eclLeakageAlgorithm Class Reference

Calculate ECL energy leakage corrections. More...

#include <eclLeakageAlgorithm.h>

Inheritance diagram for eclLeakageAlgorithm:

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

Public Member Functions
	eclLeakageAlgorithm ()
	Constructor.
virtual	~eclLeakageAlgorithm ()
	Destructor.
void	setLowEnergyThreshold (double lowEnergyThreshold)
	Setter for m_lowEnergyThreshold.
double	getLowEnergyThreshold ()
	Getter for m_lowEnergyThreshold.
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.
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
double	m_lowEnergyThreshold = 0.0
	Parameters to control fit procedure.
int	t_cellID = 0
	For TTree.
int	t_thetaID = 0
	thetaID of photon
int	t_region = 0
	region of photon 0=forward 1=barrel 2=backward
int	t_thetaBin = -1
	binned location in theta relative to crystal edge
int	t_phiBin = -1
	binned location in phi relative to crystal edge
int	t_phiMech = -1
	mechanical structure next to lower phi (0), upper phi (1), or neither (2)
int	t_energyBin = -1
	generated energy point
int	t_nCrys = -1
	number of crystals used to calculate energy
float	t_energyFrac = 0.
	measured energy after nOptimal bias and peak corrections, divided by generated
float	t_origEnergyFrac = 0.
	corrected energy at time of generation, divided by generated
float	t_locationError = 999.
	reconstructed minus generated position (cm)
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

Calculate ECL energy leakage corrections.

Definition at line 22 of file eclLeakageAlgorithm.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

eclLeakageAlgorithm()

eclLeakageAlgorithm

(

)

Constructor.

---

Definition at line 170 of file eclLeakageAlgorithm.cc.

                                        : CalibrationAlgorithm("eclLeakageCollector")
{
  setDescription(
    "Generate payload ECLLeakageCorrection from single photon MC recorded by eclLeakageCollectorModule"
  );
}

~eclLeakageAlgorithm()

virtual ~eclLeakageAlgorithm ( )

inlinevirtual

Destructor.

Definition at line 29 of file eclLeakageAlgorithm.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.

---

< number of thetaID

< 0 = forward, 1 = barrel, 2 = backward

< first thetaID of the barrel

< last thetaID of the barrel

< first thetaID to find leakage corrections

< last thetaID to find leakage correction

< locations across crystal

< number of energy points

< number of thetaID/energy combinations

< minimum allowed eta in Novosibirsk fits

< maximum allowed eta

< categories of fit status

< log(E) values for each region

Implements CalibrationAlgorithm.

Definition at line 180 of file eclLeakageAlgorithm.cc.

{
 
  //====================================================================================
  //====================================================================================
  //..Step 1. Set up prior to first loop through data
  B2INFO("starting eclLeakageAlgorithm");
 
  //-----------------------------------------------------------------------------------
  //..Read in histograms created by the collector and fix normalization
  auto inputParameters = getObjectPtr<TH1F>("inputParameters");
  int lastBin = inputParameters->GetNbinsX();
  double scale = inputParameters->GetBinContent(lastBin); // number of times inputParameters was filled
  for (int ib = 1; ib < lastBin; ib++) {
    double param = inputParameters->GetBinContent(ib);
    inputParameters->SetBinContent(ib, param / scale);
    inputParameters->SetBinError(ib, 0.);
  }
 
  //..And write to disk.
  TFile* histfile = new TFile("eclLeakageAlgorithm.root", "recreate");
  inputParameters->Write();
 
  //..Parameters
  const int nThetaID = 69; 
  const int nLeakReg = 3; 
  const int firstBarrelID = 13; 
  const int lastBarrelID = 58; 
  const int firstUsefulThID = 3; 
  const int lastUsefulThID = 66; 
  const int nPositions = (int)(inputParameters->GetBinContent(1) + 0.001); 
  const int nEnergies = (int)(inputParameters->GetBinContent(2) + 0.001); 
  const int nbinX = nEnergies * nThetaID; 
  const double etaMin = -5.; 
  const double etaMax = 5.; 
  //..Energies
  auto generatedE = create2Dvector<float>(nLeakReg, nEnergies); // 3 regions forward barrel backward
 
  int bin = 2; // bin 1 = nPositions, bin 2 = nEnergies, bin 3 = first energy
  for (int ireg = 0; ireg < nLeakReg; ireg++) {
    B2INFO("Generated energies for ireg = " << ireg);
    for (int ie = 0; ie < nEnergies; ie++) {
      bin++;
      generatedE[ireg][ie] = inputParameters->GetBinContent(bin);
      B2INFO("  " << ie << " " << generatedE[ireg][ie] << " GeV");
    }
  }
  B2INFO("Low energy threshold = " << m_lowEnergyThreshold);
 
  //..Energy per thetaID (in MeV, for use in titles etc)
  auto iEnergiesMeV = create2Dvector<int>(nEnergies, nThetaID);
  for (int thID = 0; thID < nThetaID; thID++) {
    int ireg = 0;
    if (thID >= firstBarrelID and thID <= lastBarrelID) {
      ireg = 1;
    } else if (thID > lastBarrelID) {
      ireg = 2;
    }
    for (int ie = 0; ie < nEnergies; ie++) {
      iEnergiesMeV[ie][thID] = (int)(1000.*generatedE[ireg][ie] + 0.5);
    }
  }
 
  //-----------------------------------------------------------------------------------
  //..Bins for eFrac histograms (eFrac = uncorrected reconstructed E / E true)
  const double eFracLo = 0.4; // low edge of eFrac histograms
  const double eFracHi = 1.5; // high edge of eFrac histograms
  auto nEfracBins = create2Dvector<int>(nEnergies, nThetaID);
  for (int thID = 0; thID < nThetaID; thID++) {
    B2DEBUG(25, "eFrac nBins for thetaID " << thID);
    for (int ie = 0; ie < nEnergies; ie++) {
 
