void sim(Int_t nev=1) {
gSystem->Load("liblhapdf");
gSystem->Load("libEGPythia6");
gSystem->Load("libpythia6");
gSystem->Load("libAliPythia6");
gSystem->Load("libhijing");
gSystem->Load("libTHijing");
gSystem->Load("libgeant321");
if (gSystem->Getenv("EVENT"))
nev = atoi(gSystem->Getenv("EVENT")) ;
AliSimulation simulator;
simulator.SetMakeSDigits("TRD TOF PHOS HMPID EMCAL FMD ZDC PMD T0 VZERO");
simulator.SetMakeDigitsFromHits("ITS TPC");
simulator.SetWriteRawData("ALL","raw.root",kTRUE);
simulator.SetDefaultStorage("local:///cvmfs/alice-ocdb.cern.ch/calibration/MC/Ideal");
//simulator.SetDefaultStorage(Form("local://%s/OCDB", gSystem->pwd()));
simulator.SetSpecificStorage("GRP/GRP/Data",
Form("local://%s",gSystem->pwd()));
simulator.SetRunQA("ALL:ALL") ;
simulator.SetQARefDefaultStorage("local://$ALICE_ROOT/QAref") ;
for (Int_t det = 0 ; det < AliQA::kNDET ; det++) {
simulator.SetQACycles((AliQAv1::DETECTORINDEX_t)det, nev+1) ;
}
TStopwatch timer;
timer.Start();
simulator.Run(nev);
timer.Stop();
timer.Print();
}
void write(int n) {
TRandom R;
TStopwatch timer;
TFile f1("mathcoreVectorIO_F.root","RECREATE");
// create tree
TTree t1("t1","Tree with new Float LorentzVector");
XYZTVectorF *v1 = new XYZTVectorF();
t1.Branch("LV branch","ROOT::Math::XYZTVectorF",&v1);
timer.Start();
for (int i = 0; i < n; ++i) {
double Px = R.Gaus(0,10);
double Py = R.Gaus(0,10);
double Pz = R.Gaus(0,10);
double E = R.Gaus(100,10);
//CylindricalEta4D<double> & c = v1->Coordinates();
//c.SetValues(Px,pY,pZ,E);
v1->SetCoordinates(Px,Py,Pz,E);
t1.Fill();
}
f1.Write();
timer.Stop();
std::cout << " Time for new Float Vector " << timer.RealTime() << " " << timer.CpuTime() << std::endl;
t1.Print();
}
void run_PPToGammaGammaFiles() {
gROOT->LoadMacro("FlatTreeMaker_Delphes_PPToGammaGammaFiles_C.so");
TChain* fChain = new TChain("Delphes");
ifstream sourceFiles("PPToGammaGammaFiles.txt");
char line[128];
int count = 0;
cout<< "Adding files from PPToGammaGammaFiles to chain..."<< endl;
while (sourceFiles >> line) {
fChain->Add(line);
++count;
}
cout << count<<" files added!"<<endl;
sourceFiles.close();
TStopwatch timer;
timer.Start();
fChain->Process("FlatTreeMaker_Delphes");
cout << "\n\nDone!" << endl;
cout << "CPU Time : " << timer.CpuTime() <<endl;
cout << "RealTime : " << timer.RealTime() <<endl;
cout <<"\n";
}
void makePlots() {
gROOT->LoadMacro("analyze.C+");
TStopwatch ts;
ts.Start();
TString input_ele = "ELE_FILE_TO_RUN";
TString input_muon = "MUON_FILE_TO_RUN";
bool addMC = true;
int intLumi = 19712; // quote to 19.7
double metCut = -1.;
bool displayKStest = true;
bool blinded = true;
int nPhotons_req = 0;
const int nChannels = 4;
TString channels[nChannels] = {"ele_jjj", "ele_bjj",
"muon_jjj", "muon_bjj"};
int nBtagReq[nChannels] = {0, 1,
0, 1};
for(int i = 0; i < nChannels; i++) {
if(i != 1 && i != 3) continue;
if(i < 2) analyze(input_ele, addMC, i, intLumi, metCut, nPhotons_req, nBtagReq[i], displayKStest, blinded);
else analyze(input_muon, addMC, i, intLumi, metCut, nPhotons_req, nBtagReq[i], displayKStest, blinded);
}
ts.Stop();
std::cout << "RealTime : " << ts.RealTime()/60.0 << " minutes" << std::endl;
std::cout << "CPUTime : " << ts.CpuTime()/60.0 << " minutes" << std::endl;
}
void sim(Int_t nev=1) {
AliSimulation simu;
simu.SetMakeSDigits("TRD TOF PHOS HMPID EMCAL MUON FMD PMD T0 ZDC VZERO");
simu.SetMakeDigits ("TRD TOF PHOS HMPID EMCAL MUON FMD PMD T0 ZDC VZERO");
simu.SetMakeDigitsFromHits("ITS TPC");
simu.SetWriteRawData("ALL","raw.root",kTRUE);
simu.SetDefaultStorage("local://$ALICE_ROOT/OCDB");
simu.SetSpecificStorage("GRP/GRP/Data",
Form("local://%s",gSystem->pwd()));
simu.SetRunQA("ALL:ALL") ;
simu.SetQARefDefaultStorage("local://$ALICE_ROOT/OCDB") ;
for (Int_t det = 0 ; det < AliQA::kNDET ; det++) {
simu.SetQACycles(det, 2) ;
}
TStopwatch timer;
timer.Start();
simu.Run(nev);
WriteXsection();
timer.Stop();
timer.Print();
}
int main(int argc, char **argv)
{
TStopwatch reloj;
reloj.Start();
// split by ','
string argStr = argv[1];
vector<string> fileList;
for (size_t i=0,n; i <= argStr.length(); i=n+1){
n = argStr.find_first_of(',',i);
if (n == string::npos) n = argStr.length();
string tmp = argStr.substr(i,n-i);
fileList.push_back(tmp);
}
Analysis1 o(fileList);
o.EventsLoop();
reloj.Stop();
double tiempo = reloj.CpuTime();
cout << "tiempo gastado en el calculo = " << tiempo << endl;
return 0;
}
void sim(Int_t nev=1) {
gSystem->Exec(" rm itsSegmentations.root ");
AliSimulation simulator;
// simulator.SetMakeSDigits("");
// simulator.SetMakeDigits("");
simulator.SetDefaultStorage("local://$ALICE_ROOT/OCDB");
simulator.SetSpecificStorage("GRP/GRP/Data",
Form("local://%s",gSystem->pwd()));
simulator.SetSpecificStorage("ITS/Align/Data",
Form("local://%s",gSystem->pwd()));
simulator.SetSpecificStorage("ITS/Calib/SimuParam",
Form("local://%s",gSystem->pwd()));
simulator.SetRunHLT("");
simulator.SetRunQA(":");
TStopwatch timer;
timer.Start();
simulator.Run(nev);
timer.Stop();
timer.Print();
}
void testIntegPerf(double x1, double x2, int n = 100000){
std::cout << "\n\n***************************************************************\n";
std::cout << "Test integration performances in interval [ " << x1 << " , " << x2 << " ]\n\n";
TStopwatch timer;
double dx = (x2-x1)/double(n);
//ROOT::Math::Functor1D<ROOT::Math::IGenFunction> f1(& TMath::BreitWigner);
ROOT::Math::WrappedFunction<> f1(func);
timer.Start();
ROOT::Math::Integrator ig(f1 );
double s1 = 0.0;
nc = 0;
for (int i = 0; i < n; ++i) {
double x = x1 + dx*i;
s1+= ig.Integral(x1,x);
}
timer.Stop();
std::cout << "Time using ROOT::Math::Integrator :\t" << timer.RealTime() << std::endl;
std::cout << "Number of function calls = " << nc/n << std::endl;
int pr = std::cout.precision(18); std::cout << s1 << std::endl; std::cout.precision(pr);
//TF1 *fBW = new TF1("fBW","TMath::BreitWigner(x)",x1, x2); // this is faster but cannot measure number of function calls
TF1 *fBW = new TF1("fBW",func2,x1, x2,0);
timer.Start();
nc = 0;
double s2 = 0;
for (int i = 0; i < n; ++i) {
double x = x1 + dx*i;
s2+= fBW->Integral(x1,x );
}
timer.Stop();
std::cout << "Time using TF1::Integral :\t\t\t" << timer.RealTime() << std::endl;
std::cout << "Number of function calls = " << nc/n << std::endl;
pr = std::cout.precision(18); std::cout << s1 << std::endl; std::cout.precision(pr);
}
// test using UNURAN string interface
void testStringAPI() {
TH1D * h1 = new TH1D("h1G","gaussian distribution from Unuran",100,-10,10);
TH1D * h2 = new TH1D("h2G","gaussian distribution from TRandom",100,-10,10);
cout << "\nTest using UNURAN string API \n\n";
TUnuran unr;
if (! unr.Init( "normal()", "method=arou") ) {
cout << "Error initializing unuran" << endl;
return;
}
int n = NGEN;
TStopwatch w;
w.Start();
for (int i = 0; i < n; ++i) {
double x = unr.Sample();
h1->Fill( x );
}
w.Stop();
cout << "Time using Unuran method " << unr.MethodName() << "\t=\t " << w.CpuTime() << endl;
// use TRandom::Gaus
w.Start();
for (int i = 0; i < n; ++i) {
double x = gRandom->Gaus(0,1);
h2->Fill( x );
}
w.Stop();
cout << "Time using TRandom::Gaus \t=\t " << w.CpuTime() << endl;
assert(c1 != 0);
c1->cd(++izone);
h1->Draw();
c1->cd(++izone);
h2->Draw();
}
void testDiscDistr() {
cout << "\nTest Discrete distributions\n\n";
TH1D * h1 = new TH1D("h1PS","Unuran Poisson prob",20,0,20);
TH1D * h2 = new TH1D("h2PS","Poisson dist from TRandom",20,0,20);
double mu = 5;
TF1 * f = new TF1("fps",poisson,1,0,1);
f->SetParameter(0,mu);
TUnuranDiscrDist dist2 = TUnuranDiscrDist(f);
TUnuran unr;
// dari method (needs also the mode and pmf sum)
dist2.SetMode(int(mu) );
dist2.SetProbSum(1.0);
bool ret = unr.Init(dist2,"dari");
if (!ret) return;
TStopwatch w;
w.Start();
int n = NGEN;
for (int i = 0; i < n; ++i) {
int k = unr.SampleDiscr();
h1->Fill( double(k) );
}
w.Stop();
cout << "Time using Unuran method " << unr.MethodName() << "\t=\t\t " << w.CpuTime() << endl;
w.Start();
for (int i = 0; i < n; ++i) {
h2->Fill( gRandom->Poisson(mu) );
}
cout << "Time using TRandom::Poisson " << "\t=\t\t " << w.CpuTime() << endl;
c1->cd(++izone);
h1->SetMarkerStyle(20);
h1->Draw("E");
h2->Draw("same");
std::cout << " chi2 test of UNURAN vs TRandom generated histograms: " << std::endl;
h1->Chi2Test(h2,"UUP");
}
void generate( R & r, TH1D * h) {
TStopwatch w;
r.SetSeed(0);
//r.SetSeed(int(std::pow(2.0,28)));
int m = NLOOP;
int n = NEVT;
for (int j = 0; j < m; ++j) {
//std::cout << r.GetSeed() << " ";
w.Start();
// if ( n < 40000000) iseed = std::rand();
// iseed = 0;
//TRandom3 r3(0);
//r.SetSeed( 0 ); // generate random seeds
//TRandom3 r3(0);
//r.SetSeed (static_cast<UInt_t> (4294967296.*r3.Rndm()) );
// estimate PI
double n1=0;
double rn[2000];
double x;
double y;
for (int ievt = 0; ievt < n; ievt+=1000 ) {
r.RndmArray(2000,rn);
for (int i=0; i < 1000; i++) {
x=rn[2*i];
y=rn[2*i+1];
if ( ( x*x + y*y ) <= 1.0 ) n1++;
}
}
double piEstimate = 4.0 * double(n1)/double(n);
double delta = piEstimate-PI;
h->Fill(delta);
}
w.Stop();
std::cout << std::endl;
std::cout << "Random: " << typeid(r).name()
<< "\n\tTime = " << w.RealTime() << " " << w.CpuTime() << std::endl;
std::cout << "Time/call: " << w.CpuTime()/(2*n)*1.0E9 << std::endl;
}
RooAddPdf fitZToMuMuGammaMassUnbinned(
const char *filename = "ZMuMuGammaMass_36.1ipb_EE.txt",
// const char *filename = "ZMuMuGammaMass_2.9ipb_EB.txt",
// const char *filename = "ZMuMuGammaMass_2.9ipb_EE.txt",
// const char *filename = "ZMuMuGammaMass_Zmumu_Spring10_EB.txt",
// const char *filename = "DimuonMass_data_Nov4ReReco.txt",
const char* plotOpt = "NEU",
const int nbins = 60)
{
gROOT->ProcessLine(".L tdrstyle.C");
setTDRStyle();
gStyle->SetPadRightMargin(0.05);
double minMass = 60;
double maxMass = 120;
RooRealVar mass("mass","m(#mu#mu#gamma)", minMass, maxMass,"GeV/c^{2}");
// Read data set
RooDataSet *data = RooDataSet::read(filename,RooArgSet(mass));
// RooDataSet *dataB = RooDataSet::read(filenameB,RooArgSet(mass));
// Build p.d.f.
////////////////////////////////////////////////
// Parameters //
////////////////////////////////////////////////
// Signal p.d.f. parameters
// Parameters for a Gaussian and a Crystal Ball Lineshape
RooRealVar cbBias ("#Deltam_{CB}", "CB Bias", 0.05, -2, 2,"GeV/c^{2}");
RooRealVar cbSigma("#sigma_{CB}","CB Width", 1.38, 0.01, 10.0,"GeV/c^{2}");
RooRealVar cbCut ("a_{CB}","CB Cut", 1.5, 0.1, 2.0);
RooRealVar cbPower("n_{CB}","CB Power", 1.3, 0.1, 20.0);
// cbSigma.setConstant(kTRUE);
// cbCut.setConstant(kTRUE);
// cbPower.setConstant(kTRUE);
// Parameters for Breit-Wigner
RooRealVar bwMean("m_{Z}","BW Mean", 91.1876, "GeV/c^{2}");
RooRealVar bwWidth("#Gamma_{Z}", "BW Width", 2.4952, "GeV/c^{2}");
// Keep Breit-Wigner parameters fixed to the PDG values
// bwMean.setConstant(kTRUE);
// bwWidth.setConstant(kTRUE);
// Background p.d.f. parameters
// Parameters for exponential
RooRealVar expRate("#lambda_{exp}", "Exponential Rate", -0.119, -10, 1);
// expRate.setConstant(kTRUE);
// fraction of signal
// RooRealVar frac("frac", "Signal Fraction", 0.1,0.,0.3.);
/* RooRealVar nsig("N_{S}", "#signal events", 9000, 0.,10000.);
RooRealVar nbkg("N_{B}", "#background events", 1000,2,10000.);*/
RooRealVar nsig("N_{S}", "#signal events", 29300, 0.1, 100000.);
RooRealVar nbkg("N_{B}", "#background events", 0, 0., 10000.);
// nbkg.setConstant(kTRUE);
////////////////////////////////////////////////
// P.D.F.s //
////////////////////////////////////////////////
// Di-photon mass signal p.d.f.
RooBreitWigner bw("bw", "bw", mass, bwMean, bwWidth);
// RooGaussian signal("signal", "A Gaussian Lineshape", mass, m0, sigma);
RooCBShape cball("cball", "A Crystal Ball Lineshape", mass, cbBias, cbSigma, cbCut, cbPower);
mass.setBins(100000, "fft");
RooFFTConvPdf BWxCB("BWxCB","bw (X) crystall ball", mass, bw, cball);
// Di-photon mass background p.d.f.
RooExponential bg("bg","bkgd exp", mass, expRate);
// Di-photon mass model p.d.f.
RooAddPdf model("model", "signal + background mass model", RooArgList(BWxCB, bg), RooArgList(nsig, nbkg));
TStopwatch t ;
t.Start() ;
model.fitTo(*data,FitOptions("mh"),Optimize(0),Timer(1));
// signal->fitTo(*data,FitOptions("mh"),Optimize(0),Timer(1));
t.Print() ;
TCanvas *c = new TCanvas("c","Unbinned Invariant Mass Fit", 0,0,800,600);
// Plot the fit results
RooPlot* plot = mass.frame(Range(minMass,maxMass),Bins(nbins));
// Plot 1
// dataB->plotOn(plot, MarkerColor(kRed), LineColor(kRed));
data->plotOn(plot);
// model.plotOn(plot);
model.plotOn(plot);
//model.paramOn(plot, Format(plotOpt, AutoPrecision(1)), Parameters(RooArgSet(nsig, nbkg, m0, sigma)));
model.paramOn(plot,
Format(plotOpt, AutoPrecision(2) ),
Parameters(RooArgSet(cbBias,
cbSigma,
cbCut,
cbPower,
bwMean,
bwWidth,
expRate,
nsig,
nbkg)),
Layout(.67, 0.97, 0.97),
ShowConstants(kTRUE) );
// model.plotOn(plot, Components("signal"), LineStyle(kDashed), LineColor(kRed));
model.plotOn(plot, Components("bg"), LineStyle(kDashed), LineColor(kRed));
plot->Draw();
// TLatex * tex = new TLatex(0.2,0.8,"CMS preliminary");
// tex->SetNDC();
// tex->SetTextFont(42);
// tex->SetLineWidth(2);
// tex->Draw();
// tex->DrawLatex(0.2, 0.725, "7 TeV Data, L = 258 pb^{-1}");
//
// float fsig_peak = NormalizedIntegral(model,
// mass,
// cbBias.getVal() - 2.5*cbSigma.getVal(),
// cbBias.getVal() + 2.5*cbSigma.getVal()
// );
// float fbkg_peak = NormalizedIntegral(bg,
// mass,
// m0.getVal() - 2.5*sigma.getVal(),
// m0.getVal() + 2.5*sigma.getVal()
// );
/* double nsigVal = fsig_peak * nsig.getVal();
double nsigErr = fsig_peak * nsig.getError();
double nsigErrRel = nsigErr / nsigVal;*/
// double nbkgVal = fbkg_peak * nbkg.getVal();
// double nbkgErr = fbkg_peak * nbkg.getError();
// double nbkgErrRel = nbkgErr / nbkgVal;
// cout << "nsig " << nsigVal << " +/- " << nsigErr << endl;
// cout << "S/B_{#pm2.5#sigma} " << nsigVal/nbkgVal << " +/- "
// << (nsigVal/nbkgVal)*sqrt(nsigErrRel*nsigErrRel + nbkgErrRel*nbkgErrRel)
// << endl;
// tex->DrawLatex(0.2, 0.6, Form("N_{S} = %.0f#pm%.0f", nsigVal, nsigErr) );
// tex->DrawLatex(0.2, 0.525, Form("S/B_{#pm2.5#sigma} = %.1f", nsigVal/nbkgVal) );
// tex->DrawLatex(0.2, 0.45, Form("#frac{S}{#sqrt{B}}_{#pm2.5#sigma} = %.1f", nsigVal/sqrt(nbkgVal)));
// leg = new TLegend(0.65,0.6,0.9,0.75);
// leg->SetFillColor(kWhite);
// leg->SetLineColor(kWhite);
// leg->SetShadowColor(kWhite);
// leg->SetTextFont(42);
// TLegendEntry * ldata = leg->AddEntry(data, "Opposite Sign");
// TLegendEntry * ldataB = leg->AddEntry(dataB, "Same Sign");
// ldata->SetMarkerStyle(20);
// ldataB->SetMarkerStyle(20);
// ldataB->SetMarkerColor(kRed);
// leg->Draw();
return model;
}
// internal routine to run the inverter
HypoTestInverterResult *
RooStats::HypoTestInvTool::RunInverter(RooWorkspace * w,
const char * modelSBName, const char * modelBName,
const char * dataName, int type, int testStatType,
bool useCLs, int npoints, double poimin, double poimax,
int ntoys,
bool useNumberCounting,
const char * nuisPriorName ){
std::cout << "Running HypoTestInverter on the workspace " << w->GetName() << std::endl;
w->Print();
RooAbsData * data = w->data(dataName);
if (!data) {
Error("StandardHypoTestDemo","Not existing data %s",dataName);
return 0;
}
else
std::cout << "Using data set " << dataName << std::endl;
if (mUseVectorStore) {
RooAbsData::setDefaultStorageType(RooAbsData::Vector);
data->convertToVectorStore() ;
}
// get models from WS
// get the modelConfig out of the file
ModelConfig* bModel = (ModelConfig*) w->obj(modelBName);
ModelConfig* sbModel = (ModelConfig*) w->obj(modelSBName);
if (!sbModel) {
Error("StandardHypoTestDemo","Not existing ModelConfig %s",modelSBName);
return 0;
}
// check the model
if (!sbModel->GetPdf()) {
Error("StandardHypoTestDemo","Model %s has no pdf ",modelSBName);
return 0;
}
if (!sbModel->GetParametersOfInterest()) {
Error("StandardHypoTestDemo","Model %s has no poi ",modelSBName);
return 0;
}
if (!sbModel->GetObservables()) {
Error("StandardHypoTestInvDemo","Model %s has no observables ",modelSBName);
return 0;
}
if (!sbModel->GetSnapshot() ) {
Info("StandardHypoTestInvDemo","Model %s has no snapshot - make one using model poi",modelSBName);
sbModel->SetSnapshot( *sbModel->GetParametersOfInterest() );
}
// case of no systematics
// remove nuisance parameters from model
if (noSystematics) {
const RooArgSet * nuisPar = sbModel->GetNuisanceParameters();
if (nuisPar && nuisPar->getSize() > 0) {
std::cout << "StandardHypoTestInvDemo" << " - Switch off all systematics by setting them constant to their initial values" << std::endl;
RooStats::SetAllConstant(*nuisPar);
}
if (bModel) {
const RooArgSet * bnuisPar = bModel->GetNuisanceParameters();
if (bnuisPar)
RooStats::SetAllConstant(*bnuisPar);
}
}
if (!bModel || bModel == sbModel) {
Info("StandardHypoTestInvDemo","The background model %s does not exist",modelBName);
Info("StandardHypoTestInvDemo","Copy it from ModelConfig %s and set POI to zero",modelSBName);
bModel = (ModelConfig*) sbModel->Clone();
bModel->SetName(TString(modelSBName)+TString("_with_poi_0"));
RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
if (!var) return 0;
double oldval = var->getVal();
var->setVal(0);
bModel->SetSnapshot( RooArgSet(*var) );
var->setVal(oldval);
}
else {
if (!bModel->GetSnapshot() ) {
Info("StandardHypoTestInvDemo","Model %s has no snapshot - make one using model poi and 0 values ",modelBName);
RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
if (var) {
double oldval = var->getVal();
var->setVal(0);
bModel->SetSnapshot( RooArgSet(*var) );
var->setVal(oldval);
}
else {
Error("StandardHypoTestInvDemo","Model %s has no valid poi",modelBName);
return 0;
}
}
}
// check model has global observables when there are nuisance pdf
// for the hybrid case the globobs are not needed
if (type != 1 ) {
bool hasNuisParam = (sbModel->GetNuisanceParameters() && sbModel->GetNuisanceParameters()->getSize() > 0);
bool hasGlobalObs = (sbModel->GetGlobalObservables() && sbModel->GetGlobalObservables()->getSize() > 0);
if (hasNuisParam && !hasGlobalObs ) {
// try to see if model has nuisance parameters first
RooAbsPdf * constrPdf = RooStats::MakeNuisancePdf(*sbModel,"nuisanceConstraintPdf_sbmodel");
if (constrPdf) {
Warning("StandardHypoTestInvDemo","Model %s has nuisance parameters but no global observables associated",sbModel->GetName());
Warning("StandardHypoTestInvDemo","\tThe effect of the nuisance parameters will not be treated correctly ");
}
}
}
// run first a data fit
const RooArgSet * poiSet = sbModel->GetParametersOfInterest();
RooRealVar *poi = (RooRealVar*)poiSet->first();
std::cout << "StandardHypoTestInvDemo : POI initial value: " << poi->GetName() << " = " << poi->getVal() << std::endl;
// fit the data first (need to use constraint )
TStopwatch tw;
bool doFit = initialFit;
if (testStatType == 0 && initialFit == -1) doFit = false; // case of LEP test statistic
if (type == 3 && initialFit == -1) doFit = false; // case of Asymptoticcalculator with nominal Asimov
double poihat = 0;
if (minimizerType.size()==0) minimizerType = ROOT::Math::MinimizerOptions::DefaultMinimizerType();
else
ROOT::Math::MinimizerOptions::SetDefaultMinimizer(minimizerType.c_str());
Info("StandardHypoTestInvDemo","Using %s as minimizer for computing the test statistic",
ROOT::Math::MinimizerOptions::DefaultMinimizerType().c_str() );
if (doFit) {
// do the fit : By doing a fit the POI snapshot (for S+B) is set to the fit value
// and the nuisance parameters nominal values will be set to the fit value.
