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-1546367218,https://api.github.com/repos/pydata/xarray/issues/644,1546367218,IC_kwDOAMm_X85cK7Dy,3892695,2023-05-12T22:16:16Z,2023-05-12T22:31:46Z,NONE,+1 https://stackoverflow.com/questions/76239626/xarray-preprocess-for-nearest-lat-lon-non-nan-variable,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,114773593
https://github.com/pydata/xarray/issues/644#issuecomment-1150280375,https://api.github.com/repos/pydata/xarray/issues/644,1150280375,IC_kwDOAMm_X85Ej-K3,1217238,2022-06-08T18:56:17Z,2022-06-08T18:56:17Z,MEMBER,"This might fit more naturally into interp() as a new method like ""nearest-valid"" rather than in sel().
The difference is that sel() only looks at indexes (and not the data) to select out a single value, whereas interp() can combine adjacent values in arbitrary ways.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,114773593
https://github.com/pydata/xarray/issues/644#issuecomment-1149521161,https://api.github.com/repos/pydata/xarray/issues/644,1149521161,IC_kwDOAMm_X85EhE0J,20603302,2022-06-08T06:36:30Z,2022-06-08T06:36:30Z,NONE,Just want to bump this again because I just ran into this issue too... ,"{""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,114773593
https://github.com/pydata/xarray/issues/644#issuecomment-1016260786,https://api.github.com/repos/pydata/xarray/issues/644,1016260786,IC_kwDOAMm_X848kuiy,6514690,2022-01-19T09:47:15Z,2022-01-19T09:47:15Z,NONE,"I just want to +1 this issue since I'm having the exact same problem. It would be great if the `.sel(method=""nearest"")` could ignore `NaNs` as an option","{""total_count"": 15, ""+1"": 15, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,114773593
https://github.com/pydata/xarray/issues/644#issuecomment-721441489,https://api.github.com/repos/pydata/xarray/issues/644,721441489,MDEyOklzc3VlQ29tbWVudDcyMTQ0MTQ4OQ==,57269213,2020-11-04T00:13:33Z,2020-11-04T00:13:33Z,NONE,"That's a bummer, but thanks for the fast reply","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,114773593
https://github.com/pydata/xarray/issues/644#issuecomment-721440664,https://api.github.com/repos/pydata/xarray/issues/644,721440664,MDEyOklzc3VlQ29tbWVudDcyMTQ0MDY2NA==,1217238,2020-11-04T00:10:41Z,2020-11-04T00:10:41Z,MEMBER,"There hasn't been any progress on this to my knowledge, unfortunately","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,114773593
https://github.com/pydata/xarray/issues/644#issuecomment-721437184,https://api.github.com/repos/pydata/xarray/issues/644,721437184,MDEyOklzc3VlQ29tbWVudDcyMTQzNzE4NA==,57269213,2020-11-03T23:59:06Z,2020-11-03T23:59:06Z,NONE,"I am having this exact same issue. Has there been an update that would make this task straightforward using xarray capabilities?
Thanks","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,114773593
https://github.com/pydata/xarray/issues/644#issuecomment-458802173,https://api.github.com/repos/pydata/xarray/issues/644,458802173,MDEyOklzc3VlQ29tbWVudDQ1ODgwMjE3Mw==,26384082,2019-01-30T03:51:46Z,2019-01-30T03:51:46Z,NONE,"In order to maintain a list of currently relevant issues, we mark issues as stale after a period of inactivity
If this issue remains relevant, please comment here; otherwise it will be marked as closed automatically
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,114773593
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.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,114773593
https://github.com/pydata/xarray/issues/644#issuecomment-155611625,https://api.github.com/repos/pydata/xarray/issues/644,155611625,MDEyOklzc3VlQ29tbWVudDE1NTYxMTYyNQ==,1217238,2015-11-11T00:27:10Z,2015-11-11T00:27:10Z,MEMBER,"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)
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,114773593