eclBhabhaTAlgorithm Class Reference

Calibrate ecl crystals using bhabha events. More...

#include <eclBhabhaTAlgorithm.h>

Inheritance diagram for eclBhabhaTAlgorithm:

Collaboration diagram for eclBhabhaTAlgorithm:

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

Public Member Functions
	eclBhabhaTAlgorithm ()
	..Constructor
virtual	~eclBhabhaTAlgorithm ()
	..Destructor
std::string	getPrefix () const
	Get the prefix used for getting calibration data.
bool	checkPyExpRun (PyObject *pyObj)
	Checks that a PyObject can be successfully converted to an ExpRun type. More...
Calibration::ExpRun	convertPyExpRun (PyObject *pyObj)
	Performs the conversion of PyObject to ExpRun. More...
std::string	getCollectorName () const
	Alias for prefix. More...
void	setPrefix (const std::string &prefix)
	Set the prefix used to identify datastore objects.
void	setInputFileNames (PyObject *inputFileNames)
	Set the input file names used for this algorithm from a Python list. More...
PyObject *	getInputFileNames ()
	Get the input file names used for this algorithm and pass them out as a Python list of unicode strings.
std::vector< Calibration::ExpRun >	getRunListFromAllData () const
	Get the complete list of runs from inspection of collected data.
RunRange	getRunRangeFromAllData () const
	Get the complete RunRange from inspection of collected data.
IntervalOfValidity	getIovFromAllData () const
	Get the complete IoV from inspection of collected data.
void	fillRunToInputFilesMap ()
	Fill the mapping of ExpRun -> Files.
std::string	getGranularity () const
	Get the granularity of collected data.
EResult	execute (std::vector< Calibration::ExpRun > runs={}, int iteration=0, IntervalOfValidity iov=IntervalOfValidity())
	Runs calibration over vector of runs for a given iteration. More...
EResult	execute (PyObject *runs, int iteration=0, IntervalOfValidity iov=IntervalOfValidity())
	Runs calibration over Python list of runs. Converts to C++ and then calls the other execute() function.
std::list< Database::DBImportQuery > &	getPayloads ()
	Get constants (in TObjects) for database update from last execution.
std::list< Database::DBImportQuery >	getPayloadValues ()
	Get constants (in TObjects) for database update from last execution but passed by VALUE.
bool	commit ()
	Submit constants from last calibration into database.
bool	commit (std::list< Database::DBImportQuery > payloads)
	Submit constants from a (potentially previous) set of payloads.
const std::string &	getDescription () const
	Get the description of the algoithm (set by developers in constructor)
bool	loadInputJson (const std::string &jsonString)
	Load the m_inputJson variable from a string (useful from Python interface). The rturn bool indicates success or failure.
const std::string	dumpOutputJson () const
	Dump the JSON string of the output JSON object.
const std::vector< Calibration::ExpRun >	findPayloadBoundaries (std::vector< Calibration::ExpRun > runs, int iteration=0)
	Used to discover the ExpRun boundaries that you want the Python CAF to execute on. This is optional and only used in some.
template<>
std::shared_ptr< TTree >	getObjectPtr (const std::string &name, const std::vector< Calibration::ExpRun > &requestedRuns)
	Specialization of getObjectPtr<TTree>.

Public Attributes
int	cellIDLo
	Fit crystals with cellID0 in the inclusive range [cellIDLo,cellIDHi].
int	cellIDHi
	Fit crystals with cellID0 in the inclusive range [cellIDLo,cellIDHi].
double	meanCleanRebinFactor
	Rebinning factor for mean calculation.
double	meanCleanCutMinFactor
	After rebinning, create a mask for bins that have values less than meanCleanCutMinFactor times the maximum bin value. More...
int	crateIDLo
	Fit crates with crateID0 in the inclusive range [crateIDLo,crateIDHi].
int	crateIDHi
	Fit crates with crateID0 in the inclusive range [crateIDLo,crateIDHi].
bool	debugOutput
	Save every histogram and fitted function to debugFilename.
std::string	debugFilenameBase
	Name of file with debug output, eclBhabhaTAlgorithm.root by default.
std::string	collectorName
	Name of the collector.