// This is relevant when using LEP test statistics
Info( "StandardHypoTestInvDemo"," Doing a first fit to the observed data ");
RooArgSet constrainParams;
if (sbModel->GetNuisanceParameters() ) constrainParams.add(*sbModel->GetNuisanceParameters());
RooStats::RemoveConstantParameters(&constrainParams);
tw.Start();
RooFitResult * fitres = sbModel->GetPdf()->fitTo(*data,InitialHesse(false), Hesse(false),
Minimizer(minimizerType.c_str(),"Migrad"), Strategy(0), PrintLevel(mPrintLevel), Constrain(constrainParams), Save(true) );
if (fitres->status() != 0) {
Warning("StandardHypoTestInvDemo","Fit to the model failed - try with strategy 1 and perform first an Hesse computation");
fitres = sbModel->GetPdf()->fitTo(*data,InitialHesse(true), Hesse(false),Minimizer(minimizerType.c_str(),"Migrad"), Strategy(1), PrintLevel(mPrintLevel+1), Constrain(constrainParams), Save(true) );
}
if (fitres->status() != 0)
Warning("StandardHypoTestInvDemo"," Fit still failed - continue anyway.....");
poihat = poi->getVal();
std::cout << "StandardHypoTestInvDemo - Best Fit value : " << poi->GetName() << " = "
<< poihat << " +/- " << poi->getError() << std::endl;
std::cout << "Time for fitting : "; tw.Print();
//save best fit value in the poi snapshot
sbModel->SetSnapshot(*sbModel->GetParametersOfInterest());
std::cout << "StandardHypoTestInvo: snapshot of S+B Model " << sbModel->GetName()
<< " is set to the best fit value" << std::endl;
}
// print a message in case of LEP test statistics because it affects result by doing or not doing a fit
if (testStatType == 0) {
if (!doFit)
Info("StandardHypoTestInvDemo","Using LEP test statistic - an initial fit is not done and the TS will use the nuisances at the model value");
else
Info("StandardHypoTestInvDemo","Using LEP test statistic - an initial fit has been done and the TS will use the nuisances at the best fit value");
}
// build test statistics and hypotest calculators for running the inverter
SimpleLikelihoodRatioTestStat slrts(*sbModel->GetPdf(),*bModel->GetPdf());
// null parameters must includes snapshot of poi plus the nuisance values
RooArgSet nullParams(*sbModel->GetSnapshot());
if (sbModel->GetNuisanceParameters()) nullParams.add(*sbModel->GetNuisanceParameters());
if (sbModel->GetSnapshot()) slrts.SetNullParameters(nullParams);
RooArgSet altParams(*bModel->GetSnapshot());
if (bModel->GetNuisanceParameters()) altParams.add(*bModel->GetNuisanceParameters());
if (bModel->GetSnapshot()) slrts.SetAltParameters(altParams);
// ratio of profile likelihood - need to pass snapshot for the alt
RatioOfProfiledLikelihoodsTestStat
ropl(*sbModel->GetPdf(), *bModel->GetPdf(), bModel->GetSnapshot());
ropl.SetSubtractMLE(false);
if (testStatType == 11) ropl.SetSubtractMLE(true);
ropl.SetPrintLevel(mPrintLevel);
ropl.SetMinimizer(minimizerType.c_str());
ProfileLikelihoodTestStat profll(*sbModel->GetPdf());
if (testStatType == 3) profll.SetOneSided(true);
if (testStatType == 4) profll.SetSigned(true);
profll.SetMinimizer(minimizerType.c_str());
profll.SetPrintLevel(mPrintLevel);
profll.SetReuseNLL(mOptimize);
slrts.SetReuseNLL(mOptimize);
ropl.SetReuseNLL(mOptimize);
if (mOptimize) {
profll.SetStrategy(0);
ropl.SetStrategy(0);
ROOT::Math::MinimizerOptions::SetDefaultStrategy(0);
}
if (mMaxPoi > 0) poi->setMax(mMaxPoi); // increase limit
MaxLikelihoodEstimateTestStat maxll(*sbModel->GetPdf(),*poi);
NumEventsTestStat nevtts;
AsymptoticCalculator::SetPrintLevel(mPrintLevel);
// create the HypoTest calculator class
HypoTestCalculatorGeneric * hc = 0;
if (type == 0) hc = new FrequentistCalculator(*data, *bModel, *sbModel);
else if (type == 1) hc = new HybridCalculator(*data, *bModel, *sbModel);
// else if (type == 2 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, false, mAsimovBins);
// else if (type == 3 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, true, mAsimovBins); // for using Asimov data generated with nominal values
else if (type == 2 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, false );
else if (type == 3 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, true ); // for using Asimov data generated with nominal values
else {
Error("StandardHypoTestInvDemo","Invalid - calculator type = %d supported values are only :\n\t\t\t 0 (Frequentist) , 1 (Hybrid) , 2 (Asymptotic) ",type);
return 0;
}
// set the test statistic
TestStatistic * testStat = 0;
if (testStatType == 0) testStat = &slrts;
if (testStatType == 1 || testStatType == 11) testStat = &ropl;
if (testStatType == 2 || testStatType == 3 || testStatType == 4) testStat = &profll;
if (testStatType == 5) testStat = &maxll;
if (testStatType == 6) testStat = &nevtts;
if (testStat == 0) {
Error("StandardHypoTestInvDemo","Invalid - test statistic type = %d supported values are only :\n\t\t\t 0 (SLR) , 1 (Tevatron) , 2 (PLR), 3 (PLR1), 4(MLE)",testStatType);
return 0;
}
ToyMCSampler *toymcs = (ToyMCSampler*)hc->GetTestStatSampler();
if (toymcs && (type == 0 || type == 1) ) {
// look if pdf is number counting or extended
if (sbModel->GetPdf()->canBeExtended() ) {
if (useNumberCounting) Warning("StandardHypoTestInvDemo","Pdf is extended: but number counting flag is set: ignore it ");
}
else {
// for not extended pdf
if (!useNumberCounting ) {
int nEvents = data->numEntries();
Info("StandardHypoTestInvDemo","Pdf is not extended: number of events to generate taken from observed data set is %d",nEvents);
toymcs->SetNEventsPerToy(nEvents);
}
else {
Info("StandardHypoTestInvDemo","using a number counting pdf");
toymcs->SetNEventsPerToy(1);
}
}
toymcs->SetTestStatistic(testStat);
if (data->isWeighted() && !mGenerateBinned) {
Info("StandardHypoTestInvDemo","Data set is weighted, nentries = %d and sum of weights = %8.1f but toy generation is unbinned - it would be faster to set mGenerateBinned to true\n",data->numEntries(), data->sumEntries());
}
toymcs->SetGenerateBinned(mGenerateBinned);
toymcs->SetUseMultiGen(mOptimize);
if (mGenerateBinned && sbModel->GetObservables()->getSize() > 2) {
Warning("StandardHypoTestInvDemo","generate binned is activated but the number of ovservable is %d. Too much memory could be needed for allocating all the bins",sbModel->GetObservables()->getSize() );
}
// set the random seed if needed
if (mRandomSeed >= 0) RooRandom::randomGenerator()->SetSeed(mRandomSeed);
}
// specify if need to re-use same toys
if (reuseAltToys) {
hc->UseSameAltToys();
}
if (type == 1) {
HybridCalculator *hhc = dynamic_cast<HybridCalculator*> (hc);
assert(hhc);
hhc->SetToys(ntoys,ntoys/mNToysRatio); // can use less ntoys for b hypothesis
// remove global observables from ModelConfig (this is probably not needed anymore in 5.32)
bModel->SetGlobalObservables(RooArgSet() );
sbModel->SetGlobalObservables(RooArgSet() );
// check for nuisance prior pdf in case of nuisance parameters
if (bModel->GetNuisanceParameters() || sbModel->GetNuisanceParameters() ) {
// fix for using multigen (does not work in this case)
toymcs->SetUseMultiGen(false);
ToyMCSampler::SetAlwaysUseMultiGen(false);
RooAbsPdf * nuisPdf = 0;
if (nuisPriorName) nuisPdf = w->pdf(nuisPriorName);
// use prior defined first in bModel (then in SbModel)
if (!nuisPdf) {
Info("StandardHypoTestInvDemo","No nuisance pdf given for the HybridCalculator - try to deduce pdf from the model");
if (bModel->GetPdf() && bModel->GetObservables() )
nuisPdf = RooStats::MakeNuisancePdf(*bModel,"nuisancePdf_bmodel");
else
nuisPdf = RooStats::MakeNuisancePdf(*sbModel,"nuisancePdf_sbmodel");
}
if (!nuisPdf ) {
if (bModel->GetPriorPdf()) {
nuisPdf = bModel->GetPriorPdf();
Info("StandardHypoTestInvDemo","No nuisance pdf given - try to use %s that is defined as a prior pdf in the B model",nuisPdf->GetName());
}
else {
Error("StandardHypoTestInvDemo","Cannnot run Hybrid calculator because no prior on the nuisance parameter is specified or can be derived");
return 0;
}
}
assert(nuisPdf);
Info("StandardHypoTestInvDemo","Using as nuisance Pdf ... " );
nuisPdf->Print();
const RooArgSet * nuisParams = (bModel->GetNuisanceParameters() ) ? bModel->GetNuisanceParameters() : sbModel->GetNuisanceParameters();
RooArgSet * np = nuisPdf->getObservables(*nuisParams);
if (np->getSize() == 0) {
Warning("StandardHypoTestInvDemo","Prior nuisance does not depend on nuisance parameters. They will be smeared in their full range");
}
delete np;
hhc->ForcePriorNuisanceAlt(*nuisPdf);
hhc->ForcePriorNuisanceNull(*nuisPdf);
}
}
else if (type == 2 || type == 3) {
if (testStatType == 3) ((AsymptoticCalculator*) hc)->SetOneSided(true);
if (testStatType != 2 && testStatType != 3)
Warning("StandardHypoTestInvDemo","Only the PL test statistic can be used with AsymptoticCalculator - use by default a two-sided PL");
}
else if (type == 0 || type == 1)
((FrequentistCalculator*) hc)->SetToys(ntoys,ntoys/mNToysRatio);
// Get the result
RooMsgService::instance().getStream(1).removeTopic(RooFit::NumIntegration);
HypoTestInverter calc(*hc);
calc.SetConfidenceLevel(0.95);
calc.UseCLs(useCLs);
calc.SetVerbose(true);
// can speed up using proof-lite
if (mUseProof && mNWorkers > 1) {
ProofConfig pc(*w, mNWorkers, "", kFALSE);
toymcs->SetProofConfig(&pc); // enable proof
}
if (npoints > 0) {
if (poimin > poimax) {
// if no min/max given scan between MLE and +4 sigma
poimin = int(poihat);
poimax = int(poihat + 4 * poi->getError());
}
std::cout << "Doing a fixed scan in interval : " << poimin << " , " << poimax << std::endl;
calc.SetFixedScan(npoints,poimin,poimax);
}
else {
//poi->setMax(10*int( (poihat+ 10 *poi->getError() )/10 ) );
std::cout << "Doing an automatic scan in interval : " << poi->getMin() << " , " << poi->getMax() << std::endl;
}
tw.Start();
HypoTestInverterResult * r = calc.GetInterval();
std::cout << "Time to perform limit scan \n";
tw.Print();
if (mRebuild) {
calc.SetCloseProof(1);
tw.Start();
SamplingDistribution * limDist = calc.GetUpperLimitDistribution(true,mNToyToRebuild);
std::cout << "Time to rebuild distributions " << std::endl;
tw.Print();
if (limDist) {
std::cout << "expected up limit " << limDist->InverseCDF(0.5) << " +/- "
<< limDist->InverseCDF(0.16) << " "
<< limDist->InverseCDF(0.84) << "\n";
//update r to a new updated result object containing the rebuilt expected p-values distributions
// (it will not recompute the expected limit)
if (r) delete r; // need to delete previous object since GetInterval will return a cloned copy
r = calc.GetInterval();
}
else
std::cout << "ERROR : failed to re-build distributions " << std::endl;
}
return r;
}
void inflateTree(const char *name = "h42",
const char *in = "root://eospps.cern.ch///eos/ppsscratch/test/h1big.root",
const char *out = "/tmp/h1big.root", Int_t fact = 1)
{
TStopwatch sw;
sw.Start();
// Get the input tree from the input file
TFile *fin = TFile::Open(in);
if (!fin || fin->IsZombie()) {
Printf("inflateTree", "could not open input file: %s", in);
return;
}
TTree *tin = (TTree *) fin->Get(name);
if (!tin) {
Printf("inflateTree", "could not find tree '%s' in %s", name, in);
delete fin;
return;
}
Long64_t nin = tin->GetEntriesFast();
Printf("Input tree '%s' has %lld entries", name, nin);
// Create output file
TFile *fout = TFile::Open(out, "RECREATE", 0, 1);
if (!fout || fout->IsZombie()) {
Printf("inflateTree", "could not open input file: %s", in);
delete fin;
return;
}
// Clone the header of the initial tree
TTree *tout= (TTree *) tin->CloneTree(0);
tout->SetMaxTreeSize(19000000000);
// Duplicate all entries once
#if 0
Int_t nc = fact;
while (nc--) {
Printf("Writing copy %d ...", fact - nc);
tout->CopyEntries(tin);
}
#else
for (Long64_t i = 0; i < nin; ++i) {
if (tin->LoadTree(i) < 0) {
break;
}
tin->GetEntry(i);
Int_t nc = fact;
while (nc--) {
tout->Fill();
}
if (i > 0 && !(i%1000)) {
Printf("%d copies of %lld entries filled ...", fact, i);
}
}
#endif
// Finalize the writing out
tout->Write();
// print perf stats
sw.Stop();
std::cout << "Drawing. Realtime: " << sw.RealTime() << std::endl;
std::cout << "Drawing. Cputime : " << sw.CpuTime() << std::endl;
tin->PrintCacheStats();
// Close the files
fout->Close();
fin->Close();
// Cleanup
delete fout;
delete fin;
}
void r3blandreco(Int_t nNeutrons, Int_t beamE, Int_t Erel)
{
Int_t d;
if(Erel == 100){
d = 35;
}
else{
d = 14;
}
// ----- Files ---------------------------------------------------------------
char strDir[] = ".";
char str[100];
char str2[100];
sprintf(str, "%1dAMeV.%1dn.%1dkeV.%1dm.root", beamE,nNeutrons, Erel, d);
sprintf(str2, "%1dAMeV.%1dkeV.%1dm", beamE, Erel, d);
TString inFile = TString(strDir) + "/r3bsim." + TString(str);
TString digiFile = TString(strDir) + "/r3bcalibr." + TString(str);
TString parFile = TString(strDir) + "/r3bpar." + TString(str);
TString calibrFile = TString(strDir) + "/r3bcalibr." + TString(str2) + ".txt";
TString outFile = TString(strDir) + "/r3breco." + TString(str);
// ---------------------------------------------------------------------------
// ----- Timer ---------------------------------------------------------------
TStopwatch timer;
timer.Start();
// ---------------------------------------------------------------------------
// ----- Digitization --------------------------------------------------------
FairRunAna *fRun= new FairRunAna();
fRun->SetInputFile(inFile);
fRun->AddFriend(digiFile);
fRun->SetOutputFile(outFile);
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------
Double_t beamEnergy;
Double_t beamBeta;
if(200 == beamE) {
beamEnergy=200.;
beamBeta=0.5676881;
} else if(600 == beamE) {
beamEnergy=600.;
beamBeta=0.7937626;
} else if(1000 == beamE) {
beamEnergy=1000.;
beamBeta=0.8760237;
}
// ---------------------------------------------------------------------------
// ----- Connect the Tracking Task -------------------------------------------
R3BNeutronTracker2D* tracker = new R3BNeutronTracker2D();
tracker->UseBeam(beamEnergy, beamBeta);
tracker->ReadCalibrFile(calibrFile.Data());
fRun->AddTask(tracker);
// ---------------------------------------------------------------------------
// ----- Runtime DataBase info -----------------------------------------------
FairRuntimeDb* rtdb = fRun->GetRuntimeDb();
FairParRootFileIo* parIo1 = new FairParRootFileIo();
parIo1->open(parFile.Data());
rtdb->setFirstInput(parIo1);
rtdb->setOutput(parIo1);
rtdb->saveOutput();
// ---------------------------------------------------------------------------
// ----- Number of events to process -----------------------------------------
Int_t nEvents = 10000;
// ---------------------------------------------------------------------------
// ----- Intialise and run ---------------------------------------------------
fRun->Init();
fRun->Run(0, nEvents);
// ---------------------------------------------------------------------------
// ----- Finish --------------------------------------------------------------
timer.Stop();
Double_t rtime = timer.RealTime();
Double_t ctime = timer.CpuTime();
cout << endl << endl;
cout << "Macro finished succesfully." << endl;
cout << "Output file writen: " << outFile << endl;
cout << "Parameter file writen " << parFile << endl;
cout << "Real time " << rtime << " s, CPU time " << ctime << " s" << endl;
cout << endl;
// ---------------------------------------------------------------------------
}
void run(const Char_t *files=NULL, Bool_t mc=kFALSE, Bool_t tpid=kTRUE, Bool_t tchg=kFALSE, Bool_t tpp=kTRUE, Long64_t nev=1234567890, Long64_t first = 0)
{
TStopwatch timer;
timer.Start();
// VERY GENERAL SETTINGS
//AliLog::SetGlobalLogLevel(AliLog::kError);
if(gSystem->Load("libANALYSIS.so")<0) return;
if(gSystem->Load("libANALYSISalice.so")<0) return;
if(gSystem->Load("libTender.so")<0) return;
if(gSystem->Load("libTenderSupplies.so")<0) return;
// if(gSystem->Load("libMES.so")<0) return;
if(gSystem->Load("libPWGLFspectra.so")<0) return;
// DEFINE DATA CHAIN
TChain *chain = NULL;
if(!files) chain = MakeChainLST();
else chain = MakeChainLST(files);
if(!chain) return;
chain->Lookup();
chain->GetListOfFiles()->Print();
Long64_t nfound=chain->GetEntries();
printf("\tENTRIES FOUND [%lli] REQUESTED [%lli]\n", nfound, nev>nfound?nfound:nev);
// BUILD ANALYSIS MANAGER
AliAnalysisManager *mgr = new AliAnalysisManager("Multiplicity and Event Shape");
AliESDInputHandler *esdH = new AliESDInputHandler();
AliMCEventHandler *mcH(NULL);
mgr->SetInputEventHandler(esdH);
if(mc) mgr->SetMCtruthEventHandler(mcH = new AliMCEventHandler());
//mgr->SetDebugLevel(10);
mgr->SetSkipTerminate(kTRUE);
// LOAD tasks
// ******************* PID response ******************
gROOT->LoadMacro("$ALICE_ROOT/ANALYSIS/macros/AddTaskPIDResponse.C");
if(!mc) AddTaskPIDResponse();
else AddTaskPIDResponse(kTRUE,kTRUE,kTRUE,2);
// ******************* Tenders ***********************
AliTender *aliTender(NULL);
gROOT->LoadMacro("$ALICE_PHYSICS/TENDER/TenderSupplies/AddTaskTender.C");
if(!mc){ // for DATA
aliTender = (AliTender*)AddTaskTender(!mc, kTRUE, kTRUE, kTRUE, kTRUE, kFALSE, kTRUE, kFALSE, kFALSE);
// (useV0, useTPC, !!! useTOF=kFALSE for MC !!!, useTRD, usePID, useVTX, useT0, useEmc, usePtFix)
} else { // for MC
aliTender = (AliTender*)AddTaskTender(!mc, kTRUE, kFALSE, kTRUE, kTRUE, kTRUE, kTRUE, kFALSE, kFALSE); // (useV0, useTPC, !!! useTOF=kFALSE for MC !!!, useTRD, usePID, useVTX, useT0, useEmc, usePtFix)
}
aliTender->SetHandleOCDB(kTRUE);
//aliTender->SetDefaultCDBStorage(Form("alien://folder=/alice/data/2010/OCDB?cacheFolder=%s/local", gSystem->ExpandPathName("$HOME")));
// aliTender->SetDefaultCDBStorage(Form("local://%s/local/alice/data/2010/OCDB", gSystem->ExpandPathName("$HOME")));
// ******************* Physics Selection *************
gROOT->LoadMacro("$ALICE_PHYSICS/OADB/macros/AddTaskPhysicsSelection.C");
AliPhysicsSelectionTask *physSelTask = AddTaskPhysicsSelection(mc); // 0 = real data; 1 = MC
// ******************* MES Tender ******************
gROOT->LoadMacro("$ALICE_PHYSICS/PWGLF/SPECTRA/MultEvShape/AddMEStender.C");
AddMEStender(mc);
// ******************* MES PID task ******************
if(tpid){
gROOT->LoadMacro("$ALICE_PHYSICS/PWGLF/SPECTRA/MultEvShape/AddMESpidTask.C");
AddMESpidTask(mc);
}
//
// // ******************* MES CHG task ******************
if(tchg){
gROOT->LoadMacro("$ALICE_PHYSICS/PWGLF/SPECTRA/MultEvShape/AddMESchgTask.C");
AddMESchgTask(mc);
}
//
// // ******************* MES ppCol task ******************
if(tpp){
gROOT->LoadMacro("$ALICE_PHYSICS/PWGLF/SPECTRA/MultEvShape/AddMESppColTask.C");
AddMESppColTask(mc);
}
if (!mgr->InitAnalysis()) return;
mgr->PrintStatus();
mgr->StartAnalysis("local", chain, nev, first);
timer.Stop();
timer.Print();
// verbosity
printf("\tCLEANING TASK LIST:\n");
mgr->GetTasks()->Delete();
if(mcH) delete mcH;
delete esdH;
delete chain;
}
void test(const char * sdir ="signal",
const char * bdir ="backgr") {
TStopwatch timer;
timer.Start();
TString name;
// Signal file, tree, and branch
name = sdir;
name += "/IlcESDs.root";
TFile * fSig = TFile::Open(name.Data());
TTree * tSig = (TTree*)fSig->Get("esdTree");
IlcESDEvent * esdSig = new IlcESDEvent();// The signal ESD object is put here
esdSig->ReadFromTree(tSig);
// Run loader (signal events)
name = sdir;
name += "/gilc.root";
IlcRunLoader* rlSig = IlcRunLoader::Open(name.Data());
// Run loader (underlying events)
name = bdir;
name += "/gilc.root";
IlcRunLoader* rlUnd = IlcRunLoader::Open(name.Data(),"Underlying");
// gIlc
rlSig->LoadgIlc();
rlUnd->LoadgIlc();
gIlc = rlSig->GetIlcRun();
// Now load kinematics and event header
rlSig->LoadKinematics();
rlSig->LoadHeader();
rlUnd->LoadKinematics();
rlUnd->LoadHeader();
// Loop on events: check that MC and data contain the same number of events
Long64_t nevSig = rlSig->GetNumberOfEvents();
Long64_t nevUnd = rlUnd->GetNumberOfEvents();
Long64_t nSigPerUnd = nevSig/nevUnd;
cout << nevSig << " signal events" << endl;
cout << nevUnd << " underlying events" << endl;
cout << nSigPerUnd << " signal events per one underlying" << endl;
for (Int_t iev=0; iev<nevSig; iev++) {
cout << "Signal event " << iev << endl;
Int_t ievUnd = iev/nSigPerUnd;
cout << "Underlying event " << ievUnd << endl;
// Get signal ESD
tSig->GetEntry(iev);
// Get signal kinematics
rlSig->GetEvent(iev);
// Get underlying kinematics
rlUnd->GetEvent(ievUnd);
// Particle stack
IlcStack * stackSig = rlSig->Stack();
Int_t nPartSig = stackSig->GetNtrack();
IlcStack * stackUnd = rlUnd->Stack();
Int_t nPartUnd = stackUnd->GetNtrack();
Int_t nrec = esdSig->GetNumberOfTracks();
cout << nrec << " reconstructed tracks" << endl;
for(Int_t irec=0; irec<nrec; irec++) {
IlcESDtrack * track = esdSig->GetTrack(irec);
UInt_t label = TMath::Abs(track->GetTPCLabel());
if (label>=10000000) {
// Underlying event. 10000000 is the
// value of fkMASKSTEP in IlcRunDigitizer
// cout << " Track from the underlying event" << endl;
label %=10000000;
if (label>=nPartUnd) continue;
TParticle * part = stackUnd->Particle(label);
if(part) part->Print();
}
else {
cout << " Track " << label << " from the signal event" << endl;
if (label>=nPartSig) {
cout <<"Strange, label outside the range "<< endl;
continue;
}
TParticle * part = stackSig->Particle(label);
if(part) part->Print();
}
}
}
fSig->Close();
timer.Stop();
timer.Print();
}
void csv2()
{
TString sysname ="CSV.root";
TFile *sysinput(0);
sysinput = TFile::Open( sysname ); // if not: download from ROOT server
std::vector<string> variables_;
TString name;
variables_.push_back("BDT__zjethist");
// variables_.push_back("BDT__phjethist");
variables_.push_back("BDT__tbartchhist");
variables_.push_back("BDT__tt3hist");
variables_.push_back("BDT__ttphhist");
variables_.push_back("BDT__wwphhist");
variables_.push_back("BDT__zzhist");
variables_.push_back("BDT__zgammahist");
variables_.push_back("BDT__singleantitopphotonhist");
std::vector<TH1F*> addhists;
std::vector<TH1F*> wjetandwphjet;
wjetandwphjet.push_back((TH1F*) sysinput->Get((std::string("BDT__wjet").c_str())));
wjetandwphjet.push_back((TH1F*) sysinput->Get((std::string("BDT__wphjethist").c_str())));
std::vector<TH1F*> jesuphists;
for(unsigned int i=0; i<variables_.size(); ++i){
name=variables_[i]+"__JES__plus";
jesuphists.push_back((TH1F*) sysinput->Get(name));
}
//jesuphists.push_back((TH1F*) sysinput->Get((std::string("BDT__wjet").c_str())));
//jesuphists.push_back((TH1F*) sysinput->Get((std::string("BDT__wphjethist").c_str())));
for(unsigned int idx=1; idx<variables_.size(); ++idx){
jesuphists[idx]->Add(jesuphists[idx-1]);
}
addhists.push_back(jesuphists[variables_.size()-1]);
std::vector<TH1F*> jesdownhists;
for(unsigned int i=0; i<variables_.size(); ++i){
name=variables_[i]+"__JES__minus";
jesdownhists.push_back((TH1F*) sysinput->Get(name));}
for(unsigned int idx=1; idx<variables_.size(); ++idx){
jesdownhists[idx]->Add(jesdownhists[idx-1]);}
addhists.push_back(jesdownhists[variables_.size()-1]);
std::vector<TH1F*> jeruphists;
for(unsigned int i=0; i<variables_.size(); ++i){
name=variables_[i]+"__JER__plus";
jeruphists.push_back((TH1F*) sysinput->Get(name));}
for(unsigned int idx=1; idx<variables_.size(); ++idx){
jeruphists[idx]->Add(jeruphists[idx-1]);}
addhists.push_back(jeruphists[variables_.size()-1]);
std::vector<TH1F*> jerdownhists;
for(unsigned int i=0; i<variables_.size(); ++i){
name=variables_[i]+"__JER__minus";
jerdownhists.push_back((TH1F*) sysinput->Get(name));}
for(unsigned int idx=1; idx<variables_.size(); ++idx){
jerdownhists[idx]->Add(jerdownhists[idx-1]);}
addhists.push_back(jerdownhists[variables_.size()-1]);
std::vector<TH1F*> phesuphists;
for(unsigned int i=0; i<variables_.size(); ++i){
name=variables_[i]+"__PhES__plus";
phesuphists.push_back((TH1F*) sysinput->Get(name));}
for(unsigned int idx=1; idx<variables_.size(); ++idx){
phesuphists[idx]->Add(phesuphists[idx-1]);}
addhists.push_back(phesuphists[variables_.size()-1]);
std::vector<TH1F*> phesdownhists;
for(unsigned int i=0; i<variables_.size(); ++i){
name=variables_[i]+"__PhES__minus";
phesdownhists.push_back((TH1F*) sysinput->Get(name));}
for(unsigned int idx=1; idx<variables_.size(); ++idx){
phesdownhists[idx]->Add(phesdownhists[idx-1]);}
addhists.push_back(phesdownhists[variables_.size()-1]);
std::vector<TH1F*> puuphists;
for(unsigned int i=0; i<variables_.size(); ++i){
name=variables_[i]+"__PU__plus";
puuphists.push_back((TH1F*) sysinput->Get(name));}
for(unsigned int idx=1; idx<variables_.size(); ++idx){
puuphists[idx]->Add(puuphists[idx-1]);}
addhists.push_back(puuphists[variables_.size()-1]);
std::vector<TH1F*> pudownhists;
for(unsigned int i=0; i<variables_.size(); ++i){
name=variables_[i]+"__PU__minus";
pudownhists.push_back((TH1F*) sysinput->Get(name));}
for(unsigned int idx=1; idx<variables_.size(); ++idx){
pudownhists[idx]->Add(pudownhists[idx-1]);}
addhists.push_back(pudownhists[variables_.size()-1]);
std::vector<TH1F*> triguphists;
for(unsigned int i=0; i<variables_.size(); ++i){
name=variables_[i]+"__TRIG__plus";
triguphists.push_back((TH1F*) sysinput->Get(name));}
for(unsigned int idx=1; idx<variables_.size(); ++idx){
triguphists[idx]->Add(triguphists[idx-1]);}
addhists.push_back(triguphists[variables_.size()-1]);
std::vector<TH1F*> trigdownhists;
for(unsigned int i=0; i<variables_.size(); ++i){
name=variables_[i]+"__TRIG__minus";
trigdownhists.push_back((TH1F*) sysinput->Get(name));}
for(unsigned int idx=1; idx<variables_.size(); ++idx){
trigdownhists[idx]->Add(trigdownhists[idx-1]);}
addhists.push_back(trigdownhists[variables_.size()-1]);
std::vector<TH1F*> btaguphists;
for(unsigned int i=0; i<variables_.size(); ++i){
name=variables_[i]+"__BTAG__plus";
btaguphists.push_back((TH1F*) sysinput->Get(name));}
for(unsigned int idx=1; idx<variables_.size(); ++idx){
btaguphists[idx]->Add(btaguphists[idx-1]);}
addhists.push_back(btaguphists[variables_.size()-1]);
std::vector<TH1F*> btagdownhists;
for(unsigned int i=0; i<variables_.size(); ++i){
name=variables_[i]+"__BTAG__minus";
btagdownhists.push_back((TH1F*) sysinput->Get(name));}
for(unsigned int idx=1; idx<variables_.size(); ++idx){
btagdownhists[idx]->Add(btagdownhists[idx-1]);}
addhists.push_back(btagdownhists[variables_.size()-1]);
std::vector<TH1F*> misstaguphists;
for(unsigned int i=0; i<variables_.size(); ++i){
name=variables_[i]+"__MISSTAG__plus";
misstaguphists.push_back((TH1F*) sysinput->Get(name));}
for(unsigned int idx=1; idx<variables_.size(); ++idx){
misstaguphists[idx]->Add(misstaguphists[idx-1]);}
addhists.push_back(misstaguphists[variables_.size()-1]);
std::vector<TH1F*> misstagdownhists;
for(unsigned int i=0; i<variables_.size(); ++i){
name=variables_[i]+"__MISSTAG__minus";
misstagdownhists.push_back((TH1F*) sysinput->Get(name));}
for(unsigned int idx=1; idx<variables_.size(); ++idx){
misstagdownhists[idx]->Add(misstagdownhists[idx-1]);}
addhists.push_back(misstagdownhists[variables_.size()-1]);
std::vector<TH1F*> muonuphists;
for(unsigned int i=0; i<variables_.size(); ++i){
name=variables_[i]+"__MUON__plus";
muonuphists.push_back((TH1F*) sysinput->Get(name));}
for(unsigned int idx=1; idx<variables_.size(); ++idx){
muonuphists[idx]->Add(muonuphists[idx-1]);}
addhists.push_back(muonuphists[variables_.size()-1]);
std::vector<TH1F*> muondownhists;
for(unsigned int i=0; i<variables_.size(); ++i){
name=variables_[i]+"__MUON__minus";
muondownhists.push_back((TH1F*) sysinput->Get(name));}
for(unsigned int idx=1; idx<variables_.size(); ++idx){
muondownhists[idx]->Add(muondownhists[idx-1]);}
addhists.push_back(muondownhists[variables_.size()-1]);
std::vector<TH1F*> photonuphists;
for(unsigned int i=0; i<variables_.size(); ++i){
name=variables_[i]+"__PHOTON__plus";
photonuphists.push_back((TH1F*) sysinput->Get(name));}
for(unsigned int idx=1; idx<variables_.size(); ++idx){
photonuphists[idx]->Add(photonuphists[idx-1]);}
addhists.push_back(photonuphists[variables_.size()-1]);
std::vector<TH1F*> photondownhists;
for(unsigned int i=0; i<variables_.size(); ++i){
name=variables_[i]+"__PHOTON__minus";
photondownhists.push_back((TH1F*) sysinput->Get(name));}
for(unsigned int idx=1; idx<variables_.size(); ++idx){
photondownhists[idx]->Add(photondownhists[idx-1]);}
addhists.push_back(photondownhists[variables_.size()-1]);
std::vector<std::vector<double_t> > vec(photondownhists[0]->GetNbinsX(), vector<double>(18));
for(int p = 0; p <photondownhists[0]->GetNbinsX(); p++){ //loop over bins
for(int m = 0; m < 18; m++){ //loop over systematics
vec[p][m]=addhists[m]->GetBinContent(p+1)+wjetandwphjet[0]->GetBinContent(p+1)+wjetandwphjet[1]->GetBinContent(p+1);
cout<<vec[p][m]<<endl;
}}
// Book output histograms
UInt_t nbin = 20;
double min=0;
double max=1;
// Prepare input tree (this must be replaced by your data source)
// in this example, there is a toy tree with signal and one with background events
// we'll later on use only the "signal" events for the test in this example.
