LauNovosibirskPdf.cc

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// Copyright University of Warwick 2008 - 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>

using std::cout;
using std::cerr;
using std::endl;

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

#include "LauNovosibirskPdf.hh"
#include "LauConstants.hh"

ClassImp(LauNovosibirskPdf)

LauNovosibirskPdf::LauNovosibirskPdf(const TString& theVarName, const vector<LauAbsRValue*>& params, Double_t minAbscissa, Double_t maxAbscissa) :
        LauAbsPdf(theVarName, params, minAbscissa, maxAbscissa),
        mean_(0),
        sigma_(0),
        tail_(0)
{
        // Constructor for the Novosibirsk PDF.
        //
        // The parameters in params are the mean, sigma and tail.
        // 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");
        sigma_ = this->findParameter("sigma");
        tail_ = this->findParameter("tail");

        if ((this->nParameters() != 3) || (mean_ == 0) || (sigma_ == 0) || (tail_ == 0)) {
                cerr<<"ERROR in LauNovosibirskPdf constructor: LauNovosibirskPdf requires 3 parameters: \"mean\", \"sigma\"and \"tail\" "<<endl;
                gSystem->Exit(EXIT_FAILURE);
        }

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

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

void LauNovosibirskPdf::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
        Double_t mean = mean_->unblindValue();
        Double_t sigma = sigma_->unblindValue();
        Double_t tail = tail_->unblindValue();

        // Evaluate the Novosibirsk PDF value


        Double_t qa(0.0), qb(0.0), qx(0.0), qy(0.0);
        Double_t arg(0.0);
        Double_t value(0.0);


        if(TMath::Abs(tail) < 1.e-7) 
                arg = 0.5*((abscissa - mean)/sigma)*((abscissa - mean)/sigma);
        else {
                qa = tail*TMath::Sqrt(LauConstants::log4);
                qb = TMath::SinH(qa)/qa;
                qx = (abscissa - mean)/sigma*qb;
                qy = 1.0+ tail*qx;

                //---- Cutting curve from right side

                if( qy > 1.E-7) { 
                        arg = 0.5*( (log(qy)/tail)*(log(qy)/tail) + tail*tail);
                }else{
                        arg = 15.0;
                }       
        }

        value = TMath::Exp(-arg);

        // if the parameters are floating then we
        // need to recalculate the normalisation
        if (!this->cachePDF() && !this->withinNormCalc() && !this->withinGeneration()) {
                this->calcNorm();
        }
        this->setUnNormPDFVal(value);
}

void LauNovosibirskPdf::calcPDFHeight( const LauKinematics* /*kinematics*/ )
{
        if (this->heightUpToDate()) {
                return;
        }

        // Get the up to date parameter values
        Double_t mean = mean_->unblindValue();

        LauAbscissas maxPoint(1);
        maxPoint[0] = mean;
        

        // Calculate the PDF height for the Bifurcated Gaussian function.
        
        if (mean < this->getMinAbscissa()) {
                maxPoint[0] = this->getMinAbscissa();
        } else  if (mean > 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 *= (1.0 + 1e-1);

        this->setMaxHeight(height);
}