text stringlengths 0 1.05M | meta dict |
|---|---|
__author__ = 'srio'
import json
class GFile(object):
def __init__(self,filename=None):
if filename is None:
self.gfile_as_dictionary = None
else:
self.load_gfile(filename)
def load_gfile(self,filename,use_brackets_instead_parenthesis=True):
fp = open(filena... | {
"repo_name": "srio/minishadow",
"path": "minishadow/io/gfile.py",
"copies": "1",
"size": "2044",
"license": "mit",
"hash": 363612845685609700,
"line_mean": 23.6265060241,
"line_max": 77,
"alpha_frac": 0.5151663405,
"autogenerated": false,
"ratio": 3.6565295169946332,
"config_test": false,
"h... |
from PIL import Image
print "Enter values between 0 and 255"
lightcolor_red=input("Enter red value of light color: ")
lightcolor_blue=input("Enter blue value of light color: ")
lightcolor_green=input("Enter green value of light color: ")
darkcolor_red=input("Enter red value of dark color: ")
darkcolor_blue=input... | {
"repo_name": "BhargavGamit/ImageManipulationAlgorithms",
"path": "BitonalAlgo.py",
"copies": "1",
"size": "1726",
"license": "mit",
"hash": -6684750578274544000,
"line_mean": 35.7234042553,
"line_max": 68,
"alpha_frac": 0.6123986095,
"autogenerated": false,
"ratio": 3.143897996357013,
"config_... |
from PIL import Image
i = Image.open("input.png")
pixels = i.load()
width, height = i.size
j=Image.new(i.mode,i.size)
blueLevelFloat=input("Enter the color level for blue which is ranging from 0 to 255: ")
greenLevelFloat=input("Enter the color level for green which is ranging from 0 to 255: ")
redLevelFloat=input("... | {
"repo_name": "BhargavGamit/ImageManipulationAlgorithms",
"path": "ColourBalanceAlgo.py",
"copies": "1",
"size": "1713",
"license": "mit",
"hash": 4119975504008831500,
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"line_max": 89,
"alpha_frac": 0.6152948044,
"autogenerated": false,
"ratio": 3.1316270566727606,
"c... |
from PIL import Image
import random
i = Image.open("input.png")
blueShade = input("enter the ahdingfactor for blue range:0 to 1")
redShade = input("enter the ahdingfactor for red range:0 to 1")
greenShade = input("enter the ahdingfactor for green range:0 to 1")
#pixel data is stored in pixels in form of two dimensi... | {
"repo_name": "BhargavGamit/ImageManipulationAlgorithms",
"path": "ColourShadingAlgo.py",
"copies": "1",
"size": "1026",
"license": "mit",
"hash": -5626208601291593000,
"line_mean": 33.2,
"line_max": 68,
"alpha_frac": 0.6452241715,
"autogenerated": false,
"ratio": 3.0176470588235293,
"config_te... |
from PIL import Image
i = Image.open("input.png")
print(i.format,i.size,i.mode)
pixels = i.load()
width, height = i.size
j=Image.new(i.mode,i.size)
blueTint=input("Enter the percentage of blue tint in fraction: ")
greenTint=input("Enter the percentage of green tint in fraction: ")
redTint=input("Enter the percentag... | {
"repo_name": "BhargavGamit/ImageManipulationAlgorithms",
"path": "ColorTintAlgo.py",
"copies": "1",
"size": "1474",
"license": "mit",
"hash": -2822760286382927000,
"line_mean": 36.7948717949,
"line_max": 79,
"alpha_frac": 0.6105834464,
"autogenerated": false,
"ratio": 2.9072978303747536,
"conf... |
from PIL import Image
i = Image.open("input.png")
#pixel data is stored in pixels in form of two dimensional array
pixels = i.load()
width, height = i.size
j=Image.new(i.mode,i.size)
contrast=input("enter contrast value range:-255 to 255")
def Truncate(value):
if(value < 0):
value = 0
if(value > 255):
... | {
"repo_name": "BhargavGamit/ImageManipulationAlgorithms",
"path": "ContrastAlgo.py",
"copies": "1",
"size": "1431",
"license": "mit",
"hash": 6549451475204713000,
"line_mean": 35.6923076923,
"line_max": 68,
"alpha_frac": 0.6498951782,
"autogenerated": false,
"ratio": 2.9444444444444446,
"config... |
from PIL import Image
import random
i = Image.open("input.png")
#pixel data is stored in pixels in form of two dimensional array
pixels = i.load()
width, height = i.size
j=Image.new(i.mode,i.size)
#selects the random no of shades between 2 to 256 including 2 and 256
NumberOfShades = int(random.random()*255)+2
Conv... | {
"repo_name": "BhargavGamit/ImageManipulationAlgorithms",
"path": "CustomAlgo-BW.py",
"copies": "1",
"size": "1037",
"license": "mit",
"hash": 4031717604929134600,
"line_mean": 36.0357142857,
"line_max": 78,
"alpha_frac": 0.6991321119,
"autogenerated": false,
"ratio": 3.0955223880597016,
"confi... |
from PIL import Image
i = Image.open("input.png")
#pixel data is stored in pixels in form of two dimensional array
pixels = i.load()
width, height = i.size
j=Image.new(i.mode,i.size)
threshold=128
for x in range(width):
for y in range(height):
cpixel = pixels[x, y]
#cpixel[0] contains red value cpi... | {
"repo_name": "BhargavGamit/ImageManipulationAlgorithms",
"path": "SolarisationAlgo.py",
"copies": "1",
"size": "1399",
"license": "mit",
"hash": 1258902001727141600,
"line_mean": 26.431372549,
"line_max": 71,
"alpha_frac": 0.6633309507,
"autogenerated": false,
"ratio": 2.9145833333333333,
"con... |
__author__ = 'srkiyengar'
import dynamixel
import logging
import time
from datetime import datetime
MOVE_TICKS = 130
MOVE_TICKS_SERVO4 = 70
POS_ERROR = 20
ndi_measurement = False # meaning we are not running polaris when False
log_data_to_file = False # To collect servo data without ndi measureme... | {
"repo_name": "srkiyengar/NewGripper",
"path": "src/reflex.py",
"copies": "1",
"size": "39009",
"license": "mit",
"hash": 3850495706678450000,
"line_mean": 44.201622248,
"line_max": 123,
"alpha_frac": 0.5507703351,
"autogenerated": false,
"ratio": 3.5801211453744495,
"config_test": false,
"ha... |
__author__ = 'srkiyengar'
import dynamixel
import logging
POS_ERROR = 20
CALIBRATION_TICKS = 50
MAX_FINGER_MOVEMENT = 2000
MAX_PRESHAPE_MOVEMENT = 1200
MAX_SPEED = 350 # A max speed of 1023 is allowed
# This logger is setup in the main python script
my_logger = logging.getLogger("My_Logger")
LOG_LEVEL = log... | {
"repo_name": "srkiyengar/Gripper",
"path": "src/reflex.py",
"copies": "1",
"size": "17310",
"license": "mit",
"hash": -5768638374216309000,
"line_mean": 38.7954022989,
"line_max": 122,
"alpha_frac": 0.550779896,
"autogenerated": false,
"ratio": 3.5276136132056246,
"config_test": false,
"has_... |
__author__ = 'srkiyengar'
import pygame
import logging
import logging.handlers
from datetime import datetime
import reflex
import screen_print as sp
import joystick as js
import threading
import time
import data_collect as dc
import random
import tcp_client as tc
import shutter
import serial
SOME_MIN_RANDOM_NUMBER =... | {
"repo_name": "srkiyengar/NewGripper",
"path": "src/newgripper.py",
"copies": "1",
"size": "34768",
"license": "mit",
"hash": -8260173241608401000,
"line_mean": 45.1726427623,
"line_max": 138,
"alpha_frac": 0.5532386102,
"autogenerated": false,
"ratio": 4.041850732387817,
"config_test": false,
... |
__author__ = 'srkiyengar'
import random
from datetime import datetime
import logging
# This logger is setup in the main python script
my_logger = logging.getLogger("My_Logger")
LOG_LEVEL = logging.DEBUG
class displacement_file():
def __init__(self, rand):
file_prefix = str(datetime.now())[:16]
... | {
"repo_name": "srkiyengar/NewGripper",
"path": "src/data_collect.py",
"copies": "1",
"size": "1652",
"license": "mit",
"hash": -1353553387582203000,
"line_mean": 29.0545454545,
"line_max": 82,
"alpha_frac": 0.6016949153,
"autogenerated": false,
"ratio": 3.606986899563319,
"config_test": false,
... |
__author__ = 'srkiyengar'
import socket
import logging
import struct
from datetime import datetime
import time
LOG_LEVEL = logging.DEBUG
# Set up a logger with output level set to debug; Add the handler to the logger
my_logger = logging.getLogger("My_Logger")
class make_connection:
def __init__(self, sock=No... | {
"repo_name": "srkiyengar/NewGripper",
"path": "src/tcp_client.py",
"copies": "1",
"size": "8254",
"license": "mit",
"hash": 8019758161902969000,
"line_mean": 35.6844444444,
"line_max": 128,
"alpha_frac": 0.5817785316,
"autogenerated": false,
"ratio": 3.6538291279327137,
"config_test": false,
... |
__author__ = 'srkiyengar'
# Significant joystick code or the basis of it comes from pygame.joystick sample code
import pygame
import serial
import sys
JOY_DEADZONE_A0 = 0.2
JOY_DEADZONE_A1 = 0.1
# Before invoking this class pygame.init() needs to be called
class ExtremeProJoystick():
def __init__( self):
... | {
"repo_name": "srkiyengar/NewGripper",
"path": "src/joystick.py",
"copies": "1",
"size": "6451",
"license": "mit",
"hash": -1396018073613089300,
"line_mean": 33.8702702703,
