% Demos iterative gaussian fit to two computer-generated noisy gaussian peaks.
% Variable parameters are height, position, and width of both peaks.
% Uses gaussian and fitgauss2 functions.
% T. C. O'Haver, May 2006
format compact
global c

% Create a two gaussian peaks and add random noise
k=1; % sets interval between x-values; smaller k = more points in signal
t=[-100:k:400];
noise=5;
Height1=100;
Position1=100; 
Width1=100;  
Height2=100;
Position2=200; 
Width2=200;  
y = Height1.*gaussian(t,Position1,Width1) + Height2.*gaussian(t,Position2,Width2) + noise.*randn(size(t));

% Perform an iterative fit using the FMINSEARCH function
start=[length(t)/3 length(t)/2 length(t)/1.5 length(t)/2];    % start now has guesses for BOTH peaks
options = optimset('TolX',0.1);  % Determines how close the model must fit the data
% "tic" and "toc" measure how long the iterative fit takes.
tic
parameter=FMINSEARCH('fitgauss2',start,options,t,y);
toc
Measured1=[c(1) parameter(1) parameter(2)] % Peak height 1 is returned in the global variable c(1).
Measured2=[c(2) parameter(3) parameter(4)] % Peak height 2 is returned in the global variable c(2).

% Compute a model and plot it (blue line) along with 
% the original data (red points)
peak1=c(1).*gaussian(t,parameter(1),parameter(2));
peak2=c(2).*gaussian(t,parameter(3),parameter(4));
model=peak1+peak2;
plot(t,y,'r.',t,peak1,'c',t,peak2,'m',t,model,'b')