xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

eaed71d01580e30fe1bb78678d6ff1dc2cd447e0

45

py

Python

lfp_atn_simuran/__init__.py

seankmartin/lfp_atn

647889eddfa9ba3910c74df7e61f10fd98c61854

[ "MIT" ]

1

2021-09-14T11:22:29.000Z

lfp_atn_simuran/__init__.py

seankmartin/lfp_atn

647889eddfa9ba3910c74df7e61f10fd98c61854

[ "MIT" ]

null

lfp_atn_simuran/__init__.py

seankmartin/lfp_atn

647889eddfa9ba3910c74df7e61f10fd98c61854

[ "MIT" ]

1

2021-03-11T17:07:52.000Z

"""Module for ATNx analysis with SIMURAN."""

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0

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5

eafac09b8144636b77f4f5fb69ad209928c97b98

231

py

Python

tests/2015/test_11_corporate_policy.py

wimglenn/advent-of-code-wim

6308c3fa5d29b318680419f877fd5b8ac1359b5d

[ "WTFPL" ]

20

2019-10-15T07:33:13.000Z

2022-01-19T13:40:36.000Z

tests/2015/test_11_corporate_policy.py

wimglenn/advent-of-code-wim

6308c3fa5d29b318680419f877fd5b8ac1359b5d

[ "WTFPL" ]

5

2019-02-01T23:31:27.000Z

2021-12-03T06:55:58.000Z

tests/2015/test_11_corporate_policy.py

wimglenn/advent-of-code-wim

6308c3fa5d29b318680419f877fd5b8ac1359b5d

[ "WTFPL" ]

8

2019-12-03T15:41:23.000Z

2021-12-06T17:13:57.000Z

from aoc_wim.aoc2015 import q11 def test_requirements(): assert q11.req1("hijklmmn") assert not q11.req2("hijklmmn") assert q11.req3("abbceffg") assert not q11.req1("abbceffg") assert not q11.req3("abbcegjk")

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d809f2ddf2261a18c0c013144a0e9b40a77ca15d

235

py

Python

repos/system_upgrade/el7toel8/actors/osreleasecollector/tests/test_osreleasecollector.py

Dormouse759/leapp-repository

683cd1df8fb769a8256da89bd86fd402602a8d42

[ "Apache-2.0" ]

null

repos/system_upgrade/el7toel8/actors/osreleasecollector/tests/test_osreleasecollector.py

Dormouse759/leapp-repository

683cd1df8fb769a8256da89bd86fd402602a8d42

[ "Apache-2.0" ]

1

2019-04-12T14:45:21.000Z

repos/system_upgrade/el7toel8/actors/osreleasecollector/tests/test_osreleasecollector.py

frenzymadness/leapp-repository

683cd1df8fb769a8256da89bd86fd402602a8d42

[ "Apache-2.0" ]

null

from leapp.models import OSReleaseFacts from leapp.snactor.fixture import current_actor_context def test_actor_execution(current_actor_context): current_actor_context.run() assert current_actor_context.consume(OSReleaseFacts)

33.571429

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d81f85d4c73a4adb29b2391a71d8287b8ede94eb

241

py

Python

chartilo/drawers/__init__.py

hyper-hronoz/Chartilo

92c16f367846f7a43015099907cd5bfd4976ad07

[ "MIT" ]

1

2021-11-20T15:44:02.000Z

chartilo/drawers/__init__.py

hyper-hronoz/Chartilo

92c16f367846f7a43015099907cd5bfd4976ad07

[ "MIT" ]

null

chartilo/drawers/__init__.py

hyper-hronoz/Chartilo

92c16f367846f7a43015099907cd5bfd4976ad07

[ "MIT" ]

null

from .drawer import Drawer from .candleChartDrawer import CandleChartDrawer from .lineChartDrawer import LineChartDrawer from .gridDrawer import GridDrawer from .lineDrawer import LineDrawer from .maxMinValuesDrawer import MaxMinValuesDrawer

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null

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1

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1

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1

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5

d820c11750952592fda713741123ea611488a600

95

py

Python

ycurve/ffields/utils.py

yabirgb/ycurves

4840c5a8ddd1b89a62e5b7635a78bf4a29006b84

[ "MIT" ]

2

2021-05-28T17:47:57.000Z

2021-05-30T14:50:06.000Z

ycurve/ffields/utils.py

yabirgb/ycurves

4840c5a8ddd1b89a62e5b7635a78bf4a29006b84

[ "MIT" ]

5

2021-05-31T09:05:59.000Z

2021-07-07T19:02:01.000Z

ycurve/ffields/utils.py

yabirgb/ycurves

4840c5a8ddd1b89a62e5b7635a78bf4a29006b84

[ "MIT" ]

null

from typing import List def bits(n: int) -> List[int]: return list(map(int, bin(n)[2:]))

15.833333

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0.621053

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null

0

1

0

null

0

1

0

1

0

5

dc243dad38c0ee8409f04b8a76d80115cebb9afd

438

py

Python

Geometry/CMSCommonData/python/cmsSimIdealGeometryXML_cff.py

NTrevisani/cmssw

a212a27526f34eb9507cf8b875c93896e6544781

[ "Apache-2.0" ]

3

2018-08-24T19:10:26.000Z

2019-02-19T11:45:32.000Z

Geometry/CMSCommonData/python/cmsSimIdealGeometryXML_cff.py

NTrevisani/cmssw

a212a27526f34eb9507cf8b875c93896e6544781

[ "Apache-2.0" ]

7

2016-07-17T02:34:54.000Z

2019-08-13T07:58:37.000Z

Geometry/CMSCommonData/python/cmsSimIdealGeometryXML_cff.py

NTrevisani/cmssw

a212a27526f34eb9507cf8b875c93896e6544781

[ "Apache-2.0" ]

5

2018-08-21T16:37:52.000Z

2020-01-09T13:33:17.000Z

import FWCore.ParameterSet.Config as cms from Geometry.CMSCommonData.cmsSimIdealGeometryXML_cfi import * from Geometry.TrackerNumberingBuilder.trackerNumberingGeometry_cfi import * from Geometry.HcalCommonData.hcalParameters_cfi import * from Geometry.HcalCommonData.hcalDDDSimConstants_cfi import * from Geometry.HcalCommonData.hcalDDDRecConstants_cfi import * from Geometry.MuonNumbering.muonNumberingInitialization_cfi import *

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null

0

1

0

1

0

1

0

5

dc31f817158c86e2660ac13854d3480cbee0e352

97

py

Python

defs/python-setup.py

TED-996/krait-twostones

51b27793b9cd536d680fb9a6785c57473d35cac1

[ "MIT" ]

null

defs/python-setup.py

TED-996/krait-twostones

51b27793b9cd536d680fb9a6785c57473d35cac1

[ "MIT" ]

null

defs/python-setup.py

TED-996/krait-twostones

51b27793b9cd536d680fb9a6785c57473d35cac1

[ "MIT" ]

null

import sys import os krait_py_dir = os.path.join(root_dir, "py") sys.path.append(krait_py_dir)

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null

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1

0

null

0

1

0

5

dc446aef5e283da38d6792e9f0aabdd26eb8e9ba

50

py

Python

wisdem/__init__.py

johnjasa/WISDEM

a4571e71cb5b9869c81790f8abb1bb7fba8fdb02

[ "Apache-2.0" ]

81

2015-01-19T18:17:31.000Z

2022-03-17T07:14:43.000Z

wisdem/__init__.py

johnjasa/WISDEM

a4571e71cb5b9869c81790f8abb1bb7fba8fdb02

[ "Apache-2.0" ]

159

2015-02-05T01:54:52.000Z

2022-03-30T22:44:39.000Z

wisdem/__init__.py

johnjasa/WISDEM

a4571e71cb5b9869c81790f8abb1bb7fba8fdb02

[ "Apache-2.0" ]

70

2015-01-02T15:22:39.000Z

2022-02-11T00:33:07.000Z

from wisdem.glue_code.runWISDEM import run_wisdem

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5

dc82d72404651c211a7902ccb0bfb78c20a652f2

1,792

py

Python

day-12-guessing-game/art.py

Janell-Huyck/100-Days-of-Code

c73326d779d8555d8dbbcdee3ce600807ec3bb73

[ "MIT" ]

1

2021-11-28T23:34:01.000Z

day-12-guessing-game/art.py

Janell-Huyck/100-Days-of-Code

c73326d779d8555d8dbbcdee3ce600807ec3bb73

[ "MIT" ]

null

day-12-guessing-game/art.py

Janell-Huyck/100-Days-of-Code

c73326d779d8555d8dbbcdee3ce600807ec3bb73

[ "MIT" ]

null

logo = """ ,--, ,--.'| ,---, ,--, ,--, | : ,--, ,--.' | ,--.'| ,---.'| : ' ,--.'| | | : ,---,. | | : ,---. .---. | | : _' | | |, ,----._,. : : : ,' .' | : : ' ' ,'\ /. ./| : : |.' | `--'_ / / ' / : | |,--. ,---.' , | ' | / / | .-'-. ' | | ' ' ; : ,' ,'| | : | | : ' | | | | ' | | . ; ,. : /___/ \: | ' | .'. | ' | | | | .\ . | | /' : : : .' | | : ' | |: : .-'.. ' ' . | | : | ' | | : . ; '; | ' : | | | : |.' ' : |__ ' | .; : /___/ \: ' ' : | : ; ' : |__ ' . . | | | ' | : `---' | | '.'| | : | . \ ' .\ | | ' ,/ | | '.'| `---`-'| | | : :_:,' ; : ; \ \ / \ \ ' \ | ; : ;--' ; : ; .'__/\_: | | | ,' | , / `----' \ \ |--" | ,/ | , / | : : `--'' ---`-' \ \ | '---' ---`-' \ \ / '---" `--`-' """

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null

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1

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null

0

5

76215242f3157c42478dd5b37d6c56730039146f

108

py

Python

hearauem/models/__init__.py

embeddingspace/hearauem

2f134bee6e2cebb7ea89ecc7c2d0579e81e1d7dc

[ "Apache-2.0" ]

null

hearauem/models/__init__.py

embeddingspace/hearauem

2f134bee6e2cebb7ea89ecc7c2d0579e81e1d7dc

[ "Apache-2.0" ]

null

hearauem/models/__init__.py

embeddingspace/hearauem

2f134bee6e2cebb7ea89ecc7c2d0579e81e1d7dc

[ "Apache-2.0" ]

null

from .base import AuemBaseModel from .factory import create_model from .efficientnet import MelEfficientNet

27

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7.076923

0.692308

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0.111111

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0.958333

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true

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null

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null

0

1

0

1

0

1

0

5

52297580953f50dc0e2fecb975ba2c5d136ebe89

204

py

Python

JorGpi/generate/__main__.py

adujovic/JorG

15062984e837a938819e548c83f6f5414fa47103

[ "BSD-3-Clause" ]

1

2020-07-22T11:05:03.000Z

JorGpi/generate/__main__.py

adujovic/JorG

15062984e837a938819e548c83f6f5414fa47103

[ "BSD-3-Clause" ]

2

2019-06-07T11:53:48.000Z

2019-06-24T08:20:25.000Z

JorGpi/generate/__main__.py

adujovic/JorG

15062984e837a938819e548c83f6f5414fa47103

[ "BSD-3-Clause" ]

3

2019-07-01T12:38:06.000Z

2022-02-01T21:38:12.000Z

import JorGpi.generate.run from sys import argv if __name__ == '__main__': engine = JorGpi.generate.run.JorGpi(*argv) engine.initialize_new_cell() engine.possible_configurations() exit()

22.666667

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0.730392

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0.161765

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47

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null

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5

529821fcf6a7bc8ef21301f7ece42e73a2365a47

139

py

Python

tests/__init__.py

Suor/handy

c5503bbc053724f5bef3d1c4bf3799290834c148

[ "BSD-3-Clause" ]

41

2015-03-06T13:41:58.000Z

2021-09-06T19:06:42.000Z

tests/__init__.py

Suor/handy

c5503bbc053724f5bef3d1c4bf3799290834c148

[ "BSD-3-Clause" ]

6

2015-04-19T07:02:56.000Z

2017-11-23T11:46:23.000Z

tests/__init__.py

Suor/handy

c5503bbc053724f5bef3d1c4bf3799290834c148

[ "BSD-3-Clause" ]

8

2015-04-19T04:59:24.000Z

2021-09-06T19:09:03.000Z

# Use psycopg2cffi for PyPy try: import psycopg2 # noqa except ImportError: from psycopg2cffi import compat compat.register()

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null

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1

0

1

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1

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5

bfda28ae14370e8aa52a0557dd92410bc1520d7a

229

py

Python

schoolport/app_core/admin.py

yotink522/schoolport

c6cfd0230ca05fb44f77c2f27c7e200828547bd5

[ "MIT" ]

null

schoolport/app_core/admin.py

yotink522/schoolport

c6cfd0230ca05fb44f77c2f27c7e200828547bd5

[ "MIT" ]

null

schoolport/app_core/admin.py

yotink522/schoolport

c6cfd0230ca05fb44f77c2f27c7e200828547bd5

[ "MIT" ]

null

from django.contrib import admin from schoolport.app_core.models import * # Register your models here. admin.site.register(TB_School) admin.site.register(TB_Class) admin.site.register(TB_Course) admin.site.register(TB_Student)

22.9

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9

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true

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null

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1

0

1

0

5

87064d0dafa1b8cbf50495af17c32d2eb49c398d

51

py

Python

examples/python/dst/06typed/main.py

podhmo/prestring

8a3499377d1b1b2b180809b31bd7536de5c3ec4d

[ "MIT" ]

8

2015-03-05T07:32:52.000Z

2022-03-11T09:28:21.000Z

examples/python/dst/06typed/main.py

podhmo/prestring

8a3499377d1b1b2b180809b31bd7536de5c3ec4d

[ "MIT" ]

19

2016-12-01T03:09:03.000Z

2021-03-28T05:27:35.000Z

examples/python/dst/06typed/main.py

podhmo/prestring

8a3499377d1b1b2b180809b31bd7536de5c3ec4d

[ "MIT" ]

1

2017-07-19T12:39:43.000Z

def add(x: int, y: int=0) -> int: return x + y

17

33

0.509804

11

51

2.363636

0.636364

0

0.027778

0.294118

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2

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false

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null

0

1

0

null

0

1

0

1

0

5

870b2327e612ed401043923d4980e7cbfbb42050

92

py

Python

qyburn-simulator/job.py

danielsuo/qyburn

f3f09103480fed580ebf282dc1c185281582332d

[ "MIT" ]

null

qyburn-simulator/job.py

danielsuo/qyburn

f3f09103480fed580ebf282dc1c185281582332d

[ "MIT" ]

null

qyburn-simulator/job.py

danielsuo/qyburn

f3f09103480fed580ebf282dc1c185281582332d

[ "MIT" ]

null

class Job: id = 0 def __init__(self): self.id = Job.id Job.id += 1

13.142857

24

0.467391

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92

2.785714

0.571429

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0.037037

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7

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1

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1

0

null

0

1

0

5

8769255a48e86a2f2b62f98923e4ca13ac734c5c

88

py

Python

extras/__init__.py

mikkoi/pitchfork

1f1d16549408dc29dae03fc1a479c93556239112

[ "MIT" ]

645

2018-08-24T03:29:32.000Z

2022-03-30T19:08:40.000Z

extras/__init__.py

Quincunx271/pitchfork

c68237d3dd9ad901c918495f29ca43899478c39c

[ "MIT" ]

33

2018-09-04T04:45:08.000Z

2021-08-30T08:36:58.000Z

extras/__init__.py

Quincunx271/pitchfork

c68237d3dd9ad901c918495f29ca43899478c39c

[ "MIT" ]

45

2018-08-25T04:56:48.000Z

2022-01-18T15:59:10.000Z

# This file is only here to satisfy Python 2 wanting to import extras.pf_conan.pf_conan

44

87

0.806818

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4.058824

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0.202899

0

0.013514

0.159091

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1

88

0.918919

0.965909

0

null

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true

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1

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null

0

1

0

5

5e41aa1555e40cd8c083a59f1cf6c12a74983eb7

99

py

Python

oslash/util/basic.py

stjordanis/OSlash

c271c7633daf9d72393b419cfc9229e427e6a42a

[ "MIT" ]

668

2015-03-26T12:32:55.000Z

2022-03-27T13:28:42.000Z

oslash/util/basic.py

stjordanis/OSlash

c271c7633daf9d72393b419cfc9229e427e6a42a

[ "MIT" ]

20

2016-03-01T06:56:57.000Z

2020-12-29T09:41:39.000Z

oslash/util/basic.py

stjordanis/OSlash

c271c7633daf9d72393b419cfc9229e427e6a42a

[ "MIT" ]

54

2015-03-21T08:18:12.000Z

2021-09-02T22:07:51.000Z

Unit = () def indent(level, size=2): """Return indentation.""" return ' ' * level * size

14.142857

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py

Python

gcn/metrics.py

jc-chen/gcn

ff8bbc01e53935f469e2005ff1baad1efdf652f3

[ "MIT" ]

null

gcn/metrics.py

jc-chen/gcn

ff8bbc01e53935f469e2005ff1baad1efdf652f3

[ "MIT" ]

null

gcn/metrics.py

jc-chen/gcn

ff8bbc01e53935f469e2005ff1baad1efdf652f3

[ "MIT" ]

1

2020-11-04T15:37:52.000Z

import tensorflow as tf def masked_softmax_cross_entropy(preds, labels, mask): """Softmax cross-entropy loss with masking.""" loss = tf.nn.softmax_cross_entropy_with_logits(logits=preds, labels=labels) mask = tf.cast(mask, dtype=tf.float32) mask /= tf.reduce_mean(mask) loss *= mask return tf.reduce_mean(loss) def masked_accuracy(preds, labels, mask, target_mean, target_stdev): """Accuracy with masking.""" mask=tf.transpose(mask) mask = tf.cast(mask, dtype=tf.float32) mask = tf.expand_dims(mask,-1) mask = tf.tile(mask,[1,labels.shape[1].value]) #mask /= tf.reduce_mean(mask) labels = tf.boolean_mask(labels,mask) preds = tf.boolean_mask(preds,mask) labels = tf.abs(labels + target_mean/target_stdev) preds = tf.abs(preds + target_mean/target_stdev) diff = tf.abs(tf.subtract(labels,preds)) loss = tf.divide(diff,labels) return tf.reduce_mean(loss,0) def masked_accuracy_old(preds, labels, mask, target_mean, target_stdev): """Accuracy with masking.""" mask=tf.transpose(mask) mask = tf.cast(mask, dtype=tf.float32) mask = tf.expand_dims(mask,-1) mask = tf.tile(mask,[1,labels.shape[1].value]) mask /= tf.reduce_mean(mask) #mnabserr = tf.metrics.mean_absolute_error(labels,preds) #accuracy_all = tf.multiply(mnabserr,mask) #accuracy_all *= mask labels *= mask #fixes an annoying divide by 0 problem labels = labels + target_mean/target_stdev preds = preds + target_mean/target_stdev diff = tf.abs(tf.subtract(labels,preds)) loss = tf.divide(diff,tf.abs(labels)) loss = tf.multiply(loss,mask) return tf.reduce_mean(loss,0) def mean_absolute_error(preds,labels,mask): mask = tf.cast(mask,dtype=tf.float32) mask = tf.expand_dims(mask,-1) mask = tf.tile(mask,[1,labels.shape[1].value]) mask /= tf.reduce_mean(mask) loss = tf.abs(tf.subtract(labels,preds)) loss = tf.multiply(loss,mask) return tf.reduce_mean(loss,0) def square_error_old(preds, labels, mask): """L2 loss refactored to incorporate masks""" mask = tf.cast(mask,dtype=tf.float32) mask = tf.expand_dims(mask,-1) mask = tf.tile(mask,[1,labels.shape[1].value]) mask /= tf.reduce_mean(mask) loss = tf.losses.mean_squared_error(labels,preds,reduction=tf.losses.Reduction.NONE) loss = tf.multiply(loss,mask) return tf.reduce_mean(loss) def square_error(preds, labels, mask): """L2 loss refactored to incorporate masks""" # should be equivalent to the other square error function mask = tf.cast(mask,dtype=tf.float32) mask = tf.expand_dims(mask,-1) mask = tf.tile(mask,[1,labels.shape[1].value]) loss = tf.losses.mean_squared_error(labels,preds,reduction=tf.losses.Reduction.NONE) loss = tf.boolean_mask(loss,mask) return tf.reduce_mean(loss) def square_error_3(preds, labels, mask): """L2 loss refactored to incorporate masks""" # should be equivalent to the other square error function mask = tf.cast(mask,dtype=tf.float32) mask = tf.expand_dims(mask,-1) mask = tf.tile(mask,[1,labels.shape[1].value]) loss = tf.losses.mean_squared_error(labels,preds,mask) return loss

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5e73bc267a99b739a55c3ce24bc1e30bb6a5e3a2

164

py

Python

Languages/Python/python_study_3/page5/script.py

myarist/Progate

0132583c989b5ec1805d4de1a6f6861586cf152e

[ "MIT" ]

5

2021-09-08T03:07:22.000Z

2022-03-18T13:36:27.000Z

Languages/Python/python_study_3/page5/script.py

myarist/Progate

0132583c989b5ec1805d4de1a6f6861586cf152e

[ "MIT" ]

null

Languages/Python/python_study_3/page5/script.py

myarist/Progate

0132583c989b5ec1805d4de1a6f6861586cf152e

[ "MIT" ]

12

2021-05-11T07:54:20.000Z

2022-03-27T02:55:46.000Z

# Tambahkan nilai default untuk name def print_hand(hand, name='Tamu'): print(name + ' memilih: ' + hand) # Tambahkan argument ke print_hand print_hand('Batu')

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0

1

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5e9ab7a50b99fed13a425c13bf0ffd6a3b79d236

94

py

Python

tests/test_dummy.py

selinabitting/compas_view2

cac8abaf8fbde13ceabe35324be92779ea2e535f

[ "MIT" ]

5

2021-03-03T13:07:31.000Z

2022-02-05T01:07:31.000Z

tests/test_dummy.py

selinabitting/compas_view2

cac8abaf8fbde13ceabe35324be92779ea2e535f

[ "MIT" ]

