CDCTriggerNeuroDQMModule Class Reference

CDC Trigger DQM Module. More...

#include <CDCTriggerNeuroDQMModule.h>

Inheritance diagram for CDCTriggerNeuroDQMModule:

Classes
struct	TSLine

Public Types
typedef std::vector< TSLine >	TSLines
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
	CDCTriggerNeuroDQMModule ()
	Constructor.
virtual	~CDCTriggerNeuroDQMModule ()
	Destructor.
virtual void	initialize () override
	Module functions.
virtual void	beginRun () override
	Function to process begin_run record.
virtual void	event () override
	Function to process event record.
virtual void	endRun () override
	Function to process end_run record.
virtual void	terminate () override
	Function to terminate module.
virtual void	defineHisto () override
	Histogram definitions such as TH1(), TH2(), TNtuple(), TTree()....
virtual std::vector< std::string >	getFileNames (bool outputFiles)
	Return a list of output filenames for this modules.
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.

Private Member Functions
void	condFill (TH1F *histo, float value)
	Fill a histogram only with non-zero values.
unsigned	getPattern (CDCTriggerTrack *track, std::string hitCollectionName)
	get pattern of the track
bool	isValidPattern (unsigned pattern)
	Validity of the pattern.
std::string	padto (std::string s, unsigned l)
std::string	padright (std::string s, unsigned l)
bool	have_relation (const CDCTriggerTrack &track, const CDCTriggerSegmentHit &hit, std::string &arrayname)
void	sorted_insert (TSLines &lines, TSLine &line, std::string &arrayname, std::string &firstsortarray, std::string &secondsortarray)
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_histogramDirectoryName
	Name of the histogram directory in ROOT file.
bool	m_limitedoutput
	Switch to supress output for dqm online module.
int	m_nsamets
	Number of identical track segments to be required for matching between HW and SW Neurotrigger.
std::string	m_unpackedSegmentHitsName
	Name for TS hits from unpacker.
std::string	m_unpacked2DTracksName
	Name for 2D finder tracks from unpacker.
std::string	m_unpackedNeuroTracksName
	Name for neuro tracks from unpacker.
std::string	m_unpackedNeuroInputVectorName
	Name for neuro input vector from unpacker.
std::string	m_unpackedNeuroInput2DTracksName
	Name for neuro input 2d finder tracks.
std::string	m_unpackedNeuroInputSegmentsName
	Name for neuro input Track segments.
std::string	m_simNeuroTracksName
	Name for neuro tracks from TSIM using HW TS, HW 2D.
std::string	m_simNeuroInputVectorName
	Name for simulated neuro input vector using HW TS, HW 2D.
bool	m_showRecoTracks
	Switch to turn on a comparison with the reconstruction.
int	m_recoTrackMultiplicity
	Select events with a specific RecoTrack track multiplicity.
bool	m_skipWithoutHWTS
	Switch to skip events without unpacked TS.
double	m_maxRecoZDist
	Select only RecoTracks with a maximum z distance to the IP.
double	m_maxRecoD0Dist
	Select only RecoTracks with a maximum d0 distance to the z axis.
std::string	m_simSegmentHitsName
	Name for simulated TS hits.
std::string	m_sim2DTracksSWTSName
	Name for simulated 2D finder tracks using simulated TS.
std::string	m_simNeuroTracksSWTSSW2DName
	Name for neuro tracks using simulated TS and simulated 2D.
std::string	m_simNeuroInputVectorSWTSSW2DName
	Name for neuro input vector using simulated TS and simulated 2D.
std::string	m_recoTracksName
	Name for the RecoTrack array name.
StoreArray< CDCTriggerSegmentHit >	m_unpackedSegmentHits
	StoreArray for TS hits from unpacker.
StoreArray< CDCTriggerTrack >	m_unpacked2DTracks
	StoreArray for 2D finder tracks from unpacker.
StoreArray< CDCTriggerTrack >	m_unpackedNeuroTracks
	StoreArray for neuro tracks from unpacker.
StoreArray< CDCTriggerMLPInput >	m_unpackedNeuroInputVector
	StoreArray for neuro input vector from unpacker.
StoreArray< CDCTriggerTrack >	m_unpackedNeuroInput2DTracks
	StoreArray for neuro input 2dfinder tracks.
StoreArray< CDCTriggerSegmentHit >	m_unpackedNeuroInputSegments
	StoreArray for neuro input Track segments.
StoreArray< CDCTriggerTrack >	m_simNeuroTracks
	StoreArray for neuro tracks from TSIM.
StoreArray< CDCTriggerMLPInput >	m_simNeuroInputVector
	StoreArray for neuro input vector from TSIM.
StoreArray< RecoTrack >	m_RecoTracks
	StoreArray for RecoTracks.
StoreArray< CDCTriggerSegmentHit >	m_simSegmentHits
	StoreArray for simulated TS hits.
StoreArray< CDCTriggerTrack >	m_sim2DTracksSWTS
	StoreArray for simulated 2D finder tracks using simulated TS.
StoreArray< CDCTriggerMLPInput >	m_simNeuroInputVectorSWTSSW2D
	StoreArray for neuro input vector using simulated TS and simulated 2D.
StoreArray< CDCTriggerTrack >	m_simNeuroTracksSWTSSW2D
	StoreArray for neuro tracks using simulated TS and simulated 2D.
StoreObjPtr< BinnedEventT0 >	m_eventTime
	storeobjpointer for event time
TH1F *	m_neuroHWOutZ = nullptr
	z distribution of unpacked neuro tracks
TH1F *	m_neuroHWOutCosTheta = nullptr
	cos theta distribution of unpacked neuro tracks
TH1F *	m_neuroHWOutInvPt = nullptr
	Inverse Pt distribution of unpacked neuro tracks.
TH1F *	m_neuroHWOutPt = nullptr
	Pt distribution of unpacked neuro tracks.
TH1F *	m_neuroHWOutPhi0 = nullptr
	phi distribution of unpacked neuro tracks
TH1F *	m_neuroHWOutHitPattern = nullptr
	stereo hit pattern of simulated neuro tracks (hw TS hw 2D sw NN)
TH1F *	m_neuroHWOutm_time = nullptr
	m_time distribution of unpacked neuro tracks
TH1F *	m_neuroHWOutTrackCount = nullptr
	number of unpacked tracks per event
TH1F *	m_neuroHWSector = nullptr
	sector of unpacked neuro tracks
TH1F *	m_neuroHWInInvPt = nullptr
	Inverse Pt distribution from incoming 2dtrack.
TH1F *	m_neuroHWInPhi0 = nullptr
	Phi0 of incoming 2dtrack.
TH1F *	m_neuroHWInm_time = nullptr
	m_time distribution from incoming 2dtracks
TH1F *	m_neuroHWInTrackCount = nullptr
	number of neuro input 2dtracks per event
TH1F *	m_neuroHWOutVsInTrackCount = nullptr
	neuroHWOutTracks - neuroHWInTrackCount
TH1F *	m_2DHWOutInvPt = nullptr
	Inverse Pt of 2dtracks.
TH1F *	m_2DHWOutPhi0 = nullptr
	Phi0 of 2dtracks.
TH1F *	m_2DHWOutm_time = nullptr
	m_time 2dtracks
TH1F *	m_2DHWOutTrackCount = nullptr
	number of 2dtracks per event
TH1F *	m_neuroHWInVs2DOutTrackCount = nullptr
	neuroHWInTrackCount - 2DHWOutTrackCountput
TH1F *	m_neuroSWOutZ = nullptr
	z distribution from simulation (hw TS hw 2D sw NN)
TH1F *	m_neuroSWOutCosTheta = nullptr
	cos theta distribution from simulation (hw TS hw 2D sw NN)
TH1F *	m_neuroSWOutInvPt = nullptr
	Inverse Pt distribution from simulation (hw TS hw 2D sw NN)
TH1F *	m_neuroSWOutPhi0 = nullptr
	phi distribution from simulation (hw TS hw 2D sw NN)
TH1F *	m_neuroSWOutHitPattern = nullptr
	stereo hit pattern of simulated neuro tracks (hw TS hw 2D sw NN)
TH1F *	m_neuroSWOutTrackCount = nullptr
	number of simulated tracks per event (hw TS hw 2D sw NN)
TH1F *	m_neuroSWSector = nullptr
	NN sector from simulation (hw TS hw 2D sw NN)
TH1F *	m_2DSWOutInvPt = nullptr
	Inverse Pt of 2dtracks (sw TS sw 2D)
TH1F *	m_2DSWOutPhi0 = nullptr
	Phi0 of 2dtracks (sw TS sw 2D)
TH1F *	m_2DSWOutm_time = nullptr
	m_time 2dtracks (sw TS sw 2D)
TH1F *	m_2DSWOutTrackCount = nullptr
	number of 2dtracks per event (sw TS sw 2D)
TH1F *	m_neuroSWTSSW2DOutZ = nullptr
	z distribution from simulation (sw TS sw 2D sw NN)
TH1F *	m_neuroSWTSSW2DOutCosTheta = nullptr
	cos theta distribution from simulation (sw TS sw 2D sw NN)
TH1F *	m_neuroSWTSSW2DOutInvPt = nullptr
	Inverse Pt distribution from simulation (sw TS sw 2D sw NN)
TH1F *	m_neuroSWTSSW2DOutPhi0 = nullptr
	phi distribution from simulation (sw TS sw 2D sw NN)
TH1F *	m_neuroSWTSSW2DOutHitPattern = nullptr
	stereo hit pattern of simulated neuro tracks (sw TS sw 2D sw NN)
TH1F *	m_neuroSWTSSW2DOutTrackCount = nullptr
	number of simulated tracks per event (sw TS sw 2D sw NN)
TH1F *	m_neuroSWTSSW2DSector = nullptr
	NN sector from simulation (sw TS sw 2D sw NN)
TH1F *	m_neuroDeltaZ = nullptr
	unpacked z - TSIM z
TH1F *	m_neuroDeltaTheta = nullptr
	unpacked theta - TSIM theta
TH2F *	m_neuroScatterZ = nullptr
	unpacked z vs TSIM z, scatter plot
TH2F *	m_neuroScatterTheta = nullptr
	unpacked theta vs TSIM theta, scatter plot
TH1F *	m_neuroDeltaInputID = nullptr
	unpacked ID input - TSIM ID input
TH1F *	m_neuroDeltaInputT = nullptr
	unpacked time input - TSIM time input
TH1F *	m_neuroDeltaInputAlpha = nullptr
	unpacked alpha input - TSIM alpha input
TH1F *	m_neuroDeltaTSID = nullptr
	unpacked selected TSID - TSIM selected TSID
TH1F *	m_neuroDeltaSector = nullptr
	unpacked sector - TSIM sector
TH1F *	m_simSameTS = nullptr
	number of TS selected in both, unpacked and TSIM tracks
TH1F *	m_simDiffTS = nullptr
	number of TS selcted in TSIM but not in unpacked
TH1F *	m_neuroHWOutQuad5Z = nullptr
	z distribution from unpacker (no quadrant set)
TH1F *	m_neuroHWOutQuad5CosTheta = nullptr
	cos theta distribution from unpacker (no quadrant set)
TH1F *	m_neuroHWOutQuad5Phi0 = nullptr
	phi distribution from unpacker (no quadrant set)
TH1F *	m_neuroHWOutQuad5InvPt = nullptr
	Inverse Pt distribution from unpacker (no quadrant set)
TH1F *	m_neuroHWOutQuad0Z = nullptr
	z distribution from unpacker (quadrant 0)
TH1F *	m_neuroHWOutQuad0CosTheta = nullptr
	cos theta distribution from unpacker (quadrant 0)
TH1F *	m_neuroHWOutQuad0Phi0 = nullptr
	phi distribution from unpacker (quadrant 0)
TH1F *	m_neuroHWOutQuad0InvPt = nullptr
	Inverse Pt distribution from unpacker (quadrant 0)
TH1F *	m_neuroHWOutQuad1Z = nullptr
	z distribution from unpacker (quadrant 1)
TH1F *	m_neuroHWOutQuad1CosTheta = nullptr
	cos theta distribution from unpacker (quadrant 1)
TH1F *	m_neuroHWOutQuad1Phi0 = nullptr
	phi distribution from unpacker (quadrant 1)
TH1F *	m_neuroHWOutQuad1InvPt = nullptr
	Inverse Pt distribution from unpacker (quadrant 1)
TH1F *	m_neuroHWOutQuad2Z = nullptr
	z distribution from unpacker (quadrant 2)
TH1F *	m_neuroHWOutQuad2CosTheta = nullptr
	cos theta distribution from unpacker (quadrant 2)
TH1F *	m_neuroHWOutQuad2Phi0 = nullptr
	phi distribution from unpacker (quadrant 2)
TH1F *	m_neuroHWOutQuad2InvPt = nullptr
	Inverse Pt distribution from unpacker (quadrant 2)
TH1F *	m_neuroHWOutQuad3Z = nullptr
	z distribution from unpacker (quadrant 3)
TH1F *	m_neuroHWOutQuad3CosTheta = nullptr
	cos theta distribution from unpacker (quadrant 3)
TH1F *	m_neuroHWOutQuad3Phi0 = nullptr
	phi distribution from unpacker (quadrant 3)
TH1F *	m_neuroHWOutQuad3InvPt = nullptr
	Inverse Pt distribution from unpacker (quadrant 3)
TH1F *	m_neuroHWInTSID = nullptr
	ID of incoming track segments.
TH1F *	m_neuroHWInTSCount = nullptr
	number of TS per track
TH1F *	m_neuroHWInTSPrioT_Layer0 = nullptr
	Priority time of track segments in layer 0.
TH1F *	m_neuroHWInTSPrioT_Layer1 = nullptr
	Priority time of track segments in layer 1.
TH1F *	m_neuroHWInTSPrioT_Layer2 = nullptr
	Priority time of track segments in layer 2.
TH1F *	m_neuroHWInTSPrioT_Layer3 = nullptr
	Priority time of track segments in layer 3.
TH1F *	m_neuroHWInTSPrioT_Layer4 = nullptr
	Priority time of track segments in layer 4.
TH1F *	m_neuroHWInTSPrioT_Layer5 = nullptr
	Priority time of track segments in layer 5.
TH1F *	m_neuroHWInTSPrioT_Layer6 = nullptr
	Priority time of track segments in layer 6.
TH1F *	m_neuroHWInTSPrioT_Layer7 = nullptr
	Priority time of track segments in layer 7.
TH1F *	m_neuroHWInTSPrioT_Layer8 = nullptr
	Priority time of track segments in layer 8.
TH1F *	m_neuroHWInTSFoundT_Layer0 = nullptr
	Found time of track segments in layer 0.
TH1F *	m_neuroHWInTSFoundT_Layer1 = nullptr
	Found time of track segments in layer 1.
TH1F *	m_neuroHWInTSFoundT_Layer2 = nullptr
	Found time of track segments in layer 2.
TH1F *	m_neuroHWInTSFoundT_Layer3 = nullptr
	Found time of track segments in layer 3.
TH1F *	m_neuroHWInTSFoundT_Layer4 = nullptr
	Found time of track segments in layer 4.
TH1F *	m_neuroHWInTSFoundT_Layer5 = nullptr
	Found time of track segments in layer 5.
TH1F *	m_neuroHWInTSFoundT_Layer6 = nullptr
	Found time of track segments in layer 6.
TH1F *	m_neuroHWInTSFoundT_Layer7 = nullptr
	Found time of track segments in layer 7.
TH1F *	m_neuroHWInTSFoundT_Layer8 = nullptr
	Found time of track segments in layer 8.
TH1F *	m_neuroHWInTSPrioB_Layer0 = nullptr
	Priority bits of track segments in layer 0.
TH1F *	m_neuroHWInTSPrioB_Layer1 = nullptr
	Priority bits of track segments in layer 1.
TH1F *	m_neuroHWInTSPrioB_Layer2 = nullptr
	Priority bits of track segments in layer 2.
TH1F *	m_neuroHWInTSPrioB_Layer3 = nullptr
	Priority bits of track segments in layer 3.
TH1F *	m_neuroHWInTSPrioB_Layer4 = nullptr
	Priority bits of track segments in layer 4.
TH1F *	m_neuroHWInTSPrioB_Layer5 = nullptr
	Priority bits of track segments in layer 5.
TH1F *	m_neuroHWInTSPrioB_Layer6 = nullptr
	Priority bits of track segments in layer 6.
TH1F *	m_neuroHWInTSPrioB_Layer7 = nullptr
	Priority bits of track segments in layer 7.
TH1F *	m_neuroHWInTSPrioB_Layer8 = nullptr
	Priority bits of track segments in layer 8.
TH1F *	m_neuroHWInTSLR_Layer0 = nullptr
	Left/Right of track segments in layer 0.
TH1F *	m_neuroHWInTSLR_Layer1 = nullptr
	Left/Right of track segments in layer 1.
TH1F *	m_neuroHWInTSLR_Layer2 = nullptr
	Left/Right of track segments in layer 2.
TH1F *	m_neuroHWInTSLR_Layer3 = nullptr
	Left/Right of track segments in layer 3.
TH1F *	m_neuroHWInTSLR_Layer4 = nullptr
	Left/Right of track segments in layer 4.
TH1F *	m_neuroHWInTSLR_Layer5 = nullptr
	Left/Right of track segments in layer 5.
TH1F *	m_neuroHWInTSLR_Layer6 = nullptr
	Left/Right of track segments in layer 6.
TH1F *	m_neuroHWInTSLR_Layer7 = nullptr
	Left/Right of track segments in layer 7.
TH1F *	m_neuroHWInTSLR_Layer8 = nullptr
	Left/Right of track segments in layer 8.
TH1F *	m_neuroHWSelTSID = nullptr
	ID of selected track segments.
TH1F *	m_neuroHWSelTSCount = nullptr
	number of selected TS per SL
TH1F *	m_neuroHWSelTSPrioT_Layer0 = nullptr
	Priority time of selected track segments in layer 0.
TH1F *	m_neuroHWSelTSPrioT_Layer1 = nullptr
	Priority time of selected track segments in layer 1.
TH1F *	m_neuroHWSelTSPrioT_Layer2 = nullptr
	Priority time of selected track segments in layer 2.
TH1F *	m_neuroHWSelTSPrioT_Layer3 = nullptr
	Priority time of selected track segments in layer 3.
TH1F *	m_neuroHWSelTSPrioT_Layer4 = nullptr
	Priority time of selected track segments in layer 4.
TH1F *	m_neuroHWSelTSPrioT_Layer5 = nullptr
	Priority time of selected track segments in layer 5.
TH1F *	m_neuroHWSelTSPrioT_Layer6 = nullptr
	Priority time of selected track segments in layer 6.
TH1F *	m_neuroHWSelTSPrioT_Layer7 = nullptr
	Priority time of selected track segments in layer 7.
TH1F *	m_neuroHWSelTSPrioT_Layer8 = nullptr
	Priority time of selected track segments in layer 8.
TH1F *	m_neuroHWSelTSFoundT_Layer0 = nullptr
	Found time of selected track segments in layer 0.
TH1F *	m_neuroHWSelTSFoundT_Layer1 = nullptr
	Found time of selected track segments in layer 1.
TH1F *	m_neuroHWSelTSFoundT_Layer2 = nullptr
	Found time of selected track segments in layer 2.
TH1F *	m_neuroHWSelTSFoundT_Layer3 = nullptr
	Found time of selected track segments in layer 3.
TH1F *	m_neuroHWSelTSFoundT_Layer4 = nullptr
	Found time of selected track segments in layer 4.
TH1F *	m_neuroHWSelTSFoundT_Layer5 = nullptr
	Found time of selected track segments in layer 5.
TH1F *	m_neuroHWSelTSFoundT_Layer6 = nullptr
	Found time of selected track segments in layer 6.
TH1F *	m_neuroHWSelTSFoundT_Layer7 = nullptr
	Found time of selected track segments in layer 7.
TH1F *	m_neuroHWSelTSFoundT_Layer8 = nullptr
	Found time of selected track segments in layer 8.
TH1F *	m_neuroHWSelTSPrioB_Layer0 = nullptr
	Priority bits of track segments in layer 0.
TH1F *	m_neuroHWSelTSPrioB_Layer1 = nullptr
	Priority bits of track segments in layer 1.
TH1F *	m_neuroHWSelTSPrioB_Layer2 = nullptr
	Priority bits of track segments in layer 2.
TH1F *	m_neuroHWSelTSPrioB_Layer3 = nullptr
	Priority bits of track segments in layer 3.
TH1F *	m_neuroHWSelTSPrioB_Layer4 = nullptr
	Priority bits of track segments in layer 4.
TH1F *	m_neuroHWSelTSPrioB_Layer5 = nullptr
	Priority bits of track segments in layer 5.
TH1F *	m_neuroHWSelTSPrioB_Layer6 = nullptr
	Priority bits of track segments in layer 6.
TH1F *	m_neuroHWSelTSPrioB_Layer7 = nullptr
	Priority bits of track segments in layer 7.
TH1F *	m_neuroHWSelTSPrioB_Layer8 = nullptr
	Priority bits of track segments in layer 8.
TH1F *	m_neuroHWSelTSLR_Layer0 = nullptr
	Left/Right of track segments in layer 0.
TH1F *	m_neuroHWSelTSLR_Layer1 = nullptr
	Left/Right of track segments in layer 1.
TH1F *	m_neuroHWSelTSLR_Layer2 = nullptr
	Left/Right of track segments in layer 2.
TH1F *	m_neuroHWSelTSLR_Layer3 = nullptr
	Left/Right of track segments in layer 3.
TH1F *	m_neuroHWSelTSLR_Layer4 = nullptr
	Left/Right of track segments in layer 4.
TH1F *	m_neuroHWSelTSLR_Layer5 = nullptr
	Left/Right of track segments in layer 5.
TH1F *	m_neuroHWSelTSLR_Layer6 = nullptr
	Left/Right of track segments in layer 6.
TH1F *	m_neuroHWSelTSLR_Layer7 = nullptr
	Left/Right of track segments in layer 7.
TH1F *	m_neuroHWSelTSLR_Layer8 = nullptr
	Left/Right of track segments in layer 8.
TH1F *	m_2DHWInTSPrioT_Layer0 = nullptr
	Priority time of 2D track segments in layer 0.
TH1F *	m_2DHWInTSPrioT_Layer2 = nullptr
	Priority time of 2D track segments in layer 2.
TH1F *	m_2DHWInTSPrioT_Layer4 = nullptr
	Priority time of 2D track segments in layer 4.
TH1F *	m_2DHWInTSPrioT_Layer6 = nullptr
	Priority time of 2D track segments in layer 6.
TH1F *	m_2DHWInTSPrioT_Layer8 = nullptr
	Priority time of 2D track segments in layer 8.
TH1F *	m_2DHWInTSFoundT_Layer0 = nullptr
	Found time of 2D track segments in layer 0.
TH1F *	m_2DHWInTSFoundT_Layer2 = nullptr
	Found time of 2D track segments in layer 2.
TH1F *	m_2DHWInTSFoundT_Layer4 = nullptr
	Found time of 2D track segments in layer 4.
TH1F *	m_2DHWInTSFoundT_Layer6 = nullptr
	Found time of 2D track segments in layer 6.
TH1F *	m_2DHWInTSFoundT_Layer8 = nullptr
	Found time of 2D track segments in layer 8.
TH1F *	m_2DHWInTSPrioB_Layer0 = nullptr
	Priority bits of 2D track segments in layer 0.
TH1F *	m_2DHWInTSPrioB_Layer2 = nullptr
	Priority bits of 2D track segments in layer 2.
TH1F *	m_2DHWInTSPrioB_Layer4 = nullptr
	Priority bits of 2D track segments in layer 4.
TH1F *	m_2DHWInTSPrioB_Layer6 = nullptr
	Priority bits of 2D track segments in layer 6.
TH1F *	m_2DHWInTSPrioB_Layer8 = nullptr
	Priority bits of 2D track segments in layer 8.
TH1F *	m_2DHWInTSLR_Layer0 = nullptr
	Left/Right of 2D track segments in layer 0.
TH1F *	m_2DHWInTSLR_Layer2 = nullptr
	Left/Right of 2D track segments in layer 2.
TH1F *	m_2DHWInTSLR_Layer4 = nullptr
	Left/Right of 2D track segments in layer 4.
TH1F *	m_2DHWInTSLR_Layer6 = nullptr
	Left/Right of 2D track segments in layer 6.
TH1F *	m_2DHWInTSLR_Layer8 = nullptr
	Left/Right of 2D track segments in layer 8.
TH1F *	m_neuroSWSelTSID = nullptr
	ID of incoming track segments.
TH1F *	m_neuroSWSelTSCount = nullptr
	number of TS per SL
TH1F *	m_neuroSWSelTSPrioT_Layer0 = nullptr
	Priority time of track segments in layer 0.
TH1F *	m_neuroSWSelTSPrioT_Layer1 = nullptr
	Priority time of track segments in layer 1.
TH1F *	m_neuroSWSelTSPrioT_Layer2 = nullptr
	Priority time of track segments in layer 2.
TH1F *	m_neuroSWSelTSPrioT_Layer3 = nullptr
	Priority time of track segments in layer 3.
TH1F *	m_neuroSWSelTSPrioT_Layer4 = nullptr
	Priority time of track segments in layer 4.
TH1F *	m_neuroSWSelTSPrioT_Layer5 = nullptr
	Priority time of track segments in layer 5.
TH1F *	m_neuroSWSelTSPrioT_Layer6 = nullptr
	Priority time of track segments in layer 6.
TH1F *	m_neuroSWSelTSPrioT_Layer7 = nullptr
	Priority time of track segments in layer 7.
TH1F *	m_neuroSWSelTSPrioT_Layer8 = nullptr
	Priority time of track segments in layer 8.
TH1F *	m_neuroSWSelTSFoundT_Layer0 = nullptr
	Found time of track segments in layer 0.
TH1F *	m_neuroSWSelTSFoundT_Layer1 = nullptr
	Found time of track segments in layer 1.
TH1F *	m_neuroSWSelTSFoundT_Layer2 = nullptr
	Found time of track segments in layer 2.
TH1F *	m_neuroSWSelTSFoundT_Layer3 = nullptr
	Found time of track segments in layer 3.
TH1F *	m_neuroSWSelTSFoundT_Layer4 = nullptr
	Found time of track segments in layer 4.
TH1F *	m_neuroSWSelTSFoundT_Layer5 = nullptr
	Found time of track segments in layer 5.
TH1F *	m_neuroSWSelTSFoundT_Layer6 = nullptr
	Found time of track segments in layer 6.
TH1F *	m_neuroSWSelTSFoundT_Layer7 = nullptr
	Found time of track segments in layer 7.
TH1F *	m_neuroSWSelTSFoundT_Layer8 = nullptr
	Found time of track segments in layer 8.
TH1F *	m_neuroSWSelTSPrioB_Layer0 = nullptr
	Priority bits of track segments in layer 0.
TH1F *	m_neuroSWSelTSPrioB_Layer1 = nullptr
	Priority bits of track segments in layer 1.
TH1F *	m_neuroSWSelTSPrioB_Layer2 = nullptr
	Priority bits of track segments in layer 2.
TH1F *	m_neuroSWSelTSPrioB_Layer3 = nullptr
	Priority bits of track segments in layer 3.
TH1F *	m_neuroSWSelTSPrioB_Layer4 = nullptr
	Priority bits of track segments in layer 4.
TH1F *	m_neuroSWSelTSPrioB_Layer5 = nullptr
	Priority bits of track segments in layer 5.
TH1F *	m_neuroSWSelTSPrioB_Layer6 = nullptr
	Priority bits of track segments in layer 6.
TH1F *	m_neuroSWSelTSPrioB_Layer7 = nullptr
	Priority bits of track segments in layer 7.
TH1F *	m_neuroSWSelTSPrioB_Layer8 = nullptr
	Priority bits of track segments in layer 8.
TH1F *	m_neuroSWSelTSLR_Layer0 = nullptr
	Left/Right of track segments in layer 0.
TH1F *	m_neuroSWSelTSLR_Layer1 = nullptr
	Left/Right of track segments in layer 1.
TH1F *	m_neuroSWSelTSLR_Layer2 = nullptr
	Left/Right of track segments in layer 2.
TH1F *	m_neuroSWSelTSLR_Layer3 = nullptr
	Left/Right of track segments in layer 3.
TH1F *	m_neuroSWSelTSLR_Layer4 = nullptr
	Left/Right of track segments in layer 4.
TH1F *	m_neuroSWSelTSLR_Layer5 = nullptr
	Left/Right of track segments in layer 5.
TH1F *	m_neuroSWSelTSLR_Layer6 = nullptr
	Left/Right of track segments in layer 6.
TH1F *	m_neuroSWSelTSLR_Layer7 = nullptr
	Left/Right of track segments in layer 7.
TH1F *	m_neuroSWSelTSLR_Layer8 = nullptr
	Left/Right of track segments in layer 8.
TH1F *	m_neuroSWTSSW2DInTSID = nullptr
	ID of incoming track segments.
TH1F *	m_neuroSWTSSW2DInTSCount = nullptr
	number of TS per event
TH1F *	m_neuroSWTSSW2DInTSPrioT_Layer0 = nullptr
	Priority time of track segments in layer 0.
TH1F *	m_neuroSWTSSW2DInTSPrioT_Layer1 = nullptr
	Priority time of track segments in layer 1.
TH1F *	m_neuroSWTSSW2DInTSPrioT_Layer2 = nullptr
	Priority time of track segments in layer 2.
TH1F *	m_neuroSWTSSW2DInTSPrioT_Layer3 = nullptr
	Priority time of track segments in layer 3.
TH1F *	m_neuroSWTSSW2DInTSPrioT_Layer4 = nullptr
	Priority time of track segments in layer 4.
TH1F *	m_neuroSWTSSW2DInTSPrioT_Layer5 = nullptr
	Priority time of track segments in layer 5.
TH1F *	m_neuroSWTSSW2DInTSPrioT_Layer6 = nullptr
	Priority time of track segments in layer 6.
TH1F *	m_neuroSWTSSW2DInTSPrioT_Layer7 = nullptr
	Priority time of track segments in layer 7.
TH1F *	m_neuroSWTSSW2DInTSPrioT_Layer8 = nullptr
	Priority time of track segments in layer 8.
TH1F *	m_neuroSWTSSW2DInTSFoundT_Layer0 = nullptr
	Found time of track segments in layer 0.
TH1F *	m_neuroSWTSSW2DInTSFoundT_Layer1 = nullptr
	Found time of track segments in layer 1.
TH1F *	m_neuroSWTSSW2DInTSFoundT_Layer2 = nullptr
	Found time of track segments in layer 2.
TH1F *	m_neuroSWTSSW2DInTSFoundT_Layer3 = nullptr
	Found time of track segments in layer 3.
TH1F *	m_neuroSWTSSW2DInTSFoundT_Layer4 = nullptr
	Found time of track segments in layer 4.
TH1F *	m_neuroSWTSSW2DInTSFoundT_Layer5 = nullptr
	Found time of track segments in layer 5.
TH1F *	m_neuroSWTSSW2DInTSFoundT_Layer6 = nullptr
	Found time of track segments in layer 6.
TH1F *	m_neuroSWTSSW2DInTSFoundT_Layer7 = nullptr
	Found time of track segments in layer 7.
TH1F *	m_neuroSWTSSW2DInTSFoundT_Layer8 = nullptr
	Found time of track segments in layer 8.
TH1F *	m_neuroSWTSSW2DInTSPrioB_Layer0 = nullptr
	Priority bits of track segments in layer 0.
TH1F *	m_neuroSWTSSW2DInTSPrioB_Layer1 = nullptr
	Priority bits of track segments in layer 1.
TH1F *	m_neuroSWTSSW2DInTSPrioB_Layer2 = nullptr
	Priority bits of track segments in layer 2.
TH1F *	m_neuroSWTSSW2DInTSPrioB_Layer3 = nullptr
	Priority bits of track segments in layer 3.
TH1F *	m_neuroSWTSSW2DInTSPrioB_Layer4 = nullptr
	Priority bits of track segments in layer 4.
TH1F *	m_neuroSWTSSW2DInTSPrioB_Layer5 = nullptr
	Priority bits of track segments in layer 5.
TH1F *	m_neuroSWTSSW2DInTSPrioB_Layer6 = nullptr
	Priority bits of track segments in layer 6.
TH1F *	m_neuroSWTSSW2DInTSPrioB_Layer7 = nullptr
	Priority bits of track segments in layer 7.
TH1F *	m_neuroSWTSSW2DInTSPrioB_Layer8 = nullptr
	Priority bits of track segments in layer 8.
TH1F *	m_neuroSWTSSW2DInTSLR_Layer0 = nullptr
	Left/Right of track segments in layer 0.
TH1F *	m_neuroSWTSSW2DInTSLR_Layer1 = nullptr
	Left/Right of track segments in layer 1.
TH1F *	m_neuroSWTSSW2DInTSLR_Layer2 = nullptr
	Left/Right of track segments in layer 2.
TH1F *	m_neuroSWTSSW2DInTSLR_Layer3 = nullptr
	Left/Right of track segments in layer 3.
TH1F *	m_neuroSWTSSW2DInTSLR_Layer4 = nullptr
	Left/Right of track segments in layer 4.
TH1F *	m_neuroSWTSSW2DInTSLR_Layer5 = nullptr
	Left/Right of track segments in layer 5.
TH1F *	m_neuroSWTSSW2DInTSLR_Layer6 = nullptr
	Left/Right of track segments in layer 6.
TH1F *	m_neuroSWTSSW2DInTSLR_Layer7 = nullptr
	Left/Right of track segments in layer 7.
TH1F *	m_neuroSWTSSW2DInTSLR_Layer8 = nullptr
	Left/Right of track segments in layer 8.
TH1F *	m_neuroSWTSSW2DSelTSID = nullptr
	ID of incoming track segments.
TH1F *	m_neuroSWTSSW2DSelTSCount = nullptr
	number of TS per SL
TH1F *	m_neuroSWTSSW2DSelTSPrioT_Layer0 = nullptr
	Priority time of selected track segments in layer 0.
TH1F *	m_neuroSWTSSW2DSelTSPrioT_Layer1 = nullptr
	Priority time of selected track segments in layer 1.
TH1F *	m_neuroSWTSSW2DSelTSPrioT_Layer2 = nullptr
	Priority time of selected track segments in layer 2.
TH1F *	m_neuroSWTSSW2DSelTSPrioT_Layer3 = nullptr
	Priority time of selected track segments in layer 3.
TH1F *	m_neuroSWTSSW2DSelTSPrioT_Layer4 = nullptr
	Priority time of selected track segments in layer 4.
TH1F *	m_neuroSWTSSW2DSelTSPrioT_Layer5 = nullptr
	Priority time of selected track segments in layer 5.
TH1F *	m_neuroSWTSSW2DSelTSPrioT_Layer6 = nullptr
	Priority time of selected track segments in layer 6.
TH1F *	m_neuroSWTSSW2DSelTSPrioT_Layer7 = nullptr
	Priority time of selected track segments in layer 7.
TH1F *	m_neuroSWTSSW2DSelTSPrioT_Layer8 = nullptr
	Priority time of selected track segments in layer 8.
TH1F *	m_neuroSWTSSW2DSelTSFoundT_Layer0 = nullptr
	Found time of selected track segments in layer 0.
TH1F *	m_neuroSWTSSW2DSelTSFoundT_Layer1 = nullptr
	Found time of selected track segments in layer 1.
TH1F *	m_neuroSWTSSW2DSelTSFoundT_Layer2 = nullptr
	Found time of selected track segments in layer 2.
TH1F *	m_neuroSWTSSW2DSelTSFoundT_Layer3 = nullptr
	Found time of selected track segments in layer 3.
TH1F *	m_neuroSWTSSW2DSelTSFoundT_Layer4 = nullptr
	Found time of selected track segments in layer 4.
TH1F *	m_neuroSWTSSW2DSelTSFoundT_Layer5 = nullptr
	Found time of selected track segments in layer 5.
TH1F *	m_neuroSWTSSW2DSelTSFoundT_Layer6 = nullptr
	Found time of selected track segments in layer 6.
TH1F *	m_neuroSWTSSW2DSelTSFoundT_Layer7 = nullptr
	Found time of selected track segments in layer 7.
TH1F *	m_neuroSWTSSW2DSelTSFoundT_Layer8 = nullptr
	Found time of selected track segments in layer 8.
TH1F *	m_neuroSWTSSW2DSelTSPrioB_Layer0 = nullptr
	Priority bits of track segments in layer 0.
TH1F *	m_neuroSWTSSW2DSelTSPrioB_Layer1 = nullptr
	Priority bits of track segments in layer 1.
TH1F *	m_neuroSWTSSW2DSelTSPrioB_Layer2 = nullptr
	Priority bits of track segments in layer 2.
TH1F *	m_neuroSWTSSW2DSelTSPrioB_Layer3 = nullptr
	Priority bits of track segments in layer 3.
TH1F *	m_neuroSWTSSW2DSelTSPrioB_Layer4 = nullptr
	Priority bits of track segments in layer 4.
TH1F *	m_neuroSWTSSW2DSelTSPrioB_Layer5 = nullptr
	Priority bits of track segments in layer 5.
TH1F *	m_neuroSWTSSW2DSelTSPrioB_Layer6 = nullptr
	Priority bits of track segments in layer 6.
TH1F *	m_neuroSWTSSW2DSelTSPrioB_Layer7 = nullptr
	Priority bits of track segments in layer 7.
TH1F *	m_neuroSWTSSW2DSelTSPrioB_Layer8 = nullptr
	Priority bits of track segments in layer 8.
TH1F *	m_neuroSWTSSW2DSelTSLR_Layer0 = nullptr
	Left/Right of track segments in layer 0.
TH1F *	m_neuroSWTSSW2DSelTSLR_Layer1 = nullptr
	Left/Right of track segments in layer 1.
TH1F *	m_neuroSWTSSW2DSelTSLR_Layer2 = nullptr
	Left/Right of track segments in layer 2.
TH1F *	m_neuroSWTSSW2DSelTSLR_Layer3 = nullptr
	Left/Right of track segments in layer 3.
TH1F *	m_neuroSWTSSW2DSelTSLR_Layer4 = nullptr
	Left/Right of track segments in layer 4.
TH1F *	m_neuroSWTSSW2DSelTSLR_Layer5 = nullptr
	Left/Right of track segments in layer 5.
TH1F *	m_neuroSWTSSW2DSelTSLR_Layer6 = nullptr
	Left/Right of track segments in layer 6.
TH1F *	m_neuroSWTSSW2DSelTSLR_Layer7 = nullptr
	Left/Right of track segments in layer 7.
TH1F *	m_neuroSWTSSW2DSelTSLR_Layer8 = nullptr
	Left/Right of track segments in layer 8.
TH1F *	m_neuroHWInputID_Layer0 = nullptr
	unpacked id input in layer 0
TH1F *	m_neuroHWInputT_Layer0 = nullptr
	unpacked time input in layer 0
TH1F *	m_neuroHWInputAlpha_Layer0 = nullptr
	unpacked alpha input in layer 0
TH1F *	m_neuroHWInputID_Layer1 = nullptr
	unpacked id input in layer 1
TH1F *	m_neuroHWInputT_Layer1 = nullptr
	unpacked time input in layer 1
TH1F *	m_neuroHWInputAlpha_Layer1 = nullptr
	unpacked alpha input in layer 1
TH1F *	m_neuroHWInputID_Layer2 = nullptr
	unpacked id input in layer 2
TH1F *	m_neuroHWInputT_Layer2 = nullptr
	unpacked time input in layer 2
TH1F *	m_neuroHWInputAlpha_Layer2 = nullptr
	unpacked alpha input in layer 2
TH1F *	m_neuroHWInputID_Layer3 = nullptr
	unpacked id input in layer 3
TH1F *	m_neuroHWInputT_Layer3 = nullptr
	unpacked time input in layer 3
TH1F *	m_neuroHWInputAlpha_Layer3 = nullptr
	unpacked alpha input in layer 3
TH1F *	m_neuroHWInputID_Layer4 = nullptr
	unpacked id input in layer 4
TH1F *	m_neuroHWInputT_Layer4 = nullptr
	unpacked time input in layer 4
TH1F *	m_neuroHWInputAlpha_Layer4 = nullptr
	unpacked alpha input in layer 4
TH1F *	m_neuroHWInputID_Layer5 = nullptr
	unpacked id input in layer 5
TH1F *	m_neuroHWInputT_Layer5 = nullptr
	unpacked time input in layer 5
TH1F *	m_neuroHWInputAlpha_Layer5 = nullptr
	unpacked alpha input in layer 5
TH1F *	m_neuroHWInputID_Layer6 = nullptr
	unpacked id input in layer 6
TH1F *	m_neuroHWInputT_Layer6 = nullptr
	unpacked time input in layer 6
TH1F *	m_neuroHWInputAlpha_Layer6 = nullptr
	unpacked alpha input in layer 6
TH1F *	m_neuroHWInputID_Layer7 = nullptr
	unpacked id input in layer 7
TH1F *	m_neuroHWInputT_Layer7 = nullptr
	unpacked time input in layer 7
TH1F *	m_neuroHWInputAlpha_Layer7 = nullptr
	unpacked alpha input in layer 7
TH1F *	m_neuroHWInputID_Layer8 = nullptr
	unpacked id input in layer 8
TH1F *	m_neuroHWInputT_Layer8 = nullptr
	unpacked time input in layer 8
TH1F *	m_neuroHWInputAlpha_Layer8 = nullptr
	unpacked alpha input in layer 8
TH1F *	m_neuroSWInputID_Layer0 = nullptr
	simulated id input in layer 0
TH1F *	m_neuroSWInputT_Layer0 = nullptr
	simulated time input in layer 0
TH1F *	m_neuroSWInputAlpha_Layer0 = nullptr
	simulated alpha input in layer 0
TH1F *	m_neuroSWInputID_Layer1 = nullptr
	simulated id input in layer 1
TH1F *	m_neuroSWInputT_Layer1 = nullptr
	simulated time input in layer 1
TH1F *	m_neuroSWInputAlpha_Layer1 = nullptr
	simulated alpha input in layer 1
TH1F *	m_neuroSWInputID_Layer2 = nullptr
	simulated id input in layer 2
TH1F *	m_neuroSWInputT_Layer2 = nullptr
	simulated time input in layer 2
TH1F *	m_neuroSWInputAlpha_Layer2 = nullptr
	simulated alpha input in layer 2
TH1F *	m_neuroSWInputID_Layer3 = nullptr
	simulated id input in layer 3
TH1F *	m_neuroSWInputT_Layer3 = nullptr
	simulated time input in layer 3
TH1F *	m_neuroSWInputAlpha_Layer3 = nullptr
	simulated alpha input in layer 3
TH1F *	m_neuroSWInputID_Layer4 = nullptr
	simulated id input in layer 4
TH1F *	m_neuroSWInputT_Layer4 = nullptr
	simulated time input in layer 4
TH1F *	m_neuroSWInputAlpha_Layer4 = nullptr
	simulated alpha input in layer 4
TH1F *	m_neuroSWInputID_Layer5 = nullptr
	simulated id input in layer 5
TH1F *	m_neuroSWInputT_Layer5 = nullptr
	simulated time input in layer 5
TH1F *	m_neuroSWInputAlpha_Layer5 = nullptr
	simulated alpha input in layer 5
TH1F *	m_neuroSWInputID_Layer6 = nullptr
	simulated id input in layer 6
TH1F *	m_neuroSWInputT_Layer6 = nullptr
	simulated time input in layer 6
TH1F *	m_neuroSWInputAlpha_Layer6 = nullptr
	simulated alpha input in layer 6
TH1F *	m_neuroSWInputID_Layer7 = nullptr
	simulated id input in layer 7
TH1F *	m_neuroSWInputT_Layer7 = nullptr
	simulated time input in layer 7
TH1F *	m_neuroSWInputAlpha_Layer7 = nullptr
	simulated alpha input in layer 7
TH1F *	m_neuroSWInputID_Layer8 = nullptr
	simulated id input in layer 8
TH1F *	m_neuroSWInputT_Layer8 = nullptr
	simulated time input in layer 8
TH1F *	m_neuroSWInputAlpha_Layer8 = nullptr
	simulated alpha input in layer 8
TH1F *	m_neuroSWTSSW2DInputID_Layer0 = nullptr
	simulated id input in layer 0
TH1F *	m_neuroSWTSSW2DInputT_Layer0 = nullptr
	simulated time input in layer 0
TH1F *	m_neuroSWTSSW2DInputAlpha_Layer0 = nullptr
	simulated alpha input in layer 0
TH1F *	m_neuroSWTSSW2DInputID_Layer1 = nullptr
	simulated id input in layer 1
TH1F *	m_neuroSWTSSW2DInputT_Layer1 = nullptr
	simulated time input in layer 1
TH1F *	m_neuroSWTSSW2DInputAlpha_Layer1 = nullptr
	simulated alpha input in layer 1
TH1F *	m_neuroSWTSSW2DInputID_Layer2 = nullptr
	simulated id input in layer 2
TH1F *	m_neuroSWTSSW2DInputT_Layer2 = nullptr
	simulated time input in layer 2
TH1F *	m_neuroSWTSSW2DInputAlpha_Layer2 = nullptr
	simulated alpha input in layer 2
TH1F *	m_neuroSWTSSW2DInputID_Layer3 = nullptr
	simulated id input in layer 3
TH1F *	m_neuroSWTSSW2DInputT_Layer3 = nullptr
	simulated time input in layer 3
TH1F *	m_neuroSWTSSW2DInputAlpha_Layer3 = nullptr
	simulated alpha input in layer 3
TH1F *	m_neuroSWTSSW2DInputID_Layer4 = nullptr
	simulated id input in layer 4
TH1F *	m_neuroSWTSSW2DInputT_Layer4 = nullptr
	simulated time input in layer 4
TH1F *	m_neuroSWTSSW2DInputAlpha_Layer4 = nullptr
	simulated alpha input in layer 4
TH1F *	m_neuroSWTSSW2DInputID_Layer5 = nullptr
	simulated id input in layer 5
TH1F *	m_neuroSWTSSW2DInputT_Layer5 = nullptr
	simulated time input in layer 5
TH1F *	m_neuroSWTSSW2DInputAlpha_Layer5 = nullptr
	simulated alpha input in layer 5
TH1F *	m_neuroSWTSSW2DInputID_Layer6 = nullptr
	simulated id input in layer 6
TH1F *	m_neuroSWTSSW2DInputT_Layer6 = nullptr
	simulated time input in layer 6
TH1F *	m_neuroSWTSSW2DInputAlpha_Layer6 = nullptr
	simulated alpha input in layer 6
TH1F *	m_neuroSWTSSW2DInputID_Layer7 = nullptr
	simulated id input in layer 7
TH1F *	m_neuroSWTSSW2DInputT_Layer7 = nullptr
	simulated time input in layer 7
TH1F *	m_neuroSWTSSW2DInputAlpha_Layer7 = nullptr
	simulated alpha input in layer 7
TH1F *	m_neuroSWTSSW2DInputID_Layer8 = nullptr
	simulated id input in layer 8
TH1F *	m_neuroSWTSSW2DInputT_Layer8 = nullptr
	simulated time input in layer 8
TH1F *	m_neuroSWTSSW2DInputAlpha_Layer8 = nullptr
	simulated alpha input in layer 8
TH1F *	m_RecoZ = nullptr
	reconstructed z
TH1F *	m_RecoCosTheta = nullptr
	reconstructed cos(theta)
TH1F *	m_RecoInvPt = nullptr
	reconstructed inverse Pt
TH1F *	m_RecoPhi = nullptr
	reconstructed phi
TH1F *	m_RecoD0 = nullptr
	reconstructed d0
TH1F *	m_RecoTrackCount = nullptr
	number of reconstructed tracks per event
TH1F *	m_RecoHWZ = nullptr
	matched to HW reconstructed z
TH1F *	m_RecoHWCosTheta = nullptr
	matched to HW reconstructed cos(theta)
TH1F *	m_RecoHWInvPt = nullptr
	matched to HW reconstructed inverse Pt
TH1F *	m_RecoHWPhi = nullptr
	matched to HW reconstructed phi
TH1F *	m_RecoHWD0 = nullptr
	matched to HW reconstructed d0
TH2F *	m_RecoHWZScatter = nullptr
	matched to HW reconstructed z scatter plot
TH1F *	m_neuroRecoHWOutZ = nullptr
	reco matched z distribution from unpacker
TH1F *	m_neuroRecoHWOutCosTheta = nullptr
	reco matched cos theta distribution from unpacker
TH1F *	m_neuroRecoHWOutInvPt = nullptr
	reco matched Inverse Pt distribution from unpacker
TH1F *	m_neuroRecoHWOutPhi0 = nullptr
	reco matched phi distribution from unpacker
TH1F *	m_neuroRecoHWOutHitPattern = nullptr
	reco matched stereo hit pattern from unpacker
TH1F *	m_neuroRecoHWOutTrackCount = nullptr
	reco matched number of unpacked and matched tracks per event
TH1F *	m_neuroRecoHWSector = nullptr
	reco matched NN sector from unpacker
TH1F *	m_DeltaRecoHWZ = nullptr
	matched to HW reconstructed z
TH1F *	m_DeltaRecoHWCosTheta = nullptr
	matched to HW reconstructed cos(theta)
TH1F *	m_DeltaRecoHWInvPt = nullptr
	matched to HW reconstructed inverse Pt
TH1F *	m_DeltaRecoHWPhi = nullptr
	matched to HW reconstructed phi
TH1F *	m_RecoSWZ = nullptr
	matched to SW reconstructed z
TH1F *	m_RecoSWCosTheta = nullptr
	matched to SW reconstructed cos(theta)
TH1F *	m_RecoSWInvPt = nullptr
	matched to SW reconstructed inverse Pt
TH1F *	m_RecoSWPhi = nullptr
	matched to SW reconstructed phi
TH1F *	m_RecoSWD0 = nullptr
	matched to SW reconstructed d0
TH2F *	m_RecoSWZScatter = nullptr
	matched to SW reconstructed z scatter plot
TH1F *	m_neuroRecoSWOutZ = nullptr
	reco matched z distribution from simulation (hw TS hw 2D sw NN)
TH1F *	m_neuroRecoSWOutCosTheta = nullptr
	reco matched cos theta distribution from simulation (hw TS hw 2D sw NN)
TH1F *	m_neuroRecoSWOutInvPt = nullptr
	reco matched Inverse Pt distribution from simulation (hw TS hw 2D sw NN)
TH1F *	m_neuroRecoSWOutPhi0 = nullptr
	reco matched phi distribution from simulation (hw TS hw 2D sw NN)
TH1F *	m_neuroRecoSWOutHitPattern = nullptr
	reco matched stereo hit pattern of simulated neuro tracks (hw TS hw 2D sw NN)
TH1F *	m_neuroRecoSWOutTrackCount = nullptr
	reco matched number of simulated tracks per event (hw TS hw 2D sw NN)
TH1F *	m_neuroRecoSWSector = nullptr
	reco matched NN sector from simulation (hw TS hw 2D sw NN)
TH1F *	m_DeltaRecoSWZ = nullptr
	matched to SW reconstructed z
TH1F *	m_DeltaRecoSWCosTheta = nullptr
	matched to SW reconstructed cos(theta)
TH1F *	m_DeltaRecoSWInvPt = nullptr
	matched to SW reconstructed inverse Pt
TH1F *	m_DeltaRecoSWPhi = nullptr
	matched to SW reconstructed phi
TH1F *	m_RecoSWTSSW2DZ = nullptr
	matched to SWTSSW2DSWNN reconstructed z
TH1F *	m_RecoSWTSSW2DCosTheta = nullptr
	matched to SWTSSW2DSWNN reconstructed cos(theta)
TH1F *	m_RecoSWTSSW2DInvPt = nullptr
	matched to SWTSSW2DSWNN reconstructed inverse Pt
TH1F *	m_RecoSWTSSW2DPhi = nullptr
	matched to SWTSSW2DSWNN reconstructed phi
TH1F *	m_RecoSWTSSW2DD0 = nullptr
	matched to SWTSSW2DSWNN reconstructed d0
TH2F *	m_RecoSWTSSW2DZScatter = nullptr
	matched to SWTSSW2DSWNN reconstructed z scatter plot
TH1F *	m_neuroRecoSWTSSW2DOutZ = nullptr
	reco matched z distribution from simulation (sw TS sw 2D sw NN)
TH1F *	m_neuroRecoSWTSSW2DOutCosTheta = nullptr
	reco matched cos theta distribution from simulation (sw TS sw 2D sw NN)
TH1F *	m_neuroRecoSWTSSW2DOutInvPt = nullptr
	reco matched Inverse Pt distribution from simulation (sw TS sw 2D sw NN)
TH1F *	m_neuroRecoSWTSSW2DOutPhi0 = nullptr
	reco matched phi distribution from simulation (sw TS sw 2D sw NN)
TH1F *	m_neuroRecoSWTSSW2DOutHitPattern
	reco matched stereo hit pattern of simulated neuro tracks (sw TS sw 2D sw NN)
TH1F *	m_neuroRecoSWTSSW2DOutTrackCount = nullptr
	reco matched number of simulated tracks per event (sw TS sw 2D sw NN)
TH1F *	m_neuroRecoSWTSSW2DSector = nullptr
	reco matched NN sector from simulation (sw TS sw 2D sw NN)
TH1F *	m_DeltaRecoSWTSSW2DZ = nullptr
	matched to SWTSSW2DSWNN reconstructed z
TH1F *	m_DeltaRecoSWTSSW2DCosTheta = nullptr
	matched to SWTSSW2DSWNN reconstructed cos(theta)
TH1F *	m_DeltaRecoSWTSSW2DInvPt = nullptr
	matched to SWTSSW2DSWNN reconstructed inverse Pt
TH1F *	m_DeltaRecoSWTSSW2DPhi = nullptr
	matched to SWTSSW2DSWNN reconstructed phi
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