      //..ballpark resolution
      double res_squared = 0.0001 + 0.064 / iEnergiesMeV[ie][thID];
 
      //..Convert this to an even integer to get number of bins
      double binNumOver2 = 3. / sqrt(res_squared);
      int tempNBin = (int)(binNumOver2 + 0.5);
      nEfracBins[ie][thID] = 2 * tempNBin;
      B2DEBUG(25, " ie = " << ie << " E = " << iEnergiesMeV[ie][thID] << " nBins = " << nEfracBins[ie][thID]);
    }
  }
 
  //-----------------------------------------------------------------------------------
  //..Set up Novosibirsk fit function
  TString statusString[7] = {"good", "refit", "lowStat", "lowProb", "peakAtLimit", "sigmaAtLimit", "etaAtLimit"}; 
  const double fracEnt[2] = {0.683, 0.5}; // fit range includes 68% or 50% of entries
  const double minEntries = 100.; // don't use fits with fewer entries
  const double minMaxBin = 50.; // rebin if max bin is below this value
  const double highMaxBin = 300.; // can float eta if max bin is above this value
 
  TF1* func = new TF1("eclLeakageNovo", eclLeakageNovo, eFracLo, eFracHi, 4);
  func->SetParNames("normalization", "peak", "effSigma", "eta");
  func->SetLineColor(kRed);
 
  //-----------------------------------------------------------------------------------
  //..Specify the TTree
  auto tree = getObjectPtr<TTree>("tree");
  tree->SetBranchAddress("cellID", &t_cellID);
  tree->SetBranchAddress("thetaID", &t_thetaID);
  tree->SetBranchAddress("region", &t_region);
  tree->SetBranchAddress("thetaBin", &t_thetaBin);
  tree->SetBranchAddress("phiBin", &t_phiBin);
  tree->SetBranchAddress("phiMech", &t_phiMech);
  tree->SetBranchAddress("energyBin", &t_energyBin);
  tree->SetBranchAddress("nCrys", &t_nCrys);
  tree->SetBranchAddress("energyFrac", &t_energyFrac);
  tree->SetBranchAddress("origEnergyFrac", &t_origEnergyFrac);
  tree->SetBranchAddress("locationError", &t_locationError);
 
  const int treeEntries = tree->GetEntries();
  B2INFO("eclLeakageAlgorithm entries = " << treeEntries);
 
  //-----------------------------------------------------------------------------------
  //..Get current payload to help validate the new payload
 
  //..Set event, run, exp number
  const auto exprun =  getRunList();
  const int iEvt = 1;
  const int iRun = exprun[0].second;
  const int iExp = exprun[0].first;
  StoreObjPtr<EventMetaData> evtPtr;
  DataStore::Instance().setInitializeActive(true);
  evtPtr.registerInDataStore();
  DataStore::Instance().setInitializeActive(false);
  evtPtr.construct(iEvt, iRun, iExp);
  DBStore& dbstore = DBStore::Instance();
  dbstore.update();
 
  //..Existing payload
  DBObjPtr<ECLLeakageCorrections> existingCorrections("ECLLeakageCorrections");
  TH2F existingThetaCorrection = existingCorrections->getThetaCorrections();
  existingThetaCorrection.SetName("existingThetaCorrection");
  TH2F existingPhiCorrection = existingCorrections->getPhiCorrections();
  existingPhiCorrection.SetName("existingPhiCorrection");
 
  //..Write out the correction histograms
  histfile->cd();
  existingThetaCorrection.Write();
  existingPhiCorrection.Write();
 
 
  //====================================================================================
  //====================================================================================
  //..Step 2. First loop, fill histograms of e/eTrue for each energy and thetaID
 
  //-----------------------------------------------------------------------------------
  //..One histogram of e/eTrue per energy per thetaID.
  //  Used to fix eta in subsequent fits, and to get overall correction for that thetaID
  //  (which should be very close to 1).
  TString name;
  TString title;
  auto hELabUncorr = create2Dvector<TH1F*>(nEnergies, nThetaID);
  for (int thID = firstUsefulThID; thID <= lastUsefulThID; thID++) {
    TString sthID = std::to_string(thID);
    for (int ie = 0; ie < nEnergies; ie++) {
      name = "hELabUncorr_" + std::to_string(ie) + "_" + sthID;
      title = "eFrac " + to_string(iEnergiesMeV[ie][thID]) + " MeV thetaID " + sthID + ";E/Etrue";
 
      //..High statistics for these plots; use more bins
      hELabUncorr[ie][thID] = new TH1F(name, title, 2 * nEfracBins[ie][thID], eFracLo, eFracHi);
    }
  }
 
  //-----------------------------------------------------------------------------------
  //..Loop over events and store eFrac
  for (int i = 0; i < treeEntries; i++) {
    tree->GetEntry(i);
 
    //..Only events with good reconstruction
    bool goodReco = t_thetaID >= firstUsefulThID and t_thetaID <= lastUsefulThID and t_energyBin >= 0;
    if (not goodReco) {continue;}
 
    //..Fill histogram for full thetaID
    hELabUncorr[t_energyBin][t_thetaID]->Fill(t_energyFrac);
  }
 
  //-----------------------------------------------------------------------------------
  //..Fit each thetaID/energy histogram to get peak (overall correction) and eta (fixed
  //  in subsequent fits to individual locations).
  auto peakUncorr = create2Dvector<float>(nEnergies, nThetaID); // store peak from each fit
  auto etaUncorr  = create2Dvector<float>(nEnergies, nThetaID); // store eta from each fit
 
  std::vector<TString> failedELabUncorr; // names of hists with failed fits
  std::vector<int> statusELabUncorr; // status of failed fits
  int payloadStatus = 0; // Overall status of payload determination
 
