xszheng2020/the_stack_dedup_python_hits_4 · Datasets at Hugging Face

c20cc5b9985041baad3774577e490b609d522ba0

2,094

py

Python

app/models/order/order.py

Hugking/lin-cms-flask

5f79e744e60d0f1b81c6e8bd045ab4ffa00043ce

[ "MIT" ]

null

app/models/order/order.py

Hugking/lin-cms-flask

5f79e744e60d0f1b81c6e8bd045ab4ffa00043ce

[ "MIT" ]

null

app/models/order/order.py

Hugking/lin-cms-flask

5f79e744e60d0f1b81c6e8bd045ab4ffa00043ce

[ "MIT" ]

null

from lin.exception import NotFound, ParameterException from lin.interface import InfoCrud as Base from sqlalchemy import Column, String, Integer, DECIMAL,Text, ForeignKey,DateTime,Index, Boolean class Order(Base): id = Column(Integer, primary_key=True, autoincrement=True) order_sn = Column(String(40),nullable=False,default='') member_id = Column(Integer, index=True,nullable=False,default='') order_status = Column(Integer,index=True,nullable=False,default='0') shipping_status = Column(Integer,index=True,nullable=False,default='0') pay_status = Column(Integer,index=True,nullable=False,default='0') consignee = Column(String(60),nullable=False,default='') country = Column(Integer,nullable=False,default='0') province = Column(Integer,nullable=False,default='0') city = Column(Integer,nullable=False,default='0') district = Column(Integer,nullable=False,default='0') address = Column(String(255),nullable=False,default='') mobile = Column(String(60),nullable=False,default='') postscript = Column(String(255),nullable=False,default='') shipping_fee = Column(DECIMAL(10,2),nullable=False,default='0.00') pay_name = Column(String(255),nullable=False,default='') pay_id = Column(Integer,index=True,nullable=False,default='0') actual_price = Column(DECIMAL(10,2),nullable=False,default='0.00')# 实际支付金额 integral = Column(Integer,nullable=False,default='0') integral_money = Column(Integer,nullable=False,default='0.00') order_price = Column(DECIMAL(10,2),nullable=False,default='0.00')# 订单总价 goods_price = Column(DECIMAL(10,2),nullable=False,default='0.00')# 商品总价 add_time = Column(DateTime) confirm_time = Column(DateTime) pay_time = Column(DateTime) freight_price = Column(DECIMAL(10,2),nullable=False,default='0.00')# 配送费用 coupon_id = Column(Integer,nullable=False,default='0')# 优惠卷id parent_id = Column(Integer,nullable=False,default='0') coupon_price = Column(DECIMAL(10,2),nullable=False,default='0.00') callback_status = Column(Boolean,nullable=False,default='True')

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c23062c67bdcc2fa00c5389f9be25bb77f07d3c9

17

py

Python

aiogithubapi/objects/users/__init__.py

timmo001/aiogithubapi

9d33bad77e49f8ee720bcd81c2cbab8a4cf8ebac

[ "MIT" ]

8

2019-07-24T18:14:25.000Z

2022-03-01T18:33:53.000Z

aiogithubapi/objects/users/__init__.py

timmo001/aiogithubapi

9d33bad77e49f8ee720bcd81c2cbab8a4cf8ebac

[ "MIT" ]

33

2019-12-18T22:15:06.000Z

2022-03-30T06:08:38.000Z

aiogithubapi/objects/users/__init__.py

timmo001/aiogithubapi

9d33bad77e49f8ee720bcd81c2cbab8a4cf8ebac

[ "MIT" ]

14

2019-09-02T17:50:16.000Z

2022-03-14T10:30:37.000Z

"""Deprecated"""

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1

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4

c2495098acf24554f050526c8e8dee8fd2bb78cc

312

py

Python

src/aijack/collaborative/fedgems/__init__.py

Koukyosyumei/AIJack

9545d3828907b54965ede85e0e12cb32eef54294

[ "MIT" ]

24

2021-11-17T02:16:47.000Z

2022-03-27T01:04:08.000Z

src/aijack/collaborative/fedgems/__init__.py

Koukyosyumei/AIJack

9545d3828907b54965ede85e0e12cb32eef54294

[ "MIT" ]

9

2021-12-03T06:09:27.000Z

2022-03-29T06:33:53.000Z

src/aijack/collaborative/fedgems/__init__.py

Koukyosyumei/AIJack

9545d3828907b54965ede85e0e12cb32eef54294

[ "MIT" ]

5

2022-01-12T09:58:04.000Z

2022-03-17T09:29:04.000Z

"""Implementation of `Cheng, Sijie, et al. "FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusion." arXiv preprint arXiv:2110.11027 (2021).`""" from .api import FedGEMSAPI # noqa: F401 from .client import FedGEMSClient # noqa : F401 from .server import FedGEMSServer # noqa: F401

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dfaf7c99f0b30dfa6cbcc5c9a28cd2525b87f5ce

86

py

Python

obj/Any CPU/Release/Package/PackageTmp/wsgi.py

jdehorty/python-sample-vscode-flask-tutorial

04caabe83e0bc74a79ae5f406ec9ab44284d2758

[ "MIT" ]

null

obj/Any CPU/Release/Package/PackageTmp/wsgi.py

jdehorty/python-sample-vscode-flask-tutorial

04caabe83e0bc74a79ae5f406ec9ab44284d2758

[ "MIT" ]

null

obj/Any CPU/Release/Package/PackageTmp/wsgi.py

jdehorty/python-sample-vscode-flask-tutorial

04caabe83e0bc74a79ae5f406ec9ab44284d2758

[ "MIT" ]

null

# wsgi.py # !/usr/bin/env python -W ignore::DeprecationWarning import app app.run()

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null

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1

0

1

0

4

dfc62afb9348185aa1cc0c8a9ec1ea13da157ca4

135

py

Python

Day-056/01-getpass_echo.py

arvimal/100DaysofCode-Python

01e59f45b4dc06a3be9e9900456a6bd439752911

[ "MIT" ]

1

2020-06-15T05:59:01.000Z

Day-056/01-getpass_echo.py

arvimal/100DaysofCode-Python

01e59f45b4dc06a3be9e9900456a6bd439752911

[ "MIT" ]

null

Day-056/01-getpass_echo.py

arvimal/100DaysofCode-Python

01e59f45b4dc06a3be9e9900456a6bd439752911

[ "MIT" ]

7

2020-01-24T23:03:58.000Z

2021-05-31T01:00:27.000Z

#!/usr/bin/env python3 import getpass import sys passwd = getpass.getpass(stream=sys.stderr) print("You entered: {}".format(passwd))

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null

0

1

0

4

dfe484dd3cf6da54d01fddf93e35699094c66638

160

py

Python

pedrec/models/experiments/dataset_description.py

noboevbo/PedRec

891d19bd6a2c7a7d71c2e41d37e7b4c4bfc7762e

[ "MIT" ]

1

2022-03-09T01:24:10.000Z

pedrec/models/experiments/dataset_description.py

noboevbo/PedRec

891d19bd6a2c7a7d71c2e41d37e7b4c4bfc7762e

[ "MIT" ]

null

pedrec/models/experiments/dataset_description.py

noboevbo/PedRec

891d19bd6a2c7a7d71c2e41d37e7b4c4bfc7762e

[ "MIT" ]

null

from dataclasses import dataclass @dataclass() class DatasetDescription(object): name: str subsampling: int full_length: int used_length: int

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true

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null

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null

0

1

0

1

0

4

5f1d36c1371ea5d6865431821ee84670a3052f81

61

py

Python

src/django_future/__init__.py

smartpr/django-future

8f0031994a509c3d07aa972232760331fb8e5ed1

[ "MIT" ]

null

src/django_future/__init__.py

smartpr/django-future

8f0031994a509c3d07aa972232760331fb8e5ed1

[ "MIT" ]

null

src/django_future/__init__.py

smartpr/django-future

8f0031994a509c3d07aa972232760331fb8e5ed1

[ "MIT" ]

null

default_app_config = 'django_future.apps.DjangoFutureConfig'

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0

1

null

0

4

a06cbd7686067c5d1d0d108e8a30bc4b8feb3660

120

py

Python

Instagram/Interact/urls.py

garchaaman19/Insta_clone

eccf0fd305967d8701a10e586d08cb789f03c8fa

[ "MIT" ]

null

Instagram/Interact/urls.py

garchaaman19/Insta_clone

eccf0fd305967d8701a10e586d08cb789f03c8fa

[ "MIT" ]

null

Instagram/Interact/urls.py

garchaaman19/Insta_clone

eccf0fd305967d8701a10e586d08cb789f03c8fa

[ "MIT" ]

null

from django.urls import path from .views import CreateUserAPI urlpatterns=[path('createuser',CreateUserAPI.as_view())]

24

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0

null

0

1

0

1

0

4

a07b7d3271127ec3d4df552e49911c73969b2213

260

py

Python

rul_pm/datasets/lives_dataset.py

lucianolorenti/rul_pm

da9dfad79129dd47d24923cfd6c833869ef7b6a7

[ "MIT" ]

1

2021-09-01T13:13:10.000Z

rul_pm/datasets/lives_dataset.py

lucianolorenti/rul_pm

da9dfad79129dd47d24923cfd6c833869ef7b6a7

[ "MIT" ]

3

2021-08-24T15:23:52.000Z

2021-11-09T10:28:51.000Z

rul_pm/datasets/lives_dataset.py

lucianolorenti/rul_pm

da9dfad79129dd47d24923cfd6c833869ef7b6a7

[ "MIT" ]

1

2021-12-25T14:00:16.000Z

from abc import abstractmethod, abstractproperty from temporis.dataset.ts_dataset import AbstractTimeSeriesDataset class AbstractLivesDataset(AbstractTimeSeriesDataset): @abstractproperty def rul_column(self) -> str: raise NotImplementedError

32.5

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null

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1

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1

0

4

a09e3691c7bbfadc213e666c44de0dd723708a74

146

py

Python

api_keys.py

ElenaSezionova/python-api-challenge

72f8c5fc80edca5c2bee48dee6a1adbe536b9eaa

[ "ADSL" ]

null

api_keys.py

ElenaSezionova/python-api-challenge

72f8c5fc80edca5c2bee48dee6a1adbe536b9eaa

[ "ADSL" ]

null

api_keys.py

ElenaSezionova/python-api-challenge

72f8c5fc80edca5c2bee48dee6a1adbe536b9eaa

[ "ADSL" ]

null

# OpenWeatherMap API Key weather_api_key = "9cfaeb3dbd4832137f0fb5f0e12ca0f4" # Google API Key g_key = "AIzaSyBwiWBdcMksrxnWzcOvCM1cjxhqV8017_A"

24.333333

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0.842466

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8.5

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146

5

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1

null

0

4

a09e9b2d5c80056ae4659b98de115c501db0c908

296

py

Python

crawler/oda/oda_product.py

dangrasso/oda-crawler

bb8f58c27fe762d175677b14297b3be95f77f394

[ "MIT" ]

null

crawler/oda/oda_product.py

dangrasso/oda-crawler

bb8f58c27fe762d175677b14297b3be95f77f394

[ "MIT" ]

null

crawler/oda/oda_product.py

dangrasso/oda-crawler

bb8f58c27fe762d175677b14297b3be95f77f394

[ "MIT" ]

null

from dataclasses import dataclass from typing import Optional @dataclass class Product: id: str name: Optional[str] brand: Optional[str] price: Optional[str] category_0: Optional[str] category_1: Optional[str] category_2: Optional[str] category_3: Optional[str]

19.733333

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true

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null

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1

0

1

0

4

39f9f63b8039432cf478d86e499ca57c48433431

543

py

Python

pytglib/api/functions/get_database_statistics.py

iTeam-co/pytglib

e5e75e0a85f89b77762209b32a61b0a883c0ae61

[ "MIT" ]

6

2019-10-30T08:57:27.000Z

2021-02-08T14:17:43.000Z

pytglib/api/functions/get_database_statistics.py

iTeam-co/python-telegram

e5e75e0a85f89b77762209b32a61b0a883c0ae61

[ "MIT" ]

1

2021-08-19T05:44:10.000Z

2021-08-19T07:14:56.000Z

pytglib/api/functions/get_database_statistics.py

iTeam-co/python-telegram

e5e75e0a85f89b77762209b32a61b0a883c0ae61

[ "MIT" ]

5

2019-12-04T05:30:39.000Z

2021-05-21T18:23:32.000Z

from ..utils import Object class GetDatabaseStatistics(Object): """ Returns database statistics Attributes: ID (:obj:`str`): ``GetDatabaseStatistics`` No parameters required. Returns: DatabaseStatistics Raises: :class:`telegram.Error` """ ID = "getDatabaseStatistics" def __init__(self, extra=None, **kwargs): self.extra = extra pass @staticmethod def read(q: dict, *args) -> "GetDatabaseStatistics": return GetDatabaseStatistics()

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261acd3face7f09797cd511111c8f87c994c5623

89

py

Python

src/dialogs/__init__.py

StevenBaby/chess

1aa3065d974351df58a5620c78ca80a10a385321

[ "MIT" ]

10

2021-06-17T21:45:59.000Z

2022-03-23T04:02:45.000Z

src/dialogs/__init__.py

StevenBaby/chess

1aa3065d974351df58a5620c78ca80a10a385321

[ "MIT" ]

2

2021-05-30T11:52:44.000Z

2021-06-29T11:06:31.000Z

src/dialogs/__init__.py

StevenBaby/chess

1aa3065d974351df58a5620c78ca80a10a385321

[ "MIT" ]

1

2021-06-19T03:41:23.000Z

14.833333

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265d5d0bea909e9736137b335f095e44a6475eac

93

py

Python

01_-_hello_world.py

LukaIgnjatovic/freeCodeCamp.org---Learn-Python---Full-Course-for-Beginners

6e4fc54203da15921bd6562c1fa7a915b45b4ad1

[ "MIT" ]

12

2018-11-05T10:52:19.000Z

2022-03-08T07:12:44.000Z

01_-_hello_world.py

LukaIgnjatovic/freeCodeCamp.org---Learn-Python---Full-Course-for-Beginners

6e4fc54203da15921bd6562c1fa7a915b45b4ad1

[ "MIT" ]

null

01_-_hello_world.py

LukaIgnjatovic/freeCodeCamp.org---Learn-Python---Full-Course-for-Beginners

6e4fc54203da15921bd6562c1fa7a915b45b4ad1

[ "MIT" ]

17

2019-01-13T08:09:45.000Z

2022-03-14T22:51:44.000Z

# "print" function prints the string that was given to it as an input. print("Hello world!")

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2671cd63e956feebbd09c94e1793d6e926b77359

15

py

Python

openml_data_integration/protobuf_generator/openml_1480/myconstants.py

tuix/tutorials

733d35a8a39df079e8c2432c441b70785ab08440

[ "Apache-2.0" ]

8

2020-04-21T13:29:04.000Z

2021-12-13T08:59:09.000Z

openml_data_integration/protobuf_generator/openml_1480/myconstants.py

tuix/tutorials

733d35a8a39df079e8c2432c441b70785ab08440

[ "Apache-2.0" ]

3

2021-04-27T11:03:04.000Z

2021-05-24T18:22:57.000Z

openml_data_integration/protobuf_generator/openml_1480/myconstants.py

tuix/tutorials

733d35a8a39df079e8c2432c441b70785ab08440

[ "Apache-2.0" ]

6

2020-07-06T08:23:25.000Z

2021-11-24T10:39:34.000Z

DATA_ID = 1480

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26744bbbe2d0b31d7063cbf425bd912f11dcb858

117

py

Python

optimizers.py

Judithle98/BachelorThesis

263cd589b5bfc22bdecc304430fd76816d42101b

[ "CC0-1.0" ]

null

optimizers.py

Judithle98/BachelorThesis

263cd589b5bfc22bdecc304430fd76816d42101b

[ "CC0-1.0" ]

null

optimizers.py

Judithle98/BachelorThesis

263cd589b5bfc22bdecc304430fd76816d42101b

[ "CC0-1.0" ]

null

class Optimizer: pass class RandomRestartOptimizer(Optimizer): def __init__(self, N=10): self.N=N

13

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267a24e89010dd844c50b4f3f5f5b56a241cca55

189

py

Python

merf/__init__.py

lidscott1/merf

07453e0a3968a3a5ec01c79f6ce4f97d8a74058a

[ "MIT" ]

null

merf/__init__.py

lidscott1/merf

07453e0a3968a3a5ec01c79f6ce4f97d8a74058a

[ "MIT" ]

null

merf/__init__.py

lidscott1/merf

07453e0a3968a3a5ec01c79f6ce4f97d8a74058a

[ "MIT" ]

null

import logging logging.basicConfig( format='%(levelname)-8s [%(filename)s:%(lineno)d] %(message)s', level=logging.INFO) from .merf import MERF from .utils import MERFDataGenerator

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7

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27

0.830303

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0.280423

0.132275

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1

0

true

0

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1

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null

0

1

0

1

0

4

cd003c8d4e5d8946bbbe5c83c3d9c2ff0a64b781

134

py

Python

algorithm/sort/TimSort.py

kosyachniy/dev

39bb5c5ee10780bfcd8a59cf59cfb1a348ac52a4

[ "Apache-2.0" ]

13

2018-12-17T23:30:54.000Z

2021-12-29T14:31:43.000Z

algorithm/sort/TimSort.py

kosyachniy/dev

39bb5c5ee10780bfcd8a59cf59cfb1a348ac52a4

[ "Apache-2.0" ]

36

2018-06-07T21:34:13.000Z

2022-03-13T21:01:43.000Z

algorithm/sort/TimSort.py

kosyachniy/dev

39bb5c5ee10780bfcd8a59cf59cfb1a348ac52a4

[ "Apache-2.0" ]

2

2021-01-03T11:47:20.000Z

2021-12-29T14:31:49.000Z

import timeit start=timeit.default_timer() a=[1,5,7,3,6,6,2566,774,2,0,0,0,-3] a.sort() print(a) print(timeit.default_timer()-start)

16.75

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134

3.206897

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0.27957

0.387097

0

0.142857

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134

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36

16.75

0.595238

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0.166667

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1

0

null

1

0

1

0

null

0

4

cd703f027a2b7c7b176c49220a30bea4537e094b

549

py

Python

uq360/utils/transformers/original_features.py

Sclare87/UQ360

2378bfa4a8d61f813afbf6854341888434c9eb11

[ "Apache-2.0" ]

148

2021-05-27T20:52:51.000Z

2022-03-16T22:49:48.000Z

uq360/utils/transformers/original_features.py

Sclare87/UQ360

2378bfa4a8d61f813afbf6854341888434c9eb11

[ "Apache-2.0" ]

9

2021-06-21T18:45:07.000Z

2021-11-08T14:42:30.000Z

uq360/utils/transformers/original_features.py

Sclare87/UQ360

2378bfa4a8d61f813afbf6854341888434c9eb11

[ "Apache-2.0" ]

27

2021-06-01T18:29:02.000Z

2022-03-02T06:56:03.000Z

from uq360.utils.transformers.feature_transformer import FeatureTransformer class OriginalFeaturesTransformer(FeatureTransformer): ''' Dummy/identity transformer which passes the data array through unchanged. ''' def __init__(self): super(OriginalFeaturesTransformer, self).__init__() @classmethod def name(cls): return ('original_features') def transform(self, x, predictions): return x def save(self, output_dir=None): pass def load(self, input_dir=None): pass

23.869565

77

0.686703

56

549

6.517857

0.696429

0.038356

0.060274

0

0.007092

0.229508

549

22

78

24.954545

0.855792

0.132969

0

0.153846

0

0.036957

0

1

0.384615

false

0.153846

0.076923

0.153846

0.692308

0

null

0

null

0

1

0

1

0

1

0

4

cd8de7d69923d5672ab52bad8b7dcd99d99c5cb3

1,990

py

Python

rta/read/test_isoquant_import.py

MatteoLacki/rta

93944d6fc934126e0bb4d076c8b4213cadbe49a1

[ "BSD-2-Clause" ]

