text stringlengths 0 1.05M | meta dict |
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from openstack_plugin.common import clients
from aiorchestra.core import utils
@utils.operation
async def create(node, inputs):
use_existing = node.properties['use_existing']
name = node.properties['name']
nova = clients.openstack.nova(node)
if not use_existing:
pub_key = node.properties.get... | {
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"path": "openstack_plugin/tasks/ssh.py",
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import asyncio
import collections
try:
import uvloop
except ImportError:
uvloop = None
from toscaparser import tosca_template
from aiorchestra.core import node
from aiorchestra.core import logger as log
class OrchestraContext(object):
(PENDING, RUNNING, COMPLETED, FAILED) = ('pending', 'running',
... | {
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"path": "aiorchestra/core/context.py",
"copies": "1",
"size": "14076",
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"autogenerated": false,
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"conf... |
import asyncio
class Singleton(type):
_instance = None
def __call__(cls, *args, **kwargs):
if not cls._instance:
cls._instance = super(Singleton, cls).__call__(*args, **kwargs)
return cls._instance
async def retry(fn, args=None, kwargs=None, exceptions=None,
tas... | {
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"path": "aiorchestra/core/utils.py",
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"config_... |
import importlib
import sys
from toscaparser import functions
from aiorchestra.core import noop
RELATIONSHIP_STABS = {
'link': 'aiorchestra.core.noop:link',
'unlink': 'aiorchestra.core.noop:unlink',
}
def check_for_event_definition(action):
def wraps(*args, **kwargs):
self, node, event = args... | {
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"config_... |
import logging
import sys
def common_logger_setup(
level=logging.DEBUG,
filename='/tmp/aiorchestra.log',
log_formatter='[%(asctime)s] - '
'%(name)s - '
'%(levelname)s - '
'%(module)s.py:%(lineno)d - '
'%(f... | {
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"path": "aiorchestra/core/logger.py",
"copies": "1",
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"hash": 1980757115956987400,
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"autogenerated": false,
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import os
import setuptools
def read(fname):
return open(os.path.join(os.path.dirname(__file__), fname)).read()
setuptools.setup(
name='aiorchestra-asyncssh-plugin',
version='0.1.2',
description='AsyncIO TOSCA orchestrator AsyncSSH plugin for Software configuration',
long_description=read('READM... | {
"repo_name": "aiorchestra/aiorchestra-asyncssh-plugin",
"path": "setup.py",
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"autogenerated": false,
"ratio": 4.025547445255475,
"config_t... |
import os
import setuptools
def read(fname):
return open(os.path.join(os.path.dirname(__file__), fname)).read()
setuptools.setup(
name='aiorchestra',
version='0.1.3',
description='AsyncIO TOSCA orchestrator',
long_description=read('README.rst'),
url='https://aiorchestra.io/',
author='Den... | {
"repo_name": "aiorchestra/aiorchestra",
"path": "setup.py",
"copies": "1",
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"license": "apache-2.0",
"hash": -5396597634761603000,
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"autogenerated": false,
"ratio": 4.098330241187384,
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"has... |
async def create(context, name_or_id, neutronclient,
description=None, use_existing=False):
"""
Creates security group
:param context:
:param name_or_id:
:param neutronclient:
:param description:
:param use_existing:
:return:
"""
if not use_existing:
sg... | {
"repo_name": "aiorchestra/aiorchestra-openstack-plugin",
"path": "openstack_plugin/networking/security_group_and_rules.py",
"copies": "1",
"size": "2382",
"license": "apache-2.0",
"hash": 8602192130120948000,
"line_mean": 31.6301369863,
"line_max": 78,
"alpha_frac": 0.6011754828,
"autogenerated": ... |
async def create(context,
neutronclient, floating_network_id,
port_id, use_existing=False,
existing_floating_ip_id=None):
"""
:param context:
:param neutronclient:
:param floating_ip_network:
:param port_id:
:param use_existing:
:param ex... | {
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"path": "openstack_plugin/networking/floating_ip.py",
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"rat... |
import asyncio
import os
import uvloop
import testtools
from aiorchestra.core import context
from aiorchestra.core import logger
LOG = logger.UnifiedLogger(
log_to_console=True,
level=os.environ.get('AIORCHESTRA_LOG_LEVEL', 'INFO')
).setup_logger(__name__)
def with_template(template_name):
def actio... | {
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"path": "aiorchestra/tests/base.py",
"copies": "1",
"size": "3050",
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"hash": -4754077714545244000,
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"alpha_frac": 0.6098360656,
"autogenerated": false,
"ratio": 3.865652724968314,
"config... |
__author__ = 'deonheyns'
import os
import re
class Logfind(object):
def __init__(self):
self.__dot_logfind = '.logfind'
def find(self, text, treat_as_or=False):
patterns = self.read_dot_logfind()
log_files = self.get_log_files(patterns)
matches = self.read_log_files(log_files,... | {
"repo_name": "DeonHeyns/logfind",
"path": "logfind/logfind.py",
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"has_no... |
__author__ = 'DeonHeyns'
# -*- coding: utf-8 -*-
import requests
import json as jason
class Client(object):
def __init__(self):
self._domain = 'http://data.fcc.gov/api'
self._params = None
self._url = None
self._response = None
def execute(self):
url = self._domain +... | {
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"path": "client.py",
"copies": "1",
"size": "4962",
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"autogenerated": false,
"ratio": 4.0805921052631575,
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"has_... |
import sys
import operator
import random
markov_chain = {}
#adds the specified word to the markov chain
def add_to_chain(lastword, word):
if not markov_chain.has_key(lastword):
markov_chain[lastword] = {}
if not markov_chain[lastword].has_key(word):
markov_chain[lastword][word] = 1
else:
markov_chain[lastwo... | {
"repo_name": "HCDevelopers/Snippets",
"path": "Python/glibberish.py",
"copies": "1",
"size": "2833",
"license": "bsd-3-clause",
"hash": -37209593622741770,
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"config_test"... |
__author__ = 'DeRaaf'
"""
EXAMPLE
"""
"""
These are the minimum on classes you need to import. If you want to hook up other devices than an Arduino take the
Arduino class (Ardiuno.py) as template to create a physical device with which this software can speak
"""
import sys
from SerialPort import *
from Arduino impor... | {
"repo_name": "DeRaafMedia/ProjectIRCInteractivity",
"path": "main.py",
"copies": "1",
"size": "5063",
"license": "artistic-2.0",
"hash": 5502803103746345000,
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"alpha_frac": 0.4645467114,
"autogenerated": false,
"ratio": 3.9740973312401886,
"config_te... |
__author__ = 'DeRaaf'
# TODO Clean up comments. Fix bugs. On going project!
from SerialPort import *
class Arduino(SerialPort):
def __init__(self,
serial_port_id,
physical_device_id):
"""
serial_port_id -> Give the serial port id as a variable name (i.e serial_po... | {
"repo_name": "DeRaafMedia/ProjectIRCInteractivity",
"path": "Arduino.py",
"copies": "1",
"size": "5003",
"license": "artistic-2.0",
"hash": 2466537438559972000,
"line_mean": 35.5255474453,
"line_max": 117,
"alpha_frac": 0.4401359184,
"autogenerated": false,
"ratio": 4.257872340425532,
"config_... |
__author__ = 'DeRaaf'
# TODO Clean up comments. Fix bugs. On going project!
import os
from os import system
import sys
import ConfigParser
import threading
import csv
import time
class Utilities (object):
def __init__(self):
self.preference_parser = ConfigParser.RawConfigParser()