//
TFile *input(0);
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
std::vector<string> samples_;
std::vector<string> datasamples_;
std::vector<TH1F*> hists;
std::vector<TH1F*> datahists;
std::vector<TH1F*> revDATAhists;
float scales[] = {0.628,0.0978,34.01,6.133,1.04,0.32,0.02,0.002,0.0961,0.0253,0.0224,0.0145,0.0125,0.0160,0.0158,0.0341,0.0341,0.0341,0.020,0.0017,0.0055,0.0032,0.00084,0.02,0.01139,0.01139,0.049094905/19.145};
samples_.push_back("WJET.root");
samples_.push_back("ZJET.root");
samples_.push_back("G_Pt_50to80.root");
samples_.push_back("G_Pt_80to120.root");
samples_.push_back("G_Pt_120to170.root");
samples_.push_back("G_Pt_170to300.root");
samples_.push_back("G_Pt_300to470.root");
samples_.push_back("G_Pt_470to800.root");
samples_.push_back("WPHJET.root");
samples_.push_back("T-W-CH.root");
samples_.push_back("TBAR-W-CH.root");
samples_.push_back("T-S-CH.root");
samples_.push_back("TBAR-S-CH.root");
samples_.push_back("T-T-CH.root");
samples_.push_back("TBAR-T-CH.root");
samples_.push_back("TTBAR1.root");
samples_.push_back("TTBAR2.root");
samples_.push_back("TTBAR3.root");
samples_.push_back("TTG.root");
samples_.push_back("WWG.root");
samples_.push_back("WW.root");
samples_.push_back("WZ.root");
samples_.push_back("ZZ.root");
samples_.push_back("ZGAMMA.root");
samples_.push_back("SINGLE-TOP-PH.root");
samples_.push_back("SINGLE-ANTITOP-PH.root");
samples_.push_back("SIGNALtGu.root");
datasamples_.push_back("REALDATA1.root");
datasamples_.push_back("REALDATA2.root");
datasamples_.push_back("REALDATA3.root");
std::vector<string> datasamplesreverse_;
datasamplesreverse_.push_back("etarev/REALDATA1.root");
datasamplesreverse_.push_back("etarev/REALDATA2.root");
datasamplesreverse_.push_back("etarev/REALDATA3.root");
TH1F *wphjethist(0), *zjethist(0) , *phjethist(0), *wjethist(0), *twchhist(0), *tbarwhist(0), *tschhist(0), *tbarschhist(0), *ttchhist(0), *tbartchhist(0), *tt1hist(0) ,*tt2hist(0), *tt3hist(0), *ttphhist(0), *wwphhist(0), *wwhist(0), *wzhist(0), *zzhist(0), *zgammahist(0),*singletopphotonhist(0), *singleantitopphotonhist(0), *signalhist(0), *G_Pt_50to80(0),*G_Pt_80to120(0), *G_Pt_120to170(0), *G_Pt_170to300(0) ,*G_Pt_300to470(0),*G_Pt_470to800(0) ;
TH1F *wphjethistSB(0), *zjethistSB(0) , *phjethistSB(0), *wjethistSB(0), *twchhistSB(0), *tbarwhistSB(0), *tschhistSB(0), *tbarschhistSB(0), *ttchhistSB(0), *tbartchhistSB(0), *tt1histSB(0) ,*tt2histSB(0), *tt3histSB(0), *ttphhistSB(0), *wwphhistSB(0), *wwhistSB(0), *wzhistSB(0), *zzhistSB(0), *zgammahistSB(0),*singletopphotonhistSB(0), *singleantitopphotonhistSB(0), *signalhistSB(0), *G_Pt_50to80SB(0),*G_Pt_80to120SB(0), *G_Pt_120to170SB(0), *G_Pt_170to300SB(0) ,*G_Pt_300to470SB(0),*G_Pt_470to800SB(0) ;
TH1F *data1hist(0), *data2hist(0) ,*data3hist(0) ,*datahistsideband(0);
TH1F *data1histrev(0), *data2histrev(0) ,*data3histrev(0), *datahistrevsideband(0);
wphjethist = new TH1F( "mu_BDT__wphjethist", "mu_BDT__wphjethist", nbin, min, max );
zjethist = new TH1F( "mu_BDT__zjethist", "mu_BDT__zjethist", nbin, min, max );
G_Pt_50to80= new TH1F( "mu_BDT__G_Pt_50to80", "mu_BDT__G_Pt_50to80", nbin, min, max );
G_Pt_80to120= new TH1F( "mu_BDT__G_Pt_80to120", "mu_BDT__G_Pt_80to120", nbin, min, max );
G_Pt_120to170= new TH1F( "mu_BDT__G_Pt_120to170", "mu_BDT__G_Pt_120to170", nbin, min, max );
G_Pt_170to300= new TH1F( "mu_BDT__G_Pt_170to300", "mu_BDT__G_Pt_170to300", nbin, min, max );
G_Pt_300to470= new TH1F( "mu_BDT__G_Pt_300to470", "mu_BDT__G_Pt_300to470", nbin, min, max );
G_Pt_470to800= new TH1F( "mu_BDT__G_Pt_470to800", "mu_BDT__G_Pt_470to800", nbin, min, max );
wjethist = new TH1F( "mu_BDT__wjethist", "mu_BDT__wjethist", nbin, min, max);
twchhist = new TH1F( "mu_BDT__twchhist", "mu_BDT__twchhist", nbin, min, max );
tbarwhist = new TH1F( "mu_BDT__tbarwhist", "mu_BDT__tbarwhist", nbin, min, max );
tschhist = new TH1F( "mu_BDT__tschhist", "mu_BDT__tschhist", nbin, min, max );
tbarschhist = new TH1F( "mu_BDT__tbarschhist", "mu_BDT__tbarschhist", nbin, min, max );
ttchhist = new TH1F( "mu_BDT__ttchhist", "mu_BDT__ttchhist", nbin, min, max );
tbartchhist = new TH1F( "mu_BDT__tbartchhist", "mu_BDT__tbartchhist", nbin, min, max);
tt1hist = new TH1F( "mu_BDT__tt1hist", "mu_BDT__tt1hist", nbin,min, max );
tt2hist = new TH1F( "mu_BDT__tt2hist", "mu_BDT__tt2hist", nbin, min, max);
tt3hist = new TH1F( "mu_BDT__tt3hist", "mu_BDT__tt3hist", nbin, min, max);
ttphhist = new TH1F( "mu_BDT__ttphhist", "mu_BDT__ttphhist", nbin, min, max);
wwphhist = new TH1F( "mu_BDT__wwphhist", "BDT__wwphhist", nbin,min, max );
wwhist = new TH1F( "mu_BDT__wwhist", "mu_BDT__wwhist", nbin,min, max );
wzhist = new TH1F( "mu_BDT__wzhist", "mu_BDT__wzhist", nbin, min, max );
zzhist = new TH1F( "mu_BDT__zzhist", "mu_BDT__zzhist", nbin, min, max );
zgammahist = new TH1F( "mu_BDT__zgammahist", "mu_BDT__zgammahist", nbin,min, max );
singletopphotonhist = new TH1F( "mu_BDT__singletopphotonhist", "mu_BDT__singletopphotonhist", nbin, min, max);
singleantitopphotonhist = new TH1F( "mu_BDT__singleantitopphotonhist", "mu_BDT__singleantitopphotonhist", nbin,min, max );
signalhist = new TH1F( "mu_BDT__signal100", "mu_BDT__signal100", nbin, min, max );
data1hist = new TH1F( "mu_BDT__data1hist", "mu_BDT__data1hist", nbin, min, max );
data2hist = new TH1F( "mu_BDT__data2hist", "mu_BDT__data2hist", nbin, min, max );
data3hist = new TH1F( "mu_BDT__DATA", "mu_BDT__DATA", nbin, min, max );
datahistsideband = new TH1F( "mu_BDT__DATA_sideband", "mu_BDT__DATA_sideband", nbin, min, max);
data1histrev = new TH1F( "mu_BDT__data1histrev", "mu_BDT__data1histrev", nbin, min, max );
data2histrev = new TH1F( "mu_BDT__data2histrev", "mu_BDT__data2histrev", nbin,min, max );
data3histrev = new TH1F( "mu_BDT__DATArev", "mu_BDT__DATArev", nbin, min, max );
datahistrevsideband = new TH1F( "mu_BDT__DATArevsideband", "mu_BDT__DATArevsideband", nbin, min, max );
wphjethistSB = new TH1F( "mu_BDT__wphjethist__JES__SB", "mu_BDT__wphjethist__JES__SB", nbin,min, max );
zjethistSB = new TH1F( "mu_BDT__zjethist__JES__SB", "mu_BDT__zjethist__JES__SB", nbin, min, max );
G_Pt_50to80SB= new TH1F( "mu_BDT__G_Pt_50to80SB", "mu_BDT__G_Pt_50to80SB", nbin, min, max );
G_Pt_80to120SB= new TH1F( "mu_BDT__G_Pt_80to120SB", "mu_BDT__G_Pt_80to120SB", nbin, min, max );
G_Pt_120to170SB= new TH1F( "mu_BDT__G_Pt_120to170SB", "mu_BDT__G_Pt_120to170SB", nbin, min, max );
G_Pt_170to300SB= new TH1F( "mu_BDT__G_Pt_170to300SB", "mu_BDT__G_Pt_170to300SB", nbin, min, max );
G_Pt_300to470SB= new TH1F( "mu_BDT__G_Pt_300to470SB", "mu_BDT__G_Pt_300to470SB", nbin, min, max );
G_Pt_470to800SB= new TH1F( "mu_BDT__G_Pt_470to800SB", "mu_BDT__G_Pt_470to800SB", nbin, min, max );
wjethistSB = new TH1F( "mu_BDT__wjethist__JES__SB", "mu_BDT__wjethist__JES__SB", nbin, min, max );
twchhistSB = new TH1F( "mu_BDT__twchhist__JES__SB", "mu_BDT__twchhist__JES__SB", nbin,min, max);
tbarwhistSB = new TH1F( "mu_BDT__tbarwhist__JES__SB", "mu_BDT__tbarwhist__JES__SB", nbin,min, max );
tschhistSB = new TH1F( "mu_BDT__tschhist__JES__SB", "mu_BDT__tschhist__JES__SB", nbin, min, max );
tbarschhistSB = new TH1F( "mu_BDT__tbarschhist__JES__SB", "mu_BDT__tbarschhist__JES__SB", nbin, min, max );
ttchhistSB = new TH1F( "mu_BDT__ttchhist__JES__SB", "mu_BDT__ttchhist__JES__SB", nbin, min, max );
tbartchhistSB = new TH1F( "mu_BDT__tbartchhist__JES__SB", "mu_BDT__tbartchhist__JES__SB", nbin, min, max);
tt1histSB = new TH1F( "mu_BDT__tt1hist__JES__SB", "mu_BDT__tt1hist__JES__SB", nbin, min, max );
tt2histSB = new TH1F( "mu_BDT__tt2hist__JES__SB", "mu_BDT__tt2hist__JES__SB", nbin, min, max );
tt3histSB = new TH1F( "mu_BDT__tt3hist__JES__SB", "mu_BDT__tt3hist__JES__SB", nbin, min, max );
ttphhistSB = new TH1F( "mu_BDT__ttphhist__JES__SB", "mu_BDT__ttphhist__JES__SB", nbin,min, max );
wwphhistSB = new TH1F( "mu_BDT__wwphhist__JES__SB", "BDT__wwphhist__JES__SB", nbin,min, max );
wwhistSB = new TH1F( "mu_BDT__wwhist__JES__SB", "mu_BDT__wwhist__JES__SB", nbin, min, max );
wzhistSB = new TH1F( "mu_BDT__wzhist__JES__SB", "mu_BDT__wzhist__JES__SB", nbin, min, max );
zzhistSB = new TH1F( "mu_BDT__zzhist__JES__SB", "mu_BDT__zzhist__JES__SB", nbin, min, max);
zgammahistSB = new TH1F( "mu_BDT__zgammahist__JES__SB", "mu_BDT__zgammahist__JES__SB", nbin, min, max );
singletopphotonhistSB = new TH1F( "mu_BDT__singletopphotonhistSB", "mu_BDT__singletopphotonhistSB", nbin,min, max );
singleantitopphotonhistSB = new TH1F( "mu_BDT__singleantitopphotonhistSB", "mu_BDT__singleantitopphotonhistSB", nbin, min, max);
signalhistSB = new TH1F( "mu_BDT__signal100__JES__SB", "mu_BDT__signal100__JES__SB", nbin,min, max );
std::vector<TH1F*> SBhists;
SBhists.push_back(wjethistSB);
SBhists.push_back(zjethistSB);
SBhists.push_back(G_Pt_50to80SB);
SBhists.push_back(G_Pt_80to120SB);
SBhists.push_back(G_Pt_120to170SB);
SBhists.push_back(G_Pt_170to300SB);
SBhists.push_back(G_Pt_300to470SB);
SBhists.push_back(G_Pt_470to800SB);
SBhists.push_back(wphjethistSB);
SBhists.push_back(twchhistSB);
SBhists.push_back(tbarwhistSB);
SBhists.push_back(tschhistSB);
SBhists.push_back(tbarschhistSB);
SBhists.push_back(ttchhistSB);
SBhists.push_back(tbartchhistSB);
SBhists.push_back(tt1histSB);
SBhists.push_back(tt2histSB);
SBhists.push_back(tt3histSB);
SBhists.push_back(ttphhistSB);
SBhists.push_back(wwphhistSB);
SBhists.push_back(wwhistSB);
SBhists.push_back(wzhistSB);
SBhists.push_back(zzhistSB);
SBhists.push_back(zgammahistSB);
SBhists.push_back(singletopphotonhistSB);
SBhists.push_back(singleantitopphotonhistSB);
SBhists.push_back(signalhistSB);
hists.push_back(wjethist);
hists.push_back(zjethist);
hists.push_back(G_Pt_50to80);
hists.push_back(G_Pt_80to120);
hists.push_back(G_Pt_120to170);
hists.push_back(G_Pt_170to300);
hists.push_back(G_Pt_300to470);
hists.push_back(G_Pt_470to800);
hists.push_back(wphjethist);
hists.push_back(twchhist);
hists.push_back(tbarwhist);
hists.push_back(tschhist);
hists.push_back(tbarschhist);
hists.push_back(ttchhist);
hists.push_back(tbartchhist);
hists.push_back(tt1hist);
hists.push_back(tt2hist);
hists.push_back(tt3hist);
hists.push_back(ttphhist);
hists.push_back(wwphhist);
hists.push_back(wwhist);
hists.push_back(wzhist);
hists.push_back(zzhist);
hists.push_back(zgammahist);
hists.push_back(singletopphotonhist);
hists.push_back(singleantitopphotonhist);
hists.push_back(signalhist);
for(unsigned int idx=0; idx<samples_.size(); ++idx){
hists[idx]->Sumw2();}
datahists.push_back(data1hist);
datahists.push_back(data2hist);
datahists.push_back(data3hist);
datahists.push_back(datahistsideband);
for(unsigned int idx=0; idx<datasamples_.size(); ++idx){
datahists[idx]->Sumw2();}
revDATAhists.push_back(data1histrev);
revDATAhists.push_back(data2histrev);
revDATAhists.push_back(data3histrev);
revDATAhists.push_back(datahistrevsideband);
double insidewphjet=0;
double outsidewphjet=0;
double insidewjet=0;
double outsidewjet=0;
double nsignalevent=0;
double mtopup=220;
double mtopdown=130;
//bool SR=false;
//bool SB=true;
bool SR=true;
bool SB=false;
for(unsigned int idx=0; idx<samples_.size(); ++idx){
TString fname =samples_[idx];
if (!gSystem->AccessPathName( fname )) input = TFile::Open( fname ); // check if file in local directory exists
else
input = TFile::Open( "http://root.cern.ch/files/tmva_class_example.root" ); // if not: download from ROOT server
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
std::cout << "--- TMVAClassificationApp : Using input file: " << input->GetName() << std::endl;
// --- Event loop
// Prepare the event tree
// - here the variable names have to corres[1]ponds to your tree
// - you can use the same variables as above which is slightly faster,
// but of course you can use different ones and copy the values inside the event loop
//
//Double_t myptphoton,myetaphoton,myptmuon,myetamuon,myptjet,myetajet,mymasstop,mymtw,mydeltaRphotonjet,mydeltaRphotonmuon,myht,mycostopphoton,mydeltaphiphotonmet,mycvsdiscriminant,myjetmultiplicity,mybjetmultiplicity,myleptoncharge;
std::vector<double> *myptphoton=0;
std::vector<double> *myetaphoton=0;
std::vector<double> *myptmuon=0;
std::vector<double> *myetamuon=0;
std::vector<double> *myptjet=0;
std::vector<double> *myetajet=0;
std::vector<double> *mymasstop=0;
//std::vector<double> *mymtw=0;
std::vector<double> *mydeltaRphotonjet=0;
std::vector<double> *mydeltaRphotonmuon=0;
//std::vector<double> *myht=0;
std::vector<double> *mycostopphoton=0;
std::vector<double> *mydeltaphiphotonmet=0;
std::vector<double> *mycvsdiscriminant=0;
std::vector<double> *myjetmultiplicity=0;
//std::vector<double> *mybjetmultiplicity=0;
//std::vector<double> *myleptoncharge=0;
std::vector<double> *myweight=0;
std::vector<double> *myjetmatchinginfo=0;
std::vector<double> *mycoswphoton=0;
std::cout << "--- Select signal sample" << std::endl;
TTree* theTree = (TTree*)input->Get("analyzestep2/atq");
// Int_t myjetmultiplicity, mybjetmultiplicity , myleptoncharge;
// Float_t userVar1, userVar2;
theTree->SetBranchAddress("ptphoton", &myptphoton );
theTree->SetBranchAddress( "etaphoton", &myetaphoton );
theTree->SetBranchAddress( "ptmuon", &myptmuon );
theTree->SetBranchAddress( "etamuon", &myetamuon );
theTree->SetBranchAddress( "ptjet", &myptjet );
theTree->SetBranchAddress( "etajet", &myetajet );
theTree->SetBranchAddress( "masstop", &mymasstop );
// theTree->SetBranchAddress( "mtw", &mymtw );
theTree->SetBranchAddress( "deltaRphotonjet", &mydeltaRphotonjet );
theTree->SetBranchAddress( "deltaRphotonmuon", &mydeltaRphotonmuon );
// theTree->SetBranchAddress( "ht", &myht );
theTree->SetBranchAddress( "costopphoton", &mycostopphoton );
theTree->SetBranchAddress( "jetmultiplicity", &myjetmultiplicity );
// theTree->SetBranchAddress( "bjetmultiplicity", &mybjetmultiplicity );
theTree->SetBranchAddress( "deltaphiphotonmet", &mydeltaphiphotonmet );
theTree->SetBranchAddress( "cvsdiscriminant", &mycvsdiscriminant );
// theTree->SetBranchAddress( "leptoncharge", &myleptoncharge );
theTree->SetBranchAddress( "weight", &myweight);
theTree->SetBranchAddress( "coswphoton", &mycoswphoton );
theTree->SetBranchAddress( "jetmatchinginfo", &myjetmatchinginfo );
// std::cout << "--- Processing: " << theTree->GetEntries() << " events" << std::endl;
TStopwatch sw;
sw.Start();
for (Long64_t ievt=0; ievt<theTree->GetEntries();ievt++) {
// std::cout << "--- ... Processing event: " << ievt << std::endl;
double finalweight;
if (ievt%1000 == 0) std::cout << "--- ... Processing event: " << ievt << std::endl;
theTree->GetEntry(ievt);
//for (int l=0;l<sizeof(myptphoton);l++){
//std::cout << "--- ... reza: " << myptphoton[l] <<std::endl;
//}
//std::cout << "--- ......................."<< (*mycvsdiscriminant)[0]<<std::endl;
// --- Return the MVA outputs and fill into histograms
finalweight=(*myweight)[0];
//cout<<(*myweight)[0]<<endl;
if((*mymasstop )[0]>mtopdown && (*mymasstop )[0]<mtopup){
hists[idx] ->Fill(Bmodification((*mycvsdiscriminant)[0],(*myjetmatchinginfo)[0]),finalweight );
if (samples_[idx]=="WPHJET.root")insidewphjet=insidewphjet+finalweight;
if (samples_[idx]=="SIGNALtGu.root")nsignalevent=nsignalevent+1;
//cout<<insidewphjet<<endl;
}
else {
SBhists[idx] ->Fill( Bmodification((*mycvsdiscriminant)[0],(*myjetmatchinginfo)[0]),finalweight );
if (samples_[idx]=="WPHJET.root")outsidewphjet=outsidewphjet+finalweight;}
// Retrieve also per-event error
}
delete myptphoton;
delete myetaphoton;
delete myptmuon;
delete myetamuon;
delete myptjet;
delete myetajet;
delete mymasstop;
//delete mymtw;
delete mydeltaRphotonjet;
delete mydeltaRphotonmuon;
//delete myht;
delete mycostopphoton;
delete mydeltaphiphotonmet;
delete mycvsdiscriminant;
delete myjetmultiplicity;
//delete mybjetmultiplicity;
//delete myleptoncharge;
//delete myplot;
}
for(unsigned int idx=0; idx<datasamples_.size(); ++idx){
TString fname =datasamples_[idx];
if (!gSystem->AccessPathName( fname )) input = TFile::Open( fname ); // check if file in local directory exists
else
input = TFile::Open( "http://root.cern.ch/files/tmva_class_example.root" ); // if not: download from ROOT server
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
std::cout << "--- TMVAClassificationApp : Using input file: " << input->GetName() << std::endl;
// --- Event loop
// Prepare the event tree
// - here the variable names have to corres[1]ponds to your tree
// - you can use the same variables as above which is slightly faster,
// but of course you can use different ones and copy the values inside the event loop
//
//Double_t myptphoton,myetaphoton,myptmuon,myetamuon,myptjet,myetajet,mymasstop,mymtw,mydeltaRphotonjet,mydeltaRphotonmuon,myht,mycostopphoton,mydeltaphiphotonmet,mycvsdiscriminant,myjetmultiplicity,mybjetmultiplicity,myleptoncharge;
std::vector<double> *myptphoton=0;
std::vector<double> *myetaphoton=0;
std::vector<double> *myptmuon=0;
std::vector<double> *myetamuon=0;
std::vector<double> *myptjet=0;
std::vector<double> *myetajet=0;
std::vector<double> *mymasstop=0;
//std::vector<double> *mymtw=0;
std::vector<double> *mydeltaRphotonjet=0;
std::vector<double> *mydeltaRphotonmuon=0;
//std::vector<double> *myht=0;
std::vector<double> *mycostopphoton=0;
std::vector<double> *mydeltaphiphotonmet=0;
std::vector<double> *mycvsdiscriminant=0;
std::vector<double> *myjetmultiplicity=0;
//std::vector<double> *mybjetmultiplicity=0;
//std::vector<double> *myleptoncharge=0;
std::vector<double> *mycoswphoton=0;
std::cout << "--- Select signal sample" << std::endl;
TTree* theTree = (TTree*)input->Get("analyzestep2/atq");
// Int_t myjetmultiplicity, mybjetmultiplicity , myleptoncharge;
// Float_t userVar1, userVar2;
theTree->SetBranchAddress("ptphoton", &myptphoton );
theTree->SetBranchAddress( "etaphoton", &myetaphoton );
theTree->SetBranchAddress( "ptmuon", &myptmuon );
theTree->SetBranchAddress( "etamuon", &myetamuon );
theTree->SetBranchAddress( "ptjet", &myptjet );
theTree->SetBranchAddress( "etajet", &myetajet );
theTree->SetBranchAddress( "masstop", &mymasstop );
// theTree->SetBranchAddress( "mtw", &mymtw );
theTree->SetBranchAddress( "deltaRphotonjet", &mydeltaRphotonjet );
theTree->SetBranchAddress( "deltaRphotonmuon", &mydeltaRphotonmuon );
// theTree->SetBranchAddress( "ht", &myht );
theTree->SetBranchAddress( "costopphoton", &mycostopphoton );
theTree->SetBranchAddress( "jetmultiplicity", &myjetmultiplicity );
// theTree->SetBranchAddress( "bjetmultiplicity", &mybjetmultiplicity );
theTree->SetBranchAddress( "deltaphiphotonmet", &mydeltaphiphotonmet );
theTree->SetBranchAddress( "cvsdiscriminant", &mycvsdiscriminant );
theTree->SetBranchAddress( "coswphoton", &mycoswphoton );
// theTree->SetBranchAddress( "leptoncharge", &myleptoncharge );
for (Long64_t ievt=0; ievt<theTree->GetEntries();ievt++) {
// std::cout << "--- ... Processing event: " << ievt << std::endl;
theTree->GetEntry(ievt);
// --- Return the MVA outputs and fill into histograms
//leptoncharge=(float)(*myleptoncharge )[0];
if((*mymasstop )[0]>mtopdown && (*mymasstop )[0]<mtopup) datahists[idx] ->Fill( (*mycvsdiscriminant)[0] );
else datahists[3]->Fill( (*mycvsdiscriminant)[0] );
}
delete myptphoton;
delete myetaphoton;
delete myptmuon;
delete myetamuon;
delete myptjet;
delete myetajet;
delete mymasstop;
//delete mymtw;
delete mydeltaRphotonjet;
delete mydeltaRphotonmuon;
//delete myht;
delete mycostopphoton;
delete mydeltaphiphotonmet;
delete mycvsdiscriminant;
delete myjetmultiplicity;
//delete mybjetmultiplicity;
//delete myleptoncharge;
//delete myplot;
}
for(unsigned int idx=0; idx<datasamplesreverse_.size(); ++idx){
TString fname =datasamplesreverse_[idx];
if (!gSystem->AccessPathName( fname )) input = TFile::Open( fname ); // check if file in local directory exists
else
input = TFile::Open( "http://root.cern.ch/files/tmva_class_example.root" ); // if not: download from ROOT server
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
std::cout << "--- TMVAClassificationApp : Using input file: " << input->GetName() << std::endl;
std::vector<double> *myptphoton=0;
std::vector<double> *myetaphoton=0;
std::vector<double> *myptmuon=0;
std::vector<double> *myetamuon=0;
std::vector<double> *myptjet=0;
std::vector<double> *myetajet=0;
std::vector<double> *mymasstop=0;
//std::vector<double> *mymtw=0;
std::vector<double> *mydeltaRphotonjet=0;
std::vector<double> *mydeltaRphotonmuon=0;
//std::vector<double> *myht=0;
std::vector<double> *mycostopphoton=0;
std::vector<double> *mydeltaphiphotonmet=0;
std::vector<double> *mycvsdiscriminant=0;
std::vector<double> *myjetmultiplicity=0;
std::vector<double> *mycoswphoton=0;
//std::vector<double> *mybjetmultiplicity=0;
//std::vector<double> *myleptoncharge=0;
TTree* theTree = (TTree*)input->Get("analyzestep2/atq");
theTree->SetBranchAddress("ptphoton", &myptphoton );
theTree->SetBranchAddress( "etaphoton", &myetaphoton );
theTree->SetBranchAddress( "ptmuon", &myptmuon );
theTree->SetBranchAddress( "etamuon", &myetamuon );
theTree->SetBranchAddress( "ptjet", &myptjet );
theTree->SetBranchAddress( "etajet", &myetajet );
theTree->SetBranchAddress( "masstop", &mymasstop );
// theTree->SetBranchAddress( "mtw", &mymtw );
theTree->SetBranchAddress( "deltaRphotonjet", &mydeltaRphotonjet );
theTree->SetBranchAddress( "deltaRphotonmuon", &mydeltaRphotonmuon );
// theTree->SetBranchAddress( "ht", &myht );
theTree->SetBranchAddress( "costopphoton", &mycostopphoton );
theTree->SetBranchAddress( "jetmultiplicity", &myjetmultiplicity );
// theTree->SetBranchAddress( "bjetmultiplicity", &mybjetmultiplicity );
theTree->SetBranchAddress( "deltaphiphotonmet", &mydeltaphiphotonmet );
theTree->SetBranchAddress( "cvsdiscriminant", &mycvsdiscriminant );
theTree->SetBranchAddress( "coswphoton", &mycoswphoton );
// theTree->SetBranchAddress( "leptoncharge", &myleptoncharge );
// Efficiency calculator for cut method
for (Long64_t ievt=0; ievt<theTree->GetEntries();ievt++) {
// std::cout << "--- ... Processing event: " << ievt << std::endl;
if (ievt%1000 == 0) std::cout << "--- ... Processing event: " << ievt << std::endl;
theTree->GetEntry(ievt);
if((*mymasstop )[0]>mtopdown && (*mymasstop )[0]<mtopup) {
//revDATAhists[idx]->Fill( reader->EvaluateMVA( "BDT method" ) );
insidewjet=insidewjet+1;
revDATAhists[idx]->Fill( (*mycvsdiscriminant)[0]);
}
else {
//revDATAhists[3]->Fill( reader->EvaluateMVA( "BDT method" ) );
outsidewjet=outsidewjet+1;
revDATAhists[3]->Fill( (*mycvsdiscriminant)[0]);
}
//cout<<insidewjet<<endl;
}
delete myptphoton;
delete myetaphoton;
delete myptmuon;
delete myetamuon;
delete myptjet;
delete myetajet;
delete mymasstop;
//delete mymtw;
delete mydeltaRphotonjet;
delete mydeltaRphotonmuon;
//delete myht;
delete mycostopphoton;
delete mydeltaphiphotonmet;
delete mycvsdiscriminant;
delete myjetmultiplicity;
////delete mybjetmultiplicity;
////delete myleptoncharge;
////delete myplot;
//
}
double wphjetscale;
wphjetscale=insidewphjet/(insidewphjet+outsidewphjet);
cout<<"wphjetscale= "<<wphjetscale<<endl;
double wjetscale;
wjetscale=insidewjet/(insidewjet+outsidewjet);
cout<<"wjetscale= "<<wjetscale<<endl;
cout<<"nsignalevent= "<<nsignalevent<<endl;
//cout<<insidewphjet<<"insidewphjet"<<" "<<wphjetscale<<" "<<insidewjet/(insidewjet+outsidewjet)<<endl;
float lumi = 1;
if (SR==true){
double ff=0;
for(unsigned int idx=0; idx<samples_.size(); ++idx){
hists[idx]->Scale(lumi*scales[idx]);
if (idx !=0 && idx!=3){
ff=hists[idx]->Integral()+ff;
cout<<samples_[idx]<<" = "<<hists[idx]->Integral()<<" " <<ff<<endl;}
}
for(unsigned int idx=0; idx<samples_.size(); ++idx){
SBhists[idx]->Scale(lumi*scales[idx]);}
THStack *hs1 = new THStack("hs1","BDT output");
for(unsigned int idx=1; idx<datasamplesreverse_.size(); ++idx){
revDATAhists[idx]->Add(revDATAhists[idx-1]);
}
//cout<<"*********************"<< datahists[3]->Integral()<<" "<<wphjetscale<<endl;
//cout<<"*********************"<< revDATAhists[2]->Integral()<<" "<<wjetscale<<endl;
revDATAhists[2]->Scale(219.373/revDATAhists[2]->Integral());
for(unsigned int idx=1; idx<revDATAhists[2]->GetNbinsX()+1; ++idx){
//revDATAhists[2]->SetBinError(idx,(revDATAhists[2]->GetBinContent(idx)/revDATAhists[2]->Integral())*74.84);
revDATAhists[2]->SetBinError(idx,0);
//if (revDATAhists[2]->GetBinError(idx)>revDATAhists[2]->GetBinContent(idx)) revDATAhists[2]->SetBinError(idx, revDATAhists[2]->GetBinContent(idx)/2);
}
//revDATAhists[2]->Scale(wjetscale);
revDATAhists[3]->Scale(219.373/revDATAhists[3]->Integral());
revDATAhists[3]->Scale((1-wjetscale)/wjetscale);
for(unsigned int idx=1; idx<datasamples_.size(); ++idx){
datahists[idx]->Add(datahists[idx-1]);}
cout<<" " <<datahists[2]->Integral()<<endl;
datahists[3]->Add(revDATAhists[3],-1);
datahists[3]->Add(SBhists[1],-1);
datahists[3]->Add(SBhists[2],-1);
for(unsigned int idx=9; idx<samples_.size()-1; ++idx){
datahists[3]->Add(SBhists[idx],-1);}
for(unsigned int idx=1; idx<nbin; ++idx){
if (datahists[3]->GetBinContent(idx)<0)datahists[3]->SetBinContent(idx,0);
}
datahists[3]->Scale(1112.2/datahists[3]->Integral());
for(unsigned int idx=1; idx<datahists[3]->GetNbinsX()+1; ++idx){
//datahists[3]->SetBinError(idx,(datahists[3]->GetBinContent(idx)/datahists[3]->Integral())*139.11);}
datahists[3]->SetBinError(idx,0);}
TH1F *datatoMC(0);
//datahists[3]->Scale(wphjetscale);
//hists[1]->Add(revDATAhists[2]);
//hists[2]->Add(hists[1]);
//datahists[3]->Add(hists[2]);
//hists[4]->Add(datahists[3]);
//for(unsigned int idx=5; idx<samples_.size()-1; ++idx){
// hists[idx]->Add(hists[idx-1]);}
//cout<<"**********real data***********"<< datahists[2]->Integral()<<" "<<wphjetscale<<endl;
//cout<<"********** mc ***********"<< hists[18]->Integral()<<" "<<wjetscale<<endl;
// setup the canvas and draw the histograms
TH1F *sum_h= new TH1F ( *hists[1] ) ;
sum_h->Sumw2();
for(unsigned int idx=2; idx<samples_.size()-1; ++idx){
if (idx!=8)sum_h->Add(hists[idx],1);
}
sum_h->Add(revDATAhists[2],1);
sum_h->Add(datahists[3],1);
std::vector<std::vector<double_t> > vecplus(photondownhists[0]->GetNbinsX(), vector<double>(18));
std::vector<std::vector<double_t> > vecminus(photondownhists[0]->GetNbinsX(), vector<double>(18));
for(int p = 0; p <photondownhists[0]->GetNbinsX(); p++){ //loop over bins
for(int m = 0; m < 18; m++){ //loop over systematics
vecplus[p][m]=0;
vecminus[p][m]=0;
if (vec[p][m]>sum_h->GetBinContent(p+1)) vecplus[p][m] = vec[p][m]-sum_h->GetBinContent(p+1);
else if (vec[p][m]<sum_h->GetBinContent(p+1)) vecminus[p][m] = sum_h->GetBinContent(p+1)-vec[p][m];
cout<<vecplus[p][m]<<endl;
}}
TCanvas *c1 = new TCanvas("c1","signal region",50,50,865,780);
c1->cd();
TPad *pad1 = new TPad("pad1","pad1",0,0.25,1,1);
pad1->SetFillStyle(0);
pad1->SetFrameFillStyle(0);
pad1->SetBottomMargin(0);
TPad *pad2 = new TPad("pad2","pad2",0,0,1,0.25);
pad2->SetFillStyle(0);
pad2->SetFrameFillStyle(0);
pad2->SetTopMargin(0);
pad2->SetBottomMargin(0.12/0.46);
pad2->Draw();
pad1->Draw();
pad1->cd();
//W+jet
revDATAhists[2]->SetFillColor(kBlue-2);
revDATAhists[2]->SetLineColor(kBlack);
hs1->Add(revDATAhists[2]);
//Z+jet
hists[1]->SetFillColor(kOrange-4);
