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Surface fit approximation for x,y and z data

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I have an x and y vector, of sizes n and m respectively. I also have a z matrix of size n by m.
I am trying to do one of the following, preferably both:
  • interpolate for given x and y values to find the value of z
  • find a 2D function with inputs x and y using fit
In either scenarios, I am not sure whether I can do this since my x and y's are of different sizes. For the second option, which isn't as straight forward, I have the following code:
fitType=fittype(@(a,b,c,d,e,f,g,h,i,x,y) ...
where my fit function, as seen in the code, is: (a+b*x+c*x^2)+(d+e*x+f*x^2)*sin(y)+(g+h*x+i*x^2)*cos(y). The constants a,b,c,d,e,f,g,h,i are the one which I want fit to find me. Unfortunately, this does not work for different sized arrays.
EDIT: I've found a solution that may work but it turns out I cannot call the independent variables as [M,aoa]. The code is now:
myfittype = fittype('(a+b*Mx+c*Mx^2)+(d+e*Mx+f*Mx^2)*sin(aoax)+(g+h*Mx+ii*Mx^2)*cos(aoax)',...
This returns the following error:
Operator '<' is not supported for operands of type 'fittype'.
Error in fit (line 7)
if dim_x < m
Alessandro Maria Laspina
Alessandro Maria Laspina on 13 Mar 2021
Edited: Alessandro Maria Laspina on 13 Mar 2021
@Cris LaPierre No the Z values are completely different. Z is a function of the variables X and Y. Unless by correspond to you mean that those X and Y values correspond to the value of Z at the equivalent row and column index.
And I need to interpolate no matter what. I only have a limited dataset of Z values for given X and Y, I do not have more than the given points. I need to find Z for a different subset of x and y values within the set of values I already have.
@Matt J my mistake, I repeated a,b,c
Alessandro Maria Laspina
Alessandro Maria Laspina on 21 Mar 2021
I need to correct something, the reason why I said I hav to interpolate no matter what is because my x and y data is not of the same size. This isn't difficult, and I've managed to do this via:
where x and y are sizes n and m respectively, and Z is size nxm.
Now my objective is to (hopefully) find an approximation to this interpolation. How? Well my idea is that because Y are angles between 0 and 180 degrees, and X is a dimensionless value (Z is also dimensionless), I want to fit the data to this:
f=@(a,x) a(1)+a(2).*x(:,1)+a(3).*x(:,1).^2+...
a is a vector containing the coefficients. x is a vector that is (nxm)x2 long, where the first column are the repetitions of x and y. This is not what I posted before, because that method is giving me a method. But with this formulation, I found that using lsqcurvefit I can obtain an approximation by:
options = optimoptions('lsqcurvefit','Algorithm','levenberg-marquardt');
[Zfit,resnorm,residual,exitflag,output]=lsqcurvefit(f,ones(1,9)',[X(:) Y(:)],Z(:),lb,ub,options);
Whats the problem with this? Well I get a resnorm value of over 635.8113. You can find the x,y,z (single column data) and X,Y,Z (meshgridded data) in the file attached.

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Answers (1)

Cris LaPierre
Cris LaPierre on 13 Mar 2021
Since your data appears to already be gridded, I'd suggest using interp3.
Cris LaPierre
Cris LaPierre on 15 Mar 2021
I don't see how using interp2 meets all your requirements.
Alessandro Maria Laspina
Alessandro Maria Laspina on 20 Mar 2021
I have x, y, and z data. So my set is 3 dimensional, not 4 dimensional. I have query points for x and y, and have to fiind the equivalent values for z.

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