"line_max": 110,
"alpha_frac": 0.5323205705,
"autogenerated": false,
"ratio": 4.026841448189763,
"config_test": false,
"... |
__author__ = 'srkiyengar'
# Significant joystick code or the basis of it comes from pygame.joystick sample code
import pygame
JOY_DEADZONE_A0 = 0.2
JOY_DEADZONE_A1 = 0.1
MOVE_TICKS = 15
MOVE_TICKS_SERVO4 = 10
# Before invoking this class pygame.init() needs to be called
class ExtremeProJoystick():
def __init_... | {
"repo_name": "srkiyengar/Gripper",
"path": "src/joystick.py",
"copies": "1",
"size": "3801",
"license": "mit",
"hash": -4503911433272449000,
"line_mean": 34.858490566,
"line_max": 123,
"alpha_frac": 0.531439095,
"autogenerated": false,
"ratio": 3.801,
"config_test": false,
"has_no_keywords":... |
__author__ = 's'
import os
import sys
#sys.path.append("c:/tdm-gcc-64/bin/")
print os.environ['path']
import theano
import theano.tensor as T
print theano.config.device
import numpy
import scipy
from scipy import ndimage
import numpy as np
from scipy import misc
from PIL import Image
import nu... | {
"repo_name": "serge-m/py_optical_flow",
"path": "multiscale_of_test.py",
"copies": "1",
"size": "1525",
"license": "mit",
"hash": -7034552758676476000,
"line_mean": 18.6351351351,
"line_max": 86,
"alpha_frac": 0.6439344262,
"autogenerated": false,
"ratio": 2.5501672240802677,
"config_test": fa... |
print(__doc__)
import numpy as np
import pylab as pl
from sklearn.cluster import KMeans
from sklearn.metrics import euclidean_distances
from sklearn.datasets import load_sample_image
from sklearn.utils import shuffle
from time import time
n_colors = 64
# Load the Summer Palace photo
china = load_sample_image("china.... | {
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"path": "color_quantization.py",
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"size": "2484",
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"autogenerated": false,
"ratio": 2.918918918918919,
"config_test": false,
"... |
import inspect
from textwrap import dedent
import pytest
import numpy as np
import os.path as op
from mne import create_info, EvokedArray, events_from_annotations, Epochs
from mne.channels import make_standard_montage
from mne.datasets.testing import data_path, _pytest_param
from mne.preprocessing.nirs import optica... | {
"repo_name": "kambysese/mne-python",
"path": "mne/viz/conftest.py",
"copies": "1",
"size": "3861",
"license": "bsd-3-clause",
"hash": 7309442421906218000,
"line_mean": 28.9302325581,
"line_max": 79,
"alpha_frac": 0.6275576276,
"autogenerated": false,
"ratio": 3.193548387096774,
"config_test": ... |
import numpy as np
from ... import pick_types
from ...io import BaseRaw
from ...utils import _validate_type, verbose
from ..nirs import _channel_frequencies, _check_channels_ordered
from ...filter import filter_data
@verbose
def scalp_coupling_index(raw, l_freq=0.7, h_freq=1.5,
l_trans_band... | {
"repo_name": "kambysese/mne-python",
"path": "mne/preprocessing/nirs/_scalp_coupling_index.py",
"copies": "10",
"size": "1974",
"license": "bsd-3-clause",
"hash": 5214736075912720000,
"line_mean": 28.9090909091,
"line_max": 78,
"alpha_frac": 0.5937183384,
"autogenerated": false,
"ratio": 3.53763... |
import numpy as np
from ...io import BaseRaw
from ...io.constants import FIFF
from ...utils import _validate_type, warn
from ...io.pick import _picks_to_idx
from ..nirs import _channel_frequencies, _check_channels_ordered
def optical_density(raw):
r"""Convert NIRS raw data to optical density.
Parameters
... | {
"repo_name": "drammock/mne-python",
"path": "mne/preprocessing/nirs/_optical_density.py",
"copies": "3",
"size": "1628",
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"alpha_frac": 0.6418918919,
"autogenerated": false,
"ratio": 3.30894308943... |
import numpy as np
from ...io import BaseRaw
from ...io.constants import FIFF
from ...utils import _validate_type, warn
from ...io.pick import _picks_to_idx
def optical_density(raw):
r"""Convert NIRS raw data to optical density.
Parameters
----------
raw : instance of Raw
The raw data.
... | {
"repo_name": "Eric89GXL/mne-python",
"path": "mne/preprocessing/nirs/_optical_density.py",
"copies": "7",
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"autogenerated": false,
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import numpy as np
from ...io import BaseRaw
from ...io.constants import FIFF
from ...utils import _validate_type, warn, verbose
from ...io.pick import _picks_to_idx
from ..nirs import _channel_frequencies, _check_channels_ordered
@verbose
def optical_density(raw, *, verbose=None):
r"""Convert NIRS raw data to ... | {
"repo_name": "mne-tools/mne-python",
"path": "mne/preprocessing/nirs/_optical_density.py",
"copies": "1",
"size": "1920",
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"ratio": 3.3046471600... |
import numpy as np
from ...io import BaseRaw
from ...utils import _validate_type, verbose
from ..nirs import _channel_frequencies, _check_channels_ordered
@verbose
def scalp_coupling_index(raw, l_freq=0.7, h_freq=1.5,
l_trans_bandwidth=0.3, h_trans_bandwidth=0.3,
ve... | {
"repo_name": "mne-tools/mne-python",
"path": "mne/preprocessing/nirs/_scalp_coupling_index.py",
"copies": "1",
"size": "1978",
"license": "bsd-3-clause",
"hash": 7174694667385581000,
"line_mean": 28.5223880597,
"line_max": 78,
"alpha_frac": 0.6092012133,
"autogenerated": false,
"ratio": 3.318791... |
import os.path as op
import numpy as np
from scipy import linalg
from ...io import BaseRaw
from ...io.constants import FIFF
from ...utils import _validate_type
from ..nirs import source_detector_distances, _channel_frequencies,\
_check_channels_ordered
def beer_lambert_law(raw, ppf=0.1):
r"""Convert NIRS o... | {
"repo_name": "olafhauk/mne-python",
"path": "mne/preprocessing/nirs/_beer_lambert_law.py",
"copies": "6",
"size": "2729",
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"hash": -5256050540476996000,
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"autogenerated": false,
"ratio": 3.21436984687... |
import os.path as op
import numpy as np
from ...io import BaseRaw
from ...io.constants import FIFF
from ...utils import _validate_type
from ..nirs import source_detector_distances, _channel_frequencies,\
_check_channels_ordered, _channel_chromophore
def beer_lambert_law(raw, ppf=0.1):
r"""Convert NIRS opti... | {
"repo_name": "bloyl/mne-python",
"path": "mne/preprocessing/nirs/_beer_lambert_law.py",
"copies": "4",
"size": "2992",
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import os.path as op
import pytest
import numpy as np
from numpy.testing import assert_array_equal
from mne import create_info
from mne.datasets.testing import data_path
from mne.io import read_raw_nirx, RawArray
from mne.preprocessing.nirs import (optical_density, beer_lambert_law,
... | {
"repo_name": "drammock/mne-python",
"path": "mne/preprocessing/nirs/tests/test_nirs.py",
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"hash": -3514034801553144300,
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"alpha_frac": 0.6322062758,
"autogenerated": false,
"ratio": 2.86730601569... |
import os.path as op
import pytest
import numpy as np
from mne.datasets.testing import data_path
from mne.io import read_raw_nirx, BaseRaw, read_raw_fif
from mne.preprocessing.nirs import optical_density, beer_lambert_law
from mne.utils import _validate_type
from mne.datasets import testing
from mne.externals.pymatr... | {
"repo_name": "wmvanvliet/mne-python",
"path": "mne/preprocessing/nirs/tests/test_beer_lambert_law.py",
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"autogenerated": false,
"ratio": 3... |
import pytest
import numpy as np
import os.path as op
from mne import create_info, EvokedArray, events_from_annotations, Epochs
from mne.channels import make_standard_montage
from mne.datasets.testing import data_path
from mne.preprocessing.nirs import optical_density, beer_lambert_law
from mne.io import read_raw_nir... | {
"repo_name": "cjayb/mne-python",
"path": "mne/viz/tests/conftest.py",
"copies": "1",
"size": "1608",
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"hash": -5059283885941625000,
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"autogenerated": false,
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"config_test... |
import re
import numpy as np
from scipy import linalg
from ...io.pick import _picks_to_idx
from ...utils import fill_doc
@fill_doc
def source_detector_distances(info, picks=None):
r"""Determine the distance between NIRS source and detectors.
