text stringlengths 0 1.05M | meta dict |
|---|---|
__author__ = 'mpetyx'
SPECIFIC_VOCABULARY = """
PREFIX rdf:<http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX xsd:<http://www.w3.org/2001/XMLSchema#>
PREFIX dcterms:<http://purl.org/dc/terms/>
PREFIX rdfs:<http://www.w3.org/2000/01/rdf-schema#>
PREFIX owl:<http://www.w3.org/2002/07/owl#>
PREFIX skos:<http://www.w3.o... | {
"repo_name": "LinDA-tools/LindaWorkbench",
"path": "linda/graphdb/queries.py",
"copies": "1",
"size": "2760",
"license": "mit",
"hash": 5725837093971082000,
"line_mean": 33.5125,
"line_max": 106,
"alpha_frac": 0.7231884058,
"autogenerated": false,
"ratio": 2.857142857142857,
"config_test": fal... |
__author__ = 'mpetyx'
#!/usr/bin/env python
#https://github.com/linkeddata/ldpy/blob/master/ld.py
from RDF import NS, Uri, Node, Statement, Storage, Model, SPARQLQuery, Serializer, Parser
class ns(object):
rdf = NS('http://www.w3.org/1999/02/22-rdf-syntax-ns#')
rdfs = NS('http://www.w3.org/2000/01/rdf-schema... | {
"repo_name": "Suite5/DataColibri",
"path": "linkeddata/server/id.py",
"copies": "1",
"size": "7207",
"license": "mit",
"hash": -596799356259184300,
"line_mean": 34.3333333333,
"line_max": 127,
"alpha_frac": 0.525045095,
"autogenerated": false,
"ratio": 3.3074804956402017,
"config_test": false,... |
__author__ = 'mramire8'
__copyright__ = "Copyright 2013, ML Lab"
__version__ = "0.1"
__status__ = "Development"
import sys
import os
sys.path.append(os.path.abspath("."))
sys.path.append(os.path.abspath("../aal_misc/samples/neutral"))
import argparse
import numpy as np
from sklearn import metrics
from sklearn import... | {
"repo_name": "mramire8/active",
"path": "other/neutralmodeling.py",
"copies": "1",
"size": "6496",
"license": "apache-2.0",
"hash": 6422644362712941000,
"line_mean": 35.0944444444,
"line_max": 126,
"alpha_frac": 0.5771243842,
"autogenerated": false,
"ratio": 3.1200768491834774,
"config_test": ... |
__author__ = 'mramire8'
__copyright__ = "Copyright 2013, ML Lab"
__version__ = "0.1"
__status__ = "Development"
import numpy as np
import copy
import random
class BaseLearner(object):
def __init__(self, model=None, cost_model=None, accuracy_model=None, budget=None, seed=1234567, subpool=None):
self.cur... | {
"repo_name": "mramire8/active",
"path": "strategy/baselearner.py",
"copies": "1",
"size": "1612",
"license": "apache-2.0",
"hash": 7678119458460675000,
"line_mean": 29.4150943396,
"line_max": 114,
"alpha_frac": 0.6302729529,
"autogenerated": false,
"ratio": 3.893719806763285,
"config_test": fa... |
__author__ = 'mramire8'
__copyright__ = "Copyright 2013, ML Lab"
__version__ = "0.2"
__status__ = "Development"
import sys
import os
sys.path.append(os.path.abspath("."))
sys.path.append(os.path.abspath("../"))
sys.path.append(os.path.abspath("../experiment/"))
from experiment.experiment_utils import split_data_sen... | {
"repo_name": "mramire8/active",
"path": "other/scoringsentences.py",
"copies": "1",
"size": "33085",
"license": "apache-2.0",
"hash": -2985856849839195600,
"line_mean": 36.1741573034,
"line_max": 188,
"alpha_frac": 0.5984282908,
"autogenerated": false,
"ratio": 3.45679657298088,
"config_test":... |
__author__ = 'mramire8'
__copyright__ = "Copyright 2014, ML Lab"
__version__ = "0.1"
__status__ = "Research"
import sys
import os
sys.path.append(os.path.abspath("."))
sys.path.append(os.path.abspath("../"))
sys.path.append(os.path.abspath("../experiment/"))
from experiment.experiment_utils import *
from datautil.lo... | {
"repo_name": "mramire8/active",
"path": "sentences/sent_rnd2.py",
"copies": "1",
"size": "23728",
"license": "apache-2.0",
"hash": -1766486026976623000,
"line_mean": 36.4258675079,
"line_max": 159,
"alpha_frac": 0.5777562374,
"autogenerated": false,
"ratio": 3.6781894279956595,
"config_test": ... |
__author__ = 'mramire8'
import numpy as np
def accuracy_coin(target, p, rnd):
i = rnd.random_sample()
if i < p:
return target
else:
return 1 - target
class BaseAccuracyModel(object):
def __init__(self, seed=1234567):
self.randgen = np.random.RandomState()
self.randge... | {
"repo_name": "mramire8/active",
"path": "strategy/base_models.py",
"copies": "1",
"size": "10827",
"license": "apache-2.0",
"hash": 5426257005642743000,
"line_mean": 33.0471698113,
"line_max": 118,
"alpha_frac": 0.5630368523,
"autogenerated": false,
"ratio": 3.9601316752011706,
"config_test": ... |
__author__ = 'mramire8'
import sys
import os
sys.path.append(os.path.abspath("."))
import argparse
from sklearn.naive_bayes import MultinomialNB
from datautil.textutils import StemTokenizer
from datautil.load_data import *
import ast
ap = argparse.ArgumentParser(description=__doc__,
for... | {
"repo_name": "mramire8/active",
"path": "other/important.py",
"copies": "1",
"size": "3555",
"license": "apache-2.0",
"hash": -8655789874278006000,
"line_mean": 31.027027027,
"line_max": 135,
"alpha_frac": 0.5918424754,
"autogenerated": false,
"ratio": 3.174107142857143,
"config_test": false,
... |
__author__ = 'mramire8'
from collections import defaultdict
import copy
import numpy as np
from sklearn.linear_model import LogisticRegression
from baselearner import BaseLearner
from sklearn.feature_extraction.text import CountVectorizer
from datautil.textutils import StemTokenizer
show_utilitly = False
class Ra... | {
"repo_name": "mramire8/active",
"path": "strategy/randomsampling.py",
"copies": "1",
"size": "21847",
"license": "apache-2.0",
"hash": -7264529368545010000,
"line_mean": 36.863084922,
"line_max": 172,
"alpha_frac": 0.5888222639,
"autogenerated": false,
"ratio": 3.5627853881278537,
"config_test... |
__author__ = 'mramire8'
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import train_test_split, StratifiedKFold, KFold
from sklearn.grid_search import GridSearchCV
from sklearn.base import clone
class LogisticRegressionAdaptive(LogisticRegression):
def __init__(self, penalty='l... | {
"repo_name": "mramire8/active",
"path": "learner/adaptive_lr.py",
"copies": "1",
"size": "5792",
"license": "apache-2.0",
"hash": -6720676041788742000,
"line_mean": 34.103030303,
"line_max": 108,
"alpha_frac": 0.6132596685,
"autogenerated": false,
"ratio": 3.4211458948611932,
"config_test": fa... |
__author__ = 'mramire8'
import nltk
from scipy.sparse import diags
from numpy.random import RandomState
from randomsampling import *
import random
class AALStructuredFixk(AnytimeLearner):
def __init__(self, model=None, accuracy_model=None, budget=None, seed=None, vcn=None, subpool=None,
cost_mod... | {
"repo_name": "mramire8/active",
"path": "strategy/structured.py",
"copies": "1",
"size": "47843",
"license": "apache-2.0",
"hash": 4439852097751341000,
"line_mean": 40.7478184991,
"line_max": 133,
"alpha_frac": 0.5649311289,
"autogenerated": false,
"ratio": 3.7775759968416898,
"config_test": f... |
__author__ = 'mramire8'
import numpy as np
import sys
import os
sys.path.append(os.path.abspath('../strategy'))
from strategy.base_models import *
from sklearn.linear_model import LogisticRegression
class BaseExpert(object):
def __init__(self, model=None, seed=1234567):
self.expert = model
self.... | {
"repo_name": "mramire8/active",
"path": "expert/baseexpert.py",
"copies": "1",
"size": "8692",
"license": "apache-2.0",
"hash": 7235761762871049000,
"line_mean": 35.5210084034,
"line_max": 133,
"alpha_frac": 0.6074551312,
"autogenerated": false,
"ratio": 3.8273888154997797,
"config_test": fals... |
__author__ = "mramire8"
import os, sys
sys.path.append(os.path.abspath("."))