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

Protected Attributes
std::vector< Calibration::ExpRun >	m_boundaries

Private Member Functions
std::string	getExpRunString (Calibration::ExpRun &expRun) const
	Gets the "exp.run" string repr. of (exp,run)
std::string	getFullObjectPath (std::string name, Calibration::ExpRun expRun) const
	constructs the full TDirectory + Key name of an object in a TFile based on its name and exprun

Private Attributes
std::vector< std::string >	m_inputFileNames
	List of input files to the Algorithm, will initially be user defined but then gets the wildcards expanded during execute()
std::map< Calibration::ExpRun, std::vector< std::string > >	m_runsToInputFiles
	Map of Runs to input files. Gets filled when you call getRunRangeFromAllData, gets cleared when setting input files again.
std::string	m_granularityOfData
	Granularity of input data. This only changes when the input files change so it isn't specific to an execution.
ExecutionData	m_data
	Data specific to a SINGLE execution of the algorithm. Gets reset at the beginning of execution.
std::string	m_description {""}
	Description of the algorithm.
std::string	m_prefix {""}
	The name of the TDirectory the collector objects are contained within.
nlohmann::json	m_jsonExecutionInput = nlohmann::json::object()
	Optional input JSON object used to make decisions about how to execute the algorithm code.
nlohmann::json	m_jsonExecutionOutput = nlohmann::json::object()
	Optional output JSON object that can be set during the execution by the underlying algorithm code.

Static Private Attributes
static const Calibration::ExpRun	m_allExpRun = make_pair(-1, -1)

Detailed Description

Calibrate ecl crystals using bhabha events.

Definition at line 39 of file eclBhabhaTAlgorithm.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 50 of file CalibrationAlgorithm.h.

Member Function Documentation

calibrate()

CalibrationAlgorithm::EResult calibrate ( )

protectedvirtual

..Run algorithm on events

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

Write out job parameters

---

Implements CalibrationAlgorithm.

Definition at line 36 of file eclBhabhaTAlgorithm.cc.

{
  gROOT->SetBatch();
 
 
  B2INFO("eclBhabhaTAlgorithm parameters:");
  B2INFO("cellIDLo = " << cellIDLo);
  B2INFO("cellIDHi = " << cellIDHi);
  B2INFO("meanCleanRebinFactor = " << meanCleanRebinFactor);
  B2INFO("meanCleanCutMinFactor = " << meanCleanCutMinFactor);
  B2INFO("crateIDLo = " << crateIDLo);
  B2INFO("crateIDHi = " << crateIDHi);
 
 
  /* Histogram with the data collected by eclBhabhaTCollectorModule */
 
  auto TimevsCrysPrevCrateCalibPrevCrystCalib = getObjectPtr<TH2F>("TimevsCrysPrevCrateCalibPrevCrystCalib");
  auto TimevsCratePrevCrateCalibPrevCrystCalib = getObjectPtr<TH2F>("TimevsCratePrevCrateCalibPrevCrystCalib");
  auto TimevsCrysNoCalibrations = getObjectPtr<TH2F>("TimevsCrysNoCalibrations");
  auto TimevsCrysPrevCrateCalibNoCrystCalib = getObjectPtr<TH2F>("TimevsCrysPrevCrateCalibNoCrystCalib");
  auto TimevsCrateNoCrateCalibPrevCrystCalib = getObjectPtr<TH2F>("TimevsCrateNoCrateCalibPrevCrystCalib");
 
  // Collect other plots just for reference - combines all the runs for these plots.
  auto cutflow = getObjectPtr<TH1F>("cutflow");
 
 
  if (!TimevsCrysNoCalibrations) return c_Failure;
 
  // Make tool for mapping ecl crystal to other ecl objects
  //    e.g. crates, shapers, etc.
  //    Need to load information about the event/run/experiment to get the right database information
  int expNumberForCrates = -1;
  int runNumberForCrates = -1;
  int eventNumberForCrates = 1;
  for (auto expRun : getRunList()) {
    expNumberForCrates = expRun.first;
    runNumberForCrates = expRun.second;
  }
  updateDBObjPtrs(eventNumberForCrates, runNumberForCrates, expNumberForCrates);
  unique_ptr<ECLChannelMapper> crystalMapper(new ECL::ECLChannelMapper());
  crystalMapper->initFromDB();
 
 
  TFile* histfile = 0;
  unique_ptr<TTree> tree_crystal(new TTree("tree_crystal", "Debug data from bhabha time calibration algorithm for crystals"));
 
  unique_ptr<TTree>   tree_crate(new TTree("tree_crate",   "Debug data from bhabha time calibration algorithm for crates"));
  int tree_cid;
 
