#include "AtlfastAlgs/PionBinData.h"
 
 namespace Atlfast 
 {
 
   /**
    * @brief Class to hold smearing matrix data.
    *
    * The data is provided through the constructor
    * in the format found in the flat file and the
    * correlation matrix corresponding to the bin
    * can be calculated and returned via public methods.
    */
 
 
   //-----------------------------------------------
   // PUBLIC - Constructor
   //-----------------------------------------------
   PionBinData::PionBinData( BinID& id,
                             vector< ParameterResolutions* > core,
                             vector< ParameterResolutions* > tails,
                             vector< ParameterResolutions* > fractions,
                             vector< ParameterResolutions* > correlations,
                             int randSeed
                           ) :
                m_id(id),
                m_cores(core),    
                m_tails(tails),
                m_fractions(fractions),
                m_correlations(correlations)
 {
     m_randomEngine = new HepJamesRandom(randSeed);
 }    
 
 
   PionBinData::~PionBinData()
   {
     delete m_randomEngine;
     for (size_t i=0; i < m_fractions.size(); i++)
       delete m_fractions[i];
     for (size_t i=0; i < m_correlations.size(); i++)
       delete m_correlations[i];
     for (size_t i=0; i < m_cores.size(); i++)
       delete m_cores[i];
     for (size_t i=0; i < m_tails.size(); i++)
       delete m_tails[i];
   }
   
   //--------------------------------------------------------------------
   // PUBLIC - HepSymMatrix getMatrix(random)
   // returns appropriate Sigma (=covariance) matrix
   //
   // NOTE: the representation of Sigma is determined by the track 
   //       representation implicitly given in the parameter files,
   //       i.e., (d0, z0, phi0, cot(theta0), q/pT), which is for 
   //       internal use only.
   //
   //       The main advantage of this representation is that in 
   //       the context of the ID, for a solenoidal field, the three
   //       transverse track parameters (d0, phi0, q/pT) are to a 
   //       good approximation uncorrelated with the two longitudinal 
   //       ones (z0, cot(theta0)).  
   //
   //       Sigma has to be converted into the representation used 
   //       by Common Tracking, i.e. (d0, z0, phi0, theta0, q/p), 
   //       when it is written to CBNTs or AODs.  
   //--------------------------------------------------------------------
   HepSymMatrix PionBinData::getMatrix( const TrackTrajectory& traj ) const
   {
     HepSymMatrix Sigma(5,0);
 
     double fraction, random[5];
 
     // order of parameters: d0, z0, phi0, cot(theta0), q/pT
     // introduce "correlation" between core <--> tails
     random[0] = m_randomEngine->flat();
     random[1] = m_randomEngine->flat();
     random[2] = random[0];
     random[3] = random[1];
     random[4] = random[0];
 
     // diagonals
     for ( int param = 0; param < 5; param++ )
     {
       fraction = m_fractions[param]->resolution(traj);
       if ( fraction > 1.0 )  fraction = 1.0;
       Sigma[param][param] = ( random[param] < fraction )  ?
                             std::pow( m_cores[param]->resolution(traj), 2 )  :
                             std::pow( m_tails[param]->resolution(traj), 2 );
     }
     
     // off-diagonals 
     // NOTE: m_correlations[] holds correlation coefficients, need covariances
 
     // (1,3) ... cov(d0,phi0)
     // (1,5) ... cov(d0,q/pT)
     // (3,5) ... cov(phi0,q/pT)
     double rho13 = m_correlations[0]->resolution(traj);
     double rho15 = m_correlations[1]->resolution(traj);
     double rho35 = m_correlations[2]->resolution(traj);
 
     // covariance sub-matrix of transverse parameters needs to be positive definite
     // in order that its square root (cf. PionMatrixManager) exists
     double det3 = 1 - rho13 * rho13 - rho15 * rho15 - rho35 * rho35 - 2 * rho13 * rho15 * rho35;
     if ( det3 < 0 )  rho13 = rho15 = rho35 = 0;
     
     // make sure that correlation coefficients stay within [-1,+1]
     if ( std::abs(rho13) > 1 )  rho13 *= 0.99 / std::abs(rho13);
     if ( std::abs(rho15) > 1 )  rho15 *= 0.99 / std::abs(rho15);
     if ( std::abs(rho35) > 1 )  rho35 *= 0.99 / std::abs(rho35);
     
     Sigma(1,3) = Sigma(3,1) = rho13 * std::sqrt( Sigma(1,1) * Sigma(3,3) );
     Sigma(1,5) = Sigma(5,1) = rho15 * std::sqrt( Sigma(1,1) * Sigma(5,5) );
     Sigma(3,5) = Sigma(5,3) = rho35 * std::sqrt( Sigma(3,3) * Sigma(5,5) );
 
 
     // (2,4) ... cov(z0,cot(theta0))
     double rho24 = m_correlations[3]->resolution(traj);
     // make sure that correlation coefficient stays within [-1,+1]
     if ( std::abs(rho24) > 1 )  rho24 *= 0.99 / std::abs(rho24);
     Sigma(2,4) = Sigma(4,2) = rho24 * std::sqrt( Sigma(2,2) * Sigma(4,4) );
 
     // DONE!
     return Sigma;
     
   }
   
 } //namespace bracket
 

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