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

Martin Spacek numpy at mspacek.mm.st
Sat Aug 26 05:06:42 CDT 2006

```Hello,

I'm a bit ignorant of optimization in numpy.

I have a movie with 65535 32x32 frames stored in a 3D array of uint8
with shape (65535, 32, 32). I load it from an open file f like this:

>>> import numpy as np
>>> data = np.fromfile(f, np.uint8, count=65535*32*32)
>>> data = data.reshape(65535, 32, 32)

I'm picking several thousand frames more or less randomly from
throughout the movie and finding the mean frame over those frames:

>>> meanframe = data[frameis].mean(axis=0)

frameis is a 1D array of frame indices with no repeated values in it. If
it has say 4000 indices in it, then the above line takes about 0.5 sec
to complete on my system. I'm doing this for a large number of different
frameis, some of which can have many more indices in them. All this
takes many minutes to complete, so I'm looking for ways to speed it up.

If I divide it into 2 steps:

>>> temp = data[frameis]
>>> meanframe = temp.mean(axis=0)

and time it, I find the first step takes about 0.2 sec, and the second
takes about 0.3 sec. So it's not just the mean() step, but also the
indexing step that's taking some time.

If I flatten with ravel:

>>> temp = data[frameis].ravel()
>>> meanframe = temp.mean(axis=0)

then the first step still takes about 0.2 sec, but the mean() step drops
to about 0.1 sec. But of course, this is taking a flat average across
all pixels in the movie, which isn't what I want to do.

I have a feeling that the culprit is the non contiguity of the movie
frames being averaged, but I don't know how to proceed.

Any ideas? Could reshaping the data somehow speed things up? Would
weave.blitz or weave.inline or pyrex help?

I'm running numpy 0.9.8

Thanks,

Martin

```