## Fitting data with two peaks

Asked by Niles Martinsen

### Niles Martinsen (view profile)

on 4 Jun 2012
Latest activity Edited by John Kelly

### John Kelly (view profile)

on 2 Mar 2015
Hi
I have some data, which is not too different from the top graph in this picture: <http://www.aanda.org/index.php?option=com_image&format=raw&url=/articles/aa/full/2004/12/aa0526/img14.gif>
In other words, there are two peaks that each represent a Lorentzian. I am not sure how to fit this in MatLAB. Is there a way to fit the data to one function consisting of two Lorentzians, or do I have to split the data set in two, one peak in each?
Ultimately I need to find the x-position of each peak.
Best, Niles.

Enrique

### Enrique (view profile)

on 30 Jul 2014
Niles,
Did you ever figure out how to do this and/or implement any of the solutions suggested below? I am trying to fit two Lorentzians to similar Raman data as yours (is yours a graphene Raman spectrum as well?). In the past I have done this using a single Lorentzian fitting function I found ( Lorentzian Function Fit lorentzfit), but have not tried to implement two Lorentzians. Any suggestion would help a lot.
Thanks,
Enrique

### Geoff (view profile)

on 4 Jun 2012

Define a function that accepts the parameters of the two lorenzian curves and computes the full curve.
I don't know how many parameters you need cos I'm no mathematician =) Let's say 2?
dualLorentz = @(x, a1, b1, a2, b2) = lorenz(x, a1, b1) + lorenz(x, a2, b2);
Then, define a function to generate that curve, subtract your actual dataset, square the result and sum it.... While you're at it, parameterise the whole thing (ie a vector p of [a1, b1, a2, b2])
objFn = @(p, x, y) sum( (y - dualLorentz(x, p(1), p(2), p(3), p(4))) .^ 2 );
Then chuck it at fsolve or fminsearch - assuming your dataset is in X and Y:
p0 = % some initial guess at a1, a2, b1, b2 : you can probably be very basic
p = fsolve( @(p) objFn(p, X, Y), p0 );

Answer by Image Analyst

on 4 Jun 2012

### Ryan (view profile)

on 4 Jun 2012
Edited by John Kelly

### John Kelly (view profile)

on 2 Mar 2015

You could get a close approximation of peak position with a cubic spline fit and local maxima. You could also try just smoothing the data first as well and then finding the maxima.
Cubic Spline:

Answer by Frederic Moisy

### Frederic Moisy (view profile)

on 7 Jun 2012

Hi, you can try the Ezyfit toolbox:
In particular, there is an example with two peaks (here fitted with the sum of 2 gaussian curves):