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  • shoyer · 2 ✖

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  • support dask arrays in rolling computations using bottleneck functions · 2 ✖

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329286009 https://github.com/pydata/xarray/pull/1568#issuecomment-329286009 https://api.github.com/repos/pydata/xarray/issues/1568 MDEyOklzc3VlQ29tbWVudDMyOTI4NjAwOQ== shoyer 1217238 2017-09-13T20:23:16Z 2017-09-13T20:23:16Z MEMBER

Not check the version of bottleneck at all and live with some harder to predict behavior on rolling.meadian()

Sounds good to me.

 
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   support dask arrays in rolling computations using bottleneck functions 256912384
328736801 https://github.com/pydata/xarray/pull/1568#issuecomment-328736801 https://api.github.com/repos/pydata/xarray/issues/1568 MDEyOklzc3VlQ29tbWVudDMyODczNjgwMQ== shoyer 1217238 2017-09-12T05:03:49Z 2017-09-12T05:03:49Z MEMBER

That works for me. But note that you can also use **kwargs to insert conditional keyword arguments. On Mon, Sep 11, 2017 at 10:01 PM Joe Hamman notifications@github.com wrote:

@jhamman commented on this pull request.

In xarray/core/ops.py https://github.com/pydata/xarray/pull/1568#discussion_r138251821:

  • if axis < 0:
  • axis = a.ndim + axis
  • depth = {d: 0 for d in range(a.ndim)}
  • depth[axis] = window - 1
  • boundary = {d: np.nan for d in range(a.ndim)}

  • create ghosted arrays

  • ag = da.ghost.ghost(a, depth=depth, boundary=boundary)

  • apply rolling func

  • out = ag.map_blocks(moving_func, window, min_count=min_count,
  • axis=axis, dtype=a.dtype)

  • trim array

  • result = da.ghost.trim_internal(out, depth)
  • return result + + +def dask_rolling_wrapper_without_min_count(moving_func, a, window, axis=-1):

If we update the minimum support bottleneck version to 1.1, we can do away with this special case (also present for the non-dask case) all together.

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   support dask arrays in rolling computations using bottleneck functions 256912384

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