text stringlengths 0 1.05M | meta dict |
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import requests
import json
from getpass import getpass
from ipaddress import ip_address
class NXOS():
'''
This is a class to encapsulate the information needed to connect to
an NXOS device via NXAPI. The object is instantiated with an IP
address and has setter methods to set credentials. This class... | {
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from argparse import ArgumentParser
from nxapi_base import NXOS, short_intf, short_name
def makeDescriptions(cdp, pc):
neighbor_list = cdp["TABLE_cdp_neighbor_detail_info"]["ROW_cdp_neighbor_detail_info"]
pc_list = pc["TABLE_channel"]["ROW_channel"]
if type(pc_list) == dict:
pc_list = [pc_list]
... | {
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"path": "IntfLabel.py",
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from argparse import ArgumentParser
from nxapi_base import NXOS
from ipaddress import ip_address, ip_network
from requests import Timeout
def GetRouteStats(routetable, vrf="default", addrf="ipv4"):
'''
This function takes the route-table data structure via NXAPI and returns a
new data structure that stor... | {
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... |
__author__ = 'jan.chvala'
import sqlite3
import os
import db.db_init
import json
from bottle import get, run, debug, template, request, static_file, error
DB_NAME = 'db/sessions.db'
POLYMER_ROOT = os.getcwd() + "/polymer/"
# API for getting all session in JSON
@get('/rest/sessions')
def rest_session_list():
co... | {
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"path": "src/android-ipcam-server.py",
"copies": "1",
"size": "4878",
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__author__ = 'janderson'
#!/usr/bin/env python
'''
Write a script that connects to the lab pynet-rtr1, logins, and executes the
'show ip int brief' command.
'''
import telnetlib
import time
import socket
import sys
import getpass
TELNET_PORT = 23
TELNET_TIMEOUT = 6
class TelnetConn(object):
def __init__(self,... | {
"repo_name": "ande0581/pynet",
"path": "class2/class2_ex3.py",
"copies": "1",
"size": "2122",
"license": "apache-2.0",
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__author__ = 'Janez Stupar'
from tastypie import resources, authorization, authentication, fields
from polls import models as poll_models
class PollResource(resources.ModelResource):
choices = fields.ToManyField('tp_demo.api.polls_api.ChoiceResource', 'choices',null=True, blank=True, full=True)
class Meta:
... | {
"repo_name": "JanezStupar/tastypie_demo",
"path": "tp_demo/api/polls_api.py",
"copies": "1",
"size": "4329",
"license": "mit",
"hash": -5605972320585961000,
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__author__ = 'jang@ioctl.org'
import eventlet
import eventlet.debug
# Still not sure how some of these FDs get invalidated.
# I'm pretty sure GC is not involved.
import gc
print "gc enabled?", gc.isenabled()
eventlet.monkey_patch()
#eventlet.debug.hub_listener_stacks(True)
#eventlet.debug.hub_timer_stacks(True)
#even... | {
"repo_name": "jan-g/eventlet-paramiko-test",
"path": "para.py",
"copies": "1",
"size": "3986",
"license": "apache-2.0",
"hash": -3334610459291107000,
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__author__ = 'Jan Karabas'
__project__ = 'snarkx'
__all__ = ['Cycle']
class Cycle(object):
r"""
An *immutable cycle*
"""
def __init__(self, *args, oriented=True):
self._oriented = oriented
self._mydata = []
if isinstance(args[0],list):
self._mydata = args[0][:]
... | {
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"path": "snarkx/cycle.py",
"copies": "1",
"size": "6298",
"license": "bsd-3-clause",
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"... |
__author__ = 'Jan Karabas'
__project__ = 'snarkx'
__all__ = ['PartitionsInt']
class PartitionsInt(object):
r"""
Integer partitions iterator.
Given a non-negative integer *n* it yields all partitions of that number.
The obtained partitons can be generated up to given length or
they... | {
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... |
__author__ = 'Jan Karabas'
__project__ = 'snarkx'
class BasicPermutation(object):
def __init__(self, arg):
self.__data = None
if isinstance(arg, int):
if arg < 1:
raise ValueError('Degree too small on Permutaion({0})'.format(arg))
self.__data = [_i for _i in... | {
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__author__ = 'jankuehl'
import re
from defusedxml import ElementTree as ET
from dojo.models import Finding
class XanitizerXMLParser(object):
def __init__(self, filename, test):
self.items = []
if filename is None:
return
root = self.parse_xml(filename)
if root is n... | {
"repo_name": "rackerlabs/django-DefectDojo",
"path": "dojo/tools/xanitizer/parser.py",
"copies": "1",
"size": "6355",
"license": "bsd-3-clause",
"hash": 2477606740323438600,
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import requests
import json
import os
from posixpath import join as urljoin
from requests_oauthlib import OAuth1Session
import xmltodict
CLIENT_ID = os.getenv('GOODREADS_CLIENT_ID')
CLIENT_SECRET = os.getenv('GOODREADS_CLIENT_SECRET')
# credentials stored to home folder
OAUTH_TOKEN_JSON = os.getenv('GOODREADS_OAUT... | {
"repo_name": "jmargeta/goodreads",
"path": "goodreads/goodreads.py",
"copies": "1",
"size": "3790",
"license": "mit",
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"autogenerated": false,
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"config_test": false,
... |
__author__ = 'jannis'
import pandas, os
class dummy(object):
""" an empty class used to define the states and processes attribute in the knowledgebase """
pass
class Knowledgebase(object):
"""
This class provides convenient access to data which is required to instantiate states and processes.
""... | {
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"path": "data/knowledgebase.py",
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"autogenerated": false,
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__author__ = 'jan'
import matplotlib.pyplot as plt
from prettyplotlib.utils import remove_chartjunk, maybe_get_ax
from prettyplotlib import colors as _colors
import numpy as np
import matplotlib.mlab as mlab
def _beeswarm(ax, x, notch=0, sym='b+', vert=1, whis=1.5,
positions=None, widths=None, patch_arti... | {
"repo_name": "olgabot/prettyplotlib",
"path": "prettyplotlib/_beeswarm.py",
"copies": "1",
"size": "12196",
"license": "mit",
"hash": 4303627592129842700,
"line_mean": 34.0459770115,
"line_max": 103,
"alpha_frac": 0.5420629715,
"autogenerated": false,
"ratio": 3.500574052812859,
"config_test":... |
__author__ = 'janomar'
import logging
import jaydebeapi
from airflow.hooks.base_hook import BaseHook
class JdbcHook(BaseHook):
"""
General hook for jdbc db access.
If a connection id is specified, host, port, schema, username and password will be taken from the predefined connection.
Raises an airfl... | {
"repo_name": "jbalogh/airflow",
"path": "airflow/hooks/jdbc_hook.py",
"copies": "1",
"size": "2937",
"license": "apache-2.0",
"hash": 861209922547051300,
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"alpha_frac": 0.5863125638,
"autogenerated": false,
"ratio": 3.809338521400778,
"config_test": ... |
__author__ = 'janomar'
import logging
import jaydebeapi
from airflow.hooks.dbapi_hook import DbApiHook
class JdbcHook(DbApiHook):
"""
General hook for jdbc db access.
If a connection id is specified, host, port, schema, username and password will be taken from the predefined connection.
Raises an ai... | {
"repo_name": "linearregression/airflow",
"path": "airflow/hooks/jdbc_hook.py",
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"license": "apache-2.0",
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"autogenerated": false,
"ratio": 3.8593073593073592,
"co... |
__author__ = 'janomar'
import logging
from airflow.hooks.jdbc_hook import JdbcHook
from airflow.models import BaseOperator
from airflow.utils.decorators import apply_defaults
class JdbcOperator(BaseOperator):
"""
Executes sql code in a database using jdbc driver.
