GRLNeuroTrainerModule Class Reference

The trainer module for the neural networks of the CDC trigger. More...

#include <GRLNeuroTrainerModule.h>

Inheritance diagram for GRLNeuroTrainerModule:

Public Types
enum	EModulePropFlags {   c_Input = 1 ,   c_Output = 2 ,   c_ParallelProcessingCertified = 4 ,   c_HistogramManager = 8 ,   c_InternalSerializer = 16 ,   c_TerminateInAllProcesses = 32 ,   c_DontCollectStatistics = 64 }
	Each module can be tagged with property flags, which indicate certain features of the module. More...
typedef ModuleCondition::EAfterConditionPath	EAfterConditionPath
	Forward the EAfterConditionPath definition from the ModuleCondition.

Public Member Functions
	GRLNeuroTrainerModule ()
	Constructor, for setting module description and parameters.
virtual	~GRLNeuroTrainerModule ()
	Destructor.
virtual void	initialize () override
	Initialize the module.
virtual void	event () override
	Called once for each event.
virtual void	terminate () override
	Do the training for all sectors.
void	updateRelevantID (unsigned isector)
	calculate and set the relevant id range for given sector based on hit counters of the track segments.
void	train (unsigned isector)
	Train a single MLP.
void	saveTraindata (const std::string &filename, const std::string &arrayname="trainSets")
	Save all training samples.
bool	loadTraindata (const std::string &filename, const std::string &arrayname="trainSets")
	Load saved training samples.
virtual std::vector< std::string >	getFileNames (bool outputFiles)
	Return a list of output filenames for this modules.
virtual void	beginRun ()
	Called when entering a new run.
virtual void	endRun ()
	This method is called if the current run ends.
const std::string &	getName () const
	Returns the name of the module.
const std::string &	getType () const
	Returns the type of the module (i.e.
const std::string &	getPackage () const
	Returns the package this module is in.
const std::string &	getDescription () const
	Returns the description of the module.
void	setName (const std::string &name)
	Set the name of the module.
void	setPropertyFlags (unsigned int propertyFlags)
	Sets the flags for the module properties.
LogConfig &	getLogConfig ()
	Returns the log system configuration.
void	setLogConfig (const LogConfig &logConfig)
	Set the log system configuration.
void	setLogLevel (int logLevel)
	Configure the log level.
void	setDebugLevel (int debugLevel)
	Configure the debug messaging level.
void	setAbortLevel (int abortLevel)
	Configure the abort log level.
void	setLogInfo (int logLevel, unsigned int logInfo)
	Configure the printed log information for the given level.
void	if_value (const std::string &expression, const std::shared_ptr< Path > &path, EAfterConditionPath afterConditionPath=EAfterConditionPath::c_End)
	Add a condition to the module.
void	if_false (const std::shared_ptr< Path > &path, EAfterConditionPath afterConditionPath=EAfterConditionPath::c_End)
	A simplified version to add a condition to the module.
void	if_true (const std::shared_ptr< Path > &path, EAfterConditionPath afterConditionPath=EAfterConditionPath::c_End)
	A simplified version to set the condition of the module.
bool	hasCondition () const
	Returns true if at least one condition was set for the module.
const ModuleCondition *	getCondition () const
	Return a pointer to the first condition (or nullptr, if none was set)
const std::vector< ModuleCondition > &	getAllConditions () const
	Return all set conditions for this module.
bool	evalCondition () const
	If at least one condition was set, it is evaluated and true returned if at least one condition returns true.
std::shared_ptr< Path >	getConditionPath () const
	Returns the path of the last true condition (if there is at least one, else reaturn a null pointer).
Module::EAfterConditionPath	getAfterConditionPath () const
	What to do after the conditional path is finished.
std::vector< std::shared_ptr< Path > >	getAllConditionPaths () const
	Return all condition paths currently set (no matter if the condition is true or not).
bool	hasProperties (unsigned int propertyFlags) const
	Returns true if all specified property flags are available in this module.
bool	hasUnsetForcedParams () const
	Returns true and prints error message if the module has unset parameters which the user has to set in the steering file.
const ModuleParamList &	getParamList () const
	Return module param list.
template<typename T >
ModuleParam< T > &	getParam (const std::string &name) const
	Returns a reference to a parameter.
bool	hasReturnValue () const
	Return true if this module has a valid return value set.
int	getReturnValue () const
	Return the return value set by this module.
std::shared_ptr< PathElement >	clone () const override
	Create an independent copy of this module.
std::shared_ptr< boost::python::list >	getParamInfoListPython () const
	Returns a python list of all parameters.

Static Public Member Functions
static void	exposePythonAPI ()
	Exposes methods of the Module class to Python.

Protected Member Functions
virtual void	def_initialize ()
	Wrappers to make the methods without "def_" prefix callable from Python.
virtual void	def_beginRun ()
	Wrapper method for the virtual function beginRun() that has the implementation to be used in a call from Python.
virtual void	def_event ()
	Wrapper method for the virtual function event() that has the implementation to be used in a call from Python.
virtual void	def_endRun ()
	This method can receive that the current run ends as a call from the Python side.
virtual void	def_terminate ()
	Wrapper method for the virtual function terminate() that has the implementation to be used in a call from Python.
void	setDescription (const std::string &description)
	Sets the description of the module.
void	setType (const std::string &type)
	Set the module type.
template<typename T >
void	addParam (const std::string &name, T &paramVariable, const std::string &description, const T &defaultValue)
	Adds a new parameter to the module.
template<typename T >
void	addParam (const std::string &name, T &paramVariable, const std::string &description)
	Adds a new enforced parameter to the module.
void	setReturnValue (int value)
	Sets the return value for this module as integer.
void	setReturnValue (bool value)
	Sets the return value for this module as bool.
void	setParamList (const ModuleParamList &params)
	Replace existing parameter list.

Protected Attributes
std::string	m_TrgECLClusterName
	Name of the StoreArray containing the ECL clusters.
std::string	m_2DfinderCollectionName
	Name of the StoreArray containing the input 2D tracks.
std::string	m_GRLCollectionName
	Name of the StoreObj containing the input GRL.
std::string	m_filename
	Name of file where network weights etc.
std::string	m_trainFilename
	Name of file where training samples are stored.
std::string	m_logFilename
	Name of file where training log is stored.
std::string	m_arrayname
	Name of the TObjArray holding the networks.
std::string	m_trainArrayname
	Name of the TObjArray holding the training samples.
bool	m_saveDebug
	If true, save training curve and parameter distribution of training data.
bool	m_load
	Switch to load saved parameters from a previous run.
GRLNeuro::Parameters	m_parameters
	Parameters for the NeuroTrigger.
double	m_nTrainMin
	Minimal number of training samples.
double	m_nTrainMax
	Maximal number of training samples.
bool	m_multiplyNTrain
	Switch to multiply number of samples with number of weights.
int	m_nValid
	Number of validation samples.
int	m_nTest
	Number of test samples.
double	m_wMax
	Limit for weights.
int	m_nThreads
	Number of threads for training.
int	m_checkInterval
	Training is stopped if validation error is higher than checkInterval epochs ago, i.e.
int	m_maxEpochs
	Maximal number of training epochs.
int	m_repeatTrain
	Number of training runs with different random start weights.
GRLNeuro	m_GRLNeuro
	Instance of the NeuroTrigger.
std::vector< GRLMLPData >	m_trainSets
	Sets of training data for all sectors.
std::vector< int >	TCThetaID
std::vector< float >	TCPhiLab
std::vector< float >	TCcotThetaLab
std::vector< float >	TCPhiCOM
std::vector< float >	TCThetaCOM
std::vector< float >	TC1GeV
double	radtodeg = 0
	convert radian to degree
int	n_cdc_sector = 0
	Number of CDC sectors.
int	n_ecl_sector = 0
	Number of ECL sectors.
int	n_sector = 0
	Number of Total sectors.
std::vector< TH1D * >	h_cdc2d_phi_sig
	Histograms for monitoring.
std::vector< TH1D * >	h_cdc2d_pt_sig
std::vector< TH1D * >	h_selE_sig
std::vector< TH1D * >	h_selPhi_sig
std::vector< TH1D * >	h_selTheta_sig
std::vector< TH1D * >	h_result_sig
std::vector< TH1D * >	h_cdc2d_phi_bg
std::vector< TH1D * >	h_cdc2d_pt_bg
std::vector< TH1D * >	h_selE_bg
std::vector< TH1D * >	h_selPhi_bg
std::vector< TH1D * >	h_selTheta_bg
std::vector< TH1D * >	h_result_bg
std::vector< TH1D * >	h_ncdc_sig
std::vector< TH1D * >	h_ncdcf_sig
std::vector< TH1D * >	h_ncdcs_sig
std::vector< TH1D * >	h_ncdci_sig
std::vector< TH1D * >	h_necl_sig
std::vector< TH1D * >	h_ncdc_bg
std::vector< TH1D * >	h_ncdcf_bg
std::vector< TH1D * >	h_ncdcs_bg
std::vector< TH1D * >	h_ncdci_bg
std::vector< TH1D * >	h_necl_bg
std::vector< int >	scale_bg
	BG scale factor for training.

Private Member Functions
std::list< ModulePtr >	getModules () const override
	no submodules, return empty list
std::string	getPathString () const override
	return the module name.
void	setParamPython (const std::string &name, const boost::python::object &pyObj)
	Implements a method for setting boost::python objects.
void	setParamPythonDict (const boost::python::dict &dictionary)
	Implements a method for reading the parameter values from a boost::python dictionary.

Private Attributes
std::string	m_name
	The name of the module, saved as a string (user-modifiable)
std::string	m_type
	The type of the module, saved as a string.
std::string	m_package
	Package this module is found in (may be empty).
std::string	m_description
	The description of the module.
unsigned int	m_propertyFlags
	The properties of the module as bitwise or (with |) of EModulePropFlags.
LogConfig	m_logConfig
	The log system configuration of the module.
ModuleParamList	m_moduleParamList
	List storing and managing all parameter of the module.
bool	m_hasReturnValue
	True, if the return value is set.
int	m_returnValue
	The return value.
std::vector< ModuleCondition >	m_conditions
	Module condition, only non-null if set.

Detailed Description

The trainer module for the neural networks of the CDC trigger.

Prepare training data for several neural networks and train them using the Fast Artificial Neural Network library (FANN). For documentation of FANN see http://leenissen.dk/fann/wp/

Definition at line 29 of file GRLNeuroTrainerModule.h.

Member Typedef Documentation

EAfterConditionPath

typedef ModuleCondition::EAfterConditionPath EAfterConditionPath

inherited

Forward the EAfterConditionPath definition from the ModuleCondition.

Definition at line 88 of file Module.h.

Member Enumeration Documentation

EModulePropFlags

enum EModulePropFlags

inherited

Each module can be tagged with property flags, which indicate certain features of the module.

Enumerator
c_Input	This module is an input module (reads data).
c_Output	This module is an output module (writes data).
c_ParallelProcessingCertified	This module can be run in parallel processing mode safely (All I/O must be done through the data store, in particular, the module must not write any files.)
c_HistogramManager	This module is used to manage histograms accumulated by other modules.
c_InternalSerializer	This module is an internal serializer/deserializer for parallel processing.
c_TerminateInAllProcesses	When using parallel processing, call this module's terminate() function in all processes(). This will also ensure that there is exactly one process (single-core if no parallel modules found) or at least one input, one main and one output process.
c_DontCollectStatistics	No statistics is collected for this module.

Definition at line 77 of file Module.h.

                          {
      c_Input                       = 1,  
      c_Output                      = 2,  
      c_ParallelProcessingCertified = 4,  
      c_HistogramManager            = 8, 
      c_InternalSerializer          = 16,  
      c_TerminateInAllProcesses     = 32,  
      c_DontCollectStatistics       = 64,  
    };

Constructor & Destructor Documentation

GRLNeuroTrainerModule()

GRLNeuroTrainerModule

(

)

Constructor, for setting module description and parameters.

Definition at line 46 of file GRLNeuroTrainerModule.cc.