91

2021-01-29T14:26:28.000Z

2022-03-22T17:15:58.000Z

tests/test_dummy.py

selinabitting/compas_view2

cac8abaf8fbde13ceabe35324be92779ea2e535f

[ "MIT" ]

6

2021-01-29T11:13:45.000Z

2022-02-05T00:56:24.000Z

import compas_view2 def test_trivial(): print(compas_view2.__version__) assert True

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5ea17876e52b57cd59a07067342cad7cf4197a3a

446

py

Python

python/testData/highlighting/asyncAndAwaitAsIdentifiersIn37.py

alexey-anufriev/intellij-community

ffcd46f14e630acdefcc76e2bfc7c43d2449013a

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/highlighting/asyncAndAwaitAsIdentifiersIn37.py

alexey-anufriev/intellij-community

ffcd46f14e630acdefcc76e2bfc7c43d2449013a

[ "Apache-2.0" ]

null

python/testData/highlighting/asyncAndAwaitAsIdentifiersIn37.py

alexey-anufriev/intellij-community

ffcd46f14e630acdefcc76e2bfc7c43d2449013a

[ "Apache-2.0" ]

1

2020-10-15T05:56:42.000Z

class <error descr="Identifier expected">async</error>: pass def <error descr="Identifier expected">async</error>(): pass async<error descr="'def' or 'with' or 'for' expected"> </error>=<error descr="Statement expected, found Py:EQ"> </error>10 class <error descr="Identifier expected">await</error>: pass def <error descr="Identifier expected">await</error>(): pass await<error descr="Expression expected"> </error>= 10

22.3

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5ea43478c5c1745504f22b90a3545c10e58ed26e

63

py

Python

novice/01-02/swaroopch/from_import.py

fakihAlim/zimera

69271dbcfe9d8f9b2ef72e6f6c8ce0ae4c57a9c9

[ "MIT" ]

null

novice/01-02/swaroopch/from_import.py

fakihAlim/zimera

69271dbcfe9d8f9b2ef72e6f6c8ce0ae4c57a9c9

[ "MIT" ]

null

novice/01-02/swaroopch/from_import.py

fakihAlim/zimera

69271dbcfe9d8f9b2ef72e6f6c8ce0ae4c57a9c9

[ "MIT" ]

null

from math import sqrt print("Square root of 16 is", sqrt(16))

15.75

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0.714286

12

63

3.75

0.833333

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1

0

1

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5

5ec42f8b060f3fbf8f5362a90b56437df718a7f7

165

py

Python

contract_scanner/admin.py

kimnnmadsen/eve-abyssal-market

1e07498b98be9282b969badff51d55258c72e7ed

[ "MIT" ]

13

2018-08-23T14:27:22.000Z

2020-12-07T12:35:38.000Z

contract_scanner/admin.py

kimnnmadsen/eve-abyssal-market

1e07498b98be9282b969badff51d55258c72e7ed

[ "MIT" ]

25

2018-10-09T14:37:33.000Z

2020-05-15T20:21:48.000Z

contract_scanner/admin.py

kimnnmadsen/eve-abyssal-market

1e07498b98be9282b969badff51d55258c72e7ed

[ "MIT" ]

4

2021-08-12T05:34:05.000Z

2022-01-06T05:28:36.000Z

from django.contrib import admin from contract_scanner.models import Contract, PlexPriceRecord admin.site.register(Contract) admin.site.register(PlexPriceRecord)

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5ecd234f9984153807060fabb61cec836a6e75ac

258

py

Python

pyspeckit/wrappers/__init__.py

migueldvb/pyspeckit

fa7d875da7c684c8f6aaa3ba206ef3ff2e196652

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

null

pyspeckit/wrappers/__init__.py

migueldvb/pyspeckit

fa7d875da7c684c8f6aaa3ba206ef3ff2e196652

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

null

pyspeckit/wrappers/__init__.py

migueldvb/pyspeckit

fa7d875da7c684c8f6aaa3ba206ef3ff2e196652

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

null

""" A collection of wrappers """ from showspec_splat1d import splat_1d from fit_gaussians_to_simple_spectra import fit_gaussians_to_simple_spectra import fitnh3 import fith2co from cube_fit import cube_fit from load_IRAF_multispec import load_IRAF_multispec

25.8

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5

0d79c7829ae3e5e1573601ae207685839a381f78

11,851

py

Python

cpp/pycore/test/geometry/line_segment_test.py

schroedtert/simulator

ac11876fa04f0bdc515efa224c4f74cf493bed65

[ "MIT" ]

1

2020-10-15T13:20:07.000Z

cpp/pycore/test/geometry/line_segment_test.py

schroedtert/simulator

ac11876fa04f0bdc515efa224c4f74cf493bed65

[ "MIT" ]

null

cpp/pycore/test/geometry/line_segment_test.py

schroedtert/simulator

ac11876fa04f0bdc515efa224c4f74cf493bed65

[ "MIT" ]

null

import pytest from jpscore.geometry import Coordinate, LengthUnit, Level, LineSegment, Units class TestLineSegment: @pytest.mark.parametrize( "start, end", [ ( Coordinate( LengthUnit(0.1, Units.m), LengthUnit(0.65, Units.m), Level(4), ), Coordinate( LengthUnit(0.45, Units.m), LengthUnit(61.1, Units.m), Level(4), ), ), ( Coordinate( LengthUnit(1.3, Units.m), LengthUnit(-12.1, Units.m), Level(54), ), Coordinate( LengthUnit(5.1, Units.m), LengthUnit(-41.0, Units.m), Level(54), ), ), ( Coordinate( LengthUnit(45.1, Units.m), LengthUnit(-45.11, Units.m), Level(-1), ), Coordinate( LengthUnit(7.11, Units.m), LengthUnit(45.1, Units.m), Level(-1), ), ), ( Coordinate( LengthUnit(-56.61, Units.m), LengthUnit(1.34, Units.m), Level(0), ), Coordinate( LengthUnit(-4.11, Units.m), LengthUnit(7.0001, Units.m), Level(0), ), ), ( Coordinate( LengthUnit(-41.1111, Units.m), LengthUnit(-324.11, Units.m), Level(-4), ), Coordinate( LengthUnit(-41.0, Units.m), LengthUnit(-320.11, Units.m), Level(-4), ), ), ], ) def test_constructor(self, start, end): line_segment = LineSegment(start, end) assert line_segment.start == start assert line_segment.end == end @pytest.mark.parametrize( "start, end", [ ( Coordinate( LengthUnit(0.1, Units.m), LengthUnit(0.65, Units.m), Level(4), ), Coordinate( LengthUnit(0.45, Units.m), LengthUnit(61.1, Units.m), Level(1), ), ), ( Coordinate( LengthUnit(1.1, Units.m), LengthUnit(-0.1, Units.m), Level(0), ), Coordinate( LengthUnit(1.1, Units.m), LengthUnit(-0.1, Units.m), Level(0), ), ), ], ) def test_constructor_failing(self, start, end): with pytest.raises(ValueError): LineSegment(start, end) @pytest.mark.parametrize( "line_segment", [ LineSegment( Coordinate( LengthUnit(0.1, Units.m), LengthUnit(0.65, Units.m), Level(4), ), Coordinate( LengthUnit(0.45, Units.m), LengthUnit(61.1, Units.m), Level(4), ), ), LineSegment( Coordinate( LengthUnit(36.1, Units.m), LengthUnit(45.1, Units.m), Level(1), ), Coordinate( LengthUnit(67.1, Units.m), LengthUnit(56.1, Units.m), Level(1), ), ), LineSegment( Coordinate( LengthUnit(-32.54, Units.m), LengthUnit(-61.11, Units.m), Level(-4), ), Coordinate( LengthUnit(-12.6, Units.m), LengthUnit(-718.3, Units.m), Level(-4), ), ), LineSegment( Coordinate( LengthUnit(0.1, Units.m), LengthUnit(-56.1, Units.m), Level(0), ), Coordinate( LengthUnit(-56.1, Units.m), LengthUnit(0.1, Units.m), Level(0), ), ), ], ) def test_rotate(self, line_segment): start_before = line_segment.start end_before = line_segment.end line_segment.rotate() assert line_segment.start == end_before assert line_segment.end == start_before @pytest.mark.parametrize( "line_segment, other, result", [ ( LineSegment( Coordinate( LengthUnit(0.1, Units.m), LengthUnit(0.65, Units.m), Level(4), ), Coordinate( LengthUnit(0.45, Units.m), LengthUnit(61.1, Units.m), Level(4), ), ), LineSegment( Coordinate( LengthUnit(0.1, Units.m), LengthUnit(0.65, Units.m), Level(4), ), Coordinate( LengthUnit(0.45, Units.m), LengthUnit(61.1, Units.m), Level(4), ), ), True, ), ( LineSegment( Coordinate( LengthUnit(4.145, Units.m), LengthUnit(-3.1, Units.m), Level(-2), ), Coordinate( LengthUnit(90.1, Units.m), LengthUnit(0.11, Units.m), Level(-2), ), ), LineSegment( Coordinate( LengthUnit(90.1, Units.m), LengthUnit(0.11, Units.m), Level(-2), ), Coordinate( LengthUnit(4.145, Units.m), LengthUnit(-3.1, Units.m), Level(-2), ), ), True, ), ( LineSegment( Coordinate( LengthUnit(-32.5, Units.m), LengthUnit(0.11, Units.m), Level(20), ), Coordinate( LengthUnit(-41.1, Units.m), LengthUnit(0.11, Units.m), Level(20), ), ), LineSegment( Coordinate( LengthUnit(-32.5, Units.m), LengthUnit(0.11, Units.m), Level(20), ), Coordinate( LengthUnit(-41.1, Units.m), LengthUnit(0.11, Units.m), Level(20), ), ), True, ), ( LineSegment( Coordinate( LengthUnit(34.5, Units.m), LengthUnit(56.81, Units.m), Level(1), ), Coordinate( LengthUnit(0.41, Units.m), LengthUnit(90.1, Units.m), Level(1), ), ), LineSegment( Coordinate( LengthUnit(0.001, Units.m), LengthUnit(3.1, Units.m), Level(1), ), Coordinate( LengthUnit(-41.1, Units.m), LengthUnit(8.11, Units.m), Level(1), ), ), False, ), ( LineSegment( Coordinate( LengthUnit(-32.5, Units.m), LengthUnit(0.11, Units.m), Level(20), ), Coordinate( LengthUnit(-41.1, Units.m), LengthUnit(0.11, Units.m), Level(20), ), ), LineSegment( Coordinate( LengthUnit(-32.5, Units.m), LengthUnit(0.11, Units.m), Level(1), ), Coordinate( LengthUnit(-41.1, Units.m), LengthUnit(0.11, Units.m), Level(1), ), ), False, ), ], ) def test_comparison_operators(self, line_segment, other, result): assert (line_segment == other) == result assert (line_segment != other) != result @pytest.mark.parametrize( "line_segment, new_start, new_end", [ ( LineSegment( Coordinate( LengthUnit(-32.5, Units.m), LengthUnit(0.11, Units.m), Level(20), ), Coordinate( LengthUnit(-41.1, Units.m), LengthUnit(0.11, Units.m), Level(20), ), ), Coordinate( LengthUnit(45.1, Units.m), LengthUnit(1.1, Units.m), Level(20), ), Coordinate( LengthUnit(-41.1, Units.m), LengthUnit(45.1, Units.m), Level(20), ), ), ( LineSegment( Coordinate( LengthUnit(0.1, Units.m), LengthUnit(0.65, Units.m), Level(4), ), Coordinate( LengthUnit(0.12, Units.m), LengthUnit(61.1, Units.m), Level(4), ), ), Coordinate( LengthUnit(234.1, Units.m), LengthUnit(0.11, Units.m), Level(20), ), Coordinate( LengthUnit(-41.1, Units.m), LengthUnit(0.11, Units.m), Level(20), ), ), ], ) def test_read_only(self, line_segment, new_start, new_end): with pytest.raises(AttributeError): line_segment.start = new_start with pytest.raises(AttributeError): line_segment.end = new_end

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0d8f13e5651a2ab35788b112f4b9650f00afe7df

33

py

Python

test.py

arhue/python-learning

058c93315fd5aa76584e32432e7c80cb3972478e

[ "MIT" ]

null

test.py

arhue/python-learning

058c93315fd5aa76584e32432e7c80cb3972478e

[ "MIT" ]

null

test.py

arhue/python-learning

058c93315fd5aa76584e32432e7c80cb3972478e

[ "MIT" ]

null

#!/usr/bin/python3 print("test")

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null

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0d94518eb645efa641245f97dfdbf63f3b385df9

108

py

Python

util/mathhelp.py

Gabaa/world-generation-python

2270b60fbd9915886e9223cbf014520582314dce

[ "Apache-2.0" ]

null

util/mathhelp.py

Gabaa/world-generation-python

2270b60fbd9915886e9223cbf014520582314dce

[ "Apache-2.0" ]

null

util/mathhelp.py

Gabaa/world-generation-python

2270b60fbd9915886e9223cbf014520582314dce

[ "Apache-2.0" ]

null

import math def euclidean_dist(x1, y1, x2, y2): return math.sqrt(abs(x1 - x2) ** 2 + abs(y1 - y2) ** 2)

27

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5

0da4f95683b61cc8bec8d812dfa540cfa594a9e4

193

py

Python

src/gelanis/sql/_expressions/__init__.py

svaningelgem/gelanis

12360ead579816e5a2764dc9f995449bacf67ecc

[ "Apache-2.0" ]

1

2021-07-30T11:23:43.000Z

src/gelanis/sql/_expressions/__init__.py

svaningelgem/gelanis

12360ead579816e5a2764dc9f995449bacf67ecc

[ "Apache-2.0" ]

3

2021-03-05T14:45:38.000Z

2021-03-10T16:19:38.000Z

src/gelanis/sql/_expressions/__init__.py

svaningelgem/gelanis

12360ead579816e5a2764dc9f995449bacf67ecc

[ "Apache-2.0" ]

1

2021-03-17T19:43:05.000Z

from .expressions import BinaryOperation, Expression, NullSafeColumnOperation, UnaryExpression __all__ = [ 'Expression', 'NullSafeColumnOperation', 'UnaryExpression', 'BinaryOperation', ]

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0db1acf7f479a0caaffddd1105bf0c286ad7c99e

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py

Python

descent/utilities/__init__.py

SimonBoothroyd/descent

fcefc5136bec8e2b23b3537f682c7ccfb0f30d96

[ "MIT" ]

3

2021-09-13T13:38:04.000Z

2021-12-03T03:15:07.000Z

descent/utilities/__init__.py

SimonBoothroyd/descent

fcefc5136bec8e2b23b3537f682c7ccfb0f30d96

[ "MIT" ]

17

2021-09-02T12:11:42.000Z

2022-03-01T22:31:54.000Z

descent/utilities/__init__.py

SimonBoothroyd/descent

fcefc5136bec8e2b23b3537f682c7ccfb0f30d96

[ "MIT" ]

null

from descent.utilities.utilities import value_or_list_to_list __all__ = [value_or_list_to_list]

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null

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5

0dc78a975b3ae83009e6015d13ca5ee2d08ee544

39

py

Python

apps/kba/fabfile.py

Bibiko/appconfig

356f8be5df2750dcc2faebe65adae17e550a3afe

[ "Apache-2.0" ]

null

apps/kba/fabfile.py

Bibiko/appconfig

356f8be5df2750dcc2faebe65adae17e550a3afe

[ "Apache-2.0" ]

null

apps/kba/fabfile.py

Bibiko/appconfig

356f8be5df2750dcc2faebe65adae17e550a3afe

[ "Apache-2.0" ]

null

from appconfig.tasks import * init()

7.8

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null

0

1

0

1

0

5

0def076fc874c1029f5b4f4d44b285e630e602ff

168

py

Python

anonymization/anonymizers/__init__.py

Andhs/anonymization

bb8c3e699ea150d8a294771a793c37ee47d3f885

[ "MIT" ]

null

anonymization/anonymizers/__init__.py

Andhs/anonymization

bb8c3e699ea150d8a294771a793c37ee47d3f885

[ "MIT" ]

null

anonymization/anonymizers/__init__.py

Andhs/anonymization

bb8c3e699ea150d8a294771a793c37ee47d3f885

[ "MIT" ]

null

from .fileAnonymizers import * from .internetAnonymizers import * from .spacyAnonymizers import * from .phoneNumberAnonymizers import * from .customAnonymizers import *

33.6

37

0.827381

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168

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0.466667

0.28777

0

0.113095

168

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true

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null

1

0

1

0

null

0

1

0

1

0

5

219a8951b74814ef78573362f5049c21ca3555ff

75

py

Python

docs/homepage/exampletask.py

gthb/celery

13057dc69a6ecda6aabfae7b7640d971176251fb

[ "BSD-3-Clause" ]

2

2017-05-24T13:03:30.000Z

2017-09-04T08:24:19.000Z

examples/django/demoproject/demoapp/tasks.py

winhamwr/celery

249a270301ddb9b025cf8d00400bb442df9cae62

[ "BSD-3-Clause" ]

null

examples/django/demoproject/demoapp/tasks.py

winhamwr/celery

249a270301ddb9b025cf8d00400bb442df9cae62

[ "BSD-3-Clause" ]

null

from celery.decorators import task @task def add(x, y): return x + y

10.714286

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0

1

0.25

false

0

0.25

0.75

0

1

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null

0

1

0

null

0

1

0

1

0

5

219fe8e7c0d907f56b17726e9da375879ff2d72a

119

py

Python

compra/admin.py

dusannemec/stocktracker

fd83862ce47dae1615c445a1bed1a39d3a769e80

[ "MIT" ]

null

compra/admin.py

dusannemec/stocktracker

fd83862ce47dae1615c445a1bed1a39d3a769e80

[ "MIT" ]

null

compra/admin.py

dusannemec/stocktracker

fd83862ce47dae1615c445a1bed1a39d3a769e80

[ "MIT" ]

null

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

17

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null

0

1

0

1

0

1

0

5

21a0fe847bb01c4d747b3028db9918e7c55bcecb

16,984

py

Python

Week 1 Dec 1-8/Dec 3.py

Descent098/advent-of-code-2020

74e6353248de271e233851e495f21a9589d1de36

[ "MIT" ]

null

Week 1 Dec 1-8/Dec 3.py

Descent098/advent-of-code-2020

74e6353248de271e233851e495f21a9589d1de36

[ "MIT" ]

null

Week 1 Dec 1-8/Dec 3.py

Descent098/advent-of-code-2020

74e6353248de271e233851e495f21a9589d1de36

[ "MIT" ]

