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__author__ = 'jdaniel'
# TODO (JLD): Design test cases
# TODO (JLD): Performance testing, identify crossover for model evaluation time
# TODO (JLD): Wrap this in a simple GUI
from algorithm import Algorithm
from common import WintermuteException
from common import WintermuteLogger
class Wintermute(object):
"""... | {
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"path": "wintermute.py",
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"size": "1306",
"license": "apache-2.0",
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"line_mean": 24.1153846154,
"line_max": 79,
"alpha_frac": 0.5972434916,
"autogenerated": false,
"ratio": 4.503448275862069,
"config_test": false,
... |
__author__ = 'jdaniel'
# TODO (JLD): Find some models with equality constraints
# TODO (JLD): Get 15 total models implemented
from model_base import ModelBase
from math import sqrt
from math import sin
from math import cos
from math import pi
from math import exp
from math import atan2
from functools import reduce
f... | {
"repo_name": "jldaniel/Athena",
"path": "AthenaOpt/models.py",
"copies": "1",
"size": "33891",
"license": "mit",
"hash": -8290609199654779000,
"line_mean": 26.1780272654,
"line_max": 97,
"alpha_frac": 0.5498214865,
"autogenerated": false,
"ratio": 4.18304122438904,
"config_test": true,
"has_... |
__author__ = 'Jean-Bernard Ratte - jean.bernard.ratte@unary.ca'
__version__ = '0.1'
import os
import sys
from setuptools import setup, find_packages
from setuptools.command.test import test as TestCommand
class Tox(TestCommand):
user_options = [('tox-args=', 'a', "Arguments to pass to tox")]
def initialize_... | {
"repo_name": "nap/plexcleaner",
"path": "setup.py",
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"size": "2284",
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"autogenerated": false,
"ratio": 3.631160572337043,
"config_test": true,
"has_no_k... |
__author__ = "jeanlouis.mbaka"
import csv
import sys
import pandas as pd
import numpy as np
class Pareto():
def __init__(self):
pass
def read_data(self, filename):
"""
Read data from filename
"""
results = read_csv(filename)
self.header = results[0]
self.raw_data = results[1]
self.data = self.raw... | {
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"path": "pareto.py",
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"license": "mit",
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"autogenerated": false,
"ratio": 2.8420738974970203,
"config_test": false,
"has_no_keyword... |
__author__ = 'jeanlouis.mbaka'
import dirsize
import matplotlib.pyplot as plt
import unittest
import sys, getopt
def draw_pie_chart(data_dict):
"""
Display directories and files sizes in pie chart.
:param data_dict: diction of {}
"""
labels = [key for key in data_dict.keys()]
values = [value for value in data... | {
"repo_name": "jlmbaka/directory-pie",
"path": "pie.py",
"copies": "1",
"size": "1242",
"license": "mit",
"hash": -2133022246634476800,
"line_mean": 22.9038461538,
"line_max": 99,
"alpha_frac": 0.6618357488,
"autogenerated": false,
"ratio": 2.9501187648456058,
"config_test": false,
"has_no_ke... |
__author__ = 'jeanlouis.mbaka'
import re
if __name__ == '__main__':
split_test_str = 'abc 123\n\ndef 456\n\n\tbonjour\nca va?'
print(split_test_str.split('\n\n'))
text = "1\n00:01:37,880 --> 00:01:41,726\nBack then, not sleeping,\nI'd lay awake thinking about women.\n\n"
seq = '10\n2\n 00:01:37,880... | {
"repo_name": "jlmbaka/subs-sync",
"path": "spike.py",
"copies": "1",
"size": "1378",
"license": "mit",
"hash": 2168814984341090800,
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"has_no_keywo... |
__author__ = 'jeanlouis.mbaka'
import sys, getopt
import os
import re
import time
from dateutil import parser
from datetime import datetime
from datetime import timedelta
class Subtitle():
"""
Subtitle model - Represents an .srt file unit subtitle
it is presented as follows:
sequence_no\n
start -... | {
"repo_name": "jlmbaka/subs-sync",
"path": "sub-sync.py",
"copies": "1",
"size": "5084",
"license": "mit",
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"alpha_frac": 0.5676632573,
"autogenerated": false,
"ratio": 3.4774281805745555,
"config_test": false,
"has_no_k... |
import numpy as np
from numpy.testing import (assert_array_equal, assert_array_almost_equal,
assert_equal, assert_allclose, assert_array_less)
import pytest
from mne import create_info, EpochsArray
from mne.fixes import is_regressor, is_classifier
from mne.utils import requires_sklearn, req... | {
"repo_name": "larsoner/mne-python",
"path": "mne/decoding/tests/test_base.py",
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"size": "15702",
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"hash": -8536151634660363000,
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"alpha_frac": 0.6296013247,
"autogenerated": false,
"ratio": 3.443421052631579,
"... |
import numpy as np
from numpy.testing import (assert_array_equal, assert_array_almost_equal,
assert_equal)
import pytest
from mne.fixes import is_regressor, is_classifier
from mne.utils import requires_version, check_version
from mne.decoding.base import (_get_inverse_funcs, LinearModel, ge... | {
"repo_name": "adykstra/mne-python",
"path": "mne/decoding/tests/test_base.py",
"copies": "3",
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"hash": -5867384064424200000,
"line_mean": 35.23828125,
"line_max": 79,
"alpha_frac": 0.6213215479,
"autogenerated": false,
"ratio": 3.4308431952662723,
"con... |
"""Generates 3 random 3D coordinates from a known 6x6 grid. Show how the grid
position, rotate and bending can be inferred using `ModelSurface`.
"""
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from ecoggui import ModelSurface
n_samples = 6 ** 2
# Generate a curved surfa... | {
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"path": "examples/model_surface.py",
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"autogenerated": false,
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"config_test": ... |
import numpy as np
from scipy import optimize
from scipy.spatial.distance import squareform, pdist
from sklearn.preprocessing import PolynomialFeatures
class ModelDisplacement(object):
"""Transformer to fit rigid object rotation + translation. It 1) centers
the data, 2) rotates it with SVD, and 3) fits trans... | {
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"path": "ecoggui/models.py",
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from nose.tools import assert_true
import numpy as np
from numpy.testing import assert_array_equal
from jr.stats import fast_mannwhitneyu
def _parallel_scorer(y_true, y_pred, func, n_jobs=1):
from nose.tools import assert_true
from mne.parallel import parallel_func, check_n_jobs
# check dimensionality
... | {
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"path": "jr/gat/scorers.py",
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import numpy as np
from .mixin import TransformerMixin
from .base import BaseEstimator
from ..time_frequency.tfr import _compute_tfr, _check_tfr_param
from ..utils import fill_doc, _check_option
@fill_doc
class TimeFrequency(TransformerMixin, BaseEstimator):
"""Time frequency transformer.
Time-frequency tra... | {
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"path": "mne/decoding/time_frequency.py",
"copies": "12",
"size": "5163",
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"hash": 407682331306757700,
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"alpha_frac": 0.5785396088,
"autogenerated": false,
"ratio": 4.011655011655011,
"co... |
import numpy as np
from .mixin import TransformerMixin
from .base import BaseEstimator
from ..time_frequency.tfr import _compute_tfr, _check_tfr_param
class TimeFrequency(TransformerMixin, BaseEstimator):
"""Time frequency transformer.
Time-frequency transform of times series along the last axis.
Param... | {
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"path": "mne/decoding/time_frequency.py",
"copies": "5",
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"autogenerated": false,
"ratio": 4.038632986627043,
"config_test"... |
import numpy as np
from sklearn.preprocessing import StandardScaler
def _stand_mad(a, median):
""" Fast sandard MAD
Parameters
----------
a : np.array, shape(n_samples, n_dims)
median : np.array, shape(n_dims)
Returns
-------
mad : np.array, shape(n_dims)
Adapted from based on st... | {
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"path": "jr/gat/scalers.py",
"copies": "1",
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"license": "bsd-2-clause",
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"autogenerated": false,
"ratio": 3.526553672316384,
"config_test": false,
"has_n... |
import numpy as np
from .mixin import TransformerMixin
from .base import BaseEstimator, _check_estimator, _make_scorer
from ..parallel import parallel_func
class _SearchLight(BaseEstimator, TransformerMixin):
"""Search Light.
