xszheng2020/the_stack_dedup_python_hits_3 · Datasets at Hugging Face

b157fd705dea43be298b943f4208c1336c516768

326

py

Python

pyopenproject/business/services/group_service_impl.py

webu/pyopenproject

40b2cb9fe0fa3f89bc0fe2a3be323422d9ecf966

[ "MIT" ]

5

2021-02-25T15:54:28.000Z

2021-04-22T15:43:36.000Z

pyopenproject/business/services/group_service_impl.py

webu/pyopenproject

40b2cb9fe0fa3f89bc0fe2a3be323422d9ecf966

[ "MIT" ]

7

2021-03-15T16:26:23.000Z

2022-03-16T13:45:18.000Z

pyopenproject/business/services/group_service_impl.py

webu/pyopenproject

40b2cb9fe0fa3f89bc0fe2a3be323422d9ecf966

[ "MIT" ]

6

2021-06-18T18:59:11.000Z

2022-03-27T04:58:52.000Z

from pyopenproject.business.group_service import GroupService from pyopenproject.business.services.command.group.find import Find class GroupServiceImpl(GroupService): def __init__(self, connection): super().__init__(connection) def find(self, group): return Find(self.connection, group).execute()

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b15d56db0ea737aa9405931b1b8fdc74057431ef

211

py

Python

src/domain/entities/RawDataModel.py

behrad-kzm/LocationBrain

44ef5e03cc9b240bfabad5c3c14635ef812a39ae

[ "MIT" ]

null

src/domain/entities/RawDataModel.py

behrad-kzm/LocationBrain

44ef5e03cc9b240bfabad5c3c14635ef812a39ae

[ "MIT" ]

null

src/domain/entities/RawDataModel.py

behrad-kzm/LocationBrain

44ef5e03cc9b240bfabad5c3c14635ef812a39ae

[ "MIT" ]

null

from typing import Tuple class RawDataModel: location: Tuple[float, float] label: str def __init__(self, x: float, y: float, label: str): self.location = (x, y) self.label = label

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null

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3

b160902fbb87f53408dacbc778764205ba4ad661

162

py

Python

frontend/urls.py

syqu22/django-react-blog

6c5605e1c8ef66b17d4d6453f0807947d1adfdb4

[ "MIT" ]

null

frontend/urls.py

syqu22/django-react-blog

6c5605e1c8ef66b17d4d6453f0807947d1adfdb4

[ "MIT" ]

null

frontend/urls.py

syqu22/django-react-blog

6c5605e1c8ef66b17d4d6453f0807947d1adfdb4

[ "MIT" ]

null

from os import name from django.urls import path, re_path from frontend.views import index urlpatterns = [ path('', index), re_path(r'^.*/$', index), ]

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3

b187497a3887449fad19cdd54ce4ac320171e884

219

py

Python

app/input/stdin.py

pedrolp85/pycli

469d22442de2a854aebc3354cdbf9b8fe342ee16

[ "Apache-2.0" ]

null

app/input/stdin.py

pedrolp85/pycli

469d22442de2a854aebc3354cdbf9b8fe342ee16

[ "Apache-2.0" ]

null

app/input/stdin.py

pedrolp85/pycli

469d22442de2a854aebc3354cdbf9b8fe342ee16

[ "Apache-2.0" ]

null

from typing import Iterator import fileinput from .input import Input class StdinInput(Input): def get_lines(self) -> Iterator[str]: for line in fileinput.input(): yield line.rstrip("\n")

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null

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b1a1384c55a831a6beed70e0b7bcb1ab94492a71

246

py

Python

qcloudsdkwenzhi/QuotaGetRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

qcloudsdkwenzhi/QuotaGetRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

qcloudsdkwenzhi/QuotaGetRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from qcloudsdkcore.request import Request class QuotaGetRequest(Request): def __init__(self): super(QuotaGetRequest, self).__init__( 'wenzhi', 'qcloudcliV1', 'QuotaGet', 'wenzhi.api.qcloud.com')

24.6

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null

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3

b1aa3c8e652e547b64690addc139dab66c1eb98c

155

py

Python

applications/accounts/elastic/exceptions.py

iBeCo/analytics

c71c80a7cacd55078c1a9dd463cb4e66aa868764

[ "Apache-2.0" ]

null

applications/accounts/elastic/exceptions.py

iBeCo/analytics

c71c80a7cacd55078c1a9dd463cb4e66aa868764

[ "Apache-2.0" ]

null

applications/accounts/elastic/exceptions.py

iBeCo/analytics

c71c80a7cacd55078c1a9dd463cb4e66aa868764

[ "Apache-2.0" ]

null

class StoreDoesNotExist(Exception): def __init__(self): super(StoreDoesNotExist, self).__init__("Store with the given query does not exist")

25.833333

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155

5.888889

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155

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false

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1

0

null

0

1

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null

0

1

0

3

4931b5a6ab85052a5dec41fee95ea58162682a18

1,170

py

Python

python/meanie3D/__init__.py

JuergenSimon/meanie3D

776890f6b63d735153566fecc5a76c68a23ef333

[ "MIT" ]

null

python/meanie3D/__init__.py

JuergenSimon/meanie3D

776890f6b63d735153566fecc5a76c68a23ef333

[ "MIT" ]

5

2016-09-17T13:46:23.000Z

2020-07-01T16:31:29.000Z

python/meanie3D/__init__.py

JuergenSimon/meanie3D

776890f6b63d735153566fecc5a76c68a23ef333

[ "MIT" ]

3

2016-04-18T13:13:28.000Z

2020-06-18T12:30:05.000Z

__author__ = 'Juergen Simon' __email__ = 'juergen.simon@uni-bonn.de' __version__ = '1.6.0' __url__ = 'http://git.meteo.uni-bonn.de/projects/meanie3d' __all__ = ['app', 'visualisation', 'resources'] import os.path import sys def getVersion(): ''' :return:meanie3D package version ''' from . import __version__ return __version__ def getHome(): ''' meanie3D package location :return: ''' return os.path.abspath(os.path.dirname(__file__)) def appendSystemPythonPath(): ''' Returns the system's python path to import outside modules. ''' system_python_path = ":/Users/simon/anaconda/lib/python35.zip:/Users/simon/anaconda/lib/python3.5:/Users/simon/anaconda/lib/python3.5/plat-darwin:/Users/simon/anaconda/lib/python3.5/lib-dynload:/Users/simon/anaconda/lib/python3.5/site-packages:/Users/simon/anaconda/lib/python3.5/site-packages/Sphinx-1.4.1-py3.5.egg:/Users/simon/anaconda/lib/python3.5/site-packages/aeosa:/Users/simon/anaconda/lib/python3.5/site-packages/setuptools-23.0.0-py3.5.egg".strip() paths = system_python_path.split(':') for path in paths: if path: sys.path.append(path)

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493a058b071e10afea610c82bdc7052880e29d3e

489

py

Python

coggers/register.py

restinya/Barkeep

9d6a7f47bc8e2bc3cda1ba2992a02a85d06efa7e

[ "MIT" ]

null

coggers/register.py

restinya/Barkeep

9d6a7f47bc8e2bc3cda1ba2992a02a85d06efa7e

[ "MIT" ]

null

coggers/register.py

restinya/Barkeep

9d6a7f47bc8e2bc3cda1ba2992a02a85d06efa7e

[ "MIT" ]

null

import discord import asyncio import requests import re from discord.utils import get from discord.ext import commands from math import floor from configs.settings import command_prefix from utils import accessDB, point_buy, alpha_emojis, db, VerboseMDStringifier, traceBack, checkForChar class Register(commands.Cog): def __init__ (self, bot): self.bot = bot @commands.group(aliases=['r'], case_insensitive=True) async def reward(self, ctx): pass

24.45

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null

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1

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3

4948c407586d38435e5ca768d4ac1ac6261a7430

3,561

py

Python

LOSSPhotPypeline/image/FileNames.py

xiaocong213/LOSSPhotPypeline

147630c9dbfb13005e75c661dc69ac2be58e14c9

[ "MIT" ]

null

LOSSPhotPypeline/image/FileNames.py

xiaocong213/LOSSPhotPypeline

147630c9dbfb13005e75c661dc69ac2be58e14c9

[ "MIT" ]

null

LOSSPhotPypeline/image/FileNames.py

xiaocong213/LOSSPhotPypeline

147630c9dbfb13005e75c661dc69ac2be58e14c9

[ "MIT" ]

null

class FileNames(object): '''standardize and handle all file names/types encountered by pipeline''' def __init__(self, name): '''do everything upon instantiation''' # determine root file name self.root = name self.root = self.root.replace('_c.fit','') self.root = self.root.replace('_sobj.fit','') self.root = self.root.replace('_cobj.fit','') self.root = self.root.replace('_cnew.fit','') self.root = self.root.replace('_cwcs.fit','' ) self.root = self.root.replace('_ctwp.fit','' ) self.root = self.root.replace('_cfwp.fit','' ) self.root = self.root.replace('_ctcv.fit','' ) self.root = self.root.replace('_cfcv.fit','' ) self.root = self.root.replace('_ctsb.fit','' ) self.root = self.root.replace('_cfsb.fit','' ) self.root = self.root.replace('_cph.fit','' ) self.root = self.root.replace('_ctph.fit','' ) self.root = self.root.replace('_sbph.fit','' ) self.root = self.root.replace('_cand.fit','' ) self.root = self.root.replace('_fwhm.txt','' ) self.root = self.root.replace('_obj.txt','' ) self.root = self.root.replace('_psfstar.txt','' ) self.root = self.root.replace('_apt.txt','' ) self.root = self.root.replace('_apt.dat','' ) self.root = self.root.replace('_psf.txt','' ) self.root = self.root.replace('_standrd.txt','') self.root = self.root.replace('_standxy.txt','') self.root = self.root.replace('_objectrd.txt','') self.root = self.root.replace('_objectxy.txt','') self.root = self.root.replace('_sky.txt','') self.root = self.root.replace('_apass.dat','') self.root = self.root.replace('_zero.txt','') self.root = self.root.replace('.fit','') self.root = self.root.replace('.fts','') # generate all filenames from root self.cimg = self.root + '_c.fit' self.sobj = self.root + '_sobj.fit' self.cobj = self.root + '_cobj.fit' self.cnew = self.root + '_cnew.fit' self.cwcs = self.root + '_cwcs.fit' self.ctwp = self.root + '_ctwp.fit' self.cfwp = self.root + '_cfwp.fit' self.ctcv = self.root + '_ctcv.fit' self.cfcv = self.root + '_cfcv.fit' self.ctsb = self.root + '_ctsb.fit' self.cfsb = self.root + '_cfsb.fit' self.cph = self.root + '_cph.fit' self.ctph = self.root + '_ctph.fit' self.sbph = self.root + '_sbph.fit' self.cand = self.root + '_cand.fit' self.fwhm_fl = self.root + '_fwhm.txt' self.obj = self.root + '_obj.txt' self.psfstar = self.root + '_psfstar.txt' self.apt = self.root + '_apt.txt' self.aptdat = self.root + '_apt.dat' self.psf = self.root + '_psf.txt' self.psfsub = self.root + '_psfsub.txt' self.psffitarr = self.root + '_psffitarr.fit' self.psfdat = self.root + '_psf.dat' self.psfsubdat = self.root + '_psfsub.dat' self.standrd = self.root + '_standrd.txt' self.standxy = self.root + '_standxy.txt' self.objectrd = self.root + '_objectrd.txt' self.objectxy = self.root + '_objectxy.txt' self.skytxt = self.root + '_sky.txt' self.skyfit = self.root + '_sky.fit' self.apass = self.root + '_apass.dat' self.zerotxt = self.root + '_zero.txt'

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null

1

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null

0

3

498bbd66948f733339d939ff2181af5fd1cfe721

376

py

Python

pkg/pkg/utils/utils.py

dlee0156/bilateral-connectome

26fe165341bb79379fecdd8bc5d7b5bfe3983fdc

[ "MIT" ]

2

2021-09-24T20:21:18.000Z

2022-02-08T18:31:29.000Z

pkg/pkg/utils/utils.py

dlee0156/bilateral-connectome

26fe165341bb79379fecdd8bc5d7b5bfe3983fdc

[ "MIT" ]

9

2021-09-29T17:23:41.000Z

2022-03-16T20:22:04.000Z

pkg/pkg/utils/utils.py

dlee0156/bilateral-connectome

26fe165341bb79379fecdd8bc5d7b5bfe3983fdc

[ "MIT" ]

2

2021-11-16T16:17:53.000Z

2022-03-26T01:25:10.000Z

import warnings from beartype.roar import BeartypeDecorHintPepDeprecatedWarning def set_warnings(): # warnings.filterwarnings("ignore", category=UserWarning, module="umap") # this is currently being thrown on import of graspologic (11/05/2021) warnings.filterwarnings( "ignore", module="beartype", category=BeartypeDecorHintPepDeprecatedWarning )

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3

b8d1b22a310e462a5d62d1140764a54ac0253346

34,872

py

Python

speechbrain/nnet/CNN.py

JasonSWFu/speechbrain

cb78ba2b33fceba273b055dc471535344c3053f0

[ "Apache-2.0" ]

2

2021-11-02T10:25:18.000Z

2022-03-24T05:12:05.000Z

speechbrain/nnet/CNN.py

JasonSWFu/speechbrain

cb78ba2b33fceba273b055dc471535344c3053f0

[ "Apache-2.0" ]

null

speechbrain/nnet/CNN.py

JasonSWFu/speechbrain

cb78ba2b33fceba273b055dc471535344c3053f0

[ "Apache-2.0" ]

