read_database.py.bak

#module for the application
from __future__ import division
import pickle 
from datetime import datetime,timedelta

def _openFile(filename):
    
    fileObj = open(filename,'r')
    bigList = cPickle.load(fileObj)
    return bigList

def _prepareDateList(bigList):

    dateList = [0]
    for element in range(len(bigList)):
        date = bigList[element][0].encode()
        date = date.split('-')
        epoch = datetime.datetime.utcfromtimestamp(0)
        date = map(int,date)
        dt = datetime.datetime(date[0],date[1],date[2])
        var = (dt - epoch).total_seconds() * 1000.0
        if (dateList[-1]!=var):
            dateList.append(var)
    return dateList

def _prepareCategoryList(bigList):
    '''This function would return the overall sentiment of the whole situation (categorywise) but would not return it return datewise'''
    element1 = bigList[0][1]
    categoryList = element1.keys()
    noOfCategories = len(categoryList)
    categoryListFinale = {}
    for category in categoryList:
        categoryListFinale[category] = []
    datelist = [0]
    Overall_sentiment=0
    length = 0
    for element in range(len(bigList)):
        myDict = bigList[element][1]
        date = bigList[element][0]
        date = date.split('-')
        epoch =datetime.utcfromtimestamp(0)
        date = map(int,date)
        dt = datetime(date[0],date[1],date[2])
        td = (dt - epoch)
        td = (td.microseconds + (td.seconds + td.days * 86400) * 10**6) / 10**3

        for category in categoryList:
            sentimentValue = myDict[category]
            if ((sentimentValue!=0)and(datelist[-1]!=td)):
                Overall_sentiment = Overall_sentiment + sentimentValue
                categoryListFinale[category].append([td,sentimentValue])
                length = length + 1
                datelist.append(td)
    Overall_sentiment = Overall_sentiment/length
    return (categoryList, categoryListFinale,Overall_sentiment)


def _check(filename):
	bigList = _openFile(filename)
	categoryList,categoryListFinale,Overall_sentiment = _prepareCategoryList(bigList)
	a = categoryListFinale
	return a

        
def _prepareCategoryList2(bigList):
    '''This function would return the overall sentiment of the whole situation (categorywise) but would not return it return datewise'''
    element1 = bigList[0][1]
    categoryList = element1.keys()
    noOfCategories = len(categoryList)
    categoryListFinale = {}
    for category in categoryList:
        categoryListFinale[category] = [0,0]
    datelist = [0]
    Overall_sentiment=0
    length = 0
    for element in range(len(bigList)):
        myDict = bigList[element][1]
        date = bigList[element][0]
        date = date.split('-')
        epoch =datetime.utcfromtimestamp(0)
        date = map(int,date)
        dt = datetime(date[0],date[1],date[2])
        td = (dt - epoch)
        td = (td.microseconds + (td.seconds + td.days * 86400) * 10**6) / 10**3

        for category in categoryList:
            sentimentValue = myDict[category]
            if ((sentimentValue!=0)):
                if (sentimentValue > 0 ):
                    categoryListFinale[category][0]=categoryListFinale[category][0]+sentimentValue
                if (sentimentValue < 0):
                    categoryListFinale[category][1]=categoryListFinale[category][1]+sentimentValue
    for category in categoryList:
        total = categoryListFinale[category][0] - categoryListFinale[category][1]
        categoryListFinale[category][0] = categoryListFinale[category][0]/total *100.0
        categoryListFinale[category][1] = 0 - categoryListFinale[category][1]/total*100.0
    return categoryListFinale


def _pie(filename):
	bigList = _openFile(filename)
	categoryListFinale = _prepareCategoryList2(bigList)
	return categoryListFinale