CDC Trigger DQM Module.

Definition at line 31 of file CDCTriggerNeuroDQMModule.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.

TSLines

typedef std::vector<TSLine> TSLines

Definition at line 61 of file CDCTriggerNeuroDQMModule.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

CDCTriggerNeuroDQMModule()

CDCTriggerNeuroDQMModule

(

)

Constructor.

Definition at line 29 of file CDCTriggerNeuroDQMModule.cc.

                                                   : HistoModule()
{
  //Set module properties
  setDescription("CDC Trigger DQM module");
  setPropertyFlags(c_ParallelProcessingCertified);  // specify this flag if you need parallel processing
  addParam("limitedoutput", m_limitedoutput,
           "Switch to supress output for online dqm purposes. ",
           true);
  addParam("unpackedSegmentHitsName", m_unpackedSegmentHitsName,
           "The name of the StoreArray of the unpacked CDCTriggerSegmentHits",
           string("CDCTriggerSegmentHits"));
  addParam("unpacked2DTracksName", m_unpacked2DTracksName,
           "The name of the StoreArray of the unpacked 2D finder tracks",
           string("CDCTrigger2DFinderTracks"));
  addParam("unpackedNeuroTracksName", m_unpackedNeuroTracksName,
           "The name of the StoreArray of the unpacked neurotrigger tracks",
           string("CDCTriggerNeuroTracks"));
  addParam("unpackedNeuroInput2dTracksName", m_unpackedNeuroInput2DTracksName,
           "The name of the StoreArray of the neurotrigger input 2d tracks",
           string("CDCTriggerNNInput2DFinderTracks"));
  addParam("unpackedNeuroInputSegmentHits", m_unpackedNeuroInputSegmentsName,
           "The name of the StoreArray of the neurotrigger input segment hits",
           string("CDCTriggerNNInputSegmentHits"));
  addParam("simNeuroTracksName", m_simNeuroTracksName,
           "The name of the StoreArray of the neurotrigger tracks from TSIM",
           string(""));
  addParam("histogramDirectoryName", m_histogramDirectoryName,
           "Name of the directory where histograms will be placed",
           string("TRGCDCTNN"));
  addParam("simSegmentHitsName", m_simSegmentHitsName,
           "StoreArray name for simulated TS hits", string(""));
  addParam("sim2DTracksSWTSName", m_sim2DTracksSWTSName,
           "StoreArray name for simulated 2D finder tracks using simulated TS", string(""));
  addParam("simNeuroTracksSWTSSW2DName", m_simNeuroTracksSWTSSW2DName,
           "StoreArray name for neuro tracks using simulated TS and simulated 2D", string(""));
  addParam("showRecoTracks", m_showRecoTracks,
           "switch to turn on a comparison with the reconstruction, DEPRECATED! Use RecoTracks='RecoTracks' (='') instead!",
           false);
  addParam("RecoTracks", m_recoTracksName,
           "Name of the RecoTrack StoreArray. Leave empty for skipping them.",
           string(""));
  addParam("recoTrackMultiplicity", m_recoTrackMultiplicity,
           "Select events with a specific RecoTrack track multiplicity. -1 for all events",
           -1);
  addParam("skipWithoutHWTS", m_skipWithoutHWTS,
           "Switch to skip events without unpacked TS",
           true);
  addParam("maxRecoZDist", m_maxRecoZDist,
           "Select only RecoTracks with a maximum z distance to the IP. -1.0 for all tracks",
           (double)(- 1.0));
  addParam("maxRecoD0Dist", m_maxRecoD0Dist,
           "Select only RecoTracks with a maximum d0 distance to the z axis. -1.0 for all tracks",
           (double)(- 1.0));
  addParam("nSameTS", m_nsamets,
           "Number of identical track segments to be required for matching between HW and SW Neurotrigger",
           (int)(0));
}

~CDCTriggerNeuroDQMModule()

~CDCTriggerNeuroDQMModule ( )

virtual

Destructor.

Definition at line 88 of file CDCTriggerNeuroDQMModule.cc.

{
}

Member Function Documentation

beginRun()

void beginRun ( void  )

overridevirtual

Function to process begin_run record.

Reimplemented from HistoModule.

Definition at line 1318 of file CDCTriggerNeuroDQMModule.cc.

{
  // Just to make sure, reset all the histograms.
  if (m_unpackedNeuroTracksName != "") {
 
    m_neuroHWOutZ->Reset();
    m_neuroHWOutCosTheta->Reset();
    m_neuroHWOutInvPt->Reset();
    m_neuroHWOutPt->Reset();
    m_neuroHWOutPhi0->Reset();
    m_neuroHWOutHitPattern->Reset();
    m_neuroHWOutm_time->Reset();
    m_neuroHWOutTrackCount->Reset();
    m_neuroHWOutVsInTrackCount->Reset();
    m_neuroHWSector->Reset();
 
    m_neuroHWInInvPt->Reset();
    m_neuroHWInPhi0->Reset();
    m_neuroHWInm_time->Reset();
    m_neuroHWInTrackCount->Reset();
 
    m_neuroHWInTSID->Reset();
    m_neuroHWInTSCount->Reset();
 
    m_neuroHWSelTSID->Reset();
    m_neuroHWSelTSCount->Reset();
  }
  if (m_unpacked2DTracksName != "") {
    m_2DHWOutInvPt->Reset();
    m_2DHWOutPhi0->Reset();
    m_2DHWOutm_time->Reset();
    m_2DHWOutTrackCount->Reset();
 
  }
  if (m_unpacked2DTracksName != "" && m_unpackedNeuroTracksName != "") {
    m_neuroHWInVs2DOutTrackCount->Reset();
  }
  if (!m_limitedoutput && m_unpackedNeuroTracksName != "") {
    m_neuroHWOutQuad5Z->Reset();
    m_neuroHWOutQuad5CosTheta->Reset();
    m_neuroHWOutQuad5InvPt->Reset();
    m_neuroHWOutQuad5Phi0->Reset();
    m_neuroHWOutQuad0Z->Reset();
    m_neuroHWOutQuad0CosTheta->Reset();
    m_neuroHWOutQuad0InvPt->Reset();
    m_neuroHWOutQuad0Phi0->Reset();
    m_neuroHWOutQuad1Z->Reset();
    m_neuroHWOutQuad1CosTheta->Reset();
    m_neuroHWOutQuad1InvPt->Reset();
    m_neuroHWOutQuad1Phi0->Reset();
    m_neuroHWOutQuad2Z->Reset();
    m_neuroHWOutQuad2CosTheta->Reset();
    m_neuroHWOutQuad2InvPt->Reset();
    m_neuroHWOutQuad2Phi0->Reset();
    m_neuroHWOutQuad3Z->Reset();
    m_neuroHWOutQuad3CosTheta->Reset();
    m_neuroHWOutQuad3InvPt->Reset();
    m_neuroHWOutQuad3Phi0->Reset();
 
    m_neuroHWInTSPrioT_Layer0->Reset();
    m_neuroHWInTSPrioT_Layer1->Reset();
    m_neuroHWInTSPrioT_Layer2->Reset();
    m_neuroHWInTSPrioT_Layer3->Reset();
    m_neuroHWInTSPrioT_Layer4->Reset();
    m_neuroHWInTSPrioT_Layer5->Reset();
    m_neuroHWInTSPrioT_Layer6->Reset();
    m_neuroHWInTSPrioT_Layer7->Reset();
    m_neuroHWInTSPrioT_Layer8->Reset();
    m_neuroHWInTSFoundT_Layer0->Reset();
    m_neuroHWInTSFoundT_Layer1->Reset();
    m_neuroHWInTSFoundT_Layer2->Reset();
    m_neuroHWInTSFoundT_Layer3->Reset();
    m_neuroHWInTSFoundT_Layer4->Reset();
    m_neuroHWInTSFoundT_Layer5->Reset();
    m_neuroHWInTSFoundT_Layer6->Reset();
    m_neuroHWInTSFoundT_Layer7->Reset();
    m_neuroHWInTSFoundT_Layer8->Reset();
 
    m_neuroHWInTSPrioB_Layer0->Reset();
    m_neuroHWInTSPrioB_Layer1->Reset();
    m_neuroHWInTSPrioB_Layer2->Reset();
    m_neuroHWInTSPrioB_Layer3->Reset();
    m_neuroHWInTSPrioB_Layer4->Reset();
    m_neuroHWInTSPrioB_Layer5->Reset();
    m_neuroHWInTSPrioB_Layer6->Reset();
    m_neuroHWInTSPrioB_Layer7->Reset();
    m_neuroHWInTSPrioB_Layer8->Reset();
    m_neuroHWInTSLR_Layer0->Reset();
    m_neuroHWInTSLR_Layer1->Reset();
    m_neuroHWInTSLR_Layer2->Reset();
    m_neuroHWInTSLR_Layer3->Reset();
    m_neuroHWInTSLR_Layer4->Reset();
    m_neuroHWInTSLR_Layer5->Reset();
    m_neuroHWInTSLR_Layer6->Reset();
    m_neuroHWInTSLR_Layer7->Reset();
    m_neuroHWInTSLR_Layer8->Reset();
 
    m_neuroHWSelTSPrioT_Layer0->Reset();
    m_neuroHWSelTSPrioT_Layer1->Reset();
    m_neuroHWSelTSPrioT_Layer2->Reset();
    m_neuroHWSelTSPrioT_Layer3->Reset();
    m_neuroHWSelTSPrioT_Layer4->Reset();
    m_neuroHWSelTSPrioT_Layer5->Reset();
    m_neuroHWSelTSPrioT_Layer6->Reset();
    m_neuroHWSelTSPrioT_Layer7->Reset();
    m_neuroHWSelTSPrioT_Layer8->Reset();
    m_neuroHWSelTSFoundT_Layer0->Reset();
    m_neuroHWSelTSFoundT_Layer1->Reset();
    m_neuroHWSelTSFoundT_Layer2->Reset();
    m_neuroHWSelTSFoundT_Layer3->Reset();
    m_neuroHWSelTSFoundT_Layer4->Reset();
    m_neuroHWSelTSFoundT_Layer5->Reset();
    m_neuroHWSelTSFoundT_Layer6->Reset();
    m_neuroHWSelTSFoundT_Layer7->Reset();
    m_neuroHWSelTSFoundT_Layer8->Reset();
 
    m_neuroHWSelTSPrioB_Layer0->Reset();
    m_neuroHWSelTSPrioB_Layer1->Reset();
    m_neuroHWSelTSPrioB_Layer2->Reset();
    m_neuroHWSelTSPrioB_Layer3->Reset();
    m_neuroHWSelTSPrioB_Layer4->Reset();
    m_neuroHWSelTSPrioB_Layer5->Reset();
    m_neuroHWSelTSPrioB_Layer6->Reset();
    m_neuroHWSelTSPrioB_Layer7->Reset();
    m_neuroHWSelTSPrioB_Layer8->Reset();
    m_neuroHWSelTSLR_Layer0->Reset();
    m_neuroHWSelTSLR_Layer1->Reset();
    m_neuroHWSelTSLR_Layer2->Reset();
    m_neuroHWSelTSLR_Layer3->Reset();
    m_neuroHWSelTSLR_Layer4->Reset();
    m_neuroHWSelTSLR_Layer5->Reset();
    m_neuroHWSelTSLR_Layer6->Reset();
    m_neuroHWSelTSLR_Layer7->Reset();
    m_neuroHWSelTSLR_Layer8->Reset();
 
    m_neuroHWInputID_Layer0->Reset();
    m_neuroHWInputT_Layer0->Reset();
    m_neuroHWInputAlpha_Layer0->Reset();
    m_neuroHWInputID_Layer1->Reset();
    m_neuroHWInputT_Layer1->Reset();
    m_neuroHWInputAlpha_Layer1->Reset();
    m_neuroHWInputID_Layer2->Reset();
    m_neuroHWInputT_Layer2->Reset();
    m_neuroHWInputAlpha_Layer2->Reset();
    m_neuroHWInputID_Layer3->Reset();
    m_neuroHWInputT_Layer3->Reset();
    m_neuroHWInputAlpha_Layer3->Reset();
    m_neuroHWInputID_Layer4->Reset();
    m_neuroHWInputT_Layer4->Reset();
    m_neuroHWInputAlpha_Layer4->Reset();
    m_neuroHWInputID_Layer5->Reset();
    m_neuroHWInputT_Layer5->Reset();
    m_neuroHWInputAlpha_Layer5->Reset();
    m_neuroHWInputID_Layer6->Reset();
    m_neuroHWInputT_Layer6->Reset();
    m_neuroHWInputAlpha_Layer6->Reset();
    m_neuroHWInputID_Layer7->Reset();
    m_neuroHWInputT_Layer7->Reset();
    m_neuroHWInputAlpha_Layer7->Reset();
    m_neuroHWInputID_Layer8->Reset();
    m_neuroHWInputT_Layer8->Reset();
    m_neuroHWInputAlpha_Layer8->Reset();
  }
  if (!m_limitedoutput && m_simNeuroTracksName != "") {
    m_neuroSWOutZ->Reset();
    m_neuroSWOutCosTheta->Reset();
    m_neuroSWOutPhi0->Reset();
    m_neuroSWOutInvPt->Reset();
    m_neuroSWOutHitPattern->Reset();
    m_neuroSWOutTrackCount->Reset();
    m_neuroSWSector->Reset();
  }
  if (!m_limitedoutput && m_simNeuroTracksSWTSSW2DName != "") {
    m_2DSWOutInvPt->Reset();
    m_2DSWOutPhi0->Reset();
    m_2DSWOutm_time->Reset();
    m_2DSWOutTrackCount->Reset();
 
    m_neuroSWTSSW2DOutZ->Reset();
    m_neuroSWTSSW2DOutCosTheta->Reset();
    m_neuroSWTSSW2DOutInvPt->Reset();
    m_neuroSWTSSW2DOutPhi0->Reset();
    m_neuroSWTSSW2DOutHitPattern->Reset();
    m_neuroSWTSSW2DOutTrackCount->Reset();
    m_neuroSWTSSW2DSector->Reset();
  }
  if (!m_limitedoutput && m_simNeuroTracksName != "") {
    m_neuroDeltaZ->Reset();
    m_neuroDeltaTheta->Reset();
    m_neuroScatterZ->Reset();
    m_neuroScatterTheta->Reset();
 
    m_neuroDeltaInputID->Reset();
    m_neuroDeltaInputT->Reset();
    m_neuroDeltaInputAlpha->Reset();
    m_neuroDeltaTSID->Reset();
    m_neuroDeltaSector->Reset();
    m_simSameTS->Reset();
    m_simDiffTS->Reset();
  }
  if (!m_limitedoutput && m_unpacked2DTracksName != "") {
    m_2DHWInTSPrioT_Layer0->Reset();
    m_2DHWInTSPrioT_Layer2->Reset();
    m_2DHWInTSPrioT_Layer4->Reset();
    m_2DHWInTSPrioT_Layer6->Reset();
    m_2DHWInTSPrioT_Layer8->Reset();
    m_2DHWInTSFoundT_Layer0->Reset();
    m_2DHWInTSFoundT_Layer2->Reset();
    m_2DHWInTSFoundT_Layer4->Reset();
    m_2DHWInTSFoundT_Layer6->Reset();
    m_2DHWInTSFoundT_Layer8->Reset();
 
    m_2DHWInTSPrioB_Layer0->Reset();
    m_2DHWInTSPrioB_Layer2->Reset();
    m_2DHWInTSPrioB_Layer4->Reset();
    m_2DHWInTSPrioB_Layer6->Reset();
    m_2DHWInTSPrioB_Layer8->Reset();
    m_2DHWInTSLR_Layer0->Reset();
    m_2DHWInTSLR_Layer2->Reset();
    m_2DHWInTSLR_Layer4->Reset();
    m_2DHWInTSLR_Layer6->Reset();
    m_2DHWInTSLR_Layer8->Reset();
  }
  if (!m_limitedoutput && m_simNeuroTracksName != "") {
    m_neuroSWSelTSID->Reset();
    m_neuroSWSelTSCount->Reset();
    m_neuroSWSelTSPrioT_Layer0->Reset();
    m_neuroSWSelTSPrioT_Layer1->Reset();
    m_neuroSWSelTSPrioT_Layer2->Reset();
    m_neuroSWSelTSPrioT_Layer3->Reset();
    m_neuroSWSelTSPrioT_Layer4->Reset();
    m_neuroSWSelTSPrioT_Layer5->Reset();
    m_neuroSWSelTSPrioT_Layer6->Reset();
    m_neuroSWSelTSPrioT_Layer7->Reset();
    m_neuroSWSelTSPrioT_Layer8->Reset();
    m_neuroSWSelTSFoundT_Layer0->Reset();
    m_neuroSWSelTSFoundT_Layer1->Reset();
    m_neuroSWSelTSFoundT_Layer2->Reset();
    m_neuroSWSelTSFoundT_Layer3->Reset();
    m_neuroSWSelTSFoundT_Layer4->Reset();
    m_neuroSWSelTSFoundT_Layer5->Reset();
    m_neuroSWSelTSFoundT_Layer6->Reset();
    m_neuroSWSelTSFoundT_Layer7->Reset();
    m_neuroSWSelTSFoundT_Layer8->Reset();
 
    m_neuroSWSelTSPrioB_Layer0->Reset();
    m_neuroSWSelTSPrioB_Layer1->Reset();
    m_neuroSWSelTSPrioB_Layer2->Reset();
    m_neuroSWSelTSPrioB_Layer3->Reset();
    m_neuroSWSelTSPrioB_Layer4->Reset();
    m_neuroSWSelTSPrioB_Layer5->Reset();
    m_neuroSWSelTSPrioB_Layer6->Reset();
    m_neuroSWSelTSPrioB_Layer7->Reset();
    m_neuroSWSelTSPrioB_Layer8->Reset();
 
    m_neuroSWSelTSLR_Layer0->Reset();
    m_neuroSWSelTSLR_Layer1->Reset();
    m_neuroSWSelTSLR_Layer2->Reset();
    m_neuroSWSelTSLR_Layer3->Reset();
    m_neuroSWSelTSLR_Layer4->Reset();
    m_neuroSWSelTSLR_Layer5->Reset();
    m_neuroSWSelTSLR_Layer6->Reset();
    m_neuroSWSelTSLR_Layer7->Reset();
    m_neuroSWSelTSLR_Layer8->Reset();
 
    m_neuroSWInputID_Layer0->Reset();
    m_neuroSWInputT_Layer0->Reset();
    m_neuroSWInputAlpha_Layer0->Reset();
    m_neuroSWInputID_Layer1->Reset();
    m_neuroSWInputT_Layer1->Reset();
    m_neuroSWInputAlpha_Layer1->Reset();
    m_neuroSWInputID_Layer2->Reset();
    m_neuroSWInputT_Layer2->Reset();
    m_neuroSWInputAlpha_Layer2->Reset();
    m_neuroSWInputID_Layer3->Reset();
    m_neuroSWInputT_Layer3->Reset();
    m_neuroSWInputAlpha_Layer3->Reset();
    m_neuroSWInputID_Layer4->Reset();
    m_neuroSWInputT_Layer4->Reset();
    m_neuroSWInputAlpha_Layer4->Reset();
    m_neuroSWInputID_Layer5->Reset();
    m_neuroSWInputT_Layer5->Reset();
    m_neuroSWInputAlpha_Layer5->Reset();
    m_neuroSWInputID_Layer6->Reset();
    m_neuroSWInputT_Layer6->Reset();
    m_neuroSWInputAlpha_Layer6->Reset();
    m_neuroSWInputID_Layer7->Reset();
    m_neuroSWInputT_Layer7->Reset();
    m_neuroSWInputAlpha_Layer7->Reset();
    m_neuroSWInputID_Layer8->Reset();
    m_neuroSWInputT_Layer8->Reset();
    m_neuroSWInputAlpha_Layer8->Reset();
  }
 
  if (!m_limitedoutput && m_simNeuroTracksSWTSSW2DName != "") {
    m_neuroSWTSSW2DInTSID->Reset();
    m_neuroSWTSSW2DInTSCount->Reset();
    m_neuroSWTSSW2DInTSPrioT_Layer0->Reset();
    m_neuroSWTSSW2DInTSPrioT_Layer1->Reset();
    m_neuroSWTSSW2DInTSPrioT_Layer2->Reset();
    m_neuroSWTSSW2DInTSPrioT_Layer3->Reset();
    m_neuroSWTSSW2DInTSPrioT_Layer4->Reset();
    m_neuroSWTSSW2DInTSPrioT_Layer5->Reset();
    m_neuroSWTSSW2DInTSPrioT_Layer6->Reset();
    m_neuroSWTSSW2DInTSPrioT_Layer7->Reset();
    m_neuroSWTSSW2DInTSPrioT_Layer8->Reset();
    m_neuroSWTSSW2DInTSFoundT_Layer0->Reset();
    m_neuroSWTSSW2DInTSFoundT_Layer1->Reset();
    m_neuroSWTSSW2DInTSFoundT_Layer2->Reset();
    m_neuroSWTSSW2DInTSFoundT_Layer3->Reset();
    m_neuroSWTSSW2DInTSFoundT_Layer4->Reset();
    m_neuroSWTSSW2DInTSFoundT_Layer5->Reset();
    m_neuroSWTSSW2DInTSFoundT_Layer6->Reset();
    m_neuroSWTSSW2DInTSFoundT_Layer7->Reset();
    m_neuroSWTSSW2DInTSFoundT_Layer8->Reset();
    m_neuroSWTSSW2DInTSPrioB_Layer0->Reset();
    m_neuroSWTSSW2DInTSPrioB_Layer1->Reset();
    m_neuroSWTSSW2DInTSPrioB_Layer2->Reset();
    m_neuroSWTSSW2DInTSPrioB_Layer3->Reset();
    m_neuroSWTSSW2DInTSPrioB_Layer4->Reset();
    m_neuroSWTSSW2DInTSPrioB_Layer5->Reset();
    m_neuroSWTSSW2DInTSPrioB_Layer6->Reset();
    m_neuroSWTSSW2DInTSPrioB_Layer7->Reset();
    m_neuroSWTSSW2DInTSPrioB_Layer8->Reset();
    m_neuroSWTSSW2DInTSLR_Layer0->Reset();
    m_neuroSWTSSW2DInTSLR_Layer1->Reset();
    m_neuroSWTSSW2DInTSLR_Layer2->Reset();
    m_neuroSWTSSW2DInTSLR_Layer3->Reset();
    m_neuroSWTSSW2DInTSLR_Layer4->Reset();
    m_neuroSWTSSW2DInTSLR_Layer5->Reset();
    m_neuroSWTSSW2DInTSLR_Layer6->Reset();
    m_neuroSWTSSW2DInTSLR_Layer7->Reset();
    m_neuroSWTSSW2DInTSLR_Layer8->Reset();
 
    m_neuroSWTSSW2DSelTSID->Reset();
    m_neuroSWTSSW2DSelTSCount->Reset();
    m_neuroSWTSSW2DSelTSPrioT_Layer0->Reset();
    m_neuroSWTSSW2DSelTSPrioT_Layer1->Reset();
    m_neuroSWTSSW2DSelTSPrioT_Layer2->Reset();
    m_neuroSWTSSW2DSelTSPrioT_Layer3->Reset();
    m_neuroSWTSSW2DSelTSPrioT_Layer4->Reset();
    m_neuroSWTSSW2DSelTSPrioT_Layer5->Reset();
    m_neuroSWTSSW2DSelTSPrioT_Layer6->Reset();
    m_neuroSWTSSW2DSelTSPrioT_Layer7->Reset();
    m_neuroSWTSSW2DSelTSPrioT_Layer8->Reset();
    m_neuroSWTSSW2DSelTSFoundT_Layer0->Reset();
    m_neuroSWTSSW2DSelTSFoundT_Layer1->Reset();
    m_neuroSWTSSW2DSelTSFoundT_Layer2->Reset();
    m_neuroSWTSSW2DSelTSFoundT_Layer3->Reset();
    m_neuroSWTSSW2DSelTSFoundT_Layer4->Reset();
    m_neuroSWTSSW2DSelTSFoundT_Layer5->Reset();
    m_neuroSWTSSW2DSelTSFoundT_Layer6->Reset();
    m_neuroSWTSSW2DSelTSFoundT_Layer7->Reset();
    m_neuroSWTSSW2DSelTSFoundT_Layer8->Reset();
    m_neuroSWTSSW2DSelTSPrioB_Layer0->Reset();
    m_neuroSWTSSW2DSelTSPrioB_Layer1->Reset();
    m_neuroSWTSSW2DSelTSPrioB_Layer2->Reset();
    m_neuroSWTSSW2DSelTSPrioB_Layer3->Reset();
    m_neuroSWTSSW2DSelTSPrioB_Layer4->Reset();
    m_neuroSWTSSW2DSelTSPrioB_Layer5->Reset();
    m_neuroSWTSSW2DSelTSPrioB_Layer6->Reset();
    m_neuroSWTSSW2DSelTSPrioB_Layer7->Reset();
    m_neuroSWTSSW2DSelTSPrioB_Layer8->Reset();
    m_neuroSWTSSW2DSelTSLR_Layer0->Reset();
    m_neuroSWTSSW2DSelTSLR_Layer1->Reset();
    m_neuroSWTSSW2DSelTSLR_Layer2->Reset();
    m_neuroSWTSSW2DSelTSLR_Layer3->Reset();
    m_neuroSWTSSW2DSelTSLR_Layer4->Reset();
    m_neuroSWTSSW2DSelTSLR_Layer5->Reset();
    m_neuroSWTSSW2DSelTSLR_Layer6->Reset();
    m_neuroSWTSSW2DSelTSLR_Layer7->Reset();
    m_neuroSWTSSW2DSelTSLR_Layer8->Reset();
 
    m_neuroSWTSSW2DInputID_Layer0->Reset();
    m_neuroSWTSSW2DInputT_Layer0->Reset();
    m_neuroSWTSSW2DInputAlpha_Layer0->Reset();
    m_neuroSWTSSW2DInputID_Layer1->Reset();
    m_neuroSWTSSW2DInputT_Layer1->Reset();
    m_neuroSWTSSW2DInputAlpha_Layer1->Reset();
    m_neuroSWTSSW2DInputID_Layer2->Reset();
    m_neuroSWTSSW2DInputT_Layer2->Reset();
    m_neuroSWTSSW2DInputAlpha_Layer2->Reset();
    m_neuroSWTSSW2DInputID_Layer3->Reset();
    m_neuroSWTSSW2DInputT_Layer3->Reset();
    m_neuroSWTSSW2DInputAlpha_Layer3->Reset();
    m_neuroSWTSSW2DInputID_Layer4->Reset();
    m_neuroSWTSSW2DInputT_Layer4->Reset();
    m_neuroSWTSSW2DInputAlpha_Layer4->Reset();
    m_neuroSWTSSW2DInputID_Layer5->Reset();
    m_neuroSWTSSW2DInputT_Layer5->Reset();
    m_neuroSWTSSW2DInputAlpha_Layer5->Reset();
    m_neuroSWTSSW2DInputID_Layer6->Reset();
    m_neuroSWTSSW2DInputT_Layer6->Reset();
    m_neuroSWTSSW2DInputAlpha_Layer6->Reset();
    m_neuroSWTSSW2DInputID_Layer7->Reset();
    m_neuroSWTSSW2DInputT_Layer7->Reset();
    m_neuroSWTSSW2DInputAlpha_Layer7->Reset();
    m_neuroSWTSSW2DInputID_Layer8->Reset();
    m_neuroSWTSSW2DInputT_Layer8->Reset();
    m_neuroSWTSSW2DInputAlpha_Layer8->Reset();
  }
  if (m_recoTracksName != "") {
    m_RecoZ->Reset();
    m_RecoCosTheta->Reset();
    m_RecoInvPt->Reset();
    m_RecoPhi->Reset();
    m_RecoD0->Reset();
    m_RecoTrackCount->Reset();
 
    m_RecoHWZ->Reset();
    m_RecoHWCosTheta->Reset();
    m_RecoHWInvPt->Reset();
    m_RecoHWPhi->Reset();
    m_RecoHWD0->Reset();
  }
  if (!m_limitedoutput && m_recoTracksName != "") {
    m_RecoHWZScatter->Reset();
  }
  if (m_unpackedNeuroTracksName != "" && m_recoTracksName != "") {
    m_neuroRecoHWOutZ->Reset();
    m_neuroRecoHWOutCosTheta->Reset();
    m_neuroRecoHWOutInvPt->Reset();
    m_neuroRecoHWOutPhi0->Reset();
    m_neuroRecoHWOutHitPattern->Reset();
    m_neuroRecoHWOutTrackCount->Reset();
    m_neuroRecoHWSector->Reset();
 
    m_DeltaRecoHWZ->Reset();
    m_DeltaRecoHWCosTheta->Reset();
    m_DeltaRecoHWInvPt->Reset();
    m_DeltaRecoHWPhi->Reset();
  }
  if (!m_limitedoutput && m_simNeuroTracksName != "" && m_recoTracksName != "") {
    m_RecoSWZ->Reset();
    m_RecoSWCosTheta->Reset();
    m_RecoSWInvPt->Reset();
    m_RecoSWPhi->Reset();
    m_RecoSWD0->Reset();
    m_RecoSWZScatter->Reset();
 
    m_neuroRecoSWOutZ->Reset();
    m_neuroRecoSWOutCosTheta->Reset();
    m_neuroRecoSWOutInvPt->Reset();
    m_neuroRecoSWOutPhi0->Reset();
    m_neuroRecoSWOutHitPattern->Reset();
    m_neuroRecoSWOutTrackCount->Reset();
    m_neuroRecoSWSector->Reset();
 
    m_DeltaRecoSWZ->Reset();
    m_DeltaRecoSWCosTheta->Reset();
    m_DeltaRecoSWInvPt->Reset();
    m_DeltaRecoSWPhi->Reset();
  }
  if (!m_limitedoutput && m_simNeuroTracksSWTSSW2DName != "" && m_recoTracksName != "") {
    m_RecoSWTSSW2DZ->Reset();
    m_RecoSWTSSW2DCosTheta->Reset();
    m_RecoSWTSSW2DInvPt->Reset();
    m_RecoSWTSSW2DPhi->Reset();
    m_RecoSWTSSW2DD0->Reset();
    m_RecoSWTSSW2DZScatter->Reset();
 
    m_neuroRecoSWTSSW2DOutZ->Reset();
    m_neuroRecoSWTSSW2DOutCosTheta->Reset();
    m_neuroRecoSWTSSW2DOutInvPt->Reset();
    m_neuroRecoSWTSSW2DOutPhi0->Reset();
    m_neuroRecoSWTSSW2DOutHitPattern->Reset();
    m_neuroRecoSWTSSW2DOutTrackCount->Reset();
    m_neuroRecoSWTSSW2DSector->Reset();
 
    m_DeltaRecoSWTSSW2DZ->Reset();
    m_DeltaRecoSWTSSW2DCosTheta->Reset();
    m_DeltaRecoSWTSSW2DInvPt->Reset();
    m_DeltaRecoSWTSSW2DPhi->Reset();
  }
 
}

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;
}

condFill()

void condFill ( TH1F *  histo, float  value  )

inlineprivate

Fill a histogram only with non-zero values.

Definition at line 65 of file CDCTriggerNeuroDQMModule.h.

    {
      if (value != 0) {
        histo->Fill(value);
      }
    }

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(); }

defineHisto()

void defineHisto ( )

overridevirtual

Histogram definitions such as TH1(), TH2(), TNtuple(), TTree()....

are supposed to be placed in this function.

Reimplemented from HistoModule.

Definition at line 96 of file CDCTriggerNeuroDQMModule.cc.