self.thread = t... | {
"repo_name": "DeRaafMedia/ProjectIRCInteractivity",
"path": "Utilities.py",
"copies": "1",
"size": "11689",
"license": "artistic-2.0",
"hash": 6665393720981205000,
"line_mean": 34,
"line_max": 116,
"alpha_frac": 0.5402515185,
"autogenerated": false,
"ratio": 4.097090781633368,
"config_test": f... |
__author__ = 'DeRaaf'
# TODO Clean up comments. Fix bugs. On going project!
import serial
class SerialPort(object):
def __init__(self,
serial_port,
baud_rate,
time_out,
serial_port_id):
self.serial_port = serial_port
self.baud_... | {
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"con... |
__author__ = 'DeRaaf'
# TODO Clean up comments. Fix bugs. On going project!
import socket
from time import sleep
from Utilities import *
load_imports = Utilities()
load_imports.load_skills_init('skills/')
from skills import *
class IRCBot(object):
def __init__(self,
irc_network,
... | {
"repo_name": "DeRaafMedia/ProjectIRCInteractivity",
"path": "IRCBot.py",
"copies": "1",
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"autogenerated": false,
"ratio": 4.132632430246664,
"config_t... |
__author__ = 'deranjer'
#import bs4
import datetime
import urllib
#import pprint #used for printing XML trees
import xml.etree.ElementTree as ET #cElemetTree is depreciated apparently
from Settings import serverList
from flask import Flask, render_template, redirect
app = Flask(__name__, template_folder='Templates')
... | {
"repo_name": "deranjer/Py-Multi-Monit",
"path": "main.py",
"copies": "1",
"size": "6489",
"license": "mit",
"hash": 1015333116704205400,
"line_mean": 41.1363636364,
"line_max": 250,
"alpha_frac": 0.6879334258,
"autogenerated": false,
"ratio": 3.8809808612440193,
"config_test": false,
"has_no... |
__author__ = 'derekbrameyer'
import random
import json
import datetime
def main():
maxstealcount = 2
currentturn=1
boolines = json.loads(open("boo_lines.json").read())
reportlines = json.loads(open("report_lines.json").read())
print greenify("\nWelcome to White Elephant! Please input names line b... | {
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"path": "main.py",
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"size": "8365",
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"line_mean": 38.2769953052,
"line_max": 161,
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import sys
import copy
from googledata import spreadsheetdata
from proxies import proxyassigned
from zabbixauth import serverassigned
from infotemplate import HOSTINFO
import hosts
# Server information Google spreadsheet details
GoogleSpreadsheetURL = ''
GoogleSheetName = 'servers'
worksheet, values_list = spreadshee... | {
"repo_name": "deshike22/zabbixonboarding",
"path": "zabbix_onboarding.py",
"copies": "1",
"size": "2695",
"license": "apache-2.0",
"hash": -310874566477389000,
"line_mean": 39.223880597,
"line_max": 101,
"alpha_frac": 0.5910946197,
"autogenerated": false,
"ratio": 3.8118811881188117,
"config_t... |
__author__ = 'Deus'
import unittest
from parse import parser
from ast import *
from typecheck import *
from codegen import *
def desent(level, x):
if isinstance(x, tuple):
print(" " * level, end="")
print(x)
for y in x:
if y:
desent(level + 1, y)
elif isi... | {
"repo_name": "pollow/CoreSML",
"path": "src/parser_test.py",
"copies": "1",
"size": "7786",
"license": "mit",
"hash": -5343247389395168000,
"line_mean": 31.3070539419,
"line_max": 119,
"alpha_frac": 0.4265348061,
"autogenerated": false,
"ratio": 3.152226720647773,
"config_test": true,
"has_n... |
__author__ = 'developer'
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "... | {
"repo_name": "melhindi/Assignments_ParallelDataProcessingAndAnalysis",
"path": "Assignment2/IPDPA_Assignment2_1.1_MEH.py",
"copies": "1",
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"license": "apache-2.0",
"hash": 3154802963749904400,
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"""Author:Devika Kakkar
Date: 03/07/17
Name: tweetRehydration.py
Version: 1.0
Function: This script is useful to get the details (hydrate) a collection of Tweet IDs. Returns fully-hydrated Tweet objects for up to 100 Tweets per request, as specified by comma-separated values passed to the id parameter.
Input: The scrip... | {
"repo_name": "cga-harvard/hhypermap-bop",
"path": "BOP-utilities/Twitter_Rehydration/src/Tweet_Rehydration_API.py",
"copies": "2",
"size": "3913",
"license": "apache-2.0",
"hash": 4438614660887941600,
"line_mean": 38.9285714286,
"line_max": 225,
"alpha_frac": 0.6802964477,
"autogenerated": false,
... |
"""Author:Devika Kakkar
Date: 7/18/16
Name: sentiment.py
Version: 1.0
Function: This module is used for predicting the sentiment of a tweet.
Input: Tweet from the user.
Output: The sentiment of the tweet (0 for negative and 1 for positive)
"""
#Import the required libraries
import sys
import time
import re
import nltk... | {
"repo_name": "cga-harvard/hhypermap-bop",
"path": "Twitter-Sentiment-Classifier/src/sentiment.py",
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"line_mean": 24.9369369369,
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"autogenerated": false,
"ratio": 3.47285... |
"""Author:Devika Kakkar
Date: 7/18/16
Name: training.py
Version: 1.0
Function: This module is used for defining, training
and testing the SVM classifier and then dumping it as a pickle
file which can be reused for sentiment prediction.
Input: The training datasets from various sources.
Output: The result of tesing(reca... | {
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"path": "Twitter-Sentiment-Classifier/src/training.py",
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"hash": 4704113858745154000,
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"ratio": 3.4729867482161... |
__author__ = 'devinbarry@gmail.com'
import re
import os.path
import click
from tqdm import *
from datetime import date
from datetime import datetime, timedelta
from collections import OrderedDict
from dateutil.parser import parse
from dateutil.relativedelta import relativedelta
from redmine import Redmine
from pandas ... | {
"repo_name": "devinbarry/yellow-worktracker",
"path": "work_tracker.py",
"copies": "1",
"size": "11172",
"license": "apache-2.0",
"hash": -1269924897337765600,
"line_mean": 29.0322580645,
"line_max": 94,
"alpha_frac": 0.6303258145,
"autogenerated": false,
"ratio": 3.7202797202797204,
"config_t... |
__author__ = 'Devon Timaeus'
import numpy as np
import math
import matplotlib.pyplot as plt
#First, we need a place for the bear to be seeking out, that is, a location for food to be
food_location = np.random.uniform(low=-1000, high=1000, size=2)
food_x = food_location[0]
food_y = food_location[1]
bear_location = np... | {
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"co... |
__author__ = 'Devon Timaeus'
import pandas as pd
from numpy import nan
'''
Essentially all of the data can be summed up and found via the value_counts method for each series we care about
So I'm just going to go through, grab those, store them, and then print them, with my answers/comments here in the code
'''
csv_i... | {
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"path": "In-ClassFirst/HW3.py",
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"ratio": 3.342857142857143,
"confi... |
__author__ = 'dexter'
import cx_helper
class ReachCX:
def __init__(self, output_stream):
self.cx = cx_helper.CXHelper(output_stream)
self.data = None
self.mode = 'fries'
self.cx_citation_id = None
