eclGammaGammaEAlgorithm Class Reference

Calibrate ecl crystals using gamma pair events. More...

#include <eclGammaGammaEAlgorithm.h>

Inheritance diagram for eclGammaGammaEAlgorithm:

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

Public Member Functions
	eclGammaGammaEAlgorithm ()
	..Constructor
virtual	~eclGammaGammaEAlgorithm ()
	..Destructor
void	setOutputName (const std::string &outputName)
	Setter for m_outputName.
std::string	getOutputName ()
	Getter for m_outputName.
void	setCellIDLo (int cellIDLo)
	Setter for m_cellIDLo.
int	getCellIDLo ()
	Getter for m_cellIDLo.
void	setCellIDHi (int cellIDHi)
	Setter for m_cellIDHi.
int	getCellIDHi ()
	Getter for m_cellIDHi.
void	setMinEntries (int minEntries)
	Setter for m_minEntries.
int	getMinEntries ()
	Getter for m_minEntries.
void	setMaxIterations (int maxIterations)
	Setter for m_maxIterations.
int	getMaxIterations ()
	Getter for m_maxIterations.
void	setTRatioMin (double tRatioMin)
	Setter for m_tRatioMinNom.
double	getTRatioMin ()
	Getter for m_tRatioMinNom.
void	setTRatioMax (double tRatioMax)
	Setter for m_tRatioMaxNom.
double	getTRatioMax ()
	Getter for m_tRatioMaxNom.
void	setTRatioMinHiStat (double tRatioMin)
	Setter for m_tRatioMinHiStat.
double	getTRatioMinHiStat ()
	Getter for m_tRatioMinHiStat.
void	setTRatioMaxHiStat (double tRatioMax)
	Setter for m_tRatioMaxHiStat.
double	getTRatioMaxHiStat ()
	Getter for m_tRatioMaxHiStat.
void	setUpperEdgeThresh (double upperEdgeThresh)
	Setter for m_upperEdgeThresh.
double	getUpperEdgeThresh ()
	Getter for m_upperEdgeThresh.
void	setPerformFits (bool performFits)
	Setter for m_performFits.
bool	getPerformFits ()
	Getter for m_performFits.
void	setFindExpValues (bool findExpValues)
	Setter for m_findExpValues.
bool	getFindExpValues ()
	Getter for m_findExpValues.
void	setStoreConst (int storeConst)
	Setter for m_storeConst.
int	getStoreConst ()
	Getter for m_storeConst.
std::string	getPrefix () const
	Get the prefix used for getting calibration data.
bool	checkPyExpRun (PyObject *pyObj)
	Checks that a PyObject can be successfully converted to an ExpRun type.
Calibration::ExpRun	convertPyExpRun (PyObject *pyObj)
	Performs the conversion of PyObject to ExpRun.
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.
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 ()
	Get constants (in TObjects) for database update from last execution but passed by VALUE.
bool	commit ()
	Submit constants from last calibration into database.
bool	commit (std::list< Database::DBImportQuery > payloads)
	Submit constants from a (potentially previous) set of payloads.
const std::string &	getDescription () const
	Get the description of the algoithm (set by developers in constructor)
bool	loadInputJson (const std::string &jsonString)
	Load the m_inputJson variable from a string (useful from Python interface). The rturn bool indicates success or failure.
const std::string	dumpOutputJson () const
	Dump the JSON string of the output JSON object.
const std::vector< Calibration::ExpRun >	findPayloadBoundaries (std::vector< Calibration::ExpRun > runs, int iteration=0)
	Used to discover the ExpRun boundaries that you want the Python CAF to execute on. This is optional and only used in some.
template<>
std::shared_ptr< TTree >	getObjectPtr (const std::string &name, const std::vector< Calibration::ExpRun > &requestedRuns)
	Specialization of getObjectPtr<TTree>.

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

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

Private Member Functions
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::string	m_outputName = "eclGammaGammaEAlgorithm.root"
	..Parameters to control Novosibirsk fit to energy deposited in each crystal by mu+mu- events
int	m_cellIDLo = 1
	First cellID to be fit.
int	m_cellIDHi = ECLElementNumbers::c_NCrystals
	Last cellID to be fit.
int	m_minEntries = 150
	Minimum entries to fit a crystal.
int	m_highStatEntries = 25000
	Adjust fit range above this many entries.
int	m_maxIterations = 10
	no more than maxIteration iterations
double	m_tRatioMinNom
	Fit range is adjusted so that fit at lower endpoint is between tRatioMin and tRatioMax of peak.
double	m_tRatioMaxNom = 0.70
	Fit range is adjusted so that fit at lower endpoint is between tRatioMin and tRatioMax of peak.
double	m_tRatioMinHiStat
	Fit range is adjusted so that fit at lower endpoint is between tRatioMin and tRatioMax of peak.
double	m_tRatioMaxHiStat
	Fit range is adjusted so that fit at lower endpoint is between tRatioMin and tRatioMax of peak.
double	m_upperEdgeThresh = 0.02
	Upper edge is where the fit = upperEdgeThresh * peak value.
bool	m_performFits = true
	if false, input histograms are copied to output, but no fits are done
bool	m_findExpValues
	if true, fits are used to find expected energy deposit for each crystal instead of the calibration constant
int	m_storeConst = 0
	controls which values are written to the database.
int	fitOK = 16
	Characterize fit status.
int	iterations = 8
	fit reached max number of iterations, but is useable
int	atLimit = 4
	a parameter is at the limit; upper edge is found from histogram, not fit
int	poorFit = 3
	low chi square; upper edge is found from histogram, not fit
int	noPeak = 2
	Novosibirsk component of fit is negligible; upper edge is found from histogram, not fit.
int	notFit = -1
	no fit performed; no constants found for this crystal
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 gamma pair events.

