eclTValidationAlgorithm Class Reference

Validate the ecl timing calibrations using a hadronic event selection. More...

#include <eclTValidationAlgorithm.h>

Inheritance diagram for eclTValidationAlgorithm:

Collaboration diagram for eclTValidationAlgorithm:

Public Types
enum	EResult {   c_OK ,   c_Iterate ,   c_NotEnoughData ,   c_Failure ,   c_Undefined }
	The result of calibration. More...

Public Member Functions
	eclTValidationAlgorithm ()
	..Constructor
	eclTValidationAlgorithm (std::string physicsProcessCollectorName)
	..Constructor - main one as it allows user to choose which collector data to analyse
	~eclTValidationAlgorithm ()
	..Destructor
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>.

Public Attributes
int	cellIDLo
	Fit crystals with cellID0 in the inclusive range [cellIDLo,cellIDHi].
int	cellIDHi
	Fit crystals with cellID0 in the inclusive range [cellIDLo,cellIDHi].
bool	readPrevCrysPayload
	Read the previous crystal payload values for comparison.
double	meanCleanRebinFactor
	Rebinning factor for mean calculation.
double	meanCleanCutMinFactor
	After rebinning, create a mask for bins that have values less than meanCleanCutMinFactor times the maximum bin value. More...
double	clusterTimesFractionWindow_maxtime
	Maximum time for window to calculate cluster time fraction, in ns.
bool	debugOutput
	Save every histogram and fitted function to debugFilename.
std::string	debugFilenameBase
	Name of file with debug output, eclTValidationAlgorithm.root by default.

Protected Member Functions
EResult	calibrate () override
	..Run algorithm on events 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

Private Member Functions
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::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)

Detailed Description

Validate the ecl timing calibrations using a hadronic event selection.

Definition at line 27 of file eclTValidationAlgorithm.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 events

Put root into batch mode so that we don't try to open a graphics window

Write out job parameters

---

Implements CalibrationAlgorithm.

Definition at line 82 of file eclTValidationAlgorithm.cc.

{
  gROOT->SetBatch();
 
 
  B2INFO("eclTValidationAlgorithm parameters:");
  B2INFO("cellIDLo = " << cellIDLo);
  B2INFO("cellIDHi = " << cellIDHi);
  B2INFO("readPrevCrysPayload = " << readPrevCrysPayload);
  B2INFO("meanCleanRebinFactor = " << meanCleanRebinFactor);
  B2INFO("meanCleanCutMinFactor = " << meanCleanCutMinFactor);
  B2INFO("clusterTimesFractionWindow_maxtime = " << clusterTimesFractionWindow_maxtime);
 
 
  /* Histogram with the data collected by eclTimeCalibrationValidationCollector*/
  auto clusterTime = getObjectPtr<TH1F>("clusterTime");
  auto clusterTime_cid = getObjectPtr<TH2F>("clusterTime_cid");
  auto clusterTime_run = getObjectPtr<TH2F>("clusterTime_run");
  auto clusterTimeClusterE = getObjectPtr<TH2F>("clusterTimeClusterE");
  auto dt99_clusterE = getObjectPtr<TH2F>("dt99_clusterE");
  auto eventT0 = getObjectPtr<TH1F>("eventT0");
  auto clusterTimeE0E1diff = getObjectPtr<TH1F>("clusterTimeE0E1diff");
 
  // Collect other plots just for reference - combines all the runs for these plots.
  auto cutflow = getObjectPtr<TH1F>("cutflow");
 
  vector <int> binProjectionLeft_Time_vs_E_runDep ;
  vector <int> binProjectionRight_Time_vs_E_runDep ;
 
  for (int binCounter = 1; binCounter <= clusterTimeClusterE->GetNbinsX(); binCounter++) {
    binProjectionLeft_Time_vs_E_runDep.push_back(binCounter);
    binProjectionRight_Time_vs_E_runDep.push_back(binCounter);
  }
 
  if (!clusterTime_cid) return c_Failure;
 
  TFile* histfile = 0;
 
  /* 1/(4fRF) = 0.4913 ns/clock tick, where fRF is the accelerator RF frequency.
     Same for all crystals. */
  const double TICKS_TO_NS = 1.0 / (4.0 * EclConfiguration::m_rf) * 1e3;
 
  // Vector of time offsets to track how far from nominal the cluster times are.
  vector<float> t_offsets(8736, 0.0);
  vector<float> t_offsets_unc(8736, 0.0);
  vector<long> numClusterPerCrys(8736, 0);
  vector<bool> crysHasGoodFitandStats(8736, false);
  vector<bool> crysHasGoodFit(8736, false);
  int numCrysWithNonZeroEntries = 0 ;
  int numCrysWithGoodFit = 0 ;
 
  int minNumEntries = 40;
 
  double mean;
  double sigma;
 
 
  bool minRunNumBool = false;
  bool maxRunNumBool = false;
  int minRunNum = -1;
  int maxRunNum = -1;
  int minExpNum = -1;
  int maxExpNum = -1;
  for (auto expRun : getRunList()) {
    int expNumber = expRun.first;
    int runNumber = expRun.second;
    if (!minRunNumBool) {
      minExpNum = expNumber;
      minRunNum = runNumber;
      minRunNumBool = true;
    }
    if (!maxRunNumBool) {
      maxExpNum = expNumber;
      maxRunNum = runNumber;
      maxRunNumBool = true;
    }
    if (((minRunNum > runNumber)   && (minExpNum >= expNumber))  ||
        (minExpNum > expNumber)) {
      minExpNum = expNumber;
      minRunNum = runNumber;
    }
    if (((maxRunNum < runNumber)   && (maxExpNum <= expNumber))  ||
        (maxExpNum < expNumber))
 
