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__author__ = "Sergey Aganezov"
__email__ = "aganezov(at)cs.jhu.edu"
__status__ = "production"
import unittest
from bg.genome import BGGenome, BGGenome_JSON_SCHEMA_JSON_KEY, post_load
class BGGenomeTestCase(unittest.TestCase):
def test_initialization_incorrect(self):
# empty genomes are not allowed, a na... | {
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__author__ = "Sergey Karakovskiy"
__date__ = "$Mar 18, 2010 10:48:28 PM$"
class Inspectable(object):
""" All derived classes gains the ability to print the names and values of all their fields"""
def __repr__(self):
return '<%s: %s>' % (self.__class__.__name__,
dict([(x, ... | {
"repo_name": "kavanj/marioai",
"path": "src/amico/python/agents/evaluationinfo.py",
"copies": "1",
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__author__="Sergey Karakovskiy, sergey at idsia fullstop ch"
__date__ ="$May 2, 2009 7:54:12 PM$"
class MarioAgent:
# class MarioAgent(Agent):
""" An agent is an entity capable of producing actions, based on previous observations.
Generally it will also learn from experience. It can interact directly wi... | {
"repo_name": "pgkaila/marioai",
"path": "src/ch/idsia/agents/controllers/marioagent.py",
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__author__="Sergey Karakovskiy, sergey at idsia fullstop ch"
__date__ ="$May 2, 2009 7:54:12 PM$"
import numpy as np
class MarioAgent(object):
""" An agent is an entity capable of producing actions, based on previous observations.
Generally it will also learn from experience. It can interact directly wit... | {
"repo_name": "hunse/mario-ai",
"path": "src/amico/python/agents/marioagent.py",
"copies": "1",
"size": "3416",
"license": "bsd-3-clause",
"hash": -7142293253190240000,
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"c... |
__author__ = "Sergey Karakovskiy, sergey at idsia fullstop ch"
__date__ = "$May 2, 2009 7:54:12 PM$"
class MarioAgent:
# class MarioAgent(Agent):
""" An agent is an entity capable of producing actions, based on previous observations.
Generally it will also learn from experience. It can interact dir... | {
"repo_name": "kavanj/marioai",
"path": "src/ch/idsia/agents/controllers/marioagent.py",
"copies": "1",
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"hash": 7191100334923447000,
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__author__ = 'Sergey Matyunin'
import numpy as np
def interp2linear(z, xi, yi, extrapval=np.nan):
"""
Linear interpolation equivalent to interp2(z, xi, yi,'linear') in MATLAB
@param z: function defined on square lattice [0..width(z))X[0..height(z))
@param xi: matrix of x coordinates wher... | {
"repo_name": "serge-m/pyinterp2",
"path": "interp2.py",
"copies": "1",
"size": "1870",
"license": "mit",
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"has_no_keyw... |
__author__ = 'Sergey Matyunin'
import theano
import theano.tensor as T
class TrainFunction(object):
def __init__(self, u0, v0, rate, num_steps, **kwargs):
self.rate = rate
self.gu, self.gv = None, None
self.E = None
self.count = 0
self.num_steps = n... | {
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"path": "train_function.py",
"copies": "1",
"size": "2491",
"license": "mit",
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"line_mean": 28.4024390244,
"line_max": 104,
"alpha_frac": 0.5194700923,
"autogenerated": false,
"ratio": 3.2100515463917527,
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__author__ = 'Sergey Matyunin'
import unittest
import numpy as np
from interp2 import interp2linear
from numpy import nan
from StringIO import StringIO
class Interp2TestCase(unittest.TestCase):
def test_interp2linear(self):
i1 = np.array([
[0.5, 0., 0.],
[1., 0., 0.],... | {
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"autogenerated": false,
"ratio": 3.8293991416309012,
"config_test": true,
"... |
__author__ = 'sergey'
import os
import sys
import subprocess
from distutils.core import setup
from distutils.extension import Extension
CYTHON_BUILD = 0
if "--cython-build" in sys.argv:
# Support compiling with Cython
CYTHON_BUILD = 1
sys.argv.remove("--cython-build")
from Cython.Distutils import bu... | {
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"path": "setup.py",
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"license": "mit",
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"line_mean": 32.2073732719,
"line_max": 129,
"alpha_frac": 0.6498751041,
"autogenerated": false,
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... |
__author__ = 'Sergey'
import shutil
import os
import stat
def read_all_directory_path(root_folder, final_directory_list=[], folder_for_remove='.svn'):
under_files_and_folders = os.listdir(root_folder)
if os.path.split(root_folder)[1] == folder_for_remove:
final_directory_list.append(root_folder)
... | {
"repo_name": "sdenisen/python",
"path": "remove_svn_folder.py",
"copies": "2",
"size": "1656",
"license": "unlicense",
"hash": -8296082132990228000,
"line_mean": 29.1090909091,
"line_max": 92,
"alpha_frac": 0.6322463768,
"autogenerated": false,
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__author__ = 'sergey'
class AbstractDuck:
def __init__(self, name):
self.name = name
print "I'm " + name
def setFly(self, method):
self.fly = method
def setQuack(self, method):
self.quack = method
def swim(self):
print "\tAll ducks float!"
# to call a metho... | {
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"path": "strategy.py",
"copies": "1",
"size": "1879",
"license": "mit",
"hash": -6780965194756426000,
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"autogenerated": false,
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__author__ = 'Sergey Ragatsky'
import string
from nose.tools import nottest, ok_, assert_equal, assert_false, assert_in
from nose.plugins.attrib import attr
from nose_ittr import IttrMultiplier, ittr
class TestMetaClassIttrMultiplayer(object):
def setup(self):
self.test_class_one = test_class_one()
... | {
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"path": "nose_ittr/test/test_ittr_multiplayer.py",
"copies": "1",
"size": "6099",
"license": "apache-2.0",
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... |
__author__ = 'sergio'
import os
import re
__all__ = ['PLANO_REFERENCIAL_PJ_RESUMIDO']
PLANO_REFERENCIAL_PJ_RESUMIDO = []
path_tabelas = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'tabelas')
# tabela = 'SPEDCONTABIL_DINAMICO_2014$SPEDECF_DINAMICA_P100$12$389'
tabela = 'SPEDCONTABIL_DINAMICO_2014$SPEDE... | {
"repo_name": "odoo-brazil/python-sped",
"path": "sped/ecd/tabelas.py",
"copies": "2",
"size": "1193",
"license": "mit",
"hash": -8472694926494701000,
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"autogenerated": false,
"ratio": 2.4628099173553717,
"config_test": fal... |
__author__ = 'Sergio Sicari'
__email__ = "sergiosicari@gmail.com"
import sublime, sublime_plugin
import sys,os
libraries = ["evernote", "oauth2", "httplib2", "pygments"]
abspath = os.path.abspath(os.path.dirname(__file__))
basepath = abspath
#abspath = os.path.dirname(__file__)
for library in libraries:
if abspath+"... | {
"repo_name": "sergioska/EvernoteSyntaxHighlight",
"path": "ever.py",
"copies": "1",
"size": "2681",
"license": "apache-2.0",
"hash": -5362664912667784000,
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__author__ = 'Sergio Sykes'
"""
Usage:
Add this script to your maya/scripts directory
In maya, from the Python Command line, enter:
import cogswellCoupler; reload(cogswellCoupler).GUI()
This script assumes your rig skeleton is parented directly under your world/main controller
"""
import pymel.core as pmc
class C... | {
"repo_name": "taozenforce/cogswelladvancedrigging2014",
"path": "cogswellCoupler.py",
"copies": "2",
"size": "3070",
"license": "mit",
"hash": 5576010464290777000,
"line_mean": 33.8977272727,
"line_max": 113,
"alpha_frac": 0.6228013029,
"autogenerated": false,
"ratio": 3.7167070217917675,
"con... |
import numpy as np
import vtk
def generate_annulus(r=None, theta=None, z=None):
""" Generate points for structured grid for a cylindrical annular
volume. This method is useful for generating a structured
cylindrical mesh for VTK (and perhaps other tools).
