eclNOptimalAlgorithm Class Reference

Algorithm that works with eclNOptimalCollector to find the number of crystals to be summed to get the best energy resolution for each test energy and for each group of crystals (8 groups per thetaID in the barrel). More...

#include <eclNOptimalAlgorithm.h>

Inheritance diagram for eclNOptimalAlgorithm:

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

Public Member Functions
	eclNOptimalAlgorithm ()
	..Constructor
virtual	~eclNOptimalAlgorithm ()
	..Destructor
std::string	getPrefix () const
	Get the prefix used for getting calibration data.
bool	checkPyExpRun (PyObject *pyObj)
	Checks that a PyObject can be successfully converted to an ExpRun type.
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::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

Algorithm that works with eclNOptimalCollector to find the number of crystals to be summed to get the best energy resolution for each test energy and for each group of crystals (8 groups per thetaID in the barrel).

Also finds the corresponding energy bias from beam backgrounds, and the peak fraction of energy contained in the crystals.

Contact Chris Hearty heart.nosp@m.y@ph.nosp@m.ysics.nosp@m..ubc.nosp@m..ca for questions or concerns Find optimal number of crystals to sum for cluster energy

Definition at line 30 of file eclNOptimalAlgorithm.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

eclNOptimalAlgorithm()

eclNOptimalAlgorithm

(

)

..Constructor

Definition at line 210 of file eclNOptimalAlgorithm.cc.

                                          : CalibrationAlgorithm("eclNOptimalCollector")
{
  setDescription(
    "Use single gamma MC to find the optimal number of crystals to sum for cluster energy"
  );
}

~eclNOptimalAlgorithm()

virtual ~eclNOptimalAlgorithm ( )

inlinevirtual

..Destructor

Definition at line 37 of file eclNOptimalAlgorithm.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

Implements CalibrationAlgorithm.

Definition at line 219 of file eclNOptimalAlgorithm.cc.

{
 
  //-----------------------------------------------------------------------------------
  //-----------------------------------------------------------------------------------
  //..Algorithm set up
 
  //..Get started
  B2INFO("Starting eclNOptimalAlgorithm");
  gROOT->SetBatch();
 
  //-----------------------------------------------------------------------------------
  //..Read in parameter histograms created by the collector and fix normalization
  auto inputParameters = getObjectPtr<TH1F>("inputParameters");
  const int lastBin = inputParameters->GetNbinsX();
  const 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.);
  }
 
  //..Also read in and normalize group number for each cellID
  auto groupNumberOfEachCellID = getObjectPtr<TH1F>("groupNumberOfEachCellID");
  for (int ic = 1; ic <= ECLElementNumbers::c_NCrystals; ic++) {
    const double groupNum = groupNumberOfEachCellID->GetBinContent(ic);
    groupNumberOfEachCellID->SetBinContent(ic, groupNum / scale);
    groupNumberOfEachCellID->SetBinError(ic, 0.);
  }
 
  //..Couple of diagnostic histograms
  auto entriesPerThetaIdEnergy = getObjectPtr<TH2F>("entriesPerThetaIdEnergy");
  auto mcEnergyDiff = getObjectPtr<TH2F>("mcEnergyDiff");
 
  //..Write these to disk.
  TFile* histFile = new TFile("eclNOptimalAlgorithm.root", "recreate");
  inputParameters->Write();
  groupNumberOfEachCellID->Write();
  entriesPerThetaIdEnergy->Write();
  mcEnergyDiff->Write();
 
  //-----------------------------------------------------------------------------------
  //..Parameters from the inputParameters histogram
  const int nCrystalGroups = (int)(inputParameters->GetBinContent(1) + 0.5);
  const int nEnergies = (int)(inputParameters->GetBinContent(2) + 0.5);
  const int nLeakReg = 3; // 3 regions forward barrel backward
  auto generatedE = create2Dvector<float>(nLeakReg, nEnergies);
  int bin = 2; // bin 1 = nCrystalGroups, bin 2 = nEnergies, bin 3 = first energy
  for (int ireg = 0; ireg < nLeakReg; ireg++) {
    for (int ie = 0; ie < nEnergies; ie++) {
      bin++;
      generatedE[ireg][ie] = inputParameters->GetBinContent(bin);
    }
  }
  B2INFO("nCrystalGroups = " << nCrystalGroups);
 
  //-----------------------------------------------------------------------------------
  //..Experiment and run number, for reading existing payload from database
  const auto exprun =  getRunList();
  const int iExp = exprun[0].first;
  const int iRun = exprun[0].second;
  B2INFO("Experiment, run = " << iExp << ", " << iRun);
 
  StoreObjPtr<EventMetaData> evtPtr;
  // simulate the initialize() phase where we can register objects in the DataStore
  DataStore::Instance().setInitializeActive(true);
  evtPtr.registerInDataStore();
  DataStore::Instance().setInitializeActive(false);
  // now construct the event metadata
  evtPtr.construct(1, iRun, iExp);
  // and update the database contents
  DBStore& dbstore = DBStore::Instance();
  dbstore.update();
  // this is only needed it the payload might be intra-run dependent,
  // that is if it might change during one run as well
  dbstore.updateEvent();
 
