import pylab, numpy, os, sys
from scipy import stats
from phi_driven_data import *
from math import sqrt

DATA = rundata["0"] + rundata["1"] + rundata["2"]

DATA = [elem for elem in DATA if elem[1] != None]

DATA.sort(lambda x, y: cmp(x[0], y[0]))

dat_bin_x = []
dat_bin_y = []
dat_bin_e = []
dat_bin_ssize = []
dat_bin_samp = []

samples_y = []

BINSIZE = 200

for binnum in range(10000/BINSIZE):
	points = [el for el in DATA if (el[0] >= binnum*BINSIZE and el[0] < (binnum+1)*BINSIZE and el[1] != None)]
	if len(points) == 0:
		continue
	x = [el[0] for el in points]
	y = [el[1] for el in points]
	dat_bin_x.append(sum(x)/len(x))
	dat_bin_y.append(sum(y)/len(y))
	dat_bin_ssize.append(len(x))
	#print(y)
	dat_bin_e.append(numpy.std(numpy.array(y))/sqrt(len(y)-1))
	dat_bin_samp.append(len(x))
	samples_y.append(y)


pylab.xlabel("Time Step")
pylab.ylabel(r'Integrated Information $\langle\Phi\rangle$ (bits)')
pylab.title("Average Integrated Information over Evolution")

pvalues = [ stats.ttest_ind(samples_y[0], x) for x in samples_y]
print pvalues

x = [el[0] for el in DATA if (el[1] != None)]
y = [el[1] for el in DATA if (el[1] != None)]

print(float(sum(dat_bin_ssize))/len(dat_bin_ssize))
#pylab.plot(x, y, "g,")

pylab.errorbar(dat_bin_x, dat_bin_y, yerr=dat_bin_e, fmt='b', ecolor='r')


pylab.axis([0, 10000, 0.8, 2])
pylab.show()