text stringlengths 0 1.05M | meta dict |
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import os
import warnings
import numpy as np
from numpy.testing import assert_allclose, assert_array_equal
from nose.tools import assert_true, assert_false, assert_equal
import mne
from mne.io.kit.tests import data_dir as kit_data_dir
from mne.io import Raw
from mne.utils import _TempDir, requires_traits, run_tests_... | {
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import os
import warnings
import numpy as np
from numpy.testing import assert_array_equal
from nose.tools import assert_true, assert_false
from mne.io.kit.tests import data_dir as kit_data_dir
from mne.io.kit import read_mrk
from mne.utils import _TempDir, requires_traits, run_tests_if_main
mrk_pre_path = os.path.j... | {
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import os
from numpy.testing import assert_array_equal
from nose.tools import assert_true, assert_false, assert_equal
from mne.datasets import sample
from mne.utils import _TempDir, requires_traits
sample_path = sample.data_path(download=False)
subjects_dir = os.path.join(sample_path, 'subjects')
tempdir = _TempDi... | {
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import os
from numpy.testing import assert_array_equal
from nose.tools import assert_true, assert_false, assert_equal
from mne.datasets import testing
from mne.utils import _TempDir, requires_mayavi, run_tests_if_main
sample_path = testing.data_path(download=False)
subjects_dir = os.path.join(sample_path, 'subjects... | {
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import os
from numpy.testing import assert_array_equal
from nose.tools import assert_true, assert_false, assert_equal
from mne.datasets import testing
from mne.utils import _TempDir, requires_traits
sample_path = testing.data_path(download=False)
subjects_dir = os.path.join(sample_path, 'subjects')
@testing.requi... | {
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import os
from numpy.testing import assert_array_equal
from mne.datasets import testing
from mne.utils import _TempDir, requires_mayavi, run_tests_if_main, traits_test
sample_path = testing.data_path(download=False)
subjects_dir = os.path.join(sample_path, 'subjects')
@testing.requires_testing_data
@requires_maya... | {
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import os
import numpy as np
from numpy.testing import assert_allclose, assert_array_equal
import mne
from mne.io.kit.tests import data_dir as kit_data_dir
from mne.io import read_raw_fif
from mne.utils import (requires_mayavi, run_tests_if_main, traits_test,
modified_env)
mrk_pre_path = os.p... | {
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"path": "mne/gui/tests/test_kit2fiff_gui.py",
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... |
import os
import numpy as np
from numpy.testing import assert_array_equal
from mne.io.kit.tests import data_dir as kit_data_dir
from mne.io.kit import read_mrk
from mne.utils import (requires_mayavi, run_tests_if_main, traits_test,
modified_env)
mrk_pre_path = os.path.join(kit_data_dir, 'test... | {
"repo_name": "olafhauk/mne-python",
"path": "mne/gui/tests/test_marker_gui.py",
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import numpy as np
from numpy.testing import assert_allclose
import pytest
from scipy.optimize import check_grad
from sklearn.linear_model._glm.link import (
IdentityLink,
LogLink,
LogitLink,
)
LINK_FUNCTIONS = [IdentityLink, LogLink, LogitLink]
@pytest.mark.parametrize('Link', LINK_FUNCTIONS)
def test... | {
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"path": "sklearn/linear_model/_glm/tests/test_link.py",
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import numpy as np
from numpy.testing import (
assert_allclose,
assert_array_equal,
)
from scipy.optimize import check_grad
import pytest
from sklearn._loss.glm_distribution import (
TweedieDistribution,
NormalDistribution, PoissonDistribution,
GammaDistribution, InverseGaussianDistribution,
Di... | {
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"path": "sklearn/_loss/tests/test_glm_distribution.py",
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import numpy as np
from numpy.testing import assert_allclose
import pytest
import warnings
from sklearn.datasets import make_regression
from sklearn.linear_model._glm import GeneralizedLinearRegressor
from sklearn.linear_model import (
TweedieRegressor,
PoissonRegressor,
GammaRegressor
)
from sklearn.line... | {
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import time
import copy
import numpy as np
from .. import pick_channels
from ..utils import logger, verbose
from ..epochs import _BaseEpochs
from ..event import _find_events
from ..io.proj import setup_proj
class RtEpochs(_BaseEpochs):
"""Realtime Epochs
Can receive epochs in real time from an RtClient.
... | {
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"path": "mne/realtime/epochs.py",
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"config_test": ... |
import time
import copy
import numpy as np
from .. import pick_channels
from ..utils import logger, verbose
from ..epochs import BaseEpochs
from ..event import _find_events
class RtEpochs(BaseEpochs):
"""Realtime Epochs.
Can receive epochs in real time from an RtClient.
For example, to get some epochs... | {
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"path": "mne/realtime/epochs.py",
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"config_te... |
import time
import copy
import numpy as np
from .. import pick_channels
from ..utils import logger, verbose
from ..epochs import _BaseEpochs
from ..event import _find_events
class RtEpochs(_BaseEpochs):
"""Realtime Epochs
Can receive epochs in real time from an RtClient.
For example, to get some epoch... | {
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"path": "mne/realtime/epochs.py",
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import time
import copy
import numpy as np
from .. import pick_channels
from ..utils import logger, verbose
from ..epochs import BaseEpochs
from ..event import _find_events
class RtEpochs(BaseEpochs):
"""Realtime Epochs.
Can receive epochs in real time from an RtClient.