1

2022-02-15T07:11:40.000Z

"""Library implementing convolutional neural networks. Authors * Mirco Ravanelli 2020 * Jianyuan Zhong 2020 * Cem Subakan 2021 * Davide Borra 2021 """ import math import torch import logging import numpy as np import torch.nn as nn import torch.nn.functional as F from typing import Tuple logger = logging.getLogger(__name__) class SincConv(nn.Module): """This function implements SincConv (SincNet). M. Ravanelli, Y. Bengio, "Speaker Recognition from raw waveform with SincNet", in Proc. of SLT 2018 (https://arxiv.org/abs/1808.00158) Arguments --------- input_shape : tuple The shape of the input. Alternatively use ``in_channels``. in_channels : int The number of input channels. Alternatively use ``input_shape``. out_channels : int It is the number of output channels. kernel_size: int Kernel size of the convolutional filters. stride : int Stride factor of the convolutional filters. When the stride factor > 1, a decimation in time is performed. dilation : int Dilation factor of the convolutional filters. padding : str (same, valid, causal). If "valid", no padding is performed. If "same" and stride is 1, output shape is the same as the input shape. "causal" results in causal (dilated) convolutions. padding_mode : str This flag specifies the type of padding. See torch.nn documentation for more information. groups : int This option specifies the convolutional groups. See torch.nn documentation for more information. bias : bool If True, the additive bias b is adopted. sample_rate : int, Sampling rate of the input signals. It is only used for sinc_conv. min_low_hz : float Lowest possible frequency (in Hz) for a filter. It is only used for sinc_conv. min_low_hz : float Lowest possible value (in Hz) for a filter bandwidth. Example ------- >>> inp_tensor = torch.rand([10, 16000]) >>> conv = SincConv(input_shape=inp_tensor.shape, out_channels=25, kernel_size=11) >>> out_tensor = conv(inp_tensor) >>> out_tensor.shape torch.Size([10, 16000, 25]) """ def __init__( self, out_channels, kernel_size, input_shape=None, in_channels=None, stride=1, dilation=1, padding="same", padding_mode="reflect", sample_rate=16000, min_low_hz=50, min_band_hz=50, ): super().__init__() self.out_channels = out_channels self.kernel_size = kernel_size self.stride = stride self.dilation = dilation self.padding = padding self.padding_mode = padding_mode self.sample_rate = sample_rate self.min_low_hz = min_low_hz self.min_band_hz = min_band_hz # input shape inference if input_shape is None and in_channels is None: raise ValueError("Must provide one of input_shape or in_channels") if in_channels is None: in_channels = self._check_input_shape(input_shape) # Initialize Sinc filters self._init_sinc_conv() def forward(self, x): """Returns the output of the convolution. Arguments --------- x : torch.Tensor (batch, time, channel) input to convolve. 2d or 4d tensors are expected. """ x = x.transpose(1, -1) self.device = x.device unsqueeze = x.ndim == 2 if unsqueeze: x = x.unsqueeze(1) if self.padding == "same": x = self._manage_padding( x, self.kernel_size, self.dilation, self.stride ) elif self.padding == "causal": num_pad = (self.kernel_size - 1) * self.dilation x = F.pad(x, (num_pad, 0)) elif self.padding == "valid": pass else: raise ValueError( "Padding must be 'same', 'valid' or 'causal'. Got %s." % (self.padding) ) sinc_filters = self._get_sinc_filters() wx = F.conv1d( x, sinc_filters, stride=self.stride, padding=0, dilation=self.dilation, ) if unsqueeze: wx = wx.squeeze(1) wx = wx.transpose(1, -1) return wx def _check_input_shape(self, shape): """Checks the input shape and returns the number of input channels. """ if len(shape) == 2: in_channels = 1 elif len(shape) == 3: in_channels = 1 else: raise ValueError( "sincconv expects 2d or 3d inputs. Got " + str(len(shape)) ) # Kernel size must be odd if self.kernel_size % 2 == 0: raise ValueError( "The field kernel size must be an odd number. Got %s." % (self.kernel_size) ) return in_channels def _get_sinc_filters(self,): """This functions creates the sinc-filters to used for sinc-conv. """ # Computing the low frequencies of the filters low = self.min_low_hz + torch.abs(self.low_hz_) # Setting minimum band and minimum freq high = torch.clamp( low + self.min_band_hz + torch.abs(self.band_hz_), self.min_low_hz, self.sample_rate / 2, ) band = (high - low)[:, 0] # Passing from n_ to the corresponding f_times_t domain self.n_ = self.n_.to(self.device) self.window_ = self.window_.to(self.device) f_times_t_low = torch.matmul(low, self.n_) f_times_t_high = torch.matmul(high, self.n_) # Left part of the filters. band_pass_left = ( (torch.sin(f_times_t_high) - torch.sin(f_times_t_low)) / (self.n_ / 2) ) * self.window_ # Central element of the filter band_pass_center = 2 * band.view(-1, 1) # Right part of the filter (sinc filters are symmetric) band_pass_right = torch.flip(band_pass_left, dims=[1]) # Combining left, central, and right part of the filter band_pass = torch.cat( [band_pass_left, band_pass_center, band_pass_right], dim=1 ) # Amplitude normalization band_pass = band_pass / (2 * band[:, None]) # Setting up the filter coefficients filters = band_pass.view(self.out_channels, 1, self.kernel_size) return filters def _init_sinc_conv(self): """Initializes the parameters of the sinc_conv layer.""" # Initialize filterbanks such that they are equally spaced in Mel scale high_hz = self.sample_rate / 2 - (self.min_low_hz + self.min_band_hz) mel = torch.linspace( self._to_mel(self.min_low_hz), self._to_mel(high_hz), self.out_channels + 1, ) hz = self._to_hz(mel) # Filter lower frequency and bands self.low_hz_ = hz[:-1].unsqueeze(1) self.band_hz_ = (hz[1:] - hz[:-1]).unsqueeze(1) # Maiking freq and bands learnable self.low_hz_ = nn.Parameter(self.low_hz_) self.band_hz_ = nn.Parameter(self.band_hz_) # Hamming window n_lin = torch.linspace( 0, (self.kernel_size / 2) - 1, steps=int((self.kernel_size / 2)) ) self.window_ = 0.54 - 0.46 * torch.cos( 2 * math.pi * n_lin / self.kernel_size ) # Time axis (only half is needed due to symmetry) n = (self.kernel_size - 1) / 2.0 self.n_ = ( 2 * math.pi * torch.arange(-n, 0).view(1, -1) / self.sample_rate ) def _to_mel(self, hz): """Converts frequency in Hz to the mel scale. """ return 2595 * np.log10(1 + hz / 700) def _to_hz(self, mel): """Converts frequency in the mel scale to Hz. """ return 700 * (10 ** (mel / 2595) - 1) def _manage_padding( self, x, kernel_size: int, dilation: int, stride: int, ): """This function performs zero-padding on the time axis such that their lengths is unchanged after the convolution. Arguments --------- x : torch.Tensor Input tensor. kernel_size : int Size of kernel. dilation : int Dilation used. stride : int Stride. """ # Detecting input shape L_in = x.shape[-1] # Time padding padding = get_padding_elem(L_in, stride, kernel_size, dilation) # Applying padding x = F.pad(x, padding, mode=self.padding_mode) return x class Conv1d(nn.Module): """This function implements 1d convolution. Arguments --------- out_channels : int It is the number of output channels. kernel_size : int Kernel size of the convolutional filters. input_shape : tuple The shape of the input. Alternatively use ``in_channels``. in_channels : int The number of input channels. Alternatively use ``input_shape``. stride : int Stride factor of the convolutional filters. When the stride factor > 1, a decimation in time is performed. dilation : int Dilation factor of the convolutional filters. padding : str (same, valid, causal). If "valid", no padding is performed. If "same" and stride is 1, output shape is the same as the input shape. "causal" results in causal (dilated) convolutions. groups: int Number of blocked connections from input channels to output channels. padding_mode : str This flag specifies the type of padding. See torch.nn documentation for more information. skip_transpose : bool If False, uses batch x time x channel convention of speechbrain. If True, uses batch x channel x time convention. Example ------- >>> inp_tensor = torch.rand([10, 40, 16]) >>> cnn_1d = Conv1d( ... input_shape=inp_tensor.shape, out_channels=8, kernel_size=5 ... ) >>> out_tensor = cnn_1d(inp_tensor) >>> out_tensor.shape torch.Size([10, 40, 8]) """ def __init__( self, out_channels, kernel_size, input_shape=None, in_channels=None, stride=1, dilation=1, padding="same", groups=1, bias=True, padding_mode="reflect", skip_transpose=False, ): super().__init__() self.kernel_size = kernel_size self.stride = stride self.dilation = dilation self.padding = padding self.padding_mode = padding_mode self.unsqueeze = False self.skip_transpose = skip_transpose if input_shape is None and in_channels is None: raise ValueError("Must provide one of input_shape or in_channels") if in_channels is None: in_channels = self._check_input_shape(input_shape) self.conv = nn.Conv1d( in_channels, out_channels, self.kernel_size, stride=self.stride, dilation=self.dilation, padding=0, groups=groups, bias=bias, ) def forward(self, x): """Returns the output of the convolution. Arguments --------- x : torch.Tensor (batch, time, channel) input to convolve. 2d or 4d tensors are expected. """ if not self.skip_transpose: x = x.transpose(1, -1) if self.unsqueeze: x = x.unsqueeze(1) if self.padding == "same": x = self._manage_padding( x, self.kernel_size, self.dilation, self.stride ) elif self.padding == "causal": num_pad = (self.kernel_size - 1) * self.dilation x = F.pad(x, (num_pad, 0)) elif self.padding == "valid": pass else: raise ValueError( "Padding must be 'same', 'valid' or 'causal'. Got " + self.padding ) wx = self.conv(x) if self.unsqueeze: wx = wx.squeeze(1) if not self.skip_transpose: wx = wx.transpose(1, -1) return wx def _manage_padding( self, x, kernel_size: int, dilation: int, stride: int, ): """This function performs zero-padding on the time axis such that their lengths is unchanged after the convolution. Arguments --------- x : torch.Tensor Input tensor. kernel_size : int Size of kernel. dilation : int Dilation used. stride : int Stride. """ # Detecting input shape L_in = x.shape[-1] # Time padding padding = get_padding_elem(L_in, stride, kernel_size, dilation) # Applying padding x = F.pad(x, padding, mode=self.padding_mode) return x def _check_input_shape(self, shape): """Checks the input shape and returns the number of input channels. """ if len(shape) == 2: self.unsqueeze = True in_channels = 1 elif self.skip_transpose: in_channels = shape[1] elif len(shape) == 3: in_channels = shape[2] else: raise ValueError( "conv1d expects 2d, 3d inputs. Got " + str(len(shape)) ) # Kernel size must be odd if self.kernel_size % 2 == 0: raise ValueError( "The field kernel size must be an odd number. Got %s." % (self.kernel_size) ) return in_channels class Conv2d(nn.Module): """This function implements 2d convolution. Arguments --------- out_channels : int It is the number of output channels. kernel_size : tuple Kernel size of the 2d convolutional filters over time and frequency axis. input_shape : tuple The shape of the input. Alternatively use ``in_channels``. in_channels : int The number of input channels. Alternatively use ``input_shape``. stride: int Stride factor of the 2d convolutional filters over time and frequency axis. dilation : int Dilation factor of the 2d convolutional filters over time and frequency axis. padding : str (same, valid). If "valid", no padding is performed. If "same" and stride is 1, output shape is same as input shape. padding_mode : str This flag specifies the type of padding. See torch.nn documentation for more information. groups : int This option specifies the convolutional groups. See torch.nn documentation for more information. bias : bool If True, the additive bias b is adopted. Example ------- >>> inp_tensor = torch.rand([10, 40, 16, 8]) >>> cnn_2d = Conv2d( ... input_shape=inp_tensor.shape, out_channels=5, kernel_size=(7, 3) ... ) >>> out_tensor = cnn_2d(inp_tensor) >>> out_tensor.shape torch.Size([10, 40, 16, 5]) """ def __init__( self, out_channels, kernel_size, input_shape=None, in_channels=None, stride=(1, 1), dilation=(1, 1), padding="same", groups=1, bias=True, padding_mode="reflect", ): super().__init__() # handle the case if some parameter is int if isinstance(kernel_size, int): kernel_size = (kernel_size, kernel_size) if isinstance(stride, int): stride = (stride, stride) if isinstance(dilation, int): dilation = (dilation, dilation) self.kernel_size = kernel_size self.stride = stride self.dilation = dilation self.padding = padding self.padding_mode = padding_mode self.unsqueeze = False if input_shape is None and in_channels is None: raise ValueError("Must provide one of input_shape or in_channels") if in_channels is None: in_channels = self._check_input(input_shape) # Weights are initialized following pytorch approach self.conv = nn.Conv2d( in_channels, out_channels, self.kernel_size, stride=self.stride, padding=0, dilation=self.dilation, groups=groups, bias=bias, ) def forward(self, x): """Returns the output of the convolution. Arguments --------- x : torch.Tensor (batch, time, channel) input to convolve. 2d or 4d tensors are expected. """ x = x.transpose(1, -1) if self.unsqueeze: x = x.unsqueeze(1) if self.padding == "same": x = self._manage_padding( x, self.kernel_size, self.dilation, self.stride ) elif self.padding == "valid": pass else: raise ValueError( "Padding must be 'same' or 'valid'. Got " + self.padding ) wx = self.conv(x) if self.unsqueeze: wx = wx.squeeze(1) wx = wx.transpose(1, -1) return wx def _manage_padding( self, x, kernel_size: Tuple[int, int], dilation: Tuple[int, int], stride: Tuple[int, int], ): """This function performs zero-padding on the time and frequency axes such that their lengths is unchanged after the convolution. Arguments --------- x : torch.Tensor kernel_size : int dilation : int stride: int """ # Detecting input shape L_in = x.shape[-1] # Time padding padding_time = get_padding_elem( L_in, stride[-1], kernel_size[-1], dilation[-1] ) padding_freq = get_padding_elem( L_in, stride[-2], kernel_size[-2], dilation[-2] ) padding = padding_time + padding_freq # Applying padding x = nn.functional.pad(x, padding, mode=self.padding_mode) return x def _check_input(self, shape): """Checks the input shape and returns the number of input channels. """ if len(shape) == 3: self.unsqueeze = True in_channels = 1 elif len(shape) == 4: in_channels = shape[3] else: raise ValueError("Expected 3d or 4d inputs. Got " + len(shape)) # Kernel size must be odd if self.kernel_size[0] % 2 == 0 or self.kernel_size[1] % 2 == 0: raise ValueError( "The field kernel size must be an odd number. Got %s." % (self.kernel_size) ) return in_channels class Conv2dWithConstraint(Conv2d): """This function implements 2d convolution with kernel max-norm constaint. This corresponds to set an upper bound for the kernel norm. Arguments --------- out_channels : int It is the number of output channels. kernel_size : tuple Kernel size of the 2d convolutional filters over time and frequency axis. input_shape : tuple The shape of the input. Alternatively use ``in_channels``. in_channels : int The number of input channels. Alternatively use ``input_shape``. stride: int Stride factor of the 2d convolutional filters over time and frequency axis. dilation : int Dilation factor of the 2d convolutional filters over time and frequency axis. padding : str (same, valid). If "valid", no padding is performed. If "same" and stride is 1, output shape is same as input shape. padding_mode : str This flag specifies the type of padding. See torch.nn documentation for more information. groups : int This option specifies the convolutional groups. See torch.nn documentation for more information. bias : bool If True, the additive bias b is adopted. max_norm : float kernel max-norm Example ------- >>> inp_tensor = torch.rand([10, 40, 16, 8]) >>> max_norm = 1 >>> cnn_2d_constrained = Conv2dWithConstraint( ... in_channels=inp_tensor.shape[-1], out_channels=5, kernel_size=(7, 3) ... ) >>> out_tensor = cnn_2d_constrained(inp_tensor) >>> torch.any(torch.norm(cnn_2d_constrained.conv.weight.data, p=2, dim=0)>max_norm) tensor(False) """ def __init__(self, *args, max_norm=1, **kwargs): self.max_norm = max_norm super(Conv2dWithConstraint, self).__init__(*args, **kwargs) def forward(self, x): """Returns the output of the convolution. Arguments --------- x : torch.Tensor (batch, time, channel) input to convolve. 2d or 4d tensors are expected. """ self.conv.weight.data = torch.renorm( self.conv.weight.data, p=2, dim=0, maxnorm=self.max_norm ) return super(Conv2dWithConstraint, self).forward(x) class ConvTranspose1d(nn.Module): """This class implements 1d transposed convolution with speechbrain. Transpose convolution is normally used to perform upsampling. Arguments --------- out_channels : int It is the number of output channels. kernel_size : int Kernel size of the convolutional filters. input_shape : tuple The shape of the input. Alternatively use ``in_channels``. in_channels : int The number of input channels. Alternatively use ``input_shape``. stride : int Stride factor of the convolutional filters. When the stride factor > 1, upsampling in time is performed. dilation : int Dilation factor of the convolutional filters. padding : str or int To have in output the target dimension, we suggest tuning the kernel size and the padding properly. We also support the following function to have some control over the padding and the corresponding ouput dimensionality. if "valid", no padding is applied if "same", padding amount is inferred so that the output size is closest to possible to input size. Note that for some kernel_size / stride combinations it is not possible to obtain the exact same size, but we return the closest possible size. if "factor", padding amount is inferred so that the output size is closest to inputsize*stride. Note that for some kernel_size / stride combinations it is not possible to obtain the exact size, but we return the closest possible size. if an integer value is entered, a custom padding is used. output_padding : int, Additional size added to one side of the output shape groups: int Number of blocked connections from input channels to output channels. Default: 1 bias: bool If True, adds a learnable bias to the output skip_transpose : bool If False, uses batch x time x channel convention of speechbrain. If True, uses batch x channel x time convention. Example ------- >>> from speechbrain.nnet.CNN import Conv1d, ConvTranspose1d >>> inp_tensor = torch.rand([10, 12, 40]) #[batch, time, fea] >>> convtranspose_1d = ConvTranspose1d( ... input_shape=inp_tensor.shape, out_channels=8, kernel_size=3, stride=2 ... ) >>> out_tensor = convtranspose_1d(inp_tensor) >>> out_tensor.shape torch.Size([10, 25, 8]) >>> # Combination of Conv1d and ConvTranspose1d >>> from speechbrain.nnet.CNN import Conv1d, ConvTranspose1d >>> signal = torch.tensor([1,100]) >>> signal = torch.rand([1,100]) #[batch, time] >>> conv1d = Conv1d(input_shape=signal.shape, out_channels=1, kernel_size=3, stride=2) >>> conv_out = conv1d(signal) >>> conv_t = ConvTranspose1d(input_shape=conv_out.shape, out_channels=1, kernel_size=3, stride=2, padding=1) >>> signal_rec = conv_t(conv_out, output_size=[100]) >>> signal_rec.shape torch.Size([1, 100]) >>> signal = torch.rand([1,115]) #[batch, time] >>> conv_t = ConvTranspose1d(input_shape=signal.shape, out_channels=1, kernel_size=3, stride=2, padding='same') >>> signal_rec = conv_t(signal) >>> signal_rec.shape torch.Size([1, 115]) >>> signal = torch.rand([1,115]) #[batch, time] >>> conv_t = ConvTranspose1d(input_shape=signal.shape, out_channels=1, kernel_size=7, stride=2, padding='valid') >>> signal_rec = conv_t(signal) >>> signal_rec.shape torch.Size([1, 235]) >>> signal = torch.rand([1,115]) #[batch, time] >>> conv_t = ConvTranspose1d(input_shape=signal.shape, out_channels=1, kernel_size=7, stride=2, padding='factor') >>> signal_rec = conv_t(signal) >>> signal_rec.shape torch.Size([1, 231]) >>> signal = torch.rand([1,115]) #[batch, time] >>> conv_t = ConvTranspose1d(input_shape=signal.shape, out_channels=1, kernel_size=3, stride=2, padding=10) >>> signal_rec = conv_t(signal) >>> signal_rec.shape torch.Size([1, 211]) """ def __init__( self, out_channels, kernel_size, input_shape=None, in_channels=None, stride=1, dilation=1, padding=0, output_padding=0, groups=1, bias=True, skip_transpose=False, ): super().__init__() self.kernel_size = kernel_size self.stride = stride self.dilation = dilation self.padding = padding self.unsqueeze = False self.skip_transpose = skip_transpose if input_shape is None and in_channels is None: raise ValueError("Must provide one of input_shape or in_channels") if in_channels is None: in_channels = self._check_input_shape(input_shape) if self.padding == "same": L_in = input_shape[-1] if skip_transpose else input_shape[1] padding_value = get_padding_elem_transposed( L_in, L_in, stride=stride, kernel_size=kernel_size, dilation=dilation, output_padding=output_padding, ) elif self.padding == "factor": L_in = input_shape[-1] if skip_transpose else input_shape[1] padding_value = get_padding_elem_transposed( L_in * stride, L_in, stride=stride, kernel_size=kernel_size, dilation=dilation, output_padding=output_padding, ) elif self.padding == "valid": padding_value = 0 elif type(self.padding) is int: padding_value = padding else: raise ValueError("Not supported padding type") self.conv = nn.ConvTranspose1d( in_channels, out_channels, self.kernel_size, stride=self.stride, dilation=self.dilation, padding=padding_value, groups=groups, bias=bias, ) def forward(self, x, output_size=None): """Returns the output of the convolution. Arguments --------- x : torch.Tensor (batch, time, channel) input to convolve. 2d or 4d tensors are expected. """ if not self.skip_transpose: x = x.transpose(1, -1) if self.unsqueeze: x = x.unsqueeze(1) wx = self.conv(x, output_size=output_size) if self.unsqueeze: wx = wx.squeeze(1) if not self.skip_transpose: wx = wx.transpose(1, -1) return wx def _check_input_shape(self, shape): """Checks the input shape and returns the number of input channels. """ if len(shape) == 2: self.unsqueeze = True in_channels = 1 elif self.skip_transpose: in_channels = shape[1] elif len(shape) == 3: in_channels = shape[2] else: raise ValueError( "conv1d expects 2d, 3d inputs. Got " + str(len(shape)) ) return in_channels class DepthwiseSeparableConv1d(nn.Module): """This class implements the depthwise separable 1d convolution. First, a channel-wise convolution is applied to the input Then, a point-wise convolution to project the input to output Arguments --------- out_channels : int It is the number of output channels. kernel_size : int Kernel size of the convolutional filters. input_shape : tuple Expected shape of the input. stride : int Stride factor of the convolutional filters. When the stride factor > 1, a decimation in time is performed. dilation : int Dilation factor of the convolutional filters. padding : str (same, valid, causal). If "valid", no padding is performed. If "same" and stride is 1, output shape is the same as the input shape. "causal" results in causal (dilated) convolutions. padding_mode : str This flag specifies the type of padding. See torch.nn documentation for more information. bias : bool If True, the additive bias b is adopted. Example ------- >>> inp = torch.randn([8, 120, 40]) >>> conv = DepthwiseSeparableConv1d(256, 3, input_shape=inp.shape) >>> out = conv(inp) >>> out.shape torch.Size([8, 120, 256]) """ def __init__( self, out_channels, kernel_size, input_shape, stride=1, dilation=1, padding="same", bias=True, ): super().__init__() assert len(input_shape) == 3, "input must be a 3d tensor" bz, time, chn = input_shape self.depthwise = Conv1d( chn, kernel_size, input_shape=input_shape, stride=stride, dilation=dilation, padding=padding, groups=chn, bias=bias, ) self.pointwise = Conv1d( out_channels, kernel_size=1, input_shape=input_shape, ) def forward(self, x): """Returns the output of the convolution. Arguments --------- x : torch.Tensor (batch, time, channel) input to convolve. 3d tensors are expected. """ return self.pointwise(self.depthwise(x)) class DepthwiseSeparableConv2d(nn.Module): """This class implements the depthwise separable 2d convolution. First, a channel-wise convolution is applied to the input Then, a point-wise convolution to project the input to output Arguments --------- ut_channels : int It is the number of output channels. kernel_size : int Kernel size of the convolutional filters. stride : int Stride factor of the convolutional filters. When the stride factor > 1, a decimation in time is performed. dilation : int Dilation factor of the convolutional filters. padding : str (same, valid, causal). If "valid", no padding is performed. If "same" and stride is 1, output shape is the same as the input shape. "causal" results in causal (dilated) convolutions. padding_mode : str This flag specifies the type of padding. See torch.nn documentation for more information. bias : bool If True, the additive bias b is adopted. Example ------- >>> inp = torch.randn([8, 120, 40, 1]) >>> conv = DepthwiseSeparableConv2d(256, (3, 3), input_shape=inp.shape) >>> out = conv(inp) >>> out.shape torch.Size([8, 120, 40, 256]) """ def __init__( self, out_channels, kernel_size, input_shape, stride=(1, 1), dilation=(1, 1), padding="same", bias=True, ): super().__init__() # handle the case if some parameter is int if isinstance(kernel_size, int): kernel_size = (kernel_size, kernel_size) if isinstance(stride, int): stride = (stride, stride) if isinstance(dilation, int): dilation = (dilation, dilation) assert len(input_shape) in {3, 4}, "input must be a 3d or 4d tensor" self.unsqueeze = len(input_shape) == 3 bz, time, chn1, chn2 = input_shape self.depthwise = Conv2d( chn2, kernel_size, input_shape=input_shape, stride=stride, dilation=dilation, padding=padding, groups=chn2, bias=bias, ) self.pointwise = Conv2d( out_channels, kernel_size=(1, 1), input_shape=input_shape, ) def forward(self, x): """Returns the output of the convolution. Arguments --------- x : torch.Tensor (batch, time, channel) input to convolve. 3d tensors are expected. """ if self.unsqueeze: x = x.unsqueeze(1) out = self.pointwise(self.depthwise(x)) if self.unsqueeze: out = out.squeeze(1) return out def get_padding_elem(L_in: int, stride: int, kernel_size: int, dilation: int): """This function computes the number of elements to add for zero-padding. Arguments --------- L_in : int stride: int kernel_size : int dilation : int """ if stride > 1: n_steps = math.ceil(((L_in - kernel_size * dilation) / stride) + 1) L_out = stride * (n_steps - 1) + kernel_size * dilation padding = [kernel_size // 2, kernel_size // 2] else: L_out = (L_in - dilation * (kernel_size - 1) - 1) // stride + 1 padding = [(L_in - L_out) // 2, (L_in - L_out) // 2] return padding def get_padding_elem_transposed( L_out: int, L_in: int, stride: int, kernel_size: int, dilation: int, output_padding: int, ): """This function computes the required padding size for transposed convolution Arguments --------- L_out : int L_in : int stride: int kernel_size : int dilation : int output_padding : int """ padding = -0.5 * ( L_out - (L_in - 1) * stride - dilation * (kernel_size - 1) - output_padding - 1 ) return int(padding)

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Coursera/Week.9/Task.3.py

v1nnyb0y/Coursera.BasePython

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Coursera/Week.9/Task.3.py

v1nnyb0y/Coursera.BasePython

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Coursera/Week.9/Task.3.py

v1nnyb0y/Coursera.BasePython

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[ "MIT" ]

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''' Ошибки, транспонирование ''' from sys import stdin from copy import deepcopy class MatrixError(BaseException): def __init__(self, matrix_1, matrix_2): self.matrix1 = matrix_1 self.matrix2 = matrix_2 class Matrix: def __init__(self, a): self.matr = deepcopy(a) def __str__(self): return '\n'.join(['\t'.join(map(str, list)) for list in self.matr]) def size(self): return (len(self.matr), len(self.matr[0])) def __add__(self, add_matr): if len(self.matr) == len(add_matr.matr): lenght = len(self.matr[0]) for row in self.matr: if len(row) != lenght: raise MatrixError(self, add_matr) for row2 in add_matr.matr: if len(row2) != lenght: raise MatrixError(self, add_matr) return Matrix(list(map( lambda x, y: list(map(lambda z, w: z + w, x, y)), self.matr, add_matr.matr))) else: raise MatrixError(self, add_matr) def __mul__(self, mul_matr): return Matrix([[i * mul_matr for i in list] for list in self.matr]) def transpose(self): self.matr = list(zip(*self.matr)) return Matrix(self.matr) @staticmethod def transposed(matrix): return Matrix(list(zip(*matrix.matr))) __rmul__ = __mul__ exec(stdin.read())

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1

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3

b8deea8012ac76a2d2f315bb5d55407d3bb13dd1

399

py

Python

openpyexcel/workbook/external_reference.py

sciris/openpyexcel

1fde667a1adc2f4988279fd73a2ac2660706b5ce

[ "MIT" ]

2

2019-07-03T06:37:42.000Z

2020-05-15T00:28:13.000Z

openpyexcel/workbook/external_reference.py

sciris/openpyexcel

1fde667a1adc2f4988279fd73a2ac2660706b5ce

[ "MIT" ]

null

openpyexcel/workbook/external_reference.py

sciris/openpyexcel

1fde667a1adc2f4988279fd73a2ac2660706b5ce

[ "MIT" ]

1

2020-01-06T10:01:42.000Z

from __future__ import absolute_import # Copyright (c) 2010-2019 openpyexcel from openpyexcel.descriptors.serialisable import Serialisable from openpyexcel.descriptors import ( Sequence ) from openpyexcel.descriptors.excel import ( Relation, ) class ExternalReference(Serialisable): tagname = "externalReference" id = Relation() def __init__(self, id): self.id = id

19.95

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399

7.04878

0.512195

0.155709

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null

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null

0

1

0

1

0

3

b8f537aaaa3af69181ab6e183968be0d905fb415

152

py

Python

competicao/apps.py

pedrocarvalhoaguiar/projetoOCBDMS

25182d5258affb0d93ca30266f03ab20680f6c85

[ "MIT" ]

null

competicao/apps.py

pedrocarvalhoaguiar/projetoOCBDMS

25182d5258affb0d93ca30266f03ab20680f6c85

[ "MIT" ]

null

competicao/apps.py

pedrocarvalhoaguiar/projetoOCBDMS

25182d5258affb0d93ca30266f03ab20680f6c85

[ "MIT" ]

null

from django.apps import AppConfig class CompeticaoConfig(AppConfig): default_auto_field = 'django.db.models.BigAutoField' name = 'competicao'

21.714286

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6.764706

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null

0

1

0

3

770752c5b9142fe1823fe9490d50f34218a5494a

5,042

py

Python

blazar-3.0.0/blazar/db/utils.py

scottwedge/OpenStack-Stein

7077d1f602031dace92916f14e36b124f474de15

[ "Apache-2.0" ]

null

blazar-3.0.0/blazar/db/utils.py

scottwedge/OpenStack-Stein

7077d1f602031dace92916f14e36b124f474de15

[ "Apache-2.0" ]

5

2019-08-14T06:46:03.000Z

2021-12-13T20:01:25.000Z

blazar-3.0.0/blazar/db/utils.py

scottwedge/OpenStack-Stein

7077d1f602031dace92916f14e36b124f474de15

[ "Apache-2.0" ]

2

2020-03-15T01:24:15.000Z

2020-07-22T20:34:26.000Z

# -*- coding: utf-8 -*- # # Author: François Rossigneux <francois.rossigneux@inria.fr> # # 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. """Defines interface for DB access. Functions in this module are imported into the blazar.db namespace. Call these functions from blazar.db namespace, not the blazar.db.api namespace. All functions in this module return objects that implement a dictionary-like interface. **Related Flags** :db_backend: string to lookup in the list of LazyPluggable backends. `sqlalchemy` is the only supported backend right now. :sql_connection: string specifying the sqlalchemy connection to use, like: `sqlite:///var/lib/blazar/blazar.sqlite`. """ from oslo_config import cfg from oslo_db import api as db_api from oslo_log import log as logging _BACKEND_MAPPING = { 'sqlalchemy': 'blazar.db.sqlalchemy.utils', } IMPL = db_api.DBAPI(cfg.CONF.database.backend, backend_mapping=_BACKEND_MAPPING) LOG = logging.getLogger(__name__) def setup_db(): """Set up database, create tables, etc. Return True on success, False otherwise """ return IMPL.setup_db() def drop_db(): """Drop database. Return True on success, False otherwise """ return IMPL.drop_db() # Helpers for building constraints / equality checks def constraint(**conditions): """Return a constraint object suitable for use with some updates.""" return IMPL.constraint(**conditions) def equal_any(*values): """Return an equality condition object suitable for use in a constraint. Equal_any conditions require that a model object's attribute equal any one of the given values. """ return IMPL.equal_any(*values) def not_equal(*values): """Return an inequality condition object suitable for use in a constraint. Not_equal conditions require that a model object's attribute differs from all of the given values. """ return IMPL.not_equal(*values) def to_dict(func): def decorator(*args, **kwargs): res = func(*args, **kwargs) if isinstance(res, list): return [item.to_dict() for item in res] if res: return res.to_dict() else: return None return decorator def get_reservations_by_host_id(host_id, start_date, end_date): return IMPL.get_reservations_by_host_id(host_id, start_date, end_date) def get_reservations_by_host_ids(host_ids, start_date, end_date): return IMPL.get_reservations_by_host_ids(host_ids, start_date, end_date) def get_reservation_allocations_by_host_ids(host_ids, start_date, end_date, lease_id=None, reservation_id=None): return IMPL.get_reservation_allocations_by_host_ids(host_ids, start_date, end_date, lease_id, reservation_id) def get_plugin_reservation(resource_type, resource_id): return IMPL.get_plugin_reservation(resource_type, resource_id) def get_free_periods(resource_id, start_date, end_date, duration, resource_type='host'): """Returns a list of free periods.""" return IMPL.get_free_periods(resource_id, start_date, end_date, duration, resource_type=resource_type) def get_reserved_periods(resource_id, start_date, end_date, duration, resource_type='host'): """Returns a list of reserved periods.""" return IMPL.get_reserved_periods(resource_id, start_date, end_date, duration, resource_type=resource_type) def reservation_ratio(resource_id, start_date, end_date): return IMPL.reservation_ratio(resource_id, start_date, end_date) def availability_time(resource_id, start_date, end_date): return IMPL.availability_time(resource_id, start_date, end_date) def reservation_time(resource_id, start_date, end_date): return IMPL.reservation_time(resource_id, start_date, end_date) def number_of_reservations(resource_id, start_date, end_date): return IMPL.number_of_reservations(resource_id, start_date, end_date) def longest_lease(resource_id, start_date, end_date): return IMPL.longest_lease(resource_id, start_date, end_date) def shortest_lease(resource_id, start_date, end_date): return IMPL.shortest_lease(resource_id, start_date, end_date)

30.932515

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0.694169

676

5,042

4.939349

0.284024

0.059299

0.079066

0.105421

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0.438455

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0.193471

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5,042

162

79

31.123457

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1

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0

0.048387

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0.693548

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null

0

null

0

1

0

1

0

3

7720691e7db7c7dd1f9fe2f7ea22dae29f24557f

73

py

Python

by-session/ta-921/j1/turtle7.py

amiraliakbari/sharif-mabani-python

5d14a08d165267fe71c28389ddbafe29af7078c5

[ "MIT" ]

2

2015-04-29T20:59:35.000Z

2018-09-26T13:33:43.000Z

by-session/ta-921/j1/turtle7.py

amiraliakbari/sharif-mabani-python

5d14a08d165267fe71c28389ddbafe29af7078c5

[ "MIT" ]

null

by-session/ta-921/j1/turtle7.py

amiraliakbari/sharif-mabani-python

5d14a08d165267fe71c28389ddbafe29af7078c5

[ "MIT" ]

null

x = 2 print "salam!" for i in range(10): print x, x = x * 2

12.166667

20

0.465753

14

73

2.428571

0.642857

0.117647

0

0.090909

0.39726

73

5

21

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0.681818

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0.088235

0

null

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0

null

0

1

0

null

0

1

0

3

773e5809b4337fbdb27cbd804bc562f277d2411a

11,738

py

Python

gewittergefahr/gg_utils/time_conversion_test.py

dopplerchase/GewitterGefahr

4415b08dd64f37eba5b1b9e8cc5aa9af24f96593

[ "MIT" ]

26

2018-10-04T01:07:35.000Z

2022-01-29T08:49:32.000Z

gewittergefahr/gg_utils/time_conversion_test.py

liuximarcus/GewitterGefahr

d819874d616f98a25187bfd3091073a2e6d5279e

[ "MIT" ]

4

2017-12-25T02:01:08.000Z

2018-12-19T01:54:21.000Z

gewittergefahr/gg_utils/time_conversion_test.py

liuximarcus/GewitterGefahr

d819874d616f98a25187bfd3091073a2e6d5279e

[ "MIT" ]