                                             : Module()
{
  setDescription(
    "The NeuroTriggerTrainer module of the GRL.\n"
    "Takes CDC track and ECL cluster to prepare input data\n"
    "for the training of a neural network.\n"
    "Networks are trained after the event loop and saved."
  );
  // parameters for saving / loading
  addParam("TRGECLClusters", m_TrgECLClusterName,
           "Name of the StoreArray holding the information of trigger ecl clusters ",
           string("TRGECLClusters"));
  addParam("2DfinderCollection", m_2DfinderCollectionName,
           "Name of the StoreArray holding the tracks made by the 2D finder to be used as input.",
           string("TRGCDC2DFinderTracks"));
  addParam("GRLCollection", m_GRLCollectionName,
           "Name of the StoreArray holding the tracks made by the GRL to be used as input.",
           string("TRGGRLUnpackerStore"));
  addParam("filename", m_filename,
           "Name of the root file where the NeuroTrigger parameters will be saved.",
           string("GRLNeuroTrigger.root"));
  addParam("trainFilename", m_trainFilename,
           "Name of the root file where the generated training samples will be saved.",
           string("GRLNeuroTrigger.root"));
  addParam("arrayname", m_arrayname,
           "Name of the TObjArray to hold the NeuroTrigger parameters.",
           string("MLPs"));
  addParam("trainArrayname", m_trainArrayname,
           "Name of the TObjArray to hold the training samples.",
           string("trainSets"));
  addParam("saveDebug", m_saveDebug,
           "If true, save parameter distribution of training data "
           "in train file and training curve in log file.", true);
  addParam("load", m_load,
           "Switch to load saved parameters if existing. "
           "Take care not to duplicate training sets!", false);
  // NeuroTrigger parameters
  addParam("nMLP", m_parameters.nMLP,
           "Number of expert MLPs.", m_parameters.nMLP);
  addParam("n_cdc_sector", m_parameters.n_cdc_sector,
           "Number of expert CDC MLPs.", m_parameters.n_cdc_sector);
  addParam("n_ecl_sector", m_parameters.n_ecl_sector,
           "Number of expert ECL MLPs.", m_parameters.n_ecl_sector);
  addParam("i_cdc_sector", m_parameters.i_cdc_sector,
           "#cdc track   of expert MLPs.", m_parameters.i_cdc_sector);
  addParam("i_ecl_sector", m_parameters.i_ecl_sector,
           "#ecl cluster of expert MLPs.", m_parameters.i_ecl_sector);
  addParam("nHidden", m_parameters.nHidden,
           "Number of nodes in each hidden layer for all networks "
           "or factor to multiply with number of inputs (1 list or nMLP lists). "
           "The number of layers is derived from the shape.", m_parameters.nHidden);
  addParam("multiplyHidden", m_parameters.multiplyHidden,
           "If true, multiply nHidden with number of input nodes.",
           m_parameters.multiplyHidden);
  addParam("outputScale", m_parameters.outputScale,
           "Output scale for all networks (1 value list or nMLP value lists). "
           "Output[i] of the MLP is scaled from [-1, 1] "
           "to [outputScale[2*i], outputScale[2*i+1]]. "
           "(units: z[cm] / theta[degree])", m_parameters.outputScale);
  addParam("nTrainMin", m_nTrainMin,
           "Minimal number of training samples "
           "or factor to multiply with number of weights. "
           "If the minimal number of samples is not reached, "
           "all samples are saved but no training is started.", 10.);
  addParam("nTrainMax", m_nTrainMax,
           "Maximal number of training samples "
           "or factor to multiply with number of weights. "
           "When the maximal number of samples is reached, "
           "no further samples are added.", 10.);
  addParam("multiplyNTrain", m_multiplyNTrain,
           "If true, multiply nTrainMin and nTrainMax with number of weights.",
           true);
  addParam("nValid", m_nValid,
           "Number of validation samples for training.", 1000);
  addParam("nTest", m_nTest,
           "Number of test samples to get resolution after training.", 5000);
  addParam("wMax", m_wMax,
           "Weights are limited to [-wMax, wMax] after each training epoch "
           "(for convenience of the FPGA implementation).",
           63.);
  addParam("nThreads", m_nThreads,
           "Number of threads for parallel training.", 1);
  addParam("checkInterval", m_checkInterval,
           "Training is stopped if validation error is higher than "
           "checkInterval epochs ago, i.e. either the validation error is increasing "
           "or the gain is less than the fluctuations.", 500);
  addParam("maxEpochs", m_maxEpochs,
           "Maximum number of training epochs.", 10000);
  addParam("repeatTrain", m_repeatTrain,
           "If >1, training is repeated several times with different start weights. "
           "The weights which give the best resolution on the test samples are kept.", 1);
}

~GRLNeuroTrainerModule()

virtual ~GRLNeuroTrainerModule ( )

inlinevirtual

Destructor.

Definition at line 35 of file GRLNeuroTrainerModule.h.

{}

Member Function Documentation

beginRun()

virtual void beginRun ( void  )

inlinevirtualinherited

Called when entering a new run.

Called at the beginning of each run, the method gives you the chance to change run dependent constants like alignment parameters, etc.

This method can be implemented by subclasses.

Reimplemented in ARICHBackgroundModule, BeamabortModule, BgoModule, CaveModule, ClawModule, CLAWSModule, DosiModule, FANGSModule, He3tubeModule, MicrotpcModule, Ph1bpipeModule, Ph1sustrModule, PindiodeModule, PlumeModule, QcsmonitorModule, SrsensorModule, GetEventFromSocketModule, CalibrationCollectorModule, EventsOfDoomBusterModule, CosmicsAlignmentValidationModule, EnergyBiasCorrectionModule, ChargedPidMVAModule, ChargedPidMVAMulticlassModule, CurlTaggerModule, LowEnergyPi0IdentificationExpertModule, LowEnergyPi0VetoExpertModule, ParticleVertexFitterModule, PhotonEfficiencySystematicsModule, TagVertexModule, TreeFitterModule, arichBtestModule, ARICHDigitizerModule, ARICHDQMModule, ARICHRateCalModule, ARICHReconstructorModule, B2BIIMCParticlesMonitorModule, B2BIIConvertBeamParamsModule, B2BIIConvertMdstModule, B2BIIFixMdstModule, B2BIIMdstInputModule, BelleMCOutputModule, BeamBkgGeneratorModule, BeamBkgHitRateMonitorModule, BeamBkgMixerModule, BeamBkgTagSetterModule, 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FindletModule< DATCONFPGAFindlet >, FindletModule< MCVXDCDCTrackMergerFindlet >, FindletModule< vxdHoughTracking::SVDHoughTracking >, FindletModule< CKFToCDCFindlet >, FindletModule< CKFToCDCFromEclFindlet >, FindletModule< CKFToPXDFindlet >, FindletModule< AsicBackgroundLibraryCreator >, FindletModule< CDCTrackingEventLevelMdstInfoFillerFromHitsFindlet >, FindletModule< CDCTrackingEventLevelMdstInfoFillerFromSegmentsFindlet >, FindletModule< AxialSegmentPairCreator >, FindletModule< AxialStraightTrackFinder >, FindletModule< AxialTrackCreatorMCTruth >, FindletModule< AxialTrackCreatorSegmentHough >, FindletModule< AxialTrackFinderHough >, FindletModule< AxialTrackFinderLegendre >, FindletModule< ClusterBackgroundDetector >, FindletModule< ClusterPreparer >, FindletModule< ClusterRefiner< BridgingWireHitRelationFilter > >, FindletModule< FacetCreator >, FindletModule< HitReclaimer >, FindletModule< MonopoleAxialTrackFinderLegendre >, FindletModule< MonopoleStereoHitFinder >, FindletModule< MonopoleStereoHitFinderQuadratic >, FindletModule< SegmentCreatorFacetAutomaton >, FindletModule< SegmentCreatorMCTruth >, FindletModule< SegmentFinderFacetAutomaton >, FindletModule< SegmentFitter >, FindletModule< SegmentLinker >, FindletModule< SegmentOrienter >, FindletModule< SegmentPairCreator >, FindletModule< SegmentRejecter >, FindletModule< SegmentTrackCombiner >, FindletModule< SegmentTripleCreator >, FindletModule< StereoHitFinder >, FindletModule< SuperClusterCreator >, FindletModule< TrackCombiner >, FindletModule< TrackCreatorSegmentPairAutomaton >, FindletModule< TrackCreatorSegmentTripleAutomaton >, FindletModule< TrackCreatorSingleSegments >, FindletModule< TrackExporter >, FindletModule< TrackFinderAutomaton >, FindletModule< TrackFinderCosmics >, FindletModule< TrackFinder >, FindletModule< TrackFinderSegmentPairAutomaton >, FindletModule< TrackFinderSegmentTripleAutomaton >, FindletModule< TrackFlightTimeAdjuster >, FindletModule< TrackLinker >, FindletModule< TrackOrienter >, FindletModule< TrackQualityAsserter >, FindletModule< TrackQualityEstimator >, FindletModule< TrackRejecter >, FindletModule< WireHitBackgroundDetector >, FindletModule< WireHitCreator >, FindletModule< WireHitPreparer >, CDCTriggerNeuroDQMModule, CDCTriggerNeuroDQMOnlineModule, CDCTriggerNDFinderModule, CDCTriggerTSFModule, TRGCDCModule, TRGCDCETFUnpackerModule, TRGCDCT2DDQMModule, TRGCDCT3DConverterModule, TRGCDCT3DDQMModule, TRGCDCT3DUnpackerModule, TRGCDCTSFDQMModule, TRGCDCTSFUnpackerModule, TRGCDCTSStreamModule, CDCTriggerUnpackerModule, MCMatcherTRGECLModule, TRGECLFAMModule, TRGECLModule, TRGECLBGTCHitModule, TRGECLDQMModule, TRGECLEventTimingDQMModule, TRGECLQAMModule, TRGECLRawdataAnalysisModule, TRGECLTimingCalModule, TRGECLUnpackerModule, TRGGDLModule, TRGGDLDQMModule, TRGGDLDSTModule, TRGGDLSummaryModule, TRGGDLUnpackerModule, TRGGRLMatchModule, TRGGRLModule, TRGGRLProjectsModule, TRGGRLDQMModule, TRGGRLUnpackerModule, KLMTriggerModule, TRGTOPDQMModule, TRGTOPTRD2TTSConverterModule, TRGTOPUnpackerModule, TRGTOPUnpackerWaveformModule, TRGTOPWaveformPlotterModule, TRGRAWDATAModule, VXDMisalignmentModule, DQMHistAnalysisARICHModule, DQMHistAnalysisCDCDedxModule, DQMHistAnalysisCDCEpicsModule, DQMHistAnalysisCDCMonObjModule, DQMHistAnalysisDAQMonObjModule, DQMHistAnalysisECLModule, DQMHistAnalysisECLConnectedRegionsModule, DQMHistAnalysisECLShapersModule, DQMHistAnalysisECLSummaryModule, DQMHistAnalysisEpicsExampleModule, DQMHistAnalysisEventT0EfficiencyModule, DQMHistAnalysisEventT0TriggerJitterModule, DQMHistAnalysisExampleModule, DQMHistAnalysisExampleFlagsModule, DQMHistAnalysisHLTModule, DQMHistAnalysisInput2Module, DQMHistAnalysisInputPVSrvModule, DQMHistAnalysisInputRootFileModule, DQMHistAnalysisInputTestModule, DQMHistAnalysisKLMModule, DQMHistAnalysisKLM2Module, DQMHistAnalysisMiraBelleModule, DQMHistAnalysisOutputMonObjModule, DQMHistAnalysisOutputRelayMsgModule, DQMHistAnalysisPeakModule, DQMHistAnalysisPXDERModule, DQMHistAnalysisPXDFitsModule, DQMHistAnalysisSVDClustersOnTrackModule, DQMHistAnalysisSVDDoseModule, DQMHistAnalysisSVDEfficiencyModule, DQMHistAnalysisSVDGeneralModule, DQMHistAnalysisSVDOccupancyModule, DQMHistAnalysisSVDOnMiraBelleModule, DQMHistAnalysisSVDUnpackerModule, DQMHistAnalysisTOPModule, DQMHistAnalysisTrackingAbortModule, DQMHistAnalysisTrackingHLTModule, DQMHistAnalysisTRGECLModule, DQMHistAutoCanvasModule, DQMHistComparitorModule, DQMHistDeltaHistoModule, DQMHistReferenceModule, DQMHistSnapshotsModule, DAQMonitorModule, DelayDQMModule, V0ObjectsDQMModule, ECLDQMInjectionModule, PyModule, PXDBgTupleProducerModule, PXDMCBgTupleProducerModule, PXDDAQDQMModule, PXDDQMClustersModule, PXDDQMCorrModule, PXDDQMEfficiencyModule, PXDDQMEfficiencySelftrackModule, PXDDQMExpressRecoModule, PXDGatedDHCDQMModule, PXDGatedModeDQMModule, PXDInjectionDQMModule, PXDRawDQMCorrModule, PXDRawDQMModule, PXDROIDQMModule, PXDTrackClusterDQMModule, PXDDigitizerModule, PXDPackerModule, PXDUnpackerModule, TTDDQMModule, SVDDQMClustersOnTrackModule, SVDDQMDoseModule, SVDDQMExpressRecoModule, SVDDQMInjectionModule, SVDUnpackerDQMModule, PXDclusterFilterModule, PXDdigiFilterModule, PXDROIFinderAnalysisModule, TrackingAbortDQMModule, VXDDQMExpressRecoModule, vxdDigitMaskingModule, DQMHistAnalysisDeltaEpicsMonObjExampleModule, DQMHistAnalysisDeltaTestModule, DQMHistAnalysisEpicsOutputModule, DQMHistAnalysisPhysicsModule, DQMHistAnalysisPXDChargeModule, DQMHistAnalysisPXDCMModule, DQMHistAnalysisPXDDAQModule, DQMHistAnalysisPXDEffModule, DQMHistAnalysisPXDInjectionModule, DQMHistAnalysisPXDReductionModule, DQMHistAnalysisPXDTrackChargeModule, DQMHistAnalysisRooFitExampleModule, DQMHistAnalysisRunNrModule, DQMHistAnalysisTRGModule, DQMHistInjectionModule, and DQMHistOutputToEPICSModule.