Parameters
----------
info : Info
The measurement... | {
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"path": "mne/preprocessing/nirs/nirs.py",
"copies": "6",
"size": "4857",
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"hash": 6581877465298753000,
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"autogenerated": false,
"ratio": 3.529796511627907,
"conf... |
import re
import numpy as np
from ...io.pick import _picks_to_idx
from ...utils import fill_doc
@fill_doc
def source_detector_distances(info, picks=None):
r"""Determine the distance between NIRS source and detectors.
Parameters
----------
info : Info
The measurement info.
%(picks_all)s
... | {
"repo_name": "wmvanvliet/mne-python",
"path": "mne/preprocessing/nirs/nirs.py",
"copies": "1",
"size": "4835",
"license": "bsd-3-clause",
"hash": 502128657741240770,
"line_mean": 35.6287878788,
"line_max": 79,
"alpha_frac": 0.58221303,
"autogenerated": false,
"ratio": 3.521485797523671,
"confi... |
import re
import numpy as np
from ...io.pick import _picks_to_idx
from ...utils import fill_doc
# Standardized fNIRS channel name regexs
_S_D_F_RE = re.compile(r'S(\d+)_D(\d+) (\d+\.?\d*)')
_S_D_H_RE = re.compile(r'S(\d+)_D(\d+) (\w+)')
@fill_doc
def source_detector_distances(info, picks=None):
r"""Determine ... | {
"repo_name": "drammock/mne-python",
"path": "mne/preprocessing/nirs/nirs.py",
"copies": "4",
"size": "7555",
"license": "bsd-3-clause",
"hash": -6465750751141650000,
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"alpha_frac": 0.5868960953,
"autogenerated": false,
"ratio": 3.512319851231985,
"con... |
import numpy as np
from ... import pick_types
from ...io import BaseRaw
from ...utils import _validate_type
from ...io.pick import _picks_to_idx
def temporal_derivative_distribution_repair(raw):
"""Apply temporal derivative distribution repair to data.
Applies temporal derivative distribution repair (TDDR... | {
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"path": "mne/preprocessing/nirs/_tddr.py",
"copies": "6",
"size": "4673",
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"line_max": 79,
"alpha_frac": 0.6392039375,
"autogenerated": false,
"ratio": 3.6281055900621118,
"confi... |
import numpy as np
from ... import pick_types
from ...io import BaseRaw
from ...utils import _validate_type, verbose
from ...io.pick import _picks_to_idx
from ..nirs import _channel_frequencies, _check_channels_ordered
@verbose
def temporal_derivative_distribution_repair(raw, *, verbose=None):
"""Apply tempora... | {
"repo_name": "bloyl/mne-python",
"path": "mne/preprocessing/nirs/_tddr.py",
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"size": "4877",
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import numpy as np
from ... import pick_types
from ...io import BaseRaw
from ...utils import _validate_type, verbose
from ...io.pick import _picks_to_idx
@verbose
def temporal_derivative_distribution_repair(raw, *, verbose=None):
"""Apply temporal derivative distribution repair to data.
Applies temporal d... | {
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"path": "mne/preprocessing/nirs/_tddr.py",
"copies": "1",
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... |
from configparser import ConfigParser, RawConfigParser
import glob as glob
import re as re
import os.path as op
import datetime as dt
import numpy as np
from ..base import BaseRaw
from ..utils import _mult_cal_one
from ..constants import FIFF
from ..meas_info import create_info, _format_dig_points
from ...annotation... | {
"repo_name": "kambysese/mne-python",
"path": "mne/io/nirx/nirx.py",
"copies": "3",
"size": "14845",
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"autogenerated": false,
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"config_test"... |
import os.path as op
import pytest
import numpy as np
from numpy.testing import assert_allclose
from mne.datasets.testing import data_path
from mne.io import read_raw_nirx
from mne.preprocessing.nirs import optical_density, tddr
from mne.datasets import testing
fname_nirx_15_2 = op.join(data_path(download=False),
... | {
"repo_name": "mne-tools/mne-python",
"path": "mne/preprocessing/nirs/tests/test_temporal_derivative_distribution_repair.py",
"copies": "1",
"size": "1288",
"license": "bsd-3-clause",
"hash": 7550209424660786000,
"line_mean": 31.2,
"line_max": 68,
"alpha_frac": 0.676242236,
"autogenerated": false,
... |
import os.path as op
import pytest
import numpy as np
from mne.datasets.testing import data_path
from mne.io import read_raw_nirx
from mne.preprocessing.nirs import optical_density, tddr
from mne.datasets import testing
fname_nirx_15_2 = op.join(data_path(download=False),
'NIRx', 'nirscou... | {
"repo_name": "drammock/mne-python",
"path": "mne/preprocessing/nirs/tests/test_temporal_derivative_distribution_repair.py",
"copies": "12",
"size": "1068",
"license": "bsd-3-clause",
"hash": 8425197416321507000,
"line_mean": 30.4117647059,
"line_max": 68,
"alpha_frac": 0.6853932584,
"autogenerated... |
import re
import numpy as np
import datetime
from ..base import BaseRaw
from ..meas_info import create_info, _format_dig_points
from ..utils import _mult_cal_one
from ...annotations import Annotations
from ...utils import logger, verbose, fill_doc, warn, _check_fname
from ...utils.check import _require_version
from .... | {
"repo_name": "mne-tools/mne-python",
"path": "mne/io/snirf/_snirf.py",
"copies": "1",
"size": "17350",
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"autogenerated": false,
"ratio": 4.069903823598405,
"config_tes... |
import re
import numpy as np
import datetime
from ..base import BaseRaw
from ..meas_info import create_info
from ..utils import _mult_cal_one
from ...annotations import Annotations
from ...utils import logger, verbose, fill_doc, warn, _check_fname
from ...utils.check import _require_version
from ..constants import FI... | {
"repo_name": "bloyl/mne-python",
"path": "mne/io/snirf/_snirf.py",
"copies": "1",
"size": "16414",
"license": "bsd-3-clause",
"hash": -6368891050409640000,
"line_mean": 43.6032608696,
"line_max": 79,
"alpha_frac": 0.500304618,
"autogenerated": false,
"ratio": 4.072952853598015,
"config_test": ... |
import re
import numpy as np
from ..base import BaseRaw
from ..meas_info import create_info
from ...annotations import Annotations
from ...utils import logger, verbose, fill_doc, warn
from ...utils.check import _require_version
from ..constants import FIFF
from .._digitization import _make_dig_points
from ...transfor... | {
"repo_name": "cjayb/mne-python",
"path": "mne/io/snirf/_snirf.py",
"copies": "1",
"size": "10277",
"license": "bsd-3-clause",
"hash": -3334867924638294000,
"line_mean": 39.9442231076,
"line_max": 79,
"alpha_frac": 0.4911939282,
"autogenerated": false,
"ratio": 3.9285168195718656,
"config_test"... |
from BaseHTTPServer import BaseHTTPRequestHandler, HTTPServer
import SocketServer
from signal import signal, SIGPIPE, SIG_DFL
from urlparse import parse_qs
import json
import requests
import time
import datetime
import threading, Queue
import psutil
import cPickle as pickle
import zlib
from os import listdir
from os.... | {
"repo_name": "pedrocruz/sensing_bus",
"path": "fog/stress_tests/fog_stress_test.py",
"copies": "1",
"size": "6363",
"license": "apache-2.0",
"hash": -2590577598824076000,
"line_mean": 31.6307692308,
"line_max": 97,
"alpha_frac": 0.6056891403,
"autogenerated": false,
"ratio": 3.701570680628272,
... |
from .stochastic_gradient import BaseSGDClassifier
from .stochastic_gradient import BaseSGDRegressor
from .stochastic_gradient import DEFAULT_EPSILON
class PassiveAggressiveClassifier(BaseSGDClassifier):
"""Passive Aggressive Classifier
Parameters
----------
C : float
Maximum step size (reg... | {
"repo_name": "smartscheduling/scikit-learn-categorical-tree",
"path": "sklearn/linear_model/passive_aggressive.py",
"copies": "18",
"size": "9581",
"license": "bsd-3-clause",
"hash": -4570296171679554000,
"line_mean": 34.2242647059,
"line_max": 79,
"alpha_frac": 0.5575618411,
"autogenerated": fals... |
from ._stochastic_gradient import BaseSGDClassifier
from ._stochastic_gradient import BaseSGDRegressor
from ._stochastic_gradient import DEFAULT_EPSILON
class PassiveAggressiveClassifier(BaseSGDClassifier):
"""Passive Aggressive Classifier
Read more in the :ref:`User Guide <passive_aggressive>`.
