TOPLocalCalFitter Class Reference

This module is the fitter for the CAF collector TOPLaserCalibratorCollector. More...

#include <TOPLocalCalFitter.h>

Inheritance diagram for TOPLocalCalFitter:

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

Public Member Functions
	TOPLocalCalFitter ()
	Constructor.
	~TOPLocalCalFitter () override
	Destructor.
void	setMinEntries (int minEntries)
	Sets the minimum number of entries to perform the calibration in one channel.
void	setOutputFileName (const std::string &output)
	Sets the name of the output root file.
void	setFitConstraintsFileName (const std::string &fitConstraints)
	Sets the name of the root file containing the laser MC time corrections and the fit constraints.
void	setTTSFileName (const std::string &TTSData)
	Sets the name of the root file containing the TTS parameters.
void	setFitMode (const std::string &fitterMode)
	Sets the fitter mode.
void	fitInAmpliduteBins (bool isFitInAmplitudeBins)
	Enables the fit amplitude bins.
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 algorithm (set by developers in constructor)
bool	loadInputJson (const std::string &jsonString)
	Load the m_inputJson variable from a string (useful from Python interface). The return bool indicates success or failure.
const std::string	dumpOutputJson () const
	Dump the JSON string of the output JSON object.
const std::vector< Calibration::ExpRun >	findPayloadBoundaries (std::vector< Calibration::ExpRun > runs, int iteration=0)
	Used to discover the ExpRun boundaries that you want the Python CAF to execute on. This is optional and only used in some.
template<>
std::shared_ptr< TTree >	getObjectPtr (const std::string &name, const std::vector< Calibration::ExpRun > &requestedRuns)
	Specialization of getObjectPtr<TTree>.

Protected Member Functions
void	setupOutputTreeAndFile ()
	prepares the output tree
void	loadMCInfoTrees ()
	loads the TTS parameters and the MC truth info
void	determineFitStatus ()
	determines if the constant obtained by the fit are good or not
void	fitChannel (short slot, short channel, TH1 *h)
	Fits the laser light on one channel.
void	fitChannel (short slot, short channel, TH1 *h, bool inBins, double frac)
	Fit the laser spectrum of a single channel with extra controls for amplitude-bin fits.
void	fitPulser (TH1 *, TH1 *)
	Fits the two pulsers.
void	calculateChannelT0 ()
	Calculates the commonT0 calibration after the fits have been done.
EResult	calibrate () override
	Runs the 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 internally during calibrate()
template<class T>
const T	getOutputJsonValue (const std::string &key) const
	Get a value using a key from the JSON output object, not sure why you would want to do this.
template<class T>
const T	getInputJsonValue (const std::string &key) const
	Get an input JSON value using a key. The normal exceptions are raised when the key doesn't exist.
const nlohmann::json &	getInputJsonObject () const
	Get the entire top level JSON object. We explicitly say this must be of object type so that we might pick.
bool	inputJsonKeyExists (const std::string &key) const
	Test for a key in the input JSON object.

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
short	rowOf (short slot, short ch) const noexcept
	Row index for (slot,channel), or -1 if out of bounds.
short	colOf (short slot, short ch) const noexcept
	Column index for (slot,channel), or -1 if out of bounds.
bool	areNeighbors (short slot, short a, short b, int drMax=1, int dcMax=1) const noexcept
	Return true if channels a and b are neighbors on the same slot in row/col space.
void	buildChannelMaps ()
	Build (row,col) lookup tables from the TTS tree; call after opening m_treeTTS.
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
int	m_minEntries = 50
	Minimum number of entries to perform the fit.
std::string	m_output = "laserFitResult.root"
	Name of the output file.
std::string	m_fitConstraints
	File with the Fit constraints.
std::string	m_TTSData
	File with the TTS parametrization.
std::string	m_fitterMode = "calibration"
	Fit mode.
bool	m_isFitInAmplitudeBins = false
	Enables the fit in amplitude bins.
std::vector< float >	m_binEdges = {50, 100, 150, 200, 250, 300, 350, 400, 500, 600, 800, 1000, 1500, 2000}
	Amplitude bins.
TFile *	m_inputTTS = nullptr
	File containing m_treeTTS.
TFile *	m_inputConstraints = nullptr
	File containing m_treeConstraints.
TTree *	m_treeTTS = nullptr
	Input to the fitter.
TTree *	m_treeConstraints
	Input to the fitter.
TFile *	m_histFile = nullptr
	Output of the fitter.
TTree *	m_fitTree = nullptr
	Output of the fitter.
TTree *	m_timewalkTree
	Output of the fitter.
bool	m_detectCrosstalk = false
	Enables the crosstalk detection algorithm.
TTree *	m_crosstalkTree = nullptr
	Output tree for crosstalk candidates.
TTree *	m_fitTree_noXtalk = nullptr
	Output tree for non-crosstalk candidates.
short	m_sl0 = -1
	Slot ID (1-16)
short	m_sl1 = -1
	Slot ID (1-16)
short	m_ch0 = -1
	Channel number (0-511)
short	m_ch1 = -1
	Channel number (0-511)
float	m_ht0 = NAN
	Hit time for channel 0 in pair.
float	m_ht1 = NAN
	Hit time for channel 1 in pair.
float	m_a0 = NAN
	Amplitude for channel 0 in pair.
float	m_a1 = NAN
	Amplitude for channel 1 in pair.
float	m_w0 = NAN
	Width for channel 0 in pair.
float	m_w1 = NAN
	Width for channel 1 in pair.
float	m_q0 = NAN
	Integrated charge for channel 0 in pair.
float	m_q1 = NAN
	Integrated charge for channel 1 in pair.
float	m_f_q0 = NAN
	Fraction of charge on channel 0 in pair.
float	m_mean2 = 0
	Position of the second gaussian of the TTS parametrization with respect to the first one.
float	m_sigma1 = 0
	Width of the first gaussian on the TTS parametrization.
float	m_sigma2 = 0
	Width of the second gaussian on the TTS parametrization.
float	m_f1 = 0
	Fraction of the first gaussian on the TTS parametrization.
float	m_f2 = 0
	Fraction of the second gaussian on the TTS parametrization.
short	m_pixelRow = 0
	Pixel row.
short	m_pixelCol = 0
	Pixel column.
float	m_peakTimeConstraints = 0
	Time of the main laser peak in the MC simulation (aka MC correction)
float	m_deltaTConstraints = 0
	Distance between the main and the secondary laser peak.
float	m_fractionConstraints = 0
	Fraction of the main peak.
float	m_timeExtraConstraints = 0
	Position of the gaussian used to describe the extra peak on the timing distribution tail.
float	m_sigmaExtraConstraints = 0
	Width of the gaussian used to describe the extra peak on the timing distribution tail.
float	m_alphaExtraConstraints = 0.
	alpha parameter of the tail of the extra peak.
float	m_nExtraConstraints = 0.
	parameter n of the tail of the extra peak
float	m_timeBackgroundConstraints = 0.
	Position of the gaussian used to describe the background, w/ respect to peakTime.
float	m_sigmaBackgroundConstraints = 0.
	Sigma of the gaussian used to describe the background.
float	m_binLowerEdge = 0
	Lower edge of the amplitude bin in which this fit is performed.
float	m_binUpperEdge = 0
	Upper edge of the amplitude bin in which this fit is performed.
short	m_channel = 0
	Channel number (0-511)
short	m_slot = 0
	Slot ID (1-16)
short	m_row = 0
	Pixel row.
short	m_col = 0
	Pixel column.
float	m_peakTime = 0
	Fitted time of the main (i.e.
float	m_deltaT
	Time difference between the main peak and the secondary peak.
float	m_sigma = 0.
	Gaussian time resolution, fitted.
float	m_fraction = 0.
	Fraction of events in the secondary peak.
float	m_yieldLaser = 0.
	Total number of laser hits from the fitting function integral.
float	m_histoIntegral = 0.
	Integral of the fitted histogram.
float	m_peakTimeErr = 0
	Statistical error on peakTime.
float	m_deltaTErr = 0
	Statistical error on deltaT.
float	m_sigmaErr = 0.
	Statistical error on sigma.
float	m_fractionErr = 0.
	Statistical error on fraction.
float	m_yieldLaserErr = 0.
	Statistical error on yield.
float	m_timeExtra = 0.
	Position of the extra peak seen in the timing tail, w/ respect to peakTime.
float	m_sigmaExtra = 0.
	Gaussian sigma of the extra peak in the timing tail.
float	m_yieldLaserExtra = 0.
	Integral of the extra peak.
float	m_alphaExtra = 0.
	alpha parameter of the tail of the extra peak.
float	m_nExtra = 0.
	parameter n of the tail of the extra peak
float	m_timeBackground = 0.
	Position of the gaussian used to describe the background, w/ respect to peakTime.
float	m_sigmaBackground = 0.
	Sigma of the gaussian used to describe the background.
float	m_yieldLaserBackground = 0.
	Integral of the background gaussian.
float	m_fractionMC = 0.
	Fraction of events in the secondary peak form the MC simulation.
float	m_deltaTMC = 0.
	Time difference between the main peak and the secondary peak in the MC simulation.
float	m_peakTimeMC
	Time of the main peak in the MC simulation, i.e.
float	m_chi2 = 0
	Reduced chi2 of the fit.
float	m_rms = 0
	RMS of the histogram used for the fit.
float	m_channelT0
	Raw, channelT0 calibration, defined as peakTime-peakTimeMC.
float	m_channelT0Err = 0.
	Statistical error on channelT0.
float	m_firstPulserTime = 0.
	Average time of the first electronic pulse respect to the reference pulse, from a Gaussian fit.
float	m_firstPulserSigma = 0.
	Time resolution from the fit of the first electronic pulse, from a Gaussian fit.
float	m_secondPulserTime
	Average time of the second electronic pulse respect to the reference pulse, from a gaussian fit.
float	m_secondPulserSigma = 0.
	Time resolution from the fit of the first electronic pulse, from a Gaussian fit.
short	m_fitStatus = 1
	Fit quality flag, propagated to the constants.
double	m_width = 0
	Pulse width.
double	m_amplitude = 0
	Pulse height.
std::array< std::array< short, 512 >, 16 >	m_rowOf {}
	Row index for (slot,channel), or -1 if out of bounds.
std::array< std::array< short, 512 >, 16 >	m_colOf {}
	Column index for (slot,channel), or -1 if out of bounds.
bool	m_hasChannelMaps {false}
	Flag indicating if channel->(row,col) maps have been built.
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

This module is the fitter for the CAF collector TOPLaserCalibratorCollector.

