LauAbsFitModel.cc

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// Copyright University of Warwick 2004 - 2014.
// 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 <limits>
#include <vector>

#include "TMessage.h"
#include "TMonitor.h"
#include "TServerSocket.h"
#include "TSocket.h"
#include "TSystem.h"
#include "TVectorD.h"
#include "TVirtualFitter.h"

#include "LauAbsFitModel.hh"
#include "LauAbsFitter.hh"
#include "LauAbsPdf.hh"
#include "LauComplex.hh"
#include "LauFitter.hh"
#include "LauFitDataTree.hh"
#include "LauFitNtuple.hh"
#include "LauGenNtuple.hh"
#include "LauParameter.hh"
#include "LauParamFixed.hh"
#include "LauPrint.hh"
#include "LauSPlot.hh"

ClassImp(LauAbsFitModel)


LauAbsFitModel::LauAbsFitModel() :
        storeCon_(0),
        twoStageFit_(kFALSE),
        useAsymmFitErrors_(kFALSE),
        compareFitData_(kFALSE),
        writeLatexTable_(kFALSE),
        writeSPlotData_(kFALSE),
        storeDPEff_(kFALSE),
        randomFit_(kFALSE),
        emlFit_(kFALSE),
        poissonSmear_(kFALSE),
        enableEmbedding_(kFALSE),
        usingDP_(kTRUE),
        pdfsDependOnDP_(kFALSE),
        firstExpt_(0),
        nExpt_(0),
        evtsPerExpt_(0),
        iExpt_(0),
        inputFitData_(0),
        fitNtuple_(0),
        genNtuple_(0),
        sPlotNtuple_(0),
        fitStatus_(0),
        NLL_(0),
        numberOKFits_(0),
        numberBadFits_(0),
        nParams_(0),
        nFreeParams_(0),
        worstLogLike_(std::numeric_limits<Double_t>::max()),
        withinAsymErrorCalc_(kFALSE),
        nullString_(""),
        doSFit_(kFALSE),
        sWeightBranchName_(""),
        sWeightScaleFactor_(1.0),
        fitToyMCFileName_("fitToyMC.root"),
        fitToyMCTableName_("fitToyMCTable"),
        fitToyMCScale_(10),
        fitToyMCPoissonSmear_(kFALSE),
        sPlotFileName_(""),
        sPlotTreeName_(""),
        sPlotVerbosity_(""),
        sMaster_(0),
        messageFromMaster_(0),
        slaveId_(-1),
        nSlaves_(0),
        parValues_(0)
{
}


LauAbsFitModel::~LauAbsFitModel()
{
        delete inputFitData_; inputFitData_ = 0;
        delete fitNtuple_; fitNtuple_ = 0;
        delete genNtuple_; genNtuple_ = 0;
        delete sPlotNtuple_; sPlotNtuple_ = 0;
        delete sMaster_; sMaster_ = 0;
        delete[] parValues_; parValues_ = 0;

        // Remove the components created to apply constraints to fit parameters
        for (std::vector<LauAbsRValue*>::iterator iter = conVars_.begin(); iter != conVars_.end(); ++iter){
                if ( !(*iter)->isLValue() ){
                        delete (*iter);
                        (*iter) = 0;
                }
        }
}

void LauAbsFitModel::run(const TString& applicationCode, const TString& dataFileName, const TString& dataTreeName,
                const TString& histFileName, const TString& tableFileName)
{
        // Chose whether you want to generate or fit events in the Dalitz plot.
        // To generate events choose applicationCode = "gen", to fit events choose
        // applicationCode = "fit".

        TString runCode(applicationCode);
        runCode.ToLower();

        TString histFileNameCopy(histFileName);
        TString tableFileNameCopy(tableFileName);
        TString dataFileNameCopy(dataFileName);
        TString dataTreeNameCopy(dataTreeName);

        // Initialise the fit par vectors. Each class that inherits from this one
        // must implement this sensibly for all vectors specified in clearFitParVectors,
        // i.e. specify parameter names, initial, min, max and fixed values
        this->initialise();

        // Add variables to Gaussian constrain to a list
        this->addConParameters();

        if (dataFileNameCopy == "") {dataFileNameCopy = "data.root";}
        if (dataTreeNameCopy == "") {dataTreeNameCopy = "genResults";}

        if (runCode.Contains("gen")) {

                if (histFileNameCopy == "") {histFileNameCopy = "parInfo.root";}
                if (tableFileNameCopy == "") {tableFileNameCopy = "genResults";}

                this->setGenValues();
                this->generate(dataFileNameCopy, dataTreeNameCopy, histFileNameCopy, tableFileNameCopy);

        } else if (runCode.Contains("fit")) {

                if (histFileNameCopy == "") {histFileNameCopy = "parInfo.root";}
                if (tableFileNameCopy == "") {tableFileNameCopy = "fitResults";}

                this->fit(dataFileNameCopy, dataTreeNameCopy, histFileNameCopy, tableFileNameCopy);

        } else if (runCode.Contains("reweight")) {

                this->weightEvents(dataFileNameCopy, dataTreeNameCopy);
        }
}

void LauAbsFitModel::runSlave(const TString& dataFileName, const TString& dataTreeName,
                const TString& histFileName, const TString& tableFileName,
                const TString& addressMaster, const UInt_t portMaster)
{
        if ( sMaster_ != 0 ) {
                std::cerr << "ERROR in LauAbsFitModel::runSlave : master socket already present" << std::endl;
                return;
        }

        // Open connection to master
        sMaster_ = new TSocket(addressMaster, portMaster);
        sMaster_->Recv( messageFromMaster_ );
        messageFromMaster_->ReadUInt( slaveId_ );
        messageFromMaster_->ReadUInt( nSlaves_ );

        delete messageFromMaster_;
        messageFromMaster_ = 0;

        std::cout << "INFO in LauAbsFitModel::runSlave : Established connection to master on port " << portMaster << std::endl;
        std::cout << "                                 : We are slave " << slaveId_ << " of " << nSlaves_ << std::endl;

        // Initialise the fit par vectors. Each class that inherits from this one
        // must implement this sensibly for all vectors specified in clearFitParVectors,
        // i.e. specify parameter names, initial, min, max and fixed values
        this->initialise();

