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  • fujiisoup · 2 ✖

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  • Element wise dataArray generation · 2 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
609483638 https://github.com/pydata/xarray/issues/3932#issuecomment-609483638 https://api.github.com/repos/pydata/xarray/issues/3932 MDEyOklzc3VlQ29tbWVudDYwOTQ4MzYzOA== fujiisoup 6815844 2020-04-05T21:09:58Z 2020-04-05T21:09:58Z MEMBER

An inspection of the dask dashboard indicates that the computation is not distributed among workers though. How could I make sure this happens?

Ah, I have no idea... Are you able to distribute the function some_exp without wrapping by xarray?

Within my limited knowledge, it may be better to prepare another function that distributes some_exp over the workers and put this another function into apply_ufunc, but I am not 100% sure. Probably there is a better way...

 
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   Element wise dataArray generation 593825520
609094164 https://github.com/pydata/xarray/issues/3932#issuecomment-609094164 https://api.github.com/repos/pydata/xarray/issues/3932 MDEyOklzc3VlQ29tbWVudDYwOTA5NDE2NA== fujiisoup 6815844 2020-04-04T21:54:41Z 2020-04-04T21:54:56Z MEMBER

Is python xr.apply_ufunc(some_exp, ds.x, ds.y, dask='parallelized', output_dtypes=[float], output_sizes={'stats': Nstats}, output_core_dims=[['stats']], vectorize=True) what you want? This gives ```python

<xarray.DataArray (x: 10, y: 20, stats: 5)> array([[[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.], ... [1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.]]]) Coordinates: * x (x) int64 0 1 2 3 4 5 6 7 8 9 * y (y) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Dimensions without coordinates: stats

In [26]: ```

 
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   Element wise dataArray generation 593825520

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