dqm_algorithms::TripleGaussCollFit Class Reference

#include <TripleGaussCollFit.h>

Inheritance diagram for dqm_algorithms::TripleGaussCollFit:

Collaboration diagram for dqm_algorithms::TripleGaussCollFit:

Public Member Functions
	TripleGaussCollFit (const std::string &_name)
	~TripleGaussCollFit ()
dqm_core::Result *	execute (const std::string &s, const TObject &object, const dqm_core::AlgorithmConfig &cfg)
void	printDescription (std::ostream &out)
TripleGaussCollFit *	clone ()

Private Member Functions
void	fitSingle (TH1 *hist1D)

Private Attributes
std::string	m_name
TF1 *	m_gaus3_fn

Detailed Description

Definition at line 19 of file TripleGaussCollFit.h.

Constructor & Destructor Documentation

TripleGaussCollFit()

dqm_algorithms::TripleGaussCollFit::TripleGaussCollFit

(

const std::string &

_name

)

Definition at line 169 of file TripleGaussCollFit.cxx.

                                                              : m_name(name) {
 
    //Register our instance with the Alg. Manager
    m_gaus3_fn = 0;
    dqm_core::AlgorithmManager::instance().registerAlgorithm( "Gaus3_" + m_name +"_Fit", this );
 
  }

~TripleGaussCollFit()

dqm_algorithms::TripleGaussCollFit::~TripleGaussCollFit

(

)

Definition at line 177 of file TripleGaussCollFit.cxx.

                                          {
 
    if (m_gaus3_fn != 0) {
      delete m_gaus3_fn;
      m_gaus3_fn = 0;
    }
 
  } 

Member Function Documentation

clone()

TripleGaussCollFit * dqm_algorithms::TripleGaussCollFit::clone

(

)

Definition at line 186 of file TripleGaussCollFit.cxx.

                                                {
    return new TripleGaussCollFit(m_name);
  }

execute()

dqm_core::Result * dqm_algorithms::TripleGaussCollFit::execute	(	const std::string &	s,
		const TObject &	object,
		const dqm_core::AlgorithmConfig &	cfg
	)

Definition at line 220 of file TripleGaussCollFit.cxx.

                                                                                                                            {
  
    const TH1* histogram;
    if(object.IsA()->InheritsFrom("TH1")) //or this can be done via dynamic casting
      { 
        histogram = static_cast<const TH1*>(&object);
        if (histogram->GetDimension() > 2 ){
          throw dqm_core::BadConfig( ERS_HERE, m_name, "called with histogram of dimension > 2"  );
        }
      }
    else 
      throw dqm_core::BadConfig( ERS_HERE, m_name, "called with object that does not inherit from TH1" );
 
    //Setup our TF1: find the binwidth
    Double_t binwidth = 0; 
    if (histogram->GetDimension() == 1)   //running in 1D mode
      if (histogram->GetNbinsX() >= 1) {
    binwidth = histogram->GetBinWidth(1);
      } else {
    throw dqm_core::BadConfig( ERS_HERE, m_name, "called with histogram with no bins");
      }
 
    else {   //running in 2D mode {
      const TH1* proj =  static_cast<const TH2*>(histogram)->ProjectionY("", 0, 0);
      if (proj->GetNbinsX() >= 1) {
    binwidth = proj ->GetBinWidth(1);
      } else {
    throw dqm_core::BadConfig( ERS_HERE, m_name, "called with histogram with no bins");
      }
    }
    #ifdef PARAM_IS_AREA
    m_gaus3_fn = new TF1("gaus3", Form("[3]/[2]/2.5066*%f*exp(-0.5*((x-[0]+[1])/[2])**2) + [4]/[2]/2.5066*%f*exp(-0.5*((x-[0])/[2])**2) + [5]/[2]/2.5066*%f*exp(-0.5*((x-[0]-[1])/[2])**2)", binwidth, binwidth, binwidth), -100, 100);
    #else
    m_gaus3_fn = new TF1("gaus3", "[3]*exp(-0.5*((x-[0]+[1])/[2])**2) + [4]*exp(-0.5*((x-[0])/[2])**2) + [5]*exp(-0.5*((x-[0]-[1])/[2])**2)", -100, 100);
    #endif
    m_gaus3_fn->SetParName(0, "Mean_zero_gaus");    //mean of gaussian centered at zero, should be close to zero
    m_gaus3_fn->SetParName(1, "Mean_offzero_gaus"); //mean of other two gaussians, should be close to 20
    m_gaus3_fn->SetParName(2, "Width_all"); //shared width, should be about 5
    m_gaus3_fn->SetParName(3, "N_negative_gaus"); //normalization for negative-centered gaussian
    m_gaus3_fn->SetParName(4, "N_zero_gaus"); //normalization for zero-centered gaussian
    m_gaus3_fn->SetParName(5, "N_positive_gaus"); //normalization for positive-centered gaussian 
 
