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__author__ = "mfreer"
__date__ = "2012-01-27 16:41"
__version__ = "1.0"
__all__ = ["TempPotentialCnrm"]
import egads.core.egads_core as egads_core
import egads.core.metadata as egads_metadata
class TempPotentialCnrm(egads_core.EgadsAlgorithm):
"""
FILE temp_potential_cnrm.py
VERSION 1.0
... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/algorithms/thermodynamics/temp_potential_cnrm.py",
"copies": "2",
"size": "3535",
"license": "bsd-3-clause",
"hash": -8644890247753714000,
"line_mean": 50.9852941176,
"line_max": 198,
"alpha_frac": 0.4311173975,
"autogenerated": false,
"ratio"... |
__author__ = "mfreer"
__date__ = "2012-02-07 17:23"
__version__ = "1.1"
__all__ = ['DiameterMeanRaf']
import numpy
import egads.core.egads_core as egads_core
import egads.core.metadata as egads_metadata
class DiameterMeanRaf(egads_core.EgadsAlgorithm):
"""
FILE diameter_mean_raf.py
VERSION ... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/algorithms/microphysics/diameter_mean_raf.py",
"copies": "2",
"size": "3402",
"license": "bsd-3-clause",
"hash": 7236718219970942000,
"line_mean": 49.776119403,
"line_max": 242,
"alpha_frac": 0.4285714286,
"autogenerated": false,
"ratio": 5.16... |
__author__ = "mfreer"
__date__ = "2012-02-07 17:23"
__version__ = "1.1"
__all__ = ['SampleAreaScatteringRaf']
import egads.core.egads_core as egads_core
import egads.core.metadata as egads_metadata
class SampleAreaScatteringRaf(egads_core.EgadsAlgorithm):
"""
FILE sample_area_scattering_raf.py
... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/algorithms/microphysics/sample_area_scattering_raf.py",
"copies": "2",
"size": "3161",
"license": "bsd-3-clause",
"hash": -1566287035711371000,
"line_mean": 45.4852941176,
"line_max": 198,
"alpha_frac": 0.406516925,
"autogenerated": false,
"ra... |
__author__ = "mfreer"
__date__ = "2012-02-07 17:23"
__version__ = "1.2"
__all__ = ['NumberConcTotalRaf']
import numpy
import egads.core.egads_core as egads_core
import egads.core.metadata as egads_metadata
class NumberConcTotalRaf(egads_core.EgadsAlgorithm):
"""
FILE number_conc_total_raf.py
... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/algorithms/microphysics/number_conc_total_raf.py",
"copies": "2",
"size": "3344",
"license": "bsd-3-clause",
"hash": -4835988415037447000,
"line_mean": 48.9104477612,
"line_max": 191,
"alpha_frac": 0.4258373206,
"autogenerated": false,
"ratio"... |
__author__ = "mfreer"
__date__ = "2012-06-22 17:19"
__version__ = "1.1"
__all__ = ['CorrectionSpikeSimpleCnrm']
import egads.core.egads_core as egads_core
import egads.core.metadata as egads_metadata
from copy import deepcopy
from numpy import abs
class CorrectionSpikeSimpleCnrm(egads_core.EgadsAlgorithm):
"""
... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/algorithms/corrections/correction_spike_simple_cnrm.py",
"copies": "2",
"size": "3961",
"license": "bsd-3-clause",
"hash": 5133178736790708000,
"line_mean": 50.4415584416,
"line_max": 279,
"alpha_frac": 0.4390305478,
"autogenerated": false,
"r... |
__author__ = "mfreer"
__date__ = "2012-06-22 17:19"
__version__ = "1.2"
__all__ = ['WindVector3dRaf']
from numpy import sin, cos, tan, sqrt
import egads.core.egads_core as egads_core
import egads.core.metadata as egads_metadata
class WindVector3dRaf(egads_core.EgadsAlgorithm):
"""
FILE wind_vector... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/algorithms/thermodynamics/wind_vector_3d_raf.py",
"copies": "2",
"size": "7222",
"license": "bsd-3-clause",
"hash": 1319134044673124000,
"line_mean": 65.2568807339,
"line_max": 407,
"alpha_frac": 0.4266131266,
"autogenerated": false,
"ratio": ... |
__author__ = "mfreer"
__date__ = "2012-07-06 17:42"
__version__ = "1.2"
__all__ = ['IsotimeToElements']
import egads.core.egads_core as egads_core
import egads.core.metadata as egads_metadata
import dateutil.parser
class IsotimeToElements(egads_core.EgadsAlgorithm):
"""
FILE isotime_to_elements.py
... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/algorithms/transforms/isotime_to_elements.py",
"copies": "2",
"size": "6199",
"license": "bsd-3-clause",
"hash": -5614106556239003000,
"line_mean": 52.9043478261,
"line_max": 198,
"alpha_frac": 0.3776415551,
"autogenerated": false,
"ratio": 5.... |
__author__ = "mfreer"
__date__ = "2012-07-06 17:42"
__version__ = "1.6"
__all__ = ["EgadsData", "EgadsAlgorithm"]
import logging
import weakref
import datetime
import re
import numpy
import quantities as pq # @UnresolvedImport
import metadata
from collections import defaultdict
class EgadsData(pq.Quantity):
"""
... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/core/egads_core.py",
"copies": "2",
"size": "19579",
"license": "bsd-3-clause",
"hash": -3257847947766810600,
"line_mean": 37.0914396887,
"line_max": 140,
"alpha_frac": 0.5589662393,
"autogenerated": false,
"ratio": 4.258155719878208,
"confi... |
__author__ = "mfreer"
__date__ = "2012-08-24 08:41"
__version__ = "1.3"
__all__ = ['ScatteringAngles']
import egads.core.egads_core as egads_core
import egads.core.metadata as egads_metadata
import numpy
class ScatteringAngles(egads_core.EgadsAlgorithm):
"""
FILE scattering_angles.py
VERSION ... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/algorithms/radiation/scattering_angles.py",
"copies": "2",
"size": "5046",
"license": "bsd-3-clause",
"hash": 8610973492987472000,
"line_mean": 57,
"line_max": 177,
"alpha_frac": 0.4195402299,
"autogenerated": false,
"ratio": 4.672222222222222... |
__author__ = "mfreer"
__date__ = "2013-02-17 18:01"
__version__ = "1.2"
__all__ = ['PlanckEmission']
import egads.core.egads_core as egads_core
import egads.core.metadata as egads_metadata
import numpy
class PlanckEmission(egads_core.EgadsAlgorithm):
"""
FILE planck_emission.py
VERSION 1.... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/algorithms/radiation/planck_emission.py",
"copies": "2",
"size": "3376",
"license": "bsd-3-clause",
"hash": 3619569365158737400,
"line_mean": 47.2285714286,
"line_max": 151,
"alpha_frac": 0.409063981,
"autogenerated": false,
"ratio": 5.0614692... |
__author__ = "mfreer"
__date__ = "2013-02-17 18:01"
__version__ = "1.2"
__all__ = ['TempBlackbody']
import egads.core.egads_core as egads_core
import egads.core.metadata as egads_metadata
import numpy
class TempBlackbody(egads_core.EgadsAlgorithm):
"""
FILE temp_blackbody.py
VERSION 1.2
... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/algorithms/radiation/temp_blackbody.py",
"copies": "2",
"size": "3395",
"license": "bsd-3-clause",
"hash": -6150751832552619000,
"line_mean": 46.8169014085,
"line_max": 153,
"alpha_frac": 0.412371134,
"autogenerated": false,
"ratio": 5.2070552... |
__author__ = "mfreer"
__date__ = "2013-02-17 18:01"
__version__ = "1.3"
__all__ = ['LimitAngleRange']
import egads.core.egads_core as egads_core
import egads.core.metadata as egads_metadata
import numpy
class LimitAngleRange(egads_core.EgadsAlgorithm):
"""
FILE limit_angle_range.py
VERSION 1.... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/algorithms/mathematics/limit_angle_range.py",
"copies": "2",
"size": "3485",
"license": "bsd-3-clause",
"hash": -4984711332361027000,
"line_mean": 44.2597402597,
"line_max": 172,
"alpha_frac": 0.4209469154,
"autogenerated": false,
"ratio": 5.8... |
__author__ = "mfreer"
__date__ = "2013-02-17 18:01"
__version__ = "1.3"
__all__ = ['SampleAreaOapAllInRaf']
import egads.core.egads_core as egads_core
import egads.core.metadata as egads_metadata
import numpy
class SampleAreaOapAllInRaf(egads_core.EgadsAlgorithm):
"""
FILE sample_area_oap_all_in_r... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/algorithms/microphysics/sample_area_oap_all_in_raf.py",
"copies": "2",
"size": "4151",
"license": "bsd-3-clause",
"hash": 4567412661123609000,
"line_mean": 50.2469135802,
"line_max": 261,
"alpha_frac": 0.431703204,
"autogenerated": false,
"rat... |
__author__ = "mfreer"
__date__ = "2013-03-27 20:26"
__version__ = "1.6"
__all__ = ['DiameterMedianVolumeDmt']
import numpy
import egads
import egads.core.egads_core as egads_core
import egads.core.metadata as egads_metadata
class DiameterMedianVolumeDmt(egads_core.EgadsAlgorithm):
"""
FILE diamete... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/algorithms/microphysics/diameter_median_volume_dmt.py",
"copies": "2",
"size": "5891",
"license": "bsd-3-clause",
"hash": -1166601845459324000,
"line_mean": 57.91,
"line_max": 319,
"alpha_frac": 0.4326939399,
"autogenerated": false,
"ratio": 4... |
