text stringlengths 0 1.05M | meta dict |
|---|---|
import copy
import numpy as np
from ..event import find_events
class MockRtClient(object):
"""Mock Realtime Client
Parameters
----------
raw : instance of Raw object
The raw object which simulates the RtClient
verbose : bool, str, int, or None
If not None, override default verbos... | {
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import os
import os.path as op
import glob
import warnings
import shutil
from nose.tools import assert_true, assert_equal, assert_raises
from mne import Epochs, read_events, pick_types, read_evokeds
from mne.io import Raw
from mne.datasets import testing
from mne.report import Report
from mne.utils import (_TempDir, ... | {
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import glob
import os
import os.path as op
import shutil
import warnings
from nose.tools import assert_true, assert_equal, assert_raises
from mne import Epochs, read_events, read_evokeds
from mne.io import read_raw_fif
from mne.datasets import testing
from mne.report import Report
from mne.utils import (_TempDir, re... | {
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from __future__ import division
import warnings
from math import sqrt
import numpy as np
from scipy import sparse
from ..externals.six.moves import xrange
from .hierarchical import AgglomerativeClustering
from ..base import TransformerMixin, ClusterMixin, BaseEstimator
from ..exceptions import NotFittedError
from ..... | {
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from __future__ import division
import warnings
import numpy as np
from scipy import sparse
from math import sqrt
from ..metrics.pairwise import euclidean_distances
from ..base import TransformerMixin, ClusterMixin, BaseEstimator
from ..externals.six.moves import xrange
from ..utils import check_array
from ..utils.ex... | {
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"con... |
import warnings
import numbers
import numpy as np
from scipy import sparse
from math import sqrt
from ..metrics import pairwise_distances_argmin
from ..metrics.pairwise import euclidean_distances
from ..base import TransformerMixin, ClusterMixin, BaseEstimator
from ..utils.extmath import row_norms
from ..utils import... | {
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import warnings
import numpy as np
from scipy import sparse
from math import sqrt
from ..metrics.pairwise import euclidean_distances
from ..base import TransformerMixin, ClusterMixin, BaseEstimator
from ..utils import check_array
from ..utils.extmath import row_norms, safe_sparse_dot
from ..utils.validation import ch... | {
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import numpy as np
from scipy import optimize, sparse
from sklearn.utils._testing import assert_almost_equal
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.datasets import make_regression
from sklearn.linear_model import (
HuberRegr... | {
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import numpy as np
from scipy import optimize, sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_greater
... | {
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import numpy as np
from scipy import optimize
from ..base import BaseEstimator, RegressorMixin
from ._base import LinearModel
from ..utils import axis0_safe_slice
from ..utils.validation import _check_sample_weight
from ..utils.extmath import safe_sparse_dot
from ..utils.optimize import _check_optimize_result
def ... | {
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import numpy as np
from scipy import optimize
from ..base import BaseEstimator, RegressorMixin
from .base import LinearModel
from ..utils import check_X_y
from ..utils import check_consistent_length
from ..utils import axis0_safe_slice
from ..utils.extmath import safe_sparse_dot
from ..utils.optimize import _check_o... | {
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import numpy as np
from scipy import optimize, sparse
from ..base import BaseEstimator, RegressorMixin
from .base import LinearModel
from ..utils import check_X_y
from ..utils import check_consistent_length
from ..utils import axis0_safe_slice
from ..utils.extmath import safe_sparse_dot
def _huber_loss_and_gradien... | {
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"... |
import numpy as np
import scipy.sparse as sp
from .sparsefuncs_fast import (
csr_mean_variance_axis0 as _csr_mean_var_axis0,
csc_mean_variance_axis0 as _csc_mean_var_axis0,
incr_mean_variance_axis0 as _incr_mean_var_axis0)
from .fixes import sparse_min_max, bincount
def _raise_typeerror(X):
"""Raises... | {
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import scipy.sparse as sp
import numpy as np
from .validation import _deprecate_positional_args
from .sparsefuncs_fast import (
csr_mean_variance_axis0 as _csr_mean_var_axis0,
csc_mean_variance_axis0 as _csc_mean_var_axis0,
incr_mean_variance_axis0 as _incr_mean_var_axis0)
from ..utils.validation import _c... | {
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"path": "sklearn/utils/sparsefuncs.py",
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"c... |
import scipy.sparse as sp
import numpy as np
from .fixes import sparse_min_max, bincount
from .sparsefuncs_fast import (
csr_mean_variance_axis0 as _csr_mean_var_axis0,
csc_mean_variance_axis0 as _csc_mean_var_axis0,
incr_mean_variance_axis0 as _incr_mean_var_axis0)
def _raise_typeerror(X):
"""Raises... | {
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"con... |
import scipy.sparse as sp
import numpy as np
from .fixes import sparse_min_max
from .sparsefuncs_fast import (
csr_mean_variance_axis0 as _csr_mean_var_axis0,
csc_mean_variance_axis0 as _csc_mean_var_axis0,
incr_mean_variance_axis0 as _incr_mean_var_axis0)
def _raise_typeerror(X):
"""Raises a TypeErr... | {
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import scipy.sparse as sp
import numpy as np
from .sparsefuncs_fast import (
csr_mean_variance_axis0 as _csr_mean_var_axis0,
csc_mean_variance_axis0 as _csc_mean_var_axis0,
incr_mean_variance_axis0 as _incr_mean_var_axis0)
from ..utils.validation import _check_sample_weight
def _raise_typeerror(X):
"... | {
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... |
# License: BSD 3 clause
import scipy.sparse as sp
import numpy as np
from .fixes import sparse_min_max, bincount
from .sparsefuncs_fast import csr_mean_variance_axis0 as _csr_mean_var_axis0
from .sparsefuncs_fast import csc_mean_variance_axis0 as _csc_mean_var_axis0
def _raise_typeerror(X):
"""Raises a TypeErro... | {
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"path": "sklearn/utils/sparsefuncs.py",
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... |
# License: BSD 3 clause
import scipy.sparse as sp
