LauSimpleFitModel.cc

Go to the documentation of this file.

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

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

ClassImp(LauSimpleFitModel)


        LauSimpleFitModel::LauSimpleFitModel(LauAbsDPDynamics* sigModel) : LauAbsFitModel(),
        sigDPModel_(sigModel),
        kinematics_(sigModel ? sigModel->getKinematics() : 0),
        usingBkgnd_(kFALSE),
        nSigComp_(0),
        nSigDPPar_(0),
        nExtraPdfPar_(0),
        nNormPar_(0),
        meanEff_("meanEff",0.0,0.0,1.0),
        dpRate_("dpRate",0.0,0.0,100.0),
        //signalEvents_("signalEvents",1.0,0.0,1.0),
        signalEvents_(0),
        useSCF_(kFALSE),
        useSCFHist_(kFALSE),
        scfFrac_("scfFrac",0.0,0.0,1.0),
        scfFracHist_(0),
        scfMap_(0),
        compareFitData_(kFALSE),
        signalTree_(0),
        reuseSignal_(kFALSE),
        useReweighting_(kFALSE),
        sigDPLike_(0.0),
        scfDPLike_(0.0),
        sigExtraLike_(0.0),
        scfExtraLike_(0.0),
        sigTotalLike_(0.0),
        scfTotalLike_(0.0)
{
}

LauSimpleFitModel::~LauSimpleFitModel()
{
        delete signalTree_;
        for (LauBkgndEmbDataList::iterator iter = bkgndTree_.begin(); iter != bkgndTree_.end(); ++iter) {
                delete (*iter);
        }
        delete scfFracHist_;
}

void LauSimpleFitModel::setupBkgndVectors()
{
        UInt_t nBkgnds = this->nBkgndClasses();
        bkgndDPModels_.resize( nBkgnds );
        bkgndPdfs_.resize( nBkgnds );
        bkgndEvents_.resize( nBkgnds );
        bkgndTree_.resize( nBkgnds );
        reuseBkgnd_.resize( nBkgnds );
        bkgndDPLike_.resize( nBkgnds );
        bkgndExtraLike_.resize( nBkgnds );
        bkgndTotalLike_.resize( nBkgnds );
}

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

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

        if ( ! this->validBkgndClass( nBkgndEvents->name() ) ) {
                std::cerr << "ERROR in LauSimpleFitModel::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 LauSimpleFitModel::setNBkgndEvents : You are trying to overwrite the background yield." << 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));
}

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

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

        LauDaughters* daughters = sigDPModel_->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 LauSimpleFitModel::splitSignalComponent( Double_t scfFrac, Bool_t fixed )
{
        if ( useSCF_ == kTRUE ) {
                std::cerr << "ERROR in LauSimpleFitModel::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 LauSimpleFitModel::setBkgndDPModel(const TString& bkgndClass, LauAbsBkgndDPModel* bkgndDPModel)
{
        if (bkgndDPModel == 0) {
                std::cerr << "ERROR in LauSimpleFitModel::setBkgndDPModel : The model pointer is null." << std::endl;
                return;
        }

        // check that this background name is valid
        if ( ! this->validBkgndClass( bkgndClass ) ) {
                std::cerr << "ERROR in LauSimpleFitModel::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 );
        bkgndDPModels_[bkgndID] = bkgndDPModel;

        usingBkgnd_ = kTRUE;
}

void LauSimpleFitModel::setSignalPdf(LauAbsPdf* pdf)
{
        if (pdf==0) {
                std::cerr << "ERROR in LauSimpleFitModel::setSignalPdf : The PDF pointer is null." << std::endl;
                return;
        }
        signalPdfs_.push_back(pdf);
}

void LauSimpleFitModel::setSCFPdf(LauAbsPdf* pdf)
{
        if (pdf==0) {
                std::cerr << "ERROR in LauSimpleFitModel::setSCFPdf : The PDF pointer is null." << std::endl;
                return;
        }
        scfPdfs_.push_back(pdf);
}

void LauSimpleFitModel::setBkgndPdf(const TString& bkgndClass, LauAbsPdf* pdf)
{
        if (pdf == 0) {
                std::cerr << "ERROR in LauSimpleFitModel::setBkgndPdf : The PDF pointer is null." << std::endl;
                return;
        }

