text stringlengths 0 1.05M | meta dict |
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__author__ = 'Maxim Dutkin (max@dutkin.ru)'
from unittest.mock import MagicMock
from tornado.httputil import HTTPServerRequest
class OldUserRightsFactory:
admin_permissions = ['admin', 'authenticated', ]
authenticated_permissions = ['authenticated', ]
def __init__(self):
self.default_obj = {
... | {
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__author__ = 'Maxim Dutkin (max@dutkin.ru)'
import re
from m2core.utils.decorators import classproperty
class BasePermissionRule(list):
operator_char = ''
def __new__(cls, *args, **kwargs):
return super(BasePermissionRule, cls).__new__(cls, args)
def __init__(self, *args):
super(BasePe... | {
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"path": "m2core/common/permissions.py",
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__author__ = 'Maxim Dutkin (max@dutkin.ru)'
import unittest
from m2core import M2Core
from m2core.utils.url_parser import UrlParser
from m2core.utils.tests import RESTTest
from voluptuous import Schema, Error
class UrlParserTest(unittest.TestCase, RESTTest):
def setUp(self):
m2core = M2Core()
m... | {
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__author__ = 'Maxime Le Coz'
import soundfile as sf
from math import sqrt
def load_sound(filepath):
data, samplerate = sf.read(filepath)
if len(data.shape) == 2:
data = data[:, 1]
print len(data)
return data, float(samplerate)
def cut(serie, threshold, minlen, minlensil):
segments = []
... | {
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"path": "analysers/__init__.py",
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__author__ = 'Maximilian Kurscheidt @MadMax93'
from datetime import datetime
import numpy as np
class ImportCsv(object):
"""
Imports 3D acceleration files in .csv format and saves it into a numpy array
File format: Timestamp, X-Value, Y-Value, Z-Value
"""
def __init__(self, path):
self.... | {
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__author__ = 'Maximilian Kurscheidt @MadMax93'
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import numpy as np
import scipy.signal as signal
import peakutils
import matplotlib.gridspec as gridspec
from peakutils.plot import plot as pplot
from matplotlib.offsetbox import AnchoredText
class Visual... | {
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"path": "Code/Visualization.py",
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__author__ = 'Maximilian Kurscheidt @MadMax93'
import numpy as np
import peakutils
import scipy.signal as signal
from scipy.fftpack import fft
class Signalprocessing(object):
def butter_bandpass_design(self, low_cut, high_cut, sample_rate, order=4):
"""
Defines the Butterworth bandpass filter-des... | {
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__author__ = 'Maximilian Kurscheidt @MadMax93'
import os
import matplotlib.pyplot as plt
from Code import ImportCsv
from Code import Signalprocessing
from Code import Visualization
SAMPLE_RATE = 50
LOW_CUT = 0.2
HIGH_CUT = 0.45
class Main(object):
import_csv = None
visualization = Visualization.Visualiza... | {
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__author__ = 'maximmillen'
import numpy as np
from sfsimodels import models
def add_inputs_to_object(obj, values):
"""
A generic function to load object parameters based on a dictionary list.
Parameters
----------
obj: object
values: dict
"""
for item in obj.inputs:
if hasatt... | {
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"path": "sfsimodels/loader.py",
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__author__ = 'Maxim.Rumyantsev'
# -*- coding: utf-8 -*-
from sys import maxsize
import string
class Group:
def __init__(self,
name=None,
header=None,
footer=None,
id=None):
self.name=name
self.header=header
... | {
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__author__ = 'Maxim.Rumyantsev'
# -*- coding: utf-8 -*-
from sys import maxsize
class Contact:
def __init__(self,firstname=None, middlename=None, lastname=None, nickname=None, title=None, company=None, address=None,
home=None,mobile=None,work=None,fax=None,email1=None,email2=None,email3=... | {
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... |
__author__ = 'max'
__author__ = 'Sebastian'
class State(object):
"""
Generic cell state objects. These can be used and produced by processes during simulation.
"""
def __init__(self, id, name):
"""
@param id: state id
@param name: state name
"""
self.__id = id... | {
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__author__ = 'Max'
class SessionHelper:
def __init__(self,app):
self.app = app
def login(self,user_name, password):
wd = self.app.wd
wd.get("http://localhost/addressbook/")
wd.find_element_by_id("LoginForm").click()
wd.find_element_by_name("user").click()
wd.fi... | {
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"path": "fixture/sesion.py",
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__author__ = 'max'
from datetime import datetime
def solr_now():
"""
Get the current time in UTC and format it in a way that Solr can digest.
:return: A Solr-digestible time format
:rtype: str
"""
return datetime.utcnow().isoformat() + 'Z'
class ModelField(object):
pass
class AutoDat... | {
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"path": "drow/fields.py",
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"autogenerated": false,
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"has_no_keywords": fals... |
__author__ = 'max'
from mock import MagicMock
from time import time
def create_mock_riak_object(key=None, update_time=None):
riak_object = MagicMock()
riak_object.key = key
riak_object.exists = True
riak_object.data = None
riak_object.encoded_data = None
riak_object.content_type = 'applicatio... | {
"repo_name": "Sendhub/drow",
"path": "drow/tests/mockriak.py",
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"ha... |
__author__ = 'max'
from riak import RiakError
from errors import InvalidPatch
from errors import DoesNotExist
from errors import SearchError
class QuerySet(object):
"""
Queries the database and returns instances of the associated Model
"""
def search(self, query, start=0, rows=20):
"""
... | {
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__author__ = 'max'
from state import State
import sys
from data.knowledgebase import Knowledgebase
class SingleGene(State, object):
"""
Storing the information related to each single gene of the database
"""
def __init__(self, gene_by_row):
super(SingleGene, self).__init__(gene_by_row["WholeCe... | {
"repo_name": "fugufisch/wholecell",
"path": "state/gene.py",
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__author__ = 'max'
from tensorflow.python.platform import gfile
from instance import DependencyInstance
import data_utils
class Reader(object):
def __init__(self, file_path, word_alphabet, pos_alphabet, type_alphabet):
self.__source_file = gfile.GFile(file_path, mode='r')
self.__word_alphabet = ... | {
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__author__ = 'max'
from unittest import TestCase
from mock import patch
from mock import MagicMock
import jsonpatch
from mockriak import create_mock_riak_client
from drow import models
from drow.fields import AutoDateField
from drow.fields import DefaultFalseField
class FakeModelContext(object):
def __enter__(se... | {
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"path": "drow/tests/test_fields.py",
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"autogenerated": false,
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"config_test": true,
"ha... |
__author__ = 'max'
"""
Implementation of Bi-directional LSTM-CNNs-CRF model for sequence labeling.
