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  • aggregation functions treat duck arrays differently depending on dtype · 6 ✖

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527253889 https://github.com/pydata/xarray/issues/3241#issuecomment-527253889 https://api.github.com/repos/pydata/xarray/issues/3241 MDEyOklzc3VlQ29tbWVudDUyNzI1Mzg4OQ== keewis 14808389 2019-09-02T22:48:58Z 2019-09-02T22:48:58Z MEMBER

you're right, after merging the issue is gone for me, too.

 
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   aggregation functions treat duck arrays differently depending on dtype 484097190
527253633 https://github.com/pydata/xarray/issues/3241#issuecomment-527253633 https://api.github.com/repos/pydata/xarray/issues/3241 MDEyOklzc3VlQ29tbWVudDUyNzI1MzYzMw== shoyer 1217238 2019-09-02T22:46:14Z 2019-09-02T22:46:14Z MEMBER

Can you try merging in the latest version of xarray master into your branch? I think this issue was fixed just recently by https://github.com/pydata/xarray/pull/3254. When I test this myself, both versions seem to do the right thing: ``` In [5]: xr.DataArray(data=np.arange(10).astype(float) * ureg.m).median() Out[5]: <xarray.DataArray ()> <Quantity(4.5, 'meter')>

In [6]: xr.DataArray(data=np.arange(10).astype(int) * ureg.m).median() Out[6]: <xarray.DataArray ()> <Quantity(4.5, 'meter')> ```

 
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   aggregation functions treat duck arrays differently depending on dtype 484097190
527251316 https://github.com/pydata/xarray/issues/3241#issuecomment-527251316 https://api.github.com/repos/pydata/xarray/issues/3241 MDEyOklzc3VlQ29tbWVudDUyNzI1MTMxNg== keewis 14808389 2019-09-02T22:19:16Z 2019-09-02T22:22:12Z MEMBER

that's true. I edited it, the float version should fail. The new example is actually the same as the first one, but using DataArray.median() instead of np.max()

 
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   aggregation functions treat duck arrays differently depending on dtype 484097190
527246512 https://github.com/pydata/xarray/issues/3241#issuecomment-527246512 https://api.github.com/repos/pydata/xarray/issues/3241 MDEyOklzc3VlQ29tbWVudDUyNzI0NjUxMg== keewis 14808389 2019-09-02T21:36:22Z 2019-09-02T22:18:36Z MEMBER

now that I hit this issue using the example from #3238 again, this seems to be a bug in xarray. For reference, this is the mentioned example that fails even with a pint version with __array_function__: ```python

xr.DataArray(data=np.arange(10).astype(int) * ureg.m).median() <xarray.DataArray ()> <Quantity(4.5, 'meter')> xr.DataArray(data=np.arange(10).astype(float) * ureg.m).median() <xarray.DataArray ()> array(4.5) ```

 
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   aggregation functions treat duck arrays differently depending on dtype 484097190
527250978 https://github.com/pydata/xarray/issues/3241#issuecomment-527250978 https://api.github.com/repos/pydata/xarray/issues/3241 MDEyOklzc3VlQ29tbWVudDUyNzI1MDk3OA== shoyer 1217238 2019-09-02T22:15:47Z 2019-09-02T22:15:47Z MEMBER

@keewis could you clarify that example? Both those examples appear to be the same code, with different results!

 
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   aggregation functions treat duck arrays differently depending on dtype 484097190
524278369 https://github.com/pydata/xarray/issues/3241#issuecomment-524278369 https://api.github.com/repos/pydata/xarray/issues/3241 MDEyOklzc3VlQ29tbWVudDUyNDI3ODM2OQ== keewis 14808389 2019-08-23T11:21:06Z 2019-08-23T11:21:06Z MEMBER

This seams to be an issue with pint and is worked on in hgrecco/pint#764: using that PR instead of the version available in conda-forge makes all functions fail with a TypeError regardless of dtype.

So I guess this can be closed?

 
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   aggregation functions treat duck arrays differently depending on dtype 484097190

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