LauCPFitModel.cc

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// Copyright University of Warwick 2004 - 2013.
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)

// Authors:
// Thomas Latham
// John Back
// Paul Harrison

#include <iostream>
#include <iomanip>
#include <fstream>
#include <vector>

#include "TVirtualFitter.h"
#include "TSystem.h"
#include "TMinuit.h"
#include "TRandom.h"
#include "TFile.h"
#include "TMath.h"
#include "TH2.h"

#include "LauAbsBkgndDPModel.hh"
#include "LauAbsCoeffSet.hh"
#include "LauAbsDPDynamics.hh"
#include "LauAbsPdf.hh"
#include "LauAsymmCalc.hh"
#include "LauComplex.hh"
#include "LauConstants.hh"
#include "LauCPFitModel.hh"
#include "LauDaughters.hh"
#include "LauEffModel.hh"
#include "LauEmbeddedData.hh"
#include "LauFitNtuple.hh"
#include "LauKinematics.hh"
#include "LauPrint.hh"
#include "LauRandom.hh"
#include "LauScfMap.hh"

ClassImp(LauCPFitModel)


LauCPFitModel::LauCPFitModel(LauAbsDPDynamics* negModel, LauAbsDPDynamics* posModel, Bool_t tagged, const TString& tagVarName) : LauAbsFitModel(),
        negSigModel_(negModel), posSigModel_(posModel),
        negKinematics_(negModel ? negModel->getKinematics() : 0),
        posKinematics_(posModel ? posModel->getKinematics() : 0),
        usingBkgnd_(kFALSE),
        nSigComp_(0),
        nSigDPPar_(0),
        nExtraPdfPar_(0),
        nNormPar_(0),
        negMeanEff_("negMeanEff",0.0,0.0,1.0), posMeanEff_("posMeanEff",0.0,0.0,1.0),
        negDPRate_("negDPRate",0.0,0.0,100.0), posDPRate_("posDPRate",0.0,0.0,100.0),
        signalEvents_(0),
        signalAsym_(0),
        forceAsym_(kFALSE),
        tagged_(tagged),
        tagVarName_(tagVarName),
        curEvtCharge_(0),
        useSCF_(kFALSE),
        useSCFHist_(kFALSE),
        scfFrac_("scfFrac",0.0,0.0,1.0),
        scfFracHist_(0),
        scfMap_(0),
        compareFitData_(kFALSE),
        negParent_("B-"), posParent_("B+"),
        negSignalTree_(0), posSignalTree_(0),
        reuseSignal_(kFALSE),
        useNegReweighting_(kFALSE), usePosReweighting_(kFALSE),
        sigDPLike_(0.0),
        scfDPLike_(0.0),
        sigExtraLike_(0.0),
        scfExtraLike_(0.0),
        sigTotalLike_(0.0),
        scfTotalLike_(0.0)
{
        LauDaughters* negDaug = negSigModel_->getDaughters();
        if (negDaug != 0) {negParent_ = negDaug->getNameParent();}
        LauDaughters* posDaug = posSigModel_->getDaughters();
        if (posDaug != 0) {posParent_ = posDaug->getNameParent();}
}

LauCPFitModel::~LauCPFitModel()
{
        delete negSignalTree_;
        delete posSignalTree_;
        for (LauBkgndEmbDataList::iterator iter = negBkgndTree_.begin(); iter != negBkgndTree_.end(); ++iter) {
                delete (*iter);
        }
        for (LauBkgndEmbDataList::iterator iter = posBkgndTree_.begin(); iter != posBkgndTree_.end(); ++iter) {
                delete (*iter);
        }
        delete scfFracHist_;
}

void LauCPFitModel::setupBkgndVectors()
{
        UInt_t nBkgnds = this->nBkgndClasses();
        negBkgndDPModels_.resize( nBkgnds );
        posBkgndDPModels_.resize( nBkgnds );
        negBkgndPdfs_.resize( nBkgnds );
        posBkgndPdfs_.resize( nBkgnds );
        bkgndEvents_.resize( nBkgnds );
        bkgndAsym_.resize( nBkgnds );
        negBkgndTree_.resize( nBkgnds );
        posBkgndTree_.resize( nBkgnds );
        reuseBkgnd_.resize( nBkgnds );
        bkgndDPLike_.resize( nBkgnds );
        bkgndExtraLike_.resize( nBkgnds );
        bkgndTotalLike_.resize( nBkgnds );
}

void LauCPFitModel::setNSigEvents(LauParameter* nSigEvents)
{
        if ( nSigEvents == 0 ) {
                std::cerr << "ERROR in LauCPFitModel::setNSigEvents : The LauParameter pointer is null." << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }
        if ( signalEvents_ != 0 ) {
                std::cerr << "ERROR in LauCPFitModel::setNSigEvents : You are trying to overwrite the signal yield." << std::endl;
                return;
        }
        if ( signalAsym_ != 0 ) {
                std::cerr << "ERROR in LauCPFitModel::setNSigEvents : You are trying to overwrite the signal asymmetry." << std::endl;
                return;
        }

        signalEvents_ = nSigEvents;
        TString name = signalEvents_->name();
        if ( ! name.Contains("signalEvents") && !( name.BeginsWith("signal") && name.EndsWith("Events") ) ) {
                signalEvents_->name("signalEvents");
        }
        Double_t value = nSigEvents->value();
        signalEvents_->range(-2.0*(TMath::Abs(value)+1.0), 2.0*(TMath::Abs(value)+1.0));

        signalAsym_ = new LauParameter("signalAsym",0.0,-1.0,1.0,kTRUE);
}

void LauCPFitModel::setNSigEvents( LauParameter* nSigEvents, LauParameter* sigAsym, Bool_t forceAsym )
{
        if ( nSigEvents == 0 ) {
                std::cerr << "ERROR in LauCPFitModel::setNSigEvents : The event LauParameter pointer is null." << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }
        if ( sigAsym == 0 ) {
                std::cerr << "ERROR in LauCPFitModel::setNSigEvents : The asym LauParameter pointer is null." << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }

        if ( signalEvents_ != 0 ) {
                std::cerr << "ERROR in LauCPFitModel::setNSigEvents : You are trying to overwrite the signal yield." << std::endl;
                return;
        }
        if ( signalAsym_ != 0 ) {
                std::cerr << "ERROR in LauCPFitModel::setNSigEvents : You are trying to overwrite the signal asymmetry." << std::endl;
                return;
        }

        signalEvents_ = nSigEvents;
        signalEvents_->name("signalEvents");
        Double_t value = nSigEvents->value();
        signalEvents_->range(-2.0*(TMath::Abs(value)+1.0), 2.0*(TMath::Abs(value)+1.0));

        signalAsym_ = sigAsym;
        signalAsym_->name("signalAsym");
        signalAsym_->range(-1.0,1.0);

        forceAsym_ = forceAsym;
}

void LauCPFitModel::setNBkgndEvents( LauParameter* nBkgndEvents )
{
        if ( nBkgndEvents == 0 ) {
                std::cerr << "ERROR in LauCPFitModel::setNBgkndEvents : The background yield LauParameter pointer is null." << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        } 

        if ( ! this->validBkgndClass( nBkgndEvents->name() ) ) {
                std::cerr << "ERROR in LauCPFitModel::setNBkgndEvents : Invalid background class \"" << nBkgndEvents->name() << "\"." << std::endl;
                std::cerr << "                                        : Background class names must be provided in \"setBkgndClassNames\" before any other background-related actions can be performed." << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }

        UInt_t bkgndID = this->bkgndClassID( nBkgndEvents->name() );

        if ( bkgndEvents_[bkgndID] != 0 ) {
                std::cerr << "ERROR in LauCPFitModel::setNBkgndEvents : You are trying to overwrite the background yield." << std::endl;
                return;
        }

        if ( bkgndAsym_[bkgndID] != 0 ) {
                std::cerr << "ERROR in LauCPFitModel::setNBkgndEvents : You are trying to overwrite the background asymmetry." << std::endl;
                return;
        }

        bkgndEvents_[bkgndID] = nBkgndEvents;
        bkgndEvents_[bkgndID]->name( nBkgndEvents->name()+"Events" );
        Double_t value = nBkgndEvents->value();
        bkgndEvents_[bkgndID]->range(-2.0*(TMath::Abs(value)+1.0), 2.0*(TMath::Abs(value)+1.0));

        bkgndAsym_[bkgndID] = new LauParameter(nBkgndEvents->name()+"Asym",0.0,-1.0,1.0,kTRUE);
}

void LauCPFitModel::setNBkgndEvents(LauParameter* nBkgndEvents, LauParameter* bkgndAsym)
{
        if ( nBkgndEvents == 0 ) {
                std::cerr << "ERROR in LauCPFitModel::setNBkgndEvents : The background yield LauParameter pointer is null." << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }

        if ( bkgndAsym == 0 ) {
                std::cerr << "ERROR in LauCPFitModel::setNBkgndEvents : The background asym LauParameter pointer is null." << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }

        if ( ! this->validBkgndClass( nBkgndEvents->name() ) ) {
                std::cerr << "ERROR in LauCPFitModel::setNBkgndEvents : Invalid background class \"" << nBkgndEvents->name() << "\"." << std::endl;
                std::cerr << "                                        : Background class names must be provided in \"setBkgndClassNames\" before any other background-related actions can be performed." << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }

        UInt_t bkgndID = this->bkgndClassID( nBkgndEvents->name() );

        if ( bkgndEvents_[bkgndID] != 0 ) {
                std::cerr << "ERROR in LauCPFitModel::setNBkgndEvents : You are trying to overwrite the background yield." << std::endl;
                return;
        }

        if ( bkgndAsym_[bkgndID] != 0 ) {
                std::cerr << "ERROR in LauCPFitModel::setNBkgndEvents : You are trying to overwrite the background asymmetry." << std::endl;
                return;
        }

        bkgndEvents_[bkgndID] = nBkgndEvents;
        bkgndEvents_[bkgndID]->name( nBkgndEvents->name()+"Events" );
        Double_t value = nBkgndEvents->value();
        bkgndEvents_[bkgndID]->range(-2.0*(TMath::Abs(value)+1.0), 2.0*(TMath::Abs(value)+1.0));

        bkgndAsym_[bkgndID] = bkgndAsym;
        bkgndAsym_[bkgndID]->name( nBkgndEvents->name()+"Asym" );
        bkgndAsym_[bkgndID]->range(-1.0,1.0);
}

void LauCPFitModel::splitSignalComponent( const TH2* dpHisto, Bool_t upperHalf, LauScfMap* scfMap )
{
        if ( useSCF_ == kTRUE ) {
                std::cerr << "ERROR in LauCPFitModel::splitSignalComponent : Have already setup SCF." << std::endl;
                return;
        }

        if ( dpHisto == 0 ) {
                std::cerr << "ERROR in LauCPFitModel::splitSignalComponent : The histogram pointer is null." << std::endl;
                return;
        }

        LauDaughters* daughters = negSigModel_->getDaughters();
        scfFracHist_ = new LauEffModel( daughters, 0 );
        scfFracHist_->setEffHisto( dpHisto, kTRUE, kFALSE, 0.0, 0.0, upperHalf, daughters->squareDP() );

        scfMap_ = scfMap;

        useSCF_ = kTRUE;
        useSCFHist_ = kTRUE;
}

void LauCPFitModel::splitSignalComponent( Double_t scfFrac, Bool_t fixed )
{
        if ( useSCF_ == kTRUE ) {
                std::cerr << "ERROR in LauCPFitModel::splitSignalComponent : Have already setup SCF." << std::endl;
                return;
        }

        scfFrac_.range( 0.0, 1.0 );
        scfFrac_.value( scfFrac ); scfFrac_.initValue( scfFrac ); scfFrac_.genValue( scfFrac );
        scfFrac_.fixed( fixed );

        useSCF_ = kTRUE;
        useSCFHist_ = kFALSE;
}

void LauCPFitModel::setBkgndDPModels(const TString& bkgndClass, LauAbsBkgndDPModel* negModel, LauAbsBkgndDPModel* posModel)
{
        if ((negModel==0) || (posModel==0)) {
                std::cerr << "ERROR in LauCPFitModel::setBkgndDPModels : One or both of the model pointers is null." << std::endl;
                return;
        }

        // check that this background name is valid
        if ( ! this->validBkgndClass( bkgndClass) ) {
                std::cerr << "ERROR in LauCPFitModel::setBkgndDPModel : Invalid background class \"" << bkgndClass << "\"." << std::endl;
                std::cerr << "                                        : Background class names must be provided in \"setBkgndClassNames\" before any other background-related actions can be performed." << std::endl;
                return;
        }

        UInt_t bkgndID = this->bkgndClassID( bkgndClass );
        negBkgndDPModels_[bkgndID] = negModel;
        posBkgndDPModels_[bkgndID] = posModel;

        usingBkgnd_ = kTRUE;
}

void LauCPFitModel::setSignalPdfs(LauAbsPdf* negPdf, LauAbsPdf* posPdf)
{
        if ( tagged_ ) {
                if (negPdf==0 || posPdf==0) {
                        std::cerr << "ERROR in LauCPFitModel::setSignalPdfs : One or both of the PDF pointers is null." << std::endl;
                        return;
                }
        } else {
                // if we're doing an untagged analysis we will only use the negative PDFs
                if ( negPdf==0 ) {
                        std::cerr << "ERROR in LauCPFitModel::setSignalPdfs : The negative PDF pointer is null." << std::endl;
                        return;
                }
                if ( posPdf!=0 ) {
                        std::cerr << "WARNING in LauCPFitModel::setSignalPdfs : Doing an untagged fit so will not use the positive PDF." << std::endl;
                }
        }
        negSignalPdfs_.push_back(negPdf);
        posSignalPdfs_.push_back(posPdf);
}

void LauCPFitModel::setSCFPdfs(LauAbsPdf* negPdf, LauAbsPdf* posPdf)
{
        if ( tagged_ ) {
                if (negPdf==0 || posPdf==0) {
                        std::cerr << "ERROR in LauCPFitModel::setSCFPdfs : One or both of the PDF pointers is null." << std::endl;
                        return;
                }
        } else {
                // if we're doing an untagged analysis we will only use the negative PDFs
                if ( negPdf==0 ) {
                        std::cerr << "ERROR in LauCPFitModel::setSCFPdfs : The negative PDF pointer is null." << std::endl;
                        return;
                }
                if ( posPdf!=0 ) {
                        std::cerr << "WARNING in LauCPFitModel::setSCFPdfs : Doing an untagged fit so will not use the positive PDF." << std::endl;
                }
        }
        negScfPdfs_.push_back(negPdf);
        posScfPdfs_.push_back(posPdf);
}

void LauCPFitModel::setBkgndPdfs(const TString& bkgndClass, LauAbsPdf* negPdf, LauAbsPdf* posPdf)
{
        if ( tagged_ ) {
                if (negPdf==0 || posPdf==0) {
                        std::cerr << "ERROR in LauCPFitModel::setBkgndPdfs : One or both of the PDF pointers is null." << std::endl;
                        return;
                }
        } else {
                // if we're doing an untagged analysis we will only use the negative PDFs
                if ( negPdf==0 ) {
                        std::cerr << "ERROR in LauCPFitModel::setBkgndPdfs : The negative PDF pointer is null." << std::endl;
                        return;
                }
                if ( posPdf!=0 ) {
                        std::cerr << "WARNING in LauCPFitModel::setBkgndPdfs : Doing an untagged fit so will not use the positive PDF." << std::endl;
                }
        }

