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1171932478 I_kwDOAMm_X85F2kU- 6373 Zarr backend should avoid checking for invalid encodings dcherian 2448579 closed 0     3 2022-03-17T04:55:35Z 2022-03-18T10:06:01Z 2022-03-18T04:19:48Z MEMBER      

What is your issue?

The zarr backend has a list of "valid" encodings that needs to be updated any time zarr adds something new (e.g. https://github.com/pydata/xarray/pull/6348)

https://github.com/pydata/xarray/blob/53172cb1e03a98759faf77ef48efaa64676ad24a/xarray/backends/zarr.py#L215-L234

Can we get rid of this? I don't know the backends code well, but won't all our encoding parameters have been removed by this stage? The raise_on_invalid kwarg suggests so.

@tomwhite points out that zarr will raise a warning: ``` python

zarr.create((1,), blah=1) /Users/tom/miniconda/envs/sgkit-3.8/lib/python3.8/site-packages/zarr/creation.py:221: UserWarning: ignoring keyword argument 'blah' warn('ignoring keyword argument %r' % k) <zarr.core.Array (1,) float64> ```

 
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   completed xarray 13221727 issue
1171916710 I_kwDOAMm_X85F2gem 6372 apply_ufunc + dask="parallelized" + no core dimensions should raise a nicer error about core dimensions being absent dcherian 2448579 open 0     0 2022-03-17T04:25:37Z 2022-03-17T05:10:16Z   MEMBER      

What happened?

From https://github.com/pydata/xarray/discussions/6370

Calling apply_ufunc(..., dask="parallelized") with no core dimensions and dask input "works" but raises an error on compute (ValueError: axes don't match array from np.transpose).

python xr.apply_ufunc( lambda x: np.mean(x), dt, dask="parallelized" )

What did you expect to happen?

With numpy data the apply_ufunc call does raise an error:

xr.apply_ufunc( lambda x: np.mean(x), dt.compute(), dask="parallelized" )

ValueError: applied function returned data with unexpected number of dimensions. Received 0 dimension(s) but expected 1 dimensions with names: ('x',)

Minimal Complete Verifiable Example

``` python import xarray as xr

dt = xr.Dataset( data_vars=dict( value=(["x"], [1,1,2,2,2,3,3,3,3,3]), ), coords=dict( lon=(["x"], np.linspace(0,1,10)), ), ).chunk(chunks={'x': tuple([2,3,5])}) # three chunks of different size

xr.apply_ufunc( lambda x: np.mean(x), dt, dask="parallelized" ) ```

Relevant log output

No response

Anything else we need to know?

No response

Environment

N/A

 
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     xarray 13221727 issue

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