CSCSegAlgoST.cc

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#include "CSCSegAlgoST.h"
#include "CSCSegAlgoShowering.h"

#include "DataFormats/GeometryVector/interface/GlobalPoint.h"

// // For clhep Matrix::solve
// #include "DataFormats/CLHEP/interface/AlgebraicObjects.h"
#include "Geometry/CSCGeometry/interface/CSCLayer.h"

#include "FWCore/ParameterSet/interface/ParameterSet.h"
#include "FWCore/MessageLogger/interface/MessageLogger.h"

#include "CommonTools/Statistics/interface/ChiSquaredProbability.h"

#include <algorithm>
#include <cmath>
#include <iostream>
#include <string>


/* Constructor
 *
 */
CSCSegAlgoST::CSCSegAlgoST(const edm::ParameterSet& ps) : CSCSegmentAlgorithm(ps), myName("CSCSegAlgoST") {
    
  debug                  = ps.getUntrackedParameter<bool>("CSCDebug");
  //  minLayersApart         = ps.getParameter<int>("minLayersApart");
  //  nSigmaFromSegment      = ps.getParameter<double>("nSigmaFromSegment");
  minHitsPerSegment      = ps.getParameter<int>("minHitsPerSegment");
  //  muonsPerChamberMax     = ps.getParameter<int>("CSCSegmentPerChamberMax");      
  //  chi2Max                = ps.getParameter<double>("chi2Max");
  dXclusBoxMax           = ps.getParameter<double>("dXclusBoxMax");
  dYclusBoxMax           = ps.getParameter<double>("dYclusBoxMax");
  preClustering          = ps.getParameter<bool>("preClustering");
  preClustering_useChaining    = ps.getParameter<bool>("preClusteringUseChaining");
  Pruning                = ps.getParameter<bool>("Pruning");
  BrutePruning           = ps.getParameter<bool>("BrutePruning");
  BPMinImprovement        = ps.getParameter<double>("BPMinImprovement");
  // maxRecHitsInCluster is the maximal number of hits in a precluster that is being processed
  // This cut is intended to remove messy events. Currently nothing is returned if there are
  // more that maxRecHitsInCluster hits. It could be useful to return an estimate of the 
  // cluster position, which is available.
  maxRecHitsInCluster    = ps.getParameter<int>("maxRecHitsInCluster");
  onlyBestSegment        = ps.getParameter<bool>("onlyBestSegment");

  hitDropLimit4Hits      = ps.getParameter<double>("hitDropLimit4Hits");
  hitDropLimit5Hits      = ps.getParameter<double>("hitDropLimit5Hits");
  hitDropLimit6Hits      = ps.getParameter<double>("hitDropLimit6Hits");
  
  yweightPenaltyThreshold      = ps.getParameter<double>("yweightPenaltyThreshold");
  yweightPenalty               = ps.getParameter<double>("yweightPenalty");
                                             
  curvePenaltyThreshold        = ps.getParameter<double>("curvePenaltyThreshold");
  curvePenalty                 = ps.getParameter<double>("curvePenalty");

  useShowering = ps.getParameter<bool>("useShowering");
  showering_   = new CSCSegAlgoShowering( ps );
  // std::cout<<"Constructor called..."<<std::endl;
  correctCov_     = ps.getParameter<bool>("CorrectTheErrors");
  chi2Norm_2D_        = ps.getParameter<double>("NormChi2Cut2D");
  chi2Norm_3D_        = ps.getParameter<double>("NormChi2Cut3D");
  prePrun_        = ps.getParameter<bool>("prePrun");
  prePrunLimit_   = ps.getParameter<double>("prePrunLimit");
  //
  condSeed1_  = ps.getParameter<double>("SeedSmall");
  condSeed2_  = ps.getParameter<double>("SeedBig");
  covToAnyNumber_ = ps.getParameter<bool>("ForceCovariance");
  covToAnyNumberAll_ = ps.getParameter<bool>("ForceCovarianceAll");
  covAnyNumber_ = ps.getParameter<double>("Covariance");
  passCondNumber=false;
  passCondNumber_2=false;
  protoChiUCorrection=1.0;
  maxContrIndex=0;
  protoNDF = 1.;

}

/* Destructor
 *
 */
CSCSegAlgoST::~CSCSegAlgoST() {
  delete showering_;
}


std::vector<CSCSegment> CSCSegAlgoST::run(const CSCChamber* aChamber, ChamberHitContainer rechits) {

  // Store chamber in temp memory
  theChamber = aChamber; 
  // pre-cluster rechits and loop over all sub clusters seperately
  std::vector<CSCSegment>          segments_temp;
  std::vector<CSCSegment>          segments;
  std::vector<ChamberHitContainer> rechits_clusters; // this is a collection of groups of rechits

  // Define yweight penalty depending on chamber. We fixed the relative ratios, but
  // they can be scaled by parameters:
  
  for(int a = 0; a<5; ++a) {
    for(int b = 0; b<5; ++b) {
      a_yweightPenaltyThreshold[a][b] = 0.0;
    }
  }
  
  a_yweightPenaltyThreshold[1][1] = yweightPenaltyThreshold * 10.20;
  a_yweightPenaltyThreshold[1][2] = yweightPenaltyThreshold * 14.00;
  a_yweightPenaltyThreshold[1][3] = yweightPenaltyThreshold * 20.40;
  a_yweightPenaltyThreshold[1][4] = yweightPenaltyThreshold * 10.20;
  a_yweightPenaltyThreshold[2][1] = yweightPenaltyThreshold *  7.60;
  a_yweightPenaltyThreshold[2][2] = yweightPenaltyThreshold * 20.40;
  a_yweightPenaltyThreshold[3][1] = yweightPenaltyThreshold *  7.60;
  a_yweightPenaltyThreshold[3][2] = yweightPenaltyThreshold * 20.40;
  a_yweightPenaltyThreshold[4][1] = yweightPenaltyThreshold *  6.75;
  
  if(preClustering) {
    // run a pre-clusterer on the given rechits to split obviously separated segment seeds:
    if(preClustering_useChaining){
      // it uses X,Y,Z information; there are no configurable parameters used;
      // the X, Y, Z "cuts" are just (much) wider than the LCT readout ones
      // (which are actually not step functions); this new code could accomodate
      // the clusterHits one below but we leave it for security and backward 
      // comparison reasons 
      rechits_clusters = chainHits( theChamber, rechits );
    }
    else{
      // it uses X,Y information + configurable parameters
      rechits_clusters = clusterHits( theChamber, rechits );
    }
    // loop over the found clusters:
    for(std::vector<ChamberHitContainer>::iterator sub_rechits = rechits_clusters.begin(); sub_rechits !=  rechits_clusters.end(); ++sub_rechits ) {
      // clear the buffer for the subset of segments:
      segments_temp.clear();
      // build the subset of segments:
      segments_temp = buildSegments( (*sub_rechits) );
      // add the found subset of segments to the collection of all segments in this chamber:
      segments.insert( segments.end(), segments_temp.begin(), segments_temp.end() );
    }
    // this is the place to prune:
    if( Pruning ) {
      segments_temp.clear(); // segments_temp needed?!?!
      segments_temp = prune_bad_hits( theChamber, segments );
      segments.clear(); // segments_temp needed?!?!
      segments.swap(segments_temp); // segments_temp needed?!?!
    }
    if ("ME1/a" == aChamber->specs()->chamberTypeName()){
      findDuplicates(segments);
    }
    return segments;
  }
  else {
    segments = buildSegments(rechits);
    if( Pruning ) {
      segments_temp.clear(); // segments_temp needed?!?!
      segments_temp = prune_bad_hits( theChamber, segments );
      segments.clear(); // segments_temp needed?!?!
      segments.swap(segments_temp); // segments_temp needed?!?!
    }
    if ("ME1/a" == aChamber->specs()->chamberTypeName()){
      findDuplicates(segments);
    }
    return segments;
    //return buildSegments(rechits); 
  }
}

// ********************************************************************;
// *** This method is meant to remove clear bad hits, using as      ***; 
// *** much information from the chamber as possible (e.g. charge,  ***;
// *** hit position, timing, etc.)                                  ***;
// ********************************************************************;
std::vector<CSCSegment> CSCSegAlgoST::prune_bad_hits(const CSCChamber* aChamber, std::vector<CSCSegment> & segments) {
  
  //   std::cout<<"*************************************************************"<<std::endl;
  //   std::cout<<"Called prune_bad_hits in Chamber "<< theChamber->specs()->chamberTypeName()<<std::endl;
  //   std::cout<<"*************************************************************"<<std::endl;
  
  std::vector<CSCSegment>          segments_temp;
  std::vector<ChamberHitContainer> rechits_clusters; // this is a collection of groups of rechits
  
  const float chi2ndfProbMin = 1.0e-4;
  bool   use_brute_force = BrutePruning;

  int hit_nr = 0;
  int hit_nr_worst = -1;
  int hit_nr_2ndworst = -1;
  
  for(std::vector<CSCSegment>::iterator it=segments.begin(); it != segments.end(); ++it) {
    
    // do nothing for nhit <= minHitPerSegment
    if( (*it).nRecHits() <= minHitsPerSegment ) continue;
    
    if( !use_brute_force ) {// find worst hit
      
      float chisq    = (*it).chi2();
      int nhits      = (*it).nRecHits();
      LocalPoint localPos = (*it).localPosition();
      LocalVector segDir = (*it).localDirection();
      const CSCChamber* cscchamber = theChamber;
      float globZ       ;
      
      GlobalPoint globalPosition = cscchamber->toGlobal(localPos);
      globZ = globalPosition.z();
      
      
      if( ChiSquaredProbability((double)chisq,(double)(2*nhits-4)) < chi2ndfProbMin  ) {

    // find (rough) "residuals" (NOT excluding the hit from the fit - speed!) of hits on segment
    std::vector<CSCRecHit2D> theseRecHits = (*it).specificRecHits();
    std::vector<CSCRecHit2D>::const_iterator iRH_worst;
    float xdist_local       = -99999.;

    float xdist_local_worst_sig = -99999.;
    float xdist_local_2ndworst_sig = -99999.;
    float xdist_local_sig       = -99999.;

    hit_nr = 0;
    hit_nr_worst = -1;
    hit_nr_2ndworst = -1;

    for ( std::vector<CSCRecHit2D>::const_iterator iRH = theseRecHits.begin(); iRH != theseRecHits.end(); ++iRH) {
      //mark "worst" hit:
      
      float z_at_target ;
      float radius      ;
      float loc_x_at_target ;
      float loc_y_at_target ;
      float loc_z_at_target ;

      z_at_target  = 0.;
      loc_x_at_target  = 0.;
      loc_y_at_target  = 0.;
      loc_z_at_target  = 0.;
      radius       = 0.;
      
      // set the z target in CMS global coordinates:
      const CSCLayer* csclayerRH = theChamber->layer((*iRH).cscDetId().layer());
      LocalPoint localPositionRH = (*iRH).localPosition();
      GlobalPoint globalPositionRH = csclayerRH->toGlobal(localPositionRH); 
      
      LocalError rerrlocal = (*iRH).localPositionError();  
      float xxerr = rerrlocal.xx();
      
      float target_z     = globalPositionRH.z();  // target z position in cm (z pos of the hit)
      
      if(target_z > 0.) {
        loc_x_at_target = localPos.x() + (segDir.x()/fabs(segDir.z())*( target_z - globZ ));
        loc_y_at_target = localPos.y() + (segDir.y()/fabs(segDir.z())*( target_z - globZ ));
        loc_z_at_target = target_z;
      }
      else {
        loc_x_at_target = localPos.x() + ((-1)*segDir.x()/fabs(segDir.z())*( target_z - globZ ));
        loc_y_at_target = localPos.y() + ((-1)*segDir.y()/fabs(segDir.z())*( target_z - globZ ));
        loc_z_at_target = target_z;
      }
      // have to transform the segments coordinates back to the local frame... how?!!!!!!!!!!!!
      
      xdist_local  = fabs(localPositionRH.x() - loc_x_at_target);
      xdist_local_sig  = fabs((localPositionRH.x() -loc_x_at_target)/(xxerr));
      
      if( xdist_local_sig > xdist_local_worst_sig ) {
        xdist_local_2ndworst_sig = xdist_local_worst_sig;
        xdist_local_worst_sig    = xdist_local_sig;
        iRH_worst            = iRH;
        hit_nr_2ndworst = hit_nr_worst;
        hit_nr_worst = hit_nr;
      }
      else if(xdist_local_sig > xdist_local_2ndworst_sig) {
        xdist_local_2ndworst_sig = xdist_local_sig;
        hit_nr_2ndworst = hit_nr;
      }
      ++hit_nr;
    }

    // reset worst hit number if certain criteria apply.
    // Criteria: 2nd worst hit must be at least a factor of
    // 1.5 better than the worst in terms of sigma:
    if ( xdist_local_worst_sig / xdist_local_2ndworst_sig < 1.5 ) {
      hit_nr_worst    = -1;
      hit_nr_2ndworst = -1;
    }
      }
    }

