text stringlengths 0 1.05M | meta dict |
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__author__ = 'Renato'
from time import sleep
from ctypes import *
user32 = windll.user32
from input_structures import *
import hardware_scan_code as sc
import virtual_key_code as vk
def mouse_event():
print('Mouse Event not implemented yet...')
def hardware_event():
print('Hardware event not implem... | {
"repo_name": "pastahito/PyStrokes",
"path": "pkghito/combo/pressreleasev1.py",
"copies": "1",
"size": "1722",
"license": "mit",
"hash": 299257257776584060,
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"autogenerated": false,
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__author__ = 'Renato'
from time import sleep
from input_structures import * # Here ctypes is imported implicitly and user32 is bound to windll.user32
from pyctionary import arrows, numbers, letters
def mouse_event():
print('Mouse Event not implemented yet...')
def hardware_event():
print('Hardware event n... | {
"repo_name": "pastahito/PyStrokes",
"path": "pkghito/combo/pressrelease.py",
"copies": "1",
"size": "1874",
"license": "mit",
"hash": -5934570602935164000,
"line_mean": 22.1358024691,
"line_max": 115,
"alpha_frac": 0.6478121665,
"autogenerated": false,
"ratio": 3.077175697865353,
"config_test"... |
__author__ = 'Renato'
from time import sleep
from pressreleasev2 import press_release, press, release
from Hook3v2 import execute
# print("3:", hex(17179869235), hex(17179869235>>32), hex(17179869235%256))
# print("Q:", hex(68719476817), hex(68719476817>>32), hex(68719476817%256))
# print("W:", hex(73014444119), hex... | {
"repo_name": "pastahito/PyStrokes",
"path": "pkghito/Zen.py",
"copies": "1",
"size": "2776",
"license": "mit",
"hash": 3102738032388085000,
"line_mean": 24.2363636364,
"line_max": 104,
"alpha_frac": 0.5536743516,
"autogenerated": false,
"ratio": 2.8530318602261047,
"config_test": false,
"has... |
__author__ = 'Renato'
VK_LBUTTON = 0x01 # Left mouse button
VK_RBUTTON = 0x02 # Right mouse button
VK_CANCEL = 0x03 # Control-break processing
VK_MBUTTON = 0x04 # Middle mouse button (three-button mouse)
VK_XBUTTON1 = 0x05 # X1 mouse button
VK_XBUTTON2 = 0x06 # X2 mouse button
# 0x07 Undefined
VK_BACK = 0x08 # BA... | {
"repo_name": "pastahito/PyStrokes",
"path": "pkghito/const/virtual_key_code.py",
"copies": "1",
"size": "6436",
"license": "mit",
"hash": 3954926452961271000,
"line_mean": 32.5260416667,
"line_max": 267,
"alpha_frac": 0.6472964574,
"autogenerated": false,
"ratio": 2.3801775147928996,
"config_t... |
__author__ = 'Renato'
__project__ = 'cuarentadefiebre'
__date__ = '21/02/2015'
from ctypes import *
from KeyboardHook import KeyboardHook
import win32con
user32 = windll.user32
keyboardHook = KeyboardHook()
##################################################
# returns a function pointer to the fn paramater #
# assume... | {
"repo_name": "pastahito/PyStrokes",
"path": "pkghito/KeyboardHookTest1.py",
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"size": "1244",
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__author__ = 'Renato'
__project__ = 'cuarentadefiebre'
__date__ = '21/02/2015'
from ctypes import *
import win32con
import win32api
import win32gui
class KeyboardHook:
"""
Written by: TwhK / Kheldar
What do? Installs a global keyboard hook
To install the hook, call the (gasp!) installHook() function... | {
"repo_name": "pastahito/PyStrokes",
"path": "pkghito/KeyboardHook.py",
"copies": "1",
"size": "1924",
"license": "mit",
"hash": -8696842790423289000,
"line_mean": 31.0833333333,
"line_max": 90,
"alpha_frac": 0.6216216216,
"autogenerated": false,
"ratio": 4.059071729957806,
"config_test": false... |
__author__ = 'rencui'
from afinn import Afinn
import numpy
import json
from textstat.textstat import textstat
from nltk.stem.porter import *
from tokenizer import simpleTokenize
import logging
from scipy.sparse import csr_matrix
import matplotlib.pyplot as plt
import matplotlib as mat
from sklearn.ensemble import Extra... | {
"repo_name": "renhaocui/tweetInfluencer",
"path": "featureAnalyzer.py",
"copies": "2",
"size": "7156",
"license": "mit",
"hash": -2500986423443976700,
"line_mean": 27.624,
"line_max": 188,
"alpha_frac": 0.6447736165,
"autogenerated": false,
"ratio": 3.2645985401459856,
"config_test": false,
... |
__author__ = 'rencui'
from collections import OrderedDict
import math
from nltk.corpus import stopwords
stopwordList = stopwords.words('english')
posTweetFile = open('adData/analysis/ranked/NERoutput.pos', 'r')
negTweetFile = open('adData/analysis/ranked/NERoutput.neg', 'r')
posTokenFile = open('adData/analysis/ranked... | {
"repo_name": "renhaocui/tweetInfluencer",
"path": "tweetAnalyzer.py",
"copies": "2",
"size": "1543",
"license": "mit",
"hash": -6524856584037376000,
"line_mean": 28.1320754717,
"line_max": 89,
"alpha_frac": 0.680492547,
"autogenerated": false,
"ratio": 3.129817444219067,
"config_test": false,
... |
__author__ = 'rencui'
from utilities import tokenizer
def lcsLen(a, b):
table = [[0] * (len(b) + 1) for _ in xrange(len(a) + 1)]
for i, ca in enumerate(a, 1):
for j, cb in enumerate(b, 1):
table[i][j] = (
table[i - 1][j - 1] + 1 if ca == cb else
max(table[i]... | {
"repo_name": "renhaocui/activityExtractor",
"path": "duplicate_remover.py",
"copies": "1",
"size": "2631",
"license": "mit",
"hash": -8846945236357656000,
"line_mean": 32.7435897436,
"line_max": 71,
"alpha_frac": 0.4104903079,
"autogenerated": false,
"ratio": 4.278048780487805,
"config_test": ... |
__author__ = 'rencui'
import json
import twitter
import time
from sets import Set
import os
logFile = open('log', 'w')
brandList = []
listFile = open('brand.list', 'r')
for line in listFile:
brandList.append(line.strip())
listFile.close()
for brand in brandList:
if not os.path.isdir('adData//' + brand):
... | {
"repo_name": "renhaocui/adPlatform",
"path": "adTweetTracker.py",
"copies": "2",
"size": "2817",
"license": "mit",
"hash": 7691490317504673000,
"line_mean": 30.3,
"line_max": 136,
"alpha_frac": 0.5750798722,
"autogenerated": false,
"ratio": 3.3535714285714286,
"config_test": false,
"has_no_k... |
__author__ = 'rencui'
import json
import twitter
import time
from sets import Set
brandList = ['amazon', 'dominos', 'Gap', 'Jeep', 'AppStore', 'Chilis', 'Colgate', 'Motorola', 'AmericanExpress',
'Microsoft']
logFile = open('log', 'w')
c_k = 'NNFnQv3zyeM99IDXg1IOrtMcQ'
c_s = 'GDc4vZtorwOiPgDNiaGrWDTWfqKN... | {
"repo_name": "renhaocui/adPlatform",
"path": "tweetTracker.py",
"copies": "2",
"size": "2161",
"license": "mit",
"hash": -5605170936854547000,
"line_mean": 30.3333333333,
"line_max": 112,
"alpha_frac": 0.5816751504,
"autogenerated": false,
"ratio": 3.3144171779141103,
"config_test": false,
"... |
__author__ = 'rencui'
import json
def blend(fileSize, offset):
print 'blending tweets...'