Requires jaydebeapi.
:param jdbc_u... | {
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"path": "airflow/operators/jdbc_operator.py",
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__author__ = 'janomar'
import logging
from airflow.hooks.jdbc_hook import JdbcHook
from airflow.models import BaseOperator
from airflow.utils import apply_defaults
class JdbcOperator(BaseOperator):
"""
Executes sql code in a database using jdbc driver.
Requires jaydebeapi.
:param jdbc_url: driver ... | {
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"path": "airflow/operators/jdbc_operator.py",
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__author__ = 'janosbana'
'''
this script makes an api call to ml studio web service we created and returns the predicted values
for each order item for the next seven days
Note: the code was taken from Azure's ML Studio web service information page and modified to fit our needs
'''
import json
import requests
from d... | {
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"path": "G53IDS/server/ml_helpers/ml_studio_request.py",
"copies": "1",
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__author__ = 'janos'
import csv
import re
import networkx as nx
from optparse import OptionParser
import os
import sys
import json
def export_edges_to_csv(csv_edge_file_name, provider_graph_to_export):
"""Export edges to CSV"""
with open(csv_edge_file_name, "wb") as f:
csv_edges = csv.writer(f)
... | {
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"path": "teaming/manipulate_provider_graphml.py",
"copies": "1",
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__author__ = 'janos'
from optparse import OptionParser
from teaming_extract import field_selection_with_like
if __name__ == "__main__":
parser = OptionParser()
parser.add_option("-o", "--file", dest="file_name", help="")
parser.add_option("-t", "--taxonomy_list", dest="taxonomy_list", help="")
parser... | {
"repo_name": "jhajagos/HealthcareAnalyticTools",
"path": "teaming/generate_taxonomy_binary_indicator_fields.py",
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__author__ = 'janos'
"""
Generate queries against DE-SYNPUF database or another claim's database were
the coding is flat for understanding relationships between first time of diagnosis.
"""
import sqlalchemy as sa
import re
import csv
def find_columns_that_match(table_columns, regex_field_match):
columns_that_m... | {
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"path": "scripts/generate_de_synpuf_queries.py",
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... |
__author__ = 'Jan Pecinovsky'
from opengrid.config import Config
config = Config()
import os
import sys
import json
import jsonpickle
import datetime as dt
import pandas as pd
from requests.exceptions import HTTPError
import warnings
from tqdm import tqdm
# compatibility with py3
if sys.version_info.major >= 3:
... | {
"repo_name": "WolfBerwouts/opengrid",
"path": "opengrid/library/houseprint/houseprint.py",
"copies": "2",
"size": "19273",
"license": "apache-2.0",
"hash": -4990689198843223000,
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"alpha_frac": 0.5204171639,
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__author__ = 'Jan Pecinovsky'
import datetime as dt
from copy import copy
import forecastio
import geopy
import numpy as np
import pandas as pd
from dateutil import rrule
class Weather():
"""
Object that contains Weather Data from Forecast.io for multiple days as a Pandas Dataframe.
NOTE: Foreca... | {
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"path": "opengrid/library/forecastwrapper.py",
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"autogenerated": false,
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... |
__author__ = 'Jan Pecinovsky'
import geocoder
import astral
import math
import pandas as pd
class SolarInsolation(object):
"""
Module to calculate Solar Insolation (direct intensity, global intensity, air mass) and
basic solar parameters (angle) based on a location and a date.
Formulas fro... | {
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"path": "opengrid/library/solarmodel.py",
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"c... |
__author__ = 'Jan Pecinovsky'
import pandas as pd
"""
A Device is an entity that can contain multiple sensors.
The generic Device class can be inherited by a specific device class, eg.
Fluksometer
"""
class Device(object):
def __init__(self, key=None, site=None):
self.key = key
self.site = site
... | {
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__author__ = 'Jan Pecinovsky'
import pandas as pd
"""
A Site is a physical entity (a house, appartment, school, or other building).
It may contain multiple devices and sensors.
The Site contains most of the metadata, eg. the number of inhabitants, the size
of the building, the location etc.
"""
class Site(object):
... | {
"repo_name": "JrtPec/opengrid",
"path": "opengrid/library/houseprint/site.py",
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"conf... |
__author__ = 'Jan Pecinovsky'
"""
A Device is an entity that can contain multiple sensors.
The generic Device class can be inherited by a specific device class, eg. Fluksometer
"""
import pandas as pd
class Device(object):
def __init__(self, key, site):
self.key = key
self.site = site
sel... | {
"repo_name": "MatteusDeloge/opengrid",
"path": "opengrid/library/houseprint/device.py",
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__author__ = 'Jan Pecinovsky'
"""
A sensor generates a single data stream.
It can have a parent device, but the possibility is also left open for a sensor to stand alone in a site.
It is an abstract class definition which has to be overridden (by eg. a Fluksosensor).
This class contains all metadata concerning the fu... | {
"repo_name": "MatteusDeloge/opengrid",
"path": "opengrid/library/houseprint/sensor.py",
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"size": "8288",
"license": "apache-2.0",
"hash": -3748338458504229000,
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"alpha_frac": 0.5173745174,
"autogenerated": false,
"ratio": 4.73059360730593... |
__author__ = 'Jan Pecinovsky, Roel De Coninck'
"""
A sensor generates a single data stream.
It can have a parent device, but the possibility is also left open for a sensor to stand alone in a site.
It is an abstract class definition which has to be overridden (by eg. a Fluksosensor).
This class contains all metadata ... | {
"repo_name": "WolfBerwouts/opengrid",
"path": "opengrid/library/houseprint/sensor.py",
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__author__ = 'Jan Růžička <jan.ruzicka01@gmail.com>'
__version__ = "0.5.0"
import sys
import string
import re
unicode = "".join([chr(x) for x in range(sys.maxunicode)])
ascii = string.ascii_lowercase + string.ascii_uppercase
nums = "".join(str(x) for x in range(10))
ascii_nums = ascii + nums
def expand(lst):
""... | {
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"config_test": false,
"has_no_keyw... |
__author__ = 'Jan Růžička <jan.ruzicka01@gmail.com>'
__version__ = "1.0.5"
from utils import *
import sys
import re
_ignored = " "
_recursed = {}
_keywords = []
_literals = []
_ops = {}
def _addOp(op, att):
if type(op) in [And, Or, Xor]:
_addOp(op.first, att)
_addOp(op.second, att)
else:... | {
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"has_... |
__author__="Janusz Swiatczak"
__date__ ="12/09/2012"
import re
import os
INSTANCE_START_TAG = re.compile(r'<INSTANCE CLASS="(.*)"', re.I)
INSTANCE_END_TAG = re.compile(r'</INSTANCE>', re.I)
PEOPLECODE_SECTION = 'PCM'
pathRule = lambda x: '.' if int(x) in (12,39) else ('' if not x or int(x) == 0 else os.sep)
PATH_KE... | {
"repo_name": "swiatczak/psprojectparser",
"path": "psconstants.py",
"copies": "1",
"size": "7738",
"license": "mit",
"hash": 224884070226773500,
"line_mean": 65.1367521368,
"line_max": 250,
"alpha_frac": 0.5328250194,
"autogenerated": false,
"ratio": 3.1830522418757714,
"config_test": false,
... |
__author__="Janusz Swiatczak"
__date__ ="13/07/2014"
import re
import codecs
import os
import os.path
import xml.etree.ElementTree as ET
import psconstants as ppsc
#
# Reference: David Beazley @ http://www.dabeaz.com
#
def coroutine(func):
"""
Turn function object into a coroutine.