hists[1]->SetLineColor(kBlack);
hs1->Add(hists[1]);
//photon+jet
hists[3]->Add(hists[2]);
hists[4]->Add(hists[3]);
hists[5]->Add(hists[4]);
hists[6]->Add(hists[5]);
hists[7]->Add(hists[6]);
hists[7]->SetFillColor(19);
//hs1->Add(hists[7]);
//W+photon+jet
datahists[3]->SetFillColor(kGreen-3);
datahists[3]->SetLineColor(kBlack);
hs1->Add(datahists[3]);
//single top+singletop photon
hists[5+5]->Add(hists[4+5]);
hists[6+5]->Add(hists[5+5]);
hists[7+5]->Add(hists[6+5]);
hists[8+5]->Add(hists[7+5]);
hists[9+5]->Add(hists[8+5]);
hists[19+5]->Add(hists[9+5]);
hists[20+5]->Add(hists[19+5]);
hists[20+5]->SetFillColor(kRed+3);
hists[20+5]->SetLineColor(kBlack);
hs1->Add(hists[20+5]);
//hists[9+5]->SetFillColor(kAzure+10);
//hs1->Add(hists[9+5]);
hists[11+5]->Add(hists[10+5]);
hists[12+5]->Add(hists[11+5]);
hists[13+5]->Add(hists[12+5]);
hists[13+5]->SetFillColor(kPink+1);
hists[13+5]->SetLineColor(kBlack);
hs1->Add(hists[13+5]);
//hists[13+5]->SetFillColor(17);
//hs1->Add(hists[13+5]);
//hists[14+5]->SetFillColor(kSpring-9);
//hs1->Add(hists[14+5]);
hists[15+5]->Add(hists[14+5]);
hists[16+5]->Add(hists[15+5]);
hists[17+5]->Add(hists[16+5]);
hists[17+5]->SetFillColor(kViolet-7);
hists[17+5]->SetLineColor(kBlack);
hs1->Add(hists[17+5]);
hists[18+5]->SetFillColor(kAzure+10);
hists[18+5]->SetLineColor(kBlack);
hs1->Add(hists[18+5]);
//hists[20+5]->Add(hists[19+5]);
//hists[20+5]->SetFillColor(kYellow+3);
//hs1->Add(hists[20+5]);
hs1->Draw("hist");
hs1->SetMaximum(1.6*datahists[2]->GetMaximum());
//hs1->GetXaxis()->SetTitle("BDT output");
hs1->GetYaxis()->SetTitle("Events / 0.05");
hs1->GetYaxis()->SetTitleSize(0.045);
hs1->GetYaxis()->SetTitleFont(22);
hs1->GetYaxis()->SetTitleOffset(0.8);
hs1->GetYaxis()->SetLabelSize(0.044);
hists[21+5]->SetLineColor(kRed+3);
hists[21+5]->SetLineWidth(3);
hists[21+5]->Draw("histsame");
datahists[2]->SetLineWidth(3.);
datahists[2]->SetLineColor(kBlack);
datahists[2]->SetMarkerColor(kBlack);
datahists[2]->SetMarkerStyle(20.);
datahists[2]->SetMarkerSize(1.35);
datahists[2]->Draw("esame");
sum_h->SetLineColor(kBlack);
sum_h->SetFillColor(1);
sum_h->SetFillStyle(3001);
sum_h->Draw("e2same");
TPaveText *pt = new TPaveText(0.1,0.95,0.4,0.95, "NDC"); // NDC sets coords
pt->SetLineColor(10); // relative to pad dimensions
pt->SetFillColor(10); // text is black on white
pt->SetTextSize(0.045);
pt->SetTextAlign(12);
pt->AddText("CMS Preliminary, 19.1 fb^{-1}, #sqrt{s} = 8 TeV");
pt->SetShadowColor(10);
pt->Draw("same");
std::vector<double_t> errorup(photondownhists[0]->GetNbinsX());
std::vector<double_t> errordown(photondownhists[0]->GetNbinsX());
for(int p = 0; p <photondownhists[0]->GetNbinsX(); p++){ //loop over bins
for(int m = 0; m < 18; m++){ //loop over systematics
if (m==0) {errorup[p]=0;
errordown[p]=0;}
errorup[p]=pow(vecplus[p][m],2)+errorup[p];
errordown[p]=pow(vecminus[p][m],2)+errordown[p];
}
errorup[p]=pow(0.024*sum_h->GetBinContent(p+1),2)+errorup[p];
errordown[p]=pow(0.024*sum_h->GetBinContent(p+1),2)+errordown[p];
errorup[p]=pow(0.4*wjetandwphjet[0]->GetBinContent(p+1),2)+errorup[p];
errordown[p]=pow(0.4*wjetandwphjet[0]->GetBinContent(p+1),2)+errordown[p];
errorup[p]=pow(0.3*wjetandwphjet[1]->GetBinContent(p+1),2)+errorup[p];
errordown[p]=pow(0.3*wjetandwphjet[1]->GetBinContent(p+1),2)+errordown[p];
errorup[p]=pow(0.3*hists[1]->GetBinContent(p+1),2)+errorup[p];
errordown[p]=pow(0.3*hists[1]->GetBinContent(p+1),2)+errordown[p];
errorup[p]=pow(0.3*hists[20+5]->GetBinContent(p+1),2)+errorup[p];
errordown[p]=pow(0.3*hists[20+5]->GetBinContent(p+1),2)+errordown[p];
errorup[p]=pow(0.3*hists[13+5]->GetBinContent(p+1),2)+errorup[p];
errordown[p]=pow(0.3*hists[13+5]->GetBinContent(p+1),2)+errordown[p];
errorup[p]=pow(0.3*hists[17+5]->GetBinContent(p+1),2)+errorup[p];
errordown[p]=pow(0.3*hists[17+5]->GetBinContent(p+1),2)+errordown[p];
errorup[p]=pow(0.3*hists[18+5]->GetBinContent(p+1),2)+errorup[p];
errordown[p]=pow(0.3*hists[18+5]->GetBinContent(p+1),2)+errordown[p];
cout<<errorup[p]<<endl;
cout<<errordown[p]<<endl;
}
double ax[photondownhists[0]->GetNbinsX()];
double ay[photondownhists[0]->GetNbinsX()];
double aexl[photondownhists[0]->GetNbinsX()];
double aexh[photondownhists[0]->GetNbinsX()];
double aeyl[photondownhists[0]->GetNbinsX()];
double aeyh[photondownhists[0]->GetNbinsX()];
for(int p = 0; p <photondownhists[0]->GetNbinsX(); p++){ //loop over bins
ax[p]=min+(max-min)/(2*nbin)+p*((max-min)/nbin);
ay[p]=sum_h->GetBinContent(p+1);
aexl[p]=(max-min)/(2*nbin);
aexh[p]=(max-min)/(2*nbin);
aeyl[p]=sqrt(errordown[p]);
aeyh[p]=sqrt(errorup[p]);
}
TGraphAsymmErrors* gae = new TGraphAsymmErrors(photondownhists[0]->GetNbinsX(), ax, ay, aexl, aexh, aeyl, aeyh);
gae->SetFillColor(1);
gae->SetFillStyle(3003);
gae->Draw("e2same");
TLegend* leg = new TLegend(0.60,0.40,0.89,0.87);
leg->SetFillStyle ( 0);
leg->SetFillColor ( 0);
leg->SetBorderSize( 0);
leg->AddEntry( datahists[2], "Data" , "PL");
// leg->AddEntry( hists[25], "Single top+#gamma" , "F");
leg->AddEntry( hists[23], "Z#gamma" , "F");
leg->AddEntry( hists[22], "WW,WZ,ZZ,WW#gamma " , "F");
// leg->AddEntry( hists[19], "WW#gamma" , "F");
leg->AddEntry( hists[18], "t#bar{t}, t#bar{t}#gamma" , "F");
// leg->AddEntry( hists[17], "t#bar{t}" , "F");
leg->AddEntry( hists[25], "Single top, Single top+#gamma" , "F");
// leg->AddEntry( hists[14], "Single top" , "F");
leg->AddEntry( datahists[3], "W#gamma" , "F");
// leg->AddEntry( hists[7], "#gamma+jets" , "F");
leg->AddEntry( hists[1], "Z+jets" , "F");
leg->AddEntry(revDATAhists[2], "W+jets" , "F");
leg->AddEntry( hists[26], "Signal(tu#gamma) 1 pb" , "L");
leg->AddEntry(sum_h, "Stat uncertainty" , "F");
leg->AddEntry(gae, "Syst uncertainty" , "F");
// leg->AddEntry( datahists[2], "CMS Data 2012(19.145/fb)" , "PL");
leg->Draw("same");
sum_h->Draw("AXISSAMEY+");
sum_h->Draw("AXISSAMEX+");
pad1->Draw();
TCanvas *c22 = new TCanvas("c22","signal region22",50,50,865,780);
c22->cd();
gae->Draw("a2");
gae->Draw("psame");
TH1F *h_ratio = (TH1F*)datahists[2]->Clone("h_copy");
h_ratio->Sumw2();
pad2->cd();
pad2->SetGridy();
datatoMC = new TH1F( "datatoMC", "datatoMC", nbin, min, max );
datatoMC->Sumw2();
datatoMC->Divide(datahists[2],sum_h);
h_ratio->Divide(sum_h);
h_ratio->SetFillStyle(3004);
h_ratio->GetXaxis()->SetTitle("CSV discriminator");
h_ratio->GetYaxis()->SetTitle("DATA/MC");
h_ratio->GetXaxis()->SetTitleSize(0.12);
h_ratio->GetYaxis()->SetTitleSize(0.12);
h_ratio->GetXaxis()->SetTitleFont(22);
h_ratio->GetYaxis()->SetTitleFont(22);
h_ratio->GetXaxis()->SetTickLength(0.05);
h_ratio->GetYaxis()->SetTickLength(0.05);
h_ratio->GetXaxis()->SetLabelSize(0.14);
h_ratio->GetYaxis()->SetLabelSize(0.14);
h_ratio->GetYaxis()->SetTitleOffset(0.25);
h_ratio->GetYaxis()->SetNdivisions(504);
h_ratio->SetLineWidth(2);
//h_ratio->SetStats(0);
//h_ratio->SetMarkerStyle(20);
h_ratio->SetMinimum(0);
h_ratio->SetMaximum(2);
h_ratio->Draw("E");
//datatoMC->Draw("");
TLine *l3 = new TLine(h_ratio->GetXaxis()->GetXmin(), 1.00, h_ratio->GetXaxis()->GetXmax(), 1.00);
l3->SetLineWidth(1);
//l3->SetLineStyle(7);
//l3->Draw();
h_ratio->Draw("AXISSAMEY+");
h_ratio->Draw("AXISSAMEX+");
c1->Update();
for(unsigned int idx=1; idx<nbin+1; ++idx){
cout<<"MC "<<"nbin= "<<idx<<" content= "<<sum_h->GetBinContent(idx)<<endl;
cout<<"signal "<<"nbin= "<<idx<<" content= "<<hists[21+5]->GetBinContent(idx)<<endl;
cout<<"Data "<<"nbin= "<<idx<<" content= "<<datahists[2]->GetBinContent(idx)<<endl;
}
cout<<"signal Integral "<<hists[21+5]->Integral()<<endl;
}
if (SB==true){
for(unsigned int idx=0; idx<samples_.size(); ++idx){
SBhists[idx]->Scale(lumi*scales[idx]);}
revDATAhists[3]->Scale(620.32/revDATAhists[3]->Integral());
revDATAhists[3]->Scale(1-wjetscale);
SBhists[1]->Add(revDATAhists[3]);
SBhists[2]->Add(SBhists[1]);
SBhists[4]->Add(SBhists[2]);
for(unsigned int idx=5; idx<samples_.size()-1; ++idx){
SBhists[idx]->Add(SBhists[idx-1]);}
SBhists[20]->SetMaximum(1.5*datahists[3]->GetMaximum());
SBhists[20]->SetFillColor(kMagenta+2);
SBhists[20]->Draw();
SBhists[18]->SetFillColor(kOrange+4);
SBhists[18]->Draw("same");
SBhists[17]->SetFillColor(kOrange-2);
SBhists[17]->Draw("same");
SBhists[16]->SetFillColor(kRed);
SBhists[16]->Draw("same");
SBhists[15]->SetFillColor(kViolet+1);
SBhists[15]->Draw("same");
SBhists[14]->SetFillColor(kSpring-9);
SBhists[14]->Draw("same");
SBhists[13]->SetFillColor(32);
SBhists[13]->Draw("same");
SBhists[12]->SetFillColor(6);
SBhists[12]->Draw("same");
SBhists[9]->SetFillColor(4);
SBhists[9]->Draw("same");
//hists[8]->SetFillColor(4);
//hists[8]->Draw("same");
//hists[7]->SetFillColor(3);
//hists[7]->Draw("same");
//hists[6]->SetFillColor(3);
//hists[6]->Draw("same");
//hists[5]->SetFillColor(2);
//hists[5]->Draw("same");
//hists[4]->SetFillColor(2);
//hists[4]->Draw("same");
//hists[3]->SetFillColor(5);
//hists[3]->Draw("same");
//datahists[3]->SetFillColor(5);
//datahists[3]->Draw("same");
SBhists[2]->SetFillColor(8);
SBhists[2]->Draw("same");
SBhists[1]->SetFillColor(kOrange+7);
SBhists[1]->Draw("same");
revDATAhists[3]->SetFillColor(7);
revDATAhists[3]->Draw("same");
//hists[0]->SetFillColor(7);
//hists[0]->Draw("same");
SBhists[21]->SetFillColor(1);
SBhists[21]->SetFillStyle(3004);
SBhists[21]->Draw("same");
// plot data points
datahists[3]->SetLineWidth(3.);
datahists[3]->SetLineColor(kBlack);
datahists[3]->SetMarkerColor(kBlack);
datahists[3]->SetMarkerStyle(20.);
datahists[3]->Draw("esame");
//conv->RedrawAxis();
TLegend* leg = new TLegend(0.60,0.40,0.89,0.87);
leg->SetFillStyle ( 0);
leg->SetFillColor ( 0);
leg->SetBorderSize( 0);
leg->AddEntry( revDATAhists[2], "W JET" , "F");
leg->AddEntry( SBhists[1], "Z JET" , "F");
leg->AddEntry( SBhists[2], "PH JET" , "F");
// leg->AddEntry( datahists[3], "W PH JET" , "F");
// leg->AddEntry( hists[5], "TOP-W-CH" , "F");
// leg->AddEntry( hists[5], "T-S-CH" , "F");
// leg->AddEntry( hists[7], "TOP-S-CH" , "F");
// leg->AddEntry( hists[7], "TTBAR-CH" , "F");
// leg->AddEntry( hists[8], "TBAR-W-CH" , "F");
leg->AddEntry( SBhists[9], "SINGLE TOP " , "F");
leg->AddEntry( SBhists[12], "TTBAR" , "F");
leg->AddEntry( SBhists[13], "TTG" , "F");
leg->AddEntry( SBhists[14], "WWG" , "F");
leg->AddEntry( SBhists[15], "WW" , "F");
leg->AddEntry( SBhists[16], "WZ" , "F");
leg->AddEntry( SBhists[17], "ZZ" , "F");
leg->AddEntry( SBhists[18], "ZGAMMA" , "F");
leg->AddEntry( SBhists[20], "SINGLE TOP+PHOTON" , "F");
leg->AddEntry( SBhists[21], "SIGNAL" , "F");
leg->AddEntry( datahists[3], "CMS Data 2012(19.145/fb)" , "PL");
leg->Draw("same");
}
}
int quasirandom(int n = 10000, int skip = 0) {
TH2D * h0 = new TH2D("h0","Pseudo-random Sequence",200,0,1,200,0,1);
TH2D * h1 = new TH2D("h1","Sobol Sequence",200,0,1,200,0,1);
TH2D * h2 = new TH2D("h2","Niederrer Sequence",200,0,1,200,0,1);
RandomMT r0;
// quasi random numbers need to be created giving the dimension of the sequence
// in this case we generate 2-d sequence
QuasiRandomSobol r1(2);
QuasiRandomNiederreiter r2(2);
// generate n random points
double x[2];
TStopwatch w; w.Start();
for (int i = 0; i < n; ++i) {
r0.RndmArray(2,x);
h0->Fill(x[0],x[1]);
}
std::cout << "Time for gRandom ";
w.Print();
w.Start();
if( skip>0) r1.Skip(skip);
for (int i = 0; i < n; ++i) {
r1.Next(x);
h1->Fill(x[0],x[1]);
}
std::cout << "Time for Sobol ";
w.Print();
w.Start();
if( skip>0) r2.Skip(skip);
for (int i = 0; i < n; ++i) {
r2.Next(x);
h2->Fill(x[0],x[1]);
}
std::cout << "Time for Niederreiter ";
w.Print();
TCanvas * c1 = new TCanvas("c1","Random sequence",600,1200);
c1->Divide(1,3);
c1->cd(1);
h0->Draw("COLZ");
c1->cd(2);
// check uniformity
h1->Draw("COLZ");
c1->cd(3);
h2->Draw("COLZ");
gPad->Update();
// test number of empty bins
int nzerobins0 = 0;
int nzerobins1 = 0;
int nzerobins2 = 0;
for (int i = 1; i <= h1->GetNbinsX(); ++i) {
for (int j = 1; j <= h1->GetNbinsY(); ++j) {
if (h0->GetBinContent(i,j) == 0 ) nzerobins0++;
if (h1->GetBinContent(i,j) == 0 ) nzerobins1++;
if (h2->GetBinContent(i,j) == 0 ) nzerobins2++;
}
}
std::cout << "number of empty bins for pseudo-random = " << nzerobins0 << std::endl;
std::cout << "number of empty bins for " << r1.Name() << "\t= " << nzerobins1 << std::endl;
std::cout << "number of empty bins for " << r2.Name() << "\t= " << nzerobins2 << std::endl;
int iret = 0;
if (nzerobins1 >= nzerobins0 ) iret += 1;
if (nzerobins2 >= nzerobins0 ) iret += 2;
return iret;
}
RooGaussian fitZToMuMuGammaMassUnbinned(const char *filename = "ZToMuMuGammaMass.txt",
const char* plotOpt = "NEU",
const int nbins = 25)
{
gROOT->ProcessLine(".L tdrstyle.C");
setTDRStyle();
gStyle->SetPadRightMargin(0.05);
double minMass = 60;
double maxMass = 120;
RooRealVar mass("mass","M(#mu#mu#gamma})", minMass, maxMass,"GeV/c^{2}");
// Read data set
RooDataSet *data = RooDataSet::read(filename,RooArgSet(mass));
// RooDataSet *dataB = RooDataSet::read(filenameB,RooArgSet(mass));
// Build p.d.f.
////////////////////////////////////////////////
// Parameters //
////////////////////////////////////////////////
// Signal p.d.f. parameters
// Parameters for a Gaussian and a Crystal Ball Lineshape
RooRealVar m0 ("m_{0}", "Bias", 91.19, minMass, maxMass,"GeV/c^{2}");
RooRealVar sigma("#sigma","Width", 3.0,1.0,10.0,"GeV/c^{2}");
RooRealVar cut ("#alpha","Cut", 0.6,0.6,2.0);
RooRealVar power("power","Power", 10.0, 0.5, 20.0);
// Background p.d.f. parameters
// Parameters for a polynomial lineshape
RooRealVar c0("c_{0}", "c0", 0., -10, 10);
RooRealVar c1("c_{1}", "c1", 0., -100, 0);
RooRealVar c2("c_{2}", "c2", 0., -100, 100);
// c0.setConstant();
// fraction of signal
// RooRealVar frac("frac", "Signal Fraction", 0.1,0.,0.3.);
RooRealVar nsig("N_{S}", "#signal events", 9000, 0.,10000.);
RooRealVar nbkg("N_{B}", "#background events", 1000,2,10000.);
////////////////////////////////////////////////
// P.D.F.s //
////////////////////////////////////////////////
// Di-photon mass signal p.d.f.
RooGaussian signal("signal", "A Gaussian Lineshape", mass, m0, sigma);
// RooCBShape signal("signal", "A Crystal Ball Lineshape", mass, m0,sigma, cut, power);
// Di-photon mass background p.d.f.
RooPolynomial bg("bg", "Backgroung Distribution", mass, RooArgList(c0,c1));
// Di-photon mass model p.d.f.
// RooAddPdf model("model", "Di-photon mass model", signal, bg, frac);
RooAddPdf model("model", "Di-photon mass model", RooArgList(signal, bg), RooArgList(nsig, nbkg));
TStopwatch t ;
t.Start() ;
// model->fitTo(*data,FitOptions("mh"),Optimize(0),Timer(1));
signal->fitTo(*data,FitOptions("mh"),Optimize(0),Timer(1));
t.Print() ;
c = new TCanvas("c","Unbinned Invariant Mass Fit", 0,0,800,600);
// Plot the fit results
RooPlot* plot = mass.frame(Range(minMass,maxMass),Bins(nbins));
// Plot 1
// dataB->plotOn(plot, MarkerColor(kRed), LineColor(kRed));
data->plotOn(plot);
// model.plotOn(plot);
model.plotOn(plot);
//model.paramOn(plot, Format(plotOpt, AutoPrecision(1)), Parameters(RooArgSet(nsig, nbkg, m0, sigma)));
model.paramOn(plot, Format(plotOpt, AutoPrecision(1)), Parameters(RooArgSet(m0, sigma)));
/// model.plotOn(plot, Components("signal"), LineStyle(kDashed), LineColor(kRed));
// model.plotOn(plot, Components("bg"), LineStyle(kDashed), LineColor(kRed));
plot->Draw();
TLatex * tex = new TLatex(0.2,0.8,"CMS preliminary");
tex->SetNDC();
tex->SetTextFont(42);
tex->SetLineWidth(2);
tex->Draw();
tex->DrawLatex(0.2, 0.725, "7 TeV Data, L = 258 pb^{-1}");
float fsig_peak = NormalizedIntegral(signal,
mass,
m0.getVal() - 2.5*sigma.getVal(),
m0.getVal() + 2.5*sigma.getVal()
);
// float fbkg_peak = NormalizedIntegral(bg,
// mass,
// m0.getVal() - 2.5*sigma.getVal(),
// m0.getVal() + 2.5*sigma.getVal()
// );
double nsigVal = fsig_peak * nsig.getVal();
double nsigErr = fsig_peak * nsig.getError();
double nsigErrRel = nsigErr / nsigVal;
// double nbkgVal = fbkg_peak * nbkg.getVal();
// double nbkgErr = fbkg_peak * nbkg.getError();
// double nbkgErrRel = nbkgErr / nbkgVal;
cout << "nsig " << nsigVal << " +/- " << nsigErr << endl;
// cout << "S/B_{#pm2.5#sigma} " << nsigVal/nbkgVal << " +/- "
// << (nsigVal/nbkgVal)*sqrt(nsigErrRel*nsigErrRel + nbkgErrRel*nbkgErrRel)
// << endl;
// tex->DrawLatex(0.2, 0.6, Form("N_{S} = %.0f#pm%.0f", nsigVal, nsigErr) );
// tex->DrawLatex(0.2, 0.525, Form("S/B_{#pm2.5#sigma} = %.1f", nsigVal/nbkgVal) );
// tex->DrawLatex(0.2, 0.45, Form("#frac{S}{#sqrt{B}}_{#pm2.5#sigma} = %.1f", nsigVal/sqrt(nbkgVal)));
leg = new TLegend(0.65,0.6,0.9,0.75);
leg->SetFillColor(kWhite);
leg->SetLineColor(kWhite);
leg->SetShadowColor(kWhite);
leg->SetTextFont(42);
// TLegendEntry * ldata = leg->AddEntry(data, "Opposite Sign");
// TLegendEntry * ldataB = leg->AddEntry(dataB, "Same Sign");
// ldata->SetMarkerStyle(20);
// ldataB->SetMarkerStyle(20);
// ldataB->SetMarkerColor(kRed);
leg->Draw();
return signal;
}
void run_trac_its(Int_t nEvents = 10, TString mcEngine = "TGeant3"){
// Initialize logger
FairLogger *logger = FairLogger::GetLogger();
logger->SetLogVerbosityLevel("LOW");
logger->SetLogScreenLevel("INFO");
// Input and output file name
std::stringstream inputfile, outputfile, paramfile;
inputfile << "AliceO2_" << mcEngine << ".clus_" << nEvents << "_event.root";
paramfile << "AliceO2_" << mcEngine << ".params_" << nEvents << ".root";
outputfile << "AliceO2_" << mcEngine << ".trac_" << nEvents << "_event.root";
// Setup timer
TStopwatch timer;
// Setup FairRoot analysis manager
FairRunAna * fRun = new FairRunAna();
FairFileSource *fFileSource = new FairFileSource(inputfile.str().c_str());
fRun->SetSource(fFileSource);
fRun->SetOutputFile(outputfile.str().c_str());
// Setup Runtime DB
FairRuntimeDb* rtdb = fRun->GetRuntimeDb();
FairParRootFileIo* parInput1 = new FairParRootFileIo();
parInput1->open(paramfile.str().c_str());
rtdb->setFirstInput(parInput1);
// Setup tracker
// To run with n threads call AliceO2::ITS::CookedTrackerTask(n)
AliceO2::ITS::CookedTrackerTask *trac = new AliceO2::ITS::CookedTrackerTask;
fRun->AddTask(trac);
fRun->Init();
AliceO2::Field::MagneticField* fld = (AliceO2::Field::MagneticField*)fRun->GetField();
if (!fld) {
std::cout << "Failed to get field instance from FairRunAna" << std::endl;
return;
}
trac->setBz(fld->solenoidField()); //in kG
timer.Start();
fRun->Run();
std::cout << std::endl << std::endl;
// Extract the maximal used memory an add is as Dart measurement
// This line is filtered by CTest and the value send to CDash
FairSystemInfo sysInfo;
Float_t maxMemory=sysInfo.GetMaxMemory();
std::cout << "<DartMeasurement name=\"MaxMemory\" type=\"numeric/double\">";
std::cout << maxMemory;
std::cout << "</DartMeasurement>" << std::endl;
timer.Stop();
Double_t rtime = timer.RealTime();
Double_t ctime = timer.CpuTime();
Float_t cpuUsage=ctime/rtime;
cout << "<DartMeasurement name=\"CpuLoad\" type=\"numeric/double\">";
cout << cpuUsage;
cout << "</DartMeasurement>" << endl;
cout << endl << endl;
cout << "Macro finished succesfully." << endl;
std::cout << endl << std::endl;
std::cout << "Output file is " << outputfile.str() << std::endl;
//std::cout << "Parameter file is " << parFile << std::endl;
std::cout << "Real time " << rtime << " s, CPU time " << ctime
<< "s" << endl << endl;
}
void Classify_HWW( TString myMethodList = "" )
{
#ifdef __CINT__
gROOT->ProcessLine( ".O0" ); // turn off optimization in CINT
#endif
//--------------------------------------------------------------------
// path to weights dir (this is where MVA training info is stored)
// output root file will be stored at [path]/output
//--------------------------------------------------------------------
TString path = "Trainings/v5/H160_WW_10vars_dphi10/";
//TString path = "./";
//-----------------------------------
// select samples to run over
//-----------------------------------
char* babyPath = "/tas/cerati/HtoWWmvaBabies/latest";
int mH = 160; // choose Higgs mass
vector<char*> samples;
samples.push_back("WWTo2L2Nu");
samples.push_back("GluGluToWWTo4L");
samples.push_back("WZ");
samples.push_back("ZZ");
samples.push_back("TTJets");
samples.push_back("tW");
samples.push_back("WJetsToLNu");
samples.push_back("DY");
//samples.push_back("WJetsFO3");
if ( mH == 130 ) samples.push_back("Higgs130");
else if( mH == 160 ) samples.push_back("Higgs160");
else if( mH == 200 ) samples.push_back("Higgs200");
else{
cout << "Error, unrecognized Higgs mass " << mH << " GeV, quitting" << endl;
exit(0);
}
//--------------------------------------------------------------------------------
// IMPORTANT: set the following variables to the same set used for MVA training!!!
//--------------------------------------------------------------------------------
std::map<std::string,int> mvaVar;
mvaVar[ "lephard_pt" ] = 1;
mvaVar[ "lepsoft_pt" ] = 1;
mvaVar[ "dil_dphi" ] = 1;
mvaVar[ "dil_mass" ] = 1;
mvaVar[ "event_type" ] = 0;
mvaVar[ "met_projpt" ] = 1;
mvaVar[ "met_pt" ] = 0;
mvaVar[ "mt_lephardmet" ] = 1;
mvaVar[ "mt_lepsoftmet" ] = 1;
mvaVar[ "mthiggs" ] = 1;
mvaVar[ "dphi_lephardmet" ] = 1;
mvaVar[ "dphi_lepsoftmet" ] = 1;
mvaVar[ "lepsoft_fbrem" ] = 0;
mvaVar[ "lepsoft_eOverPIn" ] = 0;
mvaVar[ "lepsoft_qdphi" ] = 0;
//---------------------------------------------------------------
// This loads the library
TMVA::Tools::Instance();
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
// --- Cut optimisation
Use["Cuts"] = 1;
Use["CutsD"] = 1;
Use["CutsPCA"] = 0;
Use["CutsGA"] = 0;
Use["CutsSA"] = 0;
//
// --- 1-dimensional likelihood ("naive Bayes estimator")
Use["Likelihood"] = 1;
Use["LikelihoodD"] = 0; // the "D" extension indicates decorrelated input variables (see option strings)
Use["LikelihoodPCA"] = 1; // the "PCA" extension indicates PCA-transformed input variables (see option strings)
Use["LikelihoodKDE"] = 0;
Use["LikelihoodMIX"] = 0;
//
// --- Mutidimensional likelihood and Nearest-Neighbour methods
Use["PDERS"] = 1;
Use["PDERSD"] = 0;
Use["PDERSPCA"] = 0;
Use["PDEFoam"] = 1;
Use["PDEFoamBoost"] = 0; // uses generalised MVA method boosting
Use["KNN"] = 1; // k-nearest neighbour method
//
// --- Linear Discriminant Analysis
Use["LD"] = 1; // Linear Discriminant identical to Fisher
Use["Fisher"] = 0;
Use["FisherG"] = 0;
Use["BoostedFisher"] = 0; // uses generalised MVA method boosting
Use["HMatrix"] = 0;
//
// --- Function Discriminant analysis
Use["FDA_GA"] = 1; // minimisation of user-defined function using Genetics Algorithm
Use["FDA_SA"] = 0;
Use["FDA_MC"] = 0;
Use["FDA_MT"] = 0;
Use["FDA_GAMT"] = 0;
Use["FDA_MCMT"] = 0;
//
// --- Neural Networks (all are feed-forward Multilayer Perceptrons)
Use["MLP"] = 0; // Recommended ANN
Use["MLPBFGS"] = 0; // Recommended ANN with optional training method
Use["MLPBNN"] = 1; // Recommended ANN with BFGS training method and bayesian regulator
Use["CFMlpANN"] = 0; // Depreciated ANN from ALEPH
Use["TMlpANN"] = 0; // ROOT's own ANN
//
// --- Support Vector Machine
Use["SVM"] = 1;
//
// --- Boosted Decision Trees
Use["BDT"] = 1; // uses Adaptive Boost
Use["BDTG"] = 0; // uses Gradient Boost
Use["BDTB"] = 0; // uses Bagging
Use["BDTD"] = 0; // decorrelation + Adaptive Boost
//
// --- Friedman's RuleFit method, ie, an optimised series of cuts ("rules")
Use["RuleFit"] = 1;
// ---------------------------------------------------------------
Use["Plugin"] = 0;
Use["Category"] = 0;
Use["SVM_Gauss"] = 0;
Use["SVM_Poly"] = 0;
Use["SVM_Lin"] = 0;
std::cout << std::endl;
std::cout << "==> Start TMVAClassificationApplication" << std::endl;
// Select methods (don't look at this code - not of interest)
if (myMethodList != "") {
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0;
std::vector<TString> mlist = gTools().SplitString( myMethodList, ',' );
for (UInt_t i=0; i<mlist.size(); i++) {
std::string regMethod(mlist[i]);
if (Use.find(regMethod) == Use.end()) {
std::cout << "Method \"" << regMethod
<< "\" not known in TMVA under this name. Choose among the following:" << std::endl;
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
std::cout << it->first << " ";
}
std::cout << std::endl;
return;
}
Use[regMethod] = 1;
}
}
// --------------------------------------------------------------------------------------------------
const unsigned int nsamples = samples.size();
for( unsigned int i = 0 ; i < nsamples ; ++i ){
// --- Create the Reader object
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
// Create a set of variables and declare them to the reader
// - the variable names MUST corresponds in name and type to those given in the weight file(s) used
// Float_t var1, var2;
// Float_t var3, var4;
// reader->AddVariable( "myvar1 := var1+var2", &var1 );
// reader->AddVariable( "myvar2 := var1-var2", &var2 );
// reader->AddVariable( "var3", &var3 );
// reader->AddVariable( "var4", &var4 );
Float_t lephard_pt;
Float_t lepsoft_pt;
Float_t dil_dphi;
Float_t dil_mass;
Float_t event_type;
Float_t met_projpt;
Float_t met_pt;
Float_t mt_lephardmet;
Float_t mt_lepsoftmet;
Float_t mthiggs;
Float_t dphi_lephardmet;
Float_t dphi_lepsoftmet;
Float_t lepsoft_fbrem;
Float_t lepsoft_eOverPIn;
Float_t lepsoft_qdphi;
if( mvaVar["lephard_pt"]) reader->AddVariable( "lephard_pt" , &lephard_pt );
if( mvaVar["lepsoft_pt"]) reader->AddVariable( "lepsoft_pt" , &lepsoft_pt );
if( mvaVar["dil_dphi"]) reader->AddVariable( "dil_dphi" , &dil_dphi );
if( mvaVar["dil_mass"]) reader->AddVariable( "dil_mass" , &dil_mass );
if( mvaVar["event_type"]) reader->AddVariable( "event_type" , &event_type );
if( mvaVar["met_projpt"]) reader->AddVariable( "met_projpt" , &met_pt );
if( mvaVar["met_pt"]) reader->AddVariable( "met_pt" , &met_pt );
if( mvaVar["mt_lephardmet"]) reader->AddVariable( "mt_lephardmet" , &mt_lephardmet );
if( mvaVar["mt_lepsoftmet"]) reader->AddVariable( "mt_lepsoftmet" , &mt_lepsoftmet );
if( mvaVar["mthiggs"]) reader->AddVariable( "mthiggs" , &mthiggs );
if( mvaVar["dphi_lephardmet"]) reader->AddVariable( "dphi_lephardmet" , &dphi_lephardmet );
if( mvaVar["dphi_lepsoftmet"]) reader->AddVariable( "dphi_lepsoftmet" , &dphi_lepsoftmet );
if( mvaVar["lepsoft_fbrem"]) reader->AddVariable( "lepsoft_fbrem" , &lepsoft_fbrem );
if( mvaVar["lepsoft_eOverPIn"]) reader->AddVariable( "lepsoft_eOverPIn" , &lepsoft_eOverPIn );
if( mvaVar["lepsoft_qdphi"]) reader->AddVariable( "lepsoft_q * lepsoft_dPhiIn" , &lepsoft_qdphi );
// Spectator variables declared in the training have to be added to the reader, too
// Float_t spec1,spec2;
// reader->AddSpectator( "spec1 := var1*2", &spec1 );
// reader->AddSpectator( "spec2 := var1*3", &spec2 );
Float_t Category_cat1, Category_cat2, Category_cat3;
if (Use["Category"]){
// Add artificial spectators for distinguishing categories
// reader->AddSpectator( "Category_cat1 := var3<=0", &Category_cat1 );
// reader->AddSpectator( "Category_cat2 := (var3>0)&&(var4<0)", &Category_cat2 );
// reader->AddSpectator( "Category_cat3 := (var3>0)&&(var4>=0)", &Category_cat3 );
}
// --- Book the MVA methods
//--------------------------------------------------------------------------------------
// tell Classify_HWW where to find the weights dir, which contains the trained MVA's.