  //..Record fit status
  TH1F* statusOfhELabUncorr = new TH1F("statusOfhELabUncorr",
                                       "status of hELabUncorr fits for each thetaID/energy. 0 = good, 1 = redo fit, 2 = lowStat, 3 = lowProb, 4 = peakAtLimit, 5 = sigmaAtLimit",
                                       6, 0,
                                       6);
 
 
  //..Fit each histogram
  for (int thID = firstUsefulThID; thID <= lastUsefulThID; thID++) {
    TString sthID = std::to_string(thID);
    for (int ie = 0; ie < nEnergies; ie++) {
      TH1F* hEnergy = (TH1F*)hELabUncorr[ie][thID];
      double peak = -1.;
      double eta = 0.;
      int fitStatus = 2;
      double entries = hEnergy->Integral();
      int nIter = 0; // keep track of attempts to fit this histogram
      double genE = iEnergiesMeV[ie][thID] / 1000.;
      bool fitHist = entries > minEntries;
 
      //..Possibly iterate fit starting from this point
      while (fitHist) {
 
        //..Fit parameters
        double norm = hEnergy->GetMaximum();
        double target = fracEnt[nIter] * entries; // fit range contains 68% or 50%
        std::vector<double> startingParameters;// peak, sigma, eta, fitLow, fitHigh, nbins
        startingParameters = eclLeakageFitParameters(hEnergy, target);
        func->SetParameters(norm, startingParameters[0], startingParameters[1], startingParameters[2]);
        func->SetParLimits(1, startingParameters[3], startingParameters[4]);
        func->SetParLimits(2, 0., startingParameters[4] - startingParameters[3]);
        func->SetParLimits(3, etaMin, etaMax);
 
        //..Fit
        name = hEnergy->GetName();
        B2DEBUG(40, "Fitting " << name.Data());
        hEnergy->Fit(func, "LIEQ", "", startingParameters[3], startingParameters[4]);
        peak = func->GetParameter(1);
        double effSigma = func->GetParameter(2);
        eta = func->GetParameter(3);
        double prob = func->GetProb();
 
        //..Check fit quality  0 = good, 1 = redo fit, 2 = lowStat, 3 = lowProb,
        //  4 = peakAtLimit, 5 = sigmaAtLimit, 6 = etaAtLimit
        double dEta = min((etaMax - eta), (eta - etaMin));
        fitStatus = eclLeakageFitQuality(startingParameters[3], startingParameters[4], peak, effSigma, dEta, prob);
 
        //..If the fit probability is low, refit using a smaller range (fracEnt)
        if ((fitStatus == 1 or fitStatus == 3) and nIter == 0) {
          nIter++;
        } else {
          fitHist = false;
        }
      }
 
      //-----------------------------------------------------------------------------------
      //..Record failures and fit results.
      //  Mark payload as failed if energy is above low-energy threshold.
      statusOfhELabUncorr->Fill(fitStatus + 0.000001);
      if (fitStatus == 2 or fitStatus >= 4) {
        statusELabUncorr.push_back(fitStatus);
        failedELabUncorr.push_back(hEnergy->GetName());
        if (genE > m_lowEnergyThreshold) {
          payloadStatus = 1; // algorithm failed
        } else {
          peak = -1.; // fix up later
        }
      }
      peakUncorr[ie][thID] = peak;
      etaUncorr[ie][thID] = eta;
 
      //..Write to disk
      if (entries > minEntries) {
        histfile->cd();
        hELabUncorr[ie][thID]->Write();
      }
    }
  }
 
  //..Write out summary of fit status
  histfile->cd();
  statusOfhELabUncorr->Write();
 
  //-----------------------------------------------------------------------------------
  //..Quit now if one of the high-energy fits failed, since we will not get a payload.
  int nbadFit = statusELabUncorr.size();
  if (nbadFit > 0) {B2ERROR("hELabUncorr fit failures (one histogram per energy/thetaID):");}
  for (int ibad = 0; ibad < nbadFit; ibad++) {
    int badStat = statusELabUncorr[ibad];
    B2ERROR(" histogram " << failedELabUncorr[ibad].Data() << " status " << badStat << " " << statusString[badStat].Data());
  }
  if (payloadStatus != 0) {
    B2ERROR("ecLeakageAlgorithm: fit to hELabUncorr failed. ");
    histfile->Close();
    return c_Failure;
  }
 
  //-----------------------------------------------------------------------------------
  //..Fix up any failed (low-energy) fits by using a neighbouring thetaID
  for (int thID = firstUsefulThID; thID <= lastUsefulThID; thID++) {
    for (int ie = 0; ie < nEnergies; ie++) {
      if (peakUncorr[ie][thID] < 0.) {
        if (thID > 40) {
          for (int nextID = thID - 1; thID >= firstUsefulThID; thID--) {
            if (peakUncorr[ie][nextID] > 0.) {
              peakUncorr[ie][thID] = peakUncorr[ie][nextID];
              break;
            }
          }
        } else {
          for (int nextID = thID + 1; thID <= lastUsefulThID; thID++) {
            if (peakUncorr[ie][nextID] > 0.) {
              peakUncorr[ie][thID] = peakUncorr[ie][nextID];
              break;
            }
          }
        }
      }
 
      //..If we were unable to get a successful fit from a neighbour, give up
      if (peakUncorr[ie][thID] < 0.) {
        B2ERROR("ecLeakageAlgorithm: unable to correct hELabUncorr for thetaID " << thID << " ie " << ie);
        histfile->Close();
        return c_Failure;
      }
    }
  }
 
  //====================================================================================
  //====================================================================================
  //..Step 3. Second loop, fill histograms of e/eTrue as a function of position.
  //  29 locations in theta, 29 in phi.
  //  Crystals next to mechanical structure in phi are treated separately from
  //  crystals without mechanical structure.
 
