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SBSGRINCH.cxx
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SBSGRINCH.cxx
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// *-- Author : Guido Maria Urciuoli 12 March 2001
//////////////////////////////////////////////////////////////////////////
//
// SBSRICH
//
// The RICH detector
// Written by Guido Maria Urciuoli, INFN
// Adapted for Hall A Analyzer by Ole Hansen, JLab
// Adapted to SBS by Seamus Riordan, ANL and Eric Fuchey, UConn
//
//////////////////////////////////////////////////////////////////////////
#include "TMath.h"
#include "SBSGRINCH.h"
#include "THaTrack.h"
#include "THaEvData.h"
#include "THaGlobals.h"
#include "THaDetMap.h"
#include "THaSpectrometer.h"
#include "TError.h"
#include "VarDef.h"
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <cmath>
#include <fstream>
#include <iostream>
#include <sstream>
#include "THaBenchmark.h"
using namespace std;
// cout<<"stay and stop here!!!"<<endl;
//_____________________________________________________________________________
SBSGRINCH::SBSGRINCH( const char* name, const char* description,
THaApparatus* apparatus ) :
SBSCherenkovDetector(name,description,apparatus)
//fTrackX(kBig), fTrackY(kBig)
{
//keep this line first
fBench = new THaBenchmark;
// Normal constructor with name and description
// default separation is 3.1 cm along X (rows) and Y (columns)
// Y positions are staggered by 1.55 cm in alternating rows but they are not
// close-packed as if in a honeycomb layout.
fMaxSep = 0.07; //3.5 cm is the max. center-to-center separation between "neighbor" PMTs, this is twice that number
// we can adjust via the DB if we want, but we'll use this as a default
fMaxSep2 = pow(fMaxSep,2);
//Set up default track match cuts based on mirror number:
fNmirror = 4;
fTrackMatchPslope = 0.1715;
fOrderTrackMatchY = 3; //default values.
fTrackMatchXslope.resize(fNmirror);
fTrackMatchX0.resize(fNmirror);
fTrackMatchXsigma.resize(fNmirror);
fTrackMatchXmin.resize(fNmirror);
fTrackMatchXmax.resize(fNmirror);
fTrackMatchYslope.resize(fNmirror*fOrderTrackMatchY);
fTrackMatchY0.resize(fNmirror);
fTrackMatchYsigma.resize(fNmirror);
// Double_t mirrx0[] = { 0.1789, 0.0052, 0.0052, -0.2576 };
//These parameters come from Maria's analysis:
Double_t mirrx0[] = {0.17, 0.005, 0.0095, -0.27}; //intercept of dx versus theta
Double_t mirrxslope[] = {1.361, 1.424, 1.3387, 1.466}; //slope of dx versus theta
Double_t mirrxmin[] = {-0.8, -0.55, -0.05, 0.45}; //mirror boundary cuts, min
Double_t mirrxmax[] = {-0.45, 0.05, 0.58, 0.85}; //mirror boundary cuts, max
Double_t mirrxsigma[] = {0.013, 0.012, 0.013, 0.011}; //sigma of corrected dx distribution
// Double_t mirrx0[] = { -0.1580, -0.0193, -0.0047, 0.1968};
// Double_t mirrxslope[] = {0.71, 0.71, 0.71, 0.71};
// Double_t mirrxmin[] = {-0.8, -0.55, -0.1, 0.45};
// Double_t mirrxmax[] = {-0.45, 0.1, 0.6, 0.85};
// Double_t mirrxsigma[] = {0.016, 0.014, 0.015, 0.016};
// Double_t mirryslope[] = {0.42, 0.48, 0.173, 0.56};
// Double_t mirry0[] = {-0.017, 0.018, 0.014, 0.046};
// Double_t mirrysigma[] = {0.022, 0.015, 0.022, 0.023};
//Let's implement Maria's 3rd-order polynomial in track phi for dy:
//Need four parameters per mirror:
Double_t mirrypar[] = { -0.002, -1.5, 10.0, -150.0,
0.041, -1.4, -5., -185.0,