Definition at line 25 of file eclGammaGammaEAlgorithm.h.

Member Enumeration Documentation

EResult

enum EResult

inherited

The result of calibration.

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

Definition at line 40 of file CalibrationAlgorithm.h.

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

Constructor & Destructor Documentation

eclGammaGammaEAlgorithm()

eclGammaGammaEAlgorithm

(

)

..Constructor

---

Definition at line 58 of file eclGammaGammaEAlgorithm.cc.

                                                : CalibrationAlgorithm("eclGammaGammaECollector")
{
  setDescription(
    "Perform energy calibration of ecl crystals by fitting a Novosibirsk function to energy deposited by photons in e+e- --> gamma gamma"
  );
}

~eclGammaGammaEAlgorithm()

virtual ~eclGammaGammaEAlgorithm ( )

inlinevirtual

..Destructor

Definition at line 32 of file eclGammaGammaEAlgorithm.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 TestBoundarySettingAlgorithm, TestCalibrationAlgorithm, PXDAnalyticGainCalibrationAlgorithm, PXDValidationAlgorithm, SVD3SampleCoGTimeCalibrationAlgorithm, SVD3SampleELSTimeCalibrationAlgorithm, and SVDCoGTimeCalibrationAlgorithm.

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

---

ranges of various fit parameters, and tolerance to determine that fit is at the limit

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

---

Write out the job parameters

---

Clean up existing histograms if necessary

---

Histograms containing the data collected by eclGammaGammaECollectorModule

---

Record the number of entries per crystal in the normalized energy histogram and calculate the average expected energy per crystal and calibration constants from Collector

---

Write out the basic histograms in all cases

---

If we have not been asked to do fits, we can quit now

---

Check that every crystal has enough entries, if so requested

Insufficient data. Quit if we are required to have a successful fit for every crystal

---

Some prep for the many fits about to follow

histograms to store results for database

Diagnostic histograms

1D summary histograms

---

Fits are requested and there is sufficient data. Loop over specified crystals and performs fits to the amplitude distributions

Project 1D histogram of energy in this crystal

Fit function (xmin, xmax, nparameters) for this histogram

Estimate initial parameters from the histogram. For peak, use maximum bin in the allowed range

Fit range is just below peak to the histogram maximum

eta and constant are nominal values

Parameters to control iterations. dIter checks if we are stuck in a loop

Use a smaller fit range for high statistics histograms

---

Iterate from this point

Set the initial parameters

Fit

The lower fit range should correspond the specified fraction of the peak. Iterate if necessary.

Check if we are oscillating between two end points

Many iterations may mean we are stuck in a loop. Try a different end point.

Set the constant term to 0 if we are close to the limit

No more than specified number of iterations

---

..Manually calculate a more meaningful fit probability. Same as P option in fit, which cannot be used with L

only include this bin if meaningful

---

Fit status

No peak; normalization of Novo component is too small

poor fit, or relatively poor fit with too many iterations

parameter at limit

---

Find upper edge of Novosibirsk fit, if possible

Look for the fit to drop to specified fraction of peak.

bins on either side of this value

look for the target value between these two points

Fit was not successful despite sufficient statistics; find upper edge from bin contents

---

fill diagnostic histograms

1D summary histograms

Store histogram with fit

---

Interpret results of fit as expected energy or calibration constant

if no fit, set upperEdge to -1, so that output calib = -1 * abs(input calib)

Find expected energies from MC, if requested

Otherwise, calibration constant

---

Write output to DB if requested and successful

Store expected energies

Store calibration constants

---

Write out diagnostic histograms

Histograms containing values written to DB

---

Clean up histograms in case Algorithm is called again

---

Set the return code appropriately

Implements CalibrationAlgorithm.

Definition at line 65 of file eclGammaGammaEAlgorithm.cc.

{
  const double limitTol = 0.0005; /*< tolerance for checking if a parameter is at the limit */
  const double minFitLimit = 1e-25; /*< cut off for labeling a fit as poor */
  const double minFitProbIter = 1e-8; /*< cut off for labeling a fit as poor if it also has many iterations */
  const double constRatio = 0.5; /*< Novosibirsk normalization must be greater than constRatio x constant term */
  const double peakMin(0.4), peakMax(2.2); /*< range for peak of normalized energy distribution */
  const double peakTol = limitTol * (peakMax - peakMin); /*< fit is at limit if it is within peakTol of min or max */
  const double effSigMin(0.02), effSigMax(0.4); /*< range for effective sigma of normalized energy distribution */
  const double effSigTol = limitTol * (effSigMax - effSigMin); /*< fit is at limit if it is within effSigTol of min or max */
  const double etaNom(0.41); /*< Nominal tail parameter */
  const double etaMin(0.), etaMax(5.0); /*< Novosibirsk tail parameter range */
  const double etaTol = limitTol * (etaMax - etaMin); /*< fit is at limit if it is within etaTol of min or max */
  const double constTol = 0.001; /*< if constant is less than constTol, it will be fixed to 0 */
 
  gROOT->SetBatch();
 
  B2INFO("eclGammaGammaAlgorithm parameters:");
  B2INFO("outputName = " << m_outputName);
  B2INFO("cellIDLo = " << m_cellIDLo);
  B2INFO("cellIDHi = " << m_cellIDHi);
  B2INFO("minEntries = " << m_minEntries);
  B2INFO("highStatEntries = " << m_highStatEntries);
  B2INFO("maxIterations = " << m_maxIterations);
  B2INFO("tRatioMinNom = " << m_tRatioMinNom);
  B2INFO("tRatioMaxNom = " << m_tRatioMaxNom);
  B2INFO("tRatioMinHiStat = " << m_tRatioMinHiStat);
  B2INFO("tRatioMaxHiStat = " << m_tRatioMaxHiStat);
  B2INFO("upperEdgeThresh = " << m_upperEdgeThresh);
  B2INFO("performFits = " << m_performFits);
  B2INFO("findExpValues = " << m_findExpValues);
  B2INFO("storeConst = " << m_storeConst);
 
