issues: 712189206

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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
712189206 MDU6SXNzdWU3MTIxODkyMDY= 4475 Preprocess function for save_mfdataset 2560426 open 0     9 2020-09-30T18:47:06Z 2020-10-15T16:32:03Z   NONE      

Is your feature request related to a problem? Please describe. I would like to supply a preprocess argument to save_mfdataset that gets applied to each dataset before getting written to disk, similar to how open_mfdataset gives you such option. Specifically, have a dataset that I want to split by unique values along dimension, apply some further logic to each sub-dataset, then save each sub-dataset to a different file. Currently I'm able to split and save using the following code provided in the API docs:

years, datasets = zip(*ds.groupby("time.year")) paths = ["%s.nc" % y for y in years] xr.save_mfdataset(datasets, paths) What's missing is the ability to insert further logic to each of the sub-datasets given by the groupby object. If I try iterating through datasets here and chain further operations to each element, the calculations begin to execute serially even though ds is a dask array:

save_mfdataset([ds.foo() for ds in datasets], paths)

Describe the solution you'd like Instead, I'd like the ability to do:

xr.save_mfdataset(datasets, paths, preprocess=lambda ds: ds.foo())

Describe alternatives you've considered Not sure.

 
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