sys.path.append(os.path.abspath("../"))
from sklearn import metrics
import utilities.experimentutils as exputil
import utilities.datautils as datautil
import utilities.configutils as cfgutil
from sklearn import cross_validation
import numpy... | {
"repo_name": "mramire8/structured",
"path": "experiment/base.py",
"copies": "1",
"size": "17811",
"license": "apache-2.0",
"hash": 8834724219444173000,
"line_mean": 36.4968421053,
"line_max": 133,
"alpha_frac": 0.5685250688,
"autogenerated": false,
"ratio": 3.6468058968058967,
"config_test": t... |
__author__ = 'mramire8'
import Tkinter as tk
import time
class App():
def __init__(self):
self.root = tk.Tk()
self.label = tk.Label(text="")
self.label.pack()
self.update_clock()
self.root.mainloop()
def update_clock(self):
now = time.strftime("%H:%M:%S")
... | {
"repo_name": "mramire8/active",
"path": "gui/guitest.py",
"copies": "1",
"size": "1073",
"license": "apache-2.0",
"hash": -4733844226173008000,
"line_mean": 23.4090909091,
"line_max": 60,
"alpha_frac": 0.5526561044,
"autogenerated": false,
"ratio": 3.625,
"config_test": false,
"has_no_keywor... |
__author__ = 'mramire8'
def unit_cost(query, cost_model=None):
n = 1
if isinstance(query, dict):
n = len(query.snippet)
else:
n = len(query)
return [1.] * n
def intra_cost(query, cost_model=None):
if cost_model is None:
raise ValueError("Cost model is not available.")
... | {
"repo_name": "mramire8/structured",
"path": "utilities/costutils.py",
"copies": "1",
"size": "1140",
"license": "apache-2.0",
"hash": 8676457512245956000,
"line_mean": 20.5094339623,
"line_max": 77,
"alpha_frac": 0.5412280702,
"autogenerated": false,
"ratio": 2.7941176470588234,
"config_test":... |
__author__ = 'mramire8'
import sys, os
sys.path.append(os.path.abspath("."))
import argparse
from sklearn.feature_extraction.text import CountVectorizer
from bunch import Bunch
from datautil.textutils import StemTokenizer
from datautil.load_data import *
from strategy import randomsampling
import codecs
import random... | {
"repo_name": "mramire8/active",
"path": "datautil/userstudy.py",
"copies": "1",
"size": "3986",
"license": "apache-2.0",
"hash": 5161713752565085000,
"line_mean": 38.0784313725,
"line_max": 156,
"alpha_frac": 0.5960863021,
"autogenerated": false,
"ratio": 3.5149911816578485,
"config_test": fal... |
__author__ = 'mramire8'
"""
ZetCode Tkinter tutorial
http://zetcode.com/gui/tkinter/
In this script, we show how to
use the Listbox widget.
author: Jan Bodar
last modified: December 2010
website: www.zetcode.com
"""
from ttk import Frame, Label, Style
from Tkinter import Tk, BOTH, Listbox, StringVar, END
class Exam... | {
"repo_name": "mramire8/active",
"path": "gui/guitest3.py",
"copies": "1",
"size": "1261",
"license": "apache-2.0",
"hash": -1302347219901735200,
"line_mean": 18.71875,
"line_max": 63,
"alpha_frac": 0.5908009516,
"autogenerated": false,
"ratio": 3.2583979328165373,
"config_test": false,
"has_... |
__author__ = 'mramire8'
"""
ZetCode Tkinter tutorial
In this program, we use the
tkFileDialog to select a file from
a filesystem.
author: Jan Bodar
last modified: January 2011
website: www.zetcode.com
"""
from Tkinter import Frame, Tk, BOTH, Text, Menu, END
import tkFileDialog
class Example(Frame):
def __init... | {
"repo_name": "mramire8/active",
"path": "gui/guitest2.py",
"copies": "1",
"size": "1369",
"license": "apache-2.0",
"hash": -5343361164987633000,
"line_mean": 19.1470588235,
"line_max": 63,
"alpha_frac": 0.5865595325,
"autogenerated": false,
"ratio": 3.4658227848101264,
"config_test": false,
... |
__author__ = "Mr.doob, Kikko"
__url__ = ['http://mrdoob.com', 'http://github.com/kikko']
__version__ = "1"
__bpydoc__ = """\
This script exports the selected object for the three.js engine.
"""
import bpy
def rvec3d(v):
return round(v[0], 6), round(v[1], 6), round(v[2], 6)
def rvec2d(v):
return round(v[0], 6... | {
"repo_name": "flyingoctopus/three.js",
"path": "utils/exporters/blender/export_threejs_2.53b.py",
"copies": "1",
"size": "7404",
"license": "mit",
"hash": -459873962509379100,
"line_mean": 33.9245283019,
"line_max": 218,
"alpha_frac": 0.5823878984,
"autogenerated": false,
"ratio": 2.995145631067... |
__author__ = 'mrichardson, Hagen Telg'
from math import fmod, sin, cos, asin
import ephem
import numpy as np
import pandas as pd
from atmPy.atmos.constants import a2r, r2a
__julian = {"day": 0., "cent": 0.}
class solar(object):
def __init__(self, ltime):
julian = solar.juliandates(l... | {
"repo_name": "msrconsulting/atm-py",
"path": "atmPy/radiation/solar.py",
"copies": "6",
"size": "4605",
"license": "mit",
"hash": 2767169806084939000,
"line_mean": 29.9791666667,
"line_max": 106,
"alpha_frac": 0.586102063,
"autogenerated": false,
"ratio": 3.376099706744868,
"config_test": fals... |
__author__ = 'mriegel'
from gge_proxy_manager.models import UnitList, UnitListRelation, Unit
from lib.core import DATA_IMPORT_LOCK_TIME
from lib.cache import cache
import logging
logger = logging.getLogger(__name__)
def import_unit_list(list, game, title="", unit_list=None):
if unit_list:
key = "-".join(... | {
"repo_name": "mrcrgl/gge-storage",
"path": "lib/core/api2object/unit.py",
"copies": "1",
"size": "1155",
"license": "mit",
"hash": -450512731711734340,
"line_mean": 32.0285714286,
"line_max": 94,
"alpha_frac": 0.6034632035,
"autogenerated": false,
"ratio": 3.701923076923077,
"config_test": fal... |
__author__ = 'mriegel'
from datetime import datetime
import urllib2
from pprint import pprint
from app.helper.string import clear_quotation_marks
from app.helper.integer import clear_float, clear_number
from app.helper.date import string_to_datetime
import re
import urllib
from datetime import datetime
REMOTE_URLS =... | {
"repo_name": "mrcrgl/stockstore",
"path": "app/remote/stocks.py",
"copies": "1",
"size": "7400",
"license": "mit",
"hash": -8077110803955787000,
"line_mean": 27.0340909091,
"line_max": 183,
"alpha_frac": 0.5404054054,
"autogenerated": false,
"ratio": 3.3035714285714284,
"config_test": false,
... |
__author__ = 'mriegel'
from django.contrib import admin
from models import *
class CurrencyAdmin(admin.ModelAdmin):
list_display = ('name', 'sign', 'base_rate', )
pass
class CurrencyExchangeRateAdmin(admin.ModelAdmin):
list_display = ('date', 'rate', )
class CompanyAdmin(admin.ModelAdmin):
list_d... | {
"repo_name": "mrcrgl/stockstore",
"path": "app/admin.py",
"copies": "1",
"size": "1640",
"license": "mit",
"hash": -4318550882803070500,
"line_mean": 24.2461538462,
"line_max": 69,
"alpha_frac": 0.6902439024,
"autogenerated": false,
"ratio": 3.4745762711864407,
"config_test": false,
"has_no_... |
__author__ = 'mriegel'
from django.db import models
from datetime import datetime
from django_countries import CountryField
class Currency(models.Model):
name = models.CharField(max_length=60, verbose_name="Name")
sign = models.CharField(max_length=5, verbose_name="Sign")
symbol = models.CharField(max_le... | {
"repo_name": "mrcrgl/stockstore",
"path": "app/models.py",
"copies": "1",
"size": "4846",
"license": "mit",
"hash": 7507505403251735000,
"line_mean": 39.731092437,
"line_max": 112,
"alpha_frac": 0.6927362773,
"autogenerated": false,
"ratio": 3.600297176820208,
"config_test": false,
"has_no_k... |
__author__ = 'mriegel'
from django import template
register = template.Library()
def unbound(form_field_obj):
if form_field_obj.__class__.__name__ == "BoundField":
return form_field_obj.field
return form_field_obj
@register.filter
def is_checkbox_multi_field(form_field_obj):
return unbound(form... | {
"repo_name": "mrcrgl/gge-storage",
"path": "templated_forms/templatetags/bs3_forms_filters.py",
"copies": "1",
"size": "1642",
"license": "mit",
"hash": 3733172312162353700,
"line_mean": 24.2769230769,
"line_max": 81,
"alpha_frac": 0.6881851401,
"autogenerated": false,
"ratio": 3.371663244353183... |
__author__ = 'mriegel'
from django.shortcuts import render_to_response, RequestContext, HttpResponseRedirect, Http404, HttpResponse