"""
import time
import sys
import argparse
import numpy as np
import lasagne
import theano
import theano.tensor as T
from lasagne.layers import Gate
from lasagne import nonlinearities
from lasagne.updates import nestero... | {
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__author__ = 'max'
import gpxpy
import pandas as pd
def parse_gpx(gpx_file_name):
return gpxpy.parse(gpx_file_name)
def data_frame_for_track_segment(segment):
seg_dict = {}
for point in segment.points:
seg_dict[point.time] = [point.latitude, point.longitude,
poi... | {
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"path": "gpxpandas/gpxreader.py",
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"alpha_frac": 0.6669505963,
"autogenerated": false,
"ratio": 3.4127906976744184,
"config_test": false,
... |
__author__ = 'max'
import lasagne
import lasagne.nonlinearities as nonlinearities
from lasagne.layers import Gate
from lasagne_nlp.networks.crf import CRFLayer
from lasagne_nlp.networks.highway import HighwayDenseLayer
def build_BiRNN(incoming, num_units, mask=None, grad_clipping=0, nonlinearity=nonlinearities.tanh,... | {
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__author__ = 'max'
import logging
import sys
import numpy as np
import lasagne
from gensim.models.word2vec import Word2Vec
import gzip
import theano
def get_logger(name, level=logging.INFO, handler=sys.stdout,
formatter='%(asctime)s - %(name)s - %(levelname)s - %(message)s'):
logger = logging.getL... | {
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"path": "lasagne_nlp/utils/utils.py",
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"autogenerated": false,
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"config_... |
__author__ = 'max'
import logging
import sys
import pickle
import numpy as np
from gensim.models.word2vec import Word2Vec
import gzip
import theano
from .io import data_utils
def load_word_embedding_dict(embedding, embedding_path, normalize_digits=True):
"""
load word embeddings from file
:param embeddi... | {
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"has... |
__author__ = 'max'
import logging
import sys
def get_logger(name, level=logging.INFO, handler=sys.stdout,
formatter='%(asctime)s - %(name)s - %(levelname)s - %(message)s'):
logger = logging.getLogger(name)
logger.setLevel(logging.INFO)
formatter = logging.Formatter(formatter)
stream_ha... | {
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"autogenerated": false,
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__author__ = 'max'
import numpy as np
import theano
import theano.tensor as T
from .nlinalg import theano_logsumexp, logabsdet
__all__ = [
"chain_crf_loss",
"chain_crf_accuracy",
"tree_crf_loss",
]
def chain_crf_loss(energies, targets, masks):
"""
compute minus log likelihood of chain crf as cha... | {
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"conf... |
__author__ = 'max'
import numpy as np
import theano
from alphabet import Alphabet
from lasagne_nlp.utils import utils as utils
root_symbol = "##ROOT##"
root_label = "<ROOT>"
word_end = "##WE##"
MAX_LENGTH = 130
MAX_CHAR_LENGTH = 45
logger = utils.get_logger("LoadData")
def read_conll_sequence_labeling(path, word_a... | {
"repo_name": "XuezheMax/LasagneNLP",
"path": "lasagne_nlp/utils/data_processor.py",
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"line_max": 136,
"alpha_frac": 0.5679417122,
"autogenerated": false,
"ratio": 3.7680164722031573,... |
__author__ = 'max'
import numpy as np
NEGTIVE_REWARD = -1
class Environment(object):
def __init__(self, heads, marks):
assert heads.ndim == 2
assert marks.ndim == 2
self.__batch_size = heads.shape[0]
self.__length = heads.shape[1]
self.__sizes = marks.sum(axis=1)
... | {
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... |
__author__ = 'max'
import numpy
class BioMolecule:
"""
A generic molecule that has basic attributes like name and
mass.
@type name: str
@type mass: float
"""
def __init__(self, mid, name, mass=0):
self.mid = mid #id ?
self.name = name
self.mas... | {
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"path": "molecules.py",
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... |
__author__ = 'max'
import numpy
import theano
import theano.tensor as T
from theano.tensor import as_tensor_variable
from theano.gof import Op, Apply
from theano.tensor.nlinalg import matrix_inverse, matrix_dot
__all__ = [
"LogAbsDet",
"logabsdet",
"theano_logsumexp",
]
class LogAbsDet(Op):
"""
... | {
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... |
__author__ = 'max'
import os.path
import re
import random
import numpy as np
from .reader import CoNLLReader
from .alphabet import Alphabet
from .logger import get_logger
# Special vocabulary symbols - we always put them at the start.
ROOT = b"_ROOT"
ROOT_POS = b"_ROOT_POS"
ROOT_TYPE = b"_<ROOT>"
ROOT_CHAR = b"_ROOT_... | {
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"c... |
__author__ = 'max'
import re
import numpy as np
def is_uni_punctuation(word):
match = re.match("^[^\w\s]+$]", word, flags=re.UNICODE)
return match is not None
def is_punctuation(word, pos, punct_set=None):
if punct_set is None:
return is_uni_punctuation(word)
else:
return pos in punc... | {
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__author__ = 'max'
import re
import numpy as np
from hanlp.metrics.metric import Metric
def is_uni_punctuation(word):
match = re.match("^[^\w\s]+$]", word, flags=re.UNICODE)
return match is not None
def is_punctuation(word, pos, punct_set=None):
if punct_set is None:
# Maybe use ispunct
... | {
"repo_name": "hankcs/HanLP",
"path": "hanlp/components/parsers/hpsg/dep_eval.py",
"copies": "1",
"size": "2872",
"license": "apache-2.0",
"hash": 2488065963378083300,
"line_mean": 26.0943396226,
"line_max": 104,
"alpha_frac": 0.5045264624,
"autogenerated": false,
"ratio": 3.451923076923077,
"c... |
__author__ = 'max'
import re
import random
import numpy as np
from tensorflow.python.platform import gfile
from reader import Reader
from alphabet import Alphabet
from .. import utils
# Special vocabulary symbols - we always put them at the start.
ROOT = b"_ROOT"
ROOT_POS = b"_ROOT_POS"
ROOT_TYPE = b"_<ROOT>"
PAD = b... | {
"repo_name": "XuezheMax/ReLiefParser",
"path": "reliefparser/io/data_utils.py",
"copies": "1",
"size": "7413",
"license": "mit",
"hash": -573655958545914940,
"line_mean": 35.1609756098,
"line_max": 116,
"alpha_frac": 0.5936867665,
"autogenerated": false,
"ratio": 3.470505617977528,
"config_tes... |
__author__ = 'max'
import socket
import sys
import threading
import time
from conn_manager import ConnManager
class Server(threading.Thread):
HOST = '0.0.0.0'
PORT = 8888
def __init__(self, host=HOST, port=PORT, max_conn=100):
super(Server, self).__init__()
self.host = host
self.... | {
"repo_name": "sk91/simplentp",
"path": "simplentp/server/server.py",
"copies": "1",
"size": "1467",
"license": "mit",
"hash": 3160655534268335000,
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"alpha_frac": 0.5739604635,
"autogenerated": false,
"ratio": 3.870712401055409,
"config_test": false,
... |
__author__ = 'max'
import sys
import time
from server import server
import signal
from client import client
def run_server(host, port):
host = host if host else server.Server.HOST
port = port if port else server.Server.PORT
