KLMTimeAlgorithm Class Reference

KLM time calibration algorithm. More...

#include <KLMTimeAlgorithm.h>

Inheritance diagram for KLMTimeAlgorithm:

Collaboration diagram for KLMTimeAlgorithm:

Classes
struct	Event
	Event data. More...

Public Types
enum	ChannelCalibrationStatus {   c_NotEnoughData = 0 ,   c_FailedFit = 1 ,   c_SuccessfulCalibration = 2 }
	Channel calibration status.
enum	EResult {   c_OK ,   c_Iterate ,   c_NotEnoughData ,   c_Failure ,   c_Undefined }
	The result of calibration. More...

Public Member Functions
	KLMTimeAlgorithm ()
	Constructor.
	~KLMTimeAlgorithm ()
	Destructor.
void	setDebug ()
	Turn on debug mode (prints histograms and output running log).
void	setMC (bool mc)
	Set flag indicating whether the input is MC sample. More...
void	useEvtT0 ()
	Use event T0 as the initial time point or not.
void	setMinimalDigitNumber (int minimalDigitNumber)
	Set minimal digit number (total).
void	setLowerLimit (int counts)
	Set the lower number of hits collected on one sigle strip. More...
void	saveHist ()
	Save histograms to file.
double	esti_timeShift (const KLMChannelIndex &klmChannel)
	Estimate value of calibration constant for uncalibrated channels. More...
std::pair< int, double >	tS_upperStrip (const KLMChannelIndex &klmChannel)
	Tracing avaiable channels with increasing strip number. More...
std::pair< int, double >	tS_lowerStrip (const KLMChannelIndex &klmChannel)
	Tracing avaiable channels with decreasing strip number. More...
double	esti_timeRes (const KLMChannelIndex &klmChannel)
	Estimate value of calibration constant for calibrated channels. More...
std::pair< int, double >	tR_upperStrip (const KLMChannelIndex &klmChannel)
	Tracing avaiable channels with increasing strip number. More...
std::pair< int, double >	tR_lowerStrip (const KLMChannelIndex &klmChannel)
	Tracing avaiable channels with decreasing strip number. More...
std::string	getPrefix () const
	Get the prefix used for getting calibration data.
bool	checkPyExpRun (PyObject *pyObj)
	Checks that a PyObject can be successfully converted to an ExpRun type. More...
Calibration::ExpRun	convertPyExpRun (PyObject *pyObj)
	Performs the conversion of PyObject to ExpRun. More...
std::string	getCollectorName () const
	Alias for prefix. More...
void	setPrefix (const std::string &prefix)
	Set the prefix used to identify datastore objects.
void	setInputFileNames (PyObject *inputFileNames)
	Set the input file names used for this algorithm from a Python list. More...
PyObject *	getInputFileNames ()
	Get the input file names used for this algorithm and pass them out as a Python list of unicode strings.
std::vector< Calibration::ExpRun >	getRunListFromAllData () const
	Get the complete list of runs from inspection of collected data.
RunRange	getRunRangeFromAllData () const
	Get the complete RunRange from inspection of collected data.
IntervalOfValidity	getIovFromAllData () const
	Get the complete IoV from inspection of collected data.
void	fillRunToInputFilesMap ()
	Fill the mapping of ExpRun -> Files.
std::string	getGranularity () const
	Get the granularity of collected data.
EResult	execute (std::vector< Calibration::ExpRun > runs={}, int iteration=0, IntervalOfValidity iov=IntervalOfValidity())
	Runs calibration over vector of runs for a given iteration. More...
EResult	execute (PyObject *runs, int iteration=0, IntervalOfValidity iov=IntervalOfValidity())
	Runs calibration over Python list of runs. Converts to C++ and then calls the other execute() function.
std::list< Database::DBImportQuery > &	getPayloads ()
	Get constants (in TObjects) for database update from last execution.
std::list< Database::DBImportQuery >	getPayloadValues ()
	Get constants (in TObjects) for database update from last execution but passed by VALUE.
bool	commit ()
	Submit constants from last calibration into database.
bool	commit (std::list< Database::DBImportQuery > payloads)
	Submit constants from a (potentially previous) set of payloads.
const std::string &	getDescription () const
	Get the description of the algoithm (set by developers in constructor)
bool	loadInputJson (const std::string &jsonString)
	Load the m_inputJson variable from a string (useful from Python interface). The rturn bool indicates success or failure.
const std::string	dumpOutputJson () const
	Dump the JSON string of the output JSON object.
const std::vector< Calibration::ExpRun >	findPayloadBoundaries (std::vector< Calibration::ExpRun > runs, int iteration=0)
	Used to discover the ExpRun boundaries that you want the Python CAF to execute on. This is optional and only used in some.
template<>
std::shared_ptr< TTree >	getObjectPtr (const std::string &name, const std::vector< Calibration::ExpRun > &requestedRuns)
	Specialization of getObjectPtr<TTree>.

Protected Member Functions
virtual EResult	calibrate () override
	Run algorithm on data. More...
void	setInputFileNames (std::vector< std::string > inputFileNames)
	Set the input file names used for this algorithm. More...
virtual bool	isBoundaryRequired (const Calibration::ExpRun &)
	Given the current collector data, make a decision about whether or not this run should be the start of a payload boundary.
virtual void	boundaryFindingSetup (std::vector< Calibration::ExpRun >, int)
	If you need to make some changes to your algorithm class before 'findPayloadBoundaries' is run, make them in this function.
virtual void	boundaryFindingTearDown ()
	Put your algorithm back into a state ready for normal execution if you need to.
const std::vector< Calibration::ExpRun > &	getRunList () const
	Get the list of runs for which calibration is called.
int	getIteration () const
	Get current iteration.
std::vector< std::string >	getVecInputFileNames () const
	Get the input file names used for this algorithm as a STL vector.
template<class T >
std::shared_ptr< T >	getObjectPtr (const std::string &name, const std::vector< Calibration::ExpRun > &requestedRuns)
	Get calibration data object by name and list of runs, the Merge function will be called to generate the overall object.
template<class T >
std::shared_ptr< T >	getObjectPtr (std::string name)
	Get calibration data object (for all runs the calibration is requested for) This function will only work during or after execute() has been called once.
template<>
shared_ptr< TTree >	getObjectPtr (const string &name, const vector< ExpRun > &requestedRuns)
	We cheekily cast the TChain to TTree for the returned pointer so that the user never knows Hopefully this doesn't cause issues if people do low level stuff to the tree...
std::string	getGranularityFromData () const
	Get the granularity of collected data.
void	saveCalibration (TClonesArray *data, const std::string &name)
	Store DBArray payload with given name with default IOV.
void	saveCalibration (TClonesArray *data, const std::string &name, const IntervalOfValidity &iov)
	Store DBArray with given name and custom IOV.
void	saveCalibration (TObject *data)
	Store DB payload with default name and default IOV.
void	saveCalibration (TObject *data, const IntervalOfValidity &iov)
	Store DB payload with default name and custom IOV.
void	saveCalibration (TObject *data, const std::string &name)
	Store DB payload with given name with default IOV.
void	saveCalibration (TObject *data, const std::string &name, const IntervalOfValidity &iov)
	Store DB payload with given name and custom IOV.
void	updateDBObjPtrs (const unsigned int event, const int run, const int experiment)
	Updates any DBObjPtrs by calling update(event) for DBStore.
void	setDescription (const std::string &description)
	Set algorithm description (in constructor)
void	clearCalibrationData ()
	Clear calibration data.
Calibration::ExpRun	getAllGranularityExpRun () const
	Returns the Exp,Run pair that means 'Everything'. Currently unused.
void	resetInputJson ()
	Clears the m_inputJson member variable.
void	resetOutputJson ()
	Clears the m_outputJson member variable.
template<class T >
void	setOutputJsonValue (const std::string &key, const T &value)
	Set a key:value pair for the outputJson object, expected to used interally during calibrate()
template<class T >
const T	getOutputJsonValue (const std::string &key) const
	Get a value using a key from the JSON output object, not sure why you would want to do this.
template<class T >
const T	getInputJsonValue (const std::string &key) const
	Get an input JSON value using a key. The normal exceptions are raised when the key doesn't exist.
const nlohmann::json &	getInputJsonObject () const
	Get the entire top level JSON object. We explicitly say this must be of object type so that we might pick.
bool	inputJsonKeyExists (const std::string &key) const
	Test for a key in the input JSON object.

