CFAlgoChargeDistrib.cxx

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#include "CFAlgoChargeDistrib.h"
#include "lardata/DetectorInfoServices/DetectorPropertiesService.h"
#include "lardata/Utilities/GeometryUtilities.h"

namespace cmtool {

  //-------------------------------------------------------
  CFAlgoChargeDistrib::CFAlgoChargeDistrib() : CFloatAlgoBase()
  //-------------------------------------------------------
  {
    SetVerbose(false);
    SetDebug(false);
  }

  //-----------------------------
  void CFAlgoChargeDistrib::Reset()
  //-----------------------------
  {

  }

  //----------------------------------------------------------------------------------------------
  float CFAlgoChargeDistrib::Float(const std::vector<const cluster::ClusterParamsAlg*> &clusters)
  //----------------------------------------------------------------------------------------------
  {

    // Code-block by Kazu starts
    // This ensures the algorithm works only if # clusters is > 2 (and not =2)
    // You may take out this block if you want to allow matching using clusters from only 2 planes.
    //if(clusters.size()==2) return -1;
    // Code-block by Kazu ends

    //This algorithm now works for 3 planes: find 3Dstart point from first 2 planes and find
    //How well that agrees with 3rd plane's start point location.

    //So first, make sure clusters vector has more than 1 element.
    //If not, return -1. Algorithm does not make sense
    if ( clusters.size() == 1 )
      return -1;

    if (_verbose) { std::cout << "Number of clusters taken into account: " << clusters.size() << std::endl; }

    //MicroBooNE Nomenclature:
    //Planes: U == 0; V == 1; Y == 2.

    //Get hits for all 3 clusters
    std::vector<std::vector<util::PxHit> > Hits;//(clusters.size(), std::vector<util::PxHit>());

    for (size_t c=0; c < clusters.size(); c++)
      Hits.push_back( clusters.at(c)->GetHitVector() );

    // return parameter is the product of
    // "convolutions" of each pair of clusters
    // return conv(a,b)*conv(b,c)*conv(c,a)
    // in the case of 3 planes with clusters
    // a, b and c.
    // The functions being convolved are the
    // charge-distribution in time of each clusters
    // The "convolution" is the common area of the
    // two distributions: the larger the charge-
    // -overlap the better the match
    // No normalization is performed.

    float totOverlap = 1;
    for (size_t c1=0; c1 < (Hits.size()-1); c1++){
      for (size_t c2=c1+1; c2 < Hits.size(); c2++){
        if (_verbose) { std::cout << "Considering Clusters: " << c1 << ", " << c2 << std::endl; }
        totOverlap *= TProfConvol(Hits.at(c1), Hits.at(c2) );
      }
    }

    if (_verbose) { std::cout << "Tot Overlap is: " << totOverlap << std::endl << std::endl; }

    return totOverlap;

  }

  //------------------------------
  void CFAlgoChargeDistrib::Report()
  //------------------------------
  {

  }


  // Function to calculate the "convolution"
  float CFAlgoChargeDistrib::TProfConvol(std::vector<util::PxHit> hA ,std::vector<util::PxHit> hB)
  {
    ::util::GeometryUtilities geou;
    const detinfo::DetectorProperties* detp = lar::providerFrom<detinfo::DetectorPropertiesService>();
    int NumTimeSamples = detp->NumberTimeSamples() * geou.TimeToCm();

    double Tmin = NumTimeSamples;
    double Tmax = 0;

    // Make two vectors to hold the Time-Profile of the Q-distribution
    // of the two clusters
    std::vector<float> QprofA(100,0.);
    std::vector<float> QprofB(100,0.);
    float QB = 0;
    float QA = 0;

    //First find min and max times
    for(auto const& hitA : hA) {
      if(hitA.t > Tmax) Tmax = hitA.t;
      if(hitA.t < Tmin) Tmin = hitA.t;
    }
    for(auto const& hitB : hB) {
      if(hitB.t > Tmax) Tmax = hitB.t;
      if(hitB.t < Tmin) Tmin = hitB.t;
    }

    //Now Fill Q-profile vectors
    for(auto const& hitA : hA){
      QprofA.at( int(99*(hitA.t-Tmin)/(Tmax-Tmin)) ) += hitA.charge;
      QA += hitA.charge;
    }
    for(auto const& hitB : hB){
      QprofB.at( int(99*(hitB.t-Tmin)/(Tmax-Tmin)) ) += hitB.charge;
      QB += hitB.charge;
    }

    /*//Normalize distributions
    for (size_t b=0; b < QprofA.size(); b++)
      QprofA.at(b) /= QA;
    for (size_t b=0; b < QprofB.size(); b++)
      QprofB.at(b) /= QB;
    */

    //print out distribution if verbose
    if (_debug){
      std::cout << "Q distribution for Cluster A:" << std::endl;
      for (size_t b=0; b < QprofA.size(); b++)
        if ( QprofA.at(b) != 0 ) { std::cout << b << "\t" << QprofA.at(b) << std::endl; }
      std::cout << "Q distribution for Cluster B:" << std::endl;
      for (size_t b=0; b < QprofB.size(); b++)
        if ( QprofB.at(b) != 0 ) { std::cout << b << "\t" << QprofB.at(b) << std::endl; }
    }

    // Now find convolution between the two distributions
    // Scan two vectors and always take smallest quantity
    // Add this smallest quantity as vector is scanned to
    // obtain the common Q-distribution for the clusters
    float conv= 0;
    for (size_t b=0; b < QprofA.size(); b++){
      if ( QprofA.at(b) < QprofB.at(b) ) { conv += QprofA.at(b); }
      else { conv += QprofB.at(b); }
    }
    if (_verbose) { std::cout << "Convolution is: " << conv << std::endl; }

    return conv;
  }


}