import numpy as np
from .fixes import sparse_min_max
from .sparsefuncs_fast import csr_mean_variance_axis0 as _csr_mean_var_axis0
from .sparsefuncs_fast import csc_mean_variance_axis0 as _csc_mean_var_axis0
def _raise_typeerror(X):
"""Raises a TypeError if X is ... | {
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"path": "sklearn/utils/sparsefuncs.py",
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# License: BSD 3 clause
import scipy.sparse as sp
import numpy as np
from .fixes import sparse_min_max
from .sparsefuncs_fast import (csr_mean_variance_axis0,
csc_mean_variance_axis0)
def _raise_typeerror(X):
"""Raises a TypeError if X is not a CSR or CSC matrix"""
input_type ... | {
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"con... |
from contextlib import nullcontext
import itertools
import os.path as op
import numpy as np
from numpy.testing import assert_array_equal, assert_allclose, assert_equal
import pytest
from mne import (pick_channels, pick_types, Epochs, read_events,
set_eeg_reference, set_bipolar_reference,
... | {
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"path": "mne/io/tests/test_reference.py",
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"confi... |
from copy import deepcopy
import numpy as np
from scipy import linalg
from .constants import FIFF
from .meas_info import _check_ch_keys
from .proj import _has_eeg_average_ref_proj, make_eeg_average_ref_proj
from .proj import setup_proj
from .pick import pick_types, pick_channels, pick_channels_forward
from .base impo... | {
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"path": "mne/io/reference.py",
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"config_test": ... |
from copy import deepcopy
import numpy as np
from scipy import linalg
from .constants import FIFF
from .proj import _has_eeg_average_ref_proj, make_eeg_average_ref_proj
from .proj import setup_proj
from .pick import pick_types, pick_channels, pick_channels_forward
from .base import BaseRaw
from ..evoked import Evoked... | {
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from copy import deepcopy
import numpy as np
from .constants import FIFF
from .meas_info import _check_ch_keys
from .proj import _has_eeg_average_ref_proj, make_eeg_average_ref_proj
from .proj import setup_proj
from .pick import pick_types, pick_channels, pick_channels_forward
from .base import BaseRaw
from ..evoked ... | {
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"config_test":... |
import itertools
import os.path as op
import numpy as np
from numpy.testing import assert_array_equal, assert_allclose, assert_equal
import pytest
from mne import (pick_channels, pick_types, Epochs, read_events,
set_eeg_reference, set_bipolar_reference,
add_reference_channels, creat... | {
"repo_name": "kambysese/mne-python",
"path": "mne/io/tests/test_reference.py",
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"alpha_frac": 0.6266880599,
"autogenerated": false,
"ratio": 3.0187689202825427,
... |
import numpy as np
from .constants import FIFF
from .meas_info import _check_ch_keys
from .proj import _has_eeg_average_ref_proj, make_eeg_average_ref_proj
from .proj import setup_proj
from .pick import pick_types, pick_channels, pick_channels_forward
from .base import BaseRaw
from ..evoked import Evoked
from ..epoch... | {
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"path": "mne/io/reference.py",
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"alpha_frac": 0.5954275263,
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"config_test":... |
from copy import deepcopy
import numpy as np
from .constants import FIFF
from .proj import _has_eeg_average_ref_proj, make_eeg_average_ref_proj
from .proj import setup_proj
from .pick import pick_types, pick_channels
from .base import BaseRaw
from ..evoked import Evoked
from ..epochs import BaseEpochs
from ..utils im... | {
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"path": "mne/io/reference.py",
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"config_test": ... |
import itertools
import os.path as op
import numpy as np
from numpy.testing import assert_array_equal, assert_allclose, assert_equal
import pytest
from mne import (pick_channels, pick_types, Epochs, read_events,
set_eeg_reference, set_bipolar_reference,
add_reference_channels, creat... | {
"repo_name": "adykstra/mne-python",
"path": "mne/io/tests/test_reference.py",
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"size": "22438",
"license": "bsd-3-clause",
"hash": -2779823950511880700,
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"line_max": 79,
"alpha_frac": 0.6303592121,
"autogenerated": false,
"ratio": 3.0842611683848795,
"c... |
import numpy as np
from .constants import FIFF
from .proj import _has_eeg_average_ref_proj, make_eeg_average_ref_proj
from .pick import pick_types
from .base import _BaseRaw
from ..evoked import Evoked
from ..epochs import _BaseEpochs
from ..utils import logger
def _apply_reference(inst, ref_from, ref_to=None, copy... | {
"repo_name": "cmoutard/mne-python",
"path": "mne/io/reference.py",
"copies": "1",
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"line_max": 79,
"alpha_frac": 0.6168742302,
"autogenerated": false,
"ratio": 3.9274388605213653,
"config_test"... |
import numpy as np
from .constants import FIFF
from .proj import _has_eeg_average_ref_proj, make_eeg_average_ref_proj
from .pick import pick_types
from .base import BaseRaw
from ..evoked import Evoked
from ..epochs import BaseEpochs
from ..utils import logger, warn, verbose
def _apply_reference(inst, ref_from, ref_... | {
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"alpha_frac": 0.6202382779,
"autogenerated": false,
"ratio": 4.054945054945055,
"config_test":... |
import numpy as np
from .constants import FIFF
from .proj import _has_eeg_average_ref_proj, make_eeg_average_ref_proj
from .pick import pick_types
from .base import _BaseRaw
from ..evoked import Evoked
from ..epochs import Epochs
from ..utils import logger
def _apply_reference(inst, ref_from, ref_to=None, copy=True... | {
"repo_name": "Odingod/mne-python",
"path": "mne/io/reference.py",
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"line_max": 79,
"alpha_frac": 0.6148814391,
"autogenerated": false,
"ratio": 3.9198717948717947,
"config_test":... |
import os.path as op
import numpy as np
from numpy.testing import assert_array_equal, assert_allclose, assert_equal
import pytest
from mne import (pick_channels, pick_types, Epochs, read_events,
set_eeg_reference, set_bipolar_reference,
add_reference_channels)
from mne.epochs import... | {
"repo_name": "teonlamont/mne-python",
"path": "mne/io/tests/test_reference.py",
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"hash": -5789615988509730000,
