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I recently wrote a version of scipy.ndimage.map_coordinates for JAX in pure NumPt that I think could be straightforwardly ported into xarray.

On Thu, Feb 6, 2020 at 7:00 AM David Huard notifications@github.com wrote:

Just got bit by this as well. Computing monthly quantile correction factors, so I have an array with dimensions (month, quantile, lon, lat). I then want to apply these correction factors to a time series (time, lon, lat), so I compute the month and quantile of my time series, and want to interp into the quantile correction factors. This doesn't work because both the factors and the time series have (lat, lon) dimensions.

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We could probably use this our own version for all linear and nearest neighbor interpolation. Then we won’t need scipy installed for that.

Linear interpolation for 1d -> nd is just a matter of averaging two indexing selections. If we leverage xarray's vectorized indexing operations to do the hard work, it should work automatically for dask arrays, sparse arrays and xarray's internal backend array types, all with any number of dimensions.

On Fri, Aug 23, 2019 at 2:17 PM Noah D Brenowitz notifications@github.com wrote:

In my experience, computing w efficiently is the tricky part. The function is slightly different, but metpy https://unidata.github.io/MetPy/latest/api/generated/metpy.interpolate.interpolate_1d.html uses a lot of tricks to make this work efficiently. A manual for-loop is much cleaner for this kind of stencil calculation IMO. What kind of duck arrays were you thinking of?

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We could implement linear interpolation just as w * array.sel(..., method='ffill') + (1 - w) * array.sel(..., method='bfill'), where w is calculated from the indexes. This is probably about as efficient as scipy. Numba could be slightly faster (by avoiding a few memory allocations) but it will be hard to make it work as generally, especially on duck arrays.