0.051, -1.9, 0.0, -120.0,
0.081, -1.8, -25.0, 10.0 };
Double_t mirrysigma[] = { 0.023, 0.020, 0.025, 0.020 };
for( int imirr=0; imirr<fNmirror; imirr++ ){
fTrackMatchXslope[imirr] = mirrxslope[imirr];
fTrackMatchX0[imirr] = mirrx0[imirr];
fTrackMatchXsigma[imirr] = mirrxsigma[imirr];
fTrackMatchXmin[imirr] = mirrxmin[imirr];
fTrackMatchXmax[imirr] = mirrxmax[imirr];
//New y params; implement Maria's third-order polynomial fit versus track phi:
fTrackMatchY0[imirr] = mirrypar[4*imirr];
fTrackMatchYsigma[imirr] = mirrysigma[imirr];
for( int ipar=1; ipar<=3; ipar++ ){
fTrackMatchYslope[3*imirr + ipar-1] = mirrypar[4*imirr+ipar];
}
}
fTrackMatchNsigmaCut = 4.5;
// fTrackMatchXslope = 0.71;
// fTrackMatchX0 = 0.0;
// fTrackMatchXsigma = 0.014;
// fTrackMatchYslope = 1.8;
// fTrackMatchY0 = -0.06;
// fTrackMatchYsigma = 0.04;
// fTrackMatchNsigmaCut = 4.5; //default to a relatively wide cut:
Clear();
}
//_____________________________________________________________________________
SBSGRINCH::~SBSGRINCH()
{
// Destructor. Remove variables from global list and free up the memory
// allocated by us.
Clear();// so the prgram doesn't complain when deleting clusters
RemoveVariables();
// delete fHits;
// delete fResolvedHits;
// delete fClusters;
// delete fResolvedClusters;
// delete [] fMIPs;
// delete [] fXseg;
// delete fBench;
}
//_____________________________________________________________________________
void SBSGRINCH::Clear( Option_t* opt )
{
// Reset event-by-event data
if(fDebug)cout << "SBSGRINCH::Clear() " << endl;
if( fDoBench ) fBench->Begin("Clear");
SBSCherenkovDetector::Clear(opt);
if( fDoBench ) fBench->Stop("Clear");
}
//_____________________________________________________________________________
Int_t SBSGRINCH::ReadDatabase( const TDatime& date )
{
//
// Read the database for this detector.
// This function is called once at the beginning of the analysis.
// 'date' contains the date/time of the run being analyzed.
//
static const char* const here = "ReadDatabase";
cout << here << endl;
// Open the database file
FILE* fi = OpenFile( date );
if( !fi ) return kFileError;
Int_t err = SBSCherenkovDetector::ReadDatabase(date);
if(err) {
return err;
}
fIsInit = false;
//add grinch specific stuff here if needed
const DBRequest request[] = {
{ "maxsep", &fMaxSep, kDouble, 0, 1, 1 },
{ "nmirror", &fNmirror, kInt, 0, 1, 1 },
{ "trackmatch_yorder", &fOrderTrackMatchY, kInt, 0, 1, 1 },
{ "trackmatch_pslope", &fTrackMatchPslope, kDouble, 0, 1, 1 },
{ "trackmatch_xslope", &fTrackMatchXslope, kDoubleV, 0, 1, 1 },
{ "trackmatch_x0", &fTrackMatchX0, kDoubleV, 0, 1, 1 },
{ "trackmatch_xsigma", &fTrackMatchXsigma, kDoubleV, 0, 1, 1 },
{ "trackmatch_yslope", &fTrackMatchYslope, kDoubleV, 0, 1, 1 },
{ "trackmatch_y0", &fTrackMatchY0, kDoubleV, 0, 1, 1 },
{ "trackmatch_ysigma", &fTrackMatchYsigma, kDoubleV, 0, 1, 1 },
{ "trackmatch_xmin", &fTrackMatchXmin, kDoubleV, 0, 1, 1 },
{ "trackmatch_xmax", &fTrackMatchXmax, kDoubleV, 0, 1, 1 },
{ "trackmatch_nsigma_cut", &fTrackMatchNsigmaCut, kDouble, 0, 1, 1 },
{0}
};
err = LoadDB( fi, date, request, fPrefix );
fMaxSep2 = pow(fMaxSep,2);
//Check size of track match cut arrays, if they were loaded from the DB they had better all be the same size:
bool sizecheck = ( fTrackMatchXslope.size() == fNmirror &&
fTrackMatchX0.size() == fNmirror &&