        // NB call to addConParameters() is intentionally not included here cf.
        // run() since this has to be dealt with by the master to avoid
        // multiple inclusions of each penalty term

        // Create array to efficiently exchange parameter values with master
        nParams_ = fitVars_.size();
        parValues_ = new Double_t[nParams_];
        for ( UInt_t iPar(0); iPar < nParams_; ++iPar ) {
                parValues_[iPar] = fitVars_[iPar]->initValue();
        }

        TString dataFileNameCopy(dataFileName);
        TString dataTreeNameCopy(dataTreeName);
        TString histFileNameCopy(histFileName);
        TString tableFileNameCopy(tableFileName);

        if (dataFileNameCopy == "")  {dataFileNameCopy = "data.root";}
        if (dataTreeNameCopy == "")  {dataTreeNameCopy = "genResults";}
        if (histFileNameCopy == "")  {histFileNameCopy = "parInfo.root";}
        if (tableFileNameCopy == "") {tableFileNameCopy = "fitResults";}

        this->fitSlave(dataFileNameCopy, dataTreeNameCopy, histFileNameCopy, tableFileNameCopy);

        std::cout << "INFO in LauAbsFitModel::runSlave : Fit slave " << slaveId_ << " has finished successfully" << std::endl;
}

void LauAbsFitModel::doSFit( const TString& sWeightBranchName, Double_t scaleFactor )
{
        if ( sWeightBranchName == "" ) {
                std::cerr << "WARNING in LauAbsFitModel::doSFit : sWeight branch name is empty string, not setting-up sFit." << std::endl;
                return;
        }

        doSFit_ = kTRUE;
        sWeightBranchName_ = sWeightBranchName;
        sWeightScaleFactor_ = scaleFactor;
}

void LauAbsFitModel::setBkgndClassNames( const std::vector<TString>& names )
{
        if ( !bkgndClassNames_.empty() ) {
                std::cerr << "WARNING in LauAbsFitModel::setBkgndClassNames : Names already stored, not changing them." << std::endl;
                return;
        }

        UInt_t nBkgnds = names.size();
        for ( UInt_t i(0); i < nBkgnds; ++i ) {
                bkgndClassNames_.insert( std::make_pair( i, names[i] ) );
        }

        this->setupBkgndVectors();
}

Bool_t LauAbsFitModel::validBkgndClass( const TString& className ) const
{
        if ( bkgndClassNames_.empty() ) {
                return kFALSE;
        }

        Bool_t found(kFALSE);
        for ( LauBkgndClassMap::const_iterator iter = bkgndClassNames_.begin(); iter != bkgndClassNames_.end(); ++iter ) {
                if ( iter->second == className ) {
                        found = kTRUE;
                        break;
                }
        }

        return found;
}

UInt_t LauAbsFitModel::bkgndClassID( const TString& className ) const
{
        if ( ! this->validBkgndClass( className ) ) {
                std::cerr << "ERROR in LauAbsFitModel::bkgndClassID : Request for ID for invalid background class \"" << className << "\"." << std::endl;
                return (bkgndClassNames_.size() + 1);
        }

        UInt_t bgID(0);
        for ( LauBkgndClassMap::const_iterator iter = bkgndClassNames_.begin(); iter != bkgndClassNames_.end(); ++iter ) {
                if ( iter->second == className ) {
                        bgID = iter->first;
                        break;
                }
        }

        return bgID;
}

const TString& LauAbsFitModel::bkgndClassName( UInt_t classID ) const
{
        LauBkgndClassMap::const_iterator iter = bkgndClassNames_.find( classID );

        if ( iter == bkgndClassNames_.end() ) {
                std::cerr << "ERROR in LauAbsFitModel::bkgndClassName : Request for name of invalid background class ID " << classID << "." << std::endl;
                return nullString_;
        }

        return iter->second;
}

void LauAbsFitModel::clearFitParVectors()
{
        std::cout << "INFO in LauAbsFitModel::clearFitParVectors : Clearing fit variable vectors" << std::endl;

        // Remove the components created to apply constraints to fit parameters
        for (std::vector<LauAbsRValue*>::iterator iter = conVars_.begin(); iter != conVars_.end(); ++iter){
                if ( !(*iter)->isLValue() ){
                        delete (*iter);
                        (*iter) = 0;
                }
        }
        conVars_.clear();
        fitVars_.clear();
}

void LauAbsFitModel::clearExtraVarVectors()
{
        std::cout << "INFO in LauAbsFitModel::clearExtraVarVectors : Clearing extra ntuple variable vectors" << std::endl;
        extraVars_.clear();
}

void LauAbsFitModel::setGenValues()
{
        // makes sure each parameter holds its genValue as its current value
        for (LauParameterPList::iterator iter = fitVars_.begin(); iter != fitVars_.end(); ++iter) {
                (*iter)->value((*iter)->genValue());
        }
        this->propagateParUpdates();
}

void LauAbsFitModel::writeSPlotData(const TString& fileName, const TString& treeName, Bool_t storeDPEfficiency, const TString& verbosity)
{
        if (this->writeSPlotData()) {
                std::cerr << "ERROR in LauAbsFitModel::writeSPlotData : Already have an sPlot ntuple setup, not doing it again." << std::endl;
                return;
        }
        writeSPlotData_ = kTRUE;
        sPlotFileName_ = fileName;
        sPlotTreeName_ = treeName;
        sPlotVerbosity_ = verbosity;
        storeDPEff_ = storeDPEfficiency;
}

// TODO : histFileName isn't used here at the moment but could be used for
//        storing the values of the parameters used in the generation.
//        These could then be read and used for setting the "true" values
//        in a subsequent fit.
void LauAbsFitModel::generate(const TString& dataFileName, const TString& dataTreeName, const TString& /*histFileName*/, const TString& tableFileNameBase)
{
        // Create the ntuple for storing the results
        std::cout << "INFO in LauAbsFitModel::generate : Creating generation ntuple." << std::endl;
        if (genNtuple_ != 0) {delete genNtuple_; genNtuple_ = 0;}
        genNtuple_ = new LauGenNtuple(dataFileName,dataTreeName);

        // add branches for storing the experiment number and the number of
        // the event within the current experiment
        this->addGenNtupleIntegerBranch("iExpt");
        this->addGenNtupleIntegerBranch("iEvtWithinExpt");
        this->setupGenNtupleBranches();