        // check that this background name is valid
        if ( ! this->validBkgndClass( bkgndClass ) ) {
                std::cerr << "ERROR in LauSimpleFitModel::setBkgndPdf : 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 );
        bkgndPdfs_[bkgndID].push_back(pdf);

        usingBkgnd_ = kTRUE;
}

void LauSimpleFitModel::setAmpCoeffSet(LauAbsCoeffSet* coeffSet)
{
        // Is there a component called compName in the signal model?
        TString compName(coeffSet->name());
        Bool_t ok = sigDPModel_->hasResonance(compName);
        if (!ok) {
                std::cerr << "ERROR in LauSimpleFitModel::setAmpCoeffSet : Signal DP model doesn't contain component \"" << compName << "\"." << 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 LauSimpleFitModel::setAmpCoeffSet : Have already set coefficients for \"" << compName << "\"." << std::endl;
                        return;
                }
        }

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

        ++nSigComp_;

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


void LauSimpleFitModel::initialise()
{
        // First of all check that, we have all the Dalitz-plot models

        if (this->useDP()) {
                if ( sigDPModel_ == 0 ) {
                        std::cerr << "ERROR in LauSimpleFitModel::initialise : The pointer to the signal DP model is null.\n";
                        std::cerr << "                                       : Removing the Dalitz Plot from the model." << std::endl;
                        this->useDP(kFALSE);
                }
                if ( usingBkgnd_ ) {
                        for (LauBkgndDPModelList::const_iterator dpmodel_iter = bkgndDPModels_.begin(); dpmodel_iter != bkgndDPModels_.end(); ++dpmodel_iter ) {
                                if ( (*dpmodel_iter) == 0 ) {
                                        std::cerr << "ERROR in LauSimpleFitModel::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 = signalPdfs_.begin(); pdf_iter != signalPdfs_.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 = scfPdfs_.begin(); pdf_iter != scfPdfs_.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 LauSimpleFitModel::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 = bkgndPdfs_.begin(); bgclass_iter != bkgndPdfs_.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 LauSimpleFitModel::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 LauSimpleFitModel::initialise : Number of fit parameters not of expected size." << 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() && signalPdfs_.empty()) {
                std::cerr << "ERROR in LauSimpleFitModel::initialise : Signal model doesn't exist for any variable." << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }

        this->setExtraNtupleVars();
}

void LauSimpleFitModel::initialiseDPModels()
{
        std::cout << "INFO in LauSimpleFitModel::initialiseDPModels : Initialising signal DP model" << std::endl;
        sigDPModel_->initialise(coeffs_);

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

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

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

        std::cout << "INFO in LauSimpleFitModel::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 nAmp = sigDPModel_->getnAmp();
        if (nAmp != nSigComp_) {
                std::cerr << "ERROR in LauSimpleFitModel::setSignalDPParameters : Number of signal DP components with magnitude and phase set not right." << 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 LauSimpleFitModel::setExtraPdfParameters()
{
        // Include all the parameters of the various PDFs 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(signalPdfs_);

        if (useSCF_ == kTRUE) {
                nExtraPdfPar_ += this->addFitParameters(scfPdfs_);
        }

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

void LauSimpleFitModel::setFitNEvents()
{
        if ( signalEvents_ == 0 ) {
                std::cerr << "ERROR in LauSimpleFitModel::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 LauSimpleFitModel::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 LauSimpleFitModel::setFitNEvents : Initialising number of events for signal and background components..." << std::endl;
                // add the signal events to the list of fit parameters
                fitVars.push_back(signalEvents_);
                ++nNormPar_;
        } else {
                std::cout << "INFO in LauSimpleFitModel::setFitNEvents : Initialising number of events for background components (and hence signal)..." << std::endl;
        }

        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_;
                }
        }
}

void LauSimpleFitModel::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();

        LauParameterList& extraVars = this->extraPars();

        // Add a fit fraction for each signal component
        fitFrac_ = sigDPModel_->getFitFractions();
        if (fitFrac_.size() != nSigComp_) {
                std::cerr << "ERROR in LauSimpleFitModel::setExtraNtupleVars : Initial Fit Fraction array of unexpected dimension: " << fitFrac_.size() << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }
        for (UInt_t i(0); i<nSigComp_; ++i) {
                if (fitFrac_[i].size() != nSigComp_) {
                        std::cerr << "ERROR in LauSimpleFitModel::setExtraNtupleVars : Initial Fit Fraction array of unexpected dimension: " << fitFrac_[i].size() << std::endl;
                        gSystem->Exit(EXIT_FAILURE);
                }
        }

        for (UInt_t i = 0; i < nSigComp_; i++) {
                for (UInt_t j = i; j < nSigComp_; j++) {
                        extraVars.push_back(fitFrac_[i][j]);
                }
        }