"line_mean": 41.9327731092,
"line_max": 79,
"alpha_frac": 0.6307007242,
"autogenerated": false,
"ratio": 3.103888213851762,
"... |
import warnings
import os.path as op
import numpy as np
from nose.tools import assert_true, assert_equal, assert_raises
from numpy.testing import assert_array_equal, assert_allclose
from mne import pick_types, Evoked, Epochs, read_events
from mne.io.constants import FIFF
from mne.io import (set_eeg_reference, set_bi... | {
"repo_name": "dimkal/mne-python",
"path": "mne/io/tests/test_reference.py",
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"con... |
import copy as cp
import os.path as op
import pytest
from numpy.testing import assert_array_equal, assert_allclose
import numpy as np
import mne
from mne.datasets import testing
from mne.beamformer import (make_dics, apply_dics, apply_dics_epochs,
apply_dics_csd, read_beamformer, Beamform... | {
"repo_name": "rkmaddox/mne-python",
"path": "mne/beamformer/tests/test_dics.py",
"copies": "4",
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"autogenerated": false,
"ratio": 3.3050445773614716,
... |
import os.path as op
import copy as cp
import pytest
from pytest import raises
from numpy.testing import assert_array_equal, assert_allclose
import numpy as np
import mne
from mne.datasets import testing
from mne.beamformer import (make_dics, apply_dics, apply_dics_epochs,
apply_dics_csd,... | {
"repo_name": "teonlamont/mne-python",
"path": "mne/beamformer/tests/test_dics.py",
"copies": "2",
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"hash": 2085097848443284500,
"line_mean": 42.7111984283,
"line_max": 79,
"alpha_frac": 0.6357589105,
"autogenerated": false,
"ratio": 3.4260856174930705,
... |
from copy import deepcopy
from os import remove
import os.path as op
from shutil import copy
import warnings
import numpy as np
from nose.tools import assert_raises, assert_true
from numpy.testing import assert_equal, assert_allclose
from mne import (make_bem_model, read_bem_surfaces, write_bem_surfaces,
... | {
"repo_name": "nicproulx/mne-python",
"path": "mne/tests/test_bem.py",
"copies": "8",
"size": "16018",
"license": "bsd-3-clause",
"hash": -3673815147041856000,
"line_mean": 41.2638522427,
"line_max": 78,
"alpha_frac": 0.6043825696,
"autogenerated": false,
"ratio": 3.1012584704743467,
"config_te... |
from copy import deepcopy
from os import remove
import os.path as op
from shutil import copy
import numpy as np
import pytest
from numpy.testing import assert_equal, assert_allclose
from mne import (make_bem_model, read_bem_surfaces, write_bem_surfaces,
make_bem_solution, read_bem_solution, write_be... | {
"repo_name": "teonlamont/mne-python",
"path": "mne/tests/test_bem.py",
"copies": "3",
"size": "15971",
"license": "bsd-3-clause",
"hash": -5507136902236938000,
"line_mean": 41.1398416887,
"line_max": 78,
"alpha_frac": 0.6017156095,
"autogenerated": false,
"ratio": 3.108407940833009,
"config_te... |
import os.path as op
from copy import deepcopy
import numpy as np
from nose.tools import assert_raises, assert_true
from numpy.testing import assert_equal, assert_allclose
from mne import (make_bem_model, read_bem_surfaces, write_bem_surfaces,
make_bem_solution, read_bem_solution, write_bem_solution... | {
"repo_name": "cmoutard/mne-python",
"path": "mne/tests/test_bem.py",
"copies": "1",
"size": "12235",
"license": "bsd-3-clause",
"hash": 8223311512214387000,
"line_mean": 39.9197324415,
"line_max": 78,
"alpha_frac": 0.6025337148,
"autogenerated": false,
"ratio": 3.0269668480950025,
"config_test... |
from __future__ import division ## removes integer division
import numpy as np
from scipy import sparse
from scipy.spatial.distance import pdist
from Mmani.geometry.cyflann.index import Index
import subprocess, os, sys, warnings
def _row_col_from_condensed_index(N,compr_ind):
# convert from pdist compressed index ... | {
"repo_name": "Jerryzcn/Mmani",
"path": "Mmani/geometry/distance.py",
"copies": "1",
"size": "6975",
"license": "bsd-2-clause",
"hash": 7661960849290114000,
"line_mean": 38.8571428571,
"line_max": 97,
"alpha_frac": 0.637562724,
"autogenerated": false,
"ratio": 3.6556603773584904,
"config_test":... |
from flask import redirect, render_template, render_template_string, Blueprint
from flask import request, url_for
from flask_user import current_user, login_required, roles_accepted
from app.init_app import app, db
from app.models import User, Role, UsersRoles
# from app.models import UserProfileForm
from flask_user... | {
"repo_name": "marcuskelly/recover",
"path": "app/views.py",
"copies": "1",
"size": "6065",
"license": "bsd-2-clause",
"hash": -7701223004239821000,
"line_mean": 33.2655367232,
"line_max": 187,
"alpha_frac": 0.6425391591,
"autogenerated": false,
"ratio": 3.203909138932911,
"config_test": false,... |
# License: BSD (3-clause)
from __future__ import division
import numpy as np
from scipy import linalg
from math import factorial
import inspect
from .. import pick_types
from ..forward._compute_forward import _concatenate_coils
from ..forward._make_forward import _prep_meg_channels
from ..io.write import _generate_... | {
"repo_name": "rajegannathan/grasp-lift-eeg-cat-dog-solution-updated",
"path": "python-packages/mne-python-0.10/mne/preprocessing/maxwell.py",
"copies": "1",
"size": "25210",
"license": "bsd-3-clause",
"hash": -1733030217104201000,
"line_mean": 38.1459627329,
"line_max": 79,
"alpha_frac": 0.599166997... |
# License: BSD (3-clause)
from __future__ import division
from os import path as op
import numpy as np
from scipy.linalg import pinv
from math import factorial
from .. import pick_types, pick_info
from ..io.constants import FIFF
from ..forward._compute_forward import _concatenate_coils
from ..forward._make_forward ... | {
"repo_name": "trachelr/mne-python",
"path": "mne/preprocessing/maxwell.py",
"copies": "3",
"size": "20214",
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"hash": 5095994424983719000,
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"autogenerated": false,
"ratio": 3.5802337938363444,
"confi... |
# License: BSD (3-clause)
from __future__ import division
import numpy as np
from scipy.linalg import pinv
from math import factorial
from ..forward._compute_forward import _concatenate_coils
from ..forward._make_forward import _prep_meg_channels
from ..io.write import _generate_meas_id, _date_now
from ..utils impo... | {
"repo_name": "rajul/mne-python",
"path": "mne/preprocessing/maxwell.py",
"copies": "1",
"size": "18428",
"license": "bsd-3-clause",
"hash": 1439921661510570000,
"line_mean": 35.3471400394,
"line_max": 79,
"alpha_frac": 0.6006077708,
"autogenerated": false,
"ratio": 3.6020328381548086,
"config_... |
# License: BSD (3-clause)
from __future__ import division
