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diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 83328d7..c93d14b 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -1,46 +1,47 @@
list(APPEND EXAMPLE_SOURCES
B3piKMatrixMassProj
B3piKMatrixPlots
CalcChiSq
GenFit3K
GenFit3KS
GenFit3pi
GenFitBelleCPKpipi
GenFitDs2KKpi
GenFitEFKLLM
GenFitIncoherent_Bs2KSKpi
GenFitKpipi
GenFitNoDP
GenFitNoDPMultiDim
GenFitTimeDep
GenFitTimeDep_Bs2KSKpi
KMatrixDto3pi
KMatrixExample
Master
MergeDataFiles
mixedSampleTest
PlotKMatrixTAmp
PlotResults
point2PointTestSample
QuasiFlatSqDalitz
ResultsExtractor
Slave
SlaveRooFit
Test_Dpipi
+ Test_Dpipi_efficiencyHist
)
if(NOT LAURA_BUILD_ROOFIT_SLAVE)
list(REMOVE_ITEM EXAMPLE_SOURCES SlaveRooFit)
endif()
foreach( _example ${EXAMPLE_SOURCES})
add_executable(${_example} ${_example}.cc)
target_link_libraries(${_example} PRIVATE Laura++)
install(TARGETS ${_example} RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR})
endforeach()
# Also install the python script version of GenFit3pi
configure_file(GenFit3pi.py.in GenFit3pi.py @ONLY)
install(PROGRAMS ${CMAKE_CURRENT_BINARY_DIR}/GenFit3pi.py DESTINATION ${CMAKE_INSTALL_BINDIR})
diff --git a/examples/Test_Dpipi.cc b/examples/Test_Dpipi.cc
index de5f315..bbfff3b 100755
--- a/examples/Test_Dpipi.cc
+++ b/examples/Test_Dpipi.cc
@@ -1,420 +1,420 @@
#include <iostream>
using std::cout;
using std::cerr;
using std::endl;
#include <vector>
#include <map>
#include "TFile.h"
#include "TH2.h"
#include "TRandom.h"
#include "TString.h"
#include "TSystem.h"
#include "LauDaughters.hh"
#include "LauDecayTimePdf.hh"
#include "LauEffModel.hh"
#include "LauIsobarDynamics.hh"
#include "LauMagPhaseCoeffSet.hh"
#include "LauRandom.hh"
#include "LauRealImagCoeffSet.hh"
#include "LauTimeDepFitModel.hh"
#include "LauVetoes.hh"
#include "LauFlavTag.hh"
#include "Lau1DHistPdf.hh"
#include "Lau1DCubicSpline.hh"
void usage(const TString& progName)
{
cerr<<"Usage:"<<endl;
cerr<<progName<<" gen D_type [firstExptGen] [nExptGen] [CP eigenvalue]"<<endl;
cerr<<"or"<<endl;
cerr<<progName<<" fit D_type [port] [iFit] [firstExpt] [nExpt] [nExptGen]"<<endl;
}
int main(const int argc, const char ** argv)
{
const TString dtype = argv[2];
Int_t port = 0;
Int_t iFit = 0;
Int_t firstExpt = 0;
Int_t firstExptGen = 0;
Int_t nExpt = 1;
Int_t nExptGen = 1;
LauTimeDepFitModel::CPEigenvalue eigenvalue = LauTimeDepFitModel::CPEven;
Bool_t fixPhiMix(kFALSE);
Bool_t useSinCos(kTRUE);
// check the command line arguments
if (argc<1) {
usage(argv[0]);
return EXIT_FAILURE;
}
TString command = argv[1];
if (command != "gen" && command != "fit") {
usage(argv[0]);
return EXIT_FAILURE;
}
if (command == "fit") {
if (argc>3) {
port = atoi(argv[3]);
if (argc>4) {
iFit = atoi(argv[4]);
if (argc>5) {
firstExpt = atoi(argv[5]);
if (argc>6) {
nExpt = atoi(argv[6]);
if (argc>7) {
nExptGen = atoi(argv[7]);
}
}
}
}
}
for (firstExptGen = 0; firstExptGen<(firstExpt+nExpt); firstExptGen+=nExptGen) {
}
firstExptGen -= nExptGen;
if ( (nExpt > nExptGen) || (nExptGen%nExpt != 0) ) {
cerr<<"Error, nExpt must be a factor of nExptGen."<<endl;
return EXIT_FAILURE;
}
} else {
if (argc>3) {
firstExptGen = atoi(argv[3]);
if (argc>4) {
nExptGen = atoi(argv[4]);
if (argc>5) {
Int_t eigval = atoi(argv[5]);
if ( eigval == 1 ) {
eigenvalue = LauTimeDepFitModel::CPOdd;
} else {
eigenvalue = LauTimeDepFitModel::CPEven;
}
}
}
}
}
cout<<"dtype "<<dtype<<" port "<<port<<" iFit "<<iFit<<" firstExpt "<<firstExpt<<" nExpt "<<nExpt<<endl;
Double_t nSigEvents(0);
if (dtype=="CPEven"){
nSigEvents = 15000;
} else {
nSigEvents = 10000;
}
LauDaughters* daughtersB0bar(0);
LauDaughters* daughtersB0(0);
LauEffModel* effModelB0bar(0);
LauEffModel* effModelB0(0);
LauVetoes* vetoes = new LauVetoes();
//vetoes->addMassVeto( 2, 2.00776, 2.01276 );
daughtersB0bar = new LauDaughters("B0_bar", "pi+", "pi-", "D0");
daughtersB0 = new LauDaughters("B0", "pi+", "pi-", "D0_bar");
// efficiency
effModelB0bar = new LauEffModel(daughtersB0bar, vetoes);
effModelB0 = new LauEffModel(daughtersB0, vetoes);
LauIsobarDynamics* sigModelB0bar(0);
LauIsobarDynamics* sigModelB0(0);
LauTimeDepFitModel* fitModel(0);
std::vector<LauParameter *> params;
LauFlavTag* flavTag = new LauFlavTag(params, kTRUE, "tagFlv", "tagCat");
flavTag->setTrueTagVarName("trueTag");
// Use alternative tagging calibration parameters?
//flavTag->useAveOmegaDeltaOmega();
TFile* etafile(0);
TH1* etahist(0);
Lau1DHistPdf* etahistpdf(0);
//if (command == "gen"){
- etafile = TFile::Open("/Users/mark/analysis/DpipiLaura/laura/examples/histogram.root");
+ etafile = TFile::Open("histogram.root");
etahist = dynamic_cast<TH1*>(etafile->Get("htemp"));
etahistpdf = new Lau1DHistPdf("eta",etahist,0,0.54,kFALSE,kFALSE);
//}
// signal dynamics
sigModelB0bar = new LauIsobarDynamics(daughtersB0bar, effModelB0bar);
sigModelB0bar->setIntFileName("integ_B0bar.dat");
sigModelB0bar->addResonance("D*+_2", 2, LauAbsResonance::RelBW);
sigModelB0bar->addResonance("D*+_0", 2, LauAbsResonance::RelBW);
sigModelB0bar->addResonance("rho0(770)", 3, LauAbsResonance::RelBW);
sigModelB0bar->addResonance("f_0(980)", 3, LauAbsResonance::RelBW);
sigModelB0bar->addResonance("f_2(1270)", 3, LauAbsResonance::RelBW);
sigModelB0 = new LauIsobarDynamics(daughtersB0, effModelB0);
sigModelB0->setIntFileName("integ_B0.dat");
sigModelB0->addResonance("D*-_2", 1, LauAbsResonance::RelBW);
sigModelB0->addResonance("D*-_0", 1, LauAbsResonance::RelBW);
sigModelB0->addResonance("rho0(770)", 3, LauAbsResonance::RelBW);
sigModelB0->addResonance("f_0(980)", 3, LauAbsResonance::RelBW);
sigModelB0->addResonance("f_2(1270)", 3, LauAbsResonance::RelBW);
// fit model
fitModel = new LauTimeDepFitModel(sigModelB0bar,sigModelB0,flavTag);
fitModel->setASqMaxValue(1.45);
std::vector<LauAbsCoeffSet*> coeffset;
coeffset.push_back( new LauRealImagCoeffSet("D*-_2", 1.00, 0.00, kTRUE, kTRUE) );
coeffset.push_back( new LauRealImagCoeffSet("D*-_0", 0.53*TMath::Cos( 3.00), 0.53*TMath::Sin( 3.00), kFALSE, kFALSE) );
coeffset.push_back( new LauRealImagCoeffSet("rho0(770)", 1.22*TMath::Cos( 2.25), 1.22*TMath::Sin( 2.25), kFALSE, kFALSE) );
coeffset.push_back( new LauRealImagCoeffSet("f_0(980)", 0.19*TMath::Cos(-2.48), 0.19*TMath::Sin(-2.48), kFALSE, kFALSE) );
coeffset.push_back( new LauRealImagCoeffSet("f_2(1270)", 0.75*TMath::Cos( 2.97), 0.75*TMath::Sin( 2.97), kFALSE, kFALSE) );
for (std::vector<LauAbsCoeffSet*>::iterator iter=coeffset.begin(); iter!=coeffset.end(); ++iter) {
fitModel->setAmpCoeffSet(*iter);
}
fitModel->setCPEigenvalue( eigenvalue );
fitModel->setPhiMix( 2.0*LauConstants::beta, fixPhiMix, useSinCos );
fitModel->setAsymmetries(0.0,kFALSE,1.0,kTRUE);
int tagCat(-1);
// Tag cat fractions, dilutions and Delta dilutions
if (dtype=="CPEven"){
flavTag->addValidTagCat(63);
flavTag->setSignalTagCatPars(63, 0.6, 1.0, 0.0, kTRUE, kTRUE,"PerEvtMistag",0.5,0.5,1.0,1.0,0.0);
tagCat=63;
} else {
// Still need tagCat 63 in QFS channels for calibration etc
flavTag->addValidTagCat(63);
flavTag->setSignalTagCatPars(63, 0.6, 1.0, 0.0, kTRUE, kTRUE,"PerEvtMistag",0.5,0.5,1.0,1.0,0.0);
tagCat=63;
}
//if (command == "gen"){
fitModel->setSignalFlavTagPdfs(63,etahistpdf);
//}
// Delta t PDFs
const Double_t minDt(0.0);
const Double_t maxDt(20.0);
const Double_t minDtErr(0.0);
const Double_t maxDtErr(2.5);
const Int_t nGauss(3);
std::vector<Bool_t> scale(nGauss);
scale[0] = kTRUE;
scale[1] = kTRUE;
scale[2] = kFALSE;
std::vector<LauAbsRValue*> dtPars(10);
std::vector<LauAbsRValue*> dtParsExp(9);
TString mean0Name("dt_"); mean0Name += "_mean_0";
TString mean1Name("dt_"); mean1Name += "_mean_1";
TString mean2Name("dt_"); mean2Name += "_mean_2";
TString sigma0Name("dt_"); sigma0Name += "_sigma_0";
TString sigma1Name("dt_"); sigma1Name += "_sigma_1";
TString sigma2Name("dt_"); sigma2Name += "_sigma_2";
TString frac1Name("dt_"); frac1Name += "_frac_1";
TString frac2Name("dt_"); frac2Name += "_frac_2";
TString tauName("dt_"); tauName += "_tau";
TString freqName("dt_"); freqName += "_deltaM";
LauParameter * mean0 = new LauParameter(mean0Name, -0.181);
LauParameter * mean1 = new LauParameter(mean1Name, -1.27);
LauParameter * mean2 = new LauParameter(mean2Name, 0.0);
LauParameter * sigma0 = new LauParameter(sigma0Name, 1.067);
LauParameter * sigma1 = new LauParameter(sigma1Name, 3.0);
LauParameter * sigma2 = new LauParameter(sigma2Name, 8.0);
LauParameter * frac1 = new LauParameter(frac1Name, 0.0930);
LauParameter * frac2 = new LauParameter(frac2Name, 0.0036);
LauParameter * tau = new LauParameter(tauName, 1.520);
LauParameter * freq = new LauParameter(freqName, 0.5064);
TString mean0tagcat63Name("dt_"); mean0tagcat63Name += 63; mean0tagcat63Name += "_mean_0";
TString sigma0tagcat63Name("dt_"); sigma0tagcat63Name += 63; sigma0tagcat63Name += "_sigma_0";
LauParameter * mean0tagcat63 = new LauParameter(mean0tagcat63Name, -0.031);
LauParameter * sigma0tagcat63 = new LauParameter(sigma0tagcat63Name, 0.972);
dtPars[0] = mean0; dtParsExp[0] = mean0;
dtPars[1] = mean1; dtParsExp[1] = mean1;
dtPars[2] = mean2; dtParsExp[2] = mean2;
dtPars[3] = sigma0; dtParsExp[3] = sigma0;
dtPars[4] = sigma1; dtParsExp[4] = sigma1;
dtPars[5] = sigma2; dtParsExp[5] = sigma2;
dtPars[6] = frac1; dtParsExp[6] = frac1;
dtPars[7] = frac2; dtParsExp[7] = frac2;
dtPars[8] = tau; dtParsExp[8] = tau;
dtPars[9] = freq;
//Decay time acceptance spline - same for all tag cats (though doesn't have to be)
std::vector<Double_t> dtvals;
dtvals.push_back(0.0); dtvals.push_back(1.0); dtvals.push_back(2.0); dtvals.push_back(4.0); dtvals.push_back(6.0);
dtvals.push_back(8.0); dtvals.push_back(11.0); dtvals.push_back(14.0); dtvals.push_back(17.0); dtvals.push_back(20.0);
std::vector<Double_t> effvals;
effvals.push_back(0.0); effvals.push_back(0.15); effvals.push_back(0.25); effvals.push_back(0.33); effvals.push_back(0.38);
effvals.push_back(0.4); effvals.push_back(0.43); effvals.push_back(0.45); effvals.push_back(0.47); effvals.push_back(0.50);
//Lau1DCubicSpline* dtEffSpline = new Lau1DCubicSpline(dtvals,effvals);
Lau1DCubicSpline* dtEffSpline = new Lau1DCubicSpline(dtvals,effvals,Lau1DCubicSpline::StandardSpline,Lau1DCubicSpline::Natural,Lau1DCubicSpline::Natural);
//LauParameters to float Spline y values
LauParameter* knot_0_0 = new LauParameter("knot_0_0",0.00,0.0,1.0,kTRUE);
LauParameter* knot_0_1 = new LauParameter("knot_0_1",0.15,0.0,1.0,kTRUE);
LauParameter* knot_0_2 = new LauParameter("knot_0_2",0.25,0.0,1.0,kTRUE);
LauParameter* knot_0_3 = new LauParameter("knot_0_3",0.33,0.0,1.0,kTRUE);
LauParameter* knot_0_4 = new LauParameter("knot_0_4",0.38,0.0,1.0,kFALSE);
LauParameter* knot_0_5 = new LauParameter("knot_0_5",0.40,0.0,1.0,kTRUE);
LauParameter* knot_0_6 = new LauParameter("knot_0_6",0.43,0.0,1.0,kTRUE);
LauParameter* knot_0_7 = new LauParameter("knot_0_7",0.45,0.0,1.0,kTRUE);
LauParameter* knot_0_8 = new LauParameter("knot_0_8",0.47,0.0,1.0,kTRUE);
LauParameter* knot_0_9 = new LauParameter("knot_0_9",0.50,0.0,1.0,kTRUE);
LauParameter* knot_63_0 = new LauParameter("knot_63_0",0.00,0.0,1.0,kTRUE);
LauParameter* knot_63_1 = new LauParameter("knot_63_1",0.15,0.0,1.0,kTRUE);
LauParameter* knot_63_2 = new LauParameter("knot_63_2",0.25,0.0,1.0,kTRUE);
LauParameter* knot_63_3 = new LauParameter("knot_63_3",0.33,0.0,1.0,kTRUE);
LauParameter* knot_63_4 = new LauParameter("knot_63_4",0.38,0.0,1.0,kFALSE);
LauParameter* knot_63_5 = new LauParameter("knot_63_5",0.40,0.0,1.0,kTRUE);
LauParameter* knot_63_6 = new LauParameter("knot_63_6",0.43,0.0,1.0,kTRUE);
LauParameter* knot_63_7 = new LauParameter("knot_63_7",0.45,0.0,1.0,kTRUE);
LauParameter* knot_63_8 = new LauParameter("knot_63_8",0.47,0.0,1.0,kTRUE);
LauParameter* knot_63_9 = new LauParameter("knot_63_9",0.50,0.0,1.0,kTRUE);
std::vector<LauParameter*> effPars0;
effPars0.reserve(10);
effPars0.push_back(knot_0_0); effPars0.push_back(knot_0_1); effPars0.push_back(knot_0_2); effPars0.push_back(knot_0_3); effPars0.push_back(knot_0_4);
effPars0.push_back(knot_0_5); effPars0.push_back(knot_0_6); effPars0.push_back(knot_0_7); effPars0.push_back(knot_0_8); effPars0.push_back(knot_0_9);
std::vector<LauParameter*> effPars63;
effPars63.reserve(10);
effPars63.push_back(knot_63_0); effPars63.push_back(knot_63_1); effPars63.push_back(knot_63_2); effPars63.push_back(knot_63_3); effPars63.push_back(knot_63_4);
effPars63.push_back(knot_63_5); effPars63.push_back(knot_63_6); effPars63.push_back(knot_63_7); effPars63.push_back(knot_63_8); effPars63.push_back(knot_63_9);
if (dtype=="CPEven"){
LauDecayTimePdf * dtPdf = new LauDecayTimePdf( "deltaTAvg", "deltaTAvgErr", dtPars, minDt, maxDt, minDtErr, maxDtErr, LauDecayTimePdf::ExpTrig, nGauss, scale, LauDecayTimePdf::DecayTime );
//LauDecayTimePdf * dtPdf = new LauDecayTimePdf( "deltaTAvg", "deltaTAvgErr", dtPars, minDt, maxDt, minDtErr, maxDtErr, LauDecayTimePdf::SimFitSigBd, nGauss, scale, LauDecayTimePdf::DecayTime );
dtPdf->doSmearing(kFALSE);
dtPdf->setEffiSpline(dtEffSpline, effPars0);
fitModel->setSignalDtPdf( 0, dtPdf );
} else {
LauDecayTimePdf * dtPdf = new LauDecayTimePdf( "deltaTAvg", "deltaTAvgErr", dtPars, minDt, maxDt, minDtErr, maxDtErr, LauDecayTimePdf::ExpTrig, nGauss, scale, LauDecayTimePdf::DecayTime );
//LauDecayTimePdf * dtPdf = new LauDecayTimePdf( "deltaTAvg", "deltaTAvgErr", dtPars, minDt, maxDt, minDtErr, maxDtErr, LauDecayTimePdf::SimFitNormBd, nGauss, scale, LauDecayTimePdf::DecayTime );
dtPdf->doSmearing(kFALSE);
dtPdf->setEffiSpline(dtEffSpline, effPars0);
fitModel->setSignalDtPdf( 0, dtPdf );
}
//if (tagCat==63){
dtPars[0] = mean0tagcat63;
dtPars[1] = mean1->createClone();
dtPars[2] = mean2->createClone();
dtPars[3] = sigma0tagcat63;
dtPars[4] = sigma1->createClone();
dtPars[5] = sigma2->createClone();
dtPars[6] = frac1->createClone();
dtPars[7] = frac2->createClone();
dtPars[8] = tau->createClone();
dtPars[9] = freq->createClone();
//LauDecayTimePdf * dtPdf = new LauDecayTimePdf( "deltaTAvg", "deltaTAvgErr", dtPars, minDt, maxDt, minDtErr, maxDtErr, LauDecayTimePdf::ExpTrig, nGauss, scale, LauDecayTimePdf::DecayTime );
if (dtype=="CPEven"){
LauDecayTimePdf * dtPdf = new LauDecayTimePdf( "deltaTAvg", "deltaTAvgErr", dtPars, minDt, maxDt, minDtErr, maxDtErr, LauDecayTimePdf::SimFitSigBd, nGauss, scale, LauDecayTimePdf::DecayTime );
dtPdf->doSmearing(kFALSE);
dtPdf->setEffiSpline(dtEffSpline, effPars63);
fitModel->setSignalDtPdf( tagCat, dtPdf );
} else {
LauDecayTimePdf * dtPdf = new LauDecayTimePdf( "deltaTAvg", "deltaTAvgErr", dtPars, minDt, maxDt, minDtErr, maxDtErr, LauDecayTimePdf::SimFitNormBd, nGauss, scale, LauDecayTimePdf::DecayTime );
dtPdf->doSmearing(kFALSE);
dtPdf->setEffiSpline(dtEffSpline, effPars63);
fitModel->setSignalDtPdf( tagCat, dtPdf );
}
//}
// set the number of signal events
cout<<"nSigEvents = "<<nSigEvents<<endl;
LauParameter* nSigPar = new LauParameter("signalEvents", nSigEvents, -2.0*nSigEvents, 2.0*nSigEvents, kTRUE);
fitModel->setNSigEvents(nSigPar);
// set the number of experiments
if (command == "fit") {
fitModel->setNExpts(nExpt, firstExpt);
} else {
fitModel->setNExpts(nExptGen, firstExptGen);
}
fitModel->useAsymmFitErrors(kFALSE);
//fitModel->useRandomInitFitPars(kTRUE);
fitModel->useRandomInitFitPars(kFALSE);
fitModel->doPoissonSmearing(kFALSE);
fitModel->doEMLFit(kFALSE);
fitModel->writeLatexTable(kFALSE);
TString dataFile("");
TString treeName("fitTree");
TString rootFileName("");
TString tableFileName("");
TString fitToyFileName("");
TString splotFileName("");
if (command == "fit") {
dataFile = "TEST-Dpipi_"+dtype;
dataFile += "_expts"; dataFile += firstExptGen; dataFile += "-"; dataFile += firstExptGen+nExptGen-1;
dataFile += "_CP";
if ( eigenvalue == LauTimeDepFitModel::CPEven ) {
dataFile += "even";
} else {
dataFile += "odd";
}
dataFile += ".root";
rootFileName = "fits/fit"; rootFileName += iFit;
rootFileName += "_expts"; rootFileName += firstExpt; rootFileName += "-"; rootFileName += firstExpt+nExpt-1;
rootFileName += ".root";
tableFileName = "fitResults_"; tableFileName += iFit;
tableFileName += "_expts"; tableFileName += firstExpt; tableFileName += "-"; tableFileName += firstExpt+nExpt-1;
fitToyFileName = "fitToyMC_"+dtype; fitToyFileName += iFit;
fitToyFileName += "_expts"; fitToyFileName += firstExpt; fitToyFileName += "-"; fitToyFileName += firstExpt+nExpt-1;
fitToyFileName += ".root";
splotFileName = "splot_"; splotFileName += iFit;
splotFileName += "_expts"; splotFileName += firstExpt; splotFileName += "-"; splotFileName += firstExpt+nExpt-1;
splotFileName += ".root";
} else {
dataFile = "TEST-Dpipi_"+dtype;
dataFile += "_expts"; dataFile += firstExptGen; dataFile += "-"; dataFile += firstExptGen+nExptGen-1; dataFile += "_CP";
if ( eigenvalue == LauTimeDepFitModel::CPEven ) {
dataFile += "even";
} else {
dataFile += "odd";
}
dataFile += ".root";
rootFileName = "dummy.root";
tableFileName = "genResults";
}
// Generate toy from the fitted parameters
fitModel->compareFitData(1, fitToyFileName);
// Write out per-event likelihoods and sWeights
//fitModel->writeSPlotData(splotFileName, "splot", kFALSE);
// Execute the generation/fit
if ( command == "fit" ){
fitModel->runSlave( dataFile, treeName, rootFileName, tableFileName, "localhost", port );
} else {
fitModel->run( command, dataFile, treeName, rootFileName, tableFileName );
}
return EXIT_SUCCESS;
}
diff --git a/examples/Test_Dpipi.cc b/examples/Test_Dpipi_efficiencyHist.cc
similarity index 80%
copy from examples/Test_Dpipi.cc
copy to examples/Test_Dpipi_efficiencyHist.cc
index de5f315..89fc7a9 100755
--- a/examples/Test_Dpipi.cc
+++ b/examples/Test_Dpipi_efficiencyHist.cc
@@ -1,420 +1,397 @@
#include <iostream>
using std::cout;
using std::cerr;
using std::endl;
#include <vector>
#include <map>
#include "TFile.h"
#include "TH2.h"
+#include "TH1D.h"
#include "TRandom.h"
#include "TString.h"
#include "TSystem.h"
#include "LauDaughters.hh"
#include "LauDecayTimePdf.hh"
#include "LauEffModel.hh"
#include "LauIsobarDynamics.hh"
#include "LauMagPhaseCoeffSet.hh"
#include "LauRandom.hh"
#include "LauRealImagCoeffSet.hh"
#include "LauTimeDepFitModel.hh"
#include "LauVetoes.hh"
#include "LauFlavTag.hh"
#include "Lau1DHistPdf.hh"
#include "Lau1DCubicSpline.hh"
void usage(const TString& progName)
{
cerr<<"Usage:"<<endl;
cerr<<progName<<" gen D_type [firstExptGen] [nExptGen] [CP eigenvalue]"<<endl;
cerr<<"or"<<endl;
cerr<<progName<<" fit D_type [port] [iFit] [firstExpt] [nExpt] [nExptGen]"<<endl;
}
int main(const int argc, const char ** argv)
{
const TString dtype = argv[2];
Int_t port = 0;
Int_t iFit = 0;
Int_t firstExpt = 0;
Int_t firstExptGen = 0;
Int_t nExpt = 1;
Int_t nExptGen = 1;
LauTimeDepFitModel::CPEigenvalue eigenvalue = LauTimeDepFitModel::CPEven;
Bool_t fixPhiMix(kFALSE);
Bool_t useSinCos(kTRUE);
// check the command line arguments
if (argc<1) {
usage(argv[0]);
return EXIT_FAILURE;
}
TString command = argv[1];
if (command != "gen" && command != "fit") {
usage(argv[0]);
return EXIT_FAILURE;
}
if (command == "fit") {
if (argc>3) {
port = atoi(argv[3]);
if (argc>4) {
iFit = atoi(argv[4]);
if (argc>5) {
firstExpt = atoi(argv[5]);
if (argc>6) {
nExpt = atoi(argv[6]);
if (argc>7) {
nExptGen = atoi(argv[7]);
}
}
}
}
}
for (firstExptGen = 0; firstExptGen<(firstExpt+nExpt); firstExptGen+=nExptGen) {
}
firstExptGen -= nExptGen;
if ( (nExpt > nExptGen) || (nExptGen%nExpt != 0) ) {
cerr<<"Error, nExpt must be a factor of nExptGen."<<endl;
return EXIT_FAILURE;
}
} else {
if (argc>3) {
firstExptGen = atoi(argv[3]);
if (argc>4) {
nExptGen = atoi(argv[4]);
if (argc>5) {
Int_t eigval = atoi(argv[5]);
if ( eigval == 1 ) {
eigenvalue = LauTimeDepFitModel::CPOdd;
} else {
eigenvalue = LauTimeDepFitModel::CPEven;
}
}
}
}
}
cout<<"dtype "<<dtype<<" port "<<port<<" iFit "<<iFit<<" firstExpt "<<firstExpt<<" nExpt "<<nExpt<<endl;
Double_t nSigEvents(0);
if (dtype=="CPEven"){
nSigEvents = 15000;
} else {
nSigEvents = 10000;
}
LauDaughters* daughtersB0bar(0);
LauDaughters* daughtersB0(0);
LauEffModel* effModelB0bar(0);
LauEffModel* effModelB0(0);
LauVetoes* vetoes = new LauVetoes();
//vetoes->addMassVeto( 2, 2.00776, 2.01276 );
daughtersB0bar = new LauDaughters("B0_bar", "pi+", "pi-", "D0");
daughtersB0 = new LauDaughters("B0", "pi+", "pi-", "D0_bar");
// efficiency
effModelB0bar = new LauEffModel(daughtersB0bar, vetoes);
effModelB0 = new LauEffModel(daughtersB0, vetoes);
LauIsobarDynamics* sigModelB0bar(0);
LauIsobarDynamics* sigModelB0(0);
LauTimeDepFitModel* fitModel(0);
std::vector<LauParameter *> params;
LauFlavTag* flavTag = new LauFlavTag(params, kTRUE, "tagFlv", "tagCat");
flavTag->setTrueTagVarName("trueTag");
// Use alternative tagging calibration parameters?
