Class for Space resolution calibration. More...

#include <SpaceResolutionCalibrationAlgorithm.h>

Inheritance diagram for SpaceResolutionCalibrationAlgorithm:

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

Public Member Functions
	SpaceResolutionCalibrationAlgorithm ()
	Constructor.

	~SpaceResolutionCalibrationAlgorithm ()
	Destructor.

void	setDebug (bool debug=false)
	Set Debug mode.

void	setMinimumNDF (double ndf)
	minimum NDF required for track

void	setMinimumPval (double pval)
	Minimum Pval required.

void	setBinWidth (double bw)
	Bin width of each slide.

void	setBField (bool bfield)
	Work with B field or not;.

void	setStoreHisto (bool storeHist=false)
	Store histograms during the calibration or not.

void	enableTextOutput (bool output=true)
	Enable text output of calibration result.

void	setOutputFileName (std::string outputname)
	output file name

void	setHistFileName (const std::string &name)
	Set name for histogram output.

void	setThreshold (double th=0.6)
	Set threshold for the fraction of fitted results.

std::string	getPrefix () const
	Get the prefix used for getting calibration data.

bool	checkPyExpRun (PyObject *pyObj)
	Checks that a PyObject can be successfully converted to an ExpRun type.

Calibration::ExpRun	convertPyExpRun (PyObject *pyObj)
	Performs the conversion of PyObject to ExpRun.

std::string	getCollectorName () const
	Alias for prefix.

void	setPrefix (const std::string &prefix)
	Set the prefix used to identify datastore objects.

void	setInputFileNames (PyObject *inputFileNames)
	Set the input file names used for this algorithm from a Python list.

PyObject *	getInputFileNames ()
	Get the input file names used for this algorithm and pass them out as a Python list of unicode strings.

std::vector< Calibration::ExpRun >	getRunListFromAllData () const
	Get the complete list of runs from inspection of collected data.

RunRange	getRunRangeFromAllData () const
	Get the complete RunRange from inspection of collected data.

IntervalOfValidity	getIovFromAllData () const
	Get the complete IoV from inspection of collected data.

void	fillRunToInputFilesMap ()
	Fill the mapping of ExpRun -> Files.

std::string	getGranularity () const
	Get the granularity of collected data.

EResult	execute (std::vector< Calibration::ExpRun > runs={}, int iteration=0, IntervalOfValidity iov=IntervalOfValidity())
	Runs calibration over vector of runs for a given iteration.

EResult	execute (PyObject *runs, int iteration=0, IntervalOfValidity iov=IntervalOfValidity())
	Runs calibration over Python list of runs. Converts to C++ and then calls the other execute() function.

std::list< Database::DBImportQuery > &	getPayloads ()
	Get constants (in TObjects) for database update from last execution.

std::list< Database::DBImportQuery >	getPayloadValues ()
	Get constants (in TObjects) for database update from last execution but passed by VALUE.

bool	commit ()
	Submit constants from last calibration into database.

bool	commit (std::list< Database::DBImportQuery > payloads)
	Submit constants from a (potentially previous) set of payloads.

const std::string &	getDescription () const
	Get the description of the algorithm (set by developers in constructor)

bool	loadInputJson (const std::string &jsonString)
	Load the m_inputJson variable from a string (useful from Python interface). The return bool indicates success or failure.

const std::string	dumpOutputJson () const
	Dump the JSON string of the output JSON object.

const std::vector< Calibration::ExpRun >	findPayloadBoundaries (std::vector< Calibration::ExpRun > runs, int iteration=0)
	Used to discover the ExpRun boundaries that you want the Python CAF to execute on. This is optional and only used in some.

template<>
std::shared_ptr< TTree >	getObjectPtr (const std::string &name, const std::vector< Calibration::ExpRun > &requestedRuns)
	Specialization of getObjectPtr<TTree>.

Protected Member Functions
EResult	calibrate () override
	Run algo on data.

void	createHisto ()
	create histogram

void	storeHisto ()
	store histogram

void	write ()
	save calibration, in text file or db

void	prepare ()
	Prepare the calibration of space resolution.

double	getUpperBoundaryForFit (TGraphErrors *graph)
	search max point at boundary region

void	setInputFileNames (std::vector< std::string > inputFileNames)
	Set the input file names used for this algorithm.

virtual bool	isBoundaryRequired (const Calibration::ExpRun &)
	Given the current collector data, make a decision about whether or not this run should be the start of a payload boundary.

virtual void	boundaryFindingSetup (std::vector< Calibration::ExpRun >, int)
	If you need to make some changes to your algorithm class before 'findPayloadBoundaries' is run, make them in this function.

virtual void	boundaryFindingTearDown ()
	Put your algorithm back into a state ready for normal execution if you need to.

const std::vector< Calibration::ExpRun > &	getRunList () const
	Get the list of runs for which calibration is called.

int	getIteration () const
	Get current iteration.

std::vector< std::string >	getVecInputFileNames () const
	Get the input file names used for this algorithm as a STL vector.

template<class T>
std::shared_ptr< T >	getObjectPtr (const std::string &name, const std::vector< Calibration::ExpRun > &requestedRuns)
	Get calibration data object by name and list of runs, the Merge function will be called to generate the overall object.

template<class T>
std::shared_ptr< T >	getObjectPtr (std::string name)
	Get calibration data object (for all runs the calibration is requested for) This function will only work during or after execute() has been called once.

template<>
shared_ptr< TTree >	getObjectPtr (const string &name, const vector< ExpRun > &requestedRuns)
	We cheekily cast the TChain to TTree for the returned pointer so that the user never knows Hopefully this doesn't cause issues if people do low level stuff to the tree...

std::string	getGranularityFromData () const
	Get the granularity of collected data.

void	saveCalibration (TClonesArray *data, const std::string &name)
	Store DBArray payload with given name with default IOV.

void	saveCalibration (TClonesArray *data, const std::string &name, const IntervalOfValidity &iov)
	Store DBArray with given name and custom IOV.

void	saveCalibration (TObject *data)
	Store DB payload with default name and default IOV.

void	saveCalibration (TObject *data, const IntervalOfValidity &iov)
	Store DB payload with default name and custom IOV.

void	saveCalibration (TObject *data, const std::string &name)
	Store DB payload with given name with default IOV.

void	saveCalibration (TObject *data, const std::string &name, const IntervalOfValidity &iov)
	Store DB payload with given name and custom IOV.

void	updateDBObjPtrs (const unsigned int event, const int run, const int experiment)
	Updates any DBObjPtrs by calling update(event) for DBStore.

void	setDescription (const std::string &description)
	Set algorithm description (in constructor)

void	clearCalibrationData ()
	Clear calibration data.

Calibration::ExpRun	getAllGranularityExpRun () const
	Returns the Exp,Run pair that means 'Everything'. Currently unused.

void	resetInputJson ()
	Clears the m_inputJson member variable.

void	resetOutputJson ()
	Clears the m_outputJson member variable.

template<class T>
void	setOutputJsonValue (const std::string &key, const T &value)
	Set a key:value pair for the outputJson object, expected to used internally during calibrate()

template<class T>
const T	getOutputJsonValue (const std::string &key) const
	Get a value using a key from the JSON output object, not sure why you would want to do this.

template<class T>
const T	getInputJsonValue (const std::string &key) const
	Get an input JSON value using a key. The normal exceptions are raised when the key doesn't exist.

const nlohmann::json &	getInputJsonObject () const
	Get the entire top level JSON object. We explicitly say this must be of object type so that we might pick.

bool	inputJsonKeyExists (const std::string &key) const
	Test for a key in the input JSON object.

Protected Attributes
std::vector< Calibration::ExpRun >	m_boundaries
	When using the boundaries functionality from isBoundaryRequired, this is used to store the boundaries. It is cleared when.

Private Member Functions
std::string	getExpRunString (Calibration::ExpRun &expRun) const
	Gets the "exp.run" string repr. of (exp,run)

std::string	getFullObjectPath (const std::string &name, Calibration::ExpRun expRun) const
	constructs the full TDirectory + Key name of an object in a TFile based on its name and exprun

Private Attributes
double	m_minNdf = 5
	Minimum NDF.

double	m_minPval = 0.
	Minimum Prob(chi2) of track.

double	m_binWidth = 0.05
	width of each bin, unit cm

bool	m_debug = false
	Debug or not.

bool	m_storeHisto = false
	Store histogram or not.

bool	m_bField = true
	Work with BField, fit range and initial parameters is different incase B and noB.

double	m_threshold = 0.6
	minimal requirement for the fraction of fitted results

double	m_sigma [56][2][18][7][8]
	new sigma parameters.

