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diff --git a/examples/GenFit3KS.cc b/examples/GenFit3KS.cc
index 40ddf66..22dcabc 100644
--- a/examples/GenFit3KS.cc
+++ b/examples/GenFit3KS.cc
@@ -1,172 +1,172 @@
#include <cstdlib>
#include <iostream>
#include <vector>
#include "TFile.h"
#include "TH2.h"
#include "TString.h"
#include "TTree.h"
#include "LauSimpleFitModel.hh"
#include "LauBkgndDPModel.hh"
#include "LauDaughters.hh"
#include "LauEffModel.hh"
#include "LauIsobarDynamics.hh"
#include "LauMagPhaseCoeffSet.hh"
#include "LauVetoes.hh"
void usage( std::ostream& out, const char* progName )
{
out<<"Usage:\n";
out<<progName<<" gen [nExpt = 1] [firstExpt = 0]\n";
out<<"or\n";
out<<progName<<" fit <iFit> [nExpt = 1] [firstExpt = 0]"<<std::endl;
}
int main( int argc, char** argv )
{
// Process command-line arguments
// Usage:
// ./GenFit3KS gen [nExpt = 1] [firstExpt = 0]
// or
// ./GenFit3KS fit <iFit> [nExpt = 1] [firstExpt = 0]
if ( argc < 2 ) {
usage( std::cerr, argv[0] );
return EXIT_FAILURE;
}
TString command = argv[1];
command.ToLower();
Int_t iFit(0);
Int_t nExpt(1);
Int_t firstExpt(0);
if ( command == "gen" ) {
if ( argc > 2 ) {
nExpt = atoi( argv[2] );
if ( argc > 3 ) {
firstExpt = atoi( argv[3] );
}
}
} else if ( command == "fit" ) {
if ( argc < 3 ) {
usage( std::cerr, argv[0] );
return EXIT_FAILURE;
}
iFit = atoi( argv[2] );
if ( argc > 3 ) {
nExpt = atoi( argv[3] );
if ( argc > 4 ) {
firstExpt = atoi( argv[4] );
}
}
} else {
usage( std::cerr, argv[0] );
return EXIT_FAILURE;
}
// If you want to use square DP histograms for efficiency,
// backgrounds or you just want the square DP co-ordinates
// stored in the toy MC ntuple then set this to kTRUE
Bool_t squareDP = kFALSE;
// This defines the DP => decay is B0 -> KS KS KS
// Particle 1 = KS
// Particle 2 = KS
// Particle 3 = KS
// The DP is defined in terms of m13Sq and m23Sq
LauDaughters* daughters = new LauDaughters("B0", "K_S0", "K_S0", "K_S0", squareDP);
// Optionally apply some vetoes to the DP
LauVetoes* vetoes = new LauVetoes();
// Define the efficiency model (defaults to unity everywhere)
// Can optionally provide a histogram to model variation over DP
LauEffModel* effModel = new LauEffModel(daughters, vetoes);
// Create the isobar model
LauIsobarDynamics* sigModel = new LauIsobarDynamics(daughters, effModel);
- LauAbsResonance* reson(0);
- reson = sigModel->addResonance("f_0(980)", 3, LauAbsResonance::Flatte);
- reson = sigModel->addResonance("f_0(1710)", 3, LauAbsResonance::RelBW);
- reson = sigModel->addResonance("f_2(2010)", 3, LauAbsResonance::RelBW);
- reson = sigModel->addResonance("chi_c0", 3, LauAbsResonance::RelBW);
+ //LauAbsResonance* reson(0);
+ /*reson =*/ sigModel->addResonance("f_0(980)", 3, LauAbsResonance::Flatte);
+ /*reson =*/ sigModel->addResonance("f_0(1710)", 3, LauAbsResonance::RelBW);
+ /*reson =*/ sigModel->addResonance("f_2(2010)", 3, LauAbsResonance::RelBW);
+ /*reson =*/ sigModel->addResonance("chi_c0", 3, LauAbsResonance::RelBW);
// Reset the maximum signal DP ASq value
// This will be automatically adjusted to avoid bias or extreme
// inefficiency if you get the value wrong but best to set this by
// hand once you've found the right value through some trial and
// error.
sigModel->setASqMaxValue(0.285);
// Create the fit model
LauSimpleFitModel* fitModel = new LauSimpleFitModel(sigModel);
// Create the complex coefficients for the isobar model
// Here we're using the magnitude and phase form:
// c_j = a_j exp(i*delta_j)
std::vector<LauAbsCoeffSet*> coeffset;
coeffset.push_back( new LauMagPhaseCoeffSet("f_0(980)", 1.00, 0.00, kTRUE, kTRUE) );
coeffset.push_back( new LauMagPhaseCoeffSet("f_0(1710)", 0.40, 1.11, kFALSE, kFALSE) );
coeffset.push_back( new LauMagPhaseCoeffSet("f_2(2010)", 0.45, 2.50, kFALSE, kFALSE) );
coeffset.push_back( new LauMagPhaseCoeffSet("chi_c0", 0.40, 0.63, kFALSE, kFALSE) );
for (std::vector<LauAbsCoeffSet*>::iterator iter=coeffset.begin(); iter!=coeffset.end(); ++iter) {
fitModel->setAmpCoeffSet(*iter);
}
// Set the signal yield and define whether it is fixed or floated
LauParameter * nSigEvents = new LauParameter("nSigEvents",10000.0,-50000.0,50000.0,kFALSE);
fitModel->setNSigEvents(nSigEvents);
// Set the number of experiments to generate or fit and which
// experiment to start with
fitModel->setNExpts( nExpt, firstExpt );
// Switch on/off calculation of asymmetric errors.
fitModel->useAsymmFitErrors(kFALSE);
// Randomise initial fit values for the signal mode
fitModel->useRandomInitFitPars(kTRUE);
// Switch on/off Poissonian smearing of total number of events
const Bool_t poissonSmear = ( fitModel->nBkgndClasses() > 0 );
fitModel->doPoissonSmearing(poissonSmear);
// Switch on/off Extended ML Fit option
const Bool_t emlFit = ( fitModel->nBkgndClasses() > 0 );
fitModel->doEMLFit(emlFit);
// Set the names of the files to read/write
TString dataFile("gen.root");
TString treeName("genResults");
TString rootFileName("");
TString tableFileName("");
TString fitToyFileName("fitToyMC_");
TString splotFileName("splot_");
if (command == "fit") {
rootFileName = "fit"; rootFileName += iFit;
rootFileName += "_expt_"; rootFileName += firstExpt;
rootFileName += "-"; rootFileName += (firstExpt+nExpt-1);
rootFileName += ".root";
tableFileName = "fitResults_"; tableFileName += iFit;
fitToyFileName += iFit;
fitToyFileName += ".root";
splotFileName += iFit;
splotFileName += ".root";
} else {
rootFileName = "dummy.root";
tableFileName = "genResults";
}
// Generate toy from the fitted parameters
//fitModel->compareFitData(100, fitToyFileName);
// Write out per-event likelihoods and sWeights
//fitModel->writeSPlotData(splotFileName, "splot", kFALSE);
// Execute the generation/fit
fitModel->run( command, dataFile, treeName, rootFileName, tableFileName );
return EXIT_SUCCESS;
}

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