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  • shoyer · 2 ✖

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  • Interpolation with multiple mutlidimensional arrays sharing dims fails · 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
700226830 https://github.com/pydata/xarray/issues/4463#issuecomment-700226830 https://api.github.com/repos/pydata/xarray/issues/4463 MDEyOklzc3VlQ29tbWVudDcwMDIyNjgzMA== shoyer 1217238 2020-09-28T19:10:55Z 2020-09-28T19:10:55Z MEMBER

I'll note that:

python da.sel(y=dy, x=dx, method='nearest')

does work, without the need to explicitly pass the shared dimension t. This is why I expected that interp would work the same.

I think interp should work in this case, too!

Let's keep this issue open to track that.

 
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   Interpolation with multiple mutlidimensional arrays sharing dims fails 709272776
699435286 https://github.com/pydata/xarray/issues/4463#issuecomment-699435286 https://api.github.com/repos/pydata/xarray/issues/4463 MDEyOklzc3VlQ29tbWVudDY5OTQzNTI4Ng== shoyer 1217238 2020-09-26T06:05:22Z 2020-09-26T06:05:22Z MEMBER

I think this gives the correct result? ```

da.interp(t=da['t'], y=dy, x=dx, method='linear') <xarray.DataArray (t: 2, u: 2)> array([[2., 3.], [2., 3.]]) Coordinates: * t (t) int64 10 12 y (u) float64 1.5 2.5 x (t, u) float64 1.5 1.5 1.5 1.5 * u (u) int64 45 55 ```

My general thought is that if an axis (like t in this case) is omitted, then should be equivalent to indexing with the existing coordinate. That is how normal indexing in xarray worked, so interpolation should work the same.

 
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   Interpolation with multiple mutlidimensional arrays sharing dims fails 709272776

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