//flavTag->useAveOmegaDeltaOmega();
TFile* etafile(0);
TH1* etahist(0);
Lau1DHistPdf* etahistpdf(0);
//if (command == "gen"){
- etafile = TFile::Open("/Users/mark/analysis/DpipiLaura/laura/examples/histogram.root");
+ etafile = TFile::Open("histogram.root");
etahist = dynamic_cast<TH1*>(etafile->Get("htemp"));
etahistpdf = new Lau1DHistPdf("eta",etahist,0,0.54,kFALSE,kFALSE);
//}
// signal dynamics
sigModelB0bar = new LauIsobarDynamics(daughtersB0bar, effModelB0bar);
sigModelB0bar->setIntFileName("integ_B0bar.dat");
sigModelB0bar->addResonance("D*+_2", 2, LauAbsResonance::RelBW);
sigModelB0bar->addResonance("D*+_0", 2, LauAbsResonance::RelBW);
sigModelB0bar->addResonance("rho0(770)", 3, LauAbsResonance::RelBW);
sigModelB0bar->addResonance("f_0(980)", 3, LauAbsResonance::RelBW);
sigModelB0bar->addResonance("f_2(1270)", 3, LauAbsResonance::RelBW);
sigModelB0 = new LauIsobarDynamics(daughtersB0, effModelB0);
sigModelB0->setIntFileName("integ_B0.dat");
sigModelB0->addResonance("D*-_2", 1, LauAbsResonance::RelBW);
sigModelB0->addResonance("D*-_0", 1, LauAbsResonance::RelBW);
sigModelB0->addResonance("rho0(770)", 3, LauAbsResonance::RelBW);
sigModelB0->addResonance("f_0(980)", 3, LauAbsResonance::RelBW);
sigModelB0->addResonance("f_2(1270)", 3, LauAbsResonance::RelBW);
// fit model
fitModel = new LauTimeDepFitModel(sigModelB0bar,sigModelB0,flavTag);
fitModel->setASqMaxValue(1.45);
std::vector<LauAbsCoeffSet*> coeffset;
coeffset.push_back( new LauRealImagCoeffSet("D*-_2", 1.00, 0.00, kTRUE, kTRUE) );
coeffset.push_back( new LauRealImagCoeffSet("D*-_0", 0.53*TMath::Cos( 3.00), 0.53*TMath::Sin( 3.00), kFALSE, kFALSE) );
coeffset.push_back( new LauRealImagCoeffSet("rho0(770)", 1.22*TMath::Cos( 2.25), 1.22*TMath::Sin( 2.25), kFALSE, kFALSE) );
coeffset.push_back( new LauRealImagCoeffSet("f_0(980)", 0.19*TMath::Cos(-2.48), 0.19*TMath::Sin(-2.48), kFALSE, kFALSE) );
coeffset.push_back( new LauRealImagCoeffSet("f_2(1270)", 0.75*TMath::Cos( 2.97), 0.75*TMath::Sin( 2.97), kFALSE, kFALSE) );
for (std::vector<LauAbsCoeffSet*>::iterator iter=coeffset.begin(); iter!=coeffset.end(); ++iter) {
fitModel->setAmpCoeffSet(*iter);
}
fitModel->setCPEigenvalue( eigenvalue );
fitModel->setPhiMix( 2.0*LauConstants::beta, fixPhiMix, useSinCos );
fitModel->setAsymmetries(0.0,kFALSE,1.0,kTRUE);
int tagCat(-1);
// Tag cat fractions, dilutions and Delta dilutions
if (dtype=="CPEven"){
flavTag->addValidTagCat(63);
flavTag->setSignalTagCatPars(63, 0.6, 1.0, 0.0, kTRUE, kTRUE,"PerEvtMistag",0.5,0.5,1.0,1.0,0.0);
tagCat=63;
} else {
// Still need tagCat 63 in QFS channels for calibration etc
flavTag->addValidTagCat(63);
flavTag->setSignalTagCatPars(63, 0.6, 1.0, 0.0, kTRUE, kTRUE,"PerEvtMistag",0.5,0.5,1.0,1.0,0.0);
tagCat=63;
}
//if (command == "gen"){
fitModel->setSignalFlavTagPdfs(63,etahistpdf);
//}
// Delta t PDFs
const Double_t minDt(0.0);
const Double_t maxDt(20.0);
const Double_t minDtErr(0.0);
const Double_t maxDtErr(2.5);
const Int_t nGauss(3);
std::vector<Bool_t> scale(nGauss);
scale[0] = kTRUE;
scale[1] = kTRUE;
scale[2] = kFALSE;
std::vector<LauAbsRValue*> dtPars(10);
std::vector<LauAbsRValue*> dtParsExp(9);
TString mean0Name("dt_"); mean0Name += "_mean_0";
TString mean1Name("dt_"); mean1Name += "_mean_1";
TString mean2Name("dt_"); mean2Name += "_mean_2";
TString sigma0Name("dt_"); sigma0Name += "_sigma_0";
TString sigma1Name("dt_"); sigma1Name += "_sigma_1";
TString sigma2Name("dt_"); sigma2Name += "_sigma_2";
TString frac1Name("dt_"); frac1Name += "_frac_1";
TString frac2Name("dt_"); frac2Name += "_frac_2";
TString tauName("dt_"); tauName += "_tau";
TString freqName("dt_"); freqName += "_deltaM";
LauParameter * mean0 = new LauParameter(mean0Name, -0.181);
LauParameter * mean1 = new LauParameter(mean1Name, -1.27);
LauParameter * mean2 = new LauParameter(mean2Name, 0.0);
LauParameter * sigma0 = new LauParameter(sigma0Name, 1.067);
LauParameter * sigma1 = new LauParameter(sigma1Name, 3.0);
LauParameter * sigma2 = new LauParameter(sigma2Name, 8.0);
LauParameter * frac1 = new LauParameter(frac1Name, 0.0930);
LauParameter * frac2 = new LauParameter(frac2Name, 0.0036);
LauParameter * tau = new LauParameter(tauName, 1.520);
LauParameter * freq = new LauParameter(freqName, 0.5064);
TString mean0tagcat63Name("dt_"); mean0tagcat63Name += 63; mean0tagcat63Name += "_mean_0";
TString sigma0tagcat63Name("dt_"); sigma0tagcat63Name += 63; sigma0tagcat63Name += "_sigma_0";
LauParameter * mean0tagcat63 = new LauParameter(mean0tagcat63Name, -0.031);
LauParameter * sigma0tagcat63 = new LauParameter(sigma0tagcat63Name, 0.972);
dtPars[0] = mean0; dtParsExp[0] = mean0;
dtPars[1] = mean1; dtParsExp[1] = mean1;
dtPars[2] = mean2; dtParsExp[2] = mean2;
dtPars[3] = sigma0; dtParsExp[3] = sigma0;
dtPars[4] = sigma1; dtParsExp[4] = sigma1;
dtPars[5] = sigma2; dtParsExp[5] = sigma2;
dtPars[6] = frac1; dtParsExp[6] = frac1;
dtPars[7] = frac2; dtParsExp[7] = frac2;
dtPars[8] = tau; dtParsExp[8] = tau;
dtPars[9] = freq;
//Decay time acceptance spline - same for all tag cats (though doesn't have to be)
std::vector<Double_t> dtvals;
dtvals.push_back(0.0); dtvals.push_back(1.0); dtvals.push_back(2.0); dtvals.push_back(4.0); dtvals.push_back(6.0);
dtvals.push_back(8.0); dtvals.push_back(11.0); dtvals.push_back(14.0); dtvals.push_back(17.0); dtvals.push_back(20.0);
std::vector<Double_t> effvals;
effvals.push_back(0.0); effvals.push_back(0.15); effvals.push_back(0.25); effvals.push_back(0.33); effvals.push_back(0.38);
effvals.push_back(0.4); effvals.push_back(0.43); effvals.push_back(0.45); effvals.push_back(0.47); effvals.push_back(0.50);
//Lau1DCubicSpline* dtEffSpline = new Lau1DCubicSpline(dtvals,effvals);
- Lau1DCubicSpline* dtEffSpline = new Lau1DCubicSpline(dtvals,effvals,Lau1DCubicSpline::StandardSpline,Lau1DCubicSpline::Natural,Lau1DCubicSpline::Natural);
-
- //LauParameters to float Spline y values
- LauParameter* knot_0_0 = new LauParameter("knot_0_0",0.00,0.0,1.0,kTRUE);
- LauParameter* knot_0_1 = new LauParameter("knot_0_1",0.15,0.0,1.0,kTRUE);
- LauParameter* knot_0_2 = new LauParameter("knot_0_2",0.25,0.0,1.0,kTRUE);
- LauParameter* knot_0_3 = new LauParameter("knot_0_3",0.33,0.0,1.0,kTRUE);
- LauParameter* knot_0_4 = new LauParameter("knot_0_4",0.38,0.0,1.0,kFALSE);
- LauParameter* knot_0_5 = new LauParameter("knot_0_5",0.40,0.0,1.0,kTRUE);
- LauParameter* knot_0_6 = new LauParameter("knot_0_6",0.43,0.0,1.0,kTRUE);
- LauParameter* knot_0_7 = new LauParameter("knot_0_7",0.45,0.0,1.0,kTRUE);
- LauParameter* knot_0_8 = new LauParameter("knot_0_8",0.47,0.0,1.0,kTRUE);
- LauParameter* knot_0_9 = new LauParameter("knot_0_9",0.50,0.0,1.0,kTRUE);
-
- LauParameter* knot_63_0 = new LauParameter("knot_63_0",0.00,0.0,1.0,kTRUE);
- LauParameter* knot_63_1 = new LauParameter("knot_63_1",0.15,0.0,1.0,kTRUE);
- LauParameter* knot_63_2 = new LauParameter("knot_63_2",0.25,0.0,1.0,kTRUE);
- LauParameter* knot_63_3 = new LauParameter("knot_63_3",0.33,0.0,1.0,kTRUE);
- LauParameter* knot_63_4 = new LauParameter("knot_63_4",0.38,0.0,1.0,kFALSE);
- LauParameter* knot_63_5 = new LauParameter("knot_63_5",0.40,0.0,1.0,kTRUE);
- LauParameter* knot_63_6 = new LauParameter("knot_63_6",0.43,0.0,1.0,kTRUE);
- LauParameter* knot_63_7 = new LauParameter("knot_63_7",0.45,0.0,1.0,kTRUE);
- LauParameter* knot_63_8 = new LauParameter("knot_63_8",0.47,0.0,1.0,kTRUE);
- LauParameter* knot_63_9 = new LauParameter("knot_63_9",0.50,0.0,1.0,kTRUE);
-
- std::vector<LauParameter*> effPars0;
- effPars0.reserve(10);
- effPars0.push_back(knot_0_0); effPars0.push_back(knot_0_1); effPars0.push_back(knot_0_2); effPars0.push_back(knot_0_3); effPars0.push_back(knot_0_4);
- effPars0.push_back(knot_0_5); effPars0.push_back(knot_0_6); effPars0.push_back(knot_0_7); effPars0.push_back(knot_0_8); effPars0.push_back(knot_0_9);
-
- std::vector<LauParameter*> effPars63;
- effPars63.reserve(10);
- effPars63.push_back(knot_63_0); effPars63.push_back(knot_63_1); effPars63.push_back(knot_63_2); effPars63.push_back(knot_63_3); effPars63.push_back(knot_63_4);
- effPars63.push_back(knot_63_5); effPars63.push_back(knot_63_6); effPars63.push_back(knot_63_7); effPars63.push_back(knot_63_8); effPars63.push_back(knot_63_9);
-
+// Lau1DCubicSpline* dtEffSpline = new Lau1DCubicSpline(dtvals,effvals,Lau1DCubicSpline::StandardSpline,Lau1DCubicSpline::Natural,Lau1DCubicSpline::Natural);
+
+ const Int_t nBins = 9;
+ const Double_t edges[nBins + 1] = {0,1,2,4,6,8,11,14,17,20};
+ const Double_t binFilling[nBins] = {0.0075,0.02,0.029,0.655,0.69,0.715,0.74,0.76,0.785};
+ TH1D effHist("effHist","Histogram of efficiencies", nBins, edges);
+
+ for( Int_t i = 1 ; i <= nBins ; ++i ){ effHist.SetBinContent(i, binFilling[i-1]); }
+
if (dtype=="CPEven"){
- LauDecayTimePdf * dtPdf = new LauDecayTimePdf( "deltaTAvg", "deltaTAvgErr", dtPars, minDt, maxDt, minDtErr, maxDtErr, LauDecayTimePdf::ExpTrig, nGauss, scale, LauDecayTimePdf::DecayTime );
- //LauDecayTimePdf * dtPdf = new LauDecayTimePdf( "deltaTAvg", "deltaTAvgErr", dtPars, minDt, maxDt, minDtErr, maxDtErr, LauDecayTimePdf::SimFitSigBd, nGauss, scale, LauDecayTimePdf::DecayTime );
+ LauDecayTimePdf * dtPdf = new LauDecayTimePdf( "deltaTAvg", "deltaTAvgErr", dtPars, minDt, maxDt, minDtErr, maxDtErr, LauDecayTimePdf::ExpTrig, nGauss, scale, LauDecayTimePdf::DecayTime, LauDecayTimePdf::Flat );
dtPdf->doSmearing(kFALSE);
- dtPdf->setEffiSpline(dtEffSpline, effPars0);
+ dtPdf->setEffiHist(&effHist);
+// dtPdf->setEffiSpline(dtEffSpline, effPars0);
fitModel->setSignalDtPdf( 0, dtPdf );
} else {
- LauDecayTimePdf * dtPdf = new LauDecayTimePdf( "deltaTAvg", "deltaTAvgErr", dtPars, minDt, maxDt, minDtErr, maxDtErr, LauDecayTimePdf::ExpTrig, nGauss, scale, LauDecayTimePdf::DecayTime );
- //LauDecayTimePdf * dtPdf = new LauDecayTimePdf( "deltaTAvg", "deltaTAvgErr", dtPars, minDt, maxDt, minDtErr, maxDtErr, LauDecayTimePdf::SimFitNormBd, nGauss, scale, LauDecayTimePdf::DecayTime );
+ LauDecayTimePdf * dtPdf = new LauDecayTimePdf( "deltaTAvg", "deltaTAvgErr", dtPars, minDt, maxDt, minDtErr, maxDtErr, LauDecayTimePdf::ExpTrig, nGauss, scale, LauDecayTimePdf::DecayTime, LauDecayTimePdf::Flat );
dtPdf->doSmearing(kFALSE);
- dtPdf->setEffiSpline(dtEffSpline, effPars0);
+ dtPdf->setEffiHist(&effHist);
+// dtPdf->setEffiSpline(dtEffSpline, effPars0);
fitModel->setSignalDtPdf( 0, dtPdf );
}
//if (tagCat==63){
dtPars[0] = mean0tagcat63;
dtPars[1] = mean1->createClone();
dtPars[2] = mean2->createClone();
dtPars[3] = sigma0tagcat63;
dtPars[4] = sigma1->createClone();
dtPars[5] = sigma2->createClone();
dtPars[6] = frac1->createClone();
dtPars[7] = frac2->createClone();
dtPars[8] = tau->createClone();
dtPars[9] = freq->createClone();
//LauDecayTimePdf * dtPdf = new LauDecayTimePdf( "deltaTAvg", "deltaTAvgErr", dtPars, minDt, maxDt, minDtErr, maxDtErr, LauDecayTimePdf::ExpTrig, nGauss, scale, LauDecayTimePdf::DecayTime );
if (dtype=="CPEven"){
- LauDecayTimePdf * dtPdf = new LauDecayTimePdf( "deltaTAvg", "deltaTAvgErr", dtPars, minDt, maxDt, minDtErr, maxDtErr, LauDecayTimePdf::SimFitSigBd, nGauss, scale, LauDecayTimePdf::DecayTime );
+ LauDecayTimePdf * dtPdf = new LauDecayTimePdf( "deltaTAvg", "deltaTAvgErr", dtPars, minDt, maxDt, minDtErr, maxDtErr, LauDecayTimePdf::SimFitSigBd, nGauss, scale, LauDecayTimePdf::DecayTime , LauDecayTimePdf::Flat );
dtPdf->doSmearing(kFALSE);
- dtPdf->setEffiSpline(dtEffSpline, effPars63);
+// dtPdf->setEffiSpline(dtEffSpline, effPars63);
+ dtPdf->setEffiHist(&effHist);
fitModel->setSignalDtPdf( tagCat, dtPdf );
} else {
- LauDecayTimePdf * dtPdf = new LauDecayTimePdf( "deltaTAvg", "deltaTAvgErr", dtPars, minDt, maxDt, minDtErr, maxDtErr, LauDecayTimePdf::SimFitNormBd, nGauss, scale, LauDecayTimePdf::DecayTime );
+ LauDecayTimePdf * dtPdf = new LauDecayTimePdf( "deltaTAvg", "deltaTAvgErr", dtPars, minDt, maxDt, minDtErr, maxDtErr, LauDecayTimePdf::SimFitNormBd, nGauss, scale, LauDecayTimePdf::DecayTime , LauDecayTimePdf::Flat);
dtPdf->doSmearing(kFALSE);
- dtPdf->setEffiSpline(dtEffSpline, effPars63);
+// dtPdf->setEffiSpline(dtEffSpline, effPars63);
+ dtPdf->setEffiHist(&effHist);
fitModel->setSignalDtPdf( tagCat, dtPdf );
}
//}
// set the number of signal events
cout<<"nSigEvents = "<<nSigEvents<<endl;
LauParameter* nSigPar = new LauParameter("signalEvents", nSigEvents, -2.0*nSigEvents, 2.0*nSigEvents, kTRUE);
fitModel->setNSigEvents(nSigPar);
// set the number of experiments
if (command == "fit") {
fitModel->setNExpts(nExpt, firstExpt);
} else {
fitModel->setNExpts(nExptGen, firstExptGen);
}
fitModel->useAsymmFitErrors(kFALSE);
//fitModel->useRandomInitFitPars(kTRUE);
fitModel->useRandomInitFitPars(kFALSE);
fitModel->doPoissonSmearing(kFALSE);
fitModel->doEMLFit(kFALSE);
fitModel->writeLatexTable(kFALSE);
TString dataFile("");
TString treeName("fitTree");
TString rootFileName("");
TString tableFileName("");
TString fitToyFileName("");
TString splotFileName("");
if (command == "fit") {
dataFile = "TEST-Dpipi_"+dtype;
dataFile += "_expts"; dataFile += firstExptGen; dataFile += "-"; dataFile += firstExptGen+nExptGen-1;
dataFile += "_CP";
if ( eigenvalue == LauTimeDepFitModel::CPEven ) {
dataFile += "even";
} else {
dataFile += "odd";
}
dataFile += ".root";
rootFileName = "fits/fit"; rootFileName += iFit;
rootFileName += "_expts"; rootFileName += firstExpt; rootFileName += "-"; rootFileName += firstExpt+nExpt-1;
rootFileName += ".root";
tableFileName = "fitResults_"; tableFileName += iFit;
tableFileName += "_expts"; tableFileName += firstExpt; tableFileName += "-"; tableFileName += firstExpt+nExpt-1;
fitToyFileName = "fitToyMC_"+dtype; fitToyFileName += iFit;
fitToyFileName += "_expts"; fitToyFileName += firstExpt; fitToyFileName += "-"; fitToyFileName += firstExpt+nExpt-1;
fitToyFileName += ".root";
splotFileName = "splot_"; splotFileName += iFit;
splotFileName += "_expts"; splotFileName += firstExpt; splotFileName += "-"; splotFileName += firstExpt+nExpt-1;
splotFileName += ".root";
} else {
dataFile = "TEST-Dpipi_"+dtype;
dataFile += "_expts"; dataFile += firstExptGen; dataFile += "-"; dataFile += firstExptGen+nExptGen-1; dataFile += "_CP";
if ( eigenvalue == LauTimeDepFitModel::CPEven ) {
dataFile += "even";
} else {
dataFile += "odd";
}
dataFile += ".root";
rootFileName = "dummy.root";
tableFileName = "genResults";
}
// Generate toy from the fitted parameters
fitModel->compareFitData(1, fitToyFileName);
// Write out per-event likelihoods and sWeights
//fitModel->writeSPlotData(splotFileName, "splot", kFALSE);
// Execute the generation/fit
if ( command == "fit" ){
fitModel->runSlave( dataFile, treeName, rootFileName, tableFileName, "localhost", port );
} else {
fitModel->run( command, dataFile, treeName, rootFileName, tableFileName );
}
return EXIT_SUCCESS;
}
diff --git a/inc/LauDecayTimePdf.hh b/inc/LauDecayTimePdf.hh
index 3065da0..f79d555 100644
--- a/inc/LauDecayTimePdf.hh
+++ b/inc/LauDecayTimePdf.hh
@@ -1,493 +1,511 @@
/*
Copyright 2006 University of Warwick
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
/*
Laura++ package authors:
John Back
Paul Harrison
Thomas Latham
*/
/*! \file LauDecayTimePdf.hh
\brief File containing declaration of LauDecayTimePdf class.
*/
/*! \class LauDecayTimePdf
\brief Class for defining the PDFs used in the time-dependent fit model to describe the decay time.
LauDecayTimePdf is a class that provides the PDFs for describing the
time-dependence of the various terms in a particle/antiparticle decay to a
common final state. The various terms have the form of exponentially
decaying trigonometric or hyperbolic functions convolved with a N-Gaussian
resolution function.
*/
#ifndef LAU_DECAYTIME_PDF
#define LAU_DECAYTIME_PDF
#include <vector>
#include "TString.h"
#include "LauAbsRValue.hh"
#include "LauFitDataTree.hh"
#include "LauComplex.hh"
class TH1;
class Lau1DHistPdf;
class Lau1DCubicSpline;
class LauDecayTimePdf {
public:
//! The functional form of the decay time PDF
enum FuncType {
Hist, /*Hist PDF for fixed background*/
Delta, /*< Delta function - for prompt background */
Exp, /*< Exponential function - for non-prompt background or charged B's */
DeltaExp, /*< Delta + Exponential function - for background with prompt and non-prompt parts */
ExpTrig, /*< Exponential function with Delta m driven mixing - for neutral B_d's */
ExpHypTrig, /*< Exponential function with both Delta m and Delta Gamma driven mixing - for neutral B_s's */
SimFitNormBd, /*< Exponential function with Delta m driven mixing - for neutral B_d's for flavour specific normalisation mode */
SimFitNormBs, /*< Exponential function with Delta m driven mixing - for neutral B_d's for CP signal modes */
SimFitSigBd, /*< Exponential function with both Delta m and Delta Gamma driven mixing - for neutral B_s's for flavour specific normalisation mode */
SimFitSigBs /*< Exponential function with both Delta m and Delta Gamma driven mixing - for neutral B_s's for CP signal modes */
};
//! State of complex error function calculation
enum State {
Good, /*< All OK */
Overflow1, /*< Overflow in term 1 */
Overflow2 /*< Overflow in term 2 */
};
//! How is the decay time measured - absolute or difference
enum TimeMeasurementMethod {
DecayTime, /*< Absolute measurement of decay time, e.g. LHCb scenario */
DecayTimeDiff /*< Measurement of the difference of two decay times, e.g. BaBar/Belle(II) scenario */
};
+ //! How is the TD efficiency information going to be given?
+ enum EfficiencyMethod {
+ Spline, /*< As a cubic spline, e.g. BaBar*/
+ dtHist, /*< As a histogram (TH1D/TH1F)*/
+ Flat /*< As a flat distribution (constant)*/
+ };
+
//! Constructor
/*!
\param [in] theVarName the name of the decay time variable in the input data
\param [in] theVarErrName the name of the decay time error variable in the input data
\param [in] params the parameters of the PDF
\param [in] minAbscissaVal the minimum value of the abscissa
\param [in] maxAbscissaVal the maximum value of the abscissa
\param [in] minAbscissaErr the minimum value of the abscissa error
\param [in] maxAbscissaErr the maximum value of the abscissa error
\param [in] type the functional form of the PDF
\param [in] nGauss the number of Gaussians in the resolution function
\param [in] scale controls whether the Gaussian parameters are scaled by the per-event error
\param [in] method set the type of the time measurement used in the given experiment
*/
LauDecayTimePdf(const TString& theVarName, const TString& theVarErrName, const std::vector<LauAbsRValue*>& params,
Double_t minAbscissaVal, Double_t maxAbscissaVal,
Double_t minAbscissaErr, Double_t maxAbscissaErr,
- const FuncType type, const UInt_t nGauss, const std::vector<Bool_t>& scale, const TimeMeasurementMethod method);
+ const FuncType type, const UInt_t nGauss, const std::vector<Bool_t>& scale, const TimeMeasurementMethod method, const EfficiencyMethod effMethod = EfficiencyMethod::Spline);
//! Constructor
/*!
\param [in] theVarName the name of the decay time variable in the input data
\param [in] theVarErrName the name of the decay time error variable in the input data
\param [in] params the parameters of the PDF
\param [in] minAbscissaVal the minimum value of the abscissa
\param [in] maxAbscissaVal the maximum value of the abscissa
\param [in] minAbscissaErr the minimum value of the abscissa error
\param [in] maxAbscissaErr the maximum value of the abscissa error
\param [in] type the functional form of the PDF
\param [in] nGauss the number of Gaussians in the resolution function
\param [in] scaleMeans controls whether the Gaussian mean parameters are scaled by the per-event error
\param [in] scaleWidths controls whether the Gaussian width parameters are scaled by the per-event error
\param [in] method set the type of the time measurement used in the given experiment
*/
LauDecayTimePdf(const TString& theVarName, const TString& theVarErrName, const std::vector<LauAbsRValue*>& params,
const Double_t minAbscissaVal, const Double_t maxAbscissaVal,
const Double_t minAbscissaErr, const Double_t maxAbscissaErr,
const FuncType type, const UInt_t nGauss, const std::vector<Bool_t>& scaleMeans,
- const std::vector<Bool_t>& scaleWidths, const TimeMeasurementMethod method);
+ const std::vector<Bool_t>& scaleWidths, const TimeMeasurementMethod method, const EfficiencyMethod effMethod = EfficiencyMethod::Spline);
//! Destructor
virtual ~LauDecayTimePdf();
//! Set the histogram to be used for generation of per-event decay time errors
/*!
If not set will fall back to using Landau distribution
\param [in] hist the histogram of the distribution
*/
void setErrorHisto(const TH1* hist);
//! Set the Histogram PDF in case of fixed background PDF
void setHistoPdf(const TH1* hist);
//! Set efficiency PDF in the form of Spline
/*!
\param [in] spline the efficiency spline function
*/
void setEffiSpline(Lau1DCubicSpline* spline);
//! Set efficiency PDF in the form of Spline
/*!
\param [in] spline the efficiency spline function
\param [in] effiPars vector of LauParameters to float the y values
*/
void setEffiSpline(Lau1DCubicSpline* spline, std::vector<LauParameter*> effiPars);
-
//! Set the parameters of the Landau distribution used to generate the per-event decay time errors
/*!
\param [in] mpv the MPV (most probable value) of the distribution
\param [in] sigma the width of the distribution
*/
void setErrorDistTerms(const Double_t mpv, const Double_t sigma) {
errorDistMPV_ = mpv;
errorDistSigma_ = sigma;
}
+ //! Set the efficiency PDF in the form of a Histogram
+ /*!
+ \param [in] hist the histogram of efficiencies
+ */
+ void setEffiHist(TH1* hist) {
+ effiHist_ = hist;
+ }
+
//! Retrieve the name of the error variable
const TString& varName() const {return varName_;}
//! Retrieve the name of the error variable
const TString& varErrName() const {return varErrName_;}
//! Turn on or off the resolution function
void doSmearing(Bool_t smear) {smear_ = smear;}
//! Determine if the resolution function is turned on or off
Bool_t doSmearing() const {return smear_;}
//! Cache information from data
/*!
Will cache, for every event, the abscissa values and, if all parameters are fixed, the PDF value.
\param [in] inputData the data to be used to calculate everything
*/
virtual void cacheInfo(const LauFitDataTree& inputData);
//! Calculate the likelihood (and all associated information) given value of the abscissa
/*!
\param [in] abscissa the value of the abscissa
*/
virtual void calcLikelihoodInfo(Double_t abscissa);
//! Calculate the likelihood (and all associated information) given value of the abscissa and its error
/*!
\param [in] abscissa the value of the abscissa
\param [in] abscissaErr the error on the abscissa
*/
virtual void calcLikelihoodInfo(Double_t abscissa, Double_t abscissaErr);
//! Retrieve the likelihood (and all associated information) given the event number
/*!
\param [in] iEvt the event number
*/
virtual void calcLikelihoodInfo(UInt_t iEvt);
//! Evaluate the complex error fonction
LauComplex ComplexErf(Double_t x, Double_t y);
//! Compute the imaginary error function: Erfi(z) = -I*Erf(iz)
LauComplex Erfi(Double_t x, Double_t y);
//! Compute the complementary complex error function
LauComplex ComplexErfc(Double_t x, Double_t y);
//! Get FuncType from model
FuncType getFuncType() const {return type_;}
//! Get the exponential term
Double_t getExpTerm() const {return expTerm_;}
//! Get the cos(Dm*t) term (multiplied by the exponential)
Double_t getCosTerm() const {return cosTerm_;}
//! Get the sin(Dm*t) term (multiplied by the exponential)
Double_t getSinTerm() const {return sinTerm_;}
//! Get the cosh(DG/2*t) term (multiplied by the exponential)
Double_t getCoshTerm() const {return coshTerm_;}
//! Get the sinh(DG/2*t) term (multiplied by the exponential)
Double_t getSinhTerm() const {return sinhTerm_;}
//! Get the normalisation related to the exponential term only
Double_t getNormTermExp() const {return normTermExp_;}
//! Get the first term in the normalisation (from integrating the cosh)
Double_t getNormTermCosh() const {return normTermCosh_;}
//! Get the second term in the normalisation (from integrating the sinh)
Double_t getNormTermSinh() const {return normTermSinh_;}
//! Get error probability density from Error distribution
Double_t getErrTerm() const{return errTerm_;}
//! Get efficiency probability density from efficiency distribution
Double_t getEffiTerm() const{return effiTerm_;}
- Double_t getEffiTermNorm() const{return effiTermN_;}
//! Retrieve the parameters of the PDF, e.g. so that they can be loaded into a fit
/*!
\return the parameters of the PDF
*/
const std::vector<LauAbsRValue*>& getParameters() const { return param_; }
//! Retrieve the parameters of the PDF, e.g. so that they can be loaded into a fit
/*!
\return the parameters of the PDF
*/
std::vector<LauAbsRValue*>& getParameters() { return param_; }
//! Update the pulls for all parameters
void updatePulls();
// Calculate the normalisation for the non smeared Hyperbolic terms
Double_t normExpHypTerm(Double_t Abs);
Double_t normExpHypTermDep(Double_t Abs);
// Store the normalisation decaytime cosh and sinh terms
void storeIntegralTimeDep(Double_t minAbs, Double_t maxAbs);
//! Generate the value of the error
/*!
If scaling by the error should call this before calling generate
\param [in] forceNew forces generation of a new value
*/
Double_t generateError(const Bool_t forceNew = kFALSE);
//! Generate an event from the PDF - TODO not clear that this is really needed, perhaps for background? commented out for now
/*!
\param [in] kinematics used by some PDFs to determine the DP position, on which they have dependence
*/
//virtual LauFitData generate(const LauKinematics* kinematics);
//! Determine the state of the calculation
State state() const {return state_;}
//! Retrieve the decay time error
Double_t abscissaError() const {return abscissaError_;}
//! Retrieve the decay time minimum value
Double_t minAbscissa() const {return minAbscissa_;}
//! Retrieve the decay time maximum value
Double_t maxAbscissa() const {return maxAbscissa_;}
//! Retrieve the decay time error minimum value
Double_t minAbscissaError() const {return minAbscissaError_;}
//! Retrieve the decay time error maximum value
Double_t maxAbscissaError() const {return maxAbscissaError_;}
virtual void checkPositiveness() {}; // Nothing to check here.
// NB calcNorm and calcPDFHeight only calculate the gaussian information for the (type_ == Delta) case
// TODO - can we delete these?
//! Calculate the normalisation factor of the PDF
//virtual void calcNorm();
//! Calculate the maximum height of the PDF
//virtual void calcPDFHeight( const LauKinematics* kinematics );
//! Get efficiency parameters to float in the fit
std::vector<LauParameter*>& getEffiPars() {return effiPars_;}
//! Update spline Y values when floating the decay time acceptance
/*!
\param [in] params the vector of LauParameters describing the Y values
*/
void updateEffiSpline(std::vector<LauParameter*> params);
protected:
typedef std::map< Int_t, LauParameter> LauTagCatParamMap;
//! Set up the initial state correctly - called by the constructors
void initialise();
//! Calculate the pure physics terms with no resolution function applied
void calcNonSmearedTerms(const Double_t abscissa);
inline void state(State s) {state_ = s;}
//! Calculate exponential auxiliary term for the convolution
void calcTrigExponent(Double_t deltaM, Double_t tau, Double_t x, Double_t sigma, Double_t& reTerm, Double_t& imTerm);
//! Calculate convolution between exponential*sin or cos with a Gaussian
void calcTrigConv(Double_t deltaM, Double_t tau, Double_t x, Double_t sigma, Double_t& reOutTerm, Double_t& imOutTerm, Bool_t trig);
//! Retrieve the number of PDF parameters
/*!
