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1 row where state_reason = "completed", "updated_at" is on date 2021-09-29 and user = 35968931 sorted by updated_at descending

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id node_id number title user state locked assignee milestone comments created_at updated_at ▲ closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
935062144 MDU6SXNzdWU5MzUwNjIxNDQ= 5559 UserWarning when wrapping pint & dask arrays together TomNicholas 35968931 closed 0     4 2021-07-01T17:25:03Z 2021-09-29T17:48:39Z 2021-09-29T17:48:39Z MEMBER      

With pint-xarray you can create a chunked, unit-aware xarray object, but calling a calculation method and then computing doesn't appear to behave as hoped.

```python da = xr.DataArray([1,2,3], attrs={'units': 'metres'})

chunked = da.chunk(1).pint.quantify() ```

python print(chunked.compute()) <xarray.DataArray (dim_0: 3)> <Quantity([1 2 3], 'meter')> Dimensions without coordinates: dim_0 So far this is fine, but if we try to take a mean before computing we get

python print(chunked.mean().compute()) <xarray.DataArray ()> <Quantity(dask.array<true_divide, shape=(), dtype=float64, chunksize=(), chunktype=numpy.ndarray>, 'meter')> /home/tegn500/miniconda3/envs/py38-mamba/lib/python3.8/site-packages/dask/array/core.py:3139: UserWarning: Passing an object to dask.array.from_array which is already a Dask collection. This can lead to unexpected behavior. warnings.warn( This is not correct: as well as the UserWarning, the return value of compute is a dask array, meaning we need to compute a second time to actually get the answer: python print(chunked.mean().compute().compute()) <xarray.DataArray ()> <Quantity(2.0, 'meter')> /home/tegn500/miniconda3/envs/py38-mamba/lib/python3.8/site-packages/dask/array/core.py:3139: UserWarning: Passing an object to dask.array.from_array which is already a Dask collection. This can lead to unexpected behavior. warnings.warn(

If we try chunking the other way (chunked = da.pint.quantify().pint.chunk(1)) then we get all the same results.

xref https://github.com/xarray-contrib/pint-xarray/issues/116 and https://github.com/pydata/xarray/pull/4972 @keewis

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  completed xarray 13221727 issue

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