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from __future__ import absolute_import, division, print_function
import logging
import docker
from docker import errors
import tempfile
import requests
from urllib3.exceptions import TimeoutError
from requests.exceptions import (RequestException, Timeout)
import json
import pprint
import time
import re
import os
import... | {
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from __future__ import absolute_import, division, print_function
import logging
import docker
import tempfile
import requests
from requests.exceptions import RequestException
import json
import pprint
import time
import re
import os
import tarfile
import sys
from cloudpickle import CloudPickler
import pickle
import num... | {
"repo_name": "dcrankshaw/clipper",
"path": "clipper_admin/clipper_admin/clipper_admin.py",
"copies": "1",
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from __future__ import absolute_import, division, print_function
import logging
import numpy as np
from scipy.optimize import leastsq
from scipy.stats import norm
log = logging.getLogger(__name__)
class IdealObs(object):
"""Statistical ideal observer.
Converts input values (usually SNRenv) to a percentage... | {
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"path": "pambox/central/decision_metrics.py",
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from __future__ import absolute_import, division, print_function
import math
from collections import defaultdict
from unittest import TestCase
from manhattan import util
class TestUtil(TestCase):
def assertRandomish(self, s, bits=4):
# Calculate entropy.
counts = defaultdict(int)
for cha... | {
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"path": "manhattan/tests/test_util.py",
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from __future__ import absolute_import, division, print_function
import math
import numpy as np
import matplotlib
from matplotlib.figure import Figure
from matplotlib.axes import Axes
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from matplotlib.collections import BrokenBarHCollection
import matpl... | {
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from __future__ import absolute_import, division, print_function
import math
import os
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import root_pandas
from uncertainties import ufloat
from histograms import histogram
from plotting_utilities import (
add_si_formatter,
COLOURS as colours... | {
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"path": "scripts/production_mass_distributions.py",
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from __future__ import absolute_import, division, print_function
import matplotlib.pyplot as plt
from astropy.io import fits
import example_helpers
import drms
# Series name, carrington rotation and data segment
series = 'hmi.synoptic_mr_720s'
cr = 2150
segname = 'synopMr'
# DRMS-Server URL (or shortcut) and data ur... | {
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"path": "examples/plot_synoptic_mr.py",
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"autogenerated": false,
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"has_... |
from __future__ import absolute_import, division, print_function
import matplotlib.pyplot as plt
import example_helpers
import drms
import pandas
pandas_version = tuple(map(int, pandas.__version__.split('.')[:2]))
if pandas_version >= (0, 22):
# Since pandas v0.22, we need to explicitely register matplotlib
# ... | {
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"path": "examples/plot_hmi_lightcurve.py",
"copies": "1",
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from __future__ import absolute_import, division, print_function
import matplotlib.pyplot as plt
import example_helpers
import drms
# Series name, timespan and wavelength
series = 'aia.lev1_euv_12s'
series_lev1 = 'aia.lev1'
wavelen = 335
#tsel = '2015-01-01T00:00:01Z/1h'
#tsel = '2015-01-01T00:00:01Z/1d'
#tsel = '201... | {
"repo_name": "kbg/drms",
"path": "examples/plot_aia_ligthcurve.py",
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"ha... |
from __future__ import absolute_import, division, print_function
import matplotlib.pyplot as plt
import numpy as np
import example_helpers
import drms
# Series name, start time and data segment
series = 'hmi.v_sht_modes'
tstart = '2014.06.20_00:00:00_TAI'
segname = 'm6' # 'm6', 'm18' or 'm36'
# DRMS-Server URL (or ... | {
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"path": "examples/plot_hmi_modes.py",
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"license": "mit",
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"autogenerated": false,
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"config_test": false,
"has_no_keyw... |
from __future__ import absolute_import, division, print_function
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import example_helpers
import drms
pandas_version = tuple(map(int, pd.__version__.split('.')[:2]))
if pandas_version >= (0, 22):
# Since pandas v0.22, we need to explicitely regis... | {
"repo_name": "kbg/drms",
"path": "examples/plot_polarfield.py",
"copies": "1",
"size": "2696",
"license": "mit",
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"ha... |
from __future__ import absolute_import, division, print_function
import numbers
from datetime import date, datetime
import toolz
from toolz import first
from ..compatibility import basestring
from ..expr import Expr, Symbol, Symbol, eval_str, Union
from ..dispatch import dispatch
__all__ = ['compute', 'compute_up']
... | {
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"path": "blaze/compute/core.py",
"copies": "1",
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... |
from __future__ import (absolute_import, division, print_function)
import numpy as np
from addie.plot.constants import BASIC_COLORS
class IndicatorManager(object):
""" Manager for all indicator lines
Indicator's Type =
- 0: horizontal. moving along Y-direction. [x_min, x_max], [y, y];
- 1: vertical.... | {
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"path": "addie/plot/indicatormanager.py",
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from __future__ import absolute_import, division, print_function
import numpy as np
from collections import OrderedDict
from sklearn.metrics import auc, log_loss, precision_recall_curve, roc_auc_score
def loss(labels, predictions):
return log_loss(labels, predictions)
def positive_accuracy(labels, predictions, ... | {
"repo_name": "agitter/dragonn",
"path": "dragonn/metrics.py",
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"has... |
from __future__ import absolute_import, division, print_function
import numpy as np
from glm import glm, glm_diagnostics, glm_multiple
def mean_underlying_noise(data_4d):
""" takes average of data_4d across the 4th dimension (time)
Parameters:
-----------
data_4d: 4 dimensional np.array
(with 4th dimension the... | {
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from __future__ import (absolute_import, division, print_function)
import numpy as np
from h5py import File
class SampleEnvironmentHandler(object):
# Specify paths in NeXus files for different sample environments
_dict_samp_env = dict()
_dict_samp_env['cryostat'] = {'samp': {'path_to_time': '/entry/DASl... | {
"repo_name": "neutrons/FastGR",
"path": "addie/processing/idl/sample_environment_handler.py",
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from __future__ import absolute_import, division, print_function
import numpy as np
from numpy.random import gamma
from scipy.special import gammainc
__all__ = ['SersicSamples']
class SersicSamples(object):
"""
Class for sampling sersic profiles in CatSim
"""
def __init__(self, rng):
"""
... | {
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"path": "snsims/samplingGalaxies.py",
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from __future__ import (absolute_import, division, print_function)
import numpy as np
from qtpy.QtWidgets import QMainWindow, QTableWidgetItem
from qtpy import QtCore, QtGui
from addie.utilities import load_ui
from addie.utilities.general import get_list_algo
from addie.processing.mantid.master_table.tree_definition i... | {
"repo_name": "neutrons/FastGR",
"path": "addie/processing/mantid/master_table/align_and_focus_args.py",
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"hash": -2265650386489986000,
"line_mean": 38.3150684932,
"line_max": 120,
"alpha_frac": 0.6159407666,
"autogenerated": false,
"ratio": 3.4227... |
from __future__ import absolute_import, division, print_function
import numpy as np
from scipy.stats import gaussian_kde
import numpy.random as rand
from scipy.integrate import quad
class KDE(object):
"""An implementation of a kernel density estimator allowing for adaptive kernels.
