AniversarioPeru aniversarioperu

## plotear_tuits.py
#! /usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import codecs
import re
import datetime
import time
from itertools import groupby
import numpy as np
import matplotlib.pyplot as plt

## tuits_incendio.csv
415727619427299328,2013-12-25 06:16:02 +0000,bomberos,INCENDIO Carreteras - Panamericanas Panamericana Sur 16.4 San Juan De Miraflores M120-3
415727115305504768,2013-12-25 06:14:02 +0000,bomberos,INCENDIO Cl. Cucarda 200 CIST176-4
415726862464483328,2013-12-25 06:13:02 +0000,bomberos,INCENDIO Av. Villa Hermosa (huascar) (coronel Francisco Bolognesi) (4) 000 San Juan De Lurigancho M65-1
415724752737955840,2013-12-25 06:04:39 +0000,bomberos,"RT @televivape: ""@bomberos: INCENDIO Pj. Pamplona Alta Sector Ollantay Mz U3 Lt 5 San Juan De Miraflores M138-4"""
415724096505516032,2013-12-25 06:02:03 +0000,bomberos,INCENDIO Cl. Emilio Fernandez 693 Lima M4-5
415723088089976832,2013-12-25 05:58:02 +0000,bomberos,INCENDIO Pj. Pamplona Alta Sector Ollantay Mz U3 Lt 5 San Juan De Miraflores M138-4
415722083558367232,2013-12-25 05:54:03 +0000,bomberos,INCENDIO Barrio Chocoy M73-3
415722082702721024,2013-12-25 05:54:02 +0000,bomberos,INCENDIO Jr. Fernandez Quiroz 000 Lima
415720575215370241,2013-12-25 05:48:03 +0000,bombero

## leeme_votaciones.py
import re
import sys

f = open(sys.argv[1].strip(), "r")
lines = f.readlines()
f.close()


def clean_line(line):
    propose = ""

## analizar_tuits.py
#! /usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import codecs
import re
import datetime
import time
from itertools import groupby
import numpy as np
import matplotlib.pyplot as plt

## datos.txt
152516
153055
144205
151560
160848
181866
206610
254405

## delitos_bayesian.py
# -*- coding: utf-8 -*-
import prettyplotlib as ppl
from prettyplotlib import plt
import sys
import pymc as pm
import numpy as np

datos = np.loadtxt("datos.txt")
alpha = 1.0/datos.mean()
print alpha

## lm.R
library(ggplot2)
y <- read.csv("datos.txt", header=FALSE)
y <- as.vector(y[,1])
x <- 2005:2012

int <- lsfit(x,y)$coefficients[1]
slope <- lsfit(x,y)$coefficients[2]
p <- ggplot(,aes(x,y))
p + geom_point() + geom_abline(intercept=int, slope=slope) +
  labs(title = "Perú: Número total de delitos por año")

## intoxicados_qali_warma.csv

          
            Loreto
            2013-10-25
            80
            http://bit.ly/18YdMbA

            
              Junin
              2013-09-28
              35
              http://bit.ly/18qqNpT

            
              Curahuasi
              2013-09-18
              50
              http://bit.ly/18qqEmx

            
              Huanuco
              2013-04-18
              17
              http://bit.ly/18qqzPL

            
              Urubamba
              2013-04-15
              100
              http://bit.ly/18qqlYX

            
              Ucayali
              2013-10-05
              15
              http://bit.ly/18qqfAm

            
              Satipo
              2013-09-27
              24
              http://bit.ly/18YeiGx

## tabla4.csv

          
            Moquegua
            24.8
            1949

            
              Tumbes
              28.4
              1839

            
              Ayacucho
              56.6
              1779

            
              Pasco
              55.8
              1769

            
              Huancavelica
              68.5
              1750

            
              Apurímac
              60.9
              1725

            
              Madre de Dios
              27.1
              1584

            
              Cusco
              38.2
              1574

            
              Amazonas
              61.7
              1404

            
              Tacna
              26.5
              1399

## top20.py
# -*- coding: utf-8 -*-
import prettyplotlib as ppl
import numpy as np
from prettyplotlib import plt
import csv
from array import array


""" Top 20 ranking """
x = []
	#! /usr/bin/env python
	# -- coding: utf-8 --
	import sys
	import codecs
	import re
	import datetime
	import time
	from itertools import groupby
	import numpy as np
	import matplotlib.pyplot as plt
	415727619427299328,2013-12-25 06:16:02 +0000,bomberos,INCENDIO Carreteras - Panamericanas Panamericana Sur 16.4 San Juan De Miraflores M120-3
	415727115305504768,2013-12-25 06:14:02 +0000,bomberos,INCENDIO Cl. Cucarda 200 CIST176-4
	415726862464483328,2013-12-25 06:13:02 +0000,bomberos,INCENDIO Av. Villa Hermosa (huascar) (coronel Francisco Bolognesi) (4) 000 San Juan De Lurigancho M65-1
	415724752737955840,2013-12-25 06:04:39 +0000,bomberos,"RT @televivape: ""@bomberos: INCENDIO Pj. Pamplona Alta Sector Ollantay Mz U3 Lt 5 San Juan De Miraflores M138-4"""
	415724096505516032,2013-12-25 06:02:03 +0000,bomberos,INCENDIO Cl. Emilio Fernandez 693 Lima M4-5
	415723088089976832,2013-12-25 05:58:02 +0000,bomberos,INCENDIO Pj. Pamplona Alta Sector Ollantay Mz U3 Lt 5 San Juan De Miraflores M138-4
	415722083558367232,2013-12-25 05:54:03 +0000,bomberos,INCENDIO Barrio Chocoy M73-3
	415722082702721024,2013-12-25 05:54:02 +0000,bomberos,INCENDIO Jr. Fernandez Quiroz 000 Lima
	415720575215370241,2013-12-25 05:48:03 +0000,bombero
	import re
	import sys

	f = open(sys.argv[1].strip(), "r")
	lines = f.readlines()
	f.close()


	def clean_line(line):
	propose = ""
	# -- coding: utf-8 --
	import prettyplotlib as ppl
	from prettyplotlib import plt
	import sys
	import pymc as pm
	import numpy as np

	datos = np.loadtxt("datos.txt")
	alpha = 1.0/datos.mean()
	print alpha
	library(ggplot2)
	y <- read.csv("datos.txt", header=FALSE)
	y <- as.vector(y[,1])
	x <- 2005:2012

	int <- lsfit(x,y)$coefficients[1]
	slope <- lsfit(x,y)$coefficients[2]
	p <- ggplot(,aes(x,y))
	p + geom_point() + geom_abline(intercept=int, slope=slope) +
	labs(title = "Perú: Número total de delitos por año")
Loreto	2013-10-25	80	http://bit.ly/18YdMbA
Junin	2013-09-28	35	http://bit.ly/18qqNpT
Curahuasi	2013-09-18	50	http://bit.ly/18qqEmx
Huanuco	2013-04-18	17	http://bit.ly/18qqzPL
Urubamba	2013-04-15	100	http://bit.ly/18qqlYX
Ucayali	2013-10-05	15	http://bit.ly/18qqfAm
Satipo	2013-09-27	24	http://bit.ly/18YeiGx
Moquegua	24.8	1949
Tumbes	28.4	1839
Ayacucho	56.6	1779
Pasco	55.8	1769
Huancavelica	68.5	1750
Apurímac	60.9	1725
Madre de Dios	27.1	1584
Cusco	38.2	1574
Amazonas	61.7	1404
Tacna	26.5	1399