  TH1F* dummy;
  dummy = (TH1F*)gROOT->FindObject("IntegralVsCrysID");
  if (dummy) {delete dummy;}
  dummy = (TH1F*)gROOT->FindObject("AverageExpECrys");
  if (dummy) {delete dummy;}
  dummy = (TH1F*)gROOT->FindObject("AverageElecCalib");
  if (dummy) {delete dummy;}
  dummy = (TH1F*)gROOT->FindObject("AverageInitCalib");
  if (dummy) {delete dummy;}
 
  auto EnVsCrysID = getObjectPtr<TH2F>("EnVsCrysID");
  auto ExpEvsCrys = getObjectPtr<TH1F>("ExpEvsCrys");
  auto ElecCalibvsCrys = getObjectPtr<TH1F>("ElecCalibvsCrys");
  auto InitialCalibvsCrys = getObjectPtr<TH1F>("InitialCalibvsCrys");
  auto CalibEntriesvsCrys = getObjectPtr<TH1F>("CalibEntriesvsCrys");
 
  TH1F* IntegralVsCrysID = new TH1F("IntegralVsCrysID", "Integral of EnVsCrysID for each crystal;crystal ID;Entries",
                                    ECLElementNumbers::c_NCrystals, 0, ECLElementNumbers::c_NCrystals);
  TH1F* AverageExpECrys = new TH1F("AverageExpECrys", "Average expected E per crys from collector;Crystal ID;Energy (GeV)",
                                   ECLElementNumbers::c_NCrystals, 0,
                                   ECLElementNumbers::c_NCrystals);
  TH1F* AverageElecCalib = new TH1F("AverageElecCalib", "Average electronics calib const vs crystal;Crystal ID;Calibration constant",
                                    ECLElementNumbers::c_NCrystals, 0, ECLElementNumbers::c_NCrystals);
  TH1F* AverageInitCalib = new TH1F("AverageInitCalib", "Average initial calib const vs crystal;Crystal ID;Calibration constant",
                                    ECLElementNumbers::c_NCrystals, 0, ECLElementNumbers::c_NCrystals);
 
  for (int crysID = 0; crysID < ECLElementNumbers::c_NCrystals; crysID++) {
    TH1D* hEnergy = EnVsCrysID->ProjectionY("hEnergy", crysID + 1, crysID + 1);
    int Integral = hEnergy->Integral();
    IntegralVsCrysID->SetBinContent(crysID + 1, Integral);
 
    double TotEntries = CalibEntriesvsCrys->GetBinContent(crysID + 1);
 
    double expectedE = 0.;
    if (TotEntries > 0.) {expectedE = ExpEvsCrys->GetBinContent(crysID + 1) / TotEntries;}
    AverageExpECrys->SetBinContent(crysID + 1, expectedE);
 
    double calibconst = 0.;
    if (TotEntries > 0.) {calibconst = ElecCalibvsCrys->GetBinContent(crysID + 1) / TotEntries;}
    AverageElecCalib->SetBinContent(crysID + 1, calibconst);
 
    calibconst = 0.;
    if (TotEntries > 0.) {calibconst = InitialCalibvsCrys->GetBinContent(crysID + 1) / TotEntries;}
    AverageInitCalib->SetBinContent(crysID + 1, calibconst);
  }
 
  TString fName = m_outputName;
  TFile* histfile = new TFile(fName, "recreate");
  EnVsCrysID->Write();
  IntegralVsCrysID->Write();
  AverageExpECrys->Write();
  AverageElecCalib->Write();
  AverageInitCalib->Write();
 
  if (!m_performFits) {
    B2INFO("eclGammaGammaEAlgorithm has not been asked to perform fits; copying input histograms and quitting");
    histfile->Close();
    return c_NotEnoughData;
  }
 
  bool sufficientData = true;
  for (int crysID = m_cellIDLo - 1; crysID < m_cellIDHi; crysID++) {
    if (IntegralVsCrysID->GetBinContent(crysID + 1) < m_minEntries) {
      if (m_storeConst == 1) {B2INFO("eclGammaGammaEAlgorithm: crystal " << crysID << " has insufficient statistics: " << IntegralVsCrysID->GetBinContent(crysID + 1) << ". Requirement is " << m_minEntries);}
      sufficientData = false;
      break;
    }
  }
 
  if (!sufficientData && m_storeConst == 1) {
    histfile->Close();
    return c_NotEnoughData;
  }
 
  TH1F* CalibVsCrysID = new TH1F("CalibVsCrysID", "Calibration constant vs crystal ID;crystal ID;counts per GeV",
                                 ECLElementNumbers::c_NCrystals, 0, ECLElementNumbers::c_NCrystals);
  TH1F* ExpEnergyperCrys = new TH1F("ExpEnergyperCrys", "Expected energy per crystal;Crystal ID;Peak energy (GeV)",
                                    ECLElementNumbers::c_NCrystals, 0, ECLElementNumbers::c_NCrystals);
 
