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
481866516,MDU6SXNzdWU0ODE4NjY1MTY=,3225,xr.DataArray.where sets valid points to nan when using several dask chunks,20225454,closed,0,,,3,2019-08-17T09:09:59Z,2022-04-18T15:58:40Z,2022-04-18T15:58:40Z,NONE,,,,"#### MCVE Code Sample
<!-- In order for the maintainers to efficiently understand and prioritize issues, we ask you post a ""Minimal, Complete and Verifiable Example"" (MCVE): http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports -->

I am trying to randomly delete a fraction of a xr.DataArray ([see identical StackOverflow question](https://stackoverflow.com/questions/56686562/xr-dataarray-where-sets-valid-points-to-nan-when-using-several-dask-chunks)) and subsequently access only the values from the original dataset data that were deleted.

This works fine as long as the data is not stored in dask arrays or in only one dask array. As soon as I define chunks smaller than the total size of the data, the original values are set to nan.

```python
data = xr.DataArray(np.arange(5*5*5.).reshape(5,5,5), dims=('time','latitude','longitude'))
data.to_netcdf('/path/to/file.nc')
#data = xr.open_dataarray('/path/to/file.nc', chunks={'time':5}) # produces expected output
data = xr.open_dataarray('/path/to/file.nc', chunks={'time':2}) # produces observed output 

def set_fraction_randomly_to_nan(data, frac_missing):
    np.random.seed(0)
    data[np.random.rand(*data.shape) < frac_missing] = np.nan
    return data

data_lost = xr.apply_ufunc(set_fraction_randomly_to_nan, data.copy(deep=True),
                           output_core_dims=[['latitude','longitude']], 
                           dask='parallelized',
                           input_core_dims=[['latitude','longitude']], 
                           output_dtypes=[data.dtype], kwargs={'frac_missing': 0.5})

print(data[0,-4:,-4:].values)
# >>
# [[ 6.  7.  8.  9.]
# [11. 12. 13. 14.]
# [16. 17. 18. 19.]
# [21. 22. 23. 24.]]

print(data.where(np.isnan(data_lost),0)[0,-4:,-4:].values)
```

#### Expected Output
expected output of the last line: keep all values where `np.isnan(data_lost)` is True and set rest to zero

```python
[[ 6.  0.  0.  9.]
[ 0.  0.  0. 14.]
[16.  0.  0.  0.]
[ 0. 22.  0. 24.]]
```

#### Problem Description
<!-- this should explain why the current behavior is a problem and why the expected output is a better solution -->
observed output of the last line: set all values where `np.isnan(data_lost)` is True to nan and set rest to zero

```python
[[nan  0.  0. nan]
[ 0.  0.  0. nan]
[nan  0.  0.  0.]
[ 0. nan  0. nan]]
```

#### Output of ``xr.show_versions()``
<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.138-59-default
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
LOCALE: de_DE.UTF-8

xarray: 0.10.2
pandas: 0.22.0
numpy: 1.14.2
scipy: 1.0.1
netCDF4: 1.3.1
h5netcdf: 0.5.0
h5py: 2.7.1
Nio: None
zarr: None
bottleneck: 1.2.1
cyordereddict: 1.0.0
dask: 0.17.2
distributed: 1.21.5
matplotlib: 2.2.2
cartopy: 0.16.0
seaborn: 0.8.1
setuptools: 39.0.1
pip: 9.0.3
conda: None
pytest: 3.5.0
IPython: 6.3.1
sphinx: 1.7.2
</details>","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3225/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue