int main(int argc, char* argv[]){
string bkgFileName;
string sigFileName;
string sigWSName;
string bkgWSName;
string outFileName;
string datFileName;
string outDir;
int cat;
int ntoys;
int jobn;
int seed;
float mu_low;
float mu_high;
float mu_step;
float expectSignal;
int expectSignalMass;
bool skipPlots=false;
int verbosity;
bool throwHybridToys=false;
vector<float> switchMass;
vector<string> switchFunc;
po::options_description desc("Allowed options");
desc.add_options()
("help,h", "Show help")
("sigfilename,s", po::value<string>(&sigFileName), "Signal file name")
("bkgfilename,b", po::value<string>(&bkgFileName), "Background file name")
("sigwsname", po::value<string>(&sigWSName)->default_value("cms_hgg_workspace"), "Signal workspace name")
("bkgwsname", po::value<string>(&bkgWSName)->default_value("cms_hgg_workspace"), "Background workspace name")
("outfilename,o", po::value<string>(&outFileName)->default_value("BiasStudyOut.root"), "Output file name")
("datfile,d", po::value<string>(&datFileName)->default_value("config.dat"), "Name of datfile containing pdf info")
("outDir,D", po::value<string>(&outDir)->default_value("./"), "Name of out directory for plots")
("cat,c", po::value<int>(&cat), "Category")
("ntoys,t", po::value<int>(&ntoys)->default_value(0), "Number of toys to run")
("jobn,j", po::value<int>(&jobn)->default_value(0), "Job number")
("seed,r", po::value<int>(&seed)->default_value(0), "Set random seed")
("mulow,L", po::value<float>(&mu_low)->default_value(-3.), "Value of mu to start scan")
("muhigh,H", po::value<float>(&mu_high)->default_value(3.), "Value of mu to end scan")
("mustep,S", po::value<float>(&mu_step)->default_value(0.01), "Value of mu step size")
("expectSignal", po::value<float>(&expectSignal)->default_value(0.), "Inject signal into toy")
("expectSignalMass", po::value<int>(&expectSignalMass)->default_value(125), "Inject signal at this mass")
("skipPlots", "Skip full profile and toy plots")
("verbosity,v", po::value<int>(&verbosity)->default_value(0), "Verbosity level")
;
po::variables_map vm;
po::store(po::parse_command_line(argc,argv,desc),vm);
po::notify(vm);
if (vm.count("help")) { cout << desc << endl; exit(1); }
if (vm.count("skipPlots")) skipPlots=true;
if (expectSignalMass!=110 && expectSignalMass!=115 && expectSignalMass!=120 && expectSignalMass!=125 && expectSignalMass!=130 && expectSignalMass!=135 && expectSignalMass!=140 && expectSignalMass!=145 && expectSignalMass!=150){
cerr << "ERROR - expectSignalMass has to be integer in range (110,150,5)" << endl;
exit(1);
}
vector<pair<int,pair<string,string> > > toysMap;
vector<pair<int,pair<string,string> > > fabianMap;
vector<pair<int,pair<string,string> > > paulMap;
readDatFile(datFileName,cat,toysMap,fabianMap,paulMap);
cout << "Toy vector.." << endl;
printOptionsMap(toysMap);
cout << "Fabian vector.." << endl;
printOptionsMap(fabianMap);
cout << "Paul vector.." << endl;
printOptionsMap(paulMap);
TStopwatch sw;
sw.Start();
if (verbosity<1) {
RooMsgService::instance().setGlobalKillBelow(RooFit::ERROR);
RooMsgService::instance().setSilentMode(true);
}
TFile *bkgFile = TFile::Open(bkgFileName.c_str());
TFile *sigFile = TFile::Open(sigFileName.c_str());
//RooWorkspace *bkgWS = (RooWorkspace*)bkgFile->Get("cms_hgg_workspace");
RooWorkspace *bkgWS = (RooWorkspace*)bkgFile->Get(bkgWSName.c_str());
RooWorkspace *sigWS = (RooWorkspace*)sigFile->Get(sigWSName.c_str());
if (!bkgWS || !sigWS){
cerr << "ERROR - one of signal or background workspace is NULL" << endl;
exit(1);
}
RooRealVar *mass = (RooRealVar*)bkgWS->var("CMS_hgg_mass");
RooRealVar *mu = new RooRealVar("mu","mu",0.,mu_low,mu_high);
TFile *outFile = new TFile(outFileName.c_str(),"RECREATE");
TTree *muTree = new TTree("muTree","muTree");
int toyn;
vector<string> truthModel;
vector<double> muFab;
vector<double> muPaul;
vector<double> muChi2;
vector<double> muAIC;
vector<double> muFabErrLow;
vector<double> muPaulErrLow;
vector<double> muChi2ErrLow;
vector<double> muAICErrLow;
vector<double> muFabErrHigh;
vector<double> muPaulErrHigh;
vector<double> muChi2ErrHigh;
vector<double> muAICErrHigh;
muTree->Branch("jobn",&jobn);
muTree->Branch("toyn",&toyn);
muTree->Branch("truthModel",&truthModel);
muTree->Branch("muFab",&muFab);
muTree->Branch("muPaul",&muPaul);
muTree->Branch("muChi2",&muChi2);
muTree->Branch("muAIC",&muAIC);
muTree->Branch("muFabErrLow",&muFabErrLow);
muTree->Branch("muPaulErrLow",&muPaulErrLow);
muTree->Branch("muChi2ErrLow",&muChi2ErrLow);
muTree->Branch("muAICErrLow",&muAICErrLow);
muTree->Branch("muFabErrHigh",&muFabErrHigh);
muTree->Branch("muPaulErrHigh",&muPaulErrHigh);
muTree->Branch("muChi2ErrHigh",&muChi2ErrHigh);
muTree->Branch("muAICErrHigh",&muAICErrHigh);
//TH1F *muDistFab = new TH1F("muDistFab","muDistFab",int(20*(mu_high-mu_low)),mu_low,mu_high);
//TH1F *muDistPaul = new TH1F("muDistPaul","muDistPaul",int(20*(mu_high-mu_low)),mu_low,mu_high);
//TH1F *muDistChi2 = new TH1F("muDistChi2","muDistChi2",int(20*(mu_high-mu_low)),mu_low,mu_high);
//TH1F *muDistAIC = new TH1F("muDistAIC","muDistAIC",int(20*(mu_high-mu_low)),mu_low,mu_high);
mass->setBins(320);
RooDataSet *data = (RooDataSet*)bkgWS->data(Form("data_mass_cat%d",cat));
//RooDataSet *data = (RooDataSet*)bkgWS->data(Form("data_cat%d_7TeV",cat));
RooDataHist *dataBinned = new RooDataHist(Form("roohist_data_mass_cat%d",cat),Form("roohist_data_mass_cat%d",cat),RooArgSet(*mass),*data);
RooDataSet *sigMC = (RooDataSet*)sigWS->data(Form("sig_ggh_mass_m%d_cat%d",expectSignalMass,cat));
RooDataSet *sigMC_vbf = (RooDataSet*)sigWS->data(Form("sig_wzh_mass_m%d_cat%d",expectSignalMass,cat));
RooDataSet *sigMC_wzh = (RooDataSet*)sigWS->data(Form("sig_vbf_mass_m%d_cat%d",expectSignalMass,cat));
RooDataSet *sigMC_tth = (RooDataSet*)sigWS->data(Form("sig_tth_mass_m%d_cat%d",expectSignalMass,cat));
sigMC->append(*sigMC_vbf);
sigMC->append(*sigMC_wzh);
sigMC->append(*sigMC_tth);
//RooExtendPdf *ggh_pdf = (RooExtendPdf*)sigWS->pdf(Form("sigpdfsmrel_cat%d_7TeV_ggh",cat));
//RooExtendPdf *vbf_pdf = (RooExtendPdf*)sigWS->pdf(Form("sigpdfsmrel_cat%d_7TeV_vbf",cat));
//RooExtendPdf *wzh_pdf = (RooExtendPdf*)sigWS->pdf(Form("sigpdfsmrel_cat%d_7TeV_wzh",cat));
//RooExtendPdf *tth_pdf = (RooExtendPdf*)sigWS->pdf(Form("sigpdfsmrel_cat%d_7TeV_tth",cat));
//RooAbsPdf *sigPdf = new RooAddPdf(Form("sigpdfsmrel_cat%d_7TeV",cat),Form("sigpdfsmrel_cat%d_7TeV",cat),RooArgList(*ggh_pdf,*vbf_pdf,*wzh_pdf,*tth_pdf));
if (!dataBinned || !sigMC){
cerr << "ERROR -- one of data or signal is NULL" << endl;
exit(1);
}
// set of truth models to throw toys from
PdfModelBuilder toysModel;
toysModel.setObsVar(mass);
toysModel.setSignalModifier(mu);
// add truth pdfs from config datfile these need to be cached
// to throw a toy from the SB fit make sure that the cache happens at makeSBPdfs
for (vector<pair<int,pair<string,string> > >::iterator it=toysMap.begin(); it!=toysMap.end(); it++){
if (it->first==-1) { // this is a hyrbid toy
throwHybridToys=true;
vector<string> temp;
split(temp,it->second.first,boost::is_any_of(","));
split(switchFunc,it->second.second,boost::is_any_of(","));
for (unsigned int i=0; i<temp.size(); i++){
switchMass.push_back(atof(temp[i].c_str()));
}
continue;
}
if (it->first==-2) { // this is a keys pdf toy
double rho = lexical_cast<double>(it->second.first);
toysModel.setKeysPdfAttributes(data,rho);
toysModel.addBkgPdf("KeysPdf",0,Form("truth_%s_cat%d",it->second.second.c_str(),cat),false);
continue;
}
if (it->first==-3) { // this is read pdf from file
toysModel.addBkgPdf(it->second.second,it->first,it->second.first,false);
continue;
}
toysModel.addBkgPdf(it->second.second,it->first,Form("truth_%s_cat%d",it->second.first.c_str(),cat),false);
}
toysModel.setSignalPdfFromMC(sigMC);
//toysModel.setSignalPdf(sigPdf);
toysModel.makeSBPdfs(true);
map<string,RooAbsPdf*> toyBkgPdfs = toysModel.getBkgPdfs();
map<string,RooAbsPdf*> toySBPdfs = toysModel.getSBPdfs();
toysModel.setSeed(seed);
// fabians chosen model
PdfModelBuilder fabianModel;
fabianModel.setObsVar(mass);
fabianModel.setSignalModifier(mu);
// add pdfs from config datfile - should be no need to cache these
for (vector<pair<int,pair<string,string> > >::iterator it=fabianMap.begin(); it!=fabianMap.end(); it++){
fabianModel.addBkgPdf(it->second.second,it->first,Form("fabian_%s_cat%d",it->second.first.c_str(),cat),false);
}
fabianModel.setSignalPdfFromMC(sigMC);
//fabianModel.setSignalPdf(sigPdf);
fabianModel.makeSBPdfs(false);
map<string,RooAbsPdf*> fabianBkgPdfs = fabianModel.getBkgPdfs();
map<string,RooAbsPdf*> fabianSBPdfs = fabianModel.getSBPdfs();
// set of models to profile
PdfModelBuilder paulModel;
paulModel.setObsVar(mass);
paulModel.setSignalModifier(mu);
// add pdfs from config datfile - should be no need to cache these
for (vector<pair<int,pair<string,string> > >::iterator it=paulMap.begin(); it!=paulMap.end(); it++){
paulModel.addBkgPdf(it->second.second,it->first,Form("paul_%s_cat%d",it->second.first.c_str(),cat),false);
}
paulModel.setSignalPdfFromMC(sigMC);
//paulModel.setSignalPdf(sigPdf);
paulModel.makeSBPdfs(false);
map<string,RooAbsPdf*> paulBkgPdfs = paulModel.getBkgPdfs();
map<string,RooAbsPdf*> paulSBPdfs = paulModel.getSBPdfs();
// set up profile for Fabians models
ProfileMultiplePdfs fabianProfiler;
for (map<string,RooAbsPdf*>::iterator pdf=fabianSBPdfs.begin(); pdf!=fabianSBPdfs.end(); pdf++){
fabianProfiler.addPdf(pdf->second);
}
cout << "Fabian profiler pdfs:" << endl;
fabianProfiler.printPdfs();
// set up profile for Pauls models
ProfileMultiplePdfs paulProfiler;
for (map<string,RooAbsPdf*>::iterator pdf=paulSBPdfs.begin(); pdf!=paulSBPdfs.end(); pdf++){
paulProfiler.addPdf(pdf->second);
}
cout << "Paul profiler pdfs:" << endl;
paulProfiler.printPdfs();
if (!skipPlots) {
system(Form("mkdir -p %s/plots/truthToData",outDir.c_str()));
system(Form("mkdir -p %s/plots/envelopeNlls",outDir.c_str()));
system(Form("mkdir -p %s/plots/toys",outDir.c_str()));
}
// throw toys - only need to fit data once as result will be cached
cout << "------ FITTING TRUTH TO DATA ------" << endl;
// sometimes useful to do best fit first to get reasonable starting value
toysModel.setSignalModifierConstant(false);
toysModel.fitToData(dataBinned,false,false,true);
// -----
toysModel.setSignalModifierVal(expectSignal);
toysModel.setSignalModifierConstant(true);
toysModel.fitToData(dataBinned,false,true,true);
if (!skipPlots) toysModel.plotPdfsToData(dataBinned,80,Form("%s/plots/truthToData/datafit_mu%3.1f",outDir.c_str(),expectSignal),false);
toysModel.setSignalModifierConstant(false);
toysModel.saveWorkspace(outFile);
for (int toy=0; toy<ntoys; toy++){
cout << "---------------------------" << endl;
cout << "--- RUNNING TOY " << toy << " / " << ntoys << " ----" << endl;
cout << "---------------------------" << endl;
// wipe stuff for tree
truthModel.clear();
muFab.clear();
muPaul.clear();
muChi2.clear();
muAIC.clear();
muFabErrLow.clear();
muPaulErrLow.clear();
muChi2ErrLow.clear();
muAICErrLow.clear();
muFabErrHigh.clear();
muPaulErrHigh.clear();
muChi2ErrHigh.clear();
muAICErrHigh.clear();
// throw toy
map<string,RooAbsData*> toys;
if (throwHybridToys) {
toysModel.throwHybridToy(Form("truth_job%d_toy%d",jobn,toy),dataBinned->sumEntries(),switchMass,switchFunc,false,true,true,true);
toys = toysModel.getHybridToyData();
if (!skipPlots) toysModel.plotToysWithPdfs(Form("%s/plots/toys/job%d_toy%d",outDir.c_str(),jobn,toy),80,false);
if (!skipPlots) toysModel.plotHybridToy(Form("%s/plots/toys/job%d_toy%d",outDir.c_str(),jobn,toy),80,switchMass,switchFunc,false);
}
else {
toysModel.throwToy(Form("truth_job%d_toy%d",jobn,toy),dataBinned->sumEntries(),false,true,true,true);
toys = toysModel.getToyData();
if (!skipPlots) toysModel.plotToysWithPdfs(Form("%s/plots/toys/job%d_toy%d",outDir.c_str(),jobn,toy),80,false);
}
for (map<string,RooAbsData*>::iterator it=toys.begin(); it!=toys.end(); it++){
// ----- USEFUL DEBUG -----------
// --- this can be a useful check that the truth model values are being cached properly ---
//toysModel.fitToData(it->second,true,false,true);
//toysModel.plotPdfsToData(it->second,80,Form("%s/plots/toys/job%d_toy%d",outDir.c_str(),jobn,toy),true,"NONE");
if (!skipPlots) fabianProfiler.plotNominalFits(it->second,mass,80,Form("%s/plots/toys/job%d_toy%d_fit_fab",outDir.c_str(),jobn,toy));
if (!skipPlots) paulProfiler.plotNominalFits(it->second,mass,80,Form("%s/plots/toys/job%d_toy%d_fit_paul",outDir.c_str(),jobn,toy));
//continue;
// --------------------------------
cout << "Fitting toy for truth model " << distance(toys.begin(),it) << "/" << toys.size() << " (" << it->first << ") " << endl;
// get Fabian envelope
pair<double,map<string,TGraph*> > fabianMinNlls = fabianProfiler.profileLikelihood(it->second,mass,mu,mu_low,mu_high,mu_step);
pair<double,map<string,TGraph*> > fabianEnvelope = fabianProfiler.computeEnvelope(fabianMinNlls,Form("fabEnvelope_job%d_%s_cat%d_toy%d",jobn,it->first.c_str(),cat,toy),0.);
if (!skipPlots) fabianProfiler.plot(fabianEnvelope.second,Form("%s/plots/envelopeNlls/nlls_fab_%s_cat%d_toy%d",outDir.c_str(),it->first.c_str(),cat,toy));
// get Paul envelopes
pair<double,map<string,TGraph*> > paulMinNlls = paulProfiler.profileLikelihood(it->second,mass,mu,mu_low,mu_high,mu_step);
pair<double,map<string,TGraph*> > paulEnvelope = paulProfiler.computeEnvelope(paulMinNlls,Form("paulEnvelope_job%d_%s_cat%d_toy%d",jobn,it->first.c_str(),cat,toy),0.);
if (!skipPlots) paulProfiler.plot(paulEnvelope.second,Form("%s/plots/envelopeNlls/nlls_paul_%s_cat%d_toy%d",outDir.c_str(),it->first.c_str(),cat,toy));
pair<double,map<string,TGraph*> > chi2Envelope = paulProfiler.computeEnvelope(paulMinNlls,Form("chi2Envelope_job%d_%s_cat%d_toy%d",jobn,it->first.c_str(),cat,toy),1.);
if (!skipPlots) paulProfiler.plot(chi2Envelope.second,Form("%s/plots/envelopeNlls/nlls_chi2_%s_cat%d_toy%d",outDir.c_str(),it->first.c_str(),cat,toy));
pair<double,map<string,TGraph*> > aicEnvelope = paulProfiler.computeEnvelope(paulMinNlls,Form("aicEnvelope_job%d_%s_cat%d_toy%d",jobn,it->first.c_str(),cat,toy),2.);
if (!skipPlots) paulProfiler.plot(aicEnvelope.second,Form("%s/plots/envelopeNlls/nlls_aic_%s_cat%d_toy%d",outDir.c_str(),it->first.c_str(),cat,toy));
pair<double,pair<double,double> > muFabInfo = ProfileMultiplePdfs::getMinAndErrorAsymm(fabianEnvelope.second["envelope"],1.);
pair<double,pair<double,double> > muPaulInfo = ProfileMultiplePdfs::getMinAndErrorAsymm(paulEnvelope.second["envelope"],1.);
pair<double,pair<double,double> > muChi2Info = ProfileMultiplePdfs::getMinAndErrorAsymm(chi2Envelope.second["envelope"],1.);
pair<double,pair<double,double> > muAICInfo = ProfileMultiplePdfs::getMinAndErrorAsymm(aicEnvelope.second["envelope"],1.);
truthModel.push_back(it->first);
muFab.push_back(muFabInfo.first);
muPaul.push_back(muPaulInfo.first);
muChi2.push_back(muChi2Info.first);
muAIC.push_back(muAICInfo.first);
muFabErrLow.push_back(muFabInfo.second.first);
muPaulErrLow.push_back(muPaulInfo.second.first);
muChi2ErrLow.push_back(muChi2Info.second.first);
muAICErrLow.push_back(muAICInfo.second.first);
muFabErrHigh.push_back(muFabInfo.second.second);
muPaulErrHigh.push_back(muPaulInfo.second.second);
muChi2ErrHigh.push_back(muChi2Info.second.second);
muAICErrHigh.push_back(muAICInfo.second.second);
cout << "Fab mu = " << muFabInfo.first << " - " << muFabInfo.second.first << " + " << muFabInfo.second.second << endl;
cout << "Paul mu = " << muPaulInfo.first << " - " << muPaulInfo.second.first << " + " << muPaulInfo.second.second << endl;
cout << "Chi2 mu = " << muChi2Info.first << " - " << muChi2Info.second.first << " + " << muChi2Info.second.second << endl;
cout << "AIC mu = " << muAICInfo.first << " - " << muAICInfo.second.first << " + " << muAICInfo.second.second << endl;
outFile->cd();
fabianEnvelope.second["envelope"]->Write();
paulEnvelope.second["envelope"]->Write();
chi2Envelope.second["envelope"]->Write();
aicEnvelope.second["envelope"]->Write();
}
toyn=toy;
muTree->Fill();
}
outFile->cd();
muTree->Write();
cout << "Done." << endl;
cout << "Whole process took..." << endl;
cout << "\t "; sw.Print();
outFile->Close();
return 0;
}
void StandardBayesianNumericalDemo(const char* infile = "",
const char* workspaceName = "combined",
const char* modelConfigName = "ModelConfig",
const char* dataName = "obsData"){
/////////////////////////////////////////////////////////////
// First part is just to access a user-defined file
// or create the standard example file if it doesn't exist
////////////////////////////////////////////////////////////
TString filename = infile;
if (filename.IsNull()) {
filename = "results/example_combined_GaussExample_model.root";
std::cout << "Use standard file generated with HistFactory : " << filename << std::endl;
}
// Check if example input file exists
TFile *file = TFile::Open(filename);
// if input file was specified but not found, quit
if(!file && !TString(infile).IsNull()){
cout <<"file " << filename << " not found" << endl;
return;
}
// if default file not found, try to create it
if(!file ){
// Normally this would be run on the command line
cout <<"will run standard hist2workspace example"<<endl;
gROOT->ProcessLine(".! prepareHistFactory .");
gROOT->ProcessLine(".! hist2workspace config/example.xml");
cout <<"\n\n---------------------"<<endl;
cout <<"Done creating example input"<<endl;
cout <<"---------------------\n\n"<<endl;
// now try to access the file again
file = TFile::Open(filename);
}
if(!file){
// if it is still not there, then we can't continue
cout << "Not able to run hist2workspace to create example input" <<endl;
return;
}
/////////////////////////////////////////////////////////////
// Tutorial starts here
////////////////////////////////////////////////////////////
// get the workspace out of the file
RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName);
if(!w){
cout <<"workspace not found" << endl;
return;
}
// get the modelConfig out of the file
ModelConfig* mc = (ModelConfig*) w->obj(modelConfigName);
// get the modelConfig out of the file
RooAbsData* data = w->data(dataName);
// make sure ingredients are found