null

""" --- Day 3: Toboggan Trajectory --- With the toboggan login problems resolved, you set off toward the airport. While travel by toboggan might be easy, it's certainly not safe: there's very minimal steering and the area is covered in trees. You'll need to see which angles will take you near the fewest trees. Due to the local geology, trees in this area only grow on exact integer coordinates in a grid. You make a map (your puzzle input) of the open squares (.) and trees (#) you can see. For example: ..##....... #...#...#.. .#....#..#. ..#.#...#.# .#...##..#. ..#.##..... .#.#.#....# .#........# #.##...#... #...##....# .#..#...#.# These aren't the only trees, though; due to something you read about once involving arboreal genetics and biome stability, the same pattern repeats to the right many times: ..##.........##.........##.........##.........##.........##....... ---> #...#...#..#...#...#..#...#...#..#...#...#..#...#...#..#...#...#.. .#....#..#..#....#..#..#....#..#..#....#..#..#....#..#..#....#..#. ..#.#...#.#..#.#...#.#..#.#...#.#..#.#...#.#..#.#...#.#..#.#...#.# .#...##..#..#...##..#..#...##..#..#...##..#..#...##..#..#...##..#. ..#.##.......#.##.......#.##.......#.##.......#.##.......#.##..... ---> .#.#.#....#.#.#.#....#.#.#.#....#.#.#.#....#.#.#.#....#.#.#.#....# .#........#.#........#.#........#.#........#.#........#.#........# #.##...#...#.##...#...#.##...#...#.##...#...#.##...#...#.##...#... #...##....##...##....##...##....##...##....##...##....##...##....# .#..#...#.#.#..#...#.#.#..#...#.#.#..#...#.#.#..#...#.#.#..#...#.# ---> You start on the open square (.) in the top-left corner and need to reach the bottom (below the bottom-most row on your map). The toboggan can only follow a few specific slopes (you opted for a cheaper model that prefers rational numbers); start by counting all the trees you would encounter for the slope right 3, down 1: From your starting position at the top-left, check the position that is right 3 and down 1. Then, check the position that is right 3 and down 1 from there, and so on until you go past the bottom of the map. The locations you'd check in the above example are marked here with O where there was an open square and X where there was a tree: ..##.........##.........##.........##.........##.........##....... ---> #..O#...#..#...#...#..#...#...#..#...#...#..#...#...#..#...#...#.. .#....X..#..#....#..#..#....#..#..#....#..#..#....#..#..#....#..#. ..#.#...#O#..#.#...#.#..#.#...#.#..#.#...#.#..#.#...#.#..#.#...#.# .#...##..#..X...##..#..#...##..#..#...##..#..#...##..#..#...##..#. ..#.##.......#.X#.......#.##.......#.##.......#.##.......#.##..... ---> .#.#.#....#.#.#.#.O..#.#.#.#....#.#.#.#....#.#.#.#....#.#.#.#....# .#........#.#........X.#........#.#........#.#........#.#........# #.##...#...#.##...#...#.X#...#...#.##...#...#.##...#...#.##...#... #...##....##...##....##...#X....##...##....##...##....##...##....# .#..#...#.#.#..#...#.#.#..#...X.#.#..#...#.#.#..#...#.#.#..#...#.# ---> In this example, traversing the map using this slope would cause you to encounter 7 trees. Starting at the top-left corner of your map and following a slope of right 3 and down 1, how many trees would you encounter? """ from pprint import pprint input_map = [ ".#..........#......#..#.....#..", "....#.............#.#....#..#..", ".....##...###....#..#.......#..", ".#....#..#......#........#.....", ".#.........###.#..........##...", "...............##........#.....", "#..#..........#..##..#....#.#..", "....#.##....#..#...#.#....#....", "...###...#............#.#......", "#.........#..#...............#.", "#.#...........#...............#", "..#.#......#..###.#...#..##....", ".....#..#..#..#............#...", "......#.......#.....#....##....", "#......#...#.......#.#.#.......", "...........##.#.............#..", ".#.........#..#.####...........", "..#...........#....##..........", "#...........#.......#..#.#.....", ".....##...#.....#..##..#..#....", "#.#..........................#.", "##.....#..........#.......##..#", "....#..#............#.#.#......", ".......#.......#..#............", "...#.#..........#..#.....#.....", ".....#...##..##.....##........#", ".#.....#........##............#", "..#....#.#...#.....#.##........", "........##.....#......##...##..", "......#..................#.....", "..##......##.....##...##.......", "......#..#...##......##........", ".#..#..#.#.....................", ".#....#.#...#....#.......##...#", ".####.#..##...#.#.#....#...#...", ".#....#.....#...#..#.........##", "...........#.#####.#.#..##..#..", ".#......##...#..###.#.#....#...", "...#.....#........#..###...#...", ".......#................##.#...", ".##...#.#..................#...", "..#........#....#..........#..#", "..#.........#..................", "...#.#..........#.#..##........", "...#.##..........##...........#", "...........#..#........#.......", ".#....#.#...........#....#.##..", ".#...#..#............#....#.#..", "...#..#...#.........####.#.#...", "..#...#...........###..#...##.#", "......##...#.#.#....##....#....", "#..#.#.....##....#.......#...#.", ".#.....#.....#..#..##..........", "................#.#.#...##.....", ".#.....#............#......#...", "...#...#..#.#....######.....#..", "..#..........##......##.....#..", "......#..#.##...#.#............", "....#.......#..#...#..#.#......", "#......##.#..#........#.....#..", "..#.........#..#.........#.....", "..#.........##.......#.#.#..##.", "...#....##.................#.#.", "...#........##.#.......#.##..##", "....#.#...#...#....#...........", ".........#....##........#......", "...#........#..#.......#...#...", "#.......#....#...#...........#.", ".......#......#...##...........", ".#.#......##.#.......#..#...#..", ".#.....##.#...#......#..#......", "........#.............#.#..#..#", "#...........#....#.....#.##.#.#", "................#...#........##", "#..#.##..#.....#...##.#........", "#.....#.#..##......#.#..#..###.", "....#...#.....#................", "......#...#..##...........#....", "......#.........##.#...#......#", "#...#.#.....#..#.#..#..#......#", "...#.#..#..#.#........###.#....", "..#...#.......#.#.......#......", "...#....#.....#.......#......#.", "#...........#....#..#..#.......", "..........##......##.........##", "##............#..#.#...#..#.#..", "..#.##....##...##..#...#.......", "............##.##..###..#..#...", "......#....##...##.........#...", "......#..#.#......####..#......", "..............#....#..#..##....", "...#.#..#...##.#.......#.#.....", "...#.#....#.......#..#..#..##..", "..........#.........#..........", "...#.....#............#.....##.", "....#.#......................#.", ".........#...#.#...#...........", "...#........#..##.....#...#.#..", "......##.....#.#..#...###.#...#", "#....#..#.#.....#...#..........", ".#.##.###.........#..##.#....#.", "#.........#....#........#...#..", "...........#...............#..#", "###....................#....#..", ".................#....#.....#..", "..........#.........#.......#..", "........#..#....#.....##.......", "#...##.#...#.#.#............#..", "....#.........##.#.#..#...###..", ".##..............#...#.....##.#", "###...#..................#...#.", ".....#..#...#..#...#...........", ".#.................#...#..#..#.", ".#.........###...#.##......###.", ".####............#......#..#...", "....#........#..#.#....#..##..#", "..#....#.#...#.#.....##....#...", "..###..#..#....##....#..#..#...", "...#.#.....#.#....#.....#......", ".....#..........#.#............", ".......#...........#.#..#..#...", "......##........#.....#.......#", "..#.#.....##............#..##..", "....#.#........#...........##..", "#......#..##........#.....#....", "#...#...###..............##....", "#..#........#........#.....##.#", "......##.####........#..#....#.", "...##..#.##.....#...#...#..#...", "#..............###.##..##......", "......................#.....#..", ".........#.#.......#...##.#....", "....#......#..........###..#...", "#...####.#.................#..#", "##.#....#....#.....##..#....#.#", "..#.....#..##.........#.#..#.#.", ".....#.....#...................", "#....##.#.........###....#.....", "#........#.#.......#.#.........", ".##.#...#.....#...#.......##.##", "#..#.............#.............", "..........#.........####.......", "..##..............#..#.#.......", "..#.#.....#........#......##...", "#.#.......#.#................#.", ".#...#........#....##....#.##..", ".#..#...#...#......#.#.........", "......##............#.........#", ".#....#.#.#.........#..#..##...", "#....#......#.......###........", ".......#........##..#...#..###.", "#.##..........#..###..#..#.#...", ".#..#....#..........#.#.##.....", "#..#...#.#...#..#..#.#...#.....", ".........#...#.#............#..", "#..#.............#......##.##..", "...##.......#..................", "....#......#...#.....#......#..", ".....##..#......#....#....#....", "....#...#...#...#.....#........", ".#....#........##....#..#.#...#", "#.......#..#......#......#...#.", "..............#......#......#..", "#......#..##...#........#....#.", "#..#..#..#.....#..#........#...", "#...#.....#...#..........#...##", "........#.......#...#.....#.#..", "...................##.......#..", ".#......#........#.##..#....#..", ".....#.....#...#..#..#......#..", "........##.#..##.........#....#", ".........#.......#.............", "............#.###.###..#.#.....", ".............#....#...........#", "..#.....#.#..##.##........#....", "...#....#....#.........#.....#.", ".#............#......#.........", "..#.#..........##.##......#.#..", "....#.........................#", "..........##...................", "#.......#.#..............#...#.", "...##..#..##...##.#..#.#.#.....", "...########.#..##....#.........", "##.#........##.....#........#..", "#.#.....#........#..#....#...#.", "..#............#.......###.##.#", "#.#............................", "...#.#.#....#..........#..#....", "..###.#.....#.#..#.............", "#........#..........#.#..#.....", "...........#..#....#.........#.", "..#............#.....#.#.......", "#.#............#..#.....#.#.#..", "...#...#.......................", ".#.#.#...##.............#..#..#", "..#.........#..#.....##....##..", ".#...#............#.......#..##", "....#..#.#.#...####............", "#.......#....#..##....##....#..", ".....##.#....#.#..#.......#....", "...........#.......#....##.#.##", "..........#...#....##...#.#....", "..#.............#.............#", "....#..#.....#....#.#..###.#...", ".......#.##.#......#...##...#.#", ".#..#.#..#.#.......#....###.#..", "#..........##...##.........##..", "##..#......##.#.####.#.....#...", "....#.#...#........#..##..#.#..", ".#.............................", ".##..#.#...##.....#....#.....#.", "..##.........#......#.........#", ".#.#........#...#.#.#....##....", ".#.................##.........#", "...#...............#....#......", "..#...#..#..........###..#...##", "..........#..#..........##..#..", "...#.............#.##.#...#....", "...#...........#...............", "......#.........##.#...#...#...", "...#.#........#..#.....#..#...#", "#.#...#....##...#.....#....#...", "#.#.#..#.....#.........#.......", "##...........#..####...........", "#..........#........###...#..#.", "#..#.......#....#......###.....", "..#.....#......#.###......##...", "...#.##..#............#...#....", ".##........#.....#.............", "#....#.##..#...........##.#.#..", "..#.....#.#....#.......#......#", "#..#.......#............#......", "#.......##....#...#..#.........", ".................#..##.........", "..............#..#..#.##.......", "#.#.......................#..#.", "..#..##...........#....#..#..#.", "...#....#.......#.......#....#.", ".....#.#..#.#.....#.........#.#", "..#.#.........#.....#..........", "...#.#.#.......#.#.......#.#..#", "...##...#.#.#.....#.....##....#", "##.......#.#.#.#.......#...##..", "....#.#...........#......#.....", ".#.....#........####...........", "#......#........#.....#..#..#..", "..#..#......#...##.......#....#", "#........#..........#.....#.#..", ".#...........#.....#.....#.....", "..........#..#...#....#....##..", ".....#.#..........#.....##..#..", "......#.........##.............", "..#..#.....##......##........#.", ".#.#.#.#..#.#..#.......#.......", "#.#...####.#.#....#.#........#.", "....#...#.....#......#..##.....", "##.........#.........#..#.#..#.", "..#.#........#.#........#.##...", "#....#......#...#....#.........", ".##.............###....###.#...", "..##.#.......#...#..#......#...", ".....#.##..................#...", ".....#.#...#..#................", "........#..#..#...........#.#.#", "....#.###.....#..#.#.....##..##", "....##.#.........#..##.........", ".##........#......#..###..#.##.", ".........##...............#.##.", "..#...............#.#...#..#.#.", "....#....##.....#...#..#.....#.", "#...#.....................#....", ".....#.#............#...##.#.#.", "...#......#.......#........##.#", ".#.#..#.#....#.##.......##....#", ".........#...#..##.........#...", ".#...#..#....................#.", ".......#...#........#.#..#.#.##", ".#.............#......#..#.#...", "............##.........#....#.#", 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".#...#..#.#.....#.##.....#.....", "...........#.............#...#.", ".#..#................#...#..#..", "#..........#......##..##....#..", "####..#...........#.#....#.....", "..#.#.##..#...##........#....##", ".#.......##........#.....#.....", "............#................#.", ".#...#...#.....#.#....#.##..#..", "..#.............#.#....#.#.....", "..............#...........#....", "..............#........#....#..", "..........##........#..#...#...", "...#.#....#.#....#..#.....#...#", "..#......#...........#..#..#.#.", ".....##.....#.####....#........",] # Get initial number of columns, and width of initial columns columns = len(input_map) width = len(input_map[0]) for index, pattern in enumerate(input_map): # Make width of each row equivalent to length of columns difference = columns - width multiplier = difference // 4 input_map[index] = input_map[index] * multiplier # Get new standard width of each row width = len(input_map[0]) def gen_new_map(right:int, down:int) -> tuple: """[summary] Parameters ---------- right : int The amount to move right on each iteration down : int The amount to move down on each iteration Returns ------- trees, map The number of trees hit, the resulting map """ trees = 0 # The number of trees hit new_map = [] # The reaulting map of each hit/miss with open(f'dec-3-output-{right}-{down}.txt', "w+") as output_file: # Write a file for each result for index in range(0, width, down): try: if index == 0: # On first iteration if input_map[0][0] == "#": # If hit a tree new_map.append("X" + input_map[0][1::]) trees += 1 else: # If missed a tree new_map.append("O" + input_map[0][1::]) else: # Every iteration but the first if input_map[index][(index//down)*right] == "#": # If hit a tree new_map.append(input_map[index][0:(index//down)*right:]+ "X" + input_map[index][(index//down)*right+1::]) trees += 1 else: # if missed a tree new_map.append(input_map[index][0:(index//down)*right:]+ "O" + input_map[index][(index//down)*right+1::]) except IndexError: # Index will always error out on final hit #print(f"For pattern \n\tright:{right}\n\tdown:{down}\n\t\tNumber of trees: {trees}") output_file.write("\n".join(new_map)) return trees, new_map trees_1, _ = gen_new_map(1,1) trees_2, _ = gen_new_map(3,1) # Part 1 answer trees_3, _ = gen_new_map(5,1) trees_4, _ = gen_new_map(7,1) trees_5, m = gen_new_map(1,2) part_2_answer = trees_1 * trees_2 *trees_3 *trees_4 * trees_5 print(f"Part 1: \n\t{trees_2}") print(f"Part 2: \n\t{trees_1} * {trees_2} *{trees_3} *{trees_4} * {trees_5} == {part_2_answer}")

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21fb55fe6677132138f5bb730c54e8a99bbb33e6

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py

Python

tests/test_length_metres.py

putridparrot/PyUnits

4f1095c6fc0bee6ba936921c391913dbefd9307c

[ "MIT" ]

null

tests/test_length_metres.py

putridparrot/PyUnits

4f1095c6fc0bee6ba936921c391913dbefd9307c

[ "MIT" ]

null

tests/test_length_metres.py

putridparrot/PyUnits

4f1095c6fc0bee6ba936921c391913dbefd9307c

[ "MIT" ]

null

# <auto-generated> # This code was generated by the UnitCodeGenerator tool # # Changes to this file will be lost if the code is regenerated # </auto-generated> import unittest import units.length.metres class TestMetresMethods(unittest.TestCase): def test_convert_known_metres_to_millimetres(self): self.assertAlmostEqual(123000.0, units.length.metres.to_millimetres(123.0), places=1) self.assertAlmostEqual(900.0, units.length.metres.to_millimetres(0.9), places=1) self.assertAlmostEqual(2.3, units.length.metres.to_millimetres(0.0023), places=1) def test_convert_known_metres_to_centimetres(self): self.assertAlmostEqual(230.0, units.length.metres.to_centimetres(2.3), places=1) self.assertAlmostEqual(3400.0, units.length.metres.to_centimetres(34.0), places=1) self.assertAlmostEqual(90.0, units.length.metres.to_centimetres(0.9), places=1) def test_convert_known_metres_to_kilometres(self): self.assertAlmostEqual(0.1, units.length.metres.to_kilometres(100.0), places=1) self.assertAlmostEqual(123.456, units.length.metres.to_kilometres(123456.0), places=1) self.assertAlmostEqual(0.0911, units.length.metres.to_kilometres(91.1), places=1) def test_convert_known_metres_to_inches(self): self.assertAlmostEqual(472.441, units.length.metres.to_inches(12.0), places=1) self.assertAlmostEqual(366.142, units.length.metres.to_inches(9.3), places=1) self.assertAlmostEqual(3.93701, units.length.metres.to_inches(0.1), places=1) def test_convert_known_metres_to_feet(self): self.assertAlmostEqual(219.816, units.length.metres.to_feet(67.0), places=1) self.assertAlmostEqual(3.93701, units.length.metres.to_feet(1.2), places=1) self.assertAlmostEqual(2.29659, units.length.metres.to_feet(0.7), places=1) def test_convert_known_metres_to_yards(self): self.assertAlmostEqual(0.874891, units.length.metres.to_yards(0.8), places=1) self.assertAlmostEqual(37.72966, units.length.metres.to_yards(34.5), places=1) self.assertAlmostEqual(1.345144, units.length.metres.to_yards(1.23), places=1) def test_convert_known_metres_to_miles(self): self.assertAlmostEqual(4.908832, units.length.metres.to_miles(7900.0), places=1) self.assertAlmostEqual(76.7120019, units.length.metres.to_miles(123456.0), places=1) self.assertAlmostEqual(0.621371, units.length.metres.to_miles(1000.0), places=1) def test_convert_known_metres_to_nautical_miles(self): self.assertAlmostEqual(0.485961, units.length.metres.to_nautical_miles(900.0), places=1) self.assertAlmostEqual(38.779158, units.length.metres.to_nautical_miles(71819.0), places=1) self.assertAlmostEqual(66.6609071, units.length.metres.to_nautical_miles(123456.0), places=1) if __name__ == '__main__': unittest.main()

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py

Python

tests/urlpatterns_reverse/nonimported_module.py

jpmallarino/django

659d2421c7adbbcd205604002d521d82d6b0b465

[ "BSD-3-Clause", "0BSD" ]

61,676

2015-01-01T00:05:13.000Z

2022-03-31T20:37:54.000Z

tests/urlpatterns_reverse/nonimported_module.py

jpmallarino/django

659d2421c7adbbcd205604002d521d82d6b0b465

[ "BSD-3-Clause", "0BSD" ]

8,884

2015-01-01T00:12:05.000Z

2022-03-31T19:53:11.000Z

tests/urlpatterns_reverse/nonimported_module.py

jpmallarino/django

659d2421c7adbbcd205604002d521d82d6b0b465

[ "BSD-3-Clause", "0BSD" ]

33,143

2015-01-01T02:04:52.000Z

2022-03-31T19:42:46.000Z

def view(request): """Stub view""" pass

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df2d1238bd04311308d932af66f431188ce35fea

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py

Python

croutera/models/tplink/__init__.py

CristianOliveiraDaRosa/urouteradmin

684f0e8d91c48df90419267a05e64c0b7e96d148

[ "MIT" ]

12

2015-12-08T19:55:03.000Z

2021-11-17T11:28:55.000Z

croutera/models/tplink/__init__.py

CristianOliveiraDaRosa/urouteradmin

684f0e8d91c48df90419267a05e64c0b7e96d148

[ "MIT" ]

11

2015-11-10T01:38:12.000Z

2020-09-05T18:09:21.000Z

croutera/models/tplink/__init__.py

CristianOliveiraDaRosa/croutera

684f0e8d91c48df90419267a05e64c0b7e96d148

[ "MIT" ]

3

2016-09-22T06:19:44.000Z

2021-04-12T01:00:51.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- from .wr340g import TplinkWR340 from .wr720n import TplinkWR720N

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py

Python

src/python-antsy/client.py

grupodyd/python-antsy

9f033cdeeedf65e6ed5721ff6af20fab5a61cdef

[ "MIT" ]

null

src/python-antsy/client.py

grupodyd/python-antsy

9f033cdeeedf65e6ed5721ff6af20fab5a61cdef

[ "MIT" ]

null

src/python-antsy/client.py

grupodyd/python-antsy

9f033cdeeedf65e6ed5721ff6af20fab5a61cdef

[ "MIT" ]

null

# -*- coding: utf-8 -*- import requests class AntsyClient(object): def __init__(self): pass

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5

df64c8970c25a715e667acfd4f828674a7c5ae77

59

py

Python

datmo/__main__.py

dmh43/datmo

e97aad4e2417a72d8f136f7afd9bfac1bf24d9f9

[ "Apache-2.0" ]

331

2018-03-30T14:33:59.000Z

2022-01-10T19:43:32.000Z

datmo/__main__.py

KIMS-Github/datmo

a456d196006b67ce56af96cb4900682eab747bef

[ "MIT" ]

274

2018-04-08T17:12:44.000Z

2020-07-29T02:45:22.000Z

datmo/__main__.py

KIMS-Github/datmo

a456d196006b67ce56af96cb4900682eab747bef

[ "MIT" ]

28

2018-05-03T21:57:22.000Z

2020-12-31T04:18:42.000Z

import sys from datmo.cli.main import main sys.exit(main())

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5

df810f23b775216d63b59d8a92645c7af2d61d00

159

py

Python

test_module.py

shikhragimov/erlpyt

71acf1ef651b100a9b142d47fce44f0b995b0973

[ "Apache-2.0" ]

null

test_module.py

shikhragimov/erlpyt

71acf1ef651b100a9b142d47fce44f0b995b0973

[ "Apache-2.0" ]

null

test_module.py

shikhragimov/erlpyt

71acf1ef651b100a9b142d47fce44f0b995b0973

[ "Apache-2.0" ]

null

from python_modules.decorators import decorate_erl_string_args @decorate_erl_string_args def test_function(data): print("The data was: {}".format(data))

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py

Python

latextools/plot/__init__.py

cduck/latextools

8161acc88d669951b2b5e1e3e6888b9fc918b49a

[ "MIT" ]

13

2020-06-02T22:57:13.000Z

2022-03-26T23:07:27.000Z

latextools/plot/__init__.py

cduck/latextools

8161acc88d669951b2b5e1e3e6888b9fc918b49a

[ "MIT" ]

3

2021-06-03T14:38:17.000Z

2022-02-28T23:05:48.000Z

latextools/plot/__init__.py

cduck/latextools

8161acc88d669951b2b5e1e3e6888b9fc918b49a

[ "MIT" ]

2

2020-08-19T05:44:23.000Z

2021-06-03T01:56:48.000Z

from .artist_wrap import ArtistContent from .scatter_plot import PgfplotsFigure, Plot, Graph from .bar_plot import BarPlot, BarGraph

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py

Python

src/text_normalizer/__init__.py

arkataev/text_normalizer

a99326e31012157980d014c9730ac94bd1d18c1d

[ "MIT" ]

null

src/text_normalizer/__init__.py

arkataev/text_normalizer

a99326e31012157980d014c9730ac94bd1d18c1d

[ "MIT" ]

null

src/text_normalizer/__init__.py

arkataev/text_normalizer

a99326e31012157980d014c9730ac94bd1d18c1d

[ "MIT" ]

null

from logging.config import dictConfig from . import settings dictConfig(settings.LOGGING) from . import stemming, tokenization, convert, normalization

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257

py

Python

erinn/python/__init__.py

swcjack6931677/ERINN

a4f3d0ad213515bc86e2a18575537d6affd472ac

[ "MIT" ]

null

erinn/python/__init__.py

swcjack6931677/ERINN

a4f3d0ad213515bc86e2a18575537d6affd472ac

[ "MIT" ]

null

erinn/python/__init__.py

swcjack6931677/ERINN

a4f3d0ad213515bc86e2a18575537d6affd472ac

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import division, absolute_import, print_function # from erinn.python import utils # from erinn.python import metrics # from erinn.python import preprocessing # from erinn.python import generator # Globally-import.

