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  • dcherian · 4 ✖

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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
1287253517 https://github.com/pydata/xarray/pull/7152#issuecomment-1287253517 https://api.github.com/repos/pydata/xarray/issues/7152 IC_kwDOAMm_X85Mue4N dcherian 2448579 2022-10-21T17:35:03Z 2022-10-21T17:35:03Z MEMBER

o you think it would be better to finish this PR as the creation of _aggregations.py to give the cum methods better documentation? Then start a new one to fix https://github.com/pydata/xarray/issues/6528?

Sure that would be a good intermediate step. Let us know if you need help.

 
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   Cumulative examples 1403614394
1287138940 https://github.com/pydata/xarray/pull/7152#issuecomment-1287138940 https://api.github.com/repos/pydata/xarray/issues/7152 IC_kwDOAMm_X85MuC58 dcherian 2448579 2022-10-21T15:42:12Z 2022-10-21T15:42:12Z MEMBER

Thanks for taking this on @patrick-naylor ! This is a decent-sized project!

Using apply_ufunc and np.cumsum/cumprod has some issues as it only finds the cumulative across one axis which makes iterating through each dimension necessary.

np.cumsum only supports an integer axis so this is OK?

flox doesn't support cumsum at the moment (https://github.com/xarray-contrib/flox/issues/91) so we can delete that bit and just have one code path.

 
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   Cumulative examples 1403614394
1275131950 https://github.com/pydata/xarray/pull/7152#issuecomment-1275131950 https://api.github.com/repos/pydata/xarray/issues/7152 IC_kwDOAMm_X85MAPgu dcherian 2448579 2022-10-11T18:51:26Z 2022-10-11T18:51:26Z MEMBER

Is there a particular reason why there is no cumprod for GroupBy objects?

Nope. Just wasn't added in :)

 
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   Cumulative examples 1403614394
1274963421 https://github.com/pydata/xarray/pull/7152#issuecomment-1274963421 https://api.github.com/repos/pydata/xarray/issues/7152 IC_kwDOAMm_X85L_mXd dcherian 2448579 2022-10-11T16:27:15Z 2022-10-11T16:30:32Z MEMBER

Thanks @patrick-naylor !

Instead of using Dataset.reduce I think we want something like python def cumsum(..., dim): return xr.apply_ufunc( np.cumsum if skipna else np.nancumsum, obj, input_core_dims=[dim], output_core_dims=[dim], kwargs={"axis": -1}, ) # now transpose dimensions back to input order

to fix #6528.

At the moment, this should also work on GroupBy objects quite nicely.

 
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   Cumulative examples 1403614394

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