brandList = []
listFile = open('brand.list', 'r')
for line in listFile:
brandList.append(line.strip())
listFile.close()
combinedOutFile = open('dataset/ConsolidatedTweets/total.json', 'w')
to... | {
"repo_name": "renhaocui/tweetInfluencer",
"path": "tweetBlender.py",
"copies": "2",
"size": "4166",
"license": "mit",
"hash": -1681307062268366000,
"line_mean": 49.8048780488,
"line_max": 157,
"alpha_frac": 0.4954392703,
"autogenerated": false,
"ratio": 4.353187042842215,
"config_test": false,... |
__author__ = 'rencui'
import numpy
import json
from textstat.textstat import textstat
import utilities
from sklearn.feature_extraction.text import *
from sklearn import svm
from tokenizer import simpleTokenize
import logging
from scipy.sparse import hstack, csr_matrix
from sklearn.externals import joblib
import pickle
... | {
"repo_name": "renhaocui/adPlatform",
"path": "modelUtility.py",
"copies": "2",
"size": "20933",
"license": "mit",
"hash": 3395404150406915000,
"line_mean": 34.9073756432,
"line_max": 146,
"alpha_frac": 0.6195958534,
"autogenerated": false,
"ratio": 3.6912361135602185,
"config_test": true,
"h... |
__author__ = 'rencui'
import statistics as stat
def hourMapper(hour):
input = int(hour)
if 0 <= input < 6:
output = 0
elif 6 <= input < 12:
output = 1
elif 12 <= input < 18:
output = 2
else:
output = 3
return output
def analyzer(groupSize, groupTitle):
# t... | {
"repo_name": "renhaocui/tweetInfluencer",
"path": "groupAnalysis.py",
"copies": "2",
"size": "9448",
"license": "mit",
"hash": -1103076897976951800,
"line_mean": 41.7556561086,
"line_max": 108,
"alpha_frac": 0.5764182896,
"autogenerated": false,
"ratio": 3.6791277258566977,
"config_test": fals... |
__author__ = 'rencui'
def contenter(indexList):
print 'generating content....'
for group in indexList:
totalRankedFile1 = open('adData/analysis/ranked/1/topic'+str(group)+'.pos', 'r')
totalRankedFile2 = open('adData/analysis/ranked/2/topic'+str(group)+'.neg', 'r')
outputFile1 = open('ad... | {
"repo_name": "renhaocui/adPlatform",
"path": "contenter.py",
"copies": "2",
"size": "1583",
"license": "mit",
"hash": -7848430473923913000,
"line_mean": 33.4347826087,
"line_max": 96,
"alpha_frac": 0.6272899558,
"autogenerated": false,
"ratio": 3.3538135593220337,
"config_test": false,
"has_... |
__author__ = 'rencui'
from nltk.corpus import wordnet as wn
from tokenizer import simpleTokenize
import string
def genSyn(word):
outputList = []
syns = wn.synsets('huge')
for item in syns:
for token in item.lemmas():
name = token.name()
if name != word:
outp... | {
"repo_name": "renhaocui/ensembleTopic",
"path": "crossRef.py",
"copies": "1",
"size": "1442",
"license": "mit",
"hash": -552496044701658050,
"line_mean": 27.2745098039,
"line_max": 60,
"alpha_frac": 0.6047156727,
"autogenerated": false,
"ratio": 3.337962962962963,
"config_test": false,
"has_... |
__author__ = 'renderle'
import logging
import json
import restcall
from openweather.OpenWeatherParser import OpenWeatherParser
def __evaluateTemp(temp):
base = 15
if temp < 0:
# seems to be very cold
# we add the negative temp to base -> return value will be lower than base
return ba... | {
"repo_name": "ralfe/wpi",
"path": "src/openweather/openweather.py",
"copies": "1",
"size": "4927",
"license": "mit",
"hash": -2988650237101780500,
"line_mean": 29.4135802469,
"line_max": 109,
"alpha_frac": 0.5384615385,
"autogenerated": false,
"ratio": 3.2824783477681545,
"config_test": false,... |
__author__ = 'rene_esteves'
class exam_01:
def somadig(self, input):
if input < 0: #base case
return int(self.somadig(self, -input))
if input == 0: #base case
return 0
return input%10 + int(self.somadig(self, input/10))
exam_01.somadig(exam_01, 10000);
##########... | {
"repo_name": "renestvs/data_structure_project",
"path": "data_structure/exam/exam_01.py",
"copies": "1",
"size": "2853",
"license": "mit",
"hash": -7563423156025508000,
"line_mean": 21.4645669291,
"line_max": 90,
"alpha_frac": 0.3473536628,
"autogenerated": false,
"ratio": 3.579673776662484,
"... |
__author__ = 'rene_esteves'
def factorial(input):
if input < 1: # base case
return 1
else:
return input * factorial(input - 1) # recursive call
def sum(input):
if input == 1: # base_case
return 1
else:
return input + sum(input - 1) # recursive call
def fibonacci... | {
"repo_name": "renestvs/data_structure_project",
"path": "data_structure/first_bim/class_02.py",
"copies": "1",
"size": "5443",
"license": "mit",
"hash": -7041359791366177000,
"line_mean": 24.3162790698,
"line_max": 100,
"alpha_frac": 0.551166636,
"autogenerated": false,
"ratio": 3.54592833876221... |
__author__ = 'rene_'
def descrente(n):
print(n)
if n == 0: #base case
return 0
return descrente(n-1) #recursive call
def crescente(n):
if n == 0:
return print (n)
crescente(n-1)
return print(n)
# def descre(n):
# print (n)
# if n == 0:
# return print(n)
# d... | {
"repo_name": "renestvs/data_structure_project",
"path": "data_structure/first_bim/exercise.py",
"copies": "1",
"size": "2507",
"license": "mit",
"hash": 203619104344941440,
"line_mean": 19.2177419355,
"line_max": 58,
"alpha_frac": 0.5125648185,
"autogenerated": false,
"ratio": 2.75192096597146,
... |
__author__ = 'rene_'
from django.test import TestCase
from data_structure.second_bim import graphs, pattern_searching, dynamic_programing
class Second_Bim_Test(TestCase):
# ==============================================
# ================= patterns ===================
# =============================================... | {
"repo_name": "renestvs/data_structure_project",
"path": "data_structure/test/second_bim_test.py",
"copies": "1",
"size": "10162",
"license": "mit",
"hash": -2515672002104182300,
"line_mean": 34.7852112676,
"line_max": 111,
"alpha_frac": 0.3515056091,
"autogenerated": false,
"ratio": 3.3165796344... |
__author__ = 'renhao.cui'
from scipy import stats
import json
from datetime import datetime
import tweetTextCleaner
import sys
reload(sys)
sys.setdefaultencoding('utf8')
filterTerms = ['iphone 7', 'pikachu', 'pokemon go', 'macbook pro']
def outlierExtractor():
print 'extracting outliers...'
brandList = []
... | {
"repo_name": "renhaocui/tweetInfluencer",
"path": "dataLabeler.py",
"copies": "2",
"size": "9812",
"license": "mit",
"hash": -5098807560071772000,
"line_mean": 45.0657276995,
"line_max": 196,
"alpha_frac": 0.5368935997,
"autogenerated": false,
"ratio": 3.8828650573802928,
"config_test": false,... |
__author__ = 'renhao.cui'
from sklearn import cross_validation
import os
import shutil
import glob
import subprocess
import readable_infer as ri
import modelUtility
import sys
def modifyFiles(fileName):
file = open(fileName, 'r')
fileContent = []
for line in file:
fileContent.append(line.replace('L... | {
"repo_name": "renhaocui/ensembleTopic",
"path": "LLDATopicModeling.py",
"copies": "1",
"size": "3628",
"license": "mit",
"hash": 5526030936372243000,
"line_mean": 37.6063829787,
"line_max": 151,
"alpha_frac": 0.6507717751,
"autogenerated": false,
"ratio": 3.28921124206709,
"config_test": true,... |
__author__ = 'renhao.cui'
import json
import os
import re
from langdetect import detect
puncList = ''' !()-[]{};:'"\,<>./?@#$%^&*_~'''
def extractLinks(input):
urls = re.findall("(?P<url>https?://[^\s]+)", input)
if len(urls) != 0:
for url in urls:
input = input.replace(url, '<URL>')
r... | {
"repo_name": "renhaocui/ensembleTopic",
"path": "dataGenerator.py",
"copies": "1",
"size": "3386",
"license": "mit",
"hash": -3735844265939051000,
"line_mean": 29.2410714286,
"line_max": 85,
"alpha_frac": 0.5005906675,
"autogenerated": false,
"ratio": 3.955607476635514,
"config_test": false,
... |
__author__ = 'renhao.cui'
import json
import statistics as stat
inputFile = open('adData//total.tweet', 'r')
favRatioFullList = []
favRetRatioFullList = []
list1=[]
list2=[]
favRatioList = []