  TH1F* PeakVsCrysID = new TH1F("PeakVsCrysID", "Peak of Novo fit vs crystal ID;crystal ID;Peak normalized energy",
                                ECLElementNumbers::c_NCrystals, 0, ECLElementNumbers::c_NCrystals);
  TH1F* EdgeVsCrysID = new TH1F("EdgeVsCrysID", "Upper edge of Novo fit vs crystal ID;crystal ID;Maximum normalized energy",
                                ECLElementNumbers::c_NCrystals, 0,
                                ECLElementNumbers::c_NCrystals);
  TH1F* effSigVsCrysID = new TH1F("effSigVsCrysID", "effSigma vs crystal ID;crystal ID;sigma)", ECLElementNumbers::c_NCrystals, 0,
                                  ECLElementNumbers::c_NCrystals);
  TH1F* etaVsCrysID = new TH1F("etaVsCrysID", "eta vs crystal ID;crystal ID;Novo eta parameter", ECLElementNumbers::c_NCrystals, 0,
                               ECLElementNumbers::c_NCrystals);
  TH1F* constVsCrysID = new TH1F("constVsCrysID", "fit constant vs crystal ID;crystal ID;fit constant",
                                 ECLElementNumbers::c_NCrystals, 0, ECLElementNumbers::c_NCrystals);
  TH1F* normVsCrysID = new TH1F("normVsCrysID", "Novosibirsk normalization vs crystal ID;crystal ID;normalization",
                                ECLElementNumbers::c_NCrystals, 0, ECLElementNumbers::c_NCrystals);
  TH1F* fitLimitVsCrysID = new TH1F("fitLimitVsCrysID", "fit range lower limit vs crystal ID;crystal ID;upper fit limit",
                                    ECLElementNumbers::c_NCrystals, 0,
                                    ECLElementNumbers::c_NCrystals);
  TH1F* StatusVsCrysID = new TH1F("StatusVsCrysID", "Fit status vs crystal ID;crystal ID;Fit status", ECLElementNumbers::c_NCrystals,
                                  0, ECLElementNumbers::c_NCrystals);
  TH1F* FitProbVsCrysID = new TH1F("FitProbVsCrysID", "Fit probability vs crystal id;crystal ID;Fit probability",
                                   ECLElementNumbers::c_NCrystals, 0, ECLElementNumbers::c_NCrystals);
 
  TH1F* hStatus = new TH1F("hStatus", "Fit status", 25, -5, 20);
  TH1F* hPeak = new TH1F("hPeak", "Peaks of normalized energy distributions, successful fits;Peak of Novosibirsk fit", 200, 0.8, 1.2);
  TH1F* fracPeakUnc = new TH1F("fracPeakUnc", "Fractional uncertainty on peak uncertainty, successful fits;Uncertainty on peak", 100,
                               0, 0.1);
  TH1F* nIterations = new TH1F("nIterations", "Number of times histogram was fit;Number of iterations", 20, -0.5, 19.5);
 
 
  bool allFitsOK = true;
  for (int crysID = m_cellIDLo - 1; crysID < m_cellIDHi; crysID++) {
 
    TString name = "Enormalized";
    name += crysID;
    TH1D* hEnergy = EnVsCrysID->ProjectionY(name, crysID + 1, crysID + 1);
 
    double histMin = hEnergy->GetXaxis()->GetXmin();
    double histMax = hEnergy->GetXaxis()->GetXmax();
    TF1* func = new TF1("eclGammaGammaNovoConst", eclGammaGammaNovoConst, histMin, histMax, 5);
    func->SetParNames("normalization", "peak", "effSigma", "eta", "const");
    func->SetParLimits(1, peakMin, peakMax);
    func->SetParLimits(2, effSigMin, effSigMax);
    func->SetParLimits(3, etaMin, etaMax);
    func->SetParLimits(4, 0., hEnergy->GetMaximum());
 
    hEnergy->GetXaxis()->SetRangeUser(peakMin, peakMax);
    int maxBin = hEnergy->GetMaximumBin();
    double peakE = hEnergy->GetBinLowEdge(maxBin);
    double peakEUnc = 0.;
    double normalization = hEnergy->GetMaximum();
    double normUnc = 0.;
    double effSigma = hEnergy->GetRMS();
    double sigmaUnc = 0.;
    hEnergy->GetXaxis()->SetRangeUser(histMin, histMax);
 
    double fitlow = peakE - effSigma;
    double fithigh = histMax;
 
    double eta = etaNom;
    double etaUnc = 0.;
    double constant = 0.01 * normalization;
    double constUnc = 0.;
 
    double dIter = 0.1 * (histMax - histMin) / hEnergy->GetNbinsX();
    double fitProb(0.);
    double fitProbDefault(0.);
    double lowold(0.), lowoldold(0.);
    bool fixConst = false;
    int nIter = 0;
    double histIntegral = IntegralVsCrysID->GetBinContent(crysID + 1);
    bool fitHist =  histIntegral >= m_minEntries; /* fit only if enough events */
 
    double m_tRatioMin = m_tRatioMinNom;
    double m_tRatioMax = m_tRatioMaxNom;
    if (histIntegral > m_highStatEntries) {
      m_tRatioMin = m_tRatioMinHiStat;
      m_tRatioMax = m_tRatioMaxHiStat;
    }
 
    while (fitHist) {
 
      func->SetParameters(normalization, peakE, effSigma, eta, constant);
      if (fixConst) { func->FixParameter(4, 0); }
 
      hEnergy->Fit(func, "LIQ", "", fitlow, fithigh);
      nIter++;
      fitHist = false;
      normalization = func->GetParameter(0);
      normUnc = func->GetParError(0);
      peakE = func->GetParameter(1);
      peakEUnc = func->GetParError(1);
      effSigma = func->GetParameter(2);
      sigmaUnc = func->GetParError(2);
      eta = func->GetParameter(3);
      etaUnc = func->GetParError(3);
      constant = func->GetParameter(4);
      constUnc = func->GetParError(4);
      fitProbDefault = func->GetProb();
 
      double peak = func->Eval(peakE) - constant;
      double tRatio = (func->Eval(fitlow) - constant) / peak;
      if (tRatio < m_tRatioMin || tRatio > m_tRatioMax) {
        double targetY = constant + 0.5 * (m_tRatioMin + m_tRatioMax) * peak;
        lowoldold = lowold;
        lowold = fitlow;
        fitlow = func->GetX(targetY, histMin, peakE);
        fitHist = true;
 
