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  • max-sixty · 4 ✖

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  • nd-rolling · 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
670819822 https://github.com/pydata/xarray/pull/4219#issuecomment-670819822 https://api.github.com/repos/pydata/xarray/issues/4219 MDEyOklzc3VlQ29tbWVudDY3MDgxOTgyMg== max-sixty 5635139 2020-08-08T04:02:09Z 2020-08-08T04:02:09Z MEMBER

Great! Thanks @fujiisoup !

Ready to go from my POV 

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   nd-rolling 655389649
670594286 https://github.com/pydata/xarray/pull/4219#issuecomment-670594286 https://api.github.com/repos/pydata/xarray/issues/4219 MDEyOklzc3VlQ29tbWVudDY3MDU5NDI4Ng== max-sixty 5635139 2020-08-07T16:13:28Z 2020-08-07T16:13:28Z MEMBER

I'm still a bit confused. How does the "roll over each dimension in turn" approach equal the "roll over both dimension together" approach with a function like std? Here's a proposed counter example:

```python import xarray as xr import numpy as np

da = xr.DataArray(np.asarray([[0,10,0],[0,10,0], [0,10,0]]), dims=list('xy')) print(da)

<xarray.DataArray (x: 3, y: 3)> array([[ 0, 10, 0], [ 0, 10, 0], [ 0, 10, 0]]) Dimensions without coordinates: x, y

x_std = da.rolling(dict(x=2)).std() print(x_std)

<xarray.DataArray (x: 3, y: 3)> array([[nan, nan, nan], [ 0., 0., 0.], [ 0., 0., 0.]]) Dimensions without coordinates: x, y

x_then_y_std = x_std.rolling(dict(y=2)).std() print(x_then_y_std)

<xarray.DataArray (x: 3, y: 3)> array([[nan, nan, nan], [nan, 0., 0.], [nan, 0., 0.]]) Dimensions without coordinates: x, y

combined_std = da.rolling(dict(x=2, y=2)).std() print(combined_std)

<xarray.DataArray (x: 3, y: 3)> array([[nan, nan, nan], [nan, 5., 5.], [nan, 5., 5.]]) Dimensions without coordinates: x, y

```

 
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   nd-rolling 655389649
657257847 https://github.com/pydata/xarray/pull/4219#issuecomment-657257847 https://api.github.com/repos/pydata/xarray/issues/4219 MDEyOklzc3VlQ29tbWVudDY1NzI1Nzg0Nw== max-sixty 5635139 2020-07-12T18:23:19Z 2020-07-12T18:23:28Z MEMBER

Re the API, I think the dict is probably the best option, although it does complicate as the arguments become differently typed depending on one vs multiple dimensions.

One alternative is to allow fluent args, like: python ( da .rolling(x=3, center=True) .rolling(y=5, min_periods=2) .mean() ) ...but does that then seem like the second rolling is operating on the result of the first?

 
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   nd-rolling 655389649
657257600 https://github.com/pydata/xarray/pull/4219#issuecomment-657257600 https://api.github.com/repos/pydata/xarray/issues/4219 MDEyOklzc3VlQ29tbWVudDY1NzI1NzYwMA== max-sixty 5635139 2020-07-12T18:20:38Z 2020-07-12T18:20:38Z MEMBER

This looks very promising; I'm surprised it was possible without more code. I'm being slow, but where is the nd-rolling algo? I had thought bottleneck didn't support more than one dimension? https://bottleneck.readthedocs.io/en/latest/bottleneck.move.html, and that we'd have to implement our own in numbagg (which would be very possible)

 
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   nd-rolling 655389649

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