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

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

        // Also add in the DP rate
        Double_t initDPRate = sigDPModel_->getDPRate().initValue();
        dpRate_.value(initDPRate); dpRate_.initValue(initDPRate); dpRate_.genValue(initDPRate);
        extraVars.push_back(dpRate_);
}

void LauSimpleFitModel::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
        // e.g. to make mag > 0.0 and 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 mag > 0.0, and phase in the right range
                        coeffPars_[i]->finaliseValues();
                }
        }

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

        // Update the pulls on all the extra PDFs' parameters
        this->updateFitParameters(signalPdfs_);
        if (useSCF_ == kTRUE) {
                this->updateFitParameters(scfPdfs_);
        }
        if (usingBkgnd_ == kTRUE) {
                for (LauBkgndPdfsList::iterator iter = bkgndPdfs_.begin(); iter != bkgndPdfs_.end(); ++iter) {
                        this->updateFitParameters(*iter);
                }
        }

        // Fill the fit results to the ntuple for current experiment

        // update the coefficients and then calculate the fit fractions
        if (this->useDP() == kTRUE) {
                this->updateCoeffs();
                sigDPModel_->updateCoeffs(coeffs_);
                sigDPModel_->calcExtraInfo();

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

                for (UInt_t i(0); i<nSigComp_; ++i) {
                        for (UInt_t j(i); j<nSigComp_; ++j) {
                                fitFrac_[i][j].value(fitFrac[i][j].value());
                        }
                }

                meanEff_.value(sigDPModel_->getMeanEff().value());
                dpRate_.value(sigDPModel_->getDPRate().value());

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

                // Then store the final fit parameters, and any extra parameters for
                // the signal model (e.g. fit fractions)
                for (UInt_t i(0); i<nSigComp_; ++i) {
                        for (UInt_t j(i); j<nSigComp_; ++j) {
                                extraVars.push_back(fitFrac_[i][j]);
                        }
                }

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

                // Now add in the DP efficiency value
                extraVars.push_back(meanEff_);

                // Also add in the DP rate
                extraVars.push_back(dpRate_);

                this->printFitFractions(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 LauSimpleFitModel::printFitFractions(std::ostream& output)
{
        // Print out Fit Fractions, total DP rate and mean efficiency
        for (UInt_t i = 0; i < nSigComp_; i++) {
                output << "FitFraction for component " << i << " (" << coeffPars_[i]->name() << ") = " << fitFrac_[i][i] << std::endl;
        }
        output << "Overall DP rate (integral of matrix element squared) = " << dpRate_ << std::endl;
        output << "Average efficiency weighted by whole DP dynamics = " << meanEff_ << std::endl;
}

void LauSimpleFitModel::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 LauSimpleFitModel::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 in another
                fout << "\\begin{tabular}{|l|c|}" << std::endl;
                fout << "\\hline" << std::endl;
                fout << "Component & FitFraction \\\\" << std::endl;
                fout << "\\hline" << std::endl;
                Double_t fitFracSum(0.0);
                for (UInt_t i = 0; i < nSigComp_; i++) {
                        TString resName = coeffPars_[i]->name();
                        resName = resName.ReplaceAll("_", "\\_");
                        fout << resName << "  &  $";
                        Double_t fitFrac = fitFrac_[i][i].value();
                        fitFracSum += fitFrac;
                        print.printFormat(fout, fitFrac);
                        fout << "$ \\\\" << std::endl;
                }
                fout << "\\hline" << std::endl;