serv = server.Server(host, port)
serv.start()
return serv
def run_clie... | {
"repo_name": "sk91/simplentp",
"path": "simplentp/__main__.py",
"copies": "1",
"size": "2229",
"license": "mit",
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"line_mean": 22.4631578947,
"line_max": 77,
"alpha_frac": 0.5697622252,
"autogenerated": false,
"ratio": 3.836488812392427,
"config_test": false,
"ha... |
__author__ = 'max'
import theano
from lasagne.random import get_rng
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
from lasagne.layers import Layer
__all__ = [
"GaussianDropoutLayer",
]
class GaussianDropoutLayer(Layer):
"""Gaussian Dropout layer
Multiply values by gaussian varia... | {
"repo_name": "XuezheMax/NeuroNLP",
"path": "neuronlp/layers/dropout.py",
"copies": "2",
"size": "1636",
"license": "mit",
"hash": 7150071743729556000,
"line_mean": 30.4615384615,
"line_max": 116,
"alpha_frac": 0.6393643032,
"autogenerated": false,
"ratio": 4.397849462365591,
"config_test": fal... |
__author__ = 'max'
import theano.tensor as T
import numpy as np
from lasagne import init
from lasagne.layers import Layer
import lasagne.nonlinearities as nonlinearities
__all__ = [
"HighwayDenseLayer",
]
class HighwayDenseLayer(Layer):
"""
lasagne_nlp.networks.highway.HighwayDenseLayer(incoming, W_h=in... | {
"repo_name": "XuezheMax/LasagneNLP",
"path": "lasagne_nlp/networks/highway.py",
"copies": "3",
"size": "3869",
"license": "apache-2.0",
"hash": 6020527581726232000,
"line_mean": 40.1595744681,
"line_max": 109,
"alpha_frac": 0.6391832515,
"autogenerated": false,
"ratio": 3.782013685239492,
"con... |
__author__ = 'max'
import theano.tensor as T
from lasagne import init
from lasagne.layers import MergeLayer
__all__ = [
"ChainCRFLayer",
"TreeAffineCRFLayer",
"TreeBiAffineCRFLayer",
]
class ChainCRFLayer(MergeLayer):
"""
ChainCRFLayer(incoming, num_labels, mask_input=None, W=init.GlorotUniform... | {
"repo_name": "adapt-sjtu/novelner",
"path": "NeuroNLP/neuronlp/layers/crf.py",
"copies": "2",
"size": "12592",
"license": "apache-2.0",
"hash": -5162687243154103000,
"line_mean": 42.1232876712,
"line_max": 117,
"alpha_frac": 0.5901365947,
"autogenerated": false,
"ratio": 3.579306424104605,
"co... |
__author__ = 'max'
import theano.tensor as T
from lasagne import init
from lasagne.layers import MergeLayer
class CRFLayer(MergeLayer):
"""
lasagne_nlp.networks.crf.CRFLayer(incoming, num_labels,
mask_input=None, W=init.GlorotUniform(), b=init.Constant(0.), **kwargs)
Parameters
----------
i... | {
"repo_name": "XuezheMax/LasagneNLP",
"path": "lasagne_nlp/networks/crf.py",
"copies": "1",
"size": "4409",
"license": "apache-2.0",
"hash": 388503183180726500,
"line_mean": 42.6534653465,
"line_max": 117,
"alpha_frac": 0.6169199365,
"autogenerated": false,
"ratio": 3.870939420544337,
"config_t... |
__author__ = 'max'
import theano.tensor as T
from lasagne import init
from lasagne.theano_extensions import conv
from lasagne.layers import Conv1DLayer, Layer, InputLayer, helper
from lasagne import nonlinearities
__all__ = [
"ConvTimeStep1DLayer",
]
class ConvTimeStep1DLayer(Layer):
"""
CNN with time ... | {
"repo_name": "XuezheMax/NeuroNLP",
"path": "neuronlp/layers/conv.py",
"copies": "2",
"size": "7382",
"license": "mit",
"hash": -7373496228209257000,
"line_mean": 43.2035928144,
"line_max": 120,
"alpha_frac": 0.6490111081,
"autogenerated": false,
"ratio": 4.196702671972711,
"config_test": false... |
__author__ = 'max'
import theano.tensor as T
from lasagne.layers import MergeLayer
from lasagne import init
import lasagne.nonlinearities as nonlinearities
from theano.tensor.sort import argsort
__all__ = [
"GraphConvLayer",
]
class GraphConvLayer(MergeLayer):
"""
lasagne_nlp.networks.graph.GraphConvLa... | {
"repo_name": "XuezheMax/LasagneNLP",
"path": "lasagne_nlp/networks/graph.py",
"copies": "3",
"size": "6385",
"license": "apache-2.0",
"hash": 3264664757309947000,
"line_mean": 43.9647887324,
"line_max": 124,
"alpha_frac": 0.6366483947,
"autogenerated": false,
"ratio": 3.834834834834835,
"confi... |
__author__ = 'max'
import theano.tensor as T
from lasagne.layers import MergeLayer
from lasagne import init
__all__ = [
"DepParserLayer",
]
class DepParserLayer(MergeLayer):
"""
"""
def __init__(self, incoming, num_labels, mask_input=None, W_h=init.GlorotUniform(), W_c=init.GlorotUniform(),
... | {
"repo_name": "XuezheMax/LasagneNLP",
"path": "lasagne_nlp/networks/parser.py",
"copies": "1",
"size": "3727",
"license": "apache-2.0",
"hash": -8466787519520523000,
"line_mean": 37.0306122449,
"line_max": 113,
"alpha_frac": 0.5744566676,
"autogenerated": false,
"ratio": 3.492970946579194,
"con... |
__author__ = 'max'
import theano.tensor as T
from lasagne.utils import as_tuple
from lasagne.layers import Pool1DLayer
__all__ = [
"PoolTimeStep1DLayer",
]
class PoolTimeStep1DLayer(Pool1DLayer):
"""
Pool with time step at axis=1. The input shape should be [batch_size, n-step, num_input_channels, input... | {
"repo_name": "adapt-sjtu/novelner",
"path": "NeuroNLP/neuronlp/layers/pool.py",
"copies": "2",
"size": "2848",
"license": "apache-2.0",
"hash": -3793149256560490000,
"line_mean": 35.987012987,
"line_max": 116,
"alpha_frac": 0.6155196629,
"autogenerated": false,
"ratio": 3.838274932614555,
"con... |
__author__ = 'max'
import time
import socket
import select
import sys
import threading
from time_sender import TimeSender
from master_time import MasterTimeRecv
class Client(threading.Thread):
def __init__(self, host="127.0.0.1", port=8888):
super(Client, self).__init__()
self.timeSender = TimeS... | {
"repo_name": "sk91/simplentp",
"path": "simplentp/client/client.py",
"copies": "1",
"size": "2107",
"license": "mit",
"hash": 4650471931920953000,
"line_mean": 27.0933333333,
"line_max": 106,
"alpha_frac": 0.5885144756,
"autogenerated": false,
"ratio": 3.967984934086629,
"config_test": false,
... |
__author__ = 'max'
import time
import sys
import argparse
from lasagne_nlp.utils import utils
from lasagne_nlp.utils.regularization import dima
import lasagne_nlp.utils.data_processor as data_processor
import theano.tensor as T
import theano
import lasagne
from lasagne_nlp.networks.networks import build_BiRNN
import l... | {
"repo_name": "XuezheMax/LasagneNLP",
"path": "bi_rnn.py",
"copies": "1",
"size": "13596",
"license": "apache-2.0",
"hash": -8837931107029125000,
"line_mean": 47.9064748201,
"line_max": 138,
"alpha_frac": 0.6008384819,
"autogenerated": false,
"ratio": 3.6905537459283386,
"config_test": true,
... |
__author__ = 'max'
import time
import sys
import argparse
from lasagne_nlp.utils import utils
import lasagne_nlp.utils.data_processor as data_processor
import theano.tensor as T
import theano
import lasagne
from lasagne_nlp.networks.networks import build_BiLSTM_CNN
import lasagne.nonlinearities as nonlinearities
def... | {
"repo_name": "XuezheMax/LasagneNLP",
"path": "bi_lstm_cnn.py",
"copies": "1",
"size": "15385",
"license": "apache-2.0",
"hash": -2835265882632765400,
"line_mean": 50.1129568106,
"line_max": 153,
"alpha_frac": 0.5944751381,
"autogenerated": false,
"ratio": 3.717081420633003,
"config_test": true... |
__author__ = 'max'
"""
Layers to construct recurrent networks. Recurrent layers can be used similarly
to feed-forward layers except that the input shape is expected to be
``(batch_size, sequence_length, num_inputs)``.