        // Start the cumulative timer
        cumulTimer_.Start();

        Bool_t genOK(kTRUE);
        do {
                // Loop over the number of experiments
                for (iExpt_ = firstExpt_; iExpt_ < (firstExpt_+nExpt_); ++iExpt_) {

                        // Start the timer to see how long each experiment takes to generate
                        timer_.Start();

                        // Store the experiment number in the ntuple
                        this->setGenNtupleIntegerBranchValue("iExpt",iExpt_);

                        // Do the generation for this experiment
                        std::cout << "INFO in LauAbsFitModel::generate : Generating experiment number " << iExpt_ << std::endl;
                        genOK = this->genExpt();

                        // Stop the timer and see how long the program took so far
                        timer_.Stop();
                        timer_.Print();

                        if (!genOK) {
                                // delete and recreate an empty tree
                                genNtuple_->deleteAndRecreateTree();

                                // then break out of the experiment loop
                                std::cerr << "WARNING in LauAbsFitModel::generate : Problem in toy MC generation.  Starting again with updated parameters..." << std::endl;
                                break;
                        }

                        if (this->writeLatexTable()) {
                                TString tableFileName(tableFileNameBase);
                                tableFileName += "_";
                                tableFileName += iExpt_;
                                tableFileName += ".tex";
                                this->writeOutTable(tableFileName);
                        }

                } // Loop over number of experiments
        } while (!genOK);

        // Print out total timing info.
        cumulTimer_.Stop();
        std::cout << "INFO in LauAbsFitModel::generate : Finished generating all experiments." << std::endl;
        std::cout << "INFO in LauAbsFitModel::generate : Cumulative timing:" << std::endl;
        cumulTimer_.Print();

        // Build the event index
        std::cout << "INFO in LauAbsFitModel::generate : Building experiment:event index." << std::endl;
        // TODO - can test this return value?
        //Int_t nIndexEntries =
        genNtuple_->buildIndex("iExpt","iEvtWithinExpt");

        // Write out toy MC ntuple
        std::cout << "INFO in LauAbsFitModel::generate : Writing data to file " << dataFileName << "." << std::endl;
        genNtuple_->writeOutGenResults();
}

void LauAbsFitModel::addGenNtupleIntegerBranch(const TString& name)
{
        genNtuple_->addIntegerBranch(name);
}

void LauAbsFitModel::addGenNtupleDoubleBranch(const TString& name)
{
        genNtuple_->addDoubleBranch(name);
}

void LauAbsFitModel::setGenNtupleIntegerBranchValue(const TString& name, Int_t value)
{
        genNtuple_->setIntegerBranchValue(name,value);
}

void LauAbsFitModel::setGenNtupleDoubleBranchValue(const TString& name, Double_t value)
{
        genNtuple_->setDoubleBranchValue(name,value);
}

Int_t LauAbsFitModel::getGenNtupleIntegerBranchValue(const TString& name) const
{
        return genNtuple_->getIntegerBranchValue(name);
}

Double_t LauAbsFitModel::getGenNtupleDoubleBranchValue(const TString& name) const
{
        return genNtuple_->getDoubleBranchValue(name);
}

void LauAbsFitModel::fillGenNtupleBranches()
{
        genNtuple_->fillBranches();
}

void LauAbsFitModel::addSPlotNtupleIntegerBranch(const TString& name)
{
        sPlotNtuple_->addIntegerBranch(name);
}

void LauAbsFitModel::addSPlotNtupleDoubleBranch(const TString& name)
{
        sPlotNtuple_->addDoubleBranch(name);
}

void LauAbsFitModel::setSPlotNtupleIntegerBranchValue(const TString& name, Int_t value)
{
        sPlotNtuple_->setIntegerBranchValue(name,value);
}

void LauAbsFitModel::setSPlotNtupleDoubleBranchValue(const TString& name, Double_t value)
{
        sPlotNtuple_->setDoubleBranchValue(name,value);
}

void LauAbsFitModel::fillSPlotNtupleBranches()
{
        sPlotNtuple_->fillBranches();
}

void LauAbsFitModel::fit(const TString& dataFileName, const TString& dataTreeName, const TString& histFileName, const TString& tableFileNameBase)
{
        // Routine to perform the total fit.

        std::cout << "INFO in LauAbsFitModel::fit : First experiment = " << firstExpt_ << std::endl;
        std::cout << "INFO in LauAbsFitModel::fit : Number of experiments = " << nExpt_ << std::endl;

        // Start the cumulative timer
        cumulTimer_.Start();

        numberOKFits_ = 0, numberBadFits_ = 0;
        fitStatus_ = -1;

        // Create and setup the fit results ntuple
        std::cout << "INFO in LauAbsFitModel::fit : Creating fit ntuple." << std::endl;
        if (fitNtuple_ != 0) {delete fitNtuple_; fitNtuple_ = 0;}
        fitNtuple_ = new LauFitNtuple(histFileName, this->useAsymmFitErrors());

        // Create and setup the sPlot ntuple
        if (this->writeSPlotData()) {
                std::cout << "INFO in LauAbsFitModel::fit : Creating sPlot ntuple." << std::endl;
                if (sPlotNtuple_ != 0) {delete sPlotNtuple_; sPlotNtuple_ = 0;}
                sPlotNtuple_ = new LauGenNtuple(sPlotFileName_,sPlotTreeName_);
                this->setupSPlotNtupleBranches();
        }

        // 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 LauAbsFitModel::fit : Problem caching the fit data." << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }

        // Loop over the number of experiments
        for (iExpt_ = firstExpt_; iExpt_ < (firstExpt_+nExpt_); ++iExpt_) {

                // Start the timer to see how long each fit takes
                timer_.Start();

                inputFitData_->readExperimentData(iExpt_);
                this->eventsPerExpt(inputFitData_->nEvents());

                if (this->eventsPerExpt() < 1) {
                        std::cerr << "WARNING in LauAbsFitModel::fit : Zero events in experiment " << iExpt_ << ", skipping..." << std::endl;
                        timer_.Stop();
                        continue;
                }

