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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).

OK, so maybe da.block({'lat': 2, 'lon': 2}).mean() would be a good way to spell this, if that's not too confusing with .chunk()? Other possible method names: groupby_block, blocked?

We could call this something like coarsen() or block_reduce() with a how='mean' or maybe func=mean argument, but I like the consistency with resample/rolling/groupby.

We can save the full coordinate based version for a later addition to .resample()

block_reduce from skimage is indeed a small function using strides/reshape, if I remember correctly. We should certainly copy or implement it ourselves rather than adding an skimage dependency. On Wed, Oct 31, 2018 at 12:36 AM Keisuke Fujii notifications@github.com wrote:

block_reduce sounds nice, but I am a little hesitating to add a soft-dependence of scikit-image only for this function... It is using the strid trick, as we are doing in rolling.construct. Maybe we can implement it by ourselves.

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I'm +1 for adding this feature in some form as well.

From an API perspective, should the window size be specified in term of integer or coordinates? - rolling is integer based - resample is coordinate based

I would lean towards a coordinate based representation since it's a little more usable/certain to be correct. It might even make sense to still call this resample, though obviously the time options would no longer apply. Also, we would almost certainly want a faster underlying implementation than what we currently use for resample().

The API for resampling to a 2x2 degree latitude/longittude grid could look something like: da.resample(lat=2, lon=2).mean()