text stringlengths 0 1.05M | meta dict |
|---|---|
import math
import numpy as np
from ..cov import compute_whitener
from ..io.pick import pick_info
from ..forward import apply_forward
from ..utils import (logger, verbose, check_random_state, _check_preload,
deprecated, _validate_type)
@verbose
def simulate_evoked(fwd, stc, info, cov, nave=30, ... | {
"repo_name": "adykstra/mne-python",
"path": "mne/simulation/evoked.py",
"copies": "1",
"size": "6662",
"license": "bsd-3-clause",
"hash": 7675482433276064000,
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"line_max": 78,
"alpha_frac": 0.618432903,
"autogenerated": false,
"ratio": 3.6584294343767163,
"config_tes... |
import warnings
import math
import numpy as np
from ..io.pick import pick_channels_cov
from ..forward import apply_forward
from ..utils import check_random_state, verbose
@verbose
def simulate_evoked(fwd, stc, info, cov, nave=30, iir_filter=None,
random_state=None, use_cps=True, verbose=None):
... | {
"repo_name": "teonlamont/mne-python",
"path": "mne/simulation/evoked.py",
"copies": "4",
"size": "4582",
"license": "bsd-3-clause",
"hash": -1563416409641500200,
"line_mean": 32.4452554745,
"line_max": 77,
"alpha_frac": 0.6246180707,
"autogenerated": false,
"ratio": 3.7101214574898784,
"config... |
from copy import deepcopy
DEFAULTS = dict(
color=dict(mag='darkblue', grad='b', eeg='k', eog='k', ecg='m', emg='k',
ref_meg='steelblue', misc='k', stim='k', resp='k', chpi='k',
exci='k', ias='k', syst='k', seeg='k', dipole='k', gof='k',
bio='k', ecog='k', hbo='darkblue... | {
"repo_name": "jmontoyam/mne-python",
"path": "mne/defaults.py",
"copies": "2",
"size": "2570",
"license": "bsd-3-clause",
"hash": 7813106122843924000,
"line_mean": 43.3103448276,
"line_max": 79,
"alpha_frac": 0.5151750973,
"autogenerated": false,
"ratio": 2.7934782608695654,
"config_test": fal... |
from copy import deepcopy
DEFAULTS = dict(
color=dict(mag='darkblue', grad='b', eeg='k', eog='k', ecg='m',
emg='k', ref_meg='steelblue', misc='k', stim='k',
resp='k', chpi='k', exci='k', ias='k', syst='k',
seeg='k', dipole='k', gof='k', bio='k', ecog='k'),
config_o... | {
"repo_name": "wronk/mne-python",
"path": "mne/defaults.py",
"copies": "2",
"size": "2407",
"license": "bsd-3-clause",
"hash": -7338710078487078000,
"line_mean": 41.2280701754,
"line_max": 78,
"alpha_frac": 0.5155795596,
"autogenerated": false,
"ratio": 2.872315035799523,
"config_test": false,
... |
from copy import deepcopy
DEFAULTS = dict(
color=dict(mag='darkblue', grad='b', eeg='k', eog='k', ecg='r',
emg='k', ref_meg='steelblue', misc='k', stim='k',
resp='k', chpi='k', exci='k', ias='k', syst='k',
seeg='k'),
config_opts=dict(),
units=dict(eeg='uV', gra... | {
"repo_name": "Odingod/mne-python",
"path": "mne/defaults.py",
"copies": "3",
"size": "1984",
"license": "bsd-3-clause",
"hash": 3088814922192486000,
"line_mean": 35.7407407407,
"line_max": 77,
"alpha_frac": 0.5171370968,
"autogenerated": false,
"ratio": 3.095163806552262,
"config_test": false,... |
import numpy as np
from .. import pick_types, pick_channels
from ..annotations import _annotations_starts_stops
from ..utils import logger, verbose, sum_squared, warn
from ..filter import filter_data
from ..epochs import Epochs, BaseEpochs
from ..io.base import BaseRaw
from ..evoked import Evoked
from ..io import Raw... | {
"repo_name": "adykstra/mne-python",
"path": "mne/preprocessing/ecg.py",
"copies": "2",
"size": "15667",
"license": "bsd-3-clause",
"hash": 7890902717778966000,
"line_mean": 36.480861244,
"line_max": 78,
"alpha_frac": 0.5851152103,
"autogenerated": false,
"ratio": 3.640948175691378,
"config_tes... |
import numpy as np
from .. import pick_types, pick_channels
from ..externals.six import string_types
from ..utils import logger, verbose, sum_squared, warn
from ..filter import band_pass_filter
from ..epochs import Epochs, _BaseEpochs
from ..io.base import _BaseRaw
from ..evoked import Evoked
from ..io import RawArra... | {
"repo_name": "wronk/mne-python",
"path": "mne/preprocessing/ecg.py",
"copies": "3",
"size": "12671",
"license": "bsd-3-clause",
"hash": -7950382688941665000,
"line_mean": 34.6929577465,
"line_max": 78,
"alpha_frac": 0.5743824481,
"autogenerated": false,
"ratio": 3.5703014933784165,
"config_tes... |
import numpy as np
from .. import pick_types, pick_channels
from ..externals.six import string_types
from ..utils import logger, verbose, sum_squared, warn
from ..filter import filter_data
from ..epochs import Epochs, BaseEpochs
from ..io.base import BaseRaw
from ..evoked import Evoked
from ..io import RawArray
from ... | {
"repo_name": "nicproulx/mne-python",
"path": "mne/preprocessing/ecg.py",
"copies": "2",
"size": "13184",
"license": "bsd-3-clause",
"hash": -3020831941395761700,
"line_mean": 35.6222222222,
"line_max": 79,
"alpha_frac": 0.5799453883,
"autogenerated": false,
"ratio": 3.6359624931053505,
"config... |
import numpy as np
from .peak_finder import peak_finder
from .. import pick_types, pick_channels
from ..utils import logger, verbose
from ..filter import band_pass_filter
from ..epochs import Epochs
from ..externals.six import string_types
@verbose
def find_eog_events(raw, event_id=998, l_freq=1, h_freq=10,
... | {
"repo_name": "alexandrebarachant/mne-python",
"path": "mne/preprocessing/eog.py",
"copies": "1",
"size": "7761",
"license": "bsd-3-clause",
"hash": -333591343658196030,
"line_mean": 35.608490566,
"line_max": 79,
"alpha_frac": 0.5768586522,
"autogenerated": false,
"ratio": 3.613128491620112,
"c... |
import numpy as np
from .peak_finder import peak_finder
from .. import pick_types, pick_channels
from ..utils import logger, verbose, _pl
from ..filter import filter_data
from ..epochs import Epochs
from ..externals.six import string_types
@verbose
def find_eog_events(raw, event_id=998, l_freq=1, h_freq=10,
... | {
"repo_name": "jaeilepp/mne-python",
"path": "mne/preprocessing/eog.py",
"copies": "2",
"size": "8899",
"license": "bsd-3-clause",
"hash": -2754296173567688000,
"line_mean": 37.6913043478,
"line_max": 79,
"alpha_frac": 0.5830992246,
"autogenerated": false,
"ratio": 3.6848861283643894,
"config_t... |
import numpy as np
from ._peak_finder import peak_finder
from .. import pick_types, pick_channels
from ..utils import logger, verbose, _pl
from ..filter import filter_data
from ..epochs import Epochs
@verbose
def find_eog_events(raw, event_id=998, l_freq=1, h_freq=10,
filter_length='10s', ch_nam... | {
"repo_name": "adykstra/mne-python",
"path": "mne/preprocessing/eog.py",
"copies": "1",
"size": "9236",
"license": "bsd-3-clause",
"hash": 7429267138000353000,
"line_mean": 36.6979591837,
"line_max": 79,
"alpha_frac": 0.5788220009,
"autogenerated": false,
"ratio": 3.730210016155089,
"config_tes... |
import warnings
from ..externals.six import string_types
import numpy as np
from .. import pick_types, pick_channels
from ..utils import logger, verbose, sum_squared
from ..filter import band_pass_filter
from ..epochs import Epochs, _BaseEpochs
from ..io.base import _BaseRaw
from ..evoked import Evoked
from ..io impo... | {
"repo_name": "yousrabk/mne-python",
"path": "mne/preprocessing/ecg.py",
"copies": "3",
"size": "12681",
"license": "bsd-3-clause",
"hash": -6153281661735857000,
"line_mean": 34.6207865169,
"line_max": 78,
"alpha_frac": 0.5743237915,
"autogenerated": false,
"ratio": 3.5701013513513513,
"config_... |
import numpy as np
from functools import reduce
from string import ascii_uppercase
from ..externals.six import string_types
from ..fixes import matrix_rank
# The following function is a rewriting of scipy.stats.f_oneway
# Contrary to the scipy.stats.f_oneway implementation it does not
# copy the data while keeping t... | {
"repo_name": "ARudiuk/mne-python",