Definition at line 147 of file Module.h.

{};

clone()

std::shared_ptr< PathElement > clone ( ) const

overridevirtualinherited

Create an independent copy of this module.

Note that parameters are shared, so changing them on a cloned module will also affect the original module.

Implements PathElement.

Definition at line 179 of file Module.cc.

{
  ModulePtr newModule = ModuleManager::Instance().registerModule(getType());
  newModule->m_moduleParamList.setParameters(getParamList());
  newModule->setName(getName());
  newModule->m_package = m_package;
  newModule->m_propertyFlags = m_propertyFlags;
  newModule->m_logConfig = m_logConfig;
  newModule->m_conditions = m_conditions;
 
  return newModule;
}

def_beginRun()

virtual void def_beginRun ( )

inlineprotectedvirtualinherited

Wrapper method for the virtual function beginRun() that has the implementation to be used in a call from Python.

Reimplemented in PyModule.

Definition at line 426 of file Module.h.

{ beginRun(); }

def_endRun()

virtual void def_endRun ( )

inlineprotectedvirtualinherited

This method can receive that the current run ends as a call from the Python side.

For regular C++-Modules that forwards the call to the regular endRun() method.

Reimplemented in PyModule.

Definition at line 439 of file Module.h.

{ endRun(); }

def_event()

virtual void def_event ( )

inlineprotectedvirtualinherited

Wrapper method for the virtual function event() that has the implementation to be used in a call from Python.

Reimplemented in PyModule.

Definition at line 432 of file Module.h.

{ event(); }

def_initialize()

virtual void def_initialize ( )

inlineprotectedvirtualinherited

Wrappers to make the methods without "def_" prefix callable from Python.

Overridden in PyModule. Wrapper method for the virtual function initialize() that has the implementation to be used in a call from Python.

Reimplemented in PyModule.

Definition at line 420 of file Module.h.

{ initialize(); }

def_terminate()

virtual void def_terminate ( )

inlineprotectedvirtualinherited

Wrapper method for the virtual function terminate() that has the implementation to be used in a call from Python.

Reimplemented in PyModule.

Definition at line 445 of file Module.h.

{ terminate(); }

endRun()

virtual void endRun ( void  )

inlinevirtualinherited

This method is called if the current run ends.

Use this method to store information, which should be aggregated over one run.

This method can be implemented by subclasses.

Reimplemented in BeamabortModule, BgoModule, CaveModule, ClawModule, CLAWSModule, DosiModule, FANGSModule, He3tubeModule, MicrotpcModule, Ph1bpipeModule, Ph1sustrModule, PindiodeModule, PlumeModule, QcsmonitorModule, SrsensorModule, GetEventFromSocketModule, CalibrationCollectorModule, AlignDQMModule, CosmicsAlignmentValidationModule, CurlTaggerModule, LowEnergyPi0IdentificationExpertModule, LowEnergyPi0VetoExpertModule, arichBtestModule, ARICHDQMModule, B2BIIMCParticlesMonitorModule, B2BIIConvertMdstModule, B2BIIMdstInputModule, BelleMCOutputModule, BeamBkgGeneratorModule, BeamBkgHitRateMonitorModule, BeamBkgMixerModule, BeamBkgTagSetterModule, BGOverlayInputModule, AnalysisPhase1StudyModule, NtuplePhase1_v6Module, ReprocessorModule, BeamabortStudyModule, BeamDigitizerModule, BgoDigitizerModule, BgoStudyModule, ClawDigitizerModule, ClawStudyModule, ClawsDigitizerModule, ClawsStudyModule, CsiDigitizer_v2Module, CsIDigitizerModule, CsiModule, CsiStudy_v2Module, CsIStudyModule, DosiDigitizerModule, DosiStudyModule, FANGSDigitizerModule, FANGSStudyModule, He3DigitizerModule, He3tubeStudyModule, MicrotpcStudyModule, TpcDigitizerModule, TPCStudyModule, PinDigitizerModule, PindiodeStudyModule, PlumeDigitizerModule, QcsmonitorDigitizerModule, QcsmonitorStudyModule, CDCCosmicAnalysisModule, CDCCRTestModule, cdcDQM7Module, CDCDQMModule, CDCPackerModule, CDCRecoTrackFilterModule, CDCUnpackerModule, DAQPerfModule, RxSocketModule, TxSocketModule, DqmHistoManagerModule, MonitorDataModule, TrackAnaModule, Ds2SampleModule, ReceiveEventModule, HLTDQM2ZMQModule, HLTDs2ZMQModule, ElapsedTimeModule, DeSerializerPXDModule, GenRawSendModule, Root2RawModule, SerializerModule, CertifyParallelModule, Ds2RawModule, Ds2RbufModule, EvReductionModule, FastRbuf2DsModule, Raw2DsModule, RawInputModule, Rbuf2DsModule, Rbuf2RbufModule, Ds2RawFileModule, PartialSeqRootReaderModule, SeqRootMergerModule, StorageDeserializerModule, StorageRootOutputModule, StorageSerializerModule, PhysicsObjectsDQMModule, PhysicsObjectsMiraBelleBhabhaModule, PhysicsObjectsMiraBelleDst2Module, PhysicsObjectsMiraBelleDstModule, PhysicsObjectsMiraBelleHadronModule, PhysicsObjectsMiraBelleModule, ECLBackgroundModule, ECLChargedPIDModule, ECLChargedPIDDataAnalysisModule, ECLChargedPIDDataAnalysisValidationModule, ECLClusterPSDModule, ECLCovarianceMatrixModule, ECLCRFinderModule, ECLDataAnalysisModule, ECLDigitCalibratorModule, ECLDigitizerModule, ECLDigitizerPureCsIModule, EclDisplayModule, ECLDQMModule, ECLDQMEXTENDEDModule, ECLDQMOutOfTimeDigitsModule, ECLFinalizerModule, ECLHitDebugModule, ECLLocalMaximumFinderModule, ECLLocalRunCalibratorModule, ECLLOMModule, ECLPackerModule, ECLShowerCorrectorModule, ECLShowerShapeModule, ECLSplitterN1Module, ECLSplitterN2Module, ECLUnpackerModule, ECLWaveformFitModule, HistoModule, SubEventModule, SwitchDataStoreModule, EventInfoPrinterModule, RandomBarrierModule, HistoManagerModule, StatisticsSummaryModule, SeqRootInputModule, SeqRootOutputModule, RxModule, TxModule, ZMQTxInputModule, ZMQTxWorkerModule, EvtGenDecayModule, OverrideGenerationFlagsModule, BKLMAnaModule, BKLMDigitAnalyzerModule, BKLMSimHistogrammerModule, BKLMTrackingModule, EKLMDataCheckerModule, KLMClusterEfficiencyModule, KLMClustersReconstructorModule, KLMDigitizerModule, KLMDQMModule, KLMDQM2Module, KLMPackerModule, KLMReconstructorModule, KLMScintillatorSimulatorModule, KLMUnpackerModule, AWESOMEBasicModule, PXDBackgroundModule, PXDClustersFromTracksModule, PXDPerformanceModule, Convert2RawDetModule, PrintDataModule, PrintEventRateModule, Root2BinaryModule, CDCDedxDQMModule, CDCDedxValidationModule, EventT0ValidationModule, DataWriterModule, KlongValidationModule, KLMMuonIDDNNExpertModule, FullSimModule, SVDBackgroundModule, SVDClusterCalibrationsMonitorModule, SVDHotStripFinderModule, SVDLatencyCalibrationModule, SVDLocalCalibrationsMonitorModule, SVDPositionErrorScaleFactorImporterModule, SVDTimeCalibrationsMonitorModule, svdDumpModule, SVDPackerModule, SVDB4CommissioningPlotsModule, SVDClusterEvaluationModule, SVDClusterEvaluationTrueInfoModule, SVDClusterFilterModule, SVDOccupancyAnalysisModule, SVDPerformanceModule, SVDShaperDigitsFromTracksModule, SVDClusterizerModule, SVDCoGTimeEstimatorModule, SVDDataFormatCheckModule, SVDRecoDigitCreatorModule, SVD3SamplesEmulatorModule, SVDTriggerQualityGeneratorModule, SVDUnpackerModule, TOPBackgroundModule, TOPChannelT0MCModule, TOPTriggerDigitizerModule, TOPDoublePulseGeneratorModule, TOPGainEfficiencyCalculatorModule, TOPLaserHitSelectorModule, TOPInterimFENtupleModule, TOPLaserCalibratorModule, TOPMCTrackMakerModule, TOPNtupleModule, TOPPackerModule, TOPRawDigitConverterModule, TOPTBCComparatorModule, TOPTimeBaseCalibratorModule, TOPUnpackerModule, TOPWaveformFeatureExtractorModule, TOPWaveformQualityPlotterModule, TOPXTalkChargeShareSetterModule, ExtModule, GenfitVisModule, MCV0MatcherModule, MCTrackCandClassifierModule, MuidModule, MCSlowPionPXDROICreatorModule, PXDROIFinderModule, SVDROIDQMModule, SVDROIFinderAnalysisModule, SVDROIFinderModule, RT2SPTCConverterModule, SPTCmomentumSeedRetrieverModule, SPTCvirtualIPRemoverModule, TrackFinderMCTruthRecoTracksModule, EffPlotsModule, HitXPModule, TrackingPerformanceEvaluationModule, V0findingPerformanceEvaluationModule, SecMapTrainerBaseModule, SecMapTrainerVXDTFModule, TrackFinderVXDAnalizerModule, VXDSimpleClusterizerModule, NoKickCutsEvalModule, SectorMapBootstrapModule, VXDTFTrainingDataCollectorModule, FindletModule< AFindlet >, FindletModule< HitBasedT0Extractor >, FindletModule< CKFToSVDSeedFindlet >, FindletModule< CKFToSVDFindlet >, FindletModule< CosmicsTrackMergerFindlet >, FindletModule< DATCONFPGAFindlet >, FindletModule< MCVXDCDCTrackMergerFindlet >, FindletModule< vxdHoughTracking::SVDHoughTracking >, FindletModule< CKFToCDCFindlet >, FindletModule< CKFToCDCFromEclFindlet >, FindletModule< CKFToPXDFindlet >, FindletModule< AsicBackgroundLibraryCreator >, FindletModule< CDCTrackingEventLevelMdstInfoFillerFromHitsFindlet >, FindletModule< CDCTrackingEventLevelMdstInfoFillerFromSegmentsFindlet >, FindletModule< AxialSegmentPairCreator >, FindletModule< AxialStraightTrackFinder >, FindletModule< AxialTrackCreatorMCTruth >, FindletModule< AxialTrackCreatorSegmentHough >, FindletModule< AxialTrackFinderHough >, FindletModule< AxialTrackFinderLegendre >, FindletModule< ClusterBackgroundDetector >, FindletModule< ClusterPreparer >, FindletModule< ClusterRefiner< BridgingWireHitRelationFilter > >, FindletModule< FacetCreator >, FindletModule< HitReclaimer >, FindletModule< MonopoleAxialTrackFinderLegendre >, FindletModule< MonopoleStereoHitFinder >, FindletModule< MonopoleStereoHitFinderQuadratic >, FindletModule< SegmentCreatorFacetAutomaton >, FindletModule< SegmentCreatorMCTruth >, FindletModule< SegmentFinderFacetAutomaton >, FindletModule< SegmentFitter >, FindletModule< SegmentLinker >, FindletModule< SegmentOrienter >, FindletModule< SegmentPairCreator >, FindletModule< SegmentRejecter >, FindletModule< SegmentTrackCombiner >, FindletModule< SegmentTripleCreator >, FindletModule< StereoHitFinder >, FindletModule< SuperClusterCreator >, FindletModule< TrackCombiner >, FindletModule< TrackCreatorSegmentPairAutomaton >, FindletModule< TrackCreatorSegmentTripleAutomaton >, FindletModule< TrackCreatorSingleSegments >, FindletModule< TrackExporter >, FindletModule< TrackFinderAutomaton >, FindletModule< TrackFinderCosmics >, FindletModule< TrackFinder >, FindletModule< TrackFinderSegmentPairAutomaton >, FindletModule< TrackFinderSegmentTripleAutomaton >, FindletModule< TrackFlightTimeAdjuster >, FindletModule< TrackLinker >, FindletModule< TrackOrienter >, FindletModule< TrackQualityAsserter >, FindletModule< TrackQualityEstimator >, FindletModule< TrackRejecter >, FindletModule< WireHitBackgroundDetector >, FindletModule< WireHitCreator >, FindletModule< WireHitPreparer >, CDCTriggerNeuroDQMModule, CDCTriggerNeuroDQMOnlineModule, CDCTriggerNDFinderModule, TRGCDCModule, TRGCDCETFUnpackerModule, TRGCDCT2DDQMModule, TRGCDCT3DConverterModule, TRGCDCT3DDQMModule, TRGCDCT3DUnpackerModule, TRGCDCTSFDQMModule, TRGCDCTSFUnpackerModule, TRGCDCTSStreamModule, MCMatcherTRGECLModule, TRGECLFAMModule, TRGECLModule, TRGECLBGTCHitModule, TRGECLDQMModule, TRGECLQAMModule, TRGECLRawdataAnalysisModule, TRGECLTimingCalModule, TRGECLUnpackerModule, TRGGDLModule, TRGGDLDQMModule, TRGGDLDSTModule, TRGGDLSummaryModule, TRGGDLUnpackerModule, TRGGRLMatchModule, TRGGRLModule, TRGGRLProjectsModule, TRGGRLDQMModule, TRGGRLUnpackerModule, KLMTriggerModule, TRGTOPDQMModule, TRGTOPTRD2TTSConverterModule, TRGTOPUnpackerModule, TRGTOPUnpackerWaveformModule, TRGTOPWaveformPlotterModule, TRGRAWDATAModule, DQMHistAnalysisARICHModule, DQMHistAnalysisARICHMonObjModule, DQMHistAnalysisCDCDedxModule, DQMHistAnalysisCDCEpicsModule, DQMHistAnalysisCDCMonObjModule, DQMHistAnalysisDAQMonObjModule, DQMHistAnalysisECLModule, DQMHistAnalysisECLConnectedRegionsModule, DQMHistAnalysisECLOutOfTimeDigitsModule, DQMHistAnalysisECLShapersModule, DQMHistAnalysisECLSummaryModule, DQMHistAnalysisEpicsExampleModule, DQMHistAnalysisExampleModule, DQMHistAnalysisExampleFlagsModule, DQMHistAnalysisHLTMonObjModule, DQMHistAnalysisInput2Module, DQMHistAnalysisInputPVSrvModule, DQMHistAnalysisInputTestModule, DQMHistAnalysisKLMModule, DQMHistAnalysisKLM2Module, DQMHistAnalysisMiraBelleModule, DQMHistAnalysisMonObjModule, DQMHistAnalysisOutputFileModule, DQMHistAnalysisOutputMonObjModule, DQMHistAnalysisOutputRelayMsgModule, DQMHistAnalysisPXDFitsModule, DQMHistAnalysisSVDClustersOnTrackModule, DQMHistAnalysisSVDDoseModule, DQMHistAnalysisSVDEfficiencyModule, DQMHistAnalysisSVDGeneralModule, DQMHistAnalysisSVDOccupancyModule, DQMHistAnalysisSVDOnMiraBelleModule, DQMHistAnalysisSVDUnpackerModule, DQMHistAnalysisTOPModule, DQMHistAnalysisTRGECLModule, DQMHistAnalysisTRGGDLModule, DQMHistComparitorModule, DQMHistDeltaHistoModule, DQMHistReferenceModule, DQMHistSnapshotsModule, PyModule, SVDUnpackerDQMModule, TrackSetEvaluatorHopfieldNNDEVModule, vxdDigitMaskingModule, DQMHistAnalysisDeltaEpicsMonObjExampleModule, DQMHistAnalysisDeltaTestModule, DQMHistAnalysisEpicsOutputModule, DQMHistAnalysisPhysicsModule, DQMHistAnalysisPXDChargeModule, DQMHistAnalysisPXDTrackChargeModule, DQMHistAnalysisRooFitExampleModule, DQMHistAnalysisTRGModule, and DQMHistOutputToEPICSModule.

