issue_comments: 404685906

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https://github.com/pydata/xarray/issues/2281#issuecomment-404685906 https://api.github.com/repos/pydata/xarray/issues/2281 404685906 MDEyOklzc3VlQ29tbWVudDQwNDY4NTkwNg== 25473287 2018-07-12T23:58:48Z 2018-07-13T18:24:02Z NONE

Do you have any proposal?

I guess it is not an API design problem yet... The algorithm is not here since interpn doesn't deal with curvilinear grids.

I think we could make dr.interp(xc=lon, yc=lat) work for the N-D -> M-D case by wrapping scipy.interpolate.griddata

My concern with scipy.interpolate.griddata is that the performance might be miserable... griddata takes an arbitrary stream of data points in a D-dimensional space. It doesn't know if those source data points have a gridded/mesh structure. A curvilinear grid mesh needs to be flatten into a stream of points before passed to griddata(). Might not be too bad for nearest-neighbour search, but very inefficient for linear/bilinear method, where knowing the mesh structure beforehand can save a lot of computation.

Utilizingscipy.interpolate.griddata would be a nice feature, but it should probably be used for data point streams (more like a Pandas dataframe method?), not as a way to handle curvilinear grids.

PS: I have some broader concerns regarding interp vs xESMF: JiaweiZhuang/xESMF#24

 
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