use as a decorat... | {
"repo_name": "swiatczak/psprojectparser",
"path": "psprojectparser.py",
"copies": "1",
"size": "4848",
"license": "mit",
"hash": 5551069796769817000,
"line_mean": 40.4358974359,
"line_max": 168,
"alpha_frac": 0.5567244224,
"autogenerated": false,
"ratio": 4.154241645244216,
"config_test": fals... |
__author__ = 'Jan Voigt'
import numpy as np
import csv as csv
from pylab import *
#from operator import add
import matplotlib.pyplot as plt
x1List = [] # ln(c)
y1List = [] # EMK Dotierseite
f = open("EMK_dotierseite_ln(c)_alle.txt")
for line in f:
line = line.rstrip()
parts = line.split()
... | {
"repo_name": "fxjung/mathphyprak",
"path": "legacy/emklnc.py",
"copies": "1",
"size": "1443",
"license": "isc",
"hash": 3206102540919071000,
"line_mean": 24.7222222222,
"line_max": 96,
"alpha_frac": 0.6209286209,
"autogenerated": false,
"ratio": 2.4457627118644067,
"config_test": false,
"has... |
__author__ = 'jan.zdunek'
# Copyright 2013-2015 Jan Zdunek
#
# 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
#
# Unless required by applicable law... | {
"repo_name": "jzdunek/robot-profiler",
"path": "src/python/robot_profiler.py",
"copies": "1",
"size": "4927",
"license": "apache-2.0",
"hash": -8934476236485787000,
"line_mean": 34.1928571429,
"line_max": 143,
"alpha_frac": 0.6141668358,
"autogenerated": false,
"ratio": 3.9196499602227526,
"co... |
__author__ = 'jan.zdunek'
# Copyright 2015 Jan Zdunek
#
# 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
#
# Unless required by applicable law or a... | {
"repo_name": "jzdunek/robot-profiler",
"path": "test/python/robot_profiler_unittests.py",
"copies": "1",
"size": "11110",
"license": "apache-2.0",
"hash": 3902919362699715000,
"line_mean": 43.44,
"line_max": 121,
"alpha_frac": 0.6606660666,
"autogenerated": false,
"ratio": 3.8859741168240642,
... |
__author__ = "Jared B Bowden"
__version__ = 1.2
"""
Plot of internet speed
"""
import matplotlib.pylab as plt
import pandas as pd
from matplotlib.dates import DateFormatter
data_path = "/Users/jaredbowden/Google Drive/cave_in_a_lake/data/"
dateparse = lambda x: pd.datetime.strptime(x, '%Y/%d/%m %H:%M:%S')
raw_data ... | {
"repo_name": "JaredBBowden/speedtest-cli",
"path": "graph_speed.py",
"copies": "1",
"size": "1589",
"license": "apache-2.0",
"hash": 4726506013344118000,
"line_mean": 21.3802816901,
"line_max": 71,
"alpha_frac": 0.6299559471,
"autogenerated": false,
"ratio": 3.0735009671179885,
"config_test": ... |
__author__ = 'Jared Jorgensen'
from slickqaweb.app import app
from slickqaweb.model.testrunGroup import TestrunGroup
from slickqaweb.model.testrun import Testrun
from slickqaweb.model.serialize import deserialize_that
from slickqaweb.model.query import queryFor
from slickqaweb.utils import *
import datetime
from flask... | {
"repo_name": "slickqa/slickqaweb",
"path": "slickqaweb/api/testrungroup.py",
"copies": "1",
"size": "4009",
"license": "apache-2.0",
"hash": 1699729544710015000,
"line_mean": 40.3298969072,
"line_max": 104,
"alpha_frac": 0.7298578199,
"autogenerated": false,
"ratio": 3.310487200660611,
"config... |
__author__ = 'Jason Crockett'
import django.db.models as datdb
import django.db.models.manager as dbiface
import django.forms.formsets as dbiform
import ebay
class Item(datdb.Model):
iid = datdb.AutoField(primary_key=True)
name = datdb.CharField(max_length=60)
image = datdb.ImageField(verbose_name="Produc... | {
"repo_name": "deddokatana/Ba3-Shop2",
"path": "Model.py",
"copies": "1",
"size": "2828",
"license": "mit",
"hash": -3867563405924629000,
"line_mean": 37.7397260274,
"line_max": 95,
"alpha_frac": 0.7241867044,
"autogenerated": false,
"ratio": 3.5086848635235732,
"config_test": false,
"has_no_... |
__author__ = 'Jason Crockett'
#username = ""
#password = ""
#authtoken = ""
#from ebaysdk import trading
from ebaysdk.trading import Connection as trading
class EbayUser:
DevID = 'f48a5c13-dca0-4077-8295-006421d6ed3b'
AppID = 'MrJasonA-67c2-4711-b4a2-51b534432c67'
CertID = 'a1ebd7a4-24e4-4a41-b657-03d45... | {
"repo_name": "deddokatana/Ba3-Shop2",
"path": "ebay.py",
"copies": "1",
"size": "3856",
"license": "mit",
"hash": 8374881862919811000,
"line_mean": 51.1216216216,
"line_max": 899,
"alpha_frac": 0.7868257261,
"autogenerated": false,
"ratio": 1.9051383399209487,
"config_test": false,
"has_no_k... |
__author__ = 'Jason Grundstad'
from django.conf import settings
from bs4 import BeautifulSoup
import json
import requests
import re
MD_ANDERSON_URL = 'https://pct.mdanderson.org'
MD_ANDERSON_OUTFILE = settings.LINKS_OUT + 'mdanderson.json'
def merge_dicts(x, y):
"""
:rtype : dict
"""
z = x.copy()
... | {
"repo_name": "jgrundstad/Variant_Report_Viewer",
"path": "igsb_report_viewer/viewer/util/link_out_scraper.py",
"copies": "1",
"size": "2209",
"license": "apache-2.0",
"hash": -6004847059391534000,
"line_mean": 23.5555555556,
"line_max": 79,
"alpha_frac": 0.5595291987,
"autogenerated": false,
"ra... |
__author__ = 'Jason Grundstad'
from django.conf import settings
from pyvirtualdisplay import Display
from selenium import webdriver
from bs4 import BeautifulSoup
import json
MD_ANDERSON_URL = 'https://pct.mdanderson.org/#/home'
MD_ANDERSON_OUTFILE = settings.LINKS_OUT + 'mdanderson.json'
def scrape_mdanderson():
... | {
"repo_name": "jgrundstad/viewer",
"path": "util/link_out_scraper.py",
"copies": "2",
"size": "1092",
"license": "apache-2.0",
"hash": 5799785715621495000,
"line_mean": 24.3953488372,
"line_max": 76,
"alpha_frac": 0.641025641,
"autogenerated": false,
"ratio": 3.1744186046511627,
"config_test": ... |
__author__ = 'Jason Grundstad'
from django.conf import settings
import json, simplejson
import os
import tablib
def add_goodies(atoms, headers, md_anderson_genes):
for i in range(0, len(headers)):
if not atoms[i]:
atoms[i] = ''
# highlight NON_SYNONYMOUS_CODING
if(headers[i] ==... | {
"repo_name": "jgrundstad/Variant_Report_Viewer",
"path": "igsb_report_viewer/viewer/util/report_parser.py",
"copies": "1",
"size": "1688",
"license": "apache-2.0",
"hash": 6468972669506762000,
"line_mean": 34.1666666667,
"line_max": 78,
"alpha_frac": 0.5710900474,
"autogenerated": false,
"ratio"... |
__author__ = 'jasonhuang'
import urllib.request as ur
import json, os, sys
import socket
import logging
import subprocess
def reporthook(blocknum, blocksize, totalsize):
readsofar = blocknum * blocksize
if totalsize > 0:
percent = readsofar * 1e2 / totalsize
s = "\r%5.1f%% %*d / %d" % (
... | {
"repo_name": "jasonleakey/douban-fm-music-downloader",
"path": "com/guchy/main.py",