For example, to get some epochs... | {
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"path": "mne/realtime/epochs.py",
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import time
import copy
import numpy as np
from .. import pick_channels, pick_types
from ..utils import logger, verbose
from ..baseline import rescale
from ..epochs import _BaseEpochs
from ..event import _find_events
from ..filter import detrend
from ..io.proj import setup_proj
class RtEpochs(_BaseEpochs):
"""R... | {
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from __future__ import print_function
import socket
import time
from ..externals.six.moves import StringIO
import threading
import numpy as np
from ..utils import logger, verbose
from ..io.constants import FIFF
from ..io.meas_info import read_meas_info
from ..io.tag import Tag, read_tag
from ..io.tree import make_d... | {
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from __future__ import division
from sys import stdout
from matplotlib import pyplot as plt
import pyrealsense as pyrs
import logging
import numpy as np
import socket
import json
import pickle
# better change this value; I don't know what address to put here
GROUND_IP = 'localhost'
# needs to be the same on the gro... | {
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"path": "Flight/RealSense.py",
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"ha... |
__author__ = 'scmason'
import random
from essentialdb import QueryFilter
from essentialdb import EssentialIndex
class LocalCollection:
"""
LocalCollection implements a simple collection store with rudimentary disk
persistence and all the logic required to query the store. This class can be
extended t... | {
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"path": "essentialdb/local_collection.py",
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""" Algorithms for Convolutional Codes """
from __future__ import division
import functools
import math
from warnings import warn
import matplotlib.colors as mcolors
import matplotlib.patches as mpatches
import matplotlib.path as mpath
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.collections i... | {
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from __future__ import division, print_function # Python 2 compatibility
import math
import matplotlib.pyplot as plt
import numpy as np
import commpy.channelcoding.convcode as cc
import commpy.channels as chan
import commpy.links as lk
import commpy.modulation as mod
import commpy.utilities as util
# ============... | {
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from numpy import array, arange, concatenate, convolve
from commpy.channelcoding.gfields import GF, poly_to_string
from commpy.utilities import dec2bitarray, bitarray2dec
__all__ = ['cyclic_code_genpoly']
def cyclic_code_genpoly(n, k):
"""
Generate all possible generator polynomials for a (n, k)-cyclic code... | {
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""" Galois Fields """
from math import gcd
from numpy import array, zeros, arange, convolve, ndarray, concatenate
from commpy.utilities import dec2bitarray, bitarray2dec
__all__ = ['GF', 'polydivide', 'polymultiply', 'poly_to_string']
class GF:
"""
Defines a Binary Galois Field of order m, containing n,
... | {
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import numpy as np
import scipy.sparse as sp
import scipy.sparse.linalg as splg
__all__ = ['build_matrix', 'get_ldpc_code_params', 'ldpc_bp_decode', 'write_ldpc_params',
'triang_ldpc_systematic_encode']
_llr_max = 500
def build_matrix(ldpc_code_params):
"""
Build the parity check and generator ma... | {
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""" Interleavers and De-interleavers """
from numpy import arange, zeros
from numpy.random import mtrand
__all__ = ['RandInterlv']
class _Interleaver:
def interlv(self, in_array):
""" Interleave input array using the specific interleaver.
Parameters
----------
in_array : 1D nda... | {
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"""
============================================
Channel Models (:mod:`commpy.channels`)
============================================
.. autosummary::
:toctree: generated/
SISOFlatChannel -- SISO Channel with Rayleigh or Rician fading.
MIMOFlatChannel -- MIMO Channel with Rayleigh or Rician fading.
... | {
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"""
============================================
Links (:mod:`commpy.links`)
============================================
.. autosummary::
:toctree: generated/
link_performance -- Estimate the BER performance of a link model with Monte Carlo simulation.
LinkModel -- Link model object.
idd_... | {
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"path": "commpy/links.py",
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"""
==================================================
Modulation Demodulation (:mod:`commpy.modulation`)
==================================================
.. autosummary::
:toctree: generated/
PSKModem -- Phase Shift Keying (PSK) Modem.
QAMModem -- Quadrature Amplitude Modulation (... | {
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"""
=============================================
Pulse Shaping Filters (:mod:`commpy.filters`)
=============================================
.. autosummary::
:toctree: generated/
rcosfilter -- Raised Cosine (RC) Filter.
rrcosfilter -- Root Raised Cosine (RRC) Filter.
gaussianfilter ... | {
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"""
==================================================
Sequences (:mod:`commpy.sequences`)
==================================================
.. autosummary::
:toctree: generated/
pnsequence -- PN Sequence Generator.
zcsequence -- Zadoff-Chu (ZC) Sequence Generator.
"""
__all__ = ['... | {
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"""
============================================
Utilities (:mod:`commpy.utilities`)
============================================
.. autosummary::
:toctree: generated/
dec2bitarray -- Integer or array-like of integers to binary (bit array).
decimal2bitarray -- Specialized version for one integer... | {
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"""
============================================
Wifi 802.11 simulation (:mod:`commpy.wifi80211`)
============================================
.. autosummary::
:toctree: generated/
Wifi80211 -- Class to simulate the transmissions and receiving parameters of physical layer 802.11
"""
import math
from typing... | {
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""" Turbo Codes """
from numpy import array, zeros, exp, log, empty
from commpy.channelcoding import conv_encode
from commpy.utilities import dec2bitarray
#from commpy.channelcoding.map_c import backward_recursion, forward_recursion_decoding
def turbo_encode(msg_bits, trellis1, trellis2, interleaver):
""" Tur... | {
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"alpha_frac": 0.6045458662,
"autogenerated": false,
"ratio": 3.7131809011432413,
"config_... |
__author__ = 'scott0012'
from tkinter import*
def iCalc(source, side):
storeObj = Frame(source, borderwidth =1, bd= 4, bg='powder blue')
storeObj.pack(side=side, expand=YES, fill=BOTH)
return storeObj
def button (source, side, text, command = None):
storeObj = Button(source, text=text, command=command... | {
"repo_name": "saintdragon2/python-3-lecture-2015",
"path": "civil-final/1st_presentation/4조/scott0012.py",
"copies": "1",
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__author__ = 'Scott Davey'
"""Simple tyre temperature plugin for Assetto Corsa"""
import sys
import ac
import acsys
import traceback
sys.path.insert(0, 'apps/python/ServerPlugin/ServerPlugin_lib/stdlib')
try:
import socketserver
import threading
from ServerPlugin_lib.UDPServer import UDPServer
except Excep... | {
"repo_name": "SDavey149/ACServerPlugin",
"path": "apps/python/ServerPlugin/ServerPlugin.py",
"copies": "1",
"size": "1139",
"license": "mit",
"hash": 5755633889458585000,
"line_mean": 24.3111111111,
"line_max": 72,
"alpha_frac": 0.6812993854,
"autogenerated": false,
"ratio": 3.4938650306748467,
... |
__author__ = 'Scott Godbold'
import subprocess
# Exceptions
class CheckZoneException(Exception):
def __init__(self, messages):
self.message = str(messages)
def __str__(self):
return self.message
# Classes
class CheckZone(object):
"""Wraps the named-checkzone utility and allows you to e... | {
"repo_name": "scgodbold/python-bindtools",
"path": "bindtools/check_zone.py",
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__author__ = 'Scott Godbold'
import subprocess
# --------------------------- #
# -------- Exceptions ------- #
# --------------------------- #
class RNDCFreezeException(Exception):
pass
class RNDCThawException(Exception):
pass
class RNDCReloadException(Exception):
pass
# ---------------------------... | {
"repo_name": "scgodbold/python-bindtools",
"path": "bindtools/reload_zone.py",
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__author__ = 'Scott Gramig'
__program__ = 'Paper-Rock-Scissors-Lizard-Spock'
import random
import os
#global variables to track w/l/t
win = 0
loss = 0
tie = 0
def greeting(): # greets player and tells rule
print("Let's play Rock-Paper-Scissors-Lizards-Spock!!")
print("Rules:")
print("0: Rock-------->beats Scis... | {
"repo_name": "p0wder/PaperRockScissorsLizardSpock",
"path": "rockPaperScissorsLizardSpock.py",
"copies": "1",
"size": "1880",
"license": "apache-2.0",
"hash": -5623072497838173000,
"line_mean": 25.1111111111,
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"autogenerated": false,
"ratio": 2.79346210... |
import sys
import numpy as np
from scipy.fftpack import ifftn
def pad(data, padding):
### padding is the number of pixels to be added to each edge
pad = np.zeros((data.shape[0]+2*padding,data.shape[1]+2*padding), float)
#print 'Padding image...'
