text stringlengths 0 1.05M | meta dict |
|---|---|
"""
This package contains Docutils Reader modules.
"""
__docformat__ = 'reStructuredText'
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 export a subcla... | {
"repo_name": "santisiri/popego",
"path": "envs/ALPHA-POPEGO/lib/python2.5/site-packages/docutils-0.4-py2.5.egg/docutils/readers/__init__.py",
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"""
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 synt... | {
"repo_name": "brownman/selenium-webdriver",
"path": "selenium/src/py/lib/docutils/transforms/__init__.py",
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"size": "6866",
"license": "apache-2.0",
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"autogenerated": false,
"ratio":... |
"""
This package contains Docutils Reader modules.
"""
__docformat__ = 'reStructuredText'
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 mus... | {
"repo_name": "mfazekas/safaridriver",
"path": "selenium/src/py/lib/docutils/readers/__init__.py",
"copies": "5",
"size": "3464",
"license": "apache-2.0",
"hash": -5549471413342098000,
"line_mean": 29.7798165138,
"line_max": 78,
"alpha_frac": 0.6273094688,
"autogenerated": false,
"ratio": 4.29776... |
"""
Transforms needed by most or all documents:
- `Decorations`: Generate a document's header & footer.
- `Messages`: Placement of system messages stored in
`nodes.document.transform_messages`.
- `TestMessages`: Like `Messages`, used on test runs.
- `FinalReferences`: Resolve remaining references.
"""
_... | {
"repo_name": "mogotest/selenium",
"path": "selenium/src/py/lib/docutils/transforms/universal.py",
"copies": "5",
"size": "5525",
"license": "apache-2.0",
"hash": 7992740192255659000,
"line_mean": 30.3099415205,
"line_max": 78,
"alpha_frac": 0.563438914,
"autogenerated": false,
"ratio": 4.6822033... |
"""
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, promote a remaining lone top-level section's
title to the document subtitle, and determine the document's t... | {
"repo_name": "hugs/selenium",
"path": "selenium/src/py/lib/docutils/transforms/frontmatter.py",
"copies": "5",
"size": "19455",
"license": "apache-2.0",
"hash": -7212737328428854000,
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"line_max": 82,
"alpha_frac": 0.5481367258,
"autogenerated": false,
"ratio": 4.768382... |
__authors__ = ["David PS"]
__email__ = "dps.helio-?-gmail.com"
import numpy as np
class Chaincode(np.ndarray):
'''
Chaincode(origin, chaincode, xdelta=1, ydelta=1)
A tool to infer some information from chaincodes produced
by HELIO Feature Catalogue or Heliphyisics Events Knowledgebase
Parameters... | {
"repo_name": "mjm159/sunpy",
"path": "sunpy/roi/chaincode.py",
"copies": "1",
"size": "3520",
"license": "bsd-2-clause",
"hash": -4948381877923156000,
"line_mean": 32.2075471698,
"line_max": 90,
"alpha_frac": 0.5650568182,
"autogenerated": false,
"ratio": 3.6213991769547325,
"config_test": fal... |
__authors__ = "David Warde-Farley, Ian Goodfellow"
__copyright__ = "Copyright 2010-2012, Universite de Montreal"
__credits__ = ["David Warde-Farley", "Ian Goodfellow"]
__license__ = "3-clause BSD"
__maintainer__ = "David Warde-Farley"
__email__ = "wardefar@iro"
from pylearn2.testing.skip import skip_if_no_gpu
skip_if_... | {
"repo_name": "ml-lab/pylearn2",
"path": "pylearn2/sandbox/cuda_convnet/tests/test_img_acts.py",
"copies": "5",
"size": "5697",
"license": "bsd-3-clause",
"hash": -396967490467963260,
"line_mean": 34.1666666667,
"line_max": 110,
"alpha_frac": 0.6294540986,
"autogenerated": false,
"ratio": 3.44437... |
__author__ = 'sdeni'
from threading import Thread, Event
import RPi.GPIO as GPIO
from WSController.common_consts import ACTION_GO_FORWARD, ACTION_GO_BACKWARD, ACTION_STOP
class Motor(Thread):
def __init__(self, pin_forward, pin_backward, pin_pwm, name="", event=None):
"""
:param pin_forward:
... | {
"repo_name": "sdenisen/test",
"path": "HomeExplorerEngine/WSController/motor.py",
"copies": "2",
"size": "1678",
"license": "unlicense",
"hash": 8416502186181592000,
"line_mean": 28.9821428571,
"line_max": 98,
"alpha_frac": 0.5822407628,
"autogenerated": false,
"ratio": 3.6798245614035086,
"co... |
__author__ = 'sdeni'
import requests
class ExplorerEngineError(Exception):
def __init__(self, msg, code, response):
self.msg = msg
self.error_code = code
self.responce = response
def __str__(self):
return repr('%s: %s %s' % (self.error_code, self.msg, self.responce))
class Engi... | {
"repo_name": "sdenisen/test",
"path": "HomeExplorerEngine/WSLib/web_client.py",
"copies": "2",
"size": "1357",
"license": "unlicense",
"hash": 8522732661538921000,
"line_mean": 26.16,
"line_max": 77,
"alpha_frac": 0.565954311,
"autogenerated": false,
"ratio": 3.5430809399477807,
"config_test":... |
__author__ = 'sdeni'
import vk_api
if __name__ == '__main__':
def main():
login, password = 'login@mail.ru', 'password'
vk_session = vk_api.VkApi(login, password, auth_handler=lambda:(47269368, True))
try:
vk_session.auth()
except vk_api.AuthError as error_msg:
... | {
"repo_name": "sdenisen/test",
"path": "vkontakte/remover_vk_group.py",
"copies": "2",
"size": "1966",
"license": "unlicense",
"hash": 3673701813753127000,
"line_mean": 53.6388888889,
"line_max": 555,
"alpha_frac": 0.6002034588,
"autogenerated": false,
"ratio": 3.492007104795737,
"config_test":... |
__author__ = 'sdeni'
from threading import Event
from HomeExplorerEngine.WSController.motor import Motor
from WSController.common_consts import *
class EngineController(object):
def __init__(self):
self.action_event = Event()
self.motor_ahead_left = Motor(PIN_AHEAD_LEFT_FORWARD, PIN_AHEAD_LEFT_B... | {
"repo_name": "sdenisen/python",
"path": "HomeExplorerEngine/WSController/engine_controller.py",
"copies": "2",
"size": "3129",
"license": "unlicense",
"hash": 1770152222534834200,
"line_mean": 37.6419753086,
"line_max": 96,
"alpha_frac": 0.6248002557,
"autogenerated": false,
"ratio": 3.523648648... |
__author__ = 'sdeni'
import time
from tkinter import *
from WSLib.web_client import EngineActions
class Application(Frame):
BTN_HEIGHT=5
BTN_WIDTH=15
BTN_NAME_LEFT = 'left'
BTN_NAME_RIGHT = 'right'
BTN_NAME_DOWN = 'down'
BTN_NAME_UP = 'up'
BTN_NAME_STOP = 'stop'
