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  • `missing_dims` option for aggregation methods like `mean` and `std` · 5 ✖

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1058577301 https://github.com/pydata/xarray/issues/5030#issuecomment-1058577301 https://api.github.com/repos/pydata/xarray/issues/5030 IC_kwDOAMm_X84_GJuV TomNicholas 35968931 2022-03-03T22:35:08Z 2022-03-03T22:37:16Z MEMBER

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).

I specifically want the user to be able to choose between different behaviours with a flag, but you're right that I could just deal with this at the datatree level instead of here. That would make a fair amount of sense, and it would cover Julius' use-case (via encouraging him to store his models in a tree, so that for ds in model_datasets would become a loop over nodes in a tree).

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.

Do you think that's a problem though? We added keep_attrs to even more of the API than this would cover. Specifically I would want to add it to the REDUCE_METHODS, the NAN_REDUCE_METHODS, and the NAN_CUM_METHODS (so {"all", "any", "max", "min", "mean", "prod", "sum", "std", "var", "median", "cumsum", "cumprod"}).

I'm fine with doing it either here or in datatree personally.

 
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   `missing_dims` option for aggregation methods like `mean` and `std` 830638672
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
1058446727 https://github.com/pydata/xarray/issues/5030#issuecomment-1058446727 https://api.github.com/repos/pydata/xarray/issues/5030 IC_kwDOAMm_X84_Fp2H TomNicholas 35968931 2022-03-03T20:19:39Z 2022-03-03T20:19:39Z MEMBER

I ran into the same sort of thing today, when trying to loop over many datasets (each of which contained the contents of a node in a datatree...).

I also think that adding a missing_dims argument to all the array reduce methods would be useful, and I plan to have a go at it.

 
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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
797842748 https://github.com/pydata/xarray/issues/5030#issuecomment-797842748 https://api.github.com/repos/pydata/xarray/issues/5030 MDEyOklzc3VlQ29tbWVudDc5Nzg0Mjc0OA== max-sixty 5635139 2021-03-13T01:30:36Z 2021-03-13T01:30:36Z MEMBER

That seems like a reasonable suggestion @jbusecke .

To confirm, would ds.groupby('lat', 'long').mean(...) work? i.e. are the dimensions you don't want to reduce over reliable?

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

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