issues: 272004812

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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
272004812 MDU6SXNzdWUyNzIwMDQ4MTI= 1699 apply_ufunc(dask='parallelized') output_dtypes for datasets 6213168 open 0     8 2017-11-07T22:18:23Z 2020-04-06T15:31:17Z   MEMBER      

When a Dataset has variables with different dtypes, there's no way to tell apply_ufunc that the same function applied to different variables will produce different dtypes:

``` ds1 = xarray.Dataset(data_vars={'a': ('x', [1, 2]), 'b': ('x', [3.0, 4.5])}).chunk() ds2 = xarray.apply_ufunc(lambda x: x + 1, ds1, dask='parallelized', output_dtypes=[float]) ds2

<xarray.Dataset> Dimensions: (x: 2) Dimensions without coordinates: x Data variables: a (x) float64 dask.array<shape=(2,), chunksize=(2,)> b (x) float64 dask.array<shape=(2,), chunksize=(2,)>

ds2.compute()

<xarray.Dataset> Dimensions: (x: 2) Dimensions without coordinates: x Data variables: a (x) int64 2 3 b (x) float64 4.0 5.5 ```

Proposed solution

When the output is a dataset, apply_ufunc could accept either output_dtypes=[t] (if all output variables will have the same dtype) or output_dtypes=[{var1: t1, var2: t2, ...}]. In the example above, it would be output_dtypes=[{'a': int, 'b': float}].

 
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     13221727 issue

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