11

2017-12-10T23:05:29.000Z

2022-01-29T08:49:33.000Z

"""Unit tests for time_conversion.py.""" import unittest from gewittergefahr.gg_utils import time_conversion TIME_FORMAT_YEAR = '%Y' TIME_FORMAT_NUMERIC_MONTH = '%m' TIME_FORMAT_3LETTER_MONTH = '%b' TIME_FORMAT_YEAR_MONTH = '%Y-%m' TIME_FORMAT_DAY_OF_MONTH = '%d' TIME_FORMAT_DATE = '%Y-%m-%d' TIME_FORMAT_HOUR = '%Y-%m-%d-%H00' TIME_FORMAT_MINUTE = '%Y-%m-%d-%H%M' TIME_FORMAT_SECOND = '%Y-%m-%d-%H%M%S' TIME_STRING_YEAR = '2017' TIME_STRING_NUMERIC_MONTH = '09' TIME_STRING_3LETTER_MONTH = 'Sep' TIME_STRING_YEAR_MONTH = '2017-09' TIME_STRING_DAY_OF_MONTH = '26' TIME_STRING_DATE = '2017-09-26' TIME_STRING_HOUR = '2017-09-26-0500' TIME_STRING_MINUTE = '2017-09-26-0520' TIME_STRING_SECOND = '2017-09-26-052033' UNIX_TIME_YEAR_SEC = 1483228800 UNIX_TIME_MONTH_SEC = 1504224000 UNIX_TIME_DATE_SEC = 1506384000 UNIX_TIME_HOUR_SEC = 1506402000 UNIX_TIME_MINUTE_SEC = 1506403200 UNIX_TIME_SEC = 1506403233 START_TIME_SEP2017_UNIX_SEC = 1504224000 END_TIME_SEP2017_UNIX_SEC = 1506815999 START_TIME_2017_UNIX_SEC = 1483228800 END_TIME_2017_UNIX_SEC = 1514764799 TIME_1200UTC_SPC_DATE_UNIX_SEC = 1506340800 TIME_0000UTC_SPC_DATE_UNIX_SEC = 1506384000 TIME_115959UTC_SPC_DATE_UNIX_SEC = 1506427199 SPC_DATE_UNIX_SEC = 1506362400 SPC_DATE_STRING = '20170925' FIRST_SPC_DATE_STRING = '20170925' LAST_SPC_DATE_STRING = '20171001' ALL_SPC_DATE_STRINGS = [ '20170925', '20170926', '20170927', '20170928', '20170929', '20170930', '20171001'] TIME_115959UTC_BEFORE_DATE_UNIX_SEC = 1506340799 TIME_1200UTC_AFTER_DATE_UNIX_SEC = 1506427200 class TimeConversionTests(unittest.TestCase): """Each method is a unit test for time_conversion.py.""" def test_string_to_unix_sec_year(self): """Ensures correctness of string_to_unix_sec; string = year only.""" this_time_unix_sec = time_conversion.string_to_unix_sec( TIME_STRING_YEAR, TIME_FORMAT_YEAR) self.assertTrue(this_time_unix_sec == UNIX_TIME_YEAR_SEC) def test_string_to_unix_sec_year_month(self): """Ensures correctness of string_to_unix_sec; string = year-month.""" this_time_unix_sec = time_conversion.string_to_unix_sec( TIME_STRING_YEAR_MONTH, TIME_FORMAT_YEAR_MONTH) self.assertTrue(this_time_unix_sec == UNIX_TIME_MONTH_SEC) def test_string_to_unix_sec_date(self): """Ensures correctness of string_to_unix_sec; string = full date.""" this_time_unix_sec = time_conversion.string_to_unix_sec( TIME_STRING_DATE, TIME_FORMAT_DATE) self.assertTrue(this_time_unix_sec == UNIX_TIME_DATE_SEC) def test_string_to_unix_sec_hour(self): """Ensures correctness of string_to_unix_sec; string = full hour.""" this_time_unix_sec = time_conversion.string_to_unix_sec( TIME_STRING_HOUR, TIME_FORMAT_HOUR) self.assertTrue(this_time_unix_sec == UNIX_TIME_HOUR_SEC) def test_string_to_unix_sec_minute(self): """Ensures correctness of string_to_unix_sec; string = full minute.""" this_time_unix_sec = time_conversion.string_to_unix_sec( TIME_STRING_MINUTE, TIME_FORMAT_MINUTE) self.assertTrue(this_time_unix_sec == UNIX_TIME_MINUTE_SEC) def test_string_to_unix_sec_second(self): """Ensures correctness of string_to_unix_sec; string = full second.""" this_time_unix_sec = time_conversion.string_to_unix_sec( TIME_STRING_SECOND, TIME_FORMAT_SECOND) self.assertTrue(this_time_unix_sec == UNIX_TIME_SEC) def test_unix_sec_to_string_year(self): """Ensures correctness of unix_sec_to_string; string = year only.""" this_time_string = time_conversion.unix_sec_to_string( UNIX_TIME_SEC, TIME_FORMAT_YEAR) self.assertTrue(this_time_string == TIME_STRING_YEAR) def test_unix_sec_to_string_numeric_month(self): """Ensures correctness of unix_sec_to_string; string = numeric month.""" this_time_string = time_conversion.unix_sec_to_string( UNIX_TIME_SEC, TIME_FORMAT_NUMERIC_MONTH) self.assertTrue(this_time_string == TIME_STRING_NUMERIC_MONTH) def test_unix_sec_to_string_3letter_month(self): """Ensures correctness of unix_sec_to_string; string = 3-lttr month.""" this_time_string = time_conversion.unix_sec_to_string( UNIX_TIME_SEC, TIME_FORMAT_3LETTER_MONTH) self.assertTrue(this_time_string == TIME_STRING_3LETTER_MONTH) def test_unix_sec_to_string_year_month(self): """Ensures correctness of unix_sec_to_string; string = year-month.""" this_time_string = time_conversion.unix_sec_to_string( UNIX_TIME_SEC, TIME_FORMAT_YEAR_MONTH) self.assertTrue(this_time_string == TIME_STRING_YEAR_MONTH) def test_unix_sec_to_string_day_of_month(self): """Ensures correctness of unix_sec_to_string; string = day of month.""" this_time_string = time_conversion.unix_sec_to_string( UNIX_TIME_SEC, TIME_FORMAT_DAY_OF_MONTH) self.assertTrue(this_time_string == TIME_STRING_DAY_OF_MONTH) def test_unix_sec_to_string_date(self): """Ensures correctness of unix_sec_to_string; string = full date.""" this_time_string = time_conversion.unix_sec_to_string( UNIX_TIME_SEC, TIME_FORMAT_DATE) self.assertTrue(this_time_string == TIME_STRING_DATE) def test_unix_sec_to_string_hour(self): """Ensures correctness of unix_sec_to_string; string = full hour.""" this_time_string = time_conversion.unix_sec_to_string( UNIX_TIME_SEC, TIME_FORMAT_HOUR) self.assertTrue(this_time_string == TIME_STRING_HOUR) def test_unix_sec_to_string_minute(self): """Ensures correctness of unix_sec_to_string; string = full minute.""" this_time_string = time_conversion.unix_sec_to_string( UNIX_TIME_SEC, TIME_FORMAT_MINUTE) self.assertTrue(this_time_string == TIME_STRING_MINUTE) def test_unix_sec_to_string_second(self): """Ensures correctness of unix_sec_to_string; string = full second.""" this_time_string = time_conversion.unix_sec_to_string( UNIX_TIME_SEC, TIME_FORMAT_SECOND) self.assertTrue(this_time_string == TIME_STRING_SECOND) def test_time_to_spc_date_unix_sec_1200utc(self): """Ensures correctness of time_to_spc_date_unix_sec; time = 1200 UTC.""" this_spc_date_unix_sec = time_conversion.time_to_spc_date_unix_sec( TIME_1200UTC_SPC_DATE_UNIX_SEC) self.assertTrue(this_spc_date_unix_sec == SPC_DATE_UNIX_SEC) def test_time_to_spc_date_unix_sec_0000utc(self): """Ensures correctness of time_to_spc_date_unix_sec; time = 0000 UTC.""" this_spc_date_unix_sec = time_conversion.time_to_spc_date_unix_sec( TIME_0000UTC_SPC_DATE_UNIX_SEC) self.assertTrue(this_spc_date_unix_sec == SPC_DATE_UNIX_SEC) def test_time_to_spc_date_unix_sec_115959utc(self): """Ensures crrctness of time_to_spc_date_unix_sec; time = 115959 UTC.""" this_spc_date_unix_sec = time_conversion.time_to_spc_date_unix_sec( TIME_115959UTC_SPC_DATE_UNIX_SEC) self.assertTrue(this_spc_date_unix_sec == SPC_DATE_UNIX_SEC) def test_time_to_spc_date_string_1200utc(self): """Ensures correctness of time_to_spc_date_string; time = 1200 UTC.""" this_spc_date_string = time_conversion.time_to_spc_date_string( TIME_1200UTC_SPC_DATE_UNIX_SEC) self.assertTrue(this_spc_date_string == SPC_DATE_STRING) def test_time_to_spc_date_string_0000utc(self): """Ensures correctness of time_to_spc_date_string; time = 0000 UTC.""" this_spc_date_string = time_conversion.time_to_spc_date_string( TIME_0000UTC_SPC_DATE_UNIX_SEC) self.assertTrue(this_spc_date_string == SPC_DATE_STRING) def test_time_to_spc_date_string_115959utc(self): """Ensures correctness of time_to_spc_date_string; time = 115959 UTC.""" this_spc_date_string = time_conversion.time_to_spc_date_string( TIME_115959UTC_SPC_DATE_UNIX_SEC) self.assertTrue(this_spc_date_string == SPC_DATE_STRING) def test_spc_date_string_to_unix_sec(self): """Ensures correct output from spc_date_string_to_unix_sec.""" this_spc_date_unix_sec = time_conversion.spc_date_string_to_unix_sec( SPC_DATE_STRING) self.assertTrue(this_spc_date_unix_sec == SPC_DATE_UNIX_SEC) def test_get_spc_dates_in_range_one_date(self): """Ensures correct output from get_spc_dates_in_range. In this case, there is only one date in the range. """ these_spc_date_strings = time_conversion.get_spc_dates_in_range( first_spc_date_string=FIRST_SPC_DATE_STRING, last_spc_date_string=FIRST_SPC_DATE_STRING) self.assertTrue(these_spc_date_strings == [FIRST_SPC_DATE_STRING]) def test_get_spc_dates_in_range_many_dates(self): """Ensures correct output from get_spc_dates_in_range. In this case, there are many dates in the range. """ these_spc_date_strings = time_conversion.get_spc_dates_in_range( first_spc_date_string=FIRST_SPC_DATE_STRING, last_spc_date_string=LAST_SPC_DATE_STRING) self.assertTrue(these_spc_date_strings == ALL_SPC_DATE_STRINGS) def test_is_time_in_spc_date_beginning(self): """Ensures correct output from is_time_in_spc_date. In this case, time is at beginning of SPC date. """ self.assertTrue(time_conversion.is_time_in_spc_date( TIME_1200UTC_SPC_DATE_UNIX_SEC, SPC_DATE_STRING)) def test_is_time_in_spc_date_middle(self): """Ensures correct output from is_time_in_spc_date. In this case, time is in middle of SPC date. """ self.assertTrue(time_conversion.is_time_in_spc_date( TIME_0000UTC_SPC_DATE_UNIX_SEC, SPC_DATE_STRING)) def test_is_time_in_spc_date_end(self): """Ensures correct output from is_time_in_spc_date. In this case, time is at end of SPC date. """ self.assertTrue(time_conversion.is_time_in_spc_date( TIME_115959UTC_SPC_DATE_UNIX_SEC, SPC_DATE_STRING)) def test_is_time_in_spc_date_before(self): """Ensures correct output from is_time_in_spc_date. In this case, time is before SPC date. """ self.assertFalse(time_conversion.is_time_in_spc_date( TIME_115959UTC_BEFORE_DATE_UNIX_SEC, SPC_DATE_STRING)) def test_is_time_in_spc_date_after(self): """Ensures correct output from is_time_in_spc_date. In this case, time is after SPC date. """ self.assertFalse(time_conversion.is_time_in_spc_date( TIME_1200UTC_AFTER_DATE_UNIX_SEC, SPC_DATE_STRING)) def test_first_and_last_times_in_month(self): """Ensures correct output from first_and_last_times_in_month.""" this_start_time_unix_sec, this_end_time_unix_sec = ( time_conversion.first_and_last_times_in_month(UNIX_TIME_MONTH_SEC)) self.assertTrue(this_start_time_unix_sec == START_TIME_SEP2017_UNIX_SEC) self.assertTrue(this_end_time_unix_sec == END_TIME_SEP2017_UNIX_SEC) def test_first_and_last_times_in_year(self): """Ensures correct output from first_and_last_times_in_year.""" this_start_time_unix_sec, this_end_time_unix_sec = ( time_conversion.first_and_last_times_in_year(2017)) self.assertTrue(this_start_time_unix_sec == START_TIME_2017_UNIX_SEC) self.assertTrue(this_end_time_unix_sec == END_TIME_2017_UNIX_SEC) if __name__ == '__main__': unittest.main()

39.521886

80

0.742205

1,739

11,738

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0.103207

0.065572

0.061562

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296

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39.655405

0.758361

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0

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1

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false

0

0.011905

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0.202381

0

null

0

1

0

null

0

3

774e41a6aa266984f80ac00aa490f63c006a7c52

1,316

py

Python

toybox/toys/crystals.py

bm424/diffraction-toybox

d37b80c8282e53a007f182318257efe78931bc00

[ "MIT" ]

null

toybox/toys/crystals.py

bm424/diffraction-toybox

d37b80c8282e53a007f182318257efe78931bc00

[ "MIT" ]

null

toybox/toys/crystals.py

bm424/diffraction-toybox

d37b80c8282e53a007f182318257efe78931bc00

[ "MIT" ]

null

import collections import numpy as np from toybox.toys.core import Pattern class BiCrystal(collections.MutableSequence): def __init__(self, pattern1, pattern2, profile=np.linspace(0, 1, 11)): self.pattern_1 = pattern1 self.pattern_2 = pattern2 self.profile = profile @property def profile(self): return self._profile @profile.setter def profile(self, profile): if np.max(profile) > 1 or np.min(profile) < 0: raise ValueError("Profile must be between 0 and 1.") self._profile = profile @property def profile_i(self): return 1. - self.profile @property def patterns(self): return np.array([p * self.pattern_2 + q * self.pattern_1 for p, q in zip(self.profile, self.profile_i)]) def __len__(self): return len(self.profile) def __getitem__(self, item): return self.patterns[item].view(Pattern) def __setitem__(self, key, value): if value > 1 or value < 0: raise ValueError("Profile must be between 0 and 1.") self.profile[key] = value def __delitem__(self, key): self.profile = np.delete(self.profile, key, None) def insert(self, index, value): self.profile = np.insert(self.profile, index, value)

20.246154

112

0.629939

173

1,316

4.630058

0.32948

0.178527

0.067416

0.064919

0.205993

0.129838

0

0.021762

0.266717

1,316

64

113

20.5625

0.80829

0

0.147059

0

0.049307

0

1

0.294118

false

0

0.088235

0.147059

0.558824

0

null

0

null

0

1

0

1

0

3

91f295cbbe2ca26555f2120e3e497e0cfde5116d

685

py

Python

samples/misc/opencv_samples/mqtt_cam/helpers.py

sintefneodroid/vision

a4e66251ead99f15f4697bfe2abd00e2f388e743

[ "Apache-2.0" ]

null

samples/misc/opencv_samples/mqtt_cam/helpers.py

sintefneodroid/vision

a4e66251ead99f15f4697bfe2abd00e2f388e743

[ "Apache-2.0" ]

1

2022-03-12T01:08:08.000Z

samples/misc/opencv_samples/mqtt_cam/helpers.py

sintefneodroid/vision

a4e66251ead99f15f4697bfe2abd00e2f388e743

[ "Apache-2.0" ]

null

""" Helper functions. Source -> https://github.com/jrosebr1/imutils/blob/master/imutils/video/webcamvideostream.py """ import datetime import io import yaml from PIL import Image DATETIME_STR_FORMAT = "%Y-%m-%d_%H:%M:%S.%f" def pil_image_to_byte_array(image): imgByteArr = io.BytesIO() image.save(imgByteArr, "PNG") return imgByteArr.getvalue() def byte_array_to_pil_image(byte_array): return Image.open(io.BytesIO(byte_array)) def get_now_string() -> str: return datetime.datetime.now().strftime(DATETIME_STR_FORMAT) def get_config(config_filepath: str) -> dict: with open(config_filepath) as f: config = yaml.safe_load(f) return config

20.757576

92

0.728467

99

685

4.828283

0.505051

0.075314

0.07113

0

0.001709

0.145985

685

32

93

21.40625

0.815385

0.162044

0

0.040636

0

1

0.235294

false

0

0.235294

0.117647

0.705882

0

null

0

null

0

1

0

1

0

3

6204f29b93c74e614de6a6106ed246b86430caa1

164

py

Python

hackerrank/4. sets/12.py

Eurydia/Xian-assignment

4a7e4bcd3d4999ea7429054fec1792064c96ff30

[ "MIT" ]

null

hackerrank/4. sets/12.py

Eurydia/Xian-assignment

4a7e4bcd3d4999ea7429054fec1792064c96ff30

[ "MIT" ]

null

hackerrank/4. sets/12.py

Eurydia/Xian-assignment

4a7e4bcd3d4999ea7429054fec1792064c96ff30

[ "MIT" ]

null

n = int(input()) for i in range(n): a = input() set1 = set(input().split()) b = input() set2 = set(input().split()) print(set1.issubset(set2))

18.222222

31

0.536585

24

164

3.666667

0.625

0.181818

0.295455

0

0.03252

0.25

164

8

32

20.5

0.682927

0

1

0

false

0

0.142857

1

0

null

0

1

0

1

0

null

0

3

6211a76f189fa8725843fac8503a99cd785e4a2f

526

py

Python

src/client/ui/widget/_widget.py

Tubular-Terriers/code-jam

be706c485110ee49727ec33d07b5d8fef7cf49e1

[ "MIT" ]

1

2021-07-20T17:01:43.000Z

src/client/ui/widget/_widget.py

Tubular-Terriers/code-jam

be706c485110ee49727ec33d07b5d8fef7cf49e1

[ "MIT" ]

null

src/client/ui/widget/_widget.py

Tubular-Terriers/code-jam

be706c485110ee49727ec33d07b5d8fef7cf49e1

[ "MIT" ]

null

# The base class for all widgets class Widget: def __init__(self, name): self.name = name pass def press_on(self, key): """Called with the argument `key`""" pass def release_on(self, k): pass # Text related methods def start_text_on(self, k): pass def update_text_on(self, k): pass def end_text_on(self, k): pass def refresh(self): if self.window: self.window.noutrefresh()

18.137931

45

0.528517

66

526

4.030303

0.454545

0.131579

0.105263

0.165414

0.203008

0

0.38403

526

28

46

18.785714

0.820988

0.157795

0

0.352941

0

1

0.411765

false

0.352941

0

0.470588

0

null

0

1

0

null

0

1

0

1

0

3

622f7389d233b8699cd1a870690fd663fb0ac244

362

py

Python

Functions/lambdaCalculator.py

poojavaibhavsahu/Pooja_Python

58122bfa8586883145042b11fe1cc013c803ab4f

[ "bzip2-1.0.6" ]

null

Functions/lambdaCalculator.py

poojavaibhavsahu/Pooja_Python

58122bfa8586883145042b11fe1cc013c803ab4f

[ "bzip2-1.0.6" ]

null

Functions/lambdaCalculator.py

poojavaibhavsahu/Pooja_Python

58122bfa8586883145042b11fe1cc013c803ab4f

[ "bzip2-1.0.6" ]

null

num1=int(input('Enter the first number:')) num2=int(input('Enter the second number:')) sum=lambda num1,num2:num1+num2 diff=lambda num1,num2:num1-num2 mul=lambda num1,num2:num1*num2 div=lambda num1,num2:num1//num2 print("Sum is:",sum(num1,num2)) print("Difference is:",diff(num1,num2)) print("Multiply is",mul(num1,num2)) print("Division is:",div(num1,num2))

24.133333

43

0.734807

62

362

4.290323

0.306452

0.360902

0.210526

0.270677

0.330827

0

0.077844

0.077348

362

14

44

25.857143

0.718563

0

0.252778

0

1

0

false

0

0.4

0

null

1

0

null

0

3

6239726a93d8621a70381897416cf3327d729c16

269

py

Python

ada/utils.py

praekeltfoundation/ndoh-hub

91d834ff8fe43b930a73d8debdaa0e6af78c5efc

[ "BSD-3-Clause" ]

null

ada/utils.py

praekeltfoundation/ndoh-hub

91d834ff8fe43b930a73d8debdaa0e6af78c5efc

[ "BSD-3-Clause" ]

126

2016-07-12T19:39:44.000Z

2022-03-24T13:39:38.000Z

ada/utils.py

praekeltfoundation/ndoh-hub

91d834ff8fe43b930a73d8debdaa0e6af78c5efc

[ "BSD-3-Clause" ]

3

2016-09-28T13:16:11.000Z

2020-11-07T15:32:37.000Z

from __future__ import absolute_import, division from django.conf import settings from temba_client.v2 import TembaClient rapidpro = None if settings.RAPIDPRO_URL and settings.RAPIDPRO_TOKEN: rapidpro = TembaClient(settings.RAPIDPRO_URL, settings.RAPIDPRO_TOKEN)

29.888889

74

0.840149

35

269

6.171429

0.514286

0.296296

0.175926

0

0.004184

0.111524

269

8

75

33.625

0.899582

0

1

0

false

0

0.5

0

0.5

0

null

1

0

1

0

null

0

1

0

3

625b0c70462ef0f9a76fdc51d6f21a49dc3ee432

3,256

py

Python

seed_services_client/tests/test_utils.py

praekeltfoundation/seed-services-client

bfb216b6b770f9433bd9cda573f13199c4afee9c

[ "BSD-3-Clause" ]

null

seed_services_client/tests/test_utils.py

praekeltfoundation/seed-services-client

bfb216b6b770f9433bd9cda573f13199c4afee9c

[ "BSD-3-Clause" ]

25

2016-06-24T14:37:51.000Z

2018-06-26T09:08:31.000Z

seed_services_client/tests/test_utils.py

praekeltfoundation/seed-services-client

bfb216b6b770f9433bd9cda573f13199c4afee9c

[ "BSD-3-Clause" ]

null

import responses from unittest import TestCase from seed_services_client.seed_services import SeedServicesApiClient from seed_services_client.utils import get_paginated_response class TestApiClient(SeedServicesApiClient): pass class TestUtils(TestCase): def setUp(self): self.api = TestApiClient( "NO", "http://test.example.org/api/v1") @responses.activate def test_get_paginated_response_single_page(self): """ The get_paginated_response function should return the content for the single page. """ responses.add( responses.GET, "http://test.example.org/api/v1/tests/", json={ "next": None, "previous": None, "results": [{"id": 1, "content": "content_for_1"}, {"id": 2, "content": "content_for_2"}] }, status=200, content_type='application/json', match_querystring=True ) res = get_paginated_response(self.api.session, "/tests/") self.assertEqual(list(res), [ {"id": 1, "content": "content_for_1"}, {"id": 2, "content": "content_for_2"} ]) @responses.activate def test_get_paginated_response_multiple_pages(self): """ The get_paginated_response function should return the content for all the pages. """ # First page responses.add( responses.GET, "http://test.example.org/api/v1/tests/", json={ "next": "http://test.example.org/api/v1/tests/?cursor=1", "previous": None, "results": [{"id": 1, "content": "content_for_1"}, {"id": 2, "content": "content_for_2"}] }, status=200, content_type='application/json', match_querystring=True ) # Second page responses.add( responses.GET, "http://test.example.org/api/v1/tests/?cursor=1", json={ "next": "http://test.example.org/api/v1/tests/?cursor=2", "previous": None, "results": [{"id": 3, "content": "content_for_3"}, {"id": 4, "content": "content_for_4"}] }, status=200, content_type='application/json', match_querystring=True ) # Thrid page responses.add( responses.GET, "http://test.example.org/api/v1/tests/?cursor=2", json={ "next": None, "previous": None, "results": [ {"id": 5, "content": "content_for_5"}, ] }, status=200, content_type='application/json', match_querystring=True ) res = get_paginated_response(self.api.session, "/tests/") self.assertEqual(list(res), [ {"id": 1, "content": "content_for_1"}, {"id": 2, "content": "content_for_2"}, {"id": 3, "content": "content_for_3"}, {"id": 4, "content": "content_for_4"}, {"id": 5, "content": "content_for_5"}, ])

33.22449

77

0.513206

327

3,256

4.923547

0.204893

0.099379

0.147826

0.078261

0.793789

0.754658

0.677019

0.640373

0

0.024023

0.347973

3,256

97

78

33.56701

0.734338

0.060811

0

0.578947

0

0.251253

0

0.026316

1

0.039474

false

0.013158

0.052632

0

0.118421

0

null

0

1

0

1

0

null

0

3

626afa8cacd56858cd402b6d197d48e732dbed0a

617

py

Python

moviepy/audio/fx/__init__.py

odidev/moviepy

b19a690fe81b17fa582622d1c0ebe73e4e6380e7

[ "MIT" ]

8,558

2015-01-03T05:14:12.000Z

2022-03-31T21:45:38.000Z

moviepy/audio/fx/__init__.py

odidev/moviepy

b19a690fe81b17fa582622d1c0ebe73e4e6380e7

[ "MIT" ]

1,592

2015-01-02T22:12:54.000Z

2022-03-30T13:10:40.000Z

moviepy/audio/fx/__init__.py

odidev/moviepy

b19a690fe81b17fa582622d1c0ebe73e4e6380e7

[ "MIT" ]

1,332

2015-01-02T18:01:53.000Z

2022-03-31T22:47:28.000Z

# import every video fx function from moviepy.audio.fx.audio_delay import audio_delay from moviepy.audio.fx.audio_fadein import audio_fadein from moviepy.audio.fx.audio_fadeout import audio_fadeout from moviepy.audio.fx.audio_loop import audio_loop from moviepy.audio.fx.audio_normalize import audio_normalize from moviepy.audio.fx.multiply_stereo_volume import multiply_stereo_volume from moviepy.audio.fx.multiply_volume import multiply_volume __all__ = ( "audio_delay", "audio_fadein", "audio_fadeout", "audio_loop", "audio_normalize", "multiply_stereo_volume", "multiply_volume", )

29.380952

74

0.802269

86

617

5.430233

0.197674

0.164882

0.239829

0.269807

0.357602

0

0.123177

617

20

75

30.85

0.863216

0.048622

0

0.167521

0.037607

0

1

0

false

0

0.4375

0

0.4375

0

null

0

1

0

null

0

1

0

3

627079ad65cb5185a29bb361e2cdd849ff8f48d5

2,638

py

Python

knowledge/urls.py

dynamicguy/treeio

4f674898cff2331711639a9b5f6812c874a2cb25

[ "MIT" ]

2

2019-02-22T16:02:19.000Z

2019-02-23T19:27:34.000Z

knowledge/urls.py

dewmal/treeio

6299fbe7826800d576f7ab68b4c1996b7194540f

[ "MIT" ]

null

knowledge/urls.py

dewmal/treeio

6299fbe7826800d576f7ab68b4c1996b7194540f

[ "MIT" ]