1

2018-05-31T14:31:18.000Z

rta/read/test_isoquant_import.py

MatteoLacki/rta

93944d6fc934126e0bb4d076c8b4213cadbe49a1

[ "BSD-2-Clause" ]

null

rta/read/test_isoquant_import.py

MatteoLacki/rta

93944d6fc934126e0bb4d076c8b4213cadbe49a1

[ "BSD-2-Clause" ]

null

"""Develop the calibrator.""" %load_ext autoreload %autoreload 2 %load_ext line_profiler import matplotlib.pyplot as plt from rta.config import * from rta.plotters.runs import plot_runs,\ plot_experiment_comparison from rta.quality_control.process_project import process_project # # the first HELA dataset I've analysed. # data_path = "../../../Data/annotated_and_unanottated_data.csv" # # Ute's data-sets for microflow. # data_path = "~/ms/Matteo/4Ute/2016-141 HYE Microflow_20180716_MF_120min_paper.csv" # data = pd.read_csv(data_path) # First shoot, than ask. mass_projects = ["Proj__15272392369260_8293106731954075_100_1", "Proj__15272392369260_8293106731954075_100_2", "Proj__15272392369260_8293106731954075_100_3", "Proj__15264893889320_6353458109334729_100_8", "Proj__15264893889320_6353458109334729_100_11", "Proj__15260213186990_6379462481554944_100_8", "Proj__15260213186990_6379462481554944_100_10", "Proj__15272392369260_8293106731954075_100_8", "Proj__15264893889320_6353458109334729_100_17", "Proj__15264893889320_6353458109334729_100_18"] results = {} for project in mass_projects: results[project] = process_project(project, password, user, ip) plot_runs(c.D, title=title, run_2_name=run_2_name) plot_experiment_comparison() c, run_2_name, project, title = results[mass_projects[-1]] plot_runs(c.D, title=title, run_2_name=run_2_name) plot_experiment_comparison(results, ["Proj__15272392369260_8293106731954075_100_8", "Proj__15264893889320_6353458109334729_100_17"]) plot_experiment_comparison(results, ["Proj__15264893889320_6353458109334729_100_8", "Proj__15260213186990_6379462481554944_100_10", "Proj__15272392369260_8293106731954075_100_1"], plt_style = 'default')

38.269231

84

0.714573

224

1,990

5.821429

0.388393

0.082822

0.156442

0.170245

0.447086

0.388037

0.304448

0

0.343038

0.20603

1,990

51

85

39.019608

0.482278

0.134673

0

0.181818

0

0.390969

0.386809

0

null

0.030303

0.121212

null

0

null

0

1

0

1

0

1

0

null

0

1

0

4

cd960014085ab7db7e7a22d23d144e71644ced73

111

py

Python

example/users/admin.py

supercodepoet/django-authority

9c17430b03ed1c0a51cf50194786100601c3ea2e

[ "BSD-3-Clause" ]

1

2015-05-26T10:16:30.000Z

example/users/admin.py

supercodepoet/django-authority

9c17430b03ed1c0a51cf50194786100601c3ea2e

[ "BSD-3-Clause" ]

null

example/users/admin.py

supercodepoet/django-authority

9c17430b03ed1c0a51cf50194786100601c3ea2e

[ "BSD-3-Clause" ]

null

from django.contrib.auth.admin import UserAdmin from example.users.User admin.site.register(User, UserAdmin)

18.5

47

0.81982

16

111

5.6875

0.75

0

0.09009

111

5

48

22.2

0.90099

0

null

0

0.333333

null

0

1

0

null

0

1

0

null

0

1

0

1

0

4

26c1a206cb4012e82c96d0597d4c93d520002991

285

py

Python

src/masonite_package_sync/utils.py

girardinsamuel/masonite-package-sync

8d3c631592c666b922bbd1b204ae4db5713ca023

[ "MIT" ]

null

src/masonite_package_sync/utils.py

girardinsamuel/masonite-package-sync

8d3c631592c666b922bbd1b204ae4db5713ca023

[ "MIT" ]

null

src/masonite_package_sync/utils.py

girardinsamuel/masonite-package-sync

8d3c631592c666b922bbd1b204ae4db5713ca023

[ "MIT" ]

null

def replace_string_in_file(filepath, searched, replaced): with open(filepath) as f: file_source = f.read() # replace all occurences replace_string = file_source.replace(searched, replaced) with open(filepath, "w") as f: f.write(replace_string)

31.666667

64

0.673684

37

285

5

0.486486

0.210811

0.216216

0.259459

0.345946

0

0.231579

285

8

65

35.625

0.844749

0.077193

0

0.003831

0

1

0.166667

false

0

0.166667

0

null

1

0

1

0

null

0

4

26cd3a2bb6ca9a6aab3f385e09bc05b0e1740331

68

py

Python

ambari-agent/src/main/python/resource_management/core/exceptions.py

boydos/incubator-ambari

e10d85756dd55729c20aeda2baa0d6c93c4ca31d

[ "Apache-2.0" ]

2

2018-06-06T14:21:11.000Z

2018-06-06T14:22:50.000Z

ambari-agent/src/main/python/resource_management/core/exceptions.py

boydos/incubator-ambari

e10d85756dd55729c20aeda2baa0d6c93c4ca31d

[ "Apache-2.0" ]

null

ambari-agent/src/main/python/resource_management/core/exceptions.py

boydos/incubator-ambari

e10d85756dd55729c20aeda2baa0d6c93c4ca31d

[ "Apache-2.0" ]

null

class Fail(Exception): pass class InvalidArgument(Fail): pass

9.714286

28

0.735294

8

68

6.25

0.625

0

0.176471

68

6

29

11.333333

0.892857

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

4

26d474883a340a3bcf89466855c18d283879ec1f

76

py

Python

organice/models.py

bittner/django-organice

7621e4cf2361db84b42d77e5e72e341559eb9906

[ "Apache-2.0" ]

34

2015-04-22T12:47:32.000Z

2022-03-18T02:16:17.000Z

organice/models.py

TebelloX/django-organice

7621e4cf2361db84b42d77e5e72e341559eb9906

[ "Apache-2.0" ]

13

2015-07-24T05:25:56.000Z

2020-09-02T17:38:35.000Z

organice/models.py

TebelloX/django-organice

7621e4cf2361db84b42d77e5e72e341559eb9906

[ "Apache-2.0" ]

14

2015-05-01T20:42:49.000Z

2022-03-25T01:12:34.000Z

""" No real model, just an empty file to make Django load the fixtures. """

19

67

0.697368

13

76

4.076923

1

0

0.197368

76

3

68

25.333333

0.868852

0.881579

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

26f05c903e8b626a141fe763eeed44e975f4a402

747

py

Python

constants.py

hbontempo-br/broker-api

992118bcb2754df34053a6dd56020d2e3002ce78

[ "MIT" ]

null

constants.py

hbontempo-br/broker-api

992118bcb2754df34053a6dd56020d2e3002ce78

[ "MIT" ]

null

constants.py

hbontempo-br/broker-api

992118bcb2754df34053a6dd56020d2e3002ce78

[ "MIT" ]

null

import os from utils.environment import get_environment_variable # ------- GENERAL ------- # SERVICE_ROOT = os.path.abspath(os.path.dirname(__file__)) PROJECT_ROOT = os.path.abspath(os.path.join(SERVICE_ROOT, os.pardir)) SERVICE_NAME = get_environment_variable("SERVICE_NAME", "broker-api") COMMIT = get_environment_variable("COMMIT", "COMMIT") # ------- DB INFO ------- # DB_USER = get_environment_variable("DB_USER") DB_PASSWORD = get_environment_variable("DB_PASSWORD") DB_ADDRESS = get_environment_variable("DB_ADDRESS") DB_PORT = get_environment_variable("DB_PORT", "3306") DB_DATABASE = get_environment_variable("DB_DATABASE", "broker") # ------- SCHEMA ------- # SCHEMA_FILE = os.path.join(SERVICE_ROOT, "documentation", "schema.yaml")

37.35

72

0.742972

96

747

5.395833

0.333333

0.216216

0.339768

0.23166

0.158301

0.088803

0

0.005848

0.084337

747

19

73

39.315789

0.751462

0.096386

0

0.170915

0

1

0

false

0.083333

0.166667

0

0.166667

0

null

1

0

null

0

1

0

4

f83fda338deb39e6752e0faedf4b5fd97009cdc9

25

py

Python

TEST3D/GUI/0010200_page_pixsel/cleanup.py

usnistgov/OOF3D

4fd423a48aea9c5dc207520f02de53ae184be74c

[ "X11" ]

31

2015-04-01T15:59:36.000Z

2022-03-18T20:21:47.000Z

TEST3D/GUI/0010200_page_pixsel/cleanup.py

usnistgov/OOF3D

4fd423a48aea9c5dc207520f02de53ae184be74c

[ "X11" ]

3

2015-02-06T19:30:24.000Z

2017-05-25T14:14:31.000Z

TEST3D/GUI/0010200_page_pixsel/cleanup.py

usnistgov/OOF3D

4fd423a48aea9c5dc207520f02de53ae184be74c

[ "X11" ]

7

2015-01-23T15:19:22.000Z

2021-06-09T09:03:59.000Z

removefile('pixsel.log')

12.5

24

0.76

3

25

6.333333

1

0

0.04

25

1

25

0.791667

0

0.4

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

4

f84ff12aad0645a3ba6d2b9f7986ea611b8c0740

48

py

Python

Grammar/02Var and types/hello_word.py

Abraham-Thomas/shiyanlou-code

ae3b940b406ba4af942dd51627fd93e85effe5b9

[ "MIT" ]

null

Grammar/02Var and types/hello_word.py

Abraham-Thomas/shiyanlou-code

ae3b940b406ba4af942dd51627fd93e85effe5b9

[ "MIT" ]

null

Grammar/02Var and types/hello_word.py

Abraham-Thomas/shiyanlou-code

ae3b940b406ba4af942dd51627fd93e85effe5b9

[ "MIT" ]

null

message = "Hello Python world!" print(message)

12

31

0.729167

6

48

5.833333

0.833333

0

0.145833

48

3

32

16

0.853659

0

0.395833

0

1

0

false

0

0.5

1

0

null

0

1

0

null

0

1

0

4

f8543075ec3280d01d66eb6454016a1c234ba89b

24

py

Python

demo/apps/hello/__init__.py

mysidewalk/pykc-meetup1

258dfb8debc46174c089cfb0583cb88b03fbbe07

[ "MIT" ]

null

demo/apps/hello/__init__.py

mysidewalk/pykc-meetup1

258dfb8debc46174c089cfb0583cb88b03fbbe07

[ "MIT" ]

null

demo/apps/hello/__init__.py

mysidewalk/pykc-meetup1

258dfb8debc46174c089cfb0583cb88b03fbbe07

[ "MIT" ]

null

""" Hello world app """

8

19

0.541667

3

24

4.333333

1

0

0.208333

24

2

20

12

0.684211

0.625

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

f86aafde96be1084e84a233789edbf89f503cde6

113

py

Python

implementation/wrong.py

sr9000/stepik_code_task_baking

60a5197f659db1734132eeb9d82624f1b7aaeb3f

[ "MIT" ]

null

implementation/wrong.py

sr9000/stepik_code_task_baking

60a5197f659db1734132eeb9d82624f1b7aaeb3f

[ "MIT" ]

null

implementation/wrong.py

sr9000/stepik_code_task_baking

60a5197f659db1734132eeb9d82624f1b7aaeb3f

[ "MIT" ]

null

from pre_definition.tag import wrong @wrong def plus1(n): print(n + 1) @wrong def noway(n): print(n)

9.416667

36

0.646018

19

113

3.789474

0.631579

0.222222

0.194444

0

0.022989

0.230089

113

11

37

10.272727

0.804598

0

0.285714

0

1

0.285714

false

0

0.142857

0

0.428571

0.285714

1

0

null

1

0

null

0

1

0

4

f882dbc0473240cba4b9cb499360bf1629372cfb

67

py

Python

aiida_castep/workflows/__init__.py

asamli/aiida-castep

893113152460a632439c91652211381091566645

[ "MIT" ]

3

2021-09-02T16:02:47.000Z

2021-12-17T22:38:20.000Z

aiida_castep/workflows/__init__.py

asamli/aiida-castep

893113152460a632439c91652211381091566645

[ "MIT" ]

16

2020-05-07T07:58:01.000Z

2022-03-21T11:35:35.000Z

aiida_castep/workflows/__init__.py

asamli/aiida-castep

893113152460a632439c91652211381091566645

[ "MIT" ]

3

2020-05-25T13:05:51.000Z

2021-12-17T22:39:12.000Z

""" Sub-package of workchain and workfuctions for aiida-castep """

16.75

58

0.746269

9

67

5.555556

1

0

0.134328

67

3

59

22.333333

0.862069

0.865672

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

f886d2525b5880e8d47bb153058d681896087d79

363

py

Python

raiden/tests/unit/utils/test_formatting.py

tirkarthi/raiden

dbd03ddda039332b54ec0c02d81cbe1100bc8028

[ "MIT" ]

2,101

2016-06-01T11:31:49.000Z

2022-03-27T20:13:19.000Z

raiden/tests/unit/utils/test_formatting.py

tirkarthi/raiden

dbd03ddda039332b54ec0c02d81cbe1100bc8028

[ "MIT" ]

5,291

2016-06-01T18:14:04.000Z

2022-03-31T11:19:09.000Z

raiden/tests/unit/utils/test_formatting.py

tirkarthi/raiden

dbd03ddda039332b54ec0c02d81cbe1100bc8028

[ "MIT" ]

484

2016-06-01T18:21:06.000Z

2022-03-22T10:29:45.000Z

import os from eth_utils import to_checksum_address as eth_utils_checksum from raiden.utils.formatting import to_checksum_address from raiden.utils.typing import Address def test_random_addresses(): for _ in range(100): address_bytes = Address(os.urandom(20)) assert eth_utils_checksum(address_bytes) == to_checksum_address(address_bytes)

27.923077

86

0.793388

52

363

5.211538

0.461538

0.221402

0.188192

0.169742

0

0.016129

0.146006

363

12

87

30.25

0.858065

0

0.125

1

0.125

false

0

0.5

0

0.625

0

null

1

0

null

0

1

0

4

f8870764592f360880ae8e4c0975132a7535ce49

89

py

Python

abc178/b.py

nishio/atcoder

8db36537b5d8580745d5f98312162506ad7d7ab4

[ "MIT" ]

1

2021-03-09T04:28:13.000Z

abc178/b.py

nishio/atcoder

8db36537b5d8580745d5f98312162506ad7d7ab4

[ "MIT" ]

null

abc178/b.py

nishio/atcoder

8db36537b5d8580745d5f98312162506ad7d7ab4

[ "MIT" ]

null

A, B, C, D = map(int, input().split()) print(max(x * y for x in [A, B] for y in [C, D]))

29.666667

49

0.516854

22

89

2.090909

0.636364

0.086957

0

0.224719

89

2

50

44.5

0.666667

0

1

0

true

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

4

f8b4fc852c49f9b7f4810fc5e30fdebee9fa3250

32

py

Python

env/lib/python3.7/__future__.py

mwikiaBundi1/Pitch-ip

16f2e2459abafeebd828dfff7cbc554db3b56b42

[ "MIT" ]

12

2019-08-02T07:58:16.000Z

2022-01-31T23:45:08.000Z

env/lib/python3.7/__future__.py

mwikiaBundi1/Pitch-ip

16f2e2459abafeebd828dfff7cbc554db3b56b42

[ "MIT" ]

9

2021-03-19T02:17:08.000Z

2022-03-12T00:01:38.000Z

env/lib/python3.7/__future__.py

mwikiaBundi1/Pitch-ip

16f2e2459abafeebd828dfff7cbc554db3b56b42

[ "MIT" ]

11

2019-07-31T16:23:36.000Z

2022-01-29T08:30:07.000Z

/usr/lib/python3.7/__future__.py

32

0.8125

6

32

3.666667

1

0

0.0625

0

32

1

32

0.625

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null

0

1

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null

0

1

0

4

3e14210cc030ce3f7670478505157f1f5ce0fafa

608

py

Python

runfile/exceptions.py

awkspace/runfile

4f457099c6b12c1d08b3bd960ee49fc3da04b0e3

[ "MIT" ]

1

2021-08-20T02:21:53.000Z

runfile/exceptions.py

awkspace/runfile

4f457099c6b12c1d08b3bd960ee49fc3da04b0e3

[ "MIT" ]

null

runfile/exceptions.py

awkspace/runfile

4f457099c6b12c1d08b3bd960ee49fc3da04b0e3

[ "MIT" ]

null

#!/usr/bin/env python3 class RunfileFormatError(Exception): pass class RunfileNotFoundError(Exception): def __init__(self, path): self.path = path class TargetNotFoundError(Exception): def __init__(self, target=None): self.target = target class TargetExecutionError(Exception): def __init__(self, exit_code): self.exit_code = exit_code class CodeBlockExecutionError(Exception): def __init__(self, exit_code): self.exit_code = exit_code class ContainerBuildError(Exception): def __init__(self, exit_code): self.exit_code = exit_code

19.612903

41

0.712171

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608

5.941176

0.308824

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0

0.002053

0.199013

608

30

42

20.266667

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false

0.058824

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null

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1

0

1

0

1

0

4

3e36349cf503a86256f690d89715bf64dcf047d4

130

py

Python

oidc_server/oidc_provider_settings.py

vicalloy/oidc-server

fbef282dc3c33f29e857d35fea05fc2a49b0a6d2

[ "MIT" ]

7

2022-02-17T07:25:39.000Z

2022-03-27T11:15:48.000Z

oidc_server/oidc_provider_settings.py

vicalloy/oidc-server

fbef282dc3c33f29e857d35fea05fc2a49b0a6d2

[ "MIT" ]

2

2022-03-27T11:15:45.000Z

2022-03-31T01:04:01.000Z

oidc_server/oidc_provider_settings.py

vicalloy/oidc-server

fbef282dc3c33f29e857d35fea05fc2a49b0a6d2

[ "MIT" ]

1

2022-03-02T11:44:01.000Z

def userinfo(claims, user): claims["name"] = user.username claims["preferred_username"] = user.username return claims

26

48

0.7

15

130

6

0.533333

0.266667

0

0.176923

130

4

49

32.5

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0

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0

null

1

0

1

0

null

0

1

0

4

3e5d9fa374f41367a44effee56611b99e5ed78e8

84

py

Python

hibro/wsgi.py

DavidMStraub/HiBro

ce08d4c091994a0e9f503712378fe357385ac4c8

[ "MIT" ]

8

2019-12-30T14:05:54.000Z

2021-09-19T21:28:36.000Z

hibro/wsgi.py

DavidMStraub/HiBro

ce08d4c091994a0e9f503712378fe357385ac4c8

[ "MIT" ]

1

2020-03-08T10:59:24.000Z

2020-06-08T18:22:23.000Z

hibro/wsgi.py

DavidMStraub/HiBro

ce08d4c091994a0e9f503712378fe357385ac4c8

[ "MIT" ]

1

2020-10-18T16:37:21.000Z

from .dashboard import create_app app = create_app(config_file="hibro-config.yaml")

28

49

0.809524

13

84

5

0.692308

0.276923

0

0.083333

84

3

49

28

0.844156

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0.2

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1

0

false

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

4

3e7d10e4d8bb197a829b29abaee322dbef27f715

131

py

Python

codes/course5/demo101.py

BigShuang/big-shuang-python-introductory-course

c4fd1343c4c539567180072c749b68bda7c28075

[ "MIT" ]

null

codes/course5/demo101.py

BigShuang/big-shuang-python-introductory-course

c4fd1343c4c539567180072c749b68bda7c28075

[ "MIT" ]

null

codes/course5/demo101.py

BigShuang/big-shuang-python-introductory-course

c4fd1343c4c539567180072c749b68bda7c28075

[ "MIT" ]

null

num = input("Please enter a num: ") while not num.isdigit(): num = input("Please enter a num: ") print("Your num: %s" % num)

18.714286

39

0.610687

21

131

3.809524

0.52381

0.2

0.35

0.475

0.575

0

0.21374

131

6

40

21.833333

0.776699

0

0.5

0

0.4

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false

0

0.25

1

0

null

0

1

0

1

0

null

0

4

3e8a37e2a2b50b7ddc45d0f1deaff0e70c0f1ebd

161

py

Python

ml_modulation/modulators/exceptions.py

DmitryBelikov/ml-modulation

510d05c751c3450c0063eee82918fb5947c55b26

[ "MIT" ]

null

ml_modulation/modulators/exceptions.py

DmitryBelikov/ml-modulation

510d05c751c3450c0063eee82918fb5947c55b26

[ "MIT" ]

null

ml_modulation/modulators/exceptions.py

DmitryBelikov/ml-modulation

510d05c751c3450c0063eee82918fb5947c55b26

[ "MIT" ]

null

class ModulationException(Exception): pass class EncodingException(ModulationException): pass class DecodingException(ModulationException): pass

