trg modules

Collaboration diagram for trg modules:

Classes
class	CDCTriggerNeuroDQMModule
	CDC Trigger DQM Module. More...
class	CDCTrigger2DFitterModule
	Module for the 2D Fitter of the CDC trigger. More...
class	CDCTrigger3DFitterModule
	Module for the 3D Fitter of the CDC trigger. More...
class	CDCTriggerHoughCand
	Hough Candidates class. More...
class	CDCTriggerHoughETFModule
class	CDCTrigger2DFinderModule
class	CDCTriggerMCMatcherModule
	A module to match CDCTriggerTracks to MCParticles. More...
class	CDCTriggerRecoHitMatcherModule
	A module to match CDCTriggerTracks to RecoTracks. More...
class	CDCTriggerRecoMatcherModule
	A module to match CDCTriggerTracks to RecoTracks. More...
class	CDCTriggerNDFinderModule
	CDC Trigger NDFinder Module. More...
class	CDCTriggerNeuroDataModule
class	CDCTriggerNeuroIDHistModule
	Description. More...
class	CDCTriggerNeuroModule
	The neural network module of the CDC trigger. More...
class	CDCTriggerETFModule
	Module for the Event Time Finder of the CDC trigger. More...
class	CDCTriggerTrackCombinerModule
	Module to combine the information from the various track trigger stages. More...
class	CDCTriggerTSFFirmwareModule
	This class is the interface between TSim/basf2 TSF module and the firmware simulation core of XSim/ISim. More...
class	CDCTriggerTSFModule
	Module for the Track Segment Finder of the CDC trigger. More...
class	TRGCDCModule
	A module to simulate the CDC trigger. More...
class	TRGCDCT2DDQMModule
	DQM module of TRGCDCT2D. More...
class	TRGCDCT3DDQMModule
	DQM module for TRGCDCT3D. More...
class	TRGCDCTSFDQMModule
	DQM module of TRGCDCTSF. More...
class	TRGCDCTSStreamModule
	A module to process CDC trigger data. More...
struct	SubTrigger
	enum class SubTriggerType : unsigned char {Merger, TSF, T2D, T3D, Neuro, ETF}; More...
class	CDCTriggerUnpackerModule
	Unpack the trigger data recorded in B2L. More...
struct	Merger
	unpacker for the merger reader (TSF which reads the merger output) More...
struct	Tracker2D
	unpacker for the 2D tracker More...
struct	Neuro
	unpacker for the Neuro More...
class	MCMatcherTRGECLModule
	Class to represent the hit of one cell. More...
class	TRGECLFAMModule
	A module of FAM. More...
class	TRGECLModule
	A module of ETM. More...
class	TRGECLBGTCHitModule
class	TRGECLDQMModule
class	TRGECLEventTimingDQMModule
	This module is for ecl trigger DQM related to L1 event timing. More...
class	TRGECLQAMModule
class	TRGECLRawdataAnalysisModule
	A module of ETM. More...
class	TRGECLTimingCalModule
	Class ECL Trigger Timiing Calibration Module. More...
class	TRGECLUnpackerModule
	A module of TRG ECL Unpacker. More...
class	TRGGDLCosmicRunModule
	Module that returns true if the trigger condition for the 2017 cosmic runs is fulfilled. More...
class	TRGGDLModule
	A module to simulate the Global Decision Logic. More...
class	TRGGDLDQMModule
class	TRGGDLDSTModule
class	TRGGDLSummaryModule
class	TRGGRLMatchModule
	Match between CDC trigger track and ECL trigger cluster. More...
class	TRGGRLModule
	A module to simulate the Global Decision Logic. More...
class	TRGGRLProjectsModule
	A module to simulate the Global Decision Logic. More...
class	TRGGRLDQMModule
class	GRLNeuroModule
	The module for application of the neural networks of the GRL. More...
class	GRLNeuroTrainerModule
	The trainer module for the neural networks of the CDC trigger. More...
class	KLM_TRG_definitions
class	KLMTriggerModule
struct	Linear_fit_of_Hits_t
class	TRGTOPDQMModule
class	TRGTOPTRD2TTSConverterModule
	TRG TOP Raw Digits to TimeStamps Converter. More...
class	TRGTOPUnpackerModule
	TRG TOP Unpacker. More...
class	TRGTOPUnpackerWaveformModule
	TRG TOP Unpacker for Timestamps. More...
class	TRGTOPWaveformPlotterModule
class	TRGRAWDATAModule