Protected Attributes
std::vector< Calibration::ExpRun >	m_boundaries
	When using the boundaries functionality from isBoundaryRequired, this is used to store the boundaries. It is cleared when.

Private Member Functions
void	setupDatabase ()
	Setup the database.
CalibrationAlgorithm::EResult	readCalibrationData ()
	Read calibration data. More...
void	createHistograms ()
	Create histograms. More...
void	fillTimeDistanceProfiles (TProfile *profileRpcPhi, TProfile *profileRpcZ, TProfile *profileBKLMScintillatorPhi, TProfile *profileBKLMScintillatorZ, TProfile *profileEKLMScintillatorPlane1, TProfile *profileEKLMScintillatorPlane2, bool fill2dHistograms)
	Fill profiles of time versus distance. More...
void	timeDistance2dFit (const std::vector< std::pair< KLMChannelNumber, unsigned int > > &channels, double &delay, double &delayError)
	Two-dimensional fit for individual channels. More...
std::string	getExpRunString (Calibration::ExpRun &expRun) const
	Gets the "exp.run" string repr. of (exp,run)
std::string	getFullObjectPath (const std::string &name, Calibration::ExpRun expRun) const
	constructs the full TDirectory + Key name of an object in a TFile based on its name and exprun

Private Attributes
std::map< KLMChannelNumber, std::vector< struct Event > >	m_evts
	Container of hit information. More...
std::map< KLMChannelNumber, int >	m_cFlag
	Calibration flag if the channel has enough hits collected and fitted OK.
std::map< KLMChannelNumber, double >	m_timeShift
	Shift values of each channel.
std::map< KLMChannelNumber, double >	m_timeRes
	Resolution values of each channel.
std::map< KLMChannelNumber, double >	m_time_channel
	Time distribution central value of each channel.
std::map< KLMChannelNumber, double >	m_etime_channel
	Time distribution central value Error of each channel.
std::map< KLMChannelNumber, double >	m_ctime_channel
	Calibrated time distribution central value of each channel.
std::map< KLMChannelNumber, double >	mc_etime_channel
	Calibrated time distribution central value Error of each channel.
double	m_LowerTimeBoundaryRPC = -10.0
	Lower time boundary for RPC.
double	m_UpperTimeBoundaryRPC = 10.0
	Upper time boundary for RPC.
double	m_LowerTimeBoundaryScintilltorsBKLM = 20.0
	Lower time boundary for BKLM scintillators.
double	m_UpperTimeBoundaryScintilltorsBKLM = 70.0
	Upper time boundary for BKLM scintillators.
double	m_LowerTimeBoundaryScintilltorsEKLM = 20.0
	Lower time boundary for EKLM scintillators.
double	m_UpperTimeBoundaryScintilltorsEKLM = 70.0
	Upper time boundary for BKLM scintillators.
double	m_LowerTimeBoundaryCalibratedRPC = -40.0
	Lower time boundary for RPC (calibrated data).
double	m_UpperTimeBoundaryCalibratedRPC = 40.0
	Upper time boundary for RPC (calibrated data).
double	m_LowerTimeBoundaryCalibratedScintilltorsBKLM = -40.0
	Lower time boundary for BKLM scintillators (calibrated data).
double	m_UpperTimeBoundaryCalibratedScintilltorsBKLM = 40.0
	Upper time boundary for BKLM scintillators (calibrated data).
double	m_LowerTimeBoundaryCalibratedScintilltorsEKLM = -40.0
	Lower time boundary for EKLM scintillators (calibrated data).
double	m_UpperTimeBoundaryCalibratedScintilltorsEKLM = 40.0
	Upper time boundary for BKLM scintillators (calibrated data).
double	m_time_channelAvg_rpc = 0.0
	Central value of the global time distribution (BKLM RPC part).
double	m_etime_channelAvg_rpc = 0.0
	Central value error of the global time distribution (BKLM RPC part).
double	m_time_channelAvg_scint = 0.0
	Central value of the global time distribution (BKLM scintillator part).
double	m_etime_channelAvg_scint = 0.0
	Central value error of the global time distribution (BKLM scintillator part).
double	m_time_channelAvg_scint_end = 0.0
	Central value of the global time distribution (EKLM scintillator part).
double	m_etime_channelAvg_scint_end = 0.0
	Central value error of the global time distribution (EKLM scintillator part).
double	m_ctime_channelAvg_rpc = 0.0
	Calibrated central value of the global time distribution (BKLM RPC part).
double	mc_etime_channelAvg_rpc = 0.0
	Calibrated central value error of the global time distribution (BKLM RPC part).
double	m_ctime_channelAvg_scint = 0.0
	Calibrated central value of the global time distribution (BKLM scintillator part).
double	mc_etime_channelAvg_scint = 0.0
	Calibrated central value error of the global time distribution (BKLM scintillator part).
double	m_ctime_channelAvg_scint_end = 0.0
	Calibrated central value of the global time distribution (EKLM scintillator part).
double	mc_etime_channelAvg_scint_end = 0.0
	Calibrated central value error of the global time distribution (EKLM scintillator part).
int	m_MinimalDigitNumber = 100000000
	Minimal digit number (total).
int	m_lower_limit_counts = 50
	Lower limit of hits collected for on single channel.
const KLMElementNumbers *	m_ElementNumbers
	Element numbers.
const bklm::GeometryPar *	m_BKLMGeometry = nullptr
	BKLM geometry data.
const EKLM::GeometryData *	m_EKLMGeometry = nullptr
	EKLM geometry data.
KLMChannelIndex	m_klmChannels
	KLM ChannelIndex object.
ROOT::Math::MinimizerOptions	m_minimizerOptions
	Minimization options.
KLMTimeConstants *	m_timeConstants = nullptr
	DBObject of time cost on some parts of the detector.
KLMTimeCableDelay *	m_timeCableDelay = nullptr
	DBObject of the calibration constant of each channel due to cable decay.
KLMTimeResolution *	m_timeResolution = nullptr
	DBObject of time resolution.
bool	m_debug = false
	Debug mode.
bool	m_mc = false
	MC or data.
bool	m_useEventT0 = true
	Whether to use event T0 from CDC.
TH1I *	h_calibrated = nullptr
	Calibration statistics for each channel.
TH1I *	hc_calibrated = nullptr
	Calibration statistics for each channel.
TH1F *	h_diff = nullptr
	Distance between global and local position.
TGraphErrors *	gre_time_channel_rpc = nullptr
	BKLM RPC.
TGraphErrors *	gre_time_channel_scint = nullptr
	BKLM Scintillator.
TGraphErrors *	gre_time_channel_scint_end = nullptr
	EKLM.
TGraphErrors *	gre_ctime_channel_rpc = nullptr
	BKLM RPC.
TGraphErrors *	gre_ctime_channel_scint = nullptr
	BKLM Scintillator.
TGraphErrors *	gre_ctime_channel_scint_end = nullptr
	EKLM.
TGraph *	gr_timeShift_channel_rpc = nullptr
	BKLM RPC.
TGraph *	gr_timeShift_channel_scint = nullptr
	BKLM scintillator.
TGraph *	gr_timeShift_channel_scint_end = nullptr