Fit, predict and score a series of models to each subset of the dataset
along... | {
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"path": "mne/decoding/search_light.py",
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"size": "25213",
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"alpha_frac": 0.5820013485,
"autogenerated": false,
"ratio": 3.989398734177215,
"con... |
import numpy as np
from .mixin import TransformerMixin
from .base import BaseEstimator, _check_estimator
from ..fixes import _get_check_scoring
from ..parallel import parallel_func
from ..utils import (_validate_type, array_split_idx, ProgressBar,
verbose, fill_doc)
@fill_doc
class SlidingEstim... | {
"repo_name": "larsoner/mne-python",
"path": "mne/decoding/search_light.py",
"copies": "6",
"size": "27438",
"license": "bsd-3-clause",
"hash": 7925074879826343000,
"line_mean": 38.6502890173,
"line_max": 114,
"alpha_frac": 0.5855383045,
"autogenerated": false,
"ratio": 3.966748590429377,
"conf... |
import numpy as np
from .mixin import TransformerMixin
from .base import BaseEstimator, _check_estimator
from ..parallel import parallel_func
from ..utils import (_validate_type, array_split_idx, ProgressBar,
verbose, fill_doc)
@fill_doc
class SlidingEstimator(BaseEstimator, TransformerMixin):
... | {
"repo_name": "adykstra/mne-python",
"path": "mne/decoding/search_light.py",
"copies": "2",
"size": "27578",
"license": "bsd-3-clause",
"hash": 1472279237485979000,
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"line_max": 114,
"alpha_frac": 0.5849952861,
"autogenerated": false,
"ratio": 3.9829578278451763,
"con... |
import numpy as np
from .mixin import TransformerMixin
from .base import BaseEstimator, _check_estimator
from ..parallel import parallel_func
from ..utils import _validate_type
class SlidingEstimator(BaseEstimator, TransformerMixin):
"""Search Light.
Fit, predict and score a series of models to each subset... | {
"repo_name": "teonlamont/mne-python",
"path": "mne/decoding/search_light.py",
"copies": "2",
"size": "25852",
"license": "bsd-3-clause",
"hash": 4954441744005651000,
"line_mean": 38.1104387292,
"line_max": 114,
"alpha_frac": 0.5943060498,
"autogenerated": false,
"ratio": 3.980292532717475,
"co... |
import numpy as np
from .mixin import TransformerMixin
from .base import BaseEstimator, _check_estimator
from ..parallel import parallel_func
class SearchLight(BaseEstimator, TransformerMixin):
"""Search Light.
Fit, predict and score a series of models to each subset of the dataset
along the last dimen... | {
"repo_name": "jniediek/mne-python",
"path": "mne/decoding/search_light.py",
"copies": "3",
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"license": "bsd-3-clause",
"hash": 6229437265381120000,
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"alpha_frac": 0.5803304623,
"autogenerated": false,
"ratio": 3.985987038010159,
"conf... |
import numpy as np
from .mixin import TransformerMixin
from .base import BaseEstimator, _check_estimator
from ..parallel import parallel_func
class SlidingEstimator(BaseEstimator, TransformerMixin):
"""Search Light.
Fit, predict and score a series of models to each subset of the dataset
along the last ... | {
"repo_name": "jaeilepp/mne-python",
"path": "mne/decoding/search_light.py",
"copies": "1",
"size": "24632",
"license": "bsd-3-clause",
"hash": 3063907341713613000,
"line_mean": 37.7295597484,
"line_max": 114,
"alpha_frac": 0.5943894121,
"autogenerated": false,
"ratio": 3.9569477911646587,
"con... |
import numpy as np
from numpy.testing import assert_array_equal
from nose.tools import assert_raises, assert_true, assert_equal
from mne.utils import requires_sklearn_0_15
from mne.decoding.search_light import SlidingEstimator, GeneralizingEstimator
from mne.decoding.transformer import Vectorizer
def make_data():
... | {
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"path": "mne/decoding/tests/test_search_light.py",
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"autogenerated": false,
"ratio": 3.13234111021721... |
import numpy as np
from numpy.testing import assert_array_equal
from nose.tools import assert_raises, assert_true, assert_equal
from ...utils import requires_sklearn_0_15
from ..search_light import _SearchLight, _GeneralizationLight
from .. import Vectorizer
def make_data():
n_epochs, n_chan, n_time = 50, 32, 1... | {
"repo_name": "nicproulx/mne-python",
"path": "mne/decoding/tests/test_search_light.py",
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"autogenerated": false,
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import numpy as np
from numpy.testing import assert_array_equal
from nose.tools import assert_raises, assert_true, assert_equal
from ...utils import requires_sklearn
from ..search_light import SearchLight, GeneralizationLight
from .. import Vectorizer
def make_data():
n_epochs, n_chan, n_time = 50, 32, 10
X... | {
"repo_name": "alexandrebarachant/mne-python",
"path": "mne/decoding/tests/test_search_light.py",
"copies": "1",
"size": "4657",
"license": "bsd-3-clause",
"hash": -5564103177497216000,
"line_mean": 31.3402777778,
"line_max": 77,
"alpha_frac": 0.6126261542,
"autogenerated": false,
"ratio": 3.1530... |
import numpy as np
from numpy.testing import assert_array_equal
from nose.tools import assert_raises
from mne.utils import requires_sklearn
from mne.decoding.time_frequency import TimeFrequency
@requires_sklearn
def test_timefrequency():
from sklearn.base import clone
# Init
n_freqs = 3
frequencies ... | {
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"ratio": 2.9452380952380... |
import numpy as np
from numpy.testing import assert_array_equal
import pytest
from mne.utils import requires_sklearn
from mne.decoding.time_frequency import TimeFrequency
@requires_sklearn
def test_timefrequency():
"""Test TimeFrequency."""
from sklearn.base import clone
# Init
n_freqs = 3
freq... | {
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"path": "mne/decoding/tests/test_time_frequency.py",
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"autogenerated": false,
"ratio": 2.88915662650602... |
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.colors as col
from matplotlib.colors import LinearSegmentedColormap
from ..utils import logcenter
from ..stats import median_abs_deviation
RdPuBu = col.LinearSegmentedColormap.from_list('RdPuBu', ['b', 'r'])
def alpha_cmap(cmap='... | {
"repo_name": "kingjr/jr-tools",
"path": "jr/plot/base.py",
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"size": "13359",
"license": "bsd-2-clause",
"hash": -9001882996455082000,
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"autogenerated": false,
"ratio": 3.10025527964725,
"config_test": false,
... |
__author__ = 'jeddy'
import sys, os, re, argparse, time
"""generateBatchSubmitParams
This script is used to generate a batch submit file for Globus Genomics Galaxy,
with all parameters specified for the selected Workflow for each library
(sample).
Inputs:
-u / --unalignedDir : directory with unaligned FASTQs fro... | {
"repo_name": "jaeddy/bripipetools",
"path": "scripts/_deprecated/generate_batch_submit_params.py",
"copies": "1",
"size": "11059",
"license": "mit",
"hash": -4913519357383324000,
"line_mean": 38.4964285714,
"line_max": 101,
"alpha_frac": 0.6048467312,
"autogenerated": false,
"ratio": 3.833275563... |
__author__ = 'jeddy'
import sys, os, re, argparse, time
"""generate_fc_batch_submit
This script is used to generate a batch submit file for Globus Genomics Galaxy,
with all parameters specified for the selected Workflow for each library
(sample).