favRetRatioList = []
followerLabelList = []
followerLabelFullList = []
listLabelList = []
listLabelFullList = []
friendLabelLi... | {
"repo_name": "renhaocui/adPlatform",
"path": "labelStatGenerator.py",
"copies": "2",
"size": "3547",
"license": "mit",
"hash": -6662066167830334000,
"line_mean": 32.4622641509,
"line_max": 77,
"alpha_frac": 0.7603608683,
"autogenerated": false,
"ratio": 3.3462264150943395,
"config_test": false... |
__author__ = 'renhao.cui'
import operator
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier
from sklearn import cross_validation
from sklearn.feature_extraction.text import *
import modelUtility
import sys
def normalization(scoreList):
outputList = []
total = s... | {
"repo_name": "renhaocui/ensembleTopic",
"path": "ensembleBaselines.py",
"copies": "1",
"size": "2327",
"license": "mit",
"hash": 3918214679644892000,
"line_mean": 34.2727272727,
"line_max": 151,
"alpha_frac": 0.6506231199,
"autogenerated": false,
"ratio": 3.7232,
"config_test": true,
"has_no... |
__author__ = 'renhao.cui'
import operator
import crossRef as cr
import math
def genFreq(corpus, data):
corpusCount = {}
for word in corpus:
corpusCount[word] = 0.0
for uttr in data:
for word in uttr:
corpusCount[word] += 1
sorted_corpusCount = sorted(corpusCount.items(), key... | {
"repo_name": "renhaocui/ensembleTopic",
"path": "utilities.py",
"copies": "1",
"size": "15809",
"license": "mit",
"hash": -2015798510082563800,
"line_mean": 33.5174672489,
"line_max": 107,
"alpha_frac": 0.5550635714,
"autogenerated": false,
"ratio": 3.865281173594132,
"config_test": true,
"h... |
__author__ = 'renhao.cui'
import re
import numpy as np
import json
def POSRatio(inputList):
out = []
temp = []
for item in inputList:
temp.append(float(item))
if sum(temp) == 0:
out = [0.0, 0.0, 0.0]
else:
for item in temp:
out.append(item/sum(temp))
return ... | {
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"path": "utilities.py",
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"autogenerated": false,
"ratio": 3.336144578313253,
"config_test": false,
"has_no... |
__author__ = 'renhao.cui'
import time
import json
import twitter
import os
import properties
def oauth_login():
# credentials for OAuth
CONSUMER_KEY = properties.twitter_cred['c_k']
CONSUMER_SECRET = properties.twitter_cred['c_s']
OAUTH_TOKEN = properties.twitter_cred['a_t']
OAUTH_TOKEN_SECRET = p... | {
"repo_name": "renhaocui/activityExtractor",
"path": "collector/placeTweetCollector.py",
"copies": "1",
"size": "5713",
"license": "mit",
"hash": 8405248559508126000,
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"line_max": 199,
"alpha_frac": 0.4901102748,
"autogenerated": false,
"ratio": 4.2007352941176475,
"c... |
__author__ = 'renhao.cui'
import time
import json
import twitter
import properties
def oauth_login():
# credentials for OAuth
CONSUMER_KEY = properties.twitter_cred['c_k']
CONSUMER_SECRET = properties.twitter_cred['c_s']
OAUTH_TOKEN = properties.twitter_cred['a_t']
OAUTH_TOKEN_SECRET = properties.... | {
"repo_name": "renhaocui/activityExtractor",
"path": "collector/followerCollecter.py",
"copies": "1",
"size": "3054",
"license": "mit",
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"alpha_frac": 0.4911591356,
"autogenerated": false,
"ratio": 4.5787106446776615,
"co... |
__author__ = 'renhao.cui'
import time
import json
import twitter
import requests
import properties
def oauth_login():
# credentials for OAuth
CONSUMER_KEY = properties.twitter_cred['c_k']
CONSUMER_SECRET = properties.twitter_cred['c_s']
OAUTH_TOKEN = properties.twitter_cred['a_t']
OAUTH_TOKEN_SECR... | {
"repo_name": "renhaocui/activityExtractor",
"path": "collector/placeCollector.py",
"copies": "1",
"size": "8299",
"license": "mit",
"hash": 8882476855936934000,
"line_mean": 39.881773399,
"line_max": 195,
"alpha_frac": 0.474033016,
"autogenerated": false,
"ratio": 4.2690329218107,
"config_test... |
__author__ = 'renhao.cui'
import time
from TwitterSearch import *
recordFile = open("tweetData//SpeedStick", 'a')
c_k = 'NNFnQv3zyeM99IDXg1IOrtMcQ'
c_s = 'GDc4vZtorwOiPgDNiaGrWDTWfqKNW2ZzMOTUkpJuF7gLdBCLZI'
a_t = '141612471-AnWgHssHt5rtOhlC8Cmy6GwEge9Z81v8MHQw6nXr'
a_t_s = 'gNE1nOhhc5CJoinMR6eUuyYBLR8YT3wK0tRb4yTUAY... | {
"repo_name": "renhaocui/ensembleTopic",
"path": "extractTweet.py",
"copies": "1",
"size": "1460",
"license": "mit",
"hash": -5779153065095448000,
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"alpha_frac": 0.6376712329,
"autogenerated": false,
"ratio": 2.8076923076923075,
"config_test": false,
... |
__author__ = 'renhao.cui'
def longestLength(input):
outputLength = 0
for key, value in input.items():
length = 0
if value != '-1' and value != '_':
length += 1
if value == '0':
if length > outputLength:
outputLength = length
... | {
"repo_name": "renhaocui/adPlatform",
"path": "parserExtractor.py",
"copies": "2",
"size": "4115",
"license": "mit",
"hash": -6558275372185734000,
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"line_max": 140,
"alpha_frac": 0.520291616,
"autogenerated": false,
"ratio": 3.5352233676975944,
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... |
"""
SCU is a module that wraps the findscu and movescu commands, which are part of DCMTK.
Usage involves the instantiation of an SCU object, which maintains knowledge of the caller and callee (data requester
and data source, respectively).
Specific Query objects are constructed (e.g., SeriesQuery, if you intend to s... | {
"repo_name": "scitran/reaper",
"path": "reaper/scu.py",
"copies": "1",
"size": "6536",
"license": "mit",
"hash": -7283210400278798000,
"line_mean": 31.8442211055,
"line_max": 127,
"alpha_frac": 0.5947062424,
"autogenerated": false,
"ratio": 3.5521739130434784,
"config_test": false,
"has_no_k... |
__author__ = 'rensholmer'
from gff import Gff
from gffsubpart import GffSubPart,TranslateError
from itertools import groupby
import uuid
import re
class Parser(object):
"""
Parser object gff3 formatted annotation files.
The parse() method return the processed Gff object.
Has several methods for known incorrectly ... | {
"repo_name": "holmrenser/gff_toolkit",
"path": "gff_toolkit/parser.py",
"copies": "1",
"size": "12368",
"license": "mit",
"hash": -593923268348445700,
"line_mean": 33.4512534819,
"line_max": 135,
"alpha_frac": 0.6697121604,
"autogenerated": false,
"ratio": 2.9773712084737602,
"config_test": fa... |
__author__ = 'rensholmer'
from gffsubpart import GffSubPart
from itertools import groupby #groupby for fasta parsing
import pprint
from intervaltree import IntervalTree, Interval
class Gff(object):
"""
Work in progess: holds GffSubParts object
"""
_combos = [{'gene':{'mRNA':['CDS','exon','five_prime_UTR','three_p... | {
"repo_name": "holmrenser/gff_toolkit",
"path": "gff_toolkit/gff.py",
"copies": "1",
"size": "17582",
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"hash": 7307567065455138000,
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"autogenerated": false,
"ratio": 3.004956417706375,
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... |
__author__ = 'rensholmer'
import re
import pprint
class TranslateError(Exception):
pass
class SeqError(Exception):
pass
class CoordinateError(Exception):
pass
class IDError(Exception):
pass
class GffSubPart(object):
"""
Work in progress: contained by Gff object
basically this is one line of a gff file, with p... | {
"repo_name": "holmrenser/gff_toolkit",
"path": "gff_toolkit/gffsubpart.py",
"copies": "1",
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__author__ = 'RetroPy'
import sys
import pygame
from pygame.locals import *
from lib.lang_en import *
from lib.colors import *
VERSION = '0'
pygame.init()
# TODO create a calculator button management screen.
SCREEN_WIDTH = 300
BUTTON_BUFFER = 2 # this is the small border separating the buttons from each other.