__author__ = "mfreer,ohenry"
__date__ = "2016-01-04 09:39"
__version__ = "1.2"
__all__ = ['InterpolationLinearOld']
import egads.core.egads_core as egads_core
import egads.core.metadata as egads_metadata
import numpy as np
class InterpolationLinearOld(egads_core.EgadsAlgorithm):
"""
FILE interpola... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/algorithms/transforms/interpolation_linear_old.py",
"copies": "2",
"size": "7136",
"license": "bsd-3-clause",
"hash": -7081527504765569000,
"line_mean": 57.9752066116,
"line_max": 297,
"alpha_frac": 0.4147982063,
"autogenerated": false,
"ratio... |
__author__ = "mfreer, ohenry"
__date__ = "2016-01-10 10:01"
__version__ = "1.2"
__all__ = ['ExtinctionCoeffDmt']
import numpy
import egads.core.egads_core as egads_core
import egads.core.metadata as egads_metadata
class ExtinctionCoeffDmt(egads_core.EgadsAlgorithm):
"""
FILE extinction_coeff_dmt.py
... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/algorithms/microphysics/extinction_coeff_dmt.py",
"copies": "2",
"size": "4265",
"license": "bsd-3-clause",
"hash": -6066900295213921000,
"line_mean": 56.6351351351,
"line_max": 319,
"alpha_frac": 0.4527549824,
"autogenerated": false,
"ratio":... |
__author__ = "mfreer, ohenry"
__date__ = "2016-01-10 11:58"
__version__ = "1.1"
__all__ = ['SurfaceAreaConcDmt']
import numpy
import egads.core.egads_core as egads_core
import egads.core.metadata as egads_metadata
class SurfaceAreaConcDmt(egads_core.EgadsAlgorithm):
"""
FILE surface_area_conc_dmt.py
... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/algorithms/microphysics/surface_area_conc_dmt.py",
"copies": "2",
"size": "4426",
"license": "bsd-3-clause",
"hash": 7311537484968453000,
"line_mean": 58.0133333333,
"line_max": 317,
"alpha_frac": 0.4577496611,
"autogenerated": false,
"ratio":... |
__author__ = "mfreer, ohenry"
__date__ = "2016-12-16 10:33"
__version__ = "1.8"
from _version import __version__
import logging
from logging.handlers import RotatingFileHandler
from threading import Thread
import os
import sys
import site
import ConfigParser
path = os.path.abspath(os.path.dirname(__file__))
config_di... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/__init__.py",
"copies": "2",
"size": "5088",
"license": "bsd-3-clause",
"hash": 5960563911135904000,
"line_mean": 41.0495867769,
"line_max": 130,
"alpha_frac": 0.6446540881,
"autogenerated": false,
"ratio": 3.4754098360655736,
"config_test":... |
__author__ = "mfreer, ohenry"
__date__ = "2017-01-11 11:12"
__version__ = "1.4"
__all__ = ['SolarVectorBlanco']
import numpy
import dateutil.parser as dateparser
import egads.core.egads_core as egads_core
import egads.core.metadata as egads_metadata
class SolarVectorBlanco(egads_core.EgadsAlgorithm):
"""
... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/algorithms/radiation/solar_vector_blanco.py",
"copies": "2",
"size": "8131",
"license": "bsd-3-clause",
"hash": 8480691351743514000,
"line_mean": 53.2066666667,
"line_max": 322,
"alpha_frac": 0.4444717747,
"autogenerated": false,
"ratio": 4.67... |
__author__ = "mfreer, ohenry"
__date__ = "2017-01-11 11:16"
__version__ = "1.5"
__all__ = ['SolarVectorReda']
import numpy
import dateutil.parser as dateparser
import egads # @UnusedImport
import egads.core.egads_core as egads_core
import egads.core.metadata as egads_metadata
import egads.algorithms.mathematics
cla... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/algorithms/radiation/solar_vector_reda.py",
"copies": "2",
"size": "36952",
"license": "bsd-3-clause",
"hash": -4030970765935391000,
"line_mean": 49.6886145405,
"line_max": 419,
"alpha_frac": 0.3285343148,
"autogenerated": false,
"ratio": 3.65... |
__author__ = "mfreer, ohenry"
__date__ = "2017-01-11 16:05"
__version__ = "1.3"
__all__ = ['Metadata', 'FileMetadata', 'VariableMetadata', 'AlgorithmMetadata']
import logging
FILE_ATTR_LIST = ['Conventions',
'title',
'source',
'institution',
'pro... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/core/metadata.py",
"copies": "2",
"size": "19343",
"license": "bsd-3-clause",
"hash": -1354366567891690800,
"line_mean": 45.2751196172,
"line_max": 135,
"alpha_frac": 0.5225146048,
"autogenerated": false,
"ratio": 4.885829754988634,
"config_... |
__author__ = "mfreer, ohenry"
__date__ = "2017-01-12 9:28"
__version__ = "1.2"
__all__ = ["AltitudePressureRaf"]
import egads.core.egads_core as egads_core
import egads.core.metadata as egads_metadata
import numpy
class AltitudePressureRaf(egads_core.EgadsAlgorithm):
"""
FILE altitude_pressure_raf... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/algorithms/thermodynamics/altitude_pressure_raf.py",
"copies": "2",
"size": "3928",
"license": "bsd-3-clause",
"hash": 5575737949570724000,
"line_mean": 46.9024390244,
"line_max": 211,
"alpha_frac": 0.4103869654,
"autogenerated": false,
"ratio... |
__author__ = "mfreer, ohenry"
__date__ = "2017-01-17 13:29"
__version__ = "1.0"
__all__ = ["VelocityTasCnrm"]
import egads.core.egads_core as egads_core
import egads.core.metadata as egads_metadata
import numpy
class VelocityTasCnrm(egads_core.EgadsAlgorithm):
"""
FILE velocity_tas_cnrm.py
VE... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/algorithms/thermodynamics/velocity_tas_cnrm.py",
"copies": "2",
"size": "3869",
"license": "bsd-3-clause",
"hash": 4144934014953865700,
"line_mean": 52.7361111111,
"line_max": 200,
"alpha_frac": 0.4326699406,
"autogenerated": false,
"ratio": 4... |
__author__ = "mfreer, ohenry"
__date__ = "2017-01-24 15:27"
__version__ = "1.2"
__all__ = ['SecondsToIsotime']
import egads.core.egads_core as egads_core
import egads.core.metadata as egads_metadata
import datetime
import dateutil.parser
from convert_time_format import convert_time_format
class SecondsToIsotime(egads... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/algorithms/transforms/seconds_to_isotime.py",
"copies": "2",
"size": "4427",
"license": "bsd-3-clause",
"hash": -4224863114299490000,
"line_mean": 48.1888888889,
"line_max": 320,
"alpha_frac": 0.4784278292,
"autogenerated": false,
"ratio": 5.1... |
__author__ = "mfreer, ohenry"
__date__ = "2017-01-26 13:07"
__version__ = "1.2"
__all__ = ['MassConcDmt']
import numpy
import egads.core.egads_core as egads_core
import egads.core.metadata as egads_metadata
class MassConcDmt(egads_core.EgadsAlgorithm):
"""
FILE mass_conc_dmt.py
VERSION 1.2
... | {
"repo_name": "eufarn7sp/egads-eufar",
"path": "egads/algorithms/microphysics/mass_conc_dmt.py",
"copies": "2",
"size": "4982",
"license": "bsd-3-clause",
"hash": -2269251180287172600,
"line_mean": 58.3095238095,
"line_max": 317,
"alpha_frac": 0.4492171819,
"autogenerated": false,
"ratio": 4.7538... |
__author__ = 'mgaldieri'
from pyaffective.emotions import PAD
from scipy.spatial.distance import sqeuclidean
from operator import itemgetter
import numpy as np
class Feature:
def __init__(self, name='', pad=PAD(), rgb=None):
if not rgb:
rgb = []
self.name = name
self.pad = pad... | {
"repo_name": "mgaldieri/flask-pyaffective",
"path": "utils/actuators.py",
"copies": "1",
"size": "1556",
"license": "mit",
"hash": 8082942856729923000,
"line_mean": 36.0476190476,
"line_max": 102,
"alpha_frac": 0.6304627249,
"autogenerated": false,
"ratio": 3.5525114155251143,
"config_test": f... |
__author__ = 'mgaldieri'
from scipy.spatial.distance import cosine
from scipy.spatial import distance
import numpy as np
# agent = Agent()
# agent.start()
#
# for i in range(5):
# sleep(1)
# print i+1
#
# agent.stop()
#
# for i in range(2):
# sleep(1)
# print i+1
# def test(*args):
# # args = arg... | {
"repo_name": "mgaldieri/flask-pyaffective",
"path": "general_tests.py",
"copies": "1",
"size": "1082",
"license": "mit",
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"alpha_frac": 0.5462107209,
"autogenerated": false,
"ratio": 2.2635983263598325,
"config_test": fa... |
__author__ = 'mgaldieri'
import time
from serial import SerialException
from pyaffective.emotions import OCEAN, PAD
from utils.sensors import Sensor, Influence, InfluenceValue
from utils.actuators import Feature, RGBled
from socketIO_client import SocketIO, BaseNamespace
import Queue
import logging
import serial
impo... | {
"repo_name": "mgaldieri/flask-pyaffective",
"path": "arduino/serialserver.py",
"copies": "1",
"size": "11530",
"license": "mit",
"hash": 4384599093110631000,
"line_mean": 48.2735042735,
"line_max": 106,
"alpha_frac": 0.6281873374,
"autogenerated": false,
"ratio": 3.6065060994682514,
"config_te... |
__author__ = 'mgaldieri'
"""
Stack tracer for multi-threaded applications.
