text stringlengths 0 1.05M | meta dict |
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import numpy as np
from scipy import sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.linear_model.randomized_l1 import (lasso_stability_path,
Randomi... | {
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from scipy import linalg
from meeg_tools import simu_meg
from bird import bird, s_bird
from joblib import Memory
if __name__ == '__main__':
white = False # change to True/False for white/pink noise
scales = [8, 16, 32, 64, 128]
n_runs = 30
# Structured sparsity parameters
n_channels = 20 # Se... | {
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"path": "demo_meg_denoising.py",
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import math
import numpy as np
from scipy import linalg
from scipy.fftpack import fft, ifft
import six
def _framing(a, L):
shape = a.shape[:-1] + (a.shape[-1] - L + 1, L)
strides = a.strides + (a.strides[-1],)
return np.lib.stride_tricks.as_strided(a, shape=shape,
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import numpy as np
import mne
from mne import pick_types_forward
from mne.datasets import sample
from mne.time_frequency import fit_iir_model_raw, morlet
from mne.simulation import simulate_sparse_stc, simulate_evoked
###############################################################################
# Load real data as... | {
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"ha... |
import numpy as np
from ..source_estimate import SourceEstimate, VolSourceEstimate
from ..source_space import _ensure_src
from ..fixes import rng_uniform
from ..utils import (check_random_state, warn, _check_option, fill_doc,
_ensure_int, _ensure_events)
from ..label import Label
from ..surface i... | {
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import numpy as np
from ..source_estimate import SourceEstimate, VolSourceEstimate
from ..source_space import _ensure_src
from ..fixes import rng_uniform
from ..utils import check_random_state, warn, _check_option, fill_doc
from ..label import Label
from ..surface import _compute_nearest
@fill_doc
def select_source... | {
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from collections import defaultdict
from colorsys import hsv_to_rgb, rgb_to_hsv
import copy as cp
import os
import os.path as op
import re
import numpy as np
from .morph_map import read_morph_map
from .parallel import parallel_func, check_n_jobs
from .source_estimate import (SourceEstimate, VolSourceEstimate,
... | {
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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 .parallel import parallel_func, check_n_jobs
from .source_estimate import (SourceEstimate, _center_of_mass,
... | {
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"... |
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 .parallel import parallel_func, check_n_jobs
from .source_estimate import (SourceEstimate, VolSourceEstimate,
_center_of_mas... | {
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"path": "mne/label.py",
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"alpha_frac": 0.5629927252,
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from collections import OrderedDict
import os
import os.path as op
import shutil
import tarfile
import stat
import sys
import zipfile
import tempfile
from distutils.version import LooseVersion
import numpy as np
from ._fsaverage.base import fetch_fsaverage
from .. import __version__ as mne_version
from ..label impor... | {
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"path": "mne/datasets/utils.py",
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from functools import partial
from ...utils import verbose, get_config
from ..utils import (has_dataset, _data_path, _data_path_doc,
_get_version, _version_doc)
has_testing_data = partial(has_dataset, name='testing')
@verbose
def data_path(path=None, force_update=False, update_path=True,
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import numpy as np
from scipy import linalg
from ..forward import is_fixed_orient
from ..minimum_norm.inverse import _check_reference, _log_exp_var
from ..utils import logger, verbose, warn
from .mxne_inverse import (_check_ori, _make_sparse_stc, _prepare_gain,
_reapply_source_weighting, _... | {
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"c... |
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 (_check_ori, _make_sparse_stc, _prepare_gain,
_reapply_source_weighting, _compute_residu... | {
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"path": "mne/inverse_sparse/_gamma_map.py",
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"con... |
import numpy as np
from ..forward import is_fixed_orient
from ..minimum_norm.inverse import _check_reference, _log_exp_var
from ..utils import logger, verbose, warn
from .mxne_inverse import (_check_ori, _make_sparse_stc, _prepare_gain,
_reapply_source_weighting, _compute_residual,
... | {
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... |
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... | {
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"con... |
import numpy as np
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 import (_psd_from_mt, _compute... | {
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... |
from __future__ import division
from itertools import chain, combinations
import numbers
import warnings
from itertools import combinations_with_replacement as combinations_w_r
from distutils.version import LooseVersion
import numpy as np
from scipy import sparse
from scipy import stats
from ..base import BaseEstim... | {
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... |
from __future__ import division
from itertools import chain, combinations
import warnings
from itertools import combinations_with_replacement as combinations_w_r
from distutils.version import LooseVersion
import numpy as np
from scipy import sparse
from scipy import stats
from scipy import optimize
from ..base impo... | {
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"path": "sklearn/preprocessing/data.py",
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"alpha_frac": 0.5848290698,
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"ratio": 4.171315984549959,
... |
from itertools import chain, combinations
import numbers
import warnings
from itertools import combinations_with_replacement as combinations_w_r
import numpy as np
from scipy import sparse
from scipy import stats
from scipy import optimize
from scipy.special import boxcox
from ..base import BaseEstimator, Transform... | {
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"con... |
from itertools import chain, combinations
import warnings
from itertools import combinations_with_replacement as combinations_w_r
import numpy as np
from scipy import sparse
from scipy import stats
from scipy import optimize
from scipy.special import boxcox
from ..base import BaseEstimator, TransformerMixin
from ..... | {
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... |
import warnings
import numpy as np
from scipy import sparse
from scipy import stats
from scipy import optimize
from scipy.special import boxcox
from ..base import BaseEstimator, TransformerMixin
from ..utils import check_array
from ..utils.deprecation import deprecated
from ..utils.extmath import row_norms
from ..u... | {
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from __future__ import division
from itertools import chain, combinations
import numbers
import warnings
from itertools import combinations_with_replacement as combinations_w_r
import numpy as np
from scipy import sparse
from scipy import stats
from ..base import BaseEstimator, TransformerMixin
from ..externals imp... | {
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"path": "sklearn/preprocessing/data.py",
"copies": "1",
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"license": "bsd-3-clause",
"hash": -5082220854051463000,
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"line_max": 84,
"alpha_frac": 0.5884911874,
"autogenerated": false,
"ratio": 4.236366518706404,
... |
from itertools import chain, combinations
import numbers
import warnings
from itertools import combinations_with_replacement as combinations_w_r
import numpy as np
from scipy import sparse
from ..base import BaseEstimator, TransformerMixin
from ..externals import six
from ..utils import check_array
from ..utils.extm... | {
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"alpha_frac": 0.5912217155,
"autogenerated": false,
"ratio": 4.169683257918552,
"co... |
from itertools import chain, combinations
import numbers
import warnings
import numpy as np
from scipy import sparse
from ..base import BaseEstimator, TransformerMixin
from ..externals import six
from ..utils import check_array
from ..utils import deprecated
from ..utils.extmath import row_norms
from ..utils.extmath... | {
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"confi... |
import numbers
import warnings
from itertools import chain, combinations
import numpy as np
from scipy import sparse
from ..utils.sparsefuncs_fast import (inplace_csr_row_normalize_l1,
inplace_csr_row_normalize_l2)
from ..base import BaseEstimator, TransformerMixin
from ..extern... | {
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"path": "01-codes/scikit-learn-master/sklearn/preprocessing/data.py",
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import numbers
import numpy as np
from scipy import sparse
from uplift.base import BaseEstimator
from uplift.base import TransformerMixin
from uplift.validation.check import check_array
FLOAT_DTYPES = (np.float64, np.float32, np.float16)
def _transform_selected(X, transform, selected="all", copy=True):
"""App... | {
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from itertools import chain, combinations
import numbers
import warnings
import numpy as np
from scipy import sparse
from ..base import BaseEstimator, TransformerMixin
from ..externals import six
from ..utils import check_array
from ..utils.extmath import row_norms
from ..utils.fixes import combinations_with_replace... | {
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"co... |
import numpy as np
from uplift.base import BaseEstimator
from uplift.base import TransformerMixin
from uplift.validation.check import check_is_fitted
from uplift.validation.check import column_or_1d
class LabelEncoder(BaseEstimator, TransformerMixin):
"""Encode labels with value between 0 and n_classes-1.