        if (abs(fitlow - lowoldold) < dIter) {fitlow = 0.5 * (lowold + lowoldold); }
 
        if (nIter > m_maxIterations - 3) {fitlow = 0.33333 * (fitlow + lowold + lowoldold); }
      }
 
      if (constant < constTol && !fixConst) {
        constant = 0;
        fixConst = true;
        fitHist = true;
      }
 
      if (nIter == m_maxIterations) {fitHist = false;}
      B2DEBUG(10, crysID << " " << nIter << " " << peakE << " " << constant << " " << tRatio << " " << fitlow);
    }
 
    fitProb = 0.;
    if (nIter > 0) {
      int lowbin = hEnergy->GetXaxis()->FindBin(fitlow);
      int highbin = hEnergy->GetXaxis()->FindBin(fithigh);
      int npar = 5;
      if (fixConst) {npar = 4;}
      int ndeg = -npar;
      double chisq = 0.;
      double binwidth = hEnergy->GetBinWidth(1);
      for (int ib = lowbin; ib <= highbin; ib++) {
        double xlow = hEnergy->GetBinLowEdge(ib);
        double yexp = func->Integral(xlow, xlow + binwidth) / binwidth;
 
        if (yexp > constTol) {
          double yobs = hEnergy->GetBinContent(ib);
          double dnom = yexp;
          if (yexp < 0.9999 && yobs > yexp) {dnom = yobs;}
          double dchi2 = (yexp - yobs) * (yexp - yobs) / dnom;
          chisq += dchi2;
          ndeg++;
        }
      }
      fitProb = TMath::Prob(chisq, ndeg);
    }
 
    int iStatus = fitOK; // success
    if (nIter == m_maxIterations) {iStatus = iterations;} // too many iterations
 
    if (normalization < constRatio * constant) {iStatus = noPeak;}
 
    if (fitProb <= minFitLimit || (fitProb < minFitProbIter && iStatus == iterations)) {iStatus = poorFit;}
 
    if ((peakE < peakMin + peakTol) || (peakE > peakMax - peakTol)) {iStatus = atLimit;}
    if ((effSigma < effSigMin + effSigTol) || (effSigma > effSigMax - effSigTol)) {iStatus = atLimit;}
    if ((eta < etaMin + etaTol) || (eta > etaMax - etaTol)) {iStatus = atLimit;}
 
    //** No fit
    if (nIter == 0) {iStatus = notFit;} // not fit
 
    double upperEdge = peakE;
    double edgeUnc = peakEUnc;
 
    if (iStatus >= iterations) {
 
      double targetY = constant + m_upperEdgeThresh * (func->Eval(peakE) - constant);
 
      int iLow = hEnergy->GetXaxis()->FindBin(peakE) + 1;
      int iHigh = hEnergy->GetNbinsX();
      int iLast = iLow;
      for (int ibin = iLow; ibin < iHigh; ibin++) {
        double xc = hEnergy->GetBinCenter(ibin);
        if (func->Eval(xc) > targetY) {iLast = ibin;}
      }
      double xLow = hEnergy->GetBinCenter(iLast);
      double xHigh = hEnergy->GetBinCenter(iLast + 1);
 
      func->SetNpx(1000);
      upperEdge = func->GetX(targetY, xLow, xHigh);
 
 
    } else if (iStatus > notFit) {
 
      int iLast = -1;
      int thisBin = hEnergy->GetBinContent(1);
      for (int ibin = 2; ibin < hEnergy->GetNbinsX(); ibin++) {
        int prevBin = thisBin;
        thisBin = hEnergy->GetBinContent(ibin);
        if (thisBin > 0 && thisBin + prevBin >= 2) {iLast = ibin;}
      }
      if (iLast > 0) {upperEdge = hEnergy->GetBinLowEdge(iLast);} // lower edge is a better estimate than center
    }
 
    int histbin = crysID + 1;
    PeakVsCrysID->SetBinContent(histbin, peakE);
    PeakVsCrysID->SetBinError(histbin, peakEUnc);
    EdgeVsCrysID->SetBinContent(histbin, upperEdge);
    EdgeVsCrysID->SetBinError(histbin, edgeUnc);
    effSigVsCrysID->SetBinContent(histbin, effSigma);
    effSigVsCrysID->SetBinError(histbin, sigmaUnc);
    etaVsCrysID->SetBinContent(histbin, eta);
    etaVsCrysID->SetBinError(histbin, etaUnc);
    constVsCrysID->SetBinContent(histbin, constant);
    constVsCrysID->SetBinError(histbin, constUnc);
    normVsCrysID->SetBinContent(histbin, normalization);
    normVsCrysID->SetBinError(histbin, normUnc);
    fitLimitVsCrysID->SetBinContent(histbin, fitlow);
    fitLimitVsCrysID->SetBinError(histbin, 0);
    StatusVsCrysID->SetBinContent(histbin, iStatus);
    StatusVsCrysID->SetBinError(histbin, 0);
    FitProbVsCrysID->SetBinContent(histbin, fitProb);
    FitProbVsCrysID->SetBinError(histbin, 0);
 
    hStatus->Fill(iStatus);
    nIterations->Fill(nIter);
    if (iStatus >= iterations) {
      hPeak->Fill(peakE);
      fracPeakUnc->Fill(peakEUnc / peakE);
    }
 
    B2INFO("cellID " << crysID + 1 << " status = "  << iStatus << " fit probability = " << fitProb << " default prob = " <<
           fitProbDefault);
    histfile->cd();
    hEnergy->Write();
 