"path": "mne/stats/parametric.py",
"copies": "2",
"size": "11717",
"license": "bsd-3-clause",
"hash": -1489239707198548200,
"line_mean": 33.6656804734,
"line_max": 78,
"alpha_frac": 0.589314671,
"autogenerated": false,
"ratio": 3.6638524077548467,
"config_tes... |
import numpy as np
from functools import reduce
from string import ascii_uppercase
from ..externals.six import string_types
from ..utils import deprecated
from ..fixes import matrix_rank
# The following function is a rewriting of scipy.stats.f_oneway
# Contrary to the scipy.stats.f_oneway implementation it does not
... | {
"repo_name": "trachelr/mne-python",
"path": "mne/stats/parametric.py",
"copies": "5",
"size": "12542",
"license": "bsd-3-clause",
"hash": 5728243986000986000,
"line_mean": 34.1316526611,
"line_max": 78,
"alpha_frac": 0.5943230745,
"autogenerated": false,
"ratio": 3.629050925925926,
"config_tes... |
import numpy as np
from functools import reduce
from string import ascii_uppercase
from ..externals.six import string_types
# The following function is a rewriting of scipy.stats.f_oneway
# Contrary to the scipy.stats.f_oneway implementation it does not
# copy the data while keeping the inputs unchanged.
def _f_on... | {
"repo_name": "jmontoyam/mne-python",
"path": "mne/stats/parametric.py",
"copies": "3",
"size": "11727",
"license": "bsd-3-clause",
"hash": 4155664342862703000,
"line_mean": 33.592920354,
"line_max": 78,
"alpha_frac": 0.5873624968,
"autogenerated": false,
"ratio": 3.6601123595505616,
"config_te... |
import os.path as op
import warnings
import numpy as np
from numpy.testing import assert_raises
from mne import read_events, Epochs, pick_types, read_cov
from mne.channels import read_layout
from mne.io import read_raw_fif
from mne.utils import slow_test, run_tests_if_main
from mne.viz.evoked import _line_plot_onse... | {
"repo_name": "nicproulx/mne-python",
"path": "mne/viz/tests/test_evoked.py",
"copies": "2",
"size": "7483",
"license": "bsd-3-clause",
"hash": -4603052892713065500,
"line_mean": 40.3425414365,
"line_max": 79,
"alpha_frac": 0.5984230923,
"autogenerated": false,
"ratio": 3.322824156305506,
"conf... |
import os.path as op
import numpy as np
from numpy.testing import assert_allclose
import pytest
import mne
from mne import (read_events, Epochs, pick_types, read_cov, compute_covariance,
make_fixed_length_events)
from mne.channels import read_layout
from mne.io import read_raw_fif
from mne.utils imp... | {
"repo_name": "teonlamont/mne-python",
"path": "mne/viz/tests/test_evoked.py",
"copies": "2",
"size": "14543",
"license": "bsd-3-clause",
"hash": 1098170884957963000,
"line_mean": 40.670487106,
"line_max": 79,
"alpha_frac": 0.6063398198,
"autogenerated": false,
"ratio": 3.160147761842677,
"conf... |
import os.path as op
import warnings
import numpy as np
from numpy.testing import assert_raises
from mne import (io, read_events, read_cov, read_source_spaces, read_evokeds,
read_dipole, SourceEstimate)
from mne.datasets import testing
from mne.minimum_norm import read_inverse_operator
from mne.viz ... | {
"repo_name": "ARudiuk/mne-python",
"path": "mne/viz/tests/test_misc.py",
"copies": "2",
"size": "4930",
"license": "bsd-3-clause",
"hash": 3486655541814997000,
"line_mean": 35.25,
"line_max": 79,
"alpha_frac": 0.6340770791,
"autogenerated": false,
"ratio": 3.1381285805219608,
"config_test": tr... |
import os.path as op
import warnings
import numpy as np
from numpy.testing import assert_raises
from mne import (read_events, read_cov, read_source_spaces, read_evokeds,
read_dipole, SourceEstimate)
from mne.datasets import testing
from mne.filter import create_filter
from mne.io import read_raw_fif... | {
"repo_name": "nicproulx/mne-python",
"path": "mne/viz/tests/test_misc.py",
"copies": "2",
"size": "6278",
"license": "bsd-3-clause",
"hash": -1792443940143136300,
"line_mean": 36.5928143713,
"line_max": 79,
"alpha_frac": 0.627588404,
"autogenerated": false,
"ratio": 3.123383084577114,
"config_... |
import os.path as op
import warnings
import numpy as np
from numpy.testing import assert_raises
from mne import io, read_events, Epochs, pick_types, read_cov
from mne.viz.evoked import _butterfly_onselect
from mne.viz.utils import _fake_click
from mne.utils import slow_test, run_tests_if_main
from mne.channels impo... | {
"repo_name": "rajegannathan/grasp-lift-eeg-cat-dog-solution-updated",
"path": "python-packages/mne-python-0.10/mne/viz/tests/test_evoked.py",
"copies": "1",
"size": "4609",
"license": "bsd-3-clause",
"hash": -5722700425189974000,
"line_mean": 32.6423357664,
"line_max": 79,
"alpha_frac": 0.6120633543... |
import os.path as op
import warnings
import numpy as np
from numpy.testing import assert_raises
from mne import io, read_events, Epochs, pick_types, read_cov
from mne.viz.utils import _fake_click
from mne.utils import slow_test, run_tests_if_main
from mne.channels import read_layout
# Set our plotters to test mode... | {
"repo_name": "lorenzo-desantis/mne-python",
"path": "mne/viz/tests/test_evoked.py",
"copies": "2",
"size": "4306",
"license": "bsd-3-clause",
"hash": 804667965398580700,
"line_mean": 32.3798449612,
"line_max": 79,
"alpha_frac": 0.6112401301,
"autogenerated": false,
"ratio": 3.1802067946824226,
... |
import os.path as op
import warnings
import numpy as np
from numpy.testing import assert_raises
# Set our plotters to test mode
import matplotlib
matplotlib.use('Agg') # for testing don't use X server
import matplotlib.pyplot as plt
from mne import io, read_events, Epochs
from mne import pick_types
from mne.layout... | {
"repo_name": "jaeilepp/eggie",
"path": "mne/viz/tests/test_evoked.py",
"copies": "2",
"size": "3508",
"license": "bsd-2-clause",
"hash": -8850186644906785000,
"line_mean": 32.0943396226,
"line_max": 77,
"alpha_frac": 0.6342645382,
"autogenerated": false,
"ratio": 3.1689250225835592,
"config_te... |
import os.path as op
import warnings
from collections import namedtuple
from nose.tools import assert_raises
import numpy as np
from mne import io, read_events, Epochs
from mne import pick_types
from mne.utils import run_tests_if_main, requires_scipy_version
from mne.channels import read_layout
from mne.viz import... | {
"repo_name": "dgwakeman/mne-python",
"path": "mne/viz/tests/test_epochs.py",
"copies": "1",
"size": "6072",
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"hash": 8565578394744661000,
"line_mean": 31.6451612903,
"line_max": 78,
"alpha_frac": 0.6205533597,
"autogenerated": false,
"ratio": 3.0651186269560826,
"conf... |
import os.path as op
import numpy as np
import pytest
import matplotlib.pyplot as plt
from mne import (make_field_map, pick_channels_evoked, read_evokeds,
read_trans, read_dipole, SourceEstimate, VectorSourceEstimate,
VolSourceEstimate, make_sphere_model, use_coil_def,
... | {
"repo_name": "adykstra/mne-python",
"path": "mne/viz/tests/test_3d.py",
"copies": "1",
"size": "22533",
"license": "bsd-3-clause",
"hash": 8243054703148467000,
"line_mean": 43.5316205534,
"line_max": 79,
"alpha_frac": 0.6062219855,
"autogenerated": false,
"ratio": 3.263287472845764,
"config_te... |
import os.path as op
import warnings
import numpy as np
from numpy.testing import assert_raises
from mne import SourceEstimate
from mne import make_field_map, pick_channels_evoked, read_evokeds
from mne.viz import (plot_sparse_source_estimates, plot_source_estimates,
plot_trans, mne_analyze_colo... | {
"repo_name": "jaeilepp/eggie",
"path": "mne/viz/tests/test_3d.py",
"copies": "1",
"size": "4270",
"license": "bsd-2-clause",
"hash": 6882349991632745000,
"line_mean": 36.1304347826,
"line_max": 79,