    {
      maxExpNum = expNumber;
      maxRunNum = runNumber;
    }
  }
 
  B2INFO("debugFilenameBase = " << debugFilenameBase);
  string runNumsString = string("_") + to_string(minExpNum) + "_" + to_string(minRunNum) + string("-") +
                         to_string(maxExpNum) + "_" + to_string(maxRunNum);
  string debugFilename = debugFilenameBase + runNumsString + string(".root");
 
 
  // Need to load information about the event/run/experiment to get the right database information
  // Will be used for:
  // * ECLChannelMapper (to map crystal to crates)
  // * crystal payload updating for iterating crystal and crate fits
  int eventNumberForCrates = 1;
 
 
  //-------------------------------------------------------------------
  /* Uploading older payloads for the current set of runs */
 
  StoreObjPtr<EventMetaData> evtPtr;
  // simulate the initialize() phase where we can register objects in the DataStore
  DataStore::Instance().setInitializeActive(true);
  evtPtr.registerInDataStore();
  DataStore::Instance().setInitializeActive(false);
  // now construct the event metadata
  evtPtr.construct(eventNumberForCrates, minRunNum, minExpNum);
  // and update the database contents
  DBStore& dbstore = DBStore::Instance();
  dbstore.update();
  // this is only needed it the payload might be intra-run dependent,
  // that is if it might change during one run as well
  dbstore.updateEvent();
 
 
  B2INFO("Uploading payload for exp " << minExpNum << ", run " << minRunNum << ", event " << eventNumberForCrates);
  updateDBObjPtrs(eventNumberForCrates, minRunNum, minExpNum);
  unique_ptr<ECLChannelMapper> crystalMapper(new ECL::ECLChannelMapper());
  crystalMapper->initFromDB();
 
  //------------------------------------------------------------------------
  //..Read payloads from database
  DBObjPtr<Belle2::ECLCrystalCalib> crystalTimeObject("ECLCrystalTimeOffset");
  B2INFO("Dumping payload");
 
  //..Get vectors of values from the payloads
  std::vector<float> currentValuesCrys = crystalTimeObject->getCalibVector();
  std::vector<float> currentUncCrys = crystalTimeObject->getCalibUncVector();
 
  //..Print out a few values for quality control
  B2INFO("Values read from database.  Write out for their values for comparison against those from tcol");
  for (int ic = 0; ic < 8736; ic += 500) {
    B2INFO("ts: cellID " << ic + 1 << " " << currentValuesCrys[ic] << " +/- " << currentUncCrys[ic]);
  }
 
 
  //..Read in the previous crystal payload values
  DBObjPtr<Belle2::ECLCrystalCalib> customPrevCrystalTimeObject("ECLCrystalTimeOffsetPreviousValues");
  vector<float> prevValuesCrys(8736);
  if (readPrevCrysPayload) {
    //..Get vectors of values from the payloads
    prevValuesCrys = customPrevCrystalTimeObject->getCalibVector();
 
    //..Print out a few values for quality control
    B2INFO("Previous values read from database.  Write out for their values for comparison against those from tcol");
    for (int ic = 0; ic < 8736; ic += 500) {
      B2INFO("ts custom previous payload: cellID " << ic + 1 << " " << prevValuesCrys[ic]);
    }
  }
 
 
  //------------------------------------------------------------------------
  //..Start looking at timing information
 
  B2INFO("Debug output rootfile: " << debugFilename);
  histfile = new TFile(debugFilename.c_str(), "recreate");
 
 
  clusterTime         ->Write();
  clusterTime_cid     ->Write();
  clusterTime_run     ->Write();
  clusterTimeClusterE ->Write();
  dt99_clusterE       ->Write();
  eventT0             ->Write();
  clusterTimeE0E1diff ->Write();
 
  cutflow->Write();
 
 
  double hist_tmin = clusterTime->GetXaxis()->GetXmin();
  double hist_tmax = clusterTime->GetXaxis()->GetXmax();
  int hist_nTbins  = clusterTime->GetNbinsX();
 
  B2INFO("hist_tmin = " << hist_tmin);
  B2INFO("hist_tmax = " << hist_tmax);
  B2INFO("hist_nTbins = " << hist_nTbins);
 
  double time_fit_min = hist_tmax;   // Set min value to largest possible value so that it gets reset
  double time_fit_max = hist_tmin;   // Set max value to smallest possible value so that it gets reset
 
 
  // define histogram for keeping track of the peak of the cluster times per crystal
  auto peakClusterTime_cid = new TH1F("peakClusterTime_cid", ";cell id;Peak cluster time [ns]", 8736, 1, 8736 + 1);
  auto peakClusterTimes = new TH1F("peakClusterTimes",
                                   "-For crystals with at least one hit-;Peak cluster time [ns];Number of crystals",
                                   hist_nTbins, hist_tmin, hist_tmax);
  auto peakClusterTimesGoodFit = new TH1F("peakClusterTimesGoodFit",
                                          "-For crystals with a good fit to distribution of hits-;Peak cluster time [ns];Number of crystals",
                                          hist_nTbins, hist_tmin, hist_tmax);
 
  auto peakClusterTimesGoodFit__cid = new TH1F("peakClusterTimesGoodFit__cid",
                                               "-For crystals with a good fit to distribution of hits-;cell id (only crystals with good fit);Peak cluster time [ns]",
                                               8736, 1, 8736 + 1);
 
