issue_comments: 1533842816

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https://github.com/pydata/xarray/issues/3213#issuecomment-1533842816 https://api.github.com/repos/pydata/xarray/issues/3213 1533842816 IC_kwDOAMm_X85bbJWA 41593244 2023-05-03T22:40:32Z 2023-05-03T22:40:32Z NONE

Hi all! As part of a research project, I'm looking to contribute to xArray's sparse capabilities, with an emphasis on sparse support for use-cases in the geosciences. I'm wondering if anyone in the geosciences (or adjacent disciplines!) has encountered problems with xArray's current level of sparse support, and what kinds of improvements they'd like to see to address those issues. From playing around, it seems the current strategy of backing DataArrays with COO sparse arrays takes care of a lot of use cases, but I have the following ideas that may (or may not) be useful to implement further:

  • Functions/methods to convert from geopandas GeoDataframes of vector data to rasterized, potentially sparse ndarrays in an xArray Dataset/DataArray (reverse direction too); this is related to the issue of converting from sparse arrays back to multi-indexed pandas objects at the top of this issue (which I believe has yet to be solved)
  • Loading sparse data from a netcdf() file directly into a Dataset/Array backed by sparse ndarray(s) (seems like the only way to get sparse backings is to either unstack or call '.from_dataframe()/series()' with the sparse flag set to True?)
  • Support for other sparse array conventions (for ex, GCXS in the sparse package for better memory efficiency; I can't find any improvements to make on the current COO backing in terms of supported arithmetic operations, merges/joins, etc.)

I'd appreciate any feedback on these ideas, as well as any other things that would be nice to have implemented!

 
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