                // Now the sub-classes must implement whatever they need to do
                // to cache any more input fit data they need in order to
                // calculate the likelihoods during the fit. 
                // They need to use the inputFitData_ tree as input. For example, 
                // inputFitData_ contains m13Sq and m23Sq. The appropriate fit model
                // then caches the resonance dynamics for the signal model, as
                // well as the background likelihood values in the Dalitz plot
                this->cacheInputFitVars();
                if ( this->doSFit() ) {
                        this->cacheInputSWeights();
                }

                // Do the fit for this experiment
                this->fitExpt();

                // Write the results into the ntuple
                this->finaliseFitResults(tableFileNameBase);

                // Stop the timer and see how long the program took so far
                timer_.Stop();
                timer_.Print();

                // Store the per-event likelihood values
                if ( this->writeSPlotData() ) {
                        this->storePerEvtLlhds();
                }

                // Create a toy MC sample using the fitted parameters so that
                // the user can compare the fit to the data.
                if (compareFitData_ == kTRUE && fitStatus_ == 3) {
                        this->createFitToyMC(fitToyMCFileName_, fitToyMCTableName_);
                }

                // Keep track of how many fits worked or failed
                // NB values of fitStatus_ now indicate the status of the error matrix:
                // 0= not calculated at all
                // 1= approximation only, not accurate
                // 2= full matrix, but forced positive-definite
                // 3= full accurate covariance matrix
                if (fitStatus_ == 3) {
                        numberOKFits_++;
                } else {
                        numberBadFits_++;
                }

        } // Loop over number of experiments

        // Print out total timing info.
        cumulTimer_.Stop();
        std::cout << "INFO in LauAbsFitModel::fit : Cumulative timing:" << std::endl;
        cumulTimer_.Print();

        // Print out stats on OK fits.
        std::cout << "INFO in LauAbsFitModel::fit : Number of OK Fits = " << numberOKFits_ << std::endl;
        std::cout << "INFO in LauAbsFitModel::fit : Number of Failed Fits = " << numberBadFits_ << std::endl;
        Double_t fitEff(0.0);
        if (nExpt_ != 0) {fitEff = numberOKFits_/(1.0*nExpt_);}
        std::cout << "INFO in LauAbsFitModel::fit : Fit efficiency = " << fitEff*100.0 << "%." << std::endl;

        // Write out any fit results (ntuples etc...).
        this->writeOutAllFitResults();
        if ( this->writeSPlotData() ) {
                this->calculateSPlotData();
        }
}

void LauAbsFitModel::fitSlave(const TString& dataFileName, const TString& dataTreeName, const TString& histFileName, const TString& tableFileNameBase)
{
        // Create and setup the fit results ntuple
        std::cout << "INFO in LauAbsFitModel::fitSlave : Creating fit ntuple." << std::endl;
        if (fitNtuple_ != 0) {delete fitNtuple_; fitNtuple_ = 0;}
        fitNtuple_ = new LauFitNtuple(histFileName, this->useAsymmFitErrors());

        // 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 LauAbsFitModel::fitSlave : Problem caching the fit data." << std::endl;
                gSystem->Exit(EXIT_FAILURE);
        }

        // Now process the various requests from the master

        TMessage messageToMaster(kMESS_ANY);

        while ( kTRUE ) {

                sMaster_->Recv( messageFromMaster_ );

                if ( messageFromMaster_->What() == kMESS_STRING ) {

                        TString msgStr;
                        messageFromMaster_->ReadTString( msgStr );

                        std::cout << "INFO in LauAbsFitModel::fitSlave : Received message from master: " << msgStr << std::endl;

                        if ( msgStr == "Send Parameters" ) {

                                // Update initial fit parameters if required (e.g. if using random numbers).
                                this->checkInitFitParams();

                                // Send the fit parameters
                                TObjArray array;
                                for ( LauParameterPList::iterator iter = fitVars_.begin(); iter != fitVars_.end(); ++iter ) {
                                        array.Add( *iter );
                                }

                                messageToMaster.Reset( kMESS_OBJECT );
                                messageToMaster.WriteObject( &array );
                                sMaster_->Send( messageToMaster );

                        } else if ( msgStr == "Read Expt" ) {

                                // Read the data for this experiment
                                messageFromMaster_->ReadUInt( iExpt_ );

                                inputFitData_->readExperimentData( iExpt_ );
                                UInt_t nEvent = inputFitData_->nEvents();
                                this->eventsPerExpt( nEvent );

                                if ( nEvent < 1 ) {
                                        std::cerr << "WARNING in LauAbsFitModel::fitSlave : Zero events in experiment " << firstExpt_ << ", the master should skip this experiment..." << std::endl;
                                }

                                messageToMaster.Reset( kMESS_ANY );
                                messageToMaster.WriteUInt( slaveId_ );
                                messageToMaster.WriteUInt( nEvent );
                                sMaster_->Send( messageToMaster );

                        } else if ( msgStr == "Cache" ) {

                                // Perform the caching

                                this->cacheInputFitVars();

                                messageToMaster.Reset( kMESS_ANY );
                                messageToMaster.WriteUInt( slaveId_ );
                                messageToMaster.WriteBool( kTRUE );
                                sMaster_->Send( messageToMaster );

                        } else if ( msgStr == "Write Results" ) {

                                this->writeOutAllFitResults();

                                messageToMaster.Reset( kMESS_ANY );
                                messageToMaster.WriteUInt( slaveId_ );
                                messageToMaster.WriteBool( kTRUE );
                                sMaster_->Send( messageToMaster );

                        } else if ( msgStr == "Finish" ) {

                                std::cout << "INFO in LauAbsFitModel::fitSlave : Message from master to finish" << std::endl;
                                break;
                        } else {
                                std::cerr << "ERROR in LauAbsFitModel::fitSlave : Unexpected message from master" << std::endl;
                                gSystem->Exit( EXIT_FAILURE );
                        }

                } else if ( messageFromMaster_->What() == kMESS_OBJECT ) {

                        std::cout << "INFO in LauAbsFitModel::fitSlave : Received message from master: Finalise" << std::endl;

                        messageFromMaster_->ReadInt( fitStatus_ );
                        messageFromMaster_->ReadDouble( NLL_ );