"alpha_frac": 0.6259953162,
"autogenerated": false,
"ratio": 3.292212798766384,
"config_test": t... |
from collections.abc import Iterable
import os
import os.path as op
import numpy as np
import xml.etree.ElementTree as ElementTree
from ..viz import plot_montage
from .channels import _contains_ch_type
from ..transforms import (apply_trans, get_ras_to_neuromag_trans, _sph_to_cart,
_topo_to_... | {
"repo_name": "adykstra/mne-python",
"path": "mne/channels/montage.py",
"copies": "1",
"size": "37080",
"license": "bsd-3-clause",
"hash": -5710891301904594000,
"line_mean": 40.4765100671,
"line_max": 79,
"alpha_frac": 0.5501618123,
"autogenerated": false,
"ratio": 3.617208077260755,
"config_te... |
from collections import Iterable
import os
import os.path as op
import numpy as np
import xml.etree.ElementTree as ElementTree
from ..viz import plot_montage
from .channels import _contains_ch_type
from ..transforms import (apply_trans, get_ras_to_neuromag_trans, _sph_to_cart,
_topo_to_sph,... | {
"repo_name": "teonlamont/mne-python",
"path": "mne/channels/montage.py",
"copies": "4",
"size": "36989",
"license": "bsd-3-clause",
"hash": 4118260909008768000,
"line_mean": 40.4674887892,
"line_max": 79,
"alpha_frac": 0.5502987375,
"autogenerated": false,
"ratio": 3.6267281105990783,
"config_... |
from collections import Iterable
import os
import os.path as op
import numpy as np
from ..viz import plot_montage
from .channels import _contains_ch_type
from ..transforms import (_sphere_to_cartesian, apply_trans,
get_ras_to_neuromag_trans, _topo_to_sphere,
_str_t... | {
"repo_name": "jniediek/mne-python",
"path": "mne/channels/montage.py",
"copies": "3",
"size": "29230",
"license": "bsd-3-clause",
"hash": -7642417481700956000,
"line_mean": 39.261707989,
"line_max": 79,
"alpha_frac": 0.5456038317,
"autogenerated": false,
"ratio": 3.683679899180844,
"config_tes... |
import os
import os.path as op
import warnings
import numpy as np
from ..viz import plot_montage
from .channels import _contains_ch_type
from ..transforms import (_sphere_to_cartesian, apply_trans,
get_ras_to_neuromag_trans, _topo_to_sphere)
from ..io.meas_info import _make_dig_points, _rea... | {
"repo_name": "cmoutard/mne-python",
"path": "mne/channels/montage.py",
"copies": "1",
"size": "23261",
"license": "bsd-3-clause",
"hash": -217986404794612930,
"line_mean": 38.358714044,
"line_max": 79,
"alpha_frac": 0.5597351791,
"autogenerated": false,
"ratio": 3.633395813808185,
"config_test... |
import os
import os.path as op
import numpy as np
from ..viz import plot_montage
from .channels import _contains_ch_type
from ..transforms import (_sphere_to_cartesian, apply_trans,
get_ras_to_neuromag_trans)
from ..io.meas_info import _make_dig_points, _read_dig_points
from ..externals.six... | {
"repo_name": "yousrabk/mne-python",
"path": "mne/channels/montage.py",
"copies": "2",
"size": "20499",
"license": "bsd-3-clause",
"hash": 8775308016053857000,
"line_mean": 37.4596622889,
"line_max": 79,
"alpha_frac": 0.5608566272,
"autogenerated": false,
"ratio": 3.6481580352375866,
"config_te... |
import logging
from collections import defaultdict
from itertools import combinations
import os.path as op
import numpy as np
from ..transforms import _polar_to_cartesian, _cartesian_to_sphere
from ..bem import fit_sphere_to_headshape
from ..io.pick import pick_types
from ..io.constants import FIFF
from ..io.meas_in... | {
"repo_name": "wronk/mne-python",
"path": "mne/channels/layout.py",
"copies": "2",
"size": "29831",
"license": "bsd-3-clause",
"hash": -2825423780847651000,
"line_mean": 32.5179775281,
"line_max": 79,
"alpha_frac": 0.568804264,
"autogenerated": false,
"ratio": 3.6088797483668036,
"config_test":... |
import logging
from collections import defaultdict
from itertools import combinations
import os.path as op
import numpy as np
from ..transforms import _pol_to_cart, _cart_to_sph
from ..bem import fit_sphere_to_headshape
from ..io.pick import pick_types
from ..io.constants import FIFF
from ..io.meas_info import Info
... | {
"repo_name": "jaeilepp/mne-python",
"path": "mne/channels/layout.py",
"copies": "1",
"size": "32417",
"license": "bsd-3-clause",
"hash": -4462968494786814000,
"line_mean": 33.0514705882,
"line_max": 79,
"alpha_frac": 0.5670481537,
"autogenerated": false,
"ratio": 3.6592166158708657,
"config_te... |
import logging
from collections import defaultdict
from itertools import combinations
import re
import os
import os.path as op
import numpy as np
from scipy.spatial.distance import pdist
from ..io.pick import pick_types
from ..io.constants import FIFF
from ..utils import _clean_names
from ..externals.six.moves import ... | {
"repo_name": "effigies/mne-python",
"path": "mne/channels/layout.py",
"copies": "1",
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"alpha_frac": 0.559301067,
"autogenerated": false,
"ratio": 3.57292817679558,
"config_test"... |
import os.path as op
from collections import namedtuple
import numpy as np
import pytest
from mne import read_events, Epochs, pick_channels_evoked, read_cov
from mne.channels import read_layout
from mne.io import read_raw_fif
from mne.time_frequency.tfr import AverageTFR
from mne.utils import run_tests_if_main
from... | {
"repo_name": "teonlamont/mne-python",
"path": "mne/viz/tests/test_topo.py",
"copies": "2",
"size": "9779",
"license": "bsd-3-clause",
"hash": -8382965036142013000,
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"line_max": 79,
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"autogenerated": false,
"ratio": 3.154516129032258,
"confi... |
import os.path as op
from functools import partial
import numpy as np
from numpy.testing import assert_array_equal, assert_equal
import pytest
from mne import (read_evokeds, read_proj, make_fixed_length_events, Epochs,
compute_proj_evoked)
from mne.io.proj import make_eeg_average_ref_proj
from mne.i... | {
"repo_name": "teonlamont/mne-python",
"path": "mne/viz/tests/test_topomap.py",
"copies": "2",
"size": "16621",
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"hash": -5133610804255975000,
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"alpha_frac": 0.6293243487,
"autogenerated": false,
"ratio": 2.992617933021246,
"c... |
import os.path as op
import warnings
from collections import namedtuple
import numpy as np
from numpy.testing import assert_raises, assert_equal
from mne import read_events, Epochs, pick_channels_evoked
from mne.channels import read_layout
from mne.io import read_raw_fif
from mne.time_frequency.tfr import AverageTFR... | {
"repo_name": "nicproulx/mne-python",
"path": "mne/viz/tests/test_topo.py",
"copies": "2",
"size": "7603",
"license": "bsd-3-clause",
"hash": -7859082971860686000,
"line_mean": 37.398989899,
"line_max": 79,
"alpha_frac": 0.6143627515,
"autogenerated": false,
"ratio": 3.130094689172499,
"config_... |
import os.path as op
import warnings
import numpy as np
from numpy.testing import assert_raises, assert_array_equal
from nose.tools import assert_true, assert_equal
from mne import io, read_evokeds, read_proj
from mne.io.constants import FIFF
from mne.channels import read_layout, make_eeg_layout
from mne.datasets ... | {
"repo_name": "rajegannathan/grasp-lift-eeg-cat-dog-solution-updated",
"path": "python-packages/mne-python-0.10/mne/viz/tests/test_topomap.py",
"copies": "3",
"size": "10198",
"license": "bsd-3-clause",
"hash": -1956801342288021200,
"line_mean": 38.5271317829,
"line_max": 79,
"alpha_frac": 0.63443812... |
import os.path as op
import warnings
import numpy as np
from numpy.testing import assert_raises
from nose.tools import assert_true, assert_equal
from mne import io, read_evokeds, read_proj
