Fast, discrete natural neighbor interpolation in 3D.
Project description
Natural neighbor interpolation is a method for interpolating scattered data (i.e. you know the values of a function at scattered locations). It is often superior to linear barycentric interpolation, which is a commonly used method of interpolation provided by Scipy’s griddata function.
There are several implementations of 2D natural neighbor interpolation in Python. We needed a fast 3D implementation that could run without a GPU, so we wrote an implementation of Discrete Sibson Interpolation (a version of natural neighbor interpolation that is fast but introduces slight errors as compared to “geometric” natural neighbor interpolation).
See https://doi.org/10.1109/TVCG.2006.27 for details.
Future Work
Change arguments so that you don’t need to pass in a full grid (i.e. change from the griddata API); this is good because it reduces memory footprint and, unlike other griddata methods, I think we really need our interpolated points to lie on a grid.
Add a bunch of tests
Check that the input dimensions are correct
Add option to avoid extrapolation
Support floats and doubles
Support 2D
Support higher dimensions (?)
Add documentation with discussion on limitations of discrete sibson’s method
Make it pip installable
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