[SciPy-user] [Fwd: 3D interpolation over irregular data]

mark starnes m.starnes05@imperial.ac...
Thu Jul 31 03:13:26 CDT 2008

Hi Stéfan, thanks for the tip.  I tried this approach before, but
couldn't get it to work, due to my installation (I broke it, got it
working again, then wasn't brave enough to tinker).  However, I got the
radial basis functions working by modifying my PYTHONPATH, rather than
with a rebuild, so may try the Delauney / natgrid approaches too.  The
radial basis function seems to have trouble with more than 5000 points
so I may need other options.

Thanks for taking the time to help!

Best regards,


Stéfan van der Walt wrote:
> Hi Mark
> 2008/7/30 mark starnes <m.starnes05@imperial.ac.uk>:
>> 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
> http://www.scipy.org/Cookbook/Matplotlib/Gridding_irregularly_spaced_data
> Robert Kern's delaunay package does natural neighbour interpolation, IIRC.
> Regards
> Stéfan
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