max_stars_repo_path stringlengths 4 286 | max_stars_repo_name stringlengths 5 119 | max_stars_count int64 0 191k | id stringlengths 1 7 | content stringlengths 6 1.03M | content_cleaned stringlengths 6 1.03M | language stringclasses 111
values | language_score float64 0.03 1 | comments stringlengths 0 556k | edu_score float64 0.32 5.03 | edu_int_score int64 0 5 |
|---|---|---|---|---|---|---|---|---|---|---|
phi/math/backend/_backend.py | marc-gav/PhiFlow | 0 | 4000 | <filename>phi/math/backend/_backend.py
from collections import namedtuple
from contextlib import contextmanager
from threading import Barrier
from typing import List, Callable
import numpy
from ._dtype import DType, combine_types
SolveResult = namedtuple('SolveResult', [
'method', 'x', 'residual', 'iterations',... | <filename>phi/math/backend/_backend.py
from collections import namedtuple
from contextlib import contextmanager
from threading import Barrier
from typing import List, Callable
import numpy
from ._dtype import DType, combine_types
SolveResult = namedtuple('SolveResult', [
'method', 'x', 'residual', 'iterations',... | en | 0.708661 | A physical device that can be selected to perform backend computations. Name of the compute device. CPUs are typically called `'CPU'`. Type of device such as `'CPU'`, `'GPU'` or `'TPU'`. Maximum memory of the device that can be allocated (in bytes). -1 for n/a. Number of CPU cores or GPU multiprocessors. -1 for n/a. Fu... | 2.842221 | 3 |
bpython/curtsiesfrontend/parse.py | dtrodrigues/bpython | 2,168 | 4001 | import re
from curtsies.formatstring import fmtstr, FmtStr
from curtsies.termformatconstants import (
FG_COLORS,
BG_COLORS,
colors as CURTSIES_COLORS,
)
from functools import partial
from ..lazyre import LazyReCompile
COLORS = CURTSIES_COLORS + ("default",)
CNAMES = dict(zip("krgybmcwd", COLORS))
# hack... | import re
from curtsies.formatstring import fmtstr, FmtStr
from curtsies.termformatconstants import (
FG_COLORS,
BG_COLORS,
colors as CURTSIES_COLORS,
)
from functools import partial
from ..lazyre import LazyReCompile
COLORS = CURTSIES_COLORS + ("default",)
CNAMES = dict(zip("krgybmcwd", COLORS))
# hack... | en | 0.742617 | # hack for finding the "inverse" Returns FmtStr constructor for a bpython-style color code Returns a FmtStr object from a bpython-formatted colored string # this isn't according to spec as I understand it # TODO figure out why boldness isn't based on presence of \x02 # hack for finding the "inverse" (?P<colormarker>\x0... | 2.522608 | 3 |
sarpy/io/general/nitf_elements/tres/unclass/BANDSA.py | pressler-vsc/sarpy | 1 | 4002 | <reponame>pressler-vsc/sarpy
# -*- coding: utf-8 -*-
from ..tre_elements import TREExtension, TREElement
__classification__ = "UNCLASSIFIED"
__author__ = "<NAME>"
class BAND(TREElement):
def __init__(self, value):
super(BAND, self).__init__()
self.add_field('BANDPEAK', 's', 5, value)
sel... | # -*- coding: utf-8 -*-
from ..tre_elements import TREExtension, TREElement
__classification__ = "UNCLASSIFIED"
__author__ = "<NAME>"
class BAND(TREElement):
def __init__(self, value):
super(BAND, self).__init__()
self.add_field('BANDPEAK', 's', 5, value)
self.add_field('BANDLBOUND', 's'... | en | 0.769321 | # -*- coding: utf-8 -*- | 2.220846 | 2 |
ktrain/graph/learner.py | husmen/ktrain | 1,013 | 4003 | from ..imports import *
from .. import utils as U
from ..core import GenLearner
class NodeClassLearner(GenLearner):
"""
```
Main class used to tune and train Keras models for node classification
Main parameters are:
model (Model): A compiled instance of keras.engine.training.Model
train_dat... | from ..imports import *
from .. import utils as U
from ..core import GenLearner
class NodeClassLearner(GenLearner):
"""
```
Main class used to tune and train Keras models for node classification
Main parameters are:
model (Model): A compiled instance of keras.engine.training.Model
train_dat... | en | 0.763768 | ``` Main class used to tune and train Keras models for node classification Main parameters are: model (Model): A compiled instance of keras.engine.training.Model train_data (Iterator): a Iterator instance for training set val_data (Iterator): A Iterator instance for validation set ``` ``` ... | 2.884221 | 3 |
VegaZero2VegaLite.py | Thanksyy/Vega-Zero | 5 | 4004 | __author__ = "<NAME>"
import json
import pandas
class VegaZero2VegaLite(object):
def __init__(self):
pass
def parse_vegaZero(self, vega_zero):
self.parsed_vegaZero = {
'mark': '',
'data': '',
'encoding': {
'x': '',
'y': {
... | __author__ = "<NAME>"
import json
import pandas
class VegaZero2VegaLite(object):
def __init__(self):
pass
def parse_vegaZero(self, vega_zero):
self.parsed_vegaZero = {
'mark': '',
'data': '',
'encoding': {
'x': '',
'y': {
... | en | 0.750747 | # replace 'and' -- 'or' # ’=‘ in SQL --to--> ’==‘ in Vega-Lite # each = '&' or '|' # only consider this case: '%a%' # only single filter condition # assign some vega-zero keywords to the VegaLiteSpec object # it seems that the group will be performed by VegaLite defaultly, in our cases. | 2.988262 | 3 |
utils/dancer.py | kmzbrnoI/ac-python | 0 | 4005 | """Library for executing user-defined dance."""
import logging
from typing import Any, Dict, Optional, Callable
import datetime
import ac
import ac.blocks
from ac import ACs, AC
JC = Dict[str, Any]
class DanceStartException(Exception):
pass
class Step:
"""Base class for all specific dance steps."""
... | """Library for executing user-defined dance."""
import logging
from typing import Any, Dict, Optional, Callable
import datetime
import ac
import ac.blocks
from ac import ACs, AC
JC = Dict[str, Any]
class DanceStartException(Exception):
pass
class Step:
"""Base class for all specific dance steps."""
... | en | 0.81087 | Library for executing user-defined dance. Base class for all specific dance steps. Process jc 'name'. If processed already, skip processing and continue. Delay any time. Wait for specific state of any block. See examples below. This AC executes predefined steps. # type: ignore | 2.637551 | 3 |
praw/models/reddit/mixins/reportable.py | zachwylde00/praw | 38 | 4006 | <gh_stars>10-100
"""Provide the ReportableMixin class."""
from ....const import API_PATH
class ReportableMixin:
"""Interface for RedditBase classes that can be reported."""
def report(self, reason):
"""Report this object to the moderators of its subreddit.
:param reason: The reason for repor... | """Provide the ReportableMixin class."""
from ....const import API_PATH
class ReportableMixin:
"""Interface for RedditBase classes that can be reported."""
def report(self, reason):
"""Report this object to the moderators of its subreddit.
:param reason: The reason for reporting.
Ra... | en | 0.7603 | Provide the ReportableMixin class. Interface for RedditBase classes that can be reported. Report this object to the moderators of its subreddit. :param reason: The reason for reporting. Raises :class:`.APIException` if ``reason`` is longer than 100 characters. Example usage: ... | 2.757044 | 3 |
defense/jpeg_compress.py | TrustworthyDL/LeBA | 19 | 4007 | def _jpeg_compression(im):
assert torch.is_tensor(im)
im = ToPILImage()(im)
savepath = BytesIO()
im.save(savepath, 'JPEG', quality=75)
im = Image.open(savepath)
im = ToTensor()(im)
return im | def _jpeg_compression(im):
assert torch.is_tensor(im)
im = ToPILImage()(im)
savepath = BytesIO()
im.save(savepath, 'JPEG', quality=75)
im = Image.open(savepath)
im = ToTensor()(im)
return im | none | 1 | 2.53273 | 3 | |
mellon/factories/filesystem/file.py | LaudateCorpus1/mellon | 5 | 4008 | <reponame>LaudateCorpus1/mellon
import collections
import os.path
from zope import component
from zope import interface
from zope.component.factory import Factory
from sparc.configuration import container
import mellon
@interface.implementer(mellon.IByteMellonFile)
class MellonByteFileFromFilePathAndConfig(object):
... | import collections
import os.path
from zope import component
from zope import interface
from zope.component.factory import Factory
from sparc.configuration import container
import mellon
@interface.implementer(mellon.IByteMellonFile)
class MellonByteFileFromFilePathAndConfig(object):
def __init__(self, file_p... | en | 0.268955 | Init Args: config: sparc.configuration.container.ISparcAppPyContainerConfiguration provider with mellon.factories.filesystem[configure.yaml:FileSystemDir] and mellon[configure.yaml:MellonSnippet] entries. #get interface-assigned s... | 2.083489 | 2 |
dltb/thirdparty/datasource/__init__.py | CogSciUOS/DeepLearningToolbox | 2 | 4009 | <gh_stars>1-10
"""Predefined Datasources.
"""
# toolbox imports
from ...datasource import Datasource
Datasource.register_instance('imagenet-val', __name__ + '.imagenet',
'ImageNet', section='val') # section='train'
Datasource.register_instance('dogsandcats', __name__ + '.dogsandcats',
... | """Predefined Datasources.
"""
# toolbox imports
from ...datasource import Datasource
Datasource.register_instance('imagenet-val', __name__ + '.imagenet',
'ImageNet', section='val') # section='train'
Datasource.register_instance('dogsandcats', __name__ + '.dogsandcats',
... | en | 0.484569 | Predefined Datasources. # toolbox imports # section='train' | 1.752405 | 2 |
tests/test_results.py | babinyurii/RECAN | 7 | 4010 | <reponame>babinyurii/RECAN
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 22 15:58:44 2019
@author: babin
"""
posits_def = [251, 501, 751, 1001, 1251, 1501, 1751, 2001, 2251, 2501, 2751, 3001, 3215]
dist_whole_align_ref = {'AB048704.1_genotype_C_':
[0.88,
0.938,
0.914,
0.886,
0.89,
0.908,
0.938,
0.9... | # -*- coding: utf-8 -*-
"""
Created on Tue Oct 22 15:58:44 2019
@author: babin
"""
posits_def = [251, 501, 751, 1001, 1251, 1501, 1751, 2001, 2251, 2501, 2751, 3001, 3215]
dist_whole_align_ref = {'AB048704.1_genotype_C_':
[0.88,
0.938,
0.914,
0.886,
0.89,
0.908,
0.938,
0.948,
0.948,
0.886,
0.8... | en | 0.808555 | # -*- coding: utf-8 -*- Created on Tue Oct 22 15:58:44 2019 @author: babin | 1.425331 | 1 |
lxmls/readers/simple_data_set.py | SimonSuster/lxmls-toolkit | 1 | 4011 | import numpy as np
# This class generates a 2D dataset with two classes, "positive" and "negative".
# Each class follows a Gaussian distribution.
class SimpleDataSet():
''' A simple two dimentional dataset for visualization purposes. The date set contains points from two gaussians with mean u_i and std_i'''
