtertest/raw_102169_debug_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

852

2015-01-11T21:03:51.000Z

2022-03-25T21:14:00.000Z

DPGAnalysis/SiStripTools/python/filtertest/raw_102169_debug_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

30,371

2015-01-02T00:14:40.000Z

2022-03-31T23:26:05.000Z

DPGAnalysis/SiStripTools/python/filtertest/raw_102169_debug_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

3,240

2015-01-02T05:53:18.000Z

2022-03-31T17:24:21.000Z

import FWCore.ParameterSet.Config as cms fileNames = cms.untracked.vstring( '/store/data/Commissioning09/Cosmics/RAW/v2/000/102/169/F6566668-4267-DE11-8354-001D09F2983F.root', )

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191

5

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0

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1

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false

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0.25

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1

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1

null

0

5

3ed9f04f476b20fbaa0a71f8b98fa4195a5078fa

298

py

Python

teen/static/js/sandboxOA-master/apps/system/forms.py

manga89/Teens

a392d615854b8340651f035c291c27dc27d1faa6

[ "bzip2-1.0.6" ]

null

teen/static/js/sandboxOA-master/apps/system/forms.py

manga89/Teens

a392d615854b8340651f035c291c27dc27d1faa6

[ "bzip2-1.0.6" ]

null

teen/static/js/sandboxOA-master/apps/system/forms.py

manga89/Teens

a392d615854b8340651f035c291c27dc27d1faa6

[ "bzip2-1.0.6" ]

null

# @Time : 2018/7/18 18:47 # @Author : RobbieHan # @File : forms.py from django import forms class LoginForm(forms.Form): username = forms.CharField(required=True, error_messages={"requeired": "请填写用户名"}) password = forms.CharField(required=True, error_messages={"requeired": "请填写密码"})

29.8

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298

5.621622

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0.25

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298

9

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3eed4c078fe6ea8006680892f099f99a4bbcad53

209

py

Python

modules/workflow_lambdas/tests/suite-db-integration/exec.py

groboclown/whimbrel

1968cccf4888ef893686a812ed729205a31d2a12

[ "Apache-2.0" ]

null

modules/workflow_lambdas/tests/suite-db-integration/exec.py

groboclown/whimbrel

1968cccf4888ef893686a812ed729205a31d2a12

[ "Apache-2.0" ]

null

modules/workflow_lambdas/tests/suite-db-integration/exec.py

groboclown/whimbrel

1968cccf4888ef893686a812ed729205a31d2a12

[ "Apache-2.0" ]

null

def setup(config): pass def teardown(config): pass def run_test(config): pass def execute(config): setup(config) try: run_test(config) finally: teardown(config)

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209

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209

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