import numpy as np
from scipy.linalg import pinv
from math import factorial
from .. import pick_types
from ..forward._compute_forward import _concatenate_coils
from ..forward._make_forward import _prep_meg_channels
from ..io.write import _generate_meas_id, _... | {
"repo_name": "dimkal/mne-python",
"path": "mne/preprocessing/maxwell.py",
"copies": "3",
"size": "19103",
"license": "bsd-3-clause",
"hash": -7137156015406971000,
"line_mean": 35.7365384615,
"line_max": 79,
"alpha_frac": 0.5961367325,
"autogenerated": false,
"ratio": 3.588765733608867,
"config... |
import os.path as op
import numpy as np
from numpy.testing import assert_allclose
import pytest
from mne import pick_types
from mne.io import read_raw_fif
from mne.datasets import testing
from mne.io.tag import _loc_to_coil_trans
from mne.preprocessing import (read_fine_calibration, write_fine_calibration,
... | {
"repo_name": "cjayb/mne-python",
"path": "mne/preprocessing/tests/test_fine_cal.py",
"copies": "1",
"size": "6097",
"license": "bsd-3-clause",
"hash": 7794832249254080000,
"line_mean": 43.8308823529,
"line_max": 79,
"alpha_frac": 0.6419550599,
"autogenerated": false,
"ratio": 2.8773006134969323,... |
import os.path as op
from copy import deepcopy
import numpy as np
from numpy.testing import assert_allclose, assert_array_equal
import pytest
from mne import (read_source_spaces, pick_types, read_trans, read_cov,
make_sphere_model, create_info, setup_volume_source_space,
find_events... | {
"repo_name": "adykstra/mne-python",
"path": "mne/simulation/tests/test_raw.py",
"copies": "1",
"size": "24904",
"license": "bsd-3-clause",
"hash": 5946442349709974000,
"line_mean": 44.4452554745,
"line_max": 79,
"alpha_frac": 0.5962897527,
"autogenerated": false,
"ratio": 3.194458696767573,
"c... |
from functools import partial
from inspect import getmembers
import numpy as np
from scipy.fftpack import fftfreq
from .utils import check_indices
from ..fixes import _get_args
from ..parallel import parallel_func
from ..source_estimate import _BaseSourceEstimate
from ..epochs import BaseEpochs
from ..time_frequency... | {
"repo_name": "jaeilepp/mne-python",
"path": "mne/connectivity/spectral.py",
"copies": "1",
"size": "43085",
"license": "bsd-3-clause",
"hash": -7270263943360127000,
"line_mean": 37.8503155996,
"line_max": 79,
"alpha_frac": 0.5658581873,
"autogenerated": false,
"ratio": 3.7011425135297653,
"con... |
from functools import partial
from inspect import getmembers
import numpy as np
from .utils import check_indices
from ..fixes import _get_args
from ..parallel import parallel_func
from ..source_estimate import _BaseSourceEstimate
from ..epochs import BaseEpochs
from ..time_frequency.multitaper import (_mt_spectra, _... | {
"repo_name": "teonlamont/mne-python",
"path": "mne/connectivity/spectral.py",
"copies": "3",
"size": "42476",
"license": "bsd-3-clause",
"hash": -8827476862137683000,
"line_mean": 38.0404411765,
"line_max": 79,
"alpha_frac": 0.5659195781,
"autogenerated": false,
"ratio": 3.678849818118829,
"co... |
from functools import partial
from inspect import getmembers
import numpy as np
from .utils import check_indices
from ..utils import _check_option
from ..fixes import _get_args, _import_fft
from ..parallel import parallel_func
from ..source_estimate import _BaseSourceEstimate
from ..epochs import BaseEpochs
from ..t... | {
"repo_name": "drammock/mne-python",
"path": "mne/connectivity/spectral.py",
"copies": "7",
"size": "42443",
"license": "bsd-3-clause",
"hash": 5381850685619240000,
"line_mean": 37.8316559927,
"line_max": 79,
"alpha_frac": 0.5667365643,
"autogenerated": false,
"ratio": 3.6350633778691335,
"conf... |
from functools import partial
from inspect import getmembers
import numpy as np
from .utils import check_indices
from ..utils import _check_option
from ..fixes import _get_args
from ..parallel import parallel_func
from ..source_estimate import _BaseSourceEstimate
from ..epochs import BaseEpochs
from ..time_frequency... | {
"repo_name": "adykstra/mne-python",
"path": "mne/connectivity/spectral.py",
"copies": "1",
"size": "40947",
"license": "bsd-3-clause",
"hash": 8945545816856438000,
"line_mean": 37.9971428571,
"line_max": 79,
"alpha_frac": 0.5683932889,
"autogenerated": false,
"ratio": 3.663177670424047,
"confi... |
from functools import partial
from inspect import getmembers
import numpy as np
from .utils import check_indices
from ..utils import _check_option
from ..fixes import _get_args, rfftfreq
from ..parallel import parallel_func
from ..source_estimate import _BaseSourceEstimate
from ..epochs import BaseEpochs
from ..time... | {
"repo_name": "larsoner/mne-python",
"path": "mne/connectivity/spectral.py",
"copies": "6",
"size": "42420",
"license": "bsd-3-clause",
"hash": 5220588452457784000,
"line_mean": 38.0598526703,
"line_max": 79,
"alpha_frac": 0.5676229991,
"autogenerated": false,
"ratio": 3.638305171970152,
"confi... |
from ..externals.six.moves import zip
import copy
import numpy as np
from ..utils import logger, verbose
from .spectral import spectral_connectivity
@verbose
def phase_slope_index(data, indices=None, sfreq=2 * np.pi,
mode='multitaper', fmin=None, fmax=np.inf,
tmin=None, t... | {
"repo_name": "teonlamont/mne-python",
"path": "mne/connectivity/effective.py",
"copies": "4",
"size": "6588",
"license": "bsd-3-clause",
"hash": 6883683973438979000,
"line_mean": 39.6666666667,
"line_max": 79,
"alpha_frac": 0.6376745598,
"autogenerated": false,
"ratio": 3.5687973997833153,
"co... |
import numpy as np
def check_indices(indices):
"""Check indices parameter."""
if not isinstance(indices, tuple) or len(indices) != 2:
raise ValueError('indices must be a tuple of length 2')
if len(indices[0]) != len(indices[1]):
raise ValueError('Index arrays indices[0] and indices[1] mus... | {
"repo_name": "wmvanvliet/mne-python",
"path": "mne/connectivity/utils.py",
"copies": "15",
"size": "2957",
"license": "bsd-3-clause",
"hash": -5826297188299216000,
"line_mean": 30.7956989247,
"line_max": 79,
"alpha_frac": 0.6141359486,
"autogenerated": false,
"ratio": 4.112656467315716,
"confi... |
import numpy as np
def check_indices(indices):
"""Check indices parameter"""
if not isinstance(indices, tuple) or len(indices) != 2:
raise ValueError('indices must be a tuple of length 2')
if len(indices[0]) != len(indices[1]):
raise ValueError('Index arrays indices[0] and indices[1] mus... | {
"repo_name": "andyh616/mne-python",
"path": "mne/connectivity/utils.py",
"copies": "29",
"size": "1196",
"license": "bsd-3-clause",
"hash": 2161845973999084800,
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"line_max": 71,
"alpha_frac": 0.6245819398,
"autogenerated": false,
"ratio": 3.986666666666667,
"config_t... |
from ..externals.six import string_types