  // Vector of time offsets to be saved in the database.
  vector<float> t_offsets;
  // Vector of time offset uncertainties to be saved in the database.
  vector<float> t_offsets_unc;
 
  vector<float> t_offsets_prev;  // previous time offsets
 
  // temp copies of offsets
  vector<float> t_offsets_temp;
  vector<float> t_offsets_unc_temp;
 
 
  int minNumEntries = 40;
 
 
  double mean = 0;
  double sigma = -1;
  double mean_unc = 0;
  int crystalCalibSaved = 0;
  double tsPrev = 0;
 
 
  bool minRunNumBool = false;
  bool maxRunNumBool = false;
  int minRunNum = -1;
  int maxRunNum = -1;
  int minExpNum = -1;
  int maxExpNum = -1;
  for (auto expRun : getRunList()) {
    int expNumber = expRun.first;
    int runNumber = expRun.second;
    if (!minRunNumBool) {
      minExpNum = expNumber;
      minRunNum = runNumber;
      minRunNumBool = true;
    }
    if (!maxRunNumBool) {
      maxExpNum = expNumber;
      maxRunNum = runNumber;
      maxRunNumBool = true;
    }
    if (((minRunNum > runNumber)   && (minExpNum >= expNumber))  ||
        (minExpNum > expNumber)) {
      minExpNum = expNumber;
      minRunNum = runNumber;
    }
    if (((maxRunNum < runNumber)   && (maxExpNum <= expNumber))  ||
        (maxExpNum < expNumber))
 
    {
      maxExpNum = expNumber;
      maxRunNum = runNumber;
    }
  }
 
  B2INFO("debugFilenameBase = " << debugFilenameBase);
  string runNumsString = string("_") + to_string(minExpNum) + "_" + to_string(minRunNum) + string("-") + to_string(
                           maxExpNum) + "_" + to_string(maxRunNum);
  string debugFilename = debugFilenameBase + runNumsString + string(".root");
 
 
  B2INFO("Debug output rootfile: " << debugFilename);
  histfile = new TFile(debugFilename.c_str(), "recreate");
 
 
  TimevsCrysPrevCrateCalibPrevCrystCalib ->Write();
  TimevsCratePrevCrateCalibPrevCrystCalib->Write();
  TimevsCrysNoCalibrations               ->Write();
  TimevsCrysPrevCrateCalibNoCrystCalib   ->Write();
  TimevsCrateNoCrateCalibPrevCrystCalib  ->Write();
 
  cutflow->Write();
 
 
  if (debugOutput) {
    tree_crystal->Branch("cid", &tree_cid)->SetTitle("Cell ID, 1..8736");
    tree_crystal->Branch("ts", &mean)->SetTitle("Time offset mean, ts, ns");
    tree_crystal->Branch("tsUnc", &mean_unc)->SetTitle("Error of time ts mean, ns.");
    tree_crystal->Branch("tsSigma", &sigma)->SetTitle("Sigma of time ts distribution, ns");
    tree_crystal->Branch("crystalCalibSaved",
                         &crystalCalibSaved)->SetTitle("0=crystal skipped, 1=crystal calib saved (num entries based)");
    tree_crystal->Branch("tsPrev", &tsPrev)->SetTitle("Previous crystal time offset, ts, ns");
    tree_crystal->SetAutoSave(10);
  }
 
 
  double hist_tmin = TimevsCrysNoCalibrations->GetYaxis()->GetXmin();
  double hist_tmax = TimevsCrysNoCalibrations->GetYaxis()->GetXmax();
 
  double time_fit_min = hist_tmax;   // Set min value to largest possible value so that it gets reset
  double time_fit_max = hist_tmin;   // Set max value to smallest possible value so that it gets reset
 
  B2INFO("hist_tmin = " << hist_tmin);
  B2INFO("hist_tmax = " << hist_tmax);
 
 
  const double TICKS_TO_NS = 1.0 / (4.0 * EclConfiguration::m_rf) *
                             1e3;  // 1/(4fRF) = 0.4913 ns/clock tick, where fRF is the accelerator RF frequency, fRF=508.889 MHz. Same for all crystals.  Proper accurate value
 
  // The ts and tcrate database values are filled once per tcol instance so count the number of times that the database values
  //    were summed together by the histogram merging process and extract out the original values again.
  auto databaseCounter = getObjectPtr<TH1F>("databaseCounter");
  float numTimesFilled = databaseCounter->GetBinContent(1);
  B2INFO("Number of times database histograms were merged = " << numTimesFilled);
 
 
  auto TsDatabase = getObjectPtr<TH1F>("TsDatabase");
  auto TsDatabaseUnc = getObjectPtr<TH1F>("TsDatabaseUnc");
  for (int i = 1; i <= 8736; i++) {
    t_offsets.push_back(TsDatabase->GetBinContent(i - 1) / numTimesFilled);
    t_offsets_prev.push_back(TsDatabase->GetBinContent(i) / numTimesFilled);
 
    B2INFO(" TsDatabase->GetBinContent(i) = " << TsDatabase->GetBinContent(i));
    B2INFO("t_offsets_prev = " << t_offsets_prev[i - 1]);
 
 
    t_offsets_unc.push_back(TsDatabaseUnc->GetBinContent(i - 1) / numTimesFilled);
  }
 
 
  for (int crys_id = cellIDLo; crys_id <= cellIDHi; crys_id++) {
    mean = 0;
    mean_unc = -1;
    sigma = -1;
    crystalCalibSaved = 0;
 
    double database_mean = 0;
    double database_mean_unc = 0;
 