  //-----------------------------------------------------------------------------------
  //..Get existing payload
  B2INFO("eclNOptimalAlgorithm: reading previous payload");
  DBObjPtr<ECLnOptimal> existingECLnOptimal;
  std::vector<float> eUpperBoundariesFwdPrev = existingECLnOptimal->getUpperBoundariesFwd();
  std::vector<float> eUpperBoundariesBrlPrev = existingECLnOptimal->getUpperBoundariesBrl();
  std::vector<float> eUpperBoundariesBwdPrev = existingECLnOptimal->getUpperBoundariesBwd();
  TH2F nOptimal2DPrev = existingECLnOptimal->getNOptimal();
  nOptimal2DPrev.SetName("nOptimal2DPrev");
 
  const int nPrevE = eUpperBoundariesFwdPrev.size();
  B2INFO("Energy upper boundaries from previous payload for each region");
  for (int ie = 0; ie < nPrevE; ie++) {
    B2INFO(" " << ie << " " << eUpperBoundariesFwdPrev[ie] << " " << eUpperBoundariesBrlPrev[ie] << " " << eUpperBoundariesBwdPrev[ie]);
  }
 
  //..Write out
  histFile->cd();
  nOptimal2DPrev.Write();
 
  //-----------------------------------------------------------------------------------
  //..Use existing payload to get the initial nOptimal value for each group
  std::vector< std::vector<int> > initialNOptimal;
  initialNOptimal.resize(nCrystalGroups, std::vector<int>(nEnergies, 0));
 
  //..Couple of histograms of relevant quantities
  TH2F* nInitialPerGroup = new TH2F("nInitialPerGroup", "initial nCrys, energy point vs group number;group;energy point",
                                    nCrystalGroups, 0., nCrystalGroups, nEnergies, 0., nEnergies);
  TH2F* generatedEPerGroup = new TH2F("generatedEPerGroup", "Generated energy, energy point vs group number;group;energy point",
                                      nCrystalGroups, 0., nCrystalGroups, nEnergies, 0., nEnergies);
  TH1F* firstCellIDPerGroup = new TH1F("firstCellIDPerGroup", "First cellID of group;group", nCrystalGroups, 0., nCrystalGroups);
  TH1F* nCrystalsPerGroup = new TH1F("nCrystalsPerGroup", "Number of crystals per group;group", nCrystalGroups, 0., nCrystalGroups);
  std::vector<int> crystalsPerGroup;
  crystalsPerGroup.resize(nCrystalGroups, 0);
 
  const int nCrystalsTotal = ECLElementNumbers::c_NCrystals;
  for (int cellID = 1; cellID <= nCrystalsTotal; cellID++) {
 
    //..Group number of this cellID
    int iGroup = (int)(0.5 + groupNumberOfEachCellID->GetBinContent(cellID));
 
    //..Energy boundaries of previous payload, which depend on the ECL region
    std::vector<float> eUpperBoundariesPrev = eUpperBoundariesBrlPrev;
    int iRegion = 1; // barrel
    if (ECLElementNumbers::isForward(cellID)) {
      eUpperBoundariesPrev = eUpperBoundariesFwdPrev;
      iRegion = 0;
    } else if (ECLElementNumbers::isBackward(cellID)) {
      eUpperBoundariesPrev = eUpperBoundariesBwdPrev;
      iRegion = 2;
    }
 
    //..For each test energy, get the nOptimal for this cellID from previous payload.
    //  iEnergy is the energy bin of the previous payload, which is equal to ie if
    //  the test energies have not changed.
    for (int ie = 0; ie < nEnergies; ie++) {
      float energy = generatedE[iRegion][ie];
      int iEnergy = 0;
      while (energy > eUpperBoundariesPrev[iEnergy]) {iEnergy++;}
      initialNOptimal[iGroup][ie] = (int)(0.5 + nOptimal2DPrev.GetBinContent(iGroup + 1, iEnergy + 1));
      if (ie != iEnergy) {
        B2INFO("ie iEnergy mismatch: cellID " << cellID << " ie " << ie << " iEnergy " << iEnergy);
      }
 
      //..Store in diagnostic histograms
      generatedEPerGroup->SetBinContent(iGroup + 1, ie + 1, energy);
      nInitialPerGroup->SetBinContent(iGroup + 1, ie + 1, initialNOptimal[iGroup][ie]);
    }
 
    //..Count crystals in this group
    if (crystalsPerGroup[iGroup] == 0) {
      firstCellIDPerGroup->SetBinContent(iGroup + 1, cellID);
      firstCellIDPerGroup->SetBinError(iGroup + 1, 0.);
    }
    crystalsPerGroup[iGroup]++;
  }
 
  //..Write out these histograms
  for (int ig = 0; ig < nCrystalGroups; ig++) {
    nCrystalsPerGroup->SetBinContent(ig + 1, crystalsPerGroup[ig]);
    nCrystalsPerGroup->SetBinError(ig + 1, 0.);
  }
  histFile->cd();
  nInitialPerGroup->Write();
  generatedEPerGroup->Write();
  firstCellIDPerGroup->Write();
  nCrystalsPerGroup->Write();
  B2INFO("Wrote initial diagnostic histograms");
 