        // check that this background name is valid
        if ( ! this->validBkgndClass( bkgndClass ) ) {
                std::cerr << "ERROR in LauCPFitModel::setBkgndPdfs : Invalid background class \"" << bkgndClass << "\"." << std::endl;
                std::cerr << "                                     : Background class names must be provided in \"setBkgndClassNames\" before any other background-related actions can be performed." << std::endl;
                return;
        }

        UInt_t bkgndID = this->bkgndClassID( bkgndClass );
        negBkgndPdfs_[bkgndID].push_back(negPdf);
        posBkgndPdfs_[bkgndID].push_back(posPdf);

        usingBkgnd_ = kTRUE;
}

void LauCPFitModel::setAmpCoeffSet(LauAbsCoeffSet* coeffSet)
{
        // Is there a component called compName in the signal model?
        TString compName(coeffSet->name());
        Bool_t negOK = negSigModel_->hasResonance(compName);
        TString conjName = negSigModel_->getConjResName(compName);
        Bool_t posOK = posSigModel_->hasResonance(conjName);
        if (!negOK) {
                std::cerr << "ERROR in LauCPFitModel::setMagPhase : " << negParent_ << " signal DP model doesn't contain component \"" << compName << "\"." << std::endl;
                return;
        }
        if (!posOK) {
                std::cerr << "ERROR in LauCPFitModel::setMagPhase : " << posParent_ << " signal DP model doesn't contain component \"" << conjName << "\"." << std::endl;
                return;
        }

        // Do we already have it in our list of names?
        for (std::vector<LauAbsCoeffSet*>::const_iterator iter=coeffPars_.begin(); iter!=coeffPars_.end(); ++iter) {
                if ((*iter)->name() == compName) {
                        std::cerr << "ERROR in LauCPFitModel::setAmpCoeffSet : Have already set coefficients for \"" << compName << "\"." << std::endl;
                        return;
                }
        }

        coeffSet->index(nSigComp_);
        coeffPars_.push_back(coeffSet);

        TString parName = coeffSet->baseName(); parName += "FitFracAsym";
        fitFracAsymm_.push_back(LauParameter(parName, 0.0, -1.0, 1.0));

        acp_.push_back(coeffSet->acp());

        ++nSigComp_;

        std::cout << "INFO in LauCPFitModel::setAmpCoeffSet : Added coefficients for component \"" << compName << "\" to the fit model." << std::endl;
        coeffSet->printParValues();
}

void LauCPFitModel::initialise()
{
        // First of all check that, if a given component is being used,
        // we've got the PDFs for all the variables involved
        if ( this->useDP() ) {
                if ((negSigModel_ == 0) || (posSigModel_ == 0)) {
                        std::cerr << "ERROR in LauCPFitModel::initialise : the pointer to one (neg or pos) of the signal DP models is null.\n";
                        std::cerr << "                                   : Removing the Dalitz Plot from the model." << std::endl;
                        this->useDP(kFALSE);
                }
                if ( usingBkgnd_ ) {
                        if ( negBkgndDPModels_.empty() || posBkgndDPModels_.empty() ) {
                                std::cerr << "ERROR in LauCPFitModel::initialise : No background DP models found.\n";
                                std::cerr << "                                   : Removing the Dalitz plot from the model." << std::endl;
                                this->useDP(kFALSE);
                        }
                        for (LauBkgndDPModelList::const_iterator dpmodel_iter = negBkgndDPModels_.begin(); dpmodel_iter != negBkgndDPModels_.end(); ++dpmodel_iter ) {
                                if ( (*dpmodel_iter) == 0 ) {
                                        std::cerr << "ERROR in LauCPFitModel::initialise : The pointer to one of the background DP models is null.\n";
                                        std::cerr << "                                   : Removing the Dalitz Plot from the model." << std::endl;
                                        this->useDP(kFALSE);
                                        break;
                                }
                        }
                        for (LauBkgndDPModelList::const_iterator dpmodel_iter = posBkgndDPModels_.begin(); dpmodel_iter != posBkgndDPModels_.end(); ++dpmodel_iter ) {
                                if ( (*dpmodel_iter) == 0 ) {
                                        std::cerr << "ERROR in LauCPFitModel::initialise : The pointer to one of the background DP models is null.\n";
                                        std::cerr << "                                   : Removing the Dalitz Plot from the model." << std::endl;
                                        this->useDP(kFALSE);
                                        break;
                                }
                        }
                }
        }

        // Next check that, if a given component is being used we've got the
        // right number of PDFs for all the variables involved
        // TODO - should probably check variable names and so on as well

        UInt_t nsigpdfvars(0);
        for ( LauPdfList::const_iterator pdf_iter = negSignalPdfs_.begin(); pdf_iter != negSignalPdfs_.end(); ++pdf_iter ) {
                std::vector<TString> varNames = (*pdf_iter)->varNames();
                for ( std::vector<TString>::const_iterator var_iter = varNames.begin(); var_iter != varNames.end(); ++var_iter ) {
                        if ( (*var_iter) != "m13Sq" && (*var_iter) != "m23Sq" ) {
                                ++nsigpdfvars;
                        }
                }
        }
        if (useSCF_) {
                UInt_t nscfpdfvars(0);
                for ( LauPdfList::const_iterator pdf_iter = negScfPdfs_.begin(); pdf_iter != negScfPdfs_.end(); ++pdf_iter ) {
                        std::vector<TString> varNames = (*pdf_iter)->varNames();
                        for ( std::vector<TString>::const_iterator var_iter = varNames.begin(); var_iter != varNames.end(); ++var_iter ) {
                                if ( (*var_iter) != "m13Sq" && (*var_iter) != "m23Sq" ) {
                                        ++nscfpdfvars;
                                }
                        }
                }
                if (nscfpdfvars != nsigpdfvars) {
                        std::cerr << "ERROR in LauCPFitModel::initialise : There are " << nsigpdfvars << " TM signal PDF variables but " << nscfpdfvars << " SCF signal PDF variables." << std::endl;
                        gSystem->Exit(EXIT_FAILURE);
                }
        }
        if (usingBkgnd_) {
                for (LauBkgndPdfsList::const_iterator bgclass_iter = negBkgndPdfs_.begin(); bgclass_iter != negBkgndPdfs_.end(); ++bgclass_iter) {
                        UInt_t nbkgndpdfvars(0);
                        const LauPdfList& pdfList = (*bgclass_iter);
                        for ( LauPdfList::const_iterator pdf_iter = pdfList.begin(); pdf_iter != pdfList.end(); ++pdf_iter ) {
                                std::vector<TString> varNames = (*pdf_iter)->varNames();
                                for ( std::vector<TString>::const_iterator var_iter = varNames.begin(); var_iter != varNames.end(); ++var_iter ) {
                                        if ( (*var_iter) != "m13Sq" && (*var_iter) != "m23Sq" ) {
                                                ++nbkgndpdfvars;
                                        }
                                }
                        }
                        if (nbkgndpdfvars != nsigpdfvars) {
                                std::cerr << "ERROR in LauCPFitModel::initialise : There are " << nsigpdfvars << " signal PDF variables but " << nbkgndpdfvars << " bkgnd PDF variables." << std::endl;
                                gSystem->Exit(EXIT_FAILURE);
                        }
                }
        }

        // Clear the vectors of parameter information so we can start from scratch
        this->clearFitParVectors();

        // Set the fit parameters for signal and background models
        this->setSignalDPParameters();

        // Set the fit parameters for the various extra PDFs
        this->setExtraPdfParameters();

        // Set the initial bg and signal events
        this->setFitNEvents();

        // Check that we have the expected number of fit variables
        const LauParameterPList& fitVars = this->fitPars();
        if (fitVars.size() != (nSigDPPar_ + nExtraPdfPar_ + nNormPar_)) {
                std::cerr << "ERROR in LauCPFitModel::initialise : Number of fit parameters not of expected size. Exiting" << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }

        // From the initial parameter values calculate the coefficients
        // so they can be passed to the signal model
        this->updateCoeffs();

        // Initialisation
        if (this->useDP() == kTRUE) {
                this->initialiseDPModels();
        }

        if (!this->useDP() && negSignalPdfs_.empty()) {
                std::cerr << "ERROR in LauCPFitModel::initialise : Signal model doesn't exist for any variable." << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }

        this->setExtraNtupleVars();
}

void LauCPFitModel::initialiseDPModels()
{
        std::cout << "INFO in LauCPFitModel::initialiseDPModels : Initialising signal DP model" << std::endl;
        negSigModel_->initialise(negCoeffs_);
        posSigModel_->initialise(posCoeffs_);

        if (usingBkgnd_ == kTRUE) {
                for (LauBkgndDPModelList::iterator iter = negBkgndDPModels_.begin(); iter != negBkgndDPModels_.end(); ++iter) {
                        (*iter)->initialise();
                }
                for (LauBkgndDPModelList::iterator iter = posBkgndDPModels_.begin(); iter != posBkgndDPModels_.end(); ++iter) {
                        (*iter)->initialise();
                }
        }
}

void LauCPFitModel::setSignalDPParameters()
{
        // Set the fit parameters for the signal model.

        nSigDPPar_ = 0;
        if ( ! this->useDP() ) {
                return;
        }

        std::cout << "INFO in LauCPFitModel::setSignalDPParameters : Setting the initial fit parameters for the signal DP model." << std::endl;

        // Need to check that the number of components we have and that the dynamics has matches up
        UInt_t nNegAmp = negSigModel_->getnAmp();
        UInt_t nPosAmp = posSigModel_->getnAmp();
        if ( nNegAmp != nPosAmp ) {
                std::cerr << "ERROR in LauCPFitModel::setSignalDPParameters : Unequal number of signal DP components in the negative and positive models: " << nNegAmp << " != " << nPosAmp << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }
        if ( nNegAmp != nSigComp_ ) {
                std::cerr << "ERROR in LauCPFitModel::setSignalDPParameters : Number of signal DP components in the model (" << nNegAmp << ") not equal to number of coefficients supplied (" << nSigComp_ << ")." << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }


        // Place signal model parameters in vector of fit variables
        LauParameterPList& fitVars = this->fitPars();
        for (UInt_t i = 0; i < nSigComp_; i++) {
                LauParameterPList pars = coeffPars_[i]->getParameters();
                for (LauParameterPList::iterator iter = pars.begin(); iter != pars.end(); ++iter) {
                        if ( !(*iter)->clone() ) {
                                fitVars.push_back(*iter);
                                ++nSigDPPar_;
                        }
                }
        }
}

void LauCPFitModel::setExtraPdfParameters()
{
        // Include all the parameters of the PDF in the fit
        // NB all of them are passed to the fit, even though some have been fixed through parameter.fixed(kTRUE)
        // With the new "cloned parameter" scheme only "original" parameters are passed to the fit.
        // Their clones are updated automatically when the originals are updated.

        nExtraPdfPar_ = 0;

        nExtraPdfPar_ += this->addFitParameters(negSignalPdfs_);
        if ( tagged_ ) {
                nExtraPdfPar_ += this->addFitParameters(posSignalPdfs_);
        }

        if (useSCF_ == kTRUE) {
                nExtraPdfPar_ += this->addFitParameters(negScfPdfs_);
                if ( tagged_ ) {
                        nExtraPdfPar_ += this->addFitParameters(posScfPdfs_);
                }
        }

        if (usingBkgnd_ == kTRUE) {
                for (LauBkgndPdfsList::iterator iter = negBkgndPdfs_.begin(); iter != negBkgndPdfs_.end(); ++iter) {
                        nExtraPdfPar_ += this->addFitParameters(*iter);
                }
                if ( tagged_ ) {
                        for (LauBkgndPdfsList::iterator iter = posBkgndPdfs_.begin(); iter != posBkgndPdfs_.end(); ++iter) {
                                nExtraPdfPar_ += this->addFitParameters(*iter);
                        }
                }
        }
}

void LauCPFitModel::setFitNEvents()
{
        if ( signalEvents_ == 0 ) {
                std::cerr << "ERROR in LauCPFitModel::setFitNEvents : Signal yield not defined." << std::endl;
                return;
        }
        nNormPar_ = 0;

        // initialise the total number of events to be the sum of all the hypotheses
        Double_t nTotEvts = signalEvents_->value();
        for (LauBkgndYieldList::const_iterator iter = bkgndEvents_.begin(); iter != bkgndEvents_.end(); ++iter) {
                nTotEvts += (*iter)->value();
                if ( (*iter) == 0 ) {
                        std::cerr << "ERROR in LauCPFitModel::setFitNEvents : Background yield not defined." << std::endl;
                        return;
                }
        }
        this->eventsPerExpt(TMath::FloorNint(nTotEvts));

        LauParameterPList& fitVars = this->fitPars();

        // if doing an extended ML fit add the number of signal events into the fit parameters
        if (this->doEMLFit()) {
                std::cout << "INFO in LauCPFitModel::setFitNEvents : Initialising number of events for signal and background components..." << std::endl;
                // add the signal fraction to the list of fit parameters
                fitVars.push_back(signalEvents_);
                ++nNormPar_;
        } else {
                std::cout << "INFO in LauCPFitModel::setFitNEvents : Initialising number of events for background components (and hence signal)..." << std::endl;
        }

        // if not using the DP in the model we need an explicit signal asymmetry parameter
        if (this->useDP() == kFALSE) {
                fitVars.push_back(signalAsym_);
                ++nNormPar_;
        }

        if (useSCF_ && !useSCFHist_) {
                fitVars.push_back(&scfFrac_);
                ++nNormPar_;
        }

        if (usingBkgnd_ == kTRUE) {
                for (LauBkgndYieldList::iterator iter = bkgndEvents_.begin(); iter != bkgndEvents_.end(); ++iter) {
                        LauParameter* parameter = (*iter);
                        fitVars.push_back(parameter);
                        ++nNormPar_;
                }
                for (LauBkgndYieldList::iterator iter = bkgndAsym_.begin(); iter != bkgndAsym_.end(); ++iter) {
                        LauParameter* parameter = (*iter);
                        fitVars.push_back(parameter);
                        ++nNormPar_;
                }
        }
}

void LauCPFitModel::setExtraNtupleVars()
{
        // Set-up other parameters derived from the fit results, e.g. fit fractions.

        if (this->useDP() != kTRUE) {
                return;
        }

        // First clear the vectors so we start from scratch
        this->clearExtraVarVectors();

        // Add the positive and negative fit fractions for each signal component
        negFitFrac_ = negSigModel_->getFitFractions();
        if (negFitFrac_.size() != nSigComp_) {
                std::cerr << "ERROR in LauCPFitModel::setExtraNtupleVars : Initial Fit Fraction array of unexpected dimension: " << negFitFrac_.size() << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }
        for (UInt_t i(0); i<nSigComp_; ++i) {
                if (negFitFrac_[i].size() != nSigComp_) {
                        std::cerr << "ERROR in LauCPFitModel::setExtraNtupleVars : Initial Fit Fraction array of unexpected dimension: " << negFitFrac_[i].size() << std::endl;
                        gSystem->Exit(EXIT_FAILURE);
                }
        }