Typedefs
typedef std::pair< unsigned short, TVector2 >	cdcPair
	Pair of <iSuperLayer, (x, y)>, for hits in conformal space.
typedef std::map< int, cdcPair >	cdcMap
	Map of <counter, cdcPair>, for hits with indices.
typedef std::pair< TVector2, TVector2 >	coord2dPair
	Hough Tuples.
using	NodeList = std::vector< std::vector< int > >
	Node list.
using	MergerBus = std::array< std::bitset< mergerWidth >, nAllMergers >
	Merger data bus.
using	MergerBits = Bitstream< MergerBus >
	Merger data bus Bitstream.

Functions
bool	operator== (const CDCTriggerHoughCand &a, const CDCTriggerHoughCand &b)
	Two cells are identical if they have the same coordinates.
	REG_MODULE (CDCTriggerNeuroData)
	REG_MODULE (CDCTriggerNeuroIDHist)
	REG_MODULE (TRGCDC)
	register the basf2 module TRGCDC
	REG_MODULE (TRGCDCTSStream)
	register the basf2 TRGCDCTSStream module
	REG_MODULE (TRGECLFAM)
	Register module name.
	REG_MODULE (TRGECL)
	Register Module.
	REG_MODULE (TRGECLBGTCHit)
	Register module name.
	REG_MODULE (TRGECLQAM)
	Register module name.
	REG_MODULE (TRGECLRawdataAnalysis)
	Register Module.
	REG_MODULE (TRGGDL)
	REG_MODULE (TRGGRL)
	Register module for TRGGRL.
Belle2::group_helper::KLM_trg_summery	make_trg (const std::vector< Belle2::group_helper::KLM_Digit_compact > &hits, int eventNR, int NLayerTrigger)
std::vector< Belle2::group_helper::KLM_trig_linear_fit >	klm_trig_linear_fit (std::vector< Belle2::group_helper::KLM_Digit_compact > &hits, const std::vector< Belle2::group_helper::KLM_geo_fit_t > &geometry)
	CDCTriggerNeuroDataModule ()
	Constructor, for setting module description and parameters.
virtual void	initialize () override
	Initialize the module. More...
virtual void	event () override
	Called once for each event. More...
virtual void	terminate () override
	Do the training for all sectors.
	CDCTriggerNeuroIDHistModule ()
	Constructor, for setting module description and parameters.
virtual void	initialize () override
	Initialize the module. More...
virtual void	event () override
	Called once for each event. More...
virtual void	terminate () override
	Do the training for all sectors.
std::string	version (void) const
	returns version of TRGCDCModule.
	TRGCDCModule ()
	Constructor.
virtual	~TRGCDCModule ()
	Destructor.
virtual void	initialize () override
	Initilizes TRGCDCModule.
virtual void	beginRun () override
	Called when new run started.
virtual void	event () override
	Called event by event.
virtual void	endRun () override
	Called when run ended.
virtual void	terminate () override
	Called when processing ended.
std::string	version (void) const
	returns version of TRGCDCTSStreamModule.
	TRGCDCTSStreamModule ()
	Constructor.
virtual	~TRGCDCTSStreamModule ()
	Destructor.
virtual void	initialize () override
	Initilizes TRGCDCTSStreamModule.
virtual void	beginRun () override
	Called when new run started.
virtual void	event () override
	Called event by event.
virtual void	endRun () override
	Called when run ended.
virtual void	terminate () override
	Called when processing ended.
std::string	version (void) const
	returns version of TRGECLFAMModule.
