LauDPDepCruijffPdf.cc

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// Copyright University of Warwick 2009 - 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 <vector>
using std::cout;
using std::cerr;
using std::endl;

#include "TMath.h"
#include "TSystem.h"

#include "LauConstants.hh"
#include "LauDaughters.hh"
#include "LauDPDepCruijffPdf.hh"
#include "LauKinematics.hh"

ClassImp(LauDPDepCruijffPdf)


LauDPDepCruijffPdf::LauDPDepCruijffPdf(const TString& theVarName, const std::vector<LauAbsRValue*>& params,
                Double_t minAbscissa, Double_t maxAbscissa,
                const LauDaughters* daughters,
                const std::vector<Double_t>& meanCoeffs,
                const std::vector<Double_t>& sigmaLCoeffs,
                const std::vector<Double_t>& sigmaRCoeffs,
                const std::vector<Double_t>& alphaLCoeffs,
                const std::vector<Double_t>& alphaRCoeffs,
                DPAxis dpAxis) :
        LauAbsPdf(theVarName, params, minAbscissa, maxAbscissa),
        kinematics_(daughters ? daughters->getKinematics() : 0),
        mean_(0),
        sigmaL_(0),
        sigmaR_(0),
        alphaL_(0),
        alphaR_(0),
        meanVal_(0.0),
        sigmaLVal_(0.0),
        sigmaRVal_(0.0),
        alphaLVal_(0.0),
        alphaRVal_(0.0),
        meanCoeffs_(meanCoeffs),
        sigmaLCoeffs_(sigmaLCoeffs),
        sigmaRCoeffs_(sigmaRCoeffs),
        alphaLCoeffs_(alphaLCoeffs),
        alphaRCoeffs_(alphaRCoeffs),
        dpAxis_(dpAxis)
{
        // Constructor for the Dalitz-plot dependent Cruijff PDF.

        // Check we have a valid kinematics object
        if ( ! kinematics_ ) {
                cerr<<"ERROR in LauDPDepCruijffPdf::LauDPDepCruijffPdf : Have not been provided with a valid DP kinematics object."<<endl;
                gSystem->Exit(EXIT_FAILURE);
        }

        // The parameters in params are the mean, sigmaR, sigmaL, alphaR and alphaL.
        // The last two arguments specify the range in which the PDF is defined, and the PDF
        // will be normalised w.r.t. these limits.

        mean_ = this->findParameter("mean");
        sigmaR_ = this->findParameter("sigmaR");
        sigmaL_ = this->findParameter("sigmaL");
        alphaR_ = this->findParameter("alphaR");
        alphaL_ = this->findParameter("alphaL");

        if ((this->nParameters() != 5) || (mean_ == 0) || (sigmaR_ == 0) || (sigmaL_ == 0) || (alphaL_ == 0) || (alphaR_ == 0)) {
                cerr<<"ERROR in LauDPDepCruijffPdf constructor: LauDPDepCruijffPdf requires 5 parameters: \"mean\", \"sigmaL\", \"sigmaR\", \"alphaR\" and \"alphaL\"."<<endl;
                gSystem->Exit(EXIT_FAILURE);
        }

        // Cache the normalisation factor
        this->calcNorm();
}

LauDPDepCruijffPdf::~LauDPDepCruijffPdf() 
{
        // Destructor
}

void LauDPDepCruijffPdf::calcNorm() 
{
        this->setNorm(1.0);
}

void LauDPDepCruijffPdf::calcLikelihoodInfo(const LauAbscissas& abscissas)
{
        // Check that the given abscissa is within the allowed range
        if (!this->checkRange(abscissas)) {
                gSystem->Exit(EXIT_FAILURE);
        }

        // Get our abscissa
        Double_t abscissa = abscissas[0];

        // Get the up to date parameter values
        meanVal_ = mean_->value();
        sigmaLVal_ = sigmaL_->value();
        sigmaRVal_ = sigmaR_->value();
        alphaLVal_ = alphaL_->value();
        alphaRVal_ = alphaR_->value();

        // Find out the DP position
        Double_t dpPos(0.0);
        UInt_t nVars = this->nInputVars();
        if ( abscissas.size() == nVars+2 ) {
                Double_t m13Sq = abscissas[nVars];
                Double_t m23Sq = abscissas[nVars+1];

                if ( dpAxis_ == M12 ) {
                        dpPos = kinematics_->calcThirdMassSq(m13Sq,m23Sq);
                } else if ( dpAxis_ == M13 ) {
                        dpPos = m13Sq;
                } else if ( dpAxis_ == M23 ) {
                        dpPos = m23Sq;
                } else if ( dpAxis_ == MMIN ) {
                        dpPos = TMath::Min( m13Sq, m23Sq );
                } else if ( dpAxis_ == MMAX ) {
                        dpPos = TMath::Max( m13Sq, m23Sq );
                } else {
                        dpPos = kinematics_->distanceFromDPCentre(m13Sq,m23Sq);
                }
        }

        // Scale the parameters according to the DP position
        this->scalePars( dpPos );

        // Calculate the PDF value
        Double_t value = this->currentPDFValue( abscissa );

