Re: surface plots from irregularly spaced data (scatter) points
- To: mathgroup at smc.vnet.net
- Subject: [mg4409] Re: surface plots from irregularly spaced data (scatter) points
- From: fschwab at polaris.cv.nrao.edu (Fred Schwab)
- Date: Fri, 19 Jul 1996 04:36:51 -0400
- Organization: National Radio Astronomy Observatory
- Sender: owner-wri-mathgroup at wolfram.com
Re: Surface plots from irregularly spaced data
Here's a quick and dirty approach that I sometimes use, which is
based on a brute-force implementation of Shepard's interpolation
formula [1]. The suitability, with particular emphasis on graphics
applications, of this and various other scattered-data interpolation
methods is discussed in Reference [2]. I chose Shepard's method
mainly on the basis of its simplicity and because it doesn't tend to
go "wild" in sparsely sampled regions. In the code below, I've
selected a weighting exponent alpha=4; see Reference [1] for
illustrations of the effect of varying this parameter.
-----------------------------------------------------------------------------
<<Graphics`Graphics3D`
f[x_,y_]:=Sin[x y]
plt0=Plot3D[f[x,y],{x,0,3},{y,0,3},PlotPoints->30]
n=500
xys=Table[3{Random[],Random[]},{n}]
data=Map[{#[[1]],#[[2]],f[#[[1]],#[[2]]]}&,xys]
shepard[x_,y_,list_]:=Module[{l,m,alpha=4,w,eps=10.^-16},
m=Length[list];
w=Map[((x-#[[1]])^2+(y-#[[2]])^2+eps)^(-alpha/2)&,list];
Sum[list[[l,3]] w[[l]],{l,m}]/Sum[w[[l]],{l,m}]]
fi=Compile[{x,y},Evaluate[shepard[x,y,data]]]
plt1=Plot3D[fi[x,y],{x,0,3},{y,0,3},PlotPoints->30]
plt2=ScatterPlot3D[data]
plt3=Show[plt1,plt2]
-----------------------------------------------------------------------------
Here, I first generate a plot of f(x,y)=sin(x y) over the
rectangle 0<x<3, 0<y<3, as in the example on page 155 of the
Mathematica book (2nd Ed.). Then I generate 500 data points randomly
distributed over the same domain, sample f at these points, construct
an interpolating function, fi, and call Plot3D to make a plot of this
interpolant. Finally, with ScatterPlot3D I show the a scatter plot of
the randomly generated data and then superimpose it upon the 3-D
perspective plot of the interpolant.
I must, of necessity, use "Compile" and "Evaluate" before calling
a plotting function, otherwise this would run too slowly. Still, this
brute-force method is too time-consuming for very large n (n>1000, say).
Probably the best approach for large-scale applications would be to
call, say, a Fortran- or C-coded mathlink application designed along
the lines of the codes in References [4,5] - which are described in
Reference [3] - in order to generate a regularly spaced table of data,
and to follow that with a call to ListPlot3D.
References:
[1] W. J. Gordon and J. A. Wixom, ``Shepard's method of `metric interpolation'
to bivariate and multivariate interpolation'', Mathematics of
Computation, 32 (1978) 253-264.
[2] R. Franke, ``Scattered data interpolation: tests of some methods'',
Mathematics of Computation, 38 (1982) 181-200.
[3] R. J. Renka, ``Multivariate interpolation of large sets of scattered
data'', ACM Transactions on Mathematical Software, 14 (1988) 139-148.
[4] ___________, ``Algorithm 660: QSHEP2D: Quadratic Shepard method for
bivariate interpolation of scattered data'', ibid., 149-150.
[5] ___________, ``Algorithm 661: QSHEP3D: Quadratic Shepard method for
trivariate interpolation of scattered data'', ibid., 151-152.
- Fred Schwab (fschwab at nrao.edu)
National Radio Astronomy Observatory
Charlottesville, VA
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