The implementation
"""
import numpy as np
import theano
import theano.tensor as T
from lasagne import... | {
"repo_name": "adapt-sjtu/novelner",
"path": "NeuroNLP/neuronlp/layers/recurrent.py",
"copies": "1",
"size": "58653",
"license": "apache-2.0",
"hash": -9006245905739404000,
"line_mean": 44.5734265734,
"line_max": 109,
"alpha_frac": 0.5874720816,
"autogenerated": false,
"ratio": 4.0350165107319755... |
__author__ = 'max'
"""
Alphabet maps objects to integer ids. It provides two way mapping from the index to the objects.
"""
import json
import os
from .. import utils
class Alphabet(object):
def __init__(self, name, keep_growing=True):
self.__name = name
self.instance2index = {}
self.ins... | {
"repo_name": "XuezheMax/ReLiefParser",
"path": "reliefparser/io/alphabet.py",
"copies": "1",
"size": "3378",
"license": "mit",
"hash": -6658582945475870000,
"line_mean": 31.7961165049,
"line_max": 116,
"alpha_frac": 0.5959147425,
"autogenerated": false,
"ratio": 4.165228113440198,
"config_test... |
__author__ = 'max'
"""
Alphabet maps objects to integer ids. It provides two way mapping from the index to the objects.
"""
import json
import os
from .logger import get_logger
class Alphabet(object):
def __init__(self, name, keep_growing=True):
self.__name = name
self.instance2index = {}
... | {
"repo_name": "XuezheMax/NeuroNLP",
"path": "neuronlp/io/alphabet.py",
"copies": "2",
"size": "3381",
"license": "mit",
"hash": -2499104638090653700,
"line_mean": 32.1470588235,
"line_max": 116,
"alpha_frac": 0.5968648329,
"autogenerated": false,
"ratio": 4.163793103448276,
"config_test": false... |
__author__ = 'max'
"""
Alphabet maps objects to integer ids. It provides two way mapping from the index to the objects.
"""
import json
import os
from lasagne_nlp.utils import utils as utils
class Alphabet:
def __init__(self, name, keep_growing=True):
self.__name = name
self.instance2index = {}... | {
"repo_name": "XuezheMax/LasagneNLP",
"path": "lasagne_nlp/utils/alphabet.py",
"copies": "1",
"size": "3173",
"license": "apache-2.0",
"hash": -2768395283755374600,
"line_mean": 31.3775510204,
"line_max": 116,
"alpha_frac": 0.6032146234,
"autogenerated": false,
"ratio": 4.153141361256544,
"conf... |
__author__ = 'max'
"""
Alphabet maps objects to integer ids. It provides two way mapping from the index to the objects.
"""
import json
import os
import utils as utils
class Alphabet:
def __init__(self, name, keep_growing=True):
self.__name = name
self.instance2index = {}
self.instances... | {
"repo_name": "adapt-sjtu/novelner",
"path": "NN_NER_tensorFlow/alphabet.py",
"copies": "1",
"size": "3221",
"license": "apache-2.0",
"hash": -82853601847652020,
"line_mean": 31.5353535354,
"line_max": 116,
"alpha_frac": 0.6047811239,
"autogenerated": false,
"ratio": 4.150773195876289,
"config_... |
__author__ = 'max'
class BioMolecule(object):
"""
A generic molecule that has basic attributes like id, name and
mass.
@type mid: int
@type name: str
@type mass: float
"""
def __init__(self, mid, name, mass=0):
self.__mid = mid
self.name = name
self.mass = mass... | {
"repo_name": "WeirdCircumstances/hu_bp_python_course",
"path": "Project/molecules.py",
"copies": "1",
"size": "4461",
"license": "mit",
"hash": -5511422685301097000,
"line_mean": 25.0935672515,
"line_max": 102,
"alpha_frac": 0.5886572517,
"autogenerated": false,
"ratio": 3.5098347757671124,
"c... |
__author__ = 'max'
from instance import DependencyInstance
import data_utils
class CoNLLReader(object):
def __init__(self, file_path, word_alphabet, char_alphabet, pos_alphabet, type_alphabet):
self.__source_file = open(file_path, 'r')
self.__word_alphabet = word_alphabet
self.__char_alp... | {
"repo_name": "XuezheMax/NeuroNLP",
"path": "neuronlp/io/reader.py",
"copies": "1",
"size": "2709",
"license": "mit",
"hash": -734667486544699400,
"line_mean": 32.8625,
"line_max": 117,
"alpha_frac": 0.5622000738,
"autogenerated": false,
"ratio": 3.675712347354138,
"config_test": false,
"has_... |
import numpy as np
from random import choice
import networkx as nx
def within_community_degree(weighted_partition, edgeless = np.nan, catch_edgeless_node=True):
''' Computes "within-module degree" (z-score) for each node (Guimera 2005, Nature)
------
Parameters
------
weighted_partition: Louvain ... | {
"repo_name": "nipy/brainx",
"path": "brainx/nodal_roles.py",
"copies": "1",
"size": "3566",
"license": "bsd-3-clause",
"hash": 7920551927316949000,
"line_mean": 39.0674157303,
"line_max": 94,
"alpha_frac": 0.6374088615,
"autogenerated": false,
"ratio": 4.103567318757192,
"config_test": false,
... |
import numpy as np
from random import choice
import networkx as nx
def within_community_degree(weighted_partition, nan = 0.0, catch_edgeless_node=True):
''' Computes "within-module degree" (z-score) for each node (Guimera 2007, J Stat Mech)
------
Parameters
------
weighted_partition: Louvain Wei... | {
"repo_name": "jrcohen02/brainx_archive2",
"path": "brainx/nodal_roles.py",
"copies": "1",
"size": "3465",
"license": "bsd-3-clause",
"hash": -4180689747965515300,
"line_mean": 37.5,
"line_max": 91,
"alpha_frac": 0.6268398268,
"autogenerated": false,
"ratio": 4.038461538461538,
"config_test": f... |
__author__ = 'm.bashari'
import numpy as np
from sklearn import datasets, linear_model
import matplotlib.pyplot as plt
# Generate a dataset and plot it
def generate_data():
np.random.seed(0)
X, y = datasets.make_moons(200, noise=0.20)
plt.scatter(X[:,0], X[:,1], s=40, c=y, cmap=plt.cm.Spectral)
# Train th... | {
"repo_name": "matthijsvk/multimodalSR",
"path": "code/Experiments/Tutorials/nn-from-scratch/linReg_classification.py",
"copies": "1",
"size": "1570",
"license": "mit",
"hash": 2686959025002732000,
"line_mean": 29.1923076923,
"line_max": 80,
"alpha_frac": 0.621656051,
"autogenerated": false,
"rat... |
__author__ = 'm.bashari'
import numpy as np
from sklearn import datasets, linear_model
import matplotlib.pyplot as plt
def generate_data():
# np.random.seed(0)
# X, y = datasets.make_moons(200, noise=0.20)
N = 100 # number of points per class
D = 2 # dimensionality
K = 3 # number of classes
... | {
"repo_name": "matthijsvk/multimodalSR",
"path": "code/Experiments/Tutorials/nn-from-scratch/simple_classification.py",
"copies": "1",
"size": "1855",
"license": "mit",
"hash": -8905488869856734000,
"line_mean": 29.4262295082,
"line_max": 82,
"alpha_frac": 0.5676549865,
"autogenerated": false,
"r... |
__author__ = 'm.bashari'
import numpy as np
from sklearn import datasets, linear_model
import matplotlib.pyplot as plt
def generate_data():
np.random.seed(0)
X, y = datasets.make_moons(200, noise=0.20)
return X, y
def visualize(X, y, clf):
# plt.scatter(X[:, 0], X[:, 1], s=40, c=y, cmap=plt.cm.Spect... | {
"repo_name": "gittyRavi/NNChess",
"path": "NN_example_new/simple_classification.py",
"copies": "1",
"size": "1363",
"license": "mit",
"hash": -7852556297469426000,
"line_mean": 26.26,
"line_max": 80,
"alpha_frac": 0.5972120323,
"autogenerated": false,
"ratio": 2.839583333333333,
"config_test":... |
__author__ = 'MBlaauw'
if __name__ == "__main__":
reload(sys)
