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  • Refactor nanops · 12 ✖

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424700785 https://github.com/pydata/xarray/pull/2236#issuecomment-424700785 https://api.github.com/repos/pydata/xarray/issues/2236 MDEyOklzc3VlQ29tbWVudDQyNDcwMDc4NQ== fujiisoup 6815844 2018-09-26T12:42:55Z 2018-09-26T12:42:55Z MEMBER

Thanks, @st-bender, for the bug report. I copied your comment to #2440.

 
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   Refactor nanops 333248242
413446001 https://github.com/pydata/xarray/pull/2236#issuecomment-413446001 https://api.github.com/repos/pydata/xarray/issues/2236 MDEyOklzc3VlQ29tbWVudDQxMzQ0NjAwMQ== fujiisoup 6815844 2018-08-16T06:59:37Z 2018-08-16T06:59:37Z MEMBER

Thanks for the review. Merging.

 
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   Refactor nanops 333248242
413431477 https://github.com/pydata/xarray/pull/2236#issuecomment-413431477 https://api.github.com/repos/pydata/xarray/issues/2236 MDEyOklzc3VlQ29tbWVudDQxMzQzMTQ3Nw== fujiisoup 6815844 2018-08-16T05:37:25Z 2018-08-16T05:37:25Z MEMBER

Thanks, @shoyer. All done.

 
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   Refactor nanops 333248242
412249461 https://github.com/pydata/xarray/pull/2236#issuecomment-412249461 https://api.github.com/repos/pydata/xarray/issues/2236 MDEyOklzc3VlQ29tbWVudDQxMjI0OTQ2MQ== fujiisoup 6815844 2018-08-11T04:15:30Z 2018-08-11T04:15:30Z MEMBER

Can anyone give further review?

 
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   Refactor nanops 333248242
412249224 https://github.com/pydata/xarray/pull/2236#issuecomment-412249224 https://api.github.com/repos/pydata/xarray/issues/2236 MDEyOklzc3VlQ29tbWVudDQxMjI0OTIyNA== fujiisoup 6815844 2018-08-11T04:08:59Z 2018-08-11T04:08:59Z MEMBER

I noticed min_count is working also on resampled object. Your issue might be for the API of resample.

 
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   Refactor nanops 333248242
412246496 https://github.com/pydata/xarray/pull/2236#issuecomment-412246496 https://api.github.com/repos/pydata/xarray/issues/2236 MDEyOklzc3VlQ29tbWVudDQxMjI0NjQ5Ng== fujiisoup 6815844 2018-08-11T03:00:13Z 2018-08-11T03:00:13Z MEMBER

@rpnaut

Thanks for testing.

Your min_count argument is not allowed for type 'dataset' but only for type 'dataarray'. Starting with the dataset located here:

I think it is not true. It works on Dataset, but not on resampled object. I will raise a issue for this later.

 
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   Refactor nanops 333248242
403272397 https://github.com/pydata/xarray/pull/2236#issuecomment-403272397 https://api.github.com/repos/pydata/xarray/issues/2236 MDEyOklzc3VlQ29tbWVudDQwMzI3MjM5Nw== fujiisoup 6815844 2018-07-08T08:40:55Z 2018-07-08T08:40:55Z MEMBER

I think this is ready for another review.

 
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   Refactor nanops 333248242
399279344 https://github.com/pydata/xarray/pull/2236#issuecomment-399279344 https://api.github.com/repos/pydata/xarray/issues/2236 MDEyOklzc3VlQ29tbWVudDM5OTI3OTM0NA== fujiisoup 6815844 2018-06-21T23:59:48Z 2018-06-21T23:59:48Z MEMBER

Thanks, @rpnaut . Actually, I'm changing the code also around sum. So it looks my change caused the bug you reported. I think we do not have a good test coverage around the dataset.reduction.

I will add the test also. Thanks again for the testing :)

 
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   Refactor nanops 333248242
399278523 https://github.com/pydata/xarray/pull/2236#issuecomment-399278523 https://api.github.com/repos/pydata/xarray/issues/2236 MDEyOklzc3VlQ29tbWVudDM5OTI3ODUyMw== fujiisoup 6815844 2018-06-21T23:54:23Z 2018-06-21T23:54:23Z MEMBER

@shoyer , thanks for the details. I think I understood your idea. This sounds a cleaner solution. I will update the code again, but it will take some more days (or a week).

 
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   Refactor nanops 333248242
398931150 https://github.com/pydata/xarray/pull/2236#issuecomment-398931150 https://api.github.com/repos/pydata/xarray/issues/2236 MDEyOklzc3VlQ29tbWVudDM5ODkzMTE1MA== shoyer 1217238 2018-06-20T23:42:04Z 2018-06-20T23:42:04Z MEMBER

A module of bottleneck/numpy functions that act on numpy arrays only. A module of functions that act on numpy or dask arrays (or these could be moved into duck_array_ops).

Could you explain more detail about this idea?

OK, let me try:

  1. On numpy arrays, we use bottleneck eqiuvalents of numpy functions when possible because bottleneck is faster than numpy
  2. On dask arrays, we use dask equivalents of numpy functions.
  3. We also want to add some extra features on top of what numpy/dask/bottleneck provide, e.g., handling of min_count

We could implement this with: - nputils.nansum() is equivalent to numpy.nansum() but uses bottleneck.nansum() internally instead when possible. - duck_array_ops.nansum() uses numpy_nansum() or dask.array.nansum(), based upon the type of the inputs. - duck_array_ops.sum() uses numpy.sum() or dask.array.sum(), based upon the type of the inputs. - duck_array_ops.sum_with_mincount() adds mincount and skipna support and is used in the Dataset.sum() implementation. Its is written using duck_array_ops.nansum(), duck_array_ops.sum(), duck_array_ops.where() and duck_array_ops.isnull().

 
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   Refactor nanops 333248242
398926990 https://github.com/pydata/xarray/pull/2236#issuecomment-398926990 https://api.github.com/repos/pydata/xarray/issues/2236 MDEyOklzc3VlQ29tbWVudDM5ODkyNjk5MA== fujiisoup 6815844 2018-06-20T23:17:12Z 2018-06-20T23:17:12Z MEMBER

I think it would make sense to restructure this a little bit to have two well defined layers:

A module of bottleneck/numpy functions that act on numpy arrays only. A module of functions that act on numpy or dask arrays (or these could be moved into duck_array_ops).

Could you explain more detail about this idea?

 
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   Refactor nanops 333248242
398150942 https://github.com/pydata/xarray/pull/2236#issuecomment-398150942 https://api.github.com/repos/pydata/xarray/issues/2236 MDEyOklzc3VlQ29tbWVudDM5ODE1MDk0Mg== shoyer 1217238 2018-06-18T18:28:58Z 2018-06-18T18:28:58Z MEMBER

Very nice!

In my implementation, bottleneck is not used when skipna=False. bottleneck would be advantageous when skipna=True as numpy needs to copy the entire array once, but I think numpy's method is still OK if skipna=False.

I think this is correct -- bottleneck does not speed up non-NaN skipping functions.

 
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   Refactor nanops 333248242

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