self.citation = None
self.text_to_support_id_map = {}
self.re... | {
"repo_name": "ndexbio/reach-util",
"path": "reach_cx.py",
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"alpha_frac": 0.5219740634,
"autogenerated": false,
"ratio": 3.7743031951053707,
"config_test": false,
... |
__author__ = 'DEXTER'
import hashlib
import base64
import os
import sqlite3
import time
import qrcode
import places
class QRencode():
def getTime(self):
t = time.localtime()
return str(t.tm_year) + '-' + str(t.tm_mon) + '-' + str(t.tm_mday) + '_' + str(t.tm_hour) + '_' + str(
t.tm_min)... | {
"repo_name": "wipxj3/EventTicket_v2",
"path": "request/qr.py",
"copies": "1",
"size": "4985",
"license": "mit",
"hash": -8285690173159170000,
"line_mean": 35.3868613139,
"line_max": 120,
"alpha_frac": 0.5209628887,
"autogenerated": false,
"ratio": 3.5130373502466528,
"config_test": false,
"h... |
__author__ = 'dexter'
import requests
import json
from requests_toolbelt import MultipartEncoder
def process_nxml(nxml, outputType):
# Note: this service does not support CORS, but Python should be ok
response = requests.post('http://agathon.sista.arizona.edu:8080/odinweb/api/nxml',
... | {
"repo_name": "ndexbio/reach-util",
"path": "reach_helper.py",
"copies": "1",
"size": "1529",
"license": "bsd-2-clause",
"hash": 1625368692512743000,
"line_mean": 34.5581395349,
"line_max": 86,
"alpha_frac": 0.6559843035,
"autogenerated": false,
"ratio": 3.490867579908676,
"config_test": false,... |
__author__ = 'dexter'
from os import listdir, makedirs
from os.path import isfile, isdir, join, abspath, dirname, exists, basename, splitext
import csv
import data_model as dm
import json
from os import remove
import os
# e_data persistence
# This persistence system uses a local directory
# has a structure of:
#
# e_... | {
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"path": "fake_persistence.py",
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"size": "4779",
"license": "bsd-2-clause",
"hash": -2506913442414314500,
"line_mean": 28.86875,
"line_max": 111,
"alpha_frac": 0.6103787403,
"autogenerated": false,
"ratio": 3.0420114576702737,
"config_test": fal... |
__author__ = 'dexter'
from scipy.stats import hypergeom
from operator import itemgetter, attrgetter
from os.path import join, isdir
from os import listdir
import fake_persistence as storage
class Gene():
def __init__(self, symbol, entrez_gene_id):
self.symbol = symbol
self.id = entrez_gene_id
cl... | {
"repo_name": "ndexbio/ndex-enrich",
"path": "data_model.py",
"copies": "1",
"size": "9670",
"license": "bsd-2-clause",
"hash": 1559797648992019500,
"line_mean": 32.2302405498,
"line_max": 122,
"alpha_frac": 0.5588417787,
"autogenerated": false,
"ratio": 3.1798750411048995,
"config_test": false... |
__author__ = 'dexter'
from scipy.stats import hypergeom
# createEnrichmentSet(setName)
# deleteEnrichmentSet(setName)
# updateEnrichmentSet(setName)
# addNetworkToEnrichmentSet(setName, NDExURI, networkId)
# removeNetworkFromEnrichmentSet(setName, networkId)
#
# getEnrichmentSet(setName)
# getEnrichmentSets()
# getEn... | {
"repo_name": "ndexbio/ndex-enrich",
"path": "enrichment_engine.py",
"copies": "1",
"size": "1880",
"license": "bsd-2-clause",
"hash": 3305594060956813000,
"line_mean": 35.8823529412,
"line_max": 126,
"alpha_frac": 0.6526595745,
"autogenerated": false,
"ratio": 3.0031948881789137,
"config_test"... |
__author__ = 'dexter'
import data_model as dm
import ndex_access as na
from operator import attrgetter
# this is currently a scratchpad script for
# testing e_service components
# This is just an internal development script
# Not intended for use by collaborators
ndex_host = "http://dev2.ndexbio.org"
ndex_e_set_acc... | {
"repo_name": "ndexbio/ndex-enrich",
"path": "test_enrich.py",
"copies": "1",
"size": "1401",
"license": "bsd-2-clause",
"hash": 5458434758005914000,
"line_mean": 23.5789473684,
"line_max": 71,
"alpha_frac": 0.6937901499,
"autogenerated": false,
"ratio": 2.7908366533864544,
"config_test": false... |
__author__ = 'dexter'
import fake_persistence as storage
class EServiceConfiguration():
def __init__(self):
self.e_set_configs =[]
self.name = None
def get_e_set_config(self, name):
for conf in self.e_set_configs:
if conf.name == name:
return conf
... | {
"repo_name": "ndexbio/ndex-enrich",
"path": "configuration.py",
"copies": "1",
"size": "1975",
"license": "bsd-2-clause",
"hash": 9101419134721907000,
"line_mean": 38.5,
"line_max": 94,
"alpha_frac": 0.573164557,
"autogenerated": false,
"ratio": 3.643911439114391,
"config_test": true,
"has_n... |
__author__ = 'dexter'
import json
import time
class CXHelper:
def __init__(self, output_stream):
self.out = output_stream
self.contexts = {}
self.citation_id_counter = 0
self.support_id_counter = 0
self.edge_id_counter = 0
self.node_id_counter = 0
self.upda... | {
"repo_name": "ndexbio/reach-util",
"path": "cx_helper.py",
"copies": "1",
"size": "6335",
"license": "bsd-2-clause",
"hash": 7044729228882891000,
"line_mean": 29.7572815534,
"line_max": 92,
"alpha_frac": 0.4842936069,
"autogenerated": false,
"ratio": 3.9299007444168734,
"config_test": false,
... |
__author__ = 'dexter'
import ndex_access
import term2gene_mapper
import json
class GeneReport():
def __init__(self, name):
self.gene_network_pairs = {}
self.name = name
self.fields = [
"Gene Symbol",
"Entrez Gene Id",
"Network Id",
"Network N... | {
"repo_name": "ndexbio/ndex-enrich",
"path": "gene_report.py",
"copies": "1",
"size": "7185",
"license": "bsd-2-clause",
"hash": -5633487706717195000,
"line_mean": 44.7643312102,
"line_max": 148,
"alpha_frac": 0.5455810717,
"autogenerated": false,
"ratio": 3.7955625990491284,
"config_test": tru... |
__author__ = 'dgc1'
import matplotlib.pyplot as plt
#from mpl_toolkits.mplot3d import Axes3D
#from matplotlib import animation
import random
#import numpy as np
#import subprocess
def TT_calc(TTs, test):
"Finds the lowest value matching a key"
TTs = sorted(TTs)
for x in TTs:
if x[0] >= ... | {
"repo_name": "Miscanthus-Germination/Model_with_Interface",
"path": "Model/test.py",
"copies": "1",
"size": "7413",
"license": "mit",
"hash": 2783730704848339000,
"line_mean": 34.3382352941,
"line_max": 124,
"alpha_frac": 0.5046539862,
"autogenerated": false,
"ratio": 3.022013860578883,
"confi... |
__author__ = 'dh1tw'
from datetime import datetime
from time import strptime, mktime
import re
import pytz
from pyhamtools.consts import LookupConventions as const
UTC = pytz.UTC
def decode_char_spot(raw_string):
"""Chop Line from DX-Cluster into pieces and return a dict with the spot data"""
data = {}... | {
"repo_name": "dh1tw/pyhamtools",
"path": "pyhamtools/dxcluster.py",
"copies": "1",
"size": "2411",
"license": "mit",
"hash": 7721320955879511000,
"line_mean": 27.7023809524,
"line_max": 106,
"alpha_frac": 0.5927001244,
"autogenerated": false,
"ratio": 2.816588785046729,
"config_test": false,
... |
__author__ = 'dhallman'
from sourcegen import LanguageSource
class ObjCSource(LanguageSource.LanguageSource):
############
# Overrides
############
def __init__(self, prefix, dateString):
super(ObjCSource, self).__init__('ObjC', prefix, dateString)
def createImplementation(self, schemas=[]):
... | {
"repo_name": "Grepstar/GSParseSchema",
"path": "sourcegen/ObjCSource.py",
"copies": "1",
"size": "6394",
"license": "mit",
"hash": 6171950607932823000,
"line_mean": 32.3072916667,
"line_max": 117,
"alpha_frac": 0.5742883954,
"autogenerated": false,
"ratio": 4.5091678420310295,