    // if worst hit was found, refit without worst hit and select if considerably better than original fit.
    // Can also use brute force: refit all n-1 hit segments and choose one over the n hit if considerably "better"
   
    std::vector< CSCRecHit2D > buffer;
    std::vector< std::vector< CSCRecHit2D > > reduced_segments;
    std::vector< CSCRecHit2D > theseRecHits = (*it).specificRecHits();
    float best_red_seg_prob = 0.0;
    // usefor chi2 1 diff   float best_red_seg_prob = 99999.;
    buffer.clear();

    if( ChiSquaredProbability((double)(*it).chi2(),(double)((2*(*it).nRecHits())-4)) < chi2ndfProbMin  ) {
    
      buffer = theseRecHits;

      // Dirty switch: here one can select to refit all possible subsets or just the one without the 
      // tagged worst hit:
      if( use_brute_force ) { // Brute force method: loop over all possible segments:
    for(size_t bi = 0; bi < buffer.size(); ++bi) {
      reduced_segments.push_back(buffer);
      reduced_segments[bi].erase(reduced_segments[bi].begin()+(bi),reduced_segments[bi].begin()+(bi+1));
    }
      }
      else { // More elegant but still biased: erase only worst hit
    // Comment: There is not a very strong correlation of the worst hit with the one that one should remove... 
    if( hit_nr_worst >= 0 && hit_nr_worst <= int(buffer.size())  ) {
      // fill segment in buffer, delete worst hit
      buffer.erase(buffer.begin()+(hit_nr_worst),buffer.begin()+(hit_nr_worst+1));
      reduced_segments.push_back(buffer);
    }
    else {
      // only fill segment in array, do not delete anything
      reduced_segments.push_back(buffer);
    }
      }
    }
      
    // Loop over the subsegments and fit (only one segment if "use_brute_force" is false):
    for(size_t iSegment=0; iSegment<reduced_segments.size(); ++iSegment) {
      // loop over hits on given segment and push pointers to hits into protosegment
      protoSegment.clear();
      for(size_t m = 0; m<reduced_segments[iSegment].size(); ++m ) {
    protoSegment.push_back(&reduced_segments[iSegment][m]);
      }
      passCondNumber=false;
      passCondNumber_2 = false;
      protoChiUCorrection=1.0;
      doSlopesAndChi2();
      // Attempt to handle numerical instability of the fit;
      // The same as in the build method;
      // Preprune is not applied;
      if(correctCov_){
    if(protoChi2/protoNDF>chi2Norm_3D_){
      passCondNumber = true;
      doSlopesAndChi2();
    }
    if((protoChiUCorrection<1.00005)&&(protoChi2/protoNDF>chi2Norm_3D_)){
      passCondNumber_2=true;
      doSlopesAndChi2();
    }
      }
      fillLocalDirection();
      // calculate error matrix
      AlgebraicSymMatrix protoErrors = calculateError();   
      // but reorder components to match what's required by TrackingRecHit interface 
      // i.e. slopes first, then positions 
      flipErrors( protoErrors ); 
      //
      CSCSegment temp(protoSegment, protoIntercept, protoDirection, protoErrors, protoChi2);

      // replace n hit segment with n-1 hit segment, if segment probability is BPMinImprovement better:
      if( ( ChiSquaredProbability((double)(*it).chi2(),(double)((2*(*it).nRecHits())-4)) 
        < 
        (1./BPMinImprovement)*(ChiSquaredProbability((double)temp.chi2(),(double)(2*temp.nRecHits()-4))) ) // was (1.e-3) 081202

      && 
      ( (ChiSquaredProbability((double)temp.chi2(),(double)(2*temp.nRecHits()-4))) 
        > best_red_seg_prob 
        )
      &&
      ( (ChiSquaredProbability((double)temp.chi2(),(double)(2*temp.nRecHits()-4))) > 1e-10 )
      ) {
    best_red_seg_prob = ChiSquaredProbability((double)temp.chi2(),(double)(2*temp.nRecHits()-4));
        // The alternative n-1 segment is much cleaner. If this segment 
        // has >= minHitsPerSegment hits exchange current n hit segment (*it) 
        // with better n-1 hit segment:
        if( temp.nRecHits() >= minHitsPerSegment ) {
          (*it) = temp;
        }
      }
    }
  }
  
  return segments;
  
}


// ********************************************************************;
std::vector< std::vector<const CSCRecHit2D*> > CSCSegAlgoST::clusterHits(const CSCChamber* aChamber, ChamberHitContainer & rechits) {
  theChamber = aChamber; 

  std::vector<ChamberHitContainer> rechits_clusters; // this is a collection of groups of rechits
  //   const float dXclus_box_cut       = 4.; // seems to work reasonably 070116
  //   const float dYclus_box_cut       = 8.; // seems to work reasonably 070116

  float dXclus = 0.0;
  float dYclus = 0.0;
  float dXclus_box = 0.0;
  float dYclus_box = 0.0;

  std::vector<const CSCRecHit2D*> temp;

  std::vector< ChamberHitContainer > seeds;

  std::vector<float> running_meanX;
  std::vector<float> running_meanY;

  std::vector<float> seed_minX;
  std::vector<float> seed_maxX;
  std::vector<float> seed_minY;
  std::vector<float> seed_maxY;

  //std::cout<<"*************************************************************"<<std::endl;
  //std::cout<<"Called clusterHits in Chamber "<< theChamber->specs()->chamberTypeName()<<std::endl;
  //std::cout<<"*************************************************************"<<std::endl;

  // split rechits into subvectors and return vector of vectors:
  // Loop over rechits 
  // Create one seed per hit
  for(unsigned int i = 0; i < rechits.size(); ++i) {

    temp.clear();

    temp.push_back(rechits[i]);

    seeds.push_back(temp);

    // First added hit in seed defines the mean to which the next hit is compared
    // for this seed.

    running_meanX.push_back( rechits[i]->localPosition().x() );
    running_meanY.push_back( rechits[i]->localPosition().y() );
    
    // set min/max X and Y for box containing the hits in the precluster:
    seed_minX.push_back( rechits[i]->localPosition().x() );
    seed_maxX.push_back( rechits[i]->localPosition().x() );
    seed_minY.push_back( rechits[i]->localPosition().y() );
    seed_maxY.push_back( rechits[i]->localPosition().y() );
  }
    
  // merge clusters that are too close
  // measure distance between final "running mean"
  for(size_t NNN = 0; NNN < seeds.size(); ++NNN) {
    
    for(size_t MMM = NNN+1; MMM < seeds.size(); ++MMM) {
      if(running_meanX[MMM] == 999999. || running_meanX[NNN] == 999999. ) {
    LogDebug("CSCSegment|CSC") << "CSCSegmentST::clusterHits: Warning: Skipping used seeds, this should happen - inform developers!";
    //  std::cout<<"We should never see this line now!!!"<<std::endl;
    continue; //skip seeds that have been used 
      }
      
      // calculate cut criteria for simple running mean distance cut:
      dXclus = fabs(running_meanX[NNN] - running_meanX[MMM]);
      dYclus = fabs(running_meanY[NNN] - running_meanY[MMM]);

      // calculate minmal distance between precluster boxes containing the hits:
      if ( running_meanX[NNN] > running_meanX[MMM] ) dXclus_box = seed_minX[NNN] - seed_maxX[MMM];
      else                                           dXclus_box = seed_minX[MMM] - seed_maxX[NNN];
      if ( running_meanY[NNN] > running_meanY[MMM] ) dYclus_box = seed_minY[NNN] - seed_maxY[MMM];
      else                                           dYclus_box = seed_minY[MMM] - seed_maxY[NNN];
      
      
      if( dXclus_box < dXclusBoxMax && dYclus_box < dYclusBoxMax ) {
    // merge clusters!
    // merge by adding seed NNN to seed MMM and erasing seed NNN
        
    // calculate running mean for the merged seed:
    running_meanX[MMM] = (running_meanX[NNN]*seeds[NNN].size() + running_meanX[MMM]*seeds[MMM].size()) / (seeds[NNN].size()+seeds[MMM].size());
    running_meanY[MMM] = (running_meanY[NNN]*seeds[NNN].size() + running_meanY[MMM]*seeds[MMM].size()) / (seeds[NNN].size()+seeds[MMM].size());
        
    // update min/max X and Y for box containing the hits in the merged cluster:
    if ( seed_minX[NNN] <= seed_minX[MMM] ) seed_minX[MMM] = seed_minX[NNN];
    if ( seed_maxX[NNN] >  seed_maxX[MMM] ) seed_maxX[MMM] = seed_maxX[NNN];
    if ( seed_minY[NNN] <= seed_minY[MMM] ) seed_minY[MMM] = seed_minY[NNN];
    if ( seed_maxY[NNN] >  seed_maxY[MMM] ) seed_maxY[MMM] = seed_maxY[NNN];
        
    // add seed NNN to MMM (lower to larger number)
    seeds[MMM].insert(seeds[MMM].end(),seeds[NNN].begin(),seeds[NNN].end());
        
    // mark seed NNN as used (at the moment just set running mean to 999999.)
    running_meanX[NNN] = 999999.;
    running_meanY[NNN] = 999999.;
    // we have merged a seed (NNN) to the highter seed (MMM) - need to contimue to 
    // next seed (NNN+1)
    break;
      }

    }
  }

  // hand over the final seeds to the output
  // would be more elegant if we could do the above step with 
  // erasing the merged ones, rather than the 
  for(size_t NNN = 0; NNN < seeds.size(); ++NNN) {
    if(running_meanX[NNN] == 999999.) continue; //skip seeds that have been marked as used up in merging
    rechits_clusters.push_back(seeds[NNN]);
  }

  //***************************************************************

      return rechits_clusters; 
}


std::vector< std::vector<const CSCRecHit2D*> > CSCSegAlgoST::chainHits(const CSCChamber* aChamber, ChamberHitContainer & rechits) {

  std::vector<ChamberHitContainer> rechits_chains; // this is a collection of groups of rechits


  std::vector<const CSCRecHit2D*> temp;

  std::vector< ChamberHitContainer > seeds;

  std::vector <bool> usedCluster;

  // split rechits into subvectors and return vector of vectors:
  // Loop over rechits
  // Create one seed per hit
  //std::cout<<" rechits.size() = "<<rechits.size()<<std::endl;
  for(unsigned int i = 0; i < rechits.size(); ++i) {
    temp.clear();
    temp.push_back(rechits[i]);
    seeds.push_back(temp);
    usedCluster.push_back(false);
  }
  bool isME11a = false;
  if ("ME1/a" == aChamber->specs()->chamberTypeName()){
    isME11a = true;
  }
  // merge chains that are too close ("touch" each other)
  for(size_t NNN = 0; NNN < seeds.size(); ++NNN) {
    for(size_t MMM = NNN+1; MMM < seeds.size(); ++MMM) {
      if(usedCluster[MMM] || usedCluster[NNN]){
        continue;
      }
      // all is in the way we define "good";
      // try not to "cluster" the hits but to "chain" them;
      // it does the clustering but also does a better job
      // for inclined tracks (not clustering them togheter;
      // crossed tracks would be still clustered together) 
      // 22.12.09: In fact it is not much more different 
      // than the "clustering", we just introduce another
      // variable in the game - Z. And it make sense 
      // to re-introduce Y (or actually wire group mumber)
      // in a similar way as for the strip number - see
      // the code below.
      bool goodToMerge  = isGoodToMerge(isME11a, seeds[NNN], seeds[MMM]);
      if(goodToMerge){
        // merge chains!
        // merge by adding seed NNN to seed MMM and erasing seed NNN

        // add seed NNN to MMM (lower to larger number)
        seeds[MMM].insert(seeds[MMM].end(),seeds[NNN].begin(),seeds[NNN].end());

        // mark seed NNN as used
        usedCluster[NNN] = true;
        // we have merged a seed (NNN) to the highter seed (MMM) - need to contimue to
        // next seed (NNN+1)
        break;
      }

    }
  }

  // hand over the final seeds to the output
  // would be more elegant if we could do the above step with
  // erasing the merged ones, rather than the

  for(size_t NNN = 0; NNN < seeds.size(); ++NNN) {
    if(usedCluster[NNN]) continue; //skip seeds that have been marked as used up in merging
    rechits_chains.push_back(seeds[NNN]);
  }