14.636364

45

0.78882

12

161

10.583333

0.5

0.141732

0

0.15528

161

10

46

16.1

0.933824

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

1

null

0

null

0

1

0

4

3e918893f0b4db7c166232c2fe9a98bc438e1e59

152

py

Python

timed while loop.py

hwdevops/pyxows

caadd107f53bef683c23ffcd84af46dde189c1f2

[ "MIT" ]

null

timed while loop.py

hwdevops/pyxows

caadd107f53bef683c23ffcd84af46dde189c1f2

[ "MIT" ]

null

timed while loop.py

hwdevops/pyxows

caadd107f53bef683c23ffcd84af46dde189c1f2

[ "MIT" ]

null

import time duration = 20 # [seconds] time_start = time.time() while time.time() < time_start + duration: print(time.time()) time.sleep(.5)

16.888889

42

0.651316

21

152

4.619048

0.47619

0.412371

0.247423

0

0.02459

0.197368

152

8

43

19

0.770492

0.059211

0

1

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false

0

0.166667

0

0.166667

1

0

null

1

0

1

0

null

0

4

e413006e6dd214b130c93f1b7bbe8b6760929831

241

py

Python

abandoned/admin.py

Kunstmord/abandoned

d6ed86ace6521144699b668d0e91497a7b3b6dc2

[ "MIT" ]

null

abandoned/admin.py

Kunstmord/abandoned

d6ed86ace6521144699b668d0e91497a7b3b6dc2

[ "MIT" ]

10

2015-01-10T15:08:50.000Z

2015-03-28T18:48:49.000Z

abandoned/admin.py

Kunstmord/abandoned

d6ed86ace6521144699b668d0e91497a7b3b6dc2

[ "MIT" ]

null

from django.contrib import admin from abandoned.models import Tag, Project, Author, Reason, Language admin.site.register(Tag) admin.site.register(Project) admin.site.register(Author) admin.site.register(Reason) admin.site.register(Language)

30.125

67

0.821577

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241

5.823529

0.411765

0.227273

0.429293

0

0.070539

241

8

68

30.125

0.883929

0

1

0

true

0

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0

0.285714

0

null

1

0

1

0

null

0

1

0

4

e4295da2f7812b00aee90ea339be9189b4602eb0

352

py

Python

LFA/class_assembly_line.py

joao-frohlich/BCC

9ed74eb6d921d1280f48680677a2140c5383368d

[ "Apache-2.0" ]

10

2020-12-08T20:18:15.000Z

2021-06-07T20:00:07.000Z

LFA/class_assembly_line.py

joao-frohlich/BCC

9ed74eb6d921d1280f48680677a2140c5383368d

[ "Apache-2.0" ]

2

2021-06-28T03:42:13.000Z

2021-06-28T16:53:13.000Z

LFA/class_assembly_line.py

joao-frohlich/BCC

9ed74eb6d921d1280f48680677a2140c5383368d

[ "Apache-2.0" ]

2

2021-01-14T19:59:20.000Z

2021-06-15T11:53:21.000Z

from utils import * from class_moore import Moore class Assembly_Line: def __init__(self, assembly_type): self.automaton = Moore(*read_7_tuple_from_data(assembly_type)) self.type = self.automaton.name def __len__(self): return self.automaton.capacity def __str__(self): return str(self.automaton.output)

23.466667

70

0.704545

46

352

4.956522

0.478261

0.22807

0.140351

0

0.00361

0.213068

352

14

71

25.142857

0.819495

0

1

0.3

false

0

0.2

0.8

0

null

1

0

null

0

1

0

1

0

4

e446c6b72b1180701247061836cd43880629e196

335

py

Python

civilpy/__init__.py

drparks71/CivilPy

96b714970d326ee1da3cc0578e05fba8ea751660

[ "MIT" ]

2

2021-09-22T00:03:20.000Z

2021-09-24T03:06:13.000Z

civilpy/__init__.py

drparks71/CivilPy

96b714970d326ee1da3cc0578e05fba8ea751660

[ "MIT" ]

null

civilpy/__init__.py

drparks71/CivilPy

96b714970d326ee1da3cc0578e05fba8ea751660

[ "MIT" ]

null

import sympy as sym import numpy as np import pint import civilpy unit = pint.UnitRegistry() from civilpy import structural from civilpy import geotechnical from civilpy import general from civilpy import environmental from civilpy import systems_management from civilpy import transportation from civilpy import water_resources

17.631579

38

0.841791

45

335

6.222222

0.444444

0.275

0.425

0

0.146269

335

18

39

18.611111

0.979021

0

1

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false

0

0.916667

0

0.916667

0

null

1

0

null

0

1

0

1

0

4

e45c7d6fa9c363e47d8b85736a918a7bd0983c29

25

py

Python

processing/__init__.py

jakebrinkmann/lagoon-emperor-penguin

03de04028ee771a635b162a29990b868504c51d5

[ "Unlicense" ]

null

processing/__init__.py

jakebrinkmann/lagoon-emperor-penguin

03de04028ee771a635b162a29990b868504c51d5

[ "Unlicense" ]

null

processing/__init__.py

jakebrinkmann/lagoon-emperor-penguin

03de04028ee771a635b162a29990b868504c51d5

[ "Unlicense" ]

null

__version__ = '3.0.dev1'

12.5

24

0.68

4

25

3.25

1

0

0.136364

0.12

25

1

25

0.454545

0

0.32

0

1

0

false

0

1

0

null

0

1

0

1

0

null

0

4

e464f66c16f718f99363b762b12f121ab2773792

6,382

py

Python

test/model/declare/node_declaration_test.py

Manu343726/biicode-common

91b32c6fd1e4a72ce5451183f1766d313cd0e420

[ "MIT" ]

17

2015-04-15T09:40:23.000Z

2017-05-17T20:34:49.000Z

test/model/declare/node_declaration_test.py

Manu343726/biicode-common

91b32c6fd1e4a72ce5451183f1766d313cd0e420

[ "MIT" ]

2

2015-04-22T11:29:36.000Z

2018-09-25T09:31:09.000Z

test/model/declare/node_declaration_test.py

bowlofstew/common

45e9ca902be7bbbdd73dafe3ab8957bc4a006020

[ "MIT" ]

22

2015-04-15T09:46:00.000Z

2020-09-29T17:03:31.000Z

from unittest import TestCase from biicode.common.model.brl.block_cell_name import BlockCellName from biicode.common.model.declare.node_declaration import NodeDeclaration from nose.plugins.attrib import attr @attr('node') class TestNodeDeclaration(TestCase): def test_usual_node_import(self): cut = NodeDeclaration("fran/noderedis") self.assertEquals("fran/noderedis", cut.block()) def test_metrics_require(self): cut = NodeDeclaration("metrics") self.assertEquals(None, cut.block()) def test_path_rel_require(self): cut = NodeDeclaration("./") self.assertEquals(None, cut.block()) def test_extension_namelist(self): cut = NodeDeclaration("fran/noderedis/index") self.assertItemsEqual(["fran/noderedis/index.json", "fran/noderedis/index.js", "fran/noderedis/index.node"], cut._extension_name) def test_extension_namelist_with_relative(self): cut = NodeDeclaration(".lib/queue") cut.extension_namelist() self.assertItemsEqual([".lib/queue.json", ".lib/queue.js", ".lib/queue.node"], cut._extension_name) def test_extension_namelist_with_extension(self): cut = NodeDeclaration("fran/noderedis/index.js") cut.extension_namelist() self.assertItemsEqual(["fran/noderedis/index.js"], cut._extension_name) def test_explicit_node_require(self): cut = NodeDeclaration("fran/noderedis/index.js") self.assertEquals("fran/noderedis", cut.block()) self.assertEquals(BlockCellName("fran/noderedis/index.js"), cut.block_cell_name()) def test_relative_require(self): cut = NodeDeclaration("../parser") origin_block_cell_name = BlockCellName("fran/noderedis/other/other.js") block_cell_names = [BlockCellName("fran/noderedis/index.js"), BlockCellName("fran/noderedis/other/other.js"), BlockCellName("fran/noderedis/parser.js")] self.assertItemsEqual(set(["fran/noderedis/parser.js"]), cut.match(block_cell_names, origin_block_cell_name)) def test_relative_require_not_found(self): cut = NodeDeclaration("../parser") origin_block_cell_name = BlockCellName("fran/noderedis/other/thing/other.js") block_cell_names = [BlockCellName("fran/noderedis/index.js"), BlockCellName("fran/noderedis/other/thing/other.js"), BlockCellName("fran/noderedis/parser.js")] self.assertItemsEqual(set(), cut.match(block_cell_names, origin_block_cell_name)) def test_one_level_relative_require(self): cut = NodeDeclaration("./lib/queue") origin_block_cell_name = BlockCellName("fran/noderedis/index.js") block_cell_names = [BlockCellName("fran/noderedis/index.js"), BlockCellName("fran/noderedis/lib/queue.js"), BlockCellName("fran/noderedis/lib/parser.js")] self.assertItemsEqual(set(["fran/noderedis/lib/queue.js"]), cut.match(block_cell_names, origin_block_cell_name)) def test_one_level_relative_require_with_depth(self): cut = NodeDeclaration("./lib/parser/queue") origin_block_cell_name = BlockCellName("fran/noderedis/index.js") block_cell_names = [BlockCellName("fran/noderedis/index.js"), BlockCellName("fran/noderedis/lib/queue.js"), BlockCellName("fran/noderedis/lib/parser/queue.js")] self.assertItemsEqual(set(["fran/noderedis/lib/parser/queue.js"]), cut.match(block_cell_names, origin_block_cell_name)) def test_match_with_implicit_require(self): cut = NodeDeclaration("fran/noderedis") block_cell_names = [BlockCellName("fran/noderedis/index.js"), BlockCellName("fran/noderedis/other.js")] self.assertItemsEqual(set([BlockCellName("fran/noderedis/index.js")]), cut.match(block_cell_names)) def test_match_with_implicit_require_with_index_and_package(self): cut = NodeDeclaration("fran/noderedis") block_cell_names = [BlockCellName("fran/noderedis/index.js"), BlockCellName("fran/noderedis/package.json"), BlockCellName("fran/noderedis/other.js")] self.assertItemsEqual(set([BlockCellName("fran/noderedis/index.js"), BlockCellName("fran/noderedis/package.json")]), cut.match(block_cell_names)) def test_match_with_implicit_require_nothing_found(self): cut = NodeDeclaration("fran/noderedis") block_cell_names = [BlockCellName("fran/noderedis/other.js")] self.assertItemsEqual(set(), cut.match(block_cell_names)) def test_same_path_relative_index_require(self): cut = NodeDeclaration("./") origin_block_cell_name = BlockCellName("fran/noderedis/bench.js") block_cell_names = [BlockCellName("fran/noderedis/index.js"), BlockCellName("fran/noderedis/client.js")] m = cut.match(block_cell_names, origin_block_cell_name) self.assertItemsEqual(set(["fran/noderedis/index.js"]), m) def test_package_json_recognition(self): cut = NodeDeclaration("fran/noderedis") block_cell_names = [BlockCellName("fran/noderedis/package.json"), BlockCellName("fran/noderedis/other.js")] self.assertItemsEqual(set([BlockCellName("fran/noderedis/package.json")]), cut.match(block_cell_names)) def test_normalize_declaration(self): cut = NodeDeclaration("fran/noderedis") block_cell_names = [BlockCellName("fran/noderedis/package.json")] self.assertEquals(cut, cut.normalize(block_cell_names)) def test_normalize_declaration_package_and_index(self): cut = NodeDeclaration("fran/noderedis") block_cell_names = [BlockCellName("fran/noderedis/index.js"), BlockCellName("fran/noderedis/package.json")] self.assertEquals(cut, cut.normalize(block_cell_names))

45.585714

90

0.646819

671

6,382

5.918033

0.101341

0.180055

0.229161

0.090657

0.833291

0.772098

0.712667

0.609922

0.608663

0.562327

0

0.235663

6,382

139

91

45.913669

0.814063

0

0.419048

0

0.219837

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0

0.180952

1

0.171429

false

0

0.047619

0

0.228571

0

null

0

1

0

1

0

null

0

4

e465bd2cd030834af54a332a451d7a70e191117a

223

py

Python

examples/flask/my_site/config.py

monokrome/patreon-python

09aad6800c6b9f33e026712a43e5f7e7c7a2a391

[ "Apache-2.0" ]

null

examples/flask/my_site/config.py

monokrome/patreon-python

09aad6800c6b9f33e026712a43e5f7e7c7a2a391

[ "Apache-2.0" ]

null

examples/flask/my_site/config.py

monokrome/patreon-python

09aad6800c6b9f33e026712a43e5f7e7c7a2a391

[ "Apache-2.0" ]

null

# Fill all this out with your information patreon_client_id = None patreon_client_secret = None patreon_creator_refresh_token = None patreon_creator_access_token = None patreon_creator_id = None patreon_redirect_uri = None

27.875

41

0.852018

33

223

5.333333

0.545455

0.3125

0.306818

0.261364

0

0.116592

223

7

42

31.857143

0.893401

0.174888

0

1

0

false

0

null

1

0

1

0

null

0

4

e47a8084bc5461fc15a8a0c6fc2ae5b6b9cc63f1

1,578

py

Python

client/clients/models.py

kim-chae-yeon/My.CL

2ca236e1791197ee331a6740bf7b5b75147fc995

[ "MIT" ]

null

client/clients/models.py

kim-chae-yeon/My.CL

2ca236e1791197ee331a6740bf7b5b75147fc995

[ "MIT" ]

8

2021-09-26T18:50:19.000Z

2021-12-09T14:38:47.000Z

client/clients/models.py

kim-chae-yeon/My.CL

2ca236e1791197ee331a6740bf7b5b75147fc995

[ "MIT" ]

2

2021-12-02T12:46:11.000Z

2021-12-11T13:31:50.000Z

from djongo import models class Recommendation(models.Model): lecture_id = models.CharField(max_length=20) lecture_name = models.CharField(max_length=100) class Meta: abstract = True class CategoryLog(models.Model): _id = models.ObjectIdField() user_id = models.IntegerField(blank=True) add_date = models.DateTimeField(auto_now_add=True) grade = models.CharField(max_length=20) subject = models.CharField(max_length=20) achivement = models.CharField(max_length=20) site = models.CharField(max_length=20) tag_jobdam = models.CharField(max_length=20) tag_pilgi = models.CharField(max_length=20) tag_jindo = models.CharField(max_length=20) objects = models.DjongoManager() class Lecture(models.Model) : title = models.CharField(max_length=100) teacher = models.CharField(max_length=100) subject = models.CharField(max_length=100) grade = models.CharField(max_length=100) link = models.CharField(max_length=200) class ReviewLog(models.Model): _id = models.ObjectIdField() user_id = models.IntegerField(blank=True) lecture_id = models.CharField(blank=True, max_length=30) lecture_title = models.CharField(blank=True, max_length=30) add_date = models.DateTimeField(auto_now_add=True) achivement = models.CharField(max_length=20) tag_jobdam = models.CharField(max_length=20) tag_pilgi = models.CharField(max_length=20) tag_jindo = models.CharField(max_length=20) lec_comment = models.CharField(max_length = 1000) objects = models.DjongoManager()

35.863636

63

0.739544

205

1,578

5.487805

0.229268

0.28

0.304

0.405333

0.729778

0.519111

0.478222

0.416

0.344889

0

0.037594

0.157161

1,578

44

64

35.863636

0.808271

0

0.444444

0

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false

0

0.027778

0

0.972222

0

null

1

0

null

0

1

0

4

e480e6d621dd50f200c7bd3b580988cca74ecc53

72

py

Python

__init__.py

KonstantinosRekoumis/CFD_CNN_Thesis

086c378df21ddac9e7902cac05daf514121c76a2

[ "MIT" ]

null

__init__.py

KonstantinosRekoumis/CFD_CNN_Thesis

086c378df21ddac9e7902cac05daf514121c76a2

[ "MIT" ]

null

__init__.py

KonstantinosRekoumis/CFD_CNN_Thesis

086c378df21ddac9e7902cac05daf514121c76a2

[ "MIT" ]

null

""" Some random dude proposed this to be the solution to my problems """

24

64

0.736111

12

72

4.416667

0.916667

0

0.180556

72

3

65

24

0.898305

0.888889

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

e49735eb562ee900157badeb515e209416e782fa

93

py

Python

mysite/pollstree/apps.py

fabioamafra/django-tutorial

b2beb7fc2d998589634611c48f22a486a4032be5

[ "MIT" ]

null

mysite/pollstree/apps.py

fabioamafra/django-tutorial

b2beb7fc2d998589634611c48f22a486a4032be5

[ "MIT" ]

16

2019-11-14T01:28:03.000Z

2022-02-10T10:23:42.000Z

mysite/pollstree/apps.py

fabioamafra/django-tutorial

b2beb7fc2d998589634611c48f22a486a4032be5

[ "MIT" ]

3

2019-11-14T00:58:54.000Z

2019-11-14T01:05:32.000Z

from django.apps import AppConfig class PollstreeConfig(AppConfig): name = 'pollstree'

15.5

33

0.763441

10

93

7.1

0.9

0

0.16129

93

5

34

18.6

0.910256

0

0.096774

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

e4b156663c690e4b425b5bfa2d41201014f307ab

333

py

Python

python/chapter2/exercise3.py

gonditeniz/cracking-coding-interview

00243f806dc88ae6edac0120188359aa1a5357a6

[ "MIT" ]

null

python/chapter2/exercise3.py

gonditeniz/cracking-coding-interview

00243f806dc88ae6edac0120188359aa1a5357a6

[ "MIT" ]

null

python/chapter2/exercise3.py

gonditeniz/cracking-coding-interview

00243f806dc88ae6edac0120188359aa1a5357a6

[ "MIT" ]

null

class Node: next_node = None data = 0 def __init__(self, value): self.data = value def run(input_data): if input_data is None or input_data.next_node is None: return False input_data.data = input_data.next_node.data input_data.next_node = input_data.next_node.next_node return True

22.2

58

0.678679

52

333

4.019231

0.346154

0.301435

0.248804

0.325359

0.200957

0

0.004032

0.255255

333

14

59

23.785714

0.83871

0

1

0.181818

false

0

0.636364

0

null

1

0

null

0

1

0

4

e4c45c3db5068e419301740fe1e740d3d2de33a7

175

py

Python

design_patterns/abstract_factory/abstract_products/coffe_table.py

amommendes/python-fluente

04b3b257802c368acd313f93ef42aee8c4564d9c

[ "Apache-2.0" ]

null

design_patterns/abstract_factory/abstract_products/coffe_table.py

amommendes/python-fluente

04b3b257802c368acd313f93ef42aee8c4564d9c

[ "Apache-2.0" ]

2

2020-04-02T06:03:34.000Z

2021-08-23T20:40:32.000Z

design_patterns/abstract_factory/abstract_products/coffe_table.py

amommendes/python-fluente

04b3b257802c368acd313f93ef42aee8c4564d9c

[ "Apache-2.0" ]

null

from abc import ABC,abstractmethod class CoffeTable(ABC): @abstractmethod def size(self): pass @abstractmethod def color(self): pass

11.666667

34

0.611429

18

175

5.944444

0.611111

0.317757

0

0.32

175

14

35

12.5

0.89916

0

0.5

0

1

0.25

false

0.25

0.125

0

0.5

0

1

0

null

1

0

null

0

1

0

1

0

4

e4e615eb535aee4734b30d6939effaec19e55cd6

137

py

Python

roll_witch/rolling/protocols/targetable.py

DavidWylie/RollWitch

9fe16db2117b1cbce02d2206cd529c4bfcc93f55

[ "BSD-3-Clause" ]

null

roll_witch/rolling/protocols/targetable.py

DavidWylie/RollWitch

9fe16db2117b1cbce02d2206cd529c4bfcc93f55

[ "BSD-3-Clause" ]

1

2020-10-26T17:29:27.000Z

2020-10-27T13:43:44.000Z

roll_witch/rolling/protocols/targetable.py

DavidWylie/RollWitch

9fe16db2117b1cbce02d2206cd529c4bfcc93f55

[ "BSD-3-Clause" ]

null

from typing import Protocol class Targetable(Protocol): target_number: int def has_target(self) -> bool: return False

15.222222

33

0.693431

17

137

5.470588

0.882353

0

0.240876

137

8

34

17.125

0.894231

0

1

0.2

false

0

0.2

1

0

1

0

null

0

1

0

null

0

1

0

4

e4f185b741c71e4e6190c8f7b1ca92caa2e5f639

111

py

Python

tests/__init__.py

sjoerdk/sitdown

2b8ba4da037e1ee098f4a88341b1202bdebde7c4

[ "MIT" ]

101

2018-04-11T14:48:04.000Z

2022-03-28T00:29:48.000Z

tests/__init__.py

sjoerdk/sitdown

2b8ba4da037e1ee098f4a88341b1202bdebde7c4

[ "MIT" ]