        LauParameterList& extraVars = this->extraPars();

        for (UInt_t i(0); i<nSigComp_; ++i) {
                for (UInt_t j(i); j<nSigComp_; ++j) {
                        TString name = negFitFrac_[i][j].name();
                        name.Insert( name.Index("FitFrac"), "Neg" );
                        negFitFrac_[i][j].name(name);
                        extraVars.push_back(negFitFrac_[i][j]);
                }
        }

        posFitFrac_ = posSigModel_->getFitFractions();
        if (posFitFrac_.size() != nSigComp_) {
                std::cerr << "ERROR in LauCPFitModel::setExtraNtupleVars : Initial Fit Fraction array of unexpected dimension: " << posFitFrac_.size() << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }
        for (UInt_t i(0); i<nSigComp_; ++i) {
                if (posFitFrac_[i].size() != nSigComp_) {
                        std::cerr << "ERROR in LauCPFitModel::setExtraNtupleVars : Initial Fit Fraction array of unexpected dimension: " << posFitFrac_[i].size() << std::endl;
                        gSystem->Exit(EXIT_FAILURE);
                }
        }
        for (UInt_t i(0); i<nSigComp_; ++i) {
                for (UInt_t j(i); j<nSigComp_; ++j) {
                        TString name = posFitFrac_[i][j].name();
                        name.Insert( name.Index("FitFrac"), "Pos" );
                        posFitFrac_[i][j].name(name);
                        extraVars.push_back(posFitFrac_[i][j]);
                }
        }

        // Store any extra parameters/quantities from the DP model (e.g. K-matrix total fit fractions)
        std::vector<LauParameter> negExtraPars = negSigModel_->getExtraParameters();
        std::vector<LauParameter>::iterator negExtraIter;
        for (negExtraIter = negExtraPars.begin(); negExtraIter != negExtraPars.end(); ++negExtraIter) {
                LauParameter negExtraParameter = (*negExtraIter);
                extraVars.push_back(negExtraParameter);
        }

        std::vector<LauParameter> posExtraPars = posSigModel_->getExtraParameters();
        std::vector<LauParameter>::iterator posExtraIter;
        for (posExtraIter = posExtraPars.begin(); posExtraIter != posExtraPars.end(); ++posExtraIter) {
                LauParameter posExtraParameter = (*posExtraIter);
                extraVars.push_back(posExtraParameter);
        }

        // Now add in the DP efficiency value
        Double_t initMeanEff = negSigModel_->getMeanEff().initValue();
        negMeanEff_.value(initMeanEff);
        negMeanEff_.genValue(initMeanEff);
        negMeanEff_.initValue(initMeanEff);
        extraVars.push_back(negMeanEff_);

        initMeanEff = posSigModel_->getMeanEff().initValue();
        posMeanEff_.value(initMeanEff);
        posMeanEff_.genValue(initMeanEff);
        posMeanEff_.initValue(initMeanEff);
        extraVars.push_back(posMeanEff_);

        // Also add in the DP rates
        Double_t initDPRate = negSigModel_->getDPRate().initValue();
        negDPRate_.value(initDPRate);
        negDPRate_.genValue(initDPRate);
        negDPRate_.initValue(initDPRate);
        extraVars.push_back(negDPRate_);

        initDPRate = posSigModel_->getDPRate().initValue();
        posDPRate_.value(initDPRate);
        posDPRate_.genValue(initDPRate);
        posDPRate_.initValue(initDPRate);
        extraVars.push_back(posDPRate_);

        // Calculate the CPC and CPV Fit Fractions, ACPs and FitFrac asymmetries
        this->calcExtraFractions(kTRUE);
        this->calcAsymmetries(kTRUE);

        // Add the CP violating and CP conserving fit fractions for each signal component
        for (UInt_t i = 0; i < nSigComp_; i++) {
                for (UInt_t j = i; j < nSigComp_; j++) {
                        extraVars.push_back(CPVFitFrac_[i][j]);
                }
        }
        for (UInt_t i = 0; i < nSigComp_; i++) {
                for (UInt_t j = i; j < nSigComp_; j++) {
                        extraVars.push_back(CPCFitFrac_[i][j]);
                }
        }

        // Add the Fit Fraction asymmetry for each signal component
        for (UInt_t i = 0; i < nSigComp_; i++) {
                extraVars.push_back(fitFracAsymm_[i]);
        }

        // Add the calculated CP asymmetry for each signal component
        for (UInt_t i = 0; i < nSigComp_; i++) {
                extraVars.push_back(acp_[i]);
        }
}

void LauCPFitModel::calcExtraFractions(Bool_t initValues)
{
        // Calculate the CP-conserving and CP-violating fit fractions

        if (initValues) {
                // create the structure
                CPCFitFrac_.clear();
                CPVFitFrac_.clear();
                CPCFitFrac_.resize(nSigComp_);
                CPVFitFrac_.resize(nSigComp_);
                for (UInt_t i(0); i<nSigComp_; ++i) {
                        CPCFitFrac_[i].resize(nSigComp_);
                        CPVFitFrac_[i].resize(nSigComp_);

                        for (UInt_t j(i); j<nSigComp_; ++j) {
                                TString name = negFitFrac_[i][j].name();
                                name.Replace( name.Index("Neg"), 3, "CPC" );
                                CPCFitFrac_[i][j].name( name );
                                CPCFitFrac_[i][j].valueAndRange( 0.0, -100.0, 100.0 );

                                name = negFitFrac_[i][j].name();
                                name.Replace( name.Index("Neg"), 3, "CPV" );
                                CPVFitFrac_[i][j].name( name );
                                CPVFitFrac_[i][j].valueAndRange( 0.0, -100.0, 100.0 );
                        }
                }
        }

        Double_t denom = negDPRate_ + posDPRate_;

        for (UInt_t i(0); i<nSigComp_; ++i) {
                for (UInt_t j(i); j<nSigComp_; ++j) {

                        Double_t negTerm = negFitFrac_[i][j]*negDPRate_;
                        Double_t posTerm = posFitFrac_[i][j]*posDPRate_;

                        Double_t cpcFitFrac = (negTerm + posTerm)/denom;
                        Double_t cpvFitFrac = (negTerm - posTerm)/denom;

                        CPCFitFrac_[i][j] = cpcFitFrac;
                        CPVFitFrac_[i][j] = cpvFitFrac;

                        if (initValues) {
                                CPCFitFrac_[i][j].genValue(cpcFitFrac);
                                CPCFitFrac_[i][j].initValue(cpcFitFrac);

                                CPVFitFrac_[i][j].genValue(cpvFitFrac);
                                CPVFitFrac_[i][j].initValue(cpvFitFrac);
                        }
                }
        }
}

void LauCPFitModel::calcAsymmetries(Bool_t initValues)
{
        // Calculate the CP asymmetries
        // Also calculate the fit fraction asymmetries

        for (UInt_t i = 0; i < nSigComp_; i++) {

                acp_[i] = coeffPars_[i]->acp();

                LauAsymmCalc asymmCalc(negFitFrac_[i][i].value(), posFitFrac_[i][i].value());
                Double_t asym = asymmCalc.getAsymmetry();
                fitFracAsymm_[i] = asym;
                if (initValues) {
                        fitFracAsymm_[i].genValue(asym);
                        fitFracAsymm_[i].initValue(asym);
                }
        }
}

void LauCPFitModel::finaliseFitResults(const TString& tablePrefixName)
{
        // Retrieve parameters from the fit results for calculations and toy generation
        // and eventually store these in output root ntuples/text files

        // Now take the fit parameters and update them as necessary
        // i.e. to make mag > 0.0, phase in the right range.
        // This function will also calculate any other values, such as the
        // fit fractions, using any errors provided by fitParErrors as appropriate.
        // Also obtain the pull values: (measured - generated)/(average error)

        if (this->useDP() == kTRUE) {
                for (UInt_t i = 0; i < nSigComp_; ++i) {
                        // Check whether we have "a/b > 0.0", and phases in the right range
                        coeffPars_[i]->finaliseValues();
                }
        }

        // update the pulls on the event fractions and asymmetries
        if (this->doEMLFit()) {
                signalEvents_->updatePull();
        }
        if (this->useDP() == kFALSE) {
                signalAsym_->updatePull();
        }
        if (useSCF_ && !useSCFHist_) {
                scfFrac_.updatePull();
        }
        if (usingBkgnd_ == kTRUE) {
                for (LauBkgndYieldList::iterator iter = bkgndEvents_.begin(); iter != bkgndEvents_.end(); ++iter) {
                        (*iter)->updatePull();
                }
                for (LauBkgndYieldList::iterator iter = bkgndAsym_.begin(); iter != bkgndAsym_.end(); ++iter) {
                        (*iter)->updatePull();
                }
        }

        // Update the pulls on all the extra PDFs' parameters
        this->updateFitParameters(negSignalPdfs_);
        this->updateFitParameters(posSignalPdfs_);
        if (useSCF_ == kTRUE) {
                this->updateFitParameters(negScfPdfs_);
                this->updateFitParameters(posScfPdfs_);
        }
        if (usingBkgnd_ == kTRUE) {
                for (LauBkgndPdfsList::iterator iter = negBkgndPdfs_.begin(); iter != negBkgndPdfs_.end(); ++iter) {
                        this->updateFitParameters(*iter);
                }
                for (LauBkgndPdfsList::iterator iter = posBkgndPdfs_.begin(); iter != posBkgndPdfs_.end(); ++iter) {
                        this->updateFitParameters(*iter);
                }
        }

        // Fill the fit results to the ntuple

        // update the coefficients and then calculate the fit fractions and ACP's
        if (this->useDP() == kTRUE) {
                this->updateCoeffs();
                negSigModel_->updateCoeffs(negCoeffs_);  negSigModel_->calcExtraInfo();
                posSigModel_->updateCoeffs(posCoeffs_);  posSigModel_->calcExtraInfo();

                LauParArray negFitFrac = negSigModel_->getFitFractions();
                if (negFitFrac.size() != nSigComp_) {
                        std::cerr << "ERROR in LauCPFitModel::finaliseFitResults : Fit Fraction array of unexpected dimension: " << negFitFrac.size() << std::endl;
                        gSystem->Exit(EXIT_FAILURE);
                }
                for (UInt_t i(0); i<nSigComp_; ++i) {
                        if (negFitFrac[i].size() != nSigComp_) {
                                std::cerr << "ERROR in LauCPFitModel::finaliseFitResults : Fit Fraction array of unexpected dimension: " << negFitFrac[i].size() << std::endl;
                                gSystem->Exit(EXIT_FAILURE);
                        }
                }

                LauParArray posFitFrac = posSigModel_->getFitFractions();
                if (posFitFrac.size() != nSigComp_) {
                        std::cerr << "ERROR in LauCPFitModel::finaliseFitResults : Fit Fraction array of unexpected dimension: " << posFitFrac.size() << std::endl;
                        gSystem->Exit(EXIT_FAILURE);
                }
                for (UInt_t i(0); i<nSigComp_; ++i) {
                        if (posFitFrac[i].size() != nSigComp_) {
                                std::cerr << "ERROR in LauCPFitModel::finaliseFitResults : Fit Fraction array of unexpected dimension: " << posFitFrac[i].size() << std::endl;
                                gSystem->Exit(EXIT_FAILURE);
                        }
                }

                for (UInt_t i(0); i<nSigComp_; ++i) {
                        for (UInt_t j(i); j<nSigComp_; ++j) {
                                negFitFrac_[i][j].value(negFitFrac[i][j].value());
                                posFitFrac_[i][j].value(posFitFrac[i][j].value());
                        }
                }

                negMeanEff_.value(negSigModel_->getMeanEff().value());
                posMeanEff_.value(posSigModel_->getMeanEff().value());
                negDPRate_.value(negSigModel_->getDPRate().value());
                posDPRate_.value(posSigModel_->getDPRate().value());

                this->calcExtraFractions();
                this->calcAsymmetries();

                // Then store the final fit parameters, and any extra parameters for
                // the signal model (e.g. fit fractions, FF asymmetries, ACPs, mean efficiency and DP rate)

                this->clearExtraVarVectors();
                LauParameterList& extraVars = this->extraPars();

                // Add the positive and negative fit fractions for each signal component
                for (UInt_t i(0); i<nSigComp_; ++i) {
                        for (UInt_t j(i); j<nSigComp_; ++j) {
                                extraVars.push_back(negFitFrac_[i][j]);
                        }
                }
                for (UInt_t i(0); i<nSigComp_; ++i) {
                        for (UInt_t j(i); j<nSigComp_; ++j) {
                                extraVars.push_back(posFitFrac_[i][j]);
                        }
                }

                // Store any extra parameters/quantities from the DP model (e.g. K-matrix total fit fractions)
                std::vector<LauParameter> negExtraPars = negSigModel_->getExtraParameters();
                std::vector<LauParameter>::iterator negExtraIter;
                for (negExtraIter = negExtraPars.begin(); negExtraIter != negExtraPars.end(); ++negExtraIter) {
                        LauParameter negExtraParameter = (*negExtraIter);
                        extraVars.push_back(negExtraParameter);
                }

                std::vector<LauParameter> posExtraPars = posSigModel_->getExtraParameters();
                std::vector<LauParameter>::iterator posExtraIter;
                for (posExtraIter = posExtraPars.begin(); posExtraIter != posExtraPars.end(); ++posExtraIter) {
                        LauParameter posExtraParameter = (*posExtraIter);
                        extraVars.push_back(posExtraParameter);
                }

                extraVars.push_back(negMeanEff_);
                extraVars.push_back(posMeanEff_);
                extraVars.push_back(negDPRate_);
                extraVars.push_back(posDPRate_);

                for (UInt_t i = 0; i < nSigComp_; i++) {
                        for (UInt_t j(i); j<nSigComp_; ++j) {
                                extraVars.push_back(CPVFitFrac_[i][j]);
                        }
                }
                for (UInt_t i = 0; i < nSigComp_; i++) {
                        for (UInt_t j(i); j<nSigComp_; ++j) {
                                extraVars.push_back(CPCFitFrac_[i][j]);
                        }
                }
                for (UInt_t i(0); i<nSigComp_; ++i) {
                        extraVars.push_back(fitFracAsymm_[i]);
                }
                for (UInt_t i(0); i<nSigComp_; ++i) {
                        extraVars.push_back(acp_[i]);
                }

                this->printFitFractions(std::cout);
                this->printAsymmetries(std::cout);
        }

        const LauParameterPList& fitVars = this->fitPars();
        const LauParameterList& extraVars = this->extraPars();
        LauFitNtuple* ntuple = this->fitNtuple();
        ntuple->storeParsAndErrors(fitVars, extraVars);

        // find out the correlation matrix for the parameters
        ntuple->storeCorrMatrix(this->iExpt(), this->nll(), this->fitStatus(), this->covarianceMatrix());

        // Fill the data into ntuple
        ntuple->updateFitNtuple();