	EKLM.
TGraph *	gr_timeRes_channel_rpc = nullptr
	BKLM RPC.
TGraph *	gr_timeRes_channel_scint = nullptr
	BKLM scintillator.
TGraph *	gr_timeRes_channel_scint_end = nullptr
	EKLM.
TProfile *	m_ProfileRpcPhi = nullptr
	For BKLM RPC phi plane.
TProfile *	m_ProfileRpcZ = nullptr
	For BKLM RPC z plane.
TProfile *	m_ProfileBKLMScintillatorPhi = nullptr
	For BKLM scintillator phi plane.
TProfile *	m_ProfileBKLMScintillatorZ = nullptr
	For BKLM scintillator z plane.
TProfile *	m_ProfileEKLMScintillatorPlane1 = nullptr
	For EKLM scintillator plane1.
TProfile *	m_ProfileEKLMScintillatorPlane2 = nullptr
	For EKLM scintillator plane2.
TProfile *	m_Profile2RpcPhi = nullptr
	For BKLM RPC phi plane.
TProfile *	m_Profile2RpcZ = nullptr
	For BKLM RPC z plane.
TProfile *	m_Profile2BKLMScintillatorPhi = nullptr
	For BKLM scintillator phi plane.
TProfile *	m_Profile2BKLMScintillatorZ = nullptr
	For BKLM scintillator z plane.
TProfile *	m_Profile2EKLMScintillatorPlane1 = nullptr
	For EKLM scintillator plane1.
TProfile *	m_Profile2EKLMScintillatorPlane2 = nullptr
	For EKLM scintillator plane2.
TH1F *	h_time_rpc_tc = nullptr
	BKLM RPC part.
TH1F *	h_time_scint_tc = nullptr
	BKLM scintillator part.
TH1F *	h_time_scint_tc_end = nullptr
	EKLM part.
TH1F *	h_time_rpc = nullptr
	BKLM RPC part.
TH1F *	h_time_scint = nullptr
	BKLM scintillator part.
TH1F *	h_time_scint_end = nullptr
	EKLM part.
TH1F *	hc_time_rpc = nullptr
	BKLM RPC part.
TH1F *	hc_time_scint = nullptr
	BKLM scintillator part.
TH1F *	hc_time_scint_end = nullptr
	EKLM part.
TH1F *	h_timeF_rpc [2] = {nullptr}
	BKLM RPC part.
TH1F *	h_timeF_scint [2] = {nullptr}
	BKLM scintillator part.
TH1F *	h_timeF_scint_end [2] = {nullptr}
	EKLM part.
TH1F *	hc_timeF_rpc [2] = {nullptr}
	BKLM RPC part.
TH1F *	hc_timeF_scint [2] = {nullptr}
	BKLM scintillator part.
TH1F *	hc_timeF_scint_end [2] = {nullptr}
	EKLM part.
TH2F *	h2_timeF_rpc [2] = {nullptr}
	BKLM RPC part.
TH2F *	h2_timeF_scint [2] = {nullptr}
	BKLM scintillator part.
TH2F *	h2_timeF_scint_end [2] = {nullptr}
	EKLM part.
TH2F *	h2c_timeF_rpc [2] = {nullptr}
	BKLM RPC part.
TH2F *	h2c_timeF_scint [2] = {nullptr}
	BKLM scintillator part.
TH2F *	h2c_timeF_scint_end [2] = {nullptr}
	EKLM part.
TH1F *	h_timeFS_rpc [2][8] = {{nullptr}}
	BKLM RPC part.
TH1F *	h_timeFS_scint [2][8] = {{nullptr}}
	BKLM scintillator part.
TH1F *	h_timeFS_scint_end [2][4] = {{nullptr}}
	EKLM part.
TH1F *	hc_timeFS_rpc [2][8] = {{nullptr}}
	BKLM RPC part.
TH1F *	hc_timeFS_scint [2][8] = {{nullptr}}
	BKLM scintillator part.
TH1F *	hc_timeFS_scint_end [2][4] = {{nullptr}}
	EKLM part.
TH2F *	h2_timeFS [2][8] = {{nullptr}}
	BKLM part.
TH2F *	h2_timeFS_end [2][4] = {{nullptr}}
	EKLM part.
TH2F *	h2c_timeFS [2][8] = {{nullptr}}
	BKLM part.
TH2F *	h2c_timeFS_end [2][4] = {{nullptr}}
	EKLM part.
TH1F *	h_timeFSL [2][8][15] = {{{nullptr}}}
	BKLM part.
TH1F *	h_timeFSL_end [2][4][14] = {{{nullptr}}}
	EKLM part.
TH1F *	hc_timeFSL [2][8][15] = {{{nullptr}}}
	BKLM part.
TH1F *	hc_timeFSL_end [2][4][14] = {{{nullptr}}}
	EKLM part.
TH1F *	h_timeFSLP [2][8][15][2] = {{{{nullptr}}}}
	BKLM part.
TH1F *	h_timeFSLP_end [2][4][14][2] = {{{{nullptr}}}}
	EKLM part.
TH1F *	hc_timeFSLP [2][8][15][2] = {{{{nullptr}}}}
	BKLM part.
TH1F *	hc_timeFSLP_end [2][4][14][2] = {{{{nullptr}}}}
	EKLM part.
TH2F *	h2_timeFSLP [2][8][15][2] = {{{{nullptr}}}}
	BKLM part.
TH2F *	h2_timeFSLP_end [2][4][14][2] = {{{{nullptr}}}}
	EKLM part.
TH2F *	h2c_timeFSLP [2][8][15][2] = {{{{nullptr}}}}
	BKLM part.
TH2F *	h2c_timeFSLP_end [2][4][14][2] = {{{{nullptr}}}}
	EKLM part.
TH1F *	h_timeFSLPC_tc [2][8][15][2][54] = {{{{{nullptr}}}}}
	BKLM part, used for effective light speed estimation.
TH1F *	h_timeFSLPC [2][8][15][2][54] = {{{{{nullptr}}}}}
	BKLM part.
TH2F *	m_HistTimeLengthBKLM [2][8][15][2][54] = {{{{{nullptr}}}}}
	Two-dimensional distributions of time versus propagation length.
TH1F *	h_timeFSLPC_tc_end [2][4][14][2][75] = {{{{{nullptr}}}}}
	EKLM part, used for effective light speed estimation.
TH1F *	h_timeFSLPC_end [2][4][14][2][75] = {{{{{nullptr}}}}}
	EKLM part.
TH2F *	m_HistTimeLengthEKLM [2][4][14][2][75] = {{{{{nullptr}}}}}
	Two-dimensional distributions of time versus propagation length.
TH1F *	hc_timeFSLPC [2][8][15][2][54] = {{{{{nullptr}}}}}
	BKLM part.
TH1F *	hc_timeFSLPC_end [2][4][14][2][75] = {{{{{nullptr}}}}}
	EKLM part.
TF1 *	fcn_pol1 = nullptr
	Pol1 function. More...
TF1 *	fcn_const = nullptr
	Const function. More...
TF1 *	fcn_gaus = nullptr
	Gaussian function. More...
TF1 *	fcn_land = nullptr
	Landau function. More...
TFile *	m_outFile = nullptr
	Output file.
std::vector< std::string >	m_inputFileNames
	List of input files to the Algorithm, will initially be user defined but then gets the wildcards expanded during execute()
std::map< Calibration::ExpRun, std::vector< std::string > >	m_runsToInputFiles
	Map of Runs to input files. Gets filled when you call getRunRangeFromAllData, gets cleared when setting input files again.
std::string	m_granularityOfData
	Granularity of input data. This only changes when the input files change so it isn't specific to an execution.
ExecutionData	m_data
	Data specific to a SINGLE execution of the algorithm. Gets reset at the beginning of execution.
std::string	m_description {""}
	Description of the algorithm.
std::string	m_prefix {""}
	The name of the TDirectory the collector objects are contained within.
nlohmann::json	m_jsonExecutionInput = nlohmann::json::object()
	Optional input JSON object used to make decisions about how to execute the algorithm code.
nlohmann::json	m_jsonExecutionOutput = nlohmann::json::object()
	Optional output JSON object that can be set during the execution by the underlying algorithm code.

Static Private Attributes
static const Calibration::ExpRun	m_allExpRun = make_pair(-1, -1)
	allExpRun

Detailed Description

KLM time calibration algorithm.

Definition at line 41 of file KLMTimeAlgorithm.h.

Member Enumeration Documentation

EResult

enum EResult

inherited

The result of calibration.

Enumerator
c_OK	Finished successfuly =0 in Python.
c_Iterate	Needs iteration =1 in Python.
c_NotEnoughData	Needs more data =2 in Python.
c_Failure	Failed =3 in Python.
c_Undefined	Not yet known (before execution) =4 in Python.

Definition at line 40 of file CalibrationAlgorithm.h.