    B2INFO("Crystal id = " << crys_id);
 
 
    vector<bool> ts_new_was_set(8736, false);
 
 
    /* Determining which bins to mask out for mean calculation
    */
 
    TH1D* h_time = TimevsCrysPrevCrateCalibNoCrystCalib->ProjectionY("h_time_psi", crys_id, crys_id);
    TH1D* h_timeMask = (TH1D*)h_time->Clone();
    TH1D* h_timeMasked = (TH1D*)h_time->Clone();
    TH1D* h_timeRebin = (TH1D*)h_time->Clone();
 
    // Do rebinning and cleaning of some bins but only if the user selection values call for it since it slows the code down
    if (meanCleanRebinFactor != 1 || meanCleanCutMinFactor != 1) {
 
      h_timeRebin->Rebin(meanCleanRebinFactor);
 
      h_timeMask->Scale(0.0);   // set all bins to being masked off
 
      time_fit_min = hist_tmax;   // Set min value to largest possible value so that it gets reset
      time_fit_max = hist_tmin;   // Set max value to smallest possible value so that it gets reset
 
      // Find value of bin with max value
      double histRebin_max = h_timeRebin->GetMaximum();
 
      bool maskedOutNonZeroBin = false;
      // Loop over all bins to find those with content less than a certain threshold.  Mask the non-rebinned histogram for the corresponding bins
      for (int bin = 1; bin <= h_timeRebin->GetNbinsX(); bin++) {
        for (int rebinCounter = 1; rebinCounter <= meanCleanRebinFactor; rebinCounter++) {
          int nonRebinnedBinNumber = (bin - 1) * meanCleanRebinFactor + rebinCounter;
          if (nonRebinnedBinNumber < h_time->GetNbinsX()) {
            if (h_timeRebin->GetBinContent(bin) >= histRebin_max * meanCleanCutMinFactor) {
              h_timeMask->SetBinContent(nonRebinnedBinNumber, 1);
 
              // Save the lower and upper edges of the rebin histogram time range for fitting purposes
              double x_lower = h_timeRebin->GetXaxis()->GetBinLowEdge(bin);
              double x_upper = h_timeRebin->GetXaxis()->GetBinUpEdge(bin);
              if (x_lower < time_fit_min) {
                time_fit_min = x_lower;
              }
              if (x_upper > time_fit_max) {
                time_fit_max = x_upper;
              }
 
            } else {
              if (h_time->GetBinContent(nonRebinnedBinNumber) > 0) {
                B2INFO("Setting bin " << nonRebinnedBinNumber << " from " << h_timeMasked->GetBinContent(nonRebinnedBinNumber) << " to 0");
                maskedOutNonZeroBin = true;
              }
              h_timeMasked->SetBinContent(nonRebinnedBinNumber, 0);
            }
          }
        }
      }
      B2INFO("Bins with non-zero values have been masked out: " << maskedOutNonZeroBin);
      h_timeMasked->ResetStats();
      h_timeMask->ResetStats();
 
    }
 
    // Calculate mean from masked histogram
    double default_meanMasked = h_timeMasked->GetMean();
    //double default_meanMasked_unc = h_timeMasked->GetMeanError();
    B2INFO("default_meanMasked = " << default_meanMasked);
 
 
    // Get the overall mean and standard deviation of the distribution within the plot.  This doesn't require a fit.
    double default_mean = h_time->GetMean();
    double default_mean_unc = h_time->GetMeanError();
    double default_sigma = h_time->GetStdDev();
 
    B2INFO("Fitting crystal between " << time_fit_min << " and " << time_fit_max);
 
    // gaus(0) is a substitute for [0]*exp(-0.5*((x-[1])/[2])**2)
    TF1* gaus = new TF1("func", "gaus(0)", time_fit_min, time_fit_max);
    gaus->SetParNames("numCrystalHitsNormalization", "mean", "sigma");
    /*
       gaus->ReleaseParameter(0);  // number of crystals
       gaus->ReleaseParameter(1);  // mean
       gaus->ReleaseParameter(2);  // standard deviation
    */
 
    double hist_max = h_time->GetMaximum();
 
    //=== Estimate initial value of sigma as std dev.
    double stddev = h_time->GetStdDev();
    sigma = stddev;
    mean = default_mean;
 