"config_test": f... |
__author__ = 'dhallman'
from sourcegen import LanguageSource
class SwiftSource(LanguageSource.LanguageSource):
############
# Overrides
############
def __init__(self, prefix, dateString, useOptionals):
super(SwiftSource, self).__init__('Swift', prefix, dateString)
self.useOptionals = useOptiona... | {
"repo_name": "Grepstar/GSParseSchema",
"path": "sourcegen/SwiftSource.py",
"copies": "1",
"size": "4098",
"license": "mit",
"hash": 8021033239602966000,
"line_mean": 33.1583333333,
"line_max": 171,
"alpha_frac": 0.5163494388,
"autogenerated": false,
"ratio": 4.341101694915254,
"config_test": f... |
__author__ = 'dhallman'
import os
class LanguageSource(object):
def __init__(self, languageName, prefix, dateString):
self.languageName = languageName
self.prefix = prefix
self.dateString = dateString
self.subclasses = []
self.parseFieldsToSkip = ['objectId', 'ACL', 'creat... | {
"repo_name": "Grepstar/GSParseSchema",
"path": "sourcegen/LanguageSource.py",
"copies": "1",
"size": "5644",
"license": "mit",
"hash": -2271796856317796000,
"line_mean": 37.924137931,
"line_max": 224,
"alpha_frac": 0.5636073707,
"autogenerated": false,
"ratio": 4.861326442721792,
"config_test"... |
__author__ = 'dhanannjay.deo'
import argparse
from app import app
def make_argument_parser():
"""
Creates command line arguments parser
"""
parser = argparse.ArgumentParser(description='Simple prototype of TileServer')
parser.add_argument("-r", "--rootpath", help='Root path of the folder hosting... | {
"repo_name": "SlideAtlas/SlideAtlas-Server",
"path": "slideatlas/ptiffstore/run.py",
"copies": "1",
"size": "1104",
"license": "apache-2.0",
"hash": -4142997461492396000,
"line_mean": 24.6744186047,
"line_max": 135,
"alpha_frac": 0.625,
"autogenerated": false,
"ratio": 3.6677740863787376,
"con... |
__author__ = 'dhanannjay.deo'
import flask
from flask import request
import os
import logging
from common_utils import get_max_depth
logger = logging.getLogger('slideatlas')
app = flask.Flask(__name__)
@app.route('/')
def index():
# return "Helllo"
return flask.send_from_directory("static","index.html")
... | {
"repo_name": "SlideAtlas/SlideAtlas-Server",
"path": "slideatlas/ptiffstore/app.py",
"copies": "1",
"size": "5851",
"license": "apache-2.0",
"hash": 8969630565622345000,
"line_mean": 28.5505050505,
"line_max": 99,
"alpha_frac": 0.6140830627,
"autogenerated": false,
"ratio": 3.1937772925764194,
... |
__author__ = 'dhanannjay.deo'
"""
Code to use pylibtiff, a wraper for libtiff 4.03 to extract tiles without
uncompressing them.
On windows requires C:\Python27\Lib\site-packages\libtiff in PATH, on might
require that in LD_LIBRARY_PATH
"""
import base64
import os
from xml.etree import cElementTree as ET
from libti... | {
"repo_name": "SlideAtlas/SlideAtlas-Server",
"path": "slideatlas/ptiffstore/tiff_reader.py",
"copies": "1",
"size": "11058",
"license": "apache-2.0",
"hash": -9147129044794382000,
"line_mean": 34.1047619048,
"line_max": 117,
"alpha_frac": 0.5680954965,
"autogenerated": false,
"ratio": 3.63869693... |
__author__ = 'dhan'
import os
import sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../..")
import slideatlas.uploader as uploader
from slideatlas import models
import logging
logger = logging.getLogger('slideatlas')
from bson import ObjectId
from bson.objectid import InvalidId
import shutil
f... | {
"repo_name": "SlideAtlas/SlideAtlas-Server",
"path": "slideatlas/tasks/dicer.py",
"copies": "1",
"size": "3555",
"license": "apache-2.0",
"hash": 1161702054857351400,
"line_mean": 32.8571428571,
"line_max": 159,
"alpha_frac": 0.6098452883,
"autogenerated": false,
"ratio": 3.6164801627670395,
"... |
__author__ = 'Dhash'
def get_storage_disks(conf_path):
import commands
trimmed_lines = commands.getoutput('cat ' + conf_path + ' | grep storage_pool_drive | sed s/storage_pool_drive//')
trimmed_lines = trimmed_lines.split('\n')
uncommented_lines = []
for elem in trimmed_lines:
if ... | {
"repo_name": "Aeolus0/Greyhole_Frontend",
"path": "util/filesystem.py",
"copies": "1",
"size": "1494",
"license": "mit",
"hash": 9189899041439076000,
"line_mean": 33.5714285714,
"line_max": 118,
"alpha_frac": 0.6010709505,
"autogenerated": false,
"ratio": 3.6174334140435835,
"config_test": fal... |
__author__ = 'DHICKMA'
import arcpy
table = "C:\\_GRID\\__V15\\V15_Final\\V15_FinalDatasets.gdb\\BAXYCRFCFDRSTLMP_ROADWAY_SUMMARY"
rows = arcpy.UpdateCursor(table)
for row in rows:
if str(row.RTE_ORDER_ID).count("-") < 2:
pass
elif str(row.RTE_ORDER_ID).split("-")[1] not in ['LG', 'MG', 'XG', 'PG', ... | {
"repo_name": "TxDOT/python",
"path": "LeftSwitch.py",
"copies": "1",
"size": "1408",
"license": "mit",
"hash": -2020767669694135300,
"line_mean": 27.18,
"line_max": 94,
"alpha_frac": 0.5404829545,
"autogenerated": false,
"ratio": 2.8273092369477912,
"config_test": false,
"has_no_keywords": f... |
__author__ = 'dhkarimi'
import cPickle
from datetime import datetime
from pprint import pprint
import json
testdict = {
'd': datetime.now(),
'f': 1.2,
'i': 1,
's': "ads"}
def test_pickle():
global testdict
pstring = cPickle.dumps(testdict)
pdict = cPickle.loads(pstring)
print("Pic... | {
"repo_name": "AlwaysTraining/bbot",
"path": "bin/prototypes/serializetest.py",
"copies": "1",
"size": "1452",
"license": "mit",
"hash": -89949262598103460,
"line_mean": 16.0941176471,
"line_max": 55,
"alpha_frac": 0.6060606061,
"autogenerated": false,
"ratio": 3.3767441860465115,
"config_test"... |
__author__ = 'dhkarimi'
#!/usr/bin/python
import time
import gdata.spreadsheet.service
email = 'derrick.karimi@gmail.com'
password = 'zzzzzzzzzzz'
weight = '180'
# Find this value in the url with 'key=XXX' and copy XXX below
spreadsheet_key = '0AlItClzrqP_edHoxMmlOcTV3NHJTbU4wZDJGQXVTTXc'
# All spreadsheets have wor... | {
"repo_name": "AlwaysTraining/bbots",
"path": "sweng/proto/gdrive.py",
"copies": "2",
"size": "1376",
"license": "mit",
"hash": 8883172682410124000,
"line_mean": 20.1846153846,
"line_max": 74,
"alpha_frac": 0.7194767442,
"autogenerated": false,
"ratio": 3.057777777777778,
"config_test": false,
... |
__author__ = 'dhruv and alex m'
from grt.core import GRTMacro, Constants
import wpilib
constants = Constants()
class DriveMacro(GRTMacro):
"""
Drive Macro; drives forwards a certain distance while
maintaining orientation
"""
leftSF = 1
rightSF = -1
DTP = constants['DTP']
DTI = consta... | {
"repo_name": "grt192/2012rebound-rumble",
"path": "py/grt/macro/drive_macro.py",
"copies": "1",
"size": "6015",
"license": "mit",
"hash": -770029041335583200,
"line_mean": 35.4545454545,
"line_max": 111,
"alpha_frac": 0.6099750623,
"autogenerated": false,
"ratio": 3.7313895781637716,
"config_t... |
__author__ = "Dhruv Govil"
__copyright__ = "Copyright 2016, Dhruv Govil"
__credits__ = ["Dhruv Govil", "John Hood", "Jason Viloria", "Adric Worley", "Alex Widener"]
__license__ = "MIT"
__version__ = "1.1.4"
__maintainer__ = "Dhruv Govil"
__email__ = "dhruvagovil@gmail.com"
__status__ = "Beta"
import inspect
import sys... | {
"repo_name": "dgovil/PySignal",
"path": "PySignal.py",
"copies": "1",
"size": "10199",
"license": "mit",
"hash": 2161358872859634000,
"line_mean": 31.7942122186,
"line_max": 120,
"alpha_frac": 0.5544661241,
"autogenerated": false,
"ratio": 4.583820224719101,
"config_test": false,
"has_no_key... |
__author__ = "dhruv, Sidd Karamcheti"
from grt.core import GRTMacro, Constants
import wpilib
constants = Constants()
class TurnMacro(GRTMacro):
"""
Macro that turns a set distance.