sys.setdefaultencoding("utf-8")
import pandas as pd
from pandas import concat
import numpy as np
from bs4 import BeautifulSoup
from urllib2 import urlopen
# Reviews
# https://boekenliefde.nl/edition_reviews_get.api?key=47L0ss6cDInejrV8SpJmPk4AgxEZT... | {
"repo_name": "mblaauw/CollectDutchBookFeatures",
"path": "scrapeBoekenLiefde.py",
"copies": "1",
"size": "3888",
"license": "mit",
"hash": 7061392752499528000,
"line_mean": 32.5172413793,
"line_max": 212,
"alpha_frac": 0.6723251029,
"autogenerated": false,
"ratio": 3.452930728241563,
"config_t... |
__author__ = 'MBlaauw'
if __name__ == "__main__":
reload(sys)
sys.setdefaultencoding("utf-8")
import re
import pandas as pd
from bs4 import BeautifulSoup
from urllib2 import urlopen
# http://www.crimezone.nl/web/Titels/Verschenen.htm?pagenr=1&queryElement=592958&sorton=rating%20desc
# http://www.crimezone.nl/w... | {
"repo_name": "mblaauw/CollectDutchBookFeatures",
"path": "scrapeCrimeZoneNL.py",
"copies": "1",
"size": "3070",
"license": "mit",
"hash": -5205651569743251000,
"line_mean": 31.670212766,
"line_max": 132,
"alpha_frac": 0.619218241,
"autogenerated": false,
"ratio": 3.2079414838035527,
"config_te... |
__author__ = 'MBlaauw'
#import libraries
import os
import cv2
import numpy as np
from scipy.sparse import lil_matrix
from scipy.stats import expon
from sklearn.decomposition import RandomizedPCA
from sklearn import cross_validation
from sklearn import svm
from sklearn import metrics
from sklearn import preprocessing
... | {
"repo_name": "mblaauw/Kaggle_CatsVsDogs",
"path": "wacax/CatsDogsBernoulli.py",
"copies": "1",
"size": "7601",
"license": "mit",
"hash": -8582476184963308000,
"line_mean": 33.2432432432,
"line_max": 160,
"alpha_frac": 0.7300355216,
"autogenerated": false,
"ratio": 3.0823195458231956,
"config_t... |
__author__ = 'MBlaauw'
print(__doc__)
# Author: Gael Varoquaux gael.varoquaux@normalesup.org
# License: BSD 3 clause
import datetime
import numpy as np
import pylab as pl
from matplotlib import finance
from matplotlib.collections import LineCollection
from sklearn import cluster, covariance, manifold
############... | {
"repo_name": "mblaauw/CollectDutchBookFeatures",
"path": "test.py",
"copies": "1",
"size": "6100",
"license": "mit",
"hash": -6280553559905134000,
"line_mean": 29.8080808081,
"line_max": 79,
"alpha_frac": 0.5759016393,
"autogenerated": false,
"ratio": 3.131416837782341,
"config_test": false,
... |
__author__ = 'MBlaauw'
#!/usr/bin/env python
# coding: utf-8
"""
prediction code without batches
see http://fastml.com/how-to-get-predictions-from-pylearn2/
author: Zygmunt Zając
"""
import sys
import os
import numpy as np
import cPickle as pickle
from pylearn2.utils import serial
from theano import tensor as T
from... | {
"repo_name": "mblaauw/Kaggle_CatsVsDogs",
"path": "kastnerkyle/predict_no_batches.py",
"copies": "1",
"size": "1206",
"license": "mit",
"hash": -5020480294041311000,
"line_mean": 19.0833333333,
"line_max": 99,
"alpha_frac": 0.6871369295,
"autogenerated": false,
"ratio": 2.7829099307159355,
"co... |
__author__ = 'MBlaauw'
#!/usr/bin/env python
from decaf.util import transform
from decaf.scripts import imagenet
import logging
import numpy as np
from glob import glob
import matplotlib.image as mpimg
from random import shuffle
import pickle
def load_and_preprocess(net, imagepath, center_only=False,
... | {
"repo_name": "mblaauw/Kaggle_CatsVsDogs",
"path": "kastnerkyle/kaggle_dataset_decaf.py",
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"license": "mit",
"hash": -1843340842178393000,
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"... |
__author__ = 'MBlaauw'
#!/usr/bin/env python
from pylearn2.datasets import DenseDesignMatrix
from pylearn2.utils import serial
from theano import tensor as T
from theano import function
import pickle
import numpy as np
import csv
def process(mdl, ds, batch_size=100):
# This batch size must be evenly divisible int... | {
"repo_name": "mblaauw/Kaggle_CatsVsDogs",
"path": "kastnerkyle/kaggle_test.py",
"copies": "1",
"size": "1308",
"license": "mit",
"hash": 9045009301930906000,
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"autogenerated": false,
"ratio": 2.9261744966442955,
"config_test... |
__author__ = 'MBlaauw'
#!/usr/bin/env python
from pylearn2.models import mlp
from pylearn2.costs.mlp.dropout import Dropout
from pylearn2.training_algorithms import sgd, learning_rule
from pylearn2.termination_criteria import EpochCounter
from pylearn2.datasets import DenseDesignMatrix
from pylearn2.train import Train
... | {
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"path": "kastnerkyle/kaggle_train_full.py",
"copies": "1",
"size": "2688",
"license": "mit",
"hash": -6075973658846753000,
"line_mean": 31.0119047619,
"line_max": 62,
"alpha_frac": 0.5319940476,
"autogenerated": false,
"ratio": 3.682191780821918,
"conf... |
__author__ = 'mbott'
# this hack is to deal with ssl errors seen on Mac OSX
import ssl
if ssl.OPENSSL_VERSION.split(" ")[1] == '0.9.8zh':
ssl._create_default_https_context = ssl._create_unverified_context
import os
import logging
import datetime
import dateutil.parser
import pytz
import stat
from tempfile import ... | {
"repo_name": "Qumulo/qftpd",
"path": "qftpd.py",
"copies": "1",
"size": "19684",
"license": "apache-2.0",
"hash": 5999150052728550000,
"line_mean": 37.0735009671,
"line_max": 82,
"alpha_frac": 0.5938325544,
"autogenerated": false,
"ratio": 3.7436287561810575,
"config_test": false,
"has_no_ke... |
__author__ = 'mcanuto, jsubirat'
__date__ ="$May 15, 2014 13:54:34 AM$"
import subprocess
import re
import gmetric
import threading
from logging import handlers
import logging
from time import sleep
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
# create a file handler
handler = handlers.Rotating... | {
"repo_name": "bsc-renewit/d2.2",
"path": "monitoringFramework/odroidXUE.py",
"copies": "1",
"size": "5261",
"license": "apache-2.0",
"hash": -4344087891269433000,
"line_mean": 43.218487395,
"line_max": 261,
"alpha_frac": 0.6215548375,
"autogenerated": false,
"ratio": 3.109338061465721,
"config... |
__author__="mcanuto"
__date__ ="$Feb 13, 2014 6:11:42 PM$"
#from subprocess import call, Popen
import subprocess
import re
import gmetric
import threading
from gmetric import GmetricConf
from time import sleep
from logging import handlers
import logging
import os
logger = logging.getLogger(__name__)
logger.setLevel(l... | {
"repo_name": "bsc-renewit/d2.2",
"path": "monitoringFramework/countersMetrics.py",
"copies": "1",
"size": "5799",
"license": "apache-2.0",
"hash": 3693280385253232600,
"line_mean": 31.3966480447,
"line_max": 201,
"alpha_frac": 0.5845835489,
"autogenerated": false,
"ratio": 3.4954792043399636,
... |
__author__ = 'mcgyver5'
import sys
import time
import datetime
import pygame
import os
import globals
pygame.init()
import surround_menu
from pygame.locals import *
import random
fade = 100
BLOCKSIZE=15
x = 100
y = 20
os.environ['SDL_VIDEO_WINDOW_POS'] = "%d,%d" % (x,y)
pygame.mixer.pre_init(44100, -16, 2, 2048) # setu... | {
