eclBhabhaTAlgorithm Class Reference

Calibrate ecl crystals using bhabha events. More...

#include <eclBhabhaTAlgorithm.h>

Inheritance diagram for eclBhabhaTAlgorithm:

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

Public Member Functions
	eclBhabhaTAlgorithm ()
	..Constructor
	~eclBhabhaTAlgorithm () override
	..Destructor
const std::string &	getPrefix () const
	Get the prefix used for getting calibration data.
const std::string &	getCollectorName () const
	Alias for prefix.
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.
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.
const 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.
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 () const
	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 algorithm (set by developers in constructor)
bool	loadInputJson (const std::string &jsonString)
	Load the m_inputJson variable from a string (useful from Python interface). The return 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>.

Static Public Member Functions
static bool	checkPyExpRun (PyObject *pyObj)
	Checks that a PyObject can be successfully converted to an ExpRun type.
static Calibration::ExpRun	convertPyExpRun (PyObject *pyObj)
	Performs the conversion of PyObject to ExpRun.

Public Attributes
int	cellIDLo = 0
	Fit crystals with cellID0 in the inclusive range [cellIDLo,cellIDHi].
int	cellIDHi = 0
	Fit crystals with cellID0 in the inclusive range [cellIDLo,cellIDHi].
double	meanCleanRebinFactor = 0.0
	Rebinning factor for mean calculation.
double	meanCleanCutMinFactor = 0.0
	After rebinning, create a mask for bins that have values less than meanCleanCutMinFactor times the maximum bin value.
int	crateIDLo = 0
	Fit crates with crateID0 in the inclusive range [crateIDLo,crateIDHi].
int	crateIDHi = 0
	Fit crates with crateID0 in the inclusive range [crateIDLo,crateIDHi].
bool	savePrevCrysPayload = false
	Save the previous crystal payload values for comparison.
bool	readPrevCrysPayload = false
	Read the previous crystal payload values for comparison.
bool	debugOutput = false
	Save every histogram and fitted function to debugFilename.
std::string	debugFilenameBase
	Name of file with debug output, eclBhabhaTAlgorithm.root by default.
std::string	collectorName
	Name of the collector.
int	refCrysPerCrate [52] = {}
	List of crystals, one per crate, used as reference time for crystal time calibration.

Protected Member Functions
EResult	calibrate () override
	..Run algorithm on events
void	setInputFileNames (const std::vector< std::string > &inputFileNames)
	Set the input file names used for this algorithm.
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.
const 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	setDescription (const std::string &description)
	Set algorithm description (in constructor)
void	clearCalibrationData ()
	Clear calibration data.
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 internally 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.

Static Protected Member Functions
static void	updateDBObjPtrs (const unsigned int event, const int run, const int experiment)
	Updates any DBObjPtrs by calling update(event) for DBStore.
static Calibration::ExpRun	getAllGranularityExpRun ()
	Returns the Exp,Run pair that means 'Everything'. Currently unused.

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
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)
	allExpRun

Detailed Description

Calibrate ecl crystals using bhabha events.

Definition at line 25 of file eclBhabhaTAlgorithm.h.

Member Enumeration Documentation

EResult

enum EResult

inherited

The result of calibration.

Enumerator
c_OK	Finished successfully =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.

                 {
      c_OK,           
      c_Iterate,      
      c_NotEnoughData,
      c_Failure,      
      c_Undefined     
    };

Constructor & Destructor Documentation

eclBhabhaTAlgorithm()

eclBhabhaTAlgorithm

(

)

..Constructor

Definition at line 36 of file eclBhabhaTAlgorithm.cc.

                                        :
  // Parameters
  CalibrationAlgorithm("ECLBhabhaTCollector"),
  cellIDLo(1),
  cellIDHi(ECLElementNumbers::c_NCrystals),
  meanCleanRebinFactor(1),
  meanCleanCutMinFactor(0),
  crateIDLo(1),
  crateIDHi(52),
  savePrevCrysPayload(false),
  readPrevCrysPayload(false),
  debugOutput(true),
  debugFilenameBase("eclBhabhaTAlgorithm"),   // base of filename (without ".root")
  collectorName("ECLBhabhaTCollector"),
  refCrysPerCrate{ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
                   -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
                   -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
                   -1, -1, -1, -1, -1, -1, -1} //,
{
  setDescription("Calculate time offsets from bhabha events by fitting gaussian function to the (t - T0) difference.");
}

~eclBhabhaTAlgorithm()

~eclBhabhaTAlgorithm ( )

inlineoverride

..Destructor

Definition at line 32 of file eclBhabhaTAlgorithm.h.

{}

Member Function Documentation

boundaryFindingSetup()

virtual void boundaryFindingSetup ( std::vector< Calibration::ExpRun > , int  )

inlineprotectedvirtualinherited

If you need to make some changes to your algorithm class before 'findPayloadBoundaries' is run, make them in this function.

Reimplemented in PXDAnalyticGainCalibrationAlgorithm, PXDValidationAlgorithm, SVD3SampleCoGTimeCalibrationAlgorithm, SVD3SampleELSTimeCalibrationAlgorithm, SVDClusterAbsoluteTimeShifterAlgorithm, SVDCoGTimeCalibrationAlgorithm, TestBoundarySettingAlgorithm, and TestCalibrationAlgorithm.

Definition at line 252 of file CalibrationAlgorithm.h.

{};

boundaryFindingTearDown()

virtual void boundaryFindingTearDown ( )

inlineprotectedvirtualinherited

Put your algorithm back into a state ready for normal execution if you need to.

Definition at line 257 of file CalibrationAlgorithm.h.

{};

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 60 of file eclBhabhaTAlgorithm.cc.

{
  gROOT->SetBatch();
 

  B2INFO("eclBhabhaTAlgorithm parameters:");
  B2INFO("cellIDLo = " << cellIDLo);
  B2INFO("cellIDHi = " << cellIDHi);
  B2INFO("meanCleanRebinFactor = " << meanCleanRebinFactor);
  B2INFO("meanCleanCutMinFactor = " << meanCleanCutMinFactor);
  B2INFO("crateIDLo = " << crateIDLo);
  B2INFO("crateIDHi = " << crateIDHi);
  B2INFO("savePrevCrysPayload = " << savePrevCrysPayload);
  B2INFO("readPrevCrysPayload = " << readPrevCrysPayload);
  B2INFO("refCrysPerCrate = {");
  for (int crateTest = 0; crateTest < 52 - 1; crateTest++) {
    B2INFO(refCrysPerCrate[crateTest] << ",");
  }
  B2INFO(refCrysPerCrate[52 - 1] << "}");
 
 
  /* Histogram with the data collected by eclBhabhaTCollectorModule */
 
  auto TimevsCrysPrevCrateCalibPrevCrystCalib = getObjectPtr<TH2F>("TimevsCrysPrevCrateCalibPrevCrystCalib");
  auto TimevsCratePrevCrateCalibPrevCrystCalib = getObjectPtr<TH2F>("TimevsCratePrevCrateCalibPrevCrystCalib");
  auto TimevsCrysNoCalibrations = getObjectPtr<TH2F>("TimevsCrysNoCalibrations");
  auto TimevsCrysPrevCrateCalibNoCrystCalib = getObjectPtr<TH2F>("TimevsCrysPrevCrateCalibNoCrystCalib");
  auto TimevsCrateNoCrateCalibPrevCrystCalib = getObjectPtr<TH2F>("TimevsCrateNoCrateCalibPrevCrystCalib");
 
  // Collect other plots just for reference - combines all the runs for these plots.
  auto cutflow = getObjectPtr<TH1F>("cutflow");
  auto svdEventT0 = getObjectPtr<TH1D>("svdEventT0");
 
 
  // Define new plots to make
  // New calibration constant values minus older values from previous iteration plotted as a function of the crystal or crate id
  unique_ptr<TH1F> tsNew_MINUS_tsOld__cid(new TH1F("TsNew_MINUS_TsOld__cid",
                                                   ";cell id; ts(new|bhabha) - ts(previous iteration|merged)  [ns]", ECLElementNumbers::c_NCrystals,
                                                   1, ECLElementNumbers::c_NCrystals + 1));
  unique_ptr<TH1F> tcrateNew_MINUS_tcrateOld__crateID(new TH1F("tcrateNew_MINUS_tcrateOld__crateID",
                                                      ";crate id; tcrate(new | bhabha) - tcrate(previous iteration | merged)  [ns]",
                                                      52, 1, 52 + 1));
  unique_ptr<TH1F> tsNew_MINUS_tsCustomPrev__cid(new TH1F("TsNew_MINUS_TsCustomPrev__cid",
                                                          ";cell id; ts(new|bhabha) - ts(old = 'before 1st iter'|merged)  [ns]",
                                                          ECLElementNumbers::c_NCrystals, 1, ECLElementNumbers::c_NCrystals + 1));
  unique_ptr<TH1F> tsNew_MINUS_tsOldBhabha__cid(new TH1F("TsNew_MINUS_TsOldBhabha__cid",
                                                         ";cell id; ts(new|bhabha) - ts(previous iteration|bhabha)  [ns]", ECLElementNumbers::c_NCrystals,
                                                         1, ECLElementNumbers::c_NCrystals + 1));
 
 
  // Histogram of the new time constant values minus values from previous iteration
  unique_ptr<TH1F> tsNew_MINUS_tsOld(new TH1F("TsNew_MINUS_TsOld",
                                              ";ts(new | bhabha) - ts(previous iteration | merged)  [ns];Number of crystals",
                                              201, -10.05, 10.05));
  unique_ptr<TH1F> tcrateNew_MINUS_tcrateOld(new TH1F("tcrateNew_MINUS_tcrateOld",
                                                      ";tcrate(new) - tcrate(previous iteration)  [ns];Number of crates",
                                                      201, -10.05, 10.05));
  unique_ptr<TH1F> tsNew_MINUS_tsCustomPrev(new TH1F("TsNew_MINUS_TsCustomPrev",
                                                     ";ts(new | bhabha) - ts(old = 'before 1st iter' | merged)  [ns];Number of crystals",
                                                     285, -69.5801, 69.5801));
  unique_ptr<TH1F> tsNew_MINUS_tsOldBhabha(new TH1F("TsNew_MINUS_TsOldBhabha",
                                                    ";ts(new | bhabha) - ts(previous iteration | bhabha)  [ns];Number of crystals",
                                                    201, -10.05, 10.05));
 