If the `adaptive` keyword i... | {
"repo_name": "timothydmorton/simpledist",
"path": "simpledist/kde.py",
"copies": "1",
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"license": "mit",
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from __future__ import absolute_import, division, print_function
import numpy as np
from sklearn.preprocessing import normalize
def crop_resample(bands, intensities, crops):
intensities = np.atleast_2d(intensities)
crops = sorted(crops)
# check that each chunk is valid and doesn't overlap with any other
prev_... | {
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"path": "superman/preprocess/utils.py",
"copies": "1",
"size": "2332",
"license": "mit",
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"line_max": 79,
"alpha_frac": 0.647084048,
"autogenerated": false,
"ratio": 2.963151207115629,
"config_test": fa... |
from __future__ import absolute_import, division, print_function
import numpy as np
import gsd.hoomd
import sklearn
import scipy.optimize as opt
import os
import pdb
from sklearn.neighbors import BallTree
from sklearn.neighbors import radius_neighbors_graph
from scipy.spatial.distance import cdist
from scipy.special im... | {
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"path": "clustering/morphology.py",
"copies": "1",
"size": "1933",
"license": "mit",
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"line_mean": 34.1454545455,
"line_max": 127,
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"autogenerated": false,
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"config_te... |
from __future__ import absolute_import, division, print_function
import numpy as np
import math
import numbers
import json
import ast
import copy
from scipy.interpolate import interp1d
from lsst.sims.catalogs.decorators import register_class, register_method, compound
from lsst.sims.catUtils.mixins import Variability, ... | {
"repo_name": "LSSTDESC/Twinkles",
"path": "python/desc/twinkles/twinklesVariabilityMixins.py",
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"ratio": 3.432005060088551... |
from __future__ import absolute_import, division, print_function
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import pdb
from scipy.optimize import curve_fit
from scipy.optimize import minimize
plt.ioff()
font = {'weight' : 'bold',
'size' : 22}... | {
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"path": "clustering/smoluchowski.py",
"copies": "1",
"size": "12388",
"license": "mit",
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"line_mean": 31.0932642487,
"line_max": 81,
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"autogenerated": false,
"ratio": 3.4748948106591864,
"config... |
from __future__ import absolute_import, division, print_function
import numpy as np
import numpy.linalg as npl
import matplotlib.pyplot as plt
import nibabel as nib
import pandas as pd # new
import sys # instead of os
import scipy.stats
from scipy.stats import gamma
import os
import scipy.stats as stats
# Relative pat... | {
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"path": "final/scripts/glm_final.py",
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"size": "2456",
"license": "bsd-3-clause",
"hash": 1985297413861462800,
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"line_max": 101,
"alpha_frac": 0.6034201954,
"autogenerated": false,
"ratio": 3.218872870249017,
"con... |
from __future__ import absolute_import, division, print_function
import numpy as np
import numpy.linalg as npl
def glm(data_4d, conv):
"""
Return a tuple of the estimated coefficients in 4 dimensions and
the design matrix.
Parameters
----------
data_4d: numpy array of 4 dimensions
... | {
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from __future__ import absolute_import, division, print_function
import numpy as np
import numpy.random
from nose.tools import assert_equal
import skimage.draw as skd
from scipy.ndimage.morphology import binary_dilation
import skbeam.core.image as nimage
def test_find_ring_center_acorr_1D():
for x in [110, 150, ... | {
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"path": "skbeam/core/tests/test_image.py",
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from __future__ import absolute_import, division, print_function
import numpy as np
import os
import logging
def _read_amira(src_file):
"""
Reads all information contained within standard AmiraMesh data sets.
Separate the header information from the image/volume, data.
Parameters
----------
s... | {
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"path": "skbeam/io/avizo_io.py",
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"config_test... |
from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
from sqlalchemy import create_engine
from lsst.sims.catalogs.db import CatalogDBObject
from lsst.sims.catUtils.utils import ObservationMetaDataGenerator
from lsst.sims.catUtils.exampleCatalogDefinitions import Defaul... | {
"repo_name": "DarkEnergyScienceCollaboration/Monitor",
"path": "python/desc/monitor/createTruthDB.py",
"copies": "2",
"size": "9341",
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"hash": 5937648328266738000,
"line_mean": 44.5658536585,
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"autogenerated": false,
"ratio": ... |
from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
import scipy.optimize as opt
from scipy.special import erf
from .due import due, Doi
__all__ = ["Model", "Fit", "opt_err_func", "transform_data", "cumgauss"]
# Use duecredit (duecredit.org) to provide a citation t... | {
"repo_name": "emaudes/bha",
"path": "bha/bha.py",
"copies": "1",
"size": "5752",
"license": "mit",
"hash": -1817461935733462500,
"line_mean": 26.2606635071,
"line_max": 85,
"alpha_frac": 0.5895340751,
"autogenerated": false,
"ratio": 4.334589299171062,
"config_test": false,
"has_no_keywords"... |
from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
def read_process(filname, sep="\t"):
col_names = ["user", "item", "rate", "st"]
df = pd.read_csv(filname, sep=sep, header=None, names=col_names, engine='python')
df["user"] -= 1
df["item"] -= 1
... | {
"repo_name": "songgc/TF-recomm",
"path": "dataio.py",
"copies": "1",
"size": "1896",
"license": "apache-2.0",
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"alpha_frac": 0.5849156118,
"autogenerated": false,
"ratio": 3.3978494623655915,
"config_test": false,
"has... |
from __future__ import absolute_import, division, print_function
import numpy as np
# import plottool_ibeis.draw_func2 as df2
from plottool_ibeis import fig_presenter
#from plottool_ibeis import custom_figure
#from plottool_ibeis import custom_constants
#from os.path import join
import utool as ut
ut.noinject(__name__,... | {
"repo_name": "Erotemic/plottool",
"path": "plottool_ibeis/plot_helpers.py",
"copies": "1",
"size": "5803",
"license": "apache-2.0",
"hash": 8426317602161440000,
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"alpha_frac": 0.5803894537,
"autogenerated": false,
"ratio": 3.2619449128724,
"config_te... |
from __future__ import (absolute_import, division, print_function)
import numpy as np
import re
import sys
import copy
from periodictable import formula
from qtpy.QtWidgets import QMainWindow, QApplication
from addie.utilities import load_ui
from qtpy import QtGui
from addie.processing.mantid.master_table.table_row_ha... | {
"repo_name": "neutrons/FastGR",
"path": "addie/processing/mantid/master_table/periodic_table/material_handler.py",
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"size": "20381",
"license": "mit",
"hash": 1706748964892499700,
"line_mean": 31.1466876972,
"line_max": 123,
"alpha_frac": 0.4795152348,
"autogenerated": false,
"rati... |
from __future__ import (absolute_import, division, print_function)
import numpy as np
import scipy.constants
# Constants
avogadro = scipy.constants.N_A
cm3_to_angstroms3 = 1e24
avogadro_term = avogadro / 1e24
PRECISION = 5
def is_int(value):
"""Checks if `value` is an integer
:param value: Input value to ch... | {
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"path": "addie/utilities/math_tools.py",
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"line_max": 103,