from mne.io.constants import FIFF
from mne.channels import read_layout, make_eeg_layout
from mne.datasets import testing
from ... | {
"repo_name": "trachelr/mne-python",
"path": "mne/viz/tests/test_topomap.py",
"copies": "2",
"size": "7202",
"license": "bsd-3-clause",
"hash": 6996322530226219000,
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"line_max": 79,
"alpha_frac": 0.6416273257,
"autogenerated": false,
"ratio": 3.106988783433995,
"confi... |
import os.path as op
import warnings
import numpy as np
from numpy.testing import assert_raises
from nose.tools import assert_true, assert_equal
# Set our plotters to test mode
import matplotlib
matplotlib.use('Agg') # for testing don't use X server
import matplotlib.pyplot as plt
from mne import io
from mne impo... | {
"repo_name": "jaeilepp/eggie",
"path": "mne/viz/tests/test_topomap.py",
"copies": "2",
"size": "4984",
"license": "bsd-2-clause",
"hash": 4283559689104494600,
"line_mean": 37.0458015267,
"line_max": 78,
"alpha_frac": 0.6045345104,
"autogenerated": false,
"ratio": 3.1887396033269355,
"config_te... |
import os.path as op
import warnings
import numpy as np
# Set our plotters to test mode
import matplotlib
matplotlib.use('Agg') # for testing don't use X server
import matplotlib.pyplot as plt
from mne import io, read_events, Epochs
from mne import pick_types
from mne.layouts import read_layout
from mne.datasets i... | {
"repo_name": "jaeilepp/eggie",
"path": "mne/viz/tests/test_epochs.py",
"copies": "2",
"size": "3478",
"license": "bsd-2-clause",
"hash": -6021106462566115000,
"line_mean": 28.7264957265,
"line_max": 76,
"alpha_frac": 0.6239217941,
"autogenerated": false,
"ratio": 2.9905417024935512,
"config_te... |
import warnings
from collections import defaultdict
import os.path as op
import numpy as np
from scipy.optimize import leastsq
from ..preprocessing.maxfilter import fit_sphere_to_headshape
from .. import pick_types
from ..io.constants import FIFF
from ..utils import _clean_names
from ..externals.six.moves import map
... | {
"repo_name": "jaeilepp/eggie",
"path": "mne/layouts/layout.py",
"copies": "1",
"size": "18279",
"license": "bsd-2-clause",
"hash": 2834508151318755300,
"line_mean": 31.4671403197,
"line_max": 80,
"alpha_frac": 0.5592756715,
"autogenerated": false,
"ratio": 3.4645564821834722,
"config_test": fa... |
import numpy as np
from numpy.testing import assert_raises
from mne.viz import plot_connectivity_circle, circular_layout
# Set our plotters to test mode
import matplotlib
matplotlib.use('Agg') # for testing don't use X server
import matplotlib.pyplot as plt
def test_plot_connectivity_circle():
"""Test plotti... | {
"repo_name": "effigies/mne-python",
"path": "mne/viz/tests/test_circle.py",
"copies": "3",
"size": "5158",
"license": "bsd-3-clause",
"hash": -9156637197271017000,
"line_mean": 53.8723404255,
"line_max": 79,
"alpha_frac": 0.5791004265,
"autogenerated": false,
"ratio": 3.4827819041188386,
"conf... |
import numpy as np
import pytest
import matplotlib.pyplot as plt
from mne.viz import plot_connectivity_circle, circular_layout
def test_plot_connectivity_circle():
"""Test plotting connectivity circle."""
node_order = ['frontalpole-lh', 'parsorbitalis-lh',
'lateralorbitofrontal-lh', 'rost... | {
"repo_name": "adykstra/mne-python",
"path": "mne/viz/tests/test_circle.py",
"copies": "2",
"size": "5036",
"license": "bsd-3-clause",
"hash": 3362197090470860000,
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"line_max": 79,
"alpha_frac": 0.5752581414,
"autogenerated": false,
"ratio": 3.4755003450655626,
"confi... |
import numpy as np
import pytest
from mne.viz import plot_connectivity_circle, circular_layout
# Set our plotters to test mode
import matplotlib
matplotlib.use('Agg') # for testing don't use X server
def test_plot_connectivity_circle():
"""Test plotting connectivity circle."""
import matplotlib.pyplot as... | {
"repo_name": "teonlamont/mne-python",
"path": "mne/viz/tests/test_circle.py",
"copies": "3",
"size": "5147",
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"autogenerated": false,
"ratio": 3.482408660351827,
"conf... |
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.time_frequency import tfr_morlet
from mne.time_frequency import single_trial_power
from mne.stats import permutation_cluster_test
import os
###############################################################################
# Set pa... | {
"repo_name": "yeatmanlab/BrainTools",
"path": "projects/NLR_MEG/nlr_frequencyanalysis.py",
"copies": "1",
"size": "5454",
"license": "bsd-3-clause",
"hash": -6606858041786952000,
"line_mean": 37.9571428571,
"line_max": 94,
"alpha_frac": 0.6217455079,
"autogenerated": false,
"ratio": 3.2483621203... |
from copy import deepcopy
from functools import partial
import re
import numpy as np
from scipy import linalg
from .cov import read_cov, _get_whitener_data
from .io.constants import FIFF
from .io.pick import pick_types, channel_type
from .io.proj import make_projector, _needs_eeg_average_ref_proj
from .bem import _f... | {
"repo_name": "jmontoyam/mne-python",
"path": "mne/dipole.py",
"copies": "3",
"size": "43452",
"license": "bsd-3-clause",
"hash": 5732499376234774000,
"line_mean": 36.817232376,
"line_max": 79,
"alpha_frac": 0.5451072448,
"autogenerated": false,
"ratio": 3.4750479846449136,
"config_test": false... |
from copy import deepcopy
import re
import numpy as np
from scipy import linalg
from .cov import read_cov, _get_whitener_data
from .io.constants import FIFF
from .io.pick import pick_types, channel_type
from .io.proj import make_projector, _needs_eeg_average_ref_proj
from .bem import _fit_sphere
from .evoked import ... | {
"repo_name": "wronk/mne-python",
"path": "mne/dipole.py",
"copies": "1",
"size": "38186",
"license": "bsd-3-clause",
"hash": -589452025232388500,
"line_mean": 36.7705242334,
"line_max": 79,
"alpha_frac": 0.5505682711,
"autogenerated": false,
"ratio": 3.465468735819947,
"config_test": false,
... |
import numpy as np
from scipy import linalg
from copy import deepcopy
import re
from .cov import read_cov, _get_whitener_data
from .io.constants import FIFF
from .io.pick import pick_types, channel_type
from .io.proj import make_projector, _has_eeg_average_ref_proj
from .bem import _fit_sphere
from .transforms import... | {
"repo_name": "andyh616/mne-python",
"path": "mne/dipole.py",
"copies": "3",
"size": "23401",
"license": "bsd-3-clause",
"hash": 2143873664493655600,
"line_mean": 36.9270664506,
"line_max": 79,
"alpha_frac": 0.5577966754,
"autogenerated": false,
"ratio": 3.43778463346555,
"config_test": false,
... |
import numpy as np
from scipy import linalg
from copy import deepcopy
import re
from .cov import read_cov, _get_whitener_data
from .io.constants import FIFF
from .io.pick import pick_types
from .io.proj import make_projector, _has_eeg_average_ref_proj
from .bem import _fit_sphere
from .transforms import (_print_coord... | {
"repo_name": "aestrivex/mne-python",
"path": "mne/dipole.py",
"copies": "2",
"size": "22795",
"license": "bsd-3-clause",
"hash": -5662276871780911000,
"line_mean": 36.928452579,
"line_max": 79,
"alpha_frac": 0.556876508,
"autogenerated": false,
"ratio": 3.442833408850627,
"config_test": false,... |
import numpy as np
from scipy import linalg
from copy import deepcopy
import re
from .cov import read_cov, _get_whitener_data
from .io.pick import pick_types, channel_type
from .io.proj import make_projector, _has_eeg_average_ref_proj
from .bem import _fit_sphere
from .transforms import (_print_coord_trans, _coord_fr... | {
"repo_name": "lorenzo-desantis/mne-python",
"path": "mne/dipole.py",
"copies": "3",
"size": "26432",