... | {
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"config_te... |
from collections import defaultdict
import itertools
import array
import warnings
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator, TransformerMixin
from ..utils.fixes import np_version
from ..utils.fixes import sparse_min_max
from ..utils.fixes import astype
from ..utils.fixes import i... | {
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from collections import defaultdict
import itertools
import array
import warnings
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator, TransformerMixin
from ..utils.sparsefuncs import min_max_axis
from ..utils import column_or_1d
from ..utils.validation import check_array
from ..utils.vali... | {
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from collections import defaultdict
import itertools
import array
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator, TransformerMixin
from ..utils.fixes import np_version
from ..utils.fixes import sparse_min_max
from ..utils.fixes import astype
from ..utils.fixes import in1d
from ..utils... | {
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from collections import defaultdict
import itertools
import array
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator, TransformerMixin
from ..utils.fixes import sparse_min_max
from ..utils import column_or_1d
from ..utils.validation import check_array
from ..utils.validation import check_... | {
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import array
import itertools
from collections import defaultdict
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator, TransformerMixin
from ..externals import six
from ..utils import column_or_1d
from ..utils.fixes import astype
from ..utils.fixes import in1d
from ..utils.fixes import np_v... | {
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"path": "01-codes/scikit-learn-master/sklearn/preprocessing/label.py",
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"ratio"... |
from itertools import chain, combinations
import numbers
import numpy as np
from scipy import sparse
from ..base import BaseEstimator, TransformerMixin
from ..externals import six
from ..utils import check_array
from ..utils import warn_if_not_float
from ..utils.extmath import row_norms
from ..utils.fixes import (co... | {
"repo_name": "mehdidc/scikit-learn",
"path": "sklearn/preprocessing/data.py",
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import numbers
import warnings
import itertools
import numpy as np
from scipy import sparse
from ..base import BaseEstimator, TransformerMixin
from ..externals import six
from ..utils import check_arrays
from ..utils import atleast2d_or_csc
from ..utils import array2d
from ..utils import atleast2d_or_csr
from ..util... | {
"repo_name": "treycausey/scikit-learn",
"path": "sklearn/preprocessing/data.py",
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"size": "38124",
"license": "bsd-3-clause",
"hash": 3897051935649942500,
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... |
import numbers
import warnings
import numpy as np
from scipy import sparse
from ..base import BaseEstimator, TransformerMixin
from ..utils import check_arrays
from ..utils import atleast2d_or_csc
from ..utils import array2d
from ..utils import atleast2d_or_csr
from ..utils import safe_asarray
from ..utils import warn... | {
"repo_name": "fspaolo/scikit-learn",
"path": "sklearn/preprocessing/data.py",
"copies": "5",
"size": "34765",
"license": "bsd-3-clause",
"hash": -8071154565273871000,
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"line_max": 79,
"alpha_frac": 0.5990507695,
"autogenerated": false,
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"co... |
import numpy as np
from ..base import BaseEstimator, TransformerMixin
from ..utils.fixes import np_version
from ..utils import deprecated, column_or_1d
from ..utils.multiclass import unique_labels
from ..utils.multiclass import type_of_target
from ..externals import six
zip = six.moves.zip
map = six.moves.map
__... | {
"repo_name": "treycausey/scikit-learn",
"path": "sklearn/preprocessing/label.py",
"copies": "3",
"size": "13306",
"license": "bsd-3-clause",
"hash": -7252821234389229000,
"line_mean": 28.7673378076,
"line_max": 79,
"alpha_frac": 0.5712460544,
"autogenerated": false,
"ratio": 4.066625916870415,
... |
import numpy as np
from ..base import BaseEstimator, TransformerMixin
from ..utils.fixes import unique
from ..utils import deprecated, column_or_1d
from ..utils.multiclass import unique_labels
from ..utils.multiclass import type_of_target
from ..externals import six
zip = six.moves.zip
map = six.moves.map
__all_... | {
"repo_name": "fspaolo/scikit-learn",
"path": "sklearn/preprocessing/label.py",
"copies": "1",
"size": "12753",
"license": "bsd-3-clause",
"hash": 8272804732549213000,
"line_mean": 28.3847926267,
"line_max": 79,
"alpha_frac": 0.5728063985,
"autogenerated": false,
"ratio": 4.075743048897412,
"co... |
import numpy as np
from ..base import BaseEstimator, TransformerMixin
from ..utils.fixes import unique, np_version
from ..utils import deprecated, column_or_1d
from ..utils.multiclass import unique_labels
from ..utils.multiclass import type_of_target
from ..externals import six
zip = six.moves.zip
map = six.moves... | {
"repo_name": "Tong-Chen/scikit-learn",
"path": "sklearn/preprocessing/label.py",
"copies": "1",
"size": "13344",
"license": "bsd-3-clause",
"hash": 1783331845472253400,
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"line_max": 79,
"alpha_frac": 0.5715677458,
"autogenerated": false,
"ratio": 4.063337393422655,
"... |
from collections import Sequence
import warnings
import numbers
import numpy as np
import scipy.sparse as sp
from .base import BaseEstimator, TransformerMixin
from .externals.six import string_types
from .utils import check_arrays, array2d, atleast2d_or_csr, safe_asarray
from .utils import warn_if_not_float
from .ut... | {
"repo_name": "maxlikely/scikit-learn",
"path": "sklearn/preprocessing.py",
"copies": "1",
