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/3932#issuecomment-610167347,https://api.github.com/repos/pydata/xarray/issues/3932,610167347,MDEyOklzc3VlQ29tbWVudDYxMDE2NzM0Nw==,11750960,2020-04-07T04:32:12Z,2020-04-07T04:32:12Z,CONTRIBUTOR,I'll close this for now as there doesn't seem to be other ideas about this,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,593825520
https://github.com/pydata/xarray/issues/3932#issuecomment-609605285,https://api.github.com/repos/pydata/xarray/issues/3932,609605285,MDEyOklzc3VlQ29tbWVudDYwOTYwNTI4NQ==,11750960,2020-04-06T07:08:19Z,2020-04-06T07:08:19Z,CONTRIBUTOR,"This sounds like method 1 (with dask delayed) to me.
There may be no faster option, thanks for giving it a thought @fujiisoup ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,593825520
https://github.com/pydata/xarray/issues/3932#issuecomment-609407162,https://api.github.com/repos/pydata/xarray/issues/3932,609407162,MDEyOklzc3VlQ29tbWVudDYwOTQwNzE2Mg==,11750960,2020-04-05T12:17:15Z,2020-04-05T12:17:47Z,CONTRIBUTOR,"thanks a lot @fujiisoup, your suggestion does help getting rid of the necessity to build the `ds['_y']` variable.
Here is the updated `apply_ufunc` solution:
```
x = np.arange(10*100)
y = np.arange(20*100)

ds = xr.Dataset(coords={'x': x, 'y': y})
# ds = ds.chunk({'x': 1, 'y':1}) # does not change anything

# let's say each experiment outputs 5 statistical diagnostics
Nstats = 5
some_exp = lambda x, y: np.ones((Nstats,))

out = xr.apply_ufunc(some_exp, ds.x, ds.y,
                      dask='parallelized',
                      vectorize=True,
                      output_dtypes=[float],
                      output_sizes={'stats': Nstats},
                      output_core_dims=[['stats']])
```
An inspection of the dask dashboard indicates that the computation is not distributed among workers though. 
How could I make sure this happens?


","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,593825520
https://github.com/pydata/xarray/issues/3932#issuecomment-609407192,https://api.github.com/repos/pydata/xarray/issues/3932,609407192,MDEyOklzc3VlQ29tbWVudDYwOTQwNzE5Mg==,11750960,2020-04-05T12:17:26Z,2020-04-05T12:17:26Z,CONTRIBUTOR,sorry closed by accident,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,593825520