  //-----------------------------------------------------------------------------------
  //..Write out all the 2D histograms
  for (int ig = 0; ig < nCrystalGroups; ig++) {
    for (int ie = 0; ie < nEnergies; ie++) {
      std::string name = "eSum_" + std::to_string(ig) + "_" + std::to_string(ie);
      auto eSum = getObjectPtr<TH2F>(name);
      name = "biasSum_" + std::to_string(ig) + "_" + std::to_string(ie);
      auto biasSum = getObjectPtr<TH2F>(name);
      histFile->cd();
      eSum->Write();
      biasSum->Write();
    }
  }
  B2INFO("Wrote eSum and biasSum histograms");
 
  //-----------------------------------------------------------------------------------
  //-----------------------------------------------------------------------------------
  //..Find nOptimal and corresponding bias for each group/energy point
 
  //..Histograms to store fit results
  TH2F* nOptimalPerGroup = new TH2F("nOptimalPerGroup", "nOptimal;group;energy point", nCrystalGroups, 0., nCrystalGroups, nEnergies,
                                    0., nEnergies);
  TH2F* changeInNOptimal = new TH2F("changeInNOptimal", "nOptimal minus nInitial;group;energy point", nCrystalGroups, 0.,
                                    nCrystalGroups, nEnergies, 0., nEnergies);
  TH2F* binsInFit = new TH2F("binsInFit", "Number of bins used in Novo fit, energy point vs group number;group;energy point",
                             nCrystalGroups, 0., nCrystalGroups, nEnergies, 0., nEnergies);
  TH2F* maxEntriesPerHist = new TH2F("maxEntriesPerHist",
                                     "Max entries in histogram bin, energy point vs group number;group;energy point",  nCrystalGroups, 0., nCrystalGroups, nEnergies, 0.,
                                     nEnergies);
  TH2F* peakPerGroup = new TH2F("peakPerGroup", "peak energy, energy point vs group number;group;energy point", nCrystalGroups, 0.,
                                nCrystalGroups, nEnergies, 0., nEnergies);
  TH2F* effSigmaPerGroup = new TH2F("effSigmaPerGroup", "fit effective sigma, energy point vs group number;group;energy point",
                                    nCrystalGroups, 0., nCrystalGroups, nEnergies, 0., nEnergies);
  TH2F* etaPerGroup = new TH2F("etaPerGroup", "fit eta, energy point vs group number;group;energy point", nCrystalGroups, 0.,
                               nCrystalGroups, nEnergies, 0., nEnergies);
  TH2F* fitProbPerGroup = new TH2F("fitProbPerGroup", "fit probability, energy point vs group number;group;energy point",
                                   nCrystalGroups, 0., nCrystalGroups, nEnergies, 0., nEnergies);
  TH2F* resolutionPerGroup = new TH2F("resolutionPerGroup", "resolution/(peak-bias), energy point vs group number;group;energy point",
                                      nCrystalGroups, 0., nCrystalGroups, nEnergies, 0., nEnergies);
  TH2F* fracContainedEPerGroup = new TH2F("fracContainedEPerGroup",
                                          "peak fraction of energy contained in nOpt crystals;group;energy point", nCrystalGroups, 0., nCrystalGroups, nEnergies, 0.,
                                          nEnergies);
 
  TH2F* biasPerGroup = new TH2F("biasPerGroup", "bias (GeV), energy point vs group number;group;energy point", nCrystalGroups, 0.,
                                nCrystalGroups, nEnergies, 0., nEnergies);
  TH2F* sigmaBiasPerGroup = new TH2F("sigmaBiasPerGroup", "sigma bias (GeV), energy point vs group number;group;energy point",
                                     nCrystalGroups, 0., nCrystalGroups, nEnergies, 0., nEnergies);
 
  TH2F* existingResolutionPerGroup = new TH2F("existingResolutionPerGroup",
                                              "existing resolution/peak, energy point vs group number;group;energy point", nCrystalGroups, 0., nCrystalGroups, nEnergies, 0.,
                                              nEnergies);
 
  //-----------------------------------------------------------------------------------
  //..Loop over all groups and energy points
 
  //..Fit function Novosibirsk (xmin, xmax, nparameters)
  TF1* func = new TF1("eclNOptimalNovo", eclNOptimalNovo, 0.4, 1.4, 4);
  func->SetLineColor(kRed);
 
  for (int ig = 0; ig < nCrystalGroups; ig++) {
    for (int ie = 0; ie < nEnergies; ie++) {
 
      //..Read in the energy sum vs nCrys for the group and energy point
      std::string name = "eSum_" + std::to_string(ig) + "_" + std::to_string(ie);
      auto eSum = getObjectPtr<TH2F>(name);
 
      //..Also the corresponding bias histgram
      std::string biasName = "biasSum_" + std::to_string(ig) + "_" + std::to_string(ie);
      auto biasSum = getObjectPtr<TH2F>(biasName);
 
      //..And the generated energy
      const double genEnergy = generatedEPerGroup->GetBinContent(ig + 1, ie + 1);
 