 
  // define histograms to keep track of the difference in the new crystal times vs the old ones
  auto tsNew_MINUS_tsCustomPrev__cid = new TH1F("TsNew_MINUS_TsCustomPrev__cid",
                                                ";cell id; ts(new|merged) - ts(old = 'pre-calib'|merged)  [ns]",
                                                8736, 1, 8736 + 1);
 
  auto tsNew_MINUS_tsCustomPrev = new TH1F("TsNew_MINUS_TsCustomPrev",
                                           ";ts(new | merged) - ts(old = 'pre-calib' | merged)  [ns];Number of crystals",
                                           285, -69.5801, 69.5801);
 
 
 
  // Histogram to keep track of the fraction of cluster times within a window.
  double timeWindow_maxTime = clusterTimesFractionWindow_maxtime;
  B2INFO("timeWindow_maxTime = " << timeWindow_maxTime);
  int binyLeft = clusterTime_cid->GetYaxis()->FindBin(-timeWindow_maxTime);
  int binyRight = clusterTime_cid->GetYaxis()->FindBin(timeWindow_maxTime);
  double windowLowTimeFromBin = clusterTime_cid->GetYaxis()->GetBinLowEdge(binyLeft);
  double windowHighTimeFromBin = clusterTime_cid->GetYaxis()->GetBinLowEdge(binyRight + 1);
  std::string s_lowTime = std::to_string(windowLowTimeFromBin);
  std::string s_highTime = std::to_string(windowHighTimeFromBin);
  TString fracWindowTitle = "Fraction of cluster times in window [" + s_lowTime + ", " + s_highTime +
                            "] ns;cell id;Fraction of cluster times in window";
  B2INFO("fracWindowTitle = " << fracWindowTitle);
  TString fracWindowInGoodECLRingsTitle = "Fraction of cluster times in window [" + s_lowTime + ", " + s_highTime +
                                          "] ns and in good ECL theta rings;cell id;Fraction cluster times in window + good ECL rings";
  B2INFO("fracWindowInGoodECLRingsTitle = " << fracWindowInGoodECLRingsTitle);
  B2INFO("Good ECL rings skip gaps in the acceptance, and includes ECL theta IDs: 3-10, 15-39, 44-56, 61-66.");
 
  TString fracWindowHistTitle = "Fraction of cluster times in window [" + s_lowTime + ", " + s_highTime +
                                "] ns;Fraction of cluster times in window;Number of crystals";
 
  auto clusterTimeNumberInWindow__cid = new TH1F("clusterTimeNumberInWindow__cid", fracWindowTitle, 8736, 1, 8736 + 1);
  auto clusterTimeNumberInWindowInGoodECLRings__cid = new TH1F("clusterTimeNumberInWindowInGoodECLRings__cid", fracWindowTitle, 8736,
      1, 8736 + 1);
  auto clusterTimeNumber__cid = new TH1F("clusterTimeNumber_cid", fracWindowTitle, 8736, 1, 8736 + 1);
  auto clusterTimeFractionInWindow = new TH1F("clusterTimeFractionInWindow", fracWindowHistTitle, 110, 0.0, 1.1);
 
  clusterTimeNumberInWindow__cid->Sumw2();
  clusterTimeNumberInWindowInGoodECLRings__cid->Sumw2();
  clusterTimeNumber__cid->Sumw2();
 
 
 
  /* CRYSTAL BY CRYSTAL VALIDATION */
 
  ECLGeometryPar* eclgeo = ECLGeometryPar::Instance();
 
  // Loop over all the crystals for doing the crystal calibation
  for (int crys_id = cellIDLo; crys_id <= cellIDHi; crys_id++) {
    double clusterTime_mean = 0;
    double clusterTime_mean_unc = 0;
 
    B2INFO("Crystal cell id = " << crys_id);
 
    eclgeo->Mapping(crys_id - 1);
    int thetaID = eclgeo->GetThetaID();
 
 
    /* Determining which bins to mask out for mean calculation
    */
 
    TH1D* h_time = clusterTime_cid->ProjectionY((std::string("h_time_psi__") + std::to_string(crys_id)).c_str(),
                                                crys_id, crys_id);
    TH1D* h_timeMask = (TH1D*)h_time->Clone();
    TH1D* h_timeMasked = (TH1D*)h_time->Clone((std::string("h_time_psi_masked__") + std::to_string(crys_id)).c_str());
    TH1D* h_timeRebin = (TH1D*)h_time->Clone();
 
    // Do rebinning and cleaning of some bins but only if the user selection values call for it since it slows the code down
    if (meanCleanRebinFactor != 1 || meanCleanCutMinFactor != 1) {
 
      h_timeRebin->Rebin(meanCleanRebinFactor);
 
      h_timeMask->Scale(0.0);   // set all bins to being masked off
 
      time_fit_min = hist_tmax;   // Set min value to largest possible value so that it gets reset
      time_fit_max = hist_tmin;   // Set max value to smallest possible value so that it gets reset
 
      // Find value of bin with max value
      double histRebin_max = h_timeRebin->GetMaximum();
 
      bool maskedOutNonZeroBin = false;
      // Loop over all bins to find those with content less than a certain threshold.  Mask the non-rebinned histogram for the corresponding bins
      for (int bin = 1; bin <= h_timeRebin->GetNbinsX(); bin++) {
        for (int rebinCounter = 1; rebinCounter <= meanCleanRebinFactor; rebinCounter++) {
          int nonRebinnedBinNumber = (bin - 1) * meanCleanRebinFactor + rebinCounter;
          if (nonRebinnedBinNumber < h_time->GetNbinsX()) {
            if (h_timeRebin->GetBinContent(bin) >= histRebin_max * meanCleanCutMinFactor) {
              h_timeMask->SetBinContent(nonRebinnedBinNumber, 1);
 