  //***************************************************************

      return rechits_chains;
}

bool CSCSegAlgoST::isGoodToMerge(bool isME11a, ChamberHitContainer & newChain, ChamberHitContainer & oldChain) {
  for(size_t iRH_new = 0;iRH_new<newChain.size();++iRH_new){
    int layer_new = newChain[iRH_new]->cscDetId().layer()-1;     
    int middleStrip_new = newChain[iRH_new]->channels().size()/2;
    int centralStrip_new = newChain[iRH_new]->channels()[middleStrip_new];
    int middleWire_new = newChain[iRH_new]->wgroups().size()/2;
    int centralWire_new = newChain[iRH_new]->wgroups()[middleWire_new];
    bool layerRequirementOK = false;
    bool stripRequirementOK = false;
    bool wireRequirementOK = false;
    bool goodToMerge = false;
    for(size_t iRH_old = 0;iRH_old<oldChain.size();++iRH_old){      
      int layer_old = oldChain[iRH_old]->cscDetId().layer()-1;
      int middleStrip_old = oldChain[iRH_old]->channels().size()/2;
      int centralStrip_old = oldChain[iRH_old]->channels()[middleStrip_old];
      int middleWire_old = oldChain[iRH_old]->wgroups().size()/2;
      int centralWire_old = oldChain[iRH_old]->wgroups()[middleWire_old];

      // to be chained, two hits need to be in neighbouring layers...
      // or better allow few missing layers (upto 3 to avoid inefficiencies);
      // however we'll not make an angle correction because it
      // worsen the situation in some of the "regular" cases 
      // (not making the correction means that the conditions for
      // forming a cluster are different if we have missing layers -
      // this could affect events at the boundaries ) 
      if(layer_new==layer_old+1 || layer_new==layer_old-1 ||
     layer_new==layer_old+2 || layer_new==layer_old-2 ||
     layer_new==layer_old+3 || layer_new==layer_old-3 ||
     layer_new==layer_old+4 || layer_new==layer_old-4 ){
        layerRequirementOK = true;
      }
      int allStrips = 48;
      //to be chained, two hits need to be "close" in strip number (can do it in phi
      // but it doesn't really matter); let "close" means upto 2 strips (3?) - 
      // this is more compared to what CLCT readout patterns allow 
      if(centralStrip_new==centralStrip_old ||
         centralStrip_new==centralStrip_old+1 || centralStrip_new==centralStrip_old-1 ||
         centralStrip_new==centralStrip_old+2 || centralStrip_new==centralStrip_old-2){
        stripRequirementOK = true;
      }
      // same for wires (and ALCT patterns)
      if(centralWire_new==centralWire_old ||
         centralWire_new==centralWire_old+1 || centralWire_new==centralWire_old-1 ||
         centralWire_new==centralWire_old+2 || centralWire_new==centralWire_old-2){
        wireRequirementOK = true;
      }

      if(isME11a){
    if(centralStrip_new==centralStrip_old+1-allStrips || centralStrip_new==centralStrip_old-1-allStrips ||
       centralStrip_new==centralStrip_old+2-allStrips || centralStrip_new==centralStrip_old-2-allStrips ||
       centralStrip_new==centralStrip_old+1+allStrips || centralStrip_new==centralStrip_old-1+allStrips ||
       centralStrip_new==centralStrip_old+2+allStrips || centralStrip_new==centralStrip_old-2+allStrips){
      stripRequirementOK = true;
    }
      }
      if(layerRequirementOK && stripRequirementOK && wireRequirementOK){
        goodToMerge = true;
        return goodToMerge;
      }
    }
  }
  return false;
}




double CSCSegAlgoST::theWeight(double coordinate_1, double coordinate_2, double coordinate_3, float layer_1, float layer_2, float layer_3) {
  double sub_weight = 0;
  sub_weight = fabs( 
            ( (coordinate_2 - coordinate_3) / (layer_2  - layer_3) ) - 
            ( (coordinate_1 - coordinate_2) / (layer_1  - layer_2) ) 
            );
  return sub_weight;
}

/* 
 * This algorithm is based on the Minimum Spanning Tree (ST) approach 
 * for building endcap muon track segments out of the rechit's in a CSCChamber.
 */
std::vector<CSCSegment> CSCSegAlgoST::buildSegments(ChamberHitContainer rechits) {

  // Clear buffer for segment vector
  std::vector<CSCSegment> segmentInChamber;
  segmentInChamber.clear(); // list of final segments

  // CSC Ring;
  unsigned int thering    = 999;
  unsigned int thestation = 999;
  unsigned int thecham    = 999;

  std::vector<int> hits_onLayerNumber(6);

  unsigned int UpperLimit = maxRecHitsInCluster;
  if (int(rechits.size()) < minHitsPerSegment) return segmentInChamber;
 
  for(int iarray = 0; iarray <6; ++iarray) { // magic number 6: number of layers in CSC chamber - not gonna change :)
    PAhits_onLayer[iarray].clear();
    hits_onLayerNumber[iarray] = 0;    
  }

  chosen_Psegments.clear();
  chosen_weight_A.clear();

  Psegments.clear();
  Psegments_noLx.clear();
  Psegments_noL1.clear();
  Psegments_noL2.clear();
  Psegments_noL3.clear();
  Psegments_noL4.clear();
  Psegments_noL5.clear();
  Psegments_noL6.clear();

  Psegments_hits.clear();
  
  weight_A.clear();
  weight_noLx_A.clear();
  weight_noL1_A.clear();
  weight_noL2_A.clear();
  weight_noL3_A.clear();
  weight_noL4_A.clear();
  weight_noL5_A.clear();
  weight_noL6_A.clear();

  weight_B.clear();
  weight_noL1_B.clear();
  weight_noL2_B.clear();
  weight_noL3_B.clear();
  weight_noL4_B.clear();
  weight_noL5_B.clear();
  weight_noL6_B.clear();

  curv_A.clear();
  curv_noL1_A.clear();
  curv_noL2_A.clear();
  curv_noL3_A.clear();
  curv_noL4_A.clear();
  curv_noL5_A.clear();
  curv_noL6_A.clear();

  // definition of middle layer for n-hit segment
  int midlayer_pointer[6] = {0,0,2,3,3,4};
  
  // int n_layers_missed_tot = 0;
  int n_layers_occupied_tot = 0;
  int n_layers_processed = 0;

  float min_weight_A = 99999.9;
  float min_weight_noLx_A = 99999.9;

  float best_weight_B = -1.;
  float best_weight_noLx_B = -1.;

  float best_curv_A = -1.;
  float best_curv_noLx_A = -1.;

  int best_pseg = -1;
  int best_noLx_pseg = -1;
  int best_Layer_noLx = -1;

  //************************************************************************;    
  //***   Start segment building   *****************************************;    
  //************************************************************************;    
  
  // Determine how many layers with hits we have
  // Fill all hits into the layer hit container:
  
  // Have 2 standard arrays: one giving the number of hits per layer. 
  // The other the corresponding hits. 
  
  // Loop all available hits, count hits per layer and fill the hits into array by layer
  for(size_t M = 0; M < rechits.size(); ++M) {
    // add hits to array per layer and count hits per layer:
    hits_onLayerNumber[ rechits[M]->cscDetId().layer()-1 ] += 1;
    if(hits_onLayerNumber[ rechits[M]->cscDetId().layer()-1 ] == 1 ) n_layers_occupied_tot += 1;
    // add hits to vector in array
    PAhits_onLayer[rechits[M]->cscDetId().layer()-1]    .push_back(rechits[M]);    
  }
 
  // We have now counted the hits per layer and filled pointers to the hits into an array
  
  int tothits = 0;
  int maxhits = 0;
  int nexthits = 0;
  int maxlayer = -1;
  int nextlayer = -1;

  for(size_t i = 0; i< hits_onLayerNumber.size(); ++i){
    //std::cout<<"We have "<<hits_onLayerNumber[i]<<" hits on layer "<<i+1<<std::endl;
    tothits += hits_onLayerNumber[i];
    if (hits_onLayerNumber[i] > maxhits) {
      nextlayer = maxlayer;
      nexthits = maxhits;
      maxlayer = i;
      maxhits = hits_onLayerNumber[i];
    }
    else if (hits_onLayerNumber[i] > nexthits) {
      nextlayer = i;
      nexthits = hits_onLayerNumber[i];
    }
  }


  if (tothits > (int)UpperLimit) {
    if (n_layers_occupied_tot > 4) {
      tothits = tothits - hits_onLayerNumber[maxlayer];
      n_layers_occupied_tot = n_layers_occupied_tot - 1;
      PAhits_onLayer[maxlayer].clear();
      hits_onLayerNumber[maxlayer] = 0;
    }
  }

  if (tothits > (int)UpperLimit) {
    if (n_layers_occupied_tot > 4) {
      tothits = tothits - hits_onLayerNumber[nextlayer];
      n_layers_occupied_tot = n_layers_occupied_tot - 1;
      PAhits_onLayer[nextlayer].clear();
      hits_onLayerNumber[nextlayer] = 0;
    }
  }

  if (tothits > (int)UpperLimit){ 

  //++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
  // Showering muon - returns nothing if chi2 == -1 (see comment in SegAlgoShowering)
  //++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++  
  if (useShowering) {
    CSCSegment segShower = showering_->showerSeg(theChamber, rechits);

    // Make sure have at least 3 hits...
    if ( segShower.nRecHits() < 3 ) return segmentInChamber;
    if ( segShower.chi2() == -1 ) return segmentInChamber;

    segmentInChamber.push_back(segShower);
    return segmentInChamber;  

  } else{
        LogDebug("CSCSegment|CSC") <<"Number of rechits in the cluster/chamber > "<< UpperLimit<<
      " ... Segment finding in the cluster/chamber canceled!";
    //     std::cout<<"Number of rechits in the cluster/chamber > "<< UpperLimit<<
    //     " ... Segment finding in the cluster/chamber canceled! "<<std::endl;
        return segmentInChamber;  
        }
  }

  // Find out which station, ring and chamber we are in 
  // Used to choose station/ring dependant y-weight cuts

  if( rechits.size() > 0 ) {
    thering = rechits[0]->cscDetId().ring();
    thestation = rechits[0]->cscDetId().station();
    thecham = rechits[0]->cscDetId().chamber();
  }

  // std::cout<<"We are in Station/ring/chamber: "<<thestation <<" "<< thering<<" "<< thecham<<std::endl;

  // Cut-off parameter - don't reconstruct segments with less than X hits
  if( n_layers_occupied_tot < minHitsPerSegment ) { 
    return segmentInChamber;
  }
  
  // Start building all possible hit combinations:

  // loop over the six chamber layers and form segment candidates from the available hits:

  for(int layer = 0; layer < 6; ++layer) {

    // *****************************************************************
    // *** Set missed layer counter here (not currently implemented) ***
    // *****************************************************************
    // if( PAhits_onLayer[layer].size() == 0 ) {
    //   n_layers_missed_tot += 1;
    // }

    if( PAhits_onLayer[layer].size() > 0 ) {
      n_layers_processed += 1;
    }

    // Save the size of the protosegment before hits were added on the current layer
    int orig_number_of_psegs = Psegments.size();
    int orig_number_of_noL1_psegs = Psegments_noL1.size();
    int orig_number_of_noL2_psegs = Psegments_noL2.size();
    int orig_number_of_noL3_psegs = Psegments_noL3.size();
    int orig_number_of_noL4_psegs = Psegments_noL4.size();
    int orig_number_of_noL5_psegs = Psegments_noL5.size();
    int orig_number_of_noL6_psegs = Psegments_noL6.size();

    // loop over the hits on the layer and initiate protosegments or add hits to protosegments
    for(int hit = 0; hit < int(PAhits_onLayer[layer].size()); ++hit) { // loop all hits on the Layer number "layer"

      // create protosegments from all hits on the first layer with hits
      if( orig_number_of_psegs == 0 ) { // would be faster to turn this around - ask for "orig_number_of_psegs != 0"

    Psegments_hits.push_back(PAhits_onLayer[layer][hit]);

    Psegments.push_back(Psegments_hits); 
    Psegments_noL6.push_back(Psegments_hits); 
    Psegments_noL5.push_back(Psegments_hits); 
    Psegments_noL4.push_back(Psegments_hits); 
    Psegments_noL3.push_back(Psegments_hits); 
    Psegments_noL2.push_back(Psegments_hits); 

    // Initialize weights corresponding to this segment for first hit (with 0)

    curv_A.push_back(0.0);
    curv_noL6_A.push_back(0.0); 
    curv_noL5_A.push_back(0.0); 
    curv_noL4_A.push_back(0.0); 
    curv_noL3_A.push_back(0.0); 
    curv_noL2_A.push_back(0.0); 

    weight_A.push_back(0.0);
    weight_noL6_A.push_back(0.0); 
    weight_noL5_A.push_back(0.0); 
    weight_noL4_A.push_back(0.0); 
    weight_noL3_A.push_back(0.0); 
    weight_noL2_A.push_back(0.0); 

    weight_B.push_back(0.0);
    weight_noL6_B.push_back(0.0); 
    weight_noL5_B.push_back(0.0); 
    weight_noL4_B.push_back(0.0); 
    weight_noL3_B.push_back(0.0); 
    weight_noL2_B.push_back(0.0); 
    
    // reset array for next hit on next layer
    Psegments_hits    .clear();
      }
      else {
    if( orig_number_of_noL1_psegs == 0 ) {