{
  // Create a separate histogram directory and cd into it.
  TDirectory* oldDir = gDirectory;
  if (m_histogramDirectoryName != "") {
    oldDir->mkdir(m_histogramDirectoryName.c_str());
    oldDir->cd(m_histogramDirectoryName.c_str());
  }
  //----------------------------------------------------------------
 
  // define neurotrigger histograms
  if (m_unpackedNeuroTracksName != "") {
    m_neuroHWOutZ = new TH1F("NeuroHWOutZ",
                             "z distribution of unpacked neuro tracks; z [cm]",
                             100, -100, 100);
    m_neuroHWOutCosTheta = new TH1F("NeuroHWOutCosTheta",
                                    "cos theta distribution of unpacked neuro tracks; cos(#theta) ",
                                    100, -1, 1);
    m_neuroHWOutPhi0 = new TH1F("NeuroHWOutPhi0",
                                "phi distribution of unpacked neuro tracks; #phi [#circ]",
                                80, 0, 360); // shift to reduce the binning error
    m_neuroHWOutInvPt = new TH1F("NeuroHWOutInvPt",
                                 "Inverse Pt distribution of unpacked neuro tracks; p_{T}^{-1} [GeV^{-1}]",
                                 50, 0, 5);
    m_neuroHWOutPt = new TH1F("NeuroHWOutPt",
                              "Pt distribution of unpacked neuro tracks; p_{T} [GeV]",
                              340, 0, 11);
    m_neuroHWOutHitPattern = new TH1F("NeuroUnpackedHitPattern",
                                      "stereo hit pattern of unpacked neuro tracks; pattern",
                                      16, 0, 16); // 4 stereo layers -> 2**4 possible patterns
    m_neuroHWOutm_time = new TH1F("NeuroHWOutM_time", "m_time distribution of unpacked neuro tracks; clock cycle",
                                  48, 0, 48);
    m_neuroHWOutTrackCount = new TH1F("NeuroHWOutTrackCount",
                                      "number of unpacked neuro tracks per event",
                                      20, 0, 20);
    m_neuroHWSector = new TH1F("NeuroHWSector",
                               "sector of unpacked neuro tracks; sector",
                               10, 0, 10);
 
 
    m_neuroHWInInvPt = new TH1F("NeuroHWInInvPt",
                                "Inverse Pt distribution from incoming 2dtrack; p_{T}^{-1} [GeV^{-1}]",
                                50, 0, 5);
    m_neuroHWInPhi0 = new TH1F("NeuroHWInPhi0", "Phi0 of incoming 2dtrack; #phi [#circ]",
                               80, 0, 360);
    m_neuroHWInm_time = new TH1F("NeuroHWInM_time", "m_time distribution from incoming 2dtracks; clock cycle",
                                 48, 0, 48);
    m_neuroHWInTrackCount = new TH1F("NeuroHWInTrackCount", "number of neuro input 2dtracks per event",
                                     20, 0, 20);
    m_neuroHWOutVsInTrackCount = new TH1F("NeuroHWOutVsInTrackCount",
                                          "number of neuroHWOutTracks - number of 2dinTracks",
                                          20, -10, 10);
    m_neuroHWInTSID = new TH1F("NeuroHWInTSID", "ID of incoming track segments",
                               2336, 0, 2335);
    m_neuroHWInTSCount = new TH1F("NeuroHWInTSCount", " number of TS per event",
                                  200, 0, 200);
    m_neuroHWSelTSID = new TH1F("NeuroHWSelTSID", "ID of selected track segments",
                                2336, 0, 2335);
    m_neuroHWSelTSCount = new TH1F("NeuroHWSelTSCount", "number of selected TS per SL; sl", 9, 0, 9);
  }
  if (!m_limitedoutput && m_unpacked2DTracksName != "") {
    m_2DHWInTSPrioT_Layer0 = new TH1F("2DHWInTSPrioT_Layer0", "Priority time of track segments in layer 0",
                                      512, 0, 511);
    m_2DHWInTSPrioT_Layer2 = new TH1F("2DHWInTSPrioT_Layer2", "Priority time of track segments in layer 2",
                                      512, 0, 511);
    m_2DHWInTSPrioT_Layer4 = new TH1F("2DHWInTSPrioT_Layer4", "Priority time of track segments in layer 4",
                                      512, 0, 511);
    m_2DHWInTSPrioT_Layer6 = new TH1F("2DHWInTSPrioT_Layer6", "Priority time of track segments in layer 6",
                                      512, 0, 511);
    m_2DHWInTSPrioT_Layer8 = new TH1F("2DHWInTSPrioT_Layer8", "Priority time of track segments in layer 8",
                                      512, 0, 511);
    m_2DHWInTSFoundT_Layer0 = new TH1F("2DHWInTSFoundT_Layer0", "Found time of track segments in layer 0",
                                       96, -48, 48);
    m_2DHWInTSFoundT_Layer2 = new TH1F("2DHWInTSFoundT_Layer2", "Found time of track segments in layer 2",
                                       96, -48, 48);
    m_2DHWInTSFoundT_Layer4 = new TH1F("2DHWInTSFoundT_Layer4", "Found time of track segments in layer 4",
                                       96, -48, 48);
    m_2DHWInTSFoundT_Layer6 = new TH1F("2DHWInTSFoundT_Layer6", "Found time of track segments in layer 6",
                                       96, -48, 48);
    m_2DHWInTSFoundT_Layer8 = new TH1F("2DHWInTSFoundT_Layer8", "Found time of track segments in layer 8",
                                       96, -48, 48);
 
 
    m_2DHWInTSPrioB_Layer0 = new TH1F("2DHWInTSPrioB_Layer0", "Priority bits of track segments in layer 0",
                                      4, 0, 4);
    m_2DHWInTSPrioB_Layer2 = new TH1F("2DHWInTSPrioB_Layer2", "Priority bits of track segments in layer 2",
                                      4, 0, 4);
    m_2DHWInTSPrioB_Layer4 = new TH1F("2DHWInTSPrioB_Layer4", "Priority bits of track segments in layer 4",
                                      4, 0, 4);
    m_2DHWInTSPrioB_Layer6 = new TH1F("2DHWInTSPrioB_Layer6", "Priority bits of track segments in layer 6",
                                      4, 0, 4);
    m_2DHWInTSPrioB_Layer8 = new TH1F("2DHWInTSPrioB_Layer8", "Priority bits of track segments in layer 8",
                                      4, 0, 4);
    m_2DHWInTSLR_Layer0 = new TH1F("2DHWInTSLR_Layer0", "Left/Right of track segments in layer 0",
                                   4, 0, 4);
    m_2DHWInTSLR_Layer2 = new TH1F("2DHWInTSLR_Layer2", "Left/Right of track segments in layer 2",
                                   4, 0, 4);
    m_2DHWInTSLR_Layer4 = new TH1F("2DHWInTSLR_Layer4", "Left/Right of track segments in layer 4",
                                   4, 0, 4);
    m_2DHWInTSLR_Layer6 = new TH1F("2DHWInTSLR_Layer6", "Left/Right of track segments in layer 6",
                                   4, 0, 4);
    m_2DHWInTSLR_Layer8 = new TH1F("2DHWInTSLR_Layer8", "Left/Right of track segments in layer 8",
                                   4, 0, 4);
  }
  if (!m_limitedoutput && m_unpackedNeuroTracksName != "") {
 
    m_neuroHWOutQuad5Z = new TH1F("NeuroHWOutQuad5Z",
                                  "z distribution of neuro tracks; z [cm]",
                                  100, -100, 100);
    m_neuroHWOutQuad5CosTheta = new TH1F("NeuroHWOutQuad5CosTheta",
                                         "cos theta distribution of neuro tracks; cos(#theta) ",
                                         100, -1, 1);
    m_neuroHWOutQuad5InvPt = new TH1F("NeuroHWOutQuad5InvPt",
                                      "Pt distribution from unpacker; p_{T}^{-1} [GeV^{-1}]",
                                      50, 0, 5);
    m_neuroHWOutQuad5Phi0 = new TH1F("NeuroHWOutQuad5Phi0",
                                     "phi distribution from unpacker; #phi [#circ]",
                                     80, 0, 360);
 
    m_neuroHWOutQuad0Z = new TH1F("NeuroHWOutQuad0Z",
                                  "z distribution of neuro tracks; z [cm]",
                                  100, -100, 100);
    m_neuroHWOutQuad0CosTheta = new TH1F("NeuroHWOutQuad0CosTheta",
                                         "cos theta distribution of neuro tracks; cos(#theta) ",
                                         100, -1, 1);
    m_neuroHWOutQuad0InvPt = new TH1F("NeuroHWOutQuad0InvPt",
                                      "Pt distribution from unpacker; p_{T}^{-1} [GeV^{-1}]",
                                      50, 0, 5);
    m_neuroHWOutQuad0Phi0 = new TH1F("NeuroHWOutQuad0Phi0",
                                     "phi distribution from unpacker; #phi [#circ]",
                                     80, 0, 360);
 
    m_neuroHWOutQuad1Z = new TH1F("NeuroHWOutQuad1Z",
                                  "z distribution of neuro tracks; z [cm]",
                                  100, -100, 100);
    m_neuroHWOutQuad1CosTheta = new TH1F("NeuroHWOutQuad1CosTheta",
                                         "cos theta distribution of neuro tracks; cos(#theta) ",
                                         100, -1, 1);
    m_neuroHWOutQuad1Phi0 = new TH1F("NeuroHWOutQuad1Phi0",
                                     "phi distribution from unpacker; #phi [#circ]",
                                     80, 0, 360);
    m_neuroHWOutQuad1InvPt = new TH1F("NeuroHWOutQuad1InvPt",
                                      "Pt distribution from unpacker; p_{T}^{-1} [GeV^{-1}]",
                                      50, 0, 5);
 
    m_neuroHWOutQuad2Z = new TH1F("NeuroHWOutQuad2Z",
                                  "z distribution of neuro tracks; z [cm]",
                                  100, -100, 100);
    m_neuroHWOutQuad2CosTheta = new TH1F("NeuroHWOutQuad2CosTheta",
                                         "cos theta distribution of neuro tracks; cos(#theta) ",
                                         100, -1, 1);
    m_neuroHWOutQuad2Phi0 = new TH1F("NeuroHWOutQuad2Phi0",
                                     "phi distribution from unpacker; #phi [#circ]",
                                     80, 0, 360);
    m_neuroHWOutQuad2InvPt = new TH1F("NeuroHWOutQuad2InvPt",
                                      "Pt distribution from unpacker; p_{T}^{-1} [GeV^{-1}]",
                                      50, 0, 5);
 
    m_neuroHWOutQuad3Z = new TH1F("NeuroHWOutQuad3Z",
                                  "z distribution of neuro tracks; z [cm]",
                                  100, -100, 100);
    m_neuroHWOutQuad3CosTheta = new TH1F("NeuroHWOutQuad3CosTheta",
                                         "cos theta distribution of neuro tracks; cos(#theta) ",
                                         100, -1, 1);
    m_neuroHWOutQuad3Phi0 = new TH1F("NeuroHWOutQuad3Phi0",
                                     "phi distribution from unpacker; #phi [#circ]",
                                     80, 0, 360);
    m_neuroHWOutQuad3InvPt = new TH1F("NeuroHWOutQuad3InvPt",
                                      "Pt distribution from unpacker; p_{T}^{-1} [GeV^{-1}]",
                                      50, 0, 5);
    m_neuroHWInTSPrioT_Layer0 = new TH1F("NeuroHWInTSPrioT_Layer0", "Priority time of track segments in layer 0",
                                         512, 0, 511);
    m_neuroHWInTSPrioT_Layer1 = new TH1F("NeuroHWInTSPrioT_Layer1", "Priority time of track segments in layer 1",
                                         512, 0, 511);
    m_neuroHWInTSPrioT_Layer2 = new TH1F("NeuroHWInTSPrioT_Layer2", "Priority time of track segments in layer 2",
                                         512, 0, 511);
    m_neuroHWInTSPrioT_Layer3 = new TH1F("NeuroHWInTSPrioT_Layer3", "Priority time of track segments in layer 3",
                                         512, 0, 511);
    m_neuroHWInTSPrioT_Layer4 = new TH1F("NeuroHWInTSPrioT_Layer4", "Priority time of track segments in layer 4",
                                         512, 0, 511);
    m_neuroHWInTSPrioT_Layer5 = new TH1F("NeuroHWInTSPrioT_Layer5", "Priority time of track segments in layer 5",
                                         512, 0, 511);
    m_neuroHWInTSPrioT_Layer6 = new TH1F("NeuroHWInTSPrioT_Layer6", "Priority time of track segments in layer 6",
                                         512, 0, 511);
    m_neuroHWInTSPrioT_Layer7 = new TH1F("NeuroHWInTSPrioT_Layer7", "Priority time of track segments in layer 7",
                                         512, 0, 511);
    m_neuroHWInTSPrioT_Layer8 = new TH1F("NeuroHWInTSPrioT_Layer8", "Priority time of track segments in layer 8",
                                         512, 0, 511);
    m_neuroHWInTSFoundT_Layer0 = new TH1F("NeuroHWInTSFoundT_Layer0", "Found time of track segments in layer 0",
                                          48, 0, 48);
    m_neuroHWInTSFoundT_Layer1 = new TH1F("NeuroHWInTSFoundT_Layer1", "Found time of track segments in layer 1",
                                          48, 0, 48);
    m_neuroHWInTSFoundT_Layer2 = new TH1F("NeuroHWInTSFoundT_Layer2", "Found time of track segments in layer 2",
                                          48, 0, 48);
    m_neuroHWInTSFoundT_Layer3 = new TH1F("NeuroHWInTSFoundT_Layer3", "Found time of track segments in layer 3",
                                          48, 0, 48);
    m_neuroHWInTSFoundT_Layer4 = new TH1F("NeuroHWInTSFoundT_Layer4", "Found time of track segments in layer 4",
                                          48, 0, 48);
    m_neuroHWInTSFoundT_Layer5 = new TH1F("NeuroHWInTSFoundT_Layer5", "Found time of track segments in layer 5",
                                          48, 0, 48);
    m_neuroHWInTSFoundT_Layer6 = new TH1F("NeuroHWInTSFoundT_Layer6", "Found time of track segments in layer 6",
                                          48, 0, 48);
    m_neuroHWInTSFoundT_Layer7 = new TH1F("NeuroHWInTSFoundT_Layer7", "Found time of track segments in layer 7",
                                          48, 0, 48);
    m_neuroHWInTSFoundT_Layer8 = new TH1F("NeuroHWInTSFoundT_Layer8", "Found time of track segments in layer 8",
                                          48, 0, 48);
 
 
    m_neuroHWInTSPrioB_Layer0 = new TH1F("NeuroHWInTSPrioB_Layer0", "Priority bits of track segments in layer 0",
                                         4, 0, 4);
    m_neuroHWInTSPrioB_Layer1 = new TH1F("NeuroHWInTSPrioB_Layer1", "Priority bits of track segments in layer 1",
                                         4, 0, 4);
    m_neuroHWInTSPrioB_Layer2 = new TH1F("NeuroHWInTSPrioB_Layer2", "Priority bits of track segments in layer 2",
                                         4, 0, 4);
    m_neuroHWInTSPrioB_Layer3 = new TH1F("NeuroHWInTSPrioB_Layer3", "Priority bits of track segments in layer 3",
                                         4, 0, 4);
    m_neuroHWInTSPrioB_Layer4 = new TH1F("NeuroHWInTSPrioB_Layer4", "Priority bits of track segments in layer 4",
                                         4, 0, 4);
    m_neuroHWInTSPrioB_Layer5 = new TH1F("NeuroHWInTSPrioB_Layer5", "Priority bits of track segments in layer 5",
                                         4, 0, 4);
    m_neuroHWInTSPrioB_Layer6 = new TH1F("NeuroHWInTSPrioB_Layer6", "Priority bits of track segments in layer 6",
                                         4, 0, 4);
    m_neuroHWInTSPrioB_Layer7 = new TH1F("NeuroHWInTSPrioB_Layer7", "Priority bits of track segments in layer 7",
                                         4, 0, 4);
    m_neuroHWInTSPrioB_Layer8 = new TH1F("NeuroHWInTSPrioB_Layer8", "Priority bits of track segments in layer 8",
                                         4, 0, 4);
 
 
    m_neuroHWInTSLR_Layer0 = new TH1F("NeuroHWInTSLR_Layer0", "Left/Right of track segments in layer 0",
                                      4, 0, 4);
    m_neuroHWInTSLR_Layer1 = new TH1F("NeuroHWInTSLR_Layer1", "Left/Right of track segments in layer 1",
                                      4, 0, 4);
    m_neuroHWInTSLR_Layer2 = new TH1F("NeuroHWInTSLR_Layer2", "Left/Right of track segments in layer 2",
                                      4, 0, 4);
    m_neuroHWInTSLR_Layer3 = new TH1F("NeuroHWInTSLR_Layer3", "Left/Right of track segments in layer 3",
                                      4, 0, 4);
    m_neuroHWInTSLR_Layer4 = new TH1F("NeuroHWInTSLR_Layer4", "Left/Right of track segments in layer 4",
                                      4, 0, 4);
    m_neuroHWInTSLR_Layer5 = new TH1F("NeuroHWInTSLR_Layer5", "Left/Right of track segments in layer 5",
                                      4, 0, 4);
    m_neuroHWInTSLR_Layer6 = new TH1F("NeuroHWInTSLR_Layer6", "Left/Right of track segments in layer 6",
                                      4, 0, 4);
    m_neuroHWInTSLR_Layer7 = new TH1F("NeuroHWInTSLR_Layer7", "Left/Right of track segments in layer 7",
                                      4, 0, 4);
    m_neuroHWInTSLR_Layer8 = new TH1F("NeuroHWInTSLR_Layer8", "Left/Right of track segments in layer 8",
                                      4, 0, 4);
    m_neuroHWSelTSPrioT_Layer0 = new TH1F("NeuroHWSelTSPrioT_Layer0", "Priority time of track segments in layer 0",
                                          512, 0, 511);
    m_neuroHWSelTSPrioT_Layer1 = new TH1F("NeuroHWSelTSPrioT_Layer1", "Priority time of track segments in layer 1",
                                          512, 0, 511);
    m_neuroHWSelTSPrioT_Layer2 = new TH1F("NeuroHWSelTSPrioT_Layer2", "Priority time of track segments in layer 2",
                                          512, 0, 511);
    m_neuroHWSelTSPrioT_Layer3 = new TH1F("NeuroHWSelTSPrioT_Layer3", "Priority time of track segments in layer 3",
                                          512, 0, 511);
    m_neuroHWSelTSPrioT_Layer4 = new TH1F("NeuroHWSelTSPrioT_Layer4", "Priority time of track segments in layer 4",
                                          512, 0, 511);
    m_neuroHWSelTSPrioT_Layer5 = new TH1F("NeuroHWSelTSPrioT_Layer5", "Priority time of track segments in layer 5",
                                          512, 0, 511);
    m_neuroHWSelTSPrioT_Layer6 = new TH1F("NeuroHWSelTSPrioT_Layer6", "Priority time of track segments in layer 6",
                                          512, 0, 511);
    m_neuroHWSelTSPrioT_Layer7 = new TH1F("NeuroHWSelTSPrioT_Layer7", "Priority time of track segments in layer 7",
                                          512, 0, 511);
    m_neuroHWSelTSPrioT_Layer8 = new TH1F("NeuroHWSelTSPrioT_Layer8", "Priority time of track segments in layer 8",
                                          512, 0, 511);
    m_neuroHWSelTSFoundT_Layer0 = new TH1F("NeuroHWSelTSFoundT_Layer0",
                                           "First found time of selected TS - found time of Neuro Track in SL 0",
                                           96, -47.99, 48.01);
    m_neuroHWSelTSFoundT_Layer1 = new TH1F("NeuroHWSelTSFoundT_Layer1",
                                           "First found time of selected TS - found time of Neuro Track in SL 1",
                                           96, -47.99, 48.01);
    m_neuroHWSelTSFoundT_Layer2 = new TH1F("NeuroHWSelTSFoundT_Layer2",
                                           "First found time of selected TS - found time of Neuro Track in SL 2",
                                           96, -47.99, 48.01);
    m_neuroHWSelTSFoundT_Layer3 = new TH1F("NeuroHWSelTSFoundT_Layer3",
                                           "First found time of selected TS - found time of Neuro Track in SL 3",
                                           96, -47.99, 48.01);
    m_neuroHWSelTSFoundT_Layer4 = new TH1F("NeuroHWSelTSFoundT_Layer4",
                                           "First found time of selected TS - found time of Neuro Track in SL 4",
                                           96, -47.99, 48.01);
    m_neuroHWSelTSFoundT_Layer5 = new TH1F("NeuroHWSelTSFoundT_Layer5",
                                           "First found time of selected TS - found time of Neuro Track in SL 5",
                                           96, -47.99, 48.01);
    m_neuroHWSelTSFoundT_Layer6 = new TH1F("NeuroHWSelTSFoundT_Layer6",
                                           "First found time of selected TS - found time of Neuro Track in SL 6",
                                           96, -47.99, 48.01);
    m_neuroHWSelTSFoundT_Layer7 = new TH1F("NeuroHWSelTSFoundT_Layer7",
                                           "First found time of selected TS - found time of Neuro Track in SL 7",
                                           96, -47.99, 48.01);
    m_neuroHWSelTSFoundT_Layer8 = new TH1F("NeuroHWSelTSFoundT_Layer8",
                                           "First found time of selected TS - found time of Neuro Track in SL 8",
                                           96, -47.99, 48.01);
 
 
    m_neuroHWSelTSPrioB_Layer0 = new TH1F("NeuroHWSelTSPrioB_Layer0", "Priority bits of track segments in layer 0",
                                          4, 0, 4);
    m_neuroHWSelTSPrioB_Layer1 = new TH1F("NeuroHWSelTSPrioB_Layer1", "Priority bits of track segments in layer 1",
                                          4, 0, 4);
    m_neuroHWSelTSPrioB_Layer2 = new TH1F("NeuroHWSelTSPrioB_Layer2", "Priority bits of track segments in layer 2",
                                          4, 0, 4);
    m_neuroHWSelTSPrioB_Layer3 = new TH1F("NeuroHWSelTSPrioB_Layer3", "Priority bits of track segments in layer 3",
                                          4, 0, 4);
    m_neuroHWSelTSPrioB_Layer4 = new TH1F("NeuroHWSelTSPrioB_Layer4", "Priority bits of track segments in layer 4",
                                          4, 0, 4);
    m_neuroHWSelTSPrioB_Layer5 = new TH1F("NeuroHWSelTSPrioB_Layer5", "Priority bits of track segments in layer 5",
                                          4, 0, 4);
    m_neuroHWSelTSPrioB_Layer6 = new TH1F("NeuroHWSelTSPrioB_Layer6", "Priority bits of track segments in layer 6",
                                          4, 0, 4);
    m_neuroHWSelTSPrioB_Layer7 = new TH1F("NeuroHWSelTSPrioB_Layer7", "Priority bits of track segments in layer 7",
                                          4, 0, 4);
    m_neuroHWSelTSPrioB_Layer8 = new TH1F("NeuroHWSelTSPrioB_Layer8", "Priority bits of track segments in layer 8",
                                          4, 0, 4);
 
 
    m_neuroHWSelTSLR_Layer0 = new TH1F("NeuroHWSelTSLR_Layer0", "Left/Right of track segments in layer 0",
                                       4, 0, 4);
    m_neuroHWSelTSLR_Layer1 = new TH1F("NeuroHWSelTSLR_Layer1", "Left/Right of track segments in layer 1",
                                       4, 0, 4);
    m_neuroHWSelTSLR_Layer2 = new TH1F("NeuroHWSelTSLR_Layer2", "Left/Right of track segments in layer 2",
                                       4, 0, 4);
    m_neuroHWSelTSLR_Layer3 = new TH1F("NeuroHWSelTSLR_Layer3", "Left/Right of track segments in layer 3",
                                       4, 0, 4);
    m_neuroHWSelTSLR_Layer4 = new TH1F("NeuroHWSelTSLR_Layer4", "Left/Right of track segments in layer 4",
                                       4, 0, 4);
    m_neuroHWSelTSLR_Layer5 = new TH1F("NeuroHWSelTSLR_Layer5", "Left/Right of track segments in layer 5",
                                       4, 0, 4);
    m_neuroHWSelTSLR_Layer6 = new TH1F("NeuroHWSelTSLR_Layer6", "Left/Right of track segments in layer 6",
                                       4, 0, 4);
    m_neuroHWSelTSLR_Layer7 = new TH1F("NeuroHWSelTSLR_Layer7", "Left/Right of track segments in layer 7",
                                       4, 0, 4);
    m_neuroHWSelTSLR_Layer8 = new TH1F("NeuroHWSelTSLR_Layer8", "Left/Right of track segments in layer 8",
                                       4, 0, 4);
    m_neuroHWInputID_Layer0 = new TH1F("NeuroHWInputID_Layer0",
                                       "unpacked id input in layer 0; id",
                                       100, -1, 1);
    m_neuroHWInputT_Layer0 = new TH1F("NeuroHWInputT_Layer0",
                                      "unpacked time input in layer 0; time",
                                      100, -1, 1);
    m_neuroHWInputAlpha_Layer0 = new TH1F("NeuroHWInputAlpha_Layer0",
                                          "unpacked alpha input in layer 0; alpha",
                                          100, -1, 1);
    m_neuroHWInputID_Layer1 = new TH1F("NeuroHWInputID_Layer1",
                                       "unpacked id input in layer 1; id",
                                       100, -1, 1);
    m_neuroHWInputT_Layer1 = new TH1F("NeuroHWInputT_Layer1",
                                      "unpacked time input in layer 1; time",
                                      100, -1, 1);
    m_neuroHWInputAlpha_Layer1 = new TH1F("NeuroHWInputAlpha_Layer1",
                                          "unpacked alpha input in layer 1; alpha",
                                          100, -1, 1);
    m_neuroHWInputID_Layer2 = new TH1F("NeuroHWInputID_Layer2",
                                       "unpacked id input in layer 2; id",
                                       100, -1, 1);
    m_neuroHWInputT_Layer2 = new TH1F("NeuroHWInputT_Layer2",
                                      "unpacked time input in layer 2; time",
                                      100, -1, 1);
    m_neuroHWInputAlpha_Layer2 = new TH1F("NeuroHWInputAlpha_Layer2",
                                          "unpacked alpha input in layer 2; alpha",
                                          100, -1, 1);
    m_neuroHWInputID_Layer3 = new TH1F("NeuroHWInputID_Layer3",
                                       "unpacked id input in layer 3; id",
                                       100, -1, 1);
    m_neuroHWInputT_Layer3 = new TH1F("NeuroHWInputT_Layer3",
                                      "unpacked time input in layer 3; time",
                                      100, -1, 1);
    m_neuroHWInputAlpha_Layer3 = new TH1F("NeuroHWInputAlpha_Layer3",
                                          "unpacked alpha input in layer 3; alpha",
                                          100, -1, 1);
    m_neuroHWInputID_Layer4 = new TH1F("NeuroHWInputID_Layer4",
                                       "unpacked id input in layer 4; id",
                                       100, -1, 1);
    m_neuroHWInputT_Layer4 = new TH1F("NeuroHWInputT_Layer4",
                                      "unpacked time input in layer 4; time",
                                      100, -1, 1);
    m_neuroHWInputAlpha_Layer4 = new TH1F("NeuroHWInputAlpha_Layer4",
                                          "unpacked alpha input in layer 4; alpha",
                                          100, -1, 1);
    m_neuroHWInputID_Layer5 = new TH1F("NeuroHWInputID_Layer5",
                                       "unpacked id input in layer 5; id",
                                       100, -1, 1);
    m_neuroHWInputT_Layer5 = new TH1F("NeuroHWInputT_Layer5",
                                      "unpacked time input in layer 5; time",
                                      100, -1, 1);
    m_neuroHWInputAlpha_Layer5 = new TH1F("NeuroHWInputAlpha_Layer5",
                                          "unpacked alpha input in layer 5; alpha",
                                          100, -1, 1);
    m_neuroHWInputID_Layer6 = new TH1F("NeuroHWInputID_Layer6",
                                       "unpacked id input in layer 6; id",
                                       100, -1, 1);
    m_neuroHWInputT_Layer6 = new TH1F("NeuroHWInputT_Layer6",
                                      "unpacked time input in layer 6; time",
                                      100, -1, 1);
    m_neuroHWInputAlpha_Layer6 = new TH1F("NeuroHWInputAlpha_Layer6",
                                          "unpacked alpha input in layer 6; alpha",
                                          100, -1, 1);
    m_neuroHWInputID_Layer7 = new TH1F("NeuroHWInputID_Layer7",
                                       "unpacked id input in layer 7; id",
                                       100, -1, 1);
    m_neuroHWInputT_Layer7 = new TH1F("NeuroHWInputT_Layer7",
                                      "unpacked time input in layer 7; time",
                                      100, -1, 1);
    m_neuroHWInputAlpha_Layer7 = new TH1F("NeuroHWInputAlpha_Layer7",
                                          "unpacked alpha input in layer 7; alpha",
                                          100, -1, 1);
    m_neuroHWInputID_Layer8 = new TH1F("NeuroHWInputID_Layer8",
                                       "unpacked id input in layer 8; id",
                                       100, -1, 1);
    m_neuroHWInputT_Layer8 = new TH1F("NeuroHWInputT_Layer8",
                                      "unpacked time input in layer 8; time",
                                      100, -1, 1);
    m_neuroHWInputAlpha_Layer8 = new TH1F("NeuroHWInputAlpha_Layer8",
                                          "unpacked alpha input in layer 8; alpha",
                                          100, -1, 1);
  }
  if (m_unpacked2DTracksName != "") {
    m_2DHWOutInvPt = new TH1F("2DHWOutInvPt",
                              "Inverse Pt of 2dtracks; p_{T}^{-1} [GeV^{-1}]",
                              50, 0, 5);
    m_2DHWOutPhi0 = new TH1F("2DHWOutPhi0", "Phi0 of 2dtracks; #phi [#circ]",
                             80, 0, 360);
    m_2DHWOutm_time = new TH1F("2DHWOutM_time", "m_time of 2dtracks; clock cycle",
                               96, -48, 48);
    m_2DHWOutTrackCount = new TH1F("2DHWOutTrackCount", "number of 2dtracks per event", 20, 0, 20);
    m_neuroHWInVs2DOutTrackCount = new TH1F("NeuroHWInVs2DOutTrackCount", "neuro in tracks - 2d out tracks",
                                            20, -10, 10);
  }
 
  if (!m_limitedoutput && m_simNeuroTracksName != "") {
    m_neuroSWOutZ = new TH1F("NeuroSWOutZ",
                             "z distribution from simulation, hw TS hw 2D; z [cm]",
                             100, -100, 100);
    m_neuroSWOutCosTheta = new TH1F("NeuroSWOutCosTheta",
                                    "cos theta distribution from simulation, hw TS hw 2D; cos(#theta) ",
                                    100, -1, 1);
    m_neuroSWOutInvPt = new TH1F("NeuroSWOutInvPt",
                                 "Inverse Pt distribution from simulation, hw TS hw 2D; p_{T}^{-1} [GeV^{-1}]",
                                 50, 0, 5);
    m_neuroSWOutPhi0 = new TH1F("NeuroSWOutPhi0",
                                "phi distribution from simulation, hw TS hw 2D; #phi [#circ]",
                                80, 0, 360); // shift to reduce the binning error
    m_neuroSWOutHitPattern = new TH1F("NeuroSWOutHitPattern",
                                      "stereo hit pattern of simulated neuro tracks, hw TS hw 2D; pattern",
                                      16, 0, 16); // 4 stereo layers -> 2**4 possible patterns
    m_neuroSWOutTrackCount = new TH1F("NeuroSWOutTrackCount",
                                      "number of neuro tracks per event from simulation, hw TS hw 2D",
                                      20, 0, 20);
    m_neuroSWSector = new TH1F("NeuroSWSector",
                               "sector of simulated neuro tracks, hw TS hw 2D; sector",
                               10, 0, 10);
    // hw TS selected by sw NN
    m_neuroSWSelTSID = new TH1F("NeuroSWSelTSID", "ID of selected track segments",
                                2336, 0, 2335);
    m_neuroSWSelTSCount = new TH1F("NeuroSWSelTSCount", "number of selected TS per SL; sl", 9, 0, 9);
    m_neuroSWSelTSPrioT_Layer0 = new TH1F("NeuroSWSelTSPrioT_Layer0", "Priority time of track segments in layer 0",
                                          512, 0, 511);
    m_neuroSWSelTSPrioT_Layer1 = new TH1F("NeuroSWSelTSPrioT_Layer1", "Priority time of track segments in layer 1",
                                          512, 0, 511);
    m_neuroSWSelTSPrioT_Layer2 = new TH1F("NeuroSWSelTSPrioT_Layer2", "Priority time of track segments in layer 2",
                                          512, 0, 511);
    m_neuroSWSelTSPrioT_Layer3 = new TH1F("NeuroSWSelTSPrioT_Layer3", "Priority time of track segments in layer 3",
                                          512, 0, 511);
    m_neuroSWSelTSPrioT_Layer4 = new TH1F("NeuroSWSelTSPrioT_Layer4", "Priority time of track segments in layer 4",
                                          512, 0, 511);
    m_neuroSWSelTSPrioT_Layer5 = new TH1F("NeuroSWSelTSPrioT_Layer5", "Priority time of track segments in layer 5",
                                          512, 0, 511);
    m_neuroSWSelTSPrioT_Layer6 = new TH1F("NeuroSWSelTSPrioT_Layer6", "Priority time of track segments in layer 6",
                                          512, 0, 511);
    m_neuroSWSelTSPrioT_Layer7 = new TH1F("NeuroSWSelTSPrioT_Layer7", "Priority time of track segments in layer 7",
                                          512, 0, 511);
    m_neuroSWSelTSPrioT_Layer8 = new TH1F("NeuroSWSelTSPrioT_Layer8", "Priority time of track segments in layer 8",
                                          512, 0, 511);
    m_neuroSWSelTSFoundT_Layer0 = new TH1F("NeuroSWSelTSFoundT_Layer0",
                                           "First found time of selected TS - found time of Neuro Track in SL 0",
                                           96, -47.99, 48.01);
    m_neuroSWSelTSFoundT_Layer1 = new TH1F("NeuroSWSelTSFoundT_Layer1",
                                           "First found time of selected TS - found time of Neuro Track in SL 1",
                                           96, -47.99, 48.01);
    m_neuroSWSelTSFoundT_Layer2 = new TH1F("NeuroSWSelTSFoundT_Layer2",
                                           "First found time of selected TS - found time of Neuro Track in SL 2",
                                           96, -47.99, 48.01);
    m_neuroSWSelTSFoundT_Layer3 = new TH1F("NeuroSWSelTSFoundT_Layer3",
                                           "First found time of selected TS - found time of Neuro Track in SL 3",
                                           96, -47.99, 48.01);
    m_neuroSWSelTSFoundT_Layer4 = new TH1F("NeuroSWSelTSFoundT_Layer4",
                                           "First found time of selected TS - found time of Neuro Track in SL 4",
                                           96, -47.99, 48.01);
    m_neuroSWSelTSFoundT_Layer5 = new TH1F("NeuroSWSelTSFoundT_Layer5",
                                           "First found time of selected TS - found time of Neuro Track in SL 5",
                                           96, -47.99, 48.01);
    m_neuroSWSelTSFoundT_Layer6 = new TH1F("NeuroSWSelTSFoundT_Layer6",
                                           "First found time of selected TS - found time of Neuro Track in SL 6",
                                           96, -47.99, 48.01);
    m_neuroSWSelTSFoundT_Layer7 = new TH1F("NeuroSWSelTSFoundT_Layer7",
                                           "First found time of selected TS - found time of Neuro Track in SL 7",
                                           96, -47.99, 48.01);
    m_neuroSWSelTSFoundT_Layer8 = new TH1F("NeuroSWSelTSFoundT_Layer8",
                                           "First found time of selected TS - found time of Neuro Track in SL 8",
                                           96, -47.99, 48.01);
 
 
    m_neuroSWSelTSPrioB_Layer0 = new TH1F("NeuroSWSelTSPrioB_Layer0", "Priority bits of track segments in layer 0",
                                          4, 0, 4);
    m_neuroSWSelTSPrioB_Layer1 = new TH1F("NeuroSWSelTSPrioB_Layer1", "Priority bits of track segments in layer 1",
                                          4, 0, 4);
    m_neuroSWSelTSPrioB_Layer2 = new TH1F("NeuroSWSelTSPrioB_Layer2", "Priority bits of track segments in layer 2",
                                          4, 0, 4);
    m_neuroSWSelTSPrioB_Layer3 = new TH1F("NeuroSWSelTSPrioB_Layer3", "Priority bits of track segments in layer 3",
                                          4, 0, 4);
    m_neuroSWSelTSPrioB_Layer4 = new TH1F("NeuroSWSelTSPrioB_Layer4", "Priority bits of track segments in layer 4",
                                          4, 0, 4);
    m_neuroSWSelTSPrioB_Layer5 = new TH1F("NeuroSWSelTSPrioB_Layer5", "Priority bits of track segments in layer 5",
                                          4, 0, 4);
    m_neuroSWSelTSPrioB_Layer6 = new TH1F("NeuroSWSelTSPrioB_Layer6", "Priority bits of track segments in layer 6",
                                          4, 0, 4);
    m_neuroSWSelTSPrioB_Layer7 = new TH1F("NeuroSWSelTSPrioB_Layer7", "Priority bits of track segments in layer 7",
                                          4, 0, 4);
    m_neuroSWSelTSPrioB_Layer8 = new TH1F("NeuroSWSelTSPrioB_Layer8", "Priority bits of track segments in layer 8",
                                          4, 0, 4);
 
 
    m_neuroSWSelTSLR_Layer0 = new TH1F("NeuroSWSelTSLR_Layer0", "Left/Right of track segments in layer 0",
                                       4, 0, 4);
    m_neuroSWSelTSLR_Layer1 = new TH1F("NeuroSWSelTSLR_Layer1", "Left/Right of track segments in layer 1",
                                       4, 0, 4);
    m_neuroSWSelTSLR_Layer2 = new TH1F("NeuroSWSelTSLR_Layer2", "Left/Right of track segments in layer 2",
                                       4, 0, 4);
    m_neuroSWSelTSLR_Layer3 = new TH1F("NeuroSWSelTSLR_Layer3", "Left/Right of track segments in layer 3",
                                       4, 0, 4);
    m_neuroSWSelTSLR_Layer4 = new TH1F("NeuroSWSelTSLR_Layer4", "Left/Right of track segments in layer 4",
                                       4, 0, 4);
    m_neuroSWSelTSLR_Layer5 = new TH1F("NeuroSWSelTSLR_Layer5", "Left/Right of track segments in layer 5",
                                       4, 0, 4);
    m_neuroSWSelTSLR_Layer6 = new TH1F("NeuroSWSelTSLR_Layer6", "Left/Right of track segments in layer 6",
                                       4, 0, 4);
    m_neuroSWSelTSLR_Layer7 = new TH1F("NeuroSWSelTSLR_Layer7", "Left/Right of track segments in layer 7",
                                       4, 0, 4);
    m_neuroSWSelTSLR_Layer8 = new TH1F("NeuroSWSelTSLR_Layer8", "Left/Right of track segments in layer 8",
                                       4, 0, 4);
 
    m_neuroSWInputID_Layer0 = new TH1F("NeuroSWInputID_Layer0",
                                       "simulated id input in layer 0; id",
                                       100, -1, 1);
    m_neuroSWInputT_Layer0 = new TH1F("NeuroSWInputT_Layer0",
                                      "simulated time input in layer 0; time",
                                      100, -1, 1);
    m_neuroSWInputAlpha_Layer0 = new TH1F("NeuroSWInputAlpha_Layer0",
                                          "simulated alpha input in layer 0; alpha",
                                          100, -1, 1);
    m_neuroSWInputID_Layer1 = new TH1F("NeuroSWInputID_Layer1",
                                       "simulated id input in layer 1; id",
                                       100, -1, 1);
    m_neuroSWInputT_Layer1 = new TH1F("NeuroSWInputT_Layer1",
                                      "simulated time input in layer 1; time",
                                      100, -1, 1);
    m_neuroSWInputAlpha_Layer1 = new TH1F("NeuroSWInputAlpha_Layer1",
                                          "simulated alpha input in layer 1; alpha",
                                          100, -1, 1);
    m_neuroSWInputID_Layer2 = new TH1F("NeuroSWInputID_Layer2",
                                       "simulated id input in layer 2; id",
                                       100, -1, 1);
    m_neuroSWInputT_Layer2 = new TH1F("NeuroSWInputT_Layer2",
                                      "simulated time input in layer 2; time",
                                      100, -1, 1);
    m_neuroSWInputAlpha_Layer2 = new TH1F("NeuroSWInputAlpha_Layer2",
                                          "simulated alpha input in layer 2; alpha",
                                          100, -1, 1);
    m_neuroSWInputID_Layer3 = new TH1F("NeuroSWInputID_Layer3",
                                       "simulated id input in layer 3; id",
                                       100, -1, 1);
    m_neuroSWInputT_Layer3 = new TH1F("NeuroSWInputT_Layer3",
                                      "simulated time input in layer 3; time",
                                      100, -1, 1);
    m_neuroSWInputAlpha_Layer3 = new TH1F("NeuroSWInputAlpha_Layer3",
                                          "simulated alpha input in layer 3; alpha",
                                          100, -1, 1);
    m_neuroSWInputID_Layer4 = new TH1F("NeuroSWInputID_Layer4",
                                       "simulated id input in layer 4; id",
                                       100, -1, 1);
    m_neuroSWInputT_Layer4 = new TH1F("NeuroSWInputT_Layer4",
                                      "simulated time input in layer 4; time",
                                      100, -1, 1);
    m_neuroSWInputAlpha_Layer4 = new TH1F("NeuroSWInputAlpha_Layer4",
                                          "simulated alpha input in layer 4; alpha",
                                          100, -1, 1);
    m_neuroSWInputID_Layer5 = new TH1F("NeuroSWInputID_Layer5",
                                       "simulated id input in layer 5; id",
                                       100, -1, 1);
    m_neuroSWInputT_Layer5 = new TH1F("NeuroSWInputT_Layer5",
                                      "simulated time input in layer 5; time",
                                      100, -1, 1);
    m_neuroSWInputAlpha_Layer5 = new TH1F("NeuroSWInputAlpha_Layer5",
                                          "simulated alpha input in layer 5; alpha",
                                          100, -1, 1);
    m_neuroSWInputID_Layer6 = new TH1F("NeuroSWInputID_Layer6",
                                       "simulated id input in layer 6; id",
                                       100, -1, 1);
    m_neuroSWInputT_Layer6 = new TH1F("NeuroSWInputT_Layer6",
                                      "simulated time input in layer 6; time",
                                      100, -1, 1);
    m_neuroSWInputAlpha_Layer6 = new TH1F("NeuroSWInputAlpha_Layer6",
                                          "simulated alpha input in layer 6; alpha",
                                          100, -1, 1);
    m_neuroSWInputID_Layer7 = new TH1F("NeuroSWInputID_Layer7",
                                       "simulated id input in layer 7; id",
                                       100, -1, 1);
    m_neuroSWInputT_Layer7 = new TH1F("NeuroSWInputT_Layer7",
                                      "simulated time input in layer 7; time",
                                      100, -1, 1);
    m_neuroSWInputAlpha_Layer7 = new TH1F("NeuroSWInputAlpha_Layer7",
                                          "simulated alpha input in layer 7; alpha",
                                          100, -1, 1);
    m_neuroSWInputID_Layer8 = new TH1F("NeuroSWInputID_Layer8",
                                       "simulated id input in layer 8; id",
                                       100, -1, 1);
    m_neuroSWInputT_Layer8 = new TH1F("NeuroSWInputT_Layer8",
                                      "simulated time input in layer 8; time",
                                      100, -1, 1);
    m_neuroSWInputAlpha_Layer8 = new TH1F("NeuroSWInputAlpha_Layer8",
                                          "simulated alpha input in layer 8; alpha",
                                          100, -1, 1);
  }
 
  if (!m_limitedoutput && m_sim2DTracksSWTSName != "") {
 
    m_2DSWOutInvPt = new TH1F("2DSWOutInvPt",
                              "Inverse Pt of 2dtracks from simulation, sw TS sw 2D; p_{T}^{-1} [GeV^{-1}]",
                              50, 0, 5);
    m_2DSWOutPhi0 = new TH1F("2DSWOutPhi0", "Phi0 of 2dtracks from simulation, sw TS sw 2D; #phi [#circ]",
                             80, 0, 360);
    m_2DSWOutm_time = new TH1F("2DSWOutM_time", "m_time of 2dtracks from simulation, sw TS sw 2D; clock cycle",
                               96, -48, 48);
    m_2DSWOutTrackCount = new TH1F("2DSWOutTrackCount", "number of 2dtracks per event from simulation, sw TS sw 2D", 20, 0, 20);
 