      //-----------------------------------------------------------------------------------
      //..Extract the eSum distributions for various nCrys (=nCrysSumToFit) and fit them
      //  to find the best resolution.
      const int nCrysBins = eSum->GetNbinsX();
      const int nCrysMax = nCrysBins - 1; // the last bin is eSum for existing reco
 
      //..Items to be found and stored in the following while loop
      TH1D* eSumOpt{nullptr}; // 1D projection with the best resolution
      int nFitBins = 0; // keep track of how many bins are included in the fit
      double bestFractionalResolution = 999.;
      double existingFractionalResolution = 999.;
      double biasForNOpt = 0.; // bias from backgrounds for optimal nCrys
      double biasSigmaForNOpt = 0.; // sigma bias
      int nOpt = 0;
 
      //..Start with the previous optimal value, then try fewer, if possible, then
      //  try more, if possible. End by finding the resolution for the
      //  reconstruction used to produce the single photon MC samples.
      const int initialnCrysSumToFit = initialNOptimal[ig][ie];
      int nCrysSumToFit = initialnCrysSumToFit;
      while (nCrysSumToFit > 0) {
 
        TH1D* hEnergy = (TH1D*)eSum->ProjectionY("hEnergy", nCrysSumToFit, nCrysSumToFit);
        TString newName = name + "_" + std::to_string(nCrysSumToFit);
        hEnergy->SetName(newName);
 
        //------------------------------------------------------------------------------
        //..Check stats, and rebin as required
        const double minESumEntries = 800.;
        if (hEnergy->GetEntries() < minESumEntries) {
          B2INFO("Insuffient entries in eSum: " << hEnergy->GetEntries() << " for group " << ig << " energy point " << ie);
          histFile->Close();
          B2INFO("closed histFile; quitting");
          return c_NotEnoughData;
        }
 
        //..Rebin if the maximum bin has too few entries
        const double minPeakValue = 300.;
        while (hEnergy->GetMaximum() < minPeakValue) {hEnergy->Rebin(2);}
 
        //------------------------------------------------------------------------------
        //..Find 50% range for Novo fit to find peak value. Rebin if there are too many bins
        double fitFraction = 0.5;
        int iLo = 1;
        int iHi = hEnergy->GetNbinsX();
        const int maxBinsToFit = 59;
        while ((iHi - iLo) > maxBinsToFit) {
          std::vector<int> iBins = eclNOptimalFitRange(hEnergy, fitFraction);
          iLo = iBins[0];
          iHi = iBins[1];
          if ((iHi - iLo) > maxBinsToFit) {hEnergy->Rebin(2);}
        }
 
        //..Fit range
        const int nBinsX = hEnergy->GetNbinsX();
        const double fitlow = hEnergy->GetBinLowEdge(iLo);
        const double fithigh = hEnergy->GetBinLowEdge(iHi + 1);
        double sum = hEnergy->Integral(iLo, iHi);
        B2INFO("group " << ig << " ie " << ie << " name " << name << " newName " << newName << " nBinsX " << nBinsX << " 50% target: " <<
               fitFraction * hEnergy->GetEntries() << " iLo: " << iLo << " iHi: " << iHi << " sum: " << sum);
 
        //------------------------------------------------------------------------------
        //..Set up the fit. Initial values
        double normalization = hEnergy->GetMaximum();
        const int iMax = hEnergy->GetMaximumBin();
        double peak = hEnergy->GetBinLowEdge(iMax);
        double effSigma = 0.5 * (fithigh - fitlow);
        double eta = 0.4; // typical value for good fits
 
        func->SetParameters(normalization, peak, effSigma, eta);
        func->SetParNames("normalization", "peak", "effSigma", "eta");
 
        //..Parameter ranges
        func->SetParLimits(1, fitlow, fithigh);
        func->SetParLimits(2, 0., 2.*effSigma);
        func->SetParLimits(3, -1, 3);
 
        //..If there is only one crystal, fix eta to a special value
        if (nCrysSumToFit == 1) {
          const double etaForOneCrystal = 0.95;
          func->SetParameter(3, etaForOneCrystal);
          func->SetParLimits(3, etaForOneCrystal, etaForOneCrystal);
        }
 
        //..Fit
        hEnergy->Fit(func, "LIEQ", "", fitlow, fithigh);
 
        //------------------------------------------------------------------------------
        //..Find the bias = sum of CalDigits minus sum of MC truth for this nCrys
        //  Bias is the mid point of the minimum range that contains 68.3% of events
        double bias = 0.;
        double biasSigma = 0.;
        if (nCrysSumToFit < nCrysBins) {
          TH1D* hBias = (TH1D*)biasSum->ProjectionY("hBias", nCrysSumToFit, nCrysSumToFit);
          fitFraction = 0.683;
          std::vector<int> jBins = eclNOptimalFitRange(hBias, fitFraction);
          const double lowEdge = hBias->GetBinLowEdge(jBins[0]);
          const double highEdge = hBias->GetBinLowEdge(jBins[1] + 1);
          bias = 0.5 * (lowEdge + highEdge);
          biasSigma = 0.5 * (highEdge - lowEdge);
        }
 