        // Print out the partial fit fractions, phases and the 
        // averaged efficiency, reweighted by the dynamics (and anything else)
        if (this->writeLatexTable()) {
                TString sigOutFileName(tablePrefixName);
                sigOutFileName += "_"; sigOutFileName += this->iExpt(); sigOutFileName += "Expt.tex";
                this->writeOutTable(sigOutFileName);
        }
}

void LauCPFitModel::printFitFractions(std::ostream& output)
{
        // Print out Fit Fractions, total DP rate and mean efficiency
        // First for the B- events
        for (UInt_t i = 0; i < nSigComp_; i++) {
                const TString compName(coeffPars_[i]->name());
                output << negParent_ << " FitFraction for component " << i << " (" << compName << ") = " << negFitFrac_[i][i] << std::endl;
        }
        output << negParent_ << " overall DP rate (integral of matrix element squared) = " << negDPRate_ << std::endl;
        output << negParent_ << " average efficiency weighted by whole DP dynamics = " << negMeanEff_ << std::endl;

        // Then for the positive sample
        for (UInt_t i = 0; i < nSigComp_; i++) {
                const TString compName(coeffPars_[i]->name());
                const TString conjName(negSigModel_->getConjResName(compName));
                output << posParent_ << " FitFraction for component " << i << " (" << conjName << ") = " << posFitFrac_[i][i] << std::endl;
        }
        output << posParent_ << " overall DP rate (integral of matrix element squared) = " << posDPRate_ << std::endl;
        output << posParent_ << " average efficiency weighted by whole DP dynamics = " << posMeanEff_ << std::endl;
}

void LauCPFitModel::printAsymmetries(std::ostream& output)
{
        for (UInt_t i = 0; i < nSigComp_; i++) {
                const TString compName(coeffPars_[i]->name());
                output << "Fit Fraction asymmetry for component " << i << " (" << compName << ") = " << fitFracAsymm_[i] << std::endl;
        }
        for (UInt_t i = 0; i < nSigComp_; i++) {
                const TString compName(coeffPars_[i]->name());
                output << "ACP for component " << i << " (" << compName << ") = " << acp_[i] << std::endl;
        }
}

void LauCPFitModel::writeOutTable(const TString& outputFile)
{
        // Write out the results of the fit to a tex-readable table
        // TODO - need to include the yields in this table
        std::ofstream fout(outputFile);
        LauPrint print;

        std::cout << "INFO in LauCPFitModel::writeOutTable : Writing out results of the fit to the tex file " << outputFile << std::endl;

        if (this->useDP() == kTRUE) {
                // print the fit coefficients in one table
                coeffPars_.front()->printTableHeading(fout);
                for (UInt_t i = 0; i < nSigComp_; i++) {
                        coeffPars_[i]->printTableRow(fout);
                }
                fout << "\\hline" << std::endl;
                fout << "\\end{tabular}" << std::endl << std::endl;

                // print the fit fractions and asymmetries in another
                fout << "\\begin{tabular}{|l|c|c|c|c|}" << std::endl;
                fout << "\\hline" << std::endl;
                fout << "Component & " << negParent_ << " Fit Fraction & " << posParent_ << " Fit Fraction & Fit Fraction Asymmetry & ACP \\\\" << std::endl;
                fout << "\\hline" << std::endl;

                Double_t negFitFracSum(0.0);
                Double_t posFitFracSum(0.0);

                for (UInt_t i = 0; i < nSigComp_; i++) {
                        TString resName = coeffPars_[i]->name();
                        resName = resName.ReplaceAll("_", "\\_");

                        Double_t negFitFrac = negFitFrac_[i][i].value();
                        Double_t posFitFrac = posFitFrac_[i][i].value();
                        negFitFracSum += negFitFrac;
                        posFitFracSum += posFitFrac;

                        Double_t fitFracAsymm = fitFracAsymm_[i].value();

                        Double_t acp = acp_[i].value();
                        Double_t acpErr = acp_[i].error();

                        fout << resName << "  &  $";

                        print.printFormat(fout, negFitFrac);
                        fout << "$  &  $";
                        print.printFormat(fout, posFitFrac);
                        fout << "$  &  $";

                        print.printFormat(fout, fitFracAsymm);
                        fout << "$  &  $";

                        print.printFormat(fout, acp);
                        fout << " \\pm ";
                        print.printFormat(fout, acpErr);
                        fout << "$ \\\\" << std::endl;
                }
                fout << "\\hline" << std::endl;

                // Also print out sum of fit fractions
                fout << "Fit Fraction Sum  &  $";
                print.printFormat(fout, negFitFracSum);
                fout << "$  &  $";
                print.printFormat(fout, posFitFracSum);
                fout << "$  &   &   \\\\" << std::endl;
                fout << "\\hline" << std::endl;

                fout << "DP rate  &  $";
                print.printFormat(fout, negDPRate_.value());
                fout << "$  &  $";
                print.printFormat(fout, posDPRate_.value());
                fout << "$  &   &   \\\\" << std::endl;

                fout << "$< \\varepsilon > $ &  $";
                print.printFormat(fout, negMeanEff_.value());
                fout << "$  &  $";
                print.printFormat(fout, posMeanEff_.value());
                fout << "$  &   &   \\\\" << std::endl;
                fout << "\\hline" << std::endl;
                fout << "\\end{tabular}" << std::endl << std::endl;
        }

        if (!negSignalPdfs_.empty()) {
                fout << "\\begin{tabular}{|l|c|}" << std::endl;
                fout << "\\hline" << std::endl;
                if (useSCF_ == kTRUE) {
                        fout << "\\Extra TM Signal PDFs' Parameters: & \\\\" << std::endl;
                } else {
                        fout << "\\Extra Signal PDFs' Parameters: & \\\\" << std::endl;
                }
                this->printFitParameters(negSignalPdfs_, fout);
                if ( tagged_ ) {
                        this->printFitParameters(posSignalPdfs_, fout);
                }
                if (useSCF_ == kTRUE && !negScfPdfs_.empty()) {
                        fout << "\\hline" << std::endl;
                        fout << "\\Extra SCF Signal PDFs' Parameters: & \\\\" << std::endl;
                        this->printFitParameters(negScfPdfs_, fout);
                        if ( tagged_ ) {
                                this->printFitParameters(posScfPdfs_, fout);
                        }
                }
                if (usingBkgnd_ == kTRUE && !negBkgndPdfs_.empty()) {
                        fout << "\\hline" << std::endl;
                        fout << "\\Extra Background PDFs' Parameters: & \\\\" << std::endl;
                        for (LauBkgndPdfsList::const_iterator iter = negBkgndPdfs_.begin(); iter != negBkgndPdfs_.end(); ++iter) {
                                this->printFitParameters(*iter, fout);
                        }
                        if ( tagged_ ) {
                                for (LauBkgndPdfsList::const_iterator iter = posBkgndPdfs_.begin(); iter != posBkgndPdfs_.end(); ++iter) {
                                        this->printFitParameters(*iter, fout);
                                }
                        }
                }
                fout << "\\hline \n\\end{tabular}" << std::endl << std::endl;
        }
}

void LauCPFitModel::checkInitFitParams()
{
        // Update the number of signal events to be total-sum(background events)
        this->updateSigEvents();

        // Check whether we want to have randomised initial fit parameters for the signal model
        if (this->useRandomInitFitPars() == kTRUE) {
                std::cout << "INFO in LauCPFitModel::checkInitFitParams : Setting random parameters for the signal model" << std::endl;
                this->randomiseInitFitPars();
        }
}

void LauCPFitModel::randomiseInitFitPars()
{
        // Only randomise those parameters that are not fixed!
        std::cout << "INFO in LauCPFitModel::randomiseInitFitPars : Randomising the initial fit magnitudes and phases of the components..." << std::endl;

        for (UInt_t i = 0; i < nSigComp_; i++) {
                coeffPars_[i]->randomiseInitValues();
        }
}

LauCPFitModel::LauGenInfo LauCPFitModel::eventsToGenerate()
{
        // Determine the number of events to generate for each hypothesis
        // If we're smearing then smear each one individually

        LauGenInfo nEvtsGen;

        // Signal
        Double_t evtWeight(1.0);
        Double_t nEvts = signalEvents_->genValue();
        if ( nEvts < 0.0 ) {
                evtWeight = -1.0;
                nEvts = TMath::Abs( nEvts );
        }
        Double_t asym(0.0);
        Double_t sigAsym(0.0);
        // need to include this as an alternative in case the DP isn't in the model
        if ( !this->useDP() || forceAsym_ ) {
                sigAsym = signalAsym_->genValue();
        } else {
                Double_t negRate = negSigModel_->getDPNorm();
                Double_t posRate = posSigModel_->getDPNorm();
                if (negRate+posRate>1e-30) {
                        sigAsym = (negRate-posRate)/(negRate+posRate);
                }
        }
        asym = sigAsym;
        Int_t nPosEvts = static_cast<Int_t>((nEvts/2.0 * (1.0 - asym)) + 0.5);
        Int_t nNegEvts = static_cast<Int_t>((nEvts/2.0 * (1.0 + asym)) + 0.5);
        if (this->doPoissonSmearing()) {
                nNegEvts = LauRandom::randomFun()->Poisson(nNegEvts);
                nPosEvts = LauRandom::randomFun()->Poisson(nPosEvts);
        }
        nEvtsGen[std::make_pair("signal",-1)] = std::make_pair(nNegEvts,evtWeight);
        nEvtsGen[std::make_pair("signal",+1)] = std::make_pair(nPosEvts,evtWeight);

        // backgrounds
        const UInt_t nBkgnds = this->nBkgndClasses();
        for ( UInt_t bkgndID(0); bkgndID < nBkgnds; ++bkgndID ) {
                const TString& bkgndClass = this->bkgndClassName(bkgndID);
                const LauParameter* evtsPar = bkgndEvents_[bkgndID];
                const LauParameter* asymPar = bkgndAsym_[bkgndID];
                evtWeight = 1.0;
                nEvts = TMath::FloorNint( evtsPar->genValue() );
                if ( nEvts < 0 ) {
                        evtWeight = -1.0;
                        nEvts = TMath::Abs( nEvts );
                }
                asym = asymPar->genValue();
                nPosEvts = static_cast<Int_t>((nEvts/2.0 * (1.0 - asym)) + 0.5);
                nNegEvts = static_cast<Int_t>((nEvts/2.0 * (1.0 + asym)) + 0.5);
                if (this->doPoissonSmearing()) {
                        nNegEvts = LauRandom::randomFun()->Poisson(nNegEvts);
                        nPosEvts = LauRandom::randomFun()->Poisson(nPosEvts);
                }
                nEvtsGen[std::make_pair(bkgndClass,-1)] = std::make_pair(nNegEvts,evtWeight);
                nEvtsGen[std::make_pair(bkgndClass,+1)] = std::make_pair(nPosEvts,evtWeight);
        }

        std::cout << "INFO in LauCPFitModel::eventsToGenerate : Generating toy MC with:" << std::endl;
        std::cout << "                                        : Signal asymmetry  = " << sigAsym << " and number of signal events = " << signalEvents_->genValue() << std::endl;
        for ( UInt_t bkgndID(0); bkgndID < nBkgnds; ++bkgndID ) {
                const TString& bkgndClass = this->bkgndClassName(bkgndID);
                const LauParameter* evtsPar = bkgndEvents_[bkgndID];
                const LauParameter* asymPar = bkgndAsym_[bkgndID];
                std::cout << "                                        : " << bkgndClass << " asymmetry  = " << asymPar->genValue() << " and number of " << bkgndClass << " events = " << evtsPar->genValue() << std::endl;
        }

        return nEvtsGen;
}

Bool_t LauCPFitModel::genExpt()
{
        // Routine to generate toy Monte Carlo events according to the various models we have defined.

        // Determine the number of events to generate for each hypothesis
        LauGenInfo nEvts = this->eventsToGenerate();

        Bool_t genOK(kTRUE);
        Int_t evtNum(0);

        const UInt_t nBkgnds = this->nBkgndClasses();
        std::vector<TString> bkgndClassNames(nBkgnds);
        std::vector<TString> bkgndClassNamesGen(nBkgnds);
        for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
                TString name( this->bkgndClassName(iBkgnd) );
                bkgndClassNames[iBkgnd] = name;
                bkgndClassNamesGen[iBkgnd] = "gen"+name;
        }

        const Bool_t storeSCFTruthInfo = ( useSCF_ || ( this->enableEmbedding() &&
                                negSignalTree_ != 0 && negSignalTree_->haveBranch("mcMatch") &&
                                posSignalTree_ != 0 && posSignalTree_->haveBranch("mcMatch") ) );

        // Loop over the hypotheses and generate the requested number of events for each one
        for (LauGenInfo::const_iterator iter = nEvts.begin(); iter != nEvts.end(); ++iter) {

                const TString& type(iter->first.first);
                curEvtCharge_ = iter->first.second;
                Double_t evtWeight( iter->second.second );
                Int_t nEvtsGen( iter->second.first );

                for (Int_t iEvt(0); iEvt<nEvtsGen; ++iEvt) {

                        this->setGenNtupleDoubleBranchValue( "evtWeight", evtWeight );

                        if (type == "signal") {
                                this->setGenNtupleIntegerBranchValue("genSig",1);
                                for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
                                        this->setGenNtupleIntegerBranchValue( bkgndClassNamesGen[iBkgnd], 0 );
                                }
                                genOK = this->generateSignalEvent();
                        } else {
                                this->setGenNtupleIntegerBranchValue("genSig",0);
                                if ( storeSCFTruthInfo ) {
                                        this->setGenNtupleIntegerBranchValue("genTMSig",0);
                                        this->setGenNtupleIntegerBranchValue("genSCFSig",0);
                                }
                                UInt_t bkgndID(0);
                                for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
                                        Int_t gen(0);
                                        if ( bkgndClassNames[iBkgnd] == type ) {
                                                gen = 1;
                                                bkgndID = iBkgnd;
                                        }
                                        this->setGenNtupleIntegerBranchValue( bkgndClassNamesGen[iBkgnd], gen );
                                }
                                genOK = this->generateBkgndEvent(bkgndID);
                        }
                        if (!genOK) {
                                // If there was a problem with the generation then break out and return.
                                // The problem model will have adjusted itself so that all should be OK next time.
                                break;
                        }
                        if (this->useDP() == kTRUE) {
                                this->setDPBranchValues();
                        }

                        // Store the event charge
                        this->setGenNtupleIntegerBranchValue(tagVarName_,curEvtCharge_);

                        // Store the event number (within this experiment)
                        // and then increment it
                        this->setGenNtupleIntegerBranchValue("iEvtWithinExpt",evtNum);
                        ++evtNum;

                        this->fillGenNtupleBranches();
                        if (iEvt%500 == 0) {std::cout << "INFO in LauCPFitModel::genExpt : Generated event number " << iEvt << " out of " << nEvtsGen << " " << type << " events." << std::endl;}
                }

                if (!genOK) {
                        break;
                }
        }

        if (this->useDP() && genOK) {

                negSigModel_->checkToyMC(kTRUE,kTRUE);
                posSigModel_->checkToyMC(kTRUE,kTRUE);

