On Fri, 2005-01-14 at 09:28 +0100, Francesc Altet wrote:

A Dijous 13 Gener 2005 23:57, Chris Barker va escriure:

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setup = 'import Numeric as na; a = na.arange(2000);a.shape= (1000,2)' Timer('for i in range(len(a)): row=a[i]', setup).timeit (number=1000) 1.97064208984375 setup = 'import numarray as na; a = na.arange(2000);a.shape= (1000,2)' Timer('for i in range(len(a)): row=a[i]', setup).timeit (number=1000) 27.220904111862183

yup! that's it. numarray's indexing is SLOW. So it's not an iterator issue. Look in the archives of this list for discussion of why numarray's generic indexing is slow. A search for "wxPython indexing" will probably turn it up.

Well, if you want to really compare generic indexing speed, you can't mix array creation objects in the process, as your example seems to do.

A pure indexing access test would look like:

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setup = 'import numarray as na; a = [i*2 for i in range(2000)]' Timer('for i in range(len(a)): row=a[i]', setup).timeit (number=1000) 0.48835396766662598 # With Python Lists setup = 'import Numeric as na; a = na.arange(2000);a.shape= (1000*2,)' Timer('for i in range(len(a)): row=a[i]', setup).timeit (number=1000) 0.65753912925720215 # With Numeric setup = 'import numarray as na; a = na.arange(2000);a.shape= (1000*2,)' Timer('for i in range(len(a)): row=a[i]', setup).timeit (number=1000) 0.89093804359436035 # With numarray

That shows that numarray indexing is slower than Numeric, but not by a large extent (just a 40%). The real problem with numarray (for Ralf's example) is, as is already known, array creation time.

I thought we were done after what Francesc pointed out above, then I tried this: from timeit import Timer setup = 'import numarray as na; a = na.arange(2000,shape=(2000,))' print "numarray iteration: ", Timer('for i in a: pass', setup).timeit(number=1000) print "numarray simple indexing, int value:", Timer('for i in range(len(a)): row=a[i]', setup).timeit(number=1000) setup = 'import Numeric as na; a = na.arange(2000); a.shape=(2000,)' print "Numeric iteration: ", Timer('for i in a: pass', setup).timeit(number=1000) print "Numeric simple indexing, int value:", Timer('for i in range(len(a)): row=a[i]', setup).timeit(number=1000) And got: numarray iteration: 8.81474900246 numarray simple indexing, int value: 3.61732387543 Numeric iteration: 1.0384759903 Numeric simple indexing, int value: 2.18056321144 This is running on Python-2.3.4 compiled --with-debug using gcc-3.4.2 on a 1 GHZ Athlon XP and FC3 Linux. Simple indexing returning an int was 66% slower for me, but iteration was 880% slower. It looks to me like there is room for significant numarray iteration improvement; I'm not sure how it needs to be done or if Numeric has any special support. Regards, Todd