  //-----------------------------------------------------------------------------------
  //..Histograms to store the energy
  const int nDir = 3;
  const TString dirName[nDir] = {"theta", "phiMech", "phiNoMech"};
 
  auto eFracPosition  = create4Dvector<TH1F*>(nEnergies, nThetaID, nDir, nPositions); // the histograms
 
 
  std::vector<TString> failedeFracPosition; // names of hists with failed fits
  std::vector<int> statuseFracPosition; // status of failed fits
 
  for (int thID = firstUsefulThID; thID <= lastUsefulThID; thID++) {
    TString sthID = std::to_string(thID);
    for (int ie = 0; ie < nEnergies; ie++) {
      TString sie = std::to_string(ie);
      for (int idir = 0; idir < nDir; idir++) {
        TString sidir = std::to_string(idir);
        for (int ipos = 0; ipos < nPositions; ipos++) {
          TString sipos = std::to_string(ipos);
          name = "eFracPosition_" + sie + "_" + sthID + "_" + sidir + "_" + sipos;
          title = "eFrac " + to_string(iEnergiesMeV[ie][thID]) + " MeV thetaID " + sthID + " " + dirName[idir] + " pos " + sipos +
                  "; E/Etrue";
          eFracPosition[ie][thID][idir][ipos] = new TH1F(name, title, nEfracBins[ie][thID], eFracLo, eFracHi);
        }
      }
    }
  }
 
  //..And some summary histograms
  TH1F* statusOfeFracPosition = new TH1F("statusOfeFracPosition",
                                         "eFrac fit status: -2 noData, -1 lowE, 0 good, 1 redo fit, 2 lowStat, 3 lowProb, 4 peakAtLimit, 5 sigmaAtLimit", 8, -2,
                                         6);
  TH1F* probOfeFracPosition = new TH1F("probOfeFracPosition", "fit probability of eFrac fits for each position;probability", 100, 0,
                                       1);
  TH1F* maxOfeFracPosition = new TH1F("maxOfeFracPosition", "max entries of eFrac histograms;maximum bin content", 100, 0, 1000);
 
  //-----------------------------------------------------------------------------------
  //..Loop over events and store eFrac
  for (int i = 0; i < treeEntries; i++) {
    tree->GetEntry(i);
 
    //..Only events with good reconstruction
    bool goodReco = t_thetaID >= firstUsefulThID and t_thetaID <= lastUsefulThID and t_energyBin >= 0;
    if (not goodReco) {continue;}
 
    //..Theta location (idir = 0)
    int idir = 0;
    eFracPosition[t_energyBin][t_thetaID][idir][t_thetaBin]->Fill(t_energyFrac);
 
    //..phi location. Note that t_phiBin starts at mechanical edge, if there is one.
    idir = t_phiMech + 1;
    eFracPosition[t_energyBin][t_thetaID][idir][t_phiBin]->Fill(t_energyFrac);
  }
 
  //-----------------------------------------------------------------------------------
  //..Now fit many many histograms to get the position-dependent corrections
  auto positionCorrection     = create4Dvector<float>(nEnergies, nThetaID, nDir, nPositions);
  auto positionCorrectionUnc  = create4Dvector<float>(nEnergies, nThetaID, nDir, nPositions);
  int nHistToFit = 0;
 
 
  //..Temp histogram of position corrections
  TH1F* thetaCorSummary = new TH1F("thetaCorSummary", "Theta dependent corrections;theta dependent correction", 100, 0.4, 1.4);
  TH1F* phiCorSummary = new TH1F("phiCorSummary", "Phi dependent corrections;phi dependent correction", 100, 0.4, 1.4);
 
  for (int thID = firstUsefulThID; thID <= lastUsefulThID; thID++) {
    for (int ie = 0; ie < nEnergies; ie++) {
      double genE = iEnergiesMeV[ie][thID] / 1000.;
      for (int idir = 0; idir < nDir; idir++) {
        for (int ipos = 0; ipos < nPositions; ipos++) {
          TH1F* hEnergy = (TH1F*)eFracPosition[ie][thID][idir][ipos];
          if (hEnergy->Integral() > minEntries) {nHistToFit++;}
 
          //..Default peak from fit to full thetaID/energy = peakUncorr;
          //  correction = peak / sqrt(peakUncorr) = sqrt(peakUncorr)
          double correction = sqrt(peakUncorr[ie][thID]);
          double corrUnc = 0.05; // arbitrary uncertainty
          double prob = -1.;
          int fitStatus = 2; // low stats
          if (genE < m_lowEnergyThreshold) {fitStatus = -1;} // low energy, don't fit, use default peak value
          if (hEnergy->GetEntries() < 0.5) {fitStatus = -2;} // unused, eg barrel pos=2
          int nIter = 0; // keep track of attempts to fit this histogram
          double entries = hEnergy->Integral();
          bool fitHist = entries > minEntries and genE >= m_lowEnergyThreshold;
 
          //..Possibly iterate fit starting from this point
          while (fitHist) {
            fitHist = false;
 
            //..Fit parameters
            double target = fracEnt[nIter] * entries; // fit range contains 68%
            std::vector<double> startingParameters;// peak, sigma, eta, fitLow, fitHigh, nbins
            bool findParm = true;
            while (findParm) {
              startingParameters = eclLeakageFitParameters(hEnergy, target);
 
              //..Rebin if stats are low and we have enough DOF
              if (hEnergy->GetMaximum()<minMaxBin and startingParameters[5]>11.9) {
                hEnergy->Rebin(2);
              } else {
                findParm = false;
              }
            }
            double norm = hEnergy->GetMaximum(); // max bin after rebinning
            double fitLow = startingParameters[3];
            double fitHigh = startingParameters[4];
 