It analyzes the tree containing the timing of the laser and pulser hits produced by the collector, returning a tree with the fit results and the histograms for each channel. It can be used to produce both channelT0 calibrations and to analyze the daily, low statistics laser runs

Definition at line 30 of file TOPLocalCalFitter.h.

Member Enumeration Documentation

EResult

enum EResult

inherited

The result of calibration.

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

Definition at line 40 of file CalibrationAlgorithm.h.

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

Constructor & Destructor Documentation

TOPLocalCalFitter()

TOPLocalCalFitter

(

)

Constructor.

Definition at line 165 of file TOPLocalCalFitter.cc.

                                             : CalibrationAlgorithm("TOPLaserCalibratorCollector")
{
  setDescription(
    "Perform the fit of the laser and pulser runs"
  );
 
}

~TOPLocalCalFitter()

~TOPLocalCalFitter ( )

override

Destructor.

Definition at line 54 of file TOPLocalCalFitter.cc.

{
  // Close & delete input files
  if (m_inputTTS) {
    m_inputTTS->Close();
    delete m_inputTTS;
    m_inputTTS = nullptr;
  }
  if (m_inputConstraints) {
    m_inputConstraints->Close();
    delete m_inputConstraints;
    m_inputConstraints = nullptr;
  }
 
  // Delete trees if still around (ROOT does not auto-delete in-memory TTrees)
  if (m_fitTree) {
    delete m_fitTree;
    m_fitTree = nullptr;
  }
  if (m_timewalkTree) {
    delete m_timewalkTree;
    m_timewalkTree = nullptr;
  }
  if (m_crosstalkTree) {
    delete m_crosstalkTree;
    m_crosstalkTree = nullptr;
  }
  if (m_fitTree_noXtalk) {
    delete m_fitTree_noXtalk;
    m_fitTree_noXtalk = nullptr;
  }
 
  // Close & delete output file last
  if (m_histFile) {
    m_histFile->Close();
    delete m_histFile;
    m_histFile = nullptr;
  }
}

Member Function Documentation

areNeighbors()

bool areNeighbors ( short slot, short a, short b, int drMax = 1, int dcMax = 1 ) const

inlineprivatenoexcept

Return true if channels a and b are neighbors on the same slot in row/col space.

Uses |Delta_row| ≤ drMax and |Delta_col| ≤ dcMax (default 1). Returns false for identical channels.

Definition at line 270 of file TOPLocalCalFitter.h.

      {
        const int dr = std::abs(rowOf(slot, a) - rowOf(slot, b));
        const int dc = std::abs(colOf(slot, a) - colOf(slot, b));
        return (dr + dc > 0) && (dr <= drMax) && (dc <= dcMax);
      }

boundaryFindingSetup()

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

inlineprotectedvirtualinherited

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

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

Definition at line 252 of file CalibrationAlgorithm.h.

{};

boundaryFindingTearDown()

virtual void boundaryFindingTearDown ( )

inlineprotectedvirtualinherited

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

Definition at line 257 of file CalibrationAlgorithm.h.

{};

buildChannelMaps()

void buildChannelMaps ( )

private

Build (row,col) lookup tables from the TTS tree; call after opening m_treeTTS.

Definition at line 34 of file TOPLocalCalFitter.cc.

{
  if (!m_treeTTS) {
    B2ERROR("TOPLocalCalFitter::buildChannelMaps called with null m_treeTTS.");
    return;
  }
 
  // The TTS tree is indexed as (channel + 512 * slot), 0-based for both.
  for (short slot = 0; slot < 16; ++slot) {
    for (short ch = 0; ch < 512; ++ch) {
      const Long64_t idx = static_cast<Long64_t>(ch) + 512LL * slot;
      m_treeTTS->GetEntry(idx);
      m_rowOf[slot][ch] = static_cast<short>(m_pixelRow);
      m_colOf[slot][ch] = static_cast<short>(m_pixelCol);
    }
  }
  m_hasChannelMaps = true;
}

calculateChannelT0()

void calculateChannelT0 ( )

protected

Calculates the commonT0 calibration after the fits have been done.

It also saves the constants in a localDB and in the output tree

Definition at line 580 of file TOPLocalCalFitter.cc.

{
  Long64_t nEntries = m_fitTree->GetEntries();
  if (nEntries != 8192) {
    B2ERROR("fitTree does not contain an entry with a fit result for each channel. Found " << nEntries <<
            " instead of 8192. Perhaps you tried to run the commonT0 calculation before finishing the fitting?");
    return;
  }
 
  // Create and fill the TOPCalChannelT0 object
  auto* channelT0 = new TOPCalChannelT0();
  short nCal[16] = {0};
  for (Long64_t i = 0; i < nEntries; i++) {
    m_fitTree->GetEntry(i);
    channelT0->setT0(m_slot, m_channel, m_peakTime - m_peakTimeMC, m_peakTimeErr);
    if (m_fitStatus == 0) {
      nCal[m_slot - 1]++;
    } else {
      channelT0->setUnusable(m_slot, m_channel);
    }
  }
 
  // Normalize the constants
  channelT0->suppressAverage();
 
  // create the localDB
  saveCalibration(channelT0);
 
  short nCalTot = 0;
  B2INFO("Summary: ");
  for (int iSlot = 1; iSlot < 17; iSlot++) {
    B2INFO("--> Number of calibrated channels on Slot " << iSlot << " : " << nCal[iSlot - 1] << "/512");
    B2INFO("--> Cal on ch 1, 256 and 511:    " << channelT0->getT0(iSlot, 0) << ", " << channelT0->getT0(iSlot,
           257) << ", " << channelT0->getT0(iSlot, 511));
    nCalTot += nCal[iSlot - 1];
  }
 
  B2RESULT("Channel T0 calibration constants imported to database, calibrated channels: " << nCalTot << "/ 8192");
 
  // Loop again on the output tree to save the constants there too, adding two more branches.
  TBranch* channelT0Branch = m_fitTree->Branch<float>("channelT0", &m_channelT0);
  TBranch* channelT0ErrBranch = m_fitTree->Branch<float>("channelT0Err", &m_channelT0Err);
 
  for (int i = 0; i < nEntries; i++) {
    m_fitTree->GetEntry(i);
    m_channelT0 = channelT0->getT0(m_slot, m_channel);
    m_channelT0Err = channelT0->getT0Error(m_slot, m_channel);
    channelT0Branch->Fill();
    channelT0ErrBranch->Fill();
  }
 
  return;
 
}

calibrate()

Belle2::CalibrationAlgorithm::EResult calibrate ( )

overrideprotectedvirtual

Runs the algorithm on events.

Currently, it always returns c_OK despite of the actual result of the fitting procedure. This is not an issue since this moduleis not intended to be used in the automatic calibration.

Implements CalibrationAlgorithm.

Definition at line 636 of file TOPLocalCalFitter.cc.

{
 
  gROOT->SetBatch();
 
  // Load MC constraints
  loadMCInfoTrees();
 
  // Prepare output
  setupOutputTreeAndFile();
 
  // Load the tree with the hits (output of TOPLaserCalibratorCollector)
  auto hitTree = getObjectPtr<TTree>("hitTree");
  int event;
  float amplitude, width, hitTime;
  short channel, slot; //, row, col;
  bool refTimeValid;
  hitTree->SetBranchAddress("event", &event);
  hitTree->SetBranchAddress("amplitude", &amplitude);
  hitTree->SetBranchAddress("width", &width);
  hitTree->SetBranchAddress("hitTime", &hitTime);
  hitTree->SetBranchAddress("channel", &channel);
  //hitTree->SetBranchAddress("row", &row);
  //hitTree->SetBranchAddress("col", &col);
  hitTree->SetBranchAddress("slot", &slot);
  hitTree->SetBranchAddress("refTimeValid", &refTimeValid);
 
  // Prepare histogram to save interesting features of each channel
  TH2F* h_hitTime = new TH2F("h_hitTime", " ", 512 * 16, 0., 512 * 16, 22000, -70, 40.); // 5 ps bins
  TH2F* h_amplitude2D = new TH2F("h_amplitude", " ", 512 * 16, 0., 512 * 16, 600, 0, 2200.);
  TH2F* h_width2D = new TH2F("h_width", " ", 512 * 16, 0., 512 * 16, 1000, 0, 2.);
 
  // Prepare vector of hitTime vs channel histograms for fits in amplitude bins
  // (attempt to speed things up looping over the hitTree only once).
 
  std::vector<TH2F*> h_hitTimeLaserHistos = {};
  for (int iLowerEdge = 0; iLowerEdge < (int)m_binEdges.size() - 1; iLowerEdge++) {
    TH2F* h_hitTimeLaser = new TH2F(("h_hitTimeLaser_" + std::to_string(iLowerEdge + 1)).c_str(), " ",
                                    512 * 16, 0., 512 * 16, 14000, -70, 0.); // 5 ps bins
    h_hitTimeLaserHistos.push_back(h_hitTimeLaser);
  }
 
  // Per-event accumulator for crosstalk logic
  struct Hit {
    short slot;
    short ch;
    float t;   // hitTime
    float a;   // amplitude
    float w;   // width
    float q;   // charge proxy = conv * a * w
  };
  std::vector<Hit> evtHits;
  //evtHits.reserve(64);  // typical multiplicity in laser runs?
 