"size": "41872",
"license": "bsd-3-clause",
"hash": 3605576705665076700,
"line_mean": 32.5781876504,
"line_max": 79,
"alpha_frac": 0.5859763088,
"autogenerated": false,
"ratio": 4.074737251848968,
"config... |
from collections import Sequence
import warnings
import numbers
import numpy as np
import scipy.sparse as sp
from .utils import check_arrays, array2d, atleast2d_or_csr, safe_asarray
from .utils import warn_if_not_float
from .utils.fixes import unique
from .base import BaseEstimator, TransformerMixin
from .utils.spa... | {
"repo_name": "seckcoder/lang-learn",
"path": "python/sklearn/sklearn/preprocessing.py",
"copies": "1",
"size": "40491",
"license": "unlicense",
"hash": 3304583940113447000,
"line_mean": 32.9974811083,
"line_max": 80,
"alpha_frac": 0.5884270579,
"autogenerated": false,
"ratio": 4.072721786360893,... |
import warnings
import numbers
import numpy as np
import scipy.sparse as sp
from .base import BaseEstimator, TransformerMixin
from .externals.six import string_types
from .utils import check_arrays, array2d, atleast2d_or_csr, safe_asarray
from .utils import warn_if_not_float
from .utils.fixes import unique
from .ut... | {
"repo_name": "kmike/scikit-learn",
"path": "sklearn/preprocessing.py",
"copies": "3",
"size": "41933",
"license": "bsd-3-clause",
"hash": 1347871312321637000,
"line_mean": 32.6271050521,
"line_max": 83,
"alpha_frac": 0.586507047,
"autogenerated": false,
"ratio": 4.085444271239283,
"config_test... |
from collections import Sequence
import warnings
import numpy as np
import scipy.sparse as sp
from .utils import check_arrays, array2d
from .utils import warn_if_not_float
from .utils.fixes import unique
from .base import BaseEstimator, TransformerMixin
from .utils.sparsefuncs import inplace_csr_row_normalize_l1
fro... | {
"repo_name": "pradyu1993/scikit-learn",
"path": "sklearn/preprocessing.py",
"copies": "1",
"size": "31914",
"license": "bsd-3-clause",
"hash": 4208630011276055600,
"line_mean": 31.7659137577,
"line_max": 79,
"alpha_frac": 0.588644482,
"autogenerated": false,
"ratio": 4.145213664112222,
"config... |
from collections import Sequence
import numpy as np
import scipy.sparse as sp
from .utils import check_arrays
from .utils import warn_if_not_float
from .base import BaseEstimator, TransformerMixin
from .utils.sparsefuncs import inplace_csr_row_normalize_l1
from .utils.sparsefuncs import inplace_csr_row_normalize_l2
... | {
"repo_name": "sgenoud/scikit-learn",
"path": "sklearn/preprocessing.py",
"copies": "1",
"size": "28614",
"license": "bsd-3-clause",
"hash": -963046273558067200,
"line_mean": 31.5529010239,
"line_max": 79,
"alpha_frac": 0.587334871,
"autogenerated": false,
"ratio": 4.142753728101925,
"config_te... |
import numpy as np
import scipy.sparse as sp
from .utils import check_arrays
from .utils import warn_if_not_float
from .base import BaseEstimator, TransformerMixin
from .utils.sparsefuncs import inplace_csr_row_normalize_l1
from .utils.sparsefuncs import inplace_csr_row_normalize_l2
from .utils.sparsefuncs import inp... | {
"repo_name": "cdegroc/scikit-learn",
"path": "sklearn/preprocessing.py",
"copies": "2",
"size": "24547",
"license": "bsd-3-clause",
"hash": 6638485830646517000,
"line_mean": 32.3972789116,
"line_max": 79,
"alpha_frac": 0.5929034098,
"autogenerated": false,
"ratio": 4.16403731976251,
"config_te... |
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 .defaults import _EXTRAPOLATE_DEFAULT, _BORDER_DEFAULT, DEFAULTS
from .io.write import start_file, end_file
from .io.proj import (make_p... | {
"repo_name": "larsoner/mne-python",
"path": "mne/cov.py",
"copies": "3",
"size": "78828",
"license": "bsd-3-clause",
"hash": -4327112750071462000,
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"line_max": 102,
"alpha_frac": 0.5706963703,
"autogenerated": false,
"ratio": 3.7726989900923753,
"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 .defaults import _EXTRAPOLATE_DEFAULT, _BORDER_DEFAULT, DEFAULTS
from .io.write import start_file, end_file
from .io.proj import (make_projector, _proj_equal, activate_p... | {
"repo_name": "mne-tools/mne-python",
"path": "mne/cov.py",
"copies": "4",
"size": "79191",
"license": "bsd-3-clause",
"hash": 8443270376986357000,
"line_mean": 36.8880382775,
"line_max": 102,
"alpha_frac": 0.5708458566,
"autogenerated": false,
"ratio": 3.7648457186326247,
"config_test": false,... |
import os
import os.path as op
import sys
from collections import OrderedDict
from copy import deepcopy
from functools import partial
import numpy as np
from scipy import sparse
from ..defaults import HEAD_SIZE_DEFAULT, _handle_default
from ..transforms import _frame_to_str
from ..utils import (verbose, logger, warn... | {
"repo_name": "larsoner/mne-python",
"path": "mne/channels/channels.py",
"copies": "3",
"size": "66166",
"license": "bsd-3-clause",
"hash": -248705718129498240,
"line_mean": 36.9375,
"line_max": 79,
"alpha_frac": 0.5684748273,
"autogenerated": false,
"ratio": 3.895378274948484,
"config_test": f... |
from copy import deepcopy
import numpy as np
from .constants import FIFF
from .tag import read_tag
from .tree import dir_tree_find
from .write import start_block, end_block, write_int
from .matrix import write_named_matrix, _read_named_matrix
from ..utils import logger, verbose, _pl
def _add_kind(one):
"""Con... | {
"repo_name": "bloyl/mne-python",
"path": "mne/io/ctf_comp.py",
"copies": "8",
"size": "5927",
"license": "bsd-3-clause",
"hash": -4879408175892087000,
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"line_max": 79,
"alpha_frac": 0.5460837272,
"autogenerated": false,
"ratio": 3.4744868035190617,
"config_test": fal... |
from copy import deepcopy
from itertools import count
from math import sqrt
import numpy as np
from scipy import linalg
from .tree import dir_tree_find
from .tag import find_tag
from .constants import FIFF
from .pick import pick_types, pick_info
from .write import (write_int, write_float, write_string, write_name_li... | {
"repo_name": "cjayb/mne-python",
"path": "mne/io/proj.py",
"copies": "2",
