function err = gaussianFunc(p,x,y,plot_handle,text_handle)
% Calculates the sum of squared error between y and the predicted function,
% which is the sum of a sinusoid and a 2nd order polynomial.  
%
% This is the required format for a function to be minimized by 'fit.m':
%1) All model parameters must be fields of the first input parameter (p)
%2) The first output parameter must be the error value to be minimized 
predY = p.AA.*exp(-(x-p.CC).^2/(2*p.BB^2));   %

% this function call above calls the model params. from the structure p
% (called initP initially in main code) and also takes independent variable x

% calculate chi-squared (this is what we want to minimize) by comparing to
% actual dependent variable (i.e. the y-data to fit to)
err = sum( (y-predY).^2);

% deal with graphics update
set(plot_handle,'YData',predY);
txt=sprintf(' ');
set(text_handle,'String',txt);
drawnow