  // Track current event id we are accumulating
  int prev_evt = std::numeric_limits<int>::min();
 
  // Conversion factor to compute approximate integrated charge (ADC·ns)
  const float conv = 1.f / (0.3989f * 2.35f);
 
  // Crosstalk tunables (consider moving to members with setters)
  const float fracMin = 0.25f;  // f_q0 < fracMin or > 1-fracMin => crosstalk
  const float dtMax   = 0.30f;  // ns, time-coincidence window for pair
  const float epsQ    = 1e-6f;  // guard against zero-sum charges
 
  // Prepares histogram to store features of each channel (if no crosstalk detected)
  // which will be fit later
  TH2F* h_hitTime_noXtalk = new TH2F("h_hitTime_noXtalk", " ", 512 * 16, 0., 512 * 16, 22000, -70, 40.); // 5 ps bins
  TH2F* h_amplitude2D_noXtalk = new TH2F("h_amplitude2D_noXtalk", " ", 512 * 16, 0., 512 * 16, 600, 0, 2200.);
  TH2F* h_width2D_noXtalk = new TH2F("h_width2D_noXtalk", " ", 512 * 16, 0., 512 * 16, 1000, 0, 2.);
 
  // Get number of entries in hitTree
  Long64_t nhits = hitTree->GetEntries();
  const Long64_t step = std::max<Long64_t>(1, nhits / 100); // for progress bar
 
  // Loop on all hits to retrieve information
  for (Long64_t i = 0; i < nhits; i++) {
 
    // Print percentage of completion
    if (i % step == 0) {
      std::cout << "Processing hit " << i << " of " << nhits << " ("
                << std::setprecision(3) << (100. * i) / nhits << " %)" << std::endl;
    }
 
    // Process entry
    hitTree->GetEntry(i);
 
    // Fill hit time histograms for each bin of pulse heigth (if activated)
    if (m_isFitInAmplitudeBins) {
      auto it = std::lower_bound(m_binEdges.cbegin(),  m_binEdges.cend(), amplitude); // std::vector iterator
      int iLowerEdge = std::distance(m_binEdges.cbegin(), it) - 1;
      if (iLowerEdge >= 0 && iLowerEdge < static_cast<int>(m_binEdges.size()) - 1 && refTimeValid)
        h_hitTimeLaserHistos[iLowerEdge]->Fill(channel + (slot - 1) * 512, hitTime);
    }
 
    // Check if pulse is at least 80 ADC and has valid reference time (suppress noise)
    if (amplitude > 80. &&  refTimeValid) {
 
      // Fill the hitTime vs channel histogram
      h_hitTime->Fill(channel + (slot - 1) * 512, hitTime);
 
      // If entry is found with -65 < hitTime < -10 (laser pulse), fill amplitude and width histograms
      if ((hitTime > -65) && (hitTime < -10)) {  // use logical &&
 
        // Fill amplitude and width histograms
        h_amplitude2D->Fill(channel + (slot - 1) * 512, amplitude);
        h_width2D->Fill(channel + (slot - 1) * 512, width);
 
        // Crosstalk finder algorithm (only on laser pulses)
        if (m_detectCrosstalk) {
 
          const int curr_evt = event;
 
          // If this entry belongs to a NEW event, process the accumulated previous event
          if (curr_evt != prev_evt && !evtHits.empty()) {
 
            // ---- step 1: find crosstalk pairs and mark involved hits ----
            std::vector<char> isXtalk(evtHits.size(), 0);
            for (size_t ii = 0; ii + 1 < evtHits.size(); ++ii) {
              const auto& hi = evtHits[ii];
              for (size_t jj = ii + 1; jj < evtHits.size(); ++jj) {
                const auto& hj = evtHits[jj];
 
                // same slot and neighboring pixels
                if (hi.slot != hj.slot) continue;
                if (!areNeighbors(hi.slot, hi.ch, hj.ch)) continue;
 
                // near-coincident in time (laser)
                if (std::fabs(hi.t - hj.t) > dtMax) continue;
 
                // robust fraction of shared charge
                const float qsum = hi.q + hj.q;
                if (qsum <= epsQ) continue;
                const float f_q0 = hi.q / qsum;
 
                if (f_q0 < fracMin || f_q0 > (1.f - fracMin)) {
                  isXtalk[ii] = 1;
                  isXtalk[jj] = 1;
 
                  // save diagnostics once per pair
                  if (m_crosstalkTree) {
                    m_sl0 = hi.slot;  m_sl1 = hj.slot;
                    m_ch0 = hi.ch;    m_ch1 = hj.ch;
                    m_ht0 = hi.t;     m_ht1 = hj.t;
                    m_a0  = hi.a;     m_a1  = hj.a;
                    m_w0  = hi.w;     m_w1  = hj.w;
                    m_q0  = hi.q;     m_q1  = hj.q;
                    m_f_q0 = f_q0;
                    m_crosstalkTree->Fill();
                  }
                }
              }
            }
 
            // ---- step 2: fill "no-crosstalk" histograms exactly once per clean hit ----
            for (size_t kk = 0; kk < evtHits.size(); ++kk) {
              if (isXtalk[kk]) continue;
              const auto& h = evtHits[kk];
              const int gch = h.ch + (h.slot - 1) * 512;
              h_hitTime_noXtalk->Fill(gch, h.t);
              h_amplitude2D_noXtalk->Fill(gch, h.a);
              h_width2D_noXtalk->Fill(gch, h.w);
            }
 
            // clear for next event
            evtHits.clear();
          }
          // accumulate the current hit for the (possibly new) event
          evtHits.push_back(Hit{slot, channel, hitTime, amplitude, width, conv* amplitude * width});
 
          // update current event id
          prev_evt = curr_evt;
        }
      }
    }
  }
 
  // Final flush for the last accumulated event (if any)
  if (m_detectCrosstalk && !evtHits.empty()) {
    std::vector<char> isXtalk(evtHits.size(), 0);
    for (size_t ll = 0; ll + 1 < evtHits.size(); ++ll) {
      const auto& hi = evtHits[ll];
      for (size_t mm = ll + 1; mm < evtHits.size(); ++mm) {
        const auto& hj = evtHits[mm];
        if (hi.slot != hj.slot) continue;
        if (!areNeighbors(hi.slot, hi.ch, hj.ch)) continue;
        if (std::fabs(hi.t - hj.t) > dtMax) continue;
        const float qsum = hi.q + hj.q;
        if (qsum <= epsQ) continue;
        const float f_q0 = hi.q / qsum;
        if (f_q0 < fracMin || f_q0 > (1.f - fracMin)) {
          isXtalk[ll] = 1;
          isXtalk[mm] = 1;
          if (m_crosstalkTree) {
            m_sl0 = hi.slot;  m_sl1 = hj.slot;
            m_ch0 = hi.ch;    m_ch1 = hj.ch;
            m_ht0 = hi.t;     m_ht1 = hj.t;
            m_a0  = hi.a;     m_a1  = hj.a;
            m_w0  = hi.w;     m_w1  = hj.w;
            m_q0  = hi.q;     m_q1  = hj.q;
            m_f_q0 = f_q0;
            m_crosstalkTree->Fill();
          }
        }
      }
    }
    for (size_t nn = 0; nn < evtHits.size(); ++nn) {
      if (isXtalk[nn]) continue;
      const auto& h = evtHits[nn];
      const int gch = h.ch + (h.slot - 1) * 512;
      h_hitTime_noXtalk->Fill(gch, h.t);
      h_amplitude2D_noXtalk->Fill(gch, h.a);
      h_width2D_noXtalk->Fill(gch, h.w);
    }
    evtHits.clear();
  }
 
  // Save tree filled with candidate crosstalks => useful for further studies
  if (m_detectCrosstalk) {
    std::cout << "Writing crosstalkTree (candidate crosstalk channel pairs) to output file" << std::endl;
    m_histFile->cd();
    m_crosstalkTree->Write();
  }
 
  // Write hitTime histograms to file
  m_histFile->cd();
  h_hitTime->Write();
 
  // After filling hitTime, amplitude, width vs. channel histograms,
  // loop on each slot and channel to perform channelT0 fits on hitTime profiles
  for (short iSlot = 0; iSlot < 16; iSlot++) {
    std::cout << "fitting slot " << iSlot + 1 << std::endl;
    for (short iChannel = 0; iChannel < 512; iChannel++) {
 
      // Project to 1-d hitTime distribution
      TH1D* h_profile = h_hitTime->ProjectionY(
                          ("profile_" + std::to_string(iSlot + 1) + "_" + std::to_string(iChannel)).c_str(),
                          iSlot * 512 + iChannel + 1, iSlot * 512 + iChannel + 1
                        );
 
      // Set hitTime range based on fitter mode
      if (m_fitterMode == "MC")
        //h_profile->GetXaxis()->SetRangeUser(-10, -10);
        h_profile->GetXaxis()->SetRangeUser(-65, -1);
      else // if you will even change the limits, make sure not to include the h_hitTime overflow bins in this range
        h_profile->GetXaxis()->SetRangeUser(-65, -5);
 
      // Run fit and determine status
      fitChannel(iSlot, iChannel, h_profile);
      determineFitStatus();
 
      // Now let's fit the pulser
      TH1D* h_profileFirstPulser = h_hitTime->ProjectionY(
                                     ("profileFirstPulser_" + std::to_string(iSlot + 1) + "_" + std::to_string(
                                        iChannel)).c_str(),  iSlot * 512 + iChannel + 1, iSlot * 512 + iChannel + 1
                                   );
      TH1D* h_profileSecondPulser = h_hitTime->ProjectionY(
                                      ("profileSecondPulser_" + std::to_string(iSlot + 1) + "_" + std::to_string(
                                         iChannel)).c_str(),  iSlot * 512 + iChannel + 1, iSlot * 512 + iChannel + 1
                                    );
      h_profileFirstPulser->GetXaxis()->SetRangeUser(-10, 10);
      h_profileSecondPulser->GetXaxis()->SetRangeUser(10, 40);
      fitPulser(h_profileFirstPulser, h_profileSecondPulser);
 
 
      // Get pulse heigth [ADC] and width [ns]
      TH1D* h_amplitude = h_amplitude2D->ProjectionY(
                            ("AmpProfile_" + std::to_string(iSlot + 1) + "_" + std::to_string(iChannel)).c_str(),
                            iSlot * 512 + iChannel + 1, iSlot * 512 + iChannel + 1
                          );
      TH1D* h_width = h_width2D->ProjectionY(
                        ("WidthProfile_" + std::to_string(iSlot + 1) + "_" + std::to_string(iChannel)).c_str(),
                        iSlot * 512 + iChannel + 1, iSlot * 512 + iChannel + 1
                      );
 
      // Set values for pulse amplitude and width (mean)
      Double_t q = 0.5; // quantile for median
      h_amplitude->GetQuantiles(1, &m_amplitude, &q);
      h_width->GetQuantiles(1, &m_width, &q);
 
      m_fitTree->Fill();
      h_profile->Write();
      h_profileFirstPulser->Write();
      h_profileSecondPulser->Write();
      h_amplitude->Write();
      h_width->Write();
 
      // Free memory: TH2D::ProjectionY allocates memory dynamically
      delete h_profile;
      delete h_profileFirstPulser;
      delete h_profileSecondPulser;
      delete h_amplitude;
      delete h_width;
 
    }
 
    // Write hitTime histogram to output tree
    h_hitTime->Write();
 
  }
 
  // Compute calibration constants (w/ error) and add corresponding branches to output fitTree
  calculateChannelT0();
 
  // Write fit results to tree
  m_fitTree->Write();
 