 
 
  // Histograms just for crates and collecting for all the different runs rather than run by run.
  unique_ptr<TH1F> tcrateNew_MINUS_tcrateOld_allRuns(new TH1F("tcrateNew_MINUS_tcrateOld_allRuns",
                                                              "Crate time constant changes over all runs : tcrate(new) uncertainty < 0.1ns;tcrate(new) - tcrate(previous iteration)  [ns];Number of crates",
                                                              201, -10.05, 10.05));
 
  unique_ptr<TH1F> tcrateNew_MINUS_tcrateOld_allRuns_allCrates(new TH1F("tcrateNew_MINUS_tcrateOld_allRuns_allCrates",
      "Crate time constant changes over all runs : all crates;tcrate(new) - tcrate(previous iteration)  [ns];Number of crates",
      201, -10.05, 10.05));
 
  unique_ptr<TH1I> num_tcrates_perRun(new TH1I("num_tcrates_perRun",
                                               "Number of good tcrates in each run;Run number;Number of good tcrates",
                                               6000, 0, 6000));
 
  unique_ptr<TH2F> tcrateNew_MINUS_tcrateOld__vs__runNum(new TH2F("tcrateNew_MINUS_tcrateOld__vs__runNum",
                                                         "Crate time constant changes vs run number : tcrate(new) uncertainty < 0.1ns;Run number;tcrate(new) - tcrate(previous iteration)  [ns]",
                                                         6000, 0, 6000, 21, -10.5, 10.5));
 
 
 
 
 
  if (!TimevsCrysNoCalibrations) return c_Failure;

 
  TFile* histfile = 0;
  TFile* histExtraCrateInfofile = 0;   // Only created if needed for crates
 
 
  unique_ptr<TTree> tree_crystal(new TTree("tree_crystal", "Debug data from bhabha time calibration algorithm for crystals"));
 
  unique_ptr<TTree>   tree_crate(new TTree("tree_crate",   "Debug data from bhabha time calibration algorithm for crates"));
  int tree_cid;
 
  // Vector of time offsets to be saved in the database.
  vector<float> t_offsets;
  // Vector of time offset uncertainties to be saved in the database.
  vector<float> t_offsets_unc;
  vector<float> t_offsets_prev;  // previous time offsets
 
 
  int minNumEntries = 40;
  int minNumEntriesCrateConvergence = 1000;
 
 
  double mean = 0;
  double sigma = -1;
  double mean_unc = 0;
  int crystalCalibSaved = 0;
  double tsPrev = 0;
 
 
  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");
  string extraCratedebugFilename = debugFilenameBase + string("_cratesAllRuns.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;
 
  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
  B2INFO("Reading payloads: ECLCrystalTimeOffset and ECLCrateTimeOffset");
  DBObjPtr<Belle2::ECLCrystalCalib> crystalTimeObject("ECLCrystalTimeOffset");
  DBObjPtr<Belle2::ECLCrystalCalib> crateTimeObject("ECLCrateTimeOffset");
 
  //..Get vectors of values from the payloads
  vector<float> currentValuesCrys = crystalTimeObject->getCalibVector();
  vector<float> currentUncCrys = crystalTimeObject->getCalibUncVector();
  vector<float> currentValuesCrate = crateTimeObject->getCalibVector();
  vector<float> currentUncCrate = crateTimeObject->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 < ECLElementNumbers::c_NCrystals; ic += 500) {
    B2INFO("ts: cellID " << ic + 1 << " " << currentValuesCrys[ic] << " +/- " << currentUncCrys[ic]);
    B2INFO("tcrate: cellID " << ic + 1 << " " << currentValuesCrate[ic] << " +/- " << currentUncCrate[ic]);
  }
 
 
  //..Read in the previous crystal payload values
  vector<float> prevValuesCrys(ECLElementNumbers::c_NCrystals);
  if (readPrevCrysPayload) {
    DBObjPtr<Belle2::ECLCrystalCalib> customPrevCrystalTimeObject("ECLCrystalTimeOffsetPreviousValues");
    //..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 < ECLElementNumbers::c_NCrystals; ic += 500) {
      B2INFO("ts custom previous payload: cellID " << ic + 1 << " " << prevValuesCrys[ic]);
    }
  }
 
 
  //..Read bhabha payloads from database
  B2INFO("Reading payloads: ECLCrystalTimeOffsetBhabha");
  DBObjPtr<Belle2::ECLCrystalCalib> crystalBhabhaTimeObject("ECLCrystalTimeOffsetBhabha");
 
  //..Get vectors of values from the payloads
  vector<float> currentBhabhaValuesCrys = crystalBhabhaTimeObject->getCalibVector();
  vector<float> currentBhabhaUncCrys = crystalBhabhaTimeObject->getCalibUncVector();
 
  //..Print out a few values for quality control
  for (int ic = 0; ic < ECLElementNumbers::c_NCrystals; ic += 500) {
    B2INFO("ts bhabha: cellID " << ic + 1 << " " << currentBhabhaValuesCrys[ic] << " +/- " << currentBhabhaUncCrys[ic]);
  }
 
 
  //------------------------------------------------------------------------
  //..Get the reference crystal information
  auto refCrysIDzeroingCrate = getObjectPtr<TH1F>("refCrysIDzeroingCrate");
 
  // crystal index for the crystals (one per crate) that is used as the reference crystal.  This one has
  //    ts defined as zero.  The crystal id runs 1...8636, not starting at 0.
  B2INFO("Extract reference crystals from collector histogram.");
  vector <short> crystalIDreferenceUntested;
  for (int bin = 1; bin <= ECLElementNumbers::c_NCrystals; bin++) {
    if (refCrysIDzeroingCrate->GetBinContent(bin) > 0.5) {
      crystalIDreferenceUntested.push_back(bin);
    }
  }
 
  // Output for the user the crystal id to be used as a reference
  //    and also state which crate the crystal is in.
  B2INFO("Reference crystals to define as having ts=0.  Base 1 counting for both crates and crystals");
  for (long unsigned int crysRefCounter = 0; crysRefCounter < crystalIDreferenceUntested.size(); crysRefCounter++) {
    int crys_id = crystalIDreferenceUntested[crysRefCounter] ;
    int crate_id_from_crystal = crystalMapper->getCrateID(crys_id);
    B2INFO("   crystal " << crys_id << " is a reference for crate " << crate_id_from_crystal);
  }
 
  // Check that the reference crystals make sense.  There should be exactly one per crate but
  //   since the crystal calibration executes over multiple runs, it is possible that the
  //   reference crystals could change but we don't want to allow this.
  B2INFO("Checking number of reference crystals");
  B2INFO("Number of reference crystals = " << crystalIDreferenceUntested.size());
 
  // Check that there are exactly 52 reference crystals
  if (crystalIDreferenceUntested.size() != 52) {
    B2FATAL("The number of reference crystals does not equal 52, which is one per crate");
    return c_Failure;
  } else {
    B2INFO("Number of reference crystals is 52 as required");
  }
 
  // Count the number of reference crystals for each crate to make sure that there is exactly
  //    one reference crystal for each crate as defined by the payload/database.
  //    also fill the final vector that maps the crate id to the reference crystal id
  vector <short> crateIDsNumRefCrystalsUntested(52, 0);
  vector <short> crystalIDReferenceForZeroTs(52, 0);
 
  for (long unsigned int crysRefCounter = 0; crysRefCounter < crystalIDreferenceUntested.size(); crysRefCounter++) {
    int crys_id = crystalIDreferenceUntested[crysRefCounter] ;
    int crate_id_from_crystal = crystalMapper->getCrateID(crys_id);
    crateIDsNumRefCrystalsUntested[crate_id_from_crystal - 1]++;
    crystalIDReferenceForZeroTs[crate_id_from_crystal - 1] = crys_id;
  }
  B2INFO("crystalIDReferenceForZeroTs = {");
  for (int crateTest = 0; crateTest < 52 - 1; crateTest++) {
    B2INFO(crystalIDReferenceForZeroTs[crateTest] << ",");
  }
  B2INFO(crystalIDReferenceForZeroTs[52 - 1] << "}");
 
 
  // Make sure that there is only one reference crystal per crate as defined by the payload/database
  for (int crateTest = 0; crateTest < 52; crateTest++) {
    if (crateIDsNumRefCrystalsUntested[crateTest] != 1) {
      B2FATAL("Crate " << crateTest + 1 << " (base 1) has " << crateIDsNumRefCrystalsUntested[crateTest] << " reference crystals");
      return c_Failure;
    }
  }
  B2INFO("All reference crystals are reasonably mapped one crystal to one crate for all crates");
 
 
 
  B2INFO("Extract reference crystals from algorithm steering script if provided.  If user inputs custom values via steering script for this algorithm, they are only applied after all the tests are performed on the values from the histogram and override the histogram valuees.  User can adjust just a single crystal if desired.  Use -1 to indicate that a crystal is not to be modified.  Position of crystal in list determines the crate to which the crystal is meant to be associated.");
 
  /* Test if the user wants to modify the reference crystals.  This will probably be done very rarely,
     perhaps less than once per year.  If the user does want to change one or more reference
     crystals then perform the checks again to make sure that there is still just one reference crystal
     per crate after the modifications to the payload values with the user values.*/
  bool userSetRefCrysPerCrate = false ;
  for (int crateTest = 0; crateTest < 52; crateTest++) {
    if (refCrysPerCrate[crateTest] != -1) {
      crystalIDReferenceForZeroTs[crateTest] = refCrysPerCrate[crateTest] ;
      B2INFO("Crate " << crateTest + 1 << " (base 1) new reference crystal = " << crystalIDReferenceForZeroTs[crateTest]);
      userSetRefCrysPerCrate = true ;
    }
  }
  if (userSetRefCrysPerCrate) {
    B2INFO("User changed reference crystals via steering script");
 
