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  • `interp` performance with chunked dimensions · 1 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1317352980 https://github.com/pydata/xarray/issues/6799#issuecomment-1317352980 https://api.github.com/repos/pydata/xarray/issues/6799 IC_kwDOAMm_X85OhTYU gjoseph92 3309802 2022-11-16T17:00:04Z 2022-11-16T17:00:04Z NONE

The current code also has the unfortunate side-effect of merging all chunks too

Don't really know what I'm talking about here, but it looks to me like the current dask-interpolation routine uses blockwise. That is, it's trying to simply map a function over each chunk in the array. To get the chunks into a structure where this is correct to do, you have to first merge all the chunks along the interpolation axis.

I would have expected interpolation to use map_overlap. You'd add some padding to each chunk, map the interpolation over each chunk (without combining them), then trim off the extra. By using overlap, you don't need to combine all the chunks into one big array first, so the operation can actually be parallel.

FYI, fixing this would probably be a big deal to geospatial people—then you could do array reprojection without GDAL! Unfortunately not something I have time to work on right now, but perhaps someone else would be interested?

 
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   `interp` performance with chunked dimensions 1307112340

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