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  • mathause · 2 ✖

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  • Optimize ndrolling nanreduce · 2 ✖

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716399575 https://github.com/pydata/xarray/issues/4325#issuecomment-716399575 https://api.github.com/repos/pydata/xarray/issues/4325 MDEyOklzc3VlQ29tbWVudDcxNjM5OTU3NQ== mathause 10194086 2020-10-26T08:40:51Z 2021-02-18T15:39:40Z MEMBER

This is already done for counts, correct? Here:

https://github.com/pydata/xarray/blob/1597e3a91eaf96626725987d23bbda2a80d2bae7/xarray/core/rolling.py#L370-L382

This should work for most of the reductions (and is a bit similar to what is done in weighted for mean and sum):

  • [x] count: isnull() -> rolling -> sum
  • [x] argmax: fillna(-inf) -> rolling -> argmax
  • [x] argmin: fillna(inf) -> rolling -> argmin
  • [x] max: fillna(-inf) -> rolling -> max (not sure about this one, need to be careful with the dtype)
  • [x] min: fillna(inf) -> rolling -> min (dito)
  • [x] mean: fillna(0) -> rolling -> sum / count (ensure nan if count == 0)
  • [x] prod: fillna(1) -> rolling -> prod
  • [x] sum: fillna(0) -> rolling -> sum
  • [ ] var: fillna(0) -> rolling -> possible (?) but a bit more involved
  • [ ] std: sqrt(var)
  • [ ] median: probably not possible

I think this should not be too difficult, the thing is that rolling itself is already quite complicated

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  Optimize ndrolling nanreduce 675482176
741734592 https://github.com/pydata/xarray/issues/4325#issuecomment-741734592 https://api.github.com/repos/pydata/xarray/issues/4325 MDEyOklzc3VlQ29tbWVudDc0MTczNDU5Mg== mathause 10194086 2020-12-09T12:17:15Z 2020-12-09T12:17:15Z MEMBER

I just saw that numpy 1.20 introduces stride_tricks.sliding_window_view. I have not looked at this yet. Just leaving this here for reference.

https://numpy.org/devdocs/reference/generated/numpy.lib.stride_tricks.sliding_window_view.html#numpy.lib.stride_tricks.sliding_window_view

https://numpy.org/devdocs/release/1.20.0-notes.html#sliding-window-view-provides-a-sliding-window-view-for-numpy-arrays

https://github.com/numpy/numpy/pull/17394

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  Optimize ndrolling nanreduce 675482176

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