#print '%s --padding--> %s' %(str(data.shape),str(pad.shape)... | {
"repo_name": "fedhere/getlucky",
"path": "LIHSPcommon/mysciutils_merged.py",
"copies": "3",
"size": "15689",
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import sys
import pyfits as PF
import numpy as np
from scipy.fftpack import ifft2, fftshift
def pad(data, padding):
### padding is the number of pixels to be added to each edge
pad = np.zeros((data.shape[0]+2*padding,data.shape[1]+2*padding), float)
#print 'Padding image...'
#print '%s --padding--> %s' %(... | {
"repo_name": "fedhere/getlucky",
"path": "LIHSPcommon/mysciutils.py",
"copies": "2",
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__author__ = 'Scott Maxwell'
__version__ = "1.04"
__project_url__ = "https://github.com/codecobblers/modified"
# Copyright (C) 2013 by Scott Maxwell
#
# 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 ... | {
"repo_name": "codecobblers/modified",
"path": "modified.py",
"copies": "2",
"size": "8710",
"license": "mit",
"hash": 4398459109681739300,
"line_mean": 37.7111111111,
"line_max": 129,
"alpha_frac": 0.6114810563,
"autogenerated": false,
"ratio": 4.527027027027027,
"config_test": false,
"has_n... |
__author__ = 'scott'
from django.conf.urls import url
from django.conf import settings
from django.conf.urls.static import static
from django.views.generic import TemplateView
from . import views
urlpatterns = [
url(r'^$', views.index, name='index'),
#This one "seems" to be correct, except for that it doesn... | {
"repo_name": "Sensorica/Sensor-Network",
"path": "DjangoServer/metadata/urls.py",
"copies": "1",
"size": "1035",
"license": "cc0-1.0",
"hash": 2815798767622094300,
"line_mean": 42.1666666667,
"line_max": 147,
"alpha_frac": 0.7265700483,
"autogenerated": false,
"ratio": 3.4966216216216215,
"con... |
__author__ = 'scott'
import boto3
import random
import botocore
s3 = boto3.resource('s3')
# print out all the buckets
def print_buckets():
for bucket in s3.buckets.all():
print(bucket.name)
# Create a bucket
def create_bucket(bucket_name):
# An alternative with a random int at the end
# bucke... | {
"repo_name": "Sensorica/Sensor-Network",
"path": "export_to_csv/create_ec2_instance.py",
"copies": "1",
"size": "1176",
"license": "cc0-1.0",
"hash": -8803433414776191000,
"line_mean": 25.1333333333,
"line_max": 74,
"alpha_frac": 0.6649659864,
"autogenerated": false,
"ratio": 3.652173913043478,
... |
__author__ = 'scott'
import csv
import json
# This python script takes the data that was outputted
# by AWS pipeline from a DynamoDB and parses the data into a CSV file.
# This is used to convert unicode json data into ASCII
def byteify(input):
if isinstance(input, dict):
return {byteify(key): byteify(v... | {
"repo_name": "Sensorica/Sensor-Network",
"path": "export_to_csv/export_to_csv.py",
"copies": "1",
"size": "3523",
"license": "cc0-1.0",
"hash": -1470477310121686000,
"line_mean": 28.6134453782,
"line_max": 126,
"alpha_frac": 0.6531365314,
"autogenerated": false,
"ratio": 3.4743589743589745,
"c... |
__author__ = 'scott'
"""
Django settings for DjangoServer project.
Generated by 'django-admin startproject' using Django 1.9.4.
For more information on this file, see
https://docs.djangoproject.com/en/1.9/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/1.9/ref/... | {
"repo_name": "Sensorica/Sensor-Network",
"path": "DjangoServer/DjangoServer/settings/base.py",
"copies": "1",
"size": "3980",
"license": "cc0-1.0",
"hash": 4238451117238626000,
"line_mean": 27.4285714286,
"line_max": 140,
"alpha_frac": 0.6997487437,
"autogenerated": false,
"ratio": 3.58235823582... |
__author__ = 'scott'
# Tested with Python 2.7.9, Linux & Mac OS X
import socket
import io.StringIO
import sys
class WSGIServer(object):
address_family = socket.AF_INET
socket_type = socket.SOCK_STREAM
request_queue_size = 1
def __init__(self, server_address):
# Create a listening socket
... | {
"repo_name": "Sensorica/Sensor-Network",
"path": "DjangoServer/DjangoServer/webserver2.py",
"copies": "1",
"size": "5236",
"license": "cc0-1.0",
"hash": -1813962093100609500,
"line_mean": 34.8698630137,
"line_max": 76,
"alpha_frac": 0.5943468296,
"autogenerated": false,
"ratio": 4.12933753943217... |
__author__ = 'scott'
"""
The standard CSVItemExporter class does not pass the kwargs through to the
CSV writer, resulting in EXPORT_FIELDS and EXPORT_ENCODING being ignored
(EXPORT_EMPTY is not used by CSV).
"""
from scrapy.conf import settings
from scrapy.contrib.exporter import CsvItemExporter
import csv
import log... | {
"repo_name": "scotm/AmazonUKWishlistScraper",
"path": "AmazonWishlistScraper/feed_exporter.py",
"copies": "1",
"size": "1608",
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"hash": -8429778309220113000,
"line_mean": 33.9565217391,
"line_max": 103,
"alpha_frac": 0.6262437811,
"autogenerated": false,
"ratio": 3.7835294117647... |
__author__ = 'scottumsted'
import gc
import resource
import time
class ResourceHelper():
def __init__(self, module=None):
self.module = '' if module is None else '\nmodule:\t'+module+'\n'
gc.disable()
self.reset()
def reset(self):
self.start_mem = resource.getrusage(res... | {
"repo_name": "sumsted/mempy-async",
"path": "mempyasync/resourcehelper.py",
"copies": "1",
"size": "1197",
"license": "apache-2.0",
"hash": -202335026683290180,
"line_mean": 45.0769230769,
"line_max": 159,
"alpha_frac": 0.6407685881,
"autogenerated": false,
"ratio": 3.2091152815013406,
"config... |
__author__ = 'scottumsted'
from PIL import Image
from io import BytesIO
SPACING = 10
VCELLS = 10
MARGIN = 20
HROTATE = (2, 10)
VROTATE = (3, 17)
ROTATE_ANGLE = 3
hcells = 0
def start(image_byte_array):
original_image = convert_bytes_to_image(image_byte_array)
