Vincent, Pauli,

From: Vincent Schut <schut@sarvision.nl>

- an other option would be some smart reshaping, which finally gives you a [y//2, x//2, 2, 2] array, which you could then reduce to calculate stats (mean, std, etc) on the last two axes. I *think* you'd have to first reshape both x and y axes, and then reposition the axes. An example: a = gdal_array.BandReadAsArray(blabla) y,x = a.shape #y and x need be divideable by 2! b = a.reshape(y/2, 2, x/2, x).transpose(0,2,1,3).reshape(y/2, x/2, 4) bMean = b.mean(axis=-1) bMax = ......etc.

You and Pauli agree on this - seems like a good option.

- a third option would be to create an index array, which has a unique value per 2x2 square, and then use histogram2d. This would, if you use its 'weight' functionality, at least enable you to get efficient counts and sums/means. Other stats might be hard, though.

Hmmmm I don't get this, but I'll experiment. I've seen some really useful things done with histogram2d so it seems worthwhile to figure out.

Message: 6 From: Pauli Virtanen <pav@iki.fi>

...

Let's say the image looks like this: np.random.randint(0,2, 16).reshape(4,4)

array([[0, 0, 0, 1], [0, 0, 1, 1], [1, 1, 0, 0], [0, 0, 0, 0]])

I want to use a square, non-overlapping moving window for resampling, so that I get a count of all the 1's in each 2x2 window.

0, 0, 0, 1 0, 0, 1, 1 0 3 => 2 0 1, 1, 0, 0 0, 0, 0, 0

In another situation with similar data I'll need the average, or the maximum value, etc..

Non-overlapping windows can be done by reshaping:

x = np.array([[0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 0, 0], [1, 1, 0, 0, 1, 1], [0, 0, 0, 0, 1, 1], [1, 0, 1, 0, 1, 1], [0, 0, 1, 0, 0, 0]])

y = x.reshape(3,2,3,2) y2 = y.sum(axis=3).sum(axis=1)

This is perfect - and so fast! Thanks! Now I just have to understand why it works ... Can anyone recommend a tutorial on working with (slicing, reshaping, etc.) multi-dimensional arrays? Stefan's slides are beautiful but my brain starts to hurt if I try to follow them line by line. -Robin