Parame... | {
"repo_name": "kevin-intel/scikit-learn",
"path": "sklearn/linear_model/_passive_aggressive.py",
"copies": "2",
"size": "17461",
"license": "bsd-3-clause",
"hash": 639101184835786500,
"line_mean": 35.605870021,
"line_max": 79,
"alpha_frac": 0.6054636046,
"autogenerated": false,
"ratio": 4.1742768... |
from .stochastic_gradient import BaseSGDClassifier
from .stochastic_gradient import BaseSGDRegressor
from .stochastic_gradient import DEFAULT_EPSILON
class PassiveAggressiveClassifier(BaseSGDClassifier):
"""Passive Aggressive Classifier
Read more in the :ref:`User Guide <passive_aggressive>`.
Parameter... | {
"repo_name": "chrsrds/scikit-learn",
"path": "sklearn/linear_model/passive_aggressive.py",
"copies": "2",
"size": "17064",
"license": "bsd-3-clause",
"hash": -5364305289353295000,
"line_mean": 36.4210526316,
"line_max": 79,
"alpha_frac": 0.6095874355,
"autogenerated": false,
"ratio": 4.213333333... |
from ..utils.validation import _deprecate_positional_args
from ._stochastic_gradient import BaseSGDClassifier
from ._stochastic_gradient import BaseSGDRegressor
from ._stochastic_gradient import DEFAULT_EPSILON
class PassiveAggressiveClassifier(BaseSGDClassifier):
"""Passive Aggressive Classifier
Read more ... | {
"repo_name": "xuewei4d/scikit-learn",
"path": "sklearn/linear_model/_passive_aggressive.py",
"copies": "6",
"size": "17375",
"license": "bsd-3-clause",
"hash": -6498191519883187000,
"line_mean": 35.9680851064,
"line_max": 79,
"alpha_frac": 0.6074244604,
"autogenerated": false,
"ratio": 4.1796968... |
from .stochastic_gradient import BaseSGDClassifier
from .stochastic_gradient import BaseSGDRegressor
from .stochastic_gradient import DEFAULT_EPSILON
class PassiveAggressiveClassifier(BaseSGDClassifier):
"""Passive Aggressive Classifier
Parameters
----------
C : float
Maximum step size (reg... | {
"repo_name": "kmike/scikit-learn",
"path": "sklearn/linear_model/passive_aggressive.py",
"copies": "7",
"size": "9780",
"license": "bsd-3-clause",
"hash": 7295000883031270000,
"line_mean": 34.1798561151,
"line_max": 79,
"alpha_frac": 0.5599182004,
"autogenerated": false,
"ratio": 4.3777976723366... |
import time
import nmx
from random import random
from simpleOSC import initOSCClient, initOSCServer, setOSCHandler, sendOSCMsg, closeOSC, \
createOSCBundle, sendOSCBundle, startOSCServer
ip = "127.0.0.1"
client_port = 8001
# takes args : ip, port
initOSCClient(ip=ip, port=client_port)
# takes args : ip, po... | {
"repo_name": "trachelr/neural-mix",
"path": "examples/play_osc_commands.py",
"copies": "1",
"size": "1340",
"license": "artistic-2.0",
"hash": -7352251961771325000,
"line_mean": 31.6829268293,
"line_max": 98,
"alpha_frac": 0.6701492537,
"autogenerated": false,
"ratio": 3.4536082474226806,
"con... |
import time
import numpy as np
from random import random
from simpleOSC import initOSCClient, initOSCServer, setOSCHandler, sendOSCMsg, closeOSC, \
createOSCBundle, sendOSCBundle, startOSCServer
import mne
from mne.realtime import FieldTripClient
from mne.filter import band_pass_filter
from mne.time_frequenc... | {
"repo_name": "trachelr/neural-mix",
"path": "examples/mne_osc_bandpower.py",
"copies": "1",
"size": "1864",
"license": "artistic-2.0",
"hash": 2188131961857349000,
"line_mean": 27.6769230769,
"line_max": 90,
"alpha_frac": 0.6464592275,
"autogenerated": false,
"ratio": 2.954041204437401,
"confi... |
import numpy as np
from scipy import linalg
from distutils.version import LooseVersion
from .mixin import TransformerMixin
class CSP(TransformerMixin):
"""M/EEG signal decomposition using the Common Spatial Patterns (CSP)
This object can be used as a supervised decomposition to estimate
spatial filters... | {
"repo_name": "effigies/mne-python",
"path": "mne/decoding/csp.py",
"copies": "1",
"size": "8389",
"license": "bsd-3-clause",
"hash": 4736148207532665000,
"line_mean": 38.2009345794,
"line_max": 78,
"alpha_frac": 0.5331982358,
"autogenerated": false,
"ratio": 4.273560876209883,
"config_test": f... |
import copy as cp
import warnings
import numpy as np
from scipy import linalg
from .mixin import TransformerMixin
from ..cov import _regularized_covariance
class CSP(TransformerMixin):
"""M/EEG signal decomposition using the Common Spatial Patterns (CSP).
This object can be used as a supervised decomposit... | {
"repo_name": "lorenzo-desantis/mne-python",
"path": "mne/decoding/csp.py",
"copies": "6",
"size": "21527",
"license": "bsd-3-clause",
"hash": 4444683564573748700,
"line_mean": 45.096359743,
"line_max": 79,
"alpha_frac": 0.575695638,
"autogenerated": false,
"ratio": 4.3418717224687375,
"config_... |
import copy as cp
import numpy as np
from scipy import linalg
from .mixin import TransformerMixin
from ..cov import _regularized_covariance
class CSP(TransformerMixin):
"""M/EEG signal decomposition using the Common Spatial Patterns (CSP).