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

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

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

        if (!signalPdfs_.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(signalPdfs_, fout);
                if (useSCF_ == kTRUE && !scfPdfs_.empty()) {
                        fout << "\\hline" << std::endl;
                        fout << "\\Extra SCF Signal PDFs' Parameters: & \\\\" << std::endl;
                        this->printFitParameters(scfPdfs_, fout);
                }
                if (usingBkgnd_ == kTRUE && !bkgndPdfs_.empty()) {
                        fout << "\\hline" << std::endl;
                        fout << "\\Extra Background PDFs' Parameters: & \\\\" << std::endl;
                        for (LauBkgndPdfsList::const_iterator iter = bkgndPdfs_.begin(); iter != bkgndPdfs_.end(); ++iter) {
                                this->printFitParameters(*iter, fout);
                        }
                }
                fout << "\\hline \n\\end{tabular}" << std::endl << std::endl;
        }
}

void LauSimpleFitModel::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 LauSimpleFitModel::checkInitFitParams : Setting random parameters for the signal DP model" << std::endl;
                this->randomiseInitFitPars();
        }
}

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

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

LauSimpleFitModel::LauGenInfo LauSimpleFitModel::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);
        Int_t nEvts = TMath::FloorNint(signalEvents_->genValue());
        if ( nEvts < 0 ) {
                evtWeight = -1.0;
                nEvts = TMath::Abs( nEvts );
        }
        if (this->doPoissonSmearing()) {
                nEvts = LauRandom::randomFun()->Poisson(nEvts);
        }
        nEvtsGen["signal"] = std::make_pair( nEvts, 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];
                evtWeight = 1.0;
                nEvts = TMath::FloorNint( evtsPar->genValue() );
                if ( nEvts < 0 ) {
                        evtWeight = -1.0;
                        nEvts = TMath::Abs( nEvts );
                }
                if (this->doPoissonSmearing()) {
                        nEvts = LauRandom::randomFun()->Poisson(nEvts);
                }
                nEvtsGen[bkgndClass] = std::make_pair( nEvts, evtWeight );
        }

        return nEvtsGen;
}

Bool_t LauSimpleFitModel::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() && signalTree_ != 0 && signalTree_->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);
                Int_t nEvtsGen( iter->second.first );
                Double_t evtWeight( iter->second.second );

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

                        this->setGenNtupleDoubleBranchValue( "evtWeight", evtWeight );
                        // Add efficiency information
                        this->setGenNtupleDoubleBranchValue( "efficiency", 1 );

                        if (type == "signal") {
                                this->setGenNtupleIntegerBranchValue("genSig",1);
                                for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
                                        this->setGenNtupleIntegerBranchValue( bkgndClassNamesGen[iBkgnd], 0 );
                                }
                                genOK = this->generateSignalEvent();
                                this->setGenNtupleDoubleBranchValue( "efficiency", sigDPModel_->getEvtEff() );
                        } 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 number (within this experiment)
                        this->setGenNtupleIntegerBranchValue("iEvtWithinExpt",evtNum);
                        // and then increment it
                        ++evtNum;

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

                if (!genOK) {
                        break;
                }
        }

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

                sigDPModel_->checkToyMC(kTRUE,kTRUE);

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

                        for (UInt_t i(0); i<nSigComp_; ++i) {
                                for (UInt_t j = i; j < nSigComp_; j++) {
                                        fitFrac_[i][j].value(fitFrac[i][j].value());
                                }
                        }
                        meanEff_.value(sigDPModel_->getMeanEff().value());
                        dpRate_.value(sigDPModel_->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 (signalTree_) {
                        signalTree_->clearUsedList();
                }
        }
        for ( UInt_t bkgndID(0); bkgndID < nBkgnds; ++bkgndID ) {
                LauEmbeddedData* data = bkgndTree_[bkgndID];
                if (reuseBkgnd_[bkgndID] || !genOK) {
                        if (data) {
                                data->clearUsedList();
                        }
                }
        }

        return genOK;
}

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

        if (this->useDP()) {
                if (this->enableEmbedding() && signalTree_) {
                        if (useReweighting_) {
                                // 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 = signalTree_->getReweightedEvent(sigDPModel_);
                        } else {