"license": "bsd-3-clause",
"hash": -2130577757315031300,
"line_mean": 35.7111111111,
"line_max": 79,
"alpha_frac": 0.5444915254,
"autogenerated": false,
"ratio": 3.3930680359435175,
"config_tes... |
import warnings
import copy
import os.path as op
from nose.tools import assert_equal, assert_true, assert_raises
import numpy as np
from numpy.testing import assert_array_equal
from mne import io, Epochs, read_events, pick_types
from mne.utils import (requires_sklearn, requires_sklearn_0_15, slow_test,
... | {
"repo_name": "jaeilepp/mne-python",
"path": "mne/decoding/tests/test_time_gen.py",
"copies": "1",
"size": "20645",
"license": "bsd-3-clause",
"hash": 4552668223974786600,
"line_mean": 41.0468431772,
"line_max": 79,
"alpha_frac": 0.6220392347,
"autogenerated": false,
"ratio": 3.4483046600968765,
... |
import warnings
import copy
import os.path as op
from nose.tools import assert_equal, assert_true, assert_raises
import numpy as np
from numpy.testing import assert_array_equal
from mne import io, Epochs, read_events, pick_types
from mne.utils import requires_sklearn, slow_test
from mne.decoding import Generalizatio... | {
"repo_name": "matthew-tucker/mne-python",
"path": "mne/decoding/tests/test_time_gen.py",
"copies": "5",
"size": "8479",
"license": "bsd-3-clause",
"hash": -454383775354871600,
"line_mean": 36.6844444444,
"line_max": 79,
"alpha_frac": 0.6108031608,
"autogenerated": false,
"ratio": 3.2624086186995... |
from __future__ import division
import numpy as np
from scipy import sparse
from sklearn.model_selection import LeaveOneOut
from sklearn.utils.testing import (assert_array_almost_equal, assert_equal,
assert_greater, assert_almost_equal,
assert_grea... | {
"repo_name": "joernhees/scikit-learn",
"path": "sklearn/tests/test_calibration.py",
"copies": "64",
"size": "12999",
"license": "bsd-3-clause",
"hash": -3957186265953042000,
"line_mean": 41.4803921569,
"line_max": 79,
"alpha_frac": 0.6017385953,
"autogenerated": false,
"ratio": 3.447096260938743... |
import numpy as np
from scipy import sparse
from sklearn.calibration import CalibratedClassifierCV
from sklearn.calibration import _sigmoid_calibration, _SigmoidCalibration
from sklearn.calibration import calibration_curve
from sklearn.datasets import make_classification, make_blobs
from sklearn.ensemble import Random... | {
"repo_name": "DailyActie/Surrogate-Model",
"path": "01-codes/scikit-learn-master/sklearn/tests/test_calibration.py",
"copies": "1",
"size": "12288",
"license": "mit",
"hash": 1178249600611695400,
"line_mean": 42.4204946996,
"line_max": 79,
"alpha_frac": 0.6000976562,
"autogenerated": false,
"rat... |
import numpy as np
from scipy import sparse
from sklearn.utils.testing import (assert_array_almost_equal, assert_equal,
assert_greater, assert_almost_equal,
assert_greater_equal,
assert_array_equal,
... | {
"repo_name": "henrykironde/scikit-learn",
"path": "sklearn/tests/test_calibration.py",
"copies": "213",
"size": "12219",
"license": "bsd-3-clause",
"hash": 4094405043979993600,
"line_mean": 42.4839857651,
"line_max": 79,
"alpha_frac": 0.6011948605,
"autogenerated": false,
"ratio": 3.473280272882... |
import pytest
import numpy as np
from scipy import sparse
from sklearn.base import BaseEstimator
from sklearn.model_selection import LeaveOneOut
from sklearn.utils._testing import (assert_array_almost_equal,
assert_almost_equal,
assert_array_equ... | {
"repo_name": "huzq/scikit-learn",
"path": "sklearn/tests/test_calibration.py",
"copies": "2",
"size": "17454",
"license": "bsd-3-clause",
"hash": -6371884424587978000,
"line_mean": 39.2165898618,
"line_max": 79,
"alpha_frac": 0.6164775983,
"autogenerated": false,
"ratio": 3.417662032504406,
"c... |
import numpy as np
from ..source_estimate import SourceEstimate, VolSourceEstimate
from ..source_space import _ensure_src
from ..utils import check_random_state, warn, _check_option
from ..label import Label
def select_source_in_label(src, label, random_state=None, location='random',
subj... | {
"repo_name": "adykstra/mne-python",
"path": "mne/simulation/source.py",
"copies": "1",
"size": "19992",
"license": "bsd-3-clause",
"hash": 8753683372250310000,
"line_mean": 37.3723608445,
"line_max": 79,
"alpha_frac": 0.5854341737,
"autogenerated": false,
"ratio": 3.9840573933838184,
"config_t... |
import numpy as np
from ..source_estimate import SourceEstimate
from ..utils import check_random_state
from ..externals.six.moves import zip
def select_source_in_label(src, label, random_state=None):
"""Select source positions using a label
Parameters
----------
src : list of dict
The sourc... | {
"repo_name": "andyh616/mne-python",
"path": "mne/simulation/source.py",
"copies": "14",
"size": "6239",
"license": "bsd-3-clause",
"hash": -7550428561856086000,
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"line_max": 79,
"alpha_frac": 0.609552813,
"autogenerated": false,
"ratio": 3.40556768558952,
"config_tes... |
import numpy as np
from ..source_estimate import SourceEstimate, VolSourceEstimate
from ..source_space import _ensure_src
from ..utils import check_random_state, deprecated, logger
from ..externals.six.moves import zip
def select_source_in_label(src, label, random_state=None):
"""Select source positions using a... | {
"repo_name": "rajegannathan/grasp-lift-eeg-cat-dog-solution-updated",
"path": "python-packages/mne-python-0.10/mne/simulation/source.py",
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"size": "11302",
"license": "bsd-3-clause",
"hash": 4525199742295740000,
"line_mean": 33.3525835866,
"line_max": 79,
"alpha_frac": 0.6080339763,
... |
import numpy as np
from ..source_estimate import SourceEstimate, VolSourceEstimate
from ..source_space import _ensure_src
from ..utils import check_random_state, logger
from ..externals.six.moves import zip
def select_source_in_label(src, label, random_state=None):
"""Select source positions using a label
... | {
"repo_name": "yousrabk/mne-python",
"path": "mne/simulation/source.py",
"copies": "3",
"size": "7751",
"license": "bsd-3-clause",
"hash": 3997633329666001400,
"line_mean": 34.2318181818,
"line_max": 79,
"alpha_frac": 0.5972132628,
"autogenerated": false,
"ratio": 3.464908359409924,
"config_tes... |
import numpy as np
from ..source_estimate import SourceEstimate, VolSourceEstimate
from ..source_space import _ensure_src
from ..utils import check_random_state, warn
from ..externals.six.moves import zip
def select_source_in_label(src, label, random_state=None):
"""Select source positions using a label
Pa... | {
"repo_name": "ARudiuk/mne-python",
"path": "mne/simulation/source.py",
"copies": "3",
"size": "7719",
"license": "bsd-3-clause",
"hash": 5131422275564644000,
"line_mean": 34.0863636364,
"line_max": 78,
"alpha_frac": 0.5982640238,
"autogenerated": false,
"ratio": 3.453691275167785,
"config_test... |
import numpy as np
from ..source_estimate import SourceEstimate, VolSourceEstimate
from ..source_space import _ensure_src
from ..utils import check_random_state, warn
from ..externals.six import string_types
from ..externals.six.moves import zip
def select_source_in_label(src, label, random_state=None, location='r... | {
"repo_name": "alexandrebarachant/mne-python",
"path": "mne/simulation/source.py",
"copies": "2",
"size": "11114",
"license": "bsd-3-clause",
"hash": -8763018956110644000,
"line_mean": 34.3949044586,
"line_max": 78,
"alpha_frac": 0.5967248515,
"autogenerated": false,
"ratio": 3.698502495840266,
... |
import numpy as np
from ..source_estimate import SourceEstimate, VolSourceEstimate
from ..utils import check_random_state, deprecated, logger
from ..externals.six.moves import zip