from django.contrib.auth.decorators import login_required
from django.core.urlresolvers import reverse
from app.helper.string import is_isin, is_wkn
from pprint import pprint
@login_req... | {
"repo_name": "mrcrgl/stockstore",
"path": "app/views.py",
"copies": "1",
"size": "5211",
"license": "mit",
"hash": 8039129480323479000,
"line_mean": 26.7180851064,
"line_max": 108,
"alpha_frac": 0.5962387258,
"autogenerated": false,
"ratio": 4.0552529182879375,
"config_test": false,
"has_no_... |
__author__ = 'mriegel'
from gge_proxy_manager.models import Alliance
from lib.cache import cache
from lib.core import DATA_IMPORT_LOCK_TIME
import logging
logger = logging.getLogger(__name__)
def import_alliance(oi, kingdom):
response = import_alliance_(oi, kingdom)
logger.info("Import alliance AID=%r respo... | {
"repo_name": "mrcrgl/gge-storage",
"path": "lib/core/api2object/alliance.py",
"copies": "1",
"size": "1835",
"license": "mit",
"hash": -2444128635900033000,
"line_mean": 37.25,
"line_max": 122,
"alpha_frac": 0.5427792916,
"autogenerated": false,
"ratio": 3.488593155893536,
"config_test": false... |
__author__ = 'mriegel'
from gge_proxy_manager.models import Player, Castle, CastleEconomy, UnitList
from lib.cache import cache
from .unit import import_unit_list
from lib.core import DATA_IMPORT_LOCK_TIME
import logging
logger = logging.getLogger(__name__)
def import_castles(ais, kingdom):
return [import_castle... | {
"repo_name": "mrcrgl/gge-storage",
"path": "lib/core/api2object/castle.py",
"copies": "1",
"size": "3849",
"license": "mit",
"hash": 5656570705764611000,
"line_mean": 26.690647482,
"line_max": 101,
"alpha_frac": 0.5674201091,
"autogenerated": false,
"ratio": 2.9584934665641813,
"config_test": ... |
__author__ = 'mriegel'
from gge_proxy_manager.models import Player, PlayerEconomy, Kingdom
from gge_proxy_manager.methods import clean_duplicate_players
from lib.cache import cache
from .alliance import import_alliance
from lib.core import DATA_IMPORT_LOCK_TIME
from django.db import IntegrityError, transaction
from dj... | {
"repo_name": "mrcrgl/gge-storage",
"path": "lib/core/api2object/player.py",
"copies": "1",
"size": "3302",
"license": "mit",
"hash": -7409026172246187000,
"line_mean": 29.8691588785,
"line_max": 100,
"alpha_frac": 0.5820714718,
"autogenerated": false,
"ratio": 3.2372549019607844,
"config_test"... |
import sys
import os.path
import encrypt
import decrypt
def main():
print("---------------------------------------------------")
print("Music Encrypt - Music Based Encryption")
print("Author: Mrinal Pande")
print("---------------------------------------------------")
if len(sys.argv) < 2:
... | {
"repo_name": "mrinalpande/Music_Encrypt",
"path": "main.py",
"copies": "1",
"size": "1949",
"license": "mit",
"hash": 641994562622039600,
"line_mean": 37.2352941176,
"line_max": 79,
"alpha_frac": 0.4730631093,
"autogenerated": false,
"ratio": 3.898,
"config_test": false,
"has_no_keywords": f... |
__author__ = 'mrr'
__version__ = '2.0'
import numpy as np; import time; import copy
from .cocoeval import COCOeval
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
plt.rcParams['xtick.labelsize'] = 16
plt.rcParams['ytick.labelsize'] = 16
plt.rcParams['font.family'] = 'monospace'
from colour i... | {
"repo_name": "matteorr/coco-analyze",
"path": "pycocotools/cocoanalyze.py",
"copies": "1",
"size": "47555",
"license": "mit",
"hash": -7841927442377336000,
"line_mean": 48.639874739,
"line_max": 148,
"alpha_frac": 0.5138260961,
"autogenerated": false,
"ratio": 3.586079481185431,
"config_test":... |
__author__ = 'mrr'
__version__ = '2.0'
import numpy as np
import datetime
import time
from collections import defaultdict
from scipy.optimize import linear_sum_assignment
from . import mask as maskUtils
import copy
class COCOeval:
# Interface for evaluating detection on the Microsoft COCO dataset.
#
# Th... | {
"repo_name": "matteorr/coco-analyze",
"path": "pycocotools/cocoeval.py",
"copies": "1",
"size": "29775",
"license": "mit",
"hash": 4728772422877512000,
"line_mean": 45.6692789969,
"line_max": 124,
"alpha_frac": 0.514256927,
"autogenerated": false,
"ratio": 3.4893941169576936,
"config_test": fa... |
__author__ = 'MrTrustworthy'
from enum import Enum
from math import pow, sqrt
from typing import List, Union
class Gender(Enum):
MALE = 0
FEMALE = 1
class Position:
"""
Position is a simple helper object for 2d-positions with an x- and y-value
"""
def __init__(self, *args: List[Union[int, ... | {
"repo_name": "MrTrustworthy/game_of_life",
"path": "gol/x_utils.py",
"copies": "1",
"size": "2071",
"license": "mit",
"hash": -1573268311661099800,
"line_mean": 27.3698630137,
"line_max": 110,
"alpha_frac": 0.5562530179,
"autogenerated": false,
"ratio": 3.9750479846449136,
"config_test": false... |
__author__ = 'MrTrustworthy'
from field import Field
class Sudoku:
"""
Represents a Sudoku with rows, cols and boxes
"""
# defines the filer-functions to determine the boxes
# box order is left->right then top->bottom
box_definition = [
lambda fld: 0 <= fld.x < 3 and 0 <= fld.y < 3,
... | {
"repo_name": "MrTrustworthy/solvr",
"path": "sudoku.py",
"copies": "1",
"size": "4327",
"license": "mit",
"hash": -3682082729855632400,
"line_mean": 30.5839416058,
"line_max": 114,
"alpha_frac": 0.5528079501,
"autogenerated": false,
"ratio": 3.7658833768494344,
"config_test": false,
"has_no_... |
__author__ = 'MrTrustworthy'
from gol.grid import Field, Grid
import unittest
from gol.gameobject import GameObject
from gol.x_utils import Position
class TestGrid(unittest.TestCase):
def test_field(self):
g = GameObject((1, 2))
f = Field(Position(1, 2), True, 2, [])
self.assertTrue(f.pa... | {
"repo_name": "MrTrustworthy/game_of_life",
"path": "tests/test_grid.py",
"copies": "1",
"size": "6367",
"license": "mit",
"hash": 8671413965834448000,
"line_mean": 51.1885245902,
"line_max": 118,
"alpha_frac": 0.6404900267,
"autogenerated": false,
"ratio": 3.3957333333333333,
"config_test": tr... |
__author__ = 'MrTrustworthy'
from gol.x_utils import Position
from typing import List, Union
class Field:
def __init__(self, position: Position, passable: bool, passing_cost: Union[int, None], objects):
self.passable = passable
self.passing_cost = passing_cost if passable else None
if no... | {
"repo_name": "MrTrustworthy/game_of_life",
"path": "gol/grid.py",
"copies": "1",
"size": "3674",
"license": "mit",
"hash": 1281650538361422300,
"line_mean": 31.5132743363,
"line_max": 100,
"alpha_frac": 0.5337506805,
"autogenerated": false,
"ratio": 4.170261066969353,
"config_test": false,
"... |
__author__ = 'MrTrustworthy'
import inspect
class Antenna:
def __init__(self):
self.listeners = {}
def add_listener(self, channel, callback):
if len(inspect.signature(callback).parameters) == 0:
raise TypeError("Callback Function needs at least 1 parameter")
if channel ... | {
"repo_name": "MrTrustworthy/game_of_life",
"path": "gol/mtAntenna.py",
"copies": "1",
"size": "1086",
"license": "mit",
"hash": -2694550715857001000,
"line_mean": 29.1666666667,
"line_max": 75,
"alpha_frac": 0.5976058932,
"autogenerated": false,
"ratio": 4.487603305785124,
"config_test": false... |
__author__ = 'MrTrustworthy'
import pdb
class Solver:
"""
The solver takes a sudoku and tries to solve it
"""
def __init__(self, sudoku):
"""
Basic constructor
:param sudoku: a "Sudoku" Object
:return: self
"""
self.sudoku = sudoku
def solve(self)... | {
"repo_name": "MrTrustworthy/solvr",
"path": "solver.py",
"copies": "1",
"size": "6533",
"license": "mit",
"hash": -7648774531242666000,
"line_mean": 32.3367346939,
"line_max": 114,
"alpha_frac": 0.5591611817,
"autogenerated": false,
"ratio": 4.361148197596796,
"config_test": false,
"has_no_k... |
__author__ = 'MrTrustworthy'
import unittest
from gol.person import Person
from gol.stat import Stat
from gol.x_utils import Position, Gender
import uuid
class TestPerson(unittest.TestCase):
def setUp(self):
self.person = Person((1, 3), 2, Gender.MALE, 1)