This object can be used as a supervised decomposition to estimate
... | {
"repo_name": "antiface/mne-python",
"path": "mne/decoding/csp.py",
"copies": "2",
"size": "20954",
"license": "bsd-3-clause",
"hash": -3681649902619304400,
"line_mean": 44.651416122,
"line_max": 79,
"alpha_frac": 0.5747828577,
"autogenerated": false,
"ratio": 4.335609352369129,
"config_test": ... |
__author__ = 'srwareham'
__all__ = ['extract_file_from_tar', 'extract_file_from_tar']
"""
Utility for managing archives
"""
from zipfile import ZipFile
import tarfile
def extract_file_from_zip(f, desired_file=None):
"""
Extracts desired file from zip file.
If no desired file is specified, the first fi... | {
"repo_name": "srwareham/CampaignAdvisor",
"path": "campaignadvisor/utilities/archiver.py",
"copies": "1",
"size": "1696",
"license": "mit",
"hash": 4290766000299703000,
"line_mean": 27.7457627119,
"line_max": 93,
"alpha_frac": 0.6780660377,
"autogenerated": false,
"ratio": 3.73568281938326,
"c... |
__author__ = 'srwareham'
"""
Utility for downloading files.
"""
import urllib2
import contextlib
import time
import socket
import StringIO
def download_file(url, max_retries=3, pause=3, timeout=socket.getdefaulttimeout()):
"""
Attempt to download a file given a url.
Note: this includes support for cont... | {
"repo_name": "srwareham/CampaignAdvisor",
"path": "campaignadvisor/utilities/downloader.py",
"copies": "1",
"size": "2757",
"license": "mit",
"hash": -2059276914870380000,
"line_mean": 33.049382716,
"line_max": 101,
"alpha_frac": 0.6401886108,
"autogenerated": false,
"ratio": 4.127245508982036,
... |
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from landlab import load_params
from landlab.plot import imshow_grid
from landlab.components import (PrecipitationDistribution, Radiation,
PotentialEvapotranspiration, SoilMoisture,
... | {
"repo_name": "landlab/drivers",
"path": "scripts/ecohydrology_flat_surface/ecohyd_functions_flat.py",
"copies": "1",
"size": "8090",
"license": "mit",
"hash": 7697456151862490000,
"line_mean": 40.2755102041,
"line_max": 84,
"alpha_frac": 0.5915945612,
"autogenerated": false,
"ratio": 3.047080979... |
"""
adapted from:
- Song and Geyer. "A neural circuitry that emphasizes
spinal feedback generates diverse behaviours of human locomotion." The
Journal of physiology, 2015.
"""
from __future__ import division # '/' always means non-truncating division
import numpy as np
from envs.control.loco_reflex_song2019 import Loc... | {
"repo_name": "stanfordnmbl/osim-rl",
"path": "osim/control/osim_loco_reflex_song2019.py",
"copies": "1",
"size": "6521",
"license": "mit",
"hash": 8220973939617085000,
"line_mean": 47.6641791045,
"line_max": 115,
"alpha_frac": 0.4628124521,
"autogenerated": false,
"ratio": 3.283484390735146,
"... |
"""
adapted from:
- Song and Geyer. "A neural circuitry that emphasizes
spinal feedback generates diverse behaviours of human locomotion." The
Journal of physiology, 2015.
- The control doesn't use muscle states if not needed
- still uses muscle force data for postivie force feedback
- Removed some control pathways
... | {
"repo_name": "stanfordnmbl/osim-rl",
"path": "envs/control/loco_reflex_song2019.py",
"copies": "1",
"size": "23705",
"license": "mit",
"hash": -102501654056675570,
"line_mean": 42.1803278689,
"line_max": 115,
"alpha_frac": 0.4815439781,
"autogenerated": false,
"ratio": 2.7557544757033248,
"con... |
"""
...
"""
from __future__ import division # '/' always means non-truncating division
import numpy as np
from scipy import interpolate
class VTgtField(object):
nn_get = np.array([11, 11]) # vtgt_field_local data is nn_get*nn_get = 121
ver = {}
# v00: constant forward velocities
ver['ver00'] = {}
... | {
"repo_name": "stanfordnmbl/osim-rl",
"path": "envs/target/v_tgt_field.py",
"copies": "1",
"size": "16837",
"license": "mit",
"hash": -4593181175045463000,
"line_mean": 44.3827493261,
"line_max": 115,
"alpha_frac": 0.4430718061,
"autogenerated": false,
"ratio": 3.0657319737800437,
"config_test"... |
import numpy as np
import pytest
from sklearn.datasets import make_blobs
from sklearn.cluster import OPTICS
from sklearn.cluster._optics import _extend_region, _extract_xi_labels
from sklearn.exceptions import DataConversionWarning
from sklearn.metrics.cluster import contingency_matrix
from sklearn.metrics.pairwise im... | {
"repo_name": "anntzer/scikit-learn",
"path": "sklearn/cluster/tests/test_optics.py",
"copies": "5",
"size": "19230",
"license": "bsd-3-clause",
"hash": 1122240322598857200,
"line_mean": 39.6553911205,
"line_max": 79,
"alpha_frac": 0.6084763391,
"autogenerated": false,
"ratio": 2.760155016506387,... |
__author__ = 'sshejko'
from PyQt4 import QtCore, QtGui
from PyQt4.QtGui import QSystemTrayIcon
import gold_time_lib
import sys
import os
import datetime
class MainWindow(QtGui.QMainWindow):
def __init__(self):
QtGui.QMainWindow.__init__(self)
# adding icon and systray icon
#if os.path.ex... | {
"repo_name": "ssic7i/Gold-time",
"path": "gold_time_main.py",
"copies": "1",
"size": "5381",
"license": "mit",
"hash": 442960616853864640,
"line_mean": 49.2990654206,
"line_max": 237,
"alpha_frac": 0.6430031593,
"autogenerated": false,
"ratio": 3.083667621776504,
"config_test": false,
"has_n... |
__author__ = 'sshejko'
import httplib, urllib
import os
import xml.etree.ElementTree as ET
class yandex_spellchecker:
base_url = r'http://speller.yandex.net/services/spellservice/checkText'
post_url = r'/services/spellservice/checkText'
def __init__(self):
pass
def get_result_post(self, te... | {
"repo_name": "ssic7i/sy_spellchecker",
"path": "sp_ch_lib.py",
"copies": "1",
"size": "1992",
"license": "mit",
"hash": -1561150063915190800,
"line_mean": 33.9473684211,
"line_max": 118,
"alpha_frac": 0.578815261,
"autogenerated": false,
"ratio": 3.5256637168141594,
"config_test": false,
"ha... |
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_spd_matrix
import gauss_utils as gauss
import pyprobml_utils as pml
def main():
np.random.seed(12)
data_dim = 8
n_data = 10
threshold_missing = 0.5
mu = np.random.randn(data_dim, 1)
sigma = make_s... | {
"repo_name": "probml/pyprobml",
"path": "scripts/gauss_imputation_known_params_demo.py",
"copies": "1",
"size": "1663",
"license": "mit",
"hash": -4271693219807021600,
"line_mean": 33.4255319149,
"line_max": 94,
"alpha_frac": 0.6542393265,
"autogenerated": false,
"ratio": 2.867241379310345,
"c... |
from __future__ import division
import logging
import copy
import numpy as np
import scipy as sp
from warnings import warn
from scipy import sparse
from scipy.sparse import lil_matrix, csr_matrix, vstack
from numpy import random
from scipy.sparse import issparse
import numbers
from sklearn.externals import six
f... | {
"repo_name": "shubhomoydas/pyaad",
"path": "pyalad/random_split_trees.py",
"copies": "1",
"size": "38022",
"license": "mit",
"hash": 3007675995184238600,
"line_mean": 34.870754717,
"line_max": 135,
"alpha_frac": 0.5701962022,
"autogenerated": false,
"ratio": 3.8687423687423688,
"config_test": ... |
__authors__ = 'shuffleres, sinkdb'
from character import Character
enemy_health = 100000
enemy_energy = 100
enemy_attack = 100
enemy_heal = 40
health = 100000
energy = 100
attack = 150
heal = 30
round_count = 0
char = Character()
print("Your stats: ", health, energy, attack, heal)
print("Enemy stats: ", enemy_hea... | {
"repo_name": "scottshuffler/Game",
"path": "game.py",
"copies": "1",
"size": "1176",
"license": "mit",
"hash": -4531118571993891000,
"line_mean": 20,
"line_max": 76,
"alpha_frac": 0.5892857143,
"autogenerated": false,
