xszheng2020/the_stack_dedup_python_hits_4 · Datasets at Hugging Face

6a6a1fa4453f1d738a6461880886fc7f27e2195d

219

py

Python

local_utils/segcomp_dataset_utils/__init__.py

Dudestin/bisenetv2-tensorflow

0c9761a7d0fd6ac4bdb3abb7195d01caf22d4716

[ "MIT" ]

194

2020-05-11T06:55:10.000Z

2022-03-31T12:39:41.000Z

local_utils/segcomp_dataset_utils/__init__.py

Dudestin/bisenetv2-tensorflow

0c9761a7d0fd6ac4bdb3abb7195d01caf22d4716

[ "MIT" ]

48

2020-05-11T06:37:28.000Z

2021-11-04T09:23:55.000Z

local_utils/segcomp_dataset_utils/__init__.py

Dudestin/bisenetv2-tensorflow

0c9761a7d0fd6ac4bdb3abb7195d01caf22d4716

[ "MIT" ]

60

2020-05-11T08:30:59.000Z

2022-02-28T06:59:27.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # @Time : 2019/12/17 下午5:46 # @Author : MaybeShewill-CV # @Site : https://github.com/MaybeShewill-CV/bisenetv2-tensorflow # @File : __init__.py.py # @IDE: PyCharm

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null

0

1

0

4

6a8b0b440835ba59efc65f89312204d6f98f2f3d

141

py

Python

test/regression/features/lists/list_setitem.py

ppelleti/berp

30925288376a6464695341445688be64ac6b2600

[ "BSD-3-Clause" ]

137

2015-02-13T21:03:23.000Z

2021-11-24T03:53:55.000Z

test/regression/features/lists/list_setitem.py

ppelleti/berp

30925288376a6464695341445688be64ac6b2600

[ "BSD-3-Clause" ]

4

2015-04-01T13:49:13.000Z

2019-07-09T19:28:56.000Z

test/regression/features/lists/list_setitem.py

bjpop/berp

30925288376a6464695341445688be64ac6b2600

[ "BSD-3-Clause" ]

8

2015-04-25T03:47:52.000Z

2019-07-27T06:33:56.000Z

x = [0] print(x) x.__setitem__(0,1) print(x) x.__setitem__(0,2) print(x) x = [4,5,6] x.__setitem__(1,7) print(x) x.__setitem__(2,8) print(x)

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null

1

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null

0

1

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4

6a92da4e6f44c4b525f7897cf4404159126ad0ec

122

py

Python

mod1.py

chidanandpujar/Python_scripts

0ee70e07ef4ab4d8c04955466ea9b305bdac0a53

[ "Unlicense" ]

null

mod1.py

chidanandpujar/Python_scripts

0ee70e07ef4ab4d8c04955466ea9b305bdac0a53

[ "Unlicense" ]

null

mod1.py

chidanandpujar/Python_scripts

0ee70e07ef4ab4d8c04955466ea9b305bdac0a53

[ "Unlicense" ]

null

a=100 class A: b=200 def fn(self,a=1): print("fn in A") def mfn(): print("Inside mfn") print("Mod1")

12.2

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122

2.909091

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0

null

0

1

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4

6ae61d4db62c9cf67dcfaf1daf5c9549356d157d

294

py

Python

denorm/__init__.py

JaGallup/is-denorm

33b3437d0d6deb09885b14733c005b3eacd9e8da

[ "Apache-2.0" ]

null

denorm/__init__.py

JaGallup/is-denorm

33b3437d0d6deb09885b14733c005b3eacd9e8da

[ "Apache-2.0" ]

null

denorm/__init__.py

JaGallup/is-denorm

33b3437d0d6deb09885b14733c005b3eacd9e8da

[ "Apache-2.0" ]

null

""" Icelandic de-normalizer Library ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ is-denorm is a denormalizer for Icelandic. Basic usage: >>> from denorm import denormalize >>> denormalize("hvað er sjö hundruð og tuttugu deilt með fjórum") "hvað er 720 / 4" """ from .denormalizer import denormalize

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4

6ae8592a9f211b4e5a748de8825fb94fad111acd

2,386

py

Python

CPAC-CNN-GPU/cnn_torch.py

wyn430/CPAC-CNN

e60901129e7784a0aef29c8b89316b7da3e54609

[ "MIT" ]

2

2021-08-29T16:45:23.000Z

2021-12-12T22:06:16.000Z

CPAC-CNN-GPU/cnn_torch.py

wyn430/CPAC-CNN

e60901129e7784a0aef29c8b89316b7da3e54609

[ "MIT" ]

null

CPAC-CNN-GPU/cnn_torch.py

wyn430/CPAC-CNN

e60901129e7784a0aef29c8b89316b7da3e54609

[ "MIT" ]

2

2020-06-24T07:52:21.000Z

2020-12-19T18:34:03.000Z

""" There are two models in this file, CPAC-CNN and CNN. The detailed model structure can be modified in this script. """ import torch from torch import nn import torch.nn.functional as F import torch.optim as optim from torch.autograd import Variable as V import numpy as np np.random.seed(48) torch.manual_seed(48) from conv_decomp_torch import Conv_Decomp class CNN_Decomp(nn.Module): def __init__(self, num_filters, filter_h, filter_w, image_channels, rank, devices, num_class, input_shape): super(CNN_Decomp, self).__init__() self.conv1 = Conv_Decomp(num_filters, filter_h, filter_w, image_channels, rank, devices) self.conv2 = Conv_Decomp(num_filters, filter_h, filter_w, num_filters, rank, devices) #self.flatten_shape = int((input_shape[-2]-2)/2) * int((input_shape[-1]-2)/2) * num_filters self.flatten_shape = int(((input_shape[-2]-2)/2-2)/2) * int(((input_shape[-1]-2)/2-2)/2) * num_filters self.fc1 = nn.Linear(self.flatten_shape, 50) self.fc2 = nn.Linear(50, num_class) self.num_class = num_class def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x),2)) x = F.relu(F.max_pool2d(self.conv2(x),2)) x = x.view(-1, self.flatten_shape) x = self.fc1(x) x = self.fc2(x) return F.log_softmax(x) class CNN(nn.Module): def __init__(self, num_filters, filter_h, filter_w, image_channels, rank, devices, num_class, input_shape): super(CNN, self).__init__() self.conv1 = nn.Conv2d(image_channels, num_filters, kernel_size=filter_h, bias=False) self.conv2 = nn.Conv2d(num_filters, num_filters, kernel_size=filter_h, bias=False) #self.flatten_shape = int((input_shape[-2]-2)/2) * int((input_shape[-1]-2)/2) * num_filters self.flatten_shape = int(((input_shape[-2]-2)/2-2)/2) * int(((input_shape[-1]-2)/2-2)/2) * num_filters #self.fc1 = nn.Linear(self.flatten_shape, num_class) self.fc1 = nn.Linear(self.flatten_shape, 50) self.fc2 = nn.Linear(50, num_class) self.num_class = num_class def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x),2)) x = F.relu(F.max_pool2d(self.conv2(x),2)) x = x.view(-1, self.flatten_shape) x = self.fc1(x) x = self.fc2(x) return F.log_softmax(x)

40.440678

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0.649204

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2,386

3.8

0.202597

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0.024607

0.016405

0.708818

0.678742

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0

0.038789

0.211232

2,386

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40.440678

0.738576

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0

null

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1

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null

0

4

0a7d7f9d45da47b459cb56f33f39fb01af030693

163

py

Python

cyber_sdk/__init__.py

SaveTheAles/cyber.py

69211d4f9e861e3c64990725a4a483d2cbee0be1

[ "MIT" ]

null

cyber_sdk/__init__.py

SaveTheAles/cyber.py

69211d4f9e861e3c64990725a4a483d2cbee0be1

[ "MIT" ]

null

cyber_sdk/__init__.py

SaveTheAles/cyber.py

69211d4f9e861e3c64990725a4a483d2cbee0be1

[ "MIT" ]

null

"""The Python SDK for Bostrom.""" # Set default logging to avoid NoHandler warnings import logging logging.getLogger(__name__).addHandler(logging.NullHandler())

23.285714

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20

163

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163

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null

0

1

0

1

0

4

0a87bb5dd451d56952614c84b8e5c1a3a8007f07

78

py

Python

scrawler.py

Samet-Aslan/shpockcrawler

9cf67fce34b07b9d5b6e378550940db9d5b8fc92

[ "MIT" ]

null

scrawler.py

Samet-Aslan/shpockcrawler

9cf67fce34b07b9d5b6e378550940db9d5b8fc92

[ "MIT" ]

null

scrawler.py

Samet-Aslan/shpockcrawler

9cf67fce34b07b9d5b6e378550940db9d5b8fc92

[ "MIT" ]

null

# Shpock Crawler # Samet Aslan 2020 import functions functions.startSearch()

13

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0.794872

9

78

6.888889

0.888889

0

0.059701

0.141026

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5

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null

0

1

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1

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4

0ab84bbc7db632acc3892ac51007052257c6a23f

2,541

py

Python

test/test_iindex.py

afourney/pyra

245f9d4ce5db8810f4a2456afc64e7ba208484a1

[ "BSD-2-Clause" ]

null

test/test_iindex.py

afourney/pyra

245f9d4ce5db8810f4a2456afc64e7ba208484a1

[ "BSD-2-Clause" ]

null

test/test_iindex.py

afourney/pyra

245f9d4ce5db8810f4a2456afc64e7ba208484a1

[ "BSD-2-Clause" ]

1

2020-01-02T19:06:20.000Z

# Load what we actually need to run the tests import unittest from pyra.iindex import InvertedIndex, INF class TestInvertedIndex(unittest.TestCase): def setUp(self): pass def test_trivial_corpus(self): corpus = "the quick brown fox jumps over the lazy dog and the brown dog runs away" tokens = corpus.split() iidx = InvertedIndex(tokens) self.assertEqual(iidx.first('dog'), 8) self.assertEqual(iidx.last('dog'), 12) self.assertEqual(iidx.next('dog', 8), 12) self.assertEqual(iidx.prev('dog', 12), 8) self.assertEqual(iidx.first('cat'), INF) self.assertEqual(iidx.last('cat'), -INF) self.assertEqual(iidx.next('cat', 8), INF) self.assertEqual(iidx.prev('cat', 12), -INF) self.assertEqual(iidx.first('fox'), 3) self.assertEqual(iidx.last('fox'), 3) self.assertEqual(iidx.frequency('dog', -INF, INF), 2) self.assertEqual(iidx.frequency('dog', -INF, 9), 1) self.assertEqual(iidx.frequency('dog', -INF, 8), 1) self.assertEqual(iidx.frequency('dog', -INF, 7), 0) self.assertEqual(iidx.frequency('dog', 7, 13), 2) self.assertEqual(iidx.frequency('dog', 8, 12), 2) self.assertEqual(iidx.frequency('dog', 12, INF), 1) self.assertEqual(iidx.frequency('dog', 13, 14), 0) self.assertEqual(iidx.frequency('cat', -INF, INF), 0) self.assertEqual(iidx.frequency('cat', 2, INF), 0) self.assertEqual(iidx.frequency('cat', -INF, 3), 0) self.assertEqual(iidx.frequency('cat', 2, 4), 0) self.assertEqual(list(iidx.postings('dog')), [8, 12]) self.assertEqual(list(iidx.postings('dog', reverse=True)), [12,8]) self.assertEqual(list(iidx.postings('dog', 12)), [12]) self.assertEqual(list(iidx.postings('cat')), []) self.assertEqual(list(iidx.postings('cat', reverse=True)), []) self.assertEqual(iidx.dictionary() ^ set(tokens), set())

59.093023

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false

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null

1

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null

0

1

0

4

0ae396133813ac7d4e192e3b35a964bd9237c777

162

py

Python

tests/test_webapps/filestotest/helpers_sample.py

KinSai1975/Menira.py

ca275ce244ee4804444e1827ba60010a55acc07c

[ "BSD-3-Clause" ]

118

2015-01-04T06:55:14.000Z

2022-01-14T08:32:41.000Z

tests/test_webapps/filestotest/helpers_sample.py

KinSai1975/Menira.py

ca275ce244ee4804444e1827ba60010a55acc07c

[ "BSD-3-Clause" ]

21

2015-01-03T02:16:28.000Z

2021-03-24T06:10:57.000Z

tests/test_webapps/filestotest/helpers_sample.py

KinSai1975/Menira.py

ca275ce244ee4804444e1827ba60010a55acc07c

[ "BSD-3-Clause" ]

53

2015-01-04T03:21:08.000Z

2021-08-04T20:52:01.000Z

"""Helper functions Consists of functions to typically be used within templates, but also available to Controllers. This module is available to both as 'h'. """

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5.208333

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162

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4

0ae7458cf85b0593c5ef45b68df926d444a33d0f

1,250

py

Python

strongr/schedulerdomain/handler/__init__.py

bigr-erasmusmc/StrongR

48573e170771a251f629f2d13dba7173f010a38c

[ "Apache-2.0" ]

null

strongr/schedulerdomain/handler/__init__.py

bigr-erasmusmc/StrongR

48573e170771a251f629f2d13dba7173f010a38c

[ "Apache-2.0" ]

null

strongr/schedulerdomain/handler/__init__.py

bigr-erasmusmc/StrongR

48573e170771a251f629f2d13dba7173f010a38c

[ "Apache-2.0" ]

null

from .schedulejobhandler import ScheduleJobHandler from .runenqueuedjobshandler import RunEnqueuedJobsHandler from .requestscheduledtaskshandler import RequestScheduledTasksHandler from .requesttaskinfohandler import RequestTaskInfoHandler from .findnodewithavailableresourceshandler import FindNodeWithAvailableResourcesHandler from .startjobonvmhandler import StartJobOnVmHandler from .checkjobsrunninghandler import CheckJobsRunningHandler from .ensureminamountofnodeshandler import EnsureMinAmountOfNodesHandler from .scaleouthandler import ScaleOutHandler from .requestfinishedjobshandler import RequestFinishedJobsHandler from .jobfinishedhandler import JobFinishedHandler from .vmcreatedhandler import VmCreatedHandler from .vmdestroyedhandler import VmDestroyedHandler from .vmreadyhandler import VmReadyHandler from .vmnewhandler import VmNewHandler from .checkscalinghandler import CheckScalingHandler from .requestresourcesrequiredhandler import RequestResourcesRequiredHandler from .cleanupnodeshandler import CleanupNodesHandler from .requestvmsbystatehandler import RequestVmsByStateHandler from .scaleinhandler import ScaleInHandler from .logstatshandler import LogStatsHandler from .cleanupoldjobshandler import CleanupOldJobsHandler

54.347826

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12.954545

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4

e40aaaa28810e84ad7bdf0c660f5588fb9e04510

346

py

Python

dirtree/models.py

LOKESWARAN-ARULJOTHI/Git-tree-app

4b240ff1471fa32eb4389a0d986bdfe1de3ff545

[ "CC-BY-2.0" ]

null

dirtree/models.py

LOKESWARAN-ARULJOTHI/Git-tree-app

4b240ff1471fa32eb4389a0d986bdfe1de3ff545

[ "CC-BY-2.0" ]

null

dirtree/models.py

LOKESWARAN-ARULJOTHI/Git-tree-app

4b240ff1471fa32eb4389a0d986bdfe1de3ff545

[ "CC-BY-2.0" ]

null

from django.db import models # Create your models here. class Number_of_trees_generated(models.Model): notg = models.IntegerField(default=0) def __str__(self): return f"{self.notg}" class User_email(models.Model): email = models.EmailField(blank=True,unique=True) def __str__(self): return self.email

21.625

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0.630435

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16

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null

0

1

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1

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4

7c12e58dd9c5fe7a6146434c27cba454b9227b92

140

py

Python

sumo_simulation/city_simulation/city_simulation/__init__.py

a-regal/tesis_pregrado

501d3a137f305d53e8b4eaec7c4ba6f18d7b7706

[ "MIT" ]

1

2019-11-16T02:32:48.000Z

sumo_simulation/city_simulation/city_simulation/__init__.py

a-regal/tesis_pregrado

501d3a137f305d53e8b4eaec7c4ba6f18d7b7706

[ "MIT" ]

null

sumo_simulation/city_simulation/city_simulation/__init__.py

a-regal/tesis_pregrado

501d3a137f305d53e8b4eaec7c4ba6f18d7b7706

[ "MIT" ]

1

2020-09-13T16:17:18.000Z

from gym.envs.registration import register register( id='city_simulation-v0', entry_point='city_simulation.envs:CitySimulation', )

20

54

0.771429

17

140

6.176471

0.764706

0.266667

0

0.00813

0.121429

140

6

55

23.333333

0.845528

0

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true

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1

0

4

7c2aee3ee01d3e1e4185da7d3ae900b967f983f5

82

py

Python

code/answer_2-1-42.py

KoyanagiHitoshi/AtCoder-Python-Introduction

6d014e333a873f545b4d32d438e57cf428b10b96

[ "MIT" ]

1

2022-03-29T13:50:12.000Z

code/answer_2-1-42.py

KoyanagiHitoshi/AtCoder-Python-Introduction

6d014e333a873f545b4d32d438e57cf428b10b96

[ "MIT" ]

null

code/answer_2-1-42.py

KoyanagiHitoshi/AtCoder-Python-Introduction

6d014e333a873f545b4d32d438e57cf428b10b96

[ "MIT" ]

null

N, X, T = map(int, input().split()) print(T*(N//X) if N % X == 0 else T*(N//X+1))

27.333333

45

0.487805

20

82

2

0.6

0.2

0.15

0

0.029851

0.182927

82

2

46

41

0.567164

0

1

0

true

0

0.5

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null

0

1

0

null

0

1

0

1

0

4

7c5f29798e914113590ce1865c50587f1da29562

151

py

Python

tests/endtoend/blob_functions/get_blob_as_bytes/main.py

yojagad/azure-functions-python-worker

d5a1587a4ccf56af64f211a64f0b7a3d6cf976c9

[ "MIT" ]

null

tests/endtoend/blob_functions/get_blob_as_bytes/main.py

yojagad/azure-functions-python-worker

d5a1587a4ccf56af64f211a64f0b7a3d6cf976c9

[ "MIT" ]

null

tests/endtoend/blob_functions/get_blob_as_bytes/main.py

yojagad/azure-functions-python-worker

d5a1587a4ccf56af64f211a64f0b7a3d6cf976c9

[ "MIT" ]

null

import azure.functions as azf def main(req: azf.HttpRequest, file: bytes) -> str: assert isinstance(file, bytes) return file.decode('utf-8')

21.571429

51

0.701987

22

151

4.818182

0.818182

0.169811

0

0.008

0.172185

151

6

52

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0

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false

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null

0

1

0

null

0

1

0

1

0

4

7c77ebe4cb27a5103ffc93d8845a02918eb95ac0

155

py

Python

example/makepython/cpto.py

jeppeter/insertcode

4dd8723b93463a39d1ddb32887529945122a5093

[ "MIT" ]

null

example/makepython/cpto.py

jeppeter/insertcode

4dd8723b93463a39d1ddb32887529945122a5093

[ "MIT" ]

null

example/makepython/cpto.py

jeppeter/insertcode

4dd8723b93463a39d1ddb32887529945122a5093

[ "MIT" ]

null

#! /usr/bin/env python import sys import shutil def main(): if len(sys.argv[1:]) >= 2: shutil.copy2(sys.argv[1],sys.argv[2]) return main()

14.090909

40

0.606452

26

155

3.615385

0.615385

0.223404

0.170213

0

0.04065

0.206452

155

11

41

14.090909

0.723577

0.135484

0

1

0.142857

true

0

0.285714

0

0.571429

0

1

0

null

1

0

null

0

1

0

1

0

4

7c8cc4ff9999f945a94bada743fc0a2cc8e422e1

216

py

Python

pybamm/models/full_battery_models/lead_acid/__init__.py

jedgedrudd/PyBaMM

79c9d34978382d50e09adaf8bf74c8fa4723f759

[ "BSD-3-Clause" ]

1

2019-10-29T19:06:04.000Z

pybamm/models/full_battery_models/lead_acid/__init__.py

jedgedrudd/PyBaMM

79c9d34978382d50e09adaf8bf74c8fa4723f759

[ "BSD-3-Clause" ]

null

pybamm/models/full_battery_models/lead_acid/__init__.py

jedgedrudd/PyBaMM

79c9d34978382d50e09adaf8bf74c8fa4723f759

[ "BSD-3-Clause" ]

null

# # Root of the lead-acid models module. # from .base_lead_acid_model import BaseModel from .loqs import LOQS from .higher_order import BaseHigherOrderModel, FOQS, Composite, CompositeExtended from .full import Full

27

82

0.814815

30

216

5.733333

0.666667

0.093023

0

0.12963

216

7

83

30.857143

0.914894

0.166667

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

4

7ca4dd559a1f077b139b0d559d5b2ecae473d7cf

43

py

Python

fastscript/__init__.py

daviddemeij/fastscript

96125fdbca57cfbdc2acbe05853d8a0a67a5ba39

[ "Apache-2.0" ]

null

fastscript/__init__.py

daviddemeij/fastscript

96125fdbca57cfbdc2acbe05853d8a0a67a5ba39

[ "Apache-2.0" ]

null

fastscript/__init__.py

daviddemeij/fastscript

96125fdbca57cfbdc2acbe05853d8a0a67a5ba39

[ "Apache-2.0" ]

null

__version__ = "0.1.5" from .core import *

10.75

21

0.651163

7

43

3.428571

1

0

0.085714

0.186047

43

3

22

14.333333

0.6

0

0.119048

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

4

7ca6419ee1ef40ab5d7ec8e6e14dbcb3301299f6

192

py

Python

mouse-now.py

Guilehm/python

ce6f8b44623cc25e9b18b2dbf8e0528096f0de96

[ "MIT" ]

null

mouse-now.py

Guilehm/python

ce6f8b44623cc25e9b18b2dbf8e0528096f0de96

[ "MIT" ]

null

mouse-now.py

Guilehm/python

ce6f8b44623cc25e9b18b2dbf8e0528096f0de96

[ "MIT" ]

null

import pyautogui try: while True: x, y = pyautogui.position() position_str = "X:" + str(x).rjust(4) + " Y:" + str(y).rjust(4) print(position_str) except: pass

19.2

71

0.557292

26

192

4.038462

0.538462

0.209524

0

0.014599

0.286458

192

9

72

21.333333

0.751825

0

0.026042

0

1

0

false

0.125

0

0.125

1

0

null

1

0

1

0

null

0

1

0

4

7cb68a5ef33b883becbc095582fc0cb573cb5371

382

py

Python

polymorphism/from_previous_lecture.py

Minkov/python-oop

db9651eef374c0e74c32cb6f2bf07c734cc1d051

[ "MIT" ]

3

2021-11-16T04:52:53.000Z

2022-02-07T20:28:41.000Z

polymorphism/from_previous_lecture.py

Minkov/python-oop

db9651eef374c0e74c32cb6f2bf07c734cc1d051

[ "MIT" ]

null

polymorphism/from_previous_lecture.py

Minkov/python-oop

db9651eef374c0e74c32cb6f2bf07c734cc1d051

[ "MIT" ]

1

2021-12-07T07:04:38.000Z

# Variant 1 - Best class Parent: _possible_drinks = ['beer', 'wine'] class Child(Parent): _possible_drinks = ['beer', 'wine', 'vodka'] # Variant 2 - Ok class Child2(Parent): def __init__(self): self._possible_drinks = super()._possible_drinks + ['vodka'] # Variant 3 - Wrong class Child3(Parent): _possible_drinks = Parent._possible_drinks + ['vodka']

20.105263

68

0.662304

45

382

5.266667

0.466667

0.35443

0.337553

0.202532

0.236287

0

0.016234

0.193717

382

18

69

21.222222

0.753247

0.128272

0

0.094225

0

1

0.111111

false

0

0.888889

0

null

1

0

null

0

1

0

4

7cc906c006f9fd901f5d6b8bea76fefd71c91e9f

109

py

Python

swappayoutubeexamplesite/swappashowcaseapp/apps.py

DeviNoles/swappa

50d2ef2bccca3030e2f39cd991a163919d8a4e23

[ "MIT" ]

1

2020-03-24T06:36:51.000Z

swappayoutubeexamplesite/swappashowcaseapp/apps.py

DeviNoles/swappa

50d2ef2bccca3030e2f39cd991a163919d8a4e23

[ "MIT" ]

5

2021-04-08T19:52:25.000Z

2021-09-22T18:47:32.000Z

swappayoutubeexamplesite/swappashowcaseapp/apps.py

DeviNoles/swappa

50d2ef2bccca3030e2f39cd991a163919d8a4e23

[ "MIT" ]

1

2021-10-31T15:16:31.000Z

from django.apps import AppConfig class SwappashowcaseappConfig(AppConfig): name = 'swappashowcaseapp'

18.166667

41

0.798165

10

109

8.7

0.9

0

0.137615

109

5

42

21.8

0.925532

0

0.155963

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

6b0ed22cfa323a34b527abdaec6e5c0bcaceeee5

362

py

Python

tests/install_tests/test_utils.py

yashasvimisra2798/cupy

ef3ce65dca455fdf2c7cb4d097fbda3328813d8a

[ "MIT" ]

1

2021-05-16T11:52:30.000Z

tests/install_tests/test_utils.py

yashasvimisra2798/cupy

ef3ce65dca455fdf2c7cb4d097fbda3328813d8a

[ "MIT" ]

8

2019-02-11T17:20:01.000Z

2021-09-08T01:14:51.000Z

tests/install_tests/test_utils.py

yashasvimisra2798/cupy

ef3ce65dca455fdf2c7cb4d097fbda3328813d8a

[ "MIT" ]