def __init__(self, pa... | {
"repo_name": "sdenisen/python",
"path": "HomeExplorerEngine/application.py",
"copies": "2",
"size": "3178",
"license": "unlicense",
"hash": 235926448746443650,
"line_mean": 32.8191489362,
"line_max": 130,
"alpha_frac": 0.59030837,
"autogenerated": false,
"ratio": 3.1716566866267466,
"config_te... |
from copy import deepcopy
import math
import numpy as np
from scipy import fftpack
# XXX explore cuda optimization at some point.
from ..io.pick import _pick_data_channels, pick_info
from ..utils import verbose, warn, fill_doc, _validate_type
from ..parallel import parallel_func, check_n_jobs
from .tfr import Average... | {
"repo_name": "cjayb/mne-python",
"path": "mne/time_frequency/_stockwell.py",
"copies": "2",
"size": "10167",
"license": "bsd-3-clause",
"hash": 8738813372946525000,
"line_mean": 36.3786764706,
"line_max": 79,
"alpha_frac": 0.5933903806,
"autogenerated": false,
"ratio": 3.2235256816740647,
"con... |
from copy import deepcopy
import numpy as np
from ..fixes import _import_fft
from ..io.pick import _pick_data_channels, pick_info
from ..utils import verbose, warn, fill_doc, _validate_type
from ..parallel import parallel_func, check_n_jobs
from .tfr import AverageTFR, _get_data
def _check_input_st(x_in, n_fft):
... | {
"repo_name": "drammock/mne-python",
"path": "mne/time_frequency/_stockwell.py",
"copies": "4",
"size": "9692",
"license": "bsd-3-clause",
"hash": -1555378386538996000,
"line_mean": 34.5018315018,
"line_max": 79,
"alpha_frac": 0.5873916632,
"autogenerated": false,
"ratio": 3.2349799732977305,
"... |
from copy import deepcopy
import numpy as np
# XXX explore cuda optimization at some point.
from ..fixes import _import_fft
from ..io.pick import _pick_data_channels, pick_info
from ..utils import verbose, warn, fill_doc, _validate_type
from ..parallel import parallel_func, check_n_jobs
from .tfr import AverageTFR, ... | {
"repo_name": "kambysese/mne-python",
"path": "mne/time_frequency/_stockwell.py",
"copies": "4",
"size": "9739",
"license": "bsd-3-clause",
"hash": 989471040886055200,
"line_mean": 34.5437956204,
"line_max": 79,
"alpha_frac": 0.5883560941,
"autogenerated": false,
"ratio": 3.238776188892584,
"co... |
from copy import deepcopy
from inspect import getargspec, isfunction
from collections import namedtuple
import os
import json
import numpy as np
from scipy import linalg
from .ecg import (qrs_detector, _get_ecg_channel_index, _make_ecg,
create_ecg_epochs)
from .eog import _find_eog_events, _get_eo... | {
"repo_name": "matthew-tucker/mne-python",
"path": "mne/preprocessing/ica.py",
"copies": "1",
"size": "89461",
"license": "bsd-3-clause",
"hash": 9088772948612729000,
"line_mean": 40.8237494156,
"line_max": 79,
"alpha_frac": 0.5739037122,
"autogenerated": false,
"ratio": 4.130812208523803,
"con... |
from copy import deepcopy
from inspect import getargspec, isfunction
from collections import namedtuple
import os
import json
import numpy as np
from scipy import stats
from scipy.spatial import distance
from scipy import linalg
from .ecg import (qrs_detector, _get_ecg_channel_index, _make_ecg,
cr... | {
"repo_name": "effigies/mne-python",
"path": "mne/preprocessing/ica.py",
"copies": "1",
"size": "87137",
"license": "bsd-3-clause",
"hash": 6409646721183541000,
"line_mean": 40.7522759943,
"line_max": 79,
"alpha_frac": 0.5773437231,
"autogenerated": false,
"ratio": 4.109072903895124,
"config_te... |
from inspect import getargspec, isfunction
from collections import namedtuple
from copy import deepcopy
import os
import json
import numpy as np
from scipy import linalg
from .ecg import (qrs_detector, _get_ecg_channel_index, _make_ecg,
create_ecg_epochs)
from .eog import _find_eog_events, _get_eo... | {
"repo_name": "rajegannathan/grasp-lift-eeg-cat-dog-solution-updated",
"path": "python-packages/mne-python-0.10/mne/preprocessing/ica.py",
"copies": "1",
"size": "102548",
"license": "bsd-3-clause",
"hash": -858262565588187100,
"line_mean": 40.8051365675,
"line_max": 79,
"alpha_frac": 0.5774369076,
... |
from inspect import getargspec, isfunction
from collections import namedtuple
import os
import json
import numpy as np
from scipy import linalg
from .ecg import (qrs_detector, _get_ecg_channel_index, _make_ecg,
create_ecg_epochs)
from .eog import _find_eog_events, _get_eog_channel_index
from .info... | {
"repo_name": "andyh616/mne-python",
"path": "mne/preprocessing/ica.py",
"copies": "1",
"size": "89879",
"license": "bsd-3-clause",
"hash": -3456838379103248000,
"line_mean": 40.9211753731,
"line_max": 79,
"alpha_frac": 0.5738937905,
"autogenerated": false,
"ratio": 4.136361544479728,
"config_t... |
from inspect import isfunction
from collections import namedtuple
from copy import deepcopy
from numbers import Integral
import os
import json
import numpy as np
from scipy import linalg
from .ecg import (qrs_detector, _get_ecg_channel_index, _make_ecg,
create_ecg_epochs)
from .eog import _find_eo... | {
"repo_name": "jaeilepp/mne-python",
"path": "mne/preprocessing/ica.py",
"copies": "1",
"size": "104711",
"license": "bsd-3-clause",
"hash": 8163275455703492000,
"line_mean": 41.20515921,
"line_max": 79,
"alpha_frac": 0.5789172102,
"autogenerated": false,
"ratio": 4.012223158862748,
"config_tes... |
from inspect import isfunction
from collections import namedtuple
from copy import deepcopy
import os
import json
import numpy as np
from scipy import linalg
from .ecg import (qrs_detector, _get_ecg_channel_index, _make_ecg,
create_ecg_epochs)