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803d3130a53e8de98544c74dbe7c1eb00e08d2ff

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py

Python

flask_site/controllers/__init__.py

wcpr740/wcpr.org

6a9120122d45a9721a7140fcc594ba5634b91f90

[ "Apache-2.0" ]

2

2019-09-10T15:53:20.000Z

2020-10-06T20:51:06.000Z

flask_site/controllers/__init__.py

wcpr740/wcpr.org

6a9120122d45a9721a7140fcc594ba5634b91f90

[ "Apache-2.0" ]

8

2020-12-18T22:13:17.000Z

2022-03-11T23:14:13.000Z

flask_site/controllers/__init__.py

wcpr740/wcpr.org

6a9120122d45a9721a7140fcc594ba5634b91f90

[ "Apache-2.0" ]

1

2018-09-19T22:58:19.000Z

from pages import * from redirects import * from legacy import * from error_pages import *

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py

Python

pyqir-parser/pyqir/parser/__init__.py

nilslice/pyqir

9ae0395e5a0fe52221be16322256629e891d63c6

[ "MIT" ]

22

2021-11-30T21:52:30.000Z

2022-03-17T02:05:17.000Z

pyqir-parser/pyqir/parser/__init__.py

nilslice/pyqir

9ae0395e5a0fe52221be16322256629e891d63c6

[ "MIT" ]

51

2021-11-24T03:42:49.000Z

2022-03-25T20:51:24.000Z

pyqir-parser/pyqir/parser/__init__.py

nilslice/pyqir

9ae0395e5a0fe52221be16322256629e891d63c6

[ "MIT" ]

6

2021-11-30T22:36:49.000Z

2022-03-17T17:21:16.000Z

# Copyright (c) Microsoft Corporation. # Licensed under the MIT License. from pyqir.parser._parser import *

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py

Python

instant/tests/__init__.py

synw/django-instant

693b5da5064a4822a3620ef0d078fac07e3d41a2

[ "MIT" ]

103

2016-08-30T16:15:41.000Z

2022-03-01T18:22:34.000Z

instant/tests/__init__.py

synw/django-instant

693b5da5064a4822a3620ef0d078fac07e3d41a2

[ "MIT" ]

22

2016-10-31T10:51:07.000Z

2021-05-31T11:44:49.000Z

instant/tests/__init__.py

synw/django-instant

693b5da5064a4822a3620ef0d078fac07e3d41a2

[ "MIT" ]

13

2017-01-20T22:05:41.000Z

2021-07-26T13:53:28.000Z

class InstantTest: pass

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3396fe6728a2a8d7c9046a1d7361770d79fb8080

193

py

Python

src/igfeedservice/IgFeedService/admin.py

MatthewWong68/Social_Media_RESTful_API_Django_Web_App

60d0f2af067071bc8fc6f9da4a810d35bfad3443

[ "MIT" ]

null

src/igfeedservice/IgFeedService/admin.py

MatthewWong68/Social_Media_RESTful_API_Django_Web_App

60d0f2af067071bc8fc6f9da4a810d35bfad3443

[ "MIT" ]

null

src/igfeedservice/IgFeedService/admin.py

MatthewWong68/Social_Media_RESTful_API_Django_Web_App

60d0f2af067071bc8fc6f9da4a810d35bfad3443

[ "MIT" ]

null

from django.contrib import admin from .models import Page, Post, Hashtag # Register your models here. admin.site.register(Page) admin.site.register(Post) admin.site.register(Hashtag)

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0

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true

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null

0

1

0

1

0

5

339f0006f9717e014019463e402dce3a3c972d2c

43

py

Python

src/scoring/__init__.py

ryanapfel/clustering

682bf22eeff2253acc0128323db97968a9a3b420

[ "MIT" ]

null

src/scoring/__init__.py

ryanapfel/clustering

682bf22eeff2253acc0128323db97968a9a3b420

[ "MIT" ]

null

src/scoring/__init__.py

ryanapfel/clustering

682bf22eeff2253acc0128323db97968a9a3b420

[ "MIT" ]

null

from src.scoring.clusterScore import Scores

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null

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1

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1

0

5

33bb886db623b159d310d68a3797b75432a2e17c

5,699

py

Python

centreon/share/python/centreonapi/webservice/configuration/host.py

VinceMacBuche/rudder-plugins

428ac654c128085cec0f2ffb9eaf59c8de467bd0

[ "Unlicense" ]

5

2018-05-28T07:38:42.000Z

2021-07-07T12:36:47.000Z

centreon/share/python/centreonapi/webservice/configuration/host.py

VinceMacBuche/rudder-plugins

428ac654c128085cec0f2ffb9eaf59c8de467bd0

[ "Unlicense" ]

9

2018-03-26T08:38:38.000Z

2019-02-25T23:03:55.000Z

centreon/share/python/centreonapi/webservice/configuration/host.py

VinceMacBuche/rudder-plugins

428ac654c128085cec0f2ffb9eaf59c8de467bd0

[ "Unlicense" ]

16

2017-03-30T11:55:34.000Z

2021-12-03T09:52:39.000Z

# -*- coding: utf-8 -*- from centreonapi.webservice import Webservice class HostObj(object): def __init__(self, properties): self.name = properties['name'] self.state = properties['activate'] self.address = properties['address'] self.alias = properties['alias'] def name(self): return self.name def address(self): return self.address() def alias(self): return self.alias() def state(self): return self.state() class Host(object): """ Centreon Web host object """ def __init__(self): """ Constructor """ self.webservice = Webservice.getInstance() def list(self): """ List hosts """ return self.webservice.call_clapi('show', 'HOST') def add(self, hostname, hostalias, hostip, hosttemplate, pollername, hgname): """ Add a host """ values = [ hostname, hostalias, hostip, '|'.join(hosttemplate), pollername, '|'.join(hgname) ] return self.webservice.call_clapi('add', 'HOST', values) def delete(self, hostname): return self.webservice.call_clapi('del', 'HOST', hostname) def setparameters(self, hostname, name, value): """ DEPRECATED """ return self.setparam(hostname, name, value) def setparam(self, hostname, name, value): values = [hostname, name, value] return self.webservice.call_clapi('setparam', 'HOST', values) def setinstance(self, hostname, instance): values = [hostname, instance] return self.webservice.call_clapi('setinstance', 'HOST', values) def getmacro(self, hostname): return self.webservice.call_clapi('getmacro', 'HOST', hostname) def setmacro(self, hostname, name, value): values = [hostname, name, value] return self.webservice.call_clapi('setmacro', 'HOST', values) def deletemacro(self, hostname, name): values = [hostname, name] return self.webservice.call_clapi('delmacro', 'HOST', values) def gettemplate(self, hostname): return self.webservice.call_clapi('gettemplate', 'HOST', hostname) def settemplate(self, hostname, template): values = [hostname, "|".join(template)] return self.webservice.call_clapi('settemplate', 'HOST', values) def addtemplate(self, hostname, template): values = [hostname, "|".join(template)] return self.webservice.call_clapi('addtemplate', 'HOST', values) def deletetemplate(self, hostname, template): values = [hostname, "|".join(template)] return self.webservice.call_clapi('delemplate', 'HOST', values) def applytemplate(self, hostname): """ Apply the host template to the host, deploy services """ return self.webservice.call_clapi('applytpl', 'HOST', hostname) def getparent(self, hostname): return self.webservice.call_clapi('getparent', 'HOST', hostname) def addparent(self, hostname, parents): return self.webservice.call_clapi('addparent', 'HOST', [hostname, "|".join(parents)]) def setparent(self, hostname, parents): return self.webservice.call_clapi('setparent', 'HOST', [hostname, "|".join(parents)]) def deleteparent(self, hostname, parents): return self.webservice.call_clapi('delparent', 'HOST', [hostname, "|".join(parents)]) def getcontactgroup(self, hostname): return self.webservice.call_clapi('getcontactgroup', 'HOST', hostname) def addcontactgroup(self, hostname, contactgroups): return self.webservice.call_clapi('addcontactgroup', 'HOST', [hostname, "|".join(contactgroups)]) def setcontactgroup(self, hostname, contactgroups): return self.webservice.call_clapi('setcontactgroup', 'HOST', [hostname, "|".join(contactgroups)]) def deletecontactgroup(self, hostname, contactgroups): return self.webservice.call_clapi('delcontactgroup', 'HOST', [hostname, "|".join(contactgroups)]) def getcontact(self, hostname): return self.webservice.call_clapi('getcontact', 'HOST', hostname) def addcontact(self, hostname, contacts): return self.webservice.call_clapi('addcontact', 'HOST', [hostname, "|".join(contacts)]) def setcontact(self, hostname, contacts): return self.webservice.call_clapi('setcontact', 'HOST', [hostname, "|".join(contacts)]) def deletecontact(self, hostname, contacts): return self.webservice.call_clapi('delcontact', 'HOST', [hostname, "|".join(contacts)]) def gethostgroup(self, hostname): return self.webservice.call_clapi('gethostgroup', 'HOST', hostname) def addhostgroup(self, hostname, hostgroups): return self.webservice.call_clapi('addhostgroup', 'HOST', [hostname, "|".join(hostgroups)]) def sethostgroup(self, hostname, hostgroups): return self.webservice.call_clapi('sethostgroup', 'HOST', [hostname, "|".join(hostgroups)]) def deletehostgroup(self, hostname, hostgroups): return self.webservice.call_clapi('delhostgroup', 'HOST', [hostname, "|".join(hostgroups)]) def setseverity(self, hostname, name): return self.webservice.call_clapi('setseverity', 'HOST', [hostname, name ]) def unsetseverity(self, hostname): return self.webservice.call_clapi('unsetseverity', 'HOST', hostname) def enable(self, hostname): return self.webservice.call_clapi('enable', 'HOST', hostname) def disable(self, hostname): return self.webservice.call_clapi('disable', 'HOST', hostname)

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0

1

0

5

33e936b6f6830c7e0b436cc0a2f7bfbd71d48d51

2,877

py

Python

hvm/chains/testnet/__init__.py

hyperevo/py-helios-node

ff417fe3fe90f85c9f95b3d8a5f0dd4c80532ee8

[ "MIT" ]

null

hvm/chains/testnet/__init__.py

hyperevo/py-helios-node

ff417fe3fe90f85c9f95b3d8a5f0dd4c80532ee8

[ "MIT" ]

null

hvm/chains/testnet/__init__.py

hyperevo/py-helios-node

ff417fe3fe90f85c9f95b3d8a5f0dd4c80532ee8

[ "MIT" ]

null

from typing import Tuple, Type # noqa: F401 from eth_utils import decode_hex from hvm.constants import TESTNET_FAUCET_PRIVATE_KEY from hvm.vm.forks.boson import BosonVM from .constants import ( HELIOS_TESTNET_TIMESTAMP, BOSON_TIMESTAMP) from hvm import constants from hvm.chains import Chain from hvm.rlp.headers import BlockHeader from hvm.vm.base import BaseVM # noqa: F401 from hvm.vm.forks import ( HeliosTestnetVM ) from eth_typing import Address from eth_keys import keys from eth_keys.datatypes import PrivateKey from hvm.types import Timestamp from eth_utils import to_wei from eth_utils import encode_hex, decode_hex TESTNET_VM_CONFIGURATION = ( (HELIOS_TESTNET_TIMESTAMP, HeliosTestnetVM), (BOSON_TIMESTAMP, BosonVM), ) TESTNET_NETWORK_ID = 2 TESTNET_GENESIS_PRIVATE_KEY = keys.PrivateKey(b'p.Oids\xedb\xa3\x93\xc5\xad\xb9\x8d\x92\x94\x00\x06\xb9\x82\xde\xb9\xbdBg\\\x82\xd4\x90W\xd0\xd5') TESTNET_GENESIS_STATE = { TESTNET_GENESIS_PRIVATE_KEY.public_key.to_canonical_address(): { "balance": 100000000000000000000000000, "code": b"", "nonce": 0, "storage": {} } } TESTNET_GENESIS_PARAMS = {'chain_address': b"\xdbL\xa4&\xd5;Y\xf6\x03p'O\xfb\x19\xf2&\x8d\xc3=\xdf", 'parent_hash': b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00', 'transaction_root': b'V\xe8\x1f\x17\x1b\xccU\xa6\xff\x83E\xe6\x92\xc0\xf8n[H\xe0\x1b\x99l\xad\xc0\x01b/\xb5\xe3c\xb4!', 'receive_transaction_root': b'V\xe8\x1f\x17\x1b\xccU\xa6\xff\x83E\xe6\x92\xc0\xf8n[H\xe0\x1b\x99l\xad\xc0\x01b/\xb5\xe3c\xb4!', 'receipt_root': b'V\xe8\x1f\x17\x1b\xccU\xa6\xff\x83E\xe6\x92\xc0\xf8n[H\xe0\x1b\x99l\xad\xc0\x01b/\xb5\xe3c\xb4!', 'bloom': 0, 'block_number': 0, 'gas_limit': 31415926, 'gas_used': 0, 'timestamp': 1543700000, 'extra_data': b'', 'reward_hash': b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00', 'account_hash': b'\x19\xfc\x94\x8d\x95\xacs\x06Db\x80\xf4\x9e\x94\x823\xa1\xe2#\x03t\x0f\x8d\\\xe9\x7f&;\xc9d\xc67', 'account_balance': 100000000000000000000000000, 'v': 38, 'r': 45034268824120027712675756355413116720367789723148269550183865435685699800523, 's': 27141080959376664758566629966709756538095401130381810437562609117602786161669} GENESIS_WALLET_ADDRESS = TESTNET_GENESIS_PARAMS['chain_address'] class BaseTestnetChain: faucet_private_key: PrivateKey = keys.PrivateKey(TESTNET_FAUCET_PRIVATE_KEY) vm_configuration: Tuple[Tuple[Timestamp, Type[BaseVM]]] = TESTNET_VM_CONFIGURATION network_id: int = TESTNET_NETWORK_ID genesis_wallet_address: Address = TESTNET_GENESIS_PARAMS['chain_address'] genesis_block_timestamp: Timestamp = TESTNET_GENESIS_PARAMS['timestamp'] class TestnetChain(BaseTestnetChain, Chain): pass

46.403226

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1

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null

0

1

0

1

0

5

1d45096e472882cf09d0bf462ba1846cc291103b

123

py

Python

di/commands/__init__.py

DataDog/di

95c45cd7715e84130535caa983bde0f1c6075d1c

[ "Apache-2.0", "MIT" ]

1

2018-02-12T05:35:35.000Z

di/commands/__init__.py

DataDog/di

95c45cd7715e84130535caa983bde0f1c6075d1c

[ "Apache-2.0", "MIT" ]

null

di/commands/__init__.py

DataDog/di

95c45cd7715e84130535caa983bde0f1c6075d1c

[ "Apache-2.0", "MIT" ]

1

2021-02-24T11:20:33.000Z

from .check import check from .config import config from .start import start from .stop import stop from .test import test

20.5

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0.796748

20

123

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123

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24.6

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null

0

1

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1

0

1

0

5

1d61a35c4075a88f757f0881f84572769f85df8e

402

py

Python

gym/gym/spaces/__init__.py

sokol1412/rllab_hierarchical_rl

6d46c02e32c3d7e9ac55d753d6a3823ff86c5a57

[ "MIT" ]

null

gym/gym/spaces/__init__.py

sokol1412/rllab_hierarchical_rl

6d46c02e32c3d7e9ac55d753d6a3823ff86c5a57

[ "MIT" ]

null

gym/gym/spaces/__init__.py

sokol1412/rllab_hierarchical_rl

6d46c02e32c3d7e9ac55d753d6a3823ff86c5a57

[ "MIT" ]

null

from gym.gym.spaces.box import Box from gym.gym.spaces.discrete import Discrete from gym.gym.spaces.multi_discrete import MultiDiscrete from gym.gym.spaces.multi_binary import MultiBinary from gym.gym.spaces.prng import seed from gym.gym.spaces.tuple_space import Tuple from gym.gym.spaces.dict_space import Dict __all__ = ["Box", "Discrete", "MultiDiscrete", "MultiBinary", "Tuple", "Dict"]

40.2

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0.783582

60

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5.116667

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0.228013

0.364821

0.136808

0

0.114428

402

9

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null

0

1

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1

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5

d52cfa9cb7a9ca6e7ae6b1f94e03008faa8e3ff1

136

py

Python

format_byte/__init__.py

ChsHub/format_byte

2828e32f4ac05d898ca5536e5e39860ff74ae7b7

[ "MIT" ]

null

format_byte/__init__.py

ChsHub/format_byte

2828e32f4ac05d898ca5536e5e39860ff74ae7b7

[ "MIT" ]

null

format_byte/__init__.py

ChsHub/format_byte

2828e32f4ac05d898ca5536e5e39860ff74ae7b7

[ "MIT" ]

null

from .format_byte import format_byte, format_bit __all__ = ['format_byte', 'format_bit'] __version__ = '1.1.1' __name__ = 'format_byte'

27.2

48

0.757353

20

136

4.25

0.45

0.470588

0.376471

0.447059

0

0.024793

0.110294

136

5

49

27.2

0.677686

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0

1

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false

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0.25

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0.25

0

1

0

null

1

0

1

0

null

0

5

d537d6c50df80f6a607c62b671672500ec2a05fd

3,268

py

Python

ops/config.py

fractal520/dbops

20c6b6b5669e09b43cd19e6f3fa0448bc7d5eaac

[ "MIT" ]

null

ops/config.py

fractal520/dbops

20c6b6b5669e09b43cd19e6f3fa0448bc7d5eaac

[ "MIT" ]

null

ops/config.py

fractal520/dbops

20c6b6b5669e09b43cd19e6f3fa0448bc7d5eaac

[ "MIT" ]

null

url_config = { 'g3': 'oracle+cx_oracle://dbmgr:Yypp8msc@10.1.5.165:1521/KM', 'vip': 'oracle+cx_oracle://dbmgr:Yypp8msc@10.1.5.160:1521/kmvip', 'scrm': 'mysql+pymysql://dbmon:dbmon1234@10.1.5.143/baihui'} sql_config = { 'SUBFHD_LSH_OLD': ''' select distinct ticketno from ( select subbh || lsh as ticketno from GZDBO.subfhd union all select subbh || lsh as ticketno from PNDBO.subfhd union all select subbh || lsh as ticketno from SZDBO.subfhd) --where rownum<10 order by ticketno''', 'SUBFHD_DH_OLD': ''' select distinct bookid from ( select subbh || dh as bookid from GZDBO.subfhd union all select subbh || dh as bookid from PNDBO.subfhd union all select subbh || dh as bookid from SZDBO.subfhd) --where rownum<10 order by bookid''', 'SUBFHD_LSH': ''' select distinct ticketno from ( select subbh || lsh as ticketno from GZDBO.subfhd where jzrq >= to_date('2016-01-01','yyyy-mm-dd') and jzrq < to_date('2017-10-12','yyyy-mm-dd') and sl <> 0 union all select subbh || lsh as ticketno from PNDBO.subfhd where jzrq >= to_date('2016-01-01','yyyy-mm-dd') and jzrq < to_date('2017-10-12','yyyy-mm-dd') and sl <> 0 union all select subbh || lsh as ticketno from SZDBO.subfhd where jzrq >= to_date('2016-01-01','yyyy-mm-dd') and jzrq < to_date('2017-10-12','yyyy-mm-dd') and sl <> 0) --where rownum<10 order by ticketno''', 'SUBFHD_DH': ''' select distinct bookid from ( select subbh || dh as bookid from GZDBO.subfhd where jzrq >= to_date('2016-01-01','yyyy-mm-dd') and jzrq < to_date('2017-10-12','yyyy-mm-dd') and sl <> 0 union all select subbh || dh as bookid from PNDBO.subfhd where jzrq >= to_date('2016-01-01','yyyy-mm-dd') and jzrq < to_date('2017-10-12','yyyy-mm-dd') and sl <> 0 union all select subbh || dh as bookid from SZDBO.subfhd where jzrq >= to_date('2016-01-01','yyyy-mm-dd') and jzrq < to_date('2017-10-12','yyyy-mm-dd') and sl <> 0) --where rownum<10 order by bookid''', 'SUBFHD_NAME': ''' select distinct lsh as name from ( select lsh , jzrq from GZDBO.subfhd where jzrq >= to_date('2017-03-01','yyyy-mm-dd') union all select lsh , jzrq from PNDBO.subfhd where jzrq >= to_date('2017-03-01','yyyy-mm-dd') union all select lsh , jzrq from SZDBO.subfhd where jzrq >= to_date('2017-03-01','yyyy-mm-dd')) order by lsh''', 'SUBFHD_ARG_A2009': ''' select distinct dh as ARG_A2009 from ( select dh , jzrq from GZDBO.subfhd where jzrq >= to_date('2017-03-01','yyyy-mm-dd') union all select dh , jzrq from PNDBO.subfhd where jzrq >= to_date('2017-03-01','yyyy-mm-dd') union all select dh , jzrq from SZDBO.subfhd where jzrq >= to_date('2017-03-01','yyyy-mm-dd')) order by dh''', 'KM_MEMBER_CONSUMEHEAD': ''' select distinct TICKETNO from kmvip.KM_MEMBER_CONSUMEHEAD --where rownum<10 order by TICKETNO''', 'KM_MEMBER_CONSUME': ''' select distinct BOOKID from kmvip.KM_MEMBER_CONSUME order by BOOKID''', 'BH_CUSTOM_MODULE_1': ''' select distinct name from bh_custom_module_1 where ARG_A2001 in (70,1562,86) and arg_a5002>= date('2017-03-01') order by name''', 'BH_CUSTOM_MODULE_2': ''' select distinct ARG_A2009 from bh_custom_module_2 where ARG_A2010 in (70,1562,86) and arg_a5002 > date('2017-03-01') order by ARG_A2009'''}

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85

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3,268

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null

0

1

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1

0

1

0

null

0

5

d53ae87b60828845487f2232ab732f815872f36b

122

py

Python

Data Scientist Career Path/3. Python Fundamentals/7. Python Strings/2. String Methods/10. format.py

myarist/Codecademy

2ba0f104bc67ab6ef0f8fb869aa12aa02f5f1efb

[ "MIT" ]

23

2021-06-06T15:35:55.000Z

2022-03-21T06:53:42.000Z

Data Scientist Career Path/3. Python Fundamentals/7. Python Strings/2. String Methods/10. format.py

shivaniverma1/Data-Scientist

f82939a411484311171465591455880c8e354750

[ "MIT" ]

null

Data Scientist Career Path/3. Python Fundamentals/7. Python Strings/2. String Methods/10. format.py

shivaniverma1/Data-Scientist

f82939a411484311171465591455880c8e354750

[ "MIT" ]

9

2021-06-08T01:32:04.000Z

2022-03-18T15:38:09.000Z

def poem_title_card(title, poet): poem_desc = "The poem \"{}\" is written by {}.".format(title, poet) return poem_desc