\return the number of PDF parameters
*/
UInt_t nParameters() const {return param_.size();}
//! Retrieve the specified parameter
/*!
\param [in] parName the parameter to retrieve
*/
LauAbsRValue* findParameter(const TString& parName);
//! Retrieve the specified parameter
/*!
\param [in] parName the parameter to retrieve
*/
const LauAbsRValue* findParameter(const TString& parName) const;
private:
//! Copy constructor (not implemented)
LauDecayTimePdf(const LauDecayTimePdf& other);
//! Copy assignment operator (not implemented)
LauDecayTimePdf& operator=(const LauDecayTimePdf& other);
//! Name of the variable
TString varName_;
//! Name of the error variable
TString varErrName_;
//! The parameters of the PDF
std::vector<LauAbsRValue*> param_;
//! Smear with the resolution model or not
Bool_t smear_;
//! The minimum value of the decay time
Double_t minAbscissa_;
//! The maximum value of the decay time
Double_t maxAbscissa_;
//! The minimum value of the decay time error
Double_t minAbscissaError_;
//! The maximum value of the decay time error
Double_t maxAbscissaError_;
//! The current value of the decay time error
Double_t abscissaError_;
//! Flag whether a value for the decay time error has been generated
Bool_t abscissaErrorGenerated_;
//! Value of the MPV of the Landau dist used to generate the Delta t error
Double_t errorDistMPV_;
//! Value of the width of the Landau dist used to generate the Delta t error
Double_t errorDistSigma_;
//! The number of gaussians in the resolution mode;
const UInt_t nGauss_;
// Parameters of the gaussian(s) that accounts for the resolution:
//! mean (offset) of each Gaussian in the resolution function
std::vector<LauAbsRValue*> mean_;
//! spread (sigma) of each Gaussian in the resolution function
std::vector<LauAbsRValue*> sigma_;
//! fraction of each Gaussian in the resolution function
std::vector<LauAbsRValue*> frac_;
// Parameters of the exponential: the mean life (tau) and the frequency of oscillation.
//! Lifetime parameter
LauAbsRValue* tau_;
//! Mass difference parameter
LauAbsRValue* deltaM_;
//! Width difference parameter
LauAbsRValue* deltaGamma_;
//! Parameter for the fraction of prompt events in DeltaExp
LauAbsRValue* fracPrompt_;
// Which type of Delta t PDF is this?
const FuncType type_;
// Which type of Delta t PDF is this?
const TimeMeasurementMethod method_;
+ // Which method for eff/dt input are we using?
+ const EfficiencyMethod effMethod_;
+
// Scale the mean and sigma by the per-event error on Delta t?
const std::vector<Bool_t> scaleMeans_;
const std::vector<Bool_t> scaleWidths_;
// The values of the Exp, ExpCos and ExpSin terms
// (NB the Delta terms, i.e. just the gaussian, are stored as the PDF value)
//! The exponential term
Double_t expTerm_;
//! The cos(Dm*t) term (multiplied by the exponential)
Double_t cosTerm_;
//! The sin(Dm*t) term (multiplied by the exponential)
Double_t sinTerm_;
//! The cosh(DG/2*t) term (multiplied by the exponential)
Double_t coshTerm_;
//! The sinh(DG/2*t) term (multiplied by the exponential)
Double_t sinhTerm_;
// Normalisation that is used in the amplitude independent of cosh/sinh term
Double_t normTermExp_;
//! The first term in the normalisation (from integrating the cosh)
Double_t normTermCosh_;
//! The second term in the normalisation (from integrating the sinh)
Double_t normTermSinh_;
//! Error Hist term
Double_t errTerm_;
//! Hist PDF term
Double_t pdfTerm_;
//! Efficiency PDF term
Double_t effiTerm_;
- Double_t effiTermN_;
//! The cache of the per-event errors on the decay time
std::vector<Double_t> abscissas_;
//! The cache of the per-event errors on the decay time
std::vector<Double_t> abscissaErrors_;
//! The cache of the exponential terms
std::vector<Double_t> expTerms_;
//! The cache of the exponential * cos(Dm*t) terms
std::vector<Double_t> cosTerms_;
//! The cache of the exponential * sin(Dm*t) terms
std::vector<Double_t> sinTerms_;
//! The cache of the exponential * cosh(DG/2*t) terms
std::vector<Double_t> coshTerms_;
//! The cache of the exponential * sinh(DG/2*t) terms
std::vector<Double_t> sinhTerms_;
//! The cache of the exponential normalisation
std::vector<Double_t> normTermsExp_;
//! The cache of the first term in the normalisation
std::vector<Double_t> normTermsCosh_;
//! The cache of the second term in the normalisation
std::vector<Double_t> normTermsSinh_;
// To be deleted once modified all the code
std::vector<Double_t> expNorms_;
// cache efficiency term
std::vector<Double_t> effiTerms_;
//! The state of the complex error function calculation
State state_;
//! Histogram PDF for abscissa error distribution
Lau1DHistPdf* errHist_;
//! Histogram PDF for abscissa distribution
Lau1DHistPdf* pdfHist_;
//! efficiency PDF in spline
Lau1DCubicSpline* effiFun_;
+ //! efficiency PDF as Histogram
+ TH1* effiHist_;
+
//! Vector of parameters to float acceptance
std::vector<LauParameter*> effiPars_;
ClassDef(LauDecayTimePdf,0) // Define the Delta t PDF
};
#endif
diff --git a/src/LauDecayTimePdf.cc b/src/LauDecayTimePdf.cc
index 343cbcb..54d3ee0 100644
--- a/src/LauDecayTimePdf.cc
+++ b/src/LauDecayTimePdf.cc
@@ -1,1142 +1,1141 @@
/*
Copyright 2006 University of Warwick
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
/*
Laura++ package authors:
John Back
Paul Harrison
Thomas Latham
*/
/*! \file LauDecayTimePdf.cc
\brief File containing implementation of LauDecayTimePdf class.
*/
#include <iostream>
#include <vector>
using std::cout;
using std::cerr;
using std::endl;
#include <complex>
using std::complex;
#include "TMath.h"
#include "TRandom.h"
#include "TSystem.h"
+#include "TH1.h"
#include "Lau1DCubicSpline.hh"
#include "Lau1DHistPdf.hh"
#include "LauConstants.hh"
#include "LauComplex.hh"
#include "LauDecayTimePdf.hh"
#include "LauFitDataTree.hh"
#include "LauParameter.hh"
#include "LauRandom.hh"
ClassImp(LauDecayTimePdf)
LauDecayTimePdf::LauDecayTimePdf(const TString& theVarName, const TString& theVarErrName, const std::vector<LauAbsRValue*>& params,
Double_t minAbscissaVal, Double_t maxAbscissaVal,
Double_t minAbscissaErr, Double_t maxAbscissaErr,
- FuncType type, UInt_t nGauss, const std::vector<Bool_t>& scale, TimeMeasurementMethod method) :
+ FuncType type, UInt_t nGauss, const std::vector<Bool_t>& scale, const TimeMeasurementMethod method, const EfficiencyMethod effMethod) :
varName_(theVarName),
varErrName_(theVarErrName),
param_(params),
smear_(kTRUE),
minAbscissa_(minAbscissaVal),
maxAbscissa_(maxAbscissaVal),
minAbscissaError_(minAbscissaErr),
maxAbscissaError_(maxAbscissaErr),
abscissaError_(0.0),
abscissaErrorGenerated_(kFALSE),
errorDistMPV_(0.230), // for signal 0.234, for qqbar 0.286
errorDistSigma_(0.075), // for signal 0.073, for qqbar 0.102
nGauss_(nGauss),
mean_(nGauss_,0),
sigma_(nGauss_,0),
frac_(nGauss_-1,0),
tau_(0),
deltaM_(0),
deltaGamma_(0),
fracPrompt_(0),
type_(type),
method_(method),
+ effMethod_(effMethod),
scaleMeans_(scale),
scaleWidths_(scale),
expTerm_(0.0),
cosTerm_(0.0),
sinTerm_(0.0),
coshTerm_(0.0),
sinhTerm_(0.0),
normTermExp_(0.0),
normTermCosh_(0.0),
normTermSinh_(0.0),
errTerm_(0.0),
pdfTerm_(0.0),
effiTerm_(0.0),
state_(Good),
- errHist_(0),
- pdfHist_(0),
- effiFun_(0),
+ errHist_(nullptr),
+ pdfHist_(nullptr),
+ effiFun_(nullptr),
+ effiHist_(nullptr),
effiPars_(0)
{
this->initialise();
// Calculate the integrals of the decay time independent of the t
this->storeIntegralTimeDep(minAbscissa_, maxAbscissa_);
}
LauDecayTimePdf::LauDecayTimePdf(const TString& theVarName, const TString& theVarErrName, const std::vector<LauAbsRValue*>& params,
Double_t minAbscissaVal, Double_t maxAbscissaVal,
Double_t minAbscissaErr, Double_t maxAbscissaErr,
- FuncType type, UInt_t nGauss, const std::vector<Bool_t>& scaleMeans, const std::vector<Bool_t>& scaleWidths, TimeMeasurementMethod method) :
+ FuncType type, UInt_t nGauss, const std::vector<Bool_t>& scaleMeans, const std::vector<Bool_t>& scaleWidths, const TimeMeasurementMethod method, const EfficiencyMethod effMethod) :
varName_(theVarName),
varErrName_(theVarErrName),
param_(params),
smear_(kTRUE),
minAbscissa_(minAbscissaVal),
maxAbscissa_(maxAbscissaVal),
minAbscissaError_(minAbscissaErr),
maxAbscissaError_(maxAbscissaErr),
abscissaError_(0.0),
abscissaErrorGenerated_(kFALSE),
errorDistMPV_(0.230), // for signal 0.234, for qqbar 0.286
errorDistSigma_(0.075), // for signal 0.073, for qqbar 0.102
nGauss_(nGauss),
mean_(nGauss_,0),
sigma_(nGauss_,0),
frac_(nGauss_-1,0),
tau_(0),
deltaM_(0),
deltaGamma_(0),
fracPrompt_(0),
type_(type),
method_(method),
+ effMethod_(effMethod),
scaleMeans_(scaleMeans),
scaleWidths_(scaleWidths),
expTerm_(0.0),
cosTerm_(0.0),
sinTerm_(0.0),
coshTerm_(0.0),
sinhTerm_(0.0),
normTermExp_(0.0),
normTermCosh_(0.0),
normTermSinh_(0.0),
errTerm_(0.0),
pdfTerm_(0.0),
effiTerm_(0.0),
state_(Good),
- errHist_(0),
- pdfHist_(0),
- effiFun_(0),
+ errHist_(nullptr),
+ pdfHist_(nullptr),
+ effiFun_(nullptr),
+ effiHist_(nullptr),
effiPars_(0)
{
this->initialise();
// Calculate the integrals of the decay time independent of the t
this->storeIntegralTimeDep(minAbscissa_, maxAbscissa_);
}
LauDecayTimePdf::~LauDecayTimePdf()
{
// Destructor
delete errHist_; errHist_ = 0;
delete pdfHist_; pdfHist_ = 0;
delete effiFun_; effiFun_ = 0;
}
void LauDecayTimePdf::initialise()
{
// The parameters are:
// - the mean and the sigma (bias and spread in resolution) of the gaussian(s)
// - the mean lifetime, denoted tau, of the exponential decay
// - the frequency of oscillation, denoted Delta m, of the cosine and sine terms
// - the decay width difference, denoted Delta Gamma, of the hyperbolic cosine and sine terms
//
// The next two arguments specify the range in which the PDF is defined,
// and the PDF will be normalised w.r.t. these limits.
//
// The final three arguments define the type of Delta t PDF (Delta, Exp, ExpTrig or ExpHypTrig ), the number of gaussians
// and whether or not the gaussian parameters should be scaled by the per-event errors on Delta t
// First check whether the scale vector is nGauss in size
if (nGauss_ != scaleMeans_.size() || nGauss_ != scaleWidths_.size()) {
cerr<<"ERROR in LauDecayTimePdf::initialise : scale vector size not the same as nGauss."<<endl;
gSystem->Exit(EXIT_FAILURE);
}
if (type_ == Hist){
if (this->nParameters() != 0){
cerr<<"ERROR in LauDecayTimePdf::initialise : Hist PDF should have 0 parameters"<<endl;
gSystem->Exit(EXIT_FAILURE);
}
}else{
TString meanName("mean_");
TString sigmaName("sigma_");
TString fracName("frac_");
Bool_t foundParams(kTRUE);
for (UInt_t i(0); i<nGauss_; ++i) {
TString tempName(meanName); tempName += i;
TString tempName2(sigmaName); tempName2 += i;
TString tempName3(fracName); tempName3 += i;
mean_[i] = this->findParameter(tempName);
foundParams &= (mean_[i] != 0);
sigma_[i] = this->findParameter(tempName2);
foundParams &= (sigma_[i] != 0);
if (i!=0) {
frac_[i-1] = this->findParameter(tempName3);
foundParams &= (frac_[i-1] != 0);
}
}
if (type_ == Delta) {
if ((this->nParameters() != (3*nGauss_-1)) || (!foundParams)) {
cerr<<"ERROR in LauDecayTimePdf::initialise : Delta type PDF requires:"<<endl;
cerr<<" - 2 parameters per Gaussian (i): \"mean_i\" and \"sigma_i\""<<endl;
cerr<<" - nGauss-1 fractions: \"frac_i\", where i!=0"<<endl;
gSystem->Exit(EXIT_FAILURE);
}
} else if (type_ == Exp) {
tau_ = this->findParameter("tau");
foundParams &= (tau_ != 0);
if ((this->nParameters() != (3*nGauss_-1+1)) || (!foundParams)) {
cerr<<"ERROR in LauDecayTimePdf::initialise : Exp type PDF requires:"<<endl;
cerr<<" - 2 parameters per Gaussian (i): \"mean_i\" and \"sigma_i\""<<endl;
cerr<<" - nGauss-1 fractions: \"frac_i\", where i!=0"<<endl;
cerr<<" - the lifetime of the exponential decay: \"tau\"."<<endl;
gSystem->Exit(EXIT_FAILURE);
}
} else if (type_ == ExpTrig) {
tau_ = this->findParameter("tau");
deltaM_ = this->findParameter("deltaM");
foundParams &= (tau_ != 0);
foundParams &= (deltaM_ != 0);
if ((this->nParameters() != (3*nGauss_-1+2)) || (!foundParams)) {
cerr<<"ERROR in LauDecayTimePdf::initialise : ExpTrig type PDF requires:"<<endl;
cerr<<" - 2 parameters per Gaussian (i): \"mean_i\" and \"sigma_i\""<<endl;
cerr<<" - nGauss-1 fractions: \"frac_i\", where i!=0"<<endl;
cerr<<" - the lifetime of the exponential decay: \"tau\""<<endl;
cerr<<" - the oscillation frequency: \"deltaM\"."<<endl;
gSystem->Exit(EXIT_FAILURE);
}
} else if (type_ == ExpHypTrig) {
tau_ = this->findParameter("tau");
deltaM_ = this->findParameter("deltaM");
deltaGamma_ = this->findParameter("deltaGamma");
foundParams &= (tau_ != 0);
foundParams &= (deltaM_ != 0);
foundParams &= (deltaGamma_ != 0);
if ((this->nParameters() != (3*nGauss_-1+3)) || (!foundParams)) {
cerr<<"ERROR in LauDecayTimePdf::initialise : ExpHypTrig type PDF requires:"<<endl;
cerr<<" - 2 parameters per Gaussian (i): \"mean_i\" and \"sigma_i\""<<endl;
cerr<<" - nGauss-1 fractions: \"frac_i\", where i!=0"<<endl;
cerr<<" - the lifetime of the exponential decay: \"tau\""<<endl;
cerr<<" - the oscillation frequency: \"deltaM\"."<<endl;
cerr<<" - the width difference: \"deltaGamma\"."<<endl;
gSystem->Exit(EXIT_FAILURE);
}
} else if (type_ == DeltaExp) {
tau_ = this->findParameter("tau");
fracPrompt_ = this->findParameter("frac_prompt");
foundParams &= (tau_ != 0);
foundParams &= (fracPrompt_ != 0);
if ((this->nParameters() != (3*nGauss_-1+2)) || (!foundParams)) {
cerr<<"ERROR in LauDecayTimePdf::initialise : DeltaExp type PDF requires:"<<endl;
cerr<<" - 2 parameters per Gaussian (i): \"mean_i\" and \"sigma_i\""<<endl;
cerr<<" - nGauss-1 fractions: \"frac_i\", where i!=0"<<endl;
cerr<<" - the lifetime of the exponential decay: \"tau\""<<endl;
cerr<<" - the fraction of the prompt part: \"frac_prompt\"."<<endl;
gSystem->Exit(EXIT_FAILURE);
}
} else if (type_ == SimFitNormBd) {
tau_ = this->findParameter("tau");
deltaM_ = this->findParameter("deltaM");
foundParams &= (tau_ != 0);
foundParams &= (deltaM_ != 0);
if ((this->nParameters() != (3*nGauss_-1+2)) || (!foundParams)) {
cerr<<"ERROR in LauDecayTimePdf::initialise : SimFitNormBd type PDF requires:"<<endl;
cerr<<" - 2 parameters per Gaussian (i): \"mean_i\" and \"sigma_i\""<<endl;
cerr<<" - nGauss-1 fractions: \"frac_i\", where i!=0"<<endl;
cerr<<" - the lifetime of the exponential decay: \"tau\""<<endl;
cerr<<" - the oscillation frequency: \"deltaM\"."<<endl;
gSystem->Exit(EXIT_FAILURE);
}
} else if (type_ == SimFitSigBd) {
tau_ = this->findParameter("tau");
deltaM_ = this->findParameter("deltaM");
foundParams &= (tau_ != 0);
foundParams &= (deltaM_ != 0);
if ((this->nParameters() != (3*nGauss_-1+2)) || (!foundParams)) {
cerr<<"ERROR in LauDecayTimePdf::initialise : SimFitSigBd type PDF requires:"<<endl;
cerr<<" - 2 parameters per Gaussian (i): \"mean_i\" and \"sigma_i\""<<endl;
cerr<<" - nGauss-1 fractions: \"frac_i\", where i!=0"<<endl;
cerr<<" - the lifetime of the exponential decay: \"tau\""<<endl;
cerr<<" - the oscillation frequency: \"deltaM\"."<<endl;
gSystem->Exit(EXIT_FAILURE);
}
} else if (type_ == SimFitNormBs) {
tau_ = this->findParameter("tau");
deltaM_ = this->findParameter("deltaM");
deltaGamma_ = this->findParameter("deltaGamma");
foundParams &= (tau_ != 0);
foundParams &= (deltaM_ != 0);
foundParams &= (deltaGamma_ != 0);
if ((this->nParameters() != (3*nGauss_-1+3)) || (!foundParams)) {
cerr<<"ERROR in LauDecayTimePdf::initialise : SimFitNormBs type PDF requires:"<<endl;
cerr<<" - 2 parameters per Gaussian (i): \"mean_i\" and \"sigma_i\""<<endl;
cerr<<" - nGauss-1 fractions: \"frac_i\", where i!=0"<<endl;
cerr<<" - the lifetime of the exponential decay: \"tau\""<<endl;
cerr<<" - the oscillation frequency: \"deltaM\"."<<endl;
cerr<<" - the width difference: \"deltaGamma\"."<<endl;
gSystem->Exit(EXIT_FAILURE);
}
} else if (type_ == SimFitSigBs) {
tau_ = this->findParameter("tau");
deltaM_ = this->findParameter("deltaM");
deltaGamma_ = this->findParameter("deltaGamma");
foundParams &= (tau_ != 0);
foundParams &= (deltaM_ != 0);
foundParams &= (deltaGamma_ != 0);
if ((this->nParameters() != (3*nGauss_-1+3)) || (!foundParams)) {
cerr<<"ERROR in LauDecayTimePdf::initialise : SimFitSigBs type PDF requires:"<<endl;
cerr<<" - 2 parameters per Gaussian (i): \"mean_i\" and \"sigma_i\""<<endl;
cerr<<" - nGauss-1 fractions: \"frac_i\", where i!=0"<<endl;
cerr<<" - the lifetime of the exponential decay: \"tau\""<<endl;
cerr<<" - the oscillation frequency: \"deltaM\"."<<endl;
cerr<<" - the width difference: \"deltaGamma\"."<<endl;
gSystem->Exit(EXIT_FAILURE);
}
}
}
// Cache the normalisation factor
//this->calcNorm();
}
void LauDecayTimePdf::cacheInfo(const LauFitDataTree& inputData)
{
Bool_t hasBranch = inputData.haveBranch(this->varName());
if (!hasBranch) {
cerr<<"ERROR in LauDecayTimePdf::cacheInfo : Input data does not contain variable \""<<this->varName()<<"\"."<<endl;
return;
}
hasBranch = inputData.haveBranch(this->varErrName());
if (!hasBranch) {
cerr<<"ERROR in LauDecayTimePdf::cacheInfo : Input data does not contain variable \""<<this->varErrName()<<"\"."<<endl;
return;
}
// Pass the data to the decay-time error PDF for caching
if ( errHist_ ) {
errHist_->cacheInfo(inputData);
}
if (type_ == Hist){
// Pass the data to the decay-time PDF for caching
if ( pdfHist_ ) {
pdfHist_->cacheInfo(inputData);
}
}else{
// determine whether we are caching our PDF value
//TODO
//Bool_t doCaching( this->nFixedParameters() == this->nParameters() );
//this->cachePDF( doCaching );
// clear the vectors and reserve enough space
const UInt_t nEvents = inputData.nEvents();
abscissas_.clear(); abscissas_.reserve(nEvents);
abscissaErrors_.clear(); abscissaErrors_.reserve(nEvents);
expTerms_.clear(); expTerms_.reserve(nEvents);
cosTerms_.clear(); cosTerms_.reserve(nEvents);
sinTerms_.clear(); sinTerms_.reserve(nEvents);
coshTerms_.clear(); coshTerms_.reserve(nEvents);
sinhTerms_.clear(); sinhTerms_.reserve(nEvents);
normTermsExp_.clear(); normTermsExp_.reserve(nEvents);
normTermsCosh_.clear(); normTermsCosh_.reserve(nEvents);
normTermsSinh_.clear(); normTermsSinh_.reserve(nEvents);
effiTerms_.clear(); effiTerms_.reserve(nEvents);
for (UInt_t iEvt = 0; iEvt < nEvents; iEvt++) {
const LauFitData& dataValues = inputData.getData(iEvt);
LauFitData::const_iterator iter = dataValues.find(this->varName());
const Double_t abscissa = iter->second;
if (abscissa > this->maxAbscissa() || abscissa < this->minAbscissa()) {
cerr<<"ERROR in LauDecayTimePdf::cacheInfo : Given value of the decay time: "<<abscissa<<
" outside allowed range: ["<<this->minAbscissa()<<","<<this->maxAbscissa()<<"]."<<endl;
gSystem->Exit(EXIT_FAILURE);
}
abscissas_.push_back( abscissa );
iter = dataValues.find(this->varErrName());
Double_t abscissaErr = iter->second;
if (abscissaErr > this->maxAbscissaError() || abscissaErr < this->minAbscissaError()) {
cerr<<"ERROR in LauDecayTimePdf::cacheInfo : Given value of the decay-time error: "<<abscissaErr<<
" outside allowed range: ["<<this->minAbscissaError()<<","<<this->maxAbscissaError()<<"]."<<endl;
gSystem->Exit(EXIT_FAILURE);
}
abscissaErrors_.push_back(abscissaErr);
this->calcLikelihoodInfo(abscissa, abscissaErr);
expTerms_.push_back(expTerm_);
cosTerms_.push_back(cosTerm_);
sinTerms_.push_back(sinTerm_);
coshTerms_.push_back(coshTerm_);
sinhTerms_.push_back(sinhTerm_);
normTermsExp_.push_back(normTermExp_);
normTermsCosh_.push_back(normTermCosh_);
normTermsSinh_.push_back(normTermSinh_);
-
- if(effiFun_) effiTerms_.push_back(effiFun_->evaluate(abscissa));
- else effiTerms_.push_back(1.0);
+ effiTerms_.push_back(effiTerm_);
}
}
}
void LauDecayTimePdf::calcLikelihoodInfo(UInt_t iEvt)
{
if (type_ == Hist){
if ( pdfHist_ ) {
pdfHist_->calcLikelihoodInfo(iEvt);
pdfTerm_ = pdfHist_->getLikelihood();
} else {
pdfTerm_ = 1.0;
}
}else{
expTerm_ = expTerms_[iEvt];
cosTerm_ = cosTerms_[iEvt];
sinTerm_ = sinTerms_[iEvt];
coshTerm_ = coshTerms_[iEvt];
sinhTerm_ = sinhTerms_[iEvt];
normTermExp_ = normTermsExp_[iEvt];
normTermCosh_ = normTermsCosh_[iEvt];
normTermSinh_ = normTermsSinh_[iEvt];
}
if ( errHist_ ) {
errHist_->calcLikelihoodInfo(iEvt);
errTerm_ = errHist_->getLikelihood();
} else {
errTerm_ = 1.0;
}
const Double_t abscissa = abscissas_[iEvt];
//Parameters will change in some cases update things!
if (type_ == SimFitNormBd || type_ == SimFitSigBd || type_ == SimFitNormBs || type_ == SimFitSigBs){
const Double_t abscissaErr = abscissaErrors_[iEvt];
this->calcLikelihoodInfo(abscissa,abscissaErr);
}
- if ( effiFun_ ) {
- this->updateEffiSpline(effiPars_);
- effiTerm_ = effiFun_->evaluate(abscissa);
- if (effiTerm_>1.0){effiTerm_=1.0;}
- if (effiTerm_<0.0){effiTerm_=0.0;}
- //effiTermN_ = effiFun_->getInt();
- effiTermN_ = 1.0;
- } else {
- effiTerm_ = 1.0;
- effiTermN_ = 1.0;
+ switch( effMethod_ ) /* < If you're going to add an effMethod, extend this switch*/
+ {
+ case EfficiencyMethod::Spline :
+ if ( effiFun_ ) {
+ this->updateEffiSpline(effiPars_);
+ effiTerm_ = effiFun_->evaluate(abscissa); //EDITED XXX
+ if (effiTerm_>1.0){effiTerm_=1.0;}
+ if (effiTerm_<0.0){effiTerm_=0.0;}
+ } else {
+ effiTerm_ = 1.0;
+ }
+ break;
+
+ default :
+ effiTerm_ = effiTerms_[iEvt];
+ break;
}
// TODO need a check in here that none of the floating parameter values have changed
// If they have, then we need to recalculate all or some of the terms
/*
if ( parsChanged ) {
const Double_t abscissa = abscissas_[iEvt][0];
const Double_t abscissaErr = abscissaErrors_[iEvt];
this->calcLikelihoodInfo(abscissa, abscissaErr);
}
*/
}
void LauDecayTimePdf::calcLikelihoodInfo(Double_t abscissa)
{
// Check whether any of the gaussians should be scaled - if any of them should we need the per-event error
Bool_t scale(kFALSE);
for (std::vector<Bool_t>::const_iterator iter = scaleMeans_.begin(); iter != scaleMeans_.end(); ++iter) {
scale |= (*iter);
}
for (std::vector<Bool_t>::const_iterator iter = scaleWidths_.begin(); iter != scaleWidths_.end(); ++iter) {
scale |= (*iter);
}
if (scale) {
cerr<<"ERROR in LauDecayTimePdf::calcLikelihoodInfo : Per-event error on Delta t not provided, cannot calculate anything."<<endl;
return;
} else {
this->calcLikelihoodInfo(abscissa, 0.0);
}
}
void LauDecayTimePdf::calcLikelihoodInfo(Double_t abscissa, Double_t abscissaErr)
{
if (abscissa > this->maxAbscissa() || abscissa < this->minAbscissa()) {
cerr<<"ERROR in LauDecayTimePdf::calcLikelihoodInfo : Given value of the decay time: "<<abscissa<<
" outside allowed range: ["<<this->minAbscissa()<<","<<this->maxAbscissa()<<"]."<<endl;
gSystem->Exit(EXIT_FAILURE);
}
if (abscissaErr > this->maxAbscissaError() || abscissaErr < this->minAbscissaError()) {
cerr<<"ERROR in LauDecayTimePdf::calcLikelihoodInfo : Given value of Delta t error: "<<abscissaErr<<
" outside allowed range: ["<<this->minAbscissaError()<<","<<this->maxAbscissaError()<<"]."<<endl;
gSystem->Exit(EXIT_FAILURE);
}
+
+ switch( effMethod_ )
+ {
+ case EfficiencyMethod::Spline : effiTerm_ = effiFun_ ? effiFun_ -> evaluate(abscissa) : 1.0 ; break;
+ case EfficiencyMethod::dtHist : effiTerm_ = effiHist_ ? effiHist_-> GetBinContent(effiHist_-> FindFixBin(abscissa)) : 1.0 ; break;
+ case EfficiencyMethod::Flat : effiTerm_ = 1.0 ; break;
+// default : cerr << "Warning: EFFICIENCY INPUT METHOD NOT SET" << endl; effiTerms_.push_back( 1.0 );
+ }
// Initialise the various terms to zero
if (type_ == Hist){
if ( pdfHist_ ) {
pdfHist_->calcLikelihoodInfo(abscissa);
pdfTerm_ = pdfHist_->getLikelihood();
} else {
pdfTerm_ = 1.0;
}
}else{
// Reset the state to Good
this->state(Good);
// If we're not using the resolution function calculate the simple terms and return
if (!this->doSmearing()) {
this->calcNonSmearedTerms(abscissa);
return;
}
//TODO how much to be added below for SimFitNormBd/SimFitNormBs/SimFitSigBd/SimFitSigBs
// Get all the up to date parameter values
std::vector<Double_t> frac(nGauss_);
std::vector<Double_t> mean(nGauss_);
std::vector<Double_t> sigma(nGauss_);
Double_t tau(0.0);
Double_t deltaM(0.0);
Double_t fracPrompt(0.0);
Double_t Delta_gamma(0.0);
frac[0] = 1.0;
for (UInt_t i(0); i<nGauss_; ++i) {
mean[i] = mean_[i]->value();
sigma[i] = sigma_[i]->value();
if (i != 0) {
frac[i] = frac_[i-1]->value();
frac[0] -= frac[i];
}
}
if (type_ != Delta) {
tau = tau_->value();
if (type_ == ExpTrig) {
deltaM = deltaM_->value();
}
if (type_ == DeltaExp) {
fracPrompt = fracPrompt_->value();
}
if (type_ == ExpHypTrig){
deltaM = deltaM_->value();
Delta_gamma = deltaGamma_->value();
}
}
// Scale the gaussian parameters by the per-event error on Delta t (if appropriate)
for (UInt_t i(0); i<nGauss_; ++i) {
if (scaleMeans_[i]) {
mean[i] *= abscissaErr;
}
if (scaleWidths_[i]) {
sigma[i] *= abscissaErr;
}
}
// Calculate term needed by every type
std::vector<Double_t> x(nGauss_);
const Double_t xMax = this->maxAbscissa();
const Double_t xMin = this->minAbscissa();
for (UInt_t i(0); i<nGauss_; ++i) {
x[i] = abscissa - mean[i];
}
// TODO, what to do with this
Double_t value(0.0);
if (type_ == Delta || type_ == DeltaExp) {
// Calculate the gaussian function(s)
for (UInt_t i(0); i<nGauss_; ++i) {
if (TMath::Abs(sigma[i]) > 1e-10) {
Double_t exponent(0.0);
Double_t norm(0.0);
Double_t scale = LauConstants::root2*sigma[i];
Double_t scale2 = LauConstants::rootPiBy2*sigma[i];
exponent = -0.5*x[i]*x[i]/(sigma[i]*sigma[i]);
norm = scale2*(TMath::Erf((xMax - mean[i])/scale)
- TMath::Erf((xMin - mean[i])/scale));
value += frac[i]*TMath::Exp(exponent)/norm;
}
}
}
if (type_ != Delta) {
std::vector<Double_t> expTerms(nGauss_);
std::vector<Double_t> cosTerms(nGauss_);
std::vector<Double_t> sinTerms(nGauss_);
std::vector<Double_t> coshTerms(nGauss_);
std::vector<Double_t> sinhTerms(nGauss_);
std::vector<Double_t> expTermsNorm(nGauss_);
// TODO - TEL changed this name to make it compile - please check!
std::vector<Double_t> SinhTermsNorm(nGauss_);
// Calculate values of the PDF convoluated with each Gaussian for a given value of the abscsissa
for (UInt_t i(0); i<nGauss_; ++i) {
// Typical case (1): B0/B0bar
if (type_ == ExpTrig) {
// LHCb convention, i.e. convolution evaluate between 0 and inf
if (method_ == DecayTime) {
// Exponential term
Double_t termExponent = (pow(sigma[i], 2) - 2.0 * tau * x[i])/(2.0 * pow(tau, 2));
Double_t termErfc = (pow(sigma[i], 2) - tau * x[i])/(LauConstants::root2 * tau * sigma[i]);
expTerms[i] = (1.0/2.0) * TMath::Exp(termExponent) * TMath::Erfc(termErfc);
Double_t exponentTermRe, exponentTermIm;
this->calcTrigExponent(deltaM, tau, x[i], sigma[i], exponentTermRe, exponentTermIm);
// Elements related to the trigonometric function, i.e. convolution of Exp*Sin or Cos with Gauss
Double_t sinTrigTermRe, sinTrigTermIm, cosTrigTermRe, cosTrigTermIm;
this->calcTrigConv(deltaM, tau, x[i], sigma[i], sinTrigTermRe, sinTrigTermIm, kFALSE);
this->calcTrigConv(deltaM, tau, x[i], sigma[i], cosTrigTermRe, cosTrigTermIm, kTRUE);
// Combining elements of the full pdf
LauComplex zExp(exponentTermRe, exponentTermIm);
LauComplex zTrigSin(sinTrigTermRe, sinTrigTermIm);
LauComplex zTrigCos(cosTrigTermRe, cosTrigTermIm);
LauComplex sinConv = zExp * zTrigSin;
LauComplex cosConv = zExp * zTrigCos;
sinConv.scale(1.0/4.0);
cosConv.scale(1.0/4.0);
// Cosine*Exp and Sine*Exp terms
cosTerms[i] = cosConv.re();
sinTerms[i] = sinConv.im();
// Normalisation
Double_t umax = xMax - mean[i];
Double_t umin = xMin - mean[i];
expTermsNorm[i] = (1.0/2.0) * tau * (-1.0 + TMath::Erf(umax/(LauConstants::root2 * sigma[i])) + TMath::Erfc(umin/(LauConstants::root2 * sigma[i])) +
TMath::Exp((pow(sigma[i], 2) - 2.0 * tau * (xMax + xMin - mean[i]))/(2.0 * pow(tau, 2))) *
(TMath::Exp(xMax/tau) * TMath::Erfc((pow(sigma[i], 2) - xMin)/(LauConstants::root2 * tau))) +
(TMath::Exp(xMin/tau) * TMath::Erfc((pow(sigma[i], 2) - xMax)/(LauConstants::root2 * tau))));
} else {
}
}
// Typical case (2): B0s/B0sbar
if (type_ == ExpHypTrig) {
// LHCb convention
if (method_ == DecayTime) {
// Convolution of Exp*cosh (Exp*sinh) with a gaussian
//Double_t OverallExpFactor = 0.25*TMath::Exp(-(x[i]-mean[i])*(x[i]-mean[i])/(2*sigma[i]*sigma[i]));
//Double_t ExpFirstTerm = TMath::Exp((2*(x[i]-mean[i])*tau+sigma[i]*sigma[i]*(-2+Delta_gamma*tau))*(2*(x[i]-mean[i])*tau+sigma[i]*sigma[i]*(-2+Delta_gamma*tau))/(8*sigma[i]*sigma[i]*tau*tau));
//Double_t ExpSecondTerm = TMath::Exp((2*(-x[i]+mean[i])*tau+sigma[i]*sigma[i]*(2+Delta_gamma*tau))*(2*(-x[i]+mean[i])*tau+sigma[i]*sigma[i]*(2+Delta_gamma*tau))/(8*sigma[i]*sigma[i]*tau*tau));
//Double_t ErfFirstTerm = TMath::Erf((2*(x[i]-mean[i])*tau+sigma[i]*sigma[i]*(-2+Delta_gamma*tau))/(2*TMath::Sqrt(2)*sigma[i]*tau));
//Double_t ErfSecondTerm = TMath::Erf((2*(-x[i]+mean[i])*tau+sigma[i]*sigma[i]*(2+Delta_gamma*tau))/(2*TMath::Sqrt(2)*sigma[i]*tau));
//Double_t sinhConv = OverallExpFactor*(ExpFirstTerm*(1+ErfFirstTerm) + ExpSecondTerm*(-1+ErfSecondTerm));
//Double_t coshConv = OverallExpFactor*(ExpFirstTerm*(1+ErfFirstTerm) - ExpSecondTerm*(-1+ErfSecondTerm));
//cosTerms[i] = sinhConv;
// sinTerms[i] = coshConv;
//TODO: check this formula and try to simplify it!
double OverallExpTerm_max = (1/(2*(-4 + Delta_gamma*Delta_gamma*tau*tau)))*tau*TMath::Exp(-0.5*Delta_gamma*(xMax + mean[i]) - xMax/tau);
double ErfTerm_max = -2*Delta_gamma*tau*TMath::Exp(0.5*Delta_gamma*(xMax+mean[i])+xMax/tau)*TMath::Erf((xMax-mean[i])/(TMath::Sqrt(2)*sigma[i]));
double ExpFirstTerm_max = TMath::Exp(xMax*Delta_gamma+(sigma[i]*sigma[i]*(-2 + Delta_gamma*tau)*(-2 + Delta_gamma*tau))/(8*tau*tau));
double ErfcFirstTerm_max = TMath::Erfc((2*(-xMax + mean[i])*tau + sigma[i]*sigma[i]*(2 - Delta_gamma*tau))/(2*TMath::Sqrt(2)*sigma[i]*tau));
double ExpSecondTerm_max = TMath::Exp(Delta_gamma*mean[i] + (sigma[i]*sigma[i]*(2 + Delta_gamma*tau)*(2 + Delta_gamma*tau))/(8*tau*tau));
double ErfcSecondTerm_max = TMath::Erfc((2*(-xMax + mean[i])*tau + sigma[i]*sigma[i]*(2 + Delta_gamma*tau))/(2*TMath::Sqrt(2)*sigma[i]*tau));
double MaxVal= OverallExpTerm_max*(ErfTerm_max + TMath::Exp(mean[i]/tau)*(ExpFirstTerm_max*(2+Delta_gamma*tau)* ErfcFirstTerm_max + ExpSecondTerm_max*(-2+Delta_gamma*tau)* ErfcSecondTerm_max));
double OverallExpTerm_min = (1/(2*(-4 + Delta_gamma*Delta_gamma*tau*tau)))*tau*TMath::Exp(-0.5*Delta_gamma*(xMin + mean[i]) - xMin/tau);
double ErfTerm_min = -2*Delta_gamma*tau*TMath::Exp(0.5*Delta_gamma*(xMin+mean[i])+xMin/tau)*TMath::Erf((xMin-mean[i])/(TMath::Sqrt(2)*sigma[i]));
double ExpFirstTerm_min = TMath::Exp(xMin*Delta_gamma+(sigma[i]*sigma[i]*(-2 + Delta_gamma*tau)*(-2 + Delta_gamma*tau))/(8*tau*tau));
double ErfcFirstTerm_min = TMath::Erfc((2*(-xMin + mean[i])*tau + sigma[i]*sigma[i]*(2 - Delta_gamma*tau))/(2*TMath::Sqrt(2)*sigma[i]*tau));
// TODO - TEL added this (currently identical to ExpSecondTerm_max) to get this to compile - please check!!
double ExpSecondTerm_min = TMath::Exp(Delta_gamma*mean[i] + (sigma[i]*sigma[i]*(2 + Delta_gamma*tau)*(2 + Delta_gamma*tau))/(8*tau*tau));
double ErfcSecondTerm_min = TMath::Erfc((2*(-xMin + mean[i])*tau + sigma[i]*sigma[i]*(2 + Delta_gamma*tau))/(2*TMath::Sqrt(2)*sigma[i]*tau));
double minVal= OverallExpTerm_min*(ErfTerm_min + TMath::Exp(mean[i]/tau)*(ExpFirstTerm_min*(2+Delta_gamma*tau)* ErfcFirstTerm_min + ExpSecondTerm_min*(-2+Delta_gamma*tau)* ErfcSecondTerm_min));
SinhTermsNorm[i] = MaxVal - minVal;
} else {
}
}
}
for (UInt_t i(0); i<nGauss_; ++i) {
expTerm_ += frac[i]*expTerms[i];
cosTerm_ += frac[i]*cosTerms[i];
sinTerm_ += frac[i]*sinTerms[i];
coshTerm_ += frac[i]*coshTerms[i];
sinhTerm_ += frac[i]*sinhTerms[i];
normTermExp_ += frac[i]*expTermsNorm[i];
//normTermSinh_ += frac[i]*SinhTermsNorm[i];
}
if (type_ == DeltaExp) {
value *= fracPrompt;
value += (1.0-fracPrompt)*expTerm_;
} else {
value = expTerm_;
}
}
}
if ( errHist_ ) {
errHist_->calcLikelihoodInfo(abscissaErr);
errTerm_ = errHist_->getLikelihood();
} else {
errTerm_ = 1.0;
}
-
- if ( effiFun_ ) {
- this->updateEffiSpline(effiPars_);
- effiTerm_ = effiFun_->evaluate(abscissa);
- if (effiTerm_>1.0){effiTerm_=1.0;}
- if (effiTerm_<0.0){effiTerm_=0.0;}
- } else {
- effiTerm_ = 1.0;
- }
}
void LauDecayTimePdf::calcTrigExponent(Double_t deltaM, Double_t tau, Double_t x, Double_t sigma, Double_t& reTerm, Double_t& imTerm)
{
Double_t exponentTerm = TMath::Exp(-(2.0 * tau * x + pow(sigma, 2) * (pow(deltaM, 2) * pow(tau, 2) - 1.0))/(2.0 * pow(tau,2)));
reTerm = exponentTerm * TMath::Cos(deltaM * (x - pow(sigma,2)/tau));
imTerm = - exponentTerm * TMath::Sin(deltaM * (x - pow(sigma,2)/tau));
}
void LauDecayTimePdf::calcTrigConv(Double_t deltaM, Double_t tau, Double_t x, Double_t sigma, Double_t& reOutTerm, Double_t& imOutTerm, Bool_t trig)
{
Double_t reExpTerm, imExpTerm;
LauComplex zExp;
LauComplex zTrig1;
LauComplex zTrig2;
// Calculation for the sine or cosine term
if (!trig) {
reExpTerm = TMath::Sin(2.0 * deltaM * (x + pow(sigma,2)/tau));
imExpTerm = 2.0 * TMath::Sin(pow(deltaM * (x + pow(sigma,2)/tau), 2));
} else {
reExpTerm = TMath::Cos(2.0 * deltaM * (x + pow(sigma,2)/tau));
imExpTerm = TMath::Sin(2.0 * deltaM * (x + pow(sigma,2)/tau));
}
// Exponential term in front of Erfc/Erfi terms
zExp.setRealPart(reExpTerm);
zExp.setImagPart(imExpTerm);
// Nominal Erfc term (common to both sine and cosine expressions
zTrig1.setRealPart(-(tau * x - pow(sigma,2))/(LauConstants::root2 * tau * sigma));
zTrig1.setImagPart(-(deltaM * sigma)/ LauConstants::root2);
// Second term for sine (Erfi) or cosine (Erfc) - notice the re-im swap and sign change
zTrig2.setRealPart(-zTrig1.im());
zTrig2.setImagPart(-zTrig1.re());
// Calculation of Erfc and Erfi (if necessary)
LauComplex term1 = ComplexErfc(zTrig1.re(), zTrig1.im());
LauComplex term2;
if (!trig) {
term2 = Erfi(zTrig2.re(), zTrig2.im());
} else {
term2 = ComplexErfc(zTrig2.re(), zTrig2.im());
}
// Multiplying all elemnets of the convolution
LauComplex output = zExp * term1 + term2;
reOutTerm = output.re();
imOutTerm = output.im();
}
LauComplex LauDecayTimePdf::ComplexErf(Double_t x, Double_t y)
{
// Evaluate Erf(x + iy) using an infinite series approximation
// From Abramowitz & Stegun (http://people.math.sfu.ca/~cbm/aands/page_299.htm)
if (x==0){
// cout << "WARNING: Set x value to 1e-100 to avoid division by 0." << endl;
x = 1e-100;
}
int n = 20; // this cotrols the number of iterations of the sum
LauComplex ErfTerm(TMath::Erf(x),0.);
LauComplex CosSineTerm(1-cos(2*x*y), sin(2*x*y));
CosSineTerm.rescale(TMath::Exp(-x*x)/(2*TMath::Pi()*x));
LauComplex firstPart = ErfTerm + CosSineTerm;
LauComplex SumTerm(0,0);
for (int k = 1; k<=n; k++){
Double_t f_k = 2*x*(1 - cos(2*x*y)*cosh(k*y)) + k*sin(2*x*y)*sinh(k*y);
Double_t g_k = 2*x*sin(2*x*y)*cosh(k*y) + k*cos(2*x*y)*sinh(k*y);
LauComplex fgTerm(f_k, g_k);
fgTerm.rescale(TMath::Exp(-0.25*k*k)/(k*k + 4*x*x));
SumTerm += fgTerm;
}
SumTerm.rescale((2/TMath::Pi())*TMath::Exp(-x*x));
LauComplex result = firstPart + SumTerm;
return result;
}
LauComplex LauDecayTimePdf::Erfi(Double_t x, Double_t y)
{
// Erfi(z) = -I*Erf(I*z) where z = x + iy
double x_prime = -y;
double y_prime = x;
LauComplex a = ComplexErf(x_prime, y_prime);
LauComplex result(a.im(), -a.re());
return result;
}
LauComplex LauDecayTimePdf::ComplexErfc(Double_t x, Double_t y)
{
// Erfc(z) = 1 - Erf(z) (z = x + iy)
LauComplex one(1., 0.);
LauComplex result = one - ComplexErf(x,y);
return result;
}
void LauDecayTimePdf::calcNonSmearedTerms(Double_t abscissa)
{
if (type_ == Hist ){
cerr << "It is a histogrammed PDF" << endl;
return;
}
if (type_ == Delta) {
return;
}
Double_t tau = tau_->value();
Double_t deltaM = deltaM_->value();
// Calculate the terms related to cosine and sine not normalised
if (type_ == ExpTrig) {
if (method_ == DecayTime) {
expTerm_ = TMath::Exp(-abscissa/tau)/(2.0*tau);
}
if (method_ == DecayTimeDiff) {
expTerm_ = TMath::Exp(-TMath::Abs(abscissa)/tau)/(2.0*tau);
}
cosTerm_ = TMath::Cos(deltaM*abscissa)*expTerm_;
sinTerm_ = TMath::Sin(deltaM*abscissa)*expTerm_;
coshTerm_ = expTerm_;
sinhTerm_ = 0.0;
}
// Calculate the terms related to cosine not normalised
if (type_ == SimFitNormBd || type_ == SimFitNormBs) {
if (method_ == DecayTime) {
expTerm_ = TMath::Exp(-abscissa/tau)/(2.0*tau);
}
if (method_ == DecayTimeDiff) {
expTerm_ = TMath::Exp(-TMath::Abs(abscissa)/tau)/(2.0*tau);
}
cosTerm_ = TMath::Cos(deltaM*abscissa)*expTerm_;
sinTerm_ = 0.0;
coshTerm_ = expTerm_;
sinhTerm_ = 0.0;
if (type_ == SimFitNormBs){
Double_t deltaGamma = deltaGamma_->value();
coshTerm_ *= TMath::CosH(deltaGamma*abscissa/2.0);
}
}
// Calculate the terms related to cosine and sine not normalised
if (type_ == SimFitSigBd || type_ == SimFitSigBs) {
if (method_ == DecayTime) {
expTerm_ = TMath::Exp(-abscissa/tau)/(2.0*tau);
}
if (method_ == DecayTimeDiff) {
expTerm_ = TMath::Exp(-TMath::Abs(abscissa)/tau)/(2.0*tau);
}
cosTerm_ = TMath::Cos(deltaM*abscissa)*expTerm_;
sinTerm_ = TMath::Sin(deltaM*abscissa)*expTerm_;
coshTerm_ = expTerm_;
sinhTerm_ = 0.0;
if (type_ == SimFitNormBs){
Double_t deltaGamma = deltaGamma_->value();
coshTerm_ *= TMath::CosH(deltaGamma*abscissa/2.0);
sinhTerm_ = TMath::SinH(deltaGamma*abscissa/2.0)*expTerm_;
}
}
// Calculate the terms related to cosine, sine, cosh and sinh not normalised (no decayTimeDiff implemented)
if (type_ == ExpHypTrig) {
Double_t deltaGamma = deltaGamma_->value();
expTerm_ = TMath::Exp(-abscissa/tau)/(2.0*tau);
cosTerm_ = TMath::Cos(deltaM*abscissa)*expTerm_;
sinTerm_ = TMath::Sin(deltaM*abscissa)*expTerm_;
coshTerm_ = TMath::CosH(deltaGamma*abscissa/2.0)*expTerm_;
sinhTerm_ = TMath::SinH(deltaGamma*abscissa/2.0)*expTerm_;
}
-
- if ( effiFun_ ) {
- this->updateEffiSpline(effiPars_);
- effiTerm_ = effiFun_->evaluate(abscissa);
- if (effiTerm_>1.0){effiTerm_=1.0;}
- if (effiTerm_<0.0){effiTerm_=0.0;}
- } else {
- effiTerm_ = 1.0;
- }
}
Double_t LauDecayTimePdf::normExpHypTerm(Double_t Abs)
{
Double_t tau = tau_->value();
Double_t deltaGamma = deltaGamma_->value();
Double_t y = tau*deltaGamma/2;
Double_t nonTrigTerm = -(TMath::Exp(-Abs/tau))/(1 - y*y);
Double_t cosHTerm = TMath::CosH(deltaGamma*Abs/2);
Double_t sinHTerm = TMath::SinH(deltaGamma*Abs/2);
Double_t normTerm = nonTrigTerm*(cosHTerm + y*sinHTerm);
return normTerm;
}
Double_t LauDecayTimePdf::normExpHypTermDep(Double_t Abs)
{
Double_t tau = tau_->value();
Double_t deltaGamma = deltaGamma_->value();
Double_t y = tau*deltaGamma/2;
Double_t nonTrigTerm = -(TMath::Exp(-Abs/tau))/(1 - y*y);
Double_t cosHTerm = TMath::CosH(deltaGamma*Abs/2);
Double_t sinHTerm = TMath::SinH(deltaGamma*Abs/2);
Double_t normTerm = nonTrigTerm*(sinHTerm + y*cosHTerm);
return normTerm;
}
void LauDecayTimePdf::storeIntegralTimeDep(Double_t minAbs, Double_t maxAbs)
{
Double_t tau = tau_->value();
// Normalisation factor for B0 decays
if (type_ == ExpTrig || type_ == SimFitNormBd || type_ == SimFitSigBd ) {
if (method_ == DecayTime) {
normTermExp_ = tau * (TMath::Exp(-minAbs/tau) - TMath::Exp(-maxAbs/tau));
}
if (method_ == DecayTimeDiff) {
normTermExp_ = tau * (2.0 - TMath::Exp(-maxAbs/tau) - TMath::Exp(-minAbs/tau));
}
}
// Normalisation factor for Bs decays
if (type_ == ExpHypTrig) {
normTermCosh_ = normExpHypTerm(maxAbs) - normExpHypTerm(minAbs);
normTermSinh_ = normExpHypTermDep( maxAbs) - normExpHypTermDep( minAbs);
}
}
Double_t LauDecayTimePdf::generateError(Bool_t forceNew)
{
if (errHist_ && (forceNew || !abscissaErrorGenerated_)) {
LauFitData errData = errHist_->generate(0);
abscissaError_ = errData.find(this->varErrName())->second;
abscissaErrorGenerated_ = kTRUE;
} else {
while (forceNew || !abscissaErrorGenerated_) {
abscissaError_ = LauRandom::randomFun()->Landau(errorDistMPV_,errorDistSigma_);
if (abscissaError_ < maxAbscissaError_ && abscissaError_ > minAbscissaError_) {
abscissaErrorGenerated_ = kTRUE;
forceNew = kFALSE;
}
}
}
return abscissaError_;
}
/*
LauFitData LauDecayTimePdf::generate(const LauKinematics* kinematics)
{
// generateError SHOULD have been called before this
// function but will call it here just to make sure
// (has ns effect if has already been called)
abscissaError_ = this->generateError();
// If the PDF is scaled by the per-event error then need to update the PDF height for each event
Bool_t scale(kFALSE);
for (std::vector<Bool_t>::const_iterator iter = scaleMeans_.begin(); iter != scaleMeans_.end(); ++iter) {
scale |= (*iter);
}
for (std::vector<Bool_t>::const_iterator iter = scaleWidths_.begin(); iter != scaleWidths_.end(); ++iter) {
scale |= (*iter);
}
if (scale || (!this->heightUpToDate() && !this->cachePDF())) {
this->calcPDFHeight(kinematics);
this->heightUpToDate(kTRUE);
}
// Generate the value of the abscissa.
Bool_t gotAbscissa(kFALSE);
Double_t genVal(0.0);
Double_t genPDFVal(0.0);
LauFitData genAbscissa;
const Double_t xMin = this->minAbscissa();
const Double_t xMax = this->maxAbscissa();
const Double_t xRange = xMax - xMin;
while (!gotAbscissa) {
genVal = LauRandom::randomFun()->Rndm()*xRange + xMin;
this->calcLikelihoodInfo(genVal, abscissaError_);
genPDFVal = this->getUnNormLikelihood();
if (LauRandom::randomFun()->Rndm() <= genPDFVal/this->getMaxHeight()) {gotAbscissa = kTRUE;}
if (genPDFVal > this->getMaxHeight()) {
cerr<<"Warning in LauDecayTimePdf::generate()."
<<" genPDFVal = "<<genPDFVal<<" is larger than the specified PDF height "
<<this->getMaxHeight()<<" for the abscissa = "<<genVal<<". Need to reset height to be larger than "
<<genPDFVal<<" by using the setMaxHeight(Double_t) function"
<<" and re-run the Monte Carlo generation!"<<endl;
}
}
genAbscissa[this->varName()] = genVal;
// mark that we need a new error to be generated next time
abscissaErrorGenerated_ = kFALSE;
return genAbscissa;
}
*/
void LauDecayTimePdf::setErrorHisto(const TH1* hist)
{
if ( errHist_ != 0 ) {
cerr<<"WARNING in LauDecayTimePdf::setErrorHisto : Error histogram already set, not doing it again."<<endl;
return;
}
errHist_ = new Lau1DHistPdf(this->varErrName(), hist, this->minAbscissaError(), this->maxAbscissaError());
}
void LauDecayTimePdf::setHistoPdf(const TH1* hist)
{
if ( pdfHist_ != 0 ) {
cerr<<"WARNING in LauDecayTimePdf::setHistoPdf : PDF histogram already set, not doing it again."<<endl;
return;
}
pdfHist_ = new Lau1DHistPdf(this->varName(), hist, this->minAbscissa(), this->maxAbscissa());
}
void LauDecayTimePdf::setEffiSpline(Lau1DCubicSpline* spline)
{
if ( effiFun_ != 0 ) {
cerr<<"WARNING in LauDecayTimePdf::setEffiPdf : efficiency function already set, not doing it again."<<endl;
return;
}
effiFun_ = spline;
}
void LauDecayTimePdf::setEffiSpline(Lau1DCubicSpline* spline, std::vector<LauParameter*> effiPars)
{
if ( effiFun_ != 0 ) {
cerr<<"WARNING in LauDecayTimePdf::setEffiPdf : efficiency function already set, not doing it again."<<endl;
return;
}
effiFun_ = spline;
if (effiPars.size() != spline->getnKnots()){
cerr<<"ERROR in LauDecayTimePdf::setEffiPdf : number of efficiency parameters is not equal to the number of spline knots."<<endl;
gSystem->Exit(EXIT_FAILURE);
}
effiPars_ = effiPars;
}
LauAbsRValue* LauDecayTimePdf::findParameter(const TString& parName)
{
for ( std::vector<LauAbsRValue*>::iterator iter = param_.begin(); iter != param_.end(); ++iter ) {
if ((*iter)->name().Contains(parName)) {
return (*iter);
}
}
std::cerr << "ERROR in LauDecayTimePdf::findParameter : Parameter \"" << parName << "\" not found." << std::endl;
return 0;
}
const LauAbsRValue* LauDecayTimePdf::findParameter(const TString& parName) const
{
for ( std::vector<LauAbsRValue*>::const_iterator iter = param_.begin(); iter != param_.end(); ++iter ) {
if ((*iter)->name().Contains(parName)) {
return (*iter);
}
}
std::cerr << "ERROR in LauDecayTimePdf::findParameter : Parameter \"" << parName << "\" not found." << std::endl;
return 0;
}
void LauDecayTimePdf::updatePulls()
{
for ( std::vector<LauAbsRValue*>::iterator iter = param_.begin(); iter != param_.end(); ++iter ) {
std::vector<LauParameter*> params = (*iter)->getPars();
for (std::vector<LauParameter*>::iterator params_iter = params.begin(); params_iter != params.end(); ++params_iter ) {
if (!(*iter)->fixed()) {
(*params_iter)->updatePull();
}
}
}
}
void LauDecayTimePdf::updateEffiSpline(std::vector<LauParameter*> effiPars)
{
if (effiPars.size() != effiFun_->getnKnots()){
cerr<<"ERROR in LauDecayTimePdf::updateEffiSpline : number of efficiency parameters is not equal to the number of spline knots."<<endl;
gSystem->Exit(EXIT_FAILURE);
}
effiFun_->updateYValues(effiPars);
}
diff --git a/src/LauTimeDepFitModel.cc b/src/LauTimeDepFitModel.cc
index b9afabe..01c6714 100644
--- a/src/LauTimeDepFitModel.cc
+++ b/src/LauTimeDepFitModel.cc
@@ -1,2979 +1,2979 @@
/*
Copyright 2006 University of Warwick
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
/*
Laura++ package authors:
John Back
Paul Harrison
Thomas Latham
*/
/*! \file LauTimeDepFitModel.cc
\brief File containing implementation of LauTimeDepFitModel class.
*/
#include <iostream>
#include <iomanip>
#include <fstream>
#include <map>
#include <vector>
#include "TFile.h"
#include "TMinuit.h"
#include "TRandom.h"
#include "TSystem.h"
#include "TVirtualFitter.h"
#include "LauAbsBkgndDPModel.hh"
#include "LauAbsCoeffSet.hh"
#include "LauAbsPdf.hh"
#include "LauAsymmCalc.hh"
#include "LauComplex.hh"
#include "LauConstants.hh"
#include "LauDPPartialIntegralInfo.hh"
#include "LauDaughters.hh"
#include "LauDecayTimePdf.hh"
#include "LauFitNtuple.hh"
#include "LauGenNtuple.hh"
#include "LauIsobarDynamics.hh"
#include "LauKinematics.hh"
#include "LauPrint.hh"
#include "LauRandom.hh"
#include "LauScfMap.hh"
#include "LauTimeDepFitModel.hh"
#include "LauFlavTag.hh"
ClassImp(LauTimeDepFitModel)
LauTimeDepFitModel::LauTimeDepFitModel(LauIsobarDynamics* modelB0bar, LauIsobarDynamics* modelB0, LauFlavTag* flavTag) : LauAbsFitModel(),
sigModelB0bar_(modelB0bar),
sigModelB0_(modelB0),
kinematicsB0bar_(modelB0bar ? modelB0bar->getKinematics() : 0),
kinematicsB0_(modelB0 ? modelB0->getKinematics() : 0),
usingBkgnd_(kFALSE),
flavTag_(flavTag),
nSigComp_(0),
nSigDPPar_(0),
nDecayTimePar_(0),
nExtraPdfPar_(0),
nNormPar_(0),
nCalibPar_(0),
nEffiPar_(0),
nAsymPar_(0),
nTagAsymPar_(0),
coeffsB0bar_(0),
coeffsB0_(0),
coeffPars_(0),
fitFracB0bar_(0),
fitFracB0_(0),
fitFracAsymm_(0),
acp_(0),
meanEffB0bar_("meanEffB0bar",0.0,0.0,1.0),
meanEffB0_("meanEffB0",0.0,0.0,1.0),
DPRateB0bar_("DPRateB0bar",0.0,0.0,100.0),
DPRateB0_("DPRateB0",0.0,0.0,100.0),
signalEvents_(0),
signalAsym_(0),
cpevVarName_(""),
cpEigenValue_(CPEven),
evtCPEigenVals_(0),
deltaM_("deltaM",0.0),
deltaGamma_("deltaGamma",0.0),
tau_("tau",LauConstants::tauB0),
phiMix_("phiMix", 2.0*LauConstants::beta, -LauConstants::threePi, LauConstants::threePi, kFALSE),
sinPhiMix_("sinPhiMix", TMath::Sin(2.0*LauConstants::beta), -1.0, 1.0, kFALSE),
cosPhiMix_("cosPhiMix", TMath::Cos(2.0*LauConstants::beta), -1.0, 1.0, kFALSE),
useSinCos_(kFALSE),
phiMixComplex_(TMath::Cos(-2.0*LauConstants::beta),TMath::Sin(-2.0*LauConstants::beta)),
signalDecayTimePdfs_(),
backgroundDecayTimePdfs_(),
curEvtDecayTime_(0.0),
curEvtDecayTimeErr_(0.0),
sigExtraPdf_(),
sigFlavTagPdf_(),
bkgdFlavTagPdf_(),
AProd_("AProd",0.0,-1.0,1.0,kTRUE),
ARaw_("ARaw",1.0,0.0,2.0,kTRUE),
iterationsMax_(500000),
nGenLoop_(0),
ASq_(0.0),
aSqMaxVar_(0.0),
aSqMaxSet_(1.25),
storeGenAmpInfo_(kFALSE),
signalTree_(),
reuseSignal_(kFALSE),
sigDPLike_(0.0),
sigExtraLike_(0.0),
sigFlavTagLike_(0.0),
bkgdFlavTagLike_(0.0),
sigTotalLike_(0.0)
{
// Set up ftag here?