if(!data || !mc){
w->Print();
cout << "data or ModelConfig was not found" <<endl;
return;
}
/////////////////////////////////////////////
// create and use the BayesianCalculator
// to find and plot the 95% credible interval
// on the parameter of interest as specified
// in the model config
// before we do that, we must specify our prior
// it belongs in the model config, but it may not have
// been specified
RooUniform prior("prior","",*mc->GetParametersOfInterest());
w->import(prior);
mc->SetPriorPdf(*w->pdf("prior"));
// do without systematics
//mc->SetNuisanceParameters(RooArgSet() );
BayesianCalculator bayesianCalc(*data,*mc);
bayesianCalc.SetConfidenceLevel(0.95); // 95% interval
// default of the calculator is central interval. here use shortest , central or upper limit depending on input
// doing a shortest interval might require a longer time since it requires a scan of the posterior function
if (intervalType == 0) bayesianCalc.SetShortestInterval(); // for shortest interval
if (intervalType == 1) bayesianCalc.SetLeftSideTailFraction(0.5); // for central interval
if (intervalType == 2) bayesianCalc.SetLeftSideTailFraction(0.); // for upper limit
if (!integrationType.IsNull() ) {
bayesianCalc.SetIntegrationType(integrationType); // set integrationType
bayesianCalc.SetNumIters(nToys); // set number of ietrations (i.e. number of toys for MC integrations)
}
// compute interval by scanning the posterior function
if (scanPosterior)
bayesianCalc.SetScanOfPosterior(nScanPoints);
SimpleInterval* interval = bayesianCalc.GetInterval();
// print out the iterval on the first Parameter of Interest
RooRealVar* firstPOI = (RooRealVar*) mc->GetParametersOfInterest()->first();
cout << "\n95% interval on " <<firstPOI->GetName()<<" is : ["<<
interval->LowerLimit() << ", "<<
interval->UpperLimit() <<"] "<<endl;
// make a plot
// since plotting may take a long time (it requires evaluating
// the posterior in many points) this command will speed up
// by reducing the number of points to plot - do 50
cout << "\nDrawing plot of posterior function....." << endl;
bayesianCalc.SetScanOfPosterior(nScanPoints);
RooPlot * plot = bayesianCalc.GetPosteriorPlot();
plot->Draw();
}
void RA2bHypoTestInvDemo(const char * fileName =0,
const char * wsName = "combined",
const char * modelSBName = "ModelConfig",
const char * modelBName = "",
const char * dataName = "obsData",
int calculatorType = 0,
int testStatType = 3,
bool useCls = true ,
int npoints = 5,
double poimin = 0,
double poimax = 5,
int ntoys=1000,
int mgl = -1,
int mlsp = -1,
const char * outFileName = "test")
{
/*
Other Parameter to pass in tutorial
apart from standard for filename, ws, modelconfig and data
type = 0 Freq calculator
type = 1 Hybrid
testStatType = 0 LEP
= 1 Tevatron
= 2 Profile Likelihood
= 3 Profile Likelihood one sided (i.e. = 0 if mu < mu_hat)
useCLs scan for CLs (otherwise for CLs+b)
npoints: number of points to scan , for autoscan set npoints = -1
poimin,poimax: min/max value to scan in case of fixed scans
(if min >= max, try to find automatically)
ntoys: number of toys to use
extra options are available as global paramters of the macro. They are:
plotHypoTestResult plot result of tests at each point (TS distributions)
useProof = true;
writeResult = true;
nworkers = 4;
*/
if (fileName==0) {
fileName = "results/example_combined_GaussExample_model.root";
std::cout << "Use standard file generated with HistFactory :" << fileName << std::endl;
}
TFile * file = new TFile(fileName);
RooWorkspace * w = dynamic_cast<RooWorkspace*>( file->Get(wsName) );
HypoTestInverterResult * r = 0;
std::cout << w << "\t" << fileName << std::endl;
if (w != NULL) {
r = RunInverter(w, modelSBName, modelBName, dataName, calculatorType, testStatType, npoints, poimin, poimax, ntoys, useCls );
if (!r) {
std::cerr << "Error running the HypoTestInverter - Exit " << std::endl;
return;
}
}
else
{
// case workspace is not present look for the inverter result
std::cout << "Reading an HypoTestInverterResult with name " << wsName << " from file " << fileName << std::endl;
r = dynamic_cast<HypoTestInverterResult*>( file->Get(wsName) ); //
if (!r) {
std::cerr << "File " << fileName << " does not contain a workspace or an HypoTestInverterResult - Exit "
<< std::endl;
file->ls();
return;
}
}
printf("\n\n") ;
HypoTestResult* htr = r->GetResult(0) ;
printf(" Data value for test stat : %7.3f\n", htr->GetTestStatisticData() ) ;
printf(" CLsplusb : %9.4f\n", r->CLsplusb(0) ) ;
printf(" CLb : %9.4f\n", r->CLb(0) ) ;
printf(" CLs : %9.4f\n", r->CLs(0) ) ;
printf("\n\n") ;
cout << flush ;
double upperLimit = r->UpperLimit();
double ulError = r->UpperLimitEstimatedError();
std::cout << "The computed upper limit is: " << upperLimit << " +/- " << ulError << std::endl;
const int nEntries = r->ArraySize();
const char * typeName = (calculatorType == 0) ? "Frequentist" : "Hybrid";
const char * resultName = (w) ? w->GetName() : r->GetName();
TString plotTitle = TString::Format("%s CL Scan for workspace %s",typeName,resultName);
HypoTestInverterPlot *plot = new HypoTestInverterPlot("HTI_Result_Plot",plotTitle,r);
TCanvas* c1 = new TCanvas() ;
plot->Draw("CLb 2CL"); // plot all and Clb
c1->Update() ;
c1->SaveAs("cls-canv1.png") ;
c1->SaveAs("cls-canv1.pdf") ;
if (plotHypoTestResult) {
TCanvas * c2 = new TCanvas();
c2->Divide( 2, TMath::Ceil(nEntries/2));
for (int i=0; i<nEntries; i++) {
c2->cd(i+1);
SamplingDistPlot * pl = plot->MakeTestStatPlot(i);
pl->SetLogYaxis(true);
pl->Draw();
}
c2->Update() ;
c2->SaveAs("cls-canv2.png") ;
c2->SaveAs("cls-canv2.pdf") ;
}
std::cout << " expected limit (median) " << r->GetExpectedUpperLimit(0) << std::endl;
std::cout << " expected limit (-1 sig) " << r->GetExpectedUpperLimit(-1) << std::endl;
std::cout << " expected limit (+1 sig) " << r->GetExpectedUpperLimit(1) << std::endl;
// save 2d histograms bin to file
TH2F *result = new TH2F("result","result",22,100,1200,23,50,1200);
TH2F *exp_res = new TH2F("exp_res","exp_res",22,100,1200,23,50,1200);
TH2F *exp_res_minus = new TH2F("exp_res_minus","exp_res_minus",22,100,1200,23,50,1200);
TH2F *exp_res_plus = new TH2F("exp_res_plus","exp_res_plus",22,100,1200,23,50,1200);
result->Fill(mgl,mlsp,upperLimit);
exp_res->Fill(mgl,mlsp,r->GetExpectedUpperLimit(0));
exp_res_minus->Fill(mgl,mlsp,r->GetExpectedUpperLimit(-1));
exp_res_plus->Fill(mgl,mlsp,r->GetExpectedUpperLimit(1));
TFile *f = new TFile(outFileName,"RECREATE");
f->cd();
result->Write();
exp_res->Write();
exp_res_minus->Write();
exp_res_plus->Write();
f->Close();
if (w != NULL && writeResult) {
// write to a file the results
const char * calcType = (calculatorType == 0) ? "Freq" : "Hybr";
const char * limitType = (useCls) ? "CLs" : "Cls+b";
const char * scanType = (npoints < 0) ? "auto" : "grid";
TString resultFileName = TString::Format("%s_%s_%s_ts%d_",calcType,limitType,scanType,testStatType);
resultFileName += fileName;
TFile * fileOut = new TFile(resultFileName,"RECREATE");
r->Write();
fileOut->Close();
}
}
double StandardFrequentistDiscovery(
const char* infile = "",
const char* workspaceName = "channel1",
const char* modelConfigNameSB = "ModelConfig",
const char* dataName = "obsData",
int toys = 1000,
double poiValueForBackground = 0.0,
double poiValueForSignal = 1.0
) {
// The workspace contains the model for s+b. The b model is "autogenerated"
// by copying s+b and setting the one parameter of interest to zero.
// To keep the script simple, multiple parameters of interest or different
// functional forms of the b model are not supported.
// for now, assume there is only one parameter of interest, and these are
// its values:
/////////////////////////////////////////////////////////////
// First part is just to access a user-defined file
// or create the standard example file if it doesn't exist
////////////////////////////////////////////////////////////
const char* filename = "";
if (!strcmp(infile,"")) {
filename = "results/example_channel1_GammaExample_model.root";
bool fileExist = !gSystem->AccessPathName(filename); // note opposite return code
// if file does not exists generate with histfactory
if (!fileExist) {
#ifdef _WIN32
cout << "HistFactory file cannot be generated on Windows - exit" << endl;
return -1;
#endif
// Normally this would be run on the command line
cout <<"will run standard hist2workspace example"<<endl;
gROOT->ProcessLine(".! prepareHistFactory .");
gROOT->ProcessLine(".! hist2workspace config/example.xml");
cout <<"\n\n---------------------"<<endl;
cout <<"Done creating example input"<<endl;
cout <<"---------------------\n\n"<<endl;
}
}
else
filename = infile;
// Try to open the file
TFile *file = TFile::Open(filename);
// if input file was specified byt not found, quit
if(!file ){
cout <<"StandardRooStatsDemoMacro: Input file " << filename << " is not found" << endl;
return -1;
}
/////////////////////////////////////////////////////////////
// Tutorial starts here
////////////////////////////////////////////////////////////
TStopwatch *mn_t = new TStopwatch;
mn_t->Start();
// get the workspace out of the file
RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName);
if (!w) {
cout << "workspace not found" << endl;
return -1.0;
}
// get the modelConfig out of the file
ModelConfig* mc = (ModelConfig*) w->obj(modelConfigNameSB);
// get the data out of the file
RooAbsData* data = w->data(dataName);
// make sure ingredients are found
if (!data || !mc) {
w->Print();
cout << "data or ModelConfig was not found" << endl;
return -1.0;
}
RooRealVar* firstPOI = (RooRealVar*) mc->GetParametersOfInterest()->first();
firstPOI->setVal(poiValueForSignal);
mc->SetSnapshot(*mc->GetParametersOfInterest());
// create null model
ModelConfig *mcNull = mc->Clone("ModelConfigNull");
firstPOI->setVal(poiValueForBackground);
mcNull->SetSnapshot(*(RooArgSet*)mcNull->GetParametersOfInterest()->snapshot());
// ----------------------------------------------------
// Configure a ProfileLikelihoodTestStat and a SimpleLikelihoodRatioTestStat
// to use simultaneously with ToyMCSampler
ProfileLikelihoodTestStat* plts = new ProfileLikelihoodTestStat(*mc->GetPdf());
plts->SetOneSidedDiscovery(true);
plts->SetVarName( "q_{0}/2" );
// ----------------------------------------------------
// configure the ToyMCImportanceSampler with two test statistics
ToyMCSampler toymcs(*plts, 50);
// Since this tool needs to throw toy MC the PDF needs to be
// extended or the tool needs to know how many entries in a dataset
// per pseudo experiment.
// In the 'number counting form' where the entries in the dataset
// are counts, and not values of discriminating variables, the
// datasets typically only have one entry and the PDF is not
// extended.
if (!mc->GetPdf()->canBeExtended()) {
if (data->numEntries() == 1) {
toymcs.SetNEventsPerToy(1);
} else cout << "Not sure what to do about this model" << endl;
}
// We can use PROOF to speed things along in parallel
// ProofConfig pc(*w, 2, "user@yourfavoriteproofcluster", false);
ProofConfig pc(*w, 2, "", false);
//toymcs.SetProofConfig(&pc); // enable proof
// instantiate the calculator
FrequentistCalculator freqCalc(*data, *mc, *mcNull, &toymcs);
freqCalc.SetToys( toys,toys ); // null toys, alt toys
// Run the calculator and print result
HypoTestResult* freqCalcResult = freqCalc.GetHypoTest();
freqCalcResult->GetNullDistribution()->SetTitle( "b only" );
freqCalcResult->GetAltDistribution()->SetTitle( "s+b" );
freqCalcResult->Print();
double pvalue = freqCalcResult->NullPValue();
// stop timing
mn_t->Stop();
cout << "total CPU time: " << mn_t->CpuTime() << endl;
cout << "total real time: " << mn_t->RealTime() << endl;
// plot
TCanvas* c1 = new TCanvas();
HypoTestPlot *plot = new HypoTestPlot(*freqCalcResult, 100, -0.49, 9.51 );
plot->SetLogYaxis(true);
// add chi2 to plot
int nPOI = 1;
TF1* f = new TF1("f", TString::Format("1*ROOT::Math::chisquared_pdf(2*x,%d,0)",nPOI), 0,20);
f->SetLineColor( kBlack );
f->SetLineStyle( 7 );
plot->AddTF1( f, TString::Format("#chi^{2}(2x,%d)",nPOI) );
plot->Draw();
c1->SaveAs("standard_discovery_output.pdf");
return pvalue;
}
MakeAICFits::MakeAICFits() {
//RooRandom::randomGenerator()->SetSeed(314159);
TFile *f = TFile::Open("/mnt/hadoop/store/user/amott/Hgg2013/Hgg/workspaces/hgg_22Jan2013_R9_CIC.root");
RooWorkspace *w = (RooWorkspace*) f->Get("cms_hgg_spin_workspace");
RooAbsData *d = w->data("Data_Combined");
RooCategory* cat = (RooCategory*)cms_hgg_spin_workspace->obj("evtcat");
RooAbsData *dc = d->reduce("evtcat==evtcat::cat4");
//RooAbsData *dc = w->data("Data_Combined")->reduce("evtcar==evtcat::cat0");
RooRealVar *mass = w->var("mass");
const Int_t nToys = 1;
std::cout<<"========== Data Set Made ==========="<<std::endl;
Double_t LogLikelihood[8] = {0,0,0,0,0,0,0,0};
Double_t AIC_bkg_array[8] = {0,0,0,0,0,0,0,0};
Double_t AICc_bkg_array[8] = {0,0,0,0,0,0,0,0};
Int_t avgcov[8] = {0,0,0,0,0,0,0};
std::cout<<"====================== Starting Toys ==============="<<std::endl;
for (Int_t i=0; i<nToys; i++) {
if (i%10==0) {
std::cout<<">> Processing Toy Trial " << i << "/" << nToys << std::endl;
}
int SampleSize = dc->sumEntries();
RooPlot* frame = mass->frame();
TLegend *leg = new TLegend(0.55,0.55,0.9,0.9, NULL, "NDC");
dc->plotOn(frame);
leg->AddEntry(dc,"Hgg Background Cat 4", "lep");
for (int type=0; type<7; type++) {
std::cout<<type<<endl;
}
int display = 7;
for (int type=0; type<display; type++) {
RooAbsPdf* ModelShape;
if (type<7) {
//std::cout<<"Model Shape: "<<type<<std::endl;
ModelShape = MakeAICFits::getBackgroundPdf(type,mass);
//std::cout<<"Model Shape made"<<std::endl;
int k = MakeAICFits::Num_Params(type);
//std::cout<<"Params counted"<<std::endl;
}
if (type==7) {
RooAbsPdf* Model1 = MakeAICFits::getBackgroundPdf(0,mass);
RooAbsPdf* Model2 = MakeAICFits::getBackgroundPdf(1,mass);
RooAbsPdf* Model3 = MakeAICFits::getBackgroundPdf(2,mass);
RooAbsPdf* Model4 = MakeAICFits::getBackgroundPdf(3,mass);
RooAbsPdf* Model5 = MakeAICFits::getBackgroundPdf(4,mass);
int k = MakeAICFits::Num_Params(3);
k+= MakeAICFits::Num_Params(1);
k+= MakeAICFits::Num_Params(0);
//k+= MakeAICFits::Num_Params(3);
//k+= MakeAICFits::Num_Params(4);
RooRealVar* modratio1 = new RooRealVar("modrat1", "modrat1", 0.62, 0.6, 0.7);
RooRealVar* modratio2 = new RooRealVar("modrat2", "modrat2", 0.29, 0.25, 0.35);
RooRealVar* modratio3 = new RooRealVar("modrat3", "modrat3", 0.01);
//RooRealVar* modratio4 = new RooRealVar("modrat4", "modrat4", 0.25);
ModelShape = new RooAddPdf("Composite", "Background Model", RooArgList(*Model1, *Model4, *Model2), RooArgList(*modratio1, *modratio2));
}
RooRealVar *Nbkg = new RooRealVar("Nbkg","N Background Events", SampleSize,0,1e9);
RooExtendPdf *BkgModel = new RooExtendPdf("BKGFIT_bkgModel", "Background Model", *ModelShape, *Nbkg);
TH1F* Model = new TH1F("Model", "Model", 100,0,100);
RooFitResult *bkg_databkg = BkgModel->fitTo(*dc, RooFit::Save(kTRUE), RooFit::Optimize(0));
if (type == 0) {
BkgModel->plotOn(frame, RooFit::LineColor(kBlue));
Model->SetLineColor(kBlue);
leg->AddEntry(Model, "Exponential Model", "l");
}
if (type == 4) {
BkgModel->plotOn(frame, RooFit::LineColor(kRed));
Model->SetLineColor(kRed);
leg->AddEntry(Model, "Polynomial Model", "l");
}
if (type == 5) {
BkgModel->plotOn(frame, RooFit::LineColor(kGreen));
Model->SetLineColor(kGreen);
leg->AddEntry(Model, "Power Model", "l");
}
if (type == 7) {
BkgModel->plotOn(frame, RooFit::LineColor(kMagenta));
Model->SetLineColor(kMagenta);
leg->AddEntry(Model, "Composite Model", "l");
}
Double_t bkg_databkg_Nll = bkg_databkg->minNll();
Int_t covariance = bkg_databkg->covQual();
avgcov[type] += covariance;
//assert (covariance == 3);
// Calculate AIC for each model
LogLikelihood[type] += -bkg_databkg_Nll;
AICc_bkg_array[type] += 2.*(k + k*(k + 1.)/(SampleSize - (k + 1.)) + bkg_databkg_Nll);
AIC_bkg_array[type] += 2.*(k + bkg_databkg_Nll);
// Clean up objects
delete bkg_databkg;
bkg_databkg_Nll = 0.;
}
//delete databkg;
TCanvas *c = new TCanvas("", "", 800, 600);
frame->Draw();
leg->Draw();
c->Update();
c->Print("HggData_cat4.pdf");
}
std::cout<<"Printing AIC Values" << std::endl;
//std::cout<<"Log Likelihood Data : " << LogLikelihood_data <<std::endl;
int display = 7;
for (int type = 0; type<display; type++) {
avgcov[type] = avgcov[type]/nToys;
LogLikelihood[type] = LogLikelihood[type]/nToys;
AIC_bkg_array[type] = AIC_bkg_array[type]/nToys;
AICc_bkg_array[type] = AICc_bkg_array[type]/nToys;
std::cout<<"average covariance quality" << type <<" ===" << avgcov[type] <<std::endl;
std::cout<<"Log Likelihood for Model " << type << " ==== " << LogLikelihood[type] <<std::endl;
std::cout<<"AICc Value for Model: " << type << " ==== " << AICc_bkg_array[type] <<std::endl;
std::cout<<"AIC Value for Model: " << type << " ==== " << AIC_bkg_array[type] << std::endl;
}
double minAIC = 10000000000.;
for (int type = 0; type<display; type++) {
if (AICc_bkg_array[type] < minAIC) {
minAIC = AICc_bkg_array[type];
}
}
std::cout<<"Minimum AIC: " << minAIC << std::endl;
double DeltaIA[8];
double sumExpA=0;
int bestmodelA;
for (int type = 0; type<display; type++) {
DeltaIA[type] = AICc_bkg_array[type] - minAIC;
if (DeltaIA[type] == 0) {
bestmodelA = type;
}
std::cout<<"Delta AIC values : " << type << " ====" << DeltaIA[type] <<std::endl;
sumExpA+= exp(-DeltaIA[type]/2);
}
double AICweights[8];
for (int type = 0; type<display; type++) {
AICweights[type] = exp(-DeltaIA[type]/2)/sumExpA;
std::cout<<"Relative Likelihood AIC " << type << " ==== " <<exp(-DeltaIA[type]/2)<<std::endl;
std::cout<<"AIC Weights : " << type << " =====" << AICweights[type] <<std::endl;
}
for (int type2 = 0; type2<display; type2++) {
std::cout<< "AIC Ratio for: " << "Model " << bestmodelA << " / " << "Model " << type2 << " = " << AICweights[bestmodelA]/AICweights[type2] <<std::endl;
}
}
void OneSidedFrequentistUpperLimitWithBands_intermediate(const char* infile = "",
const char* workspaceName = "combined",
const char* modelConfigName = "ModelConfig",
const char* dataName = "obsData"){
double confidenceLevel=0.95;
// degrade/improve number of pseudo-experiments used to define the confidence belt.