1,733

2018-03-21T11:56:16.000Z

2022-03-31T14:58:30.000Z

tests/__init__.py

sjoerdk/sitdown

2b8ba4da037e1ee098f4a88341b1202bdebde7c4

[ "MIT" ]

42

2018-06-08T05:49:07.000Z

2022-03-29T08:43:01.000Z

from pathlib import Path BASE_PATH = Path(__file__).parent.absolute() RESOURCE_PATH = BASE_PATH / "resources"

22.2

44

0.783784

15

111

5.333333

0.666667

0.2

0.3

0

0.117117

111

4

45

27.75

0.816327

0

0.081081

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

1

0

null

0

1

0

4

e4f4f26afad3b716d2e113d6809aa087ecfe6fd9

228

py

Python

Python/1013.py

lucasferreiraa/uri-judge-respostas

f5fc659d53c6b512a3624764041675e62d3fa053

[ "MIT" ]

null

Python/1013.py

lucasferreiraa/uri-judge-respostas

f5fc659d53c6b512a3624764041675e62d3fa053

[ "MIT" ]

null

Python/1013.py

lucasferreiraa/uri-judge-respostas

f5fc659d53c6b512a3624764041675e62d3fa053

[ "MIT" ]

null

# -*- coding: utf-8 -*- # URI Judge - Problema 1013 a, b, c = map(int, input().split()) if a > (b and c): print(str(a) + " eh o maior") elif b > c: print(str(b) + " eh o maior") else: print(str(c) + " eh o maior")

19

35

0.517544

41

228

2.878049

0.560976

0.20339

0

0.029762

0.263158

228

11

36

20.727273

0.672619

0.20614

0

0.185393

0

1

0

true

0

0.428571

0

null

1

0

null

0

1

0

1

0

4

9028a43c0b6bed7eb351129b3f04043099d22a1d

157

py

Python

train.py

bethskw/fakelegends

72284e8e33858a1a3efdba43e463f7c2edc66492

[ "MIT" ]

4

2018-09-03T14:36:07.000Z

2020-04-06T15:52:46.000Z

train.py

bethskw/fakelegends

72284e8e33858a1a3efdba43e463f7c2edc66492

[ "MIT" ]

null

train.py

bethskw/fakelegends

72284e8e33858a1a3efdba43e463f7c2edc66492

[ "MIT" ]

1

2019-03-24T21:48:01.000Z

from textgenrnn import textgenrnn #t=textgenrnn() t = textgenrnn('textgenrnn_weights.hdf5') t.train_from_file('legendary_creatures.txt', num_epochs=10)

15.7

59

0.789809

21

157

5.666667

0.666667

0.184874

0.352941

0

0.021127

0.095541

157

9

60

17.444444

0.816901

0.089172

0

0.333333

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

1

0

null

0

1

0

4

5fa1c64c9b8d83594f7a4af0de233d2822915be2

64

py

Python

tests/__init__.py

steven-murray/pydftools

7d3edcf0d91901a364a199fd35ac211a8bb84779

[ "MIT" ]

null

tests/__init__.py

steven-murray/pydftools

7d3edcf0d91901a364a199fd35ac211a8bb84779

[ "MIT" ]

3

2019-09-30T01:11:52.000Z

2019-10-09T04:57:57.000Z

tests/__init__.py

steven-murray/pydftools

7d3edcf0d91901a364a199fd35ac211a8bb84779

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Unit test package for pydftools."""

16

38

0.578125

8

64

4.625

1

0

0.018868

0.171875

64

3

39

21.333333

0.679245

0.859375

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

5fa3e9f4787eecea74b09aa62f1185badbf551f4

197

py

Python

itertoolsCombinationsWithReplacement.py

Pratyaksh7/PythonPrograms-Hackerrank

171108851f8f90336123e383f945e8d36922d53b

[ "MIT" ]

null

itertoolsCombinationsWithReplacement.py

Pratyaksh7/PythonPrograms-Hackerrank

171108851f8f90336123e383f945e8d36922d53b

[ "MIT" ]

null

itertoolsCombinationsWithReplacement.py

Pratyaksh7/PythonPrograms-Hackerrank

171108851f8f90336123e383f945e8d36922d53b

[ "MIT" ]

null

from itertools import combinations_with_replacement n = input().split() string = n[0].upper() k = int(n[1]) for i in combinations_with_replacement(sorted(string), k): print(''.join(i))

24.625

59

0.690355

29

197

4.551724

0.724138

0.242424

0.409091

0

0.012048

0.15736

197

8

60

24.625

0.783133

0

1

0

false

0

0.166667

0

0.166667

1

0

null

1

0

1

0

null

0

4

5faaac0726a811efcc7249f96383873171347346

99

py

Python

rsmanage/__init__.py

Kandongwe/RunestoneServer

f555868521b3717beec0ec42dbcbcb443c64686c

[ "MIT" ]

null

rsmanage/__init__.py

Kandongwe/RunestoneServer

f555868521b3717beec0ec42dbcbcb443c64686c

[ "MIT" ]

null

rsmanage/__init__.py

Kandongwe/RunestoneServer

f555868521b3717beec0ec42dbcbcb443c64686c

[ "MIT" ]

null

# ********* # |docname| # ********* # This is required by Poetry for `rsmanage.py` to be a script.

19.8

62

0.535354

13

99

4.076923

1

0

0.181818

99

4

63

24.75

0.654321

0.909091

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

5fc3fa7e2291a387816d425efb55a1d1bf29f1ac

88

py

Python

desafios/exe_001.py

jacaboyjr/pythonbirds

90be950f75514cf6c585e5cdb4d91b054fb98761

[ "MIT" ]

null

desafios/exe_001.py

jacaboyjr/pythonbirds

90be950f75514cf6c585e5cdb4d91b054fb98761

[ "MIT" ]

null

desafios/exe_001.py

jacaboyjr/pythonbirds

90be950f75514cf6c585e5cdb4d91b054fb98761

[ "MIT" ]

1

2020-09-05T14:19:42.000Z

nome = input('Digite o seu nome : ') print(f'Senha bem Vindo {nome} ao mundo do Python')

44

51

0.693182

16

88

3.8125

0.875

0

0.170455

88

2

51

44

0.835616

0

0.685393

0

1

0

false

0

0.5

1

0

null

0

1

0

1

0

null

0

1

0

4

396a70238e8de31518ed7613abd05274cedb5ba1

151

py

Python

main.py

philipgold/demo-global-variables-between-files

919728a22850c145d2dc8a14d1269fbe2da04e6d

[ "MIT" ]

null

main.py

philipgold/demo-global-variables-between-files

919728a22850c145d2dc8a14d1269fbe2da04e6d

[ "MIT" ]

null

main.py

philipgold/demo-global-variables-between-files

919728a22850c145d2dc8a14d1269fbe2da04e6d

[ "MIT" ]

null

import settings import subfile settings.init() # Call only once subfile.stuff() # Do stuff with global var print settings.myList[0] # Check the result

25.166667

43

0.774834

23

151

5.086957

0.782609

0

0.007752

0.145695

151

6

43

25.166667

0.899225

0.370861

0

null

0

0.4

null

0.2

1

0

null

0

1

0

null

0

1

0

1

0

4

396a832268dd9b37c65964b6d5caec6aa5300f45

223

py

Python

spotutil/utilities/newspot.py

wri/spotutil

6e74ce4586814b4eac868ef51c213e847c11ea13

[ "MIT" ]

1

2018-12-05T16:19:53.000Z

spotutil/utilities/newspot.py

wri/spotutil

6e74ce4586814b4eac868ef51c213e847c11ea13

[ "MIT" ]

null

spotutil/utilities/newspot.py

wri/spotutil

6e74ce4586814b4eac868ef51c213e847c11ea13

[ "MIT" ]

1

2020-12-15T18:50:33.000Z

from spotutil.utilities import spot_instance def newspot(instance_type, key_pair, price, disk_size, ami_id): instance = spot_instance.Instance(instance_type, key_pair, price, disk_size, ami_id) instance.start()

24.777778

88

0.780269

32

223

5.125

0.53125

0.146341

0.182927

0.231707

0.54878

0

0.134529

223

8

89

27.875

0.849741

0

1

0.25

false

0

0.25

0

0.5

0

null

0

1

0

1

0

null

0

1

0

4

396cf9cc09e710e1c2e9f1af5b07d31074e5ac50

159

py

Python

Lambda Functions/lamda-with-filter.py

manish1822510059/Python-1000-program

d03c1920fe63a7e32ac5bd9a13e2766d7a25756c

[ "Apache-2.0" ]

1

2021-03-06T03:33:42.000Z

Lambda Functions/lamda-with-filter.py

manish1822510059/Python-1000-programs

d03c1920fe63a7e32ac5bd9a13e2766d7a25756c

[ "Apache-2.0" ]

null

Lambda Functions/lamda-with-filter.py

manish1822510059/Python-1000-programs

d03c1920fe63a7e32ac5bd9a13e2766d7a25756c

[ "Apache-2.0" ]

null

number = [1,3,6,2,7,5,8,9,0] result = filter(lambda x:x>5,number) print("Number List",number) print("Number Smaller than 5 in the list are:",list(result))

31.8

61

0.679245

32

159

3.375

0.65625

0.203704

0.314815

0

0.07971

0.132075

159

4

62

39.75

0.702899

0

0.316129

0

1

0

false

0

0.5

0

null

1

0

1

0

null

0

1

0

4

397023375d68cbad11cd6d0c9428f9184caaade2

11

py

Python

api.py

oxff644/detector

ca3a1ac39faf6b82995b2a311f05db74762cfde3

[ "MIT" ]

6

2021-03-01T13:34:47.000Z

2022-03-02T13:58:44.000Z

api.py

oxff644/detector

ca3a1ac39faf6b82995b2a311f05db74762cfde3

[ "MIT" ]

null

api.py

oxff644/detector

ca3a1ac39faf6b82995b2a311f05db74762cfde3

[ "MIT" ]

3

2021-03-30T09:03:46.000Z

2022-03-02T18:14:31.000Z

# fast-api

5.5

10

0.636364

2

11

3.5

1

0

0.181818

11

1

11

0.777778

0.727273

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

399e0eb4c52bad899646ffaec412b5b6f17476dc

247

py

Python

001085StepikPythonIntrO/Stepik001085PythonIntrOсh01p06st01С01_20200410.py

SafonovMikhail/python_000577

739f764e80f1ca354386f00b8e9db1df8c96531d

[ "Apache-2.0" ]

null

001085StepikPythonIntrO/Stepik001085PythonIntrOсh01p06st01С01_20200410.py

SafonovMikhail/python_000577

739f764e80f1ca354386f00b8e9db1df8c96531d

[ "Apache-2.0" ]

null

001085StepikPythonIntrO/Stepik001085PythonIntrOсh01p06st01С01_20200410.py

SafonovMikhail/python_000577

739f764e80f1ca354386f00b8e9db1df8c96531d

[ "Apache-2.0" ]

null

mypass = input() if ("1234" or "qwerty" in mypass) or (len(mypass) < 8): print("Bad password") elif ("1" or "2" or "3" or "4" or "5" or "6" or "7" or "8" or "9" or "0") not in mypass: print("Bad password") else: print("Good password")

30.875

88

0.57085

45

247

3.133333

0.555556

0.113475

0.22695

0

0.078125

0.222672

247

7

89

35.285714

0.65625

0

0.285714

0

0.230769

0

1

0

false

0.857143

0

0.428571

0

null

0

1

0

1

0

null

0

1

0

1

0

4

39b5c14c9562374806c07540aab5b8f9e68390bc

2,624

py

Python

mtconnect/standard_list.py

Badzi007/MTConnect-Python-Agent

43d84c4d8c50cebdbde1976a0c9828ab49920a61

[ "Apache-2.0" ]

1

2021-02-08T16:02:06.000Z

mtconnect/standard_list.py

Badzi007/MTConnect-Python-Agent

43d84c4d8c50cebdbde1976a0c9828ab49920a61

[ "Apache-2.0" ]

1

2021-02-04T15:37:05.000Z

mtconnect/standard_list.py

Badzi007/MTConnect-Python-Agent

43d84c4d8c50cebdbde1976a0c9828ab49920a61

[ "Apache-2.0" ]

2

2022-02-28T13:30:49.000Z

2022-03-21T05:51:49.000Z

MTC_DataID_list = ['ACTUATOR','CHUCK_INTERLOCK','COMMUNICATIONS','DATA_RANGE','DIRECTION','END_OF_BAR','HARDWARE','INTERFACE_STATE','LOGIC_PROGRAM','MOTION_PROGRAM','SYSTEM','ACCELERATION','ACCUMULATED_TIME','ANGULAR_ACCELERATION','ANGULAR_VELOCITY','AMPERAGE','ALTERNATING','DIRECT','ACTUAL','TARGET','ANGLE','ACTUAL','COMMANDED','AXIS_FEEDRATE','ACTUAL','COMMANDED','JOG','PROGRAMMED','RAPID','OVERRIDE','CLOCK_TIME','CONCENTRATION','CONDUCTIVITY','DISPLACEMENT','ELECTRICAL_ENERGY','EQUIPMENT_TIMER','LOADED','WORKING','OPERATING','POWERED','DELAY','FILL_LEVEL','FLOW','FREQUENCY','LENGTH','STANDARD','REMAINING','USEABLE','LINEAR_FORCE','LOAD','MASS','PATH_FEEDRATE','ACTUAL','COMMANDED','JOG','PROGRAMMED','RAPID','OVERRIDE','PATH_POSITION','ACTUAL','COMMANDED','TARGET','PROBE','PH','POSITION','ACTUAL','COMMANDED','PROGRAMMED','TARGET','POWER_FACTOR','PRESSURE','PROCESS_TIMER','PROCESS','DELAY','RESISTANCE','ROTARY_VELOCITY','ACTUAL','COMMANDED','PROGRAMMED','OVERRIDE','SOUND_LEVEL','NO_SCALE','A_SCALE','B_SCALE','C_SCALE','D_SCALE','SPINDLE_SPEED','ACTUAL','COMMANDED','OVERRIDE','STRAIN','TEMPERATURE','TENSION','TILT','TORQUE','VOLT_AMPERE','VOLT_AMPERE_REACTIVE','VELOCITY','VISCOSITY','VOLTAGE','ALTERNATING','DIRECT','ACTUAL','TARGET','WATTAGE','ACTUAL','TARGET','ACTUATOR_STATE','ALARM','ACTIVE_AXES','','','AVAILABILITY','AXIS_COUPLING','','','AXIS_FEEDRATE_OVERRIDE','JOG','PROGRAMMED','RAPID','AXIS_INTERLOCK','AXIS_STATE','BLOCK','BLOCK_COUNT','CHUCK_INTERLOCK','MANUAL_UNCLAMP','CHUCK_STATE','CODE','COMPOSITION_STATE','ACTION','LATERAL','MOTION','SWITCHED','VERTICAL','CONTROLLER_MODE','CONTROLLER_MODE_OVERRIDE','DRY_RUN','SINGLE_BLOCK','MACHINE_AXIS_LOCK','OPTIONAL_STOP','TOOL_CHANGE_STOP','COUPLED_AXES','DIRECTION','ROTARY','LINEAR','DOOR_STATE','END_OF_BAR','PRIMARY','AUXILIARY','EMERGENCY_STOP','EQUIPMENT_MODE','LOADED','WORKING','OPERATING','POWERED','DELAY','EXECUTION','FUNCTIONAL_MODE','HARDNESS','ROCKWELL','VICKERS','SHORE','BRINELL','LEEB','MOHS','INTERFACE_STATE','LINE','MAXIMUM','MINIMUM','LINE_LABEL','LINE_NUMBER','ABSOLUTE','INCREMENTAL','MATERIAL','MESSAGE','OPERATOR_ID','PALLET_ID','PART_COUNT','ALL','GOOD','BAD','TARGET','REMAINING','PART_ID','PART_NUMBER','PATH_FEEDRATE_OVERRIDE','JOG','PROGRAMMED','RAPID','PATH_MODE','POWER_STATE','','','LINE','CONTROL','POWER_STATUS','PROGRAM','PROGRAM_EDIT','PROGRAM_EDIT_NAME','PROGRAM_COMMENT','PROGRAM_HEADER','ROTARY_MODE','ROTARY_VELOCITY_OVERRIDE','SERIAL_NUMBER','SPINDLE_INTERLOCK','TOOL_ASSET_ID','TOOL_NUMBER','TOOL_OFFSET','RADIAL','LENGTH','USER','OPERATOR','MAINTENANCE','SET_UP','WIRE','WORKHOLDING_ID','WORK_OFFSET']

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0

1

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1

0

null

0

4

39ecf72dfe73c064a4f8a15c53def0b0f510e61e

1,938

py

Python

test/test_media_api.py

CrowdEmotion/crowdemotion-api-client-python

b5ec57030e36d2b2c32cc5a43b804d7a34401c16

[ "Apache-2.0" ]

1

2018-06-14T05:12:54.000Z

test/test_media_api.py

CrowdEmotion/crowdemotion-api-client-python

b5ec57030e36d2b2c32cc5a43b804d7a34401c16

[ "Apache-2.0" ]

null

test/test_media_api.py

CrowdEmotion/crowdemotion-api-client-python

b5ec57030e36d2b2c32cc5a43b804d7a34401c16

[ "Apache-2.0" ]

null

# coding: utf-8 """ CloudEmotion API v1 CrowdEmotion API OpenAPI spec version: 1.1.0 Generated by: https://github.com/swagger-api/swagger-codegen.git Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from __future__ import absolute_import import os import sys import unittest import crowdemotion_api_client_python from crowdemotion_api_client_python.rest import ApiException from crowdemotion_api_client_python.apis.media_api import MediaApi class TestMediaApi(unittest.TestCase): """ MediaApi unit test stubs """ def setUp(self): self.api = crowdemotion_api_client_python.apis.media_api.MediaApi() def tearDown(self): pass def test_media_get(self): """ Test case for media_get Find all registered Media """ pass def test_media_media_id_delete(self): """ Test case for media_media_id_delete Delete Media """ pass def test_media_media_id_get(self): """ Test case for media_media_id_get Find a Media """ pass def test_media_media_id_put(self): """ Test case for media_media_id_put Update a Media """ pass def test_media_post(self): """ Test case for media_post Create new Media """ pass if __name__ == '__main__': unittest.main()

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0.659959

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null

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1

0

1

0

4

f2f2e9bc1828e129787a954b213cecf4f57baf29

87

py

Python

nntts/models/__init__.py

entn-at/efficient_tts

5e6ea55d0c9694f7e30eecb5048976088f1a3c66

[ "MIT" ]

111

2020-12-12T09:14:45.000Z

2022-03-31T07:44:09.000Z

nntts/models/__init__.py

entn-at/efficient_tts

5e6ea55d0c9694f7e30eecb5048976088f1a3c66

[ "MIT" ]

13

2020-12-13T06:34:17.000Z

2021-11-10T09:29:52.000Z

nntts/models/__init__.py

entn-at/efficient_tts

5e6ea55d0c9694f7e30eecb5048976088f1a3c66

[ "MIT" ]

20

2020-12-12T15:47:52.000Z

2022-03-31T03:22:13.000Z

from .efficient_tts import EfficientTTSCNN from .duration_model import DurationModel

29

43

0.862069

10

87

7.3

0.8

0

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87

2

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43.5

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0

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0

1

0

null

0

1

0

1

0

4

840beaf082639afd1f3606fd6ec6c3a661146cef

91

py

Python

pizzeriaproj/pizzeria/apps.py

generocha/pizzeria

9076d45e3ffc01ba93a7f6854db39b1005de090e

[ "MIT" ]

null

pizzeriaproj/pizzeria/apps.py

generocha/pizzeria

9076d45e3ffc01ba93a7f6854db39b1005de090e

[ "MIT" ]

null

pizzeriaproj/pizzeria/apps.py

generocha/pizzeria

9076d45e3ffc01ba93a7f6854db39b1005de090e

[ "MIT" ]

null

from django.apps import AppConfig class PizzeriaConfig(AppConfig): name = 'pizzeria'

15.166667

33

0.758242

10

91

6.9

0.9

0

0.164835

91

5

34

18.2

0.907895

0

0.087912

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

84352dba4dd9dc569cba045b10d8498dbef4e6ff

87

py

Python

chime/cogs/__init__.py

realmayus/chime

a9ad4c6e6d02ed99d45b94b6cf8ca0694ef3b6fc

[ "MIT" ]

3

2020-06-06T11:57:36.000Z

2020-06-19T09:51:56.000Z

chime/cogs/__init__.py

realmayus/chime

a9ad4c6e6d02ed99d45b94b6cf8ca0694ef3b6fc

[ "MIT" ]

4

2020-06-19T09:42:31.000Z

2020-11-08T13:10:10.000Z

chime/cogs/__init__.py

realmayus/chime

a9ad4c6e6d02ed99d45b94b6cf8ca0694ef3b6fc

[ "MIT" ]

1

2020-06-30T10:41:55.000Z

"""All the cogs that chime features. All of them interact directly with discord.py."""

