Class to fit histo with Glauber based function.
More...
#include <Fitter.hpp>
|
| | Fitter ()=default |
| |
|
| Fitter (std::unique_ptr< TTree > tree) |
| |
| virtual | ~Fitter ()=default |
| |
|
void | Init (int nEntries) |
| |
|
void | SetGlauberFitHisto (double f, double mu, double k, Int_t n=10000, Bool_t Norm2Data=true) |
| |
|
void | NormalizeGlauberFit () |
| |
| double | FitGlauber (double *par, double f0, Int_t k0, Int_t k1, Int_t nEvents) |
| |
| void | FindMuGoldenSection (double *mu, double *chi2, double mu_min, double mu_max, double f, double k, Int_t nEvents=10000, Int_t nIter=5) |
| |
| double | GetChi2 () const |
| |
| double | NBD (double n, double mu, double k) const |
| |
| void | SetNBDhist (double mu, double k) |
| |
|
double | Nancestors (double f) const |
| |
|
double | NancestorsMax (double f) const |
| |
| std::unique_ptr< TH2F > | GetModelHisto (const TString &name, const double par[3], Int_t nEvents) |
| |
|
void | SetInputHisto (const TH1F &h) |
| |
|
void | SetFitMinBin (Int_t min) |
| |
|
void | SetFitMaxBin (Int_t min) |
| |
|
void | SetNormMinBin (Int_t min) |
| |
|
void | SetBinSize (Int_t size) |
| |
|
void | SetOutDirName (const TString &name) |
| |
|
void | SetMode (const TString &mode) |
| |
|
TH1F | GetGlauberFitHisto () const |
| |
|
TH1F | GetDataHisto () const |
| |
|
TH1F | GetNBDHisto () const |
| |
|
TH1F | GetNpartHisto () const |
| |
|
TH1F | GetNcollHisto () const |
| |
|
TH1F | GetBestFiHisto () const |
| |
◆ Fitter()
| Glauber::Fitter::Fitter |
( |
| ) |
|
|
default |
◆ ~Fitter()
| virtual Glauber::Fitter::~Fitter |
( |
| ) |
|
|
virtualdefault |
◆ FindMuGoldenSection()
| void Glauber::Fitter::FindMuGoldenSection |
( |
double * | mu, |
|
|
double * | chi2, |
|
|
double | mu_min, |
|
|
double | mu_max, |
|
|
double | f, |
|
|
double | k, |
|
|
Int_t | nEvents = 10000, |
|
|
Int_t | nIter = 5 ) |
- Parameters
-
| mu | mean value of negative binominal distribution (we are looking for it) |
| chi2 | return value (indicates good match) |
| mu_min | lower search edge for mean value NBD |
| mu_max | upper search edge for mean value NBD |
| f | parameter of Na |
| k | NBD parameter |
| nEvents | |
| nIter | |
◆ FitGlauber()
| double Glauber::Fitter::FitGlauber |
( |
double * | par, |
|
|
double | f0, |
|
|
Int_t | k0, |
|
|
Int_t | k1, |
|
|
Int_t | nEvents ) |
Find the best match
- Parameters
-
| return | value of best fit parameters |
| f0 | parameter of Na, for which chi2 will be calculated |
| k0 | lower search edge for NBD parameter |
| k1 | upper search edge for NBD parameter |
| nEvents | |
◆ GetChi2()
| double Glauber::Fitter::GetChi2 |
( |
| ) |
const |
Compare fGlauberFitHisto with fDataHisto
- Returns
- chi2 value
◆ GetModelHisto()
| std::unique_ptr< TH2F > Glauber::Fitter::GetModelHisto |
( |
const TString & | name, |
|
|
const double | par[3], |
|
|
Int_t | nEvents ) |
Creates histo with a given model parameter distribution
- Parameters
-
| range | observable range |
| name | name of the MC-Glauber model parameter |
| par | array with fit parameters |
| Nevents | |
- Returns
- pointer to the histogram
◆ NBD()
| double Glauber::Fitter::NBD |
( |
double | n, |
|
|
double | mu, |
|
|
double | k ) const |
Negative Binomial Distribution (by definition)
- Parameters
-
| n | argument |
| mu | mean |
| k | argument |
- Returns
- NBD for a given parameters
◆ SetNBDhist()
| void Glauber::Fitter::SetNBDhist |
( |
double | mu, |
|
|
double | k ) |
Popuates histogram nbd_<mean>_<k> with values of NBD
- Parameters
-
The documentation for this class was generated from the following files: