issue_comments: 310032997

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https://github.com/pydata/xarray/pull/1426#issuecomment-310032997 https://api.github.com/repos/pydata/xarray/issues/1426 310032997 MDEyOklzc3VlQ29tbWVudDMxMDAzMjk5Nw== 4160723 2017-06-21T10:08:07Z 2017-06-21T10:58:29Z MEMBER

Although I haven't thought about all the details regarding this, I think that in the case of multi-dimensional coordinates a "super index" would rather allow directly using these coordinates for indexing, which is currently not possible.

In your 'rasm' example, it would rather look like

python <xarray.Dataset> Dimensions: (time: 36, x: 275, y: 205) Dimensions without coordinates: y, x Coordinates: * time (time) float64 7.226e+05 7.226e+05 7.227e+05 7.227e+05 ... * spatial_index (y, x) KDTree - xc (y, x) float64 189.2 189.4 189.6 189.7 189.9 190.1 190.2 190.4 ... - yc (y, x) float64 16.53 16.78 17.02 17.27 17.51 17.76 18.0 18.25 ... Dimensions without coordinates: x, y Data variables: Tair (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan ... Attributes: ...

and it would allow writing

python In [1]: ds.sel(xc=<...>, yc=<...>, method='nearest')

Note that x and y dimensions still don't have coordinates.

That's actually what @shoyer suggested here.

The proposal above is more about having the same API for groups of coordinates that can be indexed using a "wrapped" index object (maybe "wrapped index" is a better name than "super index"?), but the logic can be very different from one index object to another. 

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