        //------------------------------------------------------------------------------
        //..Resolution is half the smallest range that contains 68.3% of events.
        //  Speed up the process by only looking among the range that contains 75%.
        fitFraction = 0.75;
        std::vector<int> iBins = eclNOptimalFitRange(hEnergy, fitFraction);
        const double resolution = eclNOptimalResolution(hEnergy, iBins[0], iBins[1]);
 
        //..Correct for the bias from background when calculating fractional resolution.
        //  Note that variable resolution = (E_hi - E_lo)/E_gen is dimensionless,
        //  variable peak = E_peak/E_gen is dimensionless, but variable bias has units GeV
        peak = func->GetParameter(1);
        const double fractionalResolution = resolution / (peak - bias / genEnergy);
 
        //..Record information if this is the best resolution (and is not the existing
        //  resolution case).
        if (fractionalResolution < bestFractionalResolution and nCrysSumToFit != nCrysBins) {
          bestFractionalResolution = fractionalResolution;
          nOpt = nCrysSumToFit;
          eSumOpt = hEnergy;
          nFitBins = 1 + iHi - iLo;
          biasForNOpt = bias;
          biasSigmaForNOpt = biasSigma;
        } else if (nCrysSumToFit == nCrysBins) {
          existingFractionalResolution = fractionalResolution;
        }
 
        //------------------------------------------------------------------------------
        //..Logic to decide what to do next
 
        //..keep checking different N until resolution is this much worse than best value
        const double resTolerance = 1.05;
        if (nCrysSumToFit == initialnCrysSumToFit) {
 
          //..After testing the previous nOptimal, try one fewer if possible
          if (nCrysSumToFit > 1) {
            nCrysSumToFit--;
          } else {
            nCrysSumToFit++;
          }
        } else if (nCrysSumToFit < initialnCrysSumToFit) {
 
          //..Trying fewer crystals. If this is the best so far, try one
          //  fewer, if possible. Otherwise, try more crystals.
          if (fractionalResolution < resTolerance* bestFractionalResolution and nCrysSumToFit > 1) {
            nCrysSumToFit--;
          } else if (initialnCrysSumToFit != nCrysMax) {
            nCrysSumToFit = initialnCrysSumToFit + 1;
          } else {
            nCrysSumToFit = nCrysBins;
          }
        } else if (nCrysSumToFit < nCrysBins) {  // nCrysSumToFit is always > initialnCrysSumToFit
 
          //..Trying more crystals. If this is the best, try one more, if
          //  possible. Otherwise, do the current reconstruction case (nCrysSumToFit = nCrysBins)
          if (fractionalResolution < resTolerance * bestFractionalResolution and nCrysSumToFit < nCrysMax) {
            nCrysSumToFit++;
          } else {
            nCrysSumToFit = nCrysBins;
          }
        } else if (nCrysSumToFit == nCrysBins) {
 
          //..This is the current resolution case, so we are done
          nCrysSumToFit = 0;
        }
 
      } // while(nCrysSumToFit > 0)
      B2INFO(" ig: " << ig << " ie: " << ie << " initial nOpt: " << initialnCrysSumToFit << " final nOpt: " << nOpt);
 
      //-----------------------------------------------------------------------------------
      //..Store everything in diagnostic histograms
 
      //..Extract the function from the nOptimal histogram
      TF1* funcOpt = (TF1*)eSumOpt->GetFunction("eclNOptimalNovo");
 
      nOptimalPerGroup->SetBinContent(ig + 1, ie + 1, nOpt);
 
      changeInNOptimal->SetBinContent(ig + 1, ie + 1, nOpt - initialnCrysSumToFit);
      changeInNOptimal->SetBinError(ig + 1, ie + 1, 0.);
 
      biasPerGroup->SetBinContent(ig + 1, ie + 1, biasForNOpt);
      biasPerGroup->SetBinError(ig + 1, ie + 1, 0.);
 
      sigmaBiasPerGroup->SetBinContent(ig + 1, ie + 1, biasSigmaForNOpt);
      sigmaBiasPerGroup->SetBinError(ig + 1, ie + 1, 0.);
 
      binsInFit->SetBinContent(ig + 1, ie + 1, nFitBins);
      binsInFit->SetBinError(ig + 1, ie + 1, 0.);
 
      maxEntriesPerHist->SetBinContent(ig + 1, ie + 1, eSumOpt->GetMaximum());
      maxEntriesPerHist->SetBinError(ig + 1, ie + 1, 0.);
 
      const double peakOpt = funcOpt->GetParameter(1);
      const double peakOptUnc = funcOpt->GetParError(1);
      peakPerGroup->SetBinContent(ig + 1, ie + 1, peakOpt);
      peakPerGroup->SetBinError(ig + 1, ie + 1, peakOptUnc);
 
      const double genE = generatedEPerGroup->GetBinContent(ig + 1, ie + 1);
      const double peakCor = peakOpt - biasForNOpt / genE;
      fracContainedEPerGroup->SetBinContent(ig + 1, ie + 1, peakCor);
      fracContainedEPerGroup->SetBinError(ig + 1, ie + 1, peakOptUnc);
 
      effSigmaPerGroup->SetBinContent(ig + 1, ie + 1, funcOpt->GetParameter(2));
      effSigmaPerGroup->SetBinError(ig + 1, ie + 1, funcOpt->GetParError(2));
 