Definition at line 166 of file Module.h.

{};

evalCondition()

bool evalCondition ( ) const

inherited

If at least one condition was set, it is evaluated and true returned if at least one condition returns true.

If no condition or result value was defined, the method returns false. Otherwise, the condition is evaluated and true returned, if at least one condition returns true. To speed up the evaluation, the condition strings were already parsed in the method if_value().

Returns

True if at least one condition and return value exists and at least one condition expression was evaluated to true.

Definition at line 96 of file Module.cc.

{
  if (m_conditions.empty()) return false;
 
  //okay, a condition was set for this Module...
  if (!m_hasReturnValue) {
    B2FATAL("A condition was set for '" << getName() << "', but the module did not set a return value!");
  }
 
  for (const auto& condition : m_conditions) {
    if (condition.evaluate(m_returnValue)) {
      return true;
    }
  }
  return false;
}

event()

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 233 of file GRLNeuroTrainerModule.cc.

{
  //inputs and outputs
  std::vector<float> input;
  std::vector<float> output;
 
  std::vector<float> cdc2d_phi;
  std::vector<float> cdc2d_pt;
 
  //GRL input
  StoreObjPtr<TRGGRLUnpackerStore> GRLStore(m_GRLCollectionName);
  int n_cdcf = 0;
  int n_cdcs = 0;
  int n_cdci = 0;
  int n_cdc = 0;
  int map_cdcf[36];
  int map_cdcs[36];
  int map_cdci[36];
  for (int i = 0; i < 36; i++) {
    map_cdcf[i] = 0;
    map_cdcs[i] = 0;
    map_cdci[i] = 0;
  }
 
  //full track
  for (int i = 0; i < 36; i++) {
    if (GRLStore->get_phi_CDC(i)) {
      map_cdcf[i] = 1;
    }
  }
 
  //short track
  for (int i = 0; i < 64; i++) {
    if (GRLStore->get_map_ST2(i)) {
      int j = i * (36. / 64.);
      map_cdcs[j] = 1;
    }
  }
 
  //inner track
  for (int i = 0; i < 64; i++) {
    if (GRLStore->get_map_TSF0(i)) {
      int j = i * (36. / 64.);
      int j1 = i - 4;
      if (j1 < 0) j1 = j1 + 64;
      int j2 = i - 3;
      if (j2 < 0) j2 = j2 + 64;
      int j3 = i - 2;
      if (j3 < 0) j3 = j3 + 64;
      int j4 = i - 1;
      if (j4 < 0) j4 = j4 + 64;
      int j5 = i;
      int j6 = i + 1;
      if (j6 > 63) j6 = j6 - 64;
      int j7 = i + 2;
      if (j7 > 63) j7 = j7 - 64;
      if (
        (GRLStore->get_map_TSF1(j1) || GRLStore->get_map_TSF1(j2) || GRLStore->get_map_TSF1(j3) || GRLStore->get_map_TSF1(j4)
         || GRLStore->get_map_TSF1(j5))
        &&
        (GRLStore->get_map_TSF2(j3) || GRLStore->get_map_TSF2(j4) || GRLStore->get_map_TSF2(j5) || GRLStore->get_map_TSF2(j6)
         || GRLStore->get_map_TSF2(j7))
      )
        map_cdci[j] = 1;
    }
  }
 
  //avoid overlap
  for (int i = 0; i < 36; i++) {
    if (map_cdcf[i] == 1) {
      int i1 = i - 2;
      if (i1 < 0) i1 = i1 + 36;
      int i2 = i - 1;
      if (i2 < 0) i2 = i2 + 36;
      int i3 = i;
      int i4 = i + 1;
      // cppcheck-suppress knownConditionTrueFalse
      if (i4 > 36) i4 = i4 - 36;
      int i5 = i + 2;
      if (i5 > 36) i5 = i5 - 36;
      //map_cdcs[i1]=0;
      map_cdcs[i2] = 0;
      map_cdcs[i3] = 0;
      map_cdcs[i4] = 0;
      //map_cdcs[i5]=0;
      //map_cdci[i1]=0;
      map_cdci[i2] = 0;
      map_cdci[i3] = 0;
      map_cdci[i4] = 0;
      //map_cdci[i5]=0;
    }
  }
  for (int i = 0; i < 36; i++) {
    if (map_cdcs[i] == 1) {
      int i1 = i - 2;
      if (i1 < 0) i1 = i1 + 36;
      int i2 = i - 1;
      if (i2 < 0) i2 = i2 + 36;
      int i3 = i;
      int i4 = i + 1;
      // cppcheck-suppress knownConditionTrueFalse
      if (i4 > 36) i4 = i4 - 36;
      int i5 = i + 2;
      if (i5 > 36) i5 = i5 - 36;
      //map_cdci[i1]=0;
      map_cdci[i2] = 0;
      map_cdci[i3] = 0;
      map_cdci[i4] = 0;
      //map_cdci[i5]=0;
    }
  }
 
  //for (int i = 0; i < 36; i++) {
  //  std::cout << map_cdcf[i] << " " ;
  //}
  //std::cout << std::endl;
  //for (int i = 0; i < 36; i++) {
  //  std::cout << map_cdcs[i] << " " ;
  //}
  //std::cout << std::endl;
  //for (int i = 0; i < 36; i++) {
  //  std::cout << map_cdci[i] << " " ;
  //}
  //std::cout << std::endl;
 
  //count
  for (int i = 0; i < 36; i++) {
    if (map_cdcf[i] == 1) {n_cdcf++; n_cdc++;}
    if (map_cdcs[i] == 1) {n_cdcs++; n_cdc++;}
    if (map_cdci[i] == 1) {n_cdci++; n_cdc++;}
  }
 
  //input
  for (int i = 0; i < 36; i++) {
    input.push_back((map_cdcf[i] - 0.5) * 2);
  }
  for (int i = 0; i < 36; i++) {
    input.push_back((map_cdcs[i] - 0.5) * 2);
  }
  for (int i = 0; i < 36; i++) {
    input.push_back((map_cdci[i] - 0.5) * 2);
  }
 
  //ECL input
  //..Use only clusters within 100 ns of event timing (from ECL).
  StoreArray<TRGECLTrg> trgArray;
  StoreArray<TRGECLCluster> eclTrgClusterArray(m_TrgECLClusterName);
  int ntrgArray = trgArray.getEntries();
  double EventTiming = -9999.;
  if (ntrgArray > 0) {EventTiming = trgArray[0]->getEventTiming();}
  std::vector<int> selTC;
  std::vector<float> selTheta;
  std::vector<float> selPhi;
  std::vector<float> selE;
  for (int ic = 0; ic < eclTrgClusterArray.getEntries(); ic++) {
    double tcT = abs(eclTrgClusterArray[ic]->getTimeAve() - EventTiming);
    //if (tcT < 100.) {
    int TC = eclTrgClusterArray[ic]->getMaxTCId();
    selTC.push_back(TC);
    selTheta.push_back(TCcotThetaLab[TC - 1]);
    selPhi.push_back(TCPhiLab[TC - 1]);
    selE.push_back(eclTrgClusterArray[ic]->getEnergyDep() * 0.001);
    input.push_back(TCcotThetaLab[TC - 1] / TMath::Pi());
    input.push_back(TCPhiLab[TC - 1] / TMath::Pi());
    input.push_back((eclTrgClusterArray[ic]->getEnergyDep() * 0.001 - 3.5) / 3.5);
    //}
    B2DEBUG(50, "InputECL " << ic << " " << tcT << " " << TC << " " << TCcotThetaLab[TC - 1] << " " << TCPhiLab[TC - 1] << " " <<
            eclTrgClusterArray[ic]->getEnergyDep() << " " << EventTiming);
  }
 
  //output
  bool accepted_signal = false;
  bool accepted_bg     = false;
  bool accepted_hadron = false;
  bool accepted_filter = false;
  bool accepted_bhabha = false;
  StoreObjPtr<SoftwareTriggerResult> result_soft;
  if (result_soft.isValid()) {
    const std::map<std::string, int>& skim_map = result_soft->getResults();
    if (skim_map.find("software_trigger_cut&skim&accept_hadronb2") != skim_map.end()) {
      accepted_hadron = (result_soft->getResult("software_trigger_cut&skim&accept_hadronb2") == SoftwareTriggerCutResult::c_accept);
    }
    if (skim_map.find("software_trigger_cut&filter&total_result") != skim_map.end()) {
      accepted_filter = (result_soft->getResult("software_trigger_cut&filter&total_result") == SoftwareTriggerCutResult::c_accept);
    }
    if (skim_map.find("software_trigger_cut&skim&accept_bhabha") != skim_map.end()) {
      accepted_bhabha = (result_soft->getResult("software_trigger_cut&skim&accept_bhabha") == SoftwareTriggerCutResult::c_accept);
    }
  }
 
  accepted_signal = accepted_hadron && accepted_filter;
  accepted_bg     = !accepted_filter;
 
  //input and output for NN training
  int cdc_sector = cdc2d_phi.size();
  int ecl_sector = selTC.size();
  int isector = cdc_sector * n_ecl_sector + ecl_sector;
  B2DEBUG(50, "Input " << cdc_sector << " " << ecl_sector << " " << accepted_signal << " " << accepted_bg);
  if (accepted_signal
      && !accepted_filter)B2DEBUG(50, "Input " << cdc_sector << " " << ecl_sector << " " << accepted_signal << " " << accepted_filter <<
                                    " " << accepted_bhabha);
 
  if (accepted_signal) {
    output.push_back(1);
  } else if (accepted_bg) {
    scale_bg[isector]++;
    if (isector == 3) {
      if (scale_bg[isector] == 100) {
        output.push_back(-1);
        scale_bg[isector] = 1;
      } else return;
    }
    if (isector == 4) {
      if (scale_bg[isector] == 5) {
        output.push_back(-1);
        scale_bg[isector] = 1;
      } else return;
    } else {
      output.push_back(-1);
    }
  } else {
    return;
  }
 
