On 7/21/07, Eric Firing <[EMAIL PROTECTED]> wrote: > Sometimes functions from the different sources do the same thing, but > usually at different speeds.
And the difference may be *very* significant: In [1]: import numpy as N In [2]: import math as M In [3]: nsqrt = N.sqrt In [4]: msqrt = M.sqrt In [5]: def sqtest(sqrt,reps): ...: x = 99.9 ...: for i in xrange(reps): ...: a = sqrt(x) ...: In [13]: reps = int(1e6) In [14]: time sqtest(msqrt,reps) CPU times: user 0.90 s, sys: 0.00 s, total: 0.90 s Wall time: 0.90 In [15]: time sqtest(nsqrt,reps) CPU times: user 7.62 s, sys: 0.39 s, total: 8.02 s Wall time: 8.08 The overhead from numpy for scalars is not trivial at all. And as you pointed out, the semantical differences between math, cmath and numpy (ignoring ma for now) are important enough that people should know exactly what they are getting. I guess if you want to provide a 'common math functions' module with clearly defined conventions for everyday usage, you could do something like (using cos as an example, apply to all names that are common to all such modules): numpy.cos -> cos # unmodified, numpy is the 'default' a la matlab math.cos -> scos # names from math are s-prefixed for 'scalar' (could be 'm') cmath.cos -> ccos # complex names numpy.ma.cos -> macos # masked array names Just an idea... Cheers, f ------------------------------------------------------------------------- This SF.net email is sponsored by: Splunk Inc. Still grepping through log files to find problems? Stop. Now Search log events and configuration files using AJAX and a browser. Download your FREE copy of Splunk now >> http://get.splunk.com/ _______________________________________________ Matplotlib-devel mailing list Matplotlib-devel@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