// Make sure that the integration scheme will be symmetrised
sigModelB0bar_->forceSymmetriseIntegration(kTRUE);
sigModelB0_->forceSymmetriseIntegration(kTRUE);
}
LauTimeDepFitModel::~LauTimeDepFitModel()
{
for (LauTagCatEmbDataMap::iterator iter = signalTree_.begin(); iter != signalTree_.end(); ++iter){
delete iter->second;
}
for (LauTagCatEmbDataMapList::iterator iterlist = bkgndTree_.begin(); iterlist != bkgndTree_.end(); ++iterlist){
for (LauTagCatEmbDataMap::iterator iter = (*iterlist).begin(); iter != (*iterlist).end(); ++iter){
delete iter->second;
}
}
}
void LauTimeDepFitModel::setupBkgndVectors()
{
UInt_t nBkgnds = this->nBkgndClasses();
BkgndDPModels_.resize( nBkgnds );
BkgndPdfs_.resize( nBkgnds );
bkgndEvents_.resize( nBkgnds );
bkgndAsym_.resize( nBkgnds );
bkgndTree_.resize( nBkgnds );
reuseBkgnd_.resize( nBkgnds );
bkgndDPLike_.resize( nBkgnds );
bkgndExtraLike_.resize( nBkgnds );
bkgndTotalLike_.resize( nBkgnds );
}
void LauTimeDepFitModel::setNSigEvents(LauParameter* nSigEvents)
{
if ( nSigEvents == 0 ) {
std::cerr << "ERROR in LauTimeDepFitModel::setNSigEvents : The LauParameter pointer is null." << std::endl;
gSystem->Exit(EXIT_FAILURE);
}
if ( signalEvents_ != 0 ) {
std::cerr << "ERROR in LauTimeDepFitModel::setNSigEvents : You are trying to overwrite the signal yield." << std::endl;
return;
}
if ( signalAsym_ != 0 ) {
std::cerr << "ERROR in LauTimeDepFitModel::setNSigEvents : You are trying to overwrite the signal asymmetry." << std::endl;
return;
}
signalEvents_ = nSigEvents;
signalEvents_->name("signalEvents");
Double_t value = nSigEvents->value();
signalEvents_->range(-2.0*(TMath::Abs(value)+1.0),2.0*(TMath::Abs(value)+1.0));
signalAsym_ = new LauParameter("signalAsym",0.0,-1.0,1.0,kTRUE);
}
void LauTimeDepFitModel::setNSigEvents(LauParameter* nSigEvents, LauParameter* sigAsym)
{
if ( nSigEvents == 0 ) {
std::cerr << "ERROR in LauTimeDepFitModel::setNSigEvents : The event LauParameter pointer is null." << std::endl;
gSystem->Exit(EXIT_FAILURE);
}
if ( sigAsym == 0 ) {
std::cerr << "ERROR in LauTimeDepFitModel::setNSigEvents : The asym LauParameter pointer is null." << std::endl;
gSystem->Exit(EXIT_FAILURE);
}
if ( signalEvents_ != 0 ) {
std::cerr << "ERROR in LauTimeDepFitModel::setNSigEvents : You are trying to overwrite the signal yield." << std::endl;
return;
}
if ( signalAsym_ != 0 ) {
std::cerr << "ERROR in LauTimeDepFitModel::setNSigEvents : You are trying to overwrite the signal asymmetry." << std::endl;
return;
}
signalEvents_ = nSigEvents;
signalEvents_->name("signalEvents");
Double_t value = nSigEvents->value();
signalEvents_->range(-2.0*(TMath::Abs(value)+1.0), 2.0*(TMath::Abs(value)+1.0));
signalAsym_ = sigAsym;
signalAsym_->name("signalAsym");
signalAsym_->range(-1.0,1.0);
}
void LauTimeDepFitModel::setNBkgndEvents(LauAbsRValue* nBkgndEvents)
{
if ( nBkgndEvents == 0 ) {
std::cerr << "ERROR in LauTimeDepFitModel::setNBgkndEvents : The background yield LauParameter pointer is null." << std::endl;
gSystem->Exit(EXIT_FAILURE);
}
if ( ! this->validBkgndClass( nBkgndEvents->name() ) ) {
std::cerr << "ERROR in LauTimeDepFitModel::setNBkgndEvents : Invalid background class \"" << nBkgndEvents->name() << "\"." << std::endl;
std::cerr << " : Background class names must be provided in \"setBkgndClassNames\" before any other background-related actions can be performed." << std::endl;
gSystem->Exit(EXIT_FAILURE);
}
UInt_t bkgndID = this->bkgndClassID( nBkgndEvents->name() );
if ( bkgndEvents_[bkgndID] != 0 ) {
std::cerr << "ERROR in LauTimeDepFitModel::setNBkgndEvents : You are trying to overwrite the background yield." << std::endl;
return;
}
if ( bkgndAsym_[bkgndID] != 0 ) {
std::cerr << "ERROR in LauTimeDepFitModel::setNBkgndEvents : You are trying to overwrite the background asymmetry." << std::endl;
return;
}
nBkgndEvents->name( nBkgndEvents->name()+"Events" );
if ( nBkgndEvents->isLValue() ) {
Double_t value = nBkgndEvents->value();
LauParameter* yield = dynamic_cast<LauParameter*>( nBkgndEvents );
yield->range(-2.0*(TMath::Abs(value)+1.0), 2.0*(TMath::Abs(value)+1.0));
}
bkgndEvents_[bkgndID] = nBkgndEvents;
bkgndAsym_[bkgndID] = new LauParameter(nBkgndEvents->name()+"Asym",0.0,-1.0,1.0,kTRUE);
}
void LauTimeDepFitModel::setNBkgndEvents(LauAbsRValue* nBkgndEvents, LauAbsRValue* bkgndAsym)
{
if ( nBkgndEvents == 0 ) {
std::cerr << "ERROR in LauTimeDepFitModel::setNBkgndEvents : The background yield LauParameter pointer is null." << std::endl;
gSystem->Exit(EXIT_FAILURE);
}
if ( bkgndAsym == 0 ) {
std::cerr << "ERROR in LauTimeDepFitModel::setNBkgndEvents : The background asym LauParameter pointer is null." << std::endl;
gSystem->Exit(EXIT_FAILURE);
}
if ( ! this->validBkgndClass( nBkgndEvents->name() ) ) {
std::cerr << "ERROR in LauTimeDepFitModel::setNBkgndEvents : Invalid background class \"" << nBkgndEvents->name() << "\"." << std::endl;
std::cerr << " : Background class names must be provided in \"setBkgndClassNames\" before any other background-related actions can be performed." << std::endl;
gSystem->Exit(EXIT_FAILURE);
}
UInt_t bkgndID = this->bkgndClassID( nBkgndEvents->name() );
if ( bkgndEvents_[bkgndID] != 0 ) {
std::cerr << "ERROR in LauTimeDepFitModel::setNBkgndEvents : You are trying to overwrite the background yield." << std::endl;
return;
}
if ( bkgndAsym_[bkgndID] != 0 ) {
std::cerr << "ERROR in LauTimeDepFitModel::setNBkgndEvents : You are trying to overwrite the background asymmetry." << std::endl;
return;
}
bkgndEvents_[bkgndID]->name( nBkgndEvents->name()+"Events" );
if ( nBkgndEvents->isLValue() ) {
Double_t value = nBkgndEvents->value();
LauParameter* yield = dynamic_cast<LauParameter*>( nBkgndEvents );
yield->range(-2.0*(TMath::Abs(value)+1.0), 2.0*(TMath::Abs(value)+1.0));
}
bkgndEvents_[bkgndID] = nBkgndEvents;
bkgndAsym_[bkgndID]->name( nBkgndEvents->name()+"Asym" );
if ( bkgndAsym->isLValue() ) {
LauParameter* asym = dynamic_cast<LauParameter*>( bkgndAsym );
asym->range(-1.0, 1.0);
}
bkgndAsym_[bkgndID] = bkgndAsym;
}
void LauTimeDepFitModel::setSignalDtPdf(Int_t tagCat, LauDecayTimePdf* pdf)
{
if (!flavTag_->validTagCat(tagCat)) {
std::cerr<<"ERROR in LauTimeDepFitModel::setSignalDtPdf : Tagging category \""<<tagCat<<"\" not valid, not adding PDF."<<std::endl;
return;
}
if (pdf==0) {
std::cerr<<"ERROR in LauTimeDepFitModel::setSignalDtPdf : The PDF pointer is null, not adding it."<<std::endl;
return;
}
signalDecayTimePdfs_[tagCat] = pdf;
}
void LauTimeDepFitModel::setBackgroundDtPdf(Int_t tagCat, LauDecayTimePdf* pdf)
{
// TODO If these are all histograms shouldn't need to add much more code in other functions
if (!flavTag_->validTagCat(tagCat)) {
std::cerr<<"ERROR in LauTimeDepFitModel::setBackgroundDtPdf : Tagging category \""<<tagCat<<"\" not valid, not adding PDF."<<std::endl;
return;
}
if (pdf==0) {
std::cerr<<"ERROR in LauTimeDepFitModel::setBackgroundDtPdf : The PDF pointer is null, not adding it."<<std::endl;
return;
}
backgroundDecayTimePdfs_[tagCat] = pdf;
//backgroundDecayTimePdfs_[tagCat]->setAsymmetries(&AProd_, &ARaw_);
usingBkgnd_ = kTRUE;
}
void LauTimeDepFitModel::setBkgndDPModels(const TString& bkgndClass, LauAbsBkgndDPModel* model)
{
if (model==0) {
std::cerr << "ERROR in LauTimeDepFitModel::setBkgndDPModels : the model pointer is null." << std::endl;
return;
}
// check that this background name is valid
if ( ! this->validBkgndClass( bkgndClass) ) {
std::cerr << "ERROR in LauTimeDepFitModel::setBkgndDPModel : Invalid background class \"" << bkgndClass << "\"." << std::endl;
std::cerr << " : Background class names must be provided in \"setBkgndClassNames\" before any other background-related actions can be performed." << std::endl;
return;
}
UInt_t bkgndID = this->bkgndClassID( bkgndClass );
BkgndDPModels_[bkgndID] = model;
usingBkgnd_ = kTRUE;
}
void LauTimeDepFitModel::setSignalPdfs(Int_t tagCat, LauAbsPdf* pdf)
{
// These "extra variables" are assumed to be purely kinematical, like mES and DeltaE
//or making use of Rest of Event information, and therefore independent of whether
//the parent is a B0 or a B0bar. If this assupmtion doesn't hold, do modify this part!
if (!flavTag_->validTagCat(tagCat)) {
std::cerr<<"ERROR in LauTimeDepFitModel::setSignalPdfs : Tagging category \""<<tagCat<<"\" not valid, not adding models."<<std::endl;
return;
}
if (pdf==0) {
std::cerr<<"ERROR in LauTimeDepFitModel::setSignalPdfs : The PDF pointer is null."<<std::endl;
return;
}
sigExtraPdf_[tagCat].push_back(pdf);
}
void LauTimeDepFitModel::setBkgndPdf(const TString& bkgndClass, LauAbsPdf* pdf)
{
if (pdf==0) {
std::cerr << "ERROR in LauTimeDepFitModel::setBkgndPdf : PDF pointer is null." << std::endl;
return;
}
// check that this background name is valid
if ( ! this->validBkgndClass( bkgndClass ) ) {
std::cerr << "ERROR in LauTimeDepFitModel::setBkgndPdf : Invalid background class \"" << bkgndClass << "\"." << std::endl;
std::cerr << " : Background class names must be provided in \"setBkgndClassNames\" before any other background-related actions can be performed." << std::endl;
return;
}
UInt_t bkgndID = this->bkgndClassID( bkgndClass );
BkgndPdfs_[bkgndID].push_back(pdf);
usingBkgnd_ = kTRUE;
}
void LauTimeDepFitModel::setPhiMix(const Double_t phiMix, const Bool_t fixPhiMix, const Bool_t useSinCos)
{
phiMix_.value(phiMix); phiMix_.initValue(phiMix); phiMix_.genValue(phiMix); phiMix_.fixed(fixPhiMix);
const Double_t sinPhiMix = TMath::Sin(phiMix);
sinPhiMix_.value(sinPhiMix); sinPhiMix_.initValue(sinPhiMix); sinPhiMix_.genValue(sinPhiMix); sinPhiMix_.fixed(fixPhiMix);
const Double_t cosPhiMix = TMath::Cos(phiMix);
cosPhiMix_.value(cosPhiMix); cosPhiMix_.initValue(cosPhiMix); cosPhiMix_.genValue(cosPhiMix); cosPhiMix_.fixed(fixPhiMix);
useSinCos_ = useSinCos;
phiMixComplex_.setRealPart(cosPhiMix);
phiMixComplex_.setImagPart(-1.0*sinPhiMix);
}
void LauTimeDepFitModel::initialise()
{
// From the initial parameter values calculate the coefficients
// so they can be passed to the signal model
this->updateCoeffs();
// Initialisation
if (this->useDP() == kTRUE) {
this->initialiseDPModels();
}
//Flavour tagging
flavTag_->initialise();
if (!this->useDP() && sigExtraPdf_.empty()) {
std::cerr<<"ERROR in LauTimeDepFitModel::initialise : Signal model doesn't exist for any variable."<<std::endl;
gSystem->Exit(EXIT_FAILURE);
}
if (this->useDP() == kTRUE) {
// Check that we have all the Dalitz-plot models
if ((sigModelB0bar_ == 0) || (sigModelB0_ == 0)) {
std::cerr<<"ERROR in LauTimeDepFitModel::initialise : the pointer to one (particle or anti-particle) of the signal DP models is null."<<std::endl;
gSystem->Exit(EXIT_FAILURE);
}
}
// Next check that, if a given component is being used we've got the
// right number of PDFs for all the variables involved
// TODO - should probably check variable names and so on as well
//UInt_t nsigpdfvars(0);
//for ( LauPdfList::const_iterator pdf_iter = sigExtraPdf_[tagCat]_.begin(); pdf_iter != sigExtraPdf_[tagCat]_.end(); ++pdf_iter ) {
// std::vector<TString> varNames = (*pdf_iter)->varNames();
// for ( std::vector<TString>::const_iterator var_iter = varNames.begin(); var_iter != varNames.end(); ++var_iter ) {
// if ( (*var_iter) != "m13Sq" && (*var_iter) != "m23Sq" ) {
// ++nsigpdfvars;
// }
// }
//}
//if (usingBkgnd_) {
// for (LauBkgndPdfsList::const_iterator bgclass_iter = BkgndPdfsB0_.begin(); bgclass_iter != BkgndPdfsB0_.end(); ++bgclass_iter) {
// UInt_t nbkgndpdfvars(0);
// const LauPdfList& pdfList = (*bgclass_iter);
// for ( LauPdfList::const_iterator pdf_iter = pdfList.begin(); pdf_iter != pdfList.end(); ++pdf_iter ) {
// std::vector<TString> varNames = (*pdf_iter)->varNames();
// for ( std::vector<TString>::const_iterator var_iter = varNames.begin(); var_iter != varNames.end(); ++var_iter ) {
// if ( (*var_iter) != "m13Sq" && (*var_iter) != "m23Sq" ) {
// ++nbkgndpdfvars;
// }
// }
// }
// if (nbkgndpdfvars != nsigpdfvars) {
// std::cerr << "ERROR in LauTimeDepFitModel::initialise : There are " << nsigpdfvars << " signal PDF variables but " << nbkgndpdfvars << " bkgnd PDF variables." << std::endl;
// gSystem->Exit(EXIT_FAILURE);
// }
// }
//}
// Clear the vectors of parameter information so we can start from scratch
this->clearFitParVectors();
// Set the fit parameters for signal and background models
this->setSignalDPParameters();
// Set the fit parameters for the decay time models
this->setDecayTimeParameters();
// Set the fit parameters for the extra PDFs
this->setExtraPdfParameters();
// Set the initial bg and signal events
this->setFitNEvents();
// Handle flavour-tagging calibration parameters
this->setCalibParams();
// Add the efficiency parameters
this->setEffiParams();
//Asymmetry terms AProd and ARaw added in setAsymmetries()?
this->setAsymParams();
// Check that we have the expected number of fit variables
const LauParameterPList& fitVars = this->fitPars();
if (fitVars.size() != (nSigDPPar_ + nDecayTimePar_ + nExtraPdfPar_ + nNormPar_ + nCalibPar_ + nEffiPar_ + nAsymPar_ + nTagAsymPar_)) {
std::cerr<<"ERROR in LauTimeDepFitModel::initialise : Number of fit parameters not of expected size."<<std::endl;
gSystem->Exit(EXIT_FAILURE);
}
this->setExtraNtupleVars();
}
void LauTimeDepFitModel::recalculateNormalisation()
{
sigModelB0bar_->recalculateNormalisation();
sigModelB0_->recalculateNormalisation();
sigModelB0bar_->modifyDataTree();
sigModelB0_->modifyDataTree();
this->calcInterferenceTermIntegrals();
}
void LauTimeDepFitModel::initialiseDPModels()
{
if (sigModelB0bar_ == 0) {
std::cerr<<"ERROR in LauTimeDepFitModel::initialiseDPModels : B0bar signal DP model doesn't exist"<<std::endl;
gSystem->Exit(EXIT_FAILURE);
}
if (sigModelB0_ == 0) {
std::cerr<<"ERROR in LauTimeDepFitModel::initialiseDPModels : B0 signal DP model doesn't exist"<<std::endl;
gSystem->Exit(EXIT_FAILURE);
}
// Need to check that the number of components we have and that the dynamics has matches up
const UInt_t nAmpB0bar = sigModelB0bar_->getnTotAmp();
const UInt_t nAmpB0 = sigModelB0_->getnTotAmp();
if ( nAmpB0bar != nAmpB0 ) {
std::cerr << "ERROR in LauTimeDepFitModel::initialiseDPModels : Unequal number of signal DP components in the particle and anti-particle models: " << nAmpB0bar << " != " << nAmpB0 << std::endl;
gSystem->Exit(EXIT_FAILURE);
}
if ( nAmpB0bar != nSigComp_ ) {
std::cerr << "ERROR in LauTimeDepFitModel::initialiseDPModels : Number of signal DP components in the model (" << nAmpB0bar << ") not equal to number of coefficients supplied (" << nSigComp_ << ")." << std::endl;
gSystem->Exit(EXIT_FAILURE);
}
std::cout<<"INFO in LauTimeDepFitModel::initialiseDPModels : Initialising signal DP model"<<std::endl;
sigModelB0bar_->initialise(coeffsB0bar_);
sigModelB0_->initialise(coeffsB0_);
fifjEffSum_.clear();
fifjEffSum_.resize(nSigComp_);
for (UInt_t iAmp = 0; iAmp < nSigComp_; ++iAmp) {
fifjEffSum_[iAmp].resize(nSigComp_);
}
// calculate the integrals of the A*Abar terms
this->calcInterferenceTermIntegrals();
this->calcInterTermNorm();
// Add backgrounds
if (usingBkgnd_ == kTRUE) {
for (LauBkgndDPModelList::iterator iter = BkgndDPModels_.begin(); iter != BkgndDPModels_.end(); ++iter) {
(*iter)->initialise();
}
}
}
void LauTimeDepFitModel::calcInterferenceTermIntegrals()
{
const std::vector<LauDPPartialIntegralInfo*>& integralInfoListB0bar = sigModelB0bar_->getIntegralInfos();
const std::vector<LauDPPartialIntegralInfo*>& integralInfoListB0 = sigModelB0_->getIntegralInfos();
// TODO should check (first time) that they match in terms of number of entries in the vectors and that each entry has the same number of points, ranges, weights etc.
LauComplex A, Abar, fifjEffSumTerm;
for (UInt_t iAmp = 0; iAmp < nSigComp_; ++iAmp) {
for (UInt_t jAmp = 0; jAmp < nSigComp_; ++jAmp) {
fifjEffSum_[iAmp][jAmp].zero();
}
}
const UInt_t nIntegralRegions = integralInfoListB0bar.size();
for ( UInt_t iRegion(0); iRegion < nIntegralRegions; ++iRegion ) {
const LauDPPartialIntegralInfo* integralInfoB0bar = integralInfoListB0bar[iRegion];
const LauDPPartialIntegralInfo* integralInfoB0 = integralInfoListB0[iRegion];
const UInt_t nm13Points = integralInfoB0bar->getnm13Points();
const UInt_t nm23Points = integralInfoB0bar->getnm23Points();
for (UInt_t m13 = 0; m13 < nm13Points; ++m13) {
for (UInt_t m23 = 0; m23 < nm23Points; ++m23) {
const Double_t weight = integralInfoB0bar->getWeight(m13,m23);
const Double_t eff = integralInfoB0bar->getEfficiency(m13,m23);
const Double_t effWeight = eff*weight;
for (UInt_t iAmp = 0; iAmp < nSigComp_; ++iAmp) {
A = integralInfoB0->getAmplitude(m13, m23, iAmp);
for (UInt_t jAmp = 0; jAmp < nSigComp_; ++jAmp) {
Abar = integralInfoB0bar->getAmplitude(m13, m23, jAmp);
fifjEffSumTerm = Abar*A.conj();
fifjEffSumTerm.rescale(effWeight);
fifjEffSum_[iAmp][jAmp] += fifjEffSumTerm;
}
}
}
}
}
}
void LauTimeDepFitModel::calcInterTermNorm()
{
const std::vector<Double_t>& fNormB0bar = sigModelB0bar_->getFNorm();
const std::vector<Double_t>& fNormB0 = sigModelB0_->getFNorm();
LauComplex norm;
for (UInt_t iAmp = 0; iAmp < nSigComp_; ++iAmp) {
for (UInt_t jAmp = 0; jAmp < nSigComp_; ++jAmp) {
LauComplex coeffTerm = coeffsB0bar_[jAmp]*coeffsB0_[iAmp].conj();
coeffTerm *= fifjEffSum_[iAmp][jAmp];
coeffTerm.rescale(fNormB0bar[jAmp] * fNormB0[iAmp]);
norm += coeffTerm;
}
}
norm *= phiMixComplex_;
interTermReNorm_ = 2.0*norm.re();
interTermImNorm_ = 2.0*norm.im();
}
void LauTimeDepFitModel::setAmpCoeffSet(LauAbsCoeffSet* coeffSet)
{
// Is there a component called compName in the signal models?
TString compName = coeffSet->name();
TString conjName = sigModelB0bar_->getConjResName(compName);
const LauDaughters* daughtersB0bar = sigModelB0bar_->getDaughters();
const LauDaughters* daughtersB0 = sigModelB0_->getDaughters();
const Bool_t conjugate = daughtersB0bar->isConjugate( daughtersB0 );
if ( ! sigModelB0bar_->hasResonance(compName) ) {
if ( ! sigModelB0bar_->hasResonance(conjName) ) {
std::cerr<<"ERROR in LauTimeDepFitModel::setAmpCoeffSet : B0bar signal DP model doesn't contain component \""<<compName<<"\"."<<std::endl;
return;
}
std::cerr<<"WARNING in LauTimeDepFitModel::setAmpCoeffSet : B0bar signal DP model doesn't contain component \""<<compName<<"\" but does contain the conjugate \""<<conjName<<"\", resetting name to use the conjugate."<<std::endl;
TString tmp = compName;
compName = conjName;
conjName = tmp;
coeffSet->name( compName );
}
if ( conjugate ) {
if ( ! sigModelB0_->hasResonance(conjName) ) {
std::cerr<<"ERROR in LauTimeDepFitModel::setAmpCoeffSet : B0 signal DP model doesn't contain component \""<<conjName<<"\"."<<std::endl;
return;
}
} else {
if ( ! sigModelB0_->hasResonance(compName) ) {
std::cerr<<"ERROR in LauTimeDepFitModel::setAmpCoeffSet : B0 signal DP model doesn't contain component \""<<compName<<"\"."<<std::endl;
return;
}
}
// Do we already have it in our list of names?
for (std::vector<LauAbsCoeffSet*>::const_iterator iter=coeffPars_.begin(); iter!=coeffPars_.end(); ++iter) {
if ((*iter)->name() == compName) {
std::cerr<<"ERROR in LauTimeDepFitModel::setAmpCoeffSet : Have already set coefficients for \""<<compName<<"\"."<<std::endl;
return;
}
}
coeffSet->index(nSigComp_);
coeffPars_.push_back(coeffSet);
TString parName = coeffSet->baseName(); parName += "FitFracAsym";
fitFracAsymm_.push_back(LauParameter(parName, 0.0, -1.0, 1.0));
acp_.push_back(coeffSet->acp());
++nSigComp_;
std::cout<<"INFO in LauTimeDepFitModel::setAmpCoeffSet : Added coefficients for component \""<<compName<<"\" to the fit model."<<std::endl;
}
void LauTimeDepFitModel::calcAsymmetries(Bool_t initValues)
{
// Calculate the CP asymmetries
// Also calculate the fit fraction asymmetries
for (UInt_t i = 0; i < nSigComp_; i++) {
acp_[i] = coeffPars_[i]->acp();
LauAsymmCalc asymmCalc(fitFracB0bar_[i][i].value(), fitFracB0_[i][i].value());
Double_t asym = asymmCalc.getAsymmetry();
fitFracAsymm_[i].value(asym);
if (initValues) {
fitFracAsymm_[i].genValue(asym);
fitFracAsymm_[i].initValue(asym);
}
}
}
void LauTimeDepFitModel::setSignalDPParameters()
{
// Set the fit parameters for the signal model.
nSigDPPar_ = 0;
if ( ! this->useDP() ) {
return;
}
std::cout << "INFO in LauTimeDepFitModel::setSignalDPParameters : Setting the initial fit parameters for the signal DP model." << std::endl;
// Place isobar coefficient parameters in vector of fit variables
LauParameterPList& fitVars = this->fitPars();
for (UInt_t i = 0; i < nSigComp_; ++i) {
LauParameterPList pars = coeffPars_[i]->getParameters();
for (LauParameterPList::iterator iter = pars.begin(); iter != pars.end(); ++iter) {
if ( !(*iter)->clone() ) {
fitVars.push_back(*iter);
++nSigDPPar_;
}
}
}
// Obtain the resonance parameters and place them in the vector of fit variables and in a separate vector
// Need to make sure that they are unique because some might appear in both DP models
LauParameterPSet& resVars = this->resPars();
resVars.clear();
LauParameterPList& sigDPParsB0bar = sigModelB0bar_->getFloatingParameters();
LauParameterPList& sigDPParsB0 = sigModelB0_->getFloatingParameters();
for ( LauParameterPList::iterator iter = sigDPParsB0bar.begin(); iter != sigDPParsB0bar.end(); ++iter ) {
if ( resVars.insert(*iter).second ) {
fitVars.push_back(*iter);
++nSigDPPar_;
}
}
for ( LauParameterPList::iterator iter = sigDPParsB0.begin(); iter != sigDPParsB0.end(); ++iter ) {
if ( resVars.insert(*iter).second ) {
fitVars.push_back(*iter);
++nSigDPPar_;
}
}
}
UInt_t LauTimeDepFitModel::addParametersToFitList(LauTagCatDtPdfMap& theMap)
{
UInt_t counter(0);
LauParameterPList& fitVars = this->fitPars();
// loop through the map
for (LauTagCatDtPdfMap::iterator iter = theMap.begin(); iter != theMap.end(); ++iter) {
// grab the pdf and then its parameters
LauDecayTimePdf* thePdf = (*iter).second; // The first one is the tagging category
LauAbsRValuePList& rvalues = thePdf->getParameters();
// loop through the parameters
for (LauAbsRValuePList::iterator pars_iter = rvalues.begin(); pars_iter != rvalues.end(); ++pars_iter) {
LauParameterPList params = (*pars_iter)->getPars();
for (LauParameterPList::iterator params_iter = params.begin(); params_iter != params.end(); ++params_iter) {
// for each "original" parameter add it to the list of fit parameters and increment the counter
if ( !(*params_iter)->clone() && ( !(*params_iter)->fixed() ||
(this->twoStageFit() && (*params_iter)->secondStage()) ) ) {
fitVars.push_back(*params_iter);
++counter;
}
}
}
}
return counter;
}
UInt_t LauTimeDepFitModel::addParametersToFitList(LauTagCatPdfListMap& theMap)
{
UInt_t counter(0);
// loop through the map
for (LauTagCatPdfListMap::iterator iter = theMap.begin(); iter != theMap.end(); ++iter) {
counter += this->addFitParameters(iter->second); // first is the tagging category
}
return counter;
}
void LauTimeDepFitModel::setDecayTimeParameters()
{
nDecayTimePar_ = 0;
// Loop over the Dt PDFs
nDecayTimePar_ += this->addParametersToFitList(signalDecayTimePdfs_);
if (usingBkgnd_){
nDecayTimePar_ += this->addParametersToFitList(backgroundDecayTimePdfs_);
}
LauParameterPList& fitVars = this->fitPars();
if (useSinCos_) {
fitVars.push_back(&sinPhiMix_);
fitVars.push_back(&cosPhiMix_);
nDecayTimePar_ += 2;
} else {
fitVars.push_back(&phiMix_);
++nDecayTimePar_;
}
}
void LauTimeDepFitModel::setExtraPdfParameters()
{
// Include the parameters of the PDF for each tagging category in the fit
// NB all of them are passed to the fit, even though some have been fixed through parameter.fixed(kTRUE)
// With the new "cloned parameter" scheme only "original" parameters are passed to the fit.
