issues: 201617371

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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
201617371 MDU6SXNzdWUyMDE2MTczNzE= 1217 Using where() in datasets with dataarrays with different dimensions results in huge RAM consumption 4849151 closed 0     6 2017-01-18T16:09:50Z 2019-02-23T07:47:01Z 2019-02-23T07:47:01Z NONE      

I have a dataset containing groups of data with different dimensions. e.g.:

Python ds = xr.Dataset() ds['data1'] = xr.DataArray(data1, coords={'t1': t1}) ds['data2'] = xr.DataArray(data2, coords={'t2': t2})

If I do something like ds.where(ds.data1 < 0.1), Python ends up allocating huge amounts of memory (>30GB for a dataset of <1MB) and seems to loop indefinitely, until the call is interrupted with CTRL-C.

To use where() successfully, I have to use a subset of the dataset with only one dimension.

 
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