Hi Rafael,
You can try to use lsq_splin along the axes x and y.
It seems to work on the following example:
x = %pi * [-1:0.05:1]';
z = sin(x)*cos(x)';
f = gcf();
f.color_map = jetcolormap(32);
subplot(131);
xtitle("Exact values");
plot3d(x, x, z, 70, 70);
e=gce();
e.color_flag = 1;
z = z + 0.5*rand(z);
z(10,10)=5;
z(30,20)=5;
z(10,40)=5;
subplot(132);
xtitle("Perturbated values");
plot3d(x, x, z, 70, 70);
e=gce();
e.color_flag = 1;
zz=zeros(z);
w=linspace(-%pi,%pi,5)';
for i=1:size(z,'r')
[y,d]=lsq_splin(x', z(i,:), w);
zz(i,:)=interp(x', w, y, d);
end
for i=1:size(zz,'c')
[y,d]=lsq_splin(x, zz(:,i), w);
zz(:,i)=interp(x, w, y, d);
end
subplot(133);
xtitle("Smoothed values");
plot3d(x, x, zz, 70, 70);
e=gce();
e.color_flag = 1;
Best regards
Calixte
On 04/03/2013 04:36, Rafael Guerra wrote:
Hello,
Does somebody know if there are Scilab functions capable of replacing
outliers via some local robust fitting in 2D, i.e., that smooths
experimental data z=f(x,y) and is immune to strong outliers.
PS: CASCI in Atoms has a lowess function which does this via local robust
linear fitting but for functions of one variable only.
Thanks and regards,
Rafael Guerra
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Calixte Denizet
Software Development Engineer
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