    Double_t resolutionAll, meanZeroPeak, meanNonzeroPeaks, minEvents;
    param_map mymap = cfg.getParameters();
 
    meanZeroPeak = dqm_algorithms::tools::GetFirstFromMap("meanZeroPeak", mymap, 0.0);
    meanNonzeroPeaks = dqm_algorithms::tools::GetFirstFromMap("meanNonzeroPeaks", mymap, 23.2);
    resolutionAll = dqm_algorithms::tools::GetFirstFromMap("resolutionAll", mymap, 4.57);
    minEvents = dqm_algorithms::tools::GetFirstFromMap("minEvents", mymap, 5.0);
 
    //we DO NOT allow fits by default. If the parameter is not found, the retval is 0
    if ( dqm_algorithms::tools::GetFirstFromMap("allowFitMean", mymap, 0) == 1 ) {
 
      //according to Peter, this can behave strangely, so we don't allow too much horizontal translation
      m_gaus3_fn->SetParameter(0, meanZeroPeak);
      m_gaus3_fn->SetParLimits(0, meanZeroPeak-5, meanZeroPeak+5);
    }
    else
      m_gaus3_fn->FixParameter(0, meanZeroPeak);
    
    if ( dqm_algorithms::tools::GetFirstFromMap("allowFitMeanNonzeroPeaks", mymap, 0) == 1 ) {
      m_gaus3_fn->SetParameter(1, meanNonzeroPeaks);
      m_gaus3_fn->SetParLimits(1, meanNonzeroPeaks-10, meanNonzeroPeaks+10);
    }
    else 
      m_gaus3_fn->FixParameter(1, meanNonzeroPeaks);
     
    if ( dqm_algorithms::tools::GetFirstFromMap("allowFitResolution", mymap, 0) == 1 ) {
      m_gaus3_fn->SetParameter(2, resolutionAll);
      m_gaus3_fn->SetParLimits(2, 1, 10); 
    }
    else {
      m_gaus3_fn->FixParameter(2, resolutionAll);
 
    }
    // get some starting values for the histogram
 
    dqm_core::Result* result = new dqm_core::Result(); 
    
    if (histogram->GetDimension() == 1) {
      fitSingle(const_cast<TH1*>(histogram));
 
      try {
    
    result->tags_ = dqm_algorithms::tools::GetFitParams(m_gaus3_fn);
    err_map paramErrors = dqm_algorithms::tools::GetFitParamErrors(m_gaus3_fn);
    for (err_map::const_iterator peit = paramErrors.begin(); peit != paramErrors.end(); ++peit) {
      result->tags_[(*peit).first + " Error"] = (*peit).second;
    }
 
      } 
      catch ( dqm_core::Exception & ex) {
    throw dqm_core::BadConfig( ERS_HERE, m_name, ex.what(), ex);
      }
    }
    else { //working with a 2D histogram
 
      int nxbin = histogram->GetNbinsX();
      Double_t minsig = dqm_algorithms::tools::GetFirstFromMap("centralPeakMinSignificance", mymap, 3.0);
 
      bool writeSig = (dqm_algorithms::tools::GetFirstFromMap("reportSignificance", mymap, 0) == 1);
      
      
      unsigned int psz = (unsigned int) std::log10(nxbin)+1;
 
      std::vector<int> goodLB;
      char buf[10];
      const std::string sig_str = "SIG_LB_";
      
      for (int lb = 0; lb < nxbin; lb++) {
    
    sprintf(buf, fmt, psz, lb); //to avoid possible leaks (?) - should see how DQ framework behaves
    TH1D* proj = ((TH2*) histogram)->ProjectionY(buf, lb, lb);
    Double_t good_entries = proj->GetEntries() - proj->GetBinContent(0) - proj->GetBinContent(proj->GetNbinsX()+1); 
    if (good_entries < minEvents) {
      if (writeSig)
        result->tags_[sig_str+buf] = 0;
      continue; //nothing to fit here, move along
    }
 
    // I probably need to reset parameters 0-2 if they are not fixed
    if ( dqm_algorithms::tools::GetFirstFromMap("allowFitMeanZeroPeak", mymap, 0) == 1 )
      m_gaus3_fn->SetParameter(0, meanZeroPeak);
       
    if ( dqm_algorithms::tools::GetFirstFromMap("allowFitMeanNonzeroPeaks", mymap, 0) == 1 )
      m_gaus3_fn->SetParameter(1, meanNonzeroPeaks);
    
    if ( dqm_algorithms::tools::GetFirstFromMap("allowFitResolution", mymap, 0) == 1 )
      m_gaus3_fn->SetParameter(2, resolutionAll);
    
    fitSingle(proj);
 