"copies": "1",
"size": "2755",
"license": "apache-2.0",
"hash": -4880071600862845000,
"line_mean": 37.2638888889,
"line_max": 124,
"alpha_frac": 0.5626134301,
"autogenerated": false,
"ratio": 3.4610552763819094,
... |
__author__ = 'Jason Mehring'
#
# This module is only used with WingIDE debugger for testing code within
# The debugging environment
#
# Stage 1: bind /srv/...modules to cache/extmods. No sync will take place
# since files are bound
# BIND
# True : bind custom modules
# False : do not bind custom module... | {
"repo_name": "DockerNAS/yamlscript-formula",
"path": "src/salt-call.py",
"copies": "1",
"size": "7810",
"license": "mit",
"hash": 1702327962998151200,
"line_mean": 32.6637931034,
"line_max": 98,
"alpha_frac": 0.5473751601,
"autogenerated": false,
"ratio": 3.7674867342016403,
"config_test": fal... |
__author__ = 'jason'
from collections import Counter
import math
import nltk
import string
from sklearn.feature_extraction.text import TfidfVectorizer
# main functions used by others
def list_similarity(list1, list2):
c1, c2 = Counter(list1), Counter(list2)
return length_similarity(c1, c2) * counter_cosine_si... | {
"repo_name": "ArisKots1992/Similar-World-News-Articles",
"path": "Utils.py",
"copies": "1",
"size": "1873",
"license": "mit",
"hash": -8810448234906630000,
"line_mean": 25.0138888889,
"line_max": 94,
"alpha_frac": 0.6118526428,
"autogenerated": false,
"ratio": 3.2917398945518452,
"config_test"... |
__author__ = 'jason'
from textblob import TextBlob
from textblob.np_extractors import FastNPExtractor
from hashtagify import Hashtagify
from nltk.tag.stanford import NERTagger
from geonames import *
from nltk.corpus import stopwords
class NewsArticle:
def __init__(self, id, title, date, text, url, description, co... | {
"repo_name": "JasonPap/World-news-articles-matching",
"path": "NewsArticle.py",
"copies": "1",
"size": "10533",
"license": "mit",
"hash": 6339896830549414000,
"line_mean": 46.2331838565,
"line_max": 308,
"alpha_frac": 0.6478686034,
"autogenerated": false,
"ratio": 3.7929420237666545,
"config_t... |
__author__ = 'jason'
from textblob import TextBlob
from textblob.np_extractors import FastNPExtractor
from hashtagify import Hashtagify
try:
from nltk.tag.stanford import StanfordNERTagger
except:
from nltk.tag.stanford import NERTagger
from geonames import *
from nltk.corpus import stopwords
class NewsArticl... | {
"repo_name": "ArisKots1992/Similar-World-News-Articles",
"path": "NewsArticle.py",
"copies": "1",
"size": "11483",
"license": "mit",
"hash": 4341731296338985500,
"line_mean": 46.8458333333,
"line_max": 308,
"alpha_frac": 0.6426891927,
"autogenerated": false,
"ratio": 3.753841124550507,
"config... |
__author__ = 'jason'
from NewsAggregator import NewsAggregator
from NewsArticle import NewsArticle
from XMLparser import *
from nltk.corpus import stopwords
countries = ["Greece"]
aggr = NewsAggregator(0.35)
xmlfiles = ["cbsnews.xml", "Global News.xml", "chathamdailynews.xml", "Sky-News.xml", "npr.xml",
... | {
"repo_name": "JasonPap/World-news-articles-matching",
"path": "main.py",
"copies": "1",
"size": "1263",
"license": "mit",
"hash": -1009234354362083300,
"line_mean": 36.1470588235,
"line_max": 118,
"alpha_frac": 0.6516231196,
"autogenerated": false,
"ratio": 2.971764705882353,
"config_test": fa... |
__author__ = 'jason'
import imaplib
import re
import sys
import getpass
import smtplib
import time
from treelib import Tree
from os import path
from collections import defaultdict
import xml.etree.ElementTree as ET
from DatabaseLoader import DatabaseLoader
#member variables
db_file_location = "./db"
tree_location = "... | {
"repo_name": "jramapuram/rentbot",
"path": "Rentbot.py",
"copies": "1",
"size": "5659",
"license": "mit",
"hash": -3501232468076854300,
"line_mean": 35.5096774194,
"line_max": 129,
"alpha_frac": 0.5458561583,
"autogenerated": false,
"ratio": 3.3445626477541373,
"config_test": false,
"has_no_... |
__author__ = 'Jason'
import json
class JCal():
@classmethod
def from_calendar(cls, calendar):
"""Convert a Calendar instance to a json string.
:param calendar: icalendar.Calendar
:return: json str
"""
return json.dumps(JCal._from_component(calendar))
@classmetho... | {
"repo_name": "JasonCozens/CalTools",
"path": "cal_tools/jcal.py",
"copies": "1",
"size": "1093",
"license": "bsd-2-clause",
"hash": -8366995506873019000,
"line_mean": 26.35,
"line_max": 66,
"alpha_frac": 0.571820677,
"autogenerated": false,
"ratio": 4.711206896551724,
"config_test": false,
"... |
__author__ = 'Jason'
import os
import icalendar
class Examples5545():
"""Examples from RFC 5545. (http://tools.ietf.org/html/rfc5545)
.
"""
ical_str = (
'BEGIN:VCALENDAR\r\n'
'VERSION:2.0\r\n'
'PRODID:-//hacksw/handcal//NONSGML v1.0//EN\r\n'
'BEGIN:VEVENT\r\n'
... | {
"repo_name": "JasonCozens/CalTools",
"path": "cal_tools/examples/parsing_ex.py",
"copies": "1",
"size": "1521",
"license": "bsd-2-clause",
"hash": 8046122434648141000,
"line_mean": 28.2692307692,
"line_max": 71,
"alpha_frac": 0.5180802104,
"autogenerated": false,
"ratio": 3.0727272727272728,
"... |
__author__ = 'jason'
import os
import numpy as np
from skimage.io import imread
from skimage import morphology
from skimage.filter import threshold_otsu, rank
from skimage import measure
import matplotlib.pyplot as plt
def get_max_region(label_list, imagethres):
regions = measure.regionprops(label_list)
regi... | {
"repo_name": "JasonTam/ndsb2015",
"path": "feature/improc.py",
"copies": "1",
"size": "3064",
"license": "mit",
"hash": 484001441934973300,
"line_mean": 31.2526315789,
"line_max": 96,
"alpha_frac": 0.6458877285,
"autogenerated": false,
"ratio": 3.2735042735042734,
"config_test": false,
"has_... |
__author__ = 'jason'
import os.path
import bcrypt
import leveldb
import getpass
import sys
from simplecrypt import encrypt, decrypt
class DatabaseLoader:
# helper to parse out the db
def load_database(self, file_path, db_pwd):
db = leveldb.LevelDB(file_path)
db_kv_set = {}
encrypted_... | {
"repo_name": "jramapuram/rentbot",
"path": "DatabaseLoader.py",
"copies": "1",
"size": "3234",
"license": "mit",
"hash": 598254084010476800,
"line_mean": 41.5657894737,
"line_max": 117,
"alpha_frac": 0.5269016698,
"autogenerated": false,
"ratio": 3.8637992831541217,
"config_test": false,
"ha... |
__author__ = 'Jason'
import time
import unittest
class JCalTest(unittest.TestCase):
def test_from_jcal(self):
"""Example of parsing jcal dates and times.
:return: None
This examples shows the format strings to use to parse a date or time
from a jcal json stream.