1

2019-02-03T03:54:06.000Z

# encoding: utf-8 # Copyright 2011 Tree.io Limited # This file is part of Treeio. # License www.tree.io/license """ Knowledge base module URLs """ from django.conf.urls.defaults import * urlpatterns = patterns('treeio.knowledge.views', url(r'^(\.(?P<response_format>\w+))?$', 'index', name='knowledge'), url(r'^index(\.(?P<response_format>\w+))?$', 'index', name='knowledge_index'), url(r'^categories(\.(?P<response_format>\w+))?/?$', 'index_categories', name='knowledge_categories'), # Folders url(r'^folder/add(\.(?P<response_format>\w+))?/?$', 'folder_add', name='knowledge_folder_add'), url(r'^folder/add/(?P<folderPath>.(?:[a-z,0-9,-]+/)+)(\.(?P<response_format>\w+))?/?$', 'folder_add_folder', name='knowledge_folder_add_folder'), url(r'^folder/(?P<folderPath>.(?:[a-z,0-9,-]+/)+)(\.(?P<response_format>\w+))?/?$', 'folder_view', name='knowledge_folder_view'), url(r'^folder/edit/(?P<knowledgeType_id>\d+)(\.(?P<response_format>\w+))?/?$', 'folder_edit', name='knowledge_folder_edit'), url(r'^folder/delete/(?P<knowledgeType_id>\d+)(\.(?P<response_format>\w+))?/?$', 'folder_delete', name='knowledge_folder_delete'), # Knowledge Items url(r'^item/add(\.(?P<response_format>\w+))?/?$', 'item_add', name='knowledge_item_add'), url(r'^item/add/(?P<folderPath>.(?:[a-z,0-9,-]+/)+)(\.(?P<response_format>\w+))?/?$', 'item_add_folder', name='knowledge_item_add_folder'), url(r'^(?P<folderPath>.(?:[a-z,0-9,-]+/)+)(?P<itemPath>.(?:[a-z,0-9,-]+/)+)(\.(?P<response_format>\w+))?/?$', 'item_view', name='knowledge_item_view'), url(r'^item/edit/(?P<knowledgeItem_id>\d+)(\.(?P<response_format>\w+))?/?$', 'item_edit', name='knowledge_item_edit'), url(r'^item/delete/(?P<knowledgeItem_id>\d+)(\.(?P<response_format>\w+))?/?$', 'item_delete', name='knowledge_item_delete'), # Categories url(r'^category/add(\.(?P<response_format>\w+))?/?$', 'category_add', name='knowledge_category_add'), url(r'^(?P<categoryPath>.(?:[a-z,0-9,-]+/)+)(\.(?P<response_format>\w+))?/?$', 'category_view', name='knowledge_category_view'), url(r'^category/edit/(?P<knowledgeCategory_id>\d+)(\.(?P<response_format>\w+))?/?$', 'category_edit', name='knowledge_category_edit'), url(r'^category/delete/(?P<knowledgeCategory_id>\d+)(\.(?P<response_format>\w+))?/?$', 'category_delete', name='knowledge_category_delete'), )

54.958333

118

0.573541

325

2,638

4.433846

0.178462

0.047189

0.17696

0.188758

0.442054

0.374046

0.336572

0.278279

0.2644

0.07703

0

0.007731

0.166414

2,638

47

119

56.12766

0.647567

0.062926

0

0.125

0.667616

0.540277

0

1

0

false

0

0.03125

0

0.03125

0

null

0

1

0

1

null

0

3

6274964d31b044659304e43d5b84d921f0dd8a07

2,278

py

Python

200_analysis/13_standardize_master.py

cogeorg/RegulatoryComplexity_Public

c9578ce012ba1e84dbebb029e30d98eff3430fd6

[ "Apache-2.0" ]

null

200_analysis/13_standardize_master.py

cogeorg/RegulatoryComplexity_Public

c9578ce012ba1e84dbebb029e30d98eff3430fd6

[ "Apache-2.0" ]

null

200_analysis/13_standardize_master.py

cogeorg/RegulatoryComplexity_Public

c9578ce012ba1e84dbebb029e30d98eff3430fd6

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- __author__="""Co-Pierre Georg (co-pierre.georg@uct.ac.za)""" import sys import os import re # ########################################################################### # METHODS # ########################################################################### def clean(value): value = value.replace("\n", "") value = value.replace("'", "") value = value.replace(".", "") value = value.replace(",", "") value = value.replace(";", "") value = value.replace(":", "") value = value.replace('"', '') value = value.replace("`", "") value = value.replace("$", "") value = value.replace("(", "") value = value.replace(")", "") value = value.replace("``", "") value = value.replace("--", "") value = value.upper() return value # ------------------------------------------------------------------------- # do_run(file_name) # ------------------------------------------------------------------------- def do_run(input_file_name, output_file_name): out_text = "" dict_in = {} dict_cons = {} print("<<< 13_STANDARDIZE_MASTER.PY") # # LOAD DICTIONARY # dict_file = open(input_file_name, "r") for line in dict_file.readlines(): tokens = line.strip().split(";") try: dict_in[tokens[0].strip('"')] = tokens[1].strip('"') except: pass print(" <<< READ DICTIONARY: " + input_file_name + " WITH " + str(len(dict_in)) + " ENTRIES") for key in dict_in.keys(): dict_cons[key] = clean(key) for key in dict_cons.keys(): out_text += key + ";" + dict_cons[key] + ";" + dict_in[key] + "\n" out_file = open(output_file_name, "w") out_file.write(out_text) out_file.close() print(" <<< WRITTEN DICTIONARY TO: " + output_file_name) # ------------------------------------------------------------------------- # ------------------------------------------------------------------------- # # MAIN # # ------------------------------------------------------------------------- if __name__ == '__main__': # # VARIABLES # args = sys.argv input_file_name = args[1] output_file_name = args[2] # # CODE # do_run(input_file_name, output_file_name)

25.032967

98

0.43108

214

2,278

4.331776

0.35514

0.161812

0.238403

0.28479

0.299892

0.230852

0

0.004348

0.192274

2,278

90

99

25.311111

0.499457

0.209833

0

0.104487

0.031346

0

1

0.043478

false

0.021739

0.065217

0

0.130435

0.065217

0

null

0

1

0

1

0

null

0

3

6282e7956c74bf98fee7e25fcbd2cf8e7688c77a

2,934

py

Python

test/common/test_pipe.py

mountain/planetarium

14c5a75f9ac0be36f28d059c7bf7a77635d617da

[ "MIT" ]

1

2018-03-03T18:58:01.000Z

test/common/test_pipe.py

mountain/planetarium

14c5a75f9ac0be36f28d059c7bf7a77635d617da

[ "MIT" ]

null

test/common/test_pipe.py

mountain/planetarium

14c5a75f9ac0be36f28d059c7bf7a77635d617da

[ "MIT" ]

null

import unittest import flare.pipe as p import flare.dataset.decorators as d class TestPipe(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def test_roll(self): elevens = list(range(11)) self.assertListEqual([[0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10]], list(p.roll(elevens, window_size=1))) self.assertListEqual([[0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 8], [8, 9], [9, 10]], list(p.roll(elevens, window_size=2))) self.assertListEqual([[0, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 6], [5, 6, 7], [6, 7, 8], [7, 8, 9], [8, 9, 10]], list(p.roll(elevens, window_size=3))) self.assertListEqual([[0, 1, 2, 3], [1, 2, 3, 4], [2, 3, 4, 5], [3, 4, 5, 6], [4, 5, 6, 7], [5, 6, 7, 8], [6, 7, 8, 9], [7, 8, 9, 10]], list(p.roll(elevens, window_size=4))) self.assertListEqual([[0, 1, 2, 3, 4], [1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7], [4, 5, 6, 7, 8], [5, 6, 7, 8, 9], [6, 7, 8, 9, 10]], list(p.roll(elevens, window_size=5))) def test_batches(self): elevens = list(range(11)) self.assertListEqual([[0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10]], list(p.batches(elevens, batch_size=1))) self.assertListEqual([[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]], list(p.batches(elevens, batch_size=2))) self.assertListEqual([[0, 1, 2], [3, 4, 5], [6, 7, 8]], list(p.batches(elevens, batch_size=3))) self.assertListEqual([[0, 1, 2, 3], [4, 5, 6, 7]], list(p.batches(elevens, batch_size=4))) self.assertListEqual([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]], list(p.batches(elevens, batch_size=5))) def test_rolled_batches(self): elevens = list(range(11)) self.assertListEqual([[[0, 1, 2], [1, 2, 3], [2, 3, 4]], [[3, 4, 5], [4, 5, 6], [5, 6, 7]], [[6, 7, 8], [7, 8, 9], [8, 9, 10]]], list(p.batches(p.roll(elevens, window_size=3), batch_size=3))) def test_batched_roll(self): elevens = list(range(11)) self.assertListEqual([[[0, 1, 2], [3, 4, 5]], [[3, 4, 5], [6, 7, 8]]], list(p.roll(p.batches(elevens, batch_size=3), window_size=2))) def test_attributes(self): @d.attributes('x1', 'x2', 'x3') def datagen(): elevens = list(range(11)) return p.batches(elevens, batch_size=3) self.assertListEqual([{'x1': 0, 'x2': 1, 'x3': 2}, {'x1': 3, 'x2': 4, 'x3': 5}, {'x1': 6, 'x2': 7, 'x3': 8}], [d for d in datagen()]) def test_feature(self): def add(x1=0, x2=0): return [x1 + x2] @d.feature(add, ['x1', 'x2'], ['a']) @d.attributes('x1', 'x2', 'x3') def datagen(): elevens = list(range(11)) return p.batches(elevens, batch_size=3) self.assertListEqual([{'x1': 0, 'x2': 1, 'x3': 2, 'a': 1}, {'x1': 3, 'x2': 4, 'x3': 5, 'a': 7}, {'x1': 6, 'x2': 7, 'x3': 8, 'a': 13}], [d for d in datagen()])

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null

0

1

0

3

6551a175d3a0bde3d5f37c157e8cefe0f33963e1

101

py

Python

src/main/python/app/context.py

kevinyu/soundsep

58f8100e101a6302533626d2f141c86748c8dc10

[ "MIT" ]

1

2020-10-03T18:35:52.000Z

src/main/python/app/context.py

theunissenlab/soundsep

58f8100e101a6302533626d2f141c86748c8dc10

[ "MIT" ]

null

src/main/python/app/context.py

theunissenlab/soundsep

58f8100e101a6302533626d2f141c86748c8dc10

[ "MIT" ]

1

2020-08-12T17:16:15.000Z

from fbs_runtime.application_context.PyQt5 import ApplicationContext context = ApplicationContext()

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68

0.871287

10

101

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0

1

0

null

0

1

0

3

655d635191b67a99943128b5758eee46d9ab37aa

73

py

Python

src/lib/markupbase.py

timmartin/skulpt

2e3a3fbbaccc12baa29094a717ceec491a8a6750

[ "MIT" ]

10

2015-11-13T17:02:40.000Z

2021-02-09T23:21:05.000Z

src/lib/markupbase.py

timmartin/skulpt

2e3a3fbbaccc12baa29094a717ceec491a8a6750

[ "MIT" ]

43

2015-06-03T17:59:23.000Z

2021-09-17T10:45:21.000Z

src/lib/markupbase.py

timmartin/skulpt

2e3a3fbbaccc12baa29094a717ceec491a8a6750

[ "MIT" ]

13

2017-07-02T03:16:46.000Z

2021-07-05T14:53:56.000Z

raise NotImplementedError("markupbase is not yet implemented in Skulpt")

36.5

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1

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1

0

null

0

1

0

null

0

1

0

3

656bb7f2aa9858aa3a9d321ec8e20f9790fd2268

776

py

Python

test/test_script.py

kuroko1t/nne

764b903db38ffc29eec536d9b3704e9bfe8ca60f

[ "Apache-2.0" ]

10

2020-04-02T07:10:08.000Z

2022-03-09T03:36:27.000Z

test/test_script.py

kuroko1t/nne

764b903db38ffc29eec536d9b3704e9bfe8ca60f

[ "Apache-2.0" ]

2

2020-04-21T22:48:19.000Z

2021-07-18T09:53:59.000Z

test/test_script.py

kuroko1t/nne

764b903db38ffc29eec536d9b3704e9bfe8ca60f

[ "Apache-2.0" ]

2

2020-04-07T09:16:03.000Z

2020-04-26T05:34:06.000Z

import unittest import nne import torchvision import torch import numpy as np import subprocess class ScriptTests(unittest.TestCase): def __init__(self, *args, **kwargs): super(ScriptTests, self).__init__(*args, **kwargs) self.onnx_file = 'resnet.onnx' input_shape = (1, 3, 64, 64) model = torchvision.models.resnet34(pretrained=True) nne.cv2onnx(model, input_shape, self.onnx_file) def test_analyze(self): subprocess.check_call(["nne", self.onnx_file]) subprocess.check_call(["nne", self.onnx_file, "-a", "resnet.json"]) def test_convert(self): subprocess.check_call(["nne", self.onnx_file, "-s", "resnet_smip.onnx"]) subprocess.check_call(["nne", self.onnx_file, "-t", "resnet.tflite"])

33.73913

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776

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776

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0

1

0

null

0

1

0

1

0

3

6583e8b2d976d4e0deec067da02d6ba42de4de37

349

py

Python

src/flask-crud/tables.py

krishnamaram2/webapp-1

177944f8b3279343317e54e6b4e986868a98324f

[ "Apache-2.0" ]

null

src/flask-crud/tables.py

krishnamaram2/webapp-1

177944f8b3279343317e54e6b4e986868a98324f

[ "Apache-2.0" ]

null

src/flask-crud/tables.py

krishnamaram2/webapp-1

177944f8b3279343317e54e6b4e986868a98324f

[ "Apache-2.0" ]

2

2021-11-03T13:51:29.000Z

2021-11-03T13:55:21.000Z

from flask_table import Table, Col, LinkCol class Results(Table): user_id = Col('Id', show=False) user_name = Col('Name') user_email = Col('Email') user_passowrd = Col('Password', show=False) edit=LinkCol('Edit','edit_view',url_kwargs=dict(id='user_id')) delete=LinkCol('Delete','delete_user',url_kwargs=dict(id='user_id'))

34.9

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0.690544

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349

4.423077

0.423077

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0.113043

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0.182609

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349

9

73

38.777778

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null

0

1

0

null

0

1

0

1

0

3

65936de1e04cc497ac1b50e5947c8f386ccd5b0c

246

py

Python

epicteller/web/__init__.py

KawashiroNitori/epicteller

264b11e7e6eb58beb0f67ecbbb811d268a533f7a

[ "MIT" ]

null

epicteller/web/__init__.py

KawashiroNitori/epicteller

264b11e7e6eb58beb0f67ecbbb811d268a533f7a

[ "MIT" ]

null

epicteller/web/__init__.py

KawashiroNitori/epicteller

264b11e7e6eb58beb0f67ecbbb811d268a533f7a

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import asyncio from epicteller.core.config import Config from epicteller.core.kafka import Bus bus = Bus(bootstrap_servers=Config.KAFKA_SERVERS) def bus_init(): asyncio.create_task(bus.run())

18.923077

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246

4.972222

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0.156425

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0

0.004651

0.126016

246

12

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0

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null

0

1

0

null

0

1

0

1

0

3

659918da7023bbae02767596b4b07f559c7197dd

1,062

py

Python

mongoframes/factory/makers/numbers.py

tylerganter/MongoFrames

458930c74c8e2382a8bc931540254a5c38e84cba

[ "MIT" ]

52

2016-06-26T23:56:56.000Z

2022-02-07T19:12:37.000Z

mongoframes/factory/makers/numbers.py

tylerganter/MongoFrames

458930c74c8e2382a8bc931540254a5c38e84cba

[ "MIT" ]

18

2016-06-27T08:31:19.000Z

2020-06-02T20:09:04.000Z

mongoframes/factory/makers/numbers.py

tylerganter/MongoFrames

458930c74c8e2382a8bc931540254a5c38e84cba

[ "MIT" ]

6

2016-06-27T00:41:01.000Z

2022-02-16T17:32:39.000Z

import random from mongoframes.factory.makers import Maker __all__ = [ 'Counter' ] class Counter(Maker): """ Generate a sequence of numbers. """ def __init__(self, start_from=1, step=1): super().__init__() self._start_from = int(start_from) self._step = step self._counter = self._start_from def reset(self): self._counter = int(self._start_from) def _assemble(self): value = self._counter self._counter += int(self._step) return value class Float(Maker): """ Generate a random float between two values. """ def __init__(self, min_value, max_value): super().__init__() self._min_value = min_value self._max_value = max_value def _assemble(self): return random.uniform(float(self._min_value), float(self._max_value)) class Int(Float): """ Generate a random integer between two values. """ def _assemble(self): return random.randint(int(self._min_value), int(self._max_value))

20.423077

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0.627119

131

1,062

4.679389

0.274809

0.073409

0.084829

0.055465

0.088091

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0.262712

1,062

52

78

20.423077

0.780332

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null

0

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null

0

1

0

1

0

3

65ac477be277b61561c0d945ae942253728f57d2

2,091

py

Python

rocketgram/keyboards/inline.py

rocketbots/rocketgram

e509dcfad85d47a2449caf6dd302ec8581f95bf6

[ "MIT" ]

16

2019-02-27T20:15:52.000Z

2019-08-06T10:59:41.000Z

rocketgram/keyboards/inline.py

rocketbots/rocketgram

e509dcfad85d47a2449caf6dd302ec8581f95bf6

[ "MIT" ]

1

2019-04-27T06:51:57.000Z

2019-05-31T18:09:16.000Z

rocketgram/keyboards/inline.py

rocketbots/rocketgram

e509dcfad85d47a2449caf6dd302ec8581f95bf6

[ "MIT" ]

3

2019-03-19T16:01:22.000Z

2019-04-05T15:58:12.000Z

# Copyright (C) 2015-2022 by Vd. # This file is part of Rocketgram, the modern Telegram bot framework. # Rocketgram is released under the MIT License (see LICENSE). from typing import Optional from .keyboard import Keyboard from .. import api class InlineKeyboard(Keyboard): __slots__ = () def url(self, text, url) -> 'InlineKeyboard': self.add(api.InlineKeyboardButton(text=text, url=url)) return self def login(self, text: str, url: str, forward_text: Optional[str] = None, bot_username: Optional[str] = None, request_write_access: Optional[bool] = None) -> 'InlineKeyboard': lu = api.LoginUrl(url, forward_text, bot_username, request_write_access) self.add(api.InlineKeyboardButton(text=text, login_url=lu)) return self def callback(self, text: str, callback_data: str) -> 'InlineKeyboard': self.add(api.InlineKeyboardButton(text=text, callback_data=callback_data)) return self def web(self, text: str, url: str) -> 'InlineKeyboard': self.add(api.InlineKeyboardButton(text=text, web_app=api.WebAppInfo(url=url))) return self def inline(self, text: str, switch_inline_query: str = str()) -> 'InlineKeyboard': self.add(api.InlineKeyboardButton(text=text, switch_inline_query=switch_inline_query)) return self def inline_current(self, text: str, switch_inline_query_current_chat: str = str()) -> 'InlineKeyboard': self.add(api.InlineKeyboardButton(text=text, switch_inline_query_current_chat=switch_inline_query_current_chat)) return self def game(self, text: str, callback_game: str) -> 'InlineKeyboard': self.add(api.InlineKeyboardButton(text=text, callback_game=callback_game)) return self def pay(self, text: str) -> 'InlineKeyboard': self.add(api.InlineKeyboardButton(text=text, pay=True)) return self def row(self) -> 'InlineKeyboard': return super().row() def render(self) -> 'api.InlineKeyboardMarkup': return api.InlineKeyboardMarkup(self.render_buttons())

38.722222

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0.169014

0.458451

0.374648

0.308451

0.271831

0.194366

0.105634

0

0.00472

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2,091

53

121

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0.075562

0

0.228571

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0.077761

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0

1

0.285714

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0

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0.714286

0

null

0

1

0

null

0

1

0

1

0

3

65ca31621856cc059e7e7e71dd80ab961cc51ada

187

py

Python

examples/python/simple/func_param.py

airgiser/ucb

d03e62a17f35a9183ed36662352f603f0f673194

[ "MIT" ]

1

2022-01-08T14:59:44.000Z

examples/python/simple/func_param.py

airgiser/just-for-fun

d03e62a17f35a9183ed36662352f603f0f673194

[ "MIT" ]

null

examples/python/simple/func_param.py

airgiser/just-for-fun

d03e62a17f35a9183ed36662352f603f0f673194

[ "MIT" ]

null

#!/usr/bin/python # Filename: func_param.py def Max(a, b): if a > b: print 'The max one is', a else: print 'The max one is', b Max(3, 4) x = 5 y = 7 Max(x, y)

11.6875

33

0.513369

37

187

2.567568

0.621622

0.042105

0.231579

0.294737

0.336842

0

0.032

0.331551

187

15

34

12.466667

0.728

0.213904

0

0.194444

0

null

0

null

0.222222

0

null

0

1

0

null

0

1

0

3

02ef1c889f57779e2849ecff9ac93c0eb8998db2

206

py

Python

docs/examples/quickstart/bot.py

bkvalexey/aiogram_dialog

fb4a3a8c151d63f06b04e4b8641549cc7ae45c2c

[ "Apache-2.0" ]

198

2020-06-06T14:24:04.000Z

2022-03-29T16:01:30.000Z

docs/examples/quickstart/bot.py

bkvalexey/aiogram_dialog

fb4a3a8c151d63f06b04e4b8641549cc7ae45c2c

[ "Apache-2.0" ]

65

2020-06-07T19:02:42.000Z

2022-03-21T18:23:17.000Z

docs/examples/quickstart/bot.py

bkvalexey/aiogram_dialog

fb4a3a8c151d63f06b04e4b8641549cc7ae45c2c

[ "Apache-2.0" ]

48

2020-06-13T09:57:58.000Z

2022-03-11T17:59:21.000Z

from aiogram import Bot, Dispatcher, executor from aiogram.contrib.fsm_storage.memory import MemoryStorage storage = MemoryStorage() bot = Bot(token='BOT TOKEN HERE') dp = Dispatcher(bot, storage=storage)

29.428571

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6.037037

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0

0.11165

206

6

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0

null

0

1

0

null

0

1

0

3

f301ea06ddbdc6924646f7f5d90c8debf96cbb54

392

py

Python

tests/auth_helper.py

K900/httpx_auth

e8fe9d4b01e84cb707fae0c3624e3e649c602afe

[ "MIT" ]

null

tests/auth_helper.py

K900/httpx_auth

e8fe9d4b01e84cb707fae0c3624e3e649c602afe

[ "MIT" ]

null

tests/auth_helper.py

K900/httpx_auth

e8fe9d4b01e84cb707fae0c3624e3e649c602afe

[ "MIT" ]

null

import httpx from pytest_httpx import HTTPXMock # TODO Remove def get_header(httpx_mock: HTTPXMock, auth: httpx.Auth) -> dict: # Mock a dummy response httpx_mock.add_response() # Send a request to this dummy URL with authentication response = httpx.get("http://authorized_only", auth=auth) # Return headers received on this dummy URL return response.request.headers

30.153846

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0

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null

0

null

0

1

0

1

0

1

0

3

f30f312e47ccf8c3d2875b4b048be168aa978102

1,840

py

Python

qsbk/qsbk/pipelines.py

lixiang30/scripy_prj

c4db0458e47a8709286bbeaa3f91fdd1f84de151

[ "Apache-2.0" ]

null

qsbk/qsbk/pipelines.py

lixiang30/scripy_prj

c4db0458e47a8709286bbeaa3f91fdd1f84de151

[ "Apache-2.0" ]

null

qsbk/qsbk/pipelines.py

lixiang30/scripy_prj

c4db0458e47a8709286bbeaa3f91fdd1f84de151

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Define your item pipelines here # # Don't forget to add your pipeline to the ITEM_PIPELINES setting # See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html # 存储方式一　采用python自带的json模块来存储 import json class QsbkPipeline(object): def __init__(self): self.fp = open("duanzi.json",'w',encoding='utf-8') def open_spider(self,spider): print("========爬虫开始了=======") def process_item(self, item, spider): item_json = json.dumps(dict(item),ensure_ascii=False) self.fp.write(item_json+'\n') return item def close_spider(self,spider): self.fp.close() print("====爬虫结束了====") # 方式二　采用scrapy自带的ＪsonItemExporter,缺陷数据量大的时候很慢 from scrapy.exporters import JsonItemExporter class QsbkPipeline(object): def __init__(self): self.fp = open("duanzi2.json",'wb') self.exporter = JsonItemExporter(self.fp,ensure_ascii=False,encoding='utf-8') self.exporter.start_exporting() def open_spider(self,spider): print("========爬虫开始了=======") def process_item(self, item, spider): self.exporter.export_item(item) return item def close_spider(self,spider): self.exporter.finish_exporting() self.fp.close() print("====爬虫结束了====") # 方式三　　采用ＪsonLinesItemExporter方式存储 from scrapy.exporters import JsonLinesItemExporter class QsbkPipeline(object): def __init__(self): self.fp = open("duanzi3.json",'wb') self.exporter = JsonLinesItemExporter(self.fp,ensure_ascii=False,encoding='utf-8') def open_spider(self,spider): print("========爬虫开始了=======") def process_item(self, item, spider): self.exporter.export_item(item) return item def close_spider(self,spider): self.fp.close() print("====爬虫结束了====")

27.878788

90

0.645109

219

1,840

5.278539

0.324201

0.095156

0.083045

0.067474

0.532872

0.514706

0.466263

0.352076

0

0.004076

0.2

1,840

65

91

28.307692

0.78125

0.154891

0

0.707317

0

0.101036

0

1

0.292683

false

0

0.073171

0

0.512195

0.146341

0

null

0

1

0

null

0

1

0

1

0

3

b83717021e0be17e40b21f5936bab73fc9b611b7

109

py

Python

A/A 959 Mahmoud and Ehab and the even-odd game.py

zielman/Codeforces-solutions

636f11a9eb10939d09d2e50ddc5ec53327d0b7ab

[ "MIT" ]

null

A/A 959 Mahmoud and Ehab and the even-odd game.py

zielman/Codeforces-solutions

636f11a9eb10939d09d2e50ddc5ec53327d0b7ab

[ "MIT" ]