	TRGECLFAMModule ()
	Constructor.
virtual	~TRGECLFAMModule ()
	Destructor.
virtual void	initialize () override
	Initilizes TRGECLFAMModule.
virtual void	beginRun () override
	Called when new run started.
virtual void	event () override
	Called event by event.
virtual void	endRun () override
	Called when run ended.
virtual void	terminate () override
	Called when processing ended.
std::string	version (void) const
	returns version of TRGECLModule.
	TRGECLModule ()
	Constructor.
virtual	~TRGECLModule ()
	Destructor
virtual void	initialize () override
	Initilizes TRGECLModule.
virtual void	beginRun () override
	Called when new run started.
virtual void	event () override
	Called event by event.
virtual void	endRun () override
	Called when run ended.
virtual void	terminate () override
	Called when processing ended.
std::string	version (void) const
	returns version of TRGECLBGTCHitModule.
	TRGECLBGTCHitModule ()
	Constructor.
virtual	~TRGECLBGTCHitModule ()
	Destructor.
virtual void	initialize () override
	Initilizes TRGECLBGTCHitModule.
virtual void	beginRun () override
	Called when new run started.
virtual void	event () override
	Called event by event.
virtual void	endRun () override
	Called when run ended.
virtual void	terminate () override
	Called when processing ended.
void	genSimulationObj ()
	Parameters.
void	genRandomTrgObj ()
std::string	version () const
	version
	TRGECLQAMModule ()
	Costructor.
virtual	~TRGECLQAMModule ()
	Destrunctor.
virtual void	initialize () override
	initialize
virtual void	beginRun () override
	begin Run
virtual void	event () override
	Event.
virtual void	endRun () override
	End Run.
virtual void	terminate () override
	terminate
std::string	version (void) const
	returns version of TRGECLModule.
	TRGECLRawdataAnalysisModule ()
	Constructor.
virtual	~TRGECLRawdataAnalysisModule ()
	Destructor
virtual void	initialize () override
	Initilizes TRGECLModule.
virtual void	beginRun () override
	Called when new run started.
virtual void	event () override
	Called event by event.
virtual void	endRun () override
	Called when run ended.
virtual void	terminate () override
	Called when processing ended.
std::string	version (void) const
	returns version of TRGGDLModule.
	TRGGDLModule ()
	Constructor.
virtual	~TRGGDLModule ()
	Destructor.
virtual void	defineHisto () override
	Define Histogram.
virtual void	initialize () override
	Initilizes TRGGDLModule.
virtual void	beginRun () override
	Called when new run started.
virtual void	event () override
	Called event by event.
virtual void	endRun () override
	Called when run ended.
virtual void	terminate () override
	Called when processing ended.
std::string	version (void) const
	returns version of TRGGRLModule.
	TRGGRLModule ()
	Constructor.
virtual	~TRGGRLModule ()
	Destructor.
virtual void	initialize () override
	Initilizes TRGGRLModule.
virtual void	beginRun () override
	Called when new run started.
virtual void	event () override
	Called event by event.
virtual void	endRun () override
	Called when run ended.
virtual void	terminate () override
	Called when processing ended.

