issue_comments

@max-sixty thanks for the review. merged

Great! Thanks @fujiisoup !

Ready to go from my POV

Thanks @max-sixty .

You are completely correct. As the test pass, I was fooling myself.

The reason was that the dataset I was using for the test does not have time and x simultaneously. So I was not testing the 2d-rolling but just 1d-rolling.

Fixed. Now it correctly fails for mean and std, but passes with max and sum

I think it's more like da.rolling(x=2).construct("xr").rolling(y=2).construct("yr").std(["xr", "yr"])

but that gives array([[0., 5., 5.], [0., 5., 5.], [0., 5., 5.]])

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

```

Thanks @max-sixty for the review ;) I'll work for the update in a few days.

Could anyone kindly review this?

I got an error for typechecking, only in CI but not in local, from the code that I didn't change.

A possible improvement will be nan-reduction methods for nd-rolling. Currently, we just use numpy nan-reductions, which is memory consuming for strided arrays.

This issue can be solved by replacing nan by appropriate values and applying nonnan-reduction methods, e.g., python da.rolling(x=2, y=3).construct(x='xw', y='yw').sum(['xw', 'yw']) should be the same with python da.rolling(x=2, y=3).construct(x='xw', y='yw', fill_value=0).sum(['xw', 'yw'], skipna=False) and the latter is much more memory efficient.

I'd like to leave this improvement to future PR.

I think now it is ready for review, though I'm sure tests miss a lot of edge cases. Maybe we can fix them if pointed out.

I think the dictionary is OK. We can allow scalars for center, stride etc. in which case the scalar applies to all dimensions. This would preserve backward compatibility.

scalar for min_counts also seems OK to me. We can update it if someone requests it.

Another API concern. We now use min_periods, in which we implicitly assume one-dimension cases.

With nd-dimension, I think min_counts argument is more appropriate like bottleneck, which will limit the lower bound of the number of missing entries in the n-dimensional window.

Even if we leave it, we may disallow nd-argument of min_periods but keep it a scalar.

Hi @max-sixty

One alternative is to allow fluent args, like: ...but does that then seem like the second rolling is operating on the result of the first?

I couldn't think of it until just now. But yes, it sounds to me like a repeated rolling operation.

I'm being slow, but where is the nd-rolling algo? I had thought bottleneck didn't support more than one dimension?

No. With nd-rolling, we need to use numpy reductions. Its skipna=True operation is currently slow, but it can be improved replacing nan before the stride-trick.

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?

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)