d... | import numpy as np
# This class generates a 2D dataset with two classes, "positive" and "negative".
# Each class follows a Gaussian distribution.
class SimpleDataSet():
''' A simple two dimentional dataset for visualization purposes. The date set contains points from two gaussians with mean u_i and std_i'''
d... | en | 0.765766 | # This class generates a 2D dataset with two classes, "positive" and "negative". # Each class follows a Gaussian distribution. A simple two dimentional dataset for visualization purposes. The date set contains points from two gaussians with mean u_i and std_i # number of examples of "positive" class # number of example... | 3.396599 | 3 |
set1/c06_attack_repeating_key_xor.py | kangtastic/cryptopals | 1 | 4012 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Break repeating-key XOR
#
# It is officially on, now.
#
# This challenge isn't conceptually hard, but it involves actual
# error-prone coding. The other challenges in this set are there to bring
# you up to speed. This one is there to qualify you. If you can do t... | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Break repeating-key XOR
#
# It is officially on, now.
#
# This challenge isn't conceptually hard, but it involves actual
# error-prone coding. The other challenges in this set are there to bring
# you up to speed. This one is there to qualify you. If you can do t... | en | 0.912147 | #!/usr/bin/env python3 # -*- coding: utf-8 -*- # # Break repeating-key XOR # # It is officially on, now. # # This challenge isn't conceptually hard, but it involves actual # error-prone coding. The other challenges in this set are there to bring # you up to speed. This one is there to qualify you. If you can do t... | 3.679234 | 4 |
c2nl/models/transformer.py | kopf-yhs/ncscos | 22 | 4013 | import torch
import torch.nn as nn
import torch.nn.functional as f
from prettytable import PrettyTable
from c2nl.modules.char_embedding import CharEmbedding
from c2nl.modules.embeddings import Embeddings
from c2nl.modules.highway import Highway
from c2nl.encoders.transformer import TransformerEncoder
from c2nl.decoder... | import torch
import torch.nn as nn
import torch.nn.functional as f
from prettytable import PrettyTable
from c2nl.modules.char_embedding import CharEmbedding
from c2nl.modules.embeddings import Embeddings
from c2nl.modules.highway import Highway
from c2nl.encoders.transformer import TransformerEncoder
from c2nl.decoder... | en | 0.52787 | # at least one of word or char embedding options should be True # B x P x d # B x P x f # B x P x d+f # B x P x d+f # B x P x d # B x P x f # B x P x d+f # B x P x d+f # used in inference time # B x seq_len x h # B x seq_len x nlayers x h # Following (https://arxiv.org/pdf/1808.07913.pdf), we split decoder # To accompl... | 1.92915 | 2 |
cattle/plugins/docker/delegate.py | cjellick/python-agent | 8 | 4014 | import logging
from cattle import Config
from cattle.utils import reply, popen
from .compute import DockerCompute
from cattle.agent.handler import BaseHandler
from cattle.progress import Progress
from cattle.type_manager import get_type, MARSHALLER
from . import docker_client
import subprocess
import os
import time
... | import logging
from cattle import Config
from cattle.utils import reply, popen
from .compute import DockerCompute
from cattle.agent.handler import BaseHandler
from cattle.progress import Progress
from cattle.type_manager import get_type, MARSHALLER
from . import docker_client
import subprocess
import os
import time
... | en | 0.704104 | # Sleep and try again if missing | 1.896255 | 2 |
bitraider/strategy.py | ehickox2012/bitraider | 2 | 4015 | import sys
import pytz
#import xml.utils.iso8601
import time
import numpy
from datetime import date, datetime, timedelta
from matplotlib import pyplot as plt
from exchange import cb_exchange as cb_exchange
from exchange import CoinbaseExchangeAuth
from abc import ABCMeta, abstractmethod
class strategy(object):
"""... | import sys
import pytz
#import xml.utils.iso8601
import time
import numpy
from datetime import date, datetime, timedelta
from matplotlib import pyplot as plt
from exchange import cb_exchange as cb_exchange
from exchange import CoinbaseExchangeAuth
from abc import ABCMeta, abstractmethod
class strategy(object):
"""... | en | 0.717355 | #import xml.utils.iso8601 `strategy` defines an abstract base strategy class. Minimum required to create a strategy is a file with a class which inherits from strategy containing a backtest_strategy function. As a bonus, strategy includes utility functions like calculate_historic_data. Constructor for an abstract strat... | 3.40437 | 3 |
neural-networks.py | PacktPublishing/Python-Deep-Learning-for-Beginners- | 7 | 4016 | <filename>neural-networks.py
import numpy as np
# Perceptron
def predict_perceptron(inputs, weights):
if np.dot(inputs, weights) > 0:
return 1
else:
return 0
def predict_perceptron_proper(inputs, weights):
def step_function(input):
return 1 if input > 0 else 0
def linear_mode... | <filename>neural-networks.py
import numpy as np
# Perceptron
def predict_perceptron(inputs, weights):
if np.dot(inputs, weights) > 0:
return 1
else:
return 0
def predict_perceptron_proper(inputs, weights):
def step_function(input):
return 1 if input > 0 else 0
def linear_mode... | en | 0.405554 | # Perceptron | 3.598567 | 4 |
biggan_discovery/orojar_discover.py | andreasjansson/OroJaR | 47 | 4017 | """
Learns a matrix of Z-Space directions using a pre-trained BigGAN Generator.
Modified from train.py in the PyTorch BigGAN repo.
"""
import os
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.optim
import utils
import train_fns
from sync_batchnorm import patch_replication_callback
from torch.u... | """
Learns a matrix of Z-Space directions using a pre-trained BigGAN Generator.
Modified from train.py in the PyTorch BigGAN repo.
"""
import os
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.optim
import utils
import train_fns
from sync_batchnorm import patch_replication_callback
from torch.u... | en | 0.830409 | Learns a matrix of Z-Space directions using a pre-trained BigGAN Generator. Modified from train.py in the PyTorch BigGAN repo. This is simply a wrapper class to compute the OroJaR efficiently over several GPUs # The main training file. Config is a dictionary specifying the configuration # of this training run. # Downlo... | 2.347137 | 2 |
file_importer0.py | Alva789ro/Regional-Comprehensive-Economic-Partnership-RCEP-Economic-Default-Risk-Analysis | 1 | 4018 | import xlsxwriter
import pandas as pd
import numpy as np
import mysql.connector
australia=pd.read_excel(r'\Users\jesica\Desktop\RCEP_economic_analysis.xlsx', sheet_name='Australia')
brunei=pd.read_excel(r'\Users\jesica\Desktop\RCEP_economic_analysis.xlsx', sheet_name='Brunei')
cambodia=pd.read_excel(r'\Users\jesica\De... | import xlsxwriter
import pandas as pd
import numpy as np
import mysql.connector
australia=pd.read_excel(r'\Users\jesica\Desktop\RCEP_economic_analysis.xlsx', sheet_name='Australia')
brunei=pd.read_excel(r'\Users\jesica\Desktop\RCEP_economic_analysis.xlsx', sheet_name='Brunei')
cambodia=pd.read_excel(r'\Users\jesica\De... | en | 0.377304 | mydb = mysql.connector.connect( host = "localhost", user = "root", passwd = "", database = "" ) mycursor = mydb.cursor() sqlformula1 = "INSERT INTO australia VALUES(%s, %s, %s, %s, %s, %s, %s, %s)" for a, b, c, d, e, f, g, h in zip(australia['Year'], australia['RGDP'], australia['NGDP'], australia['GD... | 2.246032 | 2 |
packer/resources/bootstrap_node.py | VIOOH/nile | 4 | 4019 | #!/usr/bin/env python3
import os
import re
import glob
import boto3
import requests
import subprocess
from time import sleep
AWS_REGION = os.environ['AWS_REGION']
DEPLOY_UUID = os.environ['DEPLOY_UUID']
SERVICE_NAME = os.environ['SERVICE_NAME']
MOUNT_POINT = "/var/lib/" + SERVICE_NAME... | #!/usr/bin/env python3
import os
import re
import glob
import boto3
import requests
import subprocess
from time import sleep
AWS_REGION = os.environ['AWS_REGION']
DEPLOY_UUID = os.environ['DEPLOY_UUID']
SERVICE_NAME = os.environ['SERVICE_NAME']
MOUNT_POINT = "/var/lib/" + SERVICE_NAME... | en | 0.374695 | #!/usr/bin/env python3 # Wait to ensure device is attached # p_partprobe = subprocess.Popen('partprobe'.split(' '), stdout=subprocess.PIPE) # stdout, stderr = p_partprobe.communicate() # print(stdout) # print(stderr) # uses: DEPLOY_UUID, TAG_KEY # uses: MOUNT_POINT, SERVICE_NAME, DEPLOY_UUID, TAG_KEY # uses: NIC_IP | 2.028274 | 2 |
parsers/srum_parser.py | otoriocyber/Chronos | 12 | 4020 | import csv
import datetime
import random
import os
from parsers.parser_base import ParserBase
FILE_TIME_EPOCH = datetime.datetime(1601, 1, 1)
FILE_TIME_MICROSECOND = 10
def filetime_to_epoch_datetime(file_time):
if isinstance(file_time, int):
microseconds_since_file_time_epoch = file_time / FILE_TIME_MIC... | import csv
import datetime
import random
import os
from parsers.parser_base import ParserBase
FILE_TIME_EPOCH = datetime.datetime(1601, 1, 1)
FILE_TIME_MICROSECOND = 10
def filetime_to_epoch_datetime(file_time):
if isinstance(file_time, int):
microseconds_since_file_time_epoch = file_time / FILE_TIME_MIC... | none | 1 | 2.331956 | 2 | |
tests/csrf_tests/test_context_processor.py | Yoann-Vie/esgi-hearthstone | 0 | 4021 | from django.http import HttpRequest
from django.middleware.csrf import _compare_salted_tokens as equivalent_tokens
from django.template.context_processors import csrf
from django.test import SimpleTestCase
class TestContextProcessor(SimpleTestCase):
def test_force_token_to_string(self):
request ... | from django.http import HttpRequest
from django.middleware.csrf import _compare_salted_tokens as equivalent_tokens
from django.template.context_processors import csrf
from django.test import SimpleTestCase
class TestContextProcessor(SimpleTestCase):
def test_force_token_to_string(self):
request ... | none | 1 | 2.324888 | 2 | |
python/das/types.py | marza-animation-planet/das | 4 | 4022 | <filename>python/das/types.py
import sys
import das
import traceback
class ReservedNameError(Exception):
def __init__(self, name):
super(ReservedNameError, self).__init__("'%s' is a reserved name" % name)
class VersionError(Exception):
def __init__(self, msg=None, current_version=None, required_version=... | <filename>python/das/types.py
import sys
import das
import traceback
class ReservedNameError(Exception):
def __init__(self, name):
super(ReservedNameError, self).__init__("'%s' is a reserved name" % name)
class VersionError(Exception):
def __init__(self, msg=None, current_version=None, required_version=... | en | 0.726032 | # Always re-raise exception # run self validation first (container validation) # Skip global validaton # Funny, we need to declare *args here, but at the time we reach # the core of the method, tuple is already created # Maybe because tuple is immutable? # def __contains__(self, y): # try: # _v = self._adapt_v... | 2.401783 | 2 |
track.py | AliabbasMerchant/fileTrackAndBackup | 6 | 4023 | #! /usr/bin/python3
from help import *
import time
# short-forms are used, so as to reduce the .json file size
# t : type - d or f
# d : directory
# f : file
# ts : timestamp
# dirs : The dictionary containing info about directory contents
# time : edit time of the file/folder
# s : size of the file/folder
# p : full ... | #! /usr/bin/python3
from help import *
import time
# short-forms are used, so as to reduce the .json file size
# t : type - d or f
# d : directory
# f : file
# ts : timestamp
# dirs : The dictionary containing info about directory contents
# time : edit time of the file/folder
# s : size of the file/folder
# p : full ... | en | 0.352676 | #! /usr/bin/python3 # short-forms are used, so as to reduce the .json file size # t : type - d or f # d : directory # f : file # ts : timestamp # dirs : The dictionary containing info about directory contents # time : edit time of the file/folder # s : size of the file/folder # p : full path of the file/folder # n : na... | 2.705058 | 3 |
clang/tools/scan-build-py/libscanbuild/analyze.py | Kvarnefalk/llvm-project | 1 | 4024 | <filename>clang/tools/scan-build-py/libscanbuild/analyze.py<gh_stars>1-10
# -*- coding: utf-8 -*-
# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
""" This module implemen... | <filename>clang/tools/scan-build-py/libscanbuild/analyze.py<gh_stars>1-10
# -*- coding: utf-8 -*-
# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
""" This module implemen... | en | 0.841132 | # -*- coding: utf-8 -*- # Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. # See https://llvm.org/LICENSE.txt for license information. # SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception This module implements the 'scan-build' command API. To run the static analyzer against a build i... | 1.988493 | 2 |
tableborder.py | PIRXrav/pyhack | 0 | 4025 | <gh_stars>0
#!/usr/bin/env python3
# pylint: disable=C0103
# pylint: disable=R0902
# pylint: disable=R0903
# pylint: disable=R0913
"""
Définie la classe TableBorder
"""
class TableBorder:
"""
Facillite l'usage de l'UNICODE
"""
def __init__(self,
top_left, top_split, top_right,
... | #!/usr/bin/env python3
# pylint: disable=C0103
# pylint: disable=R0902
# pylint: disable=R0903
# pylint: disable=R0913
"""
Définie la classe TableBorder
"""
class TableBorder:
"""
Facillite l'usage de l'UNICODE
"""
def __init__(self,
top_left, top_split, top_right,
mi... | fr | 0.362801 | #!/usr/bin/env python3 # pylint: disable=C0103 # pylint: disable=R0902 # pylint: disable=R0903 # pylint: disable=R0913 Définie la classe TableBorder Facillite l'usage de l'UNICODE Constructeur | 2.64941 | 3 |
app/urls.py | tkf2019/Vue-Django-SAST-Search | 0 | 4026 | <gh_stars>0
from django.conf.urls import url
from . import views
urlpatterns = [
url(r'^register/', views.register),
url(r'^login/', views.login),
url(r'logout/', views.logout),
url(r'search/', views.search)
]
| from django.conf.urls import url
from . import views
urlpatterns = [
url(r'^register/', views.register),
url(r'^login/', views.login),
url(r'logout/', views.logout),
url(r'search/', views.search)
] | none | 1 | 1.595269 | 2 | |
custom_components/hasl/sensor.py | Ziqqo/hasl-platform | 0 | 4027 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""Simple service for SL (Storstockholms Lokaltrafik)."""
