Ralf Juengling wrote:
for data in a: do sth with data
instead of
for i in range(len(a)): data = a[i] do sth with data
Do you think it's possible to speed things up by implementing an __iter__ method?
Frankly, I seriously doubt it would make much difference, the indexing operation would have to take a comparable period of time to your: do sth with data That is unlikely. By the way, here is a test with Python lists: setup = 'import numarray as na; a = [[i*2,i*2+1] for i in range(1000)]'
Timer('for i in range(len(a)): row=a[i]', setup).timeit(number=1000) 0.37136483192443848
Much faster than the numarray examples( ~ 30 on my machine). I suspect the real delay here is that each indexing operation has to create a new array (even if they do use the same data). Lists just return the item. Also, it's been discussed that numarray's generic indexing is much slower than Numeric's, for instance. This has made a huge difference when passing arrays into wxPython, for instance. Perhaps that's relevant? Here's a test with Numeric vs. numarray:
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. -Chris -Chris -- Christopher Barker, Ph.D. Oceanographer NOAA/OR&R/HAZMAT (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chris.Barker@noaa.gov