Variables
static constexpr int	mergerWidth = 256
	Merger data width.
static constexpr int	nAllMergers = 146
	Number of Mergers.
static constexpr int	wordWidth = 32
	width of a single word in the raw int buffer
static constexpr int	nFinesse = 48
	Number of FINESSE in a PCIe40.
constexpr Linear_fit_of_Hits_t	Linear_fit_of_Hits
static const int	intNaN = std::numeric_limits<int>::quiet_NaN()
static TRGGDL *	_gdl = 0
	A pointer to a TRGGDL;.
static TRGGRL *	_grl = 0
	A pointer to a TRGGRL;.

Detailed Description

Function Documentation

event() [1/2]

void event ( void  )

overridevirtual

Called once for each event.

Prepare input and target for each track and store it.

Reimplemented from Module.

Definition at line 134 of file CDCTriggerNeuroDataModule.cc.

  {
    StoreObjPtr<EventMetaData> evtmetadata;
    for (int itrack = 0; itrack < m_tracks.getEntries(); ++itrack) {
      // get related MCParticle/RecoTrack for target
      // and retrieve track parameters
      //float phi0Target = 0;
      //float invptTarget = 0;
      float thetaTarget = 0;
      float zTarget = 0;
      if (m_trainOnRecoTracks) {
        RecoTrack* recoTrack =
          m_tracks[itrack]->getRelatedTo<RecoTrack>(m_targetCollectionName);
        if (!recoTrack) {
          B2DEBUG(150, "Skipping CDCTriggerTrack without relation to RecoTrack.");
          continue;
        }
        // a RecoTrack has multiple representations for different particle hypothesis
        // -> just take the first one that does not give errors.
        const std::vector<genfit::AbsTrackRep*>& reps = recoTrack->getRepresentations();
        bool foundValidRep = false;
        for (unsigned irep = 0; irep < reps.size() && !foundValidRep; ++irep) {
          if (!recoTrack->wasFitSuccessful(reps[irep])) {
            continue;
          }
 
          // get state (position, momentum etc.) from hit closest to IP and
          // extrapolate to z-axis (may throw an exception -> continue to next representation)
          try {
            genfit::MeasuredStateOnPlane state =
              recoTrack->getMeasuredStateOnPlaneClosestTo(ROOT::Math::XYZVector(0, 0, 0), reps[irep]);
            reps[irep]->extrapolateToLine(state, TVector3(0, 0, -1000), TVector3(0, 0, 2000));
            // flip tracks if necessary, such that trigger tracks and reco tracks
            // point in the same direction
            if (state.getMom().Dot(B2Vector3D(m_tracks[itrack]->getDirection())) < 0) {
              state.setPosMom(state.getPos(), -state.getMom());
              state.setChargeSign(-state.getCharge());
            }
            // get track parameters
            //phi0Target = state.getMom().Phi();
            //invptTarget = state.getCharge() / state.getMom().Pt();
            thetaTarget = state.getMom().Theta();
            zTarget = state.getPos().Z();
          } catch (...) {
            continue;
          }
          // break loop
          foundValidRep = true;
        }
        if (!foundValidRep) {
          B2DEBUG(150, "No valid representation found for RecoTrack, skipping.");
          continue;
        }
      } else {
        MCParticle* mcTrack =
          m_tracks[itrack]->getRelatedTo<MCParticle>(m_targetCollectionName);
        if (not mcTrack) {
          B2DEBUG(150, "Skipping CDCTriggerTrack without relation to MCParticle.");
          continue;
        }
        //phi0Target = mcTrack->getMomentum().Phi();
        //invptTarget = mcTrack->getCharge() / mcTrack->getMomentum().Pt();
        thetaTarget = mcTrack->getMomentum().Theta();
        zTarget = mcTrack->getProductionVertex().Z();
      }
      m_NeuroTrigger.updateTrack(*m_tracks[itrack]);
 
      // find all matching sectors
      float phi0 = m_tracks[itrack]->getPhi0();
      float invpt = m_tracks[itrack]->getKappa(1.5);
      float theta = atan2(1., m_tracks[itrack]->getCotTheta());
      std::vector<int> sectors = m_NeuroTrigger.selectMLPsTrain(phi0, invpt, theta);
      if (sectors.size() == 0) continue;
 
