# test_rand.py  just plot distributions
import numpy
import pylab
import math
from numpy.matlib import rand
from random import gauss

print "test_rand.py "

n=1000000 # one million samples
plotn=100
a=rand(n)
print "a=",a

y=[0 for i in range(plotn)]
x=[i/100.0 for i in range(plotn)]


for i in range(n):
  k=int(math.floor(a[0,i]*plotn))
  y[k] += 1

y[plotn-1]=n/plotn # should be average

print "generating plot test_rand_py.png"
pylab.plot(x, y)
pylab.xlabel("bin")
pylab.ylabel("hits")
pylab.grid(True)
pylab.title("hits per bin, distribution of rand("+str(n)+")")
pylab.savefig("test_rand_py") # writes .png
pylab.show()

ag=[0.0 for i in range(n)]
for i in range(n):
  ag[i]=gauss(0.0,3.0) # mean,stddev
print "ag=",ag[0],ag[1],ag[2],ag[3],ag[4]

y=[0 for i in range(plotn)]
x=[20.0*i/100.0 for i in range(-plotn/2,plotn/2)]
print "len(x)=", len(x)
print "len(y)=", len(y)


for i in range(n):
  k=int(math.floor((ag[i]+10.0)*plotn/20.0))
  if(k<0):
    k=0

  if(k>=plotn):
    k=plotn-1

  y[k] += 1


print "generating plot test_gauss_py.png"
pylab.plot(x, y)
pylab.xlabel("bin")
pylab.ylabel("hits")
pylab.grid(True)
pylab.title("hits per bin, distribution of gauss(0.0,3.0)")
pylab.savefig("test_gauss_py") # writes .png
pylab.show()