[SciPy-Dev] Review request: interp2d with partial redgrid backend

Charles R Harris charlesr.harris@gmail....
Tue Nov 13 00:14:43 CST 2012

On Mon, Nov 12, 2012 at 12:27 PM, Pauli Virtanen <pav@iki.fi> wrote:

> 12.11.2012 01:54, John Travers kirjoitti:
> [clip]
> > Following this I hope to improve the docs a little and find a better
> > solution to the scattered data problem rather than using surfit (which
> > is great for smoothing BTW).
> Currently, we have the Delaunay tesselation based interpolation routines
> (LinearNDInterpolation et al.) and RBF, in addition to Fitpack's splines.
> However, the tesselation doesn't scale very well to large datasets in
> high dimensions as the number of simplices explodes, and our RBF
> implementation would need some fine tuning (i.e. the automatic parameter
> choices it makes are not optimal). Fitpack's problem are well known. So
> there certainly would be some room for improvement here.
> We'd also need an easy-to-use gridded data intepolation routine. Tensor
> product interpolation is sort of easy [1], but I didn't immediately see
> an efficient and easy way to evaluate z(i) = interpolator(x(i), y(i))
> [as opposed to z(i,j) = interpolator(x(i), y(j))] in that way. To do
> this, one probably would have to really construct the spline
> representation rather than just reusing existing interpolators one after
> another.
I haven't looked at the spline case, but for the multidimensional numpy
polynomials there is a 'tensor' keyword in the evaluation that lets it be
used for both cases.

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