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author | Travis Oliphant <oliphant@enthought.com> | 2006-06-28 19:12:46 +0000 |
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committer | Travis Oliphant <oliphant@enthought.com> | 2006-06-28 19:12:46 +0000 |
commit | b173aa1a6dc1c361d65150c6782db7618a5ff126 (patch) | |
tree | a8c930b14c52f3ddd7b8a30c6a470e193c1f1273 /benchmarks | |
parent | 05bb6ebdf72a580c16b20a2c6b29c1a1f6c254f1 (diff) | |
download | python-numpy-b173aa1a6dc1c361d65150c6782db7618a5ff126.tar.gz python-numpy-b173aa1a6dc1c361d65150c6782db7618a5ff126.tar.bz2 python-numpy-b173aa1a6dc1c361d65150c6782db7618a5ff126.zip |
Add simple indexing test.
Diffstat (limited to 'benchmarks')
-rw-r--r-- | benchmarks/simpleindex.py | 42 |
1 files changed, 42 insertions, 0 deletions
diff --git a/benchmarks/simpleindex.py b/benchmarks/simpleindex.py new file mode 100644 index 000000000..07c57f506 --- /dev/null +++ b/benchmarks/simpleindex.py @@ -0,0 +1,42 @@ +import timeit +# This is to show that NumPy is a poorer choice than nested Python lists +# if you are writing nested for loops. +# This is slower than Numeric was but Numeric was slower than Python lists were +# in the first place. + +N = 30 +code2 = r""" +for k in xrange(%d): + for l in xrange(%d): + res = a[k,l].item() + a[l,k].item() +""" % (N,N) +code3 = r""" +for k in xrange(%d): + for l in xrange(%d): + res = a[k][l] + a[l][k] +""" % (N,N) +code = r""" +for k in xrange(%d): + for l in xrange(%d): + res = a[k,l] + a[l,k] +""" % (N,N) +setup3 = r""" +import random +a = [[None for k in xrange(%d)] for l in xrange(%d)] +for k in xrange(%d): + for l in xrange(%d): + a[k][l] = random.random() +""" % (N,N,N,N) +t1 = timeit.Timer(code, 'import numpy as N; a = N.rand(%d,%d)' % (N,N)) +t2 = timeit.Timer(code, 'import MLab as N; a=N.rand(%d,%d)' % (N,N)) +t3 = timeit.Timer(code, 'import numarray.mlab as N; a=N.rand(%d,%d)' % (N,N)) +t4 = timeit.Timer(code2, 'import numpy as N; a = N.rand(%d,%d)' % (N,N)) +t5 = timeit.Timer(code3, setup3) +t6 = timeit.Timer("res = a + a.transpose()","import numpy as N; a=N.rand(%d,%d)" % (N,N)) +print "shape = ", (N,N) +print "NumPy 1: ", t1.repeat(3,100) +print "NumPy 2: ", t4.repeat(3,100) +print "Numeric: ", t2.repeat(3,100) +print "Numarray: ", t3.repeat(3,100) +print "Python: ", t5.repeat(3,100) +print "Optimized: ", t6.repeat(3,100) |