43.5

86

0.747126

14

87

4.642857

0.928571

0

0.149425

87

1

87

0.878378

0.91954

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

845cf52003a95601241ce86de56978e65c24ce33

113

py

Python

slurry/environments/__init__.py

andersea/gasio

cc772b2611ac96c307ebc2520471eca32974987e

[ "MIT" ]

11

2020-08-16T18:10:35.000Z

2022-01-27T15:03:09.000Z

slurry/environments/__init__.py

andersea/gasio

cc772b2611ac96c307ebc2520471eca32974987e

[ "MIT" ]

1

2020-12-27T20:00:21.000Z

2021-08-05T19:46:58.000Z

slurry/environments/__init__.py

andersea/gasio

cc772b2611ac96c307ebc2520471eca32974987e

[ "MIT" ]

3

2021-01-17T01:15:08.000Z

2021-05-13T05:42:24.000Z

from ._trio import TrioSection from ._threading import ThreadSection from ._multiprocessing import ProcessSection

37.666667

44

0.876106

12

113

8

0.666667

0

0.097345

113

3

44

37.666667

0.941176

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

84650a64f53bc2af5654b5c41d0f17bd1e1d4168

261

py

Python

OpenGLCffi/GLES3/EXT/EXT/draw_transform_feedback.py

cydenix/OpenGLCffi

c78f51ae5e6b655eb2ea98f072771cf69e2197f3

[ "MIT" ]

null

OpenGLCffi/GLES3/EXT/EXT/draw_transform_feedback.py

cydenix/OpenGLCffi

c78f51ae5e6b655eb2ea98f072771cf69e2197f3

[ "MIT" ]

null

OpenGLCffi/GLES3/EXT/EXT/draw_transform_feedback.py

cydenix/OpenGLCffi

c78f51ae5e6b655eb2ea98f072771cf69e2197f3

[ "MIT" ]

null

from OpenGLCffi.GLES3 import params @params(api='gles3', prms=['mode', 'id']) def glDrawTransformFeedbackEXT(mode, id): pass @params(api='gles3', prms=['mode', 'id', 'instancecount']) def glDrawTransformFeedbackInstancedEXT(mode, id, instancecount): pass

21.75

65

0.735632

29

261

6.62069

0.482759

0.125

0.145833

0.1875

0.25

0

0.012766

0.099617

261

11

66

23.727273

0.804255

0

0.285714

0

0.135135

0

1

0.285714

false

0.285714

0.142857

0

0.428571

0

1

0

null

0

1

0

null

0

1

0

1

0

4

ffe46d375ab70f7bc12925a9d6afe4f9d279f423

133

py

Python

rover/controls-systems/mobility/GNSS/usb0ip.py

CSUFTitanRover/TitanRover2018

4926d377322a37ba644d7e852faa305fb8bb9b55

[ "Apache-2.0" ]

16

2017-09-01T23:33:17.000Z

2021-01-04T02:41:19.000Z

rover/controls-systems/mobility/GNSS/usb0ip.py

WesleyBaxter/TitanRover2018

be69fa908ed0cbb1f4fe4708d0394881b3a4b105

[ "Apache-2.0" ]

56

2017-08-30T01:14:46.000Z

2021-02-28T22:18:44.000Z

rover/controls-systems/mobility/GNSS/usb0ip.py

WesleyBaxter/TitanRover2018

be69fa908ed0cbb1f4fe4708d0394881b3a4b105

[ "Apache-2.0" ]

15

2017-09-14T19:55:55.000Z

2020-05-03T19:44:39.000Z

from subprocess import Popen from time import sleep while True: Popen(["ifconfig", "usb0", "192.168.2.2"]) sleep(5)

19

50

0.631579

19

133

4.421053

0.736842

0

0.098039

0.233083

133

7

51

19

0.72549

0

0.171642

0

1

0

true

0

0.4

0

0.4

0

1

0

null

0

1

0

null

0

1

0

1

0

4

0833cb16bacc83a912b0c97443f36b08e05758a2

294

py

Python

src/forms.py

jorzel/quotes-storage

e2b42e07d2fbf2db7c375865f06a277f15a17dd9

[ "MIT" ]

null

src/forms.py

jorzel/quotes-storage

e2b42e07d2fbf2db7c375865f06a277f15a17dd9

[ "MIT" ]

null

src/forms.py

jorzel/quotes-storage

e2b42e07d2fbf2db7c375865f06a277f15a17dd9

[ "MIT" ]

null

from flask_wtf import FlaskForm from wtforms import StringField, SubmitField from wtforms.validators import DataRequired class BookForm(FlaskForm): title = StringField(validators=[DataRequired()]) author = StringField(validators=[DataRequired()]) submit = SubmitField('Add book')

29.4

53

0.778912

30

294

7.6

0.566667

0.096491

0.289474

0

0.132653

294

9

54

32.666667

0.894118

0

0.027211

0

1

0

false

0

0.428571

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

4

f24b40ecd7bca194626e1f3db4f36a88bc8c7ca2

130

py

Python

kwat/array/check_sorted.py

KwatME/ccal

d96dfa811482eee067f346386a2181ec514625f4

[ "MIT" ]

5

2017-05-05T17:50:28.000Z

2019-01-30T19:23:02.000Z

kwat/array/check_sorted.py

KwatME/ccal

d96dfa811482eee067f346386a2181ec514625f4

[ "MIT" ]

5

2017-05-05T01:52:31.000Z

2019-04-20T21:06:05.000Z

kwat/array/check_sorted.py

KwatME/ccal

d96dfa811482eee067f346386a2181ec514625f4

[ "MIT" ]

5

2017-07-17T18:55:54.000Z

2019-02-02T04:46:19.000Z

from numpy import diff def check_sorted(nu___): di_ = diff(nu___.ravel()) return (di_ <= 0).all() or (0 <= di_).all()

14.444444

47

0.6

20

130

3.4

0.7

0

0.019802

0.223077

130

8

48

16.25

0.653465

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

f26405c56e122a934109bdf5f9d060e003308ffe

302

py

Python

pyaz/batch/location/quotas/__init__.py

py-az-cli/py-az-cli

9a7dc44e360c096a5a2f15595353e9dad88a9792

[ "MIT" ]

null

pyaz/batch/location/quotas/__init__.py

py-az-cli/py-az-cli

9a7dc44e360c096a5a2f15595353e9dad88a9792

[ "MIT" ]

null

pyaz/batch/location/quotas/__init__.py

py-az-cli/py-az-cli

9a7dc44e360c096a5a2f15595353e9dad88a9792

[ "MIT" ]

1

2022-02-03T09:12:01.000Z

''' Manage Batch service quotas at the region level. ''' from .... pyaz_utils import _call_az def show(location): ''' Required Parameters: - location -- The region for which to display the Batch service quotas. ''' return _call_az("az batch location quotas show", locals())

20.133333

75

0.665563

39

302

5.025641

0.641026

0.122449

0.183673

0

0.228477

302

14

76

21.571429

0.841202

0.466887

0

0.228346

0

1

0.333333

false

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

4

f27a0fc9e6e0539e75de96d840e99ddf1ddffd8a

4,096

py

Python

S4/S4 Library/generated/protocolbuffers/PersistenceBlobs_pb2.py

NeonOcean/Environment

ca658cf66e8fd6866c22a4a0136d415705b36d26

[ "CC-BY-4.0" ]

1

2021-05-20T19:33:37.000Z

S4/S4 Library/generated/protocolbuffers/PersistenceBlobs_pb2.py

NeonOcean/Environment

ca658cf66e8fd6866c22a4a0136d415705b36d26

[ "CC-BY-4.0" ]

null

S4/S4 Library/generated/protocolbuffers/PersistenceBlobs_pb2.py

NeonOcean/Environment

ca658cf66e8fd6866c22a4a0136d415705b36d26

[ "CC-BY-4.0" ]

null

from google.protobuf import descriptor from google.protobuf import message from google.protobuf import reflection from google.protobuf import descriptor_pb2 import protocolbuffers.Consts_pb2 as Consts_pb2 import protocolbuffers.S4Common_pb2 as S4Common_pb2 DESCRIPTOR = descriptor.FileDescriptor(name='PersistenceBlobs.proto', package='EA.Sims4.Persistence', serialized_pb='\n\x16PersistenceBlobs.proto\x12\x14EA.Sims4.Persistence\x1a\x0cConsts.proto\x1a\x0eS4Common.proto"\x8c\x02\n\x1eBlobSimFacialCustomizationData\x12\x13\n\x07sculpts\x18\x01 \x03(\x04B\x02\x10\x01\x12U\n\x0eface_modifiers\x18\x02 \x03(\x0b2=.EA.Sims4.Persistence.BlobSimFacialCustomizationData.Modifier\x12U\n\x0ebody_modifiers\x18\x03 \x03(\x0b2=.EA.Sims4.Persistence.BlobSimFacialCustomizationData.Modifier\x1a\'\n\x08Modifier\x12\x0b\n\x03key\x18\x01 \x01(\x04\x12\x0e\n\x06amount\x18\x02 \x01(\x02') _BLOBSIMFACIALCUSTOMIZATIONDATA_MODIFIER = descriptor.Descriptor(name='Modifier', full_name='EA.Sims4.Persistence.BlobSimFacialCustomizationData.Modifier', filename=None, file=DESCRIPTOR, containing_type=None, fields=[descriptor.FieldDescriptor(name='key', full_name='EA.Sims4.Persistence.BlobSimFacialCustomizationData.Modifier.key', index=0, number=1, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), descriptor.FieldDescriptor(name='amount', full_name='EA.Sims4.Persistence.BlobSimFacialCustomizationData.Modifier.amount', index=1, number=2, type=2, cpp_type=6, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None)], extensions=[], nested_types=[], enum_types=[], options=None, is_extendable=False, extension_ranges=[], serialized_start=308, serialized_end=347) _BLOBSIMFACIALCUSTOMIZATIONDATA = descriptor.Descriptor(name='BlobSimFacialCustomizationData', full_name='EA.Sims4.Persistence.BlobSimFacialCustomizationData', filename=None, file=DESCRIPTOR, containing_type=None, fields=[descriptor.FieldDescriptor(name='sculpts', full_name='EA.Sims4.Persistence.BlobSimFacialCustomizationData.sculpts', index=0, number=1, type=4, cpp_type=4, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\x10\x01')), descriptor.FieldDescriptor(name='face_modifiers', full_name='EA.Sims4.Persistence.BlobSimFacialCustomizationData.face_modifiers', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), descriptor.FieldDescriptor(name='body_modifiers', full_name='EA.Sims4.Persistence.BlobSimFacialCustomizationData.body_modifiers', index=2, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None)], extensions=[], nested_types=[_BLOBSIMFACIALCUSTOMIZATIONDATA_MODIFIER], enum_types=[], options=None, is_extendable=False, extension_ranges=[], serialized_start=79, serialized_end=347) _BLOBSIMFACIALCUSTOMIZATIONDATA_MODIFIER.containing_type = _BLOBSIMFACIALCUSTOMIZATIONDATA _BLOBSIMFACIALCUSTOMIZATIONDATA.fields_by_name['face_modifiers'].message_type = _BLOBSIMFACIALCUSTOMIZATIONDATA_MODIFIER _BLOBSIMFACIALCUSTOMIZATIONDATA.fields_by_name['body_modifiers'].message_type = _BLOBSIMFACIALCUSTOMIZATIONDATA_MODIFIER DESCRIPTOR.message_types_by_name['BlobSimFacialCustomizationData'] = _BLOBSIMFACIALCUSTOMIZATIONDATA class BlobSimFacialCustomizationData(message.Message, metaclass=reflection.GeneratedProtocolMessageType): class Modifier(message.Message, metaclass=reflection.GeneratedProtocolMessageType): DESCRIPTOR = _BLOBSIMFACIALCUSTOMIZATIONDATA_MODIFIER DESCRIPTOR = _BLOBSIMFACIALCUSTOMIZATIONDATA

195.047619

1,464

0.842529

488

4,096

6.846311

0.209016

0.040706

0.053876

0.129303

0.601018

0.500449

0.466926

0.332834

0

0.038589

0.044678

4,096

20

1,465

204.8

0.815231

0

0.470588

0.252686

0.225586

0

1

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false

0

0.352941

0

0.529412

0

null

0

1

0

1

0

null

0

1

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4

f2a559434c4bcb4195911136173049b18499c350

78

py

Python

configs/mmcls/classification_onnxruntime_dynamic.py

aegis-rider/mmdeploy

230596bad92fafadb36cf0a69c57d80522cc7c60

[ "Apache-2.0" ]

746

2021-12-27T10:50:28.000Z

2022-03-31T13:34:14.000Z

configs/mmcls/classification_onnxruntime_dynamic.py

aegis-rider/mmdeploy

230596bad92fafadb36cf0a69c57d80522cc7c60

[ "Apache-2.0" ]

253

2021-12-28T05:59:13.000Z

2022-03-31T18:22:25.000Z

configs/mmcls/classification_onnxruntime_dynamic.py

aegis-rider/mmdeploy

230596bad92fafadb36cf0a69c57d80522cc7c60

[ "Apache-2.0" ]

147

2021-12-27T10:50:33.000Z

2022-03-30T10:44:20.000Z

_base_ = ['./classification_dynamic.py', '../_base_/backends/onnxruntime.py']

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f2ac5517981b43d9b4eb5eb57a93ec2a3869ee66

366

py

Python

django/src/views.py

cloudsweb/square

5ad0b50abc182253789ccd9ae5fb6ebd19234adf

[ "MIT" ]

null

django/src/views.py

cloudsweb/square

5ad0b50abc182253789ccd9ae5fb6ebd19234adf

[ "MIT" ]

null

django/src/views.py

cloudsweb/square

5ad0b50abc182253789ccd9ae5fb6ebd19234adf

[ "MIT" ]

null

from django.shortcuts import render from django.http import HttpRequest, HttpResponse # Create your views here. def index(request: HttpRequest): return HttpResponse("Hello, world. You're at the index.") def create_user(request: HttpRequest): if request.method != 'POST': return HttpResponse("method not supported", status=405) # TODO: parse request.body

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4b37897dc4c08744bd985fc093cc3859f7f067f4

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py

Python

py_moysklad/entities/context.py

upmarket-cc/py_moysklad

e026e611344c38f8a8d4f428781fcfb315aaaa60

[ "MIT" ]

null

py_moysklad/entities/context.py

upmarket-cc/py_moysklad

e026e611344c38f8a8d4f428781fcfb315aaaa60

[ "MIT" ]

null

py_moysklad/entities/context.py

upmarket-cc/py_moysklad

e026e611344c38f8a8d4f428781fcfb315aaaa60

[ "MIT" ]

null

from pydantic import BaseModel from py_moysklad.entities.agents.employee import Employee class Context(BaseModel): employee: Employee

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4

4b4443e4c41e8f31860e575ad803d3a426bda51a

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py

Python

tests/__init__.py

danielegts/gcalendar2trello

0237441652b44686e824346b70822bd6784327ce

[ "Apache-2.0" ]

null

tests/__init__.py

danielegts/gcalendar2trello

0237441652b44686e824346b70822bd6784327ce

[ "Apache-2.0" ]

null

tests/__init__.py

danielegts/gcalendar2trello

0237441652b44686e824346b70822bd6784327ce

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """Unit test package for gcalendar2trello."""

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4

4b657e003bafddd061607a94d37a959fd863cf5e

80

py

Python

src/tests/__init__.py

bspeagle/py_git_diff

1674afc1dfac0408372e11945f4a36b297b77e66

[ "MIT" ]

null

src/tests/__init__.py

bspeagle/py_git_diff

1674afc1dfac0408372e11945f4a36b297b77e66

[ "MIT" ]

null

src/tests/__init__.py

bspeagle/py_git_diff

1674afc1dfac0408372e11945f4a36b297b77e66

[ "MIT" ]

null

""" Init stuff. """ import helpers.startup as Startup Startup.load_env_vars()

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4

4b6592c28b4eb3f74c0ba0bc03e83ecaba525e29

87

py

Python

packages/server/invites/src/domain/entities/values/__init__.py

gbartoczevicz/moosic

003ff5cff628505093cc08ad0fbd273272172a51

[ "MIT" ]

3

2021-09-30T00:41:31.000Z

2022-03-15T00:14:23.000Z

packages/server/invites/src/domain/entities/values/__init__.py

gbartoczevicz/moosic

003ff5cff628505093cc08ad0fbd273272172a51

[ "MIT" ]

13

2021-09-20T22:29:52.000Z

2021-12-05T01:59:34.000Z

packages/server/invites/src/domain/entities/values/__init__.py

gabrielbartoczevicz/moosic

003ff5cff628505093cc08ad0fbd273272172a51

[ "MIT" ]

1

2021-11-10T22:11:55.000Z

from .comment import Comment from .location import Location from .rating import Rating

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4b6e0ac9edf12eccc34065d1fc1b9cf36ac8a4e5

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py

Python

DDM/solver/__init__.py

anonymrelease/VQ-DDM

ddda1127398b217373104d8e2913549e87f585d4

[ "MIT" ]

null

DDM/solver/__init__.py

anonymrelease/VQ-DDM

ddda1127398b217373104d8e2913549e87f585d4

[ "MIT" ]

null

DDM/solver/__init__.py

anonymrelease/VQ-DDM

ddda1127398b217373104d8e2913549e87f585d4

[ "MIT" ]

2

2022-02-01T07:43:09.000Z

2022-02-27T12:02:09.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- ''' Created on: 2021/07/10 12:48 @Author: Merc2 '''

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py

Python

tests/alerts_test.py

jsonar/elastalert

1317f13bf8ed957121d88a7d013ebf53a6be6d72

[ "Apache-2.0" ]

1

2020-11-17T13:35:56.000Z

tests/alerts_test.py

jsonar/elastalert

1317f13bf8ed957121d88a7d013ebf53a6be6d72

[ "Apache-2.0" ]

3

2018-11-16T00:21:03.000Z

2019-02-08T20:31:38.000Z

tests/alerts_test.py

jsonar/elastalert

1317f13bf8ed957121d88a7d013ebf53a6be6d72

[ "Apache-2.0" ]