Member Function Documentation

calibrate()

CalibrationAlgorithm::EResult calibrate ( )

overrideprotectedvirtual

Run algorithm on data.

=======================================================================================

Implements CalibrationAlgorithm.

Definition at line 740 of file KLMTimeAlgorithm.cc.

{
  int channelId;
  gROOT->SetBatch(kTRUE);
  setupDatabase();
  m_timeCableDelay = new KLMTimeCableDelay();
  m_timeConstants = new KLMTimeConstants();
  m_timeResolution = new KLMTimeResolution();
 
  fcn_gaus = new TF1("fcn_gaus", "gaus");
  fcn_land = new TF1("fcn_land", "landau");
  fcn_pol1 = new TF1("fcn_pol1", "pol1");
  fcn_const = new TF1("fcn_const", "pol0");
 
  CalibrationAlgorithm::EResult result = readCalibrationData();
  if (result != CalibrationAlgorithm::c_OK)
    return result;
 
  /* Choose non-existing file name. */
  std::string name = "time_calibration.root";
  int i = 1;
  while (1) {
    struct stat buffer;
    if (stat(name.c_str(), &buffer) != 0)
      break;
    name = "time_calibration_" + std::to_string(i) + ".root";
    i = i + 1;
    /* Overflow. */
    if (i < 0)
      break;
  }
  m_outFile = new TFile(name.c_str(), "recreate");
  createHistograms();
 
  std::vector<struct Event>::iterator it;
  std::vector<struct Event> eventsChannel;
 
  eventsChannel.clear();
  m_cFlag.clear();
  m_minimizerOptions.SetDefaultStrategy(2);
 
  /* Sort channels by number of events. */
  std::vector< std::pair<KLMChannelNumber, unsigned int> > channelsBKLM;
  std::vector< std::pair<KLMChannelNumber, unsigned int> > channelsEKLM;
  KLMChannelIndex klmChannels;
  for (KLMChannelIndex& klmChannel : klmChannels) {
    KLMChannelNumber channel = klmChannel.getKLMChannelNumber();
    m_cFlag[channel] = ChannelCalibrationStatus::c_NotEnoughData;
    if (m_evts.find(channel) == m_evts.end())
      continue;
    int nEvents = m_evts[channel].size();
    if (nEvents < m_lower_limit_counts) {
      B2WARNING("Not enough calibration data collected."
                << LogVar("channel", channel)
                << LogVar("number of digit", nEvents));
      continue;
    }
    m_cFlag[channel] = ChannelCalibrationStatus::c_FailedFit;
    if (klmChannel.getSubdetector() == KLMElementNumbers::c_BKLM &&
        klmChannel.getLayer() < BKLMElementNumbers::c_FirstRPCLayer) {
      channelsBKLM.push_back(
        std::pair<KLMChannelNumber, unsigned int>(channel, nEvents));
    }
    if (klmChannel.getSubdetector() == KLMElementNumbers::c_EKLM) {
      channelsEKLM.push_back(
        std::pair<KLMChannelNumber, unsigned int>(channel, nEvents));
    }
  }
  std::sort(channelsBKLM.begin(), channelsBKLM.end(), compareEventNumber);
  std::sort(channelsEKLM.begin(), channelsEKLM.end(), compareEventNumber);
 
  /* Two-dimensional fit for the channel with the maximal number of events. */
  double delayBKLM, delayBKLMError;
  double delayEKLM, delayEKLMError;
  timeDistance2dFit(channelsBKLM, delayBKLM, delayBKLMError);
  timeDistance2dFit(channelsEKLM, delayEKLM, delayEKLMError);
 
  /**********************************
   * First loop
   * Estimation of effective light speed for Scintillators and RPCs, separately.
   **********************************/
  B2INFO("Effective light speed Estimation.");
  for (KLMChannelIndex klmChannel = m_klmChannels.begin(); klmChannel != m_klmChannels.end(); ++klmChannel) {
    channelId = klmChannel.getKLMChannelNumber();
    if (m_cFlag[channelId] == ChannelCalibrationStatus::c_NotEnoughData)
      continue;
 
    eventsChannel = m_evts[channelId];
    int iSub = klmChannel.getSubdetector();
 
    for (it = eventsChannel.begin(); it != eventsChannel.end(); ++it) {
      TVector3 diffD = TVector3(it->diffDistX, it->diffDistY, it->diffDistZ);
      h_diff->Fill(diffD.Mag());
      double timeHit = it->time();
      if (m_useEventT0)
        timeHit = timeHit - it->t0;
      if (iSub == KLMElementNumbers::c_BKLM) {
        int iF = klmChannel.getSection();
        int iS = klmChannel.getSector() - 1;
        int iL = klmChannel.getLayer() - 1;
        int iP = klmChannel.getPlane();
        int iC = klmChannel.getStrip() - 1;
        h_timeFSLPC_tc[iF][iS][iL][iP][iC]->Fill(timeHit);
        if (iL > 1) {
          h_time_rpc_tc->Fill(timeHit);
        } else {
          h_time_scint_tc->Fill(timeHit);
        }
      } else {
        int iF = klmChannel.getSection() - 1;
        int iS = klmChannel.getSector() - 1;
        int iL = klmChannel.getLayer() - 1;
        int iP = klmChannel.getPlane() - 1;
        int iC = klmChannel.getStrip() - 1;
        h_timeFSLPC_tc_end[iF][iS][iL][iP][iC]->Fill(timeHit);
        h_time_scint_tc_end->Fill(timeHit);
      }
    }
  }
  B2INFO("Effective light speed Estimation! Hists and Graph filling done.");
 
  m_timeShift.clear();
 
  double tmpMean_rpc_global = h_time_rpc_tc->GetMean();
  double tmpMean_scint_global = h_time_scint_tc->GetMean();
  double tmpMean_scint_global_end = h_time_scint_tc_end->GetMean();
 
  B2INFO("Global Mean for Raw." << LogVar("RPC", tmpMean_rpc_global) << LogVar("Scint BKLM",
         tmpMean_scint_global) << LogVar("Scint EKLM", tmpMean_scint_global_end));
 
  for (KLMChannelIndex klmChannel = m_klmChannels.begin(); klmChannel != m_klmChannels.end(); ++klmChannel) {
    channelId = klmChannel.getKLMChannelNumber();
    if (m_cFlag[channelId] == ChannelCalibrationStatus::c_NotEnoughData)
      continue;
 
    int iSub = klmChannel.getSubdetector();
    if (iSub == KLMElementNumbers::c_BKLM) {
      int iF = klmChannel.getSection();
      int iS = klmChannel.getSector() - 1;
      int iL = klmChannel.getLayer() - 1;
      int iP = klmChannel.getPlane();
      int iC = klmChannel.getStrip() - 1;
      h_timeFSLPC_tc[iF][iS][iL][iP][iC]->Fit(fcn_gaus, "LESQ");
      double tmpMean_channel = fcn_gaus->GetParameter(1);
      if (iL > 1) {
        m_timeShift[channelId] = tmpMean_channel - tmpMean_rpc_global;
      } else {
        m_timeShift[channelId] = tmpMean_channel - tmpMean_scint_global;
      }
    } else {
      int iF = klmChannel.getSection() - 1;
      int iS = klmChannel.getSector() - 1;
      int iL = klmChannel.getLayer() - 1;
      int iP = klmChannel.getPlane() - 1;
      int iC = klmChannel.getStrip() - 1;
      h_timeFSLPC_tc_end[iF][iS][iL][iP][iC]->Fit(fcn_gaus, "LESQ");
      double tmpMean_channel = fcn_gaus->GetParameter(1);
      m_timeShift[channelId] = tmpMean_channel - tmpMean_scint_global_end;
    }
  }
 
  delete h_time_scint_tc;
  delete h_time_scint_tc_end;
  delete h_time_rpc_tc;
  B2INFO("Effective Light m_timeShift obtained. done.");
 
  fillTimeDistanceProfiles(
    m_ProfileRpcPhi, m_ProfileRpcZ,
    m_ProfileBKLMScintillatorPhi, m_ProfileBKLMScintillatorZ,
    m_ProfileEKLMScintillatorPlane1, m_ProfileEKLMScintillatorPlane2, false);
 