  // ChannelT0 fits in bins of pulse heigth
  if (m_isFitInAmplitudeBins) {
 
    std::cout << "Fitting in bins of pulse heigth" << std::endl;
 
    for (short iSlot = 0; iSlot < 16; iSlot++) {
      std::cout << "   Fitting slot " << iSlot + 1 << std::endl;
      for (short iChannel = 0; iChannel < 512; iChannel++) {
 
        // The fraction parameter should not depend on amplitude ==> let's fix it
        // to the value we get from the fit integrated on all amplitudes
        TH1D* h_profile_full = h_hitTime->ProjectionY(
                                 ("profile_" + std::to_string(iSlot + 1) + "_" + std::to_string(iChannel)).c_str(),
                                 iSlot * 512 + iChannel + 1, iSlot * 512 + iChannel + 1
                               );
        fitChannel(iSlot, iChannel, h_profile_full);
        float ff = m_fraction;
        // Fit done, free the memory
        delete h_profile_full;
 
        // Loop on the amplitude bins
        for (int iLowerEdge = 0; iLowerEdge < (int)m_binEdges.size() - 1; iLowerEdge++) {
 
          // Get current bin edges
          m_binLowerEdge = m_binEdges[iLowerEdge];
          m_binUpperEdge = m_binEdges[iLowerEdge + 1];
          std::cout << "Fitting the amplitude interval (" <<  m_binLowerEdge << ", " << m_binUpperEdge << " )" << std::endl;
 
          // Get profile for current amplitude bin
          TH1D* h_profile = h_hitTimeLaserHistos[iLowerEdge]->ProjectionY(
                              ("profile_" + std::to_string(iSlot + 1) + "_" + std::to_string(
                                 iChannel) + "_"  + std::to_string(iLowerEdge)).c_str(),
                              iSlot * 512 + iChannel + 1, iSlot * 512 + iChannel + 1
                            );
          // Set range of fit based on fitter mode
          if (m_fitterMode == "MC")
            h_profile->GetXaxis()->SetRangeUser(-10, -10);
          else // if you will even change it, make sure not to include the h_hitTime overflow bins in this range
            h_profile->GetXaxis()->SetRangeUser(-65, -5);
 
 
          // Fit the hitTime distribution
          fitChannel(iSlot, iChannel, h_profile, true, ff);
          m_histoIntegral = h_profile->Integral();
          determineFitStatus();
 
          // Get amplitude and width profiles in order to get median amp and width of the channel
          TH1D* h_amplitude = h_amplitude2D->ProjectionY(
                                ("AmpProfile_" + std::to_string(iSlot + 1) +
                                 "_" + std::to_string(iChannel) +
                                 "_" +  std::to_string(iLowerEdge)).c_str(),
                                iSlot * 512 + iChannel + 1, iSlot * 512 + iChannel + 1
                              );
          TH1D* h_width = h_width2D->ProjectionY(
                            ("WidthProfile_" + std::to_string(iSlot + 1) +
                             "_" + std::to_string(iChannel) +
                             "_" +  std::to_string(iLowerEdge)).c_str(),
                            iSlot * 512 + iChannel + 1, iSlot * 512 + iChannel + 1
                          );
 
          // Measure the *median* amplitude and widths => less sensitive to outliers
          Double_t q = 0.5; // quantile for median
          h_amplitude->GetQuantiles(1, &m_amplitude, &q);
          h_width->GetQuantiles(1, &m_width, &q);
 
          // Fill tree and write histograms to output file
          m_timewalkTree->Fill();
          h_profile->Write();
          h_amplitude->Write();
          h_width->Write();
 
          // Try to avoid memory leaks
          delete h_profile;
          delete h_amplitude;
          delete h_width;
 
        }
      }
    }
    m_timewalkTree->Write();
  }
 
  // Run fits on channels that didn't have cross-talk
  if (m_detectCrosstalk) {
    std::cout << "Fitting channels with no crosstalk detected" << std::endl;
    for (short iSlot = 0; iSlot < 16; iSlot++) {
      std::cout << "   Fitting slot " << iSlot + 1 << std::endl;
      for (short iChannel = 0; iChannel < 512; iChannel++) {
        // Project to 1-d hitTime distribution
        TH1D* h_profile = h_hitTime_noXtalk->ProjectionY(
                            ("profile_" + std::to_string(iSlot + 1) + "_" + std::to_string(iChannel)).c_str(),
                            iSlot * 512 + iChannel + 1, iSlot * 512 + iChannel + 1
                          );
 
        // Set hitTime range based on fitter mode
        if (m_fitterMode == "MC")
          //h_profile->GetXaxis()->SetRangeUser(-10, -10);
          h_profile->GetXaxis()->SetRangeUser(-65, -1);
        else // if you will even change the limits, make sure not to include the h_hitTime overflow bins in this range
          h_profile->GetXaxis()->SetRangeUser(-65, -5);
 
        // Run fit and determine status
        fitChannel(iSlot, iChannel, h_profile);
        determineFitStatus();
 
        // Get pulse heigth [ADC] and width [ns]
        TH1D* h_amplitude = h_amplitude2D_noXtalk->ProjectionY(
                              ("AmpProfile_" + std::to_string(iSlot + 1) + "_" + std::to_string(iChannel)).c_str(),
                              iSlot * 512 + iChannel + 1, iSlot * 512 + iChannel + 1
                            );
        TH1D* h_width = h_width2D_noXtalk->ProjectionY(
                          ("WidthProfile_" + std::to_string(iSlot + 1) + "_" + std::to_string(iChannel)).c_str(),
                          iSlot * 512 + iChannel + 1, iSlot * 512 + iChannel + 1
                        );
 
        // Set values for pulse amplitude and width (mean)
        Double_t q = 0.5; // quantile for median
        h_amplitude->GetQuantiles(1, &m_amplitude, &q);
        h_width->GetQuantiles(1, &m_width, &q);
 
        m_fitTree_noXtalk->Fill();
        h_profile->Write();
        h_amplitude->Write();
        h_width->Write();
 
        // Free memory: TH2D::ProjectionY allocates memory dynamically
        delete h_profile;
        delete h_amplitude;
        delete h_width;
      }
    }
    // Write fitTree with no crosstalk (after computing calibration constants)
    calculateChannelT0();
    m_fitTree_noXtalk->Write();
  }
 
  m_histFile->Close();
 
  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();}

colOf()

short colOf ( short slot, short ch ) const

inlineprivatenoexcept

Column index for (slot,channel), or -1 if out of bounds.

Definition at line 261 of file TOPLocalCalFitter.h.

      {
        return (slot >= 0 && slot < 16 && ch >= 0 && ch < 512) ? m_colOf[slot][ch] : short(-1);
      }

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;
}

determineFitStatus()

void determineFitStatus ( )

protected

determines if the constant obtained by the fit are good or not

Definition at line 570 of file TOPLocalCalFitter.cc.

{
  if (m_chi2 < 4 && m_sigma < 0.2 && m_yieldLaser > 1000) {
    m_fitStatus = 0;
  } else {
    m_fitStatus = 1;
  }
  return;
}

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;
}

fitChannel() [1/2]

void fitChannel ( short slot, short channel, TH1 * h )

protected

Fits the laser light on one channel.

Definition at line 29 of file TOPLocalCalFitter.cc.

{
  fitChannel(iSlot, iChannel, h_profile, /*inBins=*/false, /*frac=*/0.0);
}

fitChannel() [2/2]

void fitChannel ( short slot, short channel, TH1 * h, bool inBins, double frac )

protected

Fit the laser spectrum of a single channel with extra controls for amplitude-bin fits.

slot 0-based slot index (0..15). channel 0-based channel index (0..511). h Input time profile histogram. inBins If true, fix the light-path fraction parameter. frac Fraction value used when inBins is true.

Definition at line 355 of file TOPLocalCalFitter.cc.

{
  // loads the TTS infos and the fit constraint for the given channel and slot
  if (m_fitterMode == "monitoring")
    m_treeConstraints->GetEntry(iChannel + 512 * iSlot);
  else if (m_fitterMode == "calibration") // The MC-based constraint file has only slot 1 at the moment
    m_treeConstraints->GetEntry(iChannel);
 
  m_treeTTS->GetEntry(iChannel + 512 * iSlot);
  // finds the maximum of the hit timing histogram and adjust the histogram range around it (3 ns window)
  double maxpos = h_profile->GetBinCenter(h_profile->GetMaximumBin());
  h_profile->GetXaxis()->SetRangeUser(maxpos - 1, maxpos + 2.);
 
  // gets the histogram integral to give a starting value to the fitter
  double integral = h_profile->Integral();
 
  // creates the fit function
  TF1 laser = TF1("laser", laserPDF, maxpos - 1, maxpos + 2., 16);
 
  // par[0] = peakTime
  laser.SetParameter(0, maxpos);
  laser.SetParLimits(0, maxpos - 0.06, maxpos + 0.06);
 
  // par[1] = sigma
  laser.SetParameter(1, 0.1);
  laser.SetParLimits(1, 0.05, 0.25);
  if (m_fitterMode == "MC") {
    laser.SetParameter(1, 0.02);
    laser.SetParLimits(1, 0., 0.04);
  }
 
  // par[2] = fraction of the main peak respect to the total
  laser.SetParameter(2, m_fractionConstraints);
  laser.SetParLimits(2, 0.5, 1.);
  if (inBins) {
    laser.FixParameter(2, frac);
  }
 
  // par[3]= time difference between the main and secondary path. fixed to the MC value
  laser.FixParameter(3, m_deltaTConstraints);
 
  // This is an hack: in some channels the MC sees one peak only, while in the data there are clearly
  // two well distinguished peaks. This will disappear if we'll ever get a better laser simulation.
  if (m_deltaTConstraints > -0.001) {
    laser.SetParameter(3, -0.3);
    laser.SetParLimits(3, -0.4, -0.2);
  }
 
  // par[4] is the quadratic difference of the sigmas of the two TTS gaussians (tail - core)
  laser.FixParameter(4, TMath::Sqrt(m_sigma2 * m_sigma2 - m_sigma1 * m_sigma1));
  // par[5] is the position of the second TTS gaussian w/ respect to the first one
  laser.FixParameter(5, m_mean2);
  // par[6] is the relative contribution of the second TTS gaussian
  laser.FixParameter(6, m_f1);
  if (m_fitterMode == "MC")
    laser.FixParameter(6, 0);
 
  // par[7] is the PDF normalization, = integral*bin width
  const double binw = h_profile->GetXaxis()->GetBinWidth(1);
  laser.SetParameter(7,  integral * binw);
  laser.SetParLimits(7,  0.2 * integral * binw, 2.*integral * binw);
 
