[SciPy-user] [Fwd: 3D interpolation over irregular data]
Wed Jul 30 15:32:31 CDT 2008
I'm having a go with the radial basis functions, as they seem to be
working with my Scipy (surprisingly!). Thanks for your suggestions;
I'll move onto them if I fail with this approach.
The points are partly on a structured grid, though not regular, and
partly an unstructured grid. I was hoping not to treat the regions
Thanks for your response, though. I'll be sure to write back if the
radial functions don't work.
James Turner wrote:
> Hi Mark,
>> 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.
> If you don't have any joy with the other suggestions, there are some
> useful routines by Renka in Netlib, at least one of which is 3D,
> though I believe they are also piecewise polynomials (and in ForTran,
> of course, so need wrapping).
> I also have a program implementing the Hubble Telescope's "Drizzle"
> algorithm in 3D, which is similar to nearest neighbour or linear,
> but unfortunately it's written in an obscure language that would
> make it difficult to use from Python unless you are prepared to
> install STScI's PyRAF. I also suspect it will only work well for
> data that are mildly irregular and it may be fiddly to set up for
> your application, but anyway, just so you have the option...
> The paper is at http://xxx.lanl.gov/abs/astro-ph/9708242.
> Are the points really irregular in 3D, or just in 1-2 of the
> dimensions? If any dimension is regularly spaced, you might be able
> to simplify the problem by resampling in 1-2D at a time?
> Stefan might also have suggestions ;-). But maybe the radial basis
> functions will do the trick... I'll have to look at those.
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