SVDdEdxCalibrationAlgorithm Class Reference

Class implementing the SVD dEdx calibration algorithm. More...

#include <SVDdEdxCalibrationAlgorithm.h>

Inheritance diagram for SVDdEdxCalibrationAlgorithm:

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

Public Member Functions
	SVDdEdxCalibrationAlgorithm ()
	Constructor.
virtual	~SVDdEdxCalibrationAlgorithm () override
	Destructor.
void	setMonitoringPlots (bool value=false)
	function to enable plotting
void	setNumDEdxBins (const int &value)
	set the number of dEdx bins for the payloads
void	setNumPBins (const int &value)
	set the number of momentum bins for the payloads
void	setNumBGBins (const int &value)
	set the number of beta*gamma bins for the fits
void	setDEdxCutoff (const double &value)
	set the upper edge of the dEdx binning for the payloads
void	setMinEvtsPerTree (const double &value)
	set the upper edge of the dEdx binning for the payloads
void	setCustomProfile (bool value=true)
	reimplement the profile histogram calculation
void	setFixUnstableFitParameter (bool value=true)
	In the dEdx:betagamma fit, there is one free parameter that makes fit convergence poor.
void	setUsePionBGFunctionForEverything (bool value=false)
	use the pion beta*gamma function for other hadrons
void	setUseProtonBGFunctionForEverything (bool value=false)
	use the proton beta*gamma function for other hadrons
const std::string &	getPrefix () const
	Get the prefix used for getting calibration data.
const 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.
const 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 () const
	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>.

Static Public Member Functions
static bool	checkPyExpRun (PyObject *pyObj)
	Checks that a PyObject can be successfully converted to an ExpRun type.
static Calibration::ExpRun	convertPyExpRun (PyObject *pyObj)
	Performs the conversion of PyObject to ExpRun.

Protected Member Functions
virtual EResult	calibrate () override
	run algorithm on data
void	setInputFileNames (const 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.
const 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	setDescription (const std::string &description)
	Set algorithm description (in constructor)
void	clearCalibrationData ()
	Clear calibration data.
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.

Static Protected Member Functions
static void	updateDBObjPtrs (const unsigned int event, const int run, const int experiment)
	Updates any DBObjPtrs by calling update(event) for DBStore.
static Calibration::ExpRun	getAllGranularityExpRun ()
	Returns the Exp,Run pair that means 'Everything'. Currently unused.

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
TTree *	LambdaMassFit (std::shared_ptr< TTree > preselTree)
	Mass fit for Lambda->ppi.
std::unique_ptr< TList >	LambdaHistogramming (TTree *inputTree)
	produce histograms for protons
TTree *	DstarMassFit (std::shared_ptr< TTree > preselTree)
	Mass fit for D*->Dpi.
std::unique_ptr< TList >	DstarHistogramming (TTree *inputTree)
	produce histograms for K/pi
std::unique_ptr< TList >	GammaHistogramming (std::shared_ptr< TTree > preselTree)
	produce histograms for e
std::unique_ptr< TList >	GenerateNewHistograms (std::shared_ptr< TTree > ttreeLambda, std::shared_ptr< TTree > ttreeDstar, std::shared_ptr< TTree > ttreeGamma, std::shared_ptr< TTree > ttreeGeneric)
	generate high-statistics histograms
std::vector< double >	CreatePBinningScheme ()
	build the binning scheme for the momentum
TH2F *	Normalise2DHisto (TH2F *HistoToNormalise)
	Normalise a given dEdx:momentum histogram in each momentum bin, so that sum of entries in each momentum bin is 1.
TH2F *	PrepareNewHistogram (TH2F *DataHistogram, TString NewName, TF1 *betagamma_function, TF1 *ResolutionFunctionOriginal, double bias_correction)
	Generate a new dEdx:momentum histogram from a function that encodes dEdx:momentum trend and a function that encodes dEdx resolution.
TH1D *	PrepareProfile (TH2F *DataHistogram, TString NewName)
	Reimplement the Profile histogram calculation for a 2D histogram.
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
bool	m_isMakePlots
	produce plots for monitoring
int	m_numDEdxBins = 100
	the number of dEdx bins for the payloads
int	m_numPBins = 69
	the number of momentum bins for the payloads
int	m_numBGBins = 69
	the number of beta*gamma bins for the profile and fitting
double	m_dedxCutoff = 5.e6
	the upper edge of the dEdx binning for the payloads
double	m_dedxMaxPossible = 7.e6
	the approximate max possible value of dEdx
int	m_MinEvtsPerTree
	number of events in TTree below which we don't try to fit
int	m_NToGenerate
	the number of events to be generated in each momentum bin in the new payloads.
bool	m_CustomProfile = 1
	reimplement profile histogram calculation instead of the ROOT implementation?
bool	m_UsePionBGFunctionForEverything
	Assume that the dEdx:betagamma trend is the same for all hadrons; use the pion trend as representative.
bool	m_UseProtonBGFunctionForEverything
	Assume that the dEdx:betagamma trend is the same for all hadrons; use the proton trend as representative.
bool	m_FixUnstableFitParameter
	In the dEdx:betagamma fit, there is one free parameter that makes fit convergence poor.
const double	m_ElectronPDGMass = TDatabasePDG::Instance()->GetParticle(11)->Mass()
	PDG mass for the electron.
const double	m_MuonPDGMass = TDatabasePDG::Instance()->GetParticle(13)->Mass()
	PDG mass for the muon.
const double	m_PionPDGMass = TDatabasePDG::Instance()->GetParticle(211)->Mass()
	PDG mass for the charged pion.
const double	m_KaonPDGMass = TDatabasePDG::Instance()->GetParticle(321)->Mass()
	PDG mass for the charged kaon.
const double	m_ProtonPDGMass = TDatabasePDG::Instance()->GetParticle(2212)->Mass()
	PDG mass for the proton.
const double	m_DeuteronPDGMass = TDatabasePDG::Instance()->GetParticle(1000010020)->Mass()
	PDG mass for the deuteron.
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

Class implementing the SVD dEdx calibration algorithm.

Definition at line 28 of file SVDdEdxCalibrationAlgorithm.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

SVDdEdxCalibrationAlgorithm()

SVDdEdxCalibrationAlgorithm

(

)

Constructor.

Definition at line 41 of file SVDdEdxCalibrationAlgorithm.cc.

                                                         : CalibrationAlgorithm("SVDdEdxCollector"),
  m_isMakePlots(true)
{
  setDescription("SVD dE/dx calibration algorithm");
}

~SVDdEdxCalibrationAlgorithm()

virtual ~SVDdEdxCalibrationAlgorithm ( )

inlineoverridevirtual

Destructor.

Definition at line 40 of file SVDdEdxCalibrationAlgorithm.h.

{}

Member Function Documentation

boundaryFindingSetup()

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

inlineprotectedvirtualinherited

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

Reimplemented in PXDAnalyticGainCalibrationAlgorithm, PXDValidationAlgorithm, SVD3SampleCoGTimeCalibrationAlgorithm, SVD3SampleELSTimeCalibrationAlgorithm, SVDClusterAbsoluteTimeShifterAlgorithm, 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.

{};

calibrate()

CalibrationAlgorithm::EResult calibrate ( )

overrideprotectedvirtual

run algorithm on data

Implements CalibrationAlgorithm.

Definition at line 48 of file SVDdEdxCalibrationAlgorithm.cc.

{
  gROOT->SetBatch(true);
 
  const auto exprun = getRunList()[0];
  B2INFO("ExpRun used for calibration: " << exprun.first << " " << exprun.second);
 
  auto payload = new Belle2::SVDdEdxPDFs();
 
  // Get data objects
  auto ttreeLambda = getObjectPtr<TTree>("Lambda");
  auto ttreeDstar = getObjectPtr<TTree>("Dstar");
  auto ttreeGamma = getObjectPtr<TTree>("Gamma");
  auto ttreeGeneric = getObjectPtr<TTree>("Generic");
 
  if ((ttreeLambda->GetEntries() < m_MinEvtsPerTree) || (ttreeDstar->GetEntries() < m_MinEvtsPerTree)
      || (ttreeGamma->GetEntries() < m_MinEvtsPerTree)) {
    B2WARNING("Not enough data for calibration.");
    return c_NotEnoughData;
  }
 
  // call the calibration function
  std::unique_ptr<TList> GeneratedList = GenerateNewHistograms(ttreeLambda, ttreeDstar, ttreeGamma, ttreeGeneric);
 
  TH2F* histoE = static_cast<TH2F*>(GeneratedList->FindObject("Electron2DHistogramNew"));
  TH2F* histoMu = static_cast<TH2F*>(GeneratedList->FindObject("Muon2DHistogramNew"));
  TH2F* histoPi = static_cast<TH2F*>(GeneratedList->FindObject("Pion2DHistogramNew"));
  TH2F* histoK = static_cast<TH2F*>(GeneratedList->FindObject("Kaon2DHistogramNew"));
  TH2F* histoP = static_cast<TH2F*>(GeneratedList->FindObject("Proton2DHistogramNew"));
  TH2F* histoDeut = static_cast<TH2F*>(GeneratedList->FindObject("Deuteron2DHistogramNew"));
 
  std::vector<double> pbins = CreatePBinningScheme();
  TH2F hEmpty("hEmpty", "A histogram returned if we cannot calibrate", m_numPBins, pbins.data(), m_numDEdxBins, 0, m_dedxCutoff);
  for (int pbin = 0; pbin <= m_numPBins + 1; pbin++) {
    for (int dedxbin = 0; dedxbin <= m_numDEdxBins + 1; dedxbin++) {
      hEmpty.SetBinContent(pbin, dedxbin, 0.01);
    };
  }
 
  B2INFO("Histograms are ready, proceed to creating the payload object...");
  std::vector<TH2F*> hDedxPDFs(6);
 
  std::array<std::string, 6> part = {"Electron", "Muon", "Pion", "Kaon", "Proton", "Deuteron"};
 
  std::unique_ptr<TCanvas> candEdx(new TCanvas("candEdx", "SVD dEdx payloads", 1200, 700));
  candEdx->Divide(3, 2);
  gStyle->SetOptStat(11);
 
  for (bool trunmean : {false, true}) {
    for (int iPart = 0; iPart < 6; iPart++) {
      if (iPart == 0 && trunmean) {
        hDedxPDFs[iPart] = histoE;
        hDedxPDFs[iPart]->SetName("hist_d1_11_trunc");
      } else if (iPart == 1 && trunmean) {
        hDedxPDFs[iPart] = histoMu;
        hDedxPDFs[iPart]->SetName("hist_d1_13_trunc");
      } else if (iPart == 2 && trunmean) {
        hDedxPDFs[iPart] = histoPi;
        hDedxPDFs[iPart]->SetName("hist_d1_211_trunc");
      } else if (iPart == 3 && trunmean) {
        hDedxPDFs[iPart] = histoK;
        hDedxPDFs[iPart]->SetName("hist_d1_321_trunc");
      } else if (iPart == 4 && trunmean) {
        hDedxPDFs[iPart] = histoP;
        hDedxPDFs[iPart]->SetName("hist_d1_2212_trunc");
      } else if (iPart == 5 && trunmean) {
        hDedxPDFs[iPart] = histoDeut;
        hDedxPDFs[iPart]->SetName("hist_d1_1000010020_trunc");
      } else if (iPart == 0 && !trunmean) {
        hDedxPDFs[iPart] = &hEmpty;
        hDedxPDFs[iPart]->SetName("hist_d1_11");
      } else if (iPart == 1 && !trunmean) {
        hDedxPDFs[iPart] = &hEmpty;
        hDedxPDFs[iPart]->SetName("hist_d1_13");
      } else if (iPart == 2 && !trunmean) {
        hDedxPDFs[iPart] = &hEmpty;
        hDedxPDFs[iPart]->SetName("hist_d1_211");
      } else if (iPart == 3 && !trunmean) {
        hDedxPDFs[iPart] = &hEmpty;
        hDedxPDFs[iPart]->SetName("hist_d1_321");
      } else if (iPart == 4 && !trunmean) {
        hDedxPDFs[iPart] = &hEmpty;
        hDedxPDFs[iPart]->SetName("hist_d1_2212");
      } else if (iPart == 5 && !trunmean) {
        hDedxPDFs[iPart] = &hEmpty;
        hDedxPDFs[iPart]->SetName("hist_d1_1000010020");
      } else
        hDedxPDFs[iPart] = &hEmpty;
      payload->setPDF(*hDedxPDFs[iPart], iPart, trunmean);
 
      candEdx->cd(iPart + 1);
      hDedxPDFs[iPart]->SetTitle(Form("%s; p(GeV/c) of %s; dE/dx", hDedxPDFs[iPart]->GetTitle(), part[iPart].data()));
      hDedxPDFs[iPart]->DrawCopy("colz");
    }
 
    if (m_isMakePlots) {
      candEdx->SaveAs("PlotsSVDdEdxPDFs_wTruncMean.pdf");
      std::unique_ptr<TList> l(new TList());
      for (int iPart = 0; iPart < 6; iPart++) {
        l->Add(hDedxPDFs[iPart]);
      }
 
      TFile SVDdEdxPDFsPlotFile("PlotsSVDdEdxPDFs_wTruncMean.root", "RECREATE");
      l->Write("histlist", TObject::kSingleKey);
      SVDdEdxPDFsPlotFile.Close();
    }
 
    // candEdx->SetTitle(Form("Likehood dist. of charged particles from %s, trunmean = %s", idet.data(), check.str().data()));
  }
 
  saveCalibration(payload, "SVDdEdxPDFs");
  B2INFO("SVD dE/dx calibration done!");
 
  return c_OK;
}

checkPyExpRun()

bool checkPyExpRun ( PyObject * pyObj )

staticinherited

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

Checks if the PyObject can be converted to ExpRun.

Definition at line 28 of file CalibrationAlgorithm.cc.

{
  // Is it a sequence?
  if (PySequence_Check(pyObj)) {
    Py_ssize_t nObj = PySequence_Length(pyObj);
    // Does it have 2 objects in it?
    if (nObj != 2) {
      B2DEBUG(29, "ExpRun was a Python sequence which didn't have exactly 2 entries!");
      return false;
    }
    PyObject* item1, *item2;
    item1 = PySequence_GetItem(pyObj, 0);
    item2 = PySequence_GetItem(pyObj, 1);
    // Did the GetItem work?
    if ((item1 == NULL) || (item2 == NULL)) {
      B2DEBUG(29, "A PyObject pointer was NULL in the sequence");
      return false;
    }
    // Are they longs?
    if (PyLong_Check(item1) && PyLong_Check(item2)) {
      long value1, value2;
      value1 = PyLong_AsLong(item1);
      value2 = PyLong_AsLong(item2);
      if (((value1 == -1) || (value2 == -1)) && PyErr_Occurred()) {
        B2DEBUG(29, "An error occurred while converting the PyLong to long");
        return false;
      }
    } else {
      B2DEBUG(29, "One or more of the PyObjects in the ExpRun wasn't a long");
      return false;
    }
    // Make sure to kill off the reference GetItem gave us responsibility for
    Py_DECREF(item1);
    Py_DECREF(item2);
  } else {
    B2DEBUG(29, "ExpRun was not a Python sequence.");
    return false;
  }
  return true;
}

clearCalibrationData()

void clearCalibrationData ( )

inlineprotectedinherited

Clear calibration data.

Definition at line 324 of file CalibrationAlgorithm.h.