      Psegments_hits.push_back(PAhits_onLayer[layer][hit]);

      Psegments_noL1.push_back(Psegments_hits); 

      // Initialize weight corresponding to this segment for first hit (with 0)

      curv_noL1_A.push_back(0.0);

      weight_noL1_A.push_back(0.0);

      weight_noL1_B.push_back(0.0);
    
      // reset array for next hit on next layer
      Psegments_hits    .clear();

    }

    // loop over the protosegments and create a new protosegments for each hit-1 on this layer
    
        for( int pseg = 0; pseg < orig_number_of_psegs; ++pseg ) { 

      int pseg_pos = (pseg)+((hit)*orig_number_of_psegs);
      int pseg_noL1_pos = (pseg)+((hit)*orig_number_of_noL1_psegs);
      int pseg_noL2_pos = (pseg)+((hit)*orig_number_of_noL2_psegs);
      int pseg_noL3_pos = (pseg)+((hit)*orig_number_of_noL3_psegs);
      int pseg_noL4_pos = (pseg)+((hit)*orig_number_of_noL4_psegs);
      int pseg_noL5_pos = (pseg)+((hit)*orig_number_of_noL5_psegs);
      int pseg_noL6_pos = (pseg)+((hit)*orig_number_of_noL6_psegs);

      // - Loop all psegs. 
      // - If not last hit, clone  existing protosegments  (PAhits_onLayer[layer].size()-1) times
      // - then add the new hits

      if( ! (hit == int(PAhits_onLayer[layer].size()-1)) ) { // not the last hit - prepare (copy) new protosegments for the following hits
        // clone psegs (to add next hits or last hit on layer):

        Psegments.push_back(Psegments[ pseg_pos ]); 
        if (n_layers_processed != 2 && pseg < orig_number_of_noL1_psegs) Psegments_noL1.push_back(Psegments_noL1[ pseg_noL1_pos ]); 
        if (n_layers_processed != 2 && pseg < orig_number_of_noL2_psegs) Psegments_noL2.push_back(Psegments_noL2[ pseg_noL2_pos ]); 
        if (n_layers_processed != 3 && pseg < orig_number_of_noL3_psegs) Psegments_noL3.push_back(Psegments_noL3[ pseg_noL3_pos ]); 
        if (n_layers_processed != 4 && pseg < orig_number_of_noL4_psegs) Psegments_noL4.push_back(Psegments_noL4[ pseg_noL4_pos ]); 
        if (n_layers_processed != 5 && pseg < orig_number_of_noL5_psegs) Psegments_noL5.push_back(Psegments_noL5[ pseg_noL5_pos ]); 
        if (n_layers_processed != 6 && pseg < orig_number_of_noL6_psegs) Psegments_noL6.push_back(Psegments_noL6[ pseg_noL6_pos ]); 
        // clone weight corresponding to this segment too
        weight_A.push_back(weight_A[ pseg_pos ]);
        if (n_layers_processed != 2 && pseg < orig_number_of_noL1_psegs) weight_noL1_A.push_back(weight_noL1_A[ pseg_noL1_pos ]);
        if (n_layers_processed != 2 && pseg < orig_number_of_noL2_psegs) weight_noL2_A.push_back(weight_noL2_A[ pseg_noL2_pos ]);
        if (n_layers_processed != 3 && pseg < orig_number_of_noL3_psegs) weight_noL3_A.push_back(weight_noL3_A[ pseg_noL3_pos ]);
        if (n_layers_processed != 4 && pseg < orig_number_of_noL4_psegs) weight_noL4_A.push_back(weight_noL4_A[ pseg_noL4_pos ]);
        if (n_layers_processed != 5 && pseg < orig_number_of_noL5_psegs) weight_noL5_A.push_back(weight_noL5_A[ pseg_noL5_pos ]);
        if (n_layers_processed != 6 && pseg < orig_number_of_noL6_psegs) weight_noL6_A.push_back(weight_noL6_A[ pseg_noL6_pos ]);
        // clone curvature variable corresponding to this segment too
        curv_A.push_back(curv_A[ pseg_pos ]);
        if (n_layers_processed != 2 && pseg < orig_number_of_noL1_psegs) curv_noL1_A.push_back(curv_noL1_A[ pseg_noL1_pos ]);
        if (n_layers_processed != 2 && pseg < orig_number_of_noL2_psegs) curv_noL2_A.push_back(curv_noL2_A[ pseg_noL2_pos ]);
        if (n_layers_processed != 3 && pseg < orig_number_of_noL3_psegs) curv_noL3_A.push_back(curv_noL3_A[ pseg_noL3_pos ]);
        if (n_layers_processed != 4 && pseg < orig_number_of_noL4_psegs) curv_noL4_A.push_back(curv_noL4_A[ pseg_noL4_pos ]);
        if (n_layers_processed != 5 && pseg < orig_number_of_noL5_psegs) curv_noL5_A.push_back(curv_noL5_A[ pseg_noL5_pos ]);
        if (n_layers_processed != 6 && pseg < orig_number_of_noL6_psegs) curv_noL6_A.push_back(curv_noL6_A[ pseg_noL6_pos ]);
        // clone "y"-weight corresponding to this segment too
        weight_B.push_back(weight_B[ pseg_pos ]);
        if (n_layers_processed != 2 && pseg < orig_number_of_noL1_psegs) weight_noL1_B.push_back(weight_noL1_B[ pseg_noL1_pos ]);
        if (n_layers_processed != 2 && pseg < orig_number_of_noL2_psegs) weight_noL2_B.push_back(weight_noL2_B[ pseg_noL2_pos ]);
        if (n_layers_processed != 3 && pseg < orig_number_of_noL3_psegs) weight_noL3_B.push_back(weight_noL3_B[ pseg_noL3_pos ]);
        if (n_layers_processed != 4 && pseg < orig_number_of_noL4_psegs) weight_noL4_B.push_back(weight_noL4_B[ pseg_noL4_pos ]);
        if (n_layers_processed != 5 && pseg < orig_number_of_noL5_psegs) weight_noL5_B.push_back(weight_noL5_B[ pseg_noL5_pos ]);
        if (n_layers_processed != 6 && pseg < orig_number_of_noL6_psegs) weight_noL6_B.push_back(weight_noL6_B[ pseg_noL6_pos ]);
      }
      // add hits to original pseg:
      Psegments[ pseg_pos ].push_back(PAhits_onLayer[ layer ][ hit ]);
      if (n_layers_processed != 2 && pseg < orig_number_of_noL1_psegs) Psegments_noL1[ pseg_noL1_pos ].push_back(PAhits_onLayer[ layer ][ hit ]);
      if (n_layers_processed != 2 && pseg < orig_number_of_noL2_psegs) Psegments_noL2[ pseg_noL2_pos ].push_back(PAhits_onLayer[ layer ][ hit ]);
      if (n_layers_processed != 3 && pseg < orig_number_of_noL3_psegs) Psegments_noL3[ pseg_noL3_pos ].push_back(PAhits_onLayer[ layer ][ hit ]);
      if (n_layers_processed != 4 && pseg < orig_number_of_noL4_psegs) Psegments_noL4[ pseg_noL4_pos ].push_back(PAhits_onLayer[ layer ][ hit ]);
      if (n_layers_processed != 5 && pseg < orig_number_of_noL5_psegs) Psegments_noL5[ pseg_noL5_pos ].push_back(PAhits_onLayer[ layer ][ hit ]);
      if (n_layers_processed != 6 && pseg < orig_number_of_noL6_psegs) Psegments_noL6[ pseg_noL6_pos ].push_back(PAhits_onLayer[ layer ][ hit ]);
            
      // calculate/update the weight (only for >2 hits on psegment):

      if( Psegments[ pseg_pos ].size() > 2 ) {
              
        // looks more exciting than it is. Here the weight is calculated. It is the difference in x of the last two and one but the last two hits, 
            // divided by the distance of the corresponding hits. Please refer to twiki page XXXX or CMS Note YYY (and use layer_distance)

        weight_A[ pseg_pos ] += theWeight(
                          (*(Psegments[ pseg_pos ].end()-1 ))->localPosition().x(), 
                          (*(Psegments[ pseg_pos ].end()-2 ))->localPosition().x(),
                          (*(Psegments[ pseg_pos ].end()-3 ))->localPosition().x(),
                          float((*(Psegments[ pseg_pos ].end()-1))->cscDetId().layer()),
                          float((*(Psegments[ pseg_pos ].end()-2))->cscDetId().layer()),
                          float((*(Psegments[ pseg_pos ].end()-3))->cscDetId().layer())
                          );

        weight_B[ pseg_pos ] += theWeight(
                          (*(Psegments[ pseg_pos ].end()-1 ))->localPosition().y(), 
                          (*(Psegments[ pseg_pos ].end()-2 ))->localPosition().y(),
                          (*(Psegments[ pseg_pos ].end()-3 ))->localPosition().y(),
                          float((*(Psegments[ pseg_pos ].end()-1))->cscDetId().layer()),
                          float((*(Psegments[ pseg_pos ].end()-2))->cscDetId().layer()),
                          float((*(Psegments[ pseg_pos ].end()-3))->cscDetId().layer())
                          );

        // if we have picked up the last hit go looking for pseg with the lowest (and second lowest?) weight

        if(int(Psegments[ pseg_pos ].size()) == n_layers_occupied_tot) {

          curv_A[ pseg_pos ] += theWeight(
                          (*(Psegments[ pseg_pos ].end()-1 ))->localPosition().x(), 
                          (*(Psegments[ pseg_pos ].end()-midlayer_pointer[n_layers_occupied_tot-1] ))->localPosition().x(),
                          (*(Psegments[ pseg_pos ].end()-n_layers_occupied_tot ))->localPosition().x(),
                          float((*(Psegments[ pseg_pos ].end()-1))->cscDetId().layer()),
                          float((*(Psegments[ pseg_pos ].end()-midlayer_pointer[n_layers_occupied_tot-1] ))->cscDetId().layer()),
                          float((*(Psegments[ pseg_pos ].end()-n_layers_occupied_tot ))->cscDetId().layer())
                          );

              if (curv_A[ pseg_pos ] > curvePenaltyThreshold) weight_A[ pseg_pos ] = weight_A[ pseg_pos ] * curvePenalty;

          if (weight_B[ pseg_pos ] > a_yweightPenaltyThreshold[thestation][thering]) weight_A[ pseg_pos ] = weight_A[ pseg_pos ] * yweightPenalty;
               
              if (weight_A[ pseg_pos ] < min_weight_A ) {
            min_weight_A = weight_A[ pseg_pos ];
                best_weight_B = weight_B[ pseg_pos ];
                best_curv_A = curv_A[ pseg_pos ];
            best_pseg = pseg_pos ;
              }

        }

        // alternative: fill map with weight and pseg (which is already ordered)? Seems a very good tool to go looking for segments from. 
            // As I understand, the segments would be inserted according to their weight, so the list would "automatically" be sorted.

      }

          if ( n_layers_occupied_tot > 3 ) {
        if (pseg < orig_number_of_noL1_psegs && (n_layers_processed != 2)) {
          if(( Psegments_noL1[ pseg_noL1_pos ].size() > 2 ) ) {
        
        // looks more exciting than it is. Here the weight is calculated. It is the difference in x of the last two and one but the last two hits, 
        // divided by the distance of the corresponding hits. Please refer to twiki page XXXX or CMS Note YYY (and use layer_distance)
        
        weight_noL1_A[ pseg_noL1_pos ] += theWeight(
                                (*(Psegments_noL1[ pseg_noL1_pos ].end()-1 ))->localPosition().x(), 
                                (*(Psegments_noL1[ pseg_noL1_pos ].end()-2 ))->localPosition().x(),
                                (*(Psegments_noL1[ pseg_noL1_pos ].end()-3 ))->localPosition().x(),
                                float((*(Psegments_noL1[ pseg_noL1_pos ].end()-1))->cscDetId().layer()),
                                float((*(Psegments_noL1[ pseg_noL1_pos ].end()-2))->cscDetId().layer()),
                                float((*(Psegments_noL1[ pseg_noL1_pos ].end()-3))->cscDetId().layer())
                                );

        weight_noL1_B[ pseg_noL1_pos ] += theWeight(
                                (*(Psegments_noL1[ pseg_noL1_pos ].end()-1 ))->localPosition().y(), 
                                (*(Psegments_noL1[ pseg_noL1_pos ].end()-2 ))->localPosition().y(),
                                (*(Psegments_noL1[ pseg_noL1_pos ].end()-3 ))->localPosition().y(),
                                float((*(Psegments_noL1[ pseg_noL1_pos ].end()-1))->cscDetId().layer()),
                                float((*(Psegments_noL1[ pseg_noL1_pos ].end()-2))->cscDetId().layer()),
                                float((*(Psegments_noL1[ pseg_noL1_pos ].end()-3))->cscDetId().layer())
                                );