BU... | {
"repo_name": "retropy/calculator",
"path": "caculator.py",
"copies": "1",
"size": "4457",
"license": "unlicense",
"hash": 6816022802500200000,
"line_mean": 44.0303030303,
"line_max": 130,
"alpha_frac": 0.6028718869,
"autogenerated": false,
"ratio": 3.0319727891156463,
"config_test": false,
"... |
__author__ = 'Reuven'
import sys
class GetInfo():
def __init__(self):
self.u_name = ""
self.age = ""
self.uid = ""
def get_name(self):
while self.u_name.isalpha() is False:
self.u_name = raw_input("Please enter your name:")[:20]
if self.u_name == "exit... | {
"repo_name": "reuvenderay/PythonProjects",
"path": "enterInfo.py",
"copies": "1",
"size": "1339",
"license": "mit",
"hash": 198898928807572000,
"line_mean": 30.1395348837,
"line_max": 106,
"alpha_frac": 0.5197908887,
"autogenerated": false,
"ratio": 3.3475,
"config_test": false,
"has_no_keyw... |
__author__ = 'reverendken'
import urllib.parse
import os
import json
import requests
from ws4py.client.threadedclient import WebSocketClient
import slackapi.messages
API_BASE = "https://slack.com/api/"
SLACK_TOKEN = None
def func_once(func):
"""A decorator that runs a function only once."""
def decorated... | {
"repo_name": "reverendken/slackbots",
"path": "slackapi/slackapi.py",
"copies": "1",
"size": "3122",
"license": "apache-2.0",
"hash": 844622598363946100,
"line_mean": 23.2093023256,
"line_max": 74,
"alpha_frac": 0.6092248559,
"autogenerated": false,
"ratio": 3.6772673733804475,
"config_test": ... |
__author__ = 'reverendken'
import logging
class SlackMessage(object):
def __init__(self, api, message):
self._message = message
self._api = api
@classmethod
def is_this_type(cls, message):
return message['type'] == cls.MESSAGE_TYPE
@classmethod
def get_message(cls, api,... | {
"repo_name": "reverendken/slackbots",
"path": "slackapi/messages.py",
"copies": "1",
"size": "1387",
"license": "apache-2.0",
"hash": -6112296878510480000,
"line_mean": 24.2363636364,
"line_max": 111,
"alpha_frac": 0.6164383562,
"autogenerated": false,
"ratio": 3.7486486486486488,
"config_test... |
import boto3
import collections
import datetime
import botocore
ec2_client = boto3.client('ec2',region_name='ap-southeast-2')
sns = boto3.client('sns')
retention_days = 28 # Custmised retention
timezone_offset = 11 # AEST timezone
def lambda_handler(event, context):
try:
# Get all instances in your AWS... | {
"repo_name": "RexChenjq/AWS-Image-Backup",
"path": "Lambda-AMI-Backup.py",
"copies": "1",
"size": "3554",
"license": "mit",
"hash": -7671283578384057000,
"line_mean": 41.8313253012,
"line_max": 266,
"alpha_frac": 0.5998874508,
"autogenerated": false,
"ratio": 3.9314159292035398,
"config_test":... |
import boto3
import collections
import datetime
import time
import sys
import botocore
ec2_client = boto3.client('ec2',region_name='ap-southeast-2')
ec2_resource = boto3.resource('ec2',region_name='ap-southeast-2')
sns = boto3.client('sns')
images_all = ec2_resource.images.filter(Owners=["self"]) # All AMIs belong to... | {
"repo_name": "RexChenjq/AWS-Image-Backup",
"path": "Lambda-Backup-Purge.py",
"copies": "1",
"size": "4620",
"license": "mit",
"hash": 8344895638693818000,
"line_mean": 42.5943396226,
"line_max": 137,
"alpha_frac": 0.5357142857,
"autogenerated": false,
"ratio": 4.524975514201763,
"config_test":... |
__author__ = 'reyoung'
class ICommand(object):
def __init__(self):
pass
@staticmethod
def get_key():
"""
@type return: str
"""
return None
@staticmethod
def process(*args, **kwargs):
"""
@type return: str
"""
return ""
cla... | {
"repo_name": "reyoung/SlideGen2",
"path": "slidegen2/iengine.py",
"copies": "1",
"size": "1631",
"license": "mit",
"hash": -3168098177323693600,
"line_mean": 20.76,
"line_max": 79,
"alpha_frac": 0.5248313918,
"autogenerated": false,
"ratio": 4.150127226463105,
"config_test": false,
"has_no_k... |
from __future__ import absolute_import, print_function
import tweepy
from tweepy.streaming import StreamListener
from tweepy import OAuthHandler
from tweepy import Stream
import json
import key
global api
from nltk.tag import pos_tag
from nltk.chunk import ne_chunk
from nltk import sent_tokenize, word_tokenize, po... | {
"repo_name": "rhnvrm/swdel-lost-and-found-twitter-bot",
"path": "bot.py",
"copies": "1",
"size": "4681",
"license": "mit",
"hash": 3470040683106467300,
"line_mean": 34.7328244275,
"line_max": 142,
"alpha_frac": 0.513779107,
"autogenerated": false,
"ratio": 3.827473426001635,
"config_test": fal... |
__author__ = 'rhnvrm <hello@rohanverma.net>'
import cv2
import pysrt
import argparse
def mid(x,y):
return (x+y)/2
def save_new_frame(file, ms, text):
videocapture = cv2.VideoCapture(videofile)
videocapture.set(cv2.cv.CV_CAP_PROP_POS_MSEC,ms)
success, image = videocapture.read()
height, width = image.shape[:2... | {
"repo_name": "rhnvrm/SubScribe",
"path": "script.py",
"copies": "1",
"size": "1344",
"license": "mit",
"hash": -5304574423828281000,
"line_mean": 26.4285714286,
"line_max": 106,
"alpha_frac": 0.6569940476,
"autogenerated": false,
"ratio": 2.531073446327684,
"config_test": false,
"has_no_keyw... |
import os
import re
import tweepy
from tweepy import OAuthHandler
from textblob import TextBlob
class TwitterClient(object):
'''
Generic Twitter Class for the App
'''
def __init__(self, query, retweets_only=False, with_sentiment=False):
# keys and tokens from the Twitter Dev Console
c... | {
"repo_name": "vkku/GBAS",
"path": "labeled-tweet-generator-master/twitter.py",
"copies": "1",
"size": "2849",
"license": "mit",
"hash": -1142032435606983300,
"line_mean": 33.743902439,
"line_max": 102,
"alpha_frac": 0.5584415584,
"autogenerated": false,
"ratio": 3.8604336043360434,
"config_tes... |
__author__ = 'rhowell'
from vector3 import Vec3, random_in_unit_sphere, reflect, unit_vector, dot
from ray import Ray
class Material:
def scatter(self, ray_in, hit_record):
"""
:param ray_in:
:param hit_record:
:return: Tuple of bool, Vec3 attenuation, Ray scattered
"""
... | {
"repo_name": "rustyhowell/raytracer_py",
"path": "material.py",
"copies": "1",
"size": "1197",
"license": "mit",
"hash": -2873229904224474600,
"line_mean": 30.5,
"line_max": 86,
"alpha_frac": 0.6299081036,
"autogenerated": false,
"ratio": 3.2615803814713895,
"config_test": false,
"has_no_key... |
__author__ = 'rhythmicstar'
from Bio.Seq import Seq
from Bio.Alphabet import generic_dna
def separate(geneId):
sep = '.'
splitGeneId = geneId.partition(sep)
splittingGeneId = splitGeneId[0]
splittingGeneId = splittingGeneId.strip("'")
splittingGeneId = splittingGeneId.strip('"')
return splitti... | {
"repo_name": "jessicalettes/orthoexon",
"path": "orthoexon/util.py",
"copies": "1",
"size": "2144",
"license": "bsd-3-clause",
"hash": 1613265322894580200,
"line_mean": 31.5,
"line_max": 79,
"alpha_frac": 0.6231343284,
"autogenerated": false,
"ratio": 3.4358974358974357,
"config_test": false,
... |
# AUTHOR RICCARDO BONETTO
import numpy as np
import pandas as pd
import data_utils
from sklearn import preprocessing as pre
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import confusion_matrix
from sklearn.ensemble import RandomForestClassifier
from sklearn.decomposition import KernelPCA
... | {
"repo_name": "bonettor/PubNative_Challenge",
"path": "random_forest.py",
"copies": "1",
"size": "4654",
"license": "mit",
"hash": -8929591406814065000,
"line_mean": 36.24,
"line_max": 114,
"alpha_frac": 0.6897292651,
"autogenerated": false,
"ratio": 3.037859007832898,
"config_test": true,
"h... |
# AUTHOR RICCARDO BONETTO
import numpy as np
import pandas as pd
import tensorflow as tf
import math
import os.path
import data_utils
from sklearn import preprocessing as pre
from sklearn.model_selection import StratifiedKFold
from sklearn.decomposition import PCA, KernelPCA
_batch_size = 64
_num_units = 1024
_r... | {
"repo_name": "bonettor/PubNative_Challenge",
"path": "neural_classifier_balanced_train.py",
"copies": "1",
"size": "7899",
"license": "mit",
"hash": 8691127707045802000,
"line_mean": 31.6446280992,
"line_max": 98,
"alpha_frac": 0.6802126852,
"autogenerated": false,
"ratio": 3.0241194486983156,
... |
# AUTHOR RICCARDO BONETTO
import numpy as np
import pandas as pd
import tensorflow as tf
import math
import os.path
from sklearn import preprocessing as pre
from sklearn.model_selection import StratifiedKFold
from sklearn.decomposition import PCA, KernelPCA
_batch_size = 64
_num_units = 1024
_learning_rate = 1e-... | {
"repo_name": "bonettor/PubNative_Challenge",
"path": "neural_ensemble.py",
"copies": "1",
"size": "4430",
"license": "mit",
"hash": -1086887894818395500,
"line_mean": 30.4255319149,
"line_max": 97,
"alpha_frac": 0.6902934537,
"autogenerated": false,
"ratio": 2.9932432432432434,
"config_test": ... |
# AUTHOR RICCARDO BONETTO
import numpy as np
import pandas as pd
from sklearn import preprocessing as pre
from sklearn.model_selection import StratifiedKFold
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import confusion_matrix
from sklearn import svm
from sklearn.ensemble import RandomForestC... | {
"repo_name": "bonettor/PubNative_Challenge",
"path": "random_forest_classifier.py",
"copies": "1",
"size": "6956",
"license": "mit",
"hash": 7484307017951063000,
"line_mean": 33.6119402985,
"line_max": 103,
"alpha_frac": 0.6995399655,
"autogenerated": false,
"ratio": 2.961260110685398,
"config... |
__author__="Riccardo Zaccarelli, PhD <riccardo(at)gfz-potsdam.de, riccardo.zaccarelli(at)gmail.com>"
__date__ ="Dec 3, 2014 8:37:25 PM"
""""
Global keys used for parameters I/O operations and language settings:
please PROVIDE ONLY VARIABLES WITH NO LEADING UNDERSCORES ASSOCIATED TO A STRINGS
as a convention (not manda... | {
"repo_name": "GFZ-Centre-for-Early-Warning/caravan",
"path": "caravan/settings/globalkeys.py",
"copies": "1",
"size": "1067",
"license": "bsd-3-clause",
"hash": -4291882714585654300,
"line_mean": 28.6388888889,
"line_max": 100,
"alpha_frac": 0.7066541706,
"autogenerated": false,
"ratio": 2.68765... |
from keras import backend as K
from keras.engine.topology import Layer
from keras import initializers, regularizers, constraints
class AttentionWithContext(Layer):
"""
Attention operation, with a context/query vector, for temporal data.