TGraphErrors *	m_gFit [56][2][18][7]
	sigma*sigma graph for fit

TGraphErrors *	m_graph [56][2][18][7]
	sigma graph.

TH2F *	m_hBiased [56][2][Max_nalpha][Max_ntheta]
	2D histogram of biased residual

TH2F *	m_hUnbiased [56][2][Max_nalpha][Max_ntheta]
	2D histogram of unbiased residual

TH1F *	m_hMeanBiased [56][2][Max_nalpha][Max_ntheta]
	mean histogram biased residual

TH1F *	m_hSigmaBiased [56][2][Max_nalpha][Max_ntheta]
	sigma histogram of biased residual

TH1F *	m_hMeanUnbiased [56][2][Max_nalpha][Max_ntheta]
	mean histogram of unbiased residual

TH1F *	m_hSigmaUnbiased [56][2][Max_nalpha][Max_ntheta]
	sigma histogram of ubiased residual

int	m_fitStatus [56][2][Max_nalpha][Max_ntheta] = {{{{0}}}}
	Fit flag; 1:OK ; 0:error.

int	m_nAlphaBins
	number of alpha bins

int	m_nThetaBins
	number of theta bins

float	m_lowerAlpha [18]
	Lower boundaries of alpha bins.

float	m_upperAlpha [18]
	Upper boundaries of alpha bins.

float	m_iAlpha [18]
	represented alphas of alpha bins.

float	m_lowerTheta [7]
	Lower boundaries of theta bins.

float	m_upperTheta [7]
	Upper boundaries of theta bins.

float	m_iTheta [7]
	represented alphas of theta bins.

unsigned short	m_sigmaParamMode = 0
	sigma mode for this calibration.

double	m_sigmaPost [56][2][18][7][8]
	sigma parameters before calibration

unsigned short	m_sigmaParamModePost
	sigma mode before this calibration.

bool	m_textOutput = false
	output text file if true

std::string	m_outputFileName = "sigma_new.dat"
	Output sigma filename.

std::string	m_histName = "histSigma.root"
	root file name

DBObjPtr< CDCGeometry >	m_cdcGeo
	Geometry of CDC.

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 int	Max_nalpha = 18
	Maximum alpha bin.

static const int	Max_ntheta = 7
	maximum theta bin

static const unsigned short	Max_np = 40
	Maximum number of point =1/binwidth.

static const Calibration::ExpRun	m_allExpRun = make_pair(-1, -1)
	allExpRun

Detailed Description

Class for Space resolution calibration.

Definition at line 28 of file SpaceResolutionCalibrationAlgorithm.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

◆ SpaceResolutionCalibrationAlgorithm()

SpaceResolutionCalibrationAlgorithm ( )

Constructor.

Definition at line 28 of file SpaceResolutionCalibrationAlgorithm.cc.

                                                                         :
  CalibrationAlgorithm("CDCCalibrationCollector")
{
  setDescription(
    " -------------------------- Position Resolution Calibration Algorithm -------------------------\n"
  );
}

◆ ~SpaceResolutionCalibrationAlgorithm()

~SpaceResolutionCalibrationAlgorithm ( )

inline

Destructor.

Definition at line 36 of file SpaceResolutionCalibrationAlgorithm.h.

36{}

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, SVDCoGTimeCalibrationAlgorithm, TestBoundarySettingAlgorithm, and TestCalibrationAlgorithm.

Definition at line 252 of file CalibrationAlgorithm.h.

252{};

◆ 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.

257{};

◆ calibrate()

CalibrationAlgorithm::EResult calibrate ( )

overrideprotectedvirtual

Run algo on data.

Upper limit of fitting.

Implements CalibrationAlgorithm.

Definition at line 311 of file SpaceResolutionCalibrationAlgorithm.cc.

{
 
  B2INFO("Start calibration");
  gPrintViaErrorHandler = true; // Suppress huge log output from TMinuit
  gROOT->SetBatch(1);
  gErrorIgnoreLevel = 3001;
 
  const auto exprun = getRunList()[0];
  B2INFO("ExpRun used for DB Geometry : " << exprun.first << " " << exprun.second);
  updateDBObjPtrs(1, exprun.second, exprun.first);
  //  B2INFO("Creating CDCGeometryPar object");
  //  CDC::CDCGeometryPar::Instance(&(*m_cdcGeo));
 
  prepare();
  createHisto();
 
  TF1* func = new TF1("func", "[0]/(x*x + [1])+[2]* x+[3]+[4]*exp([5]*(x-[6])*(x-[6]))", 0, 1.);
  TH1F* hprob = new TH1F("h1", "", 20, 0, 1);
  double upFit; 
  double intp6;
 
  for (int i = 0; i < 56; ++i) {
    for (int lr = 0; lr < 2; ++lr) {
      for (int al = 0; al < m_nAlphaBins; ++al) {
        for (int th = 0; th < m_nThetaBins; ++th) {
          if (!m_gFit[i][lr][al][th]) continue;
          if (m_fitStatus[i][lr][al][th] != -1) { /*if graph exist, do fitting*/
            upFit = getUpperBoundaryForFit(m_gFit[i][lr][al][th]);
            intp6 = upFit + 0.2;
            B2DEBUG(199, "xmax for fitting: " << upFit);
 
            func->SetParameters(5E-6, 0.007, 1E-4, 1E-5, 0.00008, -30, intp6);
            func->SetParLimits(0, 1E-7, 1E-4);
            func->SetParLimits(1, 0.0045, 0.02);
            func->SetParLimits(2, 1E-6, 0.0005);
            func->SetParLimits(3, 1E-8, 0.0005);
            func->SetParLimits(4, 0., 0.001);
            func->SetParLimits(5, -40, 0.);
            func->SetParLimits(6, intp6 - 0.5, intp6 + 0.2);
 
            B2DEBUG(21, "Fitting for layer: " << i << "lr: " << lr << " ial" << al << " ith:" << th);
            B2DEBUG(21, "Fit status before fit:" << m_fitStatus[i][lr][al][th]);
 
            for (int j = 0; j < 10; j++) {
 
              B2DEBUG(21, "loop: " << j);
              B2DEBUG(21, "Int p6: " << intp6);
              B2DEBUG(21, "Number of Point: " << m_gFit[i][lr][al][th]->GetN());
              Int_t stat = m_gFit[i][lr][al][th]->Fit("func", "MQE", "", 0.05, upFit);
              B2DEBUG(21, "stat of fit" << stat);
              std::string Fit_status = gMinuit->fCstatu.Data();
              B2DEBUG(21, "FIT STATUS: " << Fit_status);
              if (Fit_status == "OK" || Fit_status == "SUCCESSFUL" || Fit_status == "CALL LIMIT"
                  || Fit_status == "PROBLEMS") {//need to found better way
                if (fabs(func->Eval(0.3)) > 0.00035 || func->Eval(0.3) < 0) {
                  func->SetParameters(5E-6, 0.007, 1E-4, 1E-7, 0.0007, -30, intp6 + 0.05 * j);
                  func->SetParLimits(6, intp6 + 0.05 * j - 0.5, intp6 + 0.05 * j + 0.2);
                  //    func->SetParameters(defaultparsmall);
                  m_fitStatus[i][lr][al][th] = 0;
                } else {
                  B2DEBUG(21, "Prob of fit: " << func->GetProb());
                  m_fitStatus[i][lr][al][th] = 1;
                  break;
                }
              } else {
                m_fitStatus[i][lr][al][th] = 0;
                func->SetParameters(5E-6, 0.007, 1E-4, 1E-7, 0.0007, -30, intp6 + 0.05 * j);
                func->SetParLimits(6, intp6 + 0.05 * j - 0.5, intp6 + 0.05 * j + 0.2);
                upFit += 0.025;
                if (j == 9) {
                  // TCanvas* c1 =  new TCanvas("c1", "", 600, 600);
                  // m_gFit[i][lr][al][th]->Draw();
                  // c1->SaveAs(Form("Sigma_Fit_Error_%s_%d_%d_%d_%d.png", Fit_status.c_str(), i, lr, al, th));
                  // B2WARNING("Fit error: " << i << " " << lr << " " << al << " " << th);
                }
              }
            }
            if (m_fitStatus[i][lr][al][th] == 1) {
              B2DEBUG(21, "ProbFit: Lay_lr_al_th: " << i << " " << lr << " " << al << " " << th << func->GetProb());
              hprob->Fill(func->GetProb());
              func->GetParameters(m_sigma[i][lr][al][th]);
            }
          }
        }
      }
    }
  }
 
  write();
  storeHisto();
 
  const int nTotal = 56 * 2 * m_nAlphaBins * m_nThetaBins;
  int nFitCompleted = 0;
  for (int l = 0; l < 56; ++l) {
    for (int lr = 0; lr < 2; ++lr) {
      for (int al = 0; al < m_nAlphaBins; ++al) {
        for (int th = 0; th < m_nThetaBins; ++th) {
          if (m_fitStatus[l][lr][al][th] == 1) {
            nFitCompleted += 1;
          }
        }
      }
    }
  }
 
  if (static_cast<double>(nFitCompleted) / nTotal < m_threshold) {
    B2WARNING("Less than " << m_threshold * 100 << " % of Sigmas were fitted.");
    return c_NotEnoughData;
  }
 
  return c_OK;
}

◆ checkPyExpRun()

bool checkPyExpRun ( PyObject * pyObj )

inherited

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

Checks if the PyObject can be converted to ExpRun.