        //if we have picked up the last hit go looking for pseg with the lowest (and second lowest?) weight

        if(int(Psegments_noL1[ pseg_noL1_pos ].size()) == n_layers_occupied_tot -1 ) {

          curv_noL1_A[ pseg_noL1_pos ] += theWeight(
                                (*(Psegments_noL1[ pseg_noL1_pos ].end()-1 ))->localPosition().x(), 
                                (*(Psegments_noL1[ pseg_noL1_pos ].end()-midlayer_pointer[n_layers_occupied_tot-2] ))->localPosition().x(),
                                (*(Psegments_noL1[ pseg_noL1_pos ].end()-(n_layers_occupied_tot-1) ))->localPosition().x(),
                                float((*(Psegments_noL1[ pseg_noL1_pos ].end()-1 ))->cscDetId().layer()),
                                float((*(Psegments_noL1[ pseg_noL1_pos ].end()-midlayer_pointer[n_layers_occupied_tot-2] ))->cscDetId().layer()),
                                float((*(Psegments_noL1[ pseg_noL1_pos ].end()-(n_layers_occupied_tot-1) ))->cscDetId().layer())
                                );

          if (curv_noL1_A[ pseg_noL1_pos ] > curvePenaltyThreshold) weight_noL1_A[ pseg_noL1_pos ] = weight_noL1_A[ pseg_noL1_pos ] * curvePenalty;

          if (weight_noL1_B[ pseg_noL1_pos ] > a_yweightPenaltyThreshold[thestation][thering]) 
            weight_noL1_A[ pseg_noL1_pos ] = weight_noL1_A[ pseg_noL1_pos ] * yweightPenalty;

          if (weight_noL1_A[ pseg_noL1_pos ] < min_weight_noLx_A ) {
            min_weight_noLx_A = weight_noL1_A[ pseg_noL1_pos ];
            best_weight_noLx_B = weight_noL1_B[ pseg_noL1_pos ];
            best_curv_noLx_A = curv_noL1_A[ pseg_noL1_pos ];
            best_noLx_pseg = pseg_noL1_pos;
                    best_Layer_noLx = 1;
          }

        }

        // alternative: fill map with weight and pseg (which is already ordered)? Seems a very good tool to go looking for segments from. 
        // As I understand, the segments would be inserted according to their weight, so the list would "automatically" be sorted.
        
          }
        }
      }

          if ( n_layers_occupied_tot > 3 ) {
        if (pseg < orig_number_of_noL2_psegs && ( n_layers_processed != 2 )) {
          if(( Psegments_noL2[ pseg_noL2_pos ].size() > 2 )) {
              
        // looks more exciting than it is. Here the weight is calculated. It is the difference in x of the last two and one but the last two hits, 
        // divided by the distance of the corresponding hits. Please refer to twiki page XXXX or CMS Note YYY (and use layer_distance)

        weight_noL2_A[ pseg_noL2_pos ] += theWeight(
                                (*(Psegments_noL2[ pseg_noL2_pos ].end()-1 ))->localPosition().x(), 
                                (*(Psegments_noL2[ pseg_noL2_pos ].end()-2 ))->localPosition().x(),
                                (*(Psegments_noL2[ pseg_noL2_pos ].end()-3 ))->localPosition().x(),
                                float((*(Psegments_noL2[ pseg_noL2_pos ].end()-1))->cscDetId().layer()),
                                float((*(Psegments_noL2[ pseg_noL2_pos ].end()-2))->cscDetId().layer()),
                                float((*(Psegments_noL2[ pseg_noL2_pos ].end()-3))->cscDetId().layer())
                                );

        weight_noL2_B[ pseg_noL2_pos ] += theWeight(
                                (*(Psegments_noL2[ pseg_noL2_pos ].end()-1 ))->localPosition().y(), 
                                (*(Psegments_noL2[ pseg_noL2_pos ].end()-2 ))->localPosition().y(),
                                (*(Psegments_noL2[ pseg_noL2_pos ].end()-3 ))->localPosition().y(),
                                float((*(Psegments_noL2[ pseg_noL2_pos ].end()-1))->cscDetId().layer()),
                                float((*(Psegments_noL2[ pseg_noL2_pos ].end()-2))->cscDetId().layer()),
                                float((*(Psegments_noL2[ pseg_noL2_pos ].end()-3))->cscDetId().layer())
                                );

        //if we have picked up the last hit go looking for pseg with the lowest (and second lowest?) weight

        if(int(Psegments_noL2[ pseg_noL2_pos ].size()) == n_layers_occupied_tot -1 ) {

          curv_noL2_A[ pseg_noL2_pos ] += theWeight(
                                (*(Psegments_noL2[ pseg_noL2_pos ].end()-1 ))->localPosition().x(), 
                                (*(Psegments_noL2[ pseg_noL2_pos ].end()-midlayer_pointer[n_layers_occupied_tot-2] ))->localPosition().x(),
                                (*(Psegments_noL2[ pseg_noL2_pos ].end()-(n_layers_occupied_tot-1) ))->localPosition().x(),
                                float((*(Psegments_noL2[ pseg_noL2_pos ].end()-1 ))->cscDetId().layer()),
                                float((*(Psegments_noL2[ pseg_noL2_pos ].end()-midlayer_pointer[n_layers_occupied_tot-2] ))->cscDetId().layer()),
                                float((*(Psegments_noL2[ pseg_noL2_pos ].end()-(n_layers_occupied_tot-1) ))->cscDetId().layer())
                                );

          if (curv_noL2_A[ pseg_noL2_pos ] > curvePenaltyThreshold) weight_noL2_A[ pseg_noL2_pos ] = weight_noL2_A[ pseg_noL2_pos ] * curvePenalty;

          if (weight_noL2_B[ pseg_noL2_pos ] > a_yweightPenaltyThreshold[thestation][thering]) 
            weight_noL2_A[ pseg_noL2_pos ] = weight_noL2_A[ pseg_noL2_pos ] * yweightPenalty;

          if (weight_noL2_A[ pseg_noL2_pos ] < min_weight_noLx_A ) {
            min_weight_noLx_A = weight_noL2_A[ pseg_noL2_pos ];
            best_weight_noLx_B = weight_noL2_B[ pseg_noL2_pos ];
            best_curv_noLx_A = curv_noL2_A[ pseg_noL2_pos ];
            best_noLx_pseg = pseg_noL2_pos;
                    best_Layer_noLx = 2;
          }

        }

        // alternative: fill map with weight and pseg (which is already ordered)? Seems a very good tool to go looking for segments from. 
        // As I understand, the segments would be inserted according to their weight, so the list would "automatically" be sorted.

          }
        }
      }

          if ( n_layers_occupied_tot > 3 ) {
        if (pseg < orig_number_of_noL3_psegs && ( n_layers_processed != 3 )) {
          if(( Psegments_noL3[ pseg_noL3_pos ].size() > 2 )) {
              
        // looks more exciting than it is. Here the weight is calculated. It is the difference in x of the last two and one but the last two hits, 
        // divided by the distance of the corresponding hits. Please refer to twiki page XXXX or CMS Note YYY (and use layer_distance)

        weight_noL3_A[ pseg_noL3_pos ] += theWeight(
                                (*(Psegments_noL3[ pseg_noL3_pos ].end()-1 ))->localPosition().x(), 
                                (*(Psegments_noL3[ pseg_noL3_pos ].end()-2 ))->localPosition().x(),
                                (*(Psegments_noL3[ pseg_noL3_pos ].end()-3 ))->localPosition().x(),
                                float((*(Psegments_noL3[ pseg_noL3_pos ].end()-1))->cscDetId().layer()),
                                float((*(Psegments_noL3[ pseg_noL3_pos ].end()-2))->cscDetId().layer()),
                                float((*(Psegments_noL3[ pseg_noL3_pos ].end()-3))->cscDetId().layer())
                                );

        weight_noL3_B[ pseg_noL3_pos ] += theWeight(
                                (*(Psegments_noL3[ pseg_noL3_pos ].end()-1 ))->localPosition().y(), 
                                (*(Psegments_noL3[ pseg_noL3_pos ].end()-2 ))->localPosition().y(),
                                (*(Psegments_noL3[ pseg_noL3_pos ].end()-3 ))->localPosition().y(),
                                float((*(Psegments_noL3[ pseg_noL3_pos ].end()-1))->cscDetId().layer()),
                                float((*(Psegments_noL3[ pseg_noL3_pos ].end()-2))->cscDetId().layer()),
                                float((*(Psegments_noL3[ pseg_noL3_pos ].end()-3))->cscDetId().layer())
                                );

        //if we have picked up the last hit go looking for pseg with the lowest (and second lowest?) weight

        if(int(Psegments_noL3[ pseg_noL3_pos ].size()) == n_layers_occupied_tot -1 ) {

          curv_noL3_A[ pseg_noL3_pos ] += theWeight(
                                (*(Psegments_noL3[ pseg_noL3_pos ].end()-1 ))->localPosition().x(), 
                                (*(Psegments_noL3[ pseg_noL3_pos ].end()-midlayer_pointer[n_layers_occupied_tot-2] ))->localPosition().x(),
                                (*(Psegments_noL3[ pseg_noL3_pos ].end()-(n_layers_occupied_tot-1) ))->localPosition().x(),
                                float((*(Psegments_noL3[ pseg_noL3_pos ].end()-1 ))->cscDetId().layer()),
                                float((*(Psegments_noL3[ pseg_noL3_pos ].end()-midlayer_pointer[n_layers_occupied_tot-2] ))->cscDetId().layer()),
                                float((*(Psegments_noL3[ pseg_noL3_pos ].end()-(n_layers_occupied_tot-1) ))->cscDetId().layer())
                                );

          if (curv_noL3_A[ pseg_noL3_pos ] > curvePenaltyThreshold) weight_noL3_A[ pseg_noL3_pos ] = weight_noL3_A[ pseg_noL3_pos ] * curvePenalty;

          if (weight_noL3_B[ pseg_noL3_pos ] > a_yweightPenaltyThreshold[thestation][thering]) 
            weight_noL3_A[ pseg_noL3_pos ] = weight_noL3_A[ pseg_noL3_pos ] * yweightPenalty;

          if (weight_noL3_A[ pseg_noL3_pos ] < min_weight_noLx_A ) {
            min_weight_noLx_A = weight_noL3_A[ pseg_noL3_pos ];
            best_weight_noLx_B = weight_noL3_B[ pseg_noL3_pos ];
            best_curv_noLx_A = curv_noL3_A[ pseg_noL3_pos ];
            best_noLx_pseg = pseg_noL3_pos;
                    best_Layer_noLx = 3;
          }

        }

        // alternative: fill map with weight and pseg (which is already ordered)? Seems a very good tool to go looking for segments from. 
        // As I understand, the segments would be inserted according to their weight, so the list would "automatically" be sorted.

          }
        }
      }

          if ( n_layers_occupied_tot > 3 ) {
        if (pseg < orig_number_of_noL4_psegs && ( n_layers_processed != 4 )) {
          if(( Psegments_noL4[ pseg_noL4_pos ].size() > 2 )) {
              
        // looks more exciting than it is. Here the weight is calculated. It is the difference in x of the last two and one but the last two hits, 
        // divided by the distance of the corresponding hits. Please refer to twiki page XXXX or CMS Note YYY (and use layer_distance)
          
        weight_noL4_A[ pseg_noL4_pos ] += theWeight(
                                (*(Psegments_noL4[ pseg_noL4_pos ].end()-1 ))->localPosition().x(), 
                                (*(Psegments_noL4[ pseg_noL4_pos ].end()-2 ))->localPosition().x(),
                                (*(Psegments_noL4[ pseg_noL4_pos ].end()-3 ))->localPosition().x(),
                                float((*(Psegments_noL4[ pseg_noL4_pos ].end()-1))->cscDetId().layer()),
                                float((*(Psegments_noL4[ pseg_noL4_pos ].end()-2))->cscDetId().layer()),
                                float((*(Psegments_noL4[ pseg_noL4_pos ].end()-3))->cscDetId().layer())
                                );

        weight_noL4_B[ pseg_noL4_pos ] += theWeight(
                                (*(Psegments_noL4[ pseg_noL4_pos ].end()-1 ))->localPosition().y(), 
                                (*(Psegments_noL4[ pseg_noL4_pos ].end()-2 ))->localPosition().y(),
                                (*(Psegments_noL4[ pseg_noL4_pos ].end()-3 ))->localPosition().y(),
                                float((*(Psegments_noL4[ pseg_noL4_pos ].end()-1))->cscDetId().layer()),
                                float((*(Psegments_noL4[ pseg_noL4_pos ].end()-2))->cscDetId().layer()),
                                float((*(Psegments_noL4[ pseg_noL4_pos ].end()-3))->cscDetId().layer())
                                );