Supports Masking.
Follows the work of Yang et al. [h... | {
"repo_name": "huajianjiu/Radical_CR_Encoder",
"path": "attention.py",
"copies": "1",
"size": "4970",
"license": "apache-2.0",
"hash": 5072157338841319000,
"line_mean": 39.0725806452,
"line_max": 102,
"alpha_frac": 0.5753672771,
"autogenerated": false,
"ratio": 3.991164658634538,
"config_test":... |
__author__ = 'Richard Lincoln, r.w.lincoln@gmail.com'
from math import pi, atan, exp
import xml.etree.ElementTree as ET
from pathdata import svg
SVG_NS = "http://www.w3.org/2000/svg"
KML_NS = "http://www.opengis.net/kml/2.2"
SVG_PATH = "SYSFigureA13Ungrouped.svg"
KML_PATH = "/tmp/UKGrid.kml"
#COLOR_400 = "rgb(0%,0%... | {
"repo_name": "rwl/pylon",
"path": "examples/national_grid/svg2kml.py",
"copies": "1",
"size": "6535",
"license": "apache-2.0",
"hash": 3206892913114844000,
"line_mean": 37.6686390533,
"line_max": 87,
"alpha_frac": 0.4399387911,
"autogenerated": false,
"ratio": 3.2544820717131473,
"config_test"... |
__author__ = 'Richard Lincoln, r.w.lincoln@gmail.com'
import csv
#import kmldom
import zipfile
import xml.etree.ElementTree as ET
import geolocator.gislib
import pylon
file = zipfile.ZipFile("spt.kmz", "r")
#for name in file.namelist():
# data = file.read(name)
# print name, len(data), repr(data[:40])
#eleme... | {
"repo_name": "rwl/pylon",
"path": "examples/national_grid/national_grid.py",
"copies": "1",
"size": "24525",
"license": "apache-2.0",
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"line_mean": 42.7946428571,
"line_max": 118,
"alpha_frac": 0.6684607543,
"autogenerated": false,
"ratio": 2.259327498848457,
"conf... |
__author__ = 'Richard Lincoln, r.w.lincoln@gmail.com'
import os
import csv
import zipfile
import xml.etree.ElementTree as ET
ns = "http://www.opengis.net/kml/2.2"
file = zipfile.ZipFile("national_grid.kmz", "r")
tree = ET.parse(file.open("doc.kml")) # Python 2.6 and later.
#tree = ET.parse("doc.kml")
root = tree.ge... | {
"repo_name": "rwl/pylon",
"path": "examples/national_grid/placemark.py",
"copies": "1",
"size": "5858",
"license": "apache-2.0",
"hash": -7586042087920700000,
"line_mean": 39.965034965,
"line_max": 115,
"alpha_frac": 0.5500170707,
"autogenerated": false,
"ratio": 2.9144278606965175,
"config_te... |
__author__ = 'Richard Lincoln, r.w.lincoln@gmail.com'
import os
import sys
import math
import csv
import logging
import pylon
logger = logging.getLogger()
logger = logging.getLogger()
logger.addHandler(logging.StreamHandler(sys.stdout))
logger.setLevel(logging.DEBUG)
DATA_DIR = "./data/"
BUS_DATA = [os.path.join(D... | {
"repo_name": "rwl/pylon",
"path": "examples/national_grid/ngt.py",
"copies": "1",
"size": "15406",
"license": "apache-2.0",
"hash": 6221700435734940000,
"line_mean": 32.9339207048,
"line_max": 124,
"alpha_frac": 0.5307023238,
"autogenerated": false,
"ratio": 3.2055763628797336,
"config_test": ... |
__author__ = 'Richard Lincoln, r.w.lincoln@gmail.com'
import os.path
import math
import zipfile
import xml.etree.ElementTree as ET
NS_KML = "http://www.opengis.net/kml/2.2"
MERC_SCALE = 1e5
NGT_PATH = "national_grid.kmz"
NGT_PATH_PATH = "ngt_path.kml"
CITIES_PATH = "uk_cities.kmz"
OUT_PATH = "/home/rwl/latex/tikz/t... | {
"repo_name": "rwl/pylon",
"path": "examples/national_grid/tikz.py",
"copies": "1",
"size": "5617",
"license": "apache-2.0",
"hash": -8223690530542464000,
"line_mean": 31.6569767442,
"line_max": 151,
"alpha_frac": 0.5695210967,
"autogenerated": false,
"ratio": 2.7945273631840797,
"config_test":... |
__author__ = 'Richard Lincoln, r.w.lincoln@gmail.com'
""" This example demonstrates how optimise power flow with Pyreto. """
import sys
import logging
import numpy
import scipy.io
import pylab
import pylon
import pyreto
from pyreto.util import plotGenCost
from pybrain.rl.agents import LearningAgent
from pybrain.rl.... | {
"repo_name": "rwl/pylon",
"path": "examples/pyreto/rlopf.py",
"copies": "1",
"size": "5046",
"license": "apache-2.0",
"hash": -1618333267561434400,
"line_mean": 32.417218543,
"line_max": 78,
"alpha_frac": 0.7128418549,
"autogenerated": false,
"ratio": 2.826890756302521,
"config_test": false,
... |
__author__ = 'Richard Lincoln, r.w.lincoln@gmail.com'
""" This example demonstrates how Pyreto can simulate a discrete representation
of a power exchange auction market. """
import sys
import logging
import pylab
from scipy import array
import pylon
import pyreto.discrete
import pyreto.continuous
import pyreto.roth... | {
"repo_name": "rwl/pylon",
"path": "examples/pyreto/discrete.py",
"copies": "1",
"size": "3874",
"license": "apache-2.0",
"hash": -7894151263399118000,
"line_mean": 32.6869565217,
"line_max": 96,
"alpha_frac": 0.7222509035,
"autogenerated": false,
"ratio": 2.8997005988023954,
"config_test": fal... |
__author__ = 'Richard Lincoln, r.w.lincoln@gmail.com'
""" This example demonstrates how to compute Nash equilibria. """
import numpy
from scipy.io import mmwrite
from pyreto import SmartMarket, DISCRIMINATIVE
from pyreto.discrete import MarketEnvironment, ProfitTask
from common import setup_logging, get_case6ww
s... | {
"repo_name": "rwl/pylon",
"path": "examples/pyreto/thesis/nash.py",
"copies": "1",
"size": "2816",
"license": "apache-2.0",
"hash": -4056260172365007400,
"line_mean": 24.1428571429,
"line_max": 67,
"alpha_frac": 0.4950284091,
"autogenerated": false,
"ratio": 2.6122448979591835,
"config_test": ... |
__author__ = 'Richard Lincoln, r.w.lincoln@gmail.com'
""" This example demonstrates how to produce a publication plot of generator
costs using matplotlib. """
import matplotlib
#matplotlib.use('WXAgg')#'TkAgg')
#matplotlib.rc('font', **{'family': 'sans-serif',
# 'sans-serif': ['Computer Moder... | {
"repo_name": "rwl/pylon",
"path": "examples/pyreto/thesis/costplot.py",
"copies": "1",
"size": "3546",
"license": "apache-2.0",
"hash": -2222257614788683800,
"line_mean": 31.8333333333,
"line_max": 81,
"alpha_frac": 0.5795262267,
"autogenerated": false,
"ratio": 2.952539550374688,
"config_test... |
__author__ = 'Richard Lincoln, r.w.lincoln@gmail.com'
""" This example demonstrates how to solve an OPF problem. """