40.666667

69

0.688525

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122

4.210526

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null

0

1

0

1

0

5

d56c977e624e4537e6fb43af3cd926f4326ec610

3,031

py

Python

tests/test_spring_boot_admin_registration.py

TheCodingLand/pyctuator

a5c9f41e426b006892020ce3b98d5e56c27e9dbc

[ "Apache-2.0" ]

null

tests/test_spring_boot_admin_registration.py

TheCodingLand/pyctuator

a5c9f41e426b006892020ce3b98d5e56c27e9dbc

[ "Apache-2.0" ]

null

tests/test_spring_boot_admin_registration.py

TheCodingLand/pyctuator

a5c9f41e426b006892020ce3b98d5e56c27e9dbc

[ "Apache-2.0" ]

null

from datetime import datetime from typing import Optional, Any import pytest from pyctuator.auth import Auth, BasicAuth from pyctuator.impl.spring_boot_admin_registration import BootAdminRegistrationHandler from tests.conftest import RegistrationTrackerFixture @pytest.mark.usefixtures("boot_admin_server") def test_registration_no_auth(registration_tracker: RegistrationTrackerFixture) -> None: registration_handler = get_registration_handler("http://localhost:8001/register", None) try: registration_handler.start() assert registration_tracker.count == 1 finally: registration_handler.stop() @pytest.mark.usefixtures("boot_admin_server") def test_registration_basic_auth_no_creds(registration_tracker: RegistrationTrackerFixture, caplog: Any) -> None: registration_handler = get_registration_handler("http://localhost:8001/register-with-basic-auth", None) try: registration_handler.start() assert registration_tracker.count == 0 error_message = "Failed registering with boot-admin, got %s - %s" assert error_message in [record.msg for record in caplog.records] error_args = (401, b'{"detail":"Not authenticated"}') assert error_args in [record.args for record in caplog.records if record.msg == error_message] finally: registration_handler.stop() @pytest.mark.usefixtures("boot_admin_server") def test_registration_basic_auth_bad_creds(registration_tracker: RegistrationTrackerFixture, caplog: Any) -> None: registration_handler = get_registration_handler( "http://localhost:8001/register-with-basic-auth", BasicAuth("kuki", "puki") ) try: registration_handler.start() assert registration_tracker.count == 0 error_message = "Failed registering with boot-admin, got %s - %s" assert error_message in [record.msg for record in caplog.records] error_args = (401, b'{"detail":"Moo haha"}') assert error_args in [record.args for record in caplog.records if record.msg == error_message] finally: registration_handler.stop() @pytest.mark.usefixtures("boot_admin_server") def test_registration_basic_auth(registration_tracker: RegistrationTrackerFixture) -> None: registration_handler = get_registration_handler( "http://localhost:8001/register-with-basic-auth", BasicAuth("moo", "haha") ) try: registration_handler.start() assert registration_tracker.count == 1 finally: registration_handler.stop() def get_registration_handler(registration_url: str, registration_auth: Optional[Auth]) -> BootAdminRegistrationHandler: return BootAdminRegistrationHandler( registration_url=registration_url, registration_auth=registration_auth, application_name="noauth", pyctuator_base_url="http://whatever/pyctuator", start_time=datetime.now(), service_url="http://whatever/service", registration_interval_sec=100 )

34.83908

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3,031

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0.150723

0.05133

0.046664

0.719552

0.690154

0

0.011591

0.17453

3,031

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35.244186

0.844924

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false

0

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0.016129

0.193548

0

null

0

1

0

null

0

5

d56ed3febd2fb0b3fb1c90845168ada44fa17070

207

py

Python

scrapers/DER-derby/councillors.py

DemocracyClub/LGSF

21c2a049db08575e03db2fb63a8bccc8de0c636b

[ "MIT" ]

4

2018-10-17T13:30:08.000Z

2021-06-22T13:29:43.000Z

scrapers/DER-derby/councillors.py

DemocracyClub/LGSF

21c2a049db08575e03db2fb63a8bccc8de0c636b

[ "MIT" ]

46

2018-10-15T13:47:48.000Z

2022-03-23T10:26:18.000Z

scrapers/DER-derby/councillors.py

DemocracyClub/LGSF

21c2a049db08575e03db2fb63a8bccc8de0c636b

[ "MIT" ]

1

2018-10-15T13:36:03.000Z

from lgsf.councillors.scrapers import CMISCouncillorScraper class Scraper(CMISCouncillorScraper): base_url = "https://cmis.derby.gov.uk/cmis5/Councillors/tabid/62/ScreenMode/Alphabetical/Default.aspx"

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63946e359a87871c8d135a96b1ae514b5d7df55f

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py

Python

RL/bandit.py

alexborio/Projects

a85ad4aab370b009de14e3696e06aad92ca4859f

[ "MIT" ]

null

RL/bandit.py

alexborio/Projects

a85ad4aab370b009de14e3696e06aad92ca4859f

[ "MIT" ]

null

RL/bandit.py

alexborio/Projects

a85ad4aab370b009de14e3696e06aad92ca4859f

[ "MIT" ]

null

import numpy as np import matplotlib.pyplot as plt class Bandit: def __init__(self, m, upper_limit=0): if upper_limit > 0: mn = upper_limit N = 1 else: mn = 0 N = 0 self.m = m self.mean = mn self.N = N def pull(self): return np.random.normal() + self.m def update(self, x): self.N +=1 self.mean = (1 - 1/self.N)*self.mean + 1/self.N*x def pull_and_update(self): x = self.pull() self.update(x) return x class BayesianBandit: def __init__(self, m): mn = 0 N = 0 self.m = m self.mean = mn self.N = N self.lam = 1 self.tau = 1 self.cumsum = 0 def pull(self): return np.random.normal() + self.m def sample(self): return np.random.normal()/np.sqrt(self.lam) + self.mean def update(self, x): self.N +=1 self.cumsum += x self.mean = (self.mean*self.lam + self.tau*self.cumsum)/(self.lam + self.tau*self.N) self.lam += self.tau*self.N def pull_and_update(self): x = self.pull() self.update(x) return x def run_experiment_bayesian(means, N): n = len(means) bandits = [] data = np.empty(N) for mn in means: bandits.append(BayesianBandit(mn)) for i in range(N): j = np.argmax([bnd.sample() for bnd in bandits]) x = bandits[j].pull_and_update() data[i] = x cumulative_average = np.cumsum(data) / (np.arange(N) + 1) for bandit in bandits: print(bandit.mean) return cumulative_average def run_experiment(means, eps, N, upper_limit=0, ucb1_coeff=0): n = len(means) bandits = [] data = np.empty(N) for mn in means: bandits.append(Bandit(mn, upper_limit)) for i in range(N): rnd = np.random.random() if rnd < eps: j = np.random.choice(n) else: j = np.argmax([bnd.mean + ucb1_coeff*np.sqrt(2*np.log(i+1)/max(bnd.N, 1e-6)) for bnd in bandits]) x = bandits[j].pull_and_update() data[i] = x cumulative_average = np.cumsum(data) / (np.arange(N) + 1) for bandit in bandits: print(bandit.mean) return cumulative_average def run_experiment_deacaying(means, N, upper_limit=0, ucb1_coeff=0): n = len(means) bandits = [] data = np.empty(N) for mn in means: bandits.append(Bandit(mn, upper_limit)) for i in range(N): rnd = np.random.random() if rnd < 1/(i+1): j = np.random.choice(n) else: j = np.argmax([bnd.mean + ucb1_coeff*np.sqrt(2*np.log(i+1)/max(bnd.N, 1e-6)) for bnd in bandits]) x = bandits[j].pull_and_update() data[i] = x cumulative_average = np.cumsum(data) / (np.arange(N) + 1) for bandit in bandits: print(bandit.mean) return cumulative_average run_experiment([1, 2, 3], 0.1, 100000) plt.plot(run_experiment([1, 2, 3], 0.1, 100000), label="eps == 0.1") plt.plot(run_experiment([1, 2, 3], 0.2, 100000), label="eps == 0.2") plt.plot(run_experiment([1, 2, 3], 0.01, 100000), label="eps == 0.01") plt.legend() plt.xscale('log') plt.show() plt.figure() plt.plot(run_experiment([1, 2, 3], 0, 100000, upper_limit=10), label="optimistic") plt.plot(run_experiment([1, 2, 3], 0.1, 100000), label="eps == 0.1") plt.legend() plt.xscale('log') plt.show() plt.figure() plt.plot(run_experiment([1, 2, 3], 0, 100000, upper_limit=10, ucb1_coeff=1), label="ucb1") plt.plot(run_experiment_deacaying([1, 2, 3], 100000), label="decaying eps == 0.1") plt.plot(run_experiment([1, 2, 3], 0, 100000, upper_limit=10), label="optimistic") plt.plot(run_experiment_bayesian([1, 2, 3], 100000), label="Bayesian") plt.legend() plt.xscale('log') plt.show()

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5

63ac525c3704fe8a8e7160f4e6a55e0f401af1fd

199

py

Python

spexxy/interpolator/__init__.py

thusser/spexxy

14a8d121076b9e043bdf2e27222a65088f771ff9

[ "MIT" ]

4

2019-05-13T21:36:31.000Z

2021-09-06T01:56:36.000Z

spexxy/interpolator/__init__.py

thusser/spexxy

14a8d121076b9e043bdf2e27222a65088f771ff9

[ "MIT" ]

2

2020-02-12T14:36:39.000Z

2020-07-14T11:43:10.000Z

spexxy/interpolator/__init__.py

thusser/spexxy

14a8d121076b9e043bdf2e27222a65088f771ff9

[ "MIT" ]

1

2019-11-08T09:26:23.000Z

from .interpolator import Interpolator from .ulyss import UlyssInterpolator from .linear import LinearInterpolator from .tellurics import TelluricsInterpolator from .spline import SplineInterpolator

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5

63b8201752f5f7c5502af7a193b19a075906c61b

1,377

py

Python

desktop/core/ext-py/Django/tests/regressiontests/utils/dateformat.py

civascu/hue

82f2de44789ff5a981ed725175bae7944832d1e9

[ "Apache-2.0" ]

2

2021-04-27T03:57:00.000Z

2021-06-18T09:39:58.000Z

tests/regressiontests/utils/dateformat.py

joetyson/django

c3699190186561d5c216b2a77ecbfc487d42a734

[ "BSD-3-Clause" ]

null

tests/regressiontests/utils/dateformat.py

joetyson/django

c3699190186561d5c216b2a77ecbfc487d42a734

[ "BSD-3-Clause" ]

2

2021-09-06T18:44:45.000Z

2022-02-24T04:10:10.000Z

""" >>> from datetime import datetime, date >>> from django.utils.dateformat import format >>> from django.utils.tzinfo import FixedOffset, LocalTimezone # date >>> d = date(2009, 5, 16) >>> date.fromtimestamp(int(format(d, 'U'))) == d True # Naive datetime >>> dt = datetime(2009, 5, 16, 5, 30, 30) >>> datetime.fromtimestamp(int(format(dt, 'U'))) == dt True # datetime with local tzinfo >>> ltz = LocalTimezone(datetime.now()) >>> dt = datetime(2009, 5, 16, 5, 30, 30, tzinfo=ltz) >>> datetime.fromtimestamp(int(format(dt, 'U')), ltz) == dt True >>> datetime.fromtimestamp(int(format(dt, 'U'))) == dt.replace(tzinfo=None) True # datetime with arbitrary tzinfo >>> tz = FixedOffset(-510) >>> ltz = LocalTimezone(datetime.now()) >>> dt = datetime(2009, 5, 16, 5, 30, 30, tzinfo=tz) >>> datetime.fromtimestamp(int(format(dt, 'U')), tz) == dt True >>> datetime.fromtimestamp(int(format(dt, 'U')), ltz) == dt True >>> datetime.fromtimestamp(int(format(dt, 'U'))) == dt.astimezone(ltz).replace(tzinfo=None) True >>> datetime.fromtimestamp(int(format(dt, 'U')), tz).utctimetuple() == dt.utctimetuple() True >>> datetime.fromtimestamp(int(format(dt, 'U')), ltz).utctimetuple() == dt.utctimetuple() True # Epoch >>> utc = FixedOffset(0) >>> udt = datetime(1970, 1, 1, tzinfo=utc) >>> format(udt, 'U') u'0' """ if __name__ == "__main__": import doctest doctest.testmod()

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89487616876d213c215daa66edb5c3055c524aff

79

py

Python

python/add.py

Mem-Dixy/web

e5373ec86e3b7048c353071dc3555a96340b035e

[ "MIT" ]

null

python/add.py

Mem-Dixy/web

e5373ec86e3b7048c353071dc3555a96340b035e

[ "MIT" ]

1

2021-05-09T23:36:35.000Z

python/add.py

Mem-Dixy/web

e5373ec86e3b7048c353071dc3555a96340b035e

[ "MIT" ]

null

class add: def empty(value): return value if (value) else ' '

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8964dda237ff5b6ed2251ffafbbf16becd772163

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py

Python

katas/kyu_7/ghostbusters_whitespace_removal.py

the-zebulan/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

40

2016-03-09T12:26:20.000Z

2022-03-23T08:44:51.000Z

katas/kyu_7/ghostbusters_whitespace_removal.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

null

katas/kyu_7/ghostbusters_whitespace_removal.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

36

2016-11-07T19:59:58.000Z

2022-03-31T11:18:27.000Z

def ghostbusters(building): return building.replace(' ', '') if building.find(' ') > -1 \ else 'You just wanted my autograph didn\'t you?'

37.75

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0

1

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1

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5

89856b0379cf56a4065e78cd4f0c99b836155be3

2,119

py

Python

Part_3_advanced/m16_tests_I/pytest_fixtures/homework_1_start/estudent/tests/test_grade_calculator.py

Mikma03/InfoShareacademy_Python_Courses

3df1008c8c92831bebf1625f960f25b39d6987e6

[ "MIT" ]

null

Part_3_advanced/m16_tests_I/pytest_fixtures/homework_1_start/estudent/tests/test_grade_calculator.py

Mikma03/InfoShareacademy_Python_Courses

3df1008c8c92831bebf1625f960f25b39d6987e6

[ "MIT" ]

null

Part_3_advanced/m16_tests_I/pytest_fixtures/homework_1_start/estudent/tests/test_grade_calculator.py

Mikma03/InfoShareacademy_Python_Courses

3df1008c8c92831bebf1625f960f25b39d6987e6

[ "MIT" ]

null

from estudent.grade import Grade from estudent.grade_calculator import GradeCalculator def test_normal_policy_promote_when_there_are_less_then_3_failing_grades(): failing_grade = Grade(value=1) passing_grade = Grade(value=5) grades = [failing_grade, failing_grade, passing_grade] assert GradeCalculator.normal_promotion_policy(grades) is True def test_normal_policy_doesnt_promote_when_there_are_3_failing_grades(): failing_grade = Grade(value=1) passing_grade = Grade(value=5) grades = [failing_grade, failing_grade, failing_grade, passing_grade] assert GradeCalculator.normal_promotion_policy(grades) is False def test_strict_policy_doesnt_promote_when_there_are_3_failing_grades(): failing_grade = Grade(value=1) passing_grade = Grade(value=5) grades = [failing_grade, failing_grade, failing_grade, passing_grade, passing_grade, passing_grade] assert GradeCalculator.strict_promotion_policy(grades) is False def test_strict_policy_doesnt_promote_when_average_is_worse_then_3(): poor_grade = Grade(value=2) grades = [poor_grade, poor_grade, poor_grade] assert GradeCalculator.strict_promotion_policy(grades) is False def test_strict_policy_promote_when_average_is_3_and_only_2_failing_grades(): failing_grade = Grade(value=1) passing_grade = Grade(value=5) grades = [failing_grade, failing_grade, passing_grade, passing_grade] assert GradeCalculator.strict_promotion_policy(grades) is True def test_get_number_of_failing_grades_counts_only_failing_ones(): failing_grade = Grade(value=1) passing_grade = Grade(value=5) grades = [failing_grade, passing_grade, failing_grade] assert GradeCalculator.get_number_of_failing_grades(grades) == 2 def test_get_number_of_failing_grades_returns_0_when_no_grades(): assert GradeCalculator.get_number_of_failing_grades([]) == 0 def test_calculate_grades_avg(): failing_grade = Grade(value=1) passing_grade = Grade(value=5) grades = [failing_grade, failing_grade, passing_grade, passing_grade] assert GradeCalculator.calculate_student_avg(grades) == 3

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null

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5

89a8cb67b568ea151f4696c3efac14148df5606e

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py

Python

planning_python/cost_functions/__init__.py

daahuang/planning_python

1b9ae0346df66ed8cf2cb80a54a92fd909a578fe

[ "BSD-3-Clause" ]

null

planning_python/cost_functions/__init__.py

daahuang/planning_python

1b9ae0346df66ed8cf2cb80a54a92fd909a578fe

[ "BSD-3-Clause" ]

null

planning_python/cost_functions/__init__.py

daahuang/planning_python

1b9ae0346df66ed8cf2cb80a54a92fd909a578fe

[ "BSD-3-Clause" ]

null

import sys sys.path.insert(0, "../..") from .cost_function import CostFunction, PathLengthNoAng, PathLengthAng, DubinsPathLength, UnitCost

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89abc0b854eb7935b9aff636a6d97b8f8d51e257

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py

Python

omnibus/tests/test_check.py

wrmsr/omnibus

3c4ef5eb17b0fff8593fa6a2284337bf193c18d3

[ "BSD-3-Clause" ]

2

2020-06-17T19:54:09.000Z

2020-06-18T20:10:26.000Z

omnibus/tests/test_check.py

wrmsr/omnibus

3c4ef5eb17b0fff8593fa6a2284337bf193c18d3

[ "BSD-3-Clause" ]

null

omnibus/tests/test_check.py

wrmsr/omnibus

3c4ef5eb17b0fff8593fa6a2284337bf193c18d3

[ "BSD-3-Clause" ]

null

from .. import check def test_check(): check.none(None)

10.333333

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null

0

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1

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5

985402547b582a78e1a6af757c2bb10fb06650de

167

py

Python

app/admin.py

akimasax/boardfile

7d624ac10503d60885e7e576bd9396ba0097841e

[ "MIT" ]

null

app/admin.py

akimasax/boardfile

7d624ac10503d60885e7e576bd9396ba0097841e

[ "MIT" ]

null

app/admin.py

akimasax/boardfile

7d624ac10503d60885e7e576bd9396ba0097841e

[ "MIT" ]

null

from django.contrib import admin from .models import BoardModel,Comment,Reply admin.site.register(BoardModel) admin.site.register(Comment) admin.site.register(Reply)

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1

0

null

0

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1

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5

9869209e98b8429e6236fc538ef8a7ef9edf447c

187

py

Python

azmessaging/channels/__init__.py

ali-zahedi/az-messaging

ecc626e6be3f58a9ec166923623c144c86d2734e

[ "MIT" ]

null

azmessaging/channels/__init__.py

ali-zahedi/az-messaging

ecc626e6be3f58a9ec166923623c144c86d2734e

[ "MIT" ]

null

azmessaging/channels/__init__.py

ali-zahedi/az-messaging

ecc626e6be3f58a9ec166923623c144c86d2734e

[ "MIT" ]

null

from .base import BaseNotificationChannel from .sms import SMSNotificationChannel from .telegram import TelegramNotificationChannel from .pushnotifications import PushNotificationChannel

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987e93c1f227d3487a2b2f374beb4ac358826701

99

py

Python

mytrading/strategies/__init__.py

joeledwardson/betfair-browser

b641f134e60307250a0e51bafa849422ecf5264b

[ "MIT" ]

3

2021-11-23T19:03:02.000Z

2021-11-24T08:44:23.000Z

mytrading/strategies/__init__.py

joeledwardson/betfair-browser

b641f134e60307250a0e51bafa849422ecf5264b

[ "MIT" ]

2

2021-11-23T18:47:31.000Z

2021-12-08T15:36:11.000Z

mytrading/strategies/__init__.py

joeledwardson/betfair-browser

b641f134e60307250a0e51bafa849422ecf5264b

[ "MIT" ]

null

# import all strategies so strategy types and message formatters are registered from . import spike

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98a378edaf703895f552f67a1d84102ca77203fc

92

py

Python

Basic-Python/code/test_import/2.py

johnnynode/AI-LEARNING-MATERIAL

1719f5b6ecb9b9caf485b9d806c1211b142b8ed5

[ "MIT" ]

2

2018-06-08T00:40:17.000Z

2018-06-08T05:27:30.000Z

Basic-Python/code/test_import/2.py

johnnynode/AI-LEARNING-MATERIAL

1719f5b6ecb9b9caf485b9d806c1211b142b8ed5

[ "MIT" ]

null

Basic-Python/code/test_import/2.py

johnnynode/AI-LEARNING-MATERIAL

1719f5b6ecb9b9caf485b9d806c1211b142b8ed5

[ "MIT" ]

null

from random import random, randrange print(random()) print(randrange(1,11)) # 获取 1-10 的随机数字

23

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7fa69b58fd7f13cc99ef33f675dfa6fdc03fa156

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py

Python

Scripts/plot_InterModelBiases_v7.py

zmlabe/ModelBiasesANN

df28842a8594870db3282682b1261af5058af832

[ "MIT" ]