Usage:
import stacktracer
stacktracer.start_trace("trace.html",interval=5,auto=True) # Set auto flag to always update file!
....
stacktracer.stop_trace()
"""
import sys
import traceback
from pygments import highlight
from pygments.lexers.p... | {
"repo_name": "mgaldieri/flask-pyaffective",
"path": "utils/stacktracer.py",
"copies": "1",
"size": "2846",
"license": "mit",
"hash": -4488231220830113000,
"line_mean": 26.6310679612,
"line_max": 97,
"alpha_frac": 0.6103302881,
"autogenerated": false,
"ratio": 3.8986301369863012,
"config_test":... |
__author__ = 'mglass'
from srwlib import *
import sys
from comsyl.autocorrelation.AutocorrelationFunction import AutocorrelationFunction
from comsyl.autocorrelation.AutocorrelationFunctionPropagator import AutocorrelationFunctionPropagator
from comsyl.parallel.utils import isMaster, barrier
from comsyl.utils.Logger im... | {
"repo_name": "srio/shadow3-scripts",
"path": "COMSYL/freeSpacePropagateModes.py",
"copies": "1",
"size": "2636",
"license": "mit",
"hash": 3226840025050668500,
"line_mean": 33.2467532468,
"line_max": 138,
"alpha_frac": 0.6794385432,
"autogenerated": false,
"ratio": 3.274534161490683,
"config_t... |
__author__ = 'mglass'
# # from srwlib import *
# import sys
# from comsyl.autocorrelation.AutocorrelationFunction import AutocorrelationFunction
from orangecontrib.comsyl.util.CompactAFReader import CompactAFReader
# from comsyl.autocorrelation.AutocorrelationFunctionPropagator import AutocorrelationFunctionPropagat... | {
"repo_name": "srio/shadow3-scripts",
"path": "HIGHLIGHTS/analyze_propagated_modes.py",
"copies": "1",
"size": "3520",
"license": "mit",
"hash": 6848169781316676000,
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"line_max": 147,
"alpha_frac": 0.7244318182,
"autogenerated": false,
"ratio": 3.1150442477876106,
"co... |
__author__ = 'mglass'
from srwlib import *
from comsyl.autocorrelation.AutocorrelationFunction import AutocorrelationFunction
from comsyl.autocorrelation.AutocorrelationFunctionPropagator import AutocorrelationFunctionPropagator
from comsyl.parallel.utils import isMaster, barrier
from comsyl.utils.Logger import log
#... | {
"repo_name": "srio/shadow3-scripts",
"path": "HIGHLIGHTS/beamline_propagate_modes.py",
"copies": "1",
"size": "7384",
"license": "mit",
"hash": 8011632089908006000,
"line_mean": 35.92,
"line_max": 203,
"alpha_frac": 0.6728060672,
"autogenerated": false,
"ratio": 3.1979211779991337,
"config_tes... |
__author__ = 'mgolub2'
"""
Take photos based on rangefinder data and upload it to an azure blob container.
"""
import mraa
import time
import subprocess
import requests
import os
from azure.storage import BlobService
#Constants, not globals
backendUrl = 'http://bikeraxx.azurewebsites.net/api/Photo/'
aioPort = 0
numPh... | {
"repo_name": "mgolub2/bikeraxx",
"path": "src/bikerack.py",
"copies": "1",
"size": "3747",
"license": "mit",
"hash": -1498485585939392800,
"line_mean": 26.9701492537,
"line_max": 103,
"alpha_frac": 0.6028823058,
"autogenerated": false,
"ratio": 3.4439338235294117,
"config_test": false,
"has_... |
__author__ = 'mhan0'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import json
path = 'ch02/usagov_bitly_data2012-03-16-1331923249.txt'
records = [json.loads(line) for line in open(path)]
records[0]
records[0]['tz']
time_zones = [rec['tz'] for rec in records if 'tz' in rec]
len(time_zones)
... | {
"repo_name": "venus2247/PyS",
"path": "Test for DM&ML/PythonDataTest.py",
"copies": "1",
"size": "9998",
"license": "mit",
"hash": 2791012678909316600,
"line_mean": 20.8034934498,
"line_max": 167,
"alpha_frac": 0.6568195474,
"autogenerated": false,
"ratio": 2.6600958977091103,
"config_test": f... |
__author__ = 'mhan7'
Fulllist = []
AttendList = []
FulllistFile = open("TextReorganize\TPCFullList.txt", "r",encoding = 'ISO-8859-1')
for line in FulllistFile:
line = line.encode("utf-8",'replace')
print(line)
Fulllist.append(line)
FulllistFile.close()
len(Fulllist)
AttendListFile = open("TextReorganize\... | {
"repo_name": "venus2247/PyS",
"path": "Test for DM&ML/TextReorganize/TextReorganize.py",
"copies": "1",
"size": "1411",
"license": "mit",
"hash": -8532373956879324000,
"line_mean": 19.1714285714,
"line_max": 82,
"alpha_frac": 0.6215450035,
"autogenerated": false,
"ratio": 2.8505050505050504,
"... |
__author__ = 'mhan'
import nltk
# nltk.download()
sentence = """At eight o'clock on Thursday morning Arthur didn't feel very good."""
