I use Hardy's multiquadric interpolation to to do the math,
> then use imshow (or pcolor) to make a surface map. I only
> have data for the 120 points (where the circle are - those
> are actuators), and interpolate the rest.
> If people are interested, I can clean up the code a little
> and post it.
This sounds pretty close to matlab's griddata function. It would be
very nice to have this in matplotlib.mlab, perhaps as a wrapper to
some core scipy functionality, which could be conditionally imported.
What requirements does your code have -- pure python, extension code,
Here is the matlab docstring, FYI
GRIDDATA Data gridding and surface fitting.
ZI = GRIDDATA(X,Y,Z,XI,YI) fits a surface of the form Z = F(X,Y)
to the data in the (usually) nonuniformly-spaced vectors (X,Y,Z)
GRIDDATA interpolates this surface at the points specified by
(XI,YI) to produce ZI. The surface always goes through the data
points. XI and YI are usually a uniform grid (as produced by
MESHGRID) and is where GRIDDATA gets its name.
XI can be a row vector, in which case it specifies a matrix with
constant columns. Similarly, YI can be a column vector and it
specifies a matrix with constant rows.
[XI,YI,ZI] = GRIDDATA(X,Y,Z,XI,YI) also returns the XI and YI
formed this way (the results of [XI,YI] = MESHGRID(XI,YI)).
[...] = GRIDDATA(...,'method') where 'method' is one of
'linear' - Triangle-based linear interpolation (default).
'cubic' - Triangle-based cubic interpolation.
'nearest' - Nearest neighbor interpolation.
'v4' - MATLAB 4 griddata method.
defines the type of surface fit to the data. The 'cubic' and 'v4'
methods produce smooth surfaces while 'linear' and 'nearest' have
discontinuities in the first and zero-th derivative respectively. All
the methods except 'v4' are based on a Delaunay triangulation of the
See also GRIDDATA3, GRIDDATAN, DELAUNAY, INTERP2, MESHGRID.