                        TObjArray * objarray = dynamic_cast<TObjArray*>( messageFromMaster_->ReadObject( messageFromMaster_->GetClass() ) );
                        if ( ! objarray ) {
                                std::cerr << "ERROR in LauAbsFitModel::fitSlave : Error reading parameters from master" << std::endl;
                                gSystem->Exit( EXIT_FAILURE );
                        }

                        TMatrixD * covMat = dynamic_cast<TMatrixD*>( messageFromMaster_->ReadObject( messageFromMaster_->GetClass() ) );
                        if ( ! covMat ) {
                                std::cerr << "ERROR in LauAbsFitModel::fitSlave : Error reading covariance matrix from master" << std::endl;
                                gSystem->Exit( EXIT_FAILURE );
                        }
                        covMatrix_.Clear();
                        covMatrix_.ResizeTo( covMat->GetNrows(), covMat->GetNcols() );
                        covMatrix_.SetMatrixArray( covMat->GetMatrixArray() );
                        delete covMat; covMat = 0;


                        UInt_t nPars = objarray->GetEntries();

                        if ( nPars != nParams_ ) {
                                std::cerr << "ERROR in LauAbsFitModel::fitSlave : Unexpected number of parameters received from master" << std::endl;
                                std::cerr << "                       ::fitSlave : Received " << nPars << " when expecting " << nParams_ << std::endl;
                                gSystem->Exit( EXIT_FAILURE );
                        }

                        for ( UInt_t iPar(0); iPar < nPars; ++iPar ) {
                                LauParameter* parameter = dynamic_cast<LauParameter*>( (*objarray)[iPar] );
                                if ( ! parameter ) {
                                        std::cerr << "ERROR in LauAbsFitModel::fitSlave : Error reading parameter from master" << std::endl;
                                        gSystem->Exit( EXIT_FAILURE );
                                }

                                if ( parameter->name() != fitVars_[iPar]->name() ) {
                                        std::cerr << "ERROR in LauAbsFitModel::fitSlave : Error reading parameter from master" << std::endl;
                                        gSystem->Exit( EXIT_FAILURE );
                                }

                                *(fitVars_[iPar]) = *parameter;
                        }

                        // Write the results into the ntuple
                        this->finaliseFitResults( tableFileNameBase );

                        // Store the per-event likelihood values
                        if ( this->writeSPlotData() ) {
                          this->storePerEvtLlhds();
                        }

                        // Create a toy MC sample using the fitted parameters so that
                        // the user can compare the fit to the data.
                        if (compareFitData_ == kTRUE && fitStatus_ == 3) {
                          this->createFitToyMC(fitToyMCFileName_, fitToyMCTableName_);
                        }

                        // Send the finalised parameters
                        TObjArray array;
                        for ( LauParameterPList::iterator iter = fitVars_.begin(); iter != fitVars_.end(); ++iter ) {
                                array.Add( *iter );
                        }

                        messageToMaster.Reset( kMESS_ANY );
                        messageToMaster.WriteUInt( slaveId_ );
                        messageToMaster.WriteBool( kTRUE );
                        messageToMaster.WriteObject( &array );
                        sMaster_->Send( messageToMaster );

                } else if ( messageFromMaster_->What() == kMESS_ANY ) {

                        UInt_t nPars(0);
                        messageFromMaster_->ReadUInt( nPars );

                        if ( nPars != nParams_ ) {
                                std::cerr << "ERROR in LauAbsFitModel::fitSlave : Unexpected number of parameters received from master" << std::endl;
                                std::cerr << "                       ::fitSlave : Received " << nPars << " when expecting " << nParams_ << std::endl;
                                gSystem->Exit( EXIT_FAILURE );
                        }

                        messageFromMaster_->ReadFastArray( parValues_, nPars );

                        // Update all the floating parameters with their new values
                        // Also check if we have any parameters on which the DP integrals depend
                        // and whether they have changed since the last iteration
                        Bool_t recalcNorm(kFALSE);
                        const LauParameterPSet::const_iterator resVarsEnd = resVars_.end();
                        for ( UInt_t iPar(0); iPar < nPars; ++iPar ) {
                                if ( ! fitVars_[iPar]->fixed() ) {
                                        if ( resVars_.find( fitVars_[iPar] ) != resVarsEnd ) {
                                                if ( fitVars_[iPar]->value() != parValues_[iPar] ) {
                                                        recalcNorm = kTRUE;
                                                }
                                        }
                                        fitVars_[iPar]->value( parValues_[iPar] );
                                }
                        }

                        // If so, then recalculate the normalisation
                        if (recalcNorm) {
                                this->recalculateNormalisation();
                        }

                        this->propagateParUpdates();

                        Double_t negLogLike = this->getTotNegLogLikelihood();

                        messageToMaster.Reset( kMESS_ANY );
                        messageToMaster.WriteDouble( negLogLike );   
                        sMaster_->Send( messageToMaster );         

                } else {
                        std::cerr << "ERROR in LauAbsFitModel::fitSlave : Unexpected message type" << std::endl;
                        gSystem->Exit( EXIT_FAILURE );
                }

                delete messageFromMaster_;
                messageFromMaster_ = 0;
        }
}

Bool_t LauAbsFitModel::cacheFitData(const TString& dataFileName, const TString& dataTreeName)
{
        // From the input data stream, store the variables into the
        // internal tree inputFitData_ that can be used by the sub-classes
        // in calculating their likelihood functions for the fit
        delete inputFitData_;
        inputFitData_ = new LauFitDataTree(dataFileName,dataTreeName);
        Bool_t dataOK = inputFitData_->findBranches();

        if (!dataOK) {
                delete inputFitData_; inputFitData_ = 0;
        }

        return dataOK;
}

void LauAbsFitModel::cacheInputSWeights()
{
        Bool_t hasBranch = inputFitData_->haveBranch( sWeightBranchName_ );
        if ( ! hasBranch ) {
                std::cerr << "ERROR in LauAbsFitModel::cacheInputSWeights : Input data does not contain variable \"" << sWeightBranchName_ << "\".\n";
                std::cerr << "                                            : Turning off sFit!" << std::endl;
                doSFit_ = kFALSE;
                return;
        }

        UInt_t nEvents = this->eventsPerExpt();
        sWeights_.clear();
        sWeights_.reserve( nEvents );

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

                const LauFitData& dataValues = inputFitData_->getData(iEvt);