                                signalTree_->getEmbeddedEvent(kinematics_);
                        }
                        if (signalTree_->haveBranch("mcMatch")) {
                                Int_t match = static_cast<Int_t>(signalTree_->getValue("mcMatch"));
                                if (match) {
                                        this->setGenNtupleIntegerBranchValue("genTMSig",1);
                                        this->setGenNtupleIntegerBranchValue("genSCFSig",0);
                                        genSCF = kFALSE;
                                } else {
                                        this->setGenNtupleIntegerBranchValue("genTMSig",0);
                                        this->setGenNtupleIntegerBranchValue("genSCFSig",1);
                                        genSCF = kTRUE;
                                }
                        }
                } else {
                        genOK = sigDPModel_->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 = sigDPModel_->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 (this->enableEmbedding() && signalTree_) {
                        signalTree_->getEmbeddedEvent(0);
                        if (signalTree_->haveBranch("mcMatch")) {
                                Int_t match = static_cast<Int_t>(signalTree_->getValue("mcMatch"));
                                if (match) {
                                        this->setGenNtupleIntegerBranchValue("genTMSig",1);
                                        this->setGenNtupleIntegerBranchValue("genSCFSig",0);
                                        genSCF = kFALSE;
                                } else {
                                        this->setGenNtupleIntegerBranchValue("genTMSig",0);
                                        this->setGenNtupleIntegerBranchValue("genSCFSig",1);
                                        genSCF = kTRUE;
                                }
                        }
                } 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_, signalTree_);
                        } else {
                                this->generateExtraPdfValues(&signalPdfs_, signalTree_);
                        }
                } else {
                        this->generateExtraPdfValues(&signalPdfs_, signalTree_);
                }
        }
        // Check for problems with the embedding
        if (this->enableEmbedding() && signalTree_ && (signalTree_->nEvents() == signalTree_->nUsedEvents())) {
                std::cerr << "WARNING in LauSimpleFitModel::generateSignalEvent : Source of embedded signal events used up, clearing the list of used events." << std::endl;
                signalTree_->clearUsedList();
        }
        return genOK;
}

Bool_t LauSimpleFitModel::generateBkgndEvent(UInt_t bkgndID)
{
        // Generate background event
        Bool_t genOK(kTRUE);

        LauAbsBkgndDPModel* model = bkgndDPModels_[bkgndID];
        LauEmbeddedData* embeddedData(0);
        if (this->enableEmbedding()) {
                embeddedData = bkgndTree_[bkgndID];
        }
        LauPdfList* extraPdfs = &bkgndPdfs_[bkgndID];
        if (this->useDP()) {
                if (embeddedData) {
                        embeddedData->getEmbeddedEvent(kinematics_);
                } else {
                        if (model == 0) {
                                std::cerr << "ERROR in LauSimpleFitModel::generateBkgndEvent : Can't find the DP model for background class \"" << this->bkgndClassName(bkgndID) << "\"." << 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())) {
                std::cerr << "WARNING in LauSimpleFitModel::generateBkgndEvent : Source of embedded " << this->bkgndClassName(bkgndID) << " events used up, clearing the list of used events." << std::endl;
                embeddedData->clearUsedList();
        }
        return genOK;
}

void LauSimpleFitModel::setupGenNtupleBranches()
{
        // Setup the required ntuple branches
        this->addGenNtupleDoubleBranch("evtWeight");
        this->addGenNtupleIntegerBranch("genSig");
        this->addGenNtupleDoubleBranch("efficiency");
        if ( useSCF_ || ( this->enableEmbedding() && signalTree_ != 0 && signalTree_->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);
        }
        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 (kinematics_->squareDP()) {
                        this->addGenNtupleDoubleBranch("mPrime");
                        this->addGenNtupleDoubleBranch("thPrime");
                }
        }
        for (LauPdfList::const_iterator pdf_iter = signalPdfs_.begin(); pdf_iter != signalPdfs_.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 LauSimpleFitModel::setDPBranchValues()
{
        // 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 LauSimpleFitModel::generateExtraPdfValues(LauPdfList* extraPdfs, LauEmbeddedData* embeddedData)
{
        // Generate from the extra PDFs
        if (extraPdfs) {
                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);
                                }
                        }
                }
        }
}

void LauSimpleFitModel::propagateParUpdates()
{
        // Update the total normalisation for the signal likelihood
        if (this->useDP() == kTRUE) {
                this->updateCoeffs();
                sigDPModel_->updateCoeffs(coeffs_);
        }

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

void LauSimpleFitModel::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 LauSimpleFitModel::cacheInputFitVars()
{
        // Fill the internal data trees of the signal and backgrond models.