"alpha_frac": 0.6377229801,
"autogenerated": false,
"ratio": 3.5182279649284727,
"config_test": fal... |
from __future__ import absolute_import, division, print_function
import numpy as np
import scipy.signal
import scipy.sparse
from sklearn.preprocessing import normalize
from sklearn.decomposition import PCA
from .utils import libs_norm3, cumulative_norm
__all__ = [
'BandNormalize', 'BezierSquash', 'CosineSquash', 'C... | {
"repo_name": "all-umass/superman",
"path": "superman/preprocess/steps.py",
"copies": "1",
"size": "5980",
"license": "mit",
"hash": 2396649526054173700,
"line_mean": 25.4601769912,
"line_max": 79,
"alpha_frac": 0.6598662207,
"autogenerated": false,
"ratio": 3.066666666666667,
"config_test": fa... |
from __future__ import absolute_import, division, print_function
import numpy as np
import sys
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from scipy.signal import correlate2d
from simdna.simulations import loaded_motifs
def get_motif_scores(encoded_sequences,
motif_names,
... | {
"repo_name": "deepchem/deepchem",
"path": "contrib/dragonn/utils.py",
"copies": "6",
"size": "5203",
"license": "mit",
"hash": -7936609403675035000,
"line_mean": 34.1554054054,
"line_max": 80,
"alpha_frac": 0.6340572746,
"autogenerated": false,
"ratio": 3.480267558528428,
"config_test": false,... |
from __future__ import absolute_import, division, print_function
import numpy as np
import warnings
# from .due import due, Doi
__all__ = ["tao_impl_angle_beta"]
import astropy
import astropy.units as u
def convert_redshift_to_comoving_distance(redshifts,
cosmo=None,
... | {
"repo_name": "manodeep/lightcone",
"path": "lightcone/lightcone.py",
"copies": "1",
"size": "9662",
"license": "mit",
"hash": -3994173344389349000,
"line_mean": 34.0072463768,
"line_max": 79,
"alpha_frac": 0.5565100393,
"autogenerated": false,
"ratio": 3.614665170220726,
"config_test": false,
... |
from __future__ import absolute_import, division, print_function
import numpy as np
def adjR2(MRSS,y_1d,df,rank):
"""
Computes a single Adjusted R^2 value for a model (high is good)
Input:
------
MRSS : Mean Squared Error
y_1d : the y vector as a 1d np array ( n x 1)
df : the degrees of the model (n-p-1 gen... | {
"repo_name": "berkeley-stat159/project-alpha",
"path": "code/utils/functions/model_comparison.py",
"copies": "1",
"size": "3561",
"license": "bsd-3-clause",
"hash": 2765659924895276500,
"line_mean": 22.8993288591,
"line_max": 75,
"alpha_frac": 0.6641392867,
"autogenerated": false,
"ratio": 2.739... |
from __future__ import absolute_import, division, print_function
import numpy as np
def events2neural(task_fname, tr, n_trs):
""" Return predicted neural time course from event file `task_fname`
Parameters
----------
task_fname : str
Filename of event file
tr : float
TR in seconds
... | {
"repo_name": "berkeley-stat159/project-alpha",
"path": "code/utils/functions/stimuli.py",
"copies": "1",
"size": "1024",
"license": "bsd-3-clause",
"hash": 6001039594899577000,
"line_mean": 29.1176470588,
"line_max": 72,
"alpha_frac": 0.623046875,
"autogenerated": false,
"ratio": 3.7925925925925... |
from __future__ import absolute_import, division, print_function
import numpy as np
def multiline(ax, data, labels, line_kw=None, xlabels=None, ylabels=None):
"""Plot a number of datasets on their own line_artist
Parameters
----------
ax : iterable
List of mpl.Axes objects
data : list
... | {
"repo_name": "sameera2004/xray-vision",
"path": "xray_vision/mpl_plotting/utils.py",
"copies": "3",
"size": "2478",
"license": "bsd-3-clause",
"hash": 7959108008217354000,
"line_mean": 34.4,
"line_max": 80,
"alpha_frac": 0.5371267151,
"autogenerated": false,
"ratio": 3.7602427921092563,
"confi... |
from __future__ import absolute_import, division, print_function
import numpy as np
def present_3d(three_d_image):
""" Coverts a 3d image into a 2nd image with slices in 3rd dimension varying across the element
three_d_image: is a 3 dimensional numpy array
# might later add these in (couldn't do so at... | {
"repo_name": "berkeley-stat159/project-alpha",
"path": "code/utils/functions/Image_Visualizing.py",
"copies": "1",
"size": "4668",
"license": "bsd-3-clause",
"hash": 3281199777908891600,
"line_mean": 32.8260869565,
"line_max": 155,
"alpha_frac": 0.6214652956,
"autogenerated": false,
"ratio": 3.1... |
from __future__ import absolute_import, division, print_function
import numpy as np
def time_shift(convolved, neural_prediction, delta):
""" Returns tuple containing original convolved time course
with the correct number of volumes and a back-shifted
convolved time course.
Parameters:
-----... | {
"repo_name": "berkeley-stat159/project-alpha",
"path": "code/utils/functions/time_shift.py",
"copies": "1",
"size": "2684",
"license": "bsd-3-clause",
"hash": -1468209829183042800,
"line_mean": 26.1111111111,
"line_max": 97,
"alpha_frac": 0.6479135618,
"autogenerated": false,
"ratio": 3.85632183... |
from __future__ import (absolute_import, division, print_function)
import numpy as np
from qtpy.QtWidgets import QDialog, QTableWidgetItem, QComboBox, QCheckBox, QSpacerItem, QSizePolicy, QHBoxLayout, \
QWidget
from addie.utilities import load_ui
from qtpy import QtCore
class GlobalRuleHandler:
def __in... | {
"repo_name": "neutrons/FastGR",
"path": "addie/processing/mantid/master_table/import_from_database/global_rule_handler.py",
"copies": "1",
"size": "11730",
"license": "mit",
"hash": -2127664959525857500,
"line_mean": 38.4949494949,
"line_max": 122,
"alpha_frac": 0.5883205456,
"autogenerated": fals... |
from __future__ import (absolute_import, division, print_function)
import numpy as np
from qtpy.QtWidgets import QMainWindow
from addie.utilities import load_ui
from addie.processing.mantid.master_table.table_row_handler import \
TableRowHandler
from addie.utilities import math_tools
from addie.processing.manti... | {
"repo_name": "neutrons/FastGR",
"path": "addie/processing/mantid/master_table/mass_density_handler.py",
"copies": "1",
"size": "14472",
"license": "mit",
"hash": -6878807287725796000,
"line_mean": 40.1136363636,
"line_max": 105,
"alpha_frac": 0.6216141515,
"autogenerated": false,
"ratio": 3.7855... |
from __future__ import absolute_import, division, print_function
import numpy as np
import pdb
from cfractald import corrDim, getCOMs
__all__ = ['corrcalc','getCOMsPy','getCOMs','getCOMnumpy','methodL','fit2']
def getCOMnumpy(poslist,masslist):
#return com coordinates of a single molecule, written in python using... | {
"repo_name": "ramansbach/cluster_analysis",
"path": "clustering/fractald.py",
"copies": "1",
"size": "6320",
"license": "mit",
"hash": 3962068024579423700,
"line_mean": 29.0952380952,
"line_max": 79,
"alpha_frac": 0.5579113924,
"autogenerated": false,
"ratio": 3.28653146125845,
"config_test": ... |
from __future__ import (absolute_import, division, print_function)
import numpy as np
class DataToImportHandler:
def __init__(self, parent=None):
self.parent = parent
def is_with_filter(self):
if self.parent.ui.toolBox.currentIndex() == 0:
return False
return True
de... | {
"repo_name": "neutrons/FastGR",
"path": "addie/processing/mantid/master_table/import_from_database/data_to_import_handler.py",
"copies": "1",
"size": "1741",
"license": "mit",
"hash": -8826519960374580000,
"line_mean": 35.2708333333,
"line_max": 108,
"alpha_frac": 0.5881677197,
"autogenerated": fa... |
from __future__ import (absolute_import, division, print_function)
import numpy as np
class GuiHandler(object):
def __init__(self, parent=None):
self.parent = parent
def dropdown_get_value(self, widget_id=None):
if not widget_id:
return "N/A"
return widget_id.currentText... | {
"repo_name": "neutrons/FastGR",
"path": "addie/utilities/gui_handler.py",
"copies": "1",
"size": "1885",
"license": "mit",
"hash": 4535089010033235500,
"line_mean": 25.1805555556,
"line_max": 83,
"alpha_frac": 0.6169761273,
"autogenerated": false,
"ratio": 3.446069469835466,
"config_test": fal... |
from __future__ import absolute_import, division, print_function
import numpy as np
def bh_procedure(p_vals, Q):
"""
Return an array (mask) of the significant, valid tests
out of the p-values. not significant p-values are denoted by ones.