    //=== Setting parameters for initial iteration
    gaus->SetParameter(0, hist_max / 2.);
    gaus->SetParameter(1, mean);
    gaus->SetParameter(2, sigma);
    // L -- Use log likelihood method
    // I -- Use integral of function in bin instead of value at bin center  // not using
    // R -- Use the range specified in the function range
    // B -- Fix one or more parameters with predefined function   // not using
    // Q -- Quiet mode
 
    h_timeMasked->Fit(gaus, "LQR");  // L for likelihood, R for x-range, Q for fit quiet mode
 
    double fit_mean     = gaus->GetParameter(1);
    double fit_mean_unc = gaus->GetParError(1);
    double fit_sigma    = gaus->GetParameter(2);
 
    double meanDiff =  fit_mean - default_mean;
    double meanUncDiff = fit_mean_unc - default_mean_unc;
    double sigmaDiff = fit_sigma - default_sigma;
 
    bool good_fit = false;
 
    if ((fabs(meanDiff) > 10)      ||
        (fabs(meanUncDiff) > 10)   ||
        (fabs(sigmaDiff) > 10)     ||
        (fit_mean_unc > 3)         ||
        (fit_sigma < 0.1)          ||
        (fit_mean < time_fit_min)  ||
        (fit_mean > time_fit_max)) {
      B2INFO("Crystal id = " << crys_id);
      B2INFO("fit mean, default mean = " << fit_mean << ", " << default_mean);
      B2INFO("fit mean unc, default mean unc = " << fit_mean_unc << ", " << default_mean_unc);
      B2INFO("fit sigma, default sigma = " << fit_sigma << ", " << default_sigma);
 
      B2INFO("crystal fit mean - hist mean = " << meanDiff);
      B2INFO("fit mean unc. - hist mean unc. = " << meanUncDiff);
      B2INFO("fit sigma - hist sigma = " << sigmaDiff);
 
      B2INFO("fit_mean = " << fit_mean);
      B2INFO("time_fit_min = " << time_fit_min);
      B2INFO("time_fit_max = " << time_fit_max);
 
      if (fabs(meanDiff) > 10)       B2INFO("fit mean diff too large");
      if (fabs(meanUncDiff) > 10)    B2INFO("fit mean unc diff too large");
      if (fabs(sigmaDiff) > 10)      B2INFO("fit mean sigma diff too large");
      if (fit_mean_unc > 3)          B2INFO("fit mean unc too large");
      if (fit_sigma < 0.1)           B2INFO("fit sigma too small");
 
    } else {
      good_fit = true;
    }
 
 
 
    // Set the tree_crystal values - ignore fit values  !!!!!!!!!!!!!!!!
    sigma = default_sigma;
 
 
    int numEntries = h_time->GetEntries();
    // If number of entries in histogram is greater than X then use the statistical information from the data otherwise leave crystal uncalibrated.  Histograms are still shown though.
    //  ALSO require the that fits are good.
    if ((numEntries >= minNumEntries)  &&  good_fit) {
      crystalCalibSaved = 1;
      database_mean = fit_mean;
      database_mean_unc = fit_mean_unc;
    } else {
      database_mean = default_mean;
      database_mean_unc = default_mean_unc;
    }
 
    if (numEntries < minNumEntries)   B2INFO("Number of entries less than minimum");
    if (numEntries == 0)   B2INFO("Number of entries == 0");
 
 
    tree_cid  = crys_id;
 
    // For the database, convert back from ns to ADC ticks.
    t_offsets[crys_id - 1] = database_mean / TICKS_TO_NS;
    t_offsets_unc[crys_id - 1] = database_mean_unc / TICKS_TO_NS;
 
 
    histfile->WriteTObject(h_time, (std::string("h_time_psi") + std::to_string(crys_id)).c_str());
    histfile->WriteTObject(h_timeMasked, (std::string("h_time_psi_masked") + std::to_string(crys_id)).c_str());
 
    mean = database_mean;
    mean_unc = database_mean_unc;
 
    tsPrev = t_offsets_prev[crys_id - 1] * TICKS_TO_NS;
 
    delete gaus;
    tree_crystal->Fill();
  }
 
  ECLCrystalCalib* BhabhaTCalib = new ECLCrystalCalib();
  BhabhaTCalib->setCalibVector(t_offsets, t_offsets_unc);
 
 
  // Save the information to the payload if there is at least one crystal
  //    begin calibrated.
  if (cellIDLo <= cellIDHi) {
    saveCalibration(BhabhaTCalib, "ECLCrystalTimeOffset");
    B2DEBUG(30, "crystal payload made");
  }
 
 
  B2DEBUG(30, "end of crystal start of crate corrections .....");
 
 
  /* CRATE CORRECTIONS */
 
  hist_tmin = TimevsCrateNoCrateCalibPrevCrystCalib->GetYaxis()->GetXmin();
  hist_tmax = TimevsCrateNoCrateCalibPrevCrystCalib->GetYaxis()->GetXmax();
 