  }
  if (!m_limitedoutput && m_simNeuroTracksSWTSSW2DName != "") {
 
    m_neuroSWTSSW2DOutZ = new TH1F("NeuroSWTSSW2DOutZ",
                                   "z distribution from simulation, sw TS sw 2D; z [cm]",
                                   100, -100, 100);
    m_neuroSWTSSW2DOutCosTheta = new TH1F("NeuroSWTSSW2DOutCosTheta",
                                          "cos theta distribution from simulation, sw TS sw 2D; cos(#theta) ",
                                          100, -1, 1);
    m_neuroSWTSSW2DOutInvPt = new TH1F("NeuroSWTSSW2DOutInvPt",
                                       "Inverse Pt distribution from simulation, sw TS sw 2D; p_{T}^{-1} [GeV^{-1}]",
                                       50, 0, 5);
    m_neuroSWTSSW2DOutPhi0 = new TH1F("NeuroSWTSSW2DOutPhi0",
                                      "phi distribution from simulation, sw TS sw 2D; #phi [#circ]",
                                      80, 0, 360);
    m_neuroSWTSSW2DOutHitPattern = new TH1F("NeuroSWTSSW2DOutHitPattern",
                                            "stereo hit pattern of simulated neuro tracks, sw TS sw 2D; pattern",
                                            16, 0, 16); // 4 stereo layers -> 2**4 possible patterns
    m_neuroSWTSSW2DOutTrackCount = new TH1F("NeuroSWTSSW2DOutTrackCount",
                                            "number of simulated neuro tracks per event, sw TS sw 2D",
                                            20, 0, 20);
    m_neuroSWTSSW2DSector = new TH1F("NeuroSWTSSW2DSector",
                                     "sector of simulated neuro tracks, sw TS sw 2D; sector",
                                     10, 0, 10);
    // sw TS incoming
    m_neuroSWTSSW2DInTSID = new TH1F("NeuroSWTSSW2DInTSID", "ID of simulated track segments",
                                     2336, 0, 2335);
    m_neuroSWTSSW2DInTSCount = new TH1F("NeuroSWTSSW2DInTSCount", "number of simulated TS per event", 200, 0, 800);
    m_neuroSWTSSW2DInTSPrioT_Layer0 = new TH1F("NeuroSWTSSW2DInTSPrioT_Layer0", "Priority time of track segments in layer 0",
                                               512, 0, 511);
    m_neuroSWTSSW2DInTSPrioT_Layer1 = new TH1F("NeuroSWTSSW2DInTSPrioT_Layer1", "Priority time of track segments in layer 1",
                                               512, 0, 511);
    m_neuroSWTSSW2DInTSPrioT_Layer2 = new TH1F("NeuroSWTSSW2DInTSPrioT_Layer2", "Priority time of track segments in layer 2",
                                               512, 0, 511);
    m_neuroSWTSSW2DInTSPrioT_Layer3 = new TH1F("NeuroSWTSSW2DInTSPrioT_Layer3", "Priority time of track segments in layer 3",
                                               512, 0, 511);
    m_neuroSWTSSW2DInTSPrioT_Layer4 = new TH1F("NeuroSWTSSW2DInTSPrioT_Layer4", "Priority time of track segments in layer 4",
                                               512, 0, 511);
    m_neuroSWTSSW2DInTSPrioT_Layer5 = new TH1F("NeuroSWTSSW2DInTSPrioT_Layer5", "Priority time of track segments in layer 5",
                                               512, 0, 511);
    m_neuroSWTSSW2DInTSPrioT_Layer6 = new TH1F("NeuroSWTSSW2DInTSPrioT_Layer6", "Priority time of track segments in layer 6",
                                               512, 0, 511);
    m_neuroSWTSSW2DInTSPrioT_Layer7 = new TH1F("NeuroSWTSSW2DInTSPrioT_Layer7", "Priority time of track segments in layer 7",
                                               512, 0, 511);
    m_neuroSWTSSW2DInTSPrioT_Layer8 = new TH1F("NeuroSWTSSW2DInTSPrioT_Layer8", "Priority time of track segments in layer 8",
                                               512, 0, 511);
    m_neuroSWTSSW2DInTSFoundT_Layer0 = new TH1F("NeuroSWTSSW2DInTSFoundT_Layer0",
                                                "First found time of selected TS - found time of Neuro Track in SL 0",
                                                96, -47.99, 48.01);
    m_neuroSWTSSW2DInTSFoundT_Layer1 = new TH1F("NeuroSWTSSW2DInTSFoundT_Layer1",
                                                "First found time of selected TS - found time of Neuro Track in SL 1",
                                                96, -47.99, 48.01);
    m_neuroSWTSSW2DInTSFoundT_Layer2 = new TH1F("NeuroSWTSSW2DInTSFoundT_Layer2",
                                                "First found time of selected TS - found time of Neuro Track in SL 2",
                                                96, -47.99, 48.01);
    m_neuroSWTSSW2DInTSFoundT_Layer3 = new TH1F("NeuroSWTSSW2DInTSFoundT_Layer3",
                                                "First found time of selected TS - found time of Neuro Track in SL 3",
                                                96, -47.99, 48.01);
    m_neuroSWTSSW2DInTSFoundT_Layer4 = new TH1F("NeuroSWTSSW2DInTSFoundT_Layer4",
                                                "First found time of selected TS - found time of Neuro Track in SL 4",
                                                96, -47.99, 48.01);
    m_neuroSWTSSW2DInTSFoundT_Layer5 = new TH1F("NeuroSWTSSW2DInTSFoundT_Layer5",
                                                "First found time of selected TS - found time of Neuro Track in SL 5",
                                                96, -47.99, 48.01);
    m_neuroSWTSSW2DInTSFoundT_Layer6 = new TH1F("NeuroSWTSSW2DInTSFoundT_Layer6",
                                                "First found time of selected TS - found time of Neuro Track in SL 6",
                                                96, -47.99, 48.01);
    m_neuroSWTSSW2DInTSFoundT_Layer7 = new TH1F("NeuroSWTSSW2DInTSFoundT_Layer7",
                                                "First found time of selected TS - found time of Neuro Track in SL 7",
                                                96, -47.99, 48.01);
    m_neuroSWTSSW2DInTSFoundT_Layer8 = new TH1F("NeuroSWTSSW2DInTSFoundT_Layer8",
                                                "First found time of selected TS - found time of Neuro Track in SL 8",
                                                96, -47.99, 48.01);
 
 
    m_neuroSWTSSW2DInTSPrioB_Layer0 = new TH1F("NeuroSWTSSW2DInTSPrioB_Layer0", "Priority bits of track segments in layer 0",
                                               4, 0, 4);
    m_neuroSWTSSW2DInTSPrioB_Layer1 = new TH1F("NeuroSWTSSW2DInTSPrioB_Layer1", "Priority bits of track segments in layer 1",
                                               4, 0, 4);
    m_neuroSWTSSW2DInTSPrioB_Layer2 = new TH1F("NeuroSWTSSW2DInTSPrioB_Layer2", "Priority bits of track segments in layer 2",
                                               4, 0, 4);
    m_neuroSWTSSW2DInTSPrioB_Layer3 = new TH1F("NeuroSWTSSW2DInTSPrioB_Layer3", "Priority bits of track segments in layer 3",
                                               4, 0, 4);
    m_neuroSWTSSW2DInTSPrioB_Layer4 = new TH1F("NeuroSWTSSW2DInTSPrioB_Layer4", "Priority bits of track segments in layer 4",
                                               4, 0, 4);
    m_neuroSWTSSW2DInTSPrioB_Layer5 = new TH1F("NeuroSWTSSW2DInTSPrioB_Layer5", "Priority bits of track segments in layer 5",
                                               4, 0, 4);
    m_neuroSWTSSW2DInTSPrioB_Layer6 = new TH1F("NeuroSWTSSW2DInTSPrioB_Layer6", "Priority bits of track segments in layer 6",
                                               4, 0, 4);
    m_neuroSWTSSW2DInTSPrioB_Layer7 = new TH1F("NeuroSWTSSW2DInTSPrioB_Layer7", "Priority bits of track segments in layer 7",
                                               4, 0, 4);
    m_neuroSWTSSW2DInTSPrioB_Layer8 = new TH1F("NeuroSWTSSW2DInTSPrioB_Layer8", "Priority bits of track segments in layer 8",
                                               4, 0, 4);
 
 
    m_neuroSWTSSW2DInTSLR_Layer0 = new TH1F("NeuroSWTSSW2DInTSLR_Layer0", "Left/Right of track segments in layer 0",
                                            4, 0, 4);
    m_neuroSWTSSW2DInTSLR_Layer1 = new TH1F("NeuroSWTSSW2DInTSLR_Layer1", "Left/Right of track segments in layer 1",
                                            4, 0, 4);
    m_neuroSWTSSW2DInTSLR_Layer2 = new TH1F("NeuroSWTSSW2DInTSLR_Layer2", "Left/Right of track segments in layer 2",
                                            4, 0, 4);
    m_neuroSWTSSW2DInTSLR_Layer3 = new TH1F("NeuroSWTSSW2DInTSLR_Layer3", "Left/Right of track segments in layer 3",
                                            4, 0, 4);
    m_neuroSWTSSW2DInTSLR_Layer4 = new TH1F("NeuroSWTSSW2DInTSLR_Layer4", "Left/Right of track segments in layer 4",
                                            4, 0, 4);
    m_neuroSWTSSW2DInTSLR_Layer5 = new TH1F("NeuroSWTSSW2DInTSLR_Layer5", "Left/Right of track segments in layer 5",
                                            4, 0, 4);
    m_neuroSWTSSW2DInTSLR_Layer6 = new TH1F("NeuroSWTSSW2DInTSLR_Layer6", "Left/Right of track segments in layer 6",
                                            4, 0, 4);
    m_neuroSWTSSW2DInTSLR_Layer7 = new TH1F("NeuroSWTSSW2DInTSLR_Layer7", "Left/Right of track segments in layer 7",
                                            4, 0, 4);
    m_neuroSWTSSW2DInTSLR_Layer8 = new TH1F("NeuroSWTSSW2DInTSLR_Layer8", "Left/Right of track segments in layer 8",
                                            4, 0, 4);
 
 
    // sw TS selected
    m_neuroSWTSSW2DSelTSID = new TH1F("NeuroSWTSSW2DSelTSID", "ID of selected track segments",
                                      2336, 0, 2335);
    m_neuroSWTSSW2DSelTSCount = new TH1F("NeuroSWTSSW2DSelTSCount", "number of selected TS per SL; sl", 9, 0, 9);
    m_neuroSWTSSW2DSelTSPrioT_Layer0 = new TH1F("NeuroSWTSSW2DSelTSPrioT_Layer0", "Priority time of track segments in layer 0",
                                                512, 0, 511);
    m_neuroSWTSSW2DSelTSPrioT_Layer1 = new TH1F("NeuroSWTSSW2DSelTSPrioT_Layer1", "Priority time of track segments in layer 1",
                                                512, 0, 511);
    m_neuroSWTSSW2DSelTSPrioT_Layer2 = new TH1F("NeuroSWTSSW2DSelTSPrioT_Layer2", "Priority time of track segments in layer 2",
                                                512, 0, 511);
    m_neuroSWTSSW2DSelTSPrioT_Layer3 = new TH1F("NeuroSWTSSW2DSelTSPrioT_Layer3", "Priority time of track segments in layer 3",
                                                512, 0, 511);
    m_neuroSWTSSW2DSelTSPrioT_Layer4 = new TH1F("NeuroSWTSSW2DSelTSPrioT_Layer4", "Priority time of track segments in layer 4",
                                                512, 0, 511);
    m_neuroSWTSSW2DSelTSPrioT_Layer5 = new TH1F("NeuroSWTSSW2DSelTSPrioT_Layer5", "Priority time of track segments in layer 5",
                                                512, 0, 511);
    m_neuroSWTSSW2DSelTSPrioT_Layer6 = new TH1F("NeuroSWTSSW2DSelTSPrioT_Layer6", "Priority time of track segments in layer 6",
                                                512, 0, 511);
    m_neuroSWTSSW2DSelTSPrioT_Layer7 = new TH1F("NeuroSWTSSW2DSelTSPrioT_Layer7", "Priority time of track segments in layer 7",
                                                512, 0, 511);
    m_neuroSWTSSW2DSelTSPrioT_Layer8 = new TH1F("NeuroSWTSSW2DSelTSPrioT_Layer8", "Priority time of track segments in layer 8",
                                                512, 0, 511);
    m_neuroSWTSSW2DSelTSFoundT_Layer0 = new TH1F("NeuroSWTSSW2DSelTSFoundT_Layer0",
                                                 "First found time of selected TS - found time of Neuro Track in SL 0",
                                                 96, -47.99, 48.01);
    m_neuroSWTSSW2DSelTSFoundT_Layer1 = new TH1F("NeuroSWTSSW2DSelTSFoundT_Layer1",
                                                 "First found time of selected TS - found time of Neuro Track in SL 1",
                                                 96, -47.99, 48.01);
    m_neuroSWTSSW2DSelTSFoundT_Layer2 = new TH1F("NeuroSWTSSW2DSelTSFoundT_Layer2",
                                                 "First found time of selected TS - found time of Neuro Track in SL 2",
                                                 96, -47.99, 48.01);
    m_neuroSWTSSW2DSelTSFoundT_Layer3 = new TH1F("NeuroSWTSSW2DSelTSFoundT_Layer3",
                                                 "First found time of selected TS - found time of Neuro Track in SL 3",
                                                 96, -47.99, 48.01);
    m_neuroSWTSSW2DSelTSFoundT_Layer4 = new TH1F("NeuroSWTSSW2DSelTSFoundT_Layer4",
                                                 "First found time of selected TS - found time of Neuro Track in SL 4",
                                                 96, -47.99, 48.01);
    m_neuroSWTSSW2DSelTSFoundT_Layer5 = new TH1F("NeuroSWTSSW2DSelTSFoundT_Layer5",
                                                 "First found time of selected TS - found time of Neuro Track in SL 5",
                                                 96, -47.99, 48.01);
    m_neuroSWTSSW2DSelTSFoundT_Layer6 = new TH1F("NeuroSWTSSW2DSelTSFoundT_Layer6",
                                                 "First found time of selected TS - found time of Neuro Track in SL 6",
                                                 96, -47.99, 48.01);
    m_neuroSWTSSW2DSelTSFoundT_Layer7 = new TH1F("NeuroSWTSSW2DSelTSFoundT_Layer7",
                                                 "First found time of selected TS - found time of Neuro Track in SL 7",
                                                 96, -47.99, 48.01);
    m_neuroSWTSSW2DSelTSFoundT_Layer8 = new TH1F("NeuroSWTSSW2DSelTSFoundT_Layer8",
                                                 "First found time of selected TS - found time of Neuro Track in SL 8",
                                                 96, -47.99, 48.01);
 
 
    m_neuroSWTSSW2DSelTSPrioB_Layer0 = new TH1F("NeuroSWTSSW2DSelTSPrioB_Layer0", "Priority bits of track segments in layer 0",
                                                4, 0, 4);
    m_neuroSWTSSW2DSelTSPrioB_Layer1 = new TH1F("NeuroSWTSSW2DSelTSPrioB_Layer1", "Priority bits of track segments in layer 1",
                                                4, 0, 4);
    m_neuroSWTSSW2DSelTSPrioB_Layer2 = new TH1F("NeuroSWTSSW2DSelTSPrioB_Layer2", "Priority bits of track segments in layer 2",
                                                4, 0, 4);
    m_neuroSWTSSW2DSelTSPrioB_Layer3 = new TH1F("NeuroSWTSSW2DSelTSPrioB_Layer3", "Priority bits of track segments in layer 3",
                                                4, 0, 4);
    m_neuroSWTSSW2DSelTSPrioB_Layer4 = new TH1F("NeuroSWTSSW2DSelTSPrioB_Layer4", "Priority bits of track segments in layer 4",
                                                4, 0, 4);
    m_neuroSWTSSW2DSelTSPrioB_Layer5 = new TH1F("NeuroSWTSSW2DSelTSPrioB_Layer5", "Priority bits of track segments in layer 5",
                                                4, 0, 4);
    m_neuroSWTSSW2DSelTSPrioB_Layer6 = new TH1F("NeuroSWTSSW2DSelTSPrioB_Layer6", "Priority bits of track segments in layer 6",
                                                4, 0, 4);
    m_neuroSWTSSW2DSelTSPrioB_Layer7 = new TH1F("NeuroSWTSSW2DSelTSPrioB_Layer7", "Priority bits of track segments in layer 7",
                                                4, 0, 4);
    m_neuroSWTSSW2DSelTSPrioB_Layer8 = new TH1F("NeuroSWTSSW2DSelTSPrioB_Layer8", "Priority bits of track segments in layer 8",
                                                4, 0, 4);
 
 
    m_neuroSWTSSW2DSelTSLR_Layer0 = new TH1F("NeuroSWTSSW2DSelTSLR_Layer0", "Left/Right of track segments in layer 0",
                                             4, 0, 4);
    m_neuroSWTSSW2DSelTSLR_Layer1 = new TH1F("NeuroSWTSSW2DSelTSLR_Layer1", "Left/Right of track segments in layer 1",
                                             4, 0, 4);
    m_neuroSWTSSW2DSelTSLR_Layer2 = new TH1F("NeuroSWTSSW2DSelTSLR_Layer2", "Left/Right of track segments in layer 2",
                                             4, 0, 4);
    m_neuroSWTSSW2DSelTSLR_Layer3 = new TH1F("NeuroSWTSSW2DSelTSLR_Layer3", "Left/Right of track segments in layer 3",
                                             4, 0, 4);
    m_neuroSWTSSW2DSelTSLR_Layer4 = new TH1F("NeuroSWTSSW2DSelTSLR_Layer4", "Left/Right of track segments in layer 4",
                                             4, 0, 4);
    m_neuroSWTSSW2DSelTSLR_Layer5 = new TH1F("NeuroSWTSSW2DSelTSLR_Layer5", "Left/Right of track segments in layer 5",
                                             4, 0, 4);
    m_neuroSWTSSW2DSelTSLR_Layer6 = new TH1F("NeuroSWTSSW2DSelTSLR_Layer6", "Left/Right of track segments in layer 6",
                                             4, 0, 4);
    m_neuroSWTSSW2DSelTSLR_Layer7 = new TH1F("NeuroSWTSSW2DSelTSLR_Layer7", "Left/Right of track segments in layer 7",
                                             4, 0, 4);
    m_neuroSWTSSW2DSelTSLR_Layer8 = new TH1F("NeuroSWTSSW2DSelTSLR_Layer8", "Left/Right of track segments in layer 8",
                                             4, 0, 4);
    m_neuroSWTSSW2DInputID_Layer0 = new TH1F("NeuroSWTSSW2DInputID_Layer0",
                                             "simulated id input in layer 0; id",
                                             100, -1, 1);
    m_neuroSWTSSW2DInputT_Layer0 = new TH1F("NeuroSWTSSW2DInputT_Layer0",
                                            "simulated time input in layer 0; time",
                                            100, -1, 1);
    m_neuroSWTSSW2DInputAlpha_Layer0 = new TH1F("NeuroSWTSSW2DInputAlpha_Layer0",
                                                "simulated alpha input in layer 0; alpha",
                                                100, -1, 1);
    m_neuroSWTSSW2DInputID_Layer1 = new TH1F("NeuroSWTSSW2DInputID_Layer1",
                                             "simulated id input in layer 1; id",
                                             100, -1, 1);
    m_neuroSWTSSW2DInputT_Layer1 = new TH1F("NeuroSWTSSW2DInputT_Layer1",
                                            "simulated time input in layer 1; time",
                                            100, -1, 1);
    m_neuroSWTSSW2DInputAlpha_Layer1 = new TH1F("NeuroSWTSSW2DInputAlpha_Layer1",
                                                "simulated alpha input in layer 1; alpha",
                                                100, -1, 1);
    m_neuroSWTSSW2DInputID_Layer2 = new TH1F("NeuroSWTSSW2DInputID_Layer2",
                                             "simulated id input in layer 2; id",
                                             100, -1, 1);
    m_neuroSWTSSW2DInputT_Layer2 = new TH1F("NeuroSWTSSW2DInputT_Layer2",
                                            "simulated time input in layer 2; time",
                                            100, -1, 1);
    m_neuroSWTSSW2DInputAlpha_Layer2 = new TH1F("NeuroSWTSSW2DInputAlpha_Layer2",
                                                "simulated alpha input in layer 2; alpha",
                                                100, -1, 1);
    m_neuroSWTSSW2DInputID_Layer3 = new TH1F("NeuroSWTSSW2DInputID_Layer3",
                                             "simulated id input in layer 3; id",
                                             100, -1, 1);
    m_neuroSWTSSW2DInputT_Layer3 = new TH1F("NeuroSWTSSW2DInputT_Layer3",
                                            "simulated time input in layer 3; time",
                                            100, -1, 1);
    m_neuroSWTSSW2DInputAlpha_Layer3 = new TH1F("NeuroSWTSSW2DInputAlpha_Layer3",
                                                "simulated alpha input in layer 3; alpha",
                                                100, -1, 1);
    m_neuroSWTSSW2DInputID_Layer4 = new TH1F("NeuroSWTSSW2DInputID_Layer4",
                                             "simulated id input in layer 4; id",
                                             100, -1, 1);
    m_neuroSWTSSW2DInputT_Layer4 = new TH1F("NeuroSWTSSW2DInputT_Layer4",
                                            "simulated time input in layer 4; time",
                                            100, -1, 1);
    m_neuroSWTSSW2DInputAlpha_Layer4 = new TH1F("NeuroSWTSSW2DInputAlpha_Layer4",
                                                "simulated alpha input in layer 4; alpha",
                                                100, -1, 1);
    m_neuroSWTSSW2DInputID_Layer5 = new TH1F("NeuroSWTSSW2DInputID_Layer5",
                                             "simulated id input in layer 5; id",
                                             100, -1, 1);
    m_neuroSWTSSW2DInputT_Layer5 = new TH1F("NeuroSWTSSW2DInputT_Layer5",
                                            "simulated time input in layer 5; time",
                                            100, -1, 1);
    m_neuroSWTSSW2DInputAlpha_Layer5 = new TH1F("NeuroSWTSSW2DInputAlpha_Layer5",
                                                "simulated alpha input in layer 5; alpha",
                                                100, -1, 1);
    m_neuroSWTSSW2DInputID_Layer6 = new TH1F("NeuroSWTSSW2DInputID_Layer6",
                                             "simulated id input in layer 6; id",
                                             100, -1, 1);
    m_neuroSWTSSW2DInputT_Layer6 = new TH1F("NeuroSWTSSW2DInputT_Layer6",
                                            "simulated time input in layer 6; time",
                                            100, -1, 1);
    m_neuroSWTSSW2DInputAlpha_Layer6 = new TH1F("NeuroSWTSSW2DInputAlpha_Layer6",
                                                "simulated alpha input in layer 6; alpha",
                                                100, -1, 1);
    m_neuroSWTSSW2DInputID_Layer7 = new TH1F("NeuroSWTSSW2DInputID_Layer7",
                                             "simulated id input in layer 7; id",
                                             100, -1, 1);
    m_neuroSWTSSW2DInputT_Layer7 = new TH1F("NeuroSWTSSW2DInputT_Layer7",
                                            "simulated time input in layer 7; time",
                                            100, -1, 1);
    m_neuroSWTSSW2DInputAlpha_Layer7 = new TH1F("NeuroSWTSSW2DInputAlpha_Layer7",
                                                "simulated alpha input in layer 7; alpha",
                                                100, -1, 1);
    m_neuroSWTSSW2DInputID_Layer8 = new TH1F("NeuroSWTSSW2DInputID_Layer8",
                                             "simulated id input in layer 8; id",
                                             100, -1, 1);
    m_neuroSWTSSW2DInputT_Layer8 = new TH1F("NeuroSWTSSW2DInputT_Layer8",
                                            "simulated time input in layer 8; time",
                                            100, -1, 1);
    m_neuroSWTSSW2DInputAlpha_Layer8 = new TH1F("NeuroSWTSSW2DInputAlpha_Layer8",
                                                "simulated alpha input in layer 8; alpha",
                                                100, -1, 1);
  }
 
 
 
  if (!m_limitedoutput && m_unpackedNeuroTracksName != "" && m_simNeuroTracksName != "") {
    m_neuroDeltaZ = new TH1F("NeuroDeltaZ",
                             "difference between unpacked and simulated neuro z; #Delta z [cm]",
                             100, -100, 100); // should be bit-precise, so look at very small range
    m_neuroDeltaTheta = new TH1F("NeuroDeltaTheta",
                                 "difference between unpacked and simulated neuro theta; #Delta #theta [#circ]",
                                 100, -180, 180); // should be bit-precise, so look at very small range
    m_neuroScatterZ = new TH2F("NeuroScatterZ",
                               "unpacked z vs TSIM; hw z [cm]; sw z [cm]",
                               100, -150, 150, 100, -150, 150);
    m_neuroScatterTheta = new TH2F("NeuroScatterTheta",
                                   "unpacked theta vs TSIM; hw #theta [#circ]; sw #theta [#circ]",
                                   100, 0, 270, 100, 0, 270);
 
    m_neuroDeltaInputID = new TH1F("NeuroDeltaInputID",
                                   "difference between unpacked and simulated ID input; #Delta ID",
                                   100, -0.5, 0.5); // should be bit-precise, so look at very small range
    m_neuroDeltaInputT = new TH1F("NeuroDeltaInputT",
                                  "difference between unpacked and simulated time input; #Delta t",
                                  100, -0.5, 0.5); // should be bit-precise, so look at very small range
    m_neuroDeltaInputAlpha = new TH1F("NeuroDeltaInputAlpha",
                                      "difference between unpacked and simulated alpha input; #Delta alpha",
                                      100, -0.1, 0.1); // should be bit-precise, so look at very small range
    m_neuroDeltaTSID = new TH1F("NeuroDeltaTSID",
                                "difference between unpacked and simulated tsid; #Delta TSID",
                                100, -50, 50);
    m_neuroDeltaSector = new TH1F("NeuroDeltaSector",
                                  "difference between unpacked and simulated sector; #Delta sector",
                                  20, -10, 10);
    m_simSameTS = new TH1F("NeuroSimSameTS",
                           "number of TS selected in both, unpacked and TSIM tracks",
                           20, 0, 20);
    m_simDiffTS = new TH1F("NeuroSimDiffTS",
                           "number of TS selcted in TSIM but not in unpacker",
                           20, 0, 20);
  }
 
  if (m_recoTracksName != "") {
    //RecoTracks
    m_RecoZ = new TH1F("RecoZ",
                       "z distribution of reconstructed tracks;z [cm]",
                       100, -150, 150);
    m_RecoCosTheta = new TH1F("RecoCosTheta",
                              "cos theta distribution of reconstructed tracks; cos(#theta) ",
                              100, -1, 1);
    m_RecoInvPt = new TH1F("RecoInvPt",
                           "Pt distribution of reconstructed tracks; p_{T}^{-1} [GeV^{-1}]",
                           50, 0, 5);
    m_RecoPhi = new TH1F("RecoPhi",
                         "phi distribution of reconstructed tracks ; #phi [#circ]",
                         80, 0, 360);
    m_RecoD0 = new TH1F("RecoD0",
                        "d0 distribution of reconstructed tracks ; d_{0} [cm]",
                        100, 0, 10);
    m_RecoTrackCount = new TH1F("RecoTrackCount",
                                "number of reconstructed tracks per event",
                                20, 0, 20);
  }
  if (m_recoTracksName != "" && m_unpackedNeuroTracksName != "") {
    //RecoTracks matched to unpacked neuro tracks
    m_RecoHWZ = new TH1F("RecoHWZ",
                         "hw matched z distribution of reconstructed tracks; z [cm]",
                         100, -150, 150);
    m_RecoHWCosTheta = new TH1F("RecoHWCosTheta",
                                "hw matched cos theta distribution of reconstructed tracks; cos(#theta) ",
                                100, -1, 1);
    m_RecoHWInvPt = new TH1F("RecoHWInvPt",
                             "hw matched Pt distribution of reconstructed tracks; p_{T}^{-1} [GeV^{-1}]",
                             50, 0, 5);
    m_RecoHWPhi = new TH1F("RecoHWPhi",
                           "hw matched phi distribution of reconstructed tracks; #phi [#circ]",
                           80, 0, 360);
    m_RecoHWD0 = new TH1F("RecoHWD0",
                          "hw matched d0 distribution of reconstructed tracks ; d_{0} [cm]",
                          100, 0, 10);
    // hw neuro values for tracks matched to reco tracks
    m_neuroRecoHWOutZ = new TH1F("NeuroRecoHWOutZ",
                                 "reco matched z distribution of unpacked neuro tracks; z [cm]",
                                 100, -100, 100);
    m_neuroRecoHWOutCosTheta = new TH1F("NeuroRecoHWOutCosTheta",
                                        "reco matched cos theta distribution of unpacked neuro tracks; cos(#theta) ",
                                        100, -1, 1);
    m_neuroRecoHWOutInvPt = new TH1F("NeuroRecoHWOutInvPt",
                                     "reco matched Pt distribution of unpacked neuro tracks; p_{T}^{-1} [GeV^{-1}]",
                                     50, 0, 5);
    m_neuroRecoHWOutPhi0 = new TH1F("NeuroRecoHWOutPhi0",
                                    "reco matched phi distribution of unpacked neuro tracks; #phi [#circ]",
                                    80, 0, 360); // shift to reduce the binning error
    m_neuroRecoHWOutHitPattern = new TH1F("NeuroRecoUnpackedHitPattern",
                                          "reco matched stereo hit pattern of unpacked neuro tracks; pattern",
                                          16, 0, 16); // 4 stereo layers -> 2**4 possible patterns
    m_neuroRecoHWOutTrackCount = new TH1F("NeuroRecoHWOutTrackCount",
                                          "reco matched number of unpacked neuro tracks per event",
                                          20, 0, 20);
    m_neuroRecoHWSector = new TH1F("NeuroRecoHWSector",
                                   "reco matched sector of unpacked neuro tracks; sector",
                                   10, 0, 10);
 
 
    m_DeltaRecoHWZ = new TH1F("DeltaRecoHWZ",
                              "difference between reconstructed and unpacked neuro z; #Delta z [cm]",
                              100, -100, 100);
    m_DeltaRecoHWCosTheta = new TH1F("DeltaRecoHWCosTheta",
                                     "difference between reconstructed and unpacked neuro cos(theta); #Delta cos(#theta)",
                                     100, -1, 1);
    m_DeltaRecoHWInvPt = new TH1F("DeltaRecoHWInvPt",
                                  "difference between reconstructed and unpacked neuro Pt; #Delta p_{T}^{-1} [GeV^{-1}]",
                                  100, -10, 10);
    m_DeltaRecoHWPhi = new TH1F("DeltaRecoHWPhi",
                                "difference between reconstructed and unpacked neuro phi; #Delta #phi [#circ]",
                                80, -180, 180);
  }
  if (!m_limitedoutput && m_recoTracksName != "" && m_unpackedNeuroTracksName != "") {
    m_RecoHWZScatter = new TH2F("RecoHWZScatter",
                                "hw matched reconstruction; reco z [cm]; hw z [cm]",
                                100, -150, 150, 100, -150, 150);
  }
 
 
 
  if (!m_limitedoutput && m_simNeuroTracksName != "") {
    //RecoTracks matched to simulated neuro tracks (hw TS hw 2D sw NN)
    m_RecoSWZ = new TH1F("RecoSWZ",
                         "sw matched z distribution of reconstructed tracks; z [cm]",
                         100, -150, 150); // 1cm bins from -50cm to 50cm
    m_RecoSWCosTheta = new TH1F("RecoSWCosTheta",
                                "sw matched cos theta distribution of reconstructed tracks; cos(#theta) ",
                                100, -1, 1);
    m_RecoSWInvPt = new TH1F("RecoSWInvPt",
                             "sw matched Pt distribution of reconstructed tracks; p_{T}^{-1} [GeV^{-1}]",
                             50, 0, 5);
    m_RecoSWPhi = new TH1F("RecoSWPhi",
                           "sw matched phi distribution of reconstructed tracks ; #phi [#circ]",
                           80, 0, 360);
    m_RecoSWD0 = new TH1F("RecoSWD0",
                          "sw matched d0 distribution of reconstructed tracks ; d_{0} [cm]",
                          100, 0, 10);
    m_RecoSWZScatter = new TH2F("RecoSWZScatter",
                                "sw matched reconstruction; reco z [cm]; sw z [cm]",
                                100, -150, 150, 100, -150, 150);
 
 
    // sw neuro values for tracks matched to reco tracks
    m_neuroRecoSWOutZ = new TH1F("NeuroRecoSWOutZ",
                                 "reco matched z distribution from simulation; z [cm]",
                                 100, -100, 100);
    m_neuroRecoSWOutCosTheta = new TH1F("NeuroRecoSWOutCosTheta",
                                        "reco matched cos theta distribution from simulation; cos(#theta) ",
                                        100, -1, 1);
    m_neuroRecoSWOutInvPt = new TH1F("NeuroRecoSWOutInvPt",
                                     "reco matched Pt distribution from simulation; p_{T}^{-1} [GeV^{-1}]",
                                     50, 0, 5);
    m_neuroRecoSWOutPhi0 = new TH1F("NeuroRecoSWOutPhi0",
                                    "reco matched phi distribution from simulation; #phi [#circ]",
                                    80, 0, 360); // shift to reduce the binning error
    m_neuroRecoSWOutHitPattern = new TH1F("NeuroRecoSWHitPattern",
                                          "reco matched stereo hit pattern from simulation; pattern",
                                          16, 0, 16); // 4 stereo layers -> 2**4 possible patterns
    m_neuroRecoSWOutTrackCount = new TH1F("NeuroRecoSWOutTrackCount",
                                          "reco matched number of SW neuro tracks per event",
                                          20, 0, 20);
    m_neuroRecoSWSector = new TH1F("NeuroRecoSWSector",
                                   "reco matched sector from simulation; sector",
                                   10, 0, 10);
 
 
    m_DeltaRecoSWZ = new TH1F("DeltaRecoSWZ",
                              "difference between reconstructed and simulated neuro z; #Delta z [cm]",
                              100, -100, 100);
    m_DeltaRecoSWCosTheta = new TH1F("DeltaRecoSWCosTheta",
                                     "difference between reconstructed and simulated neuro cos(theta); #Delta cos(#theta)",
                                     100, -1, 1);
    m_DeltaRecoSWInvPt = new TH1F("DeltaRecoSWInvPt",
                                  "difference between reconstructed and simulated neuro Pt; #Delta p_{T}^{-1} [GeV^{-1}]",
                                  100, -10, 10);
    m_DeltaRecoSWPhi = new TH1F("DeltaRecoSWPhi",
                                "difference between reconstructed and simulated neuro phi; #Delta #phi [#circ]",
                                80, -180, 180);
  }
 
 
  if (!m_limitedoutput && m_simNeuroTracksSWTSSW2DName != "") {
    //RecoTracks matched to simulated neuro tracks (sw TS sw 2D sw NN)
    m_RecoSWTSSW2DZ = new TH1F("RecoSWTSSW2DZ",
                               "sw matched z distribution of reconstructed tracks; z [cm]",
                               100, -150, 150); // 1cm bins from -50cm to 50cm
    m_RecoSWTSSW2DCosTheta = new TH1F("RecoSWTSSW2DCosTheta",
                                      "sw matched cos theta distribution of reconstructed tracks; cos(#theta) ",
                                      100, -1, 1);
    m_RecoSWTSSW2DInvPt = new TH1F("RecoSWTSSW2DInvPt",
                                   "sw matched Pt distribution of reconstructed tracks; p_{T}^{-1} [GeV^{-1}]",
                                   50, 0, 5);
    m_RecoSWTSSW2DPhi = new TH1F("RecoSWTSSW2DPhi",
                                 "sw matched phi distribution of reconstructed tracks ; #phi [#circ]",
                                 80, 0, 360);
    m_RecoSWTSSW2DD0 = new TH1F("RecoSWTSSW2DD0",
                                "sw matched d0 distribution of reconstructed tracks ; d_{0} [cm]",
                                100, 0, 10);
    m_RecoSWTSSW2DZScatter = new TH2F("RecoSWTSSW2DZScatter",
                                      "sw matched reconstruction; reco z [cm]; sw z [cm]",
                                      100, -150, 150, 100, -150, 150);
 
 
    // sw neuro values for tracks matched to reco tracks (sw TS, sw 2D)
    m_neuroRecoSWTSSW2DOutZ = new TH1F("NeuroRecoSWTSSW2DOutZ",
                                       "reco matched z distribution from simulation; z [cm]",
                                       100, -100, 100);
    m_neuroRecoSWTSSW2DOutCosTheta = new TH1F("NeuroRecoSWTSSW2DOutCosTheta",
                                              "reco matched cos theta distribution from simulation; cos(#theta) ",
                                              100, -1, 1);
    m_neuroRecoSWTSSW2DOutInvPt = new TH1F("NeuroRecoSWTSSW2DOutInvPt",
                                           "reco matched Pt distribution from simulation; p_{T}^{-1} [GeV^{-1}]",
                                           50, 0, 5);
    m_neuroRecoSWTSSW2DOutPhi0 = new TH1F("NeuroRecoSWTSSW2DOutPhi0",
                                          "reco matched phi distribution from simulation; #phi [#circ]",
                                          80, 0, 360); // shift to reduce the binning error
    m_neuroRecoSWTSSW2DOutHitPattern = new TH1F("NeuroRecoSWTSSW2DHitPattern",
                                                "reco matched stereo hit pattern from simulation; pattern",
                                                16, 0, 16); // 4 stereo layers -> 2**4 possible patterns
    m_neuroRecoSWTSSW2DOutTrackCount = new TH1F("NeuroRecoSWTSSW2DOutTrackCount",
                                                "reco matched number of SW neuro tracks per event",
                                                20, 0, 20);
    m_neuroRecoSWTSSW2DSector = new TH1F("NeuroRecoSWTSSW2DSector",
                                         "reco matched sector from simulation; sector",
                                         10, 0, 10);
 
 
    m_DeltaRecoSWTSSW2DZ = new TH1F("DeltaRecoSWTSSW2DZ",
                                    "difference between reconstructed and simulated neuro z; #Delta z [cm]",
                                    100, -100, 100);
    m_DeltaRecoSWTSSW2DCosTheta = new TH1F("DeltaRecoSWTSSW2DCosTheta",
                                           "difference between reconstructed and simulated neuro cos(theta); #Delta cos(#theta)",
                                           100, -1, 1);
    m_DeltaRecoSWTSSW2DInvPt = new TH1F("DeltaRecoSWTSSW2DInvPt",
                                        "difference between reconstructed and simulated neuro Pt; #Delta p_{T}^{-1} [GeV^{-1}]",
                                        100, -10, 10);
    m_DeltaRecoSWTSSW2DPhi = new TH1F("DeltaRecoSWTSSW2DPhi",
                                      "difference between reconstructed and simulated neuro phi;#Delta #phi [#circ]",
                                      80, -180, 180);
  }
 
  // cd back to root directory
  oldDir->cd();
}

endRun()

void endRun ( void  )

overridevirtual

Function to process end_run record.

Reimplemented from HistoModule.

Definition at line 3262 of file CDCTriggerNeuroDQMModule.cc.

{
}

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

Function to process event record.

,(x," + padto(intomega.str(), 2) + "," + padto(intphi.str(), 3) + "," + padto(inttheta.str(), 3) + "," + padto(intz.str(), 3) + "),

Reimplemented from HistoModule.

Definition at line 1795 of file CDCTriggerNeuroDQMModule.cc.