from .eog import _find_eog_events, _get_eog_channel_in... | {
"repo_name": "yousrabk/mne-python",
"path": "mne/preprocessing/ica.py",
"copies": "1",
"size": "103443",
"license": "bsd-3-clause",
"hash": 8275650229113551000,
"line_mean": 40.879757085,
"line_max": 79,
"alpha_frac": 0.5771777694,
"autogenerated": false,
"ratio": 4.077374852187623,
"config_te... |
import warnings
from copy import deepcopy
from inspect import getargspec, isfunction
from collections import namedtuple
import os
import json
import numpy as np
from scipy import stats
from scipy.spatial import distance
from scipy import linalg
from .ecg import (qrs_detector, _get_ecg_channel_index, _make_ecg,
... | {
"repo_name": "jaeilepp/eggie",
"path": "mne/preprocessing/ica.py",
"copies": "1",
"size": "100890",
"license": "bsd-2-clause",
"hash": -4584618439764277000,
"line_mean": 41.3729525409,
"line_max": 82,
"alpha_frac": 0.5781246903,
"autogenerated": false,
"ratio": 4.153903162055336,
"config_test"... |
from copy import deepcopy
import math
import numpy as np
from scipy import fftpack
# XXX explore cuda optimazation at some point.
from ..io.pick import pick_types, pick_info
from ..utils import logger, verbose
from ..parallel import parallel_func, check_n_jobs
from .tfr import AverageTFR, _get_data
def _check_input... | {
"repo_name": "cmoutard/mne-python",
"path": "mne/time_frequency/_stockwell.py",
"copies": "1",
"size": "9819",
"license": "bsd-3-clause",
"hash": 7773638694256723000,
"line_mean": 37.5058823529,
"line_max": 79,
"alpha_frac": 0.5865159385,
"autogenerated": false,
"ratio": 3.3386603196191773,
"c... |
from copy import deepcopy
import math
import numpy as np
from scipy import fftpack
# XXX explore cuda optimazation at some point.
from ..io.pick import pick_types, pick_info
from ..utils import verbose, warn
from ..parallel import parallel_func, check_n_jobs
from .tfr import AverageTFR, _get_data
def _check_input_s... | {
"repo_name": "nicproulx/mne-python",
"path": "mne/time_frequency/_stockwell.py",
"copies": "2",
"size": "10146",
"license": "bsd-3-clause",
"hash": 2271781363640669200,
"line_mean": 36.717472119,
"line_max": 79,
"alpha_frac": 0.5967869111,
"autogenerated": false,
"ratio": 3.248799231508165,
"c... |
from copy import deepcopy
import math
import numpy as np
from scipy import fftpack
# XXX explore cuda optimization at some point.
from ..io.pick import _pick_data_channels, pick_info
from ..utils import verbose, warn
from ..parallel import parallel_func, check_n_jobs
from .tfr import AverageTFR, _get_data
def _chec... | {
"repo_name": "teonlamont/mne-python",
"path": "mne/time_frequency/_stockwell.py",
"copies": "4",
"size": "10180",
"license": "bsd-3-clause",
"hash": -1985150862264944600,
"line_mean": 36.7037037037,
"line_max": 79,
"alpha_frac": 0.5962671906,
"autogenerated": false,
"ratio": 3.252396166134185,
... |
import os.path as op
import warnings
from nose.tools import assert_true, assert_equal
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_allclose
from scipy import fftpack
from mne import read_events, Epochs
from mne.io import read_raw_fif
from mne.time_frequency._stockwell import (tfr_s... | {
"repo_name": "jmontoyam/mne-python",
"path": "mne/time_frequency/tests/test_stockwell.py",
"copies": "3",
"size": "4719",
"license": "bsd-3-clause",
"hash": 5576811705358190000,
"line_mean": 38,
"line_max": 79,
"alpha_frac": 0.6054248782,
"autogenerated": false,
"ratio": 3.030828516377649,
"co... |
import os.path as op
import pytest
import numpy as np
from numpy.testing import (assert_array_almost_equal, assert_allclose,
assert_equal)
from scipy import fftpack
from mne import read_events, Epochs, make_fixed_length_events
from mne.io import read_raw_fif
from mne.time_frequency._stock... | {
"repo_name": "adykstra/mne-python",
"path": "mne/time_frequency/tests/test_stockwell.py",
"copies": "5",
"size": "5196",
"license": "bsd-3-clause",
"hash": -3070265937586806300,
"line_mean": 37.2058823529,
"line_max": 79,
"alpha_frac": 0.6050808314,
"autogenerated": false,
"ratio": 3.04393673110... |
import json
import re
import textwrap
from copy import deepcopy
from .constants import PMD
from Bio import Entrez, Medline
try:
from itertools import izip_longest
except ImportError:
from itertools import zip_longest as izip_longest
try:
# For Python 3.0 and later
from urllib.request import urlopen
... | {
"repo_name": "PyMed/PyMed",
"path": "pymed/pymed.py",
"copies": "1",
"size": "19222",
"license": "bsd-3-clause",
"hash": -2419155440222871600,
"line_mean": 28.5268817204,
"line_max": 79,
"alpha_frac": 0.5409946936,
"autogenerated": false,
"ratio": 4.097633766787466,
"config_test": false,
"ha... |
import numpy as np
import os.path as op
from mne import io
from mne.io.constants import FIFF
from mne.io.proc_history import _get_sss_rank
from nose.tools import assert_true, assert_equal
base_dir = op.join(op.dirname(__file__), 'data')
raw_fname = op.join(base_dir, 'test_chpi_raw_sss.fif')
def test_maxfilter_io():... | {
"repo_name": "matthew-tucker/mne-python",
"path": "mne/io/tests/test_proc_history.py",
"copies": "18",
"size": "1741",
"license": "bsd-3-clause",
"hash": 5162203569666373000,
"line_mean": 36.0425531915,
"line_max": 76,
"alpha_frac": 0.6048248133,
"autogenerated": false,
"ratio": 2.79454253611557... |
import numpy as np
import os.path as op
from mne.io import read_info
from mne.io.constants import FIFF
from mne.io.proc_history import _get_sss_rank
from nose.tools import assert_true, assert_equal
base_dir = op.join(op.dirname(__file__), 'data')
raw_fname = op.join(base_dir, 'test_chpi_raw_sss.fif')
def test_maxfi... | {
"repo_name": "jaeilepp/mne-python",
"path": "mne/io/tests/test_proc_history.py",
"copies": "6",
"size": "1741",
"license": "bsd-3-clause",
"hash": -6199936030273108000,
"line_mean": 36.847826087,
"line_max": 76,
"alpha_frac": 0.6065479609,
"autogenerated": false,
"ratio": 2.8126009693053313,
"... |
import os.path as op
import numpy as np
from numpy.testing import assert_array_equal
from mne.io import read_info
from mne.io.constants import FIFF
base_dir = op.join(op.dirname(__file__), 'data')
raw_fname = op.join(base_dir, 'test_chpi_raw_sss.fif')
def test_maxfilter_io():
"""Test maxfilter io."""
info... | {
"repo_name": "wmvanvliet/mne-python",
"path": "mne/io/tests/test_proc_history.py",
"copies": "16",
"size": "1395",
"license": "bsd-3-clause",
"hash": 3392036214974906400,
"line_mean": 35.7105263158,
"line_max": 76,
"alpha_frac": 0.6043010753,
"autogenerated": false,
"ratio": 2.8012048192771086,
... |
import os.path as op
from io import BytesIO
from itertools import count
import numpy as np
from ...utils import logger, verbose, _stamp_to_dt
from ...transforms import (combine_transforms, invert_transform,
Transform)
from .._digitization import _make_bti_dig_points
from ..constants import... | {
"repo_name": "wmvanvliet/mne-python",
"path": "mne/io/bti/bti.py",
"copies": "4",
"size": "51168",
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"hash": 757648570328354300,
"line_mean": 38.7861586314,
"line_max": 79,
"alpha_frac": 0.4972930714,
"autogenerated": false,
"ratio": 3.620506651570903,
"config_test": t... |
import numpy as np
from numpy.testing import assert_array_equal
import pytest
from mne.time_frequency import morlet
from mne.preprocessing.ctps_ import (ctps, _prob_kuiper,
_compute_normalized_phase)
###############################################################################
... | {
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"path": "mne/preprocessing/tests/test_ctps.py",
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"""
Test the infomax algorithm.
Parts of this code are taken from scikit-learn
"""
import numpy as np
from numpy.testing import assert_almost_equal
from scipy import stats
from scipy import linalg
from mne.preprocessing.infomax_ import infomax
from mne.utils import requires_sklearn
def center_and_norm(x, axis=-1)... | {
"repo_name": "aestrivex/mne-python",
"path": "mne/preprocessing/tests/test_infomax.py",
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"autogenerated": false,
"ratio": 2.95975702353834... |
# Parts of this code are taken from scikit-learn
import pytest
import numpy as np
from numpy.testing import assert_almost_equal
from scipy import stats
from scipy import linalg
from mne.preprocessing.infomax_ import infomax
from mne.utils import requires_sklearn, run_tests_if_main, check_version
def center_and_n... | {
"repo_name": "cjayb/mne-python",
"path": "mne/preprocessing/tests/test_infomax.py",
"copies": "2",
"size": "6358",
"license": "bsd-3-clause",
"hash": -1892149528426651600,
"line_mean": 29.4210526316,
"line_max": 78,
"alpha_frac": 0.5714061025,
"autogenerated": false,
"ratio": 3.0018885741265344,... |
import numpy as np
from .constants import BTI
def bti_identity_trans(dtype='>f8'):
""" Get BTi identity transform
Parameters
----------
dtype : str | dtype object
The data format of the transform
Returns
-------
itrans : ndarray
The 4 x 4 transformation matrix.