  // par[8-10] are the relative position, the sigma and the integral of the extra peak
  laser.SetParameter(8, 1.);
  laser.SetParLimits(8, 0.3, 2.);
  laser.SetParameter(9, 0.2);
  laser.SetParLimits(9, 0.08, 1.);
  laser.SetParameter(10,  0.1 * integral * binw);
  laser.SetParLimits(10,  0., 0.2 * integral * binw);
  // par[14-15] are the tail parameters of the crystal ball function used to describe the extra peak
  laser.SetParameter(14, -2.);
  laser.SetParameter(15, 2.);
  laser.SetParLimits(15, 1.01, 20.);
 
  // par[11-13] are relative position, sigma and integral of the broad gaussian added to better describe the tail at high times
  laser.SetParameter(11, 1.);
  laser.SetParLimits(11, 0.1, 5.);
  laser.SetParameter(12, 0.8);
  laser.SetParLimits(12, 0., 5.);
  laser.SetParameter(13,  0.01 * integral * binw);
  laser.SetParLimits(13,  0., 0.2 * integral * binw);
 
  // if it's a monitoring fit, fix a buch more parameters.
  if (m_fitterMode == "monitoring") {
    laser.FixParameter(2, m_fractionConstraints);
    laser.FixParameter(3, m_deltaTConstraints);
    laser.FixParameter(8, m_timeExtraConstraints);
    laser.FixParameter(9, m_sigmaExtraConstraints);
    laser.FixParameter(14, m_alphaExtraConstraints);
    laser.FixParameter(15, m_nExtraConstraints);
    laser.FixParameter(11, m_timeBackgroundConstraints);
    laser.FixParameter(12, m_sigmaBackgroundConstraints);
  }
 
  // if it's a MC fit, fix a buch more parameters.
  if (m_fitterMode == "MC") {
    laser.SetParameter(2, 0.8);
    laser.SetParLimits(2, 0., 1.);
    laser.SetParameter(3, -0.1);
    laser.SetParLimits(3, -0.4, -0.);
    // The following are just random reasonable number, only to pin-point the tail components to some value and remove them form the fit
    laser.FixParameter(8, 0);
    laser.FixParameter(9, 0.1);
    laser.FixParameter(14, -2.);
    laser.FixParameter(15, 2);
    laser.FixParameter(11, 1.);
    laser.FixParameter(12, 0.1);
    laser.FixParameter(13, 0.);
    laser.FixParameter(10, 0.);
  }
 
  // make the plot of the fit function nice setting 2000 sampling points
  laser.SetNpx(2000);
 
  // do the fit!
  h_profile->Fit("laser", "R L Q");
 
  // Add by hand the different fit components to the histogram, mostly for debugging/presentation purposes
  TF1* peak1 = new TF1("peak1", laserPDF, maxpos - 1, maxpos + 2., 16);
  TF1* peak2 = new TF1("peak2", laserPDF, maxpos - 1, maxpos + 2., 16);
  TF1* extra = new TF1("extra", laserPDF, maxpos - 1, maxpos + 2., 16);
  TF1* background = new TF1("background", laserPDF, maxpos - 1, maxpos + 2., 16);
  for (int iPar = 0; iPar < 16; iPar++) {
    peak1->FixParameter(iPar, laser.GetParameter(iPar));
    peak2->FixParameter(iPar, laser.GetParameter(iPar));
    extra->FixParameter(iPar, laser.GetParameter(iPar));
    background->FixParameter(iPar, laser.GetParameter(iPar));
  }
  peak1->FixParameter(2, 0.);
  peak1->FixParameter(7, (1 - laser.GetParameter(2))*laser.GetParameter(7));
  peak1->FixParameter(10, 0.);
  peak1->FixParameter(13, 0.);
  peak2->FixParameter(2, 1.);
  peak2->FixParameter(7, laser.GetParameter(2)*laser.GetParameter(7));
  peak2->FixParameter(10, 0.);
  peak2->FixParameter(13, 0.);
  extra->FixParameter(7, 0.);
  extra->FixParameter(13, 0.);
  background->FixParameter(7, 0.);
  background->FixParameter(10, 0.);
 
  h_profile->GetListOfFunctions()->Add(peak1);
  h_profile->GetListOfFunctions()->Add(peak2);
  h_profile->GetListOfFunctions()->Add(extra);
  h_profile->GetListOfFunctions()->Add(background);
 
  // save the results in the variables linked to the tree branches
  m_channel = iChannel;
  m_row = rowOf(iSlot, iChannel);
  m_col = colOf(iSlot, iChannel);
  m_slot = iSlot + 1;
  m_peakTime = laser.GetParameter(0);
  m_peakTimeErr = laser.GetParError(0);
  m_deltaT = laser.GetParameter(3);
  m_deltaTErr = laser.GetParError(3);
  m_sigma = laser.GetParameter(1);
  m_sigmaErr = laser.GetParError(1);
  m_fraction = laser.GetParameter(2);
  m_fractionErr = laser.GetParError(2);
  m_yieldLaser = laser.GetParameter(7) / binw;
  m_yieldLaserErr = laser.GetParError(7) / binw;
  m_timeExtra = laser.GetParameter(8);
  m_sigmaExtra = laser.GetParameter(9);
  m_yieldLaserExtra = laser.GetParameter(10) / binw;
  m_alphaExtra = laser.GetParameter(14);
  m_nExtra = laser.GetParameter(15);
  m_timeBackground = laser.GetParameter(11);
  m_sigmaBackground = laser.GetParameter(12);
  m_yieldLaserBackground = laser.GetParameter(13) / binw;
  m_chi2 = laser.GetChisquare() / laser.GetNDF();
 
  // copy some MC information to the output tree
  m_fractionMC = m_fractionConstraints;
  m_deltaTMC = m_deltaTConstraints;
  m_peakTimeMC = m_peakTimeConstraints;
 
  return;
}

fitInAmpliduteBins()

void fitInAmpliduteBins ( bool isFitInAmplitudeBins )

inline

Enables the fit amplitude bins.

Definition at line 88 of file TOPLocalCalFitter.h.

      {
        m_isFitInAmplitudeBins = isFitInAmplitudeBins;
      }

fitPulser()

void fitPulser ( TH1 * h_profileFirstPulser, TH1 * h_profileSecondPulser )

protected

Fits the two pulsers.

Definition at line 534 of file TOPLocalCalFitter.cc.

{
  float maxpos = h_profileFirstPulser->GetBinCenter(h_profileFirstPulser->GetMaximumBin());
  h_profileFirstPulser->GetXaxis()->SetRangeUser(maxpos - 1, maxpos + 1.);
  if (h_profileFirstPulser->Integral() > 1000) {
    TF1 pulser1 = TF1("pulser1", "[0]*TMath::Gaus(x, [1], [2], kTRUE)", maxpos - 1, maxpos + 1.);
    pulser1.SetParameter(0, 1.);
    pulser1.SetParameter(1, maxpos);
    pulser1.SetParameter(2, 0.05);
    h_profileFirstPulser->Fit("pulser1", "R Q");
    m_firstPulserTime = pulser1.GetParameter(1);
    m_firstPulserSigma = pulser1.GetParameter(2);
    h_profileFirstPulser->Write();
  } else {
    m_firstPulserTime = -999;
    m_firstPulserSigma = -999;
  }
 
  maxpos = h_profileSecondPulser->GetBinCenter(h_profileSecondPulser->GetMaximumBin());
  h_profileSecondPulser->GetXaxis()->SetRangeUser(maxpos - 1, maxpos + 1.);
  if (h_profileSecondPulser->Integral() > 1000) {
    TF1 pulser2 = TF1("pulser2", "[0]*TMath::Gaus(x, [1], [2], kTRUE)", maxpos - 1, maxpos + 1.);
    pulser2.SetParameter(0, 1.);
    pulser2.SetParameter(1, maxpos);
    pulser2.SetParameter(2, 0.05);
    h_profileSecondPulser->Fit("pulser2", "R Q");
    m_secondPulserTime = pulser2.GetParameter(1);
    m_secondPulserSigma = pulser2.GetParameter(2);
    h_profileSecondPulser->Write();
  } else {
    m_secondPulserTime = -999;
    m_secondPulserSigma = -999;
  }
  return;
}

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 algorithm.

Definition at line 164 of file CalibrationAlgorithm.h.

{return getPrefix();}

getDescription()

const std::string & getDescription ( ) const

inlineinherited

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

Definition at line 216 of file CalibrationAlgorithm.h.

{return m_description;}

getExpRunString()

string getExpRunString ( Calibration::ExpRun & expRun ) const

privateinherited

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

Definition at line 254 of file CalibrationAlgorithm.cc.

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

getFullObjectPath()

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

privateinherited

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

Definition at line 263 of file CalibrationAlgorithm.cc.

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

getGranularity()

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()

template<class T>

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

inlineprotectedinherited

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

Definition at line 350 of file CalibrationAlgorithm.h.

    {
      return m_jsonExecutionInput.at(key);
    }

getIovFromAllData()

IntervalOfValidity getIovFromAllData ( ) const

inherited

Get the complete IoV from inspection of collected data.

Definition at line 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()

template<class T>

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

inlineprotectedinherited

Get calibration data object (for all runs the calibration is requested for) This function will only work during or after execute() has been called once.

Definition at line 285 of file CalibrationAlgorithm.h.

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

getOutputJsonValue()

template<class T>

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

inlineprotectedinherited

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

Definition at line 342 of file CalibrationAlgorithm.h.

    {
      return m_jsonExecutionOutput.at(key);
    }

getPayloads()

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

inlineinherited

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

Definition at line 204 of file CalibrationAlgorithm.h.

{return m_data.getPayloads();}

getPayloadValues()

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

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

Definition at line 243 of file CalibrationAlgorithm.h.

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

loadInputJson()

bool loadInputJson ( const std::string & jsonString )

inherited

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

Definition at line 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;
  }
}

loadMCInfoTrees()

void loadMCInfoTrees ( )

protected

loads the TTS parameters and the MC truth info

Definition at line 173 of file TOPLocalCalFitter.cc.