tokens = nltk.word_tokenize(sentence)
tokens
tagged = nltk.pos_tag(tokens)
tagged[0:6]
from nltk.book import *
text1.similar("monstrous")
text4.dispersion_plot(["citizens", "democr... | {
"repo_name": "venus2247/PyS",
"path": "Test for DM&ML/NLTKStudy.py",
"copies": "1",
"size": "5931",
"license": "mit",
"hash": 2359837305135665700,
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"autogenerated": false,
"ratio": 3.033759590792839,
"config_test": false,
... |
__author__ = 'mhockenberger'
#
# Copyright 2016-2020 Cuemacro - https://www.cuemacro.com / @cuemacro
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the
# License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
... | {
"repo_name": "cuemacro/finmarketpy",
"path": "finmarketpy_examples/vwap_example.py",
"copies": "1",
"size": "1446",
"license": "apache-2.0",
"hash": 4688520112216098000,
"line_mean": 27.92,
"line_max": 121,
"alpha_frac": 0.735131397,
"autogenerated": false,
"ratio": 3.293849658314351,
"config_... |
__author__ = 'miahi'
## Logger for serial APC Smart UPS
import serial
import csv
import time
PORT = 'COM2'
BAUDRATE = 2400
SLEEP_SECONDS = 3
class APCSerial(object):
def __init__(self, port, baudrate=2400):
# todo: check that port exists & init errors
self.serial = serial.Serial(port, baudrate,... | {
"repo_name": "miahi/python.apcserial",
"path": "read_serial.py",
"copies": "1",
"size": "3838",
"license": "mit",
"hash": 4402373304453716500,
"line_mean": 32.9734513274,
"line_max": 120,
"alpha_frac": 0.5643564356,
"autogenerated": false,
"ratio": 3.8418418418418416,
"config_test": false,
"... |
__author__ = 'micaela'
# !/usr/bin/env python
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
from matplotlib.pyplot import legend
import matplotlib.dates as mdates
import datetime as dt
import matplotlib.patches as mpatches
def plotMoodline (dates,moods, moodsS):
correctDates = [dt.datetime.strptime(... | {
"repo_name": "samzek/sentiment_analysis",
"path": "prgVerucchiZecchini/src/Plot.py",
"copies": "1",
"size": "1068",
"license": "apache-2.0",
"hash": -424392741194960450,
"line_mean": 26.4102564103,
"line_max": 88,
"alpha_frac": 0.65917603,
"autogenerated": false,
"ratio": 2.848,
"config_test":... |
__author__ = 'micanzhang'
from app.helper import BaseAction, ApiAction
from app.model import Post, PostTopic, Mention, User, PostGeo
import web
import time
from app.model.model import sqla_json
from app.constants import ResponseStatus
from sqlalchemy import desc
class PostAction(BaseAction):
def GET(self):
... | {
"repo_name": "micanzhang/focus",
"path": "app/controller/post.py",
"copies": "1",
"size": "5723",
"license": "mit",
"hash": 7148849683095233000,
"line_mean": 30.4450549451,
"line_max": 110,
"alpha_frac": 0.5339856719,
"autogenerated": false,
"ratio": 3.930631868131868,
"config_test": false,
... |
__author__ = 'micanzhang'
from app.helper import BaseAction
from app.model import Follow
from app.constants import Response, ResponseStatus
import web
class FollowAction(BaseAction):
def POST(self, following):
follower = self.session.user.username
exists = web.ctx.orm.query(Follow).filter(follower... | {
"repo_name": "micanzhang/focus",
"path": "app/controller/follow.py",
"copies": "1",
"size": "1605",
"license": "mit",
"hash": 1630027610059437300,
"line_mean": 32.4375,
"line_max": 101,
"alpha_frac": 0.6510903427,
"autogenerated": false,
"ratio": 4.246031746031746,
"config_test": false,
"has... |
__author__ = 'micanzhang'
from sqlalchemy import Column
from sqlalchemy.dialects import mysql
from app.model.model import Base
from sqlalchemy.ext.hybrid import hybrid_property
import hashlib
import time
import web
from app.constants import Roles
class User(Base):
__tablename__ = 'user'
id = Column(mysql.IN... | {
"repo_name": "micanzhang/focus",
"path": "app/model/user.py",
"copies": "1",
"size": "1767",
"license": "mit",
"hash": -3124658085053385000,
"line_mean": 25.7878787879,
"line_max": 81,
"alpha_frac": 0.6089417091,
"autogenerated": false,
"ratio": 3.9707865168539325,
"config_test": false,
"has... |
__author__ = 'micanzhang'
import datetime
import web
class SQLAStore(web.session.Store):
def __init__(self, table):
self.table = table
self.session = web.ctx.orm
def __contains__(self, item):
query = self.session.query(self.table).filter(self.table.session_id==item).first()
re... | {
"repo_name": "micanzhang/focus",
"path": "app/helper/DBStore.py",
"copies": "1",
"size": "1497",
"license": "mit",
"hash": -3318453584727866000,
"line_mean": 33.0227272727,
"line_max": 93,
"alpha_frac": 0.5991983968,
"autogenerated": false,
"ratio": 3.770780856423174,
"config_test": false,
"... |
__author__ = 'micanzhang'
import web
import os
from app.constants import Roles
from jinja2 import Environment,FileSystemLoader
from app.helper import filter
from app.constants import ResponseStatus, Response
class BaseAction:
access_role = Roles.AUTHROIZED
def __init__(self):
self.session = web.confi... | {
"repo_name": "micanzhang/focus",
"path": "app/helper/baseAction.py",
"copies": "1",
"size": "1307",
"license": "mit",
"hash": 164164295299613300,
"line_mean": 28.7045454545,
"line_max": 88,
"alpha_frac": 0.6388676358,
"autogenerated": false,
"ratio": 4.046439628482972,
"config_test": false,
... |
__author__ = 'Michael Andrew michael@hazardmedia.co.nz'
from nz.co.hazardmedia.sgdialer.controllers.ChevronController import ChevronController
class GateController(object):
chevron1 = None
chevron2 = None
chevron3 = None
chevron4 = None
chevron5 = None
chevron6 = None
chevron7 = None
... | {
"repo_name": "HAZARDU5/sgdialer",
"path": "src/nz/co/hazardmedia/sgdialer/controllers/GateController.py",
"copies": "1",
"size": "1373",
"license": "mit",
"hash": 7996178543995773000,
"line_mean": 23.5357142857,
"line_max": 86,
"alpha_frac": 0.5841223598,
"autogenerated": false,
"ratio": 3.27684... |
__author__ = 'Michael Andrew michael@hazardmedia.co.nz'
from nz.co.hazardmedia.sgdialer.models.SymbolModel import SymbolModel
class AddressModel(object):
name = ""
id_code = ""
symbol1 = None
symbol2 = None
symbol3 = None
symbol4 = None
symbol5 = None
symbol6 = None
symbol7 = None... | {
"repo_name": "HAZARDU5/sgdialer",
"path": "src/nz/co/hazardmedia/sgdialer/models/AddressModel.py",
"copies": "1",
"size": "1339",
"license": "mit",
"hash": 8985969865265798000,
"line_mean": 34.2631578947,
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"alpha_frac": 0.6385362211,
"autogenerated": false,
"ratio": 3.76123595505... |
__author__ = 'Michael Andrew michael@hazardmedia.co.nz'
import os
import xmltodict
from nz.co.hazardmedia.sgdialer.config.Config import Config
from nz.co.hazardmedia.sgdialer.models.AddressModel import AddressModel
class AddressBookModel(object):
addresses = []
def __init__(self):
self.import_data()... | {
"repo_name": "HAZARDU5/sgdialer",
"path": "src/nz/co/hazardmedia/sgdialer/models/AddressBookModel.py",
"copies": "1",
"size": "2908",
"license": "mit",
"hash": -4234995219409876500,
"line_mean": 22.8442622951,
"line_max": 71,
"alpha_frac": 0.4264099037,
"autogenerated": false,
"ratio": 5.2114695... |
__author__ = 'Michael Andrew michael@hazardmedia.co.nz'
import pygame
from pygame.event import Event
from pygame import key
from nz.co.hazardmedia.sgdialer.controllers.DialerController import DialerController
from nz.co.hazardmedia.sgdialer.controllers.SoundController import SoundController
from nz.co.hazardmedia.sgdi... | {
"repo_name": "HAZARDU5/sgdialer",
"path": "src/nz/co/hazardmedia/sgdialer/controllers/AppController.py",
"copies": "1",
"size": "3050",
"license": "mit",
"hash": 6060888319210298000,
"line_mean": 32.5274725275,
"line_max": 107,
"alpha_frac": 0.602295082,
"autogenerated": false,
"ratio": 3.497706... |
__author__ = 'Michael Andrew michael@hazardmedia.co.nz'
import pygame
from pygame import key
from pygame import event
from pygame.event import Event
from nz.co.hazardmedia.sgdialer.controllers.ChevronController import ChevronController
from nz.co.hazardmedia.sgdialer.models.AddressBookModel import AddressBookModel
fr... | {
"repo_name": "HAZARDU5/sgdialer",
"path": "src/nz/co/hazardmedia/sgdialer/controllers/DialerController.py",
"copies": "1",
"size": "12564",
"license": "mit",
"hash": -4195535978455707600,
"line_mean": 36.174556213,
"line_max": 287,
"alpha_frac": 0.5366921363,
"autogenerated": false,
"ratio": 4.1... |
__author__ = 'Michael Aquilina'
__email__ = 'michaelaquilina@gmail.com'
__version__ = '0.10.0'
import collections
import functools
import math
DOCUMENT_DOES_NOT_EXIST = 'The specified document does not exist'
TERM_DOES_NOT_EXIST = 'The specified term does not exist'
class HashedIndex:
"""