                // Get the fit fractions if they're to be written into the latex table
                if (this->writeLatexTable()) {
                        LauParArray negFitFrac = negSigModel_->getFitFractions();
                        if (negFitFrac.size() != nSigComp_) {
                                std::cerr << "ERROR in LauCPFitModel::genExpt : Fit Fraction array of unexpected dimension: " << negFitFrac.size() << std::endl;
                                gSystem->Exit(EXIT_FAILURE);
                        }
                        for (UInt_t i(0); i<nSigComp_; ++i) {
                                if (negFitFrac[i].size() != nSigComp_) {
                                        std::cerr << "ERROR in LauCPFitModel::genExpt : Fit Fraction array of unexpected dimension: " << negFitFrac[i].size() << std::endl;
                                        gSystem->Exit(EXIT_FAILURE);
                                }
                        }
                        LauParArray posFitFrac = posSigModel_->getFitFractions();
                        if (posFitFrac.size() != nSigComp_) {
                                std::cerr << "ERROR in LauCPFitModel::genExpt : Fit Fraction array of unexpected dimension: " << posFitFrac.size() << std::endl;
                                gSystem->Exit(EXIT_FAILURE);
                        }
                        for (UInt_t i(0); i<nSigComp_; ++i) {
                                if (posFitFrac[i].size() != nSigComp_) {
                                        std::cerr << "ERROR in LauCPFitModel::genExpt : Fit Fraction array of unexpected dimension: " << posFitFrac[i].size() << std::endl;
                                        gSystem->Exit(EXIT_FAILURE);
                                }
                        }

                        for (UInt_t i(0); i<nSigComp_; ++i) {
                                for (UInt_t j(i); j<nSigComp_; ++j) {
                                        negFitFrac_[i][j].value(negFitFrac[i][j].value());
                                        posFitFrac_[i][j].value(posFitFrac[i][j].value());
                                }
                        }
                        negMeanEff_.value(negSigModel_->getMeanEff().value());
                        posMeanEff_.value(posSigModel_->getMeanEff().value());
                        negDPRate_.value(negSigModel_->getDPRate().value());
                        posDPRate_.value(posSigModel_->getDPRate().value());
                }
        }

        // If we're reusing embedded events or if the generation is being
        // reset then clear the lists of used events
        if (reuseSignal_ || !genOK) {
                if (negSignalTree_) {
                        negSignalTree_->clearUsedList();
                }
                if (posSignalTree_) {
                        posSignalTree_->clearUsedList();
                }
        }
        for ( UInt_t bkgndID(0); bkgndID < nBkgnds; ++bkgndID ) {
                LauEmbeddedData* data = negBkgndTree_[bkgndID];
                if (reuseBkgnd_[bkgndID] || !genOK) {
                        if (data) {
                                data->clearUsedList();
                        }
                }
        }
        for ( UInt_t bkgndID(0); bkgndID < nBkgnds; ++bkgndID ) {
                LauEmbeddedData* data = posBkgndTree_[bkgndID];
                if (reuseBkgnd_[bkgndID] || !genOK) {
                        if (data) {
                                data->clearUsedList();
                        }
                }
        }

        return genOK;
}

Bool_t LauCPFitModel::generateSignalEvent()
{
        // Generate signal event
        Bool_t genOK(kTRUE);
        Bool_t genSCF(kFALSE);

        LauAbsDPDynamics* model(0);
        LauKinematics* kinematics(0);
        LauEmbeddedData* embeddedData(0);
        LauPdfList* sigPdfs(0);
        LauPdfList* scfPdfs(0);

        Bool_t doReweighting(kFALSE);

        if (curEvtCharge_<0) {
                model = negSigModel_;
                kinematics = negKinematics_;
                sigPdfs = &negSignalPdfs_;
                scfPdfs = &negScfPdfs_;
                if (this->enableEmbedding()) {
                        embeddedData = negSignalTree_;
                        doReweighting = useNegReweighting_;
                }
        } else {
                model = posSigModel_;
                kinematics = posKinematics_;
                if ( tagged_ ) {
                        sigPdfs = &posSignalPdfs_;
                        scfPdfs = &posScfPdfs_;
                } else {
                        sigPdfs = &negSignalPdfs_;
                        scfPdfs = &negScfPdfs_;
                }
                if (this->enableEmbedding()) {
                        embeddedData = posSignalTree_;
                        doReweighting = usePosReweighting_;
                }
        }

        if (this->useDP()) {
                if (embeddedData) {
                        if (doReweighting) {
                                // Select a (random) event from the generated data. Then store the
                                // reconstructed DP co-ords, together with other pdf information,
                                // as the embedded data.
                                genOK = embeddedData->getReweightedEvent(model);
                        } else {
                                // Just get the information of a (randomly) selected event in the 
                                // embedded data
                                embeddedData->getEmbeddedEvent(kinematics);
                        }
                        genSCF = this->storeSignalMCMatch( embeddedData );
                } else {
                        genOK = model->generate();
                        if ( genOK && useSCF_ ) {
                                Double_t frac(0.0);
                                if ( useSCFHist_ ) {
                                        frac = scfFracHist_->calcEfficiency( kinematics );
                                } else {
                                        frac = scfFrac_.genValue();
                                }
                                if ( frac < LauRandom::randomFun()->Rndm() ) {
                                        this->setGenNtupleIntegerBranchValue("genTMSig",1);
                                        this->setGenNtupleIntegerBranchValue("genSCFSig",0);
                                        genSCF = kFALSE;
                                } else {
                                        this->setGenNtupleIntegerBranchValue("genTMSig",0);
                                        this->setGenNtupleIntegerBranchValue("genSCFSig",1);
                                        genSCF = kTRUE;

                                        // Optionally smear the DP position
                                        // of the SCF event
                                        if ( scfMap_ != 0 ) {
                                                Double_t xCoord(0.0), yCoord(0.0);
                                                if ( kinematics->squareDP() ) {
                                                        xCoord = kinematics->getmPrime();
                                                        yCoord = kinematics->getThetaPrime();
                                                } else {
                                                        xCoord = kinematics->getm13Sq();
                                                        yCoord = kinematics->getm23Sq();
                                                }

                                                // Find the bin number where this event is generated
                                                Int_t binNo = scfMap_->binNumber( xCoord, yCoord );

                                                // Retrieve the migration histogram
                                                TH2* histo = scfMap_->trueHist( binNo );

                                                LauEffModel * effModel = model->getEffModel();
                                                do {
                                                        // Get a random point from the histogram
                                                        histo->GetRandom2( xCoord, yCoord );

                                                        // Update the kinematics
                                                        if ( kinematics->squareDP() ) {
                                                                kinematics->updateSqDPKinematics( xCoord, yCoord );
                                                        } else {
                                                                kinematics->updateKinematics( xCoord, yCoord );
                                                        }
                                                } while ( ! effModel->passVeto( kinematics ) );
                                        }
                                }
                        }
                }
        } else {
                if (embeddedData) {
                        embeddedData->getEmbeddedEvent(0);
                        genSCF = this->storeSignalMCMatch( embeddedData );
                } else if ( useSCF_ ) {
                        Double_t frac = scfFrac_.genValue();
                        if ( frac < LauRandom::randomFun()->Rndm() ) {
                                this->setGenNtupleIntegerBranchValue("genTMSig",1);
                                this->setGenNtupleIntegerBranchValue("genSCFSig",0);
                                genSCF = kFALSE;
                        } else {
                                this->setGenNtupleIntegerBranchValue("genTMSig",0);
                                this->setGenNtupleIntegerBranchValue("genSCFSig",1);
                                genSCF = kTRUE;
                        }
                }
        }
        if (genOK) {
                if ( useSCF_ ) {
                        if ( genSCF ) {
                                this->generateExtraPdfValues(scfPdfs, embeddedData);
                        } else {
                                this->generateExtraPdfValues(sigPdfs, embeddedData);
                        }
                } else {
                        this->generateExtraPdfValues(sigPdfs, embeddedData);
                }
        }
        // Check for problems with the embedding
        if (embeddedData && (embeddedData->nEvents() == embeddedData->nUsedEvents())) {
                std::cerr << "WARNING in LauCPFitModel::generateSignalEvent : Source of embedded signal events used up, clearing the list of used events." << std::endl;
                embeddedData->clearUsedList();
        }

        return genOK;
}

Bool_t LauCPFitModel::generateBkgndEvent(UInt_t bkgndID)
{
        // Generate Bkgnd event
        Bool_t genOK(kTRUE);

        LauAbsBkgndDPModel* model(0);
        LauEmbeddedData* embeddedData(0);
        LauPdfList* extraPdfs(0);
        LauKinematics* kinematics(0);

        if (curEvtCharge_<0) {
                model = negBkgndDPModels_[bkgndID];
                if (this->enableEmbedding()) {
                        embeddedData = negBkgndTree_[bkgndID];
                }
                extraPdfs = &negBkgndPdfs_[bkgndID];
                kinematics = negKinematics_;
        } else {
                model = posBkgndDPModels_[bkgndID];
                if (this->enableEmbedding()) {
                        embeddedData = posBkgndTree_[bkgndID];
                }
                if ( tagged_ ) {
                        extraPdfs = &posBkgndPdfs_[bkgndID];
                } else {
                        extraPdfs = &negBkgndPdfs_[bkgndID];
                }
                kinematics = posKinematics_;
        }

        if (this->useDP()) {
                if (embeddedData) {
                        embeddedData->getEmbeddedEvent(kinematics);
                } else {
                        if (model == 0) {
                                const TString& bkgndClass = this->bkgndClassName(bkgndID);
                                std::cerr << "ERROR in LauCPFitModel::generateBkgndEvent : Can't find the DP model for background class \"" << bkgndClass << "\"." << std::endl;
                                gSystem->Exit(EXIT_FAILURE);
                        }
                        genOK = model->generate();
                }
        } else {
                if (embeddedData) {
                        embeddedData->getEmbeddedEvent(0);
                }
        }
        if (genOK) {
                this->generateExtraPdfValues(extraPdfs, embeddedData);
        }

        // Check for problems with the embedding
        if (embeddedData && (embeddedData->nEvents() == embeddedData->nUsedEvents())) {
                const TString& bkgndClass = this->bkgndClassName(bkgndID);
                std::cerr << "WARNING in LauCPFitModel::generateBkgndEvent : Source of embedded " << bkgndClass << " events used up, clearing the list of used events." << std::endl;
                embeddedData->clearUsedList();
        }

        return genOK;
}

void LauCPFitModel::setupGenNtupleBranches()
{
        // Setup the required ntuple branches
        this->addGenNtupleDoubleBranch("evtWeight");
        this->addGenNtupleIntegerBranch("genSig");
        if ( useSCF_ || ( this->enableEmbedding() &&
                                negSignalTree_ != 0 && negSignalTree_->haveBranch("mcMatch") &&
                                posSignalTree_ != 0 && posSignalTree_->haveBranch("mcMatch") ) ) {
                this->addGenNtupleIntegerBranch("genTMSig");
                this->addGenNtupleIntegerBranch("genSCFSig");
        }
        const UInt_t nBkgnds = this->nBkgndClasses();
        for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
                TString name( this->bkgndClassName(iBkgnd) );
                name.Prepend("gen");
                this->addGenNtupleIntegerBranch(name);
        }
        this->addGenNtupleIntegerBranch("charge");
        if (this->useDP() == kTRUE) {
                this->addGenNtupleDoubleBranch("m12");
                this->addGenNtupleDoubleBranch("m23");
                this->addGenNtupleDoubleBranch("m13");
                this->addGenNtupleDoubleBranch("m12Sq");
                this->addGenNtupleDoubleBranch("m23Sq");
                this->addGenNtupleDoubleBranch("m13Sq");
                this->addGenNtupleDoubleBranch("cosHel12");
                this->addGenNtupleDoubleBranch("cosHel23");
                this->addGenNtupleDoubleBranch("cosHel13");
                if (negKinematics_->squareDP() && posKinematics_->squareDP()) {
                        this->addGenNtupleDoubleBranch("mPrime");
                        this->addGenNtupleDoubleBranch("thPrime");
                }
        }
        for (LauPdfList::const_iterator pdf_iter = negSignalPdfs_.begin(); pdf_iter != negSignalPdfs_.end(); ++pdf_iter) {
                std::vector<TString> varNames = (*pdf_iter)->varNames();
                for ( std::vector<TString>::const_iterator var_iter = varNames.begin(); var_iter != varNames.end(); ++var_iter ) {
                        if ( (*var_iter) != "m13Sq" && (*var_iter) != "m23Sq" ) {
                                this->addGenNtupleDoubleBranch( (*var_iter) );
                        }
                }
        }
}

void LauCPFitModel::setDPBranchValues()
{
        LauKinematics* kinematics(0);
        if (curEvtCharge_<0) {
                kinematics = negKinematics_;
        } else {
                kinematics = posKinematics_;
        }

        // Store all the DP information
        this->setGenNtupleDoubleBranchValue("m12", kinematics->getm12());
        this->setGenNtupleDoubleBranchValue("m23", kinematics->getm23());
        this->setGenNtupleDoubleBranchValue("m13", kinematics->getm13());
        this->setGenNtupleDoubleBranchValue("m12Sq", kinematics->getm12Sq());
        this->setGenNtupleDoubleBranchValue("m23Sq", kinematics->getm23Sq());
        this->setGenNtupleDoubleBranchValue("m13Sq", kinematics->getm13Sq());
        this->setGenNtupleDoubleBranchValue("cosHel12", kinematics->getc12());
        this->setGenNtupleDoubleBranchValue("cosHel23", kinematics->getc23());
        this->setGenNtupleDoubleBranchValue("cosHel13", kinematics->getc13());
        if (kinematics->squareDP()) {
                this->setGenNtupleDoubleBranchValue("mPrime", kinematics->getmPrime());
                this->setGenNtupleDoubleBranchValue("thPrime", kinematics->getThetaPrime());
        }
}

void LauCPFitModel::generateExtraPdfValues(LauPdfList* extraPdfs, LauEmbeddedData* embeddedData)
{
        LauKinematics* kinematics(0);
        if (curEvtCharge_<0) {
                kinematics = negKinematics_;
        } else {
                kinematics = posKinematics_;
        }

        if (!extraPdfs) {
                std::cerr << "ERROR in LauCPFitModel::generateExtraPdfValues : Null pointer to PDF list." << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }

        if (extraPdfs->empty()) {
                //std::cerr << "WARNING in LauCPFitModel::generateExtraPdfValues : PDF list is empty." << std::endl;
                return;
        }

        // Generate from the extra PDFs
        for (LauPdfList::iterator pdf_iter = extraPdfs->begin(); pdf_iter != extraPdfs->end(); ++pdf_iter) {
                LauFitData genValues;
                if (embeddedData) {
                        genValues = embeddedData->getValues( (*pdf_iter)->varNames() );
                } else {
                        genValues = (*pdf_iter)->generate(kinematics);
                }
                for ( LauFitData::const_iterator var_iter = genValues.begin(); var_iter != genValues.end(); ++var_iter ) {
                        TString varName = var_iter->first;
                        if ( varName != "m13Sq" && varName != "m23Sq" ) {
                                Double_t value = var_iter->second;
                                this->setGenNtupleDoubleBranchValue(varName,value);
                        }
                }
        }
}

