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  • Ignore missing variables when concatenating datasets? · 2 ✖

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  • CONTRIBUTOR · 2 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
553993804 https://github.com/pydata/xarray/issues/508#issuecomment-553993804 https://api.github.com/repos/pydata/xarray/issues/508 MDEyOklzc3VlQ29tbWVudDU1Mzk5MzgwNA== scottcha 775186 2019-11-14T17:30:09Z 2019-11-14T17:30:09Z CONTRIBUTOR

Ok got it, I'll take a look and spin up a PR.
Thanks

 
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   Ignore missing variables when concatenating datasets? 98587746
553709888 https://github.com/pydata/xarray/issues/508#issuecomment-553709888 https://api.github.com/repos/pydata/xarray/issues/508 MDEyOklzc3VlQ29tbWVudDU1MzcwOTg4OA== scottcha 775186 2019-11-14T03:37:59Z 2019-11-14T04:09:02Z CONTRIBUTOR

I just ran in to this issue. While the previous fix seems to handle one case it doesn't handle all the cases. Before I clean this up and open a new PR does this look like its on the right track (it worked for my issue where I was concating multiple datasets which always had the same dims and coordinates but sometimes were missing variables)?

starts at line 353 on concat.py for k in datasets[0].variables: if k in concat_over: try: #new code for ds in datasets: if k not in ds.variables: #make a new array with the same dimensions and coordinates #by default this will be initialized to np.nan which is what we want from .dataarray import DataArray new_array = DataArray(coords=ds.coords, dims=ds.dims) ds[k] = new_array #end new code vars = ensure_common_dims([ds.variables[k] for ds in datasets]) except KeyError: #this can likely be removed then raise ValueError("%r is not present in all datasets." % k) combined = concat_vars(vars, dim, positions) assert isinstance(combined, Variable) result_vars[k] = combined

 
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   Ignore missing variables when concatenating datasets? 98587746

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