        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 );
                }
                sigDPModel_->fillDataTree(*inputFitData);

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

        // ...and then the extra PDFs
        this->cacheInfo(signalPdfs_, *inputFitData);
        this->cacheInfo(scfPdfs_, *inputFitData);
        for (LauBkgndPdfsList::iterator iter = bkgndPdfs_.begin(); iter != bkgndPdfs_.end(); ++iter) {
                this->cacheInfo((*iter), *inputFitData);
        }

        // and finally the SCF fractions and jacobians
        if ( useSCF_ && useSCFHist_ ) {
                if ( !inputFitData->haveBranch( "m13Sq" )  || !inputFitData->haveBranch( "m23Sq" ) ) {
                        std::cerr << "ERROR in LauSimpleFitModel::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 ( kinematics_->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");
                        kinematics_->updateKinematics( m13_iter->second, m23_iter->second );
                        Double_t scfFrac = scfFracHist_->calcEfficiency( kinematics_ );
                        recoSCFFracs_.push_back( scfFrac );
                        if ( kinematics_->squareDP() ) {
                                recoJacobians_.push_back( kinematics_->calcSqDPJacobian() );
                        }
                }
        }
}

void LauSimpleFitModel::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 = kinematics_->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 ) {
                        kinematics_->updateSqDPKinematics(binCentresXCoords[iBin],binCentresYCoords[iBin]);
                        binCentresXCoords[iBin] = kinematics_->getm13Sq();
                        binCentresYCoords[iBin] = kinematics_->getm23Sq();
                        fakeJacobians_.push_back( kinematics_->calcSqDPJacobian() );
                } else {
                        kinematics_->updateKinematics(binCentresXCoords[iBin],binCentresYCoords[iBin]);
                }

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

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

Double_t LauSimpleFitModel::getTotEvtLikelihood(UInt_t iEvt)
{
        // 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_);
                }
        }

        // Construct the total event likelihood
        Double_t likelihood = signalEvents_->value() * sigLike;
        const UInt_t nBkgnds = this->nBkgndClasses();
        for ( UInt_t bkgndID(0); bkgndID < nBkgnds; ++bkgndID ) {
                likelihood += (bkgndEvents_[bkgndID]->value() * bkgndDPLike_[bkgndID] * bkgndExtraLike_[bkgndID]);
        }

        return likelihood;
}

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

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

        if (this->useDP() == kTRUE) {
                sigDPModel_->calcLikelihoodInfo(iEvt);
                sigDPLike_ = sigDPModel_->getEvtLikelihood();

                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);
                        }
                }

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

Double_t LauSimpleFitModel::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 = kinematics_->squareDP();
        if ( squareDP ) {
                xCoord = sigDPModel_->getEvtmPrime();
                yCoord = sigDPModel_->getEvtthPrime();
                recoJacobian = recoJacobians_[iEvt];
        } else {
                xCoord = sigDPModel_->getEvtm13Sq();
                yCoord = sigDPModel_->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);

        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 );

                // We've cached the DP amplitudes and the efficiency for the
                // true bin centres, just after the data points
                sigDPModel_->calcLikelihoodInfo( nDataEvents + trueBin );
                Double_t pTrue = sigDPModel_->getEvtLikelihood();

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

        sigExtraLike_ = 1.0;

        for (LauPdfList::iterator iter = signalPdfs_.begin(); iter != signalPdfs_.end(); ++iter) {
                (*iter)->calcLikelihoodInfo(iEvt);
                sigExtraLike_ *= (*iter)->getLikelihood();
        }
        if (useSCF_) {
                scfExtraLike_ = 1.0;
                for (LauPdfList::iterator iter = scfPdfs_.begin(); iter != scfPdfs_.end(); ++iter) {
                        (*iter)->calcLikelihoodInfo(iEvt);
                        scfExtraLike_ *= (*iter)->getLikelihood();
                }
        }

        const UInt_t nBkgnds = this->nBkgndClasses();
        for ( UInt_t bkgndID(0); bkgndID < nBkgnds; ++bkgndID ) {
                if (usingBkgnd_) {
                        bkgndExtraLike_[bkgndID] = 1.0;
                        LauPdfList& pdfList = bkgndPdfs_[bkgndID];
                        for (LauPdfList::iterator pdf_iter = pdfList.begin(); pdf_iter != pdfList.end(); ++pdf_iter) {
                                (*pdf_iter)->calcLikelihoodInfo(iEvt);
                                bkgndExtraLike_[bkgndID] *= (*pdf_iter)->getLikelihood();
                        }
                } else {
                        bkgndExtraLike_[bkgndID] = 0.0;
                }
        }
}