InvertedIndex str... | {
"repo_name": "MichaelAquilina/hashedindex",
"path": "hashedindex/__init__.py",
"copies": "1",
"size": "9293",
"license": "bsd-3-clause",
"hash": -940951527897541600,
"line_mean": 31.6070175439,
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"autogenerated": false,
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"conf... |
__author__ = 'Michael Aquilina'
# Encrypt and Decrypt messages using a very simple Caesar cipher
# NOTE: This is here just for educational purposes, you should not rely
# on the security of this cipher for sending messages. Caesar ciphers are
# very easily broken through frequency analysis.
#
# Use aes.py to encrypt a... | {
"repo_name": "MichaelAquilina/CryptoTools",
"path": "src/caesar.py",
"copies": "1",
"size": "1986",
"license": "mit",
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"autogenerated": false,
"ratio": 3.9879518072289155,
"config_test": false,
... |
__author__ = 'Michael Aquilina'
# Encrypt and decrypt messages using the AES block cipher!
# aes.py <message> <passphrase> --encrypt
# aes.py <message> <passphrase> --decrypt
# Perform preliminary check for dependencies
import sys
from utils import is_package_installed
MIN_REQUIRED_VERSION = '2.6'
if not is_packag... | {
"repo_name": "MichaelAquilina/CryptoTools",
"path": "src/aes.py",
"copies": "1",
"size": "2885",
"license": "mit",
"hash": -7010577233558761000,
"line_mean": 36.9605263158,
"line_max": 161,
"alpha_frac": 0.6994800693,
"autogenerated": false,
"ratio": 3.7960526315789473,
"config_test": false,
... |
__author__ = 'Michael Aquilina'
__desc__ = """
My attempt at writing some cryptographic block ciphers for many time key use. Makes use
of PyCrypto - specifically the AES functionality.
SPOILER: I am aware i should not be implementing ciphers myself for use in production - this code is
my way of learning how to... | {
"repo_name": "MichaelAquilina/Cryptography",
"path": "src/aes_tool.py",
"copies": "1",
"size": "7366",
"license": "mit",
"hash": -8074691085594545000,
"line_mean": 33.5845410628,
"line_max": 168,
"alpha_frac": 0.6141732283,
"autogenerated": false,
"ratio": 3.7334009123162697,
"config_test": tr... |
import adsk.core, adsk.fusion
app= adsk.core.Application.get()
design = app.activeProduct
ui = app.userInterface
#**Default User Inputs**
steps = "5 cm" #How many steps of Fibonacci would you like to plot?
#(Note, while the steps variable should be unitless, right now our API can't handle uni... | {
"repo_name": "MichaelAubry/Fusion360",
"path": "Fibonacci.py",
"copies": "1",
"size": "3602",
"license": "mit",
"hash": -4551235801623253000,
"line_mean": 30.5964912281,
"line_max": 121,
"alpha_frac": 0.7059966685,
"autogenerated": false,
"ratio": 3.0370994940978076,
"config_test": false,
"h... |
import pyb
import ugfx
import buttons
import database
ugfx.init()
ugfx.enable_tear()
buttons.init()
buttons.disable_menu_reset()
score = 0
grid_size = 8;
bird_colour = ugfx.YELLOW
back_colour = ugfx.BLACK
pipe_colour = ugfx.BLUE
gap = database.database_get("emflap.gap", 8)
pipe_diff = database.database_get("emflap.p... | {
"repo_name": "bruntonspall/emflap",
"path": "main.py",
"copies": "1",
"size": "3464",
"license": "mit",
"hash": 4459379484934788000,
"line_mean": 27.6280991736,
"line_max": 122,
"alpha_frac": 0.557448037,
"autogenerated": false,
"ratio": 3.001733102253033,
"config_test": false,
"has_no_keywo... |
__author__ = "Michael Conlon"
__copyright__ = "Copyright 2015 (c) Michael Conlon"
__license__ = "New BSD License"
__version__ = "0.01"
class TimeOut(Exception):
pass
class NoLastNameForAuthor(Exception):
pass
def get_entrez_record(pmid):
"""
Given a pmid, use Entrez to get first record from PubMed... | {
"repo_name": "indera/vivo-pump",
"path": "uf_examples/publications/add_pubmed.py",
"copies": "2",
"size": "12817",
"license": "bsd-2-clause",
"hash": -2829944658868611000,
"line_mean": 33.2727272727,
"line_max": 94,
"alpha_frac": 0.5189201841,
"autogenerated": false,
"ratio": 3.7875295508274234,... |
import math
import unittest
import pygsl
import pygsl.sum
def fn1(n):
"""Terms for the zeta function."""
return 1./n/n
class SumTest(unittest.TestCase):
def setUp(self):
self.zeta_2 = math.pi**2/6.0
self.terms = [fn1(n) for n in range(1,21)]
def test_levin_u(self):
"""Test the ... | {
"repo_name": "juhnowski/FishingRod",
"path": "production/pygsl-0.9.5/tests/sum_test.py",
"copies": "1",
"size": "1462",
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"ratio": 3.44,
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__author__ = 'Michael Harris'
import os
deprecated_functions = ".andSelf(),.context,deferred.isRejected(),deferred.isResolved(),deferred.pipe(),.die(),.error(),jQuery.boxModel,jQuery.browser,jQuery.sub(),jQuery.support,.live(),.load(),.selector,.size(),.toggle(),.unload()".split(",")
deprecated_functions = [x.strip("()... | {
"repo_name": "mlh758/jQuery-Deprecated",
"path": "FindDeprecated.py",
"copies": "1",
"size": "1471",
"license": "apache-2.0",
"hash": 2404445119300545500,
"line_mean": 31.6888888889,
"line_max": 244,
"alpha_frac": 0.6356220258,
"autogenerated": false,
"ratio": 3.623152709359606,
"config_test":... |
__author__ = "Michael Herold"
__copyright__ = "Copyright (c) 2015 Michael Herold"
__license__ = "MIT"
import os
import sys
from setuptools import setup
kwargs = {}
def get_version():
basedir = os.path.dirname(__file__)
with open(os.path.join(basedir, 'pyisemail/version.py')) as f:
locals = {}
... | {
"repo_name": "michaelherold/pyIsEmail",
"path": "setup.py",
"copies": "1",
"size": "1876",
"license": "mit",
"hash": 4072831396792549400,
"line_mean": 27,
"line_max": 74,
"alpha_frac": 0.6076759062,
"autogenerated": false,
"ratio": 3.966173361522199,
"config_test": true,
"has_no_keywords": f... |
__author__ = 'Michael Isik'
from gfilter import Filter, SimpleMutation
from variate import CauchyVariate
from population import SimplePopulation
from pybrain.tools.validation import Validator
from pybrain.tools.kwargsprocessor import KWArgsProcessor
from numpy import array, dot, concatenate, Infinity
from scipy.linal... | {
"repo_name": "rbalda/neural_ocr",
"path": "env/lib/python2.7/site-packages/pybrain/supervised/evolino/filter.py",
"copies": "3",
"size": "9764",
"license": "mit",
"hash": -2733070056839730700,
"line_mean": 31.5466666667,
"line_max": 107,
"alpha_frac": 0.6173699304,
"autogenerated": false,
"ratio... |
__author__ = 'Michael Isik'
from gpopulation import Population, SimplePopulation
from gfilter import Randomization
from individual import EvolinoIndividual, EvolinoSubIndividual
from pybrain.tools.kwargsprocessor import KWArgsProcessor
from copy import copy
from random import randrange
class EvolinoPopulation(Po... | {
"repo_name": "rbalda/neural_ocr",
"path": "env/lib/python2.7/site-packages/pybrain/supervised/evolino/population.py",
"copies": "3",
"size": "6292",
"license": "mit",
"hash": -1876318224555491600,
"line_mean": 35.7953216374,
"line_max": 124,
"alpha_frac": 0.6651303242,
"autogenerated": false,
"r... |
__author__ = 'Michael Isik'
from numpy import Infinity
class Population:
""" Abstract template for a minimal Population.
Implement just the methods you need.
