jobs = list()
#
# now send the makenums job out to the processors,
# and retrieve the results, keep track of the time
#
tic=time.clock()
for i in range(numprocs):
the_seed=seeds[i]
jobs.append(p.apply_async(makenums, (the_seed,the_size)))
#
# put the results in a new list
#
out=[j.get() for j in jobs]
p.close()
toc=time.clock()
print "two processor time is {0:5.4e} seconds".format((toc-tic))
#
# repeat this with a single process
#
tic=time.clock()
seed=12345
randomval=randomstate(seed=seed)
#
# double the size since we only have one processor
#
the_size=the_size*2.
out=randomval.uniform(size=the_size)
toc=time.clock()
print "one processor time is {0:5.4e} seconds".format((toc-tic))
def makenums(seed,the_size):
random=randomstate(seed=seed)
return random.uniform(size=the_size)
"""
Demonstrate a random number generator using the Day 4
beta example
"""
import matplotlib.pyplot as plt
from numpy.random.mtrand import RandomState as randomstate
import numpy as np
random1=randomstate(seed=5)
size=int(10.e6)
beta= 1./3.
out=random1.uniform(size=size)
xval=-1./beta*np.log( 1. - out)
fig1=plt.figure(1)
fig1.clf()
ax1=fig1.add_subplot(111)
ax1.hist(out)
fig2=plt.figure(2)
fig2.clf()
ax2=fig2.add_subplot(111)
pdf,bins,patches=ax2.hist(xval,bins=np.arange(0,30.,0.01),normed=True)
ax2.set_xlim(0,10)
test_norm=np.sum(pdf*np.diff(bins))
output=\
"""
Is the pdf normaized? Here is test_norm: {0:5.3f}"
"""
print output.format((test_norm))
xval_an=np.linspace(0.,10.,300.)
an_fun=beta*np.exp(-beta*xval_an)