Parameters
----------
p_vals: p-values from the t_stat function (1-d... | {
"repo_name": "berkeley-stat159/project-alpha",
"path": "code/utils/functions/benjamini_hochberg.py",
"copies": "1",
"size": "1604",
"license": "bsd-3-clause",
"hash": -7443446584588915000,
"line_mean": 28.1636363636,
"line_max": 80,
"alpha_frac": 0.6708229426,
"autogenerated": false,
"ratio": 2.... |
from __future__ import absolute_import, division, print_function
import numpy as np
def fit_quad_to_peak(x, y):
"""
Fits a quadratic to the data points handed in
to the from y = b[0](x-b[1])**2 + b[2] and R2
(measure of goodness of fit)
Parameters
----------
x : ndarray
locations
... | {
"repo_name": "licode/scikit-xray",
"path": "skbeam/core/fitting/funcs.py",
"copies": "7",
"size": "1084",
"license": "bsd-3-clause",
"hash": -1682874080664882000,
"line_mean": 23.0888888889,
"line_max": 67,
"alpha_frac": 0.5295202952,
"autogenerated": false,
"ratio": 3.1060171919770774,
"confi... |
from __future__ import absolute_import, division, print_function
import numpy as np, random
np.random.seed(1)
random.seed(1)
from dragonn.models import SequenceDNN
from simdna.simulations import simulate_single_motif_detection
from dragonn.utils import one_hot_encode, get_motif_scores, reverse_complement
try:
from ... | {
"repo_name": "agitter/dragonn",
"path": "examples/simple_motif_detection.py",
"copies": "2",
"size": "3903",
"license": "mit",
"hash": -3835364449215288300,
"line_mean": 37.2647058824,
"line_max": 103,
"alpha_frac": 0.685370228,
"autogenerated": false,
"ratio": 3.2579298831385644,
"config_test... |
from __future__ import absolute_import, division, print_function
import numpy as np, sys
from abc import abstractmethod, ABCMeta
class HyperparameterBackend(object):
__metaclass__ = ABCMeta
@abstractmethod
def __init__(self, grid):
"""
Parameters
----------
grid: dict
... | {
"repo_name": "agitter/dragonn",
"path": "dragonn/hyperparameter_search.py",
"copies": "2",
"size": "5278",
"license": "mit",
"hash": 7772536620244083000,
"line_mean": 41.224,
"line_max": 103,
"alpha_frac": 0.6030693444,
"autogenerated": false,
"ratio": 4.546080964685616,
"config_test": false,
... |
from __future__ import absolute_import, division, print_function
import opsimsummary as oss
from sqlalchemy import create_engine
import pandas as pd
import time
import os
import numpy as np
from opsimsummary import (OpSimOutput,
Simlibs)
logfile = 'feature_sim.simlib.log'
opsim_fname = 'feat... | {
"repo_name": "rbiswas4/simlib",
"path": "scripts/make_simlib_feature.py",
"copies": "1",
"size": "1544",
"license": "mit",
"hash": -421685907330211600,
"line_mean": 33.3111111111,
"line_max": 99,
"alpha_frac": 0.6806994819,
"autogenerated": false,
"ratio": 3.13184584178499,
"config_test": fals... |
from __future__ import absolute_import, division, print_function
import opsimsummary as oss
import opsimsummary.summarize_opsim as so
from sqlalchemy import create_engine
import pandas as pd
import os
def test_writeSimlib():
pkgDir = os.path.split(oss.__file__)[0]
dbname = os.path.join(pkgDir, 'example_data',... | {
"repo_name": "rbiswas4/simlib",
"path": "tests/test_simlibWrite.py",
"copies": "1",
"size": "1246",
"license": "mit",
"hash": -4015338789209372000,
"line_mean": 33.6111111111,
"line_max": 79,
"alpha_frac": 0.6492776886,
"autogenerated": false,
"ratio": 3.4804469273743015,
"config_test": false,... |
from __future__ import absolute_import, division, print_function
import opsimsummary as oss
import opsimsummary.summarize_opsim as so
from sqlalchemy import create_engine
import pandas as pd
import time
import os
script_start = time.time()
log_str = 'Running script with opsimsummary version {}\n'.format(oss.__VERSION... | {
"repo_name": "rbiswas4/simlib",
"path": "scripts/make_simlib_enigma.py",
"copies": "1",
"size": "2405",
"license": "mit",
"hash": -3328873828777014300,
"line_mean": 33.8550724638,
"line_max": 81,
"alpha_frac": 0.6486486486,
"autogenerated": false,
"ratio": 3.202396804260985,
"config_test": fal... |
from __future__ import (absolute_import, division, print_function)
import os
from addie.processing.idl.exp_ini_file_loader import ExpIniFileLoader
class PopulateBackgroundWidgets(object):
list_names = []
exp_ini_back_file = 'N/A'
current_folder = None
we_are_done_here = False
def __init__(self, ... | {
"repo_name": "neutrons/FastGR",
"path": "addie/processing/idl/populate_background_widgets.py",
"copies": "1",
"size": "2557",
"license": "mit",
"hash": -8628144575012708000,
"line_mean": 40.9180327869,
"line_max": 95,
"alpha_frac": 0.6765741103,
"autogenerated": false,
"ratio": 3.512362637362637... |
from __future__ import (absolute_import, division, print_function)
import os
from addie.processing.idl.table_handler import TableHandler
from addie.processing.idl.step2_gui_handler import Step2GuiHandler
class CreateNdsumFile(object):
list_selected_row = None
gui_settings = None
current_folder = None
... | {
"repo_name": "neutrons/FastGR",
"path": "addie/processing/idl/create_ndsum_file.py",
"copies": "1",
"size": "3922",
"license": "mit",
"hash": -381130720892370300,
"line_mean": 37.8316831683,
"line_max": 102,
"alpha_frac": 0.5889852116,
"autogenerated": false,
"ratio": 3.1126984126984127,
"conf... |
from __future__ import (absolute_import, division, print_function)
import os
from addie.utilities.file_handler import FileHandler
from addie.autoNOM.step1_widgets_handler import Step1WidgetsHandler
class AutoPopulateWidgets(object):
input_file_name = 'exp.ini'
file_found_message = "Config file %s has been fo... | {
"repo_name": "neutrons/FastGR",
"path": "addie/autoNOM/auto_populate_widgets.py",
"copies": "1",
"size": "3693",
"license": "mit",
"hash": 6816707094022827000,
"line_mean": 35.5643564356,
"line_max": 92,
"alpha_frac": 0.608989981,
"autogenerated": false,
"ratio": 3.5204957102001906,
"config_te... |
from __future__ import (absolute_import, division, print_function)
import os
from ansible.errors import AnsibleError
from ansible.plugins.lookup import LookupBase
__metaclass__ = type
ANSIBLE_HASHI_VAULT_ADDR = 'http://127.0.0.1:8200'
ANSIBLE_HASHI_VAULT_TOKEN = None
if os.getenv('VAULT_ADDR') is not None:
ANSIB... | {
"repo_name": "StarterSquad/prudentia",
"path": "prudentia/plugins/lookup/hashi_vault.py",
"copies": "1",
"size": "3013",
"license": "mit",
"hash": -5225251975581330000,
"line_mean": 31.0531914894,
"line_max": 97,
"alpha_frac": 0.5692001328,
"autogenerated": false,
"ratio": 3.862820512820513,
"... |
from __future__ import absolute_import, division, print_function
import os
from appr.commands.command_base import CommandBase