  B2INFO("Effective light speed fitting.");
  m_ProfileRpcPhi->Fit("fcn_pol1", "EMQ");
  double delayRPCPhi = fcn_pol1->GetParameter(1);
  double e_slope_rpc_phi = fcn_pol1->GetParError(1);
 
  m_ProfileRpcZ->Fit("fcn_pol1", "EMQ");
  double delayRPCZ = fcn_pol1->GetParameter(1);
  double e_slope_rpc_z = fcn_pol1->GetParError(1);
 
  m_ProfileBKLMScintillatorPhi->Fit("fcn_pol1", "EMQ");
  double slope_scint_phi = fcn_pol1->GetParameter(1);
  double e_slope_scint_phi = fcn_pol1->GetParError(1);
 
  m_ProfileBKLMScintillatorZ->Fit("fcn_pol1", "EMQ");
  double slope_scint_z = fcn_pol1->GetParameter(1);
  double e_slope_scint_z = fcn_pol1->GetParError(1);
 
  m_ProfileEKLMScintillatorPlane1->Fit("fcn_pol1", "EMQ");
  double slope_scint_plane1_end = fcn_pol1->GetParameter(1);
  double e_slope_scint_plane1_end = fcn_pol1->GetParError(1);
 
  m_ProfileEKLMScintillatorPlane2->Fit("fcn_pol1", "EMQ");
  double slope_scint_plane2_end = fcn_pol1->GetParameter(1);
  double e_slope_scint_plane2_end = fcn_pol1->GetParError(1);
 
  TString logStr_phi, logStr_z;
  logStr_phi = Form("%.4f +/- %.4f ns/cm", delayRPCPhi, e_slope_rpc_phi);
  logStr_z = Form("%.4f +/- %.4f ns/cm", delayRPCZ, e_slope_rpc_z);
  B2INFO("Delay in RPCs:"
         << LogVar("Fitted Value (phi readout) ", logStr_phi.Data())
         << LogVar("Fitted Value (z readout) ", logStr_z.Data()));
  logStr_phi = Form("%.4f +/- %.4f ns/cm", slope_scint_phi, e_slope_scint_phi);
  logStr_z = Form("%.4f +/- %.4f ns/cm", slope_scint_z, e_slope_scint_z);
  B2INFO("Delay in BKLM scintillators:"
         << LogVar("Fitted Value (phi readout) ", logStr_phi.Data())
         << LogVar("Fitted Value (z readout) ", logStr_z.Data()));
  logStr_phi = Form("%.4f +/- %.4f ns/cm", slope_scint_plane1_end,
                    e_slope_scint_plane1_end);
  logStr_z = Form("%.4f +/- %.4f ns/cm", slope_scint_plane2_end,
                  e_slope_scint_plane2_end);
  B2INFO("Delay in EKLM scintillators:"
         << LogVar("Fitted Value (plane1 readout) ", logStr_phi.Data())
         << LogVar("Fitted Value (plane2 readout) ", logStr_z.Data()));
 
  logStr_z = Form("%.4f +/- %.4f ns/cm", delayBKLM, delayBKLMError);
  B2INFO("Delay in BKLM scintillators:"
         << LogVar("Fitted Value (2d fit) ", logStr_z.Data()));
  logStr_z = Form("%.4f +/- %.4f ns/cm", delayEKLM, delayEKLMError);
  B2INFO("Delay in EKLM scintillators:"
         << LogVar("Fitted Value (2d fit) ", logStr_z.Data()));
 
  // Default Effective light speed in current Database
  //delayEKLM = 0.5 * (slope_scint_plane1_end + slope_scint_plane2_end);
  //delayBKLM = 0.5 * (slope_scint_phi + slope_scint_z);
 
  m_timeConstants->setDelay(delayEKLM, KLMTimeConstants::c_EKLM);
  m_timeConstants->setDelay(delayBKLM, KLMTimeConstants::c_BKLM);
  m_timeConstants->setDelay(delayRPCPhi, KLMTimeConstants::c_RPCPhi);
  m_timeConstants->setDelay(delayRPCZ, KLMTimeConstants::c_RPCZ);
 
  B2INFO("Time distribution filling begins.");
  for (KLMChannelIndex klmChannel = m_klmChannels.begin(); klmChannel != m_klmChannels.end(); ++klmChannel) {
    channelId = klmChannel.getKLMChannelNumber();
    int iSub = klmChannel.getSubdetector();
 
    if (m_cFlag[channelId] == ChannelCalibrationStatus::c_NotEnoughData)
      continue;
    eventsChannel = m_evts[channelId];
 
    for (it = eventsChannel.begin(); it != eventsChannel.end(); ++it) {
      double timeHit = it->time();
      if (m_useEventT0)
        timeHit = timeHit - it->t0;
      if (iSub == KLMElementNumbers::c_BKLM) {
        int iF = klmChannel.getSection();
        int iS = klmChannel.getSector() - 1;
        int iL = klmChannel.getLayer() - 1;
        int iP = klmChannel.getPlane();
        int iC = klmChannel.getStrip() - 1;
        if (iL > 1) {
          double propgationT;
          if (iP == BKLMElementNumbers::c_ZPlane)
            propgationT = it->dist * delayRPCZ;
          else
            propgationT = it->dist * delayRPCPhi;
          double time = timeHit - propgationT;
          h_time_rpc->Fill(time);
          h_timeF_rpc[iF]->Fill(time);
          h_timeFS_rpc[iF][iS]->Fill(time);
          h_timeFSL[iF][iS][iL]->Fill(time);
          h_timeFSLP[iF][iS][iL][iP]->Fill(time);
          h_timeFSLPC[iF][iS][iL][iP][iC]->Fill(time);
          h2_timeF_rpc[iF]->Fill(iS, time);
          h2_timeFS[iF][iS]->Fill(iL, time);
          h2_timeFSLP[iF][iS][iL][iP]->Fill(iC, time);
        } else {
          double propgationT = it->dist * delayBKLM;
          double time = timeHit - propgationT;
          h_time_scint->Fill(time);
          h_timeF_scint[iF]->Fill(time);
          h_timeFS_scint[iF][iS]->Fill(time);
          h_timeFSL[iF][iS][iL]->Fill(time);
          h_timeFSLP[iF][iS][iL][iP]->Fill(time);
          h_timeFSLPC[iF][iS][iL][iP][iC]->Fill(time);
          h2_timeF_scint[iF]->Fill(iS, time);
          h2_timeFS[iF][iS]->Fill(iL, time);
          h2_timeFSLP[iF][iS][iL][iP]->Fill(iC, time);
        }
      } else {
        int iF = klmChannel.getSection() - 1;
        int iS = klmChannel.getSector() - 1;
        int iL = klmChannel.getLayer() - 1;
        int iP = klmChannel.getPlane() - 1;
        int iC = klmChannel.getStrip() - 1;
        double propgationT = it->dist * delayEKLM;
        double time = timeHit - propgationT;
        h_time_scint_end->Fill(time);
        h_timeF_scint_end[iF]->Fill(time);
        h_timeFS_scint_end[iF][iS]->Fill(time);
        h_timeFSL_end[iF][iS][iL]->Fill(time);
        h_timeFSLP_end[iF][iS][iL][iP]->Fill(time);
        h_timeFSLPC_end[iF][iS][iL][iP][iC]->Fill(time);
        h2_timeF_scint_end[iF]->Fill(iS, time);
        h2_timeFS_end[iF][iS]->Fill(iL, time);
        h2_timeFSLP_end[iF][iS][iL][iP]->Fill(iC, time);
      }
    }
  }
 
  B2INFO("Orignal filling done.");
 
  int iChannel_rpc = 0;
  int iChannel = 0;
  int iChannel_end = 0;
  for (KLMChannelIndex klmChannel = m_klmChannels.begin(); klmChannel != m_klmChannels.end(); ++klmChannel) {
    channelId = klmChannel.getKLMChannelNumber();
    if (m_cFlag[channelId] == ChannelCalibrationStatus::c_NotEnoughData)
      continue;
    int iSub = klmChannel.getSubdetector();
 
    if (iSub == KLMElementNumbers::c_BKLM) {
      int iF = klmChannel.getSection();
      int iS = klmChannel.getSector() - 1;
      int iL = klmChannel.getLayer() - 1;
      int iP = klmChannel.getPlane();
      int iC = klmChannel.getStrip() - 1;
 