            //..Eta from the fit to the full crystal
            double etaFix = etaUncorr[ie][thID];
            func->SetParameters(norm, startingParameters[0], startingParameters[1], etaFix);
            func->SetParLimits(1, fitLow, fitHigh);
            func->SetParLimits(2, 0., fitHigh - fitLow);
 
            //..Fix eta, unless really good statistics
            if (hEnergy->GetMaximum() < highMaxBin) {
              func->SetParLimits(3, etaFix, etaFix);
            } else {
              func->SetParLimits(3, etaMin, etaMax);
            }
 
            //..Fit
            name = hEnergy->GetName();
            B2DEBUG(40, "Fitting " << name.Data());
            hEnergy->Fit(func, "LIEQ", "", fitLow, fitHigh);
            double peak = func->GetParameter(1);
            double effSigma = func->GetParameter(2);
            double eta = func->GetParameter(3);
            prob = func->GetProb();
 
            //..Check fit quality  0 = good, 1 = redo fit, 2 = lowStat, 3 = lowProb,
            //  4 = peakAtLimit, 5 = sigmaAtLimit, 6 = etaAtLimit.
            double dEta = min((etaMax - eta), (eta - etaMin));
            fitStatus = eclLeakageFitQuality(fitLow, fitHigh, peak, effSigma, dEta, prob);
 
            //..If the fit probability is low, refit using a smaller range (fracEnt)
            if ((fitStatus == 1 or fitStatus == 3) and nIter == 0) {
              nIter++;
              fitHist = true;
 
              //  Store correction except for peak or sigma at limit.
            } else if (fitStatus <= 3) {
 
              //..Divide by sqrt(peak for full crystal) to get correct average
              //  when multiplying the theta and phi corrections
              correction = peak / sqrt(peakUncorr[ie][thID]);
              corrUnc = func->GetParError(1) / sqrt(peakUncorr[ie][thID]);
            }
          }
 
          //..Store the correction for this position
          positionCorrection[ie][thID][idir][ipos] = correction;
          positionCorrectionUnc[ie][thID][idir][ipos] = corrUnc;
          statusOfeFracPosition->Fill(fitStatus + 0.00001);
          probOfeFracPosition->Fill(prob);
          maxOfeFracPosition->Fill(hEnergy->GetMaximum());
 
          //..Summary histogram for successful fits. Note that fitStatus<0 also has prob<0.
          if (prob > 0 and fitStatus <= 3) {
            if (idir == 0) {
              thetaCorSummary->Fill(correction);
            } else {
              phiCorSummary->Fill(correction);
            }
          }
 
          //..Record the failed fit details. We may still use the correction.
          if (fitStatus >= 2) {
            statuseFracPosition.push_back(fitStatus);
            failedeFracPosition.push_back(hEnergy->GetName());
            histfile->cd();
            hEnergy->Write();
          }
        }
      }
    }
  }
 
  //-----------------------------------------------------------------------------------
  //..Summarize position fit results
  B2INFO("eclLeakageAlgorithm: " << nHistToFit << " eFracPosition histograms to fit");
  nbadFit = statuseFracPosition.size();
  B2INFO("eFracPosition failed fits: " << nbadFit);
  for (int ibad = 0; ibad < nbadFit; ibad++) {
    int badStat = statuseFracPosition[ibad];
    B2ERROR(" histogram " << failedeFracPosition[ibad].Data() << " status " << badStat << " " << statusString[badStat].Data());
  }
 
  //..Write to disk
  histfile->cd();
  statusOfeFracPosition->Write();
  probOfeFracPosition->Write();
  maxOfeFracPosition->Write();
  thetaCorSummary->Write();
  phiCorSummary->Write();
 
 
  //====================================================================================
  //====================================================================================
  //..Step 4. Store quantities needed for the payloads
 
  //-----------------------------------------------------------------------------------
  //..First, we need to fix up the thetaID that have been so far missed.
  //  Just copy the values from the first or last thetaID for which corrections were found.
 
  //..ThetaID before the first useful one
  for (int thID = firstUsefulThID - 1; thID >= 0; thID--) {
    for (int ie = 0; ie < nEnergies; ie++) {
 
      for (int idir = 0; idir < 3; idir++) {
        for (int ipos = 0; ipos < nPositions; ipos++) {
          positionCorrection[ie][thID][idir][ipos] = positionCorrection[ie][thID + 1][idir][ipos];
          positionCorrectionUnc[ie][thID][idir][ipos] = positionCorrectionUnc[ie][thID + 1][idir][ipos];
        }
      }
    }
  }
 
  //..ThetaID beyond last useful one
  for (int thID = lastUsefulThID + 1; thID < nThetaID; thID++) {
    for (int ie = 0; ie < nEnergies; ie++) {
 
      for (int idir = 0; idir < 3; idir++) {
        for (int ipos = 0; ipos < nPositions; ipos++) {
          positionCorrection[ie][thID][idir][ipos] = positionCorrection[ie][thID - 1][idir][ipos];
          positionCorrectionUnc[ie][thID][idir][ipos] = positionCorrectionUnc[ie][thID - 1][idir][ipos];
        }
      }
    }
  }
 
  //-----------------------------------------------------------------------------------
  //..std::vectors of generated energies
  std::vector<float> forwardVector;
  std::vector<float> barrelVector;
  std::vector<float> backwardVector;
  for (int ie = 0; ie < nEnergies; ie++) {
    forwardVector.push_back(log(generatedE[0][ie]));
    barrelVector.push_back(log(generatedE[1][ie]));
    backwardVector.push_back(log(generatedE[2][ie]));
  }
 
  //..Store in array format for validation studies
  float leakLogE[3][12] = {}; 
  for (int ie = 0; ie < nEnergies; ie++) {
    leakLogE[0][ie] = forwardVector[ie];
    leakLogE[1][ie] = barrelVector[ie];
    leakLogE[2][ie] = backwardVector[ie];
  }
 