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

void LauSimpleFitModel::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 ( sigDPModel_->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(&signalPdfs_, "sigTM");
                this->addSPlotNtupleBranches(&scfPdfs_, "sigSCF");
        } else {
                this->addSPlotNtupleBranches(&signalPdfs_, "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 = &(bkgndPdfs_[iBkgnd]);
                        this->addSPlotNtupleBranches(pdfList, bkgndClass);
                }
        }
}

void LauSimpleFitModel::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 LauSimpleFitModel::addSPlotNtupleBranches : Can't yet deal with 3D PDFs." << std::endl;
                        }
                }
        }
}

Double_t LauSimpleFitModel::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 LauSimpleFitModel::setSPlotNtupleBranchValues : Can't yet deal with 3D PDFs." << std::endl;
                        }
                }
        }
        return totalLike;
}

LauSPlot::NameSet LauSimpleFitModel::variableNames() const
{
        LauSPlot::NameSet nameSet;
        if (this->useDP()) {
                nameSet.insert("DP");
        }
        // Loop through all the signal PDFs
        for (LauPdfList::const_iterator pdf_iter = signalPdfs_.begin(); pdf_iter != signalPdfs_.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 LauSimpleFitModel::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 LauSimpleFitModel::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 LauSimpleFitModel::twodimPDFs() const
{
        LauSPlot::TwoDMap twodimMap;

        for (LauPdfList::const_iterator pdf_iter = signalPdfs_.begin(); pdf_iter != signalPdfs_.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 = scfPdfs_.begin(); pdf_iter != scfPdfs_.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 = bkgndPdfs_[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 LauSimpleFitModel::storePerEvtLlhds()
{
        std::cout << "INFO in LauSimpleFitModel::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()) {
                sigDPModel_->initialise(coeffs_);
                sigDPModel_->fillDataTree(*inputFitData);
        }

        UInt_t evtsPerExpt(this->eventsPerExpt());

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

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

                // the DP information
                this->getEvtDPLikelihood(iEvt);
                if (this->storeDPEff()) {
                        if (!this->useDP()) {
                                sigDPModel_->calcLikelihoodInfo(iEvt);
                        }
                        this->setSPlotNtupleDoubleBranchValue("efficiency",sigDPModel_->getEvtEff());
                        if ( sigDPModel_->usingScfModel() ) {
                                this->setSPlotNtupleDoubleBranchValue("scffraction",sigDPModel_->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(&signalPdfs_, "sigTM", iEvt);
                        scfTotalLike_ *= this->setSPlotNtupleBranchValues(&scfPdfs_, "sigSCF", iEvt);
                } else {
                        sigTotalLike_ *= this->setSPlotNtupleBranchValues(&signalPdfs_, "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 LauSimpleFitModel::storePerEvtLlhds : Finished storing per-event likelihood values." << std::endl;
}

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

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

        signalTree_ = new LauEmbeddedData(fileName,treeName,reuseEventsWithinExperiment);

        Bool_t dataOK = signalTree_->findBranches();
        if (!dataOK) {
                delete signalTree_; signalTree_ = 0;
                std::cerr << "ERROR in LauSimpleFitModel::embedSignal : Problem creating data tree for embedding." << std::endl;
                return;
        }

        reuseSignal_ = reuseEventsWithinEnsemble;
        useReweighting_ = useReweighting;

        if (this->enableEmbedding() == kFALSE) {
                this->enableEmbedding(kTRUE);
        }
}

void LauSimpleFitModel::embedBkgnd(const TString& bkgndClass, const TString& fileName, const TString& treeName,
                Bool_t reuseEventsWithinEnsemble, Bool_t reuseEventsWithinExperiment)
{
        if ( ! this->validBkgndClass( bkgndClass ) ) {
                std::cerr << "ERROR in LauSimpleFitModel::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 (bkgndTree_[bkgndID]) {
                std::cerr << "ERROR in LauSimpleFitModel::embedBkgnd : Already embedding background from a file." << std::endl;
                return;
        }

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

        bkgndTree_[bkgndID] = new LauEmbeddedData(fileName,treeName,reuseEventsWithinExperiment);