{
  m_inputTTS = TFile::Open(m_TTSData.c_str());
  m_inputConstraints = TFile::Open(m_fitConstraints.c_str());
 
  B2INFO("Getting the  TTS parameters from " << m_TTSData);
  m_inputTTS->cd();
  m_inputTTS->GetObject("tree", m_treeTTS);
  m_treeTTS->SetBranchAddress("mean2", &m_mean2);
  m_treeTTS->SetBranchAddress("sigma1", &m_sigma1);
  m_treeTTS->SetBranchAddress("sigma2", &m_sigma2);
  m_treeTTS->SetBranchAddress("fraction1", &m_f1);
  m_treeTTS->SetBranchAddress("fraction2", &m_f2);
  m_treeTTS->SetBranchAddress("pixelRow", &m_pixelRow);
  m_treeTTS->SetBranchAddress("pixelCol", &m_pixelCol);
 
  buildChannelMaps(); // build the maps rowOf[slot][channel], colOf[slot][channel]
 
  if (m_fitterMode == "MC")
    std::cout << "Running in MC mode, not constraints will be set" << std::endl;
  else {
    B2INFO("Getting the laser fit parameters from " << m_fitConstraints);
    m_inputConstraints->cd();
    m_inputConstraints->GetObject("fitTree", m_treeConstraints);
    m_treeConstraints->SetBranchAddress("peakTime", &m_peakTimeConstraints);
    m_treeConstraints->SetBranchAddress("deltaT", &m_deltaTConstraints);
    m_treeConstraints->SetBranchAddress("fraction", &m_fractionConstraints);
    if (m_fitterMode == "monitoring") {
      m_treeConstraints->SetBranchAddress("timeExtra", &m_timeExtraConstraints);
      m_treeConstraints->SetBranchAddress("sigmaExtra", &m_sigmaExtraConstraints);
      m_treeConstraints->SetBranchAddress("alphaExtra", &m_alphaExtraConstraints);
      m_treeConstraints->SetBranchAddress("nExtra", &m_nExtraConstraints);
      m_treeConstraints->SetBranchAddress("timeBackground", &m_timeBackgroundConstraints);
      m_treeConstraints->SetBranchAddress("sigmaBackground", &m_sigmaBackgroundConstraints);
    }
  }
  return;
}

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();}

rowOf()

short rowOf ( short slot, short ch ) const

inlineprivatenoexcept

Row index for (slot,channel), or -1 if out of bounds.

Definition at line 256 of file TOPLocalCalFitter.h.

      {
        return (slot >= 0 && slot < 16 && ch >= 0 && ch < 512) ? m_rowOf[slot][ch] : short(-1);
      }

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;}

setFitConstraintsFileName()

void setFitConstraintsFileName ( const std::string & fitConstraints )

inline

Sets the name of the root file containing the laser MC time corrections and the fit constraints.

If the monitoringFit option is used (low statistics sample), this file must be the result of an high-statistics fit.

Definition at line 54 of file TOPLocalCalFitter.h.

      {
        m_fitConstraints = fitConstraints;
      }

setFitMode()

void setFitMode ( const std::string & fitterMode )

inline

Sets the fitter mode.

The options are 'calibration' (default), 'monitoring' or 'MC'. The mode affects the number of parameters that are fixed. Use calibration if you are fitting a large sample (1 M events or more) to derive a set of channelT0 calibrations. Use monitoring if you are fitting a smaller sample. The light path fractions and the tail parameters will be constrained according to the constraint file you passed to the fitter (usually the result of a high-statistics fit). Use MC to fit the MC sample and calculate a new set of prism corrections. No parameter is fixed, but the tail components are removed form the fit.

Definition at line 73 of file TOPLocalCalFitter.h.

      {
        if (fitterMode == "calibration")
          B2INFO("Fitter set to calibration mode");
        else if (fitterMode == "monitoring")
          B2INFO("Fitter set to monitoring mode");
        else if (fitterMode == "MC")
          B2INFO("Fitter set to MC mode");
        else
          B2ERROR("Unknown fitter type " << fitterMode  << ". The valid options are calibration, monitoring or MC");
 
        m_fitterMode = fitterMode;
      }

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();
}

setMinEntries()

void setMinEntries ( int minEntries )

inline

Sets the minimum number of entries to perform the calibration in one channel.

Definition at line 40 of file TOPLocalCalFitter.h.

      {
        m_minEntries = minEntries;
      }

setOutputFileName()

void setOutputFileName ( const std::string & output )

inline

Sets the name of the output root file.

Definition at line 46 of file TOPLocalCalFitter.h.

      {
        m_output = output;
      }

setOutputJsonValue()

template<class T>

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

inlineprotectedinherited

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

Definition at line 337 of file CalibrationAlgorithm.h.

{m_jsonExecutionOutput[key] = value;}

setPrefix()

void setPrefix ( const std::string & prefix )

inlineinherited

Set the prefix used to identify datastore objects.

Definition at line 167 of file CalibrationAlgorithm.h.

{m_prefix = prefix;}

setTTSFileName()

void setTTSFileName ( const std::string & TTSData )

inline

Sets the name of the root file containing the TTS parameters.

Definition at line 60 of file TOPLocalCalFitter.h.

      {
        m_TTSData = TTSData;
      }

setupOutputTreeAndFile()

void setupOutputTreeAndFile ( )

protected

prepares the output tree

Definition at line 212 of file TOPLocalCalFitter.cc.

{
  m_histFile = new TFile(m_output.c_str(), "recreate");
  m_histFile->cd();
  m_fitTree = new TTree("fitTree", "fitTree");
  m_fitTree->Branch<short>("channel", &m_channel);
  m_fitTree->Branch<short>("slot", &m_slot);
  m_fitTree->Branch<short>("row", &m_row);
  m_fitTree->Branch<short>("col", &m_col);
  m_fitTree->Branch<float>("peakTime", &m_peakTime);
  m_fitTree->Branch<float>("peakTimeErr", &m_peakTimeErr);
  m_fitTree->Branch<float>("deltaT", &m_deltaT);
  m_fitTree->Branch<float>("deltaTErr", &m_deltaTErr);
  m_fitTree->Branch<float>("sigma", &m_sigma);
  m_fitTree->Branch<float>("sigmaErr", &m_sigmaErr);
  m_fitTree->Branch<float>("fraction", &m_fraction);
  m_fitTree->Branch<float>("fractionErr", &m_fractionErr);
  m_fitTree->Branch<float>("yieldLaser", &m_yieldLaser);
  m_fitTree->Branch<float>("yieldLaserErr", &m_yieldLaserErr);
  m_fitTree->Branch<float>("timeExtra", &m_timeExtra);
  m_fitTree->Branch<float>("sigmaExtra", &m_sigmaExtra);
  m_fitTree->Branch<float>("nExtra", &m_nExtra);
  m_fitTree->Branch<float>("alphaExtra", &m_alphaExtra);
  m_fitTree->Branch<float>("yieldLaserExtra", &m_yieldLaserExtra);
  m_fitTree->Branch<float>("timeBackground", &m_timeBackground);
  m_fitTree->Branch<float>("sigmaBackground", &m_sigmaBackground);
  m_fitTree->Branch<float>("yieldLaserBackground", &m_yieldLaserBackground);
  m_fitTree->Branch<float>("fractionMC", &m_fractionMC);
  m_fitTree->Branch<float>("deltaTMC", &m_deltaTMC);
  m_fitTree->Branch<float>("peakTimeMC", &m_peakTimeMC);
  m_fitTree->Branch<float>("firstPulserTime", &m_firstPulserTime);
  m_fitTree->Branch<float>("firstPulserSigma", &m_firstPulserSigma);
  m_fitTree->Branch<float>("secondPulserTime", &m_secondPulserTime);
  m_fitTree->Branch<float>("secondPulserSigma", &m_secondPulserSigma);
  m_fitTree->Branch<short>("fitStatus", &m_fitStatus);
  m_fitTree->Branch<double>("width", &m_width);
  m_fitTree->Branch<double>("amplitude", &m_amplitude);
  m_fitTree->Branch<float>("chi2", &m_chi2);
  m_fitTree->Branch<float>("rms", &m_rms);
 
 
  if (m_isFitInAmplitudeBins) {
    m_timewalkTree = new TTree("timewalkTree", "timewalkTree");
    m_timewalkTree->Branch<float>("binLowerEdge", &m_binLowerEdge);
    m_timewalkTree->Branch<float>("binUpperEdge", &m_binUpperEdge);
    m_timewalkTree->Branch<short>("channel", &m_channel);
    m_timewalkTree->Branch<short>("slot", &m_slot);
    m_timewalkTree->Branch<short>("row", &m_row);
    m_timewalkTree->Branch<short>("col", &m_col);
    m_timewalkTree->Branch<float>("histoIntegral", &m_histoIntegral);
    m_timewalkTree->Branch<float>("peakTime", &m_peakTime);
    m_timewalkTree->Branch<float>("peakTimeErr", &m_peakTimeErr);
    m_timewalkTree->Branch<float>("deltaT", &m_deltaT);
    m_timewalkTree->Branch<float>("deltaTErr", &m_deltaTErr);
    m_timewalkTree->Branch<float>("sigma", &m_sigma);
    m_timewalkTree->Branch<float>("sigmaErr", &m_sigmaErr);
    m_timewalkTree->Branch<float>("fraction", &m_fraction);
    m_timewalkTree->Branch<float>("fractionErr", &m_fractionErr);
    m_timewalkTree->Branch<float>("yieldLaser", &m_yieldLaser);
    m_timewalkTree->Branch<float>("yieldLaserErr", &m_yieldLaserErr);
    m_timewalkTree->Branch<float>("timeExtra", &m_timeExtra);
    m_timewalkTree->Branch<float>("sigmaExtra", &m_sigmaExtra);
    m_timewalkTree->Branch<float>("nExtra", &m_nExtra);
    m_timewalkTree->Branch<float>("alphaExtra", &m_alphaExtra);
    m_timewalkTree->Branch<float>("yieldLaserExtra", &m_yieldLaserExtra);
    m_timewalkTree->Branch<float>("timeBackground", &m_timeBackground);
    m_timewalkTree->Branch<float>("sigmaBackground", &m_sigmaBackground);
    m_timewalkTree->Branch<float>("yieldLaserBackground", &m_yieldLaserBackground);
    m_timewalkTree->Branch<float>("fractionMC", &m_fractionMC);
    m_timewalkTree->Branch<float>("deltaTMC", &m_deltaTMC);
    m_timewalkTree->Branch<float>("peakTimeMC", &m_peakTimeMC);
    m_timewalkTree->Branch<float>("firstPulserTime", &m_firstPulserTime);
    m_timewalkTree->Branch<float>("firstPulserSigma", &m_firstPulserSigma);
    m_timewalkTree->Branch<float>("secondPulserTime", &m_secondPulserTime);
    m_timewalkTree->Branch<float>("secondPulserSigma", &m_secondPulserSigma);
    m_timewalkTree->Branch<short>("fitStatus", &m_fitStatus);
    m_timewalkTree->Branch<double>("width", &m_width);
    m_timewalkTree->Branch<double>("amplitude", &m_amplitude);
    m_timewalkTree->Branch<float>("chi2", &m_chi2);
    m_timewalkTree->Branch<float>("rms", &m_rms);
  }
 
  if (m_detectCrosstalk) {
 
    // Create tree that stores candidate crosstalk events
    m_crosstalkTree = new TTree("crosstalkTree", "Tree containing candidate crosstalks");
    m_crosstalkTree->Branch<short>("sl0", &m_sl0);
    m_crosstalkTree->Branch<short>("sl1", &m_sl1); // slot numbers (they will be the same, including them for checks)
    m_crosstalkTree->Branch<short>("ch0", &m_ch0);
    m_crosstalkTree->Branch<short>("ch1", &m_ch1); // channel numbers
    m_crosstalkTree->Branch<float>("ht0", &m_ht0);
    m_crosstalkTree->Branch<float>("ht1", &m_ht1); // hit times
    m_crosstalkTree->Branch<float>("a0", &m_a0);
    m_crosstalkTree->Branch<float>("a1", &m_a1); // amplitudes
    m_crosstalkTree->Branch<float>("w0", &m_w0);
    m_crosstalkTree->Branch<float>("w1", &m_w1); // widths
    m_crosstalkTree->Branch<float>("q0", &m_q0);
    m_crosstalkTree->Branch<float>("q1", &m_q1); // integrated charges
    m_crosstalkTree->Branch<float>("f_q0", &m_f_q0); // fraction of charge on channel 0
 