  //-----------------------------------------------------------------------------------
  //..Position dependent corrections
 
  //..2D histogram of theta-dependent corrections
  TH2F thetaCorrection("thetaCorrection", "Theta correction vs bin;bin = thetaID + 69*energyBin;local theta bin", nbinX, 0, nbinX,
                       nPositions, 0, nPositions);
  int ix = 0;
  for (int ie = 0; ie < nEnergies; ie++) {
    for (int thID = 0; thID < nThetaID; thID++) {
      ix++;
      for (int ipos = 0; ipos < nPositions; ipos++) {
        int iy = ipos + 1;
        float correction = positionCorrection[ie][thID][0][ipos];
        float corrUnc = positionCorrectionUnc[ie][thID][0][ipos];
        thetaCorrection.SetBinContent(ix, iy, correction);
        thetaCorrection.SetBinError(ix, iy, corrUnc);
      }
    }
  }
 
  //..2D histogram of phi-dependent corrections. Twice as many x bins;
  //  one set for crystals next to mechanical structure, one for otherwise.
  TH2F phiCorrection("phiCorrection", "Phi correction vs bin;bin = thetaID + 69*energyBin;local phi bin", 2 * nbinX, 0, 2 * nbinX,
                     nPositions, 0, nPositions);
  ix = 0;
  for (int ie = 0; ie < nEnergies; ie++) {
    for (int thID = 0; thID < nThetaID; thID++) {
      ix++;
      for (int ipos = 0; ipos < nPositions; ipos++) {
        int iy = ipos + 1;
 
        //..First set of corrections
        float correction = positionCorrection[ie][thID][1][ipos];
        float corrUnc = positionCorrectionUnc[ie][thID][1][ipos];
        phiCorrection.SetBinContent(ix, iy, correction);
        phiCorrection.SetBinError(ix, iy, corrUnc);
 
        //..Second set
        correction = positionCorrection[ie][thID][2][ipos];
        corrUnc = positionCorrectionUnc[ie][thID][2][ipos];
        phiCorrection.SetBinContent(ix + nbinX, iy, correction);
        phiCorrection.SetBinError(ix + nbinX, iy, corrUnc);
      }
    }
  }
 
  //-----------------------------------------------------------------------------------
  //..nCrys dependent corrections; not used since nOptimal was implemented.
  const int maxN = 21; // maximum number of crystals in a cluster
  TH2F nCrystalCorrection("nCrystalCorrection", "nCrys correction vs bin;bin = thetaID + 69*energyBin;nCrys", nbinX, 0, nbinX,
                          maxN + 1, 0, maxN + 1);
 
  ix = 0;
  for (int ie = 0; ie < nEnergies; ie++) {
    for (int thID = 0; thID < nThetaID; thID++) {
      ix++;
      for (int in = 0; in <= maxN; in++) {
        int iy = in + 1;
        float correction = 1.;
        float corrUnc = 0.;
        nCrystalCorrection.SetBinContent(ix, iy, correction);
        nCrystalCorrection.SetBinError(ix, iy, corrUnc);
      }
    }
  }
 
 
  //====================================================================================
  //====================================================================================
  //..Step 5. Diagnostic histograms
 
  //..Start by storing the payload histograms
  histfile->cd();
  thetaCorrection.Write();
  phiCorrection.Write();
  nCrystalCorrection.Write();
 
  //-----------------------------------------------------------------------------------
  //..One histogram of new and original reconstructed energy after leakage correction
  //  per generated energy per region. Also uncorrected.
  //  The "Corrected no nCrys" and "Corrected measured" are identical, but keep both
  //  for easier comparison with earlier results.
  const int nResType = 5;
  const TString resName[nResType] = {"Uncorrected", "Original", "Corrected no nCrys", "Corrected measured", "Corrected true"};
  const TString regName[nLeakReg] = {"forward", "barrel", "backward"};
  auto energyResolution = create3Dvector<TH1F*>(nLeakReg, nEnergies, nResType);
 
  //..Base number of bins on a typical thetaID for each region
  int thIDReg[nLeakReg];
  thIDReg[0] = 9;
  thIDReg[1] = 35;
  thIDReg[2] = 61;
 
  for (int ireg = 0; ireg < nLeakReg; ireg++) {
    for (int ie = 0; ie < nEnergies; ie++) {
 
      //..Titles and bins for this region and energy
      TString stireg = std::to_string(ireg);
      TString stie = std::to_string(ie);
      TString stieName = std::to_string(iEnergiesMeV[ie][thIDReg[ireg]]);
      int nbinReg = 20 * nEfracBins[ie][thIDReg[ireg]];
 
      for (int ires = 0; ires < nResType; ires++) {
        name = "energyResolution_" + stireg + "_" + stie + "_" + std::to_string(ires);
        title = resName[ires] + " energy, " + stieName + " MeV, " + regName[ireg] + ";Reconstructed E/Etrue";
        energyResolution[ireg][ie][ires] = new TH1F(name, title, nbinReg, eFracLo, eFracHi);
      }
    }
  }
 
  //-----------------------------------------------------------------------------------
  //..Loop over events and store leakage-corrected energies
  for (int i = 0; i < treeEntries; i++) {
    tree->GetEntry(i);
 
    //..Only events with good reconstruction
    bool goodReco = t_thetaID >= firstUsefulThID and t_thetaID <= lastUsefulThID and t_energyBin >= 0;
    if (not goodReco) {continue;}
 
    //-----------------------------------------------------------------------------------
    //..Corrections using true energy
 
    //..Position-dependent leakage corrections using true energy
    int idir = 0;
    float thetaPosCor = positionCorrection[t_energyBin][t_thetaID][idir][t_thetaBin];
 
    idir = t_phiMech + 1;
    float phiPosCor = positionCorrection[t_energyBin][t_thetaID][idir][t_phiBin];
    float corrTrue = thetaPosCor * phiPosCor;
 