"size": "31085",
"license": "bsd-3-clause",
"hash": -2845321749380932000,
"line_mean": 34.5222857143,
"line_max": 79,
"alpha_frac": 0.55311756,
"autogenerated": false,
"ratio": 4.006961454170427,
"config_test": false,
... |
from copy import deepcopy
from itertools import count
from math import sqrt
import numpy as np
from .tree import dir_tree_find
from .tag import find_tag, _rename_list
from .constants import FIFF
from .pick import pick_types, pick_info
from .write import (write_int, write_float, write_string, write_name_list,
... | {
"repo_name": "drammock/mne-python",
"path": "mne/io/proj.py",
"copies": "8",
"size": "31409",
"license": "bsd-3-clause",
"hash": -8213824814545371000,
"line_mean": 34.9747995418,
"line_max": 79,
"alpha_frac": 0.555976565,
"autogenerated": false,
"ratio": 3.9895833333333335,
"config_test": fals... |
import os
import os.path as op
import sys
from collections import OrderedDict
from copy import deepcopy
from functools import partial
import numpy as np
from ..defaults import HEAD_SIZE_DEFAULT, _handle_default
from ..transforms import _frame_to_str
from ..utils import (verbose, logger, warn,
_... | {
"repo_name": "pravsripad/mne-python",
"path": "mne/channels/channels.py",
"copies": "2",
"size": "74909",
"license": "bsd-3-clause",
"hash": 394357111715747200,
"line_mean": 37.1589403974,
"line_max": 79,
"alpha_frac": 0.5680719836,
"autogenerated": false,
"ratio": 3.866515253187426,
"config_t... |
import copy
import os
import os.path as op
import numpy as np
from ..constants import FIFF
from ..open import fiff_open, _fiff_get_fid, _get_next_fname
from ..meas_info import read_meas_info
from ..tree import dir_tree_find
from ..tag import read_tag, read_tag_info
from ..base import (BaseRaw, _RawShell, _check_raw_... | {
"repo_name": "cjayb/mne-python",
"path": "mne/io/fiff/raw.py",
"copies": "2",
"size": "18620",
"license": "bsd-3-clause",
"hash": 7741001825745656000,
"line_mean": 39.2965367965,
"line_max": 79,
"alpha_frac": 0.5222645969,
"autogenerated": false,
"ratio": 4.118805309734514,
"config_test": fals... |
import contextlib
import copy
import os.path as op
from types import GeneratorType
import numpy as np
from scipy import linalg, sparse
from scipy.sparse import coo_matrix, block_diag as sparse_block_diag
from .baseline import rescale
from .cov import Covariance
from .evoked import _get_peak
from .filter import resam... | {
"repo_name": "olafhauk/mne-python",
"path": "mne/source_estimate.py",
"copies": "2",
"size": "127526",
"license": "bsd-3-clause",
"hash": 6371380910515133000,
"line_mean": 36.7607343796,
"line_max": 89,
"alpha_frac": 0.5735974529,
"autogenerated": false,
"ratio": 3.9985575867799694,
"config_te... |
import contextlib
import copy
import os.path as op
from types import GeneratorType
import numpy as np
from .baseline import rescale
from .cov import Covariance
from .evoked import _get_peak
from .filter import resample
from .io.constants import FIFF
from .io.pick import pick_types
from .surface import (read_surface,... | {
"repo_name": "bloyl/mne-python",
"path": "mne/source_estimate.py",
"copies": "4",
"size": "129247",
"license": "bsd-3-clause",
"hash": -6864020388982823000,
"line_mean": 36.8348946136,
"line_max": 89,
"alpha_frac": 0.574185262,
"autogenerated": false,
"ratio": 3.9995048738975707,
"config_test"... |
from os import path
from collections import OrderedDict
import numpy as np
from .io.meas_info import Info
from .io.pick import _pick_data_channels, pick_types
from .utils import logger, verbose, _get_stim_channel
_SELECTIONS = ['Vertex', 'Left-temporal', 'Right-temporal', 'Left-parietal',
'Right-pari... | {
"repo_name": "olafhauk/mne-python",
"path": "mne/selection.py",
"copies": "4",
"size": "7056",
"license": "bsd-3-clause",
"hash": 1627357453649742800,
"line_mean": 36.3174603175,
"line_max": 79,
"alpha_frac": 0.5867006947,
"autogenerated": false,
"ratio": 3.469257255287752,
"config_test": fals... |
from os import path
import numpy as np
from .io.meas_info import Info
from .io.pick import _pick_data_channels, pick_types
from .utils import logger, verbose, _get_stim_channel
_SELECTIONS = ['Vertex', 'Left-temporal', 'Right-temporal', 'Left-parietal',
'Right-parietal', 'Left-occipital', 'Right-occi... | {
"repo_name": "cjayb/mne-python",
"path": "mne/selection.py",
"copies": "2",
"size": "6943",
"license": "bsd-3-clause",
"hash": -2201425715267995100,
"line_mean": 36.1122994652,
"line_max": 79,
"alpha_frac": 0.5900576369,
"autogenerated": false,
"ratio": 3.425468904244817,
"config_test": false,... |
import copy as cp
import numpy as np
from ..epochs import Epochs
from ..proj import compute_proj_evoked, compute_proj_epochs
from ..utils import logger, verbose, warn
from ..io.pick import pick_types
from ..io import make_eeg_average_ref_proj
from .ecg import find_ecg_events
from .eog import find_eog_events
def _s... | {
"repo_name": "mne-tools/mne-python",
"path": "mne/preprocessing/ssp.py",
"copies": "4",
"size": "14067",
"license": "bsd-3-clause",
"hash": -3948871215610789000,
"line_mean": 37.4262295082,
"line_max": 79,
"alpha_frac": 0.5802047782,
"autogenerated": false,
"ratio": 3.515121219695076,
"config_... |
import os
from ..bem import fit_sphere_to_headshape
from ..io import read_raw_fif
from ..utils import logger, verbose, warn
def _mxwarn(msg):
"""Warn about a bug."""
warn('Possible MaxFilter bug: %s, more info: '
'http://imaging.mrc-cbu.cam.ac.uk/meg/maxbugs' % msg)
@verbose
def apply_maxfilter(i... | {
"repo_name": "mne-tools/mne-python",
"path": "mne/preprocessing/maxfilter.py",
"copies": "8",
"size": "6421",
"license": "bsd-3-clause",
"hash": -5334219083585888000,
"line_mean": 31.578680203,
"line_max": 78,
"alpha_frac": 0.5735431599,
"autogenerated": false,
"ratio": 3.5615982241953383,
"co... |
from .constants import FIFF
from .tag import find_tag, has_tag
from .write import (write_int, start_block, end_block, write_float_matrix,
write_name_list)
from ..utils import logger
def _transpose_named_matrix(mat):
"""Transpose mat inplace (no copy)."""