"""
... | {
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"path": "mne/io/bti/transforms.py",
"copies": "14",
"size": "2715",
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"hash": 4344352607835268000,
"line_mean": 25.6176470588,
"line_max": 77,
"alpha_frac": 0.576427256,
"autogenerated": false,
"ratio": 2.7619532044760935,
"config_tes... |
import datetime
import os
import time
import warnings
import numpy as np
from ..base import _BaseRaw
from ..meas_info import Info
from ..constants import FIFF
from ...utils import verbose, logger
_other_fields = [
'lowpass', 'buffer_size_sec', 'dev_ctf_t',
'meas_id', 'subject_info',
'dev_head_t', 'line_... | {
"repo_name": "jaeilepp/eggie",
"path": "mne/io/egi/egi.py",
"copies": "2",
"size": "12658",
"license": "bsd-2-clause",
"hash": 7678091250508555000,
"line_mean": 38.3105590062,
"line_max": 80,
"alpha_frac": 0.5360246484,
"autogenerated": false,
"ratio": 3.640494679321254,
"config_test": false,
... |
import numpy as np
from ...externals.six import b
def _unpack_matrix(fid, rows, cols, dtype, out_dtype):
""" Aux Function """
dtype = np.dtype(dtype)
string = fid.read(int(dtype.itemsize * rows * cols))
out = np.fromstring(string, dtype=dtype).reshape(
rows, cols).astype(out_dtype)
retur... | {
"repo_name": "leggitta/mne-python",
"path": "mne/io/bti/read.py",
"copies": "5",
"size": "3205",
"license": "bsd-3-clause",
"hash": -6945226405502981000,
"line_mean": 25.7083333333,
"line_max": 75,
"alpha_frac": 0.6137285491,
"autogenerated": false,
"ratio": 3.211422845691383,
"config_test": f... |
import numpy as np
from ..utils import read_str
def _unpack_matrix(fid, rows, cols, dtype, out_dtype):
"""Unpack matrix."""
dtype = np.dtype(dtype)
string = fid.read(int(dtype.itemsize * rows * cols))
out = np.frombuffer(string, dtype=dtype).reshape(
rows, cols).astype(out_dtype)
return... | {
"repo_name": "olafhauk/mne-python",
"path": "mne/io/bti/read.py",
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"autogenerated": false,
"ratio": 3.1848290598290596,
"config_test":... |
import numpy as np
def _unpack_matrix(fid, rows, cols, dtype, out_dtype):
""" Aux Function """
dtype = np.dtype(dtype)
string = fid.read(int(dtype.itemsize * rows * cols))
out = np.fromstring(string, dtype=dtype).reshape(
rows, cols).astype(out_dtype)
return out
def _unpack_simple(fid,... | {
"repo_name": "jmontoyam/mne-python",
"path": "mne/io/bti/read.py",
"copies": "8",
"size": "2803",
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"autogenerated": false,
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"config_test": ... |
import struct
import numpy as np
from ...externals.six import b
def _unpack_matrix(fid, fmt, rows, cols, dtype):
""" Aux Function """
out = np.zeros((rows, cols), dtype=dtype)
bsize = struct.calcsize(fmt)
string = fid.read(bsize)
data = struct.unpack(fmt, string)
iter_mat = [(r, c) for r in r... | {
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"path": "mne/io/bti/read.py",
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"hash": -6501856998373276000,
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"line_max": 79,
"alpha_frac": 0.5936266215,
"autogenerated": false,
"ratio": 3.149200710479574,
"config_... |
import struct
import numpy as np
from ...externals.six import b
def _unpack_matrix(fid, format, rows, cols, dtype):
""" Aux Function """
out = np.zeros((rows, cols), dtype=dtype)
bsize = struct.calcsize(format)
string = fid.read(bsize)
data = struct.unpack(format, string)
iter_mat = [(r, c) f... | {
"repo_name": "effigies/mne-python",
"path": "mne/io/bti/read.py",
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"size": "3584",
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"hash": 6081018935588711000,
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"line_max": 79,
"alpha_frac": 0.59765625,
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"config_test": fals... |
import os.path as op
import warnings
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_array_equal
from nose.tools import assert_true, assert_raises, assert_equal
from mne import find_events, pick_types, concatenate_raws
from mne.io import read_raw_egi, Raw
from mne.io.egi import _combi... | {
"repo_name": "rajegannathan/grasp-lift-eeg-cat-dog-solution-updated",
"path": "python-packages/mne-python-0.10/mne/io/egi/tests/test_egi.py",
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"alpha_frac": 0.6452714237,... |
import datetime
import os
import time
import warnings
import numpy as np
from ..base import _BaseRaw, _check_update_montage
from ..meas_info import Info
from ..constants import FIFF
from ...utils import verbose, logger
_other_fields = [
'lowpass', 'buffer_size_sec', 'dev_ctf_t',
'meas_id', 'subject_info',
... | {
"repo_name": "effigies/mne-python",
"path": "mne/io/egi/egi.py",
"copies": "1",
"size": "13860",
"license": "bsd-3-clause",
"hash": 620872455958227300,
"line_mean": 38.9423631124,
"line_max": 79,
"alpha_frac": 0.5383116883,
"autogenerated": false,
"ratio": 3.6263736263736264,
"config_test": fa... |
import datetime
import time
import warnings
import numpy as np
from ..base import _BaseRaw, _check_update_montage
from ..meas_info import _empty_info
from ..constants import FIFF
from ...utils import verbose, logger
def _read_header(fid):
"""Read EGI binary header"""
version = np.fromfile(fid, np.int32, 1... | {
"repo_name": "andyh616/mne-python",
"path": "mne/io/egi/egi.py",
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"size": "13219",
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"hash": -3506713166610495000,
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"line_max": 79,
"alpha_frac": 0.5295408125,
"autogenerated": false,
"ratio": 3.771469329529244,
"config_test": f... |
import datetime
import time
import warnings
import numpy as np
from ..base import _BaseRaw, _check_update_montage
from ..utils import _read_segments_file
from ..meas_info import _empty_info
from ..constants import FIFF
from ...utils import verbose, logger
def _read_header(fid):
"""Read EGI binary header"""
... | {
"repo_name": "cmoutard/mne-python",
"path": "mne/io/egi/egi.py",
"copies": "1",
"size": "12461",
"license": "bsd-3-clause",
"hash": -4605983297084535000,
"line_mean": 41.5290102389,
"line_max": 79,
"alpha_frac": 0.5405665677,
"autogenerated": false,
"ratio": 3.8153704837721985,
"config_test": ... |
import datetime
import time
import numpy as np
from ..base import _BaseRaw, _check_update_montage
from ..utils import _read_segments_file, _create_chs
from ..meas_info import _empty_info
from ..constants import FIFF
from ...utils import verbose, logger, warn
def _read_header(fid):
"""Read EGI binary header"""
... | {
"repo_name": "wronk/mne-python",
"path": "mne/io/egi/egi.py",
"copies": "7",
"size": "11929",
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"hash": -1234152550887479800,
"line_mean": 40.8561403509,
"line_max": 79,
"alpha_frac": 0.5433816749,
"autogenerated": false,
"ratio": 3.8480645161290323,
"config_test": fal... |
import datetime
import time
import numpy as np
from ..base import BaseRaw, _check_update_montage
from ..utils import _read_segments_file, _create_chs
from ..meas_info import _empty_info
from ..constants import FIFF
from ...utils import verbose, logger, warn
def _read_header(fid):
"""Read EGI binary header."""