1

2021-01-08T14:16:53.000Z

import unittest from . import _from_install_import utils = _from_install_import('utils') class TestPrintWarning(unittest.TestCase): def test_print_warning(self): utils.print_warning('This is a test.') class TestSearchOnPath(unittest.TestCase): def test_exec_not_found(self): assert utils.search_on_path(['no_such_exec']) is None

19.052632

61

0.748619

48

362

5.3125

0.5625

0.086275

0.133333

0.172549

0

0.160221

362

18

62

20.111111

0.838816

0

0.088398

0

0.111111

1

0.222222

false

0

0.333333

0

0.777778

0.222222

0

null

0

1

0

null

0

1

0

1

0

1

0

4

6b41b79785f6afd51856fd3f720e2f5cd91413db

114

py

Python

recommendx/__init__.py

adrennhoff/recommendx

855f6829761caee65bf286d2996138370112e8ff

[ "BSD-3-Clause" ]

null

recommendx/__init__.py

adrennhoff/recommendx

855f6829761caee65bf286d2996138370112e8ff

[ "BSD-3-Clause" ]

null

recommendx/__init__.py

adrennhoff/recommendx

855f6829761caee65bf286d2996138370112e8ff

[ "BSD-3-Clause" ]

null

import numpy as np import pandas as pd from .reccode import RWR from .timecode import RWT __all__ = ['RWR','RWT']

19

25

0.745614

19

114

4.263158

0.631579

0

0.166667

114

6

26

19

0.852632

0

0.052174

0

1

0

false

0

0.8

0

0.8

0

1

0

null

0

1

0

null

0

1

0

1

0

4

86100c85cb3ed76eac92ccb958b933552ceb137e

168

py

Python

log.py

sdobz/http-torrent

d7a141a659cec75f039ef21b1a3a030f05869281

[ "MIT" ]

1

2016-01-27T04:53:28.000Z

log.py

sdobz/http-torrent

d7a141a659cec75f039ef21b1a3a030f05869281

[ "MIT" ]

null

log.py

sdobz/http-torrent

d7a141a659cec75f039ef21b1a3a030f05869281

[ "MIT" ]

null

import logging from logging import getLogger as get_logger logging.basicConfig( level=logging.DEBUG, format='[%(levelname)s] (%(threadName)-10s) %(message)s' )

24

60

0.732143

21

168

5.809524

0.761905

0

0.013699

0.130952

168

7

61

24

0.821918

0

0.278107

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

1

0

4

863ec91235f6b81d1545dd171eea4009ee2ed25a

440

py

Python

src/architectures/nmp/message_passing/__init__.py

isaachenrion/jets

59aeba81788d0741af448192d9dfb764fb97cf8d

[ "BSD-3-Clause" ]

9

2017-10-09T17:01:52.000Z

2018-06-12T18:06:05.000Z

src/architectures/nmp/message_passing/__init__.py

isaachenrion/jets

59aeba81788d0741af448192d9dfb764fb97cf8d

[ "BSD-3-Clause" ]

31

2017-11-01T14:39:02.000Z

2018-04-18T15:34:24.000Z

src/architectures/nmp/message_passing/__init__.py

isaachenrion/jets

59aeba81788d0741af448192d9dfb764fb97cf8d

[ "BSD-3-Clause" ]

10

2017-10-17T19:23:14.000Z

2020-07-05T04:44:45.000Z

from .message_passing_layers import MP_LAYERS #from .adjacency import construct_adjacency_matrix_layer ''' This module implements the core message passing operations. ###adjacency.py <-- compute an adjacency matrix based on vertex data. message_passing.py <-- run a single iteration of message passing. message.py <-- compute a message, given a hidden state. vertex_update.py <-- compute a vertex's new hidden state, given a message. '''

36.666667

74

0.784091

64

440

5.265625

0.53125

0.166172

0.059347

0

0.134091

440

11

75

40

0.884514

0.125

0

1

0

true

1

0

1

0

null

0

null

0

1

0

1

0

4

86792cd7ad7f766997e36b062261a9ae0c66dded

626

py

Python

aula4/aula4.py

diegocolombo1989/Trabalho-Python

4603117bebfb6e801c3289e108b4e8f29442ab6f

[ "MIT" ]

null

aula4/aula4.py

diegocolombo1989/Trabalho-Python

4603117bebfb6e801c3289e108b4e8f29442ab6f

[ "MIT" ]

null

aula4/aula4.py

diegocolombo1989/Trabalho-Python

4603117bebfb6e801c3289e108b4e8f29442ab6f

[ "MIT" ]

null

print('-'*40) numero1 = int(input('Digite o numero 1:')) numero2 = int(input('Digite o numero 2:')) print('resultado soma dos numeros:') resultado = numero1 + numero2 print(resultado) ('\n'*3) print('resultado subtração dos numeros:') resultado = numero1 - numero2 print(resultado) ('\n'*3) print('resultado divisão dos numeros:') resultado = numero1 / numero2 print(resultado) ('\n'*3) print('resultado multiplicação dos numeros:') resultado = numero1 * numero2 print(resultado) if numero1 > numero2: print('numero 1 é maior que número 2') else: print('numero 2 é maior que número 1') print('-'*40)

18.969697

45

0.688498

84

626

5.130952

0.309524

0.259861

0.220418

0.241299

0.645012

0.547564

0.438515

0

0.04771

0.162939

626

33

46

18.969697

0.774809

0

0.391304

0

0.362041

0

1

0

false

0

0.521739

0

null

1

0

null

0

1

0

4

869051c5a363a72fed4d7914f62a83814c0b7ca1

182

py

Python

jrdb/templates/MSA.py

hankehly/JRDB

ad470e867d204ea975f7b98b57881d72fcfb41c7

[ "MIT" ]

1

2022-02-19T14:44:34.000Z

jrdb/templates/MSA.py

hankehly/JRDB

ad470e867d204ea975f7b98b57881d72fcfb41c7

[ "MIT" ]

null

jrdb/templates/MSA.py

hankehly/JRDB

ad470e867d204ea975f7b98b57881d72fcfb41c7

[ "MIT" ]

1

2022-02-19T14:46:40.000Z

from jrdb.templates.MZA import MZA class MSA(MZA): """ JRDB抹消馬データ（MSA）差分 http://www.jrdb.com/program/Msa/msa_doc.txt """ description = "JRDB抹消馬データ（MSA）"

14

47

0.620879

24

182

4.666667

0.666667

0.232143

0

0.230769

182

12

48

15.166667

0.8

0.346154

0

0.157895

0

1

0

false

0

0.333333

0

1

0

1

0

null

1

0

null

0

1

0

1

0

4

869c5cbb58a908cdd11d8219406f61db301494e0

132

py

Python

modeling/dynamics/builtin/random_tst.py

takuya-ki/wrs

f6e1009b94332504042fbde9b39323410394ecde

[ "MIT" ]

23

2021-04-02T09:02:04.000Z

2022-03-22T05:31:03.000Z

modeling/dynamics/builtin/random_tst.py

takuya-ki/wrs

f6e1009b94332504042fbde9b39323410394ecde

[ "MIT" ]

35

2021-04-12T09:41:05.000Z

2022-03-26T13:32:46.000Z

modeling/dynamics/builtin/random_tst.py

takuya-ki/wrs

f6e1009b94332504042fbde9b39323410394ecde

[ "MIT" ]

16

2021-03-30T11:55:45.000Z

2022-03-30T07:10:59.000Z

# conclusions: # The builtin physics is very simple. It does not support joints and has little flexibility for different collisions.

66

117

0.810606

19

132

5.631579

1

0

0.151515

132

2

117

66

0.955357

0.969697

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

4

86ba465dbadfd5291d4d7f6eec22ee84821b4056

162

py

Python

docker/mlurlphishing/verify.py

tamarsix/dockerfiles

3ad3d4fc3e7dd55ac823bb1c5ddd530829cf0f07

[ "MIT" ]

null

docker/mlurlphishing/verify.py

tamarsix/dockerfiles

3ad3d4fc3e7dd55ac823bb1c5ddd530829cf0f07

[ "MIT" ]

null

docker/mlurlphishing/verify.py

tamarsix/dockerfiles

3ad3d4fc3e7dd55ac823bb1c5ddd530829cf0f07

[ "MIT" ]

null

import numpy as np import pandas import sklearn from bs4 import BeautifulSoup import cv2 as cv import tldextract import dill import catboost from PIL import Image

18

29

0.845679

26

162

5.269231

0.615385

0

0.014599

0.154321

162

9

30

18

0.985401

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

86bcb532ed51c6b1843ed22a561ca6db0f664b44

246

py

Python

util.py

mabruckner/rsstastic

ae00deb9bf9276df5b41be9d95b221c13ba7bf7e

[ "MIT" ]

null

util.py

mabruckner/rsstastic

ae00deb9bf9276df5b41be9d95b221c13ba7bf7e

[ "MIT" ]

null

util.py

mabruckner/rsstastic

ae00deb9bf9276df5b41be9d95b221c13ba7bf7e

[ "MIT" ]

null

import base64 def b64_to_key(data): return base64.urlsafe_b64decode(data).decode('ascii') def key_to_b64(key): return base64.urlsafe_b64encode(key.encode('ascii')) def get_url(key): return '/item/'+key_to_b64(key).decode('ascii')

20.5

57

0.727642

38

246

4.473684

0.447368

0.141176

0.223529

0.129412

0

0.074074

0.121951

246

11

58

22.363636

0.712963

0

0.085714

0

1

0.428571

false

0

0.142857

0.428571

1

0

null

0

1

0

null

0

1

0

1

0

4

86cd68b4bcbed8d6d1bac69e5fcbcdfc3fcee6b9

51

py

Python

Algorithms/python/right_angle_pattern.py

QuantzLab/QuantzPythonMath

07df4219574225633dfff8a4e2ddc01fddb2e68f

[ "CC0-1.0" ]

2

2020-10-21T06:21:27.000Z

2020-12-18T10:34:02.000Z

Algorithms/python/right_angle_pattern.py

QuantzLab/QuantzPythonMath

07df4219574225633dfff8a4e2ddc01fddb2e68f

[ "CC0-1.0" ]

2

2020-10-20T03:55:47.000Z

2020-10-28T10:35:00.000Z

Algorithms/python/right_angle_pattern.py

QuantzLab/QuantzPythonMath

07df4219574225633dfff8a4e2ddc01fddb2e68f

[ "CC0-1.0" ]

1

2020-10-16T07:21:58.000Z

d = 1 while d < 4: print("*" * d) d += 1

12.75

19

0.333333

9

51

1.888889

0.555556

0.235294

0

0.107143

0.45098

51

4

20

12.75

0.5

0

0.020408

0

1

0

false

0

0.25

1

0

1

null

1

0

1

0

null

0

4

86dd228053d335617198e5263ff26dc1de1d2406

343

py

Python

seller/admin.py

mrajeswarasai/CollegeMart

34b4087e84fd753a4796ee1cdbd53d22f637f011

[ "Apache-2.0" ]

null

seller/admin.py

mrajeswarasai/CollegeMart

34b4087e84fd753a4796ee1cdbd53d22f637f011

[ "Apache-2.0" ]

3

2021-06-08T22:29:18.000Z

2022-03-12T00:48:30.000Z

seller/admin.py

mrajeswarasai/CollegeMart

34b4087e84fd753a4796ee1cdbd53d22f637f011

[ "Apache-2.0" ]

null

from django.contrib import admin from .models import Category, Products_Selling, Products_Leasing, commonNotification, Request_table # Register your models here. admin.site.register(Products_Selling) admin.site.register(Category) admin.site.register(Products_Leasing) admin.site.register(commonNotification) admin.site.register(Request_table)

38.111111

99

0.854227

43

343

6.674419

0.395349

0.156794

0.296167

0.174216

0

0.06414

343

8

100

42.875

0.894081

0.075802

0

1

0

true

0

0.285714

0

0.285714

0

null

0

1

0

1

0

null

0

1

0

4

86e2e55f059e34a064a7f1c3670d6a30ac96c6cc

179

py

Python

Python/Tests/TestData/AstAnalysis/ReturnValues.py

techkey/PTVS

8355e67eedd8e915ca49bd38a2f36172696fd903

[ "Apache-2.0" ]

695

2019-05-06T23:49:37.000Z

2022-03-30T01:56:00.000Z

Python/Tests/TestData/AstAnalysis/ReturnValues.py

techkey/PTVS

8355e67eedd8e915ca49bd38a2f36172696fd903

[ "Apache-2.0" ]

1,672

2019-05-06T21:09:38.000Z

2022-03-31T23:16:04.000Z

Python/Tests/TestData/AstAnalysis/ReturnValues.py

techkey/PTVS

8355e67eedd8e915ca49bd38a2f36172696fd903

[ "Apache-2.0" ]

186

2019-05-13T03:17:37.000Z

2022-03-31T16:24:05.000Z

def r_a(a, b): return a def r_b(a, b): return b def r_str(): return '' def r_object(): return object() class A: def r_A(self): return type(self)()

11.1875

27

0.541899

32

179

2.875

0.3125

0.217391

0.108696

0

0.312849

179

15

28

11.933333

0.747967

0

1

0.454545

false

0

0.454545

1

0

null

1

0

null

0

1

0

1

0

4

810eb8e96f7b42c622ab36547a6754db326a2d63

248

py

Python

contact/serializers.py

omaralbeik/omaralbeik.com-api

03ce663fe2b3c52363520437d0f5b09cfcb121db

[ "MIT" ]

null

contact/serializers.py

omaralbeik/omaralbeik.com-api

03ce663fe2b3c52363520437d0f5b09cfcb121db

[ "MIT" ]

1

2018-04-05T13:44:13.000Z

2018-04-05T14:45:32.000Z

contact/serializers.py

omaralbeik/omaralbeik.com-api

03ce663fe2b3c52363520437d0f5b09cfcb121db

[ "MIT" ]

null

from rest_framework import serializers from .models import Message class MessageSerializer(serializers.ModelSerializer): class Meta: model = Message fields = ("name", "email", "phone", "country", "city", "subject", "message")

27.555556

84

0.693548

25

248

6.84

0.76

0

0.185484

248

8

85

31

0.846535

0

0.157258

0

1

0

false

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

81165b46e98e58d281946ad4238b5dde69c81a8d

259

py

Python

account_standard_report/models/res_currency.py

Chief0-0/Localizacion_ERP_V12

f59e56564e29525f772b59db7fef7c7cde347336

[ "Apache-2.0" ]

null

account_standard_report/models/res_currency.py

Chief0-0/Localizacion_ERP_V12

f59e56564e29525f772b59db7fef7c7cde347336

[ "Apache-2.0" ]

null

account_standard_report/models/res_currency.py

Chief0-0/Localizacion_ERP_V12

f59e56564e29525f772b59db7fef7c7cde347336

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from openerp import models, fields class ResCurrency(models.Model): _inherit = 'res.currency' excel_format = fields.Char(string='Excel format', default='_ * #,##0.00_) ;_ * - #,##0.00_) ;_ * "-"??_) ;_ @_ ', required=True)

25.9

132

0.606178

29

259

5.068966

0.793103

0.14966

0

0.03271

0.173745

259

9

133

28.777778

0.654206

0.081081

0

0.25

0.322034

0

1

0

false

0

0.25

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

4

8116a620e0b8ced773dff06e639403a6f630641f

2,338

py

Python

ckan/tests/legacy/lib/test_email_notifications.py

florianm/ckan

1cfd98d591ac70b4eb81048bcd227b6c1354b1bf

[ "Apache-2.0" ]

12

2015-08-28T16:59:07.000Z

2020-03-08T01:39:30.000Z

ckan/tests/legacy/lib/test_email_notifications.py

florianm/ckan

1cfd98d591ac70b4eb81048bcd227b6c1354b1bf

[ "Apache-2.0" ]

13

2019-05-02T21:01:28.000Z

2020-10-20T23:34:48.000Z

ckan/tests/legacy/lib/test_email_notifications.py

florianm/ckan

1cfd98d591ac70b4eb81048bcd227b6c1354b1bf

[ "Apache-2.0" ]

10

2015-05-08T04:33:20.000Z

2020-03-03T15:17:58.000Z

'''Tests for the ckan.lib.email_notifications module. Note that email_notifications is used by an action function, so most of the tests for the module are done by testing the action function in ckan.test.functional.api. This test module contains some additional unit tests. ''' import datetime import nose.tools import ckan.lib.email_notifications as email_notifications import ckan.logic as logic def test_string_to_time_delta(): assert email_notifications.string_to_timedelta('1 day') == ( datetime.timedelta(days=1)) assert email_notifications.string_to_timedelta('1 day') == ( datetime.timedelta(days=1)) assert email_notifications.string_to_timedelta('2 days') == ( datetime.timedelta(days=2)) assert email_notifications.string_to_timedelta('2\tdays') == ( datetime.timedelta(days=2)) assert email_notifications.string_to_timedelta('14 days') == ( datetime.timedelta(days=14)) assert email_notifications.string_to_timedelta('4:35:00') == ( datetime.timedelta(hours=4, minutes=35, seconds=00)) assert email_notifications.string_to_timedelta('4:35:12.087465') == ( datetime.timedelta(hours=4, minutes=35, seconds=12, milliseconds=87, microseconds=465)) assert email_notifications.string_to_timedelta('1 day, 3:23:34') == ( datetime.timedelta(days=1, hours=3, minutes=23, seconds=34)) assert email_notifications.string_to_timedelta('1 day, 3:23:34') == ( datetime.timedelta(days=1, hours=3, minutes=23, seconds=34)) assert email_notifications.string_to_timedelta('7 days, 3:23:34') == ( datetime.timedelta(days=7, hours=3, minutes=23, seconds=34)) assert email_notifications.string_to_timedelta('7 days,\t3:23:34') == ( datetime.timedelta(days=7, hours=3, minutes=23, seconds=34)) assert email_notifications.string_to_timedelta( '7 days, 3:23:34.087465') == datetime.timedelta(days=7, hours=3, minutes=23, seconds=34, milliseconds=87, microseconds=465) assert email_notifications.string_to_timedelta('.123456') == ( datetime.timedelta(milliseconds=123, microseconds=456)) nose.tools.assert_raises(logic.ValidationError, email_notifications.string_to_timedelta, 'foobar')

49.744681

79

0.702737

304

2,338

5.236842

0.240132

0.203518

0.211055

0.228643

0.681533

0.659548

0.609296

0.55402

0

0.065831

0.181352

2,338

46

80

50.826087

0.765935

0.115911

0

0.342857

0

0.07188

0

0.4

1

0.028571

true

0

0.114286

0

0.142857

0

null

1

0

null

0

1

0

4

8119c07cac4a8a9fcdfc7223d5f1bb4a1e28c825

159

py

Python

miniworld/repl/errors.py

miniworld-project/miniworld_core

c591bad232b78eae99e8f55cb1b907c1e228484b

[ "MIT" ]

5

2019-05-11T14:57:15.000Z

2021-07-05T00:35:25.000Z

miniworld/repl/errors.py

miniworld-project/miniworld_core

c591bad232b78eae99e8f55cb1b907c1e228484b

[ "MIT" ]

27

2017-03-17T07:11:02.000Z

2019-05-26T23:36:56.000Z

miniworld/repl/errors.py

miniworld-project/miniworld_core

c591bad232b78eae99e8f55cb1b907c1e228484b

[ "MIT" ]

6

2017-05-03T12:11:33.000Z

2020-04-03T11:44:27.000Z

from miniworld.errors import Base class REPLError(Base): pass class REPLUnexpectedResult(REPLError): pass class REPLTimeout(REPLError): pass

11.357143

38

0.742138

17

159

6.941176

0.588235

0.152542

0

0.194969

159

13

39

12.230769

0.921875

0

0.428571

0

1

0

true

0.428571

0.142857

0

0.571429

0

1

0

null

0

null

0

1

0

1

0

4

812234c94bced76272c9adad7846af89f52d9022

328

py

Python

ExifRemover/CleanExif.py

altazur/exct

cf9939218bf236404c94a4d67258ee1fdf450819

[ "MIT" ]

null

ExifRemover/CleanExif.py

altazur/exct

cf9939218bf236404c94a4d67258ee1fdf450819

[ "MIT" ]

2

2022-01-13T02:20:30.000Z

2022-03-12T00:18:03.000Z

ExifRemover/CleanExif.py

altazur/exct

cf9939218bf236404c94a4d67258ee1fdf450819

[ "MIT" ]

null

from PIL import Image import os.path as path def return_image_without_exif(image_file_input, image_file_output): """Takes an Image file as an argument and return image file without exif simply by resaving it""" image = Image.open(image_file_input) image.save(f"{image_file_output}/{path.basename(image.filename)}")

41

101

0.77439

53

328

4.584906

0.509434

0.222222

0.115226

0.156379

0

0.137195

328

7

102

46.857143

0.858657

0.277439

0

0.220779

0

1

0.2

false

0

0.4

0

0.6

0

null

1

0

1

0

null

0

1

0

1

0

4

812314297f3630a5a8dda0c327868f57d0448549

35

py

Python

hello_world.py

Vaishali3009/profiles-rest-api

200524cf716aaf80ecebd38694fa9ff5ea27cf71

[ "MIT" ]

null

hello_world.py

Vaishali3009/profiles-rest-api

200524cf716aaf80ecebd38694fa9ff5ea27cf71

[ "MIT" ]

null

hello_world.py

Vaishali3009/profiles-rest-api

200524cf716aaf80ecebd38694fa9ff5ea27cf71

[ "MIT" ]

null

print("hello") a=9 b=10 print(a+b)

7

14

0.628571

9

35

2.444444

0.666667

0

0.096774

0.114286

35

4

15

8.75

0.612903

0

0.142857

0

1

0

false

0

0.5

1

0

null

0

1

0

null

0

1

0

4

812cf23426ca275a638f0eddd39ea691ec55b088

89

py

Python

meeting/apps.py

SlapBass/nx-portal

ee262079db1e5230a24ebbc205e44926f11f8da9

[ "Apache-2.0" ]

5

2019-10-04T04:46:44.000Z

2019-10-09T10:02:01.000Z

meeting/apps.py

SlapBass/nx-portal

ee262079db1e5230a24ebbc205e44926f11f8da9

[ "Apache-2.0" ]

10

2020-02-12T00:37:45.000Z

2022-03-03T21:58:40.000Z

meeting/apps.py

SlapBass/nx-portal

ee262079db1e5230a24ebbc205e44926f11f8da9

[ "Apache-2.0" ]

1

2020-06-19T13:26:08.000Z

from django.apps import AppConfig class MeetingConfig(AppConfig): name = 'meeting'

14.833333

33

0.752809

10

89

6.7

0.9

0

0.168539

89

5

34

17.8

0.905405

0

0.078652

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

d49fe3b74021c561e38d3b698cb6d32df4e168a0

268

py

Python

jobs/models.py

IamCharlesM/Portfolio

145d06657cf47bbd1133e863ef9614e23e9ff65a

[ "MIT" ]

null

jobs/models.py

IamCharlesM/Portfolio

145d06657cf47bbd1133e863ef9614e23e9ff65a

[ "MIT" ]

null

jobs/models.py

IamCharlesM/Portfolio

145d06657cf47bbd1133e863ef9614e23e9ff65a

[ "MIT" ]

null

from django.db import models class Job(models.Model): image = models.ImageField(upload_to='images/') summary = models.CharField(max_length=255) title = models.CharField(max_length=100, default='Job Title') def __str__(self): return self.title

29.777778

65

0.712687

36

268

5.111111

0.694444

0.163043

0.195652

0.26087

0

0.027027

0.171642

268

9

66

29.777778

0.801802

0

0.05948

0

1

0.142857

false

0

0.142857

1

0

null

0

1

0

1

0

null

0

1

0

4

d4aaa18ccfd39047a41cc001c5cccac7a9f42c94

245

py

Python

control/systems/main.py

WesleyAC/toybox

c8c26ed15c1133185cbd6dd38528fbc75a8a1d1f

[ "MIT" ]

21

2017-08-21T15:29:34.000Z

2021-08-05T15:50:11.000Z

control/systems/main.py

WesleyAC/toybox

c8c26ed15c1133185cbd6dd38528fbc75a8a1d1f

[ "MIT" ]

null

control/systems/main.py

WesleyAC/toybox

c8c26ed15c1133185cbd6dd38528fbc75a8a1d1f

[ "MIT" ]

4

2017-10-02T22:10:55.000Z

2022-02-03T23:49:54.000Z

import numpy as np Kt = 1.41/89.0 Kv = 5840.0/3.0 G = 10.0 J = 4.0*(2.54**2.0)/2.0 # 4 kg on a 1 inch pully R = 12.0/89.0 A = np.asarray([[0, 1], [0, -(Kt*Kv)/((G**2)*J*R)]]) B = np.asarray([[0], [Kt/(G*J*R)]])

18.846154

48

0.432653

58

245

1.827586

0.465517

0.056604

0.188679

0

0.218391

0.289796

245

12

49

20.416667

0.390805

0.089796

0

1

0

false

0

0.1

0

0.1

0

1

null

0

1

0

1

0

1

0

null

0

4

d4b5225baee327566f0dd703c3dede1af6b58cd5

727

py

Python

individual-project/lab-src/code/controller.py

vampy/university

9496cb63594dcf1cc2cec8650b8eee603f85fdab

[ "MIT" ]

6

2015-06-22T19:43:13.000Z

2019-07-15T18:08:41.000Z

individual-project/lab-src/code/controller.py

vampy/university

9496cb63594dcf1cc2cec8650b8eee603f85fdab

[ "MIT" ]

null

individual-project/lab-src/code/controller.py

vampy/university

9496cb63594dcf1cc2cec8650b8eee603f85fdab

[ "MIT" ]