working_image = create_working_image(original_... | {
"repo_name": "sumsted/tiltedcontactsheet",
"path": "TiltedContactSheet.py",
"copies": "1",
"size": "2984",
"license": "apache-2.0",
"hash": -6566586879304900000,
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"line_max": 112,
"alpha_frac": 0.5774128686,
"autogenerated": false,
"ratio": 3.1115745568300315,
"confi... |
__author__ = 'scovetta'
import curses
screen = None
# This starts up curses.
def start():
global screen
screen = curses.initscr()
curses.noecho()
curses.cbreak()
curses.curs_set(0)
curses.nonl()
screen.keypad(1)
screen.timeout(0)
screen.scrollok(False)
# This stops curses (resets... | {
"repo_name": "scovetta/roguelike1",
"path": "core/gfx.py",
"copies": "1",
"size": "1697",
"license": "bsd-2-clause",
"hash": -4889011190149288000,
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"line_max": 74,
"alpha_frac": 0.5686505598,
"autogenerated": false,
"ratio": 3.2509578544061304,
"config_test": false,
... |
__author__ = 'scovetta'
from core import gfx
def ring(x, y, r):
report = []
odd_row = y % 2 == 1
# starting point, right, 0 degrees
x += r
report.append((x, y))
for i in range(r): # going up-left
if not odd_row: x -= 1
y -= 1
report.append((x, y))
odd_row = no... | {
"repo_name": "scovetta/roguelike1",
"path": "core/world.py",
"copies": "1",
"size": "1635",
"license": "bsd-2-clause",
"hash": -7120165779298459000,
"line_mean": 21.0945945946,
"line_max": 60,
"alpha_frac": 0.4623853211,
"autogenerated": false,
"ratio": 3.2058823529411766,
"config_test": false... |
from HTMLParser import HTMLParser
from simple_parser import CleanParser
from utils import start_str, end_str, stadardlize_text
class SentenceInfo(object):
"""
Sentence Info Dict:
sid: sentence id
related: Array of pair: (related sentence id, relationship)
content: pure sentence conte... | {
"repo_name": "zyshin/Ception",
"path": "ception/articles/content_parser.py",
"copies": "1",
"size": "8574",
"license": "mit",
"hash": 5803018013070683000,
"line_mean": 35.1772151899,
"line_max": 516,
"alpha_frac": 0.5116631677,
"autogenerated": false,
"ratio": 3.798848028356225,
"config_test":... |
from HTMLParser import HTMLParser
from utils import stadardlize_text, start_str, end_str
class SimpleParser(HTMLParser):
def __init__(self):
HTMLParser.__init__(self)
self.sentence_array = [""]
self.in_pd = False
def handle_starttag(self, tag, attrs):
if tag == 'pd':
... | {
"repo_name": "zyshin/Ception",
"path": "ception/articles/simple_parser.py",
"copies": "1",
"size": "2784",
"license": "mit",
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"line_mean": 32.5421686747,
"line_max": 387,
"alpha_frac": 0.5815373563,
"autogenerated": false,
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"config_test":... |
from HTMLParser import HTMLParser
import diff_match_patch as dmp_module
from content_parser import SentenceInfo
from utils import start_str, end_str
class DiffParser(HTMLParser):
"""
Some Constants from CKEDTIOR:
CKEDITOR.SENTENCE_NEW = -10;
CKEDITOR.SENTENCE_SPLIT = -9;
CKEDITOR.SENTENCE_UNDEFI... | {
"repo_name": "zyshin/Ception",
"path": "ception/articles/diff_parser.py",
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"size": "4850",
"license": "mit",
"hash": -5086811337142117000,
"line_mean": 46.5490196078,
"line_max": 682,
"alpha_frac": 0.6154639175,
"autogenerated": false,
"ratio": 3.5018050541516246,
"config_test": ... |
from dnnet.exception import DNNetRuntimeError
from dnnet.ext_mathlibs import cp, np
from dnnet.layers.layer import Layer
from dnnet.utils.cnn_utils import im2col, col2im
class PoolingLayer(Layer):
def __init__(self, window_shape):
self.layer_index = 0
self.window_shape = window_shape
self... | {
"repo_name": "daichi-yoshikawa/dnn",
"path": "dnnet/layers/pooling.py",
"copies": "1",
"size": "3948",
"license": "bsd-3-clause",
"hash": -6020667445547961000,
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"line_max": 72,
"alpha_frac": 0.5590172239,
"autogenerated": false,
"ratio": 3.214983713355049,
"config_te... |
from dnnet.layers.layer import Layer
from dnnet.ext_mathlibs import cp, np
from dnnet.training.weight_initialization import DefaultInitialization
from dnnet.utils.nn_utils import is_multi_channels_image
from dnnet.utils.nn_utils import prod, asnumpy, flatten, unflatten
class AffineLayer(Layer):
"""Implement affi... | {
"repo_name": "daichi-yoshikawa/dnn",
"path": "dnnet/layers/affine.py",
"copies": "1",
"size": "2777",
"license": "bsd-3-clause",
"hash": -8416870338100348000,
"line_mean": 29.5164835165,
"line_max": 87,
"alpha_frac": 0.6182931221,
"autogenerated": false,
"ratio": 3.4115479115479115,
"config_te... |
from enum import Enum
from dnnet.ext_mathlibs import cp, np
from dnnet.utils.nn_utils import asnumpy
class Optimizer:
"""
Base class for optimizers.
Warning: This class should not be used directly.
Use derived classes instead.
"""
Type = Enum(
'Type',
'sgd, momentum,... | {
"repo_name": "daichi-yoshikawa/dnn",
"path": "dnnet/training/optimizer.py",
"copies": "1",
"size": "8012",
"license": "bsd-3-clause",
"hash": 276459643742833000,
"line_mean": 28.6703703704,
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"alpha_frac": 0.5574834602,
"autogenerated": false,
"ratio": 3.1852882703777334,
"config... |
from enum import Enum
from dnnet.ext_mathlibs import cp, np
from dnnet.utils.nn_utils import prod
class WeightInitialization:
"""Base class for random initialization of weight.
Parameters
----------
Type : Enum
Enumeration of name of methods to generate random weight.