    // Create tree that stores fit results of hits without associated crosstalk
    // Unlike the "vanilla" fitTree, this doesn't contain the results of the fits to the calibration pulses
    m_fitTree_noXtalk = new TTree("fitTreeNoXTalk", "Fits to channels with no detected crosstalk");
    m_fitTree_noXtalk->Branch<short>("channel", &m_channel);
    m_fitTree_noXtalk->Branch<short>("slot", &m_slot);
    m_fitTree_noXtalk->Branch<short>("row", &m_row);
    m_fitTree_noXtalk->Branch<short>("col", &m_col);
    m_fitTree_noXtalk->Branch<float>("peakTime", &m_peakTime);
    m_fitTree_noXtalk->Branch<float>("peakTimeErr", &m_peakTimeErr);
    m_fitTree_noXtalk->Branch<float>("deltaT", &m_deltaT);
    m_fitTree_noXtalk->Branch<float>("deltaTErr", &m_deltaTErr);
    m_fitTree_noXtalk->Branch<float>("sigma", &m_sigma);
    m_fitTree_noXtalk->Branch<float>("sigmaErr", &m_sigmaErr);
    m_fitTree_noXtalk->Branch<float>("fraction", &m_fraction);
    m_fitTree_noXtalk->Branch<float>("fractionErr", &m_fractionErr);
    m_fitTree_noXtalk->Branch<float>("yieldLaser", &m_yieldLaser);
    m_fitTree_noXtalk->Branch<float>("yieldLaserErr", &m_yieldLaserErr);
    m_fitTree_noXtalk->Branch<float>("timeExtra", &m_timeExtra);
    m_fitTree_noXtalk->Branch<float>("sigmaExtra", &m_sigmaExtra);
    m_fitTree_noXtalk->Branch<float>("nExtra", &m_nExtra);
    m_fitTree_noXtalk->Branch<float>("alphaExtra", &m_alphaExtra);
    m_fitTree_noXtalk->Branch<float>("yieldLaserExtra", &m_yieldLaserExtra);
    m_fitTree_noXtalk->Branch<float>("timeBackground", &m_timeBackground);
    m_fitTree_noXtalk->Branch<float>("sigmaBackground", &m_sigmaBackground);
    m_fitTree_noXtalk->Branch<float>("yieldLaserBackground", &m_yieldLaserBackground);
    m_fitTree_noXtalk->Branch<float>("fractionMC", &m_fractionMC);
    m_fitTree_noXtalk->Branch<float>("deltaTMC", &m_deltaTMC);
    m_fitTree_noXtalk->Branch<float>("peakTimeMC", &m_peakTimeMC);
    m_fitTree_noXtalk->Branch<float>("firstPulserTime", &m_firstPulserTime);
    m_fitTree_noXtalk->Branch<float>("firstPulserSigma", &m_firstPulserSigma);
    m_fitTree_noXtalk->Branch<float>("secondPulserTime", &m_secondPulserTime);
    m_fitTree_noXtalk->Branch<float>("secondPulserSigma", &m_secondPulserSigma);
    m_fitTree_noXtalk->Branch<short>("fitStatus", &m_fitStatus);
    m_fitTree_noXtalk->Branch<double>("width", &m_width);
    m_fitTree_noXtalk->Branch<double>("amplitude", &m_amplitude);
    m_fitTree_noXtalk->Branch<float>("chi2", &m_chi2);
    m_fitTree_noXtalk->Branch<float>("rms", &m_rms);
 
  }
 
  return;
}

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 unnecessary...)
  DBStore::Instance().updateEvent(event);
}

Member Data Documentation

m_a0

float m_a0 = NAN

private

Amplitude for channel 0 in pair.

Definition at line 162 of file TOPLocalCalFitter.h.

m_a1

float m_a1 = NAN

private

Amplitude for channel 1 in pair.

Definition at line 163 of file TOPLocalCalFitter.h.

m_allExpRun

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

staticprivateinherited

allExpRun

Definition at line 364 of file CalibrationAlgorithm.h.

m_alphaExtra

float m_alphaExtra = 0.

private

alpha parameter of the tail of the extra peak.

Definition at line 215 of file TOPLocalCalFitter.h.

m_alphaExtraConstraints

float m_alphaExtraConstraints = 0.

private

alpha parameter of the tail of the extra peak.

Definition at line 185 of file TOPLocalCalFitter.h.

m_amplitude

double m_amplitude = 0

private

Pulse height.

For each pixel, it is calculated as the mean over each hit.

Definition at line 248 of file TOPLocalCalFitter.h.

m_binEdges

std::vector<float> m_binEdges = {50, 100, 150, 200, 250, 300, 350, 400, 500, 600, 800, 1000, 1500, 2000}

private

Amplitude bins.

Definition at line 138 of file TOPLocalCalFitter.h.

{50, 100, 150, 200, 250, 300, 350, 400, 500, 600, 800, 1000, 1500, 2000}; 

m_binLowerEdge

float m_binLowerEdge = 0

private

Lower edge of the amplitude bin in which this fit is performed.

Definition at line 192 of file TOPLocalCalFitter.h.

m_binUpperEdge

float m_binUpperEdge = 0

private

Upper edge of the amplitude bin in which this fit is performed.

Definition at line 193 of file TOPLocalCalFitter.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_ch0

short m_ch0 = -1

private

Channel number (0-511)

Definition at line 158 of file TOPLocalCalFitter.h.

m_ch1

short m_ch1 = -1

private

Channel number (0-511)

Definition at line 159 of file TOPLocalCalFitter.h.

m_channel

short m_channel = 0

private

Channel number (0-511)

Definition at line 194 of file TOPLocalCalFitter.h.

m_channelT0

float m_channelT0

private

Initial value:

=
        0.

Raw, channelT0 calibration, defined as peakTime-peakTimeMC.

This constant is not yet normalized to the average constant in the slot, since that part is currently done by the DB importer. When the DB import functionalities will be added to this module, it will be set to the proper channeT0

Definition at line 231 of file TOPLocalCalFitter.h.

m_channelT0Err

float m_channelT0Err = 0.

private

Statistical error on channelT0.

Definition at line 236 of file TOPLocalCalFitter.h.

m_chi2

float m_chi2 = 0

private

Reduced chi2 of the fit.

Definition at line 228 of file TOPLocalCalFitter.h.

m_col

short m_col = 0

private

Pixel column.

Definition at line 197 of file TOPLocalCalFitter.h.

m_colOf

std::array<std::array<short, 512>, 16> m_colOf {}

private

Column index for (slot,channel), or -1 if out of bounds.

Definition at line 252 of file TOPLocalCalFitter.h.

{}; 

m_crosstalkTree

TTree* m_crosstalkTree = nullptr

private

Output tree for crosstalk candidates.

Definition at line 152 of file TOPLocalCalFitter.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_deltaT

float m_deltaT

private

Initial value:

=
        0

Time difference between the main peak and the secondary peak.

Can be either fixed to the MC value or fitted.

Definition at line 199 of file TOPLocalCalFitter.h.

m_deltaTConstraints

float m_deltaTConstraints = 0

private

Distance between the main and the secondary laser peak.

Definition at line 181 of file TOPLocalCalFitter.h.

m_deltaTErr

float m_deltaTErr = 0

private

Statistical error on deltaT.

Definition at line 207 of file TOPLocalCalFitter.h.

m_deltaTMC

float m_deltaTMC = 0.

private

Time difference between the main peak and the secondary peak in the MC simulation.

Definition at line 223 of file TOPLocalCalFitter.h.

m_description

std::string m_description {""}

privateinherited

Description of the algorithm.

Definition at line 385 of file CalibrationAlgorithm.h.

{""};

m_detectCrosstalk

bool m_detectCrosstalk = false

private

Enables the crosstalk detection algorithm.

Definition at line 150 of file TOPLocalCalFitter.h.

m_f1

float m_f1 = 0

private

Fraction of the first gaussian on the TTS parametrization.

Definition at line 174 of file TOPLocalCalFitter.h.

m_f2

float m_f2 = 0

private

Fraction of the second gaussian on the TTS parametrization.

Definition at line 175 of file TOPLocalCalFitter.h.

m_f_q0

float m_f_q0 = NAN

private

Fraction of charge on channel 0 in pair.

Definition at line 168 of file TOPLocalCalFitter.h.

m_firstPulserSigma

float m_firstPulserSigma = 0.

private

Time resolution from the fit of the first electronic pulse, from a Gaussian fit.

Definition at line 239 of file TOPLocalCalFitter.h.

m_firstPulserTime

float m_firstPulserTime = 0.

private

Average time of the first electronic pulse respect to the reference pulse, from a Gaussian fit.

Definition at line 238 of file TOPLocalCalFitter.h.

m_fitConstraints

std::string m_fitConstraints

private

Initial value:

=
        "/group/belle2/group/detector/TOP/calibration/MCreferences/LaserMCParameters.root"

File with the Fit constraints.

Definition at line 132 of file TOPLocalCalFitter.h.

m_fitStatus

short m_fitStatus = 1

private

Fit quality flag, propagated to the constants.