1

2015-09-26T09:01:54.000Z

#!/usr/bin/python from repository import Repository class Controller: def __init__(self, repository): if not isinstance(repository, Repository): raise Exception("repository if not of type Repository") self.repository = repository def load_from_files(self): self.repository.load_from_files() def group_by_average(self): self.repository.group_by_average() def filter_non_passing(self): self.repository.filter_non_passing() def group_by_best_subject(self): self.repository.group_by_best_subject() def group_by_age(self): self.repository.group_by_age() def filter_passed_all(self): self.repository.filter_passed_all()

25.068966

67

0.701513

91

727

5.274725

0.340659

0.233333

0.225

0.14375

0.15625

0

0.213205

727

29

68

25.068966

0.839161

0.022008

0

0.050633

0

1

0.388889

false

0.222222

0.055556

0

0.5

0

null

1

0

null

0

1

0

1

0

4

d4df39724f7b70d9b5011504f0760b3148376f66

122

py

Python

wsgi.py

Lord-of-the-Galaxy/heroku-multi-account

9f2d8f7455ed954cc25ed905a966a6326b4d2967

[ "MIT" ]

1

2020-06-02T10:42:23.000Z

wsgi.py

Lord-of-the-Galaxy/heroku-multi-account

9f2d8f7455ed954cc25ed905a966a6326b4d2967

[ "MIT" ]

null

wsgi.py

Lord-of-the-Galaxy/heroku-multi-account

9f2d8f7455ed954cc25ed905a966a6326b4d2967

[ "MIT" ]

null

from hma_slave import app # You shouldn't need to modify anything here if __name__=='__main__': app.run(debug=True)

17.428571

44

0.737705

20

122

4.05

0.95

0

0.172131

122

6

45

20.333333

0.80198

0.344262

0

0.102564

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

1

0

4

d4e25cdd5f64b9ba2eb7854e4136a0021d03a56f

439

py

Python

program/preprocess.py

donyori/2018ccf_bdci_inter_fund_correlation_prediction

6e06a3e192e05ae1e9822111cf323eda3a61bf4e

[ "MIT" ]

null

program/preprocess.py

donyori/2018ccf_bdci_inter_fund_correlation_prediction

6e06a3e192e05ae1e9822111cf323eda3a61bf4e

[ "MIT" ]

1

2018-12-18T05:14:08.000Z

2019-01-16T06:31:35.000Z

program/preprocess.py

donyori/2018ccf_bdci_inter_fund_correlation_prediction

6e06a3e192e05ae1e9822111cf323eda3a61bf4e

[ "MIT" ]

null

from data.dataset_name import DATASET_NAME_TRAIN, DATASET_NAME_TEST, DATASET_NAME_PREDICT from data.preprocess import preprocess_data def _main(): print('Preprocess train data.') preprocess_data(DATASET_NAME_TRAIN) print('Preprocess test data.') preprocess_data(DATASET_NAME_TEST) print('Preprocess predict data.') preprocess_data(DATASET_NAME_PREDICT) print('Done.') if __name__ == '__main__': _main()

25.823529

89

0.758542

55

439

5.563636

0.254545

0.251634

0.196078

0.245098

0.284314

0

0.150342

439

16

90

27.4375

0.820375

0

0.182232

0

1

0.083333

true

0

0.166667

0

0.25

0.333333

0

null

1

0

null

0

1

0

4

d4ef2b74af165d6f79e1bdd946385a3eb5b783c8

216

py

Python

colossus/apps/accounts/models.py

CreativeWurks/emailerpro

5f8d668d1b98f5add8123794a1802b82381560eb

[ "MIT" ]

372

2018-08-13T20:51:32.000Z

2022-03-21T12:55:58.000Z

colossus/apps/accounts/models.py

CreativeWurks/emailerpro

5f8d668d1b98f5add8123794a1802b82381560eb

[ "MIT" ]

30

2018-08-13T19:34:17.000Z

2022-03-20T21:28:49.000Z

colossus/apps/accounts/models.py

CreativeWurks/emailerpro

5f8d668d1b98f5add8123794a1802b82381560eb

[ "MIT" ]

117

2018-08-13T21:54:42.000Z

2022-03-24T16:45:48.000Z

from django.contrib.auth.models import AbstractUser from django.db import models class User(AbstractUser): timezone = models.CharField(max_length=50, blank=True) class Meta: db_table = 'auth_user'

21.6

58

0.740741

29

216

5.413793

0.655172

0.127389

0

0.011236

0.175926

216

9

59

24

0.870787

0

0.041667

0

1

0

false

0

0.333333

0

0.833333

0

1

0

null

0

1

0

null

0

1

0

1

0

4

079c6119547bc4d86da2c3b98ba4a3066155a93d

182

py

Python

currencies/conf.py

CargobaseDev/django-currencies

ff722618ad5248da3b592c96c186cc93846796dc

[ "BSD-3-Clause" ]

8

2015-06-07T02:25:23.000Z

2020-10-06T05:19:59.000Z

currencies/conf.py

CargobaseDev/django-currencies

ff722618ad5248da3b592c96c186cc93846796dc

[ "BSD-3-Clause" ]

1

2015-04-03T05:40:04.000Z

2015-04-14T10:44:35.000Z

currencies/conf.py

CargobaseDev/django-currencies

ff722618ad5248da3b592c96c186cc93846796dc

[ "BSD-3-Clause" ]

4

2017-09-23T09:02:51.000Z

2021-06-25T05:21:12.000Z

# -*- coding: utf-8 -*- from django.conf import settings SESSION_PREFIX = getattr(settings, 'CURRENCY_SESSION_PREFIX', 'session') SESSION_KEY = '%s:currency_code' % SESSION_PREFIX

26

72

0.747253

23

182

5.652174

0.652174

0.3

0

0.006211

0.115385

182

6

73

30.333333

0.801242

0.115385

0

0.289308

0.144654

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

4

07d2fd3e92759421ef83a41ce768548db039d791

23

py

Python

MyLibrary/version.py

lachlangrose/python_template

9871a4ccd0e17fc87937a58e7753be54311eec9a

[ "MIT" ]

null

MyLibrary/version.py

lachlangrose/python_template

9871a4ccd0e17fc87937a58e7753be54311eec9a

[ "MIT" ]

16

2021-09-07T03:42:33.000Z

2021-12-06T04:58:43.000Z

MyLibrary/version.py

lachlangrose/python_template

9871a4ccd0e17fc87937a58e7753be54311eec9a

[ "MIT" ]

null

__version__ = "0.2.4"

7.666667

21

0.608696

4

23

2.5

1

0

0.157895

0.173913

23

2

22

11.5

0.368421

0

0.227273

0

1

0

false

0

1

0

null

0

1

0

1

0

null

0

4

07d4741930620fe4ab65b3f98decfc31421848e4

156

py

Python

config/email.py

briglass/PLAy

a78cfcf1201389f421bf393ff9c30c83a5a3ca4c

[ "BSD-3-Clause" ]

null

config/email.py

briglass/PLAy

a78cfcf1201389f421bf393ff9c30c83a5a3ca4c

[ "BSD-3-Clause" ]

null

config/email.py

briglass/PLAy

a78cfcf1201389f421bf393ff9c30c83a5a3ca4c

[ "BSD-3-Clause" ]

null

MAIL_SERVER = 'smtp.gmail.com' MAIL_PORT = 465 MAIL_USE_TLS = False MAIL_USE_SSL = True MAIL_USERNAME = 'playiq.com@gmail.com' MAIL_PASSWORD = 'notplaynet'

22.285714

38

0.775641

25

156

4.52

0.64

0.141593

0.212389

0

0.021739

0.115385

156

6

39

26

0.797101

0

0.282051

0

1

0

false

0.166667

0

1

0

null

0

1

0

1

0

null

0

1

0

4

07dbd8d2aaa4e44c6cbf3e35a4697082a9d44cb7

332

py

Python

ObasiEmmanuel/Phase 1/Python Basic 1/Day 5 task/day5-one.py

CodedLadiesInnovateTech/-python-challenge-solutions

430cd3eb84a2905a286819eef384ee484d8eb9e7

[ "MIT" ]

6

2020-05-23T19:53:25.000Z

2021-05-08T20:21:30.000Z

ObasiEmmanuel/Phase 1/Python Basic 1/Day 5 task/day5-one.py

CodedLadiesInnovateTech/-python-challenge-solutions

430cd3eb84a2905a286819eef384ee484d8eb9e7

[ "MIT" ]

8

2020-05-14T18:53:12.000Z

2020-07-03T00:06:20.000Z

ObasiEmmanuel/Phase 1/Python Basic 1/Day 5 task/day5-one.py

CodedLadiesInnovateTech/-python-challenge-solutions

430cd3eb84a2905a286819eef384ee484d8eb9e7

[ "MIT" ]

39

2020-05-10T20:55:02.000Z

2020-09-12T17:40:59.000Z

def HCF (x,y): if x > y: d=[] for i in range(1,y+1): if y % i == 0 and x % i == 0: d.append(i) print(d[-1]) elif x < y: d=[] for i in range(1,x+1): if y % i == 0 and x % i == 0: d.append(i) print(d[-1]) print(HCF(10,8))

22.133333

41

0.334337

58

332

1.913793

0.310345

0.072072

0.054054

0.108108

0.756757

0.486486

0

0.076471

0.487952

332

15

42

22.133333

0.576471

0

0.571429

0

1

0.071429

false

0

0.071429

0.214286

0

1

null

0

1

0

null

0

4

07e5d7905e87eceb1c013ca45d6ffd20a1d0a548

93

py

Python

machida/lib/wallaroo/experimental/base_meta2.py

pvmsikrsna/wallaroo

a08ef579ec809e5bf4ffe10937b2be20059a0530

[ "Apache-2.0" ]

1,459

2017-09-16T13:13:15.000Z

2020-10-05T06:19:50.000Z

machida/lib/wallaroo/experimental/base_meta2.py

pvmsikrsna/wallaroo

a08ef579ec809e5bf4ffe10937b2be20059a0530

[ "Apache-2.0" ]

1,413

2017-09-14T18:18:14.000Z

2020-09-28T08:10:30.000Z

machida/lib/wallaroo/experimental/base_meta2.py

pvmsikrsna/wallaroo

a08ef579ec809e5bf4ffe10937b2be20059a0530

[ "Apache-2.0" ]

80

2017-09-27T23:16:23.000Z

2020-06-02T09:18:53.000Z

from abc import ABCMeta, abstractmethod class BaseMeta(object): __metaclass__ = ABCMeta

18.6

39

0.784946

10

93

6.9

0.9

0

0.16129

93

4

40

23.25

0.884615

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

6af953d4ffe276b03c893a5c60d28b64f8280ca7

232

py

Python

rplanpy/metadata.py

unaisaralegui/rplanpy

eebdfde4e523c085e6309f5a35f2d2234806d898

[ "MIT" ]

1

2021-04-27T14:27:01.000Z

rplanpy/metadata.py

unaisaralegui/rplanpy

eebdfde4e523c085e6309f5a35f2d2234806d898

[ "MIT" ]

null

rplanpy/metadata.py

unaisaralegui/rplanpy

eebdfde4e523c085e6309f5a35f2d2234806d898

[ "MIT" ]

1

2021-06-25T10:20:58.000Z

major, minor, patch = (0, 1, 1) __version__ = f"{major}.{minor}.{patch}" __author__ = "Unai Saralegui" __email__ = "usaralegui@gmail.com" __credits__ = ["Unai Saralegui"] __maintainer__ = "Unai Saralegui" __status__ = "Development"

29

40

0.719828

26

232

5.5

0.692308

0.272727

0.20979

0

0.014706

0.12069

232

7

41

33.142857

0.686275

0

0.413793

0.099138

0

1

0

false

0

1

0

null

1

0

1

0

null

0

4

ed1ec1b2a698090a7f6269afe053d8d4c87d6ed6

10

py

Python

random_number.py

MightySCollins/python-challenges

1ad41a517e6320a73634862c3bd5c67b67955a69

[ "MIT" ]

1

2018-09-16T17:06:36.000Z

random_number.py

MightySCollins/python-challenges

1ad41a517e6320a73634862c3bd5c67b67955a69

[ "MIT" ]

null

random_number.py

MightySCollins/python-challenges

1ad41a517e6320a73634862c3bd5c67b67955a69

[ "MIT" ]

1

2018-09-16T17:06:22.000Z

# SoonTM

5

9

0.6

1

10

6

1

0

0.3

10

1

10

0.857143

0.6

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

ed50c9750a4662baacc6ef7f040d50ab3665f04b

95

py

Python

blog/admin.py

Kesel/django

f3fc3617c4b39b18e54bfb4c2fc8940e40f8fa25

[ "MIT" ]

null

blog/admin.py

Kesel/django

f3fc3617c4b39b18e54bfb4c2fc8940e40f8fa25

[ "MIT" ]

null

blog/admin.py

Kesel/django

f3fc3617c4b39b18e54bfb4c2fc8940e40f8fa25

[ "MIT" ]

null

from django.contrib import admin from blog.models import Article admin.site.register(Article)

19

32

0.831579

14

95

5.642857

0.714286

0

0.105263

95

4

33

23.75

0.929412

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

ed7b70b16a9db82a8cdb6661cbfbdd3ce782ab29

188

py

Python

Python/Daily Challenges/String/Binary_or_not_String.py

Aditya8821/Python

58c7ea8daf381256d8a45736832fb70f735757f7

[ "MIT" ]

2

2021-05-12T11:20:39.000Z

2021-06-17T04:35:16.000Z

Python/Daily Challenges/String/Binary_or_not_String.py

Aditya8821/Python

58c7ea8daf381256d8a45736832fb70f735757f7

[ "MIT" ]

null

Python/Daily Challenges/String/Binary_or_not_String.py

Aditya8821/Python

58c7ea8daf381256d8a45736832fb70f735757f7

[ "MIT" ]

null

def binary_or_not(str): binary="01" return all([num in binary for num in str]) str="001021010001010" if binary_or_not(str): print("Binary") else: print("Not Binary")

23.5

47

0.654255

29

188

4.103448

0.517241

0.134454

0.184874

0.235294

0

0.114865

0.212766

188

8

48

23.5

0.689189

0

0.181319

0

1

0.125

false

0

0.25

1

0

null

0

1

0

1

0

null

0

4

ed85f6b025d515a5e32ce6332153a76f3bbe95bc

224

py

Python

Chapter11_Packages2/1_NewPackageStructure/tests/__init__.py

vtolle/AdvancedPython

82c75e36fc4d6b6afc98e4d9dc0b72eaf525c434

[ "MIT" ]

null

Chapter11_Packages2/1_NewPackageStructure/tests/__init__.py

vtolle/AdvancedPython

82c75e36fc4d6b6afc98e4d9dc0b72eaf525c434

[ "MIT" ]

null

Chapter11_Packages2/1_NewPackageStructure/tests/__init__.py

vtolle/AdvancedPython

82c75e36fc4d6b6afc98e4d9dc0b72eaf525c434

[ "MIT" ]

null

"""Test code suite. """ import math import unittest from .test_computations import ComputationsTests from .test_vector import VectorTests def main_tests(): unittest.main() if __name__ == '__main__': main_tests()

14.933333

48

0.741071

27

224

5.703704

0.592593

0.103896

0

0.160714

224

14

49

16

0.819149

0.071429

0

0.039801

0

1

0.125

true

0

0.5

0

0.625

0

1

0

null

0

null

0

1

0

1

0

1

0

4

9c032a118c01b6854188b89b3c3d13b10d506a1f

642

py

Python

easycalculations/calc.py

Thatgenzgamer/easycalculations

981458d3afcc70801de476e78bfc36601a3564d4

[ "MIT" ]

null

easycalculations/calc.py

Thatgenzgamer/easycalculations

981458d3afcc70801de476e78bfc36601a3564d4

[ "MIT" ]

null

easycalculations/calc.py

Thatgenzgamer/easycalculations

981458d3afcc70801de476e78bfc36601a3564d4

[ "MIT" ]

null

class Calc: def calc(opr, num1, num2): if (opr.lower() == "+") or (opr.lower() == "sum") or (opr.lower() == "plus"): print(num1 + num2) elif (opr.lower() == "-") or (opr.lower() == 'subtract') or (opr.lower() == 'minus'): print(num1 - num2) elif (opr.lower() == "*") or (opr.lower() == "multiply") or (opr.lower() == 'product'): print(num1 * num2) elif (opr == "/") or (opr == 'divide') or (opr == 'division'): print(num1 / num2) elif (opr.lower() == "**") or (opr.lower() == 'power') or (opr.lower() == 'power_of'): print(num1 ** num2)

45.857143

95

0.462617

75

642

3.946667

0.28

0.324324

0.27027

0.175676

0.483108

0.35473

0

0.026316

0.28972

642

13

96

49.384615

0.622807

0

0.105919

0

1

0.083333

false

0

0.166667

0.416667

0

null

1

0

null

0

1

0

4

9c0c22cf66e36c4214c66074adcc86a8b8fd6af3

105

py

Python

Python 2 and 3/Medium/Compress the String/Compress the String.py

mhinzz/HackerRank

f0424288af011dcc13fd77b4fe252e56b0c8e37f

[ "MIT" ]

1

2020-10-23T18:40:20.000Z

Python 2 and 3/Medium/Compress the String/Compress the String.py

mhinzz/HackerRank

f0424288af011dcc13fd77b4fe252e56b0c8e37f

[ "MIT" ]

null

Python 2 and 3/Medium/Compress the String/Compress the String.py

mhinzz/HackerRank

f0424288af011dcc13fd77b4fe252e56b0c8e37f

[ "MIT" ]

null

from itertools import groupby if True: print(*[(len(list(c)), int(k)) for k, c in groupby(input())])

26.25

65

0.647619

18

105

3.777778

0.833333

0

0.161905

105

3

66

35

0.772727

0

1

0

true

0

0.333333

0

0.333333

1

0

null

0

1

0

null

0

1

0

1

0

4

9c450a0f30d212e1940b4f63e3d619549027d4e7

237

py

Python

toontown/toon/DistributedNPCFlippyInToonHallAI.py

TheFamiliarScoot/open-toontown

678313033174ea7d08e5c2823bd7b473701ff547

[ "BSD-3-Clause" ]

99

2019-11-02T22:25:00.000Z

2022-02-03T03:48:00.000Z

toontown/toon/DistributedNPCFlippyInToonHallAI.py

TheFamiliarScoot/open-toontown

678313033174ea7d08e5c2823bd7b473701ff547

[ "BSD-3-Clause" ]

42

2019-11-03T05:31:08.000Z

2022-03-16T22:50:32.000Z

toontown/toon/DistributedNPCFlippyInToonHallAI.py

TheFamiliarScoot/open-toontown

678313033174ea7d08e5c2823bd7b473701ff547

[ "BSD-3-Clause" ]

57

2019-11-03T07:47:37.000Z

2022-03-22T00:41:49.000Z

from .DistributedNPCToonAI import * class DistributedNPCFlippyInToonHallAI(DistributedNPCToonAI): def __init__(self, air, npcId, questCallback = None, hq = 0): DistributedNPCToonAI.__init__(self, air, npcId, questCallback)

33.857143

70

0.772152

21

237

8.333333

0.666667

0.091429

0.125714

0.182857

0.331429

0

0.004926

0.14346

237

6

71

39.5

0.857143

0

1

0.25

false

0

0.25

0

0.75

0

1

0

null

0

1

0

1

0

null

0

1

0

4

9c45a34cfa97ec0e54e1bc3be6efa291e4a12767

6,272

py

Python

cohesity_management_sdk/models/privilege_id_enum.py

chandrashekar-cohesity/management-sdk-python

9e6ec99e8a288005804b808c4e9b19fd204e3a8b

[ "Apache-2.0" ]

1

2019-11-07T23:19:32.000Z

cohesity_management_sdk/models/privilege_id_enum.py

chandrashekar-cohesity/management-sdk-python

9e6ec99e8a288005804b808c4e9b19fd204e3a8b

[ "Apache-2.0" ]

null

cohesity_management_sdk/models/privilege_id_enum.py

chandrashekar-cohesity/management-sdk-python

9e6ec99e8a288005804b808c4e9b19fd204e3a8b

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Copyright 2019 Cohesity Inc. class PrivilegeIdEnum(object): """Implementation of the 'PrivilegeId' enum. Specifies unique id for a privilege. This number must be unique when creating a new privilege. Type for unique privilege Id values. All below enum values specify a value for all uniquely defined privileges in Cohesity. Attributes: KPRINCIPALVIEW: TODO: type description here. KPRINCIPALMODIFY: TODO: type description here. KAPPLAUNCH: TODO: type description here. KAPPSMANAGEMENT: TODO: type description here. KORGANIZATIONVIEW: TODO: type description here. KORGANIZATIONMODIFY: TODO: type description here. KORGANIZATIONIMPERSONATE: TODO: type description here. KCLONEVIEW: TODO: type description here. KCLONEMODIFY: TODO: type description here. KCLUSTERVIEW: TODO: type description here. KCLUSTERMODIFY: TODO: type description here. KCLUSTERCREATE: TODO: type description here. KCLUSTERSUPPORT: TODO: type description here. KCLUSTERUPGRADE: TODO: type description here. KCLUSTERREMOTEVIEW: TODO: type description here. KCLUSTERREMOTEMODIFY: TODO: type description here. KCLUSTEREXTERNALTARGETVIEW: TODO: type description here. KCLUSTEREXTERNALTARGETMODIFY: TODO: type description here. KCLUSTERAUDIT: TODO: type description here. KALERTVIEW: TODO: type description here. KALERTMODIFY: TODO: type description here. KVLANVIEW: TODO: type description here. KVLANMODIFY: TODO: type description here. KHYBRIDEXTENDERVIEW: TODO: type description here. KHYBRIDEXTENDERDOWNLOAD: TODO: type description here. KADLDAPVIEW: TODO: type description here. KADLDAPMODIFY: TODO: type description here. KSCHEDULERVIEW: TODO: type description here. KSCHEDULERMODIFY: TODO: type description here. KPROTECTIONVIEW: TODO: type description here. KPROTECTIONMODIFY: TODO: type description here. KPROTECTIONJOBOPERATE: TODO: type description here. KPROTECTIONSOURCEMODIFY: TODO: type description here. KPROTECTIONPOLICYVIEW: TODO: type description here. KPROTECTIONPOLICYMODIFY: TODO: type description here. KRESTOREVIEW: TODO: type description here. KRESTOREMODIFY: TODO: type description here. KRESTOREDOWNLOAD: TODO: type description here. KREMOTERESTORE: TODO: type description here. KSTORAGEVIEW: TODO: type description here. KSTORAGEMODIFY: TODO: type description here. KSTORAGEDOMAINVIEW: TODO: type description here. KSTORAGEDOMAINMODIFY: TODO: type description here. KANALYTICSVIEW: TODO: type description here. KANALYTICSMODIFY: TODO: type description here. KREPORTSVIEW: TODO: type description here. KMCMMODIFY: TODO: type description here. KDATASECURITY: TODO: type description here. KSMBBACKUP: TODO: type description here. KSMBRESTORE: TODO: type description here. KSMBTAKEOWNERSHIP: TODO: type description here. KSMBAUDITING: TODO: type description here. KMCMUNREGISTER: TODO: type description here. KMCMUPGRADE: TODO: type description here. KMCMMODIFYSUPERADMIN: TODO: type description here. KMCMVIEWSUPERADMIN: TODO: type description here. KMCMMODIFYCOHESITYADMIN: TODO: type description here. KMCMVIEWCOHESITYADMIN: TODO: type description here. KOBJECTSEARCH: TODO: type description here. KFILEDATALOCKEXPIRYTIMEDECREASE: TODO: type description here. """ KPRINCIPALVIEW = 'kPrincipalView' KPRINCIPALMODIFY = 'kPrincipalModify' KAPPLAUNCH = 'kAppLaunch' KAPPSMANAGEMENT = 'kAppsManagement' KORGANIZATIONVIEW = 'kOrganizationView' KORGANIZATIONMODIFY = 'kOrganizationModify' KORGANIZATIONIMPERSONATE = 'kOrganizationImpersonate' KCLONEVIEW = 'kCloneView' KCLONEMODIFY = 'kCloneModify' KCLUSTERVIEW = 'kClusterView' KCLUSTERMODIFY = 'kClusterModify' KCLUSTERCREATE = 'kClusterCreate' KCLUSTERSUPPORT = 'kClusterSupport' KCLUSTERUPGRADE = 'kClusterUpgrade' KCLUSTERREMOTEVIEW = 'kClusterRemoteView' KCLUSTERREMOTEMODIFY = 'kClusterRemoteModify' KCLUSTEREXTERNALTARGETVIEW = 'kClusterExternalTargetView' KCLUSTEREXTERNALTARGETMODIFY = 'kClusterExternalTargetModify' KCLUSTERAUDIT = 'kClusterAudit' KALERTVIEW = 'kAlertView' KALERTMODIFY = 'kAlertModify' KVLANVIEW = 'kVlanView' KVLANMODIFY = 'kVlanModify' KHYBRIDEXTENDERVIEW = 'kHybridExtenderView' KHYBRIDEXTENDERDOWNLOAD = 'kHybridExtenderDownload' KADLDAPVIEW = 'kAdLdapView' KADLDAPMODIFY = 'kAdLdapModify' KSCHEDULERVIEW = 'kSchedulerView' KSCHEDULERMODIFY = 'kSchedulerModify' KPROTECTIONVIEW = 'kProtectionView' KPROTECTIONMODIFY = 'kProtectionModify' KPROTECTIONJOBOPERATE = 'kProtectionJobOperate' KPROTECTIONSOURCEMODIFY = 'kProtectionSourceModify' KPROTECTIONPOLICYVIEW = 'kProtectionPolicyView' KPROTECTIONPOLICYMODIFY = 'kProtectionPolicyModify' KRESTOREVIEW = 'kRestoreView' KRESTOREMODIFY = 'kRestoreModify' KRESTOREDOWNLOAD = 'kRestoreDownload' KREMOTERESTORE = 'kRemoteRestore' KSTORAGEVIEW = 'kStorageView' KSTORAGEMODIFY = 'kStorageModify' KSTORAGEDOMAINVIEW = 'kStorageDomainView' KSTORAGEDOMAINMODIFY = 'kStorageDomainModify' KANALYTICSVIEW = 'kAnalyticsView' KANALYTICSMODIFY = 'kAnalyticsModify' KREPORTSVIEW = 'kReportsView' KMCMMODIFY = 'kMcmModify' KDATASECURITY = 'kDataSecurity' KSMBBACKUP = 'kSmbBackup' KSMBRESTORE = 'kSmbRestore' KSMBTAKEOWNERSHIP = 'kSmbTakeOwnership' KSMBAUDITING = 'kSmbAuditing' KMCMUNREGISTER = 'kMcmUnregister' KMCMUPGRADE = 'kMcmUpgrade' KMCMMODIFYSUPERADMIN = 'kMcmModifySuperAdmin' KMCMVIEWSUPERADMIN = 'kMcmViewSuperAdmin' KMCMMODIFYCOHESITYADMIN = 'kMcmModifyCohesityAdmin' KMCMVIEWCOHESITYADMIN = 'kMcmViewCohesityAdmin' KOBJECTSEARCH = 'kObjectSearch' KFILEDATALOCKEXPIRYTIMEDECREASE = 'kFileDatalockExpiryTimeDecrease'