    // Validate crystals per crate again with the new user set values
    fill(crateIDsNumRefCrystalsUntested.begin(), crateIDsNumRefCrystalsUntested.end(), 0);
    for (long unsigned int crysRefCounter = 0; crysRefCounter < 52; crysRefCounter++) {
      int crys_id = crystalIDReferenceForZeroTs[crysRefCounter] ;
      int crate_id_from_crystal = crystalMapper->getCrateID(crys_id);
      crateIDsNumRefCrystalsUntested[crate_id_from_crystal - 1]++;
    }
    for (int crateTest = 0; crateTest < 52; crateTest++) {
      if (crateIDsNumRefCrystalsUntested[crateTest] != 1) {
        B2FATAL("Crate " << crateTest + 1 << " (base 1) has " << crateIDsNumRefCrystalsUntested[crateTest] << " reference crystals");
        return c_Failure;
      }
    }
    B2INFO("All reference crystals are reasonably mapped one crystal to one crate for all crates after changes made by user steering script.");
 
    // Save the information to the payload if there is at least one crate
    //    reference crystal that has been modified by the user steering file
    ECLReferenceCrystalPerCrateCalib* refCrystalsCalib = new ECLReferenceCrystalPerCrateCalib();
    refCrystalsCalib->setCalibVector(crystalIDReferenceForZeroTs);
    saveCalibration(refCrystalsCalib, "ECLReferenceCrystalPerCrateCalib");
    B2INFO("Created reference crystal per crate payload: ECLReferenceCrystalPerCrateCalib");
  } else {
    B2INFO("User did not change reference crystals via steering script");
  }
 
 
  //------------------------------------------------------------------------
  //..Start looking at timing information
 
  B2INFO("Debug output rootfile: " << debugFilename);
  histfile = new TFile(debugFilename.c_str(), "recreate");
 
 
  TimevsCrysPrevCrateCalibPrevCrystCalib ->Write();
  TimevsCratePrevCrateCalibPrevCrystCalib->Write();
  TimevsCrysNoCalibrations               ->Write();
  TimevsCrysPrevCrateCalibNoCrystCalib   ->Write();
  TimevsCrateNoCrateCalibPrevCrystCalib  ->Write();
 
  cutflow->Write();
  svdEventT0->Write();
 
 
  if (debugOutput) {
    tree_crystal->Branch("cid", &tree_cid)->SetTitle("Cell ID, 1..8736");
    tree_crystal->Branch("ts", &mean)->SetTitle("Time offset mean, ts, ns");
    tree_crystal->Branch("tsUnc", &mean_unc)->SetTitle("Error of time ts mean, ns.");
    tree_crystal->Branch("tsSigma", &sigma)->SetTitle("Sigma of time ts distribution, ns");
    tree_crystal->Branch("crystalCalibSaved",
                         &crystalCalibSaved)->SetTitle("0=crystal skipped, 1=crystal calib saved (num entries based)");
    tree_crystal->Branch("tsPrev", &tsPrev)->SetTitle("Previous crystal time offset, ts, ns");
    tree_crystal->SetAutoSave(10);
  }
 
 
  double hist_tmin = TimevsCrysNoCalibrations->GetYaxis()->GetXmin();
  double hist_tmax = TimevsCrysNoCalibrations->GetYaxis()->GetXmax();
 
  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
 
  B2INFO("hist_tmin = " << hist_tmin);
  B2INFO("hist_tmax = " << hist_tmax);
 
  /* 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::getRF()) * 1e3;
 
  // The ts and tcrate database values are filled once per tcol instance so count the number of times that the database values
  //    were summed together by the histogram merging process and extract out the original values again.
  auto databaseCounter = getObjectPtr<TH1I>("databaseCounter");
  float numTimesFilled = databaseCounter->GetBinContent(1);
  B2INFO("Number of times database histograms were merged = " << numTimesFilled);
 
 
  auto TsDatabase = getObjectPtr<TH1F>("TsDatabase");
  auto TsDatabaseUnc = getObjectPtr<TH1F>("TsDatabaseUnc");
  for (int i = 1; i <= ECLElementNumbers::c_NCrystals; i++) {
    t_offsets.push_back(TsDatabase->GetBinContent(i) / numTimesFilled);
    t_offsets_prev.push_back(TsDatabase->GetBinContent(i) / numTimesFilled);
 
    B2INFO("t_offsets_prev (last iter) at crysID " << i << " = " << t_offsets_prev[i - 1]);
 
    t_offsets_unc.push_back(TsDatabaseUnc->GetBinContent(i) / numTimesFilled);
  }
 
 
  /* CRYSTAL CORRECTIONS */
 
  /* Make a 1D histogram of the number of hits per crystal.  This will help with the validations
     to make sure that all the crystals had enough hits and to look for problems.*/
  TH1D* h_crysHits = TimevsCrysPrevCrateCalibNoCrystCalib->ProjectionX("h_crysHits");
  h_crysHits->SetTitle("Hits per crystal;Crystal id");
 
  histfile->WriteTObject(h_crysHits, "h_crysHits");
 
 
  // Loop over all the crystals for doing the crystal calibation
  for (int crys_id = cellIDLo; crys_id <= cellIDHi; crys_id++) {
    crystalCalibSaved = 0;
 
    double database_mean = 0;
    double database_mean_unc = 0;
 
    B2INFO("Crystal id = " << crys_id);
 
 
 
 
    /* Determining which bins to mask out for mean calculation
    */
 
    TH1D* h_time = TimevsCrysPrevCrateCalibNoCrystCalib->ProjectionY((string("h_time_psi__") + to_string(crys_id)).c_str(),
                   crys_id, crys_id);
    TH1D* h_timeMask = static_cast<TH1D*>(h_time->Clone());
    TH1D* h_timeMasked = static_cast<TH1D*>(h_time->Clone((string("h_time_psi_masked__") + to_string(crys_id)).c_str()));
    TH1D* h_timeRebin = static_cast<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 > 0.09)      ||
        (fit_sigma < 0.1)          ||
        (fit_mean < time_fit_min)  ||
        (fit_mean > time_fit_max)) {
      B2INFO("Crystal 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 > 0.09)       B2INFO("fit mean unc too large");
      if (fit_sigma < 0.1)           B2INFO("fit sigma too small");
 
    } else {
      good_fit = true;
    }
 
 
 
    // Set the tree_crystal values - ignore fit values  !!!!!!!!!!!!!!!!
    sigma = default_sigma;
 
 
    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) {
      crystalCalibSaved = 1;
      database_mean = fit_mean;
      database_mean_unc = fit_mean_unc;
    } else {
      database_mean = default_mean;
      database_mean_unc = -fabs(default_mean_unc);
    }
 
    if (numEntries < minNumEntries)   B2INFO("Number of entries less than minimum");
    if (numEntries == 0)   B2INFO("Number of entries == 0");
 
 
    tree_cid  = crys_id;
 
    // For the database, convert back from ns to ADC ticks.
    t_offsets[crys_id - 1] = database_mean / TICKS_TO_NS;
    t_offsets_unc[crys_id - 1] = database_mean_unc / TICKS_TO_NS;
 
 
    histfile->WriteTObject(h_time, (string("h_time_psi") + to_string(crys_id)).c_str());
    histfile->WriteTObject(h_timeMasked, (string("h_time_psi_masked") + to_string(crys_id)).c_str());
 
    mean = database_mean;
    mean_unc = database_mean_unc;
 
    tsPrev = t_offsets_prev[crys_id - 1] * TICKS_TO_NS;
 
    delete gaus;
    tree_crystal->Fill();
  }
 
 
  // Shift the crystal time calibration constants by the reference crystal calibration constant values
  if (cellIDLo <= cellIDHi) {
    vector <double> tsRefCID ;
    B2INFO("crystal times before shift");
    for (int crate_id = 1; crate_id <= 52; crate_id++) {
      tsRefCID.push_back(t_offsets[ crystalIDReferenceForZeroTs[crate_id - 1] - 1 ]);
      B2INFO("crystal time [crystal = " << crystalIDReferenceForZeroTs[crate_id - 1] << ", crate = " << crate_id  << " (base 1)] = " <<
             t_offsets[ crystalIDReferenceForZeroTs[crate_id - 1] - 1 ] << " ticks");
    }
 
    B2INFO("crystal times after shift wrt reference crystal");
    for (int crys_id = 1; crys_id <= ECLElementNumbers::c_NCrystals; crys_id++) {
      int crate_id_from_crystal = crystalMapper->getCrateID(crys_id);
      B2INFO("crystal time before shift [crystal = " << crys_id << ", crate = " << crate_id_from_crystal  << " (base 1)] = " <<
             t_offsets[crys_id - 1] << " +- " << t_offsets_unc[crys_id - 1] << " ticks");
 
      /* Shift the crystal time constant by that of the reference crystal, but only if
         there were values to shift.  If there were no entries, ts=0 and ts_unc=0, which
         are special values so do not shift these crystals. */
      if (t_offsets[crys_id - 1] == 0  && t_offsets_unc[crys_id - 1] == 0) {
        B2INFO("crystal time after shift [crystal = " << crys_id << ", crate = " << crate_id_from_crystal  << " (base 1)] = " <<
               t_offsets[crys_id - 1] << " ticks.  No change because ts=0 and ts_unc=0 (no entries).");
      } else {
        t_offsets[crys_id - 1] = t_offsets[crys_id - 1] - tsRefCID[crate_id_from_crystal - 1];
        B2INFO("crystal time after shift [crystal = " << crys_id << ", crate = " << crate_id_from_crystal  << " (base 1)] = " <<
               t_offsets[crys_id - 1] << " ticks");
      }
 
 
      // Fill histograms with the difference in the ts values between iterations
      double tsDiff_ns = (t_offsets[crys_id - 1] - t_offsets_prev[crys_id - 1]) * TICKS_TO_NS;
      double tsDiffBhabha_ns = -999;
      if (readPrevCrysPayload) {
        tsDiffBhabha_ns = (t_offsets[crys_id - 1] - currentBhabhaValuesCrys[crys_id - 1]) * TICKS_TO_NS;
      }
 
      B2INFO("Crystal " << crys_id << ": ts new bhabha - old merged = (" <<
             t_offsets[crys_id - 1]  << " - " << t_offsets_prev[crys_id - 1]  <<
             ") ticks * " << TICKS_TO_NS << " ns/tick = " << tsDiff_ns << " ns");
      B2INFO("Crystal " << crys_id << ": ts new bhabha - old bhabha = (" <<
             t_offsets[crys_id - 1]  << " - " << currentBhabhaValuesCrys[crys_id - 1]  <<
             ") ticks * " << TICKS_TO_NS << " ns/tick = " << tsDiffBhabha_ns << " ns");
 
      tsNew_MINUS_tsOld__cid->SetBinContent(crys_id, tsDiff_ns);
      tsNew_MINUS_tsOld__cid->SetBinError(crys_id, 0);
      tsNew_MINUS_tsOld__cid->ResetStats();
 
      tsNew_MINUS_tsOld->Fill(tsDiff_ns);
 
 
      tsNew_MINUS_tsOldBhabha__cid->SetBinContent(crys_id, tsDiffBhabha_ns);
      tsNew_MINUS_tsOldBhabha__cid->SetBinError(crys_id, 0);
      tsNew_MINUS_tsOldBhabha__cid->ResetStats();
 
      tsNew_MINUS_tsOldBhabha->Fill(tsDiffBhabha_ns);
 