"repo_name": "mcgyver5/surroundGame",
"path": "first_animation.py",
"copies": "1",
"size": "7615",
"license": "apache-2.0",
"hash": -3798230341988164600,
"line_mean": 33.7716894977,
"line_max": 175,
"alpha_frac": 0.6076165463,
"autogenerated": false,
"ratio": 3.354625550660793,
"config_test": ... |
__author__ = 'mcgyver5'
import sys
import time
import pygame
import os
from pygame.locals import *
import globals
def menu_render(winSurf,textList,startPos,sepDist,fontSize=40,h_offset=0):
print("MENU RENDER")
basicFont = pygame.font.SysFont(None, fontSize)
for str in textList:
tt = basicFont.render... | {
"repo_name": "mcgyver5/surroundGame",
"path": "surround_menu.py",
"copies": "1",
"size": "2431",
"license": "apache-2.0",
"hash": -2645105112749150700,
"line_mean": 31,
"line_max": 76,
"alpha_frac": 0.6104483752,
"autogenerated": false,
"ratio": 3.3670360110803323,
"config_test": false,
"has... |
__author__ = 'mckinney'
import win32service
import win32serviceutil
import win32api
import win32con
import win32event
import win32evtlogutil
import os, sys, platform, string, time
import servicemanager
class aservice(win32serviceutil.ServiceFramework):
_svc_name_ = "aiDaemonService"
_svc_display_name_ = "AI ... | {
"repo_name": "weihuali0509/appinventor-sources",
"path": "appinventor/misc/emulator-support/aiWinDaemonService.py",
"copies": "86",
"size": "1932",
"license": "apache-2.0",
"hash": -2443413117483490000,
"line_mean": 32.3275862069,
"line_max": 128,
"alpha_frac": 0.6413043478,
"autogenerated": false... |
__author__ = 'mckinney'
import win32service
import win32serviceutil
import win32api
import win32con
import win32event
import win32evtlogutil
import os, sys, platform, string, time
import servicemanager
class aservice(win32serviceutil.ServiceFramework):
_svc_name_ = "aiDaemonService"
_svc_displa... | {
"repo_name": "yflou520/appinventor-sources",
"path": "appinventor/misc/emulator-support/aiWinDaemonService.py",
"copies": "4",
"size": "1989",
"license": "apache-2.0",
"hash": -402446796115875800,
"line_mean": 32.3275862069,
"line_max": 128,
"alpha_frac": 0.6229260935,
"autogenerated": false,
"r... |
__author__ = 'mcornelio'
import socket
import sys
import math
from paramsdict import *
# Create a TCP/IP socket
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# Connect the socket to the port on the server given by the caller
server_address = (sys.argv[1], 10000)
print >>sys.stderr, 'connecting to %s port ... | {
"repo_name": "mcornelio/synapse",
"path": "tests/tcp_client.py",
"copies": "1",
"size": "1059",
"license": "mit",
"hash": 7696742424800923000,
"line_mean": 23.0681818182,
"line_max": 70,
"alpha_frac": 0.6185080264,
"autogenerated": false,
"ratio": 2.6408977556109727,
"config_test": false,
"h... |
__author__ = 'mcornelio'
import socket
import sys
import threading
from paramsdict import paramsdict
class synapse_tcp_server:
sock = None
def __init__(self, port):
def start_server():
def handle_client(connection):
print >>sys.stderr, 'client.open(%s)' % connection
try:
msg = ''
while True:... | {
"repo_name": "mcornelio/synapse",
"path": "tests/tcp_server.py",
"copies": "1",
"size": "1780",
"license": "mit",
"hash": -8624386965703239000,
"line_mean": 26.4,
"line_max": 72,
"alpha_frac": 0.6286516854,
"autogenerated": false,
"ratio": 3.2541133455210236,
"config_test": false,
"has_no_ke... |
__author__ = 'mcornelio'
import sys
import SocketServer
from paramsdict import *
class MyTCPHandler(SocketServer.StreamRequestHandler):
"""
The request handler class for our server.
It is instantiated once per connection to the server, and must
override the handle() method to implement communication to the
clie... | {
"repo_name": "mcornelio/synapse",
"path": "tests/tcpSocketServer.py",
"copies": "1",
"size": "1307",
"license": "mit",
"hash": -7628510425452826000,
"line_mean": 25.14,
"line_max": 69,
"alpha_frac": 0.6710022953,
"autogenerated": false,
"ratio": 3.3256997455470736,
"config_test": false,
"has... |
__author__ = 'mcornelio'
from setuptools import setup
from datetime import datetime
def version_string():
return "1.1.26"
readme = open('README.md', 'r')
README_TEXT = readme.read()
readme.close()
setup(name='synapse',
version=version_string(),
description='Cell-based device adapter'... | {
"repo_name": "mcornelio/synapse",
"path": "setup.py",
"copies": "1",
"size": "1302",
"license": "mit",
"hash": -4973783786143934000,
"line_mean": 28.2790697674,
"line_max": 55,
"alpha_frac": 0.5430107527,
"autogenerated": false,
"ratio": 4.383838383838384,
"config_test": false,
"has_no_keywo... |
__author__ = 'mcowger'
import logging
from pprint import pformat, pprint
class CloudFoundryDomain(object):
@classmethod
def get_class_name(cls):
return cls.__name__
def __str__(self):
# to show include all variables in sorted order
return "<{}>@0x{}:\n".format(self.get_class_name... | {
"repo_name": "mcowger/python-cloudfoundry",
"path": "cloudfoundry/domains.py",
"copies": "2",
"size": "1180",
"license": "mit",
"hash": -654760444862553600,
"line_mean": 24.6739130435,
"line_max": 170,
"alpha_frac": 0.5966101695,
"autogenerated": false,
"ratio": 3.806451612903226,
"config_test... |
__author__ = 'mcowger'
import logging
from pprint import pformat, pprint
class CloudFoundryOrg(object):
@classmethod
def get_class_name(cls):
return cls.__name__
def __str__(self):
# to show include all variables in sorted order
return "<{}>@0x{}:\n".format(self.get_class_name(),... | {
"repo_name": "ipaoTAT/python-cloudfoundry",
"path": "cloudfoundry/organizations.py",
"copies": "2",
"size": "1857",
"license": "mit",
"hash": 7215038287934154000,
"line_mean": 27.5692307692,
"line_max": 170,
"alpha_frac": 0.605277329,
"autogenerated": false,
"ratio": 3.7364185110663986,
"confi... |
__author__ = 'mcowger'
import logging
from pprint import pformat, pprint
class CloudFoundryRoute(object):
@classmethod
def get_class_name(cls):
return cls.__name__
def __str__(self):
# to show include all variables in sorted order
return "<{}>@0x{}:\n".format(self.get_class_name(... | {
"repo_name": "mcowger/python-cloudfoundry",
"path": "cloudfoundry/routes.py",
"copies": "2",
"size": "1172",
"license": "mit",
"hash": -2862294025494392000,
"line_mean": 23.4166666667,
"line_max": 170,
"alpha_frac": 0.5656996587,
"autogenerated": false,
"ratio": 3.617283950617284,
"config_test... |
__author__ = 'mcowger'
import logging
from pprint import pformat, pprint
class CloudFoundrySpace(object):
@classmethod
def get_class_name(cls):
return cls.__name__
def __str__(self):
# to show include all variables in sorted order
return "<{}>@0x{}:\n".format(self.get_class_name(... | {
"repo_name": "mcowger/python-cloudfoundry",
"path": "cloudfoundry/spaces.py",
"copies": "2",
"size": "1841",
"license": "mit",
"hash": 1191856691629405200,
"line_mean": 27.3230769231,
"line_max": 170,
"alpha_frac": 0.6045627376,
"autogenerated": false,
"ratio": 3.74949083503055,
"config_test":... |
__author__ = "mcsquaredjr"
__date__ = "12-Aug-2014"
__ver__ = "0.1"
"""Set of functions and classes to read profiling logs from a directory and to
manipulate the data.