// value of 1 corresponds to default number of toys in the tail, which is 50/(1-confidenceLevel)
double additionalToysFac = 1.;
int nPointsToScan = 30; // number of steps in the parameter of interest
int nToyMC = 100; // number of toys used to define the expected limit and band
TStopwatch t;
t.Start();
/////////////////////////////////////////////////////////////
// First part is just to access a user-defined file
// or create the standard example file if it doesn't exist
////////////////////////////////////////////////////////////
const char* filename = "";
if (!strcmp(infile,""))
filename = "results/example_combined_GaussExample_model.root";
else
filename = infile;
// Check if example input file exists
TFile *file = TFile::Open(filename);
// if input file was specified byt not found, quit
if(!file && strcmp(infile,"")){
cout <<"file not found" << endl;
return;
}
// if default file not found, try to create it
if(!file ){
// Normally this would be run on the command line
cout <<"will run standard hist2workspace example"<<endl;
gROOT->ProcessLine(".! prepareHistFactory .");
gROOT->ProcessLine(".! hist2workspace config/example.xml");
cout <<"\n\n---------------------"<<endl;
cout <<"Done creating example input"<<endl;
cout <<"---------------------\n\n"<<endl;
}
// now try to access the file again
file = TFile::Open(filename);
if(!file){
// if it is still not there, then we can't continue
cout << "Not able to run hist2workspace to create example input" <<endl;
return;
}
/////////////////////////////////////////////////////////////
// Now get the data and workspace
////////////////////////////////////////////////////////////
// get the workspace out of the file
RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName);
if(!w){
cout <<"workspace not found" << endl;
return;
}
// get the modelConfig out of the file
ModelConfig* mc = (ModelConfig*) w->obj(modelConfigName);
// get the modelConfig out of the file
RooAbsData* data = w->data(dataName);
// make sure ingredients are found
if(!data || !mc){
w->Print();
cout << "data or ModelConfig was not found" <<endl;
return;
}
cout << "Found data and ModelConfig:" <<endl;
mc->Print();
/////////////////////////////////////////////////////////////
// Now get the POI for convenience
// you may want to adjust the range of your POI
////////////////////////////////////////////////////////////
RooRealVar* firstPOI = (RooRealVar*) mc->GetParametersOfInterest()->first();
// firstPOI->setMin(0);
// firstPOI->setMax(10);
/////////////////////////////////////////////
// create and use the FeldmanCousins tool
// to find and plot the 95% confidence interval
// on the parameter of interest as specified
// in the model config
// REMEMBER, we will change the test statistic
// so this is NOT a Feldman-Cousins interval
FeldmanCousins fc(*data,*mc);
fc.SetConfidenceLevel(confidenceLevel);
fc.AdditionalNToysFactor(additionalToysFac); // improve sampling that defines confidence belt
// fc.UseAdaptiveSampling(true); // speed it up a bit, but don't use for expectd limits
fc.SetNBins(nPointsToScan); // set how many points per parameter of interest to scan
fc.CreateConfBelt(true); // save the information in the belt for plotting
/////////////////////////////////////////////
// Feldman-Cousins is a unified limit by definition
// but the tool takes care of a few things for us like which values
// of the nuisance parameters should be used to generate toys.
// so let's just change the test statistic and realize this is
// no longer "Feldman-Cousins" but is a fully frequentist Neyman-Construction.
// ProfileLikelihoodTestStatModified onesided(*mc->GetPdf());
// fc.GetTestStatSampler()->SetTestStatistic(&onesided);
// ((ToyMCSampler*) fc.GetTestStatSampler())->SetGenerateBinned(true);
ToyMCSampler* toymcsampler = (ToyMCSampler*) fc.GetTestStatSampler();
ProfileLikelihoodTestStat* testStat = dynamic_cast<ProfileLikelihoodTestStat*>(toymcsampler->GetTestStatistic());
testStat->SetOneSided(true);
// test speedups:
testStat->SetReuseNLL(true);
// toymcsampler->setUseMultiGen(true); // not fully validated
// Since this tool needs to throw toy MC the PDF needs to be
// extended or the tool needs to know how many entries in a dataset
// per pseudo experiment.
// In the 'number counting form' where the entries in the dataset
// are counts, and not values of discriminating variables, the
// datasets typically only have one entry and the PDF is not
// extended.
if(!mc->GetPdf()->canBeExtended()){
if(data->numEntries()==1)
fc.FluctuateNumDataEntries(false);
else
cout <<"Not sure what to do about this model" <<endl;
}
// We can use PROOF to speed things along in parallel
ProofConfig pc(*w, 4, "",false);
if(mc->GetGlobalObservables()){
cout << "will use global observables for unconditional ensemble"<<endl;
mc->GetGlobalObservables()->Print();
toymcsampler->SetGlobalObservables(*mc->GetGlobalObservables());
}
toymcsampler->SetProofConfig(&pc); // enable proof
// Now get the interval
PointSetInterval* interval = fc.GetInterval();
ConfidenceBelt* belt = fc.GetConfidenceBelt();
// print out the iterval on the first Parameter of Interest
cout << "\n95% interval on " <<firstPOI->GetName()<<" is : ["<<
interval->LowerLimit(*firstPOI) << ", "<<
interval->UpperLimit(*firstPOI) <<"] "<<endl;
// get observed UL and value of test statistic evaluated there
RooArgSet tmpPOI(*firstPOI);
double observedUL = interval->UpperLimit(*firstPOI);
firstPOI->setVal(observedUL);
double obsTSatObsUL = fc.GetTestStatSampler()->EvaluateTestStatistic(*data,tmpPOI);
// Ask the calculator which points were scanned
RooDataSet* parameterScan = (RooDataSet*) fc.GetPointsToScan();
RooArgSet* tmpPoint;
// make a histogram of parameter vs. threshold
TH1F* histOfThresholds = new TH1F("histOfThresholds","",
parameterScan->numEntries(),
firstPOI->getMin(),
firstPOI->getMax());
histOfThresholds->GetXaxis()->SetTitle(firstPOI->GetName());
histOfThresholds->GetYaxis()->SetTitle("Threshold");
// loop through the points that were tested and ask confidence belt
// what the upper/lower thresholds were.
// For FeldmanCousins, the lower cut off is always 0
for(Int_t i=0; i<parameterScan->numEntries(); ++i){
tmpPoint = (RooArgSet*) parameterScan->get(i)->clone("temp");
double arMax = belt->GetAcceptanceRegionMax(*tmpPoint);
double poiVal = tmpPoint->getRealValue(firstPOI->GetName()) ;
histOfThresholds->Fill(poiVal,arMax);
}
TCanvas* c1 = new TCanvas();
c1->Divide(2);
c1->cd(1);
histOfThresholds->SetMinimum(0);
histOfThresholds->Draw();
c1->cd(2);
/////////////////////////////////////////////////////////////
// Now we generate the expected bands and power-constriant
////////////////////////////////////////////////////////////
// First: find parameter point for mu=0, with conditional MLEs for nuisance parameters
RooAbsReal* nll = mc->GetPdf()->createNLL(*data);
RooAbsReal* profile = nll->createProfile(*mc->GetParametersOfInterest());
firstPOI->setVal(0.);
profile->getVal(); // this will do fit and set nuisance parameters to profiled values
RooArgSet* poiAndNuisance = new RooArgSet();
if(mc->GetNuisanceParameters())
poiAndNuisance->add(*mc->GetNuisanceParameters());
poiAndNuisance->add(*mc->GetParametersOfInterest());
w->saveSnapshot("paramsToGenerateData",*poiAndNuisance);
RooArgSet* paramsToGenerateData = (RooArgSet*) poiAndNuisance->snapshot();
cout << "\nWill use these parameter points to generate pseudo data for bkg only" << endl;
paramsToGenerateData->Print("v");
double CLb=0;
double CLbinclusive=0;
// Now we generate background only and find distribution of upper limits
TH1F* histOfUL = new TH1F("histOfUL","",100,0,firstPOI->getMax());
histOfUL->GetXaxis()->SetTitle("Upper Limit (background only)");
histOfUL->GetYaxis()->SetTitle("Entries");
for(int imc=0; imc<nToyMC; ++imc){
// set parameters back to values for generating pseudo data
w->loadSnapshot("paramsToGenerateData");
// in 5.30 there is a nicer way to generate toy data & randomize global obs
RooAbsData* toyData = toymcsampler->GenerateToyData(*paramsToGenerateData);
// get test stat at observed UL in observed data
firstPOI->setVal(observedUL);
double toyTSatObsUL = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData,tmpPOI);
// toyData->get()->Print("v");
// cout <<"obsTSatObsUL " <<obsTSatObsUL << "toyTS " << toyTSatObsUL << endl;
if(obsTSatObsUL < toyTSatObsUL) // (should be checked)
CLb+= (1.)/nToyMC;
if(obsTSatObsUL <= toyTSatObsUL) // (should be checked)
CLbinclusive+= (1.)/nToyMC;
// loop over points in belt to find upper limit for this toy data
double thisUL = 0;
for(Int_t i=0; i<parameterScan->numEntries(); ++i){
tmpPoint = (RooArgSet*) parameterScan->get(i)->clone("temp");
double arMax = belt->GetAcceptanceRegionMax(*tmpPoint);
firstPOI->setVal( tmpPoint->getRealValue(firstPOI->GetName()) );
double thisTS = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData,tmpPOI);
if(thisTS<=arMax){
thisUL = firstPOI->getVal();
} else{
break;
}
}
histOfUL->Fill(thisUL);
delete toyData;
}
histOfUL->Draw();
c1->SaveAs("one-sided_upper_limit_output.pdf");
// if you want to see a plot of the sampling distribution for a particular scan point:
// Now find bands and power constraint
Double_t* bins = histOfUL->GetIntegral();
TH1F* cumulative = (TH1F*) histOfUL->Clone("cumulative");
cumulative->SetContent(bins);
double band2sigDown=0, band1sigDown=0, bandMedian=0, band1sigUp=0,band2sigUp=0;
for(int i=1; i<=cumulative->GetNbinsX(); ++i){
if(bins[i]<RooStats::SignificanceToPValue(2))
band2sigDown=cumulative->GetBinCenter(i);
if(bins[i]<RooStats::SignificanceToPValue(1))
band1sigDown=cumulative->GetBinCenter(i);
if(bins[i]<0.5)
bandMedian=cumulative->GetBinCenter(i);
if(bins[i]<RooStats::SignificanceToPValue(-1))
band1sigUp=cumulative->GetBinCenter(i);
if(bins[i]<RooStats::SignificanceToPValue(-2))
band2sigUp=cumulative->GetBinCenter(i);
}
t.Stop();
t.Print();
cout << "-2 sigma band " << band2sigDown << endl;
cout << "-1 sigma band " << band1sigDown << endl;
cout << "median of band " << bandMedian << " [Power Constriant)]" << endl;
cout << "+1 sigma band " << band1sigUp << endl;
cout << "+2 sigma band " << band2sigUp << endl;
// print out the iterval on the first Parameter of Interest
cout << "\nobserved 95% upper-limit "<< interval->UpperLimit(*firstPOI) <<endl;
cout << "CLb strict [P(toy>obs|0)] for observed 95% upper-limit "<< CLb <<endl;
cout << "CLb inclusive [P(toy>=obs|0)] for observed 95% upper-limit "<< CLbinclusive <<endl;
delete profile;
delete nll;
}
void new_RA4(){
// let's time this challenging example
TStopwatch t;
t.Start();
// set RooFit random seed for reproducible results
RooRandom::randomGenerator()->SetSeed(4357);
// make model
RooWorkspace* wspace = new RooWorkspace("wspace");
wspace->factory("Gaussian::sigCons(prime_SigEff[0,-5,5], nom_SigEff[0,-5,5], 1)");
wspace->factory("expr::SigEff('1.0*pow(1.20,@0)',prime_SigEff)"); // // 1+-20%, 1.20=exp(20%)
wspace->factory("Poisson::on(non[0,50], sum::splusb(prod::SigUnc(s[0,0,50],SigEff),mainb[8.8,0,50],dilep[0.9,0,20],tau[2.3,0,20],QCD[0.,0,10],MC[0.1,0,4]))");
wspace->factory("Gaussian::mcCons(prime_rho[0,-5,5], nom_rho[0,-5,5], 1)");
wspace->factory("expr::rho('1.0*pow(1.39,@0)',prime_rho)"); // // 1+-39%
wspace->factory("Poisson::off(noff[0,200], prod::rhob(mainb,rho,mu_plus_e[0.74,0.01,10],1.08))");
wspace->factory("Gaussian::mcCons2(mu_plus_enom[0.74,0.01,4], mu_plus_e, sigmatwo[.05])");
wspace->factory("Gaussian::dilep_pred(dilep_nom[0.9,0,20], dilep, sigma3[2.2])");
wspace->factory("Gaussian::tau_pred(tau_nom[2.3,0,20], tau, sigma4[0.5])");
wspace->factory("Gaussian::QCD_pred(QCD_nom[0.0,0,10], QCD, sigma5[1.0])");
wspace->factory("Gaussian::MC_pred(MC_nom[0.1,0.01,4], MC, sigma7[0.14])");
wspace->factory("PROD::model(on,off,mcCons,mcCons2,sigCons,dilep_pred,tau_pred,QCD_pred,MC_pred)");
RooArgSet obs(*wspace->var("non"), *wspace->var("noff"), *wspace->var("mu_plus_enom"), *wspace->var("dilep_nom"), *wspace->var("tau_nom"), "obs");
obs.add(*wspace->var("QCD_nom")); obs.add(*wspace->var("MC_nom"));
RooArgSet globalObs(*wspace->var("nom_SigEff"), *wspace->var("nom_rho"), "global_obs");
// fix global observables to their nominal values
wspace->var("nom_SigEff")->setConstant();
wspace->var("nom_rho")->setConstant();
RooArgSet poi(*wspace->var("s"), "poi");
RooArgSet nuis(*wspace->var("mainb"), *wspace->var("prime_rho"), *wspace->var("prime_SigEff"), *wspace->var("mu_plus_e"), *wspace->var("dilep"), *wspace->var("tau"), "nuis");
nuis.add(*wspace->var("QCD")); nuis.add(*wspace->var("MC"));
wspace->factory("Uniform::prior_poi({s})");
wspace->factory("Uniform::prior_nuis({mainb,mu_plus_e,dilep,tau,QCD,MC})");
wspace->factory("PROD::prior(prior_poi,prior_nuis)");
wspace->var("non")->setVal(8); //observed
//wspace->var("non")->setVal(12); //expected observation
wspace->var("noff")->setVal(7); //observed events in control region
wspace->var("mu_plus_enom")->setVal(0.74);
wspace->var("dilep_nom")->setVal(0.9);
wspace->var("tau_nom")->setVal(2.3);
wspace->var("QCD")->setVal(0.0);
wspace->var("MC")->setVal(0.1);
RooDataSet * data = new RooDataSet("data","",obs);
data->add(obs);
wspace->import(*data);
/////////////////////////////////////////////////////
// Now the statistical tests
// model config
ModelConfig* pSbModel = new ModelConfig("SbModel");
pSbModel->SetWorkspace(*wspace);
pSbModel->SetPdf(*wspace->pdf("model"));
pSbModel->SetPriorPdf(*wspace->pdf("prior"));
pSbModel->SetParametersOfInterest(poi);
pSbModel->SetNuisanceParameters(nuis);
pSbModel->SetObservables(obs);
pSbModel->SetGlobalObservables(globalObs);
wspace->import(*pSbModel);
// set all but obs, poi and nuisance to const
SetConstants(wspace, pSbModel);
wspace->import(*pSbModel);
Double_t poiValueForBModel = 0.0;
ModelConfig* pBModel = new ModelConfig(*(RooStats::ModelConfig *)wspace->obj("SbModel"));
pBModel->SetName("BModel");
pBModel->SetWorkspace(*wspace);
wspace->import(*pBModel);
RooAbsReal * pNll = pSbModel->GetPdf()->createNLL(*data);
RooAbsReal * pProfile = pNll->createProfile(RooArgSet());
pProfile->getVal(); // this will do fit and set POI and nuisance parameters to fitted values
RooArgSet * pPoiAndNuisance = new RooArgSet();
//if(pSbModel->GetNuisanceParameters())
// pPoiAndNuisance->add(*pSbModel->GetNuisanceParameters());
pPoiAndNuisance->add(*pSbModel->GetParametersOfInterest());
cout << "\nWill save these parameter points that correspond to the fit to data" << endl;
pPoiAndNuisance->Print("v");
pSbModel->SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
pNll = pBModel->GetPdf()->createNLL(*data);
pProfile = pNll->createProfile(poi);
((RooRealVar *)poi.first())->setVal(poiValueForBModel);
pProfile->getVal(); // this will do fit and set nuisance parameters to profiled values
pPoiAndNuisance = new RooArgSet();
//if(pBModel->GetNuisanceParameters())
// pPoiAndNuisance->add(*pBModel->GetNuisanceParameters());
pPoiAndNuisance->add(*pBModel->GetParametersOfInterest());
cout << "\nShould use these parameter points to generate pseudo data for bkg only" << endl;
pPoiAndNuisance->Print("v");
pBModel->SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// inspect workspace
wspace->Print();
// save workspace to file
wspace->writeToFile("tight.root");
//wspace->writeToFile("tight_median.root");
// clean up
delete wspace;
delete data;
delete pSbModel;
delete pBModel;
}
void StandardHypoTestDemo(const char* infile = "",
const char* workspaceName = "combined",
const char* modelSBName = "ModelConfig",
const char* modelBName = "",
const char* dataName = "obsData",
int calcType = 0, // 0 freq 1 hybrid, 2 asymptotic
int testStatType = 3, // 0 LEP, 1 TeV, 2 LHC, 3 LHC - one sided
int ntoys = 5000,
bool useNC = false,
const char * nuisPriorName = 0)
{
/*
Other Parameter to pass in tutorial
apart from standard for filename, ws, modelconfig and data
type = 0 Freq calculator
type = 1 Hybrid calculator
type = 2 Asymptotic calculator
testStatType = 0 LEP
= 1 Tevatron
= 2 Profile Likelihood
= 3 Profile Likelihood one sided (i.e. = 0 if mu < mu_hat)
ntoys: number of toys to use
useNumberCounting: set to true when using number counting events
nuisPriorName: name of prior for the nnuisance. This is often expressed as constraint term in the global model
It is needed only when using the HybridCalculator (type=1)
If not given by default the prior pdf from ModelConfig is used.
