#!/usr/bin/env python
"""Simple comparison of weave.blitz versus pure Numpy """
from __future__ import division
import sys, time
import numpy as np
from scipy import weave
from pylab import figure,subplot, plot, show, legend, xlabel, ylabel, title
rand = np.random.rand
Nadds = 12
Nevals = 10
shape = 2000,2000
x = rand(*shape)
if sys.platform == 'win32':
now = time.clock
else:
now = time.time
def repeat_nadds(Nadds, Nevals, useWeave):
"""
Time the addition of i=2,Nadds arrays. Evaluate each expression
Nevals times to produce accurate timing results. If useWeave is
True, use weave to inline the addition, else use Numeric
return value is n,t where n is a list of the the number of arrays
added and t is the average time it took to add the arrays
"""
results = []
for i in range(2,Nadds):
s = 'result = %s' % '+'.join(['x']*i)
print 'evaluating: %s with weave=%s' % (s,useWeave)
# store times here
times = [None,]*(Nevals+1)
# compute all we can outside the timing loop
evalRng = range(Nevals)
blitz = weave.blitz
# Repeat the full loop to minimize non-numerical work inside, which
# can disrupt timings
if useWeave:
# only weave needs to predefine result array
result= np.empty(shape,dtype=float)
times[0] = now()
for j in evalRng:
blitz(s)
times[j+1] = now()-times[j]
else:
times[0] = now()
for j in evalRng:
exec(s)
times[j+1] = now()-times[j]
# pick the best of the running times
elapsed = min(times[1:])
print '\tNadds=%d Elapsed=%1.2f' % (i, elapsed)
results.append( (i, elapsed) )
return zip(*results)
# evaluate weave
nw, tw = repeat_nadds(Nadds, Nevals, useWeave=True)
# evaluate Numeric
nn, tn = repeat_nadds(Nadds, Nevals, useWeave=False)
# plot weave versus Numeric
figure()
ax = subplot(111)
plot(nw, tw, 'go', nn, tn, 'bs')
legend( ('Blitz', 'Numpy') )
xlabel('num adds')
ylabel('time (s)')
title('numpy vs weave; repeated adds, shape: %s' % (shape,))
ax.set_xlim( (0, Nadds+1))
show()
