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  • DataArray.sum does not respect dtype keyword · 2 ✖

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358851474 https://github.com/pydata/xarray/issues/1838#issuecomment-358851474 https://api.github.com/repos/pydata/xarray/issues/1838 MDEyOklzc3VlQ29tbWVudDM1ODg1MTQ3NA== shoyer 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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   DataArray.sum does not respect dtype keyword 289790965
358847017 https://github.com/pydata/xarray/issues/1838#issuecomment-358847017 https://api.github.com/repos/pydata/xarray/issues/1838 MDEyOklzc3VlQ29tbWVudDM1ODg0NzAxNw== fujiisoup 6815844 2018-01-19T02:21:39Z 2018-01-19T02:22:06Z MEMBER

I notice here

https://github.com/pydata/xarray/blob/f3deb2f2495220af819021b199a5305b0d62ef36/xarray/core/duck_array_ops.py#L177-L181

we removed dtype arguments from reduce methods, but I'm not sure why. This might be a bug.

As a temporal solution, python da.reduce(np.sum, dtype="i4") preserves dtype.

 
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   DataArray.sum does not respect dtype keyword 289790965

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