#ifndef MARS_MHGausEvents #define MARS_MHGausEvents #ifndef ROOT_TH1 #include #endif #ifndef ROOF_TF1 #include #endif #ifndef MARS_MH #include "MH.h" #endif #ifndef MARS_MArrayF #include "MArrayF.h" #endif class TVirtualPad; class TGraph; class TH1F; class TH1I; class TF1; class MHGausEvents : public MH { private: const static Int_t fgNDFLimit; //! Default for fNDFLimit (now set to: 2) const static Float_t fgProbLimit; //! Default for fProbLimit (now set to: 0.001) const static Int_t fgPowerProbabilityBins; //! Default for fPowerProbabilityBins (now set to: 20) private: Int_t fBinsAfterStripping; // Bins for the Gauss Histogram after stripping off the zeros at both ends UInt_t fCurrentSize; // Current size of the array fEvents Float_t fEventFrequency; // Event frequency in Hertz (to be set) Byte_t fFlags; // Bit field for the fit result bits Int_t fPowerProbabilityBins; // Bins for the projected power spectrum TH1I *fHPowerProbability; //! Fourier transform of fEvents projected on y-axis MArrayF *fPowerSpectrum; //! Fourier transform of fEvents enum { kGausFitOK, kExpFitOK, kFourierSpectrumOK, kExcluded }; // Bits for information about fit results MArrayF fEvents; // Array which holds the entries of GausHist TF1 *fFExpFit; // Exponential fit for FHPowerProbability TGraph *fGraphEvents; //! TGraph to display the event array TGraph *fGraphPowerSpectrum; //! TGraph to display the power spectrum array Axis_t fFirst; // Lower histogram edge for fHGausHist (used by InitBins()) Axis_t fLast; // Upper histogram edge for fHGausHist (used by InitBins()) Int_t fNbins; // Number histogram bins for fHGausHist (used by InitBins()) Int_t fNDFLimit; // NDF limit for judgement if fit is OK Float_t fProbLimit; // Probability limit for judgement if fit is OK protected: TF1 *fFGausFit; // Gauss fit for fHGausHist TH1F fHGausHist; // Histogram to hold the Gaussian distribution Double_t fMean; // Mean of the Gauss fit Double_t fMeanErr; // Error of the mean of the Gauss fit Double_t fSigma; // Sigma of the Gauss fit Double_t fSigmaErr; // Error of the sigma of the Gauss fit Double_t fProb; // Probability of the Gauss fit // Setters void SetBinsAfterStripping ( const Int_t nbins=0 ) { fBinsAfterStripping =nbins; } void SetPowerProbabilityBins ( const Int_t nbins=fgPowerProbabilityBins ) { fPowerProbabilityBins=nbins; } public: MHGausEvents(const char* name=NULL, const char* title=NULL); ~MHGausEvents(); void Clear(Option_t *o=""); void Reset(); void CreateFourierSpectrum(); void CreateGraphEvents(); void CreateGraphPowerSpectrum(); // Draws void Draw ( Option_t *option="" ); // *MENU* void DrawEvents ( Option_t *option="" ); // *MENU* void DrawPowerSpectrum ( Option_t *option="" ); // *MENU* void DrawPowerProjection( Option_t *option="" ); // *MENU* // Fill void FillArray ( const Float_t f ); Bool_t FillHist ( const Float_t f ); Bool_t FillHistAndArray ( const Float_t f ); // Fits Bool_t FitGaus ( Option_t *option="RQ0", const Double_t xmin=0., const Double_t xmax=0.); // *MENU* // Inits virtual void InitBins(); // Getters const Double_t GetChiSquare() const { return ( fFGausFit ? fFGausFit->GetChisquare() : 0.); } const Double_t GetExpChiSquare() const { return ( fFExpFit ? fFExpFit->GetChisquare() : 0.); } const Int_t GetExpNdf() const { return ( fFExpFit ? fFExpFit->GetNDF() : 0 ); } const Double_t GetExpProb() const { return ( fFExpFit ? fFExpFit->GetProb() : 0.); } MArrayF *GetEvents() { return &fEvents; } const MArrayF *GetEvents() const { return &fEvents; } const Float_t GetEventFrequency () const { return fEventFrequency; } TF1 *GetFExpFit() { return