1

2022-02-12T11:56:54.000Z

Scripts/plot_InterModelBiases_v7.py

zmlabe/ModelBiasesANN

df28842a8594870db3282682b1261af5058af832

[ "MIT" ]

null

Scripts/plot_InterModelBiases_v7.py

zmlabe/ModelBiasesANN

df28842a8594870db3282682b1261af5058af832

[ "MIT" ]

null

""" Script for plotting differences in models for select variables over the 1950-2019 period Author : Zachary M. Labe Date : 15 December 2021 Version : 7 - adds validation data for early stopping """ ### Import packages import sys import math import time import matplotlib.pyplot as plt import numpy as np import pandas as pd import scipy.stats as stats from mpl_toolkits.basemap import Basemap, addcyclic, shiftgrid import palettable.cubehelix as cm import palettable.scientific.sequential as sss import cmocean as cmocean import calc_Utilities as UT import calc_dataFunctions as df import calc_Stats as dSS import scipy.stats as sts ### Plotting defaults plt.rc('text',usetex=True) plt.rc('font',**{'family':'sans-serif','sans-serif':['Avant Garde']}) ############################################################################### ############################################################################### ############################################################################### ### Data preliminaries modelGCMs = ['CanESM2','MPI','CSIRO-MK3.6','KNMI-ecearth','GFDL-CM3','GFDL-ESM2M','LENS'] modelGCMsNames = ['CanESM2','MPI','CSIRO-MK3.6','KNMI-ecearth','GFDL-CM3','GFDL-ESM2M','LENS','MMmean'] letters = ["a","b","c","d","e","f","g","h","i","j","k","l","m"] datasetsingle = ['SMILE'] monthlychoiceq = ['JFM','AMJ','JAS','OND','annual'] variables = ['T2M','P','SLP'] reg_name = 'SMILEGlobe' level = 'surface' monthlychoiceq = ['annual'] variables = ['T2M'] timeper = 'historical' ############################################################################### ############################################################################### land_only = False ocean_only = False ############################################################################### ############################################################################### baseline = np.arange(1951,1980+1,1) ############################################################################### ############################################################################### window = 0 yearsall = np.arange(1950+window,2019+1,1) ############################################################################### ############################################################################### numOfEns = 16 lentime = len(yearsall) ############################################################################### ############################################################################### dataset = datasetsingle[0] lat_bounds,lon_bounds = UT.regions(reg_name) ############################################################################### ############################################################################### ravelyearsbinary = False ravelbinary = False lensalso = True randomalso = False shuffletype = 'none' ############################################################################### ############################################################################### ############################################################################### ############################################################################### ### Read in model data def read_primary_dataset(variq,dataset,monthlychoice,numOfEns,lensalso,randomalso,ravelyearsbinary,ravelbinary,shuffletype,timeper,lat_bounds=lat_bounds,lon_bounds=lon_bounds): data,lats,lons = df.readFiles(variq,dataset,monthlychoice,numOfEns,lensalso,randomalso,ravelyearsbinary,ravelbinary,shuffletype,timeper) datar,lats,lons = df.getRegion(data,lats,lons,lat_bounds,lon_bounds) print('\nOur dataset: ',dataset,' is shaped',data.shape) return datar,lats,lons ### Call functions for vv in range(len(variables)): for mo in range(len(monthlychoiceq)): variq = variables[vv] monthlychoice = monthlychoiceq[mo] directoryfigure = '/Users/zlabe/Desktop/ModelComparison_v1/Climatologies/interModel/%s/' % variq saveData = monthlychoice + '_' + variq + '_' + reg_name print('*Filename == < %s >' % saveData) ### Read data models,lats,lons = read_primary_dataset(variq,dataset,monthlychoice,numOfEns, lensalso,randomalso,ravelyearsbinary, ravelbinary,shuffletype,timeper, lat_bounds,lon_bounds) ### Calculate ensemble mean ensmean = np.nanmean(models[:,:,:,:,:],axis=1) ### Calculate multimodel mean modmean = np.nanmean(models[:,:,:,:,:],axis=0) ### Calculate difference from multimodelmean diffmod = models - modmean diffmodensm = np.nanmean(diffmod[:,:,:,:,:],axis=1) diffmodmean = np.nanmean(diffmodensm[:,:,:,:],axis=1) ### Calculate different between each model # intermodel = np.empty((models.shape[0],models.shape[0],models.shape[1], # models.shape[2],models.shape[3],models.shape[4])) # for mm in range(models.shape[0]): # for ea in range(models.shape[0]): # intermodel[mm,ea,:,:,:,:] = models[mm,:,:,:,:] - models[ea,:,:,:,:] # ensmeanintermodel = np.nanmean(intermodel[:,:,:,:,:,:],axis=2) # timeensmeanintermodel = np.nanmean(ensmeanintermodel[:,:,:,:,:],axis=2) ############################################################################### ############################################################################### ############################################################################### ####################################################################### ####################################################################### ####################################################################### ### Plot subplot of different from multimodel mean if variq == 'T2M': limit = np.arange(-6,6.01,0.25) barlim = np.round(np.arange(-6,7,2),2) cmap = cmocean.cm.balance label = r'\textbf{%s -- [$^{\circ}$C MMmean difference] -- 1950-2019}' % variq elif variq == 'P': limit = np.arange(-3,3.01,0.01) barlim = np.round(np.arange(-3,3.1,1),2) cmap = cmocean.cm.tarn label = r'\textbf{%s -- [mm/day MMmean difference] -- 1950-2019}' % variq elif variq == 'SLP': limit = np.arange(-5,5.1,0.25) barlim = np.round(np.arange(-5,6,1),2) cmap = cmocean.cm.diff label = r'\textbf{%s -- [hPa MMmean difference] -- 1950-2019}' % variq fig = plt.figure(figsize=(8,4)) for r in range(len(diffmodmean)): var = diffmodmean[r] ax1 = plt.subplot(2,4,r+2) m = Basemap(projection='moll',lon_0=0,resolution='l',area_thresh=10000) m.drawcoastlines(color='dimgrey',linewidth=0.27) var, lons_cyclic = addcyclic(var, lons) var, lons_cyclic = shiftgrid(180., var, lons_cyclic, start=False) lon2d, lat2d = np.meshgrid(lons_cyclic, lats) x, y = m(lon2d, lat2d) circle = m.drawmapboundary(fill_color='white',color='dimgray', linewidth=0.7) circle.set_clip_on(False) cs1 = m.contourf(x,y,var,limit,extend='both') cs1.set_cmap(cmap) ax1.annotate(r'\textbf{%s}' % modelGCMs[r],xy=(0,0),xytext=(0.5,1.10), textcoords='axes fraction',color='dimgrey',fontsize=8, rotation=0,ha='center',va='center') ax1.annotate(r'\textbf{[%s]}' % letters[r],xy=(0,0),xytext=(0.86,0.97), textcoords='axes fraction',color='k',fontsize=6, rotation=330,ha='center',va='center') ############################################################################### cbar_ax1 = fig.add_axes([0.36,0.11,0.3,0.03]) cbar1 = fig.colorbar(cs1,cax=cbar_ax1,orientation='horizontal', extend='both',extendfrac=0.07,drawedges=False) cbar1.set_label(label,fontsize=9,color='dimgrey',labelpad=1.4) cbar1.set_ticks(barlim) cbar1.set_ticklabels(list(map(str,barlim))) cbar1.ax.tick_params(axis='x', size=.01,labelsize=5) cbar1.outline.set_edgecolor('dimgrey') plt.tight_layout() plt.subplots_adjust(top=0.85,wspace=0.02,hspace=0.00,bottom=0.14) plt.savefig(directoryfigure + 'MultiModelBias-%s_ALL.png' % saveData,dpi=300) directorydataMS = '/Users/zlabe/Documents/Research/ModelComparison/Data/RevisitResults_v7/' np.save(directorydataMS + 'MMMeandifferences_7models.npy',diffmodmean) ############################################################################### ############################################################################### ############################################################################### fig = plt.figure(figsize=(10,2)) for r in range(len(diffmodmean)+1): if r < 7: var = diffmodmean[r] else: var = np.empty((lats.shape[0],lons.shape[0])) var[:] = np.nan ax1 = plt.subplot(1,len(diffmodmean)+1,r+1) m = Basemap(projection='npstere',boundinglat=65,lon_0=0, resolution='l',round =True,area_thresh=10000) m.drawcoastlines(color='darkgrey',linewidth=0.27) var, lons_cyclic = addcyclic(var, lons) var, lons_cyclic = shiftgrid(180., var, lons_cyclic, start=False) lon2d, lat2d = np.meshgrid(lons_cyclic, lats) x, y = m(lon2d, lat2d) circle = m.drawmapboundary(fill_color='dimgrey',color='dimgray', linewidth=0.7) circle.set_clip_on(False) cs1 = m.contourf(x,y,var,limit,extend='both') cs1.set_cmap(cmap) if ocean_only == True: m.fillcontinents(color='dimgrey',lake_color='dimgrey') elif land_only == True: m.drawlsmask(land_color=(0,0,0,0),ocean_color='darkgrey',lakes=True,zorder=5) ax1.annotate(r'\textbf{%s}' % modelGCMsNames[r],xy=(0,0),xytext=(0.5,1.10), textcoords='axes fraction',color='dimgrey',fontsize=8, rotation=0,ha='center',va='center') ax1.annotate(r'\textbf{[%s]}' % letters[r],xy=(0,0),xytext=(0.86,0.97), textcoords='axes fraction',color='k',fontsize=6, rotation=330,ha='center',va='center') ############################################################################### cbar_ax1 = fig.add_axes([0.36,0.13,0.3,0.03]) cbar1 = fig.colorbar(cs1,cax=cbar_ax1,orientation='horizontal', extend='both',extendfrac=0.07,drawedges=False) cbar1.set_label(label,fontsize=9,color='dimgrey',labelpad=1.4) cbar1.set_ticks(barlim) cbar1.set_ticklabels(list(map(str,barlim))) cbar1.ax.tick_params(axis='x', size=.01,labelsize=5) cbar1.outline.set_edgecolor('dimgrey') plt.tight_layout() plt.subplots_adjust(top=0.85,wspace=0.02,hspace=0.02,bottom=0.14) plt.savefig(directoryfigure + 'MultiModelBias-%s_ALL-Arctic.png' % saveData,dpi=300) ############################################################################### ############################################################################### ############################################################################### if variq == 'T2M': limit = np.arange(-3,3.01,0.2) barlim = np.round(np.arange(-3,4,1),2) cmap = cmocean.cm.balance label = r'\textbf{%s -- [$^{\circ}$C MMmean difference] -- 1950-2019}' % variq elif variq == 'P': limit = np.arange(-3,3.01,0.01) barlim = np.round(np.arange(-3,3.1,1),2) cmap = cmocean.cm.tarn label = r'\textbf{%s -- [mm/day MMmean difference] -- 1950-2019}' % variq elif variq == 'SLP': limit = np.arange(-5,5.1,0.25) barlim = np.round(np.arange(-5,6,1),2) cmap = cmocean.cm.diff label = r'\textbf{%s -- [hPa MMmean difference] -- 1950-2019}' % variq fig = plt.figure(figsize=(10,2)) for r in range(len(diffmodmean)+1): if r < 7: var = diffmodmean[r] else: var = np.empty((lats.shape[0],lons.shape[0])) var[:] = np.nan ax1 = plt.subplot(1,len(diffmodmean)+1,r+1) m = Basemap(projection='moll',lon_0=0,resolution='l',area_thresh=10000) m.drawcoastlines(color='darkgrey',linewidth=0.27) var, lons_cyclic = addcyclic(var, lons) var, lons_cyclic = shiftgrid(180., var, lons_cyclic, start=False) lon2d, lat2d = np.meshgrid(lons_cyclic, lats) x, y = m(lon2d, lat2d) circle = m.drawmapboundary(fill_color='dimgrey',color='dimgray', linewidth=0.7) circle.set_clip_on(False) cs1 = m.contourf(x,y,var,limit,extend='both') cs1.set_cmap(cmap) if ocean_only == True: m.fillcontinents(color='dimgrey',lake_color='dimgrey') elif land_only == True: m.drawlsmask(land_color=(0,0,0,0),ocean_color='darkgrey',lakes=True,zorder=5) ax1.annotate(r'\textbf{%s}' % modelGCMsNames[r],xy=(0,0),xytext=(0.5,1.10), textcoords='axes fraction',color='dimgrey',fontsize=8, rotation=0,ha='center',va='center') ax1.annotate(r'\textbf{[%s]}' % letters[r],xy=(0,0),xytext=(0.86,0.97), textcoords='axes fraction',color='k',fontsize=6, rotation=330,ha='center',va='center') ############################################################################### cbar_ax1 = fig.add_axes([0.36,0.13,0.3,0.03]) cbar1 = fig.colorbar(cs1,cax=cbar_ax1,orientation='horizontal', extend='both',extendfrac=0.07,drawedges=False) cbar1.set_label(label,fontsize=9,color='dimgrey',labelpad=1.4) cbar1.set_ticks(barlim) cbar1.set_ticklabels(list(map(str,barlim))) cbar1.ax.tick_params(axis='x', size=.01,labelsize=5) cbar1.outline.set_edgecolor('dimgrey') plt.tight_layout() plt.subplots_adjust(top=0.85,wspace=0.02,hspace=0.02,bottom=0.14) plt.savefig(directoryfigure + 'MultiModelBias-%s_ALL-StyleGlobe.png' % saveData,dpi=300) ############################################################################### ############################################################################### ############################################################################### if variq == 'T2M': limit = np.arange(-3,3.01,0.2) barlim = np.round(np.arange(-3,4,1),2) cmap = cmocean.cm.balance label = r'\textbf{%s -- [$^{\circ}$C MMmean difference] -- 1950-2019}' % variq elif variq == 'P': limit = np.arange(-3,3.01,0.01) barlim = np.round(np.arange(-3,3.1,1),2) cmap = cmocean.cm.tarn label = r'\textbf{%s -- [mm/day MMmean difference] -- 1950-2019}' % variq elif variq == 'SLP': limit = np.arange(-5,5.1,0.25) barlim = np.round(np.arange(-5,6,1),2) cmap = cmocean.cm.diff label = r'\textbf{%s -- [hPa MMmean difference] -- 1950-2019}' % variq fig = plt.figure(figsize=(10,2)) for r in range(len(diffmodmean)+1): if r < 7: var = diffmodmean[r] else: var = np.empty((lats.shape[0],lons.shape[0])) var[:] = np.nan latq = np.where((lats >= -20) & (lats <= 20))[0] latsqq = lats[latq] var = var[latq,:] ax1 = plt.subplot(1,len(diffmodmean)+1,r+1) m = Basemap(projection='moll',lon_0=0,resolution='l',area_thresh=10000) m.drawcoastlines(color='darkgrey',linewidth=0.27) var, lons_cyclic = addcyclic(var, lons) var, lons_cyclic = shiftgrid(180., var, lons_cyclic, start=False) lon2d, lat2d = np.meshgrid(lons_cyclic, latsqq) x, y = m(lon2d, lat2d) circle = m.drawmapboundary(fill_color='dimgrey',color='dimgray', linewidth=0.7) circle.set_clip_on(False) cs1 = m.contourf(x,y,var,limit,extend='both') cs1.set_cmap(cmap) if ocean_only == True: m.fillcontinents(color='dimgrey',lake_color='dimgrey') elif land_only == True: m.drawlsmask(land_color=(0,0,0,0),ocean_color='darkgrey',lakes=True,zorder=5) ax1.annotate(r'\textbf{%s}' % modelGCMsNames[r],xy=(0,0),xytext=(0.5,1.10), textcoords='axes fraction',color='dimgrey',fontsize=8, rotation=0,ha='center',va='center') ax1.annotate(r'\textbf{[%s]}' % letters[r],xy=(0,0),xytext=(0.86,0.97), textcoords='axes fraction',color='k',fontsize=6, rotation=330,ha='center',va='center') ############################################################################### cbar_ax1 = fig.add_axes([0.36,0.13,0.3,0.03]) cbar1 = fig.colorbar(cs1,cax=cbar_ax1,orientation='horizontal', extend='both',extendfrac=0.07,drawedges=False) cbar1.set_label(label,fontsize=9,color='dimgrey',labelpad=1.4) cbar1.set_ticks(barlim) cbar1.set_ticklabels(list(map(str,barlim))) cbar1.ax.tick_params(axis='x', size=.01,labelsize=5) cbar1.outline.set_edgecolor('dimgrey') plt.tight_layout() plt.subplots_adjust(top=0.85,wspace=0.02,hspace=0.02,bottom=0.14) plt.savefig(directoryfigure + 'MultiModelBias-%s_ALL-Tropics.png' % saveData,dpi=300) ############################################################################### ############################################################################### ############################################################################### ####################################################################### ####################################################################### ####################################################################### ### Plot subplot of different from multimodel mean if variq == 'T2M': limit = np.arange(-6,6.01,0.25) barlim = np.round(np.arange(-6,7,2),2) cmap = cmocean.cm.balance label = r'\textbf{%s -- [$^{\circ}$C difference] -- 1950-2019}' % variq elif variq == 'P': limit = np.arange(-3,3.01,0.01) barlim = np.round(np.arange(-3,3.1,1),2) cmap = cmocean.cm.tarn label = r'\textbf{%s -- [mm/day difference] -- 1950-2019}' % variq elif variq == 'SLP': limit = np.arange(-5,5.1,0.25) barlim = np.round(np.arange(-5,6,1),2) cmap = cmocean.cm.diff label = r'\textbf{%s -- [hPa difference] -- 1950-2019}' % variq for diff in range(timeensmeanintermodel.shape[0]): fig = plt.figure(figsize=(8,4)) for r in range(timeensmeanintermodel.shape[1]+1): var = timeensmeanintermodel[diff,r-1,:,:] ax1 = plt.subplot(2,4,r+1) if r == 0: varc = np.nanmean(ensmean[diff],axis=0) # average over years latsc = lats.copy() lonsc = lons.copy() if variq == 'T2M': limitc = np.arange(-35,35.01,0.5) barlimc = np.round(np.arange(-35,36,5),2) cmapc = plt.cm.CMRmap_r labelc = r'\textbf{%s -- [$^{\circ}$C mean] -- 1950-2019}' % variq elif variq == 'P': limitc = np.arange(0,10.01,0.01) barlimc = np.round(np.arange(-0,10.01,2),2) cmapc = cmocean.cm.rain labelc = r'\textbf{%s -- [mm/day mean] -- 1950-2019}' % variq elif variq == 'SLP': limitc = np.arange(990,1020.01,0.5) barlimc = np.round(np.arange(990,1021,10),2) cmapc = sss.Nuuk_20.mpl_colormap labelc = r'\textbf{%s -- [hPa : mean] -- 1950-2019}' % variq m = Basemap(projection='moll',lon_0=0,resolution='l',area_thresh=10000) m.drawcoastlines(color='dimgrey',linewidth=0.27) varc, lons_cyclicc = addcyclic(varc, lonsc) varc, lons_cyclicc = shiftgrid(180., varc, lons_cyclicc, start=False) lon2dc, lat2dc = np.meshgrid(lons_cyclicc, latsc) xc, yc = m(lon2dc, lat2dc) circle = m.drawmapboundary(fill_color='white',color='dimgray', linewidth=0.7) circle.set_clip_on(False) cs1 = m.contourf(xc,yc,varc,limitc,extend='both') cs1.set_cmap(cmapc) ax1.annotate(r'\textbf{%s Climatology}' % modelGCMs[diff],xy=(0,0),xytext=(0.5,1.10), textcoords='axes fraction',color='dimgrey',fontsize=8, rotation=0,ha='center',va='center') ax1.annotate(r'\textbf{[%s]}' % letters[r],xy=(0,0),xytext=(0.86,0.97), textcoords='axes fraction',color='k',fontsize=6, rotation=330,ha='center',va='center') else: m = Basemap(projection='moll',lon_0=0,resolution='l',area_thresh=10000) m.drawcoastlines(color='dimgrey',linewidth=0.27) var, lons_cyclic = addcyclic(var, lons) var, lons_cyclic = shiftgrid(180., var, lons_cyclic, start=False) lon2d, lat2d = np.meshgrid(lons_cyclic, lats) x, y = m(lon2d, lat2d) circle = m.drawmapboundary(fill_color='white',color='dimgray', linewidth=0.7) circle.set_clip_on(False) cs1 = m.contourf(x,y,var,limit,extend='both') cs1.set_cmap(cmap) ax1.annotate(r'\textbf{%s}' % modelGCMs[r-1],xy=(0,0),xytext=(0.5,1.10), textcoords='axes fraction',color='dimgrey',fontsize=8, rotation=0,ha='center',va='center') ax1.annotate(r'\textbf{[%s]}' % letters[r],xy=(0,0),xytext=(0.86,0.97), textcoords='axes fraction',color='k',fontsize=6, rotation=330,ha='center',va='center') ############################################################################### fig.suptitle(r'\textbf{%s minus each SMILE}' % modelGCMs[diff],color='k', fontsize=15) cbar_ax1 = fig.add_axes([0.36,0.11,0.3,0.03]) cbar1 = fig.colorbar(cs1,cax=cbar_ax1,orientation='horizontal', extend='both',extendfrac=0.07,drawedges=False) cbar1.set_label(label,fontsize=9,color='dimgrey',labelpad=1.4) cbar1.set_ticks(barlim) cbar1.set_ticklabels(list(map(str,barlim))) cbar1.ax.tick_params(axis='x', size=.01,labelsize=5) cbar1.outline.set_edgecolor('dimgrey') plt.tight_layout() plt.subplots_adjust(top=0.85,wspace=0.02,hspace=0.00,bottom=0.14) plt.savefig(directoryfigure + 'InterBias-%s_%s.png' % (saveData,modelGCMs[diff]),dpi=300)

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7fa71090ea583693a8faa04e24e03148d64d37fe

2,083

py

Python

array_api_tests/function_stubs/creation_functions.py

leofang/array-api-tests

6789a05da5595e1e53b30db22fc51206dd825c1d

[ "MIT" ]

1

2021-07-07T14:50:28.000Z

array_api_tests/function_stubs/creation_functions.py

leofang/array-api-tests

6789a05da5595e1e53b30db22fc51206dd825c1d

[ "MIT" ]

null

array_api_tests/function_stubs/creation_functions.py

leofang/array-api-tests

6789a05da5595e1e53b30db22fc51206dd825c1d

[ "MIT" ]

null

""" Function stubs for creation functions. NOTE: This file is generated automatically by the generate_stubs.py script. Do not modify it directly. See https://github.com/data-apis/array-api/blob/master/spec/API_specification/creation_functions.md """ from __future__ import annotations from ._types import Optional, Tuple, Union, array, device, dtype def arange(start: Union[int, float], /, *, stop: Optional[Union[int, float]] = None, step: Union[int, float] = 1, dtype: Optional[dtype] = None, device: Optional[device] = None) -> array: pass def empty(shape: Union[int, Tuple[int, ...]], /, *, dtype: Optional[dtype] = None, device: Optional[device] = None) -> array: pass def empty_like(x: array, /, *, dtype: Optional[dtype] = None, device: Optional[device] = None) -> array: pass def eye(N: int, /, *, M: Optional[int] = None, k: Optional[int] = 0, dtype: Optional[dtype] = None, device: Optional[device] = None) -> array: pass def full(shape: Union[int, Tuple[int, ...]], fill_value: Union[int, float], /, *, dtype: Optional[dtype] = None, device: Optional[device] = None) -> array: pass def full_like(x: array, fill_value: Union[int, float], /, *, dtype: Optional[dtype] = None, device: Optional[device] = None) -> array: pass def linspace(start: Union[int, float], stop: Union[int, float], num: int, /, *, dtype: Optional[dtype] = None, device: Optional[device] = None, endpoint: bool = True) -> array: pass def ones(shape: Union[int, Tuple[int, ...]], /, *, dtype: Optional[dtype] = None, device: Optional[device] = None) -> array: pass def ones_like(x: array, /, *, dtype: Optional[dtype] = None, device: Optional[device] = None) -> array: pass def zeros(shape: Union[int, Tuple[int, ...]], /, *, dtype: Optional[dtype] = None, device: Optional[device] = None) -> array: pass def zeros_like(x: array, /, *, dtype: Optional[dtype] = None, device: Optional[device] = None) -> array: pass __all__ = ['arange', 'empty', 'empty_like', 'eye', 'full', 'full_like', 'linspace', 'ones', 'ones_like', 'zeros', 'zeros_like']