Parameters
----------
r : ar... | {
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"path": "vtk/annulus_grid.py",
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... |
from functools import partial
import os
import numpy as np
from scipy import sparse, linalg, stats
from numpy.testing import (assert_equal, assert_array_equal,
assert_array_almost_equal, assert_allclose)
import pytest
from mne import (SourceEstimate, VolSourceEstimate, MixedSourceEstimate,... | {
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import os.path as op
from ..base import BaseRaw
from ..utils import _read_segments_file, _file_size
from ..meas_info import create_info
from ...utils import logger, verbose, warn, fill_doc, _check_fname
@fill_doc
def read_raw_eximia(fname, preload=False, verbose=None):
"""Reader for an eXimia EEG file.
Par... | {
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"path": "mne/io/eximia/eximia.py",
"copies": "7",
"size": "3002",
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"hash": -5578335259080797000,
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"autogenerated": false,
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"config_tes... |
import os.path as op
from ..base import BaseRaw
from ..utils import _read_segments_file, _file_size
from ..meas_info import create_info
from ...utils import logger, verbose, warn, fill_doc
@fill_doc
def read_raw_eximia(fname, preload=False, verbose=None):
"""Reader for an eXimia EEG file.
Parameters
--... | {
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"path": "mne/io/eximia/eximia.py",
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import os.path as op
from ..base import BaseRaw
from ..utils import _read_segments_file, _file_size
from ..meas_info import create_info
from ...utils import logger, verbose, warn
def read_raw_eximia(fname, preload=False, verbose=None):
"""Reader for an eXimia EEG file.
Parameters
----------
fname :... | {
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import glob
import os.path as op
import numpy as np
import pytest
from mne import what, create_info
from mne.datasets import testing
from mne.io import RawArray
from mne.preprocessing import ICA
from mne.utils import requires_sklearn
data_path = testing.data_path(download=False)
@pytest.mark.slowtest
@requires_sk... | {
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"path": "mne/io/tests/test_what.py",
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"hash": -1130718299946159000,
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"config_t... |
from functools import partial
import numpy as np
from ..defaults import _handle_default
from ..fixes import _safe_svd
from ..utils import warn, logger, sqrtm_sym, eigh
# For the reference implementation of eLORETA (force_equal=False),
# 0 < loose <= 1 all produce solutions that are (more or less)
# the same as free... | {
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"path": "mne/minimum_norm/_eloreta.py",
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"hash": -8815944659869095000,
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"line_max": 78,
"alpha_frac": 0.5793454345,
"autogenerated": false,
"ratio": 3.1358344113842174,
"co... |
from functools import partial
import os.path as op
from ...utils import verbose
from ..utils import (has_dataset, _data_path, _get_version, _version_doc,
_data_path_doc)
has_brainstorm_data = partial(has_dataset, name='brainstorm')
_description = u"""
URL: http://neuroimage.usc.edu/brainstorm/... | {
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"path": "mne/datasets/brainstorm/bst_phantom_ctf.py",
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... |
from functools import partial
from ...utils import verbose
from ..utils import (has_dataset, _data_path, _get_version, _version_doc,
_data_path_doc_accept)
has_brainstorm_data = partial(has_dataset, name='brainstorm.bst_phantom_ctf')
_description = u"""
URL: http://neuroimage.usc.edu/brainstor... | {
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"path": "mne/datasets/brainstorm/bst_phantom_ctf.py",
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from functools import partial
from ...utils import verbose
from ..utils import (has_dataset, _data_path, _get_version, _version_doc,
_data_path_doc)
has_brainstorm_data = partial(has_dataset, name='brainstorm.bst_phantom_ctf')
_description = u"""
URL: http://neuroimage.usc.edu/brainstorm/Tutor... | {
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from functools import partial
import numpy as np
from scipy import linalg, fftpack
from .io.pick import pick_types, pick_channels
from .io.constants import FIFF
from .forward import (_magnetic_dipole_field_vec, _create_meg_coils,
_concatenate_coils, _read_coil_defs)
from .cov import make_ad_hoc... | {
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"autogenerated": false,
"ratio": 3.2270820850808866,
"config_test": false,
... |
import copy
import os
from os import path as op
import shutil
import numpy as np
from numpy import array_equal
from numpy.testing import assert_allclose, assert_array_equal
import pytest
import mne
from mne import (pick_types, read_annotations, create_info,
events_from_annotations, make_forward_solu... | {
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"path": "mne/io/ctf/tests/test_ctf.py",
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"autogenerated": false,
"ratio": 2.9342457601904197,
"co... |
import datetime as dt
import re
import numpy as np
from ..base import BaseRaw
from ..constants import FIFF
from ..meas_info import create_info
from ..nirx.nirx import _read_csv_rows_cols
from ..utils import _mult_cal_one
from ...utils import (logger, verbose, fill_doc, warn, _check_fname,
_chec... | {
"repo_name": "rkmaddox/mne-python",
"path": "mne/io/hitachi/hitachi.py",
"copies": "4",
"size": "10281",
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"hash": -6266578948806054000,
"line_mean": 35.3286219081,
"line_max": 78,
"alpha_frac": 0.4825406089,
"autogenerated": false,
"ratio": 3.712892741061755,
"config_... |
import numpy as np
from numpy.fft import rfft, irfft
from .utils import sizeof_fmt, logger, get_config, warn, _explain_exception
# Support CUDA for FFTs; requires scikits.cuda and pycuda
_cuda_capable = False
_multiply_inplace_c128 = _halve_c128 = _double_c128 = None
def _get_cudafft():
"""Deal with scikit-cu... | {
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"config_test": true,
... |
import numpy as np