        Bool_t dataOK = bkgndTree_[bkgndID]->findBranches();
        if (!dataOK) {
                delete bkgndTree_[bkgndID]; bkgndTree_[bkgndID] = 0;
                std::cerr << "ERROR in LauSimpleFitModel::embedBkgnd : Problem creating data tree for embedding." << std::endl;
                return;
        }

        reuseBkgnd_[bkgndID] = reuseEventsWithinEnsemble;

        if (this->enableEmbedding() == kFALSE) {
                this->enableEmbedding(kTRUE);
        }
}

void LauSimpleFitModel::weightEvents( const TString& dataFileName, const TString& dataTreeName )
{
        // Routine to provide weights for events that are uniformly distributed
        // in the DP (or square DP) so as to reproduce the given DP model

        if ( kinematics_->squareDP() ) {
                std::cout << "INFO in LauSimpleFitModel::weightEvents : will create weights assuming events were generated flat in the square DP" << std::endl;
        } else {
                std::cout << "INFO in LauSimpleFitModel::weightEvents : will create weights assuming events were generated flat in phase space" << std::endl;
        }

        // This reads in the given dataFile and creates an input
        // fit data tree that stores them for all events and experiments.
        Bool_t dataOK = this->cacheFitData(dataFileName,dataTreeName);
        if (!dataOK) {
                std::cerr << "ERROR in LauSimpleFitModel::weightEvents : Problem caching the data." << std::endl;
                return;
        }

        LauFitDataTree* inputFitData = this->fitData();

        if ( ! inputFitData->haveBranch( "m13Sq_MC" ) || ! inputFitData->haveBranch( "m23Sq_MC" ) ) {
                std::cerr << "WARNING in LauSimpleFitModel::weightEvents : Cannot find MC truth DP coordinate branches in supplied data, aborting." << std::endl;
                return;
        }

        // Create the ntuple to hold the DP weights
        TString weightsFileName( dataFileName );
        Ssiz_t index = weightsFileName.Last('.');
        weightsFileName.Insert( index, "_DPweights" );
        LauGenNtuple * weightsTuple = new LauGenNtuple( weightsFileName, dataTreeName );
        weightsTuple->addIntegerBranch("iExpt");
        weightsTuple->addIntegerBranch("iEvtWithinExpt");
        weightsTuple->addDoubleBranch("dpModelWeight");

        UInt_t iExpmt = this->iExpt();
        UInt_t nExpmt = this->nExpt();
        UInt_t firstExpmt = this->firstExpt();
        for (iExpmt = firstExpmt; iExpmt < (firstExpmt+nExpmt); ++iExpmt) {

                inputFitData->readExperimentData(iExpmt);
                UInt_t nEvents = inputFitData->nEvents();

                if (nEvents < 1) {
                        std::cerr << "WARNING in LauSimpleFitModel::weightEvents : Zero events in experiment " << iExpmt << ", skipping..." << std::endl;
                        continue;
                }

                weightsTuple->setIntegerBranchValue( "iExpt", iExpmt );

                // Calculate and store the weights for the events in this experiment
                for ( UInt_t iEvent(0); iEvent < nEvents; ++iEvent ) {

                        weightsTuple->setIntegerBranchValue( "iEvtWithinExpt", iEvent );

                        const LauFitData& evtData = inputFitData->getData( iEvent );

                        Double_t m13Sq_MC = evtData.find("m13Sq_MC")->second;
                        Double_t m23Sq_MC = evtData.find("m23Sq_MC")->second;

                        Double_t dpModelWeight(0.0);

                        if ( kinematics_->withinDPLimits( m13Sq_MC, m23Sq_MC ) ) {

                                kinematics_->updateKinematics( m13Sq_MC, m23Sq_MC );
                                dpModelWeight = sigDPModel_->getEventWeight();

                                if ( kinematics_->squareDP() ) {
                                        dpModelWeight *= kinematics_->calcSqDPJacobian();
                                }

                                if (LauAbsDPDynamics::GenOK != sigDPModel_->checkToyMC(kTRUE,kFALSE)) {
                                        std::cerr << "WARNING in LauSimpleFitModel::weightEvents : Problem in calculating the weight, aborting." << std::endl;
                                        delete weightsTuple;
                                        return;
                                }
                        }

                        weightsTuple->setDoubleBranchValue( "dpModelWeight", dpModelWeight );
                        weightsTuple->fillBranches();
                }

        }

        weightsTuple->buildIndex( "iExpt", "iEvtWithinExpt" );
        weightsTuple->addFriendTree(dataFileName, dataTreeName);
        weightsTuple->writeOutGenResults();

        delete weightsTuple;
}