import sys
import logging
import numpy
import scipy.io
from os.path import join, dirname
import pylon
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
# Define a path to the data file.
C... | {
"repo_name": "rwl/pylon",
"path": "examples/load_profile.py",
"copies": "1",
"size": "2176",
"license": "apache-2.0",
"hash": 7393101631954396000,
"line_mean": 32.4769230769,
"line_max": 78,
"alpha_frac": 0.6465992647,
"autogenerated": false,
"ratio": 2.5214368482039395,
"config_test": false,
... |
__author__ = 'Richard Lincoln, r.w.lincoln@gmail.com'
""" This example demonstrates how to use Pylon to simulate a power exchange
auction market. """
#import matplotlib
#matplotlib.use('WXAgg')#'TkAgg')
#matplotlib.rc('font', **{'family': 'sans-serif',
# 'sans-serif': ['Computer Modern Sans s... | {
"repo_name": "rwl/pylon",
"path": "examples/pyreto/auction.py",
"copies": "1",
"size": "7666",
"license": "apache-2.0",
"hash": 4991188410634317000,
"line_mean": 31.4830508475,
"line_max": 81,
"alpha_frac": 0.6575789199,
"autogenerated": false,
"ratio": 3.083668543845535,
"config_test": false,... |
__author__ = 'Richard Lincoln, r.w.lincoln@gmail.com'
""" This example demonstrates how to use Pyreto to simulate an episodic power
exchange auction market. """
import sys
import logging
import pylab
import scipy
import pyreto.util
import pyreto.continuous
from pylon import Case, OPF
from pybrain.rl.agents import ... | {
"repo_name": "rwl/pylon",
"path": "examples/pyreto/episodic.py",
"copies": "1",
"size": "4892",
"license": "apache-2.0",
"hash": 3114543547355565600,
"line_mean": 32.7379310345,
"line_max": 84,
"alpha_frac": 0.6860179886,
"autogenerated": false,
"ratio": 3.0594121325828643,
"config_test": fals... |
__author__ = 'Richard Lincoln, r.w.lincoln@gmail.com'
""" This example demonstrates how to use the discrete Roth-Erev reinforcement
learning algorithms to learn the n-armed bandit task. """
import pylab
import scipy
from pybrain.rl.agents import LearningAgent
from pybrain.rl.explorers import BoltzmannExplorer #@Unus... | {
"repo_name": "rwl/pylon",
"path": "examples/pyreto/bandit.py",
"copies": "1",
"size": "1933",
"license": "apache-2.0",
"hash": -6356878838941284000,
"line_mean": 30.6885245902,
"line_max": 77,
"alpha_frac": 0.651319193,
"autogenerated": false,
"ratio": 2.924357034795764,
"config_test": false,
... |
__author__ = 'Richard Lincoln, r.w.lincoln@gmail.com'
""" This script creates the plots published in Learning to Trade Power by
Richard Lincoln. """
import matplotlib
#matplotlib.use('WXAgg')#'TkAgg')
#matplotlib.rc('font', **{'family': 'sans-serif',
# 'sans-serif': ['Computer Modern Sans ser... | {
"repo_name": "rwl/pylon",
"path": "examples/pyreto/thesis/plot.py",
"copies": "1",
"size": "21533",
"license": "apache-2.0",
"hash": -2250265373309537300,
"line_mean": 31.4781297134,
"line_max": 81,
"alpha_frac": 0.5429805415,
"autogenerated": false,
"ratio": 2.9396587030716725,
"config_test":... |
__author__ = 'Richard Lincoln, r.w.lincoln@gmail.com'
""" This script runs the first experiment from chapter 5 of Learning to Trade
Power by Richard Lincoln. """
from numpy import array, zeros, mean, std
from pyreto import DISCRIMINATIVE, FIRST_PRICE #@UnusedImport
import pyreto.continuous
import pyreto.roth_erev #... | {
"repo_name": "rwl/pylon",
"path": "examples/pyreto/thesis/ex5_1.py",
"copies": "1",
"size": "11771",
"license": "apache-2.0",
"hash": 820916370239000800,
"line_mean": 27.1602870813,
"line_max": 81,
"alpha_frac": 0.6033472092,
"autogenerated": false,
"ratio": 3.4825443786982246,
"config_test": ... |
__author__ = 'Richard Lincoln, r.w.lincoln@gmail.com'
""" This script runs the first experiment from chapter 6 of Learning to Trade
Power by Richard Lincoln. """
from time import time
from numpy import zeros, mean, std
from pylon import PQ
import pyreto.continuous
from pyreto import DISCRIMINATIVE, FIRST_PRICE #@U... | {
"repo_name": "rwl/pylon",
"path": "examples/pyreto/thesis/ex6_1.py",
"copies": "1",
"size": "12162",
"license": "apache-2.0",
"hash": 3403872204168090000,
"line_mean": 27.7517730496,
"line_max": 79,
"alpha_frac": 0.600148002,
"autogenerated": false,
"ratio": 3.244930629669157,
"config_test": f... |
__author__ = "Richard Lindsley"
import sys
import os
import argparse
import logging
PY3 = sys.version_info.major == 3
try:
if not PY3:
import sip
sip.setapi('QDate', 2)
sip.setapi('QDateTime', 2)
sip.setapi('QString', 2)
sip.setapi('QTextStream', 2)
sip.setapi('QTi... | {
"repo_name": "richli/dame",
"path": "dame/dame.py",
"copies": "1",
"size": "1829",
"license": "mit",
"hash": 7447573134521931000,
"line_mean": 28.9836065574,
"line_max": 74,
"alpha_frac": 0.6014215418,
"autogenerated": false,
"ratio": 3.579256360078278,
"config_test": false,
"has_no_keywords... |
__author__ = 'richard'
import cPickle as pickle
import os
from roboskeeter.io import i_o
EXPERIMENT_PATH = i_o.get_directory('EXPERIMENT_PATH')
def store_mosquito_pickle(experiment):
pickle.dump(experiment, open(os.path.join(EXPERIMENT_PATH, "controls.p"), "wb"))
ref_data = score.get_data(exper.kinematics)... | {
"repo_name": "isomerase/RoboSkeeter",
"path": "roboskeeter/io/pickle_experiments.py",
"copies": "2",
"size": "1205",
"license": "mit",
"hash": -6245025051093537000,
"line_mean": 33.4285714286,
"line_max": 110,
"alpha_frac": 0.712033195,
"autogenerated": false,
"ratio": 3.0352644836272042,
"con... |
__author__ = 'richard'
import numpy as np
from roboskeeter.math import math_toolbox
class Flight():
def __init__(self, random_f_strength, stim_f_strength, damping_coeff):
self.random_f_strength = random_f_strength
self.stim_f_strength = stim_f_strength # TODO: separate surge strength, cast streng... | {
"repo_name": "isomerase/RoboSkeeter",
"path": "roboskeeter/flight.py",
"copies": "2",
"size": "3556",
"license": "mit",
"hash": -3536590644379134500,
"line_mean": 29.1355932203,
"line_max": 113,
"alpha_frac": 0.5292463442,
"autogenerated": false,
"ratio": 4.390123456790123,
"config_test": fals... |
__author__ = 'richard'
import os
import pickle
import score_y
from roboskeeter.io import i_o
reload(score_y)
reload(kinematics)
EXPERIMENT_PATH = i_o.get_directory('EXPERIMENT_PATH')
def store_mosquito_pickle():
MOSQUITOES = kinematics.ExperimentalKinematics()
MOSQUITOES.experiment_data_to_DF(experimental... | {
"repo_name": "isomerase/RoboSkeeter",
"path": "roboskeeter/io/pickle_experimentsY.py",
"copies": "2",
"size": "1407",
"license": "mit",
"hash": -3268704572040629000,
"line_mean": 32.5,
"line_max": 111,
"alpha_frac": 0.7192608387,
"autogenerated": false,
"ratio": 2.9010309278350515,
"config_tes... |
__author__ = 'richard'
from scipy.stats import ks_2samp
from roboskeeter.experiments import load_experiment
class Scoring():
def __init__(self,
condition='Control',
score_weights=None,
reference_data=None
):
self.condition = condition
... | {
"repo_name": "isomerase/mozziesniff",