"ratio": 3.230769230769231,
"config_test": false,
"has_no_keywords": false... |
from PIL import Image
import random
i = Image.open("input.png")
#pixel data is stored in pixels in form of two dimensional array
pixels = i.load()
width, height = i.size
j=Image.new(i.mode,i.size)
gamma=input("Enter the value of gamma range:0.25 to 2.0: ")
for x in range(width):
for y in range(height):
cpixe... | {
"repo_name": "BhargavGamit/ImageManipulationAlgorithms",
"path": "GammaCorrectionAlgo.py",
"copies": "1",
"size": "1211",
"license": "mit",
"hash": -3199115733037002000,
"line_mean": 40.7586206897,
"line_max": 68,
"alpha_frac": 0.681255161,
"autogenerated": false,
"ratio": 3.042713567839196,
"... |
__authors__ = ['Stew Francis']
import csv
import time
import math
import colorsys
import pygame
import os
from DDRPi import FloorCanvas
from VisualisationPlugin import VisualisationPlugin
# from DDRPi import DDRPiPlugin
#from lib.utils import ColourUtils
import logging
class Filter(object):
def process(self,... | {
"repo_name": "fraz3alpha/led-disco-dancefloor",
"path": "software/controller/visualisation_plugins/patterns.py",
"copies": "2",
"size": "15741",
"license": "mit",
"hash": -1760023774891280400,
"line_mean": 34.0579064588,
"line_max": 117,
"alpha_frac": 0.5479956801,
"autogenerated": false,
"ratio... |
__authors__ = ['Stew Francis']
import csv
import time
import math
import colorsys
from DDRPi import DDRPiPlugin
from lib.utils import ColourUtils
class Filter(object):
def process(self, frame):
raise NotImplementedError
class Pattern(object):
def __init__(self, patternFile):
with open(patternFile) as csvFil... | {
"repo_name": "joel-wright/DDRPi",
"path": "plugins/patterns.py",
"copies": "1",
"size": "8735",
"license": "mit",
"hash": 2683730695489037300,
"line_mean": 25.7125382263,
"line_max": 138,
"alpha_frac": 0.65930166,
"autogenerated": false,
"ratio": 2.9155540720961284,
"config_test": false,
"ha... |
__author__ = 'sstober'
from deepthought.analysis.tempo.autocorrelation import *
import scipy.stats as stats
def sliding_window_tempo_analysis(trial, sfreq,
bpm_min, bpm_max,
start_sample, stop_sample,
window_length, ... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/experiments/bcmi2015/tempo_prediction.py",
"copies": "1",
"size": "3381",
"license": "bsd-3-clause",
"hash": -3382258128420631000,
"line_mean": 33.8659793814,
"line_max": 122,
"alpha_frac": 0.5811889973,
"autogenerated": false,
"ratio": 3.... |
__author__ = 'sstober'
from deepthought.datasets.eeg.EEGEpochsDataset import DataFile
class DataFileWithMetaClasses(DataFile):
def __init__(self, filepath, meta_classes=dict()):
super(DataFileWithMetaClasses, self).__init__(filepath)
for class_name, classes in meta_classes.iteritems():
... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/datasets/eeg/meta_class.py",
"copies": "1",
"size": "1503",
"license": "bsd-3-clause",
"hash": -9077261800644021000,
"line_mean": 26.3272727273,
"line_max": 63,
"alpha_frac": 0.5409181637,
"autogenerated": false,
"ratio": 4.040322580645161... |
__author__ = 'sstober'
from pylearn2.train_extensions.best_params import MonitorBasedSaveBest
from copy import deepcopy
from pylearn2.utils import serial
from pylearn2.utils.timing import log_timing
import logging
log = logging.getLogger(__name__)
class MonitorBasedSaveBestMod(MonitorBasedSaveBest):
def on_mo... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/pylearn2ext/best_params.py",
"copies": "1",
"size": "1573",
"license": "bsd-3-clause",
"hash": -2261956944791149000,
"line_mean": 33.2173913043,
"line_max": 77,
"alpha_frac": 0.6185632549,
"autogenerated": false,
"ratio": 4.333333333333333... |
__author__ = 'sstober'
import logging
from pylearn2.utils.logger import CustomFormatter, CustomStreamHandler
def configure_custom(debug=False, stdout=None, stderr=None):
"""
copied from pylearn2.utils.logger to obtain similar behavior for deepthought extensions
Configure the logging module to output logg... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/util/logging_util.py",
"copies": "1",
"size": "2139",
"license": "bsd-3-clause",
"hash": 5725851138043827000,
"line_mean": 36.5438596491,
"line_max": 91,
"alpha_frac": 0.6979897148,
"autogenerated": false,
"ratio": 4.347560975609756,
"co... |
__author__ = 'sstober'
import logging
log = logging.getLogger(__name__)
from deepthought.datasets.openmiir.preprocessing.events import generate_beat_events
from deepthought.datasets.openmiir.metadata import get_stimuli_version
import deepthought.datasets.openmiir.preprocessing as preprocessing
# alias
preload = prep... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/experiments/bcmi2015/preprocessing.py",
"copies": "1",
"size": "1928",
"license": "bsd-3-clause",
"hash": 2547759787302493000,
"line_mean": 36.8039215686,
"line_max": 116,
"alpha_frac": 0.5368257261,
"autogenerated": false,
"ratio": 4.5258... |
__author__ = 'sstober'
import logging
log = logging.getLogger(__name__)
from deepthought.util.fs_util import load
from deepthought.datasets.selection import DatasetMetaDB
from pylearn2.utils.timing import log_timing
import os
class Datasource(object):
def __init__(self, data, metadata, targets=None):
s... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/datasets/datasources.py",
"copies": "1",
"size": "2379",
"license": "bsd-3-clause",
"hash": 1120393440154615800,
"line_mean": 32.9857142857,
"line_max": 86,
"alpha_frac": 0.604035309,
"autogenerated": false,
"ratio": 3.8125,
"config_test... |
__author__ = 'sstober'
import logging
log = logging.getLogger(__name__)
from mne import pick_types
import numpy as np
import librosa
import mne
## old interface from mne/filter.py:
# def resample(self, sfreq, npad=100, window='boxcar',
# stim_picks=None, n_jobs=1, verbose=None):
def fast_resample_mne(ra... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/mneext/resample.py",
"copies": "1",
"size": "6815",
"license": "bsd-3-clause",
"hash": -7696827608279479000,
"line_mean": 39.8083832335,
"line_max": 109,
"alpha_frac": 0.5973587674,
"autogenerated": false,
"ratio": 3.809390721073225,
"co... |
__author__ = 'sstober'
import logging
log = logging.getLogger(__name__)
from pylearn2.costs.cost import Cost, DefaultDataSpecsMixin
from pylearn2.space import CompositeSpace, Conv2DSpace, VectorSpace
import theano.tensor as T
class MeanCrossCorrelation(DefaultDataSpecsMixin, Cost):
def __init__(self, supervise... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/pylearn2ext/costs/correlation.py",
"copies": "1",
"size": "3880",
"license": "bsd-3-clause",
"hash": 5712632885647795000,
"line_mean": 27.1231884058,
"line_max": 103,
"alpha_frac": 0.5309278351,
"autogenerated": false,
"ratio": 3.353500432... |
__author__ = 'sstober'
import logging
log = logging.getLogger(__name__)
from pylearn2.costs.cost import DefaultDataSpecsMixin, Cost
import theano.tensor as T
from deepthought.util.axes_util import symbolic_to_b01c
class MeanSquaredReconstructionError(DefaultDataSpecsMixin, Cost):
"""
for each input instan... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/pylearn2ext/costs/reconstruct.py",
"copies": "1",
"size": "5324",
"license": "bsd-3-clause",
"hash": 4716424403258711000,
"line_mean": 25.8939393939,
"line_max": 81,
"alpha_frac": 0.5533433509,
"autogenerated": false,
"ratio": 3.6021650879... |
__author__ = 'sstober'
import logging
log = logging.getLogger(__name__)
import mne
from deepthought.datasets.openmiir.preprocessing.pipeline import load_raw, load_ica
from deepthought.datasets.openmiir.preprocessing.events import generate_beat_events