 
 
      /* Fill histograms with the difference in the ts values from this iteration
         and the previous values read in from the payload. */
      double tsDiffCustomOld_ns = -999;
      if (readPrevCrysPayload) {
        tsDiffCustomOld_ns = (t_offsets[crys_id - 1] - prevValuesCrys[crys_id - 1]) * TICKS_TO_NS;
        B2INFO("Crystal " << crys_id << ": ts new bhabha - 'before 1st iter' merged = (" <<
               t_offsets[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);
 
    }
 
    // Save the histograms to the output root file
    histfile->WriteTObject(tsNew_MINUS_tsOld__cid.get(), "tsNew_MINUS_tsOld__cid");
    histfile->WriteTObject(tsNew_MINUS_tsOld.get(), "tsNew_MINUS_tsOld");
 
    histfile->WriteTObject(tsNew_MINUS_tsCustomPrev__cid.get(), "tsNew_MINUS_tsCustomPrev__cid");
    histfile->WriteTObject(tsNew_MINUS_tsCustomPrev.get(), "tsNew_MINUS_tsCustomPrev");
 
    histfile->WriteTObject(tsNew_MINUS_tsOldBhabha__cid.get(), "tsNew_MINUS_tsOldBhabha__cid");
    histfile->WriteTObject(tsNew_MINUS_tsOldBhabha.get(), "tsNew_MINUS_tsOldBhabha");
  }
 
 
  //..Store previous crystal calibration constants to payload under different
  // names so that they can be read in for comparison later.  These are temporary
  // payloads that are only used for plotting purposes while running the calibration
  // and does not need to be added to any global tag.
  if (savePrevCrysPayload) {
    ECLCrystalCalib* crysTCalib_prev = new ECLCrystalCalib();
    crysTCalib_prev->setCalibVector(currentValuesCrys, currentUncCrys);
 
    ECLCrystalCalib* crysBhabhaTCalib_prev = new ECLCrystalCalib();
    crysBhabhaTCalib_prev->setCalibVector(currentBhabhaValuesCrys, currentBhabhaUncCrys);
 
 
    // Save the information to the payload if there is at least one crystal
    //    begin calibrated.
    if (cellIDLo <= cellIDHi) {
      saveCalibration(crysTCalib_prev, "ECLCrystalTimeOffsetPreviousValues");
      B2INFO("Previous overall crystal payload made");
 
      saveCalibration(crysBhabhaTCalib_prev, "ECLCrystalTimeOffsetBhabhaPreviousValues");
      B2INFO("Previous bhabha crystal payload made");
    }
  }
 
 
  ECLCrystalCalib* BhabhaTCalib = new ECLCrystalCalib();
  BhabhaTCalib->setCalibVector(t_offsets, t_offsets_unc);
 
  // Save the information to the payload if there is at least one crystal
  //    begin calibrated.
  if (cellIDLo <= cellIDHi) {
    saveCalibration(BhabhaTCalib, "ECLCrystalTimeOffset");
    saveCalibration(BhabhaTCalib, "ECLCrystalTimeOffsetBhabha");
    B2DEBUG(22, "crystal payload made");
  }
 
 
  B2DEBUG(22, "end of crystal start of crate corrections .....");
 
 
  //==============================================================
  /* CRATE CORRECTIONS */
 
  hist_tmin = TimevsCrateNoCrateCalibPrevCrystCalib->GetYaxis()->GetXmin();
  hist_tmax = TimevsCrateNoCrateCalibPrevCrystCalib->GetYaxis()->GetXmax();
 
  B2DEBUG(22, "Found min/max of X axis of TimevsCrateNoCrateCalibPrevCrystCalib");
 
  // Vector of time offsets to be saved in the database.
 
  auto TcrateDatabase = getObjectPtr<TH1F>("TcrateDatabase");
 
 
  B2DEBUG(22, "Retrieved Ts and Tcrate histograms from tcol root file");
 
 
  vector<float> tcrate_mean_new(52, 0.0);
  vector<float> tcrate_mean_unc_new(52, 0.0);
  vector<float> tcrate_sigma_new(52, 0.0);
  vector<float> tcrate_mean_prev(52, 0.0);
  // vector<float> tcrate_sigma_prev(52, 0.0); // currently not used
  vector<bool> tcrate_new_was_set(52, false);
  vector<bool> tcrate_new_goodQuality(52, false);
 
  B2DEBUG(22, "crate vectors set");
 
 
 
  // Crate time calibration constants are saved per crystal so read them per crystal
  //    and save as one entry per crate in the array
  for (int crys_id = 1; crys_id <= ECLElementNumbers::c_NCrystals; crys_id++) {
    int crate_id_from_crystal = crystalMapper->getCrateID(crys_id);
    tcrate_mean_prev[crate_id_from_crystal - 1] = TcrateDatabase->GetBinContent(crys_id) / numTimesFilled;
  }
 
 
  B2INFO("Print out previous crate time calibration constants to make sure they match from the two different sources.");
  for (int crate_id = 1; crate_id <= 52; crate_id++) {
    B2INFO("tcrate_mean_prev[crate " << crate_id << " (base 1)] = " << tcrate_mean_prev[crate_id - 1]);
 
    int thisRefCellID = crystalIDReferenceForZeroTs[crate_id - 1];
    B2INFO("tcrate from payload: ref cellID " << thisRefCellID << " " << currentValuesCrate[thisRefCellID - 1] << " +/- " <<
           currentUncCrate[thisRefCellID - 1]);
  }
 
 
  /* Read in the histogram about the crate calibration constant differences between iterations
     if it exists.  This way the histograms can be updated after each run.*/
  if (crateIDLo <= crateIDHi) {
    TFile* histExtraCrateInfofile_dummy = 0;   // Only created if needed for crates
    B2INFO("Debug output rootfile used for crate iterations: " << extraCratedebugFilename);
    histExtraCrateInfofile_dummy = new TFile(extraCratedebugFilename.c_str(), "UPDATE");
 
 
    // If the histogram already exists then read it in and recreate the file so that we can save an updated version of the histogram.
    const TKey* key = histExtraCrateInfofile_dummy->FindKey("tcrateNew_MINUS_tcrateOld_allRuns");
    if (key != 0) {
      TH1F* h = static_cast<TH1F*>(histExtraCrateInfofile_dummy->Get("tcrateNew_MINUS_tcrateOld_allRuns"));
      tcrateNew_MINUS_tcrateOld_allRuns->Add(h);
    }
 
    key = histExtraCrateInfofile_dummy->FindKey("tcrateNew_MINUS_tcrateOld_allRuns_allCrates");
    if (key != 0) {
      TH1F* h = static_cast<TH1F*>(histExtraCrateInfofile_dummy->Get("tcrateNew_MINUS_tcrateOld_allRuns_allCrates"));
      tcrateNew_MINUS_tcrateOld_allRuns_allCrates->Add(h);
    }
 
    key = histExtraCrateInfofile_dummy->FindKey("num_tcrates_perRun");
    if (key != 0) {
      TH1F* h = static_cast<TH1F*>(histExtraCrateInfofile_dummy->Get("num_tcrates_perRun"));
      num_tcrates_perRun->Add(h);
    }
 
    key = histExtraCrateInfofile_dummy->FindKey("tcrateNew_MINUS_tcrateOld__vs__runNum");
    if (key != 0) {
      TH1F* h = static_cast<TH1F*>(histExtraCrateInfofile_dummy->Get("tcrateNew_MINUS_tcrateOld__vs__runNum"));
      tcrateNew_MINUS_tcrateOld__vs__runNum->Add(h);
    }
 
    histExtraCrateInfofile_dummy->Close();
 
    /* After reading in all the histograms, recreate the root file from empty so that the
       histograms can be made again with the updated values.*/
    histExtraCrateInfofile = new TFile(extraCratedebugFilename.c_str(), "recreate");
  }
 
 
 
  for (int crate_id = crateIDLo; crate_id <= crateIDHi; crate_id++) {
 
    B2DEBUG(22, "Start of crate id = " << crate_id);
 
    TH1D* h_time_crate = TimevsCrateNoCrateCalibPrevCrystCalib->ProjectionY("h_time_psi_crate", crate_id, crate_id);
    TH1D* h_time_crate_mask = static_cast<TH1D*>(h_time_crate->Clone());
    TH1D* h_time_crate_masked = static_cast<TH1D*>(h_time_crate->Clone());
    TH1D* h_time_crate_rebin = static_cast<TH1D*>(h_time_crate->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_time_crate_rebin->Rebin(meanCleanRebinFactor);
      h_time_crate_mask->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_time_crate_rebin->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_time_crate_rebin->GetNbinsX(); bin++) {
        for (int rebinCounter = 1; rebinCounter <= meanCleanRebinFactor; rebinCounter++) {
          int nonRebinnedBinNumber = (bin - 1) * meanCleanRebinFactor + rebinCounter;
          if (nonRebinnedBinNumber < h_time_crate->GetNbinsX()) {
            if (h_time_crate_rebin->GetBinContent(bin) >= histRebin_max * meanCleanCutMinFactor) {
              h_time_crate_mask->SetBinContent(nonRebinnedBinNumber, 1);
 
              // Save the lower and upper edges of the rebin histogram time range for fitting purposes
              double x_lower = h_time_crate_rebin->GetXaxis()->GetBinLowEdge(bin);
              double x_upper = h_time_crate_rebin->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_crate->GetBinContent(nonRebinnedBinNumber) > 0) {
                B2DEBUG(22, "Setting bin " << nonRebinnedBinNumber << " from " << h_time_crate_masked->GetBinContent(
                          nonRebinnedBinNumber) << " to 0");
                maskedOutNonZeroBin = true;
              }
              h_time_crate_masked->SetBinContent(nonRebinnedBinNumber, 0);
            }
          }
        }
      }
      B2INFO("Bins with non-zero values have been masked out: " << maskedOutNonZeroBin);
      h_time_crate_masked->ResetStats();
      h_time_crate_mask->ResetStats();
 
