Python: downsample 2D numpy array by a non-integer factor

Question

I need to downsample a 2D numpy array by a non-integer factor (e.g. 100x100 array to a 45x45 array) in a way that performs local averaging, just like Photoshop/gimp would do that for an image. I need double precision. Current options can't do it well.

scipy.ndimage.zoom does not perform averaging, and basically uses nearest-neighbor sampling (see previous question scipy.ndimage.interpolation.zoom uses nearest-neighbor-like algorithm for scaling-down )
scipy.misc.imresize converts an array to an int8; I need more precision and floating point
skimage.transform.rescale also uses nearest-neighbor and forwards you to skimage.transform.downscale_local_mean for local averaging,
skimage.transform.downscale_local_mean can only perform integer scaling factor (and pads image with zeros if the factor is non-integer). Integer scaling factor is a trivial numpy excersice.

Did I miss any other options?

not the most elegant and I'm not sure of the math here but if you just want something that works I would scale it up by 9 (900X900) and then down by 20 (45X45) so you can do scaling by integer at both steps — OMRY VOLK
– OMRY VOLK, Commented Dec 6, 2015 at 22:06
I am currently using up-scaling to the nearest multiplier of the final size (135 in that case), and then down-scaling by block-averaging. It works, but it is kind of a ugly, especially for huge matrices. — Maxim Imakaev
– Maxim Imakaev, Commented Dec 6, 2015 at 22:12

Maxim Imakaev · Accepted Answer · 2015-12-12 22:02:01Z

I ended up writing a small function that upscales the image using scipy.ndimage.zoom, but for downscaling it first upscales it to be the multiple of the original shape, and then downscales by block-averaging. It accepts any other keyword arguments for scipy.zoom (order and prefilter)

I'm still looking for a cleaner solution using available packages.

def zoomArray(inArray, finalShape, sameSum=False, **zoomKwargs):
    inArray = np.asarray(inArray, dtype = np.double)
    inShape = inArray.shape
    assert len(inShape) == len(finalShape)
    mults = []
    for i in range(len(inShape)):
        if finalShape[i] < inShape[i]:
            mults.append(int(np.ceil(inShape[i]/finalShape[i])))
        else:
            mults.append(1)
    tempShape = tuple([i * j for i,j in zip(finalShape, mults)])

    zoomMultipliers = np.array(tempShape) / np.array(inShape) + 0.0000001
    rescaled = zoom(inArray, zoomMultipliers, **zoomKwargs)

    for ind, mult in enumerate(mults):
        if mult != 1:
            sh = list(rescaled.shape)
            assert sh[ind] % mult == 0
            newshape = sh[:ind] + [sh[ind] / mult, mult] + sh[ind+1:]
            rescaled.shape = newshape
            rescaled = np.mean(rescaled, axis = ind+1)
    assert rescaled.shape == finalShape

    if sameSum:
        extraSize = np.prod(finalShape) / np.prod(inShape)
        rescaled /= extraSize
    return rescaled

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