{m_data.clearCalibrationData();}

commit() [1/2]

bool commit ( )

inherited

Submit constants from last calibration into database.

Definition at line 302 of file CalibrationAlgorithm.cc.

{
  if (getPayloads().empty())
    return false;
  list<Database::DBImportQuery> payloads = getPayloads();
  B2INFO("Committing " << payloads.size()  << " payloads to database.");
  return Database::Instance().storeData(payloads);
}

commit() [2/2]

bool commit ( std::list< Database::DBImportQuery > payloads )

inherited

Submit constants from a (potentially previous) set of payloads.

Definition at line 312 of file CalibrationAlgorithm.cc.

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

convertPyExpRun()

ExpRun convertPyExpRun ( PyObject * pyObj )

staticinherited

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;
}

CreatePBinningScheme()

std::vector< double > CreatePBinningScheme ( )

inlineprivate

build the binning scheme for the momentum

Definition at line 135 of file SVDdEdxCalibrationAlgorithm.h.

    {
      std::vector<double> pbins;
      pbins.reserve(m_numPBins + 1);
      pbins.push_back(0.0);
      pbins.push_back(0.05);
 
      for (int iBin = 2; iBin <= m_numPBins; iBin++) {
        if (iBin <= 19)
          pbins.push_back(0.025 + 0.025 * iBin);
        else if (iBin <= 59)
          pbins.push_back(pbins.at(19) + 0.05 * (iBin - 19));
        else
          pbins.push_back(pbins.at(59) + 0.3 * (iBin - 59));
      }
 
      return pbins;
    }

DstarHistogramming()

std::unique_ptr< TList > DstarHistogramming ( TTree * inputTree )

private

produce histograms for K/pi

Definition at line 525 of file SVDdEdxCalibrationAlgorithm.cc.

{
  gROOT->SetBatch(true);
  inputTree->SetEstimate(-1);
  std::vector<double> pbins = CreatePBinningScheme();
 
  TH2F* hDstarKMomentum = new TH2F("hist_d1_321_truncMomentum", "hist_d1_321_trunc;Momentum [GeV/c];dEdx [arb. units]", m_numPBins,
                                   pbins.data(),
                                   m_numDEdxBins, 0, m_dedxCutoff);
  // the pion payload
  TH2F* hDstarPiMomentum = new TH2F("hist_d1_211_truncMomentum", "hist_d1_211_trunc;Momentum [GeV/c];dEdx [arb. units]", m_numPBins,
                                    pbins.data(),
                                    m_numDEdxBins, 0, m_dedxCutoff);
 
  inputTree->Draw("KaonSVDdEdx:KaonSVDdEdxTrackMomentum>>hist_d1_321_truncMomentum",
                  "nSignalDstar_sw * (KaonSVDdEdx>0) * (KaonnSVDHits>4)", "goff");
  // the pion one will be built from both pions in the Dstar decay tree
  TH2F* hDstarPiPart1Momentum = static_cast<TH2F*>(hDstarPiMomentum->Clone("hist_d1_211_truncPart1Momentum"));
  TH2F* hDstarPiPart2Momentum = static_cast<TH2F*>(hDstarPiMomentum->Clone("hist_d1_211_truncPart2Momentum"));
 
  inputTree->Draw("PionDSVDdEdx:PionDSVDdEdxTrackMomentum>>hist_d1_211_truncPart1Momentum",
                  "nSignalDstar_sw * (PionDSVDdEdx>0) * (PionDnSVDHits>4)",
                  "goff");
  inputTree->Draw("SlowPionSVDdEdx:SlowPionSVDdEdxTrackMomentum>>hist_d1_211_truncPart2Momentum",
                  "nSignalDstar_sw * (SlowPionSVDdEdx>0) * (SlowPionnSVDHits>4)",
                  "goff");
  hDstarPiMomentum->Add(hDstarPiPart1Momentum);
  hDstarPiMomentum->Add(hDstarPiPart2Momentum);
 
  inputTree->Draw(Form("KaonSVDdEdxTrackMomentum/%f", m_KaonPDGMass), "", "goff",
                  ((inputTree->GetEntries()) / m_numBGBins)*m_numBGBins);
  double* KaonMomentumDataset = inputTree->GetV1();
  TKDTreeBinning* kdBinsK = new TKDTreeBinning(((inputTree->GetEntries()) / m_numBGBins)*m_numBGBins, 1, KaonMomentumDataset,
                                               m_numBGBins);
  const double* binsMinEdgesKOriginal = kdBinsK->SortOneDimBinEdges();
  double* binsMinEdgesK =  const_cast<double*>(binsMinEdgesKOriginal);
  binsMinEdgesK[0] = 0.1;
  binsMinEdgesK[m_numBGBins + 1] = 50.;
 
// get a distribution that contains both pions to get a typical kinematics for the binning scheme
  inputTree->Draw(Form("SlowPionSVDdEdxTrackMomentum/%f * (event %% 2 == 0) + PionDSVDdEdxTrackMomentum/%f * (event %% 2 ==1)",
                       m_PionPDGMass, m_PionPDGMass), "", "goff",
                  ((inputTree->GetEntries()) / m_numBGBins)*m_numBGBins);
  double* PionMomentumDataset = inputTree->GetV1();
 
  TKDTreeBinning* kdBinsPi = new TKDTreeBinning(((inputTree->GetEntries()) / m_numBGBins)*m_numBGBins, 1, PionMomentumDataset,
                                                m_numBGBins);
  const double* binsMinEdgesPiOriginal = kdBinsPi->SortOneDimBinEdges();
  double* binsMinEdgesPi =  const_cast<double*>(binsMinEdgesPiOriginal);
  binsMinEdgesPi[0] = 0.1;
  binsMinEdgesPi[m_numBGBins + 1] = 50.;
 
  TH2F* hDstarKBetaGamma = new TH2F("hist_d1_321_truncBetaGamma", "hist_d1_321_truncBetaGamma;#beta*#gamma;dEdx [arb. units]",
                                    m_numBGBins,
                                    binsMinEdgesK,
                                    m_numDEdxBins, 0, m_dedxMaxPossible);
  // the pion payload
  TH2F* hDstarPiBetaGamma = new TH2F("hist_d1_211_truncBetaGamma", "hist_d1_211_truncBetaGamma;#beta*#gamma;dEdx [arb. units]",
                                     m_numBGBins,
                                     binsMinEdgesPi,
                                     m_numDEdxBins, 0, m_dedxMaxPossible);
 
  inputTree->Draw(Form("KaonSVDdEdx:KaonSVDdEdxTrackMomentum/%f>>hist_d1_321_truncBetaGamma", m_KaonPDGMass),
                  "nSignalDstar_sw * (KaonSVDdEdx>0) * (KaonnSVDHits>4)", "goff");
  // the pion one will be built from both pions in the Dstar decay tree
  TH2F* hDstarPiPart1BetaGamma = static_cast<TH2F*>(hDstarPiBetaGamma->Clone("hist_d1_211_truncPart1BetaGamma"));
  TH2F* hDstarPiPart2BetaGamma = static_cast<TH2F*>(hDstarPiBetaGamma->Clone("hist_d1_211_truncPart2BetaGamma"));
 
  inputTree->Draw(Form("PionDSVDdEdx:PionDSVDdEdxTrackMomentum/%f>>hist_d1_211_truncPart1BetaGamma", m_PionPDGMass),
                  "nSignalDstar_sw * (PionDSVDdEdx>0) * (PionDnSVDHits>4)",
                  "goff");
  inputTree->Draw(Form("SlowPionSVDdEdx:SlowPionSVDdEdxTrackMomentum/%f>>hist_d1_211_truncPart2BetaGamma", m_PionPDGMass),
                  "nSignalDstar_sw * (SlowPionSVDdEdx>0) * (SlowPionnSVDHits>4)", "goff");
  hDstarPiBetaGamma->Add(hDstarPiPart1BetaGamma);
  hDstarPiBetaGamma->Add(hDstarPiPart2BetaGamma);
 
 
 
  // produce the 1D profiles
 
 
  TH1D* PionProfileMomentum = static_cast<TH1D*>(hDstarPiMomentum->ProfileX("PionProfileMomentum"));
  PionProfileMomentum->SetTitle("PionProfile");
  PionProfileMomentum->GetYaxis()->SetRangeUser(0, m_dedxCutoff);
  PionProfileMomentum->GetXaxis()->SetTitle("Momentum, GeV/c");
  PionProfileMomentum->GetYaxis()->SetTitle("dE/dx");
  PionProfileMomentum->SetLineColor(kRed);
 
  TH1D* PionProfileBetaGamma = static_cast<TH1D*>(hDstarPiBetaGamma->ProfileX("PionProfileBetaGamma"));
  if (m_CustomProfile) {
    PionProfileBetaGamma = PrepareProfile(hDstarPiBetaGamma, "PionProfileBetaGamma");
  }
  PionProfileBetaGamma->SetTitle("PionProfile");
  PionProfileBetaGamma->GetYaxis()->SetRangeUser(0, m_dedxCutoff);
  PionProfileBetaGamma->GetXaxis()->SetTitle("#beta*#gamma");
  PionProfileBetaGamma->GetYaxis()->SetTitle("dE/dx");
  PionProfileBetaGamma->SetLineColor(kRed);
 
 
  TH1D* KaonProfileMomentum = static_cast<TH1D*>(hDstarKMomentum->ProfileX("KaonProfileMomentum"));
  KaonProfileMomentum->SetTitle("KaonProfile");
  KaonProfileMomentum->GetYaxis()->SetRangeUser(0, m_dedxCutoff);
  KaonProfileMomentum->GetXaxis()->SetTitle("Momentum, GeV/c");
  KaonProfileMomentum->GetYaxis()->SetTitle("dE/dx");
  KaonProfileMomentum->SetLineColor(kRed);
 
 
  TH1D* KaonProfileBetaGamma = static_cast<TH1D*>(hDstarKBetaGamma->ProfileX("KaonProfileBetaGamma"));
  if (m_CustomProfile) {
    KaonProfileBetaGamma = PrepareProfile(hDstarKBetaGamma, "KaonProfileBetaGamma");
  }
  KaonProfileBetaGamma->SetTitle("KaonProfile");
 
  KaonProfileBetaGamma->GetYaxis()->SetRangeUser(0, m_dedxCutoff);
  KaonProfileBetaGamma->GetXaxis()->SetTitle("#beta*#gamma");
  KaonProfileBetaGamma->GetYaxis()->SetTitle("dE/dx");
  KaonProfileBetaGamma->SetLineColor(kRed);
 
 
 
  //  normalisation
  hDstarKMomentum->Sumw2();
  hDstarKMomentum = Normalise2DHisto(hDstarKMomentum);
 
  hDstarPiMomentum->Sumw2();
  hDstarPiMomentum = Normalise2DHisto(hDstarPiMomentum);
 
  std::unique_ptr<TList> histList(new TList);
  histList->Add(KaonProfileMomentum);
  histList->Add(KaonProfileBetaGamma);
  histList->Add(hDstarKMomentum);
 
  histList->Add(PionProfileMomentum);
  histList->Add(PionProfileBetaGamma);
  histList->Add(hDstarPiMomentum);
 
  if (m_isMakePlots) {
    TFile DstarHistogrammingPlotFile("SVDdEdxCalibrationDstarHistogramming.root", "RECREATE");
    histList->Write();
    DstarHistogrammingPlotFile.Close();
  }
 
  return histList;
}

DstarMassFit()

TTree * DstarMassFit ( std::shared_ptr< TTree > preselTree )

private

Mass fit for D*->Dpi.

Definition at line 386 of file SVDdEdxCalibrationAlgorithm.cc.

{
  B2INFO("Configuring the Dstar fit...");
  gROOT->SetBatch(true);
  RooMsgService::instance().setGlobalKillBelow(RooFit::WARNING);
 
  RooRealVar deltaM("deltaM", "m(D*)-m(D^{0})", 0.139545, 0.151, "GeV/c^{2}");
 
  RooRealVar KaonMomentum("KaonMomentum", "momentum for Kaon (GeV)", -1.e8, 1.e8);
  RooRealVar KaonSVDdEdxTrackMomentum("KaonSVDdEdxTrackMomentum", "momentum for Kaon (GeV), from the track", -1.e8, 1.e8);
  RooRealVar KaonSVDdEdx("KaonSVDdEdx", "", -1.e8, 1.e8);
  RooRealVar PionDMomentum("PionDMomentum", "momentum for pion (GeV)", -1.e8, 1.e8);
  RooRealVar PionDSVDdEdxTrackMomentum("PionDSVDdEdxTrackMomentum", "momentum for pion (GeV), from the track", -1.e8, 1.e8);
  RooRealVar PionDSVDdEdx("PionDSVDdEdx", "", -1.e8, 1.e8);
  RooRealVar SlowPionMomentum("SlowPionMomentum", "momentum for slow pion (GeV)", -1.e8, 1.e8);
  RooRealVar SlowPionSVDdEdxTrackMomentum("SlowPionSVDdEdxTrackMomentum", "momentum for slow pion (GeV), from the track", -1.e8,
                                          1.e8);
  RooRealVar SlowPionSVDdEdx("SlowPionSVDdEdx", "", -1.e8, 1.e8);
  RooRealVar KaonnSVDHits("KaonnSVDHits", "", -1.e8, 1.e8);
  RooRealVar PionDnSVDHits("PionDnSVDHits", "", -1.e8, 1.e8);
  RooRealVar SlowPionnSVDHits("SlowPionnSVDHits", "", -1.e8, 1.e8);
 
  RooRealVar exp("exp", "experiment number", 0, 1.e5);
  RooRealVar run("run", "run number", 0, 1.e8);
  RooRealVar event("event", "event number", 0, 1.e10);
 
  auto variables = new RooArgSet();
  variables->add(deltaM);
  variables->add(KaonMomentum);
  variables->add(KaonSVDdEdxTrackMomentum);
  variables->add(KaonSVDdEdx);
  variables->add(PionDMomentum);
  variables->add(PionDSVDdEdxTrackMomentum);
  variables->add(PionDSVDdEdx);
  variables->add(SlowPionMomentum);
  variables->add(SlowPionSVDdEdxTrackMomentum);
  variables->add(SlowPionSVDdEdx);
  variables->add(KaonnSVDHits);
  variables->add(PionDnSVDHits);
  variables->add(SlowPionnSVDHits);
  variables->add(exp);
  variables->add(run);
  variables->add(event);
 
  RooDataSet* DstarDataset = new RooDataSet("DstarDataset", "DstarDataset", *variables, Import(*preselTree));
 
  if (DstarDataset->sumEntries() == 0) {
    B2FATAL("The Dstar dataset is empty, stopping here");
  }
 
  RooPlot* DstarFitFrame = DstarDataset->plotOn(deltaM.frame());
 
  RooRealVar GaussMean("GaussMean", "GaussMean", 0.145, 0.140, 0.150);
  RooRealVar GaussSigma1("GaussSigma1", "GaussSigma1", 0.01, 1.e-4, 1.0);
  RooGaussian DstarGauss1("DstarGauss1", "DstarGauss1", deltaM, GaussMean, GaussSigma1);
  RooRealVar GaussSigma2("GaussSigma2", "GaussSigma2", 0.001, 1.e-4, 1.0);
  RooGaussian DstarGauss2("DstarGauss2", "DstarGauss2", deltaM, GaussMean, GaussSigma2);
  RooRealVar fracGaussYield("fracGaussYield", "Fraction of two Gaussians", 0.75, 0.0, 1.0);
  RooAddPdf DstarSignalPDF("DstarSignalPDF", "DstarGauss1+DstarGauss2", RooArgList(DstarGauss1, DstarGauss2), fracGaussYield);
 
