Menu
▾
▴
Re: [Numpy-discussion] treating numpy arrays like lists is slow]
|
From: Jeff W. <js...@fa...> - 2006-02-04 03:05:31
|
Travis Oliphant wrote: > Jeff Whitaker wrote: > >> >> Hi: >> >> I've noticed that code like this is really slow in numpy (0.9.4): >> >> import numpy as NP >> a = NP.ones(10000,'d') >> a = [2.*a1 for a1 in a] >> >> >> the last line takes 0.17 seconds on my G5, while for Numeric and >> numarray it takes only 0.01. Anyone know the reason for this? >> > We could actually change this right now, before the introduction of > scalar math by using the standard float table for the corresponding > array scalars. The only reason I didn't do this initially was that I > wanted consistency in behavior for "division-by-zero" between arrays > and scalars. > Using the Python float math you will get divide-by-zero errors whereas > you don't (unless you ask for them), with numpy arrays. > > Thus, current scalars are treated as 0-d arrays in the internals and > go through the entire ufunc machinery for every operation. > Now, the real question is why are you doing this? Using arrays in > this way defeats their purpose :-) > > What is wrong with 2*a? Now, of course there will be situations that > require this. > > -Travis > Travis: Of course I know this is a dumb thing to do - but sometimes it does happen that a function that expects a list actually gets a rank-1 array. The workaround in that case is to just pass it a.tolist() instead of a. -Jeff -- Jeffrey S. Whitaker Phone : (303)497-6313 Meteorologist FAX : (303)497-6449 NOAA/OAR/PSD R/PSD1 Email : Jef...@no... 325 Broadway Office : Skaggs Research Cntr 1D-124 Boulder, CO, USA 80303-3328 Web : http://tinyurl.com/5telg -- Jeffrey S. Whitaker Phone : (303)497-6313 Meteorologist FAX : (303)497-6449 NOAA/OAR/PSD R/PSD1 Email : Jef...@no... 325 Broadway Office : Skaggs Research Cntr 1D-124 Boulder, CO, USA 80303-3328 Web : http://tinyurl.com/5telg |