source: MML/trunk/machine/SOLEIL/common/toolbox/ezyfit/ezyfit/demo/efdemo.m @ 4

%% Discover Ezyfit: A free curve fitting toolbox for Matlab
% F. Moisy, 19 nov 2008.
%
% Laboratory FAST, University Paris Sud.

%% About the Ezyfit Toolbox
% The EzyFit toolbox for Matlab enables you to perform simple curve fitting
% of one-dimensional data using arbitrary fitting functions. It provides
% command-line functions and a basic graphical user interface for interactive
% selection of the data.

%% Simple fit: exponential decay
% 
% First plot some data, say, an exponential decay.

plotsample exp nodisp

%%
% A predefined fit called 'exp' allows you to fit your data:

showfit exp

%%
% Suppose now you want to use your own variable and function names.
% Let's fit this data with the function f(t)=a*exp(-t/tau), and show the
% fit with a bold red line:

undofit  % deletes the previous fit
showfit('f(t)=a*exp(-t/tau)','fitlinewidth',2,'fitcolor','red');

%%
% Note that showfit recognizes that t is the variable, and the coefficients
% of the fit are named a and tau.
%
% If you want to use the values of the coefficients t and tau into Matlab,
% you need to creates these variables into the base workspace:

makevarfit
a
tau


%% Initial guesses
% Now suppose you want to fit more complex data, like a distribution
% showing two peaks. Let's try to fit these peaks with two gaussians, each
% of height a, mean m and width s.

plotsample hist2 nodisp
showfit('a_1*exp(-(x-x_1)^2/(2*s_1^2)) + a_2*exp(-(x-x_2)^2/(2*s_2^2))');

%%
% The solver obviously get lost in our 6-dimensional space. Let's help it,
% by providing initial guesses

undofit
showfit('a_1*exp(-(x-m_1)^2/(2*s_1^2)) + a_2*exp(-(x-m_2)^2/(2*s_2^2)); a_1=120; m_1=7; a_2 = 100; m_2=15', 'fitcolor','blue','fitlinewidth',2);

%%
% The result seems to be correct now. Note that only 4 initial guesses are
% given here; the two other ones, s_1 and s_2, are taken as 1 -- which is
% close to the expected solution.


%% Fitting in linear or in log scale
% Suppose you want to fit a power law in logarithmic scale:

plotsample power nodisp
showfit power

%%
% would you have obtained the same result in linear scale? No:
swy    % this shortcut turns the Y-axis to linear scale
showfit('power','fitcolor','red');

%%
% The value of the coefficients have changed. In the first case, LOG(Y) was
% fitted, whereas in the second case Y was fitted, because the Y-axis has
% been changed.
%
% You may however force showfit to fit LOG(Y) or Y whatever the Y axis, by
% specifying 'lin' or 'log' in the first input argument:

rmfit % this removes all the fits
showfit('power; lin','fitcolor','red');
showfit('power; log','fitcolor','blue');

%%
% In the equation information, it is specified (lin) or (log) after the R
% coefficient.




%% Using the fit structure f

%%
% You can fit your the data without displaying it:
x=1:10;
y=[15 14.2 13.6 13.2 12.9 12.7 12.5 12.4 12.4 12.2];
f = ezfit(x,y,'beta(rho) = beta_0 + Delta * exp(-rho * mu);  beta_0 = 12');

%%
% f is a structure that contains all the informations about the fit:

f

%%
% From this structure, you can plot the data and the fit:

clf
plot(x,y,'r*');
showfit(f)

%%
% you can also display the result of the fit

dispeqfit(f)

%%
% or create the variables in the base workspace

makevarfit(f)
beta_0
mu
Delta

%% Weigthed fit
% Suppose now we want to fit data with unequal weights, shown here as error
% bars of different lengths:

x =  1:10;
y =  [1.56 1.20 1.10 0.74 0.57 0.55 0.31 0.27 0.28 0.11];
dy = [0.02 0.02 0.20 0.03 0.03 0.10 0.05 0.02 0.10 0.05];
clf, errorbar(x,y,dy,'o');

%%
% In order to perform a weighted fit on this data, the vectors y and dy
% have to be merged into a 2-by-N matrix and given as the second input
% argument to ezfit. Compare the results for the usual and weighted fits:

fw = ezfit(x, [y;dy], 'exp');
showfit(fw,'fitcolor','red');
f = ezfit(x, y, 'exp');
showfit(f,'fitcolor','blue');

%%
% The red curve (weighted fit) tends to go through the data with smaller
% error bars.