    }
 
 
 
    B2DEBUG(22, "crate loop - projected h_time_psi_crate");
 
 
    double default_mean_crate = h_time_crate_masked->GetMean();
    double default_mean_crate_unc = h_time_crate_masked->GetMeanError();
    double default_sigma_crate = h_time_crate_masked->GetStdDev();
    B2INFO("Fitting crate between " << time_fit_min << " and " << time_fit_max);
    TF1* gaus = new TF1("func", "gaus(0)", time_fit_min, time_fit_max);
    gaus->SetParNames("numCrateHisNormalization", "mean", "sigma");
    double hist_max = h_time_crate->GetMaximum();
    double stddev = h_time_crate->GetStdDev();
    double sigma_crate = stddev;
    double mean_crate = default_mean_crate;
    gaus->SetParameter(0, hist_max / 2.);
    gaus->SetParameter(1, mean_crate);
    gaus->SetParameter(2, sigma_crate);
 
    h_time_crate_masked->Fit(gaus, "LQR");  // L for likelihood, R for x-range, Q for fit quiet mode
 
    double fit_mean_crate     = gaus->GetParameter(1);
    double fit_mean_crate_unc = gaus->GetParError(1);
    double fit_sigma_crate    = gaus->GetParameter(2);
 
    double meanDiff =  fit_mean_crate - default_mean_crate;
    double meanUncDiff = fit_mean_crate_unc - default_mean_crate_unc;
    double sigmaDiff = fit_sigma_crate - default_sigma_crate;
 
    bool good_fit = false;
 
    B2DEBUG(22, "Crate id = " << crate_id << " with crate mean = " << default_mean_crate << " +- " << fit_mean_crate_unc);
 
    if ((fabs(meanDiff) > 7)            ||
        (fabs(meanUncDiff) > 7)         ||
        (fabs(sigmaDiff) > 7)           ||
        (fit_mean_crate_unc > 3)        ||
        (fit_sigma_crate < 0.1)         ||
        (fit_mean_crate < time_fit_min) ||
        (fit_mean_crate > time_fit_max)) {
      B2INFO("Crate id = " << crate_id);
      B2INFO("fit mean, default mean = " << fit_mean_crate << ", " << default_mean_crate);
      B2INFO("fit sigma, default sigma = " << fit_sigma_crate << ", " << default_sigma_crate);
 
      B2INFO("crate fit mean - hist mean = " << meanDiff);
      B2INFO("fit mean unc. - hist mean unc. = " << meanUncDiff);
      B2INFO("fit sigma - hist sigma = " << sigmaDiff);
      B2INFO("fit_mean_crate = " << fit_mean_crate);
      B2INFO("time_fit_min = " << time_fit_min);
      B2INFO("time_fit_max = " << time_fit_max);
    } else {
      good_fit = true;
    }
 
    int numEntries = h_time_crate->GetEntries();
    B2INFO("Number entries = " << numEntries);
    double database_mean_crate = 0;
    double database_mean_crate_unc = 0;
    tcrate_new_was_set[crate_id - 1] = false;
    tcrate_new_goodQuality[crate_id - 1] = false;
    if ((numEntries >= minNumEntries)  &&  good_fit) {
      database_mean_crate = fit_mean_crate;
      database_mean_crate_unc = fit_mean_crate_unc;
      tcrate_new_was_set[crate_id - 1] = true;
 
      if ((numEntries >= minNumEntriesCrateConvergence)  && (fit_mean_crate_unc < 0.1)) {
        tcrate_new_goodQuality[crate_id - 1] = true;
      }
    }
 
    tcrate_mean_new[crate_id - 1] = database_mean_crate;
    tcrate_mean_unc_new[crate_id - 1] = database_mean_crate_unc;
    tcrate_sigma_new[crate_id - 1] = fit_sigma_crate;
 
    histfile->WriteTObject(h_time_crate, (string("h_time_psi_crate") + to_string(crate_id)).c_str());
    histfile->WriteTObject(h_time_crate_masked, (string("h_time_psi_crate_masked") + to_string(crate_id)).c_str());
    histfile->WriteTObject(h_time_crate_rebin, (string("h_time_psi_crate_rebinned") + to_string(crate_id)).c_str());
 
    delete gaus;
  }
 
  B2DEBUG(22, "crate histograms made");
 
 
  // Save database for crates
  // Vector of time offsets to be saved in the database.
  vector<float> t_offsets_crate;
  // Vector of time offset uncertainties to be saved in the database.
  vector<float> t_offsets_crate_unc;
  for (int i = 1; i <= ECLElementNumbers::c_NCrystals; i++) {
    t_offsets_crate.push_back(0);
    t_offsets_crate_unc.push_back(0);
  }
 
 
  for (int crys_id = 1; crys_id <= ECLElementNumbers::c_NCrystals; crys_id++) {
    int crate_id_from_crystal = crystalMapper->getCrateID(crys_id);
    if (tcrate_new_was_set[crate_id_from_crystal - 1]) {
      t_offsets_crate[crys_id - 1] = tcrate_mean_new[crate_id_from_crystal - 1] / TICKS_TO_NS;
      t_offsets_crate_unc[crys_id - 1] = tcrate_mean_unc_new[crate_id_from_crystal - 1] / TICKS_TO_NS;
 
    } else {
      t_offsets_crate[crys_id - 1] = tcrate_mean_prev[crate_id_from_crystal - 1];
      B2INFO("used old crate mean but zeroed uncertainty since not saved in root file");
    }
  }
 
 
  // Fill histograms with the difference in the tcrate values
  if (crateIDLo <= crateIDHi) {
    for (int crate_id = crateIDLo; crate_id <= crateIDHi; crate_id++) {
      // tcrate_mean_new already in ns, but tcrate_mean_prev in ticks.
      double tCrateDiff_ns = tcrate_mean_new[crate_id - 1] - (tcrate_mean_prev[crate_id - 1] * TICKS_TO_NS);
      B2INFO("Crate " << crate_id << ": tcrate new - previous iteration = "
             << tcrate_mean_new[crate_id - 1]
             << " - " << tcrate_mean_prev[crate_id - 1] * TICKS_TO_NS
             << " = " << tCrateDiff_ns << " ns");
      tcrateNew_MINUS_tcrateOld__crateID->SetBinContent(crate_id, tCrateDiff_ns);
      tcrateNew_MINUS_tcrateOld__crateID->SetBinError(crate_id, 0);
      tcrateNew_MINUS_tcrateOld__crateID->ResetStats();
 
      tcrateNew_MINUS_tcrateOld->Fill(tCrateDiff_ns);
 
      // Save the histograms monitoring the change between iterations
      tcrateNew_MINUS_tcrateOld_allRuns_allCrates->Fill(tCrateDiff_ns);
      if (tcrate_new_goodQuality[crate_id - 1]) {
        tcrateNew_MINUS_tcrateOld_allRuns->Fill(tCrateDiff_ns);
        num_tcrates_perRun->Fill(minRunNum);
        tcrateNew_MINUS_tcrateOld__vs__runNum->Fill(minRunNum, tCrateDiff_ns);
      }
    }
 
    // Save the histograms to the output root file
    histfile->WriteTObject(tcrateNew_MINUS_tcrateOld__crateID.get(), "tcrateNew_MINUS_tcrateOld__crateID");
    histfile->WriteTObject(tcrateNew_MINUS_tcrateOld.get(), "tcrateNew_MINUS_tcrateOld");
 
    // Save the histograms to the crate iterations file
    histExtraCrateInfofile->WriteTObject(tcrateNew_MINUS_tcrateOld_allRuns.get(), "tcrateNew_MINUS_tcrateOld_allRuns");
    histExtraCrateInfofile->WriteTObject(tcrateNew_MINUS_tcrateOld_allRuns_allCrates.get(),
                                         "tcrateNew_MINUS_tcrateOld_allRuns_allCrates");
    histExtraCrateInfofile->WriteTObject(num_tcrates_perRun.get(), "num_tcrates_perRun");
    histExtraCrateInfofile->WriteTObject(tcrateNew_MINUS_tcrateOld__vs__runNum.get(), "tcrateNew_MINUS_tcrateOld__vs__runNum");
  }
 
 
  ECLCrystalCalib* BhabhaTCrateCalib = new ECLCrystalCalib();
  BhabhaTCrateCalib->setCalibVector(t_offsets_crate, t_offsets_crate_unc);
 
 
  // Save the information to the payload if there is at least one crate
  //    begin calibrated.
  if (crateIDLo <= crateIDHi) {
    saveCalibration(BhabhaTCrateCalib, "ECLCrateTimeOffset");
    B2DEBUG(22, "crate payload made");
 
    histExtraCrateInfofile->Close();
  }
 
 
  int tree_crateid;
  int tree_runNum;
  double tree_tcrate_mean;
  double tree_tcrate_mean_unc;
  double tree_tcrate_sigma;
  double tree_tcrate_meanPrev;
 
  tree_crate->Branch("runNum", &tree_runNum)->SetTitle("Run number, 0..infinity and beyond!");
  tree_crate->Branch("crateid", &tree_crateid)->SetTitle("Crate id, 1..52");
  tree_crate->Branch("tcrate", &tree_tcrate_mean)->SetTitle("Crate time offset mean, tcrate, ns");
  tree_crate->Branch("tcratePrev", &tree_tcrate_meanPrev)->SetTitle("Previous crate time offset mean, tcrate, ns");
  tree_crate->Branch("tcrate_unc", &tree_tcrate_mean_unc)->SetTitle("Error of time tcrate mean, ns.");
  tree_crate->Branch("tcrate_sigma", &tree_tcrate_sigma)->SetTitle("Sigma of time tcrate distribution, ns");
  tree_crate->SetAutoSave(10);
 
 
  for (auto expRun : getRunList()) {
    // Key command to make sure your DBObjPtrs are correct
    B2INFO("run num, exp num:    " << expRun.second << ", " <<  expRun.first);
    int runNumber = expRun.second;
 
    for (int crate_id = 1; crate_id <= 52; crate_id++) {
      if (tcrate_new_was_set[crate_id - 1]) {
        tree_runNum = runNumber;
        tree_crateid = crate_id;
        tree_tcrate_mean = tcrate_mean_new[crate_id - 1];
        tree_tcrate_mean_unc = tcrate_mean_unc_new[crate_id - 1];
        tree_tcrate_sigma = tcrate_sigma_new[crate_id - 1];
        tree_tcrate_meanPrev = tcrate_mean_prev[crate_id - 1] * TICKS_TO_NS;
        tree_crate->Fill();
      }
    }
  }
 
  B2DEBUG(22, "end of crate corrections .....");
 
  tree_crystal->Write();
  tree_crate->Write();
 
  histfile->Close();
 
  B2INFO("Finished talgorithm");
  return c_OK;
}

checkPyExpRun()

bool checkPyExpRun ( PyObject * pyObj )

staticinherited

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.