  B2DEBUG(30, "Found min/max of X axis of TimevsCrateNoCrateCalibPrevCrystCalib");
 
  // Vector of time offsets to be saved in the database.
 
  auto TcrateDatabase = getObjectPtr<TH1F>("TcrateDatabase");
 
 
  B2DEBUG(30, "Retrieved Ts and Tcrate histograms from tcol root file");
 
  double database_mean_crate = 0;
  double database_mean_crate_unc = 0;
 
  double mean_crate = 0;
  double sigma_crate = -1;
 
  vector<float> tcrate_mean_new(52, 0.0);
  vector<float> tcrate_mean_unc_new(52, 0.0);
  vector<float> tcrate_sigma_new(52, 0.0);
  vector<float> tcrate_mean_prev(52, 0.0);
  vector<float> tcrate_sigma_prev(52, 0.0);
  vector<bool> tcrate_new_was_set(52, false);
 
  B2DEBUG(30, "crate vectors set");
 
 
 
  // Crate time calibration constants are saved per crystal so read them per crystal
  //    and save as one entry per crate in the array
  for (int crys_id = 1; crys_id <= 8736; crys_id++) {
    int crate_id_from_crystal = crystalMapper->getCrateID(crys_id);
    tcrate_mean_prev[crate_id_from_crystal - 1] = TcrateDatabase->GetBinContent(crys_id) / numTimesFilled;
  }
 
  for (int crate_id = crateIDLo; crate_id <= crateIDHi; crate_id++) {
 
    B2DEBUG(30, "Start of crate id = " << crate_id);
 
    TH1D* h_time_crate = TimevsCrateNoCrateCalibPrevCrystCalib->ProjectionY("h_time_psi_crate", crate_id, crate_id);
    TH1D* h_time_crate_mask = (TH1D*)h_time_crate->Clone();
    TH1D* h_time_crate_masked = (TH1D*)h_time_crate->Clone();
    TH1D* h_time_crate_rebin = (TH1D*)h_time_crate->Clone();
 
 
 
    // Do rebinning and cleaning of some bins but only if the user selection values call for it since it slows the code down
    if (meanCleanRebinFactor != 1 || meanCleanCutMinFactor != 1) {
 
      h_time_crate_rebin->Rebin(meanCleanRebinFactor);
      h_time_crate_mask->Scale(0.0);   // set all bins to being masked off
 
      time_fit_min = hist_tmax;   // Set min value to largest possible value so that it gets reset
      time_fit_max = hist_tmin;   // Set max value to smallest possible value so that it gets reset
 
      // Find value of bin with max value
      double histRebin_max = h_time_crate_rebin->GetMaximum();
 
      bool maskedOutNonZeroBin = false;
      // Loop over all bins to find those with content less than a certain threshold.  Mask the non-rebinned histogram for the corresponding bins
      for (int bin = 1; bin <= h_time_crate_rebin->GetNbinsX(); bin++) {
        for (int rebinCounter = 1; rebinCounter <= meanCleanRebinFactor; rebinCounter++) {
          int nonRebinnedBinNumber = (bin - 1) * meanCleanRebinFactor + rebinCounter;
          if (nonRebinnedBinNumber < h_time_crate->GetNbinsX()) {
            if (h_time_crate_rebin->GetBinContent(bin) >= histRebin_max * meanCleanCutMinFactor) {
              h_time_crate_mask->SetBinContent(nonRebinnedBinNumber, 1);
 
              // Save the lower and upper edges of the rebin histogram time range for fitting purposes
              double x_lower = h_time_crate_rebin->GetXaxis()->GetBinLowEdge(bin);
              double x_upper = h_time_crate_rebin->GetXaxis()->GetBinUpEdge(bin);
              if (x_lower < time_fit_min) {
                time_fit_min = x_lower;
              }
              if (x_upper > time_fit_max) {
                time_fit_max = x_upper;
              }
            } else {
              if (h_time_crate->GetBinContent(nonRebinnedBinNumber) > 0) {
                B2INFO("Setting bin " << nonRebinnedBinNumber << " from " << h_time_crate_masked->GetBinContent(nonRebinnedBinNumber) << " to 0");
                maskedOutNonZeroBin = true;
              }
              h_time_crate_masked->SetBinContent(nonRebinnedBinNumber, 0);
            }
          }
        }
      }
      B2INFO("Bins with non-zero values have been masked out: " << maskedOutNonZeroBin);
      h_time_crate_masked->ResetStats();
      h_time_crate_mask->ResetStats();
 
    }
 
 
 
