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  • chrisroat · 2 ✖

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  • multiple arrays with common nan-shaped dimension · 2 ✖

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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
821655160 https://github.com/pydata/xarray/issues/5168#issuecomment-821655160 https://api.github.com/repos/pydata/xarray/issues/5168 MDEyOklzc3VlQ29tbWVudDgyMTY1NTE2MA== chrisroat 1053153 2021-04-16T22:38:08Z 2021-04-16T22:38:08Z CONTRIBUTOR

It may run even deeper -- there seem to be several checks on dimension sizes that would need special casing. Even simply doing a variable[dim] lookup fails! 

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   multiple arrays with common nan-shaped dimension 859577556
821285344 https://github.com/pydata/xarray/issues/5168#issuecomment-821285344 https://api.github.com/repos/pydata/xarray/issues/5168 MDEyOklzc3VlQ29tbWVudDgyMTI4NTM0NA== chrisroat 1053153 2021-04-16T16:13:09Z 2021-04-16T16:13:09Z CONTRIBUTOR

There seems to be some support, but now you have me worried. I have a used xarray mainly for labelling, but not for much computation -- I'm dropping into dask because I need map_overlap.

FWIW, calling dask.compute(arr) works with unknown chunk sizes, but now I see arr.compute() does not. This fooled me into thinking I could use unknown chunk sizes. Now I see that writing to zarr does not work, either. This might torpedo my current design.

I see the compute_chunk_sizes method, but that seems to trigger computation. I'm running on a dask cluster -- is there anything I can do to salvage the pattern arr_with_nan_shape.to_dataset().to_zarr(compute=False) (with our without xarray)?

 
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   multiple arrays with common nan-shaped dimension 859577556

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