html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/3168#issuecomment-521017662,https://api.github.com/repos/pydata/xarray/issues/3168,521017662,MDEyOklzc3VlQ29tbWVudDUyMTAxNzY2Mg==,22258697,2019-08-13T21:33:04Z,2019-08-13T21:34:33Z,NONE,"I am not sure if this is related or not, but my dask array has a different shape before and after computing. After computing by converting to a numpy array, it looks like the time dimension (44) is still there, which is expected but I would also expect this to show in the xarray metadata.

```
result
<xarray.DataArray 'reflectance' (y: 1082, x: 1084)>
dask.array<shape=(1082, 1084), dtype=uint16, chunksize=(1082, 1084)>
Coordinates:
    band     int64 1
  * y        (y) float64 9.705e+05 9.705e+05 9.705e+05 ... 9.673e+05 9.672e+05
  * x        (x) float64 4.889e+05 4.889e+05 4.889e+05 ... 4.922e+05 4.922e+05

[87]
# the shape of the xarray and numpy array do not match after conversion to numpy array, the time dimension reappears
np.array(result).shape
(1082, 1084, 44) 
```

See: https://stackoverflow.com/questions/57419541/how-to-use-apply-ufunc-with-numpy-digitize-for-each-image-along-time-dimension-o

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