// Their clones are updated automatically when the originals are updated.
nExtraPdfPar_ = 0;
nExtraPdfPar_ += this->addParametersToFitList(sigExtraPdf_);
if (usingBkgnd_ == kTRUE) {
for (LauBkgndPdfsList::iterator iter = BkgndPdfs_.begin(); iter != BkgndPdfs_.end(); ++iter) {
nExtraPdfPar_ += this->addFitParameters(*iter);
}
}
}
void LauTimeDepFitModel::setFitNEvents()
{
nNormPar_ = 0;
// Initialise the total number of events to be the sum of all the hypotheses
Double_t nTotEvts = signalEvents_->value();
this->eventsPerExpt(TMath::FloorNint(nTotEvts));
LauParameterPList& fitVars = this->fitPars();
// if doing an extended ML fit add the signal fraction into the fit parameters
if (this->doEMLFit()) {
std::cout<<"INFO in LauTimeDepFitModel::setFitNEvents : Initialising number of events for signal and background components..."<<std::endl;
fitVars.push_back(signalEvents_);
++nNormPar_;
} else {
std::cout<<"INFO in LauTimeDepFitModel::setFitNEvents : Initialising number of events for background components (and hence signal)..."<<std::endl;
}
// if not using the DP in the model we need an explicit signal asymmetry parameter
if (this->useDP() == kFALSE) {
fitVars.push_back(signalAsym_);
++nNormPar_;
}
// TODO arguably should delegate this
LauTagCatParamMap& signalTagCatFrac = flavTag_->getSignalTagCatFrac();
// tagging-category fractions for signal events
for (LauTagCatParamMap::iterator iter = signalTagCatFrac.begin(); iter != signalTagCatFrac.end(); ++iter) {
if (iter == signalTagCatFrac.begin()) {
continue;
}
LauParameter* par = &((*iter).second);
fitVars.push_back(par);
++nNormPar_;
}
// Backgrounds
if (usingBkgnd_ == kTRUE) {
for (LauBkgndYieldList::iterator iter = bkgndEvents_.begin(); iter != bkgndEvents_.end(); ++iter) {
std::vector<LauParameter*> parameters = (*iter)->getPars();
for ( LauParameter* parameter : parameters ) {
if(!parameter->clone()) {
fitVars.push_back(parameter);
++nNormPar_;
}
}
}
for (LauBkgndYieldList::iterator iter = bkgndAsym_.begin(); iter != bkgndAsym_.end(); ++iter) {
std::vector<LauParameter*> parameters = (*iter)->getPars();
for ( LauParameter* parameter : parameters ) {
if(!parameter->clone()) {
fitVars.push_back(parameter);
++nNormPar_;
}
}
}
}
}
void LauTimeDepFitModel::setAsymParams()
{
LauParameterPList& fitVars = this->fitPars();
fitVars.push_back(&AProd_);
fitVars.push_back(&ARaw_);
nAsymPar_+=2;
}
void LauTimeDepFitModel::setCalibParams()
{
Bool_t useAltPars = flavTag_->getUseAveOmegaDeltaOmega();
if (useAltPars){
LauTagCatParamMap& p0pars_ave = flavTag_->getCalibP0Ave();
LauTagCatParamMap& p0pars_delta = flavTag_->getCalibP0Delta();
LauTagCatParamMap& p1pars_ave = flavTag_->getCalibP1Ave();
LauTagCatParamMap& p1pars_delta = flavTag_->getCalibP1Delta();
LauParameterPList& fitVars = this->fitPars();
for(LauTagCatParamMap::iterator iter = p0pars_ave.begin(); iter != p0pars_ave.end(); ++iter){
LauParameter* p0 = &((*iter).second);
if (p0->fixed()){continue;}
fitVars.push_back(p0);
++nCalibPar_;
}
for(LauTagCatParamMap::iterator iter = p0pars_delta.begin(); iter != p0pars_delta.end(); ++iter){
LauParameter* p0 = &((*iter).second);
if (p0->fixed()){continue;}
fitVars.push_back(p0);
++nCalibPar_;
}
for(LauTagCatParamMap::iterator iter = p1pars_ave.begin(); iter != p1pars_ave.end(); ++iter){
LauParameter* p1 = &((*iter).second);
if (p1->fixed()){continue;}
fitVars.push_back(p1);
++nCalibPar_;
}
for(LauTagCatParamMap::iterator iter = p1pars_delta.begin(); iter != p1pars_delta.end(); ++iter){
LauParameter* p1 = &((*iter).second);
if (p1->fixed()){continue;}
fitVars.push_back(p1);
++nCalibPar_;
}
} else {
LauTagCatParamMap& p0pars_b0 = flavTag_->getCalibP0B0();
LauTagCatParamMap& p0pars_b0bar = flavTag_->getCalibP0B0bar();
LauTagCatParamMap& p1pars_b0 = flavTag_->getCalibP1B0();
LauTagCatParamMap& p1pars_b0bar = flavTag_->getCalibP1B0bar();
LauParameterPList& fitVars = this->fitPars();
for(LauTagCatParamMap::iterator iter = p0pars_b0.begin(); iter != p0pars_b0.end(); ++iter){
LauParameter* p0 = &((*iter).second);
if (p0->fixed()){continue;}
fitVars.push_back(p0);
++nCalibPar_;
}
for(LauTagCatParamMap::iterator iter = p0pars_b0bar.begin(); iter != p0pars_b0bar.end(); ++iter){
LauParameter* p0 = &((*iter).second);
if (p0->fixed()){continue;}
fitVars.push_back(p0);
++nCalibPar_;
}
for(LauTagCatParamMap::iterator iter = p1pars_b0.begin(); iter != p1pars_b0.end(); ++iter){
LauParameter* p1 = &((*iter).second);
if (p1->fixed()){continue;}
fitVars.push_back(p1);
++nCalibPar_;
}
for(LauTagCatParamMap::iterator iter = p1pars_b0bar.begin(); iter != p1pars_b0bar.end(); ++iter){
LauParameter* p1 = &((*iter).second);
if (p1->fixed()){continue;}
fitVars.push_back(p1);
++nCalibPar_;
}
}
}
void LauTimeDepFitModel::setFlavTagAsymParams(){
LauParameterPList& fitVars = this->fitPars();
LauTagCatParamMap& tagAsyms = flavTag_ -> getTagAsym();
for(LauTagCatParamMap::iterator iter = tagAsyms.begin(); iter != tagAsyms.end(); ++iter){
LauParameter* At = &((*iter).second);
if (At->fixed()){continue;}
fitVars.push_back(At);
++nTagAsymPar_;
}
}
void LauTimeDepFitModel::setEffiParams()
{
LauParameterPList& fitVars = this->fitPars();
LauTagCatDtPdfMap& signalDecayTimePdfs = this->getSignalDecayTimePdfs();
// Loop over DecayTimePdfs
for (LauTagCatDtPdfMap::iterator iter = signalDecayTimePdfs.begin(); iter != signalDecayTimePdfs.end(); ++iter){
LauDecayTimePdf* pdf = (*iter).second;
std::vector<LauParameter*>& effiPars = pdf->getEffiPars();
for(std::vector<LauParameter*>::iterator iter2 = effiPars.begin(); iter2 != effiPars.end(); ++iter2){
LauParameter* par = *iter2;
if (par->fixed()){continue;}
fitVars.push_back(par);
++nEffiPar_;
}
}
}
void LauTimeDepFitModel::setExtraNtupleVars()
{
// Set-up other parameters derived from the fit results, e.g. fit fractions.
if (this->useDP() != kTRUE) {
return;
}
// First clear the vectors so we start from scratch
this->clearExtraVarVectors();
LauParameterList& extraVars = this->extraPars();
// Add the B0 and B0bar fit fractions for each signal component
fitFracB0bar_ = sigModelB0bar_->getFitFractions();
if (fitFracB0bar_.size() != nSigComp_) {
std::cerr<<"ERROR in LauTimeDepFitModel::setExtraNtupleVars : Initial Fit Fraction array of unexpected dimension: "<<fitFracB0bar_.size()<<std::endl;
gSystem->Exit(EXIT_FAILURE);
}
for (UInt_t i(0); i<nSigComp_; ++i) {
if (fitFracB0bar_[i].size() != nSigComp_) {
std::cerr<<"ERROR in LauTimeDepFitModel::setExtraNtupleVars : Initial Fit Fraction array of unexpected dimension: "<<fitFracB0bar_[i].size()<<std::endl;
gSystem->Exit(EXIT_FAILURE);
}
}
for (UInt_t i(0); i<nSigComp_; ++i) {
for (UInt_t j = i; j < nSigComp_; j++) {
TString name = fitFracB0bar_[i][j].name();
name.Insert( name.Index("FitFrac"), "B0bar" );
fitFracB0bar_[i][j].name(name);
extraVars.push_back(fitFracB0bar_[i][j]);
}
}
fitFracB0_ = sigModelB0_->getFitFractions();
if (fitFracB0_.size() != nSigComp_) {
std::cerr<<"ERROR in LauTimeDepFitModel::setExtraNtupleVars : Initial Fit Fraction array of unexpected dimension: "<<fitFracB0_.size()<<std::endl;
gSystem->Exit(EXIT_FAILURE);
}
for (UInt_t i(0); i<nSigComp_; ++i) {
if (fitFracB0_[i].size() != nSigComp_) {
std::cerr<<"ERROR in LauTimeDepFitModel::setExtraNtupleVars : Initial Fit Fraction array of unexpected dimension: "<<fitFracB0_[i].size()<<std::endl;
gSystem->Exit(EXIT_FAILURE);
}
}
for (UInt_t i(0); i<nSigComp_; ++i) {
for (UInt_t j = i; j < nSigComp_; j++) {
TString name = fitFracB0_[i][j].name();
name.Insert( name.Index("FitFrac"), "B0" );
fitFracB0_[i][j].name(name);
extraVars.push_back(fitFracB0_[i][j]);
}
}
// Calculate the ACPs and FitFrac asymmetries
this->calcAsymmetries(kTRUE);
// Add the Fit Fraction asymmetry for each signal component
for (UInt_t i = 0; i < nSigComp_; i++) {
extraVars.push_back(fitFracAsymm_[i]);
}
// Add the calculated CP asymmetry for each signal component
for (UInt_t i = 0; i < nSigComp_; i++) {
extraVars.push_back(acp_[i]);
}
// Now add in the DP efficiency values
Double_t initMeanEffB0bar = sigModelB0bar_->getMeanEff().initValue();
meanEffB0bar_.value(initMeanEffB0bar); meanEffB0bar_.initValue(initMeanEffB0bar); meanEffB0bar_.genValue(initMeanEffB0bar);
extraVars.push_back(meanEffB0bar_);
Double_t initMeanEffB0 = sigModelB0_->getMeanEff().initValue();
meanEffB0_.value(initMeanEffB0); meanEffB0_.initValue(initMeanEffB0); meanEffB0_.genValue(initMeanEffB0);
extraVars.push_back(meanEffB0_);
// Also add in the DP rates
Double_t initDPRateB0bar = sigModelB0bar_->getDPRate().initValue();
DPRateB0bar_.value(initDPRateB0bar); DPRateB0bar_.initValue(initDPRateB0bar); DPRateB0bar_.genValue(initDPRateB0bar);
extraVars.push_back(DPRateB0bar_);
Double_t initDPRateB0 = sigModelB0_->getDPRate().initValue();
DPRateB0_.value(initDPRateB0); DPRateB0_.initValue(initDPRateB0); DPRateB0_.genValue(initDPRateB0);
extraVars.push_back(DPRateB0_);
}
void LauTimeDepFitModel::setAsymmetries(const Double_t AProd, const Bool_t AProdFix, const Double_t ARaw, const Bool_t ARawFix){
AProd_.value(AProd);
AProd_.fixed(AProdFix);
ARaw_.value(ARaw);
ARaw_.fixed(ARawFix);
//this->setAsymParams();
}
void LauTimeDepFitModel::finaliseFitResults(const TString& tablePrefixName)
{
// Retrieve parameters from the fit results for calculations and toy generation
// and eventually store these in output root ntuples/text files
// Now take the fit parameters and update them as necessary
// i.e. to make mag > 0.0, phase in the right range.
// This function will also calculate any other values, such as the
// fit fractions, using any errors provided by fitParErrors as appropriate.
// Also obtain the pull values: (measured - generated)/(average error)
if (this->useDP() == kTRUE) {
for (UInt_t i = 0; i < nSigComp_; ++i) {
// Check whether we have "a > 0.0", and phases in the right range
coeffPars_[i]->finaliseValues();
}
}
// update the pulls on the event fractions and asymmetries
if (this->doEMLFit()) {
signalEvents_->updatePull();
}
if (this->useDP() == kFALSE) {
signalAsym_->updatePull();
}
// Finalise the pulls on the decay time parameters
for (LauTagCatDtPdfMap::iterator iter = signalDecayTimePdfs_.begin(); iter != signalDecayTimePdfs_.end(); ++iter) {
LauDecayTimePdf* pdf = (*iter).second;
pdf->updatePulls();
}
// and for backgrounds if required
if (usingBkgnd_){
for (LauTagCatDtPdfMap::iterator iter = backgroundDecayTimePdfs_.begin(); iter != backgroundDecayTimePdfs_.end(); ++iter) {
LauDecayTimePdf* pdf = (*iter).second;
pdf->updatePulls();
}
}
if (useSinCos_) {
cosPhiMix_.updatePull();
sinPhiMix_.updatePull();
} else {
this->checkMixingPhase();
}
if (usingBkgnd_ == kTRUE) {
for (LauBkgndYieldList::iterator iter = bkgndEvents_.begin(); iter != bkgndEvents_.end(); ++iter) {
std::vector<LauParameter*> parameters = (*iter)->getPars();
for ( LauParameter* parameter : parameters ) {
parameter->updatePull();
}
}
for (LauBkgndYieldList::iterator iter = bkgndAsym_.begin(); iter != bkgndAsym_.end(); ++iter) {
std::vector<LauParameter*> parameters = (*iter)->getPars();
for ( LauParameter* parameter : parameters ) {
parameter->updatePull();
}
}
}
// Update the pulls on all the extra PDFs' parameters
for (LauTagCatPdfListMap::iterator iter = sigExtraPdf_.begin(); iter != sigExtraPdf_.end(); ++iter) {
this->updateFitParameters(iter->second);
}
if (usingBkgnd_ == kTRUE) {
for (LauBkgndPdfsList::iterator iter = BkgndPdfs_.begin(); iter != BkgndPdfs_.end(); ++iter) {
this->updateFitParameters(*iter);
}
}
LauTagCatParamMap& signalTagCatFrac = flavTag_->getSignalTagCatFrac();
// Tagging-category fractions for signal and background events
Double_t firstCatFrac(1.0);
Int_t firstCat(0);
for (LauTagCatParamMap::iterator iter = signalTagCatFrac.begin(); iter != signalTagCatFrac.end(); ++iter) {
if (iter == signalTagCatFrac.begin()) {
firstCat = iter->first;
continue;
}
LauParameter& par = (*iter).second;
firstCatFrac -= par.value();
// update the parameter pull
par.updatePull();
}
signalTagCatFrac[firstCat].value(firstCatFrac);
signalTagCatFrac[firstCat].updatePull();
// Fill the fit results to the ntuple
// update the coefficients and then calculate the fit fractions and ACP's
if (this->useDP() == kTRUE) {
this->updateCoeffs();
sigModelB0bar_->updateCoeffs(coeffsB0bar_); sigModelB0bar_->calcExtraInfo();
sigModelB0_->updateCoeffs(coeffsB0_); sigModelB0_->calcExtraInfo();
LauParArray fitFracB0bar = sigModelB0bar_->getFitFractions();
if (fitFracB0bar.size() != nSigComp_) {
std::cerr<<"ERROR in LauTimeDepFitModel::finaliseFitResults : Fit Fraction array of unexpected dimension: "<<fitFracB0bar.size()<<std::endl;
gSystem->Exit(EXIT_FAILURE);
}
for (UInt_t i(0); i<nSigComp_; ++i) {
if (fitFracB0bar[i].size() != nSigComp_) {
std::cerr<<"ERROR in LauTimeDepFitModel::finaliseFitResults : Fit Fraction array of unexpected dimension: "<<fitFracB0bar[i].size()<<std::endl;
gSystem->Exit(EXIT_FAILURE);
}
}
LauParArray fitFracB0 = sigModelB0_->getFitFractions();
if (fitFracB0.size() != nSigComp_) {
std::cerr<<"ERROR in LauTimeDepFitModel::finaliseFitResults : Fit Fraction array of unexpected dimension: "<<fitFracB0.size()<<std::endl;
gSystem->Exit(EXIT_FAILURE);
}
for (UInt_t i(0); i<nSigComp_; ++i) {
if (fitFracB0[i].size() != nSigComp_) {
std::cerr<<"ERROR in LauTimeDepFitModel::finaliseFitResults : Fit Fraction array of unexpected dimension: "<<fitFracB0[i].size()<<std::endl;
gSystem->Exit(EXIT_FAILURE);
}
}
for (UInt_t i(0); i<nSigComp_; ++i) {
for (UInt_t j(i); j<nSigComp_; ++j) {
fitFracB0bar_[i][j].value(fitFracB0bar[i][j].value());
fitFracB0_[i][j].value(fitFracB0[i][j].value());
}
}
meanEffB0bar_.value(sigModelB0bar_->getMeanEff().value());
meanEffB0_.value(sigModelB0_->getMeanEff().value());
DPRateB0bar_.value(sigModelB0bar_->getDPRate().value());
DPRateB0_.value(sigModelB0_->getDPRate().value());
this->calcAsymmetries();
// Then store the final fit parameters, and any extra parameters for
// the signal model (e.g. fit fractions, FF asymmetries, ACPs, mean efficiency and DP rate)
this->clearExtraVarVectors();
LauParameterList& extraVars = this->extraPars();
for (UInt_t i(0); i<nSigComp_; ++i) {
for (UInt_t j(i); j<nSigComp_; ++j) {
extraVars.push_back(fitFracB0bar_[i][j]);
}
}
for (UInt_t i(0); i<nSigComp_; ++i) {
for (UInt_t j(i); j<nSigComp_; ++j) {
extraVars.push_back(fitFracB0_[i][j]);
}
}
for (UInt_t i(0); i<nSigComp_; ++i) {
extraVars.push_back(fitFracAsymm_[i]);
}
for (UInt_t i(0); i<nSigComp_; ++i) {
extraVars.push_back(acp_[i]);
}
extraVars.push_back(meanEffB0bar_);
extraVars.push_back(meanEffB0_);
extraVars.push_back(DPRateB0bar_);
extraVars.push_back(DPRateB0_);
this->printFitFractions(std::cout);
this->printAsymmetries(std::cout);
}
const LauParameterPList& fitVars = this->fitPars();
const LauParameterList& extraVars = this->extraPars();
LauFitNtuple* ntuple = this->fitNtuple();
ntuple->storeParsAndErrors(fitVars, extraVars);
// find out the correlation matrix for the parameters
ntuple->storeCorrMatrix(this->iExpt(), this->fitStatus(), this->covarianceMatrix());
// Fill the data into ntuple
ntuple->updateFitNtuple();
// Print out the partial fit fractions, phases and the
// averaged efficiency, reweighted by the dynamics (and anything else)
if (this->writeLatexTable()) {
TString sigOutFileName(tablePrefixName);
sigOutFileName += "_"; sigOutFileName += this->iExpt(); sigOutFileName += "Expt.tex";
this->writeOutTable(sigOutFileName);
}
}
void LauTimeDepFitModel::printFitFractions(std::ostream& output)
{
// Print out Fit Fractions, total DP rate and mean efficiency
// First for the B0bar events
for (UInt_t i = 0; i < nSigComp_; i++) {
const TString compName(coeffPars_[i]->name());
output<<"B0bar FitFraction for component "<<i<<" ("<<compName<<") = "<<fitFracB0bar_[i][i]<<std::endl;
}
output<<"B0bar overall DP rate (integral of matrix element squared) = "<<DPRateB0bar_<<std::endl;
output<<"B0bar average efficiency weighted by whole DP dynamics = "<<meanEffB0bar_<<std::endl;
// Then for the B0 sample
for (UInt_t i = 0; i < nSigComp_; i++) {
const TString compName(coeffPars_[i]->name());
const TString conjName(sigModelB0bar_->getConjResName(compName));
output<<"B0 FitFraction for component "<<i<<" ("<<conjName<<") = "<<fitFracB0_[i][i]<<std::endl;
}
output<<"B0 overall DP rate (integral of matrix element squared) = "<<DPRateB0_<<std::endl;
output<<"B0 average efficiency weighted by whole DP dynamics = "<<meanEffB0_<<std::endl;
}
void LauTimeDepFitModel::printAsymmetries(std::ostream& output)
{
for (UInt_t i = 0; i < nSigComp_; i++) {
const TString compName(coeffPars_[i]->name());
output<<"Fit Fraction asymmetry for component "<<i<<" ("<<compName<<") = "<<fitFracAsymm_[i]<<std::endl;
}
for (UInt_t i = 0; i < nSigComp_; i++) {
const TString compName(coeffPars_[i]->name());
output<<"ACP for component "<<i<<" ("<<compName<<") = "<<acp_[i].value()<<" +- "<<acp_[i].error()<<std::endl;
}
}
void LauTimeDepFitModel::writeOutTable(const TString& outputFile)
{
// Write out the results of the fit to a tex-readable table
std::ofstream fout(outputFile);
LauPrint print;
std::cout<<"INFO in LauTimeDepFitModel::writeOutTable : Writing out results of the fit to the tex file "<<outputFile<<std::endl;
if (this->useDP() == kTRUE) {
// print the fit coefficients in one table
coeffPars_.front()->printTableHeading(fout);
for (UInt_t i = 0; i < nSigComp_; i++) {
coeffPars_[i]->printTableRow(fout);
}
fout<<"\\hline"<<std::endl;
fout<<"\\end{tabular}"<<std::endl<<std::endl;
// print the fit fractions in another
fout<<"\\begin{tabular}{|l|c|c|c|c|}"<<std::endl;
fout<<"\\hline"<<std::endl;
fout<<"Component & \\Bzb\\ Fit Fraction & \\Bz\\ Fit Fraction & Fit Fraction Asymmetry & $A_{\\CP}$ \\\\"<<std::endl;
fout<<"\\hline"<<std::endl;
Double_t fitFracSumB0bar(0.0);
Double_t fitFracSumB0(0.0);
for (UInt_t i = 0; i < nSigComp_; i++) {
TString resName = coeffPars_[i]->name();
resName = resName.ReplaceAll("_", "\\_");
fout<<resName<<" & $";
Double_t fitFracB0bar = fitFracB0bar_[i][i].value();
fitFracSumB0bar += fitFracB0bar;
print.printFormat(fout, fitFracB0bar);
fout << "$ & $" << std::endl;
Double_t fitFracB0 = fitFracB0_[i][i].value();
fitFracSumB0 += fitFracB0;
print.printFormat(fout, fitFracB0);
fout << "$ & $" << std::endl;
Double_t fitFracAsymm = fitFracAsymm_[i].value();
print.printFormat(fout, fitFracAsymm);
fout << "$ & $" << std::endl;
Double_t acp = acp_[i].value();
Double_t acpErr = acp_[i].error();
print.printFormat(fout, acp);
fout<<" \\pm ";
print.printFormat(fout, acpErr);
fout<<"$ \\\\"<<std::endl;
}
fout<<"\\hline"<<std::endl;
// Also print out sum of fit fractions
fout << "Fit Fraction Sum = & $";
print.printFormat(fout, fitFracSumB0bar);
fout << "$ & $";
print.printFormat(fout, fitFracSumB0);
fout << "$ & & \\\\" << std::endl;
fout << "\\hline \n\\hline" << std::endl;
fout << "DP rate = & $";
print.printFormat(fout, DPRateB0bar_.value());
fout << "$ & $";
print.printFormat(fout, DPRateB0_.value());
fout << "$ & & \\\\" << std::endl;
fout << "$< \\varepsilon > =$ & $";
print.printFormat(fout, meanEffB0bar_.value());
fout << "$ & $";
print.printFormat(fout, meanEffB0_.value());
fout << "$ & & \\\\" << std::endl;
if (useSinCos_) {
fout << "$\\sinPhiMix =$ & $";
print.printFormat(fout, sinPhiMix_.value());
fout << " \\pm ";
print.printFormat(fout, sinPhiMix_.error());
fout << "$ & & & & & & & \\\\" << std::endl;
fout << "$\\cosPhiMix =$ & $";
print.printFormat(fout, cosPhiMix_.value());
fout << " \\pm ";
print.printFormat(fout, cosPhiMix_.error());
fout << "$ & & & & & & & \\\\" << std::endl;
} else {
fout << "$\\phiMix =$ & $";
print.printFormat(fout, phiMix_.value());
fout << " \\pm ";
print.printFormat(fout, phiMix_.error());
fout << "$ & & & & & & & \\\\" << std::endl;
}
fout << "\\hline \n\\end{tabular}" << std::endl;
}
if (!sigExtraPdf_.empty()) {
fout<<"\\begin{tabular}{|l|c|}"<<std::endl;
fout<<"\\hline"<<std::endl;
fout<<"\\Extra Signal PDFs' Parameters: & \\\\"<<std::endl;
for (LauTagCatPdfListMap::const_iterator iter = sigExtraPdf_.begin(); iter != sigExtraPdf_.end(); ++iter) {
this->printFitParameters(iter->second, fout);
}
if (usingBkgnd_ == kTRUE && !BkgndPdfs_.empty()) {
fout << "\\hline" << std::endl;
fout << "\\Extra Background PDFs' Parameters: & \\\\" << std::endl;
for (LauBkgndPdfsList::const_iterator iter = BkgndPdfs_.begin(); iter != BkgndPdfs_.end(); ++iter) {
this->printFitParameters(*iter, fout);
}
}
fout<<"\\hline \n\\end{tabular}"<<std::endl<<std::endl;
}
}
void LauTimeDepFitModel::checkInitFitParams()
{
// Update the number of signal events to be total-sum(background events)
this->updateSigEvents();
// Check whether we want to have randomised initial fit parameters for the signal model
if (this->useRandomInitFitPars() == kTRUE) {
this->randomiseInitFitPars();
}
}
void LauTimeDepFitModel::randomiseInitFitPars()
{
// Only randomise those parameters that are not fixed!
std::cout<<"INFO in LauTimeDepFitModel::randomiseInitFitPars : Randomising the initial values of the coefficients of the DP components (and phiMix)..."<<std::endl;
for (UInt_t i = 0; i < nSigComp_; i++) {
coeffPars_[i]->randomiseInitValues();
}
phiMix_.randomiseValue(-LauConstants::pi, LauConstants::pi);
if (useSinCos_) {
sinPhiMix_.initValue(TMath::Sin(phiMix_.initValue()));
cosPhiMix_.initValue(TMath::Cos(phiMix_.initValue()));
}
}
LauTimeDepFitModel::LauGenInfo LauTimeDepFitModel::eventsToGenerate()
{
// Determine the number of events to generate for each hypothesis
// If we're smearing then smear each one individually
// NB this individual smearing has to be done individually per tagging category as well
LauGenInfo nEvtsGen;
LauTagCatGenInfo eventsB0, eventsB0bar;
// Signal
// If we're including the DP and decay time we can't decide on the tag
// yet, since it depends on the whole DP+dt PDF, however, if
// we're not then we need to decide.
Double_t evtWeight(1.0);
Double_t nEvts = signalEvents_->genValue();
if ( nEvts < 0.0 ) {
evtWeight = -1.0;
nEvts = TMath::Abs( nEvts );
}
LauTagCatParamMap& signalTagCatFrac = flavTag_->getSignalTagCatFrac();
Double_t sigAsym(0.0);
if (this->useDP() == kFALSE) {
sigAsym = signalAsym_->genValue();
for (LauTagCatParamMap::const_iterator iter = signalTagCatFrac.begin(); iter != signalTagCatFrac.end(); ++iter) {
const LauParameter& par = iter->second;
Double_t eventsbyTagCat = par.value() * nEvts;
Double_t eventsB0byTagCat = TMath::Nint(eventsbyTagCat/2.0 * (1.0 - sigAsym));
Double_t eventsB0barbyTagCat = TMath::Nint(eventsbyTagCat/2.0 * (1.0 + sigAsym));
if (this->doPoissonSmearing()) {
eventsB0byTagCat = LauRandom::randomFun()->Poisson(eventsB0byTagCat);
eventsB0barbyTagCat = LauRandom::randomFun()->Poisson(eventsB0barbyTagCat);
}
eventsB0[iter->first] = std::make_pair( TMath::Nint(eventsB0byTagCat), evtWeight );
eventsB0bar[iter->first] = std::make_pair( TMath::Nint(eventsB0barbyTagCat), evtWeight );
}
// CONVENTION WARNING
nEvtsGen[std::make_pair("signal",-1)] = eventsB0;
nEvtsGen[std::make_pair("signal",+1)] = eventsB0bar;
} else {
Double_t rateB0bar = sigModelB0bar_->getDPRate().value();
Double_t rateB0 = sigModelB0_->getDPRate().value();
if ( rateB0bar+rateB0 > 1e-30) {
sigAsym = (rateB0bar-rateB0)/(rateB0bar+rateB0);
}
for (LauTagCatParamMap::const_iterator iter = signalTagCatFrac.begin(); iter != signalTagCatFrac.end(); ++iter) {
const LauParameter& par = iter->second;
Double_t eventsbyTagCat = par.value() * nEvts;
if (this->doPoissonSmearing()) {
eventsbyTagCat = LauRandom::randomFun()->Poisson(eventsbyTagCat);
}
eventsB0[iter->first] = std::make_pair( TMath::Nint(eventsbyTagCat), evtWeight );
}
nEvtsGen[std::make_pair("signal",0)] = eventsB0; // generate signal event, decide tag later.
}
std::cout<<"INFO in LauTimeDepFitModel::eventsToGenerate : Generating toy MC with:"<<std::endl;
std::cout<<" : Signal asymmetry = "<<sigAsym<<" and number of signal events = "<<signalEvents_->genValue()<<std::endl;
return nEvtsGen;
}
Bool_t LauTimeDepFitModel::genExpt()
{
// Routine to generate toy Monte Carlo events according to the various models we have defined.