              // Save the lower and upper edges of the rebin histogram time range for fitting purposes
              double x_lower = h_timeRebin->GetXaxis()->GetBinLowEdge(bin);
              double x_upper = h_timeRebin->GetXaxis()->GetBinUpEdge(bin);
              if (x_lower < time_fit_min) {
                time_fit_min = x_lower;
              }
              if (x_upper > time_fit_max) {
                time_fit_max = x_upper;
              }
 
            } else {
              if (h_time->GetBinContent(nonRebinnedBinNumber) > 0) {
                B2DEBUG(22, "Setting bin " << nonRebinnedBinNumber << " from " << h_timeMasked->GetBinContent(nonRebinnedBinNumber) << " to 0");
                maskedOutNonZeroBin = true;
              }
              h_timeMasked->SetBinContent(nonRebinnedBinNumber, 0);
            }
          }
        }
      }
      B2INFO("Bins with non-zero values have been masked out: " << maskedOutNonZeroBin);
      h_timeMasked->ResetStats();
      h_timeMask->ResetStats();
 
    }
 
    // Calculate mean from masked histogram
    double default_meanMasked = h_timeMasked->GetMean();
    //double default_meanMasked_unc = h_timeMasked->GetMeanError();
    B2INFO("default_meanMasked = " << default_meanMasked);
 
 
    // Get the overall mean and standard deviation of the distribution within the plot.  This doesn't require a fit.
    double default_mean = h_time->GetMean();
    double default_mean_unc = h_time->GetMeanError();
    double default_sigma = h_time->GetStdDev();
 
    B2INFO("Fitting crystal between " << time_fit_min << " and " << time_fit_max);
 
    // gaus(0) is a substitute for [0]*exp(-0.5*((x-[1])/[2])**2)
    TF1* gaus = new TF1("func", "gaus(0)", time_fit_min, time_fit_max);
    gaus->SetParNames("numCrystalHitsNormalization", "mean", "sigma");
    /*
       gaus->ReleaseParameter(0);  // number of crystals
       gaus->ReleaseParameter(1);  // mean
       gaus->ReleaseParameter(2);  // standard deviation
    */
 
    double hist_max = h_time->GetMaximum();
 
    //=== Estimate initial value of sigma as std dev.
    double stddev = h_time->GetStdDev();
    sigma = stddev;
    mean = default_mean;
 
    //=== Setting parameters for initial iteration
    gaus->SetParameter(0, hist_max / 2.);
    gaus->SetParameter(1, mean);
    gaus->SetParameter(2, sigma);
    // L -- Use log likelihood method
    // I -- Use integral of function in bin instead of value at bin center  // not using
    // R -- Use the range specified in the function range
    // B -- Fix one or more parameters with predefined function   // not using
    // Q -- Quiet mode
 
    h_timeMasked->Fit(gaus, "LQR");  // L for likelihood, R for x-range, Q for fit quiet mode
 
    double fit_mean     = gaus->GetParameter(1);
    double fit_mean_unc = gaus->GetParError(1);
    double fit_sigma    = gaus->GetParameter(2);
 
    double meanDiff =  fit_mean - default_mean;
    double meanUncDiff = fit_mean_unc - default_mean_unc;
    double sigmaDiff = fit_sigma - default_sigma;
 
    bool good_fit = false;
 
    if ((fabs(meanDiff) > 10)      ||
        (fabs(meanUncDiff) > 10)   ||
        (fabs(sigmaDiff) > 10)     ||
        (fit_mean_unc > 3)         ||
        (fit_sigma < 0.1)          ||
        (fit_mean < time_fit_min)  ||
        (fit_mean > time_fit_max)) {
      B2INFO("Crystal cell id = " << crys_id);
      B2INFO("fit mean, default mean = " << fit_mean << ", " << default_mean);
      B2INFO("fit mean unc, default mean unc = " << fit_mean_unc << ", " << default_mean_unc);
      B2INFO("fit sigma, default sigma = " << fit_sigma << ", " << default_sigma);
 
      B2INFO("crystal fit mean - hist mean = " << meanDiff);
      B2INFO("fit mean unc. - hist mean unc. = " << meanUncDiff);
      B2INFO("fit sigma - hist sigma = " << sigmaDiff);
 
      B2INFO("fit_mean = " << fit_mean);
      B2INFO("time_fit_min = " << time_fit_min);
      B2INFO("time_fit_max = " << time_fit_max);
 
      if (fabs(meanDiff) > 10)       B2INFO("fit mean diff too large");
      if (fabs(meanUncDiff) > 10)    B2INFO("fit mean unc diff too large");
      if (fabs(sigmaDiff) > 10)      B2INFO("fit mean sigma diff too large");
      if (fit_mean_unc > 3)          B2INFO("fit mean unc too large");
      if (fit_sigma < 0.1)           B2INFO("fit sigma too small");
 
    } else {
      good_fit = true;
      numCrysWithGoodFit++;
      crysHasGoodFit[crys_id - 1] = true ;
    }
 
 
    int numEntries = h_time->GetEntries();
    // If number of entries in histogram is greater than X then use the statistical information from the data otherwise leave crystal uncalibrated.  Histograms are still shown though.
    //  ALSO require the that fits are good.
    if ((numEntries >= minNumEntries)  &&  good_fit) {
      clusterTime_mean = fit_mean;
      clusterTime_mean_unc = fit_mean_unc;
      crysHasGoodFitandStats[crys_id - 1] = true ;
    } else {
      clusterTime_mean = default_mean;
      clusterTime_mean_unc = default_mean_unc;
    }
 
    if (numEntries < minNumEntries)   B2INFO("Number of entries less than minimum");
    if (numEntries == 0)   B2INFO("Number of entries == 0");
 
 
    t_offsets[crys_id - 1] = clusterTime_mean ;
    t_offsets_unc[crys_id - 1] = clusterTime_mean_unc ;
    numClusterPerCrys[crys_id - 1] = numEntries ;
 
    histfile->WriteTObject(h_time, (std::string("h_time_psi") + std::to_string(crys_id)).c_str());
    histfile->WriteTObject(h_timeMasked, (std::string("h_time_psi_masked") + std::to_string(crys_id)).c_str());
 