Definition at line 28 of file CalibrationAlgorithm.cc.

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

◆ clearCalibrationData()

void clearCalibrationData ( )

inlineprotectedinherited

Clear calibration data.

Definition at line 324 of file CalibrationAlgorithm.h.

324{m_data.clearCalibrationData();}

◆ commit() [1/2]

bool commit ( )

inherited

Submit constants from last calibration into database.

Definition at line 302 of file CalibrationAlgorithm.cc.

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

◆ commit() [2/2]

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

inherited

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

Definition at line 311 of file CalibrationAlgorithm.cc.

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

◆ convertPyExpRun()

ExpRun convertPyExpRun ( PyObject * pyObj )

inherited

Performs the conversion of PyObject to ExpRun.

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

Definition at line 70 of file CalibrationAlgorithm.cc.

{
  ExpRun expRun;
  PyObject* itemExp, *itemRun;
  itemExp = PySequence_GetItem(pyObj, 0);
  itemRun = PySequence_GetItem(pyObj, 1);
  expRun.first = PyLong_AsLong(itemExp);
  Py_DECREF(itemExp);
  expRun.second = PyLong_AsLong(itemRun);
  Py_DECREF(itemRun);
  return expRun;
}

◆ createHisto()

void createHisto ( )

protected

create histogram

Definition at line 35 of file SpaceResolutionCalibrationAlgorithm.cc.

{
  B2INFO("Creating histograms");
  const int np = floor(1 / m_binWidth);
 
  vector<double> yu;
  vector <double> yb;
  for (int i = 0; i < 50; ++i) {
    yb.push_back(-0.07 + i * (0.14 / 50));
  }
  for (int i = 0; i < 50; ++i) {
    yu.push_back(-0.08 + i * (0.16 / 50));
  }
 
  vector<double> xbin;
  xbin.push_back(0.);
  xbin.push_back(0.02);
  for (int i = 1; i < np; ++i) {
    xbin.push_back(i * m_binWidth);
  }
 
  for (int il = 0; il < 56; ++il) {
    for (int lr = 0; lr < 2; ++lr) {
      for (int al = 0; al < m_nAlphaBins; ++al) {
        for (int th = 0; th < m_nThetaBins; ++th) {
          m_hBiased[il][lr][al][th] = new TH2F(Form("hb_%d_%d_%d_%d", il, lr, al, th),
                                               Form("lay_%d_lr%d_al_%3.0f_th_%3.0f;Drift Length [cm];#DeltaX", il, lr, m_iAlpha[al], m_iTheta[th]),
                                               xbin.size() - 1, &xbin.at(0), yb.size() - 1, &yb.at(0));
          m_hUnbiased[il][lr][al][th] = new TH2F(Form("hu_%d_%d_%d_%d", il, lr, al, th),
                                                 Form("lay_%d_lr%d_al_%3.0f_th_%3.0f;Drift Length [cm];#DeltaX", il, lr, m_iAlpha[al], m_iTheta[th]),
                                                 xbin.size() - 1, &xbin.at(0), yu.size() - 1, &yu.at(0));
        }
      }
    }
  }
 
 
  auto tree = getObjectPtr<TTree>("tree");
 
  UChar_t lay;
  Float_t w;
  Float_t x_u;
  Float_t x_b;
  Float_t x_mea;
  Float_t Pval;
  Float_t alpha;
  Float_t theta;
  Float_t ndf;
  Float_t absRes_u;
  Float_t absRes_b;
  tree->SetBranchAddress("lay", &lay);
  tree->SetBranchAddress("ndf", &ndf);
  tree->SetBranchAddress("Pval", &Pval);
  tree->SetBranchAddress("x_u", &x_u);
  tree->SetBranchAddress("x_b", &x_b);
  tree->SetBranchAddress("x_mea", &x_mea);
  tree->SetBranchAddress("weight", &w);
  tree->SetBranchAddress("alpha", &alpha);
  tree->SetBranchAddress("theta", &theta);
 
  /* Disable unused branch */
  std::vector<TString> list_vars = {"lay", "ndf", "Pval", "x_u", "x_b", "x_mea", "weight", "alpha", "theta"};
  tree->SetBranchStatus("*", 0);
 
  for (TString brname : list_vars) {
    tree->SetBranchStatus(brname, 1);
  }
 
 
  const Long64_t nEntries = tree->GetEntries();
  B2INFO("Number of entries: " << nEntries);
  int ith = -99;
  int ial = -99;
  TStopwatch timer;
  timer.Start();
  for (Long64_t i = 0; i < nEntries; ++i) {
    tree->GetEntry(i);
    if (std::fabs(x_b) < 0.02 || std::fabs(x_u) < 0.02) continue;
    if (Pval < m_minPval || ndf < m_minNdf) continue;
 
    for (int k = 0; k < m_nAlphaBins; ++k) {
      if (alpha < m_upperAlpha[k]) {
        ial = k;
        break;
      }
    }
 
    for (int j = 0; j < m_nThetaBins; ++j) {
      if (theta < m_upperTheta[j]) {
        ith = j;
        break;
      }
    }
 
    int ilr = x_u > 0 ? 1 : 0;
 
    if (ial == -99 || ith == -99) {
      TString command = Form("Error in alpha=%3.2f and theta = %3.2f>> error", alpha, theta);
      B2FATAL("ERROR" << command);
    }
 
    absRes_u = fabs(x_mea) - fabs(x_u);
    absRes_b = fabs(x_mea) - fabs(x_b);
 
    int ilay = static_cast<int>(lay);
    m_hUnbiased[ilay][ilr][ial][ith]->Fill(fabs(x_u), absRes_u, w);
    m_hBiased[ilay][ilr][ial][ith]->Fill(fabs(x_b), absRes_b, w);
  }
 
  timer.Stop();
  B2INFO("Time to fill histograms: " << timer.RealTime() << "s");
 
  B2INFO("Start to obtain the biased and unbiased sigmas...");
  TF1* gb = new TF1("gb", "gaus", -0.05, 0.05);
  TF1* gu = new TF1("gu", "gaus", -0.06, 0.06);
  TF1* g0b = new TF1("g0b", "gaus", -0.015, 0.07);
  TF1* g0u = new TF1("g0u", "gaus", -0.015, 0.08);
 
  std::vector<double> sigma;
  std::vector<double> dsigma;
  std::vector<double> s2;
  std::vector<double> ds2;
  std::vector<double> xl;
  std::vector<double> dxl;
  std::vector<double> dxl0;
 
  ofstream ofss("IntReso.dat");
  const int ib1 = int(0.1 / m_binWidth) + 1;
  int firstbin = 1;
  int minEntry = 10;
  for (int il = 0; il < 56; ++il) {
    for (int lr = 0; lr < 2; ++lr) {
      for (int al = 0; al < m_nAlphaBins; ++al) {
        for (int th = 0; th < m_nThetaBins; ++th) {
 
          B2DEBUG(21, "layer-lr-al-th " << il << " - " << lr << " - " << al << " - " << th);
          if (m_hBiased[il][lr][al][th]->GetEntries() < 5000) {
            m_fitStatus[il][lr][al][th] = -1;
            continue;
          }
 
          auto* proYb = m_hBiased[il][lr][al][th]->ProjectionY();
          auto* proYu = m_hUnbiased[il][lr][al][th]->ProjectionY();
 
          g0b->SetParLimits(0, 0, m_hBiased[il][lr][al][th]->GetEntries() * 5);
          g0u->SetParLimits(0, 0, m_hUnbiased[il][lr][al][th]->GetEntries() * 5);
          g0b->SetParLimits(1, -0.01, 0.004);
          g0u->SetParLimits(1, -0.01, 0.004);
          g0b->SetParLimits(2, 0.0, proYb->GetRMS() * 5);
          g0u->SetParLimits(2, 0.0, proYu->GetRMS() * 5);
 
          g0b->SetParameter(0, m_hBiased[il][lr][al][th]->GetEntries());
          g0u->SetParameter(0, m_hUnbiased[il][lr][al][th]->GetEntries());
          g0b->SetParameter(1, 0);
          g0u->SetParameter(1, 0);
          g0b->SetParameter(2, proYb->GetRMS());
          g0u->SetParameter(2, proYu->GetRMS());
 