from inspect import getmembers
import numpy as np
from scipy.fftpack import fftfreq
from .utils import check_indices
from ..fixes import tril_indices, partial, _get_args
from ..parallel import parallel_func
from ..source_estimate import _BaseSourceEstimate
from ..epochs impor... | {
"repo_name": "ARudiuk/mne-python",
"path": "mne/connectivity/spectral.py",
"copies": "4",
"size": "41506",
"license": "bsd-3-clause",
"hash": 1159133162822191000,
"line_mean": 38.1196983977,
"line_max": 79,
"alpha_frac": 0.5463547439,
"autogenerated": false,
"ratio": 3.8089382398825364,
"confi... |
from ..externals.six import string_types
from warnings import warn
from inspect import getargspec, getmembers
import numpy as np
from scipy.fftpack import fftfreq
from .utils import check_indices
from ..fixes import tril_indices, partial
from ..parallel import parallel_func
from ..source_estimate import _BaseSourceE... | {
"repo_name": "effigies/mne-python",
"path": "mne/connectivity/spectral.py",
"copies": "3",
"size": "41291",
"license": "bsd-3-clause",
"hash": 6404810913027974000,
"line_mean": 37.9170593779,
"line_max": 80,
"alpha_frac": 0.5503136277,
"autogenerated": false,
"ratio": 3.791295565145533,
"confi... |
from ..externals.six import string_types
from warnings import warn
from inspect import getmembers
import numpy as np
from scipy.fftpack import fftfreq
from .utils import check_indices
from ..fixes import tril_indices, partial, _get_args
from ..parallel import parallel_func
from ..source_estimate import _BaseSourceEs... | {
"repo_name": "cmoutard/mne-python",
"path": "mne/connectivity/spectral.py",
"copies": "1",
"size": "41513",
"license": "bsd-3-clause",
"hash": -8960145771459558000,
"line_mean": 38.0894538606,
"line_max": 79,
"alpha_frac": 0.5464553272,
"autogenerated": false,
"ratio": 3.810629704424454,
"conf... |
from functools import partial
from inspect import getmembers
import numpy as np
from scipy.fftpack import fftfreq
from .utils import check_indices
from ..fixes import _get_args
from ..parallel import parallel_func
from ..source_estimate import _BaseSourceEstimate
from ..epochs import BaseEpochs
from ..time_frequency... | {
"repo_name": "nicproulx/mne-python",
"path": "mne/connectivity/spectral.py",
"copies": "2",
"size": "41514",
"license": "bsd-3-clause",
"hash": -154956221753801340,
"line_mean": 37.7619047619,
"line_max": 79,
"alpha_frac": 0.5452618394,
"autogenerated": false,
"ratio": 3.8082744702320888,
"con... |
import copy
import numpy as np
from ..utils import logger, verbose
from .spectral import spectral_connectivity
@verbose
def phase_slope_index(data, indices=None, sfreq=2 * np.pi,
mode='multitaper', fmin=None, fmax=np.inf,
tmin=None, tmax=None, mt_bandwidth=None,
... | {
"repo_name": "mne-tools/mne-python",
"path": "mne/connectivity/effective.py",
"copies": "14",
"size": "6416",
"license": "bsd-3-clause",
"hash": -5430914496757996000,
"line_mean": 39.1,
"line_max": 79,
"alpha_frac": 0.6349750623,
"autogenerated": false,
"ratio": 3.556541019955654,
"config_test... |
__author__ = 'smartschat'
class CorefMultigraphCreator:
def __init__(self,
positive_features,
negative_features,
weighting_function,
relation_weights,
construct_when_negative=False):
self.positive_features = positive_feat... | {
"repo_name": "smartschat/cort",
"path": "cort/coreference/multigraph/multigraphs.py",
"copies": "2",
"size": "3132",
"license": "mit",
"hash": 4286816954963982000,
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"line_max": 80,
"alpha_frac": 0.5657726692,
"autogenerated": false,
"ratio": 4.204026845637584,
"confi... |
__author__ = 'smartschat'
class MultigraphDecoder:
def __init__(self, multigraph_creator):
self.coref_multigraph_creator = multigraph_creator
def decode(self, corpus):
for doc in corpus:
for mention in doc.system_mentions:
mention.attributes["set_id"] = None
... | {
"repo_name": "smartschat/cort",
"path": "cort/coreference/multigraph/decoders.py",
"copies": "2",
"size": "1601",
"license": "mit",
"hash": -7471679639505907000,
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"line_max": 79,
"alpha_frac": 0.5965021861,
"autogenerated": false,
"ratio": 4.042929292929293,
"config_... |
"""
=========================================
Plot Hierarchical Clustering Dendrogram
=========================================
This example plots the corresponding dendrogram of a hierarchical clustering
using AgglomerativeClustering and the dendrogram method available in scipy.
"""
import numpy as np
from matplotli... | {
"repo_name": "bnaul/scikit-learn",
"path": "examples/cluster/plot_agglomerative_dendrogram.py",
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"alpha_frac": 0.67147806,
"autogenerated": false,
"ratio": 3.9633867... |
import logging
import json
import telepot
logger = logging.getLogger(__name__)
defaultBotUserSettings = {'enabled': True, 'notifications': True}
class TelegramBot:
def __init__(self, token, irc):
self.telegram = telepot.Bot(token)
logger.debug('starting telegram msg loop.')
self.teleg... | {
"repo_name": "mpunkenhofer/irc-telegram-bot",
"path": "telegrambot.py",
"copies": "1",
"size": "5694",
"license": "mit",
"hash": -1485655149641286700,
"line_mean": 42.1363636364,
"line_max": 115,
"alpha_frac": 0.5402177731,
"autogenerated": false,
"ratio": 4.0042194092827,
"config_test": false... |
import numpy as np
import pylab as pl
from setup import *
from sklearn.decomposition import PCA, KernelPCA
from sklearn.datasets import make_circles
np.random.seed(0)
X, y = make_circles(n_samples=400, factor=.3, noise=.05)
kpca = KernelPCA(kernel="rbf", fit_inverse_transform=True, gamma=10)
X_kpca = kpca.fit_tran... | {
"repo_name": "AndreasMadsen/grace",
"path": "Code/figures/pca_kernel_example.py",
"copies": "1",
"size": "1867",
"license": "mit",
"hash": 5346799080493974000,
"line_mean": 28.171875,
"line_max": 76,
"alpha_frac": 0.6679164435,
"autogenerated": false,
"ratio": 2.4630606860158313,
"config_test"... |
"""
Class for reading and writing numpy arrays from / to binary files
"""
import sys
import numpy as np
__all__ = ['sys_endian_code', 'npfile']
sys_endian_code = (sys.byteorder == 'little') and '<' or '>'
class npfile(object):
''' Class for reading and writing numpy arrays to/from files
Inputs:
fil... | {
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"path": "scipy/io/npfile.py",
"copies": "2",
"size": "8176",
"license": "bsd-3-clause",
"hash": 8125958531044385000,
"line_mean": 34.2413793103,
"line_max": 77,
"alpha_frac": 0.541462818,
"autogenerated": false,
"ratio": 4.1820971867007675,
"config_test": false... |
__authors__ = ["Matthias Feurer"]
import numpy as np
import scipy.stats
import sklearn.cluster
import sklearn.manifold
import sklearn.preprocessing
import sklearn.utils
class GMeans(object):