"""
import os
import time
import re
import json
START_MARKER = "START "
END_MARKER = "END "
ARGS_MARKER = "args"
SYNC_MARKER = "SYNCHRONIZATION "
c... | {
"repo_name": "SKA-ScienceDataProcessor/RC",
"path": "MS1/visualize/SKA/readlog.py",
"copies": "1",
"size": "8466",
"license": "apache-2.0",
"hash": -3360429804353321000,
"line_mean": 29.4532374101,
"line_max": 136,
"alpha_frac": 0.5356721002,
"autogenerated": false,
"ratio": 3.718050065876153,
... |
__author__ = 'mcsquaredjr'
import os
import sys
import socket
CAD_FILE = os.environ["CAD"]
NODE_FILE = os.environ["NODE_FILE"]
PROCS_PER_NODE = os.environ["PROCS_PER_NODE"]
ITEMCOUNT = os.environ["ITEMCOUNT"]
DDP = os.environ["DDP"]
GHC_EVENTS = os.environ["GHC_EVENTS"]
DDP_OPTS = os.environ["DDP_OPTS"]
MIN_PORT = os... | {
"repo_name": "SKA-ScienceDataProcessor/RC",
"path": "MS1/ddp-erlang-style/dna_lib.py",
"copies": "1",
"size": "1705",
"license": "apache-2.0",
"hash": 2721369736416662000,
"line_mean": 21.1428571429,
"line_max": 93,
"alpha_frac": 0.5278592375,
"autogenerated": false,
"ratio": 3.1227106227106227,... |
__author__ = "mcsquaredjr"
import sys
import matplotlib
matplotlib.use('pdf')
matplotlib.rcParams['axes.linewidth'] = 0.5
import matplotlib.pyplot as plt
import readlog
RED = [0.77, 0.16, 0.31]
BLUE = [0.33, 0.63, 0.82]
GREEN = [0.1, 0.66, 0.53]
YELLOW = [0.93, 0.67, 0.34]
DARKBLUE = [0.27, 0.55, 0.78]
REDDISH = [0... | {
"repo_name": "SKA-ScienceDataProcessor/RC",
"path": "MS1/visualize/SKA/logs2plot.py",
"copies": "1",
"size": "11513",
"license": "apache-2.0",
"hash": 9548247426008538,
"line_mean": 30.9833333333,
"line_max": 152,
"alpha_frac": 0.5450360462,
"autogenerated": false,
"ratio": 3.247672778561354,
... |
__author__ = "mcsquaredjr"
import matplotlib
matplotlib.use('pdf')
matplotlib.rcParams['axes.linewidth'] = 0.5
import matplotlib.pyplot as plt
import readlog
RED = [0.77, 0.16, 0.31]
BLUE = [0.33, 0.63, 0.82]
GREEN = [0.1, 0.66, 0.53]
YELLOW = [0.93, 0.67, 0.34]
DARKBLUE = [0.27, 0.55, 0.78]
SEABLUE = [0.38, 0.75, ... | {
"repo_name": "SKA-ScienceDataProcessor/RC",
"path": "MS1/visualize/SKA/logs2plot_b.py",
"copies": "1",
"size": "4264",
"license": "apache-2.0",
"hash": 872358706655358100,
"line_mean": 31.8076923077,
"line_max": 140,
"alpha_frac": 0.5037523452,
"autogenerated": false,
"ratio": 3.144542772861357,... |
__author__ = 'mcsquaredjr'
import xlwt
TITLE = xlwt.easyxf('font: bold True, height 320;')
HEADER = xlwt.easyxf('font: bold True, height 240;')
HEADER_C = xlwt.easyxf('font: bold True, height 240; alignment: horizontal center;')
WRAPPED = xlwt.easyxf('alignment: wrap True;')
class XLS_Writer(object):
def __... | {
"repo_name": "SKA-ScienceDataProcessor/RC",
"path": "MS1/visualize/SKA/writexls.py",
"copies": "1",
"size": "3358",
"license": "apache-2.0",
"hash": -39330175177877680,
"line_mean": 31.6019417476,
"line_max": 104,
"alpha_frac": 0.5512209649,
"autogenerated": false,
"ratio": 3.3850806451612905,
... |
__author__ = 'mdavid'
from BaseResolver import BaseResolver
from addressimo.data import IdObject
from addressimo.util import LogUtil
from uuid import uuid4
log = LogUtil.setup_logging()
class LocalResolver(BaseResolver):
local_ir = {
"id": "b17330349ca44abb9c086e81987045b6",
"requester_pubkey":... | {
"repo_name": "netkicorp/addressimo",
"path": "addressimo/resolvers/LocalResolver.py",
"copies": "1",
"size": "6709",
"license": "bsd-3-clause",
"hash": -3352124557810540500,
"line_mean": 37.3371428571,
"line_max": 147,
"alpha_frac": 0.7625577582,
"autogenerated": false,
"ratio": 2.10842237586423... |
__author__ = 'mdavid'
from bcresolver import *
if __name__ == '__main__':
nc_resolver = NamecoinResolver(
host='127.0.0.1',
user='rpcuser',
password='rpcpassword',
port=8336,
temp_dir='/tmp'
)
try:
txt_result = nc_resolver.resolve('_wallet.wallet.mattdavid... | {
"repo_name": "netkicorp/blockchain-resolver",
"path": "examples/resolve_namecoin_name.py",
"copies": "1",
"size": "2219",
"license": "bsd-3-clause",
"hash": -8623268470211466000,
"line_mean": 35.393442623,
"line_max": 96,
"alpha_frac": 0.6538981523,
"autogenerated": false,
"ratio": 3.56179775280... |
__author__ = 'mdavid'
from flask import request, Response
from redis import Redis
from addressimo.blockchain import cache_up_to_date
from addressimo.config import config
from addressimo.crypto import generate_bip32_address_from_extended_pubkey, generate_payment_request, get_unused_presigned_payment_request, derive_br... | {
"repo_name": "netkicorp/addressimo",
"path": "addressimo/resolvers/__init__.py",
"copies": "1",
"size": "9135",
"license": "bsd-3-clause",
"hash": 7947193102148449000,
"line_mean": 45.8461538462,
"line_max": 180,
"alpha_frac": 0.6553913519,
"autogenerated": false,
"ratio": 3.6467065868263475,
... |
__author__ = 'mdavid'
from wnsresolver import *
if __name__ == '__main__':
wallet_name = 'wallet.franksisf.bit'
currency = 'btc'
retries = 3
resolved_address = None
resolver = WalletNameResolver(
resolv_conf='/etc/resolv.conf',
dnssec_root_key='/usr/local/etc/unbound/root.key'
... | {
"repo_name": "netkicorp/wns-resolver",
"path": "examples/resolve_namecoin_walletname.py",
"copies": "1",
"size": "1826",
"license": "bsd-3-clause",
"hash": 6665933314547674000,
"line_mean": 34.1346153846,
"line_max": 176,
"alpha_frac": 0.6462212486,
"autogenerated": false,
"ratio": 3.99562363238... |
__author__ = 'mdavid'
from wnsresolver import *