          B2DEBUG(21, "Nentries: " << m_hBiased[il][lr][al][th]->GetEntries());
          m_hBiased[il][lr][al][th]->SetDirectory(0);
          m_hUnbiased[il][lr][al][th]->SetDirectory(0);
 
          // With biased track fit result
 
          // Apply slice fit for the region near sense wire
          m_hBiased[il][lr][al][th]->FitSlicesY(g0b, firstbin, ib1, minEntry);
 
          // mean
          m_hMeanBiased[il][lr][al][th] = (TH1F*)gDirectory->Get(Form("hb_%d_%d_%d_%d_1", il, lr, al, th))->Clone(Form("hb_%d_%d_%d_%d_m", il,
                                          lr, al,
                                          th));
          // sigma
          m_hSigmaBiased[il][lr][al][th] = (TH1F*)gDirectory->Get(Form("hb_%d_%d_%d_%d_2", il, lr, al, th))->Clone(Form("hb_%d_%d_%d_%d_s",
                                           il, lr, al,
                                           th));
          m_hMeanBiased[il][lr][al][th]->SetDirectory(0);
          m_hSigmaBiased[il][lr][al][th]->SetDirectory(0);
 
          //Apply slice fit for other regions
          m_hBiased[il][lr][al][th]->FitSlicesY(gb, ib1 + 1, np, minEntry);
          // mean
          m_hMeanBiased[il][lr][al][th]->Add((TH1F*)gDirectory->Get(Form("hb_%d_%d_%d_%d_1", il, lr, al, th)));
          //sigma
          m_hSigmaBiased[il][lr][al][th]->Add((TH1F*)gDirectory->Get(Form("hb_%d_%d_%d_%d_2", il, lr, al, th)));
          B2DEBUG(21, "entries (2nd): " << m_hSigmaBiased[il][lr][al][th]->GetEntries());
 
          // With unbiased track fit result
 
          // Apply slice fit for the region near sense wire
          m_hUnbiased[il][lr][al][th]->FitSlicesY(g0u, firstbin, ib1, minEntry);
          // mean
          m_hMeanUnbiased[il][lr][al][th] = (TH1F*)gDirectory->Get(Form("hu_%d_%d_%d_%d_1", il, lr, al, th))->Clone(Form("hu_%d_%d_%d_%d_m",
                                            il, lr, al,
                                            th));
          // sigma
          m_hSigmaUnbiased[il][lr][al][th] = (TH1F*)gDirectory->Get(Form("hu_%d_%d_%d_%d_2", il, lr, al, th))->Clone(Form("hu_%d_%d_%d_%d_s",
                                             il, lr, al,
                                             th));
          m_hMeanUnbiased[il][lr][al][th]->SetDirectory(0);
          m_hSigmaUnbiased[il][lr][al][th]->SetDirectory(0);
 
 
          //Apply slice fit for other regions
          m_hUnbiased[il][lr][al][th]->FitSlicesY(gu, ib1 + 1, np, minEntry);
          //mean
          m_hMeanUnbiased[il][lr][al][th]->Add((TH1F*)gDirectory->Get(Form("hu_%d_%d_%d_%d_1", il, lr, al, th)));
          //sigma
          m_hSigmaUnbiased[il][lr][al][th]->Add((TH1F*)gDirectory->Get(Form("hu_%d_%d_%d_%d_2", il, lr, al, th)));
          if (!m_hSigmaUnbiased[il][lr][al][th] || !m_hSigmaBiased[il][lr][al][th]) {
            B2WARNING("sliced histo  not found");
            m_fitStatus[il][lr][al][th] = -1;
            continue;
          }
          //clean up container before adding new values.
          xl.clear();
          dxl.clear();
          dxl0.clear();
          sigma.clear();
          dsigma.clear();
          s2.clear();
          ds2.clear();
 
 
          for (int j = 1; j < m_hSigmaUnbiased[il][lr][al][th]->GetNbinsX(); j++) {
            if (m_hSigmaUnbiased[il][lr][al][th]->GetBinContent(j) == 0) continue;
            if (m_hSigmaBiased[il][lr][al][th]->GetBinContent(j) == 0) continue;
            double sb = m_hSigmaBiased[il][lr][al][th]->GetBinContent(j);
            double su = m_hSigmaUnbiased[il][lr][al][th]->GetBinContent(j);
 
            double dsb = m_hSigmaBiased[il][lr][al][th]->GetBinError(j);
            double dsu = m_hSigmaUnbiased[il][lr][al][th]->GetBinError(j);
            double XL = m_hSigmaBiased[il][lr][al][th]->GetXaxis()->GetBinCenter(j);
            double dXL = (m_hSigmaBiased[il][lr][al][th]->GetXaxis()->GetBinWidth(j)) / 2;
            double  s_int = std::sqrt(sb * su);
            double  ds_int = 0.5 * s_int * (dsb / sb + dsu / su);
            if (ds_int > 0.02) continue;
            xl.push_back(XL);
            dxl.push_back(dXL);
            dxl0.push_back(0.);
            sigma.push_back(s_int);
            dsigma.push_back(ds_int);
            s2.push_back(s_int * s_int);
            ds2.push_back(2 * s_int * ds_int);
            ofss << il << "   " << lr << "  " << al << "  " << th << "  " << j << "   " << XL << "   " << dXL << "   " << s_int << "   " <<
                 ds_int << endl;
          }
 
          if (xl.size() < 7 || xl.size() > Max_np) {
            m_fitStatus[il][lr][al][th] = -1;
            B2WARNING("number of element might out of range"); continue;
          }
 
          //Intrinsic resolution
          B2DEBUG(21, "Create Histo for layer-lr: " << il << " " << lr);
          m_graph[il][lr][al][th] = new TGraphErrors(xl.size(), &xl.at(0), &sigma.at(0), &dxl.at(0), &dsigma.at(0));
          m_graph[il][lr][al][th]->SetMarkerSize(0.5);
          m_graph[il][lr][al][th]->SetMarkerStyle(8);
          m_graph[il][lr][al][th]->SetTitle(Form("Layer_%d lr%d #alpha = %3.0f #theta = %3.0f", il, lr, m_iAlpha[al], m_iTheta[th]));
          m_graph[il][lr][al][th]->SetName(Form("lay%d_lr%d_al%d_th%d", il, lr, al, th));
 
          //square of sigma for fitting
          m_gFit[il][lr][al][th] = new TGraphErrors(xl.size(), &xl.at(0), &s2.at(0), &dxl0.at(0), &ds2.at(0));
          m_gFit[il][lr][al][th]->SetMarkerSize(0.5);
          m_gFit[il][lr][al][th]->SetMarkerStyle(8);
          m_gFit[il][lr][al][th]->SetTitle(Form("L%d lr%d #alpha = %3.0f #theta = %3.0f ", il, lr, m_iAlpha[al], m_iTheta[th]));
          m_gFit[il][lr][al][th]->SetName(Form("sigma2_lay%d_lr%d_al%d_th%d", il, lr, al, th));
 
          gDirectory->Delete("hu_%d_%d_%d_%d_0");
        }
      }
    }
  }
  ofss.close();
 
}

◆ dumpOutputJson()

const std::string dumpOutputJson ( ) const

inlineinherited

Dump the JSON string of the output JSON object.

Definition at line 223 of file CalibrationAlgorithm.h.

223{return m_jsonExecutionOutput.dump();}

◆ enableTextOutput()

void enableTextOutput ( bool output = true )

inline

Enable text output of calibration result.

Definition at line 57 of file SpaceResolutionCalibrationAlgorithm.h.

57{m_textOutput = output;}

◆ execute() [1/2]

CalibrationAlgorithm::EResult execute	(	PyObject *	runs,
		int	iteration = 0,
		IntervalOfValidity	iov = IntervalOfValidity() )

inherited

Runs calibration over Python list of runs. Converts to C++ and then calls the other execute() function.

Definition at line 83 of file CalibrationAlgorithm.cc.