    // Set this for each crystal even if there are zero entries
    peakClusterTime_cid->SetBinContent(crys_id, t_offsets[crys_id - 1]);
    peakClusterTime_cid->SetBinError(crys_id, t_offsets_unc[crys_id - 1]);
 
    /* Store mean cluster time info in a separate histogram but only if there is at
       least one entry for that crystal.  */
    if (numEntries > 0) {
      peakClusterTimes->Fill(t_offsets[crys_id - 1]);
      numCrysWithNonZeroEntries++ ;
    }
    if ((numEntries >= minNumEntries)  &&  good_fit) {
      peakClusterTimesGoodFit->Fill(t_offsets[crys_id - 1]);
      peakClusterTimesGoodFit__cid->SetBinContent(crys_id, t_offsets[crys_id - 1]);
      peakClusterTimesGoodFit__cid->SetBinError(crys_id, t_offsets_unc[crys_id - 1]);
    }
 
 
    // Find the fraction of cluster times within +-X ns and fill histograms
    double numClusterTimesWithinWindowFraction = h_time->Integral(binyLeft, binyRight) ;
    double clusterTimesWithinWindowFraction = numClusterTimesWithinWindowFraction;
    if (numEntries > 0) {
      clusterTimesWithinWindowFraction /= numEntries;
    } else {
      clusterTimesWithinWindowFraction = -0.1;
    }
 
    B2INFO("Crystal cell id = " << crys_id << ", theta id = " <<
           thetaID << ", clusterTimesWithinWindowFraction = " <<
           numClusterTimesWithinWindowFraction << " / " << numEntries << " = " <<
           clusterTimesWithinWindowFraction);
 
    clusterTimeFractionInWindow->Fill(clusterTimesWithinWindowFraction);
    clusterTimeNumberInWindow__cid->SetBinContent(crys_id, numClusterTimesWithinWindowFraction);
    clusterTimeNumber__cid->SetBinContent(crys_id, numEntries);
 
    if ((thetaID >= 3   &&  thetaID <= 10)    ||
        (thetaID >= 15  &&  thetaID <= 39)    ||
        (thetaID >= 44  &&  thetaID <= 56)    ||
        (thetaID >= 61  &&  thetaID <= 66)) {
      clusterTimeNumberInWindowInGoodECLRings__cid->SetBinContent(crys_id, numClusterTimesWithinWindowFraction);
    }
 
 
    delete gaus;
  }
 
  // Find the fraction of cluster times within +-X ns and fill histogram
  auto g_clusterTimeFractionInWindow__cid = new TGraphAsymmErrors(clusterTimeNumberInWindow__cid, clusterTimeNumber__cid, "w");
  auto g_clusterTimeFractionInWindowInGoodECLRings__cid = new TGraphAsymmErrors(clusterTimeNumberInWindowInGoodECLRings__cid,
      clusterTimeNumber__cid, "w");
  g_clusterTimeFractionInWindow__cid->SetTitle(fracWindowTitle);
  g_clusterTimeFractionInWindowInGoodECLRings__cid->SetTitle(fracWindowInGoodECLRingsTitle);
 
 
  peakClusterTime_cid->ResetStats();
  peakClusterTimesGoodFit__cid->ResetStats();
 
  histfile->WriteTObject(peakClusterTime_cid, "peakClusterTime_cid");
  histfile->WriteTObject(peakClusterTimes, "peakClusterTimes");
  histfile->WriteTObject(peakClusterTimesGoodFit__cid, "peakClusterTimesGoodFit__cid");
  histfile->WriteTObject(peakClusterTimesGoodFit, "peakClusterTimesGoodFit");
  histfile->WriteTObject(g_clusterTimeFractionInWindow__cid, "g_clusterTimeFractionInWindow__cid");
  histfile->WriteTObject(g_clusterTimeFractionInWindowInGoodECLRings__cid, "g_clusterTimeFractionInWindowInGoodECLRings__cid");
  histfile->WriteTObject(clusterTimeFractionInWindow, "clusterTimeFractionInWindow");
 
 
 
  /*  -----------------------------------------------------------
      Fit the time histograms for different energy slices  */
 
  vector <int> binProjectionLeft  = binProjectionLeft_Time_vs_E_runDep;
  vector <int> binProjectionRight = binProjectionRight_Time_vs_E_runDep;
 
  auto h2 = clusterTimeClusterE;
 
 
  double max_E = h2->GetXaxis()->GetXmax();
 