1

2021-12-08T10:27:37.000Z

# -*- coding: utf-8 -*- import datetime import json import mock import pytest from elastalert.alerts import Alerter from elastalert.alerts import BasicMatchString from elastalert.alerts import EmailAlerter from elastalert.opsgenie import OpsGenieAlerter from elastalert.util import ts_add class mock_rule: def get_match_str(self, event): return str(event) def test_basic_match_string(ea): ea.rules[0]['top_count_keys'] = ['username'] match = {'@timestamp': '1918-01-17', 'field': 'value', 'top_events_username': {'bob': 10, 'mallory': 5}} alert_text = unicode(BasicMatchString(ea.rules[0], match)) assert 'anytest' in alert_text assert 'some stuff happened' in alert_text assert 'username' in alert_text assert 'bob: 10' in alert_text assert 'field: value' in alert_text # Non serializable objects don't cause errors match['non-serializable'] = {open: 10} alert_text = unicode(BasicMatchString(ea.rules[0], match)) # unicode objects dont cause errors match['snowman'] = u'☃' alert_text = unicode(BasicMatchString(ea.rules[0], match)) # Pretty printed objects match.pop('non-serializable') match['object'] = {'this': {'that': [1, 2, "3"]}} alert_text = unicode(BasicMatchString(ea.rules[0], match)) assert '"this": {\n "that": [\n 1, \n 2, \n "3"\n ]\n }' in alert_text ea.rules[0]['alert_text'] = 'custom text' alert_text = unicode(BasicMatchString(ea.rules[0], match)) assert 'custom text' in alert_text assert 'anytest' not in alert_text ea.rules[0]['alert_text_type'] = 'alert_text_only' alert_text = unicode(BasicMatchString(ea.rules[0], match)) assert 'custom text' in alert_text assert 'some stuff happened' not in alert_text assert 'username' not in alert_text assert 'field: value' not in alert_text ea.rules[0]['alert_text_type'] = 'exclude_fields' alert_text = unicode(BasicMatchString(ea.rules[0], match)) assert 'custom text' in alert_text assert 'some stuff happened' in alert_text assert 'username' in alert_text assert 'field: value' not in alert_text def test_email(): rule = {'name': 'test alert', 'email': ['testing@test.test', 'test@test.test'], 'from_addr': 'testfrom@test.test', 'type': mock_rule(), 'timestamp_field': '@timestamp', 'email_reply_to': 'test@example.com', 'owner': 'owner_value', 'alert_subject': 'Test alert for {0}, owned by {1}', 'alert_subject_args': ['test_term', 'owner'], 'snowman': u'☃'} with mock.patch('elastalert.alerts.SMTP') as mock_smtp: mock_smtp.return_value = mock.Mock() alert = EmailAlerter(rule) alert.alert([{'test_term': 'test_value'}]) expected = [mock.call('localhost'), mock.call().ehlo(), mock.call().has_extn('STARTTLS'), mock.call().starttls(certfile=None, keyfile=None), mock.call().sendmail(mock.ANY, ['testing@test.test', 'test@test.test'], mock.ANY), mock.call().close()] assert mock_smtp.mock_calls == expected body = mock_smtp.mock_calls[4][1][2] assert 'Reply-To: test@example.com' in body assert 'To: testing@test.test' in body assert 'From: testfrom@test.test' in body assert 'Subject: Test alert for test_value, owned by owner_value' in body def test_email_from_field(): rule = {'name': 'test alert', 'email': ['testing@test.test'], 'email_add_domain': 'example.com', 'type': mock_rule(), 'timestamp_field': '@timestamp', 'email_from_field': 'data.user', 'owner': 'owner_value'} # Found, without @ with mock.patch('elastalert.alerts.SMTP') as mock_smtp: mock_smtp.return_value = mock.Mock() alert = EmailAlerter(rule) alert.alert([{'data': {'user': 'qlo'}}]) assert mock_smtp.mock_calls[4][1][1] == ['qlo@example.com'] # Found, with @ rule['email_add_domain'] = '@example.com' with mock.patch('elastalert.alerts.SMTP') as mock_smtp: mock_smtp.return_value = mock.Mock() alert = EmailAlerter(rule) alert.alert([{'data': {'user': 'qlo'}}]) assert mock_smtp.mock_calls[4][1][1] == ['qlo@example.com'] # Found, list with mock.patch('elastalert.alerts.SMTP') as mock_smtp: mock_smtp.return_value = mock.Mock() alert = EmailAlerter(rule) alert.alert([{'data': {'user': ['qlo', 'foo']}}]) assert mock_smtp.mock_calls[4][1][1] == ['qlo@example.com', 'foo@example.com'] # Not found with mock.patch('elastalert.alerts.SMTP') as mock_smtp: mock_smtp.return_value = mock.Mock() alert = EmailAlerter(rule) alert.alert([{'data': {'foo': 'qlo'}}]) assert mock_smtp.mock_calls[4][1][1] == ['testing@test.test'] # Found, wrong type with mock.patch('elastalert.alerts.SMTP') as mock_smtp: mock_smtp.return_value = mock.Mock() alert = EmailAlerter(rule) alert.alert([{'data': {'user': 17}}]) assert mock_smtp.mock_calls[4][1][1] == ['testing@test.test'] def test_email_with_unicode_strings(): rule = {'name': 'test alert', 'email': u'testing@test.test', 'from_addr': 'testfrom@test.test', 'type': mock_rule(), 'timestamp_field': '@timestamp', 'email_reply_to': 'test@example.com', 'owner': 'owner_value', 'alert_subject': 'Test alert for {0}, owned by {1}', 'alert_subject_args': ['test_term', 'owner'], 'snowman': u'☃'} with mock.patch('elastalert.alerts.SMTP') as mock_smtp: mock_smtp.return_value = mock.Mock() alert = EmailAlerter(rule) alert.alert([{'test_term': 'test_value'}]) expected = [mock.call('localhost'), mock.call().ehlo(), mock.call().has_extn('STARTTLS'), mock.call().starttls(certfile=None, keyfile=None), mock.call().sendmail(mock.ANY, [u'testing@test.test'], mock.ANY), mock.call().close()] assert mock_smtp.mock_calls == expected body = mock_smtp.mock_calls[4][1][2] assert 'Reply-To: test@example.com' in body assert 'To: testing@test.test' in body assert 'From: testfrom@test.test' in body assert 'Subject: Test alert for test_value, owned by owner_value' in body def test_email_with_auth(): rule = {'name': 'test alert', 'email': ['testing@test.test', 'test@test.test'], 'from_addr': 'testfrom@test.test', 'type': mock_rule(), 'timestamp_field': '@timestamp', 'email_reply_to': 'test@example.com', 'alert_subject': 'Test alert for {0}', 'alert_subject_args': ['test_term'], 'smtp_auth_file': 'file.txt', 'rule_file': '/tmp/foo.yaml'} with mock.patch('elastalert.alerts.SMTP') as mock_smtp: with mock.patch('elastalert.alerts.yaml_loader') as mock_open: mock_open.return_value = {'user': 'someone', 'password': 'hunter2'} mock_smtp.return_value = mock.Mock() alert = EmailAlerter(rule) alert.alert([{'test_term': 'test_value'}]) expected = [mock.call('localhost'), mock.call().ehlo(), mock.call().has_extn('STARTTLS'), mock.call().starttls(certfile=None, keyfile=None), mock.call().login('someone', 'hunter2'), mock.call().sendmail(mock.ANY, ['testing@test.test', 'test@test.test'], mock.ANY), mock.call().close()] assert mock_smtp.mock_calls == expected def test_email_with_cert_key(): rule = {'name': 'test alert', 'email': ['testing@test.test', 'test@test.test'], 'from_addr': 'testfrom@test.test', 'type': mock_rule(), 'timestamp_field': '@timestamp', 'email_reply_to': 'test@example.com', 'alert_subject': 'Test alert for {0}', 'alert_subject_args': ['test_term'], 'smtp_auth_file': 'file.txt', 'smtp_cert_file': 'dummy/cert.crt', 'smtp_key_file': 'dummy/client.key', 'rule_file': '/tmp/foo.yaml'} with mock.patch('elastalert.alerts.SMTP') as mock_smtp: with mock.patch('elastalert.alerts.yaml_loader') as mock_open: mock_open.return_value = {'user': 'someone', 'password': 'hunter2'} mock_smtp.return_value = mock.Mock() alert = EmailAlerter(rule) alert.alert([{'test_term': 'test_value'}]) expected = [mock.call('localhost'), mock.call().ehlo(), mock.call().has_extn('STARTTLS'), mock.call().starttls(certfile='dummy/cert.crt', keyfile='dummy/client.key'), mock.call().login('someone', 'hunter2'), mock.call().sendmail(mock.ANY, ['testing@test.test', 'test@test.test'], mock.ANY), mock.call().close()] assert mock_smtp.mock_calls == expected def test_email_with_cc(): rule = {'name': 'test alert', 'email': ['testing@test.test', 'test@test.test'], 'from_addr': 'testfrom@test.test', 'type': mock_rule(), 'timestamp_field': '@timestamp', 'email_reply_to': 'test@example.com', 'cc': 'tester@testing.testing'} with mock.patch('elastalert.alerts.SMTP') as mock_smtp: mock_smtp.return_value = mock.Mock() alert = EmailAlerter(rule) alert.alert([{'test_term': 'test_value'}]) expected = [mock.call('localhost'), mock.call().ehlo(), mock.call().has_extn('STARTTLS'), mock.call().starttls(certfile=None, keyfile=None), mock.call().sendmail(mock.ANY, ['testing@test.test', 'test@test.test', 'tester@testing.testing'], mock.ANY), mock.call().close()] assert mock_smtp.mock_calls == expected body = mock_smtp.mock_calls[4][1][2] assert 'Reply-To: test@example.com' in body assert 'To: testing@test.test' in body assert 'CC: tester@testing.testing' in body assert 'From: testfrom@test.test' in body def test_email_with_bcc(): rule = {'name': 'test alert', 'email': ['testing@test.test', 'test@test.test'], 'from_addr': 'testfrom@test.test', 'type': mock_rule(), 'timestamp_field': '@timestamp', 'email_reply_to': 'test@example.com', 'bcc': 'tester@testing.testing'} with mock.patch('elastalert.alerts.SMTP') as mock_smtp: mock_smtp.return_value = mock.Mock() alert = EmailAlerter(rule) alert.alert([{'test_term': 'test_value'}]) expected = [mock.call('localhost'), mock.call().ehlo(), mock.call().has_extn('STARTTLS'), mock.call().starttls(certfile=None, keyfile=None), mock.call().sendmail(mock.ANY, ['testing@test.test', 'test@test.test', 'tester@testing.testing'], mock.ANY), mock.call().close()] assert mock_smtp.mock_calls == expected body = mock_smtp.mock_calls[4][1][2] assert 'Reply-To: test@example.com' in body assert 'To: testing@test.test' in body assert 'CC: tester@testing.testing' not in body assert 'From: testfrom@test.test' in body def test_email_with_cc_and_bcc(): rule = {'name': 'test alert', 'email': ['testing@test.test', 'test@test.test'], 'from_addr': 'testfrom@test.test', 'type': mock_rule(), 'timestamp_field': '@timestamp', 'email_reply_to': 'test@example.com', 'cc': ['test1@test.com', 'test2@test.com'], 'bcc': 'tester@testing.testing'} with mock.patch('elastalert.alerts.SMTP') as mock_smtp: mock_smtp.return_value = mock.Mock() alert = EmailAlerter(rule) alert.alert([{'test_term': 'test_value'}]) expected = [mock.call('localhost'), mock.call().ehlo(), mock.call().has_extn('STARTTLS'), mock.call().starttls(certfile=None, keyfile=None), mock.call().sendmail( mock.ANY, [ 'testing@test.test', 'test@test.test', 'test1@test.com', 'test2@test.com', 'tester@testing.testing' ], mock.ANY ), mock.call().close()] assert mock_smtp.mock_calls == expected body = mock_smtp.mock_calls[4][1][2] assert 'Reply-To: test@example.com' in body assert 'To: testing@test.test' in body assert 'CC: test1@test.com,test2@test.com' in body assert 'From: testfrom@test.test' in body def test_email_with_args(): rule = { 'name': 'test alert', 'email': ['testing@test.test', 'test@test.test'], 'from_addr': 'testfrom@test.test', 'type': mock_rule(), 'timestamp_field': '@timestamp', 'email_reply_to': 'test@example.com', 'alert_subject': 'Test alert for {0} {1}', 'alert_subject_args': ['test_term', 'test.term'], 'alert_text': 'Test alert for {0} and {1} {2}', 'alert_text_args': ['test_arg1', 'test_arg2', 'test.arg3'], 'alert_missing_value': '<CUSTOM MISSING VALUE>' } with mock.patch('elastalert.alerts.SMTP') as mock_smtp: mock_smtp.return_value = mock.Mock() alert = EmailAlerter(rule) alert.alert([{'test_term': 'test_value', 'test_arg1': 'testing', 'test': {'term': ':)', 'arg3': u'☃'}}]) expected = [mock.call('localhost'), mock.call().ehlo(), mock.call().has_extn('STARTTLS'), mock.call().starttls(certfile=None, keyfile=None), mock.call().sendmail(mock.ANY, ['testing@test.test', 'test@test.test'], mock.ANY), mock.call().close()] assert mock_smtp.mock_calls == expected body = mock_smtp.mock_calls[4][1][2] # Extract the MIME encoded message body body_text = body.split('\n\n')[-1][:-1].decode('base64') assert 'testing' in body_text assert '<CUSTOM MISSING VALUE>' in body_text assert '☃' in body_text assert 'Reply-To: test@example.com' in body assert 'To: testing@test.test' in body assert 'From: testfrom@test.test' in body assert 'Subject: Test alert for test_value :)' in body def test_email_query_key_in_subject(): rule = {'name': 'test alert', 'email': ['testing@test.test', 'test@test.test'], 'type': mock_rule(), 'timestamp_field': '@timestamp', 'email_reply_to': 'test@example.com', 'query_key': 'username'} with mock.patch('elastalert.alerts.SMTP') as mock_smtp: mock_smtp.return_value = mock.Mock() alert = EmailAlerter(rule) alert.alert([{'test_term': 'test_value', 'username': 'werbenjagermanjensen'}]) body = mock_smtp.mock_calls[4][1][2] lines = body.split('\n') found_subject = False for line in lines: if line.startswith('Subject'): assert 'werbenjagermanjensen' in line found_subject = True assert found_subject def test_opsgenie_basic(): rule = {'name': 'testOGalert', 'opsgenie_key': 'ogkey', 'opsgenie_account': 'genies', 'opsgenie_addr': 'https://api.opsgenie.com/v2/alerts', 'opsgenie_recipients': ['lytics'], 'type': mock_rule()} with mock.patch('requests.post') as mock_post: alert = OpsGenieAlerter(rule) alert.alert([{'@timestamp': '2014-10-31T00:00:00'}]) print("mock_post: {0}".format(mock_post._mock_call_args_list)) mcal = mock_post._mock_call_args_list print('mcal: {0}'.format(mcal[0])) assert mcal[0][0][0] == ('https://api.opsgenie.com/v2/alerts') assert mock_post.called assert mcal[0][1]['headers']['Authorization'] == 'GenieKey ogkey' assert mcal[0][1]['json']['source'] == 'ElastAlert' assert mcal[0][1]['json']['responders'] == [{'id': 'lytics', 'type': 'user'}] assert mcal[0][1]['json']['source'] == 'ElastAlert' def test_opsgenie_frequency(): rule = {'name': 'testOGalert', 'opsgenie_key': 'ogkey', 'opsgenie_account': 'genies', 'opsgenie_addr': 'https://api.opsgenie.com/v2/alerts', 'opsgenie_recipients': ['lytics'], 'type': mock_rule(), 'filter': [{'query': {'query_string': {'query': '*hihi*'}}}], 'alert': 'opsgenie'} with mock.patch('requests.post') as mock_post: alert = OpsGenieAlerter(rule) alert.alert([{'@timestamp': '2014-10-31T00:00:00'}]) assert alert.get_info()['recipients'] == rule['opsgenie_recipients'] print("mock_post: {0}".format(mock_post._mock_call_args_list)) mcal = mock_post._mock_call_args_list print('mcal: {0}'.format(mcal[0])) assert mcal[0][0][0] == ('https://api.opsgenie.com/v2/alerts') assert mock_post.called assert mcal[0][1]['headers']['Authorization'] == 'GenieKey ogkey' assert mcal[0][1]['json']['source'] == 'ElastAlert' assert mcal[0][1]['json']['responders'] == [{'id': 'lytics', 'type': 'user'}] assert mcal[0][1]['json']['source'] == 'ElastAlert' assert mcal[0][1]['json']['source'] == 'ElastAlert' def test_kibana(ea): rule = {'filter': [{'query': {'query_string': {'query': 'xy:z'}}}], 'name': 'Test rule!', 'es_host': 'test.testing', 'es_port': 12345, 'timeframe': datetime.timedelta(hours=1), 'index': 'logstash-test', 'include': ['@timestamp'], 'timestamp_field': '@timestamp'} match = {'@timestamp': '2014-10-10T00:00:00'} with mock.patch("elastalert.elastalert.elasticsearch_client") as mock_es: mock_create = mock.Mock(return_value={'_id': 'ABCDEFGH'}) mock_es_inst = mock.Mock() mock_es_inst.index = mock_create mock_es_inst.host = 'test.testing' mock_es_inst.port = 12345 mock_es.return_value = mock_es_inst link = ea.generate_kibana_db(rule, match) assert 'http://test.testing:12345/_plugin/kibana/#/dashboard/temp/ABCDEFGH' == link # Name and index dashboard = json.loads(mock_create.call_args_list[0][1]['body']['dashboard']) assert dashboard['index']['default'] == 'logstash-test' assert 'Test rule!' in dashboard['title'] # Filters and time range filters = dashboard['services']['filter']['list'] assert 'xy:z' in filters['1']['query'] assert filters['1']['type'] == 'querystring' time_range = filters['0'] assert time_range['from'] == ts_add(match['@timestamp'], -rule['timeframe']) assert time_range['to'] == ts_add(match['@timestamp'], datetime.timedelta(minutes=10)) # Included fields active in table assert dashboard['rows'][1]['panels'][0]['fields'] == ['@timestamp'] def test_alert_text_kw(ea): rule = ea.rules[0].copy() rule['alert_text'] = '{field} at {time}' rule['alert_text_kw'] = { '@timestamp': 'time', 'field': 'field', } match = {'@timestamp': '1918-01-17', 'field': 'value'} alert_text = unicode(BasicMatchString(rule, match)) body = '{field} at {@timestamp}'.format(**match) assert body in alert_text def test_alert_text_global_substitution(ea): rule = ea.rules[0].copy() rule['owner'] = 'the owner from rule' rule['priority'] = 'priority from rule' rule['abc'] = 'abc from rule' rule['alert_text'] = 'Priority: {0}; Owner: {1}; Abc: {2}' rule['alert_text_args'] = ['priority', 'owner', 'abc'] match = { '@timestamp': '2016-01-01', 'field': 'field_value', 'abc': 'abc from match', } alert_text = unicode(BasicMatchString(rule, match)) assert 'Priority: priority from rule' in alert_text assert 'Owner: the owner from rule' in alert_text # When the key exists in both places, it will come from the match assert 'Abc: abc from match' in alert_text def test_alert_text_kw_global_substitution(ea): rule = ea.rules[0].copy() rule['foo_rule'] = 'foo from rule' rule['owner'] = 'the owner from rule' rule['abc'] = 'abc from rule' rule['alert_text'] = 'Owner: {owner}; Foo: {foo}; Abc: {abc}' rule['alert_text_kw'] = { 'owner': 'owner', 'foo_rule': 'foo', 'abc': 'abc', } match = { '@timestamp': '2016-01-01', 'field': 'field_value', 'abc': 'abc from match', } alert_text = unicode(BasicMatchString(rule, match)) assert 'Owner: the owner from rule' in alert_text assert 'Foo: foo from rule' in alert_text # When the key exists in both places, it will come from the match assert 'Abc: abc from match' in alert_text def test_resolving_rule_references(ea): rule = { 'name': 'test_rule', 'type': mock_rule(), 'owner': 'the_owner', 'priority': 2, 'list_of_things': [ '1', '$owner$', [ '11', '$owner$', ], ], 'nested_dict': { 'nested_one': '1', 'nested_owner': '$owner$', }, 'resolved_string_reference': '$owner$', 'resolved_int_reference': '$priority$', 'unresolved_reference': '$foo$', } alert = Alerter(rule) assert 'the_owner' == alert.rule['resolved_string_reference'] assert 2 == alert.rule['resolved_int_reference'] assert '$foo$' == alert.rule['unresolved_reference'] assert 'the_owner' == alert.rule['list_of_things'][1] assert 'the_owner' == alert.rule['list_of_things'][2][1] assert 'the_owner' == alert.rule['nested_dict']['nested_owner']

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29953c6d280174491558ecdcf1eedbb1435e38e9

115

py

Python

pra subir/pythonexercicios/ex3.py

daianebandeira88/curso-python

763f5f36b6d7329549ad861c63acc3c84aade887

[ "MIT" ]

null

pra subir/pythonexercicios/ex3.py

daianebandeira88/curso-python

763f5f36b6d7329549ad861c63acc3c84aade887

[ "MIT" ]

null

pra subir/pythonexercicios/ex3.py

daianebandeira88/curso-python

763f5f36b6d7329549ad861c63acc3c84aade887

[ "MIT" ]

null

l=[0]*10 for i in range(len(l)): l[i]=i*2 print(0 in l) print(1 in l) print(2 in l) print(3 in l) print(4 in l)

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29a5e93ccc398aea2cf6b054918df1113146f8ee

167

py

Python

venv/Lib/site-packages/problog/nnf_formula.py

szervoudakis/OpinionMine

70ab18747ec0b32b59ed1936a04872acffdbd2f9

[ "Apache-2.0" ]

1

2021-06-01T06:03:00.000Z

venv/Lib/site-packages/problog/nnf_formula.py

szervoudakis/OpinionMine

70ab18747ec0b32b59ed1936a04872acffdbd2f9

[ "Apache-2.0" ]

null

venv/Lib/site-packages/problog/nnf_formula.py

szervoudakis/OpinionMine

70ab18747ec0b32b59ed1936a04872acffdbd2f9

[ "Apache-2.0" ]

1

2021-06-25T14:59:34.000Z

from ddnnf_formula import DDNNF as NNF import warnings warnings.warn("The class nnf_formula.NNF has been renamed to ddnnf_formula.DDNNF. Please update your code!")