{
  B2DEBUG(29, "Running execute() using Python Object as input argument");
  // Reset the execution specific data in case the algorithm was previously called
  m_data.reset();
  m_data.setIteration(iteration);
  vector<ExpRun> vecRuns;
  // Is it a list?
  if (PySequence_Check(runs)) {
    boost::python::handle<> handle(boost::python::borrowed(runs));
    boost::python::list listRuns(handle);
 
    int nList = boost::python::len(listRuns);
    for (int iList = 0; iList < nList; ++iList) {
      boost::python::object pyExpRun(listRuns[iList]);
      if (!checkPyExpRun(pyExpRun.ptr())) {
        B2ERROR("Received Python ExpRuns couldn't be converted to C++");
        m_data.setResult(c_Failure);
        return c_Failure;
      } else {
        vecRuns.push_back(convertPyExpRun(pyExpRun.ptr()));
      }
    }
  } else {
    B2ERROR("Tried to set the input runs but we didn't receive a Python sequence object (list,tuple).");
    m_data.setResult(c_Failure);
    return c_Failure;
  }
  return execute(vecRuns, iteration, iov);
}

◆ execute() [2/2]

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

inherited

Runs calibration over vector of runs for a given iteration.

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

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

Definition at line 114 of file CalibrationAlgorithm.cc.

{
  // Check if we are calling this function directly and need to reset, or through Python where it was already done.
  if (m_data.getResult() != c_Undefined) {
    m_data.reset();
    m_data.setIteration(iteration);
  }
 
  if (m_inputFileNames.empty()) {
    B2ERROR("There aren't any input files set. Please use CalibrationAlgorithm::setInputFiles()");
    m_data.setResult(c_Failure);
    return c_Failure;
  }
 
  // Did we receive runs to execute over explicitly?
  if (!(runs.empty())) {
    for (auto expRun : runs) {
      B2DEBUG(29, "ExpRun requested = (" << expRun.first << ", " << expRun.second << ")");
    }
    // We've asked explicitly for certain runs, but we should check if the data granularity is 'run'
    if (strcmp(getGranularity().c_str(), "all") == 0) {
      B2ERROR(("The data is collected with granularity=all (exp=-1,run=-1), but you seem to request calibration for specific runs."
               " We'll continue but using ALL the input data given instead of the specific runs requested."));
    }
  } else {
    // If no runs are provided, infer the runs from all collected data
    runs = getRunListFromAllData();
    // Let's check that we have some now
    if (runs.empty()) {
      B2ERROR("No collected data in input files.");
      m_data.setResult(c_Failure);
      return c_Failure;
    }
    for (auto expRun : runs) {
      B2DEBUG(29, "ExpRun requested = (" << expRun.first << ", " << expRun.second << ")");
    }
  }
 
  m_data.setRequestedRuns(runs);
  if (iov.empty()) {
    // If no user specified IoV we use the IoV from the executed run list
    iov = IntervalOfValidity(runs[0].first, runs[0].second, runs[runs.size() - 1].first, runs[runs.size() - 1].second);
  }
  m_data.setRequestedIov(iov);
  // After here, the getObject<...>(...) helpers start to work
 
  CalibrationAlgorithm::EResult result = calibrate();
  m_data.setResult(result);
  return result;
}

◆ fillRunToInputFilesMap()

void fillRunToInputFilesMap ( )

inherited

Fill the mapping of ExpRun -> Files.

Definition at line 330 of file CalibrationAlgorithm.cc.

{
  m_runsToInputFiles.clear();
  // Save TDirectory to change back at the end
  TDirectory* dir = gDirectory;
  RunRange* runRange;
  // Construct the TDirectory name where we expect our objects to be
  string runRangeObjName(getPrefix() + "/" + RUN_RANGE_OBJ_NAME);
  for (const auto& fileName : m_inputFileNames) {
    //Open TFile to get the objects
    unique_ptr<TFile> f;
    f.reset(TFile::Open(fileName.c_str(), "READ"));
    runRange = dynamic_cast<RunRange*>(f->Get(runRangeObjName.c_str()));
    if (runRange) {
      // Insert or extend the run -> file mapping for this ExpRun
      auto expRuns = runRange->getExpRunSet();
      for (const auto& expRun : expRuns) {
        auto runFiles = m_runsToInputFiles.find(expRun);
        if (runFiles != m_runsToInputFiles.end()) {
          (runFiles->second).push_back(fileName);
        } else {
          m_runsToInputFiles.insert(std::make_pair(expRun, std::vector<std::string> {fileName}));
        }
      }
    } else {
      B2WARNING("Missing a RunRange object for file: " << fileName);
    }
  }
  dir->cd();
}

◆ findPayloadBoundaries()

const std::vector< ExpRun > findPayloadBoundaries	(	std::vector< Calibration::ExpRun >	runs,
		int	iteration = 0 )

inherited

Used to discover the ExpRun boundaries that you want the Python CAF to execute on. This is optional and only used in some.

Definition at line 520 of file CalibrationAlgorithm.cc.

{
  m_boundaries.clear();
  if (m_inputFileNames.empty()) {
    B2ERROR("There aren't any input files set. Please use CalibrationAlgorithm::setInputFiles()");
    return m_boundaries;
  }
  // Reset the internal execution data just in case something is hanging around
  m_data.reset();
  if (runs.empty()) {
    // Want to loop over all runs we could possibly know about
    runs = getRunListFromAllData();
  }
  // Let's check that we have some now
  if (runs.empty()) {
    B2ERROR("No collected data in input files.");
    return m_boundaries;
  }
  // In order to find run boundaries we must have collected with data granularity == 'run'
  if (strcmp(getGranularity().c_str(), "all") == 0) {
    B2ERROR("The data is collected with granularity='all' (exp=-1,run=-1), and we can't use that to find run boundaries.");
    return m_boundaries;
  }
  m_data.setIteration(iteration);
  // User defined setup function
  boundaryFindingSetup(runs, iteration);
  std::vector<ExpRun> runList;
  // Loop over run list and call derived class "isBoundaryRequired" member function
  for (auto currentRun : runs) {
    runList.push_back(currentRun);
    m_data.setRequestedRuns(runList);
    // After here, the getObject<...>(...) helpers start to work
    if (isBoundaryRequired(currentRun)) {
      m_boundaries.push_back(currentRun);
    }
    // Only want run-by-run
    runList.clear();
    // Don't want memory hanging around
    m_data.clearCalibrationData();
  }
  m_data.reset();
  boundaryFindingTearDown();
  return m_boundaries;
}

◆ getAllGranularityExpRun()

Calibration::ExpRun getAllGranularityExpRun ( ) const

inlineprotectedinherited

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

Definition at line 327 of file CalibrationAlgorithm.h.

327{return m_allExpRun;}

◆ getCollectorName()

std::string getCollectorName ( ) const

inlineinherited

Alias for prefix.

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

Definition at line 164 of file CalibrationAlgorithm.h.

164{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.

216{return m_description;}

◆ getExpRunString()

string getExpRunString ( Calibration::ExpRun & expRun ) const

privateinherited

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

Definition at line 254 of file CalibrationAlgorithm.cc.

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

◆ getFullObjectPath()

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

privateinherited

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

Definition at line 263 of file CalibrationAlgorithm.cc.

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

◆ getGranularity()

std::string getGranularity ( ) const

inlineinherited

Get the granularity of collected data.

Definition at line 188 of file CalibrationAlgorithm.h.

188{return m_granularityOfData;};

◆ getGranularityFromData()

string getGranularityFromData ( ) const

protectedinherited

Get the granularity of collected data.

Definition at line 383 of file CalibrationAlgorithm.cc.

{
  // Save TDirectory to change back at the end
  TDirectory* dir = gDirectory;
  RunRange* runRange;
  string runRangeObjName(getPrefix() + "/" + RUN_RANGE_OBJ_NAME);
  // We only check the first file
  string fileName = m_inputFileNames[0];
  unique_ptr<TFile> f;
  f.reset(TFile::Open(fileName.c_str(), "READ"));
  runRange = dynamic_cast<RunRange*>(f->Get(runRangeObjName.c_str()));
  if (!runRange) {
    B2FATAL("The input file " << fileName << " does not contain a RunRange object at "
            << runRangeObjName << ". Please set your input files to exclude it.");
    return "";
  }
  string granularity = runRange->getGranularity();
  dir->cd();
  return granularity;
}

◆ getInputFileNames()

PyObject * getInputFileNames ( )

inherited

Get the input file names used for this algorithm and pass them out as a Python list of unicode strings.

Definition at line 245 of file CalibrationAlgorithm.cc.

{
  PyObject* objInputFileNames = PyList_New(m_inputFileNames.size());
  for (size_t i = 0; i < m_inputFileNames.size(); ++i) {
    PyList_SetItem(objInputFileNames, i, Py_BuildValue("s", m_inputFileNames[i].c_str()));
  }
  return objInputFileNames;
}

◆ getInputJsonObject()

const nlohmann::json & getInputJsonObject ( ) const

inlineprotectedinherited

Get the entire top level JSON object. We explicitly say this must be of object type so that we might pick.

