LauParametricStepFuncPdf.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

/*****************************************************************************
 * Class based on RooFit/RooParametricStepFunction.                          *
 * Original copyright given below.                                           *
 *****************************************************************************
 * Authors:                                                                  *
 *   WV, Wouter Verkerke, UC Santa Barbara, verkerke@slac.stanford.edu       *
 *   DK, David Kirkby,    UC Irvine,         dkirkby@uci.edu                 *
 *                                                                           *
 * Copyright (c) 2000-2005, Regents of the University of California          *
 *                          and Stanford University. All rights reserved.    *
 *                                                                           *
 * Redistribution and use in source and binary forms,                        *
 * with or without modification, are permitted according to the terms        *
 * listed in LICENSE (http://roofit.sourceforge.net/license.txt)             *
 *****************************************************************************/


#include <iostream>
#include <vector>
using std::cout;
using std::cerr;
using std::endl;
using std::vector;

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

#include "LauConstants.hh"
#include "LauParametricStepFuncPdf.hh"

ClassImp(LauParametricStepFuncPdf)


LauParametricStepFuncPdf::LauParametricStepFuncPdf(const TString& theVarName, const vector<LauAbsRValue*>& params, const vector<Double_t>& limits, NormBin normalisationBin) :
        LauAbsPdf(theVarName, params, limits.front(), limits.back()),
        normBin_(normalisationBin),
        limits_(limits)
{
        // Constructor for the PSF PDF.
        //
        // The parameters in params are the bin contents of all but the
        // normalisation bin, so has N_bins-1 entries.
        // The last argument specifies the limits of the bins and the range
        // as a whole, so has N_bins+1 entries.

        if (this->nParameters() != this->nBins()-1) {
                cerr<<"ERROR in LauParametricStepFuncPdf constructor: LauParametricStepFuncPdf requires N-1 parameters, where N is the number of bins."<<endl;
                gSystem->Exit(EXIT_FAILURE);
        }

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

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

void LauParametricStepFuncPdf::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 parameters
        const vector<LauAbsRValue*>& pars = this->getParameters();

        // Calculate value
        Double_t value(0.0);
        const UInt_t numBins = this->nBins();

        if ( this->normBin() == Last ) {
                // the last bin is our normalisation bin
                for ( UInt_t i(1); i<=numBins; ++i ) {
                        if ( abscissa < limits_[i] ) {
                                // in bin i-1 (starting with bin 0)
                                if ( i < numBins ) {
                                        // not in last bin
                                        value = pars[i-1]->unblindValue();
                                        break;
                                } else {
                                        // in last bin
                                        Double_t sum(0.0);
                                        Double_t binSize(0.0);
                                        for ( UInt_t j(1); j<numBins; ++j ) {
                                                binSize = limits_[j] - limits_[j-1];
                                                sum += ( pars[j-1]->unblindValue() * binSize );
                                        }
                                        binSize = limits_[numBins] - limits_[numBins-1];
                                        value = ( 1.0 - sum ) / binSize;
                                        break;
                                }
                        }
                }
        } else {
                // the first bin is our normalisation bin
                for ( UInt_t i(1); i<=numBins; ++i ) {
                        if ( abscissa < limits_[i] ) {
                                // in bin i-1 (starting with bin 0)
                                if ( i > 1 ) {
                                        // not in first bin
                                        value = pars[i-2]->unblindValue();
                                        break;
                                } else {
                                        // in first bin
                                        Double_t sum(0.0);
                                        Double_t binSize(0.0);
                                        for ( UInt_t j(2); j<=numBins; ++j ) {
                                                binSize = limits_[j] - limits_[j-1];
                                                sum += ( pars[j-2]->unblindValue() * binSize );
                                        }
                                        binSize = limits_[1] - limits_[0];
                                        value = ( 1.0 - sum ) / binSize;
                                        break;
                                }
                        }
                }
        }

        this->setUnNormPDFVal(value);

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

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

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

        // Get the parameters
        const vector<LauAbsRValue*>& pars = this->getParameters();

        // Find the PDF height
        Double_t height(0.0);
        Double_t value(0.0);
        const UInt_t numBins = this->nBins();

        if ( this->normBin() == Last ) {
                // the last bin is our normalisation bin

                // Check through all the parameterised bins
                for ( UInt_t i(0); i<numBins-1; ++i ) {
                        value = pars[i]->unblindValue();
                        if ( height < value ) {
                                height = value;
                        }
                }

                // Check the last bin
                Double_t sum(0.0);
                Double_t binSize(0.0);
                for ( UInt_t j(1); j<numBins; ++j ) {
                        binSize = limits_[j] - limits_[j-1];
                        sum += ( pars[j-1]->unblindValue() * binSize );
                }
                binSize = limits_[numBins] - limits_[numBins-1];
                value = ( 1.0 - sum ) / binSize;
                if ( height < value ) {
                        height = value;
                }
        } else {
                // the first bin is our normalisation bin

                // Check through all the parameterised bins
                for ( UInt_t i(1); i<numBins; ++i ) {
                        value = pars[i-1]->unblindValue();
                        if ( height < value ) {
                                height = value;
                        }
                }

                // Check the first bin
                Double_t sum(0.0);
                Double_t binSize(0.0);
                for ( UInt_t j(2); j<=numBins; ++j ) {
                        binSize = limits_[j] - limits_[j-1];
                        sum += ( pars[j-2]->unblindValue() * binSize );
                }
                binSize = limits_[1] - limits_[0];
                value = ( 1.0 - sum ) / binSize;
                if ( height < value ) {
                        height = value;
                }
        }

        this->setMaxHeight(height);
}