"""
Type = Enu... | {
"repo_name": "daichi-yoshikawa/dnn",
"path": "dnnet/training/weight_initialization.py",
"copies": "1",
"size": "2713",
"license": "bsd-3-clause",
"hash": 1371191847518494700,
"line_mean": 24.5943396226,
"line_max": 77,
"alpha_frac": 0.6140803539,
"autogenerated": false,
"ratio": 4.00147492625368... |
from enum import Enum
import dnnet.utils.numcupy as ncp
from dnnet.ext_mathlibs import cp, np
from dnnet.layers.layer import Layer
from dnnet.utils.nn_utils import is_multi_channels_image
from dnnet.utils.nn_utils import asnumpy, flatten, unflatten
class ActivationLayer(Layer):
"""Implements layer which convert... | {
"repo_name": "daichi-yoshikawa/dnn",
"path": "dnnet/layers/activation.py",
"copies": "1",
"size": "7601",
"license": "bsd-3-clause",
"hash": -6821316902629174000,
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"line_max": 79,
"alpha_frac": 0.5861070912,
"autogenerated": false,
"ratio": 3.528783658310121,
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import dnnet.utils.numcupy as ncp
from dnnet.ext_mathlibs import cp, np
from dnnet.exception import DNNetRuntimeError
from dnnet.layers.layer import Layer
from dnnet.training.weight_initialization import DefaultInitialization
from dnnet.utils.nn_utils import asnumpy
from dnnet.utils.cnn_utils import pad_img, im2col, c... | {
"repo_name": "daichi-yoshikawa/dnn",
"path": "dnnet/layers/convolution.py",
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"size": "5223",
"license": "bsd-3-clause",
"hash": -8672797328452431000,
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"line_max": 88,
"alpha_frac": 0.5718935478,
"autogenerated": false,
"ratio": 3.1925427872860634,
"conf... |
import dnnet.utils.numcupy as ncp
from dnnet.ext_mathlibs import cp, np
from dnnet.layers.layer import Layer
from dnnet.utils.nn_utils import is_multi_channels_image
from dnnet.utils.nn_utils import asnumpy, flatten, unflatten
class BatchNormLayer(Layer):
"""Implementation of Batch Normalization.
Derived cl... | {
"repo_name": "daichi-yoshikawa/dnn",
"path": "dnnet/layers/batch_norm.py",
"copies": "1",
"size": "5516",
"license": "bsd-3-clause",
"hash": 317742389187953000,
"line_mean": 32.0299401198,
"line_max": 76,
"alpha_frac": 0.59862219,
"autogenerated": false,
"ratio": 3.3986444855206406,
"config_te... |
import matplotlib.pyplot as plt
import dnnet.utils.numcupy as ncp
from dnnet.ext_mathlibs import cp, np
from dnnet.utils.nn_utils import prod
def pad_img(img, pad_rows, pad_cols):
"""Returns padded matrix which represents image.
1d matrix is not supported.
Shape must be in forms of (***, ***, ... , ***... | {
"repo_name": "daichi-yoshikawa/dnn",
"path": "dnnet/utils/cnn_utils.py",
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"size": "5098",
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"autogenerated": false,
"ratio": 3.158612143742255,
"config_te... |
import operator
from collections.abc import Iterable
from functools import reduce
import numpy as np
import cupy as cp
def prod(x):
if isinstance(x, Iterable):
return reduce(operator.mul, x, 1)
else:
return x
def asnumpy(x):
if isinstance(type(x), np.ndarray):
return x
else... | {
"repo_name": "daichi-yoshikawa/dnn",
"path": "dnnet/utils/nn_utils.py",
"copies": "1",
"size": "7037",
"license": "bsd-3-clause",
"hash": -5965496406333297000,
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"line_max": 75,
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"autogenerated": false,
"ratio": 3.6385729058945193,
"config_t... |
from math import sqrt
import numpy as np
from scipy import linalg
from ..utils import check_random_state, logger, verbose, fill_doc
@fill_doc
def power_iteration_kron(A, C, max_iter=1000, tol=1e-3, random_state=0):
"""Find the largest singular value for the matrix kron(C.T, A).
It uses power iterations.
... | {
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"path": "mne/inverse_sparse/mxne_debiasing.py",
"copies": "6",
"size": "3545",
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"ratio": 3.153914590747331,
... |
from math import sqrt
import numpy as np
from ..utils import check_random_state, logger, verbose, fill_doc
@fill_doc
def power_iteration_kron(A, C, max_iter=1000, tol=1e-3, random_state=0):
"""Find the largest singular value for the matrix kron(C.T, A).
It uses power iterations.
Parameters
-------... | {
"repo_name": "mne-tools/mne-python",
"path": "mne/inverse_sparse/mxne_debiasing.py",
"copies": "8",
"size": "3532",
"license": "bsd-3-clause",
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"autogenerated": false,
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from math import sqrt
import numpy as np
from scipy import linalg
from ..utils import check_random_state, logger, verbose
def power_iteration_kron(A, C, max_iter=1000, tol=1e-3, random_state=0):
"""Find the largest singular value for the matrix kron(C.T, A).
It uses power iterations.
Parameters
--... | {
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"path": "mne/inverse_sparse/mxne_debiasing.py",
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import numpy as np
from ..evoked import Evoked
from ..epochs import _BaseEpochs
from ..io import _BaseRaw
from ..event import find_events
from ..io.pick import pick_channels
from ..utils import _check_copy_dep
def _get_window(start, end):
"""Return window which has length as much as parameter start - end"""
... | {
"repo_name": "alexandrebarachant/mne-python",
"path": "mne/preprocessing/stim.py",
"copies": "7",
"size": "4785",
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"hash": 9011898282917975000,
"line_mean": 36.0930232558,
"line_max": 79,
"alpha_frac": 0.5962382445,
"autogenerated": false,
"ratio": 3.6526717557251906,
... |
import numpy as np
from ..evoked import Evoked
from ..epochs import _BaseEpochs
from ..io import _BaseRaw
from ..event import find_events
from ..io.pick import pick_channels
def _get_window(start, end):
"""Return window which has length as much as parameter start - end"""
from scipy.signal import hann
w... | {
"repo_name": "cmoutard/mne-python",
"path": "mne/preprocessing/stim.py",
"copies": "2",
"size": "4752",
"license": "bsd-3-clause",
"hash": -9033815929276663000,
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"line_max": 79,
"alpha_frac": 0.5936447811,
"autogenerated": false,
"ratio": 3.6666666666666665,
"config_... |
import numpy as np
from ..evoked import Evoked
from ..epochs import BaseEpochs
from ..io import BaseRaw
from ..event import find_events
from ..io.pick import _pick_data_channels
from ..io.base import _check_preload
def _get_window(start, end):
"""Return window which has length as much as parameter start - end."... | {
"repo_name": "nicproulx/mne-python",
"path": "mne/preprocessing/stim.py",
"copies": "2",
"size": "4386",
"license": "bsd-3-clause",
"hash": 4396537651135174000,
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"line_max": 79,
"alpha_frac": 0.5973552212,
"autogenerated": false,