from numpy.testing import assert_allclose, assert_equal, assert_array_equal
from scipy import linalg
from .. import pick_types, Evoked
from ..io import BaseRaw
from ..io.constants import FIFF
from ..bem import fit_sphere_to_headshape
def _get_data(x, ch_idx):
"""Get the (n_ch, n_times) data ... | {
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import numpy as np
from numpy.testing import assert_allclose, assert_equal, assert_array_equal
from scipy import linalg
from .. import pick_types, Evoked
from ..io import _BaseRaw
from ..io.constants import FIFF
from ..bem import fit_sphere_to_headshape
def _get_data(x, ch_idx):
"""Helper to get the (n_ch, n_t... | {
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import numpy as np
from numpy.testing import assert_allclose, assert_equal, assert_array_equal
from scipy import linalg
from .. import pick_types, Evoked
from ..io import BaseRaw
from ..io.constants import FIFF
from ..bem import fit_sphere_to_headshape
def _get_data(x, ch_idx):
"""Helper to get the (n_ch, n_ti... | {
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"size": "5327",
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"hash": 2171307211807102500,
"line_mean": 40.6171875,
"line_max": 78,
"alpha_frac": 0.6054064201,
"autogenerated": false,
"ratio": 3.150206978119456,
"config_test": fal... |
import numpy as np
from numpy.testing import assert_allclose, assert_equal
from .. import pick_types, Evoked
from ..io import _BaseRaw
from ..io.constants import FIFF
from ..bem import fit_sphere_to_headshape
def _get_data(x, ch_idx):
"""Helper to get the (n_ch, n_times) data array"""
if isinstance(x, _Base... | {
"repo_name": "cmoutard/mne-python",
"path": "mne/tests/common.py",
"copies": "1",
"size": "2915",
"license": "bsd-3-clause",
"hash": 6304353307285652000,
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"line_max": 74,
"alpha_frac": 0.5996569468,
"autogenerated": false,
"ratio": 2.880434782608696,
"config_test": f... |
import numpy as np
from os import path as op
from scipy import linalg
from .io.pick import pick_types, pick_channels
from .io.base import _BaseRaw
from .io.constants import FIFF
from .forward import (_magnetic_dipole_field_vec, _create_meg_coils,
_concatenate_coils)
from .cov import make_ad_hoc_... | {
"repo_name": "cmoutard/mne-python",
"path": "mne/chpi.py",
"copies": "1",
"size": "19468",
"license": "bsd-3-clause",
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"line_mean": 39.643006263,
"line_max": 79,
"alpha_frac": 0.5404766797,
"autogenerated": false,
"ratio": 3.131917631917632,
"config_test": false,
... |
import numpy as np
from os import path as op
from .pick import pick_types
from .base import _BaseRaw
from ..utils import verbose
from ..externals.six import string_types
@verbose
def get_chpi_positions(raw, t_step=None, verbose=None):
"""Extract head positions
Note that the raw instance must have CHPI chan... | {
"repo_name": "antiface/mne-python",
"path": "mne/io/chpi.py",
"copies": "12",
"size": "4695",
"license": "bsd-3-clause",
"hash": 5123663161728791000,
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"alpha_frac": 0.5710330138,
"autogenerated": false,
"ratio": 3.372844827586207,
"config_test": false... |
import numpy as np
from scipy.fftpack import fft, ifft
from .utils import sizeof_fmt, logger, get_config
# Support CUDA for FFTs; requires scikits.cuda and pycuda
_cuda_capable = False
_multiply_inplace_c128 = _halve_c128 = _real_c128 = None
def get_cuda_memory():
"""Get the amount of free memory for CUDA ope... | {
"repo_name": "Odingod/mne-python",
"path": "mne/cuda.py",
"copies": "3",
"size": "14260",
"license": "bsd-3-clause",
"hash": -3026360379545724400,
"line_mean": 37.8555858311,
"line_max": 79,
"alpha_frac": 0.5664796634,
"autogenerated": false,
"ratio": 3.7915448019143843,
"config_test": true,
... |
import numpy as np
from scipy.fftpack import fft, ifft
try:
import pycuda.gpuarray as gpuarray
from pycuda.driver import mem_get_info
from scikits.cuda import fft as cudafft
except (ImportError, OSError):
# need OSError because scikits.cuda throws it if cufft not found
pass
from .utils import size... | {
"repo_name": "effigies/mne-python",
"path": "mne/cuda.py",
"copies": "1",
"size": "13806",
"license": "bsd-3-clause",
"hash": 3376977842394831000,
"line_mean": 37.7808988764,
"line_max": 79,
"alpha_frac": 0.569317688,
"autogenerated": false,
"ratio": 3.7876543209876545,
"config_test": false,
... |
import numpy as np
from scipy.fftpack import fft, ifft, rfft, irfft
from .utils import sizeof_fmt, logger, get_config, warn, _explain_exception
# Support CUDA for FFTs; requires scikits.cuda and pycuda
_cuda_capable = False
_multiply_inplace_c128 = _halve_c128 = _real_c128 = None
def _get_cudafft():
"""Deal w... | {
"repo_name": "nicproulx/mne-python",
"path": "mne/cuda.py",
"copies": "3",
"size": "15322",
"license": "bsd-3-clause",
"hash": -385450249221753340,
"line_mean": 37.8883248731,
"line_max": 79,
"alpha_frac": 0.5629160684,
"autogenerated": false,
"ratio": 3.6965018094089266,
"config_test": true,
... |
import numpy as np
from scipy import linalg, fftpack
from .io.pick import pick_types, pick_channels
from .io.base import _BaseRaw
from .io.constants import FIFF
from .forward import (_magnetic_dipole_field_vec, _create_meg_coils,
_concatenate_coils, _read_coil_defs)
from .cov import make_ad_hoc_... | {
"repo_name": "wronk/mne-python",
"path": "mne/chpi.py",
"copies": "2",
"size": "27605",
"license": "bsd-3-clause",
"hash": 645893909027766700,
"line_mean": 39.3581871345,
"line_max": 79,
"alpha_frac": 0.547835537,
"autogenerated": false,
"ratio": 3.278114238213989,
"config_test": false,
"has... |
import numpy as np
from scipy import linalg, fftpack
from .io.pick import pick_types, pick_channels
from .io.constants import FIFF
from .forward import (_magnetic_dipole_field_vec, _create_meg_coils,
_concatenate_coils, _read_coil_defs)
from .cov import make_ad_hoc_cov, _get_whitener_data
from .... | {
"repo_name": "alexandrebarachant/mne-python",
"path": "mne/chpi.py",
"copies": "3",
"size": "24853",
"license": "bsd-3-clause",
"hash": 3013125602009355000,
"line_mean": 39.7426229508,
"line_max": 79,
"alpha_frac": 0.5397738704,
"autogenerated": false,
"ratio": 3.2289203585812656,
"config_test... |
import numpy as np
import pytest
from mne import create_info
from mne.io import RawArray
from mne.utils import logger, catch_logging, run_tests_if_main
def bad_1(x):
"""Fail."""
return # bad return type
def bad_2(x):
"""Fail."""
return x[:-1] # bad shape
def bad_3(x):
"""Fail."""