                LauFitData::const_iterator iter = dataValues.find( sWeightBranchName_ );

                sWeights_.push_back( iter->second * sWeightScaleFactor_ );
        }
}

void LauAbsFitModel::fitExpt()
{
        // Routine to perform the actual fit for the given experiment

        // Reset the worst likelihood found to its catch-all value
        worstLogLike_ = std::numeric_limits<Double_t>::max();

        // Update initial fit parameters if required (e.g. if using random numbers).
        this->checkInitFitParams();

        // Initialise the fitter
        LauFitter::fitter()->useAsymmFitErrors( this->useAsymmFitErrors() );
        LauFitter::fitter()->twoStageFit( this->twoStageFit() );
        LauFitter::fitter()->initialise( this, fitVars_ );

        nParams_ = LauFitter::fitter()->nParameters();
        nFreeParams_ = LauFitter::fitter()->nFreeParameters();

        // Now ready for minimisation step
        std::cout << "\nINFO in LauAbsFitModel::fitExpt : Start minimisation...\n";
        std::pair<Int_t,Double_t> fitResult = LauFitter::fitter()->minimise();

        fitStatus_ = fitResult.first;
        NLL_       = fitResult.second;

        // If we're doing a two stage fit we can now release (i.e. float)
        // the 2nd stage parameters and re-fit
        if (this->twoStageFit()) {

                if ( fitStatus_ != 3 ) {
                        std::cerr << "WARNING in LauAbsFitModel:fitExpt : Not running second stage fit since first stage failed." << std::endl;
                        LauFitter::fitter()->releaseSecondStageParameters();
                } else {
                        LauFitter::fitter()->fixFirstStageParameters();
                        LauFitter::fitter()->releaseSecondStageParameters();
                        nParams_ = LauFitter::fitter()->nParameters();
                        nFreeParams_ = LauFitter::fitter()->nFreeParameters();
                        fitResult = LauFitter::fitter()->minimise();
                }
        }

        fitStatus_ = fitResult.first;
        NLL_       = fitResult.second;
        const TMatrixD& covMat = LauFitter::fitter()->covarianceMatrix();
        covMatrix_.Clear();
        covMatrix_.ResizeTo( covMat.GetNrows(), covMat.GetNcols() );
        covMatrix_.SetMatrixArray( covMat.GetMatrixArray() );

        // Store the final fit results and errors into protected internal vectors that
        // all sub-classes can use within their own finalFitResults implementation
        // used below (e.g. putting them into an ntuple in a root file)
        LauFitter::fitter()->updateParameters();
        LauFitter::fitter()->releaseFirstStageParameters();
}

void LauAbsFitModel::writeOutAllFitResults()
{
        // Write out histograms at end
        if (fitNtuple_ != 0) {
                fitNtuple_->writeOutFitResults();
        }
}

void LauAbsFitModel::calculateSPlotData()
{
        if (sPlotNtuple_ != 0) {
                sPlotNtuple_->addFriendTree(inputFitData_->fileName(), inputFitData_->treeName());
                sPlotNtuple_->writeOutGenResults();
                LauSPlot splot(sPlotNtuple_->fileName(), sPlotNtuple_->treeName(), this->firstExpt(), this->nExpt(),
                                this->variableNames(), this->freeSpeciesNames(), this->fixdSpeciesNames(), this->twodimPDFs(),
                                this->splitSignal(), this->scfDPSmear());
                splot.runCalculations(sPlotVerbosity_);
                splot.writeOutResults();
        }
}

void LauAbsFitModel::compareFitData(UInt_t toyMCScale, const TString& mcFileName, const TString& tableFileName, Bool_t poissonSmearing) 
{
        compareFitData_ = kTRUE;
        fitToyMCScale_ = toyMCScale;
        fitToyMCFileName_ = mcFileName;
        fitToyMCTableName_ = tableFileName;
        fitToyMCPoissonSmear_ = poissonSmearing;
}

void LauAbsFitModel::createFitToyMC(const TString& mcFileName, const TString& tableFileName)
{
        // Create a toy MC sample so that the user can compare the fitted
        // result with the data.
        // Generate more toy MC to reduce statistical fluctuations:
        // - use the rescaling value fitToyMCScale_

        // Store the info on the number of experiments, first expt and current expt
        const UInt_t oldNExpt(this->nExpt());
        const UInt_t oldFirstExpt(this->firstExpt());
        const UInt_t oldIExpt(iExpt_);

        // Turn off Poisson smearing if required
        const Bool_t poissonSmearing(this->doPoissonSmearing());
        this->doPoissonSmearing(fitToyMCPoissonSmear_);

        // Turn off embedding, since we need toy MC, not reco'ed events
        const Bool_t enableEmbeddingOrig(this->enableEmbedding());
        this->enableEmbedding(kFALSE);

        // Need to make sure that the generation of the DP co-ordinates is
        // switched on if any of our PDFs depend on it
        const Bool_t origUseDP = this->useDP();
        if ( !origUseDP && this->pdfsDependOnDP() ) {
                this->useDP( kTRUE );
                this->initialiseDPModels();
        }

        // Construct a unique filename for this experiment
        TString exptString("_expt");
        exptString += oldIExpt;
        TString fileName( mcFileName );
        fileName.Insert( fileName.Last('.'), exptString );

        // Generate the toy MC
        std::cout << "INFO in LauAbsFitModel::createFitToyMC : Generating toy MC in " << fileName << " to compare fit with data..." << std::endl;
        std::cout << "                                       : Number of experiments to generate = " << fitToyMCScale_ << "." << std::endl;
        std::cout << "                                       : This is to allow the toy MC to be made with reduced statistical fluctuations." << std::endl;

        // Set the genValue of each parameter to its current (fitted) value
        // but first store the original genValues for restoring later
        std::vector<Double_t> origGenValues;  origGenValues.reserve(nParams_);
        for (LauParameterPList::iterator iter = fitVars_.begin(); iter != fitVars_.end(); ++iter) {
                origGenValues.push_back((*iter)->genValue());
                (*iter)->genValue((*iter)->value());
        }

        // If we're asked to generate more than 100 experiments then split it
        // up into multiple files since otherwise can run into memory issues
        // when building the index

