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  • shoyer · 2 ✖

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  • Memory usage of `da.rolling().construct` · 2 ✖

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  • MEMBER · 2 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
705098106 https://github.com/pydata/xarray/issues/3332#issuecomment-705098106 https://api.github.com/repos/pydata/xarray/issues/3332 MDEyOklzc3VlQ29tbWVudDcwNTA5ODEwNg== shoyer 1217238 2020-10-07T17:54:32Z 2020-10-07T17:54:32Z MEMBER

The loop via slicing is not a terrible option. The trick construct() uses with views only really makes sense with NumPy arrays, not with dask.

There are also true streaming moving window algorithms that work very well for computing various statistics (e.g., mean and variance). These are implemented in bottleneck (e.g., move_mean) and could be wrapped in xarray if desired for methods like rolling(...).mean(). These aren't implemented in dask yet, though.

 
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   Memory usage of `da.rolling().construct` 496809167
534709955 https://github.com/pydata/xarray/issues/3332#issuecomment-534709955 https://api.github.com/repos/pydata/xarray/issues/3332 MDEyOklzc3VlQ29tbWVudDUzNDcwOTk1NQ== shoyer 1217238 2019-09-24T19:21:22Z 2019-09-24T19:21:22Z MEMBER

It uses a view for allocating the initial result, but I think applying boundary conditions means that we end up doing a copy.

 
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   Memory usage of `da.rolling().construct` 496809167

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