from appr.display import print_package_info
class ShowCmd(CommandBase):
name = 'show'
help_message = "print the package manifest"
default_media_type = None
def __init__(self, ... | {
"repo_name": "app-registry/appr",
"path": "appr/commands/show.py",
"copies": "2",
"size": "1696",
"license": "apache-2.0",
"hash": -5353150050260842000,
"line_mean": 38.4418604651,
"line_max": 92,
"alpha_frac": 0.6515330189,
"autogenerated": false,
"ratio": 3.889908256880734,
"config_test": fa... |
from __future__ import absolute_import, division, print_function
import os
from appr.commands.command_base import CommandBase
from appr.display import print_packages
class ListPackageCmd(CommandBase):
name = 'list'
help_message = "list packages"
default_media_type = None
def __init__(self, options):
... | {
"repo_name": "app-registry/appr",
"path": "appr/commands/list_package.py",
"copies": "2",
"size": "1923",
"license": "apache-2.0",
"hash": -6172326961104096000,
"line_mean": 35.9807692308,
"line_max": 93,
"alpha_frac": 0.6391055642,
"autogenerated": false,
"ratio": 4.023012552301255,
"config_t... |
from __future__ import absolute_import, division, print_function
import os
from appr.commands.command_base import CommandBase
from appr.pack import ApprPackage
class InspectCmd(CommandBase):
name = 'inspect'
help_message = "Browse package files"
def __init__(self, options):
super(InspectCmd, self... | {
"repo_name": "cn-app-registry/cnr-server",
"path": "appr/commands/inspect.py",
"copies": "2",
"size": "2043",
"license": "apache-2.0",
"hash": 5051356730435240000,
"line_mean": 37.5471698113,
"line_max": 96,
"alpha_frac": 0.6377875673,
"autogenerated": false,
"ratio": 3.944015444015444,
"confi... |
from __future__ import absolute_import, division, print_function
import os
from appr.commands.command_base import CommandBase
class DeletePackageCmd(CommandBase):
name = 'delete-package'
help_message = 'delete package from the registry'
def __init__(self, options):
super(DeletePackageCmd, self)._... | {
"repo_name": "app-registry/appr",
"path": "appr/commands/delete_package.py",
"copies": "2",
"size": "1432",
"license": "apache-2.0",
"hash": 8746054122297890000,
"line_mean": 36.6842105263,
"line_max": 92,
"alpha_frac": 0.656424581,
"autogenerated": false,
"ratio": 3.934065934065934,
"config_t... |
from __future__ import (absolute_import, division, print_function)
import os
from .compilation import compile_run_strings
from .util import CompilerNotFoundError
def has_fortran():
if not hasattr(has_fortran, 'result'):
try:
(stdout, stderr), info = compile_run_strings(
[('main.... | {
"repo_name": "kaushik94/sympy",
"path": "sympy/utilities/_compilation/availability.py",
"copies": "3",
"size": "2951",
"license": "bsd-3-clause",
"hash": 7723583287475822000,
"line_mean": 36.8333333333,
"line_max": 100,
"alpha_frac": 0.4778041342,
"autogenerated": false,
"ratio": 3.9770889487870... |
from __future__ import absolute_import, division, print_function
import os
from jinja2 import Environment, PackageLoader
import click
import boto3
from botocore.client import ClientError
DEFAULT_AWS_PROFILE = 'default'
DEFAULT_BACKUP_CREDENTIAL_FILE = 'credentials'
env = Environment(loader=PackageLoader('awsbackup',... | {
"repo_name": "dantagg/awsbackup",
"path": "awsbackup/__main__.py",
"copies": "1",
"size": "4227",
"license": "mit",
"hash": -7658046417920336000,
"line_mean": 40.8514851485,
"line_max": 143,
"alpha_frac": 0.6832268749,
"autogenerated": false,
"ratio": 3.6283261802575106,
"config_test": false,
... |
from __future__ import absolute_import, division, print_function
import os
from ply.yacc import yacc
from .lexer import tokens as lexer_tokens
from .elements import (ID, LongString, ShortString, Float, Integer, Boolean,
List, Dict, TestCase)
__all__ = ['create_parser']
tokens = lexer_tokens
s... | {
"repo_name": "huntzhan/tcg",
"path": "tcg/ast/parser.py",
"copies": "1",
"size": "3549",
"license": "mit",
"hash": 8544055120927019000,
"line_mean": 19.3965517241,
"line_max": 76,
"alpha_frac": 0.5083122006,
"autogenerated": false,
"ratio": 3.217588395285585,
"config_test": true,
"has_no_key... |
from __future__ import (absolute_import, division, print_function)
import os
from qtpy import QtGui, QtCore
from addie.utilities.file_handler import FileHandler
class ImportTable(object):
file_contain = []
table_contain = []
contain_parsed = []
full_contain_parsed = []
def __init__(self, parent... | {
"repo_name": "neutrons/FastGR",
"path": "addie/processing/mantid/master_table/import_table.py",
"copies": "1",
"size": "4792",
"license": "mit",
"hash": 2309817004641425000,
"line_mean": 30.7350993377,
"line_max": 104,
"alpha_frac": 0.5287979967,
"autogenerated": false,
"ratio": 4.02689075630252... |
from __future__ import (absolute_import, division, print_function)
import os
from qtpy.QtCore import Qt
from addie.processing.idl.step2_gui_handler import Step2GuiHandler
class RunSumScans(object):
script = 'python /SNS/NOM/shared/autoNOM/stable/sumscans.py '
output_file = ''
def __init__(self, parent=... | {
"repo_name": "neutrons/FastGR",
"path": "addie/processing/idl/run_sum_scans.py",
"copies": "1",
"size": "3773",
"license": "mit",
"hash": -1131044315973222500,
"line_mean": 34.261682243,
"line_max": 91,
"alpha_frac": 0.5743440233,
"autogenerated": false,
"ratio": 3.396039603960396,
"config_tes... |
from __future__ import (absolute_import, division, print_function)
import os
from qtpy.QtCore import Qt
from qtpy.QtWidgets import (QCheckBox, QComboBox, QHBoxLayout, QMessageBox, QTableWidgetItem, QWidget)
from addie.processing.idl.generate_sumthing import GenerateSumthing
class PopulateMasterTable(object):
au... | {
"repo_name": "neutrons/FastGR",
"path": "addie/processing/idl/populate_master_table.py",
"copies": "1",
"size": "5527",
"license": "mit",
"hash": -1297704840560142800,
"line_mean": 33.9810126582,
"line_max": 102,
"alpha_frac": 0.5778903564,
"autogenerated": false,
"ratio": 3.8867791842475388,
... |
from __future__ import (absolute_import, division, print_function)
import os
from qtpy.QtWidgets import (QMessageBox)
import glob
from addie.autoNOM.make_exp_ini_file_and_run_autonom import MakeExpIniFileAndRunAutonom
class RunStep1(object):
keep_running_status = True
folder = None
auto_folder_base_name... | {
"repo_name": "neutrons/FastGR",
"path": "addie/autoNOM/run_step1.py",
"copies": "1",
"size": "3476",
"license": "mit",
"hash": -12565472475804572,
"line_mean": 33.76,
"line_max": 116,
"alpha_frac": 0.585155351,
"autogenerated": false,
"ratio": 3.7376344086021507,
"config_test": false,
"has_n... |