      etaPerGroup->SetBinContent(ig + 1, ie + 1, funcOpt->GetParameter(3));
      etaPerGroup->SetBinError(ig + 1, ie + 1, funcOpt->GetParError(3));
 
      fitProbPerGroup->SetBinContent(ig + 1, ie + 1, funcOpt->GetProb());
      fitProbPerGroup->SetBinError(ig + 1, ie + 1, 0.);
 
      resolutionPerGroup->SetBinContent(ig + 1, ie + 1, bestFractionalResolution);
      resolutionPerGroup->SetBinError(ig + 1, ie + 1, 0.);
 
      existingResolutionPerGroup->SetBinContent(ig + 1, ie + 1, existingFractionalResolution);
      existingResolutionPerGroup->SetBinError(ig + 1, ie + 1, 0.);
 
      //..Write out to disk
      histFile->cd();
      eSumOpt->Write();
 
    }
  }
 
  //-----------------------------------------------------------------------------------
  //..Write out fit summary histograms
  histFile->cd();
  changeInNOptimal->Write();
  biasPerGroup->Write();
  sigmaBiasPerGroup->Write();
  binsInFit->Write();
  maxEntriesPerHist->Write();
  peakPerGroup->Write();
  effSigmaPerGroup->Write();
  etaPerGroup->Write();
  fitProbPerGroup->Write();
  resolutionPerGroup->Write();
  existingResolutionPerGroup->Write();
  fracContainedEPerGroup->Write();
 
  B2INFO("Wrote fit summary histograms");
 
  //-----------------------------------------------------------------------------------
  //-----------------------------------------------------------------------------------
  //..Find bias and fraction contained energy in nOpt crystals for the energy bins
  //  adjacent to each of the group / energy points.
 
  //..Histograms to store results
  TH2F* fracContainedAdjacent[2];
  TH2F* biasAdjacent[2];
  const TString updown[2] = {"lower", "higher"};
  for (int ia = 0; ia < 2; ia++) {
    TString name = "fracContainedAdjacent_" + std::to_string(ia);
    TString title = "peak fraction of energy contained in nOpt crystals, " + updown[ia] + " E;group;energy point";
    fracContainedAdjacent[ia] = new TH2F(name, title, nCrystalGroups, 0., nCrystalGroups, nEnergies, 0., nEnergies);
 
    name = "biasAdjacent_" + std::to_string(ia);
    title = "bias (GeV) in nOpt crystals, " + updown[ia] + " E;group;energy point";
    biasAdjacent[ia] = new TH2F(name, title, nCrystalGroups, 0., nCrystalGroups, nEnergies, 0., nEnergies);
  }
 
  //-----------------------------------------------------------------------------------
  //..Loop over all groups and energy points
  for (int ig = 0; ig < nCrystalGroups; ig++) {
    for (int ie = 0; ie < nEnergies; ie++) {
 
      //..nOpt for this point
      const int nOpt = nOptimalPerGroup->GetBinContent(ig + 1, ie + 1);
 
      //-----------------------------------------------------------------------------------
      //..Two adjacent energy points
      for (int ia = 0; ia < 2; ia++) {
        const int ieAdj = ie + 2 * ia - 1; // ie +/- 1
        double eFracPeakAdj = fracContainedEPerGroup->GetBinContent(ig + 1, ie + 1);
        double biasAdj = biasPerGroup->GetBinContent(ig + 1, ie + 1);
 
        //..Need this to be a valid energy point to do anything more
        if (ieAdj >= 0 and ieAdj < nEnergies) {
 
          //----------------------------------------------------------------------------
          //..Find the bias
          std::string biasName = "biasSum_" + std::to_string(ig) + "_" + std::to_string(ieAdj);
          auto biasSum = getObjectPtr<TH2F>(biasName);
          TH1D* hBias = (TH1D*)biasSum->ProjectionY("hBias", nOpt, nOpt);
 
          //..Bias is the mid-point of the range containing 68.3% of events
          double fitFraction = 0.683;
          std::vector<int> jBins = eclNOptimalFitRange(hBias, fitFraction);
          const double lowEdge = hBias->GetBinLowEdge(jBins[0]);
          const double highEdge = hBias->GetBinLowEdge(jBins[1] + 1);
          biasAdj = 0.5 * (lowEdge + highEdge);
 
          //----------------------------------------------------------------------------
          //..Get the eSum distribution to be fit
          std::string name = "eSum_" + std::to_string(ig) + "_" + std::to_string(ieAdj);
          auto eSum = getObjectPtr<TH2F>(name);
          TH1D* hEnergy = (TH1D*)eSum->ProjectionY("hEnergy", nOpt, nOpt);
          TString newName = name + "_" + std::to_string(nOpt);
          hEnergy->SetName(newName);
 
          B2INFO("fit adj ig = " << ig << " ie = " << ie << " ia = " << ia << " " << newName << " " << " entries = " << hEnergy->GetEntries()
                 << " GetMaxiumum = " << hEnergy->GetMaximum());
 
          //..Rebin if the maximum bin has too few entries
          const double minPeakValue = 300.;
          while (hEnergy->GetMaximum() < minPeakValue) {hEnergy->Rebin(2);}
 