Bool_t LauCPFitModel::storeSignalMCMatch(LauEmbeddedData* embeddedData)
{
        // Default to TM
        Bool_t genSCF(kFALSE);
        Int_t match(1);

        // Check that we have a valid pointer and that embedded data has
        // the mcMatch branch.  If so then get the match value.
        if ( embeddedData && embeddedData->haveBranch("mcMatch") ) {
                match = TMath::Nint( embeddedData->getValue("mcMatch") );
        }

        // Set the variables accordingly.
        if (match) {
                this->setGenNtupleIntegerBranchValue("genTMSig",1);
                this->setGenNtupleIntegerBranchValue("genSCFSig",0);
                genSCF = kFALSE;
        } else {
                this->setGenNtupleIntegerBranchValue("genTMSig",0);
                this->setGenNtupleIntegerBranchValue("genSCFSig",1);
                genSCF = kTRUE;
        }

        return genSCF;
}

void LauCPFitModel::propagateParUpdates()
{
        // Update the signal parameters and then the total normalisation for the signal likelihood
        if (this->useDP() == kTRUE) {
                this->updateCoeffs();
                negSigModel_->updateCoeffs(negCoeffs_);
                posSigModel_->updateCoeffs(posCoeffs_);
        }

        // Update the signal fraction from the background fractions if not doing an extended fit
        if ( !this->doEMLFit() && !signalEvents_->fixed() ) {
                this->updateSigEvents();
        }
}

void LauCPFitModel::updateSigEvents()
{
        // The background parameters will have been set from Minuit.
        // We need to update the signal events using these.
        Double_t nTotEvts = this->eventsPerExpt();

        signalEvents_->range(-2.0*nTotEvts,2.0*nTotEvts);
        for (LauBkgndYieldList::iterator iter = bkgndEvents_.begin(); iter != bkgndEvents_.end(); ++iter) {
                (*iter)->range(-2.0*nTotEvts,2.0*nTotEvts);
        }

        if (signalEvents_->fixed()) {
                return;
        }

        // Subtract background events (if any) from signal.
        Double_t signalEvents = nTotEvts;
        if (usingBkgnd_ == kTRUE) {
                for (LauBkgndYieldList::const_iterator iter = bkgndEvents_.begin(); iter != bkgndEvents_.end(); ++iter) {
                        signalEvents -= (*iter)->value();
                }
        }
        signalEvents_->value(signalEvents);
}

void LauCPFitModel::cacheInputFitVars()
{
        // Fill the internal data trees of the signal and background models.
        // Note that we store the events of both charges in both the
        // negative and the positive models. It's only later, at the stage
        // when the likelihood is being calculated, that we separate them.

        LauFitDataTree* inputFitData = this->fitData();

        // First the Dalitz plot variables (m_ij^2)
        if (this->useDP() == kTRUE) {

                // need to append SCF smearing bins before caching DP amplitudes
                if ( scfMap_ != 0 ) {
                        this->appendBinCentres( inputFitData );
                }

                negSigModel_->fillDataTree(*inputFitData);
                posSigModel_->fillDataTree(*inputFitData);

                if (usingBkgnd_ == kTRUE) {
                        for (LauBkgndDPModelList::iterator iter = negBkgndDPModels_.begin(); iter != negBkgndDPModels_.end(); ++iter) {
                                (*iter)->fillDataTree(*inputFitData);
                        }
                        for (LauBkgndDPModelList::iterator iter = posBkgndDPModels_.begin(); iter != posBkgndDPModels_.end(); ++iter) {
                                (*iter)->fillDataTree(*inputFitData);
                        }
                }
        }

        // ...and then the extra PDFs
        this->cacheInfo(negSignalPdfs_, *inputFitData);
        this->cacheInfo(negScfPdfs_, *inputFitData);
        for (LauBkgndPdfsList::iterator iter = negBkgndPdfs_.begin(); iter != negBkgndPdfs_.end(); ++iter) {
                this->cacheInfo((*iter), *inputFitData);
        }
        if ( tagged_ ) {
                this->cacheInfo(posSignalPdfs_, *inputFitData);
                this->cacheInfo(posScfPdfs_, *inputFitData);
                for (LauBkgndPdfsList::iterator iter = posBkgndPdfs_.begin(); iter != posBkgndPdfs_.end(); ++iter) {
                        this->cacheInfo((*iter), *inputFitData);
                }
        }

        // the SCF fractions and jacobians
        if ( useSCF_ && useSCFHist_ ) {
                if ( !inputFitData->haveBranch( "m13Sq" )  || !inputFitData->haveBranch( "m23Sq" ) ) {
                        std::cerr << "ERROR in LauCPFitModel::cacheInputFitVars : Input data does not contain DP branches and so can't cache the SCF fraction." << std::endl;
                        gSystem->Exit(EXIT_FAILURE);
                }

                UInt_t nEvents = inputFitData->nEvents();
                recoSCFFracs_.clear();
                recoSCFFracs_.reserve( nEvents );
                if ( negKinematics_->squareDP() ) {
                        recoJacobians_.clear();
                        recoJacobians_.reserve( nEvents );
                }
                for (UInt_t iEvt = 0; iEvt < nEvents; iEvt++) {
                        const LauFitData& dataValues = inputFitData->getData(iEvt);
                        LauFitData::const_iterator m13_iter = dataValues.find("m13Sq");
                        LauFitData::const_iterator m23_iter = dataValues.find("m23Sq");
                        negKinematics_->updateKinematics( m13_iter->second, m23_iter->second );
                        Double_t scfFrac = scfFracHist_->calcEfficiency( negKinematics_ );
                        recoSCFFracs_.push_back( scfFrac );
                        if ( negKinematics_->squareDP() ) {
                                recoJacobians_.push_back( negKinematics_->calcSqDPJacobian() );
                        }
                }
        }

        // finally cache the event charge
        evtCharges_.clear();
        if ( tagged_ ) {
                if ( !inputFitData->haveBranch( tagVarName_ ) ) {
                        std::cerr << "ERROR in LauCPFitModel::cacheInputFitVars : Input data does not contain branch \"" << tagVarName_ << "\"." << std::endl;
                        gSystem->Exit(EXIT_FAILURE);
                }
                UInt_t nEvents = inputFitData->nEvents();
                evtCharges_.reserve( nEvents );
                for (UInt_t iEvt = 0; iEvt < nEvents; iEvt++) {
                        const LauFitData& dataValues = inputFitData->getData(iEvt);
                        LauFitData::const_iterator iter = dataValues.find( tagVarName_ );
                        curEvtCharge_ = static_cast<Int_t>( iter->second );
                        evtCharges_.push_back( curEvtCharge_ );
                }
        }
}

void LauCPFitModel::appendBinCentres( LauFitDataTree* inputData )
{
        // We'll be caching the DP amplitudes and efficiencies of the centres of the true bins.
        // To do so, we attach some fake points at the end of inputData, the number of the entry
        // minus the total number of events corresponding to the number of the histogram for that
        // given true bin in the LauScfMap object. (What this means is that when Laura is provided with
        // the LauScfMap object by the user, it's the latter who has to make sure that it contains the
        // right number of histograms and in exactly the right order!) 

        // Get the x and y co-ordinates of the bin centres
        std::vector<Double_t> binCentresXCoords;
        std::vector<Double_t> binCentresYCoords;
        scfMap_->listBinCentres(binCentresXCoords, binCentresYCoords);

        // The SCF histograms could be in square Dalitz plot histograms.
        // The dynamics takes normal Dalitz plot coords, so we might have to convert them back.
        Bool_t sqDP = negKinematics_->squareDP();
        UInt_t nBins = binCentresXCoords.size();
        fakeSCFFracs_.clear();
        fakeSCFFracs_.reserve( nBins );
        if ( sqDP ) {
                fakeJacobians_.clear();
                fakeJacobians_.reserve( nBins );
        }

        for (UInt_t iBin = 0; iBin < nBins; ++iBin) {

                if ( sqDP ) {
                        negKinematics_->updateSqDPKinematics(binCentresXCoords[iBin],binCentresYCoords[iBin]);
                        binCentresXCoords[iBin] = negKinematics_->getm13Sq();
                        binCentresYCoords[iBin] = negKinematics_->getm23Sq();
                        fakeJacobians_.push_back( negKinematics_->calcSqDPJacobian() );
                } else {
                        negKinematics_->updateKinematics(binCentresXCoords[iBin],binCentresYCoords[iBin]);
                }

                fakeSCFFracs_.push_back( scfFracHist_->calcEfficiency( negKinematics_ ) );
        }

        // Set up inputFitVars_ object to hold the fake events
        inputData->appendFakePoints(binCentresXCoords,binCentresYCoords);
}

Double_t LauCPFitModel::getTotEvtLikelihood(UInt_t iEvt)
{
        // Find out whether we have B- or B+
        if ( tagged_ ) {
                curEvtCharge_ = evtCharges_[iEvt];

                // check that the charge is either +1 or -1
                if (TMath::Abs(curEvtCharge_)!=1) {
                        std::cerr << "ERROR in LauCPFitModel::getTotEvtLikelihood : Charge/tag not accepted value: " << curEvtCharge_ << std::endl;
                        if (curEvtCharge_>0) {
                                curEvtCharge_ = +1;
                        } else {
                                curEvtCharge_ = -1;
                        }
                        std::cerr << "                                            : Making it: " << curEvtCharge_ << "." << std::endl;
                }
        }

        // Get the DP likelihood for signal and backgrounds
        this->getEvtDPLikelihood(iEvt);

        // Get the combined extra PDFs likelihood for signal and backgrounds
        this->getEvtExtraLikelihoods(iEvt);

        // If appropriate, combine the TM and SCF likelihoods
        Double_t sigLike = sigDPLike_ * sigExtraLike_;
        if ( useSCF_ ) {
                Double_t scfFrac(0.0);
                if (useSCFHist_) {
                        scfFrac = recoSCFFracs_[iEvt];
                } else {
                        scfFrac = scfFrac_.value();
                }
                sigLike *= (1.0 - scfFrac);
                if ( (scfMap_ != 0) && (this->useDP() == kTRUE) ) {
                        // if we're smearing the SCF DP PDF then the SCF frac
                        // is already included in the SCF DP likelihood
                        sigLike += (scfDPLike_ * scfExtraLike_);
                } else {
                        sigLike += (scfFrac * scfDPLike_ * scfExtraLike_);
                }
        }

        // Get the correct event fractions depending on the charge
        // Signal asymmetry is built into the DP model... but when the DP
        // isn't in the fit we need an explicit parameter
        Double_t signalEvents = signalEvents_->value() * 0.5;
        if (this->useDP() == kFALSE) {
                signalEvents *= (1.0 - curEvtCharge_ * signalAsym_->value());
        }

        // Construct the total event likelihood
        Double_t likelihood(0.0);
        if (usingBkgnd_) {
                likelihood = sigLike*signalEvents;
                const UInt_t nBkgnds = this->nBkgndClasses();
                for ( UInt_t bkgndID(0); bkgndID < nBkgnds; ++bkgndID ) {
                        Double_t bkgndEvents  = bkgndEvents_[bkgndID]->value() * 0.5 * (1.0 - curEvtCharge_ * bkgndAsym_[bkgndID]->value());
                        likelihood += bkgndEvents*bkgndDPLike_[bkgndID]*bkgndExtraLike_[bkgndID];
                }
        } else {
                likelihood = sigLike*0.5;
        }
        return likelihood;
}

Double_t LauCPFitModel::getEventSum() const
{
        Double_t eventSum(0.0);
        eventSum += signalEvents_->value();
        if (usingBkgnd_) {
                for (LauBkgndYieldList::const_iterator iter = bkgndEvents_.begin(); iter != bkgndEvents_.end(); ++iter) {
                        eventSum += (*iter)->value();
                }
        }
        return eventSum;
}

void LauCPFitModel::getEvtDPLikelihood(UInt_t iEvt)
{
        // Function to return the signal and background likelihoods for the
        // Dalitz plot for the given event evtNo.

        if ( ! this->useDP() ) {
                // There's always going to be a term in the likelihood for the
                // signal, so we'd better not zero it.
                sigDPLike_ = 1.0;
                scfDPLike_ = 1.0;

                const UInt_t nBkgnds = this->nBkgndClasses();
                for ( UInt_t bkgndID(0); bkgndID < nBkgnds; ++bkgndID ) {
                        if (usingBkgnd_ == kTRUE) {
                                bkgndDPLike_[bkgndID] = 1.0;
                        } else {
                                bkgndDPLike_[bkgndID] = 0.0;
                        }
                }

                return;
        }

        const UInt_t nBkgnds = this->nBkgndClasses();
        if ( tagged_ ) {
                if (curEvtCharge_==+1) {
                        posSigModel_->calcLikelihoodInfo(iEvt);
                        sigDPLike_ = posSigModel_->getEvtDPAmp().abs2();
                        sigDPLike_ *= posSigModel_->getEvtEff();

                        for ( UInt_t bkgndID(0); bkgndID < nBkgnds; ++bkgndID ) {
                                if (usingBkgnd_ == kTRUE) {
                                        bkgndDPLike_[bkgndID] = posBkgndDPModels_[bkgndID]->getLikelihood(iEvt);
                                } else {
                                        bkgndDPLike_[bkgndID] = 0.0;
                                }
                        }
                } else {
                        negSigModel_->calcLikelihoodInfo(iEvt);
                        sigDPLike_ = negSigModel_->getEvtDPAmp().abs2();
                        sigDPLike_ *= negSigModel_->getEvtEff();

                        for ( UInt_t bkgndID(0); bkgndID < nBkgnds; ++bkgndID ) {
                                if (usingBkgnd_ == kTRUE) {
                                        bkgndDPLike_[bkgndID] = negBkgndDPModels_[bkgndID]->getLikelihood(iEvt);
                                } else {
                                        bkgndDPLike_[bkgndID] = 0.0;
                                }
                        }
                }
        } else {
                posSigModel_->calcLikelihoodInfo(iEvt);
                negSigModel_->calcLikelihoodInfo(iEvt);

                sigDPLike_ = 0.5 * ( posSigModel_->getEvtDPAmp().abs2() * posSigModel_->getEvtEff() +
                                negSigModel_->getEvtDPAmp().abs2() * negSigModel_->getEvtEff() );

                for ( UInt_t bkgndID(0); bkgndID < nBkgnds; ++bkgndID ) {
                        if (usingBkgnd_ == kTRUE) {
                                bkgndDPLike_[bkgndID] = 0.5 * ( posBkgndDPModels_[bkgndID]->getLikelihood(iEvt) +
                                                negBkgndDPModels_[bkgndID]->getLikelihood(iEvt) );
                        } else {
                                bkgndDPLike_[bkgndID] = 0.0;
                        }
                }
        }

        if ( useSCF_ == kTRUE ) {
                if ( scfMap_ == 0 ) {
                        // we're not smearing the SCF DP position
                        // so the likelihood is the same as the TM
                        scfDPLike_ = sigDPLike_;
                } else {
                        // calculate the smeared SCF DP likelihood
                        scfDPLike_ = this->getEvtSCFDPLikelihood(iEvt);
                }
        }