Definition at line 357 of file CalibrationAlgorithm.h.

357{return m_jsonExecutionInput;}

◆ getInputJsonValue()

template<class T>

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

inlineprotectedinherited

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

Definition at line 350 of file CalibrationAlgorithm.h.

    {
      return m_jsonExecutionInput.at(key);
    }

◆ getIovFromAllData()

IntervalOfValidity getIovFromAllData ( ) const

inherited

Get the complete IoV from inspection of collected data.

Definition at line 325 of file CalibrationAlgorithm.cc.

{
  return getRunRangeFromAllData().getIntervalOfValidity();
}

◆ getIteration()

int getIteration ( ) const

inlineprotectedinherited

Get current iteration.

Definition at line 269 of file CalibrationAlgorithm.h.

269{ 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.

204{return m_data.getPayloads();}

◆ getPayloadValues()

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

inlineinherited

Get constants (in TObjects) for database update from last execution but passed by VALUE.

Definition at line 207 of file CalibrationAlgorithm.h.

207{return m_data.getPayloadValues();}

◆ getPrefix()

std::string getPrefix ( ) const

inlineinherited

Get the prefix used for getting calibration data.

Definition at line 146 of file CalibrationAlgorithm.h.

146{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.

266{return m_data.getRequestedRuns();}

◆ getRunListFromAllData()

vector< ExpRun > getRunListFromAllData ( ) const

inherited

Get the complete list of runs from inspection of collected data.

Definition at line 318 of file CalibrationAlgorithm.cc.

{
  RunRange runRange = getRunRangeFromAllData();
  set<ExpRun> expRunSet = runRange.getExpRunSet();
  return vector<ExpRun>(expRunSet.begin(), expRunSet.end());
}

◆ getRunRangeFromAllData()

RunRange getRunRangeFromAllData ( ) const

inherited

Get the complete RunRange from inspection of collected data.

Definition at line 361 of file CalibrationAlgorithm.cc.

{
  // Save TDirectory to change back at the end
  TDirectory* dir = gDirectory;
  RunRange runRange;
  // Construct the TDirectory name where we expect our objects to be
  string runRangeObjName(getPrefix() + "/" + RUN_RANGE_OBJ_NAME);
  for (const auto& fileName : m_inputFileNames) {
    //Open TFile to get the objects
    unique_ptr<TFile> f;
    f.reset(TFile::Open(fileName.c_str(), "READ"));
    RunRange* runRangeOther = dynamic_cast<RunRange*>(f->Get(runRangeObjName.c_str()));
    if (runRangeOther) {
      runRange.merge(runRangeOther);
    } else {
      B2WARNING("Missing a RunRange object for file: " << fileName);
    }
  }
  dir->cd();
  return runRange;
}

◆ getUpperBoundaryForFit()

double getUpperBoundaryForFit ( TGraphErrors * graph )

inlineprotected

search max point at boundary region

Definition at line 81 of file SpaceResolutionCalibrationAlgorithm.h.

      {
        double ymax = 0;
        double xmax = 0;
        int imax = 0;
        double x, y;
        int unCount = floor(0.05 / m_binWidth);
        int N = graph->GetN();
        int Nstart = floor(0.5 * (N - unCount));
        int Nend = N - unCount;
        for (int i  = Nstart; i < Nend; ++i) {
          graph->GetPoint(i, x, y);
          if (graph->GetErrorY(i) > 0.06E-3) continue;
          if (y > ymax) {
            xmax = x; ymax = y;
            imax = i;
          }
        }
        if (imax <= Nstart) {
          graph->GetPoint(Nend, x, y);
          xmax = x;
        }
        return xmax;
      }

◆ getVecInputFileNames()

std::vector< std::string > getVecInputFileNames ( ) const

inlineprotectedinherited

Get the input file names used for this algorithm as a STL vector.

Definition at line 275 of file CalibrationAlgorithm.h.

275{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.

360{return m_jsonExecutionInput.count(key);}

◆ isBoundaryRequired()

virtual bool isBoundaryRequired ( const Calibration::ExpRun & )

inlineprotectedvirtualinherited

Given the current collector data, make a decision about whether or not this run should be the start of a payload boundary.

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

Definition at line 243 of file CalibrationAlgorithm.h.

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

◆ loadInputJson()

bool loadInputJson ( const std::string & jsonString )

inherited

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

Definition at line 502 of file CalibrationAlgorithm.cc.

{
  try {
    auto jsonInput = nlohmann::json::parse(jsonString);
    // Input string has an object (dict) as the top level object?
    if (jsonInput.is_object()) {
      m_jsonExecutionInput = jsonInput;
      return true;
    } else {
      B2ERROR("JSON input string isn't an object type i.e. not a '{}' at the top level.");
      return false;
    }
  } catch (nlohmann::json::parse_error&) {
    B2ERROR("Parsing of JSON input string failed");
    return false;
  }
}

◆ prepare()

void prepare ( )

protected

Prepare the calibration of space resolution.

Definition at line 537 of file SpaceResolutionCalibrationAlgorithm.cc.

{
  B2INFO("Prepare calibration of space resolution");
 
  const double rad2deg = 180 / M_PI;
 
  DBObjPtr<CDCSpaceResols> dbSigma;
 
  m_nAlphaBins = dbSigma->getNoOfAlphaBins();
  m_nThetaBins = dbSigma->getNoOfThetaBins();
 
  B2INFO("Number of alpha bins: " << m_nAlphaBins);
  for (int i = 0; i < m_nAlphaBins; ++i) {
    array3 alpha = dbSigma->getAlphaBin(i);
    m_lowerAlpha[i] = alpha[0] * rad2deg;
    m_upperAlpha[i] = alpha[1] * rad2deg;
    m_iAlpha[i] = alpha[2] * rad2deg;
  }
 
  B2INFO("Number of theta bins: " << m_nThetaBins);
  for (int i = 0; i < m_nThetaBins; ++i) {
    array3 theta = dbSigma->getThetaBin(i);
    m_lowerTheta[i] = theta[0] * rad2deg;
    m_upperTheta[i] = theta[1] * rad2deg;
    m_iTheta[i] = theta[2] * rad2deg;
  }
  m_sigmaParamModePost = dbSigma->getSigmaParamMode();
 
  for (unsigned short iCL = 0; iCL < 56; ++iCL) {
    for (unsigned short iLR = 0; iLR < 2; ++iLR) {
      for (unsigned short iA = 0; iA < m_nAlphaBins; ++iA) {
        for (unsigned short iT = 0; iT < m_nThetaBins; ++iT) {
          const std::vector<float> params = dbSigma->getSigmaParams(iCL, iLR, iA, iT);
          unsigned short np = params.size();
          //    std::cout <<"np4sigma= " << np << std::endl;
          for (unsigned short i = 0; i < np; ++i) {
            m_sigmaPost[iCL][iLR][iA][iT][i] = params[i];
          }
        }
      }
    }
  }
}

◆ resetInputJson()

void resetInputJson ( )

inlineprotectedinherited

Clears the m_inputJson member variable.

Definition at line 330 of file CalibrationAlgorithm.h.

330{m_jsonExecutionInput.clear();}

◆ resetOutputJson()

void resetOutputJson ( )

inlineprotectedinherited

Clears the m_outputJson member variable.

Definition at line 333 of file CalibrationAlgorithm.h.

333{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);
}

◆ setBField()

void setBField ( bool bfield )

inline

Work with B field or not;.

Definition at line 51 of file SpaceResolutionCalibrationAlgorithm.h.

51{m_bField = bfield;}

◆ setBinWidth()

void setBinWidth ( double bw )

inline

Bin width of each slide.

Definition at line 48 of file SpaceResolutionCalibrationAlgorithm.h.

48{m_binWidth = bw;}

◆ setDebug()

void setDebug ( bool debug = false )

inline

Set Debug mode.

Definition at line 39 of file SpaceResolutionCalibrationAlgorithm.h.

39{m_debug = debug; }

◆ setDescription()

void setDescription ( const std::string & description )

inlineprotectedinherited

Set algorithm description (in constructor)

Definition at line 321 of file CalibrationAlgorithm.h.

321{m_description = description;}

◆ setHistFileName()

void setHistFileName ( const std::string & name )

inline

Set name for histogram output.

Definition at line 63 of file SpaceResolutionCalibrationAlgorithm.h.

63{m_histName = "histSigma_" + name + ".root";}

◆ setInputFileNames() [1/2]

void setInputFileNames ( PyObject * inputFileNames )

inherited

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

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

Definition at line 166 of file CalibrationAlgorithm.cc.