"""
TP = constants['TP']
TI = constants['TI']
TD = constants['TD']
TOLERANCE = constants['TMtol']
class... | {
"repo_name": "grt192/2012rebound-rumble",
"path": "py/grt/macro/turn_macro.py",
"copies": "1",
"size": "2642",
"license": "mit",
"hash": -8838689309437595000,
"line_mean": 31.2195121951,
"line_max": 80,
"alpha_frac": 0.5794852385,
"autogenerated": false,
"ratio": 3.736916548797737,
"config_tes... |
__author__ = 'diana'
import logging
from dataset_manager.models import Video, Dataset
from django.db import models
from django_enumfield import enum
from jsonfield.fields import JSONField
from django.conf import settings
from emotion_annotator.enums import EmotionType
from arousal_modeler.utils import list_normalizati... | {
"repo_name": "dumoulinj/ers",
"path": "ers_backend/emotion_annotator/models.py",
"copies": "1",
"size": "2727",
"license": "mit",
"hash": 4300270198341504500,
"line_mean": 33.5316455696,
"line_max": 120,
"alpha_frac": 0.6123945728,
"autogenerated": false,
"ratio": 4.661538461538462,
"config_te... |
__author__ = 'Dichild'
# ver 0.1
import os
import urllib
import urllib2
import sys
import re
import urlparse
from collections import Counter
from sys import exit
import MySQLdb
import pygame
import semantic
global LEFT_PAGES
global InitialIndex
def check_en(word):
for chara in word:
if chara not in [u... | {
"repo_name": "dichild/Searching",
"path": "crawler.py",
"copies": "2",
"size": "6490",
"license": "mit",
"hash": 3239274968513882000,
"line_mean": 33.8924731183,
"line_max": 251,
"alpha_frac": 0.5317411402,
"autogenerated": false,
"ratio": 3.883901855176541,
"config_test": false,
"has_no_key... |
__author__ = 'Diego'
import PyQt4.QtCore as QtCore
from PyQt4.QtCore import QAbstractListModel
from PyQt4.QtCore import QAbstractTableModel, QAbstractItemModel
import pandas as pd
class VarListModel(QAbstractListModel):
CheckedChanged = QtCore.pyqtSignal(list)
def __init__(self, outcome_var=None, parent=None,... | {
"repo_name": "diego0020/correlation_viewer",
"path": "vcorr/qt_models.py",
"copies": "1",
"size": "2903",
"license": "mit",
"hash": 7621714038312744000,
"line_mean": 33.1529411765,
"line_max": 86,
"alpha_frac": 0.6028246641,
"autogenerated": false,
"ratio": 3.8706666666666667,
"config_test": f... |
__author__ = 'digao'
from collections import defaultdict
import sys
import re
def rec_depth(graph,k,seen,parents,cycles):
parents.append(k)
seen.add(k)
for other in graph[k]:
if len(parents)>1 and other in parents:
index = parents.index(other)
if index < len(parents)-2:
... | {
"repo_name": "digaobarbosa/algorithms",
"path": "peak_traffic.py",
"copies": "1",
"size": "1133",
"license": "mit",
"hash": -4500399111532453000,
"line_mean": 21.2352941176,
"line_max": 54,
"alpha_frac": 0.5939982348,
"autogenerated": false,
"ratio": 3.191549295774648,
"config_test": false,
... |
__author__ = 'digao'
from flask import Blueprint, render_template,request,redirect
from flask.views import MethodView
from models import Post,Comment
from flask.ext.mongoengine.wtf import model_form
import logging
log = logging.getLogger('postviews')
posts = Blueprint('posts', __name__, template_folder='templates')
... | {
"repo_name": "digaobarbosa/tumblog",
"path": "views.py",
"copies": "1",
"size": "1533",
"license": "mit",
"hash": 5544744263895554000,
"line_mean": 25.8947368421,
"line_max": 70,
"alpha_frac": 0.6242661448,
"autogenerated": false,
"ratio": 3.803970223325062,
"config_test": false,
"has_no_key... |
__author__ = 'digao'
from StringIO import StringIO
import sys,re
def add_it(numbers,n):
for i in xrange(len(numbers)):
k = numbers[i]
if k>n:
return numbers[:i]+[n]+numbers[i:]
numbers.append(n)
return numbers
def del_it(numbers,n):
numbers.remove(n)
return numb... | {
"repo_name": "digaobarbosa/algorithms",
"path": "hackerrank/algorithms/median.py",
"copies": "1",
"size": "1091",
"license": "mit",
"hash": 2418916785004677600,
"line_mean": 16.3333333333,
"line_max": 47,
"alpha_frac": 0.5380384968,
"autogenerated": false,
"ratio": 3.227810650887574,
"config_t... |
__author__ = 'digao'
import math
a²t/2+vt = D
def respToA(a,other,D):
possible = [(t,p) for (t,p) in other if p<D]
if possible and len(other)>len(possible):
(at,ap)=other[len(possible)]
(bt,bp) = possible[-1]
v = (ap-bp)/(at-bt)
timeo = D==ap and at or bt + (D-bp)/v
la... | {
"repo_name": "digaobarbosa/algorithms",
"path": "out_gas.py",
"copies": "1",
"size": "1393",
"license": "mit",
"hash": -683756458086147700,
"line_mean": 24.3272727273,
"line_max": 66,
"alpha_frac": 0.4920977011,
"autogenerated": false,
"ratio": 2.870103092783505,
"config_test": false,
"has_n... |
__author__ = 'digao'
import sys
R,D,L,U = 0,1,2,3
def incr(i,j,op,round,n,m):
r = None
bop = op
if op==R:
if i<m-round-1:
i+=1
r = (i,j,op,round)
else:
op = D
if op==D:
if j<n-round-1:
j+=1
r = (i,j,op,round)
e... | {
"repo_name": "digaobarbosa/algorithms",
"path": "spiral.py",
"copies": "1",
"size": "1373",
"license": "mit",
"hash": 8919024061021806000,
"line_mean": 15.5542168675,
"line_max": 61,
"alpha_frac": 0.4078659869,
"autogenerated": false,
"ratio": 2.9337606837606836,
"config_test": false,
"has_n... |
__author__ = 'digao'
import datetime
from flask import url_for
from . import db
class TestCase(db.Document):
created_at = db.DateTimeField(default=datetime.datetime.now, required=True)
title = db.StringField(max_length=255, required=True)
class Comment(db.EmbeddedDocument):
created_at = db.DateTimeField(... | {
"repo_name": "digaobarbosa/tumblog",
"path": "models.py",
"copies": "1",
"size": "1120",
"license": "mit",
"hash": 7907494741561205000,
"line_mean": 30.1111111111,
"line_max": 79,
"alpha_frac": 0.6866071429,
"autogenerated": false,
"ratio": 3.6363636363636362,
"config_test": false,
"has_no_k... |
from functools import partial
import json
import os
from typing import Dict, Tuple
import numpy as np
import pandas as pd
# import plotly.plotly as py
import plotly.graph_objs as go
import visdom
vis = visdom.Visdom()
# TODO: Make this not hardcoded
COLORS = {
'e_coli_core': '#beaed4',
'iAF1260b': '#fdc086'
... | {
"repo_name": "dileep-kishore/microbial-ai",
"path": "microbial_ai/visualization/plot_results.py",
"copies": "1",
"size": "8810",
"license": "mit",
"hash": 7830807725495335000,
"line_mean": 28.3666666667,
"line_max": 90,
"alpha_frac": 0.4909194098,
"autogenerated": false,
"ratio": 3.9684684684684... |
from typing import List, Dict
import numpy as np
from .memory import Action, Memory
# from sklearn.preprocessing import minmax_scale, maxabs_scale
class Regulator:
"""
Base regulator class
Parameters
---------
dfba_obj : DFBA
DFBA instance
Attributes
... | {
"repo_name": "dileep-kishore/microbial-ai",
"path": "microbial_ai/regulation/regulator.py",
"copies": "1",
"size": "4426",
"license": "mit",
"hash": -5254637941063211000,
"line_mean": 33.0461538462,
"line_max": 98,
"alpha_frac": 0.5542250339,
"autogenerated": false,
"ratio": 3.962399283795882,
... |
from typing import List, Dict, Tuple
import torch
import torch.nn.functional as F
from torch.autograd import Variable
from .memory import Action, Event
from .regulator import Regulator
from .dqn.dddqn import Network
StateType = Tuple[Dict[str, float], Dict[str, float]]
class DQNRegulator(Regulator):