Inputs:
-u / --unalignedDir : directory with unaligned FASTQs from... | {
"repo_name": "jaeddy/bripipetools",
"path": "scripts/_deprecated/generate_fc_batch_submit.py",
"copies": "1",
"size": "16066",
"license": "mit",
"hash": 1942194530735209000,
"line_mean": 40.5142118863,
"line_max": 121,
"alpha_frac": 0.577492842,
"autogenerated": false,
"ratio": 3.673068129858253... |
__author__ = 'jedi'
from controller.cocheController import *
import sys
import re
sys.path.insert(0, '../model')
class DbController:
def __init__(self, pathToDbCoches="../database/coches.txt", pathToDbClientes="../database/coches.txt", pathToDbTransacciones="../database/coches.txt"):
self.pathToDbCoches ... | {
"repo_name": "nejogaro/carruajes_barbie",
"path": "controller/dbController.py",
"copies": "1",
"size": "1946",
"license": "apache-2.0",
"hash": -1890861996541742300,
"line_mean": 35.0185185185,
"line_max": 155,
"alpha_frac": 0.5956790123,
"autogenerated": false,
"ratio": 3.272727272727273,
"co... |
__author__ = 'jedi'
import sys
sys.path.insert(0, '../controller')
from controller.dbController import *
from datetime import datetime
class Alquileres:
def __init__(self, matricula, nif, fechaalquiler, fecharetorno, importe, compleatada):
self.matricula = matricula
self.nif = nif
self.fe... | {
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"path": "model/alquileres.py",
"copies": "1",
"size": "1198",
"license": "apache-2.0",
"hash": 6304530593967764000,
"line_mean": 29.7179487179,
"line_max": 90,
"alpha_frac": 0.6611018364,
"autogenerated": false,
"ratio": 3.0876288659793816,
"config_tes... |
__author__ = "Jeff Nelson"
__copyright__ = "Copyright 2015, Cinchapi Inc."
__license__ = "Apache, Version 2.0"
from thrift import Thrift
from thrift.transport import TSocket
from thriftapi import ConcourseService
from thriftapi.shared.ttypes import *
from utils import *
from collections import OrderedDict
import ujson... | {
"repo_name": "prateek135/concourse",
"path": "concourse-driver-python/concourse/concourse.py",
"copies": "7",
"size": "49719",
"license": "apache-2.0",
"hash": -4488985652129412600,
"line_mean": 53.8169790518,
"line_max": 121,
"alpha_frac": 0.6391922605,
"autogenerated": false,
"ratio": 4.593827... |
__author__ = "Jeff Nelson"
__copyright__ = "Copyright 2015, Cinchapi, Inc."
__license__ = "Apache, Version 2.0"
from thrift import Thrift
from thrift.transport import TSocket
from thriftapi import ConcourseService
from thriftapi.shared.ttypes import *
from utils import *
import ujson
class Concourse(object):
"""... | {
"repo_name": "mAzurkovic/concourse",
"path": "concourse-driver-python/concourse/concourse.py",
"copies": "2",
"size": "23365",
"license": "apache-2.0",
"hash": -4882017675951950000,
"line_mean": 42.5914179104,
"line_max": 120,
"alpha_frac": 0.6213995292,
"autogenerated": false,
"ratio": 4.583169... |
__author__ = 'Jeff Nelson'
#
# The MIT License (MIT)
#
# Copyright (c) 2013-2016 Cinchapi Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitat... | {
"repo_name": "kylycht/concourse",
"path": "examples/quickstart/python/utils.py",
"copies": "4",
"size": "1661",
"license": "apache-2.0",
"hash": 7174430099093112000,
"line_mean": 32.24,
"line_max": 79,
"alpha_frac": 0.7182420229,
"autogenerated": false,
"ratio": 4.081081081081081,
"config_test... |
__author__ = 'Jeff Nelson'
#
# The MIT License (MIT)
#
# Copyright (c) 2013-2017 Cinchapi Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitat... | {
"repo_name": "dubex/concourse",
"path": "examples/quickstart/python/utils.py",
"copies": "2",
"size": "1661",
"license": "apache-2.0",
"hash": -1892514470561715700,
"line_mean": 32.24,
"line_max": 79,
"alpha_frac": 0.7182420229,
"autogenerated": false,
"ratio": 4.081081081081081,
"config_test"... |
__author__ = 'Jeff Nelson'
#
# The MIT License (MIT)
#
# Copyright (c) 2015 Cinchapi Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation t... | {
"repo_name": "hcuffy/concourse",
"path": "examples/quickstart/python/utils.py",
"copies": "1",
"size": "1656",
"license": "apache-2.0",
"hash": 1381186296613207000,
"line_mean": 32.14,
"line_max": 79,
"alpha_frac": 0.7179951691,
"autogenerated": false,
"ratio": 4.088888888888889,
"config_test"... |
__author__ = 'jeff'
from abc import ABCMeta, abstractmethod, abstractproperty
import subprocess, os
class ToolboxPlugin(object):
"""
Abstract base class for an Toolbox plugin
"""
__metaclass__ = ABCMeta
name = None
description = None
@abstractmethod
def prepare_parser(self, parser):
... | {
"repo_name": "jeff-99/toolbox",
"path": "toolbox/plugin.py",
"copies": "1",
"size": "4245",
"license": "isc",
"hash": -1937592033911492900,
"line_mean": 27.4899328859,
"line_max": 97,
"alpha_frac": 0.6124852768,
"autogenerated": false,
"ratio": 4.644420131291028,
"config_test": false,
"has_n... |
__author__ = 'jeff'
from .plugin import ToolboxPlugin
from .mixins import RegistryMixin, ConfigMixin, LogMixin
from .config import ConfigManager
import importlib, inspect, logging
class NoPluginException(Exception):
pass
class Registry(object):
"""
Registry of all available plugins
Setup the config ... | {
"repo_name": "jeff-99/toolbox",
"path": "toolbox/registry.py",
"copies": "1",
"size": "4603",
"license": "isc",
"hash": -3494148414318483500,
"line_mean": 33.8712121212,
"line_max": 103,
"alpha_frac": 0.6124266783,
"autogenerated": false,
"ratio": 4.5574257425742575,
"config_test": true,
"ha... |
__author__ = 'jeff'
import os
import re
from .renderer import ALIASES
class Parser(object):
def __init__(self, template_dir, dest_dir, args):
self.template_dir = template_dir
self.dest_dir = dest_dir
self.args = args
def resolve_key(self, match):
"""
Resolve the matche... | {
"repo_name": "jeff-99/toolbox",
"path": "toolbox/contrib/create/parser.py",
"copies": "1",
"size": "2276",
"license": "isc",
"hash": 3622082948719634400,
"line_mean": 29.3466666667,
"line_max": 114,
"alpha_frac": 0.5711775044,
"autogenerated": false,
"ratio": 4.035460992907802,
"config_test": ... |
__author__ = 'jeff'
import pkgutil
import re
import os
import sys
def find_contrib_modules():
"""
Find all core modules in the contrib package and return a list of importable packages
:return: A list of importable packages
:rtype: list
"""
contrib_dir = os.path.join(
os.path.dirname(os... | {
"repo_name": "jeff-99/toolbox",
"path": "toolbox/scanner.py",
"copies": "1",
"size": "1615",
"license": "isc",
"hash": -4610528536217598000,
"line_mean": 25.4754098361,
"line_max": 89,
"alpha_frac": 0.6235294118,
"autogenerated": false,
"ratio": 3.8822115384615383,
"config_test": false,
"has... |
__author__ = 'Jeff'
import re
import os
import zipfile
import smtplib
import getpass
from email import encoders
from email.mime.base import MIMEBase
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
userEmail = ''
toEmail = ''
while 1:
userEmail = input("Please enter your email ... | {
"repo_name": "jregistr/Academia",
"path": "CSC344-Programming-Languages/A5-Python/csc344/a5/a5Python.py",
"copies": "1",
"size": "7280",
"license": "mit",
"hash": -8046319550130659000,
"line_mean": 29.5882352941,
"line_max": 108,
"alpha_frac": 0.5677197802,
"autogenerated": false,
"ratio": 3.623... |
__author__ = 'jeff'
from energetic import EnergeticNetwork
import numpy as np
import math
