[SciPy-user] [Fwd: 3D interpolation over irregular data]
José María García Pérez
Thu Jul 31 04:18:50 CDT 2008
>From my experience working with RBF, they work pretty well even when you use
few points for the interpolation. They track non linear behavior very well.
I have worked with RBF with very big FEM models (200000-500000 grid points)
and with more than 3D (in other disciplines), but I don't take all the
points at the same. What I would do is to use for example the 100 nearest
points to the geometric point where you want to interpolate (probably with
10 would be enough). That's something you can try: test using 10, 20, 50,
100 points, and you will see that the difference is small pretty soon, and
for sure smaller that the error you may expect from a CFD simulation.
Hope this tip helps!
2008/7/31 mark starnes <email@example.com>
> Hi again, Stéfan.
> Is Robert Kern's package limited to two-dimensional data? I've had a
> look and can't see any three-dimensional options.
> Best regards,
> Stéfan van der Walt wrote:
> > Hi Mark
> > 2008/7/30 mark starnes <firstname.lastname@example.org>:
> >> Hi everyone,
> >> I've looked through the list here and in Numpy-users, and checked the
> >> 'net but can't find an answer to this problem (with luck, I've missed
> >> something obvious!).
> >> I've an array of velocities at 80,000 points, irregularly spaced (from a
> >> CFD analysis). I'd like to generate the interpolated velocity at any
> >> position in the domain, to map the data to an acoustics analysis on a
> >> different mesh.
> >> I tried a least squares approach but the errors are too large using
> >> polynomials and trigonometric functions. My conclusion is that I need a
> >> nearest-neighbour type interpolation routine.
> > Also take a look at
> > Robert Kern's delaunay package does natural neighbour interpolation,
> > Regards
> > Stéfan
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