31.676768

72

0.722417

472

6,272

9.599576

0.216102

0.105937

0.2516

0.304569

0

0.001016

0.215721

6,272

197

73

31.837563

0.920106

0.539222

0

0.366003

0.101072

0

0.304569

0

1

0

false

0

1

0

null

0

1

0

null

0

1

0

1

0

4

9c4bc69b869e9334e64fb3b148c127249184ef09

60

py

Python

Analyze_APK/GetMessage/__init__.py

AmandaLingJ/Anaylze-Features-of-APP

4893cdbdac860b00008d335af1fec6025b449c1d

[ "Apache-2.0" ]

1

2019-10-19T02:33:56.000Z

Analyze_APK/GetMessage/__init__.py

AmandaLingJ/Anaylze-Features-of-APP

4893cdbdac860b00008d335af1fec6025b449c1d

[ "Apache-2.0" ]

null

Analyze_APK/GetMessage/__init__.py

AmandaLingJ/Anaylze-Features-of-APP

4893cdbdac860b00008d335af1fec6025b449c1d

[ "Apache-2.0" ]

1

2019-10-21T08:34:24.000Z

import os from GetMessage.AboutMessage import Message

12

44

0.783333

7

60

6.714286

0.857143

0

0.2

60

4

45

15

0.979167

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

9c5948ff86cefbe938b8c8fb7873335fa1887bec

87

py

Python

learnedevolution/targets/covariance/covariance_target.py

realtwister/LearnedEvolution

2ec49b50a49acae9693cfb05ac114dfbcc4aa337

[ "MIT" ]

null

learnedevolution/targets/covariance/covariance_target.py

realtwister/LearnedEvolution

2ec49b50a49acae9693cfb05ac114dfbcc4aa337

[ "MIT" ]

null

learnedevolution/targets/covariance/covariance_target.py

realtwister/LearnedEvolution

2ec49b50a49acae9693cfb05ac114dfbcc4aa337

[ "MIT" ]

null

from ..target import Target; class CovarianceTarget(Target): _type = "covariance"

17.4

31

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0.777778

0

0.16092

87

4

32

21.75

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null

0

1

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1

0

4

92bf8d9ebb59933cb4672d4ece29b25f5d070a1e

419

py

Python

click_project/monkeypatch.py

hobeika/click-project

216da0a3b3551cc06324c98f295c90176380f201

[ "MIT" ]

null

click_project/monkeypatch.py

hobeika/click-project

216da0a3b3551cc06324c98f295c90176380f201

[ "MIT" ]

1

2021-03-17T10:39:54.000Z

2021-03-17T10:40:39.000Z

click_project/monkeypatch.py

hobeika/click-project

216da0a3b3551cc06324c98f295c90176380f201

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding:utf-8 -*- from click import Context old_lookup_default = Context.lookup_default def context_lookup_default(self, name): if not hasattr(self, "click_project_default_catch"): self.click_project_default_catch = set() self.click_project_default_catch.add(name) return old_lookup_default(self, name) def do(): Context.lookup_default = context_lookup_default

22.052632

56

0.749403

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419

5.086207

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0.264407

0.271186

0.233898

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0.150358

419

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null

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null

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1

0

4

92d37185b4d25339b132694f2cae7b200e571095

82,101

pyp

Python

main-python-backend/pipeline/mi-offline.pyp

paulbaniqued/BCI-VR-Robot_Integration

6a2466d8787997651f6f75270cf44d98baa93fcf

[ "BSD-4-Clause-UC" ]

null

main-python-backend/pipeline/mi-offline.pyp

paulbaniqued/BCI-VR-Robot_Integration

6a2466d8787997651f6f75270cf44d98baa93fcf

[ "BSD-4-Clause-UC" ]

null

main-python-backend/pipeline/mi-offline.pyp

paulbaniqued/BCI-VR-Robot_Integration

6a2466d8787997651f6f75270cf44d98baa93fcf

[ "BSD-4-Clause-UC" ]

null

<?xml version='1.0' encoding='utf-8'?> <scheme description="This pipeline predicts imagined motor actions using neural oscillatory pattern classification. The main node of this pipeline is the Common Spatial Pattern (CSP) filter, which is used to retrieve the components or patterns in the signal that are most suitable to represent desired categories or classes. CSP and its various extensions (available through NeuroPype) provide a powerful tool for building applications based on neural oscillations.
This pipeline can be divided into 4 main parts, which we discuss in the following:

Data acquisition:
Includes : Import Data (here titled “Import SET”), LSL input/output, Stream Data and Inject Calibration Data nodes.
In general you can process your data online or offline. For developing and testing purposes you will be mostly performing offline process using a pre-recorded file.

- The “Import Data” nodes (here titled “Import Set”) are used to connect the pipeline to files.

- The “LSL input” and “LSL output” nodes are used to get data stream into the pipeline, or send the data out to the network from the pipeline. (If you are sending markers make sure to check the “send marker” option in “LSL output” node)

- The “Inject Calibration Data” node is used to pass the initial calibration data into the pipeline before the actual data is processed. The calibration data (Calib Data) is used by adaptive and machine learning algorithms to train and set their parameters initially. The main data is connected to the “Streaming Data” port.

NOTE regarding “Inject Calibration Data”: 
In case you would like to train and test your pipeline using files (without using streaming node), you need to set the “Delay streaming packets” in this node. This enables the “Inject Calibration Data” node to buffer the test data that is pushed into it for one cycle and transfer it to the output port in the next cycle. It should be noted that the first cycle is used to push the calibration data through the pipeline.

Data preprocess:
Includes: Assign Targets, Select Range, FIR filter and Segmentation nodes

- The “Assign Target” node is mostly useful for the supervised learning algorithms, where target values are assigned to specific markers present in the EEG signal. In order for this node to operate correctly you need to know the label for the markers in the data.

- The “Select Range” node is used to specify certain parts of the data stream. For example, if we have a headset that contains certain bad channels, you can manually remove them here. That is the case for our example here where only data from the last 6 channels are used.

- The “FIR Filter” node is used to remove the unwanted signals components outside of the EEG signal frequencies, e.g. to keep the 6-30 Hz frequency window.

- The “Segmentation” node performs the epoching process, where the streamed data is divided into segments of the predefined window-length around the markers on the EEG data.

NOTE regarding "Segmentation" node:
The epoching process can be either done relative to the marker or the time window. When Processing a large file you should set the epoching relative to markers and while processing the streaming data, you should set it to sliding which chooses a single window at the end of the data.

Feature extraction:

Includes: Common Spatial Patterns (CSP) node
As discussed above the spectral and spatial patterns in the data can be extracted by the CSP filters and its extensions.

Classification:
Includes: Variance, Logarithm, Logistic Regression and Measure Loss

- The “Logistic Regression” node is used to perform the classification, where supervised learning methods is used to train the classifier. in this node you can choose the type of regularization and the regularization coefficient. You can also set the number of the folds for cross-validation in this node.

- The “Measure Loss” node is used to measure various performance criteria. Here we use misclassification rate (MCR)." title="Simple Motor Imagery Prediction with CSP" version="2.0"> <nodes> <node id="0" name="Assign Target Values" position="(515.0, 143.0)" project_name="NeuroPype" qualified_name="widgets.machine_learning.owassigntargets.OWAssignTargets" title="Assign Targets" uuid="50fc60e0-68ae-4f5f-9df0-2c665fcec642" version="1.0.0" /> <node id="1" name="Segmentation" position="(725.0, 271.0)" project_name="NeuroPype" qualified_name="widgets.formatting.owsegmentation.OWSegmentation" title="Segmentation" uuid="a354922a-b063-4d8f-90ad-a44a96f4d06e" version="1.0.1" /> <node id="2" name="Common Spatial Patterns" position="(825.0, 271.0)" project_name="NeuroPype" qualified_name="widgets.neural.owcommonspatialpatterns.OWCommonSpatialPatterns" title="Common Spatial Patterns" uuid="fc44e467-8b0d-4105-8ba2-f9e3156be6e0" version="1.0.0" /> <node id="3" name="Variance" position="(925.0, 271.0)" project_name="NeuroPype" 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qualified_name="widgets.visualization.owbarplot.OWBarPlot" title="Streaming Bar Plot" uuid="59e63e6c-7285-42d2-bce1-a843844d84dd" version="1.0.2" /> <node id="12" name="Print To Console" position="(1246.0, 424.0)" project_name="NeuroPype" qualified_name="widgets.diagnostics.owprinttoconsole.OWPrintToConsole" title="Print To Console" uuid="f4456a97-0035-4569-81b8-d1a8c0666637" version="1.0.0" /> <node id="13" name="Import XDF" position="(45.0, 7.0)" project_name="NeuroPype" qualified_name="widgets.file_system.owimportxdf.OWImportXDF" title="Import XDF" uuid="2f01fab8-6e7d-4390-b557-76c83e4024e3" version="1.0.0" /> <node id="14" name="Override Axis" position="(1229.0, 170.0)" project_name="NeuroPype" qualified_name="widgets.tensor_math.owoverrideaxis.OWOverrideAxis" title="Override Axis" uuid="bde71859-dd2d-4489-b29b-7d05b401a1ef" version="1.0.2" /> <node id="15" name="Rewrite Markers" position="(63.0, 216.0)" project_name="NeuroPype" 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"customized": false, "type": "BoolPort", "value": false }, "warmup_samples": { "customized": false, "type": "IntPort", "value": -1 } }, "uuid": "8a92baff-12bd-41ed-89dd-ff3ad2074cff" }, "node26": { "class": "AssignTargets", "module": "neuropype.nodes.machine_learning.AssignTargets", "params": { "also_legacy_output": { "customized": false, "type": "BoolPort", "value": false }, "is_categorical": { "customized": false, "type": "BoolPort", "value": false }, "iv_column": { "customized": false, "type": "StringPort", "value": "Marker" }, "mapping": { "customized": true, "type": "Port", "value": { "0": 0, "1": 1 } }, "mapping_format": { "customized": false, "type": "EnumPort", "value": "compat" }, "set_breakpoint": { "customized": false, "type": "BoolPort", "value": false }, "support_wildcards": { "customized": false, "type": "BoolPort", "value": false }, "use_numbers": { "customized": false, "type": "BoolPort", "value": false }, "verbose": { "customized": false, "type": "BoolPort", "value": false } }, "uuid": "86544d61-58db-404a-b625-f90dc1d40cf6" }, "node27": { "class": "RewriteMarkers", "module": "neuropype.nodes.markers.RewriteMarkers", "params": { "iv_column": { "customized": false, "type": "StringPort", "value": "Marker" }, "pattern_syntax": { "customized": false, "type": "EnumPort", "value": "wildcards" }, "regex_sub": { "customized": false, "type": "BoolPort", "value": false }, "remove_all_others": { "customized": true, "type": "BoolPort", "value": true }, "rules": { "customized": true, "type": "Port", "value": "{'3': '0', '4': '1'}" }, "set_breakpoint": { "customized": false, "type": "BoolPort", "value": false } }, "uuid": "7766f1f8-b8b6-4da7-b3bf-bad7f87ed83c" }, "node3": { "class": "CommonSpatialPatterns", "module": "neuropype.nodes.neural.CommonSpatialPatterns", "params": { "cond_field": { "customized": false, "type": "StringPort", "value": "TargetValue" }, "initialize_once": { "customized": false, "type": "BoolPort", "value": true }, "nof": { "customized": false, "type": "IntPort", "value": 3 }, "set_breakpoint": { "customized": false, "type": "BoolPort", "value": false } }, "uuid": "fc44e467-8b0d-4105-8ba2-f9e3156be6e0" }, "node4": { "class": "Variance", "module": "neuropype.nodes.statistics.Variance", "params": { "axis": { "customized": true, "type": "EnumPort", "value": "time" }, "degrees_of_freedom": { "customized": false, "type": "IntPort", "value": 0 }, "force_feature_axis": { "customized": false, "type": "BoolPort", "value": false }, "set_breakpoint": { "customized": false, "type": "BoolPort", "value": false } }, "uuid": "dde3b169-5e5e-4656-ab61-8c5d4ec14b1e" }, "node5": { "class": "Logarithm", "module": "neuropype.nodes.elementwise_math.Logarithm", "params": { "base": { "customized": false, "type": "FloatPort", "value": null }, "set_breakpoint": { "customized": false, "type": "BoolPort", "value": false } }, "uuid": "52007b84-ac4c-49fb-a4e6-5f463d4d972f" }, "node6": { "class": "SelectRange", "module": "neuropype.nodes.tensor_math.SelectRange", "params": { "apply_multiple_axes": { "customized": false, "type": "BoolPort", "value": false }, "axis": { "customized": true, "type": "EnumPort", "value": "space" }, "selection": { "customized": true, "type": "Port", "value": "0:8" }, "set_breakpoint": { "customized": false, "type": "BoolPort", "value": false }, "unit": { "customized": false, "type": "EnumPort", "value": "indices" } }, "uuid": "0d5bcb38-5560-447d-8f52-f66ea28429f9" }, "node7": { "class": "LogisticRegression", "module": "neuropype.nodes.machine_learning.LogisticRegression", "params": { "alphas": { "customized": false, "type": "ListPort", "value": [ 0.1, 0.5, 1.0, 5, 10.0 ] }, "bias_scaling": { "customized": false, "type": "FloatPort", "value": 1.0 }, "class_weights": { "customized": true, "type": "Port", "value": "auto" }, "cond_field": { "customized": false, "type": "StringPort", "value": "TargetValue" }, "dont_reset_model": { "customized": false, "type": "BoolPort", "value": false }, "dual_formulation": { "customized": false, "type": "BoolPort", "value": false }, "include_bias": { "customized": false, "type": "BoolPort", "value": true }, "initialize_once": { "customized": false, "type": "BoolPort", "value": true }, "max_iter": { "customized": false, "type": "IntPort", "value": 100 }, "multiclass": { "customized": false, "type": "EnumPort", "value": "ovr" }, "num_folds": { "customized": false, "type": "IntPort", "value": 5 }, "num_jobs": { "customized": false, "type": "IntPort", "value": 1 }, "probabilistic": { "customized": false, "type": "BoolPort", "value": true }, "regularizer": { "customized": false, "type": "EnumPort", "value": "l2" }, "search_metric": { "customized": false, "type": "EnumPort", "value": "accuracy" }, "set_breakpoint": { "customized": false, "type": "BoolPort", "value": false }, "solver": { "customized": false, "type": "EnumPort", "value": "lbfgs" }, "tolerance": { "customized": false, "type": "FloatPort", "value": 0.0001 }, "verbosity": { "customized": false, "type": "IntPort", "value": 0 } }, "uuid": "60a38b0d-6e4a-45fa-bcd3-2135d16ab720" }, "node8": { "class": "FIRFilter", "module": "neuropype.nodes.signal_processing.FIRFilter", "params": { "antisymmetric": { "customized": false, "type": "BoolPort", "value": false }, "axis": { "customized": false, "type": "EnumPort", "value": "time" }, "convolution_method": { "customized": false, "type": "EnumPort", "value": "standard" }, "cut_preringing": { "customized": false, "type": "BoolPort", "value": false }, "frequencies": { "customized": true, "type": "ListPort", "value": [ 6, 7, 30, 32 ] }, "minimum_phase": { "customized": false, "type": "BoolPort", "value": true }, "mode": { "customized": false, "type": "EnumPort", "value": "bandpass" }, "order": { "customized": false, "type": "IntPort", "value": null }, "set_breakpoint": { "customized": false, "type": "BoolPort", "value": false }, "stop_atten": { "customized": false, "type": "FloatPort", "value": 50.0 } }, "uuid": "1941778b-c500-4140-ba42-ad02d92a500c" }, "node9": { "class": "LSLInput", "module": "neuropype.nodes.network.LSLInput", "params": { "channel_names": { "customized": false, "type": "ListPort", "value": [] }, "diagnostics": { "customized": false, "type": "BoolPort", "value": false }, "marker_query": { "customized": true, "type": "StringPort", "value": "name='cue_markers'" }, "max_blocklen": { "customized": false, "type": "IntPort", "value": 1024 }, "max_buflen": { "customized": false, "type": "IntPort", "value": 30 }, "max_chunklen": { "customized": false, "type": "IntPort", "value": 0 }, "nominal_rate": { "customized": false, "type": "FloatPort", "value": null }, "query": { "customized": true, "type": "StringPort", "value": "name='EEG'" }, "recover": { "customized": false, "type": "BoolPort", "value": true }, "resolve_minimum_time": { "customized": false, "type": "FloatPort", "value": 0.5 }, "set_breakpoint": { "customized": false, "type": "BoolPort", "value": false } }, "uuid": "c77c08bd-2ca0-43f6-af5f-d7f4396301c1" } }, "version": 1.1 }</patch> </scheme>

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null

1

0

1

0

4

130df3a7747d341b2f305e2053f88d4ccdc18e65

4,311

py

Python

stacker/tests/test_lookups.py

GoodRx/stacker

0cf1df67b4ae5aeda5845442c84905909101c238

[ "BSD-2-Clause" ]

1

2021-11-06T17:01:01.000Z

stacker/tests/test_lookups.py

GoodRx/stacker

0cf1df67b4ae5aeda5845442c84905909101c238

[ "BSD-2-Clause" ]

null

stacker/tests/test_lookups.py

GoodRx/stacker

0cf1df67b4ae5aeda5845442c84905909101c238

[ "BSD-2-Clause" ]

1

2021-11-06T17:00:53.000Z

import unittest from stacker.lookups import extract_lookups class TestLookupExtraction(unittest.TestCase): def test_no_lookups(self): lookups = extract_lookups("value") self.assertEqual(lookups, set()) def test_single_lookup_string(self): lookups = extract_lookups("${output fakeStack::FakeOutput}") self.assertEqual(len(lookups), 1) def test_multiple_lookups_string(self): lookups = extract_lookups( "url://${fakeStack::FakeOutput}@${fakeStack::FakeOutput2}" ) self.assertEqual(len(lookups), 2) self.assertEqual(list(lookups)[0].type, "output") def test_lookups_list(self): lookups = extract_lookups(["something", "${fakeStack::FakeOutput}"]) self.assertEqual(len(lookups), 1) def test_lookups_dict(self): lookups = extract_lookups({ "something": "${fakeStack::FakeOutput}", "other": "value", }) self.assertEqual(len(lookups), 1) def test_lookups_mixed(self): lookups = extract_lookups({ "something": "${fakeStack::FakeOutput}", "list": ["value", "${fakeStack::FakeOutput2}"], "dict": { "other": "value", "another": "${fakeStack::FakeOutput3}", }, }) self.assertEqual(len(lookups), 3) def test_nested_lookups_string(self): lookups = extract_lookups( "${noop ${output stack::Output},${output stack::Output2}}" ) self.assertEqual(len(lookups), 2) def test_comma_delimited(self): lookups = extract_lookups("${noop val1,val2}") self.assertEqual(len(lookups), 1) def test_kms_lookup(self): lookups = extract_lookups("${kms CiADsGxJp1mCR21fjsVjVxr7RwuO2FE3ZJqC4iG0Lm+HkRKwAQEBAgB4A7BsSadZgkdtX47FY1ca+0cLjthRN2SaguIhtC5vh5EAAACHMIGEBgkqhkiG9w0BBwagdzB1AgEAMHAGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQM3IKyEoNEQVxN3BaaAgEQgEOpqa0rcl3WpHOmblAqL1rOPRyokO3YXcJAAB37h/WKLpZZRAWV2h9C67xjlsj3ebg+QIU91T/}") # NOQA self.assertEqual(len(lookups), 1) lookup = list(lookups)[0] self.assertEqual(lookup.type, "kms") self.assertEqual(lookup.input, "CiADsGxJp1mCR21fjsVjVxr7RwuO2FE3ZJqC4iG0Lm+HkRKwAQEBAgB4A7BsSadZgkdtX47FY1ca+0cLjthRN2SaguIhtC5vh5EAAACHMIGEBgkqhkiG9w0BBwagdzB1AgEAMHAGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQM3IKyEoNEQVxN3BaaAgEQgEOpqa0rcl3WpHOmblAqL1rOPRyokO3YXcJAAB37h/WKLpZZRAWV2h9C67xjlsj3ebg+QIU91T/") # NOQA def test_kms_lookup_with_equals(self): lookups = extract_lookups("${kms us-east-1@AQECAHjLp186mZ+mgXTQSytth/ibiIdwBm8CZAzZNSaSkSRqswAAAG4wbAYJKoZIhvcNAQcGoF8wXQIBADBYBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDLNmhGU6fe4vp175MAIBEIAr+8tUpi7SDzOZm+FFyYvWXhs4hEEyaazIn2dP8a+yHzZYDSVYGRpfUz34bQ==}") # NOQA self.assertEqual(len(lookups), 1) lookup = list(lookups)[0] self.assertEqual(lookup.type, "kms") self.assertEqual(lookup.input, "us-east-1@AQECAHjLp186mZ+mgXTQSytth/ibiIdwBm8CZAzZNSaSkSRqswAAAG4wbAYJKoZIhvcNAQcGoF8wXQIBADBYBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDLNmhGU6fe4vp175MAIBEIAr+8tUpi7SDzOZm+FFyYvWXhs4hEEyaazIn2dP8a+yHzZYDSVYGRpfUz34bQ==") # NOQA def test_kms_lookup_with_region(self): lookups = extract_lookups("${kms us-west-2@CiADsGxJp1mCR21fjsVjVxr7RwuO2FE3ZJqC4iG0Lm+HkRKwAQEBAgB4A7BsSadZgkdtX47FY1ca+0cLjthRN2SaguIhtC5vh5EAAACHMIGEBgkqhkiG9w0BBwagdzB1AgEAMHAGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQM3IKyEoNEQVxN3BaaAgEQgEOpqa0rcl3WpHOmblAqL1rOPRyokO3YXcJAAB37h/WKLpZZRAWV2h9C67xjlsj3ebg+QIU91T/}") # NOQA self.assertEqual(len(lookups), 1) lookup = list(lookups)[0] self.assertEqual(lookup.type, "kms") self.assertEqual(lookup.input, "us-west-2@CiADsGxJp1mCR21fjsVjVxr7RwuO2FE3ZJqC4iG0Lm+HkRKwAQEBAgB4A7BsSadZgkdtX47FY1ca+0cLjthRN2SaguIhtC5vh5EAAACHMIGEBgkqhkiG9w0BBwagdzB1AgEAMHAGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQM3IKyEoNEQVxN3BaaAgEQgEOpqa0rcl3WpHOmblAqL1rOPRyokO3YXcJAAB37h/WKLpZZRAWV2h9C67xjlsj3ebg+QIU91T/") # NOQA def test_kms_file_lookup(self): lookups = extract_lookups("${kms file://path/to/some/file.txt}") self.assertEqual(len(lookups), 1) lookup = list(lookups)[0] self.assertEqual(lookup.type, "kms") self.assertEqual(lookup.input, "file://path/to/some/file.txt")

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0.227273

0

null

0

1

0

1

null

0

4

1346233f45637a240358bc12d1960b79691ced42

84

py

Python

index/apps.py

zhaoo/zhaooBlog

cc20315e2045ad4094b4b25dc0ca17992eb45a00

[ "MIT" ]

1

2022-03-03T16:51:03.000Z

index/apps.py

zhaoo/zhaooBlog

cc20315e2045ad4094b4b25dc0ca17992eb45a00

[ "MIT" ]

null

index/apps.py

zhaoo/zhaooBlog

cc20315e2045ad4094b4b25dc0ca17992eb45a00

[ "MIT" ]

null

from django.apps import AppConfig class IndexConfig(AppConfig): name = 'index'

16.8

33

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10

84

6.3

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0.333333

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null

0

1

0

1

0

4

136351ebee692bd9c83522986ff7e92a3df28c70

180

py

Python

python/check_ml_device/check_torch_device.py

kierenAW/snippets_and_boilerplate_code

806f75a0607c6a23dcc8c50d6abfbb5dc6a7e009

[ "MIT" ]