"""
... | {
"repo_name": "dileep-kishore/microbial-ai",
"path": "microbial_ai/regulation/dqnregulator.py",
"copies": "1",
"size": "5810",
"license": "mit",
"hash": 820092432926419500,
"line_mean": 37.4768211921,
"line_max": 98,
"alpha_frac": 0.5993115318,
"autogenerated": false,
"ratio": 3.9794520547945207,... |
from typing import List
from collections import namedtuple
import random
Action = namedtuple("Action", ['type', 'phi'])
Event = namedtuple("Event", ['state', 'action', 'next_state', 'reward'])
# TODO: Replace with ringbuffer
class Memory:
"""
Stores events
Parameters
---------
c... | {
"repo_name": "dileep-kishore/microbial-ai",
"path": "microbial_ai/regulation/memory.py",
"copies": "1",
"size": "1399",
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"hash": -7951941910569546000,
"line_mean": 27.5510204082,
"line_max": 76,
"alpha_frac": 0.5796997856,
"autogenerated": false,
"ratio": 4.176119402985075,
"c... |
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
class Network(nn.Module):
"""
Double-Dueling Deep Q-network
"""
def __init__(self, state_size, action_size):
super().__init__()
self.input_size = state_size
self.output_size = action_... | {
"repo_name": "dileep-kishore/microbial-ai",
"path": "microbial_ai/regulation/dqn/dddqn.py",
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"line_max": 95,
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"autogenerated": false,
"ratio": 2.8498542274052476,
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import pytest
from microbial_ai import Simulation
@pytest.mark.usefixtures("microbe_dict", "core_media", "model_table")
class TestSimulation:
"""
Tests for the simulation class
"""
def test_initialization(self, microbe_dict, core_media, model_table):
simulator = Simulation(microbe_dict, c... | {
"repo_name": "dileep-kishore/microbial-ai",
"path": "tests/metabolism/test_simulation.py",
"copies": "1",
"size": "1082",
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"line_max": 86,
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"ratio": 3.3190184049079754,
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import random
import numpy as np
import pytest
from microbial_ai.regulation import Regulator, Action
from microbial_ai.metabolism import Microbe, Media, Microbiota
@pytest.mark.usefixtures("random_model")
@pytest.fixture
def random_regulator_inst(random_model):
random_microbe = Microbe(random_model)
rxn_list... | {
"repo_name": "dileep-kishore/microbial-ai",
"path": "tests/regulation/test_regulator.py",
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"line_max": 89,
"alpha_frac": 0.6604921205,
"autogenerated": false,
"ratio": 3.2122557726465364,
"... |
import random
import pytest
import numpy as np
from microbial_ai.metabolism import Microbe, Component, Microbiota, Media
@pytest.fixture
def random_met(random_model):
random_microbe = Microbe(random_model)
ex_mets = random_microbe.ex_metabolites
mid = random.randint(0, len(ex_mets) - 1)
return list(e... | {
"repo_name": "dileep-kishore/microbial-ai",
"path": "tests/metabolism/test_media.py",
"copies": "1",
"size": "3106",
"license": "mit",
"hash": -342865839720220600,
"line_mean": 33.8988764045,
"line_max": 73,
"alpha_frac": 0.6394075982,
"autogenerated": false,
"ratio": 3.2626050420168067,
"conf... |
"""
Module that encodes the functionality and behavior of a reaction
"""
from functools import partial
import operator as op
import re
from typing import Tuple, Union, FrozenSet
import warnings
import numpy as np
from cobra import Reaction as CobraRxn
from .metabolite import Metabolite, ExMetabolite, MetType
Rxn... | {
"repo_name": "dileep-kishore/microbial-ai",
"path": "microbial_ai/metabolism/reaction.py",
"copies": "1",
"size": "13719",
"license": "mit",
"hash": 4294374833089424000,
"line_mean": 31.8205741627,
"line_max": 97,
"alpha_frac": 0.5439171951,
"autogenerated": false,
"ratio": 4.167375455650061,
... |
"""
Module that encodes the media components
"""
from collections import defaultdict
from typing import Dict, Iterable, FrozenSet, Set
import numpy as np
from .microbe import Microbiota
from .reaction import ExReaction
from .metabolite import ExMetabolite
class Component:
"""
Class that represents a... | {
"repo_name": "dileep-kishore/microbial-ai",
"path": "microbial_ai/metabolism/media.py",
"copies": "1",
"size": "7658",
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"hash": 6145099353337001000,
"line_mean": 32.0086206897,
"line_max": 96,
"alpha_frac": 0.5652911987,
"autogenerated": false,
"ratio": 4.49149560117302,
"conf... |
"""
Module that handles the dFBA simulation of the genome scale metabolic network
"""
from typing import Iterable, Dict, Tuple
from cobra import Model as CobraModel
from numpy import isclose
from .microbe import Microbe, Microbiota
from .media import Media
from ..regulation import RegType
StepReturn = Tuple[Dict... | {
"repo_name": "dileep-kishore/microbial-ai",
"path": "microbial_ai/metabolism/dfba.py",
"copies": "1",
"size": "6003",
"license": "mit",
"hash": 4499789308140924400,
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"line_max": 93,
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"""
Module that handles the simulation of the entire framework
"""
import csv
import string
import os
import random
from typing import Dict, Optional
from halo import Halo
import numpy as np
import torch
from microbial_ai import DFBA
from microbial_ai.metabolism import Media
from ..io import ModelTable
from ..reg... | {
"repo_name": "dileep-kishore/microbial-ai",
"path": "microbial_ai/simulation/simulation.py",
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"autogenerated": false,
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"""
Module the encodes the functionality and the behavior of a metabolite
"""
import re
from typing import Union, Tuple
from cobra import Metabolite as CobraMet
import warnings
MetType = Union["Metabolite", "ExMetabolite"]
class Metabolite:
"""
Class that represents a metabolite in the dFBA simulat... | {
"repo_name": "dileep-kishore/microbial-ai",
"path": "microbial_ai/metabolism/metabolite.py",
"copies": "1",
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"hash": -4545734467830244400,
"line_mean": 28.859375,
"line_max": 106,
"alpha_frac": 0.5428222571,
"autogenerated": false,
"ratio": 4.221649484536083,
"... |
"""
Module the encodes the functionality and the behavior of a microbe
"""
from collections import namedtuple, deque
from typing import FrozenSet, Dict, Iterable, Deque, Optional, Set
from cobra import Model as CobraModel
from cobra.util import OptimizationError
from cobra.flux_analysis.variability import find_es... | {
"repo_name": "dileep-kishore/microbial-ai",
"path": "microbial_ai/metabolism/microbe.py",
"copies": "1",
"size": "16243",
"license": "mit",
"hash": -8422565535053139000,
"line_mean": 33.3403805497,
"line_max": 102,
"alpha_frac": 0.5601797697,
"autogenerated": false,
"ratio": 4.22444733420026,
... |
"""
Module to create the Model table in the database
"""
import os
from typing import Iterable, Union
from cobra.io import read_sbml_model
from cobra import Model as CobraModel
from sqlalchemy import Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