retur... | {
"repo_name": "adykstra/mne-python",
"path": "mne/io/tests/test_apply_function.py",
"copies": "2",
"size": "1942",
"license": "bsd-3-clause",
"hash": -7775078403417984000,
"line_mean": 27.5588235294,
"line_max": 75,
"alpha_frac": 0.6441812564,
"autogenerated": false,
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... |
import numpy as np
from ..annotations import _annotations_starts_stops, Annotations
from ..io import BaseRaw
from ..io.pick import _picks_to_idx
from ..utils import (_validate_type, verbose, logger, _pl,
_mask_to_onsets_offsets, ProgressBar)
@verbose
def annotate_flat(raw, bad_percent=5., min_d... | {
"repo_name": "Eric89GXL/mne-python",
"path": "mne/preprocessing/flat.py",
"copies": "12",
"size": "4302",
"license": "bsd-3-clause",
"hash": 9063035851091252000,
"line_mean": 39.9523809524,
"line_max": 79,
"alpha_frac": 0.5918604651,
"autogenerated": false,
"ratio": 3.7005163511187606,
"config... |
import numpy as np
from ..annotations import _annotations_starts_stops
from ..io import BaseRaw
from ..io.pick import _picks_to_idx
from ..utils import (_validate_type, verbose, logger, _pl,
_mask_to_onsets_offsets, ProgressBar)
@verbose
def mark_flat(raw, bad_percent=5., min_duration=0.005, pi... | {
"repo_name": "cjayb/mne-python",
"path": "mne/preprocessing/flat.py",
"copies": "2",
"size": "4273",
"license": "bsd-3-clause",
"hash": -7035439548041356000,
"line_mean": 40.0673076923,
"line_max": 79,
"alpha_frac": 0.5902598923,
"autogenerated": false,
"ratio": 3.6598114824335903,
"config_tes... |
import numpy as np
from ..defaults import _handle_default
from ..fixes import _safe_svd
from ..utils import warn, logger, sqrtm_sym, eigh
# For the reference implementation of eLORETA (force_equal=False),
# 0 < loose <= 1 all produce solutions that are (more or less)
# the same as free orientation (loose=1) and qui... | {
"repo_name": "cjayb/mne-python",
"path": "mne/minimum_norm/_eloreta.py",
"copies": "2",
"size": "6990",
"license": "bsd-3-clause",
"hash": -446005704841424400,
"line_mean": 38.4915254237,
"line_max": 78,
"alpha_frac": 0.573676681,
"autogenerated": false,
"ratio": 3.1903240529438612,
"config_te... |
import numpy as np
from ..epochs import BaseEpochs
from ..io.pick import _picks_to_idx
from ..io.base import BaseRaw
from ..utils import _check_preload, _validate_type, _check_option, verbose
@verbose
def regress_artifact(inst, picks=None, picks_artifact='eog', betas=None,
copy=True, verbose=No... | {
"repo_name": "larsoner/mne-python",
"path": "mne/preprocessing/_regress.py",
"copies": "5",
"size": "3402",
"license": "bsd-3-clause",
"hash": 1837960521152907,
"line_mean": 38.1034482759,
"line_max": 93,
"alpha_frac": 0.6378600823,
"autogenerated": false,
"ratio": 3.562303664921466,
"config_t... |
import numpy as np
from ..io import BaseRaw
from ..io.pick import _picks_to_idx
from ..utils import (_validate_type, verbose, logger, _pl,
_mask_to_onsets_offsets, ProgressBar)
@verbose
def mark_flat(raw, bad_percent=5., min_duration=0.005, picks=None,
verbose=None):
r"""Mark ... | {
"repo_name": "adykstra/mne-python",
"path": "mne/preprocessing/flat.py",
"copies": "1",
"size": "4008",
"license": "bsd-3-clause",
"hash": -2572319409584508000,
"line_mean": 39.4646464646,
"line_max": 79,
"alpha_frac": 0.5788816775,
"autogenerated": false,
"ratio": 3.7474275023386343,
"config_... |
import numpy as np
from scipy import linalg
from ..defaults import _handle_default
from ..fixes import _safe_svd
from ..utils import warn, logger
# For the reference implementation of eLORETA (force_equal=False),
# 0 < loose <= 1 all produce solutions that are (more or less)
# the same as free orientation (loose=1... | {
"repo_name": "adykstra/mne-python",
"path": "mne/minimum_norm/_eloreta.py",
"copies": "6",
"size": "6031",
"license": "bsd-3-clause",
"hash": -5013574865267384000,
"line_mean": 39.75,
"line_max": 79,
"alpha_frac": 0.5708837672,
"autogenerated": false,
"ratio": 3.2285867237687365,
"config_test"... |
import os
from os import path as op
import shutil
import warnings
import numpy as np
from nose.tools import assert_raises, assert_true, assert_false
from numpy.testing import assert_allclose, assert_array_equal, assert_equal
from mne import pick_types
from mne.tests.common import assert_dig_allclose
from mne.transfo... | {
"repo_name": "alexandrebarachant/mne-python",
"path": "mne/io/ctf/tests/test_ctf.py",
"copies": "5",
"size": "10354",
"license": "bsd-3-clause",
"hash": 4175182335623569400,
"line_mean": 47.3831775701,
"line_max": 79,
"alpha_frac": 0.579679351,
"autogenerated": false,
"ratio": 3.0372543267820475... |
import os.path as op
from ...utils import verbose
from ...fixes import partial
from ..utils import (has_dataset, _data_path, _get_version, _version_doc,
_data_path_doc)
has_brainstorm_data = partial(has_dataset, name='brainstorm')
_description = u"""
URL: http://neuroimage.usc.edu/brainstorm/T... | {
"repo_name": "alexandrebarachant/mne-python",
"path": "mne/datasets/brainstorm/bst_phantom_ctf.py",
"copies": "2",
"size": "1561",
"license": "bsd-3-clause",
"hash": -9044289993659191000,
"line_mean": 30.22,
"line_max": 79,
"alpha_frac": 0.6047405509,
"autogenerated": false,
"ratio": 3.342612419... |
import os.path as op
import pytest
import numpy as np
from numpy.fft import rfft, rfftfreq
from mne import create_info
from mne.datasets import testing
from mne.io import RawArray, read_raw_fif
from mne.io.pick import _pick_data_channels
from mne.preprocessing import oversampled_temporal_projection
from mne.utils i... | {
"repo_name": "wmvanvliet/mne-python",
"path": "mne/preprocessing/tests/test_otp.py",
"copies": "17",
"size": "3871",
"license": "bsd-3-clause",
"hash": -4067715987699951600,
"line_mean": 36.2211538462,
"line_max": 79,
"alpha_frac": 0.6238698011,
"autogenerated": false,
"ratio": 3.074662430500397... |
import numpy as np
import os.path as op
import itertools
from distutils.version import LooseVersion
from numpy.testing import assert_allclose
import pytest
import matplotlib
import matplotlib.pyplot as plt
from mne import read_events, pick_types, Annotations, create_info
from mne.datasets import testing
from mne.io ... | {
"repo_name": "cjayb/mne-python",
"path": "mne/viz/tests/test_raw.py",
"copies": "1",
"size": "21152",
"license": "bsd-3-clause",
"hash": 6253209279162296000,
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"line_max": 79,
"alpha_frac": 0.6000094572,
"autogenerated": false,
"ratio": 3.0224381877947692,
"config_tes... |
import os.path as op
import itertools
import numpy as np
from numpy.testing import assert_allclose
import pytest
import matplotlib
import matplotlib.pyplot as plt
from mne import read_events, pick_types, Annotations, create_info
from mne.datasets import testing
from mne.fixes import _close_event
from mne.io import r... | {
"repo_name": "mne-tools/mne-python",
"path": "mne/viz/tests/test_raw.py",
"copies": "2",
"size": "31759",
"license": "bsd-3-clause",
"hash": 3854354668758932500,
"line_mean": 41.5402144772,
"line_max": 79,
"alpha_frac": 0.6205766504,
"autogenerated": false,
"ratio": 3.0502691272587468,
"config... |
import os.path as op
import warnings
from numpy.testing import assert_raises
from mne import io, read_events, pick_types
from mne.utils import requires_version, run_tests_if_main
from mne.viz.utils import _fake_click
# Set our plotters to test mode
import matplotlib
matplotlib.use('Agg') # for testing don't use X ... | {
"repo_name": "rajegannathan/grasp-lift-eeg-cat-dog-solution-updated",
"path": "python-packages/mne-python-0.10/mne/viz/tests/test_raw.py",
"copies": "1",
"size": "4667",
"license": "bsd-3-clause",
"hash": 5461002826466056000,
"line_mean": 36.336,
"line_max": 79,
"alpha_frac": 0.611527748,
"autogen... |
import numpy as np
from numpy.testing import assert_allclose
import pytest
from scipy.signal import hilbert
from mne.connectivity import envelope_correlation
def _compute_corrs_orig(data):
# This is the version of the code by Sheraz and Denis.
# For this version (epochs, labels, time) must be -> (labels, ti... | {
"repo_name": "olafhauk/mne-python",
"path": "mne/connectivity/tests/test_envelope.py",
"copies": "4",
"size": "3228",
"license": "bsd-3-clause",
"hash": 3394203897486447000,
"line_mean": 42.6216216216,
"line_max": 79,
"alpha_frac": 0.6366171004,
"autogenerated": false,
"ratio": 3.359001040582726... |
import numpy as np
from ..filter import next_fast_len
from ..source_estimate import _BaseSourceEstimate
from ..utils import verbose, _check_combine, _check_option
@verbose
def envelope_correlation(data, combine='mean', orthogonalize="pairwise",
log=False, absolute=True, verbose=None):
"... | {
"repo_name": "drammock/mne-python",
"path": "mne/connectivity/envelope.py",
"copies": "8",
"size": "6302",
"license": "bsd-3-clause",
"hash": 145122297214886000,
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"line_max": 79,
"alpha_frac": 0.5964773088,
"autogenerated": false,
"ratio": 3.963522012578616,
"config_... |
import numpy as np
from ..filter import next_fast_len
from ..source_estimate import _BaseSourceEstimate
from ..utils import verbose, _check_combine
@verbose
def envelope_correlation(data, combine='mean', verbose=None):
"""Compute the envelope correlation.