import datetime
import json
import logging
from datetime import timedelta
import homeassistant.helpers.config_validation as cv
import voluptuous as vol
from homeassistant.components.sensor import PLATFORM_SCHEMA
from ho... | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""Simple service for SL (Storstockholms Lokaltrafik)."""
import datetime
import json
import logging
from datetime import timedelta
import homeassistant.helpers.config_validation as cv
import voluptuous as vol
from homeassistant.components.sensor import PLATFORM_SCHEMA
from ho... | en | 0.685498 | #!/usr/bin/python # -*- coding: utf-8 -*- Simple service for SL (Storstockholms Lokaltrafik). # Keys used in the configuration. # Default values for configuration. # Defining the configuration schema. # API Keys Setup the sensors. Trafic Situation Sensor. Return the name of the sensor. Return the icon for the frontend.... | 1.940315 | 2 |
simbad_tools.py | ishivvers/astro | 1 | 4028 | """
A quick library to deal with searching simbad for info
about a SN and parsing the results.
Author: <NAME>, <EMAIL>, 2014
example SIMBAD uri query:
http://simbad.u-strasbg.fr/simbad/sim-id?output.format=ASCII&Ident=sn%201998S
"""
import re
from urllib2 import urlopen
def get_SN_info( name ):
"""
Querie... | """
A quick library to deal with searching simbad for info
about a SN and parsing the results.
Author: <NAME>, <EMAIL>, 2014
example SIMBAD uri query:
http://simbad.u-strasbg.fr/simbad/sim-id?output.format=ASCII&Ident=sn%201998S
"""
import re
from urllib2 import urlopen
def get_SN_info( name ):
"""
Querie... | en | 0.736947 | A quick library to deal with searching simbad for info about a SN and parsing the results. Author: <NAME>, <EMAIL>, 2014 example SIMBAD uri query: http://simbad.u-strasbg.fr/simbad/sim-id?output.format=ASCII&Ident=sn%201998S Queries simbad for SN coords, redshift, and host galaxy. If redshift is not given for S... | 3.154911 | 3 |
robots/environments.py | StanfordASL/soft-robot-control | 5 | 4029 | import os
from math import cos
from math import sin
import Sofa.Core
from splib.numerics import Quat, Vec3
from sofacontrol import measurement_models
path = os.path.dirname(os.path.abspath(__file__))
class TemplateEnvironment:
def __init__(self, name='Template', rayleighMass=0.1, rayleighStiffness=0.1, dt=0.01... | import os
from math import cos
from math import sin
import Sofa.Core
from splib.numerics import Quat, Vec3
from sofacontrol import measurement_models
path = os.path.dirname(os.path.abspath(__file__))
class TemplateEnvironment:
def __init__(self, name='Template', rayleighMass=0.1, rayleighStiffness=0.1, dt=0.01... | en | 0.57657 | # set-up solvers # default # Without premultiplication with dt # Option 1: # Option 2: Equivalent to option 1 (we believe) # self.robot.addObject('MechanicalObject', src='@loader') # Gives a mass to the model # Add a TetrahedronFEMForceField componant which implement an elastic material model solved using the Finite # ... | 2.102496 | 2 |
default.py | SimonPreissner/get-shifty | 0 | 4030 | """
This file contains meta information and default configurations of the project
"""
RSC_YEARS = [1660, 1670, 1680, 1690,
1700, 1710, 1720, 1730, 1740, 1750, 1760, 1770, 1780, 1790,
1800, 1810, 1820, 1830, 1840, 1850, 1860, 1870, 1880, 1890,
1900, 1910, 1920]
# cf. Chapter 4.... | """
This file contains meta information and default configurations of the project
"""
RSC_YEARS = [1660, 1670, 1680, 1690,
1700, 1710, 1720, 1730, 1740, 1750, 1760, 1770, 1780, 1790,
1800, 1810, 1820, 1830, 1840, 1850, 1860, 1870, 1880, 1890,
1900, 1910, 1920]
# cf. Chapter 4.... | en | 0.674842 | This file contains meta information and default configurations of the project # cf. Chapter 4.4.1 of the thesis # Alternatives # parameters passed to the GWOT object # 'euclidian', # 'mean', 'whiten', 'whiten_zca' # 'max', 'median' # #TODO fill in the rest of the options in the comments # 'csls', ... # 'custom', 'zipf'... | 1.652773 | 2 |
generate_training_data_drb.py | SimonTopp/Graph-WaveNet | 0 | 4031 | <reponame>SimonTopp/Graph-WaveNet
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import numpy as np
import os
import pandas as pd
import util
import os.path
import pandas as pd
import n... | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import numpy as np
import os
import pandas as pd
import util
import os.path
import pandas as pd
import numpy as np
import yaml
import xa... | en | 0.600793 | scale the data so it has a standard deviation of 1 and a mean of zero
:param dataset: [xr dataset] input or output data
:param std: [xr dataset] standard deviation if scaling test data with dims
:param mean: [xr dataset] mean if scaling test data with dims
:return: scaled data with original dims # a... | 2.75891 | 3 |
Phase-1/Python Basic 1/Day-3.py | CodedLadiesInnovateTech/python-challenges | 11 | 4032 | <reponame>CodedLadiesInnovateTech/python-challenges
<<<<<<< HEAD
"""
1. Write a Python program to print the documents (syntax, description etc.) of Python built-in function(s).
Sample function : abs()
Expected Result :
abs(number) -> number
Return the absolute value of the argument.
... | <<<<<<< HEAD
"""
1. Write a Python program to print the documents (syntax, description etc.) of Python built-in function(s).
Sample function : abs()
Expected Result :
abs(number) -> number
Return the absolute value of the argument.
Tools: help function
2. Write a Python program to p... | en | 0.732932 | 1. Write a Python program to print the documents (syntax, description etc.) of Python built-in function(s). Sample function : abs() Expected Result : abs(number) -> number Return the absolute value of the argument. Tools: help function 2. Write a Python program to print the calendar... | 4.767434 | 5 |
tests/python/metaclass_inheritance.py | gmgunter/pyre | 25 | 4033 | <filename>tests/python/metaclass_inheritance.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# <NAME>. aïvázis
# orthologue
# (c) 1998-2021 all rights reserved
#
#
"""
When a metaclass understands the extra keywords that can be passed during class declaration,
it has to override all these to accommodate the chang... | <filename>tests/python/metaclass_inheritance.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# <NAME>. aïvázis
# orthologue
# (c) 1998-2021 all rights reserved
#
#
"""
When a metaclass understands the extra keywords that can be passed during class declaration,
it has to override all these to accommodate the chang... | en | 0.83108 | #!/usr/bin/env python3 # -*- coding: utf-8 -*- # # <NAME>. aïvázis # orthologue # (c) 1998-2021 all rights reserved # # When a metaclass understands the extra keywords that can be passed during class declaration, it has to override all these to accommodate the change in signature # main # end of file | 2.934571 | 3 |
cs101/module8/8-1/chroma1.py | idsdlab/basicai_sp21 | 1 | 4034 |
from cs1media import *
import math
def dist(c1, c2):
r1, g1, b1 = c1
r2, g2, b2 = c2
return math.sqrt((r1-r2)**2 + (g1-g2)**2 + (b1-b2)**2)
def chroma(img, key, threshold):
w, h = img.size()
for y in range(h):
for x in range(w):
p = img.get(x, y)
if dist(p, key) < threshold:
img.set... |
from cs1media import *
import math
def dist(c1, c2):
r1, g1, b1 = c1
r2, g2, b2 = c2
return math.sqrt((r1-r2)**2 + (g1-g2)**2 + (b1-b2)**2)
def chroma(img, key, threshold):
w, h = img.size()
for y in range(h):
for x in range(w):
p = img.get(x, y)
if dist(p, key) < threshold:
img.set... | none | 1 | 3.396655 | 3 | |
wfirst_stars/mklc.py | RuthAngus/wfirst_stars | 0 | 4035 | import numpy as np
import scipy
import scipy.io
import pylab
import numpy
import glob
import pyfits
def mklc(t, nspot=200, incl=(scipy.pi)*5./12., amp=1., tau=30.5, p=10.0):
diffrot = 0.
''' This is a simplified version of the class-based routines in
spot_model.py. It generates a light curves for dark, p... | import numpy as np
import scipy
import scipy.io
import pylab
import numpy
import glob
import pyfits
def mklc(t, nspot=200, incl=(scipy.pi)*5./12., amp=1., tau=30.5, p=10.0):
diffrot = 0.