extra options are available as global paramwters of the macro. They major ones are:
generateBinned generate binned data sets for toys (default is false) - be careful not to activate with
a too large (>=3) number of observables
nToyRatio ratio of S+B/B toys (default is 2)
printLevel
*/
// disable - can cause some problems
//ToyMCSampler::SetAlwaysUseMultiGen(true);
SimpleLikelihoodRatioTestStat::SetAlwaysReuseNLL(true);
ProfileLikelihoodTestStat::SetAlwaysReuseNLL(true);
RatioOfProfiledLikelihoodsTestStat::SetAlwaysReuseNLL(true);
//RooRandom::randomGenerator()->SetSeed(0);
// to change minimizers
// ROOT::Math::MinimizerOptions::SetDefaultStrategy(0);
// ROOT::Math::MinimizerOptions::SetDefaultMinimizer("Minuit2");
// ROOT::Math::MinimizerOptions::SetDefaultTolerance(1);
/////////////////////////////////////////////////////////////
// First part is just to access a user-defined file
// or create the standard example file if it doesn't exist
////////////////////////////////////////////////////////////
const char* filename = "";
if (!strcmp(infile,""))
filename = "results/example_combined_GaussExample_model.root";
else
filename = infile;
// Check if example input file exists
TFile *file = TFile::Open(filename);
// if input file was specified byt not found, quit
if(!file && strcmp(infile,"")){
cout <<"file not found" << endl;
return;
}
// if default file not found, try to create it
if(!file ){
// Normally this would be run on the command line
cout <<"will run standard hist2workspace example"<<endl;
gROOT->ProcessLine(".! prepareHistFactory .");
gROOT->ProcessLine(".! hist2workspace config/example.xml");
cout <<"\n\n---------------------"<<endl;
cout <<"Done creating example input"<<endl;
cout <<"---------------------\n\n"<<endl;
}
// now try to access the file again
file = TFile::Open(filename);
if(!file){
// if it is still not there, then we can't continue
cout << "Not able to run hist2workspace to create example input" <<endl;
return;
}
/////////////////////////////////////////////////////////////
// Tutorial starts here
////////////////////////////////////////////////////////////
// get the workspace out of the file
RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName);
if(!w){
cout <<"workspace not found" << endl;
return;
}
w->Print();
// get the modelConfig out of the file
ModelConfig* sbModel = (ModelConfig*) w->obj(modelSBName);
// get the modelConfig out of the file
RooAbsData* data = w->data(dataName);
// make sure ingredients are found
if(!data || !sbModel){
w->Print();
cout << "data or ModelConfig was not found" <<endl;
return;
}
// make b model
ModelConfig* bModel = (ModelConfig*) w->obj(modelBName);
// 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 ) {
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("B_only"));
RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
if (!var) return;
double oldval = var->getVal();
var->setVal(0);
//bModel->SetSnapshot( RooArgSet(*var, *w->var("lumi")) );
bModel->SetSnapshot( RooArgSet(*var) );
var->setVal(oldval);
}
if (!sbModel->GetSnapshot() || poiValue > 0) {
Info("StandardHypoTestDemo","Model %s has no snapshot - make one using model poi",modelSBName);
RooRealVar * var = dynamic_cast<RooRealVar*>(sbModel->GetParametersOfInterest()->first());
if (!var) return;
double oldval = var->getVal();
if (poiValue > 0) var->setVal(poiValue);
//sbModel->SetSnapshot( RooArgSet(*var, *w->var("lumi") ) );
sbModel->SetSnapshot( RooArgSet(*var) );
if (poiValue > 0) var->setVal(oldval);
//sbModel->SetSnapshot( *sbModel->GetParametersOfInterest() );
}
// part 1, hypothesis testing
SimpleLikelihoodRatioTestStat * slrts = new SimpleLikelihoodRatioTestStat(*bModel->GetPdf(), *sbModel->GetPdf());
// null parameters must includes snapshot of poi plus the nuisance values
RooArgSet nullParams(*bModel->GetSnapshot());
if (bModel->GetNuisanceParameters()) nullParams.add(*bModel->GetNuisanceParameters());
slrts->SetNullParameters(nullParams);
RooArgSet altParams(*sbModel->GetSnapshot());
if (sbModel->GetNuisanceParameters()) altParams.add(*sbModel->GetNuisanceParameters());
slrts->SetAltParameters(altParams);
ProfileLikelihoodTestStat * profll = new ProfileLikelihoodTestStat(*bModel->GetPdf());
RatioOfProfiledLikelihoodsTestStat *
ropl = new RatioOfProfiledLikelihoodsTestStat(*bModel->GetPdf(), *sbModel->GetPdf(), sbModel->GetSnapshot());
ropl->SetSubtractMLE(false);
if (testStatType == 3) profll->SetOneSidedDiscovery(1);
profll->SetPrintLevel(printLevel);
// profll.SetReuseNLL(mOptimize);
// slrts.SetReuseNLL(mOptimize);
// ropl.SetReuseNLL(mOptimize);
AsymptoticCalculator::SetPrintLevel(printLevel);
HypoTestCalculatorGeneric * hypoCalc = 0;
// note here Null is B and Alt is S+B
if (calcType == 0) hypoCalc = new FrequentistCalculator(*data, *sbModel, *bModel);
else if (calcType == 1) hypoCalc= new HybridCalculator(*data, *sbModel, *bModel);
else if (calcType == 2) hypoCalc= new AsymptoticCalculator(*data, *sbModel, *bModel);
if (calcType == 0)
((FrequentistCalculator*)hypoCalc)->SetToys(ntoys, ntoys/nToysRatio);
if (calcType == 1)
((HybridCalculator*)hypoCalc)->SetToys(ntoys, ntoys/nToysRatio);
if (calcType == 2 ) {
if (testStatType == 3) ((AsymptoticCalculator*) hypoCalc)->SetOneSidedDiscovery(true);
if (testStatType != 2 && testStatType != 3)
Warning("StandardHypoTestDemo","Only the PL test statistic can be used with AsymptoticCalculator - use by default a two-sided PL");
}
// check for nuisance prior pdf in case of nuisance parameters
if (calcType == 1 && (bModel->GetNuisanceParameters() || sbModel->GetNuisanceParameters() )) {
RooAbsPdf * nuisPdf = 0;
if (nuisPriorName) nuisPdf = w->pdf(nuisPriorName);
// use prior defined first in bModel (then in SbModel)
if (!nuisPdf) {
Info("StandardHypoTestDemo","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("StandardHypoTestDemo","No nuisance pdf given - try to use %s that is defined as a prior pdf in the B model",nuisPdf->GetName());
}
else {
Error("StandardHypoTestDemo","Cannnot run Hybrid calculator because no prior on the nuisance parameter is specified or can be derived");
return;
}
}
assert(nuisPdf);
Info("StandardHypoTestDemo","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("StandardHypoTestDemo","Prior nuisance does not depend on nuisance parameters. They will be smeared in their full range");
}
delete np;
((HybridCalculator*)hypoCalc)->ForcePriorNuisanceAlt(*nuisPdf);
((HybridCalculator*)hypoCalc)->ForcePriorNuisanceNull(*nuisPdf);
}
// hypoCalc->ForcePriorNuisanceAlt(*sbModel->GetPriorPdf());
// hypoCalc->ForcePriorNuisanceNull(*bModel->GetPriorPdf());
ToyMCSampler * sampler = (ToyMCSampler *)hypoCalc->GetTestStatSampler();
if (sampler && (calcType == 0 || calcType == 1) ) {
// look if pdf is number counting or extended
if (sbModel->GetPdf()->canBeExtended() ) {
if (useNC) Warning("StandardHypoTestDemo","Pdf is extended: but number counting flag is set: ignore it ");
}
else {
// for not extended pdf
if (!useNC) {
int nEvents = data->numEntries();
Info("StandardHypoTestDemo","Pdf is not extended: number of events to generate taken from observed data set is %d",nEvents);
sampler->SetNEventsPerToy(nEvents);
}
else {
Info("StandardHypoTestDemo","using a number counting pdf");
sampler->SetNEventsPerToy(1);
}
}
if (data->isWeighted() && !generateBinned) {
Info("StandardHypoTestDemo","Data set is weighted, nentries = %d and sum of weights = %8.1f but toy generation is unbinned - it would be faster to set generateBinned to true\n",data->numEntries(), data->sumEntries());
}
if (generateBinned) sampler->SetGenerateBinned(generateBinned);
// set the test statistic
if (testStatType == 0) sampler->SetTestStatistic(slrts);
if (testStatType == 1) sampler->SetTestStatistic(ropl);
if (testStatType == 2 || testStatType == 3) sampler->SetTestStatistic(profll);
}
HypoTestResult * htr = hypoCalc->GetHypoTest();
htr->SetPValueIsRightTail(true);
htr->SetBackgroundAsAlt(false);
htr->Print(); // how to get meaningfull CLs at this point?
delete sampler;
delete slrts;
delete ropl;
delete profll;
if (calcType != 2) {
HypoTestPlot * plot = new HypoTestPlot(*htr,100);
plot->SetLogYaxis(true);
plot->Draw();
}
else {
std::cout << "Asymptotic results " << std::endl;
}
// look at expected significances
// found median of S+B distribution
if (calcType != 2) {
SamplingDistribution * altDist = htr->GetAltDistribution();
HypoTestResult htExp("Expected Result");
htExp.Append(htr);
// find quantiles in alt (S+B) distribution
double p[5];
double q[5];
for (int i = 0; i < 5; ++i) {
double sig = -2 + i;
p[i] = ROOT::Math::normal_cdf(sig,1);
}
std::vector<double> values = altDist->GetSamplingDistribution();
TMath::Quantiles( values.size(), 5, &values[0], q, p, false);
for (int i = 0; i < 5; ++i) {
htExp.SetTestStatisticData( q[i] );
double sig = -2 + i;
std::cout << " Expected p -value and significance at " << sig << " sigma = "
<< htExp.NullPValue() << " significance " << htExp.Significance() << " sigma " << std::endl;
}
}
else {
// case of asymptotic calculator
for (int i = 0; i < 5; ++i) {
double sig = -2 + i;
// sigma is inverted here
double pval = AsymptoticCalculator::GetExpectedPValues( htr->NullPValue(), htr->AlternatePValue(), -sig, false);
std::cout << " Expected p -value and significance at " << sig << " sigma = "
<< pval << " significance " << ROOT::Math::normal_quantile_c(pval,1) << " sigma " << std::endl;
}
}
}
void asimov() {
std::string InputFile = "./data/example.root";
// Create the measurement
Measurement meas("meas", "meas");
meas.SetOutputFilePrefix( "./results/asimov_UsingC" );
meas.SetPOI( "SigXsecOverSM" );
meas.AddConstantParam("alpha_syst1");
meas.AddConstantParam("Lumi");
meas.SetLumi( 1.0 );
meas.SetLumiRelErr( 0.10 );
meas.SetExportOnly( false );
meas.SetBinHigh( 2 );
// Create a channel
Channel chan( "channel1" );
chan.SetData( "data", InputFile );
chan.SetStatErrorConfig( 0.05, "Poisson" );
// Now, create some samples
// Create the signal sample
Sample signal( "signal", "signal", InputFile );
signal.AddOverallSys( "syst1", 0.95, 1.05 );
signal.AddNormFactor( "SigXsecOverSM", 1, 0, 3 );
chan.AddSample( signal );
// Background 1
Sample background1( "background1", "background1", InputFile );
background1.ActivateStatError( "background1_statUncert", InputFile );
background1.AddOverallSys( "syst2", 0.95, 1.05 );
chan.AddSample( background1 );
// Background 1
Sample background2( "background2", "background2", InputFile );
background2.ActivateStatError();
background2.AddOverallSys( "syst3", 0.95, 1.05 );
chan.AddSample( background2 );
// Done with this channel
// Add it to the measurement:
meas.AddChannel( chan );
// Collect the histograms from their files,
// print some output,
meas.CollectHistograms();
//meas.PrintTree();
// One can print XML code to an
// output directory:
// meas.PrintXML( "xmlFromCCode", meas.GetOutputFilePrefix() );
RooWorkspace* wspace = RooStats::HistFactory::HistoToWorkspaceFactoryFast::MakeCombinedModel(meas);
// Get the pdf, obs
RooAbsPdf* pdf = wspace->pdf("simPdf");
RooDataSet* obsData = (RooDataSet*) wspace->data("obsData");
RooDataSet* asimovData = (RooDataSet*) wspace->data("asimovData");
// fit the pdf to the asimov data
std::cout << "Fitting to Observed Data" << std::endl;
pdf->fitTo(*obsData);
std::cout << "Fitting to Asimov Data" << std::endl;
pdf->fitTo(*asimovData);
return;
}
int main (int argc, char **argv)
{
const char* chInFile = "ws.root";
const char* chOutFile = "ws_data.root";
const char* chRootFile = "BDT20.root";
const char* chCut = "";
char option_char;
while ( (option_char = getopt(argc,argv, "i:o:r:c:")) != EOF )
switch (option_char)
{
case 'i': chInFile = optarg; break;
case 'o': chOutFile = optarg; break;
case 'r': chRootFile = optarg; break;
case 'c': chCut = optarg; break;
case '?': fprintf (stderr,
"usage: %s [i<input file> o<output file>]\n", argv[0]);
}
cout << "In Ws = " << chInFile << endl;
cout << "Out Ws = " << chOutFile << endl;
cout << "Data From = " << chRootFile << endl;
cout << "Extra Cut = " << chCut << endl;
TFile* in_file = new TFile(chInFile);
RooWorkspace *rws = (RooWorkspace*) in_file->Get("rws");
TFile* tree_file = new TFile(chRootFile);
TTree* tree = (TTree*) tree_file->Get("tree");
TFile* out_file = new TFile(chOutFile, "RECREATE");
RooArgSet allVars(*rws->var("m"),*rws->var("t"),*rws->var("et"),*rws->var("cpsi"),*rws->var("ctheta"),*rws->var("phi"),*rws->var("d"),*rws->cat("dilution"));
RooDataSet* data = new RooDataSet("data","data",allVars);
RooDataSet* dataBkg = new RooDataSet("dataBkg","dataBkg",allVars);
//TCut* cut = new TCut("5.17<bs_mass && bs_mass<5.57 && bs_pdl>-0.044 && bs_pdl<0.3 ");
TCut* cut = new TCut("5.17<bs_mass && bs_mass<5.57 && bs_epdl<0.025 && bs_pdl<0.4 && bs_pdl>-0.44");
*cut += chCut;
tree->Draw(">>entry_list", *cut, "entrylist");
TEntryList* event_list = (TEntryList*) out_file->Get("entry_list");
Double_t dM, dT, dEt, dCpsi, dCtheta, dPhi, dd;
Int_t ddDefined;
tree->SetBranchAddress("bs_mass", &dM);
tree->SetBranchAddress("bs_pdl", &dT);
tree->SetBranchAddress("bs_epdl", &dEt);
tree->SetBranchAddress("bs_angle_cpsi", &dCpsi);
tree->SetBranchAddress("bs_angle_ctheta", &dCtheta);
tree->SetBranchAddress("bs_angle_phi", &dPhi);
tree->SetBranchAddress("newtag_ost", &dd);
tree->SetBranchAddress("newtag_ost_defined", &ddDefined);
for (Long_t i=0; i<event_list->GetN(); i++){
tree->GetEntry(event_list->GetEntry(i));
*rws->var("m")=dM;
*rws->var("t")=dT/0.0299792458;
*rws->var("et")=dEt/0.0299792458;
*rws->var("cpsi")=dCpsi;
*rws->var("ctheta")=dCtheta;
*rws->var("phi")=dPhi;
*rws->var("d")=0;
rws->cat("dilution")->setIndex(0);
if ( ddDefined==1 ){
rws->cat("dilution")->setIndex(1);
*rws->var("d")=dd;
}
data->add(allVars);
if (dM<5.29 || dM>5.44)
dataBkg->add(allVars);
}
rws->import(*data);
rws->import(*dataBkg);
rws->Write("rws");
out_file->Close();
in_file->Close();
tree_file->Close();
cout << endl << "Done." << endl;
}
void makeWorkspace2015(RooWorkspace& ws, const TString FileName, struct InputOpt opt){
double binw=0.05;
std::string finput(FileName);
TFile *f = new TFile(finput.c_str());
TTree* theTree = (TTree*)gROOT->FindObject("myTree"); // OS --- all mass
RooRealVar* mass = new RooRealVar("invariantMass","#mu#mu mass", opt.dMuon.M.Min, opt.dMuon.M.Max, "GeV/c^{2}");
// ws.import(*mass);
RooRealVar* dimuPt = new RooRealVar("dimuPt","p_{T}(#DiMuon)",0,60,"GeV/c");
RooRealVar* dimuRapidity = new RooRealVar("dimuRapidity", "dimuRapidity",-2.4, 2.4);
RooRealVar* vProb = new RooRealVar("vProb", "vProb" ,0.01,1.00);
RooRealVar* QQsign = new RooRealVar("QQsign", "QQsign" ,-1,5);
RooRealVar* Centrality = new RooRealVar("Centrality","Centrality",0,200);
RooRealVar* RunNb = new RooRealVar("RunNb","RunNb",0,350000);
RooRealVar* muPlusPt = new RooRealVar("muPlusPt","muPlusPt", 0, 1000);
RooRealVar* muMinusPt = new RooRealVar("muMinusPt","muMinusPt", 0, 1000);
RooRealVar* muPlusEta = new RooRealVar("muPlusEta","muPlusEta", -2.4, 2.4);
RooRealVar* muMinusEta = new RooRealVar("muMinusEta","muMinusEta", -2.4, 2.4);
RooDataSet* data0, *dataOS, *dataSS;
RooArgSet cols(*mass,*dimuPt,*dimuRapidity,*muPlusPt,*muMinusPt,*muPlusEta,*muMinusEta, *RunNb, *QQsign);
data0 = new RooDataSet("data","data",cols);
// import the tree to the RooDataSet
UInt_t runNb;
Int_t centrality;
ULong64_t HLTriggers;
Int_t Reco_QQ_size;
Int_t Reco_QQ_sign[99]; //[Reco_QQ_size]
TClonesArray *Reco_QQ_4mom;
TClonesArray *Reco_QQ_mupl_4mom;
TClonesArray *Reco_QQ_mumi_4mom;
ULong64_t Reco_QQ_trig[99]; //[Reco_QQ_size]
Float_t Reco_QQ_VtxProb[99]; //[Reco_QQ_size]
TBranch *b_runNb; //!
TBranch *b_centrality; //!
TBranch *b_HLTriggers; //!
TBranch *b_Reco_QQ_size; //!
TBranch *b_Reco_QQ_sign; //!
TBranch *b_Reco_QQ_4mom; //!