 
 
  if (cdc_sector < n_cdc_sector && ecl_sector < n_ecl_sector) {
    m_trainSets[isector].addSample(input, output);
    if (m_saveDebug) {
      if (accepted_signal) {
        for (int i = 0; i < cdc_sector; i++)     h_cdc2d_phi_sig[isector]->Fill(cdc2d_phi[i]);
        for (int i = 0; i < cdc_sector; i++)     h_cdc2d_pt_sig[isector]->Fill(cdc2d_pt[i]);
        for (int i = 0; i < ecl_sector; i++)     h_selTheta_sig[isector]->Fill(selTheta[i]);
        for (int i = 0; i < ecl_sector; i++)     h_selPhi_sig[isector]->Fill(selPhi[i]);
        for (int i = 0; i < ecl_sector; i++)     h_selE_sig[isector]->Fill(selE[i]);
        h_ncdcf_sig[0]->Fill(n_cdcf);
        h_ncdcs_sig[0]->Fill(n_cdcs);
        h_ncdci_sig[0]->Fill(n_cdci);
        h_ncdc_sig[0]->Fill(n_cdc);
        h_necl_sig[0]->Fill(ecl_sector);
      } else if (accepted_bg) {
        for (int i = 0; i < cdc_sector; i++)     h_cdc2d_phi_bg[isector]->Fill(cdc2d_phi[i]);
        for (int i = 0; i < cdc_sector; i++)     h_cdc2d_pt_bg[isector]->Fill(cdc2d_pt[i]);
        for (int i = 0; i < ecl_sector; i++)     h_selTheta_bg[isector]->Fill(selTheta[i]);
        for (int i = 0; i < ecl_sector; i++)     h_selPhi_bg[isector]->Fill(selPhi[i]);
        for (int i = 0; i < ecl_sector; i++)     h_selE_bg[isector]->Fill(selE[i]);
        h_ncdcf_bg[0]->Fill(n_cdcf);
        h_ncdcs_bg[0]->Fill(n_cdcs);
        h_ncdci_bg[0]->Fill(n_cdci);
        h_ncdc_bg[0]->Fill(n_cdc);
        h_necl_bg[0]->Fill(ecl_sector);
      }
    }
  }
}

exposePythonAPI()

void exposePythonAPI ( )

staticinherited

Exposes methods of the Module class to Python.

Definition at line 325 of file Module.cc.

{
  // to avoid confusion between std::arg and boost::python::arg we want a shorthand namespace as well
  namespace bp = boost::python;
 
  docstring_options options(true, true, false); //userdef, py sigs, c++ sigs
 
  void (Module::*setReturnValueInt)(int) = &Module::setReturnValue;
 
  enum_<Module::EAfterConditionPath>("AfterConditionPath",
                                     R"(Determines execution behaviour after a conditional path has been executed:
 
.. attribute:: END
 
  End processing of this path after the conditional path. (this is the default for if_value() etc.)
 
.. attribute:: CONTINUE
 
  After the conditional path, resume execution after this module.)")
  .value("END", Module::EAfterConditionPath::c_End)
  .value("CONTINUE", Module::EAfterConditionPath::c_Continue)
  ;
 
  /* Do not change the names of >, <, ... we use them to serialize conditional pathes */
  enum_<Belle2::ModuleCondition::EConditionOperators>("ConditionOperator")
  .value(">", Belle2::ModuleCondition::EConditionOperators::c_GT)
  .value("<", Belle2::ModuleCondition::EConditionOperators::c_ST)
  .value(">=", Belle2::ModuleCondition::EConditionOperators::c_GE)
  .value("<=", Belle2::ModuleCondition::EConditionOperators::c_SE)
  .value("==", Belle2::ModuleCondition::EConditionOperators::c_EQ)
  .value("!=", Belle2::ModuleCondition::EConditionOperators::c_NE)
  ;
 
  enum_<Module::EModulePropFlags>("ModulePropFlags",
                                  R"(Flags to indicate certain low-level features of modules, see :func:`Module.set_property_flags()`, :func:`Module.has_properties()`. Most useful flags are:
 
.. attribute:: PARALLELPROCESSINGCERTIFIED
 
    This module can be run in parallel processing mode safely (All I/O must be done through the data store, in particular, the module must not write any files.)
 
.. attribute:: HISTOGRAMMANAGER
 
  This module is used to manage histograms accumulated by other modules
 
.. attribute:: TERMINATEINALLPROCESSES
 
  When using parallel processing, call this module's terminate() function in all processes. This will also ensure that there is exactly one process (single-core if no parallel modules found) or at least one input, one main and one output process.
)")
  .value("INPUT", Module::EModulePropFlags::c_Input)
  .value("OUTPUT", Module::EModulePropFlags::c_Output)
  .value("PARALLELPROCESSINGCERTIFIED", Module::EModulePropFlags::c_ParallelProcessingCertified)
  .value("HISTOGRAMMANAGER", Module::EModulePropFlags::c_HistogramManager)
  .value("INTERNALSERIALIZER", Module::EModulePropFlags::c_InternalSerializer)
  .value("TERMINATEINALLPROCESSES", Module::EModulePropFlags::c_TerminateInAllProcesses)
  ;
 
  //Python class definition
  class_<Module, PyModule> module("Module", R"(
Base class for Modules.
 
A module is the smallest building block of the framework.
A typical event processing chain consists of a Path containing
modules. By inheriting from this base class, various types of
modules can be created. To use a module, please refer to
:func:`Path.add_module()`.  A list of modules is available by running
``basf2 -m`` or ``basf2 -m package``, detailed information on parameters is
given by e.g. ``basf2 -m RootInput``.
 
The 'Module Development' section in the manual provides detailed information
on how to create modules, setting parameters, or using return values/conditions:
https://confluence.desy.de/display/BI/Software+Basf2manual#Module_Development
 
)");
  module
  .def("__str__", &Module::getPathString)
  .def("name", &Module::getName, return_value_policy<copy_const_reference>(),
       "Returns the name of the module. Can be changed via :func:`set_name() <Module.set_name()>`, use :func:`type() <Module.type()>` for identifying a particular module class.")
  .def("type", &Module::getType, return_value_policy<copy_const_reference>(),
       "Returns the type of the module (i.e. class name minus 'Module')")
  .def("set_name", &Module::setName, args("name"), R"(
Set custom name, e.g. to distinguish multiple modules of the same type.
 
>>> path.add_module('EventInfoSetter')
>>> ro = path.add_module('RootOutput', branchNames=['EventMetaData'])
>>> ro.set_name('RootOutput_metadata_only')
>>> print(path)
[EventInfoSetter -> RootOutput_metadata_only]
 
)")
  .def("description", &Module::getDescription, return_value_policy<copy_const_reference>(),
       "Returns the description of this module.")
  .def("package", &Module::getPackage, return_value_policy<copy_const_reference>(),
       "Returns the package this module belongs to.")
  .def("available_params", &_getParamInfoListPython,
       "Return list of all module parameters as `ModuleParamInfo` instances")
  .def("has_properties", &Module::hasProperties, (bp::arg("properties")),
       R"DOCSTRING(Allows to check if the module has the given properties out of `ModulePropFlags` set.
 
>>> if module.has_properties(ModulePropFlags.PARALLELPROCESSINGCERTIFIED):
>>>     ...
 
Parameters:
  properties (int): bitmask of `ModulePropFlags` to check for.
)DOCSTRING")
  .def("set_property_flags", &Module::setPropertyFlags, args("property_mask"),
       "Set module properties in the form of an OR combination of `ModulePropFlags`.");
  {
    // python signature is too crowded, make ourselves
    docstring_options subOptions(true, false, false); //userdef, py sigs, c++ sigs
    module
    .def("if_value", &Module::if_value,
         (bp::arg("expression"), bp::arg("condition_path"), bp::arg("after_condition_path")= Module::EAfterConditionPath::c_End),
         R"DOCSTRING(if_value(expression, condition_path, after_condition_path=AfterConditionPath.END)
 
Sets a conditional sub path which will be executed after this
module if the return value set in the module passes the given ``expression``.
 
Modules can define a return value (int or bool) using ``setReturnValue()``,
which can be used in the steering file to split the Path based on this value, for example
 
>>> module_with_condition.if_value("<1", another_path)
 
In case the return value of the ``module_with_condition`` for a given event is
less than 1, the execution will be diverted into ``another_path`` for this event.
 
You could for example set a special return value if an error occurs, and divert
the execution into a path containing :b2:mod:`RootOutput` if it is found;
saving only the data producing/produced by the error.
 
After a conditional path has executed, basf2 will by default stop processing
the path for this event. This behaviour can be changed by setting the
``after_condition_path`` argument.
 
Parameters:
  expression (str): Expression to determine if the conditional path should be executed.
      This should be one of the comparison operators ``<``, ``>``, ``<=``,
      ``>=``, ``==``, or ``!=`` followed by a numerical value for the return value
  condition_path (Path): path to execute in case the expression is fulfilled
  after_condition_path (AfterConditionPath): What to do once the ``condition_path`` has been executed.
)DOCSTRING")
    .def("if_false", &Module::if_false,
         (bp::arg("condition_path"), bp::arg("after_condition_path")= Module::EAfterConditionPath::c_End),
         R"DOC(if_false(condition_path, after_condition_path=AfterConditionPath.END)
 
Sets a conditional sub path which will be executed after this module if
the return value of the module evaluates to False. This is equivalent to
calling `if_value` with ``expression=\"<1\"``)DOC")
    .def("if_true", &Module::if_true,
         (bp::arg("condition_path"), bp::arg("after_condition_path")= Module::EAfterConditionPath::c_End),
         R"DOC(if_true(condition_path, after_condition_path=AfterConditionPath.END)
 
Sets a conditional sub path which will be executed after this module if
the return value of the module evaluates to True. It is equivalent to
calling `if_value` with ``expression=\">=1\"``)DOC");
  }
  module
  .def("has_condition", &Module::hasCondition,
       "Return true if a conditional path has been set for this module "
       "using `if_value`, `if_true` or `if_false`")
  .def("get_all_condition_paths", &_getAllConditionPathsPython,
       "Return a list of all conditional paths set for this module using "
       "`if_value`, `if_true` or `if_false`")
  .def("get_all_conditions", &_getAllConditionsPython,
       "Return a list of all conditional path expressions set for this module using "
       "`if_value`, `if_true` or `if_false`")
  .add_property("logging", make_function(&Module::getLogConfig, return_value_policy<reference_existing_object>()),
                &Module::setLogConfig)

getAfterConditionPath()

Module::EAfterConditionPath getAfterConditionPath ( ) const

inherited

What to do after the conditional path is finished.

(defaults to c_End if no condition is set)

Definition at line 133 of file Module.cc.

{
  if (m_conditions.empty()) return EAfterConditionPath::c_End;
 
  //okay, a condition was set for this Module...
  if (!m_hasReturnValue) {
    B2FATAL("A condition was set for '" << getName() << "', but the module did not set a return value!");
  }
 
  for (const auto& condition : m_conditions) {
    if (condition.evaluate(m_returnValue)) {
      return condition.getAfterConditionPath();
    }
  }
 
  return EAfterConditionPath::c_End;
}

getAllConditionPaths()

std::vector< std::shared_ptr< Path > > getAllConditionPaths ( ) const

inherited

Return all condition paths currently set (no matter if the condition is true or not).

Definition at line 150 of file Module.cc.

{
  std::vector<std::shared_ptr<Path>> allConditionPaths;
  for (const auto& condition : m_conditions) {
    allConditionPaths.push_back(condition.getPath());
  }
 
  return allConditionPaths;
}

getAllConditions()

const std::vector< ModuleCondition > & getAllConditions ( ) const

inlineinherited

Return all set conditions for this module.

Definition at line 324 of file Module.h.

    {
      return m_conditions;
    }

getCondition()

const ModuleCondition * getCondition ( ) const

inlineinherited

Return a pointer to the first condition (or nullptr, if none was set)

Definition at line 314 of file Module.h.

    {
      if (m_conditions.empty()) {
        return nullptr;
      } else {
        return &m_conditions.front();
      }
    }

getConditionPath()

std::shared_ptr< Path > getConditionPath ( ) const

inherited

Returns the path of the last true condition (if there is at least one, else reaturn a null pointer).

Definition at line 113 of file Module.cc.

{
  PathPtr p;
  if (m_conditions.empty()) return p;
 
  //okay, a condition was set for this Module...
  if (!m_hasReturnValue) {
    B2FATAL("A condition was set for '" << getName() << "', but the module did not set a return value!");
  }
 
  for (const auto& condition : m_conditions) {
    if (condition.evaluate(m_returnValue)) {
      return condition.getPath();
    }
  }
 
  // if none of the conditions were true, return a null pointer.
  return p;
}

getDescription()

const std::string & getDescription ( ) const

inlineinherited

Returns the description of the module.

Definition at line 202 of file Module.h.

{return m_description;}

getFileNames()

virtual std::vector< std::string > getFileNames ( bool  outputFiles )

inlinevirtualinherited

Return a list of output filenames for this modules.

This will be called when basf2 is run with "--dry-run" if the module has set either the c_Input or c_Output properties.

If the parameter outputFiles is false (for modules with c_Input) the list of input filenames should be returned (if any). If outputFiles is true (for modules with c_Output) the list of output files should be returned (if any).

If a module has sat both properties this member is called twice, once for each property.