// In this example, the weights dir is located at [path]/[dir]
// and the output root file is written to [path]/[output]
//--------------------------------------------------------------------------------------
TString dir = path + "weights/";
TString outdir = path + "output/";
TString prefix = "TMVAClassification";
// Book method(s)
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
if (it->second) {
TString methodName = TString(it->first) + TString(" method");
TString weightfile = dir + prefix + TString("_") + TString(it->first) + TString(".weights.xml");
reader->BookMVA( methodName, weightfile );
}
}
// Book output histograms
UInt_t nbin = 1000;
TH1F *histLk(0), *histLkD(0), *histLkPCA(0), *histLkKDE(0), *histLkMIX(0), *histPD(0), *histPDD(0);
TH1F *histPDPCA(0), *histPDEFoam(0), *histPDEFoamErr(0), *histPDEFoamSig(0), *histKNN(0), *histHm(0);
TH1F *histFi(0), *histFiG(0), *histFiB(0), *histLD(0), *histNn(0),*histNnbfgs(0),*histNnbnn(0);
TH1F *histNnC(0), *histNnT(0), *histBdt(0), *histBdtG(0), *histBdtD(0), *histRf(0), *histSVMG(0);
TH1F *histSVMP(0), *histSVML(0), *histFDAMT(0), *histFDAGA(0), *histCat(0), *histPBdt(0);
if (Use["Likelihood"]) histLk = new TH1F( "MVA_Likelihood", "MVA_Likelihood", nbin, -1, 1 );
if (Use["LikelihoodD"]) histLkD = new TH1F( "MVA_LikelihoodD", "MVA_LikelihoodD", nbin, -1, 0.9999 );
if (Use["LikelihoodPCA"]) histLkPCA = new TH1F( "MVA_LikelihoodPCA", "MVA_LikelihoodPCA", nbin, -1, 1 );
if (Use["LikelihoodKDE"]) histLkKDE = new TH1F( "MVA_LikelihoodKDE", "MVA_LikelihoodKDE", nbin, -0.00001, 0.99999 );
if (Use["LikelihoodMIX"]) histLkMIX = new TH1F( "MVA_LikelihoodMIX", "MVA_LikelihoodMIX", nbin, 0, 1 );
if (Use["PDERS"]) histPD = new TH1F( "MVA_PDERS", "MVA_PDERS", nbin, 0, 1 );
if (Use["PDERSD"]) histPDD = new TH1F( "MVA_PDERSD", "MVA_PDERSD", nbin, 0, 1 );
if (Use["PDERSPCA"]) histPDPCA = new TH1F( "MVA_PDERSPCA", "MVA_PDERSPCA", nbin, 0, 1 );
if (Use["KNN"]) histKNN = new TH1F( "MVA_KNN", "MVA_KNN", nbin, 0, 1 );
if (Use["HMatrix"]) histHm = new TH1F( "MVA_HMatrix", "MVA_HMatrix", nbin, -0.95, 1.55 );
if (Use["Fisher"]) histFi = new TH1F( "MVA_Fisher", "MVA_Fisher", nbin, -4, 4 );
if (Use["FisherG"]) histFiG = new TH1F( "MVA_FisherG", "MVA_FisherG", nbin, -1, 1 );
if (Use["BoostedFisher"]) histFiB = new TH1F( "MVA_BoostedFisher", "MVA_BoostedFisher", nbin, -2, 2 );
if (Use["LD"]) histLD = new TH1F( "MVA_LD", "MVA_LD", nbin, -2, 2 );
if (Use["MLP"]) histNn = new TH1F( "MVA_MLP", "MVA_MLP", nbin, -1.25, 1.5 );
if (Use["MLPBFGS"]) histNnbfgs = new TH1F( "MVA_MLPBFGS", "MVA_MLPBFGS", nbin, -1.25, 1.5 );
if (Use["MLPBNN"]) histNnbnn = new TH1F( "MVA_MLPBNN", "MVA_MLPBNN", nbin, -1.25, 1.5 );
if (Use["CFMlpANN"]) histNnC = new TH1F( "MVA_CFMlpANN", "MVA_CFMlpANN", nbin, 0, 1 );
if (Use["TMlpANN"]) histNnT = new TH1F( "MVA_TMlpANN", "MVA_TMlpANN", nbin, -1.3, 1.3 );
if (Use["BDT"]) histBdt = new TH1F( "MVA_BDT", "MVA_BDT", nbin, -1. , 1. );
if (Use["BDTD"]) histBdtD = new TH1F( "MVA_BDTD", "MVA_BDTD", nbin, -0.8, 0.8 );
if (Use["BDTG"]) histBdtG = new TH1F( "MVA_BDTG", "MVA_BDTG", nbin, -1.0, 1.0 );
if (Use["RuleFit"]) histRf = new TH1F( "MVA_RuleFit", "MVA_RuleFit", nbin, -2.0, 2.0 );
if (Use["SVM_Gauss"]) histSVMG = new TH1F( "MVA_SVM_Gauss", "MVA_SVM_Gauss", nbin, 0.0, 1.0 );
if (Use["SVM_Poly"]) histSVMP = new TH1F( "MVA_SVM_Poly", "MVA_SVM_Poly", nbin, 0.0, 1.0 );
if (Use["SVM_Lin"]) histSVML = new TH1F( "MVA_SVM_Lin", "MVA_SVM_Lin", nbin, 0.0, 1.0 );
if (Use["FDA_MT"]) histFDAMT = new TH1F( "MVA_FDA_MT", "MVA_FDA_MT", nbin, -2.0, 3.0 );
if (Use["FDA_GA"]) histFDAGA = new TH1F( "MVA_FDA_GA", "MVA_FDA_GA", nbin, -2.0, 3.0 );
if (Use["Category"]) histCat = new TH1F( "MVA_Category", "MVA_Category", nbin, -2., 2. );
if (Use["Plugin"]) histPBdt = new TH1F( "MVA_PBDT", "MVA_BDT", nbin, -0.8, 0.8 );
if (Use["Likelihood"]) histLk ->Sumw2();
if (Use["LikelihoodD"]) histLkD ->Sumw2();
if (Use["LikelihoodPCA"]) histLkPCA ->Sumw2();
if (Use["LikelihoodKDE"]) histLkKDE ->Sumw2();
if (Use["LikelihoodMIX"]) histLkMIX ->Sumw2();
if (Use["PDERS"]) histPD ->Sumw2();
if (Use["PDERSD"]) histPDD ->Sumw2();
if (Use["PDERSPCA"]) histPDPCA ->Sumw2();
if (Use["KNN"]) histKNN ->Sumw2();
if (Use["HMatrix"]) histHm ->Sumw2();
if (Use["Fisher"]) histFi ->Sumw2();
if (Use["FisherG"]) histFiG ->Sumw2();
if (Use["BoostedFisher"]) histFiB ->Sumw2();
if (Use["LD"]) histLD ->Sumw2();
if (Use["MLP"]) histNn ->Sumw2();
if (Use["MLPBFGS"]) histNnbfgs ->Sumw2();
if (Use["MLPBNN"]) histNnbnn ->Sumw2();
if (Use["CFMlpANN"]) histNnC ->Sumw2();
if (Use["TMlpANN"]) histNnT ->Sumw2();
if (Use["BDT"]) histBdt ->Sumw2();
if (Use["BDTD"]) histBdtD ->Sumw2();
if (Use["BDTG"]) histBdtG ->Sumw2();
if (Use["RuleFit"]) histRf ->Sumw2();
if (Use["SVM_Gauss"]) histSVMG ->Sumw2();
if (Use["SVM_Poly"]) histSVMP ->Sumw2();
if (Use["SVM_Lin"]) histSVML ->Sumw2();
if (Use["FDA_MT"]) histFDAMT ->Sumw2();
if (Use["FDA_GA"]) histFDAGA ->Sumw2();
if (Use["Category"]) histCat ->Sumw2();
if (Use["Plugin"]) histPBdt ->Sumw2();
// PDEFoam also returns per-event error, fill in histogram, and also fill significance
if (Use["PDEFoam"]) {
histPDEFoam = new TH1F( "MVA_PDEFoam", "MVA_PDEFoam", nbin, 0, 1 );
histPDEFoamErr = new TH1F( "MVA_PDEFoamErr", "MVA_PDEFoam error", nbin, 0, 1 );
histPDEFoamSig = new TH1F( "MVA_PDEFoamSig", "MVA_PDEFoam significance", nbin, 0, 10 );
}
// Book example histogram for probability (the other methods are done similarly)
TH1F *probHistFi(0), *rarityHistFi(0);
if (Use["Fisher"]) {
probHistFi = new TH1F( "MVA_Fisher_Proba", "MVA_Fisher_Proba", nbin, 0, 1 );
rarityHistFi = new TH1F( "MVA_Fisher_Rarity", "MVA_Fisher_Rarity", nbin, 0, 1 );
}
// Prepare input tree (this must be replaced by your data source)
// in this example, there is a toy tree with signal and one with background events
// we'll later on use only the "signal" events for the test in this example.
//
TChain *ch = new TChain("Events");
if( strcmp( samples.at(i) , "DY" ) == 0 ){
ch -> Add( Form("%s/DYToMuMuM20_PU_testFinal_baby.root",babyPath) );
ch -> Add( Form("%s/DYToMuMuM10To20_PU_testFinal_baby.root",babyPath) );
ch -> Add( Form("%s/DYToEEM20_PU_testFinal_baby.root",babyPath) );
ch -> Add( Form("%s/DYToEEM10To20_PU_testFinal_baby.root",babyPath) );
ch -> Add( Form("%s/DYToTauTauM20_PU_testFinal_baby.root",babyPath) );
ch -> Add( Form("%s/DYToTauTauM10To20_PU_testFinal_baby.root",babyPath) );
}
if( strcmp( samples.at(i) , "WJetsFO3" ) == 0 ){
ch -> Add( Form("%s/WJetsToLNu_FOv3_PU_testFinal_baby.root",babyPath) );
ch -> Add( Form("%s/WToLNu_FOv3_testFinal_baby.root",babyPath) );
}
else if( strcmp( samples.at(i) , "Higgs130" ) == 0 ){
ch -> Add( Form("%s/HToWWTo2L2NuM130_PU_testFinal_baby.root",babyPath) );
ch -> Add( Form("%s/HToWWToLNuTauNuM130_PU_testFinal_baby.root",babyPath) );
ch -> Add( Form("%s/HToWWTo2Tau2NuM130_PU_testFinal_baby.root",babyPath) );
}
else if( strcmp( samples.at(i) , "Higgs160" ) == 0 ){
ch -> Add( Form("%s/HToWWTo2L2NuM160_PU_testFinal_baby.root",babyPath) );
ch -> Add( Form("%s/HToWWToLNuTauNuM160_PU_testFinal_baby.root",babyPath) );
ch -> Add( Form("%s/HToWWTo2Tau2NuM160_PU_testFinal_baby.root",babyPath) );
}
else if( strcmp( samples.at(i) , "Higgs200" ) == 0 ){
ch -> Add( Form("%s/HToWWTo2L2NuM200_PU_testFinal_baby.root",babyPath) );
ch -> Add( Form("%s/HToWWToLNuTauNuM200_PU_testFinal_baby.root",babyPath) );
ch -> Add( Form("%s/HToWWTo2Tau2NuM200_PU_testFinal_baby.root",babyPath) );
}
else{
ch -> Add( Form("%s/%s_PU_testFinal_baby.root",babyPath,samples.at(i)) );
}
// --- Event loop
// Prepare the event tree
// - here the variable names have to corresponds to your tree
// - you can use the same variables as above which is slightly faster,
// but of course you can use different ones and copy the values inside the event loop
//
TTree *theTree = (TTree*) ch;
std::cout << "--- Using input files: -------------------" << std::endl;
TObjArray *listOfFiles = ch->GetListOfFiles();
TIter fileIter(listOfFiles);
TChainElement* currentFile = 0;
while((currentFile = (TChainElement*)fileIter.Next())) {
std::cout << currentFile->GetTitle() << std::endl;
}
Float_t lephard_pt_;
Float_t lepsoft_pt_;
Float_t lepsoft_fr_;
Float_t dil_dphi_;
Float_t dil_mass_;
Float_t event_type_;
Float_t met_projpt_;
Int_t jets_num_;
Int_t extralep_num_;
Int_t lowptbtags_num_;
Int_t softmu_num_;
Float_t event_scale1fb_;
Float_t met_pt_;
Int_t lepsoft_passTighterId_;
Float_t mt_lephardmet_;
Float_t mt_lepsoftmet_;
Float_t mthiggs_;
Float_t dphi_lephardmet_;
Float_t dphi_lepsoftmet_;
Float_t lepsoft_fbrem_;
Float_t lepsoft_eOverPIn_;
Float_t lepsoft_q_;
Float_t lepsoft_dPhiIn_;
theTree->SetBranchAddress( "lephard_pt_" , &lephard_pt_ );
theTree->SetBranchAddress( "lepsoft_pt_" , &lepsoft_pt_ );
theTree->SetBranchAddress( "lepsoft_fr_" , &lepsoft_fr_ );
theTree->SetBranchAddress( "dil_dphi_" , &dil_dphi_ );
theTree->SetBranchAddress( "dil_mass_" , &dil_mass_ );
theTree->SetBranchAddress( "event_type_" , &event_type_ );
theTree->SetBranchAddress( "met_projpt_" , &met_projpt_ );
theTree->SetBranchAddress( "jets_num_" , &jets_num_ );
theTree->SetBranchAddress( "extralep_num_" , &extralep_num_ );
theTree->SetBranchAddress( "lowptbtags_num_" , &lowptbtags_num_ );
theTree->SetBranchAddress( "softmu_num_" , &softmu_num_ );
theTree->SetBranchAddress( "event_scale1fb_" , &event_scale1fb_ );
theTree->SetBranchAddress( "lepsoft_passTighterId_" , &lepsoft_passTighterId_ );
theTree->SetBranchAddress( "met_pt_" , &met_pt_ );
theTree->SetBranchAddress( "mt_lephardmet_" , &mt_lephardmet_ );
theTree->SetBranchAddress( "mt_lepsoftmet_" , &mt_lepsoftmet_ );
theTree->SetBranchAddress( "mthiggs_" , &mthiggs_ );
theTree->SetBranchAddress( "dphi_lephardmet_" , &dphi_lephardmet_ );
theTree->SetBranchAddress( "dphi_lepsoftmet_" , &dphi_lepsoftmet_ );
theTree->SetBranchAddress( "lepsoft_fbrem_" , &lepsoft_fbrem_ );
theTree->SetBranchAddress( "lepsoft_eOverPIn_" , &lepsoft_eOverPIn_ );
theTree->SetBranchAddress( "lepsoft_q_" , &lepsoft_q_ );
theTree->SetBranchAddress( "lepsoft_dPhiIn_" , &lepsoft_dPhiIn_ );
// Efficiency calculator for cut method
Int_t nSelCutsGA = 0;
Double_t effS = 0.7;
std::vector<Float_t> vecVar(4); // vector for EvaluateMVA tests
std::cout << "--- Processing: " << theTree->GetEntries() << " events" << std::endl;
TStopwatch sw;
sw.Start();
int npass = 0;
float yield = 0.;
for (Long64_t ievt=0; ievt<theTree->GetEntries();ievt++) {
if (ievt%1000 == 0) std::cout << "--- ... Processing event: " << ievt << std::endl;
theTree->GetEntry(ievt);
//-------------------------------------------------------
// event selection
//-------------------------------------------------------
if( dil_dphi_ > 1. ) continue;
//em
if( event_type_ > 0.5 && event_type_ < 2.5 ){
if( met_projpt_ < 20. ) continue;
}
//ee/mm
if( event_type_ < 0.5 || event_type_ > 2.5 ){
if( met_projpt_ < 35. ) continue;
}
if( lephard_pt_ < 20. ) continue;
if( jets_num_ > 0 ) continue;
if( extralep_num_ > 0 ) continue;
if( lowptbtags_num_ > 0 ) continue;
if( softmu_num_ > 0 ) continue;
if( dil_mass_ < 12. ) continue;
if( lepsoft_passTighterId_ == 0 ) continue;
//if( event_type_ < 1.5 ) continue;
//if( event_type > 1.5 && lepsoft_pt_ < 15. ) continue;
//mH-dependent selection
if( mH == 130 ){
if( lepsoft_pt_ < 10. ) continue;
if( dil_mass_ > 90. ) continue;
}
else if( mH == 160 ){
if( lepsoft_pt_ < 20. ) continue;
if( dil_mass_ > 100. ) continue;
}
else if( mH == 200 ){
if( lepsoft_pt_ < 20. ) continue;
if( dil_mass_ > 130. ) continue;
}
float weight = event_scale1fb_ * lepsoft_fr_ * 0.5;
//--------------------------------------------------------
// important: here we associate branches to MVA variables
//--------------------------------------------------------
lephard_pt = lephard_pt_;
lepsoft_pt = lepsoft_pt_;
dil_mass = dil_mass_;
dil_dphi = dil_dphi_;
event_type = event_type_;
met_pt = met_pt_;
met_projpt = met_projpt_;
mt_lephardmet = mt_lephardmet_;
mt_lepsoftmet = mt_lepsoftmet_;
mthiggs = mthiggs_;
dphi_lephardmet = dphi_lephardmet_;
dphi_lepsoftmet = dphi_lepsoftmet_;
lepsoft_fbrem = lepsoft_fbrem_;
lepsoft_eOverPIn = lepsoft_eOverPIn_;
lepsoft_qdphi = lepsoft_q_ * lepsoft_dPhiIn_;
npass++;
yield+=weight;
// var1 = userVar1 + userVar2;
// var2 = userVar1 - userVar2;
// --- Return the MVA outputs and fill into histograms
if (Use["CutsGA"]) {
// Cuts is a special case: give the desired signal efficienciy
Bool_t passed = reader->EvaluateMVA( "CutsGA method", effS );
if (passed) nSelCutsGA++;
}
if (Use["Likelihood" ]) histLk ->Fill( reader->EvaluateMVA( "Likelihood method" ) , weight);
if (Use["LikelihoodD" ]) histLkD ->Fill( reader->EvaluateMVA( "LikelihoodD method" ) , weight);
if (Use["LikelihoodPCA"]) histLkPCA ->Fill( reader->EvaluateMVA( "LikelihoodPCA method" ) , weight);
if (Use["LikelihoodKDE"]) histLkKDE ->Fill( reader->EvaluateMVA( "LikelihoodKDE method" ) , weight);
if (Use["LikelihoodMIX"]) histLkMIX ->Fill( reader->EvaluateMVA( "LikelihoodMIX method" ) , weight);
if (Use["PDERS" ]) histPD ->Fill( reader->EvaluateMVA( "PDERS method" ) , weight);
if (Use["PDERSD" ]) histPDD ->Fill( reader->EvaluateMVA( "PDERSD method" ) , weight);
if (Use["PDERSPCA" ]) histPDPCA ->Fill( reader->EvaluateMVA( "PDERSPCA method" ) , weight);
if (Use["KNN" ]) histKNN ->Fill( reader->EvaluateMVA( "KNN method" ) , weight);
if (Use["HMatrix" ]) histHm ->Fill( reader->EvaluateMVA( "HMatrix method" ) , weight);
if (Use["Fisher" ]) histFi ->Fill( reader->EvaluateMVA( "Fisher method" ) , weight);
if (Use["FisherG" ]) histFiG ->Fill( reader->EvaluateMVA( "FisherG method" ) , weight);
if (Use["BoostedFisher"]) histFiB ->Fill( reader->EvaluateMVA( "BoostedFisher method" ) , weight);
if (Use["LD" ]) histLD ->Fill( reader->EvaluateMVA( "LD method" ) , weight);
if (Use["MLP" ]) histNn ->Fill( reader->EvaluateMVA( "MLP method" ) , weight);
if (Use["MLPBFGS" ]) histNnbfgs ->Fill( reader->EvaluateMVA( "MLPBFGS method" ) , weight);
if (Use["MLPBNN" ]) histNnbnn ->Fill( reader->EvaluateMVA( "MLPBNN method" ) , weight);
if (Use["CFMlpANN" ]) histNnC ->Fill( reader->EvaluateMVA( "CFMlpANN method" ) , weight);
if (Use["TMlpANN" ]) histNnT ->Fill( reader->EvaluateMVA( "TMlpANN method" ) , weight);
if (Use["BDT" ]) histBdt ->Fill( reader->EvaluateMVA( "BDT method" ) , weight);
if (Use["BDTD" ]) histBdtD ->Fill( reader->EvaluateMVA( "BDTD method" ) , weight);
if (Use["BDTG" ]) histBdtG ->Fill( reader->EvaluateMVA( "BDTG method" ) , weight);
if (Use["RuleFit" ]) histRf ->Fill( reader->EvaluateMVA( "RuleFit method" ) , weight);
if (Use["SVM_Gauss" ]) histSVMG ->Fill( reader->EvaluateMVA( "SVM_Gauss method" ) , weight);
if (Use["SVM_Poly" ]) histSVMP ->Fill( reader->EvaluateMVA( "SVM_Poly method" ) , weight);
if (Use["SVM_Lin" ]) histSVML ->Fill( reader->EvaluateMVA( "SVM_Lin method" ) , weight);
if (Use["FDA_MT" ]) histFDAMT ->Fill( reader->EvaluateMVA( "FDA_MT method" ) , weight);
if (Use["FDA_GA" ]) histFDAGA ->Fill( reader->EvaluateMVA( "FDA_GA method" ) , weight);
if (Use["Category" ]) histCat ->Fill( reader->EvaluateMVA( "Category method" ) , weight);
if (Use["Plugin" ]) histPBdt ->Fill( reader->EvaluateMVA( "P_BDT method" ) , weight);
// Retrieve also per-event error
if (Use["PDEFoam"]) {
Double_t val = reader->EvaluateMVA( "PDEFoam method" );
Double_t err = reader->GetMVAError();
histPDEFoam ->Fill( val );
histPDEFoamErr->Fill( err );
if (err>1.e-50) histPDEFoamSig->Fill( val/err , weight);
}
// Retrieve probability instead of MVA output
if (Use["Fisher"]) {
probHistFi ->Fill( reader->GetProba ( "Fisher method" ) , weight);
rarityHistFi->Fill( reader->GetRarity( "Fisher method" ) , weight);
}
}
std::cout << npass << " events passing selection, yield " << yield << std::endl;
// Get elapsed time
sw.Stop();
std::cout << "--- End of event loop: "; sw.Print();
// Get efficiency for cuts classifier
if (Use["CutsGA"]) std::cout << "--- Efficiency for CutsGA method: " << double(nSelCutsGA)/theTree->GetEntries()
<< " (for a required signal efficiency of " << effS << ")" << std::endl;
if (Use["CutsGA"]) {
// test: retrieve cuts for particular signal efficiency
// CINT ignores dynamic_casts so we have to use a cuts-secific Reader function to acces the pointer
TMVA::MethodCuts* mcuts = reader->FindCutsMVA( "CutsGA method" ) ;
if (mcuts) {
std::vector<Double_t> cutsMin;
std::vector<Double_t> cutsMax;
mcuts->GetCuts( 0.7, cutsMin, cutsMax );
std::cout << "--- -------------------------------------------------------------" << std::endl;
std::cout << "--- Retrieve cut values for signal efficiency of 0.7 from Reader" << std::endl;
for (UInt_t ivar=0; ivar<cutsMin.size(); ivar++) {
std::cout << "... Cut: "
<< cutsMin[ivar]
<< " < \""
<< mcuts->GetInputVar(ivar)
<< "\" <= "
<< cutsMax[ivar] << std::endl;
}
std::cout << "--- -------------------------------------------------------------" << std::endl;
}
}
// --- Write histograms
cout << "dir " << dir << endl;
char* mydir = outdir;
TFile *target = new TFile( Form("%s/%s.root",mydir,samples.at(i) ) ,"RECREATE" );
cout << "Writing to file " << Form("%s/%s.root",mydir,samples.at(i) ) << endl;
if (Use["Likelihood" ]) histLk ->Write();
if (Use["LikelihoodD" ]) histLkD ->Write();
if (Use["LikelihoodPCA"]) histLkPCA ->Write();
if (Use["LikelihoodKDE"]) histLkKDE ->Write();
if (Use["LikelihoodMIX"]) histLkMIX ->Write();
if (Use["PDERS" ]) histPD ->Write();
if (Use["PDERSD" ]) histPDD ->Write();
if (Use["PDERSPCA" ]) histPDPCA ->Write();
if (Use["KNN" ]) histKNN ->Write();
if (Use["HMatrix" ]) histHm ->Write();
if (Use["Fisher" ]) histFi ->Write();
if (Use["FisherG" ]) histFiG ->Write();
if (Use["BoostedFisher"]) histFiB ->Write();
if (Use["LD" ]) histLD ->Write();
if (Use["MLP" ]) histNn ->Write();
if (Use["MLPBFGS" ]) histNnbfgs ->Write();
if (Use["MLPBNN" ]) histNnbnn ->Write();
if (Use["CFMlpANN" ]) histNnC ->Write();
if (Use["TMlpANN" ]) histNnT ->Write();
if (Use["BDT" ]) histBdt ->Write();
if (Use["BDTD" ]) histBdtD ->Write();
if (Use["BDTG" ]) histBdtG ->Write();
if (Use["RuleFit" ]) histRf ->Write();
if (Use["SVM_Gauss" ]) histSVMG ->Write();
if (Use["SVM_Poly" ]) histSVMP ->Write();
if (Use["SVM_Lin" ]) histSVML ->Write();
if (Use["FDA_MT" ]) histFDAMT ->Write();
if (Use["FDA_GA" ]) histFDAGA ->Write();
if (Use["Category" ]) histCat ->Write();
if (Use["Plugin" ]) histPBdt ->Write();
// Write also error and significance histos
if (Use["PDEFoam"]) { histPDEFoam->Write(); histPDEFoamErr->Write(); histPDEFoamSig->Write(); }
// Write also probability hists
if (Use["Fisher"]) { if (probHistFi != 0) probHistFi->Write(); if (rarityHistFi != 0) rarityHistFi->Write(); }
target->Close();
delete reader;
std::cout << "==> TMVAClassificationApplication is done with sample " << samples.at(i) << endl << std::endl;
}
}
void califaAna_batch(Int_t nEvents=1, Int_t fGeoVer=1, Double_t fThres=0.000050,
Double_t fExpRes=5., Double_t fDelPolar=3.2, Double_t fDelAzimuthal=3.2) {
cout << "Running califaAna_batch with arguments:" <<endl;
cout << "Number of events: " << nEvents <<endl;
cout << "CALIFA geo version: " << fGeoVer <<endl;
cout << "Threshold: " << fThres <<endl<<endl;
cout << "Experimental resolution: " << fExpRes <<endl<<endl;
// In general, the following parts need not be touched
// ========================================================================
// ---- Debug option -------------------------------------------------
gDebug = 0;
// ------------------------------------------------------------------------
// ----- Timer --------------------------------------------------------
TStopwatch timer;
timer.Start();
// ------------------------------------------------------------------------
// ----- Create analysis run ----------------------------------------
FairRunAna* fRun = new FairRunAna();
FairRuntimeDb* rtdb = fRun->GetRuntimeDb();
FairParRootFileIo* parIo1 = new FairParRootFileIo();
parIo1->open("r3bpar.root");
rtdb->setFirstInput(parIo1);
rtdb->print();
fRun->SetInputFile("r3bsim.root");
fRun->SetOutputFile("califaAna.root");
// ----- Analysis routines for CALIFA
R3BCaloHitFinder* caloHF = new R3BCaloHitFinder();
//Selecting the geometry version
// 0- CALIFA 5.0, including BARREL and ENDCAP.