1

2021-05-05T17:05:03.000Z

A/A 959 Mahmoud and Ehab and the even-odd game.py

zielman/Codeforces-solutions

636f11a9eb10939d09d2e50ddc5ec53327d0b7ab

[ "MIT" ]

null

# https://codeforces.com/problemset/problem/959/A n = int(input()) print('Mahmoud' if n%2 == 0 else 'Ehab')

21.8

49

0.669725

18

109

4.055556

0.944444

0

0.052083

0.119266

109

5

50

21.8

0.708333

0.431193

0

0.180328

0

1

0

false

0

0.5

1

0

null

0

1

0

null

0

1

0

3

b8471548cda2fa7089c2b432725294f5dc1f74fd

340

py

Python

stock deep learning/Practice/checkdevice.py

nosy0411/Deep-learning-project

b0864579ec1fef4c6224397e3c39e4fce051c93a

[ "MIT" ]

null

stock deep learning/Practice/checkdevice.py

nosy0411/Deep-learning-project

b0864579ec1fef4c6224397e3c39e4fce051c93a

[ "MIT" ]

null

stock deep learning/Practice/checkdevice.py

nosy0411/Deep-learning-project

b0864579ec1fef4c6224397e3c39e4fce051c93a

[ "MIT" ]

null

import numpy as np import pandas as pd import keras import tensorflow as tf from IPython.display import display import PIL # How to check if the code is running on GPU or CPU? from tensorflow.python.client import device_lib print(device_lib.list_local_devices()) from keras import backend as K K.tensorflow_backend._get_available_gpus()

22.666667

52

0.817647

58

340

4.655172

0.655172

0.081481

0

0.141176

340

15

53

22.666667

0.924658

0.147059

0

1

0

true

0

0.8

0

0.8

0.1

0

null

0

null

0

1

0

1

0

1

0

3

b85118753b5540690f9157e0cb5c8fced69b1e4c

3,929

py

Python

watcher_test.py

coveritytest/website-watcher

c3f17061600c3b7ff1b2c55121ec43310965b8b0

[ "MIT" ]

29

2020-04-26T17:47:42.000Z

2022-03-24T21:52:01.000Z

watcher_test.py

coveritytest/website-watcher

c3f17061600c3b7ff1b2c55121ec43310965b8b0

[ "MIT" ]

23

2020-04-05T19:24:47.000Z

2022-02-26T14:23:41.000Z

watcher_test.py

coveritytest/website-watcher

c3f17061600c3b7ff1b2c55121ec43310965b8b0

[ "MIT" ]

8

2021-01-30T13:28:42.000Z

2022-01-31T18:00:19.000Z

import os import uuid import unittest from unittest.mock import Mock, patch import watcher from adapters import SendAdapter class NoopAdapter(SendAdapter): def __init__(self): self.calls = { 'send': [] } def send(self, data): self.calls['send'].append(data) return True @classmethod def get_parser(cls): pass @classmethod def get_name(cls): pass @classmethod def get_description(cls): pass class Args: def __init__(self, url=None, adapter='noop', user_agent='firefox', xpath='//body', tolerance=0): self.url = url if url is not None else f'https://{uuid.uuid4()}.org' self.adapter = adapter self.user_agent = user_agent self.xpath = xpath self.tolerance = tolerance doc1 = ''' <html> <body> <div id="d1">Some text including umlauts äöü.</div> <div id="d2">Not changing</div> </body> </html> ''' doc2 = ''' <html> <body> <div id="d1">Some text including umlauts äöü (changed)</div> <div id="d2">Not changing</div> </body> </html> ''' class WatcherTests(unittest.TestCase): @patch('watcher.requests.get', autospec=True) @patch('adapters.SendAdapterFactory.get', autospec=True) def test_ignore_change_in_different_xpath(self, adapter_factory_mock, request_mock): noop_adapter = NoopAdapter() # Set up mocks adapter_factory_mock.return_value = noop_adapter request_mock.return_value.status_code = 200 request_mock.return_value.text = doc1 args = Args(xpath='//div[@id="d2"]') # First call watcher.main(args, None) self.assertEqual(len(noop_adapter.calls['send']), 1) self.assertEqual(noop_adapter.calls['send'][0].diff, 12) # 'A' self.assertEqual(noop_adapter.calls['send'][0].url, args.url) request_mock.return_value.text = doc2 # Second call watcher.main(args, None) self.assertEqual(len(noop_adapter.calls['send']), 1) # same as before @patch('watcher.requests.get', autospec=True) @patch('adapters.SendAdapterFactory.get', autospec=True) def test_detect_change_in_same_xpath(self, adapter_factory_mock, request_mock): noop_adapter = NoopAdapter() # Set up mocks adapter_factory_mock.return_value = noop_adapter request_mock.return_value.status_code = 200 request_mock.return_value.text = doc1 args = Args(xpath='//div[@id="d1"]') # First call watcher.main(args, None) self.assertEqual(len(noop_adapter.calls['send']), 1) self.assertEqual(noop_adapter.calls['send'][0].diff, 32) self.assertEqual(noop_adapter.calls['send'][0].url, args.url) request_mock.return_value.text = doc2 # Second call watcher.main(args, None) self.assertEqual(len(noop_adapter.calls['send']), 2) self.assertEqual(noop_adapter.calls['send'][1].diff, 11) # 9 new chars, minus '.', plus ' ' self.assertEqual(noop_adapter.calls['send'][1].url, args.url) @patch('watcher.requests.get', autospec=True) @patch('adapters.SendAdapterFactory.get', autospec=True) def test_ignore_changes_below_tolerance(self, adapter_factory_mock, request_mock): noop_adapter = NoopAdapter() # Set up mocks adapter_factory_mock.return_value = noop_adapter request_mock.return_value.status_code = 200 request_mock.return_value.text = doc1 args = Args(xpath='/*', tolerance=12) # First call watcher.main(args, None) self.assertEqual(len(noop_adapter.calls['send']), 1) self.assertEqual(noop_adapter.calls['send'][0].url, args.url) request_mock.return_value.text = doc2 # Second call watcher.main(args, None) self.assertEqual(len(noop_adapter.calls['send']), 1)

30.223077

100

0.642912

492

3,929

4.95935

0.213415

0.085656

0.085246

0.106557

0.747951

0.728279

0.69877

0.672541

0

0.015506

0.228557

3,929

130

101

30.223077

0.789508

0.040468

0

0.569892

0

0.144492

0.024747

0

0.139785

1

0.096774

false

0.032258

0.064516

0

0.204301

0

null

0

1

0

1

0

null

0

3

b854035f0df74dfb7297d6e6114af3c4a636d039

131

py

Python

acmicpc/5596.py

juseongkr/BOJ

8f10a2bf9a7d695455493fbe7423347a8b648416

[ "Apache-2.0" ]

7

2020-02-03T10:00:19.000Z

2021-11-16T11:03:57.000Z

acmicpc/5596.py

juseongkr/Algorithm-training

8f10a2bf9a7d695455493fbe7423347a8b648416

[ "Apache-2.0" ]

1

2021-01-03T06:58:24.000Z

acmicpc/5596.py

juseongkr/Algorithm-training

8f10a2bf9a7d695455493fbe7423347a8b648416

[ "Apache-2.0" ]

1

2020-01-22T14:34:03.000Z

a, b, c, d = map(int, input().split()) s = a + b + c + d a, b, c, d = map(int, input().split()) t = a + b + c + d print(max(s, t))

21.833333

38

0.458015

30

131

2

0.4

0.133333

0.2

0.266667

0.666667

0

0.259542

131

5

39

26.2

0.618557

0

0.4

0

1

0

false

0

0.2

0

null

0

1

0

1

0

null

0

3

b8577a63805ae444b7886eb719d04c5883b98098

3,823

py

Python

resources/test_cases/python/PyNaCl/TestRule4.py

stg-tud/licma

b899e6e682f7716d19e79d6ce7b73c28c6efd4cf

[ "MIT" ]

5

2021-09-13T11:24:13.000Z

2022-03-18T21:56:58.000Z

resources/test_cases/python/PyNaCl/TestRule4.py

stg-tud/licma

b899e6e682f7716d19e79d6ce7b73c28c6efd4cf

[ "MIT" ]

null

resources/test_cases/python/PyNaCl/TestRule4.py

stg-tud/licma

b899e6e682f7716d19e79d6ce7b73c28c6efd4cf

[ "MIT" ]

1

2021-09-13T06:02:20.000Z

from nacl.utils import random from nacl.secret import SecretBox from nacl.pwhash.argon2i import kdf g_salt1 = b"1234567812345678" g_salt2 = bytes("1234567812345678", "utf8") nonce = b"123456781234567812345678" # 24 byte def p_example1_hard_coded1(password, data): key = kdf(32, password, b"1234567812345678") secret_box = SecretBox(key) cipher_text = secret_box.encrypt(data, nonce) return cipher_text def p_example2_hard_coded2(password, data): key = kdf(32, password, bytes("1234567812345678", "utf8")) secret_box = SecretBox(key) cipher_text = secret_box.encrypt(data, nonce) return cipher_text def p_example3_local_variable1(password, data): salt = b"1234567812345678" key = kdf(32, password, salt) secret_box = SecretBox(key) cipher_text = secret_box.encrypt(data, nonce) return cipher_text def p_example4_local_variable2(password, data): salt = bytes("1234567812345678", "utf8") key = kdf(32, password, salt) secret_box = SecretBox(key) cipher_text = secret_box.encrypt(data, nonce) return cipher_text def p_example5_nested_local_variable1(password, data): salt1 = b"1234567812345678" salt2 = salt1 salt3 = salt2 key = kdf(32, password, salt3) secret_box = SecretBox(key) cipher_text = secret_box.encrypt(data, nonce) return cipher_text def p_example6_nested_local_variable2(password, data): salt1 = bytes("1234567812345678", "utf8") salt2 = salt1 salt3 = salt2 key = kdf(32, password, salt3) secret_box = SecretBox(key) cipher_text = secret_box.encrypt(data, nonce) return cipher_text def p_example_method_call(password, salt, data): key = kdf(32, password, salt) secret_box = SecretBox(key) cipher_text = secret_box.encrypt(data, nonce) return cipher_text def p_example_nested_method_call(password, salt, data): return p_example_method_call(password, salt, data) def p_example7_direct_method_call1(password, data): salt = b"1234567812345678" return p_example_method_call(password, salt, data) def p_example8_direct_method_call2(password, data): salt = bytes("1234567812345678", "utf8") return p_example_method_call(password, salt, data) def p_example9_nested_method_call1(password, data): salt = b"1234567812345678" return p_example_nested_method_call(password, salt, data) def p_example10_nested_method_call2(password, data): salt = bytes("1234567812345678", "utf8") return p_example_nested_method_call(password, salt, data) def p_example11_direct_g_variable_access1(password, data): key = kdf(32, password, g_salt1) secret_box = SecretBox(key) cipher_text = secret_box.encrypt(data, nonce) return cipher_text def p_example12_direct_g_variable_access2(password, data): key = kdf(32, password, g_salt2) secret_box = SecretBox(key) cipher_text = secret_box.encrypt(data, nonce) return cipher_text def p_example13_indirect_g_variable_access1(password, data): salt = g_salt1 key = kdf(32, password, salt) secret_box = SecretBox(key) cipher_text = secret_box.encrypt(data, nonce) return cipher_text def p_example14_indirect_g_variable_access2(password, data): salt = g_salt2 key = kdf(32, password, salt) secret_box = SecretBox(key) cipher_text = secret_box.encrypt(data, nonce) return cipher_text def p_example15_warning_parameter_not_resolvable(password, salt, data): key = kdf(32, password, salt) secret_box = SecretBox(key) cipher_text = secret_box.encrypt(data, nonce) return cipher_text def n_example1_random_salt(password, data): salt = random(16) key = kdf(32, password, salt) secret_box = SecretBox(key) cipher_text = secret_box.encrypt(data, nonce) return cipher_text

25.657718

71

0.733194

515

3,823

5.159223

0.139806

0.088069

0.039142

0.078284

0.751976

0.704554

0.668047

0.642454

0.63041

0

0.097522

0.176563

3,823

148

72

25.831081

0.746506

0.001831

0

0.65625

0

0.062926

0.006293

0

1

0.1875

false

0.375

0.03125

0.010417

0.40625

0

null

0

1

0

1

0

null

0

1

0

3

b8579d992a0fe67398ed86e6a7492481685d93c0

270

py

Python

typed_models/fields/float.py

lockhaty/typed-models

75005b84cf78bc58d9a760eef34d42095ca4f726

[ "MIT" ]

1

2020-09-06T13:55:58.000Z

typed_models/fields/float.py

lockhaty/typed-models

75005b84cf78bc58d9a760eef34d42095ca4f726

[ "MIT" ]

3

2020-09-06T13:54:33.000Z

2020-10-13T10:57:15.000Z

typed_models/fields/float.py

lockhaty/typed-models

75005b84cf78bc58d9a760eef34d42095ca4f726

[ "MIT" ]

1

2020-10-05T11:29:17.000Z

from ..base import Field class FloatField(Field): def parse(self, value): try: return float(value) except (TypeError, ValueError): self._raise_value_error(value) def default_serializer(self, value): return value

22.5

42

0.622222

30

270

5.466667

0.666667

0.109756

0

0.292593

270

12

43

22.5

0.858639

0

1

0.222222

false

0

0.111111

0.666667

0

null

0

null

0

1

0

1

0

3

b8652a77ad6115eccd982364431e150eb06ccb2a

250

py

Python

djcalcilib.py

Dasham007/Dasham007

596cd5db00661718f33bbba0b3e0d0f16c373efc

[ "MIT" ]

null

djcalcilib.py

Dasham007/Dasham007

596cd5db00661718f33bbba0b3e0d0f16c373efc

[ "MIT" ]

null

djcalcilib.py

Dasham007/Dasham007

596cd5db00661718f33bbba0b3e0d0f16c373efc

[ "MIT" ]

null

def sum(x,y): print("sum"," =",(x+y)) def subtract(x,y): print("difference"," =",(x-y)) def divide(x,y): print("division"," =",(x/y)) def multiply(x,y): print("multiplication"," =",(x/y))

14.705882

42

0.432

32

250

3.375

0.34375

0.148148

0.259259

0

0.312

250

17

43

14.705882

0.627907

0

0.171315

0

1

0.5

false

0

0.5

0

null

0

1

0

null

0

1

0

1

0

3

b86c6122e79ffe56338f938187626fa6fb3e14e5

180

py

Python

solutions/python3/780.py

sm2774us/amazon_interview_prep_2021

f580080e4a6b712b0b295bb429bf676eb15668de

[ "MIT" ]

42

2020-08-02T07:03:49.000Z

2022-03-26T07:50:15.000Z

solutions/python3/780.py

ajayv13/leetcode

de02576a9503be6054816b7444ccadcc0c31c59d

[ "MIT" ]

null

solutions/python3/780.py

ajayv13/leetcode

de02576a9503be6054816b7444ccadcc0c31c59d

[ "MIT" ]

40

2020-02-08T02:50:24.000Z

2022-03-26T15:38:10.000Z

class Solution: def reachingPoints(self, sx, sy, tx, ty): while sx<tx and sy<ty: tx,ty = tx%ty,ty%tx return sx==tx and (ty-sy)%sx==0 or sy==ty and (tx-sx)%sy==0

45

67

0.588889

37

180

2.864865

0.378378

0.113208

0.132075

0

0.014493

0.233333

180

4

67

45

0.753623

0

1

0.25

false

0

0.75

0

null

0

1

0

null

0

1

0

1

0

3

b87c0ecd787dc3725a835ebd8de33f9460a75e6d

222

py

Python

spec/fixtures/issue12/backends/Bla.py

Askaholic/linter-mypy

97978c5c9455d4215ea0cd0395e34b8eb118feca

[ "MIT" ]

33

2016-12-08T14:53:50.000Z

2022-02-22T20:56:49.000Z

spec/fixtures/issue12/backends/Bla.py

Askaholic/linter-mypy

97978c5c9455d4215ea0cd0395e34b8eb118feca

[ "MIT" ]

27

2017-03-12T01:18:05.000Z

2021-01-27T14:59:54.000Z

spec/fixtures/issue12/backends/Bla.py

Askaholic/linter-mypy

97978c5c9455d4215ea0cd0395e34b8eb118feca

[ "MIT" ]

7

2017-03-12T01:56:07.000Z

2022-03-24T18:09:00.000Z

from .. import * from typing import Iterable class Bla: def __init__(self, path: str) -> None: self.path = path def method(self, options: Iterable[str]) -> str: return call_command.call(["BLA"])

20.181818

52

0.626126

29

222

4.62069

0.586207

0.119403

0

0.243243

222

10

53

22.2

0.797619

0

0.013575

0

1

0.285714

false

0

0.285714

0.142857

0.857143

0

1

0

null

0

null

0

1

0

1

0

3

b87d9dd5000109799ee73ef17f4575f815da85b3

89

py

Python

checkov/openapi/checks/registry.py

peaudecastor/checkov

a4804b61c1b1390b7abd44ab53285fcbc3e7e80b

[ "Apache-2.0" ]

null

checkov/openapi/checks/registry.py

peaudecastor/checkov

a4804b61c1b1390b7abd44ab53285fcbc3e7e80b

[ "Apache-2.0" ]

null

checkov/openapi/checks/registry.py

peaudecastor/checkov

a4804b61c1b1390b7abd44ab53285fcbc3e7e80b

[ "Apache-2.0" ]

null

from checkov.openapi.checks.base_registry import Registry openapi_registry = Registry()

22.25

57

0.842697

11

89

6.636364

0.636364

0

0.089888

89

3

58

29.666667

0.901235

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

3

b88f0922be73df21e36c5b7c066b667d6da6fcaf

137

py

Python

Flappybird/main.py

Yuconium/Flappy-Bird

2fe6c4e6004d85c2267577e9a548021510f41e84

[ "MIT" ]

null

Flappybird/main.py

Yuconium/Flappy-Bird

2fe6c4e6004d85c2267577e9a548021510f41e84

[ "MIT" ]

null

Flappybird/main.py

Yuconium/Flappy-Bird

2fe6c4e6004d85c2267577e9a548021510f41e84

[ "MIT" ]

null

import pygame import mainwindow if __name__ == "__main__": pygame.init() Screen = mainwindow.Screen(700, 500) Screen.mainloop()

19.571429

38

0.715328

16

137

5.625

0.6875

0

0.052632

0.167883

137

7

39

19.571429

0.736842

0

0.060606

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

3

b89df135d78183c9e3732c93c605e990693b9397

186

py

Python

Data Science With Python/06-importing-data-in-python-(part-2)/3-diving-deep-into-the-twitter-api/twitter-data-into-dataframe.py

aimanahmedmoin1997/DataCamp

c6a6c4d59b83f14854bd76ed5c0c7f2dddd6de1d

[ "MIT" ]

3

2019-05-12T04:49:24.000Z

2020-05-06T00:40:28.000Z

Data Science With Python/06-importing-data-in-python-(part-2)/3-diving-deep-into-the-twitter-api/twitter-data-into-dataframe.py

aimanahmedmoin1997/DataCamp

c6a6c4d59b83f14854bd76ed5c0c7f2dddd6de1d

[ "MIT" ]

null

Data Science With Python/06-importing-data-in-python-(part-2)/3-diving-deep-into-the-twitter-api/twitter-data-into-dataframe.py

aimanahmedmoin1997/DataCamp

c6a6c4d59b83f14854bd76ed5c0c7f2dddd6de1d

[ "MIT" ]

7

2018-11-06T17:43:31.000Z

2020-11-07T21:08:16.000Z

# Import package import pandas as pd # Build DataFrame of tweet texts and languages df = pd.DataFrame(tweets_data, columns=['text', 'lang']) # Print head of DataFrame print(df.head())

20.666667

56

0.736559

28

186

4.857143

0.714286

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0.150538

186

8

57

23.25

0.860759

0.446237

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0.333333

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0.333333

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null

0

1

0

null

0

1

0

3

b89f350961634c88641cc94778e605609adb9a9c

214

py

Python

Leetcode/0190. Reverse Bits.py

luckyrabbit85/Python

ed134fd70b4a7b84b183b87b85ad5190f54c9526

[ "MIT" ]

1

2021-07-15T18:40:26.000Z

Leetcode/0190. Reverse Bits.py

luckyrabbit85/Python

ed134fd70b4a7b84b183b87b85ad5190f54c9526

[ "MIT" ]

null

Leetcode/0190. Reverse Bits.py

luckyrabbit85/Python

ed134fd70b4a7b84b183b87b85ad5190f54c9526

[ "MIT" ]

null

class Solution: def reverseBits(self, n): result = 0 for i in range(32): result <<= 1 if n & 1 > 0: result += 1 n >>= 1 return result

21.4

29

0.397196

25

214

3.4

0.64

0.164706

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0.07619

0.509346

214

9

30

23.777778

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0.111111

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0

0.333333

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1

0

null

0

1

0

1

0

null

0

3

b8a89c3d89dd76973c278244a6b74336b3ca2365

114

py

Python

iniciante/1156.py

samucosta13/URI-Online-Judge

d3dc0c4c3ccf260e02cb3705a11226cbddffb90b

[ "MIT" ]

2

2021-05-28T18:52:53.000Z

2021-06-04T19:30:39.000Z

iniciante/1156.py

samucosta13/URI-Online-Judge

d3dc0c4c3ccf260e02cb3705a11226cbddffb90b

[ "MIT" ]

null

iniciante/1156.py

samucosta13/URI-Online-Judge

d3dc0c4c3ccf260e02cb3705a11226cbddffb90b

[ "MIT" ]

null

i = 1 n = 1 S = int(0) while i <= 39: somar = i/n S = S + somar n = n*2 i = i + 2 print('%.2f'%S)

11.4

17

0.394737

25

114

1.8

0.48

0

0.117647

0.403509

114

9

18

12.666667

0.544118

0

0.035088

0

1

0

false

0

0.111111

1

0

1

null

0

1

0

null

0

3

b218a3c421b56d4fd39eadfd560d29222487724f

1,081

py

Python

quarkc/test/generate_docs.py

datawire/quark

df0058a148b077c0aff535eb6ee382605c556273

[ "Apache-2.0" ]

112

2015-10-02T19:51:51.000Z

2022-03-07T06:29:44.000Z

quarkc/test/generate_docs.py

datawire/quark

df0058a148b077c0aff535eb6ee382605c556273

[ "Apache-2.0" ]

181

2015-10-01T20:23:38.000Z

2016-12-07T17:21:26.000Z

quarkc/test/generate_docs.py

datawire/quark

df0058a148b077c0aff535eb6ee382605c556273

[ "Apache-2.0" ]

31

2015-10-13T22:10:00.000Z

2020-08-03T02:50:12.000Z

# Copyright 2016 datawire. 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. """ Helpers for generating documentation using Jinja2 templates. """ import jinja2 def filter_commandline(value, prompt="$ "): return prompt + value.command def filter_output(value): return "\n ".join(value.output.split("\n")) jinja2_filters = dict(commandline=filter_commandline, output=filter_output) def make_env(): env = jinja2.Environment() for key, value in jinja2_filters.items(): env.filters[key] = value return env

27.717949

74

0.721554

149

1,081

5.187919

0.604027

0.07762

0.033635

0.041397

0

0.014874

0.191489

1,081

38

75

28.447368

0.869565

0.581869

0

0.023148

0

1

0.25

false

0

0.083333

0.166667

0.583333

0

null

0

null

0

1

0

1

0

3

b21acb0eec9f0dcaa682203204a56f7c23c5c5bb

69

py

Python

SAMPLE_config.py

declasm/binance_harvester

3aa8237a4d12eaaec6966057a191a6228a285295

[ "Apache-2.0" ]

64

2022-01-17T17:45:37.000Z

2022-03-11T22:56:08.000Z

SAMPLE_config.py

declasm/binance_harvester

3aa8237a4d12eaaec6966057a191a6228a285295

[ "Apache-2.0" ]

1

2022-01-23T13:03:34.000Z

2022-01-24T16:21:39.000Z

SAMPLE_config.py

declasm/binance_harvester

3aa8237a4d12eaaec6966057a191a6228a285295

[ "Apache-2.0" ]

11

2022-01-17T17:39:26.000Z

2022-03-23T15:49:03.000Z

API_KEY = 'YOUR API KEY HERE' API_SECRET = 'YOUR API SECRET KEY HERE'

34.5

39

0.73913

13

69

3.769231

0.384615

0.244898

0

0.173913

69

2

39

34.5

0.859649

0

0.585714

0

1

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1

0

null

1

0

1

0

null

0

3

b22792c88ca23359aeb0281fb1c7e65d7afc13ef

9,630

py

Python

halla/tests/basic_tests_stats.py

bmpbos/halla

d51315a2905e282a250a6c7f5c6c7a7c4e180b6d

[ "MIT" ]

null

halla/tests/basic_tests_stats.py

bmpbos/halla

d51315a2905e282a250a6c7f5c6c7a7c4e180b6d

[ "MIT" ]