        // Calculate the normalisation
        IntMethod sumMethod = this->integMethod();
        Double_t normFac = (sumMethod == GaussLegendre) ? this->integrGaussLegendre() : this->integTrapezoid();

        value /= normFac;

        this->setUnNormPDFVal(value);
}

Double_t LauDPDepCruijffPdf::currentPDFValue(Double_t abscissa) const
{
        Double_t arg = abscissa - meanVal_;
        Double_t coef(0.0);
        if (arg < 0.0){
                if (TMath::Abs(sigmaLVal_) > 1e-30) {
                        coef = -1.0/(2.0*sigmaLVal_*sigmaLVal_ + alphaLVal_*arg*arg);
                }
        } else {
                if (TMath::Abs(sigmaRVal_) > 1e-30) {
                        coef = -1.0/(2.0*sigmaRVal_*sigmaRVal_ + alphaRVal_*arg*arg);
                }
        }
        return TMath::Exp(coef*arg*arg);
}

Double_t LauDPDepCruijffPdf::integrGaussLegendre()
{
        if (!this->normWeightsDone()) {
                this->getNormWeights();
        }

        const std::vector<LauAbscissas>& norm_abscissas = this->normAbscissas();
        const std::vector<Double_t>& norm_weights = this->normWeights();

        // Now compute the integral
        Double_t norm(0.0);
        for (UInt_t i = 0; i < norm_weights.size(); i++) {
                Double_t fun = this->currentPDFValue( norm_abscissas[i][0] );
                Double_t intFactor = 0.5 * this->getRange();
                norm += norm_weights[i]*intFactor*fun;
        } 
        
        return norm;
}

Double_t LauDPDepCruijffPdf::integTrapezoid()
{
        static Double_t norm(0.0);
        Double_t range = this->getRange();

        if (this->nNormPoints()==1){

                Double_t abscissa = this->getMinAbscissa();
                Double_t funAbsMin = this->currentPDFValue(abscissa);

                abscissa = this->getMaxAbscissa();
                Double_t funAbsMax = this->currentPDFValue(abscissa);

                norm = 0.5*range*(funAbsMin+funAbsMax);
                return norm;

        } else {

                Double_t abscVal(0.0), tnm(0.0), sum(0.0), del(0.0);
                Int_t it(0), j(0);

                for (it=1, j=1; j< this->nNormPoints()-1; j++) {it<<=1;}
                tnm=it;
                del=range/tnm;
                abscVal= this->getMinAbscissa()+ 0.5*del;

                for (sum = 0.0, j=1; j<it; j++, abscVal+=del) {

                        Double_t funVal = this->currentPDFValue(abscVal);
                        sum+=funVal;
                }

                norm = 0.5*(norm + sum*range/tnm);
                return norm;
        }
}

void LauDPDepCruijffPdf::scalePars(Double_t dpPos)
{
        Int_t power = 1;
        for (std::vector<Double_t>::const_iterator iter = meanCoeffs_.begin(); iter != meanCoeffs_.end(); ++iter) {
                Double_t coeff = (*iter);
                meanVal_ += coeff * TMath::Power(dpPos,power);
                ++power;
        }

        power = 1;
        for (std::vector<Double_t>::const_iterator iter = sigmaLCoeffs_.begin(); iter != sigmaLCoeffs_.end(); ++iter) {
                Double_t coeff = (*iter);
                sigmaLVal_ += coeff * TMath::Power(dpPos,power);
                ++power;
        }

        power = 1;
        for (std::vector<Double_t>::const_iterator iter = sigmaRCoeffs_.begin(); iter != sigmaRCoeffs_.end(); ++iter) {
                Double_t coeff = (*iter);
                sigmaRVal_ += coeff * TMath::Power(dpPos,power);
                ++power;
        }

        power = 1;
        for (std::vector<Double_t>::const_iterator iter = alphaLCoeffs_.begin(); iter != alphaLCoeffs_.end(); ++iter) {
                Double_t coeff = (*iter);
                alphaLVal_ += coeff * TMath::Power(dpPos,power);
                ++power;
        }

        power = 1;
        for (std::vector<Double_t>::const_iterator iter = alphaRCoeffs_.begin(); iter != alphaRCoeffs_.end(); ++iter) {
                Double_t coeff = (*iter);
                alphaRVal_ += coeff * TMath::Power(dpPos,power);
                ++power;
        }
}

void LauDPDepCruijffPdf::calcPDFHeight( const LauKinematics* kinematics )
{
        // Get the up to date parameter values
        meanVal_ = mean_->value();
        sigmaLVal_ = sigmaL_->value();
        sigmaRVal_ = sigmaR_->value();
        alphaLVal_ = alphaL_->value();
        alphaRVal_ = alphaR_->value();

        // Find out the DP position
        Double_t dpPos(0.0);
        if ( dpAxis_ == M12 ) {
                dpPos = kinematics->getm12Sq();
        } else if ( dpAxis_ == M13 ) {
                dpPos = kinematics->getm13Sq();
        } else if ( dpAxis_ == M23 ) {
                dpPos = kinematics->getm23Sq();
        } else {
                dpPos = kinematics->distanceFromDPCentre();
        }

        // Scale the parameters according to the DP position
        this->scalePars( dpPos );

        // Calculate the PDF height
        
        LauAbscissas maxPoint(3);
        maxPoint[0] = meanVal_;
        maxPoint[1] = kinematics->getm13Sq();
        maxPoint[2] = kinematics->getm23Sq();

        if (meanVal_ < this->getMinAbscissa()) {
                maxPoint[0] = this->getMinAbscissa();
        } else  if (meanVal_ > this->getMaxAbscissa()) {
                maxPoint[0] = this->getMaxAbscissa();
        }

        this->calcLikelihoodInfo(maxPoint);
        Double_t height = this->getUnNormLikelihood();

        // Multiply by a small factor to avoid problems from rounding errors
        height *= 1.01;

        this->setMaxHeight(height);
}