issue_comments: 358851474

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https://github.com/pydata/xarray/issues/1838#issuecomment-358851474 https://api.github.com/repos/pydata/xarray/issues/1838 358851474 MDEyOklzc3VlQ29tbWVudDM1ODg1MTQ3NA== 1217238 2018-01-19T02:50:13Z 2018-01-19T02:50:13Z MEMBER

We do this for two reasons: - bottleneck's aggregation functions like bottleneck.nansum() don't have a dtype argument, so passing on a dtype argument causes an error to be raised. - If you call a numpy function like numpy.sum() on an xarray object, it calls the appropriate method with all keyword arguments, e.g., numpy.sum(xarray_obj) -> xarray_obj.sum(axis=None, dtype=None, out=None).

What we should probably do here instead of ignoring dtype and out entirely is to look at their values: - If dtype is not None, use numpy's aggregation function instead of bottleneck's. - If out is not None, raise an error.

 
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