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  • shoyer · 3 ✖

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  • apply_ufunc(dask='parallelized') output_dtypes for datasets · 3 ✖

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386835630 https://github.com/pydata/xarray/issues/1699#issuecomment-386835630 https://api.github.com/repos/pydata/xarray/issues/1699 MDEyOklzc3VlQ29tbWVudDM4NjgzNTYzMA== shoyer 1217238 2018-05-05T21:17:20Z 2018-05-05T21:17:20Z MEMBER

dtype = [getattr(x, 'dtype', getattr(x, 'dtypes'))] would be another alternative, but I agree it's ugly. The ternary expression dtype = x.dtypes if isinstance(x, xarray.Dataset) else x.dtype would also work.

I agree with the concern about duck typing, but my concern with Dataset.dtype is that there is strong convention for a dtype attribute to be an actual NumPy dtype.

Another option would be accept either objects with a dtype or dtypes in output_dtypes, like np.result_type(). Then you could write output_dtype=[x].

 
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   apply_ufunc(dask='parallelized') output_dtypes for datasets 272004812
384134364 https://github.com/pydata/xarray/issues/1699#issuecomment-384134364 https://api.github.com/repos/pydata/xarray/issues/1699 MDEyOklzc3VlQ29tbWVudDM4NDEzNDM2NA== shoyer 1217238 2018-04-25T01:42:36Z 2018-04-25T01:42:36Z MEMBER

I'm not sure about adding Dataset.dtype. Certainly Dataset.dtypes returning a dict would make sense -- that would match how pandas defines DataFrame.dtypes.

Anyways, I agree that output_dtypes=[{var1: t1, var2: t2, ...}, ...] is most natural, because it matches the structure of the outputs.

 
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   apply_ufunc(dask='parallelized') output_dtypes for datasets 272004812
342670406 https://github.com/pydata/xarray/issues/1699#issuecomment-342670406 https://api.github.com/repos/pydata/xarray/issues/1699 MDEyOklzc3VlQ29tbWVudDM0MjY3MDQwNg== shoyer 1217238 2017-11-08T00:32:45Z 2017-11-08T00:32:45Z MEMBER

Yes, I like this. Though it's worth considering whether the syntax should reverse the list/dict nesting, e.g., output_dtypes={var1: [t1, ...], var2: [t2, ...], ...}.

 
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   apply_ufunc(dask='parallelized') output_dtypes for datasets 272004812

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