"""
def __init__(self):pass
def getIndividuals(self):
""" Should return a shallow copy of the individuals container, so that
... | {
"repo_name": "daanwierstra/pybrain",
"path": "pybrain/rl/learners/blackboxoptimizers/evolution/population.py",
"copies": "1",
"size": "5294",
"license": "bsd-3-clause",
"hash": 91786760372147780,
"line_mean": 33.6013071895,
"line_max": 88,
"alpha_frac": 0.648469966,
"autogenerated": false,
"rati... |
__author__ = 'Michael Isik'
from pybrain.datasets import SupervisedDataSet
class SVMData(SupervisedDataSet):
""" Reads data files in LIBSVM/SVMlight format """
def __init__(self, filename=None):
SupervisedDataSet.__init__(self,0,0)
self.nCls = 0
self.nSamples = 0
self.classHi... | {
"repo_name": "daanwierstra/pybrain",
"path": "pybrain/tools/svmdata.py",
"copies": "1",
"size": "4844",
"license": "bsd-3-clause",
"hash": -3144246091219823000,
"line_mean": 29.275,
"line_max": 88,
"alpha_frac": 0.5208505367,
"autogenerated": false,
"ratio": 4.023255813953488,
"config_test": f... |
__author__ = 'Michael Isik'
from pybrain.rl.learners.blackboxoptimizers.evolution.filter import Filter, SimpleMutation
from pybrain.rl.learners.blackboxoptimizers.evolution.variate import CauchyVariate
from pybrain.rl.learners.blackboxoptimizers.evolution.population import SimplePopulation
from pybrain.tools.va... | {
"repo_name": "daanwierstra/pybrain",
"path": "pybrain/rl/learners/blackboxoptimizers/evolino/filter.py",
"copies": "1",
"size": "9964",
"license": "bsd-3-clause",
"hash": -4968932330584951000,
"line_mean": 32.6621621622,
"line_max": 109,
"alpha_frac": 0.6213368125,
"autogenerated": false,
"ratio... |
__author__ = 'Michael Isik'
from pybrain.rl.learners.blackboxoptimizers.evolution.population import Population, SimplePopulation
from pybrain.rl.learners.blackboxoptimizers.evolution.filter import Randomization
from individual import EvolinoIndividual, EvolinoSubIndividual
from pybrain.tools.kwargsprocessor impor... | {
"repo_name": "daanwierstra/pybrain",
"path": "pybrain/rl/learners/blackboxoptimizers/evolino/population.py",
"copies": "1",
"size": "6423",
"license": "bsd-3-clause",
"hash": -4831736206448438000,
"line_mean": 36.5614035088,
"line_max": 123,
"alpha_frac": 0.6671337381,
"autogenerated": false,
"r... |
__author__ = 'Michael Isik'
from pybrain.supervised.evolino.gpopulation import Population, SimplePopulation
from pybrain.supervised.evolino.gfilter import Randomization
from pybrain.supervised.evolino.individual import EvolinoIndividual, EvolinoSubIndividual
from pybrain.tools.kwargsprocessor import KWArgsProcessor
... | {
"repo_name": "Neural-Network/TicTacToe",
"path": "pybrain/supervised/evolino/population.py",
"copies": "25",
"size": "6360",
"license": "bsd-3-clause",
"hash": -227599376368216480,
"line_mean": 36.1929824561,
"line_max": 124,
"alpha_frac": 0.6687106918,
"autogenerated": false,
"ratio": 4.5854361... |
__author__ = 'Michael Isik'
from random import uniform, random, gauss
from numpy import tan, pi
class UniformVariate:
def __init__(self, min_val=0., max_val=1.):
""" Initializes the uniform variate with a min and a max value.
"""
self._min_val = min_val
self._max_val = max_val
... | {
"repo_name": "pybrain2/pybrain2",
"path": "pybrain/supervised/evolino/variate.py",
"copies": "32",
"size": "1438",
"license": "bsd-3-clause",
"hash": 4935352897720510000,
"line_mean": 27.76,
"line_max": 71,
"alpha_frac": 0.5681502086,
"autogenerated": false,
"ratio": 3.3598130841121496,
"confi... |
__author__ = 'Michael Isik'
class KWArgDsc(object):
def __init__(self, name, **kwargs):
self.name = name
self.private = False
self.mandatory = False
keys=['private','default','mandatory']
for key in keys:
if kwargs.has_key(key):
setattr(self,key,... | {
"repo_name": "daanwierstra/pybrain",
"path": "pybrain/tools/kwargsprocessor.py",
"copies": "1",
"size": "2178",
"license": "bsd-3-clause",
"hash": 8529483407545799000,
"line_mean": 24.3255813953,
"line_max": 83,
"alpha_frac": 0.5394857668,
"autogenerated": false,
"ratio": 3.594059405940594,
"c... |
__author__ = 'Michael Isik'
from gindividual import Individual
from copy import copy, deepcopy
class EvolinoIndividual(Individual):
""" Individual of the Evolino framework, that consists of a list of
sub-individuals. The genomes of the sub-individuals are used as
the cromosomes for the main indi... | {
"repo_name": "hassaanm/stock-trading",
"path": "pybrain-pybrain-87c7ac3/pybrain/supervised/evolino/individual.py",
"copies": "8",
"size": "1899",
"license": "apache-2.0",
"hash": -2195757104026411800,
"line_mean": 28.671875,
"line_max": 80,
"alpha_frac": 0.6219062665,
"autogenerated": false,
"ra... |
__author__ = 'Michael Isik'
from numpy.random import permutation
from numpy import array, array_split, apply_along_axis, concatenate, ones, dot, delete, append, zeros, argmax
import copy
from pybrain.datasets.importance import ImportanceDataSet
from pybrain.datasets.sequential import SequentialDataSet
from pybrain.da... | {
"repo_name": "rbalda/neural_ocr",
"path": "env/lib/python2.7/site-packages/pybrain/tools/validation.py",
"copies": "3",
"size": "14579",
"license": "mit",
"hash": 3752695342580046300,
"line_mean": 35.6306532663,
"line_max": 113,
"alpha_frac": 0.6196584128,
"autogenerated": false,
"ratio": 4.6608... |
__author__ = 'Michael Isik'
from pybrain.rl.learners.blackboxoptimizers.evolution.individual import Individual
from copy import copy, deepcopy
class EvolinoIndividual(Individual):
""" Individual of the Evolino framework, that consists of a list of
sub-individuals. The genomes of the sub-individuals are ... | {
"repo_name": "daanwierstra/pybrain",
"path": "pybrain/rl/learners/blackboxoptimizers/evolino/individual.py",
"copies": "1",
"size": "1953",
"license": "bsd-3-clause",
"hash": 7907448646505026000,
"line_mean": 29.515625,
"line_max": 82,
"alpha_frac": 0.6287762417,
"autogenerated": false,
"ratio":... |
__author__ = 'Michael Isik'
from pybrain.rl.learners.blackboxoptimizers.evolution.variate import UniformVariate, GaussianVariate
class Filter(object):
""" Base class for all kinds of operators on the population during the
evolutionary process like mutation, selection or evaluation.
"""
def __init... | {
"repo_name": "daanwierstra/pybrain",
"path": "pybrain/rl/learners/blackboxoptimizers/evolution/filter.py",
"copies": "1",
"size": "3931",
"license": "bsd-3-clause",
"hash": 8919398915806850000,
"line_mean": 31.7583333333,
"line_max": 100,
"alpha_frac": 0.6207071992,
"autogenerated": false,
"rati... |
__author__ = 'Michael Isik'
from pybrain.structure.networks.network import Network
from pybrain.structure.modules.lstm import LSTMLayer
from pybrain.structure.modules.linearlayer import LinearLayer
from pybrain.structure.connections.full import FullConnection
from pybrain.structure.modules.module ... | {
"repo_name": "rbalda/neural_ocr",
"path": "env/lib/python2.7/site-packages/pybrain/supervised/evolino/networkwrapper.py",
"copies": "3",
"size": "18026",
"license": "mit",
"hash": -8914195882001892000,
"line_mean": 31.1892857143,
"line_max": 103,
"alpha_frac": 0.5783867747,
"autogenerated": false,... |
__author__ = 'Michael Isik'
from pybrain.structure.networks.recurrent import RecurrentNetwork
from pybrain.structure.modules.lstm import LSTMLayer
from pybrain.structure.modules.linearlayer import LinearLayer
from pybrain.structure.connections.full import FullConnection
from pybrain.structure.modules.module import Mo... | {
"repo_name": "RatulGhosh/pybrain",
"path": "pybrain/structure/modules/evolinonetwork.py",
"copies": "25",
"size": "8236",
"license": "bsd-3-clause",
"hash": 7061523227544870000,
"line_mean": 36.6073059361,
"line_max": 103,
"alpha_frac": 0.6062408936,
"autogenerated": false,
"ratio": 4.3692307692... |
__author__ = 'Michael Isik'
from pybrain.supervised.evolino.gindividual import Individual
from copy import copy, deepcopy
class EvolinoIndividual(Individual):
""" Individual of the Evolino framework, that consists of a list of
sub-individuals. The genomes of the sub-individuals are used as
the c... | {
"repo_name": "RafaelCosman/pybrain",
"path": "pybrain/supervised/evolino/individual.py",
"copies": "26",
"size": "1926",
"license": "bsd-3-clause",
"hash": 3329765999790135000,
"line_mean": 29.09375,
"line_max": 80,
"alpha_frac": 0.6256490135,
"autogenerated": false,
"ratio": 4.196078431372549,
... |
__author__ = 'Michael Isik'
from pybrain.supervised.evolino.variate import UniformVariate, GaussianVariate
class Filter(object):
""" Base class for all kinds of operators on the population during the
evolutionary process like mutation, selection or evaluation.
