[SciPy-User] down-sampling an array by averaging - vectorized form?

[andrew giessel]

Hello all,

I'm looking to down-sample an image by averaging.  I quickly hacked up the
following code, which does exactly what I want, but the double loop is slow
(the images I'm working with are ~2000x2000 pixels).  Is there a nice way
to vectorize this?  A quick profile showed that most of the time is spend
averaging- perhaps there is a way to utilize np.sum or np.cumsum, divide
the whole array, and then take every so many pixels?

This method of down-sampling (spatial averaging) makes sense for the type
of data I'm using and yields good results, but I'm also open to
alternatives.  Thanks in advance!

Andrew

######################
import numpy as np

def downsample(array, reduction):
    """example call for 2fold size reduction:  newImage = downsample(image,
2)"""

    newArray = np.empty(array.shape[0]/reduction, array.shape[1]/reduction)

    for x in range(newArray.shape[0]):
        for y in range(newArray.shape[1]):
            newArray[x,y] =
np.mean(array[x*reduction:((x+1)*reduction)-1, y*reduction:((y+1)*reduction)-1])

    return newArray
######################

-- 
Andrew Giessel, PhD

Department of Neurobiology, Harvard Medical School
220 Longwood Ave Boston, MA 02115
ph: 617.432.7971 email: andrew_giessel at hms.harvard.edu
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