{
  // The reasoning for this very 'manual' approach to extending the Python interface
  // (instead of using boost::python) is down to my fear of putting off final users with
  // complexity on their side.
  //
  // I didn't want users that inherit from this class to be forced to use boost and
  // to have to define a new python module just to use the CAF. A derived class from
  // from a boost exposed class would need to have its own boost python module definition
  // to allow access from a steering file and to the base class functions (I think).
  // I also couldn't be bothered to write a full framework to get around the issue in a similar
  // way to Module()...maybe there's an easy way.
  //
  // But this way we can allow people to continue using their ROOT implemented classes and inherit
  // easily from this one. But add in a few helper functions that work with Python objects
  // created in their steering file i.e. instead of being forced to use STL objects as input
  // to the algorithm.
  if (PyList_Check(inputFileNames)) {
    boost::python::handle<> handle(boost::python::borrowed(inputFileNames));
    boost::python::list listInputFileNames(handle);
    auto vecInputFileNames = PyObjConvUtils::convertPythonObject(listInputFileNames, vector<string>());
    setInputFileNames(vecInputFileNames);
  } else {
    B2ERROR("Tried to set the input files but we didn't receive a Python list.");
  }
}

◆ setInputFileNames() [2/2]

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

protectedinherited

Set the input file names used for this algorithm.

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

Definition at line 194 of file CalibrationAlgorithm.cc.

{
  // A lot of code below is tweaked from RootInputModule::initialize,
  // since we're basically copying the functionality anyway.
  if (inputFileNames.empty()) {
    B2WARNING("You have called setInputFileNames() with an empty list. Did you mean to do that?");
    return;
  }
  auto tmpInputFileNames = RootIOUtilities::expandWordExpansions(inputFileNames);
 
  // We'll use a set to enforce sorted unique file paths as we check them
  set<string> setInputFileNames;
  // Check that files exist and convert to absolute paths
  for (auto path : tmpInputFileNames) {
    string fullPath = fs::absolute(path).string();
    if (fs::exists(fullPath)) {
      setInputFileNames.insert(fs::canonical(fullPath).string());
    } else {
      B2WARNING("Couldn't find the file " << path);
    }
  }
 
  if (setInputFileNames.empty()) {
    B2WARNING("No valid files specified!");
    return;
  } else {
    // Reset the run -> files map as our files are likely different
    m_runsToInputFiles.clear();
  }
 
  // Open TFile to check they can be accessed by ROOT
  TDirectory* dir = gDirectory;
  for (const string& fileName : setInputFileNames) {
    unique_ptr<TFile> f;
    try {
      f.reset(TFile::Open(fileName.c_str(), "READ"));
    } catch (logic_error&) {
      //this might happen for ~invaliduser/foo.root
      //actually undefined behaviour per standard, reported as ROOT-8490 in JIRA
    }
    if (!f || !f->IsOpen()) {
      B2FATAL("Couldn't open input file " + fileName);
    }
  }
  dir->cd();
 
  // Copy the entries of the set to a vector
  m_inputFileNames = vector<string>(setInputFileNames.begin(), setInputFileNames.end());
  m_granularityOfData = getGranularityFromData();
}

◆ setMinimumNDF()

void setMinimumNDF ( double ndf )

inline

minimum NDF required for track

Definition at line 42 of file SpaceResolutionCalibrationAlgorithm.h.

42{m_minNdf = ndf;}

◆ setMinimumPval()

void setMinimumPval ( double pval )

inline

Minimum Pval required.

Definition at line 45 of file SpaceResolutionCalibrationAlgorithm.h.

45{m_minPval = pval;}

◆ setOutputFileName()

void setOutputFileName ( std::string outputname )

inline

output file name

Definition at line 60 of file SpaceResolutionCalibrationAlgorithm.h.

60{m_outputFileName.assign(outputname);}

◆ 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.

337{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.

167{m_prefix = prefix;}

◆ setStoreHisto()

void setStoreHisto ( bool storeHist = false )

inline

Store histograms during the calibration or not.

Definition at line 54 of file SpaceResolutionCalibrationAlgorithm.h.

54{m_storeHisto = storeHist;}

◆ setThreshold()

void setThreshold ( double th = 0.6 )

inline

Set threshold for the fraction of fitted results.

Definition at line 66 of file SpaceResolutionCalibrationAlgorithm.h.

66{m_threshold = th;}

◆ storeHisto()

void storeHisto ( )

protected

store histogram

Definition at line 425 of file SpaceResolutionCalibrationAlgorithm.cc.

{
  B2INFO("saving histograms");
 
  TFile*  ff = new TFile(m_histName.c_str(), "RECREATE");
  TDirectory* top = gDirectory;
  TDirectory* Direct[56];
 
  auto hNDF =   getObjectPtr<TH1F>("hNDF");
  auto hPval =   getObjectPtr<TH1F>("hPval");
  auto hEvtT0 =   getObjectPtr<TH1F>("hEventT0");
  //store NDF, P-val. EventT0 histogram for monitoring during calibration
  if (hNDF && hPval && hEvtT0) {
    hEvtT0->Write();
    hPval->Write();
    hNDF->Write();
  }
 
 
  for (int il = 0; il < 56; ++il) {
    top->cd();
    Direct[il] = gDirectory->mkdir(Form("lay_%d", il));
    Direct[il]->cd();
 
    for (int lr = 0; lr < 2; ++lr) {
      for (int al = 0; al < m_nAlphaBins; ++al) {
        for (int th = 0; th < m_nThetaBins; ++th) {
          if (!m_graph[il][lr][al][th]) continue;
          if (!m_gFit[il][lr][al][th]) continue;
          if (m_fitStatus[il][lr][al][th] == 1) {
            m_hBiased[il][lr][al][th]->Write();
            m_hUnbiased[il][lr][al][th]->Write();
            m_hMeanBiased[il][lr][al][th]->Write();
            m_hSigmaBiased[il][lr][al][th]->Write();
            m_hMeanUnbiased[il][lr][al][th]->Write();
            m_hSigmaUnbiased[il][lr][al][th]->Write();
            m_graph[il][lr][al][th]->Write();
            m_gFit[il][lr][al][th]->Write();
          }
        }
      }
    }
  }
  ff->Close();
  B2INFO("Finish store histogram");
 
}

◆ updateDBObjPtrs()

void updateDBObjPtrs	(	const unsigned int	event = 1,
		const int	run = 0,
		const int	experiment = 0 )

protectedinherited

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

Definition at line 404 of file CalibrationAlgorithm.cc.

{
  // Construct an EventMetaData object but NOT in the Datastore
  EventMetaData emd(event, run, experiment);
  // Explicitly update while avoiding registering a Datastore object
  DBStore::Instance().update(emd);
  // Also update the intra-run objects to the event at the same time (maybe unnecessary...)
  DBStore::Instance().updateEvent(event);
}

◆ write()

void write ( )

protected

save calibration, in text file or db

Definition at line 472 of file SpaceResolutionCalibrationAlgorithm.cc.

{
  B2INFO("Writing calibrated sigma's");
  int nfitted = 0;
  int nfailure = 0;
 
  CDCSpaceResols* dbSigma = new CDCSpaceResols();
 
  const float deg2rad = M_PI / 180.0;
 
  for (unsigned short i = 0; i < m_nAlphaBins; ++i) {
    std::array<float, 3> alpha3 = {m_lowerAlpha[i]* deg2rad,
                                   m_upperAlpha[i]* deg2rad,
                                   m_iAlpha[i]*   deg2rad
                                  };
    dbSigma->setAlphaBin(alpha3);
  }
 
 
  for (unsigned short i = 0; i < m_nThetaBins; ++i) {
    std::array<float, 3> theta3 = {m_lowerTheta[i]* deg2rad,
                                   m_upperTheta[i]* deg2rad,
                                   m_iTheta[i]* deg2rad
                                  };
    dbSigma->setThetaBin(theta3);
  }
 
  dbSigma->setSigmaParamMode(m_sigmaParamMode);
  for (int ialpha = 0; ialpha < m_nAlphaBins; ++ialpha) {
    for (int itheta = 0; itheta < m_nThetaBins; ++itheta) {
      for (int iCL = 0; iCL < 56; ++iCL) {
        for (int iLR = 1; iLR >= 0; --iLR) {
          std::vector<float> sgbuff;
          if (m_fitStatus[iCL][iLR][ialpha][itheta] == 1) {
            nfitted += 1;  // inclement number of successfully fitted sigma's
            for (int i = 0; i < 7; ++i) {
              sgbuff.push_back(m_sigma[iCL][iLR][ialpha][itheta][i]);
            }
          } else {
            //B2WARNING("Fitting error and old sigma will be used. (Layer " << iCL << ") (lr = " << iLR <<
            //                      ") (al = " << ialpha << ") (th = " << itheta << ")");
            nfailure += 1; // inclement number of fit failed sigma's
            for (int i = 0; i < 7; ++i) {
              sgbuff.push_back(m_sigmaPost[iCL][iLR][ialpha][itheta][i]);
            }
          }
          dbSigma->setSigmaParams(iCL, iLR, ialpha, itheta, sgbuff);
        }
      }
    }
  }
 
  if (m_textOutput == true) {
    dbSigma->outputToFile(m_outputFileName);
  }
 
  saveCalibration(dbSigma, "CDCSpaceResols");
 
  B2RESULT("Number of histogram: " << 56 * 2 * m_nAlphaBins * m_nThetaBins);
  B2RESULT("Histos successfully fitted: " << nfitted);
  B2RESULT("Histos fit failure: " << nfailure);
 
 
}

Member Data Documentation

◆ m_allExpRun

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

staticprivateinherited

allExpRun

Definition at line 364 of file CalibrationAlgorithm.h.