{
  // Is it a sequence?
  if (PySequence_Check(pyObj)) {
    Py_ssize_t nObj = PySequence_Length(pyObj);
    // Does it have 2 objects in it?
    if (nObj != 2) {
      B2DEBUG(29, "ExpRun was a Python sequence which didn't have exactly 2 entries!");
      return false;
    }
    PyObject* item1, *item2;
    item1 = PySequence_GetItem(pyObj, 0);
    item2 = PySequence_GetItem(pyObj, 1);
    // Did the GetItem work?
    if ((item1 == NULL) || (item2 == NULL)) {
      B2DEBUG(29, "A PyObject pointer was NULL in the sequence");
      return false;
    }
    // Are they longs?
    if (PyLong_Check(item1) && PyLong_Check(item2)) {
      long value1, value2;
      value1 = PyLong_AsLong(item1);
      value2 = PyLong_AsLong(item2);
      if (((value1 == -1) || (value2 == -1)) && PyErr_Occurred()) {
        B2DEBUG(29, "An error occurred while converting the PyLong to long");
        return false;
      }
    } else {
      B2DEBUG(29, "One or more of the PyObjects in the ExpRun wasn't a long");
      return false;
    }
    // Make sure to kill off the reference GetItem gave us responsibility for
    Py_DECREF(item1);
    Py_DECREF(item2);
  } else {
    B2DEBUG(29, "ExpRun was not a Python sequence.");
    return false;
  }
  return true;
}

clearCalibrationData()

void clearCalibrationData ( )

inlineprotectedinherited

Clear calibration data.

Definition at line 324 of file CalibrationAlgorithm.h.

{m_data.clearCalibrationData();}

commit() [1/2]

bool commit ( )

inherited

Submit constants from last calibration into database.

Definition at line 302 of file CalibrationAlgorithm.cc.

{
  if (getPayloads().empty())
    return false;
  list<Database::DBImportQuery> payloads = getPayloads();
  B2INFO("Committing " << payloads.size()  << " payloads to database.");
  return Database::Instance().storeData(payloads);
}

commit() [2/2]

bool commit ( std::list< Database::DBImportQuery > payloads )

inherited

Submit constants from a (potentially previous) set of payloads.

Definition at line 312 of file CalibrationAlgorithm.cc.

{
  if (payloads.empty())
    return false;
  return Database::Instance().storeData(payloads);
}

convertPyExpRun()

ExpRun convertPyExpRun ( PyObject * pyObj )

staticinherited

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.

{
  ExpRun expRun;
  PyObject* itemExp, *itemRun;
  itemExp = PySequence_GetItem(pyObj, 0);
  itemRun = PySequence_GetItem(pyObj, 1);
  expRun.first = PyLong_AsLong(itemExp);
  Py_DECREF(itemExp);
  expRun.second = PyLong_AsLong(itemRun);
  Py_DECREF(itemRun);
  return expRun;
}

dumpOutputJson()

const std::string dumpOutputJson ( ) const

inlineinherited

Dump the JSON string of the output JSON object.

Definition at line 223 of file CalibrationAlgorithm.h.

{return m_jsonExecutionOutput.dump();}

execute() [1/2]

CalibrationAlgorithm::EResult execute ( PyObject * runs, int iteration = 0, IntervalOfValidity iov = IntervalOfValidity() )

inherited

Runs calibration over Python list of runs. Converts to C++ and then calls the other execute() function.

Definition at line 83 of file CalibrationAlgorithm.cc.

{
  B2DEBUG(29, "Running execute() using Python Object as input argument");
  // Reset the execution specific data in case the algorithm was previously called
  m_data.reset();
  m_data.setIteration(iteration);
  vector<ExpRun> vecRuns;
  // Is it a list?
  if (PySequence_Check(runs)) {
    boost::python::handle<> handle(boost::python::borrowed(runs));
    boost::python::list listRuns(handle);
 
    int nList = boost::python::len(listRuns);
    for (int iList = 0; iList < nList; ++iList) {
      boost::python::object pyExpRun(listRuns[iList]);
      if (!checkPyExpRun(pyExpRun.ptr())) {
        B2ERROR("Received Python ExpRuns couldn't be converted to C++");
        m_data.setResult(c_Failure);
        return c_Failure;
      } else {
        vecRuns.push_back(convertPyExpRun(pyExpRun.ptr()));
      }
    }
  } else {
    B2ERROR("Tried to set the input runs but we didn't receive a Python sequence object (list,tuple).");
    m_data.setResult(c_Failure);
    return c_Failure;
  }
  return execute(vecRuns, iteration, iov);
}

execute() [2/2]

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.

{
  // Check if we are calling this function directly and need to reset, or through Python where it was already done.
  if (m_data.getResult() != c_Undefined) {
    m_data.reset();
    m_data.setIteration(iteration);
  }
 
  if (m_inputFileNames.empty()) {
    B2ERROR("There aren't any input files set. Please use CalibrationAlgorithm::setInputFiles()");
    m_data.setResult(c_Failure);
    return c_Failure;
  }
 
  // Did we receive runs to execute over explicitly?
  if (!(runs.empty())) {
    for (auto expRun : runs) {
      B2DEBUG(29, "ExpRun requested = (" << expRun.first << ", " << expRun.second << ")");
    }
    // We've asked explicitly for certain runs, but we should check if the data granularity is 'run'
    if (strcmp(getGranularity().c_str(), "all") == 0) {
      B2ERROR(("The data is collected with granularity=all (exp=-1,run=-1), but you seem to request calibration for specific runs."
               " We'll continue but using ALL the input data given instead of the specific runs requested."));
    }
  } else {
    // If no runs are provided, infer the runs from all collected data
    runs = getRunListFromAllData();
    // Let's check that we have some now
    if (runs.empty()) {
      B2ERROR("No collected data in input files.");
      m_data.setResult(c_Failure);
      return c_Failure;
    }
    for (auto expRun : runs) {
      B2DEBUG(29, "ExpRun requested = (" << expRun.first << ", " << expRun.second << ")");
    }
  }
 
  m_data.setRequestedRuns(runs);
  if (iov.empty()) {
    // If no user specified IoV we use the IoV from the executed run list
    iov = IntervalOfValidity(runs[0].first, runs[0].second, runs[runs.size() - 1].first, runs[runs.size() - 1].second);
  }
  m_data.setRequestedIov(iov);
  // After here, the getObject<...>(...) helpers start to work
 
  CalibrationAlgorithm::EResult result = calibrate();
  m_data.setResult(result);
  return result;
}

fillRunToInputFilesMap()

void fillRunToInputFilesMap ( )

inherited

Fill the mapping of ExpRun -> Files.

Definition at line 331 of file CalibrationAlgorithm.cc.

{
  m_runsToInputFiles.clear();
  // Save TDirectory to change back at the end
  TDirectory* dir = gDirectory;
  RunRange* runRange;
  // Construct the TDirectory name where we expect our objects to be
  string runRangeObjName(getPrefix() + "/" + RUN_RANGE_OBJ_NAME);
  for (const auto& fileName : m_inputFileNames) {
    //Open TFile to get the objects
    unique_ptr<TFile> f;
    f.reset(TFile::Open(fileName.c_str(), "READ"));
    runRange = dynamic_cast<RunRange*>(f->Get(runRangeObjName.c_str()));
    if (runRange) {
      // Insert or extend the run -> file mapping for this ExpRun
      auto expRuns = runRange->getExpRunSet();
      for (const auto& expRun : expRuns) {
        auto runFiles = m_runsToInputFiles.find(expRun);
        if (runFiles != m_runsToInputFiles.end()) {
          (runFiles->second).push_back(fileName);
        } else {
          m_runsToInputFiles.insert(std::make_pair(expRun, std::vector<std::string> {fileName}));
        }
      }
    } else {
      B2WARNING("Missing a RunRange object for file: " << fileName);
    }
  }
  dir->cd();
}

findPayloadBoundaries()

const std::vector< ExpRun > findPayloadBoundaries ( std::vector< Calibration::ExpRun > runs, int iteration = 0 )

inherited

Used to discover the ExpRun boundaries that you want the Python CAF to execute on. This is optional and only used in some.

Definition at line 521 of file CalibrationAlgorithm.cc.

{
  m_boundaries.clear();
  if (m_inputFileNames.empty()) {
    B2ERROR("There aren't any input files set. Please use CalibrationAlgorithm::setInputFiles()");
    return m_boundaries;
  }
  // Reset the internal execution data just in case something is hanging around
  m_data.reset();
  if (runs.empty()) {
    // Want to loop over all runs we could possibly know about
    runs = getRunListFromAllData();
  }
  // Let's check that we have some now
  if (runs.empty()) {
    B2ERROR("No collected data in input files.");
    return m_boundaries;
  }
  // In order to find run boundaries we must have collected with data granularity == 'run'
  if (strcmp(getGranularity().c_str(), "all") == 0) {
    B2ERROR("The data is collected with granularity='all' (exp=-1,run=-1), and we can't use that to find run boundaries.");
    return m_boundaries;
  }
  m_data.setIteration(iteration);
  // User defined setup function
  boundaryFindingSetup(runs, iteration);
  std::vector<ExpRun> runList;
  // Loop over run list and call derived class "isBoundaryRequired" member function
  for (auto currentRun : runs) {
    runList.push_back(currentRun);
    m_data.setRequestedRuns(runList);
    // After here, the getObject<...>(...) helpers start to work
    if (isBoundaryRequired(currentRun)) {
      m_boundaries.push_back(currentRun);
    }
    // Only want run-by-run
    runList.clear();
    // Don't want memory hanging around
    m_data.clearCalibrationData();
  }
  m_data.reset();
  boundaryFindingTearDown();
  return m_boundaries;
}

getAllGranularityExpRun()

static Calibration::ExpRun getAllGranularityExpRun ( )

inlinestaticprotectedinherited

Returns the Exp,Run pair that means 'Everything'. Currently unused.