... | {
"repo_name": "nicproulx/mne-python",
"path": "mne/io/egi/egi.py",
"copies": "2",
"size": "12333",
"license": "bsd-3-clause",
"hash": 925437345772160000,
"line_mean": 41.0921501706,
"line_max": 79,
"alpha_frac": 0.545366091,
"autogenerated": false,
"ratio": 3.84804992199688,
"config_test": fals... |
import datetime
import time
import numpy as np
from .egimff import _read_raw_egi_mff
from .events import _combine_triggers
from ..base import BaseRaw, _check_update_montage
from ..utils import _read_segments_file, _create_chs
from ..meas_info import _empty_info
from ..constants import FIFF
from ...utils import verbo... | {
"repo_name": "adykstra/mne-python",
"path": "mne/io/egi/egi.py",
"copies": "1",
"size": "11784",
"license": "bsd-3-clause",
"hash": 2282699542068004900,
"line_mean": 41.3884892086,
"line_max": 79,
"alpha_frac": 0.549389002,
"autogenerated": false,
"ratio": 3.8148268047911946,
"config_test": fa... |
import datetime
import time
import numpy as np
from .egimff import _read_raw_egi_mff
from .events import _combine_triggers
from ..base import BaseRaw
from ..utils import _read_segments_file, _create_chs
from ..meas_info import _empty_info
from ..constants import FIFF
from ...utils import verbose, logger, warn
def ... | {
"repo_name": "larsoner/mne-python",
"path": "mne/io/egi/egi.py",
"copies": "3",
"size": "11041",
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"hash": -5965550940930296000,
"line_mean": 40.197761194,
"line_max": 79,
"alpha_frac": 0.5404401775,
"autogenerated": false,
"ratio": 3.7863511659807956,
"config_test": f... |
import os.path as op
import numpy as np
from numpy.testing import assert_equal, assert_array_equal
import pytest
import matplotlib.pyplot as plt
from mne import read_events, Epochs, read_cov, pick_types, Annotations
from mne.io import read_raw_fif
from mne.preprocessing import ICA, create_ecg_epochs, create_eog_epoc... | {
"repo_name": "cjayb/mne-python",
"path": "mne/viz/tests/test_ica.py",
"copies": "1",
"size": "14300",
"license": "bsd-3-clause",
"hash": 3186809201282301000,
"line_mean": 36.1428571429,
"line_max": 79,
"alpha_frac": 0.6170629371,
"autogenerated": false,
"ratio": 2.9935105714883816,
"config_tes... |
import os.path as op
import numpy as np
from numpy.testing import assert_equal, assert_array_equal
import pytest
import matplotlib.pyplot as plt
from mne import (read_events, Epochs, read_cov, pick_types, Annotations,
make_fixed_length_events)
from mne.fixes import _close_event
from mne.io import re... | {
"repo_name": "mne-tools/mne-python",
"path": "mne/viz/tests/test_ica.py",
"copies": "4",
"size": "15646",
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"hash": -3553316918172171300,
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"line_max": 79,
"alpha_frac": 0.6186245686,
"autogenerated": false,
"ratio": 3.0268910814470886,
"confi... |
import os.path as op
import warnings
from numpy.testing import assert_raises, assert_equal, assert_array_equal
from nose.tools import assert_true
from mne import read_events, Epochs, read_cov, pick_types
from mne.io import read_raw_fif
from mne.preprocessing import ICA, create_ecg_epochs, create_eog_epochs
from mne.... | {
"repo_name": "jniediek/mne-python",
"path": "mne/viz/tests/test_ica.py",
"copies": "3",
"size": "11395",
"license": "bsd-3-clause",
"hash": -1077357929454064900,
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"line_max": 79,
"alpha_frac": 0.6245721808,
"autogenerated": false,
"ratio": 3.058239398819109,
"config_... |
import os.path as op
import warnings
from numpy.testing import assert_raises
from mne import io, read_events, Epochs, read_cov
from mne import pick_types
from mne.utils import run_tests_if_main, requires_sklearn
from mne.viz.utils import _fake_click
from mne.preprocessing import ICA, create_ecg_epochs, create_eog_ep... | {
"repo_name": "wronk/mne-python",
"path": "mne/viz/tests/test_ica.py",
"copies": "2",
"size": "8043",
"license": "bsd-3-clause",
"hash": 480034513146126700,
"line_mean": 35.2297297297,
"line_max": 79,
"alpha_frac": 0.629615815,
"autogenerated": false,
"ratio": 2.991074748977315,
"config_test": ... |
import os.path as op
import numpy as np
from numpy.testing import assert_equal, assert_array_equal
import pytest
import matplotlib.pyplot as plt
from mne import read_events, Epochs, read_cov, pick_types
from mne.io import read_raw_fif
from mne.preprocessing import ICA, create_ecg_epochs, create_eog_epochs
from mne.u... | {
"repo_name": "adykstra/mne-python",
"path": "mne/viz/tests/test_ica.py",
"copies": "2",
"size": "11544",
"license": "bsd-3-clause",
"hash": 8724859535264600000,
"line_mean": 35.6476190476,
"line_max": 79,
"alpha_frac": 0.6163374913,
"autogenerated": false,
"ratio": 2.984488107549121,
"config_t... |
import os.path as op
import warnings
from nose.tools import assert_raises, assert_equals
import numpy as np
from mne.epochs import equalize_epoch_counts, concatenate_epochs
from mne.decoding import GeneralizationAcrossTime
from mne import io, Epochs, read_events, pick_types
from mne.utils import requires_sklearn, r... | {
"repo_name": "rajul/mne-python",
"path": "mne/viz/tests/test_decoding.py",
"copies": "10",
"size": "3823",
"license": "bsd-3-clause",
"hash": -6100704335478914000,
"line_mean": 29.8306451613,
"line_max": 75,
"alpha_frac": 0.6544598483,
"autogenerated": false,
"ratio": 3.0221343873517785,
"conf... |
import numpy as np
def find_outliers(X, threshold=3.0, max_iter=2):
"""Find outliers based on iterated Z-scoring.
This procedure compares the absolute z-score against the threshold.
After excluding local outliers, the comparison is repeated until no
local outlier is present any more.
Parameter... | {
"repo_name": "adykstra/mne-python",
"path": "mne/preprocessing/bads.py",
"copies": "10",
"size": "1161",
"license": "bsd-3-clause",
"hash": -6155192602671369000,
"line_mean": 28.025,
"line_max": 71,
"alpha_frac": 0.6279069767,
"autogenerated": false,
"ratio": 3.561349693251534,
"config_test": ... |
import numpy as np
def find_outliers(X, threshold=3.0, max_iter=2):
"""Find outliers based on iterated Z-scoring
This procedure compares the absolute z-score against the threshold.
After excluding local outliers, the comparison is repeated until no
local outlier is present any more.
Parameters... | {
"repo_name": "Odingod/mne-python",
"path": "mne/preprocessing/bads.py",
"copies": "24",
"size": "1160",
"license": "bsd-3-clause",
"hash": 8755576337321423000,
"line_mean": 28,
"line_max": 71,
"alpha_frac": 0.6284482759,
"autogenerated": false,
"ratio": 3.5692307692307694,
"config_test": false... |
import numpy as np
def _find_outliers(X, threshold=3.0, max_iter=2, tail=0):
"""Find outliers based on iterated Z-scoring.
This procedure compares the absolute z-score against the threshold.
After excluding local outliers, the comparison is repeated until no
local outlier is present any more.
