mobility-Centrality

import pandas as pd

from pprint import pprint

import numpy as np

import glob

from matplotlib import pyplot

from statsmodels.graphics.tsaplots import plot_acf, acf

import sys

import os

from collections import defaultdict

import matplotlib.pyplot as plt

import code  # code.interact(local=dict(globals(), **locals()))

folder = sys.argv[1]

lags = int(sys.argv[2])

os.chdir(folder)

timeBC = defaultdict(list)

print "Loading data from", folder, "..."

for snap in sorted(glob.glob('./BC*')):

    # print snap

    df = pd.read_csv(snap, names=['Node', 'BC'], skiprows=1)

    for index, row in df.iterrows():

        timeBC[int(row.Node)].append(row.BC)

print "Processing data..."

lags2NodeACF = defaultdict(list)

for node in timeBC:

    nodeACF = acf(timeBC[node], nlags=lags)

    for lag in range(0, len(nodeACF)):

        lags2NodeACF[lag].append(nodeACF[lag])

#code.interact(local=dict(globals(), **locals()))

os.chdir("./..")

# Plotting

#lags=20

x = range(0, lags)

y = []

for i in x:

    y.append(np.mean(lags2NodeACF[i]))

#plt.plot(x, y, marker=".", lw="1.5")

#plt.stem(x, y, markerfmt=' ')

plt.stem(x, y, s=0.1)

plt.ylabel('Mean Autocorrelation')

plt.xlabel('Time-lags')

#plt.ylim(-1.0,1.0)

plt.xlim(0,lags)

#plt.title("Mean Autocorrelation X time-lag", y=1.04)

plt.savefig("autoCorrMean.pdf", format='pdf')

plt.clf()

print "Plot saved in", folder+".."

print "THE END"

#plot_acf(timeBC[0.0], lags=lags)

#pyplot.show()