      TFitResultPtr r = h_timeFSLPC[iF][iS][iL][iP][iC]->Fit(fcn_gaus, "LESQ");
      if (int(r) != 0)
        continue;
      if (int(r) == 0)
        m_cFlag[channelId] = ChannelCalibrationStatus::c_SuccessfulCalibration;
      m_time_channel[channelId] = fcn_gaus->GetParameter(1);
      m_etime_channel[channelId] = fcn_gaus->GetParError(1);
      if (iL > 1) {
        gre_time_channel_rpc->SetPoint(iChannel_rpc, channelId, m_time_channel[channelId]);
        gre_time_channel_rpc->SetPointError(iChannel_rpc, 0., m_etime_channel[channelId]);
        iChannel_rpc++;
      } else {
        gre_time_channel_scint->SetPoint(iChannel, channelId, m_time_channel[channelId]);
        gre_time_channel_scint->SetPointError(iChannel, 0., m_etime_channel[channelId]);
        iChannel++;
      }
    } else {
      int iF = klmChannel.getSection() - 1;
      int iS = klmChannel.getSector() - 1;
      int iL = klmChannel.getLayer() - 1;
      int iP = klmChannel.getPlane() - 1;
      int iC = klmChannel.getStrip() - 1;
 
      TFitResultPtr r = h_timeFSLPC_end[iF][iS][iL][iP][iC]->Fit(fcn_gaus, "LESQ");
      if (int(r) != 0)
        continue;
      if (int(r) == 0)
        m_cFlag[channelId] = ChannelCalibrationStatus::c_SuccessfulCalibration;
      m_time_channel[channelId] = fcn_gaus->GetParameter(1);
      m_etime_channel[channelId] = fcn_gaus->GetParError(1);
      gre_time_channel_scint_end->SetPoint(iChannel_end, channelId, m_time_channel[channelId]);
      gre_time_channel_scint_end->SetPointError(iChannel_end, 0., m_etime_channel[channelId]);
      iChannel_end++;
    }
  }
 
  gre_time_channel_scint->Fit("fcn_const", "EMQ");
  m_time_channelAvg_scint = fcn_const->GetParameter(0);
  m_etime_channelAvg_scint = fcn_const->GetParError(0);
 
  gre_time_channel_scint_end->Fit("fcn_const", "EMQ");
  m_time_channelAvg_scint_end = fcn_const->GetParameter(0);
  m_etime_channelAvg_scint_end = fcn_const->GetParError(0);
 
  gre_time_channel_rpc->Fit("fcn_const", "EMQ");
  m_time_channelAvg_rpc = fcn_const->GetParameter(0);
  m_etime_channelAvg_rpc = fcn_const->GetParError(0);
 
  B2INFO("Channel's time distribution fitting done.");
  B2DEBUG(20, LogVar("Average time (RPC)", m_time_channelAvg_rpc)
          << LogVar("Average time (BKLM scintillators)", m_time_channelAvg_scint)
          << LogVar("Average time (EKLM scintillators)", m_time_channelAvg_scint_end));
 
  B2INFO("Calibrated channel's time distribution filling begins.");
 
  m_timeShift.clear();
  for (KLMChannelIndex klmChannel = m_klmChannels.begin(); klmChannel != m_klmChannels.end(); ++klmChannel) {
    channelId = klmChannel.getKLMChannelNumber();
    h_calibrated->Fill(m_cFlag[channelId]);
    if (m_time_channel.find(channelId) == m_time_channel.end())
      continue;
    double timeShift = m_time_channel[channelId];
    int iSub = klmChannel.getSubdetector();
    if (iSub == KLMElementNumbers::c_BKLM) {
      int iL = klmChannel.getLayer() - 1;
      if (iL > 1)
        m_timeShift[channelId] = timeShift;
      else
        m_timeShift[channelId] = timeShift;
    } else {
      m_timeShift[channelId] = timeShift;
    }
    m_timeCableDelay->setTimeDelay(channelId, m_timeShift[channelId]);
  }
 
  for (KLMChannelIndex klmChannel = m_klmChannels.begin(); klmChannel != m_klmChannels.end(); ++klmChannel) {
    channelId = klmChannel.getKLMChannelNumber();
    if (m_timeShift.find(channelId) != m_timeShift.end())
      continue;
    m_timeShift[channelId] = esti_timeShift(klmChannel);
    m_timeCableDelay->setTimeDelay(channelId, m_timeShift[channelId]);
    B2DEBUG(20, "Uncalibrated Estimation " << LogVar("Channel", channelId) << LogVar("Estimated value", m_timeShift[channelId]));
  }
 
  iChannel_rpc = 0;
  iChannel = 0;
  iChannel_end = 0;
  for (KLMChannelIndex klmChannel = m_klmChannels.begin(); klmChannel != m_klmChannels.end(); ++klmChannel) {
    channelId = klmChannel.getKLMChannelNumber();
    if (m_timeShift.find(channelId) == m_timeShift.end()) {
      B2ERROR("!!! Not All Channels Calibration Constant Set. Error Happended on " << LogVar("Channel", channelId));
      continue;
    }
    int iSub = klmChannel.getSubdetector();
    if (iSub == KLMElementNumbers::c_BKLM) {
      int iL = klmChannel.getLayer();
      if (iL > 2) {
        gr_timeShift_channel_rpc->SetPoint(iChannel_rpc, channelId, m_timeShift[channelId]);
        iChannel_rpc++;
      } else {
        gr_timeShift_channel_scint->SetPoint(iChannel, channelId, m_timeShift[channelId]);
        iChannel++;
      }
    } else {
      gr_timeShift_channel_scint_end->SetPoint(iChannel_end, channelId, m_timeShift[channelId]);
      iChannel_end++;
    }
  }
 
  fillTimeDistanceProfiles(
    m_Profile2RpcPhi, m_Profile2RpcZ,
    m_Profile2BKLMScintillatorPhi, m_Profile2BKLMScintillatorZ,
    m_Profile2EKLMScintillatorPlane1,  m_Profile2EKLMScintillatorPlane2, true);
 
  for (KLMChannelIndex klmChannel = m_klmChannels.begin(); klmChannel != m_klmChannels.end(); ++klmChannel) {
    channelId = klmChannel.getKLMChannelNumber();
    int iSub = klmChannel.getSubdetector();
    eventsChannel = m_evts[channelId];
    for (it = eventsChannel.begin(); it != eventsChannel.end(); ++it) {
      double timeHit = it->time();
      if (m_useEventT0)
        timeHit = timeHit - it->t0;
      if (iSub == KLMElementNumbers::c_BKLM) {
        int iF = klmChannel.getSection();
        int iS = klmChannel.getSector() - 1;
        int iL = klmChannel.getLayer() - 1;
        int iP = klmChannel.getPlane();
        int iC = klmChannel.getStrip() - 1;
        if (iL > 1) {
          double propgationT;
          if (iP == BKLMElementNumbers::c_ZPlane)
            propgationT = it->dist * delayRPCZ;
          else
            propgationT = it->dist * delayRPCPhi;
          double time = timeHit - propgationT - m_timeShift[channelId];
          hc_time_rpc->Fill(time);
          hc_timeF_rpc[iF]->Fill(time);
          hc_timeFS_rpc[iF][iS]->Fill(time);
          hc_timeFSL[iF][iS][iL]->Fill(time);
          hc_timeFSLP[iF][iS][iL][iP]->Fill(time);
          hc_timeFSLPC[iF][iS][iL][iP][iC]->Fill(time);
          h2c_timeF_rpc[iF]->Fill(iS, time);
          h2c_timeFS[iF][iS]->Fill(iL, time);
          h2c_timeFSLP[iF][iS][iL][iP]->Fill(iC, time);
        } else {
          double propgationT = it->dist * delayBKLM;
          double time = timeHit - propgationT - m_timeShift[channelId];
          hc_time_scint->Fill(time);
          hc_timeF_scint[iF]->Fill(time);
          hc_timeFS_scint[iF][iS]->Fill(time);
          hc_timeFSL[iF][iS][iL]->Fill(time);
          hc_timeFSLP[iF][iS][iL][iP]->Fill(time);
          hc_timeFSLPC[iF][iS][iL][iP][iC]->Fill(time);
          h2c_timeF_scint[iF]->Fill(iS, time);
          h2c_timeFS[iF][iS]->Fill(iL, time);
          h2c_timeFSLP[iF][iS][iL][iP]->Fill(iC, time);
        }
      } else {
        int iF = klmChannel.getSection() - 1;
        int iS = klmChannel.getSector() - 1;
        int iL = klmChannel.getLayer() - 1;
        int iP = klmChannel.getPlane() - 1;
        int iC = klmChannel.getStrip() - 1;
        double propgationT = it->dist * delayEKLM;
        double time = timeHit - propgationT - m_timeShift[channelId];
        hc_time_scint_end->Fill(time);
        hc_timeF_scint_end[iF]->Fill(time);
        hc_timeFS_scint_end[iF][iS]->Fill(time);
        hc_timeFSL_end[iF][iS][iL]->Fill(time);
        hc_timeFSLP_end[iF][iS][iL][iP]->Fill(time);
        hc_timeFSLPC_end[iF][iS][iL][iP][iC]->Fill(time);
        h2c_timeF_scint_end[iF]->Fill(iS, time);
        h2c_timeFS_end[iF][iS]->Fill(iL, time);
        h2c_timeFSLP_end[iF][iS][iL][iP]->Fill(iC, time);
      }
    }
  }
 