          //..Find 50% range for Novo fit. Rebin if there are too many bins
          fitFraction = 0.5;
          int iLo = 1;
          int iHi = hEnergy->GetNbinsX();
          const int maxBinsToFit = 59;
          while ((iHi - iLo) > maxBinsToFit) {
            std::vector<int> iBins = eclNOptimalFitRange(hEnergy, fitFraction);
            iLo = iBins[0];
            iHi = iBins[1];
            if ((iHi - iLo) > maxBinsToFit) {hEnergy->Rebin(2);}
          }
 
          //..Fit parameters
          const double fitlow = hEnergy->GetBinLowEdge(iLo);
          const double fithigh = hEnergy->GetBinLowEdge(iHi + 1);
          double normalization = hEnergy->GetMaximum();
          const int iMax = hEnergy->GetMaximumBin();
          double peak = hEnergy->GetBinLowEdge(iMax);
          double effSigma = 0.5 * (fithigh - fitlow);
          double eta = 0.4; // typical value for good fits
 
          func->SetParameters(normalization, peak, effSigma, eta);
          func->SetParNames("normalization", "peak", "effSigma", "eta");
 
          //..Parameter ranges
          func->SetParLimits(1, fitlow, fithigh);
          func->SetParLimits(2, 0., 2.*effSigma);
          func->SetParLimits(3, -1, 3);
 
          //..If there is only one crystal, fix eta to a special value
          if (nOpt == 1) {
            const double etaForOneCrystal = 0.95;
            func->SetParameter(3, etaForOneCrystal);
            func->SetParLimits(3, etaForOneCrystal, etaForOneCrystal);
          }
 
          //..Fit
          hEnergy->Fit(func, "LIEQ", "", fitlow, fithigh);
 
          //..Fraction of contained energy = peak of fit corrected for bias
          const double peakAdj = func->GetParameter(1);
          const double genE = generatedEPerGroup->GetBinContent(ig + 1, ieAdj + 1);
          eFracPeakAdj = peakAdj - biasAdj / genE;
        }
 
        //..Store
        fracContainedAdjacent[ia]->SetBinContent(ig + 1, ie + 1, eFracPeakAdj);
        biasAdjacent[ia]->SetBinContent(ig + 1, ie + 1, biasAdj);
      } // loop over adjacent energy points
 
      B2INFO(" ig " << ig << " ie " << ie << " eFrac (3): "
             << fracContainedAdjacent[0]->GetBinContent(ig + 1, ie + 1) << " "
             << fracContainedEPerGroup->GetBinContent(ig + 1, ie + 1) << " "
             << fracContainedAdjacent[1]->GetBinContent(ig + 1, ie + 1)
             << " biases (3): "
             << biasAdjacent[0]->GetBinContent(ig + 1, ie + 1) << " "
             << biasPerGroup->GetBinContent(ig + 1, ie + 1) << " "
             << biasAdjacent[1]->GetBinContent(ig + 1, ie + 1)
            );
    } // loop over ie
  } // loop over ig
 
  //-----------------------------------------------------------------------------------
  //..Write out histograms with results for adjacent energy points
  histFile->cd();
  fracContainedAdjacent[0]->Write();
  fracContainedAdjacent[1]->Write();
  biasAdjacent[0]->Write();
  biasAdjacent[1]->Write();
 
  B2INFO("Wrote adjacent energy point summary histograms");
 
 
  //-----------------------------------------------------------------------------------
  //-----------------------------------------------------------------------------------
  //..Prepare the payload contents
  //..Upper energy boundaries are the mid-point of the log energies
  auto  boundaryE = create2Dvector<float>(nLeakReg, nEnergies);
  for (int ireg = 0; ireg < nLeakReg; ireg++) {
    B2INFO("Generated energies and boundaries for region = " << ireg);
    for (int ie = 0; ie < nEnergies - 1; ie++) {
      double logE0 = log(generatedE[ireg][ie]);
      double logE1 = log(generatedE[ireg][ie + 1]);
      double logBoundary = 0.5 * (logE0 + logE1);
      boundaryE[ireg][ie] = exp(logBoundary);
      B2INFO("  " << ie << " " << generatedE[ireg][ie] <<  " " << boundaryE[ireg][ie] << " GeV");
    }
 
    //..Last boundary is an arbitrarily large number
    boundaryE[ireg][nEnergies - 1] = 9999.;
    B2INFO("  " << nEnergies - 1 << " " << generatedE[ireg][nEnergies - 1] <<  " " << boundaryE[ireg][nEnergies - 1] << " GeV");
  }
 
  //..Group number of each cellID
  std::vector<int> groupNumber;
  for (int cellID = 1; cellID <= ECLElementNumbers::c_NCrystals; cellID++) {
    const int iGroup = (int)(0.5 + groupNumberOfEachCellID->GetBinContent(cellID));
    groupNumber.push_back(iGroup);
  }
 