''' This is a simplified version of the class-based routines in
spot_model.py. It generates a light curves for dark, p... | en | 0.840359 | This is a simplified version of the class-based routines in spot_model.py. It generates a light curves for dark, point like spots with no limb-darkening. Parameters: nspot = desired number of spots present on star at any one time amp = desired light curve amplitude tau = characteris... | 2.618161 | 3 |
bin/sort.py | pelavarre/pybashish | 4 | 4036 | <filename>bin/sort.py
#!/usr/bin/env python3
"""
usage: sort.py [-h]
sort lines
options:
-h, --help show this help message and exit
quirks:
sorts tabs as different than spaces
sorts some spaces ending a line as different than none ending a line
examples:
Oh no! No examples disclosed!! 💥 💔 💥
"""
# FIXME... | <filename>bin/sort.py
#!/usr/bin/env python3
"""
usage: sort.py [-h]
sort lines
options:
-h, --help show this help message and exit
quirks:
sorts tabs as different than spaces
sorts some spaces ending a line as different than none ending a line
examples:
Oh no! No examples disclosed!! 💥 💔 💥
"""
# FIXME... | en | 0.798646 | #!/usr/bin/env python3 usage: sort.py [-h] sort lines options: -h, --help show this help message and exit quirks: sorts tabs as different than spaces sorts some spaces ending a line as different than none ending a line examples: Oh no! No examples disclosed!! 💥 💔 💥 # FIXME: doc -k$N,$N and -n and maybe ... | 3.207767 | 3 |
davan/http/service/telldus/tdtool.py | davandev/davanserver | 0 | 4037 | <reponame>davandev/davanserver<gh_stars>0
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys, getopt, httplib, urllib, json, os
import oauth.oauth as oauth
import datetime
from configobj import ConfigObj
import logging
global logger
logger = logging.getLogger(os.path.basename(__file__))
import davan.util.appli... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys, getopt, httplib, urllib, json, os
import oauth.oauth as oauth
import datetime
from configobj import ConfigObj
import logging
global logger
logger = logging.getLogger(os.path.basename(__file__))
import davan.util.application_logger as log_manager
#insert your ... | en | 0.339509 | #!/usr/bin/env python # -*- coding: utf-8 -*- #insert your own public_key and private_key | 2.249626 | 2 |
ichnaea/data/export.py | rajreet/ichnaea | 348 | 4038 | from collections import defaultdict
import json
import re
import time
from urllib.parse import urlparse
import uuid
import boto3
import boto3.exceptions
import botocore.exceptions
import markus
import redis.exceptions
import requests
import requests.exceptions
from sqlalchemy import select
import sqlalchemy.exc
from ... | from collections import defaultdict
import json
import re
import time
from urllib.parse import urlparse
import uuid
import boto3
import boto3.exceptions
import botocore.exceptions
import markus
import redis.exceptions
import requests
import requests.exceptions
from sqlalchemy import select
import sqlalchemy.exc
from ... | en | 0.780739 | The incoming queue contains the data collected in the web application. It is the single entrypoint from which all other data pipelines get their data. It distributes the data into the configured export queues, checks those queues and if they contain enough or old enough data schedules an async expo... | 2.174272 | 2 |
test/inference_correctness/dcn_multi_hot.py | x-y-z/HugeCTR | 130 | 4039 | <filename>test/inference_correctness/dcn_multi_hot.py
import hugectr
from mpi4py import MPI
solver = hugectr.CreateSolver(model_name = "dcn",
max_eval_batches = 1,
batchsize_eval = 16384,
batchsize = 16384,
... | <filename>test/inference_correctness/dcn_multi_hot.py
import hugectr
from mpi4py import MPI
solver = hugectr.CreateSolver(model_name = "dcn",
max_eval_batches = 1,
batchsize_eval = 16384,
batchsize = 16384,
... | none | 1 | 1.85315 | 2 | |
bindings/pydrake/systems/perception.py | RobotLocomotion/drake-python3.7 | 2 | 4040 | <reponame>RobotLocomotion/drake-python3.7
import numpy as np
from pydrake.common.value import AbstractValue
from pydrake.math import RigidTransform
from pydrake.perception import BaseField, Fields, PointCloud
from pydrake.systems.framework import LeafSystem
def _TransformPoints(points_Ci, X_CiSi):
# Make homogen... | import numpy as np
from pydrake.common.value import AbstractValue
from pydrake.math import RigidTransform
from pydrake.perception import BaseField, Fields, PointCloud
from pydrake.systems.framework import LeafSystem
def _TransformPoints(points_Ci, X_CiSi):
# Make homogeneous copy of points.
points_h_Ci = np.... | en | 0.795661 | # Make homogeneous copy of points. # Need manual broadcasting. .. pydrake_system:: name: PointCloudConcatenation input_ports: - point_cloud_CiSi_id0 - X_FCi_id0 - ... - point_cloud_CiSi_idN - X_FCi_idN output_ports: - point_cloud_FS A system that ... | 2.304374 | 2 |
experiments/db_test.py | mit-ll/CATAN | 15 | 4041 | <gh_stars>10-100
#!/usr/bin/env python
"""
@author <NAME>
© 2015 Massachusetts Institute of Technology
"""
import argparse
import random
import catan.db
from catan.data import NodeMessage
# test data
STATUS_LIST = ['ok', 'injured', 'deceased']
# nodes
def gen_nodes(n, db, start_lat, stop_lat, start_long, stop_long... | #!/usr/bin/env python
"""
@author <NAME>
© 2015 Massachusetts Institute of Technology
"""
import argparse
import random
import catan.db
from catan.data import NodeMessage
# test data
STATUS_LIST = ['ok', 'injured', 'deceased']
# nodes
def gen_nodes(n, db, start_lat, stop_lat, start_long, stop_long):
assert n ... | en | 0.701928 | #!/usr/bin/env python @author <NAME> © 2015 Massachusetts Institute of Technology # test data # nodes # generate n random nodes, centered around Cambridge # random lat, long # node_id, gps_lat, gps_long, gps_acc, path, timestamp # people Generates n people, random male/female ratio between 5 and 90 years of age # open... | 2.861026 | 3 |
Medium/200.py | Hellofafar/Leetcode | 6 | 4042 | <gh_stars>1-10
# ------------------------------
# 200. Number of Islands
#
# Description:
# Given a 2d grid map of '1's (land) and '0's (water), count the number of islands. An island is surrounded by water and is formed by connecting adjacent lands horizontally or vertically. You may assume all four edges of the grid... | # ------------------------------
# 200. Number of Islands
#
# Description:
# Given a 2d grid map of '1's (land) and '0's (water), count the number of islands. An island is surrounded by water and is formed by connecting adjacent lands horizontally or vertically. You may assume all four edges of the grid are all surrou... | en | 0.629582 | # ------------------------------ # 200. Number of Islands # # Description: # Given a 2d grid map of '1's (land) and '0's (water), count the number of islands. An island is surrounded by water and is formed by connecting adjacent lands horizontally or vertically. You may assume all four edges of the grid are all surroun... | 3.942081 | 4 |
tests/formatters/fseventsd.py | SamuelePilleri/plaso | 0 | 4043 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Tests for the fseventsd record event formatter."""
from __future__ import unicode_literals
import unittest
from plaso.formatters import fseventsd
from tests.formatters import test_lib
class FseventsdFormatterTest(test_lib.EventFormatterTestCase):
"""Tests for the... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Tests for the fseventsd record event formatter."""
from __future__ import unicode_literals
import unittest
from plaso.formatters import fseventsd
from tests.formatters import test_lib
class FseventsdFormatterTest(test_lib.EventFormatterTestCase):
"""Tests for the... | en | 0.602035 | #!/usr/bin/env python # -*- coding: utf-8 -*- Tests for the fseventsd record event formatter. Tests for the fseventsd record event formatter. Tests the initialization. Tests the GetFormatStringAttributeNames function. # TODO: add test for GetSources. | 2.480531 | 2 |
train.py | Farzin-Negahbani/PathoNet | 0 | 4044 | from keras.callbacks import ModelCheckpoint,Callback,LearningRateScheduler,TensorBoard
from keras.models import load_model
import random
import numpy as np
from scipy import misc
import gc
from keras.optimizers import Adam
from imageio import imread
from datetime import datetime
import os
import json
import models
from... | from keras.callbacks import ModelCheckpoint,Callback,LearningRateScheduler,TensorBoard
from keras.models import load_model
import random
import numpy as np
from scipy import misc
import gc
from keras.optimizers import Adam
from imageio import imread
from datetime import datetime
import os
import json
import models
from... | none | 1 | 2.036933 | 2 | |
tests/chainer_tests/functions_tests/array_tests/test_flatten.py | mingxiaoh/chainer-v3 | 7 | 4045 | import unittest
import numpy
import chainer
from chainer import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
@testing.parameterize(*testing.product({
'shape': [(3, 4), ()],
'dtype': [numpy.float16, numpy.float32, numpy.flo... | import unittest
import numpy
import chainer
from chainer import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
@testing.parameterize(*testing.product({
'shape': [(3, 4), ()],
'dtype': [numpy.float16, numpy.float32, numpy.flo... | none | 1 | 2.58771 | 3 | |
categories/migrations/0001_initial.py | snoop2head/exercise_curation_django | 3 | 4046 | # Generated by Django 3.0.3 on 2020-03-24 09:59
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
('exercises', '0018_photo_file'),
]
operations = [
migrations.CreateModel(
na... | # Generated by Django 3.0.3 on 2020-03-24 09:59
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
('exercises', '0018_photo_file'),
]
operations = [
migrations.CreateModel(
na... | en | 0.747411 | # Generated by Django 3.0.3 on 2020-03-24 09:59 | 1.778413 | 2 |
src/metarl/envs/dm_control/dm_control_env.py | neurips2020submission11699/metarl | 2 | 4047 | <filename>src/metarl/envs/dm_control/dm_control_env.py<gh_stars>1-10
from dm_control import suite
from dm_control.rl.control import flatten_observation
from dm_env import StepType
import gym
import numpy as np
from metarl.envs import Step
from metarl.envs.dm_control.dm_control_viewer import DmControlViewer
class DmC... | <filename>src/metarl/envs/dm_control/dm_control_env.py<gh_stars>1-10
from dm_control import suite
from dm_control.rl.control import flatten_observation
from dm_env import StepType
import gym
import numpy as np
from metarl.envs import Step
from metarl.envs.dm_control.dm_control_viewer import DmControlViewer
class DmC... | en | 0.482278 | Binding for `dm_control <https://arxiv.org/pdf/1801.00690.pdf>`_ # pylint: disable=inconsistent-return-statements | 2.299811 | 2 |
python_modules/lakehouse/lakehouse/snowflake_table.py | vatervonacht/dagster | 3 | 4048 | from dagster import check
from .house import Lakehouse
from .table import create_lakehouse_table_def
class SnowflakeLakehouse(Lakehouse):
def __init__(self):
pass
def hydrate(self, _context, _table_type, _table_metadata, table_handle, _dest_metadata):
return None
def materialize(self, c... | from dagster import check
from .house import Lakehouse
from .table import create_lakehouse_table_def
class SnowflakeLakehouse(Lakehouse):
def __init__(self):
pass
def hydrate(self, _context, _table_type, _table_metadata, table_handle, _dest_metadata):
return None
def materialize(self, c... | none | 1 | 2.264205 | 2 | |
pype/plugins/maya/publish/validate_look_no_default_shaders.py | tokejepsen/pype | 0 | 4049 | <reponame>tokejepsen/pype
from maya import cmds
import pyblish.api
import pype.api
import pype.maya.action
class ValidateLookNoDefaultShaders(pyblish.api.InstancePlugin):
"""Validate if any node has a connection to a default shader.
