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plot_twitter.py
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plot_twitter.py
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import os
import json
from matplotlib import pyplot as plt
import datetime
import pandas as pd
from matplotlib import dates
import xlrd
from matplotlib import numpy as np
import sys
reload(sys) # Reload does the trick!
sys.setdefaultencoding('utf-8')
import dabase
class PlotTwitter:
before =[]
after=[]
rep_dic={}
erro =[]
def __init__(self, dir_in, dir_out,excel_path, sheet_name, col):
self.dir_in = dir_in
self.dir_out = dir_out
self.excel_path = excel_path
self.sheet_name = sheet_name
self.col = col
def run(self):
fnames = ([file for root, dirs, files in os.walk(self.dir_in)
for file in files if file.endswith('.json') ])
self.create_dic(fnames)
self.save_db()
print("No file for these Ids:")
print(self.erro)
np.savetxt(self.dir_out+'Ids_not_found.txt', self.erro, delimiter=",", fmt="%s")
def save(self):
xls = xlrd.open_workbook(self.excel_path)
sheet = xls.sheet_by_name(self.sheet_name)
for i in xrange(sheet.nrows):
id_rep = str(int(sheet.cell_value(rowx= i, colx=self.col)))
user_name=""
date_fq =[]
if (self.rep_dic.has_key(id_rep)):
with open(self.dir_in+self.rep_dic[id_rep]) as data_file:
for line in data_file:
tweet = json.loads(line)
date_fq.append(pd.to_datetime(tweet['created_at']*1000000))
user_name = tweet['user_name']
print("processing: "+user_name+" id "+id_rep)
# self.save_before(id_rep, user_name,date_fq)
#self.save_after(id_rep, user_name,date_fq)
self.cumulative_hist(id_rep, user_name, date_fq)
#self.save_all(id_rep, user_name,date_fq)
else:
self.erro.append(id_rep)
def save_db(self):
xls = xlrd.open_workbook(self.excel_path)
sheet = xls.sheet_by_name(self.sheet_name)
for i in xrange(sheet.nrows):
id_rep = str(int(sheet.cell_value(rowx= i, colx=self.col)))
condicao = str(sheet.cell_value(rowx= i, colx=self.col+1))
user_name=""
if (self.rep_dic.has_key(id_rep)):
with open(self.dir_in+self.rep_dic[id_rep]) as data_file:
for line in data_file:
tweet = json.loads(line)
date = pd.to_datetime(tweet['created_at']*1000000)
user_name = tweet['user_name']
tw = dabase.Twitter(user_name=user_name, id_parlamentar=id_rep, condicao=condicao, tweet_data=date.strftime("%y-%m-%d"), tweet_hora=date.strftime("%H:%M:%S"))
tw.save()
print("processing: "+user_name+" id "+id_rep)
else:
self.erro.append(id_rep)
def create_dic(self, fnames):
for fname in fnames:
self.rep_dic[fname.split('_',1)[0]] = fname
def save_before(self,id_rep, user_name, date_fq):
self.id_rep = id_rep
self.user_name = user_name
self.date_fq = date_fq
before = [date_fq[i] for i in xrange(0,len(date_fq)) if date_fq[i] >= (datetime.datetime(2013,10,04)) and date_fq[i] <= (datetime.datetime(2014,10,04))]
if len(before) == 0: before.append(datetime.datetime(2100,01,01))
ones = [1]*len(before)
idx = pd.DatetimeIndex(before)
serie = pd.Series(ones, index=idx)
per_week = serie.resample('W', how='count').fillna(0)
per_week.plot(figsize=(9,7),color="blue")
#per_week.plot( marker="o", color="green")
plt.title(user_name+" - A Year Before Election")
plt.ylabel("Number of Posts")
plt.xlabel("Date (Week)")
plt.ylim(0,150)
plt.grid(True, which='both')
plt.xlim(datetime.datetime(2013,10,04), datetime.datetime(2014,10,4))
plt.savefig(self.dir_out+self.id_rep+"_"+self.user_name+"_before"+".png")
plt.clf()
def save_after(self, id_rep, user_name, data):
self.id_rep = id_rep
self.user_name = user_name
self.data = data
after = [data[i] for i in xrange(0,len(data)) if data[i] >= (datetime.datetime(2014,10,04,)) and data[i] <= (datetime.datetime(2015,10,04))]
if len(after) == 0: after.append(datetime.datetime(2100,01,01))
ones = [1]*len(after)
idx = pd.DatetimeIndex(after)
serie = pd.Series(ones, index=idx)
per_week = serie.resample('W', how='count').fillna(0)
per_week.plot(figsize=(9,7),color="red")
#per_week.plot( marker="o", color="green")
plt.title(user_name+" - A Year After Election")
plt.ylabel("Number of Posts")
plt.xlabel("Date (Week)")
plt.ylim(0,150)
plt.grid(True, which='both')
#plt.axvspan('10/04/2014','10/04/2014', color='blue')
plt.xlim(datetime.datetime(2014,10,04), datetime.datetime(2015,10,04))
#plt.savefig(self.dir_out+self.id_rep+"_"+self.user_name+"_after"".png")
plt.clf()
def save_all(self, id_rep, user_name, data):
self.id_rep = id_rep
self.user_name = user_name
self.data = data
whole_period = [data[i] for i in xrange(0,len(data)) if data[i] >= (datetime.datetime(2014,10,04,)) and data[i] <= (datetime.datetime(2015,10,04))]
if len(whole_period) == 0: whole_period.append(datetime.datetime(2100,01,01))
ones = [1]*len(whole_period)
idx = pd.DatetimeIndex(whole_period)
serie = pd.Series(ones, index=idx)
per_week = serie.resample('W', how='count').fillna(0)
per_week.plot(figsize=(9,7),color="red")
#per_week.plot( marker="o", color="green")
plt.title(user_name+" - A Year Before and After Election")
plt.ylabel("Number of Posts")
plt.xlabel("Date (Week)")
plt.ylim(0,150)
plt.grid(True, which='both')
plt.axvspan('10/04/2014','10/04/2014', color='blue')
plt.xlim(datetime.datetime(2013,10,04), datetime.datetime(2015,10,04))
plt.show()
#plt.savefig(self.dir_out+self.id_rep+"_"+self.user_name+"_after"".png")
plt.clf()
def cumulative_hist(self, id_rep, user_name, data):
self.id_rep = id_rep
self.user_name = user_name
self.data = data
after = [data[i] for i in xrange(0,len(data)) if data[i] >= (datetime.datetime(2013,10,04,)) and data[i] <= (datetime.datetime(2014,10,04))]
if len(after) == 0: after.append(datetime.datetime(2100,01,01))
plt.title(user_name+" - A Year Before Election ")
plt.hist(dates.date2num(after), cumulative=True)
plt.savefig(self.dir_out+self.id_rep+"_"+self.user_name+"_before"".png")
plt.show()
plt.clf()
if __name__=='__main__':
dir_in = "/Users/lucasso/Dropbox/Twitter_Marcelo/Report/coleta_pedro/"
dir_out = "/Users/lucasso/Dropbox/Twitter_Marcelo/Report/plot/"
excel_path = "/Users/lucasso/Dropbox/Twitter_Marcelo/Arquivo Principal da Pesquisa - Quatro Etapas.xls"
sheet_name = "nao_eleitos"
col = 4
pt = PlotTwitter(dir_in, dir_out, excel_path, sheet_name, col )
pt.run()