1

2020-02-26T22:21:24.000Z

python/check_ml_device/check_torch_device.py

kierenAW/snippets_and_boilerplate_code

806f75a0607c6a23dcc8c50d6abfbb5dc6a7e009

[ "MIT" ]

null

python/check_ml_device/check_torch_device.py

kierenAW/snippets_and_boilerplate_code

806f75a0607c6a23dcc8c50d6abfbb5dc6a7e009

[ "MIT" ]

null

print("Checking which device Torch is using....") import torch print("Is CUDA available?", torch.cuda.is_available()) print(torch.cuda.get_device_name(torch.cuda.current_device()))

45

62

0.777778

27

180

5.037037

0.481481

0.198529

0

0.066667

180

4

62

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0

1

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true

0

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0.25

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null

0

1

0

null

0

1

0

1

0

4

13697a9275ef727f277913a26e999c41c32aadeb

2,896

py

Python

lBeaufifulSoup/resource.py

jieshenboy/jeckstockpick

39219722a78212fa39eba860b2e945e45df58bff

[ "MIT" ]

1

2018-03-24T10:03:27.000Z

lBeaufifulSoup/resource.py

jieshenboy/jeckstockpick

39219722a78212fa39eba860b2e945e45df58bff

[ "MIT" ]

null

lBeaufifulSoup/resource.py

jieshenboy/jeckstockpick

39219722a78212fa39eba860b2e945e45df58bff

[ "MIT" ]

null

#!/usr/bin/env python #-*- coding: utf-8 -*- UserAgents = [ "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; AcooBrowser; .NET CLR 1.1.4322; .NET CLR 2.0.50727)", "Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.0; Acoo Browser; SLCC1; .NET CLR 2.0.50727; Media Center PC 5.0; .NET CLR 3.0.04506)", "Mozilla/4.0 (compatible; MSIE 7.0; AOL 9.5; AOLBuild 4337.35; Windows NT 5.1; .NET CLR 1.1.4322; .NET CLR 2.0.50727)", "Mozilla/5.0 (Windows; U; MSIE 9.0; Windows NT 9.0; en-US)", "Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Win64; x64; Trident/5.0; .NET CLR 3.5.30729; .NET CLR 3.0.30729; .NET CLR 2.0.50727; Media Center PC 6.0)", "Mozilla/5.0 (compatible; MSIE 8.0; Windows NT 6.0; Trident/4.0; WOW64; Trident/4.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; .NET CLR 1.0.3705; .NET CLR 1.1.4322)", "Mozilla/4.0 (compatible; MSIE 7.0b; Windows NT 5.2; .NET CLR 1.1.4322; .NET CLR 2.0.50727; InfoPath.2; .NET CLR 3.0.04506.30)", "Mozilla/5.0 (Windows; U; Windows NT 5.1; zh-CN) AppleWebKit/523.15 (KHTML, like Gecko, Safari/419.3) Arora/0.3 (Change: 287 c9dfb30)", "Mozilla/5.0 (X11; U; Linux; en-US) AppleWebKit/527+ (KHTML, like Gecko, Safari/419.3) Arora/0.6", "Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.8.1.2pre) Gecko/20070215 K-Ninja/2.1.1", "Mozilla/5.0 (Windows; U; Windows NT 5.1; zh-CN; rv:1.9) Gecko/20080705 Firefox/3.0 Kapiko/3.0", "Mozilla/5.0 (X11; Linux i686; U;) Gecko/20070322 Kazehakase/0.4.5", "Mozilla/5.0 (X11; U; Linux i686; en-US; rv:1.9.0.8) Gecko Fedora/1.9.0.8-1.fc10 Kazehakase/0.5.6", "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.56 Safari/535.11", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_3) AppleWebKit/535.20 (KHTML, like Gecko) Chrome/19.0.1036.7 Safari/535.20", "Opera/9.80 (Macintosh; Intel Mac OS X 10.6.8; U; fr) Presto/2.9.168 Version/11.52", ] PROXIES = [ '122.72.18.34:80', '183.159.91.21:18118', '114.113.126.82:80', '114.252.210.119:8118', '211.159.177.212:3128', '202.96.86.59:61202', '58.19.80.168:18118', '58.19.81.39:18118', '180.121.128.64:3128', '183.94.64.144:61202', '115.46.64.52:8123', '183.23.72.62:61234', '210.5.149.43:8090', '14.153.52.214:3128', '219.135.164.245:3128', '59.55.161.174:61202', '183.159.85.169:18118', '123.180.69.195:6666', '183.33.192.134:9797', '125.125.140.237:61202', '42.87.73.98:61202', '120.92.119.20:10000', '106.14.146.58:3128', '120.77.254.116:3128', '49.70.52.234:8888', '117.69.41.164:61202', '115.235.174.60:61202', '220.164.220.160:61202', '119.28.138.104:3128', '58.19.63.182:18118', '14.153.53.167:3128', '202.111.7.150:1080', '120.77.159.228:3128', '58.19.15.44:18118', '139.196.138.249:8080', '125.125.140.237:61202', '124.193.37.5:8888', '223.240.208.249:18118', '221.225.15.56:61202', '180.116.122.64:6666', '122.72.18.35:80', ]

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138607496668e07a46b0811aa485f90af88dc300

250

py

Python

src/gui/developerWindow.py

michael-stanin/Subtitles-Distributor

e4638d952235f96276729239596dc31d9ccc2ee1

[ "MIT" ]

1

2017-06-03T19:42:05.000Z

src/gui/developerWindow.py

michael-stanin/Subtitles-Distributor

e4638d952235f96276729239596dc31d9ccc2ee1

[ "MIT" ]

null

src/gui/developerWindow.py

michael-stanin/Subtitles-Distributor

e4638d952235f96276729239596dc31d9ccc2ee1

[ "MIT" ]

null

from .helpDialog import HelpDialog from .ui.developerWindowUi import Ui_DeveloperWindow class DeveloperWindow(HelpDialog, Ui_DeveloperWindow): def __init__(self, *args, **kwargs): super(DeveloperWindow, self).__init__(*args, **kwargs)

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null

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1

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4

1387a0a127c2bd81ee61ce4b8ccf08a1de02d6cc

221

py

Python

fabfile.py

dhruv-aggarwal/omnichannel

a39e6092b382572241629f9ee892f8bb6e10f4de

[ "MIT" ]

2

2018-06-06T04:35:17.000Z

2021-08-11T16:15:35.000Z

fabfile.py

dhruv-aggarwal/omnichannel

a39e6092b382572241629f9ee892f8bb6e10f4de

[ "MIT" ]

1

2018-05-11T07:49:20.000Z

2018-05-13T17:45:54.000Z

fabfile.py

dhruv-aggarwal/omnichannel

a39e6092b382572241629f9ee892f8bb6e10f4de

[ "MIT" ]

1

2018-05-17T07:53:04.000Z

# from fabpolish import polish, sniff, info, local # from fabpolish.contrib import ( # find_merge_conflict_leftovers, find_pep8_violations, fix_file_permission, # python_code_analyzer, check_python_debug_info # )

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1399a7c5dfc980be51bb59a6438671f93ea30116

67

py

Python

hello.py

dyrbrm/pynet-test

78c600c35865810403ce6a4901635796fe22c65d

[ "Apache-2.0" ]

null

hello.py

dyrbrm/pynet-test

78c600c35865810403ce6a4901635796fe22c65d

[ "Apache-2.0" ]

null

hello.py

dyrbrm/pynet-test

78c600c35865810403ce6a4901635796fe22c65d

[ "Apache-2.0" ]

null

#!/usr/bin/env python for i in range(1): print "Hello world!"

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null

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4

13a00441693c4933c8684c388134787212b7b60d

195

py

Python

pages/forms.py

bineetgh/examday

c9dc1e3268237dbfff60df91c257b514ef4e0227

[ "MIT" ]

null

pages/forms.py

bineetgh/examday

c9dc1e3268237dbfff60df91c257b514ef4e0227

[ "MIT" ]

null

pages/forms.py

bineetgh/examday

c9dc1e3268237dbfff60df91c257b514ef4e0227

[ "MIT" ]

null

from django import forms from .models import Post class PostCreationForm(forms.ModelForm): class Meta: model = Post fields = ('exam', 'title', 'subtitle', 'content', 'url')

21.666667

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0.651282

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null

0

1

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1

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4

13b708912bf22a449e7551a97fff687b0e4e19f3

23

py

Python

t_eda_analysis/__init__.py

marvinmin/test_edapython

3b759141acba56aea5f11aff45105c9be87aec3b

[ "MIT" ]

41

2016-08-07T21:22:44.000Z

2022-03-08T17:45:36.000Z

t_eda_analysis/__init__.py

marvinmin/test_edapython

3b759141acba56aea5f11aff45105c9be87aec3b

[ "MIT" ]

44

2021-02-26T19:15:19.000Z

2021-03-20T00:07:51.000Z

t_eda_analysis/__init__.py

marvinmin/test_edapython

3b759141acba56aea5f11aff45105c9be87aec3b

[ "MIT" ]

16

2016-12-09T22:57:07.000Z

2020-05-14T05:21:16.000Z

__version__ = '0.1.11'

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0

1

0

1

0

null

0

4

13cab9be46d37bd96f6993e4a1d2d8d231782db1

51,000

py

Python

pandas/tests/tseries/offsets/test_yqm_offsets.py

gsyqax/pandas

cb35d8a938c9222d903482d2f66c62fece5a7aae

[ "PSF-2.0", "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "MIT", "MIT-0", "ECL-2.0", "BSD-3-Clause" ]

1

2020-04-26T22:11:21.000Z

pandas/tests/tseries/offsets/test_yqm_offsets.py

gsyqax/pandas

cb35d8a938c9222d903482d2f66c62fece5a7aae

[ "PSF-2.0", "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "MIT", "MIT-0", "ECL-2.0", "BSD-3-Clause" ]

null

pandas/tests/tseries/offsets/test_yqm_offsets.py

gsyqax/pandas

cb35d8a938c9222d903482d2f66c62fece5a7aae

[ "PSF-2.0", "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "MIT", "MIT-0", "ECL-2.0", "BSD-3-Clause" ]

