html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/2525#issuecomment-435213658,https://api.github.com/repos/pydata/xarray/issues/2525,435213658,MDEyOklzc3VlQ29tbWVudDQzNTIxMzY1OA==,1217238,2018-11-01T22:51:55Z,2018-11-01T22:51:55Z,MEMBER,"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`).","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,375126758
https://github.com/pydata/xarray/issues/2525#issuecomment-435192382,https://api.github.com/repos/pydata/xarray/issues/2525,435192382,MDEyOklzc3VlQ29tbWVudDQzNTE5MjM4Mg==,1217238,2018-11-01T21:24:15Z,2018-11-01T21:24:15Z,MEMBER,"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()`","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,375126758
https://github.com/pydata/xarray/issues/2525#issuecomment-434705757,https://api.github.com/repos/pydata/xarray/issues/2525,434705757,MDEyOklzc3VlQ29tbWVudDQzNDcwNTc1Nw==,1217238,2018-10-31T14:22:07Z,2018-10-31T14:22:07Z,MEMBER,"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.
>
> —
> You are receiving this because you commented.
> Reply to this email directly, view it on GitHub
> <https://github.com/pydata/xarray/issues/2525#issuecomment-434589377>, or mute
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","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,375126758
https://github.com/pydata/xarray/issues/2525#issuecomment-434477550,https://api.github.com/repos/pydata/xarray/issues/2525,434477550,MDEyOklzc3VlQ29tbWVudDQzNDQ3NzU1MA==,1217238,2018-10-30T21:31:18Z,2018-10-30T21:31:18Z,MEMBER,"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()`","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,375126758