{
  double phinorm ; /* intermediate name for phi to transfrom zo 0->360 */
  if (m_skipWithoutHWTS and m_unpackedNeuroInputSegments.getEntries() == 0) {
    //if (m_unpackedNeuroInputSegments.getEntries() == 0) {
    B2DEBUG(150, "No unpacked TS found, skipping event.");
    return;
  }
  if (m_recoTrackMultiplicity != -1 and m_RecoTracks.getEntries() != m_recoTrackMultiplicity) {
    B2DEBUG(150, "Wrong track multiplicity " << m_RecoTracks.getEntries() << ", skipping event.");
    return;
  }
 
  StoreObjPtr<EventMetaData> eventMetaData;
 
  if (m_recoTracksName != "") {
    // a RecoTrack has multiple representations for different particle hypothesis
    // -> just take the first one that does not give errors.
    m_RecoTrackCount->Fill(m_RecoTracks.getEntries());
    bool foundValidRep = false;
 
    for (RecoTrack& recoTrack : m_RecoTracks) {
      int nhwmatched = 0;
      int nswmatched = 0;
      int nswtssw2dmatched = 0;
      double phi0Target = 0;
      double invptTarget = 0;
      double cosThetaTarget = 0;
      double zTarget = 0;
      double d0Target = 0;
      for (genfit::AbsTrackRep* rep : recoTrack.getRepresentations()) {
        if (!recoTrack.wasFitSuccessful(rep))
          continue;
        // get state (position, momentum etc.) from hit closest to IP and
        // extrapolate to z-axis (may throw an exception -> continue to next representation)
        try {
          genfit::MeasuredStateOnPlane state =
            recoTrack.getMeasuredStateOnPlaneClosestTo(ROOT::Math::XYZVector(0, 0, 0), rep);
          rep->extrapolateToLine(state, TVector3(0, 0, -1000), TVector3(0, 0, 2000));
          // TODO check after matching
          //  // flip tracks if necessary, such that trigger tracks and reco tracks
          //  // point in the same direction
          //  if (state.getMom().Dot(m_tracks[itrack]->getDirection()) < 0) {
          //    state.setPosMom(state.getPos(), -state.getMom());
          //    state.setChargeSign(-state.getCharge());
          //  }
          // get track parameters
          phi0Target = state.getMom().Phi() * 180. / M_PI ;
          if (phi0Target < 0.) {phi0Target = phi0Target + 360. ;}
          invptTarget = state.getCharge() * state.getMom().Pt();
          cosThetaTarget = state.getMom().CosTheta();
          zTarget = state.getPos().Z();
          d0Target = state.getPos().Perp();
        } catch (...) {
          continue;
        }
        // break loop
        foundValidRep = true;
        break;
      }
      if (!foundValidRep) {
        B2DEBUG(150, "No valid representation found for RecoTrack, skipping.");
        continue;
      } else {
        if (m_maxRecoZDist != -1.0 and abs(zTarget) > m_maxRecoZDist) {
          B2DEBUG(150, "RecoTrack not close to IP (maxRecoZDist), zReco = " << zTarget);
          continue;
        }
        if (m_maxRecoD0Dist != -1.0 and abs(d0Target) > m_maxRecoD0Dist) {
          B2DEBUG(150, "RecoTrack not close to the z axis (maxRecoD0Dist), d0Reco = " << d0Target);
          continue;
        }
        m_RecoZ->Fill(zTarget);
        m_RecoCosTheta->Fill(cosThetaTarget);
        m_RecoPhi->Fill(phi0Target);
        m_RecoD0->Fill(d0Target);
        m_RecoInvPt->Fill(invptTarget);
        if (m_unpackedNeuroTracksName != "") {
          CDCTriggerTrack* neuroHWTrack = recoTrack.getRelatedTo<CDCTriggerTrack>(m_unpackedNeuroTracksName);
 
          if (neuroHWTrack) {
            bool valtrack = false;
            try {
              valtrack = neuroHWTrack->getValidStereoBit();
            } catch (...) {
              B2WARNING("HWTrack doesn't have 'valid bit', get it from relations now... ");
              unsigned checkpattern = getPattern(neuroHWTrack, m_unpackedNeuroInputSegmentsName);
              valtrack = isValidPattern(checkpattern);
            }
            if (!valtrack) {
              continue;
            }
 
            m_RecoHWZ->Fill(zTarget);
            m_RecoHWCosTheta->Fill(cosThetaTarget);
            m_RecoHWPhi->Fill(phi0Target);
            m_RecoHWD0->Fill(d0Target);
            m_RecoHWInvPt->Fill(invptTarget);
 
            double neuroHWZ = neuroHWTrack->getZ0() ;
            m_neuroRecoHWOutZ->Fill(neuroHWZ);
            double cotTh = neuroHWTrack->getCotTheta() ;
            double cosTh = copysign(1.0, cotTh) / sqrt(1. / (cotTh * cotTh) + 1);
            double neuroHWcosTh = cosTh ;
            m_neuroRecoHWOutCosTheta->Fill(neuroHWcosTh);
            double neuroHWPt = neuroHWTrack->getPt() ;
            m_neuroRecoHWOutInvPt->Fill(neuroHWPt);
            phinorm = neuroHWTrack->getPhi0() * 180 / M_PI ;
            if (phinorm < 0.) {phinorm = phinorm + 360. ;}
            double neuroHWPhi0 = phinorm;
            m_neuroRecoHWOutPhi0->Fill(neuroHWPhi0);
            m_neuroRecoHWOutHitPattern->Fill(getPattern(neuroHWTrack, m_unpackedNeuroInputSegmentsName));
            nhwmatched++;
            unsigned hwMatchedSector =
              neuroHWTrack->getRelatedTo<CDCTriggerMLPInput>(m_unpackedNeuroInputVectorName)->getSector();
            m_neuroRecoHWSector->Fill(hwMatchedSector);
 
            m_DeltaRecoHWZ->Fill(zTarget - neuroHWZ);
            m_DeltaRecoHWCosTheta->Fill(cosThetaTarget - neuroHWcosTh);
            m_DeltaRecoHWPhi->Fill(phi0Target - neuroHWPhi0);
            m_DeltaRecoHWInvPt->Fill(invptTarget - neuroHWPt);
            if (!m_limitedoutput) {
              m_RecoHWZScatter->Fill(zTarget, neuroHWZ);
            }
          }
          if (!m_limitedoutput && m_simNeuroTracksName != "") {
            CDCTriggerTrack* neuroSWTrack = recoTrack.getRelatedTo<CDCTriggerTrack>(m_simNeuroTracksName);
            if (neuroSWTrack) {
              bool valtrack = false;
              try {
                valtrack = neuroSWTrack->getValidStereoBit();
              } catch (...) {
                B2INFO("SWTrack doesn't have 'valid bit', get it from relations now... ");
                unsigned checkpattern = getPattern(neuroSWTrack, m_unpackedNeuroInputSegmentsName);
                valtrack = isValidPattern(checkpattern);
              }
              if (!valtrack) {
                continue;
              }
 
              m_RecoSWZ->Fill(zTarget);
              m_RecoSWCosTheta->Fill(cosThetaTarget);
              m_RecoSWPhi->Fill(phi0Target);
              m_RecoSWD0->Fill(d0Target);
              m_RecoSWInvPt->Fill(invptTarget);
 
              double neuroSWZ = neuroSWTrack->getZ0() ;
              m_neuroRecoSWOutZ->Fill(neuroSWZ);
              double cotTh = neuroSWTrack->getCotTheta() ;
              double cosTh = copysign(1.0, cotTh) / sqrt(1. / (cotTh * cotTh) + 1);
              double neuroSWcosTh = cosTh ;
              m_neuroRecoSWOutCosTheta->Fill(neuroSWcosTh);
              double neuroSWPt = neuroSWTrack->getPt() ;
              m_neuroRecoSWOutInvPt->Fill(neuroSWPt);
              phinorm = neuroSWTrack->getPhi0() * 180 / M_PI ;
              if (phinorm < 0.) {phinorm = phinorm + 360. ;}
              double neuroSWPhi0 = phinorm ;
              m_neuroRecoSWOutPhi0->Fill(neuroSWPhi0);
              m_neuroRecoSWOutHitPattern->Fill(getPattern(neuroSWTrack, m_unpackedNeuroInputSegmentsName));
              nswmatched++;
              unsigned swMatchedSector =
                neuroSWTrack->getRelatedTo<CDCTriggerMLPInput>(m_simNeuroInputVectorName)->getSector();
              m_neuroRecoSWSector->Fill(swMatchedSector);
 
              m_DeltaRecoSWZ->Fill(zTarget - neuroSWZ);
              m_DeltaRecoSWCosTheta->Fill(cosThetaTarget - neuroSWcosTh);
              m_DeltaRecoSWPhi->Fill(phi0Target - neuroSWPhi0);
              m_DeltaRecoSWInvPt->Fill(invptTarget - neuroSWPt);
              m_RecoSWZScatter->Fill(zTarget, neuroSWZ);
            }
          }
          if (!m_limitedoutput && m_simNeuroTracksSWTSSW2DName != "") {
            CDCTriggerTrack* neuroSWTSSW2DTrack = recoTrack.getRelatedTo<CDCTriggerTrack>(m_simNeuroTracksSWTSSW2DName);
            if (neuroSWTSSW2DTrack) {
              m_RecoSWTSSW2DZ->Fill(zTarget);
              m_RecoSWTSSW2DCosTheta->Fill(cosThetaTarget);
              m_RecoSWTSSW2DPhi->Fill(phi0Target);
              m_RecoSWTSSW2DD0->Fill(d0Target);
              m_RecoSWTSSW2DInvPt->Fill(invptTarget);
 
              m_neuroRecoSWTSSW2DOutZ->Fill(neuroSWTSSW2DTrack->getZ0());
 
              double cotTh = neuroSWTSSW2DTrack->getCotTheta();
              double cosTh = copysign(1.0, cotTh) / sqrt(1. / (cotTh * cotTh) + 1);
              m_neuroRecoSWTSSW2DOutCosTheta->Fill(cosTh);
              phinorm = neuroSWTSSW2DTrack->getPhi0() * 180 / M_PI;
              if (phinorm < 0.) {phinorm = phinorm + 360. ;}
              double phiNeuro = phinorm ;
              double invptNeuro = neuroSWTSSW2DTrack->getPt() ;
              m_neuroRecoSWTSSW2DOutInvPt->Fill(invptNeuro);
              m_neuroRecoSWTSSW2DOutPhi0->Fill(phiNeuro);
              m_neuroRecoSWTSSW2DOutHitPattern->Fill(getPattern(neuroSWTSSW2DTrack, m_simSegmentHitsName));
              nswtssw2dmatched++;
              unsigned swtssw2dMatchedSector =
                neuroSWTSSW2DTrack->getRelatedTo<CDCTriggerMLPInput>(m_simNeuroInputVectorSWTSSW2DName)->getSector();
              m_neuroRecoSWTSSW2DSector->Fill(swtssw2dMatchedSector);
 
              m_DeltaRecoSWTSSW2DZ->Fill(zTarget - neuroSWTSSW2DTrack->getZ0());
 
 
              m_DeltaRecoSWTSSW2DCosTheta->Fill(cosThetaTarget - cosTh);
              phinorm = neuroSWTSSW2DTrack->getPhi0() * 180. / M_PI ;
              if (phinorm < 0.) {phinorm = phinorm  + 360. ;}
              m_DeltaRecoSWTSSW2DPhi->Fill(phi0Target - phinorm);
              m_DeltaRecoSWTSSW2DInvPt->Fill(invptTarget - neuroSWTSSW2DTrack->getPt());
              m_RecoSWTSSW2DZScatter->Fill(zTarget, neuroSWTSSW2DTrack->getZ0());
            }
          }
        }
      }
      if (m_unpackedNeuroTracksName != "") {
        m_neuroRecoHWOutTrackCount->Fill(nhwmatched);
        if (!m_limitedoutput && m_simNeuroTracksName != "") {
          m_neuroRecoSWOutTrackCount->Fill(nswmatched);
        }
        if (!m_limitedoutput && m_simNeuroTracksSWTSSW2DName != "") {
          m_neuroRecoSWTSSW2DOutTrackCount->Fill(nswtssw2dmatched);
        }
      }
    }
  }
 
  if (!m_limitedoutput && m_simNeuroTracksSWTSSW2DName != "") {
    m_neuroSWTSSW2DOutTrackCount->Fill(m_simNeuroTracksSWTSSW2D.getEntries());
    for (CDCTriggerTrack& neuroswTrack : m_simNeuroTracksSWTSSW2D) {
      m_neuroSWTSSW2DOutZ->Fill(neuroswTrack.getZ0());
      double cotThSW = neuroswTrack.getCotTheta();
      double cosThSW = copysign(1.0, cotThSW) / sqrt(1. / (cotThSW * cotThSW) + 1);
      m_neuroSWTSSW2DOutCosTheta->Fill(cosThSW);
      phinorm = neuroswTrack.getPhi0() * 180. / M_PI  ;
      if (phinorm < 0.) {
        phinorm = phinorm + 360. ;
      }
      m_neuroSWTSSW2DOutPhi0->Fill(phinorm);
      m_neuroSWTSSW2DOutInvPt->Fill(neuroswTrack.getPt());
      unsigned simSWTSSW2DSector =
        neuroswTrack.getRelatedTo<CDCTriggerMLPInput>(m_simNeuroInputVectorSWTSSW2DName)->getSector();
      m_neuroSWTSSW2DSector->Fill(simSWTSSW2DSector);
 
      // sw NN selected sw ts
      unsigned pattern = 0;
      for (const CDCTriggerSegmentHit& hit :
           neuroswTrack.getRelationsTo<CDCTriggerSegmentHit>(m_simSegmentHitsName)) {
        m_neuroSWTSSW2DSelTSID->Fill(hit.getSegmentID());
        unsigned int sl = hit.getISuperLayer();
        m_neuroSWTSSW2DSelTSCount->Fill(sl);
        float neuroTime = neuroswTrack.getTime();
 
        // find first occurence of hit (used to debug the selcted TS field)
        CDCTriggerSegmentHit firstHit = hit;
        for (CDCTriggerSegmentHit compare : m_simSegmentHits) {
          if (compare.getISuperLayer()       == hit.getISuperLayer()      &&
              compare.getIWireCenter()       == hit.getIWireCenter()      &&
              compare.getPriorityPosition()  == hit.getPriorityPosition() &&
              compare.getLeftRight()         == hit.getLeftRight()        &&
              compare.priorityTime()         == hit.priorityTime()        &&
              compare.foundTime()            < firstHit.foundTime()) {
            firstHit = compare;
          }
        }
 
        switch (sl) {
          case 0: m_neuroSWTSSW2DSelTSPrioT_Layer0->Fill(hit.priorityTime());
            m_neuroSWTSSW2DSelTSFoundT_Layer0->Fill(firstHit.foundTime() - neuroTime);
            m_neuroSWTSSW2DSelTSPrioB_Layer0->Fill(hit.getPriorityPosition());
            m_neuroSWTSSW2DSelTSLR_Layer0->Fill(hit.getLeftRight());
            break;
          case 1: m_neuroSWTSSW2DSelTSPrioT_Layer1->Fill(hit.priorityTime());
            m_neuroSWTSSW2DSelTSFoundT_Layer1->Fill(firstHit.foundTime() - neuroTime);
            m_neuroSWTSSW2DSelTSPrioB_Layer1->Fill(hit.getPriorityPosition());
            m_neuroSWTSSW2DSelTSLR_Layer1->Fill(hit.getLeftRight());
            break;
          case 2: m_neuroSWTSSW2DSelTSPrioT_Layer2->Fill(hit.priorityTime());
            m_neuroSWTSSW2DSelTSFoundT_Layer2->Fill(firstHit.foundTime() - neuroTime);
            m_neuroSWTSSW2DSelTSPrioB_Layer2->Fill(hit.getPriorityPosition());
            m_neuroSWTSSW2DSelTSLR_Layer2->Fill(hit.getLeftRight());
            break;
          case 3: m_neuroSWTSSW2DSelTSPrioT_Layer3->Fill(hit.priorityTime());
            m_neuroSWTSSW2DSelTSFoundT_Layer3->Fill(firstHit.foundTime() - neuroTime);
            m_neuroSWTSSW2DSelTSPrioB_Layer3->Fill(hit.getPriorityPosition());
            m_neuroSWTSSW2DSelTSLR_Layer3->Fill(hit.getLeftRight());
            break;
          case 4: m_neuroSWTSSW2DSelTSPrioT_Layer4->Fill(hit.priorityTime());
            m_neuroSWTSSW2DSelTSFoundT_Layer4->Fill(firstHit.foundTime() - neuroTime);
            m_neuroSWTSSW2DSelTSPrioB_Layer4->Fill(hit.getPriorityPosition());
            m_neuroSWTSSW2DSelTSLR_Layer4->Fill(hit.getLeftRight());
            break;
          case 5: m_neuroSWTSSW2DSelTSPrioT_Layer5->Fill(hit.priorityTime());
            m_neuroSWTSSW2DSelTSFoundT_Layer5->Fill(firstHit.foundTime() - neuroTime);
            m_neuroSWTSSW2DSelTSPrioB_Layer5->Fill(hit.getPriorityPosition());
            m_neuroSWTSSW2DSelTSLR_Layer5->Fill(hit.getLeftRight());
            break;
          case 6: m_neuroSWTSSW2DSelTSPrioT_Layer6->Fill(hit.priorityTime());
            m_neuroSWTSSW2DSelTSFoundT_Layer6->Fill(firstHit.foundTime() - neuroTime);
            m_neuroSWTSSW2DSelTSPrioB_Layer6->Fill(hit.getPriorityPosition());
            m_neuroSWTSSW2DSelTSLR_Layer6->Fill(hit.getLeftRight());
            break;
          case 7: m_neuroSWTSSW2DSelTSPrioT_Layer7->Fill(hit.priorityTime());
            m_neuroSWTSSW2DSelTSFoundT_Layer7->Fill(firstHit.foundTime() - neuroTime);
            m_neuroSWTSSW2DSelTSPrioB_Layer7->Fill(hit.getPriorityPosition());
            m_neuroSWTSSW2DSelTSLR_Layer7->Fill(hit.getLeftRight());
            break;
          case 8: m_neuroSWTSSW2DSelTSPrioT_Layer8->Fill(hit.priorityTime());
            m_neuroSWTSSW2DSelTSFoundT_Layer8->Fill(firstHit.foundTime() - neuroTime);
            m_neuroSWTSSW2DSelTSPrioB_Layer8->Fill(hit.getPriorityPosition());
            m_neuroSWTSSW2DSelTSLR_Layer8->Fill(hit.getLeftRight());
            break;
        }
        if (sl % 2 == 1) pattern |= (1 << ((sl - 1) / 2));
      }
      m_neuroSWTSSW2DOutHitPattern->Fill(pattern);
 
      // plot input vector
      vector<float> nnInput =
        neuroswTrack.getRelatedTo<CDCTriggerMLPInput>(m_simNeuroInputVectorSWTSSW2DName)->getInput();
      condFill(m_neuroSWTSSW2DInputID_Layer0, nnInput[0]);
      condFill(m_neuroSWTSSW2DInputT_Layer0, nnInput[1]);
      condFill(m_neuroSWTSSW2DInputAlpha_Layer0, nnInput[2]);
      condFill(m_neuroSWTSSW2DInputID_Layer1, nnInput[3]);
      condFill(m_neuroSWTSSW2DInputT_Layer1, nnInput[4]);
      condFill(m_neuroSWTSSW2DInputAlpha_Layer1, nnInput[5]);
      condFill(m_neuroSWTSSW2DInputID_Layer2, nnInput[6]);
      condFill(m_neuroSWTSSW2DInputT_Layer2, nnInput[7]);
      condFill(m_neuroSWTSSW2DInputAlpha_Layer2, nnInput[8]);
      condFill(m_neuroSWTSSW2DInputID_Layer3, nnInput[9]);
      condFill(m_neuroSWTSSW2DInputT_Layer3, nnInput[10]);
      condFill(m_neuroSWTSSW2DInputAlpha_Layer3, nnInput[11]);
      condFill(m_neuroSWTSSW2DInputID_Layer4, nnInput[12]);
      condFill(m_neuroSWTSSW2DInputT_Layer4, nnInput[13]);
      condFill(m_neuroSWTSSW2DInputAlpha_Layer4, nnInput[14]);
      condFill(m_neuroSWTSSW2DInputID_Layer5, nnInput[15]);
      condFill(m_neuroSWTSSW2DInputT_Layer5, nnInput[16]);
      condFill(m_neuroSWTSSW2DInputAlpha_Layer5, nnInput[17]);
      condFill(m_neuroSWTSSW2DInputID_Layer6, nnInput[18]);
      condFill(m_neuroSWTSSW2DInputT_Layer6, nnInput[19]);
      condFill(m_neuroSWTSSW2DInputAlpha_Layer6, nnInput[20]);
      condFill(m_neuroSWTSSW2DInputID_Layer7, nnInput[21]);
      condFill(m_neuroSWTSSW2DInputT_Layer7, nnInput[22]);
      condFill(m_neuroSWTSSW2DInputAlpha_Layer7, nnInput[23]);
      condFill(m_neuroSWTSSW2DInputID_Layer8, nnInput[24]);
      condFill(m_neuroSWTSSW2DInputT_Layer8, nnInput[25]);
      condFill(m_neuroSWTSSW2DInputAlpha_Layer8, nnInput[26]);
    }
  }
  if (!m_limitedoutput && m_simNeuroTracksName != "") {
    m_neuroSWOutTrackCount->Fill(m_simNeuroTracks.getEntries());
    for (CDCTriggerTrack& neuroswTrack : m_simNeuroTracks) {
      bool valtrack = false;
      try {
        valtrack = neuroswTrack.getValidStereoBit();
      } catch (...) {
        B2INFO("HWTrack doesn't have 'valid bit', get it from relations now... ");
        unsigned checkpattern = getPattern(&neuroswTrack, m_unpackedNeuroInputSegmentsName);
        valtrack = isValidPattern(checkpattern);
      }
      if (!valtrack) {
        continue;
      }
 
      m_neuroSWOutZ->Fill(neuroswTrack.getZ0());
      double cotThSW = neuroswTrack.getCotTheta();
      double cosThSW = copysign(1.0, cotThSW) / sqrt(1. / (cotThSW * cotThSW) + 1);
      m_neuroSWOutCosTheta->Fill(cosThSW);
      phinorm = neuroswTrack.getPhi0() * 180. / M_PI ;
      if (phinorm < 0.) {phinorm = phinorm + 360. ;}
      m_neuroSWOutPhi0->Fill(phinorm);
      m_neuroSWOutInvPt->Fill(neuroswTrack.getPt());
 
      unsigned simSector =
        neuroswTrack.getRelatedTo<CDCTriggerMLPInput>(m_simNeuroInputVectorName)->getSector();
      m_neuroSWSector->Fill(simSector);
 
      // sw NN selected sw ts
      unsigned pattern = 0;
      for (const CDCTriggerSegmentHit& hit :
           neuroswTrack.getRelationsTo<CDCTriggerSegmentHit>(m_unpackedNeuroInputSegmentsName)) {
        m_neuroSWSelTSID->Fill(hit.getSegmentID());
        unsigned int sl = hit.getISuperLayer();
        m_neuroSWSelTSCount->Fill(sl);
        float neuroTime = neuroswTrack.getTime();
 
        // find first occurence of hit (used to debug the selcted TS field)
        CDCTriggerSegmentHit firstHit = hit;
        for (CDCTriggerSegmentHit compare : m_simSegmentHits) {
          if (compare.getISuperLayer()       == hit.getISuperLayer()      &&
              compare.getIWireCenter()       == hit.getIWireCenter()      &&
              compare.getPriorityPosition()  == hit.getPriorityPosition() &&
              compare.getLeftRight()         == hit.getLeftRight()        &&
              compare.priorityTime()         == hit.priorityTime()        &&
              compare.foundTime()            < firstHit.foundTime()) {
            firstHit = compare;
          }
        }
 
        switch (sl) {
          case 0: m_neuroSWSelTSPrioT_Layer0->Fill(hit.priorityTime());
            m_neuroSWSelTSFoundT_Layer0->Fill(firstHit.foundTime() - neuroTime);
            m_neuroSWSelTSPrioB_Layer0->Fill(hit.getPriorityPosition());
            m_neuroSWSelTSLR_Layer0->Fill(hit.getLeftRight());
            break;
          case 1: m_neuroSWSelTSPrioT_Layer1->Fill(hit.priorityTime());
            m_neuroSWSelTSFoundT_Layer1->Fill(firstHit.foundTime() - neuroTime);
            m_neuroSWSelTSPrioB_Layer1->Fill(hit.getPriorityPosition());
            m_neuroSWSelTSLR_Layer1->Fill(hit.getLeftRight());
            break;
          case 2: m_neuroSWSelTSPrioT_Layer2->Fill(hit.priorityTime());
            m_neuroSWSelTSFoundT_Layer2->Fill(firstHit.foundTime() - neuroTime);
            m_neuroSWSelTSPrioB_Layer2->Fill(hit.getPriorityPosition());
            m_neuroSWSelTSLR_Layer2->Fill(hit.getLeftRight());
            break;
          case 3: m_neuroSWSelTSPrioT_Layer3->Fill(hit.priorityTime());
            m_neuroSWSelTSFoundT_Layer3->Fill(firstHit.foundTime() - neuroTime);
            m_neuroSWSelTSPrioB_Layer3->Fill(hit.getPriorityPosition());
            m_neuroSWSelTSLR_Layer3->Fill(hit.getLeftRight());
            break;
          case 4: m_neuroSWSelTSPrioT_Layer4->Fill(hit.priorityTime());
            m_neuroSWSelTSFoundT_Layer4->Fill(firstHit.foundTime() - neuroTime);
            m_neuroSWSelTSPrioB_Layer4->Fill(hit.getPriorityPosition());
            m_neuroSWSelTSLR_Layer4->Fill(hit.getLeftRight());
            break;
          case 5: m_neuroSWSelTSPrioT_Layer5->Fill(hit.priorityTime());
            m_neuroSWSelTSFoundT_Layer5->Fill(firstHit.foundTime() - neuroTime);
            m_neuroSWSelTSPrioB_Layer5->Fill(hit.getPriorityPosition());
            m_neuroSWSelTSLR_Layer5->Fill(hit.getLeftRight());
            break;
          case 6: m_neuroSWSelTSPrioT_Layer6->Fill(hit.priorityTime());
            m_neuroSWSelTSFoundT_Layer6->Fill(firstHit.foundTime() - neuroTime);
            m_neuroSWSelTSPrioB_Layer6->Fill(hit.getPriorityPosition());
            m_neuroSWSelTSLR_Layer6->Fill(hit.getLeftRight());
            break;
          case 7: m_neuroSWSelTSPrioT_Layer7->Fill(hit.priorityTime());
            m_neuroSWSelTSFoundT_Layer7->Fill(firstHit.foundTime() - neuroTime);
            m_neuroSWSelTSPrioB_Layer7->Fill(hit.getPriorityPosition());
            m_neuroSWSelTSLR_Layer7->Fill(hit.getLeftRight());
            break;
          case 8: m_neuroSWSelTSPrioT_Layer8->Fill(hit.priorityTime());
            m_neuroSWSelTSFoundT_Layer8->Fill(firstHit.foundTime() - neuroTime);
            m_neuroSWSelTSPrioB_Layer8->Fill(hit.getPriorityPosition());
            m_neuroSWSelTSLR_Layer8->Fill(hit.getLeftRight());
            break;
        }
        if (sl % 2 == 1) pattern |= (1 << ((sl - 1) / 2));
      }
      m_neuroSWOutHitPattern->Fill(pattern);
 
 
      // plot input vector
      vector<float> nnInput =
        neuroswTrack.getRelatedTo<CDCTriggerMLPInput>(m_simNeuroInputVectorName)->getInput();
      condFill(m_neuroSWInputID_Layer0, nnInput[0]);
      condFill(m_neuroSWInputT_Layer0, nnInput[1]);
      condFill(m_neuroSWInputAlpha_Layer0, nnInput[2]);
      condFill(m_neuroSWInputID_Layer1, nnInput[3]);
      condFill(m_neuroSWInputT_Layer1, nnInput[4]);
      condFill(m_neuroSWInputAlpha_Layer1, nnInput[5]);
      condFill(m_neuroSWInputID_Layer2, nnInput[6]);
      condFill(m_neuroSWInputT_Layer2, nnInput[7]);
      condFill(m_neuroSWInputAlpha_Layer2, nnInput[8]);
      condFill(m_neuroSWInputID_Layer3, nnInput[9]);
      condFill(m_neuroSWInputT_Layer3, nnInput[10]);
      condFill(m_neuroSWInputAlpha_Layer3, nnInput[11]);
      condFill(m_neuroSWInputID_Layer4, nnInput[12]);
      condFill(m_neuroSWInputT_Layer4, nnInput[13]);
      condFill(m_neuroSWInputAlpha_Layer4, nnInput[14]);
      condFill(m_neuroSWInputID_Layer5, nnInput[15]);
      condFill(m_neuroSWInputT_Layer5, nnInput[16]);
      condFill(m_neuroSWInputAlpha_Layer5, nnInput[17]);
      condFill(m_neuroSWInputID_Layer6, nnInput[18]);
      condFill(m_neuroSWInputT_Layer6, nnInput[19]);
      condFill(m_neuroSWInputAlpha_Layer6, nnInput[20]);
      condFill(m_neuroSWInputID_Layer7, nnInput[21]);
      condFill(m_neuroSWInputT_Layer7, nnInput[22]);
      condFill(m_neuroSWInputAlpha_Layer7, nnInput[23]);
      condFill(m_neuroSWInputID_Layer8, nnInput[24]);
      condFill(m_neuroSWInputT_Layer8, nnInput[25]);
      condFill(m_neuroSWInputAlpha_Layer8, nnInput[26]);
    }
  }
 
  int nofintracks = 0;
  if (m_unpackedNeuroTracksName != "") {
    int nofouttracks = 0;
    int nofinsegments = 0;
    // fill neurotrigger histograms
    for (CDCTriggerTrack& neuroTrack : m_unpackedNeuroTracks) {
      bool valtrack = false;
      try {
        valtrack = neuroTrack.getValidStereoBit();
      } catch (...) {
        B2WARNING("NeuroTrack doesn't have 'valid bit', get it from relations now... ");
        unsigned checkpattern = getPattern(&neuroTrack, m_unpackedNeuroInputSegmentsName);
        valtrack = isValidPattern(checkpattern);
      }
      if (!valtrack) {
        continue;
      }
      // count number of tracks
      ++nofouttracks;
      // fill raw distributions
      m_neuroHWOutZ->Fill(neuroTrack.getZ0());
      double cotTh = neuroTrack.getCotTheta();
      double cosTh = copysign(1.0, cotTh) / sqrt(1. / (cotTh * cotTh) + 1);
      m_neuroHWOutCosTheta->Fill(cosTh);
      phinorm = neuroTrack.getPhi0() * 180. / M_PI ;
      if (phinorm < 0.) {phinorm = phinorm + 360. ;}
      m_neuroHWOutPhi0->Fill(phinorm);
      m_neuroHWOutInvPt->Fill(neuroTrack.getPt());
      m_neuroHWOutPt->Fill(neuroTrack.getPt());
      m_neuroHWOutm_time->Fill(neuroTrack.getTime());
 
      unsigned hwSector =
        neuroTrack.getRelatedTo<CDCTriggerMLPInput>(m_unpackedNeuroInputVectorName)->getSector();
      m_neuroHWSector->Fill(hwSector);
 
      // fill hists per quadrant
      if (!m_limitedoutput) {
        switch (neuroTrack.getQuadrant()) {
          case -1:
            m_neuroHWOutQuad5Z->Fill(neuroTrack.getZ0());
            m_neuroHWOutQuad5CosTheta->Fill(cosTh);
            phinorm = neuroTrack.getPhi0() * 180. / M_PI ;
            if (phinorm < 0.) { phinorm = phinorm + 360. ;}
            m_neuroHWOutQuad5Phi0->Fill(phinorm);
            m_neuroHWOutQuad5InvPt->Fill(neuroTrack.getPt());
            break;
          case 0:
            m_neuroHWOutQuad0Z->Fill(neuroTrack.getZ0());
            m_neuroHWOutQuad0CosTheta->Fill(cosTh);
            phinorm = neuroTrack.getPhi0() * 180. / M_PI ;
            if (phinorm < 0.) { phinorm = phinorm + 360. ;}
            m_neuroHWOutQuad0Phi0->Fill(phinorm);
            m_neuroHWOutQuad0InvPt->Fill(neuroTrack.getPt());
            break;
          case 1:
            m_neuroHWOutQuad1Z->Fill(neuroTrack.getZ0());
            m_neuroHWOutQuad1CosTheta->Fill(cosTh);
            phinorm = neuroTrack.getPhi0() * 180. / M_PI ;
            if (phinorm < 0.) { phinorm = phinorm + 360. ;}
            m_neuroHWOutQuad1Phi0->Fill(phinorm);
            m_neuroHWOutQuad1InvPt->Fill(neuroTrack.getPt());
            break;
          case 2:
            m_neuroHWOutQuad2Z->Fill(neuroTrack.getZ0());
            m_neuroHWOutQuad2CosTheta->Fill(cosTh);
            phinorm = neuroTrack.getPhi0() * 180. / M_PI ;
            if (phinorm < 0.) { phinorm = phinorm + 360. ;}
            m_neuroHWOutQuad2Phi0->Fill(phinorm);
            m_neuroHWOutQuad2InvPt->Fill(neuroTrack.getPt());
            break;
          case 3:
            m_neuroHWOutQuad3Z->Fill(neuroTrack.getZ0());
            m_neuroHWOutQuad3CosTheta->Fill(cosTh);
            phinorm = neuroTrack.getPhi0() * 180. / M_PI ;
            if (phinorm < 0.) { phinorm = phinorm + 360. ;}
            m_neuroHWOutQuad3Phi0->Fill(phinorm);
            m_neuroHWOutQuad3InvPt->Fill(neuroTrack.getPt());
            break;
        }
      }
 
      // get related stereo hits
      unsigned pattern = 0;
      for (const CDCTriggerSegmentHit& hit :
           neuroTrack.getRelationsTo<CDCTriggerSegmentHit>(m_unpackedNeuroInputSegmentsName)) {
        m_neuroHWSelTSID->Fill(hit.getSegmentID());
        unsigned int sl = hit.getISuperLayer();
        m_neuroHWSelTSCount->Fill(sl);
        float neuroTime = neuroTrack.getTime();
        // find first occurence of hit (used to debug the selcted TS field)
        CDCTriggerSegmentHit firstHit = hit;
        for (CDCTriggerSegmentHit compare : m_unpackedNeuroInputSegments) {
          if (compare.getISuperLayer()       == hit.getISuperLayer()      &&
              compare.getIWireCenter()       == hit.getIWireCenter()      &&
              compare.getPriorityPosition()  == hit.getPriorityPosition() &&
              compare.getLeftRight()         == hit.getLeftRight()        &&
              compare.priorityTime()         == hit.priorityTime()        &&
              compare.foundTime()            < firstHit.foundTime()) {
            firstHit = compare;
          }
        }
 
        if (!m_limitedoutput) {
          switch (sl) {
            case 0: m_neuroHWSelTSPrioT_Layer0->Fill(hit.priorityTime());
              m_neuroHWSelTSFoundT_Layer0->Fill(firstHit.foundTime() - neuroTime);
              m_neuroHWSelTSPrioB_Layer0->Fill(hit.getPriorityPosition());
              m_neuroHWSelTSLR_Layer0->Fill(hit.getLeftRight());
              break;
            case 1: m_neuroHWSelTSPrioT_Layer1->Fill(hit.priorityTime());
              m_neuroHWSelTSFoundT_Layer1->Fill(firstHit.foundTime() - neuroTime);
              m_neuroHWSelTSPrioB_Layer1->Fill(hit.getPriorityPosition());
              m_neuroHWSelTSLR_Layer1->Fill(hit.getLeftRight());
              break;
            case 2: m_neuroHWSelTSPrioT_Layer2->Fill(hit.priorityTime());
              m_neuroHWSelTSFoundT_Layer2->Fill(firstHit.foundTime() - neuroTime);
              m_neuroHWSelTSPrioB_Layer2->Fill(hit.getPriorityPosition());
              m_neuroHWSelTSLR_Layer2->Fill(hit.getLeftRight());
              break;
            case 3: m_neuroHWSelTSPrioT_Layer3->Fill(hit.priorityTime());
              m_neuroHWSelTSFoundT_Layer3->Fill(firstHit.foundTime() - neuroTime);
              m_neuroHWSelTSPrioB_Layer3->Fill(hit.getPriorityPosition());
              m_neuroHWSelTSLR_Layer3->Fill(hit.getLeftRight());
              break;
            case 4: m_neuroHWSelTSPrioT_Layer4->Fill(hit.priorityTime());
              m_neuroHWSelTSFoundT_Layer4->Fill(firstHit.foundTime() - neuroTime);
              m_neuroHWSelTSPrioB_Layer4->Fill(hit.getPriorityPosition());
              m_neuroHWSelTSLR_Layer4->Fill(hit.getLeftRight());
              break;
            case 5: m_neuroHWSelTSPrioT_Layer5->Fill(hit.priorityTime());
              m_neuroHWSelTSFoundT_Layer5->Fill(firstHit.foundTime() - neuroTime);
              m_neuroHWSelTSPrioB_Layer5->Fill(hit.getPriorityPosition());
              m_neuroHWSelTSLR_Layer5->Fill(hit.getLeftRight());
              break;
            case 6: m_neuroHWSelTSPrioT_Layer6->Fill(hit.priorityTime());
              m_neuroHWSelTSFoundT_Layer6->Fill(firstHit.foundTime() - neuroTime);
              m_neuroHWSelTSPrioB_Layer6->Fill(hit.getPriorityPosition());
              m_neuroHWSelTSLR_Layer6->Fill(hit.getLeftRight());
              break;
            case 7: m_neuroHWSelTSPrioT_Layer7->Fill(hit.priorityTime());
              m_neuroHWSelTSFoundT_Layer7->Fill(firstHit.foundTime() - neuroTime);
              m_neuroHWSelTSPrioB_Layer7->Fill(hit.getPriorityPosition());
              m_neuroHWSelTSLR_Layer7->Fill(hit.getLeftRight());
              break;
            case 8: m_neuroHWSelTSPrioT_Layer8->Fill(hit.priorityTime());
              m_neuroHWSelTSFoundT_Layer8->Fill(firstHit.foundTime() - neuroTime);
              m_neuroHWSelTSPrioB_Layer8->Fill(hit.getPriorityPosition());
              m_neuroHWSelTSLR_Layer8->Fill(hit.getLeftRight());
              break;
          }
        }
        if (sl % 2 == 1) pattern |= (1 << ((sl - 1) / 2));
      }
      m_neuroHWOutHitPattern->Fill(pattern);
 
      // plot input vector
      if (!m_limitedoutput) {
        vector<float> nnInput =
          neuroTrack.getRelatedTo<CDCTriggerMLPInput>(m_unpackedNeuroInputVectorName)->getInput();
        condFill(m_neuroHWInputID_Layer0, nnInput[0]);
        condFill(m_neuroHWInputT_Layer0, nnInput[1]);
        condFill(m_neuroHWInputAlpha_Layer0, nnInput[2]);
        condFill(m_neuroHWInputID_Layer1, nnInput[3]);
        condFill(m_neuroHWInputT_Layer1, nnInput[4]);
        condFill(m_neuroHWInputAlpha_Layer1, nnInput[5]);
        condFill(m_neuroHWInputID_Layer2, nnInput[6]);
        condFill(m_neuroHWInputT_Layer2, nnInput[7]);
        condFill(m_neuroHWInputAlpha_Layer2, nnInput[8]);
        condFill(m_neuroHWInputID_Layer3, nnInput[9]);
        condFill(m_neuroHWInputT_Layer3, nnInput[10]);
        condFill(m_neuroHWInputAlpha_Layer3, nnInput[11]);
        condFill(m_neuroHWInputID_Layer4, nnInput[12]);
        condFill(m_neuroHWInputT_Layer4, nnInput[13]);
        condFill(m_neuroHWInputAlpha_Layer4, nnInput[14]);
        condFill(m_neuroHWInputID_Layer5, nnInput[15]);
        condFill(m_neuroHWInputT_Layer5, nnInput[16]);
        condFill(m_neuroHWInputAlpha_Layer5, nnInput[17]);
        condFill(m_neuroHWInputID_Layer6, nnInput[18]);
        condFill(m_neuroHWInputT_Layer6, nnInput[19]);
        condFill(m_neuroHWInputAlpha_Layer6, nnInput[20]);
        condFill(m_neuroHWInputID_Layer7, nnInput[21]);
        condFill(m_neuroHWInputT_Layer7, nnInput[22]);
        condFill(m_neuroHWInputAlpha_Layer7, nnInput[23]);
        condFill(m_neuroHWInputID_Layer8, nnInput[24]);
        condFill(m_neuroHWInputT_Layer8, nnInput[25]);
        condFill(m_neuroHWInputAlpha_Layer8, nnInput[26]);
 
 
        if (m_simNeuroTracksName != "") {
          // get related track from TSIM (via 2D finder track)
          CDCTriggerTrack* finderTrack =
            neuroTrack.getRelatedFrom<CDCTriggerTrack>(m_unpackedNeuroInput2DTracksName);
          if (finderTrack) {
            CDCTriggerTrack* neuroSimTrack =
              finderTrack->getRelatedTo<CDCTriggerTrack>(m_simNeuroTracksName);
            if (neuroSimTrack) {
              // check if they same TS are selected in the unpacked and TSIM track
              int nsameTS = 0;
              int ndiffTS = 0;
              for (const CDCTriggerSegmentHit& simhit :
                   neuroSimTrack->getRelationsTo<CDCTriggerSegmentHit>(m_unpackedNeuroInputSegmentsName)) {
                unsigned int simsl = simhit.getISuperLayer();
                for (const CDCTriggerSegmentHit& hit :
                     neuroTrack.getRelationsTo<CDCTriggerSegmentHit>(m_unpackedNeuroInputSegmentsName)) {
                  unsigned int sl = hit.getISuperLayer();
                  if (sl == simsl) {
                    m_neuroDeltaTSID->Fill(hit.getSegmentID() - simhit.getSegmentID());
                    if (simhit.getSegmentID() == hit.getSegmentID() &&
                        simhit.getPriorityPosition() == hit.getPriorityPosition() &&
                        simhit.getLeftRight() == hit.getLeftRight() &&
                        simhit.priorityTime() == hit.priorityTime()
                       ) {
                      nsameTS += 1;
                    } else {
                      ndiffTS += 1;
                    }
                  }
                }
              }
              m_simSameTS->Fill(nsameTS);
              m_simDiffTS->Fill(ndiffTS);
              // only calculate deltas if the same TS are selected in unpacker and TSIM
 
 
              if (nsameTS >= m_nsamets) {
                if (abs(neuroTrack.getZ0() - neuroSimTrack->getZ0()) > 1) {
                  neuroSimTrack->setQualityVector(2);
                  neuroTrack.setQualityVector(2);
                }
                m_neuroDeltaZ->Fill(neuroTrack.getZ0() - neuroSimTrack->getZ0());
                double nnHWtheta = neuroTrack.getDirection().Theta() * 180. / M_PI;
                double nnSWtheta = neuroSimTrack->getDirection().Theta() * 180. / M_PI;
                m_neuroDeltaTheta->Fill(nnHWtheta - nnSWtheta);
                m_neuroScatterZ->Fill(neuroTrack.getZ0(), neuroSimTrack->getZ0());
                m_neuroScatterTheta->Fill(nnHWtheta, nnSWtheta);
                vector<float> unpackedInput =
                  neuroTrack.getRelatedTo<CDCTriggerMLPInput>(m_unpackedNeuroInputVectorName)->getInput();
                vector<float> simInput =
                  neuroSimTrack->getRelatedTo<CDCTriggerMLPInput>(m_simNeuroInputVectorName)->getInput();
                unsigned unpackedSector =
                  neuroTrack.getRelatedTo<CDCTriggerMLPInput>(m_unpackedNeuroInputVectorName)->getSector();
                unsigned simSector =
                  neuroSimTrack->getRelatedTo<CDCTriggerMLPInput>(m_simNeuroInputVectorName)->getSector();
                m_neuroDeltaSector->Fill(unpackedSector - simSector);
                bool sameInputId = true;
                bool sameInputAlpha = true;
                bool scaleErr = false;
                bool missingTS = false;
                bool timeErr = false;
                for (unsigned ii = 0; ii < unpackedInput.size(); ii += 3) {
                  m_neuroDeltaInputID->Fill(unpackedInput[ii] - simInput[ii]);
                  m_neuroDeltaInputT->Fill(unpackedInput[ii + 1] - simInput[ii + 1]);
                  m_neuroDeltaInputAlpha->Fill(unpackedInput[ii + 2] - simInput[ii + 2]);
                  bool hwZero = false;
                  bool hwSimZero = false;
                  if (unpackedInput[ii] != simInput[ii]) {sameInputId = false;}
                  if (unpackedInput[ii + 2] != simInput[ii + 2]) {sameInputAlpha = false;}
                  if (unpackedInput[ii + 1] != simInput[ii + 1]) {timeErr = true;}
                  if (unpackedInput[ii + 1] == 0 && fabs(simInput[ii + 1] > 0.99)) {scaleErr = true;}
                  if (simInput[ii + 1] == 0 && fabs(unpackedInput[ii + 1] > 0.99)) {scaleErr = true;}
                  if (unpackedInput[ii] == 0 && unpackedInput[ii + 1] == 0 && unpackedInput[ii + 2] == 0) {hwZero = true;}
                  if (simInput[ii] == 0 && simInput[ii + 1] == 0 && simInput[ii + 2] == 0) {hwSimZero = true;}
                  if (hwZero != hwSimZero) {missingTS = true;}
                }
                if (!sameInputId) {neuroTrack.setQualityVector(4);}
                if (!sameInputAlpha) {neuroTrack.setQualityVector(8);}
                if (scaleErr) {neuroTrack.setQualityVector(16);}
                if (missingTS) {neuroTrack.setQualityVector(32);}
                if (timeErr) {neuroTrack.setQualityVector(64);}
 