fTrackMatchXsigma.size() == fNmirror &&
fTrackMatchYslope.size() == fNmirror*fOrderTrackMatchY &&
fTrackMatchY0.size() == fNmirror &&
fTrackMatchYsigma.size() == fNmirror &&
fTrackMatchXmin.size() == fNmirror &&
fTrackMatchXmax.size() == fNmirror );
if( !sizecheck ){
Error(Here("SBSGRINCH::ReadDatabase"), TString::Format("Size of track match cut parameters does not match number of GRINCH mirrors (%d): Fix Database!",fNmirror) );
return kInitError;
}
if( err != 0 ){
fclose(fi);
return err;
}
fIsInit = true;
fclose(fi);
return kOK;
}
//_____________________________________________________________________________
Int_t SBSGRINCH::DefineVariables( EMode mode )
{
// Define (or delete) global variables of the detector
if( mode == kDefine && fIsSetup ) return kOK;
//fIsSetup = ( mode == kDefine );
Int_t err = SBSCherenkovDetector::DefineVariables(mode);
if(err)
return err;
//Add here the cluster output variables? maybe
RVarDef var2[] = {
{ "nclus", " number of GRINCH PMT clusters", "GetNumClusters()" },
{ "allclus.size", " GRINCH cluster size", "fClusters.SBSCherenkov_Cluster.GetNHits()" },
{ "allclus.trackindex", "Index of matched track", "fClusters.SBSCherenkov_Cluster.GetTrackIndex()" },
{ "allclus.mirrorindex", "Mirror index of matched track", "fClusters.SBSCherenkov_Cluster.GetMirrorIndex()" },
{ "allclus.x_mean", " GRINCH cluster X center", "fClusters.SBSCherenkov_Cluster.GetXcenter()" },
{ "allclus.y_mean", " GRINCH cluster Y center", "fClusters.SBSCherenkov_Cluster.GetYcenter()" },
{ "allclus.t_mean", " GRINCH cluster mean time", "fClusters.SBSCherenkov_Cluster.GetMeanTime()" },
{ "allclus.t_rms", "RMS of GRINCH cluster hit times", "fClusters.SBSCherenkov_Cluster.GetTimeRMS()" },
{ "allclus.tot_mean", " GRINCH cluster mean time over threshold", "fClusters.SBSCherenkov_Cluster.GetMeanAmp()" },
{ "allclus.adc", " GRINCH cluster total charge", "fClusters.SBSCherenkov_Cluster.GetCharge()" },
{ "allclus.dx", " GRINCH cluster corrected dx wrt track projection", "fClusters.SBSCherenkov_Cluster.GetDX()" },
{ "allclus.dy", " GRINCH cluster corrected dy wrt track projection", "fClusters.SBSCherenkov_Cluster.GetDY()" },
{ "bestcluster", "Index of best cluster", "fBestClusterIndex" },
{ 0 }
};
//Define global variables for "best cluster" properties:
RVarDef var3[] = {
{ "clus.size", " GRINCH best cluster size", "fBestCluster.GetNHits()" },
{ "clus.trackindex", "Index of best cluster matched track", "fBestCluster.GetTrackIndex()" },
{ "clus.mirrorindex", "Mirror index of best cluster", "fBestCluster.GetMirrorIndex()" },
{ "clus.x_mean", " GRINCH best cluster X center", "fBestCluster.GetXcenter()" },
{ "clus.y_mean", " GRINCH best cluster Y center", "fBestCluster.GetYcenter()" },
{ "clus.t_mean", " GRINCH best cluster mean time", "fBestCluster.GetMeanTime()" },
{ "clus.t_rms", "RMS of GRINCH best cluster hit times", "fBestCluster.GetTimeRMS()" },
{ "clus.tot_mean", " GRINCH best cluster mean time over threshold", "fBestCluster.GetMeanAmp()" },
{ "clus.adc", " GRINCH best cluster total charge", "fBestCluster.GetCharge()" },
{ "clus.dx", " GRINCH cluster corrected dx wrt track projection", "fBestCluster.GetDX()" },
{ "clus.dy", " GRINCH cluster corrected dy wrt track projection", "fBestCluster.GetDY()" },
{ 0 }
};
DefineVarsFromList( var2, mode, "" );// (re)define path here...