"""
... | {
"repo_name": "JasonCozens/CalTools",
"path": "cal_tools/examples/time_ex.py",
"copies": "1",
"size": "1264",
"license": "bsd-2-clause",
"hash": 2845770478971544600,
"line_mean": 33.1891891892,
"line_max": 77,
"alpha_frac": 0.5965189873,
"autogenerated": false,
"ratio": 3.0384615384615383,
"con... |
__author__ = 'Jason'
import unittest
from icalendar import Calendar
from cal_tools import model
from cal_tools.model import CalendarModel
print_help = True
class CalendarModelTest(unittest.TestCase):
def test_calendar_type_incorrect(self):
# Act.
with self.assertRaises(AssertionError) as ex:
... | {
"repo_name": "JasonCozens/CalTools",
"path": "cal_tools/test/model_test/calendar_model_test.py",
"copies": "1",
"size": "1561",
"license": "bsd-2-clause",
"hash": -1508134833989146400,
"line_mean": 26.875,
"line_max": 56,
"alpha_frac": 0.5707879564,
"autogenerated": false,
"ratio": 3.85432098765... |
__author__ = 'Jason'
import unittest
import icalendar
import icalendar.cal
import icalendar.parser_tools
import icalendar.parser
import icalendar.prop
import cal_tools.ijconvert
import json
import yaml
class IJConvertTest(unittest.TestCase):
def test_empty_vcalendar(self):
# Arrange.
expected_r... | {
"repo_name": "JasonCozens/CalTools",
"path": "cal_tools/test/ijconvert_test.py",
"copies": "1",
"size": "3668",
"license": "bsd-2-clause",
"hash": -5911728690271333000,
"line_mean": 22.6709677419,
"line_max": 84,
"alpha_frac": 0.5155398037,
"autogenerated": false,
"ratio": 3.43767572633552,
"c... |
__author__ = 'jason'
import Utils
class Classifier:
def __init__(self, content_type, initial_content):
self.content_type = content_type # a string with that describes the type of data to be stored
self.content = initial_content
# This function compute the similarity of the object stored wit... | {
"repo_name": "JasonPap/World-news-articles-matching",
"path": "Classifier.py",
"copies": "1",
"size": "1948",
"license": "mit",
"hash": 764715219768870800,
"line_mean": 41.347826087,
"line_max": 102,
"alpha_frac": 0.6262833676,
"autogenerated": false,
"ratio": 4.198275862068965,
"config_test":... |
__author__ = 'jason'
def geo_search(query):
results = dict()
# use the geonames webAPI to fill dictionary with data
results["country"] = "Greece"
return results
import urllib2
from bs4 import BeautifulSoup
Cache = dict()
def getCountry(query):
if query in Cache:
return Cache[query... | {
"repo_name": "ArisKots1992/Similar-World-News-Articles",
"path": "geonames.py",
"copies": "1",
"size": "1105",
"license": "mit",
"hash": 6135644727705165000,
"line_mean": 24.6976744186,
"line_max": 128,
"alpha_frac": 0.563800905,
"autogenerated": false,
"ratio": 4.003623188405797,
"config_test... |
__author__ = 'Jason Piper'
import imp
current_version = imp.load_source('lol', 'pyDNase/_version.py').__version__
try:
from setuptools import setup, Extension
except ImportError:
from distutils.core import setup
from distutils.extension import Extension
setup(
name='pyDNase',
version=current_vers... | {
"repo_name": "jpiper/pyDNase",
"path": "setup.py",
"copies": "1",
"size": "1690",
"license": "mit",
"hash": 217815065132853020,
"line_mean": 32.137254902,
"line_max": 149,
"alpha_frac": 0.6313609467,
"autogenerated": false,
"ratio": 3.1766917293233083,
"config_test": false,
"has_no_keywords"... |
__author__ = 'Jason'
import os, re, csv, ast, csv
import math
import jinja2
jinja_environment = jinja2.Environment(autoescape=True, loader=jinja2.FileSystemLoader(os.path.join(os.path.dirname(__file__), 'templates')))
import flask
from features import attr_str2dict
from utils import *
MAX_MONTH = 1000
def _a... | {
"repo_name": "zwChan/VATEC",
"path": "~/eb-flask/analysis.py",
"copies": "1",
"size": "9079",
"license": "apache-2.0",
"hash": 6690114786800579000,
"line_mean": 53.0242424242,
"line_max": 530,
"alpha_frac": 0.5825531446,
"autogenerated": false,
"ratio": 3.0954653937947496,
"config_test": false... |
__author__ = 'Jason'
from collections import deque
from Node import Node
from Edge import Edge
class Graph:
def __init__(self):
self.dictionary = dict()
self.number_of_nodes = 0
self.number_of_edges = 0
def insert_node(self, node):
"""
Insert a node in ... | {
"repo_name": "JasonPap/graphproject",
"path": "python/Graph.py",
"copies": "1",
"size": "7894",
"license": "mit",
"hash": 684801361661715800,
"line_mean": 32.480349345,
"line_max": 120,
"alpha_frac": 0.5153280973,
"autogenerated": false,
"ratio": 4.126502875065342,
"config_test": false,
"has... |
__author__ = 'Jason'
from collections import deque
import itertools
def degree_distribution(graph):
distribution = dict()
for node_id in graph.dictionary:
n = len(graph.dictionary[node_id].links)
if n in distribution:
val = distribution[n]
val += 1
... | {
"repo_name": "JasonPap/graphproject",
"path": "python/GraphStatistics.py",
"copies": "1",
"size": "8961",
"license": "mit",
"hash": 3086861111594786300,
"line_mean": 26.179245283,
"line_max": 101,
"alpha_frac": 0.4747238031,
"autogenerated": false,
"ratio": 3.6952577319587627,
"config_test": f... |
__author__ = 'Jason'
from Graph import Graph
from Node import Node
from Edge import Edge
def create_graph_from_file(filename):
"""
Create a graph from a CSV file. Column[0] -> Column[1]
:param filename: CSV filename
:return: Graph
"""
graph = Graph()
with open(filename, 'r')... | {
"repo_name": "JasonPap/graphproject",
"path": "python/FileOperations.py",
"copies": "1",
"size": "4102",
"license": "mit",
"hash": -6314599435570171000,
"line_mean": 31.6393442623,
"line_max": 112,
"alpha_frac": 0.5402242808,
"autogenerated": false,
"ratio": 4.077534791252485,
"config_test": f... |
__author__ = 'Jason'
from GraphStatistics import *
from Edge import *
import itertools
import copy
def girvan_newman(given_graph, limit):
graph = copy.deepcopy(given_graph)
ecb = edge_betweeness_centrality_slow(graph) # ecb is a dictionary (edge start, edge end) : centrality
Q = -1
prev_Q... | {
"repo_name": "JasonPap/graphproject",
"path": "python/Girvan_Newman.py",
"copies": "1",
"size": "3555",
"license": "mit",
"hash": -8662023135947273000,
"line_mean": 29.4601769912,
"line_max": 117,
"alpha_frac": 0.5676511955,
"autogenerated": false,
"ratio": 3.562124248496994,
"config_test": fa... |
__author__ = 'Jason'
import itertools
