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  • wholmgren · 2 ✖

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  • API design for pointwise indexing · 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
121790688 https://github.com/pydata/xarray/issues/475#issuecomment-121790688 https://api.github.com/repos/pydata/xarray/issues/475 MDEyOklzc3VlQ29tbWVudDEyMTc5MDY4OA== wholmgren 4383303 2015-07-16T00:42:08Z 2015-07-16T00:42:08Z NONE

Unidata also has a blog post benchmarking cKDTree and other methods and concludes "Your Mileage May Vary". I'd probably just go with a KDTree, but something to aware of.

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  API design for pointwise indexing 95114700
121701139 https://github.com/pydata/xarray/issues/475#issuecomment-121701139 https://api.github.com/repos/pydata/xarray/issues/475 MDEyOklzc3VlQ29tbWVudDEyMTcwMTEzOQ== wholmgren 4383303 2015-07-15T18:15:49Z 2015-07-15T18:15:49Z NONE

Seems like if your method is going to be named sel_points then points is a reasonable dimension name. Maybe support a name kwarg?

One thing to keep in mind is that for many of us the "nearest-neighbor" part isn't really method='nearest', but instead more like, method='ingridcell' where the grid cell might be roughly square or might be something pretty different. At least that's how I think of my data. Maybe what I really want is some other kind of more explicit support for gridded data, although my thoughts on this are too half-baked to clearly write down. I thought there was another issue related to this, but I couldn't find it.

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  API design for pointwise indexing 95114700

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   [html_url] TEXT,
   [issue_url] TEXT,
   [id] INTEGER PRIMARY KEY,
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   [user] INTEGER REFERENCES [users]([id]),
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   [reactions] TEXT,
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   [issue] INTEGER REFERENCES [issues]([id])
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