This checks whether the look has any members of:
- lambert1
- initi... | from maya import cmds
import pyblish.api
import pype.api
import pype.maya.action
class ValidateLookNoDefaultShaders(pyblish.api.InstancePlugin):
"""Validate if any node has a connection to a default shader.
This checks whether the look has any members of:
- lambert1
- initialShadingGroup
- initi... | en | 0.846313 | Validate if any node has a connection to a default shader. This checks whether the look has any members of: - lambert1 - initialShadingGroup - initialParticleSE - particleCloud1 If any of those is present it will raise an error. A look is not allowed to have any of the "default" shaders pr... | 2.57053 | 3 |
data_science_app/app.py | Johne-DuChene/data_science_learning_app | 0 | 4050 | <gh_stars>0
from flask import Flask
# initialize the app
app = Flask(__name__)
# execute iris function at /iris route
@app.route("/iris")
def iris():
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
X, y = load_iris(return_X_y=True)
clf = LogisticRegression(
... | from flask import Flask
# initialize the app
app = Flask(__name__)
# execute iris function at /iris route
@app.route("/iris")
def iris():
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
X, y = load_iris(return_X_y=True)
clf = LogisticRegression(
rando... | en | 0.530076 | # initialize the app # execute iris function at /iris route | 2.758127 | 3 |
vbdiar/scoring/normalization.py | VarunSrivastava19/VBDiarization | 101 | 4051 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2018 Brno University of Technology FIT
# Author: <NAME> <<EMAIL>>
# All Rights Reserved
import os
import logging
import pickle
import multiprocessing
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from vbdiar.features.segments... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2018 Brno University of Technology FIT
# Author: <NAME> <<EMAIL>>
# All Rights Reserved
import os
import logging
import pickle
import multiprocessing
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from vbdiar.features.segments... | en | 0.635916 | #!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright (C) 2018 Brno University of Technology FIT # Author: <NAME> <<EMAIL>> # All Rights Reserved Args: fns: speakers_dict: features_extractor: embedding_extractor: audio_dir: wav_suffix: in_rttm_dir: r... | 2.038463 | 2 |
agent_based_models/abm_allelopathy/plot_data.py | mattsmart/biomodels | 0 | 4052 | <filename>agent_based_models/abm_allelopathy/plot_data.py
import matplotlib.pyplot as plt
import os
def data_plotter(lattice_dict, datafile_dir, plot_dir):
# total spaces on grid implies grid size
total_cells = lattice_dict['E'][0] + lattice_dict['D_a'][0] + lattice_dict['D_b'][0] + lattice_dict['B'][0]
... | <filename>agent_based_models/abm_allelopathy/plot_data.py
import matplotlib.pyplot as plt
import os
def data_plotter(lattice_dict, datafile_dir, plot_dir):
# total spaces on grid implies grid size
total_cells = lattice_dict['E'][0] + lattice_dict['D_a'][0] + lattice_dict['D_b'][0] + lattice_dict['B'][0]
... | en | 0.546043 | # total spaces on grid implies grid size # alternative: 20.0, 8.0 | 2.334128 | 2 |
azure-mgmt-network/azure/mgmt/network/v2018_10_01/models/virtual_wan_security_providers.py | JonathanGailliez/azure-sdk-for-python | 1 | 4053 | # coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes ... | # coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes ... | en | 0.553409 | # coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes ... | 1.8679 | 2 |
jsonresume_theme_stackoverflow/filters.py | flowgunso/jsonresume-theme-stackoverflow | 0 | 4054 | import datetime
import re
from .exceptions import ObjectIsNotADate
def format_date(value, format="%d %M %Y"):
regex = re.match(r"(?P<year>\d{4})-(?P<month>\d{2})-(?P<day>\d{2})", value)
if regex is not None:
date = datetime.date(
int(regex.group("year")),
int(regex.group("mont... | import datetime
import re
from .exceptions import ObjectIsNotADate
def format_date(value, format="%d %M %Y"):
regex = re.match(r"(?P<year>\d{4})-(?P<month>\d{2})-(?P<day>\d{2})", value)
if regex is not None:
date = datetime.date(
int(regex.group("year")),
int(regex.group("mont... | none | 1 | 3.472229 | 3 | |
ipec/data/core.py | wwwbbb8510/ippso | 9 | 4055 | import numpy as np
import os
import logging
from sklearn.model_selection import train_test_split
DATASET_ROOT_FOLDER = os.path.abspath('datasets')
class DataLoader:
train = None
validation = None
test = None
mode = None
partial_dataset = None
@staticmethod
def load(train_path=None, valid... | import numpy as np
import os
import logging
from sklearn.model_selection import train_test_split
DATASET_ROOT_FOLDER = os.path.abspath('datasets')
class DataLoader:
train = None
validation = None
test = None
mode = None
partial_dataset = None
@staticmethod
def load(train_path=None, valid... | en | 0.588679 | get training data :return: dict of (images, labels) :rtype: dict get validation data :return: dict of (images, labels) :rtype: dict get test data :return: dict of (images, labels) :rtype: dict # randomly pick partial dataset | 2.619869 | 3 |
FOR/Analisador-completo/main.py | lucasf5/Python | 1 | 4056 | <filename>FOR/Analisador-completo/main.py<gh_stars>1-10
# Exercício Python 56: Desenvolva um programa que leia o nome, idade e sexo de 4 pessoas. No final do programa, mostre: a média de idade do grupo, qual é o nome do homem mais velho e quantas mulheres têm menos de 20 anos.
mediaidade = ''
nomelista = []
idadelista... | <filename>FOR/Analisador-completo/main.py<gh_stars>1-10
# Exercício Python 56: Desenvolva um programa que leia o nome, idade e sexo de 4 pessoas. No final do programa, mostre: a média de idade do grupo, qual é o nome do homem mais velho e quantas mulheres têm menos de 20 anos.
mediaidade = ''
nomelista = []
idadelista... | pt | 0.973299 | # Exercício Python 56: Desenvolva um programa que leia o nome, idade e sexo de 4 pessoas. No final do programa, mostre: a média de idade do grupo, qual é o nome do homem mais velho e quantas mulheres têm menos de 20 anos. # ------------------------------------------------------------------- # Adcionei todas idades em u... | 3.712274 | 4 |
test/python/quantum_info/operators/test_operator.py | EnriqueL8/qiskit-terra | 2 | 4057 | <reponame>EnriqueL8/qiskit-terra<filename>test/python/quantum_info/operators/test_operator.py
# -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2017, 2019.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the ... | # -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2017, 2019.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any... | en | 0.583342 | # -*- coding: utf-8 -*- # This code is part of Qiskit. # # (C) Copyright IBM 2017, 2019. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any ... | 2.291649 | 2 |
pages/feature_modal.py | jack-skerrett-bluefruit/Python-ScreenPlay | 0 | 4058 | <gh_stars>0
from selenium.webdriver.common.by import By
class feature_modal:
title_textbox = (By.ID, "feature-name")
description_textbox = (By.ID, "description")
save_button = (By.XPATH, "/html/body/app/div[3]/div[2]/div/div/div/button[1]")
| from selenium.webdriver.common.by import By
class feature_modal:
title_textbox = (By.ID, "feature-name")
description_textbox = (By.ID, "description")
save_button = (By.XPATH, "/html/body/app/div[3]/div[2]/div/div/div/button[1]") | none | 1 | 2.131076 | 2 | |
liststations.py | CrookedY/AirPollutionBot | 1 | 4059 | from urllib2 import Request, urlopen, URLError
import json
request = Request('https://uk-air.defra.gov.uk/sos-ukair/api/v1/stations/')
try:
response = urlopen(request)
data = response.read()
except URLError, e:
print 'error:', e
stations= json.loads (data)
#extract out station 2
stations2 = stations [7]
prope... | from urllib2 import Request, urlopen, URLError
import json
request = Request('https://uk-air.defra.gov.uk/sos-ukair/api/v1/stations/')
try:
response = urlopen(request)
data = response.read()
except URLError, e:
print 'error:', e
stations= json.loads (data)
#extract out station 2
stations2 = stations [7]
prope... | en | 0.832328 | #extract out station 2 #extract ID so can be use in link #print ID #contains station properties data. Need to get to timecourse ID #ID is a key in dictionary so need to extract as a key | 3.116202 | 3 |
pyfinancials/engine.py | kmiller96/PyFinancials | 1 | 4060 | <filename>pyfinancials/engine.py
def hello_world():
"""Tests the import."""
return "Hello world!"
| <filename>pyfinancials/engine.py
def hello_world():
"""Tests the import."""
return "Hello world!"
| en | 0.527007 | Tests the import. | 1.443302 | 1 |
core/migrations/0002_auto_20180702_1913.py | mertyildiran/echo | 5 | 4061 | <reponame>mertyildiran/echo<gh_stars>1-10
# Generated by Django 2.0.6 on 2018-07-02 19:13
import core.models
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('core', '0001_initial'),
]
operations = [
migrations.RenameField(
mo... | # Generated by Django 2.0.6 on 2018-07-02 19:13
import core.models
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('core', '0001_initial'),
]
operations = [
migrations.RenameField(
model_name='echo',
old_name='own... | en | 0.693346 | # Generated by Django 2.0.6 on 2018-07-02 19:13 | 1.87034 | 2 |
tests/test_helpers.py | ajdavis/aiohttp | 1 | 4062 | <filename>tests/test_helpers.py
import pytest
from unittest import mock
from aiohttp import helpers
import datetime
def test_parse_mimetype_1():
assert helpers.parse_mimetype('') == ('', '', '', {})
def test_parse_mimetype_2():
assert helpers.parse_mimetype('*') == ('*', '*', '', {})
def test_parse_mimety... | <filename>tests/test_helpers.py
import pytest
from unittest import mock
from aiohttp import helpers
import datetime
def test_parse_mimetype_1():
assert helpers.parse_mimetype('') == ('', '', '', {})
def test_parse_mimetype_2():
assert helpers.parse_mimetype('*') == ('*', '*', '', {})
def test_parse_mimety... | en | 0.898726 | application/json; charset=utf-8; # missing password here # 2-char str is not allowed Docstring. # Ensure we handle unquoted percent signs in redirects. # Ensure requoting doesn't break expectations. | 2.413228 | 2 |
GenConfigs.py | truls/faas-profiler | 0 | 4063 | from os.path import join
FAAS_ROOT="/lhome/trulsas/faas-profiler"
WORKLOAD_SPECS=join(FAAS_ROOT, "specs", "workloads")
#FAAS_ROOT="/home/truls/uni/phd/faas-profiler"
WSK_PATH = "wsk"
OPENWHISK_PATH = "/lhome/trulsas/openwhisk"
#: Location of output data
DATA_DIR = join(FAAS_ROOT, "..", "profiler_results")
SYSTEM_CPU... | from os.path import join
FAAS_ROOT="/lhome/trulsas/faas-profiler"
WORKLOAD_SPECS=join(FAAS_ROOT, "specs", "workloads")
#FAAS_ROOT="/home/truls/uni/phd/faas-profiler"
WSK_PATH = "wsk"
OPENWHISK_PATH = "/lhome/trulsas/openwhisk"
#: Location of output data
DATA_DIR = join(FAAS_ROOT, "..", "profiler_results")
SYSTEM_CPU... | en | 0.548451 | #FAAS_ROOT="/home/truls/uni/phd/faas-profiler" #: Location of output data | 1.469259 | 1 |
Chapter09/calc.py | LuisPereda/Learning_Python | 0 | 4064 |
def sum1(a,b):
try:
c = a+b
return c
except :
print "Error in sum1 function"
def divide(a,b):
try:
c = a/b
return c
except :
print "Error in divide function"
print divide(10,0)
print sum1(10,0) |
def sum1(a,b):
try:
c = a+b
return c
except :
print "Error in sum1 function"
def divide(a,b):
try:
c = a/b
return c
except :
print "Error in divide function"
print divide(10,0)