The module should return the actual list of requested input or produced output filenames (including handling of input/output overrides) so that the grid system can handle input/output files correctly.

This function should return the same value when called multiple times. This is especially important when taking the input/output overrides from Environment as they get consumed when obtained so the finalized list of output files should be stored for subsequent calls.

Reimplemented in RootInputModule, StorageRootOutputModule, and RootOutputModule.

Definition at line 134 of file Module.h.

    {
      return std::vector<std::string>();
    }

getLogConfig()

LogConfig & getLogConfig ( )

inlineinherited

Returns the log system configuration.

Definition at line 225 of file Module.h.

{return m_logConfig;}

getModules()

std::list< ModulePtr > getModules ( ) const

inlineoverrideprivatevirtualinherited

no submodules, return empty list

Implements PathElement.

Definition at line 506 of file Module.h.

{ return std::list<ModulePtr>(); }

getName()

const std::string & getName ( ) const

inlineinherited

Returns the name of the module.

This can be changed via e.g. set_name() in the steering file to give more useful names if there is more than one module of the same type.

For identifying the type of a module, using getType() (or type() in Python) is recommended.

Definition at line 187 of file Module.h.

{return m_name;}

getPackage()

const std::string & getPackage ( ) const

inlineinherited

Returns the package this module is in.

Definition at line 197 of file Module.h.

{return m_package;}

getParamInfoListPython()

std::shared_ptr< boost::python::list > getParamInfoListPython ( ) const

inherited

Returns a python list of all parameters.

Each item in the list consists of the name of the parameter, a string describing its type, a python list of all default values and the description of the parameter.

Returns

A python list containing the parameters of this parameter list.

Definition at line 279 of file Module.cc.

{
  return m_moduleParamList.getParamInfoListPython();
}

getParamList()

const ModuleParamList & getParamList ( ) const

inlineinherited

Return module param list.

Definition at line 363 of file Module.h.

{ return m_moduleParamList; }

getPathString()

std::string getPathString ( ) const

overrideprivatevirtualinherited

return the module name.

Implements PathElement.

Definition at line 192 of file Module.cc.

{
 
  std::string output = getName();
 
  for (const auto& condition : m_conditions) {
    output += condition.getString();
  }
 
  return output;
}

getReturnValue()

int getReturnValue ( ) const

inlineinherited

Return the return value set by this module.

This value is only meaningful if hasReturnValue() is true

Definition at line 381 of file Module.h.

{ return m_returnValue; }

getType()

const std::string & getType ( ) const

inherited

Returns the type of the module (i.e.

class name minus 'Module')

Definition at line 41 of file Module.cc.

{
  if (m_type.empty())
    B2FATAL("Module type not set for " << getName());
  return m_type;
}

hasCondition()

bool hasCondition ( ) const

inlineinherited

Returns true if at least one condition was set for the module.

Definition at line 311 of file Module.h.

{ return not m_conditions.empty(); };

hasProperties()

bool hasProperties ( unsigned int  propertyFlags ) const

inherited

Returns true if all specified property flags are available in this module.

Parameters

propertyFlags

Ored EModulePropFlags which should be compared with the module flags.

Definition at line 160 of file Module.cc.

{
  return (propertyFlags & m_propertyFlags) == propertyFlags;
}

hasReturnValue()

bool hasReturnValue ( ) const

inlineinherited

Return true if this module has a valid return value set.

Definition at line 378 of file Module.h.

{ return m_hasReturnValue; }

hasUnsetForcedParams()

bool hasUnsetForcedParams ( ) const

inherited

Returns true and prints error message if the module has unset parameters which the user has to set in the steering file.

Definition at line 166 of file Module.cc.

{
  auto missing = m_moduleParamList.getUnsetForcedParams();
  std::string allMissing = "";
  for (const auto& s : missing)
    allMissing += s + " ";
  if (!missing.empty())
    B2ERROR("The following required parameters of Module '" << getName() << "' were not specified: " << allMissing <<
            "\nPlease add them to your steering file.");
  return !missing.empty();
}

if_false()

void if_false ( const std::shared_ptr< Path > &  path, EAfterConditionPath  afterConditionPath = EAfterConditionPath::c_End  )

inherited

A simplified version to add a condition to the module.

Please note that successive calls of this function will add more than one condition to the module. If more than one condition results in true, only the last of them will be used.

Please be careful: Avoid creating cyclic paths, e.g. by linking a condition to a path which is processed before the path where this module is located in.

It is equivalent to the if_value() method, using the expression "<1". This method is meant to be used together with the setReturnValue(bool value) method.

Parameters

path	Shared pointer to the Path which will be executed if the return value is false.
afterConditionPath	What to do after executing 'path'.

Definition at line 85 of file Module.cc.

{
  if_value("<1", path, afterConditionPath);
}

if_true()

void if_true ( const std::shared_ptr< Path > &  path, EAfterConditionPath  afterConditionPath = EAfterConditionPath::c_End  )

inherited

A simplified version to set the condition of the module.

Please note that successive calls of this function will add more than one condition to the module. If more than one condition results in true, only the last of them will be used.

Please be careful: Avoid creating cyclic paths, e.g. by linking a condition to a path which is processed before the path where this module is located in.

It is equivalent to the if_value() method, using the expression ">=1". This method is meant to be used together with the setReturnValue(bool value) method.

Parameters

path	Shared pointer to the Path which will be executed if the return value is true.
afterConditionPath	What to do after executing 'path'.

Definition at line 90 of file Module.cc.

{
  if_value(">=1", path, afterConditionPath);
}

if_value()

void if_value ( const std::string &  expression, const std::shared_ptr< Path > &  path, EAfterConditionPath  afterConditionPath = EAfterConditionPath::c_End  )

inherited

Add a condition to the module.

Please note that successive calls of this function will add more than one condition to the module. If more than one condition results in true, only the last of them will be used.

See https://confluence.desy.de/display/BI/Software+ModCondTut or ModuleCondition for a description of the syntax.

Please be careful: Avoid creating cyclic paths, e.g. by linking a condition to a path which is processed before the path where this module is located in.

Parameters

expression	The expression of the condition.
path	Shared pointer to the Path which will be executed if the condition is evaluated to true.
afterConditionPath	What to do after executing 'path'.

Definition at line 79 of file Module.cc.

{
  m_conditions.emplace_back(expression, path, afterConditionPath);
}

initialize()

void initialize ( void  )

overridevirtual

Initialize the module.

Initialize the networks and register datastore objects.

Reimplemented from Module.

Definition at line 141 of file GRLNeuroTrainerModule.cc.

{
  //initialize with input parameter
  m_GRLNeuro.initialize(m_parameters);
  n_cdc_sector = m_parameters.n_cdc_sector;
  n_ecl_sector = m_parameters.n_ecl_sector;
  n_sector = m_parameters.nMLP;
  m_trainSets.clear();
  for (unsigned iMLP = 0; iMLP < (unsigned)n_sector; ++iMLP) {
    m_trainSets.push_back(GRLMLPData());
    scale_bg.push_back(0);
  }
  if (m_nTrainMin > m_nTrainMax) {
    m_nTrainMin = m_nTrainMax;
    B2WARNING("nTrainMin set to " << m_nTrainMin << " (was larger than nTrainMax)");
  }
 
  //initializa histograms
  for (int isector = 0; isector < n_sector; isector++) {
    h_cdc2d_phi_sig  .push_back(new TH1D(("h_cdc2d_phi_sig_" + to_string(isector)).c_str(),
                                         ("h_cdc2d_phi_sig_" + to_string(isector)).c_str(),     64, -3.2, 3.2));
    h_cdc2d_pt_sig   .push_back(new TH1D(("h_cdc2d_pt_sig_" + to_string(isector)).c_str(),
                                         ("h_cdc2d_pt_sig_" + to_string(isector)).c_str(),       100, -5, 5));
    h_selTheta_sig.push_back(new TH1D(("h_selTheta_sig_" + to_string(isector)).c_str(),
                                      ("h_selTheta_sig_" + to_string(isector)).c_str(), 64, -3.2, 3.2));
    h_selPhi_sig  .push_back(new TH1D(("h_selPhi_sig_" + to_string(isector)).c_str(), ("h_selPhi_sig_" + to_string(isector)).c_str(),
                                      64, -3.2, 3.2));
    h_selE_sig    .push_back(new TH1D(("h_selE_sig_" + to_string(isector)).c_str(), ("h_selE_sig_" + to_string(isector)).c_str(),
                                      100, 0, 10));
    h_result_sig  .push_back(new TH1D(("h_result_sig_" + to_string(isector)).c_str(), ("h_result_sig_" + to_string(isector)).c_str(),
                                      100, -1, 1));
    h_cdc2d_phi_bg   .push_back(new TH1D(("h_cdc2d_phi_bg_" + to_string(isector)).c_str(),
                                         ("h_cdc2d_phi_bg_" + to_string(isector)).c_str(),       64, -3.2, 3.2));
    h_cdc2d_pt_bg    .push_back(new TH1D(("h_cdc2d_pt_bg_" + to_string(isector)).c_str(),
                                         ("h_cdc2d_pt_bg_" + to_string(isector)).c_str(),         100, -5, 5));
    h_selTheta_bg .push_back(new TH1D(("h_selTheta_bg_" + to_string(isector)).c_str(), ("h_selTheta_bg_" + to_string(isector)).c_str(),
                                      64, -3.2, 3.2));
    h_selPhi_bg   .push_back(new TH1D(("h_selPhi_bg_" + to_string(isector)).c_str(), ("h_selPhi_bg_" + to_string(isector)).c_str(),
                                      64, -3.2, 3.2));
    h_selE_bg     .push_back(new TH1D(("h_selE_bg_" + to_string(isector)).c_str(), ("h_selE_bg_" + to_string(isector)).c_str(),
                                      100, 0, 10));
    h_result_bg   .push_back(new TH1D(("h_result_bg_" + to_string(isector)).c_str(), ("h_result_bg_" + to_string(isector)).c_str(),
                                      100, -1, 1));
  }
  h_ncdcf_sig.push_back(new TH1D("h_ncdcf_sig", "h_ncdcf_sig", 10, 0, 10));
  h_ncdcs_sig.push_back(new TH1D("h_ncdcs_sig", "h_ncdcs_sig", 10, 0, 10));
  h_ncdci_sig.push_back(new TH1D("h_ncdci_sig", "h_ncdci_sig", 10, 0, 10));
  h_ncdc_sig.push_back(new TH1D("h_ncdc_sig", "h_ncdc_sig", 10, 0, 10));
  h_necl_sig.push_back(new TH1D("h_necl_sig", "h_necl_sig", 10, 0, 10));
  h_ncdcf_bg.push_back(new TH1D("h_ncdcf_bg", "h_ncdcf_bg", 10, 0, 10));
  h_ncdcs_bg.push_back(new TH1D("h_ncdcs_bg", "h_ncdcs_bg", 10, 0, 10));
  h_ncdci_bg.push_back(new TH1D("h_ncdci_bg", "h_ncdci_bg", 10, 0, 10));
  h_ncdc_bg.push_back(new TH1D("h_ncdc_bg", "h_ncdc_bg", 10, 0, 10));
  h_necl_bg.push_back(new TH1D("h_necl_bg", "h_necl_bg", 10, 0, 10));
 
  //..Trigger ThetaID for each trigger cell. Could be replaced by getMaxThetaId() for newer MC
  TrgEclMapping* trgecl_obj = new TrgEclMapping();
  for (int tc = 1; tc <= 576; tc++) {
    TCThetaID.push_back(trgecl_obj->getTCThetaIdFromTCId(tc));
  }
 
  //-----------------------------------------------------------------------------------------
  //..ECL look up tables
  PCmsLabTransform boostrotate;
  for (int tc = 1; tc <= 576; tc++) {
 
    //..Four vector of a 1 GeV lab photon at this TC
    ROOT::Math::XYZVector CellPosition = trgecl_obj->getTCPosition(tc);
    ROOT::Math::PxPyPzEVector CellLab;
    CellLab.SetPx(CellPosition.Unit().X());
    CellLab.SetPy(CellPosition.Unit().Y());
    CellLab.SetPz(CellPosition.Unit().Z());
    CellLab.SetE(1.);
 
    //..cotan Theta and phi in lab
    TCPhiLab.push_back(CellPosition.Phi());
    double tantheta = tan(CellPosition.Theta());
    TCcotThetaLab.push_back(1. / tantheta);
 
    //..Corresponding 4 vector in the COM frame
    ROOT::Math::PxPyPzEVector CellCOM = boostrotate.rotateLabToCms() * CellLab;
    TCThetaCOM.push_back(CellCOM.Theta()*TMath::RadToDeg());
    TCPhiCOM.push_back(CellCOM.Phi()*TMath::RadToDeg());
 
    //..Scale to give 1 GeV in the COM frame
    TC1GeV.push_back(1. / CellCOM.E());
  }
 
  delete trgecl_obj;
}

loadTraindata()

bool loadTraindata	(	const std::string &	filename,
		const std::string &	arrayname = "trainSets"
	)

Load saved training samples.