def select_source_in_label(src, label, random_state=None):
"""Select source positions using a label
Parameters
----------
... | {
"repo_name": "lorenzo-desantis/mne-python",
"path": "mne/simulation/source.py",
"copies": "2",
"size": "11179",
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"line_max": 79,
"alpha_frac": 0.607567761,
"autogenerated": false,
"ratio": 3.5264984227129337,
"c... |
from collections import defaultdict
from colorsys import hsv_to_rgb, rgb_to_hsv
from os import path as op
import os
import copy as cp
import re
from warnings import warn
import numpy as np
from scipy import linalg, sparse
from .fixes import digitize, in1d
from .utils import (get_subjects_dir, _check_subject, logger,... | {
"repo_name": "jaeilepp/eggie",
"path": "mne/label.py",
"copies": "1",
"size": "72262",
"license": "bsd-2-clause",
"hash": 8660809716116498000,
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"line_max": 79,
"alpha_frac": 0.5627854197,
"autogenerated": false,
"ratio": 3.9427106067219553,
"config_test": false,
"h... |
from collections import defaultdict
from colorsys import hsv_to_rgb, rgb_to_hsv
from os import path as op
import os
import copy as cp
import re
import numpy as np
from scipy import linalg, sparse
from .fixes import digitize, in1d
from .utils import get_subjects_dir, _check_subject, logger, verbose
from .source_estim... | {
"repo_name": "aestrivex/mne-python",
"path": "mne/label.py",
"copies": "3",
"size": "73309",
"license": "bsd-3-clause",
"hash": 6938240650517609000,
"line_mean": 36.5366103431,
"line_max": 79,
"alpha_frac": 0.5587990561,
"autogenerated": false,
"ratio": 3.9517546223923237,
"config_test": false... |
from collections import defaultdict
from colorsys import hsv_to_rgb, rgb_to_hsv
import os
import os.path as op
import copy as cp
import re
import numpy as np
from scipy import linalg, sparse
from .fixes import _sparse_argmax
from .parallel import parallel_func, check_n_jobs
from .source_estimate import (SourceEstima... | {
"repo_name": "adykstra/mne-python",
"path": "mne/label.py",
"copies": "1",
"size": "95006",
"license": "bsd-3-clause",
"hash": -4934769954930889000,
"line_mean": 36.6559651209,
"line_max": 79,
"alpha_frac": 0.5654274467,
"autogenerated": false,
"ratio": 3.9531477551699745,
"config_test": false... |
from collections import OrderedDict
import os
import os.path as op
import shutil
import tarfile
import stat
import sys
import zipfile
from distutils.version import LooseVersion
import numpy as np
from .. import __version__ as mne_version
from ..label import read_labels_from_annot, Label, write_labels_to_annot
from .... | {
"repo_name": "teonlamont/mne-python",
"path": "mne/datasets/utils.py",
"copies": "2",
"size": "30305",
"license": "bsd-3-clause",
"hash": -5078841463562041000,
"line_mean": 41.8036723164,
"line_max": 107,
"alpha_frac": 0.5907606006,
"autogenerated": false,
"ratio": 3.4154175588865097,
"config_... |
from ..externals.six import string_types
import os
import os.path as op
import shutil
import tarfile
from warnings import warn
from .. import __version__ as mne_version
from ..utils import get_config, set_config, _fetch_file, logger
_doc = """Get path to local copy of {name} dataset
Parameters
----------
... | {
"repo_name": "jaeilepp/eggie",
"path": "mne/datasets/utils.py",
"copies": "2",
"size": "8107",
"license": "bsd-2-clause",
"hash": 1673799284564399900,
"line_mean": 38.1642512077,
"line_max": 80,
"alpha_frac": 0.5687677316,
"autogenerated": false,
"ratio": 3.833096926713948,
"config_test": true... |
import os
import os.path as op
import shutil
import tarfile
from warnings import warn
import stat
from .. import __version__ as mne_version
from ..utils import get_config, set_config, _fetch_file, logger
from ..externals.six import string_types
from ..externals.six.moves import input
_data_path_doc = """Get path to... | {
"repo_name": "cmoutard/mne-python",
"path": "mne/datasets/utils.py",
"copies": "1",
"size": "12897",
"license": "bsd-3-clause",
"hash": -8925987822764475000,
"line_mean": 37.3839285714,
"line_max": 79,
"alpha_frac": 0.5664107932,
"autogenerated": false,
"ratio": 3.7721556010529396,
"config_tes... |
import os
import os.path as op
import shutil
import tarfile
from warnings import warn
from .. import __version__ as mne_version
from ..utils import get_config, set_config, _fetch_file, logger
from ..externals.six import string_types
from ..externals.six.moves import input
_doc = """Get path to local copy of {name} ... | {
"repo_name": "effigies/mne-python",
"path": "mne/datasets/utils.py",
"copies": "2",
"size": "8571",
"license": "bsd-3-clause",
"hash": -8573757099596130000,
"line_mean": 38.1369863014,
"line_max": 79,
"alpha_frac": 0.5687784389,
"autogenerated": false,
"ratio": 3.7575624725997367,
"config_test... |
import os
import os.path as op
import shutil
import tarfile
import stat
import sys
from .. import __version__ as mne_version
from ..utils import get_config, set_config, _fetch_file, logger, warn, verbose
from ..externals.six import string_types
from ..externals.six.moves import input
_data_path_doc = """Get path to... | {
"repo_name": "jmontoyam/mne-python",
"path": "mne/datasets/utils.py",
"copies": "2",
"size": "14930",
"license": "bsd-3-clause",
"hash": 4673488202615354000,
"line_mean": 37.3804627249,
"line_max": 79,
"alpha_frac": 0.5890823845,
"autogenerated": false,
"ratio": 3.5777618020608677,
"config_tes... |
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_sample_data = partial(has_dataset, name='sample')
@verbose
def data_path(path=None, force_update=False, upda... | {
"repo_name": "jaeilepp/mne-python",
"path": "mne/datasets/sample/sample.py",
"copies": "5",
"size": "1385",
"license": "bsd-3-clause",
"hash": 2893616096189931000,
"line_mean": 31.2093023256,
"line_max": 77,
"alpha_frac": 0.6151624549,
"autogenerated": false,
"ratio": 3.4711779448621556,
"conf... |
import numpy as np
from ...utils import get_config, verbose
from ...fixes import partial
from ..utils import has_dataset, _data_path, _doc
has_sample_data = partial(has_dataset, name='sample')
@verbose
def data_path(path=None, force_update=False, update_path=True,
download=True, verbose=None):
r... | {
"repo_name": "jaeilepp/eggie",
"path": "mne/datasets/sample/sample.py",
"copies": "2",
"size": "1218",
"license": "bsd-2-clause",
"hash": -6704209759494905000,
"line_mean": 33.8,
"line_max": 80,
"alpha_frac": 0.6264367816,
"autogenerated": false,
"ratio": 3.489971346704871,
"config_test": fals... |
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_sample_data = partial(has_dataset, name='sample')
@verbose
def data_path(path=None, force_update=False, update... | {
"repo_name": "rajegannathan/grasp-lift-eeg-cat-dog-solution-updated",
"path": "python-packages/mne-python-0.10/mne/datasets/sample/sample.py",
"copies": "9",
"size": "1355",
"license": "bsd-3-clause",
"hash": -3616707157725063000,
"line_mean": 31.2619047619,
"line_max": 77,
"alpha_frac": 0.614022140... |
import numpy as np
from ...utils import verbose, get_config
from ...fixes import partial
from ..utils import has_dataset, _data_path, _doc
has_testing_data = partial(has_dataset, name='testing')
@verbose
def data_path(path=None, force_update=False, update_path=True,
download=True, verbose=None):
... | {
"repo_name": "effigies/mne-python",
"path": "mne/datasets/testing/_testing.py",
"copies": "1",
"size": "1447",
"license": "bsd-3-clause",
"hash": 3726207550251721000,
"line_mean": 35.175,
"line_max": 76,
"alpha_frac": 0.6323427782,
"autogenerated": false,
"ratio": 3.5816831683168315,
"config_t... |
from copy import deepcopy
import numpy as np
from scipy import linalg
