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/*
Copyright 2006 University of Warwick
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
/*
Laura++ package authors:
John Back
Paul Harrison
Thomas Latham
*/
/*! \file LauArgusPdf.cc
\brief File containing implementation of LauArgusPdf class.
*/
#include <iostream>
#include <vector>
#include "TMath.h"
#include "TSystem.h"
#include "LauArgusPdf.hh"
#include "LauConstants.hh"
ClassImp(LauArgusPdf)
LauArgusPdf::LauArgusPdf(const TString& theVarName, const std::vector<LauAbsRValue*>& params, Double_t minAbscissa, Double_t maxAbscissa) :
LauAbsPdf(theVarName, params, minAbscissa, maxAbscissa),
xi_(0),
m0_(0)
{
// Constructor for the ARGUS PDF.
//
// The parameters in params are the shape, xi, and the end-point of the curve.
// The last two arguments specify the range in which the PDF is defined, and the PDF
// will be normalised w.r.t. these limits.
xi_ = this->findParameter("xi");
m0_ = this->findParameter("m0");
if ((this->nParameters() != 2) || (xi_ == 0) || (m0_ == 0)) {
std::cerr << "ERROR in LauArgusPdf constructor: LauArgusPdf requires 2 parameters: argus shape parameter, \"xi\", and end-point, \"m0\"." << std::endl;
gSystem->Exit(EXIT_FAILURE);
}
// Cache the normalisation factor.
this->calcNorm();
}
LauArgusPdf::~LauArgusPdf()
{
// Destructor
}
void LauArgusPdf::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 xi = xi_->unblindValue();
Double_t m0 = m0_->unblindValue();
// Calculate the value of the ARGUS function for the given value of the abscissa.
Double_t x = abscissa/m0;
Double_t term = 1.0 - x*x;
if (term < 0.0) {term = 0.0;} // In case |x| > 1.0 (which should happen rarely).
Double_t value = abscissa*TMath::Sqrt(term)*TMath::Exp(-xi*term);
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 LauArgusPdf::calcNorm()
{
// Calculate the PDF normalisation and cache it
// Get the up to date parameter values
Double_t xi = xi_->unblindValue();
Double_t m0 = m0_->unblindValue();
// Since the PDF is 0 above m0 by definition need to check whether m0 is within the range, above it or below it
Double_t min = (this->getMinAbscissa() < m0) ? this->getMinAbscissa() : m0;
Double_t max = (this->getMaxAbscissa() < m0) ? this->getMaxAbscissa() : m0;
// Define variables equivalent to "term" in calcLikelihoodInfo above but at the min and max points
Double_t termMin = 1.0 - (min/m0)*(min/m0);
Double_t termMax = 1.0 - (max/m0)*(max/m0);
// Calculate the various terms in the integrals
Double_t norm1 = TMath::Sqrt(termMax)*TMath::Exp(-xi*termMax) - TMath::Sqrt(termMin)*TMath::Exp(-xi*termMin);
Double_t norm2 = LauConstants::rootPi/(2.0*TMath::Sqrt(xi)) * ( TMath::Erf(TMath::Sqrt(xi*termMax)) - TMath::Erf(TMath::Sqrt(xi*termMin)) );
// Combine them and set the normalisation
Double_t norm = m0*m0*(norm1 - norm2)/(2.0*xi);
this->setNorm(norm);
}
void LauArgusPdf::calcPDFHeight( const LauKinematics* /*kinematics*/ )
{
if (this->heightUpToDate()) {
return;
}
// Calculate the PDF height of the ARGUS function.
// Get the up to date parameter values
Double_t xi = xi_->unblindValue();
Double_t m0 = m0_->unblindValue();
// First make sure that the limits are not larger than the end-point.
// (Btw, use the logarithmic derivative to derive this formula)
Double_t term = xi*xi + 1.0;
Double_t x = TMath::Sqrt((TMath::Sqrt(term) - 1.0 + xi)/(2.0*xi));
x = (x*m0 >= this->getMinAbscissa()) ? x*m0 : this->getMinAbscissa();
LauAbscissas abscissa(1);
abscissa[0] = x;
this->calcLikelihoodInfo(abscissa);
Double_t height = this->getUnNormLikelihood();
this->setMaxHeight(height);
}

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