  int icChannel_rpc = 0;
  int icChannel = 0;
  int icChannel_end = 0;
  for (KLMChannelIndex klmChannel = m_klmChannels.begin(); klmChannel != m_klmChannels.end(); ++klmChannel) {
    channelId = klmChannel.getKLMChannelNumber();
    if (m_cFlag[channelId] == ChannelCalibrationStatus::c_NotEnoughData)
      continue;
    int iSub = klmChannel.getSubdetector();
 
    if (iSub == KLMElementNumbers::c_BKLM) {
      int iF = klmChannel.getSection();
      int iS = klmChannel.getSector() - 1;
      int iL = klmChannel.getLayer() - 1;
      int iP = klmChannel.getPlane();
      int iC = klmChannel.getStrip() - 1;
 
      TFitResultPtr rc = hc_timeFSLPC[iF][iS][iL][iP][iC]->Fit(fcn_gaus, "LESQ");
      if (int(rc) != 0)
        continue;
      if (int(rc) == 0)
        m_cFlag[channelId] = ChannelCalibrationStatus::c_SuccessfulCalibration;
      m_ctime_channel[channelId] = fcn_gaus->GetParameter(1);
      mc_etime_channel[channelId] = fcn_gaus->GetParError(1);
      if (iL > 1) {
        gre_ctime_channel_rpc->SetPoint(icChannel_rpc, channelId, m_ctime_channel[channelId]);
        gre_ctime_channel_rpc->SetPointError(icChannel_rpc, 0., mc_etime_channel[channelId]);
        icChannel_rpc++;
      } else {
        gre_ctime_channel_scint->SetPoint(icChannel, channelId, m_ctime_channel[channelId]);
        gre_ctime_channel_scint->SetPointError(icChannel, 0., mc_etime_channel[channelId]);
        icChannel++;
      }
    } else {
      int iF = klmChannel.getSection() - 1;
      int iS = klmChannel.getSector() - 1;
      int iL = klmChannel.getLayer() - 1;
      int iP = klmChannel.getPlane() - 1;
      int iC = klmChannel.getStrip() - 1;
 
      TFitResultPtr rc = hc_timeFSLPC_end[iF][iS][iL][iP][iC]->Fit(fcn_gaus, "LESQ");
      if (int(rc) != 0)
        continue;
      if (int(rc) == 0)
        m_cFlag[channelId] = ChannelCalibrationStatus::c_SuccessfulCalibration;
      m_ctime_channel[channelId] = fcn_gaus->GetParameter(1);
      mc_etime_channel[channelId] = fcn_gaus->GetParError(1);
      gre_ctime_channel_scint_end->SetPoint(icChannel_end, channelId, m_ctime_channel[channelId]);
      gre_ctime_channel_scint_end->SetPointError(icChannel_end, 0., mc_etime_channel[channelId]);
      icChannel_end++;
    }
  }
 
  gre_ctime_channel_scint->Fit("fcn_const", "EMQ");
  m_ctime_channelAvg_scint = fcn_const->GetParameter(0);
  mc_etime_channelAvg_scint = fcn_const->GetParError(0);
 
  gre_ctime_channel_scint_end->Fit("fcn_const", "EMQ");
  m_ctime_channelAvg_scint_end = fcn_const->GetParameter(0);
  mc_etime_channelAvg_scint_end = fcn_const->GetParError(0);
 
  gre_ctime_channel_rpc->Fit("fcn_const", "EMQ");
  m_ctime_channelAvg_rpc = fcn_const->GetParameter(0);
  mc_etime_channelAvg_rpc = fcn_const->GetParError(0);
 
  B2INFO("Channel's time distribution fitting done.");
  B2DEBUG(20, LogVar("Average calibrated time (RPC)", m_ctime_channelAvg_rpc)
          << LogVar("Average calibrated time (BKLM scintillators)", m_ctime_channelAvg_scint)
          << LogVar("Average calibrated time (EKLM scintillators)", m_ctime_channelAvg_scint_end));
 
  B2INFO("Calibrated channel's time distribution filling begins.");
 
  m_timeRes.clear();
  for (KLMChannelIndex klmChannel = m_klmChannels.begin(); klmChannel != m_klmChannels.end(); ++klmChannel) {
    channelId = klmChannel.getKLMChannelNumber();
    hc_calibrated->Fill(m_cFlag[channelId]);
    if (m_ctime_channel.find(channelId) == m_ctime_channel.end())
      continue;
    double timeRes = m_ctime_channel[channelId];
    int iSub = klmChannel.getSubdetector();
    if (iSub == KLMElementNumbers::c_BKLM) {
      int iL = klmChannel.getLayer() - 1;
      if (iL > 1)
        m_timeRes[channelId] = timeRes;
      else
        m_timeRes[channelId] = timeRes;
    } else {
      m_timeRes[channelId] = timeRes;
    }
    m_timeResolution->setTimeResolution(channelId, m_timeRes[channelId]);
  }
 
  for (KLMChannelIndex klmChannel = m_klmChannels.begin(); klmChannel != m_klmChannels.end(); ++klmChannel) {
    channelId = klmChannel.getKLMChannelNumber();
    if (m_timeRes.find(channelId) != m_timeRes.end())
      continue;
    m_timeRes[channelId] = esti_timeRes(klmChannel);
    m_timeResolution->setTimeResolution(channelId, m_timeRes[channelId]);
    B2DEBUG(20, "Calibrated Estimation " << LogVar("Channel", channelId) << LogVar("Estimated value", m_timeRes[channelId]));
  }
 
  icChannel_rpc = 0;
  icChannel = 0;
  icChannel_end = 0;
  for (KLMChannelIndex klmChannel = m_klmChannels.begin(); klmChannel != m_klmChannels.end(); ++klmChannel) {
    channelId = klmChannel.getKLMChannelNumber();
    if (m_timeRes.find(channelId) == m_timeRes.end()) {
      B2ERROR("!!! Not All Channels Calibration Constant Set. Error Happended on " << LogVar("Channel", channelId));
      continue;
    }
    int iSub = klmChannel.getSubdetector();
    if (iSub == KLMElementNumbers::c_BKLM) {
      int iL = klmChannel.getLayer();
      if (iL > 2) {
        gr_timeRes_channel_rpc->SetPoint(icChannel_rpc, channelId, m_timeRes[channelId]);
        icChannel_rpc++;
      } else {
        gr_timeRes_channel_scint->SetPoint(icChannel, channelId, m_timeRes[channelId]);
        icChannel++;
      }
    } else {
      gr_timeRes_channel_scint_end->SetPoint(icChannel_end, channelId, m_timeRes[channelId]);
      icChannel_end++;
    }
  }
  saveHist();
 
  delete fcn_const;
  m_evts.clear();
  m_timeShift.clear();
  m_timeRes.clear();
  m_cFlag.clear();
 
  saveCalibration(m_timeCableDelay, "KLMTimeCableDelay");
  saveCalibration(m_timeConstants, "KLMTimeConstants");
  saveCalibration(m_timeResolution, "KLMTimeResolution");
  return CalibrationAlgorithm::c_OK;
}

checkPyExpRun()

bool checkPyExpRun ( PyObject *  pyObj )

inherited

Checks that a PyObject can be successfully converted to an ExpRun type.