from ..forward import is_fixed_orient, _to_fixed_ori
from ..io.pick import pick_channels_evoked
from ..minimum_norm.inverse import _prepare_forward
from ..utils import logger, verbose
from .mxne_inverse import _make_sparse_stc, _prepare_gain
@ver... | {
"repo_name": "effigies/mne-python",
"path": "mne/inverse_sparse/_gamma_map.py",
"copies": "2",
"size": "10817",
"license": "bsd-3-clause",
"hash": 4548092139406641000,
"line_mean": 34.9368770764,
"line_max": 79,
"alpha_frac": 0.5923084034,
"autogenerated": false,
"ratio": 3.654391891891892,
"c... |
from copy import deepcopy
import numpy as np
from scipy import linalg
from ..forward import is_fixed_orient, _to_fixed_ori
from ..minimum_norm.inverse import _check_reference
from ..utils import logger, verbose
from ..externals.six.moves import xrange as range
from .mxne_inverse import (_make_sparse_stc, _prepare_ga... | {
"repo_name": "trachelr/mne-python",
"path": "mne/inverse_sparse/_gamma_map.py",
"copies": "16",
"size": "10643",
"license": "bsd-3-clause",
"hash": 1647359252421232600,
"line_mean": 34.3588039867,
"line_max": 79,
"alpha_frac": 0.6010523349,
"autogenerated": false,
"ratio": 3.6151494565217392,
... |
import numpy as np
from scipy import linalg
from ..forward import is_fixed_orient, convert_forward_solution
from ..minimum_norm.inverse import _check_reference
from ..utils import logger, verbose, warn
from ..externals.six.moves import xrange as range
from .mxne_inverse import (_make_sparse_stc, _prepare_gain,
... | {
"repo_name": "teonlamont/mne-python",
"path": "mne/inverse_sparse/_gamma_map.py",
"copies": "2",
"size": "11583",
"license": "bsd-3-clause",
"hash": 1077904770940450300,
"line_mean": 35.5394321767,
"line_max": 79,
"alpha_frac": 0.5963049296,
"autogenerated": false,
"ratio": 3.6050420168067228,
... |
import numpy as np
from scipy import linalg
from ..forward import is_fixed_orient
from ..minimum_norm.inverse import _check_reference
from ..utils import logger, verbose, warn
from .mxne_inverse import (_make_sparse_stc, _prepare_gain,
_reapply_source_weighting, _compute_residual,
... | {
"repo_name": "adykstra/mne-python",
"path": "mne/inverse_sparse/_gamma_map.py",
"copies": "2",
"size": "10771",
"license": "bsd-3-clause",
"hash": -602535427196261200,
"line_mean": 34.4309210526,
"line_max": 79,
"alpha_frac": 0.5898245288,
"autogenerated": false,
"ratio": 3.5891369543485503,
"... |
from warnings import warn
import numpy as np
from scipy import linalg, fftpack
import warnings
from ..io.constants import FIFF
from ..source_estimate import _make_stc
from ..time_frequency.tfr import cwt, morlet
from ..time_frequency.multitaper import (dpss_windows, _psd_from_mt,
... | {
"repo_name": "andyh616/mne-python",
"path": "mne/minimum_norm/time_frequency.py",
"copies": "7",
"size": "26364",
"license": "bsd-3-clause",
"hash": 3026910853196262000,
"line_mean": 37.3197674419,
"line_max": 79,
"alpha_frac": 0.5736989835,
"autogenerated": false,
"ratio": 3.773293258909403,
... |
from warnings import warn
import numpy as np
from scipy import linalg, signal, fftpack
import warnings
from ..io.constants import FIFF
from ..source_estimate import _make_stc
from ..time_frequency.tfr import cwt, morlet
from ..time_frequency.multitaper import (dpss_windows, _psd_from_mt,
... | {
"repo_name": "effigies/mne-python",
"path": "mne/minimum_norm/time_frequency.py",
"copies": "2",
"size": "26554",
"license": "bsd-3-clause",
"hash": 8577103849338623000,
"line_mean": 36.611898017,
"line_max": 80,
"alpha_frac": 0.5615726444,
"autogenerated": false,
"ratio": 3.8815962578570384,
... |
from warnings import warn
import numpy as np
from scipy import linalg, signal, fftpack
from ..io.constants import FIFF
from ..source_estimate import _make_stc
from ..time_frequency.tfr import cwt, morlet
from ..time_frequency.multitaper import (dpss_windows, _psd_from_mt,
_ps... | {
"repo_name": "jaeilepp/eggie",
"path": "mne/minimum_norm/time_frequency.py",
"copies": "1",
"size": "25272",
"license": "bsd-2-clause",
"hash": -3658411442483756500,
"line_mean": 36.1647058824,
"line_max": 79,
"alpha_frac": 0.5737179487,
"autogenerated": false,
"ratio": 3.773066587040908,
"con... |
import numpy as np
from scipy import linalg, fftpack
from ..io.constants import FIFF
from ..source_estimate import _make_stc
from ..time_frequency.tfr import cwt, morlet
from ..time_frequency.multitaper import (dpss_windows, _psd_from_mt,
_psd_from_mt_adaptive, _mt_spectra)
fr... | {
"repo_name": "jaeilepp/mne-python",
"path": "mne/minimum_norm/time_frequency.py",
"copies": "1",
"size": "27129",
"license": "bsd-3-clause",
"hash": -5159326162484254000,
"line_mean": 38.1471861472,
"line_max": 93,
"alpha_frac": 0.5760625161,
"autogenerated": false,
"ratio": 3.76112574518231,
... |
import numpy as np
from scipy import linalg
from ..epochs import Epochs, make_fixed_length_events
from ..evoked import EvokedArray
from ..io.constants import FIFF
from ..io.pick import pick_info
from ..source_estimate import _make_stc
from ..time_frequency.tfr import cwt, morlet
from ..time_frequency.multitaper impor... | {
"repo_name": "adykstra/mne-python",
"path": "mne/minimum_norm/time_frequency.py",
"copies": "1",
"size": "30168",
"license": "bsd-3-clause",
"hash": 2364186732508894700,
"line_mean": 38.1284046693,
"line_max": 86,
"alpha_frac": 0.5912224874,
"autogenerated": false,
"ratio": 3.7313543599257883,
... |
from copy import deepcopy
from distutils.version import LooseVersion
from glob import glob
from functools import partial
import os
from os import path as op
import sys
from struct import pack
import numpy as np
from scipy.sparse import coo_matrix, csr_matrix, eye as speye
from .io.constants import FIFF
from .io.open... | {
"repo_name": "adykstra/mne-python",
"path": "mne/surface.py",
"copies": "1",
"size": "50956",
"license": "bsd-3-clause",
"hash": -4724236009094248000,
"line_mean": 35.0878186969,
"line_max": 79,
"alpha_frac": 0.5464910904,
"autogenerated": false,
"ratio": 3.372336201191264,
"config_test": fals... |
from copy import deepcopy
from distutils.version import LooseVersion
from glob import glob
import os
from os import path as op
import sys
from struct import pack
import numpy as np
from scipy.sparse import coo_matrix, csr_matrix, eye as speye
from .io.constants import FIFF
from .io.open import fiff_open
from .io.tre... | {
"repo_name": "jaeilepp/mne-python",
"path": "mne/surface.py",
"copies": "1",
"size": "46597",
"license": "bsd-3-clause",
"hash": 2751789278274323500,
"line_mean": 35.8064770932,
"line_max": 79,
"alpha_frac": 0.5492413675,
"autogenerated": false,
"ratio": 3.385179803850345,
"config_test": false... |
from copy import deepcopy
from distutils.version import LooseVersion
import itertools as itt
from math import log
import os
import numpy as np
from scipy import linalg
from .io.write import start_file, end_file
from .io.proj import (make_projector, _proj_equal, activate_proj,
_needs_eeg_average... | {
"repo_name": "nicproulx/mne-python",
"path": "mne/cov.py",
"copies": "2",
"size": "77577",
"license": "bsd-3-clause",
"hash": 5169403612513926000,
"line_mean": 37.7110778443,
"line_max": 113,
"alpha_frac": 0.5689830749,
"autogenerated": false,
"ratio": 3.843109085504805,
"config_test": false,
... |
from copy import deepcopy
from distutils.version import LooseVersion
import itertools as itt
from math import log
import os
import numpy as np
from scipy import linalg, sparse
from .io.write import start_file, end_file
from .io.proj import (make_projector, _proj_equal, activate_proj,
_check_pro... | {
"repo_name": "adykstra/mne-python",
"path": "mne/cov.py",
"copies": "1",
"size": "74094",
"license": "bsd-3-clause",