  RooRealVar dm0Bkg("dm0Bkg", "dm0", 0.13957018, 0.130, 0.140);
  RooRealVar aBkg("aBkg", "a", -0.0784, -0.08, 3.0);
  RooRealVar bBkg("bBkg", "b", -0.444713, -0.5, 0.4);
  RooRealVar cBkg("cBkg", "c", 0.3);
  RooDstD0BG DstarBkgPDF("DstarBkgPDF", "DstarBkgPDF", deltaM, dm0Bkg, cBkg, aBkg, bBkg);
  RooRealVar nSignalDstar("nSignalDstar", "signal yield", 0.5 * preselTree->GetEntries(), 0, preselTree->GetEntries());
  RooRealVar nBkgDstar("nBkgDstar", "background yield", 0.5 * preselTree->GetEntries(), 0, preselTree->GetEntries());
  RooAddPdf totalPDFDstar("totalPDFDstar", "totalPDFDstar pdf", RooArgList(DstarSignalPDF, DstarBkgPDF),
                          RooArgList(nSignalDstar, nBkgDstar));
 
  B2INFO("Dstar: Start fitting...");
  RooFitResult* DstarFitResult = totalPDFDstar.fitTo(*DstarDataset, Save(kTRUE), PrintLevel(-1));
 
  int status = DstarFitResult->status();
  int covqual = DstarFitResult->covQual();
  double diff = nSignalDstar.getValV() + nBkgDstar.getValV() - DstarDataset->sumEntries();
 
  B2INFO("Dstar: Fit status: " << status << "; covariance quality: " << covqual);
  // if the fit is not healthy, try again once before giving up, with a slightly different setup:
  if ((status > 0) || (TMath::Abs(diff) > 1.) || (nSignalDstar.getError() < sqrt(nSignalDstar.getValV()))
      || (nSignalDstar.getError() > (nSignalDstar.getValV()))) {
 
    DstarFitResult = totalPDFDstar.fitTo(*DstarDataset, Save(), Strategy(2), Offset(1));
    status = DstarFitResult->status();
    covqual = DstarFitResult->covQual();
    diff = nSignalDstar.getValV() + nBkgDstar.getValV() - DstarDataset->sumEntries();
    B2INFO("Dstar: Updated fit status: " << status << "; covariance quality: " << covqual);
  }
 
  if ((status > 0) || (TMath::Abs(diff) > 1.) || (nSignalDstar.getError() < sqrt(nSignalDstar.getValV()))
      || (nSignalDstar.getError() > (nSignalDstar.getValV()))) {
    B2WARNING("Dstar: Fit problem: fit status " << status << "; sum of component yields minus the dataset yield is " << diff <<
              "; signal yield is " << nSignalDstar.getValV() << ", while its uncertainty is " << nSignalDstar.getError());
  }
  if (covqual < 2) {
    B2INFO("Dstar: Fit warning: covariance quality " << covqual);
  }
 
  totalPDFDstar.plotOn(DstarFitFrame, LineColor(TColor::GetColor("#4575b4")));
 
  double chisquare = DstarFitFrame->chiSquare();
  B2INFO("Dstar: Fit chi2 = " << chisquare);
  totalPDFDstar.paramOn(DstarFitFrame, Layout(0.63, 0.96, 0.93), Format("NEU", AutoPrecision(2)));
  DstarFitFrame->getAttText()->SetTextSize(0.03);
 
  totalPDFDstar.plotOn(DstarFitFrame, Components("DstarSignalPDF"), LineColor(TColor::GetColor("#d73027")));
  totalPDFDstar.plotOn(DstarFitFrame, Components("DstarBkgPDF"), LineColor(TColor::GetColor("#fc8d59")));
  totalPDFDstar.plotOn(DstarFitFrame, LineColor(TColor::GetColor("#4575b4")));
 
  DstarFitFrame->GetXaxis()->SetTitle("#Deltam [GeV/c^{2}]");
  if (m_isMakePlots) {
    std::unique_ptr<TCanvas> canvDstar(new TCanvas("canvDstar", "canvDstar"));
    canvDstar->cd();
 
    DstarFitFrame->Draw();
 
    canvDstar->Print("SVDdEdxCalibrationFitDstar.pdf");
    TFile DstarFitPlotFile("SVDdEdxCalibrationDstarFitPlotFile.root", "RECREATE");
    canvDstar->Write();
    DstarFitPlotFile.Close();
  }

 
  RooStats::SPlot* sPlotDatasetDstar = new RooStats::SPlot("sData", "An SPlot", *DstarDataset, &totalPDFDstar,
                                                           RooArgList(nSignalDstar, nBkgDstar));
 
  for (int iEvt = 0; iEvt < 5; iEvt++) {
    if (TMath::Abs(sPlotDatasetDstar->GetSWeight(iEvt, "nSignalDstar") + sPlotDatasetDstar->GetSWeight(iEvt, "nBkgDstar") - 1) > 5.e-3)
      B2FATAL("Dstar: sPlot error: sum of weights not equal to 1");
  }
 
  TTree* treeDstarSWeighted = DstarDataset->GetClonedTree();
  treeDstarSWeighted->SetName("treeDstarSWeighted");
 
  B2INFO("Dstar: sPlot done. Proceed to histogramming");
  return treeDstarSWeighted;
}

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 331 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 521 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;
}

GammaHistogramming()

std::unique_ptr< TList > GammaHistogramming ( std::shared_ptr< TTree > preselTree )

private

produce histograms for e

Definition at line 671 of file SVDdEdxCalibrationAlgorithm.cc.

{
  B2INFO("Histogramming the converted photon selection...");
  gROOT->SetBatch(true);
 
 
  if (preselTree->GetEntries() == 0) {
    B2FATAL("The Gamma tree is empty, stopping here");
  }
  preselTree->SetEstimate(-1);
  std::vector<double> pbins = CreatePBinningScheme();
 
 
  TH2F* hGammaEMomentum = new TH2F("hist_d1_11_truncMomentum", "hist_d1_11_trunc;Momentum [GeV/c];dEdx [arb. units]", m_numPBins,
                                   pbins.data(), m_numDEdxBins, 0, m_dedxCutoff);
 
  TH2F* hGammaEPart1Momentum = static_cast<TH2F*>(hGammaEMomentum->Clone("hist_d1_11_truncPart1Momentum"));
  TH2F* hGammaEPart2Momentum = static_cast<TH2F*>(hGammaEMomentum->Clone("hist_d1_11_truncPart2Momentum"));
 
  preselTree->Draw("FirstElectronSVDdEdx:FirstElectronSVDdEdxTrackMomentum>>hist_d1_11_truncPart1Momentum",
                   "FirstElectronSVDdEdx>0 && FirstElectronnSVDHits>4 && DIRA>0.995 && dr>1.2", "goff");
  preselTree->Draw("SecondElectronSVDdEdx:SecondElectronSVDdEdxTrackMomentum>>hist_d1_11_truncPart2Momentum",
                   "SecondElectronSVDdEdx>0 && SecondElectronnSVDHits>4 && DIRA>0.995 && dr>1.2", "goff");
  hGammaEMomentum->Add(hGammaEPart1Momentum);
  hGammaEMomentum->Add(hGammaEPart2Momentum);
 
// get a distribution that contains both pions to get a typical kinematics for the binning scheme
  preselTree->Draw(
    Form("FirstElectronSVDdEdxTrackMomentum/%f* (event %% 2==0) + SecondElectronSVDdEdxTrackMomentum/%f* (event %% 2==1)",
         m_ElectronPDGMass, m_ElectronPDGMass),
    "", "goff", ((preselTree->GetEntries()) / m_numBGBins)*m_numBGBins);
  double* ElectronMomentumDataset = preselTree->GetV1();
 
  TKDTreeBinning* kdBinsE = new TKDTreeBinning(((preselTree->GetEntries()) / m_numBGBins)*m_numBGBins, 1, ElectronMomentumDataset,
                                               m_numBGBins);
  const double* binsMinEdgesEOriginal = kdBinsE->SortOneDimBinEdges();
  double* binsMinEdgesE =  const_cast<double*>(binsMinEdgesEOriginal);
  binsMinEdgesE[0] = 0.;
  binsMinEdgesE[m_numBGBins + 1] = 10000.;
 
 
  TH2F* hGammaEBetaGamma = new TH2F("hist_d1_11_truncBetaGamma", "hist_d1_11_truncBetaGamma;#beta*#gamma;dEdx [arb. units]",
                                    m_numBGBins,
                                    binsMinEdgesE,
                                    m_numDEdxBins, 0, m_dedxMaxPossible);
  TH2F* hGammaEPart1BetaGamma = static_cast<TH2F*>(hGammaEBetaGamma->Clone("hist_d1_11_truncPart1BetaGamma"));
  TH2F* hGammaEPart2BetaGamma = static_cast<TH2F*>(hGammaEBetaGamma->Clone("hist_d1_11_truncPart2BetaGamma"));
 
  preselTree->Draw(Form("FirstElectronSVDdEdx:FirstElectronSVDdEdxTrackMomentum/%f>>hist_d1_11_truncPart1BetaGamma",
                        m_ElectronPDGMass),
                   "FirstElectronSVDdEdx>0 && FirstElectronnSVDHits>4 && DIRA>0.995 && dr>1.2 && FirstElectronSVDdEdx<1.8e6", "goff");
  preselTree->Draw(Form("SecondElectronSVDdEdx:SecondElectronSVDdEdxTrackMomentum/%f>>hist_d1_11_truncPart2BetaGamma",
                        m_ElectronPDGMass),
                   "SecondElectronSVDdEdx>0 && SecondElectronnSVDHits>4 && DIRA>0.995 && dr>1.2 && SecondElectronSVDdEdx<1.8e6", "goff");
  hGammaEBetaGamma->Add(hGammaEPart1BetaGamma);
  hGammaEBetaGamma->Add(hGammaEPart2BetaGamma);
 
 
  // produce the 1D profile (for data-MC comparisons)
  TH1D* ElectronProfileMomentum = static_cast<TH1D*>(hGammaEMomentum->ProfileX("ElectronProfileMomentum"));
  ElectronProfileMomentum->SetTitle("ElectronProfile");
  ElectronProfileMomentum->GetYaxis()->SetRangeUser(0, m_dedxCutoff);
  ElectronProfileMomentum->GetXaxis()->SetTitle("Momentum, GeV/c");
  ElectronProfileMomentum->GetYaxis()->SetTitle("dE/dx");
  ElectronProfileMomentum->SetLineColor(kRed);
 
// beta*gamma profile for the fit
  TH1D* ElectronProfileBetaGamma = static_cast<TH1D*>(hGammaEBetaGamma->ProfileX("ElectronProfileBetaGamma"));
  if (m_CustomProfile) {
    ElectronProfileBetaGamma = PrepareProfile(hGammaEBetaGamma, "ElectronProfileBetaGamma");
  }
  ElectronProfileBetaGamma->SetTitle("ElectronProfile");
 
  ElectronProfileBetaGamma->GetYaxis()->SetRangeUser(0, m_dedxCutoff);
  ElectronProfileBetaGamma->GetXaxis()->SetTitle("#beta*#gamma");
  ElectronProfileBetaGamma->GetYaxis()->SetTitle("dE/dx");
  ElectronProfileBetaGamma->SetLineColor(kRed);
 
 
  hGammaEMomentum = Normalise2DHisto(hGammaEMomentum);
 
  std::unique_ptr<TList> histList(new TList);
  histList->Add(ElectronProfileMomentum);
  histList->Add(ElectronProfileBetaGamma);
  histList->Add(hGammaEMomentum);
 
  if (m_isMakePlots) {
    TFile GammaHistogrammingPlotFile("SVDdEdxCalibrationGammaHistogramming.root", "RECREATE");
    histList->Write();
    GammaHistogrammingPlotFile.Close();
  }
 
  return histList;
 
}

GenerateNewHistograms()

std::unique_ptr< TList > GenerateNewHistograms ( std::shared_ptr< TTree > ttreeLambda, std::shared_ptr< TTree > ttreeDstar, std::shared_ptr< TTree > ttreeGamma, std::shared_ptr< TTree > ttreeGeneric )

private

generate high-statistics histograms

Definition at line 767 of file SVDdEdxCalibrationAlgorithm.cc.

{
  gROOT->SetBatch(true);
  gStyle->SetOptStat(0);
// run the background subtraction and histogramming parts
  TTree* treeLambda = LambdaMassFit(ttreeLambda);
  std::unique_ptr<TList> HistListLambda = LambdaHistogramming(treeLambda);
  TH1D* ProtonProfileBetaGamma = static_cast<TH1D*>(HistListLambda->FindObject("ProtonProfileBetaGamma"));
  TH2F* Proton2DHistogram = static_cast<TH2F*>(HistListLambda->FindObject("hist_d1_2212_truncMomentum"));
 
  TTree* treeDstar = DstarMassFit(ttreeDstar);
  std::unique_ptr<TList> HistListDstar = DstarHistogramming(treeDstar);
  TH1D* PionProfileBetaGamma = static_cast<TH1D*>(HistListDstar->FindObject("PionProfileBetaGamma"));
  TH2F* Pion2DHistogram = static_cast<TH2F*>(HistListDstar->FindObject("hist_d1_211_truncMomentum"));
  TH1D* KaonProfileBetaGamma = static_cast<TH1D*>(HistListDstar->FindObject("KaonProfileBetaGamma"));
  TH2F* Kaon2DHistogram = static_cast<TH2F*>(HistListDstar->FindObject("hist_d1_321_truncMomentum"));
 
  std::unique_ptr<TList> HistListGamma = GammaHistogramming(ttreeGamma);
  TH1D* ElectronProfileBetaGamma = static_cast<TH1D*>(HistListGamma->FindObject("ElectronProfileBetaGamma"));
  TH2F* Electron2DHistogram = static_cast<TH2F*>(HistListGamma->FindObject("hist_d1_11_truncMomentum"));
 
  int cred = TColor::GetColor("#e31a1c");
  PionProfileBetaGamma->SetMarkerSize(4);
  PionProfileBetaGamma->SetLineWidth(2);
  PionProfileBetaGamma->SetMarkerColor(cred);
  PionProfileBetaGamma->SetLineColor(cred);
 
  int cpink = TColor::GetColor("#807dba");
  KaonProfileBetaGamma->SetMarkerSize(4);
  KaonProfileBetaGamma->SetLineWidth(2);
  KaonProfileBetaGamma->SetMarkerColor(cpink);
  KaonProfileBetaGamma->SetLineColor(cpink);
 
  int cblue = TColor::GetColor("#084594");
  ProtonProfileBetaGamma->SetMarkerSize(4);
  ProtonProfileBetaGamma->SetLineWidth(2);
  ProtonProfileBetaGamma->SetMarkerColor(cblue);
  ProtonProfileBetaGamma->SetLineColor(cblue);
 
  int cgreen = TColor::GetColor("#238b45");
  ElectronProfileBetaGamma->SetMarkerSize(4);
  ElectronProfileBetaGamma->SetLineWidth(2);
  ElectronProfileBetaGamma->SetMarkerColor(cgreen);
  ElectronProfileBetaGamma->SetLineColor(cgreen);
 
