#!/usr/bin/python

import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt

n = 100
mu, sigma = 0, 5
x = mu + sigma*np.random.randn(n)

s_mean  = np.mean(x)
s_sigma = np.std(x) 
print 'sample mean' , s_mean
print 'sample sigma', s_sigma
print 
#for i in x:
#    print x

n,bins,patches = plt.hist(x,30,range=(-15,15),normed=1,facecolor='green',alpha=0.75)

y = mlab.normpdf(bins,mu,sigma)
l = plt.plot(bins, y, 'r--', linewidth=2)

plt.xlabel('Value')
plt.ylabel('Probability')
plt.title(r'Gaussian Distribution')
plt.axis([-15, 15, 0, 0.15])
plt.grid(True)

mean =  '\mu='
std  = '\sigma='

pdl = r'Parent Distribution $' + mean + str(mu) + '\ ' + std + str(sigma) +'$'
sdl = r'Sample Distribution $' + mean + str(s_mean) + '\ ' + std + str(s_sigma) + '$'

plt.legend((pdl,
            sdl),
           'upper center',shadow=False,fancybox=False)

plt.show()