Definition at line 327 of file CalibrationAlgorithm.h.

{return m_allExpRun;}

getCollectorName()

const 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 algorithm.

Definition at line 164 of file CalibrationAlgorithm.h.

{return getPrefix();}

getDescription()

const std::string & getDescription ( ) const

inlineinherited

Get the description of the algorithm (set by developers in constructor)

Definition at line 216 of file CalibrationAlgorithm.h.

{return m_description;}

getExpRunString()

string getExpRunString ( Calibration::ExpRun & expRun ) const

privateinherited

Gets the "exp.run" string repr. of (exp,run)

Definition at line 254 of file CalibrationAlgorithm.cc.

{
  string expRunString;
  expRunString += to_string(expRun.first);
  expRunString += ".";
  expRunString += to_string(expRun.second);
  return expRunString;
}

getFullObjectPath()

string getFullObjectPath ( const std::string & name, Calibration::ExpRun expRun ) const

privateinherited

constructs the full TDirectory + Key name of an object in a TFile based on its name and exprun

Definition at line 263 of file CalibrationAlgorithm.cc.

{
  string dirName = getPrefix() + "/" + name;
  string objName = name + "_" + getExpRunString(expRun);
  return dirName + "/" + objName;
}

getGranularity()

const std::string & getGranularity ( ) const

inlineinherited

Get the granularity of collected data.

Definition at line 188 of file CalibrationAlgorithm.h.

{return m_granularityOfData;};

getGranularityFromData()

string getGranularityFromData ( ) const

protectedinherited

Get the granularity of collected data.

Definition at line 384 of file CalibrationAlgorithm.cc.

{
  // Save TDirectory to change back at the end
  TDirectory* dir = gDirectory;
  const RunRange* runRange;
  string runRangeObjName(getPrefix() + "/" + RUN_RANGE_OBJ_NAME);
  // We only check the first file
  string fileName = m_inputFileNames[0];
  unique_ptr<TFile> f;
  f.reset(TFile::Open(fileName.c_str(), "READ"));
  runRange = dynamic_cast<RunRange*>(f->Get(runRangeObjName.c_str()));
  if (!runRange) {
    B2FATAL("The input file " << fileName << " does not contain a RunRange object at "
            << runRangeObjName << ". Please set your input files to exclude it.");
    return "";
  }
  string granularity = runRange->getGranularity();
  dir->cd();
  return granularity;
}

getInputFileNames()

PyObject * getInputFileNames ( )

inherited

Get the input file names used for this algorithm and pass them out as a Python list of unicode strings.

Definition at line 245 of file CalibrationAlgorithm.cc.

{
  PyObject* objInputFileNames = PyList_New(m_inputFileNames.size());
  for (size_t i = 0; i < m_inputFileNames.size(); ++i) {
    PyList_SetItem(objInputFileNames, i, Py_BuildValue("s", m_inputFileNames[i].c_str()));
  }
  return objInputFileNames;
}

getInputJsonObject()

const nlohmann::json & getInputJsonObject ( ) const

inlineprotectedinherited

Get the entire top level JSON object. We explicitly say this must be of object type so that we might pick.

Definition at line 357 of file CalibrationAlgorithm.h.

{return m_jsonExecutionInput;}

getInputJsonValue()

template<class T>

const T getInputJsonValue ( const std::string & key ) const

inlineprotectedinherited

Get an input JSON value using a key. The normal exceptions are raised when the key doesn't exist.

Definition at line 350 of file CalibrationAlgorithm.h.

    {
      return m_jsonExecutionInput.at(key);
    }

getIovFromAllData()

IntervalOfValidity getIovFromAllData ( ) const

inherited

Get the complete IoV from inspection of collected data.

Definition at line 326 of file CalibrationAlgorithm.cc.

{
  return getRunRangeFromAllData().getIntervalOfValidity();
}

getIteration()

int getIteration ( ) const

inlineprotectedinherited

Get current iteration.

Definition at line 269 of file CalibrationAlgorithm.h.

{ return m_data.getIteration(); }

getObjectPtr()

template<class T>

std::shared_ptr< T > getObjectPtr ( std::string name )

inlineprotectedinherited

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.

Definition at line 285 of file CalibrationAlgorithm.h.

    {
      if (m_runsToInputFiles.size() == 0)
        fillRunToInputFilesMap();
      return getObjectPtr<T>(name, m_data.getRequestedRuns());
    }

getOutputJsonValue()

template<class T>

const T getOutputJsonValue ( const std::string & key ) const

inlineprotectedinherited

Get a value using a key from the JSON output object, not sure why you would want to do this.

Definition at line 342 of file CalibrationAlgorithm.h.

    {
      return m_jsonExecutionOutput.at(key);
    }

getPayloads()

std::list< Database::DBImportQuery > & getPayloads ( )

inlineinherited

Get constants (in TObjects) for database update from last execution.

Definition at line 204 of file CalibrationAlgorithm.h.

{return m_data.getPayloads();}

getPayloadValues()

std::list< Database::DBImportQuery > getPayloadValues ( ) const

inlineinherited

Get constants (in TObjects) for database update from last execution but passed by VALUE.

Definition at line 207 of file CalibrationAlgorithm.h.

{return m_data.getPayloadValues();}

getPrefix()

const std::string & getPrefix ( ) const

inlineinherited

Get the prefix used for getting calibration data.

Definition at line 146 of file CalibrationAlgorithm.h.

{return m_prefix;}

getRunList()

const std::vector< Calibration::ExpRun > & getRunList ( ) const

inlineprotectedinherited

Get the list of runs for which calibration is called.

Definition at line 266 of file CalibrationAlgorithm.h.

{return m_data.getRequestedRuns();}

getRunListFromAllData()

vector< ExpRun > getRunListFromAllData ( ) const

inherited

Get the complete list of runs from inspection of collected data.

Definition at line 319 of file CalibrationAlgorithm.cc.

{
  RunRange runRange = getRunRangeFromAllData();
  set<ExpRun> expRunSet = runRange.getExpRunSet();
  return vector<ExpRun>(expRunSet.begin(), expRunSet.end());
}

getRunRangeFromAllData()

RunRange getRunRangeFromAllData ( ) const

inherited

Get the complete RunRange from inspection of collected data.

Definition at line 362 of file CalibrationAlgorithm.cc.

{
  // Save TDirectory to change back at the end
  TDirectory* dir = gDirectory;
  RunRange runRange;
  // Construct the TDirectory name where we expect our objects to be
  string runRangeObjName(getPrefix() + "/" + RUN_RANGE_OBJ_NAME);
  for (const auto& fileName : m_inputFileNames) {
    //Open TFile to get the objects
    unique_ptr<TFile> f;
    f.reset(TFile::Open(fileName.c_str(), "READ"));
    const RunRange* runRangeOther = dynamic_cast<RunRange*>(f->Get(runRangeObjName.c_str()));
    if (runRangeOther) {
      runRange.merge(runRangeOther);
    } else {
      B2WARNING("Missing a RunRange object for file: " << fileName);
    }
  }
  dir->cd();
  return runRange;
}

getVecInputFileNames()

const std::vector< std::string > & getVecInputFileNames ( ) const

inlineprotectedinherited

Get the input file names used for this algorithm as a STL vector.

Definition at line 275 of file CalibrationAlgorithm.h.

{return m_inputFileNames;}

inputJsonKeyExists()

bool inputJsonKeyExists ( const std::string & key ) const

inlineprotectedinherited

Test for a key in the input JSON object.

Definition at line 360 of file CalibrationAlgorithm.h.

{return m_jsonExecutionInput.count(key);}

isBoundaryRequired()

virtual bool isBoundaryRequired ( const Calibration::ExpRun & )

inlineprotectedvirtualinherited

Given the current collector data, make a decision about whether or not this run should be the start of a payload boundary.

Reimplemented in PXDAnalyticGainCalibrationAlgorithm, PXDValidationAlgorithm, SVD3SampleCoGTimeCalibrationAlgorithm, SVD3SampleELSTimeCalibrationAlgorithm, SVDClusterAbsoluteTimeShifterAlgorithm, SVDCoGTimeCalibrationAlgorithm, TestBoundarySettingAlgorithm, and TestCalibrationAlgorithm.

Definition at line 243 of file CalibrationAlgorithm.h.

    {
      B2ERROR("You didn't implement a isBoundaryRequired() member function in your CalibrationAlgorithm but you are calling it!");
      return false;
    }

loadInputJson()

bool loadInputJson ( const std::string & jsonString )

inherited

Load the m_inputJson variable from a string (useful from Python interface). The return bool indicates success or failure.

Definition at line 503 of file CalibrationAlgorithm.cc.

{
  try {
    auto jsonInput = nlohmann::json::parse(jsonString);
    // Input string has an object (dict) as the top level object?
    if (jsonInput.is_object()) {
      m_jsonExecutionInput = jsonInput;
      return true;
    } else {
      B2ERROR("JSON input string isn't an object type i.e. not a '{}' at the top level.");
      return false;
    }
  } catch (nlohmann::json::parse_error&) {
    B2ERROR("Parsing of JSON input string failed");
    return false;
  }
}

resetInputJson()

void resetInputJson ( )

inlineprotectedinherited

Clears the m_inputJson member variable.

Definition at line 330 of file CalibrationAlgorithm.h.

{m_jsonExecutionInput.clear();}

resetOutputJson()

void resetOutputJson ( )

inlineprotectedinherited

Clears the m_outputJson member variable.

Definition at line 333 of file CalibrationAlgorithm.h.

{m_jsonExecutionOutput.clear();}

saveCalibration() [1/6]

void saveCalibration ( TClonesArray * data, const std::string & name )

protectedinherited

Store DBArray payload with given name with default IOV.

Definition at line 297 of file CalibrationAlgorithm.cc.

{
  saveCalibration(data, name, m_data.getRequestedIov());
}

saveCalibration() [2/6]

void saveCalibration ( TClonesArray * data, const std::string & name, const IntervalOfValidity & iov )

protectedinherited

Store DBArray with given name and custom IOV.