1

2022-03-20T12:02:41.000Z

halla/tests/basic_tests_stats.py

bmpbos/halla

d51315a2905e282a250a6c7f5c6c7a7c4e180b6d

[ "MIT" ]

null

import sys import unittest from halla import stats try: from numpy import array except ImportError: sys.exit("Please install numpy") class TestHAllAStatsFunctions(unittest.TestCase): """ Test the functions found in halla.stats """ def test_discretize_tenths(self): """ Test the discretize function on four values of tenths """ expected_result=[0, 0, 1, 1] result=stats.discretize([0.1, 0.2, 0.3, 0.4]) self.assertEqual(expected_result,result) def test_discretize_squares(self): """ Test the discretize function on four values of squares """ expected_result=[0, 0, 1, 1] result=stats.discretize([0.01, 0.04, 0.09, 0.16]) self.assertEqual(expected_result,result) def test_discretize_negatives(self): """ Test the discretize function on all negative values """ expected_result=[1, 1, 0, 0] result=stats.discretize([-0.1, -0.2, -0.3, -0.4]) self.assertEqual(expected_result,result) def test_discretize_quarters(self): """ Test the discretize function on four values of quarters """ expected_result=[0, 0, 1, 1] result=stats.discretize([0.25, 0.5, 0.75, 1.00]) self.assertEqual(expected_result,result) def test_discretize_eights(self): """ Test the discretize function on four values of eigths """ expected_result=[0, 0, 1, 1] result=stats.discretize([0.015625, 0.125, 0.421875, 1]) self.assertEqual(expected_result,result) def test_discretize_zero(self): """ Test the discretize function on an array containing a single zero """ expected_result=[0] result=stats.discretize([0]) self.assertEqual(expected_result,result) def test_discretize_one(self): """ Test the discretize function on an array of [0,1] """ expected_result=[0,0] result=stats.discretize([0,1]) self.assertEqual(expected_result,result) def test_discretize_two_bins_two_values(self): """ Test the discretize function two values with two bins """ expected_result=[0,1] result=stats.discretize([0, 1], 2) self.assertEqual(expected_result,result) def test_discretize_two_bins_two_values_reverse(self): """ Test the discretize function on two values (revered order) with two bins """ expected_result=[1,0] result=stats.discretize([1, 0], 2) self.assertEqual(expected_result,result) def test_discretize_three_bins_three_values(self): """ Test the discretize function on three values with three bins """ expected_result=[1, 0, 2] result=stats.discretize([0.2, 0.1, 0.3], 3) self.assertEqual(expected_result,result) def test_discretize_one_bin_three_values(self): """ Test the discretize function on three values with one bin """ expected_result=[0, 0, 0] result=stats.discretize([0.2, 0.1, 0.3], 1) self.assertEqual(expected_result,result) def test_discretize_two_bins_three_values(self): """ Test the discretize function on three values with two bins """ expected_result=[0, 0, 1] result=stats.discretize([0.2, 0.1, 0.3], 2) self.assertEqual(expected_result,result) def test_discretize_two_bins_four_values_all_floats(self): """ Test the discretize function on four values (all floats) with two bins """ expected_result=[1, 0, 0, 1] result=stats.discretize([0.4, 0.2, 0.1, 0.3], 2) self.assertEqual(expected_result,result) def test_discretize_two_bins_four_values_one_int(self): """ Test the discretize function on four values (one int) with two bins """ expected_result=[1, 0, 0, 1] result=stats.discretize([4, 0.2, 0.1, 0.3], 2) self.assertEqual(expected_result,result) def test_discretize_five_values(self): """ Test the discretize function on five values with default bins """ expected_result=[1, 0, 0, 0, 1] result=stats.discretize([0.4, 0.2, 0.1, 0.3, 0.5]) self.assertEqual(expected_result,result) def test_discretize_three_bins_five_values(self): """ Test the discretize function on five values with three bins """ expected_result=[1, 0, 0, 1, 2] result=stats.discretize([0.4, 0.2, 0.1, 0.3, 0.5], 3) self.assertEqual(expected_result,result) def test_discretize_six_values(self): """ Test the discretize function on six values with default bins """ expected_result=[1, 0, 1, 0, 0, 1] result=stats.discretize([0.4, 0.2, 0.6, 0.1, 0.3, 0.5]) self.assertEqual(expected_result,result) def test_discretize_three_bins_six_values(self): """ Test the discretize function six values with three bins """ expected_result=[1, 0, 2, 0, 1, 2] result=stats.discretize([0.4, 0.2, 0.6, 0.1, 0.3, 0.5], 3) self.assertEqual(expected_result,result) def test_discretize_zero_bins_six_values(self): """ Test the discretize function on six values with zero bins """ expected_result=[3, 1, 5, 0, 2, 4] result=stats.discretize([0.4, 0.2, 0.6, 0.1, 0.3, 0.5], 0) self.assertEqual(expected_result,result) def test_discretize_six_bins_six_values(self): """ Test the discretize function on six values with six bins """ expected_result=[3, 1, 5, 0, 2, 4] result=stats.discretize([0.4, 0.2, 0.6, 0.1, 0.3, 0.5], 6) self.assertEqual(expected_result,result) def test_discretize_sixty_bins_six_values(self): """ Test the discretize function on six values with sixty bins """ expected_result=[3, 1, 5, 0, 2, 4] result=stats.discretize([0.4, 0.2, 0.6, 0.1, 0.3, 0.5], 60) self.assertEqual(expected_result,result) def test_discretize_two_bins_eight_values(self): """ Test the discretize function on eight values with two bins """ expected_result=[0, 0, 0, 0, 0, 0, 1, 1] result=stats.discretize([0, 0, 0, 0, 0, 0, 1, 2], 2) self.assertEqual(expected_result,result) def test_discretize_three_bins_ten_values(self): """ Test the discretize function on ten values with three bins """ expected_result=[0, 0, 0, 0, 1, 1, 1, 1, 1, 2] result=stats.discretize([0, 0, 0, 0, 1, 2, 2, 2, 2, 3], 3) self.assertEqual(expected_result,result) def test_discretize_nine_values(self): """ Test the discretize function on nine values which are mostly zero """ expected_result=[1, 0, 0, 0, 0, 0, 0, 0, 0] result=stats.discretize([0.1, 0, 0, 0, 0, 0, 0, 0, 0]) self.assertEqual(expected_result,result) def test_discretize_fifty_one_values(self): """ Test the discretize function on a large set of values """ expected_result=[3, 6, 6, 5, 5, 0, 2, 2, 3, 5, 2, 4, 4, 2, 3, 5, 0, 4, 0, 6, 0, 1, 6, 1, 5, 3, 0, 3, 2, 1, 3, 0, 6, 3, 2, 0, 6, 5, 1, 3, 6, 4, 1, 1, 4, 5, 0, 4, 2, 4, 1] input_values=[0.992299, 1, 1, 0.999696, 0.999605, 0.663081, 0.978293, 0.987621, 0.997237, 0.999915, 0.984792, 0.998338, 0.999207, 0.98051, 0.997984, 0.999219, 0.579824, 0.998983, 0.720498, 1, 0.803619, 0.970992, 1, 0.952881, 0.999866, 0.997153, 0.014053, 0.998049, 0.977727, 0.971233, 0.995309, 0.0010376, 1, 0.989373, 0.989161, 0.91637, 1, 0.99977, 0.960816, 0.998025, 1, 0.998852, 0.960849, 0.957963, 0.998733, 0.999426, 0.876182, 0.998509, 0.988527, 0.998265, 0.943673] result=stats.discretize(input_values) self.assertEqual(expected_result,result) def test_discretize_array_skip_one(self): """ Test the discretize function with a numpy array and one skip """ expected_result=array([ [ 1., 1., 0., 0.], [ 1., 1., 0., 0.], [ 0., 0., 1., 1.], [ 0., 0., 1., 1.]]) y = array([[-0.1,-0.2,-0.3,-0.4],[1,1,0,0],[0.25,0.5,0.75,1.0], [0.015625,0.125,0.421875,1.0]]) result=stats.discretize(y, aiSkip = [1]) self.assertEqual(expected_result.all(),result.all()) def test_discretize_array_skip_two(self): """ Test the discretize function with a numpy array and two skips """ expected_result=array([ [ 0., 0., 1., 1.], [ 1., 1., 1., 0.], [ 0., 0., 1., 1.], [ 0., 0., 0., 1.]]) x = array([[0.1,0.2,0.3,0.4],[1,1,1,0],[0.01,0.04,0.09,0.16],[0,0,0,1]]) result=stats.discretize(x, aiSkip = [1,3]) self.assertEqual(expected_result.all(),result.all())

34.028269

81

0.558775

1,327

9,630

3.929917

0.104748

0.023394

0.018408

0.108725

0.804794

0.781783

0.752445

0.659252

0.541898

0.435666

0

0.121777

0.315265

9,630

282

82

34.148936

0.669093

0.170717

0

0.309353

0

0.002721

0

0.194245

1

0.194245

false

0

0.035971

0

0.23741

0

null

0

1

0

null

0

3

b248bfb3b8553a3a84656d5a830ee64841a0dac1

336

py

Python

MathematicalChallenges/8_AmstrongNumbers/task8.py

kamil2789/TasksCollection

ed4f84b431b42a4649a7ac042c07fe7e27a71c40

[ "MIT" ]

1

2021-07-12T17:14:53.000Z

MathematicalChallenges/8_AmstrongNumbers/task8.py

kamil2789/TasksCollection

ed4f84b431b42a4649a7ac042c07fe7e27a71c40

[ "MIT" ]

null

MathematicalChallenges/8_AmstrongNumbers/task8.py

kamil2789/TasksCollection

ed4f84b431b42a4649a7ac042c07fe7e27a71c40

[ "MIT" ]

null

def calculate_amstrong_numbers_3_digits(): result = [] for item in range(100, 1000): sum = 0 for number in str(item): sum += int(number) ** 3 if sum == item: result.append(item) return result print("3-digit Amstrong Numbers:") print(calculate_amstrong_numbers_3_digits())

21

44

0.604167

43

336

4.534884

0.534884

0.230769

0.246154

0.25641

0.317949

0

0.050209

0.28869

336

15

45

22.4

0.76569

0

0.074627

0

1

0.090909

false

0

0.181818

0

null

1

0

null

0

3

b259e7e90f024b23f55b2459153b276bfa49b8fa

1,101

py

Python

test/test_tabs.py

volfpeter/markyp-bootstrap4

1af5a1f9dc861a14323706ace28882ef6555739a

[ "MIT" ]

21

2019-07-16T15:03:43.000Z

2021-11-16T10:51:58.000Z

test/test_tabs.py

volfpeter/markyp-bootstrap4

1af5a1f9dc861a14323706ace28882ef6555739a

[ "MIT" ]

null

test/test_tabs.py

volfpeter/markyp-bootstrap4

1af5a1f9dc861a14323706ace28882ef6555739a

[ "MIT" ]

null

from markyp_bootstrap4.tabs import * def test_tab_content(): assert tab_content().markup ==\ '<div class="tab-content"></div>' assert tab_content("First", "Second").markup ==\ '<div class="tab-content">\nFirst\nSecond\n</div>' assert tab_content("First", "Second", class_="my-tc", attr=42).markup ==\ '<div attr="42" class="tab-content my-tc">\nFirst\nSecond\n</div>' def test_tab_pane(): assert tab_pane().markup ==\ '<div role="tabpanel" class="tab-pane fade"></div>' assert tab_pane("First", "Second").markup ==\ '<div role="tabpanel" class="tab-pane fade">\nFirst\nSecond\n</div>' assert tab_pane("First", "Second", active=True).markup ==\ '<div role="tabpanel" class="tab-pane fade show active">\nFirst\nSecond\n</div>' assert tab_pane("First", "Second", fade=False).markup ==\ '<div role="tabpanel" class="tab-pane">\nFirst\nSecond\n</div>' assert tab_pane("First", "Second", class_="my-tp", attr=42).markup ==\ '<div role="tabpanel" attr="42" class="tab-pane fade my-tp">\nFirst\nSecond\n</div>'

50.045455

92

0.631244

153

1,101

4.444444

0.202614

0.113235

0.105882

0.15

0.611765

0.541176

0.392647

0.344118

0.180882

0

0.009698

0.15713

1,101

21

93

52.428571

0.72306

0

0.210526

0.504087

0.207993

0

0.421053

1

0.105263

true

0

0.052632

0

0.157895

0

null

0

1

0

null

0

1

0

1

0

3

b28518ddb45134ab65646282ab0f4d3a2914a8de

94

py

Python

src/apps/climsoft/schemas/__init__.py

opencdms/opencdms-api

f1ed6e1d883025a8658746fe457e0c975718c7be

[ "MIT" ]

3

2020-12-01T09:25:18.000Z

2022-02-14T23:57:34.000Z

src/common_schemas.py

opencdms/opencdms-api

f1ed6e1d883025a8658746fe457e0c975718c7be

[ "MIT" ]

11

2021-12-05T10:09:00.000Z

2022-02-17T08:11:22.000Z

src/apps/climsoft/schemas/__init__.py

opencdms/opencdms-api

f1ed6e1d883025a8658746fe457e0c975718c7be

[ "MIT" ]

2

2021-03-10T19:03:05.000Z

2021-12-11T08:36:04.000Z

from pydantic import BaseModel class Response(BaseModel): message: str status: str

11.75

30

0.723404

11

94

6.181818

0.818182

0

0.223404

94

7

31

13.428571

0.931507

0

1

0

true

0

0.25

0

1

0

1

0

null

0

1

0

null

0

1

0

3

b2969f4b0a3f60b6fbbaaddfe8aab6f5a266a908

65

py

Python

string_calculator/__init__.py

suradet/string-calculator-kata

6b87fcfad609f5c7c8e35d5392f7faceb650a5ef

[ "Apache-2.0" ]

null

string_calculator/__init__.py

suradet/string-calculator-kata

6b87fcfad609f5c7c8e35d5392f7faceb650a5ef

[ "Apache-2.0" ]

null

string_calculator/__init__.py

suradet/string-calculator-kata

6b87fcfad609f5c7c8e35d5392f7faceb650a5ef

[ "Apache-2.0" ]

null

"""The string_calculator package.""" NAME = "string_calculator"

16.25

36

0.738462

7

65

6.571429

0.714286

0.695652

0

0.107692

65

3

37

21.666667

0.793103

0.461538

0

0.586207

0

1

0

false

0

1

0

null

1

0

1

0

null

0

3

b2b0f179656973feff8b88ab10ae2e6225127d32

125

py

Python

logstash/datadog_checks/logstash/__init__.py

zparnold/integrations-extras

3558ec40cdc07230bf85cd5b77874110b33abf99

[ "BSD-3-Clause" ]

null

logstash/datadog_checks/logstash/__init__.py

zparnold/integrations-extras

3558ec40cdc07230bf85cd5b77874110b33abf99

[ "BSD-3-Clause" ]

null

logstash/datadog_checks/logstash/__init__.py

zparnold/integrations-extras

3558ec40cdc07230bf85cd5b77874110b33abf99

[ "BSD-3-Clause" ]

null

from .__about__ import __version__ from .logstash import LogstashCheck __all__ = [ '__version__', 'LogstashCheck' ]

15.625

35

0.736

11

125

6.909091

0.636364

0

0.184

125

7

36

17.857143

0.745098

0

0.192

0

1

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false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

3

a23462d942a5277ec1b78d08914770952fb018e4

1,051

py

Python

source/parse/xwset.py

ucabops/robbie

f74aefbdb9069d62188d4bb820bf91fa50f73b8c

[ "OML" ]

null

source/parse/xwset.py

ucabops/robbie

f74aefbdb9069d62188d4bb820bf91fa50f73b8c

[ "OML" ]

null

source/parse/xwset.py

ucabops/robbie

f74aefbdb9069d62188d4bb820bf91fa50f73b8c

[ "OML" ]

null

from xwentry import CrosswordEntry from xwpuzzle import Crossword class CrosswordSet: def __init__(self, crosswords): self.crosswords = crosswords self._entries = None @classmethod def from_dict(cls, data): return CrosswordSet({xw_id: Crossword(xw_dict) for xw_id, xw_dict in data.items()}) def __len__(self): return len(self.crosswords) def __iter__(self): return iter(self.crosswords.items()) def __getitem__(self, id): """The crossword with the given id. e.g. 'xw = xwset[12000]' for Guardian quick crossword no. 12000 """ return self.crosswords[id] @property def crosswords_as_list(self): """All the crosswords as one long list.""" return self.crosswords.values() @property def entries(self): """All the crossword entries as one long list.""" if self._entries is None: self._entries = [xw.entries for xw in self.crosswords] return self._entries

26.948718

71

0.623216

127

1,051

4.944882

0.362205

0.156051

0.063694

0.041401

0

0.013298

0.284491

1,051

38

72

27.657895

0.821809

0.169363

0

0.083333

0

1

0.291667

false

0

0.083333

0.125

0.666667

0

null

0

null

0

1

0

1

0

3

a23e3a1fa898b65875a730d72038c5215e5e420f

314

py

Python

nlp/__init__.py

kirollosHossam/MachineLearningTask

3780513af04cf7bb97432436b4714c32d1c271e6

[ "MIT" ]

null

nlp/__init__.py

kirollosHossam/MachineLearningTask

3780513af04cf7bb97432436b4714c32d1c271e6

[ "MIT" ]

null

nlp/__init__.py

kirollosHossam/MachineLearningTask

3780513af04cf7bb97432436b4714c32d1c271e6

[ "MIT" ]

null

'''To tell Python that a particular directory is a package, \ we create a file named __init__.py inside it and then it is considered as a package \ and we may create other modules and sub-packages within it. This __init__.py file can be left \ blank or can be coded with the initialization code for the package.'''

78.5

95

0.773885

57

314

4.122807

0.684211

0.068085

0

0.178344

314

4

96

78.5

0.910853

0.961783

0

null

0

null

0

null

0

null

1

null

true

0

null

0

null

0

1

0

1

0

null

0

1

0

3

a25b4ae46be92fe5cd32e9a6df9839b7efea2f90

1,497

py

Python

tests/js2py_test.py

eniraa/python-aternos

0407e6e6e8c932265c32ef779b004bd6a6ed5af1

[ "Apache-2.0" ]

null

tests/js2py_test.py

eniraa/python-aternos

0407e6e6e8c932265c32ef779b004bd6a6ed5af1

[ "Apache-2.0" ]

null

tests/js2py_test.py

eniraa/python-aternos

0407e6e6e8c932265c32ef779b004bd6a6ed5af1

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 import re import base64 import js2py # Use tests from a file tests = [] with open('../token.txt', 'rt') as f: lines = re.split(r'[\r\n]', f.read()) del lines[len(lines)-1] # Remove empty string tests = lines arrowre = re.compile(r'(\w+?|$\w+?(?:,\s*\w+?)*$|)\s*=>\s*({\s*[\s\S]+\s*}|[^\r\n]+?(?:;|$))') def to_ecma5_function(f): # return "(function() { " + f[f.index("{")+1 : f.index("}")] + "})();" fnstart = f.find('{')+1 fnend = f.rfind('}') f = arrow_conv(f[fnstart:fnend]) return f def atob(s): return base64.standard_b64decode(str(s)).decode('utf-8') def arrow_conv(f): m = arrowre.search(f) while m != None: print(f) params = m.group(1).strip('()') body = m.group(2) if body.startswith('{')\ and body.endswith('}'): body = body.strip('{}') else: body = f'return {body}' f = arrowre.sub(f'function({params}){{{body}}}', f) m = arrowre.search(f) print(f) #print('function(' + m.group(1).strip("()") + '){return ' + m.group(2) + ';}') return f ctx = js2py.EvalJs({'atob': atob}) for f in tests: c = to_ecma5_function(f) ctx.execute(c) print(ctx.window['AJAX_TOKEN']) # Expected output: # 2rKOA1IFdBcHhEM616cb # 2rKOA1IFdBcHhEM616cb # 2rKOA1IFdBcHhEM616cb # 2rKOA1IFdBcHhEM616cb # 2rKOA1IFdBcHhEM616cb # 2rKOA1IFdBcHhEM616cb # 2rKOA1IFdBcHhEM616cb # 2rKOA1IFdBcHhEM616cb # 2rKOA1IFdBcHhEM616cb # 2iXh5W5uEYq5fWJIazQ6 # CuUcmZ27Fb8bVBNw12Vj # YPPe8Ph7vzYaZ9PF9oQP # ... # (Note: The last four # tokens are different)

23.030769

100

0.632599

202

1,497

4.648515

0.450495

0.340788

0.447284

0.511182

0.232162

0.191693

0

0.060795

0.142953

1,497

64

101

23.390625

0.671083

0.351369

0

0.166667

0

0.027778

0.171579

0.108421

0

1

0.083333

false

0

0.083333

0.027778

0.25

0.083333

0

null

1

0

null

0

3

a25c8e4bf295efd799ee8e81460c6139425b5ec0

152

py

Python

Modulo-01/ex024/ex024.py

Matheus-Henrique-Burey/Curso-de-Python

448aebaab96527affa1e45897a662bb0407c11c6

[ "MIT" ]

null

Modulo-01/ex024/ex024.py

Matheus-Henrique-Burey/Curso-de-Python

448aebaab96527affa1e45897a662bb0407c11c6

[ "MIT" ]

null

Modulo-01/ex024/ex024.py

Matheus-Henrique-Burey/Curso-de-Python

448aebaab96527affa1e45897a662bb0407c11c6

[ "MIT" ]

null

nome = str(input('Dogite o nome de sua cidade: ')).lower().strip() city = nome.split() print('Sua cidade começa com santos?') print("santo" in city[0])

30.4

66

0.677632

25

152

4.12

0.76

0.174757

0

0.007576

0.131579

152

4

67

38

0.772727

0

0.414474

0

1

0

false

0

0.5

1

0

null

0

1

0

null

0

1

0

3

a272af6535c72273b6bbeb242f6b00fef1282fd3

21

py

Python

apps/games/__init__.py

LouisPi/PiPortableRecorder

430a4b6e1e869cbd68fd89bbf97261710fd7db6b

[ "Apache-2.0", "MIT" ]

51

2017-12-03T21:59:13.000Z

2021-01-02T17:13:34.000Z

apps/games/__init__.py

LouisPi/PiPortableRecorder

430a4b6e1e869cbd68fd89bbf97261710fd7db6b

[ "Apache-2.0", "MIT" ]

153

2017-10-27T19:59:46.000Z

2020-01-14T23:58:57.000Z

apps/games/__init__.py

LouisPi/PiPortableRecorder

430a4b6e1e869cbd68fd89bbf97261710fd7db6b

[ "Apache-2.0", "MIT" ]

26

2017-11-16T11:10:56.000Z

2022-03-29T18:44:48.000Z

_menu_name = "Games"

10.5

20

0.714286

3

21

4.333333

1

0

0.142857

21

1

21

0.722222

0

0.238095

0

1

0

false

0

1

0

null

0

1

0

null

0

3

a280558f96d9619cfa49d379ac06204560efe06f

1,113

py

Python

template_formatter/AppContext.py

Koldar/template-formatter

bb55b0ccbe1f5a6c8f0ba187765bbd41836f7c54

[ "Apache-2.0" ]

1

2021-03-08T01:31:19.000Z

template_formatter/AppContext.py

Koldar/template-formatter

bb55b0ccbe1f5a6c8f0ba187765bbd41836f7c54

[ "Apache-2.0" ]

null

template_formatter/AppContext.py

Koldar/template-formatter

bb55b0ccbe1f5a6c8f0ba187765bbd41836f7c54

[ "Apache-2.0" ]

1

2021-03-07T15:39:08.000Z

from typing import Optional from template_formatter.Jinja2Model import Jinja2Model class AppContext: def __init__(self): self.model = Jinja2Model() self.input_file: Optional[str] = None self.input_directory: Optional[str] = None self.output_directory: Optional[str] = None self.trailing_string_template_file: Optional[str] = None self.output_file_format: Optional[str] = None self.log_level: Optional[str] = None self.block_start_string: Optional[str] = None self.block_end_string: Optional[str] = None self.comment_start_string: Optional[str] = None self.comment_end_string: Optional[str] = None self.expression_start_string: Optional[str] = None self.expression_end_string: Optional[str] = None self.line_statement_prefix: Optional[str] = None self.input_file_encoding: Optional[str] = None self.output_file_encoding: Optional[str] = None self.write_on_stdout: bool = False self.template_string: Optional[str] = None self.format: Optional[str] = None

39.75

64

0.690925

137

1,113

5.357664

0.277372

0.254768

0.347411

0.414169

0.649864

0.415531

0

0.003464

0.221923

1,113

27

65

41.222222

0.844111

0

1

0.043478

false

0

0.086957

0

0.173913

0

null

1

0

null

0

3

a28beeac7a0a0d5624b95294751aa487d171a7a2

2,403

py

Python

worker/tasks/cv_task.py

Dev-Jahn/cms

84ea115bdb865daff83d069502f6f0dd105fc4f0

[ "RSA-MD" ]

null

worker/tasks/cv_task.py

Dev-Jahn/cms

84ea115bdb865daff83d069502f6f0dd105fc4f0

[ "RSA-MD" ]

9

2021-01-05T07:48:28.000Z

2021-05-14T06:38:27.000Z

worker/tasks/cv_task.py

Dev-Jahn/cms

84ea115bdb865daff83d069502f6f0dd105fc4f0

[ "RSA-MD" ]

4

2021-01-05T06:46:09.000Z

2021-05-06T01:44:28.000Z

import os import traceback from PIL import Image from celery.utils.log import get_task_logger from celery.exceptions import TaskError import numpy as np import cv2 from app import app from cv import blur, color, detection, normalize, segmentation, threshold logger = get_task_logger(__name__) # TODO # task chaining 구현 # image caching 방법 고안 @app.task(name='cv_task.cv_color') def cv_color(path, **kwargs) -> np.ndarray: try: src = np.array(Image.open('/data/' + path)) output_path = '/data/cv/'+path if not os.path.exists('/data/cv'): os.mkdir('/data/cv') Image.fromarray(color.apply(src, **kwargs)).save(output_path) return output_path except Exception as e: logger.error(traceback.format_exc()) raise TaskError(e) @app.task(name='cv_task.cv_blur') def cv_blur(path, **kwargs) -> np.ndarray: try: src = np.array(Image.open('/data/' + path)) output_path = '/data/cv/'+path if not os.path.exists('/data/cv'): os.mkdir('/data/cv') Image.fromarray(blur.apply(src, **kwargs)).save(output_path) return output_path except Exception as e: logger.error(traceback.format_exc()) raise TaskError(e) @app.task(name='cv_task.cv_normalize') def cv_normalize(path, **kwargs) -> np.ndarray: try: src = np.array(Image.open('/data/' + path)) output_path = '/data/cv/'+path if not os.path.exists('/data/cv'): os.mkdir('/data/cv') Image.fromarray(normalize.apply(src, **kwargs)).save(output_path) return output_path except Exception as e: logger.error(traceback.format_exc()) raise TaskError(e) @app.task(name='cv_task.cv_threshold') def cv_threshold(path, **kwargs) -> np.ndarray: try: src = np.array(Image.open('/data/' + path)) output_path = '/data/cv/'+path if not os.path.exists('/data/cv'): os.mkdir('/data/cv') Image.fromarray(threshold.apply(src, **kwargs)).save(output_path) return output_path except Exception as e: logger.error(traceback.format_exc()) raise TaskError(e) @app.task(name='cv_task.cv_detection') def cv_detection(src: np.ndarray, **kwargs) -> np.ndarray: pass @app.task(name='cv_task.cv_segmentation') def cv_segmentation(src: np.ndarray, **kwargs) -> np.ndarray: pass