def test_init(self):
with self.ass... | {
"repo_name": "MrTrustworthy/game_of_life",
"path": "tests/test_person.py",
"copies": "1",
"size": "2000",
"license": "mit",
"hash": -7748034996955142000,
"line_mean": 43.4444444444,
"line_max": 113,
"alpha_frac": 0.6955,
"autogenerated": false,
"ratio": 3.629764065335753,
"config_test": false,... |
__author__ = 'MrTrustworthy'
class Field:
"""
Represents a Field inside the Sudoku. A Field has:
Value [Integer]
X and Y Positions [Integer]
possibles [Array] containing the possible values for a field
solved [Boolean] Indicating whether it already has a fixed value
"""
def __init... | {
"repo_name": "MrTrustworthy/solvr",
"path": "field.py",
"copies": "1",
"size": "1718",
"license": "mit",
"hash": 7912431467660162000,
"line_mean": 28.1186440678,
"line_max": 105,
"alpha_frac": 0.5337601863,
"autogenerated": false,
"ratio": 3.9403669724770642,
"config_test": false,
"has_no_ke... |
__author__ = 'mruskov'
from estimator_interface import estimator_interface
from numpy import genfromtxt
from sklearn import tree
import StringIO
import cPickle
class estimator_sklearn_tree(estimator_interface):
def __init__(self):
# columns in train_data and test CSV file: plen, psets, pdict, phist, pren... | {
"repo_name": "mapto/sprks",
"path": "sim/estimator_sklearn_tree.py",
"copies": "1",
"size": "6466",
"license": "mit",
"hash": 5604162966741461000,
"line_mean": 42.1133333333,
"line_max": 139,
"alpha_frac": 0.60392824,
"autogenerated": false,
"ratio": 3.694857142857143,
"config_test": true,
"... |
__author__ = 'mruskov'
from localsys.storage import db
from models.policies import policies_model
from models.incident import incident
from sim.model_sklearn import classifier_sklearn
from sim.model_sklearn import regression_sklearn
from models.company import company
class simulation:
ordered_context = ['employee... | {
"repo_name": "mapto/sprks",
"path": "models/simulation.py",
"copies": "1",
"size": "8168",
"license": "mit",
"hash": 2943068499493576000,
"line_mean": 43.3967391304,
"line_max": 136,
"alpha_frac": 0.5963516161,
"autogenerated": false,
"ratio": 4.188717948717949,
"config_test": false,
"has_no... |
__author__ = 'mruskov'
from sklearn import svm
from sklearn import __version__ as sklearn_ver
import cPickle
import numpy
import glob
import csv
import json
import os
from localsys import storage
from models.company import company
from models.policies import policies_model as policy_model
from sim.model_sklearn import... | {
"repo_name": "mapto/sprks",
"path": "sim/train_sklearn.py",
"copies": "1",
"size": "12099",
"license": "mit",
"hash": 3892382578152172000,
"line_mean": 36.9278996865,
"line_max": 119,
"alpha_frac": 0.5919497479,
"autogenerated": false,
"ratio": 4.3900580551523944,
"config_test": false,
"has_... |
__author__ = 'mruskov'
import web
import json
from localsys.environment import context
from models.incident import incident as model
from models.policies import policies_model
from models.simulation import simulation as sim_model
class incident_rest:
def GET(self, id=0):
"""
If given ID, returns... | {
"repo_name": "mapto/sprks",
"path": "controllers/incident.py",
"copies": "1",
"size": "1309",
"license": "mit",
"hash": 7503785933005297000,
"line_mean": 36.4,
"line_max": 98,
"alpha_frac": 0.6424751719,
"autogenerated": false,
"ratio": 4.3344370860927155,
"config_test": false,
"has_no_keywo... |
# Import CSV controls
import csv
# Uses 'deque' objects to handle first-in last out type queues
from collections import deque
# Uses randomisation controls
from random import shuffle
attVal = 1
attDesc = 0
gameDeck = []
# Declare the variable 'deck' as an empty Set
deck = []
cardAtts = 0
# file name of where to find... | {
"repo_name": "the-mandeville-school/computerClub",
"path": "super_heros.py",
"copies": "1",
"size": "5299",
"license": "unlicense",
"hash": 8094151560472618000,
"line_mean": 35.7986111111,
"line_max": 160,
"alpha_frac": 0.6193621438,
"autogenerated": false,
"ratio": 2.9786396852164136,
"config... |
__author__ = 'mrx'
import utils.pyutils as ut
import operator
#neuron interface
def neuron_create(name, model_coeffs, features_order):
model_coeffs_list = ut.dict_to_list(model_coeffs, features_order)
return name, map(lambda x: float(x), model_coeffs_list)
def neuron_name(neuron):
return neuron[0]
... | {
"repo_name": "sayon/ignoreme",
"path": "model.py",
"copies": "1",
"size": "1704",
"license": "mit",
"hash": -20410768582782304,
"line_mean": 28.3793103448,
"line_max": 99,
"alpha_frac": 0.6531690141,
"autogenerated": false,
"ratio": 3.51340206185567,
"config_test": false,
"has_no_keywords": ... |
__author__ = 'msei'
import serial
import random
import time
LF = serial.to_bytes([10])
CR = serial.to_bytes([13])
CRLF = serial.to_bytes([13, 10])
LENGTH = 4 * 60 * 3
TERM = 13
class LEDCommunication:
def __init__(self, port, baudrate):
self.port = serial.Serial(port=port, baudrate=baudrate, timeout=0.... | {
"repo_name": "ma-ver-ick/pyaphrodite",
"path": "led.py",
"copies": "1",
"size": "2080",
"license": "mit",
"hash": 2484243760541419000,
"line_mean": 18.0917431193,
"line_max": 76,
"alpha_frac": 0.5283653846,
"autogenerated": false,
"ratio": 3.1137724550898205,
"config_test": false,
"has_no_ke... |
__author__ = 'msei'
import time
import math
class Point:
def __init__(self, x, y, z):
self.x = x
self.y = y
self.z = z
def __sub__(self, other):
return Point(self.x - other.x, self.y - other.y, self.z - other.z)
def __add__(self, other):
return Point(self.x + oth... | {
"repo_name": "ma-ver-ick/pyaphrodite",
"path": "animation.py",
"copies": "1",
"size": "7054",
"license": "mit",
"hash": -7706735004581584000,
"line_mean": 25.2230483271,
"line_max": 117,
"alpha_frac": 0.5620924298,
"autogenerated": false,
"ratio": 3.687401986408782,
"config_test": false,
"ha... |
__author__ = 'msergeyx'
from fixture.application import Application
import pytest
import json
import importlib
import jsonpickle
import os.path
from fixture.db import DbFixture
fixture = None
target = None
def load_config(file):
global target
if target is None:
config_file = os.path.join(os.path.dir... | {
"repo_name": "msergeyx/python_training",
"path": "conftest.py",
"copies": "1",
"size": "2475",
"license": "apache-2.0",
"hash": -8297996971041053000,
"line_mean": 29.1829268293,
"line_max": 100,
"alpha_frac": 0.6715151515,
"autogenerated": false,
"ratio": 3.7050898203592815,
"config_test": tru... |
__author__ = 'msergeyx'
from model.contact import Contact
from selenium.webdriver.support.ui import Select
import re
class ContactHelper:
def __init__(self, app):
self.app = app
def return_to_homepage(self):
wd = self.app.wd
wd.find_element_by_link_text("home page").click()
def g... | {
"repo_name": "msergeyx/python_training",
"path": "fixture/contact.py",
"copies": "1",
"size": "10147",
"license": "apache-2.0",
"hash": -175313140686755600,
"line_mean": 43.5043859649,
"line_max": 137,
"alpha_frac": 0.6143687789,
"autogenerated": false,
"ratio": 3.4061765693185633,
"config_tes... |
__author__ = 'msergeyx'
from model.contact import Contact
import random
def test_full_modify_some_contact(app, db, check_ui):
if len(db.get_cont_list()) == 0:
app.contact.create(Contact(firstname="hghjdsfni4389"))
old_contacts = db.get_cont_list()
index = random.choice(old_contacts)
contact = ... | {
"repo_name": "msergeyx/python_training",
"path": "test/test_modify_contact.py",
"copies": "1",
"size": "2276",
"license": "apache-2.0",
"hash": -5983420327025120000,
"line_mean": 51.9302325581,
"line_max": 132,
"alpha_frac": 0.5008787346,
"autogenerated": false,
"ratio": 4.021201413427562,
"co... |
__author__ = 'msergeyx'
from model.group import Group
import random
def test_modify_some_group(app, db, check_ui):
if len(db.get_group_list()) == 0:
app.group.create(Group(gr_name="ghj", gr_header="gnkl", gr_footer="jhb"))
old_groups = db.get_group_list()
index = random.choice(old_groups)
grou... | {
"repo_name": "msergeyx/python_training",
