Aggregate Numpy Array By Summing

Question

I have a Numpy array of shape (4320,8640). I would like to have an array of shape (2160,4320).

You'll notice that each cell of the new array maps to a 2x2 set of cells in the old array. I would like a cell's value in the new array to be the sum of the values in this block in the old array.

I can achieve this as follows:

import numpy

#Generate an example array
arr = numpy.random.randint(10,size=(4320,8640))

#Perform the transformation
arrtrans = numpy.array([ [ arr[y][x]+arr[y+1][x]+arr[y][x+1]+arr[y+1][x+1] for x in range(0,8640,2)] for y in range(0,4320,2)])

But this is slow and more than a little ugly.

Is there a way to do this using Numpy (or an interoperable package)?

Answer 1

When the window fits exactly into the array, reshaping to more dimensions and collapsing the extra dimensions with np.sum is sort of the canonical way of doing this with numpy:

>>> a = np.random.rand(4320,8640)
>>> a.shape
(4320, 8640)
>>> a_small = a.reshape(2160, 2, 4320, 2).sum(axis=(1, 3))
>>> a_small.shape
(2160, 4320)
>>> np.allclose(a_small[100, 203], a[200:202, 406:408].sum())
True