28.270588

73

0.641282

335

2,403

4.474627

0.176119

0.080053

0.044029

0.052035

0.717812

0.692462

0.651101

0

0.000529

0.213899

2,403

84

74

28.607143

0.793012

0.017062

0

0.59375

0

0.100933

0.009754

0

0.011905

0

1

0.09375

false

0.03125

0.140625

0

0.296875

0

null

0

1

0

1

0

null

0

1

0

3

a29775b58402c1f6096066a15d4a7c30f11ab140

1,388

py

Python

q2_shogun/_formats.py

ChrisKeefe/q2-shogun

8d5547ce43d4915b2474fa2f7721313918af36c8

[ "BSD-3-Clause" ]

null

q2_shogun/_formats.py

ChrisKeefe/q2-shogun

8d5547ce43d4915b2474fa2f7721313918af36c8

[ "BSD-3-Clause" ]

null

q2_shogun/_formats.py

ChrisKeefe/q2-shogun

8d5547ce43d4915b2474fa2f7721313918af36c8

[ "BSD-3-Clause" ]

null

# ---------------------------------------------------------------------------- # Copyright (c) 2018, QIIME 2 development team. # # Distributed under the terms of the Modified BSD License. # # The full license is in the file LICENSE, distributed with this software. # ---------------------------------------------------------------------------- from qiime2.plugin import model class Bowtie2IndexFileFormat(model.BinaryFileFormat): def _validate_(self, level): # It's not clear if there is any way to tell if a Bowtie2 index is # correct or not. # bowtie2 does have an inspect method — this inspects at the dir level # not on the file level. # may also want to validate that all files have the same basename pass class Bowtie2IndexDirFmt(model.DirectoryFormat): idx1 = model.File('.+(?<!\.rev)\.1\.bt2', format=Bowtie2IndexFileFormat) idx2 = model.File('.+(?<!\.rev)\.2\.bt2', format=Bowtie2IndexFileFormat) ref3 = model.File('.+\.3\.bt2', format=Bowtie2IndexFileFormat) ref4 = model.File('.+\.4\.bt2', format=Bowtie2IndexFileFormat) rev1 = model.File('.+\.rev\.1\.bt2', format=Bowtie2IndexFileFormat) rev2 = model.File('.+\.rev\.2\.bt2', format=Bowtie2IndexFileFormat) def get_name(self): filename = str(self.idx1.path_maker().relative_to(self.path)) return filename.rsplit('.1.bt2')[0]

42.060606

78

0.605187

161

1,388

5.192547

0.571429

0.064593

0.222488

0.0311

0.210526

0

0.032958

0.169308

1,388

32

79

43.375

0.69124

0.407781

0

0.118665

0

1

0.142857

false

0.071429

0

0.857143

0

null

0

1

0

null

0

1

0

1

0

3

a29a77680cc4363c3143328281e289f220011909

1,225

py

Python

tests/unit/wallet/test_dewies.py

vyaspranjal33/lbry

e03e41ad3105ccc0d8d8891b0e9fa63f9bbfce34

[ "MIT" ]

null

tests/unit/wallet/test_dewies.py

vyaspranjal33/lbry

e03e41ad3105ccc0d8d8891b0e9fa63f9bbfce34

[ "MIT" ]

null

tests/unit/wallet/test_dewies.py

vyaspranjal33/lbry

e03e41ad3105ccc0d8d8891b0e9fa63f9bbfce34

[ "MIT" ]

null

import unittest from lbrynet.wallet.dewies import lbc_to_dewies as l2d, dewies_to_lbc as d2l class TestDeweyConversion(unittest.TestCase): def test_good_output(self): self.assertEqual(d2l(1), "0.00000001") self.assertEqual(d2l(10**7), "0.1") self.assertEqual(d2l(2*10**8), "2.0") self.assertEqual(d2l(2*10**17), "2000000000.0") def test_good_input(self): self.assertEqual(l2d("0.00000001"), 1) self.assertEqual(l2d("0.1"), 10**7) self.assertEqual(l2d("1.0"), 10**8) self.assertEqual(l2d("2.00000000"), 2*10**8) self.assertEqual(l2d("2000000000.0"), 2*10**17) def test_bad_input(self): with self.assertRaises(ValueError): l2d("1") with self.assertRaises(ValueError): l2d("-1.0") with self.assertRaises(ValueError): l2d("10000000000.0") with self.assertRaises(ValueError): l2d("1.000000000") with self.assertRaises(ValueError): l2d("-0") with self.assertRaises(ValueError): l2d("1") with self.assertRaises(ValueError): l2d(".1") with self.assertRaises(ValueError): l2d("1e-7")

32.236842

76

0.594286

153

1,225

4.69281

0.248366

0.188022

0.222841

0.334262

0.495822

0.332869

0.285515

0.235376

0

0.147702

0.253878

1,225

37

77

33.108108

0.637856

0

0.322581

0

0.084898

0

0.548387

1

0.096774

false

0

0.064516

0

0.193548

0

null

0

1

0

null

0

1

0

3

a2da81a22ce348712a67db4cad0b4cdb6fc85be7

111

py

Python

back-end/tests/__init__.py

JAYqq/MonGo

e33c9f62c2cf494af2b2d33408853294f3aed168

[ "MIT" ]

1

2019-03-26T04:44:59.000Z

back-end/tests/__init__.py

JAYqq/MonGo

e33c9f62c2cf494af2b2d33408853294f3aed168

[ "MIT" ]

5

2020-02-12T13:32:08.000Z

2021-06-02T00:27:16.000Z

back-end/tests/__init__.py

JAYqq/MonGo

e33c9f62c2cf494af2b2d33408853294f3aed168

[ "MIT" ]

null

from config import Config class TestConfig(Config): TESTING=True SQLALCHEMY_DATABASE_URI = 'sqlite://'

22.2

41

0.747748

13

111

6.230769

0.846154

0

0.162162

111

4

42

27.75

0.870968

0

0.081081

0

1

0

false

0

0.25

0

1

0

1

0

null

0

1

0

null

0

1

0

3

a2f57d7d746f1e55e4a6459c04ec7e10b1601869

103

py

Python

helper2.py

adamghx/cs3240-labdemo

4c722e07f027b3c7a3e06c7532e351b11ec06ce5

[ "MIT" ]

null

helper2.py

adamghx/cs3240-labdemo

4c722e07f027b3c7a3e06c7532e351b11ec06ce5

[ "MIT" ]

null

helper2.py

adamghx/cs3240-labdemo

4c722e07f027b3c7a3e06c7532e351b11ec06ce5

[ "MIT" ]

null

from helper import greeting def main(): greeting("Hi, there!") if __name__ == "__main__": main()

12.875

27

0.660194

13

103

4.615385

0.769231

0

0.184466

103

7

28

14.714286

0.714286

0

0.174757

0

1

0.2

true

0

0.2

0

0.4

0

1

0

null

0

1

0

null

0

1

0

3

a2fe7ff3f3f5a50621997877e7d0619e72a59577

185

py

Python

abc/abc161/abc161b.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

1

2019-08-21T00:49:34.000Z

abc/abc161/abc161b.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

null

abc/abc161/abc161b.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

null

N, M = map(int, input().split()) A = list(map(int, input().split())) threshold = sum(A) / (4 * M) if len([a for a in A if a >= threshold]) >= M: print('Yes') else: print('No')

20.555556

46

0.535135

33

185

3

0.575758

0.121212

0.222222

0.323232

0

0.006897

0.216216

185

8

47

23.125

0.675862

0

0.027027

0

1

0

false

0

0.285714

0

null

0

1

0

1

0

null

0

3

0c12171ce127330c68d4e836d71474508956428b

184

py

Python

currency_converter/converter/models.py

jbhayback/currency-converter

c7e0bf2663ee9d60a97cb9f76269691574fce0a0

[ "MIT" ]

null

currency_converter/converter/models.py

jbhayback/currency-converter

c7e0bf2663ee9d60a97cb9f76269691574fce0a0

[ "MIT" ]

null

currency_converter/converter/models.py

jbhayback/currency-converter

c7e0bf2663ee9d60a97cb9f76269691574fce0a0

[ "MIT" ]

null

from django.db import models # Create your models here. class Currencies(models.Model): id = models.AutoField(primary_key=True) currency_name = models.CharField(max_length=3)

26.285714

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0.766304

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184

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0.846154

0

0.006329

0.141304

184

6

51

30.666667

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0.130435

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1

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1

0

null

0

1

0

null

0

1

0

3

0c33ad46502ed2ff065d77b0d178ca74fc10e9b3

344

py

Python

kicker/SymbolMapper.py

KI-cker/Ki-cker

b48ae75bfeea970940ad657c73d71438531259c6

[ "Apache-2.0" ]

null

kicker/SymbolMapper.py

KI-cker/Ki-cker

b48ae75bfeea970940ad657c73d71438531259c6

[ "Apache-2.0" ]

14

2018-02-21T17:58:33.000Z

2022-03-11T23:16:09.000Z

kicker/SymbolMapper.py

KI-cker/Ki-cker

b48ae75bfeea970940ad657c73d71438531259c6

[ "Apache-2.0" ]

1

2018-02-22T09:28:26.000Z

class SymbolMapper(object): def __init__(self): self.symbolmap = {0: '0', 1: '+', -1: '-'} @staticmethod def normalize(value): return 0 if value == 0 else value / abs(value) def inputs2symbols(self, inputs): return map( lambda value: self.symbolmap[SymbolMapper.normalize(value)], inputs)

26.461538

80

0.604651

39

344

5.230769

0.512821

0.127451

0

0.027451

0.258721

344

12

81

28.666667

0.772549

0

0.008721

0

1

0.333333

false

0

0.222222

0.666667

0

null

0

null

0

1

0

1

0

3

0c3f2039bbc159a5bdba79a6798166e1423783aa

118

py

Python

pokedex/abilities/urls.py

ToniIvars/django-pokedex

37c8b5011658ee23f5df4db8a26db044a4fcb35f

[ "MIT" ]

null

pokedex/abilities/urls.py

ToniIvars/django-pokedex

37c8b5011658ee23f5df4db8a26db044a4fcb35f

[ "MIT" ]

null

pokedex/abilities/urls.py

ToniIvars/django-pokedex

37c8b5011658ee23f5df4db8a26db044a4fcb35f

[ "MIT" ]

null

from django.urls import path from . import views urlpatterns = [ path('<name>', views.ability, name='ability'), ]

19.666667

50

0.686441

15

118

5.4

0.6

0

0.161017

118

6

51

19.666667

0.818182

0

0.109244

0

1

0

false

0

0.4

0

0.4

0

1

0

null

0

1

0

null

0

1

0

3

0c3fbd64e82cc62cfa9a15428e01856cbf3f81f6

96

py

Python

accounting_integrations/fyle/apps.py

fylein/fyle-accounting-integrations

f1b4d01c815235dff9070f3f79313a3234be9b66

[ "MIT" ]

1

2019-05-22T06:17:24.000Z

accounting_integrations/fyle/apps.py

fylein/fyle-accounting-integrations

f1b4d01c815235dff9070f3f79313a3234be9b66

[ "MIT" ]

null

accounting_integrations/fyle/apps.py

fylein/fyle-accounting-integrations

f1b4d01c815235dff9070f3f79313a3234be9b66

[ "MIT" ]

null

from django.apps import AppConfig class FyleImportConfig(AppConfig): name = 'fyle_import'

16

34

0.770833

11

96

6.636364

0.818182

0

0.15625

96

5

35

19.2

0.901235

0

0.114583

0

1

0

false

0

1

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1.666667

0

1

0

null

0

1

0

null

0

1

0

3

0c456d009fbfe2369189c452116cd3c667067330

219

py

Python

desafio017.py

mario-nobre/python-guanabara

2048d833f61c8545ced2163ae1b6b0d844494ada

[ "MIT" ]

null

desafio017.py

mario-nobre/python-guanabara

2048d833f61c8545ced2163ae1b6b0d844494ada

[ "MIT" ]

null

desafio017.py

mario-nobre/python-guanabara

2048d833f61c8545ced2163ae1b6b0d844494ada

[ "MIT" ]

null

co=float(input('digite a medida do cateto oposto ')) ca=float(input('digite a medida do cateto adjacente ')) from math import hypot h=hypot(co,ca) print('a hipotenusa do triângulo retângulo tem medida de {}'.format(h))

36.5

71

0.748858

37

219

4.432432

0.621622

0.121951

0.195122

0.207317

0.378049

0

0.127854

219

5

72

43.8

0.858639

0

0.552511

0

1

0

false

0

0.2

0

0.2

0

null

0

1

0

1

0

null

0

3

0c45b31cc4a618854a9460b669d36749ed40e8e9

5,937

py

Python

storefront/boto/sqs/20070501/message.py

linkedin/indextank-service

880c6295ce8e7a3a55bf9b3777cc35c7680e0d7e

[ "Apache-2.0" ]

26

2015-06-15T11:21:09.000Z

2020-12-27T19:42:14.000Z

storefront/boto/sqs/20070501/message.py

LinkedInAttic/indextank-service

880c6295ce8e7a3a55bf9b3777cc35c7680e0d7e

[ "Apache-2.0" ]

1

2020-09-15T19:34:38.000Z

storefront/boto/sqs/20070501/message.py

LinkedInAttic/indextank-service

880c6295ce8e7a3a55bf9b3777cc35c7680e0d7e

[ "Apache-2.0" ]

12

2015-03-17T17:14:19.000Z

2019-12-21T13:26:23.000Z

# Copyright (c) 2006,2007 Mitch Garnaat http://garnaat.org/ # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the # "Software"), to deal in the Software without restriction, including # without limitation the rights to use, copy, modify, merge, publish, dis- # tribute, sublicense, and/or sell copies of the Software, and to permit # persons to whom the Software is furnished to do so, subject to the fol- # lowing conditions: # # The above copyright notice and this permission notice shall be included # in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS # OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABIL- # ITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT # SHALL THE AUTHOR BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, # WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS # IN THE SOFTWARE. """ Represents an SQS Message """ import base64 import StringIO class RawMessage: """ Base class for SQS messages. RawMessage does not encode the message in any way. Whatever you store in the body of the message is what will be written to SQS and whatever is returned from SQS is stored directly into the body of the message. """ def __init__(self, queue=None, body=''): self.queue = queue self._body = '' self.set_body(body) self.id = None def __len__(self): return len(self._body) def startElement(self, name, attrs, connection): return None def endElement(self, name, value, connection): if name == 'MessageBody': self.set_body(value) elif name == 'MessageId': self.id = value else: setattr(self, name, value) def set_body(self, body): """ Set the body of the message. You should always call this method rather than setting the attribute directly. """ self._body = body def get_body(self): """ Retrieve the body of the message. """ return self._body def get_body_encoded(self): """ This method is really a semi-private method used by the Queue.write method when writing the contents of the message to SQS. The RawMessage class does not encode the message in any way so this just calls get_body(). You probably shouldn't need to call this method in the normal course of events. """ return self.get_body() def change_visibility(self, vtimeout): """ Convenience function to allow you to directly change the invisibility timeout for an individual message that has been read from an SQS queue. This won't affect the default visibility timeout of the queue. """ return self.queue.connection.change_message_visibility(self.queue.id, self.id, vtimeout) class Message(RawMessage): """ The default Message class used for SQS queues. This class automatically encodes/decodes the message body using Base64 encoding to avoid any illegal characters in the message body. See: http://developer.amazonwebservices.com/connect/thread.jspa?messageID=49680%EC%88%90 for details on why this is a good idea. The encode/decode is meant to be transparent to the end-user. """ def endElement(self, name, value, connection): if name == 'MessageBody': # Decode the message body returned from SQS using base64 self.set_body(base64.b64decode(value)) elif name == 'MessageId': self.id = value else: setattr(self, name, value) def get_body_encoded(self): """ Because the Message class encodes the message body in base64 this private method used by queue.write needs to perform the encoding. """ return base64.b64encode(self.get_body()) class MHMessage(Message): """ The MHMessage class provides a message that provides RFC821-like headers like this: HeaderName: HeaderValue The encoding/decoding of this is handled automatically and after the message body has been read, the message instance can be treated like a mapping object, i.e. m['HeaderName'] would return 'HeaderValue'. """ def __init__(self, queue=None, body='', xml_attrs=None): self._dict = {} Message.__init__(self, queue, body) def set_body(self, body): fp = StringIO.StringIO(body) line = fp.readline() while line: delim = line.find(':') key = line[0:delim] value = line[delim+1:].strip() self._dict[key.strip()] = value.strip() line = fp.readline() def get_body(self): s = '' for key,value in self._dict.items(): s = s + '%s: %s\n' % (key, value) return s def __len__(self): return len(self.get_body()) def __getitem__(self, key): if self._dict.has_key(key): return self._dict[key] else: raise KeyError(key) def __setitem__(self, key, value): self._dict[key] = value def keys(self): return self._dict.keys() def values(self): return self._dict.values() def items(self): return self._dict.items() def has_key(self, key): return self._dict.has_key(key) def update(self, d): return self._dict.update(d) def get(self, key, default=None): return self._dict.get(key, default)

32.801105

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783

5,937

4.689655

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0.038126

0.026688

0.013072

0.153595

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0.078431

0.061547

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0.290045

5,937

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0.481051

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1

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0.538462

0

null

0

null

0

1

0

1

0

3

0c6080d52c807df7818b3a0e9f216e74d3c0861b

697

py

Python

17_logging_and_monitoring/start_17_blue_yellow_app_monitoring/blue_yellow_app/data/purchase.py

g2gcio/course-demo

b0d00a6ac7a6a6a17af963cee67cf13dc5941e95

[ "MIT" ]

276

2016-04-04T20:57:36.000Z

2022-03-12T02:42:46.000Z

17_logging_and_monitoring/start_17_blue_yellow_app_monitoring/blue_yellow_app/data/purchase.py

g2gcio/course-demo

b0d00a6ac7a6a6a17af963cee67cf13dc5941e95

[ "MIT" ]

37

2016-10-13T12:04:27.000Z

2020-11-22T10:36:53.000Z

17_logging_and_monitoring/start_17_blue_yellow_app_monitoring/blue_yellow_app/data/purchase.py

g2gcio/course-demo

b0d00a6ac7a6a6a17af963cee67cf13dc5941e95

[ "MIT" ]

163

2016-10-03T02:10:00.000Z

2022-03-25T03:43:01.000Z

import datetime import sqlalchemy import sqlalchemy.orm from sqlalchemy import Column from sqlalchemy import DateTime from sqlalchemy import ForeignKey from sqlalchemy import Integer from sqlalchemy import String from blue_yellow_app.data.modelbase import SqlAlchemyBase class AlbumPurchase(SqlAlchemyBase): __tablename__ = 'AlbumPurchase' id = Column(Integer, primary_key=True, autoincrement=True) created = Column(DateTime, default=datetime.datetime.now, index=True) description = Column(String) amount_paid = sqlalchemy.Column(sqlalchemy.Float, index=True) album_id = Column(String, ForeignKey('Album.id')) user_id = Column(String, ForeignKey('Account.id'))

26.807692

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0.786227

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697

6.560976

0.414634

0.178439

0.185874

0.089219

0

0.137733

697

25

74

27.88

0.895175

0

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0

1

0

false

0

0.529412

0

1

0

null

0

1

0

null

0

1

0

1

0

3

0c62aabb759b7d13357748f636d492ce98395509

279

py

Python

evaluation/migrations/0004_merge_20181223_1602.py

AymenQ/tarteel.io

a72150bca90b5244580daf172d5f8d738ba98c1b

[ "MIT" ]

null

evaluation/migrations/0004_merge_20181223_1602.py

AymenQ/tarteel.io

a72150bca90b5244580daf172d5f8d738ba98c1b

[ "MIT" ]

null

evaluation/migrations/0004_merge_20181223_1602.py

AymenQ/tarteel.io

a72150bca90b5244580daf172d5f8d738ba98c1b

[ "MIT" ]

null

# Generated by Django 2.1.3 on 2018-12-23 21:02 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('evaluation', '0003_auto_20181210_0816'), ('evaluation', '0003_auto_20181202_1602'), ] operations = [ ]

18.6

50

0.655914

33

279

5.363636

0.818182

0.158192

0.20339

0

0.217593

0.225806

279

14

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19.928571

0.601852

0.16129

0

1

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0.198276

0

1

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false

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0.125

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0.5

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null

0

1

0

1

0

1

0

null

0

3

a7444113d46bf5f5d39b968eb2273ff8dd343777

218

py

Python

tests/__main__.py

Privex/python-helpers

1c976ce5b0e2c5241ea0bdf330bd6701b5e31153

[ "X11" ]

12

2019-06-18T11:17:41.000Z

2021-09-13T23:00:21.000Z

tests/__main__.py

Privex/python-coinhandlers

b24c0c3f7d81cedefd52a5837a371cfef2f83e97

[ "X11" ]

1

2019-10-13T07:34:44.000Z

tests/__main__.py

Privex/python-coinhandlers

b24c0c3f7d81cedefd52a5837a371cfef2f83e97

[ "X11" ]

4

2019-10-10T10:15:09.000Z

2021-05-16T01:55:48.000Z

""" This file exists to allow for ``python3 -m tests`` to work, as python's module execution option attempts to load ``__main__`` from a package. """ from tests import * if __name__ == '__main__': unittest.main()

24.222222

95

0.697248

32

218

4.375

0.8125

0

0.005587

0.178899

218

8

96

27.25

0.776536

0.646789

0

0.115942

0

1

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true

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0.333333

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0.333333

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null

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1

0

null

0

1

0

1

0

3

a769df3f78292663e6a4c642a787b894aa8d8eb9

108

py

Python

novaposhta/__init__.py

last-partizan/novaposhta-api-client

db21a1b112631ee3c4bbd66e5b88ca3ca993d976

[ "MIT" ]

1

2017-11-20T15:04:57.000Z

novaposhta/__init__.py

last-partizan/novaposhta-api-client

db21a1b112631ee3c4bbd66e5b88ca3ca993d976

[ "MIT" ]

null

novaposhta/__init__.py

last-partizan/novaposhta-api-client

db21a1b112631ee3c4bbd66e5b88ca3ca993d976

[ "MIT" ]

1

2021-04-10T19:08:57.000Z

from . import models # noqa from .api import NovaPoshta __author__ = 'semolex' __all__ = ['NovaPoshta']

18

27

0.712963

12

108

5.75

0.75

0

0.185185

108

5

28

21.6

0.784091

0.037037

0

0.166667

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

3

a76bb4dff1fe0879751b999f54a3655021638b4f

274

py

Python

blog/posts/urls.py

abdullah1107/Django_RestApi-New-

b4fd65d73ca95d1a373758f46093e5fe28786980

[ "MIT" ]

1

2019-12-14T06:09:01.000Z

blog/posts/urls.py

abdullah1107/Django_RestApi-New-

b4fd65d73ca95d1a373758f46093e5fe28786980

[ "MIT" ]

null

blog/posts/urls.py

abdullah1107/Django_RestApi-New-

b4fd65d73ca95d1a373758f46093e5fe28786980

[ "MIT" ]

null

from django.urls import path from . import views from .api import views urlpatterns = [ path('', views.PostListView.as_view(), name=None), path('create/', views.PostCreateView.as_view(), name=None), path('<int:pk>/', views.PostDetailView.as_view(), name=None) ]

30.444444

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0.69708

37

274

5.081081

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0.095745

0.159574

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0.191489

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0.135037

274

9

65

30.444444

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0.058182

0

1

0

false

0

0.375

0

0.375

0

null

0

1

0

1

0

null

0

1

0

3

a76ce790b74a4c30effb8092ca591a01c2299f84

262

py

Python

registry/permissions.py

Nephilim-Jack/regUserBack

00bc28088ea9d4c681c26d1317da6a14b36e38c5

[ "MIT" ]

null

registry/permissions.py

Nephilim-Jack/regUserBack

00bc28088ea9d4c681c26d1317da6a14b36e38c5

[ "MIT" ]

null

registry/permissions.py

Nephilim-Jack/regUserBack

00bc28088ea9d4c681c26d1317da6a14b36e38c5

[ "MIT" ]

null

from rest_framework.permissions import BasePermission class BaseUserPermission(BasePermission): def has_permission(self, request, view): if view.action == 'getUserLogin': return True return super().has_permission(request, view)

29.111111

53

0.717557

27

262

6.851852

0.740741

0.140541

0

0.20229

262

8

54

32.75

0.885167

0

0.045802

0

1

0.166667

false

0

0.166667

0

0.833333

0

1

0

null

0

1

0

null

0

1

0

3

a77246c6713e7ec7497193420e2218d7550101ac

1,914

py

Python

xero_python/accounting/models/repeating_invoices.py

sromero84/xero-python

89558c0baa8080c3f522701eb1b94f909248dbd7

[ "MIT" ]

null

xero_python/accounting/models/repeating_invoices.py

sromero84/xero-python

89558c0baa8080c3f522701eb1b94f909248dbd7

[ "MIT" ]

null

xero_python/accounting/models/repeating_invoices.py

sromero84/xero-python

89558c0baa8080c3f522701eb1b94f909248dbd7

[ "MIT" ]

null

# coding: utf-8 """ Accounting API No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501 OpenAPI spec version: 2.3.4 Contact: api@xero.com Generated by: https://openapi-generator.tech """ import re # noqa: F401 from xero_python.models import BaseModel class RepeatingInvoices(BaseModel): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. """ """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ openapi_types = {"repeating_invoices": "list[RepeatingInvoice]"} attribute_map = {"repeating_invoices": "RepeatingInvoices"} def __init__(self, repeating_invoices=None): # noqa: E501 """RepeatingInvoices - a model defined in OpenAPI""" # noqa: E501 self._repeating_invoices = None self.discriminator = None if repeating_invoices is not None: self.repeating_invoices = repeating_invoices @property def repeating_invoices(self): """Gets the repeating_invoices of this RepeatingInvoices. # noqa: E501 :return: The repeating_invoices of this RepeatingInvoices. # noqa: E501 :rtype: list[RepeatingInvoice] """ return self._repeating_invoices @repeating_invoices.setter def repeating_invoices(self, repeating_invoices): """Sets the repeating_invoices of this RepeatingInvoices. :param repeating_invoices: The repeating_invoices of this RepeatingInvoices. # noqa: E501 :type: list[RepeatingInvoice] """ self._repeating_invoices = repeating_invoices