from Graph import *
from GraphStatistics import *
def get_cliques(graph, k, proc_pool):
persons = []
for idx in graph.dictionary:
persons.append(idx)
cliques = []
combinations = itertools.combinations(persons, k)
arg = []
for comb ... | {
"repo_name": "JasonPap/graphproject",
"path": "python/Clique_Percolation_Method.py",
"copies": "1",
"size": "2475",
"license": "mit",
"hash": 5090507665947763000,
"line_mean": 23,
"line_max": 54,
"alpha_frac": 0.5373737374,
"autogenerated": false,
"ratio": 3.7218045112781954,
"config_test": fa... |
__author__ = 'Jason'
import csv
import sys,os,re
with open(r'C:\fsu\ra\data\201708\Copy of Botanical_with_dsld_cat_termlist.csv', 'w+') as output:
with open(r'C:\fsu\ra\data\201708\Copy of Botanical_with_dsld_cat.csv', 'rb') as csvfile:
spamreader = csv.reader(csvfile, delimiter=',', quotechar=... | {
"repo_name": "zwChan/Clinical-Text-Mining",
"path": "py/preprocess_index.py",
"copies": "1",
"size": "1746",
"license": "apache-2.0",
"hash": 8593496552647507000,
"line_mean": 34.375,
"line_max": 97,
"alpha_frac": 0.4450171821,
"autogenerated": false,
"ratio": 3.779220779220779,
"config_test":... |
__author__ = 'Jason'
import urllib2
from urllib2 import urlopen
from bs4 import BeautifulSoup
import re
def visible(element):
if element.parent.name in ['style', 'script', '[document]', 'head', 'title']:
return False
# elif re.match("<!--.*-->", str(element)):
# return False
... | {
"repo_name": "zwChan/Clinical-Text-Mining",
"path": "py/get_ct.py",
"copies": "1",
"size": "1562",
"license": "apache-2.0",
"hash": -458659385224633500,
"line_mean": 33.5,
"line_max": 123,
"alpha_frac": 0.5550576184,
"autogenerated": false,
"ratio": 3.2073921971252566,
"config_test": false,
... |
__author__ = 'Jason Romero'
from pyPdf import PdfFileWriter, PdfFileReader
output = PdfFileWriter()
input1 = PdfFileReader(file("document1.pdf", "rb"))
# print the title of document1.pdf
print "title = %s" % (input1.getDocumentInfo().title)
# add page 1 from input1 to output document, unchanged
output.addPage(input1... | {
"repo_name": "codenamejason/data_from_pdf",
"path": "pyPDF.py",
"copies": "1",
"size": "1268",
"license": "artistic-2.0",
"hash": -3882264183226830300,
"line_mean": 31.5128205128,
"line_max": 69,
"alpha_frac": 0.7492113565,
"autogenerated": false,
"ratio": 2.990566037735849,
"config_test": fal... |
__author__ = 'jasonrudy'
import unittest
import numpy
from choldate import cholupdate, choldowndate
class TestCholdate(unittest.TestCase):
def setUp(self):
numpy.random.seed(1)
self.X = numpy.random.normal(size=(100,10))
def test_update(self):
V = numpy.dot(self.X.transpose... | {
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"path": "choldate/test/test.py",
"copies": "1",
"size": "1127",
"license": "bsd-3-clause",
"hash": 4055990410864702000,
"line_mean": 27.8974358974,
"line_max": 79,
"alpha_frac": 0.5749778172,
"autogenerated": false,
"ratio": 3.0876712328767124,
"config_test": tr... |
__author__ = 'Jason Vanzin forked by kujiy'
import sys #used to get commandline arguments
import subprocess
import os
def update(ipaddress, hostname):
cmd = 'hostile set ' + ipaddress + ' ' + hostname
print(cmd)
os.system(cmd)
def validIP(ipaddress):
""" str -> bool
Found this on http://stackoverflow.com/ques... | {
"repo_name": "kujiy/docker-connect-host-to-guest",
"path": "docker-connect-host-to-guest.py",
"copies": "1",
"size": "3295",
"license": "mit",
"hash": 8252807859000254000,
"line_mean": 29.5092592593,
"line_max": 161,
"alpha_frac": 0.6801213961,
"autogenerated": false,
"ratio": 3.18973862536302,
... |
__author__ = 'Jason Wang'
import numpy as np
def nMathar(wv, P, T, H=10):
"""
Calculate the index of refraction as given by Mathar (2008): http://arxiv.org/pdf/physics/0610256v2.pdf
***Only valid for between 1.3 and 2.5 microns!
Inputs:
wv: wavelength in microns
P: Pressure in Pa
... | {
"repo_name": "semaphoreP/nair",
"path": "nair.py",
"copies": "2",
"size": "9649",
"license": "bsd-3-clause",
"hash": 535032433317127940,
"line_mean": 37.4462151394,
"line_max": 148,
"alpha_frac": 0.5750855011,
"autogenerated": false,
"ratio": 2.412853213303326,
"config_test": false,
"has_no_... |
import random,copy, statistics, timeit, threading, math
from math import *
import numpy as np
import matplotlib.pyplot as plt
import plot as pt
import queue as Queue
print("SIMULATED ANNEALING BASED PLACER")
files = ['cm138a.txt', 'cm150a.txt', 'cm151a.txt', 'cm162a.txt', 'alu2.txt', 'C880.txt',
... | {
"repo_name": "jaspreetj/SA_based_FPGA_placer",
"path": "SAv3.py",
"copies": "1",
"size": "10052",
"license": "mit",
"hash": -4039079754577298000,
"line_mean": 29.3146417445,
"line_max": 100,
"alpha_frac": 0.5021886192,
"autogenerated": false,
"ratio": 3.973122529644269,
"config_test": false,
... |
__author__ = 'jasrub'
import json
"""
Parameter class for defining parameters for a Deep Learning model
"""
class Parameter(object):
def __init__(self, name, value, p_type=None,
p_min=0, p_max=0, step=0, description="",
size_change=False, list_type=None, is_path=False):
se... | {
"repo_name": "artBoffin/GooeyBrain",
"path": "models/parameter.py",
"copies": "1",
"size": "1108",
"license": "apache-2.0",
"hash": 5558352260417176000,
"line_mean": 29.8055555556,
"line_max": 67,
"alpha_frac": 0.523465704,
"autogenerated": false,
"ratio": 3.705685618729097,
"config_test": fal... |
__author__ = 'jasrub'
import json
import os
import signal
import subprocess
import time
from flask import (Flask,jsonify, render_template, request, send_file)
import models_manager
from models.util import log
# webapp
app = Flask(__name__,static_folder='app/static', template_folder='app/templates')
# for debugging... | {
"repo_name": "artBoffin/GooeyBrain",
"path": "main.py",
"copies": "1",
"size": "3182",
"license": "apache-2.0",
"hash": -9044894453814023000,
"line_mean": 29.8932038835,
"line_max": 104,
"alpha_frac": 0.6634192332,
"autogenerated": false,
"ratio": 3.563269876819709,
"config_test": false,
"ha... |
__author__ = 'jasrub'
"""
This file is called from main.py whan "Train" is clicked.
It recieves as arguments a path to parameters json file and a flag weather this is train or not
TODO: add generate button, then call this with "--train False" flag on click
"""