        UInt_t totalExpts = fitToyMCScale_;
        if ( totalExpts > 100 ) {
                UInt_t nFiles = totalExpts/100;
                if ( totalExpts%100 ) {
                        nFiles += 1;
                }
                for ( UInt_t iFile(0); iFile < nFiles; ++iFile ) {

                        UInt_t firstExp( iFile*100 );

                        // Set number of experiments and first experiment to generate
                        UInt_t nExp = ((firstExp + 100)>totalExpts) ? totalExpts-firstExp : 100;
                        this->setNExpts(nExp, firstExp);

                        // Create a unique filename and generate the events
                        TString extraname = "_file";
                        extraname += iFile;
                        fileName.Insert( fileName.Last('.'), extraname );
                        this->generate(fileName, "genResults", "dummy.root", tableFileName);
                }
        } else {
                // Set number of experiments to new value
                this->setNExpts(fitToyMCScale_, 0);
                // Generate the toy
                this->generate(fileName, "genResults", "dummy.root", tableFileName);
        }

        // Reset number of experiments to original value
        iExpt_ = oldIExpt;
        this->setNExpts(oldNExpt, oldFirstExpt);

        // Restore the Poisson smearing to its former value
        this->doPoissonSmearing(poissonSmearing);

        // Restore the embedding status
        this->enableEmbedding(enableEmbeddingOrig);

        // Restore "useDP" to its former status
        this->useDP( origUseDP );

        // Restore the original genValue to each parameter
        for (UInt_t i(0); i<nParams_; ++i) {
                fitVars_[i]->genValue(origGenValues[i]);
        }

        std::cout << "INFO in LauAbsFitModel::createFitToyMC : Finished in createFitToyMC." << std::endl;
}

Double_t LauAbsFitModel::getTotNegLogLikelihood()
{
        // Calculate the total negative log-likelihood over all events.
        // This function assumes that the fit parameters and data tree have
        // already been set-up correctly.

        // Loop over the data points to calculate the log likelihood
        Double_t logLike = this->getLogLikelihood( 0, this->eventsPerExpt() );

        // Include the Poisson term in the extended likelihood if required
        if (this->doEMLFit()) {
                logLike -= this->getEventSum();
        }

        // Calculate any penalty terms from Gaussian constrained variables
        if ( ! conVars_.empty() ){
                logLike -= this->getLogLikelihoodPenalty();
        }

        Double_t totNegLogLike = -logLike;
        return totNegLogLike;
}

Double_t LauAbsFitModel::getLogLikelihoodPenalty()
{
        Double_t penalty(0.0);

        for ( LauAbsRValuePList::const_iterator iter = conVars_.begin(); iter != conVars_.end(); ++iter ) {
                Double_t val = (*iter)->value();
                Double_t mean = (*iter)->constraintMean();
                Double_t width = (*iter)->constraintWidth();

                Double_t term = ( val - mean )*( val - mean );
                penalty += term/( 2*width*width );
        }

        return penalty;
}

Double_t LauAbsFitModel::getLogLikelihood( UInt_t iStart, UInt_t iEnd )
{
        // Calculate the total negative log-likelihood over all events.
        // This function assumes that the fit parameters and data tree have
        // already been set-up correctly.

        // Loop over the data points to calculate the log likelihood
        Double_t logLike(0.0);

        // Loop over the number of events in this experiment
        Bool_t ok(kTRUE);
        for (UInt_t iEvt = iStart; iEvt < iEnd; ++iEvt) {

                Double_t likelihood = this->getTotEvtLikelihood(iEvt);

                if (likelihood > DBL_MIN) {     // Is the likelihood zero?
                        Double_t evtLogLike = TMath::Log(likelihood);
                        if ( doSFit_ ) {
                                evtLogLike *= sWeights_[iEvt];
                        }
                        logLike += evtLogLike;
                } else {
                        ok = kFALSE;
                        std::cerr << "WARNING in LauAbsFitModel::getLogLikelihood : Strange likelihood value for event " << iEvt << ": " << likelihood << "\n";
                        this->printEventInfo(iEvt);
                        this->printVarsInfo();  //Write the values of the floated variables for which the likelihood is zero
                        break;
                }       
        }

        if (!ok) {
                std::cerr << "                                                  : Returning worst NLL found so far to force MINUIT out of this region." << std::endl;
                logLike = worstLogLike_;
        } else if (logLike < worstLogLike_) {
                worstLogLike_ = logLike;
        }

        return logLike;
}

void LauAbsFitModel::setParsFromMinuit(Double_t* par, Int_t npar)
{
        // This function sets the internal parameters based on the values
        // that Minuit is using when trying to minimise the total likelihood function.

        // MINOS reports different numbers of free parameters depending on the
        // situation, so disable this check
        if ( ! withinAsymErrorCalc_ ) {
                if (static_cast<UInt_t>(npar) != nFreeParams_) {
                        std::cerr << "ERROR in LauAbsFitModel::setParsFromMinuit : Unexpected number of free parameters: " << npar << ".\n";
                        std::cerr << "                                             Expected: " << nFreeParams_ << ".\n" << std::endl;
                        gSystem->Exit(EXIT_FAILURE);
                }
        }

        // Despite npar being the number of free parameters
        // the par array actually contains all the parameters,
        // free and floating...

        // Update all the floating ones with their new values
        // Also check if we have any parameters on which the DP integrals depend
        // and whether they have changed since the last iteration
        Bool_t recalcNorm(kFALSE);
        const LauParameterPSet::const_iterator resVarsEnd = resVars_.end();
        for (UInt_t i(0); i<nParams_; ++i) {
                if (!fitVars_[i]->fixed()) {
                        if ( resVars_.find( fitVars_[i] ) != resVarsEnd ) {
                                if ( fitVars_[i]->value() != par[i] ) {
                                        recalcNorm = kTRUE;
                                }
                        }
                        fitVars_[i]->value(par[i]);
                }
        }