Parameters
----------
data : array-like, sh... | {
"repo_name": "adykstra/mne-python",
"path": "mne/connectivity/envelope.py",
"copies": "1",
"size": "4787",
"license": "bsd-3-clause",
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"alpha_frac": 0.6114596403,
"autogenerated": false,
"ratio": 3.6869699306090977,
"conf... |
import os
from os import 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
##############################################################################
# FAST LEGENDRE (DERI... | {
"repo_name": "teonlamont/mne-python",
"path": "mne/forward/_lead_dots.py",
"copies": "5",
"size": "18857",
"license": "bsd-3-clause",
"hash": -2680866822702062600,
"line_mean": 35.6155339806,
"line_max": 79,
"alpha_frac": 0.5697088614,
"autogenerated": false,
"ratio": 3.0822164105916965,
"conf... |
import os
from os import path as op
import numpy as np
from numpy.polynomial import legendre
from ..parallel import parallel_func
from ..utils import logger, _get_extra_data_path
##############################################################################
# FAST LEGENDRE (DERIVATIVE) POLYNOMIALS USING LOOKUP TAB... | {
"repo_name": "matthew-tucker/mne-python",
"path": "mne/forward/_lead_dots.py",
"copies": "7",
"size": "19257",
"license": "bsd-3-clause",
"hash": 1181619228166365200,
"line_mean": 36.247582205,
"line_max": 79,
"alpha_frac": 0.5538765124,
"autogenerated": false,
"ratio": 3.1424608355091386,
"co... |
import os
from os import 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
##############################################################################
# FAST LEGENDRE (DERIVATIVE) POLYNOMIALS USING L... | {
"repo_name": "nicproulx/mne-python",
"path": "mne/forward/_lead_dots.py",
"copies": "3",
"size": "18857",
"license": "bsd-3-clause",
"hash": -6416217600729951000,
"line_mean": 35.686770428,
"line_max": 79,
"alpha_frac": 0.5694437079,
"autogenerated": false,
"ratio": 3.081712698153293,
"config_... |
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
##############################################################################
# FAST LEGENDRE (DERIVATIV... | {
"repo_name": "adykstra/mne-python",
"path": "mne/forward/_lead_dots.py",
"copies": "1",
"size": "18846",
"license": "bsd-3-clause",
"hash": -9124618697016250000,
"line_mean": 35.6653696498,
"line_max": 79,
"alpha_frac": 0.5698291415,
"autogenerated": false,
"ratio": 3.0763956904995102,
"config... |
__author__ = 'seriouschicken'
# This file serve as a data extractor from the gdp_overtime. This file will open the file and scan through the
# Data of countries and their GDP overtime. Then, we will inject these data into the existing geoJSON
# The resulting geoJSON will contains the countries, their geographic data a... | {
"repo_name": "ExtremelySeriousChicken/WorldInD3",
"path": "data_extractor.py",
"copies": "1",
"size": "1610",
"license": "mit",
"hash": 8224374997291042000,
"line_mean": 30.5882352941,
"line_max": 111,
"alpha_frac": 0.6776397516,
"autogenerated": false,
"ratio": 3.561946902654867,
"config_test... |
__author__ = "Services team"
import ceilometerclient.client
import argparse
import os
import datetime
dir_path = os.environ['PWD']+"/billing/"
parser = argparse.ArgumentParser()
parser.add_argument("--project_id", dest='project_id', help="write id of project, which statistics you want to get")
parser.add_argument("--... | {
"repo_name": "vkuspits/ceilometer-billing",
"path": "billing.py",
"copies": "1",
"size": "6244",
"license": "apache-2.0",
"hash": -5658521248269627000,
"line_mean": 49.3548387097,
"line_max": 118,
"alpha_frac": 0.6356502242,
"autogenerated": false,
"ratio": 3.584385763490241,
"config_test": fa... |
__author__ = 'Servy'
import re
with open("FullSpellList.txt") as f:
lines = f.readlines()
def normalize_name(s):
res = []
buf = ""
for c in str(s):
if c.isupper():
res.append(buf)
buf = str(c)
elif c != ' ':
buf += c
res.append(buf)
buf = st... | {
"repo_name": "servy/dnd5spellbook",
"path": "util/ocr_spells_to_html.py",
"copies": "1",
"size": "2126",
"license": "mit",
"hash": 7725387014813634000,
"line_mean": 21.6276595745,
"line_max": 88,
"alpha_frac": 0.4557855127,
"autogenerated": false,
"ratio": 3.2808641975308643,
"config_test": fa... |
__author__ = 'setten'
import numpy
from pymatgen.util.testing import PymatgenTest
from pymatgen.util.convergence import determine_convergence
class ConvergenceTest(PymatgenTest):
def test_determine_convergence(self):
self.maxDiff = None
xs = [1, 2, 3, 4, 5, 6]
# a converging example:
... | {
"repo_name": "mbkumar/pymatgen",
"path": "pymatgen/util/tests/test_convergence.py",
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"line_max": 91,
"alpha_frac": 0.5664379289,
"autogenerated": false,
"ratio": 3.2253521126760565,
"config... |
__author__ = "setten"
import numpy
from pymatgen.util.convergence import determine_convergence
from pymatgen.util.testing import PymatgenTest
class ConvergenceTest(PymatgenTest):
def test_determine_convergence(self):
self.maxDiff = None
xs = [1, 2, 3, 4, 5, 6]
# a converging example:
... | {
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"confi... |
__author__ = 'Sevak Mardirosian'
import sys
from PyQt4 import QtGui, uic
class DialogDemo(QtGui.QWidget):
#----------------------------------------------------------------------
def __init__(self):
"""Constructor"""
# super(DialogDemo, self).__init__()
QtGui.QWidget.__init__(self)
... | {
"repo_name": "sevmardi/University-of-Applied-Sciences-Leiden",
"path": "Python/ifscp_Opdrachten/Opdracht3.py",
"copies": "1",
"size": "1376",
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__author__ = 'SEVAK MARDIROSIAN'
import os
import sys
import sqlite3
from PyQt4.QtGui import *
from PyQt4.QCore import *
import re
import logging
class Main(QMainWindow ):
dbPath = "data.db"
dbConn = sqlite3.connect(dbPath)
def __init__(self, parant=None):
super(Main, self).__init__(parant)
... | {
"repo_name": "sevmardi/University-of-Applied-Sciences-Leiden",
"path": "Python/iscp_keuzemodule/Controller/main.py",
"copies": "1",
"size": "2982",
"license": "mit",
"hash": 8288776370551684000,
"line_mean": 27.4,
"line_max": 109,
"alpha_frac": 0.619047619,
"autogenerated": false,
"ratio": 3.755... |
__author__ = 'sevas'
from BeautifulSoup import BeautifulSoup
from geholexceptions import *
TYPE_TO_DESCR = {
'THE':u'Theorie',
'EXE':u'Exercices',
'EXC':u'Excursion',
'GDC':u'Guidance',
'STG':u'Stage',
'TPR':u'Laboratoire',
'AGD':u'Agenda',
'GLB':u'Theorie ou Exercices',
'PRS':u'T... | {
"repo_name": "sevas/geholimportapp",
"path": "dependencies/gehol/basecalendar.py",
"copies": "2",
