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.