Downsample array in Python

scikit-image has implemented a working version of downsampling here, although they shy away from calling it downsampling for it not being a downsampling in terms of DSP, if I understand correctly:

http://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.block_reduce

but it works very well, and it is the only downsampler that I found in Python that can deal with np.nan in the image. I have downsampled gigantic images with this very quickly.

When downsampling, interpolation is the wrong thing to do. Always use an aggregated approach.

I use block means to do this, using a "factor" to reduce the resolution.

import numpy as npfrom scipy import ndimagedef block_mean(ar, fact):    assert isinstance(fact, int), type(fact)    sx, sy = ar.shape    X, Y = np.ogrid[0:sx, 0:sy]    regions = sy/fact * (X/fact) + Y/fact    res = ndimage.mean(ar, labels=regions, index=np.arange(regions.max() + 1))    res.shape = (sx/fact, sy/fact)    return res

E.g., a (100, 200) shape array using a factor of 5 (5x5 blocks) results in a (20, 40) array result:

ar = np.random.rand(20000).reshape((100, 200))block_mean(ar, 5).shape  # (20, 40)

imresize and ndimage.interpolation.zoom look like they do what you want

I haven't tried imresize before but here is how I have used ndimage.interpolation.zoom

a = np.array(64).reshape(8,8)a = ndimage.interpolation.zoom(a,.5) #decimate resolution

a is then a 4x4 matrix with interpolated values in it