"""
def __init__(self):
pass... | {
"repo_name": "jlegendary/pybrain",
"path": "pybrain/supervised/evolino/gfilter.py",
"copies": "26",
"size": "3909",
"license": "bsd-3-clause",
"hash": 5411041705667345000,
"line_mean": 30.7804878049,
"line_max": 85,
"alpha_frac": 0.6165259657,
"autogenerated": false,
"ratio": 4.47766323024055,
... |
__author__ = 'Michael Isik'
from pybrain.tools.validation import CrossValidator
from numpy import linspace, append, ones, zeros, array, where, apply_along_axis
import copy
class GridSearch2D:
""" Abstract class providing a method for searching optimal metaparmeters
of a training algorithm.
It is... | {
"repo_name": "hassaanm/stock-trading",
"path": "src/pybrain/tools/gridsearch.py",
"copies": "5",
"size": "13402",
"license": "apache-2.0",
"hash": -6574863065882017000,
"line_mean": 36.4357541899,
"line_max": 115,
"alpha_frac": 0.5599910461,
"autogenerated": false,
"ratio": 4.1646985705407085,
... |
__author__ = 'Michael Isik'
from random import uniform, random, gauss
from numpy import tan,pi
class UniformVariate:
def __init__(self, min_val=0., max_val=1.):
""" Initializes the uniform variate with a min and a max value.
"""
self._min_val = min_val
self._max_val = max_val
... | {
"repo_name": "daanwierstra/pybrain",
"path": "pybrain/rl/learners/blackboxoptimizers/evolution/variate.py",
"copies": "1",
"size": "1474",
"license": "bsd-3-clause",
"hash": -1920630046282309400,
"line_mean": 27.9019607843,
"line_max": 75,
"alpha_frac": 0.5597014925,
"autogenerated": false,
"rat... |
__author__ = 'Michael Isik'
from variate import UniformVariate, GaussianVariate
class Filter(object):
""" Base class for all kinds of operators on the population during the
evolutionary process like mutation, selection or evaluation.
"""
def __init__(self):
pass
def apply(self, popula... | {
"repo_name": "rbalda/neural_ocr",
"path": "env/lib/python2.7/site-packages/pybrain/supervised/evolino/gfilter.py",
"copies": "3",
"size": "3909",
"license": "mit",
"hash": 4096476929748194300,
"line_mean": 30.7804878049,
"line_max": 85,
"alpha_frac": 0.6103862881,
"autogenerated": false,
"ratio"... |
__author__ = 'Michael Isik'
from pybrain.datasets.sequential import SequentialDataSet
from numpy import array, sin, apply_along_axis, ones
class SuperimposedSine(object):
""" Small class for generating superimposed sine signals
"""
def __init__(self, lambdas=[1.]):
self.lambdas = array(lambda... | {
"repo_name": "fxsjy/pybrain",
"path": "examples/supervised/evolino/lib/data_generator.py",
"copies": "4",
"size": "1030",
"license": "bsd-3-clause",
"hash": 2339344701655340000,
"line_mean": 23.5238095238,
"line_max": 67,
"alpha_frac": 0.6495145631,
"autogenerated": false,
"ratio": 3.30128205128... |
__author__ = 'Michael Isik'
__version__ = '$Id$'
from trainer import Trainer
from pybrain.rl.learners.blackboxoptimizers.evolino.population import EvolinoPopulation
from pybrain.rl.learners.blackboxoptimizers.evolino.individual import EvolinoSubIndividual
from pybrain.rl.learners.blackboxoptimizers.evolino... | {
"repo_name": "daanwierstra/pybrain",
"path": "pybrain/supervised/trainers/evolino.py",
"copies": "1",
"size": "6647",
"license": "bsd-3-clause",
"hash": -5728340431366732000,
"line_mean": 43.9189189189,
"line_max": 152,
"alpha_frac": 0.661651873,
"autogenerated": false,
"ratio": 4.05552165954850... |
import re
import time
from datetime import date
from datetime import timedelta
from juriscraper.OpinionSite import OpinionSite
from juriscraper.lib.string_utils import titlecase
class Site(OpinionSite):
def __init__(self, *args, **kwargs):
super(Site, self).__init__(*args, **kwargs)
self.court_i... | {
"repo_name": "m4h7/juriscraper",
"path": "juriscraper/opinions/united_states/state/mont.py",
"copies": "2",
"size": "4112",
"license": "bsd-2-clause",
"hash": -3273360543909708000,
"line_mean": 42.7446808511,
"line_max": 118,
"alpha_frac": 0.5588521401,
"autogenerated": false,
"ratio": 3.4965986... |
import time
from datetime import date
from juriscraper.OpinionSite import OpinionSite
class Site(OpinionSite):
def __init__(self, *args, **kwargs):
super(Site, self).__init__(*args, **kwargs)
self.court_id = self.__module__
today = date.today()
self.url = 'http://www.nmcompcomm.u... | {
"repo_name": "m4h7/juriscraper",
"path": "juriscraper/opinions/united_states/state/nm_p.py",
"copies": "2",
"size": "1564",
"license": "bsd-2-clause",
"hash": -7246885828312263000,
"line_mean": 34.5454545455,
"line_max": 113,
"alpha_frac": 0.6093350384,
"autogenerated": false,
"ratio": 3.0134874... |
import time
from datetime import date
from juriscraper.OpinionSite import OpinionSite
class Site(OpinionSite):
def __init__(self):
super(Site, self).__init__()
self.court_id = self.__module__
today = date.today()
self.url = 'http://www.nmcompcomm.us/nmcases/NMARYear.aspx?db=scr&y... | {
"repo_name": "brianwc/juriscraper",
"path": "opinions/united_states/state/nm_p.py",
"copies": "1",
"size": "1532",
"license": "bsd-2-clause",
"hash": -9059193381974460000,
"line_mean": 33.8181818182,
"line_max": 113,
"alpha_frac": 0.6090078329,
"autogenerated": false,
"ratio": 3.015748031496063,... |
import re
import requests
from datetime import date
from datetime import datetime
from juriscraper.opinions.united_states.state import nd
from juriscraper.DeferringList import DeferringList
class Site(nd.Site):
def __init__(self, *args, **kwargs):
super(Site, self).__init__(*args, **kwargs)
sel... | {
"repo_name": "m4h7/juriscraper",
"path": "juriscraper/opinions/united_states_backscrapers/state/nd.py",
"copies": "2",
"size": "6042",
"license": "bsd-2-clause",
"hash": -5754587924455750000,
"line_mean": 37.9806451613,
"line_max": 114,
"alpha_frac": 0.5019860973,
"autogenerated": false,
"ratio"... |
from datetime import date
from datetime import datetime
import re
from lxml import html
from juriscraper.OpinionSite import OpinionSite
class Site(OpinionSite):
def __init__(self, *args, **kwargs):
super(Site, self).__init__(*args, **kwargs)
self.court_id = self.__module__
today = date.t... | {
"repo_name": "Andr3iC/juriscraper",
"path": "opinions/united_states/state/nd.py",
"copies": "1",
"size": "4009",
"license": "bsd-2-clause",
"hash": -2293317433579659000,
"line_mean": 37.9223300971,
"line_max": 114,
"alpha_frac": 0.5487652781,
"autogenerated": false,
"ratio": 3.722376973073352,
... |
import re
from datetime import date
from datetime import datetime
from lxml import html
from juriscraper.OpinionSite import OpinionSite
class Site(OpinionSite):
def __init__(self):
super(Site, self).__init__()
self.court_id = self.__module__
today = date.today()
now = datetime.no... | {
"repo_name": "brianwc/juriscraper",
"path": "opinions/united_states/state/nd.py",
"copies": "1",
"size": "3918",
"license": "bsd-2-clause",
"hash": 2083366774099746000,
"line_mean": 37.7920792079,
"line_max": 114,
"alpha_frac": 0.55079122,
"autogenerated": false,
"ratio": 3.7278782112274023,
"... |
import re
from lxml import html
from datetime import date
from datetime import datetime
from juriscraper.OpinionSite import OpinionSite
from juriscraper.lib.string_utils import convert_date_string
class Site(OpinionSite):
"""This will scrape all cases, excluding Appeal
cases from the ND court site
"""
... | {
"repo_name": "m4h7/juriscraper",
"path": "juriscraper/opinions/united_states/state/nd.py",
"copies": "1",
"size": "3607",
"license": "bsd-2-clause",