29

124

0.671369

210

1,914

5.971429

0.37619

0.257576

0.100478

0.070175

0.307815

0.216906

0.182616

0.060606

0

0.017349

0.247126

1,914

65

125

29.446154

0.85288

0.421108

0

1

0

0.102881

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0

1

0.1875

false

0

0.125

0

0.5625

0

null

1

0

1

0

null

0

1

0

3

a7908ab8c52c1e6e3c2a925752339a22c07cce0d

130

py

Python

script/tools/ignore_file.py

evoldourden/FrackinUniverse-sChinese-Project

db33417f7693df2a8ed0055afc48dbe30c3da0c0

[ "MIT" ]

null

script/tools/ignore_file.py

evoldourden/FrackinUniverse-sChinese-Project

db33417f7693df2a8ed0055afc48dbe30c3da0c0

[ "MIT" ]

null

script/tools/ignore_file.py

evoldourden/FrackinUniverse-sChinese-Project

db33417f7693df2a8ed0055afc48dbe30c3da0c0

[ "MIT" ]

null

# Ignore file list ignore_filelist = [ 'teslagun.activeitem', 'teslagun2.activeitem', ] ignore_filelist_patch = [ ]

13

27

0.669231

12

130

7

0.666667

0.333333

0

0.009804

0.215385

130

9

28

14.444444

0.813725

0.123077

0

0.348214

0

1

0

false

0

1

0

null

1

0

1

0

null

0

3

a79454eb94163d981cff288c16d13accb25d3dfc

729

py

Python

aspdotnet/datadog_checks/aspdotnet/aspdotnet.py

tcpatterson/integrations-core

3692601de09f8db60f42612b0d623509415bbb53

[ "BSD-3-Clause" ]

null

aspdotnet/datadog_checks/aspdotnet/aspdotnet.py

tcpatterson/integrations-core

3692601de09f8db60f42612b0d623509415bbb53

[ "BSD-3-Clause" ]

null

aspdotnet/datadog_checks/aspdotnet/aspdotnet.py

tcpatterson/integrations-core

3692601de09f8db60f42612b0d623509415bbb53

[ "BSD-3-Clause" ]

null

# (C) Datadog, Inc. 2013-present # All rights reserved # Licensed under Simplified BSD License (see LICENSE) from six import PY3 from datadog_checks.base import PDHBaseCheck from .metrics import DEFAULT_COUNTERS EVENT_TYPE = SOURCE_TYPE_NAME = 'aspdotnet' class AspdotnetCheck(PDHBaseCheck): def __new__(cls, name, init_config, instances): if PY3: from .check import AspdotnetCheckV2 return AspdotnetCheckV2(name, init_config, instances) else: return super(AspdotnetCheck, cls).__new__(cls) def __init__(self, name, init_config, instances=None): super(AspdotnetCheck, self).__init__(name, init_config, instances=instances, counter_list=DEFAULT_COUNTERS)

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3

a79d1a25e0fd12d08cb7790e77ab5c85c084c39f

212

py

Python

dev_up/models/vk/search_audio.py

lordralinc/dev_up

e035afd386c8a16c574aaa7615c263f1c1369911

[ "MIT" ]

2

2021-01-10T15:44:41.000Z

2021-01-10T15:59:48.000Z

dev_up/models/vk/search_audio.py

lordralinc/dev_up

e035afd386c8a16c574aaa7615c263f1c1369911

[ "MIT" ]

null

dev_up/models/vk/search_audio.py

lordralinc/dev_up

e035afd386c8a16c574aaa7615c263f1c1369911

[ "MIT" ]

4

2021-01-10T15:45:19.000Z

2021-03-05T20:09:57.000Z

from pydantic import BaseModel class VkSearchAudioResponse(BaseModel): q: str count: int attachments: str msg_response: str class VkSearchAudio(BaseModel): response: VkSearchAudioResponse

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a7b9297b2d74a3a4fbac1f2e93de298645ba2b9a

304

py

Python

01-logica-de-programacao-e-algoritmos/Aula 05/5 Recursos avancados com funcoes/5.2 Funcao como parametro de funcao/ex01.py

rafaelbarretomg/Uninter

1f84b0103263177122663e991db3a8aeb106a959

[ "MIT" ]

null

01-logica-de-programacao-e-algoritmos/Aula 05/5 Recursos avancados com funcoes/5.2 Funcao como parametro de funcao/ex01.py

rafaelbarretomg/Uninter

1f84b0103263177122663e991db3a8aeb106a959

[ "MIT" ]

null

01-logica-de-programacao-e-algoritmos/Aula 05/5 Recursos avancados com funcoes/5.2 Funcao como parametro de funcao/ex01.py

rafaelbarretomg/Uninter

1f84b0103263177122663e991db3a8aeb106a959

[ "MIT" ]

null

# funcao como parametro de funcao # so imprime se o numero estiver correto def imprime_com_condicao(num, fcond): if fcond(num): print(num) def par(x): return x % 2 == 0 def impar(x): return not par(x) # Programa Principal # neste caso nao imprimira imprime_com_condicao(5, par)

16

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null

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a7be76ee5cd3a975a32123a5d2dab80b2c1c629e

417

py

Python

libp2p/stream_muxer/mplex/exceptions.py

g-r-a-n-t/py-libp2p

36a4a9150dcc53b42315b5c6868fccde5083963b

[ "Apache-2.0", "MIT" ]

315

2019-02-13T01:29:09.000Z

2022-03-28T13:44:07.000Z

libp2p/stream_muxer/mplex/exceptions.py

pipermerriam/py-libp2p

379a157d6b67e86a616b2458af519bbe5fb26a51

[ "Apache-2.0", "MIT" ]

249

2019-02-22T05:00:07.000Z

2022-03-29T16:30:46.000Z

libp2p/stream_muxer/mplex/exceptions.py

ralexstokes/py-libp2p

5144ab82894623969cb17baf0d4c64bd0a274068

[ "Apache-2.0", "MIT" ]

77

2019-02-24T19:45:17.000Z

2022-03-30T03:20:09.000Z

from libp2p.stream_muxer.exceptions import ( MuxedConnError, MuxedConnUnavailable, MuxedStreamClosed, MuxedStreamEOF, MuxedStreamReset, ) class MplexError(MuxedConnError): pass class MplexUnavailable(MuxedConnUnavailable): pass class MplexStreamReset(MuxedStreamReset): pass class MplexStreamEOF(MuxedStreamEOF): pass class MplexStreamClosed(MuxedStreamClosed): pass

14.892857

45

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417

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417

27

46

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0

1

null

0

null

0

1

0

3

a7d1a09e420232e6175c5cd0f1e6a7faf572e4aa

126

py

Python

imagepy/menus/__init__.py

siyemuxu888/imagepy

a933526483a15da282bacac54608d44d2173beb4

[ "BSD-4-Clause" ]

null

imagepy/menus/__init__.py

siyemuxu888/imagepy

a933526483a15da282bacac54608d44d2173beb4

[ "BSD-4-Clause" ]

null

imagepy/menus/__init__.py

siyemuxu888/imagepy

a933526483a15da282bacac54608d44d2173beb4

[ "BSD-4-Clause" ]

null

catlog = ['File','Edit','Image','Process','Selection', 'Analysis','Kit3D', 'Plugins','Window','Skimage','Opencv','ITK','Help']

126

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126

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1

0

false

0

1

0

null

0

1

0

1

0

null

0

3

a7d6694743abbc2f9c473a8c591d8d24000ac031

6,960

py

Python

garden_calendar_time/equinox_solstice.py

gossrock/garden_calendar_time

1cabcbd3bd20614a2adb1110ac994b04a7f666c9

[ "MIT" ]

null

garden_calendar_time/equinox_solstice.py

gossrock/garden_calendar_time

1cabcbd3bd20614a2adb1110ac994b04a7f666c9

[ "MIT" ]

null

garden_calendar_time/equinox_solstice.py

gossrock/garden_calendar_time

1cabcbd3bd20614a2adb1110ac994b04a7f666c9

[ "MIT" ]

null

import csv from pathlib import Path from typing import NamedTuple from typing import Dict, Callable from functools import partial from garden_calendar_time.location import LatLong from garden_calendar_time.utcdatetime import UTCDateTime, Time, TimeDelta, parse_iso_date class YearEquinoxSolsticData(NamedTuple): year: int march_equinox: UTCDateTime june_solstice: UTCDateTime september_equinox: UTCDateTime december_solstice: UTCDateTime def nearest_day_at_time_to_datetime(time: Time, target_datetime: UTCDateTime) -> UTCDateTime: half_day_in_seconds = 12 * 60 * 60 target_date = target_datetime.date() choice1 = UTCDateTime.combine(target_date, time) choice1_diff = abs(target_datetime - choice1) if choice1_diff.days == 0 and choice1_diff.seconds <= half_day_in_seconds: return choice1 choice2 = choice1 + TimeDelta(1) choice2_diff = abs(target_datetime - choice2) if choice2_diff.days == 0 and choice2_diff.seconds <= half_day_in_seconds: return choice2 choice3 = choice1 - TimeDelta(1) choice3_diff = abs(target_datetime - choice3) if choice3_diff.days == 0 and choice3_diff.seconds <= half_day_in_seconds: return choice3 DATABASE: Dict[int, YearEquinoxSolsticData] = {} DEFAULT_DATA_FILE = Path(__file__).parent.joinpath('data/equinox_solstice_data.csv') # csv columns YEAR = 0 MARCH_EQUINOX = 1 JUNE_SOLSTICE = 2 SEPTEMBER_EQUINOX = 3 DECEMBER_SOLSTICE = 4 def load_data_from_csv(csv_file_name: str = DEFAULT_DATA_FILE) -> None: with open(csv_file_name) as data_file: csv_reader = csv.reader(data_file) for row in csv_reader: year = int(row[YEAR]) march_equinox = parse_iso_date(row[MARCH_EQUINOX]) june_solstice = parse_iso_date(row[JUNE_SOLSTICE]) september_equinox = parse_iso_date(row[SEPTEMBER_EQUINOX]) december_solstice = parse_iso_date(row[DECEMBER_SOLSTICE]) DATABASE[year] = YearEquinoxSolsticData(year, march_equinox, june_solstice, september_equinox, december_solstice) def year_data(year: int) -> YearEquinoxSolsticData: if DATABASE == {}: load_data_from_csv() return DATABASE[year] # specific equinoxes and solstices def march_equinox(year: int, location: LatLong = LatLong(0,0)) -> UTCDateTime: return year_data(year).march_equinox def june_solstice(year: int, location: LatLong = LatLong(0,0)) -> UTCDateTime: return year_data(year).june_solstice def september_equinox(year: int, location: LatLong = LatLong(0,0)) -> UTCDateTime: return year_data(year).september_equinox def december_solstice(year:int, location: LatLong = LatLong(0,0)) -> UTCDateTime: return year_data(year).december_solstice # seasonal equinoxes and solstices def spring_equinox(year: int, location: LatLong = LatLong(0,0)) -> UTCDateTime: if location.lat >= 0: return march_equinox(year) else: return september_equinox(year) def summer_solstice(year: int, location: LatLong = LatLong(0,0)) -> UTCDateTime: if location.lat >= 0: return june_solstice(year) else: return december_solstice(year) def fall_equinox(year: int, location: LatLong = LatLong(0,0)) -> UTCDateTime: if location.lat >= 0: return september_equinox(year) else: return march_equinox(year) def winter_solstice(year: int, location: LatLong = LatLong(0,0)) -> UTCDateTime: if location.lat >= 0: return december_solstice(year) else: return june_solstice(year) # reletive specific equnoxes and solstices def _after(datetime:UTCDateTime, event_function: Callable, location: LatLong = LatLong(0,0)) -> UTCDateTime: if (event_function(datetime.year, location) - datetime).days >= 0: return event_function(datetime.year, location) else: return event_function(datetime.year + 1, location) def _before(datetime:UTCDateTime, event_function: Callable, location: LatLong = LatLong(0,0)) -> UTCDateTime: if (event_function(datetime.year, location) - datetime).days < 0: return event_function(datetime.year, location) else: return event_function(datetime.year - 1, location) def march_equinox_after(datetime: UTCDateTime, location: LatLong = LatLong(0,0)) -> UTCDateTime: return _after(datetime, march_equinox, location) def march_equinox_before(datetime: UTCDateTime, location: LatLong = LatLong(0,0)) -> UTCDateTime: return _before(datetime, march_equinox, location) def june_solstice_after(datetime: UTCDateTime, location: LatLong = LatLong(0,0)) -> UTCDateTime: return _after(datetime, june_solstice, location) def june_solstice_before(datetime: UTCDateTime, location: LatLong = LatLong(0,0)) -> UTCDateTime: return _before(datetime, june_solstice, location) def september_equinox_after(datetime: UTCDateTime, location: LatLong = LatLong(0,0)) -> UTCDateTime: return _after(datetime, september_equinox, location) def september_equinox_before(datetime: UTCDateTime, location: LatLong = LatLong(0,0)) -> UTCDateTime: return _before(datetime, september_equinox, location) def december_solstice_after(datetime: UTCDateTime, location: LatLong = LatLong(0,0)) -> UTCDateTime: return _after(datetime, december_solstice, location) def december_solstice_before(datetime: UTCDateTime, location: LatLong = LatLong(0,0)) -> UTCDateTime: return _before(datetime, december_solstice, location) # reletive seasonal equinoxes and solstices def spring_equinox_after(datetime: UTCDateTime, location: LatLong = LatLong(0,0)) -> UTCDateTime: return _after(datetime, spring_equinox, location) def spring_equinox_before(datetime: UTCDateTime, location: LatLong = LatLong(0,0)) -> UTCDateTime: return _before(datetime, spring_equinox, location) def summer_solstice_after(datetime: UTCDateTime, location: LatLong = LatLong(0,0)) -> UTCDateTime: return _after(datetime, summer_solstice, location) def summer_solstice_before(datetime: UTCDateTime, location: LatLong = LatLong(0,0)) -> UTCDateTime: return _before(datetime, summer_solstice, location) def fall_equinox_after(datetime: UTCDateTime, location: LatLong = LatLong(0,0)) -> UTCDateTime: return _after(datetime, fall_equinox, location) def fall_equinox_before(datetime: UTCDateTime, location: LatLong = LatLong(0,0)) -> UTCDateTime: return _before(datetime, fall_equinox, location) def winter_solstice_after(datetime: UTCDateTime, location: LatLong = LatLong(0,0)) -> UTCDateTime: return _after(datetime, winter_solstice, location) def winter_solstice_before(datetime: UTCDateTime, location: LatLong = LatLong(0,0)) -> UTCDateTime: return _before(datetime, winter_solstice, location)

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1

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ac04e45fb7efd16eef5f170ebdb869d511eddd3d

637

py

Python

msgraph/sharepoint/abstractions/ISharepointSite.py

SWB-Dev/microsoft-graph-api

07fef0071852a697bdf0b0a28a4758214621d5b2

[ "MIT" ]

null

msgraph/sharepoint/abstractions/ISharepointSite.py

SWB-Dev/microsoft-graph-api

07fef0071852a697bdf0b0a28a4758214621d5b2

[ "MIT" ]

null

msgraph/sharepoint/abstractions/ISharepointSite.py

SWB-Dev/microsoft-graph-api

07fef0071852a697bdf0b0a28a4758214621d5b2

[ "MIT" ]

null

from typing import Protocol, Callable from ... import IGraphResponse, IGraphFilter, IGraphAction from .ISharepointDocumentLibrary import ISharepointDocumentLibrary from .ISharepointList import ISharepointList class ISharepointSite(Protocol): def lists(self, list_name:str = None) -> ISharepointList: """""" def filters(self, filter_func:Callable[...,list[IGraphFilter]]) -> IGraphAction: """""" def documents(self, library_name:str) -> ISharepointDocumentLibrary: """""" def get(self, url:str = None) -> IGraphResponse: """""" def build_url(self) -> str: """"""

27.695652

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1

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ac1c85668a9bd509748b1a53906039614d33f427

95

py

Python

modelflow/notused__init__.py

IbHansen/ModelFlow

09b1f911332f3d0af700ec65d46e8d4a53335e19

[ "X11" ]

2

2019-06-13T15:50:42.000Z

2019-06-13T15:51:05.000Z

modelflow/notused__init__.py

IbHansen/modelflow

09b1f911332f3d0af700ec65d46e8d4a53335e19

[ "X11" ]

null

modelflow/notused__init__.py

IbHansen/modelflow

09b1f911332f3d0af700ec65d46e8d4a53335e19

[ "X11" ]

1

2019-05-10T09:35:59.000Z

# -*- coding: utf-8 -*- """ Created on Sat Feb 29 09:54:51 2020 @author: bruger """

10.555556

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ac2187369c5deea320983291a63708dfc0270f00

123

py

Python

GithubTest.py

akanchhaS/python-goof

9ec011f459c0c8a7b9b58b4fe0a65255c2a5368e

[ "MIT" ]

null

GithubTest.py

akanchhaS/python-goof

9ec011f459c0c8a7b9b58b4fe0a65255c2a5368e

[ "MIT" ]

7

2020-02-22T18:04:41.000Z

2020-09-02T12:26:19.000Z

GithubTest.py

akanchhaS/python-goof

9ec011f459c0c8a7b9b58b4fe0a65255c2a5368e

[ "MIT" ]

8

2020-10-30T18:44:03.000Z

2022-02-24T22:15:47.000Z

from github import Github g = Github( ${{Pygithub.secrets}} ) for repo in g.get_user().get_repos(): print(repo.name)

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0

1

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3

ac23907ddad7e1df5b3643f0643f45035b761f25

270

py

Python

datastructure/practice/c1/r_1_10.py

stoneyangxu/python-kata

979af91c74718a525dcd2a83fe53ec6342af9741

[ "MIT" ]

null

datastructure/practice/c1/r_1_10.py

stoneyangxu/python-kata

979af91c74718a525dcd2a83fe53ec6342af9741

[ "MIT" ]

null

datastructure/practice/c1/r_1_10.py

stoneyangxu/python-kata

979af91c74718a525dcd2a83fe53ec6342af9741

[ "MIT" ]

null

import unittest def range_8() -> list: return [n for n in range(8, -10, -2)] class MyTestCase(unittest.TestCase): def test_something(self): self.assertEqual(range_8(), [8, 6, 4, 2, 0, -2, -4, -6, -8]) if __name__ == '__main__': unittest.main()

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0

null

0

null

0

1

0

1

0

3

ac28126ae19b3128bf5f85a601b84c3cefe050ba

253

py

Python

src/Python/101-200/151.ReverseWordsInString.py

Peefy/PeefyLeetCode

92156e4b48ba19e3f02e4286b9f733e9769a1dee

[ "Apache-2.0" ]

2

2018-05-03T07:50:03.000Z

2018-06-17T04:32:13.000Z

src/Python/101-200/151.ReverseWordsInString.py

Peefy/PeefyLeetCode

92156e4b48ba19e3f02e4286b9f733e9769a1dee

[ "Apache-2.0" ]

null

src/Python/101-200/151.ReverseWordsInString.py

Peefy/PeefyLeetCode

92156e4b48ba19e3f02e4286b9f733e9769a1dee

[ "Apache-2.0" ]

3

2018-11-09T14:18:11.000Z

2021-11-17T15:23:52.000Z

class Solution: def reverseWords(self, set): return ' '.join(set.split()[::-1]) if __name__ == "__main__": solution = Solution() print(solution.reverseWords("the sky is blue")) print(solution.reverseWords(" hello world! "))

23

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253

5.428571

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0

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10

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0

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1

0

null

0

1

0

null

0

1

0

3

ac50dc05118a4a710ef6149c65fa8ce6bba0eb20

2,845

py

Python

backend/backend/settings/__init__.py

ambikads/portunus

a9c9047757528ef5667fcfda2eb43f313281a3c9

[ "MIT" ]

null

backend/backend/settings/__init__.py

ambikads/portunus

a9c9047757528ef5667fcfda2eb43f313281a3c9

[ "MIT" ]

null

backend/backend/settings/__init__.py

ambikads/portunus

a9c9047757528ef5667fcfda2eb43f313281a3c9

[ "MIT" ]

null

from .zygoat_settings import * # noqa AUTH_USER_MODEL = "authentication.User" INSTALLED_APPS = [ *INSTALLED_APPS, "rest_framework", "authentication", "rest_framework_simplejwt.token_blacklist", ] MIDDLEWARE = [ "corsheaders.middleware.CorsMiddleware", *MIDDLEWARE, ] REST_FRAMEWORK = { "DEFAULT_AUTHENTICATION_CLASSES": ( "simplejwt_extensions.authentication.JWTAuthentication", ), } DEFAULT_SIGNING_KEY = """-----BEGIN RSA PRIVATE KEY----- MIICXQIBAAKBgQC91RWCawEvxQj+tigRvuHxouO8jKd35ukUxFBFRAGcI57firbA kFII6zPIiWAENGMqtjX57hk9EjAZ27XvQ4SQACvD5j7htsJT31bZbVUH7a3JEDpx a02VXpXdfPYSs8umZkdxMxxmiD9uH9VmLN3VS14lxQlyJdlvbLmNCAf6uwIDAQAB AoGACXFcDIy+Fl46wFDXVWqlWpu7sFleyzwVRA8v3wIvAlFTSdNgm9uR+ReaD9Ol jw/8DtfZf4E0iDEra13egvRc16byYQ4qv0l7xvn3ATomxcPwbdvkfYE4C0EZFuXx ZZwSQwWsNl/36BSyZErw8y/THMIkOKRNFuJarK4P3aYppEECQQDpvRnL2kbbXbqW uB8Pt2hMfBXW/byCcctoI+cuqYAUJ/J1tLPe+q7sAW6Fr0WpYTpjljC+UCqu/a6a YgG0L6I5AkEAz+l7QfyFKk7jl05hEUly0CNshgJ8jLPcF2ZXILrcUwGxbpdyjmCJ ExiKpfzPufYeu7qLwhyaHniR9huSZIx0kwJBALMRTFIAR4iHpgsRw7omqKDv70tl 2KWWyF5gIxx8fsLyV64VYjfRlXD5J9MDFDtPYYwp4+3pPMoTT1C3BNcmJwECQEYV Ero0b4LKYsce4XNdSblFJ5Coh+k5u2eb1KSwuBG20WNQ44mAmtP4AsxewnqRrtxi zjdZQs4goDrQInGIMscCQQCIT5jvRb197iRinBqpNy01i7GdlLtMC7Z9V/PV0YW1 GmX50gvd7aA+i2UuZj7BxapFStyEGl4Nggglnn+QqQ+L -----END RSA PRIVATE KEY-----""" DEFAULT_VERIFYING_KEY = """-----BEGIN PUBLIC KEY----- MIGfMA0GCSqGSIb3DQEBAQUAA4GNADCBiQKBgQC91RWCawEvxQj+tigRvuHxouO8 jKd35ukUxFBFRAGcI57firbAkFII6zPIiWAENGMqtjX57hk9EjAZ27XvQ4SQACvD 5j7htsJT31bZbVUH7a3JEDpxa02VXpXdfPYSs8umZkdxMxxmiD9uH9VmLN3VS14l xQlyJdlvbLmNCAf6uwIDAQAB -----END PUBLIC KEY-----""" SIMPLE_JWT = { "USER_ID_FIELD": "portunus_uuid", "NEW_USER_CALLBACK": "backend.utils.create_user", "ALGORITHM": "RS512", "SIGNING_KEY": prod_required_env("DJANGO_JWT_SIGNING_KEY", DEFAULT_SIGNING_KEY), "VERIFYING_KEY": prod_required_env("DJANGO_JWT_VEFIFYING_KEY", DEFAULT_VERIFYING_KEY), } CORS_ORIGIN_ALLOW_ALL = DEBUG CORS_ALLOW_CREDENTIALS = True BASE_URL = env("DJANGO_BASE_URL", default="http://localhost:3000/") AUTH_PASSWORD_VALIDATORS = [ { "NAME": "django.contrib.auth.password_validation.MinimumLengthValidator", "OPTIONS": {"min_length": 7,}, }, {"NAME": "django.contrib.auth.password_validation.CommonPasswordValidator",}, {"NAME": "authentication.password_validators.AlphaNumericPasswordValidator",}, ] DEFAULT_REDIRECT_URL = prod_required_env( "DJANGO_DEFAULT_REDIRECT_URL", "http://localhost:3000" ) VALID_REDIRECT_HOSTNAMES = ["localhost"] GOOGLE_APP_ID = prod_required_env("DJANGO_GOOGLE_APP_ID", default=None) FACEBOOK_CLIENT_ID = prod_required_env("DJANGO_FACEBOOK_CLIENT_ID", default=None) FACEBOOK_CLIENT_SECRET = prod_required_env("DJANGO_FACEBOOK_CLIENT_SECRET", default=None) SESSION_COOKIE_SAMESITE = None

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ac5470c4220c7cd784739a9a92c687cedbf7cf9f

355

py

Python

tests/items.py

castares/scrapy-sqlitem

169e46d0e10e13270c947b96f2674c80813d46db

[ "BSD-3-Clause" ]

41

2015-08-14T23:58:48.000Z

2022-01-08T16:56:57.000Z

tests/items.py

castares/scrapy-sqlitem

169e46d0e10e13270c947b96f2674c80813d46db

[ "BSD-3-Clause" ]

2

2016-08-28T21:51:33.000Z

2017-09-23T15:48:49.000Z

tests/items.py

ryancerf/scrapy-sqlitem

0650a5fb91b56cb1fae7837bd74a159a61b51bf9

[ "BSD-3-Clause" ]

16

2015-08-14T23:52:50.000Z

2020-08-27T14:33:43.000Z

from scrapy import Field from scrapy_sqlitem import SqlItem from . models import User, Address class UserItem(SqlItem): sqlmodel = User class AddressItem(SqlItem): sqlmodel = Address class NewFieldItemUser(SqlItem): sqlmodel = User first_joined = Field() class OverrideFieldItemUser(SqlItem): sqlmodel = User id = Field()

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