  // Determine the energy bins.  Save the left edge for histogram purposes
  vector <double> E_binEdges(binProjectionLeft.size() + 1);
  for (long unsigned int x_bin = 0; x_bin < binProjectionLeft.size(); x_bin++) {
    TH1D* h_E_t_slice = h2->ProjectionX("h_E_t_slice", 1, 1) ;
    E_binEdges[x_bin] = h_E_t_slice->GetXaxis()->GetBinLowEdge(binProjectionLeft[x_bin]) ;
    B2INFO("E_binEdges[" << x_bin << "] = " << E_binEdges[x_bin]);
    if (x_bin == binProjectionLeft.size() - 1) {
      E_binEdges[x_bin + 1] = max_E ;
      B2INFO("E_binEdges[" << x_bin + 1 << "] = " << E_binEdges[x_bin + 1]);
    }
  }
 
 
  auto clusterTimePeak_ClusterEnergy_varBin = new TH1F("clusterTimePeak_ClusterEnergy_varBin",
                                                       ";ECL cluster energy [GeV];Cluster time fit position [ns]", E_binEdges.size() - 1, &(E_binEdges[0]));
  auto clusterTimePeakWidth_ClusterEnergy_varBin = new TH1F("clusterTimePeakWidth_ClusterEnergy_varBin",
                                                            ";ECL cluster energy [GeV];Cluster time fit width [ns]", E_binEdges.size() - 1, &(E_binEdges[0]));
 
  int Ebin_counter = 1 ;
 
  // Loop over all the energy bins
  for (long unsigned int x_bin = 0; x_bin < binProjectionLeft.size(); x_bin++) {
    double clusterTime_mean = 0;
    double clusterTime_mean_unc = 0;
    double clusterTime_sigma = 0;
 
    B2INFO("x_bin = " << x_bin);
 
    /* Determining which bins to mask out for mean calculation
    */
    TH1D* h_time = h2->ProjectionY(("h_time_E_slice_" + std::to_string(x_bin)).c_str(), binProjectionLeft[x_bin],
                                   binProjectionRight[x_bin]) ;
 
 
    TH1D* h_E_t_slice = h2->ProjectionX("h_E_t_slice", 1, 1) ;
    double lowE  = h_E_t_slice->GetXaxis()->GetBinLowEdge(binProjectionLeft[x_bin]) ;
    double highE = h_E_t_slice->GetXaxis()->GetBinUpEdge(binProjectionRight[x_bin]) ;
    double meanE = (lowE + highE) / 2.0 ;
 
    B2INFO("bin " << Ebin_counter << ": low E = " << lowE << ", high E = " << highE << " GeV");
 
    TH1D* h_timeMask = (TH1D*)h_time->Clone();
    TH1D* h_timeMasked = (TH1D*)h_time->Clone((std::string("h_time_E_slice_masked__") + std::to_string(meanE)).c_str());
    TH1D* h_timeRebin = (TH1D*)h_time->Clone();
 
 
    if (meanCleanRebinFactor != 1 || meanCleanCutMinFactor != 1) {
 
      h_timeRebin->Rebin(meanCleanRebinFactor);
 
      h_timeMask->Scale(0.0);   // set all bins to being masked off
 
      time_fit_min = hist_tmax;   // Set min value to largest possible value so that it gets reset
      time_fit_max = hist_tmin;   // Set max value to smallest possible value so that it gets reset
 
      // Find value of bin with max value
      double histRebin_max = h_timeRebin->GetMaximum();
 
      bool maskedOutNonZeroBin = false;
      // Loop over all bins to find those with content less than a certain threshold.  Mask the non-rebinned histogram for the corresponding bins
      for (int bin = 1; bin <= h_timeRebin->GetNbinsX(); bin++) {
        for (int rebinCounter = 1; rebinCounter <= meanCleanRebinFactor; rebinCounter++) {
          int nonRebinnedBinNumber = (bin - 1) * meanCleanRebinFactor + rebinCounter;
          if (nonRebinnedBinNumber < h_time->GetNbinsX()) {
            if (h_timeRebin->GetBinContent(bin) >= histRebin_max * meanCleanCutMinFactor) {
              h_timeMask->SetBinContent(nonRebinnedBinNumber, 1);
 
              // Save the lower and upper edges of the rebin histogram time range for fitting purposes
              double x_lower = h_timeRebin->GetXaxis()->GetBinLowEdge(bin);
              double x_upper = h_timeRebin->GetXaxis()->GetBinUpEdge(bin);
              if (x_lower < time_fit_min) {
                time_fit_min = x_lower;
              }
              if (x_upper > time_fit_max) {
                time_fit_max = x_upper;
              }
 
            } else {
              if (h_time->GetBinContent(nonRebinnedBinNumber) > 0) {
                B2DEBUG(22, "Setting bin " << nonRebinnedBinNumber << " from " << h_timeMasked->GetBinContent(nonRebinnedBinNumber) << " to 0");
                maskedOutNonZeroBin = true;
              }
              h_timeMasked->SetBinContent(nonRebinnedBinNumber, 0);
            }
          }
        }
      }
      B2INFO("Bins with non-zero values have been masked out: " << maskedOutNonZeroBin);
      h_timeMasked->ResetStats();
      h_timeMask->ResetStats();
 
    }
 
 
    // Calculate mean from masked histogram
    double default_meanMasked = h_timeMasked->GetMean();
    //double default_meanMasked_unc = h_timeMasked->GetMeanError();
    B2INFO("default_meanMasked = " << default_meanMasked);
 
 
    // Get the overall mean and standard deviation of the distribution within the plot.  This doesn't require a fit.
    double default_mean = h_time->GetMean();
    double default_mean_unc = h_time->GetMeanError();
    double default_sigma = h_time->GetStdDev();
 
    B2INFO("Fitting crystal between " << time_fit_min << " and " << time_fit_max);
 