    B2DEBUG(30, "crate loop - projected h_time_psi_crate");
 
 
    double default_mean_crate = h_time_crate_masked->GetMean();
    double default_mean_crate_unc = h_time_crate_masked->GetMeanError();
    double default_sigma_crate = h_time_crate_masked->GetStdDev();
    B2INFO("Fitting crate between " << time_fit_min << " and " << time_fit_max);
    TF1* gaus = new TF1("func", "gaus(0)", time_fit_min, time_fit_max);
    gaus->SetParNames("numCrateHisNormalization", "mean", "sigma");
    double hist_max = h_time_crate->GetMaximum();
    double stddev = h_time_crate->GetStdDev();
    sigma_crate = stddev;
    mean_crate = default_mean_crate;
    gaus->SetParameter(0, hist_max / 2.);
    gaus->SetParameter(1, mean_crate);
    gaus->SetParameter(2, sigma_crate);
 
    h_time_crate_masked->Fit(gaus, "LQR");  // L for likelihood, R for x-range, Q for fit quiet mode
 
    double fit_mean_crate     = gaus->GetParameter(1);
    double fit_mean_crate_unc = gaus->GetParError(1);
    double fit_sigma_crate    = gaus->GetParameter(2);
 
    double meanDiff =  fit_mean_crate - default_mean_crate;
    double meanUncDiff = fit_mean_crate_unc - default_mean_crate_unc;
    double sigmaDiff = fit_sigma_crate - default_sigma_crate;
 
    bool good_fit = false;
 
    B2DEBUG(30, "Crate id = " << crate_id << " with crate mean = " << default_mean_crate << " +- " << fit_mean_crate_unc);
 
    if ((fabs(meanDiff) > 7)            ||
        (fabs(meanUncDiff) > 7)         ||
        (fabs(sigmaDiff) > 7)           ||
        (fit_mean_crate_unc > 3)        ||
        (fit_sigma_crate < 0.1)         ||
        (fit_mean_crate < time_fit_min) ||
        (fit_mean_crate > time_fit_max)) {
      B2INFO("Crate id = " << crate_id);
      B2INFO("fit mean, default mean = " << fit_mean_crate << ", " << default_mean_crate);
      B2INFO("fit sigma, default sigma = " << fit_sigma_crate << ", " << default_sigma_crate);
 
      B2INFO("crate fit mean - hist mean = " << meanDiff);
      B2INFO("fit mean unc. - hist mean unc. = " << meanUncDiff);
      B2INFO("fit sigma - hist sigma = " << sigmaDiff);
      B2INFO("fit_mean_crate = " << fit_mean_crate);
      B2INFO("time_fit_min = " << time_fit_min);
      B2INFO("time_fit_max = " << time_fit_max);
    } else {
      good_fit = true;
    }
 
    int numEntries = h_time_crate->GetEntries();
    B2INFO("Number entries = " << numEntries);
    if ((numEntries >= minNumEntries)  &&  good_fit) {
 
      database_mean_crate = fit_mean_crate;
      database_mean_crate_unc = fit_mean_crate_unc;
 
    } else {
      database_mean_crate = default_mean_crate;
      database_mean_crate_unc = default_mean_crate_unc;
    }
    if (numEntries == 0) {
      database_mean_crate = 0;
      database_mean_crate_unc = 0;
    }
 
    tcrate_mean_new[crate_id - 1] = database_mean_crate;
    tcrate_mean_unc_new[crate_id - 1] = database_mean_crate_unc;
    tcrate_sigma_new[crate_id - 1] = fit_sigma_crate;
    tcrate_new_was_set[crate_id - 1] = true;
 
 
    histfile->WriteTObject(h_time_crate, (std::string("h_time_psi_crate") + std::to_string(crate_id)).c_str());
    histfile->WriteTObject(h_time_crate_masked, (std::string("h_time_psi_crate_masked") + std::to_string(crate_id)).c_str());
    histfile->WriteTObject(h_time_crate_rebin, (std::string("h_time_psi_crate_rebinned") + std::to_string(crate_id)).c_str());
 
    delete gaus;
  }
 
  B2DEBUG(30, "crate histograms made");
 
 
  // Save database for crates
  // Vector of time offsets to be saved in the database.
  vector<float> t_offsets_crate;
  // Vector of time offset uncertainties to be saved in the database.
  vector<float> t_offsets_crate_unc;
  for (int i = 1; i <= 8736; i++) {
    t_offsets_crate.push_back(0);
    t_offsets_crate_unc.push_back(0);
  }
 
 
  for (int crys_id = 1; crys_id <= 8736; crys_id++) {
 
    int crate_id_from_crystal = crystalMapper->getCrateID(crys_id);
    if (tcrate_new_was_set[crate_id_from_crystal - 1]) {
      t_offsets_crate[crys_id - 1] = tcrate_mean_new[crate_id_from_crystal - 1] / TICKS_TO_NS;
      t_offsets_crate_unc[crys_id - 1] = tcrate_mean_unc_new[crate_id_from_crystal - 1] / TICKS_TO_NS;
 