DefineVarsFromList( var3, mode, "" );
return kOK;
}
//_____________________________________________________________________________
Int_t SBSGRINCH::Decode( const THaEvData& evdata )
{
//Decode RICH data and fill hit array
if(fDebug){
cout << "SBSGRINCH::Decode " << endl;
cout << " Is Init ? " << fIsInit << " Is Physics Trigger ? " << evdata.IsPhysicsTrigger() << endl;
}
return SBSCherenkovDetector::Decode(evdata);
}
//_____________________________________________________________________________
Int_t SBSGRINCH::CoarseProcess( TClonesArray& tracks )
{
if(fDebug)cout << "Begin Coarse Process" << endl;
if( fDoBench ) fBench->Begin("CoarseProcess");
SBSCherenkovDetector::CoarseProcess(tracks);
// add clustering here: hits have been filtered by SBSCherenkovDetector::CoarseProcess()
// clustering ought to be coded in function "FindClusters" below
/*int err = */FindClusters();
if( fDoBench ) fBench->Stop("CoarseProcess");
if(fDebug)cout << "End Coarse Process" << endl;
return 0;
}
//_____________________________________________________________________________
Int_t SBSGRINCH::FineProcess( TClonesArray& tracks )
{
if(fDebug)cout << "Begin Fine Process" << endl;
// The main GRINCH processing method.
// Here we attempt to match a track to each cluster
if( !fIsInit ) return -255;
if( fDoBench ) fBench->Begin("FineProcess");
SBSCherenkovDetector::FineProcess(tracks);
//Int_t nmatch = 0;
fNtrackMatch = 0;
// Clusters matched with tracks here (only if there are any tracks to match)
if(tracks.GetLast()>=0){
fNtrackMatch = MatchClustersWithTracks(tracks);
}
fBestClusterIndex = SelectBestCluster(fNtrackMatch);
if( fBestClusterIndex >= 0 && fBestClusterIndex < fClusters->GetLast()+1 && tracks.GetLast() >= 0 ){
fBestCluster = *(GetBestCluster());
// std::cout << "ntrackmatch, best cluster index, best cluster track index = "
// << fNtrackMatch << ", " << fBestClusterIndex << ", " << fBestCluster.GetTrackIndex()
// << std::endl;
}
if( fDoBench ) fBench->Stop("FineProcess");
if(fDebug)cout << "Done fine process " << endl;
return 0;
}
//__________________________________________________________________________
Int_t SBSGRINCH::FindClusters()
{
// here will be implemented the clustering for the GRINCH
// Basic idea: loop on all the good hits; group all contiguous nearest-neighbor hits into clusters based on position and/or row/col number;
// when this is called, the "fHits" array has already been filled by CoarseProcess
Int_t ngoodhits = GetNumHits();
Int_t nclust = 0;
std::set<Int_t> list_pmts; //list of all fired PMTs
std::set<Int_t> unused_pmts; //list of all PMTs that aren't part of clusters
std::map<Int_t,Int_t> hitindex_pmts; //index in hit array of fired PMTs
//std::map<Int_t,Bool_t> hitused_pmts; //flag to indicate a PMT has already been used in a cluster
std::map<Int_t,Int_t> pmt_row;
std::map<Int_t,Int_t> pmt_col;
std::map<Int_t,Double_t> xpmt;
std::map<Int_t,Double_t> ypmt;
std::map<Int_t,Int_t> clustindex_pmts; //cluster index of pmts (maybe redundant, unclear if necessary yet)
std::vector<std::set<Int_t> > pmtlist_clusters;
pmtlist_clusters.clear();
for( int ihit=0; ihit<ngoodhits; ihit++ ){
int pmtnum = GetHit(ihit)->GetPMTNum();
list_pmts.insert( pmtnum );
unused_pmts.insert( pmtnum );
pmt_row[pmtnum] = GetHit(ihit)->GetRow();
pmt_col[pmtnum] = GetHit(ihit)->GetCol();
xpmt[pmtnum] = GetHit(ihit)->GetX();
ypmt[pmtnum] = GetHit(ihit)->GetY();
hitindex_pmts[pmtnum] = ihit;
//hitused_pmts[pmtnum] = false;
clustindex_pmts[pmtnum] = -1;
}
nclust = 0; //for readability
//Start "new" improved clustering algorithm here:
while( !( unused_pmts.empty() ) ){
//grab the first unused PMT:
auto ipmt = *(unused_pmts.begin());
//Each iteration of this loop is guaranteed to remove at least one pmt from the set otherwise we would get stuck in an infinite loop. The first pmt in the set is always either used to create a new cluster or added to an existing one!
