issue_comments: 155611625
This data as json
| 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-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 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) |
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