from __future__ import (absolute_import, division, print_function)
import os
import configparser
import numpy as np
class FileHandler(object):
file_contain = []
def __init__(self, filename=None):
self.filename = filename
@staticmethod
def is_file_correct_extension(filename='', ext_requested... | {
"repo_name": "neutrons/FastGR",
"path": "addie/utilities/file_handler.py",
"copies": "1",
"size": "2300",
"license": "mit",
"hash": -7503333513245508000,
"line_mean": 29.6666666667,
"line_max": 83,
"alpha_frac": 0.5643478261,
"autogenerated": false,
"ratio": 3.9383561643835616,
"config_test": ... |
from __future__ import absolute_import, division, print_function
import os
import hashlib
from cbopensource.tools.eventduplicator.utils import get_process_id, json_encode
import json
import codecs
from collections import defaultdict
import logging
__author__ = 'jgarman'
log = logging.getLogger(__name__)
def get_pro... | {
"repo_name": "carbonblack/cb-event-duplicator",
"path": "cbopensource/tools/eventduplicator/file_endpoint.py",
"copies": "1",
"size": "6176",
"license": "mit",
"hash": -7864493006316601000,
"line_mean": 38.8451612903,
"line_max": 119,
"alpha_frac": 0.6081606218,
"autogenerated": false,
"ratio": ... |
from __future__ import absolute_import, division, print_function
import os
import hashlib
import json
import random
import string
from base64 import b64decode, b64encode
import jinja2
import yaml
def get_hash(data, hashtype='sha1'):
h = hashlib.new(hashtype)
h.update(data)
return h.hexdigest()
def rand... | {
"repo_name": "app-registry/appr",
"path": "appr/template_filters.py",
"copies": "2",
"size": "6642",
"license": "apache-2.0",
"hash": -2550097358742126000,
"line_mean": 28.52,
"line_max": 93,
"alpha_frac": 0.6371574827,
"autogenerated": false,
"ratio": 3.712688652878703,
"config_test": false,
... |
from __future__ import absolute_import, division, print_function
import os
import importlib
import logging
logger = logging.getLogger(__name__)
filetypes = ['py', 'txt', 'dat']
blacklisted = [' his ', ' him ', ' guys ', ' guy ']
class ValuesError(ValueError):
pass
class UnwelcomenessError(ValuesError):
pas... | {
"repo_name": "licode/scikit-beam",
"path": "skbeam/tests/test_openness.py",
"copies": "7",
"size": "4309",
"license": "bsd-3-clause",
"hash": -3835814145431209000,
"line_mean": 30.2246376812,
"line_max": 97,
"alpha_frac": 0.5785565096,
"autogenerated": false,
"ratio": 3.8473214285714286,
"conf... |
from __future__ import absolute_import, division, print_function
import os
import io
import hashlib
import json
import tempfile
import matplotlib.pyplot as plt
HASH_LIBRARY_NAME = 'figure_hashes.json'
# Load the hash library if it exists
try:
with open(os.path.join(os.path.dirname(__file__), HASH_LIBRARY_NAME)) ... | {
"repo_name": "Alex-Ian-Hamilton/sunpy",
"path": "sunpy/tests/hash.py",
"copies": "1",
"size": "1941",
"license": "bsd-2-clause",
"hash": -1094753007994943900,
"line_mean": 26.338028169,
"line_max": 99,
"alpha_frac": 0.6687274601,
"autogenerated": false,
"ratio": 4.112288135593221,
"config_test... |
from __future__ import absolute_import, division, print_function
import os
import math
import hmac
import json
import hashlib
import argparse
from random import shuffle
from pathlib2 import Path
import numpy as np
import tensorflow as tf
from tensorflow.data import Dataset
def info(msg, char="#", width=75):
print... | {
"repo_name": "kubeflow/examples",
"path": "pipelines/azurepipeline/code/training/train.py",
"copies": "1",
"size": "6509",
"license": "apache-2.0",
"hash": -4263225679812289500,
"line_mean": 29.8483412322,
"line_max": 98,
"alpha_frac": 0.5954831771,
"autogenerated": false,
"ratio": 3.62618384401... |
from __future__ import absolute_import, division, print_function
import os
import multiprocessing
import threading
import chainlet
import chainlet.dataflow
import chainlet.chainlink
import chainlet.primitives.link
import chainlet.signals
class NamedChainlet(chainlet.dataflow.NoOp):
"""Chainlet with nice represen... | {
"repo_name": "maxfischer2781/chainlet",
"path": "chainlet_unittests/utility.py",
"copies": "1",
"size": "3107",
"license": "mit",
"hash": -381563194153056260,
"line_mean": 24.6776859504,
"line_max": 83,
"alpha_frac": 0.6121660766,
"autogenerated": false,
"ratio": 3.922979797979798,
"config_tes... |
from __future__ import (absolute_import, division, print_function)
import os
import numpy as np
from collections import OrderedDict
import copy
import simplejson
from qtpy.QtWidgets import QDialog
from addie.utilities import load_ui
from qtpy import QtCore, QtGui
from addie.utilities.file_handler import FileHandler
f... | {
"repo_name": "neutrons/FastGR",
"path": "addie/processing/mantid/master_table/master_table_loader.py",
"copies": "1",
"size": "29420",
"license": "mit",
"hash": 346060110386451300,
"line_mean": 37.0595084088,
"line_max": 118,
"alpha_frac": 0.5706662135,
"autogenerated": false,
"ratio": 3.9628232... |
from __future__ import absolute_import, division, print_function
import os
import numpy as np
import bha
data_path = os.path.join(bha.__path__[0], 'data')
#from .due import due, Doi
#
#
## Use duecredit (duecredit.org) to provide a citation to relevant work to
## be cited. This does nothing, unless the user has duecre... | {
"repo_name": "christiancarballo/bha",
"path": "bha/bha.py",
"copies": "1",
"size": "5070",
"license": "mit",
"hash": -3981557117561869300,
"line_mean": 30.8867924528,
"line_max": 114,
"alpha_frac": 0.6291913215,
"autogenerated": false,
"ratio": 3.391304347826087,
"config_test": false,
"has_n... |
from __future__ import absolute_import, division, print_function
import os
import numpy as np
import pandas as pd
from sqlalchemy import create_engine
from lsst.utils import getPackageDir
from .phosim_cpu_pred import CpuPred
__all__ = ['OpSimOrdering']
class OpSimOrdering(object):
"""
Code to split the Twink... | {
"repo_name": "LSSTDESC/Twinkles",
"path": "python/desc/twinkles/obsHistIDOrdering.py",
"copies": "2",
"size": "10882",
"license": "mit",
"hash": 642907513084858600,
"line_mean": 40.3764258555,
"line_max": 103,
"alpha_frac": 0.6285609263,
"autogenerated": false,
"ratio": 4.014016967908521,
"con... |
from __future__ import absolute_import, division, print_function
import os
import numpy as np
# Define DB information
BASE_PATH = 'D:/DB/IQA/LIVE/LIVE IQA DB'
LIST_FILE_NAME = 'LIVE_IQA.txt'
ALL_SCENES = list(range(29))
ALL_DIST_TYPES = list(range(5))
def make_image_list(scenes, dist_types=None, show_info=True):
... | {
"repo_name": "jongyookim/IQA_BIECON_release",
"path": "IQA_BIECON_release/data_load/LIVE.py",
"copies": "1",
