issue_comments: 435213658

skimage implements block_reduce via the view_as_blocks utility function: https://github.com/scikit-image/scikit-image/blob/62e29cd89dc858d8fb9d3578034a2f456f298ed3/skimage/util/shape.py#L9-L103

But given that it doesn't actually duplicate any elements and needs a C-order array to work, I think it's actually just equivalent to use use reshape + transpose, e.g., B = A.reshape(4, 1, 2, 2, 3, 2).transpose([0, 2, 4, 1, 3, 5]) reproduces skimage.util.view_as_blocks(A, (1, 2, 2)) from the docstring example.

So the super-simple version of block-reduce looks like: python def block_reduce(image, block_size, func=np.sum): # TODO: input validation # TODO: consider copying padding from skimage blocked_shape = [] for existing_size, block_size in zip(image.shape, block_size): blocked_shape.extend([existing_size // block_size, block_size]) blocked = np.reshape(image, tuple(blocked_shape)) return func(blocked, axis=tuple(range(1, blocked.ndim, 2)))

This would work on dask arrays out of the box but it's probably worth benchmarking whether you'd get better performance doing the operation chunk-wise (e.g., with map_blocks).