1 if fit did not converge, 0 if it is fine.

Definition at line 245 of file TOPLocalCalFitter.h.

m_fitterMode

std::string m_fitterMode = "calibration"

private

Fit mode.

Can be 'calibration', 'monitoring' or 'MC'

Definition at line 136 of file TOPLocalCalFitter.h.

m_fitTree

TTree* m_fitTree = nullptr

private

Output of the fitter.

The tree containing the fit results.

Definition at line 145 of file TOPLocalCalFitter.h.

m_fitTree_noXtalk

TTree* m_fitTree_noXtalk = nullptr

private

Output tree for non-crosstalk candidates.

Definition at line 153 of file TOPLocalCalFitter.h.

m_fraction

float m_fraction = 0.

private

Fraction of events in the secondary peak.

Definition at line 202 of file TOPLocalCalFitter.h.

m_fractionConstraints

float m_fractionConstraints = 0

private

Fraction of the main peak.

Definition at line 182 of file TOPLocalCalFitter.h.

m_fractionErr

float m_fractionErr = 0.

private

Statistical error on fraction.

Definition at line 209 of file TOPLocalCalFitter.h.

m_fractionMC

float m_fractionMC = 0.

private

Fraction of events in the secondary peak form the MC simulation.

Definition at line 222 of file TOPLocalCalFitter.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_hasChannelMaps

bool m_hasChannelMaps {false}

private

Flag indicating if channel->(row,col) maps have been built.

True after m_rowOf/m_colOf have been built.

Definition at line 253 of file TOPLocalCalFitter.h.

{false}; 

m_histFile

TFile* m_histFile = nullptr

private

Output of the fitter.

The file containing the output trees and histograms

Definition at line 144 of file TOPLocalCalFitter.h.

m_histoIntegral

float m_histoIntegral = 0.

private

Integral of the fitted histogram.

Definition at line 204 of file TOPLocalCalFitter.h.

m_ht0

float m_ht0 = NAN

private

Hit time for channel 0 in pair.

Definition at line 160 of file TOPLocalCalFitter.h.

m_ht1

float m_ht1 = NAN

private

Hit time for channel 1 in pair.

Definition at line 161 of file TOPLocalCalFitter.h.

m_inputConstraints

TFile* m_inputConstraints = nullptr

private

File containing m_treeConstraints.

Definition at line 140 of file TOPLocalCalFitter.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_inputTTS

TFile* m_inputTTS = nullptr

private

File containing m_treeTTS.

Definition at line 139 of file TOPLocalCalFitter.h.

m_isFitInAmplitudeBins

bool m_isFitInAmplitudeBins = false

private

Enables the fit in amplitude bins.

Definition at line 137 of file TOPLocalCalFitter.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_mean2

float m_mean2 = 0

private

Position of the second gaussian of the TTS parametrization with respect to the first one.

Definition at line 171 of file TOPLocalCalFitter.h.

m_minEntries

int m_minEntries = 50

private

Minimum number of entries to perform the fit.

Currently not used

Definition at line 130 of file TOPLocalCalFitter.h.

m_nExtra

float m_nExtra = 0.

private

parameter n of the tail of the extra peak

Definition at line 216 of file TOPLocalCalFitter.h.

m_nExtraConstraints

float m_nExtraConstraints = 0.

private

parameter n of the tail of the extra peak

Definition at line 186 of file TOPLocalCalFitter.h.

m_output

std::string m_output = "laserFitResult.root"

private

Name of the output file.

Definition at line 131 of file TOPLocalCalFitter.h.

m_peakTime

float m_peakTime = 0

private

Fitted time of the main (i.e.

latest) peak

Definition at line 198 of file TOPLocalCalFitter.h.

m_peakTimeConstraints

float m_peakTimeConstraints = 0

private

Time of the main laser peak in the MC simulation (aka MC correction)

Definition at line 180 of file TOPLocalCalFitter.h.

m_peakTimeErr

float m_peakTimeErr = 0

private

Statistical error on peakTime.

Definition at line 206 of file TOPLocalCalFitter.h.

m_peakTimeMC

float m_peakTimeMC

private

Initial value:

=
        0.

Time of the main peak in the MC simulation, i.e.

time of propagation of the light in the prism. This factor is used to get the channelT0 calibration

Definition at line 224 of file TOPLocalCalFitter.h.

m_pixelCol

short m_pixelCol = 0

private

Pixel column.

Definition at line 177 of file TOPLocalCalFitter.h.

m_pixelRow

short m_pixelRow = 0

private

Pixel row.

Definition at line 176 of file TOPLocalCalFitter.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_q0

float m_q0 = NAN

private

Integrated charge for channel 0 in pair.

Definition at line 166 of file TOPLocalCalFitter.h.

m_q1

float m_q1 = NAN

private

Integrated charge for channel 1 in pair.

Definition at line 167 of file TOPLocalCalFitter.h.

m_rms

float m_rms = 0

private

RMS of the histogram used for the fit.

Definition at line 229 of file TOPLocalCalFitter.h.

m_row

short m_row = 0

private

Pixel row.

Definition at line 196 of file TOPLocalCalFitter.h.

m_rowOf

std::array<std::array<short, 512>, 16> m_rowOf {}

private

Row index for (slot,channel), or -1 if out of bounds.

Definition at line 251 of file TOPLocalCalFitter.h.

{}; 

m_runsToInputFiles

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

privateinherited

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

Definition at line 376 of file CalibrationAlgorithm.h.

m_secondPulserSigma

float m_secondPulserSigma = 0.

private

Time resolution from the fit of the first electronic pulse, from a Gaussian fit.

Definition at line 243 of file TOPLocalCalFitter.h.

m_secondPulserTime

float m_secondPulserTime

private

Initial value:

=
        0.

Average time of the second electronic pulse respect to the reference pulse, from a gaussian fit.

Definition at line 241 of file TOPLocalCalFitter.h.

m_sigma

float m_sigma = 0.

private

Gaussian time resolution, fitted.

Definition at line 201 of file TOPLocalCalFitter.h.

m_sigma1

float m_sigma1 = 0

private

Width of the first gaussian on the TTS parametrization.

Definition at line 172 of file TOPLocalCalFitter.h.

m_sigma2

float m_sigma2 = 0

private

Width of the second gaussian on the TTS parametrization.

Definition at line 173 of file TOPLocalCalFitter.h.

m_sigmaBackground

float m_sigmaBackground = 0.

private

Sigma of the gaussian used to describe the background.

Definition at line 219 of file TOPLocalCalFitter.h.

m_sigmaBackgroundConstraints

float m_sigmaBackgroundConstraints = 0.

private

Sigma of the gaussian used to describe the background.

Definition at line 188 of file TOPLocalCalFitter.h.

m_sigmaErr

float m_sigmaErr = 0.

private

Statistical error on sigma.

Definition at line 208 of file TOPLocalCalFitter.h.

m_sigmaExtra

float m_sigmaExtra = 0.

private

Gaussian sigma of the extra peak in the timing tail.

Definition at line 213 of file TOPLocalCalFitter.h.

m_sigmaExtraConstraints

float m_sigmaExtraConstraints = 0

private

Width of the gaussian used to describe the extra peak on the timing distribution tail.

Definition at line 184 of file TOPLocalCalFitter.h.

m_sl0

short m_sl0 = -1

private

Slot ID (1-16)

Definition at line 156 of file TOPLocalCalFitter.h.

m_sl1

short m_sl1 = -1

private

Slot ID (1-16)

Definition at line 157 of file TOPLocalCalFitter.h.

m_slot

short m_slot = 0

private

Slot ID (1-16)

Definition at line 195 of file TOPLocalCalFitter.h.

m_timeBackground

float m_timeBackground = 0.

private

Position of the gaussian used to describe the background, w/ respect to peakTime.

Definition at line 218 of file TOPLocalCalFitter.h.

m_timeBackgroundConstraints

float m_timeBackgroundConstraints = 0.

private

Position of the gaussian used to describe the background, w/ respect to peakTime.

Definition at line 187 of file TOPLocalCalFitter.h.

m_timeExtra

float m_timeExtra = 0.

private

Position of the extra peak seen in the timing tail, w/ respect to peakTime.

Definition at line 212 of file TOPLocalCalFitter.h.

m_timeExtraConstraints

float m_timeExtraConstraints = 0

private

Position of the gaussian used to describe the extra peak on the timing distribution tail.

Definition at line 183 of file TOPLocalCalFitter.h.

m_timewalkTree

TTree* m_timewalkTree

private

Initial value:

=
        nullptr

Output of the fitter.

The tree containing the fit results to be used to study timewalk and asymptotic time resolution.

Definition at line 146 of file TOPLocalCalFitter.h.

m_treeConstraints

TTree* m_treeConstraints

private

Initial value:

=
        nullptr

Input to the fitter.

A tree containing the laser MC corrections and all the parameters to be fixed in the fit

Definition at line 142 of file TOPLocalCalFitter.h.

m_treeTTS

TTree* m_treeTTS = nullptr

private

Input to the fitter.

A tree containing the TTS parametrization for each channel

Definition at line 141 of file TOPLocalCalFitter.h.

m_TTSData

std::string m_TTSData

private

Initial value:

=
        "/group/belle2/group/detector/TOP/calibration/MCreferences/TTSParametrization.root"

File with the TTS parametrization.

Definition at line 134 of file TOPLocalCalFitter.h.

m_w0

float m_w0 = NAN

private

Width for channel 0 in pair.

Definition at line 164 of file TOPLocalCalFitter.h.

m_w1

float m_w1 = NAN

private

Width for channel 1 in pair.

Definition at line 165 of file TOPLocalCalFitter.h.

m_width

double m_width = 0

private

Pulse width.

For each pixel, it is calculated as the mean over each hit.

Definition at line 247 of file TOPLocalCalFitter.h.

m_yieldLaser

float m_yieldLaser = 0.

private

Total number of laser hits from the fitting function integral.

Definition at line 203 of file TOPLocalCalFitter.h.

m_yieldLaserBackground

float m_yieldLaserBackground = 0.

private

Integral of the background gaussian.

Definition at line 220 of file TOPLocalCalFitter.h.

m_yieldLaserErr

float m_yieldLaserErr = 0.

private

Statistical error on yield.

Definition at line 210 of file TOPLocalCalFitter.h.

m_yieldLaserExtra

float m_yieldLaserExtra = 0.

private

Integral of the extra peak.

Definition at line 214 of file TOPLocalCalFitter.h.

---

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

• top/calibration/include/TOPLocalCalFitter.h
• top/calibration/src/TOPLocalCalFitter.cc