"size": "3621",
"license": "mit",
"hash": 7921870828939956000,
"line_mean": 28.4390243902,
"line_max": 113,
"alpha_frac": 0.5887876277,
"autogenerated": false,
"ratio": 4.096153846153846,
"config_... |
__author__ = 'sevas'
from datetime import datetime
import json
def make_dict_keys_str(a_dict):
"""
Takes a dictionary with unicode strings as keys
and returns a new dict with str keys instead
"""
items = [(str(k), v) for (k, v) in a_dict.items()]
return dict(items)
class ProviderStats(objec... | {
"repo_name": "sevas/csxj-crawler",
"path": "csxj/db/providerstats.py",
"copies": "1",
"size": "2579",
"license": "mit",
"hash": -3049225006810453000,
"line_mean": 29.7142857143,
"line_max": 78,
"alpha_frac": 0.5746413339,
"autogenerated": false,
"ratio": 3.81508875739645,
"config_test": false,... |
__author__ = 'sevas'
from datetime import datetime, timedelta
from StringIO import StringIO
class Event(object):
def __init__(self):
self.summary = ""
self.organizer = ""
self.location = ""
self.description = ""
self.dtstart = None
self.dtend = None
self.dt... | {
"repo_name": "Psycojoker/geholparser",
"path": "src/gehol/converters/rfc5545icalwriter.py",
"copies": "2",
"size": "3959",
"license": "mit",
"hash": 342836418449509600,
"line_mean": 32.2773109244,
"line_max": 104,
"alpha_frac": 0.5791866633,
"autogenerated": false,
"ratio": 3.326890756302521,
... |
__author__ = 'sevas'
import os, os.path
import argparse
from csxj.db.providerstats import ProviderStats
def print_report(stats):
print 'Number of articles :', stats.n_articles
print 'Number of links : ', stats.n_links
print 'Number of errors ', stats.n_errors
print 'Start date : ', stats.... | {
"repo_name": "sevas/csxj-crawler",
"path": "scripts/show_stats.py",
"copies": "1",
"size": "1590",
"license": "mit",
"hash": -1966225983940230700,
"line_mean": 29,
"line_max": 104,
"alpha_frac": 0.6301886792,
"autogenerated": false,
"ratio": 3.3473684210526318,
"config_test": false,
"has_no_... |
__author__ = 'seyriz'
from json import loads, dumps
from flask import *
from naver_login import flask_naver
app = Flask(__name__)
app.config['CLIENT_ID'] = "t4zhYRQ2RoZwVXAXzL5V"
app.config['CLIENT_SECRET'] = "iqANBHZRnq"
app.config['CALLBACK'] = '/callback'
app.config['SECRET_KEY'] = 'THIS_IS_NOT_SECRET_KEY'
naver ... | {
"repo_name": "seyriz/flask-naver",
"path": "test.py",
"copies": "1",
"size": "1592",
"license": "bsd-3-clause",
"hash": -1172644828605585700,
"line_mean": 34.4,
"line_max": 112,
"alpha_frac": 0.608040201,
"autogenerated": false,
"ratio": 3.365750528541226,
"config_test": false,
"has_no_keywo... |
__author__ = 'seyriz'
"""
Flask-Naver
-----------
Oauth2 wraper for Naver login
"""
from setuptools import setup
setup(
name='Flask-naver',
version='1.0',
url='http://github.com/seyriz/flask-naver',
license='BSD',
author='HanWool Lee',
author_email='kudnya@gmail.com',
description='Oauth2 ... | {
"repo_name": "seyriz/flask-naver",
"path": "setup.py",
"copies": "1",
"size": "1046",
"license": "bsd-3-clause",
"hash": 4211457237002715000,
"line_mean": 25.175,
"line_max": 70,
"alpha_frac": 0.6051625239,
"autogenerated": false,
"ratio": 3.8036363636363637,
"config_test": false,
"has_no_ke... |
import math
import warnings
import numpy as np
from scipy import interpolate
from scipy.stats import spearmanr
from ._isotonic import _isotonic_regression, _make_unique
from .base import BaseEstimator, TransformerMixin, RegressorMixin
from .utils import as_float_array, check_array, check_consistent_length
from .util... | {
"repo_name": "DailyActie/Surrogate-Model",
"path": "01-codes/scikit-learn-master/sklearn/isotonic.py",
"copies": "1",
"size": "14182",
"license": "mit",
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"line_mean": 31.6774193548,
"line_max": 79,
"alpha_frac": 0.5744605838,
"autogenerated": false,
"ratio": 3.883351... |
import numpy as np
from scipy import interpolate
from .base import BaseEstimator, TransformerMixin, RegressorMixin
from .utils import as_float_array, check_arrays
from ._isotonic import _isotonic_regression
import warnings
def isotonic_regression(y, sample_weight=None, y_min=None, y_max=None,
... | {
"repo_name": "JT5D/scikit-learn",
"path": "sklearn/isotonic.py",
"copies": "10",
"size": "8166",
"license": "bsd-3-clause",
"hash": 7121951687345838000,
"line_mean": 30.6511627907,
"line_max": 79,
"alpha_frac": 0.5489835905,
"autogenerated": false,
"ratio": 3.9430226943505553,
"config_test": f... |
import numpy as np
from scipy import interpolate
from scipy.stats import spearmanr
from .base import BaseEstimator, TransformerMixin, RegressorMixin
from .utils import as_float_array, check_array, check_consistent_length
from ._isotonic import _inplace_contiguous_isotonic_regression, _make_unique
import warnings
impor... | {
"repo_name": "vortex-ape/scikit-learn",
"path": "sklearn/isotonic.py",
"copies": "9",
"size": "13391",
"license": "bsd-3-clause",
"hash": -4119819817062420000,
"line_mean": 33.2480818414,
"line_max": 79,
"alpha_frac": 0.5805391681,
"autogenerated": false,
"ratio": 3.897264260768335,
"config_te... |
import numpy as np
from scipy import interpolate
from scipy.stats import spearmanr
from .base import BaseEstimator, TransformerMixin, RegressorMixin
from .utils import as_float_array, check_arrays
from ._isotonic import _isotonic_regression
import warnings
import math
def check_increasing(x, y):
"""Determine whe... | {
"repo_name": "chaluemwut/fbserver",
"path": "venv/lib/python2.7/site-packages/sklearn/isotonic.py",
"copies": "2",
"size": "12370",
"license": "apache-2.0",
"hash": -3987862562301471000,
"line_mean": 30.881443299,
"line_max": 79,
"alpha_frac": 0.5619240097,
"autogenerated": false,
"ratio": 3.905... |
import numpy as np
from scipy import interpolate
from scipy.stats import spearmanr
from .base import BaseEstimator, TransformerMixin, RegressorMixin
from .utils import check_array, check_consistent_length
from ._isotonic import _inplace_contiguous_isotonic_regression, _make_unique
import warnings
import math
__all__... | {
"repo_name": "chrsrds/scikit-learn",
"path": "sklearn/isotonic.py",
"copies": "2",
"size": "14210",
"license": "bsd-3-clause",
"hash": -8104718181716458000,
"line_mean": 32.9140811456,
"line_max": 79,
"alpha_frac": 0.5781843772,
"autogenerated": false,
"ratio": 3.8772169167803545,
"config_test... |
import numpy as np
from scipy import interpolate
from scipy.stats import spearmanr
import warnings
import math
from .base import BaseEstimator, TransformerMixin, RegressorMixin
from .utils import check_array, check_consistent_length
from .utils.validation import _check_sample_weight, _deprecate_positional_args