print sum1(10,0) | none | 1 | 3.525509 | 4 | |
radssh/hostkey.py | Eli-Tarrago/radssh | 39 | 4065 | <gh_stars>10-100
#
# Copyright (c) 2014, 2016, 2018, 2020 LexisNexis Risk Data Management Inc.
#
# This file is part of the RadSSH software package.
#
# RadSSH is free software, released under the Revised BSD License.
# You are permitted to use, modify, and redsitribute this software
# according to the Revised BSD Lice... | #
# Copyright (c) 2014, 2016, 2018, 2020 LexisNexis Risk Data Management Inc.
#
# This file is part of the RadSSH software package.
#
# RadSSH is free software, released under the Revised BSD License.
# You are permitted to use, modify, and redsitribute this software
# according to the Revised BSD License, a copy of wh... | en | 0.83345 | # # Copyright (c) 2014, 2016, 2018, 2020 LexisNexis Risk Data Management Inc. # # This file is part of the RadSSH software package. # # RadSSH is free software, released under the Revised BSD License. # You are permitted to use, modify, and redsitribute this software # according to the Revised BSD License, a copy of wh... | 2.059211 | 2 |
nuke/pymmh3.py | jfpanisset/Cryptomatte | 543 | 4066 | '''
pymmh3 was written by <NAME> and enhanced by <NAME>, and is placed in the public
domain. The authors hereby disclaim copyright to this source code.
pure python implementation of the murmur3 hash algorithm
https://code.google.com/p/smhasher/wiki/MurmurHash3
This was written for the times when you do not want to c... | '''
pymmh3 was written by <NAME> and enhanced by <NAME>, and is placed in the public
domain. The authors hereby disclaim copyright to this source code.
pure python implementation of the murmur3 hash algorithm
https://code.google.com/p/smhasher/wiki/MurmurHash3
This was written for the times when you do not want to c... | en | 0.721885 | pymmh3 was written by <NAME> and enhanced by <NAME>, and is placed in the public domain. The authors hereby disclaim copyright to this source code. pure python implementation of the murmur3 hash algorithm https://code.google.com/p/smhasher/wiki/MurmurHash3 This was written for the times when you do not want to compi... | 3.456221 | 3 |
bindings/python/tests/test_factory.py | pscff/dlite | 10 | 4067 | <reponame>pscff/dlite<gh_stars>1-10
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import dlite
thisdir = os.path.abspath(os.path.dirname(__file__))
class Person:
def __init__(self, name, age, skills):
self.name = name
self.age = age
self.skills = skills
def __repr__(self):... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import dlite
thisdir = os.path.abspath(os.path.dirname(__file__))
class Person:
def __init__(self, name, age, skills):
self.name = name
self.age = age
self.skills = skills
def __repr__(self):
return 'Person(%r, %r, %r)'... | en | 0.453372 | #!/usr/bin/env python # -*- coding: utf-8 -*- # Print json-representation of person2 using dlite | 3.112059 | 3 |
week_11_DS_N_Algorithm/03_Thr_Lecture/실습6_연속 부분 최대합.py | bky373/elice-racer-1st | 1 | 4068 | '''
연속 부분 최대합
nn개의 숫자가 주어질 때, 연속 부분을 선택하여 그 합을 최대화 하는 프로그램을 작성하시오.
예를 들어, 다음과 같이 8개의 숫자가 있다고 하자.
1 2 -4 5 3 -2 9 -10
이 때, 연속 부분이란 연속하여 숫자를 선택하는 것을 말한다.
가능한 연속 부분으로써 [1, 2, -4], [5, 3, -2, 9], [9, -10] 등이 있을 수 있다.
이 연속 부분들 중에서 가장 합이 큰 연속 부분은 [5, 3, -2, 9] 이며,
이보다 더 합을 크게 할 수는 없다.
따라서 연속 부분 최대합은 5+3+(-2)+9 = 15 이다... | '''
연속 부분 최대합
nn개의 숫자가 주어질 때, 연속 부분을 선택하여 그 합을 최대화 하는 프로그램을 작성하시오.
예를 들어, 다음과 같이 8개의 숫자가 있다고 하자.
1 2 -4 5 3 -2 9 -10
이 때, 연속 부분이란 연속하여 숫자를 선택하는 것을 말한다.
가능한 연속 부분으로써 [1, 2, -4], [5, 3, -2, 9], [9, -10] 등이 있을 수 있다.
이 연속 부분들 중에서 가장 합이 큰 연속 부분은 [5, 3, -2, 9] 이며,
이보다 더 합을 크게 할 수는 없다.
따라서 연속 부분 최대합은 5+3+(-2)+9 = 15 이다... | ko | 1.000069 | 연속 부분 최대합 nn개의 숫자가 주어질 때, 연속 부분을 선택하여 그 합을 최대화 하는 프로그램을 작성하시오. 예를 들어, 다음과 같이 8개의 숫자가 있다고 하자. 1 2 -4 5 3 -2 9 -10 이 때, 연속 부분이란 연속하여 숫자를 선택하는 것을 말한다. 가능한 연속 부분으로써 [1, 2, -4], [5, 3, -2, 9], [9, -10] 등이 있을 수 있다. 이 연속 부분들 중에서 가장 합이 큰 연속 부분은 [5, 3, -2, 9] 이며, 이보다 더 합을 크게 할 수는 없다. 따라서 연속 부분 최대합은 5+3+(-2)+9 = 15 이다. 입... | 2.816516 | 3 |
tests/test_dns.py | jensstein/mockdock | 0 | 4069 | <reponame>jensstein/mockdock
#!/usr/bin/env python3
import unittest
from mockdock import dns
class DNSTest(unittest.TestCase):
def test_build_packet(self):
data = b"^4\x01\x00\x00\x01\x00\x00\x00\x00\x00\x00\x06google\x03com\x00\x00\x01\x00\x01"
packet = dns.build_packet(data, "192.168.0.1")
... | #!/usr/bin/env python3
import unittest
from mockdock import dns
class DNSTest(unittest.TestCase):
def test_build_packet(self):
data = b"^4\x01\x00\x00\x01\x00\x00\x00\x00\x00\x00\x06google\x03com\x00\x00\x01\x00\x01"
packet = dns.build_packet(data, "192.168.0.1")
expeced_result = b"^4\x81... | fr | 0.221828 | #!/usr/bin/env python3 | 2.780838 | 3 |
tests/conftest.py | zhongnansu/es-cli | 6 | 4070 | <filename>tests/conftest.py
"""
Copyright 2019, Amazon Web Services Inc.
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 ... | <filename>tests/conftest.py
"""
Copyright 2019, Amazon Web Services Inc.
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 ... | en | 0.839811 | Copyright 2019, Amazon Web Services Inc. 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, sof... | 2.02809 | 2 |
Cogs/ServerStats.py | Damiian1/techwizardshardware | 0 | 4071 | import asyncio
import discord
from datetime import datetime
from operator import itemgetter
from discord.ext import commands
from Cogs import Nullify
from Cogs import DisplayName
from Cogs import UserTime
from Cogs import Message
def setup(bot):
# Add the bot... | import asyncio
import discord
from datetime import datetime
from operator import itemgetter
from discord.ext import commands
from Cogs import Nullify
from Cogs import DisplayName
from Cogs import UserTime
from Cogs import Message
def setup(bot):
# Add the bot... | en | 0.815187 | # Add the bot and deps # Check the message and see if we should allow it - always yes. # This module doesn't need to cancel messages. # Don't count your own, Pooter Lists some info about the current or passed server. # Check if we passed another guild # We didn't find it # Get localized user time # bot_percent = "{:,g}... | 2.535952 | 3 |
chess_commentary_model/transformers_model/dataset_preprocessing.py | Rseiji/TCC-2020 | 0 | 4072 | <filename>chess_commentary_model/transformers_model/dataset_preprocessing.py
"""Métodos de preprocessamento de testes individuais
"""
import pandas as pd
import numpy as np
import math
def test_1(df, seed=0):
"""training: balanced; test: balanced
training: 80k (40k 0, 40k 1)
test: 20k (10k 0, 10k... | <filename>chess_commentary_model/transformers_model/dataset_preprocessing.py
"""Métodos de preprocessamento de testes individuais
"""
import pandas as pd
import numpy as np
import math
def test_1(df, seed=0):
"""training: balanced; test: balanced
training: 80k (40k 0, 40k 1)
test: 20k (10k 0, 10k... | en | 0.710715 | Métodos de preprocessamento de testes individuais training: balanced; test: balanced training: 80k (40k 0, 40k 1) test: 20k (10k 0, 10k 1) training: balanced; test: unbalanced training: 80k (40k 0, 40k 1) test: 20k (4k 0, 16k 1) training: unbalanced; test: unbalanced training: 80k ... | 2.775193 | 3 |
venv/Lib/site-packages/CoolProp/constants.py | kubakoziczak/gasSteamPowerPlant | 0 | 4073 | # This file is automatically generated by the generate_constants_module.py script in wrappers/Python.
# DO NOT MODIFY THE CONTENTS OF THIS FILE!
from __future__ import absolute_import
from . import _constants
INVALID_PARAMETER = _constants.INVALID_PARAMETER
igas_constant = _constants.igas_constant
imolar_mass = _cons... | # This file is automatically generated by the generate_constants_module.py script in wrappers/Python.