mat['nrow'], mat['ncol'] = mat['nc... | {
"repo_name": "olafhauk/mne-python",
"path": "mne/io/matrix.py",
"copies": "14",
"size": "4437",
"license": "bsd-3-clause",
"hash": 5293997076232905000,
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"line_max": 78,
"alpha_frac": 0.5631483987,
"autogenerated": false,
"ratio": 3.499605367008682,
"config_test": false,
... |
from functools import partial
import struct
import numpy as np
from scipy import sparse
from .constants import (FIFF, _dig_kind_named, _dig_cardinal_named,
_ch_kind_named, _ch_coil_type_named, _ch_unit_named,
_ch_unit_mul_named)
from ..utils.numerics import _julian_to_... | {
"repo_name": "olafhauk/mne-python",
"path": "mne/io/tag.py",
"copies": "5",
"size": "17464",
"license": "bsd-3-clause",
"hash": 6871479615683388000,
"line_mean": 33.7137176938,
"line_max": 79,
"alpha_frac": 0.5656033446,
"autogenerated": false,
"ratio": 3.2649588631264024,
"config_test": false... |
from functools import partial
import struct
import numpy as np
from .constants import (FIFF, _dig_kind_named, _dig_cardinal_named,
_ch_kind_named, _ch_coil_type_named, _ch_unit_named,
_ch_unit_mul_named)
from ..utils.numerics import _julian_to_cal
###################... | {
"repo_name": "wmvanvliet/mne-python",
"path": "mne/io/tag.py",
"copies": "4",
"size": "17633",
"license": "bsd-3-clause",
"hash": -3940569949739112400,
"line_mean": 33.5009784736,
"line_max": 79,
"alpha_frac": 0.566477595,
"autogenerated": false,
"ratio": 3.257575757575758,
"config_test": fals... |
from gzip import GzipFile
import os.path as op
import re
import time
import uuid
import numpy as np
from scipy import linalg, sparse
from .constants import FIFF
from ..utils import logger, _file_like
from ..utils.numerics import _cal_to_julian
# We choose a "magic" date to store (because meas_date is obligatory)
# ... | {
"repo_name": "cjayb/mne-python",
"path": "mne/io/write.py",
"copies": "2",
"size": "16281",
"license": "bsd-3-clause",
"hash": -5354026379320738000,
"line_mean": 34.0818965517,
"line_max": 79,
"alpha_frac": 0.6123602408,
"autogenerated": false,
"ratio": 3.048314606741573,
"config_test": false,... |
from gzip import GzipFile
import os.path as op
import re
import time
import uuid
import numpy as np
from .constants import FIFF
from ..utils import logger, _file_like
from ..utils.numerics import _cal_to_julian
# We choose a "magic" date to store (because meas_date is obligatory)
# to treat as meas_date=None. This ... | {
"repo_name": "mne-tools/mne-python",
"path": "mne/io/write.py",
"copies": "8",
"size": "16738",
"license": "bsd-3-clause",
"hash": -8024500651996100000,
"line_mean": 34.0838574423,
"line_max": 79,
"alpha_frac": 0.6108754108,
"autogenerated": false,
"ratio": 3.061653860226857,
"config_test": fa... |
from ..utils._bunch import BunchConstNamed
FIFF = BunchConstNamed()
#
# FIFF version number in use
#
FIFF.FIFFC_MAJOR_VERSION = 1
FIFF.FIFFC_MINOR_VERSION = 4
FIFF.FIFFC_VERSION = FIFF.FIFFC_MAJOR_VERSION << 16 | FIFF.FIFFC_MINOR_VERSION
#
# Blocks
#
FIFF.FIFFB_ROOT = 999
FIFF.FIFFB_MEAS ... | {
"repo_name": "bloyl/mne-python",
"path": "mne/io/constants.py",
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"alpha_frac": 0.650382852,
"autogenerated": false,
"ratio": 2.667101179391682,
"config_test": fa... |
import numpy as np
from .constants import FIFF
from .tag import Tag
from .tag import read_tag
from .write import write_id, start_block, end_block, _write
from ..utils import logger, verbose
def dir_tree_find(tree, kind):
"""Find nodes of the given kind from a directory tree structure.
Parameters
------... | {
"repo_name": "bloyl/mne-python",
"path": "mne/io/tree.py",
"copies": "14",
"size": "4647",
"license": "bsd-3-clause",
"hash": -39406610005262280,
"line_mean": 29.3529411765,
"line_max": 79,
"alpha_frac": 0.5161498708,
"autogenerated": false,
"ratio": 3.7451612903225806,
"config_test": false,
... |
from numpy.testing import assert_array_equal, assert_allclose
import numpy as np
from scipy import stats, sparse
from mne.stats import permutation_cluster_1samp_test
from mne.stats.permutations import (permutation_t_test, _ci,
bootstrap_confidence_interval)
from mne.utils import ru... | {
"repo_name": "wmvanvliet/mne-python",
"path": "mne/stats/tests/test_permutations.py",
"copies": "10",
"size": "3198",
"license": "bsd-3-clause",
"hash": -1287712449007004000,
"line_mean": 37.0714285714,
"line_max": 75,
"alpha_frac": 0.6138211382,
"autogenerated": false,
"ratio": 3.26993865030674... |
import numpy as np
from scipy import linalg
from .epochs import Epochs
from .utils import check_fname, logger, verbose, _check_option
from .io.open import fiff_open
from .io.pick import pick_types, pick_types_forward
from .io.proj import (Projection, _has_eeg_average_ref_proj, _read_proj,
make_p... | {
"repo_name": "olafhauk/mne-python",
"path": "mne/proj.py",
"copies": "3",
"size": "15969",
"license": "bsd-3-clause",
"hash": -659565971651633200,
"line_mean": 34.1740088106,
"line_max": 79,
"alpha_frac": 0.5600225437,
"autogenerated": false,
"ratio": 3.606368563685637,
"config_test": false,
... |
import numpy as np
from .epochs import Epochs
from .utils import check_fname, logger, verbose, _check_option
from .io.open import fiff_open
from .io.pick import pick_types, pick_types_forward
from .io.proj import (Projection, _has_eeg_average_ref_proj, _read_proj,
make_projector, make_eeg_averag... | {
"repo_name": "pravsripad/mne-python",
"path": "mne/proj.py",
"copies": "8",
"size": "15951",
"license": "bsd-3-clause",
"hash": -6966721053444967000,
"line_mean": 34.1343612335,
"line_max": 79,
"alpha_frac": 0.5610933484,
"autogenerated": false,
"ratio": 3.6039313149570718,
"config_test": fals... |
import os.path as op
import numpy as np
from numpy.testing import assert_allclose
import pytest
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from mne.viz.utils import (compare_fiff, _fake_click, _compute_scalings,
_validate_if_list_of_axes, _get_color_list,
... | {
"repo_name": "olafhauk/mne-python",
"path": "mne/viz/tests/test_utils.py",
"copies": "8",
"size": "7259",
"license": "bsd-3-clause",
"hash": 1167379688048297200,
"line_mean": 34.5833333333,
"line_max": 78,
"alpha_frac": 0.6163383386,
"autogenerated": false,
"ratio": 3.136992221261884,
"config_... |
from __future__ import print_function