Definition at line 276 of file CalibrationAlgorithm.cc.

{
  B2DEBUG(29, "Saving calibration TClonesArray '" <<  name << "' to payloads list.");
  getPayloads().emplace_back(name, data, iov);
}

saveCalibration() [3/6]

void saveCalibration ( TObject * data )

protectedinherited

Store DB payload with default name and default IOV.

Definition at line 287 of file CalibrationAlgorithm.cc.

{
  saveCalibration(data, DataStore::objectName(data->IsA(), ""));
}

saveCalibration() [4/6]

void saveCalibration ( TObject * data, const IntervalOfValidity & iov )

protectedinherited

Store DB payload with default name and custom IOV.

Definition at line 282 of file CalibrationAlgorithm.cc.

{
  saveCalibration(data, DataStore::objectName(data->IsA(), ""), iov);
}

saveCalibration() [5/6]

void saveCalibration ( TObject * data, const std::string & name )

protectedinherited

Store DB payload with given name with default IOV.

Definition at line 292 of file CalibrationAlgorithm.cc.

{
  saveCalibration(data, name, m_data.getRequestedIov());
}

saveCalibration() [6/6]

void saveCalibration ( TObject * data, const std::string & name, const IntervalOfValidity & iov )

protectedinherited

Store DB payload with given name and custom IOV.

Definition at line 270 of file CalibrationAlgorithm.cc.

{
  B2DEBUG(29, "Saving calibration TObject = '" <<  name << "' to payloads list.");
  getPayloads().emplace_back(name, data, iov);
}

setDescription()

void setDescription ( const std::string & description )

inlineprotectedinherited

Set algorithm description (in constructor)

Definition at line 321 of file CalibrationAlgorithm.h.

{m_description = description;}

setInputFileNames() [1/2]

void setInputFileNames ( const 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.

{
  // A lot of code below is tweaked from RootInputModule::initialize,
  // since we're basically copying the functionality anyway.
  if (inputFileNames.empty()) {
    B2WARNING("You have called setInputFileNames() with an empty list. Did you mean to do that?");
    return;
  }
  auto tmpInputFileNames = RootIOUtilities::expandWordExpansions(inputFileNames);
 
  // We'll use a set to enforce sorted unique file paths as we check them
  set<string> setInputFileNames;
  // Check that files exist and convert to absolute paths
  for (auto path : tmpInputFileNames) {
    string fullPath = fs::absolute(path).string();
    if (fs::exists(fullPath)) {
      setInputFileNames.insert(fs::canonical(fullPath).string());
    } else {
      B2WARNING("Couldn't find the file " << path);
    }
  }
 
  if (setInputFileNames.empty()) {
    B2WARNING("No valid files specified!");
    return;
  } else {
    // Reset the run -> files map as our files are likely different
    m_runsToInputFiles.clear();
  }
 
  // Open TFile to check they can be accessed by ROOT
  TDirectory* dir = gDirectory;
  for (const string& fileName : setInputFileNames) {
    unique_ptr<TFile> f;
    try {
      f.reset(TFile::Open(fileName.c_str(), "READ"));
    } catch (logic_error&) {
      //this might happen for ~invaliduser/foo.root
      //actually undefined behaviour per standard, reported as ROOT-8490 in JIRA
    }
    if (!f || !f->IsOpen()) {
      B2FATAL("Couldn't open input file " + fileName);
    }
  }
  dir->cd();
 
  // Copy the entries of the set to a vector
  m_inputFileNames = vector<string>(setInputFileNames.begin(), setInputFileNames.end());
  m_granularityOfData = getGranularityFromData();
}

setInputFileNames() [2/2]

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.

{
  // The reasoning for this very 'manual' approach to extending the Python interface
  // (instead of using boost::python) is down to my fear of putting off final users with
  // complexity on their side.
  //
  // I didn't want users that inherit from this class to be forced to use boost and
  // to have to define a new python module just to use the CAF. A derived class from
  // from a boost exposed class would need to have its own boost python module definition
  // to allow access from a steering file and to the base class functions (I think).
  // I also couldn't be bothered to write a full framework to get around the issue in a similar
  // way to Module()...maybe there's an easy way.
  //
  // But this way we can allow people to continue using their ROOT implemented classes and inherit
  // easily from this one. But add in a few helper functions that work with Python objects
  // created in their steering file i.e. instead of being forced to use STL objects as input
  // to the algorithm.
  if (PyList_Check(inputFileNames)) {
    boost::python::handle<> handle(boost::python::borrowed(inputFileNames));
    boost::python::list listInputFileNames(handle);
    auto vecInputFileNames = PyObjConvUtils::convertPythonObject(listInputFileNames, vector<string>());
    setInputFileNames(vecInputFileNames);
  } else {
    B2ERROR("Tried to set the input files but we didn't receive a Python list.");
  }
}

setOutputJsonValue()

template<class T>

void setOutputJsonValue ( const std::string & key, const T & value )

inlineprotectedinherited

Set a key:value pair for the outputJson object, expected to used internally during calibrate()

Definition at line 337 of file CalibrationAlgorithm.h.

{m_jsonExecutionOutput[key] = value;}

setPrefix()

void setPrefix ( const std::string & prefix )

inlineinherited

Set the prefix used to identify datastore objects.

Definition at line 167 of file CalibrationAlgorithm.h.

{m_prefix = prefix;}

updateDBObjPtrs()

void updateDBObjPtrs ( const unsigned int event, const int run, const int experiment )

staticprotectedinherited

Updates any DBObjPtrs by calling update(event) for DBStore.

Definition at line 405 of file CalibrationAlgorithm.cc.

{
  // Construct an EventMetaData object but NOT in the Datastore
  EventMetaData emd(event, run, experiment);
  // Explicitly update while avoiding registering a Datastore object
  DBStore::Instance().update(emd);
  // Also update the intra-run objects to the event at the same time (maybe unnecessary...)
  DBStore::Instance().updateEvent(event);
}

Member Data Documentation

cellIDHi

int cellIDHi = 0

Fit crystals with cellID0 in the inclusive range [cellIDLo,cellIDHi].

Definition at line 37 of file eclBhabhaTAlgorithm.h.

cellIDLo

int cellIDLo = 0

Fit crystals with cellID0 in the inclusive range [cellIDLo,cellIDHi].

Definition at line 36 of file eclBhabhaTAlgorithm.h.

collectorName

std::string collectorName

Name of the collector.

Definition at line 49 of file eclBhabhaTAlgorithm.h.

crateIDHi

int crateIDHi = 0

Fit crates with crateID0 in the inclusive range [crateIDLo,crateIDHi].

Definition at line 43 of file eclBhabhaTAlgorithm.h.

crateIDLo

int crateIDLo = 0

Fit crates with crateID0 in the inclusive range [crateIDLo,crateIDHi].

Definition at line 42 of file eclBhabhaTAlgorithm.h.

debugFilenameBase

std::string debugFilenameBase

Name of file with debug output, eclBhabhaTAlgorithm.root by default.

Definition at line 48 of file eclBhabhaTAlgorithm.h.

debugOutput

bool debugOutput = false

Save every histogram and fitted function to debugFilename.

Definition at line 46 of file eclBhabhaTAlgorithm.h.

m_allExpRun

const ExpRun m_allExpRun = make_pair(-1, -1)

staticprivateinherited

allExpRun

Definition at line 364 of file CalibrationAlgorithm.h.

m_boundaries

std::vector<Calibration::ExpRun> m_boundaries

protectedinherited

When using the boundaries functionality from isBoundaryRequired, this is used to store the boundaries. It is cleared when.

Definition at line 261 of file CalibrationAlgorithm.h.

m_data

ExecutionData m_data

privateinherited

Data specific to a SINGLE execution of the algorithm. Gets reset at the beginning of execution.

Definition at line 382 of file CalibrationAlgorithm.h.

m_description

std::string m_description {""}

privateinherited

Description of the algorithm.

Definition at line 385 of file CalibrationAlgorithm.h.

{""};

m_granularityOfData

std::string m_granularityOfData

privateinherited

Granularity of input data. This only changes when the input files change so it isn't specific to an execution.

Definition at line 379 of file CalibrationAlgorithm.h.

m_inputFileNames

std::vector<std::string> m_inputFileNames

privateinherited

List of input files to the Algorithm, will initially be user defined but then gets the wildcards expanded during execute()

Definition at line 373 of file CalibrationAlgorithm.h.

m_jsonExecutionInput

nlohmann::json m_jsonExecutionInput = nlohmann::json::object()

privateinherited

Optional input JSON object used to make decisions about how to execute the algorithm code.

Definition at line 397 of file CalibrationAlgorithm.h.

m_jsonExecutionOutput

nlohmann::json m_jsonExecutionOutput = nlohmann::json::object()

privateinherited

Optional output JSON object that can be set during the execution by the underlying algorithm code.

Definition at line 403 of file CalibrationAlgorithm.h.

m_prefix

std::string m_prefix {""}

privateinherited

The name of the TDirectory the collector objects are contained within.

Definition at line 388 of file CalibrationAlgorithm.h.

{""};

m_runsToInputFiles

std::map<Calibration::ExpRun, std::vector<std::string> > m_runsToInputFiles

privateinherited

Map of Runs to input files. Gets filled when you call getRunRangeFromAllData, gets cleared when setting input files again.

Definition at line 376 of file CalibrationAlgorithm.h.

meanCleanCutMinFactor

double meanCleanCutMinFactor = 0.0

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 39 of file eclBhabhaTAlgorithm.h.

meanCleanRebinFactor

double meanCleanRebinFactor = 0.0

Rebinning factor for mean calculation.

Definition at line 38 of file eclBhabhaTAlgorithm.h.

readPrevCrysPayload

bool readPrevCrysPayload = false

Read the previous crystal payload values for comparison.

Definition at line 45 of file eclBhabhaTAlgorithm.h.

refCrysPerCrate

int refCrysPerCrate[52] = {}

List of crystals, one per crate, used as reference time for crystal time calibration.

Definition at line 50 of file eclBhabhaTAlgorithm.h.

{};  

savePrevCrysPayload

bool savePrevCrysPayload = false

Save the previous crystal payload values for comparison.

Definition at line 44 of file eclBhabhaTAlgorithm.h.

---

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

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