//Check if this pmt is not already in a cluster:
//if( unused_pmts.find(ipmt) != unused_pmts.end() ){ //compare this pmt to all existing clusters.
if( nclust == 0 ){ //create first cluster
std::set<Int_t> pmtlist_temp;
pmtlist_temp.insert( ipmt );
pmtlist_clusters.push_back( pmtlist_temp );
clustindex_pmts[ipmt] = nclust;
unused_pmts.erase( ipmt );
//Now loop on all OTHER unused PMTs and add any immediate neighbors of ipmt found to this cluster:
for( auto jpmt : unused_pmts ){
//if( jpmt != ipmt ){ //In principle this check is unnecessary since we already erased ipmt
Double_t sep2 = pow( xpmt[ipmt] - xpmt[jpmt], 2 ) + pow( ypmt[ipmt] - ypmt[jpmt], 2 );
if( sep2 <= fMaxSep2 ){ //neighbor!
pmtlist_clusters[nclust].insert( jpmt );
//unused_pmts.erase( jpmt ); DON'T modify the set while iterating on it, this could have unintended consequences
clustindex_pmts[jpmt] = nclust;
}
}
// NOW go back over all the PMTs in the cluster and erase them from the unused PMTs list. This is a safe operation
for( auto jpmt : pmtlist_clusters[nclust] ){
unused_pmts.erase(jpmt);
}
nclust++; //increment cluster count. IMPORTANT!
} else { //nclust > 0. loop on all existing clusters. Compare this PMT to all PMTs in all existing clusters, add it to the first cluster containing a neighbor of this PMT according to MaxSep!
int iclust=0;
while( unused_pmts.find( ipmt ) != unused_pmts.end() && iclust < nclust ){
//for( int iclust=0; iclust<nclust; iclust++ ){
for( auto jpmt : pmtlist_clusters[iclust] ){ //loop on all PMTs in the cluster and check for a match:
Double_t sep2 = pow( xpmt[ipmt]-xpmt[jpmt],2 ) + pow( ypmt[ipmt]-ypmt[jpmt],2);
if( sep2 <= fMaxSep2 ){ //iclust contains a neighbor of ipmt, add ipmt to iclust!
pmtlist_clusters[iclust].insert( ipmt );
clustindex_pmts[ipmt] = iclust;
unused_pmts.erase(ipmt);
break; //if we found a match, we don't need to iterate any more
}
}
iclust++; //increment cluster index regardless of match. If no match found in existing clusters then this will eventually force us to exit the loop
}
//check whether the current pmt was matched to any existing cluster:
if( unused_pmts.find( ipmt ) != unused_pmts.end() ){ //this PMT was not matched to any existing cluster; start new one!
std::set<Int_t> pmtlist_temp;
pmtlist_temp.insert( ipmt );
pmtlist_clusters.push_back( pmtlist_temp );
clustindex_pmts[ipmt] = nclust;
unused_pmts.erase( ipmt );
//after creating a new cluster, loop on all remaining unused pmts and check for immediate neighbors of this one!
for( auto jpmt : unused_pmts ){
//if( jpmt != ipmt ){ //In principle this check is unnecessary since we already erased ipmt
Double_t sep2 = pow( xpmt[ipmt] - xpmt[jpmt], 2 ) + pow( ypmt[ipmt] - ypmt[jpmt], 2 );
if( sep2 <= fMaxSep2 ){ //neighbor!
pmtlist_clusters[nclust].insert( jpmt );
//unused_pmts.erase( jpmt ); DON'T modify the set while iterating on it, this could have unintended consequences
clustindex_pmts[jpmt] = nclust;
}
}
//go back over all pmts added to the current cluster and erase them from the unused pmts list
for( auto jpmt : pmtlist_clusters[nclust] ){
unused_pmts.erase(jpmt);
}
nclust++; //increment cluster count! IMPORTANT!