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29dc3a4a4625a80e8d004c0715022823260db52f

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py

Python

common/validators/__init__.py

oil-rope/oil-and-rope

6d59c87d4809f120417a90c1624952085486bb06

[ "MIT" ]

8

2019-08-27T20:08:22.000Z

2021-07-23T22:49:47.000Z

common/validators/__init__.py

oil-rope/oil-and-rope

6d59c87d4809f120417a90c1624952085486bb06

[ "MIT" ]

73

2020-03-11T18:07:29.000Z

2022-03-28T18:07:47.000Z

common/validators/__init__.py

oil-rope/oil-and-rope

6d59c87d4809f120417a90c1624952085486bb06

[ "MIT" ]

4

2020-02-22T19:44:17.000Z

2022-03-08T09:42:45.000Z

from .files import validate_file_size __all__ = [ 'validate_file_size' ]

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4b00bf3794d63e54df7152f704bccaf31317ca2a

140

py

Python

chord.py

Gredelston/johann

2c85238ad603987686d2d6c7be6abe806b963e02

[ "MIT" ]

2

2019-06-19T14:09:09.000Z

2019-06-19T14:21:05.000Z

chord.py

Gredelston/johann

2c85238ad603987686d2d6c7be6abe806b963e02

[ "MIT" ]

1

2019-06-19T02:18:34.000Z

chord.py

Gredelston/johann

2c85238ad603987686d2d6c7be6abe806b963e02

[ "MIT" ]

null

class Chord(object): def __init__(self): self.tones = [] def add_tone(self, tone): self.tones.append(tone)

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1

0

null

0

1

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4

4b044dbd0738df756528fbf83b5e521118db09e9

2,911

py

Python

ansuite.py

anonik9900/Anonik-Suite

c47099e96dac5e6e1eec68637b41de33ff57e058

[ "Apache-2.0" ]

null

ansuite.py

anonik9900/Anonik-Suite

c47099e96dac5e6e1eec68637b41de33ff57e058

[ "Apache-2.0" ]

null

ansuite.py

anonik9900/Anonik-Suite

c47099e96dac5e6e1eec68637b41de33ff57e058

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 import random import string import subprocess #Process commands import socket #Process socket data import pyfiglet import sys import os from subprocess import call def MenuShell(): ascii_banner = pyfiglet.figlet_format("Anonik Suite") print(ascii_banner) print ("{< Devolved By Anonik V 0.1>}") print("") def SplashScreen(): print (" . ) )") print (" ( (| .") print (" ) )\/ ( ( (") print (" * ( (( / ))\)) ( ) )") print (" ( \ )$ | ))( ) (|") print (" >) ))/ | )/ \(( ) \ ") print (" ( ( . -. V )/ )( ( ") print (" \ / . \ . $) ))") print (" )( ( | | ) . ( /") print (" )( ,')) \ / $ `. )") print (" (\> ,'/__ )) __`. /") print (" ( \ | / ___ ( \/ ___ \ | ( (") print (" \.) |/ / \__ __/ \ \| ))") print (" . \. |> \ | __ | / <| /") print (" )/ \____/ :..: \____/ \ <") print (" ) \ (|__ . / ;: \ __| ) (") print (" (( )$ ~--_ -- -- _--~ / ))") print (" \ ( | || || | ( /") print (" \. | ||_ _|| | /") print (" > : | ~V+-I_I_I-+V~ | : (.") print (" ( \: T\ _ _ /T : ./") print (" \ : T^T T-+-T T^T ;<") print (" \..`_ -+- _' )") print (" ) . `--=.._____..=--'. ./ (") def listatool(): print("YouTube Downloader | digita - ytdown \n") print("Backdoor Backfucker | digita - backfucker \n") print("Password Generator | digita - makepsw \n") return MenuPrincipale() def Funzioni(): print ("Ciao") def MenuPrincipale(): ascii_banner = pyfiglet.figlet_format("Anonik Suite") print(ascii_banner) print("") print ("============================================") print(" MENU PRINCIPALE ") print ("") print ("Seleziona (1) per la lista dei tools") print("") print ("Altrimenti digita il comando da lanciare") print ("") print ("============================================") sceltaMenu = input("root@anoniksuite: ") if sceltaMenu == str("1"): return (listatool()) if sceltaMenu == str("ytdown"): print("") call(["python3", "tools/ytd.py"]) #xec(open('test1.py').read()) #call(["python3", "test/test1.py"]) if sceltaMenu == str("backfucker"): print("") call(["python3", "tools/backfucker.py"]) if sceltaMenu == str("makepsw"): print("") call(["python3", "tools/pswf.py"]) else: print("Errore: Invalid Input") os.system('clear') return MenuPrincipale() print (MenuShell()) print (SplashScreen()) print (MenuPrincipale())

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1

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null

0

1

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4

4b0d0cdfeb09bf4c03f462fe1bf9725daa564acb

285

py

Python

05.Drawing Figures with Loops/squareFrame.py

nikolayvutov/Python

55163496dac452a7110b7f76edc6894ee195f1fe

[ "MIT" ]

null

05.Drawing Figures with Loops/squareFrame.py

nikolayvutov/Python

55163496dac452a7110b7f76edc6894ee195f1fe

[ "MIT" ]

null

05.Drawing Figures with Loops/squareFrame.py

nikolayvutov/Python

55163496dac452a7110b7f76edc6894ee195f1fe

[ "MIT" ]

null

n = int(input()) print('+', end='') for i in range(n-2): print(' -', end='') print(' +') for k in range(n-2): print('|', end='') for row in range(n-2): print(' -', end='') print(' |') print('+', end='') for j in range(n-2): print(' -', end='') print(' +')

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null

0

1

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4

4b20bba0a75a955a524ef7557476f245038e6fad

136

py

Python

testing/ejemplo/app.py

rauljrz/curso_python

f241125f0a51c39899f5d59537dca9e7b4c53489

[ "Apache-2.0" ]

null

testing/ejemplo/app.py

rauljrz/curso_python

f241125f0a51c39899f5d59537dca9e7b4c53489

[ "Apache-2.0" ]

null

testing/ejemplo/app.py

rauljrz/curso_python

f241125f0a51c39899f5d59537dca9e7b4c53489

[ "Apache-2.0" ]

null

import requests def bajar_datos(url): """Hace un GET del endpoint y devuelve JSON.""" res = requests.get(url) return res.json()

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0

1

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1

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4

d9a10352c4f69b557de904553a42e7cdb227d69c

174

py

Python

bayesnet/tensor/constant.py

ctgk/bayes

96eab9305eaeecc5a5b032cdf92a8285de4f60bf

[ "MIT" ]

21

2019-01-08T05:58:41.000Z

2021-11-26T14:24:11.000Z

bayesnet/tensor/constant.py

ctgk/bayes

96eab9305eaeecc5a5b032cdf92a8285de4f60bf

[ "MIT" ]

null

bayesnet/tensor/constant.py

ctgk/bayes

96eab9305eaeecc5a5b032cdf92a8285de4f60bf

[ "MIT" ]

11

2019-05-04T13:44:19.000Z

2021-08-05T04:26:19.000Z

from bayesnet.tensor.tensor import Tensor class Constant(Tensor): """ constant tensor class """ def __init__(self, value): super().__init__(value)

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d9a46f53b76e4fe134bcd74f1104e73dcec986aa

82

py

Python

ckanapi/version.py

muccg/ckanapi

f26fb83a8287b41a18994b36b22693c95f8f3516

[ "MIT" ]

128

2015-01-13T05:18:31.000Z

2022-03-18T03:15:43.000Z

ckanapi/version.py

muccg/ckanapi

f26fb83a8287b41a18994b36b22693c95f8f3516

[ "MIT" ]

134

2015-02-04T15:22:25.000Z

2022-03-18T19:14:12.000Z

ckanapi/version.py

muccg/ckanapi

f26fb83a8287b41a18994b36b22693c95f8f3516

[ "MIT" ]

80

2015-01-09T20:47:41.000Z

2022-03-25T15:04:32.000Z

import pkg_resources __version__ = pkg_resources.require("ckanapi")[0].version

13.666667

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d9e18d8cb1ac415f958aca109314f19d0ba5c0df

59

py

Python

gym_tetris/_constants/__init__.py

Olloxan/gym-tetris

87424d8813eac100d360ec2ce6df6dcf0e3ef9d3

[ "MIT" ]

null

gym_tetris/_constants/__init__.py

Olloxan/gym-tetris

87424d8813eac100d360ec2ce6df6dcf0e3ef9d3

[ "MIT" ]

null

gym_tetris/_constants/__init__.py

Olloxan/gym-tetris

87424d8813eac100d360ec2ce6df6dcf0e3ef9d3

[ "MIT" ]

null

"""Modules with constant values for the parent package."""

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8a177472ca8bbdf37a4c74135a86b7a1448276bb

36,896

py

Python

horovod/torch/mpi_ops.py

heyfey/horovod

7a697111eef7d88899551c176e31cde5ab61545c

[ "Apache-2.0" ]

2

2021-04-03T13:53:21.000Z

2021-04-03T13:53:26.000Z

horovod/torch/mpi_ops.py

heyfey/horovod

7a697111eef7d88899551c176e31cde5ab61545c

[ "Apache-2.0" ]

4

2021-04-15T15:14:24.000Z

2021-05-25T10:53:23.000Z

horovod/torch/mpi_ops.py

heyfey/horovod

7a697111eef7d88899551c176e31cde5ab61545c

[ "Apache-2.0" ]