// Determine the number of events to generate for each hypothesis
LauGenInfo nEvts = this->eventsToGenerate();
Bool_t genOK(kTRUE);
Int_t evtNum(0);
const UInt_t nBkgnds = this->nBkgndClasses();
std::vector<TString> bkgndClassNames(nBkgnds);
std::vector<TString> bkgndClassNamesGen(nBkgnds);
for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
TString name( this->bkgndClassName(iBkgnd) );
bkgndClassNames[iBkgnd] = name;
bkgndClassNamesGen[iBkgnd] = "gen"+name;
}
// Loop over the hypotheses and generate the appropriate number of
// events for each one
for (LauGenInfo::const_iterator iter = nEvts.begin(); iter != nEvts.end(); ++iter) {
// find the category of events (e.g. signal)
const TString& evtCategory(iter->first.first);
// Type
const TString& type(iter->first.first);
// find the true flavour
curEvtTagFlv_ = iter->first.second;
// loop through each tagging category
const LauTagCatGenInfo& eventsByTagCat = iter->second;
for (LauTagCatGenInfo::const_iterator tagCatIter = eventsByTagCat.begin(); tagCatIter != eventsByTagCat.end(); ++tagCatIter) {
// get the tagging category
curEvtTagCat_ = tagCatIter->first;
// get the event weight for this category
const Double_t evtWeight( tagCatIter->second.second );
// get the number of events to generate in this configuration
const Int_t nEvtsGen( tagCatIter->second.first );
for (Int_t iEvt(0); iEvt<nEvtsGen; ++iEvt) {
this->setGenNtupleDoubleBranchValue( "evtWeight", evtWeight );
if (evtCategory == "signal") {
this->setGenNtupleIntegerBranchValue("genSig",1);
for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
this->setGenNtupleIntegerBranchValue( bkgndClassNamesGen[iBkgnd], 0 );
}
// All the generate*Event() methods have to fill in curEvtDecayTime_ and curEvtDecayTimeErr_
// In addition, generateSignalEvent has to decide on the tag and fill in curEvtTagFlv_
genOK = this->generateSignalEvent();
} else {
this->setGenNtupleIntegerBranchValue("genSig",0);
UInt_t bkgndID(0);
for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
Int_t gen(0);
if ( bkgndClassNames[iBkgnd] == type ) {
gen = 1;
bkgndID = iBkgnd;
}
this->setGenNtupleIntegerBranchValue( bkgndClassNamesGen[iBkgnd], gen );
}
genOK = this->generateBkgndEvent(bkgndID);
}
if (!genOK) {
// If there was a problem with the generation then break out and return.
// The problem model will have adjusted itself so that all should be OK next time.
break;
}
if (this->useDP() == kTRUE) {
this->setDPDtBranchValues(); // store DP, decay time and tagging variables in the ntuple
}
// If the tagging category is 0, we don't know what the flavour is...
if (curEvtTagCat_==0){
curEvtTagFlv_=0;
}
// Store the event's tag and tagging category
this->setGenNtupleIntegerBranchValue("cpEigenvalue", cpEigenValue_);
this->setGenNtupleIntegerBranchValue("tagCat",curEvtTagCat_);
this->setGenNtupleIntegerBranchValue("tagFlv",curEvtTagFlv_);
this->setGenNtupleDoubleBranchValue(flavTag_->getMistagVarName(),curEvtMistag_);
this->setGenNtupleDoubleBranchValue(flavTag_->getTrueTagVarName(),curEvtTrueTagFlv_);
//Store calibration parameters and asymmetries during debugging at least
//this->setGenNtupleDoubleBranchValue("AProd", AProd_->unblindValue());
//this->setGenNtupleDoubleBranchValue("ARaw", ARaw_->unblindValue());
// Store the event number (within this experiment)
// and then increment it
this->setGenNtupleIntegerBranchValue("iEvtWithinExpt",evtNum);
++evtNum;
// Write the values into the tree
this->fillGenNtupleBranches();
// Print an occasional progress message
if (iEvt%1000 == 0) {std::cout<<"INFO in LauTimeDepFitModel::genExpt : Generated event number "<<iEvt<<" out of "<<nEvtsGen<<" "<<evtCategory<<" events."<<std::endl;}
} // end of loop over events for given species, tagCat and tagFlv
if (!genOK) {
break;
}
} // end of loop over tagCats for the given species and tagFlv
if (!genOK) {
break;
}
} //end of loop over species and tagFlv.
if (this->useDP() && genOK) {
sigModelB0bar_->checkToyMC(kTRUE);
sigModelB0_->checkToyMC(kTRUE);
std::cout<<"aSqMaxSet = "<<aSqMaxSet_<<" and aSqMaxVar = "<<aSqMaxVar_<<std::endl;
// Get the fit fractions if they're to be written into the latex table
if (this->writeLatexTable()) {
LauParArray fitFracB0bar = sigModelB0bar_->getFitFractions();
if (fitFracB0bar.size() != nSigComp_) {
std::cerr<<"ERROR in LauTimeDepFitModel::generate : Fit Fraction array of unexpected dimension: "<<fitFracB0bar.size()<<std::endl;
gSystem->Exit(EXIT_FAILURE);
}
for (UInt_t i(0); i<nSigComp_; ++i) {
if (fitFracB0bar[i].size() != nSigComp_) {
std::cerr<<"ERROR in LauTimeDepFitModel::generate : Fit Fraction array of unexpected dimension: "<<fitFracB0bar[i].size()<<std::endl;
gSystem->Exit(EXIT_FAILURE);
}
}
LauParArray fitFracB0 = sigModelB0_->getFitFractions();
if (fitFracB0.size() != nSigComp_) {
std::cerr<<"ERROR in LauTimeDepFitModel::generate : Fit Fraction array of unexpected dimension: "<<fitFracB0.size()<<std::endl;
gSystem->Exit(EXIT_FAILURE);
}
for (UInt_t i(0); i<nSigComp_; ++i) {
if (fitFracB0[i].size() != nSigComp_) {
std::cerr<<"ERROR in LauTimeDepFitModel::generate : Fit Fraction array of unexpected dimension: "<<fitFracB0[i].size()<<std::endl;
gSystem->Exit(EXIT_FAILURE);
}
}
for (UInt_t i(0); i<nSigComp_; ++i) {
for (UInt_t j(i); j<nSigComp_; ++j) {
fitFracB0bar_[i][j].value(fitFracB0bar[i][j].value());
fitFracB0_[i][j].value(fitFracB0[i][j].value());
}
}
meanEffB0bar_.value(sigModelB0bar_->getMeanEff().value());
meanEffB0_.value(sigModelB0_->getMeanEff().value());
DPRateB0bar_.value(sigModelB0bar_->getDPRate().value());
DPRateB0_.value(sigModelB0_->getDPRate().value());
}
}
// If we're reusing embedded events or if the generation is being
// reset then clear the lists of used events
//if (!signalTree_.empty() && (reuseSignal_ || !genOK)) {
if (reuseSignal_ || !genOK) {
for(LauTagCatEmbDataMap::const_iterator iter = signalTree_.begin(); iter != signalTree_.end(); ++iter) {
(iter->second)->clearUsedList();
}
}
for ( UInt_t bkgndID(0); bkgndID < nBkgnds; ++bkgndID ) {
if (reuseBkgnd_[bkgndID] || !genOK) {
for (LauTagCatEmbDataMapList::iterator iterlist = bkgndTree_.begin(); iterlist != bkgndTree_.end(); ++iterlist){
for (LauTagCatEmbDataMap::iterator iter = (*iterlist).begin(); iter != (*iterlist).end(); ++iter){
(iter->second)->clearUsedList();
}
}
}
}
return genOK;
}
Bool_t LauTimeDepFitModel::generateSignalEvent()
{
// Generate signal event, including SCF if necessary.
// DP:DecayTime generation follows.
// If it's ok, we then generate mES, DeltaE, Fisher/NN...
Bool_t genOK(kTRUE);
Bool_t generatedEvent(kFALSE);
Bool_t doSquareDP = kinematicsB0bar_->squareDP();
doSquareDP &= kinematicsB0_->squareDP();
LauKinematics* kinematics(kinematicsB0bar_);
// find the right decay time PDF for the current tagging category
LauTagCatDtPdfMap::const_iterator dt_iter = signalDecayTimePdfs_.find(curEvtTagCat_);
LauDecayTimePdf* decayTimePdf = (dt_iter != signalDecayTimePdfs_.end()) ? dt_iter->second : 0;
// find the right embedded data for the current tagging category
LauTagCatEmbDataMap::const_iterator emb_iter = signalTree_.find(curEvtTagCat_);
LauEmbeddedData* embeddedData = (emb_iter != signalTree_.end()) ? emb_iter->second : 0;
// find the right extra PDFs for the current tagging category
LauTagCatPdfListMap::iterator extra_iter = sigExtraPdf_.find(curEvtTagCat_);
LauPdfList* extraPdfs = (extra_iter != sigExtraPdf_.end()) ? &(extra_iter->second) : 0;
if (this->useDP()) {
if (embeddedData) {
embeddedData->getEmbeddedEvent(kinematics);
curEvtTagFlv_ = TMath::Nint(embeddedData->getValue("tagFlv"));
curEvtDecayTimeErr_ = embeddedData->getValue(decayTimePdf->varErrName());
curEvtDecayTime_ = embeddedData->getValue(decayTimePdf->varName());
if (embeddedData->haveBranch("mcMatch")) {
Int_t match = TMath::Nint(embeddedData->getValue("mcMatch"));
if (match) {
this->setGenNtupleIntegerBranchValue("genTMSig",1);
this->setGenNtupleIntegerBranchValue("genSCFSig",0);
} else {
this->setGenNtupleIntegerBranchValue("genTMSig",0);
this->setGenNtupleIntegerBranchValue("genSCFSig",1);
}
}
} else {
nGenLoop_ = 0;
// generate the decay time error (NB the kTRUE forces the generation of a new value)
curEvtDecayTimeErr_ = decayTimePdf->generateError(kTRUE);
while (generatedEvent == kFALSE && nGenLoop_ < iterationsMax_) {
// Calculate the unnormalised truth-matched signal likelihood
// First let define the tag flavour CONVENTION WARNING
Double_t randNo = LauRandom::randomFun()->Rndm();
if (randNo < 0.5) {
curEvtTagFlv_ = +1; // B0bar tag
} else {
curEvtTagFlv_ = -1; // B0 tag
}
curEvtTrueTagFlv_ = 0;
if (decayTimePdf->getFuncType() == LauDecayTimePdf::SimFitNormBd || decayTimePdf->getFuncType() == LauDecayTimePdf::SimFitNormBs || curEvtTagCat_==0){
// First let define the tag flavour CONVENTION WARNING
Double_t rand = LauRandom::randomFun()->Rndm();
if (rand < 0.5) {
curEvtTrueTagFlv_ = +1; // B0bar tag
} else {
curEvtTrueTagFlv_ = -1; // B0 tag
}
}
// Calculate event quantities that depend only on the tagCat and tagFlv
Double_t qD(0.);
Double_t qDDo2(0.);
if(flavTag_->getUsePerEvtMistag() && curEvtTagCat_!=0 ){
curEvtMistag_ = flavTag_->getEtaGen(sigFlavTagPdf_[curEvtTagCat_]);
Double_t omega = flavTag_->getOmegaGen(curEvtMistag_,curEvtTagFlv_,curEvtTagCat_);
qD = curEvtTagFlv_*(1.0-2.0*omega);
//qD = curEvtTagFlv_*(1-2*curEvtMistag_);
}else{
curEvtMistag_ = 0.5;
LauTagCatParamMap dilution_ = flavTag_->getDilution();
LauTagCatParamMap deltaDilution_ = flavTag_->getDeltaDilution();
qD = curEvtTagFlv_*dilution_[curEvtTagCat_].unblindValue();
qDDo2 = curEvtTagFlv_*0.5*deltaDilution_[curEvtTagCat_].unblindValue();
}
// Generate the DP position
Double_t m13Sq(0.0), m23Sq(0.0);
kinematicsB0bar_->genFlatPhaseSpace(m13Sq, m23Sq);
// Next, calculate the total A and Abar for the given DP position
sigModelB0_->calcLikelihoodInfo(m13Sq, m23Sq);
sigModelB0bar_->calcLikelihoodInfo(m13Sq, m23Sq);
// Retrieve the amplitudes and efficiency from the dynamics
const LauComplex& Abar = sigModelB0bar_->getEvtDPAmp();
const LauComplex& A = sigModelB0_->getEvtDPAmp();
Double_t eff = sigModelB0bar_->getEvtEff();
// Next calculate the DP terms
Double_t aSqSum = A.abs2() + Abar.abs2();
Double_t aSqDif = A.abs2() - Abar.abs2();
LauComplex inter = Abar * A.conj() * phiMixComplex_;
Double_t interTermIm = 2.0 * inter.im();
Double_t interTermRe = 2.0 * inter.re();
// Generate decay time
const Double_t tMin = decayTimePdf->minAbscissa();
const Double_t tMax = decayTimePdf->maxAbscissa();
curEvtDecayTime_ = LauRandom::randomFun()->Rndm()*(tMax-tMin) + tMin;
// Calculate all the decay time info
decayTimePdf->calcLikelihoodInfo(curEvtDecayTime_,curEvtDecayTimeErr_);
//decayTimePdf->calcLikelihoodInfoGen(curEvtDecayTime_,curEvtDecayTimeErr_,curEvtTagFlv_,curEvtMistag_,curEvtTagCat_,curEvtTrueTagFlv_);
// ...and check that the calculation went ok, otherwise loop again
if (decayTimePdf->state() != LauDecayTimePdf::Good) {
std::cout<<"decayTimePdf state is bad"<<std::endl;
++nGenLoop_;
continue;
}
// Get the decay time acceptance
Double_t dtEff = decayTimePdf->getEffiTerm();
// First get all the decay time terms
Double_t dtCos = decayTimePdf->getCosTerm();
Double_t dtSin = decayTimePdf->getSinTerm();
Double_t dtCosh = decayTimePdf->getCoshTerm();
Double_t dtSinh = decayTimePdf->getSinhTerm();
// Get flavour tagging terms
Double_t omega(0);
Double_t omegabar(0);
if (curEvtTagFlv_==1){
omegabar = flavTag_->getOmegaGen(curEvtMistag_,curEvtTagFlv_,curEvtTagCat_);
} else if (curEvtTagFlv_==-1){
omega = flavTag_->getOmegaGen(curEvtMistag_,curEvtTagFlv_,curEvtTagCat_);
}
Double_t ftOmegaTrig(1.);
Double_t ftOmegaHyp(1.);
if (curEvtTagCat_!=0){
ftOmegaTrig = (1 - AProd_.unblindValue())*omegabar - (1 + AProd_.unblindValue())*omega;
ftOmegaHyp = (1 - AProd_.unblindValue())*omegabar + (1 + AProd_.unblindValue())*omega;
}
// Combine all terms
Double_t cosTerm = dtCos * ftOmegaTrig * ARaw_.unblindValue() * qD * aSqDif;
Double_t sinTerm = dtSin * ftOmegaTrig * ARaw_.unblindValue() * qD * interTermIm;
Double_t coshTerm = dtCosh * ftOmegaHyp * ARaw_.unblindValue() * (1.0 + qDDo2) * aSqSum;
Double_t sinhTerm = dtSinh * ftOmegaHyp * ARaw_.unblindValue() * (1.0 + qDDo2) * interTermRe;
if (decayTimePdf->getFuncType() == LauDecayTimePdf::SimFitNormBd || decayTimePdf->getFuncType() == LauDecayTimePdf::SimFitNormBs){
cosTerm *= curEvtTrueTagFlv_;
sinTerm *= curEvtTrueTagFlv_;
}
if ( cpEigenValue_ == CPOdd ) {
sinTerm *= -1.0;
sinhTerm *= -1.0;
}
// ... to get the total and multiply by the efficiency
Double_t ASq = coshTerm + cosTerm - sinTerm + sinhTerm;
//if (curEvtTrueTagFlv_ !=0 && curEvtTagCat_>0 && curEvtTagFlv_==-1){
// std::cout<<"##### Event details #####"<<std::endl;
// std::cout<< "dtCos dtSin dtCosh dtSinh " << dtCos << " " << dtSin << " " << dtCosh << " " << dtSinh << std::endl;
// std::cout<< "qD aSqDif dtSin interTermIm " << qD << " " << aSqDif << " " << dtSin << " " << interTermIm << std::endl;
// std::cout<<"Cosh Cos Sin Sinh "<< coshTerm<< " " << cosTerm << " " << sinTerm << " " << sinhTerm << std::endl;
// std::cout<< "Total Amplitude : " << ASq << std::endl;
//}
//ASq /= decayTimePdf->getNormTerm();
ASq *= eff;
ASq *= dtEff;
//std::cout << "Total Amplitude Eff: " << ASq << std::endl;
//Finally we throw the dice to see whether this event should be generated
//We make a distinction between the likelihood of TM and SCF to tag the SCF events as such
randNo = LauRandom::randomFun()->Rndm();
if (randNo <= ASq/aSqMaxSet_ ) {
generatedEvent = kTRUE;
nGenLoop_ = 0;
if (ASq > aSqMaxVar_) {aSqMaxVar_ = ASq;}
} else {
nGenLoop_++;
}
} // end of while !generatedEvent loop
} // end of if (embeddedData) else control
} else {
if ( embeddedData ) {
embeddedData->getEmbeddedEvent(0);
curEvtTagFlv_ = TMath::Nint(embeddedData->getValue("tagFlv"));
curEvtDecayTimeErr_ = embeddedData->getValue(decayTimePdf->varErrName());
curEvtDecayTime_ = embeddedData->getValue(decayTimePdf->varName());
}
}
// Check whether we have generated the toy MC OK.
if (nGenLoop_ >= iterationsMax_) {
aSqMaxSet_ = 1.01 * aSqMaxVar_;
genOK = kFALSE;
std::cerr<<"WARNING in LauTimeDepFitModel::generateSignalEvent : Hit max iterations: setting aSqMaxSet_ to "<<aSqMaxSet_<<std::endl;
} else if (aSqMaxVar_ > aSqMaxSet_) {
aSqMaxSet_ = 1.01 * aSqMaxVar_;
genOK = kFALSE;
std::cerr<<"WARNING in LauTimeDepFitModel::generateSignalEvent : Found a larger ASq value: setting aSqMaxSet_ to "<<aSqMaxSet_<<std::endl;
}
if (genOK) {
//Some variables, like Fisher or NN, might use m13Sq and m23Sq from the kinematics
//kinematicsB0bar_ is up to date, update kinematicsB0_
kinematicsB0_->updateKinematics(kinematicsB0bar_->getm13Sq(), kinematicsB0bar_->getm23Sq() );
this->generateExtraPdfValues(extraPdfs, embeddedData);
}
// Check for problems with the embedding
if (embeddedData && (embeddedData->nEvents() == embeddedData->nUsedEvents())) {
std::cerr<<"WARNING in LauTimeDepFitModel::generateSignalEvent : Source of embedded signal events used up, clearing the list of used events."<<std::endl;
embeddedData->clearUsedList();
}
return genOK;
}
Bool_t LauTimeDepFitModel::generateBkgndEvent(UInt_t bkgndID)
{
// Generate Bkgnd event
Bool_t genOK(kTRUE);
LauAbsBkgndDPModel* model(0);
LauEmbeddedData* embeddedData(0);
LauPdfList* extraPdfs(0);
LauKinematics* kinematics(0);
model = BkgndDPModels_[bkgndID];
if (this->enableEmbedding()) {
// find the right embedded data for the current tagging category
LauTagCatEmbDataMap::const_iterator emb_iter = bkgndTree_[bkgndID].find(curEvtTagCat_);
embeddedData = (emb_iter != bkgndTree_[bkgndID].end()) ? emb_iter->second : 0;
}
extraPdfs = &BkgndPdfs_[bkgndID];
kinematics = kinematicsB0bar_;
if (this->useDP()) {
if (embeddedData) {
embeddedData->getEmbeddedEvent(kinematics);
} else {
if (model == 0) {
const TString& bkgndClass = this->bkgndClassName(bkgndID);
std::cerr << "ERROR in LauCPFitModel::generateBkgndEvent : Can't find the DP model for background class \"" << bkgndClass << "\"." << std::endl;
gSystem->Exit(EXIT_FAILURE);
}
genOK = model->generate();
}
} else {
if (embeddedData) {
embeddedData->getEmbeddedEvent(0);
}
}
if (genOK) {
this->generateExtraPdfValues(extraPdfs, embeddedData);
}
// Check for problems with the embedding
if (embeddedData && (embeddedData->nEvents() == embeddedData->nUsedEvents())) {
const TString& bkgndClass = this->bkgndClassName(bkgndID);
std::cerr << "WARNING in LauCPFitModel::generateBkgndEvent : Source of embedded " << bkgndClass << " events used up, clearing the list of used events." << std::endl;
embeddedData->clearUsedList();
}
return genOK;
}
void LauTimeDepFitModel::setupGenNtupleBranches()
{
// Setup the required ntuple branches
this->addGenNtupleDoubleBranch("evtWeight");
this->addGenNtupleIntegerBranch("genSig");
this->addGenNtupleIntegerBranch("cpEigenvalue");
this->addGenNtupleIntegerBranch("tagFlv");
this->addGenNtupleIntegerBranch("tagCat");
if (this->useDP() == kTRUE) {
// Let's add the decay time variables.
if (signalDecayTimePdfs_.begin() != signalDecayTimePdfs_.end()) {
LauDecayTimePdf* pdf = signalDecayTimePdfs_.begin()->second;
this->addGenNtupleDoubleBranch(pdf->varName());
this->addGenNtupleDoubleBranch(pdf->varErrName());
}
this->addGenNtupleDoubleBranch("m12");
this->addGenNtupleDoubleBranch("m23");
this->addGenNtupleDoubleBranch("m13");
this->addGenNtupleDoubleBranch("m12Sq");
this->addGenNtupleDoubleBranch("m23Sq");
this->addGenNtupleDoubleBranch("m13Sq");
this->addGenNtupleDoubleBranch("cosHel12");
this->addGenNtupleDoubleBranch("cosHel23");
this->addGenNtupleDoubleBranch("cosHel13");
if (kinematicsB0bar_->squareDP() && kinematicsB0_->squareDP()) {
this->addGenNtupleDoubleBranch("mPrime");
this->addGenNtupleDoubleBranch("thPrime");
}
// Can add the real and imaginary parts of the B0 and B0bar total
// amplitudes seen in the generation (restrict this with a flag
// that defaults to false)
if ( storeGenAmpInfo_ ) {
this->addGenNtupleDoubleBranch("reB0Amp");
this->addGenNtupleDoubleBranch("imB0Amp");
this->addGenNtupleDoubleBranch("reB0barAmp");
this->addGenNtupleDoubleBranch("imB0barAmp");
}
}
// Let's look at the extra variables for signal in one of the tagging categories
if ( ! sigExtraPdf_.empty() ) {
LauPdfList oneTagCatPdfList = sigExtraPdf_.begin()->second;
for (LauPdfList::const_iterator pdf_iter = oneTagCatPdfList.begin(); pdf_iter != oneTagCatPdfList.end(); ++pdf_iter) {
for ( std::vector<TString>::const_iterator var_iter = (*pdf_iter)->varNames().begin(); var_iter != (*pdf_iter)->varNames().end(); ++var_iter ) {
if ( (*var_iter) != "m13Sq" && (*var_iter) != "m23Sq" ) {
this->addGenNtupleDoubleBranch( (*var_iter) );
}
}
}
}
}
void LauTimeDepFitModel::setDPDtBranchValues()
{
// Store the decay time variables.
if (signalDecayTimePdfs_.begin() != signalDecayTimePdfs_.end()) {
LauDecayTimePdf* pdf = signalDecayTimePdfs_.begin()->second;
this->setGenNtupleDoubleBranchValue(pdf->varName(),curEvtDecayTime_);
this->setGenNtupleDoubleBranchValue(pdf->varErrName(),curEvtDecayTimeErr_);
}
// CONVENTION WARNING
LauKinematics* kinematics(0);
if (curEvtTagFlv_<0) {
kinematics = kinematicsB0_;
} else {
kinematics = kinematicsB0bar_;
}
// Store all the DP information
this->setGenNtupleDoubleBranchValue("m12", kinematics->getm12());
this->setGenNtupleDoubleBranchValue("m23", kinematics->getm23());
this->setGenNtupleDoubleBranchValue("m13", kinematics->getm13());
this->setGenNtupleDoubleBranchValue("m12Sq", kinematics->getm12Sq());
this->setGenNtupleDoubleBranchValue("m23Sq", kinematics->getm23Sq());
this->setGenNtupleDoubleBranchValue("m13Sq", kinematics->getm13Sq());
this->setGenNtupleDoubleBranchValue("cosHel12", kinematics->getc12());
this->setGenNtupleDoubleBranchValue("cosHel23", kinematics->getc23());
this->setGenNtupleDoubleBranchValue("cosHel13", kinematics->getc13());
if (kinematics->squareDP()) {
this->setGenNtupleDoubleBranchValue("mPrime", kinematics->getmPrime());
this->setGenNtupleDoubleBranchValue("thPrime", kinematics->getThetaPrime());
}
// Can add the real and imaginary parts of the B0 and B0bar total
// amplitudes seen in the generation (restrict this with a flag
// that defaults to false)
if ( storeGenAmpInfo_ ) {
if ( this->getGenNtupleIntegerBranchValue("genSig")==1 ) {
LauComplex Abar = sigModelB0bar_->getEvtDPAmp();
LauComplex A = sigModelB0_->getEvtDPAmp();
this->setGenNtupleDoubleBranchValue("reB0Amp", A.re());
this->setGenNtupleDoubleBranchValue("imB0Amp", A.im());
this->setGenNtupleDoubleBranchValue("reB0barAmp", Abar.re());
this->setGenNtupleDoubleBranchValue("imB0barAmp", Abar.im());
} else {
this->setGenNtupleDoubleBranchValue("reB0Amp", 0.0);
this->setGenNtupleDoubleBranchValue("imB0Amp", 0.0);
this->setGenNtupleDoubleBranchValue("reB0barAmp", 0.0);
this->setGenNtupleDoubleBranchValue("imB0barAmp", 0.0);
}
}
}
void LauTimeDepFitModel::generateExtraPdfValues(LauPdfList* extraPdfs, LauEmbeddedData* embeddedData)
{
// CONVENTION WARNING
LauKinematics* kinematics(0);
if (curEvtTagFlv_<0) {
kinematics = kinematicsB0_;
} else {
kinematics = kinematicsB0bar_;
}
// Generate from the extra PDFs
if (extraPdfs) {
for (LauPdfList::iterator pdf_iter = extraPdfs->begin(); pdf_iter != extraPdfs->end(); ++pdf_iter) {
LauFitData genValues;
if (embeddedData) {
genValues = embeddedData->getValues( (*pdf_iter)->varNames() );
} else {
genValues = (*pdf_iter)->generate(kinematics);
}
for ( LauFitData::const_iterator var_iter = genValues.begin(); var_iter != genValues.end(); ++var_iter ) {
TString varName = var_iter->first;
if ( varName != "m13Sq" && varName != "m23Sq" ) {
Double_t value = var_iter->second;
this->setGenNtupleDoubleBranchValue(varName,value);
}
}
}
}
}
void LauTimeDepFitModel::propagateParUpdates()
{
// Update the complex mixing phase
if (useSinCos_) {
phiMixComplex_.setRealPart(cosPhiMix_.unblindValue());
phiMixComplex_.setImagPart(-1.0*sinPhiMix_.unblindValue());
} else {
phiMixComplex_.setRealPart(TMath::Cos(-1.0*phiMix_.unblindValue()));
phiMixComplex_.setImagPart(TMath::Sin(-1.0*phiMix_.unblindValue()));
}
// Update the total normalisation for the signal likelihood
if (this->useDP() == kTRUE) {
this->updateCoeffs();
sigModelB0bar_->updateCoeffs(coeffsB0bar_);
sigModelB0_->updateCoeffs(coeffsB0_);
this->calcInterTermNorm();
}
// Update the signal events from the background numbers if not doing an extended fit
// And update the tagging category fractions
this->updateSigEvents();
}
void LauTimeDepFitModel::updateSigEvents()
{
// The background parameters will have been set from Minuit.
// We need to update the signal events using these.
if (!this->doEMLFit()) {
Double_t nTotEvts = this->eventsPerExpt();
Double_t signalEvents = nTotEvts;
signalEvents_->range(-2.0*nTotEvts,2.0*nTotEvts);
for (LauBkgndYieldList::iterator iter = bkgndEvents_.begin(); iter != bkgndEvents_.end(); ++iter) {
LauAbsRValue* nBkgndEvents = (*iter);
if ( nBkgndEvents->isLValue() ) {
LauParameter* yield = dynamic_cast<LauParameter*>( nBkgndEvents );
yield->range(-2.0*nTotEvts,2.0*nTotEvts);
}
}
// Subtract background events (if any) from signal.
if (usingBkgnd_ == kTRUE) {
for (LauBkgndYieldList::const_iterator iter = bkgndEvents_.begin(); iter != bkgndEvents_.end(); ++iter) {
signalEvents -= (*iter)->value();
}
}
if ( ! signalEvents_->fixed() ) {
signalEvents_->value(signalEvents);
}
}
// tagging-category fractions for signal events
flavTag_->setFirstTagCatFractions();
}
void LauTimeDepFitModel::cacheInputFitVars()
{
// Fill the internal data trees of the signal and background models.
// Note that we store the events of both charges in both the
// negative and the positive models. It's only later, at the stage
// when the likelihood is being calculated, that we separate them.
LauFitDataTree* inputFitData = this->fitData();
evtCPEigenVals_.clear();
const Bool_t hasCPEV = ( (cpevVarName_ != "") && inputFitData->haveBranch( cpevVarName_ ) );
UInt_t nEvents = inputFitData->nEvents();
evtCPEigenVals_.reserve( nEvents );
LauFitData::const_iterator fitdata_iter;
for (UInt_t iEvt = 0; iEvt < nEvents; iEvt++) {
const LauFitData& dataValues = inputFitData->getData(iEvt);
// if the CP-eigenvalue is in the data use those, otherwise use the default
if ( hasCPEV ) {
fitdata_iter = dataValues.find( cpevVarName_ );
const Int_t cpEV = static_cast<Int_t>( fitdata_iter->second );
if ( cpEV == 1 ) {
cpEigenValue_ = CPEven;
} else if ( cpEV == -1 ) {
cpEigenValue_ = CPOdd;
} else {
std::cerr<<"WARNING in LauTimeDepFitModel::cacheInputFitVars : Unknown value: "<<cpEV<<" for CP eigenvalue, setting to CP-even"<<std::endl;
cpEigenValue_ = CPEven;
}
}
evtCPEigenVals_.push_back( cpEigenValue_ );
}
// We'll cache the DP amplitudes at the end because we'll
// append some points that the other PDFs won't deal with.