// prepare the fitting
 
  PionProfileBetaGamma->GetYaxis()->SetRangeUser(5.e5, 5.5e6);
  KaonProfileBetaGamma->GetYaxis()->SetRangeUser(5.e5, 5.5e6);
  ProtonProfileBetaGamma->GetYaxis()->SetRangeUser(5.e5, 5.5e6);
 
// enhance the proton histogram (which ends at beta*gamma around 3) by adding pion data above this value – otherwise the fit is very unstable
  auto PionEdges = PionProfileBetaGamma->GetXaxis()->GetXbins()->GetArray();
  auto ProtonEdges = ProtonProfileBetaGamma->GetXaxis()->GetXbins()->GetArray();
 
  std::vector<float> CombinedEdgesVector;
 
  double borderline = 3.;
 
  for (int i = 0; i < ProtonProfileBetaGamma->GetNbinsX() + 1; i++)
    if (ProtonEdges[i] < borderline) CombinedEdgesVector.push_back(ProtonEdges[i]);
 
 
  for (int i = 0; i < PionProfileBetaGamma->GetNbinsX() + 1; i++)
    if (PionEdges[i] > borderline) CombinedEdgesVector.push_back(PionEdges[i]);
 
 
  TH1D* CombinedHistogramPAndPi = new TH1D("CombinedHistogramPAndPi", "histo_for_fit", CombinedEdgesVector.size() - 1,
                                           CombinedEdgesVector.data());
 
  int iterator = 1;
  for (int i = 1; i < ProtonProfileBetaGamma->GetNbinsX() + 1; i++)
    if (ProtonEdges[i - 1] < borderline) {
      CombinedHistogramPAndPi->SetBinContent(i, ProtonProfileBetaGamma->GetBinContent(i));
      CombinedHistogramPAndPi->SetBinError(i, ProtonProfileBetaGamma->GetBinError(i));
      iterator++;
    }
 
  for (int i = 1; i < PionProfileBetaGamma->GetNbinsX() + 1; i++)
    if (PionEdges[i - 1] > borderline) {
 
      CombinedHistogramPAndPi->SetBinContent(iterator, PionProfileBetaGamma->GetBinContent(i));
      CombinedHistogramPAndPi->SetBinError(iterator, PionProfileBetaGamma->GetBinError(i));
      iterator++;
    }
 
// define the beta*gamma vs momentum function
  TF1* BetaGammaFunctionPion = new TF1("BetaGammaFunctionPion", "[0] + [1] * x/[2] +  [5]/(x^2/[2]^2 + [3])**[4] + [6]* (x/[2])**0.5",
                                       0.01, 25.);
 
  BetaGammaFunctionPion->SetNpx(1000);
 
  BetaGammaFunctionPion->SetParameters(5.e5, 2.e3, 1, 0.15, 1.2, 6.e5, 3.e5);
 
  BetaGammaFunctionPion->SetParLimits(0, 3.e5, 7.e5);
  BetaGammaFunctionPion->SetParLimits(1, -3.e4, 1.e4);
  BetaGammaFunctionPion->SetParLimits(3, 0.1, 0.2);
  BetaGammaFunctionPion->SetParLimits(4, 0.9, 1.6);
  BetaGammaFunctionPion->SetParLimits(5, 3.e5, 7.e5);
  BetaGammaFunctionPion->SetParLimits(6, 0., 1.e6);
  BetaGammaFunctionPion->FixParameter(2, 1);
  if (m_FixUnstableFitParameter) BetaGammaFunctionPion->FixParameter(3, 0.15);
 
// fit it to the pion data
  ROOT::Math::MinimizerOptions::SetDefaultMinimizer("Minuit2", "Migrad");
  auto FitResultBetaGammaPion = PionProfileBetaGamma->Fit("BetaGammaFunctionPion", "0SI", "", 0.4, 25);
 
  if ((FitResultBetaGammaPion->Status() > 1) || (BetaGammaFunctionPion->Eval(1) < 5.e5) || (BetaGammaFunctionPion->Eval(1) > 5.e6)) {
    BetaGammaFunctionPion->FixParameter(3, 0.15);
    FitResultBetaGammaPion = PionProfileBetaGamma->Fit("BetaGammaFunctionPion", "0SI", "", 0.4, 25);
  }
  if ((FitResultBetaGammaPion->Status() > 1) || (BetaGammaFunctionPion->Eval(1) < 5.e5) || (BetaGammaFunctionPion->Eval(1) > 5.e6)) {
    BetaGammaFunctionPion->FixParameter(3, 0.15);
    FitResultBetaGammaPion = PionProfileBetaGamma->Fit("BetaGammaFunctionPion", "0SI", "", 0.45, 25);
  }
  if ((FitResultBetaGammaPion->Status() > 1) || (BetaGammaFunctionPion->Eval(1) < 5.e5) || (BetaGammaFunctionPion->Eval(1) > 5.e6)) {
    BetaGammaFunctionPion->FixParameter(3, 0.15);
    FitResultBetaGammaPion = PionProfileBetaGamma->Fit("BetaGammaFunctionPion", "0S", "", 0.5, 25);
  }
 
  B2INFO("BetaGamma fit for pions done. Fit status: " <<  FitResultBetaGammaPion->Status());
  // B2INFO(FitResultBetaGammaPion->Print(Belle2::LogConfig::c_Info));
  B2INFO("Fit parameters:");
  B2INFO("p0: " << BetaGammaFunctionPion->GetParameter(0) << " +- " << BetaGammaFunctionPion->GetParError(0));
  B2INFO("p1: " << BetaGammaFunctionPion->GetParameter(1) << " +- " << BetaGammaFunctionPion->GetParError(1));
  B2INFO("p2: " << BetaGammaFunctionPion->GetParameter(2) << " +- " << BetaGammaFunctionPion->GetParError(2));
  B2INFO("p3: " << BetaGammaFunctionPion->GetParameter(3) << " +- " << BetaGammaFunctionPion->GetParError(3));
  B2INFO("p4: " << BetaGammaFunctionPion->GetParameter(4) << " +- " << BetaGammaFunctionPion->GetParError(4));
  B2INFO("p5: " << BetaGammaFunctionPion->GetParameter(5) << " +- " << BetaGammaFunctionPion->GetParError(5));
  B2INFO("p6: " << BetaGammaFunctionPion->GetParameter(6) << " +- " << BetaGammaFunctionPion->GetParError(6));
 
  // repeat the same for kaons
  TF1* BetaGammaFunctionKaon = new TF1("BetaGammaFunctionKaon", "[0] + [1] * x/[2] +  [5]/(x^2/[2]^2 + [3])**[4]+ [6]* (x/[2])**0.5",
                                       0.01, 25.);
 
  BetaGammaFunctionKaon->SetNpx(1000);
  BetaGammaFunctionKaon->SetParameters(5.e5, 2.e3, 1, 0.15, 1.2, 6.e5, 3.e5);
 
  BetaGammaFunctionKaon->SetParLimits(0, 3.e5, 7.e5);
  BetaGammaFunctionKaon->SetParLimits(1, -3.e4, 1.e4);
  BetaGammaFunctionKaon->SetParLimits(3, 0.1, 0.2);
  BetaGammaFunctionKaon->SetParLimits(4, 0.9, 1.6);
  BetaGammaFunctionKaon->SetParLimits(5, 3.e5, 7.e5);
  BetaGammaFunctionKaon->SetParLimits(6, 0., 1.e6);
 
  BetaGammaFunctionKaon->FixParameter(2, 1);
  if (m_FixUnstableFitParameter) BetaGammaFunctionKaon->FixParameter(3, 0.15);
 
  BetaGammaFunctionKaon->SetLineColor(KaonProfileBetaGamma->GetMarkerColor());
 
  auto FitResultBetaGammaKaon = KaonProfileBetaGamma->Fit("BetaGammaFunctionKaon", "0SI", "", 0.4, 8.5);
 
  if ((FitResultBetaGammaKaon->Status() > 1) || (BetaGammaFunctionKaon->Eval(1) < 5.e5) || (BetaGammaFunctionKaon->Eval(1) > 5.e6)) {
    BetaGammaFunctionKaon->FixParameter(3, 0.15);
    FitResultBetaGammaKaon = KaonProfileBetaGamma->Fit("BetaGammaFunctionKaon", "0SI", "", 0.4, 8.5);
  }
  if ((FitResultBetaGammaKaon->Status() > 1) || (BetaGammaFunctionKaon->Eval(1) < 5.e5) || (BetaGammaFunctionKaon->Eval(1) > 5.e6)) {
    BetaGammaFunctionKaon->FixParameter(3, 0.15);
    FitResultBetaGammaKaon = KaonProfileBetaGamma->Fit("BetaGammaFunctionKaon", "0SI", "", 0.45, 8);
  }
  if ((FitResultBetaGammaKaon->Status() > 1) || (BetaGammaFunctionKaon->Eval(1) < 5.e5) || (BetaGammaFunctionKaon->Eval(1) > 5.e6)) {
    BetaGammaFunctionKaon->FixParameter(3, 0.15);
    FitResultBetaGammaKaon = KaonProfileBetaGamma->Fit("BetaGammaFunctionKaon", "0S", "", 0.5, 8);
  }
 
  B2INFO("BetaGamma fit for kaons done. Fit status: " <<  FitResultBetaGammaKaon->Status());
  B2INFO("Fit parameters:");
  B2INFO("p0: " << BetaGammaFunctionKaon->GetParameter(0) << " +- " << BetaGammaFunctionKaon->GetParError(0));
  B2INFO("p1: " << BetaGammaFunctionKaon->GetParameter(1) << " +- " << BetaGammaFunctionKaon->GetParError(1));
  B2INFO("p2: " << BetaGammaFunctionKaon->GetParameter(2) << " +- " << BetaGammaFunctionKaon->GetParError(2));
  B2INFO("p3: " << BetaGammaFunctionKaon->GetParameter(3) << " +- " << BetaGammaFunctionKaon->GetParError(3));
  B2INFO("p4: " << BetaGammaFunctionKaon->GetParameter(4) << " +- " << BetaGammaFunctionKaon->GetParError(4));
  B2INFO("p5: " << BetaGammaFunctionKaon->GetParameter(5) << " +- " << BetaGammaFunctionKaon->GetParError(5));
  B2INFO("p6: " << BetaGammaFunctionKaon->GetParameter(6) << " +- " << BetaGammaFunctionKaon->GetParError(6));
 
  // repeat the same for protons
  TF1* BetaGammaFunctionProton = new TF1("BetaGammaFunctionProton",
                                         "[0] + [1] * x/[2] +  [5]/(x^2/[2]^2 + [3])**[4]+ [6]* (x/[2])**0.5", 0.01, 25.);
 
  BetaGammaFunctionProton->SetNpx(1000);
 
  BetaGammaFunctionProton->SetParameters(5.e5, 2.e3, 1, 0.15, 1.2, 6.e5, 3.e5);
 
  BetaGammaFunctionProton->SetParLimits(0, 3.e5, 7.e5);
  BetaGammaFunctionProton->SetParLimits(1, -3.e4, 1.e4);
  BetaGammaFunctionProton->SetParLimits(3, 0.1, 0.2);
  BetaGammaFunctionProton->SetParLimits(4, 0.9, 1.6);
  BetaGammaFunctionProton->SetParLimits(5, 3.e5, 7.e5);
  BetaGammaFunctionProton->SetParLimits(6, 0., 1.e6);
 
  BetaGammaFunctionProton->FixParameter(2, 1);
  if (m_FixUnstableFitParameter) BetaGammaFunctionProton->FixParameter(3, 0.15);
 
  BetaGammaFunctionProton->SetLineColor(ProtonProfileBetaGamma->GetMarkerColor());
 
  auto FitResultBetaGammaProton = CombinedHistogramPAndPi->Fit("BetaGammaFunctionProton", "0SI", "", 0.45, 15);
 
  if ((FitResultBetaGammaProton->Status() > 1) || (BetaGammaFunctionProton->Eval(1) < 5.e5)
      || (BetaGammaFunctionProton->Eval(1) > 5.e6)) {
    BetaGammaFunctionProton->FixParameter(3, 0.15);
    FitResultBetaGammaProton = CombinedHistogramPAndPi->Fit("BetaGammaFunctionProton", "0SI", "", 0.45, 15);
  }
  if ((FitResultBetaGammaProton->Status() > 1) || (BetaGammaFunctionProton->Eval(1) < 5.e5)
      || (BetaGammaFunctionProton->Eval(1) > 5.e6)) {
    BetaGammaFunctionProton->FixParameter(3, 0.15);
    FitResultBetaGammaProton = CombinedHistogramPAndPi->Fit("BetaGammaFunctionProton", "0SI", "", 0.45, 10);
  }
  if ((FitResultBetaGammaProton->Status() > 1) || (BetaGammaFunctionProton->Eval(1) < 5.e5)
      || (BetaGammaFunctionProton->Eval(1) > 5.e6)) {
    BetaGammaFunctionProton->FixParameter(3, 0.15);
    FitResultBetaGammaProton = CombinedHistogramPAndPi->Fit("BetaGammaFunctionProton", "0S", "", 0.5, 10);
  }
 
  B2INFO("BetaGamma fit for protons done. Fit status: " <<  FitResultBetaGammaProton->Status());
  B2INFO("Fit parameters:");
  B2INFO("p0: " << BetaGammaFunctionProton->GetParameter(0) << " +- " << BetaGammaFunctionProton->GetParError(0));
  B2INFO("p1: " << BetaGammaFunctionProton->GetParameter(1) << " +- " << BetaGammaFunctionProton->GetParError(1));
  B2INFO("p2: " << BetaGammaFunctionProton->GetParameter(2) << " +- " << BetaGammaFunctionProton->GetParError(2));
  B2INFO("p3: " << BetaGammaFunctionProton->GetParameter(3) << " +- " << BetaGammaFunctionProton->GetParError(3));
  B2INFO("p4: " << BetaGammaFunctionProton->GetParameter(4) << " +- " << BetaGammaFunctionProton->GetParError(4));
  B2INFO("p5: " << BetaGammaFunctionProton->GetParameter(5) << " +- " << BetaGammaFunctionProton->GetParError(5));
  B2INFO("p6: " << BetaGammaFunctionProton->GetParameter(6) << " +- " << BetaGammaFunctionProton->GetParError(6));
 
  if (m_isMakePlots) {
// plot a summary of all beta*gamma fits for hadrons
    std::unique_ptr<TCanvas> CombinedCanvasHadrons(new TCanvas("CombinedCanvasHadrons", "Hadron beta*gamma fits", 10, 10, 1000, 700));
    gStyle->SetOptFit(1111);
 
    PionProfileBetaGamma->Draw();
    PionProfileBetaGamma->GetListOfFunctions()->Add(BetaGammaFunctionPion);
    KaonProfileBetaGamma->Draw("SAME");
    KaonProfileBetaGamma->GetListOfFunctions()->Add(BetaGammaFunctionKaon);
    ProtonProfileBetaGamma->Draw("SAME");
    ProtonProfileBetaGamma->GetListOfFunctions()->Add(BetaGammaFunctionProton);
// BetaGammaFunctionPion->Draw("SAME");
// BetaGammaFunctionKaon->Draw("SAME");
// BetaGammaFunctionProton->Draw("SAME");
    auto legend = new TLegend(0.4, 0.7, 0.65, 0.9);
    legend->AddEntry(PionProfileBetaGamma, "Pions", "lep");
    legend->AddEntry(KaonProfileBetaGamma, "Kaons", "lep");
    legend->AddEntry(ProtonProfileBetaGamma, "Protons", "lep");
    legend->Draw();
 
    gPad->SetLogx();
    gPad->SetLogy();
 