        // If so, then recalculate the normalisation
        if (recalcNorm) {
                this->recalculateNormalisation();
        }

        this->propagateParUpdates();
}

UInt_t LauAbsFitModel::addFitParameters(LauPdfList& pdfList)
{
        UInt_t nParsAdded(0);
        for (LauPdfList::iterator pdf_iter = pdfList.begin(); pdf_iter != pdfList.end(); ++pdf_iter) {
                LauAbsPdf* pdf = (*pdf_iter);
                if ( pdf->isDPDependent() ) {
                        this->pdfsDependOnDP( kTRUE );
                }
                LauAbsRValuePList& pars = pdf->getParameters();
                for (LauAbsRValuePList::iterator pars_iter = pars.begin(); pars_iter != pars.end(); ++pars_iter) {
                        LauParameterPList params = (*pars_iter)->getPars();
                        for (LauParameterPList::iterator params_iter = params.begin(); params_iter != params.end(); ++params_iter) {
                                if ( !(*params_iter)->clone() && ( !(*params_iter)->fixed() ||
                                                        (this->twoStageFit() && ( (*params_iter)->secondStage() || (*params_iter)->firstStage())) ) ) {
                                        fitVars_.push_back(*params_iter);
                                        ++nParsAdded;
                                }
                        }
                }
        }
        return nParsAdded;
}

void LauAbsFitModel::addConstraint(const TString& formula, const std::vector<TString>& pars, const Double_t mean, const Double_t width)
{
        StoreConstraints newCon;
        newCon.formula_ = formula;
        newCon.conPars_ = pars;
        newCon.mean_ = mean;
        newCon.width_ = width;
        storeCon_.push_back(newCon);
}

void LauAbsFitModel::addConParameters()
{
        for ( LauParameterPList::const_iterator iter = fitVars_.begin(); iter != fitVars_.end(); ++iter ) {
                if ( (*iter)->gaussConstraint() ) {
                        conVars_.push_back( *iter );
                        std::cout << "INFO in LauAbsFitModel::addConParameters : Added Gaussian constraint to parameter "<< (*iter)->name() << std::endl;
                }
        }

        // Add penalties from the constraints to fit parameters
        for ( std::vector<StoreConstraints>::iterator iter = storeCon_.begin(); iter != storeCon_.end(); ++iter ) {
                std::vector<TString> names = (*iter).conPars_;
                std::vector<LauParameter*> params;
                for ( std::vector<TString>::iterator iternames = names.begin(); iternames != names.end(); ++iternames ) { 
                        for ( LauParameterPList::const_iterator iterfit = fitVars_.begin(); iterfit != fitVars_.end(); ++iterfit ) {
                                if ( (*iternames) == (*iterfit)->name() ){
                                        params.push_back(*iterfit);
                                }
                        }
                }

                // If the parameters are not found, skip it
                if ( params.size() != (*iter).conPars_.size() ) {
                        std::cerr << "WARNING in LauAbsFitModel::addConParameters: Could not find parameters to constrain in the formula... skipping" << std::endl;
                        continue;
                }

                LauFormulaPar* formPar = new LauFormulaPar( (*iter).formula_, (*iter).formula_, params );
                formPar->addGaussianConstraint( (*iter).mean_, (*iter).width_ );
                conVars_.push_back(formPar);

                std::cout << "INFO in LauAbsFitModel::addConParameters : Added Gaussian constraint to formula\n";
                std::cout << "                                         : Formula: " << (*iter).formula_ << std::endl;
                for ( std::vector<LauParameter*>::iterator iterparam = params.begin(); iterparam != params.end(); ++iterparam ) {
                        std::cout << "                                         : Parameter: " << (*iterparam)->name() << std::endl;
                }
        }
        
}

void LauAbsFitModel::updateFitParameters(LauPdfList& pdfList)
{
        for (LauPdfList::iterator pdf_iter = pdfList.begin(); pdf_iter != pdfList.end(); ++pdf_iter) {
                (*pdf_iter)->updatePulls();
        }
}

void LauAbsFitModel::printFitParameters(const LauPdfList& pdfList, std::ostream& fout) const
{
        LauPrint print;
        for (LauPdfList::const_iterator pdf_iter = pdfList.begin(); pdf_iter != pdfList.end(); ++pdf_iter) {
                const LauAbsRValuePList& pars = (*pdf_iter)->getParameters();
                for (LauAbsRValuePList::const_iterator pars_iter = pars.begin(); pars_iter != pars.end(); ++pars_iter) {
                        LauParameterPList params = (*pars_iter)->getPars();
                        for (LauParameterPList::iterator params_iter = params.begin(); params_iter != params.end(); ++params_iter) {
                                if (!(*params_iter)->clone()) {
                                        fout << (*params_iter)->name() << "  &  $";
                                        print.printFormat(fout, (*params_iter)->value());
                                        if ((*params_iter)->fixed() == kTRUE) {
                                                fout << "$ (fixed) \\\\";
                                        } else {
                                                fout << " \\pm ";
                                                print.printFormat(fout, (*params_iter)->error());
                                                fout << "$ \\\\" << std::endl;
                                        }
                                }
                        }
                }
        }
}

void LauAbsFitModel::cacheInfo(LauPdfList& pdfList, const LauFitDataTree& theData)
{
        for (LauPdfList::iterator pdf_iter = pdfList.begin(); pdf_iter != pdfList.end(); ++pdf_iter) {
                (*pdf_iter)->cacheInfo(theData);
        }
}

Double_t LauAbsFitModel::prodPdfValue(LauPdfList& pdfList, UInt_t iEvt)
{
        Double_t pdfVal = 1.0;
        for (LauPdfList::iterator pdf_iter = pdfList.begin(); pdf_iter != pdfList.end(); ++pdf_iter) {
                (*pdf_iter)->calcLikelihoodInfo(iEvt);
                pdfVal *= (*pdf_iter)->getLikelihood();
        }
        return pdfVal;
}

void LauAbsFitModel::printEventInfo(UInt_t iEvt) const
{
        const LauFitData& data = inputFitData_->getData(iEvt);
        std::cerr << "                                                  : Input data values for this event:" << std::endl;
        for (LauFitData::const_iterator iter = data.begin(); iter != data.end(); ++iter) {
                std::cerr << " " << iter->first << " = " << iter->second << std::endl;
        }
}

void LauAbsFitModel::printVarsInfo() const
{
        std::cerr << "                                                  : Current values of fit parameters:" << std::endl;
        for (UInt_t i(0); i<nParams_; ++i) {
                std::cerr << " " << (fitVars_[i]->name()).Data() << " = " << fitVars_[i]->value() << std::endl;
        }
}