              }
            }
          }
        }
      }
    }
    for (CDCTriggerTrack& neuroinput2dtrack : m_unpackedNeuroInput2DTracks) {
      nofintracks ++;
      phinorm = neuroinput2dtrack.getPhi0() * 180. / M_PI ;
      if (phinorm < 0.) { phinorm = phinorm + 360. ;}
      m_neuroHWInPhi0->Fill(phinorm);
      m_neuroHWInm_time->Fill(neuroinput2dtrack.getTime());
      m_neuroHWInInvPt->Fill(neuroinput2dtrack.getPt());
    }
    if (nofintracks > 0 || nofouttracks > 0) {
      m_neuroHWInTrackCount->Fill(nofintracks);
      m_neuroHWOutTrackCount->Fill(nofouttracks);
      m_neuroHWOutVsInTrackCount->Fill((nofouttracks - nofintracks));
    }
    for (CDCTriggerSegmentHit& neuroinputsegment : m_unpackedNeuroInputSegments) {
      ++nofinsegments;
      m_neuroHWInTSID->Fill(neuroinputsegment.getSegmentID());
      if (!m_limitedoutput) {
        unsigned int sl = neuroinputsegment.getISuperLayer();
        switch (sl) {
          case 0: m_neuroHWInTSPrioT_Layer0->Fill(neuroinputsegment.priorityTime());
            m_neuroHWInTSFoundT_Layer0->Fill(neuroinputsegment.foundTime());
            m_neuroHWInTSPrioB_Layer0->Fill(neuroinputsegment.getPriorityPosition());
            m_neuroHWInTSLR_Layer0->Fill(neuroinputsegment.getLeftRight());
            break;
          case 1: m_neuroHWInTSPrioT_Layer1->Fill(neuroinputsegment.priorityTime());
            m_neuroHWInTSFoundT_Layer1->Fill(neuroinputsegment.foundTime());
            m_neuroHWInTSPrioB_Layer1->Fill(neuroinputsegment.getPriorityPosition());
            m_neuroHWInTSLR_Layer1->Fill(neuroinputsegment.getLeftRight());
            break;
          case 2: m_neuroHWInTSPrioT_Layer2->Fill(neuroinputsegment.priorityTime());
            m_neuroHWInTSFoundT_Layer2->Fill(neuroinputsegment.foundTime());
            m_neuroHWInTSPrioB_Layer2->Fill(neuroinputsegment.getPriorityPosition());
            m_neuroHWInTSLR_Layer2->Fill(neuroinputsegment.getLeftRight());
            break;
          case 3: m_neuroHWInTSPrioT_Layer3->Fill(neuroinputsegment.priorityTime());
            m_neuroHWInTSFoundT_Layer3->Fill(neuroinputsegment.foundTime());
            m_neuroHWInTSPrioB_Layer3->Fill(neuroinputsegment.getPriorityPosition());
            m_neuroHWInTSLR_Layer3->Fill(neuroinputsegment.getLeftRight());
            break;
          case 4: m_neuroHWInTSPrioT_Layer4->Fill(neuroinputsegment.priorityTime());
            m_neuroHWInTSFoundT_Layer4->Fill(neuroinputsegment.foundTime());
            m_neuroHWInTSPrioB_Layer4->Fill(neuroinputsegment.getPriorityPosition());
            m_neuroHWInTSLR_Layer4->Fill(neuroinputsegment.getLeftRight());
            break;
          case 5: m_neuroHWInTSPrioT_Layer5->Fill(neuroinputsegment.priorityTime());
            m_neuroHWInTSFoundT_Layer5->Fill(neuroinputsegment.foundTime());
            m_neuroHWInTSPrioB_Layer5->Fill(neuroinputsegment.getPriorityPosition());
            m_neuroHWInTSLR_Layer5->Fill(neuroinputsegment.getLeftRight());
            break;
          case 6: m_neuroHWInTSPrioT_Layer6->Fill(neuroinputsegment.priorityTime());
            m_neuroHWInTSFoundT_Layer6->Fill(neuroinputsegment.foundTime());
            m_neuroHWInTSPrioB_Layer6->Fill(neuroinputsegment.getPriorityPosition());
            m_neuroHWInTSLR_Layer6->Fill(neuroinputsegment.getLeftRight());
            break;
          case 7: m_neuroHWInTSPrioT_Layer7->Fill(neuroinputsegment.priorityTime());
            m_neuroHWInTSFoundT_Layer7->Fill(neuroinputsegment.foundTime());
            m_neuroHWInTSPrioB_Layer7->Fill(neuroinputsegment.getPriorityPosition());
            m_neuroHWInTSLR_Layer7->Fill(neuroinputsegment.getLeftRight());
            break;
          case 8: m_neuroHWInTSPrioT_Layer8->Fill(neuroinputsegment.priorityTime());
            m_neuroHWInTSFoundT_Layer8->Fill(neuroinputsegment.foundTime());
            m_neuroHWInTSPrioB_Layer8->Fill(neuroinputsegment.getPriorityPosition());
            m_neuroHWInTSLR_Layer8->Fill(neuroinputsegment.getLeftRight());
            break;
        }
      }
    }
    if (nofinsegments > 0) {
      m_neuroHWInTSCount->Fill(nofinsegments);
    }
  }
 
 
  if (!m_limitedoutput && m_simSegmentHitsName != "" && m_sim2DTracksSWTSName != "") {
    m_2DSWOutTrackCount->Fill(m_sim2DTracksSWTS.getEntries());
    for (CDCTriggerTrack& sim2dtrack : m_sim2DTracksSWTS) {
      phinorm = sim2dtrack.getPhi0() * 180. / M_PI ;
      if (phinorm < 0.) { phinorm = phinorm + 360. ;}
      m_2DSWOutPhi0->Fill(phinorm);
      m_2DSWOutm_time->Fill(sim2dtrack.getTime());
      m_2DSWOutInvPt->Fill(sim2dtrack.getPt());
    }
  }
  if (m_unpacked2DTracksName != "") {
    int nof2douttracks = 0;
    for (CDCTriggerTrack& finder2dtrack : m_unpacked2DTracks) {
      ++nof2douttracks;
      phinorm = finder2dtrack.getPhi0() * 180. / M_PI ;
      if (phinorm < 0.) {phinorm = phinorm + 360. ;}
      m_2DHWOutPhi0->Fill(phinorm);
      m_2DHWOutm_time->Fill(finder2dtrack.getTime());
      m_2DHWOutInvPt->Fill(finder2dtrack.getPt());
    }
    if (nof2douttracks > 0) {
      m_2DHWOutTrackCount->Fill(nof2douttracks);
      if (m_unpackedNeuroTracksName != "") {
        m_neuroHWInVs2DOutTrackCount->Fill((nofintracks - nof2douttracks));
      }
    }
 
    int nof2dinsegments = 0;
 
    for (CDCTriggerSegmentHit& hit : m_unpackedSegmentHits) {
      ++nof2dinsegments;
      if (!m_limitedoutput) {
        unsigned int sl = hit.getISuperLayer();
        switch (sl) {
          case 0: m_2DHWInTSPrioT_Layer0->Fill(hit.priorityTime());
            m_2DHWInTSFoundT_Layer0->Fill(hit.foundTime());
            m_2DHWInTSPrioB_Layer0->Fill(hit.getPriorityPosition());
            m_2DHWInTSLR_Layer0->Fill(hit.getLeftRight());
            break;
          case 2: m_2DHWInTSPrioT_Layer2->Fill(hit.priorityTime());
            m_2DHWInTSFoundT_Layer2->Fill(hit.foundTime());
            m_2DHWInTSPrioB_Layer2->Fill(hit.getPriorityPosition());
            m_2DHWInTSLR_Layer2->Fill(hit.getLeftRight());
            break;
          case 4: m_2DHWInTSPrioT_Layer4->Fill(hit.priorityTime());
            m_2DHWInTSFoundT_Layer4->Fill(hit.foundTime());
            m_2DHWInTSPrioB_Layer4->Fill(hit.getPriorityPosition());
            m_2DHWInTSLR_Layer4->Fill(hit.getLeftRight());
            break;
          case 6: m_2DHWInTSPrioT_Layer6->Fill(hit.priorityTime());
            m_2DHWInTSFoundT_Layer6->Fill(hit.foundTime());
            m_2DHWInTSPrioB_Layer6->Fill(hit.getPriorityPosition());
            m_2DHWInTSLR_Layer6->Fill(hit.getLeftRight());
            break;
          case 8: m_2DHWInTSPrioT_Layer8->Fill(hit.priorityTime());
            m_2DHWInTSFoundT_Layer8->Fill(hit.foundTime());
            m_2DHWInTSPrioB_Layer8->Fill(hit.getPriorityPosition());
            m_2DHWInTSLR_Layer8->Fill(hit.getLeftRight());
            break;
        }
      }
    }
    if (nof2dinsegments > 0) {
    }
  }
 
 
  // sw TS
  if (!m_limitedoutput && m_simNeuroTracksSWTSSW2DName != "") {
    m_neuroSWTSSW2DInTSCount->Fill(m_simSegmentHits.getEntries());
    for (CDCTriggerSegmentHit& hit : m_simSegmentHits) {
      m_neuroSWTSSW2DInTSID->Fill(hit.getSegmentID());
      unsigned int sl = hit.getISuperLayer();
      switch (sl) {
        case 0: m_neuroSWTSSW2DInTSPrioT_Layer0->Fill(hit.priorityTime());
          m_neuroSWTSSW2DInTSFoundT_Layer0->Fill(hit.foundTime());
          m_neuroSWTSSW2DInTSPrioB_Layer0->Fill(hit.getPriorityPosition());
          m_neuroSWTSSW2DInTSLR_Layer0->Fill(hit.getLeftRight());
          break;
        case 1: m_neuroSWTSSW2DInTSPrioT_Layer1->Fill(hit.priorityTime());
          m_neuroSWTSSW2DInTSFoundT_Layer1->Fill(hit.foundTime());
          m_neuroSWTSSW2DInTSPrioB_Layer1->Fill(hit.getPriorityPosition());
          m_neuroSWTSSW2DInTSLR_Layer1->Fill(hit.getLeftRight());
          break;
        case 2: m_neuroSWTSSW2DInTSPrioT_Layer2->Fill(hit.priorityTime());
          m_neuroSWTSSW2DInTSFoundT_Layer2->Fill(hit.foundTime());
          m_neuroSWTSSW2DInTSPrioB_Layer2->Fill(hit.getPriorityPosition());
          m_neuroSWTSSW2DInTSLR_Layer2->Fill(hit.getLeftRight());
          break;
        case 3: m_neuroSWTSSW2DInTSPrioT_Layer3->Fill(hit.priorityTime());
          m_neuroSWTSSW2DInTSFoundT_Layer3->Fill(hit.foundTime());
          m_neuroSWTSSW2DInTSPrioB_Layer3->Fill(hit.getPriorityPosition());
          m_neuroSWTSSW2DInTSLR_Layer3->Fill(hit.getLeftRight());
          break;
        case 4: m_neuroSWTSSW2DInTSPrioT_Layer4->Fill(hit.priorityTime());
          m_neuroSWTSSW2DInTSFoundT_Layer4->Fill(hit.foundTime());
          m_neuroSWTSSW2DInTSPrioB_Layer4->Fill(hit.getPriorityPosition());
          m_neuroSWTSSW2DInTSLR_Layer4->Fill(hit.getLeftRight());
          break;
        case 5: m_neuroSWTSSW2DInTSPrioT_Layer5->Fill(hit.priorityTime());
          m_neuroSWTSSW2DInTSFoundT_Layer5->Fill(hit.foundTime());
          m_neuroSWTSSW2DInTSPrioB_Layer5->Fill(hit.getPriorityPosition());
          m_neuroSWTSSW2DInTSLR_Layer5->Fill(hit.getLeftRight());
          break;
        case 6: m_neuroSWTSSW2DInTSPrioT_Layer6->Fill(hit.priorityTime());
          m_neuroSWTSSW2DInTSFoundT_Layer6->Fill(hit.foundTime());
          m_neuroSWTSSW2DInTSPrioB_Layer6->Fill(hit.getPriorityPosition());
          m_neuroSWTSSW2DInTSLR_Layer6->Fill(hit.getLeftRight());
          break;
        case 7: m_neuroSWTSSW2DInTSPrioT_Layer7->Fill(hit.priorityTime());
          m_neuroSWTSSW2DInTSFoundT_Layer7->Fill(hit.foundTime());
          m_neuroSWTSSW2DInTSPrioB_Layer7->Fill(hit.getPriorityPosition());
          m_neuroSWTSSW2DInTSLR_Layer7->Fill(hit.getLeftRight());
          break;
        case 8: m_neuroSWTSSW2DInTSPrioT_Layer8->Fill(hit.priorityTime());
          m_neuroSWTSSW2DInTSFoundT_Layer8->Fill(hit.foundTime());
          m_neuroSWTSSW2DInTSPrioB_Layer8->Fill(hit.getPriorityPosition());
          m_neuroSWTSSW2DInTSLR_Layer8->Fill(hit.getLeftRight());
          break;
      }
    }
  }
 
  static constexpr std::array<int, 9> nWiresInSuperLayer = {
    160, 160, 192, 224, 256, 288, 320, 352, 384
  };
  unsigned axhwts = 0;
  unsigned sthwts = 0;
  unsigned axswts = 0;
  unsigned stswts = 0;
 
  std::vector<TSLine> hwtsoutput;
  std::vector<TSLine> swtsoutput;
  if (m_unpackedSegmentHitsName != "") {
    for (const CDCTriggerSegmentHit& xhit : m_unpackedNeuroInputSegments) {
      if (xhit.getISuperLayer() % 2 == 0) {
        axhwts ++;
      } else {
        sthwts ++;
      }
      int iSL = xhit.getISuperLayer();
      int iTS = xhit.getIWire();
      int nwires = nWiresInSuperLayer[ iSL ];
      if (iSL == 8) {
        iTS += 16;
        if (iTS > nwires) {
          iTS -= nwires;
        }
      }
      int tsIDInTracker = iTS - nwires * xhit.getQuadrant() / 4;
      if (tsIDInTracker < 0) {
        tsIDInTracker += nwires;
      }
      TSLine l(xhit);
      l.strline = "("
                  + padto(std::to_string(xhit.getISuperLayer()), 2) + ", "
                  + padto(std::to_string(xhit.getQuadrant()), 1) + ", "
                  + padto(std::to_string(xhit.getSegmentID()), 4) + ", "
                  + padto(std::to_string(xhit.getIWire()), 5) + ", "
                  + padto(std::to_string(xhit.getPriorityPosition()), 2) + ", "
                  + padto(std::to_string(xhit.getLeftRight()), 2) + ", "
                  + padto(std::to_string(xhit.priorityTime()), 4) + ", "
                  + padto(std::to_string(xhit.foundTime()), 3) + ", "
                  + padto(std::to_string(tsIDInTracker), 4) + ") | ";
 
      if (m_unpackedNeuroTracksName != "") {
        unsigned count2d = 0;
        for (CDCTriggerTrack& track : m_unpackedNeuroInput2DTracks) {
          count2d++;
          if (have_relation(track, xhit, m_unpackedNeuroInputSegmentsName)) {
            l.strline += std::to_string(count2d);
          } else {
            l.strline += ".";
          }
        }
        l.strline += " | ";
        unsigned counthwn = 0;
        for (CDCTriggerTrack& track : m_unpackedNeuroTracks) {
          counthwn++;
          if (have_relation(track, xhit, m_unpackedNeuroInputSegmentsName)) {
            if (track.getValidStereoBit()) {
              l.strline += std::to_string(counthwn);
            } else {
              l.strline += "x";
            }
          } else {
            l.strline += ".";
          }
        }
        l.strline += " | ";
      }
      if (m_simNeuroTracksName != "") {
        unsigned countswn = 0;
        for (CDCTriggerTrack& track : m_simNeuroTracks) {
          countswn++;
          if (have_relation(track, xhit, m_unpackedNeuroInputSegmentsName)) {
            l.strline += std::to_string(countswn);
          } else {
            l.strline += ".";
          }
        }
        l.strline += " | ";
      }
      if (m_recoTracksName != "") {
        unsigned countreco = 0;
        for (const auto& track : m_RecoTracks) {
          countreco++;
          bool related = false;
          for (const CDCTriggerSegmentHit& ts : track.getRelationsTo<CDCTriggerSegmentHit>(m_unpackedNeuroInputSegmentsName)) {
            if (&ts == &xhit) {related = true;}
          }
          if (related) {
            l.strline += std::to_string(countreco);
          } else {
            l.strline += ".";
          }
        }
        l.strline += " | ";
      }
      if (m_unpackedNeuroTracksName != "") {
        sorted_insert(hwtsoutput, l, m_unpackedNeuroInputSegmentsName, m_unpackedNeuroInput2DTracksName, m_unpackedNeuroTracksName);
      } else {
        hwtsoutput.push_back(l);
      }
    }
  }
  std::string axhw = std::to_string(axhwts) + " / " + std::to_string(sthwts);
  if (!m_limitedoutput && m_simSegmentHitsName != "") {
    for (const CDCTriggerSegmentHit& xhit : m_simSegmentHits) {
      if (xhit.getISuperLayer() % 2 == 0) {
        axswts ++;
      } else {
        stswts ++;
      }
      int iSL = xhit.getISuperLayer();
      int iTS = xhit.getIWire();
      int nwires = nWiresInSuperLayer[ iSL ];
      if (iSL == 8) {
        iTS += 16;
        if (iTS > nwires) {
          iTS -= nwires;
        }
      }
      int tsIDInTracker = iTS - nwires * xhit.getQuadrant() / 4;
      if (tsIDInTracker < 0) {
        tsIDInTracker += nwires;
      }
      TSLine l(xhit);
      l.strline = "("
                  + padto(std::to_string(xhit.getISuperLayer()), 2) + ", "
                  + padto(std::to_string(xhit.getSegmentID()), 4) + ", "
                  + padto(std::to_string(xhit.getIWire()), 5) + ", "
                  + padto(std::to_string(xhit.getPriorityPosition()), 2) + ", "
                  + padto(std::to_string(xhit.getLeftRight()), 2) + ", "
                  + padto(std::to_string(xhit.priorityTime()), 4) + ", "
                  + padto(std::to_string(xhit.foundTime()), 3) + ", "
                  + padto(std::to_string(tsIDInTracker), 4) + ") ";
 
      if (m_sim2DTracksSWTSName != "") {
        unsigned count2d = 0;
        for (CDCTriggerTrack& track : m_sim2DTracksSWTS) {
          count2d++;
          if (have_relation(track, xhit, m_simSegmentHitsName)) {
            l.strline += std::to_string(count2d);
          } else {
            l.strline += ".";
          }
        }
        l.strline += " | ";
      }
      if (m_simNeuroTracksSWTSSW2DName != "") {
        unsigned countswn = 0;
        for (CDCTriggerTrack& track : m_simNeuroTracksSWTSSW2D) {
          countswn++;
          if (have_relation(track, xhit, m_simSegmentHitsName)) {
            l.strline += std::to_string(countswn);
          } else {
            l.strline += ".";
          }
        }
        l.strline += " | ";
      }
      if (m_recoTracksName != "") {
        unsigned countreco = 0;
        for (const auto& track : m_RecoTracks) {
          countreco++;
          bool related = false;
          for (const CDCTriggerSegmentHit& ts : track.getRelationsTo<CDCTriggerSegmentHit>(m_simSegmentHitsName)) {
            if (&ts == &xhit) {related = true;}
          }
          if (related) {
            l.strline += std::to_string(countreco);
          } else {
            l.strline += ".";
          }
        }
        l.strline += " | ";
      }
      if (m_simNeuroTracksSWTSSW2DName != "") {
        sorted_insert(swtsoutput, l, m_simSegmentHitsName, m_sim2DTracksSWTSName, m_simNeuroTracksSWTSSW2DName);
      } else {
        swtsoutput.push_back(l);
      }
    }
  }
  std::string hwtsstring = "(SL, Q, SID , WID  , PP, LR, pT , fT , TSID) | HWNNIn2D | HWNeuro | SWNeuro | ";
  std::string swtsstring = "(SL, Q, SID , WID  , PP, LR, pT , fT , TSID) | SW2D | SWNeuro | ";
  if (m_recoTracksName != "") {
    hwtsstring += "Reco |  ";
    swtsstring += "Reco |  ";
  }
  B2DEBUG(10, padright(" ", 100));
  B2DEBUG(10, "----------------------------------------------------------------------------------------------------");
  B2DEBUG(10, padright(" ", 100));
  std::string experimentstring = "Experiment " + std::to_string(eventMetaData->getExperiment()) + "  Run " +
                                 std::to_string(eventMetaData->getRun()) + "  Event " + std::to_string(eventMetaData->getEvent());
  B2DEBUG(10, padright(experimentstring, 100));
  B2DEBUG(10, padright(" ", 100));
  B2DEBUG(10, padright(" ", 100));
  if (m_unpackedNeuroTracksName != "" && m_unpacked2DTracksName != "") {
    B2DEBUG(10,  padright("Number of NN HW TS (Axial/Stereo): ", 40) << padright(axhw, 60));
    B2DEBUG(10,  padright("Number of HW 2DFinderTracks: ", 40)            << padright(std::to_string(m_unpacked2DTracks.getEntries()),
            60));
    unsigned f2dtrn = 0;
    for (CDCTriggerTrack& ltrack : m_unpacked2DTracks) {
      f2dtrn++;
      std::stringstream strpt;
      std::stringstream stromega;
      std::stringstream strphi;
      strpt       << std::fixed << std::setprecision(2)   << ltrack.getPt();
      stromega    << std::fixed << std::setprecision(2)   << ltrack.getOmega();
      strphi      << std::fixed << std::setprecision(2)   << (ltrack.getPhi0() * 180. / M_PI);
      std::string trs  = "    HW2DFinderTrack Nr. " + std::to_string(f2dtrn) + " (pt, omega, phi) = ";
      trs += padto(strpt.str(), 6) + ", " + padto(stromega.str(), 6) + ", " + padto(strphi.str(), 6) + ")";
      B2DEBUG(15, padright(trs, 100));
    }
    B2DEBUG(10,  padright("Number of HW NNInput2DFinderTracks: ",
                          40)     << padright(std::to_string(m_unpackedNeuroInput2DTracks.getEntries()), 60));
    unsigned n2dtrn = 0;
    for (CDCTriggerTrack& ltrack : m_unpackedNeuroInput2DTracks) {
      n2dtrn++;
      std::stringstream strpt;
      std::stringstream stromega;
      std::stringstream strphi;
      strpt       << std::fixed << std::setprecision(2)   << ltrack.getPt();
      stromega    << std::fixed << std::setprecision(2)   << ltrack.getOmega();
      strphi      << std::fixed << std::setprecision(2)   << (ltrack.getPhi0() * 180. / M_PI);
      std::string trs  = "    HWNeuroInput2DTrack Nr. " + std::to_string(n2dtrn) + " (pt, omega, phi) = ";
      trs += padto(strpt.str(), 6) + ", " + padto(stromega.str(), 6) + ", " + padto(strphi.str(), 6) + ", " + ")";
      B2DEBUG(15, padright(trs, 100));
    }
    B2DEBUG(10,  padright("Number of HW NeuroTracks: ",
                          40)               << padright(std::to_string(m_unpackedNeuroTracks.getEntries()), 60));
    unsigned hwntrn = 0;
    for (CDCTriggerTrack& ltrack : m_unpackedNeuroTracks) {
      hwntrn++;
      std::stringstream strpt;
      std::stringstream stromega;
      std::stringstream strphi;
      std::stringstream strtheta;
      std::stringstream strz;
      std::stringstream hwomega;
      std::stringstream hwphi;
      std::stringstream hwtheta;
      std::stringstream hwz;
      strpt       << std::fixed << std::setprecision(2)   << ltrack.getPt();
      stromega    << std::fixed << std::setprecision(2)   << ltrack.getOmega();
      strphi      << std::fixed << std::setprecision(2)   << (ltrack.getPhi0() * 180. / M_PI);
      strtheta    << std::fixed << std::setprecision(2)   << (ltrack.getDirection().Theta() * 180. / M_PI);
      strz        << std::fixed << std::setprecision(2)   << ltrack.getZ0();
      hwomega     << std::fixed << std::setprecision(0)   << ltrack.getRawOmega();
      hwphi       << std::fixed << std::setprecision(0)   << ltrack.getRawPhi0();
      hwtheta     << std::fixed << std::setprecision(0)   << ltrack.getRawTheta();
      hwz         << std::fixed << std::setprecision(0)   << ltrack.getRawZ();
      std::string trs  = "    HWNeuroTrack Nr. " + std::to_string(hwntrn) + " (pt,om,phi,theta,z)=(";
      trs += padto(strpt.str(), 4) + "," + padto(stromega.str(), 6) + "," + padto(strphi.str(), 6) + "," + padto(strtheta.str(),
             6) + "," + padto(strz.str(), 6) + "),(x," + padto(hwomega.str(), 3) + "," + padto(hwphi.str(), 3) + "," + padto(hwtheta.str(),
                 3) + "," + padto(hwz.str(), 3) + ")";
      B2DEBUG(15, padright(trs, 100));
      std::string infostr = ", Found old track: ( ";
      for (bool x : ltrack.getFoundOldTrack()) {
        infostr += std::to_string(x);
      }
      infostr += "), ";
      infostr = padright(infostr, 50);
      infostr += "Drift threshold: ( ";
      for (bool x : ltrack.getDriftThreshold()) {
        infostr += std::to_string(x);
      }
      infostr += ")";
      infostr += (ltrack.getValidStereoBit()) ? " valid" : " NOT valid";
      B2DEBUG(15, padright(infostr, 100));
      std::string infostr2 = "     std. ETF vld:";
      infostr2 += std::to_string(m_eventTime.isValid());
      infostr2 += ", ETFT0: ";
      if (m_eventTime.isValid()) {
        infostr2 += std::to_string(m_eventTime->getBinnedEventT0(Const::CDC));
      } else {
        infostr2 += "  ";
      }
      infostr2 += ", ETF in CC: ";
      infostr2 += std::to_string(ltrack.getETF_unpacked());
      infostr2 += ", ETF recalculated: ";
      infostr2 += std::to_string(ltrack.getETF_recalced());
      B2DEBUG(15, padright(infostr2, 100));
      std::string info2str = "      Expert Network Number: " + std::to_string(ltrack.getExpert());
      info2str += ", TSVector: (";
      for (unsigned x : ltrack.getTSVector()) {
        info2str += std::to_string(x) + " ";
      }
      info2str += ")";
      info2str += ", Quality=";
      info2str += std::to_string(ltrack.getQualityVector());
      B2DEBUG(15, padright(info2str, 100));
      CDCTriggerTrack* ftrack = ltrack.getRelatedFrom<CDCTriggerTrack>(m_unpackedNeuroInput2DTracksName);
      CDCTriggerTrack* strack = ftrack->getRelatedTo<CDCTriggerTrack>(m_simNeuroTracksName);
      if (strack) {
        vector<float> unpackedInput =
          ltrack.getRelatedTo<CDCTriggerMLPInput>(m_unpackedNeuroInputVectorName)->getInput();
        // vector<float> simInput = ltrack.getRelatedTo<CDCTriggerMLPInput>(m_unpackedNeuroInputVectorName)->getInput();
        vector<float> simInput =
          strack->getRelatedTo<CDCTriggerMLPInput>(m_simNeuroInputVectorName)->getInput();
        B2DEBUG(20, padright("      Input Vector unpacked (id, t, alpha), sim (id, t, alpha), delta (id, t, alpha):", 100));
        for (unsigned ii = 0; ii < unpackedInput.size(); ii += 3) {
          std::string lla = "      " + std::to_string(ii / 3) + ")";
          std::string llb = "      " + std::to_string(ii / 3) + ")";
          lla += "(" + padright(std::to_string(unpackedInput[ii]), 8) + " " + padright(std::to_string(unpackedInput[ii + 1]),
                 8) + " " + padright(std::to_string(unpackedInput[ii + 2]), 8) + "),(" + padright(std::to_string(simInput[ii]),
                     8) + " " + padright(std::to_string(simInput[ii + 1]), 8) + " " + padright(std::to_string(simInput[ii + 2]),
                         8) + "),(" + padright(std::to_string(unpackedInput[ii] - simInput[ii]),
                                               8) + " " + padright(std::to_string(unpackedInput[ii + 1] - simInput[ii + 1]),
                                                                   8) + " " + padright(std::to_string(unpackedInput[ii + 2] - simInput[ii + 2]), 8) + ")";
          llb += "  (" + padright(std::to_string(int(unpackedInput[ii] * 4096)),
                                  8) + " " + padright(std::to_string(int(unpackedInput[ii + 1] * 4096)),
                                                      8) + " " + padright(std::to_string(int(unpackedInput[ii + 2] * 4096)),
                                                          8) + "),(" + padright(std::to_string(int(simInput[ii] * 4096)), 8) + " " + padright(std::to_string(int(simInput[ii + 1] * 4096)),
                                                              8) + " " + padright(std::to_string(int(simInput[ii + 2] * 4096)),
                                                                  8) + "),(" + padright(std::to_string(int(unpackedInput[ii] * 4096 - simInput[ii] * 4096)),
                                                                      8) + " " + padright(std::to_string(int(unpackedInput[ii + 1] * 4096 - simInput[ii + 1] * 4096)),
                                                                          8) + " " + padright(std::to_string(int(unpackedInput[ii + 2] * 4096 - simInput[ii + 2] * 4096)), 8) + ")";
 
          //  cout << hex;
          //  cout.setf(ios::showbase);
          //  cout << "   (" << setw(11) << (int)(unpackedInput[ii]  * 4096) << ", "                       << setw(11) << (int)(
          //         unpackedInput[ii + 1] * 4096) << ", "                            << setw(11) << (int)(unpackedInput[ii + 2] * 4096) << "), ";
          //  cout << "(" << setw(11) << (int)(simInput[ii]       * 4096) << ", "                       << setw(11) << (int)(
          //         simInput[ii + 1]      * 4096) << ", "                            << setw(11) << (int)(simInput[ii + 2]      * 4096) << "), ";
          //  cout << "(" << setw(11) << (int)(unpackedInput[ii] * 4096 - simInput[ii] * 4096)  << ", " << setw(11) << (int)(
          //         unpackedInput[ii + 1] * 4096 - simInput[ii + 1] * 4096) << ", "  << setw(11) << (int)(unpackedInput[ii + 2] * 4096 - simInput[ii +
          //             2] * 4096) << "), " << endl;
          //  cout.unsetf(ios::showbase);
          //  cout << dec;
          //std::cout << " (" << simInput[ii] / unpackedInput[ii] << std::endl << ", " << simInput[ii + 1] /  unpackedInput[ii + 1] << ", " <<
          //          simInput[ii + 2] / unpackedInput[ii + 2] << ")" << std::endl;
          B2DEBUG(30, padright(lla, 100));
          B2DEBUG(20, padright(llb, 100));
        }
      }
    }
 
 
  }
  if (!m_limitedoutput && m_simNeuroTracksName != "") {
    B2DEBUG(10,  padright("Number of SW NeuroTracks: ", 40)             << padright(std::to_string(m_simNeuroTracks.getEntries()), 60));
    unsigned swntrn = 0;
    for (CDCTriggerTrack& ltrack : m_simNeuroTracks) {
      swntrn++;
      std::stringstream strpt;
      std::stringstream stromega;
      std::stringstream strphi;
      std::stringstream strtheta;
      std::stringstream strz;
      std::stringstream strquality;
      strpt       << std::fixed << std::setprecision(2)   << ltrack.getPt();
      stromega    << std::fixed << std::setprecision(2)   << ltrack.getOmega();
      strphi      << std::fixed << std::setprecision(2)   << (ltrack.getPhi0() * 180. / M_PI);
      strtheta    << std::fixed << std::setprecision(2)   << (ltrack.getDirection().Theta() * 180. / M_PI);
      strz        << std::fixed << std::setprecision(2)   << ltrack.getZ0();
      strquality  << std::fixed << ltrack.getQualityVector();
 
      std::string trs  = "    SWNeuroTrack Nr. " + std::to_string(swntrn) + " (pt,om,phi,theta,z)=(";
      trs += padto(strpt.str(), 5) + "," + padto(stromega.str(), 5) + "," + padto(strphi.str(), 6) + "," + padto(strtheta.str(),
             6) + ", " + padto(strz.str(), 6) + ")"
             +  " Q=" + strquality.str();
      B2DEBUG(15, padright(trs, 100));
    }
  }
  if (!m_limitedoutput && m_simSegmentHitsName != "") {
    std::string axsw = std::to_string(axswts) + " / " + std::to_string(stswts);
    B2DEBUG(10,  padright("Number of AllSW TS (Axial/Stereo): ", 40)    << padright(axsw, 60));
    if (m_sim2DTracksSWTSName != "" && m_simNeuroTracksSWTSSW2DName != "") {
      B2DEBUG(10,  padright("Number of SW 2DFinderSWTSTracks: ", 40)          << padright(std::to_string(m_sim2DTracksSWTS.getEntries()),
              60));
      unsigned sf2dtrn = 0;
      for (CDCTriggerTrack& ltrack : m_sim2DTracksSWTS) {
        sf2dtrn++;
        std::stringstream strpt;
        std::stringstream stromega;
        std::stringstream strphi;
        strpt       << std::fixed << std::setprecision(2)   << ltrack.getPt();
        stromega    << std::fixed << std::setprecision(2)   << ltrack.getOmega();
        strphi      << std::fixed << std::setprecision(2)   << (ltrack.getPhi0() * 180. / M_PI);
        std::string trs  = "    SW2DFinderSWTSTrack Nr. " + std::to_string(sf2dtrn) + " (pt, omega, phi) = ";
        trs += padto(strpt.str(), 6) + ", " + padto(stromega.str(), 6) + ", " + padto(strphi.str(), 6) + ")";
        B2DEBUG(15, padright(trs, 100));
      }
      B2DEBUG(10,  padright("Number of SW NeuroSWTSSW2DTracks: ",
                            40)          << padright(std::to_string(m_simNeuroTracksSWTSSW2D.getEntries()), 60));
      unsigned aswntrn = 0;
      for (CDCTriggerTrack& ltrack : m_simNeuroTracksSWTSSW2D) {
        aswntrn++;
        std::stringstream strpt;
        std::stringstream stromega;
        std::stringstream strphi;
        std::stringstream strtheta;
        std::stringstream strz;
        strpt       << std::fixed << std::setprecision(2)   << ltrack.getPt();
        stromega    << std::fixed << std::setprecision(2)   << ltrack.getOmega();
        strphi      << std::fixed << std::setprecision(2)   << (ltrack.getPhi0() * 180. / M_PI);
        strtheta    << std::fixed << std::setprecision(2)   << (ltrack.getDirection().Theta() * 180. / M_PI);
        strz        << std::fixed << std::setprecision(2)   << ltrack.getZ0();
        std::string trs  = "    SWNeuroSWTSSW2DTrack Nr. " + std::to_string(aswntrn) + " (pt, omega, phi, theta, z) = ";
        trs += padto(strpt.str(), 6) + ", " + padto(stromega.str(), 6) + ", " + padto(strphi.str(), 6) + ", " + padto(strtheta.str(),
               6) + ", " + padto(strz.str(), 6) + ")";
        B2DEBUG(15, padright(trs, 100));
        vector<float> simInputSWTSSW2D =
          ltrack.getRelatedTo<CDCTriggerMLPInput>(m_simNeuroInputVectorSWTSSW2DName)->getInput();
        B2DEBUG(20, padright("      Input Vector simulated (id, t, alpha):", 100));
        for (unsigned ii = 0; ii < simInputSWTSSW2D.size(); ii += 3) {
          std::string lla = "      " + std::to_string(ii / 3) + ")";
          std::string llb = "      " + std::to_string(ii / 3) + ")";
          lla += "(" + padright(std::to_string(simInputSWTSSW2D[ii]),
                                8) + " " + padright(std::to_string(simInputSWTSSW2D[ii + 1]), 8) + " " + padright(std::to_string(simInputSWTSSW2D[ii + 2]),
                                    8) + ")";
          llb += "  (" + padright(std::to_string(int(simInputSWTSSW2D[ii] * 4096)),
                                  8) + " " + padright(std::to_string(int(simInputSWTSSW2D[ii + 1] * 4096)),
                                                      8) + " " + padright(std::to_string(int(simInputSWTSSW2D[ii + 2] * 4096)),
                                                          8) + ")";
          B2DEBUG(30, padright(lla, 100));
          B2DEBUG(20, padright(llb, 100));
        }
 
      }
    }
  }
  if (m_recoTracksName != "") {
    B2DEBUG(10,  padright("Number of  RecoTracks: ", 40)                  << padright(std::to_string(m_RecoTracks.getEntries()), 60));
    unsigned recotrn = 0;
    for (RecoTrack& ltrack : m_RecoTracks) {
      double phi0Target = 0;
      double invptTarget = 0;
      double thetaTarget = 0;
      double zTarget = 0;
      double d0Target = 0;
      bool foundValidRep = false;
      for (genfit::AbsTrackRep* rep : ltrack.getRepresentations()) {
        if (!ltrack.wasFitSuccessful(rep))
          continue;
        // get state (position, momentum etc.) from hit closest to IP and
        // extrapolate to z-axis (may throw an exception -> continue to next representation)
        try {
          genfit::MeasuredStateOnPlane state =
            ltrack.getMeasuredStateOnPlaneClosestTo(ROOT::Math::XYZVector(0, 0, 0), rep);
          rep->extrapolateToLine(state, TVector3(0, 0, -1000), TVector3(0, 0, 2000));
          // TODO check after matching
          //  // flip tracks if necessary, such that trigger tracks and reco tracks
          //  // point in the same direction
          //  if (state.getMom().Dot(m_tracks[itrack]->getDirection()) < 0) {
          //    state.setPosMom(state.getPos(), -state.getMom());
          //    state.setChargeSign(-state.getCharge());
          //  }
          // get track parameters
          phi0Target = state.getMom().Phi() * 180. / M_PI ;
          if (phi0Target < 0.) {phi0Target = phi0Target + 360. ;}
          invptTarget = state.getCharge() * state.getMom().Pt();
          thetaTarget = acos(state.getMom().CosTheta()) * 180 / M_PI;
          zTarget = state.getPos().Z();
          d0Target = state.getPos().Perp();
        } catch (...) {
          continue;
        }
        // break loop
        foundValidRep = true;
        break;
      }
      if (!foundValidRep) {
        B2DEBUG(150, "No valid representation found for RecoTrack, skipping.");
        continue;
      } else {
        if (m_maxRecoZDist != -1.0 and abs(zTarget) > m_maxRecoZDist) {
          B2DEBUG(150, "RecoTrack not close to IP (maxRecoZDist), zReco = " << zTarget);
          continue;
        }
        if (m_maxRecoD0Dist != -1.0 and abs(d0Target) > m_maxRecoD0Dist) {
          B2DEBUG(150, "RecoTrack not close to the z axis (maxRecoD0Dist), d0Reco = " << d0Target);
          continue;
        }
      }
      recotrn++;
      std::stringstream strpt;
      std::stringstream stromega;
      std::stringstream strphi;
      std::stringstream strtheta;
      std::stringstream strz;
      strpt       << std::fixed << std::setprecision(2)   << invptTarget;
      stromega    << std::fixed << std::setprecision(2)   << d0Target;
      strphi      << std::fixed << std::setprecision(2)   << phi0Target;
      strtheta    << std::fixed << std::setprecision(2)   << thetaTarget;
      strz        << std::fixed << std::setprecision(2)   << zTarget;
      std::string trs  = "    RecoTrack Nr. " + std::to_string(recotrn) + " (invpt, d0, phi, theta, z) = ";
      trs += padto(strpt.str(), 6) + ", " + padto(stromega.str(), 6) + ", " + padto(strphi.str(), 6) + ", " + padto(strtheta.str(),
             6) + ", " + padto(strz.str(), 6) + ")";
      B2DEBUG(15, padright(trs, 100));
    }
  }
  B2DEBUG(10, padright(" ", 100));
  B2DEBUG(10, padright(" ", 100));
  if (m_unpackedSegmentHitsName != "") {
    B2DEBUG(15, padright("Detailed information about HW TS ", 100));
    B2DEBUG(15, padright(" ", 100));
    B2DEBUG(15, padright(hwtsstring, 100));
    for (auto x : hwtsoutput) {
      B2DEBUG(15, padright(x.strline, 100));
    }
    B2DEBUG(15, padright(" ", 100));
  }
  if (!m_limitedoutput && m_simSegmentHitsName != "") {
    B2DEBUG(15, padright(" ", 100));
    B2DEBUG(15, padright("Detailed information about SW TS ", 100));
    B2DEBUG(15, padright(" ", 100));
    B2DEBUG(15, padright(swtsstring, 100));
    for (auto x : swtsoutput) {
      B2DEBUG(15, padright(x.strline, 100));
    }
  }
}

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://xwiki.desy.de/xwiki/rest/p/f4fa4/#HModuleDevelopment
 
)");
  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;
}

getPattern()

unsigned getPattern ( CDCTriggerTrack *  track, std::string  hitCollectionName  )

inlineprivate

get pattern of the track

Definition at line 72 of file CDCTriggerNeuroDQMModule.h.

    {
      unsigned pattern = 0;
      for (const CDCTriggerSegmentHit& hit : track->getRelationsTo<CDCTriggerSegmentHit>(hitCollectionName)) {
        unsigned sl = hit.getISuperLayer();
        if (sl % 2 == 1) pattern |= (1 << ((sl - 1) / 2));
      }
      return pattern;
    }

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();
}

have_relation()

bool have_relation ( const CDCTriggerTrack &  track, const CDCTriggerSegmentHit &  hit, std::string &  arrayname  )

inlineprivate

Definition at line 109 of file CDCTriggerNeuroDQMModule.h.

    {
      bool related = false;
      for (const CDCTriggerSegmentHit& ts : track.getRelationsTo<CDCTriggerSegmentHit>(arrayname)) {
        if (&ts == &hit) {related = true;}
      }
      return related;
    }

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://xwiki.desy.de/xwiki/rest/p/a94f2 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

Module functions.

Reimplemented from HistoModule.

Definition at line 1263 of file CDCTriggerNeuroDQMModule.cc.

