html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/644#issuecomment-155698426,https://api.github.com/repos/pydata/xarray/issues/644,155698426,MDEyOklzc3VlQ29tbWVudDE1NTY5ODQyNg==,13906519,2015-11-11T08:02:56Z,2015-11-11T08:02:56Z,NONE,"Ah, ok, cool.

Thanks for the pointers and getting back to me. Looking forward to any future xray improvements. It’s really becoming my goto to for netcdf stuff (in addition to cdo).

Christian

> On 11 Nov 2015, at 01:27, Stephan Hoyer notifications@github.com wrote:
> 
> This is tricky to put into .sel because that method currently works by only looking at coordinate labels, not at data values.
> 
> One way to fix this would be to unravel your two dimensions corresponding to latitude and longitude into a single ""lat_lon"" dimension. At this point, you could apply a sea mask, to produce a compressed lat_lon coordinate corresponding to only unmasked points. Now, it's relatively straightforward to imagine doing nearest neighbor lookups on this set of labels.
> 
> This later solution will require a few steps (all of which are on the ""to do"" list, but without any immediate timelines):
> 1. support for multi-level indexes in xray
> 2. support for ""unraveling"" multiple dimensions into 1-dimension
> 3. support for looking up nearest locations in multiple dimensions via some sort of spatial index (e.g., a KD tree)
> 
> —
> Reply to this email directly or view it on GitHub https://github.com/xray/xray/issues/644#issuecomment-155611625.
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