    // gaus(0) is a substitute for [0]*exp(-0.5*((x-[1])/[2])**2)
    TF1* gaus = new TF1("func", "gaus(0)", time_fit_min, time_fit_max);
    gaus->SetParNames("numCrystalHitsNormalization", "mean", "sigma");
    /*
       gaus->ReleaseParameter(0);  // number of crystals
       gaus->ReleaseParameter(1);  // mean
       gaus->ReleaseParameter(2);  // standard deviation
    */
 
    double hist_max = h_time->GetMaximum();
 
    //=== Estimate initial value of sigma as std dev.
    double stddev = h_time->GetStdDev();
    sigma = stddev;
    mean = default_mean;
 
    //=== Setting parameters for initial iteration
    gaus->SetParameter(0, hist_max / 2.);
    gaus->SetParameter(1, mean);
    gaus->SetParameter(2, sigma);
    // L -- Use log likelihood method
    // I -- Use integral of function in bin instead of value at bin center  // not using
    // R -- Use the range specified in the function range
    // B -- Fix one or more parameters with predefined function   // not using
    // Q -- Quiet mode
 
    h_timeMasked->Fit(gaus, "LQR");  // L for likelihood, R for x-range, Q for fit quiet mode
 
    double fit_mean     = gaus->GetParameter(1);
    double fit_mean_unc = gaus->GetParError(1);
    double fit_sigma    = gaus->GetParameter(2);
 
    double meanDiff =  fit_mean - default_mean;
    double meanUncDiff = fit_mean_unc - default_mean_unc;
    double sigmaDiff = fit_sigma - default_sigma;
 
    bool good_fit = false;
 
    if ((fabs(meanDiff) > 10)      ||
        (fabs(meanUncDiff) > 10)   ||
        (fabs(sigmaDiff) > 10)     ||
        (fit_mean_unc > 3)         ||
        (fit_sigma < 0.1)          ||
        (fit_mean < time_fit_min)  ||
        (fit_mean > time_fit_max)) {
      B2INFO("x_bin = " << x_bin);
      B2INFO("fit mean, default mean = " << fit_mean << ", " << default_mean);
      B2INFO("fit mean unc, default mean unc = " << fit_mean_unc << ", " << default_mean_unc);
      B2INFO("fit sigma, default sigma = " << fit_sigma << ", " << default_sigma);
 
      B2INFO("crystal fit mean - hist mean = " << meanDiff);
      B2INFO("fit mean unc. - hist mean unc. = " << meanUncDiff);
      B2INFO("fit sigma - hist sigma = " << sigmaDiff);
 
      B2INFO("fit_mean = " << fit_mean);
      B2INFO("time_fit_min = " << time_fit_min);
      B2INFO("time_fit_max = " << time_fit_max);
 
      if (fabs(meanDiff) > 10)       B2INFO("fit mean diff too large");
      if (fabs(meanUncDiff) > 10)    B2INFO("fit mean unc diff too large");
      if (fabs(sigmaDiff) > 10)      B2INFO("fit mean sigma diff too large");
      if (fit_mean_unc > 3)          B2INFO("fit mean unc too large");
      if (fit_sigma < 0.1)           B2INFO("fit sigma too small");
 
    } else {
      good_fit = true;
    }
 
 
    int numEntries = h_time->GetEntries();
    /* If number of entries in histogram is greater than X then use the statistical information
       from the data otherwise leave crystal uncalibrated.  Histograms are still shown though.
       ALSO require the that fits are good. */
    if ((numEntries >= minNumEntries)  &&  good_fit) {
      clusterTime_mean = fit_mean;
      clusterTime_mean_unc = fit_mean_unc;
      clusterTime_sigma = fit_sigma;
    } else {
      clusterTime_mean = default_mean;
      clusterTime_mean_unc = default_mean_unc;
      clusterTime_sigma = default_sigma;
    }
 
    if (numEntries < minNumEntries)   B2INFO("Number of entries less than minimum");
    if (numEntries == 0)   B2INFO("Number of entries == 0");
 
    histfile->WriteTObject(h_time, (std::string("h_time_E_slice") + std::to_string(meanE)).c_str());
    histfile->WriteTObject(h_timeMasked, (std::string("h_time_E_slice_masked") + std::to_string(meanE)).c_str());
 
    // store mean cluster time info in a separate histogram
    clusterTimePeak_ClusterEnergy_varBin->SetBinContent(Ebin_counter, clusterTime_mean);
    clusterTimePeak_ClusterEnergy_varBin->SetBinError(Ebin_counter, clusterTime_mean_unc);
 
    clusterTimePeakWidth_ClusterEnergy_varBin->SetBinContent(Ebin_counter, clusterTime_sigma);
    clusterTimePeakWidth_ClusterEnergy_varBin->SetBinError(Ebin_counter, 0);
 
    Ebin_counter++;
 
    delete gaus;
  }
 
 
 
  /***************************************************************************
  For the user, print out some information about the peak cluster times.
  It is sorted by the absolute value of the peak cluster time so that the
  worst times are at the end.
  ***************************************************************************/
 
  // Vector to store element with respective present index
  vector< pair<double, int> > fitClusterTime__crystalIDBase0__pairs;
 
  // Prepare a vector of pairs containing the fitted cluster time and cell ID (base 0)
  for (int cid = 0; cid < 8736; cid++) {
    fitClusterTime__crystalIDBase0__pairs.push_back(make_pair(0.0, cid));
  }
 
  // Inserting element in pair vector to keep track of crystal id.
  for (int crys_id = cellIDLo; crys_id <= cellIDHi; crys_id++) {
    fitClusterTime__crystalIDBase0__pairs[crys_id - 1] = make_pair(fabs(t_offsets[crys_id - 1]), crys_id - 1) ;
  }
 