null

""" Tests for Year, Quarter, and Month-based DateOffset subclasses """ from datetime import datetime import pytest import pandas as pd from pandas import Timestamp from pandas.tseries.offsets import ( BMonthBegin, BMonthEnd, BQuarterBegin, BQuarterEnd, BYearBegin, BYearEnd, MonthBegin, MonthEnd, QuarterBegin, QuarterEnd, YearBegin, YearEnd, ) from .common import assert_is_on_offset, assert_offset_equal from .test_offsets import Base # -------------------------------------------------------------------- # Misc def test_quarterly_dont_normalize(): date = datetime(2012, 3, 31, 5, 30) offsets = (QuarterBegin, QuarterEnd, BQuarterEnd, BQuarterBegin) for klass in offsets: result = date + klass() assert result.time() == date.time() @pytest.mark.parametrize("n", [-2, 1]) @pytest.mark.parametrize( "cls", [ MonthBegin, MonthEnd, BMonthBegin, BMonthEnd, QuarterBegin, QuarterEnd, BQuarterBegin, BQuarterEnd, YearBegin, YearEnd, BYearBegin, BYearEnd, ], ) def test_apply_index(cls, n): offset = cls(n=n) rng = pd.date_range(start="1/1/2000", periods=100000, freq="T") ser = pd.Series(rng) res = rng + offset assert res.freq is None # not retained res_v2 = offset.apply_index(rng) assert (res == res_v2).all() assert res[0] == rng[0] + offset assert res[-1] == rng[-1] + offset res2 = ser + offset # apply_index is only for indexes, not series, so no res2_v2 assert res2.iloc[0] == ser.iloc[0] + offset assert res2.iloc[-1] == ser.iloc[-1] + offset @pytest.mark.parametrize( "offset", [QuarterBegin(), QuarterEnd(), BQuarterBegin(), BQuarterEnd()] ) def test_on_offset(offset): dates = [ datetime(2016, m, d) for m in [10, 11, 12] for d in [1, 2, 3, 28, 29, 30, 31] if not (m == 11 and d == 31) ] for date in dates: res = offset.is_on_offset(date) slow_version = date == (date + offset) - offset assert res == slow_version # -------------------------------------------------------------------- # Months class TestMonthBegin(Base): _offset = MonthBegin offset_cases = [] # NOTE: I'm not entirely happy with the logic here for Begin -ss # see thread 'offset conventions' on the ML offset_cases.append( ( MonthBegin(), { datetime(2008, 1, 31): datetime(2008, 2, 1), datetime(2008, 2, 1): datetime(2008, 3, 1), datetime(2006, 12, 31): datetime(2007, 1, 1), datetime(2006, 12, 1): datetime(2007, 1, 1), datetime(2007, 1, 31): datetime(2007, 2, 1), }, ) ) offset_cases.append( ( MonthBegin(0), { datetime(2008, 1, 31): datetime(2008, 2, 1), datetime(2008, 1, 1): datetime(2008, 1, 1), datetime(2006, 12, 3): datetime(2007, 1, 1), datetime(2007, 1, 31): datetime(2007, 2, 1), }, ) ) offset_cases.append( ( MonthBegin(2), { datetime(2008, 2, 29): datetime(2008, 4, 1), datetime(2008, 1, 31): datetime(2008, 3, 1), datetime(2006, 12, 31): datetime(2007, 2, 1), datetime(2007, 12, 28): datetime(2008, 2, 1), datetime(2007, 1, 1): datetime(2007, 3, 1), datetime(2006, 11, 1): datetime(2007, 1, 1), }, ) ) offset_cases.append( ( MonthBegin(-1), { datetime(2007, 1, 1): datetime(2006, 12, 1), datetime(2008, 5, 31): datetime(2008, 5, 1), datetime(2008, 12, 31): datetime(2008, 12, 1), datetime(2006, 12, 29): datetime(2006, 12, 1), datetime(2006, 1, 2): datetime(2006, 1, 1), }, ) ) @pytest.mark.parametrize("case", offset_cases) def test_offset(self, case): offset, cases = case for base, expected in cases.items(): assert_offset_equal(offset, base, expected) class TestMonthEnd(Base): _offset = MonthEnd def test_day_of_month(self): dt = datetime(2007, 1, 1) offset = MonthEnd() result = dt + offset assert result == Timestamp(2007, 1, 31) result = result + offset assert result == Timestamp(2007, 2, 28) def test_normalize(self): dt = datetime(2007, 1, 1, 3) result = dt + MonthEnd(normalize=True) expected = dt.replace(hour=0) + MonthEnd() assert result == expected offset_cases = [] offset_cases.append( ( MonthEnd(), { datetime(2008, 1, 1): datetime(2008, 1, 31), datetime(2008, 1, 31): datetime(2008, 2, 29), datetime(2006, 12, 29): datetime(2006, 12, 31), datetime(2006, 12, 31): datetime(2007, 1, 31), datetime(2007, 1, 1): datetime(2007, 1, 31), datetime(2006, 12, 1): datetime(2006, 12, 31), }, ) ) offset_cases.append( ( MonthEnd(0), { datetime(2008, 1, 1): datetime(2008, 1, 31), datetime(2008, 1, 31): datetime(2008, 1, 31), datetime(2006, 12, 29): datetime(2006, 12, 31), datetime(2006, 12, 31): datetime(2006, 12, 31), datetime(2007, 1, 1): datetime(2007, 1, 31), }, ) ) offset_cases.append( ( MonthEnd(2), { datetime(2008, 1, 1): datetime(2008, 2, 29), datetime(2008, 1, 31): datetime(2008, 3, 31), datetime(2006, 12, 29): datetime(2007, 1, 31), datetime(2006, 12, 31): datetime(2007, 2, 28), datetime(2007, 1, 1): datetime(2007, 2, 28), datetime(2006, 11, 1): datetime(2006, 12, 31), }, ) ) offset_cases.append( ( MonthEnd(-1), { datetime(2007, 1, 1): datetime(2006, 12, 31), datetime(2008, 6, 30): datetime(2008, 5, 31), datetime(2008, 12, 31): datetime(2008, 11, 30), datetime(2006, 12, 29): datetime(2006, 11, 30), datetime(2006, 12, 30): datetime(2006, 11, 30), datetime(2007, 1, 1): datetime(2006, 12, 31), }, ) ) @pytest.mark.parametrize("case", offset_cases) def test_offset(self, case): offset, cases = case for base, expected in cases.items(): assert_offset_equal(offset, base, expected) on_offset_cases = [ (MonthEnd(), datetime(2007, 12, 31), True), (MonthEnd(), datetime(2008, 1, 1), False), ] @pytest.mark.parametrize("case", on_offset_cases) def test_is_on_offset(self, case): offset, dt, expected = case assert_is_on_offset(offset, dt, expected) class TestBMonthBegin(Base): _offset = BMonthBegin def test_offsets_compare_equal(self): # root cause of #456 offset1 = BMonthBegin() offset2 = BMonthBegin() assert not offset1 != offset2 offset_cases = [] offset_cases.append( ( BMonthBegin(), { datetime(2008, 1, 1): datetime(2008, 2, 1), datetime(2008, 1, 31): datetime(2008, 2, 1), datetime(2006, 12, 29): datetime(2007, 1, 1), datetime(2006, 12, 31): datetime(2007, 1, 1), datetime(2006, 9, 1): datetime(2006, 10, 2), datetime(2007, 1, 1): datetime(2007, 2, 1), datetime(2006, 12, 1): datetime(2007, 1, 1), }, ) ) offset_cases.append( ( BMonthBegin(0), { datetime(2008, 1, 1): datetime(2008, 1, 1), datetime(2006, 10, 2): datetime(2006, 10, 2), datetime(2008, 1, 31): datetime(2008, 2, 1), datetime(2006, 12, 29): datetime(2007, 1, 1), datetime(2006, 12, 31): datetime(2007, 1, 1), datetime(2006, 9, 15): datetime(2006, 10, 2), }, ) ) offset_cases.append( ( BMonthBegin(2), { datetime(2008, 1, 1): datetime(2008, 3, 3), datetime(2008, 1, 15): datetime(2008, 3, 3), datetime(2006, 12, 29): datetime(2007, 2, 1), datetime(2006, 12, 31): datetime(2007, 2, 1), datetime(2007, 1, 1): datetime(2007, 3, 1), datetime(2006, 11, 1): datetime(2007, 1, 1), }, ) ) offset_cases.append( ( BMonthBegin(-1), { datetime(2007, 1, 1): datetime(2006, 12, 1), datetime(2008, 6, 30): datetime(2008, 6, 2), datetime(2008, 6, 1): datetime(2008, 5, 1), datetime(2008, 3, 10): datetime(2008, 3, 3), datetime(2008, 12, 31): datetime(2008, 12, 1), datetime(2006, 12, 29): datetime(2006, 12, 1), datetime(2006, 12, 30): datetime(2006, 12, 1), datetime(2007, 1, 1): datetime(2006, 12, 1), }, ) ) @pytest.mark.parametrize("case", offset_cases) def test_offset(self, case): offset, cases = case for base, expected in cases.items(): assert_offset_equal(offset, base, expected) on_offset_cases = [ (BMonthBegin(), datetime(2007, 12, 31), False), (BMonthBegin(), datetime(2008, 1, 1), True), (BMonthBegin(), datetime(2001, 4, 2), True), (BMonthBegin(), datetime(2008, 3, 3), True), ] @pytest.mark.parametrize("case", on_offset_cases) def test_is_on_offset(self, case): offset, dt, expected = case assert_is_on_offset(offset, dt, expected) class TestBMonthEnd(Base): _offset = BMonthEnd def test_normalize(self): dt = datetime(2007, 1, 1, 3) result = dt + BMonthEnd(normalize=True) expected = dt.replace(hour=0) + BMonthEnd() assert result == expected def test_offsets_compare_equal(self): # root cause of #456 offset1 = BMonthEnd() offset2 = BMonthEnd() assert not offset1 != offset2 offset_cases = [] offset_cases.append( ( BMonthEnd(), { datetime(2008, 1, 1): datetime(2008, 1, 31), datetime(2008, 1, 31): datetime(2008, 2, 29), datetime(2006, 12, 29): datetime(2007, 1, 31), datetime(2006, 12, 31): datetime(2007, 1, 31), datetime(2007, 1, 1): datetime(2007, 1, 31), datetime(2006, 12, 1): datetime(2006, 12, 29), }, ) ) offset_cases.append( ( BMonthEnd(0), { datetime(2008, 1, 1): datetime(2008, 1, 31), datetime(2008, 1, 31): datetime(2008, 1, 31), datetime(2006, 12, 29): datetime(2006, 12, 29), datetime(2006, 12, 31): datetime(2007, 1, 31), datetime(2007, 1, 1): datetime(2007, 1, 31), }, ) ) offset_cases.append( ( BMonthEnd(2), { datetime(2008, 1, 1): datetime(2008, 2, 29), datetime(2008, 1, 31): datetime(2008, 3, 31), datetime(2006, 12, 29): datetime(2007, 2, 28), datetime(2006, 12, 31): datetime(2007, 2, 28), datetime(2007, 1, 1): datetime(2007, 2, 28), datetime(2006, 11, 1): datetime(2006, 12, 29), }, ) ) offset_cases.append( ( BMonthEnd(-1), { datetime(2007, 1, 1): datetime(2006, 12, 29), datetime(2008, 6, 30): datetime(2008, 5, 30), datetime(2008, 12, 31): datetime(2008, 11, 28), datetime(2006, 12, 29): datetime(2006, 11, 30), datetime(2006, 12, 30): datetime(2006, 12, 29), datetime(2007, 1, 1): datetime(2006, 12, 29), }, ) ) @pytest.mark.parametrize("case", offset_cases) def test_offset(self, case): offset, cases = case for base, expected in cases.items(): assert_offset_equal(offset, base, expected) on_offset_cases = [ (BMonthEnd(), datetime(2007, 12, 31), True), (BMonthEnd(), datetime(2008, 1, 1), False), ] @pytest.mark.parametrize("case", on_offset_cases) def test_is_on_offset(self, case): offset, dt, expected = case assert_is_on_offset(offset, dt, expected) # -------------------------------------------------------------------- # Quarters class TestQuarterBegin(Base): def test_repr(self): expected = "<QuarterBegin: startingMonth=3>" assert repr(QuarterBegin()) == expected expected = "<QuarterBegin: startingMonth=3>" assert repr(QuarterBegin(startingMonth=3)) == expected expected = "<QuarterBegin: startingMonth=1>" assert repr(QuarterBegin(startingMonth=1)) == expected def test_is_anchored(self): assert QuarterBegin(startingMonth=1).is_anchored() assert QuarterBegin().is_anchored() assert not QuarterBegin(2, startingMonth=1).is_anchored() def test_offset_corner_case(self): # corner offset = QuarterBegin(n=-1, startingMonth=1) assert datetime(2010, 2, 1) + offset == datetime(2010, 1, 1) offset_cases = [] offset_cases.append( ( QuarterBegin(startingMonth=1), { datetime(2007, 12, 1): datetime(2008, 1, 1), datetime(2008, 1, 1): datetime(2008, 4, 1), datetime(2008, 2, 15): datetime(2008, 4, 1), datetime(2008, 2, 29): datetime(2008, 4, 1), datetime(2008, 3, 15): datetime(2008, 4, 1), datetime(2008, 3, 31): datetime(2008, 4, 1), datetime(2008, 4, 15): datetime(2008, 7, 1), datetime(2008, 4, 1): datetime(2008, 7, 1), }, ) ) offset_cases.append( ( QuarterBegin(startingMonth=2), { datetime(2008, 1, 1): datetime(2008, 2, 1), datetime(2008, 1, 31): datetime(2008, 2, 1), datetime(2008, 1, 15): datetime(2008, 2, 1), datetime(2008, 2, 29): datetime(2008, 5, 1), datetime(2008, 3, 15): datetime(2008, 5, 1), datetime(2008, 3, 31): datetime(2008, 5, 1), datetime(2008, 4, 15): datetime(2008, 5, 1), datetime(2008, 4, 30): datetime(2008, 5, 1), }, ) ) offset_cases.append( ( QuarterBegin(startingMonth=1, n=0), { datetime(2008, 1, 1): datetime(2008, 1, 1), datetime(2008, 12, 1): datetime(2009, 1, 1), datetime(2008, 1, 1): datetime(2008, 1, 1), datetime(2008, 2, 15): datetime(2008, 4, 1), datetime(2008, 2, 29): datetime(2008, 4, 1), datetime(2008, 3, 15): datetime(2008, 4, 1), datetime(2008, 3, 31): datetime(2008, 4, 1), datetime(2008, 4, 15): datetime(2008, 7, 1), datetime(2008, 4, 30): datetime(2008, 7, 1), }, ) ) offset_cases.append( ( QuarterBegin(startingMonth=1, n=-1), { datetime(2008, 1, 1): datetime(2007, 10, 1), datetime(2008, 1, 31): datetime(2008, 1, 1), datetime(2008, 2, 15): datetime(2008, 1, 1), datetime(2008, 2, 29): datetime(2008, 1, 1), datetime(2008, 3, 15): datetime(2008, 1, 1), datetime(2008, 3, 31): datetime(2008, 1, 1), datetime(2008, 4, 15): datetime(2008, 4, 1), datetime(2008, 4, 30): datetime(2008, 4, 1), datetime(2008, 7, 1): datetime(2008, 4, 1), }, ) ) offset_cases.append( ( QuarterBegin(startingMonth=1, n=2), { datetime(2008, 1, 1): datetime(2008, 7, 1), datetime(2008, 2, 15): datetime(2008, 7, 1), datetime(2008, 2, 29): datetime(2008, 7, 1), datetime(2008, 3, 15): datetime(2008, 7, 1), datetime(2008, 3, 31): datetime(2008, 7, 1), datetime(2008, 4, 15): datetime(2008, 10, 1), datetime(2008, 4, 1): datetime(2008, 10, 1), }, ) ) @pytest.mark.parametrize("case", offset_cases) def test_offset(self, case): offset, cases = case for base, expected in cases.items(): assert_offset_equal(offset, base, expected) class TestQuarterEnd(Base): _offset = QuarterEnd def test_repr(self): expected = "<QuarterEnd: startingMonth=3>" assert repr(QuarterEnd()) == expected expected = "<QuarterEnd: startingMonth=3>" assert repr(QuarterEnd(startingMonth=3)) == expected expected = "<QuarterEnd: startingMonth=1>" assert repr(QuarterEnd(startingMonth=1)) == expected def test_is_anchored(self): assert QuarterEnd(startingMonth=1).is_anchored() assert QuarterEnd().is_anchored() assert not QuarterEnd(2, startingMonth=1).is_anchored() def test_offset_corner_case(self): # corner offset = QuarterEnd(n=-1, startingMonth=1) assert datetime(2010, 2, 1) + offset == datetime(2010, 1, 31) offset_cases = [] offset_cases.append( ( QuarterEnd(startingMonth=1), { datetime(2008, 1, 1): datetime(2008, 1, 31), datetime(2008, 1, 31): datetime(2008, 4, 30), datetime(2008, 2, 15): datetime(2008, 4, 30), datetime(2008, 2, 29): datetime(2008, 4, 30), datetime(2008, 3, 15): datetime(2008, 4, 30), datetime(2008, 3, 31): datetime(2008, 4, 30), datetime(2008, 4, 15): datetime(2008, 4, 30), datetime(2008, 4, 30): datetime(2008, 7, 31), }, ) ) offset_cases.append( ( QuarterEnd(startingMonth=2), { datetime(2008, 1, 1): datetime(2008, 2, 29), datetime(2008, 1, 31): datetime(2008, 2, 29), datetime(2008, 2, 15): datetime(2008, 2, 29), datetime(2008, 2, 29): datetime(2008, 5, 31), datetime(2008, 3, 15): datetime(2008, 5, 31), datetime(2008, 3, 31): datetime(2008, 5, 31), datetime(2008, 4, 15): datetime(2008, 5, 31), datetime(2008, 4, 30): datetime(2008, 5, 31), }, ) ) offset_cases.append( ( QuarterEnd(startingMonth=1, n=0), { datetime(2008, 1, 1): datetime(2008, 1, 31), datetime(2008, 1, 31): datetime(2008, 1, 31), datetime(2008, 2, 15): datetime(2008, 4, 30), datetime(2008, 2, 29): datetime(2008, 4, 30), datetime(2008, 3, 15): datetime(2008, 4, 30), datetime(2008, 3, 31): datetime(2008, 4, 30), datetime(2008, 4, 15): datetime(2008, 4, 30), datetime(2008, 4, 30): datetime(2008, 4, 30), }, ) ) offset_cases.append( ( QuarterEnd(startingMonth=1, n=-1), { datetime(2008, 1, 1): datetime(2007, 10, 31), datetime(2008, 1, 31): datetime(2007, 10, 31), datetime(2008, 2, 15): datetime(2008, 1, 31), datetime(2008, 2, 29): datetime(2008, 1, 31), datetime(2008, 3, 15): datetime(2008, 1, 31), datetime(2008, 3, 31): datetime(2008, 1, 31), datetime(2008, 4, 15): datetime(2008, 1, 31), datetime(2008, 4, 30): datetime(2008, 1, 31), datetime(2008, 7, 1): datetime(2008, 4, 30), }, ) ) offset_cases.append( ( QuarterEnd(startingMonth=1, n=2), { datetime(2008, 1, 31): datetime(2008, 7, 31), datetime(2008, 2, 15): datetime(2008, 7, 31), datetime(2008, 2, 29): datetime(2008, 7, 31), datetime(2008, 3, 15): datetime(2008, 7, 31), datetime(2008, 3, 31): datetime(2008, 7, 31), datetime(2008, 4, 15): datetime(2008, 7, 31), datetime(2008, 4, 30): datetime(2008, 10, 31), }, ) ) @pytest.mark.parametrize("case", offset_cases) def test_offset(self, case): offset, cases = case for base, expected in cases.items(): assert_offset_equal(offset, base, expected) on_offset_cases = [ (QuarterEnd(1, startingMonth=1), datetime(2008, 1, 31), True), (QuarterEnd(1, startingMonth=1), datetime(2007, 12, 31), False), (QuarterEnd(1, startingMonth=1), datetime(2008, 2, 29), False), (QuarterEnd(1, startingMonth=1), datetime(2007, 3, 30), False), (QuarterEnd(1, startingMonth=1), datetime(2007, 3, 31), False), (QuarterEnd(1, startingMonth=1), datetime(2008, 4, 30), True), (QuarterEnd(1, startingMonth=1), datetime(2008, 5, 30), False), (QuarterEnd(1, startingMonth=1), datetime(2008, 5, 31), False), (QuarterEnd(1, startingMonth=1), datetime(2007, 6, 29), False), (QuarterEnd(1, startingMonth=1), datetime(2007, 6, 30), False), (QuarterEnd(1, startingMonth=2), datetime(2008, 1, 31), False), (QuarterEnd(1, startingMonth=2), datetime(2007, 12, 31), False), (QuarterEnd(1, startingMonth=2), datetime(2008, 2, 29), True), (QuarterEnd(1, startingMonth=2), datetime(2007, 3, 30), False), (QuarterEnd(1, startingMonth=2), datetime(2007, 3, 31), False), (QuarterEnd(1, startingMonth=2), datetime(2008, 4, 30), False), (QuarterEnd(1, startingMonth=2), datetime(2008, 5, 30), False), (QuarterEnd(1, startingMonth=2), datetime(2008, 5, 31), True), (QuarterEnd(1, startingMonth=2), datetime(2007, 6, 29), False), (QuarterEnd(1, startingMonth=2), datetime(2007, 6, 30), False), (QuarterEnd(1, startingMonth=3), datetime(2008, 1, 31), False), (QuarterEnd(1, startingMonth=3), datetime(2007, 12, 31), True), (QuarterEnd(1, startingMonth=3), datetime(2008, 2, 29), False), (QuarterEnd(1, startingMonth=3), datetime(2007, 3, 30), False), (QuarterEnd(1, startingMonth=3), datetime(2007, 3, 31), True), (QuarterEnd(1, startingMonth=3), datetime(2008, 4, 30), False), (QuarterEnd(1, startingMonth=3), datetime(2008, 5, 30), False), (QuarterEnd(1, startingMonth=3), datetime(2008, 5, 31), False), (QuarterEnd(1, startingMonth=3), datetime(2007, 6, 29), False), (QuarterEnd(1, startingMonth=3), datetime(2007, 6, 30), True), ] @pytest.mark.parametrize("case", on_offset_cases) def test_is_on_offset(self, case): offset, dt, expected = case assert_is_on_offset(offset, dt, expected) class TestBQuarterBegin(Base): _offset = BQuarterBegin def test_repr(self): expected = "<BusinessQuarterBegin: startingMonth=3>" assert repr(BQuarterBegin()) == expected expected = "<BusinessQuarterBegin: startingMonth=3>" assert repr(BQuarterBegin(startingMonth=3)) == expected expected = "<BusinessQuarterBegin: startingMonth=1>" assert repr(BQuarterBegin(startingMonth=1)) == expected def test_is_anchored(self): assert BQuarterBegin(startingMonth=1).is_anchored() assert BQuarterBegin().is_anchored() assert not BQuarterBegin(2, startingMonth=1).is_anchored() def test_offset_corner_case(self): # corner offset = BQuarterBegin(n=-1, startingMonth=1) assert datetime(2007, 4, 3) + offset == datetime(2007, 4, 2) offset_cases = [] offset_cases.append( ( BQuarterBegin(startingMonth=1), { datetime(2008, 1, 1): datetime(2008, 4, 1), datetime(2008, 1, 31): datetime(2008, 4, 1), datetime(2008, 2, 15): datetime(2008, 4, 1), datetime(2008, 2, 29): datetime(2008, 4, 1), datetime(2008, 3, 15): datetime(2008, 4, 1), datetime(2008, 3, 31): datetime(2008, 4, 1), datetime(2008, 4, 15): datetime(2008, 7, 1), datetime(2007, 3, 15): datetime(2007, 4, 2), datetime(2007, 2, 28): datetime(2007, 4, 2), datetime(2007, 1, 1): datetime(2007, 4, 2), datetime(2007, 4, 15): datetime(2007, 7, 2), datetime(2007, 7, 1): datetime(2007, 7, 2), datetime(2007, 4, 1): datetime(2007, 4, 2), datetime(2007, 4, 2): datetime(2007, 7, 2), datetime(2008, 4, 30): datetime(2008, 7, 1), }, ) ) offset_cases.append( ( BQuarterBegin(startingMonth=2), { datetime(2008, 1, 1): datetime(2008, 2, 1), datetime(2008, 1, 31): datetime(2008, 2, 1), datetime(2008, 1, 15): datetime(2008, 2, 1), datetime(2008, 2, 29): datetime(2008, 5, 1), datetime(2008, 3, 15): datetime(2008, 5, 1), datetime(2008, 3, 31): datetime(2008, 5, 1), datetime(2008, 4, 15): datetime(2008, 5, 1), datetime(2008, 8, 15): datetime(2008, 11, 3), datetime(2008, 9, 15): datetime(2008, 11, 3), datetime(2008, 11, 1): datetime(2008, 11, 3), datetime(2008, 4, 30): datetime(2008, 5, 1), }, ) ) offset_cases.append( ( BQuarterBegin(startingMonth=1, n=0), { datetime(2008, 1, 1): datetime(2008, 1, 1), datetime(2007, 12, 31): datetime(2008, 1, 1), datetime(2008, 2, 15): datetime(2008, 4, 1), datetime(2008, 2, 29): datetime(2008, 4, 1), datetime(2008, 1, 15): datetime(2008, 4, 1), datetime(2008, 2, 27): datetime(2008, 4, 1), datetime(2008, 3, 15): datetime(2008, 4, 1), datetime(2007, 4, 1): datetime(2007, 4, 2), datetime(2007, 4, 2): datetime(2007, 4, 2), datetime(2007, 7, 1): datetime(2007, 7, 2), datetime(2007, 4, 15): datetime(2007, 7, 2), datetime(2007, 7, 2): datetime(2007, 7, 2), }, ) ) offset_cases.append( ( BQuarterBegin(startingMonth=1, n=-1), { datetime(2008, 1, 1): datetime(2007, 10, 1), datetime(2008, 1, 31): datetime(2008, 1, 1), datetime(2008, 2, 15): datetime(2008, 1, 1), datetime(2008, 2, 29): datetime(2008, 1, 1), datetime(2008, 3, 15): datetime(2008, 1, 1), datetime(2008, 3, 31): datetime(2008, 1, 1), datetime(2008, 4, 15): datetime(2008, 4, 1), datetime(2007, 7, 3): datetime(2007, 7, 2), datetime(2007, 4, 3): datetime(2007, 4, 2), datetime(2007, 7, 2): datetime(2007, 4, 2), datetime(2008, 4, 1): datetime(2008, 1, 1), }, ) ) offset_cases.append( ( BQuarterBegin(startingMonth=1, n=2), { datetime(2008, 1, 1): datetime(2008, 7, 1), datetime(2008, 1, 15): datetime(2008, 7, 1), datetime(2008, 2, 29): datetime(2008, 7, 1), datetime(2008, 3, 15): datetime(2008, 7, 1), datetime(2007, 3, 31): datetime(2007, 7, 2), datetime(2007, 4, 15): datetime(2007, 10, 1), datetime(2008, 4, 30): datetime(2008, 10, 1), }, ) ) @pytest.mark.parametrize("case", offset_cases) def test_offset(self, case): offset, cases = case for base, expected in cases.items(): assert_offset_equal(offset, base, expected) class TestBQuarterEnd(Base): _offset = BQuarterEnd def test_repr(self): expected = "<BusinessQuarterEnd: startingMonth=3>" assert repr(BQuarterEnd()) == expected expected = "<BusinessQuarterEnd: startingMonth=3>" assert repr(BQuarterEnd(startingMonth=3)) == expected expected = "<BusinessQuarterEnd: startingMonth=1>" assert repr(BQuarterEnd(startingMonth=1)) == expected def test_is_anchored(self): assert BQuarterEnd(startingMonth=1).is_anchored() assert BQuarterEnd().is_anchored() assert not BQuarterEnd(2, startingMonth=1).is_anchored() def test_offset_corner_case(self): # corner offset = BQuarterEnd(n=-1, startingMonth=1) assert datetime(2010, 1, 31) + offset == datetime(2010, 1, 29) offset_cases = [] offset_cases.append( ( BQuarterEnd(startingMonth=1), { datetime(2008, 1, 1): datetime(2008, 1, 31), datetime(2008, 1, 31): datetime(2008, 4, 30), datetime(2008, 2, 15): datetime(2008, 4, 30), datetime(2008, 2, 29): datetime(2008, 4, 30), datetime(2008, 3, 15): datetime(2008, 4, 30), datetime(2008, 3, 31): datetime(2008, 4, 30), datetime(2008, 4, 15): datetime(2008, 4, 30), datetime(2008, 4, 30): datetime(2008, 7, 31), }, ) ) offset_cases.append( ( BQuarterEnd(startingMonth=2), { datetime(2008, 1, 1): datetime(2008, 2, 29), datetime(2008, 1, 31): datetime(2008, 2, 29), datetime(2008, 2, 15): datetime(2008, 2, 29), datetime(2008, 2, 29): datetime(2008, 5, 30), datetime(2008, 3, 15): datetime(2008, 5, 30), datetime(2008, 3, 31): datetime(2008, 5, 30), datetime(2008, 4, 15): datetime(2008, 5, 30), datetime(2008, 4, 30): datetime(2008, 5, 30), }, ) ) offset_cases.append( ( BQuarterEnd(startingMonth=1, n=0), { datetime(2008, 1, 1): datetime(2008, 1, 31), datetime(2008, 1, 31): datetime(2008, 1, 31), datetime(2008, 2, 15): datetime(2008, 4, 30), datetime(2008, 2, 29): datetime(2008, 4, 30), datetime(2008, 3, 15): datetime(2008, 4, 30), datetime(2008, 3, 31): datetime(2008, 4, 30), datetime(2008, 4, 15): datetime(2008, 4, 30), datetime(2008, 4, 30): datetime(2008, 4, 30), }, ) ) offset_cases.append( ( BQuarterEnd(startingMonth=1, n=-1), { datetime(2008, 1, 1): datetime(2007, 10, 31), datetime(2008, 1, 31): datetime(2007, 10, 31), datetime(2008, 2, 15): datetime(2008, 1, 31), datetime(2008, 2, 29): datetime(2008, 1, 31), datetime(2008, 3, 15): datetime(2008, 1, 31), datetime(2008, 3, 31): datetime(2008, 1, 31), datetime(2008, 4, 15): datetime(2008, 1, 31), datetime(2008, 4, 30): datetime(2008, 1, 31), }, ) ) offset_cases.append( ( BQuarterEnd(startingMonth=1, n=2), { datetime(2008, 1, 31): datetime(2008, 7, 31), datetime(2008, 2, 15): datetime(2008, 7, 31), datetime(2008, 2, 29): datetime(2008, 7, 31), datetime(2008, 3, 15): datetime(2008, 7, 31), datetime(2008, 3, 31): datetime(2008, 7, 31), datetime(2008, 4, 15): datetime(2008, 7, 31), datetime(2008, 4, 30): datetime(2008, 10, 31), }, ) ) @pytest.mark.parametrize("case", offset_cases) def test_offset(self, case): offset, cases = case for base, expected in cases.items(): assert_offset_equal(offset, base, expected) on_offset_cases = [ (BQuarterEnd(1, startingMonth=1), datetime(2008, 1, 31), True), (BQuarterEnd(1, startingMonth=1), datetime(2007, 12, 31), False), (BQuarterEnd(1, startingMonth=1), datetime(2008, 2, 29), False), (BQuarterEnd(1, startingMonth=1), datetime(2007, 3, 30), False), (BQuarterEnd(1, startingMonth=1), datetime(2007, 3, 31), False), (BQuarterEnd(1, startingMonth=1), datetime(2008, 4, 30), True), (BQuarterEnd(1, startingMonth=1), datetime(2008, 5, 30), False), (BQuarterEnd(1, startingMonth=1), datetime(2007, 6, 29), False), (BQuarterEnd(1, startingMonth=1), datetime(2007, 6, 30), False), (BQuarterEnd(1, startingMonth=2), datetime(2008, 1, 31), False), (BQuarterEnd(1, startingMonth=2), datetime(2007, 12, 31), False), (BQuarterEnd(1, startingMonth=2), datetime(2008, 2, 29), True), (BQuarterEnd(1, startingMonth=2), datetime(2007, 3, 30), False), (BQuarterEnd(1, startingMonth=2), datetime(2007, 3, 31), False), (BQuarterEnd(1, startingMonth=2), datetime(2008, 4, 30), False), (BQuarterEnd(1, startingMonth=2), datetime(2008, 5, 30), True), (BQuarterEnd(1, startingMonth=2), datetime(2007, 6, 29), False), (BQuarterEnd(1, startingMonth=2), datetime(2007, 6, 30), False), (BQuarterEnd(1, startingMonth=3), datetime(2008, 1, 31), False), (BQuarterEnd(1, startingMonth=3), datetime(2007, 12, 31), True), (BQuarterEnd(1, startingMonth=3), datetime(2008, 2, 29), False), (BQuarterEnd(1, startingMonth=3), datetime(2007, 3, 30), True), (BQuarterEnd(1, startingMonth=3), datetime(2007, 3, 31), False), (BQuarterEnd(1, startingMonth=3), datetime(2008, 4, 30), False), (BQuarterEnd(1, startingMonth=3), datetime(2008, 5, 30), False), (BQuarterEnd(1, startingMonth=3), datetime(2007, 6, 29), True), (BQuarterEnd(1, startingMonth=3), datetime(2007, 6, 30), False), ] @pytest.mark.parametrize("case", on_offset_cases) def test_is_on_offset(self, case): offset, dt, expected = case assert_is_on_offset(offset, dt, expected) # -------------------------------------------------------------------- # Years class TestYearBegin(Base): _offset = YearBegin def test_misspecified(self): with pytest.raises(ValueError, match="Month must go from 1 to 12"): YearBegin(month=13) offset_cases = [] offset_cases.append( ( YearBegin(), { datetime(2008, 1, 1): datetime(2009, 1, 1), datetime(2008, 6, 30): datetime(2009, 1, 1), datetime(2008, 12, 31): datetime(2009, 1, 1), datetime(2005, 12, 30): datetime(2006, 1, 1), datetime(2005, 12, 31): datetime(2006, 1, 1), }, ) ) offset_cases.append( ( YearBegin(0), { datetime(2008, 1, 1): datetime(2008, 1, 1), datetime(2008, 6, 30): datetime(2009, 1, 1), datetime(2008, 12, 31): datetime(2009, 1, 1), datetime(2005, 12, 30): datetime(2006, 1, 1), datetime(2005, 12, 31): datetime(2006, 1, 1), }, ) ) offset_cases.append( ( YearBegin(3), { datetime(2008, 1, 1): datetime(2011, 1, 1), datetime(2008, 6, 30): datetime(2011, 1, 1), datetime(2008, 12, 31): datetime(2011, 1, 1), datetime(2005, 12, 30): datetime(2008, 1, 1), datetime(2005, 12, 31): datetime(2008, 1, 1), }, ) ) offset_cases.append( ( YearBegin(-1), { datetime(2007, 1, 1): datetime(2006, 1, 1), datetime(2007, 1, 15): datetime(2007, 1, 1), datetime(2008, 6, 30): datetime(2008, 1, 1), datetime(2008, 12, 31): datetime(2008, 1, 1), datetime(2006, 12, 29): datetime(2006, 1, 1), datetime(2006, 12, 30): datetime(2006, 1, 1), datetime(2007, 1, 1): datetime(2006, 1, 1), }, ) ) offset_cases.append( ( YearBegin(-2), { datetime(2007, 1, 1): datetime(2005, 1, 1), datetime(2008, 6, 30): datetime(2007, 1, 1), datetime(2008, 12, 31): datetime(2007, 1, 1), }, ) ) offset_cases.append( ( YearBegin(month=4), { datetime(2007, 4, 1): datetime(2008, 4, 1), datetime(2007, 4, 15): datetime(2008, 4, 1), datetime(2007, 3, 1): datetime(2007, 4, 1), datetime(2007, 12, 15): datetime(2008, 4, 1), datetime(2012, 1, 31): datetime(2012, 4, 1), }, ) ) offset_cases.append( ( YearBegin(0, month=4), { datetime(2007, 4, 1): datetime(2007, 4, 1), datetime(2007, 3, 1): datetime(2007, 4, 1), datetime(2007, 12, 15): datetime(2008, 4, 1), datetime(2012, 1, 31): datetime(2012, 4, 1), }, ) ) offset_cases.append( ( YearBegin(4, month=4), { datetime(2007, 4, 1): datetime(2011, 4, 1), datetime(2007, 4, 15): datetime(2011, 4, 1), datetime(2007, 3, 1): datetime(2010, 4, 1), datetime(2007, 12, 15): datetime(2011, 4, 1), datetime(2012, 1, 31): datetime(2015, 4, 1), }, ) ) offset_cases.append( ( YearBegin(-1, month=4), { datetime(2007, 4, 1): datetime(2006, 4, 1), datetime(2007, 3, 1): datetime(2006, 4, 1), datetime(2007, 12, 15): datetime(2007, 4, 1), datetime(2012, 1, 31): datetime(2011, 4, 1), }, ) ) offset_cases.append( ( YearBegin(-3, month=4), { datetime(2007, 4, 1): datetime(2004, 4, 1), datetime(2007, 3, 1): datetime(2004, 4, 1), datetime(2007, 12, 15): datetime(2005, 4, 1), datetime(2012, 1, 31): datetime(2009, 4, 1), }, ) ) @pytest.mark.parametrize("case", offset_cases) def test_offset(self, case): offset, cases = case for base, expected in cases.items(): assert_offset_equal(offset, base, expected) on_offset_cases = [ (YearBegin(), datetime(2007, 1, 3), False), (YearBegin(), datetime(2008, 1, 1), True), (YearBegin(), datetime(2006, 12, 31), False), (YearBegin(), datetime(2006, 1, 2), False), ] @pytest.mark.parametrize("case", on_offset_cases) def test_is_on_offset(self, case): offset, dt, expected = case assert_is_on_offset(offset, dt, expected) class TestYearEnd(Base): _offset = YearEnd def test_misspecified(self): with pytest.raises(ValueError, match="Month must go from 1 to 12"): YearEnd(month=13) offset_cases = [] offset_cases.append( ( YearEnd(), { datetime(2008, 1, 1): datetime(2008, 12, 31), datetime(2008, 6, 30): datetime(2008, 12, 31), datetime(2008, 12, 31): datetime(2009, 12, 31), datetime(2005, 12, 30): datetime(2005, 12, 31), datetime(2005, 12, 31): datetime(2006, 12, 31), }, ) ) offset_cases.append( ( YearEnd(0), { datetime(2008, 1, 1): datetime(2008, 12, 31), datetime(2008, 6, 30): datetime(2008, 12, 31), datetime(2008, 12, 31): datetime(2008, 12, 31), datetime(2005, 12, 30): datetime(2005, 12, 31), }, ) ) offset_cases.append( ( YearEnd(-1), { datetime(2007, 1, 1): datetime(2006, 12, 31), datetime(2008, 6, 30): datetime(2007, 12, 31), datetime(2008, 12, 31): datetime(2007, 12, 31), datetime(2006, 12, 29): datetime(2005, 12, 31), datetime(2006, 12, 30): datetime(2005, 12, 31), datetime(2007, 1, 1): datetime(2006, 12, 31), }, ) ) offset_cases.append( ( YearEnd(-2), { datetime(2007, 1, 1): datetime(2005, 12, 31), datetime(2008, 6, 30): datetime(2006, 12, 31), datetime(2008, 12, 31): datetime(2006, 12, 31), }, ) ) @pytest.mark.parametrize("case", offset_cases) def test_offset(self, case): offset, cases = case for base, expected in cases.items(): assert_offset_equal(offset, base, expected) on_offset_cases = [ (YearEnd(), datetime(2007, 12, 31), True), (YearEnd(), datetime(2008, 1, 1), False), (YearEnd(), datetime(2006, 12, 31), True), (YearEnd(), datetime(2006, 12, 29), False), ] @pytest.mark.parametrize("case", on_offset_cases) def test_is_on_offset(self, case): offset, dt, expected = case assert_is_on_offset(offset, dt, expected) class TestYearEndDiffMonth(Base): offset_cases = [] offset_cases.append( ( YearEnd(month=3), { datetime(2008, 1, 1): datetime(2008, 3, 31), datetime(2008, 2, 15): datetime(2008, 3, 31), datetime(2008, 3, 31): datetime(2009, 3, 31), datetime(2008, 3, 30): datetime(2008, 3, 31), datetime(2005, 3, 31): datetime(2006, 3, 31), datetime(2006, 7, 30): datetime(2007, 3, 31), }, ) ) offset_cases.append( ( YearEnd(0, month=3), { datetime(2008, 1, 1): datetime(2008, 3, 31), datetime(2008, 2, 28): datetime(2008, 3, 31), datetime(2008, 3, 31): datetime(2008, 3, 31), datetime(2005, 3, 30): datetime(2005, 3, 31), }, ) ) offset_cases.append( ( YearEnd(-1, month=3), { datetime(2007, 1, 1): datetime(2006, 3, 31), datetime(2008, 2, 28): datetime(2007, 3, 31), datetime(2008, 3, 31): datetime(2007, 3, 31), datetime(2006, 3, 29): datetime(2005, 3, 31), datetime(2006, 3, 30): datetime(2005, 3, 31), datetime(2007, 3, 1): datetime(2006, 3, 31), }, ) ) offset_cases.append( ( YearEnd(-2, month=3), { datetime(2007, 1, 1): datetime(2005, 3, 31), datetime(2008, 6, 30): datetime(2007, 3, 31), datetime(2008, 3, 31): datetime(2006, 3, 31), }, ) ) @pytest.mark.parametrize("case", offset_cases) def test_offset(self, case): offset, cases = case for base, expected in cases.items(): assert_offset_equal(offset, base, expected) on_offset_cases = [ (YearEnd(month=3), datetime(2007, 3, 31), True), (YearEnd(month=3), datetime(2008, 1, 1), False), (YearEnd(month=3), datetime(2006, 3, 31), True), (YearEnd(month=3), datetime(2006, 3, 29), False), ] @pytest.mark.parametrize("case", on_offset_cases) def test_is_on_offset(self, case): offset, dt, expected = case assert_is_on_offset(offset, dt, expected) class TestBYearBegin(Base): _offset = BYearBegin def test_misspecified(self): msg = "Month must go from 1 to 12" with pytest.raises(ValueError, match=msg): BYearBegin(month=13) with pytest.raises(ValueError, match=msg): BYearEnd(month=13) offset_cases = [] offset_cases.append( ( BYearBegin(), { datetime(2008, 1, 1): datetime(2009, 1, 1), datetime(2008, 6, 30): datetime(2009, 1, 1), datetime(2008, 12, 31): datetime(2009, 1, 1), datetime(2011, 1, 1): datetime(2011, 1, 3), datetime(2011, 1, 3): datetime(2012, 1, 2), datetime(2005, 12, 30): datetime(2006, 1, 2), datetime(2005, 12, 31): datetime(2006, 1, 2), }, ) ) offset_cases.append( ( BYearBegin(0), { datetime(2008, 1, 1): datetime(2008, 1, 1), datetime(2008, 6, 30): datetime(2009, 1, 1), datetime(2008, 12, 31): datetime(2009, 1, 1), datetime(2005, 12, 30): datetime(2006, 1, 2), datetime(2005, 12, 31): datetime(2006, 1, 2), }, ) ) offset_cases.append( ( BYearBegin(-1), { datetime(2007, 1, 1): datetime(2006, 1, 2), datetime(2009, 1, 4): datetime(2009, 1, 1), datetime(2009, 1, 1): datetime(2008, 1, 1), datetime(2008, 6, 30): datetime(2008, 1, 1), datetime(2008, 12, 31): datetime(2008, 1, 1), datetime(2006, 12, 29): datetime(2006, 1, 2), datetime(2006, 12, 30): datetime(2006, 1, 2), datetime(2006, 1, 1): datetime(2005, 1, 3), }, ) ) offset_cases.append( ( BYearBegin(-2), { datetime(2007, 1, 1): datetime(2005, 1, 3), datetime(2007, 6, 30): datetime(2006, 1, 2), datetime(2008, 12, 31): datetime(2007, 1, 1), }, ) ) @pytest.mark.parametrize("case", offset_cases) def test_offset(self, case): offset, cases = case for base, expected in cases.items(): assert_offset_equal(offset, base, expected) class TestBYearEnd(Base): _offset = BYearEnd offset_cases = [] offset_cases.append( ( BYearEnd(), { datetime(2008, 1, 1): datetime(2008, 12, 31), datetime(2008, 6, 30): datetime(2008, 12, 31), datetime(2008, 12, 31): datetime(2009, 12, 31), datetime(2005, 12, 30): datetime(2006, 12, 29), datetime(2005, 12, 31): datetime(2006, 12, 29), }, ) ) offset_cases.append( ( BYearEnd(0), { datetime(2008, 1, 1): datetime(2008, 12, 31), datetime(2008, 6, 30): datetime(2008, 12, 31), datetime(2008, 12, 31): datetime(2008, 12, 31), datetime(2005, 12, 31): datetime(2006, 12, 29), }, ) ) offset_cases.append( ( BYearEnd(-1), { datetime(2007, 1, 1): datetime(2006, 12, 29), datetime(2008, 6, 30): datetime(2007, 12, 31), datetime(2008, 12, 31): datetime(2007, 12, 31), datetime(2006, 12, 29): datetime(2005, 12, 30), datetime(2006, 12, 30): datetime(2006, 12, 29), datetime(2007, 1, 1): datetime(2006, 12, 29), }, ) ) offset_cases.append( ( BYearEnd(-2), { datetime(2007, 1, 1): datetime(2005, 12, 30), datetime(2008, 6, 30): datetime(2006, 12, 29), datetime(2008, 12, 31): datetime(2006, 12, 29), }, ) ) @pytest.mark.parametrize("case", offset_cases) def test_offset(self, case): offset, cases = case for base, expected in cases.items(): assert_offset_equal(offset, base, expected) on_offset_cases = [ (BYearEnd(), datetime(2007, 12, 31), True), (BYearEnd(), datetime(2008, 1, 1), False), (BYearEnd(), datetime(2006, 12, 31), False), (BYearEnd(), datetime(2006, 12, 29), True), ] @pytest.mark.parametrize("case", on_offset_cases) def test_is_on_offset(self, case): offset, dt, expected = case assert_is_on_offset(offset, dt, expected) class TestBYearEndLagged(Base): _offset = BYearEnd def test_bad_month_fail(self): msg = "Month must go from 1 to 12" with pytest.raises(ValueError, match=msg): BYearEnd(month=13) with pytest.raises(ValueError, match=msg): BYearEnd(month=0) offset_cases = [] offset_cases.append( ( BYearEnd(month=6), { datetime(2008, 1, 1): datetime(2008, 6, 30), datetime(2007, 6, 30): datetime(2008, 6, 30), }, ) ) offset_cases.append( ( BYearEnd(n=-1, month=6), { datetime(2008, 1, 1): datetime(2007, 6, 29), datetime(2007, 6, 30): datetime(2007, 6, 29), }, ) ) @pytest.mark.parametrize("case", offset_cases) def test_offset(self, case): offset, cases = case for base, expected in cases.items(): assert_offset_equal(offset, base, expected) def test_roll(self): offset = BYearEnd(month=6) date = datetime(2009, 11, 30) assert offset.rollforward(date) == datetime(2010, 6, 30) assert offset.rollback(date) == datetime(2009, 6, 30) on_offset_cases = [ (BYearEnd(month=2), datetime(2007, 2, 28), True), (BYearEnd(month=6), datetime(2007, 6, 30), False), ] @pytest.mark.parametrize("case", on_offset_cases) def test_is_on_offset(self, case): offset, dt, expected = case assert_is_on_offset(offset, dt, expected)