"path": "test/test_modify_group.py",
"copies": "1",
"size": "1125",
"license": "apache-2.0",
"hash": 4832891583265652000,
"line_mean": 39.2142857143,
"line_max": 117,
"alpha_frac": 0.6506666667,
"autogenerated": false,
"ratio": 2.8553299492385786,
"conf... |
__author__ = 'msergeyx'
from model.group import Group
class GroupHelper:
def __init__(self, app):
self.app = app
def return_to_group_page(self):
wd = self.app.wd
wd.find_element_by_link_text("group page").click()
def open_group_page(self):
wd = self.app.wd
if wd.c... | {
"repo_name": "msergeyx/python_training",
"path": "fixture/group.py",
"copies": "1",
"size": "3905",
"license": "apache-2.0",
"hash": -4742568334723606000,
"line_mean": 31.5416666667,
"line_max": 94,
"alpha_frac": 0.5825864277,
"autogenerated": false,
"ratio": 3.410480349344978,
"config_test": ... |
__author__ = 'msergeyx'
from sys import maxsize
class Contact:
def __init__(self, firstname=None, middlename=None, lastname=None, nick=None, tytle=None, company=None, address=None, home_tel=None, mobile_tel=None, work_tel=None,
fax=None, birth_day=None, birth_month=None, birth_year=None, second_a... | {
"repo_name": "msergeyx/python_training",
"path": "model/contact.py",
"copies": "1",
"size": "1553",
"license": "apache-2.0",
"hash": 5406571084877045000,
"line_mean": 38.8205128205,
"line_max": 180,
"alpha_frac": 0.6188023181,
"autogenerated": false,
"ratio": 3.435840707964602,
"config_test": ... |
__author__ = 'msergeyx'
import mysql.connector
from model.group import Group
from model.contact import Contact
class DbFixture:
def __init__(self, host, name, user, password):
self.host = host
self.name = name
self.user = user
self.password = password
self.connection = mys... | {
"repo_name": "msergeyx/python_training",
"path": "fixture/db.py",
"copies": "1",
"size": "1618",
"license": "apache-2.0",
"hash": 106487683928804370,
"line_mean": 36.6279069767,
"line_max": 160,
"alpha_frac": 0.6001236094,
"autogenerated": false,
"ratio": 4.0148883374689825,
"config_test": fal... |
__author__ = 'msergeyx'
import re
from random import randrange
from model.contact import Contact
def test_compare_contact_on_home_page(app):
all_contacts = app.contact.get_cont_list()
index = randrange(len(all_contacts))
cont_from_home_page = app.contact.get_contact_info_from_home_page(index)
cont_fro... | {
"repo_name": "msergeyx/python_training",
"path": "test/test_compare_contact.py",
"copies": "1",
"size": "2538",
"license": "apache-2.0",
"hash": 5883492409782247000,
"line_mean": 54.1956521739,
"line_max": 128,
"alpha_frac": 0.5776201734,
"autogenerated": false,
"ratio": 3.683599419448476,
"co... |
__author__ = 'msergeyx'
class SessionHelper:
def __init__(self, app):
self.app = app
def login(self, username, password):
wd = self.app.wd
self.app.open_homepage()
wd.find_element_by_name("user").click()
wd.find_element_by_name("user").click()
wd.find_element_b... | {
"repo_name": "msergeyx/python_training",
"path": "fixture/session.py",
"copies": "1",
"size": "1401",
"license": "apache-2.0",
"hash": -9162125492706835000,
"line_mean": 30.1333333333,
"line_max": 73,
"alpha_frac": 0.5731620271,
"autogenerated": false,
"ratio": 3.3759036144578314,
"config_test... |
__author__ = 'msimonin'
import unittest
import uuid as stdlib_uuid
from nova.db.discovery import context
from nova import db
from nova import objects
objects.register_all()
class CommonMixin:
def _create_instance(self, **kwargs):
instance = db.instance_create(self.ctxt, kwargs)
return instance['u... | {
"repo_name": "BeyondTheClouds/nova",
"path": "nova/tests/unit/db/test_object_discovery.py",
"copies": "1",
"size": "3325",
"license": "apache-2.0",
"hash": 1782268010371766300,
"line_mean": 36.7954545455,
"line_max": 109,
"alpha_frac": 0.6168421053,
"autogenerated": false,
"ratio": 3.89800703399... |
__author__ = 'mslabicki'
import numpy as np
class maximalThroughputProblemRR:
def __init__(self, networkInstance, dim, lowerTxLimit, upperTxLimit):
self.dim = dim
self.networkInstance = networkInstance
self.lowerLimitsVector = []
self.upperLimitsVector = []
for i in range(d... | {
"repo_name": "iitis/PyLTEs",
"path": "pyltes/powerOptimizationProblemsDef.py",
"copies": "1",
"size": "5265",
"license": "mit",
"hash": 8913924411922015000,
"line_mean": 35.5694444444,
"line_max": 102,
"alpha_frac": 0.6685660019,
"autogenerated": false,
"ratio": 3.8628026412325753,
"config_tes... |
__author__ = 'mslabicki'
import pygmo as pg
#
from pyltes.powerOptimizationProblemsDef import maximalThroughputProblemRR
from pyltes.powerOptimizationProblemsDef import local_maximalThroughputProblemRR
from pyltes.powerOptimizationProblemsDef import maximalMedianThrProblemRR
from pyltes.powerOptimizationProblem... | {
"repo_name": "iitis/PyLTEs",
"path": "pyltes/powerConfigurator.py",
"copies": "1",
"size": "5818",
"license": "mit",
"hash": -2458547869363342300,
"line_mean": 43.811023622,
"line_max": 222,
"alpha_frac": 0.6172224132,
"autogenerated": false,
"ratio": 3.513285024154589,
"config_test": false,
... |
__author__ = 'm.stanford'
import cv2
import cv2.cv as cv
import numpy as np
class Circles():
accumulator_threshold = 75
minCircleDist = 100
maxCannyThresh = 175
minRadius = 10
maxRadius = 0
def __init__(self):
pass
def getCircles(self, img):
smoothed = cv2.medianBlur(im... | {
"repo_name": "MaStanford/PingPongOpenCV",
"path": "shapes/Circles.py",
"copies": "1",
"size": "1235",
"license": "apache-2.0",
"hash": 3004479450862178000,
"line_mean": 27.0681818182,
"line_max": 71,
"alpha_frac": 0.5190283401,
"autogenerated": false,
"ratio": 3.7652439024390243,
"config_test"... |
__author__ = 'm.stanford'
import numpy as np
import cv2
class Lines():
minEdge = 200
maxEdge = 300
minLineLength = 1000
maxLineGap = 100
p = 1
theta = np.pi/180
threshhold = 200
probability = False
def __init__(self):
pass
def getLines(self, grayImage):
edges ... | {
"repo_name": "MaStanford/PingPongOpenCV",
"path": "shapes/Lines.py",
"copies": "1",
"size": "1653",
"license": "apache-2.0",
"hash": 1714953822255195000,
"line_mean": 27.5172413793,
"line_max": 116,
"alpha_frac": 0.4736842105,
"autogenerated": false,
"ratio": 3.5472103004291844,
"config_test":... |
__author__ = 'm.stanford'
__author__ = 'm.stanford'
import string
import json, httplib
nameheader = 'Naming Words'
pageheader = 'Page'
maleheader = 'Male Names'
femaleheader = 'Female Names'
flushline = ' . '
lines = []
wordType = ['NameWord', 'FemaleName', 'MaleName']
section = wordType[0]
def buildNameDef(proc... | {
"repo_name": "MaStanford/AnglishWordbook",
"path": "Anglish/UploadNamingWords.py",
"copies": "1",
"size": "4626",
"license": "apache-2.0",
"hash": -7090787360318512000,
"line_mean": 21.4611650485,
"line_max": 87,
"alpha_frac": 0.5214007782,
"autogenerated": false,
"ratio": 3.528604118993135,
"... |
__author__ = 'mstipanov'
class ApiRequestErrorDetails(object):
messageId = ""
text = ""
variables = ""
additionalDescription = ""
def __init__(self, text=""):
self.text = text
def __str__(self):
return "ApiRequestErrorDetails: {" \
"messageId = \"" + str(self.m... | {
"repo_name": "infobip/infobip-api-python-client",
"path": "infobip/util/exception.py",
"copies": "1",
"size": "1760",
"license": "apache-2.0",
"hash": 4972748802176346000,
"line_mean": 35.6875,
"line_max": 110,
"alpha_frac": 0.4397727273,
"autogenerated": false,
"ratio": 5.191740412979351,
"co... |
__author__ = 'msullivan'
import os
import sys
import json
import contextlib
import subprocess
dot_dir = '~/.volt' #get_config['dot_dir']
volt_config_filename = 'config.json'
def get_path_to_volt():
return os.path.dirname(os.path.realpath(sys.argv[0]))
def get_dot_dir():
expanded_dot_dir = os.path.expanduser... | {
"repo_name": "MichaelSullivan/volt",
"path": "volt_modules/utils.py",
"copies": "1",
"size": "1307",
"license": "apache-2.0",
"hash": 1845831266119618300,
"line_mean": 26.2291666667,
"line_max": 80,
"alpha_frac": 0.635807192,
"autogenerated": false,
"ratio": 3.2271604938271605,