  } /* end of loop over crystals */
 
  for (int crysID = 0; crysID < ECLElementNumbers::c_NCrystals; crysID++) {
    int histbin = crysID + 1;
    double fitstatus = StatusVsCrysID->GetBinContent(histbin);
    double upperEdge = EdgeVsCrysID->GetBinContent(histbin);
    double fracEdgeUnc = EdgeVsCrysID->GetBinError(histbin) / upperEdge;
 
    if (fitstatus < 0) {
      upperEdge = -1.;
      fracEdgeUnc = 0.;
      if (histbin >= m_cellIDLo && histbin <= m_cellIDHi) {
        B2INFO("eclGammaGammaEAlgorithm: cellID " << histbin << " is not a successful fit. Status = " << fitstatus);
        allFitsOK = false;
      }
    }
 
    if (m_findExpValues) {
      double inputExpE = abs(AverageExpECrys->GetBinContent(histbin));
      ExpEnergyperCrys->SetBinContent(histbin, inputExpE * upperEdge);
      ExpEnergyperCrys->SetBinError(histbin, fracEdgeUnc * inputExpE * upperEdge);
    } else {
 
      double inputCalib = abs(AverageInitCalib->GetBinContent(histbin));
      CalibVsCrysID->SetBinContent(histbin, inputCalib / upperEdge);
      CalibVsCrysID->SetBinError(histbin, fracEdgeUnc * inputCalib / upperEdge);
    }
  }
 
  bool DBsuccess = false;
  if (m_storeConst == 0 || (m_storeConst == 1 && allFitsOK)) {
    DBsuccess = true;
    if (m_findExpValues) {
 
      std::vector<float> tempE;
      std::vector<float> tempUnc;
      for (int crysID = 0; crysID < ECLElementNumbers::c_NCrystals; crysID++) {
        tempE.push_back(ExpEnergyperCrys->GetBinContent(crysID + 1));
        tempUnc.push_back(ExpEnergyperCrys->GetBinError(crysID + 1));
      }
      ECLCrystalCalib* ExpectedE = new ECLCrystalCalib();
      ExpectedE->setCalibVector(tempE, tempUnc);
      saveCalibration(ExpectedE, "ECLExpGammaGammaE");
      B2INFO("eclCosmicEAlgorithm: successfully stored expected energies ECLExpGammaGammaE");
 
    } else {
 
      std::vector<float> tempCalib;
      std::vector<float> tempCalibUnc;
      for (int crysID = 0; crysID < ECLElementNumbers::c_NCrystals; crysID++) {
        tempCalib.push_back(CalibVsCrysID->GetBinContent(crysID + 1));
        tempCalibUnc.push_back(CalibVsCrysID->GetBinError(crysID + 1));
      }
      ECLCrystalCalib* GammaGammaECalib = new ECLCrystalCalib();
      GammaGammaECalib->setCalibVector(tempCalib, tempCalibUnc);
      saveCalibration(GammaGammaECalib, "ECLCrystalEnergyGammaGamma");
      B2INFO("eclGammaGammaEAlgorithm: successfully stored ECLCrystalEnergyGammaGamma calibration constants");
    }
  }
 
  PeakVsCrysID->Write();
  EdgeVsCrysID->Write();
  effSigVsCrysID->Write();
  etaVsCrysID->Write();
  constVsCrysID->Write();
  normVsCrysID->Write();
  fitLimitVsCrysID->Write();
  StatusVsCrysID->Write();
  FitProbVsCrysID->Write();
  hPeak->Write();
  fracPeakUnc->Write();
  nIterations->Write();
  hStatus->Write();
 
  if (m_findExpValues) {
    ExpEnergyperCrys->Write();
  } else {
    CalibVsCrysID->Write();
  }
  histfile->Close();
 
  dummy = (TH1F*)gROOT->FindObject("PeakVsCrysID"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("EdgeVsCrysID"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("effSigVsCrysID"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("etaVsCrysID"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("constVsCrysID"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("normVsCrysID"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("fitLimitVsCrysID"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("StatusVsCrysID"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("FitProbVsCrysID"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("fracPeakUnc"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("nIterations"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("hStatus"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("ExpEnergyperCrys"); delete dummy;
  dummy = (TH1F*)gROOT->FindObject("CalibVsCrysID"); delete dummy;
 
 
  if (m_storeConst == -1) {
    B2INFO("eclGammaGammaEAlgorithm performed fits but was not asked to store contants");
    return c_Failure;
  } else if (!DBsuccess) {
    if (m_findExpValues) { B2INFO("eclGammaGammaEAlgorithm: failed to store expected values"); }
    else { B2INFO("eclGammaGammaEAlgorithm: failed to store calibration constants"); }
    return c_Failure;
  }
  return c_OK;
}

checkPyExpRun()

bool checkPyExpRun ( PyObject *  pyObj )

inherited

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

Checks if the PyObject can be converted to ExpRun.

Definition at line 28 of file CalibrationAlgorithm.cc.

{
  // 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 311 of file CalibrationAlgorithm.cc.

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

convertPyExpRun()

ExpRun convertPyExpRun ( PyObject *  pyObj )

inherited

Performs the conversion of PyObject to ExpRun.

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

Definition at line 70 of file CalibrationAlgorithm.cc.

{
  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 330 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 520 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()

Calibration::ExpRun getAllGranularityExpRun ( ) const

inlineprotectedinherited

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

Definition at line 327 of file CalibrationAlgorithm.h.

{return m_allExpRun;}

getCellIDHi()

int getCellIDHi ( )

inline

Getter for m_cellIDHi.

Definition at line 50 of file eclGammaGammaEAlgorithm.h.

{return m_cellIDHi;}

getCellIDLo()

int getCellIDLo ( )

inline

Getter for m_cellIDLo.

Definition at line 44 of file eclGammaGammaEAlgorithm.h.