class BoltzmannMachine(EnergeticNetwork):
def __init__(self, neuron_count):
super(BoltzmannMachine, self).__init__(neuron_count)
# The current temperature of the neural network. The higher the
# temp... | {
"repo_name": "JPMoresmau/aifh",
"path": "vol3/vol3-python-examples/lib/aifh/boltzmann.py",
"copies": "1",
"size": "3070",
"license": "apache-2.0",
"hash": -1236250327910696400,
"line_mean": 29.396039604,
"line_max": 84,
"alpha_frac": 0.5583061889,
"autogenerated": false,
"ratio": 3.8664987405541... |
__author__ = 'jeff'
from toolbox.plugin import ToolboxPlugin
from toolbox.mixins import ConfigMixin, RegistryMixin
from toolbox.utils import generate_name
from .parser import Parser
import os, tempfile, zipfile, shutil
class CreatePlugin(RegistryMixin, ConfigMixin, ToolboxPlugin):
name = 'create'
description... | {
"repo_name": "jeff-99/toolbox",
"path": "toolbox/contrib/create/create.py",
"copies": "1",
"size": "2428",
"license": "isc",
"hash": 5636337552002174000,
"line_mean": 34.1884057971,
"line_max": 75,
"alpha_frac": 0.5189456343,
"autogenerated": false,
"ratio": 4.529850746268656,
"config_test": f... |
__author__ = 'jeff'
from toolbox.plugin import ToolboxPlugin
from toolbox.mixins import RegistryMixin
from toolbox.scanner import find_modules
from terminaltables import AsciiTable
class ListPlugin(RegistryMixin, ToolboxPlugin):
name = 'list'
description = 'List all plugins'
def prepare_parser(self, par... | {
"repo_name": "jeff-99/toolbox",
"path": "toolbox/contrib/list/list.py",
"copies": "1",
"size": "1679",
"license": "isc",
"hash": 7210080269583072000,
"line_mean": 33.2653061224,
"line_max": 76,
"alpha_frac": 0.5372245384,
"autogenerated": false,
"ratio": 4.650969529085873,
"config_test": false... |
__author__ = 'jeff'
import json
class BucketAlreadyExists(Exception):
@property
def response(self):
return json.dumps(dict(error={
"errors": [
{
"domain": "global",
"reason": "conflict",
"message": "You already ow... | {
"repo_name": "sir-wiggles/moogle",
"path": "moogle/storage/errors.py",
"copies": "1",
"size": "1813",
"license": "apache-2.0",
"hash": -424314801739311700,
"line_mean": 24.9142857143,
"line_max": 89,
"alpha_frac": 0.4070601213,
"autogenerated": false,
"ratio": 5.050139275766017,
"config_test":... |
__author__ = 'jeff'
import numpy as np
from energetic import EnergeticNetwork
class HopfieldNetwork(EnergeticNetwork):
def __init__(self, neuron_count):
super(HopfieldNetwork, self).__init__(neuron_count)
self.input_count = neuron_count
self.output_count = neuron_count
self.activat... | {
"repo_name": "JPMoresmau/aifh",
"path": "vol3/vol3-python-examples/lib/aifh/hopfield.py",
"copies": "1",
"size": "4835",
"license": "apache-2.0",
"hash": 5414791547444788000,
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"line_max": 98,
"alpha_frac": 0.5604963806,
"autogenerated": false,
"ratio": 3.87109687750200... |
__author__ = 'jeff'
import numpy as np
class EnergeticNetwork:
"""
The energetic network forms the base class for Hopfield and Boltzmann machines.
"""
def __init__(self, neuron_count):
"""
Construct the network with the specified neuron count.
:param neuron_count: The number o... | {
"repo_name": "JPMoresmau/aifh",
"path": "vol3/vol3-python-examples/lib/aifh/energetic.py",
"copies": "1",
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"license": "apache-2.0",
"hash": 4054352349920913000,
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"line_max": 120,
"alpha_frac": 0.5609954017,
"autogenerated": false,
"ratio": 3.970998925886... |
__author__ = 'jeff'
import re
from jinja2 import Template
from .models import gcs_backend
from .errors import *
# https://www.googleapis.com/
# storage/v1beta2/b/<bucket>/o/<object>
# ?project=mock_project&alt=json
BASE_RE = re.compile("(?:/upload)?/storage/v1beta2/b/?(?P<bucket>[0-9a-zA-Z_-]+)?(?:/o/?(?P<object>[... | {
"repo_name": "sir-wiggles/moogle",
"path": "moogle/storage/responses.py",
"copies": "1",
"size": "5835",
"license": "apache-2.0",
"hash": -7381851207829086000,
"line_mean": 32.7283236994,
"line_max": 141,
"alpha_frac": 0.5904027421,
"autogenerated": false,
"ratio": 3.412280701754386,
"config_t... |
from __future__ import division, print_function, absolute_import
import numpy as np
from numpy.testing import TestCase, run_module_suite, assert_equal, \
assert_array_almost_equal, assert_array_equal, \
assert_allclose
from scipy.signal import dlsim, dstep, dimpulse... | {
"repo_name": "sargas/scipy",
"path": "scipy/signal/tests/test_dltisys.py",
"copies": "4",
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"hash": 2768271142162465000,
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"line_max": 78,
"alpha_frac": 0.4694618021,
"autogenerated": false,
"ratio": 2.9733293377284986,
"config... |
from __future__ import division, print_function, absolute_import
import warnings
import numpy as np
from numpy.testing import (TestCase, run_module_suite, assert_equal,
assert_array_almost_equal, assert_array_equal,
assert_allclose, assert_, assert_raises,
... | {
"repo_name": "DailyActie/Surrogate-Model",
"path": "01-codes/scipy-master/scipy/signal/tests/test_dltisys.py",
"copies": "1",
"size": "24143",
"license": "mit",
"hash": 7480495294300126000,
"line_mean": 35.1422155689,
"line_max": 78,
"alpha_frac": 0.5201093485,
"autogenerated": false,
"ratio": 3... |
import numpy as np
from numpy.testing import (assert_equal,
assert_array_almost_equal, assert_array_equal,
assert_allclose, assert_, assert_almost_equal,
suppress_warnings)
from pytest import raises as assert_raises
from scipy.signal impo... | {
"repo_name": "WarrenWeckesser/scipy",
"path": "scipy/signal/tests/test_dltisys.py",
"copies": "12",
"size": "21558",
"license": "bsd-3-clause",
"hash": -9150498401333489000,
"line_mean": 35.0501672241,
"line_max": 78,
"alpha_frac": 0.5143334261,
"autogenerated": false,
"ratio": 3.059174116645381... |
__author__ = 'jeffrey creighton & anand patel'
import random
import Player
import Message
class JCAPPlayer(Player.Player):
def __init__(self):
self.name = "JCAP"
self.moves = [0, 0, 0]
self.pre_smart = 3
self.decrementing = 45
# Decide to make a smart or random move and
#... | {
"repo_name": "geebzter/game-framework",
"path": "JCAPPlayer.py",
"copies": "1",
"size": "2351",
"license": "apache-2.0",
"hash": -2628894994493330400,
"line_mean": 28.7594936709,
"line_max": 77,
"alpha_frac": 0.5614632071,
"autogenerated": false,
"ratio": 3.8477905073649756,
"config_test": fal... |
__author__ = 'jeffrey creighton & anand patel'
# Purpose: to collect and store scores from all players and matches
import ScoreKeeperHistoryItem
import ScoreKeeperListItem
class ScoreKeeper(object):
"""
Consisting of two lists, will track all players and their scores
as well as each match and their ou... | {
"repo_name": "geebzter/game-framework",
"path": "ScoreKeeper.py",
"copies": "1",
"size": "4081",
"license": "apache-2.0",
"hash": 5448344656816630000,
"line_mean": 28.7883211679,
"line_max": 96,
"alpha_frac": 0.6086743445,
"autogenerated": false,
"ratio": 4.160040774719674,
"config_test": fals... |
__author__ = 'jeffrey creighton & anand patel'
# Purpose: to collect and store scores from all players and matches
import ScorekeeperHistoryItem
import ScoreKeeperListItem
class Scorekeeper:
leader_board = []
match_history = []