34.78854

76

0.506922

5,816

51,000

4.389615

0.031809

0.234548

0.075872

0.041676

0.870427

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0.79765

0.698786

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0.19306

0.345627

51,000

1,465

77

34.812287

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0.083203

0

null

1

0

1

0

null

0

4

b915ac63e71e9e031c172100035dcc29d8c1b248

180

py

Python

dpylint/checkers/basechecker.py

wasi-master/dpylint

521db6085c1a4f981a59e5169acb50cfd04b89fd

[ "MIT" ]

2

2021-08-10T16:43:34.000Z

2022-03-14T08:41:12.000Z

dpylint/checkers/basechecker.py

wasi-master/dpylint

521db6085c1a4f981a59e5169acb50cfd04b89fd

[ "MIT" ]

null

dpylint/checkers/basechecker.py

wasi-master/dpylint

521db6085c1a4f981a59e5169acb50cfd04b89fd

[ "MIT" ]

null

import astroid from pylint.checkers import BaseChecker from pylint.interfaces import IAstroidChecker class DiscordBaseChecker(BaseChecker): __implements__ = IAstroidChecker

20

45

0.844444

17

180

8.705882

0.647059

0.135135

0

0.122222

180

8

46

22.5

0.936709

0

1

0

false

0

0.6

0

1

0

1

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null

0

1

0

null

0

1

0

1

0

4

b93a6ae4cb36e3530132e41283fbf43476e724b3

111

py

Python

readthestuff/__init__.py

playpauseandstop/readthestuff

13024fef364cd6770326c48eb7806a6a9a75abb8

[ "BSD-3-Clause" ]

2

2015-10-27T07:23:19.000Z

2015-11-05T16:50:04.000Z

readthestuff/__init__.py

playpauseandstop/readthestuff

13024fef364cd6770326c48eb7806a6a9a75abb8

[ "BSD-3-Clause" ]

null

readthestuff/__init__.py

playpauseandstop/readthestuff

13024fef364cd6770326c48eb7806a6a9a75abb8

[ "BSD-3-Clause" ]

null

""" ============ readthestuff ============ Yet another Google Reader alternative built on top of Python. """

12.333333

61

0.567568

11

111

5.727273

1

0

0.153153

111

8

62

13.875

0.670213

0.90991

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

4

b96bd25f85a2b0a985b147e15d0e58ef9ae18a24

153

py

Python

Algoritimos/uri1019.py

mathspin/Algoritimos-py

3a814dd924d9ee4c15ee4734170ed82f70e95479

[ "MIT" ]

null

Algoritimos/uri1019.py

mathspin/Algoritimos-py

3a814dd924d9ee4c15ee4734170ed82f70e95479

[ "MIT" ]

null

Algoritimos/uri1019.py

mathspin/Algoritimos-py

3a814dd924d9ee4c15ee4734170ed82f70e95479

[ "MIT" ]

null

s = input("digite um valor em segundos: ") h = int(int(s)/3600) m = int((int(s)%3600)/60) s = int((int(s)%3600)%60) print (str(h)+":"+str(m)+":"+str(s))

25.5

42

0.562092

31

153

2.774194

0.451613

0.209302

0.244186

0.383721

0.302326

0

0.119403

0.124183

153

5

43

30.6

0.522388

0

0.202614

0

1

0

false

0

0.2

0

null

1

0

1

0

null

0

4

b97b428482bcf516996e391f3b4734f973e52b1d

15

py

Python

typewriter/annotations/tests/__init__.py

ezragoss/typewriter

7d4e70864036190cc705dc22c465f838b522f3fe

[ "Apache-2.0" ]

1,363

2017-11-13T23:46:52.000Z

2022-03-31T17:23:58.000Z

typewriter/annotations/tests/__init__.py

ezragoss/typewriter

7d4e70864036190cc705dc22c465f838b522f3fe

[ "Apache-2.0" ]

91

2017-11-14T18:48:00.000Z

2022-03-10T09:21:27.000Z

typewriter/annotations/tests/__init__.py

ezragoss/typewriter

7d4e70864036190cc705dc22c465f838b522f3fe

[ "Apache-2.0" ]

65

2017-11-16T05:38:02.000Z

2022-02-11T15:38:21.000Z

# type: ignore

7.5

14

0.666667

2

15

5

1

0

0.2

15

1

15

0.833333

0.8

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

b9806e7bba2a089ec10d5b23d92d1e0683a7f091

61

py

Python

src/helloworld.py

krzychb/readme-code-testing

5ba816d64595fd04039db69047dc6d5bb3517f51

[ "MIT" ]

null

src/helloworld.py

krzychb/readme-code-testing

5ba816d64595fd04039db69047dc6d5bb3517f51

[ "MIT" ]

null

src/helloworld.py

krzychb/readme-code-testing

5ba816d64595fd04039db69047dc6d5bb3517f51

[ "MIT" ]

null

def hello(): message = "v1.0.0 world" return message

15.25

28

0.606557

9

61

4.111111

0.777778

0

0.066667

0.262295

61

3

29

20.333333

0.755556

0

0.196721

0

1

0.333333

false

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

b9b77b94fa8d16d03628a356c57dde6fcfa91f8d

539

py

Python

tools/getcolor.py

cmhello/Material-Design-Avatars

a30e5fd168a7a9e149e3c28cb5cc2895cb5745b2

[ "Apache-2.0" ]

304

2015-04-30T03:45:47.000Z

2022-03-01T16:17:55.000Z

tools/getcolor.py

cmhello/Material-Design-Avatars

a30e5fd168a7a9e149e3c28cb5cc2895cb5745b2

[ "Apache-2.0" ]

10

2015-04-30T07:26:24.000Z

2019-01-02T13:06:50.000Z

tools/getcolor.py

cmhello/Material-Design-Avatars

a30e5fd168a7a9e149e3c28cb5cc2895cb5745b2

[ "Apache-2.0" ]

86

2015-05-01T06:32:32.000Z

2020-12-11T12:35:17.000Z

#encoding=utf-8 from xml.dom import minidom doc = minidom.parse("color.xml") root = doc.documentElement resources = root.getElementsByTagName("color") for color in resources: #print "0x"+color.childNodes[0].nodeValue.replace("#","") print "array(", print int("0x"+color.childNodes[0].nodeValue.replace("#","")[0:2],16), print ",", print int("0x"+color.childNodes[0].nodeValue.replace("#","")[2:4],16), print ",", print int("0x"+color.childNodes[0].nodeValue.replace("#","")[4:6],16), print "),"

38.5

75

0.628942

69

539

4.913043

0.42029

0.082596

0.20059

0.212389

0.513274

0.412979

0.289086

0

0.045553

0.144712

539

14

76

38.5

0.689805

0.128015

0

0.166667

0

0.072527

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null

0

0.083333

null

0.583333

0

null

0

1

0

null

0

1

0

1

0

4

b9cce9415d41fa5e811592051a61e31fcc7b5674

238

py

Python

rest_server/books/serializers.py

Air-t/bookstore-backend

b648f25d746b9f6fba3d8c7d7f45d8a8345b94be

[ "MIT" ]

null

rest_server/books/serializers.py

Air-t/bookstore-backend

b648f25d746b9f6fba3d8c7d7f45d8a8345b94be

[ "MIT" ]

null

rest_server/books/serializers.py

Air-t/bookstore-backend

b648f25d746b9f6fba3d8c7d7f45d8a8345b94be

[ "MIT" ]

null

from .models import Book from rest_framework import serializers class BookSerializer(serializers.HyperlinkedModelSerializer): class Meta: model = Book fields = ("id", "author", "title", "isbn", "publisher", "genre")

26.444444

72

0.697479

24

238

6.875

0.791667

0

0.189076

238

8

73

29.75

0.854922

0

0.130252

0

1

0

false

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

b9e2fab4234308af87bf277b6f03180485948fd4

431

py

Python

foodelivery/ext/config/__init__.py

araneto/foodelivery

9c8c587307286d9f0b79206bf8464d8fff9073fa

[ "MIT" ]

null

foodelivery/ext/config/__init__.py

araneto/foodelivery

9c8c587307286d9f0b79206bf8464d8fff9073fa

[ "MIT" ]

1

2020-09-14T22:09:03.000Z

foodelivery/ext/config/__init__.py

araneto/foodelivery

9c8c587307286d9f0b79206bf8464d8fff9073fa

[ "MIT" ]

null

def init_app(app): app.config["SECRET_KEY"] = "passW0rd01" app.config["SQLALCHEMY_DATABASE_URI"] = "sqlite:///foodelivery.db" app.config["SQLALCHEMY_TRACK_MODIFICATIONS"] = False app.config["FLASK_ADMIN_SWATCH"] = "cerulean" app.config["DEBUG_TB_INTERCEPT_REDIRECTS"] = False if app.debug: app.config["DEBUG_TB_TEMPLATE_EDITOR_ENABLED"] = True app.config["DEBUG_TB_PROFILER_ENABLED"] = True

43.1

70

0.714617

54

431

5.37037

0.555556

0.217241

0.144828

0.165517

0

0.008197

0.150812

431

9

71

47.888889

0.784153

0

0.482599

0.37587

0

1

0.111111

false

0.111111

0

0.111111

0

null

1

0

1

0

1

0

null

0

1

0

4

b9e854937fcf4de165bf8e2d59b5a553159c31c7

194

py

Python

doges/serializers/role_serializer.py

Nunuzac/doges

fcd0343946bf0cb4f4a80bb910acea44dfa71b37

[ "Apache-2.0" ]

null

doges/serializers/role_serializer.py

Nunuzac/doges

fcd0343946bf0cb4f4a80bb910acea44dfa71b37

[ "Apache-2.0" ]

null

doges/serializers/role_serializer.py

Nunuzac/doges

fcd0343946bf0cb4f4a80bb910acea44dfa71b37

[ "Apache-2.0" ]

null

from rest_framework import serializers from doges.models import Role class RoleSerializer(serializers.HyperlinkedModelSerializer): class Meta: model = Role fields = ['id', 'name']

21.555556

61

0.752577

21

194

6.904762

0.761905

0

0.170103

194

8

62

24.25

0.900621

0

0.030928

0

1

0

false

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

b9f40ef2de3fd222559441bc2c6b9535e17570ea

134

py

Python

tests/__init__.py

geektutor/credo-python

955b0c11af5bf170e2ba302a7857da49a330ce0b

[ "MIT" ]

2

2022-03-07T21:10:00.000Z

2022-03-13T12:38:06.000Z

tests/__init__.py

BdVade/credo-python

d9dc0cfb346fdfb6d389bb294ca7d0ea4cb15acf

[ "MIT" ]

null

tests/__init__.py

BdVade/credo-python

d9dc0cfb346fdfb6d389bb294ca7d0ea4cb15acf

[ "MIT" ]

null

SECRET_KEY = "sk_demo-dmq2ZsiZ23sKbgHBAZvRhQ25qBtnD1.7HWfBSGEZX-d" PUBLIC_KEY = "pk_demo-fKq5DnKgI3ISchDxVEySOS4Z9X4hck.D1gJjoVG5p-d"

44.666667

66

0.865672

14

134

8

0.785714

0

0.109375

0.044776

134

2

67

0.765625

0

0.761194

0

1

0

false

0

1

0

null

0

1

0

1

null

0

4

b9f58633ed3c365d97648336da2fccb1bea99586

19,157

py

Python

modules/chempy/protein.py

markdoerr/pymol-open-source

b891b59ffaea812600648aa131ea2dbecd59a199

[ "CNRI-Python" ]

null

modules/chempy/protein.py

markdoerr/pymol-open-source

b891b59ffaea812600648aa131ea2dbecd59a199

[ "CNRI-Python" ]

null

modules/chempy/protein.py

markdoerr/pymol-open-source

b891b59ffaea812600648aa131ea2dbecd59a199

[ "CNRI-Python" ]

null

#A* ------------------------------------------------------------------- #B* This file contains source code for the PyMOL computer program #C* copyright 1998-2000 by Warren Lyford Delano of DeLano Scientific. #D* ------------------------------------------------------------------- #E* It is unlawful to modify or remove this copyright notice. #F* ------------------------------------------------------------------- #G* Please see the accompanying LICENSE file for further information. #H* ------------------------------------------------------------------- #I* Additional authors of this source file include: #-* #-* #-* #Z* ------------------------------------------------------------------- # # # from __future__ import print_function from . import bond_amber from . import protein_residues from . import protein_amber import chempy.models from chempy.neighbor import Neighbor from chempy.models import Connected from chempy import Bond,place,feedback from chempy.cpv import * MAX_BOND_LEN = 2.2 PEPT_CUTOFF = 1.7 N_TERMINAL_ATOMS = ('HT','HT1','HT2','HT3','H1','H2','H3', '1H','2H','3H','1HT','2HT','3HT') C_TERMINAL_ATOMS = ('OXT','O2','OT1','OT2') #--------------------------------------------------------------------------------- # NOTE: right now, the only way to get N-terminal residues is to # submit a structure which contains at least one N_TERMINAL hydrogens def generate(model, forcefield = protein_amber, histidine = 'HIE', skip_sort=None, bondfield = bond_amber ): strip_atom_bonds(model) # remove bonds between non-hetatms (ATOM) add_bonds(model,forcefield=forcefield) connected = model.convert_to_connected() add_hydrogens(connected,forcefield=forcefield,skip_sort=skip_sort) place.simple_unknowns(connected,bondfield = bondfield) return connected.convert_to_indexed() #--------------------------------------------------------------------------------- def strip_atom_bonds(model): new_bond = [] matom = model.atom for a in model.bond: if matom[a.index[0]].hetatm or matom[a.index[1]].hetatm: new_bond.append(a) model.bond = new_bond #--------------------------------------------------------------------------------- def assign_types(model, forcefield = protein_amber, histidine = 'HIE' ): ''' assigns types: takes HIS -> HID,HIE,HIP and CYS->CYX where appropriate but does not add any bonds! ''' if feedback['actions']: print(" "+str(__name__)+": assigning types...") if not isinstance(model, chempy.models.Indexed): raise ValueError('model is not an "Indexed" model object') if model.nAtom: crd = model.get_coord_list() nbr = Neighbor(crd,MAX_BOND_LEN) res_list = model.get_residues() if len(res_list): for a in res_list: base = model.atom[a[0]] if not base.hetatm: resn = base.resn if resn == 'HIS': for c in range(a[0],a[1]): # this residue model.atom[c].resn = histidine resn = histidine if resn == 'N-M': # N-methyl from Insight II, for c in range(a[0],a[1]): # this residue model.atom[c].resn = 'NME' resn = 'NME' # find out if this is n or c terminal residue names = [] for b in range(a[0],a[1]): names.append(model.atom[b].name) tmpl = protein_residues.normal if forcefield: ffld = forcefield.normal for b in N_TERMINAL_ATOMS: if b in names: tmpl = protein_residues.n_terminal if forcefield: ffld = forcefield.n_terminal break for b in C_TERMINAL_ATOMS: if b in names: tmpl = protein_residues.c_terminal if forcefield: ffld = forcefield.c_terminal break if resn not in tmpl: raise RuntimeError("unknown residue type '"+resn+"'") else: # reassign atom names and build dictionary dict = {} aliases = tmpl[resn]['aliases'] for b in range(a[0],a[1]): at = model.atom[b] if at.name in aliases: at.name = aliases[at.name] dict[at.name] = b if forcefield: k = (resn,at.name) if k in ffld: at.text_type = ffld[k]['type'] at.partial_charge = ffld[k]['charge'] else: raise RuntimeError("no parameters for '"+str(k)+"'") if 'SG' in dict: # cysteine cur = dict['SG'] at = model.atom[cur] lst = nbr.get_neighbors(at.coord) for b in lst: if b>cur: # only do this once (only when b>cur - i.e. this is 1st CYS) at2 = model.atom[b] if at2.name=='SG': if not at2.in_same_residue(at): dst = distance(at.coord,at2.coord) if dst<=MAX_BOND_LEN: if forcefield: for c in range(a[0],a[1]): # this residue atx = model.atom[c] atx.resn = 'CYX' resn = atx.resn if (c<=b): k = ('CYX',atx.name) if k in ffld: atx.text_type = ffld[k]['type'] atx.partial_charge = ffld[k]['charge'] else: raise RuntimeError("no parameters for '"+str(k)+"'") for d in res_list: # other residue if (b>=d[0]) and (b<d[1]): for c in range(d[0],d[1]): atx = model.atom[c] atx.resn = 'CYX' # since b>cur, assume assignment later on break #--------------------------------------------------------------------------------- def add_bonds(model, forcefield = protein_amber, histidine = 'HIE' ): ''' add_bonds(model, forcefield = protein_amber, histidine = 'HIE' ) (1) fixes aliases, assigns types, makes HIS into HIE,HID, or HIP and changes cystine to CYX (2) adds bonds between existing atoms ''' if feedback['actions']: print(" "+str(__name__)+": assigning types and bonds...") if not isinstance(model, chempy.models.Indexed): raise ValueError('model is not an "Indexed" model object') if model.nAtom: crd = model.get_coord_list() nbr = Neighbor(crd,MAX_BOND_LEN) res_list = model.get_residues() if len(res_list): for a in res_list: base = model.atom[a[0]] if not base.hetatm: resn = base.resn if resn == 'HIS': for c in range(a[0],a[1]): # this residue model.atom[c].resn = histidine resn = histidine if resn == 'N-M': # N-methyl from Insight II, for c in range(a[0],a[1]): # this residue model.atom[c].resn = 'NME' resn = 'NME' # find out if this is n or c terminal residue names = [] for b in range(a[0],a[1]): names.append(model.atom[b].name) tmpl = protein_residues.normal if forcefield: ffld = forcefield.normal for b in N_TERMINAL_ATOMS: if b in names: tmpl = protein_residues.n_terminal if forcefield: ffld = forcefield.n_terminal break for b in C_TERMINAL_ATOMS: if b in names: tmpl = protein_residues.c_terminal if forcefield: ffld = forcefield.c_terminal break if resn not in tmpl: raise RuntimeError("unknown residue type '"+resn+"'") else: # reassign atom names and build dictionary dict = {} aliases = tmpl[resn]['aliases'] for b in range(a[0],a[1]): at = model.atom[b] if at.name in aliases: at.name = aliases[at.name] dict[at.name] = b if forcefield: k = (resn,at.name) if k in ffld: at.text_type = ffld[k]['type'] at.partial_charge = ffld[k]['charge'] else: raise RuntimeError("no parameters for '"+str(k)+"'") # now add bonds for atoms which are present bonds = tmpl[resn]['bonds'] mbond = model.bond for b in list(bonds.keys()): if b[0] in dict and b[1] in dict: bnd = Bond() bnd.index = [ dict[b[0]], dict[b[1]] ] bnd.order = bonds[b]['order'] mbond.append(bnd) if 'N' in dict: # connect residues N-C based on distance cur_n = dict['N'] at = model.atom[cur_n] lst = nbr.get_neighbors(at.coord) for b in lst: at2 = model.atom[b] if at2.name=='C': if not at2.in_same_residue(at): dst = distance(at.coord,at2.coord) if dst<=PEPT_CUTOFF: bnd=Bond() bnd.index = [cur_n,b] bnd.order = 1 mbond.append(bnd) break if 'SG' in dict: # cysteine cur = dict['SG'] at = model.atom[cur] lst = nbr.get_neighbors(at.coord) for b in lst: if b>cur: # only do this once (only when b>cur - i.e. this is 1st CYS) at2 = model.atom[b] if at2.name=='SG': if not at2.in_same_residue(at): dst = distance(at.coord,at2.coord) if dst<=MAX_BOND_LEN: bnd=Bond() bnd.index = [cur,b] bnd.order = 1 mbond.append(bnd) if forcefield: for c in range(a[0],a[1]): # this residue atx = model.atom[c] atx.resn = 'CYX' resn = atx.resn k = ('CYX',atx.name) if k in ffld: atx.text_type = ffld[k]['type'] atx.partial_charge = ffld[k]['charge'] else: raise RuntimeError("no parameters for '"+str(k)+"'") for d in res_list: if (b>=d[0]) and (b<d[1]): # find other residue for c in range(d[0],d[1]): atx = model.atom[c] atx.resn = 'CYX' # since b>cur, assume assignment later on break #--------------------------------------------------------------------------------- def add_hydrogens(model,forcefield=protein_amber,skip_sort=None): # assumes no bonds between non-hetatms if feedback['actions']: print(" "+str(__name__)+": adding hydrogens...") if not isinstance(model, chempy.models.Connected): raise ValueError('model is not a "Connected" model object') if model.nAtom: if not model.index: model.update_index() res_list = model.get_residues() if len(res_list): for a in res_list: base = model.atom[a[0]] if not base.hetatm: resn = base.resn # find out if this is n or c terminal residue names = [] for b in range(a[0],a[1]): names.append(model.atom[b].name) tmpl = protein_residues.normal if forcefield: ffld = forcefield.normal for b in N_TERMINAL_ATOMS: if b in names: tmpl = protein_residues.n_terminal if forcefield: ffld = forcefield.n_terminal break for b in C_TERMINAL_ATOMS: if b in names: tmpl = protein_residues.c_terminal if forcefield: ffld = forcefield.c_terminal break if resn not in tmpl: raise RuntimeError("unknown residue type '"+resn+"'") else: # build dictionary dict = {} for b in range(a[0],a[1]): at = model.atom[b] dict[at.name] = b # find missing bonds with hydrogens bonds = tmpl[resn]['bonds'] mbond = model.bond for b in list(bonds.keys()): if b[0] in dict and (b[1] not in dict): at = model.atom[dict[b[0]]] if at.symbol != 'H': name = b[1] symbol = tmpl[resn]['atoms'][name]['symbol'] if symbol == 'H': newat = at.new_in_residue() newat.name = name newat.symbol = symbol k = (resn,newat.name) newat.text_type = ffld[k]['type'] newat.partial_charge = ffld[k]['charge'] idx1 = model.index[id(at)] idx2 = model.add_atom(newat) bnd = Bond() bnd.index = [ idx1, idx2 ] bnd.order = bonds[b]['order'] mbond[idx1].append(bnd) mbond[idx2].append(bnd) if (b[0] not in dict) and b[1] in dict: at = model.atom[dict[b[1]]] if at.symbol != 'H': name = b[0] symbol = tmpl[resn]['atoms'][name]['symbol'] if symbol == 'H': newat = at.new_in_residue() newat.name = name newat.symbol = symbol k = (resn,newat.name) newat.text_type = ffld[k]['type'] newat.partial_charge = ffld[k]['charge'] idx1 = model.index[id(at)] idx2 = model.add_atom(newat) bnd = Bond() bnd.index = [ idx1, idx2 ] bnd.order = bonds[b]['order'] mbond[idx1].append(bnd) mbond[idx2].append(bnd) if not skip_sort: model.sort()