def __init__(self, minimum_samples_per_cluster=2, n_init=10, significance=4,
restarts=10, random_state=No... | {
"repo_name": "hmendozap/auto-sklearn",
"path": "autosklearn/metalearning/metalearning/clustering/gmeans.py",
"copies": "1",
"size": "3508",
"license": "bsd-3-clause",
"hash": -2671809588806584300,
"line_mean": 37.9777777778,
"line_max": 88,
"alpha_frac": 0.4717787913,
"autogenerated": false,
"ra... |
# The computations in this code were primarily derived from Matti Hämäläinen's
# C code.
from collections import OrderedDict
from functools import partial
import glob
import os
import os.path as op
import shutil
from copy import deepcopy
import numpy as np
from .io.constants import FIFF, FWD
from .io._digitization ... | {
"repo_name": "bloyl/mne-python",
"path": "mne/bem.py",
"copies": "2",
"size": "81008",
"license": "bsd-3-clause",
"hash": 7159269123889141000,
"line_mean": 36.4657724329,
"line_max": 79,
"alpha_frac": 0.5684868088,
"autogenerated": false,
"ratio": 3.387462361993978,
"config_test": false,
"ha... |
# The computations in this code were primarily derived from Matti Hämäläinen's
# C code.
from functools import partial
import glob
import os
import os.path as op
import shutil
from copy import deepcopy
import numpy as np
from scipy import linalg
from .io.constants import FIFF, FWD
from .io._digitization import _dig... | {
"repo_name": "olafhauk/mne-python",
"path": "mne/bem.py",
"copies": "4",
"size": "74177",
"license": "bsd-3-clause",
"hash": 4780944563070036000,
"line_mean": 36.2713567839,
"line_max": 79,
"alpha_frac": 0.5636375893,
"autogenerated": false,
"ratio": 3.404948813294771,
"config_test": false,
... |
# The computations in this code were primarily derived from Matti Hämäläinen's
# C code.
from copy import deepcopy
from contextlib import contextmanager
import os
import os.path as op
import numpy as np
from ._compute_forward import _compute_forwards
from ..io import read_info, _loc_to_coil_trans, _loc_to_eeg_loc, ... | {
"repo_name": "bloyl/mne-python",
"path": "mne/forward/_make_forward.py",
"copies": "14",
"size": "30593",
"license": "bsd-3-clause",
"hash": 1241965821223875300,
"line_mean": 37.5226700252,
"line_max": 79,
"alpha_frac": 0.5907084709,
"autogenerated": false,
"ratio": 3.5745004090218533,
"config... |
# The computations in this code were primarily derived from Matti Hämäläinen's
# C code.
from time import time
from copy import deepcopy
import re
import numpy as np
from scipy import sparse
import shutil
import os
from os import path as op
import tempfile
from ..io import RawArray, Info
from ..io.constants import... | {
"repo_name": "cjayb/mne-python",
"path": "mne/forward/forward.py",
"copies": "1",
"size": "73746",
"license": "bsd-3-clause",
"hash": -2880703840255505000,
"line_mean": 36.9320987654,
"line_max": 79,
"alpha_frac": 0.5717249797,
"autogenerated": false,
"ratio": 3.704410730433035,
"config_test":... |
# The computations in this code were primarily derived from Matti Hämäläinen's
# C code.
from time import time
from copy import deepcopy
import re
import numpy as np
import shutil
import os
from os import path as op
import tempfile
from ..io import RawArray, Info
from ..io.constants import FIFF
from ..io.open impo... | {
"repo_name": "mne-tools/mne-python",
"path": "mne/forward/forward.py",
"copies": "1",
"size": "74446",
"license": "bsd-3-clause",
"hash": 8153890565818525000,
"line_mean": 37.0183861083,
"line_max": 79,
"alpha_frac": 0.5710908114,
"autogenerated": false,
"ratio": 3.694292803970223,
"config_tes... |
# Many of the computations in this code were derived from Matti Hämäläinen's
# C code.
from copy import deepcopy
from distutils.version import LooseVersion
from functools import partial, lru_cache
from glob import glob
from os import path as op
from struct import pack
import warnings
import numpy as np
from .channe... | {
"repo_name": "bloyl/mne-python",
"path": "mne/surface.py",
"copies": "2",
"size": "61761",
"license": "bsd-3-clause",
"hash": -7062352747725824000,
"line_mean": 34.066439523,
"line_max": 146,
"alpha_frac": 0.5478041197,
"autogenerated": false,
"ratio": 3.4608529955724934,
"config_test": false,... |
# Many of the computations in this code were derived from Matti Hämäläinen's
# C code.
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 warnings
from struct import pack
import numpy as np
from scipy.spa... | {
"repo_name": "larsoner/mne-python",
"path": "mne/surface.py",
"copies": "4",
"size": "65829",
"license": "bsd-3-clause",
"hash": -4854420799811581000,
"line_mean": 34.7328990228,
"line_max": 146,
"alpha_frac": 0.5503798238,
"autogenerated": false,
"ratio": 3.4174454828660434,
"config_test": fa... |
# Many of the computations in this code were derived from Matti Hämäläinen's
# C code.
import os
import os.path as op
import numpy as np
from .io.constants import FIFF
from .io.open import fiff_open
from .io.tag import find_tag
from .io.tree import dir_tree_find
from .io.write import (start_block, end_block, write_... | {
"repo_name": "drammock/mne-python",
"path": "mne/morph_map.py",
"copies": "4",
"size": "9180",
"license": "bsd-3-clause",
"hash": 6408151871001787000,
"line_mean": 38.5301724138,
"line_max": 78,
"alpha_frac": 0.6061498201,
"autogenerated": false,
"ratio": 3.2440749911567033,
"config_test": fal... |
# Many of the computations in this code were derived from Matti Hämäläinen's
# C code.
from copy import deepcopy
from functools import partial
from gzip import GzipFile
import os
import os.path as op
import numpy as np
from scipy import sparse, linalg
from .io.constants import FIFF
from .io.meas_info import create_... | {
"repo_name": "Eric89GXL/mne-python",
"path": "mne/source_space.py",
"copies": "3",
"size": "125513",
"license": "bsd-3-clause",
"hash": -2753095413879890000,
"line_mean": 37.4754751686,
"line_max": 79,
"alpha_frac": 0.5564789215,
"autogenerated": false,
"ratio": 3.5364046210200057,
"config_tes... |
# The computations in this code were primarily derived from Matti Hämäläinen's
# C code.
import os
import os.path as op
import numpy as np
from numpy.polynomial import legendre
from ..fixes import einsum
from ..parallel import parallel_func
from ..utils import logger, verbose, _get_extra_data_path, fill_doc
#####... | {
"repo_name": "olafhauk/mne-python",
"path": "mne/forward/_lead_dots.py",
"copies": "10",
"size": "18952",
"license": "bsd-3-clause",
"hash": -6792430455760441000,
"line_mean": 35.5048169557,
"line_max": 79,
"alpha_frac": 0.5704634224,
"autogenerated": false,
"ratio": 3.0642083131166102,
"confi... |
# The computations in this code were primarily derived from Matti Hämäläinen's
# C code.
import os
import os.path as op
import numpy as np
from numpy.polynomial import legendre
from ..parallel import parallel_func
from ..utils import logger, verbose, _get_extra_data_path, fill_doc
################################... | {
"repo_name": "rkmaddox/mne-python",
"path": "mne/forward/_lead_dots.py",
"copies": "4",
"size": "18952",
"license": "bsd-3-clause",
"hash": -7324027621941174000,
"line_mean": 35.5752895753,
"line_max": 79,
"alpha_frac": 0.5701995144,
"autogenerated": false,
"ratio": 3.063712807244502,
"config_... |
__author__ = 'smck'
import json
import requests
from ifind.search.engine import Engine
from ifind.search.response import Response