        //if we have picked up the last hit go looking for pseg with the lowest (and second lowest?) weight

        if(int(Psegments_noL4[ pseg_noL4_pos ].size()) == n_layers_occupied_tot -1 ) {

          curv_noL4_A[ pseg_noL4_pos ] += theWeight(
                                (*(Psegments_noL4[ pseg_noL4_pos ].end()-1 ))->localPosition().x(), 
                                (*(Psegments_noL4[ pseg_noL4_pos ].end()-midlayer_pointer[n_layers_occupied_tot-2] ))->localPosition().x(),
                                (*(Psegments_noL4[ pseg_noL4_pos ].end()-(n_layers_occupied_tot-1) ))->localPosition().x(),
                                float((*(Psegments_noL4[ pseg_noL4_pos ].end()-1 ))->cscDetId().layer()),
                                float((*(Psegments_noL4[ pseg_noL4_pos ].end()-midlayer_pointer[n_layers_occupied_tot-2] ))->cscDetId().layer()),
                                float((*(Psegments_noL4[ pseg_noL4_pos ].end()-(n_layers_occupied_tot-1) ))->cscDetId().layer())
                                );

          if (curv_noL4_A[ pseg_noL4_pos ] > curvePenaltyThreshold) weight_noL4_A[ pseg_noL4_pos ] = weight_noL4_A[ pseg_noL4_pos ] * curvePenalty;

          if (weight_noL4_B[ pseg_noL4_pos ] > a_yweightPenaltyThreshold[thestation][thering]) 
            weight_noL4_A[ pseg_noL4_pos ] = weight_noL4_A[ pseg_noL4_pos ] * yweightPenalty;

          if (weight_noL4_A[ pseg_noL4_pos ] < min_weight_noLx_A ) {
            min_weight_noLx_A = weight_noL4_A[ pseg_noL4_pos ];
            best_weight_noLx_B = weight_noL4_B[ pseg_noL4_pos ];
            best_curv_noLx_A = curv_noL4_A[ pseg_noL4_pos ];
            best_noLx_pseg = pseg_noL4_pos;
                    best_Layer_noLx = 4;
          }

        }

        // alternative: fill map with weight and pseg (which is already ordered)? Seems a very good tool to go looking for segments from. 
        // As I understand, the segments would be inserted according to their weight, so the list would "automatically" be sorted.

          }
        }
      }

          if ( n_layers_occupied_tot > 4 ) {
        if (pseg < orig_number_of_noL5_psegs && ( n_layers_processed != 5 )) {
          if(( Psegments_noL5[ pseg_noL5_pos ].size() > 2 )){
              
        // looks more exciting than it is. Here the weight is calculated. It is the difference in x of the last two and one but the last two hits, 
        // divided by the distance of the corresponding hits. Please refer to twiki page XXXX or CMS Note YYY (and use layer_distance)

        weight_noL5_A[ pseg_noL5_pos ] += theWeight(
                                (*(Psegments_noL5[ pseg_noL5_pos ].end()-1 ))->localPosition().x(), 
                                (*(Psegments_noL5[ pseg_noL5_pos ].end()-2 ))->localPosition().x(),
                                (*(Psegments_noL5[ pseg_noL5_pos ].end()-3 ))->localPosition().x(),
                                float((*(Psegments_noL5[ pseg_noL5_pos ].end()-1))->cscDetId().layer()),
                                float((*(Psegments_noL5[ pseg_noL5_pos ].end()-2))->cscDetId().layer()),
                                float((*(Psegments_noL5[ pseg_noL5_pos ].end()-3))->cscDetId().layer())
                                );

        weight_noL5_B[ pseg_noL5_pos ] += theWeight(
                                (*(Psegments_noL5[ pseg_noL5_pos ].end()-1 ))->localPosition().y(), 
                                (*(Psegments_noL5[ pseg_noL5_pos ].end()-2 ))->localPosition().y(),
                                (*(Psegments_noL5[ pseg_noL5_pos ].end()-3 ))->localPosition().y(),
                                float((*(Psegments_noL5[ pseg_noL5_pos ].end()-1))->cscDetId().layer()),
                                float((*(Psegments_noL5[ pseg_noL5_pos ].end()-2))->cscDetId().layer()),
                                float((*(Psegments_noL5[ pseg_noL5_pos ].end()-3))->cscDetId().layer())
                                );

        //if we have picked up the last hit go looking for pseg with the lowest (and second lowest?) weight

        if(int(Psegments_noL5[ pseg_noL5_pos ].size()) == n_layers_occupied_tot -1 ) {

          curv_noL5_A[ pseg_noL5_pos ] += theWeight(
                                (*(Psegments_noL5[ pseg_noL5_pos ].end()-1 ))->localPosition().x(), 
                                (*(Psegments_noL5[ pseg_noL5_pos ].end()-midlayer_pointer[n_layers_occupied_tot-2] ))->localPosition().x(),
                                (*(Psegments_noL5[ pseg_noL5_pos ].end()-(n_layers_occupied_tot-1) ))->localPosition().x(),
                                float((*(Psegments_noL5[ pseg_noL5_pos ].end()-1 ))->cscDetId().layer()),
                                float((*(Psegments_noL5[ pseg_noL5_pos ].end()-midlayer_pointer[n_layers_occupied_tot-2] ))->cscDetId().layer()),
                                float((*(Psegments_noL5[ pseg_noL5_pos ].end()-(n_layers_occupied_tot-1) ))->cscDetId().layer())
                                );

          if (curv_noL5_A[ pseg_noL5_pos ] > curvePenaltyThreshold) weight_noL5_A[ pseg_noL5_pos ] = weight_noL5_A[ pseg_noL5_pos ] * curvePenalty;

          if (weight_noL5_B[ pseg_noL5_pos ] > a_yweightPenaltyThreshold[thestation][thering]) 
            weight_noL5_A[ pseg_noL5_pos ] = weight_noL5_A[ pseg_noL5_pos ] * yweightPenalty;

          if (weight_noL5_A[ pseg_noL5_pos ] < min_weight_noLx_A ) {
            min_weight_noLx_A = weight_noL5_A[ pseg_noL5_pos ];
            best_weight_noLx_B = weight_noL5_B[ pseg_noL5_pos ];
            best_curv_noLx_A = curv_noL5_A[ pseg_noL5_pos ];
            best_noLx_pseg = pseg_noL5_pos;
                    best_Layer_noLx = 5;
          }

        }

        // alternative: fill map with weight and pseg (which is already ordered)? Seems a very good tool to go looking for segments from. 
        // As I understand, the segments would be inserted according to their weight, so the list would "automatically" be sorted.

          }
        }
      }

          if ( n_layers_occupied_tot > 5 ) {
        if (pseg < orig_number_of_noL6_psegs && ( n_layers_processed != 6 )) {
          if(( Psegments_noL6[ pseg_noL6_pos ].size() > 2 )){
              
        // looks more exciting than it is. Here the weight is calculated. It is the difference in x of the last two and one but the last two hits, 
        // divided by the distance of the corresponding hits. Please refer to twiki page XXXX or CMS Note YYY (and use layer_distance)

        weight_noL6_A[ pseg_noL6_pos ] += theWeight(
                                (*(Psegments_noL6[ pseg_noL6_pos ].end()-1 ))->localPosition().x(), 
                                (*(Psegments_noL6[ pseg_noL6_pos ].end()-2 ))->localPosition().x(),
                                (*(Psegments_noL6[ pseg_noL6_pos ].end()-3 ))->localPosition().x(),
                                float((*(Psegments_noL6[ pseg_noL6_pos ].end()-1))->cscDetId().layer()),
                                float((*(Psegments_noL6[ pseg_noL6_pos ].end()-2))->cscDetId().layer()),
                                float((*(Psegments_noL6[ pseg_noL6_pos ].end()-3))->cscDetId().layer())
                                );

        weight_noL6_B[ pseg_noL6_pos ] += theWeight(
                                (*(Psegments_noL6[ pseg_noL6_pos ].end()-1 ))->localPosition().y(), 
                                (*(Psegments_noL6[ pseg_noL6_pos ].end()-2 ))->localPosition().y(),
                                (*(Psegments_noL6[ pseg_noL6_pos ].end()-3 ))->localPosition().y(),
                                float((*(Psegments_noL6[ pseg_noL6_pos ].end()-1))->cscDetId().layer()),
                                float((*(Psegments_noL6[ pseg_noL6_pos ].end()-2))->cscDetId().layer()),
                                float((*(Psegments_noL6[ pseg_noL6_pos ].end()-3))->cscDetId().layer())
                                );

        //if we have picked up the last hit go looking for pseg with the lowest (and second lowest?) weight

        if(int(Psegments_noL6[ pseg_noL6_pos ].size()) == n_layers_occupied_tot -1 ) {

          curv_noL6_A[ pseg_noL6_pos ] += theWeight(
                                (*(Psegments_noL6[ pseg_noL6_pos ].end()-1 ))->localPosition().x(), 
                                (*(Psegments_noL6[ pseg_noL6_pos ].end()-midlayer_pointer[n_layers_occupied_tot-2] ))->localPosition().x(),
                                (*(Psegments_noL6[ pseg_noL6_pos ].end()-(n_layers_occupied_tot-1) ))->localPosition().x(),
                                float((*(Psegments_noL6[ pseg_noL6_pos ].end()-1 ))->cscDetId().layer()),
                                float((*(Psegments_noL6[ pseg_noL6_pos ].end()-midlayer_pointer[n_layers_occupied_tot-2] ))->cscDetId().layer()),
                                float((*(Psegments_noL6[ pseg_noL6_pos ].end()-(n_layers_occupied_tot-1) ))->cscDetId().layer())
                                );

          if (curv_noL6_A[ pseg_noL6_pos ] > curvePenaltyThreshold) weight_noL6_A[ pseg_noL6_pos ] = weight_noL6_A[ pseg_noL6_pos ] * curvePenalty;

          if (weight_noL6_B[ pseg_noL6_pos ] > a_yweightPenaltyThreshold[thestation][thering]) 
            weight_noL6_A[ pseg_noL6_pos ] = weight_noL6_A[ pseg_noL6_pos ] * yweightPenalty;

          if (weight_noL6_A[ pseg_noL6_pos ] < min_weight_noLx_A ) {
            min_weight_noLx_A = weight_noL6_A[ pseg_noL6_pos ];
            best_weight_noLx_B = weight_noL6_B[ pseg_noL6_pos ];
            best_curv_noLx_A = curv_noL6_A[ pseg_noL6_pos ];
            best_noLx_pseg = pseg_noL6_pos;
                    best_Layer_noLx = 6;
          }

        }

        // alternative: fill map with weight and pseg (which is already ordered)? Seems a very good tool to go looking for segments from. 
        // As I understand, the segments would be inserted according to their weight, so the list would "automatically" be sorted.

          }
        }
      }

    }
      }
    }
  }

  //************************************************************************;    
  //***   End segment building   *******************************************;    
  //************************************************************************;    

  // Important part! Here segment(s) are actually chosen. All the good segments
  // could be chosen or some (best) ones only (in order to save time).

  // Check if there is a segment with n-1 hits that has a signifcantly better 
  // weight than the best n hit segment

  // IBL 070828: implicit assumption here is that there will always only be one segment per 
  // cluster - if there are >1 we will need to find out which segment the alternative n-1 hit 
  // protosegment belongs to!


  float chosen_weight = min_weight_A;
  float chosen_ywgt = best_weight_B;
  float chosen_curv = best_curv_A;
  int chosen_nlayers = n_layers_occupied_tot;
  int chosen_pseg = best_pseg;
  if (best_pseg<0) { 
    return segmentInChamber; 
  }
  chosen_Psegments = (Psegments);
  chosen_weight_A = (weight_A);

  float hit_drop_limit = -999999.999;

  // define different weight improvement requirements depending on how many layers are in the segment candidate
  switch ( n_layers_processed ) {
  case 1 : 
    // do nothing;
    break;
  case 2 :
    // do nothing;
    break;
  case 3 : 
    // do nothing;
    break;
  case 4 : 
    hit_drop_limit =  hitDropLimit6Hits * (1./2.) * hitDropLimit4Hits;
    if ((best_Layer_noLx < 1) || (best_Layer_noLx > 4)) {
      //      std::cout<<"CSCSegAlgoST: For four layers, best_Layer_noLx = "<< best_Layer_noLx << std::endl;
    }
    if ((best_Layer_noLx == 2) || (best_Layer_noLx == 3)) hit_drop_limit = hit_drop_limit * (1./2.); 
    break;
  case 5 : 
    hit_drop_limit =  hitDropLimit6Hits * (2./3.) * hitDropLimit5Hits;
    if ((best_Layer_noLx < 1) || (best_Layer_noLx > 5)) {
      //      std::cout<<"CSCSegAlgoST: For five layers, best_Layer_noLx = "<< best_Layer_noLx << std::endl;
    }
    if ((best_Layer_noLx == 2) || (best_Layer_noLx == 4)) hit_drop_limit = hit_drop_limit * (1./2.); 
    if (best_Layer_noLx == 3) hit_drop_limit = hit_drop_limit * (1./3.); 
    break;
  case 6 : 
    hit_drop_limit =  hitDropLimit6Hits * (3./4.);
    if ((best_Layer_noLx < 1) || (best_Layer_noLx > 6)) {
      //      std::cout<<"CSCSegAlgoST: For six layers, best_Layer_noLx = "<< best_Layer_noLx << std::endl;
    }
    if ((best_Layer_noLx == 2) || (best_Layer_noLx == 5)) hit_drop_limit = hit_drop_limit * (1./2.); 
    if ((best_Layer_noLx == 3) || (best_Layer_noLx == 4)) hit_drop_limit = hit_drop_limit * (1./3.); 
    break;
    
  default : 
    // Fallback - should never occur.
    LogDebug("CSCSegment|CSC") <<"CSCSegAlgoST: Unexpected number of layers with hits - please inform developers.";
    //     std::cout<<"CSCSegAlgoST: Unexpected number of layers with hits - please inform developers."<<std::endl;
    hit_drop_limit = 0.1;
  }