    //-----------------------------------------------------------------------------------
    //..Find correction using measured energy. The correction is a function of corrected
    //  energy, so will need to iterate
    float corrMeasured = 0.96; // typical correction as starting point
    for (int iter = 0; iter < 2; iter++) {
 
 
      //..Energy points that bracket this value
      float energyRaw = t_energyFrac * generatedE[t_region][t_energyBin];
      float logEnergy = log(energyRaw / corrMeasured);
      int ie0 = 0; // lower energy point
      if (logEnergy < leakLogE[t_region][0]) {
        ie0 = 0;
      } else if (logEnergy > leakLogE[t_region][nEnergies - 1]) {
        ie0 = nEnergies - 2;
      } else {
        while (logEnergy > leakLogE[t_region][ie0 + 1]) {ie0++;}
      }
 
      //..Corrections from lower and upper energy points
      float cor0 = positionCorrection[ie0][t_thetaID][0][t_thetaBin] * positionCorrection[ie0][t_thetaID][t_phiMech + 1][t_phiBin];
      float cor1 = positionCorrection[ie0 + 1][t_thetaID][0][t_thetaBin] * positionCorrection[ie0 + 1][t_thetaID][t_phiMech +
                   1][t_phiBin];
 
      //..Interpolate in logE
      corrMeasured = cor0 + (cor1 - cor0) * (logEnergy - leakLogE[t_region][ie0]) / (leakLogE[t_region][ie0 + 1] -
                     leakLogE[t_region][ie0]);
    }
 
    //..No longer have a separate case that excludes the nCrys correction
    float corrNonCrys = corrMeasured;
 
    //-----------------------------------------------------------------------------------
    //..Fill the histograms
    energyResolution[t_region][t_energyBin][0]->Fill(t_energyFrac); // uncorrected
    energyResolution[t_region][t_energyBin][1]->Fill(t_origEnergyFrac); // original payload
    energyResolution[t_region][t_energyBin][2]->Fill(t_energyFrac / corrNonCrys); // no nCrys, measured energy
    energyResolution[t_region][t_energyBin][3]->Fill(t_energyFrac / corrMeasured); // corrected, measured energy
    energyResolution[t_region][t_energyBin][4]->Fill(t_energyFrac / corrTrue); // corrected, true energy
  }
 
  //-----------------------------------------------------------------------------------
  //..Fit each histogram to find peak, and extract resolution.
 
  //..Store the peak and resolution values for each histogram
  auto peakEnergy = create3Dvector<float>(nLeakReg, nEnergies, nResType);
  auto energyRes  = create3Dvector<float>(nLeakReg, nEnergies, nResType);
 
  //..Loop over the histograms to be fit
  for (int ireg = 0; ireg < nLeakReg; ireg++) {
    for (int ie = 0; ie < nEnergies; ie++) {
 
      //..Base the resolution on the number of entries in the corrected plot for all 4 cases.
      double entries = energyResolution[ireg][ie][nResType - 1]->Integral();
      for (int ires = 0; ires < nResType; ires++) {
        TH1F* hEnergy = (TH1F*)energyResolution[ireg][ie][ires];
        double peak = -1.;
        double res68 = 99.; // resolution based on 68.3% of the entries
        int nIter = 0; // keep track of attempts to fit this histogram
        bool fitHist = entries > minEntries;
 
        //..Possibly iterate fit starting from this point
        while (fitHist) {
 
          //..Fit parameters
          double norm = hEnergy->GetMaximum();
          double target = fracEnt[nIter] * entries; // fit range contains 68.3% or 50%
          std::vector<double> startingParameters;// peak, sigma, eta, fitLow, fitHigh, nbins
          startingParameters = eclLeakageFitParameters(hEnergy, target);
          func->SetParameters(norm, startingParameters[0], startingParameters[1], startingParameters[2]);
          func->SetParLimits(1, startingParameters[3], startingParameters[4]);
          func->SetParLimits(2, 0., startingParameters[4] - startingParameters[3]);
          func->SetParLimits(3, etaMin, etaMax);
 
          //..First iteration the fitting range contains >68.3% of the histogram integral.
          if (nIter == 0) {
 
            //..Find the bin numbers used in the fit
            int minLo = hEnergy->GetXaxis()->FindBin(startingParameters[3] + 0.0001);
            int minHi = hEnergy->GetXaxis()->FindBin(startingParameters[4] - 0.0001);
 
            //..Subtract a partial bin to get to exactly 68.3%
            double dx = hEnergy->GetBinLowEdge(minLo + 1) - hEnergy->GetBinLowEdge(minLo);
            double overage = hEnergy->Integral(minLo, minHi) - target;
            double subLo = overage / hEnergy->GetBinContent(minLo);
            double subHi = overage / hEnergy->GetBinContent(minHi);
 
            //..Resolution is half the range that contains 68.3% of the events
            res68 = 0.5 * dx * (1 + minHi - minLo - max(subLo, subHi));
          }
 
          //..Fit
          name = hEnergy->GetName();
          B2DEBUG(40, "Fitting " << name.Data());
          hEnergy->Fit(func, "LIEQ", "", startingParameters[3], startingParameters[4]);
          peak = func->GetParameter(1);
          double effSigma = func->GetParameter(2);
          double eta = func->GetParameter(3);
          double prob = func->GetProb();
 
          //..Check fit quality  0 = good, 1 = redo fit, 2 = lowStat, 3 = lowProb,
          //  4 = peakAtLimit, 5 = sigmaAtLimit, 6 = etaAtLimit
          double dEta = min((etaMax - eta), (eta - etaMin));
          int fitStatus = eclLeakageFitQuality(startingParameters[3], startingParameters[4], peak, effSigma, dEta, prob);
 
