Improve perf of xr.corr #6638
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Improve perf of xr.corr #6638
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Because we want an all-NaN slice to return NaN for If there were a numba implementation of corr or cov, that would be far more performant than these, which are doing multiple passes & copies of each array, as well as blowing out any single-dimensions with the I'll close this. If anyone has alternative ideas, please feel free to raise. |
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* main: (95 commits) Use `zarr` to validate attrs when writing to zarr (pydata#6636) Add pre-commit hook to check CITATION.cff (pydata#6658) Fix kwargs used for extrapolation in docs (pydata#6639) Fix notebooks' HTML links (pydata#6655) Doc index update (pydata#6530) CFTime support for polyval (pydata#6624) Support dask arrays in datetime_to_numeric (pydata#6556) [pre-commit.ci] pre-commit autoupdate (pydata#6654) 0-padded month. (pydata#6653) [test-upstream] import `cleanup` fixture from `distributed` (pydata#6650) Allow all interp methods in typing (pydata#6647) Typing support for custom backends (pydata#6651) Improved DataArray typing (pydata#6637) Adjust code comments & types from pydata#6638 (pydata#6642) Typing of `str` and `dt` accessors (pydata#6641) Feature/to dict encoding (pydata#6635) fix {full,zeros,ones}_like overloads (pydata#6630) Mypy badge (pydata#6626) concatenate docs style (pydata#6621) Typing for open_dataset/array/mfdataset and to_netcdf/zarr (pydata#6612) ...
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I think this should improve the performance of
xr.corr&xr.cov, but I'm not sure about dask arrays. I haven't been able to do tests on them, but I can do that soon unless anyone has an a priori view.