    CombinedCanvasHadrons->Print("HadronBetaGammaFits.pdf");
    TFile HadronFitPlotFile("SVDdEdxCalibrationHadronFitPlotFile.root", "RECREATE");
    PionProfileBetaGamma->Write();
    KaonProfileBetaGamma->Write();
    ProtonProfileBetaGamma->Write();
    CombinedHistogramPAndPi->Write();
    BetaGammaFunctionPion->Write();
    BetaGammaFunctionKaon->Write();
    BetaGammaFunctionProton->Write();
    CombinedCanvasHadrons->Write();
    HadronFitPlotFile.Close();
  }
 
 
  // in case we assume that all hadrons are equal
  if (m_UsePionBGFunctionForEverything) {
    BetaGammaFunctionKaon = static_cast<TF1*>(BetaGammaFunctionPion->Clone("BetaGammaFunctionKaon"));
    BetaGammaFunctionProton = static_cast<TF1*>(BetaGammaFunctionPion->Clone("BetaGammaFunctionProton"));
  }
  if (m_UseProtonBGFunctionForEverything) {
    BetaGammaFunctionKaon = static_cast<TF1*>(BetaGammaFunctionProton->Clone("BetaGammaFunctionKaon"));
    BetaGammaFunctionPion = static_cast<TF1*>(BetaGammaFunctionProton->Clone("BetaGammaFunctionPion"));
  }
 
  // sanity checks: are all fits ok?
  if ((FitResultBetaGammaProton->Status() > 1) || (BetaGammaFunctionProton->Eval(1) < 5.e5)
      || (BetaGammaFunctionProton->Eval(1) > 5.e6)) {
    if (FitResultBetaGammaPion->Status() == 0) {
      BetaGammaFunctionProton = static_cast<TF1*>(BetaGammaFunctionPion->Clone("BetaGammaFunctionProton"));
    } else if (FitResultBetaGammaKaon->Status() == 0) {
      BetaGammaFunctionProton = static_cast<TF1*>(BetaGammaFunctionKaon->Clone("BetaGammaFunctionProton"));
    } else {
      B2WARNING("Problem with the beta*gamma fit for protons, reverting to the default values");
      BetaGammaFunctionProton->SetParameters(450258, -10900.8, 1, 0.126797, 1.155, 641907, 86304.5);
    }
  }
 
  if ((FitResultBetaGammaKaon->Status() > 1) || (BetaGammaFunctionKaon->Eval(1) < 5.e5) || (BetaGammaFunctionKaon->Eval(1) > 5.e6)) {
    if (FitResultBetaGammaProton->Status() == 0) {
      BetaGammaFunctionKaon = static_cast<TF1*>(BetaGammaFunctionProton->Clone("BetaGammaFunctionKaon"));
    } else if (FitResultBetaGammaPion->Status() == 0) {
      BetaGammaFunctionKaon = static_cast<TF1*>(BetaGammaFunctionPion->Clone("BetaGammaFunctionKaon"));
    } else {
      B2WARNING("Problem with the beta*gamma fit for kaons, reverting to the default values");
      BetaGammaFunctionKaon->SetParameters(543386, 3013.81, 1, 0.135517, 1.19742, 619509, 15484.4);
    }
  }
 
  if ((FitResultBetaGammaPion->Status() > 1) || (BetaGammaFunctionPion->Eval(1) < 5.e5) || (BetaGammaFunctionPion->Eval(1) > 5.e6)) {
    if (FitResultBetaGammaKaon->Status() == 0) {
      BetaGammaFunctionPion = static_cast<TF1*>(BetaGammaFunctionKaon->Clone("BetaGammaFunctionPion"));
    } else if (FitResultBetaGammaProton->Status() == 0) {
      BetaGammaFunctionPion = static_cast<TF1*>(BetaGammaFunctionProton->Clone("BetaGammaFunctionPion"));
    } else {
      B2WARNING("Problem with the beta*gamma fit for pions, reverting to the default values");
      BetaGammaFunctionPion->SetParameters(537623, -1937.62, 1, 0.15292, 1.23803, 623678, 30400.9);
    }
  }
 
 
// electrons
 
  TF1* BetaGammaFunctionElectron = new TF1("BetaGammaFunctionElectron", "[0] + [1]* x", 1, 10000.);
  BetaGammaFunctionElectron->SetParameters(6.e5, -1);
  BetaGammaFunctionElectron->SetParLimits(0, 3.e5, 8.e5);
  BetaGammaFunctionElectron->SetParLimits(1, -1.e5, 1.e5);
  auto FitResultBetaGammaElectron = ElectronProfileBetaGamma->Fit("BetaGammaFunctionElectron", "0SI", "", 100, 8000);
 
 
  if ((FitResultBetaGammaElectron->Status() > 1) || (BetaGammaFunctionElectron->Eval(1) < 3.e5)
      || (BetaGammaFunctionElectron->Eval(1) > 5.e6)) {
    FitResultBetaGammaElectron = ElectronProfileBetaGamma->Fit("BetaGammaFunctionElectron", "0S", "", 100, 10000);
  }
  B2INFO("BetaGamma fit for electrons done. Fit status: " <<  FitResultBetaGammaElectron->Status());
  B2INFO("Fit parameters:");
  B2INFO("p0: " << BetaGammaFunctionElectron->GetParameter(0) << " +- " << BetaGammaFunctionElectron->GetParError(0));
  B2INFO("p1: " << BetaGammaFunctionElectron->GetParameter(1) << " +- " << BetaGammaFunctionElectron->GetParError(1));
 
 
  ElectronProfileBetaGamma->SetMarkerSize(4);
  ElectronProfileBetaGamma->SetLineWidth(2);
  ElectronProfileBetaGamma->GetYaxis()->SetRangeUser(5e5, 1e6);
  ElectronProfileBetaGamma->GetListOfFunctions()->Add(BetaGammaFunctionElectron);
 
  if (m_isMakePlots) {
    std::unique_ptr<TCanvas> ElectronCanvas(new TCanvas("ElectronCanvas", "Electron histogram", 10, 10, 1000, 700));
    ElectronProfileBetaGamma->Draw();
 
    gPad->SetLogx();
 
    ElectronCanvas->Print("ElectronBetaGammaFits.pdf");
    TFile ElectronFitPlotFile("SVDdEdxCalibrationElectronFitPlotFile.root", "RECREATE");
    ElectronProfileBetaGamma->Write();
    BetaGammaFunctionElectron->Write();
    ElectronCanvas->Write();
    ElectronFitPlotFile.Close();
  }
 
  TF1* MomentumFunctionElectron = static_cast<TF1*>(BetaGammaFunctionElectron->Clone("MomentumFunctionElectron"));
  MomentumFunctionElectron->SetParameter(2, m_ElectronPDGMass);
  MomentumFunctionElectron->SetRange(0.01, 5.5);
  MomentumFunctionElectron->SetLineColor(kRed);
  MomentumFunctionElectron->SetLineWidth(4);
 
  TF1* MomentumFunctionPion = static_cast<TF1*>(BetaGammaFunctionPion->Clone("MomentumFunctionPion"));
  MomentumFunctionPion->SetParameter(2, m_PionPDGMass);
  MomentumFunctionPion->SetRange(0.01, 5.5);
  MomentumFunctionPion->SetLineColor(kRed);
  MomentumFunctionPion->SetLineWidth(4);
 
  TF1* MomentumFunctionProton = static_cast<TF1*>(BetaGammaFunctionProton->Clone("MomentumFunctionProton"));
  MomentumFunctionProton->SetParameter(2, m_ProtonPDGMass);
  MomentumFunctionProton->SetRange(0.01, 5.5);
  MomentumFunctionProton->SetLineColor(kRed);
  MomentumFunctionProton->SetLineWidth(4);
 
  TF1* MomentumFunctionKaon = static_cast<TF1*>(BetaGammaFunctionKaon->Clone("MomentumFunctionKaon"));
  MomentumFunctionKaon->SetParameter(2, m_KaonPDGMass);
  MomentumFunctionKaon->SetRange(0.01, 5.5);
  MomentumFunctionKaon->SetLineColor(kRed);
  MomentumFunctionKaon->SetLineWidth(4);
 
  gStyle->SetOptFit(1111);
  std::unique_ptr<TCanvas> CanvasOverlays(new TCanvas("CanvasOverlays", "overlays", 1300, 1000));
  CanvasOverlays->Divide(2, 2);
  CanvasOverlays->cd(1); Electron2DHistogram->Draw();   MomentumFunctionElectron->Draw("SAME");
  CanvasOverlays->cd(2); Pion2DHistogram->Draw();  MomentumFunctionPion->Draw("SAME");
  CanvasOverlays->cd(3); Kaon2DHistogram->Draw();  MomentumFunctionKaon->Draw("SAME");
  CanvasOverlays->cd(4); Proton2DHistogram->Draw();  MomentumFunctionProton->Draw("SAME");
  CanvasOverlays->Print("SVDdEdxOverlaysFitsHistos.pdf");
 
  TF1* MomentumFunctionDeuteron = static_cast<TF1*>(BetaGammaFunctionProton->Clone("MomentumFunctionDeuteron"));
  MomentumFunctionDeuteron->SetParameter(2, m_DeuteronPDGMass);
  MomentumFunctionDeuteron->SetRange(0.01, 5.5);
  MomentumFunctionDeuteron->SetLineColor(kRed);
 
  TF1* MomentumFunctionMuon = static_cast<TF1*>(BetaGammaFunctionPion->Clone("MomentumFunctionMuon"));
  MomentumFunctionMuon->SetParameter(2, m_MuonPDGMass);
  MomentumFunctionMuon->SetRange(0.01, 5.5);
  MomentumFunctionMuon->SetLineColor(kRed);
 
// overlay all fits in one plot
 
  std::unique_ptr<TCanvas> OverlayAllTracksCanvas(new TCanvas("OverlayAllTracksCanvas", "The Ultimate Plot", 10, 10, 1000, 700));
 
  TH2F* AllTracksHistogram = new TH2F("AllTracksHistogram", "AllTracksHistogram;Momentum [GeV/c];dEdx [arb. units]", 1000, 0.05, 5,
                                      1000, 2.e5, 6.e6);
 
  ttreeGeneric->Draw("TrackSVDdEdx:TrackSVDdEdxTrackMomentum>>AllTracksHistogram", "TracknSVDHits>7", "goff");
  AllTracksHistogram->Draw("COLZ");
  AllTracksHistogram->GetXaxis()->SetTitle("Momentum [GeV/c]");
  AllTracksHistogram->GetYaxis()->SetTitle("dE/dx [arbitrary units]");
  MomentumFunctionElectron->Draw("SAME");
  MomentumFunctionMuon->Draw("SAME");
  MomentumFunctionPion->Draw("SAME");
  MomentumFunctionKaon->Draw("SAME");
  MomentumFunctionProton->Draw("SAME");
  MomentumFunctionDeuteron->Draw("SAME");
  OverlayAllTracksCanvas->SetLogx();
  OverlayAllTracksCanvas->SetLogz();
 
  OverlayAllTracksCanvas->Print("SVDdEdxAllTracksWithFits.pdf");
  TFile OverlayAllTracksPlotFile("SVDdEdxCalibrationOverlayAllTracks.root", "RECREATE");
  AllTracksHistogram->Write();
  MomentumFunctionElectron->Write();
  MomentumFunctionMuon->Write();
  MomentumFunctionPion->Write();
  MomentumFunctionKaon->Write();
  MomentumFunctionProton->Write();
  MomentumFunctionDeuteron->Write();
  OverlayAllTracksCanvas->Write();
  OverlayAllTracksPlotFile.Close();
 
 
// resolution studies //
 
// For resolution measurement, we need to take a ProjectionY of the data histograms in the momentum range where the dEdx is flat vs momentum. We use our educated guess of the flat range (e.g. 0.6-1 GeV for pions) and FindBin to figure out which bin numbers those momentum values correspond to.
  double PionRangeMin = 0.6;
  double PionRangeMax = 1.;
  double KaonRangeMin = 1.9;
  double KaonRangeMax = 3;
  double ElectronRangeMin = 1.;
  double ElectronRangeMax = 1.4;
 
  auto PionResolutionHistogram = Pion2DHistogram->ProjectionY("PionResolutionHistogram",
                                                              Pion2DHistogram->GetXaxis()->FindBin(PionRangeMin),
                                                              Pion2DHistogram->GetXaxis()->FindBin(PionRangeMax));
  auto ElectronResolutionHistogram = Electron2DHistogram->ProjectionY("ElectronResolutionHistogram",
                                     Electron2DHistogram->GetXaxis()->FindBin(ElectronRangeMin), Electron2DHistogram->GetXaxis()->FindBin(ElectronRangeMax));
  auto KaonResolutionHistogram = Kaon2DHistogram->ProjectionY("KaonResolutionHistogram",
                                                              Kaon2DHistogram->GetXaxis()->FindBin(KaonRangeMin),
                                                              Kaon2DHistogram->GetXaxis()->FindBin(KaonRangeMax));
// for protons, there is not enough data in the flat range.
 
 
  TF1* PionResolutionFunction = new TF1("PionResolutionFunction",
                                        "[0]*TMath::Landau(x, [1], [1]*[2])*TMath::Gaus(x, [1], [1]*[2]*[4]) + [3]*TMath::Gaus(x, [1], [1]*[2]*[5])", 100e3, 1500e3);
// parameter [1] is the mean of the Landau
// parameter [2] is the relative resolution (w.r.t. the mean) of the Landau
// parameters [4]-[5] are the relative resolution of Gauss contributions w.r.t. that of Landau
// parameters [0] and [3] are fractions of the two components
  PionResolutionFunction->SetParameters(1, 6.e5, 0.1, 0.5, 2, 1);
  PionResolutionFunction->SetParLimits(0, 0, 500);
  PionResolutionFunction->SetParLimits(1, 3.e5, 8.e5);
  PionResolutionFunction->SetParLimits(2, 0, 1);
  PionResolutionFunction->SetParLimits(3, 0, 500);
  PionResolutionFunction->SetParLimits(4, 0, 7);
  PionResolutionFunction->SetParLimits(5, 1, 7);
  PionResolutionFunction->SetNpx(1000);
  auto FitResultResolutionPion = PionResolutionHistogram->Fit(PionResolutionFunction, "RSI");
 
  B2INFO("relative resolution for pions: " << PionResolutionFunction->GetParameter(2));
  B2INFO("resolution for pions: fit status" << FitResultResolutionPion->Status());
 
  TF1* KaonResolutionFunction = new TF1("KaonResolutionFunction",
                                        "[0]*TMath::Landau(x, [1], [1]*[2])*TMath::Gaus(x, [1], [1]*[2]*[4]) + [3]*TMath::Gaus(x, [1], [1]*[2]*[5])", 100e3, 1500e3);
 
 
  KaonResolutionFunction->SetParameters(1, 6.e5, 0.1, 0.5, 2, 1);
  KaonResolutionFunction->SetParLimits(0, 0, 500);
  KaonResolutionFunction->SetParLimits(1, 3.e5, 8.e5);
  KaonResolutionFunction->SetParLimits(2, 0, 1);
  KaonResolutionFunction->SetParLimits(3, 0, 500);
  KaonResolutionFunction->SetParLimits(4, 0, 7);
  KaonResolutionFunction->SetParLimits(5, 1, 7);
  KaonResolutionFunction->SetNpx(1000);
  auto FitResultResolutionKaon = KaonResolutionHistogram->Fit(KaonResolutionFunction, "RSI");
 