"size": "2166",
"license": "mit",
"hash": 5657474054917497000,
"line_mean": 34.5081967213,
"line_max": 75,
"alpha_frac": 0.5692520776,
"autogenerated": false,
"ratio": 3.081081081081081,
... |
from __future__ import absolute_import, division, print_function
import os
import numpy as np
# Define DB information
BASE_PATH = 'D:/DB/IQA/TID2008'
LIST_FILE_NAME = 'TID2008.txt'
ALL_SCENES = list(range(24))
# ALL_SCENES = list(range(25))
ALL_DIST_TYPES = list(range(17))
def make_image_list(scenes, dist_types=None... | {
"repo_name": "jongyookim/IQA_BIECON_release",
"path": "IQA_BIECON_release/data_load/TID2008.py",
"copies": "1",
"size": "1761",
"license": "mit",
"hash": -9135426908207386000,
"line_mean": 32.8653846154,
"line_max": 68,
"alpha_frac": 0.5718341851,
"autogenerated": false,
"ratio": 3.0894736842105... |
from __future__ import absolute_import, division, print_function
import os
import numpy as np
# Define DB information
BASE_PATH = 'D:/DB/IQA/TID2013'
LIST_FILE_NAME = 'TID2013.txt'
ALL_SCENES = list(range(24))
# ALL_SCENES = list(range(25))
ALL_DIST_TYPES = list(range(24))
def make_image_list(scenes, dist_types=None... | {
"repo_name": "jongyookim/IQA_BIECON_release",
"path": "IQA_BIECON_release/data_load/TID2013.py",
"copies": "1",
"size": "1761",
"license": "mit",
"hash": 5494124675876890000,
"line_mean": 32.8653846154,
"line_max": 68,
"alpha_frac": 0.5718341851,
"autogenerated": false,
"ratio": 3.08947368421052... |
from __future__ import absolute_import, division, print_function
import os
import os.path as op
import sys
import six
def ask_for_export_email():
"""Ask for a registered email address."""
print('You have not set the email variable at the top of this script.')
print('Please set this variable in the script,... | {
"repo_name": "kbg/drms",
"path": "examples/example_helpers.py",
"copies": "1",
"size": "2150",
"license": "mit",
"hash": 1763578232245692000,
"line_mean": 31.0895522388,
"line_max": 78,
"alpha_frac": 0.6688372093,
"autogenerated": false,
"ratio": 3.8053097345132745,
"config_test": false,
"ha... |
from __future__ import absolute_import, division, print_function
import os
import os.path
import time
import glob
from fcntl import flock, LOCK_EX, LOCK_UN
from threading import Event
from .text import TextLog
class TimeRotatingLog(TextLog):
"""
A type of log which writes records as individual lines to a ser... | {
"repo_name": "storborg/manhattan",
"path": "manhattan/log/timerotating.py",
"copies": "1",
"size": "4291",
"license": "mit",
"hash": -6828685286597927000,
"line_mean": 32.2635658915,
"line_max": 78,
"alpha_frac": 0.5292472617,
"autogenerated": false,
"ratio": 3.9621421975992615,
"config_test":... |
from __future__ import absolute_import, division, print_function
import os
import re
import logging
from watchdog.observers import Observer as FSObserver # Auto-detect best fs event api according to OS
from watchdog.observers.polling import PollingObserver
from watchdog.events import RegexMatchingEventHandler, FileSy... | {
"repo_name": "stcorp/legato",
"path": "legato/filesystem.py",
"copies": "1",
"size": "5056",
"license": "bsd-3-clause",
"hash": -2661348680554804000,
"line_mean": 38.811023622,
"line_max": 105,
"alpha_frac": 0.5719936709,
"autogenerated": false,
"ratio": 4.035115722266561,
"config_test": false... |
from __future__ import absolute_import, division, print_function
import os
import sys
import mock
import pytest
from _pytest.mark import (
MarkGenerator as Mark,
ParameterSet,
transfer_markers,
EMPTY_PARAMETERSET_OPTION,
)
from _pytest.nodes import Node
ignore_markinfo = pytest.mark.filterwarnings(
... | {
"repo_name": "sadmansk/servo",
"path": "tests/wpt/web-platform-tests/tools/third_party/pytest/testing/test_mark.py",
"copies": "30",
"size": "32326",
"license": "mpl-2.0",
"hash": -4967787228656274000,
"line_mean": 27.5061728395,
"line_max": 88,
"alpha_frac": 0.5387613686,
"autogenerated": false,
... |
from __future__ import absolute_import, division, print_function
import os
import sys
import pprint
import random
import socket
import docker
import logging
import time
import tempfile
cur_dir = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, os.path.abspath("%s/../clipper_admin" % cur_dir))
from clipper_... | {
"repo_name": "dcrankshaw/clipper",
"path": "integration-tests/test_utils.py",
"copies": "1",
"size": "4856",
"license": "apache-2.0",
"hash": 3090184084945704000,
"line_mean": 32.958041958,
"line_max": 90,
"alpha_frac": 0.6091433278,
"autogenerated": false,
"ratio": 3.8448139350752175,
"config... |
from __future__ import absolute_import, division, print_function
import os
import sys
import requests
import json
import tempfile
import shutil
import numpy as np
import time
import logging
from test_utils import (create_docker_connection, BenchmarkException,
fake_model_data, headers, log_clippe... | {
"repo_name": "dcrankshaw/clipper",
"path": "integration-tests/many_apps_many_models.py",
"copies": "1",
"size": "4569",
"license": "apache-2.0",
"hash": 5173512916932833000,
"line_mean": 32.5955882353,
"line_max": 79,
"alpha_frac": 0.576274896,
"autogenerated": false,
"ratio": 3.6817082997582595... |
from __future__ import absolute_import, division, print_function
import os
import sys
import textwrap
import pytest
from _pytest.monkeypatch import MonkeyPatch
@pytest.fixture
def mp():
cwd = os.getcwd()
sys_path = list(sys.path)
yield MonkeyPatch()
sys.path[:] = sys_path
os.chdir(cwd)
def test... | {
"repo_name": "flub/pytest",
"path": "testing/test_monkeypatch.py",
"copies": "1",
"size": "8416",
"license": "mit",
"hash": 8503717498844262000,
"line_mean": 24.6585365854,
"line_max": 75,
"alpha_frac": 0.6222671103,
"autogenerated": false,
"ratio": 3.5661016949152544,
"config_test": true,
"... |
from __future__ import absolute_import, division, print_function
import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), "..", "functions"))
import time as time
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
from sklearn.feature_extraction.image import grid_to_graph
from skl... | {
"repo_name": "berkeley-stat159/project-alpha",
"path": "code/utils/scripts/cluster.py",
"copies": "1",
"size": "1770",
"license": "bsd-3-clause",
"hash": -1195223848598151400,
"line_mean": 28.0163934426,
"line_max": 102,
"alpha_frac": 0.6785310734,
"autogenerated": false,
"ratio": 3.041237113402... |
from __future__ import (absolute_import, division, print_function)
import os
import time
import simplejson
from datetime import datetime
import re
from addie.autoNOM.step1_gui_handler import Step1GuiHandler
class MakeExpIniFileAndRunAutonom(object):
