/usr/include/root/TSpectrumFit.h is in libroot-hist-spectrum-dev 5.34.30-0ubuntu8.
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// Author: Miroslav Morhac 25/09/06
/*************************************************************************
* Copyright (C) 1995-2006, Rene Brun and Fons Rademakers. *
* All rights reserved. *
* *
* For the licensing terms see $ROOTSYS/LICENSE. *
* For the list of contributors see $ROOTSYS/README/CREDITS. *
*************************************************************************/
#ifndef ROOT_TSpectrumFit
#define ROOT_TSpectrumFit
//////////////////////////////////////////////////////////////////////////
// //
// TSpectrumFit //
// //
// Class for fitting 1D spectra using AWMI (algorithm without matrix //
// inversion) and conjugate gradient algorithms for symmetrical //
// matrices (Stiefel-Hestens method). AWMI method allows to fit //
// simulaneously 100s up to 1000s peaks. Stiefel method is very stable, //
// it converges faster, but is more time consuming //
// //
//////////////////////////////////////////////////////////////////////////
#ifndef ROOT_TNamed
#include "TNamed.h"
#endif
class TH1;
class TSpectrumFit : public TNamed {
protected:
Int_t fNPeaks; //number of peaks present in fit, input parameter, it should be > 0
Int_t fNumberIterations; //number of iterations in fitting procedure, input parameter, it should be > 0
Int_t fXmin; //first fitted channel
Int_t fXmax; //last fitted channel
Int_t fStatisticType; //type of statistics, possible values kFitOptimChiCounts (chi square statistics with counts as weighting coefficients), kFitOptimChiFuncValues (chi square statistics with function values as weighting coefficients),kFitOptimMaxLikelihood
Int_t fAlphaOptim; //optimization of convergence algorithm, possible values kFitAlphaHalving, kFitAlphaOptimal
Int_t fPower; //possible values kFitPower2,4,6,8,10,12, for details see references. It applies only for Awmi fitting function.
Int_t fFitTaylor; //order of Taylor expansion, possible values kFitTaylorOrderFirst, kFitTaylorOrderSecond. It applies only for Awmi fitting function.
Double_t fAlpha; //convergence coefficient, input parameter, it should be positive number and <=1, for details see references
Double_t fChi; //here the fitting functions return resulting chi square
Double_t *fPositionInit; //[fNPeaks] array of initial values of peaks positions, input parameters
Double_t *fPositionCalc; //[fNPeaks] array of calculated values of fitted positions, output parameters
Double_t *fPositionErr; //[fNPeaks] array of position errors
Double_t *fAmpInit; //[fNPeaks] array of initial values of peaks amplitudes, input parameters
Double_t *fAmpCalc; //[fNPeaks] array of calculated values of fitted amplitudes, output parameters
Double_t *fAmpErr; //[fNPeaks] array of amplitude errors
Double_t *fArea; //[fNPeaks] array of calculated areas of peaks
Double_t *fAreaErr; //[fNPeaks] array of errors of peak areas
Double_t fSigmaInit; //initial value of sigma parameter
Double_t fSigmaCalc; //calculated value of sigma parameter
Double_t fSigmaErr; //error value of sigma parameter
Double_t fTInit; //initial value of t parameter (relative amplitude of tail), for details see html manual and references
Double_t fTCalc; //calculated value of t parameter
Double_t fTErr; //error value of t parameter
Double_t fBInit; //initial value of b parameter (slope), for details see html manual and references
Double_t fBCalc; //calculated value of b parameter
Double_t fBErr; //error value of b parameter
Double_t fSInit; //initial value of s parameter (relative amplitude of step), for details see html manual and references
Double_t fSCalc; //calculated value of s parameter
Double_t fSErr; //error value of s parameter
Double_t fA0Init; //initial value of background a0 parameter(background is estimated as a0+a1*x+a2*x*x)
Double_t fA0Calc; //calculated value of background a0 parameter
Double_t fA0Err; //error value of background a0 parameter
Double_t fA1Init; //initial value of background a1 parameter(background is estimated as a0+a1*x+a2*x*x)
Double_t fA1Calc; //calculated value of background a1 parameter
Double_t fA1Err; //error value of background a1 parameter
Double_t fA2Init; //initial value of background a2 parameter(background is estimated as a0+a1*x+a2*x*x)
Double_t fA2Calc; //calculated value of background a2 parameter
Double_t fA2Err; //error value of background a2 parameter
Bool_t *fFixPosition; //[fNPeaks] array of logical values which allow to fix appropriate positions (not fit). However they are present in the estimated functional
Bool_t *fFixAmp; //[fNPeaks] array of logical values which allow to fix appropriate amplitudes (not fit). However they are present in the estimated functional
Bool_t fFixSigma; //logical value of sigma parameter, which allows to fix the parameter (not to fit).
Bool_t fFixT; //logical value of t parameter, which allows to fix the parameter (not to fit).
