Did you try larger arrays/tuples? I would guess that makes a significant difference. On Fri, Jan 7, 2011 at 7:58 AM, EMMEL Thomas wrote:

Hi,

There are some discussions on the speed of numpy compared to Numeric in this list, however I have a topic I don't understand in detail, maybe someone can enlighten me... I use python 2.6 on a SuSE installation and test this:

#Python 2.6 (r26:66714, Mar 30 2010, 00:29:28) #[GCC 4.3.2 [gcc-4_3-branch revision 141291]] on linux2 #Type "help", "copyright", "credits" or "license" for more information.

import timeit

#creation of arrays and tuples (timeit number=1000000 by default)

timeit.Timer('a((1.,2.,3.))','from numpy import array as a').timeit() #8.2061841487884521 timeit.Timer('a((1.,2.,3.))','from Numeric import array as a').timeit() #9.6958281993865967 timeit.Timer('a((1.,2.,3.))','a=tuple').timeit() #0.13814711570739746

#Result: tuples - of course - are much faster than arrays and numpy is a bit faster in creating arrays than Numeric

#working with arrays

timeit.Timer('d=x1-x2;sum(d*d)','from Numeric import array as a; x1=a((1.,2.,3.));x2=a((2.,4.,6.))').timeit() #3.263314962387085 timeit.Timer('d=x1-x2;sum(d*d)','from numpy import array as a; x1=a((1.,2.,3.));x2=a((2.,4.,6.))').timeit() #9.7236979007720947

#Result: Numeric is three times faster than numpy! Why?

#working with components:

timeit.Timer('d0=x1[0]-x2[0];d1=x1[1]-x2[1];d2=x1[2]-x2[2];d0*d0+d1*d1+d2*d2','a=tuple; x1=a((1.,2.,3.));x2=a((2.,4.,6.))').timeit() #0.64785194396972656 timeit.Timer('d0=x1[0]-x2[0];d1=x1[1]-x2[1];d2=x1[2]-x2[2];d0*d0+d1*d1+d2*d2','from numpy import array as a; x1=a((1.,2.,3.));x2=a((2.,4.,6.))').timeit() #3.4181499481201172 timeit.Timer('d0=x1[0]-x2[0];d1=x1[1]-x2[1];d2=x1[2]-x2[2];d0*d0+d1*d1+d2*d2','from Numeric import array as a; x1=a((1.,2.,3.));x2=a((2.,4.,6.))').timeit() #0.97426199913024902

Result: tuples are again the fastest variant, Numeric is faster than numpy and both are faster than the variant above using the high-level functions! Why?

For various reasons I need to use numpy in the future where I used Numeric before. Is there any better solution in numpy I missed?

Kind regards and thanks in advance

Thomas

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