[SciPy-Dev] Problem with N-dimensional interpolation using a new griddata function for N>=3

[Adam Machnik]

Thank you very much for your explanations. Indeed, I would like to use
the n-linear
interpolation.The problem is that I cannot find any python package that
can do this on N-D dimension on the incomplete and irregular set of points.
For regular grids I use "scipy.ndimage.interpolation.map_coordinates" and it
works perfectly. I use also "scipy.interpolate.Rbf " to perform interpolation
on irregular set of points using radial basis functions and it works
also great.
But when using Rbf the interpolation is nonlinear, and often inconsistent.
The only package that I have found up to now is this new griddata() function.
I hope that it will work for me when fixed these issues with the
handling of degenerated
zero volume cases on interpolation level.
Pauli Vitranen, who wrote this function, says that it should be
possible, so I wait with impatience
to test it when available.

Thanks,

Adam

On Tue, Sep 21, 2010 at 4:57 PM, Robert Kern <robert.kern at gmail.com> wrote:
> On Tue, Sep 21, 2010 at 09:17, Adam Machnik <amachnik at gmail.com> wrote:
>> Hello,
>> I am concerned in the difference in triangulation. I think that on
>> such a simple grid there should be no difference from qhull results.
>
> What you call a "simple grid" is actually a degenerate set of points
> for Delaunay triangulation. There are a a large number of valid
> triangulations that satisfy the Delaunay condition. The way that qhull
> deals with degeneracy is to randomly perturb the points. While perhaps
> qhull should pick a consistent random seed in order to reproduce
> whichever of the arbitrarily chosen triangulations to return in your
> degenerate case, please be aware that interpolation using
> triangulation is not going to work very well for your case. Trilinear
> interpolation would be better.
>
> --
> Robert Kern
>
> "I have come to believe that the whole world is an enigma, a harmless
> enigma that is made terrible by our own mad attempt to interpret it as
> though it had an underlying truth."
>   -- Umberto Eco
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