} //end check on need to start a new cluster
} //end check on any existing clusters to start first cluster
} //end while ( !(unused_pmts.empty()))
//ALL PMTs will be part of at least one "cluster". Later we will also consider timing here; for now we allow clusters to grow to arbitrary size in any direction; we'll probably want to revisit that later:
// if( ngoodhits > 0 ){
// for( auto ipmt : list_pmts ){
// nclust = pmtlist_clusters.size();
// //if( !hitused_pmts[ipmt] ){ //find a cluster to put this PMT into!
// // Check existing clusters to see if this is a nearest-neighbor of
// // any PMT in an existing cluster:
// Double_t xpmt = GetHit(hitindex_pmts[ipmt])->GetX();
// Double_t ypmt = GetHit(hitindex_pmts[ipmt])->GetY();
// if( nclust == 0 ){ //first pmt starts a cluster
// std::set<Int_t> pmtlist_temp;
// pmtlist_temp.insert(ipmt);
// pmtlist_clusters.push_back( pmtlist_temp );
// clustindex_pmts[ipmt] = 0;
// } else {
// //if( clustindex_pmts[ipmt] < 0 ){ //if this pmt is not already part of a cluster, check it against existing clusters:
// for( int iclust=0; iclust<nclust; iclust++ ){ //loop over all existing clusters
// bool isneighbor = false;
// for( int ihit=0; ihit<pmtlist_clusters[iclust].size(); ihit++ ){ //loop over pmts in the cluster:
// //Check if this PMT is within maxsep of any hit in an existing cluster:
// Double_t xtest = GetHit(hitindex_pmts[pmtlist_clusters[iclust][ihit]])->GetX();
// Double_t ytest = GetHit(hitindex_pmts[pmtlist_clusters[iclust][ihit]])->GetY();
// Double_t sep2 = pow( xpmt - xtest, 2 ) + pow( ypmt - ytest, 2 );
// if( sep2 <= fMaxSep2 ){ // this is a neighbor to at least one of the hits in an existing cluster! --> Add it!
// clustindex_pmts[ipmt] = iclust;
// //hitused_pmts[ipmt] = true;
// isneighbor = true;
// break; //exit the loop, we don't need to consider any more pmts
// }
// }
// if( isneighbor ){
// pmtlist_clusters[iclust].push_back( ipmt );
// break; //this insures that every PMT is part of one and ONLY one cluster;
// // the first cluster containing one of its neighbors.
// }
// }
// }
// //
// if( clustindex_pmts[ipmt] < 0 ){ //PMT is neither the first pmt nor the neighbor to any existing cluster; create a new one!
// std::vector<Int_t> pmtlist_temp(1,ipmt);
// pmtlist_clusters.push_back( pmtlist_temp );
// }
// }
nclust = pmtlist_clusters.size();
for( int iclust=0; iclust<nclust; iclust++ ){
new( (*fClusters)[iclust] ) SBSCherenkov_Cluster();
SBSCherenkov_Cluster *clusttemp = GetCluster(iclust);
//for( int ihit=0; ihit<pmtlist_clusters[iclust].size(); ihit++ ){
for( auto ipmt : pmtlist_clusters[iclust] ){
//insert the hit into the cluster. This automatically updates the cluster properties
SBSCherenkov_Hit *hittemp = GetHit( hitindex_pmts[ipmt] );
clusttemp->Insert( hittemp );
hittemp->SetClustIndex( iclust );
}
}
return 0;
}
//_____________________________________________________________________________
Int_t SBSGRINCH::MatchClustersWithTracks( TClonesArray& tracks )
{
// what we actually want to do at this stage is to find the closest TRACK to each GRINCH CLUSTER
// consistent with the cuts; and assign a track to each cluster that is "close enough" to the track:
// Later we will choose the biggest one that has a track match.
Int_t ntracks = tracks.GetLast() + 1;
Int_t nclust = GetNumClusters();
Int_t nmatch = 0;
for( int iclust=0; iclust<nclust; iclust++ ){
//Int_t nmatch=0;
Int_t itrmin=-1;
Int_t bestmirror=-1;
Double_t dxbest = kBig;
Double_t dybest = kBig;
SBSCherenkov_Cluster *clusttemp = GetCluster( iclust );
for( int itrack=0; itrack<ntracks; itrack++ ){
auto* theTrack = static_cast<THaTrack*>( tracks.At(itrack) );
// Now we need to come up with some sort of matching criterion for GRINCH clusters and tracks.