  B2INFO("relative resolution for kaons: " << KaonResolutionFunction->GetParameter(2));
  B2INFO("resolution for kaons: fit status" << FitResultResolutionKaon->Status());
 
  if ((FitResultResolutionKaon->Status() > 1)
      && (FitResultResolutionPion->Status() <= 1)) KaonResolutionFunction = static_cast<TF1*>
            (PionResolutionFunction->Clone("KaonResolutionFunction"));
 
 
 
 
  TF1* ElectronResolutionFunction = new TF1("ElectronResolutionFunction",
                                            "[0]*TMath::Landau(x, [1], [1]*[2])*TMath::Gaus(x, [1], [1]*[2]*[4]) + [3]*TMath::Gaus(x, [1], [1]*[2]*[5])", 50e3, 1500e3);
 
 
  ElectronResolutionFunction->SetParameters(1, 6.e5, 0.1, 0.5, 2, 1);
  ElectronResolutionFunction->SetParLimits(0, 0, 500);
  ElectronResolutionFunction->SetParLimits(1, 3.e5, 8.e5);
  ElectronResolutionFunction->SetParLimits(2, 0, 1);
  ElectronResolutionFunction->SetParLimits(3, 0, 500);
  ElectronResolutionFunction->SetParLimits(4, 0, 7);
  ElectronResolutionFunction->SetParLimits(5, 1, 7);
  ElectronResolutionFunction->SetNpx(1000);
  auto FitResultResolutionElectron = ElectronResolutionHistogram->Fit(ElectronResolutionFunction, "RSI");
 
  B2INFO("relative resolution for electrons: " << ElectronResolutionFunction->GetParameter(2));
  B2INFO("resolution for electrons: fit status" << FitResultResolutionElectron->Status());
 
  // plot all the resolution fits
  if (m_isMakePlots) {
    TCanvas* CanvasResolutions = new TCanvas("CanvasResolutions", "Resolutions", 1200, 650);
    CanvasResolutions->Divide(3, 1);
    CanvasResolutions->cd(1); PionResolutionHistogram->Draw();
    CanvasResolutions->cd(2); KaonResolutionHistogram->Draw();
    CanvasResolutions->cd(3); ElectronResolutionHistogram->Draw();
 
    CanvasResolutions->Print("SVDdEdxResolutions.pdf");
    TFile OverlayResolutionsPlotFile("SVDdEdxCalibrationResolutions.root", "RECREATE");
    PionResolutionHistogram->Write();
    KaonResolutionHistogram->Write();
    ElectronResolutionHistogram->Write();
    CanvasResolutions->Write();
    OverlayResolutionsPlotFile.Close();
  }
 
// evaluate the bias correction:
// difference between the MomentumFunction prediction and the mean of the resolution function in the flat part
// it should be of the order -1e4, i.e. about -1% of the absolute dEdx value
  double BiasCorrectionPion = PionResolutionFunction->GetParameter(1) - MomentumFunctionPion->Eval((
                                PionRangeMax + PionRangeMin) / 2.);
  B2INFO("BiasCorrectionPion = " << BiasCorrectionPion);
 
// generate a new pion payload using the MomentumFunctionPion, PionResolutionFunction and the bias correction
  TH2F* Pion2DHistogramNew = PrepareNewHistogram(Pion2DHistogram, Form("%sNew", Pion2DHistogram->GetName()), MomentumFunctionPion,
                                                 PionResolutionFunction, BiasCorrectionPion);
 
// sanity check: residual between the generated distribution and the data one
  TH2F* Pion2DHistogramResidual = static_cast<TH2F*>(Pion2DHistogram->Clone("Pion2DHistogramResidual"));
  Pion2DHistogramResidual->Add(Pion2DHistogramNew, Pion2DHistogram, 1, -1);
  Pion2DHistogramResidual->SetMinimum(-0.15);
  Pion2DHistogramResidual->SetMaximum(0.15);
 
  // repeat, for kaons
  double BiasCorrectionKaon = KaonResolutionFunction->GetParameter(1) - MomentumFunctionKaon->Eval((
                                KaonRangeMax + KaonRangeMin) / 2.);
  B2INFO("BiasCorrectionKaon = " << BiasCorrectionKaon);
 
  // for protons, we compare the flat part of the MomentumFunction (~3 GeV) with the mean of the kaon resolution function
  // as there's not enough stats in the flat part to extract proton resolution from data
  double BiasCorrectionProton = KaonResolutionFunction->GetParameter(1) - MomentumFunctionProton->Eval(3.);
  B2INFO("BiasCorrectionProton = " << BiasCorrectionProton);
 
  if ((BiasCorrectionProton / BiasCorrectionKaon)  > 1.5) BiasCorrectionProton =
      BiasCorrectionKaon; // probably something went wrong due to low statistics
 
  // back to kaons: generate a new payload
  TH2F* Kaon2DHistogramNew = PrepareNewHistogram(Kaon2DHistogram, Form("%sNew", Kaon2DHistogram->GetName()), MomentumFunctionKaon,
                                                 KaonResolutionFunction, BiasCorrectionKaon);
// residual generated - data for kaons
  TH2F* Kaon2DHistogramResidual = static_cast<TH2F*>(Kaon2DHistogram->Clone("Kaon2DHistogramResidual"));
  Kaon2DHistogramResidual->Add(Kaon2DHistogramNew, Kaon2DHistogram, 1, -1);
  Kaon2DHistogramResidual->SetMinimum(-0.15);
  Kaon2DHistogramResidual->SetMaximum(0.15);
 
  // same for protons (we use the kaon resolution function as explained above)
  TH2F* Proton2DHistogramNew = PrepareNewHistogram(Proton2DHistogram, Form("%sNew", Proton2DHistogram->GetName()),
                                                   MomentumFunctionProton,
                                                   KaonResolutionFunction, BiasCorrectionProton);
 
// residual for protons
  TH2F* Proton2DHistogramResidual = static_cast<TH2F*>(Proton2DHistogram->Clone("Proton2DHistogramResidual"));
  Proton2DHistogramResidual->Add(Proton2DHistogramNew, Proton2DHistogram, 1, -1);
  Proton2DHistogramResidual->SetMinimum(-0.15);
  Proton2DHistogramResidual->SetMaximum(0.15);
 
// deuterons: same as protons, but use the MomentumFunctionDeuteron
  TH2F* Deuteron2DHistogramNew = PrepareNewHistogram(Proton2DHistogram, "Deuteron2DHistogramNew", MomentumFunctionDeuteron,
                                                     KaonResolutionFunction,
                                                     BiasCorrectionKaon);
  Deuteron2DHistogramNew->SetTitle("hist_d1_1000010020_trunc");
 
// muons: same as pions, but use the MomentumFunctionMuon
  TH2F* Muon2DHistogramNew = PrepareNewHistogram(Pion2DHistogram, "Muon2DHistogramNew", MomentumFunctionMuon, PionResolutionFunction,
                                                 BiasCorrectionPion);
  Muon2DHistogramNew->SetTitle("hist_d1_13_trunc");
 
// same for electrons
  double BiasCorrectionElectron = ElectronResolutionFunction->GetParameter(1) - MomentumFunctionElectron->Eval((
                                    ElectronRangeMax + ElectronRangeMin) / 2.);
  B2INFO("BiasCorrectionElectron = " << BiasCorrectionElectron);
  TH2F* Electron2DHistogramNew = PrepareNewHistogram(Electron2DHistogram, Form("%sNew", Electron2DHistogram->GetName()),
                                                     MomentumFunctionElectron,
                                                     ElectronResolutionFunction, BiasCorrectionElectron);
 
  TH2F* Electron2DHistogramResidual = static_cast<TH2F*>(Electron2DHistogram->Clone("Electron2DHistogramResidual"));
  Electron2DHistogramResidual->Add(Electron2DHistogramNew, Electron2DHistogram, 1, -1);
  Electron2DHistogramResidual->SetMinimum(-0.15);
  Electron2DHistogramResidual->SetMaximum(0.15);
 
  Electron2DHistogramNew->SetName("Electron2DHistogramNew");
  Muon2DHistogramNew->SetName("Muon2DHistogramNew");
  Pion2DHistogramNew->SetName("Pion2DHistogramNew");
  Kaon2DHistogramNew->SetName("Kaon2DHistogramNew");
  Proton2DHistogramNew->SetName("Proton2DHistogramNew");
  Deuteron2DHistogramNew->SetName("Deuteron2DHistogramNew");
 
// plot the summary of all the distributions
  if (m_isMakePlots) {
    TCanvas* CanvasSummaryGenerated = new TCanvas("CanvasSummaryGenerated", "Generated payloads", 1700, 850);
    CanvasSummaryGenerated->Divide(3, 2);
    CanvasSummaryGenerated->cd(1); Electron2DHistogramNew->Draw("COLZ");
    CanvasSummaryGenerated->cd(2); Muon2DHistogramNew->Draw("COLZ");
    CanvasSummaryGenerated->cd(3); Pion2DHistogramNew->Draw("COLZ");
    CanvasSummaryGenerated->cd(4); Kaon2DHistogramNew->Draw("COLZ");
    CanvasSummaryGenerated->cd(5); Proton2DHistogramNew->Draw("COLZ");
    CanvasSummaryGenerated->cd(6); Deuteron2DHistogramNew->Draw("COLZ");
 
    CanvasSummaryGenerated->Print("SVDdEdxGeneratedPayloads.pdf");
    TFile SummaryGeneratedPlotFile("SVDdEdxCalibrationSummaryGenerated.root", "RECREATE");
    Electron2DHistogramNew->Write();
    Muon2DHistogramNew->Write();
    Pion2DHistogramNew->Write();
    Kaon2DHistogramNew->Write();
    Proton2DHistogramNew->Write();
    Deuteron2DHistogramNew->Write();
    SummaryGeneratedPlotFile.Close();
 
 
    TCanvas* CanvasSummaryData = new TCanvas("CanvasSummaryData", "Data distributions", 1700, 850);
    CanvasSummaryData->Divide(3, 2);
    CanvasSummaryData->cd(1); Electron2DHistogram->Draw("COLZ");
    CanvasSummaryData->cd(3); Pion2DHistogram->Draw("COLZ");
    CanvasSummaryData->cd(4); Kaon2DHistogram->Draw("COLZ");
    CanvasSummaryData->cd(5); Proton2DHistogram->Draw("COLZ");
 
    CanvasSummaryData->Print("SVDdEdxDataDistributions.pdf");
    TFile SummaryDataPlotFile("SVDdEdxCalibrationSummaryData.root", "RECREATE");
    Electron2DHistogram->Write();
    Pion2DHistogram->Write();
    Kaon2DHistogram->Write();
    Proton2DHistogram->Write();
    SummaryDataPlotFile.Close();
 
 
    TCanvas* CanvasSummaryResiduals = new TCanvas("CanvasSummaryResiduals", "Residuals", 1700, 850);
    CanvasSummaryResiduals->Divide(3, 2);
    CanvasSummaryResiduals->cd(1); Electron2DHistogramResidual->Draw("COLZ");
    CanvasSummaryResiduals->cd(3); Pion2DHistogramResidual->Draw("COLZ");
    CanvasSummaryResiduals->cd(4); Kaon2DHistogramResidual->Draw("COLZ");
    CanvasSummaryResiduals->cd(5); Proton2DHistogramResidual->Draw("COLZ");
 
 
    CanvasSummaryResiduals->Print("SVDdEdxResiduals.pdf");
    TFile SummaryResidualsPlotFile("SVDdEdxCalibrationSummaryResiduals.root", "RECREATE");
    Electron2DHistogramResidual->Write();
    Pion2DHistogramResidual->Write();
    Kaon2DHistogramResidual->Write();
    Proton2DHistogramResidual->Write();
    SummaryResidualsPlotFile.Close();
  }
 
 
  // return all the generated payloads
  std::unique_ptr<TList> histList(new TList);
  histList->Add(Electron2DHistogramNew);
  histList->Add(Muon2DHistogramNew);
  histList->Add(Pion2DHistogramNew);
  histList->Add(Kaon2DHistogramNew);
  histList->Add(Proton2DHistogramNew);
  histList->Add(Deuteron2DHistogramNew);
 
  return histList;
 
}

getAllGranularityExpRun()

static Calibration::ExpRun getAllGranularityExpRun ( )

inlinestaticprotectedinherited

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

Definition at line 327 of file CalibrationAlgorithm.h.

{return m_allExpRun;}

getCollectorName()

const 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()

const 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 384 of file CalibrationAlgorithm.cc.

{
  // Save TDirectory to change back at the end
  TDirectory* dir = gDirectory;
  const 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 326 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 ( ) const

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()

const 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 319 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 362 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"));
    const 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()

const 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, SVDClusterAbsoluteTimeShifterAlgorithm, 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;
    }

LambdaHistogramming()

std::unique_ptr< TList > LambdaHistogramming ( TTree * inputTree )

private

produce histograms for protons

Definition at line 310 of file SVDdEdxCalibrationAlgorithm.cc.

{
  gROOT->SetBatch(true);
  inputTree->SetEstimate(-1);
  std::vector<double> pbins = CreatePBinningScheme();
 
  TH2F* hLambdaPMomentum = new TH2F("hist_d1_2212_truncMomentum", "hist_d1_2212_trunc;Momentum [GeV/c];dEdx [arb. units]",
                                    m_numPBins, pbins.data(), m_numDEdxBins, 0,
                                    m_dedxCutoff);
 
  inputTree->Draw("ProtonSVDdEdx:ProtonSVDdEdxTrackMomentum>>hist_d1_2212_truncMomentum",
                  "nSignalLambda_sw * (ProtonSVDdEdx>0) * (ProtonnSVDHits>4) * (ProtonSVDdEdxTrackMomentum>0.13)", "goff");
 
// create isopopulated beta*gamma binning
  inputTree->Draw(Form("ProtonSVDdEdxTrackMomentum/%f", m_ProtonPDGMass), "", "goff",
                  ((inputTree->GetEntries()) / m_numBGBins)*m_numBGBins);
  double* ProtonMomentumDataset = inputTree->GetV1();
 
  TKDTreeBinning* kdBinsP = new TKDTreeBinning(((inputTree->GetEntries()) / m_numBGBins)*m_numBGBins, 1, ProtonMomentumDataset,
                                               m_numBGBins);
  const double* binsMinEdgesP_pointer = kdBinsP->SortOneDimBinEdges();
  double* binsMinEdgesP =  const_cast<double*>(binsMinEdgesP_pointer);
 
 
  binsMinEdgesP[0] = 0.1;
  binsMinEdgesP[m_numBGBins + 1] = 50.;
 
 
  TH2F* hLambdaPBetaGamma = new TH2F("hist_d1_2212_truncBetaGamma", "hist_d1_2212_truncBetaGamma;#beta*#gamma;dEdx [arb. units]",
                                     m_numBGBins, binsMinEdgesP, m_numDEdxBins,
                                     0,
                                     m_dedxMaxPossible);
 
  inputTree->Draw(Form("ProtonSVDdEdx:ProtonSVDdEdxTrackMomentum/%f>>hist_d1_2212_truncBetaGamma", m_ProtonPDGMass),
                  "nSignalLambda_sw * (ProtonSVDdEdx>0) * (ProtonnSVDHits>4) * (ProtonSVDdEdxTrackMomentum>0.13) * (ProtonSVDdEdx>1.2e6 - 1.e6*ProtonSVDdEdxTrackMomentum)",
                  "goff");
 
  // produce the 1D profile
  // momentum: for data-MC comparisons
 
  TH1D* ProtonProfileMomentum = static_cast<TH1D*>(hLambdaPMomentum->ProfileX("ProtonProfileMomentum"));
  ProtonProfileMomentum->SetTitle("ProtonProfile");
  ProtonProfileMomentum->GetYaxis()->SetRangeUser(0, m_dedxCutoff);
  ProtonProfileMomentum->GetXaxis()->SetTitle("Momentum, GeV/c");
  ProtonProfileMomentum->GetYaxis()->SetTitle("dE/dx");
  ProtonProfileMomentum->SetLineColor(kRed);
 
  //beta*gamma: for the fit
  TH1D* ProtonProfileBetaGamma = static_cast<TH1D*>(hLambdaPBetaGamma->ProfileX("ProtonProfileBetaGamma"));
  if (m_CustomProfile) {
    ProtonProfileBetaGamma = PrepareProfile(hLambdaPBetaGamma, "ProtonProfileBetaGamma");
  }
  ProtonProfileBetaGamma->SetTitle("ProtonProfile");
  ProtonProfileBetaGamma->GetYaxis()->SetRangeUser(0, m_dedxCutoff);
  ProtonProfileBetaGamma->GetXaxis()->SetTitle("#beta*#gamma");
  ProtonProfileBetaGamma->GetYaxis()->SetTitle("dE/dx");
  ProtonProfileBetaGamma->SetLineColor(kRed);
 
 
  // for each momentum bin, normalize the pdf
  hLambdaPMomentum = Normalise2DHisto(hLambdaPMomentum);
 
  std::unique_ptr<TList> histList(new TList);
  histList->Add(ProtonProfileMomentum);
  histList->Add(ProtonProfileBetaGamma);
  histList->Add(hLambdaPMomentum);
 
  if (m_isMakePlots) {
    TFile LambdaHistogrammingPlotFile("SVDdEdxCalibrationLambdaHistogramming.root", "RECREATE");
    histList->Write();
    LambdaHistogrammingPlotFile.Close();
  }
 
  return histList;
}

LambdaMassFit()

TTree * LambdaMassFit ( std::shared_ptr< TTree > preselTree )

private

Mass fit for Lambda->ppi.