from .db_connect import db_connect... | {
"repo_name": "dileep-kishore/microbial-ai",
"path": "microbial_ai/io/model_table.py",
"copies": "1",
"size": "6757",
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"hash": 8329892671462657000,
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"line_max": 97,
"alpha_frac": 0.5511321592,
"autogenerated": false,
"ratio": 4.189088654680719,
"confi... |
"""
Module to make a connection to the database
"""
from typing import Tuple
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
def parse_dburl(dburi: str) -> Tuple[str, str, str]:
"""
Given a database url parse it into database type, hostname and database location
... | {
"repo_name": "dileep-kishore/microbial-ai",
"path": "microbial_ai/io/db_connect.py",
"copies": "1",
"size": "1382",
"license": "mit",
"hash": -8842437862996523000,
"line_mean": 24.1272727273,
"line_max": 88,
"alpha_frac": 0.5615050651,
"autogenerated": false,
"ratio": 4.213414634146342,
"confi... |
"""
Script to download models from the BIGG database
"""
import asyncio
import json
import os
import requests
from typing import List
import aiohttp
import aiofiles
import async_timeout
MODEL_LIST = 'http://bigg.ucsd.edu/api/v2/models'
MODEL_DWNLD = 'http://bigg.ucsd.edu/static/models/*.xml'
def dwnld_model_li... | {
"repo_name": "dileep-kishore/microbial-ai",
"path": "microbial_ai/scripts/download_models.py",
"copies": "1",
"size": "2900",
"license": "mit",
"hash": -9063793127097377000,
"line_mean": 26.619047619,
"line_max": 80,
"alpha_frac": 0.5906896552,
"autogenerated": false,
"ratio": 3.877005347593583,... |
__author__ = 'dima'
import os
import re
import yaml
_DIGITS_RE = re.compile(r"^\d+$")
def read_binary_file(path):
if not os.path.exists(path):
return None
with open(path, 'rb') as f:
return f.read()
def write_binary_file(path, data, quite=True):
if quite:
previous_data = read_... | {
"repo_name": "open-epicycle/epicycle.derkonfigurator-py",
"path": "projects/epicycle.derkonfigurator-py/epicycle/derkonfigurator/utils.py",
"copies": "1",
"size": "2928",
"license": "apache-2.0",
"hash": 8071486192458581000,
"line_mean": 19.6197183099,
"line_max": 89,
"alpha_frac": 0.6219262295,
"... |
__author__ = 'Dima Potekhin'
from DirectoryBasedObject import DirectoryBasedObject
from epicycle.derkonfigurator.insertoid import has_insertoid, set_insertoid
class WorkspaceEntity(DirectoryBasedObject):
def __init__(self, path, environment, workspace, reporter):
super(WorkspaceEntity, self).__init__(pat... | {
"repo_name": "open-epicycle/epicycle.derkonfigurator-py",
"path": "projects/epicycle.derkonfigurator-py/epicycle/derkonfigurator/WorkspaceEntity.py",
"copies": "1",
"size": "1492",
"license": "apache-2.0",
"hash": 6688864502918417000,
"line_mean": 28.84,
"line_max": 86,
"alpha_frac": 0.677613941,
... |
__author__ = 'Dima Potekhin'
from epicycle.derkonfigurator.utils import nget, xml_escape, parse_versioned_name
from epicycle.derkonfigurator.externals.DotNetLib import DotNetLib
class NuGetPackager(object):
def __init__(self, repository):
self._repository = repository
self._package_name = "%s.%s... | {
"repo_name": "open-epicycle/epicycle.derkonfigurator-py",
"path": "projects/epicycle.derkonfigurator-py/epicycle/derkonfigurator/packaging/NuGetPackager.py",
"copies": "1",
"size": "5030",
"license": "apache-2.0",
"hash": 7972025970611591000,
"line_mean": 35.7153284672,
"line_max": 120,
"alpha_frac"... |
__author__ = 'Dima Potekhin'
import os
from epicycle.derkonfigurator.DirectoryBasedObject import DirectoryBasedObject
from epicycle.derkonfigurator.utils import listdir_full, join_ipath
from DotNetLib import DotNetLib
from DotNetSystemLib import DotNetSystemLib
class ExternalsManager(DirectoryBasedObject):
NUGET... | {
"repo_name": "open-epicycle/epicycle.derkonfigurator-py",
"path": "projects/epicycle.derkonfigurator-py/epicycle/derkonfigurator/externals/ExternalsManager.py",
"copies": "1",
"size": "3394",
"license": "apache-2.0",
"hash": 7516274345041708000,
"line_mean": 34.7263157895,
"line_max": 116,
"alpha_fr... |
__author__ = 'Dima Potekhin'
import os
from epicycle.derkonfigurator.DirectoryBasedObject import DirectoryBasedObject
from epicycle.derkonfigurator.utils import listdir_full, join_ipath, parse_versioned_name
class DotNetLib(DirectoryBasedObject):
LIB_DIR = "lib"
def __init__(self, repository, repository_lev... | {
"repo_name": "open-epicycle/epicycle.derkonfigurator-py",
"path": "projects/epicycle.derkonfigurator-py/epicycle/derkonfigurator/externals/DotNetLib.py",
"copies": "1",
"size": "3257",
"license": "apache-2.0",
"hash": 7468817576668422000,
"line_mean": 30.3173076923,
"line_max": 110,
"alpha_frac": 0.... |
__author__ = 'Dima Potekhin'
import os
from utils import read_binary_file, write_binary_file, read_unicode_file, write_unicode_file, read_yaml, join_ipath, compare_paths, has_extension, ensure_dir, listdir_full
class Directory(object):
def __init__(self, path):
self._path = path
@property
def pa... | {
"repo_name": "open-epicycle/epicycle.derkonfigurator-py",
"path": "projects/epicycle.derkonfigurator-py/epicycle/derkonfigurator/Directory.py",
"copies": "1",
"size": "2869",
"license": "apache-2.0",
"hash": -7936860572899775000,
"line_mean": 33.987804878,
"line_max": 171,
"alpha_frac": 0.6406413384... |
__author__ = 'Dima Potekhin'
import os
import re
from epicycle.derkonfigurator.WorkspaceEntity import WorkspaceEntity
from epicycle.derkonfigurator.utils import nget
from ProjectConfiguratorCs import ProjectConfiguratorCs
class Project(WorkspaceEntity):
CONFIG_FILE_NAME = "project_config.yaml"
_FULL_NAME_PAR... | {
"repo_name": "open-epicycle/epicycle.derkonfigurator-py",
"path": "projects/epicycle.derkonfigurator-py/epicycle/derkonfigurator/project/Project.py",
"copies": "1",
"size": "3502",
"license": "apache-2.0",
"hash": -2603393701859166000,
"line_mean": 26.1472868217,
"line_max": 110,
"alpha_frac": 0.627... |
__author__ = 'Dima Potekhin'
import re
from utils import prefix_lines
def resolve_templates(data, template_provider):
if "[###" not in data:
return None
lines = data.split("\n")
output_parts = []
skip_until_template_end = False
for line in lines[:-1]:
if "###]" in line:
... | {
"repo_name": "open-epicycle/epicycle.derkonfigurator-py",
"path": "projects/epicycle.derkonfigurator-py/epicycle/derkonfigurator/temploid.py",
"copies": "1",
"size": "6769",
"license": "apache-2.0",
"hash": -4917809456993844000,
"line_mean": 25.5490196078,
"line_max": 99,
"alpha_frac": 0.5729058945,... |
#IMPORT STATEMENTS
import json
from PIL import Image
#OBJECT DICTIONARY DECLARATIONS
objects = {
'car': 1,
'truck': 2,
'semi': 3,
'streetsign': 4, #ex.stop sign
'trafficlight': 5, #red,yellow,green
'streetlight': 6,
'firehydrant': 7,
'person': 8,
'door': 9,
'window': 10,
'b... | {
"repo_name": "dimensiondetector/VGGAnnotator",
"path": "Programs/JsonToText.py",
"copies": "1",
"size": "7008",
"license": "bsd-2-clause",
"hash": 610929702856127500,
"line_mean": 39.7441860465,
"line_max": 106,
"alpha_frac": 0.5480878995,
"autogenerated": false,
"ratio": 4.402010050251256,
"c... |
from calls import APICalls
class CanvasReader(object):
"""
Class that contains functions useful for downloading (reading) entities for a course.
