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https://github.com/pydata/xarray/issues/1077#issuecomment-645416425 https://api.github.com/repos/pydata/xarray/issues/1077 645416425 MDEyOklzc3VlQ29tbWVudDY0NTQxNjQyNQ== 2448579 2020-06-17T14:40:19Z 2020-06-17T14:40:19Z MEMBER

@shoyer I now understand your earlier comment.

I agree that it should work with both sparse and MultiIndex but as such there's no way to decide whether this should be decoded to a sparse array or a MultiIndexed dense array.

Following your comment in https://github.com/pydata/xarray/issues/3213#issuecomment-521533999

Fortunately, there does seems to be a CF convention that would be a good fit for for sparse data in COO format, namely the indexed ragged array representation (example, note the instance_dimension attribute). That's probably the right thing to use for sparse arrays in xarray.

How about using this "compression by gathering" idea for MultiIndexed dense arrays and "indexed ragged arrays" for sparse arrays? I do not know the internals of sparse or the details of the CF conventions to have a strong opinion on which representation to prefer for sparse.COO arrays.

PS: CF convention for "indexed ragged arrays" is here: http://cfconventions.org/cf-conventions/cf-conventions.html#_indexed_ragged_array_representation

 
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