import argparse
import json
from pprint import pformat
... | {
"repo_name": "artBoffin/GooeyBrain",
"path": "run_tf.py",
"copies": "1",
"size": "1240",
"license": "apache-2.0",
"hash": -1738717137634039000,
"line_mean": 27.1818181818,
"line_max": 103,
"alpha_frac": 0.6459677419,
"autogenerated": false,
"ratio": 3.604651162790698,
"config_test": false,
"... |
__author__ = 'jatwood'
import lasagne
import lasagne.layers as layers
import theano
import theano.tensor as T
import numpy as np
import matplotlib.pyplot as plt
import util
# This class is not user facing; it contains the Lasagne internals for the SCNN model.
class SearchConvolution(layers.MergeLayer):
"""
... | {
"repo_name": "jcatw/scnn",
"path": "scnn/scnn.py",
"copies": "1",
"size": "11080",
"license": "mit",
"hash": 2983355005087330300,
"line_mean": 36.9452054795,
"line_max": 142,
"alpha_frac": 0.5825812274,
"autogenerated": false,
"ratio": 3.4239802224969096,
"config_test": false,
"has_no_keywor... |
__author__ = 'jatwood'
import numpy as np
import cPickle as cp
import inspect
import os
current_dir = os.path.dirname(os.path.abspath(inspect.stack()[0][1]))
def parse_cora(plot=False):
path = "%s/data/cora/" % (current_dir,)
id2index = {}
label2index = {
'Case_Based': 0,
'Genetic_Algo... | {
"repo_name": "jcatw/scnn",
"path": "scnn/data.py",
"copies": "1",
"size": "6116",
"license": "mit",
"hash": -697243858439341700,
"line_mean": 26.0619469027,
"line_max": 84,
"alpha_frac": 0.5021255723,
"autogenerated": false,
"ratio": 3.397777777777778,
"config_test": false,
"has_no_keywords"... |
__author__ = 'jatwood'
import numpy as np
import sys
from sklearn.metrics import f1_score, accuracy_score
import util, data
from graph_scnn import GraphSCNN
def graph_experiment(data_fn, name, n_hops, transform_fn=util.rw_laplacian, transform_name='rwl'):
A, X, Y = data_fn()
n_graphs = len(A)
indices ... | {
"repo_name": "jcatw/scnn",
"path": "scnn/graph_experiment.py",
"copies": "1",
"size": "2497",
"license": "mit",
"hash": -8979454289852689000,
"line_mean": 34.6714285714,
"line_max": 113,
"alpha_frac": 0.6063275931,
"autogenerated": false,
"ratio": 2.9940047961630696,
"config_test": false,
"h... |
__author__ = 'jatwood'
import numpy as np
def rw_laplacian(A):
Dm1 = np.zeros(A.shape)
degree = A.sum(0)
for i in range(A.shape[0]):
if degree[i] == 0:
Dm1[i,i] = 0.
else:
Dm1[i,i] = - 1. / degree[i]
return -np.asarray(Dm1.dot(A),dtype='float32')
def laplac... | {
"repo_name": "jcatw/scnn",
"path": "scnn/util.py",
"copies": "1",
"size": "1181",
"license": "mit",
"hash": 4972983377597312000,
"line_mean": 19.0169491525,
"line_max": 51,
"alpha_frac": 0.5190516511,
"autogenerated": false,
"ratio": 2.727482678983834,
"config_test": false,
"has_no_keywords"... |
__author__ = 'jatwood'
import sys
import numpy as np
from sklearn.metrics import f1_score, accuracy_score
from sklearn.linear_model import LogisticRegression
import data
import kernel
def baseline_edge_experiment(model_fn, data_fn, data_name, model_name):
print 'Running edge experiment (%s)...' % (data_name,)
... | {
"repo_name": "jcatw/scnn",
"path": "scnn/baseline_edge_experiment.py",
"copies": "1",
"size": "2403",
"license": "mit",
"hash": 3738545890330624500,
"line_mean": 26.3068181818,
"line_max": 103,
"alpha_frac": 0.6125676238,
"autogenerated": false,
"ratio": 3.12890625,
"config_test": false,
"ha... |
__author__ = 'jatwood'
import sys
import numpy as np
from sklearn.metrics import f1_score, accuracy_score
from sklearn.linear_model import LogisticRegression
import data
import util
import kernel
import structured
def node_proportion_baseline_experiment(model_fn, data_fn, data_name, model_name, prop_valid, prop_tes... | {
"repo_name": "jcatw/scnn",
"path": "scnn/node_proportion_baseline_experiment.py",
"copies": "1",
"size": "6747",
"license": "mit",
"hash": -329463364221982850,
"line_mean": 31.4375,
"line_max": 124,
"alpha_frac": 0.6094560545,
"autogenerated": false,
"ratio": 3.117837338262477,
"config_test": ... |
__author__ = 'jatwood'
import sys
import numpy as np
from sklearn.metrics import f1_score, accuracy_score
from edge_scnn import EdgeSCNN
import data
import util
def edge_experiment(data_fn, name, n_hops, transform_fn=util.rw_laplacian, transform_name='rwl'):
print 'Running edge experiment (%s)...' % (name,)
... | {
"repo_name": "jcatw/scnn",
"path": "scnn/edge_experiment.py",
"copies": "1",
"size": "2490",
"license": "mit",
"hash": -4330952920816972300,
"line_mean": 27.9534883721,
"line_max": 113,
"alpha_frac": 0.6012048193,
"autogenerated": false,
"ratio": 3.0778739184178,
"config_test": false,
"has_n... |
__author__ = 'jatwood'
import sys
import numpy as np
from sklearn.metrics import f1_score, accuracy_score
from scnn import SCNN, DeepSCNN, DeepFeedForwardSCNN
import data
import util
def node_experiment(data_fn, name, n_hops, transform_fn=util.rw_laplacian, transform_name='rwl'):
print 'Running node experiment ... | {
"repo_name": "jcatw/scnn",
"path": "scnn/node_experiment.py",
"copies": "1",
"size": "3295",
"license": "mit",
"hash": -8004710738341169000,
"line_mean": 31.3039215686,
"line_max": 115,
"alpha_frac": 0.6209408194,
"autogenerated": false,
"ratio": 2.9445933869526364,
"config_test": true,
"has... |
__author__ = 'jatwood'
# Always prefer setuptools over distutils
from setuptools import setup, find_packages
# To use a consistent encoding
from codecs import open
from os import path
here = path.abspath(path.dirname(__file__))
setup(
name='scnn',
# Versions should comply with PEP440. For a discussion on ... | {
"repo_name": "jcatw/scnn",
"path": "setup.py",
"copies": "1",
"size": "4846",
"license": "mit",
"hash": 6922065338169591000,
"line_mean": 32.4206896552,
"line_max": 101,
"alpha_frac": 0.6438299629,
"autogenerated": false,
"ratio": 3.9430431244914566,
"config_test": true,
"has_no_keywords": f... |
__author__ = 'Javad Arjmandi'
from random import randint
def player_choice_input():
choice = input("Welcome to The Game! Please Choose: \n1. Rock\n2. Paper\n3. Scissor\n>>>")
if choice == "1":
return "Rock"
elif choice == "2":
return "Paper"
elif choice == "3":
return "Scissor"
... | {
"repo_name": "La-Volpe/RoPaSci",
"path": "RoPaSci.py",
"copies": "1",
"size": "1360",
"license": "mit",
"hash": 4473146328923384300,
"line_mean": 29.2444444444,
"line_max": 94,
"alpha_frac": 0.5316176471,
"autogenerated": false,
"ratio": 3.215130023640662,
"config_test": false,
"has_no_keywo... |
__author__ = 'Javier Domingo Cansino <javierdo1@gmail.com>'
__version__ = '0.0.1'
import ast
import logging
from functools import partial
logging.basicConfig(level=logging.DEBUG,
format='%(levelname)-8s %(asctime)s %(message)s')
logger = logging.getLogger()
DQ = '"'
SQ = "'"
class StringChecker... | {
"repo_name": "txomon/flake8-quotes",
"path": "flake8_quotes.py",
"copies": "1",
"size": "4511",
"license": "mit",
"hash": -4947451979116135000,
"line_mean": 32.4148148148,
"line_max": 79,
"alpha_frac": 0.5455553092,
"autogenerated": false,
"ratio": 3.9294425087108014,
"config_test": false,
"... |