_dict_mandatory = None
_dict_optional = None
EXP_INI_F... | {
"repo_name": "neutrons/FastGR",
"path": "addie/autoNOM/make_exp_ini_file_and_run_autonom.py",
"copies": "1",
"size": "9557",
"license": "mit",
"hash": -3395861440926234000,
"line_mean": 38.0081632653,
"line_max": 116,
"alpha_frac": 0.5679606571,
"autogenerated": false,
"ratio": 3.598268072289156... |
from __future__ import absolute_import, division, print_function
import os
import warnings
import six
import py
import attr
import _pytest
import _pytest._code
from _pytest.compat import getfslineno
from _pytest.outcomes import fail
from _pytest.mark.structures import NodeKeywords, MarkInfo
SEP = "/"
tracebackcutd... | {
"repo_name": "ddboline/pytest",
"path": "src/_pytest/nodes.py",
"copies": "1",
"size": "17771",
"license": "mit",
"hash": -1199841808011707600,
"line_mean": 32.3414634146,
"line_max": 119,
"alpha_frac": 0.6009791233,
"autogenerated": false,
"ratio": 4.30082284607938,
"config_test": true,
"ha... |
from __future__ import (absolute_import, division, print_function)
import os
from addie.processing.idl.table_handler import TableHandler
class CreateSampleFiles(object):
list_selected_row = None
file_extension = '.ini'
list_sample_files = None
def __init__(self, parent=None):
self.parent = ... | {
"repo_name": "neutrons/FastGR",
"path": "addie/processing/idl/create_sample_files.py",
"copies": "1",
"size": "2149",
"license": "mit",
"hash": 2990628454749937700,
"line_mean": 36.0517241379,
"line_max": 102,
"alpha_frac": 0.6258724988,
"autogenerated": false,
"ratio": 3.4773462783171523,
"co... |
from __future__ import absolute_import, division, print_function
import os
from .common import Benchmark
import numpy as np
class Records(Benchmark):
def setup(self):
self.l50 = np.arange(1000)
self.fields_number = 10000
self.arrays = [self.l50 for _ in range(self.fields_number)]
... | {
"repo_name": "shoyer/numpy",
"path": "benchmarks/benchmarks/bench_records.py",
"copies": "8",
"size": "1472",
"license": "bsd-3-clause",
"hash": -6493422900236288000,
"line_mean": 33.2325581395,
"line_max": 78,
"alpha_frac": 0.65625,
"autogenerated": false,
"ratio": 3.5047619047619047,
"config... |
from __future__ import (absolute_import, division, print_function)
import os
import addie.processing.idl.table_handler
from addie.processing.idl.mantid_reduction_dialogbox import MantidReductionDialogbox
from addie.processing.idl.mantid_reduction_view import MantidReductionView
class GlobalMantidReduction(object):
... | {
"repo_name": "neutrons/FastGR",
"path": "addie/mantid_handler/mantid_reduction.py",
"copies": "1",
"size": "4931",
"license": "mit",
"hash": -4481171405131095600,
"line_mean": 44.6574074074,
"line_max": 127,
"alpha_frac": 0.6090042588,
"autogenerated": false,
"ratio": 3.6498889711324947,
"conf... |
from __future__ import absolute_import, division, print_function
import os
import matplotlib.pyplot as plt
import ROOT
import root_pandas
from histograms import histogram
from root_converters import roocurve, tgraphasymerrors
from plotting_utilities import (
COLOURS as colours,
set_axis_labels
)
PREFIX = 'ro... | {
"repo_name": "alexpearce/thesis",
"path": "scripts/background_categories.py",
"copies": "1",
"size": "3206",
"license": "mit",
"hash": -1967345471581070600,
"line_mean": 30.431372549,
"line_max": 118,
"alpha_frac": 0.6250779788,
"autogenerated": false,
"ratio": 2.8221830985915495,
"config_test... |
from __future__ import absolute_import, division, print_function
import os.path as op
from os.path import join as pjoin
import glob
# Format expected by setup.py and doc/source/conf.py: string of form "X.Y.Z"
_version_major = 0
_version_minor = 3
_version_micro = '' # use '' for first of series, number for 1 and abov... | {
"repo_name": "richford/AFQ-Browser",
"path": "afqbrowser/version.py",
"copies": "3",
"size": "2793",
"license": "bsd-3-clause",
"hash": -6113664906693592000,
"line_mean": 36.24,
"line_max": 93,
"alpha_frac": 0.6147511636,
"autogenerated": false,
"ratio": 3.6701708278580814,
"config_test": fals... |
from __future__ import absolute_import, division, print_function
import os.path as op
import numpy as np
import numpy.testing as npt
import pdb
import gsd.hoomd
import sys
import clustering as cl
#from context import clustering as cl
#from context import smoluchowski as smol
from cdistances import conOptDistanceCython... | {
"repo_name": "ramansbach/cluster_analysis",
"path": "clustering/tests/test_visualization.py",
"copies": "1",
"size": "1247",
"license": "mit",
"hash": -5758892858615032000,
"line_mean": 33.6666666667,
"line_max": 116,
"alpha_frac": 0.6920609463,
"autogenerated": false,
"ratio": 2.976133651551313... |
from __future__ import absolute_import, division, print_function
import os.path as op
import numpy as np
import pandas as pd
import numpy.testing as npt
import bha as sb
data_path = op.join(sb.__path__[0], 'data')
def test_transform_data():
"""
Testing the transformation of the data from raw data to function... | {
"repo_name": "christiancarballo/bha",
"path": "bha/tests/tests_bha.py",
"copies": "1",
"size": "3977",
"license": "mit",
"hash": 3365397824534920700,
"line_mean": 35.1545454545,
"line_max": 77,
"alpha_frac": 0.618556701,
"autogenerated": false,
"ratio": 3.0174506828528074,
"config_test": true,... |
from __future__ import absolute_import, division, print_function
import os.path as op
# Format expected by setup.py and doc/source/conf.py: string of form "X.Y.Z"
_version_major = 0
_version_minor = 3
_version_micro = 7 # use '' for first of series, number for 1 and above
# _version_extra = 'dev'
_version_extra = '' ... | {
"repo_name": "lukassnoek/BidsConverter",
"path": "bidsify/version.py",
"copies": "1",
"size": "1482",
"license": "bsd-3-clause",
"hash": 877323328785897100,
"line_mean": 31.9333333333,
"line_max": 76,
"alpha_frac": 0.6639676113,
"autogenerated": false,
"ratio": 3.3080357142857144,
"config_test... |
from __future__ import absolute_import, division, print_function
import os.path
import subprocess
from typing import List, Optional, cast
_HAS_ARMOR = {".gpg": False, ".asc": True}
_EXTENSIONS = _HAS_ARMOR.keys()
_OVERRIDE_HOMEDIR = None # type: Optional[str] # useful for unit tests
def is_encrypted(path: str) -> ... | {
"repo_name": "catch22/pw",
"path": "pw/_gpg.py",
"copies": "1",
"size": "1613",
"license": "mit",
"hash": 6977568754076554000,
"line_mean": 28.3272727273,
"line_max": 82,
"alpha_frac": 0.6255424675,
"autogenerated": false,
"ratio": 3.431914893617021,
"config_test": false,
"has_no_keywords": ... |
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