TBranch *b_Reco_QQ_mupl_4mom; //!
TBranch *b_Reco_QQ_mumi_4mom; //!
TBranch *b_Reco_QQ_trig; //!
TBranch *b_Reco_QQ_VtxProb; //!
Reco_QQ_4mom = 0;
Reco_QQ_mupl_4mom = 0;
Reco_QQ_mumi_4mom = 0;
theTree->SetBranchAddress("runNb", &runNb, &b_runNb);
theTree->SetBranchAddress("Centrality", ¢rality, &b_centrality);
theTree->SetBranchAddress("HLTriggers", &HLTriggers, &b_HLTriggers);
theTree->SetBranchAddress("Reco_QQ_size", &Reco_QQ_size, &b_Reco_QQ_size);
theTree->SetBranchAddress("Reco_QQ_sign", Reco_QQ_sign, &b_Reco_QQ_sign);
theTree->GetBranch("Reco_QQ_4mom")->SetAutoDelete(kFALSE);
theTree->SetBranchAddress("Reco_QQ_4mom", &Reco_QQ_4mom, &b_Reco_QQ_4mom);
theTree->GetBranch("Reco_QQ_mupl_4mom")->SetAutoDelete(kFALSE);
theTree->SetBranchAddress("Reco_QQ_mupl_4mom", &Reco_QQ_mupl_4mom, &b_Reco_QQ_mupl_4mom);
theTree->GetBranch("Reco_QQ_mumi_4mom")->SetAutoDelete(kFALSE);
theTree->SetBranchAddress("Reco_QQ_mumi_4mom", &Reco_QQ_mumi_4mom, &b_Reco_QQ_mumi_4mom);
theTree->SetBranchAddress("Reco_QQ_trig", Reco_QQ_trig, &b_Reco_QQ_trig);
theTree->SetBranchAddress("Reco_QQ_VtxProb", Reco_QQ_VtxProb, &b_Reco_QQ_VtxProb);
if (theTree == 0) return;
Long64_t nentries = theTree->GetEntriesFast();
Long64_t nbytes = 0, nb = 0;
for (Long64_t jentry=0; jentry<nentries;jentry++) {
// Long64_t ientry = LoadTree(jentry);
// if (ientry < 0) break;
nb = theTree->GetEntry(jentry); nbytes += nb;
// if (Cut(ientry) < 0) continue;
for (int i=0; i<Reco_QQ_size; i++) {
TLorentzVector *qq4mom = (TLorentzVector*) Reco_QQ_4mom->At(i);
TLorentzVector *qq4mupl = (TLorentzVector*) Reco_QQ_mupl_4mom->At(i);
TLorentzVector *qq4mumi = (TLorentzVector*) Reco_QQ_mumi_4mom->At(i);
mass->setVal(qq4mom->M());
dimuPt->setVal(qq4mom->Pt());
dimuRapidity->setVal(qq4mom->Rapidity());
vProb->setVal(Reco_QQ_VtxProb[i]);
QQsign->setVal(Reco_QQ_sign[i]);
Centrality->setVal(centrality);
muPlusPt->setVal(qq4mupl->Pt());
muMinusPt->setVal(qq4mumi->Pt());
muPlusEta->setVal(qq4mupl->Eta());
muMinusEta->setVal(qq4mumi->Eta());
RunNb->setVal(runNb);
data0->add(cols);
}
}
TString cut_ap(Form("(%.2f<invariantMass && invariantMass<%.2f) &&"
"(%.2f<muPlusEta && muPlusEta < %.2f) &&"
"(%.2f<muMinusEta && muMinusEta < %.2f) &&"
"(%.2f<dimuPt && dimuPt<%.2f) &&"
"(abs(dimuRapidity)>%.2f && abs(dimuRapidity)<%.2f) &&"
"(muPlusPt > %.2f && muMinusPt > %.2f) &&"
"(%d<=RunNb && RunNb<=%d)",
opt.dMuon.M.Min, opt.dMuon.M.Max,
opt.sMuon.Eta.Min, opt.sMuon.Eta.Max,
opt.sMuon.Eta.Min, opt.sMuon.Eta.Max,
opt.dMuon.Pt.Min, opt.dMuon.Pt.Max,
opt.dMuon.AbsRap.Min, opt.dMuon.AbsRap.Max,
opt.sMuon.Pt.Min, opt.sMuon.Pt.Min,
opt.RunNb.Start, opt.RunNb.End));
TString cut_ap_OS(TString("(QQsign==0) && ") + cut_ap);
TString cut_ap_SS(TString("(QQsign<0 || QQsign>0) && ") + cut_ap);
dataOS = ( RooDataSet*) data0->reduce(Name("dataOS"), Cut(cut_ap_OS));
dataSS = ( RooDataSet*) data0->reduce(Name("dataSS"), Cut(cut_ap_SS));
ws.import(*dataSS);
ws.import(*dataOS);
f->Close();
}
void combinedWorkspace_4WS(const char* name_pbpb_pass="******", const char* name_pbpb_fail="fitresult_pbpb_fail.root", const char* name_pp_pass="******", const char* name_pp_fail="fitresult_pp_fail.root", const char* name_out="fitresult_combo.root", const float systval = 0., const char* subDirName ="wsTest", int nCPU=2){
// subdir: Directory to save workspaces under currentPATH/CombinedWorkspaces/subDir/
// set things silent
gErrorIgnoreLevel=kError;
RooMsgService::instance().setGlobalKillBelow(RooFit::ERROR);
bool dosyst = (systval > 0.);
TString nameOut(name_out);
RooWorkspace * ws = test_combine_4WS(name_pbpb_pass, name_pp_pass, name_pbpb_fail, name_pp_fail, false, nCPU);
RooAbsData * data = ws->data("dOS_DATA");
RooRealVar* RFrac2Svs1S_PbPbvsPP_P = ws->var("RFrac2Svs1S_PbPbvsPP_P");
RooRealVar* leftEdge = new RooRealVar("leftEdge","leftEdge",-10);
RooRealVar* rightEdge = new RooRealVar("rightEdge","rightEdge",10);
RooGenericPdf step("step", "step", "(@0 >= @1) && (@0 < @2)", RooArgList(*RFrac2Svs1S_PbPbvsPP_P, *leftEdge, *rightEdge));
ws->import(step);
ws->factory( "Uniform::flat(RFrac2Svs1S_PbPbvsPP_P)" );
// systematics
if (dosyst) {
ws->factory( Form("kappa_syst[%f]",systval) );
ws->factory( "expr::alpha_syst('kappa_syst*beta_syst',kappa_syst,beta_syst[0,-5,5])" );
ws->factory( "Gaussian::constr_syst(beta_syst,glob_syst[0,-5,5],1)" );
// add systematics into the double ratio
ws->factory( "expr::RFrac2Svs1S_PbPbvsPP_P_syst('@0+@1',RFrac2Svs1S_PbPbvsPP_P,alpha_syst)" );
// build the pbpb pdf
RooRealVar* effjpsi_pp_P = (RooRealVar*)ws->var("effjpsi_pp_P");
RooRealVar* effpsip_pp_P = (RooRealVar*)ws->var("effpsip_pp_P");
RooRealVar* effjpsi_pp_NP = (RooRealVar*)ws->var("effjpsi_pp_NP");
Double_t Npsi2SPbPbPass = npsip_pbpb_pass_from_doubleratio_prompt(ws, RooArgList(*effjpsi_pp_P,*effpsip_pp_P,*effjpsi_pp_NP),true); // Create and import N_Psi2S_PbPb_pass_syst
ws->factory( "SUM::pdfMASS_Tot_PbPb_pass_syst(N_Jpsi_PbPb_pass * pdfMASS_Jpsi_PbPb_pass, N_Psi2S_PbPb_pass_syst * pdfMASS_Psi2S_PbPb_pass, N_Bkg_PbPb_pass * pdfMASS_Bkg_PbPb_pass)" );
ws->factory( "PROD::pdfMASS_Tot_PbPb_pass_constr(pdfMASS_Tot_PbPb_pass_syst,constr_syst)" );
// build the combined pdf
ws->factory("SIMUL::simPdf_syst_noconstr(sample,PbPb_pass=pdfMASS_Tot_PbPb_pass_syst,PbPb_fail=pdfMASS_Tot_PbPb_fail,PP_pass=pdfMASS_Tot_PP_pass,PP_fail=pdfMASS_Tot_PP_fail)");
RooSimultaneous *simPdf = (RooSimultaneous*) ws->pdf("simPdf_syst_noconstr");
RooGaussian *constr_syst = (RooGaussian*) ws->pdf("constr_syst");
RooProdPdf *simPdf_constr = new RooProdPdf("simPdf_syst","simPdf_syst",RooArgSet(*simPdf,*constr_syst));
ws->import(*simPdf_constr);
} else {
ws->factory("SIMUL::simPdf_syst(sample,PbPb_pass=pdfMASS_Tot_PbPb_pass,PbPb_fail=pdfMASS_Tot_PbPb_fail,PP_pass=pdfMASS_Tot_PP_pass,PP_fail=pdfMASS_Tot_PP_fail)");
}
ws->Print();
if (dosyst) ws->var("beta_syst")->setConstant(kFALSE);
/////////////////////////////////////////////////////////////////////
RooRealVar * pObs = ws->var("invMass"); // get the pointer to the observable
RooArgSet obs("observables");
obs.add(*pObs);
obs.add( *ws->cat("sample"));
// /////////////////////////////////////////////////////////////////////
if (dosyst) ws->var("glob_syst")->setConstant(true);
RooArgSet globalObs("global_obs");
if (dosyst) globalObs.add( *ws->var("glob_syst") );
// ws->Print();
RooArgSet poi("poi");
poi.add( *ws->var("RFrac2Svs1S_PbPbvsPP_P") );
// create set of nuisance parameters
RooArgSet nuis("nuis");
if (dosyst) nuis.add( *ws->var("beta_syst") );
// set parameters constant
RooArgSet allVars = ws->allVars();
TIterator* it = allVars.createIterator();
RooRealVar *theVar = (RooRealVar*) it->Next();
while (theVar) {
TString varname(theVar->GetName());
// if (varname != "RFrac2Svs1S_PbPbvsPP"
// && varname != "invMass"
// && varname != "sample"
// )
// theVar->setConstant();
if ( varname.Contains("f_Jpsi_PP") || varname.Contains("f_Jpsi_PbPb") ||
varname.Contains("rSigma21_Jpsi_PP") ||
varname.Contains("m_Jpsi_PP") || varname.Contains("m_Jpsi_PbPb") ||
varname.Contains("sigma1_Jpsi_PP") || varname.Contains("sigma1_Jpsi_PbPb") ||
(varname.Contains("lambda")) ||
(varname.Contains("_fail") && !varname.Contains("RFrac2Svs1S")))
{
theVar->setConstant();
}
if (varname=="glob_syst"
|| varname=="beta_syst"
) {
cout << varname << endl;
theVar->setConstant(!dosyst);
}
theVar = (RooRealVar*) it->Next();
}
// create signal+background Model Config
RooStats::ModelConfig sbHypo("SbHypo");
sbHypo.SetWorkspace( *ws );
sbHypo.SetPdf( *ws->pdf("simPdf_syst") );
sbHypo.SetObservables( obs );
sbHypo.SetGlobalObservables( globalObs );
sbHypo.SetParametersOfInterest( poi );
sbHypo.SetNuisanceParameters( nuis );
sbHypo.SetPriorPdf( *ws->pdf("step") ); // this is optional
/////////////////////////////////////////////////////////////////////
RooAbsReal * pNll = sbHypo.GetPdf()->createNLL( *data,NumCPU(nCPU) );
RooMinuit(*pNll).migrad(); // minimize likelihood wrt all parameters before making plots
if (controlPlots)
{
RooPlot *framepoi = ((RooRealVar *)poi.first())->frame(Bins(10),Range(0.,1),Title("LL and profileLL in RFrac2Svs1S_PbPbvsPP_P"));
pNll->plotOn(framepoi,ShiftToZero());
framepoi->SetMinimum(0);
framepoi->SetMaximum(10);
TCanvas *cpoi = new TCanvas();
cpoi->cd(); framepoi->Draw();
cpoi->SaveAs("cpoi.pdf");
}
((RooRealVar *)poi.first())->setMin(0.);
RooArgSet * pPoiAndNuisance = new RooArgSet("poiAndNuisance");
pPoiAndNuisance->add( nuis );
pPoiAndNuisance->add( poi );
sbHypo.SetSnapshot(*pPoiAndNuisance);
if (controlPlots)
{
RooPlot* xframeSB_PP_pass = pObs->frame(Title("SBhypo_PP_pass"));
data->plotOn(xframeSB_PP_pass,Cut("sample==sample::PP_pass"));
RooAbsPdf *pdfSB_PP_pass = sbHypo.GetPdf();
RooCategory *sample = ws->cat("sample");
pdfSB_PP_pass->plotOn(xframeSB_PP_pass,Slice(*sample,"PP_pass"),ProjWData(*sample,*data));
TCanvas *c1 = new TCanvas();
c1->cd(); xframeSB_PP_pass->Draw();
c1->SaveAs("c1.pdf");
RooPlot* xframeSB_PP_fail = pObs->frame(Title("SBhypo_PP_fail"));
data->plotOn(xframeSB_PP_fail,Cut("sample==sample::PP_fail"));
RooAbsPdf *pdfSB_PP_fail = sbHypo.GetPdf();
pdfSB_PP_fail->plotOn(xframeSB_PP_fail,Slice(*sample,"PP_fail"),ProjWData(*sample,*data));
TCanvas *c2 = new TCanvas();
c2->cd(); xframeSB_PP_fail->Draw();
c2->SaveAs("c1.pdf");
RooPlot* xframeB_PbPb_pass = pObs->frame(Title("SBhypo_PbPb_pass"));
data->plotOn(xframeB_PbPb_pass,Cut("sample==sample::PbPb_pass"));
RooAbsPdf *pdfB_PbPb_pass = sbHypo.GetPdf();
pdfB_PbPb_pass->plotOn(xframeB_PbPb_pass,Slice(*sample,"PbPb_pass"),ProjWData(*sample,*data));
TCanvas *c3 = new TCanvas();
c3->cd(); xframeB_PbPb_pass->Draw();
c3->SetLogy();
c3->SaveAs("c2.pdf");
RooPlot* xframeB_PbPb_fail = pObs->frame(Title("SBhypo_PbPb_fail"));
data->plotOn(xframeB_PbPb_fail,Cut("sample==sample::PbPb_fail"));
RooAbsPdf *pdfB_PbPb_fail = sbHypo.GetPdf();
pdfB_PbPb_fail->plotOn(xframeB_PbPb_fail,Slice(*sample,"PbPb_fail"),ProjWData(*sample,*data));
TCanvas *c4 = new TCanvas();
c4->cd(); xframeB_PbPb_fail->Draw();
c4->SetLogy();
c4->SaveAs("c2.pdf");
}
delete pNll;
delete pPoiAndNuisance;
ws->import( sbHypo );
/////////////////////////////////////////////////////////////////////
RooStats::ModelConfig bHypo = sbHypo;
bHypo.SetName("BHypo");
bHypo.SetWorkspace(*ws);
pNll = bHypo.GetPdf()->createNLL( *data,NumCPU(nCPU) );
// RooMinuit(*pNll).migrad(); // minimize likelihood wrt all parameters before making plots
RooArgSet poiAndGlobalObs("poiAndGlobalObs");
poiAndGlobalObs.add( poi );
poiAndGlobalObs.add( globalObs );
RooAbsReal * pProfile = pNll->createProfile( poiAndGlobalObs ); // do not profile POI and global observables
((RooRealVar *)poi.first())->setVal( 0 ); // set RFrac2Svs1S_PbPbvsPP=0 here
pProfile->getVal(); // this will do fit and set nuisance parameters to profiled values
pPoiAndNuisance = new RooArgSet( "poiAndNuisance" );
pPoiAndNuisance->add( nuis );
pPoiAndNuisance->add( poi );
bHypo.SetSnapshot(*pPoiAndNuisance);
delete pNll;
delete pPoiAndNuisance;
// import model config into workspace
bHypo.SetWorkspace(*ws);
ws->import( bHypo );
/////////////////////////////////////////////////////////////////////
ws->Print();
bHypo.Print();
sbHypo.Print();
// save workspace to file
string mainDIR = gSystem->ExpandPathName(gSystem->pwd());
string wsDIR = mainDIR + "/CombinedWorkspaces/";
string ssubDirName="";
if (subDirName) ssubDirName.append(subDirName);
string subDIR = wsDIR + ssubDirName;
void * dirp = gSystem->OpenDirectory(wsDIR.c_str());
if (dirp) gSystem->FreeDirectory(dirp);
else gSystem->mkdir(wsDIR.c_str(), kTRUE);
void * dirq = gSystem->OpenDirectory(subDIR.c_str());
if (dirq) gSystem->FreeDirectory(dirq);
else gSystem->mkdir(subDIR.c_str(), kTRUE);
const char* saveName = Form("%s/%s",subDIR.c_str(),nameOut.Data());
ws->writeToFile(saveName);
}
int main(int argc, char *argv[]){
OptionParser(argc,argv);
TStopwatch sw;
sw.Start();
RooMsgService::instance().setGlobalKillBelow(RooFit::ERROR);
RooMsgService::instance().setSilentMode(true);
system("mkdir -p plots/fTest");
vector<string> procs;
split(procs,procString_,boost::is_any_of(","));
TFile *inFile = TFile::Open(filename_.c_str());
RooWorkspace *inWS = (RooWorkspace*)inFile->Get("cms_hgg_workspace");
RooRealVar *mass = (RooRealVar*)inWS->var("CMS_hgg_mass");
//mass->setBins(320);
//mass->setRange(mass_-10,mass_+10);
//mass->setBins(20);
RooRealVar *MH = new RooRealVar("MH","MH",mass_);
MH->setVal(mass_);
MH->setConstant(true);
map<string,pair<int,int> > choices;
map<string,vector<RooPlot*> > plotsRV;
map<string,vector<RooPlot*> > plotsWV;
for (unsigned int p=0; p<procs.size(); p++){
vector<RooPlot*> tempRV;
vector<RooPlot*> tempWV;
for (int cat=0; cat<ncats_; cat++){
RooPlot *plotRV = mass->frame(Range(mass_-10,mass_+10));
plotRV->SetTitle(Form("%s_cat%d_RV",procs[p].c_str(),cat));
tempRV.push_back(plotRV);
RooPlot *plotWV = mass->frame(Range(mass_-10,mass_+10));
plotWV->SetTitle(Form("%s_cat%d_WV",procs[p].c_str(),cat));
tempWV.push_back(plotWV);
}
plotsRV.insert(pair<string,vector<RooPlot*> >(procs[p],tempRV));
plotsWV.insert(pair<string,vector<RooPlot*> >(procs[p],tempWV));
}
vector<int> colors;
colors.push_back(kBlue);
colors.push_back(kRed);
colors.push_back(kGreen+2);
colors.push_back(kMagenta+1);
for (int cat=0; cat<ncats_; cat++){
for (unsigned int p=0; p<procs.size(); p++){
string proc = procs[p];
RooDataSet *dataRV = (RooDataSet*)inWS->data(Form("sig_%s_mass_m%d_rv_cat%d",proc.c_str(),mass_,cat));
RooDataSet *dataWV = (RooDataSet*)inWS->data(Form("sig_%s_mass_m%d_wv_cat%d",proc.c_str(),mass_,cat));
//mass->setBins(160);
//RooDataHist *dataRV = dataRVtemp->binnedClone();
//RooDataHist *dataWV = dataWVtemp->binnedClone();
//RooDataSet *dataRVw = (RooDataSet*)dataRVtemp->reduce(Form("CMS_hgg_mass>=%3d && CMS_hgg_mass<=%3d",mass_-10,mass_+10));
//RooDataSet *dataWVw = (RooDataSet*)dataWVtemp->reduce(Form("CMS_hgg_mass>=%3d && CMS_hgg_mass<=%3d",mass_-10,mass_+10));
//RooDataHist *dataRV = new RooDataHist(Form("roohist_%s",dataRVtemp->GetName()),Form("roohist_%s",dataRVtemp->GetName()),RooArgSet(*mass),*dataRVtemp);
//RooDataHist *dataWV = new RooDataHist(Form("roohist_%s",dataWVtemp->GetName()),Form("roohist_%s",dataWVtemp->GetName()),RooArgSet(*mass),*dataWVtemp);
//RooDataSet *dataRV = stripWeights(dataRVweight,mass);
//RooDataSet *dataWV = stripWeights(dataWVweight,mass);