Parameters

filename	name of the TFile to read from
arrayname	name of the TObjArray holding the training samples in the file

Returns

true if the training sets were loaded correctly

saveTraindata()

void saveTraindata	(	const std::string &	filename,
		const std::string &	arrayname = "trainSets"
	)

Save all training samples.

Parameters

filename	name of the TFile to write to
arrayname	name of the TObjArray holding the training samples in the file

Definition at line 653 of file GRLNeuroTrainerModule.cc.

{
  B2INFO("Saving traindata to file " << filename << ", array " << arrayname);
  TFile datafile(filename.c_str(), "RECREATE");
  //TObjArray* trainSets = new TObjArray(m_trainSets.size());
  for (int isector = 0; isector < n_sector; ++isector) {
    //trainSets->Add(&m_trainSets[isector]);
    if (m_saveDebug) {
      h_cdc2d_phi_sig[isector]->Write();
      h_cdc2d_pt_sig[isector]->Write();
      h_selTheta_sig[isector]->Write();
      h_selPhi_sig[isector]->Write();
      h_selE_sig[isector]->Write();
      h_result_sig[isector]->Write();
      h_cdc2d_phi_bg[isector]->Write();
      h_cdc2d_pt_bg[isector]->Write();
      h_selTheta_bg[isector]->Write();
      h_selPhi_bg[isector]->Write();
      h_selE_bg[isector]->Write();
      h_result_bg[isector]->Write();
    }
    h_ncdcf_sig[0]->Write();
    h_ncdcs_sig[0]->Write();
    h_ncdci_sig[0]->Write();
    h_ncdc_sig[0]->Write();
    h_necl_sig[0]->Write();
    h_ncdcf_bg[0]->Write();
    h_ncdcs_bg[0]->Write();
    h_ncdci_bg[0]->Write();
    h_ncdc_bg[0]->Write();
    h_necl_bg[0]->Write();
  }
  //trainSets->Write(arrayname.c_str(), TObject::kSingleKey | TObject::kOverwrite);
  //datafile.Close();
  //trainSets->Clear();
  //delete trainSets;
  for (int isector = 0; isector < n_sector; ++ isector) {
    delete h_cdc2d_phi_sig[isector];
    delete h_cdc2d_pt_sig[isector];
    delete h_selTheta_sig[isector];
    delete h_selPhi_sig[isector];
    delete h_selE_sig[isector];
    delete h_result_sig[isector];
    delete h_cdc2d_phi_bg[isector];
    delete h_cdc2d_pt_bg[isector];
    delete h_selTheta_bg[isector];
    delete h_selPhi_bg[isector];
    delete h_selE_bg[isector];
    delete h_result_bg[isector];
  }
  delete h_ncdcf_sig[0];
  delete h_ncdcs_sig[0];
  delete h_ncdci_sig[0];
  delete h_ncdc_sig[0];
  delete h_necl_sig[0];
  delete h_ncdcf_bg[0];
  delete h_ncdcs_bg[0];
  delete h_ncdci_bg[0];
  delete h_ncdc_bg[0];
  delete h_necl_bg[0];
  h_cdc2d_phi_sig.clear();
  h_cdc2d_pt_sig.clear();
  h_selTheta_sig.clear();
  h_selPhi_sig.clear();
  h_selE_sig.clear();
  h_result_sig.clear();
  h_cdc2d_phi_bg.clear();
  h_cdc2d_pt_bg.clear();
  h_selTheta_bg.clear();
  h_selPhi_bg.clear();
  h_selE_bg.clear();
  h_result_bg.clear();
  h_ncdcf_sig.clear();
  h_ncdcs_sig.clear();
  h_ncdci_sig.clear();
  h_ncdc_sig.clear();
  h_necl_sig.clear();
  h_ncdcf_bg.clear();
  h_ncdcs_bg.clear();
  h_ncdci_bg.clear();
  h_necl_bg.clear();
}

setAbortLevel()

void setAbortLevel ( int  abortLevel )

inherited

Configure the abort log level.

Definition at line 67 of file Module.cc.

{
  m_logConfig.setAbortLevel(static_cast<LogConfig::ELogLevel>(abortLevel));
}

setDebugLevel()

void setDebugLevel ( int  debugLevel )

inherited

Configure the debug messaging level.

Definition at line 61 of file Module.cc.

{
  m_logConfig.setDebugLevel(debugLevel);
}

setDescription()

void setDescription ( const std::string &  description )

protectedinherited

Sets the description of the module.

Parameters

description

A description of the module.

Definition at line 214 of file Module.cc.

{
  m_description = description;
}

setLogConfig()

void setLogConfig ( const LogConfig &  logConfig )

inlineinherited

Set the log system configuration.

Definition at line 230 of file Module.h.

{m_logConfig = logConfig;}

setLogInfo()

void setLogInfo ( int  logLevel, unsigned int  logInfo  )

inherited

Configure the printed log information for the given level.

Parameters

logLevel	The log level (one of LogConfig::ELogLevel)
logInfo	What kind of info should be printed? ORed combination of LogConfig::ELogInfo flags.

Definition at line 73 of file Module.cc.

{
  m_logConfig.setLogInfo(static_cast<LogConfig::ELogLevel>(logLevel), logInfo);
}

setLogLevel()

void setLogLevel ( int  logLevel )

inherited

Configure the log level.

Definition at line 55 of file Module.cc.

{
  m_logConfig.setLogLevel(static_cast<LogConfig::ELogLevel>(logLevel));
}

setName()

void setName ( const std::string &  name )

inlineinherited

Set the name of the module.

Note

The module name is set when using the REG_MODULE macro, but the module can be renamed before calling process() using the set_name() function in your steering file.

Parameters

name	The name of the module

Definition at line 214 of file Module.h.

{ m_name = name; };

setParamList()

void setParamList ( const ModuleParamList &  params )

inlineprotectedinherited

Replace existing parameter list.

Definition at line 501 of file Module.h.

{ m_moduleParamList = params; }

setParamPython()

void setParamPython ( const std::string &  name, const boost::python::object &  pyObj  )

privateinherited

Implements a method for setting boost::python objects.

The method supports the following types: list, dict, int, double, string, bool The conversion of the python object to the C++ type and the final storage of the parameter value is done in the ModuleParam class.

Parameters

name	The unique name of the parameter.
pyObj	The object which should be converted and stored as the parameter value.

Definition at line 234 of file Module.cc.

{
  LogSystem& logSystem = LogSystem::Instance();
  logSystem.updateModule(&(getLogConfig()), getName());
  try {
    m_moduleParamList.setParamPython(name, pyObj);
  } catch (std::runtime_error& e) {
    throw std::runtime_error("Cannot set parameter '" + name + "' for module '"
                             + m_name + "': " + e.what());
  }
 
  logSystem.updateModule(nullptr);
}

setParamPythonDict()

void setParamPythonDict ( const boost::python::dict &  dictionary )

privateinherited

Implements a method for reading the parameter values from a boost::python dictionary.

The key of the dictionary has to be the name of the parameter and the value has to be of one of the supported parameter types.

Parameters

dictionary

The python dictionary from which the parameter values are read.

Definition at line 249 of file Module.cc.

{
 
  LogSystem& logSystem = LogSystem::Instance();
  logSystem.updateModule(&(getLogConfig()), getName());
 
  boost::python::list dictKeys = dictionary.keys();
  int nKey = boost::python::len(dictKeys);
 
  //Loop over all keys in the dictionary
  for (int iKey = 0; iKey < nKey; ++iKey) {
    boost::python::object currKey = dictKeys[iKey];
    boost::python::extract<std::string> keyProxy(currKey);
 
    if (keyProxy.check()) {
      const boost::python::object& currValue = dictionary[currKey];
      setParamPython(keyProxy, currValue);
    } else {
      B2ERROR("Setting the module parameters from a python dictionary: invalid key in dictionary!");
    }
  }
 
  logSystem.updateModule(nullptr);
}

setPropertyFlags()

void setPropertyFlags ( unsigned int  propertyFlags )

inherited

Sets the flags for the module properties.

Parameters

propertyFlags

bitwise OR of EModulePropFlags

Definition at line 208 of file Module.cc.

{
  m_propertyFlags = propertyFlags;
}

setReturnValue() [1/2]

void setReturnValue ( bool  value )

protectedinherited

Sets the return value for this module as bool.

The bool value is saved as an integer with the convention 1 meaning true and 0 meaning false. The value can be used in the steering file to divide the analysis chain into several paths.

Parameters

value

The value of the return value.

Definition at line 227 of file Module.cc.

{
  m_hasReturnValue = true;
  m_returnValue = value;
}

setReturnValue() [2/2]

void setReturnValue ( int  value )

protectedinherited

Sets the return value for this module as integer.

The value can be used in the steering file to divide the analysis chain into several paths.

Parameters

value

The value of the return value.

Definition at line 220 of file Module.cc.

{
  m_hasReturnValue = true;
  m_returnValue = value;
}

setType()

void setType ( const std::string &  type )

protectedinherited

Set the module type.

Only for use by internal modules (which don't use the normal REG_MODULE mechanism).

Definition at line 48 of file Module.cc.

{
  if (!m_type.empty())
    B2FATAL("Trying to change module type from " << m_type << " is not allowed, the value is assumed to be fixed.");
  m_type = type;
}

terminate()

void terminate ( void  )

overridevirtual

Do the training for all sectors.

Reimplemented from Module.

Definition at line 492 of file GRLNeuroTrainerModule.cc.

{
  // do training for all sectors with sufficient training samples
  for (unsigned isector = 0; isector < m_GRLNeuro.nSectors(); ++isector) {
    // skip sectors that have already been trained
    if (m_GRLNeuro[isector].isTrained())
      continue;
    float nTrainMin = m_multiplyNTrain ? m_nTrainMin * m_GRLNeuro[isector].getNumberOfWeights() : m_nTrainMin;
    std::cout << m_nTrainMin << " " << m_nValid << " " << m_nTest << std::endl;
    if (m_trainSets[isector].getNumberOfSamples() < (nTrainMin + m_nValid + m_nTest)) {
      B2WARNING("Not enough training samples for sector " << isector << " (" << (nTrainMin + m_nValid + m_nTest)
                << " requested, " << m_trainSets[isector].getNumberOfSamples() << " found)");
      continue;
    }
    train(isector);
    m_GRLNeuro[isector].Trained(true);
    // save all networks (including the newly trained)
    //m_GRLNeuro.save(m_filename, m_arrayname);
  }
 
  // save the training data
  saveTraindata(m_trainFilename, m_trainArrayname);
}

train()

void train

(

unsigned

isector

)

Train a single MLP.

Definition at line 519 of file GRLNeuroTrainerModule.cc.

