#ifndef MARS_MHGausEvents #define MARS_MHGausEvents #ifndef ROOT_TH1 #include #endif #ifndef MARS_MH #include "MH.h" #endif class TVirtualPad; class TGraph; class TArrayF; class TH1F; class TH1I; class TF1; class MHGausEvents : public MH { private: const static Int_t fgBinsAfterStripping; //! Default for fBinsAfterStripping (now set to: 40) const static Float_t fgBlackoutLimit; //! Default for fBlackoutLimit (now set to: 5. ) 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 Float_t fgPickupLimit; //! Default for fPickupLimit (now set to: 5. ) const static Int_t fgPowerProbabilityBins; //! Default for fPowerProbabilityBins (now set to: 20) Int_t fBinsAfterStripping; // Bins for the Gauss Histogram after stripping off the zeros at both ends Int_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 TArrayF *fPowerSpectrum; // Fourier transform of fEvents TGraph *fGraphEvents; //! TGraph to display the event array (will not be cloned!!) TGraph *fGraphPowerSpectrum; //! TGraph to display the power spectrum array (will not be cloned!!) enum { kGausFitOK, kExpFitOK, kFourierSpectrumOK, kExcluded }; // Bits for information about fit results protected: Float_t fBlackoutLimit; // Lower number sigmas from mean until event is considered blackout TArrayF fEvents; // Array which holds the entries of GausHist TF1 *fFGausFit; // Gauss fit for fHGausHist TF1 *fFExpFit; // Exponential fit for FHPowerProbability Axis_t fFirst; // Lower histogram edge for fHGausHist (used by InitBins()) TH1F fHGausHist; // Histogram to hold the Gaussian distribution Axis_t fLast; // Upper histogram edge for fHGausHist (used by InitBins()) Double_t fMean; // Mean of the Gauss fit Double_t fMeanErr; // Error of the mean of the Gauss fit Int_t fNbins; // Number histogram bins for fHGausHist (used by InitBins()) Int_t fNDFLimit; // NDF limit for judgement if fit is OK Float_t fSaturated; // Number of events classified as saturated Double_t fSigma; // Sigma of the Gauss fit Double_t fSigmaErr; // Error of the sigma of the Gauss fit Float_t fPickupLimit; // Upper number sigmas from mean until event is considered pickup Int_t fPixId; // Pixel ID Double_t fProb; // Probability of the Gauss fit Float_t fProbLimit; // Probability limit for judgement if fit is OK Float_t *CreateEventXaxis(Int_t n); // Create an x-axis for the Event TGraphs Float_t *CreatePSDXaxis(Int_t n); // Create an x-axis for the PSD TGraphs void DrawEvents(); // Draw graph of fEvents void DrawPowerSpectrum(TVirtualPad &pad, Int_t i); // Draw graph of fPowerSpectrum and fHPowerProbability // Setters void SetBinsAfterStripping ( const Int_t nbins=fgBinsAfterStripping ) { fBinsAfterStripping =nbins; } void SetPowerProbabilityBins ( const Int_t nbins=fgPowerProbabilityBins ) { fPowerProbabilityBins=nbins; } public: MHGausEvents(const char* name=NULL, const char* title=NULL); ~MHGausEvents(); virtual void Clear(Option_t *o=""); virtual void Reset(); virtual void InitBins(); // Draws virtual void Draw(Option_t *option=""); // Default Draw // Getters const Double_t GetBlackout() const; const Double_t GetChiSquare() const; const Double_t GetExpChiSquare() const; const Int_t GetExpNdf() const; const Double_t GetExpProb() const; TArrayF *GetEvents() { return &fEvents; } const TArrayF *GetEvents() const { return &fEvents; } 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 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 GetMean() const { return fMean; } const Double_t GetMeanErr() const { return fMeanErr; } const Int_t GetNdf() const; const Double_t GetOffset() const; const Double_t GetPickup() const; const Int_t GetPixId() const { return fPixId; } TArrayF *GetPowerSpectrum() { return fPowerSpectrum; } const TArrayF *GetPowerSpectrum() const { return fPowerSpectrum; } const Double_t GetProb() const { return fProb; } const Float_t GetSaturated() const { return fSaturated; } const Double_t GetSigma() const { return fSigma; } const Double_t GetSigmaErr() const { return fSigmaErr; } const Double_t GetSlope() const; const Bool_t IsExcluded() const; const Bool_t IsExpFitOK() const; const Bool_t IsEmpty() const; const Bool_t IsFourierSpectrumOK() const; const Bool_t IsGausFitOK() const; // Fill void FillArray ( const Float_t f ); // Fill only the array fEvents Bool_t FillHist ( const Float_t f ); // Fill only the histogram HGausHist Bool_t FillHistAndArray( const Float_t f ); // Fill bothe the array fEvents and the histogram HGausHist // Fits Bool_t FitGaus( Option_t *option="RQ0", const Double_t xmin=0., const Double_t xmax=0.); // Fit the histogram HGausHist with a Gaussian Bool_t RepeatFit(const Option_t *option="RQ0"); // Repeat fit within limits defined by fPickupLimit void BypassFit(); // Take mean and RMS from the histogram // Prints virtual void Print(const Option_t *o="") const; // Default Print // Setters void SetBlackoutLimit ( const Float_t lim=fgBlackoutLimit ) { fBlackoutLimit = lim; } void SetEventFrequency ( const Float_t f ) { fEventFrequency = f; } void SetExcluded ( const Bool_t b=kTRUE ); void SetExpFitOK ( const Bool_t b=kTRUE ); void SetFourierSpectrumOK( const Bool_t b=kTRUE ); void SetGausFitOK ( const Bool_t b=kTRUE ); void SetLast ( const Double_t d ) { fLast = d; } void SetFirst ( const Double_t d ) { fFirst = d; } void SetMean ( const Double_t d ) { fMean = d; } void SetMeanErr ( const Double_t d ) { fMeanErr = d; } void SetNbins ( const Int_t i ) { fNbins = i; } void SetNDFLimit ( const Int_t lim=fgNDFLimit ) { fNDFLimit = lim; } void SetPickupLimit ( const Float_t lim=fgPickupLimit ) { fPickupLimit = lim; } void SetPixId ( const Int_t i ) { fPixId = i; } void SetProb ( const Double_t d ) { fProb = d; } void SetProbLimit ( const Float_t lim=fgProbLimit ) { fProbLimit = lim; } void SetSaturated ( const Float_t f ) { fSaturated += f; } void SetSigma ( const Double_t d ) { fSigma = d; } void SetSigmaErr ( const Double_t d ) { fSigmaErr = d; } // Miscelleaneous virtual void ChangeHistId(const Int_t id); // Changes names and titles of the histogram virtual void Renorm(); // Re-normalize the results void CreateFourierSpectrum(); // Create the fourier spectrum out of fEvents void CreateGraphEvents(); // Create the TGraph fGraphEvents of fEvents void CreateGraphPowerSpectrum(); // Create the TGraph fGraphPowerSpectrum out of fPowerSpectrum ClassDef(MHGausEvents, 1) // Base class for events with Gaussian distributed values }; #endif