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  • shoyer · 4 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
711461331 https://github.com/pydata/xarray/issues/4473#issuecomment-711461331 https://api.github.com/repos/pydata/xarray/issues/4473 MDEyOklzc3VlQ29tbWVudDcxMTQ2MTMzMQ== shoyer 1217238 2020-10-19T01:30:48Z 2020-10-19T01:30:48Z MEMBER
  • What's the best way of reconstituting the coords etc, after npg produces the array?

I think we can reuse the existing logic from the _combine method here: https://github.com/pydata/xarray/blob/97e26257e81b0ba35af4a34be43a3e9cc666b9bc/xarray/core/groupby.py#L830

This just gives us an alternative way to calculate applied.

  • Presumably we're going to have a fairly different design for this than the existing groupby operations — that design is very nested — wrapping functions and eventually calling .map to loop over each group in python.

Agreed. Hopefully this can live alongside in the GroupBy objects.

  • Presumably we're going to need to keep the existing logic around for dask — is it reasonable for an initial version to defer to the existing logic for all dask arrays? (+ @shoyer 's thoughts above on this)

Yes, I agree that we should do this incrementally.

 
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   Wrap numpy-groupies to speed up Xarray's groupby aggregations 711626733
711460703 https://github.com/pydata/xarray/issues/4473#issuecomment-711460703 https://api.github.com/repos/pydata/xarray/issues/4473 MDEyOklzc3VlQ29tbWVudDcxMTQ2MDcwMw== shoyer 1217238 2020-10-19T01:27:50Z 2020-10-19T01:27:50Z MEMBER

Something like the resample test case from https://github.com/pydata/xarray/issues/4498 might be a good example for finding 100x speed-ups. The main feature of that case is that there are a very large number of groups (only slightly fewer groups than original data points).

 
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   Wrap numpy-groupies to speed up Xarray's groupby aggregations 711626733
701961598 https://github.com/pydata/xarray/issues/4473#issuecomment-701961598 https://api.github.com/repos/pydata/xarray/issues/4473 MDEyOklzc3VlQ29tbWVudDcwMTk2MTU5OA== shoyer 1217238 2020-10-01T07:57:58Z 2020-10-01T07:57:58Z MEMBER

Highly speculative, but would this also be a faster approach to stacking & unstacking? "Form ~5~ 4" in the readme.

I'm not entirely sure, but I suspect something like the approach in https://github.com/pydata/xarray/pull/4184 might be more directly relevant for speeding up unstack (at least with NumPy arrays).

 
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   Wrap numpy-groupies to speed up Xarray's groupby aggregations 711626733
701609035 https://github.com/pydata/xarray/issues/4473#issuecomment-701609035 https://api.github.com/repos/pydata/xarray/issues/4473 MDEyOklzc3VlQ29tbWVudDcwMTYwOTAzNQ== shoyer 1217238 2020-09-30T19:52:05Z 2020-09-30T19:52:05Z MEMBER

A prototype implementation of the core functionality here can be found in: https://nbviewer.jupyter.org/gist/shoyer/6d6c82bbf383fb717cc8631869678737

 
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   Wrap numpy-groupies to speed up Xarray's groupby aggregations 711626733

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