'''
Created on Dec 22, 2014
@author: Brett Paufler
Copyright Brett Paufler

This is about swear words and their usage, among other things, so no reading beyond this point if that offends, you're under 18, it's illegal in your neck of the woods, and so on.


Reddit - Summarize Front Page
    1) Use tally_report to gain inspiration


Garbage (Here Down)
# -*- coding: utf-8 -*-
#print "${:,}".format(12345.67)
#clip = mpy.VideoClip([img1,img2], duration=2) # 2 seconds
#clip.write_gif("./imageIn/circle.gif",fps=15)
'''


#47361 raw of which 30413 are unique (by-id)



from reddit_offline import thousands_to_dataframe, quick_report, tally_report

import pandas as pd
import re


regex_groups = {
             'reddit':'^reddit$',
             'redditIn':'reddit',
             'bots': '(?<!ro)bot[s]?$',
             'fuck': 'fuck',
             'assh': 'assh[a|o]',
             'fuck': 'fuck',
             'shit': 'shit',
             'bitch': 'bitch',
             'cunt': 'cunt',
             'penis': 'penis',
             'dick': 'dick',
             'cock': '(?<!ppy|pea)cock(?!y)',
             'vagina': 'vagina',
             'nigger': 'nigger',
             #'nigR': 'nig(.){0,3}r', #worthless
             #'pussy': 'pussy', # 1 in 5 naughty
             #'pee' : 'pee', #speed
             
             'swear': 'fuck|shit|assh[a|o]|bitch|dick|penis|(?<!ppy|pea)cock(?!y)|vagina',
             'fuck_shit' : 'fuck|shit',
             'stupid': 'cute|funny|interesting'
             }


def load_data(category='front_page', test_run=True):
    '''Returns a dataFrame of all unique items in category (front_page or new)
        if test_run = True, then latest pull only
            otherwise, for all
    
    '''
    mFiles = 1 if test_run else 1000
    dF = thousands_to_dataframe(last_first=True, max_num_files=mFiles) #, max_num_files=1)
    dF = dF[dF['category']==category]
    dF = dF.drop_duplicates(subset='id', take_last=True)
    quick_report(dF, 2, 10)
    return dF

def author_title_review(regDict, test=True):
    '''Prints Raw Data Pre-Analysis for Website
    (throw away code)
    '''
    
    #front_page
    fP = load_data(category='front_page', test_run=test)
    #author
    aF = [a.lower() for a in list(fP['author'])]
    aFL = len(aF)
    #title
    tF = [a.lower() for a in list(fP['title'])]
    tFL = len(tF)
    
    #new
    nP = load_data(category='new', test_run=test)
    #author
    aN =  [a.lower() for a in list(nP['author'])]
    aNL = len(aN)
    #title
    tN = [a.lower() for a in list(nP['title'])]
    tNL = len(tN)
    
    for k,v in regDict.items():
        fA = [a for a in aF if re.search(v,a)] #front author
        nA = [a for a in aN if re.search(v,a)] #new author
        fT = [a for a in tF if re.search(v,a)] #front title
        nT = [a for a in tN if re.search(v,a)] #new title
        
        #report
        print "\n%s" % k
        #author
        print "author: (frontPage, newPage, ratio)" 
        print "fP: %d, %.4f %s" % (len(fA), len(fA)/float(aFL), fA)
        print "nP: %d, %.4f %s" % (len(nA), len(nA)/float(aNL), nA)
        if fA and nA:
            print "Ratio fp/nP: %.4f ::fP(%d/%d), nP(%d/%d)" % (len(fA) * aNL / float(aFL) / len(nA),
                                            len(fA), aFL, len(nA), aNL)
        
        #title
        print "title: (frontPage, newPage, ratio)" 
        print "fP: %d, %.4f %s" % (len(fT), len(fT)/float(tFL), fT[:10])
        print "nP: %d, %.4f %s" % (len(nT), len(nT)/float(tNL), nT[:10])
        if fT and nT:
            print "Ratio fp/nP: %.4f ::fP(%d/%d), nP(%d/%d)" % (len(fT) * tNL / float(tFL) / len(nT),
                                            len(fT), tFL, len(nT), tNL)
    
    

regex_domains = {'imgur': 'gur',
                 'youtube': 'youtu',
                 'soundcloud': 'soundcloud',
                 'gfycat': 'gfycat',
                 'tumblr': 'blr',
                 'twitter': 'twit',
                 'wiki': 'wiki',
                 'self': 'self'
                 }

def domain_review(regDict=regex_domains, test=True):
    
    fP = load_data(category='front_page', test_run=test)
    fDL = [a.lower() for a in list(fP['domain'])]
    fNum = len(fDL)
    
    nP = load_data(category='new', test_run=test)
    nDL = [a.lower() for a in list(nP['domain'])]
    nNum = len(nDL)

    
    for k,v in regDict.items():
        fD = [a for a in fDL if re.search(v,a)] #front domain
        nD = [a for a in nDL if re.search(v,a)] #front domain
        
        #Domain Report
        print "\n%s" % k
        print "Domain: (frontPage, newPage, ratio)" 
        print "fP: %d, %.4f %s" % (len(fD), len(fD)/float(fNum), fD[:10])
        print "nP: %d, %.4f %s" % (len(nD), len(nD)/float(len(nDL)), nD[:10])
        if fD and nD:
            print "Ratio fp/nP: %.4f ::fP(%d/%d), nP(%d/%d)" % (len(fD) * nNum / float(fNum) / len(nD),
                                            len(fD), fNum, len(nD), nNum)
        
        

  
    
  
if __name__ == '__main__':

    #domain_review(test=False)
    author_title_review(regex_groups, test=False)