Definition at line 164 of file SVDdEdxCalibrationAlgorithm.cc.

{
  B2INFO("Configuring the Lambda fit...");
  gROOT->SetBatch(true);
  RooMsgService::instance().setGlobalKillBelow(RooFit::WARNING);
 
  RooRealVar InvM("InvM", "m(p^{+}#pi^{-})", 1.1, 1.13, "GeV/c^{2}");
 
  RooRealVar ProtonMomentum("ProtonMomentum", "momentum for p", -1.e8, 1.e8);
  RooRealVar ProtonSVDdEdxTrackMomentum("ProtonSVDdEdxTrackMomentum", "momentum for p", -1.e8, 1.e8);
  RooRealVar ProtonSVDdEdx("ProtonSVDdEdx", "", -1.e8, 1.e8);
  RooRealVar ProtonSVDdEdxTrackCosTheta("ProtonSVDdEdxTrackCosTheta", "", -10., 10.);
  RooRealVar ProtonnSVDHits("ProtonnSVDHits", "", -1.e8, 1.e8);
 
  RooRealVar exp("exp", "experiment number", 0, 1.e5);
  RooRealVar run("run", "run number", 0, 1.e7);
 
  auto variables = new RooArgSet();
 
  variables->add(InvM);
 
  variables->add(ProtonMomentum);
  variables->add(ProtonSVDdEdxTrackMomentum);
  variables->add(ProtonSVDdEdx);
  variables->add(ProtonSVDdEdxTrackCosTheta);
  variables->add(ProtonnSVDHits);
  variables->add(exp);
  variables->add(run);
 
  RooDataSet* LambdaDataset = new RooDataSet("LambdaDataset", "LambdaDataset", *variables, Import(*preselTree));
 
  if (LambdaDataset->sumEntries() == 0) {
    B2FATAL("The Lambda dataset is empty, stopping here");
  }
 
  // the signal PDF; might be revisited at a later point
 
  RooRealVar GaussMean("GaussMean", " GaussMean", 1.116, 1.111, 1.12);
  RooRealVar GaussSigma("GaussSigma", "#sigma_{1}", 3.e-3, 3.e-5, 10.e-3);
  RooGaussian LambdaGauss("LambdaGauss", "LambdaGauss", InvM, GaussMean, GaussSigma);
 
  /* temporary RooRealVar sigmaBifurGaussL1 and sigmaBifurGaussR1 to replace
   * RooRealVar resolutionParamL("resolutionParamL", "resolutionParamL", 0.4, 5.e-4, 1.0);
   * RooRealVar resolutionParamR("resolutionParamR", "resolutionParamR", 0.4, 5.e-4, 1.0);
   * RooFormulaVar sigmaBifurGaussL1("sigmaBifurGaussL1", "resolutionParamL*GaussSigma", RooArgSet(resolutionParamL, GaussSigma));
   * RooFormulaVar sigmaBifurGaussR1("sigmaBifurGaussR1", "resolutionParamR*GaussSigma", RooArgSet(resolutionParamR, GaussSigma));
   */
  RooRealVar sigmaBifurGaussL1("sigmaBifurGaussL1", "sigma left", 0.4 * 3.e-3, 3.e-5, 10.e-3);
  RooRealVar sigmaBifurGaussR1("sigmaBifurGaussR1", "sigma right", 0.4 * 3.e-3, 3.e-5, 10.e-3);
  RooBifurGauss LambdaBifurGauss("LambdaBifurGauss", "LambdaBifurGauss", InvM, GaussMean, sigmaBifurGaussL1, sigmaBifurGaussR1);
 
  /* temporary RooRealVar sigmaBifurGaussL2 to replace
   * RooRealVar resolutionParam2("resolutionParam2", "resolutionParam2", 0.2, 5.e-4, 1.0);
   * sigmaBifurGaussL2("sigmaBifurGaussL2", "resolutionParam2*GaussSigma", RooArgSet(resolutionParam2, GaussSigma));
   */
  RooRealVar sigmaBifurGaussL2("sigmaBifurGaussL2", "sigmaBifurGaussL2", 0.2 * 3.e-3, 3.e-5, 10.e-3);
  RooGaussian LambdaBifurGauss2("LambdaBifurGauss2", "LambdaBifurGauss2", InvM, GaussMean, sigmaBifurGaussL2);
 
  RooRealVar fracBifurGaussYield("fracBifurGaussYield", "fracBifurGaussYield", 0.3, 5.e-4, 1.0);
  RooRealVar fracGaussYield("fracGaussYield", "fracGaussYield", 0.8, 5.e-4, 1.0);
 
  RooAddPdf LambdaCombinedBifurGauss("LambdaCombinedBifurGauss", "LambdaBifurGauss + LambdaBifurGauss2 ", RooArgList(LambdaBifurGauss,
                                     LambdaBifurGauss2), RooArgList(fracBifurGaussYield));
 
  RooAddPdf LambdaSignalPDF("LambdaSignalPDF", "LambdaCombinedBifurGauss + LambdaGauss", RooArgList(LambdaCombinedBifurGauss,
                            LambdaGauss), RooArgList(fracGaussYield));
 
  // Background PDF
  RooRealVar BkgPolyCoef0("BkgPolyCoef0", "BkgPolyCoef0", 0.1, 0., 1.5);
  RooRealVar BkgPolyCoef1("BkgPolyCoef1", "BkgPolyCoef1", -0.5, -1.5, -1.e-3);
  RooChebychev BkgPolyPDF("BkgPolyPDF", "BkgPolyPDF", InvM, RooArgList(BkgPolyCoef0, BkgPolyCoef1));
 
  RooRealVar nSignalLambda("nSignalLambda", "nSignalLambda", 0.6 * preselTree->GetEntries(), 0., 0.99 * preselTree->GetEntries());
  RooRealVar nBkgLambda("nBkgLambda", "nBkgLambda", 0.4 * preselTree->GetEntries(), 0., 0.99 * preselTree->GetEntries());
  RooAddPdf totalPDFLambda("totalPDFLambda", "totalPDFLambda pdf", RooArgList(LambdaSignalPDF, BkgPolyPDF),
                           RooArgList(nSignalLambda, nBkgLambda));
 
  B2INFO("Lambda: Start fitting...");
  RooFitResult* LambdaFitResult = totalPDFLambda.fitTo(*LambdaDataset, Save(kTRUE), PrintLevel(-1));
 
  int status = LambdaFitResult->status();
  int covqual = LambdaFitResult->covQual();
  double diff = nSignalLambda.getValV() + nBkgLambda.getValV() - LambdaDataset->sumEntries();
 
  B2INFO("Lambda: Fit status: " << status << "; covariance quality: " << covqual);
  // if the fit is not healthy, try again once before giving up, with a slightly different setup:
  if ((status > 0) || (TMath::Abs(diff) > 1.) || (nSignalLambda.getError() < sqrt(nSignalLambda.getValV()))
      || (nSignalLambda.getError() > (nSignalLambda.getValV()))) {
 
    LambdaFitResult = totalPDFLambda.fitTo(*LambdaDataset, Save(), Strategy(2), Offset(1));
    status = LambdaFitResult->status();
    covqual = LambdaFitResult->covQual();
    diff = nSignalLambda.getValV() + nBkgLambda.getValV() - LambdaDataset->sumEntries();
    B2INFO("Lambda: updated fit status: " << status << "; covariance quality: " << covqual);
  }
 
  if ((status > 0) || (TMath::Abs(diff) > 1.) || (nSignalLambda.getError() < sqrt(nSignalLambda.getValV()))
      || (nSignalLambda.getError() > (nSignalLambda.getValV()))) {
    B2WARNING("Lambda: Fit problem: fit status " << status << "; sum of component yields minus the dataset yield is " << diff <<
              "; signal yield is " << nSignalLambda.getValV() << ", while its uncertainty is " << nSignalLambda.getError());
  }
  if (covqual < 2) {
    B2INFO("Lambda: Fit warning: covariance quality " << covqual);
  }
 
  if (m_isMakePlots) {
    std::unique_ptr<TCanvas> canvLambda(new TCanvas("canvLambda", "canvLambda"));
    canvLambda->cd();
    RooPlot* LambdaFitFrame = LambdaDataset->plotOn(InvM.frame(130));
    totalPDFLambda.plotOn(LambdaFitFrame, LineColor(TColor::GetColor("#4575b4")));
 
    double chisquare = LambdaFitFrame->chiSquare();
    B2INFO("Lambda: Fit chi2 = " << chisquare);
    totalPDFLambda.paramOn(LambdaFitFrame, Layout(0.6, 0.96, 0.93), Format("NEU", AutoPrecision(2)));
    LambdaFitFrame->getAttText()->SetTextSize(0.03);
 
    totalPDFLambda.plotOn(LambdaFitFrame, Components("LambdaSignalPDF"), LineColor(TColor::GetColor("#d73027")));
    totalPDFLambda.plotOn(LambdaFitFrame, Components("BkgPolyPDF"), LineColor(TColor::GetColor("#fc8d59")));
    totalPDFLambda.plotOn(LambdaFitFrame, LineColor(TColor::GetColor("#4575b4")));
 
    LambdaFitFrame->GetXaxis()->SetTitle("m(p#pi^{-}) (GeV/c^{2})");
 
    LambdaFitFrame->Draw();
 
 
    canvLambda->Print("SVDdEdxCalibrationFitLambda.pdf");
    TFile LambdaFitPlotFile("SVDdEdxCalibrationLambdaFitPlotFile.root", "RECREATE");
    canvLambda->Write();
    LambdaFitPlotFile.Close();
  }
  RooStats::SPlot* sPlotDatasetLambda = new RooStats::SPlot("sData", "An SPlot", *LambdaDataset, &totalPDFLambda,
                                                            RooArgList(nSignalLambda, nBkgLambda));
 
  for (int iEvt = 0; iEvt < 5; iEvt++) {
    if (TMath::Abs(sPlotDatasetLambda->GetSWeight(iEvt, "nSignalLambda") + sPlotDatasetLambda->GetSWeight(iEvt,
                   "nBkgLambda") - 1) > 5.e-3)
      B2FATAL("Lambda: sPlot error: sum of weights not equal to 1");
  }
 
  TTree* treeLambdaSWeighted = LambdaDataset->GetClonedTree();
  treeLambdaSWeighted->SetName("treeLambdaSWeighted");
 
  B2INFO("Lambda: sPlot done. Proceed to histogramming");
  return treeLambdaSWeighted;
}

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 503 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;
  }
}

Normalise2DHisto()

TH2F * Normalise2DHisto ( TH2F * HistoToNormalise )

inlineprivate

Normalise a given dEdx:momentum histogram in each momentum bin, so that sum of entries in each momentum bin is 1.

Note that this accounts for entries in the underflow/overflow bins.

Definition at line 158 of file SVDdEdxCalibrationAlgorithm.h.

    {
      for (int pbin = 0; pbin <= m_numPBins + 1; pbin++) {
        for (int dedxbin = 0; dedxbin <= m_numDEdxBins + 1; dedxbin++) {
          // get rid of the bins with negative weights
          if (HistoToNormalise->GetBinContent(pbin, dedxbin) <= 1) {
            HistoToNormalise->SetBinContent(pbin, dedxbin, 0);
          };
        }
        // create a projection (1D histogram) in a given momentum bin
 
        TH1D* MomentumSlice = static_cast<TH1D*>(HistoToNormalise->ProjectionY("slice_tr", pbin, pbin));
        // normalise, but ignore the cases with empty histograms
        if (MomentumSlice->Integral(0, m_numDEdxBins + 1) > 0) {
          MomentumSlice->Scale(1. / MomentumSlice->Integral(0, m_numDEdxBins + 1));
        }
        // fill back the 2D histogram with the result
        for (int dedxbin = 0; dedxbin <= m_numDEdxBins + 1; dedxbin++) {
          HistoToNormalise->SetBinContent(pbin, dedxbin, MomentumSlice->GetBinContent(dedxbin));
          HistoToNormalise->SetBinError(pbin, dedxbin, MomentumSlice->GetBinError(dedxbin));
        }
      }
      return HistoToNormalise;
    }

PrepareNewHistogram()

TH2F * PrepareNewHistogram ( TH2F * DataHistogram, TString NewName, TF1 * betagamma_function, TF1 * ResolutionFunctionOriginal, double bias_correction )

inlineprivate

Generate a new dEdx:momentum histogram from a function that encodes dEdx:momentum trend and a function that encodes dEdx resolution.

Definition at line 186 of file SVDdEdxCalibrationAlgorithm.h.