{
  // Register histograms (calls back defineHisto)
  REG_HISTOGRAM
  m_eventTime.isRequired("CDCTriggerNeuroETFT0");
  if (m_showRecoTracks && m_recoTracksName == "") {
    m_recoTracksName = "RecoTracks";
  }
  if (m_unpackedSegmentHitsName != "") {
    m_unpackedSegmentHits.isRequired(m_unpackedSegmentHitsName);
  }
  if (m_unpacked2DTracksName != "") {
    m_unpacked2DTracks.isRequired(m_unpacked2DTracksName);
  }
  if (m_unpackedNeuroTracksName != "") {
    m_unpackedNeuroTracks.isRequired(m_unpackedNeuroTracksName);
    m_unpackedNeuroInput2DTracks.isRequired(m_unpackedNeuroInput2DTracksName);
    m_unpackedNeuroInputSegments.isRequired(m_unpackedNeuroInputSegmentsName);
    m_unpackedNeuroTracks.requireRelationTo(m_unpackedNeuroInputSegments);
    m_unpackedNeuroInput2DTracks.requireRelationTo(m_unpackedNeuroTracks);
    m_unpackedNeuroInputVectorName = m_unpackedNeuroTracksName + "Input";
    m_unpackedNeuroInputVector.isRequired(m_unpackedNeuroInputVectorName);
    m_unpackedNeuroTracks.requireRelationTo(m_unpackedNeuroInputVector);
  }
 
  if (!m_limitedoutput && m_simNeuroTracksName != "") {
    m_simNeuroInputVectorName = m_simNeuroTracksName + "Input";
    m_simNeuroTracks.isRequired(m_simNeuroTracksName);
    m_simNeuroInputVector.isRequired(m_simNeuroInputVectorName);
    m_unpackedNeuroInput2DTracks.requireRelationTo(m_simNeuroTracks);
    m_simNeuroTracks.requireRelationTo(m_simNeuroInputVector);
    m_simNeuroTracks.requireRelationTo(m_unpackedNeuroInputSegments);
  }
  if (!m_limitedoutput && m_simNeuroTracksSWTSSW2DName != "") {
    m_simSegmentHits.isRequired(m_simSegmentHitsName);
    m_sim2DTracksSWTS.isRequired(m_sim2DTracksSWTSName);
    m_simNeuroInputVectorSWTSSW2DName = m_simNeuroTracksSWTSSW2DName + "Input";
    m_simNeuroTracksSWTSSW2D.isRequired(m_simNeuroTracksSWTSSW2DName);
    m_simNeuroInputVectorSWTSSW2D.isRequired(m_simNeuroInputVectorSWTSSW2DName);
    m_simNeuroTracksSWTSSW2D.requireRelationTo(m_simNeuroInputVectorSWTSSW2D);
    m_simNeuroTracksSWTSSW2D.requireRelationTo(m_simSegmentHits);
    m_sim2DTracksSWTS.requireRelationTo(m_simNeuroTracksSWTSSW2D);
  }
  if (m_recoTracksName != "") {
    m_RecoTracks.isRequired("RecoTracks");
    m_RecoTracks.requireRelationTo(m_unpackedNeuroTracks);
  }
  if (!m_limitedoutput && m_recoTracksName != "" && m_simNeuroTracksName != "") {
    m_RecoTracks.requireRelationTo(m_simNeuroTracks);
  }
  if (!m_limitedoutput && m_recoTracksName != "" && m_simNeuroTracksSWTSSW2DName != "") {
    m_RecoTracks.requireRelationTo(m_simNeuroTracksSWTSSW2D);
  }
}

isValidPattern()

bool isValidPattern ( unsigned  pattern )

inlineprivate

Validity of the pattern.

Definition at line 82 of file CDCTriggerNeuroDQMModule.h.

    {
      bool valid = false;
      switch (pattern) {
        case 7:
        case 11:
        case 13:
        case 14:
        case 15:
          valid = true;
      }
      return valid;
    }

padright()

std::string padright ( std::string  s, unsigned  l  )

inlineprivate

Definition at line 102 of file CDCTriggerNeuroDQMModule.h.

    {
      if (s.size() < l) {
        s.insert(s.end(), l - s.size(), ' ');
      }
      return s;
    }

padto()

std::string padto ( std::string  s, unsigned  l  )

inlineprivate

Definition at line 95 of file CDCTriggerNeuroDQMModule.h.

    {
      if (s.size() < l) {
        s.insert(s.begin(), l - s.size(), ' ');
      }
      return s;
    }

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;
}

sorted_insert()

void sorted_insert ( TSLines &  lines, TSLine &  line, std::string &  arrayname, std::string &  firstsortarray, std::string &  secondsortarray  )

inlineprivate

Definition at line 117 of file CDCTriggerNeuroDQMModule.h.

    {
      bool inserted = false;
      bool related = false;
      TSLines::iterator it = lines.begin();
      for (const CDCTriggerTrack& track : line.hit->getRelationsFrom<CDCTriggerTrack>(firstsortarray)) {
        if (!inserted) {
          for (TSLines::iterator i = lines.begin(); i < lines.end(); ++i) {
            if (i->hit->getISuperLayer() % 2 != line.hit->getISuperLayer() % 2) {
              continue;
            }
            if (have_relation(track, *(i->hit), arrayname)) {
              it = i;
              related = true;
              if (i->hit->getSegmentID() > line.hit->getSegmentID()) {
                inserted = true;
                break;
              }
            }
          }
        } else { break; }
      }
      if (related) {
        if (!inserted) {++it; }
        lines.insert(it, line);
      } else {
        for (const CDCTriggerTrack& track : line.hit->getRelationsFrom<CDCTriggerTrack>(secondsortarray)) {
          if (!inserted) {
            for (TSLines::iterator i = it; i < lines.end(); ++i) {
              if (i->hit->getISuperLayer() % 2 != line.hit->getISuperLayer() % 2) {
                continue;
              }
              if (have_relation(track, *(i->hit), arrayname)) {
                it = i;
                related = true;
                if (i->hit->getSegmentID() > line.hit->getSegmentID()) {
                  inserted = true;
                  break;
                }
              }
            }
          } else { break; }
        }
        if (related) {
          if (!inserted) {++it; }
          lines.insert(it, line);
        } else {
          lines.push_back(line);
        }
      }
    }

terminate()

void terminate ( void  )

overridevirtual

Function to terminate module.

Reimplemented from HistoModule.

Definition at line 3267 of file CDCTriggerNeuroDQMModule.cc.

{
}

Member Data Documentation

m_2DHWInTSFoundT_Layer0

TH1F* m_2DHWInTSFoundT_Layer0 = nullptr

private

Found time of 2D track segments in layer 0.

Definition at line 423 of file CDCTriggerNeuroDQMModule.h.

m_2DHWInTSFoundT_Layer2

TH1F* m_2DHWInTSFoundT_Layer2 = nullptr

private

Found time of 2D track segments in layer 2.

Definition at line 424 of file CDCTriggerNeuroDQMModule.h.

m_2DHWInTSFoundT_Layer4

TH1F* m_2DHWInTSFoundT_Layer4 = nullptr

private

Found time of 2D track segments in layer 4.

Definition at line 425 of file CDCTriggerNeuroDQMModule.h.

m_2DHWInTSFoundT_Layer6

TH1F* m_2DHWInTSFoundT_Layer6 = nullptr

private

Found time of 2D track segments in layer 6.

Definition at line 426 of file CDCTriggerNeuroDQMModule.h.

m_2DHWInTSFoundT_Layer8

TH1F* m_2DHWInTSFoundT_Layer8 = nullptr

private

Found time of 2D track segments in layer 8.

Definition at line 427 of file CDCTriggerNeuroDQMModule.h.

m_2DHWInTSLR_Layer0

TH1F* m_2DHWInTSLR_Layer0 = nullptr

private

Left/Right of 2D track segments in layer 0.

Definition at line 434 of file CDCTriggerNeuroDQMModule.h.

m_2DHWInTSLR_Layer2

TH1F* m_2DHWInTSLR_Layer2 = nullptr

private

Left/Right of 2D track segments in layer 2.

Definition at line 435 of file CDCTriggerNeuroDQMModule.h.

m_2DHWInTSLR_Layer4

TH1F* m_2DHWInTSLR_Layer4 = nullptr

private

Left/Right of 2D track segments in layer 4.

Definition at line 436 of file CDCTriggerNeuroDQMModule.h.

m_2DHWInTSLR_Layer6

TH1F* m_2DHWInTSLR_Layer6 = nullptr

private

Left/Right of 2D track segments in layer 6.

Definition at line 437 of file CDCTriggerNeuroDQMModule.h.

m_2DHWInTSLR_Layer8

TH1F* m_2DHWInTSLR_Layer8 = nullptr

private

Left/Right of 2D track segments in layer 8.

Definition at line 438 of file CDCTriggerNeuroDQMModule.h.

m_2DHWInTSPrioB_Layer0

TH1F* m_2DHWInTSPrioB_Layer0 = nullptr

private

Priority bits of 2D track segments in layer 0.

Definition at line 429 of file CDCTriggerNeuroDQMModule.h.

m_2DHWInTSPrioB_Layer2

TH1F* m_2DHWInTSPrioB_Layer2 = nullptr

private

Priority bits of 2D track segments in layer 2.

Definition at line 430 of file CDCTriggerNeuroDQMModule.h.

m_2DHWInTSPrioB_Layer4

TH1F* m_2DHWInTSPrioB_Layer4 = nullptr

private

Priority bits of 2D track segments in layer 4.

Definition at line 431 of file CDCTriggerNeuroDQMModule.h.

m_2DHWInTSPrioB_Layer6

TH1F* m_2DHWInTSPrioB_Layer6 = nullptr

private

Priority bits of 2D track segments in layer 6.

Definition at line 432 of file CDCTriggerNeuroDQMModule.h.

m_2DHWInTSPrioB_Layer8

TH1F* m_2DHWInTSPrioB_Layer8 = nullptr

private

Priority bits of 2D track segments in layer 8.

Definition at line 433 of file CDCTriggerNeuroDQMModule.h.

m_2DHWInTSPrioT_Layer0

TH1F* m_2DHWInTSPrioT_Layer0 = nullptr

private

Priority time of 2D track segments in layer 0.

Definition at line 418 of file CDCTriggerNeuroDQMModule.h.

m_2DHWInTSPrioT_Layer2

TH1F* m_2DHWInTSPrioT_Layer2 = nullptr

private

Priority time of 2D track segments in layer 2.

Definition at line 419 of file CDCTriggerNeuroDQMModule.h.

m_2DHWInTSPrioT_Layer4

TH1F* m_2DHWInTSPrioT_Layer4 = nullptr

private

Priority time of 2D track segments in layer 4.

Definition at line 420 of file CDCTriggerNeuroDQMModule.h.

m_2DHWInTSPrioT_Layer6

TH1F* m_2DHWInTSPrioT_Layer6 = nullptr

private

Priority time of 2D track segments in layer 6.

Definition at line 421 of file CDCTriggerNeuroDQMModule.h.

m_2DHWInTSPrioT_Layer8

TH1F* m_2DHWInTSPrioT_Layer8 = nullptr

private

Priority time of 2D track segments in layer 8.

Definition at line 422 of file CDCTriggerNeuroDQMModule.h.

m_2DHWOutInvPt

TH1F* m_2DHWOutInvPt = nullptr

private

Inverse Pt of 2dtracks.

Definition at line 264 of file CDCTriggerNeuroDQMModule.h.

m_2DHWOutm_time

TH1F* m_2DHWOutm_time = nullptr

private

m_time 2dtracks

Definition at line 266 of file CDCTriggerNeuroDQMModule.h.

m_2DHWOutPhi0

TH1F* m_2DHWOutPhi0 = nullptr

private

Phi0 of 2dtracks.

Definition at line 265 of file CDCTriggerNeuroDQMModule.h.

m_2DHWOutTrackCount

TH1F* m_2DHWOutTrackCount = nullptr

private

number of 2dtracks per event

Definition at line 267 of file CDCTriggerNeuroDQMModule.h.

m_2DSWOutInvPt

TH1F* m_2DSWOutInvPt = nullptr

private

Inverse Pt of 2dtracks (sw TS sw 2D)

Definition at line 280 of file CDCTriggerNeuroDQMModule.h.

m_2DSWOutm_time

TH1F* m_2DSWOutm_time = nullptr

private

m_time 2dtracks (sw TS sw 2D)

Definition at line 282 of file CDCTriggerNeuroDQMModule.h.

m_2DSWOutPhi0

TH1F* m_2DSWOutPhi0 = nullptr

private

Phi0 of 2dtracks (sw TS sw 2D)

Definition at line 281 of file CDCTriggerNeuroDQMModule.h.

m_2DSWOutTrackCount

TH1F* m_2DSWOutTrackCount = nullptr

private

number of 2dtracks per event (sw TS sw 2D)

Definition at line 283 of file CDCTriggerNeuroDQMModule.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_DeltaRecoHWCosTheta

TH1F* m_DeltaRecoHWCosTheta = nullptr

private

matched to HW reconstructed cos(theta)

Definition at line 685 of file CDCTriggerNeuroDQMModule.h.

m_DeltaRecoHWInvPt

TH1F* m_DeltaRecoHWInvPt = nullptr

private

matched to HW reconstructed inverse Pt

Definition at line 686 of file CDCTriggerNeuroDQMModule.h.

m_DeltaRecoHWPhi

TH1F* m_DeltaRecoHWPhi = nullptr

private

matched to HW reconstructed phi

Definition at line 687 of file CDCTriggerNeuroDQMModule.h.

m_DeltaRecoHWZ

TH1F* m_DeltaRecoHWZ = nullptr

private

matched to HW reconstructed z

Definition at line 684 of file CDCTriggerNeuroDQMModule.h.

m_DeltaRecoSWCosTheta

TH1F* m_DeltaRecoSWCosTheta = nullptr

private

matched to SW reconstructed cos(theta)

Definition at line 708 of file CDCTriggerNeuroDQMModule.h.

m_DeltaRecoSWInvPt

TH1F* m_DeltaRecoSWInvPt = nullptr

private

matched to SW reconstructed inverse Pt

Definition at line 709 of file CDCTriggerNeuroDQMModule.h.

m_DeltaRecoSWPhi

TH1F* m_DeltaRecoSWPhi = nullptr

private

matched to SW reconstructed phi

Definition at line 710 of file CDCTriggerNeuroDQMModule.h.

m_DeltaRecoSWTSSW2DCosTheta

TH1F* m_DeltaRecoSWTSSW2DCosTheta = nullptr

private

matched to SWTSSW2DSWNN reconstructed cos(theta)

Definition at line 732 of file CDCTriggerNeuroDQMModule.h.

m_DeltaRecoSWTSSW2DInvPt

TH1F* m_DeltaRecoSWTSSW2DInvPt = nullptr

private

matched to SWTSSW2DSWNN reconstructed inverse Pt

Definition at line 733 of file CDCTriggerNeuroDQMModule.h.

m_DeltaRecoSWTSSW2DPhi

TH1F* m_DeltaRecoSWTSSW2DPhi = nullptr

private

matched to SWTSSW2DSWNN reconstructed phi

Definition at line 734 of file CDCTriggerNeuroDQMModule.h.

m_DeltaRecoSWTSSW2DZ

TH1F* m_DeltaRecoSWTSSW2DZ = nullptr

private

matched to SWTSSW2DSWNN reconstructed z

Definition at line 731 of file CDCTriggerNeuroDQMModule.h.

m_DeltaRecoSWZ

TH1F* m_DeltaRecoSWZ = nullptr

private

matched to SW reconstructed z

Definition at line 707 of file CDCTriggerNeuroDQMModule.h.

m_description

std::string m_description

privateinherited

The description of the module.

Definition at line 511 of file Module.h.

m_eventTime

StoreObjPtr<BinnedEventT0> m_eventTime

private

storeobjpointer for event time

Definition at line 246 of file CDCTriggerNeuroDQMModule.h.

m_hasReturnValue

bool m_hasReturnValue

privateinherited

True, if the return value is set.

Definition at line 518 of file Module.h.

m_histogramDirectoryName

std::string m_histogramDirectoryName

private

Name of the histogram directory in ROOT file.

Definition at line 169 of file CDCTriggerNeuroDQMModule.h.

m_limitedoutput

bool m_limitedoutput

private

Switch to supress output for dqm online module.

Definition at line 171 of file CDCTriggerNeuroDQMModule.h.

m_logConfig

LogConfig m_logConfig

privateinherited

The log system configuration of the module.

Definition at line 514 of file Module.h.

m_maxRecoD0Dist

double m_maxRecoD0Dist

private

Select only RecoTracks with a maximum d0 distance to the z axis.

-1 for all tracks

Definition at line 202 of file CDCTriggerNeuroDQMModule.h.

m_maxRecoZDist

double m_maxRecoZDist

private

Select only RecoTracks with a maximum z distance to the IP.

-1 for all tracks

Definition at line 200 of file CDCTriggerNeuroDQMModule.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_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_neuroDeltaInputAlpha

TH1F* m_neuroDeltaInputAlpha = nullptr

private

unpacked alpha input - TSIM alpha input

Definition at line 302 of file CDCTriggerNeuroDQMModule.h.

m_neuroDeltaInputID

TH1F* m_neuroDeltaInputID = nullptr

private

unpacked ID input - TSIM ID input

Definition at line 300 of file CDCTriggerNeuroDQMModule.h.

m_neuroDeltaInputT

TH1F* m_neuroDeltaInputT = nullptr

private

unpacked time input - TSIM time input

Definition at line 301 of file CDCTriggerNeuroDQMModule.h.

m_neuroDeltaSector

TH1F* m_neuroDeltaSector = nullptr

private

unpacked sector - TSIM sector

Definition at line 304 of file CDCTriggerNeuroDQMModule.h.

m_neuroDeltaTheta

TH1F* m_neuroDeltaTheta = nullptr

private

unpacked theta - TSIM theta

Definition at line 296 of file CDCTriggerNeuroDQMModule.h.

m_neuroDeltaTSID

TH1F* m_neuroDeltaTSID = nullptr

private

unpacked selected TSID - TSIM selected TSID

Definition at line 303 of file CDCTriggerNeuroDQMModule.h.

m_neuroDeltaZ

TH1F* m_neuroDeltaZ = nullptr

private

unpacked z - TSIM z

Definition at line 295 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInInvPt

TH1F* m_neuroHWInInvPt = nullptr

private

Inverse Pt distribution from incoming 2dtrack.

Definition at line 258 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInm_time

TH1F* m_neuroHWInm_time = nullptr

private

m_time distribution from incoming 2dtracks

Definition at line 260 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInPhi0

TH1F* m_neuroHWInPhi0 = nullptr

private

Phi0 of incoming 2dtrack.

Definition at line 259 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputAlpha_Layer0

TH1F* m_neuroHWInputAlpha_Layer0 = nullptr

private

unpacked alpha input in layer 0

Definition at line 573 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputAlpha_Layer1

TH1F* m_neuroHWInputAlpha_Layer1 = nullptr

private

unpacked alpha input in layer 1

Definition at line 576 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputAlpha_Layer2

TH1F* m_neuroHWInputAlpha_Layer2 = nullptr

private

unpacked alpha input in layer 2

Definition at line 579 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputAlpha_Layer3

TH1F* m_neuroHWInputAlpha_Layer3 = nullptr

private

unpacked alpha input in layer 3

Definition at line 582 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputAlpha_Layer4

TH1F* m_neuroHWInputAlpha_Layer4 = nullptr

private

unpacked alpha input in layer 4

Definition at line 585 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputAlpha_Layer5

TH1F* m_neuroHWInputAlpha_Layer5 = nullptr

private

unpacked alpha input in layer 5

Definition at line 588 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputAlpha_Layer6

TH1F* m_neuroHWInputAlpha_Layer6 = nullptr

private

unpacked alpha input in layer 6

Definition at line 591 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputAlpha_Layer7

TH1F* m_neuroHWInputAlpha_Layer7 = nullptr

private

unpacked alpha input in layer 7

Definition at line 594 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputAlpha_Layer8

TH1F* m_neuroHWInputAlpha_Layer8 = nullptr

private

unpacked alpha input in layer 8

Definition at line 597 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputID_Layer0

TH1F* m_neuroHWInputID_Layer0 = nullptr

private

unpacked id input in layer 0

Definition at line 571 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputID_Layer1

TH1F* m_neuroHWInputID_Layer1 = nullptr

private

unpacked id input in layer 1

Definition at line 574 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputID_Layer2

TH1F* m_neuroHWInputID_Layer2 = nullptr

private

unpacked id input in layer 2

Definition at line 577 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputID_Layer3

TH1F* m_neuroHWInputID_Layer3 = nullptr

private

unpacked id input in layer 3

Definition at line 580 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputID_Layer4

TH1F* m_neuroHWInputID_Layer4 = nullptr

private

unpacked id input in layer 4

Definition at line 583 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputID_Layer5

TH1F* m_neuroHWInputID_Layer5 = nullptr

private

unpacked id input in layer 5

Definition at line 586 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputID_Layer6

TH1F* m_neuroHWInputID_Layer6 = nullptr

private

unpacked id input in layer 6

Definition at line 589 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputID_Layer7

TH1F* m_neuroHWInputID_Layer7 = nullptr

private

unpacked id input in layer 7

Definition at line 592 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputID_Layer8

TH1F* m_neuroHWInputID_Layer8 = nullptr

private

unpacked id input in layer 8

Definition at line 595 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputT_Layer0

TH1F* m_neuroHWInputT_Layer0 = nullptr

private

unpacked time input in layer 0

Definition at line 572 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputT_Layer1

TH1F* m_neuroHWInputT_Layer1 = nullptr

private

unpacked time input in layer 1

Definition at line 575 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputT_Layer2

TH1F* m_neuroHWInputT_Layer2 = nullptr

private

unpacked time input in layer 2

Definition at line 578 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputT_Layer3

TH1F* m_neuroHWInputT_Layer3 = nullptr

private

unpacked time input in layer 3

Definition at line 581 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputT_Layer4

TH1F* m_neuroHWInputT_Layer4 = nullptr

private

unpacked time input in layer 4

Definition at line 584 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputT_Layer5

TH1F* m_neuroHWInputT_Layer5 = nullptr

private

unpacked time input in layer 5

Definition at line 587 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputT_Layer6

TH1F* m_neuroHWInputT_Layer6 = nullptr

private

unpacked time input in layer 6

Definition at line 590 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputT_Layer7

TH1F* m_neuroHWInputT_Layer7 = nullptr

private

unpacked time input in layer 7

Definition at line 593 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInputT_Layer8

TH1F* m_neuroHWInputT_Layer8 = nullptr

private

unpacked time input in layer 8

Definition at line 596 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTrackCount

TH1F* m_neuroHWInTrackCount = nullptr

private

number of neuro input 2dtracks per event

Definition at line 261 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSCount

TH1F* m_neuroHWInTSCount = nullptr

private

number of TS per track

Definition at line 332 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSFoundT_Layer0

TH1F* m_neuroHWInTSFoundT_Layer0 = nullptr

private

Found time of track segments in layer 0.

Definition at line 342 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSFoundT_Layer1

TH1F* m_neuroHWInTSFoundT_Layer1 = nullptr

private

Found time of track segments in layer 1.

Definition at line 343 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSFoundT_Layer2

TH1F* m_neuroHWInTSFoundT_Layer2 = nullptr

private

Found time of track segments in layer 2.

Definition at line 344 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSFoundT_Layer3

TH1F* m_neuroHWInTSFoundT_Layer3 = nullptr

private

Found time of track segments in layer 3.

Definition at line 345 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSFoundT_Layer4

TH1F* m_neuroHWInTSFoundT_Layer4 = nullptr

private

Found time of track segments in layer 4.

Definition at line 346 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSFoundT_Layer5

TH1F* m_neuroHWInTSFoundT_Layer5 = nullptr

private

Found time of track segments in layer 5.

Definition at line 347 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSFoundT_Layer6

TH1F* m_neuroHWInTSFoundT_Layer6 = nullptr

private

Found time of track segments in layer 6.

Definition at line 348 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSFoundT_Layer7

TH1F* m_neuroHWInTSFoundT_Layer7 = nullptr

private

Found time of track segments in layer 7.

Definition at line 349 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSFoundT_Layer8

TH1F* m_neuroHWInTSFoundT_Layer8 = nullptr

private

Found time of track segments in layer 8.

Definition at line 350 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSID

TH1F* m_neuroHWInTSID = nullptr

private

ID of incoming track segments.

Definition at line 331 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSLR_Layer0

TH1F* m_neuroHWInTSLR_Layer0 = nullptr

private

Left/Right of track segments in layer 0.

Definition at line 362 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSLR_Layer1

TH1F* m_neuroHWInTSLR_Layer1 = nullptr

private

Left/Right of track segments in layer 1.

Definition at line 363 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSLR_Layer2

TH1F* m_neuroHWInTSLR_Layer2 = nullptr

private

Left/Right of track segments in layer 2.

Definition at line 364 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSLR_Layer3

TH1F* m_neuroHWInTSLR_Layer3 = nullptr

private

Left/Right of track segments in layer 3.

Definition at line 365 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSLR_Layer4

TH1F* m_neuroHWInTSLR_Layer4 = nullptr

private

Left/Right of track segments in layer 4.

Definition at line 366 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSLR_Layer5

TH1F* m_neuroHWInTSLR_Layer5 = nullptr

private

Left/Right of track segments in layer 5.

Definition at line 367 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSLR_Layer6

TH1F* m_neuroHWInTSLR_Layer6 = nullptr

private

Left/Right of track segments in layer 6.

Definition at line 368 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSLR_Layer7

TH1F* m_neuroHWInTSLR_Layer7 = nullptr

private

Left/Right of track segments in layer 7.

Definition at line 369 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSLR_Layer8

TH1F* m_neuroHWInTSLR_Layer8 = nullptr

private

Left/Right of track segments in layer 8.

Definition at line 370 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSPrioB_Layer0

TH1F* m_neuroHWInTSPrioB_Layer0 = nullptr

private

Priority bits of track segments in layer 0.

Definition at line 352 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSPrioB_Layer1

TH1F* m_neuroHWInTSPrioB_Layer1 = nullptr

private

Priority bits of track segments in layer 1.

Definition at line 353 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSPrioB_Layer2

TH1F* m_neuroHWInTSPrioB_Layer2 = nullptr

private

Priority bits of track segments in layer 2.

Definition at line 354 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSPrioB_Layer3

TH1F* m_neuroHWInTSPrioB_Layer3 = nullptr

private

Priority bits of track segments in layer 3.

Definition at line 355 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSPrioB_Layer4

TH1F* m_neuroHWInTSPrioB_Layer4 = nullptr

private

Priority bits of track segments in layer 4.

Definition at line 356 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSPrioB_Layer5

TH1F* m_neuroHWInTSPrioB_Layer5 = nullptr

private

Priority bits of track segments in layer 5.

Definition at line 357 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSPrioB_Layer6

TH1F* m_neuroHWInTSPrioB_Layer6 = nullptr

private

Priority bits of track segments in layer 6.

Definition at line 358 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSPrioB_Layer7

TH1F* m_neuroHWInTSPrioB_Layer7 = nullptr

private

Priority bits of track segments in layer 7.

Definition at line 359 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSPrioB_Layer8

TH1F* m_neuroHWInTSPrioB_Layer8 = nullptr

private

Priority bits of track segments in layer 8.

Definition at line 360 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSPrioT_Layer0

TH1F* m_neuroHWInTSPrioT_Layer0 = nullptr

private

Priority time of track segments in layer 0.

Definition at line 333 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSPrioT_Layer1

TH1F* m_neuroHWInTSPrioT_Layer1 = nullptr

private

Priority time of track segments in layer 1.

Definition at line 334 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSPrioT_Layer2

TH1F* m_neuroHWInTSPrioT_Layer2 = nullptr

private

Priority time of track segments in layer 2.

Definition at line 335 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSPrioT_Layer3

TH1F* m_neuroHWInTSPrioT_Layer3 = nullptr

private

Priority time of track segments in layer 3.

Definition at line 336 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSPrioT_Layer4

TH1F* m_neuroHWInTSPrioT_Layer4 = nullptr

private

Priority time of track segments in layer 4.

Definition at line 337 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSPrioT_Layer5

TH1F* m_neuroHWInTSPrioT_Layer5 = nullptr

private

Priority time of track segments in layer 5.

Definition at line 338 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSPrioT_Layer6

TH1F* m_neuroHWInTSPrioT_Layer6 = nullptr

private

Priority time of track segments in layer 6.

Definition at line 339 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSPrioT_Layer7

TH1F* m_neuroHWInTSPrioT_Layer7 = nullptr

private

Priority time of track segments in layer 7.

Definition at line 340 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInTSPrioT_Layer8

TH1F* m_neuroHWInTSPrioT_Layer8 = nullptr

private

Priority time of track segments in layer 8.

Definition at line 341 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWInVs2DOutTrackCount

TH1F* m_neuroHWInVs2DOutTrackCount = nullptr

private

neuroHWInTrackCount - 2DHWOutTrackCountput

Definition at line 268 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutCosTheta

TH1F* m_neuroHWOutCosTheta = nullptr

private

cos theta distribution of unpacked neuro tracks

Definition at line 249 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutHitPattern

TH1F* m_neuroHWOutHitPattern = nullptr

private

stereo hit pattern of simulated neuro tracks (hw TS hw 2D sw NN)

Definition at line 253 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutInvPt

TH1F* m_neuroHWOutInvPt = nullptr

private

Inverse Pt distribution of unpacked neuro tracks.

Definition at line 250 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutm_time

TH1F* m_neuroHWOutm_time = nullptr

private

m_time distribution of unpacked neuro tracks

Definition at line 254 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutPhi0

TH1F* m_neuroHWOutPhi0 = nullptr

private

phi distribution of unpacked neuro tracks

Definition at line 252 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutPt

TH1F* m_neuroHWOutPt = nullptr

private

Pt distribution of unpacked neuro tracks.

Definition at line 251 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutQuad0CosTheta

TH1F* m_neuroHWOutQuad0CosTheta = nullptr

private

cos theta distribution from unpacker (quadrant 0)

Definition at line 314 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutQuad0InvPt

TH1F* m_neuroHWOutQuad0InvPt = nullptr

private

Inverse Pt distribution from unpacker (quadrant 0)

Definition at line 316 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutQuad0Phi0

TH1F* m_neuroHWOutQuad0Phi0 = nullptr

private

phi distribution from unpacker (quadrant 0)

Definition at line 315 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutQuad0Z

TH1F* m_neuroHWOutQuad0Z = nullptr

private

z distribution from unpacker (quadrant 0)

Definition at line 313 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutQuad1CosTheta

TH1F* m_neuroHWOutQuad1CosTheta = nullptr

private

cos theta distribution from unpacker (quadrant 1)

Definition at line 318 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutQuad1InvPt

TH1F* m_neuroHWOutQuad1InvPt = nullptr

private

Inverse Pt distribution from unpacker (quadrant 1)

Definition at line 320 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutQuad1Phi0

TH1F* m_neuroHWOutQuad1Phi0 = nullptr

private

phi distribution from unpacker (quadrant 1)

Definition at line 319 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutQuad1Z

TH1F* m_neuroHWOutQuad1Z = nullptr

private

z distribution from unpacker (quadrant 1)

Definition at line 317 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutQuad2CosTheta

TH1F* m_neuroHWOutQuad2CosTheta = nullptr

private

cos theta distribution from unpacker (quadrant 2)

Definition at line 322 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutQuad2InvPt

TH1F* m_neuroHWOutQuad2InvPt = nullptr

private

Inverse Pt distribution from unpacker (quadrant 2)

Definition at line 324 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutQuad2Phi0

TH1F* m_neuroHWOutQuad2Phi0 = nullptr

private

phi distribution from unpacker (quadrant 2)

Definition at line 323 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutQuad2Z

TH1F* m_neuroHWOutQuad2Z = nullptr

private

z distribution from unpacker (quadrant 2)

Definition at line 321 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutQuad3CosTheta

TH1F* m_neuroHWOutQuad3CosTheta = nullptr

private

cos theta distribution from unpacker (quadrant 3)

Definition at line 326 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutQuad3InvPt

TH1F* m_neuroHWOutQuad3InvPt = nullptr

private

Inverse Pt distribution from unpacker (quadrant 3)

Definition at line 328 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutQuad3Phi0

TH1F* m_neuroHWOutQuad3Phi0 = nullptr

private

phi distribution from unpacker (quadrant 3)

Definition at line 327 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutQuad3Z

TH1F* m_neuroHWOutQuad3Z = nullptr

private

z distribution from unpacker (quadrant 3)

Definition at line 325 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutQuad5CosTheta

TH1F* m_neuroHWOutQuad5CosTheta = nullptr

private

cos theta distribution from unpacker (no quadrant set)

Definition at line 310 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutQuad5InvPt

TH1F* m_neuroHWOutQuad5InvPt = nullptr

private

Inverse Pt distribution from unpacker (no quadrant set)

Definition at line 312 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutQuad5Phi0

TH1F* m_neuroHWOutQuad5Phi0 = nullptr

private

phi distribution from unpacker (no quadrant set)

Definition at line 311 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutQuad5Z

TH1F* m_neuroHWOutQuad5Z = nullptr

private

z distribution from unpacker (no quadrant set)

Definition at line 309 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutTrackCount

TH1F* m_neuroHWOutTrackCount = nullptr

private

number of unpacked tracks per event

Definition at line 255 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutVsInTrackCount

TH1F* m_neuroHWOutVsInTrackCount = nullptr

private

neuroHWOutTracks - neuroHWInTrackCount

Definition at line 262 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWOutZ

TH1F* m_neuroHWOutZ = nullptr

private

z distribution of unpacked neuro tracks

Definition at line 248 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSector

TH1F* m_neuroHWSector = nullptr

private

sector of unpacked neuro tracks

Definition at line 256 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSCount

TH1F* m_neuroHWSelTSCount = nullptr

private

number of selected TS per SL

Definition at line 374 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSFoundT_Layer0

TH1F* m_neuroHWSelTSFoundT_Layer0 = nullptr

private

Found time of selected track segments in layer 0.

Definition at line 384 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSFoundT_Layer1

TH1F* m_neuroHWSelTSFoundT_Layer1 = nullptr

private

Found time of selected track segments in layer 1.

Definition at line 385 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSFoundT_Layer2

TH1F* m_neuroHWSelTSFoundT_Layer2 = nullptr

private

Found time of selected track segments in layer 2.

Definition at line 386 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSFoundT_Layer3

TH1F* m_neuroHWSelTSFoundT_Layer3 = nullptr

private

Found time of selected track segments in layer 3.

Definition at line 387 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSFoundT_Layer4

TH1F* m_neuroHWSelTSFoundT_Layer4 = nullptr

private

Found time of selected track segments in layer 4.

Definition at line 388 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSFoundT_Layer5

TH1F* m_neuroHWSelTSFoundT_Layer5 = nullptr

private

Found time of selected track segments in layer 5.

Definition at line 389 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSFoundT_Layer6

TH1F* m_neuroHWSelTSFoundT_Layer6 = nullptr

private

Found time of selected track segments in layer 6.

Definition at line 390 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSFoundT_Layer7

TH1F* m_neuroHWSelTSFoundT_Layer7 = nullptr

private

Found time of selected track segments in layer 7.

Definition at line 391 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSFoundT_Layer8

TH1F* m_neuroHWSelTSFoundT_Layer8 = nullptr

private

Found time of selected track segments in layer 8.

Definition at line 392 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSID

TH1F* m_neuroHWSelTSID = nullptr

private

ID of selected track segments.

Definition at line 373 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSLR_Layer0

TH1F* m_neuroHWSelTSLR_Layer0 = nullptr

private

Left/Right of track segments in layer 0.

Definition at line 404 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSLR_Layer1

TH1F* m_neuroHWSelTSLR_Layer1 = nullptr

private

Left/Right of track segments in layer 1.

Definition at line 405 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSLR_Layer2

TH1F* m_neuroHWSelTSLR_Layer2 = nullptr

private

Left/Right of track segments in layer 2.

Definition at line 406 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSLR_Layer3

TH1F* m_neuroHWSelTSLR_Layer3 = nullptr

private

Left/Right of track segments in layer 3.

Definition at line 407 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSLR_Layer4

TH1F* m_neuroHWSelTSLR_Layer4 = nullptr

private

Left/Right of track segments in layer 4.

Definition at line 408 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSLR_Layer5

TH1F* m_neuroHWSelTSLR_Layer5 = nullptr

private

Left/Right of track segments in layer 5.

Definition at line 409 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSLR_Layer6

TH1F* m_neuroHWSelTSLR_Layer6 = nullptr

private

Left/Right of track segments in layer 6.

Definition at line 410 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSLR_Layer7

TH1F* m_neuroHWSelTSLR_Layer7 = nullptr

private

Left/Right of track segments in layer 7.

Definition at line 411 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSLR_Layer8

TH1F* m_neuroHWSelTSLR_Layer8 = nullptr

private

Left/Right of track segments in layer 8.

Definition at line 412 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSPrioB_Layer0

TH1F* m_neuroHWSelTSPrioB_Layer0 = nullptr

private

Priority bits of track segments in layer 0.

Definition at line 394 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSPrioB_Layer1

TH1F* m_neuroHWSelTSPrioB_Layer1 = nullptr

private

Priority bits of track segments in layer 1.

Definition at line 395 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSPrioB_Layer2

TH1F* m_neuroHWSelTSPrioB_Layer2 = nullptr

private

Priority bits of track segments in layer 2.

Definition at line 396 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSPrioB_Layer3

TH1F* m_neuroHWSelTSPrioB_Layer3 = nullptr

private

Priority bits of track segments in layer 3.

Definition at line 397 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSPrioB_Layer4

TH1F* m_neuroHWSelTSPrioB_Layer4 = nullptr

private

Priority bits of track segments in layer 4.

Definition at line 398 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSPrioB_Layer5

TH1F* m_neuroHWSelTSPrioB_Layer5 = nullptr

private

Priority bits of track segments in layer 5.

Definition at line 399 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSPrioB_Layer6

TH1F* m_neuroHWSelTSPrioB_Layer6 = nullptr

private

Priority bits of track segments in layer 6.

Definition at line 400 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSPrioB_Layer7

TH1F* m_neuroHWSelTSPrioB_Layer7 = nullptr

private

Priority bits of track segments in layer 7.

Definition at line 401 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSPrioB_Layer8

TH1F* m_neuroHWSelTSPrioB_Layer8 = nullptr

private

Priority bits of track segments in layer 8.

Definition at line 402 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSPrioT_Layer0

TH1F* m_neuroHWSelTSPrioT_Layer0 = nullptr

private

Priority time of selected track segments in layer 0.

Definition at line 375 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSPrioT_Layer1

TH1F* m_neuroHWSelTSPrioT_Layer1 = nullptr

private

Priority time of selected track segments in layer 1.

Definition at line 376 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSPrioT_Layer2

TH1F* m_neuroHWSelTSPrioT_Layer2 = nullptr

private

Priority time of selected track segments in layer 2.

Definition at line 377 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSPrioT_Layer3

TH1F* m_neuroHWSelTSPrioT_Layer3 = nullptr

private

Priority time of selected track segments in layer 3.

Definition at line 378 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSPrioT_Layer4

TH1F* m_neuroHWSelTSPrioT_Layer4 = nullptr

private

Priority time of selected track segments in layer 4.

Definition at line 379 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSPrioT_Layer5

TH1F* m_neuroHWSelTSPrioT_Layer5 = nullptr

private

Priority time of selected track segments in layer 5.

Definition at line 380 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSPrioT_Layer6

TH1F* m_neuroHWSelTSPrioT_Layer6 = nullptr

private

Priority time of selected track segments in layer 6.

Definition at line 381 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSPrioT_Layer7

TH1F* m_neuroHWSelTSPrioT_Layer7 = nullptr

private

Priority time of selected track segments in layer 7.

Definition at line 382 of file CDCTriggerNeuroDQMModule.h.

m_neuroHWSelTSPrioT_Layer8

TH1F* m_neuroHWSelTSPrioT_Layer8 = nullptr

private

Priority time of selected track segments in layer 8.