Bool_t fFixB; //logical value of b parameter, which allows to fix the parameter (not to fit).
Bool_t fFixS; //logical value of s parameter, which allows to fix the parameter (not to fit).
Bool_t fFixA0; //logical value of a0 parameter, which allows to fix the parameter (not to fit).
Bool_t fFixA1; //logical value of a1 parameter, which allows to fix the parameter (not to fit).
Bool_t fFixA2; //logical value of a2 parameter, which allows to fix the parameter (not to fit).
public:
enum {
kFitOptimChiCounts =0,
kFitOptimChiFuncValues =1,
kFitOptimMaxLikelihood =2,
kFitAlphaHalving =0,
kFitAlphaOptimal =1,
kFitPower2 =2,
kFitPower4 =4,
kFitPower6 =6,
kFitPower8 =8,
kFitPower10 =10,
kFitPower12 =12,
kFitTaylorOrderFirst =0,
kFitTaylorOrderSecond =1,
kFitNumRegulCycles =100
};
TSpectrumFit(void); //default constructor
TSpectrumFit(Int_t numberPeaks);
virtual ~TSpectrumFit();
//auxiliary functions for 1. parameter fit functions
protected:
Double_t Area(Double_t a,Double_t sigma,Double_t t,Double_t b);
Double_t Dera1(Double_t i);
Double_t Dera2(Double_t i);
Double_t Deramp(Double_t i,Double_t i0,Double_t sigma,Double_t t,Double_t s,Double_t b);
Double_t Derb(Int_t num_of_fitted_peaks,Double_t i,const Double_t* parameter,Double_t sigma,Double_t t,Double_t b);
Double_t Derderi0(Double_t i,Double_t amp,Double_t i0,Double_t sigma);
Double_t Derdersigma(Int_t num_of_fitted_peaks,Double_t i,const Double_t* parameter,Double_t sigma);
Double_t Derfc(Double_t x);
Double_t Deri0(Double_t i,Double_t amp,Double_t i0,Double_t sigma,Double_t t,Double_t s,Double_t b);
Double_t Derpa(Double_t sigma,Double_t t,Double_t b);
Double_t Derpb(Double_t a,Double_t sigma,Double_t t,Double_t b);
Double_t Derpsigma(Double_t a,Double_t t,Double_t b);
Double_t Derpt(Double_t a,Double_t sigma,Double_t b);
Double_t Ders(Int_t num_of_fitted_peaks,Double_t i,const Double_t* parameter,Double_t sigma);
Double_t Dersigma(Int_t num_of_fitted_peaks,Double_t i,const Double_t* parameter,Double_t sigma,Double_t t,Double_t s,Double_t b);
Double_t Dert(Int_t num_of_fitted_peaks,Double_t i,const Double_t* parameter,Double_t sigma,Double_t b);
Double_t Erfc(Double_t x);
Double_t Ourpowl(Double_t a,Int_t pw);
Double_t Shape(Int_t num_of_fitted_peaks,Double_t i,const Double_t *parameter,Double_t sigma,Double_t t,Double_t s,Double_t b,Double_t a0,Double_t a1,Double_t a2);
void StiefelInversion(Double_t **a,Int_t rozmer);
public:
void FitAwmi(float *source);
void FitStiefel(float *source);
Double_t *GetAmplitudes() const {return fAmpCalc;}
Double_t *GetAmplitudesErrors() const {return fAmpErr;}
Double_t *GetAreas() const {return fArea;}
Double_t *GetAreasErrors() const {return fAreaErr;}
void GetBackgroundParameters(Double_t &a0, Double_t &a0Err, Double_t &a1, Double_t &a1Err, Double_t &a2, Double_t &a2Err);
Double_t GetChi() const {return fChi;}
Double_t *GetPositions() const {return fPositionCalc;}
Double_t *GetPositionsErrors() const {return fPositionErr;}
void GetSigma(Double_t &sigma, Double_t &sigmaErr);
void GetTailParameters(Double_t &t, Double_t &tErr, Double_t &b, Double_t &bErr, Double_t &s, Double_t &sErr);
void SetBackgroundParameters(Double_t a0Init, Bool_t fixA0, Double_t a1Init, Bool_t fixA1, Double_t a2Init, Bool_t fixA2);
void SetFitParameters(Int_t xmin,Int_t xmax, Int_t numberIterations, Double_t alpha, Int_t statisticType, Int_t alphaOptim, Int_t power, Int_t fitTaylor);
void SetPeakParameters(Double_t sigma, Bool_t fixSigma, const Float_t *positionInit, const Bool_t *fixPosition, const Float_t *ampInit, const Bool_t *fixAmp);
void SetTailParameters(Double_t tInit, Bool_t fixT, Double_t bInit, Bool_t fixB, Double_t sInit, Bool_t fixS);
ClassDef(TSpectrumFit,1) //Spectrum Fitter using algorithm without matrix inversion and conjugate gradient method for symmetrical matrices (Stiefel-Hestens method)
};
#endif
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