% your vectors
Wpig=0.5:0.5:7;
Chla=0.5:0.01:0.6;
Starch=0:0.04:0.24;
Now you need to sample uniformly the space using your vectors. Use may use ndgrid for this
[X,Y,Z] = ndgrid(Wpig,Chla,Starch);
X, Y, and Z are now 3d matrices, where
- X: Wpig
- Y: Chla
- Z: Starch
Note that you have numel(Wpig)*numel(Chla)*numel(Starch) = 1078 elements in these matrices.
You must now evaluate your function. If it admits matrices as inputs, you can supply directly the 3d matrices. For example
Ea = cos(X)+sin(Y).*tan(Z);
If it admits vectors as inputs or you evaluate each value of Ea providing only scalar values, e.g. in a for loop, you can create Ea as a vector, then reshape it. For example
% this three lines are not necessary, but makes things more clear
X1 = X(:);
Y1 = Y(:);
Z1 = Z(:);
% preallocation
Ea = zeros(numel(X),1);
% evaluation
for i = numel(X1)
Ea = cos(X1(i))+sin(Y1(i)).*tan(Z1(i));
end
% reshape the result vector to fit X,Y,Z dimensions
Ea = reshape(Ea,size(X));
Now you can use intepolation. interpn can interpolate n-d gridded data in WpigQ, ChlaQ, StarchQ query points
% query points
WpigQ = [5.5; 6.3];
ChlaQ = [0.53; 0.58];
StarchQ = [0.111; 0.134];
% interp
EaInterp = interpn(X,Y,Z,Ea,WpigQ,ChlaQ,StarchQ);