import inspect
import sys
import warnings
import importlib
from pkgutil import walk_packages
from inspect import getsource
import sklearn
from sklearn.base import signature
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing import check_docstring_parameters
f... | {
"repo_name": "wazeerzulfikar/scikit-learn",
"path": "sklearn/tests/test_docstring_parameters.py",
"copies": "1",
"size": "5172",
"license": "bsd-3-clause",
"hash": -2742763066606637600,
"line_mean": 33.9459459459,
"line_max": 74,
"alpha_frac": 0.5852668213,
"autogenerated": false,
"ratio": 4.390... |
import inspect
import sys
import warnings
import importlib
from pkgutil import walk_packages
from inspect import getsource, isabstract
from sklearn.base import signature
from sklearn.utils.testing import SkipTest, _get_args
from sklearn.utils.testing import _get_func_name
from sklearn.utils.testing import ignore_war... | {
"repo_name": "MrNuggelz/sklearn-glvq",
"path": "sklearn_lvq/tests/test_docstring_parameters.py",
"copies": "1",
"size": "7619",
"license": "bsd-3-clause",
"hash": -538141614691765900,
"line_mean": 36.1658536585,
"line_max": 82,
"alpha_frac": 0.5536159601,
"autogenerated": false,
"ratio": 4.33636... |
import inspect
import warnings
import importlib
from pkgutil import walk_packages
from inspect import signature
import numpy as np
import sklearn
from sklearn.utils import IS_PYPY
from sklearn.utils._testing import check_docstring_parameters
from sklearn.utils._testing import _get_func_name
from sklearn.utils._test... | {
"repo_name": "kevin-intel/scikit-learn",
"path": "sklearn/tests/test_docstring_parameters.py",
"copies": "2",
"size": "11956",
"license": "bsd-3-clause",
"hash": -4784139205237933000,
"line_mean": 34.0615835777,
"line_max": 79,
"alpha_frac": 0.6015389762,
"autogenerated": false,
"ratio": 4.20985... |
import inspect
import warnings
import importlib
from pkgutil import walk_packages
from inspect import signature
import sklearn
from sklearn.utils import IS_PYPY
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing import check_docstring_parameters
from sklearn.utils.testing import _get_func_name
fro... | {
"repo_name": "chrsrds/scikit-learn",
"path": "sklearn/tests/test_docstring_parameters.py",
"copies": "1",
"size": "5292",
"license": "bsd-3-clause",
"hash": 643307069478975400,
"line_mean": 35.2465753425,
"line_max": 78,
"alpha_frac": 0.5789871504,
"autogenerated": false,
"ratio": 4.421052631578... |
def parse_config(fname):
"""Parse a config file (like .ave and .cov files).
Parameters
----------
fname : str
Config file name.
Returns
-------
conditions : list of dict
Each condition is indexed by the event type.
A condition contains as keys::
tmin,... | {
"repo_name": "bloyl/mne-python",
"path": "mne/misc.py",
"copies": "14",
"size": "2972",
"license": "bsd-3-clause",
"hash": -8654074402788345000,
"line_mean": 28.4257425743,
"line_max": 79,
"alpha_frac": 0.5057200538,
"autogenerated": false,
"ratio": 3.738364779874214,
"config_test": false,
"... |
import numpy as np
from scipy import linalg
from ..defaults import _handle_default
from ..io.pick import _picks_to_idx, _picks_by_type, pick_info
from ..utils import verbose, _apply_scaling_array
def _yule_walker(X, order=1):
"""Compute Yule-Walker (adapted from statsmodels).
Operates in-place.
"""
... | {
"repo_name": "cjayb/mne-python",
"path": "mne/time_frequency/ar.py",
"copies": "6",
"size": "2352",
"license": "bsd-3-clause",
"hash": 240536473676881200,
"line_mean": 29.1538461538,
"line_max": 77,
"alpha_frac": 0.6016156463,
"autogenerated": false,
"ratio": 3.186991869918699,
"config_test": ... |
import numpy as np
from ..defaults import _handle_default
from ..io.pick import _picks_to_idx, _picks_by_type, pick_info
from ..utils import verbose, _apply_scaling_array
def _yule_walker(X, order=1):
"""Compute Yule-Walker (adapted from statsmodels).
Operates in-place.
"""
from scipy import linalg... | {
"repo_name": "bloyl/mne-python",
"path": "mne/time_frequency/ar.py",
"copies": "8",
"size": "2356",
"license": "bsd-3-clause",
"hash": 1511024463549959400,
"line_mean": 29.2051282051,
"line_max": 77,
"alpha_frac": 0.6005942275,
"autogenerated": false,
"ratio": 3.192411924119241,
"config_test":... |
"""Recursive feature elimination for feature ranking"""
import numpy as np
from ..utils import check_arrays, safe_sqr
from ..base import BaseEstimator
from ..base import MetaEstimatorMixin
from ..base import clone
from ..base import is_classifier
from ..cross_validation import _check_cv as check_cv
from ..cross_valid... | {
"repo_name": "flightgong/scikit-learn",
"path": "sklearn/feature_selection/rfe.py",
"copies": "1",
"size": "14108",
"license": "bsd-3-clause",
"hash": -52572571382255180,
"line_mean": 36.7219251337,
"line_max": 79,
"alpha_frac": 0.6141196484,
"autogenerated": false,
"ratio": 4.095210449927431,
... |
"""Recursive feature elimination for feature ranking"""
import numpy as np
from ..utils import check_X_y, safe_sqr
from ..utils.metaestimators import if_delegate_has_method
from ..base import BaseEstimator
from ..base import MetaEstimatorMixin
from ..base import clone
from ..base import is_classifier
from ..externals... | {
"repo_name": "nelson-liu/scikit-learn",
"path": "sklearn/feature_selection/rfe.py",
"copies": "33",
"size": "16667",
"license": "bsd-3-clause",
"hash": -5693669969430803000,
"line_mean": 35.7924944812,
"line_max": 84,
"alpha_frac": 0.6145677086,
"autogenerated": false,
"ratio": 4.053258754863813... |
"""Recursive feature elimination for feature ranking"""
import numpy as np
from ..utils import check_X_y, safe_sqr
from ..utils.metaestimators import if_delegate_has_method
from ..utils.validation import check_is_fitted
from ..base import BaseEstimator
from ..base import MetaEstimatorMixin
from ..base import clone
fr... | {
"repo_name": "herilalaina/scikit-learn",
"path": "sklearn/feature_selection/rfe.py",
"copies": "13",
"size": "17310",
"license": "bsd-3-clause",
"hash": 3782124377635216400,
"line_mean": 35.5961945032,
"line_max": 84,
"alpha_frac": 0.6132871173,
"autogenerated": false,
"ratio": 4.052915008194802... |
"""Recursive feature elimination for feature ranking"""
import numpy as np
import numbers
from joblib import Parallel, delayed, effective_n_jobs
from ..utils.metaestimators import if_delegate_has_method