// 1- CALIFA 7.05, only BARREL
// 2- CALIFA 7.07, only BARREL
// 3- CALIFA 7.09, only BARREL (ongoing work)
// 4- CALIFA 7.17, only ENDCAP (in CsI[Tl])
// 5- CALIFA 7.07+7.17,
// 6- CALIFA 7.09+7.17, (ongoing work)
// 10- CALIFA 8.11, only BARREL (ongoing work)
// ...
caloHF->SelectGeometryVersion(fGeoVer);
//caloHF->SelectGeometryVersion(10);
caloHF->SetDetectionThreshold(fThres); //50 KeV [fThres in GeV]
caloHF->SetExperimentalResolution(fExpRes); //5% at 1 MeV
caloHF->SetAngularWindow(fDelPolar,fDelAzimuthal); //[0.25 around 14.3 degrees, 3.2 for the complete calorimeter]
fRun->AddTask(caloHF);
fRun->Init();
fRun->Run(0, nEvents);
// ----- Finish -------------------------------------------------------
timer.Stop();
Double_t rtime = timer.RealTime();
Double_t ctime = timer.CpuTime();
cout << endl << endl;
cout << "Macro finished succesfully." << endl;
cout << "Real time " << rtime << " s, CPU time " << ctime << " s" << endl;
cout << endl;
// ------------------------------------------------------------------------
}
void TMVAClassificationApplication_TX(TString myMethodList = "" , TString iFileName = "", TString sampleLocation = "", TString outputLocation = "")
{
#ifdef __CINT__
gROOT->ProcessLine( ".O0" ); // turn off optimization in CINT
#endif
//---------------------------------------------------------------
// This loads the library
TMVA::Tools::Instance();
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
// --- Cut optimisation
Use["Cuts"] = 0;
Use["CutsD"] = 0;
Use["CutsPCA"] = 0;
Use["CutsGA"] = 0;
Use["CutsSA"] = 0;
//
//
// --- Boosted Decision Trees
Use["BDT"] = 1; // uses Adaptive Boost
std::cout << std::endl;
std::cout << "==> Start TMVAClassificationApplication" << std::endl;
// Select methods (don't look at this code - not of interest)
if (myMethodList != "") {
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0;
std::vector<TString> mlist = gTools().SplitString( myMethodList, ',' );
for (UInt_t i=0; i<mlist.size(); i++) {
std::string regMethod(mlist[i]);
if (Use.find(regMethod) == Use.end()) {
std::cout << "Method \"" << regMethod
<< "\" not known in TMVA under this name. Choose among the following:" << std::endl;
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
std::cout << it->first << " ";
}
std::cout << std::endl;
return;
}
Use[regMethod] = 1;
}
}
// --------------------------------------------------------------------------------------------------
// --- Create the Reader object
TMVA::Reader *reader = new TMVA::Reader("!Color:!Silent" );
// Create a set of variables and declare them to the reader
// - the variable names MUST corresponds in name and type to those given in the weight file(s) used
Float_t var1, var2, var3, var4, var5, var6, var7, var8, var9, var10, var11, var12, var13, var14, var15, var16, var17, var18, var19, var20, var21, var22, var23, var24, var25, var26, var27, var28, var29;
//reader->AddVariable( "Alt$(jet_pt_singleLepCalc[0],0)", &var1);
//reader->AddVariable( "Alt$(jet_pt_singleLepCalc[1],0)", &var2 );
//reader->AddVariable( "Alt$(jet_pt_singleLepCalc[2],0)", &var3 );
reader->AddVariable( "Alt$(bJetPt_CATopoCalc[0],0)", &var4 );
reader->AddVariable( "Alt$(bJetPt_CATopoCalc[1],0)", &var5 );
//reader->AddVariable( "corr_met_singleLepCalc", &var6 );
//reader->AddVariable( "muon_1_pt_singleLepCalc", &var7 );
//reader->AddVariable( "nBJets_CATopoCalc", &var8 );
//reader->AddVariable( "nSelJets_CommonCalc", &var9 );
//reader->AddVariable( "LeptonJet_DeltaR_LjetsTopoCalcNew", &var10);
reader->AddVariable( "Mevent_LjetsTopoCalcNew", &var11);
//reader->AddVariable( "W_Pt_LjetsTopoCalcNew", &var12 );
reader->AddVariable( "Jet1Jet2_Pt_LjetsTopoCalcNew", &var13 );
//reader->AddVariable( "BestTop_LjetsTopoCalcNew", &var14 );
//reader->AddVariable( "BTagTopMass_LjetsTopoCalcNew", &var15 );
//reader->AddVariable( "Alt$(CAHEPTopJetMass_JetSubCalc[0],0)", &var16 );
//reader->AddVariable( "Alt$(CAWCSVMSubJets_JetSubCalc[0],0)", &var17 );
//reader->AddVariable( "Alt$(CAWCSVLSubJets_JetSubCalc[0],0)", &var18 );
reader->AddVariable( "Alt$(CAWJetPt_JetSubCalc[0],0)", &var19 );
reader->AddVariable( "Alt$(CAWJetMass_JetSubCalc[0],0)", &var20 );
//reader->AddVariable( "Alt$(CAHEPTopJetMass_JetSubCalc[1],0)", &var21 );
//reader->AddVariable( "Hz_LjetsTopoCalcNew", &var22 );
//reader->AddVariable( "Centrality_LjetsTopoCalcNew", &var23 );
reader->AddVariable( "SqrtsT_LjetsTopoCalcNew", &var24 );
reader->AddVariable( "CAMindrBMass_CATopoCalc", &var28 );
reader->AddVariable( "minDRCAtoB_CATopoCalc", &var29 );
//reader->AddVariable( "HT2prime_LjetsTopoCalcNew", &var25 );
reader->AddVariable( "HT2_LjetsTopoCalcNew", &var26 );
//reader->AddVariable( "dphiLepMet_LjetsTopoCalcNew", &var27 );
// Spectator variables declared in the training have to be added to the reader, too
// Float_t spec1,spec2;
// reader->AddSpectator( "spec1 := var1*2", &spec1 );
// reader->AddSpectator( "spec2 := var1*3", &spec2 );
// Float_t Category_cat1, Category_cat2, Category_cat3;
// if (Use["Category"]){
// // Add artificial spectators for distinguishing categories
// reader->AddSpectator( "Category_cat1 := var3<=0", &Category_cat1 );
// reader->AddSpectator( "Category_cat2 := (var3>0)&&(var4<0)", &Category_cat2 );
// reader->AddSpectator( "Category_cat3 := (var3>0)&&(var4>=0)", &Category_cat3 );
// }
// --- Book the MVA methods
// Book method(s)
TString weightFileName = "weights/TMVAClassification_BDT.weights";
reader->BookMVA("BDT method", weightFileName+".xml" );
// Book output histograms
UInt_t nbin = 100;
TH1F *histBdt(0);
histBdt = new TH1F( "MVA_BDT_TX", "MVA_BDT_TX", nbin, -1.0, 1.0);
// Prepare input tree (this must be replaced by your data source)
// in this example, there is a toy tree with signal and one with background events
// we'll later on use only the "signal" events for the test in this example.
//
TFile *input(0);
TString fileName = iFileName;
TString fname = sampleLocation+"/";
fname += fileName;
TString oFileName = fileName;
if (!gSystem->AccessPathName( fname ))
input = TFile::Open( fname ); // check if file in local directory exists
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
std::cout << "--- TMVAClassificationApp : Using input file: " << input->GetName() << std::endl;
// --- Event loop
// Prepare the event tree
// - here the variable names have to corresponds to your tree
// - you can use the same variables as above which is slightly faster,
// but of course you can use different ones and copy the values inside the event loop
//
std::cout << "--- Select signal sample" << std::endl;
TTree* theTree = (TTree*)input->Get("ljmet");
gSystem->mkdir( outputLocation );
TFile *target = new TFile( outputLocation+"/"+oFileName,"RECREATE" );
TTree *newTree = theTree->CloneTree();
Float_t BDT;
TBranch *branchBDT = newTree->Branch("__BDT_TX__",&BDT,"__BDT_TX__/F");
std::vector<Double_t> *vecVar1;
std::vector<Double_t> *vecVar4;
std::vector<Double_t> *vecVar16;
std::vector<Int_t> *vecVar17;
std::vector<Int_t> *vecVar18;
std::vector<Double_t> *vecVar19;
std::vector<Double_t> *vecVar20;
Int_t *intVar5, *intVar8, *intVar9;
Double_t *dVar2, *dVar3, *dVar6, *dVar7, *dVar10, *dVar11, *dVar12, *dVar13, *dVar14, *dVar15, *dVar22, *dVar23, *dVar24, dVar25, *dVar26, *dVar27, *dVar28, *dVar29;
theTree->SetBranchAddress( "jet_pt_singleLepCalc", &vecVar1);
theTree->SetBranchAddress( "bJetPt_CATopoCalc", &vecVar4 );
theTree->SetBranchAddress( "corr_met_singleLepCalc", &dVar6 );
theTree->SetBranchAddress( "muon_1_pt_singleLepCalc", &dVar7 );
theTree->SetBranchAddress( "nBJets_CATopoCalc", &intVar8 );
theTree->SetBranchAddress( "nSelJets_CommonCalc", &intVar9 );
theTree->SetBranchAddress( "LeptonJet_DeltaR_LjetsTopoCalcNew", &dVar10);
theTree->SetBranchAddress( "Mevent_LjetsTopoCalcNew", &dVar11);
theTree->SetBranchAddress( "W_Pt_LjetsTopoCalcNew", &dVar12 );
theTree->SetBranchAddress( "Jet1Jet2_Pt_LjetsTopoCalcNew", &dVar13 );
theTree->SetBranchAddress( "BestTop_LjetsTopoCalcNew", &dVar14 );
theTree->SetBranchAddress( "BTagTopMass_LjetsTopoCalcNew", &dVar15 );
theTree->SetBranchAddress( "CAHEPTopJetMass_JetSubCalc", &vecVar16 );
theTree->SetBranchAddress( "CAWCSVMSubJets_JetSubCalc", &vecVar17 );
theTree->SetBranchAddress( "CAWCSVLSubJets_JetSubCalc", &vecVar18 );
theTree->SetBranchAddress( "CAWJetPt_JetSubCalc", &vecVar19 );
theTree->SetBranchAddress( "CAWJetMass_JetSubCalc", &vecVar20 );
theTree->SetBranchAddress( "Hz_LjetsTopoCalcNew", &dVar22 );
theTree->SetBranchAddress( "Centrality_LjetsTopoCalcNew", &dVar23 );
theTree->SetBranchAddress( "SqrtsT_LjetsTopoCalcNew", &dVar24 );
theTree->SetBranchAddress( "HT2prime_LjetsTopoCalcNew", &dVar25 );
theTree->SetBranchAddress( "HT2_LjetsTopoCalcNew", &dVar26 );
theTree->SetBranchAddress( "dphiLepMet_LjetsTopoCalcNew", &dVar27 );
theTree->SetBranchAddress( "CAMindrBMass_CATopoCalc", &dVar28 );
theTree->SetBranchAddress( "minDRCAtoB_CATopoCalc", &dVar29 );
// Efficiency calculator for cut method
Int_t nSelCutsGA = 0;
Double_t effS = 0.7;
std::vector<Float_t> vecVar(4); // vector for EvaluateMVA tests
std::cout << "--- Processing: " << theTree->GetEntries() << " events" << std::endl;
TStopwatch sw;
sw.Start();
for (Long64_t ievt=0; ievt<theTree->GetEntries();ievt++) {
if (ievt%1000 == 0) std::cout << "--- ... Processing event: " << ievt << std::endl;
theTree->GetEntry(ievt);
if(vecVar1->size()>0){
var1 = vecVar1->at(0);
}
if(vecVar1->size()>1){
var2 = vecVar1->at(1);
}
if(vecVar1->size()>2){
var3 = vecVar1->at(2);
}
if(vecVar4->size()>0){
var4 = vecVar4->at(0);
}
if(vecVar4->size()>1){
var5 = vecVar4->at(1);
}
var6 = dVar6;
var7 = dVar7;
var8 = intVar8;
var9 = intVar9;
var10 = dVar10;
var11 = dVar11;
var12 = dVar12;
var13 = dVar13;
var14 = dVar14;
var15 = dVar15;
if(vecVar16->size()>0){
var16 = vecVar16->at(0);
}
else{
var16 = 0;
}
if(vecVar17->size()>0){
var17 = vecVar17->at(0);
}
else{
var18 = 0;
}
if(vecVar19->size()>0){
var19 = vecVar19->at(0);
}
else{
var19 = 0;
}
if(vecVar20->size()>0){
var20 = vecVar20->at(0);
}
else{
var20 = 0;
}
if(vecVar16->size()>1){
var21 = vecVar16->at(1);
}
else{
var21 = 0;
}
var22 = dVar22;
var23 = dVar23;
var24 = dVar24;
var25 = dVar25;
var26 = dVar26;
var27 = dVar27;
var28 = dVar28;
var29 = dVar29; // --- Return the MVA outputs and fill into histograms
if (Use["CutsGA"]) {
// Cuts is a special case: give the desired signal efficienciy
Bool_t passed = reader->EvaluateMVA( "CutsGA method", effS );
if (passed) nSelCutsGA++;
}
BDT = reader->EvaluateMVA( "BDT method");
histBdt->Fill(BDT);
branchBDT->Fill();
}
// Get elapsed time
sw.Stop();
std::cout << "--- End of event loop: "; sw.Print();
// Get efficiency for cuts classifier
if (Use["CutsGA"]) std::cout << "--- Efficiency for CutsGA method: " << double(nSelCutsGA)/theTree->GetEntries()
<< " (for a required signal efficiency of " << effS << ")" << std::endl;
if (Use["CutsGA"]) {
// test: retrieve cuts for particular signal efficiency
// CINT ignores dynamic_casts so we have to use a cuts-secific Reader function to acces the pointer
TMVA::MethodCuts* mcuts = reader->FindCutsMVA( "CutsGA method" ) ;
if (mcuts) {
std::vector<Double_t> cutsMin;
std::vector<Double_t> cutsMax;
mcuts->GetCuts( 0.7, cutsMin, cutsMax );
std::cout << "--- -------------------------------------------------------------" << std::endl;
std::cout << "--- Retrieve cut values for signal efficiency of 0.7 from Reader" << std::endl;
for (UInt_t ivar=0; ivar<cutsMin.size(); ivar++) {
std::cout << "... Cut: "
<< cutsMin[ivar]
<< " < \""
<< mcuts->GetInputVar(ivar)
<< "\" <= "
<< cutsMax[ivar] << std::endl;
}
std::cout << "--- -------------------------------------------------------------" << std::endl;
}
}
// --- Write histograms
newTree->Write("",TObject::kOverwrite);
target->Close();
std::cout << "--- Created root file: \""<<oFileName<<"\" containing the MVA output histograms" << std::endl;
delete reader;
std::cout << "==> TMVAClassificationApplication is done!" << endl << std::endl;
}
void TMVAClassificationApplication_new(TString myMethodList = "" , TString iFileName = "", TString bkgSample = "", TString sampleLocation = "", TString massPoint = "", TString oFileLocation = "")
{
#ifdef __CINT__
gROOT->ProcessLine( ".O0" ); // turn off optimization in CINT
#endif
//---------------------------------------------------------------
// This loads the library
TMVA::Tools::Instance();
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
// --- Cut optimisation
Use["Cuts"] = 0;
Use["CutsD"] = 0;
Use["CutsPCA"] = 0;
Use["CutsGA"] = 0;
Use["CutsSA"] = 0;
//
//
// --- Boosted Decision Trees
Use["BDT"] = 1; // uses Adaptive Boost
std::cout << std::endl;
std::cout << "==> Start TMVAClassificationApplication" << std::endl;
// Select methods (don't look at this code - not of interest)
if (myMethodList != "") {
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0;
std::vector<TString> mlist = gTools().SplitString( myMethodList, ',' );
for (UInt_t i=0; i<mlist.size(); i++) {
std::string regMethod(mlist[i]);
if (Use.find(regMethod) == Use.end()) {
std::cout << "Method \"" << regMethod
<< "\" not known in TMVA under this name. Choose among the following:" << std::endl;
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
std::cout << it->first << " ";
}
std::cout << std::endl;
return;
}
Use[regMethod] = 1;
}
}
// --------------------------------------------------------------------------------------------------
// --- Create the Reader object
TMVA::Reader *reader = new TMVA::Reader("!Color:!Silent" );
TString weightTail = "_";
weightTail = weightTail + massPoint;
// Create a set of variables and declare them to the reader
// - the variable names MUST corresponds in name and type to those given in the weight file(s) used
Float_t var1, var2, var3, var4, var5, var6, var7, var8, var9, var10, var11, var12, var13, var14, var15, var16, var17, var18;
reader->AddVariable( "svMass", &var1);
reader->AddVariable( "dRTauTau", &var3 );
reader->AddVariable( "dRJJ", &var4 );
// reader->AddVariable( "svPt", &var5 );
// reader->AddVariable( "dRhh", &var6 );
reader->AddVariable( "met", &var7 );
reader->AddVariable( "mJJ", &var8 );
// reader->AddVariable( "metTau1DPhi", &var9 );
// reader->AddVariable( "metTau2DPhi", &var10);
// reader->AddVariable( "metJ1DPhi", &var11);
// reader->AddVariable( "metJ2DPhi", &var12 );
// reader->AddVariable( "metTauPairDPhi", &var13 );
// reader->AddVariable( "metSvTauPairDPhi", &var14 );
// reader->AddVariable( "metJetPairDPhi", &var15 );
// reader->AddVariable( "CSVJ1", &var16 );
// reader->AddVariable( "CSVJ2", &var17 );
reader->AddVariable( "fMassKinFit", &var2 );
reader->AddVariable( "chi2KinFit2", &var18 );
// Spectator variables declared in the training have to be added to the reader, too
// Float_t spec1,spec2;
// reader->AddSpectator( "spec1 := var1*2", &spec1 );
// reader->AddSpectator( "spec2 := var1*3", &spec2 );
// Float_t Category_cat1, Category_cat2, Category_cat3;
// if (Use["Category"]){
// // Add artificial spectators for distinguishing categories
// reader->AddSpectator( "Category_cat1 := var3<=0", &Category_cat1 );
// reader->AddSpectator( "Category_cat2 := (var3>0)&&(var4<0)", &Category_cat2 );
// reader->AddSpectator( "Category_cat3 := (var3>0)&&(var4>=0)", &Category_cat3 );
// }
// --- Book the MVA methods
// Book method(s)
TString weightFileName = "/nfs_scratch/zmao/test/CMSSW_5_3_15/src/TMVA-v4.2.0/test/weights/TMVAClassification_BDT.weights_";
weightFileName += bkgSample;
weightFileName += weightTail;
reader->BookMVA("BDT method", weightFileName+".xml" );
// Book output histograms
UInt_t nbin = 200;
TH1F *histBdt(0);
histBdt = new TH1F( "MVA_BDT", "MVA_BDT", nbin, -1.0, 1.0);
// Prepare input tree (this must be replaced by your data source)
// in this example, there is a toy tree with signal and one with background events
// we'll later on use only the "signal" events for the test in this example.
//
TFile *input(0);
TString fileName = iFileName;
TString fname = sampleLocation;
fname += fileName;
TString oFileName = oFileLocation;
oFileName += "ClassApp_" + bkgSample;
oFileName += "_";
oFileName += fileName;
if (!gSystem->AccessPathName( fname ))
input = TFile::Open(fname); // check if file in local directory exists
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
std::cout << "--- TMVAClassificationApp : Using input file: " << input->GetName() << std::endl;
// --- Event loop
// Prepare the event tree
// - here the variable names have to corresponds to your tree
// - you can use the same variables as above which is slightly faster,
// but of course you can use different ones and copy the values inside the event loop
//
std::cout << "--- Select signal sample" << std::endl;
TTree* theTree = (TTree*)input->Get("eventTree");
TFile *target = new TFile( oFileName,"RECREATE" );
TTree *newTree = theTree->CloneTree();
Float_t BDT;
TBranch *branchBDT = newTree->Branch("BDT_"+bkgSample,&BDT,"BDT/F");
std::vector<Double_t> *vecVar1;
std::vector<Double_t> *vecVar5;
std::vector<Double_t> *vecVar7;
theTree->SetBranchAddress( "svMass", &vecVar1);
theTree->SetBranchAddress( "dRTauTau", &var3);
theTree->SetBranchAddress( "dRJJ", &var4 );
// theTree->SetBranchAddress( "svPt", &vecVar5 );
// theTree->SetBranchAddress( "dRhh", &var6 );
theTree->SetBranchAddress( "met", &vecVar7 );
theTree->SetBranchAddress( "mJJ", &var8 );
// theTree->SetBranchAddress( "metTau1DPhi", &var9 );
// theTree->SetBranchAddress( "metTau2DPhi", &var10);
// theTree->SetBranchAddress( "metJ1DPhi", &var11);
// theTree->SetBranchAddress( "metJ2DPhi", &var12 );
// theTree->SetBranchAddress( "metTauPairDPhi", &var13 );
// theTree->SetBranchAddress( "metSvTauPairDPhi", &var14 );
// theTree->SetBranchAddress( "metJetPairDPhi", &var15 );
// theTree->SetBranchAddress( "CSVJ1", &var16 );
// theTree->SetBranchAddress( "CSVJ2", &var17 );
theTree->SetBranchAddress( "fMassKinFit", &var2);
theTree->SetBranchAddress( "chi2KinFit2", &var18);
//to get initial pre-processed events
TH1F* cutFlow = (TH1F*)input->Get("preselection");
// Efficiency calculator for cut method
Int_t nSelCutsGA = 0;
Double_t effS = 0.7;
std::vector<Float_t> vecVar(4); // vector for EvaluateMVA tests
std::cout << "--- Processing: " << theTree->GetEntries() << " events" << std::endl;
TStopwatch sw;
sw.Start();
for (Long64_t ievt=0; ievt<theTree->GetEntries();ievt++) {
if (ievt%1000 == 0) std::cout << "--- ... Processing event: " << ievt << std::endl;
theTree->GetEntry(ievt);
var1 = vecVar1->at(0);
// var5 = vecVar5->at(0);
var7 = vecVar7->at(0);
// --- Return the MVA outputs and fill into histograms
if (Use["CutsGA"]) {
// Cuts is a special case: give the desired signal efficienciy
Bool_t passed = reader->EvaluateMVA( "CutsGA method", effS );
if (passed) nSelCutsGA++;
}
BDT = reader->EvaluateMVA( "BDT method");
histBdt->Fill(BDT);
branchBDT->Fill();
}
// Get elapsed time
sw.Stop();
std::cout << "--- End of event loop: "; sw.Print();
// Get efficiency for cuts classifier
if (Use["CutsGA"]) std::cout << "--- Efficiency for CutsGA method: " << double(nSelCutsGA)/theTree->GetEntries()
<< " (for a required signal efficiency of " << effS << ")" << std::endl;
if (Use["CutsGA"]) {
// test: retrieve cuts for particular signal efficiency
// CINT ignores dynamic_casts so we have to use a cuts-secific Reader function to acces the pointer
TMVA::MethodCuts* mcuts = reader->FindCutsMVA( "CutsGA method" ) ;
if (mcuts) {
std::vector<Double_t> cutsMin;
std::vector<Double_t> cutsMax;
mcuts->GetCuts( 0.7, cutsMin, cutsMax );
std::cout << "--- -------------------------------------------------------------" << std::endl;
std::cout << "--- Retrieve cut values for signal efficiency of 0.7 from Reader" << std::endl;
for (UInt_t ivar=0; ivar<cutsMin.size(); ivar++) {
std::cout << "... Cut: "
<< cutsMin[ivar]
<< " < \""
<< mcuts->GetInputVar(ivar)
<< "\" <= "
<< cutsMax[ivar] << std::endl;
}
std::cout << "--- -------------------------------------------------------------" << std::endl;
}
}
// --- Write histograms
histBdt->Write();
cutFlow->Write();
newTree->Write();
target->Close();
std::cout << "--- Created root file: \""<<oFileName<<"\" containing the MVA output histograms" << std::endl;
delete reader;
std::cout << "==> TMVAClassificationApplication is done!" << endl << std::endl;
}
void TMVAClassificationApplication( TString myMethodList = "" )
{
//---------------------------------------------------------------
// default MVA methods to be trained + tested
// this loads the library
TMVA::Tools::Instance();
std::map<std::string,int> Use;
Use["CutsGA"] = 0; // other "Cuts" methods work identically
// ---
Use["Likelihood"] = 1;
Use["LikelihoodD"] = 0; // the "D" extension indicates decorrelated input variables (see option strings)
Use["LikelihoodPCA"] = 0; // the "PCA" extension indicates PCA-transformed input variables (see option strings)
Use["LikelihoodKDE"] = 0;
Use["LikelihoodMIX"] = 0;
// ---
Use["PDERS"] = 0;
Use["PDERSD"] = 0;
Use["PDERSPCA"] = 0;
Use["PDERSkNN"] = 0; // depreciated until further notice
Use["PDEFoam"] = 0;
// --
Use["KNN"] = 0;
// ---
Use["HMatrix"] = 0;
Use["Fisher"] = 0;
Use["FisherG"] = 0;
Use["BoostedFisher"] = 0;
Use["LD"] = 0;
// ---
Use["FDA_GA"] = 0;
Use["FDA_SA"] = 0;
Use["FDA_MC"] = 0;
Use["FDA_MT"] = 0;
Use["FDA_GAMT"] = 0;
Use["FDA_MCMT"] = 0;
// ---
Use["MLP"] = 0; // this is the recommended ANN
Use["MLPBFGS"] = 0; // recommended ANN with optional training method
Use["MLPBNN"] = 0; //
Use["CFMlpANN"] = 0; // *** missing
Use["TMlpANN"] = 0;
// ---
Use["SVM"] = 0;
// ---
Use["BDT"] = 1;
Use["BDTD"] = 0;
Use["BDTG"] = 0;
Use["BDTB"] = 0;
// ---
Use["RuleFit"] = 0;
// ---
Use["Category"] = 0;
// ---
Use["Plugin"] = 0;
// ---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVAClassificationApplication" << std::endl;
if (myMethodList != "") {
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0;
std::vector<TString> mlist = gTools().SplitString( myMethodList, ',' );
for (UInt_t i=0; i<mlist.size(); i++) {
std::string regMethod(mlist[i]);
if (Use.find(regMethod) == Use.end()) {
std::cout << "Method \"" << regMethod << "\" not known in TMVA under this name. Choose among the following:" << std::endl;
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) std::cout << it->first << " ";
std::cout << std::endl;
return;
}
Use[regMethod] = 0;
}
}
//
// create the Reader object
//
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
Float_t Z_rapidity_z;
reader->AddVariable("Z_rapidity_z",&Z_rapidity_z);
Float_t THRUST_2D;
reader->AddVariable("THRUST_2D",&THRUST_2D);
Float_t L1_L2_cosangle;
reader->AddVariable("L1_L2_cosangle",&L1_L2_cosangle);
Float_t TransMass_ZH150_uncl;
reader->AddVariable("TransMass_ZH150_uncl",&TransMass_ZH150_uncl);
Float_t TransMass_ZH150;
reader->AddVariable("TransMass_ZH150",&TransMass_ZH150);
Float_t DeltaPhi_ZH;
reader->AddVariable("DeltaPhi_ZH",&DeltaPhi_ZH);
Float_t DeltaPhi_ZH_uncl;
reader->AddVariable("DeltaPhi_ZH_uncl",&DeltaPhi_ZH_uncl);
Float_t CMAngle;
reader->AddVariable("CMAngle",&CMAngle);
Float_t CS_cosangle;
reader->AddVariable("CS_cosangle",&CS_cosangle);
// create a set of variables and declare them to the reader
// - the variable names must corresponds in name and type to
// those given in the weight file(s) that you use
Float_t var1, var2;
Float_t var3, var4;
// reader->AddVariable( "myvar1 := var1+var2", &var1 );
// reader->AddVariable( "myvar2 := var1-var2", &var2 );
// reader->AddVariable( "var3", &var3 );
// reader->AddVariable( "var4", &var4 );
//Spectator variables declared in the training have to be added to the reader, too
Float_t spec1,spec2;
// reader->AddSpectator( "spec1 := var1*2", &spec1 );
float nonsense =0;
// reader->AddSpectator( "spec2 := var1*3", &spec2 );
float nonsense =0;
Float_t Category_cat1, Category_cat2, Category_cat3;
if (Use["Category"]){
// add artificial spectators for distinguishing categories
// reader->AddSpectator( "Category_cat1 := var3<=0", &Category_cat1 );
float nonsense =0;
// reader->AddSpectator( "Category_cat2 := (var3>0)&&(var4<0)", &Category_cat2 );
float nonsense =0;
// reader->AddSpectator( "Category_cat3 := (var3>0)&&(var4>=0)", &Category_cat3 );
float nonsense =0;
}
//
// book the MVA methods
//
TString dir = "weights/";
TString prefix = "TMVAClassification";
// book method(s)
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
if (it->second) {
TString methodName = it->first + " method";
TString weightfile = dir + prefix + "_" + TString(it->first) + ".weights.xml";
reader->BookMVA( methodName, weightfile );
}
}
// example how to use your own method as plugin
if (Use["Plugin"]) {
// the weight file contains a line
// Method : MethodName::InstanceName
// if MethodName is not a known TMVA method, it is assumed to be
// a user implemented method which has to be loaded via the
// plugin mechanism
// for user implemented methods the line in the weight file can be
// Method : PluginName::InstanceName
// where PluginName can be anything
// before usage the plugin has to be defined, which can happen
// either through the following line in .rootrc:
// # plugin handler plugin class library constructor format
// Plugin.TMVA@@MethodBase: PluginName MethodClassName UserPackage "MethodName(DataSet&,TString)"
//
// or by telling the global plugin manager directly
gPluginMgr->AddHandler("TMVA@@MethodBase", "PluginName", "MethodClassName", "UserPackage", "MethodName(DataSet&,TString)");
// the class is then looked for in libUserPackage.so
// now the method can be booked like any other
reader->BookMVA( "User method", dir + prefix + "_User.weights.txt" );
}
// book output histograms
UInt_t nbin = 100;
TH1F *histLk(0), *histLkD(0), *histLkPCA(0), *histLkKDE(0), *histLkMIX(0), *histPD(0), *histPDD(0);
TH1F *histPDPCA(0), *histPDEFoam(0), *histPDEFoamErr(0), *histPDEFoamSig(0), *histKNN(0), *histHm(0);
TH1F *histFi(0), *histFiG(0), *histFiB(0), *histLD(0), *histNn(0),*histNnbfgs(0),*histNnbnn(0), *histNnC(0), *histNnT(0), *histBdt(0), *histBdtG(0), *histBdtD(0);
TH1F *histRf(0), *histSVMG(0), *histSVMP(0), *histSVML(0), *histFDAMT(0), *histFDAGA(0), *histCat(0), *histPBdt(0);
if (Use["Likelihood"]) histLk = new TH1F( "MVA_Likelihood", "MVA_Likelihood", nbin, -1, 1 );
if (Use["LikelihoodD"]) histLkD = new TH1F( "MVA_LikelihoodD", "MVA_LikelihoodD", nbin, -1, 0.9999 );
if (Use["LikelihoodPCA"]) histLkPCA = new TH1F( "MVA_LikelihoodPCA", "MVA_LikelihoodPCA", nbin, -1, 1 );
if (Use["LikelihoodKDE"]) histLkKDE = new TH1F( "MVA_LikelihoodKDE", "MVA_LikelihoodKDE", nbin, -0.00001, 0.99999 );
if (Use["LikelihoodMIX"]) histLkMIX = new TH1F( "MVA_LikelihoodMIX", "MVA_LikelihoodMIX", nbin, 0, 1 );
if (Use["PDERS"]) histPD = new TH1F( "MVA_PDERS", "MVA_PDERS", nbin, 0, 1 );
if (Use["PDERSD"]) histPDD = new TH1F( "MVA_PDERSD", "MVA_PDERSD", nbin, 0, 1 );
if (Use["PDERSPCA"]) histPDPCA = new TH1F( "MVA_PDERSPCA", "MVA_PDERSPCA", nbin, 0, 1 );
if (Use["KNN"]) histKNN = new TH1F( "MVA_KNN", "MVA_KNN", nbin, 0, 1 );
if (Use["HMatrix"]) histHm = new TH1F( "MVA_HMatrix", "MVA_HMatrix", nbin, -0.95, 1.55 );
if (Use["Fisher"]) histFi = new TH1F( "MVA_Fisher", "MVA_Fisher", nbin, -4, 4 );
if (Use["FisherG"]) histFiG = new TH1F( "MVA_FisherG", "MVA_FisherG", nbin, -1, 1 );
if (Use["BoostedFisher"]) histFiB = new TH1F( "MVA_BoostedFisher", "MVA_BoostedFisher", nbin, -2, 2 );
if (Use["LD"]) histLD = new TH1F( "MVA_LD", "MVA_LD", nbin, -2, 2 );
if (Use["MLP"]) histNn = new TH1F( "MVA_MLP", "MVA_MLP", nbin, -1.25, 1.5 );
if (Use["MLPBFGS"]) histNnbfgs = new TH1F( "MVA_MLPBFGS", "MVA_MLPBFGS", nbin, -1.25, 1.5 );
if (Use["MLPBNN"]) histNnbnn = new TH1F( "MVA_MLPBNN", "MVA_MLPBNN", nbin, -1.25, 1.5 );
if (Use["CFMlpANN"]) histNnC = new TH1F( "MVA_CFMlpANN", "MVA_CFMlpANN", nbin, 0, 1 );
if (Use["TMlpANN"]) histNnT = new TH1F( "MVA_TMlpANN", "MVA_TMlpANN", nbin, -1.3, 1.3 );
if (Use["BDT"]) histBdt = new TH1F( "MVA_BDT", "MVA_BDT", nbin, -0.8, 0.8 );
if (Use["BDTD"]) histBdtD = new TH1F( "MVA_BDTD", "MVA_BDTD", nbin, -0.8, 0.8 );
if (Use["BDTG"]) histBdtG = new TH1F( "MVA_BDTG", "MVA_BDTG", nbin, -1.0, 1.0 );
if (Use["RuleFit"]) histRf = new TH1F( "MVA_RuleFit", "MVA_RuleFit", nbin, -2.0, 2.0 );
if (Use["SVM_Gauss"]) histSVMG = new TH1F( "MVA_SVM_Gauss", "MVA_SVM_Gauss", nbin, 0.0, 1.0 );
if (Use["SVM_Poly"]) histSVMP = new TH1F( "MVA_SVM_Poly", "MVA_SVM_Poly", nbin, 0.0, 1.0 );
if (Use["SVM_Lin"]) histSVML = new TH1F( "MVA_SVM_Lin", "MVA_SVM_Lin", nbin, 0.0, 1.0 );
if (Use["FDA_MT"]) histFDAMT = new TH1F( "MVA_FDA_MT", "MVA_FDA_MT", nbin, -2.0, 3.0 );
if (Use["FDA_GA"]) histFDAGA = new TH1F( "MVA_FDA_GA", "MVA_FDA_GA", nbin, -2.0, 3.0 );
if (Use["Category"]) histCat = new TH1F( "MVA_Category", "MVA_Category", nbin, -2., 2. );
if (Use["Plugin"]) histPBdt = new TH1F( "MVA_PBDT", "MVA_BDT", nbin, -0.8, 0.8 );
// PDEFoam also returns per-event error, fill in histogram, and also fill significance
if (Use["PDEFoam"]) {
histPDEFoam = new TH1F( "MVA_PDEFoam", "MVA_PDEFoam", nbin, 0, 1 );
histPDEFoamErr = new TH1F( "MVA_PDEFoamErr", "MVA_PDEFoam error", nbin, 0, 1 );
histPDEFoamSig = new TH1F( "MVA_PDEFoamSig", "MVA_PDEFoam significance", nbin, 0, 10 );
}
// book example histogram for probability (the other methods are done similarly)
TH1F *probHistFi(0), *rarityHistFi(0);
if (Use["Fisher"]) {
probHistFi = new TH1F( "MVA_Fisher_Proba", "MVA_Fisher_Proba", nbin, 0, 1 );
rarityHistFi = new TH1F( "MVA_Fisher_Rarity", "MVA_Fisher_Rarity", nbin, 0, 1 );
}
// Prepare input tree (this must be replaced by your data source)
// in this example, there is a toy tree with signal and one with background events
// we'll later on use only the "signal" events for the test in this example.