//RooDataSet *data = (RooDataSet*)inWS->data(Form("sig_%s_mass_m%d_cat%d",proc.c_str(),mass_,cat));
int rvChoice=0;
int wvChoice=0;
// right vertex
int order=1;
int prev_order=0;
int cache_order=0;
double thisNll=0.;
double prevNll=1.e6;
double chi2=0.;
double prob=0.;
dataRV->plotOn(plotsRV[proc][cat]);
//while (prob<0.8) {
while (order<5) {
RooAddPdf *pdf = buildSumOfGaussians(Form("cat%d_g%d",cat,order),mass,MH,order);
RooFitResult *fitRes = pdf->fitTo(*dataRV,Save(true),SumW2Error(true));//,Range(mass_-10,mass_+10));
double myNll=0.;
thisNll = fitRes->minNll();
//double myNll = getMyNLL(mass,pdf,dataRV);
//thisNll = getMyNLL(mass,pdf,dataRV);
//RooAbsReal *nll = pdf->createNLL(*dataRV);
//RooMinuit m(*nll);
//m.migrad();
//thisNll = nll->getVal();
//plot(Form("plots/fTest/%s_cat%d_g%d_rv",proc.c_str(),cat,order),mass_,mass,dataRV,pdf);
pdf->plotOn(plotsRV[proc][cat],LineColor(colors[order-1]));
chi2 = 2.*(prevNll-thisNll);
//if (chi2<0. && order>1) chi2=0.;
int diffInDof = (2*order+(order-1))-(2*prev_order+(prev_order-1));
prob = TMath::Prob(chi2,diffInDof);
cout << "\t RV: " << cat << " " << order << " " << diffInDof << " " << prevNll << " " << thisNll << " " << myNll << " " << chi2 << " " << prob << endl;
prevNll=thisNll;
cache_order=prev_order;
prev_order=order;
order++;
}
rvChoice=cache_order;
// wrong vertex
order=1;
prev_order=0;
cache_order=0;
thisNll=0.;
prevNll=1.e6;
chi2=0.;
prob=0.;
dataWV->plotOn(plotsWV[proc][cat]);
while (order<4) {
//while (prob<0.8) {
RooAddPdf *pdf = buildSumOfGaussians(Form("cat%d_g%d",cat,order),mass,MH,order);
RooFitResult *fitRes = pdf->fitTo(*dataWV,Save(true),SumW2Error(true));//,Range(mass_-10,mass_+10));
double myNll=0.;
thisNll = fitRes->minNll();
//double myNll = getMyNLL(mass,pdf,dataRV);
//thisNll = getMyNLL(mass,pdf,dataRV);
//RooAbsReal *nll = pdf->createNLL(*dataWV);
//RooMinuit m(*nll);
//m.migrad();
//thisNll = nll->getVal();
//plot(Form("plots/fTest/%s_cat%d_g%d_wv",proc.c_str(),cat,order),mass_,mass,dataWV,pdf);
pdf->plotOn(plotsWV[proc][cat],LineColor(colors[order-1]));
chi2 = 2.*(prevNll-thisNll);
//if (chi2<0. && order>1) chi2=0.;
int diffInDof = (2*order+(order-1))-(2*prev_order+(prev_order-1));
prob = TMath::Prob(chi2,diffInDof);
cout << "\t WV: " << cat << " " << order << " " << diffInDof << " " << prevNll << " " << thisNll << " " << myNll << " " << chi2 << " " << prob << endl;
prevNll=thisNll;
cache_order=prev_order;
prev_order=order;
order++;
}
wvChoice=cache_order;
choices.insert(pair<string,pair<int,int> >(Form("%s_cat%d",proc.c_str(),cat),make_pair(rvChoice,wvChoice)));
}
}
TLegend *leg = new TLegend(0.6,0.6,0.89,0.89);
leg->SetFillColor(0);
leg->SetLineColor(0);
TH1F *h1 = new TH1F("h1","",1,0,1);
h1->SetLineColor(colors[0]);
leg->AddEntry(h1,"1st order","L");
TH1F *h2 = new TH1F("h2","",1,0,1);
h2->SetLineColor(colors[1]);
leg->AddEntry(h2,"2nd order","L");
TH1F *h3 = new TH1F("h3","",1,0,1);
h3->SetLineColor(colors[2]);
leg->AddEntry(h3,"3rd order","L");
TH1F *h4 = new TH1F("h4","",1,0,1);
h4->SetLineColor(colors[3]);
leg->AddEntry(h4,"4th order","L");
TCanvas *canv = new TCanvas();
for (map<string,vector<RooPlot*> >::iterator plotIt=plotsRV.begin(); plotIt!=plotsRV.end(); plotIt++){
string proc = plotIt->first;
for (int cat=0; cat<ncats_; cat++){
RooPlot *plot = plotIt->second.at(cat);
plot->Draw();
leg->Draw();
canv->Print(Form("plots/fTest/rv_%s_cat%d.pdf",proc.c_str(),cat));
}
}
for (map<string,vector<RooPlot*> >::iterator plotIt=plotsWV.begin(); plotIt!=plotsWV.end(); plotIt++){
string proc = plotIt->first;
for (int cat=0; cat<ncats_; cat++){
RooPlot *plot = plotIt->second.at(cat);
plot->Draw();
leg->Draw();
canv->Print(Form("plots/fTest/wv_%s_cat%d.pdf",proc.c_str(),cat));
}
}
delete canv;
cout << "Recommended options" << endl;
for (map<string,pair<int,int> >::iterator it=choices.begin(); it!=choices.end(); it++){
cout << "\t " << it->first << " - " << it->second.first << " " << it->second.second << endl;
}
inFile->Close();
return 0;
}
void eregtraining_fixalpha(bool dobarrel, bool doele) {
// gSystem->Setenv("OMP_WAIT_POLICY","PASSIVE");
//candidate to set fixed alpha values (0.9,3.8)
//TString dirname = TString::Format("/afs/cern.ch/work/b/bendavid/bare/eregtesteleJul30_sig5_01_alphafloat5_%i/",int(minevents));
TString dirname = "/afs/cern.ch/work/b/bendavid/bare/eregAug10RCalphafix/";
gSystem->mkdir(dirname,true);
gSystem->cd(dirname);
std::vector<std::string> *varsf = new std::vector<std::string>;
varsf->push_back("ph.scrawe");
varsf->push_back("ph.sceta");
varsf->push_back("ph.scphi");
varsf->push_back("ph.r9");
varsf->push_back("ph.scetawidth");
varsf->push_back("ph.scphiwidth");
varsf->push_back("ph.scnclusters");
varsf->push_back("ph.hoveretower");
varsf->push_back("rho");
varsf->push_back("nVtx");
varsf->push_back("ph.etaseed-ph.sceta");
varsf->push_back("atan2(sin(ph.phiseed-ph.scphi),cos(ph.phiseed-ph.scphi))");
varsf->push_back("ph.eseed/ph.scrawe");
varsf->push_back("ph.e3x3seed/ph.e5x5seed");
varsf->push_back("ph.sigietaietaseed");
varsf->push_back("ph.sigiphiphiseed");
varsf->push_back("ph.covietaiphiseed");
varsf->push_back("ph.emaxseed/ph.e5x5seed");
varsf->push_back("ph.e2ndseed/ph.e5x5seed");
varsf->push_back("ph.etopseed/ph.e5x5seed");
varsf->push_back("ph.ebottomseed/ph.e5x5seed");
varsf->push_back("ph.eleftseed/ph.e5x5seed");
varsf->push_back("ph.erightseed/ph.e5x5seed");
varsf->push_back("ph.e2x5maxseed/ph.e5x5seed");
varsf->push_back("ph.e2x5topseed/ph.e5x5seed");
varsf->push_back("ph.e2x5bottomseed/ph.e5x5seed");
varsf->push_back("ph.e2x5leftseed/ph.e5x5seed");
varsf->push_back("ph.e2x5rightseed/ph.e5x5seed");
std::vector<std::string> *varseb = new std::vector<std::string>(*varsf);
std::vector<std::string> *varsee = new std::vector<std::string>(*varsf);
varseb->push_back("ph.e5x5seed/ph.eseed");
varseb->push_back("ph.ietaseed");
varseb->push_back("ph.iphiseed");
varseb->push_back("ph.ietaseed%5");
varseb->push_back("ph.iphiseed%2");
varseb->push_back("(abs(ph.ietaseed)<=25)*(ph.ietaseed%25) + (abs(ph.ietaseed)>25)*((ph.ietaseed-25*abs(ph.ietaseed)/ph.ietaseed)%20)");
varseb->push_back("ph.iphiseed%20");
varseb->push_back("ph.etacryseed");
varseb->push_back("ph.phicryseed");
varsee->push_back("ph.scpse/ph.scrawe");
std::vector<std::string> *varslist;
if (dobarrel) varslist = varseb;
else varslist = varsee;
RooArgList vars;
for (unsigned int ivar=0; ivar<varslist->size(); ++ivar) {
RooRealVar *var = new RooRealVar(TString::Format("var_%i",ivar),varslist->at(ivar).c_str(),0.);
vars.addOwned(*var);
}
RooArgList condvars(vars);
// RooRealVar *tgtvar = new RooRealVar("tgtvar","ph.scrawe/ph.gene",1.);
// if (!dobarrel) tgtvar->SetTitle("(ph.scrawe + ph.scpse)/ph.gene");
RooRealVar *tgtvar = new RooRealVar("tgtvar","ph.gene/ph.scrawe",1.);
if (!dobarrel) tgtvar->SetTitle("ph.gene/(ph.scrawe + ph.scpse)");
//tgtvar->setRange(0.,5.);
vars.addOwned(*tgtvar);
//varstest.add(*tgtvar);
RooRealVar weightvar("weightvar","",1.);
//TFile *fdin = TFile::Open("/home/mingyang/cms/hist/hgg-2013Moriond/merged/hgg-2013Moriond_s12-diphoj-3-v7a_noskim.root");
// TFile *fdin = TFile::Open("root://eoscms.cern.ch//eos/cms/store/cmst3/user/bendavid/trainingtreesJul1/hgg-2013Final8TeV_s12-zllm50-v7n_noskim.root");
// TDirectory *ddir = (TDirectory*)fdin->FindObjectAny("PhotonTreeWriterSingleInvert");
// TTree *dtree = (TTree*)ddir->Get("hPhotonTreeSingle");
/* TFile *fdinsig = TFile::Open("root://eoscms.cern.ch//eos/cms/store/cmst3/user/bendavid/trainingtreesJul1/hgg-2013Moriond_s12-h125gg-gf-v7a_noskim.root");
TDirectory *ddirsig = (TDirectory*)fdinsig->FindObjectAny("PhotonTreeWriterPreselNoSmear");
TTree *dtreesig = (TTree*)ddirsig->Get("hPhotonTreeSingle"); */
TString treeloc;
if (doele) {
treeloc = "RunLumiSelectionMod/MCProcessSelectionMod/HLTModP/GoodPVFilterMod/PhotonIDModPreselInvert/PhotonTreeWriterSingleInvert/hPhotonTreeSingle";
}
else {
treeloc = "RunLumiSelectionMod/MCProcessSelectionMod/HLTModP/GoodPVFilterMod/PhotonIDModPresel/PhotonTreeWriterSingle/hPhotonTreeSingle";
}
TChain *tree;
float xsecs[50];
if (doele) {
tree = new TChain("RunLumiSelectionMod/MCProcessSelectionMod/HLTModP/GoodPVFilterMod/PhotonIDModPreselInvert/PhotonTreeWriterSingleInvert/hPhotonTreeSingle");
tree->Add("root://eoscms.cern.ch//eos/cms/store/cmst3/user/bendavid/regTreesAug1/hgg-2013Final8TeV_reg_s12-zllm50-v7n_noskim.root");
xsecs[0] = 1.;
initweights(tree,xsecs,1.);
xsecweights[0] = 1.0;
}
else {
tree = new TChain("RunLumiSelectionMod/MCProcessSelectionMod/HLTModP/GoodPVFilterMod/PhotonIDModPresel/PhotonTreeWriterSingle/hPhotonTreeSingle");
tree->Add("root://eoscms.cern.ch//eos/cms/store/cmst3/user/bendavid/regTreesAug1/hgg-2013Final8TeV_reg_s12-pj20_40-2em-v7n_noskim.root");
tree->Add("root://eoscms.cern.ch//eos/cms/store/cmst3/user/bendavid/regTreesAug1/hgg-2013Final8TeV_reg_s12-pj40-2em-v7n_noskim.root");
xsecs[0] = 0.001835*81930.0;
xsecs[1] = 0.05387*8884.0;
initweights(tree,xsecs,1.);
double weightscale = xsecweights[1];
xsecweights[0] /= weightscale;
xsecweights[1] /= weightscale;
}
TCut selcut;
if (dobarrel) {
selcut = "ph.genpt>16. && ph.isbarrel && ph.ispromptgen";
}
else {
selcut = "ph.genpt>16. && !ph.isbarrel && ph.ispromptgen";
}
TCut selweight = "xsecweight(procidx)";
TCut prescale10 = "(evt%10==0)";
TCut prescale25 = "(evt%25==0)";
TCut prescale50 = "(evt%50==0)";
TCut prescale100 = "(evt%100==0)";
TCut prescale1000 = "(evt%1000==0)";
TCut evenevents = "(evt%2==0)";
TCut oddevents = "(evt%2==1)";
//TCut oddevents = prescale100;
//weightvar.SetTitle(prescale10*selcut);
/* new TCanvas;
tree->Draw("ph.genpt>>hpt(200,0.,100.)",selweight*selcut);
return;*/
if (doele) {
weightvar.SetTitle(evenevents*selcut);
}
else {
weightvar.SetTitle(selweight*selcut);
}
RooDataSet *hdata = RooTreeConvert::CreateDataSet("hdata",tree,vars,weightvar);
// weightvar.SetTitle(prescale1000*selcut);
// RooDataSet *hdatasig = RooTreeConvert::CreateDataSet("hdatasig",dtree,vars,weightvar);
// RooDataSet *hdatasigtest = RooTreeConvert::CreateDataSet("hdatasigtest",dtree,varstest,weightvar);
RooDataSet *hdatasig = 0;
RooDataSet *hdatasigtest = 0;
// weightvar.SetTitle(prescale10*selcut);
// RooDataSet *hdatasigsmall = RooTreeConvert::CreateDataSet("hdatasigsmall",dtreesig,vars,weightvar);
RooRealVar sigwidthtvar("sigwidthtvar","",0.01);
sigwidthtvar.setConstant(false);
RooRealVar sigmeantvar("sigmeantvar","",1.);
sigmeantvar.setConstant(false);
RooRealVar sigalphavar("sigalphavar","",1.);
sigalphavar.setConstant(false);
RooRealVar signvar("signvar","",2.);
signvar.setConstant(false);
RooRealVar sigalpha2var("sigalpha2var","",1.);
sigalpha2var.setConstant(false);
RooRealVar sign2var("sign2var","",2.);
sign2var.setConstant(false);
RooArgList tgts;
RooGBRFunction func("func","",condvars,4);
RooGBRTarget sigwidtht("sigwidtht","",func,0,sigwidthtvar);
RooGBRTarget sigmeant("sigmeant","",func,1,sigmeantvar);
RooGBRTarget signt("signt","",func,2,signvar);
RooGBRTarget sign2t("sign2t","",func,3,sign2var);
tgts.add(sigwidtht);
tgts.add(sigmeant);
tgts.add(signt);
tgts.add(sign2t);
RooRealConstraint sigwidthlim("sigwidthlim","",sigwidtht,0.0002,0.5);
RooRealConstraint sigmeanlim("sigmeanlim","",sigmeant,0.2,2.0);
//RooRealConstraint sigmeanlim("sigmeanlim","",sigmeant,-2.0,-0.2);
RooRealConstraint signlim("signlim","",signt,1.01,110.);
RooRealConstraint sign2lim("sign2lim","",sign2t,1.01,110.);
RooLinearVar tgtscaled("tgtscaled","",*tgtvar,sigmeanlim,RooConst(0.));
RooDoubleCBFast sigpdf("sigpdf","",tgtscaled,RooConst(1.),sigwidthlim,RooConst(2.0),signlim,RooConst(1.0),sign2lim);
//RooDoubleCBFast sigpdf("sigpdf","",tgtscaled,RooConst(1.),sigwidthlim,RooConst(2.0),signlim,RooConst(1.0),sign2lim);
//RooCBExp sigpdf("sigpdf","",tgtscaled,RooConst(-1.),sigwidthlim,sigalpha2lim,sign2lim,sigalphalim);
//RooDoubleCBFast sigpdf("sigpdf","",tgtscaled,RooConst(1.),sigwidthlim,RooConst(100.),RooConst(100.),sigalpha2lim,sign2lim);
//RooDoubleCBFast sigpdf("sigpdf","",tgtscaled,RooConst(1.),sigwidthlim,sigalphalim,signlim,RooConst(3.),sign2lim);
//RooCBShape sigpdf("sigpdf","",tgtscaled,RooConst(1.),sigwidthlim,sigalphalim,signlim);
RooConstVar etermconst("etermconst","",0.);
//RooFormulaVar etermconst("etermconst","","1000.*(@0-1.)*(@0-1.)",RooArgList(tgtscaled));
RooRealVar r("r","",1.);
r.setConstant();
std::vector<RooAbsReal*> vpdf;
vpdf.push_back(&sigpdf);
double minweight = 200.;
std::vector<double> minweights;
minweights.push_back(minweight);
//ntot.setConstant();
TFile *fres = new TFile("fres.root","RECREATE");
if (1) {
std::vector<RooAbsData*> vdata;
vdata.push_back(hdata);
RooHybridBDTAutoPdf bdtpdfdiff("bdtpdfdiff","",func,tgts,etermconst,r,vdata,vpdf);
bdtpdfdiff.SetMinCutSignificance(5.);
bdtpdfdiff.SetPrescaleInit(100);
// bdtpdfdiff.SetPrescaleInit(10);
//bdtpdfdiff.SetMaxNSpurious(300.);
//bdtpdfdiff.SetMaxNSpurious(2400.);
bdtpdfdiff.SetShrinkage(0.1);
bdtpdfdiff.SetMinWeights(minweights);
//bdtpdfdiff.SetMaxNodes(270);
//bdtpdfdiff.SetMaxNodes(750);
bdtpdfdiff.SetMaxNodes(500);
//bdtpdfdiff.SetMaxDepth(8);
bdtpdfdiff.TrainForest(1e6);
}
RooWorkspace *wereg = new RooWorkspace("wereg");
wereg->import(sigpdf);
if (doele && dobarrel)
wereg->writeToFile("wereg_ele_eb.root");
else if (doele && !dobarrel)
wereg->writeToFile("wereg_ele_ee.root");
else if (!doele && dobarrel)
wereg->writeToFile("wereg_ph_eb.root");
else if (!doele && !dobarrel)
wereg->writeToFile("wereg_ph_ee.root");
return;
}
void fastEfficiencyNadir(unsigned int iEG, int iECAL1, int iColl1, int iECAL2, int iColl2,
TString dirIn="result/",
TString lumi="200 pb", int nCPU=4,
int color1=kBlack, int style1=kFullCircle, int color2=kRed, int style2=kOpenSquare,
TString probe="WP80", TString tag="WP80", TString fileIn="tree_effi_TagProbe.root")
{
// STYLE //
gROOT->Reset();
loadPresentationStyle();
gROOT->ForceStyle();
// EG THRESHOLDS //
const int nEG = 71;
double thres[nEG];
for(int i=0 ; i<nEG ; i++) thres[i]=i;
TString names[nEG];
ostringstream ossi;
for(int i=0;i<(int)nEG;i++) {
ossi.str("");
ossi << thres[i] ;
names[i] = ossi.str();
}
// NAMES //
const int nECAL=2;
const int nColl=2;
TString name_leg_ecal[nECAL] = {"Barrel","Endcaps"};
TString name_leg_coll[nColl] = {"Online","Emulation"};
TString name_ecal[nECAL] = {"_EB","_EE"};