  // choose the NoLx collection (the one that contains the best N-1 candidate)
  switch ( best_Layer_noLx ) {
  case 1 : 
    Psegments_noLx.clear();
    Psegments_noLx = Psegments_noL1;
    weight_noLx_A.clear();
    weight_noLx_A = weight_noL1_A;
    break;
  case 2 :
    Psegments_noLx.clear();
    Psegments_noLx = Psegments_noL2;
    weight_noLx_A.clear();
    weight_noLx_A = weight_noL2_A;
    break;
  case 3 : 
    Psegments_noLx.clear();
    Psegments_noLx = Psegments_noL3;
    weight_noLx_A.clear();
    weight_noLx_A = weight_noL3_A;
    break;
  case 4 : 
    Psegments_noLx.clear();
    Psegments_noLx = Psegments_noL4;
    weight_noLx_A.clear();
    weight_noLx_A = weight_noL4_A;
    break;
  case 5 : 
    Psegments_noLx.clear();
    Psegments_noLx = Psegments_noL5;
    weight_noLx_A.clear();
    weight_noLx_A = weight_noL5_A;
    break;
  case 6 : 
    Psegments_noLx.clear();
    Psegments_noLx = Psegments_noL6;
    weight_noLx_A.clear();
    weight_noLx_A = weight_noL6_A;
    break;
    
  default : 
    // Fallback - should occur only for preclusters with only 3 layers with hits.
    Psegments_noLx.clear();
    weight_noLx_A.clear();
  }
  
  if( min_weight_A > 0. ) {
    if ( min_weight_noLx_A/min_weight_A < hit_drop_limit ) {
      chosen_weight = min_weight_noLx_A;
      chosen_ywgt = best_weight_noLx_B;
      chosen_curv = best_curv_noLx_A;
      chosen_nlayers = n_layers_occupied_tot-1;
      chosen_pseg = best_noLx_pseg;
      chosen_Psegments.clear();
      chosen_weight_A.clear();
      chosen_Psegments = (Psegments_noLx);
      chosen_weight_A = (weight_noLx_A);
    }
  }

  if(onlyBestSegment) {
    ChooseSegments2a( chosen_Psegments, chosen_pseg );
  }
  else {
    ChooseSegments3( chosen_Psegments, chosen_weight_A, chosen_pseg ); 
  }

  for(unsigned int iSegment=0; iSegment<GoodSegments.size();++iSegment){
    protoSegment = GoodSegments[iSegment];
    passCondNumber=false;
    passCondNumber_2 = false;
    protoChiUCorrection=1.0;
    doSlopesAndChi2();
    // Attempt to handle numerical instability of the fit;
    // Any segment with protoChi2/protoNDF>chi2Norm_3D_
    // considered as that one potentially suffering from
    // numerical instability in fit.
    if(correctCov_){
    // Call the fit with prefitting option;
    // First fit a straight line to X-Z coordinates
    // and calculate chi^2 (chiUZ in correctTheCovX(void)) for X-Z fit;
    // Scale up errors in X if chiUZ too big (default 20);
    // Refit XY-Z with the scaled up X errors 
      if(protoChi2/protoNDF>chi2Norm_3D_){
    passCondNumber = true;
    doSlopesAndChi2();
      }
      if(protoChiUCorrection<1.00005){
        LogDebug("CSCSegment|segmWierd") << "Wierd segment, ErrXX scaled, refit " <<std::endl;
        if(protoChi2/protoNDF>chi2Norm_3D_){
     // Call the fit with direct adjustment of condition number;
     // If the attempt to improve fit by scaling up X error fails
     // call the procedure to make the condition number of M compatible with
     // the precision of X and Y measurements;
     // Achieved by decreasing abs value of the Covariance
          LogDebug("CSCSegment|segmWierd") << "Wierd segment, ErrXY changed to match cond. number, refit  " << std::endl;
      passCondNumber_2=true;
      doSlopesAndChi2();
        }
      }
      // Call the pre-pruning procedure;
      // If the attempt to improve fit by scaling up X error is successfull,
      // while scale factor for X errors is too big.
      // Prune the recHit inducing the biggest contribution into X-Z chi^2
      // and refit;
      if(prePrun_ && (sqrt(protoChiUCorrection)>prePrunLimit_) &&
     (protoSegment.size()>3)){   
        LogDebug("CSCSegment|segmWierd") << "Scale factor protoChiUCorrection too big, pre-Prune, refit  " << std::endl;
    protoSegment.erase(protoSegment.begin()+(maxContrIndex),
               protoSegment.begin()+(maxContrIndex+1));                 
    doSlopesAndChi2();
      }
    }

    fillLocalDirection();
    // calculate error matrix
    AlgebraicSymMatrix protoErrors = calculateError();   
    // but reorder components to match what's required by TrackingRecHit interface 
    // i.e. slopes first, then positions 
    flipErrors( protoErrors ); 
    //
    CSCSegment temp(protoSegment, protoIntercept, protoDirection, protoErrors, protoChi2);
    segmentInChamber.push_back(temp); 
  }
  return segmentInChamber;
}

void CSCSegAlgoST::ChooseSegments2a(std::vector< ChamberHitContainer > & chosen_segments, int chosen_seg) {
  // just return best segment
  GoodSegments.clear();
  GoodSegments.push_back( chosen_segments[chosen_seg] );
}

void CSCSegAlgoST::ChooseSegments3(std::vector< ChamberHitContainer > & chosen_segments, std::vector< float > & chosen_weight, int chosen_seg) {

  int SumCommonHits = 0;
  GoodSegments.clear();
  int nr_remaining_candidates;
  unsigned int nr_of_segment_candidates;
  
  nr_remaining_candidates = nr_of_segment_candidates = chosen_segments.size();

  // always select and return best protosegment:  
  GoodSegments.push_back( chosen_segments[ chosen_seg ] );

  float chosen_weight_temp = 999999.;
  int chosen_seg_temp = -1;

  // try to find further segment candidates:
  while( nr_remaining_candidates > 0 ) {

    for(unsigned int iCand=0; iCand < nr_of_segment_candidates; ++iCand) {
      //only compare current best to psegs that have not been marked bad:
      if( chosen_weight[iCand] < 0. ) continue;
      SumCommonHits = 0;

      for( int ihits = 0; ihits < int(chosen_segments[iCand].size()); ++ihits ) { // iCand and iiCand NEED to have same nr of hits! (always have by construction)
    if( chosen_segments[iCand][ihits] == chosen_segments[chosen_seg][ihits]) {
      ++SumCommonHits;
    }
      }

      //mark a pseg bad:
      if(SumCommonHits>1) { // needs to be a card; should be investigated first
    chosen_weight[iCand] = -1.;
    nr_remaining_candidates -= 1;
      }
      else {
    // save the protosegment with the smallest weight
    if( chosen_weight[ iCand ] < chosen_weight_temp ) {
      chosen_weight_temp = chosen_weight[ iCand ];
      chosen_seg_temp = iCand ;
    }
      }
    }

    if( chosen_seg_temp > -1 ) GoodSegments.push_back( chosen_segments[ chosen_seg_temp ] );

    chosen_seg = chosen_seg_temp;
    // re-initialze temporary best parameters
    chosen_weight_temp = 999999;
    chosen_seg_temp = -1;
  }
}

void CSCSegAlgoST::ChooseSegments2(int best_seg) {
  //  std::vector <int> CommonHits(6); // nice  concept :)
  std::vector <unsigned int> BadCandidate;
  int SumCommonHits =0;
  GoodSegments.clear();
  BadCandidate.clear();
  for(unsigned int iCand=0;iCand<Psegments.size();++iCand) {
    // skip here if segment was marked bad
    for(unsigned int iiCand=iCand+1;iiCand<Psegments.size();++iiCand){
      // skip here too if segment was marked bad
      SumCommonHits =0;
      if( Psegments[iCand].size() != Psegments[iiCand].size() ) {
    LogDebug("CSCSegment|CSC") <<"CSCSegmentST::ChooseSegments2: ALARM!! THIS should not happen!!";
//  std::cout<<"CSCSegmentST::ChooseSegments2: ALARM!! THIS should not happen!!"<<std::endl;
      }
      else {
    for( int ihits = 0; ihits < int(Psegments[iCand].size()); ++ihits ) { // iCand and iiCand NEED to have same nr of hits! (alsways have by construction)
      if( Psegments[iCand][ihits] == Psegments[iiCand][ihits]) {
        ++SumCommonHits;
      }
    }
      }
      if(SumCommonHits>1) {
    if( weight_A[iCand]>weight_A[iiCand] ) { // use weight_A here
      BadCandidate.push_back(iCand);
      // rather mark segment bad by an array which is in sync with protosegments!! e.g. set weight = weight*1000 or have an addidional array or set it to weight *= -1
    }
    else{
      BadCandidate.push_back(iiCand);
      // rather mark segment bad by an array which is in sync with protosegments!! e.g. set weight = weight*1000 or have an addidional array or set it to weight *= -1
    }
      }
    }
  }
  bool discard;
  for(unsigned int isegm=0;isegm<Psegments.size();++isegm) {
    // For best results another iteration/comparison over Psegments 
    //should be applied here... It would make the program much slower.
    discard = false;
    for(unsigned int ibad=0;ibad<BadCandidate.size();++ibad) {
      // can save this loop if we used an array in sync with Psegments!!!!
      if(isegm == BadCandidate[ibad]) {
    discard = true;
      }
    }
    if(!discard) {
      GoodSegments.push_back( Psegments[isegm] );
    }
  }
}
//Method doSlopesAndChi2
// fitSlopes() and  fillChiSquared() are always called one after the other 
// In fact the code is duplicated in the two functions (as we need 2 loops) - 
// it is much better to fix that at some point 
void CSCSegAlgoST::doSlopesAndChi2(){
  fitSlopes();
  fillChiSquared();
}
/* Method fitSlopes
 *
 * Perform a Least Square Fit on a segment as per SK algo
 *
 */
void CSCSegAlgoST::fitSlopes() {
  e_Cxx.clear(); 
  if(passCondNumber && !passCondNumber_2){
    correctTheCovX();
    if(e_Cxx.size()!=protoSegment.size()){
      LogDebug("CSCSegment|segmWierd") << "e_Cxx.size()!=protoSegment.size() IT IS A SERIOUS PROBLEM!!! " <<std::endl;
    }
  }
  CLHEP::HepMatrix M(4,4,0);
  CLHEP::HepVector B(4,0);
  ChamberHitContainer::const_iterator ih = protoSegment.begin();
  for (ih = protoSegment.begin(); ih != protoSegment.end(); ++ih) {
    const CSCRecHit2D& hit = (**ih);
    const CSCLayer* layer  = theChamber->layer(hit.cscDetId().layer());
    GlobalPoint gp         = layer->toGlobal(hit.localPosition());
    LocalPoint  lp         = theChamber->toLocal(gp); 
    // ptc: Local position of hit w.r.t. chamber
    double u = lp.x();
    double v = lp.y();
    double z = lp.z();
    // ptc: Covariance matrix of local errors 
    CLHEP::HepMatrix IC(2,2);
    if(passCondNumber&& !passCondNumber_2){
      IC(1,1) = e_Cxx.at(ih-protoSegment.begin());
    }
    else{
      IC(1,1) = hit.localPositionError().xx();
    }
    //    IC(1,1) = hit.localPositionError().xx();
    IC(1,2) = hit.localPositionError().xy();
    IC(2,2) = hit.localPositionError().yy();
    IC(2,1) = IC(1,2); // since Cov is symmetric
    if(passCondNumber_2){
      correctTheCovMatrix(IC);
    }
    // ptc: Invert covariance matrix (and trap if it fails!)
    int ierr = 0;
    IC.invert(ierr); // inverts in place
    if (ierr != 0) {
      LogDebug("CSCSegment|CSC") << "CSCSegment::fitSlopes: failed to invert covariance matrix=\n" << IC;      
      //       std::cout<< "CSCSegment::fitSlopes: failed to invert covariance matrix=\n" << IC << "\n"<<std::endl;
    }
    