"config_test": ... |
__author__ = 'mtambos'
from __future__ import division, print_function
import os
import pandas as pd
from PIL import Image
import numpy as np
def to_gray_scale(img):
img_array = np.asarray(img)
luminosity = lambda x: 0.21*x[0] + 0.72*x[1] + 0.07*x[2]
return np.apply_along_axis(func1d=luminosity, axis=2... | {
"repo_name": "mtambos/character_identification",
"path": "src/knn_classifier.py",
"copies": "1",
"size": "4630",
"license": "mit",
"hash": -7843783641043690000,
"line_mean": 37.2644628099,
"line_max": 103,
"alpha_frac": 0.6265658747,
"autogenerated": false,
"ratio": 3.457804331590739,
"config_... |
__author__ = 'mthompson'
import base64
import httplib
import json
import mimetypes
import random
import string
import urllib
import urlparse
class DeviceProxy():
def __init__(self, host, port, username, password, use_https=True, keep_alive=False, forwarded_ip=""):
self.username = username
self.pass... | {
"repo_name": "amwelch/a10sdk-python",
"path": "a10sdk/common/device_proxy.py",
"copies": "1",
"size": "12572",
"license": "apache-2.0",
"hash": 6798103401894910000,
"line_mean": 40.2196721311,
"line_max": 149,
"alpha_frac": 0.5679287305,
"autogenerated": false,
"ratio": 4.035955056179775,
"con... |
__author__ = 'mthompson'
import copy
import inspect
import importlib
import urllib
import json
from urlparse import urlparse
import collections
import re
import os
import keyword
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
class A10BaseClass(object):
is_POST = True
def __init__(self):
self... | {
"repo_name": "a10networks/a10sdk-python",
"path": "a10sdk/common/A10BaseClass.py",
"copies": "2",
"size": "37904",
"license": "apache-2.0",
"hash": 8913666667968505000,
"line_mean": 42.2200684151,
"line_max": 120,
"alpha_frac": 0.4406395103,
"autogenerated": false,
"ratio": 4.550846440148877,
... |
__author__ = 'Mufid'
# Every import needed, listed
import numpy as np
import pylab as pl
import random
import math
from matplotlib.backends.backend_pdf import PdfPages
# ------------------------
# All configuration.
# ------------------------
# Alpha: manufacture-specific variable
# Please use 1 as its base bound
alp... | {
"repo_name": "mufid/berkilau",
"path": "ws/fidz/velocitymodel/run.py",
"copies": "1",
"size": "3172",
"license": "mit",
"hash": -3007881703340357000,
"line_mean": 29.7961165049,
"line_max": 80,
"alpha_frac": 0.6390290038,
"autogenerated": false,
"ratio": 2.8195555555555556,
"config_test": fals... |
from __future__ import division
import logging
import time
import math
import wiringpi as wp
# Registers/etc:
PCA9685_ADDRESS = 0x40
MODE1 = 0x00
MODE2 = 0x01
SUBADR1 = 0x02
SUBADR2 = 0x03
SUBADR3 = 0x04
PRESCALE = 0xFE
LED0_ON_L = 0x06
LED0_ON_H = 0x07
LED0_OFF_L = 0x08
LED0_OFF_H = 0x09
ALL_LED_ON_L = 0xFA
ALL_LED_O... | {
"repo_name": "IsmoilovMuhriddin/allgo",
"path": "rasp/allgo_utils/raspcar.py",
"copies": "1",
"size": "9333",
"license": "mit",
"hash": -1481846949031950600,
"line_mean": 33.5703703704,
"line_max": 125,
"alpha_frac": 0.6160934319,
"autogenerated": false,
"ratio": 2.9247884675650266,
"config_te... |
__author__ = "Muin Momin"
from flask import Flask, request, render_template, redirect, url_for, session, jsonify
from functools import wraps
from controls import *
# Initializing things
app = Flask(__name__)
app.secret_key = "secret"
GPIO.setwarnings(False)
initpins()
error = None
def login_required(f):
@wraps... | {
"repo_name": "muinmomin/PiCar",
"path": "main.py",
"copies": "1",
"size": "2343",
"license": "mit",
"hash": -6639280169600024000,
"line_mean": 20.504587156,
"line_max": 89,
"alpha_frac": 0.5983781477,
"autogenerated": false,
"ratio": 3.6438569206842923,
"config_test": false,
"has_no_keywords... |
__author__ = 'Muin'
import RPi.GPIO as GPIO
import time
#SETUP GPIO PINS
def initpins():
GPIO.setmode(GPIO.BOARD)
GPIO.setup(7, GPIO.OUT)
GPIO.setup(11, GPIO.OUT)
GPIO.setup(13, GPIO.OUT)
GPIO.setup(15, GPIO.OUT)
#MOTOR CONTROL FUNCTIONS
def forward():
initpins()
GPIO.output(7, False)
... | {
"repo_name": "muinmomin/PiCar",
"path": "controls.py",
"copies": "1",
"size": "1414",
"license": "mit",
"hash": -1759273112687791600,
"line_mean": 18.3698630137,
"line_max": 63,
"alpha_frac": 0.6230551627,
"autogenerated": false,
"ratio": 3.0408602150537636,
"config_test": false,
"has_no_key... |
__author__ = 'mukesh'
# In this stock data is scrapped from one of the Indian markets website for any particular stock.
import urllib
from bs4 import BeautifulSoup
import re
base_url = 'http://money.rediff.com/companies/'
class StockAPI:
def __init__(self, symbol):
self.symbol = symbol
def get_con... | {
"repo_name": "mukeshthawani/StockAPI",
"path": "StockAPI.py",
"copies": "1",
"size": "4464",
"license": "mit",
"hash": -7351187520658276000,
"line_mean": 30.6595744681,
"line_max": 97,
"alpha_frac": 0.5591397849,
"autogenerated": false,
"ratio": 4.259541984732825,
"config_test": false,
"has_... |
__author__ = 'mukesh'
"""
*
* ================================
*
* Author List: Mukesh P
* Filename: Kinect.py
* Date: June 20, 2015
* Functions: get_depth()
filter_smooth()
filter_noise()
contours_re... | {
"repo_name": "eyantrainternship/eYSIP_2015_Depth_Mapping_Kinect",
"path": "Research/Tests/Final.py",
"copies": "2",
"size": "19875",
"license": "cc0-1.0",
"hash": 7198903914902910000,
"line_mean": 35.204007286,
"line_max": 438,
"alpha_frac": 0.5789685535,
"autogenerated": false,
"ratio": 3.16935... |
__author__="Mukesh Tiwari"
__date__ ="$Feb 10, 2010 1:35:26 AM$"
import random
from Queue import Queue
def gcd(a,b):
while b:
a,b=b,a%b
return a
def rabin_miller(p):
if(p<2):
return False
if(p!=2 and p%2==0):
return False
s=p-1
while(s%2==0):
s>>=1
for i in xrange(10):
a=random.randrange(... | {
"repo_name": "winxos/python",
"path": "reptend/rho.py",
"copies": "1",
"size": "2107",
"license": "mit",
"hash": 8706870553441621000,
"line_mean": 19.0666666667,
"line_max": 75,
"alpha_frac": 0.4323682962,
"autogenerated": false,
"ratio": 2.5446859903381642,
"config_test": false,
"has_no_key... |
__author__ = 'multiangle'
import datetime
from ..Basehandler import BaseHandler
from ..databases.tables import ActivCommitUser, Activity
from ..return_code_config import codeTable
from sqlalchemy.orm.exc import NoResultFound
class HuoException(RuntimeError):
def __init__(self, code):
self.code = code
c... | {
"repo_name": "HeraldStudio/HeraldAppApi",
"path": "mod/huodong/HuodongCommit.py",
"copies": "1",
"size": "4023",
"license": "mpl-2.0",
"hash": -9198309401370789000,
"line_mean": 40.0510204082,
"line_max": 120,
"alpha_frac": 0.5090728312,
"autogenerated": false,
"ratio": 4.4453038674033145,
"co... |
__author__ = 'muntaserahmed'
import sys
import Leap
from Leap import ScreenTapGesture, SwipeGesture
from pymouse import PyMouse
from pykeyboard import PyKeyboard
from numpy import interp
import threading
from threading import Thread
# Initialize PyMouse object
AERO_MOUSE = PyMouse()
AERO_GESTURES = PyKeyboard()
... | {
"repo_name": "mantazer/AeroMouse",
"path": "AeroMouse.py",
"copies": "1",
"size": "5926",
"license": "mit",
"hash": -7952635919148586000,
"line_mean": 31.2065217391,
"line_max": 117,
"alpha_frac": 0.5919676004,
"autogenerated": false,
"ratio": 3.5828295042321643,
"config_test": false,
"has_n... |
__author__ = 'Murat Derya Ozen'
import glob
import gzip
import logging
import os
import shutil
TEMP_DIR_NAME = "temp"
OUTPUT_DIR_NAME = "diff"
OUTPUT_DIFF_LEFT_FILENAME = "diff-left.gz"
OUTPUT_DIFF_RIGHT_FILENAME = "diff-right.gz"
BUCKET_FILE_NAME_PREFIX = "bucket-"
def join_abs_path(x, y):
"""
Join two p... | {
"repo_name": "muratdozen/bigdiff",
"path": "bigdiff/application.py",
"copies": "1",
"size": "10253",
"license": "mit",
"hash": -4530553772065472500,
"line_mean": 36.6985294118,
"line_max": 79,
"alpha_frac": 0.651224032,
"autogenerated": false,
"ratio": 3.77087164398676,
"config_test": false,
... |
__author__ = 'Murat Derya Ozen'
PROGRAM_DESCRIPTION = """
TODO !!!
