Sparse regridding without using ESMF #130
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andersy005
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Jun 3, 2024
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| def xr_regridder( | ||
| ds: xr.Dataset, | ||
| grid: xr.Dataset, | ||
| weights: xr.DataArray, | ||
| out_grid_shape: tuple[int, int], | ||
| ) -> xr.Dataset: | ||
| """ |
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this is very neat, @TomNicholas! thank you for this addition.
we should definitely figure out how to generalize this...
Cc @norlandrhagen / @maxrjones
TomNicholas
commented
Jun 3, 2024
| latlon_dims = ['nlat', 'nlon'] | ||
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| shape_in = (ds.sizes['nlat'], ds.sizes['nlon']) | ||
| shape_out = out_grid_shape |
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This out_grid_shape argument is probably superfluous - you could just read it from the grid object.
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This PR shows how one can avoid using xESMF for the application of the regridding weights, by using sparse matrix multiplication instead. The motivation for this is errors when using ndpyramid with a dask PBSCluster at scale due to ESMF not being thread-safe (see pangeo-data/xESMF#307 (comment)).
To be clear: This still uses xESMF to generate the regridding weights, it just allows you to generate them once outside of dask and then use them repeatedly without invoking
xESMFon you dask cluster, which is enough to get around the threading errors (because you can completely avoid importing xESMF on the dask worker at all).This notebook shows that this function gives the same results as using xESMF for the application of the weights.
I'm not expecting this to be merged - I just wanted to put it up here in case it's useful to anyone.