"path": "roboskeeter/math/scoring/scoring.py",
"copies": "2",
"size": "2563",
"license": "mit",
"hash": 2628433812028469000,
"line_mean": 36.1449275362,
"line_max": 105,
"alpha_frac": 0.5864221615,
"autogenerated": false,
"ratio": 4.4342560553633215,
"conf... |
__author__ = 'Richard'
from threading import Thread
TRUPPENPREIS = 100
UEBERNAHMEPREIS = 1000
LOOT = 200
MODEEFFEKT = 0.3
MODERABATT = 1 - MODEEFFEKT
class DummyAI (object):
def __init__(self):
self.player = None
def add_player(self, player):
self.player = player
@staticmethod
def... | {
"repo_name": "TurnTheTideTM/AI",
"path": "ProjectNeurons/core.py",
"copies": "1",
"size": "14436",
"license": "mit",
"hash": -6265866879669322000,
"line_mean": 29.7803837953,
"line_max": 96,
"alpha_frac": 0.5289553893,
"autogenerated": false,
"ratio": 3.5723830734966593,
"config_test": false,
... |
__author__ = 'richard'
import logging
from datetime import datetime
from scipy.optimize import minimize_scalar, basinhopping, brute
from roboskeeter import experiments
from roboskeeter.math.scoring import scoring
logging.basicConfig(filename='basin_hopping.log', level=logging.DEBUG)
# TODO: save guesses and their ... | {
"repo_name": "isomerase/RoboSkeeter",
"path": "roboskeeter/math/optimizers/optimizer.py",
"copies": "2",
"size": "10155",
"license": "mit",
"hash": -9188319098102907000,
"line_mean": 36.8955223881,
"line_max": 172,
"alpha_frac": 0.5179714426,
"autogenerated": false,
"ratio": 3.737578211262422,
... |
__author__ = 'richard'
import logging
from datetime import datetime
from scipy.optimize import minimize_scalar
from roboskeeter.experiments import start_simulation
from roboskeeter.scripts import load_mosquito_kde_data_dicts
# wrapper func for agent 3D
def fly_wrapper(BOUNCE_COEFF, *args):
"""
:param bias_... | {
"repo_name": "isomerase/mozziesniff",
"path": "roboskeeter/math/optimizers/optimizer_y.py",
"copies": "2",
"size": "3179",
"license": "mit",
"hash": 1887114110103095000,
"line_mean": 31.1111111111,
"line_max": 120,
"alpha_frac": 0.509594212,
"autogenerated": false,
"ratio": 3.762130177514793,
... |
__author__ = 'richard'
import numpy as np
from numpy import *
from matplotlib import pyplot as plt
from matplotlib.patches import FancyArrowPatch
from mpl_toolkits.mplot3d import proj3d
class Arrow3D(FancyArrowPatch):
def __init__(self, xs, ys, zs, *args, **kwargs):
FancyArrowPatch.__init__(self, (0, 0)... | {
"repo_name": "isomerase/mozziesniff",
"path": "roboskeeter/plotting/simple_3D_vectors.py",
"copies": "2",
"size": "2196",
"license": "mit",
"hash": 7632813179791669000,
"line_mean": 30.3714285714,
"line_max": 117,
"alpha_frac": 0.5869763206,
"autogenerated": false,
"ratio": 2.783269961977186,
... |
__author__ = 'richard'
"""
"""
from roboskeeter.math.kinematic_math import DoMath
# from roboskeeter.math.scoring.scoring import Scoring
from roboskeeter.simulator import Simulator
from roboskeeter.environment import Environment
from roboskeeter.observations import Observations
from roboskeeter.plotting.plot_funcs_wra... | {
"repo_name": "isomerase/mozziesniff",
"path": "roboskeeter/experiments.py",
"copies": "2",
"size": "8222",
"license": "mit",
"hash": -5902927779284093000,
"line_mean": 40.9489795918,
"line_max": 141,
"alpha_frac": 0.5488932133,
"autogenerated": false,
"ratio": 4.288993218570683,
"config_test":... |
__author__ = "Richard O'Dwyer"
__email__ = "richard@richard.do"
__license__ = "None"
import re
def process_log(log):
requests = get_requests(log)
files = get_files(requests)
totals = file_occur(files)
return totals
def get_requests(f):
log_line = f.read()
pat = (r''
'(\d+.\d+.\d+.\... | {
"repo_name": "GeekyTheory/Nginx-Log-Parser",
"path": "main.py",
"copies": "1",
"size": "2340",
"license": "apache-2.0",
"hash": -1548898559441928200,
"line_mean": 22.6363636364,
"line_max": 84,
"alpha_frac": 0.5260683761,
"autogenerated": false,
"ratio": 3.611111111111111,
"config_test": false... |
from __future__ import with_statement
import os
import threading
import logging
import phantom
from sickbeard import classes
# number of log files to keep
NUM_LOGS = 3
# log size in bytes
LOG_SIZE = 10000000 # 10 megs
ERROR = logging.ERROR
WARNING = logging.WARNING
MESSAGE = logging.INFO
DEBUG = logging.DEBUG... | {
"repo_name": "rosterloh/Phantom",
"path": "phantom/logger.py",
"copies": "1",
"size": "5092",
"license": "mit",
"hash": 5747848737475109000,
"line_mean": 30.245398773,
"line_max": 118,
"alpha_frac": 0.5551846033,
"autogenerated": false,
"ratio": 4.0284810126582276,
"config_test": false,
"has... |
import re
from scrapy.spider import BaseSpider
from scrapy.selector import HtmlXPathSelector
from crawly.items import CrawlyItem
from crawly.models import db, Domain, Template
def get_urls():
"""get stored urls from database (tb_domain)"""
for url in db.session.query(Domain).outerjoin(Template).\
f... | {
"repo_name": "icyrizard/webcrawler",
"path": "crawly/crawly/spiders/crawly_spider.py",
"copies": "1",
"size": "2925",
"license": "mit",
"hash": 1440366602569276400,
"line_mean": 30.7934782609,
"line_max": 78,
"alpha_frac": 0.5733333333,
"autogenerated": false,
"ratio": 4.0344827586206895,
"con... |
__author__ = 'richardxx'
import mongoctl.repository as repository
from mongoctl.utils import document_pretty_string
from mongoctl.mongoctl_logging import log_info, log_error
from mongoctl.objects.sharded_cluster import ShardedCluster
from mongoctl.objects.replicaset_cluster import ReplicaSetCluster
from mongoctl.er... | {
"repo_name": "richardxx/mongoctl-service",
"path": "mongoctl/commands/cluster/control.py",
"copies": "2",
"size": "3639",
"license": "mit",
"hash": 77479544986945020,
"line_mean": 30.1025641026,
"line_max": 90,
"alpha_frac": 0.6281945589,
"autogenerated": false,
"ratio": 4.443223443223443,
"co... |
__author__ = "Richie Foreman <richie.foreman@gmail.com>"
import os
import time
import logging
import httplib2
import json
import base64
try:
from Crypto.Signature import PKCS1_v1_5
from Crypto.Hash import SHA256
from Crypto.PublicKey import RSA
except:
ImportError("You need to enable or install PyCryp... | {
"repo_name": "timblakely/bigbrain",
"path": "bigbrain/bigbrain/oauth2/PyCryptoSignedJWT.py",
"copies": "1",
"size": "11121",
"license": "apache-2.0",
"hash": -759607463517080200,
"line_mean": 31.1416184971,
"line_max": 260,
"alpha_frac": 0.6238647604,
"autogenerated": false,
"ratio": 3.970367725... |
__author__ = 'richss'
"""
The MIT License (MIT)
Copyright (c) 2015 Richard S. Stansbury
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the righ... | {
"repo_name": "richss/TwitterBots",
"path": "InitialTwitterBot/QueryBot.py",
"copies": "1",
"size": "5742",
"license": "mit",
"hash": 7135950102573124000,
"line_mean": 33.5963855422,
"line_max": 109,
"alpha_frac": 0.6649251132,
"autogenerated": false,
"ratio": 4.136887608069165,
"config_test": ... |
__author__ = 'Rick Dean'
__doc__ = ' '
#Created by Rick on 2017-01-02.