{
#ifdef HAS_OPENMP
  B2INFO("Training network for sector " << isector << " with OpenMP");
#else
  B2INFO("Training network for sector " << isector << " without OpenMP");
#endif
  // initialize network
  unsigned nLayers = m_GRLNeuro[isector].getNumberOfLayers();
  unsigned* nNodes = new unsigned[nLayers];
  for (unsigned il = 0; il < nLayers; ++il) {
    nNodes[il] = m_GRLNeuro[isector].getNumberOfNodesLayer(il);
  }
  struct fann* ann = fann_create_standard_array(nLayers, nNodes);
  // initialize training and validation data
  GRLMLPData currentData = m_trainSets[isector];
  // train set
  unsigned nTrain = m_trainSets[isector].getNumberOfSamples() - m_nValid - m_nTest;
  struct fann_train_data* train_data =
    fann_create_train(nTrain, nNodes[0], nNodes[nLayers - 1]);
  for (unsigned i = 0; i < nTrain; ++i) {
    vector<float> input = currentData.getInput(i);
    for (unsigned j = 0; j < input.size(); ++j) {
      train_data->input[i][j] = input[j];
    }
    vector<float> target = currentData.getTarget(i);
    for (unsigned j = 0; j < target.size(); ++j) {
      train_data->output[i][j] = target[j];
    }
  }
  // validation set
  struct fann_train_data* valid_data =
    fann_create_train(m_nValid, nNodes[0], nNodes[nLayers - 1]);
  for (unsigned i = nTrain; i < nTrain + m_nValid; ++i) {
    vector<float> input = currentData.getInput(i);
    for (unsigned j = 0; j < input.size(); ++j) {
      valid_data->input[i - nTrain][j] = input[j];
    }
    vector<float> target = currentData.getTarget(i);
    for (unsigned j = 0; j < target.size(); ++j) {
      valid_data->output[i - nTrain][j] = target[j];
    }
  }
  // set network parameters
  fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
  fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
  fann_set_training_algorithm(ann, FANN_TRAIN_RPROP);
  // keep train error of optimum for all runs
  vector<double> trainOptLog = {};
  vector<double> validOptLog = {};
  // repeat training several times with different random start weights
  for (int irun = 0; irun < m_repeatTrain; ++irun) {
    double bestValid = 999.;
    vector<double> trainLog = {};
    vector<double> validLog = {};
    trainLog.assign(m_maxEpochs, 0.);
    validLog.assign(m_maxEpochs, 0.);
    int breakEpoch = 0;
    int bestEpoch = 0;
    vector<fann_type> bestWeights = {};
    bestWeights.assign(m_GRLNeuro[isector].getNumberOfWeights(), 0.);
    fann_randomize_weights(ann, -0.1, 0.1);
    // train and save the network
    for (int epoch = 1; epoch <= m_maxEpochs; ++epoch) {
#ifdef HAS_OPENMP
      double mse = parallel_fann::train_epoch_irpropm_parallel(ann, train_data, m_nThreads);
#else
      double mse = fann_train_epoch(ann, train_data);
#endif
      trainLog[epoch - 1] = mse;
      // reduce weights that got too large
      for (unsigned iw = 0; iw < ann->total_connections; ++iw) {
        if (ann->weights[iw] > m_wMax)
          ann->weights[iw] = m_wMax;
        else if (ann->weights[iw] < -m_wMax)
          ann->weights[iw] = -m_wMax;
      }
      // evaluate validation set
      fann_reset_MSE(ann);
#ifdef HAS_OPENMP
      double valid_mse = parallel_fann::test_data_parallel(ann, valid_data, m_nThreads);
#else
      double valid_mse = fann_test_data(ann, valid_data);
#endif
      validLog[epoch - 1] = valid_mse;
      // keep weights for lowest validation error
      if (valid_mse < bestValid) {
        bestValid = valid_mse;
        for (unsigned iw = 0; iw < ann->total_connections; ++iw) {
          bestWeights[iw] = ann->weights[iw];
        }
        bestEpoch = epoch;
      }
      // break when validation error increases
      if (epoch > m_checkInterval && valid_mse > validLog[epoch - m_checkInterval]) {
        B2INFO("Training run " << irun << " stopped in epoch " << epoch);
        B2INFO("Train error: " << mse << ", valid error: " << valid_mse <<
               ", best valid: " << bestValid);
        breakEpoch = epoch;
        break;
      }
      // print current status
      if (epoch == 1 || (epoch < 100 && epoch % 10 == 0) || epoch % 100 == 0) {
        B2INFO("Epoch " << epoch << ": Train error = " << mse <<
               ", valid error = " << valid_mse << ", best valid = " << bestValid);
      }
    }
    if (breakEpoch == 0) {
      B2INFO("Training run " << irun << " finished in epoch " << m_maxEpochs);
      breakEpoch = m_maxEpochs;
    }
    trainOptLog.push_back(trainLog[bestEpoch - 1]);
    validOptLog.push_back(validLog[bestEpoch - 1]);
    vector<float> oldWeights = m_GRLNeuro[isector].getWeights();
    m_GRLNeuro[isector].m_weights = bestWeights;
  }
  if (m_saveDebug) {
    for (unsigned i = nTrain + m_nValid; i < m_trainSets[isector].getNumberOfSamples(); ++i) {
      float output = m_GRLNeuro.runMLP(isector, m_trainSets[isector].getInput(i));
      vector<float> target = m_trainSets[isector].getTarget(i);
      //for (unsigned iout = 0; iout < output.size(); ++iout) {
      if (((int)target[0]) == 1)h_result_sig[isector]->Fill(output);
      else                    h_result_bg[isector]->Fill(output);
      //}
    }
  }
  // free memory
  fann_destroy_train(train_data);
  fann_destroy_train(valid_data);
  fann_destroy(ann);
  delete[] nNodes;
}

Member Data Documentation

h_cdc2d_phi_bg

std::vector<TH1D*> h_cdc2d_phi_bg

protected

Definition at line 146 of file GRLNeuroTrainerModule.h.

h_cdc2d_phi_sig

std::vector<TH1D*> h_cdc2d_phi_sig

protected

Histograms for monitoring.

Definition at line 140 of file GRLNeuroTrainerModule.h.

h_cdc2d_pt_bg

std::vector<TH1D*> h_cdc2d_pt_bg

protected

Definition at line 147 of file GRLNeuroTrainerModule.h.

h_cdc2d_pt_sig

std::vector<TH1D*> h_cdc2d_pt_sig

protected

Definition at line 141 of file GRLNeuroTrainerModule.h.

h_ncdc_bg

std::vector<TH1D*> h_ncdc_bg

protected

Definition at line 157 of file GRLNeuroTrainerModule.h.

h_ncdc_sig

std::vector<TH1D*> h_ncdc_sig

protected

Definition at line 152 of file GRLNeuroTrainerModule.h.

h_ncdcf_bg

std::vector<TH1D*> h_ncdcf_bg

protected

Definition at line 158 of file GRLNeuroTrainerModule.h.

h_ncdcf_sig

std::vector<TH1D*> h_ncdcf_sig

protected

Definition at line 153 of file GRLNeuroTrainerModule.h.

h_ncdci_bg

std::vector<TH1D*> h_ncdci_bg

protected

Definition at line 160 of file GRLNeuroTrainerModule.h.

h_ncdci_sig

std::vector<TH1D*> h_ncdci_sig

protected

Definition at line 155 of file GRLNeuroTrainerModule.h.

h_ncdcs_bg

std::vector<TH1D*> h_ncdcs_bg

protected

Definition at line 159 of file GRLNeuroTrainerModule.h.

h_ncdcs_sig

std::vector<TH1D*> h_ncdcs_sig

protected

Definition at line 154 of file GRLNeuroTrainerModule.h.

h_necl_bg

std::vector<TH1D*> h_necl_bg

protected

Definition at line 161 of file GRLNeuroTrainerModule.h.

h_necl_sig

std::vector<TH1D*> h_necl_sig

protected

Definition at line 156 of file GRLNeuroTrainerModule.h.

h_result_bg

std::vector<TH1D*> h_result_bg

protected

Definition at line 151 of file GRLNeuroTrainerModule.h.

h_result_sig

std::vector<TH1D*> h_result_sig

protected

Definition at line 145 of file GRLNeuroTrainerModule.h.

h_selE_bg

std::vector<TH1D*> h_selE_bg

protected

Definition at line 148 of file GRLNeuroTrainerModule.h.

h_selE_sig

std::vector<TH1D*> h_selE_sig

protected

Definition at line 142 of file GRLNeuroTrainerModule.h.

h_selPhi_bg

std::vector<TH1D*> h_selPhi_bg

protected

Definition at line 149 of file GRLNeuroTrainerModule.h.

h_selPhi_sig

std::vector<TH1D*> h_selPhi_sig

protected

Definition at line 143 of file GRLNeuroTrainerModule.h.

h_selTheta_bg

std::vector<TH1D*> h_selTheta_bg

protected

Definition at line 150 of file GRLNeuroTrainerModule.h.

h_selTheta_sig

std::vector<TH1D*> h_selTheta_sig

protected

Definition at line 144 of file GRLNeuroTrainerModule.h.

m_2DfinderCollectionName

std::string m_2DfinderCollectionName

protected

Name of the StoreArray containing the input 2D tracks.

Definition at line 75 of file GRLNeuroTrainerModule.h.

m_arrayname

std::string m_arrayname

protected

Name of the TObjArray holding the networks.

Definition at line 85 of file GRLNeuroTrainerModule.h.

m_checkInterval

int m_checkInterval

protected

Training is stopped if validation error is higher than checkInterval epochs ago, i.e.

either the validation error is increasing or the gain is less than the fluctuations.

Definition at line 111 of file GRLNeuroTrainerModule.h.

m_conditions

std::vector<ModuleCondition> m_conditions

privateinherited

Module condition, only non-null if set.

Definition at line 521 of file Module.h.

m_description

std::string m_description

privateinherited

The description of the module.

Definition at line 511 of file Module.h.

m_filename

std::string m_filename

protected

Name of file where network weights etc.

are stored after training.

Definition at line 79 of file GRLNeuroTrainerModule.h.

m_GRLCollectionName

std::string m_GRLCollectionName

protected

Name of the StoreObj containing the input GRL.

Definition at line 77 of file GRLNeuroTrainerModule.h.

m_GRLNeuro

GRLNeuro m_GRLNeuro

protected

Instance of the NeuroTrigger.

Definition at line 118 of file GRLNeuroTrainerModule.h.

m_hasReturnValue

bool m_hasReturnValue

privateinherited

True, if the return value is set.

Definition at line 518 of file Module.h.

m_load

bool m_load

protected

Switch to load saved parameters from a previous run.

Definition at line 91 of file GRLNeuroTrainerModule.h.

m_logConfig

LogConfig m_logConfig

privateinherited

The log system configuration of the module.

Definition at line 514 of file Module.h.

m_logFilename

std::string m_logFilename

protected

Name of file where training log is stored.

Definition at line 83 of file GRLNeuroTrainerModule.h.

m_maxEpochs

int m_maxEpochs

protected

Maximal number of training epochs.

Definition at line 113 of file GRLNeuroTrainerModule.h.

m_moduleParamList

ModuleParamList m_moduleParamList

privateinherited

List storing and managing all parameter of the module.

Definition at line 516 of file Module.h.

m_multiplyNTrain

bool m_multiplyNTrain

protected

Switch to multiply number of samples with number of weights.

Definition at line 99 of file GRLNeuroTrainerModule.h.

m_name

std::string m_name

privateinherited

The name of the module, saved as a string (user-modifiable)

Definition at line 508 of file Module.h.

m_nTest

int m_nTest

protected

Number of test samples.

Definition at line 103 of file GRLNeuroTrainerModule.h.

m_nThreads

int m_nThreads

protected

Number of threads for training.

Definition at line 107 of file GRLNeuroTrainerModule.h.

m_nTrainMax

double m_nTrainMax

protected

Maximal number of training samples.

Definition at line 97 of file GRLNeuroTrainerModule.h.

m_nTrainMin

double m_nTrainMin

protected

Minimal number of training samples.

Definition at line 95 of file GRLNeuroTrainerModule.h.

m_nValid

int m_nValid

protected

Number of validation samples.

Definition at line 101 of file GRLNeuroTrainerModule.h.

m_package

std::string m_package

privateinherited

Package this module is found in (may be empty).

Definition at line 510 of file Module.h.

m_parameters

GRLNeuro::Parameters m_parameters

protected

Parameters for the NeuroTrigger.

Definition at line 93 of file GRLNeuroTrainerModule.h.

m_propertyFlags

unsigned int m_propertyFlags

privateinherited

The properties of the module as bitwise or (with |) of EModulePropFlags.

Definition at line 512 of file Module.h.

m_repeatTrain

int m_repeatTrain

protected

Number of training runs with different random start weights.

Definition at line 115 of file GRLNeuroTrainerModule.h.

m_returnValue

int m_returnValue

privateinherited

The return value.

Definition at line 519 of file Module.h.

m_saveDebug

bool m_saveDebug

protected

If true, save training curve and parameter distribution of training data.

Definition at line 89 of file GRLNeuroTrainerModule.h.

m_trainArrayname

std::string m_trainArrayname

protected

Name of the TObjArray holding the training samples.

Definition at line 87 of file GRLNeuroTrainerModule.h.

m_trainFilename

std::string m_trainFilename

protected

Name of file where training samples are stored.

Definition at line 81 of file GRLNeuroTrainerModule.h.

m_trainSets

std::vector<GRLMLPData> m_trainSets

protected

Sets of training data for all sectors.

Definition at line 120 of file GRLNeuroTrainerModule.h.

m_TrgECLClusterName

std::string m_TrgECLClusterName

protected

Name of the StoreArray containing the ECL clusters.

Definition at line 73 of file GRLNeuroTrainerModule.h.

m_type

std::string m_type

privateinherited

The type of the module, saved as a string.

Definition at line 509 of file Module.h.

m_wMax

double m_wMax

protected

Limit for weights.

Definition at line 105 of file GRLNeuroTrainerModule.h.

n_cdc_sector

int n_cdc_sector = 0

protected

Number of CDC sectors.

Definition at line 133 of file GRLNeuroTrainerModule.h.

n_ecl_sector

int n_ecl_sector = 0

protected

Number of ECL sectors.

Definition at line 135 of file GRLNeuroTrainerModule.h.

n_sector

int n_sector = 0

protected

Number of Total sectors.

Definition at line 137 of file GRLNeuroTrainerModule.h.

radtodeg

double radtodeg = 0

protected

convert radian to degree

Definition at line 130 of file GRLNeuroTrainerModule.h.

scale_bg

std::vector<int> scale_bg

protected

BG scale factor for training.

Definition at line 164 of file GRLNeuroTrainerModule.h.

TC1GeV

std::vector<float> TC1GeV

protected

Definition at line 128 of file GRLNeuroTrainerModule.h.

TCcotThetaLab

std::vector<float> TCcotThetaLab

protected

Definition at line 125 of file GRLNeuroTrainerModule.h.

TCPhiCOM

std::vector<float> TCPhiCOM

protected

Definition at line 126 of file GRLNeuroTrainerModule.h.

TCPhiLab

std::vector<float> TCPhiLab

protected

Definition at line 124 of file GRLNeuroTrainerModule.h.

TCThetaCOM

std::vector<float> TCThetaCOM

protected

Definition at line 127 of file GRLNeuroTrainerModule.h.

TCThetaID

std::vector<int> TCThetaID

protected

Definition at line 123 of file GRLNeuroTrainerModule.h.

---

The documentation for this class was generated from the following files:

• trg/grl/modules/trggrlneuralnet/include/GRLNeuroTrainerModule.h
• trg/grl/modules/trggrlneuralnet/src/GRLNeuroTrainerModule.cc