SpaceResolutionCalibrationAlgorithm Class Reference

Class for Space resolution calibration. More...

#include <SpaceResolutionCalibrationAlgorithm.h>

Inheritance diagram for SpaceResolutionCalibrationAlgorithm:

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

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

Protected Attributes
std::vector< Calibration::ExpRun >	m_boundaries
	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 prameters.
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 boundays of alpha bins.
float	m_upperAlpha [18]
	Upper boundays of alpha bins.
float	m_iAlpha [18]
	represented alphas of alpha bins.
float	m_lowerTheta [7]
	Lower boundays of theta bins.
float	m_upperTheta [7]
	Upper boundays 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 prameters 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 successfuly =0 in Python.
c_Iterate	Needs iteration =1 in Python.
c_NotEnoughData	Needs more data =2 in Python.
c_Failure	Failed =3 in Python.
c_Undefined	Not yet known (before execution) =4 in Python.

Definition at line 40 of file CalibrationAlgorithm.h.

Member Function Documentation

calibrate()

CalibrationAlgorithm::EResult calibrate ( )

overrideprotectedvirtual

Run algo on data.

Upper limit of fitting.

Implements CalibrationAlgorithm.

Definition at line 312 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.

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.

execute()

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.

getCollectorName()

std::string getCollectorName ( ) const

inlineinherited

Alias for prefix.

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

Definition at line 164 of file CalibrationAlgorithm.h.

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.

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.

---

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

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