def __init__(self):
self.leader_board = []
self.match_histor... | {
"repo_name": "PaulieC/sprint1_Council_a",
"path": "Scorekeeper.py",
"copies": "2",
"size": "2202",
"license": "apache-2.0",
"hash": 8972126061509615000,
"line_mean": 32.3787878788,
"line_max": 98,
"alpha_frac": 0.6285195277,
"autogenerated": false,
"ratio": 3.7900172117039586,
"config_test": f... |
_author__ = 'jeff roy'
from mi.core.log import get_logger
log = get_logger()
from mi.idk.config import Config
import unittest
import os
from mi.dataset.driver.ctdbp_p.dcl.ctdbp_p_dcl_recovered_driver import parse
from mi.dataset.dataset_driver import ParticleDataHandler
class DriverTest(unittest.TestCase):
d... | {
"repo_name": "JeffRoy/mi-dataset",
"path": "mi/dataset/driver/ctdbp_p/dcl/test/test_ctdbp_p_dcl_recovered_driver.py",
"copies": "1",
"size": "1092",
"license": "bsd-2-clause",
"hash": 143350989086735180,
"line_mean": 25.0238095238,
"line_max": 99,
"alpha_frac": 0.6016483516,
"autogenerated": false... |
# Import the cscCommonScript utility module which, in turn, imports the
# standard library modules and imports arcpy
#EXPECTS 3 PARAMETERS, input bathy and output grid and neighborhood
# If running in PythonWin use C:\arcgis\data\BTM_Data\crml_bth C:\arcgis\data\mypig3 Annulus 1 3 CELL
import arcpy,sys, trac... | {
"repo_name": "EsriOceans/btm",
"path": "legacy/10.0/scripts_ags10/CreatePosIndexGrid.py",
"copies": "1",
"size": "3236",
"license": "mpl-2.0",
"hash": 4136667055698592000,
"line_mean": 30.7070707071,
"line_max": 101,
"alpha_frac": 0.6983930779,
"autogenerated": false,
"ratio": 3.8341232227488153... |
__author__ = 'JennyYueJin'
import json, sys
reload(sys)
sys.setdefaultencoding('UTF-8')
from pprint import pprint
import os
import re
import shutil
import requests
import urlparse
from bs4 import BeautifulSoup
import json
def get_soup(url):
"""
:param url: link to the page
:return: soup object
"""
... | {
"repo_name": "jennyyuejin/projectFox",
"path": "nonWebCode/crawlBBS/crawl.py",
"copies": "1",
"size": "5292",
"license": "unlicense",
"hash": -2995566967285780500,
"line_mean": 27.9234972678,
"line_max": 128,
"alpha_frac": 0.585978836,
"autogenerated": false,
"ratio": 3.3095684803001877,
"conf... |
__author__ = 'Jens Nevens'
from railfetcher import railtimeFetcher
from database import railDB
import pickle
from datetime import timedelta, date, datetime
def toUnix(timestamp):
if timestamp is None:
return None
else:
stamp = timestamp[6 : len(timestamp)-2]
(unix, tz) = stamp.split('+')
unix = float(unix)
... | {
"repo_name": "JensNevens/Bachelorproject",
"path": "railSQL/rail.py",
"copies": "1",
"size": "4608",
"license": "mit",
"hash": -1446887470269852200,
"line_mean": 25.6358381503,
"line_max": 216,
"alpha_frac": 0.6293402778,
"autogenerated": false,
"ratio": 2.996098829648895,
"config_test": false... |
__author__ = 'Jens Nevens'
import pymysql
import uuid
import json
json_data = open('oAuth')
oAuth = json.load(json_data)
class railDB():
def __init__(self, period='new'):
if period is 'new':
self.conn = pymysql.connect(host='localhost', port=3306, user=oAuth['USER'], passwd=oAuth['PASSWD'], db='newrailDB')
... | {
"repo_name": "JensNevens/Bachelorproject",
"path": "railSQL/database.py",
"copies": "1",
"size": "4374",
"license": "mit",
"hash": 1626786406300383200,
"line_mean": 30.4676258993,
"line_max": 159,
"alpha_frac": 0.443301326,
"autogenerated": false,
"ratio": 3.969147005444646,
"config_test": fal... |
__author__ = 'Jens Nevens'
import time
import json
from twython import Twython
from pymongo import MongoClient
json_data = open('oAuth')
oAuth = json.load(json_data)
CONSUMER_KEY = oAuth['CONS_KEY']
CONSUMER_SECRET = oAuth['CONS_SECRET']
ACCESS_TOKEN_KEY = oAuth['TOKEN_KEY']
ACCESS_TOKEN_SECRET = oAuth['TOKEN_SECRET... | {
"repo_name": "JensNevens/Bachelorproject",
"path": "twitter/tweet_mining.py",
"copies": "1",
"size": "3503",
"license": "mit",
"hash": 5169491154471578000,
"line_mean": 32.6826923077,
"line_max": 156,
"alpha_frac": 0.686554382,
"autogenerated": false,
"ratio": 3.0674255691768826,
"config_test"... |
__author__ = "Jens Thomas, Felix Simkovic & Adam Simpkin"
__date__ = "10 June 2019"
__version__ = "1.0"
import argparse
import os
from ample.modelling.multimer_definitions import MULTIMER_MODES
from pyjob.factory import TASK_PLATFORMS
class BoolAction(argparse.Action):
"""Class to set a boolean value either form... | {
"repo_name": "rigdenlab/ample",
"path": "ample/util/argparse_util.py",
"copies": "1",
"size": "26506",
"license": "bsd-3-clause",
"hash": -6199217811404720000,
"line_mean": 42.1693811075,
"line_max": 187,
"alpha_frac": 0.6482305893,
"autogenerated": false,
"ratio": 3.8825252673209314,
"config_... |
__author__ = "Jens Thomas & Felix Simkovic"
__date__ = "10 June 2019"
__version__ = "1.0"
import argparse
import os
from ample.modelling.multimer_definitions import MULTIMER_MODES
class BoolAction(argparse.Action):
"""Class to set a boolean value either form a string or just from the use of the command-line flag... | {
"repo_name": "linucks/ample",
"path": "ample/util/argparse_util.py",
"copies": "1",
"size": "25356",
"license": "bsd-3-clause",
"hash": -3824050381052637700,
"line_mean": 41.7588532884,
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"alpha_frac": 0.6580690961,
"autogenerated": false,
"ratio": 3.8250113139236688,
"config_te... |
__author__ = "Jens Thomas"
import glob
import logging
import os
import shutil
import tarfile
import zipfile
import iotbx.pdb
from ample.util import ample_util, exit_util, pdb_edit, sequence_util
logger = logging.getLogger(__name__)
class CheckModelsResult:
def __init__(self):
self.created_updated_mode... | {
"repo_name": "linucks/ample",
"path": "ample/util/process_models.py",
"copies": "2",
"size": "14356",
"license": "bsd-3-clause",
"hash": 1885631828696821500,
"line_mean": 40.8542274052,
"line_max": 163,
"alpha_frac": 0.6134020619,
"autogenerated": false,
"ratio": 3.9482948294829483,
"config_te... |
__author__ = 'jeremiahd'
import curses
import redis
import argparse
import sys
import datetime
DEFAULT_TEXT = 0
GREEN_TEXT = 1
RED_TEXT = 2
class RedisList:
def __init__(self, display_name, key_name):
self.display_name = display_name
self.key_name = key_name
self.size = 0
self.la... | {
"repo_name": "dangler/rlisty",
"path": "rlisty/main.py",
"copies": "1",
"size": "5150",
"license": "apache-2.0",
"hash": 870158369562503200,
"line_mean": 30.4024390244,
"line_max": 120,
"alpha_frac": 0.5980582524,
"autogenerated": false,
"ratio": 3.428761651131824,
"config_test": false,
"has... |
import sys
def read_file(file_str):
""" Return file lines for a file """
file_obj = open(file_str, "r")
file_lines = file_obj.readlines()
return file_lines
def extract_column_names(file_lines):
""" Return a list of tuples containing column names and its respective index. Mutates the file lines list by extrac... | {
"repo_name": "jlant/readmeasurements",
"path": "read_measurements.py",
"copies": "1",
"size": "3537",
"license": "mit",
"hash": -6212130393804891000,
"line_mean": 30.8738738739,
"line_max": 163,
"alpha_frac": 0.7028555273,
"autogenerated": false,
"ratio": 3.287174721189591,
"config_test": fals... |
"""Cards, hand and deck models. A hand contains 2 or more cards. A deck contains a multiple of 52 cards."""