        // Calculate the signal normalisation
        // NB the 2.0 is there so that the 0.5 factor is applied to
        // signal and background in the same place otherwise you get
        // normalisation problems when you switch off the DP in the fit
        Double_t norm = negSigModel_->getDPNorm() + posSigModel_->getDPNorm();
        sigDPLike_ *= 2.0/norm;
        scfDPLike_ *= 2.0/norm;
}

Double_t LauCPFitModel::getEvtSCFDPLikelihood(UInt_t iEvt)
{
        Double_t scfDPLike(0.0);

        Double_t recoJacobian(1.0);
        Double_t xCoord(0.0);
        Double_t yCoord(0.0);

        Bool_t squareDP = negKinematics_->squareDP();
        if ( squareDP ) {
                xCoord = negSigModel_->getEvtmPrime();
                yCoord = negSigModel_->getEvtthPrime();
                recoJacobian = recoJacobians_[iEvt];
        } else {
                xCoord = negSigModel_->getEvtm13Sq();
                yCoord = negSigModel_->getEvtm23Sq();
        }

        // Find the bin that our reco event falls in
        Int_t recoBin = scfMap_->binNumber( xCoord, yCoord );

        // Find out which true Bins contribute to the given reco bin
        const std::vector<Int_t>* trueBins = scfMap_->trueBins(recoBin);

        const Int_t nDataEvents = this->eventsPerExpt();

        // Loop over the true bins
        for (std::vector<Int_t>::const_iterator iter = trueBins->begin(); iter != trueBins->end(); ++iter)
        {
                Int_t trueBin = (*iter);

                // prob of a true event in the given true bin migrating to the reco bin
                Double_t pRecoGivenTrue =  scfMap_->prob( recoBin, trueBin );
                Double_t pTrue(0.0);

                // We've cached the DP amplitudes and the efficiency for the
                // true bin centres, just after the data points
                if ( tagged_ ) {
                        LauAbsDPDynamics* sigModel(0);
                        if (curEvtCharge_<0) {
                                sigModel = negSigModel_;
                        } else {
                                sigModel = posSigModel_;
                        }

                        sigModel->calcLikelihoodInfo( nDataEvents + trueBin );

                        pTrue = sigModel->getEvtDPAmp().abs2() * sigModel->getEvtEff();
                } else {
                        posSigModel_->calcLikelihoodInfo( nDataEvents + trueBin );
                        negSigModel_->calcLikelihoodInfo( nDataEvents + trueBin );

                        pTrue = 0.5 * ( posSigModel_->getEvtDPAmp().abs2() * posSigModel_->getEvtEff() +
                                        negSigModel_->getEvtDPAmp().abs2() * negSigModel_->getEvtEff() );
                }

                // Get the cached SCF fraction (and jacobian if we're using the square DP)
                Double_t scfFraction = fakeSCFFracs_[ trueBin ];
                Double_t jacobian(1.0);
                if ( squareDP ) {
                        jacobian = fakeJacobians_[ trueBin ];
                }

                scfDPLike += pTrue * jacobian * scfFraction * pRecoGivenTrue;
        }

        // Divide by the reco jacobian
        scfDPLike /= recoJacobian;

        return scfDPLike;
}

void LauCPFitModel::getEvtExtraLikelihoods(UInt_t iEvt)
{
        // Function to return the signal and background likelihoods for the
        // extra variables for the given event evtNo.

        sigExtraLike_ = 1.0;

        const UInt_t nBkgnds = this->nBkgndClasses();

        if ( ! tagged_  || curEvtCharge_ < 0 ) {
                sigExtraLike_ = this->prodPdfValue( negSignalPdfs_, iEvt );

                if (useSCF_) {
                        scfExtraLike_ = this->prodPdfValue( negScfPdfs_, iEvt );
                }

                for ( UInt_t bkgndID(0); bkgndID < nBkgnds; ++bkgndID ) {
                        if (usingBkgnd_) {
                                bkgndExtraLike_[bkgndID] = this->prodPdfValue( negBkgndPdfs_[bkgndID], iEvt );
                        } else {
                                bkgndExtraLike_[bkgndID] = 0.0;
                        }
                }
        } else {
                sigExtraLike_ = this->prodPdfValue( posSignalPdfs_, iEvt );

                if (useSCF_) {
                        scfExtraLike_ = this->prodPdfValue( posScfPdfs_, iEvt );
                }

                for ( UInt_t bkgndID(0); bkgndID < nBkgnds; ++bkgndID ) {
                        if (usingBkgnd_) {
                                bkgndExtraLike_[bkgndID] = this->prodPdfValue( posBkgndPdfs_[bkgndID], iEvt );
                        } else {
                                bkgndExtraLike_[bkgndID] = 0.0;
                        }
                }
        }
}

void LauCPFitModel::updateCoeffs() 
{
        negCoeffs_.clear(); posCoeffs_.clear();
        negCoeffs_.reserve(nSigComp_); posCoeffs_.reserve(nSigComp_);
        for (UInt_t i = 0; i < nSigComp_; i++) {
                negCoeffs_.push_back(coeffPars_[i]->antiparticleCoeff());
                posCoeffs_.push_back(coeffPars_[i]->particleCoeff());
        }
}

void LauCPFitModel::setupSPlotNtupleBranches()
{
        // add branches for storing the experiment number and the number of
        // the event within the current experiment
        this->addSPlotNtupleIntegerBranch("iExpt");
        this->addSPlotNtupleIntegerBranch("iEvtWithinExpt");

        // Store the efficiency of the event (for inclusive BF calculations).
        if (this->storeDPEff()) {
                this->addSPlotNtupleDoubleBranch("efficiency");
                if ( negSigModel_->usingScfModel() && posSigModel_->usingScfModel() ) {
                        this->addSPlotNtupleDoubleBranch("scffraction");
                }
        }

        // Store the total event likelihood for each species.
        if (useSCF_) {
                this->addSPlotNtupleDoubleBranch("sigTMTotalLike");
                this->addSPlotNtupleDoubleBranch("sigSCFTotalLike");
                this->addSPlotNtupleDoubleBranch("sigSCFFrac");
        } else {
                this->addSPlotNtupleDoubleBranch("sigTotalLike");
        }
        if (usingBkgnd_) {
                const UInt_t nBkgnds = this->nBkgndClasses();
                for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
                        TString name( this->bkgndClassName(iBkgnd) );
                        name += "TotalLike";
                        this->addSPlotNtupleDoubleBranch(name);
                }
        }

        // Store the DP likelihoods
        if (this->useDP()) {
                if (useSCF_) {
                        this->addSPlotNtupleDoubleBranch("sigTMDPLike");
                        this->addSPlotNtupleDoubleBranch("sigSCFDPLike");
                } else {
                        this->addSPlotNtupleDoubleBranch("sigDPLike");
                }
                if (usingBkgnd_) {
                        const UInt_t nBkgnds = this->nBkgndClasses();
                        for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
                                TString name( this->bkgndClassName(iBkgnd) );
                                name += "DPLike";
                                this->addSPlotNtupleDoubleBranch(name);
                        }
                }
        }

        // Store the likelihoods for each extra PDF
        if (useSCF_) {
                this->addSPlotNtupleBranches(&negSignalPdfs_, "sigTM");
                this->addSPlotNtupleBranches(&negScfPdfs_, "sigSCF");
        } else {
                this->addSPlotNtupleBranches(&negSignalPdfs_, "sig");
        }
        if (usingBkgnd_) {
                const UInt_t nBkgnds = this->nBkgndClasses();
                for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
                        const TString& bkgndClass = this->bkgndClassName(iBkgnd);
                        const LauPdfList* pdfList = &(negBkgndPdfs_[iBkgnd]);
                        this->addSPlotNtupleBranches(pdfList, bkgndClass);
                }
        }
}

void LauCPFitModel::addSPlotNtupleBranches(const LauPdfList* extraPdfs, const TString& prefix)
{
        if (extraPdfs) {
                // Loop through each of the PDFs
                for (LauPdfList::const_iterator pdf_iter = extraPdfs->begin(); pdf_iter != extraPdfs->end(); ++pdf_iter) {

                        // Count the number of input variables that are not
                        // DP variables (used in the case where there is DP
                        // dependence for e.g. MVA)
                        UInt_t nVars(0);
                        std::vector<TString> varNames = (*pdf_iter)->varNames();
                        for ( std::vector<TString>::const_iterator var_iter = varNames.begin(); var_iter != varNames.end(); ++var_iter ) {
                                if ( (*var_iter) != "m13Sq" && (*var_iter) != "m23Sq" ) {
                                        ++nVars;
                                }
                        }

                        if ( nVars == 1 ) {
                                // If the PDF only has one variable then
                                // simply add one branch for that variable
                                TString varName = (*pdf_iter)->varName();
                                TString name(prefix);
                                name += varName;
                                name += "Like";
                                this->addSPlotNtupleDoubleBranch(name);
                        } else if ( nVars == 2 ) {
                                // If the PDF has two variables then we
                                // need a branch for them both together and
                                // branches for each
                                TString allVars("");
                                for ( std::vector<TString>::const_iterator var_iter = varNames.begin(); var_iter != varNames.end(); ++var_iter ) {
                                        allVars += (*var_iter);
                                        TString name(prefix);
                                        name += (*var_iter);
                                        name += "Like";
                                        this->addSPlotNtupleDoubleBranch(name);
                                }
                                TString name(prefix);
                                name += allVars;
                                name += "Like";
                                this->addSPlotNtupleDoubleBranch(name);
                        } else {
                                std::cerr << "WARNING in LauCPFitModel::addSPlotNtupleBranches : Can't yet deal with 3D PDFs." << std::endl;
                        }
                }
        }
}

Double_t LauCPFitModel::setSPlotNtupleBranchValues(LauPdfList* extraPdfs, const TString& prefix, UInt_t iEvt)
{
        // Store the PDF value for each variable in the list
        Double_t totalLike(1.0);
        Double_t extraLike(0.0);
        if (extraPdfs) {
                for (LauPdfList::iterator pdf_iter = extraPdfs->begin(); pdf_iter != extraPdfs->end(); ++pdf_iter) {

                        // calculate the likelihood for this event
                        (*pdf_iter)->calcLikelihoodInfo(iEvt);
                        extraLike = (*pdf_iter)->getLikelihood();
                        totalLike *= extraLike;

                        // Count the number of input variables that are not
                        // DP variables (used in the case where there is DP
                        // dependence for e.g. MVA)
                        UInt_t nVars(0);
                        std::vector<TString> varNames = (*pdf_iter)->varNames();
                        for ( std::vector<TString>::const_iterator var_iter = varNames.begin(); var_iter != varNames.end(); ++var_iter ) {
                                if ( (*var_iter) != "m13Sq" && (*var_iter) != "m23Sq" ) {
                                        ++nVars;
                                }
                        }

                        if ( nVars == 1 ) {
                                // If the PDF only has one variable then
                                // simply store the value for that variable
                                TString varName = (*pdf_iter)->varName();
                                TString name(prefix);
                                name += varName;
                                name += "Like";
                                this->setSPlotNtupleDoubleBranchValue(name, extraLike);
                        } else if ( nVars == 2 ) {
                                // If the PDF has two variables then we
                                // store the value for them both together
                                // and for each on their own
                                TString allVars("");
                                for ( std::vector<TString>::const_iterator var_iter = varNames.begin(); var_iter != varNames.end(); ++var_iter ) {
                                        allVars += (*var_iter);
                                        TString name(prefix);
                                        name += (*var_iter);
                                        name += "Like";
                                        Double_t indivLike = (*pdf_iter)->getLikelihood( (*var_iter) );
                                        this->setSPlotNtupleDoubleBranchValue(name, indivLike);
                                }
                                TString name(prefix);
                                name += allVars;
                                name += "Like";
                                this->setSPlotNtupleDoubleBranchValue(name, extraLike);
                        } else {
                                std::cerr << "WARNING in LauCPFitModel::setSPlotNtupleBranchValues : Can't yet deal with 3D PDFs." << std::endl;
                        }
                }
        }
        return totalLike;
}

LauSPlot::NameSet LauCPFitModel::variableNames() const
{
        LauSPlot::NameSet nameSet;
        if (this->useDP()) {
                nameSet.insert("DP");
        }
        // Loop through all the signal PDFs
        for (LauPdfList::const_iterator pdf_iter = negSignalPdfs_.begin(); pdf_iter != negSignalPdfs_.end(); ++pdf_iter) {
                // Loop over the variables involved in each PDF
                std::vector<TString> varNames = (*pdf_iter)->varNames();
                for ( std::vector<TString>::const_iterator var_iter = varNames.begin(); var_iter != varNames.end(); ++var_iter ) {
                        // If they are not DP coordinates then add them
                        if ( (*var_iter) != "m13Sq" && (*var_iter) != "m23Sq" ) {
                                nameSet.insert( (*var_iter) );
                        }
                }
        }
        return nameSet;
}

LauSPlot::NumbMap LauCPFitModel::freeSpeciesNames() const
{
        LauSPlot::NumbMap numbMap;
        if (!signalEvents_->fixed() && this->doEMLFit()) {
                numbMap["sig"] = signalEvents_->genValue();
        }
        if ( usingBkgnd_ ) {
                const UInt_t nBkgnds = this->nBkgndClasses();
                for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
                        const TString& bkgndClass = this->bkgndClassName(iBkgnd);
                        const LauParameter* par = bkgndEvents_[iBkgnd];
                        if (!par->fixed()) {
                                numbMap[bkgndClass] = par->genValue();
                        }
                }
        }
        return numbMap;
}

LauSPlot::NumbMap LauCPFitModel::fixdSpeciesNames() const
{
        LauSPlot::NumbMap numbMap;
        if (signalEvents_->fixed() && this->doEMLFit()) {
                numbMap["sig"] = signalEvents_->genValue();
        }
        if ( usingBkgnd_ ) {
                const UInt_t nBkgnds = this->nBkgndClasses();
                for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
                        const TString& bkgndClass = this->bkgndClassName(iBkgnd);
                        const LauParameter* par = bkgndEvents_[iBkgnd];
                        if (par->fixed()) {
                                numbMap[bkgndClass] = par->genValue();
                        }
                }
        }
        return numbMap;
}

LauSPlot::TwoDMap LauCPFitModel::twodimPDFs() const
{
        // This makes the assumption that the form of the positive and
        // negative PDFs are the same, which seems reasonable to me