from .... | {
"repo_name": "huzq/scikit-learn",
"path": "sklearn/isotonic.py",
"copies": "2",
"size": "14050",
"license": "bsd-3-clause",
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"line_max": 79,
"alpha_frac": 0.5910320285,
"autogenerated": false,
"ratio": 3.8962839711591792,
"config_test": ... |
import numpy as np
from scipy import interpolate
from .base import BaseEstimator, TransformerMixin, RegressorMixin
from .utils import as_float_array, check_arrays
from ._isotonic import _isotonic_regression
def isotonic_regression(y, weight=None, y_min=None, y_max=None):
"""Solve the isotonic regression model::
... | {
"repo_name": "florian-f/sklearn",
"path": "sklearn/isotonic.py",
"copies": "3",
"size": "6601",
"license": "bsd-3-clause",
"hash": 2541344111893143000,
"line_mean": 29.0045454545,
"line_max": 79,
"alpha_frac": 0.5559763672,
"autogenerated": false,
"ratio": 3.742063492063492,
"config_test": fal... |
import numpy as np
from scipy import interpolate
from ..base import BaseEstimator, TransformerMixin, RegressorMixin
from ..utils import as_float_array, check_arrays
def isotonic_regression(y, weight=None, y_min=None, y_max=None):
"""Solve the isotonic regression model:
min sum w[i] (y[i] - y_[i]) ** 2
... | {
"repo_name": "pradyu1993/scikit-learn",
"path": "sklearn/linear_model/isotonic_regression_.py",
"copies": "1",
"size": "7808",
"license": "bsd-3-clause",
"hash": -7462030916173699000,
"line_mean": 30.232,
"line_max": 82,
"alpha_frac": 0.549692623,
"autogenerated": false,
"ratio": 3.7592681752527... |
import numpy as np
from scipy import interpolate
from .base import BaseEstimator, TransformerMixin, RegressorMixin
from .utils import as_float_array, check_arrays
def isotonic_regression(y, weight=None, y_min=None, y_max=None):
"""Solve the isotonic regression model:
min sum w[i] (y[i] - y_[i]) ** 2
... | {
"repo_name": "seckcoder/lang-learn",
"path": "python/sklearn/sklearn/isotonic.py",
"copies": "1",
"size": "7816",
"license": "unlicense",
"hash": -6959316593877802000,
"line_mean": 30.1394422311,
"line_max": 79,
"alpha_frac": 0.5491299898,
"autogenerated": false,
"ratio": 3.7613089509143407,
"... |
import numpy as np
from .base import LinearClassifierMixin, SparseCoefMixin
from ..feature_selection.from_model import _LearntSelectorMixin
from ..svm.base import BaseLibLinear
class LogisticRegression(BaseLibLinear, LinearClassifierMixin,
_LearntSelectorMixin, SparseCoefMixin):
"""Logi... | {
"repo_name": "treycausey/scikit-learn",
"path": "sklearn/linear_model/logistic.py",
"copies": "1",
"size": "5401",
"license": "bsd-3-clause",
"hash": 7550543199677424000,
"line_mean": 36.5069444444,
"line_max": 78,
"alpha_frac": 0.6574708387,
"autogenerated": false,
"ratio": 4.317346123101519,
... |
from collections import OrderedDict
from datetime import datetime, timezone
from pathlib import Path
import numpy as np
from ...utils import fill_doc, logger, verbose, warn, _check_fname
from ..base import BaseRaw
from ..meas_info import create_info
from ...annotations import Annotations
from ..utils import _mult_ca... | {
"repo_name": "drammock/mne-python",
"path": "mne/io/nihon/nihon.py",
"copies": "1",
"size": "14267",
"license": "bsd-3-clause",
"hash": 2604703604825078000,
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"autogenerated": false,
"ratio": 3.3704228679423576,
"config_test"... |
from collections import OrderedDict
from datetime import datetime, timezone
from pathlib import Path
import numpy as np
from ...utils import fill_doc, logger, verbose, warn
from ..base import BaseRaw
from ..meas_info import create_info
from ...annotations import Annotations
from ..utils import _mult_cal_one
def _e... | {
"repo_name": "olafhauk/mne-python",
"path": "mne/io/nihon/nihon.py",
"copies": "5",
"size": "13801",
"license": "bsd-3-clause",
"hash": -598941590953933000,
"line_mean": 35.2230971129,
"line_max": 79,
"alpha_frac": 0.5544525759,
"autogenerated": false,
"ratio": 3.3255421686746987,
"config_test... |
import numpy as np
from math import *
def arc_distance_python_nested_for_loops(a, b):
"""
Calculates the pairwise arc distance between all points in vector a and b.
"""
a_nrows = a.shape[0]
b_nrows = b.shape[0]
distance_matrix = np.zeros([a_nrows, b_nrows])
for i in range(a_nrows):
... | {
"repo_name": "numfocus/python-benchmarks",
"path": "arc_distance/arc_distance_python.py",
"copies": "1",
"size": "2068",
"license": "mit",
"hash": -4007261665644278300,
"line_mean": 28.1267605634,
"line_max": 78,
"alpha_frac": 0.5299806576,
"autogenerated": false,
"ratio": 2.933333333333333,
"... |
"""
Auxiliary transforms mainly to be used by Writer components.
This module is called "writer_aux" because otherwise there would be
conflicting imports like this one::
from docutils import writers
from docutils.transforms import writers
"""
__docformat__ = 'reStructuredText'
from docutils import nodes, ut... | {
"repo_name": "santisiri/popego",
"path": "envs/ALPHA-POPEGO/lib/python2.5/site-packages/docutils-0.4-py2.5.egg/docutils/transforms/writer_aux.py",
"copies": "6",
"size": "1390",
"license": "bsd-3-clause",
"hash": -2016183546753311200,
"line_mean": 25.7307692308,
"line_max": 67,
"alpha_frac": 0.61798... |
"""
Auxiliary transforms mainly to be used by Writer components.
This module is called "writer_aux" because otherwise there would be
conflicting imports like this one::
from docutils import writers
from docutils.transforms import writers
"""
__docformat__ = 'reStructuredText'
from docutils imp... | {
"repo_name": "mogotest/selenium",
"path": "selenium/src/py/lib/docutils/transforms/writer_aux.py",
"copies": "5",
"size": "1442",
"license": "apache-2.0",
"hash": -4273884996950406700,
"line_mean": 25.7307692308,
"line_max": 67,
"alpha_frac": 0.5957004161,
"autogenerated": false,
"ratio": 4.3963... |
__author__ = 'sfranky'
try:
import ujson as json
except ImportError:
import json
import logging
import sys
from qtop_py.serialiser import StatExtractor, GenericBatchSystem
from xml.etree import ElementTree as etree
import qtop_py.fileutils as fileutils
class SGEStatExtractor(StatExtractor):
def __init__(s... | {
"repo_name": "sfranky/qtop",
"path": "qtop_py/plugins/sge.py",
"copies": "3",
"size": "14194",
"license": "mit",
"hash": 9064954991036475000,
"line_mean": 42.4067278287,
"line_max": 125,
"alpha_frac": 0.5498802311,
"autogenerated": false,
"ratio": 3.84244721169464,
"config_test": true,
"has_... |
import theano
import theano.tensor as tensor
def arc_distance_theano_alloc_prepare(dtype='float64'):
"""
Calculates the pairwise arc distance between all points in vector a and b.
