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  • dcherian · 2 ✖

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  • `missing_dims` option for aggregation methods like `mean` and `std` · 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
1058532014 https://github.com/pydata/xarray/issues/5030#issuecomment-1058532014 https://api.github.com/repos/pydata/xarray/issues/5030 IC_kwDOAMm_X84_F-qu dcherian 2448579 2022-03-03T21:56:55Z 2022-03-03T21:57:21Z MEMBER

My concern is that we could conceivably adding missing_dims to any function that takes a dim argument, which is pretty much the whole API.

For datatree, you could apply the reduction with the set-intersection of provided dims and dims present in a node (if that's the right term).

 
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   `missing_dims` option for aggregation methods like `mean` and `std` 830638672
797850555 https://github.com/pydata/xarray/issues/5030#issuecomment-797850555 https://api.github.com/repos/pydata/xarray/issues/5030 MDEyOklzc3VlQ29tbWVudDc5Nzg1MDU1NQ== dcherian 2448579 2021-03-13T02:23:38Z 2021-03-13T02:23:38Z MEMBER

Alternatively, you could run the following at the beginning

``` python

not sure if syntax is right

model_datasets = [ ds.expand_dims('member_id') if "member_id" not in ds.coords else ds for ds in model_datasets ] ```

so all your datasets are consistent.

 
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   `missing_dims` option for aggregation methods like `mean` and `std` 830638672

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