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  • Add an Cumulative aggregation, similar to Rolling · 3 ✖

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826374418 https://github.com/pydata/xarray/issues/5215#issuecomment-826374418 https://api.github.com/repos/pydata/xarray/issues/5215 MDEyOklzc3VlQ29tbWVudDgyNjM3NDQxOA== max-sixty 5635139 2021-04-25T19:10:36Z 2021-04-25T19:10:36Z MEMBER

.cumulative is great! Much better.

The benefit is that the API surface is reduced — e.g. we can have a .cumulative().integrate(), rather than a separate .cumulative_integrate (and so on, for each aggregation), from https://github.com/pydata/xarray/pull/5153.

The implementation could be as simple as da.rolling(dim=da.sizes[dim]). How compatible would dask be with that? How does it compare to the numpy.ufunc.accumulate(...) suggestion?

 
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   Add an Cumulative aggregation, similar to Rolling 866826033
826364666 https://github.com/pydata/xarray/issues/5215#issuecomment-826364666 https://api.github.com/repos/pydata/xarray/issues/5215 MDEyOklzc3VlQ29tbWVudDgyNjM2NDY2Ng== dcherian 2448579 2021-04-25T18:05:55Z 2021-04-25T18:05:55Z MEMBER

IIUC da.expanding(dim=dim).sum() is da.cumsum(dim) with support for min_periods and center like rolling.

I guess all expanding reductions are basically numpy.ufunc.accumulate(...). The dask versions will be "interesting" to write. https://github.com/dask/dask/blob/f1f37cae96d5e98f8043ea430539a4fffbe62661/dask/array/reductions.py#L1389-L1413

Like @mathause I find "expanding" confusing. .accumulate().sum() or .cumulative().sum() sounds much better to me.

 
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   Add an Cumulative aggregation, similar to Rolling 866826033
826345774 https://github.com/pydata/xarray/issues/5215#issuecomment-826345774 https://api.github.com/repos/pydata/xarray/issues/5215 MDEyOklzc3VlQ29tbWVudDgyNjM0NTc3NA== mathause 10194086 2021-04-25T15:49:56Z 2021-04-25T15:49:56Z MEMBER

I don't entirely get what the function is supposed to do (even after looking at the pandas docstring) - can you give a an example or two? 

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   Add an Cumulative aggregation, similar to Rolling 866826033

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