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Reference Guide
minuit2GausFit.C
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1 /// \file
2 /// \ingroup tutorial_fit
3 /// \notebook -js
4 /// Perform fits with different configurations using Minuit2
5 ///
6 /// \macro_image
7 /// \macro_output
8 /// \macro_code
9 ///
10 /// \author Lorenzo Moneta
11 
12 #include "TH1.h"
13 #include "TF1.h"
14 #include "TCanvas.h"
15 #include "TRandom3.h"
16 #include "TVirtualFitter.h"
17 #include "TPaveLabel.h"
18 #include "TStyle.h"
19 
20 #include <iostream>
21 #include <string>
22 
23 
24 void testGausFit( std::string type = "Minuit2", int n = 1000) {
25 
26  gRandom = new TRandom3();
27 
29 
30  std::string name;
31  name = "h1_" + type;
32  TH1D * h1 = new TH1D(name.c_str(),"Chi2 Fit",100, -5, 5. );
33  name = "h2_" + type;
34  TH1D * h2 = new TH1D(name.c_str(),"Chi2 Fit with Minos Error",100, -5, 5. );
35  name = "h3_" + type;
36  TH1D * h3 = new TH1D(name.c_str(),"Chi2 Fit with Integral and Minos",100, -5, 5. );
37  name = "h4_" + type;
38  TH1D * h4 = new TH1D(name.c_str(),"Likelihood Fit with Minos Error",100, -5, 5. );
39 
40  gStyle->SetOptStat(1111111);
41  gStyle->SetOptFit(1111111);
42 
43  for (int i = 0; i < n; ++i) {
44  double x = gRandom->Gaus(0,1);
45  h1->Fill( x );
46  h2->Fill( x );
47  h3->Fill( x );
48  h4->Fill( x );
49  }
50 
51  std::string cname = type + "Canvas" ;
52  std::string ctitle = type + " Gaussian Fit" ;
53  TCanvas *c1 = new TCanvas(cname.c_str(),cname.c_str(),10,10,900,900);
54  c1->Divide(2,2);
55 
56  c1->cd(1);
57  cout << "\nDo Fit 1\n";
58  h1->Fit("gaus","Q");
59  h1->Draw();
60  c1->cd(2);
61  cout << "\nDo Fit 2\n";
62  h2->Fit("gaus","VE");
63  h2->Draw();
64  c1->cd(3);
65  cout << "\nDo Fit 3\n";
66  h3->Fit("gaus","IE");
67  h3->Draw();
68  c1->cd(4);
69  cout << "\nDo Fit 4\n";
70  h4->Fit("gaus","VLE");
71  h4->Draw();
72 
73 }
74 
75 void minuit2GausFit() {
76 
77  int n = 1000;
78  testGausFit("Minuit2",n);
79  testGausFit("Fumili2",n);
80 
81 }
82 
83 
84 
virtual Int_t Fill(Double_t x)
Increment bin with abscissa X by 1.
Definition: TH1.cxx:3251
Random number generator class based on M.
Definition: TRandom3.h:27
virtual Double_t Gaus(Double_t mean=0, Double_t sigma=1)
Samples a random number from the standard Normal (Gaussian) Distribution with the given mean and sigm...
Definition: TRandom.cxx:256
return c1
Definition: legend1.C:41
R__EXTERN TStyle * gStyle
Definition: TStyle.h:406
static void SetDefaultFitter(const char *name="")
static: set name of default fitter
TVirtualPad * cd(Int_t subpadnumber=0)
Set current canvas & pad.
Definition: TCanvas.cxx:688
Double_t x[n]
Definition: legend1.C:17
TH1F * h1
Definition: legend1.C:5
virtual void Draw(Option_t *option="")
Draw this histogram with options.
Definition: TH1.cxx:2974
void SetOptFit(Int_t fit=1)
The type of information about fit parameters printed in the histogram statistics box can be selected ...
Definition: TStyle.cxx:1396
R__EXTERN TRandom * gRandom
Definition: TRandom.h:62
1-D histogram with a double per channel (see TH1 documentation)}
Definition: TH1.h:610
The Canvas class.
Definition: TCanvas.h:31
int type
Definition: TGX11.cxx:120
virtual void Divide(Int_t nx=1, Int_t ny=1, Float_t xmargin=0.01, Float_t ymargin=0.01, Int_t color=0)
Automatic pad generation by division.
Definition: TPad.cxx:1162
void SetOptStat(Int_t stat=1)
The type of information printed in the histogram statistics box can be selected via the parameter mod...
Definition: TStyle.cxx:1444
THist< 1, double, THistStatContent, THistStatUncertainty > TH1D
Definition: THist.hxx:284
virtual TFitResultPtr Fit(const char *formula, Option_t *option="", Option_t *goption="", Double_t xmin=0, Double_t xmax=0)
Fit histogram with function fname.
Definition: TH1.cxx:3695
const Int_t n
Definition: legend1.C:16
char name[80]
Definition: TGX11.cxx:109