// @(#)root/spectrum:$Id: TSpectrumFit.h 20882 2007-11-19 11:31:26Z rdm $ // 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(backgroud 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(backgroud 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(backgroud 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