if __name__ == '__main__':
wallet_name = 'wallet.justinnewton.me'
currency = 'btc'
retries = 3
resolved_address = None
resolver = WalletNameResolver(
resolv_conf='/etc/resolv.conf',
dnssec_root_key='/usr/local/etc/unbound/root.key'
... | {
"repo_name": "netkicorp/wns-resolver",
"path": "examples/resolve_icann_walletname.py",
"copies": "1",
"size": "1547",
"license": "bsd-3-clause",
"hash": 58493280401784856,
"line_mean": 35.8333333333,
"line_max": 176,
"alpha_frac": 0.6535229476,
"autogenerated": false,
"ratio": 4.049738219895288,... |
__author__ = 'mdavid'
import base64
import json
import requests
class NamecoinException(Exception):
def __init__(self, message=None, code=0):
self.message = message
self.code = code
def __str__(self):
return 'NamecoinException [Code: %d | Message: %s]' % (self.code, self.message)
cl... | {
"repo_name": "netkicorp/blockchain-resolver",
"path": "bcresolver/namecoin.py",
"copies": "1",
"size": "2082",
"license": "bsd-3-clause",
"hash": -3546690829653018000,
"line_mean": 30.5606060606,
"line_max": 144,
"alpha_frac": 0.5384245917,
"autogenerated": false,
"ratio": 4.0984251968503935,
... |
__author__ = 'mdavid'
import hashlib
import iptools
import os
import re
import requests
import socket
from base64 import b64decode
from dns import rdatatype
from flask import request
from unbound import ub_ctx, RR_CLASS_IN
from urlparse import urlparse
class WalletNameLookupError(Exception):
pass
class WalletNa... | {
"repo_name": "netkicorp/wns-resolver",
"path": "wnsresolver/__init__.py",
"copies": "1",
"size": "7723",
"license": "bsd-3-clause",
"hash": 4551310697143017500,
"line_mean": 33.0220264317,
"line_max": 188,
"alpha_frac": 0.5897967111,
"autogenerated": false,
"ratio": 4.1655879180151025,
"config... |
__author__ = 'mdavid'
import json
from mock import *
from unittest import TestCase
from bcresolver.namecoin import NamecoinClient, NamecoinException
class TestNamecoinException(TestCase):
def test_go_right(self):
ex = NamecoinException(message='test_message', code=42)
msg = str(ex)
self.... | {
"repo_name": "netkicorp/blockchain-resolver",
"path": "tests/test_namecoin_client.py",
"copies": "1",
"size": "7363",
"license": "bsd-3-clause",
"hash": -7127519601273204000,
"line_mean": 43.0898203593,
"line_max": 299,
"alpha_frac": 0.647562135,
"autogenerated": false,
"ratio": 3.22797018851381... |
__author__ = 'mdavid'
import json
from urlparse import urlparse
from flask import Response, request, current_app
from netki.util.logutil import LogUtil
log = LogUtil.setup_logging('util_api')
def create_json_response(success=True, message='', status=200, data={}):
allowed_origins = ['localhost', '127.0.0.... | {
"repo_name": "netkicorp/wns-api-server",
"path": "netki/api/util.py",
"copies": "1",
"size": "1698",
"license": "bsd-3-clause",
"hash": -8097464138963220000,
"line_mean": 29.8909090909,
"line_max": 107,
"alpha_frac": 0.6219081272,
"autogenerated": false,
"ratio": 3.8503401360544216,
"config_te... |
__author__ = 'mdavid'
import json
import logging
import logging.handlers
import os
import sys
import urllib
from datetime import datetime
from ecdsa import curves
from ecdsa.der import UnexpectedDER
from ecdsa.keys import VerifyingKey, BadDigestError, BadSignatureError
from ecdsa.util import sigdecode_der
from flask ... | {
"repo_name": "netkicorp/addressimo",
"path": "addressimo/util.py",
"copies": "1",
"size": "7020",
"license": "bsd-3-clause",
"hash": 3728025890262776000,
"line_mean": 31.9577464789,
"line_max": 145,
"alpha_frac": 0.6276353276,
"autogenerated": false,
"ratio": 3.7782561894510227,
"config_test":... |
__author__ = 'mdavid'
import logging
import logging.handlers
import os
import sys
class LogUtil:
loggers = {}
@classmethod
def setup_logging(cls, app_name='app', log_to_file=False):
if LogUtil.loggers.get(app_name):
return cls.loggers.get(app_name)
logdir = os.path.abspath(... | {
"repo_name": "netkicorp/wns-api-server",
"path": "netki/util/logutil.py",
"copies": "1",
"size": "1044",
"license": "bsd-3-clause",
"hash": -8333012083890823000,
"line_mean": 28,
"line_max": 126,
"alpha_frac": 0.6111111111,
"autogenerated": false,
"ratio": 3.5876288659793816,
"config_test": fa... |
__author__ = 'mdavid'
import string
import re
class InputValidation:
@staticmethod
def is_valid_domain(text):
if not text:
return False
if '.' not in text:
return False
allowed = set(string.ascii_letters + string.digits + '-.')
if set(text) - allowed:... | {
"repo_name": "netkicorp/wns-api-server",
"path": "netki/util/validation.py",
"copies": "1",
"size": "1225",
"license": "bsd-3-clause",
"hash": 211966335809604600,
"line_mean": 21.6851851852,
"line_max": 75,
"alpha_frac": 0.5534693878,
"autogenerated": false,
"ratio": 4.375,
"config_test": fals... |
__author__ = 'mdavid'
# System Imports
import binascii
from datetime import datetime
import multiprocessing
from multiprocessing.queues import Empty
import os
import pybitcointools
from redis import Redis
import resource
import simpleflock
import socket
import struct
import sys
import time
# Addressimo Imports
from a... | {
"repo_name": "netkicorp/addressimo",
"path": "jobs/build_address_cache.py",
"copies": "1",
"size": "9041",
"license": "bsd-3-clause",
"hash": 5718433925829167000,
"line_mean": 31.642599278,
"line_max": 309,
"alpha_frac": 0.6167459352,
"autogenerated": false,
"ratio": 3.6677484787018257,
"confi... |
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