... | {
"repo_name": "mne-tools/mne-python",
"path": "mne/preprocessing/bads.py",
"copies": "14",
"size": "1550",
"license": "bsd-3-clause",
"hash": -2099492783344691200,
"line_mean": 30,
"line_max": 76,
"alpha_frac": 0.5974193548,
"autogenerated": false,
"ratio": 3.5632183908045976,
"config_test": fa... |
from ..constants import BunchConst
BTI = BunchConst()
BTI.ELEC_STATE_NOT_COLLECTED = 0
BTI.ELEC_STATE_COLLECTED = 1
BTI.ELEC_STATE_SKIPPED = 2
BTI.ELEC_STATE_NOT_APPLICABLE = 3
#
## Byte offesets and data sizes for different files
#
BTI.FILE_MASK ... | {
"repo_name": "jniediek/mne-python",
"path": "mne/io/bti/constants.py",
"copies": "23",
"size": "3533",
"license": "bsd-3-clause",
"hash": 37121818094067890,
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"line_max": 75,
"alpha_frac": 0.4616473252,
"autogenerated": false,
"ratio": 2.8911620294599016,
"config_test... |
from ..constants import Bunch
BTI = Bunch()
BTI.ELEC_STATE_NOT_COLLECTED = 0
BTI.ELEC_STATE_COLLECTED = 1
BTI.ELEC_STATE_SKIPPED = 2
BTI.ELEC_STATE_NOT_APPLICABLE = 3
#
## Byte offesets and data sizes for different files
#
BTI.FILE_MASK = 214... | {
"repo_name": "effigies/mne-python",
"path": "mne/io/bti/constants.py",
"copies": "14",
"size": "3806",
"license": "bsd-3-clause",
"hash": -1735108956367718400,
"line_mean": 34.5794392523,
"line_max": 75,
"alpha_frac": 0.4603258014,
"autogenerated": false,
"ratio": 2.75,
"config_test": false,
... |
from ...utils import BunchConst
BTI = BunchConst()
BTI.ELEC_STATE_NOT_COLLECTED = 0
BTI.ELEC_STATE_COLLECTED = 1
BTI.ELEC_STATE_SKIPPED = 2
BTI.ELEC_STATE_NOT_APPLICABLE = 3
#
## Byte offesets and data sizes for different files
#
BTI.FILE_MASK ... | {
"repo_name": "wmvanvliet/mne-python",
"path": "mne/io/bti/constants.py",
"copies": "15",
"size": "3530",
"license": "bsd-3-clause",
"hash": -5733000386991262000,
"line_mean": 34.6565656566,
"line_max": 75,
"alpha_frac": 0.4609065156,
"autogenerated": false,
"ratio": 2.888707037643208,
"config_... |
import numpy as np
from ._fixes import string_types
class Discrete(list):
""" Simple Container for discrete data based on Python list
"""
def __init__(self, *args):
list.__init__(self, *args)
def __repr__(self):
s = '<Discrete | {0} epochs; {1} events>'
return s.format(len(s... | {
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"path": "pyeparse/_event.py",
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"alpha_frac": 0.6051893408,
"autogenerated": false,
"ratio": 3.665809768637532,
"config_test": false,
"ha... |
import numpy as np
from numpy.polynomial.legendre import legval
from scipy import linalg
from ..fixes import einsum
from ..utils import logger, warn, verbose
from ..io.pick import pick_types, pick_channels, pick_info
from ..surface import _normalize_vectors
from ..bem import _fit_sphere
from ..forward import _map_meg... | {
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"hash": 6685831616059805000,
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"line_max": 78,
"alpha_frac": 0.6217119605,
"autogenerated": false,
"ratio": 3.399703557312253,
"confi... |
import numpy as np
from numpy.polynomial.legendre import legval
from scipy import linalg
from ..utils import logger
from ..io.pick import pick_types, pick_channels
from ..surface import _normalize_vectors
from ..bem import _fit_sphere
from ..forward import _map_meg_channels
def _calc_g(cosang, stiffness=4, num_lter... | {
"repo_name": "rajegannathan/grasp-lift-eeg-cat-dog-solution-updated",
"path": "python-packages/mne-python-0.10/mne/channels/interpolation.py",
"copies": "5",
"size": "7012",
"license": "bsd-3-clause",
"hash": 8708012161889161000,
"line_mean": 32.8743961353,
"line_max": 79,
"alpha_frac": 0.6263548203... |
import numpy as np
from numpy.polynomial.legendre import legval
from scipy import linalg
from ..utils import logger
from ..io.pick import pick_types, pick_channels, pick_info
from ..surface import _normalize_vectors
from ..bem import _fit_sphere
from ..forward import _map_meg_channels
def _calc_g(cosang, stiffness=... | {
"repo_name": "cmoutard/mne-python",
"path": "mne/channels/interpolation.py",
"copies": "2",
"size": "7129",
"license": "bsd-3-clause",
"hash": 3796100436552039000,
"line_mean": 33.4396135266,
"line_max": 79,
"alpha_frac": 0.6272969561,
"autogenerated": false,
"ratio": 3.4208253358925145,
"conf... |
import numpy as np
from numpy.polynomial.legendre import legval
from scipy import linalg
from ..utils import logger, warn
from ..io.pick import pick_types, pick_channels, pick_info
from ..surface import _normalize_vectors
from ..bem import _fit_sphere
from ..forward import _map_meg_channels
def _calc_g(cosang, stif... | {
"repo_name": "wronk/mne-python",
"path": "mne/channels/interpolation.py",
"copies": "4",
"size": "6510",
"license": "bsd-3-clause",
"hash": -3185875129857627000,
"line_mean": 33.0837696335,
"line_max": 78,
"alpha_frac": 0.625499232,
"autogenerated": false,
"ratio": 3.3870967741935485,
"config_... |
import numpy as np
from numpy.polynomial.legendre import legval
from scipy import linalg
from ..utils import logger, warn, verbose
from ..io.meas_info import _simplify_info
from ..io.pick import pick_types, pick_channels, pick_info
from ..surface import _normalize_vectors
from ..forward import _map_meg_or_eeg_channel... | {
"repo_name": "cjayb/mne-python",
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import numpy as np
from numpy.polynomial.legendre import legval
from ..utils import logger, warn, verbose
from ..io.meas_info import _simplify_info
from ..io.pick import pick_types, pick_channels, pick_info
from ..surface import _normalize_vectors
from ..forward import _map_meg_or_eeg_channels
from ..utils import _ch... | {
"repo_name": "bloyl/mne-python",
"path": "mne/channels/interpolation.py",
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"hash": -3691005618495827500,
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"alpha_frac": 0.612404862,
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"config_t... |
import numpy as np
import math
from collections import deque
from functools import partial
from .utils import create_chunks, fwhm_kernel_2d
from ._fixes import string_types
def plot_raw(raw, events=None, title='Raw', show=True):
"""Visualize raw data traces
Parameters
----------
raw : instance of py... | {
"repo_name": "teonlamont/pyeparse",
"path": "pyeparse/viz.py",
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import numpy as np
import matplotlib.pyplot as plt
import pyeparse as pp
fname = '../pyeparse/tests/data/test_raw.edf'
raw = pp.read_raw(fname)
# visualize initial calibration
raw.plot_calibration(title='5-Point Calibration')
# create heatmap
raw.plot_heatmap(start=3., stop=60.)
# find events and epoch data
even... | {
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"path": "examples/plot_from_raw_to_epochs.py",
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"size": "1328",
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"ratio": 2.778242677824268,
... |
from functools import partial
import numpy as np
from ...utils import verbose, get_config
from ..utils import (has_dataset, _data_path, _data_path_doc,
_get_version, _version_doc)
has_spm_data = partial(has_dataset, name='spm')
@verbose
def data_path(path=None, force_update=False, update_pat... | {
"repo_name": "nicproulx/mne-python",
"path": "mne/datasets/spm_face/spm_data.py",
"copies": "5",
"size": "1192",
"license": "bsd-3-clause",
"hash": 8798457057004321000,
"line_mean": 28.0731707317,
"line_max": 77,
"alpha_frac": 0.5939597315,
"autogenerated": false,
"ratio": 3.32033426183844,
"c... |
import numpy as np
from ...utils import get_config, verbose
from ...fixes import partial
from ..utils import has_dataset, _data_path, _doc
has_spm_data = partial(has_dataset, name='spm')
@verbose
def data_path(path=None, force_update=False, update_path=True,
download=True, verbose=None):
return ... | {
"repo_name": "jaeilepp/eggie",
"path": "mne/datasets/spm_face/spm_data.py",
"copies": "2",
"size": "1074",
"license": "bsd-2-clause",
"hash": 7318852926376268000,
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"autogenerated": false,
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import numpy as np
from ...utils import verbose, get_config
from ...fixes import partial
from ..utils import (has_dataset, _data_path, _data_path_doc,
_get_version, _version_doc)
has_spm_data = partial(has_dataset, name='spm')
@verbose
def data_path(path=None, force_update=False, update_path=... | {
"repo_name": "alexandrebarachant/mne-python",
"path": "mne/datasets/spm_face/spm_data.py",
"copies": "5",
"size": "1162",
"license": "bsd-3-clause",
"hash": -6228227320812005000,
"line_mean": 28.05,
"line_max": 77,
"alpha_frac": 0.5920826162,
"autogenerated": false,
"ratio": 3.3487031700288186,
... |
import numpy as np
from os import path as op
from copy import deepcopy
from ._event import find_events
from ._fixes import string_types
from .viz import plot_calibration, plot_heatmap_raw, plot_raw
class _BaseRaw(object):
"""Base class for Raw"""
def __init__(self):
assert self._samples.shape[0] == ... | {
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"path": "pyeparse/_baseraw.py",
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__author__ = 'sdenisenko'
target_number = 600851475143
def isNatural(item, naturals):
for nat_number in naturals:
if not item % nat_number:
break
else:
return True
return False
def getNaturals(n):
naturals = []
for i in range(2, n):
if isNatural(i, naturals):
... | {
"repo_name": "sdenisen/python",
"path": "euler-problems/euler-problem-3.py",
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"alpha_frac": 0.5786471068,
"autogenerated": false,
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"config_... |
from base64 import b64encode, b64decode
class B64VariantEncoder:
def __init__(self, translation):
base = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/=".encode("utf-8")
self.__alphabet = translation
self.__lookup = dict(zip(base, translation))
self.__revlookup ... | {
"repo_name": "HCDevelopers/MultiEncoder",
"path": "hcdev/encodings/b64variant.py",
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"line_max": 98,
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"autogenerated": false,
"ratio": 3.008313539192399,
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""" Implements a Hidden Alignment Conditional Random Field (HACRF). """
from __future__ import absolute_import
import numpy as np
import lbfgs
from .algorithms import forward, backward
from .algorithms import forward_predict, forward_max_predict
from .algorithms import gradient, gradient_sparse, populate_sparse_featu... | {
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"path": "pyhacrf/pyhacrf.py",
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"alpha_frac": 0.5777283809,
"autogenerated": false,
"ratio": 4.458439897698209,
"config_test": fals... |
""" Implements a Hidden Alignment Conditional Random Field (HACRF). """
import numpy as np
import lbfgs
from .algorithms import forward, backward
from .algorithms import forward_predict, forward_max_predict
from .algorithms import gradient, gradient_sparse, populate_sparse_features, sparse_multiply
from .state_machin... | {
"repo_name": "pombredanne/pyhacrf",
"path": "pyhacrf/pyhacrf.py",
"copies": "2",
"size": "13105",
"license": "bsd-3-clause",
"hash": 6900185469994923000,
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"line_max": 129,
"alpha_frac": 0.593819153,
"autogenerated": false,
"ratio": 4.316534914361001,
"config_test": f... |
""" Implements feature extraction methods to use with HACRF models. """
import numpy as np
import functools
import itertools
class PairFeatureExtractor(object):
"""Extract features from sequence pairs.
