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/191#issuecomment-50401000,https://api.github.com/repos/pydata/xarray/issues/191,50401000,MDEyOklzc3VlQ29tbWVudDUwNDAxMDAw,2835718,2014-07-28T21:07:30Z,2014-07-28T21:07:30Z,NONE,"Stephan,

I think that I could contribute some functions to do 'nearest' and linear interpolation in n-dimensions; these should be able to take advantage of the indexing afforded by `xray` and not have to deal with Scipy's functions, because they only require selecting values in the neighborhood of the points.  

As far as I can tell, higher-order interpolation (spline, etc.) requires fitting functions to the entirety of the dataset, which is pretty slow/ram-intensive with large datasets, and many of the fuctions require the data to be on a regular grid (I am not sure what the `xray / netcdf` requirements are here).  For this, it's probably better to use the scipy functions (at least I personally don't have the knowledge to write anything comparable without more study).

For the function signature, I was thinking about something simple, like:

`xray.interpolate(point[s], array, field[s], order)`, or
`DataArray.interpolate(point[s], order)` where `points` are the points to interpolate `field` values from the `array`, and `order` is like in [map_coordinates](http://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.interpolation.map_coordinates.html#scipy.ndimage.interpolation.map_coordinates) (0=nearest, 1=linear, 3=cubic, etc.).

This could return a Series or DataFrame.

But thinking about this a little more, there are kind of two sides to interpolation:  What I think of as 'sampling', where we pull values at points from within a grid or structured array (like in `map_coordinates`), and then the creation of arrays from unstructured (or just smaller) point sets.  Both are equally common in geoscience, and probably require pretty different treatment programmatically, so it might be wise to make room for both early.
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