# DO NOT MODIFY THE CONTENTS OF THIS FILE!
from __future__ import absolute_import
from . import _constants
INVALID_PARAMETER = _constants.INVALID_PARAMETER
igas_constant = _constants.igas_constant
imolar_mass = _cons... | en | 0.505264 | # This file is automatically generated by the generate_constants_module.py script in wrappers/Python. # DO NOT MODIFY THE CONTENTS OF THIS FILE! | 1.214307 | 1 |
torch_datasets/samplers/balanced_batch_sampler.py | mingruimingrui/torch-datasets | 0 | 4074 | import random
import torch.utils.data.sampler
class BalancedBatchSampler(torch.utils.data.sampler.BatchSampler):
def __init__(
self,
dataset_labels,
batch_size=1,
steps=None,
n_classes=0,
n_samples=2
):
""" Create a balanced batch sampler for label based... | import random
import torch.utils.data.sampler
class BalancedBatchSampler(torch.utils.data.sampler.BatchSampler):
def __init__(
self,
dataset_labels,
batch_size=1,
steps=None,
n_classes=0,
n_samples=2
):
""" Create a balanced batch sampler for label based... | en | 0.679254 | Create a balanced batch sampler for label based datasets Args dataset_labels : Labels of every entry from a dataset (in the same sequence) batch_size : batch_size no explaination needed step_size : Number of batches to generate (if None, then dataset_size / batch_siz... | 3.151266 | 3 |
ambari-common/src/main/python/resource_management/libraries/functions/get_bare_principal.py | likenamehaojie/Apache-Ambari-ZH | 1,664 | 4075 | #!/usr/bin/env python
"""
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
distributed with this work for additional information
regarding copyright ownership. The ASF licenses this file
to you under the Apache License, Version 2.0 (the
"License");... | #!/usr/bin/env python
"""
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
distributed with this work for additional information
regarding copyright ownership. The ASF licenses this file
to you under the Apache License, Version 2.0 (the
"License");... | en | 0.799659 | #!/usr/bin/env python Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you... | 2.298561 | 2 |
04/cross_validation.01.py | study-machine-learning/dongheon.shin | 2 | 4076 | <filename>04/cross_validation.01.py<gh_stars>1-10
from sklearn import svm, metrics
import random
import re
def split(rows):
data = []
labels = []
for row in rows:
data.append(row[0:4])
labels.append(row[4])
return (data, labels)
def calculate_score(train, test):
train_data,... | <filename>04/cross_validation.01.py<gh_stars>1-10
from sklearn import svm, metrics
import random
import re
def split(rows):
data = []
labels = []
for row in rows:
data.append(row[0:4])
labels.append(row[4])
return (data, labels)
def calculate_score(train, test):
train_data,... | none | 1 | 2.870315 | 3 | |
third_party/org_specs2.bzl | wix/wix-oss-infra | 3 | 4077 | <gh_stars>1-10
load("@wix_oss_infra//:import_external.bzl", import_external = "safe_wix_scala_maven_import_external")
def dependencies():
import_external(
name = "org_specs2_specs2_fp_2_12",
artifact = "org.specs2:specs2-fp_2.12:4.8.3",
artifact_sha256 = "777962ca58054a9ea86e294e025453ecf3... | load("@wix_oss_infra//:import_external.bzl", import_external = "safe_wix_scala_maven_import_external")
def dependencies():
import_external(
name = "org_specs2_specs2_fp_2_12",
artifact = "org.specs2:specs2-fp_2.12:4.8.3",
artifact_sha256 = "777962ca58054a9ea86e294e025453ecf394c60084c28bd61... | none | 1 | 1.453159 | 1 | |
task/w2/trenirovka/12-rivnist 2.py | beregok/pythontask | 1 | 4078 | a = int(input())
b = int(input())
c = int(input())
d = int(input())
if a == 0 and b == 0:
print("INF")
else:
if (d - b * c / a) != 0 and (- b / a) == (- b // a):
print(- b // a)
else:
print("NO")
| a = int(input())
b = int(input())
c = int(input())
d = int(input())
if a == 0 and b == 0:
print("INF")
else:
if (d - b * c / a) != 0 and (- b / a) == (- b // a):
print(- b // a)
else:
print("NO")
| none | 1 | 3.536719 | 4 | |
src/reg_resampler.py | atif-hassan/Regression_ReSampling | 15 | 4079 | class resampler:
def __init__(self):
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from collections import Counter
import numpy as np
self.bins = 3
self.pd = pd
self.LabelEncoder = LabelEncoder
self.Counter = Counter
... | class resampler:
def __init__(self):
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from collections import Counter
import numpy as np
self.bins = 3
self.pd = pd
self.LabelEncoder = LabelEncoder
self.Counter = Counter
... | en | 0.865244 | # This function adds classes to each sample and returns the class list as a dataframe/numpy array (as per input) # It also merges classes as and when required # If data is numpy, then convert it into pandas # Use qcut if balanced binning is required # Pandas outputs ranges after binning. Convert ranges to classes # Mer... | 3.191765 | 3 |
get_data/speech_commands.py | patrick-kidger/generalised_shapelets | 32 | 4080 | <reponame>patrick-kidger/generalised_shapelets
import os
import pathlib
import sklearn.model_selection
import tarfile
import torch
import torchaudio
import urllib.request
here = pathlib.Path(__file__).resolve().parent
def _split_data(tensor, stratify):
# 0.7/0.15/0.15 train/val/test split
(train_tensor, test... | import os
import pathlib
import sklearn.model_selection
import tarfile
import torch
import torchaudio
import urllib.request
here = pathlib.Path(__file__).resolve().parent
def _split_data(tensor, stratify):
# 0.7/0.15/0.15 train/val/test split
(train_tensor, testval_tensor,
train_stratify, testval_strati... | en | 0.901854 | # 0.7/0.15/0.15 train/val/test split # for forward compatbility if they fix it # Normalization argument doesn't seem to work so we do it manually. # A few samples are shorter than the full length; for simplicity we discard them. # X is of shape (batch=34975, length=16000, channels=1) # X is of shape (batch=34975, lengt... | 2.885396 | 3 |
app/endpoints/products.py | duch94/spark_crud_test | 0 | 4081 | <reponame>duch94/spark_crud_test
from datetime import datetime
from typing import List
from flask import Blueprint, jsonify, request, json
from app.models.products import Product, Category, products_categories
from app import db
products_blueprint = Blueprint('products', __name__)
def create_or_get_cate... | from datetime import datetime
from typing import List
from flask import Blueprint, jsonify, request, json
from app.models.products import Product, Category, products_categories
from app import db
products_blueprint = Blueprint('products', __name__)
def create_or_get_categories(p: dict) -> List[Category]... | en | 0.455655 | Func to get existing categories objects or create new otherwise
:param p: payload of request
:return: list of categories | 2.91144 | 3 |
util/config/validators/test/test_validate_bitbucket_trigger.py | giuseppe/quay | 2,027 | 4082 | import pytest
from httmock import urlmatch, HTTMock
from util.config import URLSchemeAndHostname
from util.config.validator import ValidatorContext
from util.config.validators import ConfigValidationException
from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator
from test.fixtures i... | import pytest
from httmock import urlmatch, HTTMock
from util.config import URLSchemeAndHostname
from util.config.validator import ValidatorContext
from util.config.validators import ConfigValidationException
from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator
from test.fixtures i... | none | 1 | 2.057448 | 2 | |
Refraction.py | silkoch42/Geometric-Optics-from-QM | 0 | 4083 | <reponame>silkoch42/Geometric-Optics-from-QM
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 15 16:51:16 2019
@author: Silvan
"""
import numpy as np
import scipy
import matplotlib.pyplot as plt
k=1000
n1=2.0
n2=1.0
alpha=np.pi/6.0
beta=np.arcsin(n2/n1*np.sin(alpha))
ya=1.0
xa=-ya*np.tan(alpha)
... | # -*- coding: utf-8 -*-
"""
Created on Fri Mar 15 16:51:16 2019
@author: Silvan
"""
import numpy as np
import scipy
import matplotlib.pyplot as plt
k=1000
n1=2.0
n2=1.0
alpha=np.pi/6.0
beta=np.arcsin(n2/n1*np.sin(alpha))
ya=1.0
xa=-ya*np.tan(alpha)
yb=-1.0
xb=-yb*np.tan(beta)
def s(x):
... | en | 0.207945 | # -*- coding: utf-8 -*- Created on Fri Mar 15 16:51:16 2019
@author: Silvan #Maximum Number of subdivisions for integral calculations #plt.errorbar(x,K2/M,0.1*K2/M) #plt.text(1.1,0.5,r'$|\int_{-R}^{R}e^{i k s(x)}dx|^2$',fontsize=20) #N=20 # #dx=np.linspace(0,10,N) # #P=np.ones(N) # #for i in range(N): # print(i+1... | 2.601061 | 3 |
readthedocs/docsitalia/management/commands/clear_elasticsearch.py | italia/readthedocs.org | 19 | 4084 | <reponame>italia/readthedocs.org<gh_stars>10-100
"""Remove the readthedocs elasticsearch index."""
from __future__ import absolute_import
from django.conf import settings
from django.core.management.base import BaseCommand
from elasticsearch import Elasticsearch
class Command(BaseCommand):
"""Clear elasticsea... | """Remove the readthedocs elasticsearch index."""
from __future__ import absolute_import
from django.conf import settings
from django.core.management.base import BaseCommand
from elasticsearch import Elasticsearch
class Command(BaseCommand):
"""Clear elasticsearch index."""
def handle(self, *args, **opti... | en | 0.643325 | Remove the readthedocs elasticsearch index. Clear elasticsearch index. handle command. | 1.656461 | 2 |
train.py | vnbot2/BigGAN-PyTorch | 0 | 4085 | """ BigGAN: The Authorized Unofficial PyTorch release
Code by <NAME> and <NAME>
This code is an unofficial reimplementation of
"Large-Scale GAN Training for High Fidelity Natural Image Synthesis,"
by <NAME>, <NAME>, and <NAME> (arXiv 1809.11096).
Let's go.
"""
import datetime
import time
import torc... | """ BigGAN: The Authorized Unofficial PyTorch release
Code by <NAME> and <NAME>
This code is an unofficial reimplementation of
"Large-Scale GAN Training for High Fidelity Natural Image Synthesis,"
by <NAME>, <NAME>, and <NAME> (arXiv 1809.11096).
Let's go.
"""
import datetime
import time
import torc... | en | 0.848216 | BigGAN: The Authorized Unofficial PyTorch release Code by <NAME> and <NAME> This code is an unofficial reimplementation of "Large-Scale GAN Training for High Fidelity Natural Image Synthesis," by <NAME>, <NAME>, and <NAME> (arXiv 1809.11096). Let's go. # IMG_SIZE = 64 # IMG_SIZE_2 = IMG_SIZE * 2 # U... | 2.43992 | 2 |
geocamUtil/tempfiles.py | geocam/geocamUtilWeb | 4 | 4086 | <reponame>geocam/geocamUtilWeb<gh_stars>1-10
# __BEGIN_LICENSE__
#Copyright (c) 2015, United States Government, as represented by the
#Administrator of the National Aeronautics and Space Administration.
#All rights reserved.
# __END_LICENSE__
import os
import time
import random
import shutil
from glob import glob
im... | # __BEGIN_LICENSE__
#Copyright (c) 2015, United States Government, as represented by the
#Administrator of the National Aeronautics and Space Administration.
#All rights reserved.
# __END_LICENSE__
import os
import time
import random
import shutil
from glob import glob
import traceback
import sys
from geocamUtil im... | en | 0.892245 | # __BEGIN_LICENSE__ #Copyright (c) 2015, United States Government, as represented by the #Administrator of the National Aeronautics and Space Administration. #All rights reserved. # __END_LICENSE__ | 1.923968 | 2 |
Ex1:Tests/ex2.py | Lludion/Exercises-SE | 0 | 4087 | # Ce fichier contient (au moins) cinq erreurs.
# Instructions:
# - tester jusqu'à atteindre 100% de couverture;
# - corriger les bugs;"
# - envoyer le diff ou le dépôt git par email."""
import hypothesis
from hypothesis import given, settings
from hypothesis.strategies import integers, lists
class BinHeap:
#st... | # Ce fichier contient (au moins) cinq erreurs.
# Instructions:
# - tester jusqu'à atteindre 100% de couverture;
# - corriger les bugs;"
# - envoyer le diff ou le dépôt git par email."""
import hypothesis
from hypothesis import given, settings
from hypothesis.strategies import integers, lists
class BinHeap:
#st... | fr | 0.410245 | # Ce fichier contient (au moins) cinq erreurs. # Instructions: # - tester jusqu'à atteindre 100% de couverture; # - corriger les bugs;" # - envoyer le diff ou le dépôt git par email.""" #structure de tas binaires d'entiers #initialise un tas binaire d'entiers avec un element 0 #taille de la liste heapList (invariant... | 3.563407 | 4 |
python/snewpy/snowglobes.py | svalder/snewpy | 0 | 4088 | <gh_stars>0
# -*- coding: utf-8 -*-
"""The ``snewpy.snowglobes`` module contains functions for interacting with SNOwGLoBES.
`SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected
event rates from a given input supernova neutrino flux. It supports many
different neutrino detectors, detector mate... | # -*- coding: utf-8 -*-
"""The ``snewpy.snowglobes`` module contains functions for interacting with SNOwGLoBES.
`SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected
event rates from a given input supernova neutrino flux. It supports many
different neutrino detectors, detector materials and in... | en | 0.75036 | # -*- coding: utf-8 -*- The ``snewpy.snowglobes`` module contains functions for interacting with SNOwGLoBES. `SNOwGLoBES <https://github.com/SNOwGLoBES/snowglobes>`_ can estimate detected event rates from a given input supernova neutrino flux. It supports many different neutrino detectors, detector materials and inter... | 2.568571 | 3 |
rlcycle/dqn_base/loss.py | cyoon1729/Rlcycle | 128 | 4089 | from typing import List, Tuple
from omegaconf import DictConfig
import torch
import torch.nn as nn
import torch.nn.functional as F
from rlcycle.common.abstract.loss import Loss
class DQNLoss(Loss):
"""Compute double DQN loss"""
def __init__(self, hyper_params: DictConfig, use_cuda: bool):
Loss.__in... | from typing import List, Tuple
from omegaconf import DictConfig
import torch
import torch.nn as nn
import torch.nn.functional as F
from rlcycle.common.abstract.loss import Loss
class DQNLoss(Loss):
"""Compute double DQN loss"""
def __init__(self, hyper_params: DictConfig, use_cuda: bool):
Loss.__in... | en | 0.398257 | Compute double DQN loss Compute quantile regression loss Compute C51 loss | 2.337731 | 2 |
scripts/gap_filling_viewer.py | raphischer/probgf | 3 | 4090 | <gh_stars>1-10
"""viewer application which allows to interactively view spatio-temporal gap filling results"""
import os
import argparse
from datetime import datetime, timedelta
from tkinter import Canvas, Tk, Button, RAISED, DISABLED, SUNKEN, NORMAL
import numpy as np
from PIL import Image, ImageTk
import probgf.media... | """viewer application which allows to interactively view spatio-temporal gap filling results"""
import os
import argparse
from datetime import datetime, timedelta
from tkinter import Canvas, Tk, Button, RAISED, DISABLED, SUNKEN, NORMAL
import numpy as np
from PIL import Image, ImageTk
import probgf.media as media
cla... | en | 0.731141 | viewer application which allows to interactively view spatio-temporal gap filling results # setup images # width of each displayed image # masked full images # unmasked full images # text labels and logos # image timeline # images and buttons # full image width # images for visualization # bind buttons and keys | 2.68058 | 3 |
paypal/pro/tests.py | pdfcrowd/django-paypal | 1 | 4091 | <gh_stars>1-10
#!/usr/bin/python
# -*- coding: utf-8 -*-
from django.conf import settings
from django.core.handlers.wsgi import WSGIRequest
from django.forms import ValidationError
from django.http import QueryDict
from django.test import TestCase
from django.test.client import Client
from paypal.pro.fields import Cre... | #!/usr/bin/python
# -*- coding: utf-8 -*-
from django.conf import settings
from django.core.handlers.wsgi import WSGIRequest
from django.forms import ValidationError
from django.http import QueryDict
from django.test import TestCase
from django.test.client import Client
from paypal.pro.fields import CreditCardField
fr... | en | 0.373435 | #!/usr/bin/python # -*- coding: utf-8 -*- # Used to generate request objects. # """Dummy class for testing PayPalWPP.""" # responses = { # # @@@ Need some reals data here. # "DoDirectPayment": """ack=Success×tamp=2009-03-12T23%3A52%3A33Z&l_severitycode0=Error&l_shortmessage0=Security+error&... | 2.17433 | 2 |
Hackerrank_Bot_Saves_Princess.py | madhurgupta96/Algorithmic-Journey | 0 | 4092 | # -*- coding: utf-8 -*-
"""
Created on Mon Dec 7 19:46:40 2020
@author: Intel
"""
def displayPathtoPrincess(n,grid):
me_i=n//2
me_j=n//2
for i in range(n):
if 'p' in grid[i]:
pe_i=i
for j in range(n):
if 'p'==grid[i][j]:
pe... | # -*- coding: utf-8 -*-
"""
Created on Mon Dec 7 19:46:40 2020
@author: Intel
"""
def displayPathtoPrincess(n,grid):
me_i=n//2
me_j=n//2
for i in range(n):
if 'p' in grid[i]:
pe_i=i
for j in range(n):
if 'p'==grid[i][j]:
pe... | en | 0.721303 | # -*- coding: utf-8 -*- Created on Mon Dec 7 19:46:40 2020
@author: Intel | 3.692683 | 4 |
gelviz/basic.py | HiDiHlabs/gelviz | 0 | 4093 | <gh_stars>0
import matplotlib.pyplot as plt
import pybedtools
import pandas as pnd
import numpy as np
import tabix
import matplotlib.ticker as ticker
from matplotlib.patches import Rectangle
from matplotlib.patches import Arrow
from matplotlib.path import Path
from matplotlib.patches import PathPatch
import matplotlib.... | import matplotlib.pyplot as plt
import pybedtools
import pandas as pnd
import numpy as np
import tabix
import matplotlib.ticker as ticker
from matplotlib.patches import Rectangle
from matplotlib.patches import Arrow
from matplotlib.path import Path
from matplotlib.patches import PathPatch
import matplotlib.cm as cm
imp... | en | 0.587636 | Function for plotting gene structures, i.e. introns exons of genes. :param genes_bed: :class:`pybedtools.BedTool` object containing TX start, and TX end of genes. :type genes_bed: :class:`pybedtools.BedTool` :param exons_bed: :class:`pybedtools.BedTool` object containing exons of genes. ... | 2.580891 | 3 |
toc/fsa/fsa.py | djrochford/toc | 0 | 4094 | <filename>toc/fsa/fsa.py<gh_stars>0
"""
File containing DFA and NFA public classes
"""
import collections.abc
from itertools import product, chain, combinations
from string import printable
from typing import (
AbstractSet,
Container,
FrozenSet,
Iterable,
List,
Mapping,
MutableMapping,
O... | <filename>toc/fsa/fsa.py<gh_stars>0
"""
File containing DFA and NFA public classes
"""
import collections.abc
from itertools import product, chain, combinations
from string import printable
from typing import (
AbstractSet,
Container,
FrozenSet,
Iterable,
List,
Mapping,
MutableMapping,
O... | en | 0.860103 | File containing DFA and NFA public classes Output a GNFA equivalent to `self` with one less state in it. A nondeterministic finite automaton class. Takes three keyword arguments: - `transition_function`: Mapping[Tuple[State, Symbol], AbstractSet[State]] - `start_state`: State - `accept_states`: AbstractS... | 2.673539 | 3 |
Numbers/Roman Number Generator/tests.py | fossabot/IdeaBag2-Solutions | 10 | 4095 | <reponame>fossabot/IdeaBag2-Solutions<filename>Numbers/Roman Number Generator/tests.py
#!/usr/bin/env python3
import unittest
from roman_number_generator import arabic_to_roman
class Test(unittest.TestCase):
def _start_arabic_to_roman(self):
self.assertRaises(ValueError, arabic_to_roman, 4000)
s... | Number Generator/tests.py
#!/usr/bin/env python3
import unittest
from roman_number_generator import arabic_to_roman
class Test(unittest.TestCase):
def _start_arabic_to_roman(self):
self.assertRaises(ValueError, arabic_to_roman, 4000)
self.assertEqual(arabic_to_roman(4), "IV")
self.assert... | fr | 0.221828 | #!/usr/bin/env python3 | 3.35965 | 3 |
modules/moduleBase.py | saintaardvark/glouton-satnogs-data-downloader | 0 | 4096 | from infrastructure.satnogClient import SatnogClient
import os
class ModuleBase:
def __init__(self, working_dir):
self.working_dir = working_dir
def runAfterDownload(self, file_name, full_path, observation):
raise NotImplementedError() | from infrastructure.satnogClient import SatnogClient
import os
class ModuleBase:
def __init__(self, working_dir):
self.working_dir = working_dir
def runAfterDownload(self, file_name, full_path, observation):
raise NotImplementedError() | none | 1 | 1.960945 | 2 | |
oregano_plugins/fusion/server.py | MrNaif2018/Oregano | 0 | 4097 | <reponame>MrNaif2018/Oregano
#!/usr/bin/env python3
#
# Oregano - a lightweight Ergon client
# CashFusion - an advanced coin anonymizer
#
# Copyright (C) 2020 <NAME>
#
# Permission is hereby granted, free of charge, to any person
# obtaining a copy of this software and associated documentation files
# (the "Software"),... | #!/usr/bin/env python3
#
# Oregano - a lightweight Ergon client
# CashFusion - an advanced coin anonymizer
#
# Copyright (C) 2020 <NAME>
#
# 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 with... | en | 0.912131 | #!/usr/bin/env python3 # # Oregano - a lightweight Ergon client # CashFusion - an advanced coin anonymizer # # Copyright (C) 2020 <NAME> # # 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 with... | 1.578609 | 2 |
Excercici4Package/ex4.py | jtorrenth/CienciaDades | 0 | 4098 | <gh_stars>0
import matplotlib.pyplot as plt
def countvalues(dataframe, subject):
# Filtrem i tractem el dataset
economydf = filtrar(dataframe, "economy")
# el printem
printar(economydf, subject)
# Filtrem ara per subject infected i ho desem en un altre df
infectedf = filtrar(dataframe, "infe... | import matplotlib.pyplot as plt
def countvalues(dataframe, subject):
# Filtrem i tractem el dataset
economydf = filtrar(dataframe, "economy")
# el printem
printar(economydf, subject)
# Filtrem ara per subject infected i ho desem en un altre df
infectedf = filtrar(dataframe, "infected")
#... | ca | 0.944866 | # Filtrem i tractem el dataset # el printem # Filtrem ara per subject infected i ho desem en un altre df # Calculem els percentatjes # Els printem # Printem a la consola els valors # Finalment, grafiquem # Cal tancar el grafic per a seguir amb l'execució # Afegim els valors en funció del samplesize a dues noves columne... | 3.449601 | 3 |
build/rules.bzl | filmil/bazel-ebook | 9 | 4099 | <gh_stars>1-10
# Copyright (C) 2020 Google Inc.
#
# This file has been licensed under Apache 2.0 license. Please see the LICENSE
# file at the root of the repository.
# Build rules for building ebooks.
# This is the container
CONTAINER = "filipfilmar/ebook-buildenv:1.1"
# Use this for quick local runs.
#CONTAINER =... | # Copyright (C) 2020 Google Inc.
#
# This file has been licensed under Apache 2.0 license. Please see the LICENSE
# file at the root of the repository.
# Build rules for building ebooks.
# This is the container
CONTAINER = "filipfilmar/ebook-buildenv:1.1"
# Use this for quick local runs.
#CONTAINER = "ebook-builden... | en | 0.596982 | # Copyright (C) 2020 Google Inc. # # This file has been licensed under Apache 2.0 license. Please see the LICENSE # file at the root of the repository. # Build rules for building ebooks. # This is the container # Use this for quick local runs. #CONTAINER = "ebook-buildenv:local" # Returns the docker_run script invocat... | 2.434261 | 2 |