Re: [Numpy-discussion] Optimizing mean(axis=0) on a 3D array

[Francesc A. <fa...@ca...>]

A Dissabte 26 Agost 2006 12:26, Travis Oliphant va escriure:
> If frameis is 1-D, then you should be able to use
>
> temp =3D data.take(frameis,axis=3D0)
>
> for the first step.   This can be quite a bit faster (and is a big
> reason why take is still around).   There are several reasons for this
> (one of which is that index checking is done over the entire list when
> using indexing).

Well, some days ago I've stumbled on this as well. NumPy manual says=20
that .take() is usually faster than fancy indexing, but my timings shows th=
at=20
this is no longer true in recent versions of NumPy:

In [56]: Timer("b.take(a)","import numpy; a=3Dnumpy.arange(999,-1,-1,=20
dtype=3D'l');b=3Da[:]").repeat(3,1000)
Out[56]: [0.28740906715393066, 0.20345211029052734, 0.20371079444885254]

In [57]: Timer("b[a]","import numpy; a=3Dnumpy.arange(999,-1,-1,=20
dtype=3D'l');b=3Da[:]").repeat(3,1000)
Out[57]: [0.20807695388793945, 0.11684703826904297, 0.11686491966247559]

I've done some profiling on this and it seems that take is using C memmove=
=20
call so as to copy the data, and this is *very* slow, at least in my platfo=
rm=20
(Linux on Intel). On its hand, fancy indexing seems to use an iterator and=
=20
copying the elements one-by-one seems faster. I'd say that replacing memmov=
e=20
by memcpy would make .take() much faster.

Regards,

=2D-=20
>0,0<   Francesc Altet =A0 =A0 http://www.carabos.com/
V   V   C=E1rabos Coop. V. =A0=A0Enjoy Data
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