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"config_t... |
import numpy as np
from ..evoked import Evoked
from ..epochs import BaseEpochs
from ..io import BaseRaw
from ..event import find_events
from ..io.pick import _pick_data_channels
from ..utils import _check_preload, _check_option
def _get_window(start, end):
"""Return window which has length as much as parameter ... | {
"repo_name": "cjayb/mne-python",
"path": "mne/preprocessing/stim.py",
"copies": "2",
"size": "4333",
"license": "bsd-3-clause",
"hash": 2268957477645044000,
"line_mean": 35.7203389831,
"line_max": 79,
"alpha_frac": 0.5977382876,
"autogenerated": false,
"ratio": 3.6108333333333333,
"config_test... |
import numpy as np
from ..evoked import Evoked
from ..epochs import BaseEpochs
from ..io import BaseRaw
from ..event import find_events
from ..io.pick import _picks_to_idx
from ..utils import _check_preload, _check_option, fill_doc
def _get_window(start, end):
"""Return window which has length as much as parame... | {
"repo_name": "larsoner/mne-python",
"path": "mne/preprocessing/stim.py",
"copies": "12",
"size": "4425",
"license": "bsd-3-clause",
"hash": 287475228802522240,
"line_mean": 35.5702479339,
"line_max": 79,
"alpha_frac": 0.5943502825,
"autogenerated": false,
"ratio": 3.585899513776337,
"config_te... |
import numpy as np
from ..evoked import Evoked
from ..epochs import Epochs
from ..io import Raw
from ..event import find_events
from ..io.pick import pick_channels
def _get_window(start, end):
"""Return window which has length as much as parameter start - end"""
from scipy.signal import hann
window = 1 ... | {
"repo_name": "Odingod/mne-python",
"path": "mne/preprocessing/stim.py",
"copies": "7",
"size": "4735",
"license": "bsd-3-clause",
"hash": 8781463452764286000,
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"line_max": 79,
"alpha_frac": 0.5915522703,
"autogenerated": false,
"ratio": 3.664860681114551,
"config_tes... |
import numpy as np
from scipy import signal, interpolate
from .. import pick_types
def eliminate_stim_artifact(raw, events, event_id, tmin=-0.005,
tmax=0.01, mode='linear'):
"""Eliminates stimulations artifacts from raw data
The raw object will be modified in place (no copy)
... | {
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"path": "mne/preprocessing/stim.py",
"copies": "4",
"size": "2477",
"license": "bsd-3-clause",
"hash": 728945459613086300,
"line_mean": 34.8985507246,
"line_max": 75,
"alpha_frac": 0.5672184094,
"autogenerated": false,
"ratio": 3.569164265129683,
"config_tes... |
import os.path as op
import numpy as np
from numpy.testing import assert_array_almost_equal
from nose.tools import assert_true, assert_raises
from mne.io import Raw
from mne.io.pick import pick_types
from mne.event import read_events
from mne.epochs import Epochs
from mne.preprocessing.stim import fix_stim_artifact
... | {
"repo_name": "rajul/mne-python",
"path": "mne/preprocessing/tests/test_stim.py",
"copies": "14",
"size": "3917",
"license": "bsd-3-clause",
"hash": -5239542652210452000,
"line_mean": 39.8020833333,
"line_max": 77,
"alpha_frac": 0.6339034976,
"autogenerated": false,
"ratio": 2.8281588447653427,
... |
import os.path as op
import numpy as np
from numpy.testing import assert_array_almost_equal
from nose.tools import assert_true, assert_raises
from mne.io import read_raw_fif
from mne.io.pick import pick_types
from mne.event import read_events
from mne.epochs import Epochs
from mne.preprocessing.stim import fix_stim_... | {
"repo_name": "jniediek/mne-python",
"path": "mne/preprocessing/tests/test_stim.py",
"copies": "3",
"size": "4026",
"license": "bsd-3-clause",
"hash": -7347796885080884000,
"line_mean": 40.5051546392,
"line_max": 77,
"alpha_frac": 0.6328862394,
"autogenerated": false,
"ratio": 2.8153846153846156,... |
import os.path as op
import numpy as np
from numpy.testing import assert_array_almost_equal
import pytest
from mne.io import read_raw_fif
from mne.event import read_events
from mne.epochs import Epochs
from mne.preprocessing.stim import fix_stim_artifact
data_path = op.join(op.dirname(__file__), '..', '..', 'io', '... | {
"repo_name": "olafhauk/mne-python",
"path": "mne/preprocessing/tests/test_stim.py",
"copies": "8",
"size": "4073",
"license": "bsd-3-clause",
"hash": 7774227364715470000,
"line_mean": 38.931372549,
"line_max": 77,
"alpha_frac": 0.6211637614,
"autogenerated": false,
"ratio": 2.8245492371705962,
... |
import os.path as op
import numpy as np
from numpy.testing import assert_array_almost_equal
import pytest
from mne.io import read_raw_fif
from mne.io.pick import pick_types
from mne.event import read_events
from mne.epochs import Epochs
from mne.preprocessing.stim import fix_stim_artifact
data_path = op.join(op.dir... | {
"repo_name": "teonlamont/mne-python",
"path": "mne/preprocessing/tests/test_stim.py",
"copies": "7",
"size": "3910",
"license": "bsd-3-clause",
"hash": -4667381468140810000,
"line_mean": 39.3092783505,
"line_max": 77,
"alpha_frac": 0.6319693095,
"autogenerated": false,
"ratio": 2.831281679942071... |
import math
import numpy as np
import scipy.sparse as sp
from sklearn.linear_model.sag import get_auto_step_size
from sklearn.linear_model.sag_fast import _multinomial_grad_loss_all_samples
from sklearn.linear_model import LogisticRegression, Ridge
from sklearn.linear_model.base import make_dataset
from sklearn.linea... | {
"repo_name": "rvraghav93/scikit-learn",
"path": "sklearn/linear_model/tests/test_sag.py",
"copies": "36",
"size": "30671",
"license": "bsd-3-clause",
"hash": -2016440539763605000,
"line_mean": 36.1319612591,
"line_max": 79,
"alpha_frac": 0.5371197548,
"autogenerated": false,
"ratio": 3.561839507... |
import math
import re
import pytest
import numpy as np
import scipy.sparse as sp
from scipy.special import logsumexp
from sklearn.linear_model._sag import get_auto_step_size
from sklearn.linear_model._sag_fast import _multinomial_grad_loss_all_samples
from sklearn.linear_model import LogisticRegression, Ridge
from sk... | {
"repo_name": "kevin-intel/scikit-learn",
"path": "sklearn/linear_model/tests/test_sag.py",
"copies": "2",
"size": "32004",
"license": "bsd-3-clause",
"hash": 7994714089285005000,
"line_mean": 36.6517647059,
"line_max": 79,
"alpha_frac": 0.5411511061,
"autogenerated": false,
"ratio": 3.5818690542... |
import warnings
import numpy as np
from scipy.optimize import linprog
from ..base import BaseEstimator, RegressorMixin
from ._base import LinearModel
from ..exceptions import ConvergenceWarning
from ..utils.validation import _check_sample_weight
from ..utils.fixes import sp_version, parse_version
class QuantileRegr... | {
"repo_name": "kevin-intel/scikit-learn",
"path": "sklearn/linear_model/_quantile.py",
"copies": "2",
"size": "9383",
"license": "bsd-3-clause",
"hash": -9218322689303483000,
"line_mean": 31.9228070175,
"line_max": 78,
"alpha_frac": 0.5393797293,
"autogenerated": false,
"ratio": 4.042654028436019... |
"""
Directives for table elements.