          //..If the fit probability is low, refit using a smaller range (fracEnt)
          if ((fitStatus == 1 or fitStatus == 3) and nIter == 0) {
            nIter++;
          } else {
            fitHist = false;
          }
 
        } // end of while
 
        //..Record peak and resolution
        peakEnergy[ireg][ie][ires] = peak;
        energyRes[ireg][ie][ires] = res68;
 
        //..And write to disk
        histfile->cd();
        hEnergy->Write();
      }
    }
  }
 
  //-----------------------------------------------------------------------------------
  //..Summarize resolution
  int nresBins = nEnergies * nLeakReg * (nResType + 1); // +1 to add an empty bin after each set
  TH1F* peakSummary = new TH1F("peakSummary", "Peak E/Etrue for each method, region, energy;Energy energy point", nresBins, 0,
                               nEnergies);
  TH1F* resolutionSummary = new TH1F("resolutionSummary", "Resolution/peak for each method, region, energy;Energy energy point",
                                     nresBins, 0, nEnergies);
 
  B2INFO("Resolution divided by peak for each energy bin and region " << nResType << " ways");
  for (int ires = 0; ires < nResType; ires++) {B2INFO(" " << resName[ires]);}
  ix = 0;
  for (int ie = 0; ie < nEnergies; ie++) {
    B2INFO("  energy point " << ie);
    for (int ireg = 0; ireg < nLeakReg; ireg++) {
      B2INFO("    region " << ireg);
      for (int ires = 0; ires < nResType; ires++) {
 
        //..Store in summary histograms
        ix++;
        peakSummary->SetBinContent(ix, peakEnergy[ireg][ie][ires]);
        peakSummary->SetBinError(ix, 0.);
        resolutionSummary->SetBinContent(ix, energyRes[ireg][ie][ires] / peakEnergy[ireg][ie][ires]);
        resolutionSummary->SetBinError(ix, 0.);
 
        //..Print out as well
        B2INFO("      " << ires << " " << energyRes[ireg][ie][ires] / peakEnergy[ireg][ie][ires]);
      }
      ix++;
    }
  }
 
  //..Write the summary histograms to disk
  histfile->cd();
  peakSummary->Write();
  resolutionSummary->Write();
 
 
  //====================================================================================
  //====================================================================================
  //..Step 6. Finish up.
 
  //-----------------------------------------------------------------------------------
  //..Close histogram file
  histfile->Close();
 
  //------------------------------------------------------------------------
  //..Create and store payload
  ECLLeakageCorrections* leakagePayload = new  ECLLeakageCorrections();
  leakagePayload->setlogEnergiesFwd(forwardVector);
  leakagePayload->setlogEnergiesBrl(barrelVector);
  leakagePayload->setlogEnergiesBwd(backwardVector);
  leakagePayload->setThetaCorrections(thetaCorrection);
  leakagePayload->setPhiCorrections(phiCorrection);
  leakagePayload->setnCrystalCorrections(nCrystalCorrection);
  saveCalibration(leakagePayload, "ECLLeakageCorrections");
 
  B2INFO("eclLeakageAlgorithm: successfully stored payload ECLLeakageCorrections");
  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;}

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;
}

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(); }

getLowEnergyThreshold()

double getLowEnergyThreshold ( )

inline

Getter for m_lowEnergyThreshold.

Definition at line 35 of file eclLeakageAlgorithm.h.

{return m_lowEnergyThreshold;}

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);
    }

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();}

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;
}

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);
}

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 ( 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();
}

setLowEnergyThreshold()

void setLowEnergyThreshold ( double  lowEnergyThreshold )

inline

Setter for m_lowEnergyThreshold.

Definition at line 32 of file eclLeakageAlgorithm.h.

{m_lowEnergyThreshold = lowEnergyThreshold;}

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;}

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 = 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

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_lowEnergyThreshold

double m_lowEnergyThreshold = 0.0

private

Parameters to control fit procedure.

only minimal fits below this value

Definition at line 45 of file eclLeakageAlgorithm.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.

t_cellID

int t_cellID = 0

private

For TTree.

cellID of photon

Definition at line 48 of file eclLeakageAlgorithm.h.

t_energyBin

int t_energyBin = -1

private

generated energy point

Definition at line 54 of file eclLeakageAlgorithm.h.

t_energyFrac

float t_energyFrac = 0.

private

measured energy after nOptimal bias and peak corrections, divided by generated

Definition at line 56 of file eclLeakageAlgorithm.h.

t_locationError

float t_locationError = 999.

private

reconstructed minus generated position (cm)

Definition at line 58 of file eclLeakageAlgorithm.h.

t_nCrys

int t_nCrys = -1

private

number of crystals used to calculate energy

Definition at line 55 of file eclLeakageAlgorithm.h.

t_origEnergyFrac

float t_origEnergyFrac = 0.

private

corrected energy at time of generation, divided by generated

Definition at line 57 of file eclLeakageAlgorithm.h.

t_phiBin

int t_phiBin = -1

private

binned location in phi relative to crystal edge

Definition at line 52 of file eclLeakageAlgorithm.h.

t_phiMech

int t_phiMech = -1

private

mechanical structure next to lower phi (0), upper phi (1), or neither (2)

Definition at line 53 of file eclLeakageAlgorithm.h.

t_region

int t_region = 0

private

region of photon 0=forward 1=barrel 2=backward

Definition at line 50 of file eclLeakageAlgorithm.h.

t_thetaBin

int t_thetaBin = -1

private

binned location in theta relative to crystal edge

Definition at line 51 of file eclLeakageAlgorithm.h.

t_thetaID

int t_thetaID = 0

private

thetaID of photon

Definition at line 49 of file eclLeakageAlgorithm.h.

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

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