For each feature, a grid is constructed for a sequency pair. The
features are stacked, producing a 3 ... | {
"repo_name": "pombredanne/pyhacrf",
"path": "pyhacrf/feature_extraction.py",
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"con... |
__author__ = 'sdjoum'
# Copyright 2016 DECaF Project Group
# This file is part of the DECaF project and originally derives from OpenMANO
#
# 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
#
# ... | {
"repo_name": "CN-UPB/OpenBarista",
"path": "components/decaf-specification/decaf_specification/schemas/basic_schema.py",
"copies": "1",
"size": "3059",
"license": "mpl-2.0",
"hash": -253113790210712160,
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"autogenerated": false,
... |
__author__ = 'sdjoum'
import basic_schema as bs
#Network scenario descriptor schema. This schema will be considered as the data structure of our network scenario
'''
class NetworkScenario {
public string name;
public string description;
public Topology topology;
}
class Topology {
... | {
"repo_name": "CN-UPB/OpenBarista",
"path": "components/schemas/scenario_schema.py",
"copies": "1",
"size": "5796",
"license": "mpl-2.0",
"hash": -6271554635913654000,
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"line_max": 121,
"alpha_frac": 0.4097653554,
"autogenerated": false,
"ratio": 4.790082644628099,
"config_... |
__author__ = 'sdjoum'
# decaf_specification = Specification()
# # res, object1 = decaf_specification.parser('dataplaneVNF2.yaml', vnfd_schema_v01)
# res, object1 = decaf_specification.parser('complex.yaml', nsd_schema)
# print object1
# placement = Placement()
# print placement.new_vnf(nfvo_tenant="d9a225dc-69ef-11... | {
"repo_name": "CN-UPB/OpenBarista",
"path": "components/decaf-specification/decaf_specification/tests.py",
"copies": "1",
"size": "1254",
"license": "mpl-2.0",
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"line_mean": 23.5882352941,
"line_max": 110,
"alpha_frac": 0.7272727273,
"autogenerated": false,
"ratio": 2.... |
__author__ = 'sdk'
from time import strftime, gmtime, mktime
import datetime
from xml.dom import minidom
import numpy as np
import cx_Oracle
from password import databaseSCO as database
import pandas as pd
pd.options.mode.chained_assignment = None
tables = {"ASDM": "XML_ASDM_ENTITIES", "Main": "XML_MAINTABLE_ENTITIES"... | {
"repo_name": "SDK/metadatachecker",
"path": "sacm/utils.py",
"copies": "1",
"size": "14423",
"license": "mit",
"hash": 8487430013924369000,
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"line_max": 168,
"alpha_frac": 0.6104832559,
"autogenerated": false,
"ratio": 3.1470652411084443,
"config_test": false,
"has... |
author__ = 'seamaster'
import configparser
import os
def builddec(currentlevels):
"""Decides, what to build next. Based on an input-dict and a build_guide.txt.
Returns the name of the building as a string"""
build_guide = open("settings" + os.sep + "build_guide.txt",'r').readlines()
for line in build_... | {
"repo_name": "erstis-go-botting/sexy-bot",
"path": "builder.py",
"copies": "1",
"size": "2359",
"license": "mit",
"hash": -4786493950055323000,
"line_mean": 31.7638888889,
"line_max": 81,
"alpha_frac": 0.6222975837,
"autogenerated": false,
"ratio": 3.4793510324483776,
"config_test": true,
"h... |
__author__ = 'sean-abbott'
from setuptools import setup
from distutils.cmd import Command
import subprocess
import sys
import os
import versioneer
def readme():
with open('README.rst') as f:
return f.read()
class BaseCommand(Command):
user_options = []
def initialize_options(self):
pass... | {
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"ratio": 3.871024734982332,
"config_test": false,
"has_... |
__author__ = 'sean-abbott'
# RED this whole thing should be swapped out for cookiecutter
# https://github.com/audreyr/cookiecutter
import os
import sys
import shutil
import inspect
from pkg_resources import resource_filename
import click
from jinja2 import Environment, PackageLoader
import utils
# YELLOW create_b... | {
"repo_name": "sean-abbott/auto_build_env",
"path": "src/abe/create_skeleton.py",
"copies": "1",
"size": "3540",
"license": "mit",
"hash": 5920461728152859000,
"line_mean": 27.32,
"line_max": 84,
"alpha_frac": 0.5790960452,
"autogenerated": false,
"ratio": 3.5435435435435436,
"config_test": fal... |
__author__ = 'sean.braley'
import os
import csv
import sys
#########################################################
## Change these to match the folder and scenario you want
#########################################################
folder = "size-100-75"
scenario = "04"
if len(sys.argv) == 3:
folder = sys.argv[... | {
"repo_name": "seanbraley/ndn-split-caching",
"path": "get-stats.py",
"copies": "1",
"size": "4156",
"license": "mit",
"hash": -383391744469452500,
"line_mean": 35.7876106195,
"line_max": 166,
"alpha_frac": 0.5399422522,
"autogenerated": false,
"ratio": 3.221705426356589,
"config_test": false,
... |
__author__ = 'sean.braley'
exclusion_words = (
'Whose', 'What', "ever'", 'Whosever', 'thees', 'its', 'whose', 'His', 'enny', 'anye',
'Both', 'them', 'his', 'whichever', 'every', 'thet', 'ther', 'Whatever', 'these',
'hys', 'either', 'each', "another's", 'some', 'Which', 'our', 'Neither', 'out',
'what', ... | {
"repo_name": "seanbraley/ebook-fixer",
"path": "knowledge_base.py",
"copies": "1",
"size": "5737",
"license": "mit",
"hash": -2226697974053565200,
"line_mean": 23.1092436975,
"line_max": 89,
"alpha_frac": 0.4551159142,
"autogenerated": false,
"ratio": 2.9405433111225014,
"config_test": false,
... |
__author__ = 'sean.braley'
import re
import math
import nltk
import string
import progressbar
from knowledge_base import genres_pulp, authors_pulp, transition_words, exclusion_words
from utils import sylco
def average(l):
return reduce(lambda x, y: x + y, l) / float(len(l))
