FittingDemo.C

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/// \file
/// \ingroup tutorial_fit
/// \notebook -js
/// Example for fitting signal/background.
/// This example can be executed with:
///
/// ~~~{.cpp}
/// root > .x FittingDemo.C  (using the cling interpreter)
/// root > .x FittingDemo.C+ (using the native complier via ACLIC)
/// ~~~
///
/// \macro_image
/// \macro_output
/// \macro_code
///
/// \author Rene Brun
 
#include "TH1.h"
#include "TMath.h"
#include "TF1.h"
#include "TLegend.h"
#include "TCanvas.h"
 
// Quadratic background function
double background(double *x, double *par) {
   return par[0] + par[1]*x[0] + par[2]*x[0]*x[0];
}
 
 
// Lorenzian Peak function
double lorentzianPeak(double *x, double *par) {
  return (0.5*par[0]*par[1]/TMath::Pi()) /
    TMath::Max( 1.e-10,(x[0]-par[2])*(x[0]-par[2])
   + .25*par[1]*par[1]);
}
 
// Sum of background and peak function
double fitFunction(double *x, double *par) {
  return background(x,par) + lorentzianPeak(x,&par[3]);
}
 
void FittingDemo() {
 //Bevington Exercise by Peter Malzacher, modified by Rene Brun
 
   const int nBins = 60;
 
   double data[nBins] = { 6, 1,10,12, 6,13,23,22,15,21,
                           23,26,36,25,27,35,40,44,66,81,
                           75,57,48,45,46,41,35,36,53,32,
                           40,37,38,31,36,44,42,37,32,32,
                           43,44,35,33,33,39,29,41,32,44,
                           26,39,29,35,32,21,21,15,25,15};
   TCanvas *c1 = new TCanvas("c1","Fitting Demo",10,10,700,500);
   c1->SetFillColor(33);
   c1->SetFrameFillColor(41);
   c1->SetGrid();
 
   TH1F *histo = new TH1F("histo",
      "Lorentzian Peak on Quadratic Background",60,0,3);
   histo->SetMarkerStyle(21);
   histo->SetMarkerSize(0.8);
   histo->SetStats(false);
 
   for(int i=0; i < nBins;  i++) histo->SetBinContent(i+1,data[i]);
 
   // create a TF1 with the range from 0 to 3 and 6 parameters
   TF1 *fitFcn = new TF1("fitFcn",fitFunction,0,3,6);
   fitFcn->SetNpx(500);
   fitFcn->SetLineWidth(4);
   fitFcn->SetLineColor(kMagenta);
 
   // first try without starting values for the parameters
   // This defaults to 1 for each param.
   // this results in an ok fit for the polynomial function
   // however the non-linear part (lorenzian) does not
   // respond well.
   fitFcn->SetParameters(1,1,1,1,1,1);
   histo->Fit("fitFcn","0");
 
   // second try: set start values for some parameters
   fitFcn->SetParameter(4,0.2); // width
   fitFcn->SetParameter(5,1);   // peak
 
   histo->Fit("fitFcn","V+","ep");
 
   // improve the picture:
   TF1 *backFcn = new TF1("backFcn",background,0,3,3);
   backFcn->SetLineColor(kRed);
   TF1 *signalFcn = new TF1("signalFcn",lorentzianPeak,0,3,3);
   signalFcn->SetLineColor(kBlue);
   signalFcn->SetNpx(500);
   double par[6];
 
   // writes the fit results into the par array
   fitFcn->GetParameters(par);
 
   backFcn->SetParameters(par);
   backFcn->Draw("same");
 
   signalFcn->SetParameters(&par[3]);
   signalFcn->Draw("same");
 
   // draw the legend
   TLegend *legend=new TLegend(0.6,0.65,0.88,0.85);
   legend->SetTextFont(72);
   legend->SetTextSize(0.04);
   legend->AddEntry(histo,"Data","lpe");
   legend->AddEntry(backFcn,"Background fit","l");
   legend->AddEntry(signalFcn,"Signal fit","l");
   legend->AddEntry(fitFcn,"Global Fit","l");
   legend->Draw();
 
}