fFExpFit; } const TF1 *GetFExpFit() const { return fFExpFit; } TF1 *GetFGausFit() { return fFGausFit; } const TF1 *GetFGausFit() const { return fFGausFit; } TGraph *GetGraphEvents() { return fGraphEvents; } const Double_t GetFirst() const { return fFirst; } const Double_t GetLast () const { return fLast ; } const TGraph *GetGraphEvents() const { return fGraphEvents; } TGraph *GetGraphPowerSpectrum() { return fGraphPowerSpectrum; } const TGraph *GetGraphPowerSpectrum() const { return fGraphPowerSpectrum; } TH1F *GetHGausHist() { return &fHGausHist; } const TH1F *GetHGausHist() const { return &fHGausHist; } TH1I *GetHPowerProbability() { return fHPowerProbability; } const TH1I *GetHPowerProbability() const { return fHPowerProbability; } const Double_t GetHistRms() const { return fHGausHist.GetRMS(); } const Double_t GetMean() const { return fMean; } const Double_t GetMeanErr() const { return fMeanErr; } const Int_t GetNdf() const { return ( fFGausFit ? fFGausFit->GetNDF() : 0); } const Int_t GetNbins() const { return fNbins; } const Double_t GetOffset() const { return ( fFExpFit ? fFExpFit->GetParameter(0) : 0.); } MArrayF *GetPowerSpectrum() { return fPowerSpectrum; } const MArrayF *GetPowerSpectrum() const { return fPowerSpectrum; } const Double_t GetProb() const { return fProb; } const Double_t GetSigma() const { return fSigma; } const Double_t GetSigmaErr() const { return fSigmaErr; } const Double_t GetSlope() const { return ( fFExpFit ? fFExpFit->GetParameter(1) : 0.); } const Int_t GetNDFLimit() const { return fNDFLimit; } const Float_t GetProbLimit() const { return fProbLimit; } const Bool_t IsValid() const { return fMean!=0 || fSigma!=0; } const Bool_t IsExcluded() const { return TESTBIT(fFlags,kExcluded); } const Bool_t IsExpFitOK() const { return TESTBIT(fFlags,kExpFitOK); } const Bool_t IsEmpty() const { return !(fHGausHist.GetEntries()); } const Bool_t IsFourierSpectrumOK() const { return TESTBIT(fFlags,kFourierSpectrumOK); } const Bool_t IsGausFitOK() const { return TESTBIT(fFlags,kGausFitOK); } const Bool_t IsOnlyOverflow() const { return fHGausHist.GetEntries()>0 && fHGausHist.GetEntries() == fHGausHist.GetBinContent(fNbins+1); } const Bool_t IsOnlyUnderflow() const { return fHGausHist.GetEntries()>0 && fHGausHist.GetEntries() == fHGausHist.GetBinContent(0); } // Prints void Print(const Option_t *o="") const; // *MENU* // Setters void SetEventFrequency ( const Float_t f ) { fEventFrequency = f; } void SetExcluded ( const Bool_t b=kTRUE ) { b ? SETBIT(fFlags,kExcluded) : CLRBIT(fFlags,kExcluded); } void SetExpFitOK ( const Bool_t b=kTRUE ) { b ? SETBIT(fFlags,kExpFitOK) : CLRBIT(fFlags,kExpFitOK); } void SetFourierSpectrumOK( const Bool_t b=kTRUE ) { b ? SETBIT(fFlags,kFourierSpectrumOK) : CLRBIT(fFlags,kFourierSpectrumOK); } void SetGausFitOK ( const Bool_t b=kTRUE ) { b ? SETBIT(fFlags,kGausFitOK) : CLRBIT(fFlags,kGausFitOK);} void SetMean ( const Double_t d ) { fMean = d; } void SetMeanErr ( const Double_t d ) { fMeanErr = d; } void SetNDFLimit ( const Int_t lim=fgNDFLimit ) { fNDFLimit = lim; } void SetProb ( const Double_t d ) { fProb = d; } void SetProbLimit ( const Float_t lim=fgProbLimit ) { fProbLimit = lim; } void SetSigma ( const Double_t d ) { fSigma = d; } void SetSigmaErr ( const Double_t d ) { fSigmaErr = d; } void SetBinning(Int_t i, Axis_t lo, Axis_t up) { fNbins=i; fFirst=lo; fLast=up; } // Simulates void SimulateGausEvents(const Float_t mean, const Float_t sigma, const Int_t nevts=4096); // *MENU* ClassDef(MHGausEvents, 4) // Base class for events with Gaussian distributed values }; #endif