Checks if the PyObject can be converted to ExpRun.

Definition at line 28 of file CalibrationAlgorithm.cc.

convertPyExpRun()

ExpRun convertPyExpRun ( PyObject *  pyObj )

inherited

Performs the conversion of PyObject to ExpRun.

Converts the PyObject to an ExpRun. We've preoviously checked the object so this assumes a lot about the PyObject.

Definition at line 70 of file CalibrationAlgorithm.cc.

createHistograms()

void createHistograms ( )

private

Create histograms.

Hist declaration Global time distribution

Definition at line 174 of file KLMTimeAlgorithm.cc.

esti_timeRes()

double esti_timeRes

(

const KLMChannelIndex &

klmChannel

)

Estimate value of calibration constant for calibrated channels.

Parameters

[in]

klmChannel

KLM channel index.

Definition at line 1633 of file KLMTimeAlgorithm.cc.

esti_timeShift()

double esti_timeShift

(

const KLMChannelIndex &

klmChannel

)

Estimate value of calibration constant for uncalibrated channels.

Parameters

[in]

klmChannel

KLM channel index.

Definition at line 1553 of file KLMTimeAlgorithm.cc.

execute()

CalibrationAlgorithm::EResult execute ( std::vector< Calibration::ExpRun >  runs = {}, int  iteration = 0, IntervalOfValidity  iov = IntervalOfValidity()  )

inherited

Runs calibration over vector of runs for a given iteration.

You can also specify the IoV to save the database payload as. By default the Algorithm will create an IoV from your requested ExpRuns, or from the overall ExpRuns of the input data if you haven't specified ExpRuns in this function.

No checks are performed to make sure that a IoV you specify matches the data you ran over, it simply labels the IoV to commit to the database later.

Definition at line 114 of file CalibrationAlgorithm.cc.

fillTimeDistanceProfiles()

void fillTimeDistanceProfiles ( TProfile *  profileRpcPhi, TProfile *  profileRpcZ, TProfile *  profileBKLMScintillatorPhi, TProfile *  profileBKLMScintillatorZ, TProfile *  profileEKLMScintillatorPlane1, TProfile *  profileEKLMScintillatorPlane2, bool  fill2dHistograms  )

private

Fill profiles of time versus distance.

Parameters

[out]	profileRpcPhi	BKLM RPC phi plane.
[out]	profileRpcZ	BKLM RPC z plane.
[out]	profileBKLMScintillatorPhi	BKLM scintillator phi plane.
[out]	profileBKLMScintillatorZ	BKLM scintillator z plane.
[out]	profileEKLMScintillatorPlane1	EKLM scintillator plane1.
[out]	profileEKLMScintillatorPlane2	EKLM scintillator plane2.
[in]	fill2dHistograms	Whether to fill 2d histograms.

Definition at line 595 of file KLMTimeAlgorithm.cc.

getCollectorName()

std::string getCollectorName ( ) const

inlineinherited

Alias for prefix.

For convenience and less writing, we say developers to set this to default collector module name in constructor of base class. One can however use the dublets of collector+algorithm multiple times with different settings. To bind these together correctly, the prefix has to be set the same for algo and collector. So we call the setter setPrefix rather than setModuleName or whatever. This getter will work out of the box for default cases -> return the name of module you have to add to your path to collect data for this algorihtm.

Definition at line 164 of file CalibrationAlgorithm.h.

readCalibrationData()

CalibrationAlgorithm::EResult readCalibrationData ( )

private

Read calibration data.

Returns

CalibrationAlgorithm::c_OK if the amount of data is sufficient, CalibrationAlgorithm::c_NotEnoughData otherwise.

Definition at line 141 of file KLMTimeAlgorithm.cc.

setInputFileNames() [1/2]

void setInputFileNames ( PyObject *  inputFileNames )

inherited

Set the input file names used for this algorithm from a Python list.

Set the input file names used for this algorithm and resolve the wildcards.

Definition at line 166 of file CalibrationAlgorithm.cc.

setInputFileNames() [2/2]

void setInputFileNames ( std::vector< std::string >  inputFileNames )

protectedinherited

Set the input file names used for this algorithm.

Set the input file names used for this algorithm and resolve the wildcards.

Definition at line 194 of file CalibrationAlgorithm.cc.

setLowerLimit()

void setLowerLimit ( int  counts )

inline

Set the lower number of hits collected on one sigle strip.

If the hit number is lower than the limit, the strip will not be calibrated and set the average value of the calibration constant.

Definition at line 161 of file KLMTimeAlgorithm.h.

    {
      m_lower_limit_counts = counts;
    }

setMC()

void setMC ( bool  mc )

inline

Set flag indicating whether the input is MC sample.

The histogram ranges are different for data and MC. This setting cannot be determined automatically, because the collector output does not contain metadata.

Definition at line 135 of file KLMTimeAlgorithm.h.

timeDistance2dFit()

void timeDistance2dFit ( const std::vector< std::pair< KLMChannelNumber, unsigned int > > &  channels, double &  delay, double &  delayError  )

private

Two-dimensional fit for individual channels.

Parameters

[in]	channels	Channels.
[out]	delay	Delay (ns / cm).
[out]	delayError	Delay error.

Definition at line 656 of file KLMTimeAlgorithm.cc.

tR_lowerStrip()

std::pair< int, double > tR_lowerStrip

(

const KLMChannelIndex &

klmChannel

)

Tracing avaiable channels with decreasing strip number.

Parameters

[in]

klmChannel

KLM channel index.

Definition at line 1690 of file KLMTimeAlgorithm.cc.

tR_upperStrip()

std::pair< int, double > tR_upperStrip

(

const KLMChannelIndex &

klmChannel

)

Tracing avaiable channels with increasing strip number.

Parameters

[in]

klmChannel

KLM channel index.

Definition at line 1664 of file KLMTimeAlgorithm.cc.

tS_lowerStrip()

std::pair< int, double > tS_lowerStrip

(

const KLMChannelIndex &

klmChannel

)

Tracing avaiable channels with decreasing strip number.

Parameters

[in]

klmChannel

KLM channel index.

Definition at line 1610 of file KLMTimeAlgorithm.cc.

tS_upperStrip()

std::pair< int, double > tS_upperStrip

(

const KLMChannelIndex &

klmChannel

)

Tracing avaiable channels with increasing strip number.

Parameters

[in]

klmChannel

KLM channel index.

Definition at line 1584 of file KLMTimeAlgorithm.cc.

Member Data Documentation

fcn_const

TF1* fcn_const = nullptr

private

Const function.

Global time distribution fitting.

Definition at line 744 of file KLMTimeAlgorithm.h.

fcn_gaus

TF1* fcn_gaus = nullptr

private

Gaussian function.

Scitillator time ditribution fitting.

Definition at line 747 of file KLMTimeAlgorithm.h.

fcn_land

TF1* fcn_land = nullptr

private

Landau function.

RPC time ditribution fitting.

Definition at line 750 of file KLMTimeAlgorithm.h.

fcn_pol1

TF1* fcn_pol1 = nullptr

private

Pol1 function.

Effective light speed fitting.

Definition at line 741 of file KLMTimeAlgorithm.h.

m_evts

std::map<KLMChannelNumber, std::vector<struct Event> > m_evts

private

Container of hit information.

the global element number of the strip is used as the key.

Definition at line 279 of file KLMTimeAlgorithm.h.

---

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

• klm/calibration/include/KLMTimeAlgorithm.h
• klm/calibration/src/KLMTimeAlgorithm.cc