      // get target values
      std::vector<float> targetRaw = {};
      if (m_neuroParameters.targetZ)
        targetRaw.push_back(zTarget);
      if (m_neuroParameters.targetTheta)
        targetRaw.push_back(thetaTarget);
      for (unsigned i = 0; i < sectors.size(); ++i) {
        int isector = sectors[i];
        std::vector<float> target = m_NeuroTrigger[isector].scaleTarget(targetRaw);
        // skip out of range targets or rescale them
        bool outOfRange = false;
        for (unsigned itarget = 0; itarget < target.size(); ++itarget) {
          if (fabs(target[itarget]) > 1.) {
            outOfRange = true;
            target[itarget] /= fabs(target[itarget]);
          }
        }
        if (!m_neuroParameters.rescaleTarget && outOfRange) {
          continue;
        }
        //
        // read out or determine event time
        m_NeuroTrigger.getEventTime(isector, *m_tracks[itrack], m_neuroParameters.et_option(), m_neuroTrackInputMode);
        // check hit pattern
        unsigned long hitPattern = m_NeuroTrigger.getCompleteHitPattern(isector, *m_tracks[itrack], m_neuroTrackInputMode); // xxxxx0xxx
        // sectorpattern holds the absolut necessary SLs for the expert
        unsigned long sectorPattern = m_NeuroTrigger[isector].getSLpattern(); // 010100010
        // sectorpatternmask holds the SLs, which are generally used to determine the right expert
        unsigned long sectorPatternMask = m_NeuroTrigger[isector].getSLpatternMask(); // 010101010
        B2DEBUG(250, "hitPattern " << hitPattern << " sectorPattern " << sectorPattern);
        // if multiple experts should be trained with one track, the necessary SLs have to be in the hitpattern
        if (!m_singleUse && sectorPattern > 0 && (sectorPattern & hitPattern) != sectorPattern) {
          B2DEBUG(250, "hitPattern not matching " << (sectorPattern & hitPattern));
          continue;
          // if one track should only be used for one expert, the absolute necessary SLs for this expert should match exactly the set within the hitpattern of the generally used SLs for determining the expert.
        } else if (m_singleUse && sectorPattern > 0 && (sectorPattern) != (hitPattern & sectorPatternMask)) {
          // 010100010 != 0x0x0x0x0
          B2DEBUG(250, "hitPattern not matching " << (sectorPatternMask & hitPattern));
          continue;
        }
        // check, if enough axials are there. first, we select the axial bits from the
        // hitpattern (341 = int('101010101',base=2)) and then check if the number of
        // ones is equal or greater than 4.
        if ((hitPattern & 341) != 341 && // this is an ugly workaround, because popcount is only
            (hitPattern & 341) != 340 && // available with c++20 and newer
            (hitPattern & 341) != 337 &&
            (hitPattern & 341) != 325 &&
            (hitPattern & 341) != 277 &&
            (hitPattern & 341) != 85) {
          B2DEBUG(250, "Not enough axial hits (<4), skipping!");
          continue;
        }
        // get training data
        std::vector<unsigned> hitIds;
        if (m_neuroTrackInputMode) {
          hitIds = m_NeuroTrigger.selectHitsHWSim(isector, *m_tracks[itrack]);
        } else {
          hitIds = m_NeuroTrigger.selectHits(isector, *m_tracks[itrack]);
        }
        // add a "sample" it is a full training set including some zeroes for the neurotrigger values (raw and scaled z and theta). Those are sometimes used in the training for reference, but cannot be added here without running *some* neuotrigger instance.
        CDCTriggerMLPData::NeuroSet<27, 2> sample(m_NeuroTrigger.getInputVector(isector, hitIds).data(), target.data(),
                                                  evtmetadata->getExperiment(), evtmetadata->getRun(), evtmetadata->getSubrun(), evtmetadata->getEvent(), itrack, i,
                                                  m_tracks.getEntries(), 0, 0, 0, 0, phi0, theta, invpt);
        //check whether we already have enough samples
        m_trainSet[isector].addSample(sample);
        if ((m_trainSet)[isector].nSamples() % 1000 == 0) {
          B2DEBUG(50, m_trainSet[isector].nSamples() << " samples for training collected for sector " << isector);
        }
        std::ofstream gzipfile4(m_filename, std::ios_base::app | std::ios_base::binary);
        boost::iostreams::filtering_ostream outStream;
        outStream.push(boost::iostreams::gzip_compressor());
        outStream.push(gzipfile4);
        outStream << sample << std::endl;
        m_trackcounter++;
      }
 
    }
  }

event() [2/2]

void event ( void  )

overridevirtual

Called once for each event.

Prepare input and target for each track and store it.

Reimplemented from Module.

Definition at line 126 of file CDCTriggerNeuroIDHistModule.cc.