from ifind.search.exceptions import EngineAPIKeyException, QueryParamException, EngineConnectionException
from ifind.utils.encoding import encode_symbols
from string import maketrans
API_E... | {
"repo_name": "leifos/ifind",
"path": "ifind/search/engines/neutrinogeoaddress.py",
"copies": "1",
"size": "8686",
"license": "mit",
"hash": 100518723702244850,
"line_mean": 34.5983606557,
"line_max": 119,
"alpha_frac": 0.5964770896,
"autogenerated": false,
"ratio": 4.521603331598126,
"config_t... |
from __future__ import print_function
import theano
import numbers
import numpy as np
import theano.tensor as T
from theano.tensor.signal.downsample import max_pool_2d
def _relu(x):
return T.maximum(x, 0)
def _identity(x):
return x
ACTIVATIONS = {'sigmoid': T.nnet.sigmoid,
'identity': _iden... | {
"repo_name": "JesseLivezey/sklearn-theano",
"path": "sklearn_theano/base.py",
"copies": "7",
"size": "20301",
"license": "bsd-3-clause",
"hash": 2907664373663387600,
"line_mean": 36.045620438,
"line_max": 82,
"alpha_frac": 0.5612531402,
"autogenerated": false,
"ratio": 4.009678056488248,
"conf... |
import os
import theano
# Required to avoid fuse errors... very strange
theano.config.floatX = 'float32'
import zipfile
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
from .overfeat_class_labels import get_overfeat_class_label
from .overfeat_class_labels import get_all_overfeat_labels
from... | {
"repo_name": "kastnerkyle/sklearn-theano",
"path": "sklearn_theano/feature_extraction/overfeat.py",
"copies": "2",
"size": "20679",
"license": "bsd-3-clause",
"hash": 4920201388587353000,
"line_mean": 35.4708994709,
"line_max": 81,
"alpha_frac": 0.5316021084,
"autogenerated": false,
"ratio": 4.0... |
import os
import zipfile
import theano
import numpy as np
from sklearn.externals.joblib import load
from sklearn.utils import check_random_state
from ..base import (Feedforward, fuse)
from ..datasets import get_dataset_dir, download
GENERATORS_PATH = get_dataset_dir("adversarial_weights")
MNIST_NETWORK_FILE = os.pa... | {
"repo_name": "JesseLivezey/sklearn-theano",
"path": "sklearn_theano/datasets/generators.py",
"copies": "9",
"size": "4460",
"license": "bsd-3-clause",
"hash": -8189453845780850000,
"line_mean": 31.3188405797,
"line_max": 90,
"alpha_frac": 0.63632287,
"autogenerated": false,
"ratio": 3.3787878787... |
import theano
import numbers
import numpy as np
import theano.tensor as T
from theano.tensor.signal.downsample import max_pool_2d
def _relu(x):
return T.maximum(x, 0)
def _identity(x):
return x
ACTIVATIONS = {'sigmoid': T.nnet.sigmoid,
'identity': _identity,
'linear': _identi... | {
"repo_name": "eickenberg/sklearn-theano",
"path": "sklearn_theano/base.py",
"copies": "2",
"size": "20305",
"license": "bsd-3-clause",
"hash": 5018707340699999000,
"line_mean": 36.052919708,
"line_max": 82,
"alpha_frac": 0.5597143561,
"autogenerated": false,
"ratio": 4.016815034619189,
"config... |
from __future__ import generators
Any = None
from rdflib.term import BNode
from rdflib.store import Store
class IOMemory(Store):
"""\
An integer-key-optimized-context-aware-in-memory store.
Uses nested dictionaries to store triples and context. Each triple
is stored in six such indices as follows cs... | {
"repo_name": "alcides/rdflib",
"path": "rdflib/store/IOMemory.py",
"copies": "1",
"size": "14379",
"license": "bsd-3-clause",
"hash": 1014150899694771500,
"line_mean": 35.1281407035,
"line_max": 92,
"alpha_frac": 0.5070589053,
"autogenerated": false,
"ratio": 4.2031569716457176,
"config_test":... |
__author__ = 'SmileyBarry'
from .core import APIConnection, SteamObject, chunker
from .app import SteamApp
from .decorators import cached_property, INFINITE, MINUTE, HOUR
from .errors import *
import datetime
import itertools
class SteamUserBadge(SteamObject):
def __init__(self, badge_id, level, completion_tim... | {
"repo_name": "smiley/steamapi",
"path": "steamapi/user.py",
"copies": "1",
"size": "16340",
"license": "mit",
"hash": 5277093850970363000,
"line_mean": 31.3564356436,
"line_max": 104,
"alpha_frac": 0.54124847,
"autogenerated": false,
"ratio": 4.186523187291827,
"config_test": false,
"has_no_... |
__author__ = 'SmileyBarry'
from .core import APIConnection, SteamObject
from .app import SteamApp
from .decorators import cached_property, INFINITE, MINUTE, HOUR
from .errors import *
import datetime
class SteamUserBadge(SteamObject):
def __init__(self, badge_id, level, completion_time, xp, scarcity, appid=Non... | {
"repo_name": "James-Firth/steam-download-notifier",
"path": "steamapi/user.py",
"copies": "1",
"size": "13089",
"license": "mit",
"hash": -2432565235210457600,
"line_mean": 31.6433915212,
"line_max": 114,
"alpha_frac": 0.5417526167,
"autogenerated": false,
"ratio": 4.222258064516129,
"config_t... |
__author__ = 'SmileyBarry'
from .core import APIConnection, SteamObject, store
from .decorators import cached_property, INFINITE
class SteamApp(SteamObject):
def __init__(self, appid, name=None, owner=None):
self._id = appid
if name is not None:
import time
self._cache = d... | {
"repo_name": "James-Firth/steam-download-notifier",
"path": "steamapi/app.py",
"copies": "1",
"size": "5327",
"license": "mit",
"hash": -5497057292712649000,
"line_mean": 37.6086956522,
"line_max": 118,
"alpha_frac": 0.5445841937,
"autogenerated": false,
"ratio": 4.40976821192053,
"config_test... |
__author__ = 'SmileyBarry'
from .decorators import debug
class APIException(Exception):
"""
Base class for all API exceptions.
"""
pass
class AccessException(APIException):
"""
You are attempting to query an object that you have no permission to query. (E.g.: private user,
hidden screen... | {
"repo_name": "smiley/steamapi",
"path": "steamapi/errors.py",
"copies": "1",
"size": "3290",
"license": "mit",
"hash": 2921715295660746000,
"line_mean": 26.1900826446,
"line_max": 111,
"alpha_frac": 0.6425531915,
"autogenerated": false,
"ratio": 4.363395225464191,
"config_test": false,
"has_... |
__author__ = 'SmileyBarry'
from .decorators import debug
class APIException(Exception):
"""
Base class for all API exceptions.
"""
pass
class APIUserError(APIException):
"""
An API error caused by a user error, like wrong data or just empty results for a query.
"""
pass
class User... | {
"repo_name": "James-Firth/steam-download-notifier",
"path": "steamapi/errors.py",
"copies": "1",
"size": "1961",
"license": "mit",
"hash": -2062507403276751600,
"line_mean": 21.5402298851,
"line_max": 105,
"alpha_frac": 0.6455889852,
"autogenerated": false,
"ratio": 4.137130801687764,
"config_... |
__author__ = 'SmileyBarry'
import requests
import sys
import time
from .consts import API_CALL_DOCSTRING_TEMPLATE, API_CALL_PARAMETER_TEMPLATE, IPYTHON_PEEVES, IPYTHON_MODE
from .decorators import Singleton, cached_property, INFINITE
from .errors import APIException, APIUnauthorized, APIKeyRequired, APIPrivate, APICo... | {
"repo_name": "smiley/steamapi",
"path": "steamapi/core.py",
"copies": "1",
"size": "27034",
"license": "mit",
"hash": -5120185184894570000,
"line_mean": 38.4656934307,
"line_max": 120,
"alpha_frac": 0.5747207221,
"autogenerated": false,
"ratio": 4.535906040268457,
"config_test": false,
"has_... |
__author__ = 'SmileyBarry'
import threading
import time
class debug(object):
@staticmethod
def no_return(originalFunction, *args, **kwargs):
def callNoReturn(*args, **kwargs):
originalFunction(*args, **kwargs)