from ..utils.metaestimators import _safe_split
from ..utils.validation import check_is_fitted
from ..utils.validat... | {
"repo_name": "huzq/scikit-learn",
"path": "sklearn/feature_selection/_rfe.py",
"copies": "2",
"size": "23472",
"license": "bsd-3-clause",
"hash": -2919371433948654600,
"line_mean": 36.7363344051,
"line_max": 79,
"alpha_frac": 0.6172886844,
"autogenerated": false,
"ratio": 4.151397240891404,
"c... |
"""Recursive feature elimination for feature ranking"""
import numpy as np
import numbers
from joblib import Parallel, effective_n_jobs
from ..utils.metaestimators import if_delegate_has_method
from ..utils.metaestimators import _safe_split
from ..utils._tags import _safe_tags
from ..utils.validation import check_i... | {
"repo_name": "kevin-intel/scikit-learn",
"path": "sklearn/feature_selection/_rfe.py",
"copies": "2",
"size": "24324",
"license": "bsd-3-clause",
"hash": 1537635074615302700,
"line_mean": 36.4791987673,
"line_max": 79,
"alpha_frac": 0.6185660253,
"autogenerated": false,
"ratio": 4.16935207404868,... |
"""Recursive feature elimination for feature ranking"""
import numpy as np
from .base import SelectorMixin
from ..base import BaseEstimator
from ..base import MetaEstimatorMixin
from ..base import clone
from ..base import is_classifier
from ..externals.joblib import Parallel, delayed
from ..metrics.scorer import che... | {
"repo_name": "DailyActie/Surrogate-Model",
"path": "01-codes/scikit-learn-master/sklearn/feature_selection/rfe.py",
"copies": "1",
"size": "16747",
"license": "mit",
"hash": 6839980075935600000,
"line_mean": 36.133037694,
"line_max": 84,
"alpha_frac": 0.6116319341,
"autogenerated": false,
"ratio... |
"""Recursive feature elimination for feature ranking"""
import warnings
import numpy as np
from ..utils import check_X_y, safe_sqr
from ..utils.metaestimators import if_delegate_has_method
from ..base import BaseEstimator
from ..base import MetaEstimatorMixin
from ..base import clone
from ..base import is_classifier
... | {
"repo_name": "arahuja/scikit-learn",
"path": "sklearn/feature_selection/rfe.py",
"copies": "8",
"size": "16893",
"license": "bsd-3-clause",
"hash": -3802300234611313700,
"line_mean": 37.745412844,
"line_max": 79,
"alpha_frac": 0.607529746,
"autogenerated": false,
"ratio": 4.2032843991042546,
"... |
"""Recursive feature elimination for feature ranking"""
import numpy as np
from ..base import BaseEstimator
from ..base import MetaEstimatorMixin
from ..base import clone
from ..base import is_classifier
from ..cross_validation import check_cv
class RFE(BaseEstimator, MetaEstimatorMixin):
"""Feature ranking wit... | {
"repo_name": "sgenoud/scikit-learn",
"path": "sklearn/feature_selection/rfe.py",
"copies": "1",
"size": "12391",
"license": "bsd-3-clause",
"hash": 9083485903887772000,
"line_mean": 34.2017045455,
"line_max": 79,
"alpha_frac": 0.5918812041,
"autogenerated": false,
"ratio": 4.155264922870557,
"... |
"""Recursive feature elimination for feature ranking"""
import numpy as np
from ..utils import check_arrays, safe_sqr, safe_mask
from ..base import BaseEstimator
from ..base import MetaEstimatorMixin
from ..base import clone
from ..base import is_classifier
from ..cross_validation import check_cv
class RFE(BaseEsti... | {
"repo_name": "mrshu/scikit-learn",
"path": "sklearn/feature_selection/rfe.py",
"copies": "2",
"size": "14194",
"license": "bsd-3-clause",
"hash": 8323172117959356000,
"line_mean": 35.4884318766,
"line_max": 79,
"alpha_frac": 0.602508102,
"autogenerated": false,
"ratio": 4.126162790697674,
"con... |
import numpy as np
from ..parallel import parallel_func
from ..io.pick import _pick_data_channels
from ..utils import logger, verbose, deprecated, _time_mask
from .multitaper import _psd_multitaper
@deprecated('This will be deprecated in release v0.12, see psd_welch.')
@verbose
def compute_raw_psd(raw, tmin=0., tma... | {
"repo_name": "wronk/mne-python",
"path": "mne/time_frequency/psd.py",
"copies": "2",
"size": "15617",
"license": "bsd-3-clause",
"hash": -8422619715411310000,
"line_mean": 34.9838709677,
"line_max": 78,
"alpha_frac": 0.5942882756,
"autogenerated": false,
"ratio": 3.5712325634575808,
"config_te... |
import numpy as np
from ..parallel import parallel_func
from ..io.pick import _pick_data_channels
from ..utils import logger, verbose, _time_mask
from ..fixes import get_spectrogram
from .multitaper import psd_array_multitaper
def _psd_func(epoch, noverlap, n_per_seg, nfft, fs, freq_mask, func):
"""Aux function... | {
"repo_name": "teonlamont/mne-python",
"path": "mne/time_frequency/psd.py",
"copies": "4",
"size": "11492",
"license": "bsd-3-clause",
"hash": 3120919758231846400,
"line_mean": 37.6936026936,
"line_max": 79,
"alpha_frac": 0.6043334494,
"autogenerated": false,
"ratio": 3.571162212554382,
"config... |
import numpy as np
from ..parallel import parallel_func
from ..io.pick import _pick_data_channels
from ..utils import logger, verbose, _time_mask
from .multitaper import _psd_multitaper
def _pwelch(epoch, noverlap, nfft, fs, freq_mask, welch_fun):
"""Aux function"""
return welch_fun(epoch, nperseg=nfft, nov... | {
"repo_name": "jmontoyam/mne-python",
"path": "mne/time_frequency/psd.py",
"copies": "5",
"size": "9524",
"license": "bsd-3-clause",
"hash": -7047507374337346000,
"line_mean": 35.2129277567,
"line_max": 78,
"alpha_frac": 0.6148677026,
"autogenerated": false,
"ratio": 3.4746442904049615,
"config... |
import numpy as np
from ..parallel import parallel_func
from ..io.pick import _picks_to_idx
from ..utils import logger, verbose, _time_mask
from .multitaper import psd_array_multitaper
def _psd_func(epoch, noverlap, n_per_seg, nfft, fs, freq_mask, func):
"""Aux function."""
return func(epoch, fs=fs, nperseg... | {
"repo_name": "adykstra/mne-python",
"path": "mne/time_frequency/psd.py",
"copies": "1",
"size": "10728",
"license": "bsd-3-clause",
"hash": -3419160527660801500,
"line_mean": 36.6421052632,
"line_max": 79,
"alpha_frac": 0.5997390007,
"autogenerated": false,
"ratio": 3.511620294599018,
"config_... |
import numpy as np
from ..parallel import parallel_func
from ..io.pick import pick_types
from ..utils import logger, verbose, deprecated, _time_mask
from .multitaper import _psd_multitaper
@deprecated('This will be deprecated in release v0.12, see psd_welch.')