import random
SUITLIST = ('heart', 'diamond', 'spade', 'club')
RANKLIST = ('A ', '2 ', '3 ', '4 ', '5 ', '6 ', '7 ',
'8 ', '9 ', '10', 'J ', 'Q ', 'K ')
VALUEMAP = {'A ':1, '2 ':2, '3 ':3, '4 ':4, '5... | {
"repo_name": "jercoh/pyBlackJack",
"path": "cards.py",
"copies": "1",
"size": "5235",
"license": "mit",
"hash": -5527588919997746000,
"line_mean": 30.9207317073,
"line_max": 107,
"alpha_frac": 0.4660936008,
"autogenerated": false,
"ratio": 3.480718085106383,
"config_test": false,
"has_no_key... |
"""pyBlackJack main file"""
from cards import Deck
from players import Player, Dealer
import asciiArts
import utils
#####################################################
class BlackJack:
"""Main class of pyBlackJack. Define a BlackJack game with a 6-deck shoe, one player and one dealer."""
def __init__(self):
# C... | {
"repo_name": "jercoh/pyBlackJack",
"path": "pyblackjack.py",
"copies": "1",
"size": "6812",
"license": "mit",
"hash": 3907384833485959700,
"line_mean": 26.0317460317,
"line_max": 110,
"alpha_frac": 0.6285965942,
"autogenerated": false,
"ratio": 3.0698512843623256,
"config_test": false,
"has_... |
"""User model. PLayer and Dealer class inherits from User."""
from cards import Hand
#####################################################
class User:
"""Define a User"""
def __init__(self, deck):
self.deck = deck
def hit(self):
"""The user takes a hit. Add a card to user's hand."""
... | {
"repo_name": "jercoh/pyBlackJack",
"path": "players.py",
"copies": "1",
"size": "2965",
"license": "mit",
"hash": -6218941051146210000,
"line_mean": 28.3564356436,
"line_max": 123,
"alpha_frac": 0.5365935919,
"autogenerated": false,
"ratio": 3.911609498680739,
"config_test": false,
"has_no_k... |
"""Various utility functions for handling prompt messages and console prints."""
def read_integer(message):
"""Prompt message until the user types an integer"""
while True:
user_input = raw_input(message)
try:
return int(user_input)
except ValueError:
continue
def read_integer_in_range(message, min, max... | {
"repo_name": "jercoh/pyBlackJack",
"path": "utils.py",
"copies": "1",
"size": "1057",
"license": "mit",
"hash": -9216233960880478000,
"line_mean": 28.3611111111,
"line_max": 80,
"alpha_frac": 0.6773888363,
"autogenerated": false,
"ratio": 3.546979865771812,
"config_test": false,
"has_no_keyw... |
__author__ = "Jeremy Carbaugh (jcarbaugh@sunlightfoundation.com)"
__version__ = "0.1"
__copyright__ = "Copyright (c) 2008 Sunlight Labs"
__license__ = "BSD"
from django.conf import settings
from django.contrib.auth.models import User
from django.contrib.contenttypes.models import ContentType
from django.contrib.sites.... | {
"repo_name": "uclastudentmedia/django-gatekeeper",
"path": "gatekeeper/__init__.py",
"copies": "1",
"size": "7984",
"license": "bsd-3-clause",
"hash": 6271360302482584000,
"line_mean": 42.8736263736,
"line_max": 191,
"alpha_frac": 0.622745491,
"autogenerated": false,
"ratio": 3.99799699549324,
... |
__author__ = "Jeremy Carbaugh (jcarbaugh@sunlightfoundation.com)"
__version__ = "0.1"
__copyright__ = "Copyright (c) 2010 Sunlight Labs"
__license__ = "BSD"
import sys
if sys.version_info[0] == 3:
from urllib.parse import urlencode, urljoin
from urllib.request import urlopen
from urllib.error import HTTPE... | {
"repo_name": "aaronsw/python-transparencydata",
"path": "transparencydata.py",
"copies": "1",
"size": "3571",
"license": "bsd-3-clause",
"hash": -4877899059528567000,
"line_mean": 30.3245614035,
"line_max": 100,
"alpha_frac": 0.5527863344,
"autogenerated": false,
"ratio": 4.236061684460261,
"c... |
__author__ = "Jeremy Carbaugh (jcarbaugh@sunlightfoundation.com)"
__version__ = "0.4.0a"
__copyright__ = "Copyright (c) 2010 Sunlight Labs"
__license__ = "BSD"
from django.conf import settings
from django.contrib.auth.models import User
from django.contrib.contenttypes.models import ContentType
from django.core.mail i... | {
"repo_name": "sunlightlabs/django-gatekeeper",
"path": "gatekeeper/__init__.py",
"copies": "1",
"size": "9293",
"license": "bsd-3-clause",
"hash": 6029347955654389000,
"line_mean": 38.2109704641,
"line_max": 127,
"alpha_frac": 0.6282147853,
"autogenerated": false,
"ratio": 3.8850334448160537,
... |
__author__="JeremyNelson, Mike Stabile"
import argparse
import datetime
import requests
import rdflib
import json
from elasticsearch import Elasticsearch
from elasticsearch import helpers
from sparql.general import*
from sparql.languages import workflow as languages
from sparql.subjects import workflow as subjects
fro... | {
"repo_name": "KnowledgeLinks/graph-utilities",
"path": "run.py",
"copies": "1",
"size": "11973",
"license": "apache-2.0",
"hash": -8376886935261175000,
"line_mean": 37.8733766234,
"line_max": 124,
"alpha_frac": 0.5841476656,
"autogenerated": false,
"ratio": 4.023185483870968,
"config_test": fa... |
__author__ = "Jeremy Nelson, Mike Stabile"
import os
import sys
import unittest
from rdfframework.validators import *
from rdfframework.processors import csv_to_multi_prop_processor, \
email_verification_processor, password_processor, run_processor,\
salt_processor
##
##
##
##class Test_email_verificati... | {
"repo_name": "KnowledgeLinks/rdfframework",
"path": "tests/test_validators.py",
"copies": "1",
"size": "4180",
"license": "mit",
"hash": 5923503884397710000,
"line_mean": 30.6666666667,
"line_max": 104,
"alpha_frac": 0.5488038278,
"autogenerated": false,
"ratio": 3.2939322301024427,
"config_te... |
{
"repo_name": "KnowledgeLinks/rdfframework",
"path": "tests/test_rdfclass.py",
"copies": "1",
"size": "4114",
"license": "mit",
"hash": 5280747529036805000,
"line_mean": 33.8644067797,
"line_max": 104,
"alpha_frac": 0.5269810404,
"autogenerated": false,
"ratio": 3.2470402525651143,
"config_test... | |
__author__ = "Jeremy Nelson"
from instance import config
from collections import OrderedDict
import requests
PREFIX = """PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX bf: <http://id.loc.gov/ontologies/bibframe/>
PREFIX schema: <http://schema.org/... | {
"repo_name": "KnowledgeLinks/dpla-service-hub",
"path": "reports/__init__.py",
"copies": "1",
"size": "2912",
"license": "apache-2.0",
"hash": 722498185623610400,
"line_mean": 33.2588235294,
"line_max": 109,
"alpha_frac": 0.6212225275,
"autogenerated": false,
"ratio": 3.599505562422744,
"confi... |
__author__ = "Jeremy Nelson"
import argparse
import datetime
import logging
import pymarc
import sys
from rda_enhancement import pcc_conversion
logging.basicConfig(
filename='error.log',
format='%(asctime)s %(message)s',
level=logging.ERROR)
def convert(input_mrc_filename, output_mrc_filename):
"""F... | {
"repo_name": "Tutt-Library/rda-enhancement",
"path": "run.py",
"copies": "1",
"size": "2416",
"license": "mit",
"hash": 2310226674400042000,
"line_mean": 31.2133333333,
"line_max": 80,
"alpha_frac": 0.565397351,
"autogenerated": false,
"ratio": 4.151202749140894,
"config_test": false,
"has_n... |
__author__ = "Jeremy Nelson"
import datetime