//
TFile *input(0);
TString fname = "/tmp/chasco/ORIGINAL//Data_MuEG2011B_1.root";
if (!gSystem->AccessPathName( fname )) {
input = TFile::Open( fname ); // check if file in local directory exists
}
else {
input = TFile::Open( "http://root.cern.ch/files/tmva_class_example.root" ); // if not: download from ROOT server
}
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
std::cout << "--- TMVAClassificationApp : Using input file: " << input->GetName() << std::endl;
//
// prepare the tree
// - here the variable names have to corresponds to your tree
// - you can use the same variables as above which is slightly faster,
// but of course you can use different ones and copy the values inside the event loop
//
TTree* BigTree = (TTree*)input->Get("data");
TFile *tmp = new TFile( "tmp.root","RECREATE" );
TTree* theTree = BigTree->CopyTree("((cat == 1) + (cat == 2))*(ln==0)*(Cosmic==0)*(fabs(Mass_Z - 91.18)<10)*(Pt_Z>30)*(DeltaPhi_metjet>0.5)*(Pt_J1 < 30)*(pfMEToverPt_Z > 0.4)*(pfMEToverPt_Z < 1.8)*((Pt_Jet_btag_CSV_max > 20)*(btag_CSV_max < 0.244) + (1-(Pt_Jet_btag_CSV_max > 20)))*(sqrt(pow(dilepPROJLong + 1.25*recoilPROJLong + 0.0*uncertPROJLong,2)*(dilepPROJLong + 1.25*recoilPROJLong + 0.0*uncertPROJLong > 0) + 1.0*pow(dilepPROJPerp + 1.25*recoilPROJPerp + 0.0*uncertPROJPerp,2)*(dilepPROJPerp + 1.25*recoilPROJPerp + 0.0*uncertPROJPerp > 0)) > 45.0)");
std::cout << "--- Select signal sample" << std::endl;
Float_t userVar1, userVar2;
// theTree->SetBranchAddress( "var1", &userVar1 );
// theTree->SetBranchAddress( "var2", &userVar2 );
// theTree->SetBranchAddress( "var3", &var3 );
// theTree->SetBranchAddress( "var4", &var4 );
theTree->SetBranchAddress( " Z_rapidity_z", &Z_rapidity_z);
theTree->SetBranchAddress( " THRUST_2D", &THRUST_2D);
theTree->SetBranchAddress( " L1_L2_cosangle", &L1_L2_cosangle);
theTree->SetBranchAddress( " TransMass_ZH150_uncl", &TransMass_ZH150_uncl);
theTree->SetBranchAddress( " TransMass_ZH150", &TransMass_ZH150);
theTree->SetBranchAddress( " DeltaPhi_ZH", &DeltaPhi_ZH);
theTree->SetBranchAddress( " DeltaPhi_ZH_uncl", &DeltaPhi_ZH_uncl);
theTree->SetBranchAddress( " CMAngle", &CMAngle);
theTree->SetBranchAddress( " CS_cosangle", &CS_cosangle);
// efficiency calculator for cut method
Int_t nSelCutsGA = 0;
Double_t effS = 0.7;
std::vector<Float_t> vecVar(9); // vector for EvaluateMVA tests
std::cout << "--- Processing: " << theTree->GetEntries() << " events" << std::endl;
TStopwatch sw;
sw.Start();
for (Long64_t ievt=0; ievt<theTree->GetEntries();ievt++) {
if (ievt%1000 == 0){
std::cout << "--- ... Processing event: " << ievt << std::endl;
}
theTree->GetEntry(ievt);
var1 = userVar1 + userVar2;
var2 = userVar1 - userVar2;
if (ievt <20){
// test the twodifferent Reader::EvaluateMVA functions
// access via registered variables compared to access via vector<float>
// vecVar[0]=var1;
// vecVar[1]=var2;
// vecVar[2]=var3;
// vecVar[3]=var4;
vecVar[0]=Z_rapidity_z;
vecVar[1]=THRUST_2D;
vecVar[2]=L1_L2_cosangle;
vecVar[3]=TransMass_ZH150_uncl;
vecVar[4]=TransMass_ZH150;
vecVar[5]=DeltaPhi_ZH;
vecVar[6]=DeltaPhi_ZH_uncl;
vecVar[7]=CMAngle;
vecVar[8]=CS_cosangle;
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
if (it->second) {
TString mName = it->first + " method";
Double_t mva1 = reader->EvaluateMVA( mName);
Double_t mva2 = reader->EvaluateMVA( vecVar, mName);
if (mva1 != mva2) {
std::cout << "++++++++++++++ ERROR in "<< mName <<", comparing different EvaluateMVA results val1=" << mva1 << " val2="<<mva2<<std::endl;
}
}
}
// now test that the inputs do matter
TRandom3 rand(0);
// vecVar[0]=rand.Rndm();
// vecVar[1]=rand.Rndm();
// vecVar[2]=rand.Rndm();
// vecVar[3]=rand.Rndm();
vecVar[0]=rand.Rndm();
vecVar[1]=rand.Rndm();
vecVar[2]=rand.Rndm();
vecVar[3]=rand.Rndm();
vecVar[4]=rand.Rndm();
vecVar[5]=rand.Rndm();
vecVar[6]=rand.Rndm();
vecVar[7]=rand.Rndm();
vecVar[8]=rand.Rndm();
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
if (it->second) {
TString mName = it->first + " method";
Double_t mva1 = reader->EvaluateMVA( mName);
Double_t mva2 = reader->EvaluateMVA( vecVar, mName);
if (mva1 == mva2) {
std::cout << "++++++++++++++ ERROR in "<< mName <<", obtaining idnetical output for different inputs" <<std::endl;
}
}
}
}
//
// return the MVAs and fill to histograms
//
if (Use["CutsGA"]) {
// Cuts is a special case: give the desired signal efficienciy
Bool_t passed = reader->EvaluateMVA( "CutsGA method", effS );
if (passed) nSelCutsGA++;
}
if (Use["Likelihood" ]) histLk ->Fill( reader->EvaluateMVA( "Likelihood method" ) );
if (Use["LikelihoodD" ]) histLkD ->Fill( reader->EvaluateMVA( "LikelihoodD method" ) );
if (Use["LikelihoodPCA"]) histLkPCA ->Fill( reader->EvaluateMVA( "LikelihoodPCA method" ) );
if (Use["LikelihoodKDE"]) histLkKDE ->Fill( reader->EvaluateMVA( "LikelihoodKDE method" ) );
if (Use["LikelihoodMIX"]) histLkMIX ->Fill( reader->EvaluateMVA( "LikelihoodMIX method" ) );
if (Use["PDERS" ]) histPD ->Fill( reader->EvaluateMVA( "PDERS method" ) );
if (Use["PDERSD" ]) histPDD ->Fill( reader->EvaluateMVA( "PDERSD method" ) );
if (Use["PDERSPCA" ]) histPDPCA ->Fill( reader->EvaluateMVA( "PDERSPCA method" ) );
if (Use["KNN" ]) histKNN ->Fill( reader->EvaluateMVA( "KNN method" ) );
if (Use["HMatrix" ]) histHm ->Fill( reader->EvaluateMVA( "HMatrix method" ) );
if (Use["Fisher" ]) histFi ->Fill( reader->EvaluateMVA( "Fisher method" ) );
if (Use["FisherG" ]) histFiG ->Fill( reader->EvaluateMVA( "FisherG method" ) );
if (Use["BoostedFisher"]) histFiB ->Fill( reader->EvaluateMVA( "BoostedFisher method" ) );
if (Use["LD" ]) histLD ->Fill( reader->EvaluateMVA( "LD method" ) );
if (Use["MLP" ]) histNn ->Fill( reader->EvaluateMVA( "MLP method" ) );
if (Use["MLPBFGS" ]) histNnbfgs ->Fill( reader->EvaluateMVA( "MLPBFGS method" ) );
if (Use["MLPBNN" ]) histNnbnn ->Fill( reader->EvaluateMVA( "MLPBNN method" ) );
if (Use["CFMlpANN" ]) histNnC ->Fill( reader->EvaluateMVA( "CFMlpANN method" ) );
if (Use["TMlpANN" ]) histNnT ->Fill( reader->EvaluateMVA( "TMlpANN method" ) );
if (Use["BDT" ]) histBdt ->Fill( reader->EvaluateMVA( "BDT method" ) );
if (Use["BDTD" ]) histBdtD ->Fill( reader->EvaluateMVA( "BDTD method" ) );
if (Use["BDTG" ]) histBdtG ->Fill( reader->EvaluateMVA( "BDTG method" ) );
if (Use["RuleFit" ]) histRf ->Fill( reader->EvaluateMVA( "RuleFit method" ) );
if (Use["SVM_Gauss" ]) histSVMG ->Fill( reader->EvaluateMVA( "SVM_Gauss method" ) );
if (Use["SVM_Poly" ]) histSVMP ->Fill( reader->EvaluateMVA( "SVM_Poly method" ) );
if (Use["SVM_Lin" ]) histSVML ->Fill( reader->EvaluateMVA( "SVM_Lin method" ) );
if (Use["FDA_MT" ]) histFDAMT ->Fill( reader->EvaluateMVA( "FDA_MT method" ) );
if (Use["FDA_GA" ]) histFDAGA ->Fill( reader->EvaluateMVA( "FDA_GA method" ) );
if (Use["Category" ]) histCat ->Fill( reader->EvaluateMVA( "Category method" ) );
if (Use["Plugin" ]) histPBdt ->Fill( reader->EvaluateMVA( "P_BDT method" ) );
// retrieve also per-event error
if (Use["PDEFoam"]) {
Double_t val = reader->EvaluateMVA( "PDEFoam method" );
Double_t err = reader->GetMVAError();
histPDEFoam ->Fill( val );
histPDEFoamErr->Fill( err );
histPDEFoamSig->Fill( val/err );
}
// retrieve probability instead of MVA output
if (Use["Fisher"]) {
probHistFi ->Fill( reader->GetProba ( "Fisher method" ) );
rarityHistFi->Fill( reader->GetRarity( "Fisher method" ) );
}
}
// get elapsed time
sw.Stop();
std::cout << "--- End of event loop: "; sw.Print();
// get efficiency for cuts classifier
if (Use["CutsGA"]) std::cout << "--- Efficiency for CutsGA method: " << double(nSelCutsGA)/theTree->GetEntries()
<< " (for a required signal efficiency of " << effS << ")" << std::endl;
if (Use["CutsGA"]) {
// test: retrieve cuts for particular signal efficiency
// CINT ignores dynamic_casts so we have to use a cuts-secific Reader function to acces the pointer
TMVA::MethodCuts* mcuts = reader->FindCutsMVA( "CutsGA method" ) ;
if (mcuts) {
std::vector<Double_t> cutsMin;
std::vector<Double_t> cutsMax;
mcuts->GetCuts( 0.7, cutsMin, cutsMax );
std::cout << "--- -------------------------------------------------------------" << std::endl;
std::cout << "--- Retrieve cut values for signal efficiency of 0.7 from Reader" << std::endl;
for (UInt_t ivar=0; ivar<cutsMin.size(); ivar++) {
std::cout << "... Cut: "
<< cutsMin[ivar]
<< " < \""
<< mcuts->GetInputVar(ivar)
<< "\" <= "
<< cutsMax[ivar] << std::endl;
}
std::cout << "--- -------------------------------------------------------------" << std::endl;
}
}
//
// write histograms
//
TFile *target = new TFile( "TMVApp.root","RECREATE" );
if (Use["Likelihood" ]) histLk ->Write();
if (Use["LikelihoodD" ]) histLkD ->Write();
if (Use["LikelihoodPCA"]) histLkPCA ->Write();
if (Use["LikelihoodKDE"]) histLkKDE ->Write();
if (Use["LikelihoodMIX"]) histLkMIX ->Write();
if (Use["PDERS" ]) histPD ->Write();
if (Use["PDERSD" ]) histPDD ->Write();
if (Use["PDERSPCA" ]) histPDPCA ->Write();
if (Use["KNN" ]) histKNN ->Write();
if (Use["HMatrix" ]) histHm ->Write();
if (Use["Fisher" ]) histFi ->Write();
if (Use["FisherG" ]) histFiG ->Write();
if (Use["BoostedFisher"]) histFiB ->Write();
if (Use["LD" ]) histLD ->Write();
if (Use["MLP" ]) histNn ->Write();
if (Use["MLPBFGS" ]) histNnbfgs ->Write();
if (Use["MLPBNN" ]) histNnbnn ->Write();
if (Use["CFMlpANN" ]) histNnC ->Write();
if (Use["TMlpANN" ]) histNnT ->Write();
if (Use["BDT" ]) histBdt ->Write();
if (Use["BDTD" ]) histBdtD ->Write();
if (Use["BDTG" ]) histBdtG ->Write();
if (Use["RuleFit" ]) histRf ->Write();
if (Use["SVM_Gauss" ]) histSVMG ->Write();
if (Use["SVM_Poly" ]) histSVMP ->Write();
if (Use["SVM_Lin" ]) histSVML ->Write();
if (Use["FDA_MT" ]) histFDAMT ->Write();
if (Use["FDA_GA" ]) histFDAGA ->Write();
if (Use["Category" ]) histCat ->Write();
if (Use["Plugin" ]) histPBdt ->Write();
// write also error and significance histos
if (Use["PDEFoam"]) { histPDEFoam->Write(); histPDEFoamErr->Write(); histPDEFoamSig->Write(); }
// write also probability hists
if (Use["Fisher"]) { if (probHistFi != 0) probHistFi->Write(); if (rarityHistFi != 0) rarityHistFi->Write(); }
target->Close();
std::cout << "--- Created root file: \"TMVApp.root\" containing the MVA output histograms" << std::endl;
delete reader;
std::cout << "==> TMVAClassificationApplication is done!" << endl << std::endl;
}
Int_t MergeV1(TString fileNameDigits="digits.root",
TString fileNameSDigitsSig="sig.sdigits.root",
TString fileNameSDigitsBgr="bgr.sdigits.root",
Int_t nEvents = 1, Int_t iITS = 2, Int_t iTPC = 0,
Int_t iTRD = 0, Int_t iPHOS = 0, Int_t iMUON = 0,
Int_t iRICH = 0, Int_t iCopy = 1)
{
// delete the current gAlice object, the one from input file
// will be used
if(gAlice){
delete gAlice;
gAlice = 0;
} // end if gAlice
// Connect the Root Galice file containing Geometry, Kine and Hits
TFile *file = (TFile*)gROOT->GetListOfFiles()->FindObject(fileNameSDigitsSig.Data());
if(!file) file = new TFile(fileNameSDigitsSig.Data());
TDatime *ct0 = new TDatime(2002,04,26,00,00,00), ct = file->GetCreationDate();
// Get AliRun object from file or create it if not on file
if(!gAlice) {
gAlice = (AliRun*)file->Get("gAlice");
if(gAlice) printf("AliRun object found on file\n");
if(!gAlice) gAlice = new AliRun("gAlice","Alice test program");
} // end if !gAlice
AliRunDigitizer * manager = new AliRunDigitizer(2,1);
manager->SetInputStream(0,fileNameSDigitsSig.Data());
manager->SetInputStream(1,fileNameSDigitsBgr.Data());
if (fileNameDigits != "") {
// if (iCopy) {
// AliCopyN(fileNameSDigitsSig,fileNameDigits);
// }
manager->SetOutputFile(fileNameDigits);
}
manager->SetNrOfEventsToWrite(nEvents);
if (iITS) {
AliITSDigitizer *dITS = new AliITSDigitizer(manager);
if (iITS == 2) dITS->SetByRegionOfInterestFlag(1);
if(ct0->GetDate()>ct.GetDate()){
// For old files, must change SDD noise.
AliITS *ITS = (AliITS*) gAlice->GetDetector("ITS");
AliITSresponseSDD *resp1 = ITS->DetType(1)->GetResponseModel();
resp1->SetNoiseParam();
resp1->SetNoiseAfterElectronics();
Float_t n,b;
Int_t cPar[8];
resp1->GetNoiseParam(n,b);
n = resp1->GetNoiseAfterElectronics();
cPar[0]=0;
cPar[1]=0;
cPar[2]=(Int_t)(b + 2.*n + 0.5);
cPar[3]=(Int_t)(b + 2.*n + 0.5);
cPar[4]=0;
cPar[5]=0;
cPar[6]=0;
cPar[7]=0;
resp1->SetCompressParam(cPar);
} // end if
}
if (iTPC) AliTPCDigitizer *dTPC = new AliTPCDigitizer(manager);
if (iTRD) AliTRDdigitizer *dTRD = new AliTRDdigitizer(manager);
if (iPHOS) AliPHOSDigitizer *dPHOS = new AliPHOSDigitizer(manager);
if (iMUON) AliMUONDigitizer *dMUON = new AliMUONDigitizer(manager);
if (iRICH) AliRICHDigitizer *dRICH = new AliRICHDigitizer(manager);
TStopwatch timer;
timer.Start();
manager->Exec("deb all");
timer.Stop();
timer.Print();
// delete gAlice;
// gAlice = 0;
delete manager;
}
void varsig0196_4()
{
TStopwatch timer;
timer.Start();
// Define histogarams
gStyle->SetOptFit(1111);
gStyle->SetOptStat(111111);
TGraphErrors *g1 = new TGraphErrors(20);
g1->SetName("Dilvsdm");
g1->SetTitle("Dilution vs. dm");
TGraph *g2 = new TGraph(20);
g2->SetName("DilErrvsdm");
g2->SetTitle("Dil Err vs. dm");
for(Int_t i=0.;i<20.;i++){
oscpar1_init = 0.5+(0.33*i);
cout << "============== dm fixed at "<< oscpar1_init <<" ===================="<<endl;
//TH1F *h1 = new TH1F("h1","Gaussian Dist",50, 4.8, 5.8);
TH1F *h2 = new TH1F("h2","Lifetime Dist",nBins, min, max);
TH1F *h3 = new TH1F("h3","Lifetime Dist tag=1",nBins, min, max);
TH1F *h4 = new TH1F("h4","Lifetime Dist tag=-1",nBins, min, max);
// Get data
// Generate events
mixmasta_mc();
for (Int_t ja=0; ja<nEvts; ja++){
h2->Fill(lifetime[ja]);
if (tag[ja] == 1){
h3->Fill(lifetime[ja]);
}else if(tag[ja] == -1){
h4->Fill(lifetime[ja]);
}else{
cout << "Tag value "<<tag[ja]<< " out of range, should be -1 or 1" << endl;
break;
}
}
// Do unbinned likelihood fit
TF1 *f3 = new TF1("f3", lftmosc_plt_d, min, max, 5);
TF1 *f4 = new TF1("f4", lftmosc_plt_d, min, max, 5);
unbinFitosc_d();
for (Int_t j=0; j<4; j++){
f3->SetParameter(j,fitpar[j]);
f4->SetParameter(j,fitpar[j]);
}
f3->SetParameter(4,1);
f4->SetParameter(4,-1);
g1->SetPoint(i,fitpar[1],-(1-2*fitpar[3])/dilfit);
g1->SetPointError(i,fiterr[1],2*fiterr[3]/dilfit);
g2->SetPoint(i,fitpar[1],1.65*2*fiterr[3]/dilfit);
delete h2;
delete h3;
delete h4;
delete f3;
delete f4;
}
TCanvas *vardm = new TCanvas("vardm","varsig0196_4",800,400);
vardm->Divide(2,1);
vardm->cd(1);
g1->GetXaxis()->SetTitle("dm");
g1->GetXaxis()->CenterTitle();
g1->GetYaxis()->SetTitle("Dilution (1-2alpha)");
g1->GetYaxis()->CenterTitle();
//g1->SetMarkerStyle(21);
//g1->SetMarkerSize(1);
g1->Draw("AP*");
vardm->cd(2);
gStyle->SetPadColor(10);
gStyle->SetCanvasColor(10);
vardm->SetGrid();
g2->GetXaxis()->SetTitle("dm");
g2->GetXaxis()->CenterTitle();
g2->GetYaxis()->SetTitle("Dil. Err");
g2->GetYaxis()->CenterTitle();
//g2->SetMarkerStyle(21);
//g2->SetMarkerSize(1);
g2->Draw("AP*");
TObjArray a1(0);
a1.Add(g1);
a1.Add(g2);
a1.Add(vardm);
TFile var_dm("varsig0196_4.root", "recreate");
a1.Write();
var_dm.Close();
timer.Stop();
Double_t rtime = timer.RealTime();
Double_t ctime = timer.CpuTime();
cout << "Real time " << rtime << endl;
cout << "CPU time " << ctime << endl;
}
void RAA_dataDrivenUnfoldingErrorCheck(int radius = 4, int radiusPP = 4, char* algo = (char*) "Pu", char *jet_type = (char*) "PF", int unfoldingCut = 30, char* etaWidth = (char*) "n20_eta_p20", double deltaEta = 4.0){
TStopwatch timer;
timer.Start();
TH1::SetDefaultSumw2();
TH2::SetDefaultSumw2();
bool printDebug = true;
// get the data and mc histograms from the output of the read macro.
TDatime date;//this is just here to get them to run optimized.
// Raghav's files:
//TFile * fPbPb_in = TFile::Open(Form("/afs/cern.ch/work/r/rkunnawa/WORK/RAA/CMSSW_5_3_18/src/Output/PbPb_CutEfficiency_YetkinCuts_matched_slantedlinecalopfpt_addingunmatched_exclusionhighertriggers_eMaxSumcand_A_R0p%d.root",radius));
// //TFile * fPP_in = TFile::Open(Form("/afs/cern.ch/work/r/rkunnawa/WORK/RAA/CMSSW_5_3_18/src/Output/Pp_CutEfficiency_YetkinCuts_matched_slantedlinecalopfpt_addingunmatched_exclusionhighertriggers_eMaxSumcand_A_R0p%d.root",radius));
//TFile * fPP_in = TFile::Open(Form("/afs/cern.ch/work/r/rkunnawa/WORK/RAA/CMSSW_5_3_18/src/Output/Pp_CutEfficiency_noJetID_exclusionhighertriggers_A_R0p%d.root",radius));
// Pawan's files:
TFile * fPbPb_in = TFile::Open(Form("/afs/cern.ch/work/r/rkunnawa/WORK/RAA/CMSSW_5_3_18/src/Output/Pawan_ntuplehistograms/PbPb_CutEfficiency_YetkinCuts_matched_slantedlinecalopfpt_addingunmatched_exclusionhighertriggers_eMaxSumcand_A_R0p%d.root",radius));
//TFile * fPP_in = TFile::Open(Form("/afs/cern.ch/work/r/rkunnawa/WORK/RAA/CMSSW_5_3_18/src/Output/Pp_CutEfficiency_YetkinCuts_matched_slantedlinecalopfpt_addingunmatched_exclusionhighertriggers_eMaxSumcand_A_R0p%d.root",radius));
TFile * fPP_in = TFile::Open(Form("/afs/cern.ch/work/r/rkunnawa/WORK/RAA/CMSSW_5_3_18/src/Output/Pawan_ntuplehistograms/Pp_CutEfficiency_YetkinCuts_matched_slantedlinecalopfpt_addingunmatched_exclusionhighertriggers_eMaxSumcand_A_R0p%d.root",radius));
TFile * fPbPb_MB_in = TFile::Open(Form("/afs/cern.ch/work/r/rkunnawa/WORK/RAA/CMSSW_5_3_18/src/Output/PbPb_MinBiasUPC_CutEfficiency_YetkinCuts_matched_slantedlinecalopfpt_addingunmatched_exclusionhighertriggers_eMaxSumcand_A_R0p%d.root",radius));
//TH1F * htest = new TH1F("htest","",nbins_pt, boundaries_pt);
//Int_t unfoldingCutBin = htest->FindBin(unfoldingCut);
cout<<"after input file declaration"<<endl;
// need to make sure that the file names are in prefect order so that i can run them one after another.
// for the above condition, i might have to play with the date stamp.
const int nbins_cent = 6;
double boundaries_cent[nbins_cent+1] = {0,2,4,12,20,28,36};
double ncoll[nbins_cent+1] = {1660,1310,745,251,62.8,10.8,362.24};
// histogram declarations with the following initial appendage: d - Data, m - MC, u- Unfolded
// for the MC closure test, ive kept separate
// setup the radius and the eta bin loop here later. not for the time being. Aug 20th. only run the -2 < eta < 2 with the differenent centrality bins
TH1F *dPbPb_TrgComb[nbins_cent+1], *dPbPb_Comb[nbins_cent+1], *dPbPb_Trg80[nbins_cent+1], *dPbPb_Trg65[nbins_cent+1], *dPbPb_Trg55[nbins_cent+1], *dPbPb_1[nbins_cent+1], *dPbPb_2[nbins_cent+1], *dPbPb_3[nbins_cent+1], *dPbPb_80[nbins_cent+1], *dPbPb_65[nbins_cent+1], *dPbPb_55[nbins_cent+1];
TH1F *mPbPb_Gen[nbins_cent+1], *mPbPb_Reco[nbins_cent+1];
TH2F *mPbPb_Matrix[nbins_cent+1], *mPbPb_Response[nbins_cent+1], *mPbPb_ResponseNorm[nbins_cent+1];
TH1F *mPbPb_mcclosure_data[nbins_cent+1];
TH2F *mPbPb_mcclosure_Matrix[nbins_cent+1],*mPbPb_mcclosure_Response[nbins_cent+1], *mPbPb_mcclosure_ResponseNorm[nbins_cent+1];
TH1F *mPbPb_mcclosure_gen[nbins_cent+1];
const int Iterations = 20; //for unfolding systematics.
const int BayesIter = 4;
TH1F *uPbPb_Bayes[nbins_cent+1], *uPbPb_BinByBin[nbins_cent+1], *uPbPb_SVD[nbins_cent+1];
TH1F *uPbPb_BayesianIter[nbins_cent+1][Iterations];
TH1F *dPbPb_MinBias[nbins_cent];
TH1F *dPP_1, *dPP_2, *dPP_3, *dPP_Comb;
TH1F *mPP_Gen, *mPP_Reco;
TH2F *mPP_Matrix, *mPP_Response,*mPP_ResponseNorm;
TH1F *mPP_mcclosure_data;
TH2F *mPP_mcclosure_Matrix, *mPP_mcclosure_Response,*mPP_mcclosure_ResponseNorm;
TH1F *mPP_mcclosure_Gen;
TH1F *uPP_Bayes, *uPP_BinByBin, *uPP_SVD;
TH1F *uPP_BayesianIter[Iterations];
// would be better to read in the histograms and rebin them. come to think of it, it would be better to have them already rebinned (and properly scaled - to the level of differential cross section in what ever barns (inverse micro barns) but keep it consistent) from the read macro.