{return m_cellIDLo;}

getCollectorName()

std::string getCollectorName ( ) const

inlineinherited

Alias for prefix.

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

Definition at line 164 of file CalibrationAlgorithm.h.

{return getPrefix();}

getDescription()

const std::string & getDescription ( ) const

inlineinherited

Get the description of the algoithm (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;
}

getFindExpValues()

bool getFindExpValues ( )

inline

Getter for m_findExpValues.

Definition at line 104 of file eclGammaGammaEAlgorithm.h.

{return m_findExpValues;}

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()

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 383 of file CalibrationAlgorithm.cc.

{
  // Save TDirectory to change back at the end
  TDirectory* dir = gDirectory;
  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()

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 325 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(); }

getMaxIterations()

int getMaxIterations ( )

inline

Getter for m_maxIterations.

Definition at line 62 of file eclGammaGammaEAlgorithm.h.

{return m_maxIterations;}

getMinEntries()

int getMinEntries ( )

inline

Getter for m_minEntries.

Definition at line 56 of file eclGammaGammaEAlgorithm.h.

{return m_minEntries;}

getObjectPtr()

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()

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);
    }

getOutputName()

std::string getOutputName ( )

inline

Getter for m_outputName.

Definition at line 38 of file eclGammaGammaEAlgorithm.h.

{return m_outputName;}

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 ( )

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();}

getPerformFits()

bool getPerformFits ( )

inline

Getter for m_performFits.

Definition at line 98 of file eclGammaGammaEAlgorithm.h.

{return m_performFits;}

getPrefix()

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 318 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 361 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"));
    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;
}

getStoreConst()

int getStoreConst ( )

inline

Getter for m_storeConst.

Definition at line 110 of file eclGammaGammaEAlgorithm.h.

{return m_storeConst;}

getTRatioMax()

double getTRatioMax ( )

inline

Getter for m_tRatioMaxNom.

Definition at line 74 of file eclGammaGammaEAlgorithm.h.

{return m_tRatioMaxNom;}

getTRatioMaxHiStat()

double getTRatioMaxHiStat ( )

inline

Getter for m_tRatioMaxHiStat.

Definition at line 86 of file eclGammaGammaEAlgorithm.h.

{return m_tRatioMaxHiStat;}

getTRatioMin()

double getTRatioMin ( )

inline

Getter for m_tRatioMinNom.

Definition at line 68 of file eclGammaGammaEAlgorithm.h.

{return m_tRatioMinNom;}

getTRatioMinHiStat()

double getTRatioMinHiStat ( )

inline

Getter for m_tRatioMinHiStat.

Definition at line 80 of file eclGammaGammaEAlgorithm.h.

{return m_tRatioMinHiStat;}

getUpperEdgeThresh()

double getUpperEdgeThresh ( )

inline

Getter for m_upperEdgeThresh.

Definition at line 92 of file eclGammaGammaEAlgorithm.h.

{return m_upperEdgeThresh;}

getVecInputFileNames()

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 TestBoundarySettingAlgorithm, PXDAnalyticGainCalibrationAlgorithm, PXDValidationAlgorithm, TestCalibrationAlgorithm, SVD3SampleCoGTimeCalibrationAlgorithm, SVD3SampleELSTimeCalibrationAlgorithm, and SVDCoGTimeCalibrationAlgorithm.

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 rturn bool indicates success or failure.

Definition at line 502 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);
}

setCellIDHi()

void setCellIDHi ( int  cellIDHi )

inline

Setter for m_cellIDHi.

Definition at line 47 of file eclGammaGammaEAlgorithm.h.

{m_cellIDHi = cellIDHi;}

setCellIDLo()

void setCellIDLo ( int  cellIDLo )

inline

Setter for m_cellIDLo.

Definition at line 41 of file eclGammaGammaEAlgorithm.h.

{m_cellIDLo = cellIDLo;}

setDescription()

void setDescription ( const std::string &  description )

inlineprotectedinherited

Set algorithm description (in constructor)

Definition at line 321 of file CalibrationAlgorithm.h.

{m_description = description;}

setFindExpValues()

void setFindExpValues ( bool  findExpValues )

inline

Setter for m_findExpValues.

Definition at line 101 of file eclGammaGammaEAlgorithm.h.

{m_findExpValues = findExpValues;}

setInputFileNames() [1/2]

void setInputFileNames ( PyObject *  inputFileNames )

inherited

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

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

Definition at line 166 of file CalibrationAlgorithm.cc.

{
  // 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.");
  }
}

setInputFileNames() [2/2]

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

protectedinherited

Set the input file names used for this algorithm.

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

Definition at line 194 of file CalibrationAlgorithm.cc.

{
  // 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();
}

setMaxIterations()

void setMaxIterations ( int  maxIterations )

inline

Setter for m_maxIterations.

Definition at line 59 of file eclGammaGammaEAlgorithm.h.

{m_maxIterations = maxIterations;}

setMinEntries()

void setMinEntries ( int  minEntries )

inline

Setter for m_minEntries.

Definition at line 53 of file eclGammaGammaEAlgorithm.h.

{m_minEntries = minEntries;}

setOutputJsonValue()

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

inlineprotectedinherited

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

Definition at line 337 of file CalibrationAlgorithm.h.

{m_jsonExecutionOutput[key] = value;}

setOutputName()

void setOutputName ( const std::string &  outputName )

inline

Setter for m_outputName.

Definition at line 35 of file eclGammaGammaEAlgorithm.h.

{m_outputName = outputName;}

setPerformFits()

void setPerformFits ( bool  performFits )

inline

Setter for m_performFits.

Definition at line 95 of file eclGammaGammaEAlgorithm.h.

{m_performFits = performFits;}

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;}

setStoreConst()

void setStoreConst ( int  storeConst )

inline

Setter for m_storeConst.