TString name_coll[nColl] = {"_N","_M"};
TString dirResults = dirIn + "/turnons/EG"+names[iEG]+"/" ;
TString name_image =
dirResults + "eff_EG"+names[iEG]+"_tag"+tag+"_probe"+probe+name_ecal[iECAL1]+name_coll[iColl1]+"_vs"+name_ecal[iECAL2]+name_coll[iColl2] ;
// Output log //
ofstream fichier(name_image+".txt", ios::out);
// BINNING //
const int nbins[nEG] = {29,29,29,29,21,21,21,22,22,21,22,21,22,18,19,18,18,18,18,20,20,20,20,19,20,20,20,20,21,21,
21,21,21,21,21,21,21,21,21,21, //EG30
22,22,22,22,22,22,22,22,22,22, //EG40
29,29,29,29,29,29,29,29,29,29, //EG50
29,29,29,29,29,29,29,29,29,29};//EG60
Double_t bins_0[29] = {1,1.5,1.8,2,2.2,2.4,2.6,2.8, 3, 3.5, 4,4.2,4.5,4.7,5,5.5,6,6.5,7,7.5,8,8.5,9,10,12,15,20,50,150};// EG0
Double_t bins_1[29] = {1,1.5,1.8,2,2.2,2.4,2.6,2.8, 3, 3.5, 4,4.2,4.5,4.7,5,5.5,6,6.5,7,7.5,8,8.5,9,10,12,15,20,50,150};// EG1
Double_t bins_2[29] = {1,1.5,1.8,2,2.2,2.4,2.6,2.8, 3, 3.5, 4,4.2,4.5,4.7,5,5.5,6,6.5,7,7.5,8,8.5,9,10,12,15,20,50,150};// EG2
Double_t bins_3[29] = {1,1.5,1.8,2,2.2,2.4,2.6,2.8, 3, 3.5, 4,4.2,4.5,4.7,5,5.5,6,6.5,7,7.5,8,8.5,9,10,12,15,20,50,150};// EG3
Double_t bins_4[21] = {1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15, 17, 19, 21, 27, 32, 41, 50, 60, 70, 150}; // EG4
Double_t bins_5[21] = {2, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 22, 24, 31, 40, 50, 60, 70, 150}; // EG5
Double_t bins_6[21] = {3, 4, 5, 6, 7, 8, 9, 11, 13, 15, 17, 19, 21, 23, 27, 32, 41, 50, 60, 70, 150}; // EG6
Double_t bins_7[22] = {2, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 22, 24, 26, 31, 40, 50, 60, 70, 150}; // EG7
Double_t bins_8[22] = {3, 5, 6, 7, 8, 9, 10, 11, 13, 15, 17, 19, 21, 23, 25, 27, 32, 41, 50, 60, 70, 150}; // EG8
Double_t bins_9[21] = {4, 6, 7, 8, 9, 10, 11, 12, 14, 16, 18, 20, 22, 24, 26, 31, 40, 50, 60, 70, 150}; // EG9
Double_t bins_10[22] = {5, 7, 8, 9, 10, 11, 12, 13, 15, 17, 19, 21, 23, 25, 27, 29, 32, 41, 50, 60, 70, 150}; // EG10
Double_t bins_11[21] = {6, 8, 9, 10, 11, 12, 13, 14, 16, 18, 20, 22, 24, 26, 28, 31, 40, 50, 60, 70, 150}; // EG11
Double_t bins_12[22] = {5, 7, 9, 10, 11, 12, 13, 14, 15, 17, 19, 21, 23, 25, 27, 29, 32, 41, 50, 60, 70, 150}; // EG12
Double_t bins_13[18] = {5, 7, 9, 11, 12, 13, 14, 15, 17, 19, 22, 25, 29, 37, 50, 60, 70, 150}; // EG13
Double_t bins_14[19] = {6, 8, 10, 12, 13, 14, 15, 16, 18, 20, 22, 25, 30, 35, 40, 50, 60, 70, 150}; // EG14
Double_t bins_15[18] = {5, 7, 9, 11, 13, 14, 15, 16, 17, 19, 22, 25, 29, 37, 50, 60, 70, 150}; // EG15
Double_t bins_16[18] = {8, 10, 12, 14, 16, 17, 18, 19, 20, 22, 25, 30, 35, 40, 50, 60, 70, 150}; // EG16
Double_t bins_17[18] = {9, 11, 13, 15, 16, 17, 18, 19, 21, 23, 25, 30, 35, 40, 50, 60, 70, 150}; // EG17
Double_t bins_18[18] = {8, 10, 12, 14, 16, 17, 18, 19, 20, 22, 25, 30, 35, 40, 50, 60, 70, 150}; // EG18
Double_t bins_19[20] = {9, 11, 13, 15, 17, 18, 19, 20, 21, 23, 25, 27, 30, 35, 40, 45, 50, 60, 70, 150}; // EG19
Double_t bins_20[20] = {8, 10, 12, 14, 16, 18, 19, 20, 21, 22, 24, 26, 30, 35, 40, 45, 50, 60, 70, 100}; // EG20
Double_t bins_21[20] = {9, 11, 13, 15, 17, 19, 20, 21, 22, 23, 25, 27, 30, 35, 40, 45, 50, 60, 70, 150}; // EG21
Double_t bins_22[20] = {10, 12, 14, 16, 18, 20, 21, 22, 23, 24, 26, 28, 30, 35, 40, 45, 50, 60, 70, 150}; // EG22
Double_t bins_23[19] = {11, 13, 15, 17, 19, 21, 22, 23, 24, 25, 27, 30, 35, 40, 45, 50, 60, 70, 150}; // EG23
Double_t bins_24[20] = {10, 12, 14, 16, 18, 20, 22, 23, 24, 25, 26, 28, 30, 35, 40, 45, 50, 60, 70, 150}; // EG24
Double_t bins_25[20] = {11, 13, 15, 17, 19, 21, 23, 24, 25, 26, 27, 29, 30, 35, 40, 45, 50, 60, 70, 150}; // EG25
Double_t bins_26[20] = {10, 12, 14, 16, 18, 20, 22, 24, 25, 26, 27, 28, 30, 35, 40, 45, 50, 60, 70, 150}; // EG26
Double_t bins_27[20] = {11, 13, 15, 17, 19, 21, 23, 25, 26, 27, 28, 29, 33, 35, 40, 45, 50, 60, 70, 150}; // EG27
Double_t bins_28[21] = {10, 12, 14, 16, 18, 20, 22, 24, 26, 27, 28, 29, 30, 32, 35, 40, 45, 50, 60, 70, 150}; // EG28
Double_t bins_29[21] = {11, 13, 15, 17, 19, 21, 23, 25, 27, 28, 29, 30, 31, 33, 35, 40, 45, 50, 60, 70, 150}; // EG29
Double_t bins_30[21] = {10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 29, 30, 31, 32, 35, 40, 45, 50, 60, 70, 150}; // EG30
Double_t bins_40[22] = {10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 38, 39, 40, 42, 45, 50, 60, 70, 150}; // EG40
Double_t bins_50[29] = {10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 48, 50, 55, 60, 70, 90, 110, 130, 150, 170, 190}; // EG50
Double_t bins_60[29] = {10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 48, 50, 55, 60, 70, 90, 110, 130, 150, 170, 190}; // EG60
vector< Double_t* > bins;
bins.push_back( bins_0 ); bins.push_back( bins_1 ); bins.push_back( bins_2 ); bins.push_back( bins_3 ); bins.push_back( bins_4 );
bins.push_back( bins_5 ); bins.push_back( bins_6 ); bins.push_back( bins_7 ); bins.push_back( bins_8 ); bins.push_back( bins_9 );
bins.push_back( bins_10 ); bins.push_back( bins_11 ); bins.push_back( bins_12 ); bins.push_back( bins_13 ); bins.push_back( bins_14 );
bins.push_back( bins_15 ); bins.push_back( bins_16 ); bins.push_back( bins_17 ); bins.push_back( bins_18 ); bins.push_back( bins_19 );
bins.push_back( bins_20 ); bins.push_back( bins_21 ); bins.push_back( bins_22 ); bins.push_back( bins_23 ); bins.push_back( bins_24 );
bins.push_back( bins_25 ); bins.push_back( bins_26 ); bins.push_back( bins_27 ); bins.push_back( bins_28 ); bins.push_back( bins_29 );
for(int iV=0 ; iV<10 ; iV++) bins.push_back( bins_30 );
for(int iV=0 ; iV<10 ; iV++) bins.push_back( bins_40 );
for(int iV=0 ; iV<10 ; iV++) bins.push_back( bins_50 );
for(int iV=0 ; iV<10 ; iV++) bins.push_back( bins_60 );
RooBinning binning = RooBinning(nbins[iEG]-1, bins[iEG], "binning");
// INPUT DATA //
TFile* f1 = TFile::Open(dirIn+"/"+fileIn);
TTree* treenew;
TTree* treenew_2;
treenew = (TTree*) gDirectory->Get( "treenew"+name_ecal[iECAL1]+name_coll[iColl1] ) ;
treenew_2 = (TTree*) gDirectory->Get( "treenew"+name_ecal[iECAL2]+name_coll[iColl2] ) ;
TString name_scet[2], name_scdr[2], name_l1bin[2];
name_scet[0] = "sc_et"+name_ecal[iECAL1]+name_coll[iColl1];
name_scet[1] = "sc_et"+name_ecal[iECAL2]+name_coll[iColl2];
name_scdr[0] = "sc_dr"+name_ecal[iECAL1]+name_coll[iColl1];
name_scdr[1] = "sc_dr"+name_ecal[iECAL2]+name_coll[iColl2];
name_l1bin[0] = "l1_"+names[iEG]+name_ecal[iECAL1]+name_coll[iColl1];
name_l1bin[1] = "l1_"+names[iEG]+name_ecal[iECAL2]+name_coll[iColl2];
RooRealVar et_plot(name_scet[0],name_scet[0],0,150) ;
RooRealVar dr(name_scdr[0],name_scdr[0],0.5,1.5) ;
RooRealVar et_plot2(name_scet[1],name_scet[1],0,150) ;
RooRealVar dr2(name_scdr[1],name_scdr[1],0.5,1.5) ;
// Acceptance state cut (1 or 0)
RooCategory cut(name_l1bin[0],name_l1bin[0]) ;
cut.defineType("accept",1) ;
cut.defineType("reject",0) ;
RooCategory cut2(name_l1bin[1],name_l1bin[1]) ;
cut2.defineType("accept",1) ;
cut2.defineType("reject",0) ;
// PARAMETRES ROOFIT CRYSTAL BALL
RooRealVar norm("norm","N",1,0.6,1);
RooRealVar alpha("alpha","#alpha",0.671034,0.01,8);
RooRealVar n("n","n",4.07846,1.1,35);
RooRealVar mean("mean","mean",20.8,0,100);
//mean.setVal(thres[iEG]);
RooRealVar sigma("sigma","#sigma",0.972825,0.01,5);
//RooRealVar pedestal("pedestal","pedestal",0.01,0,0.4);
RooRealVar norm2("norm2","N",0.999069,0.6,1);
RooRealVar alpha2("alpha2","#alpha",0.492303,0.01,8);
RooRealVar n2("n2","n",11.6694,1.1,35);
RooRealVar mean2("mean2","mean",21.4582,0,100);
//mean2.setVal(thres[iEG]);
RooRealVar sigma2("sigma2","#sigma",1.19,0.01,5);
//RooRealVar pedestal2("pedestal2","pedestal",0.01,0,0.4);
FuncCB cb("cb","Crystal Ball Integree",et_plot,mean,sigma,alpha,n,norm) ;
FuncCB cb2("cb2","Crystal Ball Integree",et_plot2,mean2,sigma2,alpha2,n2,norm2) ;
// EFFICIENCY //
RooEfficiency eff("eff","efficiency",cb,cut,"accept");
RooEfficiency eff2("eff2","efficiency",cb2,cut2,"accept");
// DATASETS //
RooDataSet dataSet("data","data",RooArgSet(et_plot, cut,dr),Import(*treenew));
RooDataSet dataSet2("data2","data2",RooArgSet(et_plot2, cut2,dr2),Import(*treenew_2));
dataSet.Print();
dataSet2.Print();
// PLOT //
RooPlot* frame = et_plot.frame(Bins(18000),Title("Fitted efficiency")) ;
RooPlot* frame2 = et_plot2.frame(Bins(18000),Title("Fitted efficiency")) ;
dataSet.plotOn(frame, Binning(binning), Efficiency(cut), MarkerColor(color1), LineColor(color1), MarkerStyle(style1) );
dataSet2.plotOn(frame2, Binning(binning), Efficiency(cut2), MarkerColor(color2), LineColor(color2), MarkerStyle(style2) );
/////////////////////// FITTING /////////////////////////////
double fit_cuts_min = thres[iEG]-1.5 ;
double fit_cuts_max = 150;
et_plot.setRange("interesting",fit_cuts_min,fit_cuts_max);
et_plot2.setRange("interesting",fit_cuts_min,fit_cuts_max);
RooFitResult* roofitres1 = new RooFitResult("roofitres1","roofitres1");
RooFitResult* roofitres2 = new RooFitResult("roofitres2","roofitres2");
fichier << "Fit characteristics :" << endl ;
fichier << "EG " << names[iEG] << endl ;
fichier << "Fit Range , EB Coll : [" << fit_cuts_min << "," << fit_cuts_max << "]" << endl ;
fichier << "Fit Range , EE Coll : [" << fit_cuts_min << "," << fit_cuts_max << "]" << endl ;
fichier << "----------------------" << endl ;
// Fit #1 //
roofitres1 = eff.fitTo(dataSet,ConditionalObservables(et_plot),Range("interesting"),Minos(kTRUE),Warnings(kFALSE),NumCPU(nCPU),Save(kTRUE));
cb.plotOn(frame,LineColor(color1),LineWidth(2));
double res_norm1 = norm.getVal();
double err_norm1 = norm.getErrorLo();
double res_mean1 = mean.getVal();
double err_mean1 = mean.getError();
double res_sigma1 = sigma.getVal();
double err_sigma1 = sigma.getError();
double res_n1 = n.getVal();
double err_n1 = n.getError();
double res_alpha1 = alpha.getVal();
double err_alpha1 = alpha.getError();
fichier << "<----------------- EB ----------------->" << endl
<< "double res_mean=" << res_mean1 << "; "
<< "double res_sigma=" << res_sigma1 << "; "
<< "double res_alpha=" << res_alpha1 << "; "
<< "double res_n=" << res_n1 << "; "
<< "double res_norm=" << res_norm1 << "; "
<< endl
<< "double err_mean=" << err_mean1 << "; "
<< "double err_sigma=" << err_sigma1 << "; "
<< "double err_alpha=" << err_alpha1 << "; "
<< "double err_n=" << err_n1 << "; "
<< "double err_norm=" << err_norm1 << "; "
<< endl;
// Fit #2 //
roofitres2 = eff2.fitTo(dataSet2,ConditionalObservables(et_plot2),Range("interesting"),Minos(kTRUE),Warnings(kFALSE),NumCPU(nCPU),Save(kTRUE));
cb2.plotOn(frame2,LineColor(color2),LineWidth(2));
double res_norm2 = norm2.getVal();
double err_norm2 = norm2.getErrorLo();
double res_mean2 = mean2.getVal();
double err_mean2 = mean2.getError();
double res_sigma2 = sigma2.getVal();
double err_sigma2 = sigma2.getError();
double res_n2 = n2.getVal();
double err_n2 = n2.getError();
double res_alpha2 = alpha2.getVal();
double err_alpha2 = alpha2.getError();
fichier << "<----------------- EE ----------------->" << endl
<< "double res_mean=" << res_mean2 << "; "
<< "double res_sigma=" << res_sigma2 << "; "
<< "double res_alpha=" << res_alpha2 << "; "
<< "double res_n=" << res_n2 << "; "
<< "double res_norm=" << res_norm2 << "; "
<< endl
<< "double err_mean=" << err_mean2 << "; "
<< "double err_sigma=" << err_sigma2 << "; "
<< "double err_alpha=" << err_alpha2 << "; "
<< "double err_n=" << err_n2 << "; "
<< "double err_norm=" << err_norm2 << "; "
<< endl;
//////////////////////////// DRAWING PLOTS AND LEGENDS /////////////////////////////////
TCanvas* ca = new TCanvas("ca","Trigger Efficiency") ;
ca->SetGridx();
ca->SetGridy();
ca->cd();
gPad->SetLogx();
gPad->SetObjectStat(1);
frame->GetYaxis()->SetRangeUser(0,1.05);
frame->GetXaxis()->SetRangeUser(1,100.);
frame->GetYaxis()->SetTitle("Efficiency");
frame->GetXaxis()->SetTitle("E_{T} [GeV]");
frame->Draw() ;
frame2->GetYaxis()->SetRangeUser(0,1.05);
frame2->GetXaxis()->SetRangeUser(1,100.);
frame2->GetYaxis()->SetTitle("Efficiency");
frame2->GetXaxis()->SetTitle("E_{T} [GeV]");
frame2->Draw("same") ;
TH1F *SCeta1 = new TH1F("SCeta1","SCeta1",50,-2.5,2.5);
TH1F *SCeta2 = new TH1F("SCeta2","SCeta2",50,-2.5,2.5);
SCeta1->SetLineColor(color1) ;
SCeta1->SetMarkerColor(color1);
SCeta1->SetMarkerStyle(style1);
SCeta2->SetLineColor(color2) ;
SCeta2->SetMarkerColor(color2);
SCeta2->SetMarkerStyle(style2);
TLegend *leg = new TLegend(0.246,0.435,0.461,0.560,NULL,"brNDC"); // mid : x=353.5
leg->SetLineColor(1);
leg->SetTextColor(1);
leg->SetTextFont(42);
leg->SetTextSize(0.03);
leg->SetShadowColor(kWhite);
leg->SetFillColor(kWhite);
leg->SetMargin(0.25);
TLegendEntry *entry=leg->AddEntry("NULL","L1_SingleEG"+names[iEG],"h");
// leg->AddEntry(SCeta1,name_leg_ecal[iECAL1]+" "+name_leg_coll[iColl1],"p");
// leg->AddEntry(SCeta2,name_leg_ecal[iECAL2]+" "+name_leg_coll[iColl2],"p");
leg->AddEntry(SCeta1,name_leg_ecal[iECAL1],"p");
leg->AddEntry(SCeta2,name_leg_ecal[iECAL2],"p");
leg->Draw();
leg = new TLegend(0.16,0.725,0.58,0.905,NULL,"brNDC");
leg->SetBorderSize(0);
leg->SetTextFont(62);
leg->SetTextSize(0.03);
leg->SetLineColor(0);
leg->SetLineStyle(1);
leg->SetLineWidth(1);
leg->SetFillColor(0);
leg->SetFillStyle(0);
leg->AddEntry("NULL","CMS Preliminary 2012 pp #sqrt{s}=8 TeV","h");
leg->AddEntry("NULL","#int L dt = "+lumi+"^{-1}","h");
leg->AddEntry("NULL","Threshold : "+names[iEG]+" GeV","h");
leg->Draw();
TPaveText *pt2 = new TPaveText(0.220,0.605,0.487,0.685,"brNDC"); // mid : x=353.5
pt2->SetLineColor(1);
pt2->SetTextColor(1);
pt2->SetTextFont(42);
pt2->SetTextSize(0.03);
pt2->SetFillColor(kWhite);
pt2->SetShadowColor(kWhite);
pt2->AddText("L1 E/Gamma Trigger");
pt2->AddText("Electrons from Z");
pt2->Draw();
//TString name_image="eff_EG20_2012_12fb";
ca->Print(name_image+".cxx","cxx");
ca->Print(name_image+".png","png");
ca->Print(name_image+".gif","gif");
ca->Print(name_image+".pdf","pdf");
ca->Print(name_image+".ps","ps");
ca->Print(name_image+".eps","eps");
/////////////////////////////
// SAVE THE ROO FIT RESULT //
/////////////////////////////
RooWorkspace *w = new RooWorkspace("workspace","workspace") ;
w->import(dataSet);
w->import(dataSet2);
w->import(*roofitres1,"roofitres1");
w->import(*roofitres2,"roofitres2");
cout << "CREATES WORKSPACE : " << endl;
w->Print();
w->writeToFile(name_image+"_fitres.root") ;
//gDirectory->Add(w) ;
//f1->Close();
}
///
/// Fit to the minimum using an improve method.