"hash": 8025152181226910000,
"line_mean": 36.9579918033,
"line_max": 113,
"alpha_frac": 0.5725564823,
"autogenerated": false,
"ratio": 3.7845540913269997,
"config_test": false,
... |
from copy import deepcopy
import itertools as itt
from math import log
import os
import numpy as np
from scipy import linalg
from .io.write import start_file, end_file
from .io.proj import (make_projector, _proj_equal, activate_proj,
_needs_eeg_average_ref_proj, _check_projs,
... | {
"repo_name": "teonlamont/mne-python",
"path": "mne/cov.py",
"copies": "2",
"size": "78672",
"license": "bsd-3-clause",
"hash": -600152030658387500,
"line_mean": 37.3205065757,
"line_max": 113,
"alpha_frac": 0.581871568,
"autogenerated": false,
"ratio": 3.7786743515850145,
"config_test": false,... |
from .externals.six import string_types
import os
from os import path as op
import sys
from struct import pack
from glob import glob
import numpy as np
from scipy.spatial.distance import cdist
from scipy import sparse
from .io.constants import FIFF
from .io.open import fiff_open
from .io.tree import dir_tree_find
fr... | {
"repo_name": "effigies/mne-python",
"path": "mne/surface.py",
"copies": "1",
"size": "47438",
"license": "bsd-3-clause",
"hash": 7204894894356967000,
"line_mean": 35.9742790335,
"line_max": 79,
"alpha_frac": 0.5493486235,
"autogenerated": false,
"ratio": 3.3570164885712264,
"config_test": fals... |
from .externals.six import string_types
import os
from os import path as op
import sys
from struct import pack
import numpy as np
from scipy.spatial.distance import cdist
from scipy import sparse
from fnmatch import fnmatch
from .io.constants import FIFF
from .io.open import fiff_open
from .io.tree import dir_tree_fi... | {
"repo_name": "jaeilepp/eggie",
"path": "mne/surface.py",
"copies": "1",
"size": "47507",
"license": "bsd-2-clause",
"hash": -294141215309423700,
"line_mean": 35.0721336371,
"line_max": 87,
"alpha_frac": 0.5476877092,
"autogenerated": false,
"ratio": 3.345327793817337,
"config_test": false,
"... |
import copy as cp
from distutils.version import LooseVersion
import itertools as itt
from math import log
import os
import numpy as np
from scipy import linalg
from .io.write import start_file, end_file
from .io.proj import (make_projector, _proj_equal, activate_proj,
_needs_eeg_average_ref_pro... | {
"repo_name": "wronk/mne-python",
"path": "mne/cov.py",
"copies": "1",
"size": "75382",
"license": "bsd-3-clause",
"hash": 7771026926811778000,
"line_mean": 37.0717171717,
"line_max": 95,
"alpha_frac": 0.5673900931,
"autogenerated": false,
"ratio": 3.8417082866170627,
"config_test": false,
"h... |
import copy as cp
import os
from math import floor, ceil, log
import itertools as itt
import warnings
from copy import deepcopy
from distutils.version import LooseVersion
import numpy as np
from scipy import linalg
from .io.write import start_file, end_file
from .io.proj import (make_projector, _proj_equal, activate... | {
"repo_name": "yousrabk/mne-python",
"path": "mne/cov.py",
"copies": "2",
"size": "71662",
"license": "bsd-3-clause",
"hash": 5949022882991684000,
"line_mean": 36.6970015781,
"line_max": 95,
"alpha_frac": 0.5668555162,
"autogenerated": false,
"ratio": 3.8105923641391044,
"config_test": false,
... |
import copy as cp
import os
from math import floor, ceil, log
import itertools as itt
import warnings
import six
from distutils.version import LooseVersion
import numpy as np
from scipy import linalg
from .io.write import start_file, end_file
from .io.proj import (make_projector, _proj_equal, activate_proj,
... | {
"repo_name": "trachelr/mne-python",
"path": "mne/cov.py",
"copies": "2",
"size": "70653",
"license": "bsd-3-clause",
"hash": -7085427137996675000,
"line_mean": 36.7218366257,
"line_max": 95,
"alpha_frac": 0.5664303002,
"autogenerated": false,
"ratio": 3.7932460002147534,
"config_test": false,
... |
import copy as cp
import os
from math import floor, ceil, log
import itertools as itt
import warnings
from copy import deepcopy
import six
from distutils.version import LooseVersion
import numpy as np
from scipy import linalg
from .io.write import start_file, end_file
from .io.proj import (make_projector, _proj_eq... | {
"repo_name": "rajegannathan/grasp-lift-eeg-cat-dog-solution-updated",
"path": "python-packages/mne-python-0.10/mne/cov.py",
"copies": "1",
"size": "72176",
"license": "bsd-3-clause",
"hash": -5691242028707969000,
"line_mean": 36.6898172324,
"line_max": 95,
"alpha_frac": 0.5670444469,
"autogenerate... |
import copy as cp
import os
from math import floor, ceil, log
import itertools as itt
import warnings
import numpy as np
from scipy import linalg
from .io.write import start_file, end_file
from .io.proj import (make_projector, _proj_equal, activate_proj,
_has_eeg_average_ref_proj)
from .io impo... | {
"repo_name": "dgwakeman/mne-python",
"path": "mne/cov.py",
"copies": "4",
"size": "66658",
"license": "bsd-3-clause",
"hash": -6401433666353132000,
"line_mean": 36.5961646926,
"line_max": 95,
"alpha_frac": 0.5674787722,
"autogenerated": false,
"ratio": 3.7484114041500307,
"config_test": false,... |
import os
from os import path as op
import sys
from struct import pack
from glob import glob
from distutils.version import LooseVersion
import numpy as np
from scipy.sparse import coo_matrix, csr_matrix, eye as speye
from .bem import read_bem_surfaces
from .io.constants import FIFF
from .io.open import fiff_open
fro... | {
"repo_name": "jniediek/mne-python",
"path": "mne/surface.py",
"copies": "5",
"size": "45614",
"license": "bsd-3-clause",
"hash": 4054251795818808300,
"line_mean": 35.7262479871,
"line_max": 79,
"alpha_frac": 0.544657342,
"autogenerated": false,
"ratio": 3.4073354747142752,
"config_test": false... |
import os
from os import path as op
import sys
from struct import pack
from glob import glob
import numpy as np
from scipy import sparse
from .bem import read_bem_surfaces
from .io.constants import FIFF
from .io.open import fiff_open
from .io.tree import dir_tree_find
from .io.tag import find_tag
from .io.write impo... | {
"repo_name": "matthew-tucker/mne-python",
"path": "mne/surface.py",
"copies": "1",
"size": "39112",
"license": "bsd-3-clause",
"hash": -801022969498496800,
"line_mean": 36.6801541426,
"line_max": 79,
"alpha_frac": 0.540089998,
"autogenerated": false,
"ratio": 3.3610036951104236,
"config_test":... |
import os
from os import path as op
import sys
from struct import pack
from glob import glob
import numpy as np
from scipy import sparse
from .io.constants import FIFF
from .io.open import fiff_open
from .io.tree import dir_tree_find
from .io.tag import find_tag
from .io.write import (write_int, write_float, write_f... | {
"repo_name": "aestrivex/mne-python",
"path": "mne/surface.py",
"copies": "4",
"size": "49208",
"license": "bsd-3-clause",
"hash": 407580138413851700,
"line_mean": 35.9706987228,
"line_max": 79,
"alpha_frac": 0.5470451959,
"autogenerated": false,
"ratio": 3.3605135559653077,
"config_test": fals... |
import os
from os import path as op
import sys
from struct import pack
from glob import glob
import numpy as np
from scipy.sparse import coo_matrix, csr_matrix, eye as speye
from .bem import read_bem_surfaces
from .io.constants import FIFF
from .io.open import fiff_open
from .io.tree import dir_tree_find
from .io.ta... | {
"repo_name": "rajegannathan/grasp-lift-eeg-cat-dog-solution-updated",
"path": "python-packages/mne-python-0.10/mne/surface.py",
"copies": "4",
"size": "41230",
"license": "bsd-3-clause",
"hash": -9186197072195736000,
"line_mean": 36.0440251572,
"line_max": 79,
"alpha_frac": 0.5425418385,
"autogene... |
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