        LauSPlot::TwoDMap twodimMap;

        for (LauPdfList::const_iterator pdf_iter = negSignalPdfs_.begin(); pdf_iter != negSignalPdfs_.end(); ++pdf_iter) {
                // Count the number of input variables that are not DP variables
                UInt_t nVars(0);
                std::vector<TString> varNames = (*pdf_iter)->varNames();
                for ( std::vector<TString>::const_iterator var_iter = varNames.begin(); var_iter != varNames.end(); ++var_iter ) {
                        if ( (*var_iter) != "m13Sq" && (*var_iter) != "m23Sq" ) {
                                ++nVars;
                        }
                }
                if ( nVars == 2 ) {
                        if (useSCF_) {
                                twodimMap.insert( std::make_pair( "sigTM", std::make_pair( varNames[0], varNames[1] ) ) );
                        } else {
                                twodimMap.insert( std::make_pair( "sig", std::make_pair( varNames[0], varNames[1] ) ) );
                        }
                }
        }

        if ( useSCF_ ) {
                for (LauPdfList::const_iterator pdf_iter = negScfPdfs_.begin(); pdf_iter != negScfPdfs_.end(); ++pdf_iter) {
                        // Count the number of input variables that are not DP variables
                        UInt_t nVars(0);
                        std::vector<TString> varNames = (*pdf_iter)->varNames();
                        for ( std::vector<TString>::const_iterator var_iter = varNames.begin(); var_iter != varNames.end(); ++var_iter ) {
                                if ( (*var_iter) != "m13Sq" && (*var_iter) != "m23Sq" ) {
                                        ++nVars;
                                }
                        }
                        if ( nVars == 2 ) {
                                twodimMap.insert( std::make_pair( "sigSCF", std::make_pair( varNames[0], varNames[1] ) ) );
                        }
                }
        }

        if (usingBkgnd_) {
                const UInt_t nBkgnds = this->nBkgndClasses();
                for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
                        const TString& bkgndClass = this->bkgndClassName(iBkgnd);
                        const LauPdfList& pdfList = negBkgndPdfs_[iBkgnd];
                        for (LauPdfList::const_iterator pdf_iter = pdfList.begin(); pdf_iter != pdfList.end(); ++pdf_iter) {
                                // Count the number of input variables that are not DP variables
                                UInt_t nVars(0);
                                std::vector<TString> varNames = (*pdf_iter)->varNames();
                                for ( std::vector<TString>::const_iterator var_iter = varNames.begin(); var_iter != varNames.end(); ++var_iter ) {
                                        if ( (*var_iter) != "m13Sq" && (*var_iter) != "m23Sq" ) {
                                                ++nVars;
                                        }
                                }
                                if ( nVars == 2 ) {
                                        twodimMap.insert( std::make_pair( bkgndClass, std::make_pair( varNames[0], varNames[1] ) ) );
                                }
                        }
                }
        }

        return twodimMap;
}

void LauCPFitModel::storePerEvtLlhds()
{
        std::cout << "INFO in LauCPFitModel::storePerEvtLlhds : Storing per-event likelihood values..." << std::endl;

        // if we've not been using the DP model then we need to cache all
        // the info here so that we can get the efficiency from it

        LauFitDataTree* inputFitData = this->fitData();

        if (!this->useDP() && this->storeDPEff()) {
                negSigModel_->initialise(negCoeffs_);
                posSigModel_->initialise(posCoeffs_);
                negSigModel_->fillDataTree(*inputFitData);
                posSigModel_->fillDataTree(*inputFitData);
        }

        UInt_t evtsPerExpt(this->eventsPerExpt());

        LauAbsDPDynamics* sigModel(0);
        LauPdfList* sigPdfs(0);
        LauPdfList* scfPdfs(0);
        LauBkgndPdfsList* bkgndPdfs(0);

        for (UInt_t iEvt = 0; iEvt < evtsPerExpt; ++iEvt) {

                this->setSPlotNtupleIntegerBranchValue("iExpt",this->iExpt());
                this->setSPlotNtupleIntegerBranchValue("iEvtWithinExpt",iEvt);

                // Find out whether we have B- or B+
                if ( tagged_ ) {
                        const LauFitData& dataValues = inputFitData->getData(iEvt);
                        LauFitData::const_iterator iter = dataValues.find("charge");
                        curEvtCharge_ = static_cast<Int_t>(iter->second);

                        if (curEvtCharge_==+1) {
                                sigModel = posSigModel_;
                                sigPdfs = &posSignalPdfs_;
                                scfPdfs = &posScfPdfs_;
                                bkgndPdfs = &posBkgndPdfs_;
                        } else {
                                sigModel = negSigModel_;
                                sigPdfs = &negSignalPdfs_;
                                scfPdfs = &negScfPdfs_;
                                bkgndPdfs = &negBkgndPdfs_;
                        }
                } else {
                        sigPdfs = &negSignalPdfs_;
                        scfPdfs = &negScfPdfs_;
                        bkgndPdfs = &negBkgndPdfs_;
                }

                // the DP information
                this->getEvtDPLikelihood(iEvt);
                if (this->storeDPEff()) {
                        if (!this->useDP()) {
                                posSigModel_->calcLikelihoodInfo(iEvt);
                                negSigModel_->calcLikelihoodInfo(iEvt);
                        }
                        if ( tagged_ ) {
                                this->setSPlotNtupleDoubleBranchValue("efficiency",sigModel->getEvtEff());
                                if ( negSigModel_->usingScfModel() && posSigModel_->usingScfModel() ) {
                                        this->setSPlotNtupleDoubleBranchValue("scffraction",sigModel->getEvtScfFraction());
                                }
                        } else {
                                this->setSPlotNtupleDoubleBranchValue("efficiency",0.5*(posSigModel_->getEvtEff() + negSigModel_->getEvtEff()) );
                                if ( negSigModel_->usingScfModel() && posSigModel_->usingScfModel() ) {
                                        this->setSPlotNtupleDoubleBranchValue("scffraction",0.5*(posSigModel_->getEvtScfFraction() + negSigModel_->getEvtScfFraction()));
                                }
                        }
                }
                if (this->useDP()) {
                        sigTotalLike_ = sigDPLike_;
                        if (useSCF_) {
                                scfTotalLike_ = scfDPLike_;
                                this->setSPlotNtupleDoubleBranchValue("sigTMDPLike",sigDPLike_);
                                this->setSPlotNtupleDoubleBranchValue("sigSCFDPLike",scfDPLike_);
                        } else {
                                this->setSPlotNtupleDoubleBranchValue("sigDPLike",sigDPLike_);
                        }
                        if (usingBkgnd_) {
                                const UInt_t nBkgnds = this->nBkgndClasses();
                                for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
                                        TString name = this->bkgndClassName(iBkgnd);
                                        name += "DPLike";
                                        this->setSPlotNtupleDoubleBranchValue(name,bkgndDPLike_[iBkgnd]);
                                }
                        }
                } else {
                        sigTotalLike_ = 1.0;
                        if (useSCF_) {
                                scfTotalLike_ = 1.0;
                        }
                        if (usingBkgnd_) {
                                const UInt_t nBkgnds = this->nBkgndClasses();
                                for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
                                        bkgndTotalLike_[iBkgnd] = 1.0;
                                }
                        }
                }

                // the signal PDF values
                if ( useSCF_ ) {
                        sigTotalLike_ *= this->setSPlotNtupleBranchValues(sigPdfs, "sigTM", iEvt);
                        scfTotalLike_ *= this->setSPlotNtupleBranchValues(scfPdfs, "sigSCF", iEvt);
                } else {
                        sigTotalLike_ *= this->setSPlotNtupleBranchValues(sigPdfs, "sig", iEvt);
                }

                // the background PDF values
                if (usingBkgnd_) {
                        const UInt_t nBkgnds = this->nBkgndClasses();
                        for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
                                const TString& bkgndClass = this->bkgndClassName(iBkgnd);
                                LauPdfList& pdfs = (*bkgndPdfs)[iBkgnd];
                                bkgndTotalLike_[iBkgnd] *= this->setSPlotNtupleBranchValues(&(pdfs), bkgndClass, iEvt);
                        }
                }

                // the total likelihoods
                if (useSCF_) {
                        Double_t scfFrac(0.0);
                        if ( useSCFHist_ ) {
                                scfFrac = recoSCFFracs_[iEvt];
                        } else {
                                scfFrac = scfFrac_.value();
                        }
                        this->setSPlotNtupleDoubleBranchValue("sigSCFFrac",scfFrac);
                        sigTotalLike_ *= ( 1.0 - scfFrac );
                        if ( scfMap_ == 0 ) {
                                scfTotalLike_ *= scfFrac;
                        }
                        this->setSPlotNtupleDoubleBranchValue("sigTMTotalLike",sigTotalLike_);
                        this->setSPlotNtupleDoubleBranchValue("sigSCFTotalLike",scfTotalLike_);
                } else {
                        this->setSPlotNtupleDoubleBranchValue("sigTotalLike",sigTotalLike_);
                }
                if (usingBkgnd_) {
                        const UInt_t nBkgnds = this->nBkgndClasses();
                        for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
                                TString name = this->bkgndClassName(iBkgnd);
                                name += "TotalLike";
                                this->setSPlotNtupleDoubleBranchValue(name,bkgndTotalLike_[iBkgnd]);
                        }
                }
                // fill the tree
                this->fillSPlotNtupleBranches();
        }
        std::cout << "INFO in LauCPFitModel::storePerEvtLlhds : Finished storing per-event likelihood values." << std::endl;
}

void LauCPFitModel::embedNegSignal(const TString& fileName, const TString& treeName,
                Bool_t reuseEventsWithinEnsemble, Bool_t reuseEventsWithinExperiment, 
                Bool_t useReweighting)
{
        if (negSignalTree_) {
                std::cerr << "ERROR in LauCPFitModel::embedNegSignal : Already embedding signal from a file." << std::endl;
                return;
        }

        if (!reuseEventsWithinEnsemble && reuseEventsWithinExperiment) {
                std::cerr << "WARNING in LauCPFitModel::embedNegSignal : Conflicting options provided, will not reuse events at all." << std::endl;
                reuseEventsWithinExperiment = kFALSE;
        }

        negSignalTree_ = new LauEmbeddedData(fileName,treeName,reuseEventsWithinExperiment);
        Bool_t dataOK = negSignalTree_->findBranches();
        if (!dataOK) {
                delete negSignalTree_; negSignalTree_ = 0;
                std::cerr << "ERROR in LauCPFitModel::embedNegSignal : Problem creating data tree for embedding." << std::endl;
                return;
        }
        reuseSignal_ = reuseEventsWithinEnsemble;
        useNegReweighting_ = useReweighting;
        if (this->enableEmbedding() == kFALSE) {this->enableEmbedding(kTRUE);}
}

void LauCPFitModel::embedNegBkgnd(const TString& bkgndClass, const TString& fileName, const TString& treeName,
                Bool_t reuseEventsWithinEnsemble, Bool_t reuseEventsWithinExperiment)
{
        if ( ! this->validBkgndClass( bkgndClass ) ) {
                std::cerr << "ERROR in LauCPFitModel::embedBkgnd : Invalid background class \"" << bkgndClass << "\"." << std::endl;
                std::cerr << "                                   : Background class names must be provided in \"setBkgndClassNames\" before any other background-related actions can be performed." << std::endl;
                return;
        }

        UInt_t bkgndID = this->bkgndClassID( bkgndClass );

        if (negBkgndTree_[bkgndID]) {
                std::cerr << "ERROR in LauCPFitModel::embedNegBkgnd : Already embedding background from a file." << std::endl;
                return;
        }

        if (!reuseEventsWithinEnsemble && reuseEventsWithinExperiment) {
                std::cerr << "WARNING in LauCPFitModel::embedNegBkgnd : Conflicting options provided, will not reuse events at all." << std::endl;
                reuseEventsWithinExperiment = kFALSE;
        }

        negBkgndTree_[bkgndID] = new LauEmbeddedData(fileName,treeName,reuseEventsWithinExperiment);
        Bool_t dataOK = negBkgndTree_[bkgndID]->findBranches();
        if (!dataOK) {
                delete negBkgndTree_[bkgndID]; negBkgndTree_[bkgndID] = 0;
                std::cerr << "ERROR in LauCPFitModel::embedNegBkgnd : Problem creating data tree for embedding." << std::endl;
                return;
        }
        reuseBkgnd_[bkgndID] = reuseEventsWithinEnsemble;
        if (this->enableEmbedding() == kFALSE) {this->enableEmbedding(kTRUE);}
}

void LauCPFitModel::embedPosSignal(const TString& fileName, const TString& treeName,
                Bool_t reuseEventsWithinEnsemble, Bool_t reuseEventsWithinExperiment,
                Bool_t useReweighting)
{
        if (posSignalTree_) {
                std::cerr << "ERROR in LauCPFitModel::embedPosSignal : Already embedding signal from a file." << std::endl;
                return;
        }

        if (!reuseEventsWithinEnsemble && reuseEventsWithinExperiment) {
                std::cerr << "WARNING in LauCPFitModel::embedPosSignal : Conflicting options provided, will not reuse events at all." << std::endl;
                reuseEventsWithinExperiment = kFALSE;
        }

        posSignalTree_ = new LauEmbeddedData(fileName,treeName,reuseEventsWithinExperiment);
        Bool_t dataOK = posSignalTree_->findBranches();
        if (!dataOK) {
                delete posSignalTree_; posSignalTree_ = 0;
                std::cerr << "ERROR in LauCPFitModel::embedPosSignal : Problem creating data tree for embedding." << std::endl;
                return;
        }
        reuseSignal_ = reuseEventsWithinEnsemble;
        usePosReweighting_ = useReweighting;
        if (this->enableEmbedding() == kFALSE) {this->enableEmbedding(kTRUE);}
}

void LauCPFitModel::embedPosBkgnd(const TString& bkgndClass, const TString& fileName, const TString& treeName,
                Bool_t reuseEventsWithinEnsemble, Bool_t reuseEventsWithinExperiment)
{
        if ( ! this->validBkgndClass( bkgndClass ) ) {
                std::cerr << "ERROR in LauCPFitModel::embedBkgnd : Invalid background class \"" << bkgndClass << "\"." << std::endl;
                std::cerr << "                                   : Background class names must be provided in \"setBkgndClassNames\" before any other background-related actions can be performed." << std::endl;
                return;
        }

        UInt_t bkgndID = this->bkgndClassID( bkgndClass );

        if (posBkgndTree_[bkgndID]) {
                std::cerr << "ERROR in LauCPFitModel::embedPosBkgnd : Already embedding background from a file." << std::endl;
                return;
        }

        if (!reuseEventsWithinEnsemble && reuseEventsWithinExperiment) {
                std::cerr << "WARNING in LauCPFitModel::embedPosBkgnd : Conflicting options provided, will not reuse events at all." << std::endl;
                reuseEventsWithinExperiment = kFALSE;
        }

        posBkgndTree_[bkgndID] = new LauEmbeddedData(fileName,treeName,reuseEventsWithinExperiment);
        Bool_t dataOK = posBkgndTree_[bkgndID]->findBranches();
        if (!dataOK) {
                delete posBkgndTree_[bkgndID]; posBkgndTree_[bkgndID] = 0;
                std::cerr << "ERROR in LauCPFitModel::embedPosBkgnd : Problem creating data tree for embedding." << std::endl;
                return;
        }
        reuseBkgnd_[bkgndID] = reuseEventsWithinEnsemble;
        if (this->enableEmbedding() == kFALSE) {this->enableEmbedding(kTRUE);}
}

void LauCPFitModel::weightEvents( const TString& /*dataFileName*/, const TString& /*dataTreeName*/ )
{
        std::cerr << "ERROR in LauCPFitModel::weightEvents : Method not available for this fit model." << std::endl;
        return;
}