  //..Copy results for each group into the payload histogram
  TH2F nOptimal2D("nOptimal2D", "Optimal nCrys, energy bin vs group number", nCrystalGroups, 0, nCrystalGroups, nEnergies, 0.,
                  nEnergies);
  for (int ig = 0; ig < nCrystalGroups; ig++) {
    for (int ie = 0; ie < nEnergies; ie++) {
      double nOpt = nOptimalPerGroup->GetBinContent(ig + 1, ie + 1);
      nOptimal2D.SetBinContent(ig + 1, ie + 1, nOpt);
    }
  }
 
  //..2D histograms for peak and bias corrections.
  //  Note that these have 3 x bins per group = nominal energy plus adjacent energies.
  TH2F peakFracEnergy("peakFracEnergy", "peak contained energy over generated E, energy bin vs group number", 3 * nCrystalGroups, 0,
                      nCrystalGroups, nEnergies, 0., nEnergies);
  TH2F bias("bias", "median bias (GeV), energy bin vs group number", 3 * nCrystalGroups, 0, nCrystalGroups, nEnergies, 0., nEnergies);
  TH2F logPeakEnergy("logPeakEnergy", "log peak Energy (GeV), energy bin vs group number", 3 * nCrystalGroups, 0, nCrystalGroups,
                     nEnergies, 0., nEnergies);
  for (int ig = 0; ig < nCrystalGroups; ig++) {
    for (int ie = 0; ie < nEnergies; ie++) {
      const int iy = ie + 1;
      const int ix = 1 + 3 * ig; // three bins per group in payload histograms
 
      //..Peak fractional energy and log peak contained energy (GeV)
 
      //..Nominal ig and and ie
      double peakAdj =  fracContainedEPerGroup->GetBinContent(ig + 1, ie + 1);
      const double genE = generatedEPerGroup->GetBinContent(ig + 1, ie + 1);
      double logE = log(genE * peakAdj);
 
      peakFracEnergy.SetBinContent(ix, iy, peakAdj);
      logPeakEnergy.SetBinContent(ix, iy, logE);
 
      //..Lower E adjacent point
      double peakAdj0 = peakAdj;
      double logE0 = logE;
      if (ie > 1) {
        peakAdj0 = fracContainedAdjacent[0]->GetBinContent(ig + 1, ie + 1);
        const double genE0 = generatedEPerGroup->GetBinContent(ig + 1, ie);
        logE0 = log(genE0 * peakAdj0);
      }
 
      peakFracEnergy.SetBinContent(ix + 1, iy, peakAdj0);
      logPeakEnergy.SetBinContent(ix + 1, iy, logE0);
 
      //..Higher E adjacent point
      double peakAdj1 = peakAdj;
      double logE1 = logE;
      if (ie < nEnergies - 1) {
        peakAdj1 = fracContainedAdjacent[1]->GetBinContent(ig + 1, ie + 1);
        const double genE1 = generatedEPerGroup->GetBinContent(ig + 1, ie + 2);
        logE1 = log(genE1 * peakAdj1);
      }
 
      peakFracEnergy.SetBinContent(ix + 2, iy, peakAdj1);
      logPeakEnergy.SetBinContent(ix + 2, iy, logE1);
 
      //..Bias
      double medianBias = biasPerGroup->GetBinContent(ig + 1, ie + 1);
      bias.SetBinContent(ix, iy, medianBias);
      medianBias = biasAdjacent[0]->GetBinContent(ig + 1, ie + 1);
      bias.SetBinContent(ix + 1, iy, medianBias);
      medianBias = biasAdjacent[1]->GetBinContent(ig + 1, ie + 1);
      bias.SetBinContent(ix + 2, iy, medianBias);
    }
  }
 
  //..Store the histograms
  histFile->cd();
  nOptimal2D.Write();
  peakFracEnergy.Write();
  logPeakEnergy.Write();
  bias.Write();
  histFile->Close();
 
  //-----------------------------------------------------------------------------------
  //..The payload itself
  ECLnOptimal* optimalNCrys = new ECLnOptimal();
 
  //..Energy boundaries
  for (int ireg = 0; ireg < nLeakReg; ireg++) {
    std::vector<float> eUpperBoundaries;
    for (int ie = 0; ie < nEnergies; ie++) {eUpperBoundaries.push_back(boundaryE[ireg][ie]);}
    if (ireg == 0) {
      optimalNCrys->setUpperBoundariesFwd(eUpperBoundaries);
    } else if (ireg == 1) {
      optimalNCrys->setUpperBoundariesBrl(eUpperBoundaries);
    } else {
      optimalNCrys->setUpperBoundariesBwd(eUpperBoundaries);
    }
  }
 
  //..Group number of each cellID
  optimalNCrys->setGroupNumber(groupNumber);
 
  //..nOptimal histogram
  optimalNCrys->setNOptimal(nOptimal2D);
 
  //..Peak and bias correction histograms
  optimalNCrys->setPeakFracEnergy(peakFracEnergy);
  optimalNCrys->setBias(bias);
  optimalNCrys->setLogPeakEnergy(logPeakEnergy);
 
  //..Save the calibration
  saveCalibration(optimalNCrys, "ECLnOptimal");
  B2RESULT("eclNOptimalAlgorithm: successfully stored payload ECLnOptimal");
 
  //..Done
  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(); }

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();
}

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_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.

---

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

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