#include <GenericTnPFitter.h>

Inheritance diagram for dqmTnP::GaussianPlusLinearFitter:

Public Member Functions
void	fit (TH1 pass, TH1 all)
	GaussianPlusLinearFitter (bool verbose=false)
	~GaussianPlusLinearFitter ()
Protected Attributes
RooGaussian	gaussian
RooChebychev	linearFail
RooChebychev	linearPass
RooAddPdf	pdfFail
RooAddPdf	pdfPass
RooRealVar	slopeFail
RooRealVar	slopePass

Detailed Description

Definition at line 240 of file GenericTnPFitter.h.

Constructor & Destructor Documentation

dqmTnP::GaussianPlusLinearFitter::GaussianPlusLinearFitter ( bool verbose = false ) [inline]

Definition at line 250 of file GenericTnPFitter.h.

References dqmTnP::AbstractFitter::simPdf.

                                                :
    AbstractFitter(verbose),
    gaussian("gaussian","gaussian",mass,mean,sigma),
    slopeFail("slopeFail","slopeFail",0.,-1.,1.),
    linearFail("linearFail","linearFail",mass,slopeFail),
    slopePass("slopePass","slopePass",0.,-1.,1.),
    linearPass("linearPass","linearPass",mass,slopePass),
    pdfFail("pdfFail","pdfFail", RooArgList(gaussian,linearFail), RooArgList(nSignalFail,nBackgroundFail)),
    pdfPass("pdfPass","pdfPass", RooArgList(gaussian,linearPass), RooArgList(nSignalPass,nBackgroundPass))
  {
    simPdf.addPdf(pdfFail,"fail");
    simPdf.addPdf(pdfPass,"pass");
  };

dqmTnP::GaussianPlusLinearFitter::~GaussianPlusLinearFitter ( ) [inline]

Definition at line 263 of file GenericTnPFitter.h.

{};

Member Function Documentation

void dqmTnP::GaussianPlusLinearFitter::fit	(	TH1 *	pass,
		TH1 *	all
	)		`[inline, virtual]`

Implements dqmTnP::AbstractFitter.

Definition at line 264 of file GenericTnPFitter.h.

                               {
    using namespace RooFit;
    all->Add(pass,-1);
    TH1* &fail = all;
    if(!data) delete data;
    data = new RooDataHist("data", "data", mass, Index(category), Import("fail",*fail), Import("pass",*pass) );
    if(pass->Integral()+fail->Integral() < 5){
      efficiency.setVal(0.5);
      efficiency.setError(0.5);
      chi2 = 0;
      return;
    }
    mean.setVal(expectedMean);
    sigma.setVal(expectedSigma);
    efficiency.setVal(pass->Integral()/(pass->Integral()+fail->Integral()));
    nSignalAll.setVal(0.5*(fail->Integral()+pass->Integral()));
    nBackgroundFail.setVal(0.5*fail->Integral());
    nBackgroundPass.setVal(0.5*pass->Integral());
    slopeFail.setVal(0.);
    slopePass.setVal(0.);
    if(verbose){
      simPdf.fitTo( *data );
    }else{
      simPdf.fitTo( *data, Verbose(kFALSE), PrintLevel(-1), Warnings(kFALSE), PrintEvalErrors(-1) );
    }
    RooDataHist dataFail("fail", "fail", mass, fail );
    RooDataHist dataPass("pass", "pass", mass, pass );
    chi2 = ( RooChi2Var("chi2Fail","chi2Fail",pdfFail,dataFail,DataError(RooAbsData::Poisson)).getVal()
      +RooChi2Var("chi2Pass","chi2Pass",pdfPass,dataPass,DataError(RooAbsData::Poisson)).getVal() )/(2*pass->GetNbinsX()-8);
    if(chi2>3){
      efficiency.setVal(0.5);
      efficiency.setError(0.5);
    }
  }