// get PbPb data
for(int i = 0;i<nbins_cent;i++){
if(printDebug) cout<<"cent_"<<i<<endl;
dPbPb_TrgComb[i] = (TH1F*)fPbPb_in->Get(Form("hpbpb_HLTComb_R%d_n20_eta_p20_cent%d",radius,i));
//dPbPb_TrgComb[i]->Scale(4*145.156*1e6);
dPbPb_TrgComb[i]->Print("base");
dPbPb_Trg80[i] = (TH1F*)fPbPb_in->Get(Form("hpbpb_HLT80_R%d_n20_eta_p20_cent%d",radius,i));
//dPbPb_Trg80[i]->Scale(4*145.156*1e6);
dPbPb_Trg80[i]->Print("base");
dPbPb_Trg65[i] = (TH1F*)fPbPb_in->Get(Form("hpbpb_HLT65_R%d_n20_eta_p20_cent%d",radius,i));
//dPbPb_Trg65[i]->Scale(4*145.156*1e6);
dPbPb_Trg65[i]->Print("base");
dPbPb_Trg55[i] = (TH1F*)fPbPb_in->Get(Form("hpbpb_HLT55_R%d_n20_eta_p20_cent%d",radius,i));
//dPbPb_Trg55[i]->Scale(4*145.156*1e6);
dPbPb_Trg55[i]->Print("base");
//dPbPb_TrgComb[i] = (TH1F*)dPbPb_Trg80[i]->Clone(Form("Jet_80_triggered_spectra_data_PbPb_cent%d",i));
//dPbPb_MinBias[i] = (TH1F*)fPbPb_MB_in->Get(Form("hpbpb_HLTComb_R%d_n20_eta_p20_cent%d",radius,i));
//dPbPb_MinBias[i]->Print("base");
dPbPb_TrgComb[i]->Scale(1./(145.156 * 1e9));
//dPbPb_MinBias[i]->Scale(1./(161.939 * 1e9));
//dPbPb_TrgComb[i]->Add(dPbPb_MinBias[i]);
for(int k = 1;k<=unfoldingCut;k++) {
dPbPb_TrgComb[i]->SetBinContent(k,0);
dPbPb_Trg80[i]->SetBinContent(k,0);
dPbPb_Trg65[i]->SetBinContent(k,0);
dPbPb_Trg55[i]->SetBinContent(k,0);
}
}
//Int_t nSVDIter = 4;
if(printDebug)cout<<"loaded the data histograms PbPb"<<endl;
// get PbPb MC
for(int i = 0;i<nbins_cent;i++){
mPbPb_Gen[i] = (TH1F*)fPbPb_in->Get(Form("hpbpb_JetComb_gen_R%d_n20_eta_p20_cent%d",radius,i));
//mPbPb_Gen[i] = (TH1F*)fPbPb_in->Get(Form("hpbpb_gen_R%d_n20_eta_p20_cent%d",radius,i));
mPbPb_Gen[i]->Print("base");
mPbPb_Reco[i] = (TH1F*)fPbPb_in->Get(Form("hpbpb_JetComb_reco_R%d_n20_eta_p20_cent%d",radius,i));
//mPbPb_Reco[i] = (TH1F*)fPbPb_in->Get(Form("hpbpb_reco_R%d_n20_eta_p20_cent%d",radius,i));
mPbPb_Reco[i]->Print("base");
mPbPb_Matrix[i] = (TH2F*)fPbPb_in->Get(Form("hpbpb_matrix_HLT_R%d_n20_eta_p20_cent%d",radius,i));
//mPbPb_Matrix[i] = (TH2F*)fPbPb_in->Get(Form("hpbpb_matrix_R%d_n20_eta_p20_cent%d",radius,i));
mPbPb_Matrix[i]->Print("base");
mPbPb_mcclosure_data[i] = (TH1F*)fPbPb_in->Get(Form("hpbpb_mcclosure_JetComb_data_R%d_n20_eta_p20_cent%d",radius,i));
mPbPb_mcclosure_data[i]->Print("base");
mPbPb_mcclosure_gen[i] = (TH1F*)fPbPb_in->Get(Form("hpbpb_mcclosure_gen_JetComb_R%d_n20_eta_p20_cent%d",radius,i));
mPbPb_mcclosure_gen[i]->Print("base");
mPbPb_mcclosure_Matrix[i] = (TH2F*)fPbPb_in->Get(Form("hpbpb_mcclosure_matrix_HLT_R%d_n20_eta_p20_cent%d",radius,i));
mPbPb_mcclosure_Matrix[i]->Print("base");
//since SVD is very straight forward, lets do it rignt here:
//get the SVD response matrix:
//RooUnfoldResponse ruResponse(mPbPb_Matrix[i]->ProjectionY(),mPbPb_Matrix[i]->ProjectionX(), mPbPb_Matrix[i],"","");
//regularization parameter definition:
//RooUnfoldSvd unfoldSvd(&ruResponse, dPbPb_TrgComb[i], nSVDIter);
//uPbPb_SVD[i] = (TH1F*)unfoldSvd.Hreco();
// for(int k = 1;k<=unfoldingCut;k++){
// mPbPb_Gen[i]->SetBinContent(k,0);
// mPbPb_Reco[i]->SetBinContent(k,0);
// mPbPb_mcclosure_data[i]->SetBinContent(k,0);
// mPbPb_mcclosure_gen[i]->SetBinContent(k,0);
// for(int l = 1;l<=1000;l++){
// mPbPb_Matrix[i]->SetBinContent(k,l,0);
// mPbPb_mcclosure_Matrix[i]->SetBinContent(k,l,0);
// mPbPb_Matrix[i]->SetBinContent(l,k,0);
// mPbPb_mcclosure_Matrix[i]->SetBinContent(l,k,0);
// }
// }
//mPbPb_Response[i] = new TH2F(Form("mPbPb_Response_cent%d",i),"Response Matrix",nbins_pt,boundaries_pt,nbins_pt,boundaries_pt);
//mPbPb_ResponseNorm[i] = new TH2F(Form("mPbPb_ResponseNorm_cent%d",i),"Normalized Response Matrix",nbins_pt,boundaries_pt,nbins_pt,boundaries_pt);
}
if(printDebug) cout<<"loaded the data and mc PbPb histograms from the files"<<endl;
// get PP data
if(printDebug) cout<<"Getting PP data and MC"<<endl;
dPP_1 = (TH1F*)fPP_in->Get(Form("hpp_HLT80_R%d_%s",radiusPP,etaWidth));
dPP_1->Print("base");
dPP_2 = (TH1F*)fPP_in->Get(Form("hpp_HLT60_R%d_%s",radiusPP,etaWidth));
dPP_2->Print("base");
dPP_3 = (TH1F*)fPP_in->Get(Form("hpp_HLT40_R%d_%s",radiusPP,etaWidth));
dPP_3->Print("base");
dPP_Comb = (TH1F*)fPP_in->Get(Form("hpp_HLTComb_R%d_%s",radiusPP,etaWidth));
//dPP_Comb = (TH1F*)dPP_1->Clone(Form("hpp_TrgComb_R%d_n20_eta_p20",radiusPP,etaWidth));
dPP_Comb->Print("base");
dPP_Comb->Scale(1./(5.3 * 1e9));
for(int k = 1;k<=unfoldingCut;k++) {
dPP_Comb->SetBinContent(k,0);
dPP_1->SetBinContent(k,0);
dPP_2->SetBinContent(k,0);
dPP_3->SetBinContent(k,0);
}
// get PP MC
mPP_Gen = (TH1F*)fPP_in->Get(Form("hpp_JetComb_gen_R%d_%s",radiusPP,etaWidth));
mPP_Gen->Print("base");
mPP_Reco = (TH1F*)fPP_in->Get(Form("hpp_JetComb_reco_R%d_%s",radiusPP,etaWidth));
mPP_Reco->Print("base");
mPP_Matrix = (TH2F*)fPP_in->Get(Form("hpp_matrix_HLT_R%d_%s",radiusPP,etaWidth));
mPP_Matrix->Print("base");
mPP_mcclosure_data = (TH1F*)fPP_in->Get(Form("hpp_mcclosure_JetComb_data_R%d_%s",radiusPP,etaWidth));
mPP_mcclosure_data->Print("base");
mPP_mcclosure_Matrix = (TH2F*)fPP_in->Get(Form("hpp_mcclosure_matrix_HLT_R%d_%s",radiusPP,etaWidth));
mPP_mcclosure_Matrix->Print("base");
//RooUnfoldResponse ruResponsePP(mPP_Matrix->ProjectionY(),mPP_Matrix->ProjectionX(), mPP_Matrix,"","");
//regularization parameter definition:
//RooUnfoldSvd unfoldSvdPP(&ruResponsePP, dPP_Comb, nSVDIter);
//uPP_SVD = (TH1F*)unfoldSvdPP.Hreco();
// for(int k = 1;k<=unfoldingCut;k++){
// mPP_Gen->SetBinContent(k,0);
// mPP_Reco->SetBinContent(k,0);
// mPP_mcclosure_data->SetBinContent(k,0);
// for(int l = 1;l<=1000;l++){
// mPP_Matrix->SetBinContent(k,l,0);
// mPP_mcclosure_Matrix->SetBinContent(k,l,0);
// mPP_Matrix->SetBinContent(l,k,0);
// mPP_mcclosure_Matrix->SetBinContent(l,k,0);
// }
// }
if(printDebug) cout<<"Filling the PbPb response Matrix"<<endl;
// response matrix and unfolding for PbPb
// going to try it the way kurt has it.
for(int i = 0;i<nbins_cent;i++){
if(printDebug) cout<<"centrality bin iteration = "<<i<<endl;
TF1 *f = new TF1("f","[0]*pow(x+[2],[1])");
f->SetParameters(1e10,-8.8,40);
// TH1F *hGenSpectraCorr = (TH1F*)mPbPb_Matrix[i]->ProjectionX()->Clone(Form("hGenSpectraCorr_cent%d",i));
// hGenSpectraCorr->Fit("f"," ");
// hGenSpectraCorr->Fit("f","","");
// hGenSpectraCorr->Fit("f","LL");
// TH1F *fHist = functionHist(f,hGenSpectraCorr,Form("fHist_cent%d",i));// function that you get from the fitting
// hGenSpectraCorr->Divide(fHist);
for (int y=1;y<=mPbPb_Matrix[i]->GetNbinsY();y++) {
double sum=0;
for (int x=1;x<=mPbPb_Matrix[i]->GetNbinsX();x++) {
if (mPbPb_Matrix[i]->GetBinContent(x,y)<=1*mPbPb_Matrix[i]->GetBinError(x,y)) {
//in the above line mine had 0*getbinerror while Kurt's had 1*.
mPbPb_Matrix[i]->SetBinContent(x,y,0);
mPbPb_Matrix[i]->SetBinError(x,y,0);
}
sum+=mPbPb_Matrix[i]->GetBinContent(x,y);
}
for (int x=1;x<=mPbPb_Matrix[i]->GetNbinsX();x++) {
double ratio = 1;
// if (hGenSpectraCorr->GetBinContent(x)!=0) ratio = 1e5/hGenSpectraCorr->GetBinContent(x);
mPbPb_Matrix[i]->SetBinContent(x,y,mPbPb_Matrix[i]->GetBinContent(x,y)*ratio);
mPbPb_Matrix[i]->SetBinError(x,y,mPbPb_Matrix[i]->GetBinError(x,y)*ratio);
}
}
//mPbPb_Matrix[i]->Smooth(0);
// Ok major differences here between my code and Kurt in b-jet Tools under Unfold - lines 469 and above.
mPbPb_Response[i] = (TH2F*)mPbPb_Matrix[i]->Clone(Form("mPbPb_Response_cent%d",i));
TH1F *hProj = (TH1F*)mPbPb_Response[i]->ProjectionY()->Clone(Form("hProj_cent%d",i));
for (int y=1;y<=mPbPb_Response[i]->GetNbinsY();y++) {
double sum=0;
for (int x=1;x<=mPbPb_Response[i]->GetNbinsX();x++) {
if (mPbPb_Response[i]->GetBinContent(x,y)<=1*mPbPb_Response[i]->GetBinError(x,y)) {
// in the above if loop, kurt has 1*error and my old had 0*error
mPbPb_Response[i]->SetBinContent(x,y,0);
mPbPb_Response[i]->SetBinError(x,y,0);
}
sum+=mPbPb_Response[i]->GetBinContent(x,y);
}
for (int x=1;x<=mPbPb_Response[i]->GetNbinsX();x++) {
if (sum==0) continue;
double ratio = 1;
//if(dPbPb_TrgComb[i]->GetBinContent(y)==0) ratio = 1e-100/sum;
// else ratio = dPbPb_TrgComb[i]->GetBinContent(y)/sum
ratio = 1./sum;
if (hProj->GetBinContent(y)==0) ratio = 1e-100/sum;
else ratio = hProj->GetBinContent(y)/sum;
mPbPb_Response[i]->SetBinContent(x,y,mPbPb_Response[i]->GetBinContent(x,y)*ratio);
mPbPb_Response[i]->SetBinError(x,y,mPbPb_Response[i]->GetBinError(x,y)*ratio);
}
}
mPbPb_ResponseNorm[i] = (TH2F*)mPbPb_Matrix[i]->Clone(Form("mPbPb_ResponseNorm_cent%d",i));
for (int x=1;x<=mPbPb_ResponseNorm[i]->GetNbinsX();x++) {
double sum=0;
for (int y=1;y<=mPbPb_ResponseNorm[i]->GetNbinsY();y++) {
if (mPbPb_ResponseNorm[i]->GetBinContent(x,y)<=1*mPbPb_ResponseNorm[i]->GetBinError(x,y)) {
mPbPb_ResponseNorm[i]->SetBinContent(x,y,0);
mPbPb_ResponseNorm[i]->SetBinError(x,y,0);
}
sum+=mPbPb_ResponseNorm[i]->GetBinContent(x,y);
}
for (int y=1;y<=mPbPb_ResponseNorm[i]->GetNbinsY();y++) {
if (sum==0) continue;
double ratio = 1./sum;
mPbPb_ResponseNorm[i]->SetBinContent(x,y,mPbPb_ResponseNorm[i]->GetBinContent(x,y)*ratio);
mPbPb_ResponseNorm[i]->SetBinError(x,y,mPbPb_ResponseNorm[i]->GetBinError(x,y)*ratio);
}
}
}
if(printDebug) cout<<"Filling PP response Matrix"<<endl;
// response matrix for pp.
// Kurt doesnt have this whole hGenSpectraCorr thing in his macro. need to check why the difference exists between out codes
TF1 *fpp = new TF1("fpp","[0]*pow(x+[2],[1])");
fpp->SetParameters(1e10,-8.8,40);
// if(printDebug) cout<<"before getting the gen spectra corr matrix"<<endl;
// TH1F *hGenSpectraCorrPP = (TH1F*)mPP_Matrix->ProjectionX()->Clone("hGenSpectraCorrPP");
// if(printDebug) cout<<"after gettign the gen spectra corr matrix"<<endl;
// hGenSpectraCorrPP->Fit("f"," ");
// hGenSpectraCorrPP->Fit("f","","");
// hGenSpectraCorrPP->Fit("f","LL");
// TH1F *fHistPP = functionHist(fpp,hGenSpectraCorrPP,"fHistPP");// that the function that you get from the fitting
// hGenSpectraCorrPP->Divide(fHistPP);
for (int y=1;y<=mPP_Matrix->GetNbinsY();y++) {
double sum=0;
for (int x=1;x<=mPP_Matrix->GetNbinsX();x++) {
if (mPP_Matrix->GetBinContent(x,y)<=1*mPP_Matrix->GetBinError(x,y)) {
mPP_Matrix->SetBinContent(x,y,0);
mPP_Matrix->SetBinError(x,y,0);
}
sum+=mPP_Matrix->GetBinContent(x,y);
}
for (int x=1;x<=mPP_Matrix->GetNbinsX();x++) {
double ratio = 1;
// if (hGenSpectraCorrPP->GetBinContent(x)!=0) ratio = 1e5/hGenSpectraCorrPP->GetBinContent(x);
mPP_Matrix->SetBinContent(x,y,mPP_Matrix->GetBinContent(x,y)*ratio);
mPP_Matrix->SetBinError(x,y,mPP_Matrix->GetBinError(x,y)*ratio);
}
}
// mPbPb_Matrix[i]->Smooth(0);
// Ok major differences here between my code and Kurt in b-jet Tools under Unfold - lines 469 and above.
if(printDebug) cout<<"getting the response matrix"<<endl;
mPP_Response = (TH2F*)mPP_Matrix->Clone("mPP_Response");
TH1F *hProjPP = (TH1F*)mPP_Response->ProjectionY()->Clone("hProjPP");
for (int y=1;y<=mPP_Response->GetNbinsY();y++) {
double sum=0;
for (int x=1;x<=mPP_Response->GetNbinsX();x++) {
if (mPP_Response->GetBinContent(x,y)<=1*mPP_Response->GetBinError(x,y)) {
// in the above if statement, kurt has 1*error and my old has 0*error
mPP_Response->SetBinContent(x,y,0);
mPP_Response->SetBinError(x,y,0);
}
sum+=mPP_Response->GetBinContent(x,y);
}
for (int x=1;x<=mPP_Response->GetNbinsX();x++) {
if (sum==0) continue;
double ratio = 1;
//if(dPbPb_TrgComb[i]->GetBinContent(y)==0) ratio = 1e-100/sum;
// else ratio = dPbPb_TrgComb[i]->GetBinContent(y)/sum
ratio = 1./sum;
if (hProjPP->GetBinContent(y)==0) ratio = 1e-100/sum;
else ratio = hProjPP->GetBinContent(y)/sum;
mPP_Response->SetBinContent(x,y,mPP_Response->GetBinContent(x,y)*ratio);
mPP_Response->SetBinError(x,y,mPP_Response->GetBinError(x,y)*ratio);
}
}
if(printDebug) cout<<"getting the normalized response matrix"<<endl;
mPP_ResponseNorm = (TH2F*)mPP_Matrix->Clone("mPP_ResponseNorm");
for (int x=1;x<=mPP_ResponseNorm->GetNbinsX();x++) {
double sum=0;
for (int y=1;y<=mPP_ResponseNorm->GetNbinsY();y++) {
if (mPP_ResponseNorm->GetBinContent(x,y)<=1*mPP_ResponseNorm->GetBinError(x,y)) {
mPP_ResponseNorm->SetBinContent(x,y,0);
mPP_ResponseNorm->SetBinError(x,y,0);
}
sum+=mPP_ResponseNorm->GetBinContent(x,y);
}
for (int y=1;y<=mPP_ResponseNorm->GetNbinsY();y++) {
if (sum==0) continue;
double ratio = 1./sum;
mPP_ResponseNorm->SetBinContent(x,y,mPP_ResponseNorm->GetBinContent(x,y)*ratio);
mPP_ResponseNorm->SetBinError(x,y,mPP_ResponseNorm->GetBinError(x,y)*ratio);
}
}
// scale the spectra to the respective units
// for(int i = 0;i<nbins_cent;++i){
// dPbPb_TrgComb[i] = (TH1F*)dPbPb_TrgComb[i]->Rebin(nbins_pt,Form("PbPb_measured_spectra_combined_cent%d",i),boundaries_pt);
// divideBinWidth(dPbPb_TrgComb[i]);
// }
// dPP_Comb = (TH1F*)dPP_Comb->Rebin(nbins_pt,"pp_measured_spectra_combined",boundaries_pt);
// divideBinWidth(dPP_Comb);
// dPP_Comb->Scale(1./ dPP_Comb->GetBinContent(nbins_pt));
// Now that we have all the response matrix for the 6 centralities in PbPb and one pp spectra lets start doing the steps:
// we have 39 pt bins, so we need 1000 gaussian functions for each pt bin.
Int_t unfoldingTrials = 200;
Double_t meanMeasPbPb[nbins_pt][nbins_cent], sigmaMeasPbPb[nbins_pt][nbins_cent];
Double_t meanMeasPP[nbins_pt], sigmaMeasPP[nbins_pt];
Double_t meanUnfoldPbPb[nbins_pt][nbins_cent][unfoldingTrials], sigmaUnfoldPbPb[nbins_pt][nbins_cent][unfoldingTrials];
Double_t meanUnfoldPP[nbins_pt][unfoldingTrials], sigmaUnfoldPP[nbins_pt][unfoldingTrials];
TRandom3 *random = new TRandom3(0);
for(int u = 0;u<unfoldingTrials;++u){
cout<<"unfolding trial no = "<<u+1<<endl;
for(int j = 0;j<nbins_pt;++j){
for(int i = 0;i<nbins_cent;++i){
meanMeasPbPb[j][i] = dPbPb_TrgComb[i]->GetBinContent(j+1);
sigmaMeasPbPb[j][i] = dPbPb_TrgComb[i]->GetBinError(j+1);
}// centrality loop
meanMeasPP[j] = dPP_Comb->GetBinContent(j+1);
sigmaMeasPP[j] = dPP_Comb->GetBinContent(j+1);
}// nbins_pt loop
// now proceed to unfolding for each trial.
for(int i = 0;i<nbins_cent;++i){
//cout<<"centrality = "<<i<<endl;
TH1F * hPreUnfoldingSpectra = new TH1F("hPreUnfoldingSpectra","",nbins_pt,0,nbins_pt);
TH1F * hAfterUnfoldingSpectra;
for(int j = 0;j<nbins_pt;++j){
hPreUnfoldingSpectra->SetBinContent(j+1, random->Gaus(meanMeasPbPb[j][i], sigmaMeasPbPb[j][i]));
hPreUnfoldingSpectra->SetBinError(j+1, sigmaMeasPbPb[j][i]/sqrt(unfoldingTrials));
//if(j==100)cout << " before unfolding bin " << j << " value = " << hPreUnfoldingSpectra->GetBinContent(j+1)<<endl;
//if(j==100)cout << " before unfolding bin " << j << " error = " << hPreUnfoldingSpectra->GetBinError(j+1)<<endl;
}// nbins_pt loop
TH1F* hMCGen = (TH1F*)mPbPb_Response[i]->ProjectionX();
removeZero(hMCGen);
//cout << " MC bin " << 100 << " value = " << hMCGen->GetBinContent(100)<<endl;
bayesianUnfold myUnfoldingMulti(mPbPb_Matrix[i], hMCGen, 0);
myUnfoldingMulti.unfold(hPreUnfoldingSpectra, BayesIter);
hAfterUnfoldingSpectra = (TH1F*) myUnfoldingMulti.hPrior->Clone("hAfterUnfoldingSpectra");
for(int j = 0;j<nbins_pt;++j){
//if(j==100)cout << " before unfolding bin " << j << " value = " << hPreUnfoldingSpectra->GetBinContent(j+1)<<endl;
//if(j==100)cout << " after unfolding bin " << j << " value = " << hAfterUnfoldingSpectra->GetBinContent(j+1)<<endl;
meanUnfoldPbPb[j][i][u] = hAfterUnfoldingSpectra->GetBinContent(j+1);
sigmaUnfoldPbPb[j][i][u] = hAfterUnfoldingSpectra->GetBinError(j+1);
// cout << "after unfolding meanUnfoldPbPb[" << j << "][" << i << "][" << u<< "] = " <<meanUnfoldPbPb[j][i][u]<<" ";
// cout << "after unfolding meanUnfoldPbPb[" << j << "][" << i << "][" << u<< "] = " <<sigmaUnfoldPbPb[j][i][u]<<endl;
}// nbins_pt loop
//hPreUnfoldingSpectra->Print("base");
//hAfterUnfoldingSpectra->Print("base");
delete hPreUnfoldingSpectra;
delete hAfterUnfoldingSpectra;
delete hMCGen;
}// centrality loop
cout<<"pp "<<endl;
// now do it for the pp:
TH1F * hPreUnfoldingSpectraPP = new TH1F("hPreUnfoldingSpectraPP","",nbins_pt,0,nbins_pt);
TH1F * hAfterUnfoldingSpectraPP;
for(int j = 0;j<nbins_pt;++j){
hPreUnfoldingSpectraPP->SetBinContent(j+1, random->Gaus(meanMeasPP[j], sigmaMeasPP[j]));
hPreUnfoldingSpectraPP->SetBinError(j+1, sigmaMeasPP[j]/sqrt(unfoldingTrials));
}// nbins_pt loop
TH1F* hMCGenPP = (TH1F*)mPP_Response->ProjectionX();
removeZero(hMCGenPP);
bayesianUnfold myUnfoldingMultiPP(mPP_Matrix, hMCGenPP, 0);
myUnfoldingMultiPP.unfold(hPreUnfoldingSpectraPP, BayesIter);
hAfterUnfoldingSpectraPP = (TH1F*) myUnfoldingMultiPP.hPrior->Clone("hAfterUnfoldingSpectraPP");
for(int j = 0;j<nbins_pt;++j){
meanUnfoldPP[j][u] = hAfterUnfoldingSpectraPP->GetBinContent(j+1);
sigmaUnfoldPP[j][u] = hAfterUnfoldingSpectraPP->GetBinError(j+1);
}// nbins_pt loop
delete hPreUnfoldingSpectraPP;
delete hAfterUnfoldingSpectraPP;
delete hMCGenPP;
}// unfolding trials loop
// Now that we have all the necesary values we need, lets proceed to fill a histogram with the mean values for each ptbin and get the corrected values.
TH1F * hAfterUnfoldingptBinDistribution[nbins_pt];
TH1F * hCorrUnfoldingPbPb[nbins_cent];
for(int i = 0;i<nbins_cent;++i){
hCorrUnfoldingPbPb[i] = new TH1F(Form("PbPb_BayesianUnfolded_cent%d",i),"Spectra after correction", nbins_pt, 0, nbins_pt);
for(int j = 0;j<nbins_pt;++j){
//hAfterUnfoldingptBinDistribution[j] = new TH1F(Form("hAfterUnfoldingptBinDistribution_ptBin%d",j),"",100, (meanMeasPbPb[j][i]-10) * sigmaMeasPbPb[j][i], (meanMeasPbPb[j][i]+10) * sigmaMeasPbPb[j][i]);
hAfterUnfoldingptBinDistribution[j] = new TH1F(Form("hAfterUnfoldingptBinDistribution_ptBin%d",j),"",100, 0, 1);
for(int u = 0;u<unfoldingTrials;++u){
hAfterUnfoldingptBinDistribution[j]->Fill(meanUnfoldPbPb[j][i][u]);
//if(j==100) cout<< "unfolding_trial = " << u+1 << " mean unfold value = "<< meanUnfoldPbPb[j][i][u] <<endl;
}// unfolding trials loop
//if(j==100) cout<<"Mean of that value for pt=100 = "<< (Float_t)hAfterUnfoldingptBinDistribution[j]->GetMean() <<endl;
hCorrUnfoldingPbPb[i]->SetBinContent(j+1, hAfterUnfoldingptBinDistribution[j]->GetMean());
//cout<<"centrality bin "<<i<<", pT bin "<<j<<" bin Content = "<<hCorrUnfoldingPbPb[i]->GetBinContent(j+1)<<endl;
hCorrUnfoldingPbPb[i]->SetBinError(j+1, hAfterUnfoldingptBinDistribution[j]->GetRMS());
//cout<<"centrality bin "<<i<<", pT bin "<<j<<" bin Error = "<<hCorrUnfoldingPbPb[i]->GetBinError(j+1)<<endl;
delete hAfterUnfoldingptBinDistribution[j];
}// nbins_pt loop
}// centrality loop
// similar for the pp:
TH1F * hAfterUnfoldingptBinDistributionPP[nbins_pt];
TH1F * hCorrUnfoldingPP;
hCorrUnfoldingPP = new TH1F("PP_BayesianUnfolded","Spectra after unfolding error correction",nbins_pt, 0, nbins_pt);
for(int j = 0;j<nbins_pt;++j){
//hAfterUnfoldingptBinDistributionPP[j] = new TH1F(Form("hAfterUnfoldingptBinDistributionPP_ptBin%d",j),"",1000,(meanMeasPP[j]-10) * sigmaMeasPP[j], (meanMeasPP[j]+10) * sigmaMeasPP[j]);
hAfterUnfoldingptBinDistributionPP[j] = new TH1F(Form("hAfterUnfoldingptBinDistributionPP_ptBin%d",j),"",100, 0, 1);
for(int u = 0;u<unfoldingTrials;++u){
hAfterUnfoldingptBinDistributionPP[j]->Fill(meanUnfoldPP[j][u]);
}// unfolding trials loop
hCorrUnfoldingPP->SetBinContent(j+1, hAfterUnfoldingptBinDistributionPP[j]->GetMean());
//cout<<"PP pT bin "<<j<<" bin Content = "<<hCorrUnfoldingPP->GetBinContent(j+1)<<endl;
hCorrUnfoldingPP->SetBinError(j+1, hAfterUnfoldingptBinDistributionPP[j]->GetRMS());
//cout<<"PP pT bin "<<j<<" bin Error = "<<hCorrUnfoldingPP->GetBinError(j+1)<<endl;
delete hAfterUnfoldingptBinDistributionPP[j];
}// nbins_pt loop
TFile f(Form("../../Output/Pawan_ntuple_PbPb_R%d_pp_R%d_%s_unfoldingCut_%d_data_driven_correction_ak%s%s_%d.root",radius, radiusPP, etaWidth ,unfoldingCut,algo,jet_type,date.GetDate()),"RECREATE");
f.cd();
for(int i = 0;i<nbins_cent;i++) {
hCorrUnfoldingPbPb[i]->Scale(145.156 * 1e9);
//hCorrUnfoldingPbPb[i] = (TH1F*)hCorrUnfoldingPbPb[i]->Rebin(nbins_pt_coarse, Form("PbPb_BayesianUnfolded_cent%d",i), boundaries_pt_coarse);
hCorrUnfoldingPbPb[i]->Write();
hCorrUnfoldingPbPb[i]->Print("base");
dPbPb_TrgComb[i]->Scale(145.156 * 1e9);
//dPbPb_TrgComb[i] = (TH1F*)dPbPb_TrgComb[i]->Rebin(nbins_pt_coarse, Form("PbPb_measured_cent%d",i), boundaries_pt_coarse);
dPbPb_TrgComb[i]->Write();
dPbPb_TrgComb[i]->Print("base");
}
hCorrUnfoldingPP->Scale(5.3 * 1e9);
//hCorrUnfoldingPP = (TH1F*)hCorrUnfoldingPP->Rebin(nbins_pt_coarse, "PP_BayesianUnfolded", boundaries_pt_coarse);
hCorrUnfoldingPP->Write();
hCorrUnfoldingPP->Print("base");
dPP_Comb->Scale(5.3 * 1e9);
//dPP_Comb = (TH1F*)dPP_Comb->Rebin(nbins_pt_coarse, "PP_measured", boundaries_pt_coarse);
dPP_Comb->Write();
dPP_Comb->Print("base");
f.Write();
f.Close();
timer.Stop();
if(printDebug) cout<<"CPU time (mins) = "<<(Float_t)timer.CpuTime()/60<<endl;
if(printDebug) cout<<"Real tile (mins) = "<<(Float_t)timer.RealTime()/60<<endl;
}
void ana_Main_MC(TString ds="relval", TString physics="ttbar") {
gSystem->Load("libSusyEvent.so");
// Look ../jec/JetMETObjects/README
gSystem->Load("../jec/lib/libJetMETObjects.so");
// Printing utility for ntuple variables
gROOT->LoadMacro("SusyEventPrinter.cc+");
// Main analysis code
gROOT->LoadMacro("SusyMainAna_MC.cc+");
// chain of inputs
TChain* chain = new TChain("susyTree");
//////////////// MC files /////////////////
cout<<"I survive this long1 "<< which_MC_to_use<< endl;
MCpoint* thisMCpoint = setupMCpoint(which_MC_to_use);
cout<<"I survive this long2"<<endl;
chain->Add(thisMCpoint->filepath.c_str());
cout<<"I survive this long"<<endl;
//chain->Add("../susyEvents_AB_1M_ho200_v2.root");
//chain->Add("../susyEvents_newNatural.root"); //last used!!
//chain->Add("/eos/uscms/store/user/abarker/MC/newNat350_225/MC_AB_2500k_NEWnaturalHiggsinoNLSPout_mst_350_M3_5025_mu_225.root");//same thing as ../susyEvents_newNatural.root
//chain->Add("/eos/uscms/store/user/abarker/MC/st_250_ho_150/MC_AB_2500k_st_250_ho_150.root");
//chain->Add("/eos/uscms/store/user/abarker/MC/st_250_ho_200/MC_AB_2500k_st_250_ho_200.root");
//chain->Add("/eos/uscms/store/user/abarker/MC/st_350_ho_200/MC_AB_2500k_mst_350_mu_200.root");
//chain->Add("/eos/uscms/store/user/abarker/MC/ho_140/MC_AB_2500k_ho_140.root");
//chain->Add("/eos/uscms/store/user/abarker/MC/ho_200/MC_AB_2500k_ho_200.root");
//chain->Add("../susyEvents_newNatural.root");
//chain->Add("dcache:/pnfs/cms/WAX/resilient/abarker/MC/MC_AB_2500k_NEWnaturalHiggsinoNLSPout_mst_350_M3_5025_mu_225.root");
//chain->Add("dcache:/pnfs/cms/WAX/resilient/abarker/MC/MC_AB_2500k_st_250_ho_150.root");
//chain->Add("dcache:/pnfs/cms/WAX/resilient/abarker/MC/MC_AB_2500k_st_250_ho_200.root");
//chain->Add("dcache:/pnfs/cms/WAX/resilient/abarker/MC/MC_AB_2500k_mst_350_mu_200.root");
//chain->Add("dcache:/pnfs/cms/WAX/resilient/abarker/MC/MC_AB_2500k_ho_140.root");
//chain->Add("dcache:/pnfs/cms/WAX/resilient/abarker/MC/MC_AB_2500k_ho_200.root");
SusyMainAna_MC* sea = new SusyMainAna_MC(chain);
// configuration parameters
// any values given here will replace the default values
sea->SetDataset(physics+"_"+ds); // dataset name
sea->SetPrintInterval(1e4); // print frequency
sea->SetPrintLevel(0); // print level for event contents
sea->SetUseTrigger(false);
/*
sea->AddHltName("HLT_Photon36_CaloIdL_Photon22_CaloIdL"); // add HLT trigger path name
sea->AddHltName("HLT_Photon32_CaloIdL_Photon26_CaloIdL"); // add HLT trigger path name
sea->AddHltName("HLT_Photon26_R9Id85_Photon18_R9Id85_Mass60");
sea->AddHltName("HLT_Photon26_R9Id85_Photon18_CaloId10_Iso50_Mass60");
sea->AddHltName("HLT_Photon26_CaloId10_Iso50_Photon18_R9Id85_Mass60");
sea->AddHltName("HLT_Photon26_CaloId10_Iso50_Photon18_CaloId10_Iso50_Mass60");
sea->AddHltName("HLT_Photon26_R9Id85_OR_CaloId10_Iso50_Photon18_R9Id85_OR_CaloId10_Iso50_Mass60");
sea->AddHltName("HLT_Photon26_R9Id85_OR_CaloId10_Iso50_Photon18_R9Id85_OR_CaloId10_Iso50_Mass70");
sea->AddHltName("HLT_Photon36_R9Id85_Photon22_R9Id85");
sea->AddHltName("HLT_Photon36_R9Id85_Photon22_CaloId10_Iso50");
sea->AddHltName("HLT_Photon36_CaloId10_Iso50_Photon22_R9Id85");
sea->AddHltName("HLT_Photon36_CaloId10_Iso50_Photon22_CaloId10_Iso50");
sea->AddHltName("HLT_Photon36_R9Id85_OR_CaloId10_Iso50_Photon22_R9Id85_OR_CaloId10_Iso50");
*/
sea->SetFilter(false); // filter events passing final cuts
sea->SetProcessNEvents(-1); // number of events to be processed
// as an example -- add your favorite Json here. More than one can be "Include"ed
// sea->IncludeAJson("Cert_161079-161352_7TeV_PromptReco_Collisions11_JSON_noESpbl_v2.txt");
//sea->IncludeAJson("anotherJSON.txt");
TStopwatch ts;
ts.Start();
sea->Loop();
ts.Stop();
std::cout << "RealTime : " << ts.RealTime()/60.0 << " minutes" << std::endl;
std::cout << "CPUTime : " << ts.CpuTime()/60.0 << " minutes" << std::endl;
}