// I suppose what we want for now is the cluster closest to the track along X and/or Y This could become more
// sophisticated over time
//Project track to GRINCH entrance window:
double zGRINCH = GetOrigin().Z();
double xtrack = theTrack->GetX(zGRINCH);
double ytrack = theTrack->GetY(zGRINCH);
double thetatrack = theTrack->GetTheta();
double phitrack = theTrack->GetPhi();
double ptrack = theTrack->GetP();
double minxdiff = 0.0;
double mindiff2 = 0.0;
//int iclmin=-1;
// for( int iclust=0; iclust<nclust; iclust++ ){
//SBSCherenkov_Cluster *clusttemp = ( (SBSCherenkov_Cluster*) (*fClusters)[iclust] );
double xGRINCH = clusttemp->GetXcenter();
double yGRINCH = clusttemp->GetYcenter();
for( int imirr=0; imirr<fNmirror; imirr++ ){
if( xtrack >= fTrackMatchXmin[imirr] && xtrack < fTrackMatchXmax[imirr] ){
//double xdiff = fabs(xtrack - fTrackMatchXslope[imirr] * xGRINCH - fTrackMatchX0[imirr]-fTrackMatchPslope/ptrack);
//Implement new x-matching criterion parametrized based on track theta:
double xdiff = xGRINCH - xtrack - ( fTrackMatchX0[imirr] + fTrackMatchXslope[imirr]*thetatrack );
//double ydiff = fabs(ytrack - fTrackMatchYslope[imirr] * yGRINCH - fTrackMatchY0[imirr]);
//The lines below implement Maria's 3rd-order polynomial in track phi for the y correlation:
double ypredict = fTrackMatchY0[imirr];
for( int ipar=1; ipar<fOrderTrackMatchY; ipar++ ){
ypredict += fTrackMatchYslope[3*imirr + ipar-1] * pow( phitrack, ipar );
}
double ydiff = yGRINCH - ytrack - ypredict;
double diff2 = pow( xdiff/fTrackMatchXsigma[imirr], 2 ) + pow( ydiff/fTrackMatchYsigma[imirr], 2 );
// if( xdiff <= fTrackMatchXcut ){
if( diff2 <= pow( fTrackMatchNsigmaCut, 2 ) ){
//if( iclmin < 0 || xdiff < minxdiff ){
if( itrmin < 0 || diff2 < mindiff2 ){
itrmin = itrack;
bestmirror = imirr+1; //keep Maria's numbering convention of 1 to 4
// minxdiff = xdiff;
mindiff2 = diff2;
dxbest = xdiff;
dybest = ydiff;
}
}
}
}
}
if( itrmin >= 0 ){ //match:
//SBSCherenkov_Cluster *clusttemp = ( (SBSCherenkov_Cluster*) (*fClusters)[iclmin] );
//SBSCherenkov_Cluster *clusttemp = GetCluster( iclmin );
auto* theTrack = static_cast<THaTrack*>( tracks.At(itrmin) );
clusttemp->SetTrackIndex( itrmin );
clusttemp->SetTrack( theTrack );
clusttemp->SetMirrorIndex( bestmirror );
clusttemp->SetDX( dxbest );
clusttemp->SetDY( dybest );
//We also need to loop on all the hits and set the track index for those hits:
TIter next(clusttemp->GetHitList());
SBSCherenkov_Hit *hittemp;
while( ( hittemp = static_cast<SBSCherenkov_Hit*>( next() ) ) ){
hittemp->SetTrackIndex( itrmin );
}
nmatch++;
}
}
// here will be implemented the matching of GRINCH clusters with tracks
return nmatch;
}
Int_t SBSGRINCH::SelectBestCluster(Int_t nmatch){
Int_t nclusters = GetNumClusters();
Int_t best=-1;
Int_t maxsize=0;
for( int iclust=0; iclust<nclusters; iclust++ ){
SBSCherenkov_Cluster *clusttemp = GetCluster(iclust);
if( clusttemp->GetTrackIndex() >= 0 || nmatch <= 0 ){ //either this cluster is matched with a track or there are no clusters matched with tracks and we just pick the biggest one:
if( best < 0 || clusttemp->GetNHits() > maxsize ){
best = iclust;
maxsize = clusttemp->GetNHits();
//fBestCluster = clusttemp;
}
}
}
return best;
}
ClassImp(SBSGRINCH)