# http://rosettacode.org/wiki/Map_... | {
"repo_name": "seanbraley/ebook-fixer",
"path": "models.py",
"copies": "1",
"size": "12568",
"license": "mit",
"hash": -909720841757191800,
"line_mean": 33.3415300546,
"line_max": 146,
"alpha_frac": 0.5089910885,
"autogenerated": false,
"ratio": 3.704096669613911,
"config_test": false,
"has_n... |
__author__ = "Sean Davis"
__email__ = "sdavis2@mail.nih.gov"
__license__ = "MIT"
from snakemake.shell import shell
# deal with issue of memory size for small files where
# VEP tries to load all the caches at once.
# Arrange buffer_size so that no more than 1/20 of the
# caches are open at one time
n = 0
with open(sn... | {
"repo_name": "seandavi/snakewrappers",
"path": "bio/bw_vep_annotate/wrapper.py",
"copies": "1",
"size": "1753",
"license": "mit",
"hash": -681669664495927900,
"line_mean": 33.3333333333,
"line_max": 125,
"alpha_frac": 0.6676185037,
"autogenerated": false,
"ratio": 2.487215909090909,
"config_te... |
__author__ = "Sean Davis"
__email__ = "seandavi@gmail.com"
__license__ = "MIT"
from snakemake.shell import shell
# inputs:
# fastq:
# gtf:
# params:
# genomeDir
#
# expects a list of string, ['read1','read2','read1_2','read2_2',...]
x = snakemake.input.fastqs
fastqs=' '.join([','.join([x[i] for i in... | {
"repo_name": "seandavi/snakewrappers",
"path": "bio/bw_star/wrapper.py",
"copies": "1",
"size": "1431",
"license": "mit",
"hash": 6529724116454184000,
"line_mean": 30.8,
"line_max": 106,
"alpha_frac": 0.6645702306,
"autogenerated": false,
"ratio": 2.8792756539235413,
"config_test": false,
"h... |
__author__ = 'seanfitz'
"""
A sample intent that uses a fixed vocabulary to extract entities for an intent
try with the following:
PYTHONPATH=. python examples/single_intent_parser.py "what's the weather like in tokyo"
"""
import json
import sys
from adapt.intent import IntentBuilder
from adapt.engine import IntentDet... | {
"repo_name": "MycroftAI/adapt",
"path": "examples/single_intent_parser.py",
"copies": "1",
"size": "1157",
"license": "apache-2.0",
"hash": -8514165414865222000,
"line_mean": 20.8301886792,
"line_max": 87,
"alpha_frac": 0.6776145203,
"autogenerated": false,
"ratio": 3.363372093023256,
"config_... |
__author__ = 'seanfitz'
"""
A sample intent that uses a regular expression entity to
extract location from a query
try with the following:
PYTHONPATH=. python examples/regex_intent_parser.py "what's the weather like in tokyo"
"""
import json
import sys
from adapt.intent import IntentBuilder
from adapt.engine import I... | {
"repo_name": "MycroftAI/adapt",
"path": "examples/regex_intent_parser.py",
"copies": "1",
"size": "1184",
"license": "apache-2.0",
"hash": -5035510107343504000,
"line_mean": 22.2156862745,
"line_max": 86,
"alpha_frac": 0.6959459459,
"autogenerated": false,
"ratio": 3.4823529411764707,
"config_... |
__author__ = 'seanfitz'
"""
A sample program that uses multiple intents and disambiguates by
intent confidence
try with the following:
PYTHONPATH=. python examples/multi_intent_parser.py "what's the weather like in tokyo"
PYTHONPATH=. python examples/multi_intent_parser.py "play some music by the clash"
"""
import js... | {
"repo_name": "MycroftAI/adapt",
"path": "examples/multi_intent_parser.py",
"copies": "1",
"size": "1934",
"license": "apache-2.0",
"hash": -5786551223930879000,
"line_mean": 18.9381443299,
"line_max": 86,
"alpha_frac": 0.6680455016,
"autogenerated": false,
"ratio": 3.2779661016949153,
"config_... |
__author__ = 'Sean Griffin'
__version__ = '1.0.0'
__email__ = 'sean@thoughtbot.com'
import sys
import os.path
import json
import shutil
from pymel.core import *
from maya.OpenMaya import *
from maya.OpenMayaMPx import *
kPluginTranslatorTypeName = 'Three.js'
kOptionScript = 'ThreeJsExportScript'
kDefaultOptionsStri... | {
"repo_name": "matgr1/three.js",
"path": "utils/exporters/maya/plug-ins/threeJsFileTranslator.py",
"copies": "10",
"size": "23827",
"license": "mit",
"hash": 798612753138363500,
"line_mean": 34.6691616766,
"line_max": 208,
"alpha_frac": 0.5557141058,
"autogenerated": false,
"ratio": 4.07159945317... |
__author__ = 'Sean Griffin'
__version__ = '1.0.0'
__email__ = 'sean@thoughtbot.com'
import sys
import os.path
import json
import shutil
from pymel.core import *
from maya.OpenMaya import *
from maya.OpenMayaMPx import *
kPluginTranslatorTypeName = 'Three.js'
kOptionScript = 'ThreeJsExportScript'
kDe... | {
"repo_name": "Jerdak/three.js",
"path": "utils/exporters/maya/plug-ins/threeJsFileTranslator.py",
"copies": "1",
"size": "16605",
"license": "mit",
"hash": -7060811712062927000,
"line_mean": 35.3146067416,
"line_max": 208,
"alpha_frac": 0.5595904848,
"autogenerated": false,
"ratio": 4.0166908563... |
__author__ = 'Sean'
import os, sys
INTERP = os.path.expanduser("/home/thorub2/MOcrime.thomasruble.com/env/bin/python")
if sys.executable != INTERP: os.execl(INTERP, INTERP, *sys.argv)
from sqlalchemy import Column, ForeignKey, Integer, String, UniqueConstraint, DateTime
from sqlalchemy.ext.declarative import declarati... | {
"repo_name": "siucacm/GlobalHackV",
"path": "python/database_setup.py",
"copies": "1",
"size": "2297",
"license": "mit",
"hash": 8303503224609126000,
"line_mean": 31.8142857143,
"line_max": 103,
"alpha_frac": 0.700043535,
"autogenerated": false,
"ratio": 3.363103953147877,
"config_test": false... |
__author__ = 'Sean'
import itertools
import csv
from sqlalchemy import Column, ForeignKey, Integer, String, UniqueConstraint, DateTime
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relationship
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
Base = dec... | {
"repo_name": "siucacm/GlobalHackV",
"path": "python/csvreader.py",
"copies": "1",
"size": "1842",
"license": "mit",
"hash": 6911330894519031000,
"line_mean": 30.7586206897,
"line_max": 103,
"alpha_frac": 0.6655808903,
"autogenerated": false,
"ratio": 3.2372583479789103,
"config_test": false,
... |
__author__ = 'Sean'
import re
# RETRIEVED FROM: http://eayd.in/?p=232
# ALL CREDIT TO THAT AUTHOR
def sylco(word) :
word = word.lower()
# exception_add are words that need extra syllables
# exception_del are words that need less syllables
exception_add = ['serious','crucial']
exception_del = [... | {
"repo_name": "seanbraley/ebook-fixer",
"path": "utils.py",
"copies": "1",
"size": "4163",
"license": "mit",
"hash": -229676433682789600,
"line_mean": 30.3082706767,
"line_max": 168,
"alpha_frac": 0.5472015374,
"autogenerated": false,
"ratio": 3.099776619508563,
"config_test": false,
"has_no_... |
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