import os
import sys
import requests
import click
from zipfile import ZipFile, ZIP_DEFLATED
from multiprocessing import Pool
import bibcat.rml.processor as processor
BF2MAP4 = processor.SPARQLBatchProcessor(
rml_rules=['bf-to-map4-alt.ttl'],
triplestore_url='')
C... | {
"repo_name": "KnowledgeLinks/dpla-service-hub",
"path": "output.py",
"copies": "1",
"size": "3510",
"license": "apache-2.0",
"hash": 8368740396271991000,
"line_mean": 31.2018348624,
"line_max": 80,
"alpha_frac": 0.6,
"autogenerated": false,
"ratio": 3.5240963855421685,
"config_test": false,
... |
import os
import jinja2
import csv
TEMPLATE_FILENAME = 'cisco.j2'
CSVDATA_FILENAME = 'hosts_data.csv'
## ---------------------------------------------------------------------------
## define a function that will transform the "vlan_name_<n>" and "vlan_id_<n>"
## into a new dictionary called 'vlans'
## -------------... | {
"repo_name": "jeremyschulman/demo_host_csv_template_render",
"path": "render.py",
"copies": "1",
"size": "3083",
"license": "mit",
"hash": -8769440057608319000,
"line_mean": 40.1066666667,
"line_max": 78,
"alpha_frac": 0.5870904963,
"autogenerated": false,
"ratio": 4.166216216216216,
"config_t... |
__author__ = 'jerickson'
import netCDF4
import shutil
import types
import os
import uuid
import time
### PRMS File representation
class PRMSFile:
def __init__(self, basefile):
self.basefile = basefile
self.workingscenario = None
def begin_scenario(self, scenarioname):
'''
Star... | {
"repo_name": "ruiwu1990/fire_simulation_education",
"path": "app/api/PRMSCoverageTool.py",
"copies": "1",
"size": "5316",
"license": "bsd-3-clause",
"hash": -4319937112369216000,
"line_mean": 36.4436619718,
"line_max": 138,
"alpha_frac": 0.574303988,
"autogenerated": false,
"ratio": 3.8521739130... |
__author__ = 'jerickson'
# Lookup table for snow_intcp variable
def lookup_snow_intcp(covtype):
snow_intcp = [0.01, 0.01, 0.002, 0.01, 0.0]
return snow_intcp[covtype]
lookup_snow_intcp_ref = lookup_snow_intcp
# Lookup table for srain_intcp variable
def lookup_srain_intcp(covtype):
srain_intcp = [0.05, 0.0... | {
"repo_name": "ruiwu1990/fire_simulation_education",
"path": "app/api/PRMSLookup.py",
"copies": "1",
"size": "2560",
"license": "bsd-3-clause",
"hash": -3721779543451168300,
"line_mean": 32.2597402597,
"line_max": 75,
"alpha_frac": 0.6265625,
"autogenerated": false,
"ratio": 2.622950819672131,
... |
__author__ = 'Jernej'
from camera import camera
from elevons import elevons
from motor_handler import motor_handler
from sensors import sensors
import commands as c
MODE="M"
CONTROL="C"
HOLD="H"
ALT="T"
AUTO="A"
CAMERA="S"
RECORD="R"
MANUAL="m"
STABILIZED="s"
RESQUE="r"
DISCONNECT = "X"
SERVO_INIT = "SI"
SERVO_LI... | {
"repo_name": "jeryfast/piflyer",
"path": "piflyer/old/commander.py",
"copies": "1",
"size": "4610",
"license": "apache-2.0",
"hash": 2531574690891434500,
"line_mean": 28.5512820513,
"line_max": 99,
"alpha_frac": 0.5436008677,
"autogenerated": false,
"ratio": 3.489780469341408,
"config_test": f... |
__author__ = 'Jernej'
from commander import commander
from comm import comm
import threading
import time
class mainserver():
def __init__(self):
self.client=comm()
self.commander=commander()
#self.sendThread=dataSendingThread(self.client, self.commander)
#self.sendThread = m.Process... | {
"repo_name": "jeryfast/piflyer",
"path": "piflyer/old/mainserver.py",
"copies": "1",
"size": "2075",
"license": "apache-2.0",
"hash": 4384593151617850000,
"line_mean": 29.0724637681,
"line_max": 88,
"alpha_frac": 0.578313253,
"autogenerated": false,
"ratio": 3.929924242424242,
"config_test": f... |
__author__ = 'Jernej'
import number_range as n
import time
import Adafruit_PCA9685
import delays
MIN=0
MAX=100
# Initialise the PWM device using the default address
pwm = Adafruit_PCA9685.PCA9685(0x41)
# Note if you'd like more debug output you can instead run:
# pwm = PWM(0x40, debug=True)
# Set frequency to 60 Hz
... | {
"repo_name": "jeryfast/piflyer",
"path": "piflyer/motor_handler.py",
"copies": "1",
"size": "1901",
"license": "apache-2.0",
"hash": -1911645288464448300,
"line_mean": 25.7746478873,
"line_max": 99,
"alpha_frac": 0.6449237244,
"autogenerated": false,
"ratio": 3.468978102189781,
"config_test": ... |
__author__ = 'Jernej'
import os
BITRATE_MAX=17000000
HEIGHT_MAX=1080
class streamer:
def __init__(self,ip):
self.height=720
self.width=1080
self.fps=25
self.bitrate=8000000
self.ipAddress=ip
def run(self):
os.system("raspivid -t 0 -h "+self.height+" -w "+self.wi... | {
"repo_name": "jeryfast/piflyer",
"path": "piflyer/old/draft/streamer.py",
"copies": "1",
"size": "1096",
"license": "apache-2.0",
"hash": 5326636074673150000,
"line_mean": 28.6216216216,
"line_max": 254,
"alpha_frac": 0.5711678832,
"autogenerated": false,
"ratio": 3.2046783625730995,
"config_t... |
__author__ = 'Jernej'
import socket
from piflyer.commander import commander
import piflyer.commands as c
TCP_PORT = 13000
BUFFER_SIZE = 20 # Normally 1024, but we want fast response
class mainserver:
def __init__(self):
self.conn=""
self.addr=""
def run(self):
#create an INET, STREA... | {
"repo_name": "jeryfast/piflyer",
"path": "piflyer/old/draft/mainserver.py",
"copies": "1",
"size": "1160",
"license": "apache-2.0",
"hash": -2861965244931458600,
"line_mean": 25.976744186,
"line_max": 67,
"alpha_frac": 0.5905172414,
"autogenerated": false,
"ratio": 3.7419354838709675,
"config_... |
__author__ = "Jerome Kieffer"
__license__ = "MIT"
__copyright__ = "2017, ESRF"
import numpy
from math import log
from .collections import GOF
from ._cormap import measure_longest
class LongestRunOfHeads(object):
"""Implements the "longest run of heads" by Mark F. Schilling
The College Mathematics Journal, V... | {
"repo_name": "kif/freesas",
"path": "freesas/cormap.py",
"copies": "1",
"size": "4026",
"license": "mit",
"hash": 2966892423137648000,
"line_mean": 32,
"line_max": 106,
"alpha_frac": 0.5705414804,
"autogenerated": false,
"ratio": 3.528483786152498,
"config_test": false,
"has_no_keywords": fa... |
__author__ = 'jerrico'
from datetime import date
import arrow
from phishnetpy.exceptions import *
from phishnetpy.decorators import check_api_key, check_authorized_user
import requests
class PhishNetAPI(object):
DEFAULT_VERSION = '2.0'
DEFAULT_RETRY = 3
FORMAT = 'json'
def __init__(self, api_key=N... | {
"repo_name": "meg2208/phishnetpy",
"path": "phishnetpy/phishnet_api.py",
"copies": "2",
"size": "27027",
"license": "mit",
"hash": -6920461131981221000,
"line_mean": 44.4235294118,
"line_max": 127,
"alpha_frac": 0.6143856144,
"autogenerated": false,
"ratio": 4.20065278209512,
"config_test": fa... |
import random,string,hashlib
import requests
from urllib import request
import urllib
from bs4 import BeautifulSoup
def haslib():
a = ''.join(random.choice(string.ascii_letters + string.digits)
for _ in range(10))
print (a)
b = hashlib.md5("".join("str_args").encode('utf-8')).hexdigest()... | {