"""
__docformat__ = 'reStructuredText'
import sys
import os.path
from docutils import io, nodes, statemachine, utils
from docutils.utils import SystemMessagePropagation
from docutils.parsers.rst import directives
try:
import csv # new in Python 2.3
ex... | {
"repo_name": "jmchilton/galaxy-central",
"path": "modules/docutils/parsers/rst/directives/tables.py",
"copies": "1",
"size": "19085",
"license": "mit",
"hash": -5406349568338067000,
"line_mean": 42.2766439909,
"line_max": 80,
"alpha_frac": 0.5933455593,
"autogenerated": false,
"ratio": 4.0212810... |
"""
Directives for table elements.
"""
__docformat__ = 'reStructuredText'
import sys
import os.path
from docutils import io, nodes, statemachine, utils
from docutils.utils import SystemMessagePropagation
from docutils.parsers.rst import directives
try:
import csv # new i... | {
"repo_name": "epall/selenium",
"path": "selenium/src/py/lib/docutils/parsers/rst/directives/tables.py",
"copies": "5",
"size": "19737",
"license": "apache-2.0",
"hash": -8784400199170375000,
"line_mean": 42.4527027027,
"line_max": 80,
"alpha_frac": 0.5803820236,
"autogenerated": false,
"ratio": ... |
"""Miscellaneous directives."""
__docformat__ = 'reStructuredText'
import sys
import os.path
import re
from docutils import io, nodes, statemachine, utils
from docutils.parsers.rst import directives, roles, states
from docutils.transforms import misc
try:
import urllib2
except ImportError:
urllib2 = None
... | {
"repo_name": "jmchilton/galaxy-central",
"path": "modules/docutils/parsers/rst/directives/misc.py",
"copies": "1",
"size": "15276",
"license": "mit",
"hash": 5238963673416450000,
"line_mean": 42.770773639,
"line_max": 83,
"alpha_frac": 0.5953783713,
"autogenerated": false,
"ratio": 4.04020100502... |
"""Miscellaneous directives."""
__docformat__ = 'reStructuredText'
import sys
import os.path
import re
import time
from docutils import io, nodes, statemachine, utils
from docutils.parsers.rst import directives, roles, states
from docutils.transforms import misc
try:
import urllib2
except ImportError:
urlli... | {
"repo_name": "pombreda/django-hotclub",
"path": "libs/external_libs/docutils-0.4/docutils/parsers/rst/directives/misc.py",
"copies": "6",
"size": "17513",
"license": "mit",
"hash": -1340080511913611300,
"line_mean": 41.9240196078,
"line_max": 83,
"alpha_frac": 0.5978416034,
"autogenerated": false,... |
"""Miscellaneous directives."""
__docformat__ = 'reStructuredText'
import sys
import os.path
import re
import time
from docutils import io, nodes, statemachine, utils
from docutils.parsers.rst import directives, roles, states
from docutils.transforms import misc
try:
import urllib2
except ImportE... | {
"repo_name": "hugs/selenium",
"path": "selenium/src/py/lib/docutils/parsers/rst/directives/misc.py",
"copies": "5",
"size": "17921",
"license": "apache-2.0",
"hash": 8769480392152779000,
"line_mean": 41.9240196078,
"line_max": 83,
"alpha_frac": 0.5842307907,
"autogenerated": false,
"ratio": 4.09... |
"""
Exports the following:
:Modules:
- `statemachine` is 'docutils.statemachine'
- `nodes` is 'docutils.nodes'
- `urischemes` is 'docutils.urischemes'
- `utils` is 'docutils.utils'
- `transforms` is 'docutils.transforms'
- `states` is 'docutils.parsers.rst.states'
- `tableparser` is 'docut... | {
"repo_name": "google-code-export/django-hotclub",
"path": "libs/external_libs/docutils-0.4/test/DocutilsTestSupport.py",
"copies": "5",
"size": "30800",
"license": "mit",
"hash": -7013983339805034000,
"line_mean": 34.4838709677,
"line_max": 121,
"alpha_frac": 0.5839285714,
"autogenerated": false,
... |
"""
This module contains practical examples of Docutils client code.
Importing this module from client code is not recommended; its contents are
subject to change in future Docutils releases. Instead, it is recommended
that you copy and paste the parts you need into your own code, modifying as
necessary.
"""
from d... | {
"repo_name": "jmchilton/galaxy-central",
"path": "modules/docutils/examples.py",
"copies": "1",
"size": "3908",
"license": "mit",
"hash": 8395440392592869000,
"line_mean": 40.1368421053,
"line_max": 77,
"alpha_frac": 0.6862845445,
"autogenerated": false,
"ratio": 4.113684210526316,
"config_tes... |
"""
Simple internal document tree Writer, writes Docutils XML.
"""
__docformat__ = 'reStructuredText'
import docutils
from docutils import frontend, writers
class Writer(writers.Writer):
supported = ('xml',)
"""Formats this writer supports."""
settings_spec = (
'"Docutils XML" Writer Options... | {
"repo_name": "jmchilton/galaxy-central",
"path": "modules/docutils/writers/docutils_xml.py",
"copies": "1",
"size": "2701",
"license": "mit",
"hash": 6660557657403665000,
"line_mean": 36,
"line_max": 79,
"alpha_frac": 0.6019992595,
"autogenerated": false,
"ratio": 4.14900153609831,
"config_tes... |
"""
This package contains Docutils Writer modules.
"""
__docformat__ = 'reStructuredText'
import sys
import docutils
from docutils import languages, Component
from docutils.transforms import universal
class Writer(Component):
"""
Abstract base class for docutils Writers.
Each writer module or packag... | {
"repo_name": "jmchilton/galaxy-central",
"path": "modules/docutils/writers/__init__.py",
"copies": "1",
"size": "3465",
"license": "mit",
"hash": -4824820657030973000,
"line_mean": 30.7889908257,
"line_max": 78,
"alpha_frac": 0.6507936508,
"autogenerated": false,
"ratio": 4.288366336633663,
"c... |
"""
Simple internal document tree Writer, writes Docutils XML.