"hash": -7495281990356177000,
"line_mean": 40.4597701149,
"line_max": 114,
"alpha_frac": 0.5633490435,
"autogenerated": false,
"ratio": 3.866023579... |
from datetime import date
from juriscraper.opinions.united_states_backscrapers.state import nd
class Site(nd.Site):
def __init__(self, *args, **kwargs):
super(Site, self).__init__(*args, **kwargs)
self.court_id = self.__module__
today = date.today()
self.url = 'http://www.ndcourts... | {
"repo_name": "m4h7/juriscraper",
"path": "juriscraper/opinions/united_states_backscrapers/state/ndctapp.py",
"copies": "2",
"size": "1637",
"license": "bsd-2-clause",
"hash": -8069427070269341000,
"line_mean": 37.0697674419,
"line_max": 112,
"alpha_frac": 0.565058033,
"autogenerated": false,
"ra... |
from datetime import date
from juriscraper.opinions.united_states_backscrapers.state import nd
class Site(nd.Site):
def __init__(self):
super(Site, self).__init__()
self.court_id = self.__module__
today = date.today()
self.url = 'http://www.ndcourts.gov/opinions/month/%s.htm' % (t... | {
"repo_name": "brianwc/juriscraper",
"path": "opinions/united_states_backscrapers/state/ndctapp.py",
"copies": "1",
"size": "1605",
"license": "bsd-2-clause",
"hash": -1167487387366738000,
"line_mean": 36.3255813953,
"line_max": 112,
"alpha_frac": 0.5638629283,
"autogenerated": false,
"ratio": 3.... |
import re
from datetime import datetime
from lxml import html
from selenium import webdriver
from time import sleep
from juriscraper.OpinionSite import OpinionSite
from juriscraper.lib.string_utils import titlecase
class Site(OpinionSite):
def __init__(self, *args, **kwargs):
super(Site, self).__init__(*... | {
"repo_name": "Andr3iC/juriscraper",
"path": "opinions/united_states_backscrapers/state/sd.py",
"copies": "2",
"size": "10286",
"license": "bsd-2-clause",
"hash": 5555318260351098000,
"line_mean": 50.43,
"line_max": 109,
"alpha_frac": 0.5293602955,
"autogenerated": false,
"ratio": 3.5604015230183... |
from datetime import date
from datetime import datetime
from juriscraper.opinions.united_states.state import nd
from lxml import html
class Site(nd.Site):
def __init__(self, *args, **kwargs):
super(Site, self).__init__(*args, **kwargs)
self.court_id = self.__module__
today = date.today(... | {
"repo_name": "Andr3iC/juriscraper",
"path": "opinions/united_states/state/ndctapp.py",
"copies": "1",
"size": "2213",
"license": "bsd-2-clause",
"hash": -2288618632458812400,
"line_mean": 38.5178571429,
"line_max": 93,
"alpha_frac": 0.5716222323,
"autogenerated": false,
"ratio": 3.81551724137931... |
from juriscraper.opinions.united_states.state import nd
from datetime import date
from datetime import datetime
from lxml import html
class Site(nd.Site):
def __init__(self):
super(Site, self).__init__()
self.court_id = self.__module__
today = date.today()
now = datetime.now()
... | {
"repo_name": "brianwc/juriscraper",
"path": "opinions/united_states/state/ndctapp.py",
"copies": "1",
"size": "1998",
"license": "bsd-2-clause",
"hash": 1774818048948713700,
"line_mean": 38.1764705882,
"line_max": 114,
"alpha_frac": 0.5820820821,
"autogenerated": false,
"ratio": 3.81297709923664... |
from datetime import date
from datetime import datetime
from juriscraper.OpinionSite import OpinionSite
class Site(OpinionSite):
def __init__(self, *args, **kwargs):
super(Site, self).__init__(*args, **kwargs)
self.court_id = self.__module__
today = date.today()
self.crawl_date =... | {
"repo_name": "m4h7/juriscraper",
"path": "juriscraper/opinions/united_states/state/neb.py",
"copies": "2",
"size": "2335",
"license": "bsd-2-clause",
"hash": 3455796131265861600,
"line_mean": 38.5762711864,
"line_max": 112,
"alpha_frac": 0.5490364026,
"autogenerated": false,
"ratio": 3.138440860... |
from datetime import date
from datetime import datetime
from juriscraper.OpinionSite import OpinionSite
class Site(OpinionSite):
def __init__(self):
super(Site, self).__init__()
self.court_id = self.__module__
today = date.today()
self.crawl_date = today
self.url = 'http:... | {
"repo_name": "brianwc/juriscraper",
"path": "opinions/united_states/state/neb.py",
"copies": "1",
"size": "2303",
"license": "bsd-2-clause",
"hash": -2278175528625964500,
"line_mean": 38.0338983051,
"line_max": 112,
"alpha_frac": 0.5479808945,
"autogenerated": false,
"ratio": 3.141882673942701,
... |
from datetime import date
from datetime import datetime
from juriscraper.OpinionSite import OpinionSite
from lxml import html
class Site(OpinionSite):
def __init__(self, *args, **kwargs):
super(Site, self).__init__(*args, **kwargs)
self.court_id = self.__module__
today = date.today()
... | {
"repo_name": "m4h7/juriscraper",
"path": "juriscraper/opinions/united_states/state/nebctapp.py",
"copies": "1",
"size": "2410",
"license": "bsd-2-clause",
"hash": 6269601626679415000,
"line_mean": 36.65625,
"line_max": 92,
"alpha_frac": 0.556846473,
"autogenerated": false,
"ratio": 3.19205298013... |
from datetime import date
from datetime import datetime
from juriscraper.OpinionSite import OpinionSite
from lxml import html
class Site(OpinionSite):
def __init__(self):
super(Site, self).__init__()
self.court_id = self.__module__
today = date.today()
self.crawl_date = today
... | {
"repo_name": "brianwc/juriscraper",
"path": "opinions/united_states/state/nebctapp.py",
"copies": "1",
"size": "2805",
"license": "bsd-2-clause",
"hash": 3563918322613020700,
"line_mean": 35.9078947368,
"line_max": 92,
"alpha_frac": 0.544741533,
"autogenerated": false,
"ratio": 3.250289687137891... |
from datetime import datetime
import os
import re
from juriscraper.AbstractSite import logger
from juriscraper.OpinionSite import OpinionSite
from juriscraper.lib.string_utils import titlecase
from lxml import html
from selenium import webdriver
class Site(OpinionSite):
def __init__(self, *args, **kwargs):
... | {
"repo_name": "m4h7/juriscraper",
"path": "juriscraper/opinions/united_states/state/sd.py",
"copies": "2",
"size": "3519",
"license": "bsd-2-clause",
"hash": -5064155649565304000,
"line_mean": 36.8387096774,
"line_max": 132,
"alpha_frac": 0.5868144359,
"autogenerated": false,
"ratio": 3.316682375... |
import os
import re
from datetime import datetime
from juriscraper.AbstractSite import logger
from juriscraper.OpinionSite import OpinionSite
from juriscraper.lib.string_utils import titlecase
from urlparse import urlsplit, urljoin, urlunsplit
from lxml import html
from selenium import webdriver
class Site(OpinionS... | {
"repo_name": "brianwc/juriscraper",
"path": "opinions/united_states/state/sd.py",
"copies": "1",
"size": "3506",
"license": "bsd-2-clause",
"hash": 963470318764018400,
"line_mean": 36.2978723404,
"line_max": 132,
"alpha_frac": 0.5901312037,
"autogenerated": false,
"ratio": 3.3295346628679963,
... |
# import re
import time
from datetime import date
from lxml import html
from juriscraper.OpinionSite import OpinionSite
class Site(OpinionSite):
def __init__(self, *args, **kwargs):
super(Site, self).__init__(*args, **kwargs)
self.url = 'http://www.courts.state.hi.us/opinions_and_ord... | {
"repo_name": "m4h7/juriscraper",
"path": "juriscraper/opinions/united_states/state/haw.py",
"copies": "2",
"size": "2328",
"license": "bsd-2-clause",
"hash": -1556508579950352400,
"line_mean": 42.7692307692,
"line_max": 146,
"alpha_frac": 0.5768900344,
"autogenerated": false,
"ratio": 3.02337662... |
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