    M(1,1) += IC(1,1);
    M(1,2) += IC(1,2);
    M(1,3) += IC(1,1) * z;
    M(1,4) += IC(1,2) * z;
    B(1)   += u * IC(1,1) + v * IC(1,2);
    
    M(2,1) += IC(2,1);
    M(2,2) += IC(2,2);
    M(2,3) += IC(2,1) * z;
    M(2,4) += IC(2,2) * z;
    B(2)   += u * IC(2,1) + v * IC(2,2);
    
    M(3,1) += IC(1,1) * z;
    M(3,2) += IC(1,2) * z;
    M(3,3) += IC(1,1) * z * z;
    M(3,4) += IC(1,2) * z * z;
    B(3)   += ( u * IC(1,1) + v * IC(1,2) ) * z;
    
    M(4,1) += IC(2,1) * z;
    M(4,2) += IC(2,2) * z;
    M(4,3) += IC(2,1) * z * z;
    M(4,4) += IC(2,2) * z * z;
    B(4)   += ( u * IC(2,1) + v * IC(2,2) ) * z;
  }
  CLHEP::HepVector p = solve(M, B);
  
  // Update member variables 
  // Note that origin has local z = 0
  protoIntercept = LocalPoint(p(1), p(2), 0.);
  protoSlope_u = p(3);
  protoSlope_v = p(4);
}
/* Method fillChiSquared
 *
 * Determine Chi^2 for the proto wire segment
 *
 */
void CSCSegAlgoST::fillChiSquared() {
  
  double chsq = 0.;
  
  ChamberHitContainer::const_iterator ih;
  for (ih = protoSegment.begin(); ih != protoSegment.end(); ++ih) {
    
    const CSCRecHit2D& hit = (**ih);
    const CSCLayer* layer  = theChamber->layer(hit.cscDetId().layer());
    GlobalPoint gp         = layer->toGlobal(hit.localPosition());
    LocalPoint lp          = theChamber->toLocal(gp);
    
    double u = lp.x();
    double v = lp.y();
    double z = lp.z();
    
    double du = protoIntercept.x() + protoSlope_u * z - u;
    double dv = protoIntercept.y() + protoSlope_v * z - v;
    
    CLHEP::HepMatrix IC(2,2);
    if(passCondNumber&& !passCondNumber_2){
      IC(1,1) = e_Cxx.at(ih-protoSegment.begin());
    }
    else{
      IC(1,1) = hit.localPositionError().xx();
    }
    //    IC(1,1) = hit.localPositionError().xx();
    IC(1,2) = hit.localPositionError().xy();
    IC(2,2) = hit.localPositionError().yy();
    IC(2,1) = IC(1,2);
    if(passCondNumber_2){
      correctTheCovMatrix(IC);
    }
    
    // Invert covariance matrix
    int ierr = 0;
    IC.invert(ierr);
    if (ierr != 0) {
      LogDebug("CSCSegment|CSC") << "CSCSegment::fillChiSquared: failed to invert covariance matrix=\n" << IC;
      //       std::cout << "CSCSegment::fillChiSquared: failed to invert covariance matrix=\n" << IC << "\n";
      
    }
    
    chsq += du*du*IC(1,1) + 2.*du*dv*IC(1,2) + dv*dv*IC(2,2);
  }

  protoChi2 = chsq;
  protoNDF = 2.*protoSegment.size() - 4;
}
/* fillLocalDirection
 *
 */
void CSCSegAlgoST::fillLocalDirection() {
  // Always enforce direction of segment to point from IP outwards
  // (Incorrect for particles not coming from IP, of course.)
  
  double dxdz = protoSlope_u;
  double dydz = protoSlope_v;
  double dz   = 1./sqrt(1. + dxdz*dxdz + dydz*dydz);
  double dx   = dz*dxdz;
  double dy   = dz*dydz;
  LocalVector localDir(dx,dy,dz);
  
  // localDir may need sign flip to ensure it points outward from IP
  // ptc: Examine its direction and origin in global z: to point outward
  // the localDir should always have same sign as global z...
  
  double globalZpos    = ( theChamber->toGlobal( protoIntercept ) ).z();
  double globalZdir    = ( theChamber->toGlobal( localDir ) ).z();
  double directionSign = globalZpos * globalZdir;
  protoDirection       = (directionSign * localDir).unit();
}
/* weightMatrix
 *   
 */
AlgebraicSymMatrix CSCSegAlgoST::weightMatrix() const {
  
  std::vector<const CSCRecHit2D*>::const_iterator it;
  int nhits = protoSegment.size();
  AlgebraicSymMatrix matrix(2*nhits, 0);
  int row = 0;
  
  for (it = protoSegment.begin(); it != protoSegment.end(); ++it) {
    
    const CSCRecHit2D& hit = (**it);
    ++row;
    matrix(row, row)   = protoChiUCorrection*hit.localPositionError().xx();
    matrix(row, row+1) = hit.localPositionError().xy();
    ++row;
    matrix(row, row-1) = hit.localPositionError().xy();
    matrix(row, row)   = hit.localPositionError().yy();
  }
  int ierr;
  matrix.invert(ierr);
  return matrix;
}


/* derivativeMatrix
 *
 */
CLHEP::HepMatrix CSCSegAlgoST::derivativeMatrix() const {
  
  ChamberHitContainer::const_iterator it;
  int nhits = protoSegment.size();
  CLHEP::HepMatrix matrix(2*nhits, 4);
  int row = 0;
  
  for(it = protoSegment.begin(); it != protoSegment.end(); ++it) {
    
    const CSCRecHit2D& hit = (**it);
    const CSCLayer* layer = theChamber->layer(hit.cscDetId().layer());
    GlobalPoint gp = layer->toGlobal(hit.localPosition());      
    LocalPoint lp = theChamber->toLocal(gp); 
    float z = lp.z();
    ++row;
    matrix(row, 1) = 1.;
    matrix(row, 3) = z;
    ++row;
    matrix(row, 2) = 1.;
    matrix(row, 4) = z;
  }
  return matrix;
}


/* calculateError
 *
 */
AlgebraicSymMatrix CSCSegAlgoST::calculateError() const {
  
  AlgebraicSymMatrix weights = weightMatrix();
  AlgebraicMatrix A = derivativeMatrix();
  
  // (AT W A)^-1
  // from http://www.phys.ufl.edu/~avery/fitting.html, part I
  int ierr;
  AlgebraicSymMatrix result = weights.similarityT(A);
  result.invert(ierr);
  
  // blithely assuming the inverting never fails...
  return result;
}


void CSCSegAlgoST::flipErrors( AlgebraicSymMatrix& a ) const { 
    
  // The CSCSegment needs the error matrix re-arranged to match
  //  parameters in order (uz, vz, u0, v0) where uz, vz = slopes, u0, v0 = intercepts
    
  AlgebraicSymMatrix hold( a ); 
    
  // errors on slopes into upper left 
  a(1,1) = hold(3,3); 
  a(1,2) = hold(3,4); 
  a(2,1) = hold(4,3); 
  a(2,2) = hold(4,4); 
    
  // errors on positions into lower right 
  a(3,3) = hold(1,1); 
  a(3,4) = hold(1,2); 
  a(4,3) = hold(2,1); 
  a(4,4) = hold(2,2); 
    
  // must also interchange off-diagonal elements of off-diagonal 2x2 submatrices
  a(4,1) = hold(2,3);
  a(3,2) = hold(1,4);
  a(2,3) = hold(4,1); // = hold(1,4)
  a(1,4) = hold(3,2); // = hold(2,3)
} 
//
void CSCSegAlgoST::correctTheCovX(void){
  std::vector<double> uu, vv, zz;  
  //std::vector<double> e_Cxx;
  e_Cxx.clear();
  double sum_U_err=0.0;
  double sum_Z_U_err=0.0; 
  double sum_Z2_U_err=0.0;
  double sum_U_U_err=0.0;
  double sum_UZ_U_err=0.0;
  std::vector<double> chiUZind;
  std::vector<double>::iterator chiContribution;
  double chiUZ=0.0;
  ChamberHitContainer::const_iterator ih = protoSegment.begin();
  for (ih = protoSegment.begin(); ih != protoSegment.end(); ++ih) {
    const CSCRecHit2D& hit = (**ih);
    e_Cxx.push_back(hit.localPositionError().xx());
    // 
    const CSCLayer* layer  = theChamber->layer(hit.cscDetId().layer());
    GlobalPoint gp         = layer->toGlobal(hit.localPosition());
    LocalPoint  lp         = theChamber->toLocal(gp); 
    // ptc: Local position of hit w.r.t. chamber
    double u = lp.x();
    double v = lp.y();
    double z = lp.z();
    uu.push_back(u); 
    vv.push_back(v); 
    zz.push_back(z);
    sum_U_err += 1./e_Cxx.back();
    sum_Z_U_err += z/e_Cxx.back();
    sum_Z2_U_err += (z*z)/e_Cxx.back();
    sum_U_U_err += u/e_Cxx.back();
    sum_UZ_U_err += (u*z)/e_Cxx.back();
  }
 
  
  double denom=sum_U_err*sum_Z2_U_err-pow(sum_Z_U_err,2);
  double U0=(sum_Z2_U_err*sum_U_U_err-sum_Z_U_err*sum_UZ_U_err)/denom;
  double UZ=(sum_U_err*sum_UZ_U_err-sum_Z_U_err*sum_U_U_err)/denom;
  
  
  for(unsigned i=0; i<uu.size(); ++i){
    double uMean = U0+UZ*zz[i];
    chiUZind.push_back((pow((uMean-uu[i]),2))/e_Cxx[i]);
    chiUZ += (pow((uMean-uu[i]),2))/e_Cxx[i];
  }
  chiUZ = chiUZ/(uu.size()-2);
  
  if(chiUZ>=chi2Norm_2D_){
    protoChiUCorrection = chiUZ/chi2Norm_2D_;
    for(unsigned i=0; i<uu.size(); ++i)
      e_Cxx[i]=e_Cxx[i]*protoChiUCorrection;
  }
  
  
  if(sqrt(protoChiUCorrection)>prePrunLimit_){
    chiContribution=max_element(chiUZind.begin(),chiUZind.end());
    maxContrIndex = chiContribution - chiUZind.begin();
    /*
    for(unsigned i=0; i<chiUZind.size();++i){
      if(*chiContribution==chiUZind[i]){
    maxContrIndex=i;
      }
    }
    */
  }
  //  
  //return e_Cxx;
}
//
void CSCSegAlgoST::correctTheCovMatrix(CLHEP::HepMatrix &IC){
  double condNumberCorr1=0.0;
  double condNumberCorr2=0.0; 
  double detCov=0.0;
  double diag1=0.0;
  double diag2=0.0;
  double IC_12_corr=0.0;
  double  IC_11_corr=0.0;
  if(!covToAnyNumberAll_){
    condNumberCorr1=condSeed1_*IC(2,2);
    condNumberCorr2=condSeed2_*IC(2,2);
    diag1=IC(1,1)*IC(2,2);
    diag2=IC(1,2)*IC(1,2);
    detCov=fabs(diag1-diag2);
    if((diag1<condNumberCorr2)&&(diag2<condNumberCorr2)){
      if(covToAnyNumber_)
        IC(1,2)=covAnyNumber_;
      else{ 
        IC_11_corr=condSeed1_+fabs(IC(1,2))/IC(2,2);
        IC(1,1)=IC_11_corr;
      }
    }
    
    if(((detCov<condNumberCorr2)&&(diag1>condNumberCorr2))||
       ((diag2>condNumberCorr2)&&(detCov<condNumberCorr2)
    )){
      if(covToAnyNumber_)
    IC(1,2)=covAnyNumber_;
      else{ 
    IC_12_corr=sqrt(fabs(diag1-condNumberCorr2));
    if(IC(1,2)<0)
      IC(1,2)=(-1)*IC_12_corr;
    else
      IC(1,2)=IC_12_corr;
      }
    }
  }
  else{
    IC(1,2)=covAnyNumber_;
  }
}
//
void CSCSegAlgoST::findDuplicates(std::vector<CSCSegment>  & segments ){
  // this is intended for ME1/1a only - we have ghost segments because of the strips ganging 
  // this function finds them (first the rechits by sharesInput() )
  // if a segment shares all the rechits with another segment it is a duplicate (even if
  // it has less rechits) 
  
  for(std::vector<CSCSegment>::iterator it=segments.begin(); it != segments.end(); ++it) {
    std::vector<CSCSegment*> duplicateSegments;
    for(std::vector<CSCSegment>::iterator it2=segments.begin(); it2 != segments.end(); ++it2) {
      //
      bool allShared = true;
      if(it!=it2){
    allShared = it->sharesRecHits(*it2);
      }
      else{
        allShared = false;
      }
      //
      if(allShared){
        duplicateSegments.push_back(&(*it2));
      }
    }
    it->setDuplicateSegments(duplicateSegments);
  }

}
//