/* ======================================================================== *\ ! ! * ! * This file is part of MARS, the MAGIC Analysis and Reconstruction ! * Software. It is distributed to you in the hope that it can be a useful ! * and timesaving tool in analysing Data of imaging Cerenkov telescopes. ! * It is distributed WITHOUT ANY WARRANTY. ! * ! * Permission to use, copy, modify and distribute this software and its ! * documentation for any purpose is hereby granted without fee, ! * provided that the above copyright notice appear in all copies and ! * that both that copyright notice and this permission notice appear ! * in supporting documentation. It is provided "as is" without express ! * or implied warranty. ! * ! ! ! Author(s): Markus Gaug 02/2005 ! ! Copyright: MAGIC Software Development, 2000-2005 ! ! \* ======================================================================== */ ////////////////////////////////////////////////////////////////////////////// // // MHPedestalPix // // A base class for events which are believed to follow a Gaussian distribution // with time, e.g. calibration events, observables containing white noise, ... // // MHPedestalPix derives from MHGausEvents, thus all features of // MHGausEvents can be used by a class deriving from MHPedestalPix // // As an additional feature to MHGausEvents, this class offers to skip the fitting // to set mean, sigma and its errors directly from the histograms with the function // BypassFit() // // See also: MHGausEvents // ////////////////////////////////////////////////////////////////////////////// #include "MHPedestalPix.h" #include #include #include #include "MLog.h" #include "MLogManip.h" ClassImp(MHPedestalPix); using namespace std; // -------------------------------------------------------------------------- // // Default Constructor. // MHPedestalPix::MHPedestalPix(const char *name, const char *title) { fName = name ? name : "MHPedestalPix"; fTitle = title ? title : "Pedestal histogram events"; } // ------------------------------------------------------------------------------- // // Fits the histogram to a double Gauss. // // Bool_t MHPedestalPix::FitDoubleGaus(const Double_t xmin, const Double_t xmax, Option_t *option) { if (IsGausFitOK()) return kTRUE; StripZeros(&fHGausHist,0); TAxis *axe = fHGausHist.GetXaxis(); // // Get the fitting ranges // Axis_t rmin = ((xmin==0.) && (xmax==0.)) ? fHGausHist.GetBinCenter(axe->GetFirst()) : xmin; Axis_t rmax = ((xmin==0.) && (xmax==0.)) ? fHGausHist.GetBinCenter(axe->GetLast()) : xmax; // // First guesses for the fit (should be as close to reality as possible, // const Stat_t entries = fHGausHist.Integral(axe->FindBin(rmin),axe->FindBin(rmax),"width"); const Double_t sigma_guess = fHGausHist.GetRMS(); const Double_t area_guess = entries/TMath::Sqrt(TMath::TwoPi())/sigma_guess; fFGausFit = new TF1("GausFit","gaus(0)+gaus(3)",rmin,rmax); if (!fFGausFit) { *fLog << warn << dbginf << "WARNING: Could not create fit function for Gauss fit " << "in: " << fName << endl; return kFALSE; } // // For the fits, we have to take special care since ROOT // has stored the function pointer in a global list which // lead to removing the object twice. We have to take out // the following functions of the global list of functions // as well: // gROOT->GetListOfFunctions()->Remove(fFGausFit); fFGausFit->SetParameters(area_guess/2.,0.,sigma_guess/2.,area_guess/2.,25.,sigma_guess/2.); fFGausFit->SetParNames("Area_{0}","#mu_{0}","#sigma_{0}","Area_{1}","#mu_{1}","#sigma_{1}"); fFGausFit->SetParLimits(0,0.,area_guess*5.); fFGausFit->SetParLimits(1,rmin,0.); fFGausFit->SetParLimits(2,0.,rmax-rmin); fFGausFit->SetParLimits(3,0.,area_guess*10.); fFGausFit->SetParLimits(4,0.,rmax/2.); fFGausFit->SetParLimits(5,0.,rmax-rmin); fFGausFit->SetRange(rmin,rmax); fHGausHist.Fit(fFGausFit,option); SetMean (fFGausFit->GetParameter(4)-fFGausFit->GetParameter(1)); SetSigma (TMath::Sqrt(fFGausFit->GetParameter(5)*fFGausFit->GetParameter(5) +fFGausFit->GetParameter(2)*fFGausFit->GetParameter(2))); SetMeanErr (TMath::Sqrt(fFGausFit->GetParError(4)*fFGausFit->GetParError(4) +fFGausFit->GetParError(1)*fFGausFit->GetParError(1))); SetSigmaErr (TMath::Sqrt(fFGausFit->GetParError(5)*fFGausFit->GetParError(5) +fFGausFit->GetParError(2)*fFGausFit->GetParError(2))); SetProb (fFGausFit->GetProb()); // // The fit result is accepted under condition: // 1) The results are not nan's // 2) The NDF is not smaller than fNDFLimit (default: fgNDFLimit) // 3) The Probability is greater than fProbLimit (default: fgProbLimit) // // !Finitite means either infinite or not-a-number if ( !TMath::Finite(GetMean()) || !TMath::Finite(GetMeanErr()) || !TMath::Finite(GetProb()) || !TMath::Finite(GetSigma()) || !TMath::Finite(GetSigmaErr()) || fProb < GetProbLimit()) return kFALSE; SetGausFitOK(kTRUE); return kTRUE; } // ------------------------------------------------------------------------------- // // Fits the histogram to a triple Gauss. // Bool_t MHPedestalPix::FitTripleGaus(const Double_t xmin, const Double_t xmax, Option_t *option) { if (IsGausFitOK()) return kTRUE; StripZeros(&fHGausHist,0); TAxis *axe = fHGausHist.GetXaxis(); // // Get the fitting ranges // Axis_t rmin = ((xmin==0.) && (xmax==0.)) ? fHGausHist.GetBinCenter(axe->GetFirst()) : xmin; Axis_t rmax = ((xmin==0.) && (xmax==0.)) ? fHGausHist.GetBinCenter(axe->GetLast()) : xmax; // // First guesses for the fit (should be as close to reality as possible, // const Stat_t entries = fHGausHist.Integral(axe->FindBin(rmin),axe->FindBin(rmax),"width"); const Double_t sigma_guess = fHGausHist.GetRMS(); const Double_t area_guess = entries/TMath::Sqrt(TMath::TwoPi())/sigma_guess; fFGausFit = new TF1("GausFit","gaus(0)+gaus(3)+gaus(6)",rmin,rmax); if (!fFGausFit) { *fLog << warn << dbginf << "WARNING: Could not create fit function for Gauss fit " << "in: " << fName << endl; return kFALSE; } // // For the fits, we have to take special care since ROOT // has stored the function pointer in a global list which // lead to removing the object twice. We have to take out // the following functions of the global list of functions // as well: // gROOT->GetListOfFunctions()->Remove(fFGausFit); fFGausFit->SetParameters(10.,-4.0,1.5,70.,1.5,6.,5.,7.,7.); fFGausFit->SetParNames("Area_{0}","#mu_{0}","#sigma_{0}","Area_{1}","#mu_{1}","#sigma_{1}","Area_{2}","#mu_{2}","#sigma_{2}"); fFGausFit->SetParLimits(0,0.,area_guess*2.5); fFGausFit->SetParLimits(1,-9.0,-2.2); fFGausFit->SetParLimits(2,-1.0,15.); fFGausFit->SetParLimits(3,0.,area_guess*10.); fFGausFit->SetParLimits(4,-4.5,2.); fFGausFit->SetParLimits(5,0.,(rmax-rmin)/3.); fFGausFit->SetParLimits(6,0.,area_guess*5.); fFGausFit->SetParLimits(7,6.,20.); fFGausFit->SetParLimits(8,5.,40.); fFGausFit->SetRange(rmin,rmax); fHGausHist.Fit(fFGausFit,option); SetMean (fFGausFit->GetParameter(4)-fFGausFit->GetParameter(1)); SetSigma (TMath::Sqrt(fFGausFit->GetParameter(5)*fFGausFit->GetParameter(5) +fFGausFit->GetParameter(2)*fFGausFit->GetParameter(2))); SetMeanErr (TMath::Sqrt(fFGausFit->GetParError(4)*fFGausFit->GetParError(4) +fFGausFit->GetParError(1)*fFGausFit->GetParError(1))); SetSigmaErr (TMath::Sqrt(fFGausFit->GetParError(5)*fFGausFit->GetParError(5) +fFGausFit->GetParError(2)*fFGausFit->GetParError(2))); SetProb (fFGausFit->GetProb()); // // The fit result is accepted under condition: // 1) The results are not nan's // 2) The NDF is not smaller than fNDFLimit (default: fgNDFLimit) // 3) The Probability is greater than fProbLimit (default: fgProbLimit) // // !Finitite means either infinite or not-a-number if ( !TMath::Finite(GetMean()) || !TMath::Finite(GetMeanErr()) || !TMath::Finite(GetProb()) || !TMath::Finite(GetSigma()) || !TMath::Finite(GetSigmaErr()) || fProb < GetProbLimit() ) return kFALSE; SetGausFitOK(kTRUE); return kTRUE; }