    } else {
      t_offsets_crate[crys_id - 1] = tcrate_mean_prev[crate_id_from_crystal - 1];
      B2INFO("used old crate mean but zeroed uncertainty since not saved in root file");
    }
 
  }
 
  ECLCrystalCalib* BhabhaTCrateCalib = new ECLCrystalCalib();
  BhabhaTCrateCalib->setCalibVector(t_offsets_crate, t_offsets_crate_unc);
 
 
  // Save the information to the payload if there is at least one crate
  //    begin calibrated.
  if (crateIDLo <= crateIDHi) {
    saveCalibration(BhabhaTCrateCalib, "ECLCrateTimeOffset");
    B2DEBUG(30, "crate payload made");
  }
 
 
  int tree_crateid;
  int tree_runNum;
  double tree_tcrate_mean;
  double tree_tcrate_mean_unc;
  double tree_tcrate_sigma;
  double tree_tcrate_meanPrev;
 
  tree_crate->Branch("runNum", &tree_runNum)->SetTitle("Run number, 0..infinity and beyond!");
  tree_crate->Branch("crateid", &tree_crateid)->SetTitle("Crate id, 1..52");
  tree_crate->Branch("tcrate", &tree_tcrate_mean)->SetTitle("Crate time offset mean, tcrate, ns");
  tree_crate->Branch("tcratePrev", &tree_tcrate_meanPrev)->SetTitle("Previous crate time offset mean, tcrate, ns");
  tree_crate->Branch("tcrate_unc", &tree_tcrate_mean_unc)->SetTitle("Error of time tcrate mean, ns.");
  tree_crate->Branch("tcrate_sigma", &tree_tcrate_sigma)->SetTitle("Sigma of time tcrate distribution, ns");
  tree_crate->SetAutoSave(10);
 
 
  for (auto expRun : getRunList()) {
    // Key command to make sure your DBObjPtrs are correct
    B2INFO("run num, exp num:    " << expRun.second << ", " <<  expRun.first);
    int runNumber = expRun.second;
 
    for (int crate_id = 1; crate_id <= 52; crate_id++) {
      if (tcrate_new_was_set[crate_id - 1]) {
        tree_runNum = runNumber;
        tree_crateid = crate_id;
        tree_tcrate_mean = tcrate_mean_new[crate_id - 1];
        tree_tcrate_mean_unc = tcrate_mean_unc_new[crate_id - 1];
        tree_tcrate_sigma = tcrate_sigma_new[crate_id - 1];
        tree_tcrate_meanPrev = tcrate_mean_prev[crate_id - 1] * TICKS_TO_NS;
        tree_crate->Fill();
      }
    }
  }
 
  B2DEBUG(30, "end of crate corrections .....");
 
  tree_crystal->Write();
  tree_crate->Write();
 
  histfile->Close();
 
  tree_crystal = 0;
  tree_crate = 0;
 
  B2INFO("Finished talgorithm");
  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 21 of file CalibrationAlgorithm.cc.

convertPyExpRun()

ExpRun convertPyExpRun ( PyObject *  pyObj )

inherited

Performs the conversion of PyObject to ExpRun.

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

Definition at line 63 of file CalibrationAlgorithm.cc.

execute()

EResult execute ( std::vector< Calibration::ExpRun >  runs = {}, int  iteration = 0, IntervalOfValidity  iov = IntervalOfValidity()  )

inherited

Runs calibration over vector of runs for a given iteration.

You can also specify the IoV to save the database payload as. By default the Algorithm will create an IoV from your requested ExpRuns, or from the overall ExpRuns of the input data if you haven't specified ExpRuns in this function.

No checks are performed to make sure that a IoV you specify matches the data you ran over, it simply labels the IoV to commit to the database later.

getCollectorName()

std::string getCollectorName ( ) const

inlineinherited

Alias for prefix.

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

Definition at line 174 of file CalibrationAlgorithm.h.

setInputFileNames() [1/2]

void setInputFileNames ( PyObject *  inputFileNames )

inherited

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

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

Definition at line 159 of file CalibrationAlgorithm.cc.

setInputFileNames() [2/2]

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

protectedinherited

Set the input file names used for this algorithm.

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

Definition at line 187 of file CalibrationAlgorithm.cc.

Member Data Documentation

meanCleanCutMinFactor

double meanCleanCutMinFactor

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

Expand mask and apply to non-rebinned histogram.

Definition at line 53 of file eclBhabhaTAlgorithm.h.

---

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

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