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But I mistakenly thought that there was a performance penalty to doing this.

Yes, construct(stride=2) does exactly the same thing before returning an array. https://github.com/pydata/xarray/blob/ff75081304eb2e2784dcb229cc48a532da557896/xarray/core/rolling.py#L242

Hi @fujiisoup, thx for your response.

That is exactly what I needed and what I used to do. But I mistakenly thought that there was a performance penalty to doing this. But the performance decrease turned out to be the result of the order in which I rolled and sliced.

Hi @mark-boer for raising an issue. I am not sure if I got the point exactly, but the following is similar to what you want? python In [81]: arr = xr.DataArray(np.arange(4), dims=("x",)) ...: arr.rolling(x=2).construct("roll_x").isel(x=slice(1, None, 2)) Out[81]: <xarray.DataArray (x: 2, roll_x: 2)> array([[0., 1.], [2., 3.]]) Dimensions without coordinates: x, roll_x

Small update:

I was currently using: data = ( data.rolling(x=window_size ).construct("roll_x") .rolling(y=window_size ).construct("roll_y") .isel(x=slice(window_size - 1, None, stride), y=slice(window_size - 1, None, stride)) .stack(n=("x", "y")) )

but this was performing quite badly for larger arrays. However after having a look at DataArrayRolling and rewriting this piece of code to the following, performance was good.

data = ( data.rolling(x=window_size ).construct("roll_x") .isel(x=slice(window_size - 1, None, stride)) .rolling(y=window_size ).construct("roll_y") .isel(y=slice(window_size - 1, None, stride)) .stack(n=("x", "y")) )

I saw that DataArrayRolling.construct() also uses a slice (isel) to create the strided array, so there is not much of a performance penalty of first creating a rolling window with stride 1 and then slicing the data.

However, if you want to add strides to the 'Strided rolling' (#3607) it would be nice to still be able to create rolling windows that start at index 0.

By the way: feel free to close this issue, if you do not see a need for an alignment option.