from deepthought.mneext.resample import fast_resample_mne, resample... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/datasets/openmiir/preprocessing/__init__.py",
"copies": "1",
"size": "3360",
"license": "bsd-3-clause",
"hash": 9011408628370962000,
"line_mean": 39.0119047619,
"line_max": 105,
"alpha_frac": 0.5827380952,
"autogenerated": false,
"ratio": ... |
__author__ = 'sstober'
import logging
log = logging.getLogger(__name__)
import numpy as np
import mne
from deepthought.datasets.openmiir.preprocessing.events \
import filter_trial_events, generate_beat_events, filter_beat_events
from deepthought.datasets.openmiir.metadata \
import get_stimuli_version, load_st... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/datasets/openmiir/epochs.py",
"copies": "1",
"size": "4813",
"license": "bsd-3-clause",
"hash": 8223452683909073000,
"line_mean": 28.3475609756,
"line_max": 102,
"alpha_frac": 0.5763557033,
"autogenerated": false,
"ratio": 3.62971342383107... |
__author__ = 'sstober'
import logging
log = logging.getLogger(__name__)
import numpy as np
import functools
# from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix
from pylearn2.datasets import Dataset
from pylearn2.format.target_format import OneHotFormatter
from pylearn2.space import CompositeSpace,... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/datasets/eeg/EEGEpochsDataset.py",
"copies": "1",
"size": "15136",
"license": "bsd-3-clause",
"hash": 1562818688248836900,
"line_mean": 34.9548693587,
"line_max": 112,
"alpha_frac": 0.5529201903,
"autogenerated": false,
"ratio": 4.50074338... |
__author__ = 'sstober'
import logging
log = logging.getLogger(__name__)
import numpy as np
KEYSTROKE_BASE_ID = 2000
def get_event_id(stimulus_id, condition):
return stimulus_id * 10 + condition
def decode_event_id(event_id):
if event_id < 1000:
stimulus_id = event_id / 10
condition = event_... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/datasets/openmiir/events.py",
"copies": "1",
"size": "1421",
"license": "bsd-3-clause",
"hash": -8572622694799107000,
"line_mean": 28.625,
"line_max": 73,
"alpha_frac": 0.5763546798,
"autogenerated": false,
"ratio": 3.690909090909091,
"c... |
__author__ = 'sstober'
import logging
log = logging.getLogger(__name__)
import os
import datetime
import numpy as np
import mne
from mne.io.edf.edf import read_raw_edf
from scipy.io import loadmat
from deepthought.util.fs_util import ensure_parent_dir_exists
from deepthought.datasets.openmiir.preprocessing.keystr... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/datasets/openmiir/preprocessing/events.py",
"copies": "1",
"size": "20385",
"license": "bsd-3-clause",
"hash": -8217116019613545000,
"line_mean": 34.8908450704,
"line_max": 120,
"alpha_frac": 0.5682609762,
"autogenerated": false,
"ratio": ... |
__author__ = 'sstober'
import logging
log = logging.getLogger(__name__)
import os
import numpy as np
import xlrd
import deepthought
from deepthought.util.fs_util import ensure_parent_dir_exists
from deepthought.datasets.openmiir.constants import STIMULUS_IDS
DEFAULT_VERSION = 1
def get_stimuli_version(subject):
... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/datasets/openmiir/metadata.py",
"copies": "1",
"size": "5592",
"license": "bsd-3-clause",
"hash": -5838849774872429000,
"line_mean": 31.8941176471,
"line_max": 111,
"alpha_frac": 0.55472103,
"autogenerated": false,
"ratio": 3.4928169893816... |
__author__ = 'sstober'
import logging
log = logging.getLogger(__name__)
import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import mne
from mne.io import read_raw_edf
from mne.channels import rename_channels
from mne.preprocessing import ICA, read_ica
from mne.viz.top... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/datasets/openmiir/preprocessing/pipeline.py",
"copies": "1",
"size": "40580",
"license": "bsd-3-clause",
"hash": 6403651418171538000,
"line_mean": 37.103286385,
"line_max": 139,
"alpha_frac": 0.5782897979,
"autogenerated": false,
"ratio": ... |
__author__ = 'sstober'
import logging
log = logging.getLogger(__name__)
from pylearn2.utils.timing import log_timing
from pylearn2.train_extensions import TrainExtension
from pylearn2.space import CompositeSpace
import theano
from sklearn.metrics import confusion_matrix, classification_report
import numpy as np
c... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/pylearn2ext/monitor/rnn.py",
"copies": "1",
"size": "4858",
"license": "bsd-3-clause",
"hash": -8027333924418754000,
"line_mean": 31.8243243243,
"line_max": 92,
"alpha_frac": 0.5282009057,
"autogenerated": false,
"ratio": 4.089225589225589... |
__author__ = 'sstober'
import numpy as np
import matplotlib.pyplot as plt
from itertools import cycle
from mne.io.pick import channel_type
from mne.externals.six import string_types
from mne.defaults import _handle_default
from mne.viz.utils import tight_layout
def plot_ica_overlay_evoked(evoked, evoked_cln, title, s... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/mneext/viz.py",
"copies": "1",
"size": "7947",
"license": "bsd-3-clause",
"hash": 2709513191120352000,
"line_mean": 36.6682464455,
"line_max": 89,
"alpha_frac": 0.5467472002,
"autogenerated": false,
"ratio": 3.6238030095759233,
"config_t... |
__author__ = 'sstober'
import numpy as np
import theano
class WindowingProcessor(object):
def __init__(self, window_size, hop_size=None, stack_frames=False):
if hop_size is None:
hop_size = window_size // 4
self.hop_size = hop_size
self.window_size = window_size
self.... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/datasets/eeg/trial_processors.py",
"copies": "1",
"size": "9085",
"license": "bsd-3-clause",
"hash": -4295414609910383000,
"line_mean": 31.5663082437,
"line_max": 96,
"alpha_frac": 0.5277930655,
"autogenerated": false,
"ratio": 4.055803571... |
__author__ = 'sstober'
import numpy as np
# default settings
from deepthought.datasets.eeg.biosemi64 import Biosemi64Layout as Biosemi64
def compute_plot_xy_mapping(xy_layout, layout=Biosemi64()):
channel_xy_positions = np.zeros((layout.num_channels(),2), dtype=np.int)
channel_name_to_number = dict()
f... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/datasets/eeg/channel_util.py",
"copies": "1",
"size": "4997",
"license": "bsd-3-clause",
"hash": -8835042100520183000,
"line_mean": 30.0434782609,
"line_max": 78,
"alpha_frac": 0.6331799079,
"autogenerated": false,
"ratio": 3.5667380442541... |
__author__ = 'sstober'
import numpy as np
def compute_autocorrelation(data):
# from pandas.tools.plotting import autocorrelation_plot
# see http://pandas.pydata.org/pandas-docs/dev/visualization.html#autocorrelation-plot
# see http://www.itl.nist.gov/div898/handbook/eda/section3/autocopl.htm
from pan... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/analysis/tempo/autocorrelation.py",
"copies": "1",
"size": "2768",
"license": "bsd-3-clause",
"hash": -6333841718031703000,
"line_mean": 29.7666666667,
"line_max": 91,
"alpha_frac": 0.6062138728,
"autogenerated": false,
"ratio": 3.06194690... |
__author__ = 'sstober'
import collections
class DatasetMetaDB(object):
def __init__(self, metadata, attributes):
def multi_dimensions(n, dtype):
""" Creates an n-dimension dictionary where the n-th dimension is of type 'type'
"""
if n == 0:
return dtyp... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/datasets/selection.py",
"copies": "1",
"size": "1726",
"license": "bsd-3-clause",
"hash": 711897120121752300,
"line_mean": 32.862745098,
"line_max": 92,
"alpha_frac": 0.5382387022,
"autogenerated": false,