"""
import argparse
import logging
import mmh3
from application import bigdiff
def get_argparser():
""" Defines and returns the ArgumentParser for this program. """
parser = argparse.ArgumentParser(description=PROGRAM_DESCRIPTION)
par... | {
"repo_name": "muratdozen/bigdiff",
"path": "bigdiff/__main__.py",
"copies": "1",
"size": "1241",
"license": "mit",
"hash": 6391955631366602000,
"line_mean": 27.8837209302,
"line_max": 94,
"alpha_frac": 0.5995165189,
"autogenerated": false,
"ratio": 4.235494880546075,
"config_test": false,
"h... |
__author__ = 'Murat Derya Ozen'
PROGRAM_DESCRIPTION = """
Spot the ugliest files in a code repository.
Given a path to a file or a directory, this program parses source files to extract,
pretty print and calculate the number of curly brackets, i.e braces.
*** Samples usages ***
> python pretty_print_braces file --pa... | {
"repo_name": "muratdozen/playground",
"path": "scripts/pretty_print_braces/__main__.py",
"copies": "1",
"size": "9057",
"license": "unlicense",
"hash": 85368432466029760,
"line_mean": 39.7972972973,
"line_max": 115,
"alpha_frac": 0.6297891134,
"autogenerated": false,
"ratio": 4.10934664246824,
... |
import random
import logging
import sys, os
import pprint
logger = logging.getLogger(__name__)
logger.addHandler(logging.StreamHandler(sys.stdout))
logger.setLevel(logging.INFO)
debug = True
class DNASegment(object):
def __init__(self, contig_sequence, filename, bufferPlaces):
self.sequence = contig_se... | {
"repo_name": "bakedintime/bioalignment",
"path": "GenAlgorithm.py",
"copies": "1",
"size": "7306",
"license": "mit",
"hash": 2943325238795119600,
"line_mean": 31.9144144144,
"line_max": 90,
"alpha_frac": 0.592800438,
"autogenerated": false,
"ratio": 4.196438828259621,
"config_test": false,
"... |
__author__ = "Music"
# Learning Basic Usage of Tf
# https://www.tensorflow.org/versions/r0.9/get_started/basic_usage.html
import tensorflow as tf
print "Construct Graph"
matrix1 = tf.constant([[3,3]])
matrix2 = tf.constant([[2],[2]])
# TO MY UNDERSTANDING: constant is added to the default graph as op (node), matrix1 r... | {
"repo_name": "pkumusic/AI",
"path": "learn_tf/basic_usage.py",
"copies": "1",
"size": "1509",
"license": "mit",
"hash": 6427372199125899000,
"line_mean": 23.3387096774,
"line_max": 116,
"alpha_frac": 0.6752816435,
"autogenerated": false,
"ratio": 2.8258426966292136,
"config_test": false,
"ha... |
__author__ = 'mustafaduran'
import math
import numpy as np
from numpy import array
from numpy import matrix
from numpy import linalg as LA
def myptw (a,sample_profile,target_profile):
n=len(sample_profile)
print n
A_=array([1,1,1])
dA=array([A_]).T
while LA.norm(dA)>0.01:
... | {
"repo_name": "mduranmustafa/Python",
"path": "ptw.py",
"copies": "1",
"size": "2489",
"license": "epl-1.0",
"hash": -891411925838580400,
"line_mean": 21.9326923077,
"line_max": 109,
"alpha_frac": 0.4443551627,
"autogenerated": false,
"ratio": 2.917936694021102,
"config_test": false,
"has_no_... |
from PIL import Image
import Tkinter, tkFileDialog
import math
import random
def default (pixels):
# Returns pixels list of tuples (R,G,B,Cluster Assignment('x' by default))
pixels = [list(x)+ list(('x',)) for x in pixels]
return pixels
def centroids (pixels,k):
# Checks whether the pix... | {
"repo_name": "Mustaf2501/Dominant-Colors",
"path": "dominant_colors.py",
"copies": "1",
"size": "3155",
"license": "mit",
"hash": -9017552353171799000,
"line_mean": 29.6310679612,
"line_max": 121,
"alpha_frac": 0.5553090333,
"autogenerated": false,
"ratio": 3.4861878453038675,
"config_test": f... |
__author__ = 'Mustafa ÇELİK'
class Deque:
""" Dequeue is common usage of Stack and Queue in the same data structure.
Items can be added both beginning and end of dequeue.
Items can be removed both beginning and end of dequeue.
Example Usage Scenario: Palindrome Checker is a great example.
( basta... | {
"repo_name": "celikmustafa89/python_work",
"path": "dequeue.py",
"copies": "1",
"size": "1670",
"license": "apache-2.0",
"hash": -223749537687531680,
"line_mean": 26.8,
"line_max": 90,
"alpha_frac": 0.5983213429,
"autogenerated": false,
"ratio": 3.0831792975970425,
"config_test": false,
"has... |
__author__ = 'mwagner'
from qgis.core import QgsVectorLayer, QgsGeometry, QgsMapLayerRegistry, QgsPoint, QgsFeatureRequest,\
QgsMessageLog, QGis, QgsVectorFileWriter
from PyQt4.Qt import QThread, pyqtSignal, QMutex, QMutexLocker, QDir
class SnapToGrid(QThread):
run_finished = pyqtSignal(str, bool)
run_p... | {
"repo_name": "allspatial/vertex-tools",
"path": "controller/SnapToGrid.py",
"copies": "1",
"size": "6663",
"license": "mit",
"hash": 4853717422286398000,
"line_mean": 35.8121546961,
"line_max": 111,
"alpha_frac": 0.5853219271,
"autogenerated": false,
"ratio": 4.097785977859779,
"config_test": ... |
__author__ = 'mwagner'
from qgis.core import QgsVectorLayer, QgsMapLayerRegistry, QgsMessageLog
from PyQt4.Qt import QThread, pyqtSignal, QMutex, QMutexLocker, QDir, QFile
class RestoreGeometry(QThread):
run_finished = pyqtSignal(str, bool)
run_progressed = pyqtSignal(str, int, int)
report_message = pyq... | {
"repo_name": "allspatial/vertex-tools",
"path": "controller/RestoreGeometry.py",
"copies": "1",
"size": "2541",
"license": "mit",
"hash": -4697653698336651000,
"line_mean": 32,
"line_max": 107,
"alpha_frac": 0.6033057851,
"autogenerated": false,
"ratio": 3.9517884914463455,
"config_test": fals... |
__author__ = 'mwagner'
from ..view.Ui_SnapToGridWidget import Ui_SnapToGridWidget
from qgis.core import QgsMapLayerRegistry, QgsMapLayer, QgsVectorDataProvider
from PyQt4.Qt import Qt, pyqtSlot, QReadWriteLock, QReadLocker, QSettings
from PyQt4.QtGui import QDockWidget, QListWidgetItem, QMessageBox, QFileDialog
from S... | {
"repo_name": "allspatial/vertex-tools",
"path": "controller/SnapToGridWidget.py",
"copies": "1",
"size": "11888",
"license": "mit",
"hash": 7448048099681821000,
"line_mean": 36.0373831776,
"line_max": 114,
"alpha_frac": 0.6069986541,
"autogenerated": false,
"ratio": 3.883698137863443,
"config_... |
__author__ = 'mwalker'
from cms.models.pluginmodel import CMSPlugin
from django.db import models
from django.utils.translation import ugettext_lazy as _
class HomeMultiArticle(CMSPlugin):
"""
Home page articles, displayed in two columns.