__author__ = 'Javier'
import unittest
from gindex import GIndex, Project, ProjectRepositoryService, ProjectFactory
from gindex_presenter import GIndexPresenter
from gindex_conectors import GithubConnector
from unittest.mock import MagicMock
import httpretty
class TestProject(unittest.TestCase):
def test_init_co... | {
"repo_name": "javierj/kobudo-katas",
"path": "Kata-RestConsumer/testcases.py",
"copies": "1",
"size": "3673",
"license": "apache-2.0",
"hash": -1258656430675613700,
"line_mean": 35.0098039216,
"line_max": 93,
"alpha_frac": 0.6319085216,
"autogenerated": false,
"ratio": 3.5283381364073008,
"con... |
__author__ = 'Javier'
import urllib.request
import json
class Repo(object):
def __init__(self, fork, stars, watchers):
self._fork = int(fork)
self._stars = int(stars)
self._watchers = int(watchers)
@property
def forks(self):
return self._fork
@property
def stars(s... | {
"repo_name": "javierj/kobudo-katas",
"path": "Kata-RestConsumer/DjangoGIndexDemo/gindex/gindex_logic/gindex.py",
"copies": "1",
"size": "1609",
"license": "apache-2.0",
"hash": -7977808212470422000,
"line_mean": 25.3770491803,
"line_max": 90,
"alpha_frac": 0.6059664388,
"autogenerated": false,
"... |
__author__ = 'Javier'
class Project(object):
def __init__(self, forks, stars, watchs):
self._forks = int(forks)
self._stars = int(stars)
self._watchs = int(watchs)
@property
def forks(self):
return self._forks
@property
def stars(self):
return self._stars... | {
"repo_name": "javierj/kobudo-katas",
"path": "Kata-RestConsumer/gindex.py",
"copies": "1",
"size": "1259",
"license": "apache-2.0",
"hash": -5008096293509698000,
"line_mean": 22.7735849057,
"line_max": 65,
"alpha_frac": 0.5909451946,
"autogenerated": false,
"ratio": 3.724852071005917,
"config_... |
__author__ = 'javon'
import itertools
import math
import heapq
def func():
options = []
n = raw_input()
a,b = n.split()
a,b = int(a),int(b)
return (a+b)+(a-b)+(b+a)+(b-a)
evaluations = {"+": lambda num1, num2, ans: (num1 + num2) == ans,
"-": lambda num1, num2, ans: (num1 - num2) ==... | {
"repo_name": "JA-VON/python-helpers-msbm",
"path": "main.py",
"copies": "1",
"size": "24289",
"license": "mit",
"hash": 8788391688941698000,
"line_mean": 31.2576361222,
"line_max": 304,
"alpha_frac": 0.4431224011,
"autogenerated": false,
"ratio": 3.7774494556765164,
"config_test": false,
"ha... |
def ranges(upvotes, N, K):
i = 1
count = 0
helper1 = {}
helper2 = {}
n = N - K + 1
if upvotes[0] < upvotes[1]:
helper1.extend([1, 0])
helper2.extend([1, 0])
elif upvotes[0] > upvotes[1]:
helper1.extend([0, 1])
helper2.extend([0, 1])
else:
hel... | {
"repo_name": "JA-VON/python-helpers-msbm",
"path": "quora_subrange.py",
"copies": "1",
"size": "4950",
"license": "mit",
"hash": -3204781393106124000,
"line_mean": 23.8793969849,
"line_max": 72,
"alpha_frac": 0.4797979798,
"autogenerated": false,
"ratio": 3.435114503816794,
"config_test": fals... |
__author__ = 'javon'
import Queue
class MutableQueue(Queue.Queue):
def change(self, index, new_item):
with self.mutex:
self.queue[index] = new_item
def show(self, index):
with self.mutex:
return self.queue[index]
def ranges_with_queue(upvotes, N, K):
i = 1
... | {
"repo_name": "JA-VON/python-helpers-msbm",
"path": "quora_subranges_simple.py",
"copies": "1",
"size": "2882",
"license": "mit",
"hash": 1175338326680779000,
"line_mean": 26.4571428571,
"line_max": 76,
"alpha_frac": 0.4580152672,
"autogenerated": false,
"ratio": 3.709137709137709,
"config_test... |
__author__ = 'javon'
"""
Recursive power implementation O(log n)
"""
def pow_recur(a, n):
if n == 0:
return 1
if n == 1:
return a
elif n % 2 == 0:
return pow_recur(a, n / 2) * pow_recur(a, n / 2)
else:
return a * pow_recur(a, (n - 1) / 2) * pow_recur(a, (n - 1) / 2)
... | {
"repo_name": "JA-VON/python-helpers-msbm",
"path": "implementations.py",
"copies": "1",
"size": "18945",
"license": "mit",
"hash": 4425060195597715000,
"line_mean": 25.7207334274,
"line_max": 122,
"alpha_frac": 0.4775930325,
"autogenerated": false,
"ratio": 3.4215279031966768,
"config_test": f... |
__author__ = 'jawaad'
import codecs
import logging
from tbar import YuubinBango, unicode_csv_reader
class RedisYuubinBango(YuubinBango):
"""Enables the use of Redis-DB with Japanese Postal Code data."""
def _save(self, pipeline):
"""Uses pipeline / connection sent by default.
You should use... | {
"repo_name": "jmahmood/pytbar",
"path": "pytbar/redis_tbar.py",
"copies": "1",
"size": "1373",
"license": "mit",
"hash": 5380473099949217000,
"line_mean": 31.6904761905,
"line_max": 98,
"alpha_frac": 0.6474872542,
"autogenerated": false,
"ratio": 3.671122994652406,
"config_test": false,
"has... |
__author__ = 'jay7958'
from xml.dom import NamespaceErr
from defusedxml import ElementTree
from datetime import datetime
from dojo.models import Finding
class VeracodeXMLParser(object):
def __init__(self, filename, test):
vscan = ElementTree.parse(filename)
root = vscan.getroot()
if 'ht... | {
"repo_name": "yan99uic/django-DefectDojo",
"path": "dojo/tools/veracode/parser.py",
"copies": "2",
"size": "4974",
"license": "bsd-3-clause",
"hash": -5897367039127475000,
"line_mean": 48.2475247525,
"line_max": 142,
"alpha_frac": 0.4969843185,
"autogenerated": false,
"ratio": 4.07037643207856,
... |
class HashTable():
LOAD_THRESHOLD = 0.3
GOLDEN_VALUE = 0.618034
LOW_CAP = 10
def __init__(self, capacity=-1):
self.size = 0
self.capacity = max(self.LOW_CAP, capacity)
self.table = [None] * self.capacity
def loadFactor(self):
return self.size / self.capacity
def resize(s... | {
"repo_name": "JDNdeveloper/Interview-Practice-Python",
"path": "src/HashTable.py",
"copies": "1",
"size": "1734",
"license": "mit",
"hash": -8238702344313458000,
"line_mean": 23.4225352113,
"line_max": 64,
"alpha_frac": 0.598615917,
"autogenerated": false,
"ratio": 3.4404761904761907,
"config_... |
def quickSort(data):
sortedData = list(data)
_quickSort(sortedData, 0, len(data) - 1)
return sortedData
def _quickSort(data, left, right):
if (right - left) < 1:
return
partitionIndex = _partition(data, left, right)
_quickSort(data, left, partitionIndex - 1)
_quickSort(data, partitionIndex ... | {
"repo_name": "JDNdeveloper/Interview-Practice-Python",
"path": "src/QuickSort.py",
"copies": "1",
"size": "1566",
"license": "mit",
"hash": 3241390101307373600,
"line_mean": 26,
"line_max": 70,
"alpha_frac": 0.6123882503,
"autogenerated": false,
"ratio": 3.157258064516129,
"config_test": false... |
from collections import Counter
class Arc:
def __init__(self):
self.dest = None
self.weight = 1
def __eq__(self, other):
return self.dest == other.dest
def __repr__(self):
return str(self.weight) + "->" + str(self.dest)
def __hash__(self):
return hash(self.dest)
class Gr... | {
"repo_name": "JDNdeveloper/Interview-Practice-Python",
"path": "src/GraphADT.py",
"copies": "1",
"size": "2846",
"license": "mit",
"hash": -3869290566667396600,
"line_mean": 23.9649122807,
"line_max": 66,
"alpha_frac": 0.5270555165,
"autogenerated": false,
"ratio": 3.2525714285714287,
"config_... |
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