@verbose
def compute_raw_psd(raw, tmin=0., tmax=None, p... | {
"repo_name": "cmoutard/mne-python",
"path": "mne/time_frequency/psd.py",
"copies": "1",
"size": "15621",
"license": "bsd-3-clause",
"hash": -1924970241268516000,
"line_mean": 34.9103448276,
"line_max": 78,
"alpha_frac": 0.5930478202,
"autogenerated": false,
"ratio": 3.5754177157244222,
"config... |
import numpy as np
from ..parallel import parallel_func
from ..io.proj import make_projector_info
from ..io.pick import pick_types
from ..utils import logger, verbose
@verbose
def compute_raw_psd(raw, tmin=0., tmax=None, picks=None, fmin=0,
fmax=np.inf, n_fft=2048, n_overlap=0,
... | {
"repo_name": "leggitta/mne-python",
"path": "mne/time_frequency/psd.py",
"copies": "2",
"size": "6609",
"license": "bsd-3-clause",
"hash": 8277740628495266000,
"line_mean": 33.9682539683,
"line_max": 78,
"alpha_frac": 0.5609018006,
"autogenerated": false,
"ratio": 3.5061007957559682,
"config_t... |
import numpy as np
from ..parallel import parallel_func
from ..io.proj import make_projector_info
from ..io.pick import pick_types
from ..utils import logger, verbose, _time_mask
@verbose
def compute_raw_psd(raw, tmin=0., tmax=None, picks=None, fmin=0,
fmax=np.inf, n_fft=2048, n_overlap=0,
... | {
"repo_name": "yousrabk/mne-python",
"path": "mne/time_frequency/psd.py",
"copies": "2",
"size": "6933",
"license": "bsd-3-clause",
"hash": 4852385736223343000,
"line_mean": 33.8391959799,
"line_max": 78,
"alpha_frac": 0.560363479,
"autogenerated": false,
"ratio": 3.508603238866397,
"config_tes... |
import os
from os import path as op
import glob
import numpy as np
from numpy import sin, cos
from scipy import linalg
from .io.constants import FIFF
from .io.open import fiff_open
from .io.tag import read_tag
from .io.write import start_file, end_file, write_coord_trans
from .utils import check_fname, logger, deprec... | {
"repo_name": "rajegannathan/grasp-lift-eeg-cat-dog-solution-updated",
"path": "python-packages/mne-python-0.10/mne/transforms.py",
"copies": "1",
"size": "21706",
"license": "bsd-3-clause",
"hash": 5796601336823749000,
"line_mean": 30.503628447,
"line_max": 79,
"alpha_frac": 0.5328019902,
"autogen... |
import os
import glob
import numpy as np
from numpy import sin, cos
from scipy import linalg
from .io.constants import FIFF
from .io.open import fiff_open
from .io.tag import read_tag
from .io.write import start_file, end_file, write_coord_trans
from .utils import check_fname, logger
from .externals.six import string... | {
"repo_name": "jaeilepp/eggie",
"path": "mne/transforms.py",
"copies": "2",
"size": "17252",
"license": "bsd-2-clause",
"hash": 1061990408675354500,
"line_mean": 31.0074211503,
"line_max": 80,
"alpha_frac": 0.5170994667,
"autogenerated": false,
"ratio": 3.403432629710002,
"config_test": false,
... |
import copy
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, _time_mask, warn, deprecated
@verbose
def simulate_evoked(fwd, stc, info, cov, nave=30, tmin=None, tmax=None,
ii... | {
"repo_name": "jaeilepp/mne-python",
"path": "mne/simulation/evoked.py",
"copies": "1",
"size": "7031",
"license": "bsd-3-clause",
"hash": -2774582244778623500,
"line_mean": 33.2975609756,
"line_max": 77,
"alpha_frac": 0.6050348457,
"autogenerated": false,
"ratio": 3.733935209771641,
"config_te... |
import copy
import warnings
import numpy as np
from ..io.pick import pick_channels_cov
from ..forward import apply_forward
from ..utils import check_random_state, verbose, _time_mask, deprecated
@deprecated('"generate_evoked" is deprecated and will be removed in '
'MNE-0.11. Please use simulate_evoked i... | {
"repo_name": "leggitta/mne-python",
"path": "mne/simulation/evoked.py",
"copies": "3",
"size": "7043",
"license": "bsd-3-clause",
"hash": -4977758031428607000,
"line_mean": 31.9112149533,
"line_max": 78,
"alpha_frac": 0.6243078234,
"autogenerated": false,
"ratio": 3.7764075067024128,
"config_t... |
import copy
import warnings
import numpy as np
from ..io.pick import pick_channels_cov
from ..forward import apply_forward
from ..utils import check_random_state, verbose, _time_mask
@verbose
def simulate_evoked(fwd, stc, info, cov, snr=3., tmin=None, tmax=None,
iir_filter=None, random_state=Non... | {
"repo_name": "jmontoyam/mne-python",
"path": "mne/simulation/evoked.py",
"copies": "5",
"size": "5459",
"license": "bsd-3-clause",
"hash": -1649951815206569000,
"line_mean": 32.0848484848,
"line_max": 78,
"alpha_frac": 0.6122000366,
"autogenerated": false,
"ratio": 3.6960054163845633,
"config_... |
import copy
import numpy as np
from scipy import signal
from ..io.pick import pick_channels_cov
from ..utils import check_random_state
from ..forward import apply_forward
def generate_evoked(fwd, stc, evoked, cov, snr=3, tmin=None, tmax=None,
iir_filter=None, random_state=None):
"""Generate ... | {
"repo_name": "effigies/mne-python",
"path": "mne/simulation/evoked.py",
"copies": "3",
"size": "3960",
"license": "bsd-3-clause",
"hash": -9209853237643729000,
"line_mean": 30.68,
"line_max": 77,
"alpha_frac": 0.6204545455,
"autogenerated": false,
"ratio": 3.6666666666666665,
"config_test": fa... |
import copy
import numpy as np
from ..io.pick import pick_channels_cov
from ..forward import apply_forward
from ..utils import check_random_state, verbose, _time_mask
@verbose
def generate_evoked(fwd, stc, evoked, cov, snr=3, tmin=None, tmax=None,
iir_filter=None, random_state=None, verbose=None... | {
"repo_name": "dimkal/mne-python",
"path": "mne/simulation/evoked.py",
"copies": "3",
"size": "4355",
"license": "bsd-3-clause",
"hash": -397218394132361540,
"line_mean": 32.2442748092,
"line_max": 78,
"alpha_frac": 0.6323765786,
"autogenerated": false,
"ratio": 3.5991735537190084,
"config_test... |
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