# This code should never return!
raise AssertionError("... | {
"repo_name": "balohmatevz/steamapi",
"path": "steamapi/decorators.py",
"copies": "1",
"size": "3834",
"license": "mit",
"hash": -31086386285991290,
"line_mean": 31.7777777778,
"line_max": 78,
"alpha_frac": 0.6171100678,
"autogenerated": false,
"ratio": 4.548042704626335,
"config_test": false,
... |
__author__ = 'SmileyBarry'
class Enum(object):
def __init__(self):
raise TypeError("Enums cannot be instantiated, use their attributes instead")
class CommunityVisibilityState(Enum):
PRIVATE = 1
FRIENDS_ONLY = 2
FRIENDS_OF_FRIENDS = 3
USERS_ONLY = 4
PUBLIC = 5
class OnlineState(Enu... | {
"repo_name": "balohmatevz/steamapi",
"path": "steamapi/consts.py",
"copies": "1",
"size": "1119",
"license": "mit",
"hash": 4020544255870311400,
"line_mean": 23.8888888889,
"line_max": 117,
"alpha_frac": 0.6657730116,
"autogenerated": false,
"ratio": 3.262390670553936,
"config_test": false,
... |
__author__ = 'SmileyBarry'
class Enum(object):
def __init__(self):
raise TypeError(
"Enums cannot be instantiated, use their attributes instead")
class CommunityVisibilityState(Enum):
PRIVATE = 1
FRIENDS_ONLY = 2
FRIENDS_OF_FRIENDS = 3
USERS_ONLY = 4
PUBLIC = 5
class On... | {
"repo_name": "smiley/steamapi",
"path": "steamapi/consts.py",
"copies": "1",
"size": "1144",
"license": "mit",
"hash": 187071039163490000,
"line_mean": 22.8333333333,
"line_max": 117,
"alpha_frac": 0.6512237762,
"autogenerated": false,
"ratio": 3.2873563218390807,
"config_test": false,
"has_... |
from m5.objects import *
class Port0_FU(FUDesc):
opList = [ OpDesc(opClass="IntAlu", opLat=1),
OpDesc(opClass="IntDiv", opLat=20, pipelined=True),
OpDesc(opClass="FloatMult", opLat=5),
OpDesc(opClass="FloatCvt", opLat=3),
OpDesc(opClass="FloatDiv", opLat=... | {
"repo_name": "alianmohammad/pd-gem5-latest",
"path": "configs/common/Nehalem.py",
"copies": "2",
"size": "4722",
"license": "bsd-3-clause",
"hash": 181839231029388600,
"line_mean": 26.9408284024,
"line_max": 66,
"alpha_frac": 0.5965692503,
"autogenerated": false,
"ratio": 3.040566645202833,
"c... |
__authors__ = ["M Sanchez del Rio - ESRF ISDD Advanced Analysis and Modelling"]
__license__ = "MIT"
__date__ = "12/01/2017"
#
# load/write files and plot facilities for the undul_phot and undul_cdf shadow3/undulator preprocessors
#
import numpy
import h5py
import time
class SourceUndulatorInputOutput(object):
... | {
"repo_name": "srio/minishadow",
"path": "minishadow/undulator/source_undulator_input_output.py",
"copies": "1",
"size": "13362",
"license": "mit",
"hash": -5672264668205481000,
"line_mean": 30.7387173397,
"line_max": 148,
"alpha_frac": 0.5011974255,
"autogenerated": false,
"ratio": 3.10599721059... |
__authors__ = ["M Sanchez del Rio - ESRF ISDD Advanced Analysis and Modelling"]
__license__ = "MIT"
__date__ = "12/01/2017"
#
# SHADOW Undulator preprocessors implemented in python
#
# this code replaces SHADOW's undul_phot and undul_cdf
#
# It calculates the undulator radiation as a function of energy, theta and phi.... | {
"repo_name": "srio/minishadow",
"path": "minishadow/undulator/source_undulator_factory.py",
"copies": "1",
"size": "9970",
"license": "mit",
"hash": 9055067470325614000,
"line_mean": 39.0401606426,
"line_max": 184,
"alpha_frac": 0.5171514544,
"autogenerated": false,
"ratio": 2.7872518870561924,
... |
__authors__ = ["M Sanchez del Rio - ESRF ISDD Advanced Analysis and Modelling"]
__license__ = "MIT"
__date__ = "30-08-2018"
"""
"""
import numpy
from srxraylib.util.inverse_method_sampler import Sampler1D, Sampler2D
import scipy.constants as codata
from scipy import interpolate
from scipy.interpolate import inte... | {
"repo_name": "srio/minishadow",
"path": "minishadow/bending_magnet/source_bending_magnet.py",
"copies": "1",
"size": "27423",
"license": "mit",
"hash": 4895192648452276000,
"line_mean": 39.268722467,
"line_max": 148,
"alpha_frac": 0.5022061773,
"autogenerated": false,
"ratio": 3.296429859358096,... |
__authors__ = ["M Sanchez del Rio - ESRF ISDD Advanced Analysis and Modelling"]
__license__ = "MIT"
__date__ = "30-08-2018"
"""
Undulator code: computes undulator radiation distributions and samples rays according to them.
Fully replaces and upgrades the shadow3 undulator model.
The radiation is calculating using o... | {
"repo_name": "srio/minishadow",
"path": "minishadow/undulator/source_undulator.py",
"copies": "1",
"size": "35609",
"license": "mit",
"hash": -5923595346378268000,
"line_mean": 40.5992990654,
"line_max": 156,
"alpha_frac": 0.5355387683,
"autogenerated": false,
"ratio": 3.4010506208213944,
"con... |
__authors__ = ["M Sanchez del Rio - ESRF ISDD Advanced Analysis and Modelling"]
__license__ = "MIT"
__date__ = "30-08-2018"
"""
Wiggler code: computes wiggler radiation distributions and samples rays according to them.
Fully replaces and upgrades the shadow3 wiggler model.
The radiation is calculating using sr-xray... | {
"repo_name": "srio/minishadow",
"path": "minishadow/wiggler/source_wiggler.py",
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"config_... |
__author__ = 'smurpheus'
import sqlite3
from listing_manager import Listing
class DBConnector(object):
def __init__(self):
self.con = sqlite3.connect('itemstorage.db')
def create_listing(self, listing):
statement = "INSERT INTO listings VALUES(%s,%s,%s,'%s',%s,%s,%s,%s,%s, '%s', %s)" % (
... | {
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"path": "dbconnector.py",
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"... |
__author__ = 'smurpheus'
import struct
from getpass import getpass
from steam import SteamClient
from steam.enums import EResult
from steam.enums.emsg import EMsg
from eventemitter import EventEmitter
import gevent
from listing_manager import Listing, ListingReceiver
from csgo.enums import ECsgoGCMsg
from dbconnector i... | {
"repo_name": "smurpheus/sneakyfloat",
"path": "manager.py",
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"ratio": 3.3476084538375974,
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"has_... |
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