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  • `interp` performance with chunked dimensions · 3 ✖

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  • CONTRIBUTOR · 3 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1188348238 https://github.com/pydata/xarray/issues/6799#issuecomment-1188348238 https://api.github.com/repos/pydata/xarray/issues/6799 IC_kwDOAMm_X85G1MFO slevang 39069044 2022-07-18T21:43:11Z 2022-07-18T21:43:11Z CONTRIBUTOR

The chunking structure on disk is pretty instrumental to my application, which requires fast retrievals of full slices in the time dimension. The loop option in my first post only takes about 10 seconds with ni=1000 which is fine for my use case, so I'll probably go with that for now. It would be interesting to dig deeper though and see if there is a way to handle this better in the interp logic.

 
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   `interp` performance with chunked dimensions 1307112340
1187755023 https://github.com/pydata/xarray/issues/6799#issuecomment-1187755023 https://api.github.com/repos/pydata/xarray/issues/6799 IC_kwDOAMm_X85Gy7QP pums974 1005109 2022-07-18T16:55:42Z 2022-07-18T16:55:42Z CONTRIBUTOR

You are right about the behavior of the code. I don't see any way to enhance that in the general case.

Maybe, in your case, rechunking before interpolating might be a good idea

 
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   `interp` performance with chunked dimensions 1307112340
1187729258 https://github.com/pydata/xarray/issues/6799#issuecomment-1187729258 https://api.github.com/repos/pydata/xarray/issues/6799 IC_kwDOAMm_X85Gy09q slevang 39069044 2022-07-18T16:44:40Z 2022-07-18T16:44:40Z CONTRIBUTOR

Interpolating on chunked dimensions doesn't work at all prior to #4155. The changes in #4069 are also relevant.

 
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   `interp` performance with chunked dimensions 1307112340

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