Essentially a wrapper for API get calls for groups of data
Input always contains a course_id which is a string eg '1112'
Token that authorises this, has ... | {
"repo_name": "dkloz/canvas-api-python",
"path": "read.py",
"copies": "1",
"size": "7066",
"license": "mit",
"hash": -7952201032986123000,
"line_mean": 48.4125874126,
"line_max": 132,
"alpha_frac": 0.6437871497,
"autogenerated": false,
"ratio": 3.5940996948118005,
"config_test": false,
"has_n... |
import requests
import itertools
class APICalls(object):
"""
Class that simulates the low level API calls.
For now, only implements get (for reading only purposes)
Code based on https://github.com/hawesie/python-canvas-api
Canvas API returns a responses which contain several data points in them. T... | {
"repo_name": "dkloz/canvas-api-python",
"path": "calls.py",
"copies": "1",
"size": "2819",
"license": "mit",
"hash": -1684507799824041000,
"line_mean": 35.1538461538,
"line_max": 119,
"alpha_frac": 0.6154664775,
"autogenerated": false,
"ratio": 4.4604430379746836,
"config_test": false,
"has_... |
import pickle
import time
import numpy as np
import sys
import simplejson as json
import os
import csv
def file_exists(filename):
return os.path.isfile(filename)
def make_dir(filename):
dir_path = os.path.dirname(filename)
if not os.path.exists(dir_path):
os.makedirs(dir_path)
def save_pickle... | {
"repo_name": "dkloz/canvas-api-python",
"path": "utils/file_utilities.py",
"copies": "1",
"size": "2756",
"license": "mit",
"hash": -8067285996259756000,
"line_mean": 21.7768595041,
"line_max": 63,
"alpha_frac": 0.6008708273,
"autogenerated": false,
"ratio": 3.36919315403423,
"config_test": fa... |
__author__ = 'dimitris'
import abc
class AbstractImporterState:
__metaclass__ = abc.ABCMeta
def __init__(self, importer_object, object_factory):
self.importer_object = importer_object
self.factory = object_factory
@abc.abstractmethod
def handle_whitespace_line(self, line):
pa... | {
"repo_name": "gdimitris/ChessPuzzler",
"path": "Application/importer_states.py",
"copies": "2",
"size": "1588",
"license": "mit",
"hash": 6592460585307361000,
"line_mean": 30.78,
"line_max": 86,
"alpha_frac": 0.669395466,
"autogenerated": false,
"ratio": 3.7016317016317015,
"config_test": fals... |
__author__ = 'dimitris'
from Application import db
class ChessPuzzle(db.Model):
__tablename__ = 'Puzzles'
puzzle_id = db.Column(db.Integer, primary_key=True, autoincrement=True)
description = db.Column(db.String(200))
fen = db.Column(db.String(250), nullable=False)
solution = db.Column(db.String... | {
"repo_name": "gdimitris/ChessPuzzlerBackend",
"path": "Application/Models.py",
"copies": "2",
"size": "1191",
"license": "mit",
"hash": 7852752586038266000,
"line_mean": 35.0909090909,
"line_max": 90,
"alpha_frac": 0.6481947943,
"autogenerated": false,
"ratio": 3.1096605744125325,
"config_test... |
__author__ = 'dimitris'
import os
import glob
from Application.models import PuzzleType
from Application import db
from Application.chess_game_importer import ChessGameParser
def populate_db():
populate_puzzle_types()
populate_puzzles()
def populate_puzzle_types():
descriptions = ['Mate in 2', 'Mate i... | {
"repo_name": "gdimitris/ChessPuzzler",
"path": "Application/populate_db.py",
"copies": "2",
"size": "1154",
"license": "mit",
"hash": 4057463414876866600,
"line_mean": 24.6444444444,
"line_max": 89,
"alpha_frac": 0.6620450607,
"autogenerated": false,
"ratio": 3.2055555555555557,
"config_test":... |
__author__ = 'dimitris'
import string
import codecs
from Application.importer_states import HasEntryState, NoEntryState
from Application.wtharvey_factory import WTHarveyFactory
def is_whitespace(line):
return all(c in string.whitespace for c in line)
class ChessGameParser:
def __init__(self):
fact... | {
"repo_name": "gdimitris/ChessPuzzlerBackend",
"path": "Application/chess_game_importer.py",
"copies": "2",
"size": "1245",
"license": "mit",
"hash": 650265745773728100,
"line_mean": 26.6666666667,
"line_max": 67,
"alpha_frac": 0.6465863454,
"autogenerated": false,
"ratio": 3.672566371681416,
"... |
__author__ = 'dimitrovdr'
import re
from collections import Counter
class FedTextException(Exception):
pass
class WikipediaFedTextParser():
def __init__(self, fed_text):
self.fed_text = self.__set_fed_text(fed_text)
def reset(self):
self.fed_text = None
def get_text_only(self, data... | {
"repo_name": "trovdimi/wikilinks",
"path": "WikipediaFedTextParser.py",
"copies": "1",
"size": "9389",
"license": "mit",
"hash": -6273569699502331000,
"line_mean": 35.9645669291,
"line_max": 117,
"alpha_frac": 0.5389285334,
"autogenerated": false,
"ratio": 3.852687730816578,
"config_test": fal... |
__author__ = 'dimitrovdr'
from HTMLParser import HTMLParser
class WikipediaHTMLParser(HTMLParser):
def __init__(self):
HTMLParser.__init__(self)
self.fed = []
self.fed_in_section = []
self.fed_text = None
self.section_found = False
self.section_name = False
... | {
"repo_name": "trovdimi/wikilinks",
"path": "WikipediaHTMLParser.py",
"copies": "1",
"size": "5479",
"license": "mit",
"hash": -5999806021744601000,
"line_mean": 38.4172661871,
"line_max": 170,
"alpha_frac": 0.4816572367,
"autogenerated": false,
"ratio": 3.941726618705036,
"config_test": false,... |
__author__ = 'dimitrovdr'
from HTMLParser import HTMLParser
class WikipediaHTMLTableParser(HTMLParser):
def __init__(self):
HTMLParser.__init__(self)
self.fed = []
self.fed_text = None
self.table_counter = 0
def reset(self):
self.fed = []
self.fed_... | {
"repo_name": "trovdimi/wikilinks",
"path": "WikipediaHTMLTableParser.py",
"copies": "1",
"size": "1410",
"license": "mit",
"hash": 4586775343187801000,
"line_mean": 28,
"line_max": 62,
"alpha_frac": 0.495035461,
"autogenerated": false,
"ratio": 4.017094017094017,
"config_test": false,
"has_n... |
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