"""
__docformat__ = 'reStructuredText'
import docutils
from docutils import frontend, writers
class Writer(writers.Writer):
supported = ('xml',)
"""Formats this writer supports."""
settings_spec = (
'"Docutils XML" Writer Options... | {
"repo_name": "indro/t2c",
"path": "libs/external_libs/docutils-0.4/docutils/writers/docutils_xml.py",
"copies": "6",
"size": "2781",
"license": "mit",
"hash": -6091358762444960000,
"line_mean": 36.08,
"line_max": 79,
"alpha_frac": 0.6058971593,
"autogenerated": false,
"ratio": 4.150746268656716,... |
"""
This package contains Docutils Writer modules.
"""
__docformat__ = 'reStructuredText'
import os.path
import docutils
from docutils import languages, Component
from docutils.transforms import universal
class Writer(Component):
"""
Abstract base class for docutils Writers.
Each writer module or pa... | {
"repo_name": "pombreda/django-hotclub",
"path": "libs/external_libs/docutils-0.4/docutils/writers/__init__.py",
"copies": "6",
"size": "4164",
"license": "mit",
"hash": -6109601571537053000,
"line_mean": 30.3082706767,
"line_max": 78,
"alpha_frac": 0.6496157541,
"autogenerated": false,
"ratio": ... |
"""
This module contains practical examples of Docutils client code.
Importing this module from client code is not recommended; its contents are
subject to change in future Docutils releases. Instead, it is recommended
that you copy and paste the parts you need into your own code, modifying as
necessary.
"""... | {
"repo_name": "mogotest/selenium",
"path": "selenium/src/py/lib/docutils/examples.py",
"copies": "5",
"size": "3952",
"license": "apache-2.0",
"hash": -3505303712548716000,
"line_mean": 40.0425531915,
"line_max": 77,
"alpha_frac": 0.6710526316,
"autogenerated": false,
"ratio": 4.16,
"config_tes... |
"""
Simple internal document tree Writer, writes Docutils XML.
"""
__docformat__ = 'reStructuredText'
import docutils
from docutils import frontend, writers
class Writer(writers.Writer):
supported = ('xml',)
"""Formats this writer supports."""
settings_spec = (
'"Docutils X... | {
"repo_name": "mogotest/selenium",
"path": "selenium/src/py/lib/docutils/writers/docutils_xml.py",
"copies": "5",
"size": "2856",
"license": "apache-2.0",
"hash": -7841262473559943000,
"line_mean": 36.08,
"line_max": 79,
"alpha_frac": 0.5899859944,
"autogenerated": false,
"ratio": 4.2123893805309... |
"""
This package contains Docutils Writer modules.
"""
__docformat__ = 'reStructuredText'
import os.path
import docutils
from docutils import languages, Component
from docutils.transforms import universal
class Writer(Component):
"""
Abstract base class for docutils Writers.
Each ... | {
"repo_name": "hugs/selenium",
"path": "selenium/src/py/lib/docutils/writers/__init__.py",
"copies": "5",
"size": "4424",
"license": "apache-2.0",
"hash": 1043719074089768600,
"line_mean": 30.7703703704,
"line_max": 78,
"alpha_frac": 0.6299728752,
"autogenerated": false,
"ratio": 4.41958041958041... |
"""
Transforms related to document parts.
"""
__docformat__ = 'reStructuredText'
import re
import sys
from docutils import nodes, utils
from docutils.transforms import TransformError, Transform
class SectNum(Transform):
"""
Automatically assigns numbers to the titles of document sections.
It is poss... | {
"repo_name": "jmchilton/galaxy-central",
"path": "modules/docutils/transforms/parts.py",
"copies": "1",
"size": "6361",
"license": "mit",
"hash": 8331340569922773000,
"line_mean": 36.1988304094,
"line_max": 78,
"alpha_frac": 0.6054079547,
"autogenerated": false,
"ratio": 4.252005347593583,
"co... |
"""
Transforms related to document parts.
"""
__docformat__ = 'reStructuredText'
import re
import sys
from docutils import nodes, utils
from docutils.transforms import TransformError, Transform
class SectNum(Transform):
"""
Automatically assigns numbers to the titles of document sections... | {
"repo_name": "brownman/selenium-webdriver",
"path": "selenium/src/py/lib/docutils/transforms/parts.py",
"copies": "5",
"size": "6490",
"license": "apache-2.0",
"hash": -299470105536012540,
"line_mean": 35.9532163743,
"line_max": 78,
"alpha_frac": 0.5882896764,
"autogenerated": false,
"ratio": 4.... |
"""
This package contains Docutils Reader modules.
"""
__docformat__ = 'reStructuredText'
import sys
from docutils import utils, parsers, Component
from docutils.transforms import universal
class Reader(Component):
"""
Abstract base class for docutils Readers.
Each reader module or package must expo... | {
"repo_name": "jmchilton/galaxy-central",
"path": "modules/docutils/readers/__init__.py",
"copies": "1",
"size": "2791",
"license": "mit",
"hash": 8381648186903637000,
"line_mean": 30.3595505618,
"line_max": 78,
"alpha_frac": 0.6481547832,
"autogenerated": false,
"ratio": 4.147102526002972,
"co... |
"""
This package contains modules for standard tree transforms available
to Docutils components. Tree transforms serve a variety of purposes:
- To tie up certain syntax-specific "loose ends" that remain after the
initial parsing of the input plaintext. These transforms are used to
supplement a limited syntax.
- ... | {
"repo_name": "jmchilton/galaxy-central",
"path": "modules/docutils/transforms/__init__.py",
"copies": "1",
"size": "6733",
"license": "mit",
"hash": 3952262699929310000,
"line_mean": 36.4055555556,
"line_max": 79,
"alpha_frac": 0.6551314422,
"autogenerated": false,
"ratio": 4.721598877980365,
... |
"""
Transforms related to the front matter of a document or a section
(information found before the main text):
- `DocTitle`: Used to transform a lone top level section's title to
the document title, and promote a remaining lone top-level section's
title to the document subtitle.
- `SectionTitle`: Used to transf... | {
"repo_name": "jmchilton/galaxy-central",
"path": "modules/docutils/transforms/frontmatter.py",
"copies": "1",
"size": "17801",
"license": "mit",
"hash": 1650798511989161500,
"line_mean": 35.6275720165,
"line_max": 78,
"alpha_frac": 0.5572720634,
"autogenerated": false,
"ratio": 4.731791600212653... |
"""
This package contains modules for standard tree transforms available
to Docutils components. Tree transforms serve a variety of purposes:
- To tie up certain syntax-specific "loose ends" that remain after the
initial parsing of the input plaintext. These transforms are used to
supplement a limited syntax.
- ... | {
"repo_name": "santisiri/popego",
"path": "envs/ALPHA-POPEGO/lib/python2.5/site-packages/docutils-0.4-py2.5.egg/docutils/transforms/__init__.py",
"copies": "6",
"size": "6690",
"license": "bsd-3-clause",
"hash": 8157385663687641000,
"line_mean": 37.0113636364,
"line_max": 79,
"alpha_frac": 0.65485799... |
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