"ratio": 4.590425531914893,
"con... |
__author__ = 'sstober'
import logging
logger = logging.getLogger('test')
from mne.preprocessing import ICA
from mne.preprocessing.ica import _check_start_stop, _reject_data_segments
from mne.evoked import Evoked,EvokedArray
from mne.io.pick import pick_types, pick_info
class EvokedICA(ICA):
def fit(self, inst, ... | {
"repo_name": "sstober/deepthought",
"path": "deepthought/mneext/ica.py",
"copies": "1",
"size": "2403",
"license": "bsd-3-clause",
"hash": 7008167170156417000,
"line_mean": 37.7741935484,
"line_max": 101,
"alpha_frac": 0.5530586767,
"autogenerated": false,
"ratio": 3.714064914992272,
"config_t... |
__author__ = 'sstreiner'
import re
import socket
import nis
import sys
from collections import OrderedDict
def checkdns(host):
try:
dnsraw = socket.gethostbyaddr(host)
dns = dnsraw[2]
if dns:
return True
except:
return False
def checkipaddr(host):
try:
... | {
"repo_name": "sstreiner/nastools",
"path": "cdotmigration/libcdotmigration/libcdotmigration.py",
"copies": "1",
"size": "7451",
"license": "bsd-2-clause",
"hash": 5687160616729370000,
"line_mean": 29.412244898,
"line_max": 116,
"alpha_frac": 0.5412696282,
"autogenerated": false,
"ratio": 4.06270... |
__author__ = "Stacy Smith"
__credits__ = "Jeremy Schulman, Nitin Kumar"
import unittest2 as unittest
from nose.plugins.attrib import attr
from jnpr.junos.facts.swver import version_info
import jnpr.junos.facts.swver
@attr('unit')
class TestVersionInfo(unittest.TestCase):
def test_version_info_after_type_len_els... | {
"repo_name": "spidercensus/py-junos-eznc",
"path": "tests/unit/facts/test_swver.py",
"copies": "1",
"size": "2674",
"license": "apache-2.0",
"hash": 5894308679564512000,
"line_mean": 35.1486486486,
"line_max": 83,
"alpha_frac": 0.5792819746,
"autogenerated": false,
"ratio": 3.15702479338843,
"... |
__author__ = "Stacy Smith"
__credits__ = "Jeremy Schulman, Nitin Kumar"
import unittest
from nose.plugins.attrib import attr
from mock import patch, MagicMock
import os
from lxml import etree
from jnpr.junos import Device
from jnpr.junos.exception import RpcError
from ncclient.manager import Manager, make_device_han... | {
"repo_name": "spidercensus/py-junos-eznc",
"path": "tests/unit/facts/test_get_software_information.py",
"copies": "1",
"size": "9963",
"license": "apache-2.0",
"hash": -2407025879152036000,
"line_mean": 43.6771300448,
"line_max": 80,
"alpha_frac": 0.5666967781,
"autogenerated": false,
"ratio": 3... |
__author__ = "Stacy Smith"
__credits__ = "Jeremy Schulman, Nitin Kumar"
import unittest
from nose.plugins.attrib import attr
from mock import patch, MagicMock
import os
from jnpr.junos import Device
from ncclient.manager import Manager, make_device_handler
from ncclient.transport import SSHSession
@attr('unit')
cl... | {
"repo_name": "spidercensus/py-junos-eznc",
"path": "tests/unit/facts/test_current_re.py",
"copies": "1",
"size": "1810",
"license": "apache-2.0",
"hash": -4899721735222327000,
"line_mean": 33.1698113208,
"line_max": 78,
"alpha_frac": 0.591160221,
"autogenerated": false,
"ratio": 3.80252100840336... |
from collections import namedtuple
from inspect import isgenerator
import warnings
from ..externals.six import string_types
import numpy as np
from scipy import linalg
from ..source_estimate import SourceEstimate
from ..epochs import _BaseEpochs
from ..evoked import Evoked, EvokedArray
from ..utils import logger, _r... | {
"repo_name": "antiface/mne-python",
"path": "mne/stats/regression.py",
"copies": "2",
"size": "15231",
"license": "bsd-3-clause",
"hash": -7366613527551673000,
"line_mean": 43.9292035398,
"line_max": 79,
"alpha_frac": 0.6172280218,
"autogenerated": false,
"ratio": 3.8001497005988023,
"config_t... |
from collections import namedtuple
from inspect import isgenerator
import warnings
from ..externals.six import string_types
import numpy as np
from scipy import linalg, sparse
from ..source_estimate import SourceEstimate
from ..epochs import _BaseEpochs
from ..evoked import Evoked, EvokedArray
from ..utils import lo... | {
"repo_name": "rajegannathan/grasp-lift-eeg-cat-dog-solution-updated",
"path": "python-packages/mne-python-0.10/mne/stats/regression.py",
"copies": "3",
"size": "14875",
"license": "bsd-3-clause",
"hash": -5020865233275059000,
"line_mean": 43.1394658754,
"line_max": 79,
"alpha_frac": 0.6086722689,
... |
from inspect import isgenerator
from collections import namedtuple
import numpy as np
from scipy import linalg, sparse
from ..externals.six import string_types
from ..source_estimate import SourceEstimate
from ..epochs import _BaseEpochs
from ..evoked import Evoked, EvokedArray
from ..utils import logger, _reject_da... | {
"repo_name": "ARudiuk/mne-python",
"path": "mne/stats/regression.py",
"copies": "4",
"size": "17595",
"license": "bsd-3-clause",
"hash": -2331784790605169000,
"line_mean": 43.6573604061,
"line_max": 79,
"alpha_frac": 0.6144359193,
"autogenerated": false,
"ratio": 3.7468057921635434,
"config_te... |
from inspect import isgenerator
from collections import namedtuple
import numpy as np
from scipy import linalg, sparse
from ..source_estimate import SourceEstimate
from ..epochs import BaseEpochs
from ..evoked import Evoked, EvokedArray
from ..utils import logger, _reject_data_segments, warn, fill_doc
from ..io.pick... | {
"repo_name": "adykstra/mne-python",
"path": "mne/stats/regression.py",
"copies": "2",
"size": "17731",
"license": "bsd-3-clause",
"hash": -5213919070938516000,
"line_mean": 43.5502512563,
"line_max": 79,
"alpha_frac": 0.6114714342,
"autogenerated": false,
"ratio": 3.725,
"config_test": false,
... |
from inspect import isgenerator
from collections import namedtuple
import numpy as np
from ..source_estimate import SourceEstimate
from ..epochs import BaseEpochs
from ..evoked import Evoked, EvokedArray
from ..utils import logger, _reject_data_segments, warn, fill_doc
from ..io.pick import pick_types, pick_info, _p... | {
"repo_name": "kambysese/mne-python",
"path": "mne/stats/regression.py",
"copies": "7",
"size": "17734",
"license": "bsd-3-clause",
"hash": -1958960874206965000,
"line_mean": 43.4310776942,
"line_max": 79,
"alpha_frac": 0.6086416968,
"autogenerated": false,
"ratio": 3.723587481621508,
"config_t... |
from collections import namedtuple
from inspect import isgenerator
import warnings
import numpy as np
from scipy import linalg
from ..source_estimate import SourceEstimate
from ..epochs import _BaseEpochs
from ..evoked import Evoked, EvokedArray
from ..utils import logger
from ..io.pick import pick_types
def linea... | {
"repo_name": "Odingod/mne-python",
"path": "mne/stats/regression.py",
"copies": "5",
"size": "5681",
"license": "bsd-3-clause",
"hash": -8515773475633695000,
"line_mean": 40.7720588235,
"line_max": 79,
"alpha_frac": 0.6236578067,
"autogenerated": false,
"ratio": 3.725245901639344,
"config_test... |
from collections import namedtuple
from inspect import isgenerator
import warnings
import numpy as np
from scipy import linalg, stats
from ..source_estimate import SourceEstimate
from ..epochs import _BaseEpochs
from ..evoked import Evoked, EvokedArray
from ..utils import logger
from ..io.pick import pick_types
de... | {
"repo_name": "effigies/mne-python",
"path": "mne/stats/regression.py",
"copies": "2",
"size": "5660",
"license": "bsd-3-clause",
"hash": 7437852305340747000,
"line_mean": 40.9259259259,
"line_max": 79,
"alpha_frac": 0.6233215548,
"autogenerated": false,
"ratio": 3.7187910643889617,
"config_tes... |
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