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f6b4a90bcc49028259f06a6df2bda7d738d7c0a9

160

py

Python

settings_files/gamess_dft.py

tommason14/monash2018

8fc2de97172130ed5d532deb6f5bcca39ef3a6e3

[ "MIT" ]

7

2020-06-05T01:55:09.000Z

2021-12-20T19:32:36.000Z

settings_files/gamess_dft.py

tommason14/monash2018

8fc2de97172130ed5d532deb6f5bcca39ef3a6e3

[ "MIT" ]

null

settings_files/gamess_dft.py

tommason14/monash2018

8fc2de97172130ed5d532deb6f5bcca39ef3a6e3

[ "MIT" ]

8

2020-06-06T10:03:17.000Z

2022-03-18T14:47:33.000Z

from autochem import Settings sett=Settings() sett.input.mp2=None sett.input.contrl.mplevl=None sett.input.contrl.dfttyp='m06-2x' sett.input.dft.method='grid'

20

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0.79375

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0.615385

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null

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f6c6621e9cca945cb68eddd758e327a9c2c9eb12

245

py

Python

src/models/__init__.py

angelvillar96/super-resolution-noisy-images

ad5954e226f2c9d887735d76e48379b5c0ff8f77

[ "MIT" ]

6

2021-02-14T10:45:40.000Z

2021-11-10T02:58:44.000Z

src/models/__init__.py

angelvillar96/super-resolution-noisy-images

ad5954e226f2c9d887735d76e48379b5c0ff8f77

[ "MIT" ]

null

src/models/__init__.py

angelvillar96/super-resolution-noisy-images

ad5954e226f2c9d887735d76e48379b5c0ff8f77

[ "MIT" ]

1

2022-01-27T08:02:56.000Z

from .wdsr_new import MODEL as WDSR # from .wdsr_a import MODEL as WDSR_A # from .wdsr_b import MODEL as WDSR_B from .denoising_autoencoder import Autoencoder as Autoencoder from .denoising_autoencoder import ConvAutoencoder as ConvAutoencoder

35

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245

5.351351

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0.121212

0.19697

0.257576

0

0.134694

245

6

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40.833333

0.933962

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1

0

null

0

1

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1

0

5

10023fc11e70998a42db0afe0b46964926cdb1a9

295

py

Python

pytext/optimizer/__init__.py

twild-fb/pytext

07cadc0d130dac30d71d9da70380f124b3f5ac59

[ "BSD-3-Clause" ]

1

2022-03-27T18:56:21.000Z

pytext/optimizer/__init__.py

twild-fb/pytext

07cadc0d130dac30d71d9da70380f124b3f5ac59

[ "BSD-3-Clause" ]

null

pytext/optimizer/__init__.py

twild-fb/pytext

07cadc0d130dac30d71d9da70380f124b3f5ac59

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved from pytext.optimizer.activations import get_activation from pytext.optimizer.optimizers import SGD, Adagrad, Adam, Optimizer, learning_rates from pytext.optimizer.swa import StochasticWeightAveraging

49.166667

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0.830508

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295

6.394737

0.763158

0.123457

0.234568

0

0.003759

0.098305

295

5

86

59

0.909774

0.305085

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null

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null

0

1

0

1

0

1

0

5

63ff4cf695e84aaed952ab467575e9dd939d4c99

699

py

Python

app/content/factories/__init__.py

TIHLDE/Lepton

60ec0793381f1c1b222f305586e8c2d4345fb566

[ "MIT" ]

7

2021-03-04T18:49:12.000Z

2021-03-08T18:25:51.000Z

app/content/factories/__init__.py

TIHLDE/Lepton

60ec0793381f1c1b222f305586e8c2d4345fb566

[ "MIT" ]

251

2021-03-04T19:19:14.000Z

2022-03-31T14:47:53.000Z

app/content/factories/__init__.py

tihlde/Lepton

5cab3522c421b76373a5c25f49267cfaef7b826a

[ "MIT" ]

3

2021-10-05T19:03:04.000Z

2022-02-25T13:32:09.000Z

from app.content.factories.event_factory import ( EventFactory, EventWithSignalsFactory, ) from app.content.factories.priority_factory import PriorityFactory from app.content.factories.user_factory import UserFactory from app.content.factories.registration_factory import RegistrationFactory from app.content.factories.cheatsheet_factory import CheatsheetFactory from app.content.factories.news_factory import NewsFactory from app.content.factories.notification_factory import NotificationFactory from app.content.factories.page_factory import PageFactory from app.content.factories.page_factory import ParentPageFactory from app.content.factories.short_link_factory import ShortLinkFactory

49.928571

74

0.878398

82

699

7.353659

0.329268

0.116086

0.232172

0.381426

0.13267

0

0.072961

699

13

75

53.769231

0.930556

0

1

0

true

0

0.769231

0

0.769231

0

null

0

1

0

null

0

1

0

1

0

1

0

5

12133d6bec300a53c20e6d45f29d7879c10c65a8

48

py

Python

backend/app/db/__init__.py

shvixxl/tablic

3ca2f026d84fab9692e7e5adde74a9716266ff5e

[ "MIT" ]

2

2021-02-05T16:55:41.000Z

2021-02-07T21:46:37.000Z

backend/app/db/__init__.py

shvixxl/tablic

3ca2f026d84fab9692e7e5adde74a9716266ff5e

[ "MIT" ]

1

2021-10-30T15:42:53.000Z

backend/app/db/__init__.py

shvixxl/tablic

3ca2f026d84fab9692e7e5adde74a9716266ff5e

[ "MIT" ]

null

"""Database module.""" from .mongodb import db

12

23

0.6875

6

48

5.5

1

0

0.145833

48

3

24

16

0.804878

0.333333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

121add0170aa88b1c64de975433800aca18fd2a5

1,315

py

Python

test/ts_eraser.py

zoumingzhe/FileShredder

e9488370a819ccd8d734bde54a14523959371d3e

[ "MIT" ]

null

test/ts_eraser.py

zoumingzhe/FileShredder

e9488370a819ccd8d734bde54a14523959371d3e

[ "MIT" ]

null

test/ts_eraser.py

zoumingzhe/FileShredder

e9488370a819ccd8d734bde54a14523959371d3e

[ "MIT" ]

null

import os import sys sys.path.insert(0, r'..\pyshredder') from pyshredder.core.eraser import eraser from ztools import fbasic f = fbasic() e = eraser() old_path = '.\\ts_file\\eraser_testfile.txt' f.ensure('.\\ts_file\\eraser') new_path = '.\\ts_file\\eraser\\eraser_testfile1.txt' f.copy(old_path, new_path) print("before file size:", os.path.getsize(new_path)) e.random(new_path) print("after file size:", os.path.getsize(new_path)) new_path = '.\\ts_file\\eraser\\eraser_testfile2.txt' f.copy(old_path, new_path) print("before file size:", os.path.getsize(new_path)) e.random_block(new_path) print("after file size:", os.path.getsize(new_path)) new_path = '.\\ts_file\\eraser\\eraser_testfile3.txt' f.copy(old_path, new_path) print("before file size:", os.path.getsize(new_path)) e.fill(new_path) print("after file size:", os.path.getsize(new_path)) new_path = '.\\ts_file\\eraser\\eraser_testfile4.txt' f.copy(old_path, new_path) print("before file size:", os.path.getsize(new_path)) e.fill(new_path, [1]) print("after file size:", os.path.getsize(new_path)) new_path = '.\\ts_file\\eraser\\eraser_testfile5.txt' f.copy(old_path, new_path) print("before file size:", os.path.getsize(new_path)) e.fill(new_path, [1, 2, 3, 4, 5]) print("after file size:", os.path.getsize(new_path)) input("按回车（Enter）继续")

29.886364

53

0.732319

227

1,315

4.044053

0.198238

0.190632

0.108932

0.152505

0.747277

0.720044

0.678649

0

0.00995

0.08289

1,315

43

54

30.581395

0.751244

0

0.428571

0

0.33384

0.175665

0

1

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false

0

0.114286

0

0.114286

0.285714

0

null

0

1

0

null

0

5

121f34447e60a25638efba803ae519ea691610f9

20

py

Python

Python books/info.py

AnilNITT/PyTHON-Books

86108cb8e5940772c53c7b0233876d732836f269

[ "Apache-2.0" ]

null

Python books/info.py

AnilNITT/PyTHON-Books

86108cb8e5940772c53c7b0233876d732836f269

[ "Apache-2.0" ]

1

2019-11-02T10:56:54.000Z

2019-11-02T11:00:20.000Z

Python books/info.py

AnilNITT/PyTHON-Books

86108cb8e5940772c53c7b0233876d732836f269

[ "Apache-2.0" ]

null

# Best python books

10

19

0.75

3

20

5

1

0

0.2

20

1

20

0.9375

0.85

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

126b4bc58631c0f9f5a9c764e6032c8ba3ee1d7c

44

py

Python

07_user_input_and_while_loops/7_7_infinity.py

simonhoch/python_basics

4ecf12c074e641e3cdeb0a6690846eb9133f96af

[ "MIT" ]

null

07_user_input_and_while_loops/7_7_infinity.py

simonhoch/python_basics

4ecf12c074e641e3cdeb0a6690846eb9133f96af

[ "MIT" ]

null

07_user_input_and_while_loops/7_7_infinity.py

simonhoch/python_basics

4ecf12c074e641e3cdeb0a6690846eb9133f96af

[ "MIT" ]

null

n = 1 while n >= 1: print(n) n += 1

8.8

13

0.386364

9

44

1.888889

0.444444

0.352941

0

0.12

0.431818

44

4

14

11

0.56

0

1

0

false

0

0.25

1

null

1

0

1

0

null

0

5

127167355cbe343d0f2cc6796ef9bd9083343d76

42

py

Python

test_pyramids/__init__.py

leomauro/pyramids

4f7a8e97e13a5ee0b037dc528e5ba72f31ac36e5

[ "MIT" ]

9

2015-09-04T22:33:40.000Z

2019-04-11T14:05:11.000Z

test_pyramids/__init__.py

leomauro/pyramids

4f7a8e97e13a5ee0b037dc528e5ba72f31ac36e5

[ "MIT" ]

2

2015-09-04T22:31:44.000Z

2017-07-29T04:11:53.000Z

test_pyramids/__init__.py

hosford42/pyramids

4f7a8e97e13a5ee0b037dc528e5ba72f31ac36e5

[ "MIT" ]

3

2015-10-14T12:41:26.000Z

2022-01-08T19:43:47.000Z

"""Test suite for the Pyramids parser."""

21

41

0.690476

6

42

4.833333

1

0

0.142857

42

1

42

0.805556

0.833333

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

89e1a92dcda27a6ac7777e2ad00663974171a9eb

498

py

Python

example/Polynomial.py

vyattalee/HowToBePythonExpert

64a4ffbe6eec0d11af870de39c1de8232a81d8d5

[ "MIT" ]

null

example/Polynomial.py

vyattalee/HowToBePythonExpert

64a4ffbe6eec0d11af870de39c1de8232a81d8d5

[ "MIT" ]

null

example/Polynomial.py

vyattalee/HowToBePythonExpert

64a4ffbe6eec0d11af870de39c1de8232a81d8d5

[ "MIT" ]

null

class Polynomial: def __init__(self, *coeffs): self.coeffs = coeffs def __repr__(self): return 'Polynomial(*{!r})'.format(self.coeffs) def __add__(self, other): return Polynomial(*(x + y for x, y in zip(self.coeffs, other.coeffs))) def __sub__(self, other): return Polynomial(*(x - y for x, y in zip(self.coeffs, other.coeffs))) def __len__(self): return len(self.coeffs) def __call__(self): return

23.714286

79

0.582329

63

498

4.222222

0.31746

0.225564

0.097744

0.18797

0.458647

0

0.289157

498

20

80

24.9

0.751412

0

0.035714

0

1

0.461538

false

0

0.384615

0.923077

0

null

1

0

1

0

null

0

1

0

1

0

5

89e9752823844dae9d874ab3fc605b31ca50dfb3

124

py

Python

example/whitespaceTokenizer.py

pontakornth/pythainlp

26ec714b7fb2a3e9081b1db6d0bcb95f742a45f3

[ "Apache-2.0" ]

1

2018-10-10T19:01:43.000Z

example/whitespaceTokenizer.py

pontakornth/pythainlp

26ec714b7fb2a3e9081b1db6d0bcb95f742a45f3

[ "Apache-2.0" ]

null

example/whitespaceTokenizer.py

pontakornth/pythainlp

26ec714b7fb2a3e9081b1db6d0bcb95f742a45f3

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from pythainlp.tokenize import WhitespaceTokenizer print(WhitespaceTokenizer("ทดสอบ ตัดคำช่องว่าง"))

41.333333

50

0.766129

20

124

4.95

0.9

0

0.008772

0.080645

124

3

51

41.333333

0.824561

0.169355

0

0.186275

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

89f94c1d0348eee0e76e564a1338c0cd90057a40

541

py

Python

tests/conftest.py

uploadcare/heif-image-plugin

164230d08472403b709e2d0c78e8de0207e9312a

[ "MIT" ]

6

2021-12-09T16:57:55.000Z

2022-03-22T13:34:53.000Z

tests/conftest.py

uploadcare/heif-image-plugin

164230d08472403b709e2d0c78e8de0207e9312a

[ "MIT" ]

5

2021-11-24T15:59:35.000Z

2022-03-11T16:29:53.000Z

tests/conftest.py

uploadcare/heif-image-plugin

164230d08472403b709e2d0c78e8de0207e9312a

[ "MIT" ]

1

2022-02-07T11:59:30.000Z

import pytest from PIL import Image from . import respath @pytest.fixture(scope="session") def orientation_ref_image(): return Image.open(respath('orientation', 'Landscape_1.heic')) @pytest.fixture(scope="session") def fox_ref_image(): return Image.open(respath('avif-sample-images', 'fox.jpg')) @pytest.fixture(scope="session") def jungle_ref_image(fox_ref_image): return Image.open(respath('jungle.png')) @pytest.fixture(scope="session") def dices_ref_image(fox_ref_image): return Image.open(respath('dices.png'))

21.64

65

0.744917

76

541

5.131579

0.328947

0.123077

0.184615

0.25641

0.658974

0.371795

0.294872

0.210256

0

0.002066

0.10536

541

24

66

22.541667

0.803719

0

0.266667

0

0.182994

0

1

0.266667

false

0

0.2

0.266667

0.733333

0

null

0

1

0

null

0

1

0

1

0

5

c3e01a350bc02b1a5083456c7ef4fcbcc01c2682

150

py

Python

tests/conftest.py

aracaarja12/logParserUtility

68a5d79ba2ba6856efac77f60709d8370e824691

[ "MIT" ]

null

tests/conftest.py

aracaarja12/logParserUtility

68a5d79ba2ba6856efac77f60709d8370e824691

[ "MIT" ]

null

tests/conftest.py

aracaarja12/logParserUtility

68a5d79ba2ba6856efac77f60709d8370e824691

[ "MIT" ]

null

import pytest import sys from pathlib import PurePath @pytest.fixture(scope="session") def script_name(): return PurePath(sys.argv[0]).parts[-1]

21.428571

42

0.753333

22

150

5.090909

0.772727

0

0.015152

0.12

150

7

42

21.428571

0.833333

0

0.046358

0

1

0.166667

true

0

0.5

0.166667

0.833333

0

1

0

null

0

1

0

null

0

1

0

1

0

5

c3f21a5131265993429db1bce653d20a23ff401e

125

py

Python

pythonequipmentdrivers/utility/__init__.py

admleman/PythonEquipmentDrivers

1e1fbf96ae372757ad90339af5863ab64daef2a0

[ "MIT" ]

6

2021-02-01T14:23:22.000Z

2021-12-31T12:26:07.000Z

pythonequipmentdrivers/utility/__init__.py

admleman/PythonEquipmentDrivers

1e1fbf96ae372757ad90339af5863ab64daef2a0

[ "MIT" ]

5

2020-08-17T12:59:06.000Z

2022-03-24T02:19:31.000Z

pythonequipmentdrivers/utility/__init__.py

admleman/PythonEquipmentDrivers

1e1fbf96ae372757ad90339af5863ab64daef2a0

[ "MIT" ]

3

2020-12-30T17:25:55.000Z

2021-07-27T13:52:44.000Z

from .data_management import (log_data, dump_data, create_test_log) __all__ = ['log_data', 'dump_data', 'create_test_log']

25

67

0.768

19

125

4.368421

0.473684

0.168675

0.26506

0.361446

0.674699

0

0.104

125

4

68

31.25

0.741071

0

0.256

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

5

7f09f1d01803153b37e9585276d631c8a2243570

282

py

Python

apps/names/models.py

kamranhossain/tracking_creator_django_obj

aafd945048e362ef6fdfe88a82adfe1014dce7fa

[ "MIT" ]

null

apps/names/models.py

kamranhossain/tracking_creator_django_obj

aafd945048e362ef6fdfe88a82adfe1014dce7fa

[ "MIT" ]

5

2020-06-07T17:57:59.000Z

2021-06-10T20:11:41.000Z

apps/names/models.py

kamranhossain/tracking_creator_django_obj

aafd945048e362ef6fdfe88a82adfe1014dce7fa

[ "MIT" ]

null

from django.db import models from project.base import BaseModel class Name(BaseModel): english_representation = models.CharField(max_length=100) vernacular_representation = models.CharField(max_length=100) def __str__(self): return self.english_representation

28.2

64

0.783688

34

282

6.235294

0.617647

0.198113

0.273585

0.301887

0.386792

0

0.025

0.148936

282

9

65

31.333333

0.858333

0

1

0.142857

false

0

0.285714

0.142857

1

0

null

0

1

0

1

0

null

0

1

0

5

6141e9877bd45523dab344023639c58e40f8c5c3

16,111

py

Python

bin/Python27/Lib/site-packages/numpy/doc/indexing.py

lefevre-fraser/openmeta-mms

08f3115e76498df1f8d70641d71f5c52cab4ce5f

[ "MIT" ]

null

bin/Python27/Lib/site-packages/numpy/doc/indexing.py

lefevre-fraser/openmeta-mms

08f3115e76498df1f8d70641d71f5c52cab4ce5f

[ "MIT" ]

null

bin/Python27/Lib/site-packages/numpy/doc/indexing.py

lefevre-fraser/openmeta-mms

08f3115e76498df1f8d70641d71f5c52cab4ce5f

[ "MIT" ]