  {
    StoreObjPtr<EventMetaData> evtmetadata;
    for (int itrack = 0; itrack < m_tracks.getEntries(); ++itrack) {
      // get related MCParticle/RecoTrack for target
      // and retrieve track parameters
 
      std::vector<float> targetvector = NeuroTrainer::getTrainTargets(m_trainOnRecoTracks, m_tracks[itrack], m_targetCollectionName);
      if (targetvector[4] == 0) {
        continue;
      } // no valid representation found
      float phi0Target = targetvector[0];
      float invptTarget = targetvector[1];
      float thetaTarget = targetvector[2];
      float zTarget = targetvector[3];
 
 
      // update 2D track variables
      m_NeuroTrigger.updateTrack(*m_tracks[itrack]);
 
      // find all matching sectors
      float phi0 = m_tracks[itrack]->getPhi0();
      float invpt = m_tracks[itrack]->getKappa(1.5);
      float theta = atan2(1., m_tracks[itrack]->getCotTheta());
      std::vector<int> sectors = m_NeuroTrigger.selectMLPsTrain(phi0, invpt, theta);
      if (sectors.size() == 0) continue;
      // get target values
      std::vector<float> targetRaw = {};
      if (m_neuroParameters.targetZ)
        targetRaw.push_back(zTarget);
      if (m_neuroParameters.targetTheta)
        targetRaw.push_back(thetaTarget);
      for (unsigned i = 0; i < sectors.size(); ++i) {
        int isector = sectors[i];
        std::vector<float> target = m_NeuroTrigger[isector].scaleTarget(targetRaw);
        // skip out of range targets or rescale them
        bool outOfRange = false;
        for (unsigned itarget = 0; itarget < target.size(); ++itarget) {
          if (fabs(target[itarget]) > 1.) {
            outOfRange = true;
            target[itarget] /= fabs(target[itarget]);
          }
        }
        if (!m_neuroParameters.rescaleTarget && outOfRange) continue;
        if (m_nPrepare == 0 || m_trainSets_prepare[isector].getTrackCounter() < m_nPrepare) {
          // get relative ids for all hits related to the MCParticle / RecoTrack
          // and count them to find relevant id range
          // using only related hits suppresses background EXCEPT for curling tracks
          if (m_trainOnRecoTracks) {
            RecoTrack* recoTrack =
              m_tracks[itrack]->getRelatedTo<RecoTrack>(m_targetCollectionName);
            for (const CDCTriggerSegmentHit& hit :
                 recoTrack->getRelationsTo<CDCTriggerSegmentHit>(m_hitCollectionName)) {
              // get relative id
              double relId = m_NeuroTrigger.getRelId(hit);
              m_trainSets_prepare[isector].addHit(hit.getISuperLayer(), round(relId));
            }
          } else {
            MCParticle* mcTrack =
              m_tracks[itrack]->getRelatedTo<MCParticle>(m_targetCollectionName);
            for (const CDCTriggerSegmentHit& hit :
                 mcTrack->getRelationsTo<CDCTriggerSegmentHit>(m_hitCollectionName)) {
              // get relative id
              double relId = m_NeuroTrigger.getRelId(hit);
              m_trainSets_prepare[isector].addHit(hit.getISuperLayer(), round(relId));
            }
          }
          m_trainSets_prepare[isector].countTrack();
        }
      }
    }
    bool stop = true;
    for (unsigned isector = 0; isector < m_trainSets_prepare.size(); ++isector) {
      if (m_nPrepare == 0 || m_trainSets_prepare[isector].getTrackCounter() < m_nPrepare) {
        stop = false;
        break;
      }
    }
    if (stop) {
      B2INFO("Training sample preparation for NeuroTrigger finished, stopping event loop.");
      // if required hit number is reached, get relevant ids
      StoreObjPtr<EventMetaData> eventMetaData;
      eventMetaData->setEndOfData();
    }
  }

initialize() [1/2]

void initialize ( void  )

overridevirtual

Initialize the module.

Initialize the networks and register datastore objects.

Reimplemented from Module.

Definition at line 83 of file CDCTriggerNeuroDataModule.cc.

initialize() [2/2]

void initialize ( void  )

overridevirtual

Initialize the module.

Initialize the networks and register datastore objects.

First Initialize the neurotrigger object, which is needed because of the original structure of the training procedure in the old module. Second, the dataset for the idhist data will be initialized and set up.

Reimplemented from Module.

Definition at line 70 of file CDCTriggerNeuroIDHistModule.cc.