51.915989

116

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null

0

4

b9f82399bd13c1d0207096414c6576f4517afaf6

100

py

Python

executors/python-django/sample/core/models.py

omaraboumrad/djanground

f153aabf68f8d500317d357ceaa558da61380b2a

[ "MIT" ]

1

2017-11-25T20:22:14.000Z

executors/python-django/sample/core/models.py

omaraboumrad/dryorm

f153aabf68f8d500317d357ceaa558da61380b2a

[ "MIT" ]

8

2017-11-26T21:57:16.000Z

2017-12-26T08:53:17.000Z

executors/python-django/sample/core/models.py

omaraboumrad/djanground

f153aabf68f8d500317d357ceaa558da61380b2a

[ "MIT" ]

null

from django.db import models class Question(models.Model): name = models.TextField(null=True)

16.666667

38

0.75

14

100

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null

0

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6a017025f8f0e30cc6bf48fcac31107e75e2940b

3,099

py

Python

Funny_Js_Crack/19-慕课网登陆破解/imooc.py

qqizai/Func_Js_Crack

8cc8586107fecace4b71d0519cfbc760584171b1

[ "MIT" ]

18

2020-12-09T06:49:46.000Z

2022-01-27T03:20:36.000Z

Funny_Js_Crack/19-慕课网登陆破解/imooc.py

sumerzhang/Func_Js_Crack

8cc8586107fecace4b71d0519cfbc760584171b1

[ "MIT" ]

null

Funny_Js_Crack/19-慕课网登陆破解/imooc.py

sumerzhang/Func_Js_Crack

8cc8586107fecace4b71d0519cfbc760584171b1

[ "MIT" ]

9

2020-12-20T08:52:09.000Z

2021-12-19T09:13:09.000Z

import execjs import requests def get_js_function(js_path, func_name, func_args): ''' 获取指定目录下的js代码, 并且指定js代码中函数的名字以及函数的参数。 :param js_path: js代码的位置 :param func_name: js代码中函数的名字 :param func_args: js代码中函数的参数 :return: 返回调用js函数的结果 ''' with open(js_path) as fp: js = fp.read() ctx = execjs.compile(js) return ctx.call(func_name, func_args) def login(passwd): url = 'https://www.imooc.com/passport/user/login' session = requests.Session() headers = { 'Accept': 'application/json, text/javascript, */*; q=0.01', 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh;q=0.9', 'Cache-Control': 'no-cache', 'Connection': 'keep-alive', 'Content-Length': '327', 'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8', 'Cookie': 'imooc_uuid=698163be-752c-437d-979f-a024be53a993; imooc_isnew_ct=1539541237; imooc_isnew=2; zg_did=%7B%22did%22%3A%20%22166e7f069a10-0b251bb7adae18-36664c08-100200-166e7f069a49b%22%7D; dist_id=a7ZE0dF1uNW8enTrUenFBYTjbeAWKkSx; IMCDNS=0; Hm_lvt_f0cfcccd7b1393990c78efdeebff3968=1562464352,1563851968,1564150581; PHPSESSID=fc6rttd0j0orp63jlpqqr47qi3; PSEID=2ef87c0ecfdb8e233fdb7bcf67c89ae4; Hm_lpvt_f0cfcccd7b1393990c78efdeebff3968=1564194896; cvde=5d3b0b34657b9-22; zg_f375fe2f71e542a4b890d9a620f9fb32=%7B%22sid%22%3A%201564194229399%2C%22updated%22%3A%201564195619895%2C%22info%22%3A%201563851967607%2C%22superProperty%22%3A%20%22%7B%5C%22%E5%BA%94%E7%94%A8%E5%90%8D%E7%A7%B0%5C%22%3A%20%5C%22%E6%85%95%E8%AF%BE%E7%BD%91%E6%95%B0%E6%8D%AE%E7%BB%9F%E8%AE%A1%5C%22%2C%5C%22Platform%5C%22%3A%20%5C%22web%5C%22%7D%22%2C%22platform%22%3A%20%22%7B%7D%22%2C%22utm%22%3A%20%22%7B%7D%22%2C%22referrerDomain%22%3A%20%22www.imooc.com%22%2C%22zs%22%3A%200%2C%22sc%22%3A%200%2C%22firstScreen%22%3A%201564194229399%2C%22cuid%22%3A%20%22zo4kcpAhzmU%2C%22%7D', 'Host': 'www.imooc.com', 'Origin': 'https://www.imooc.com', 'Pragma': 'no-cache', 'Referer': 'https://www.imooc.com/user/newlogin', 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36', 'X-Requested-With': 'XMLHttpRequest', } url_get_cookie = 'https://www.imooc.com/user/newlogin' # 先访问这个获取Cookie 记录Cookie # session.get(url_get_cookie, headers=headers) data = { 'username': '13298307816-我的微信-填写你的微信', 'password': passwd, 'verify': "zxkp", 'remember': '1', 'pwencode': '1', 'browser_key': 'b3d1f46398d13c2608889e5f91c197f3', 'referer': 'https://www.imooc.com', } response = requests.post(url, data=data, headers=headers) with open('imooc.html', 'wb') as fp: fp.write(response.content) if __name__ == '__main__': params = '填写你的密码' # 加密密码 passwd = get_js_function('imooc.js', 'login', params) print(passwd) login(str(passwd)) ''' 目前存在的问题是: 加密搞出来了但是在发送请求的时候出现的大都是非法请求的错误其实在浏览器里面有时候即使密码账号正确也会出现这样的错误也登陆不进去。 '''

45.573529

1,062

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0.023166

0.03861

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null

0

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0

4

6a09db8130e206475f86639f4ebda4fe788235fd

1,728

py

Python

sdks/python/apache_beam/typehints/row_type.py

rehmanmuradali/beam

de8ff705145cbbc41bea7750a0a5d3553924ab3a

[ "Apache-2.0" ]

1

2021-06-28T17:49:58.000Z

sdks/python/apache_beam/typehints/row_type.py

rehmanmuradali/beam

de8ff705145cbbc41bea7750a0a5d3553924ab3a

[ "Apache-2.0" ]

9

2020-06-03T12:34:25.000Z

2020-08-11T12:18:22.000Z

sdks/python/apache_beam/typehints/row_type.py

rehmanmuradali/beam

de8ff705145cbbc41bea7750a0a5d3553924ab3a

[ "Apache-2.0" ]

1

2020-11-11T18:45:54.000Z

# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # pytype: skip-file from __future__ import absolute_import from apache_beam.typehints import typehints class RowTypeConstraint(typehints.TypeConstraint): def __init__(self, fields): self._fields = tuple(fields) def _consistent_with_check_(self, sub): return self == sub def type_check(self, instance): from apache_beam import Row return isinstance(instance, Row) def _inner_types(self): """Iterates over the inner types of the composite type.""" return [field[1] for field in self._fields] def __eq__(self, other): return type(self) == type(other) and self._fields == other._fields def __ne__(self, other): # TODO(BEAM-5949): Needed for Python 2 compatibility. return not self == other def __hash__(self): return hash(self._fields) def __repr__(self): return 'Row(%s)' % ', '.join( '%s=%s' % (name, typehints._unified_repr(t)) for name, t in self._fields)

32

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4.927126

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false

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0.227273

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0

null

0

null

0

1

0

1

0

1

0

4

6a14bc88dbbdf32942a6544839e13e36df38a5a4

333

py

Python

dev_up/models/utils/number_identifier.py

lordralinc/dev_up

e035afd386c8a16c574aaa7615c263f1c1369911

[ "MIT" ]

2

2021-01-10T15:44:41.000Z

2021-01-10T15:59:48.000Z

dev_up/models/utils/number_identifier.py

lordralinc/dev_up

e035afd386c8a16c574aaa7615c263f1c1369911

[ "MIT" ]

null

dev_up/models/utils/number_identifier.py

lordralinc/dev_up

e035afd386c8a16c574aaa7615c263f1c1369911

[ "MIT" ]

4

2021-01-10T15:45:19.000Z

2021-03-05T20:09:57.000Z

from pydantic import BaseModel class UtilsNumberIdentifierResponseGeo(BaseModel): region: str class UtilsNumberIdentifierResponse(BaseModel): number: str operator: str operator_id: str geo: UtilsNumberIdentifierResponseGeo class UtilsNumberIdentifier(BaseModel): response: UtilsNumberIdentifierResponse

19.588235

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0.798799

26

333

10.192308

0.576923

0.083019

0

0.156156

333

16

51

20.8125

0.943061

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true

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null

0

null

0

1

0

1

0

4

6a1cf97386d34211a81e62092331222bcbf7e8fc

52

py

Python

test.py

schliffen/QuantResearch

5df74a30c89151cf0019a4853d21a401ff0b8821

[ "MIT" ]

null

test.py

schliffen/QuantResearch

5df74a30c89151cf0019a4853d21a401ff0b8821

[ "MIT" ]

null

test.py

schliffen/QuantResearch

5df74a30c89151cf0019a4853d21a401ff0b8821

[ "MIT" ]

null

# # # import numpy as np if __name__=='__main__':

6.5

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7

52

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1

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52

7

25

7.428571

0.609756

0

0.163265

0

null

0

0.5

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null

0

1

0

null

0

1

0

1

0

4

6a2316efc93d7a185148b02a69ff4767e85bde60

231

py

Python

crescent/functions/ref.py

mpolatcan/zepyhrus

2fd0b1b9b21613b5876a51fe8b5f9e3afbec1b67

[ "Apache-2.0" ]

1

2020-03-26T19:20:03.000Z

crescent/functions/ref.py

mpolatcan/zepyhrus

2fd0b1b9b21613b5876a51fe8b5f9e3afbec1b67

[ "Apache-2.0" ]

null

crescent/functions/ref.py

mpolatcan/zepyhrus

2fd0b1b9b21613b5876a51fe8b5f9e3afbec1b67

[ "Apache-2.0" ]

null

from .fn import FnSingleValue class Ref(FnSingleValue): def __init__(self): super(Ref, self).__init__(fn_name=Ref.__name__) def Value(self, value: str): return self._set_field(self.Value.__name__, value)

23.1

58

0.69697

31

231

4.580645

0.516129

0.126761

0

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e01ea800ab2285e98024ac692c88eac405df57cc

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py

Python

networkx/algorithms/bipartite/__init__.py

LamprosYfantis/networkx

4f957ad8abef63f0933dcc198468897fbcdabce2

[ "BSD-3-Clause" ]

null

networkx/algorithms/bipartite/__init__.py

LamprosYfantis/networkx

4f957ad8abef63f0933dcc198468897fbcdabce2

[ "BSD-3-Clause" ]

null

networkx/algorithms/bipartite/__init__.py

LamprosYfantis/networkx

4f957ad8abef63f0933dcc198468897fbcdabce2

[ "BSD-3-Clause" ]

null

r""" This module provides functions and operations for bipartite graphs. Bipartite graphs `B = (U, V, E)` have two node sets `U,V` and edges in `E` that only connect nodes from opposite sets. It is common in the literature to use an spatial analogy referring to the two node sets as top and bottom nodes. The bipartite algorithms are not imported into the networkx namespace at the top level so the easiest way to use them is with: >>> import networkx as nx >>> from networkx import bipartite NetworkX does not have a custom bipartite graph class but the Graph() or DiGraph() classes can be used to represent bipartite graphs. However, you have to keep track of which set each node belongs to, and make sure that there is no edge between nodes of the same set. The convention used in NetworkX is to use a node attribute named `bipartite` with values 0 or 1 to identify the sets each node belongs to. This convention is not enforced in the source code of bipartite functions, it's only a recommendation. For example: >>> B = nx.Graph() >>> # Add nodes with the node attribute "bipartite" >>> B.add_nodes_from([1, 2, 3, 4], bipartite=0) >>> B.add_nodes_from(['a', 'b', 'c'], bipartite=1) >>> # Add edges only between nodes of opposite node sets >>> B.add_edges_from([(1, 'a'), (1, 'b'), (2, 'b'), (2, 'c'), (3, 'c'), (4, 'a')]) Many algorithms of the bipartite module of NetworkX require, as an argument, a container with all the nodes that belong to one set, in addition to the bipartite graph `B`. The functions in the bipartite package do not check that the node set is actually correct nor that the input graph is actually bipartite. If `B` is connected, you can find the two node sets using a two-coloring algorithm: >>> nx.is_connected(B) True >>> bottom_nodes, top_nodes = bipartite.sets(B) However, if the input graph is not connected, there are more than one possible colorations. This is the reason why we require the user to pass a container with all nodes of one bipartite node set as an argument to most bipartite functions. In the face of ambiguity, we refuse the temptation to guess and raise an :exc:`AmbiguousSolution <networkx.AmbiguousSolution>` Exception if the input graph for :func:`bipartite.sets <networkx.algorithms.bipartite.basic.sets>` is disconnected. Using the `bipartite` node attribute, you can easily get the two node sets: >>> top_nodes = {n for n, d in B.nodes(data=True) if d['bipartite']==0} >>> bottom_nodes = set(B) - top_nodes So you can easily use the bipartite algorithms that require, as an argument, a container with all nodes that belong to one node set: >>> print(round(bipartite.density(B, bottom_nodes), 2)) 0.5 >>> G = bipartite.projected_graph(B, top_nodes) All bipartite graph generators in NetworkX build bipartite graphs with the `bipartite` node attribute. Thus, you can use the same approach: >>> RB = bipartite.random_graph(5, 7, 0.2) >>> RB_top = {n for n, d in RB.nodes(data=True) if d['bipartite']==0} >>> RB_bottom = set(RB) - RB_top >>> list(RB_top) [0, 1, 2, 3, 4] >>> list(RB_bottom) [5, 6, 7, 8, 9, 10, 11] For other bipartite graph generators see :mod:`Generators <networkx.algorithms.bipartite.generators>`. """ from networkx.algorithms.bipartite.basic import * from networkx.algorithms.bipartite.centrality import * from networkx.algorithms.bipartite.cluster import * from networkx.algorithms.bipartite.covering import * from networkx.algorithms.bipartite.edgelist import * from networkx.algorithms.bipartite.matching import * from networkx.algorithms.bipartite.matrix import * from networkx.algorithms.bipartite.projection import * from networkx.algorithms.bipartite.redundancy import * from networkx.algorithms.bipartite.spectral import * from networkx.algorithms.bipartite.generators import *

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e02306c8bca73b51863ccbc9382ed1c8febcd572

68

py

Python

Python/Topics/Escape sequences/Printing the path/main.py

drtierney/hyperskill-problems

b74da993f0ac7bcff1cbd5d89a3a1b06b05f33e0

[ "MIT" ]

5

2020-08-29T15:15:31.000Z

2022-03-01T18:22:34.000Z

Python/Topics/Escape sequences/Printing the path/main.py

drtierney/hyperskill-problems

b74da993f0ac7bcff1cbd5d89a3a1b06b05f33e0

[ "MIT" ]

null

Python/Topics/Escape sequences/Printing the path/main.py

drtierney/hyperskill-problems

b74da993f0ac7bcff1cbd5d89a3a1b06b05f33e0

[ "MIT" ]

1

2020-12-02T11:13:14.000Z

path = 'C:\\Users\\Public\\Desktop\\Temporary\\Newsletters'.lower()

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e02371ddebd886ca53f97e92686e47b54ec775c2

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py

Python

apps/links/models.py

nmoeller/DjangoAngularJsWebsite

e77167f817171943eb4213b4101d93023e84058e

[ "MIT" ]

null

apps/links/models.py

nmoeller/DjangoAngularJsWebsite

e77167f817171943eb4213b4101d93023e84058e

[ "MIT" ]

4

2020-06-05T17:34:55.000Z

2021-09-07T23:47:10.000Z

apps/links/models.py

nmoeller/WebsiteWithDjangoAndAngular

4310b1272a87161b9e70b2f5172afbc74570d7ff

[ "MIT" ]

null

from django.db import models from ckeditor_uploader.fields import RichTextUploadingField class Link(models.Model): text = models.CharField(max_length=200) link = models.CharField(max_length=200) def __str__(self): return self.text

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e02915cf701e1d7853e5f09b2f50e655272fb355

79

py

Python

inept/__init__.py

StefanJMU/INEPT

8af27b068c9e18e5b75204d7727c0d56ca2a8feb

[ "MIT" ]

null

inept/__init__.py

StefanJMU/INEPT

8af27b068c9e18e5b75204d7727c0d56ca2a8feb

[ "MIT" ]

null

inept/__init__.py

StefanJMU/INEPT

8af27b068c9e18e5b75204d7727c0d56ca2a8feb

[ "MIT" ]

null

from ._inept import interval_partitioning __all__ = ['interval_partitioning']

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e0374bbd498641a2fa1c7cef52c5dfab8b317499

393

py

Python

examples/sqlalchemy/booksapp/database/books.py

mwilliamson/python-graphlayer

d71d99c314aca07816ce6a1a7329d0d7fecdfb2f

[ "BSD-2-Clause" ]

25

2019-03-11T16:48:52.000Z

2021-05-02T03:23:20.000Z

examples/sqlalchemy/booksapp/database/books.py

mwilliamson/python-graphlayer

d71d99c314aca07816ce6a1a7329d0d7fecdfb2f

[ "BSD-2-Clause" ]

9

2019-03-24T10:43:44.000Z

2021-11-09T23:02:20.000Z

examples/sqlalchemy/booksapp/database/books.py

mwilliamson/python-graphlayer

d71d99c314aca07816ce6a1a7329d0d7fecdfb2f

[ "BSD-2-Clause" ]

7

2018-12-30T17:52:07.000Z

2021-05-02T03:23:35.000Z

import sqlalchemy from .base import Base class Book(Base): __tablename__ = "book" id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True) title = sqlalchemy.Column(sqlalchemy.Unicode, nullable=False) genre = sqlalchemy.Column(sqlalchemy.Unicode, nullable=False) author_id = sqlalchemy.Column(sqlalchemy.Integer, sqlalchemy.ForeignKey("author.id"), nullable=False)

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e07180470821bff8460bde88ff1f49eeb76c160f

167

py

Python

entmoot/optimizer/__init__.py

DavidWalz/entmoot-1

4fad534a569673c2254cae8e870b8bcd70fc6ccf

[ "BSD-3-Clause" ]

27

2020-08-31T13:30:14.000Z

2022-03-21T11:35:05.000Z

entmoot/optimizer/__init__.py

DavidWalz/entmoot-1

4fad534a569673c2254cae8e870b8bcd70fc6ccf

[ "BSD-3-Clause" ]

2

2021-02-16T11:27:53.000Z

2021-04-20T19:50:53.000Z

entmoot/optimizer/__init__.py

DavidWalz/entmoot-1

4fad534a569673c2254cae8e870b8bcd70fc6ccf

[ "BSD-3-Clause" ]

6

2020-10-22T11:45:43.000Z

2022-03-28T17:42:53.000Z

from .optimizer import Optimizer from .entmoot_minimize import entmoot_minimize from .entmootopti import EntmootOpti __all__ = [ "Optimizer","entmoot_minimize" ]

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0ed28aa43faa86af019bb0af2221f612ffe28618

241

py

Python

plouflib/checksum.py

ravelsoft/ploufseo

4f66286821d7d39219a2ee633a6f52b385023869

[ "MIT" ]

1

2020-12-09T06:29:12.000Z

plouflib/checksum.py

ravelsoft/ploufseo

4f66286821d7d39219a2ee633a6f52b385023869

[ "MIT" ]

null

plouflib/checksum.py

ravelsoft/ploufseo

4f66286821d7d39219a2ee633a6f52b385023869

[ "MIT" ]

1

2020-12-09T06:29:14.000Z

import hashlib class CheckSum: def __init__(self,options): self.options = options def headers(self): return ['Hash SHA1'] def process(self,request): return [hashlib.sha1(request.HTML).hexdigest()]

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null

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null

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4

0ee56d1c7c51e54b55e8978d94b25e3da6cc18dc

27

py

Python

data/studio21_generated/introductory/4598/starter_code.py

vijaykumawat256/Prompt-Summarization

614f5911e2acd2933440d909de2b4f86653dc214

[ "Apache-2.0" ]

null

data/studio21_generated/introductory/4598/starter_code.py

vijaykumawat256/Prompt-Summarization

614f5911e2acd2933440d909de2b4f86653dc214

[ "Apache-2.0" ]

null

data/studio21_generated/introductory/4598/starter_code.py

vijaykumawat256/Prompt-Summarization

614f5911e2acd2933440d909de2b4f86653dc214

[ "Apache-2.0" ]

null

def calculate(n1, n2, o):

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null

0

1

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4

163df5ea39a647b0c36ed26f14512001fd9edf6e

164

py

Python

slrd/__init__.py

slro/slrd-backend

dc6029198d229c3df01f8d56a13cf7793b2fa927

[ "Unlicense" ]

2

2017-12-02T20:59:45.000Z

2019-01-20T02:12:20.000Z

slrd/__init__.py

slro/slrd-backend

dc6029198d229c3df01f8d56a13cf7793b2fa927

[ "Unlicense" ]

4

2017-11-23T13:57:11.000Z

2018-02-04T17:05:38.000Z

slrd/__init__.py

slro/slrd-backend

dc6029198d229c3df01f8d56a13cf7793b2fa927

[ "Unlicense" ]

null

""".""" from flask import Flask import logging slrd = Flask(__name__) from slrd.views import views logging.getLogger(__name__).addHandler(logging.NullHandler())

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1

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16605332597d0c0bb2829620e09f2712ef1a5bbf

395

py

Python

vns_web3/txpool.py

AMTcommunity/vns-web3.py

9966be02be9f33c0341cf0abad59b7bf61e1ca92

[ "MIT" ]

null

vns_web3/txpool.py

AMTcommunity/vns-web3.py

9966be02be9f33c0341cf0abad59b7bf61e1ca92

[ "MIT" ]

null

vns_web3/txpool.py

AMTcommunity/vns-web3.py

9966be02be9f33c0341cf0abad59b7bf61e1ca92

[ "MIT" ]

null

from vns_web3.module import ( Module, ) class TxPool(Module): @property def content(self): return self.web3.manager.request_blocking("txpool_content", []) @property def inspect(self): return self.web3.manager.request_blocking("txpool_inspect", []) @property def status(self): return self.web3.manager.request_blocking("txpool_status", [])

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