1

2020-08-08T12:44:48.000Z

"""============== Array indexing ============== Array indexing refers to any use of the square brackets ([]) to index array values. There are many options to indexing, which give numpy indexing great power, but with power comes some complexity and the potential for confusion. This section is just an overview of the various options and issues related to indexing. Aside from single element indexing, the details on most of these options are to be found in related sections. Assignment vs referencing ========================= Most of the following examples show the use of indexing when referencing data in an array. The examples work just as well when assigning to an array. See the section at the end for specific examples and explanations on how assignments work. Single element indexing ======================= Single element indexing for a 1-D array is what one expects. It work exactly like that for other standard Python sequences. It is 0-based, and accepts negative indices for indexing from the end of the array. :: >>> x = np.arange(10) >>> x[2] 2 >>> x[-2] 8 Unlike lists and tuples, numpy arrays support multidimensional indexing for multidimensional arrays. That means that it is not necessary to separate each dimension's index into its own set of square brackets. :: >>> x.shape = (2,5) # now x is 2-dimensional >>> x[1,3] 8 >>> x[1,-1] 9 Note that if one indexes a multidimensional array with fewer indices than dimensions, one gets a subdimensional array. For example: :: >>> x[0] array([0, 1, 2, 3, 4]) That is, each index specified selects the array corresponding to the rest of the dimensions selected. In the above example, choosing 0 means that the remaining dimension of length 5 is being left unspecified, and that what is returned is an array of that dimensionality and size. It must be noted that the returned array is not a copy of the original, but points to the same values in memory as does the original array. In this case, the 1-D array at the first position (0) is returned. So using a single index on the returned array, results in a single element being returned. That is: :: >>> x[0][2] 2 So note that ``x[0,2] = x[0][2]`` though the second case is more inefficient as a new temporary array is created after the first index that is subsequently indexed by 2. Note to those used to IDL or Fortran memory order as it relates to indexing. Numpy uses C-order indexing. That means that the last index usually represents the most rapidly changing memory location, unlike Fortran or IDL, where the first index represents the most rapidly changing location in memory. This difference represents a great potential for confusion. Other indexing options ====================== It is possible to slice and stride arrays to extract arrays of the same number of dimensions, but of different sizes than the original. The slicing and striding works exactly the same way it does for lists and tuples except that they can be applied to multiple dimensions as well. A few examples illustrates best: :: >>> x = np.arange(10) >>> x[2:5] array([2, 3, 4]) >>> x[:-7] array([0, 1, 2]) >>> x[1:7:2] array([1, 3, 5]) >>> y = np.arange(35).reshape(5,7) >>> y[1:5:2,::3] array([[ 7, 10, 13], [21, 24, 27]]) Note that slices of arrays do not copy the internal array data but also produce new views of the original data. It is possible to index arrays with other arrays for the purposes of selecting lists of values out of arrays into new arrays. There are two different ways of accomplishing this. One uses one or more arrays of index values. The other involves giving a boolean array of the proper shape to indicate the values to be selected. Index arrays are a very powerful tool that allow one to avoid looping over individual elements in arrays and thus greatly improve performance. It is possible to use special features to effectively increase the number of dimensions in an array through indexing so the resulting array aquires the shape needed for use in an expression or with a specific function. Index arrays ============ Numpy arrays may be indexed with other arrays (or any other sequence- like object that can be converted to an array, such as lists, with the exception of tuples; see the end of this document for why this is). The use of index arrays ranges from simple, straightforward cases to complex, hard-to-understand cases. For all cases of index arrays, what is returned is a copy of the original data, not a view as one gets for slices. Index arrays must be of integer type. Each value in the array indicates which value in the array to use in place of the index. To illustrate: :: >>> x = np.arange(10,1,-1) >>> x array([10, 9, 8, 7, 6, 5, 4, 3, 2]) >>> x[np.array([3, 3, 1, 8])] array([7, 7, 9, 2]) The index array consisting of the values 3, 3, 1 and 8 correspondingly create an array of length 4 (same as the index array) where each index is replaced by the value the index array has in the array being indexed. Negative values are permitted and work as they do with single indices or slices: :: >>> x[np.array([3,3,-3,8])] array([7, 7, 4, 2]) It is an error to have index values out of bounds: :: >>> x[np.array([3, 3, 20, 8])] <type 'exceptions.IndexError'>: index 20 out of bounds 0<=index<9 Generally speaking, what is returned when index arrays are used is an array with the same shape as the index array, but with the type and values of the array being indexed. As an example, we can use a multidimensional index array instead: :: >>> x[np.array([[1,1],[2,3]])] array([[9, 9], [8, 7]]) Indexing Multi-dimensional arrays ================================= Things become more complex when multidimensional arrays are indexed, particularly with multidimensional index arrays. These tend to be more unusal uses, but theyare permitted, and they are useful for some problems. We'll start with thesimplest multidimensional case (using the array y from the previous examples): :: >>> y[np.array([0,2,4]), np.array([0,1,2])] array([ 0, 15, 30]) In this case, if the index arrays have a matching shape, and there is an index array for each dimension of the array being indexed, the resultant array has the same shape as the index arrays, and the values correspond to the index set for each position in the index arrays. In this example, the first index value is 0 for both index arrays, and thus the first value of the resultant array is y[0,0]. The next value is y[2,1], and the last is y[4,2]. If the index arrays do not have the same shape, there is an attempt to broadcast them to the same shape. If they cannot be broadcast to the same shape, an exception is raised: :: >>> y[np.array([0,2,4]), np.array([0,1])] <type 'exceptions.ValueError'>: shape mismatch: objects cannot be broadcast to a single shape The broadcasting mechanism permits index arrays to be combined with scalars for other indices. The effect is that the scalar value is used for all the corresponding values of the index arrays: :: >>> y[np.array([0,2,4]), 1] array([ 1, 15, 29]) Jumping to the next level of complexity, it is possible to only partially index an array with index arrays. It takes a bit of thought to understand what happens in such cases. For example if we just use one index array with y: :: >>> y[np.array([0,2,4])] array([[ 0, 1, 2, 3, 4, 5, 6], [14, 15, 16, 17, 18, 19, 20], [28, 29, 30, 31, 32, 33, 34]]) What results is the construction of a new array where each value of the index array selects one row from the array being indexed and the resultant array has the resulting shape (size of row, number index elements). An example of where this may be useful is for a color lookup table where we want to map the values of an image into RGB triples for display. The lookup table could have a shape (nlookup, 3). Indexing such an array with an image with shape (ny, nx) with dtype=np.uint8 (or any integer type so long as values are with the bounds of the lookup table) will result in an array of shape (ny, nx, 3) where a triple of RGB values is associated with each pixel location. In general, the shape of the resulant array will be the concatenation of the shape of the index array (or the shape that all the index arrays were broadcast to) with the shape of any unused dimensions (those not indexed) in the array being indexed. Boolean or "mask" index arrays ============================== Boolean arrays used as indices are treated in a different manner entirely than index arrays. Boolean arrays must be of the same shape as the initial dimensions of the array being indexed. In the most straightforward case, the boolean array has the same shape: :: >>> b = y>20 >>> y[b] array([21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34]) Unlike in the case of integer index arrays, in the boolean case, the result is a 1-D array containing all the elements in the indexed array corresponding to all the true elements in the boolean array. The elements in the indexed array are always iterated and returned in :term:`row-major` (C-style) order. The result is also identical to ``y[np.nonzero(b)]``. As with index arrays, what is returned is a copy of the data, not a view as one gets with slices. The result will be multidimensional if y has more dimensions than b. For example: :: >>> b[:,5] # use a 1-D boolean whose first dim agrees with the first dim of y array([False, False, False, True, True], dtype=bool) >>> y[b[:,5]] array([[21, 22, 23, 24, 25, 26, 27], [28, 29, 30, 31, 32, 33, 34]]) Here the 4th and 5th rows are selected from the indexed array and combined to make a 2-D array. In general, when the boolean array has fewer dimensions than the array being indexed, this is equivalent to y[b, ...], which means y is indexed by b followed by as many : as are needed to fill out the rank of y. Thus the shape of the result is one dimension containing the number of True elements of the boolean array, followed by the remaining dimensions of the array being indexed. For example, using a 2-D boolean array of shape (2,3) with four True elements to select rows from a 3-D array of shape (2,3,5) results in a 2-D result of shape (4,5): :: >>> x = np.arange(30).reshape(2,3,5) >>> x array([[[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]], [[15, 16, 17, 18, 19], [20, 21, 22, 23, 24], [25, 26, 27, 28, 29]]]) >>> b = np.array([[True, True, False], [False, True, True]]) >>> x[b] array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [20, 21, 22, 23, 24], [25, 26, 27, 28, 29]]) For further details, consult the numpy reference documentation on array indexing. Combining index arrays with slices ================================== Index arrays may be combined with slices. For example: :: >>> y[np.array([0,2,4]),1:3] array([[ 1, 2], [15, 16], [29, 30]]) In effect, the slice is converted to an index array np.array([[1,2]]) (shape (1,2)) that is broadcast with the index array to produce a resultant array of shape (3,2). Likewise, slicing can be combined with broadcasted boolean indices: :: >>> y[b[:,5],1:3] array([[22, 23], [29, 30]]) Structural indexing tools ========================= To facilitate easy matching of array shapes with expressions and in assignments, the np.newaxis object can be used within array indices to add new dimensions with a size of 1. For example: :: >>> y.shape (5, 7) >>> y[:,np.newaxis,:].shape (5, 1, 7) Note that there are no new elements in the array, just that the dimensionality is increased. This can be handy to combine two arrays in a way that otherwise would require explicitly reshaping operations. For example: :: >>> x = np.arange(5) >>> x[:,np.newaxis] + x[np.newaxis,:] array([[0, 1, 2, 3, 4], [1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7], [4, 5, 6, 7, 8]]) The ellipsis syntax maybe used to indicate selecting in full any remaining unspecified dimensions. For example: :: >>> z = np.arange(81).reshape(3,3,3,3) >>> z[1,...,2] array([[29, 32, 35], [38, 41, 44], [47, 50, 53]]) This is equivalent to: :: >>> z[1,:,:,2] array([[29, 32, 35], [38, 41, 44], [47, 50, 53]]) Assigning values to indexed arrays ================================== As mentioned, one can select a subset of an array to assign to using a single index, slices, and index and mask arrays. The value being assigned to the indexed array must be shape consistent (the same shape or broadcastable to the shape the index produces). For example, it is permitted to assign a constant to a slice: :: >>> x = np.arange(10) >>> x[2:7] = 1 or an array of the right size: :: >>> x[2:7] = np.arange(5) Note that assignments may result in changes if assigning higher types to lower types (like floats to ints) or even exceptions (assigning complex to floats or ints): :: >>> x[1] = 1.2 >>> x[1] 1 >>> x[1] = 1.2j <type 'exceptions.TypeError'>: can't convert complex to long; use long(abs(z)) Unlike some of the references (such as array and mask indices) assignments are always made to the original data in the array (indeed, nothing else would make sense!). Note though, that some actions may not work as one may naively expect. This particular example is often surprising to people: :: >>> x = np.arange(0, 50, 10) >>> x array([ 0, 10, 20, 30, 40]) >>> x[np.array([1, 1, 3, 1])] += 1 >>> x array([ 0, 11, 20, 31, 40]) Where people expect that the 1st location will be incremented by 3. In fact, it will only be incremented by 1. The reason is because a new array is extracted from the original (as a temporary) containing the values at 1, 1, 3, 1, then the value 1 is added to the temporary, and then the temporary is assigned back to the original array. Thus the value of the array at x[1]+1 is assigned to x[1] three times, rather than being incremented 3 times. Dealing with variable numbers of indices within programs ======================================================== The index syntax is very powerful but limiting when dealing with a variable number of indices. For example, if you want to write a function that can handle arguments with various numbers of dimensions without having to write special case code for each number of possible dimensions, how can that be done? If one supplies to the index a tuple, the tuple will be interpreted as a list of indices. For example (using the previous definition for the array z): :: >>> indices = (1,1,1,1) >>> z[indices] 40 So one can use code to construct tuples of any number of indices and then use these within an index. Slices can be specified within programs by using the slice() function in Python. For example: :: >>> indices = (1,1,1,slice(0,2)) # same as [1,1,1,0:2] >>> z[indices] array([39, 40]) Likewise, ellipsis can be specified by code by using the Ellipsis object: :: >>> indices = (1, Ellipsis, 1) # same as [1,...,1] >>> z[indices] array([[28, 31, 34], [37, 40, 43], [46, 49, 52]]) For this reason it is possible to use the output from the np.where() function directly as an index since it always returns a tuple of index arrays. Because the special treatment of tuples, they are not automatically converted to an array as a list would be. As an example: :: >>> z[[1,1,1,1]] # produces a large array array([[[[27, 28, 29], [30, 31, 32], ... >>> z[(1,1,1,1)] # returns a single value 40 """ from __future__ import division, absolute_import, print_function

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Python

include/tclap-1.4.0-rc1/tests/test91.py

SpaceKatt/cpp-cli-poc

02ffefea2fc6e999fa2b27d08a8b3be6830b1b97

[ "BSL-1.0" ]

62

2021-09-21T18:58:02.000Z

2022-03-07T02:17:43.000Z

third_party/tclap-1.4.0-rc1/tests/test91.py

Vertexwahn/FlatlandRT

37d09fde38b25eff5f802200b43628efbd1e3198

[ "Apache-2.0" ]

null

third_party/tclap-1.4.0-rc1/tests/test91.py

Vertexwahn/FlatlandRT

37d09fde38b25eff5f802200b43628efbd1e3198

[ "Apache-2.0" ]

null

#!/usr/bin/python import simple_test simple_test.test("test29", ["-a", "5", ])

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py

Python

src/constellix/domains/record/type/__init__.py

aperim/python-constellix

11cb19fce5cc00aefd14f8ac6bf63dc2f98731ae

[ "CC0-1.0" ]

null

src/constellix/domains/record/type/__init__.py

aperim/python-constellix

11cb19fce5cc00aefd14f8ac6bf63dc2f98731ae

[ "CC0-1.0" ]

null

src/constellix/domains/record/type/__init__.py

aperim/python-constellix

11cb19fce5cc00aefd14f8ac6bf63dc2f98731ae

[ "CC0-1.0" ]

null

"""Domain Record Data""" from .main import DomainRecord from .a import A from .aaaa import AAAA

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py

Python

isopy/toolbox/__init__.py

mattias-ek/isopy

96d5530034655c7f9559568ab9b0879b978ef566

[ "MIT" ]

null

isopy/toolbox/__init__.py

mattias-ek/isopy

96d5530034655c7f9559568ab9b0879b978ef566

[ "MIT" ]

1

2021-08-23T08:48:04.000Z

isopy/toolbox/__init__.py

mattias-ek/isopy

96d5530034655c7f9559568ab9b0879b978ef566

[ "MIT" ]

null

from . import doublespike from . import isotope from . import plotting from . import regress

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py

Python

anonymization/__init__.py

Andhs/anonymization

bb8c3e699ea150d8a294771a793c37ee47d3f885

[ "MIT" ]

15

2020-06-18T12:29:55.000Z

2021-12-14T16:31:26.000Z

anonymization/__init__.py

Andhs/anonymization

bb8c3e699ea150d8a294771a793c37ee47d3f885

[ "MIT" ]

3

2021-03-20T17:47:03.000Z

2021-09-01T14:36:03.000Z

anonymization/__init__.py

Andhs/anonymization

bb8c3e699ea150d8a294771a793c37ee47d3f885

[ "MIT" ]

4

2019-05-22T15:50:42.000Z

2021-02-06T23:44:49.000Z

from .anonymizers import * from .Anonymization import *

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py

Python

ciphey/basemods/__init__.py

emadfuel/Ciphey

323fe3aa5eac07a8c25354bdb19fe8a860c11958

[ "MIT" ]

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2020-06-06T01:06:50.000Z

2022-03-31T21:22:57.000Z

ciphey/basemods/__init__.py

emadfuel/Ciphey

323fe3aa5eac07a8c25354bdb19fe8a860c11958

[ "MIT" ]

423

2020-05-30T11:44:37.000Z

2022-03-18T03:15:30.000Z

ciphey/basemods/__init__.py

emadfuel/Ciphey

323fe3aa5eac07a8c25354bdb19fe8a860c11958

[ "MIT" ]

714

2020-06-09T20:24:41.000Z

2022-03-29T15:28:53.000Z

from . import Checkers, Crackers, Decoders, Resources, Searchers

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py

Python

src/database/__init__.py

0417taehyun/studeep-backend

5a13fd6b20b8fda8adceb7c82e44efe87b644da0

[ "Apache-2.0" ]

1

2021-07-05T06:25:43.000Z

src/database/__init__.py

0417taehyun/studeep-backend

5a13fd6b20b8fda8adceb7c82e44efe87b644da0

[ "Apache-2.0" ]

26

2021-05-01T05:56:34.000Z

2021-05-21T10:07:32.000Z

app/database/__init__.py

YAPP-18th/ML-Team-Backend

7da5430ab07e180d88ca62d005d760c729f1de9c

[ "Apache-2.0" ]

null

from app.database.base_class import Base from app.database.session import SessionLocal

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9cef99bf03d636cbf8da10189695ace642d92cb8

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py

Python

py_wake/utils/ieawind37_utils.py

aemoser/PyWake

889a2c10882195af21339e9bcf2ede0db9b58319

[ "MIT" ]

30

2019-03-18T14:10:27.000Z

2022-03-13T17:39:04.000Z

py_wake/utils/ieawind37_utils.py

aemoser/PyWake

889a2c10882195af21339e9bcf2ede0db9b58319

[ "MIT" ]

1

2020-11-12T06:13:00.000Z

2020-11-12T06:43:26.000Z

py_wake/utils/ieawind37_utils.py

aemoser/PyWake

889a2c10882195af21339e9bcf2ede0db9b58319

[ "MIT" ]

20

2019-01-11T14:45:13.000Z

2021-12-13T19:55:29.000Z

def iea37_names(): return [('P', 'probability'), ('TI', 'turbulence_intensity'), ('wd', 'wind_direction'), ('ws', 'wind_speed'), ('Sector_frequency', 'sector_probability'), ('Weibull_A', 'weibull_a'), ('Weibull_k', 'weibull_k')]

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py

Python

tests/__init__.py

apiology/op_env

84269db5ca58801bff3ea4f0ab56b6ac74132806

[ "MIT" ]

11

2021-02-03T14:50:47.000Z

2021-11-09T15:23:40.000Z

tests/__init__.py

apiology/op_env

84269db5ca58801bff3ea4f0ab56b6ac74132806

[ "MIT" ]

2

2021-02-06T02:28:58.000Z

2021-03-25T22:13:08.000Z

tests/__init__.py

apiology/op_env

84269db5ca58801bff3ea4f0ab56b6ac74132806

[ "MIT" ]

null

"""Unit test package for op_env."""

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py

Python

__init__.py

mperov/generatorConstructionsC

0ff4b1d72ddc021d7fd3b3e46b917ec71a0807dd

[ "MIT" ]

4

2017-03-27T10:46:29.000Z

2020-12-22T14:16:34.000Z

__init__.py

mperov/generatorConstructionsC

0ff4b1d72ddc021d7fd3b3e46b917ec71a0807dd

[ "MIT" ]

null

__init__.py

mperov/generatorConstructionsC

0ff4b1d72ddc021d7fd3b3e46b917ec71a0807dd

[ "MIT" ]

null

# File for: import codegen.codegen

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py

Python

conditioner/conditions/__init__.py

omni-digital/django-conditioner

d5d2ad1f016bc3e6b34c74ff68cd024e8fad5125

[ "MIT" ]

3

2019-03-02T21:59:50.000Z

2020-02-19T04:38:13.000Z

conditioner/conditions/__init__.py

omni-digital/django-conditioner

d5d2ad1f016bc3e6b34c74ff68cd024e8fad5125

[ "MIT" ]

1

2018-08-30T02:52:24.000Z

2018-09-03T02:30:47.000Z

conditioner/conditions/__init__.py

omni-digital/django-conditioner

d5d2ad1f016bc3e6b34c74ff68cd024e8fad5125

[ "MIT" ]

2

2017-03-17T11:22:48.000Z

2019-12-05T11:48:53.000Z

""" Conditioner module conditions All available conditions should be imported here for ease of use. """ from conditioner.conditions.dates import DayOfMonthCondition, DayOfWeekCondition # noqa from conditioner.conditions.signals import ModelSignalCondition # noqa

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Python

appengine/monorail/api/api_proto/features_prpc_pb2.py

asdfghjjklllllaaa/infra

8f63af54e46194cd29291813f2790ff6e986804d

[ "BSD-3-Clause" ]

1

2020-11-11T06:25:13.000Z

appengine/monorail/api/api_proto/features_prpc_pb2.py

asdfghjjklllllaaa/infra

8f63af54e46194cd29291813f2790ff6e986804d

[ "BSD-3-Clause" ]

21

2020-09-06T02:41:05.000Z

2022-03-02T04:40:01.000Z

appengine/monorail/api/api_proto/features_prpc_pb2.py

asdfghjjklllllaaa/infra

8f63af54e46194cd29291813f2790ff6e986804d

[ "BSD-3-Clause" ]

null

# Generated by the pRPC protocol buffer compiler plugin. DO NOT EDIT! # source: api/api_proto/features.proto import base64 import zlib from google.protobuf import descriptor_pb2 # Includes description of the api/api_proto/features.proto and all of its transitive # dependencies. Includes source code info. FILE_DESCRIPTOR_SET = descriptor_pb2.FileDescriptorSet() FILE_DESCRIPTOR_SET.ParseFromString(zlib.decompress(base64.b64decode( 'eJztfW1wW9l1GN57wANwwQ/wAQRBkJKeIGklUdQXJdm7+thdfkmEliK5AClZ692lQOKRhBYEuH' 'iAtFp/JXZS107s2o6ddpJNvN7MZNf54SRNvGnHTRvHbZy0v9rfnenPzHQ6/dXpdDrTTs8599z3' 'HiiCVL3tdCbTndkRzrv3nq977rkf59xL8e+mxWh5p3oe/l/daTZajfMbTrnVbjruOQKt2Haj3m' 'iWq7VcrrPeemMbimSt3PG9caw21h466y3GlZ8Sw/NVtzXXaNXgH3fqyYrrNIvO223HbVknRLgN' 'YFa3tVOJiYFziu45WWmjSMX5V0RuLxzuTqPuOtZZEdvikqxmG52IuE3Rq5K/uRtZwXXbjuLolI' 'hUEWaWLB8TV9soygr5eTGyJ55fjKvjIo/Yis66U2/VntytutWWU1HImbv8sji2b61fjPao1Eip' 'VW42n6bJcj5V+ovRKoncLUcpDXFON9r1ltL+FZHgmqtNZ4P7IP00PugFseX9zl8XI3siZRYPCe' 'HCx9V1/ApMaqd6i3FXVcs7wsI2Hu5PwoqVFVFXaiprQJNYUYH5yyLVQebZmPuqJrIBMyMbcz8h' 'j5eF2ClvVuvlVrVRJzY7Wi15ZcVAvfxcx0BWjLAUZ2DYtJxtZQKDT/FQgNKirJMfEwMzVXcbht' 'G0P/AGhbnedlarFRIjXowAVKjk08IK1pXk8v9JE+npJjgcZ1evWSJcL287jIN+U5e0t7fLzSck' 'a7yoQMsWiYrjrjerO6SJMJUGP1kTIuFUqq1GExXsZiO7DVz5KSFrwU/XuigEeQjZxKQme/mReJ' 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_INDEX[u'api/api_proto/features.proto']['descriptor'], 'service_descriptor': _INDEX[u'api/api_proto/features.proto']['services'][u'Features'], }

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Python

mayan/apps/user_management/models.py

mbehrle/mayan-edms

9ebf27d2ea1666eaa36ad6ddc0fb9c6accf5cced

[ "Apache-2.0" ]

null

mayan/apps/user_management/models.py

mbehrle/mayan-edms

9ebf27d2ea1666eaa36ad6ddc0fb9c6accf5cced

[ "Apache-2.0" ]

1

2022-03-12T01:03:39.000Z

mayan/apps/user_management/models.py

mbehrle/mayan-edms

9ebf27d2ea1666eaa36ad6ddc0fb9c6accf5cced

[ "Apache-2.0" ]

null

from django.db import models from django.contrib.auth.models import User, Group from actstream import registry registry.register(User) registry.register(Group)

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1496fd962bd8e83ff252382f6d8dfde39c0c9732

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py

Python

backend/visualset/__init__.py

steinitzu/visualset

379f00759151dba8f80d13f396f05a764aaaa3c8

[ "MIT" ]

null

backend/visualset/__init__.py

steinitzu/visualset

379f00759151dba8f80d13f396f05a764aaaa3c8

[ "MIT" ]

null

backend/visualset/__init__.py

steinitzu/visualset

379f00759151dba8f80d13f396f05a764aaaa3c8

[ "MIT" ]

null

from gevent import monkey monkey.patch_all(thread=False) from . import dependencies

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