1

2021-04-16T06:28:54.000Z

# Copyright 2019 Uber Technologies, Inc. All Rights Reserved. # Modifications copyright Microsoft # Modifications copyright (C) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # Load all the necessary PyTorch C types. import torch import warnings from horovod.common.basics import HorovodBasics as _HorovodBasics from horovod.common.exceptions import HorovodInternalError from horovod.common.util import check_installed_version, get_average_backwards_compatibility_fun, gpu_available, num_rank_is_power_2 from horovod.torch.compression import Compression # Check possible symbol not found error from pytorch version mismatch try: from horovod.torch import mpi_lib_v2 as mpi_lib except Exception as e: check_installed_version('pytorch', torch.__version__, e) raise e else: check_installed_version('pytorch', torch.__version__) _NULL = "" _basics = _HorovodBasics(__file__, 'mpi_lib_v2') # import basic methods init = _basics.init is_initialized = _basics.is_initialized start_timeline = _basics.start_timeline stop_timeline = _basics.stop_timeline size = _basics.size local_size = _basics.local_size cross_size = _basics.cross_size rank = _basics.rank local_rank = _basics.local_rank cross_rank = _basics.cross_rank mpi_threads_supported = _basics.mpi_threads_supported mpi_enabled = _basics.mpi_enabled mpi_built = _basics.mpi_built gloo_enabled = _basics.gloo_enabled gloo_built = _basics.gloo_built nccl_built = _basics.nccl_built ddl_built = _basics.ddl_built ccl_built = _basics.ccl_built cuda_built = _basics.cuda_built rocm_built = _basics.rocm_built def shutdown(*args, **kwargs): mpi_lib.horovod_torch_reset() return _basics.shutdown(*args, **kwargs) # import reduction op values Average = _basics.Average Sum = _basics.Sum Adasum = _basics.Adasum is_homogeneous = _basics.is_homogeneous handle_average_backwards_compatibility = get_average_backwards_compatibility_fun(_basics) # Schema: handle -> input, output # We keep input in order to make sure it does not get garbage collected # before the operation is finished. _handle_map = {} def _check_function(function_factory, tensor): function = function_factory(tensor) if not hasattr(mpi_lib, function): raise ValueError('Tensor type %s is not supported.' % tensor.type()) if not tensor.is_contiguous(): raise ValueError('Tensor is required to be contiguous.') return function def _allreduce_function_factory(tensor): return 'horovod_torch_allreduce_async_' + tensor.type().replace('.', '_') def _allreduce_async(tensor, output, name, op, prescale_factor, postscale_factor): # Set the divisor for reduced gradients to average when necessary if op == Average: if rocm_built(): # For ROCm, perform averaging at framework level divisor = size() op = Sum else: divisor = 1 elif op == Adasum: if tensor.device.type != 'cpu' and gpu_available('torch'): if nccl_built(): if not is_homogeneous(): raise NotImplementedError('Running GPU Adasum on heterogeneous cluster is not supported yet.') elif not num_rank_is_power_2(int(size() / local_size())): raise NotImplementedError('Running GPU Adasum with non-power of 2 nodes is not supported yet.') if rocm_built(): # For ROCm, perform averaging at framework level divisor = local_size() else: divisor = 1 else: warnings.warn('Adasum reduction does not currently support GPU reduction using MPI. Tensors are ' 'copied to CPU memory instead. To use Adasum for GPU reduction, please compile Horovod ' 'with HOROVOD_GPU_OPERATIONS=NCCL.') divisor = 1 else: if not num_rank_is_power_2(size()): raise NotImplementedError('Running Adasum with non-power of 2 ranks is not supported yet.') divisor = 1 else: divisor = 1 function = _check_function(_allreduce_function_factory, tensor) try: handle = getattr(mpi_lib, function)(tensor, output, divisor, name.encode() if name is not None else _NULL, op, prescale_factor, postscale_factor) except RuntimeError as e: raise HorovodInternalError(e) _handle_map[handle] = (tensor, output) return handle def allreduce_async(tensor, average=None, name=None, op=None, prescale_factor=1.0, postscale_factor=1.0): """ A function that performs asynchronous averaging or summation of the input tensor over all the Horovod processes. The input tensor is not modified. The reduction operation is keyed by the name. If name is not provided, an incremented auto-generated name is used. The tensor type and shape must be the same on all Horovod processes for a given name. The reduction will not start until all processes are ready to send and receive the tensor. Arguments: tensor: A tensor to reduce. average: .. warning:: .. deprecated:: 0.19.0 Use `op` instead. Will be removed in v0.21.0. name: A name of the reduction operation. op: The reduction operation to combine tensors across different ranks. Defaults to Average if None is given. prescale_factor: Multiplicative factor to scale tensor before allreduce. postscale_factor: Multiplicative factor to scale tensor after allreduce. Returns: A handle to the allreduce operation that can be used with `poll()` or `synchronize()`. """ op = handle_average_backwards_compatibility(op, average) output = tensor.new(tensor.shape) return _allreduce_async(tensor, output, name, op, prescale_factor, postscale_factor) class HorovodAllreduce(torch.autograd.Function): """An autograd function that performs allreduce on a tensor.""" @staticmethod def forward(ctx, tensor, average, name, op, prescale_factor, postscale_factor): ctx.average = average ctx.op = op ctx.prescale_factor = prescale_factor ctx.postscale_factor = postscale_factor handle = allreduce_async(tensor, average, name, op, prescale_factor, postscale_factor) return synchronize(handle) @staticmethod def backward(ctx, grad_output): return allreduce(grad_output, average=ctx.average, op=ctx.op, prescale_factor=ctx.prescale_factor, postscale_factor=ctx.postscale_factor), None, None, None, None, None def allreduce(tensor, average=None, name=None, compression=Compression.none, op=None, prescale_factor=1.0, postscale_factor=1.0): """ A function that performs averaging or summation of the input tensor over all the Horovod processes. The input tensor is not modified. The reduction operation is keyed by the name. If name is not provided, an incremented auto-generated name is used. The tensor type and shape must be the same on all Horovod processes for a given name. The reduction will not start until all processes are ready to send and receive the tensor. This acts as a thin wrapper around an autograd function. If your input tensor requires gradients, then callings this function will allow gradients to be computed and backpropagated. Arguments: tensor: A tensor to reduce. average: .. warning:: .. deprecated:: 0.19.0 Use `op` instead. Will be removed in v0.21.0. name: A name of the reduction operation. compression: Compression algorithm used during allreduce to reduce the amount of data sent during the each parameter update step. Defaults to not using compression. op: The reduction operation to combine tensors across different ranks. Defaults to Average if None is given. prescale_factor: Multiplicative factor to scale tensor before allreduce. postscale_factor: Multiplicative factor to scale tensor after allreduce. Returns: A tensor of the same shape and type as `tensor`, averaged or summed across all processes. """ tensor_compressed, ctx = compression.compress(tensor) summed_tensor_compressed = HorovodAllreduce.apply(tensor_compressed, average, name, op, prescale_factor, postscale_factor) return compression.decompress(summed_tensor_compressed, ctx) def allreduce_async_(tensor, average=None, name=None, op=None, prescale_factor=1.0, postscale_factor=1.0): """ A function that performs asynchronous in-place averaging or summation of the input tensor over all the Horovod processes. The reduction operation is keyed by the name. If name is not provided, an incremented auto-generated name is used. The tensor type and shape must be the same on all Horovod processes for a given name. The reduction will not start until all processes are ready to send and receive the tensor. Arguments: tensor: A tensor to reduce. average: .. warning:: .. deprecated:: 0.19.0 Use `op` instead. Will be removed in v0.21.0. name: A name of the reduction operation. op: The reduction operation to combine tensors across different ranks. Defaults to Average if None is given. prescale_factor: Multiplicative factor to scale tensor before allreduce. postscale_factor: Multiplicative factor to scale tensor after allreduce. Returns: A handle to the allreduce operation that can be used with `poll()` or `synchronize()`. """ op = handle_average_backwards_compatibility(op, average) return _allreduce_async(tensor, tensor, name, op, prescale_factor, postscale_factor) def allreduce_(tensor, average=None, name=None, op=None, prescale_factor=1.0, postscale_factor=1.0): """ A function that performs in-place averaging or summation of the input tensor over all the Horovod processes. The reduction operation is keyed by the name. If name is not provided, an incremented auto-generated name is used. The tensor type and shape must be the same on all Horovod processes for a given name. The reduction will not start until all processes are ready to send and receive the tensor. Arguments: tensor: A tensor to reduce. average: .. warning:: .. deprecated:: 0.19.0 Use `op` instead. Will be removed in v0.21.0. name: A name of the reduction operation. op: The reduction operation to combine tensors across different ranks. Defaults to Average if None is given. prescale_factor: Multiplicative factor to scale tensor before allreduce. postscale_factor: Multiplicative factor to scale tensor after allreduce. Returns: A tensor of the same shape and type as `tensor`, averaged or summed across all processes. """ handle = allreduce_async_(tensor, average, name, op, prescale_factor, postscale_factor) return synchronize(handle) def _grouped_allreduce_function_factory(tensor): return 'horovod_torch_grouped_allreduce_async_' + tensor.type().replace('.', '_') def _grouped_allreduce_async(tensors, outputs, name, op, prescale_factor, postscale_factor): # Set the divisor for reduced gradients to average when necessary if op == Average: if rocm_built(): # For ROCm, perform averaging at framework level divisor = size() op = Sum else: divisor = 1 elif op == Adasum: if tensors[0].device.type != 'cpu' and gpu_available('torch'): if nccl_built(): if not is_homogeneous(): raise NotImplementedError('Running GPU Adasum on heterogeneous cluster is not supported yet.') elif not num_rank_is_power_2(int(size() / local_size())): raise NotImplementedError('Running GPU Adasum with non-power of 2 nodes is not supported yet.') if rocm_built(): # For ROCm, perform averaging at framework level divisor = local_size() else: divisor = 1 else: warnings.warn('Adasum reduction does not currently support GPU reduction using MPI. Tensors are ' 'copied to CPU memory instead. To use Adasum for GPU reduction, please compile Horovod ' 'with HOROVOD_GPU_OPERATIONS=NCCL.') divisor = 1 else: if not num_rank_is_power_2(size()): raise NotImplementedError('Running Adasum with non-power of 2 ranks is not supported yet.') divisor = 1 else: divisor = 1 function = _check_function(_grouped_allreduce_function_factory, tensors[0]) try: handle = getattr(mpi_lib, function)(tensors, outputs, divisor, name.encode() if name is not None else _NULL, op, prescale_factor, postscale_factor) except RuntimeError as e: raise HorovodInternalError(e) _handle_map[handle] = (tuple(tensors), tuple(outputs)) return handle def grouped_allreduce_async(tensors, average=None, name=None, op=None, prescale_factor=1.0, postscale_factor=1.0): """ A function that performs asynchronous averaging or summation of the input tensor list over all the Horovod processes. The input tensors are not modified. The reduction operations are keyed by the base name. If a base name is not provided, an incremented auto-generated base name is used. Reductions are performed across tensors in the same list position. The tensor type and shape must be the same on all Horovod processes for tensors sharing positions in the input tensor list. The reduction will not start until all processes are ready to send and receive the tensors. Arguments: tensors: A list of tensors to reduce. average: .. warning:: .. deprecated:: 0.19.0 Use `op` instead. Will be removed in v0.21.0. name: A base name to use for the group reduction operation. op: The reduction operation to combine tensors across different ranks. Defaults to Average if None is given. prescale_factor: Multiplicative factor to scale tensor before allreduce. postscale_factor: Multiplicative factor to scale tensor after allreduce. Returns: A handle to the group allreduce operation that can be used with `poll()` or `synchronize()`. """ op = handle_average_backwards_compatibility(op, average) outputs = [t.new(t.shape) for t in tensors] return _grouped_allreduce_async(tensors, outputs, name, op, prescale_factor, postscale_factor) class HorovodGroupedAllreduce(torch.autograd.Function): """An autograd function that performs allreduce on a list of tensors.""" @staticmethod def forward(ctx, average, name, op, prescale_factor, postscale_factor, *tensors): ctx.average = average ctx.op = op ctx.prescale_factor = prescale_factor ctx.postscale_factor = postscale_factor handle = grouped_allreduce_async(list(tensors), average, name, op, prescale_factor, postscale_factor) return synchronize(handle) @staticmethod def backward(ctx, *grad_output): grad_reduced = grouped_allreduce(list(grad_output), average=ctx.average, op=ctx.op, prescale_factor=ctx.prescale_factor, postscale_factor=ctx.postscale_factor) return (None, None, None, None, None, *grad_reduced) def grouped_allreduce(tensors, average=None, name=None, compression=Compression.none, op=None, prescale_factor=1.0, postscale_factor=1.0): """ A function that performs averaging or summation of the input tensor list over all the Horovod processes. The input tensors are not modified. The reduction operations are keyed by the base name. If a base name is not provided, an incremented auto-generated base name is used. Reductions are performed across tensors in the same list position. The tensor type and shape must be the same on all Horovod processes for tensors sharing positions in the input tensor list. The reduction will not start until all processes are ready to send and receive the tensors. This acts as a thin wrapper around an autograd function. If your input tensors require gradients, then calling this function will allow gradients to be computed and backpropagated. Arguments: tensors: A list of tensors to reduce. average: .. warning:: .. deprecated:: 0.19.0 Use `op` instead. Will be removed in v0.21.0. name: A base name to use for the group reduction operation. compression: Compression algorithm used during allreduce to reduce the amount of data sent during the each parameter update step. Defaults to not using compression. op: The reduction operation to combine tensors across different ranks. Defaults to Average if None is given. prescale_factor: Multiplicative factor to scale tensor before allreduce. postscale_factor: Multiplicative factor to scale tensor after allreduce. Returns: A list containing tensors of the same shape and type as in `tensors`, averaged or summed across all processes. """ tensors_compressed, ctxs = zip(*[compression.compress(t) for t in tensors]) summed_tensors_compressed = HorovodGroupedAllreduce.apply(average, name, op, prescale_factor, postscale_factor, *tensors_compressed) return [compression.decompress(t, ctx) for t, ctx in zip(summed_tensors_compressed, ctxs)] def grouped_allreduce_async_(tensors, average=None, name=None, op=None, prescale_factor=1.0, postscale_factor=1.0): """ A function that performs asynchronous in-place averaging or summation of the input tensors over all the Horovod processes. The reduction operations are keyed by the base name. If a base name is not provided, an incremented auto-generated base name is used. Reductions are performed across tensors in the same list position. The tensor type and shape must be the same on all Horovod processes for tensors sharing positions in the input tensor list. The reduction will not start until all processes are ready to send and receive the tensors. Arguments: tensors: A list of tensors to reduce. average: .. warning:: .. deprecated:: 0.19.0 Use `op` instead. Will be removed in v0.21.0. name: A base name to use for the group reduction operation. op: The reduction operation to combine tensors across different ranks. Defaults to Average if None is given. prescale_factor: Multiplicative factor to scale tensor before allreduce. postscale_factor: Multiplicative factor to scale tensor after allreduce. Returns: A handle to the group allreduce operation that can be used with `poll()` or `synchronize()`. """ op = handle_average_backwards_compatibility(op, average) return _grouped_allreduce_async(tensors, tensors, name, op, prescale_factor, postscale_factor) def grouped_allreduce_(tensors, average=None, name=None, op=None, prescale_factor=1.0, postscale_factor=1.0): """ A function that performs in-place averaging or summation of the input tensors over all the Horovod processes. The reduction operations are keyed by the base name. If a base name is not provided, an incremented auto-generated base name is used. Reductions are performed across tensors in the same list position. The tensor type and shape must be the same on all Horovod processes for tensors sharing positions in the input tensor list. The reduction will not start until all processes are ready to send and receive the tensors. Arguments: tensors: A list of tensors to reduce. average: .. warning:: .. deprecated:: 0.19.0 Use `op` instead. Will be removed in v0.21.0. name: A base name to use for the group reduction operation. op: The reduction operation to combine tensors across different ranks. Defaults to Average if None is given. prescale_factor: Multiplicative factor to scale tensor before allreduce. postscale_factor: Multiplicative factor to scale tensor after allreduce. Returns: A list containing tensors of the same shape and type as in `tensors`, averaged or summed across all processes. """ handle = grouped_allreduce_async_(tensors, average, name, op, prescale_factor, postscale_factor) return synchronize(handle) def sparse_allreduce_async(tensor, name, op): # Allgather aggregates along the first dimension, so we need to transpose the # indices to enforce correct concatenation behavior, then transpose back prior to # constructing the new aggregated sparse gradient t = tensor indices_handle = allgather_async(t._indices().transpose(0, 1).contiguous(), name=f'{name}.indices') values_handle = allgather_async(t._values(), name=f'{name}.values') def handle(): indices = synchronize(indices_handle) values = synchronize(values_handle) values = (values / size()) if op == Average else values if indices.dim() == 0 or values.dim() == 0: return t.new().resize_as_(t) return t.new(indices.transpose(0, 1), values, t.size()) return handle def _allgather_function_factory(tensor): return 'horovod_torch_allgather_async_' + tensor.type().replace('.', '_') def _allgather_async(tensor, output, name): function = _check_function(_allgather_function_factory, tensor) try: handle = getattr(mpi_lib, function)( tensor, output, name.encode() if name is not None else _NULL) except RuntimeError as e: raise HorovodInternalError(e) _handle_map[handle] = (tensor, output) return handle def allgather_async(tensor, name=None): """ A function that asynchronously concatenates the input tensor with the same input tensor on all other Horovod processes. The input tensor is not modified. The concatenation is done on the first dimension, so the input tensors on the different processes must have the same rank and shape, except for the first dimension, which is allowed to be different. Arguments: tensor: A tensor to allgather. name: A name of the allgather operation. Returns: A handle to the allgather operation that can be used with `poll()` or `synchronize()`. """ output = tensor.new() return _allgather_async(tensor, output, name) class HorovodAllgather(torch.autograd.Function): """An autograd function that performs allgather on a tensor.""" @staticmethod def forward(ctx, tensor, name): ctx.dim = tensor.shape[0] handle = allgather_async(tensor, name) return synchronize(handle) @staticmethod def backward(ctx, grad_output): grad_reduced = allreduce(grad_output, average=True) dim_t = torch.IntTensor([ctx.dim]) dim = allgather(dim_t).view(size()) r = rank() offset = torch.sum(dim.narrow(0, 0, r)).item() if r != 0 else 0 return grad_reduced.narrow(0, offset, ctx.dim), None def allgather(tensor, name=None): """ A function that concatenates the input tensor with the same input tensor on all other Horovod processes. The input tensor is not modified. The concatenation is done on the first dimension, so the input tensors on the different processes must have the same rank and shape, except for the first dimension, which is allowed to be different. This acts as a thin wrapper around an autograd function. If your input tensor requires gradients, then callings this function will allow gradients to be computed and backpropagated. Arguments: tensor: A tensor to allgather. name: A name of the allgather operation. Returns: A tensor of the same type as `tensor`, concatenated on dimension zero across all processes. The shape is identical to the input shape, except for the first dimension, which may be greater and is the sum of all first dimensions of the tensors in different Horovod processes. """ return HorovodAllgather.apply(tensor, name) def _broadcast_function_factory(tensor): return 'horovod_torch_broadcast_async_' + tensor.type().replace('.', '_') def _broadcast_async(tensor, output, root_rank, name): function = _check_function(_broadcast_function_factory, tensor) try: handle = getattr(mpi_lib, function)( tensor, output, root_rank, name.encode() if name is not None else _NULL) except RuntimeError as e: raise HorovodInternalError(e) _handle_map[handle] = (tensor, output) return handle def broadcast_async(tensor, root_rank, name=None): """ A function that asynchronously broadcasts the input tensor on root rank to the same input tensor on all other Horovod processes. The input tensor is not modified. The broadcast operation is keyed by the name. If name is not provided, an incremented auto-generated name is used. The tensor type and shape must be the same on all Horovod processes for a given name. The broadcast will not start until all processes are ready to send and receive the tensor. Arguments: tensor: A tensor to broadcast. root_rank: The rank to broadcast the value from. name: A name of the broadcast operation. Returns: A handle to the broadcast operation that can be used with `poll()` or `synchronize()`. """ output = tensor.new(tensor.shape) return _broadcast_async(tensor, output, root_rank, name) class HorovodBroadcast(torch.autograd.Function): """An autograd function that broadcasts a tensor.""" @staticmethod def forward(ctx, tensor, root_rank, name): ctx.root_rank = root_rank handle = broadcast_async(tensor, root_rank, name) return synchronize(handle) @staticmethod def backward(ctx, grad_output): grad_reduced = allreduce(grad_output, average=True) if rank() != ctx.root_rank: grad_reduced *= 0 return grad_reduced, None, None def broadcast(tensor, root_rank, name=None): """ A function that broadcasts the input tensor on root rank to the same input tensor on all other Horovod processes. The input tensor is not modified. The broadcast operation is keyed by the name. If name is not provided, an incremented auto-generated name is used. The tensor type and shape must be the same on all Horovod processes for a given name. The broadcast will not start until all processes are ready to send and receive the tensor. This acts as a thin wrapper around an autograd function. If your input tensor requires gradients, then callings this function will allow gradients to be computed and backpropagated. Arguments: tensor: A tensor to broadcast. root_rank: The rank to broadcast the value from. name: A name of the broadcast operation. Returns: A tensor of the same shape and type as `tensor`, with the value broadcasted from root rank. """ return HorovodBroadcast.apply(tensor, root_rank, name) def broadcast_async_(tensor, root_rank, name=None): """ A function that asynchronously broadcasts the input tensor on root rank to the same input tensor on all other Horovod processes. The operation is performed in-place. The broadcast operation is keyed by the name. If name is not provided, an incremented auto-generated name is used. The tensor type and shape must be the same on all Horovod processes for a given name. The broadcast will not start until all processes are ready to send and receive the tensor. Arguments: tensor: A tensor to broadcast. root_rank: The rank to broadcast the value from. name: A name of the broadcast operation. Returns: A handle to the broadcast operation that can be used with `poll()` or `synchronize()`. """ return _broadcast_async(tensor, tensor, root_rank, name) def broadcast_(tensor, root_rank, name=None): """ A function that broadcasts the input tensor on root rank to the same input tensor on all other Horovod processes. The operation is performed in-place. The broadcast operation is keyed by the name. If name is not provided, an incremented auto-generated name is used. The tensor type and shape must be the same on all Horovod processes for a given name. The broadcast will not start until all processes are ready to send and receive the tensor. Arguments: tensor: A tensor to broadcast. root_rank: The rank to broadcast the value from. name: A name of the broadcast operation. Returns: A tensor of the same shape and type as `tensor`, with the value broadcasted from root rank. """ handle = broadcast_async_(tensor, root_rank, name) return synchronize(handle) def _alltoall_function_factory(tensor): return 'horovod_torch_alltoall_async_' + tensor.type().replace('.', '_') def _alltoall_async(tensor, splits, output, output_received_splits, name): if splits is None: # If splits not provided, create empty tensor as placeholder splits = torch.tensor([], dtype=torch.int32, device='cpu') elif not isinstance(splits, torch.Tensor): splits = torch.tensor(splits, dtype=torch.int32, device='cpu') function = _check_function(_alltoall_function_factory, tensor) try: handle = getattr(mpi_lib, function)( tensor, splits, output, output_received_splits, name.encode() if name is not None else _NULL) except RuntimeError as e: raise HorovodInternalError(e) _handle_map[handle] = (tensor, splits, (output, output_received_splits)) return handle def alltoall_async(tensor, splits=None, name=None): """ A function that scatters slices of the input tensor to all other Horovod processes and returns a tensor of gathered slices from all other Horovod processes. The input tensor is not modified. The slicing is done on the first dimension, so the input tensors on the different processes must have the same rank and shape, except for the first dimension, which is allowed to be different. Arguments: tensor: A tensor to distribute with alltoall. splits: A tensor of integers in rank order describing how many elements in `tensor` to send to each worker. Splitting is applied along the first dimension of `tensor`. If `splits` is not provided, the first dimension is split equally by the number of Horovod processes. name: A name of the alltoall operation. Returns: A handle to the alltoall operation that can be used with `poll()` or `synchronize()`. """ output = tensor.new() if isinstance(splits, torch.Tensor): output_received_splits = splits.new() else: output_received_splits = torch.empty(size(), dtype=torch.int32, device='cpu') return _alltoall_async(tensor, splits, output, output_received_splits, name) class HorovodAlltoall(torch.autograd.Function): """An autograd function that performs alltoall on a tensor.""" @staticmethod def forward(ctx, tensor, splits, name): handle = alltoall_async(tensor, splits, name) output, received_splits = synchronize(handle) ctx.recvsplits = received_splits if splits is None: return output else: ctx.mark_non_differentiable(received_splits) return output, received_splits @staticmethod def backward(ctx, grad_output, *dead_gradients): grad_wrt_tensor, _ = alltoall(grad_output, splits=ctx.recvsplits) return grad_wrt_tensor, None, None def alltoall(tensor, splits=None, name=None): """ A function that scatters slices of the input tensor to all other Horovod processes and returns a tensor of gathered slices from all other Horovod processes. The input tensor is not modified. The slicing is done on the first dimension, so the input tensors on the different processes must have the same rank and shape, except for the first dimension, which is allowed to be different. This acts as a thin wrapper around an autograd function. If your input tensor requires gradients, then callings this function will allow gradients to be computed and backpropagated. Arguments: tensor: A tensor to distribute with alltoall. splits: A tensor of integers in rank order describing how many elements in `tensor` to send to each worker. Splitting is applied along the first dimension of `tensor`. If `splits` is not provided, the first dimension is split equally by the number of Horovod processes. name: A name of the alltoall operation. Returns: 1) A tensor containing the gathered tensor data from all workers. 2) If `splits` has been provided: A tensor of integers in rank order describing how many elements in the output tensor have been received from each worker. """ return HorovodAlltoall.apply(tensor, splits, name) def poll(handle): """ Polls an allreduce, allgather or broadcast handle to determine whether underlying asynchronous operation has completed. After `poll()` returns `True`, `synchronize()` will return without blocking. Arguments: handle: A handle returned by an allreduce, allgather or broadcast asynchronous operation. Returns: A flag indicating whether the operation has completed. """ return mpi_lib.horovod_torch_poll(handle) != 0 def synchronize(handle): """ Synchronizes an asynchronous allreduce, allgather, alltoall or broadcast operation until it's completed. Returns the result of the operation. Arguments: handle: A handle returned by an allreduce, allgather, alltoall or broadcast asynchronous operation. Returns: A single output tensor of the operation or a tuple of multiple output tensors. """ if handle not in _handle_map: return try: mpi_lib.horovod_torch_wait_and_clear(handle) output = _handle_map.pop(handle)[-1] return output except RuntimeError as e: raise HorovodInternalError(e) def join(device=-1): """A function that indicates that the rank finished processing data. All ranks that did not call join() continue to process allreduce operations. This function blocks Python thread until all ranks join. Arguments: device: An id of the device to create temprorary zero tensors (default -1, CPU) Returns: Id of the rank that joined last. """ try: return mpi_lib.horovod_torch_join(device) except RuntimeError as e: raise HorovodInternalError(e)

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8a2c109b5e273d0a69f2c463552597c6ca6fbda4

222

py

Python

nimare/base/annotate.py

Julio-Yanes/NiMARE

36bb05034041998519814b55fe402489147fdd63

[ "MIT" ]

null

nimare/base/annotate.py

Julio-Yanes/NiMARE

36bb05034041998519814b55fe402489147fdd63

[ "MIT" ]

null

nimare/base/annotate.py

Julio-Yanes/NiMARE

36bb05034041998519814b55fe402489147fdd63

[ "MIT" ]

null

""" Base classes for meta-analytic annotation methods. """ from .base import NiMAREBase class AnnotationModel(NiMAREBase): """ Base class for topic and vector models. """ def __init__(self): pass

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8a4d38c0db2017ef38f5a1f2eaaacc5251512936

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py

Python

security/backends/logging/__init__.py

druids/django-security

bca889ff1a58378a038a08ca365162d9e3ef3fbf

[ "MIT" ]

9

2019-03-12T12:31:20.000Z

2021-01-22T13:31:36.000Z

security/backends/logging/__init__.py

druids/django-security

bca889ff1a58378a038a08ca365162d9e3ef3fbf

[ "MIT" ]

28

2019-12-05T12:20:49.000Z

2022-03-25T08:15:10.000Z

security/backends/logging/__init__.py

druids/django-security

bca889ff1a58378a038a08ca365162d9e3ef3fbf

[ "MIT" ]

5

2019-07-10T15:29:44.000Z

2021-02-01T12:50:56.000Z

default_app_config = 'security.backends.logging.app.SecurityLoggingBackend'

38

75

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null

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null

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8aa0625d67ae374942ecb586fde9a37dd06e794b

63

py

Python

Retired/Differences of Triangles.py

mwk0408/codewars_solutions

9b4f502b5f159e68024d494e19a96a226acad5e5

[ "MIT" ]

6

2020-09-03T09:32:25.000Z

2020-12-07T04:10:01.000Z

Retired/Differences of Triangles.py

mwk0408/codewars_solutions

9b4f502b5f159e68024d494e19a96a226acad5e5

[ "MIT" ]

1

2021-12-13T15:30:21.000Z

Retired/Differences of Triangles.py

mwk0408/codewars_solutions

9b4f502b5f159e68024d494e19a96a226acad5e5

[ "MIT" ]

null

def diff_of_tri_numbers(n, m): return n*(n+1)//2-m*(m+1)//2

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8aa7b77687addd0d37ec023a06eb5efeaf919379

174

py

Python

Admin_Automation-master/www/cgi-bin/caas/caas_pyton.py

agirishkumar/Automate-admin-tasks

1eb9a6f0aa8a65d09abef69b9b1cc42816c4043f

[ "MIT" ]

null

Admin_Automation-master/www/cgi-bin/caas/caas_pyton.py

agirishkumar/Automate-admin-tasks

1eb9a6f0aa8a65d09abef69b9b1cc42816c4043f

[ "MIT" ]

null

Admin_Automation-master/www/cgi-bin/caas/caas_pyton.py

agirishkumar/Automate-admin-tasks

1eb9a6f0aa8a65d09abef69b9b1cc42816c4043f

[ "MIT" ]

null

#!/usr/bin/python36 print("content-type: text/html") import subprocess as sp import cgi import cgitb cgitb.enable() print("location: http://192.168.43.125:3200") print()

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8abc74711c870ba2d5649f99ad630f849f79254f

152

py

Python

__init__.py

lz677/Alien-Invasion

a9f4e84944bfee2136cd7abacf71a77cc15e2275

[ "MIT" ]

null

__init__.py

lz677/Alien-Invasion

a9f4e84944bfee2136cd7abacf71a77cc15e2275

[ "MIT" ]

null

__init__.py

lz677/Alien-Invasion

a9f4e84944bfee2136cd7abacf71a77cc15e2275

[ "MIT" ]

null

# /usr/bin/python3 # -*- coding: utf-8 -*- # @Author : Zhe LIU # @Email : 937150058@qq.com # @File : __init__.py.py # @Time : 2019/8/28 15:28

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8acd78f2f9ec3e8b967e8a292da4e943feb79771

171

py

Python

emol/emol/views/__init__.py

lrt512/emol

e1dd3462632a525c3b9701d4fd9a332d19c93b85

[ "MIT" ]

null

emol/emol/views/__init__.py

lrt512/emol

e1dd3462632a525c3b9701d4fd9a332d19c93b85

[ "MIT" ]

null

emol/emol/views/__init__.py

lrt512/emol

e1dd3462632a525c3b9701d4fd9a332d19c93b85

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Module for flask views. Only the home page view is defined in this scope. All other views are defined in nested modules for partitioning. """

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