Definition at line 383 of file CDCTriggerNeuroDQMModule.h.

m_neuroRecoHWOutCosTheta

TH1F* m_neuroRecoHWOutCosTheta = nullptr

private

reco matched cos theta distribution from unpacker

Definition at line 676 of file CDCTriggerNeuroDQMModule.h.

m_neuroRecoHWOutHitPattern

TH1F* m_neuroRecoHWOutHitPattern = nullptr

private

reco matched stereo hit pattern from unpacker

Definition at line 679 of file CDCTriggerNeuroDQMModule.h.

m_neuroRecoHWOutInvPt

TH1F* m_neuroRecoHWOutInvPt = nullptr

private

reco matched Inverse Pt distribution from unpacker

Definition at line 677 of file CDCTriggerNeuroDQMModule.h.

m_neuroRecoHWOutPhi0

TH1F* m_neuroRecoHWOutPhi0 = nullptr

private

reco matched phi distribution from unpacker

Definition at line 678 of file CDCTriggerNeuroDQMModule.h.

m_neuroRecoHWOutTrackCount

TH1F* m_neuroRecoHWOutTrackCount = nullptr

private

reco matched number of unpacked and matched tracks per event

Definition at line 680 of file CDCTriggerNeuroDQMModule.h.

m_neuroRecoHWOutZ

TH1F* m_neuroRecoHWOutZ = nullptr

private

reco matched z distribution from unpacker

Definition at line 675 of file CDCTriggerNeuroDQMModule.h.

m_neuroRecoHWSector

TH1F* m_neuroRecoHWSector = nullptr

private

reco matched NN sector from unpacker

Definition at line 681 of file CDCTriggerNeuroDQMModule.h.

m_neuroRecoSWOutCosTheta

TH1F* m_neuroRecoSWOutCosTheta = nullptr

private

reco matched cos theta distribution from simulation (hw TS hw 2D sw NN)

Definition at line 699 of file CDCTriggerNeuroDQMModule.h.

m_neuroRecoSWOutHitPattern

TH1F* m_neuroRecoSWOutHitPattern = nullptr

private

reco matched stereo hit pattern of simulated neuro tracks (hw TS hw 2D sw NN)

Definition at line 702 of file CDCTriggerNeuroDQMModule.h.

m_neuroRecoSWOutInvPt

TH1F* m_neuroRecoSWOutInvPt = nullptr

private

reco matched Inverse Pt distribution from simulation (hw TS hw 2D sw NN)

Definition at line 700 of file CDCTriggerNeuroDQMModule.h.

m_neuroRecoSWOutPhi0

TH1F* m_neuroRecoSWOutPhi0 = nullptr

private

reco matched phi distribution from simulation (hw TS hw 2D sw NN)

Definition at line 701 of file CDCTriggerNeuroDQMModule.h.

m_neuroRecoSWOutTrackCount

TH1F* m_neuroRecoSWOutTrackCount = nullptr

private

reco matched number of simulated tracks per event (hw TS hw 2D sw NN)

Definition at line 703 of file CDCTriggerNeuroDQMModule.h.

m_neuroRecoSWOutZ

TH1F* m_neuroRecoSWOutZ = nullptr

private

reco matched z distribution from simulation (hw TS hw 2D sw NN)

Definition at line 698 of file CDCTriggerNeuroDQMModule.h.

m_neuroRecoSWSector

TH1F* m_neuroRecoSWSector = nullptr

private

reco matched NN sector from simulation (hw TS hw 2D sw NN)

Definition at line 704 of file CDCTriggerNeuroDQMModule.h.

m_neuroRecoSWTSSW2DOutCosTheta

TH1F* m_neuroRecoSWTSSW2DOutCosTheta = nullptr

private

reco matched cos theta distribution from simulation (sw TS sw 2D sw NN)

Definition at line 722 of file CDCTriggerNeuroDQMModule.h.

m_neuroRecoSWTSSW2DOutHitPattern

TH1F* m_neuroRecoSWTSSW2DOutHitPattern

private

Initial value:

=
      nullptr

reco matched stereo hit pattern of simulated neuro tracks (sw TS sw 2D sw NN)

Definition at line 725 of file CDCTriggerNeuroDQMModule.h.

m_neuroRecoSWTSSW2DOutInvPt

TH1F* m_neuroRecoSWTSSW2DOutInvPt = nullptr

private

reco matched Inverse Pt distribution from simulation (sw TS sw 2D sw NN)

Definition at line 723 of file CDCTriggerNeuroDQMModule.h.

m_neuroRecoSWTSSW2DOutPhi0

TH1F* m_neuroRecoSWTSSW2DOutPhi0 = nullptr

private

reco matched phi distribution from simulation (sw TS sw 2D sw NN)

Definition at line 724 of file CDCTriggerNeuroDQMModule.h.

m_neuroRecoSWTSSW2DOutTrackCount

TH1F* m_neuroRecoSWTSSW2DOutTrackCount = nullptr

private

reco matched number of simulated tracks per event (sw TS sw 2D sw NN)

Definition at line 727 of file CDCTriggerNeuroDQMModule.h.

m_neuroRecoSWTSSW2DOutZ

TH1F* m_neuroRecoSWTSSW2DOutZ = nullptr

private

reco matched z distribution from simulation (sw TS sw 2D sw NN)

Definition at line 721 of file CDCTriggerNeuroDQMModule.h.

m_neuroRecoSWTSSW2DSector

TH1F* m_neuroRecoSWTSSW2DSector = nullptr

private

reco matched NN sector from simulation (sw TS sw 2D sw NN)

Definition at line 728 of file CDCTriggerNeuroDQMModule.h.

m_neuroScatterTheta

TH2F* m_neuroScatterTheta = nullptr

private

unpacked theta vs TSIM theta, scatter plot

Definition at line 298 of file CDCTriggerNeuroDQMModule.h.

m_neuroScatterZ

TH2F* m_neuroScatterZ = nullptr

private

unpacked z vs TSIM z, scatter plot

Definition at line 297 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputAlpha_Layer0

TH1F* m_neuroSWInputAlpha_Layer0 = nullptr

private

simulated alpha input in layer 0

Definition at line 602 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputAlpha_Layer1

TH1F* m_neuroSWInputAlpha_Layer1 = nullptr

private

simulated alpha input in layer 1

Definition at line 605 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputAlpha_Layer2

TH1F* m_neuroSWInputAlpha_Layer2 = nullptr

private

simulated alpha input in layer 2

Definition at line 608 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputAlpha_Layer3

TH1F* m_neuroSWInputAlpha_Layer3 = nullptr

private

simulated alpha input in layer 3

Definition at line 611 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputAlpha_Layer4

TH1F* m_neuroSWInputAlpha_Layer4 = nullptr

private

simulated alpha input in layer 4

Definition at line 614 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputAlpha_Layer5

TH1F* m_neuroSWInputAlpha_Layer5 = nullptr

private

simulated alpha input in layer 5

Definition at line 617 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputAlpha_Layer6

TH1F* m_neuroSWInputAlpha_Layer6 = nullptr

private

simulated alpha input in layer 6

Definition at line 620 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputAlpha_Layer7

TH1F* m_neuroSWInputAlpha_Layer7 = nullptr

private

simulated alpha input in layer 7

Definition at line 623 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputAlpha_Layer8

TH1F* m_neuroSWInputAlpha_Layer8 = nullptr

private

simulated alpha input in layer 8

Definition at line 626 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputID_Layer0

TH1F* m_neuroSWInputID_Layer0 = nullptr

private

simulated id input in layer 0

Definition at line 600 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputID_Layer1

TH1F* m_neuroSWInputID_Layer1 = nullptr

private

simulated id input in layer 1

Definition at line 603 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputID_Layer2

TH1F* m_neuroSWInputID_Layer2 = nullptr

private

simulated id input in layer 2

Definition at line 606 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputID_Layer3

TH1F* m_neuroSWInputID_Layer3 = nullptr

private

simulated id input in layer 3

Definition at line 609 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputID_Layer4

TH1F* m_neuroSWInputID_Layer4 = nullptr

private

simulated id input in layer 4

Definition at line 612 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputID_Layer5

TH1F* m_neuroSWInputID_Layer5 = nullptr

private

simulated id input in layer 5

Definition at line 615 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputID_Layer6

TH1F* m_neuroSWInputID_Layer6 = nullptr

private

simulated id input in layer 6

Definition at line 618 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputID_Layer7

TH1F* m_neuroSWInputID_Layer7 = nullptr

private

simulated id input in layer 7

Definition at line 621 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputID_Layer8

TH1F* m_neuroSWInputID_Layer8 = nullptr

private

simulated id input in layer 8

Definition at line 624 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputT_Layer0

TH1F* m_neuroSWInputT_Layer0 = nullptr

private

simulated time input in layer 0

Definition at line 601 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputT_Layer1

TH1F* m_neuroSWInputT_Layer1 = nullptr

private

simulated time input in layer 1

Definition at line 604 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputT_Layer2

TH1F* m_neuroSWInputT_Layer2 = nullptr

private

simulated time input in layer 2

Definition at line 607 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputT_Layer3

TH1F* m_neuroSWInputT_Layer3 = nullptr

private

simulated time input in layer 3

Definition at line 610 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputT_Layer4

TH1F* m_neuroSWInputT_Layer4 = nullptr

private

simulated time input in layer 4

Definition at line 613 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputT_Layer5

TH1F* m_neuroSWInputT_Layer5 = nullptr

private

simulated time input in layer 5

Definition at line 616 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputT_Layer6

TH1F* m_neuroSWInputT_Layer6 = nullptr

private

simulated time input in layer 6

Definition at line 619 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputT_Layer7

TH1F* m_neuroSWInputT_Layer7 = nullptr

private

simulated time input in layer 7

Definition at line 622 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWInputT_Layer8

TH1F* m_neuroSWInputT_Layer8 = nullptr

private

simulated time input in layer 8

Definition at line 625 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWOutCosTheta

TH1F* m_neuroSWOutCosTheta = nullptr

private

cos theta distribution from simulation (hw TS hw 2D sw NN)

Definition at line 272 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWOutHitPattern

TH1F* m_neuroSWOutHitPattern = nullptr

private

stereo hit pattern of simulated neuro tracks (hw TS hw 2D sw NN)

Definition at line 275 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWOutInvPt

TH1F* m_neuroSWOutInvPt = nullptr

private

Inverse Pt distribution from simulation (hw TS hw 2D sw NN)

Definition at line 273 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWOutPhi0

TH1F* m_neuroSWOutPhi0 = nullptr

private

phi distribution from simulation (hw TS hw 2D sw NN)

Definition at line 274 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWOutTrackCount

TH1F* m_neuroSWOutTrackCount = nullptr

private

number of simulated tracks per event (hw TS hw 2D sw NN)

Definition at line 276 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWOutZ

TH1F* m_neuroSWOutZ = nullptr

private

z distribution from simulation (hw TS hw 2D sw NN)

Definition at line 271 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSector

TH1F* m_neuroSWSector = nullptr

private

NN sector from simulation (hw TS hw 2D sw NN)

Definition at line 277 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSCount

TH1F* m_neuroSWSelTSCount = nullptr

private

number of TS per SL

Definition at line 443 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSFoundT_Layer0

TH1F* m_neuroSWSelTSFoundT_Layer0 = nullptr

private

Found time of track segments in layer 0.

Definition at line 453 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSFoundT_Layer1

TH1F* m_neuroSWSelTSFoundT_Layer1 = nullptr

private

Found time of track segments in layer 1.

Definition at line 454 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSFoundT_Layer2

TH1F* m_neuroSWSelTSFoundT_Layer2 = nullptr

private

Found time of track segments in layer 2.

Definition at line 455 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSFoundT_Layer3

TH1F* m_neuroSWSelTSFoundT_Layer3 = nullptr

private

Found time of track segments in layer 3.

Definition at line 456 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSFoundT_Layer4

TH1F* m_neuroSWSelTSFoundT_Layer4 = nullptr

private

Found time of track segments in layer 4.

Definition at line 457 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSFoundT_Layer5

TH1F* m_neuroSWSelTSFoundT_Layer5 = nullptr

private

Found time of track segments in layer 5.

Definition at line 458 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSFoundT_Layer6

TH1F* m_neuroSWSelTSFoundT_Layer6 = nullptr

private

Found time of track segments in layer 6.

Definition at line 459 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSFoundT_Layer7

TH1F* m_neuroSWSelTSFoundT_Layer7 = nullptr

private

Found time of track segments in layer 7.

Definition at line 460 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSFoundT_Layer8

TH1F* m_neuroSWSelTSFoundT_Layer8 = nullptr

private

Found time of track segments in layer 8.

Definition at line 461 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSID

TH1F* m_neuroSWSelTSID = nullptr

private

ID of incoming track segments.

Definition at line 442 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSLR_Layer0

TH1F* m_neuroSWSelTSLR_Layer0 = nullptr

private

Left/Right of track segments in layer 0.

Definition at line 473 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSLR_Layer1

TH1F* m_neuroSWSelTSLR_Layer1 = nullptr

private

Left/Right of track segments in layer 1.

Definition at line 474 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSLR_Layer2

TH1F* m_neuroSWSelTSLR_Layer2 = nullptr

private

Left/Right of track segments in layer 2.

Definition at line 475 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSLR_Layer3

TH1F* m_neuroSWSelTSLR_Layer3 = nullptr

private

Left/Right of track segments in layer 3.

Definition at line 476 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSLR_Layer4

TH1F* m_neuroSWSelTSLR_Layer4 = nullptr

private

Left/Right of track segments in layer 4.

Definition at line 477 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSLR_Layer5

TH1F* m_neuroSWSelTSLR_Layer5 = nullptr

private

Left/Right of track segments in layer 5.

Definition at line 478 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSLR_Layer6

TH1F* m_neuroSWSelTSLR_Layer6 = nullptr

private

Left/Right of track segments in layer 6.

Definition at line 479 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSLR_Layer7

TH1F* m_neuroSWSelTSLR_Layer7 = nullptr

private

Left/Right of track segments in layer 7.

Definition at line 480 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSLR_Layer8

TH1F* m_neuroSWSelTSLR_Layer8 = nullptr

private

Left/Right of track segments in layer 8.

Definition at line 481 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSPrioB_Layer0

TH1F* m_neuroSWSelTSPrioB_Layer0 = nullptr

private

Priority bits of track segments in layer 0.

Definition at line 463 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSPrioB_Layer1

TH1F* m_neuroSWSelTSPrioB_Layer1 = nullptr

private

Priority bits of track segments in layer 1.

Definition at line 464 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSPrioB_Layer2

TH1F* m_neuroSWSelTSPrioB_Layer2 = nullptr

private

Priority bits of track segments in layer 2.

Definition at line 465 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSPrioB_Layer3

TH1F* m_neuroSWSelTSPrioB_Layer3 = nullptr

private

Priority bits of track segments in layer 3.

Definition at line 466 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSPrioB_Layer4

TH1F* m_neuroSWSelTSPrioB_Layer4 = nullptr

private

Priority bits of track segments in layer 4.

Definition at line 467 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSPrioB_Layer5

TH1F* m_neuroSWSelTSPrioB_Layer5 = nullptr

private

Priority bits of track segments in layer 5.

Definition at line 468 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSPrioB_Layer6

TH1F* m_neuroSWSelTSPrioB_Layer6 = nullptr

private

Priority bits of track segments in layer 6.

Definition at line 469 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSPrioB_Layer7

TH1F* m_neuroSWSelTSPrioB_Layer7 = nullptr

private

Priority bits of track segments in layer 7.

Definition at line 470 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSPrioB_Layer8

TH1F* m_neuroSWSelTSPrioB_Layer8 = nullptr

private

Priority bits of track segments in layer 8.

Definition at line 471 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSPrioT_Layer0

TH1F* m_neuroSWSelTSPrioT_Layer0 = nullptr

private

Priority time of track segments in layer 0.

Definition at line 444 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSPrioT_Layer1

TH1F* m_neuroSWSelTSPrioT_Layer1 = nullptr

private

Priority time of track segments in layer 1.

Definition at line 445 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSPrioT_Layer2

TH1F* m_neuroSWSelTSPrioT_Layer2 = nullptr

private

Priority time of track segments in layer 2.

Definition at line 446 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSPrioT_Layer3

TH1F* m_neuroSWSelTSPrioT_Layer3 = nullptr

private

Priority time of track segments in layer 3.

Definition at line 447 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSPrioT_Layer4

TH1F* m_neuroSWSelTSPrioT_Layer4 = nullptr

private

Priority time of track segments in layer 4.

Definition at line 448 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSPrioT_Layer5

TH1F* m_neuroSWSelTSPrioT_Layer5 = nullptr

private

Priority time of track segments in layer 5.

Definition at line 449 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSPrioT_Layer6

TH1F* m_neuroSWSelTSPrioT_Layer6 = nullptr

private

Priority time of track segments in layer 6.

Definition at line 450 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSPrioT_Layer7

TH1F* m_neuroSWSelTSPrioT_Layer7 = nullptr

private

Priority time of track segments in layer 7.

Definition at line 451 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWSelTSPrioT_Layer8

TH1F* m_neuroSWSelTSPrioT_Layer8 = nullptr

private

Priority time of track segments in layer 8.

Definition at line 452 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputAlpha_Layer0

TH1F* m_neuroSWTSSW2DInputAlpha_Layer0 = nullptr

private

simulated alpha input in layer 0

Definition at line 631 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputAlpha_Layer1

TH1F* m_neuroSWTSSW2DInputAlpha_Layer1 = nullptr

private

simulated alpha input in layer 1

Definition at line 634 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputAlpha_Layer2

TH1F* m_neuroSWTSSW2DInputAlpha_Layer2 = nullptr

private

simulated alpha input in layer 2

Definition at line 637 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputAlpha_Layer3

TH1F* m_neuroSWTSSW2DInputAlpha_Layer3 = nullptr

private

simulated alpha input in layer 3

Definition at line 640 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputAlpha_Layer4

TH1F* m_neuroSWTSSW2DInputAlpha_Layer4 = nullptr

private

simulated alpha input in layer 4

Definition at line 643 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputAlpha_Layer5

TH1F* m_neuroSWTSSW2DInputAlpha_Layer5 = nullptr

private

simulated alpha input in layer 5

Definition at line 646 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputAlpha_Layer6

TH1F* m_neuroSWTSSW2DInputAlpha_Layer6 = nullptr

private

simulated alpha input in layer 6

Definition at line 649 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputAlpha_Layer7

TH1F* m_neuroSWTSSW2DInputAlpha_Layer7 = nullptr

private

simulated alpha input in layer 7

Definition at line 652 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputAlpha_Layer8

TH1F* m_neuroSWTSSW2DInputAlpha_Layer8 = nullptr

private

simulated alpha input in layer 8

Definition at line 655 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputID_Layer0

TH1F* m_neuroSWTSSW2DInputID_Layer0 = nullptr

private

simulated id input in layer 0

Definition at line 629 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputID_Layer1

TH1F* m_neuroSWTSSW2DInputID_Layer1 = nullptr

private

simulated id input in layer 1

Definition at line 632 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputID_Layer2

TH1F* m_neuroSWTSSW2DInputID_Layer2 = nullptr

private

simulated id input in layer 2

Definition at line 635 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputID_Layer3

TH1F* m_neuroSWTSSW2DInputID_Layer3 = nullptr

private

simulated id input in layer 3

Definition at line 638 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputID_Layer4

TH1F* m_neuroSWTSSW2DInputID_Layer4 = nullptr

private

simulated id input in layer 4

Definition at line 641 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputID_Layer5

TH1F* m_neuroSWTSSW2DInputID_Layer5 = nullptr

private

simulated id input in layer 5

Definition at line 644 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputID_Layer6

TH1F* m_neuroSWTSSW2DInputID_Layer6 = nullptr

private

simulated id input in layer 6

Definition at line 647 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputID_Layer7

TH1F* m_neuroSWTSSW2DInputID_Layer7 = nullptr

private

simulated id input in layer 7

Definition at line 650 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputID_Layer8

TH1F* m_neuroSWTSSW2DInputID_Layer8 = nullptr

private

simulated id input in layer 8

Definition at line 653 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputT_Layer0

TH1F* m_neuroSWTSSW2DInputT_Layer0 = nullptr

private

simulated time input in layer 0

Definition at line 630 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputT_Layer1

TH1F* m_neuroSWTSSW2DInputT_Layer1 = nullptr

private

simulated time input in layer 1

Definition at line 633 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputT_Layer2

TH1F* m_neuroSWTSSW2DInputT_Layer2 = nullptr

private

simulated time input in layer 2

Definition at line 636 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputT_Layer3

TH1F* m_neuroSWTSSW2DInputT_Layer3 = nullptr

private

simulated time input in layer 3

Definition at line 639 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputT_Layer4

TH1F* m_neuroSWTSSW2DInputT_Layer4 = nullptr

private

simulated time input in layer 4

Definition at line 642 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputT_Layer5

TH1F* m_neuroSWTSSW2DInputT_Layer5 = nullptr

private

simulated time input in layer 5

Definition at line 645 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputT_Layer6

TH1F* m_neuroSWTSSW2DInputT_Layer6 = nullptr

private

simulated time input in layer 6

Definition at line 648 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputT_Layer7

TH1F* m_neuroSWTSSW2DInputT_Layer7 = nullptr

private

simulated time input in layer 7

Definition at line 651 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInputT_Layer8

TH1F* m_neuroSWTSSW2DInputT_Layer8 = nullptr

private

simulated time input in layer 8

Definition at line 654 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSCount

TH1F* m_neuroSWTSSW2DInTSCount = nullptr

private

number of TS per event

Definition at line 486 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSFoundT_Layer0

TH1F* m_neuroSWTSSW2DInTSFoundT_Layer0 = nullptr

private

Found time of track segments in layer 0.

Definition at line 496 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSFoundT_Layer1

TH1F* m_neuroSWTSSW2DInTSFoundT_Layer1 = nullptr

private

Found time of track segments in layer 1.

Definition at line 497 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSFoundT_Layer2

TH1F* m_neuroSWTSSW2DInTSFoundT_Layer2 = nullptr

private

Found time of track segments in layer 2.

Definition at line 498 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSFoundT_Layer3

TH1F* m_neuroSWTSSW2DInTSFoundT_Layer3 = nullptr

private

Found time of track segments in layer 3.

Definition at line 499 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSFoundT_Layer4

TH1F* m_neuroSWTSSW2DInTSFoundT_Layer4 = nullptr

private

Found time of track segments in layer 4.

Definition at line 500 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSFoundT_Layer5

TH1F* m_neuroSWTSSW2DInTSFoundT_Layer5 = nullptr

private

Found time of track segments in layer 5.

Definition at line 501 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSFoundT_Layer6

TH1F* m_neuroSWTSSW2DInTSFoundT_Layer6 = nullptr

private

Found time of track segments in layer 6.

Definition at line 502 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSFoundT_Layer7

TH1F* m_neuroSWTSSW2DInTSFoundT_Layer7 = nullptr

private

Found time of track segments in layer 7.

Definition at line 503 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSFoundT_Layer8

TH1F* m_neuroSWTSSW2DInTSFoundT_Layer8 = nullptr

private

Found time of track segments in layer 8.

Definition at line 504 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSID

TH1F* m_neuroSWTSSW2DInTSID = nullptr

private

ID of incoming track segments.

Definition at line 485 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSLR_Layer0

TH1F* m_neuroSWTSSW2DInTSLR_Layer0 = nullptr

private

Left/Right of track segments in layer 0.

Definition at line 516 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSLR_Layer1

TH1F* m_neuroSWTSSW2DInTSLR_Layer1 = nullptr

private

Left/Right of track segments in layer 1.

Definition at line 517 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSLR_Layer2

TH1F* m_neuroSWTSSW2DInTSLR_Layer2 = nullptr

private

Left/Right of track segments in layer 2.

Definition at line 518 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSLR_Layer3

TH1F* m_neuroSWTSSW2DInTSLR_Layer3 = nullptr

private

Left/Right of track segments in layer 3.

Definition at line 519 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSLR_Layer4

TH1F* m_neuroSWTSSW2DInTSLR_Layer4 = nullptr

private

Left/Right of track segments in layer 4.

Definition at line 520 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSLR_Layer5

TH1F* m_neuroSWTSSW2DInTSLR_Layer5 = nullptr

private

Left/Right of track segments in layer 5.

Definition at line 521 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSLR_Layer6

TH1F* m_neuroSWTSSW2DInTSLR_Layer6 = nullptr

private

Left/Right of track segments in layer 6.

Definition at line 522 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSLR_Layer7

TH1F* m_neuroSWTSSW2DInTSLR_Layer7 = nullptr

private

Left/Right of track segments in layer 7.

Definition at line 523 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSLR_Layer8

TH1F* m_neuroSWTSSW2DInTSLR_Layer8 = nullptr

private

Left/Right of track segments in layer 8.

Definition at line 524 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSPrioB_Layer0

TH1F* m_neuroSWTSSW2DInTSPrioB_Layer0 = nullptr

private

Priority bits of track segments in layer 0.

Definition at line 506 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSPrioB_Layer1

TH1F* m_neuroSWTSSW2DInTSPrioB_Layer1 = nullptr

private

Priority bits of track segments in layer 1.

Definition at line 507 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSPrioB_Layer2

TH1F* m_neuroSWTSSW2DInTSPrioB_Layer2 = nullptr

private

Priority bits of track segments in layer 2.

Definition at line 508 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSPrioB_Layer3

TH1F* m_neuroSWTSSW2DInTSPrioB_Layer3 = nullptr

private

Priority bits of track segments in layer 3.

Definition at line 509 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSPrioB_Layer4

TH1F* m_neuroSWTSSW2DInTSPrioB_Layer4 = nullptr

private

Priority bits of track segments in layer 4.

Definition at line 510 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSPrioB_Layer5

TH1F* m_neuroSWTSSW2DInTSPrioB_Layer5 = nullptr

private

Priority bits of track segments in layer 5.

Definition at line 511 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSPrioB_Layer6

TH1F* m_neuroSWTSSW2DInTSPrioB_Layer6 = nullptr

private

Priority bits of track segments in layer 6.

Definition at line 512 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSPrioB_Layer7

TH1F* m_neuroSWTSSW2DInTSPrioB_Layer7 = nullptr

private

Priority bits of track segments in layer 7.

Definition at line 513 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSPrioB_Layer8

TH1F* m_neuroSWTSSW2DInTSPrioB_Layer8 = nullptr

private

Priority bits of track segments in layer 8.

Definition at line 514 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSPrioT_Layer0

TH1F* m_neuroSWTSSW2DInTSPrioT_Layer0 = nullptr

private

Priority time of track segments in layer 0.

Definition at line 487 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSPrioT_Layer1

TH1F* m_neuroSWTSSW2DInTSPrioT_Layer1 = nullptr

private

Priority time of track segments in layer 1.

Definition at line 488 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSPrioT_Layer2

TH1F* m_neuroSWTSSW2DInTSPrioT_Layer2 = nullptr

private

Priority time of track segments in layer 2.

Definition at line 489 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSPrioT_Layer3

TH1F* m_neuroSWTSSW2DInTSPrioT_Layer3 = nullptr

private

Priority time of track segments in layer 3.

Definition at line 490 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSPrioT_Layer4

TH1F* m_neuroSWTSSW2DInTSPrioT_Layer4 = nullptr

private

Priority time of track segments in layer 4.

Definition at line 491 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSPrioT_Layer5

TH1F* m_neuroSWTSSW2DInTSPrioT_Layer5 = nullptr

private

Priority time of track segments in layer 5.

Definition at line 492 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSPrioT_Layer6

TH1F* m_neuroSWTSSW2DInTSPrioT_Layer6 = nullptr

private

Priority time of track segments in layer 6.

Definition at line 493 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSPrioT_Layer7

TH1F* m_neuroSWTSSW2DInTSPrioT_Layer7 = nullptr

private

Priority time of track segments in layer 7.

Definition at line 494 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DInTSPrioT_Layer8

TH1F* m_neuroSWTSSW2DInTSPrioT_Layer8 = nullptr

private

Priority time of track segments in layer 8.

Definition at line 495 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DOutCosTheta

TH1F* m_neuroSWTSSW2DOutCosTheta = nullptr

private

cos theta distribution from simulation (sw TS sw 2D sw NN)

Definition at line 287 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DOutHitPattern

TH1F* m_neuroSWTSSW2DOutHitPattern = nullptr

private

stereo hit pattern of simulated neuro tracks (sw TS sw 2D sw NN)

Definition at line 290 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DOutInvPt

TH1F* m_neuroSWTSSW2DOutInvPt = nullptr

private

Inverse Pt distribution from simulation (sw TS sw 2D sw NN)

Definition at line 288 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DOutPhi0

TH1F* m_neuroSWTSSW2DOutPhi0 = nullptr

private

phi distribution from simulation (sw TS sw 2D sw NN)

Definition at line 289 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DOutTrackCount

TH1F* m_neuroSWTSSW2DOutTrackCount = nullptr

private

number of simulated tracks per event (sw TS sw 2D sw NN)

Definition at line 291 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DOutZ

TH1F* m_neuroSWTSSW2DOutZ = nullptr

private

z distribution from simulation (sw TS sw 2D sw NN)

Definition at line 286 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSector

TH1F* m_neuroSWTSSW2DSector = nullptr

private

NN sector from simulation (sw TS sw 2D sw NN)

Definition at line 292 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSCount

TH1F* m_neuroSWTSSW2DSelTSCount = nullptr

private

number of TS per SL

Definition at line 529 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSFoundT_Layer0

TH1F* m_neuroSWTSSW2DSelTSFoundT_Layer0 = nullptr

private

Found time of selected track segments in layer 0.

Definition at line 539 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSFoundT_Layer1

TH1F* m_neuroSWTSSW2DSelTSFoundT_Layer1 = nullptr

private

Found time of selected track segments in layer 1.

Definition at line 540 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSFoundT_Layer2

TH1F* m_neuroSWTSSW2DSelTSFoundT_Layer2 = nullptr

private

Found time of selected track segments in layer 2.

Definition at line 541 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSFoundT_Layer3

TH1F* m_neuroSWTSSW2DSelTSFoundT_Layer3 = nullptr

private

Found time of selected track segments in layer 3.

Definition at line 542 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSFoundT_Layer4

TH1F* m_neuroSWTSSW2DSelTSFoundT_Layer4 = nullptr

private

Found time of selected track segments in layer 4.

Definition at line 543 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSFoundT_Layer5

TH1F* m_neuroSWTSSW2DSelTSFoundT_Layer5 = nullptr

private

Found time of selected track segments in layer 5.

Definition at line 544 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSFoundT_Layer6

TH1F* m_neuroSWTSSW2DSelTSFoundT_Layer6 = nullptr

private

Found time of selected track segments in layer 6.

Definition at line 545 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSFoundT_Layer7

TH1F* m_neuroSWTSSW2DSelTSFoundT_Layer7 = nullptr

private

Found time of selected track segments in layer 7.

Definition at line 546 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSFoundT_Layer8

TH1F* m_neuroSWTSSW2DSelTSFoundT_Layer8 = nullptr

private

Found time of selected track segments in layer 8.

Definition at line 547 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSID

TH1F* m_neuroSWTSSW2DSelTSID = nullptr

private

ID of incoming track segments.

Definition at line 528 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSLR_Layer0

TH1F* m_neuroSWTSSW2DSelTSLR_Layer0 = nullptr

private

Left/Right of track segments in layer 0.

Definition at line 559 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSLR_Layer1

TH1F* m_neuroSWTSSW2DSelTSLR_Layer1 = nullptr

private

Left/Right of track segments in layer 1.

Definition at line 560 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSLR_Layer2

TH1F* m_neuroSWTSSW2DSelTSLR_Layer2 = nullptr

private

Left/Right of track segments in layer 2.

Definition at line 561 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSLR_Layer3

TH1F* m_neuroSWTSSW2DSelTSLR_Layer3 = nullptr

private

Left/Right of track segments in layer 3.

Definition at line 562 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSLR_Layer4

TH1F* m_neuroSWTSSW2DSelTSLR_Layer4 = nullptr

private

Left/Right of track segments in layer 4.

Definition at line 563 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSLR_Layer5

TH1F* m_neuroSWTSSW2DSelTSLR_Layer5 = nullptr

private

Left/Right of track segments in layer 5.

Definition at line 564 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSLR_Layer6

TH1F* m_neuroSWTSSW2DSelTSLR_Layer6 = nullptr

private

Left/Right of track segments in layer 6.

Definition at line 565 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSLR_Layer7

TH1F* m_neuroSWTSSW2DSelTSLR_Layer7 = nullptr

private

Left/Right of track segments in layer 7.

Definition at line 566 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSLR_Layer8

TH1F* m_neuroSWTSSW2DSelTSLR_Layer8 = nullptr

private

Left/Right of track segments in layer 8.

Definition at line 567 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSPrioB_Layer0

TH1F* m_neuroSWTSSW2DSelTSPrioB_Layer0 = nullptr

private

Priority bits of track segments in layer 0.

Definition at line 549 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSPrioB_Layer1

TH1F* m_neuroSWTSSW2DSelTSPrioB_Layer1 = nullptr

private

Priority bits of track segments in layer 1.

Definition at line 550 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSPrioB_Layer2

TH1F* m_neuroSWTSSW2DSelTSPrioB_Layer2 = nullptr

private

Priority bits of track segments in layer 2.

Definition at line 551 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSPrioB_Layer3

TH1F* m_neuroSWTSSW2DSelTSPrioB_Layer3 = nullptr

private

Priority bits of track segments in layer 3.

Definition at line 552 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSPrioB_Layer4

TH1F* m_neuroSWTSSW2DSelTSPrioB_Layer4 = nullptr

private

Priority bits of track segments in layer 4.

Definition at line 553 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSPrioB_Layer5

TH1F* m_neuroSWTSSW2DSelTSPrioB_Layer5 = nullptr

private

Priority bits of track segments in layer 5.

Definition at line 554 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSPrioB_Layer6

TH1F* m_neuroSWTSSW2DSelTSPrioB_Layer6 = nullptr

private

Priority bits of track segments in layer 6.

Definition at line 555 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSPrioB_Layer7

TH1F* m_neuroSWTSSW2DSelTSPrioB_Layer7 = nullptr

private

Priority bits of track segments in layer 7.

Definition at line 556 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSPrioB_Layer8

TH1F* m_neuroSWTSSW2DSelTSPrioB_Layer8 = nullptr

private

Priority bits of track segments in layer 8.

Definition at line 557 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSPrioT_Layer0

TH1F* m_neuroSWTSSW2DSelTSPrioT_Layer0 = nullptr

private

Priority time of selected track segments in layer 0.

Definition at line 530 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSPrioT_Layer1

TH1F* m_neuroSWTSSW2DSelTSPrioT_Layer1 = nullptr

private

Priority time of selected track segments in layer 1.

Definition at line 531 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSPrioT_Layer2

TH1F* m_neuroSWTSSW2DSelTSPrioT_Layer2 = nullptr

private

Priority time of selected track segments in layer 2.

Definition at line 532 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSPrioT_Layer3

TH1F* m_neuroSWTSSW2DSelTSPrioT_Layer3 = nullptr

private

Priority time of selected track segments in layer 3.

Definition at line 533 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSPrioT_Layer4

TH1F* m_neuroSWTSSW2DSelTSPrioT_Layer4 = nullptr

private

Priority time of selected track segments in layer 4.

Definition at line 534 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSPrioT_Layer5

TH1F* m_neuroSWTSSW2DSelTSPrioT_Layer5 = nullptr

private

Priority time of selected track segments in layer 5.

Definition at line 535 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSPrioT_Layer6

TH1F* m_neuroSWTSSW2DSelTSPrioT_Layer6 = nullptr

private

Priority time of selected track segments in layer 6.

Definition at line 536 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSPrioT_Layer7

TH1F* m_neuroSWTSSW2DSelTSPrioT_Layer7 = nullptr

private

Priority time of selected track segments in layer 7.

Definition at line 537 of file CDCTriggerNeuroDQMModule.h.

m_neuroSWTSSW2DSelTSPrioT_Layer8

TH1F* m_neuroSWTSSW2DSelTSPrioT_Layer8 = nullptr

private

Priority time of selected track segments in layer 8.

Definition at line 538 of file CDCTriggerNeuroDQMModule.h.

m_nsamets

int m_nsamets

private

Number of identical track segments to be required for matching between HW and SW Neurotrigger.

Definition at line 173 of file CDCTriggerNeuroDQMModule.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_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_RecoCosTheta

TH1F* m_RecoCosTheta = nullptr

private

reconstructed cos(theta)

Definition at line 660 of file CDCTriggerNeuroDQMModule.h.

m_RecoD0

TH1F* m_RecoD0 = nullptr

private

reconstructed d0

Definition at line 663 of file CDCTriggerNeuroDQMModule.h.

m_RecoHWCosTheta

TH1F* m_RecoHWCosTheta = nullptr

private

matched to HW reconstructed cos(theta)

Definition at line 668 of file CDCTriggerNeuroDQMModule.h.

m_RecoHWD0

TH1F* m_RecoHWD0 = nullptr

private

matched to HW reconstructed d0

Definition at line 671 of file CDCTriggerNeuroDQMModule.h.

m_RecoHWInvPt

TH1F* m_RecoHWInvPt = nullptr

private

matched to HW reconstructed inverse Pt

Definition at line 669 of file CDCTriggerNeuroDQMModule.h.

m_RecoHWPhi

TH1F* m_RecoHWPhi = nullptr

private

matched to HW reconstructed phi

Definition at line 670 of file CDCTriggerNeuroDQMModule.h.

m_RecoHWZ

TH1F* m_RecoHWZ = nullptr

private

matched to HW reconstructed z

Definition at line 667 of file CDCTriggerNeuroDQMModule.h.

m_RecoHWZScatter

TH2F* m_RecoHWZScatter = nullptr

private

matched to HW reconstructed z scatter plot

Definition at line 672 of file CDCTriggerNeuroDQMModule.h.

m_RecoInvPt

TH1F* m_RecoInvPt = nullptr

private

reconstructed inverse Pt

Definition at line 661 of file CDCTriggerNeuroDQMModule.h.

m_RecoPhi

TH1F* m_RecoPhi = nullptr

private

reconstructed phi

Definition at line 662 of file CDCTriggerNeuroDQMModule.h.

m_RecoSWCosTheta

TH1F* m_RecoSWCosTheta = nullptr

private

matched to SW reconstructed cos(theta)

Definition at line 691 of file CDCTriggerNeuroDQMModule.h.

m_RecoSWD0

TH1F* m_RecoSWD0 = nullptr

private

matched to SW reconstructed d0

Definition at line 694 of file CDCTriggerNeuroDQMModule.h.

m_RecoSWInvPt

TH1F* m_RecoSWInvPt = nullptr

private

matched to SW reconstructed inverse Pt

Definition at line 692 of file CDCTriggerNeuroDQMModule.h.

m_RecoSWPhi

TH1F* m_RecoSWPhi = nullptr

private

matched to SW reconstructed phi

Definition at line 693 of file CDCTriggerNeuroDQMModule.h.

m_RecoSWTSSW2DCosTheta

TH1F* m_RecoSWTSSW2DCosTheta = nullptr

private

matched to SWTSSW2DSWNN reconstructed cos(theta)

Definition at line 714 of file CDCTriggerNeuroDQMModule.h.

m_RecoSWTSSW2DD0

TH1F* m_RecoSWTSSW2DD0 = nullptr

private

matched to SWTSSW2DSWNN reconstructed d0

Definition at line 717 of file CDCTriggerNeuroDQMModule.h.

m_RecoSWTSSW2DInvPt

TH1F* m_RecoSWTSSW2DInvPt = nullptr

private

matched to SWTSSW2DSWNN reconstructed inverse Pt

Definition at line 715 of file CDCTriggerNeuroDQMModule.h.

m_RecoSWTSSW2DPhi

TH1F* m_RecoSWTSSW2DPhi = nullptr

private

matched to SWTSSW2DSWNN reconstructed phi

Definition at line 716 of file CDCTriggerNeuroDQMModule.h.

m_RecoSWTSSW2DZ

TH1F* m_RecoSWTSSW2DZ = nullptr

private

matched to SWTSSW2DSWNN reconstructed z

Definition at line 713 of file CDCTriggerNeuroDQMModule.h.

m_RecoSWTSSW2DZScatter

TH2F* m_RecoSWTSSW2DZScatter = nullptr

private

matched to SWTSSW2DSWNN reconstructed z scatter plot

Definition at line 718 of file CDCTriggerNeuroDQMModule.h.

m_RecoSWZ

TH1F* m_RecoSWZ = nullptr

private

matched to SW reconstructed z

Definition at line 690 of file CDCTriggerNeuroDQMModule.h.

m_RecoSWZScatter

TH2F* m_RecoSWZScatter = nullptr

private

matched to SW reconstructed z scatter plot

Definition at line 695 of file CDCTriggerNeuroDQMModule.h.

m_RecoTrackCount

TH1F* m_RecoTrackCount = nullptr

private

number of reconstructed tracks per event

Definition at line 664 of file CDCTriggerNeuroDQMModule.h.

m_recoTrackMultiplicity

int m_recoTrackMultiplicity

private

Select events with a specific RecoTrack track multiplicity.

-1 for all events

Definition at line 196 of file CDCTriggerNeuroDQMModule.h.

m_RecoTracks

StoreArray<RecoTrack> m_RecoTracks

private

StoreArray for RecoTracks.

Definition at line 235 of file CDCTriggerNeuroDQMModule.h.

m_recoTracksName

std::string m_recoTracksName

private

Name for the RecoTrack array name.

Definition at line 214 of file CDCTriggerNeuroDQMModule.h.

m_RecoZ

TH1F* m_RecoZ = nullptr

private

reconstructed z

Definition at line 659 of file CDCTriggerNeuroDQMModule.h.

m_returnValue

int m_returnValue

privateinherited

The return value.

Definition at line 519 of file Module.h.

m_showRecoTracks

bool m_showRecoTracks

private

Switch to turn on a comparison with the reconstruction.

Definition at line 194 of file CDCTriggerNeuroDQMModule.h.

m_sim2DTracksSWTS

StoreArray<CDCTriggerTrack> m_sim2DTracksSWTS

private

StoreArray for simulated 2D finder tracks using simulated TS.

Definition at line 240 of file CDCTriggerNeuroDQMModule.h.

m_sim2DTracksSWTSName

std::string m_sim2DTracksSWTSName

private

Name for simulated 2D finder tracks using simulated TS.

Definition at line 207 of file CDCTriggerNeuroDQMModule.h.

m_simDiffTS

TH1F* m_simDiffTS = nullptr

private

number of TS selcted in TSIM but not in unpacked

Definition at line 306 of file CDCTriggerNeuroDQMModule.h.

m_simNeuroInputVector

StoreArray<CDCTriggerMLPInput> m_simNeuroInputVector

private

StoreArray for neuro input vector from TSIM.

Definition at line 233 of file CDCTriggerNeuroDQMModule.h.

m_simNeuroInputVectorName

std::string m_simNeuroInputVectorName

private

Name for simulated neuro input vector using HW TS, HW 2D.

Definition at line 192 of file CDCTriggerNeuroDQMModule.h.

m_simNeuroInputVectorSWTSSW2D

StoreArray<CDCTriggerMLPInput> m_simNeuroInputVectorSWTSSW2D

private

StoreArray for neuro input vector using simulated TS and simulated 2D.

Definition at line 242 of file CDCTriggerNeuroDQMModule.h.

m_simNeuroInputVectorSWTSSW2DName

std::string m_simNeuroInputVectorSWTSSW2DName

private

Name for neuro input vector using simulated TS and simulated 2D.

Definition at line 211 of file CDCTriggerNeuroDQMModule.h.

m_simNeuroTracks

StoreArray<CDCTriggerTrack> m_simNeuroTracks

private

StoreArray for neuro tracks from TSIM.

Definition at line 231 of file CDCTriggerNeuroDQMModule.h.

m_simNeuroTracksName

std::string m_simNeuroTracksName

private

Name for neuro tracks from TSIM using HW TS, HW 2D.

Definition at line 190 of file CDCTriggerNeuroDQMModule.h.

m_simNeuroTracksSWTSSW2D

StoreArray<CDCTriggerTrack> m_simNeuroTracksSWTSSW2D

private

StoreArray for neuro tracks using simulated TS and simulated 2D.

Definition at line 244 of file CDCTriggerNeuroDQMModule.h.

m_simNeuroTracksSWTSSW2DName

std::string m_simNeuroTracksSWTSSW2DName

private

Name for neuro tracks using simulated TS and simulated 2D.

Definition at line 209 of file CDCTriggerNeuroDQMModule.h.

m_simSameTS

TH1F* m_simSameTS = nullptr

private

number of TS selected in both, unpacked and TSIM tracks

Definition at line 305 of file CDCTriggerNeuroDQMModule.h.

m_simSegmentHits

StoreArray<CDCTriggerSegmentHit> m_simSegmentHits

private

StoreArray for simulated TS hits.

Definition at line 238 of file CDCTriggerNeuroDQMModule.h.

m_simSegmentHitsName

std::string m_simSegmentHitsName

private

Name for simulated TS hits.

Definition at line 205 of file CDCTriggerNeuroDQMModule.h.

m_skipWithoutHWTS

bool m_skipWithoutHWTS

private

Switch to skip events without unpacked TS.

Definition at line 198 of file CDCTriggerNeuroDQMModule.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_unpacked2DTracks

StoreArray<CDCTriggerTrack> m_unpacked2DTracks

private

StoreArray for 2D finder tracks from unpacker.

Definition at line 221 of file CDCTriggerNeuroDQMModule.h.

m_unpacked2DTracksName

std::string m_unpacked2DTracksName

private

Name for 2D finder tracks from unpacker.

Definition at line 178 of file CDCTriggerNeuroDQMModule.h.

m_unpackedNeuroInput2DTracks

StoreArray<CDCTriggerTrack> m_unpackedNeuroInput2DTracks

private

StoreArray for neuro input 2dfinder tracks.

Definition at line 227 of file CDCTriggerNeuroDQMModule.h.

m_unpackedNeuroInput2DTracksName

std::string m_unpackedNeuroInput2DTracksName

private

Name for neuro input 2d finder tracks.

Definition at line 184 of file CDCTriggerNeuroDQMModule.h.

m_unpackedNeuroInputSegments

StoreArray<CDCTriggerSegmentHit> m_unpackedNeuroInputSegments

private

StoreArray for neuro input Track segments.

Definition at line 229 of file CDCTriggerNeuroDQMModule.h.

m_unpackedNeuroInputSegmentsName

std::string m_unpackedNeuroInputSegmentsName

private

Name for neuro input Track segments.

Definition at line 186 of file CDCTriggerNeuroDQMModule.h.

m_unpackedNeuroInputVector

StoreArray<CDCTriggerMLPInput> m_unpackedNeuroInputVector

private

StoreArray for neuro input vector from unpacker.

Definition at line 225 of file CDCTriggerNeuroDQMModule.h.

m_unpackedNeuroInputVectorName

std::string m_unpackedNeuroInputVectorName

private

Name for neuro input vector from unpacker.

Definition at line 182 of file CDCTriggerNeuroDQMModule.h.

m_unpackedNeuroTracks

StoreArray<CDCTriggerTrack> m_unpackedNeuroTracks

private

StoreArray for neuro tracks from unpacker.

Definition at line 223 of file CDCTriggerNeuroDQMModule.h.

m_unpackedNeuroTracksName

std::string m_unpackedNeuroTracksName

private

Name for neuro tracks from unpacker.

Definition at line 180 of file CDCTriggerNeuroDQMModule.h.

m_unpackedSegmentHits

StoreArray<CDCTriggerSegmentHit> m_unpackedSegmentHits

private

StoreArray for TS hits from unpacker.

Definition at line 219 of file CDCTriggerNeuroDQMModule.h.

m_unpackedSegmentHitsName

std::string m_unpackedSegmentHitsName

private

Name for TS hits from unpacker.

Definition at line 176 of file CDCTriggerNeuroDQMModule.h.

---

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

• trg/cdc/modules/dqmneuro/include/CDCTriggerNeuroDQMModule.h
• trg/cdc/modules/dqmneuro/src/CDCTriggerNeuroDQMModule.cc