◆ m_bField

bool m_bField = true

private

Work with BField, fit range and initial parameters is different incase B and noB.

Definition at line 116 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_binWidth

double m_binWidth = 0.05

private

width of each bin, unit cm

Definition at line 113 of file SpaceResolutionCalibrationAlgorithm.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_cdcGeo

DBObjPtr<CDCGeometry> m_cdcGeo

private

Geometry of CDC.

Definition at line 145 of file SpaceResolutionCalibrationAlgorithm.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_debug

bool m_debug = false

private

Debug or not.

Definition at line 114 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_description

std::string m_description {""}

privateinherited

Description of the algorithm.

Definition at line 385 of file CalibrationAlgorithm.h.

385{""};

◆ m_fitStatus

int m_fitStatus[56][2][Max_nalpha][Max_ntheta] = {{{{0}}}}

private

Fit flag; 1:OK ; 0:error.

Definition at line 127 of file SpaceResolutionCalibrationAlgorithm.h.

127{{{{0}}}} ;

◆ m_gFit

TGraphErrors* m_gFit[56][2][18][7]

private

sigma*sigma graph for fit

Definition at line 119 of file SpaceResolutionCalibrationAlgorithm.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_graph

TGraphErrors* m_graph[56][2][18][7]

private

sigma graph.

Definition at line 120 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_hBiased

TH2F* m_hBiased[56][2][Max_nalpha][Max_ntheta]

private

2D histogram of biased residual

Definition at line 121 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_histName

std::string m_histName = "histSigma.root"

private

root file name

Definition at line 144 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_hMeanBiased

TH1F* m_hMeanBiased[56][2][Max_nalpha][Max_ntheta]

private

mean histogram biased residual

Definition at line 123 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_hMeanUnbiased

TH1F* m_hMeanUnbiased[56][2][Max_nalpha][Max_ntheta]

private

mean histogram of unbiased residual

Definition at line 125 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_hSigmaBiased

TH1F* m_hSigmaBiased[56][2][Max_nalpha][Max_ntheta]

private

sigma histogram of biased residual

Definition at line 124 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_hSigmaUnbiased

TH1F* m_hSigmaUnbiased[56][2][Max_nalpha][Max_ntheta]

private

sigma histogram of ubiased residual

Definition at line 126 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_hUnbiased

TH2F* m_hUnbiased[56][2][Max_nalpha][Max_ntheta]

private

2D histogram of unbiased residual

Definition at line 122 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_iAlpha

float m_iAlpha[18]

private

represented alphas of alpha bins.

Definition at line 133 of file SpaceResolutionCalibrationAlgorithm.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_iTheta

float m_iTheta[7]

private

represented alphas of theta bins.

Definition at line 136 of file SpaceResolutionCalibrationAlgorithm.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_lowerAlpha

float m_lowerAlpha[18]

private

Lower boundaries of alpha bins.

Definition at line 131 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_lowerTheta

float m_lowerTheta[7]

private

Lower boundaries of theta bins.

Definition at line 134 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_minNdf

double m_minNdf = 5

private

Minimum NDF.

Definition at line 111 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_minPval

double m_minPval = 0.

private

Minimum Prob(chi2) of track.

Definition at line 112 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_nAlphaBins

int m_nAlphaBins

private

number of alpha bins

Definition at line 129 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_nThetaBins

int m_nThetaBins

private

number of theta bins

Definition at line 130 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_outputFileName

std::string m_outputFileName = "sigma_new.dat"

private

Output sigma filename.

Definition at line 143 of file SpaceResolutionCalibrationAlgorithm.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.

388{""};

◆ 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_sigma

double m_sigma[56][2][18][7][8]

private

new sigma parameters.

Definition at line 118 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_sigmaParamMode

unsigned short m_sigmaParamMode = 0

private

sigma mode for this calibration.

Definition at line 137 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_sigmaParamModePost

unsigned short m_sigmaParamModePost

private

sigma mode before this calibration.

Definition at line 140 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_sigmaPost

double m_sigmaPost[56][2][18][7][8]

private

sigma parameters before calibration

Definition at line 139 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_storeHisto

bool m_storeHisto = false

private

Store histogram or not.

Definition at line 115 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_textOutput

bool m_textOutput = false

private

output text file if true

Definition at line 142 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_threshold

double m_threshold = 0.6

private

minimal requirement for the fraction of fitted results

Definition at line 117 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_upperAlpha

float m_upperAlpha[18]

private

Upper boundaries of alpha bins.

Definition at line 132 of file SpaceResolutionCalibrationAlgorithm.h.

◆ m_upperTheta

float m_upperTheta[7]

private

Upper boundaries of theta bins.

Definition at line 135 of file SpaceResolutionCalibrationAlgorithm.h.

◆ Max_nalpha

const int Max_nalpha = 18

staticprivate

Maximum alpha bin.

Definition at line 107 of file SpaceResolutionCalibrationAlgorithm.h.

◆ Max_np

const unsigned short Max_np = 40

staticprivate

Maximum number of point =1/binwidth.

Definition at line 109 of file SpaceResolutionCalibrationAlgorithm.h.

◆ Max_ntheta

const int Max_ntheta = 7

staticprivate

maximum theta bin

Definition at line 108 of file SpaceResolutionCalibrationAlgorithm.h.

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

cdc/calibration/include/SpaceResolutionCalibrationAlgorithm.h
cdc/calibration/src/SpaceResolutionCalibrationAlgorithm.cc

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Private Member Functions

Private Attributes

Static Private Attributes

Detailed Description

Member Enumeration Documentation

◆ EResult

Constructor & Destructor Documentation

◆ SpaceResolutionCalibrationAlgorithm()

◆ ~SpaceResolutionCalibrationAlgorithm()

Member Function Documentation

◆ boundaryFindingSetup()

◆ boundaryFindingTearDown()

◆ calibrate()

◆ checkPyExpRun()

◆ clearCalibrationData()

◆ commit() [1/2]

◆ commit() [2/2]

◆ convertPyExpRun()

◆ createHisto()

◆ dumpOutputJson()

◆ enableTextOutput()

◆ execute() [1/2]

◆ execute() [2/2]

◆ fillRunToInputFilesMap()

◆ findPayloadBoundaries()

◆ getAllGranularityExpRun()

◆ getCollectorName()

◆ getDescription()

◆ getExpRunString()

◆ getFullObjectPath()

◆ getGranularity()

◆ getGranularityFromData()

◆ getInputFileNames()

◆ getInputJsonObject()

◆ getInputJsonValue()

◆ getIovFromAllData()

◆ getIteration()

◆ getObjectPtr()

◆ getOutputJsonValue()

◆ getPayloads()

◆ getPayloadValues()

◆ getPrefix()

◆ getRunList()

◆ getRunListFromAllData()

◆ getRunRangeFromAllData()

◆ getUpperBoundaryForFit()

◆ getVecInputFileNames()

◆ inputJsonKeyExists()

◆ isBoundaryRequired()

◆ loadInputJson()

◆ prepare()

◆ resetInputJson()

◆ resetOutputJson()

◆ saveCalibration() [1/6]

◆ saveCalibration() [2/6]

◆ saveCalibration() [3/6]

◆ saveCalibration() [4/6]

◆ saveCalibration() [5/6]

◆ saveCalibration() [6/6]

◆ setBField()

◆ setBinWidth()

◆ setDebug()

◆ setDescription()

◆ setHistFileName()

◆ setInputFileNames() [1/2]

◆ setInputFileNames() [2/2]

◆ setMinimumNDF()

◆ setMinimumPval()

◆ setOutputFileName()

◆ setOutputJsonValue()

◆ setPrefix()

◆ setStoreHisto()

◆ setThreshold()

◆ storeHisto()

◆ updateDBObjPtrs()

◆ write()