Is there a way to reduce compilation time for this segment?

tic

    iscorr = 1;
    myunc = zeros(1,length(Kurzschluss.Plateaus_I));

    myfunction = '(X2/X1)*(sqrt(1 - X3^2/(X2^2 * X1^2)))';  

    for i=1:length(mystruct.Plateaus_I)

         x1 = mystruct.Plateaus_I{1,i};
         x2 = mystruct.Plateaus_U{1,i};
         x3 = mystruct.Plateaus_P{1,i};
         myunc(i) =  calculateEndUncertainty_3(x1,x2,x3,iscorr,myfunction);

    end 
    
    toc

[darova]

Can you show calculateEndUncertainty_3 function?

[Souheil Araoud]

function [unc] = CalculateEndUncertainty_3(x1,x2,x3,iscorr,myfunction) 

N = 3; 

if iscorr
Enable_correlation  = 1;
else
Enable_correlation = 0;
end

StrModelFunction = myfunction;

StrInput = '';
StrMean = '';

for c1 = 1:N
    StrInput = [StrInput 'X' num2str(c1) ','];    
    StrMean = [StrMean 'mean(x' num2str(c1) '),']; 
end

StrInput(end) = [];
StrMean(end) = [];

eval(['syms f(' StrInput ')'])

eval(['f(' StrInput ') ='  StrModelFunction ';'])

u2 = 0;
for c1 = 1:N
    
    temp = diff(f,eval(['X' num2str(c1)]));
    df_dx = double(eval(['temp(' StrMean ')']));
    
    u2 = u2 + df_dx^2*std(eval(['x' num2str(c1)]))^2/length(x1) ;
    
end

 if Enable_correlation
    for c1 = 1:N-1
        for c2 = c1+1:N    
            temp_c1 = diff(f,eval(['X' num2str(c1)]));
            df_dx_c1 = double(eval(['temp_c1(' StrMean ')']));

            temp_c2 = diff(f,eval(['X' num2str(c2)]));
            df_dx_c2 = double(eval(['temp_c2(' StrMean ')']));

            temp = cov(eval(['x' num2str(c1)]),eval(['x' num2str(c2)]));
            u2 = u2 + 2*df_dx_c1*df_dx_c2*temp(2)/length(x1); 
        end
    end
end

unc = sqrt(abs(u2));

res = double(eval(['f(' StrMean ')']));

end

[darova]

You have a lot of eval() that is why script is slow

The function below depends on f(), temp(), temp_c1(), temp_c2()

Maybe you can attach whole your project? Any description?

[Souheil Araoud]

The whole project is way too long. This function is supposed to take 3 inputs of a function as vectors (in my case containing about 5 lightly diffefrent values) and calculate the uncertainty of the result based on the mean value of each input vector etc.. The function has been applied on 3 unidimensional cells named Plateaus_I, Plateaus_U and Plateaus_P containing a vector in each cell.

Answer 1

[darova]

See if it works

[Souheil Araoud]

Great! Thanks a lot

Answer 2

[Jackson Burns]

Hi Souheil!

If you have access to the parallel computing toolbox, you can improve execution time with a parfor loop.

tic
iscorr = 1;
myunc = zeros(1,length(Kurzschluss.Plateaus_I));
myfunction = '(X2/X1)*(sqrt(1 - X3^2/(X2^2 * X1^2)))';  
parfor i=1:length(mystruct.Plateaus_I)
     x1 = mystruct.Plateaus_I{1,i};
     x2 = mystruct.Plateaus_U{1,i};
     x3 = mystruct.Plateaus_P{1,i};
     myunc(i) =  calculateEndUncertainty_3(x1,x2,x3,iscorr,myfunction);
end 
toc

Other optimizations will likely come from improving your functions, such as calculateEndUncertainty_3. This page from MathWorks has some great advice on how to do this. I would reccomend looking at vectorization in particular, as MATLAB is extremely powerful when properly vectorized.

Hope this helps, good luck!