// Flavour tagging information
flavTag_->cacheInputFitVars(inputFitData);
if (this->useDP() == kTRUE) {
// DecayTime and SigmaDecayTime
for (LauTagCatDtPdfMap::iterator dt_iter = signalDecayTimePdfs_.begin(); dt_iter != signalDecayTimePdfs_.end(); ++dt_iter) {
(*dt_iter).second->cacheInfo(*inputFitData);
}
}
// ...and then the extra PDFs
for (LauTagCatPdfListMap::iterator pdf_iter = sigExtraPdf_.begin(); pdf_iter != sigExtraPdf_.end(); ++pdf_iter) {
this->cacheInfo(pdf_iter->second, *inputFitData);
}
if(usingBkgnd_ == kTRUE){
for (LauBkgndPdfsList::iterator iter = BkgndPdfs_.begin(); iter != BkgndPdfs_.end(); ++iter) {
this->cacheInfo((*iter), *inputFitData);
}
}
if (this->useDP() == kTRUE) {
sigModelB0bar_->fillDataTree(*inputFitData);
sigModelB0_->fillDataTree(*inputFitData);
if (usingBkgnd_ == kTRUE) {
for (LauBkgndDPModelList::iterator iter = BkgndDPModels_.begin(); iter != BkgndDPModels_.end(); ++iter) {
(*iter)->fillDataTree(*inputFitData);
}
}
}
}
Double_t LauTimeDepFitModel::getTotEvtLikelihood(const UInt_t iEvt)
{
// Find out whether the tag-side B was a B0 or a B0bar.
curEvtTagFlv_ = flavTag_->getEvtTagFlvVals(iEvt);
// Also get the tagging category.
curEvtTagCat_ = flavTag_->getEvtTagCatVals(iEvt);
// Get the CP eigenvalue of the current event
cpEigenValue_ = evtCPEigenVals_[iEvt];
// Get the DP and DecayTime likelihood for signal (TODO and eventually backgrounds)
this->getEvtDPDtLikelihood(iEvt);
// Get the flavour tagging likelihood from eta PDFs (per tagging category - TODO backgrounds to come later)
sigFlavTagLike_ = 1.0;
//this->getEvtFlavTagLikelihood(iEvt);
// Get the combined extra PDFs likelihood for signal (TODO and eventually backgrounds)
this->getEvtExtraLikelihoods(iEvt);
// Construct the total likelihood for signal, qqbar and BBbar backgrounds
Double_t sigLike = sigDPLike_ * sigFlavTagLike_ * sigExtraLike_;
//std::cout << "DP like = " << sigDPLike_ << std::endl;
//std::cout << "flav tag like = " << sigFlavTagLike_ << std::endl;
//std::cout << "extra like = " << sigExtraLike_ << std::endl;
Double_t signalEvents = signalEvents_->unblindValue();
if (this->useDP() == kFALSE) {
signalEvents *= 0.5 * (1.0 + curEvtTagFlv_ * signalAsym_->unblindValue());
}
// TODO better to store this info for each event
LauTagCatParamMap& signalTagCatFrac = flavTag_->getSignalTagCatFrac();
const Double_t sigTagCatFrac = signalTagCatFrac[curEvtTagCat_].unblindValue();
//std::cout << "Signal tag cat frac = " << sigTagCatFrac << std::endl;
// Construct the total event likelihood
Double_t likelihood(sigLike*sigTagCatFrac);
//std::cout << "Likelihoood = " << likelihood << std::endl;
if ( ! signalEvents_->fixed() ) {
likelihood *= signalEvents;
}
return likelihood;
}
Double_t LauTimeDepFitModel::getEventSum() const
{
Double_t eventSum(0.0);
eventSum += signalEvents_->unblindValue();
return eventSum;
}
void LauTimeDepFitModel::getEvtDPDtLikelihood(const UInt_t iEvt)
{
// Function to return the signal and background likelihoods for the
// Dalitz plot for the given event evtNo.
if ( ! this->useDP() ) {
// There's always going to be a term in the likelihood for the
// signal, so we'd better not zero it.
sigDPLike_ = 1.0;
const UInt_t nBkgnds = this->nBkgndClasses();
for ( UInt_t bkgndID(0); bkgndID < nBkgnds; ++bkgndID ) {
if (usingBkgnd_ == kTRUE) {
bkgndDPLike_[bkgndID] = 1.0;
} else {
bkgndDPLike_[bkgndID] = 0.0;
}
}
return;
}
// Mistag probabilities. Defined as: omega = prob of the tagging B0 being reported as B0bar
// Whether we want omega or omegaBar depends on q_tag, hence curEvtTagFlv_*... in the previous lines
//Double_t misTagFrac = 0.5 * (1.0 - dilution_[curEvtTagCat_] - qDDo2);
//Double_t misTagFracBar = 0.5 * (1.0 - dilution_[curEvtTagCat_] + qDDo2);
// Calculate event quantities
Double_t qD(0);
Double_t qDDo2(0);
if (flavTag_->getUsePerEvtMistag()){
//qDDo2 term accounted for automatically with per event information
Double_t omega = flavTag_->getOmega(iEvt);
qD = curEvtTagFlv_*(1.0-2.0*omega);
} else {
// TODO need to sort out references here
LauTagCatParamMap dilution_ = flavTag_->getDilution();
LauTagCatParamMap deltaDilution_ = flavTag_->getDeltaDilution();
qD = curEvtTagFlv_*dilution_[curEvtTagCat_].unblindValue();
qDDo2 = curEvtTagFlv_*0.5*deltaDilution_[curEvtTagCat_].unblindValue();
}
// Get the dynamics to calculate everything required for the likelihood calculation
sigModelB0bar_->calcLikelihoodInfo(iEvt);
sigModelB0_->calcLikelihoodInfo(iEvt);
// Background part
// TODO add them into the actual Likelihood calculations
const UInt_t nBkgnds = this->nBkgndClasses();
for ( UInt_t bkgndID(0); bkgndID < nBkgnds; ++bkgndID ) {
if (usingBkgnd_ == kTRUE) {
bkgndDPLike_[bkgndID] = BkgndDPModels_[bkgndID]->getLikelihood(iEvt);
} else {
bkgndDPLike_[bkgndID] = 0.0;
}
}
// Retrieve the amplitudes and efficiency from the dynamics
const LauComplex& Abar = sigModelB0bar_->getEvtDPAmp();
const LauComplex& A = sigModelB0_->getEvtDPAmp();
Double_t eff = sigModelB0bar_->getEvtEff();
// Next calculate the DP terms
Double_t aSqSum = A.abs2() + Abar.abs2();
Double_t aSqDif = A.abs2() - Abar.abs2();
LauComplex inter = Abar * A.conj() * phiMixComplex_;
Double_t interTermIm = 2.0 * inter.im();
Double_t interTermRe = 2.0 * inter.re();
// First get all the decay time terms
LauDecayTimePdf* decayTimePdf = signalDecayTimePdfs_[curEvtTagCat_];
decayTimePdf->calcLikelihoodInfo(iEvt);
// TODO Backgrounds
// Get the decay time acceptance
Double_t dtEff = decayTimePdf->getEffiTerm();
- Double_t dtEffN = decayTimePdf->getEffiTermNorm();
+ //Double_t dtEffN = decayTimePdf->getEffiTermNorm();
// First get all the decay time terms
Double_t dtCos = decayTimePdf->getCosTerm();
Double_t dtSin = decayTimePdf->getSinTerm();
Double_t dtCosh = decayTimePdf->getCoshTerm();
Double_t dtSinh = decayTimePdf->getSinhTerm();
// Get flavour tagging terms
Double_t omega(0.);
Double_t omegabar(0.);
if (curEvtTagFlv_==1){
omegabar = flavTag_->getOmega(iEvt);
} else if (curEvtTagFlv_==-1){
omega = flavTag_->getOmega(iEvt);
}
Double_t ftOmegaTrig(1.);
Double_t ftOmegaHyp(1.);
if (curEvtTagCat_!=0){
ftOmegaTrig = (1 - AProd_.unblindValue())*omegabar - (1 + AProd_.unblindValue())*omega;
ftOmegaHyp = (1 - AProd_.unblindValue())*omegabar + (1 + AProd_.unblindValue())*omega;
}
//std::cout << dtCos << " " << dtSin << " " << dtCosh << " " << dtSinh << std::endl;
Double_t cosTerm = dtCos * ftOmegaTrig * ARaw_.unblindValue() * qD * aSqDif;
Double_t sinTerm = dtSin * ftOmegaTrig * ARaw_.unblindValue() * qD * interTermIm;
Double_t coshTerm = dtCosh * ftOmegaHyp * ARaw_.unblindValue() * (1.0 + qDDo2) * aSqSum;
Double_t sinhTerm = dtSinh * ftOmegaHyp * ARaw_.unblindValue() * (1.0 + qDDo2) * interTermRe;
curEvtTrueTagFlv_ = flavTag_->getEvtTrueTagVals(iEvt);
if (decayTimePdf->getFuncType() == LauDecayTimePdf::SimFitNormBd || decayTimePdf->getFuncType() == LauDecayTimePdf::SimFitNormBs){
cosTerm *= curEvtTrueTagFlv_;
sinTerm *= curEvtTrueTagFlv_;
}
if ( cpEigenValue_ == CPOdd ) {
sinTerm *= -1.0;
sinhTerm *= -1.0;
}
// ... to get the total and multiply by the efficiency (DP and decay time)
Double_t ASq = coshTerm + cosTerm - sinTerm + sinhTerm;
ASq *= eff;
ASq *= dtEff;
//std::cout<<"Cosh Cos Sin Sinh "<< coshTerm<< " " << cosTerm << " " << sinTerm << " " << sinhTerm << std::endl;
//std::cout << "Total Amplitude : " << ASq << std::endl;
// Calculate the DP and time normalisation
Double_t normTermIndep = sigModelB0bar_->getDPNorm() + sigModelB0_->getDPNorm();
Double_t normTermCosh(0.0);
Double_t norm(0.0);
if (decayTimePdf->getFuncType() == LauDecayTimePdf::ExpTrig || decayTimePdf->getFuncType() == LauDecayTimePdf::SimFitNormBd || decayTimePdf->getFuncType() == LauDecayTimePdf::SimFitSigBd){
normTermCosh = decayTimePdf->getNormTermExp();
norm = normTermIndep*normTermCosh;
//Test
- norm *= dtEffN;
+// norm *= dtEffN;
}
if (decayTimePdf->getFuncType() == LauDecayTimePdf::ExpHypTrig){
normTermCosh = decayTimePdf->getNormTermCosh();
Double_t normTermDep = interTermReNorm_;
Double_t normTermSinh = decayTimePdf->getNormTermSinh();
norm = normTermIndep*normTermCosh + normTermDep*normTermSinh;
}
// Calculate the normalised signal likelihood
//std::cout << "ASq = " << ASq << std::endl;
//std::cout << "norm = " << norm << std::endl;
sigDPLike_ = ASq / norm;
}
void LauTimeDepFitModel::getEvtExtraLikelihoods(const UInt_t iEvt)
{
// Function to return the signal and background likelihoods for the
// extra variables for the given event evtNo.
sigExtraLike_ = 1.0; //There's always a likelihood term for signal, so we better not zero it.
// First, those independent of the tagging of the event:
// signal
LauTagCatPdfListMap::iterator sig_iter = sigExtraPdf_.find(curEvtTagCat_);
LauPdfList* pdfList = (sig_iter != sigExtraPdf_.end())? &(sig_iter->second) : 0;
if (pdfList) {
sigExtraLike_ = this->prodPdfValue( *pdfList, iEvt );
}
// Background
const UInt_t nBkgnds = this->nBkgndClasses();
for ( UInt_t bkgndID(0); bkgndID < nBkgnds; ++bkgndID ) {
if (usingBkgnd_) {
bkgndExtraLike_[bkgndID] = this->prodPdfValue( BkgndPdfs_[bkgndID], iEvt );
} else {
bkgndExtraLike_[bkgndID] = 0.0;
}
}
}
void LauTimeDepFitModel::getEvtFlavTagLikelihood(const UInt_t iEvt)
{
// Function to return the signal and background likelihoods for the
// extra variables for the given event evtNo.
sigFlavTagLike_ = 1.0; //There's always a likelihood term for signal, so we better not zero it.
// First, those independent of the tagging of the event:
// signal
LauAbsPdf* pdf = sigFlavTagPdf_[curEvtTagCat_];
if (pdf) {
pdf->calcLikelihoodInfo(iEvt);
sigFlavTagLike_ = pdf->getLikelihood();
}
if (sigFlavTagLike_<=0){
std::cout<<"INFO in LauTimeDepFitModel::getEvtFlavTagLikelihood : Event with 0 FlavTag Liklihood"<<std::endl;
}
// TODO Add in the background components too
}
void LauTimeDepFitModel::updateCoeffs()
{
coeffsB0bar_.clear(); coeffsB0_.clear();
coeffsB0bar_.reserve(nSigComp_); coeffsB0_.reserve(nSigComp_);
for (UInt_t i = 0; i < nSigComp_; ++i) {
coeffsB0bar_.push_back(coeffPars_[i]->antiparticleCoeff());
coeffsB0_.push_back(coeffPars_[i]->particleCoeff());
}
}
void LauTimeDepFitModel::checkMixingPhase()
{
Double_t phase = phiMix_.value();
Double_t genPhase = phiMix_.genValue();
// Check now whether the phase lies in the right range (-pi to pi).
Bool_t withinRange(kFALSE);
while (withinRange == kFALSE) {
if (phase > -LauConstants::pi && phase < LauConstants::pi) {
withinRange = kTRUE;
} else {
// Not within the specified range
if (phase > LauConstants::pi) {
phase -= LauConstants::twoPi;
} else if (phase < -LauConstants::pi) {
phase += LauConstants::twoPi;
}
}
}
// A further problem can occur when the generated phase is close to -pi or pi.
// The phase can wrap over to the other end of the scale -
// this leads to artificially large pulls so we wrap it back.
Double_t diff = phase - genPhase;
if (diff > LauConstants::pi) {
phase -= LauConstants::twoPi;
} else if (diff < -LauConstants::pi) {
phase += LauConstants::twoPi;
}
// finally store the new value in the parameter
// and update the pull
phiMix_.value(phase);
phiMix_.updatePull();
}
void LauTimeDepFitModel::embedSignal(Int_t tagCat, const TString& fileName, const TString& treeName,
Bool_t reuseEventsWithinEnsemble, Bool_t reuseEventsWithinExperiment)
{
if (signalTree_[tagCat]) {
std::cerr<<"ERROR in LauTimeDepFitModel::embedSignal : Already embedding signal from file for tagging category "<<tagCat<<"."<<std::endl;
return;
}
if (!reuseEventsWithinEnsemble && reuseEventsWithinExperiment) {
std::cerr<<"WARNING in LauTimeDepFitModel::embedSignal : Conflicting options provided, will not reuse events at all."<<std::endl;
reuseEventsWithinExperiment = kFALSE;
}
signalTree_[tagCat] = new LauEmbeddedData(fileName,treeName,reuseEventsWithinExperiment);
Bool_t dataOK = signalTree_[tagCat]->findBranches();
if (!dataOK) {
delete signalTree_[tagCat]; signalTree_[tagCat] = 0;
std::cerr<<"ERROR in LauTimeDepFitModel::embedSignal : Problem creating data tree for embedding."<<std::endl;
return;
}
reuseSignal_ = reuseEventsWithinEnsemble;
}
void LauTimeDepFitModel::embedBkgnd(Int_t tagCat, const TString& bkgndClass, const TString& fileName, const TString& treeName,
Bool_t reuseEventsWithinEnsemble, Bool_t reuseEventsWithinExperiment)
{
if ( ! this->validBkgndClass( bkgndClass ) ) {
std::cerr << "ERROR in LauSimpleFitModel::embedBkgnd : Invalid background class \"" << bkgndClass << "\"." << std::endl;
std::cerr << " : Background class names must be provided in \"setBkgndClassNames\" before any other background-related actions can be performed." << std::endl;
return;
}
UInt_t bkgndID = this->bkgndClassID( bkgndClass );
LauTagCatEmbDataMap bkgTree = bkgndTree_[bkgndID];
if (bkgTree[tagCat]) {
std::cerr << "ERROR in LauSimpleFitModel::embedBkgnd : Already embedding background from a file." << std::endl;
return;
}
bkgTree[tagCat] = new LauEmbeddedData(fileName,treeName,reuseEventsWithinExperiment);
Bool_t dataOK = bkgTree[tagCat]->findBranches();
if (!dataOK) {
delete bkgTree[tagCat]; bkgTree[tagCat] = 0;
std::cerr << "ERROR in LauSimpleFitModel::embedBkgnd : Problem creating data tree for embedding." << std::endl;
return;
}
reuseBkgnd_[bkgndID] = reuseEventsWithinEnsemble;
if (this->enableEmbedding() == kFALSE) {
this->enableEmbedding(kTRUE);
}
}
void LauTimeDepFitModel::setupSPlotNtupleBranches()
{
// add branches for storing the experiment number and the number of
// the event within the current experiment
this->addSPlotNtupleIntegerBranch("iExpt");
this->addSPlotNtupleIntegerBranch("iEvtWithinExpt");
// Store the efficiency of the event (for inclusive BF calculations).
if (this->storeDPEff()) {
this->addSPlotNtupleDoubleBranch("efficiency");
}
// Store the total event likelihood for each species.
this->addSPlotNtupleDoubleBranch("sigTotalLike");
if (usingBkgnd_) {
const UInt_t nBkgnds = this->nBkgndClasses();
for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
TString name( this->bkgndClassName(iBkgnd) );
name += "TotalLike";
this->addSPlotNtupleDoubleBranch(name);
}
}
// Store the DP likelihoods
if (this->useDP()) {
this->addSPlotNtupleDoubleBranch("sigDPLike");
if (usingBkgnd_) {
const UInt_t nBkgnds = this->nBkgndClasses();
for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
TString name( this->bkgndClassName(iBkgnd) );
name += "DPLike";
this->addSPlotNtupleDoubleBranch(name);
}
}
}
// Store the likelihoods for each extra PDF
const LauPdfList* pdfList( &(sigExtraPdf_.begin()->second) );
this->addSPlotNtupleBranches(pdfList, "sig");
if (usingBkgnd_) {
const UInt_t nBkgnds = this->nBkgndClasses();
for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
const TString& bkgndClass = this->bkgndClassName(iBkgnd);
const LauPdfList* pdfList2 = &(BkgndPdfs_[iBkgnd]);
this->addSPlotNtupleBranches(pdfList2, bkgndClass);
}
}
}
void LauTimeDepFitModel::addSPlotNtupleBranches(const LauPdfList* extraPdfs, const TString& prefix)
{
if (!extraPdfs) {
return;
}
// Loop through each of the PDFs
for (LauPdfList::const_iterator pdf_iter = extraPdfs->begin(); pdf_iter != extraPdfs->end(); ++pdf_iter) {
// Count the number of input variables that are not
// DP variables (used in the case where there is DP
// dependence for e.g. MVA)
UInt_t nVars(0);
for ( std::vector<TString>::const_iterator var_iter = (*pdf_iter)->varNames().begin(); var_iter != (*pdf_iter)->varNames().end(); ++var_iter ) {
if ( (*var_iter) != "m13Sq" && (*var_iter) != "m23Sq" ) {
++nVars;
}
}
if ( nVars == 1 ) {
// If the PDF only has one variable then
// simply add one branch for that variable
TString varName = (*pdf_iter)->varName();
TString name(prefix);
name += varName;
name += "Like";
this->addSPlotNtupleDoubleBranch(name);
} else if ( nVars == 2 ) {
// If the PDF has two variables then we
// need a branch for them both together and
// branches for each
TString allVars("");
for ( std::vector<TString>::const_iterator var_iter = (*pdf_iter)->varNames().begin(); var_iter != (*pdf_iter)->varNames().end(); ++var_iter ) {
allVars += (*var_iter);
TString name(prefix);
name += (*var_iter);
name += "Like";
this->addSPlotNtupleDoubleBranch(name);
}
TString name(prefix);
name += allVars;
name += "Like";
this->addSPlotNtupleDoubleBranch(name);
} else {
std::cerr<<"WARNING in LauTimeDepFitModel::addSPlotNtupleBranches : Can't yet deal with 3D PDFs."<<std::endl;
}
}
}
Double_t LauTimeDepFitModel::setSPlotNtupleBranchValues(LauPdfList* extraPdfs, const TString& prefix, UInt_t iEvt)
{
// Store the PDF value for each variable in the list
Double_t totalLike(1.0);
Double_t extraLike(0.0);
if ( !extraPdfs ) {
return totalLike;
}
for (LauPdfList::iterator pdf_iter = extraPdfs->begin(); pdf_iter != extraPdfs->end(); ++pdf_iter) {
// calculate the likelihood for this event
(*pdf_iter)->calcLikelihoodInfo(iEvt);
extraLike = (*pdf_iter)->getLikelihood();
totalLike *= extraLike;
// Count the number of input variables that are not
// DP variables (used in the case where there is DP
// dependence for e.g. MVA)
UInt_t nVars(0);
for ( std::vector<TString>::const_iterator var_iter = (*pdf_iter)->varNames().begin(); var_iter != (*pdf_iter)->varNames().end(); ++var_iter ) {
if ( (*var_iter) != "m13Sq" && (*var_iter) != "m23Sq" ) {
++nVars;
}
}
if ( nVars == 1 ) {
// If the PDF only has one variable then
// simply store the value for that variable
TString varName = (*pdf_iter)->varName();
TString name(prefix);
name += varName;
name += "Like";
this->setSPlotNtupleDoubleBranchValue(name, extraLike);
} else if ( nVars == 2 ) {
// If the PDF has two variables then we
// store the value for them both together
// and for each on their own
TString allVars("");
for ( std::vector<TString>::const_iterator var_iter = (*pdf_iter)->varNames().begin(); var_iter != (*pdf_iter)->varNames().end(); ++var_iter ) {
allVars += (*var_iter);
TString name(prefix);
name += (*var_iter);
name += "Like";
Double_t indivLike = (*pdf_iter)->getLikelihood( (*var_iter) );
this->setSPlotNtupleDoubleBranchValue(name, indivLike);
}
TString name(prefix);
name += allVars;
name += "Like";
this->setSPlotNtupleDoubleBranchValue(name, extraLike);
} else {
std::cerr<<"WARNING in LauAllFitModel::setSPlotNtupleBranchValues : Can't yet deal with 3D PDFs."<<std::endl;
}
}
return totalLike;
}
LauSPlot::NameSet LauTimeDepFitModel::variableNames() const
{
LauSPlot::NameSet nameSet;
if (this->useDP()) {
nameSet.insert("DP");
}
LauPdfList pdfList( (sigExtraPdf_.begin()->second) );
for (LauPdfList::const_iterator pdf_iter = pdfList.begin(); pdf_iter != pdfList.end(); ++pdf_iter) {
// Loop over the variables involved in each PDF
for ( std::vector<TString>::const_iterator var_iter = (*pdf_iter)->varNames().begin(); var_iter != (*pdf_iter)->varNames().end(); ++var_iter ) {
// If they are not DP coordinates then add them
if ( (*var_iter) != "m13Sq" && (*var_iter) != "m23Sq" ) {
nameSet.insert( (*var_iter) );
}
}
}
return nameSet;
}
LauSPlot::NumbMap LauTimeDepFitModel::freeSpeciesNames() const
{
LauSPlot::NumbMap numbMap;
if (!signalEvents_->fixed() && this->doEMLFit()) {
numbMap["sig"] = signalEvents_->genValue();
}
if ( usingBkgnd_ ) {
const UInt_t nBkgnds = this->nBkgndClasses();
for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
const TString& bkgndClass = this->bkgndClassName(iBkgnd);
const LauAbsRValue* par = bkgndEvents_[iBkgnd];
if (!par->fixed()) {
numbMap[bkgndClass] = par->genValue();
if ( ! par->isLValue() ) {
std::cerr << "WARNING in LauTimeDepFitModel::freeSpeciesNames : \"" << par->name() << "\" is a LauFormulaPar, which implies it is perhaps not entirely free to float in the fit, so the sWeight calculation may not be reliable" << std::endl;
}
}
}
}
return numbMap;
}
LauSPlot::NumbMap LauTimeDepFitModel::fixdSpeciesNames() const
{
LauSPlot::NumbMap numbMap;
if (signalEvents_->fixed() && this->doEMLFit()) {
numbMap["sig"] = signalEvents_->genValue();
}
if ( usingBkgnd_ ) {
const UInt_t nBkgnds = this->nBkgndClasses();
for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
const TString& bkgndClass = this->bkgndClassName(iBkgnd);
const LauAbsRValue* par = bkgndEvents_[iBkgnd];
if (par->fixed()) {
numbMap[bkgndClass] = par->genValue();
}
}
}
return numbMap;
}
LauSPlot::TwoDMap LauTimeDepFitModel::twodimPDFs() const
{
LauSPlot::TwoDMap twodimMap;
const LauPdfList* pdfList = &(sigExtraPdf_.begin()->second);
for (LauPdfList::const_iterator pdf_iter = pdfList->begin(); pdf_iter != pdfList->end(); ++pdf_iter) {
// Count the number of input variables that are not DP variables
UInt_t nVars(0);
for ( std::vector<TString>::const_iterator var_iter = (*pdf_iter)->varNames().begin(); var_iter != (*pdf_iter)->varNames().end(); ++var_iter ) {
if ( (*var_iter) != "m13Sq" && (*var_iter) != "m23Sq" ) {
++nVars;
}
}
if ( nVars == 2 ) {
twodimMap.insert( std::make_pair( "sig", std::make_pair( (*pdf_iter)->varNames()[0], (*pdf_iter)->varNames()[1] ) ) );
}
}
if (usingBkgnd_) {
const UInt_t nBkgnds = this->nBkgndClasses();
for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
const TString& bkgndClass = this->bkgndClassName(iBkgnd);
const LauPdfList& pdfList2 = BkgndPdfs_[iBkgnd];
for (LauPdfList::const_iterator pdf_iter = pdfList2.begin(); pdf_iter != pdfList2.end(); ++pdf_iter) {
// Count the number of input variables that are not DP variables
UInt_t nVars(0);
std::vector<TString> varNames = (*pdf_iter)->varNames();
for ( std::vector<TString>::const_iterator var_iter = varNames.begin(); var_iter != varNames.end(); ++var_iter ) {
if ( (*var_iter) != "m13Sq" && (*var_iter) != "m23Sq" ) {
++nVars;
}
}
if ( nVars == 2 ) {
twodimMap.insert( std::make_pair( bkgndClass, std::make_pair( varNames[0], varNames[1] ) ) );
}
}
}
}
return twodimMap;
}
void LauTimeDepFitModel::storePerEvtLlhds()
{
std::cout<<"INFO in LauTimeDepFitModel::storePerEvtLlhds : Storing per-event likelihood values..."<<std::endl;
LauFitDataTree* inputFitData = this->fitData();
// if we've not been using the DP model then we need to cache all
// the info here so that we can get the efficiency from it
if (!this->useDP() && this->storeDPEff()) {
sigModelB0bar_->initialise(coeffsB0bar_);
sigModelB0_->initialise(coeffsB0_);
sigModelB0bar_->fillDataTree(*inputFitData);
sigModelB0_->fillDataTree(*inputFitData);
}
UInt_t evtsPerExpt(this->eventsPerExpt());
LauIsobarDynamics* sigModel(sigModelB0bar_);
for (UInt_t iEvt = 0; iEvt < evtsPerExpt; ++iEvt) {
// Find out whether we have B0bar or B0
curEvtTagFlv_ = flavTag_->getEvtTagFlvVals(iEvt);
curEvtTagCat_ = flavTag_->getEvtTagCatVals(iEvt);
curEvtMistag_ = flavTag_->getOmega(iEvt);
LauTagCatPdfListMap::iterator sig_iter = sigExtraPdf_.find(curEvtTagCat_);
LauPdfList* sigPdfs = (sig_iter != sigExtraPdf_.end())? &(sig_iter->second) : 0;
// the DP information
this->getEvtDPDtLikelihood(iEvt);
if (this->storeDPEff()) {
if (!this->useDP()) {
sigModel->calcLikelihoodInfo(iEvt);
}
this->setSPlotNtupleDoubleBranchValue("efficiency",sigModel->getEvtEff());
}
if (this->useDP()) {
sigTotalLike_ = sigDPLike_;
this->setSPlotNtupleDoubleBranchValue("sigDPLike",sigDPLike_);
if (usingBkgnd_) {
const UInt_t nBkgnds = this->nBkgndClasses();
for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
TString name = this->bkgndClassName(iBkgnd);
name += "DPLike";
this->setSPlotNtupleDoubleBranchValue(name,bkgndDPLike_[iBkgnd]);
}
}
} else {
sigTotalLike_ = 1.0;
}
// the signal PDF values
sigTotalLike_ *= this->setSPlotNtupleBranchValues(sigPdfs, "sig", iEvt);
// the background PDF values
LauBkgndPdfsList* bkgndPdfs(0);
if (usingBkgnd_) {
const UInt_t nBkgnds = this->nBkgndClasses();
for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
const TString& bkgndClass = this->bkgndClassName(iBkgnd);
LauPdfList& pdfs = (*bkgndPdfs)[iBkgnd];
bkgndTotalLike_[iBkgnd] *= this->setSPlotNtupleBranchValues(&(pdfs), bkgndClass, iEvt);
}
}
// the total likelihoods
this->setSPlotNtupleDoubleBranchValue("sigTotalLike",sigTotalLike_);
if (usingBkgnd_) {
const UInt_t nBkgnds = this->nBkgndClasses();
for ( UInt_t iBkgnd(0); iBkgnd < nBkgnds; ++iBkgnd ) {
TString name = this->bkgndClassName(iBkgnd);
name += "TotalLike";
this->setSPlotNtupleDoubleBranchValue(name,bkgndTotalLike_[iBkgnd]);
}
}
// fill the tree
this->fillSPlotNtupleBranches();
}
std::cout<<"INFO in LauTimeDepFitModel::storePerEvtLlhds : Finished storing per-event likelihood values."<<std::endl;
}
void LauTimeDepFitModel::weightEvents( const TString& /*dataFileName*/, const TString& /*dataTreeName*/ )
{
std::cerr << "ERROR in LauTimeDepFitModel::weightEvents : Method not available for this fit model." << std::endl;
return;
}
void LauTimeDepFitModel::savePDFPlots(const TString& /*label*/)
{
}
void LauTimeDepFitModel::savePDFPlotsWave(const TString& /*label*/, const Int_t& /*spin*/)
{
}
void LauTimeDepFitModel::setSignalFlavTagPdfs(const Int_t tagCat, LauAbsPdf* pdf)
{
if (!flavTag_->validTagCat(tagCat)) {
std::cerr<<"ERROR in LauTimeDepFitModel::setSignalFlavTagPdfs : Tagging category \""<<tagCat<<"\" not valid, not adding models."<<std::endl;
return;
}
if (pdf==0) {
std::cerr<<"ERROR in LauTimeDepFitModel::setSignalFlavTagPdfs : The PDF pointer is null."<<std::endl;
return;
}
sigFlavTagPdf_[tagCat] = pdf;
}
void LauTimeDepFitModel::setBkgdFlavTagPdfs(const TString name, LauAbsPdf* pdf)
{
// TODO - when backgrounds are added - implement a check that "name" really is the name of a background category
if (pdf==0) {
std::cerr<<"ERROR in LauTimeDepFitModel::setBkgdFlavTagPdfs : The PDF pointer is null."<<std::endl;
return;
}
bkgdFlavTagPdf_[name] = pdf;
}

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