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  • rolling.construct alignment · 2 ✖

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  • CONTRIBUTOR · 2 ✖
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572931220 https://github.com/pydata/xarray/issues/3671#issuecomment-572931220 https://api.github.com/repos/pydata/xarray/issues/3671 MDEyOklzc3VlQ29tbWVudDU3MjkzMTIyMA== mark-boer 12862013 2020-01-10T08:40:35Z 2020-01-10T08:40:35Z CONTRIBUTOR

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.

 
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   rolling.construct alignment 546791416
572556658 https://github.com/pydata/xarray/issues/3671#issuecomment-572556658 https://api.github.com/repos/pydata/xarray/issues/3671 MDEyOklzc3VlQ29tbWVudDU3MjU1NjY1OA== mark-boer 12862013 2020-01-09T13:17:29Z 2020-01-09T13:17:29Z CONTRIBUTOR

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.

 
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   rolling.construct alignment 546791416

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