Definition at line 107 of file eclGammaGammaEAlgorithm.h.

{m_storeConst = storeConst;}

setTRatioMax()

void setTRatioMax ( double  tRatioMax )

inline

Setter for m_tRatioMaxNom.

Definition at line 71 of file eclGammaGammaEAlgorithm.h.

{m_tRatioMaxNom = tRatioMax;}

setTRatioMaxHiStat()

void setTRatioMaxHiStat ( double  tRatioMax )

inline

Setter for m_tRatioMaxHiStat.

Definition at line 83 of file eclGammaGammaEAlgorithm.h.

{m_tRatioMaxHiStat = tRatioMax;}

setTRatioMin()

void setTRatioMin ( double  tRatioMin )

inline

Setter for m_tRatioMinNom.

Definition at line 65 of file eclGammaGammaEAlgorithm.h.

{m_tRatioMinNom = tRatioMin;}

setTRatioMinHiStat()

void setTRatioMinHiStat ( double  tRatioMin )

inline

Setter for m_tRatioMinHiStat.

Definition at line 77 of file eclGammaGammaEAlgorithm.h.

{m_tRatioMinHiStat = tRatioMin;}

setUpperEdgeThresh()

void setUpperEdgeThresh ( double  upperEdgeThresh )

inline

Setter for m_upperEdgeThresh.

Definition at line 89 of file eclGammaGammaEAlgorithm.h.

{m_upperEdgeThresh = upperEdgeThresh;}

updateDBObjPtrs()

void updateDBObjPtrs ( const unsigned int  event = 1, const int  run = 0, const int  experiment = 0  )

protectedinherited

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

Definition at line 404 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 unnessary...)
  DBStore::Instance().updateEvent(event);
}

Member Data Documentation

atLimit

int atLimit = 4

private

a parameter is at the limit; upper edge is found from histogram, not fit

Definition at line 146 of file eclGammaGammaEAlgorithm.h.

fitOK

int fitOK = 16

private

Characterize fit status.

fit is OK

Definition at line 144 of file eclGammaGammaEAlgorithm.h.

iterations

int iterations = 8

private

fit reached max number of iterations, but is useable

Definition at line 145 of file eclGammaGammaEAlgorithm.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_cellIDHi

int m_cellIDHi = ECLElementNumbers::c_NCrystals

private

Last cellID to be fit.

Definition at line 123 of file eclGammaGammaEAlgorithm.h.

m_cellIDLo

int m_cellIDLo = 1

private

First cellID to be fit.

Definition at line 122 of file eclGammaGammaEAlgorithm.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_findExpValues

bool m_findExpValues

private

Initial value:

=
        false

if true, fits are used to find expected energy deposit for each crystal instead of the calibration constant

Definition at line 136 of file eclGammaGammaEAlgorithm.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_highStatEntries

int m_highStatEntries = 25000

private

Adjust fit range above this many entries.

Definition at line 125 of file eclGammaGammaEAlgorithm.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_maxIterations

int m_maxIterations = 10

private

no more than maxIteration iterations

Definition at line 126 of file eclGammaGammaEAlgorithm.h.

m_minEntries

int m_minEntries = 150

private

Minimum entries to fit a crystal.

Definition at line 124 of file eclGammaGammaEAlgorithm.h.

m_outputName

std::string m_outputName = "eclGammaGammaEAlgorithm.root"

private

..Parameters to control Novosibirsk fit to energy deposited in each crystal by mu+mu- events

file name for histogram output

Definition at line 121 of file eclGammaGammaEAlgorithm.h.

m_performFits

bool m_performFits = true

private

if false, input histograms are copied to output, but no fits are done

Definition at line 135 of file eclGammaGammaEAlgorithm.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.

m_storeConst

int m_storeConst = 0

private

controls which values are written to the database.

0 : store value found by successful fits, or -|input value| otherwise; -1 : do not store values 1 : store values if every fit for [cellIDLo,cellIDHi] was successful

Definition at line 138 of file eclGammaGammaEAlgorithm.h.

m_tRatioMaxHiStat

double m_tRatioMaxHiStat

private

Initial value:

=
        0.95

Fit range is adjusted so that fit at lower endpoint is between tRatioMin and tRatioMax of peak.

Definition at line 132 of file eclGammaGammaEAlgorithm.h.

m_tRatioMaxNom

double m_tRatioMaxNom = 0.70

private

Fit range is adjusted so that fit at lower endpoint is between tRatioMin and tRatioMax of peak.

Definition at line 129 of file eclGammaGammaEAlgorithm.h.

m_tRatioMinHiStat

double m_tRatioMinHiStat

private

Initial value:

=
        0.70

Fit range is adjusted so that fit at lower endpoint is between tRatioMin and tRatioMax of peak.

Definition at line 130 of file eclGammaGammaEAlgorithm.h.

m_tRatioMinNom

double m_tRatioMinNom

private

Initial value:

=
        0.45

Fit range is adjusted so that fit at lower endpoint is between tRatioMin and tRatioMax of peak.

Definition at line 127 of file eclGammaGammaEAlgorithm.h.

m_upperEdgeThresh

double m_upperEdgeThresh = 0.02

private

Upper edge is where the fit = upperEdgeThresh * peak value.

Definition at line 134 of file eclGammaGammaEAlgorithm.h.

noPeak

int noPeak = 2

private

Novosibirsk component of fit is negligible; upper edge is found from histogram, not fit.

Definition at line 148 of file eclGammaGammaEAlgorithm.h.

notFit

int notFit = -1

private

no fit performed; no constants found for this crystal

Definition at line 149 of file eclGammaGammaEAlgorithm.h.

poorFit

int poorFit = 3

private

low chi square; upper edge is found from histogram, not fit

Definition at line 147 of file eclGammaGammaEAlgorithm.h.

---

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

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