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  • fujiisoup · 2 ✖

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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
357907365 https://github.com/pydata/xarray/issues/1831#issuecomment-357907365 https://api.github.com/repos/pydata/xarray/issues/1831 MDEyOklzc3VlQ29tbWVudDM1NzkwNzM2NQ== fujiisoup 6815844 2018-01-16T09:48:56Z 2018-01-16T09:49:21Z MEMBER

Thanks for the information. I will look into the issue.

I think the sliding-and-stack method itself would be also handy. I will start from this.

 
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   Slow performance of rolling.reduce 288567090
357814170 https://github.com/pydata/xarray/issues/1831#issuecomment-357814170 https://api.github.com/repos/pydata/xarray/issues/1831 MDEyOklzc3VlQ29tbWVudDM1NzgxNDE3MA== fujiisoup 6815844 2018-01-15T23:47:06Z 2018-01-15T23:47:06Z MEMBER

I'm thinking to use the fancy indexing to speed up rolling.reduce, by temporary constructing a single large array with da.ndim+1 dimensions,

E.g. for the following DataArray python In [2]: da Out [2]: <xarray.DataArray (x: 4)> array([0, 1, 2, 3]) Dimensions without coordinates: x a larger array with additional dimension (x_rolling) would be created python <xarray.DataArray (x: 4, x_rolling: 3)> array([[ nan, 0., 1.], [ 0., 1., 2.], [ 1., 2., 3.], [ 2., 3., nan]]) Dimensions without coordinates: x, x_rolling then reduce along x_rolling dimension.

The advantages would be + Indexing occurs only once. + Reducing operation can be easily vectorized.

The disadvantages would be + It constructs a huge array with size of (window_size - 1) * da.size, consuming a lot of memory.

I think this disadvantage would be solved if we could use np.lib.stride_trick.as_strided for np.ndarray backend case. (how about with dask?)

 
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   Slow performance of rolling.reduce 288567090

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