import numpy as np
from bokeh.resources import CDN
from bokeh.embed import file_html
from bokeh.plotting import figure
from bokeh.layouts import column
from sklearn.cluster import KMeans
class Visuals:
@staticmethod
def k_means_2(data):
tr... | {
"repo_name": "deandevl/num_sci_server_projects",
"path": "Fleet Drivers K-Means/Code/Visuals.py",
"copies": "1",
"size": "1366",
"license": "mit",
"hash": -7728043479034923000,
"line_mean": 31.5238095238,
"line_max": 103,
"alpha_frac": 0.6383601757,
"autogenerated": false,
"ratio": 3.17674418604... |
__author__ = 'Rick Dean'
__doc__ = ' '
#Created by Rick on 2017-03-16.
import numpy as np
import numpy.ma as ma
import statsmodels.api as sm
from scipy import stats
from statsmodels.sandbox.regression.predstd import wls_prediction_std
from bokeh.layouts import column,layout
from bokeh.resources import CDN
from bokeh.... | {
"repo_name": "deandevl/num_sci_server_projects",
"path": "Predicting Home Prices/Code/Visuals.py",
"copies": "1",
"size": "8017",
"license": "mit",
"hash": -8065592742411729000,
"line_mean": 40.3298969072,
"line_max": 120,
"alpha_frac": 0.6357739803,
"autogenerated": false,
"ratio": 3.0367424242... |
__author__ = 'Rick Dean'
__doc__ = ' '
#Created by Rick on 2017-03-19.
import re
import csv
type_map = dict(byte=int, int=int, long=int, float=float, double=float)
var_info = []
names = []
for line in open('2002FemPreg.dct'):
match = re.search( r'_column\(([^)]*)\)', line)
if match:
start = int(match.group(1... | {
"repo_name": "deandevl/num_sci_server_projects",
"path": "ThinkStats/Code/convert_dat_to_csv_file.py",
"copies": "1",
"size": "1228",
"license": "mit",
"hash": -1735799985719256000,
"line_mean": 24.6041666667,
"line_max": 71,
"alpha_frac": 0.6083061889,
"autogenerated": false,
"ratio": 2.6929824... |
__author__ = 'Rick Qiu'
from bs4 import BeautifulSoup
def parse_problem_archive(htmldoc):
"""
This function is to handle below page:
http://community.topcoder.com/tc?module=ProblemArchive&sc=0&sd=asc&er=10000
it returns a list and each element is a diction like below:
{'name':'Aaagmnrs',
'url'... | {
"repo_name": "rick-qiu/tcgrabber",
"path": "parser.py",
"copies": "1",
"size": "2481",
"license": "mit",
"hash": 5625924998386320000,
"line_mean": 34.4428571429,
"line_max": 202,
"alpha_frac": 0.6473196292,
"autogenerated": false,
"ratio": 2.8848837209302327,
"config_test": false,
"has_no_ke... |
__author__ = 'Rick Qiu'
import configuration
import urllib.request
import urllib.response
import urllib.parse
class FileGrabber:
def __init__(self):
data = urllib.parse.urlencode(query={'username':configuration.username,
'password':configuration.password,
... | {
"repo_name": "rick-qiu/tcgrabber",
"path": "filegrabber.py",
"copies": "1",
"size": "1324",
"license": "mit",
"hash": -4106924808885736000,
"line_mean": 36.8571428571,
"line_max": 101,
"alpha_frac": 0.6246223565,
"autogenerated": false,
"ratio": 3.8941176470588235,
"config_test": false,
"has... |
__author__ = "Rick Sherman"
__credits__ = "Jeremy Schulman, Nitin Kumar"
import unittest
from nose.plugins.attrib import attr
from jnpr.junos import Device
from jnpr.junos.utils.start_shell import StartShell
from mock import patch, MagicMock, call
@attr('unit')
class TestStartShell(unittest.TestCase):
@patch('... | {
"repo_name": "JamesNickerson/py-junos-eznc",
"path": "tests/unit/utils/test_start_shell.py",
"copies": "1",
"size": "1815",
"license": "apache-2.0",
"hash": 5519609518240943000,
"line_mean": 34.5882352941,
"line_max": 87,
"alpha_frac": 0.6567493113,
"autogenerated": false,
"ratio": 3.31204379562... |
__author__ = "Rick Sherman"
__credits__ = "Jeremy Schulman"
import unittest
import os
from nose.plugins.attrib import attr
from jnpr.junos import Device
from jnpr.junos.op.phyport import PhyPortStatsTable
from jnpr.junos.op.ethport import EthPortTable
from ncclient.manager import Manager, make_device_handler
from nc... | {
"repo_name": "JamesNickerson/py-junos-eznc",
"path": "tests/unit/factory/test_optable.py",
"copies": "1",
"size": "3679",
"license": "apache-2.0",
"hash": 2455931987863321600,
"line_mean": 32.1441441441,
"line_max": 78,
"alpha_frac": 0.6121228595,
"autogenerated": false,
"ratio": 3.3907834101382... |
__author__ = "Rick Sherman, Nitin Kumar"
__credits__ = "Jeremy Schulman"
import unittest
from nose.plugins.attrib import attr
from mock import MagicMock, patch
from jnpr.junos import Device
from jnpr.junos.factory.view import View
from jnpr.junos.op.phyport import PhyPortStatsTable, PhyPortStatsView
from lxml import e... | {
"repo_name": "pklimai/py-junos-eznc",
"path": "tests/unit/factory/test_view.py",
"copies": "3",
"size": "7145",
"license": "apache-2.0",
"hash": -7663567226459055000,
"line_mean": 36.6052631579,
"line_max": 78,
"alpha_frac": 0.5321203639,
"autogenerated": false,
"ratio": 3.729123173277662,
"co... |
__author__ = "Rick Sherman, Nitin Kumar"
__credits__ = "Jeremy Schulman"
import unittest2 as unittest
from nose.plugins.attrib import attr
from mock import MagicMock, patch, mock_open
import os
from lxml import etree
from ncclient.manager import Manager, make_device_handler
from ncclient.transport import SS... | {
"repo_name": "JamesNickerson/py-junos-eznc",
"path": "tests/unit/test_device.py",
"copies": "1",
"size": "19176",
"license": "apache-2.0",
"hash": 6140360169622676000,
"line_mean": 39.3275862069,
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"alpha_frac": 0.6009073842,
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"ratio": 3.808540218470705,
"... |
__author__ = "Rick Sherman"
import unittest2 as unittest
from nose.plugins.attrib import attr
from mock import patch
import os
import json
from jnpr.junos import Device
from jnpr.junos.factory.to_json import PyEzJSONEncoder, TableJSONEncoder, TableViewJSONEncoder
from jnpr.junos.op.routes import RouteSummaryTable
fro... | {
"repo_name": "b1naryth1ef/py-junos-eznc",
"path": "tests/unit/factory/test_to_json.py",
"copies": "4",
"size": "3883",
"license": "apache-2.0",
"hash": 4896008544439438000,
"line_mean": 41.6703296703,
"line_max": 153,
"alpha_frac": 0.5912953902,
"autogenerated": false,
"ratio": 3.454626334519573... |
__author__ = 'Ricky'
from flask import Flask, render_template
from flask_mail import Mail, Message
#*** Remember to change the email sender at production time! ***
def send_welcome_email(email, plan):
app = Flask(__name__)
mail = Mail()
mail.init_app(app)
msg = Message("You've successfully signed up f... | {
"repo_name": "rcharp/Simple",
"path": "app/pages/emails.py",
"copies": "1",
"size": "2665",
"license": "bsd-2-clause",
"hash": 5112438206537319000,
"line_mean": 34.0657894737,
"line_max": 117,
"alpha_frac": 0.5909943715,
"autogenerated": false,
"ratio": 4.056316590563166,
"config_test": false,... |
__author__ = 'Ricky'
import stripe
from flask import session
import time
from date import *
from eventClass import Event
import threading
import Queue
from calc import calculate, chartify
import inspect
from flask_cache import Cache
from flask_user import current_user
from app.app_and_db import app
from stripeErrorCla... | {
"repo_name": "rcharp/Simple",
"path": "app/pages/events.py",
"copies": "1",
"size": "6300",
"license": "bsd-2-clause",
"hash": 4319358812082459000,
"line_mean": 31.6476683938,
"line_max": 168,
"alpha_frac": 0.6233333333,
"autogenerated": false,
"ratio": 3.884093711467324,
"config_test": false,... |
__author__ = 'rico jonschkowski'
import cv2
import scipy.ndimage
import numpy as np
from sklearn import preprocessing # @UnresolvedImport
from sklearn.svm import SVC # @UnresolvedImport
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline imp... | {
"repo_name": "start-jsk/jsk_apc",
"path": "jsk_apc2016_common/python/jsk_apc2016_common/rbo_segmentation/probabilistic_segmentation.py",
"copies": "1",
"size": "30350",
"license": "bsd-3-clause",
"hash": 6323820982267696000,
"line_mean": 48.7540983607,
"line_max": 209,
"alpha_frac": 0.6198682043,
... |
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