"""
left_title_1 = models.CharField(
_("Left side, Sli... | {
"repo_name": "marksweb/django-cms-app-examples",
"path": "homepage/models.py",
"copies": "1",
"size": "6447",
"license": "mit",
"hash": -2439728645060225000,
"line_mean": 21.7007042254,
"line_max": 67,
"alpha_frac": 0.5221033039,
"autogenerated": false,
"ratio": 3.7094361334867663,
"config_tes... |
__author__ = 'mwalker'
from cms.plugin_base import CMSPluginBase
from cms.plugin_pool import plugin_pool
from django.utils.translation import ugettext_lazy as _
from .forms import HomeMultiArticleAdminForm, HomeFullArticleAdminForm
from .models import HomeMultiArticle, HomeFullArticle
class HomeMultiArticlePlugin(... | {
"repo_name": "marksweb/django-cms-app-examples",
"path": "homepage/cms_plugins.py",
"copies": "1",
"size": "1123",
"license": "mit",
"hash": 8884296372611701000,
"line_mean": 26.4146341463,
"line_max": 70,
"alpha_frac": 0.7310774711,
"autogenerated": false,
"ratio": 4.269961977186312,
"config_... |
__author__ = 'mwalker'
# from dateutil.relativedelta import relativedelta
from django.db import models
from django.utils.translation import gettext as _
AUS = 'AUS'
NZ = 'NZ'
UK = 'UK'
USA = 'USA'
COUNTRY_CHOICES = (
(AUS, _("Australia")),
(NZ, _("New Zealand")),
(UK, _("United Kingdom")),
(USA, _("... | {
"repo_name": "marksweb/django-cms-app-examples",
"path": "listings/models.py",
"copies": "1",
"size": "3764",
"license": "mit",
"hash": -8180725960951889000,
"line_mean": 21.9512195122,
"line_max": 79,
"alpha_frac": 0.541445271,
"autogenerated": false,
"ratio": 3.588179218303146,
"config_test"... |
__author__ = 'mwalker'
from dateutil.relativedelta import relativedelta
from django.db import models
from django.utils.translation import gettext as _
TIER_CHOICES = (
(1, 1),
(2, 2),
(3, 3)
)
class Clinic(models.Model):
"""
Model for storing a clinic.
"""
name = models.CharField(
... | {
"repo_name": "marksweb/django-cms-app-examples",
"path": "clinics/models.py",
"copies": "1",
"size": "2003",
"license": "mit",
"hash": 6671204912269754000,
"line_mean": 19.4387755102,
"line_max": 78,
"alpha_frac": 0.5127309036,
"autogenerated": false,
"ratio": 3.851923076923077,
"config_test":... |
__author__ = 'mwalker'
from django.core.urlresolvers import reverse
from django.db.models import F, Q
from django.shortcuts import (
render_to_response, get_object_or_404, HttpResponseRedirect
)
from django.template import RequestContext
from django.utils import timezone
from .models import Listing, Tracking, COU... | {
"repo_name": "marksweb/django-cms-app-examples",
"path": "listings/views.py",
"copies": "1",
"size": "2669",
"license": "mit",
"hash": -2187613345790148900,
"line_mean": 27.6989247312,
"line_max": 77,
"alpha_frac": 0.6328212814,
"autogenerated": false,
"ratio": 3.856936416184971,
"config_test"... |
__author__ = 'mwalker'
from django.db.models import F
from django.shortcuts import (
render_to_response, get_object_or_404, HttpResponseRedirect
)
from django.template import RequestContext
from .models import Clinic
def index(request):
"""
Display the injury clinics by tier in random order.
@param... | {
"repo_name": "marksweb/django-cms-app-examples",
"path": "clinics/views.py",
"copies": "1",
"size": "2068",
"license": "mit",
"hash": 3031167551545737700,
"line_mean": 24.2317073171,
"line_max": 69,
"alpha_frac": 0.6044487427,
"autogenerated": false,
"ratio": 3.577854671280277,
"config_test": ... |
__author__ = 'mwalker'
from django import forms
from .models import HomeMultiArticle, HomeFullArticle
class HomeMultiArticleAdminForm(forms.ModelForm):
left_url_1 = forms.URLField(
max_length=200,
help_text="Input full URL e.g. http://www.google.com",
initial='http://'
)
left_url... | {
"repo_name": "marksweb/django-cms-app-examples",
"path": "homepage/forms.py",
"copies": "1",
"size": "5585",
"license": "mit",
"hash": 7320946706767587000,
"line_mean": 28.5555555556,
"line_max": 69,
"alpha_frac": 0.4879140555,
"autogenerated": false,
"ratio": 3.8019060585432265,
"config_test"... |
"@Author Mwaruwa Chaka, JKUAT ECE Final Year Project 2014"
"This code takes videos from the Rpi and transports them to a local folder via a network"
import pysftp
import time
def Transfer():
try:
with pysftp.Connection('192.168.60.102',username = 'sysop', password = 'posys') as sftp:
working_di... | {
"repo_name": "mwaruwa/jkuat-projects",
"path": "Final-Project/Transport/RpiTransmission.py",
"copies": "1",
"size": "1451",
"license": "apache-2.0",
"hash": 3330720720714606600,
"line_mean": 41.6764705882,
"line_max": 136,
"alpha_frac": 0.5561681599,
"autogenerated": false,
"ratio": 4.4783950617... |
__author__ = 'mworden'
import ntplib
import re
from mi.core.exceptions import RecoverableSampleException
from mi.core.log import get_logger
from mi.dataset.dataset_parser import DataSetDriverConfigKeys
log = get_logger()
from mi.dataset.dataset_parser import SimpleParser
from mi.dataset.parser.pco2w_abc_particles im... | {
"repo_name": "oceanobservatories/mi-instrument",
"path": "mi/dataset/parser/pco2w_abc_imodem.py",
"copies": "5",
"size": "20618",
"license": "bsd-2-clause",
"hash": -1729002443305780700,
"line_mean": 37.3234200743,
"line_max": 95,
"alpha_frac": 0.5479192938,
"autogenerated": false,
"ratio": 4.21... |
__author__ = 'mworden'
# A regex used to match any characters
ANY_CHARS_REGEX = r'.*'
# A regex used to match any characters
ANY_NON_SPACE_CHARS_REGEX = r'([^\s]*)'
# A regex used to match a single space
SPACE_REGEX = ' '
# A regex used to match the end of a line
END_OF_LINE_REGEX = r'(?:\r\n|\n)'
# A regex used ... | {
"repo_name": "renegelinas/mi-instrument",
"path": "mi/dataset/parser/common_regexes.py",
"copies": "2",
"size": "1902",
"license": "bsd-2-clause",
"hash": -2308483000332407000,
"line_mean": 30.7,
"line_max": 109,
"alpha_frac": 0.6451104101,
"autogenerated": false,
"ratio": 2.6054794520547944,
... |
__author__ = 'mworden'
"""
@package mi.dataset.parser.nutnr_b_particles
@file mi/dataset/parser/nutnr_b_particles.py
@author Mark Worden
@brief Parser for the nutnr_b_particles dataset driver
This file contains the particles that are applicable
nutnr_b_dcl_conc, nutnr_b_dcl_full and nutnr_b.
"""
from mi.core.log imp... | {
"repo_name": "JeffRoy/mi-dataset",
"path": "mi/dataset/parser/nutnr_b_particles.py",
"copies": "3",
"size": "12615",
"license": "bsd-2-clause",
"hash": 6982931587896350000,
"line_mean": 42.2054794521,
"line_max": 107,
"alpha_frac": 0.6198176774,
"autogenerated": false,
"ratio": 3.82504548211037,... |
__author__ = 'myang'
from a10networks.a10api.login import *
import unittest
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.common.alert import Alert
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.support.ui import W... | {
"repo_name": "PyLearner/myworks",
"path": "selenium/network/adc/templates/slb_server_template.py",
"copies": "1",
"size": "2036",
"license": "apache-2.0",
"hash": -8787725440773535000,
"line_mean": 36.1509433962,
"line_max": 124,
"alpha_frac": 0.6320474777,
"autogenerated": false,
"ratio": 3.610... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.