  // Sorting by the absolute value of the fitted cluster time for the crystal
  sort(fitClusterTime__crystalIDBase0__pairs.begin(), fitClusterTime__crystalIDBase0__pairs.end());
 
 
  // Print out the fitted peak cluster time values sorted by their absolute value
  B2INFO("-------- List of the (fitted) peak cluster times sorted by their absolute value ----------");
  B2INFO("------------------------------------------------------------------------------------------");
  B2INFO("------------------------------------------------------------------------------------------");
  B2INFO("Quoted # of clusters is before the cutting off of the distribution tails, cellID=1..8736, crysID=0..8735");
 
  bool hasHitThresholdBadTimes = false ;
  for (int iSortedTimes = 0; iSortedTimes < 8736; iSortedTimes++) {
    int cid = fitClusterTime__crystalIDBase0__pairs[iSortedTimes].second ;
    if (!hasHitThresholdBadTimes && fitClusterTime__crystalIDBase0__pairs[iSortedTimes].first > 2) {
      B2INFO("======== |t_fit| > Xns threshold ======");
      hasHitThresholdBadTimes = true;
    }
    //B2INFO("crystal ID = " << cid << ", peak clust t = " << t_offsets[cid] << " +- " << t_offsets_unc[cid] << ", # clusters = " << numClusterPerCrys[cid] << ", fabs(t) = " << fitClusterTime__crystalIDBase0__pairs[iSortedTimes].first );
    B2INFO("cid = " << cid << ", peak clust t = " << t_offsets[cid] << " +- " << t_offsets_unc[cid] << " ns, # clust = " <<
           numClusterPerCrys[cid] << ", good fit = " << crysHasGoodFit[cid] << ", good fit & stats = " << crysHasGoodFitandStats[cid]);
  }
 
 
 
 
 
  // Print out just a subset that definitely don't look good even though they have good stats.
  B2INFO("######## List of poor (fitted) peak cluster times sorted by their absolute value #########");
  B2INFO("##########################################################################################");
  B2INFO("##########################################################################################");
 
  for (int iSortedTimes = 0; iSortedTimes < 8736; iSortedTimes++) {
    int cid = fitClusterTime__crystalIDBase0__pairs[iSortedTimes].second ;
    if (fitClusterTime__crystalIDBase0__pairs[iSortedTimes].first > 2  && crysHasGoodFitandStats[cid]) {
      B2INFO("WARNING: cid = " << cid << ", peak clust t = " << t_offsets[cid] << " +- " << t_offsets_unc[cid] << " ns, # clust = " <<
             numClusterPerCrys[cid] << ", good fit = " << crysHasGoodFit[cid] << ", good fit & stats = " << crysHasGoodFitandStats[cid]);
    }
  }
 
 
  B2INFO("~~~~~~~~");
  B2INFO("Number of crystals with non-zero number of hits = " << numCrysWithNonZeroEntries);
  B2INFO("Number of crystals with good quality fits = " << numCrysWithGoodFit);
 
 
  clusterTimePeak_ClusterEnergy_varBin->ResetStats();
  clusterTimePeakWidth_ClusterEnergy_varBin->ResetStats();
 
  histfile->WriteTObject(clusterTimePeak_ClusterEnergy_varBin, "clusterTimePeak_ClusterEnergy_varBin");
  histfile->WriteTObject(clusterTimePeakWidth_ClusterEnergy_varBin, "clusterTimePeakWidth_ClusterEnergy_varBin");
 
 
 
  /* Fill histograms with the difference in the ts values from this iteration
     and the previous values read in from the payload. */
  B2INFO("Filling histograms for difference in crystal payload values and the pre-calibration values.  These older values may be from a previous bucket or older reprocessing of the data.");
  for (int crys_id = 1; crys_id <= 8736; crys_id++) {
    double tsDiffCustomOld_ns = -999;
    if (readPrevCrysPayload) {
      tsDiffCustomOld_ns = (currentValuesCrys[crys_id - 1] - prevValuesCrys[crys_id - 1]) * TICKS_TO_NS;
      B2INFO("Crystal " << crys_id << ": ts new merged - 'before 1st iter' merged = (" <<
             currentValuesCrys[crys_id - 1]  << " - " << prevValuesCrys[crys_id - 1]  <<
             ") ticks * " << TICKS_TO_NS << " ns/tick = " << tsDiffCustomOld_ns << " ns");
 
    }
    tsNew_MINUS_tsCustomPrev__cid->SetBinContent(crys_id, tsDiffCustomOld_ns);
    tsNew_MINUS_tsCustomPrev__cid->SetBinError(crys_id, 0);
    tsNew_MINUS_tsCustomPrev__cid->ResetStats();
 
    tsNew_MINUS_tsCustomPrev->Fill(tsDiffCustomOld_ns);
    tsNew_MINUS_tsCustomPrev->ResetStats();
  }
 
  histfile->WriteTObject(tsNew_MINUS_tsCustomPrev__cid, "tsNew_MINUS_tsCustomPrev__cid");
  histfile->WriteTObject(tsNew_MINUS_tsCustomPrev, "tsNew_MINUS_tsCustomPrev");
 
 
  histfile->Close();
 
  B2INFO("Finished validations algorithm");
  return 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.

execute()

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.

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.

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.

Member Data Documentation

meanCleanCutMinFactor

double meanCleanCutMinFactor

After rebinning, create a mask for bins that have values less than meanCleanCutMinFactor times the maximum bin value.

Expand mask and apply to non-rebinned histogram.

Definition at line 45 of file eclTValidationAlgorithm.h.

---

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

• ecl/calibration/include/eclTValidationAlgorithm.h
• ecl/calibration/src/eclTValidationAlgorithm.cc