"""
a = tensor.matrix(dtype=str(dtype))
b = tensor.matrix(dtype=str(dtype))
# Theano don't implement all case of t... | {
"repo_name": "numfocus/python-benchmarks",
"path": "arc_distance/arc_distance_theano.py",
"copies": "1",
"size": "2175",
"license": "mit",
"hash": 2800028464632271000,
"line_mean": 31.9545454545,
"line_max": 82,
"alpha_frac": 0.5448275862,
"autogenerated": false,
"ratio": 3.3931357254290173,
"... |
import time
import json
import gspread
from oauth2client.client import SignedJwtAssertionCredentials
from sneakers.modules import Channel, Parameter
class GoogleSpread(Channel):
description = """\
Posts data to Google Spreadsheets.
"""
params = {
'sending': [
Parameter('clien... | {
"repo_name": "DakotaNelson/sneaky-creeper",
"path": "sneakers/channels/googleSpread.py",
"copies": "1",
"size": "2909",
"license": "mit",
"hash": 8002043135516924000,
"line_mean": 40.5571428571,
"line_max": 202,
"alpha_frac": 0.6428325885,
"autogenerated": false,
"ratio": 4.017955801104972,
"c... |
from myapp.models import Mode, State
from rest_framework import viewsets
from django.shortcuts import render_to_response
from django.template import RequestContext
from myapp.serializers import ModeSerializer, StateSerializer
import requests
import json
class ModeViewSet(viewsets.ModelViewSet):
queryset = Mode.ob... | {
"repo_name": "gabimachado/cooktop-IoT",
"path": "views.py",
"copies": "1",
"size": "2902",
"license": "mit",
"hash": -4034952807924310500,
"line_mean": 32.7441860465,
"line_max": 67,
"alpha_frac": 0.5727084769,
"autogenerated": false,
"ratio": 3.803407601572739,
"config_test": false,
"has_no... |
import time
import datetime
import sqlite3
import os
import glob
import RPi.GPIO as GPIO
import httplib, urllib
os.system('modprobe w1-gpio')
os.system('modprobe w1-therm')
base_dir = '/sys/bus/w1/devices/'
device_folder = glob.glob(base_dir + '28*')[0]
device_file = device_folder + '/w1_slave'
# Initialize SQLite
... | {
"repo_name": "gabimachado/cooktop-IoT",
"path": "controller.py",
"copies": "1",
"size": "3734",
"license": "mit",
"hash": -2338988457264141300,
"line_mean": 22.9358974359,
"line_max": 86,
"alpha_frac": 0.6751472951,
"autogenerated": false,
"ratio": 2.7618343195266273,
"config_test": false,
"... |
import math
import numpy as np
import pytest
import scipy.stats
from sklearn.utils._testing import assert_array_equal
from sklearn.utils.fixes import _joblib_parallel_args
from sklearn.utils.fixes import _object_dtype_isnan
from sklearn.utils.fixes import loguniform
from sklearn.utils.fixes import linspace, parse_v... | {
"repo_name": "kevin-intel/scikit-learn",
"path": "sklearn/utils/tests/test_fixes.py",
"copies": "2",
"size": "4164",
"license": "bsd-3-clause",
"hash": -1705870794047414300,
"line_mean": 33.7,
"line_max": 79,
"alpha_frac": 0.5958213256,
"autogenerated": false,
"ratio": 3.5109612141652615,
"con... |
import numpy as np
from nose.tools import assert_equal
from nose.tools import assert_false
from nose.tools import assert_true
from numpy.testing import (assert_almost_equal,
assert_array_almost_equal)
from sklearn.utils.fixes import astype
from sklearn.utils.fixes import divide, expit
def ... | {
"repo_name": "DailyActie/Surrogate-Model",
"path": "01-codes/scikit-learn-master/sklearn/utils/tests/test_fixes.py",
"copies": "1",
"size": "1828",
"license": "mit",
"hash": -4015477119821063700,
"line_mean": 33.4905660377,
"line_max": 80,
"alpha_frac": 0.6794310722,
"autogenerated": false,
"rat... |
import numpy as np
from nose.tools import assert_equal
from nose.tools import assert_false
from nose.tools import assert_true
from numpy.testing import (assert_almost_equal,
assert_array_almost_equal)
from sklearn.utils.fixes import divide, expit
from sklearn.utils.fixes import astype
de... | {
"repo_name": "JeanKossaifi/scikit-learn",
"path": "sklearn/utils/tests/test_fixes.py",
"copies": "281",
"size": "1829",
"license": "bsd-3-clause",
"hash": -6774058060806427000,
"line_mean": 32.2545454545,
"line_max": 79,
"alpha_frac": 0.6790595954,
"autogenerated": false,
"ratio": 3.073949579831... |
import numpy as np
from numpy.testing import (assert_almost_equal,
assert_array_almost_equal)
from sklearn.utils.fixes import divide, expit
from sklearn.utils.fixes import astype
from sklearn.utils.testing import assert_equal, assert_false, assert_true
def test_expit():
# Check numeri... | {
"repo_name": "giorgiop/scikit-learn",
"path": "sklearn/utils/tests/test_fixes.py",
"copies": "2",
"size": "1795",
"license": "bsd-3-clause",
"hash": -2754112918705978400,
"line_mean": 33.5192307692,
"line_max": 79,
"alpha_frac": 0.6763231198,
"autogenerated": false,
"ratio": 3.0631399317406145,
... |
import pickle
import numpy as np
import math
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.fixes import divide
from sklearn.utils.fixes import astype
f... | {
"repo_name": "wlamond/scikit-learn",
"path": "sklearn/utils/tests/test_fixes.py",
"copies": "3",
"size": "2492",
"license": "bsd-3-clause",
"hash": 67900575195911630,
"line_mean": 31.3636363636,
"line_max": 73,
"alpha_frac": 0.6512841091,
"autogenerated": false,
"ratio": 2.9808612440191387,
"c... |
import pickle
import numpy as np
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import asse... | {
"repo_name": "glennq/scikit-learn",
"path": "sklearn/utils/tests/test_fixes.py",
"copies": "8",
"size": "2324",
"license": "bsd-3-clause",
"hash": -51542771912559544,
"line_mean": 33.1764705882,
"line_max": 79,
"alpha_frac": 0.6966437177,
"autogenerated": false,
"ratio": 3.1278600269179004,
"c... |
import pickle
import numpy as np
import pytest
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_allclose
from sklearn.utils.fixes import divide
from sklearn.utils.fixes import MaskedArray
from sklearn.utils.fixes import nanm... | {
"repo_name": "vortex-ape/scikit-learn",
"path": "sklearn/utils/tests/test_fixes.py",
"copies": "7",
"size": "1778",
"license": "bsd-3-clause",
"hash": -3886781721488860000,
"line_mean": 29.1355932203,
"line_max": 73,
"alpha_frac": 0.6484814398,
"autogenerated": false,
"ratio": 3.0761245674740483... |
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