"repo_name": "tencrance/cool-config",
"path": "python3/playground.py",
"copies": "1",
"size": "1091",
"license": "mit",
"hash": 3687607227569992000,
"line_mean": 21.7291666667,
"line_max": 68,
"alpha_frac": 0.5783684693,
"autogenerated": false,
"ratio": 3.1623188405797102,
"config_test": false... |
from urllib import request
import morse_talk as mtalk
from bs4 import BeautifulSoup
from requests_toolbelt import MultipartEncoder
import requests
def get0():
m = MultipartEncoder(
fields={'field0': 'value', 'field1': 'value',
'field2': ('filename', open('/Users/yang/Desktop/Screen Shot 2... | {
"repo_name": "niasand/cool-config",
"path": "python_tricks/playground.py",
"copies": "1",
"size": "1331",
"license": "mit",
"hash": 7292362919999146000,
"line_mean": 23.6481481481,
"line_max": 129,
"alpha_frac": 0.5927873779,
"autogenerated": false,
"ratio": 3.0318906605922553,
"config_test": ... |
__author__ = 'Jerry'
import os, imp
def preBuild(channel, project, client):
path = os.path.join(client, 'channelinfo', channel, 'script')
if not os.path.exists(os.path.join(path, 'build.py')):
pass
else:
fp, pathname, description = imp.find_module('build', [path])
try:
m... | {
"repo_name": "ucloud/chameleon",
"path": "client/tools/buildtool/chameleon_tool/chameleon_script.py",
"copies": "3",
"size": "1267",
"license": "mit",
"hash": -6685826988733499000,
"line_mean": 34.2222222222,
"line_max": 71,
"alpha_frac": 0.5382794002,
"autogenerated": false,
"ratio": 3.91049382... |
__author__ = 'jerry'
import os, sys
import codecs
TOKEN_START = 'STARTCHAR'
TOKEN_UNICODE = 'U_'
TOKEN_ENCODING = 'ENCODING'
TOKEN_UNICODE_START = 'STARTCHAR U_'
TOKEN_BITMAP = 'BITMAP'
TOKEN_END = 'ENDCHAR'
class BDFReader(object):
def load(self):
if not os.path.exists(self.bdffile):
raise Exc... | {
"repo_name": "jerryshang/led",
"path": "loveapp/modules/bdfreader.py",
"copies": "1",
"size": "3210",
"license": "mit",
"hash": 9031032965185641000,
"line_mean": 32.8,
"line_max": 98,
"alpha_frac": 0.4919003115,
"autogenerated": false,
"ratio": 4.058154235145386,
"config_test": false,
"has_n... |
__author__ = 'jerry'
import cStringIO
import contextlib
import logging
import pstats
import time
@contextlib.contextmanager
def measure_time(label=None, logger=None, loglevel=logging.INFO, precision=2):
""" Measure how much time has passed between beginning and end of a block """
start = time.clock()
tr... | {
"repo_name": "MillionIntegrals/ESL",
"path": "common/profiling.py",
"copies": "1",
"size": "1511",
"license": "mit",
"hash": 6323895909069897000,
"line_mean": 22.9841269841,
"line_max": 82,
"alpha_frac": 0.6101919259,
"autogenerated": false,
"ratio": 4.280453257790368,
"config_test": false,
... |
__author__ = 'Jerry'
import os, sys, shutil, zipfile, re
from optparse import OptionParser
def merge(dir1, dir2, difflist):
print('difflist: ', difflist)
for d in difflist:
if d.endswith('.dex') or d.endswith('.arsc'):
return
joind1 = os.path.join(dir1, d)
joind2 = os.path.... | {
"repo_name": "uclouddotcn/chameleon",
"path": "client/tools/buildtool/chameleon_tool/diff_file.py",
"copies": "3",
"size": "2355",
"license": "mit",
"hash": -3908297395942522000,
"line_mean": 29.6438356164,
"line_max": 86,
"alpha_frac": 0.6079570854,
"autogenerated": false,
"ratio": 2.8424396442... |
__author__ = 'jerry'
import sys,urllib
import time
def get_title(page):
title_pos = page.find("article-header")
title = page[title_pos + 43 + len(url):]
return title[:title.find("</h1>")]
def get_content(page):
content_pos = page.find("article-content")
content = page[content_pos:]
content = c... | {
"repo_name": "jerrynlp/Linear_Models",
"path": "Data_Sets/Crawler.py",
"copies": "1",
"size": "1423",
"license": "apache-2.0",
"hash": -4269289182336668000,
"line_mean": 28.6666666667,
"line_max": 99,
"alpha_frac": 0.6170063247,
"autogenerated": false,
"ratio": 3.0934782608695652,
"config_test... |
__author__ = 'Jerry'
#!/usr/bin/env python
import zipfile, sys, os, shutil
from optparse import OptionParser
BASEDIR = os.path.split(os.path.realpath(__file__))[0]
APK_TOOL_PATH = os.path.join(BASEDIR, 'apktool')
DIFF_TOOL_PATH = os.path.join(BASEDIR, 'diff_file.py')
unzipPath = 'unzipPath'
apktoolPath = 'apktoolPat... | {
"repo_name": "uclouddotcn/chameleon",
"path": "client/tools/buildtool/chameleon_tool/build_channel_new.py",
"copies": "3",
"size": "3887",
"license": "mit",
"hash": -3531329843229568000,
"line_mean": 29.6062992126,
"line_max": 103,
"alpha_frac": 0.576794443,
"autogenerated": false,
"ratio": 3.52... |
__author__ = 'jerzydem'
def stripNonAlphaNum(text):
import re
return re.compile(r'\W+', re.UNICODE).split(text)
def contains_digits(d):
# DUPLICATE FUNCTION IN extractor.py
# TODO - remove duplication
import re
_digits = re.compile('\d')
return bool(_digits.search(d))
def get_csv_heade... | {
"repo_name": "DigitalHistorians/bs_scraper",
"path": "cleaner.py",
"copies": "1",
"size": "15700",
"license": "mit",
"hash": -8337668112393296000,
"line_mean": 31.6857749469,
"line_max": 153,
"alpha_frac": 0.4945433286,
"autogenerated": false,
"ratio": 2.8318616629874906,
"config_test": false,... |
__author__ = 'jerzydem'
EXTRA_COLUMNS_HEADERS = [
'osoba',
'daty-zycia',
'rok-urodzenia',
'miejsce-i-rok-urodzenia',
'rok-smierci',
'miejsce-i-rok-smierci',
'baza'
]
def contains_digits(d):
import re
_digits = re.compile('\d')
return bool(_digits.search(d))
def containsAny(... | {
"repo_name": "DigitalHistorians/bs_scraper",
"path": "extractor.py",
"copies": "1",
"size": "7744",
"license": "mit",
"hash": 1529244611820446700,
"line_mean": 26.6428571429,
"line_max": 126,
"alpha_frac": 0.5414136193,
"autogenerated": false,
"ratio": 3.8445106805762546,
"config_test": false,... |
__author__ = 'jerzydem'
# TODO
# def get_image
def prepare_page_url(name):
import urllib
refine_name = name.replace('?', ' ').replace('-', ' ').replace('(', ' ').replace(')', ' ');
# add name values to table
names_table = refine_name.split()
# prepare url query
name_query = ''
for token... | {
"repo_name": "DigitalHistorians/bs_scraper",
"path": "get_files.py",
"copies": "1",
"size": "4214",
"license": "mit",
"hash": 9088004090286811000,
"line_mean": 27.8493150685,
"line_max": 95,
"alpha_frac": 0.599002849,
"autogenerated": false,
"ratio": 3.394037066881547,
"config_test": false,
... |
# This tic-tac-toe game requires the colorama Python library, which is
# available here: http://pypi.python.org/pypi/colorama or can be downloaded
# through pip by typing "sudo pip install colorama" into a terminal window
from colorama import init, Fore, Back, Style
import os
init()
# Initializes board
board1 = [1, ... | {
"repo_name": "jessebikman/Tictactoe-colorama",
"path": "ultimate_game.py",
"copies": "1",
"size": "12724",
"license": "mit",
"hash": 5305826810082694000,
"line_mean": 49.2924901186,
"line_max": 103,
"alpha_frac": 0.5149324112,
"autogenerated": false,
"ratio": 3.1148102815177476,
"config_test":... |
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