    {
      TF1* ResolutionFunction = static_cast<TF1*>(ResolutionFunctionOriginal->Clone(Form("%sClone",
                                                  ResolutionFunctionOriginal->GetName()))); // to avoid modifying the resolution function
      ResolutionFunction->SetRange(0, m_dedxMaxPossible); // allow the function to take values outside the histogram range
      TH2F* DataHistogramNew = static_cast<TH2F*>(DataHistogram->Clone(NewName));
 
      DataHistogramNew->Reset();
 
      for (int pbin = 1; pbin <= m_numPBins + 1; pbin++) {
        double mean_dEdx_value = betagamma_function->Eval(DataHistogramNew->GetXaxis()->GetBinCenter(pbin));
        ResolutionFunction->FixParameter(1, mean_dEdx_value + bias_correction);
 
        // create a projection (1D histogram) in a given momentum bin
        TH1D* MomentumSlice = static_cast<TH1D*>(DataHistogramNew->ProjectionY("slice", pbin, pbin));
 
        // fill manually (instead of FillRandom) to also preserve events in the overflow bin
        // this is needed for the correct normalisation
        for (int iEvent = 0; iEvent < m_NToGenerate; iEvent++) {
          MomentumSlice->Fill(ResolutionFunction->GetRandom());
        }
 
        // get rid of the empty bins: set their bin content to 0.5 (i.e. smaller than 1 event)
        // this is to allow for a well-defined log-likelihood calculation
        for (int dedxbin = 0; dedxbin <= m_numDEdxBins + 1; dedxbin++) {
          if (MomentumSlice->GetBinContent(dedxbin) < 1) {
            MomentumSlice->SetBinContent(dedxbin, 0.5);
          };
        }
 
        // normalise each momentum slice to unity, but ignore the cases with empty histograms
        if (MomentumSlice->Integral(0, m_numDEdxBins + 1) > 0) {
          MomentumSlice->Scale(1. / MomentumSlice->Integral(0, m_numDEdxBins + 1));
        }
        // fill back the 2D histo with the result
        for (int dedxbin = 0; dedxbin <= m_numDEdxBins + 1; dedxbin++) {
          DataHistogramNew->SetBinContent(pbin, dedxbin, MomentumSlice->GetBinContent(dedxbin));
          DataHistogramNew->SetBinError(pbin, dedxbin, MomentumSlice->GetBinError(dedxbin));
        }
      }
 
      return DataHistogramNew;
 
    }

PrepareProfile()

TH1D * PrepareProfile ( TH2F * DataHistogram, TString NewName )

inlineprivate

Reimplement the Profile histogram calculation for a 2D histogram.

The standard ROOT implementation takes the mean Y at a given X, which produces biased results in case of rapidly-rising distributions with non-Gaussian resolutions. We fit in slices to extract the mean more precisely.

Definition at line 235 of file SVDdEdxCalibrationAlgorithm.h.

    {
// define our resolution function: Crystal Ball. Parameter [1] is the mean and [2] is the relative width.
      TF1* ResolutionFunction = new TF1("ResolutionFunction", "[0]*ROOT::Math::crystalball_function(x,[4],[3],[2]*[1],[1])", 100e3,
                                        7000e3);
 
 
      ResolutionFunction->SetNpx(1000);
 
      ResolutionFunction->SetParameters(1000, 6.e5, 0.1, 1, 1);
      ResolutionFunction->SetParLimits(0, 0, 1.e6);
      ResolutionFunction->SetParLimits(1, 3.e5, 7.e6);
      ResolutionFunction->SetParLimits(2, 0, 10);
      ResolutionFunction->SetParLimits(3, 0.01, 100);
      ResolutionFunction->SetParLimits(4, 0.01, 100);
 
      ResolutionFunction->SetRange(0, m_dedxMaxPossible); // allow the function to take values outside the histogram range
 
      TH1D* DataHistogramNew = static_cast<TH1D*>(DataHistogram->ProfileX()->ProjectionX());
      TH1D* DataHistogramClone = static_cast<TH1D*>(DataHistogramNew->Clone(Form("%sClone",
                                                    DataHistogramNew->GetName()))); // preserve the original profile for uncertainty cross-checks
      DataHistogramNew->SetName(NewName);
      DataHistogramNew->SetTitle(NewName);
      DataHistogramNew->Reset();
 
      for (int pbin = 1; pbin <= m_numBGBins; pbin++) {
        // create a projection (1D histogram) in a given momentum bin
        TH1D* MomentumSlice = static_cast<TH1D*>(DataHistogram->ProjectionY("slice", pbin, pbin));
 
        if (MomentumSlice->Integral() < 1) continue;
// guesstimate the starting fit values
        ResolutionFunction->SetParameter(1, MomentumSlice->GetMean());
        ResolutionFunction->SetParameter(2, MomentumSlice->GetStdDev() / MomentumSlice->GetMean());
        ResolutionFunction->SetRange(MomentumSlice->GetMean() * 0.2, MomentumSlice->GetMean() * 1.75);
// fit each slice to extract the mean
        MomentumSlice->Fit(ResolutionFunction, "RQI");
 
        double stat_error = DataHistogramClone->GetBinError(pbin);
// fill back the 1D histo with the result
        double bincontent = ResolutionFunction->GetParameter(1);
        double binerror = ResolutionFunction->GetParError(1);
 
        binerror = std::max(binerror, stat_error);
 
        DataHistogramNew->SetBinContent(pbin, bincontent);
        DataHistogramNew->SetBinError(pbin, binerror);
      }
 
      return DataHistogramNew;
 
    }

resetInputJson()

void resetInputJson ( )

inlineprotectedinherited

Clears the m_inputJson member variable.

Definition at line 330 of file CalibrationAlgorithm.h.

{m_jsonExecutionInput.clear();}

resetOutputJson()

void resetOutputJson ( )

inlineprotectedinherited

Clears the m_outputJson member variable.

Definition at line 333 of file CalibrationAlgorithm.h.

{m_jsonExecutionOutput.clear();}

saveCalibration() [1/6]

void saveCalibration ( TClonesArray * data, const std::string & name )

protectedinherited

Store DBArray payload with given name with default IOV.

Definition at line 297 of file CalibrationAlgorithm.cc.

{
  saveCalibration(data, name, m_data.getRequestedIov());
}

saveCalibration() [2/6]

void saveCalibration ( TClonesArray * data, const std::string & name, const IntervalOfValidity & iov )

protectedinherited

Store DBArray with given name and custom IOV.

Definition at line 276 of file CalibrationAlgorithm.cc.

{
  B2DEBUG(29, "Saving calibration TClonesArray '" <<  name << "' to payloads list.");
  getPayloads().emplace_back(name, data, iov);
}

saveCalibration() [3/6]

void saveCalibration ( TObject * data )

protectedinherited

Store DB payload with default name and default IOV.

Definition at line 287 of file CalibrationAlgorithm.cc.

{
  saveCalibration(data, DataStore::objectName(data->IsA(), ""));
}

saveCalibration() [4/6]

void saveCalibration ( TObject * data, const IntervalOfValidity & iov )

protectedinherited

Store DB payload with default name and custom IOV.

Definition at line 282 of file CalibrationAlgorithm.cc.

{
  saveCalibration(data, DataStore::objectName(data->IsA(), ""), iov);
}

saveCalibration() [5/6]

void saveCalibration ( TObject * data, const std::string & name )

protectedinherited

Store DB payload with given name with default IOV.

Definition at line 292 of file CalibrationAlgorithm.cc.

{
  saveCalibration(data, name, m_data.getRequestedIov());
}

saveCalibration() [6/6]

void saveCalibration ( TObject * data, const std::string & name, const IntervalOfValidity & iov )

protectedinherited

Store DB payload with given name and custom IOV.

Definition at line 270 of file CalibrationAlgorithm.cc.

{
  B2DEBUG(29, "Saving calibration TObject = '" <<  name << "' to payloads list.");
  getPayloads().emplace_back(name, data, iov);
}

setCustomProfile()

void setCustomProfile ( bool value = true )

inline

reimplement the profile histogram calculation

Definition at line 75 of file SVDdEdxCalibrationAlgorithm.h.

{ m_CustomProfile = value; }

setDEdxCutoff()

void setDEdxCutoff ( const double & value )

inline

set the upper edge of the dEdx binning for the payloads

Definition at line 65 of file SVDdEdxCalibrationAlgorithm.h.

{ m_dedxCutoff = value; }

setDescription()

void setDescription ( const std::string & description )

inlineprotectedinherited

Set algorithm description (in constructor)

Definition at line 321 of file CalibrationAlgorithm.h.

{m_description = description;}

setFixUnstableFitParameter()

void setFixUnstableFitParameter ( bool value = true )

inline

In the dEdx:betagamma fit, there is one free parameter that makes fit convergence poor.

It is ok to fix it, unless the dEdx behavior changes radically with time.

Definition at line 80 of file SVDdEdxCalibrationAlgorithm.h.

{ m_FixUnstableFitParameter = value; }

setInputFileNames() [1/2]

void setInputFileNames ( const 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();
}

setInputFileNames() [2/2]

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.");
  }
}

setMinEvtsPerTree()

void setMinEvtsPerTree ( const double & value )

inline

set the upper edge of the dEdx binning for the payloads

Definition at line 70 of file SVDdEdxCalibrationAlgorithm.h.

{ m_MinEvtsPerTree = value; }

setMonitoringPlots()

void setMonitoringPlots ( bool value = false )

inline

function to enable plotting

Definition at line 45 of file SVDdEdxCalibrationAlgorithm.h.

{ m_isMakePlots = value; }

setNumBGBins()

void setNumBGBins ( const int & value )

inline

set the number of beta*gamma bins for the fits

Definition at line 60 of file SVDdEdxCalibrationAlgorithm.h.

{ m_numBGBins = value; }

setNumDEdxBins()

void setNumDEdxBins ( const int & value )

inline

set the number of dEdx bins for the payloads

Definition at line 50 of file SVDdEdxCalibrationAlgorithm.h.

{ m_numDEdxBins = value; }

setNumPBins()

void setNumPBins ( const int & value )

inline

set the number of momentum bins for the payloads

Definition at line 55 of file SVDdEdxCalibrationAlgorithm.h.

{ m_numPBins = value; }

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;}

setUsePionBGFunctionForEverything()

void setUsePionBGFunctionForEverything ( bool value = false )

inline

use the pion beta*gamma function for other hadrons

Definition at line 85 of file SVDdEdxCalibrationAlgorithm.h.

{ m_UsePionBGFunctionForEverything = value; }

setUseProtonBGFunctionForEverything()

void setUseProtonBGFunctionForEverything ( bool value = false )

inline

use the proton beta*gamma function for other hadrons

Definition at line 90 of file SVDdEdxCalibrationAlgorithm.h.

{ m_UseProtonBGFunctionForEverything = value; }

updateDBObjPtrs()

void updateDBObjPtrs ( const unsigned int event, const int run, const int experiment )

staticprotectedinherited

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

Definition at line 405 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_allExpRun

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

staticprivateinherited

allExpRun

Definition at line 364 of file CalibrationAlgorithm.h.

m_boundaries

std::vector<Calibration::ExpRun> m_boundaries

protectedinherited

When using the boundaries functionality from isBoundaryRequired, this is used to store the boundaries. It is cleared when.

Definition at line 261 of file CalibrationAlgorithm.h.

m_CustomProfile

bool m_CustomProfile = 1

private

reimplement profile histogram calculation instead of the ROOT implementation?

Definition at line 117 of file SVDdEdxCalibrationAlgorithm.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_dedxCutoff

double m_dedxCutoff = 5.e6

private

the upper edge of the dEdx binning for the payloads

Definition at line 111 of file SVDdEdxCalibrationAlgorithm.h.

m_dedxMaxPossible

double m_dedxMaxPossible = 7.e6

private

the approximate max possible value of dEdx

Definition at line 112 of file SVDdEdxCalibrationAlgorithm.h.

m_description

std::string m_description {""}

privateinherited

Description of the algorithm.

Definition at line 385 of file CalibrationAlgorithm.h.

{""};

m_DeuteronPDGMass

const double m_DeuteronPDGMass = TDatabasePDG::Instance()->GetParticle(1000010020)->Mass()

private

PDG mass for the deuteron.

Definition at line 130 of file SVDdEdxCalibrationAlgorithm.h.

m_ElectronPDGMass

const double m_ElectronPDGMass = TDatabasePDG::Instance()->GetParticle(11)->Mass()

private

PDG mass for the electron.

Definition at line 125 of file SVDdEdxCalibrationAlgorithm.h.

m_FixUnstableFitParameter

bool m_FixUnstableFitParameter

private

Initial value:

=
      1

In the dEdx:betagamma fit, there is one free parameter that makes fit convergence poor.

It is ok to fix it, unless the dEdx behavior changes radically with time.

Definition at line 122 of file SVDdEdxCalibrationAlgorithm.h.

m_granularityOfData

std::string m_granularityOfData

privateinherited

Granularity of input data. This only changes when the input files change so it isn't specific to an execution.

Definition at line 379 of file CalibrationAlgorithm.h.

m_inputFileNames

std::vector<std::string> m_inputFileNames

privateinherited

List of input files to the Algorithm, will initially be user defined but then gets the wildcards expanded during execute()

Definition at line 373 of file CalibrationAlgorithm.h.

m_isMakePlots

bool m_isMakePlots

private

produce plots for monitoring

Definition at line 99 of file SVDdEdxCalibrationAlgorithm.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_KaonPDGMass

const double m_KaonPDGMass = TDatabasePDG::Instance()->GetParticle(321)->Mass()

private

PDG mass for the charged kaon.

Definition at line 128 of file SVDdEdxCalibrationAlgorithm.h.

m_MinEvtsPerTree

int m_MinEvtsPerTree

private

Initial value:

=
      100

number of events in TTree below which we don't try to fit

Definition at line 113 of file SVDdEdxCalibrationAlgorithm.h.

m_MuonPDGMass

const double m_MuonPDGMass = TDatabasePDG::Instance()->GetParticle(13)->Mass()

private

PDG mass for the muon.

Definition at line 126 of file SVDdEdxCalibrationAlgorithm.h.

m_NToGenerate

int m_NToGenerate

private

Initial value:

=
      5e6

the number of events to be generated in each momentum bin in the new payloads.

Please do not change this number unless it's really needed. It is crucial that this is consistent between data and MC payloads, as it affects the minimal possible PID probability value after normalisation.

Definition at line 115 of file SVDdEdxCalibrationAlgorithm.h.

m_numBGBins

int m_numBGBins = 69

private

the number of beta*gamma bins for the profile and fitting

Definition at line 110 of file SVDdEdxCalibrationAlgorithm.h.

m_numDEdxBins

int m_numDEdxBins = 100

private

the number of dEdx bins for the payloads

Definition at line 108 of file SVDdEdxCalibrationAlgorithm.h.

m_numPBins

int m_numPBins = 69

private

the number of momentum bins for the payloads

Definition at line 109 of file SVDdEdxCalibrationAlgorithm.h.

m_PionPDGMass

const double m_PionPDGMass = TDatabasePDG::Instance()->GetParticle(211)->Mass()

private

PDG mass for the charged pion.

Definition at line 127 of file SVDdEdxCalibrationAlgorithm.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_ProtonPDGMass

const double m_ProtonPDGMass = TDatabasePDG::Instance()->GetParticle(2212)->Mass()

private

PDG mass for the proton.

Definition at line 129 of file SVDdEdxCalibrationAlgorithm.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_UsePionBGFunctionForEverything

bool m_UsePionBGFunctionForEverything

private

Initial value:

=
      0

Assume that the dEdx:betagamma trend is the same for all hadrons; use the pion trend as representative.

Definition at line 118 of file SVDdEdxCalibrationAlgorithm.h.

m_UseProtonBGFunctionForEverything

bool m_UseProtonBGFunctionForEverything

private

Initial value:

=
      0

Assume that the dEdx:betagamma trend is the same for all hadrons; use the proton trend as representative.

Definition at line 120 of file SVDdEdxCalibrationAlgorithm.h.

---

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

• svd/calibration/include/SVDdEdxCalibrationAlgorithm.h
• svd/calibration/src/SVDdEdxCalibrationAlgorithm.cc