#ifdef TC_WORKBENCH
    result->histos_.push_back((TH1D*)proj->Clone());
#endif
 
    if (m_gaus3_fn->GetParameter(4) == 0) {
      if (writeSig)
        result->tags_[sig_str+buf] = 0;
      continue;
    }
    Double_t sig = std::abs(m_gaus3_fn->GetParameter(4) / m_gaus3_fn->GetParError(4));
    if (writeSig)
      result->tags_[sig_str+buf] = sig;
 
    if (sig > minsig) 
      goodLB.push_back(lb); //this is grossly non-optimal
 
    
      } //end loop over lumi blocks
 
      if (goodLB.size() != 0) 
    multLB_writeTags(&goodLB, result, histogram);
 
    }
    return result; 
  }

fitSingle()

void dqm_algorithms::TripleGaussCollFit::fitSingle ( TH1 *  hist1D )

private

Definition at line 385 of file TripleGaussCollFit.cxx.

                                                {
     
    Double_t startVal;
    const Double_t window = 5.0;
 
    Double_t offset_all = m_gaus3_fn->GetParameter(0);
    Double_t offset_nz  = m_gaus3_fn->GetParameter(1);
 
    startVal = getStartValue(hist1D, offset_all-offset_nz-window, offset_all-offset_nz+window); ADJUST_VAL; 
    if (startVal == 0.0)
      m_gaus3_fn->FixParameter(3, 0.0);
    else {
      m_gaus3_fn->SetParameter(3, startVal);
      // DO NOT do something like this if we are using areas!!
      // m_gaus3_fn->SetParLimits(3, 0, 5 * startVal);  
    }
 
    startVal = getStartValue(hist1D, offset_all-window, offset_all+window); ADJUST_VAL; 
    if (startVal == 0.0)
      m_gaus3_fn->FixParameter(4, 0.0);
    else {
      m_gaus3_fn->SetParameter(4, startVal);
      //   m_gaus3_fn->SetParLimits(4, 0, 100 * startVal); 
    }
    startVal = getStartValue(hist1D, offset_all+offset_nz-window , offset_all+offset_nz+window); ADJUST_VAL; 
    if (startVal == 0.0)
      m_gaus3_fn->FixParameter(5, 0.0);
    else {
      m_gaus3_fn->SetParameter(5, startVal);
    }
 
 
      
      hist1D->Fit(m_gaus3_fn, "QN");
 
  }

printDescription()

void dqm_algorithms::TripleGaussCollFit::printDescription

(

std::ostream &

out

)

Definition at line 191 of file TripleGaussCollFit.cxx.

                                                           {
 
    out << "Gaus3_"+ m_name +"_Fit: specialized fit for the Tile timining difference plot." << std::endl;
    out << "Current parameter values: " << std::endl;
 
    if  (m_gaus3_fn != 0)
      for (int i=0; i < m_gaus3_fn->GetNpar(); i++) 
    out << " Param. " << i << ": " << m_gaus3_fn->GetParName(i) << " , current value: " 
          << m_gaus3_fn->GetParameter(i) << std::endl; 
 
    else 
      out << "    execute() has not been called" << std::endl;
 
    out << "Parameter list: " << std::endl
          << " allowFitMean: 1 allows fitting for a shift in the entire function around zero by up to 5ns" << std::endl
          << " allowFitMeanNonzeroPeaks: 1 allows fitting of the distance between center and off-center Gaussians" << std::endl
          << " allowFitResolution: 1 allows fitting the (shared) width of the Gaussians" << std::endl
          << " reportSignificance: 1 reports the central peak significance for each LB as a tag" << std::endl
              << " meanZeroPeak: starting or fixed mean of the Gaussian centered at zero" << std::endl
          << " meanNonZeroPeaks: starting or fixed mean of the other Gaussians" << std::endl      
          << " resolutionAll: starting or fixed value of the width of the Gaussians" << std::endl       
          << " minSignificance: number of events required for checks to be valid, undefined if not set." << std::endl
              << " centralPeakMinSignificance: significance of central peak required for a LB to pass threshold; default is 3.0" << std::endl
          << " minEvents: number of events required to attempt a fit; default is 5." << std::endl ;
    
    out << "Currently, no thresholds are defined." << std::endl;
 
  }

Member Data Documentation

m_gaus3_fn

TF1* dqm_algorithms::TripleGaussCollFit::m_gaus3_fn

private

Definition at line 33 of file TripleGaussCollFit.h.

m_name

std::string dqm_algorithms::TripleGaussCollFit::m_name

private

Definition at line 32 of file TripleGaussCollFit.h.

---

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

• TripleGaussCollFit.h
• TripleGaussCollFit.cxx