/// The idea is to kill known minima. For this, we add a multivariate
/// Gaussian to the chi2 at the position of the minimum. Ideally it should
/// be a parabola shaped function, but the Gaussian is readily available.
/// A true parabola like in the original IMPROVE algorithm doesn't work
/// because apparently it affects the other regions of
/// the chi2 too much. There are two parameters to tune: the width of the
/// Gaussian, which is controlled by the error definition of the first fit,
/// and the height of the Gaussian, which is set to 16 (=4 sigma).
/// Three fits are performed: 1) initial fit, 2) fit with improved FCN,
/// 3) fit of the non-improved FCN using the result from step 2 as the start
/// parameters.
/// So far it is only available for the Prob method, via the probimprove
/// command line flag.
///
RooFitResult* Utils::fitToMinImprove(RooWorkspace *w, TString name)
{
TString parsName = "par_"+name;
TString obsName = "obs_"+name;
TString pdfName = "pdf_"+name;
int printlevel = -1;
RooMsgService::instance().setGlobalKillBelow(ERROR);
// step 1: find a minimum to start with
RooFitResult *r1 = 0;
{
RooFormulaVar ll("ll", "ll", "-2*log(@0)", RooArgSet(*w->pdf(pdfName)));
// RooFitResult* r1 = fitToMin(&ll, printlevel);
RooMinuit m(ll);
m.setPrintLevel(-2);
m.setNoWarn();
m.setErrorLevel(4.0); ///< define 2 sigma errors. This will make the hesse PDF 2 sigma wide!
int status = m.migrad();
r1 = m.save();
// if ( 102<RadToDeg(w->var("g")->getVal())&&RadToDeg(w->var("g")->getVal())<103 )
// {
// cout << "step 1" << endl;
// r1->Print("v");
// gStyle->SetPalette(1);
// float xmin = 0.;
// float xmax = 3.14;
// float ymin = 0.;
// float ymax = 0.2;
// TH2F* histo = new TH2F("histo", "histo", 100, xmin, xmax, 100, ymin, ymax);
// for ( int ix=0; ix<100; ix++ )
// for ( int iy=0; iy<100; iy++ )
// {
// float x = xmin + (xmax-xmin)*(double)ix/(double)100;
// float y = ymin + (ymax-ymin)*(double)iy/(double)100;
// w->var("d_dk")->setVal(x);
// w->var("r_dk")->setVal(y);
// histo->SetBinContent(ix+1,iy+1,ll.getVal());
// }
// newNoWarnTCanvas("c2");
// histo->GetZaxis()->SetRangeUser(0,20);
// histo->Draw("colz");
// setParameters(w, parsName, r1);
// }
}
// step 2: build and fit the improved fcn
RooFitResult *r2 = 0;
{
// create a Hesse PDF, import both PDFs into a new workspace,
// so that their parameters are linked
RooAbsPdf* hessePdf = r1->createHessePdf(*w->set(parsName));
if ( !hessePdf ) return r1;
// RooWorkspace* wImprove = (RooWorkspace*)w->Clone();
RooWorkspace* wImprove = new RooWorkspace();
wImprove->import(*w->pdf(pdfName));
wImprove->import(*hessePdf);
hessePdf = wImprove->pdf(hessePdf->GetName());
RooAbsPdf* fullPdf = wImprove->pdf(pdfName);
RooFormulaVar ll("ll", "ll", "-2*log(@0) +16*@1", RooArgSet(*fullPdf, *hessePdf));
// RooFitResult *r2 = fitToMin(&ll, printlevel);
RooMinuit m(ll);
m.setPrintLevel(-2);
m.setNoWarn();
m.setErrorLevel(1.0);
int status = m.migrad();
r2 = m.save();
// if ( 102<RadToDeg(w->var("g")->getVal())&&RadToDeg(w->var("g")->getVal())<103 )
// {
// cout << "step 3" << endl;
// r2->Print("v");
//
// gStyle->SetPalette(1);
// float xmin = 0.;
// float xmax = 3.14;
// float ymin = 0.;
// float ymax = 0.2;
// TH2F* histo = new TH2F("histo", "histo", 100, xmin, xmax, 100, ymin, ymax);
// for ( int ix=0; ix<100; ix++ )
// for ( int iy=0; iy<100; iy++ )
// {
// float x = xmin + (xmax-xmin)*(double)ix/(double)100;
// float y = ymin + (ymax-ymin)*(double)iy/(double)100;
// wImprove->var("d_dk")->setVal(x);
// wImprove->var("r_dk")->setVal(y);
// histo->SetBinContent(ix+1,iy+1,ll.getVal());
// }
// newNoWarnTCanvas("c7");
// histo->GetZaxis()->SetRangeUser(0,20);
// histo->Draw("colz");
// // setParameters(wImprove, parsName, r1);
// }
delete wImprove;
}
// step 3: use the fit result of the improved fit as
// start parameters for the nominal fcn
RooFitResult* r3;
{
setParameters(w, parsName, r2);
RooFormulaVar ll("ll", "ll", "-2*log(@0)", RooArgSet(*w->pdf(pdfName)));
RooMinuit m(ll);
m.setPrintLevel(-2);
m.setNoWarn();
m.setErrorLevel(1.0);
int status = m.migrad();
r3 = m.save();
// if ( 102<RadToDeg(w->var("g")->getVal())&&RadToDeg(w->var("g")->getVal())<103 )
// {
// cout << "step 3" << endl;
// r3->Print("v");
// }
}
// step 5: chose better minimum
// cout << r1->minNll() << " " << r3->minNll() << endl;
RooFitResult* r = 0;
if ( r1->minNll()<r3->minNll() )
{
delete r3;
r = r1;
}
else
{
delete r1;
r = r3;
// cout << "Utils::fitToMinImprove() : improved fit is better!" << endl;
}
RooMsgService::instance().setGlobalKillBelow(INFO);
// set to best parameters
setParameters(w, parsName, r);
return r;
}
void buildPdf()
{
Double_t lorange = 100.;
Double_t hirange = 140.;
// Import data
TFile *file = new TFile("/atlas/data18a/yupan/HZZ4l2012/MiniTree/data12.root");
TTree *tree = (TTree*)file->Get("tree_incl_4mu");
// should include all channels, for now just testing...
// Define variables
Float_t m4l = 0;
Float_t m4lerr = 0;
// Float_t wgt = 0;
// Get number of entries and setbranchaddress
Int_t nevents = tree->GetEntries();
tree->SetBranchStatus("*",0);
tree->SetBranchStatus("m4l_unconstrained",1);
tree->SetBranchStatus("m4lerr_unconstrained",1);
//tree->SetBranchStatus("weight",1);
tree->SetBranchAddress("m4l_unconstrained",&m4l);
tree->SetBranchAddress("m4lerr_unconstrained",&m4lerr);
//tree->SetBranchAddress("weight",&wgt);
///////////////
// Build pdfs
//////////////
RooRealVar* mass = new RooRealVar("m4l","mass",lorange,hirange,"GeV");
RooRealVar merr("m4lerr","mass err",0.1,4.0,"GeV");
//RooRealVar weight("weight","weight",0,10);
RooRealVar scale("scale","per-event error scale factor",1.0,0.2,4.0);
RooProduct sigmaErr("sigmaErr","sigmaErr",RooArgSet(scale,merr));
/*
float totalwgt(0);
for (Int_t i=0; i<nevents; i++) {
tree->GetEntry(i);
totalwgt+=wgt;
}
std::cout<<"total weight = "<<totalwgt<<std::endl;
*/
/// Make DataSet
RooDataSet signal("signal","signal",RooArgSet(*mass,merr));
std::cout<<"Reading in events from signal minitree"<<std::endl;
for (Int_t i=0; i<nevents; i++) {
tree->GetEntry(i);
mass->setVal(m4l);
merr.setVal(m4lerr);
//weight.setVal(wgt/totalwgt);
signal.add(RooArgSet(*mass,merr));
}
// Create 1D kernel estimation for signal mass
std::cout<<"Building mass keys"<<std::endl;
RooKeysPdf kestsig("kestsig","kestsig",*mass,signal);
TH1F* hm = (TH1F*)kestsig.createHistogram("hm",*mass);
kestsig.fillHistogram(hm,RooArgList(*mass));
std::cout<<"Building mass pdf"<<std::endl;
RooDataHist* dmass = new RooDataHist("dmass","binned dataset",*mass,hm);
RooHistPdf* masspdf = new RooHistPdf("masspdf","pdf(dm)",*mass,*dmass,2);
// Create 1D kernel estimation for mass err
std::cout<<"Building error keys"<<std::endl;
RooKeysPdf kestsigerr("kestsigerr","kestsigerr",merr,signal,RooKeysPdf::MirrorBoth,2);
TH1F* herr = (TH1F*)kestsigerr.createHistogram("herr",merr);
kestsigerr.fillHistogram(herr,RooArgList(merr));
std::cout<<"Integral "<<herr->Integral()<<std::endl;
std::cout<<"Building error pdf"<<std::endl;
RooDataHist* derr = new RooDataHist("derr","binned dataset",merr,herr);
RooHistPdf* errpdf = new RooHistPdf("errpdf","pdf(de)",merr,*derr,2);
//Make the crystal ball resolution model with CB sigma = mass error
RooRealVar meanCB ("meanCB", "mean CB", hmass, hmass-10., hmass+5.);
RooRealVar alphaCB ("alphaCB", "alpha CB", 7, 0., 10.);
RooRealVar nnCB ("nnCB", "nn CB", 1.5, 0., 15.);
RooCBShape* shapeCB = new RooCBShape("shapeCB", "crystal ball pdf", *mass, meanCB, sigmaErr, alphaCB, nnCB);
// Make conditional pdf
RooProdPdf* sigmodel = new RooProdPdf("sigmodel","sigmodel", *errpdf, Conditional(*shapeCB, *mass));
// Make a workspace to store the fit model
RooWorkspace* pdfWsp = new RooWorkspace("pdfWspCB");
pdfWsp->import(*sigmodel,RecycleConflictNodes());
pdfWsp->import(*masspdf);
pdfWsp->import(*errpdf,RecycleConflictNodes());
pdfWsp->import(signal);
pdfWsp->Print();
pdfWsp->writeToFile("pdfWspCB.root");
// Make some plots
TCanvas *c = new TCanvas("c","c",500,500);
c->Divide(2);
RooPlot* frame = merr.frame(Title("keys signal error"));
signal.plotOn(frame);
kestsigerr.plotOn(frame,LineColor(kRed));
errpdf->plotOn(frame);
RooPlot* frame2 = mass->frame(Title("keys signal"));
signal.plotOn(frame2);
kestsig.plotOn(frame2,LineColor(kRed));
masspdf->plotOn(frame2);
c->cd(2);
frame->Draw();
c->cd(1);
grame2->Draw();
c->Print("testPdf.png");
}
void ZeeGammaMassFitSystematicStudy(string workspaceFile, const Int_t seed = 1234,
Int_t Option = 0, Int_t NToys = 1) {
//--------------------------------------------------------------------------------------------------------------
// Settings
//==============================================================================================================
TRandom3 *randomnumber = new TRandom3(seed);
// RooRealVar m("m","mass",60,130);
RooCategory sample("sample","");
sample.defineType("Pass",1);
sample.defineType("Fail",2);
//--------------------------------------------------------------------------------------------------------------
//Load Workspace
//==============================================================================================================
TFile *f = new TFile (workspaceFile.c_str(), "READ");
RooWorkspace *w = (RooWorkspace*)f->Get("MassFitWorkspace");
//--------------------------------------------------------------------------------------------------------------
//Setup output tree
//==============================================================================================================
TFile *outputfile = new TFile (Form("EffToyResults_Option%d_Seed%d.root",Option, seed), "RECREATE");
float varEff = 0;
float varEffErrL = 0;
float varEffErrH = 0;
TTree *outTree = new TTree("eff","eff");
outTree->Branch("eff",&varEff, "eff/F");
outTree->Branch("efferrl",&varEffErrL, "efferrl/F");
outTree->Branch("efferrh",&varEffErrH, "efferrh/F");
//--------------------------------------------------------------------------------------------------------------
//Load Model
//==============================================================================================================
RooSimultaneous *totalPdf = (RooSimultaneous*)w->pdf("totalPdf");
RooRealVar *m_default = (RooRealVar*)w->var("m");
m_default->setRange("signalRange",85, 95);
//get default models
RooAddPdf *modelPass_default = (RooAddPdf*)w->pdf("modelPass");
RooAddPdf *modelFail_default = (RooAddPdf*)w->pdf("modelFail");
//get variables
RooRealVar *Nsig = (RooRealVar*)w->var("Nsig");
RooRealVar *eff = (RooRealVar*)w->var("eff");
RooRealVar *NbkgFail = (RooRealVar*)w->var("NbkgFail");
RooFormulaVar NsigPass("NsigPass","eff*Nsig",RooArgList(*eff,*Nsig));
RooFormulaVar NsigFail("NsigFail","(1.0-eff)*Nsig",RooArgList(*eff,*Nsig));
//get number of expected events
Double_t npass = 100;
Double_t nfail = 169;
//*************************************************************************************
//make alternative model
//*************************************************************************************
RooRealVar *tFail_default = (RooRealVar*)w->var("tFail");
RooRealVar *fracFail_default = (RooRealVar*)w->var("fracFail");
RooRealVar *meanFail_default = (RooRealVar*)w->var("meanFail");
RooRealVar *sigmaFail_default = (RooRealVar*)w->var("sigmaFail");
RooHistPdf *bkgFailTemplate_default = (RooHistPdf*)w->pdf("bkgHistPdfFail");
RooFFTConvPdf *sigFail_default = (RooFFTConvPdf*)w->pdf("signalFail");
RooFFTConvPdf *bkgFail_default = (RooFFTConvPdf*)w->pdf("bkgConvPdfFail");
RooExtendPdf *esignalFail_default = (RooExtendPdf *)w->pdf("esignalFail");
RooExtendPdf *ebackgroundFail_default = (RooExtendPdf *)w->pdf("ebackgroundFail");
RooExponential *bkgexpFail_default = (RooExponential*)w->pdf("bkgexpFail");
RooAddPdf *backgroundFail_default = (RooAddPdf*)w->pdf("backgroundFail");
RooGaussian *bkggausFail_default = (RooGaussian*)w->pdf("bkggausFail");
//shifted mean
RooRealVar *meanFail_shifted = new RooRealVar("meanFail_shifted","meanFail_shifted", 0, -5, 5);
meanFail_shifted->setVal(meanFail_default->getVal());
if (Option == 1) meanFail_shifted->setVal(meanFail_default->getVal()-1.0);
else if (Option == 2) meanFail_shifted->setVal(meanFail_default->getVal()+1.0);
else if (Option == 11) meanFail_shifted->setVal(meanFail_default->getVal()-2.0);
else if (Option == 12) meanFail_shifted->setVal(meanFail_default->getVal()+2.0);
RooRealVar *sigmaFail_shifted = new RooRealVar("sigmaFail_shifted","sigmaFail_shifted", 0, -5, 5);
sigmaFail_shifted->setVal(sigmaFail_default->getVal());
if (Option == 3) sigmaFail_shifted->setVal(sigmaFail_default->getVal()*1.2);
else if (Option == 4) sigmaFail_shifted->setVal(sigmaFail_default->getVal()*0.8);
CMCBkgTemplateConvGaussianPlusExp *bkgFailModel = new CMCBkgTemplateConvGaussianPlusExp(*m_default,bkgFailTemplate_default,false,meanFail_shifted,sigmaFail_shifted, "shifted");
bkgFailModel->t->setVal(tFail_default->getVal());
bkgFailModel->frac->setVal(fracFail_default->getVal());
cout << "mean : " << meanFail_default->getVal() << " - " << meanFail_shifted->getVal() << endl;
cout << "sigma : " << sigmaFail_default->getVal() << " - " << sigmaFail_shifted->getVal() << endl;
cout << "t: " << tFail_default->getVal() << " - " << bkgFailModel->t->getVal() << endl;
cout << "frac: " << fracFail_default->getVal() << " - " << bkgFailModel->frac->getVal() << endl;
cout << "eff: " << eff->getVal() << " : " << NsigPass.getVal() << " / " << (NsigPass.getVal() + NsigFail.getVal()) << endl;
cout << "NbkgFail: " << NbkgFail->getVal() << endl;
//make alternative fail model
RooAddPdf *modelFail=0;
RooExtendPdf *esignalFail=0, *ebackgroundFail=0;
ebackgroundFail = new RooExtendPdf("ebackgroundFail_shifted","ebackgroundFail_shifted",*(bkgFailModel->model),*NbkgFail,"signalRange");
modelFail = new RooAddPdf("modelFail","Model for FAIL sample", RooArgList(*esignalFail_default,*ebackgroundFail));
cout << "*************************************\n";
ebackgroundFail->Print();
cout << "*************************************\n";
ebackgroundFail_default->Print();
cout << "*************************************\n";
modelFail->Print();
cout << "*************************************\n";
modelFail_default->Print();
cout << "*************************************\n";
TCanvas *cv = new TCanvas("cv","cv",800,600);
RooPlot *mframeFail_default = m_default->frame(Bins(Int_t(130-60)/2));
modelFail_default->plotOn(mframeFail_default);
modelFail_default->plotOn(mframeFail_default,Components("ebackgroundFail"),LineStyle(kDashed),LineColor(kRed));
modelFail_default->plotOn(mframeFail_default,Components("bkgexpFail"),LineStyle(kDashed),LineColor(kGreen+2));
mframeFail_default->GetYaxis()->SetTitle("");
mframeFail_default->GetYaxis()->SetTitleOffset(1.2);
mframeFail_default->GetXaxis()->SetTitle("m_{ee#gamma} [GeV/c^{2}]");
mframeFail_default->GetXaxis()->SetTitleOffset(1.05);
mframeFail_default->SetTitle("");
mframeFail_default->Draw();
cv->SaveAs("DefaultModel.gif");
RooPlot *mframeFail = m_default->frame(Bins(Int_t(130-60)/2));
modelFail->plotOn(mframeFail);
modelFail->plotOn(mframeFail,Components("ebackgroundFail_shifted"),LineStyle(kDashed),LineColor(kRed));
modelFail->plotOn(mframeFail,Components("bkgexpFail_shifted"),LineStyle(kDashed),LineColor(kGreen+2));
mframeFail->GetYaxis()->SetTitle("");
mframeFail->GetYaxis()->SetTitleOffset(1.2);
mframeFail->GetXaxis()->SetTitle("m_{ee#gamma} [GeV/c^{2}]");
mframeFail->GetXaxis()->SetTitleOffset(1.05);
mframeFail->SetTitle("");
mframeFail->Draw();
cv->SaveAs(Form("ShiftedModel_%d.gif",Option));
//*************************************************************************************
//Do Toys
//*************************************************************************************
for(uint t=0; t < NToys; ++t) {
RooDataSet *pseudoData_pass = modelPass_default->generate(*m_default, randomnumber->Poisson(npass));
RooDataSet *pseudoData_fail = 0;
pseudoData_fail = modelFail->generate(*m_default, randomnumber->Poisson(nfail));
RooDataSet *pseudoDataCombined = new RooDataSet("pseudoDataCombined","pseudoDataCombined",RooArgList(*m_default),
RooFit::Index(sample),
RooFit::Import("Pass",*pseudoData_pass),
RooFit::Import("Fail",*pseudoData_fail));
pseudoDataCombined->write(Form("toy%d.txt",t));
RooFitResult *fitResult=0;
fitResult = totalPdf->fitTo(*pseudoDataCombined,
RooFit::Extended(),
RooFit::Strategy(2),
//RooFit::Minos(RooArgSet(eff)),
RooFit::Save());
cout << "\n\n";
cout << "Eff Fit: " << eff->getVal() << " -" << fabs(eff->getErrorLo()) << " +" << eff->getErrorHi() << endl;
//Fill Tree
varEff = eff->getVal();
varEffErrL = fabs(eff->getErrorLo());
varEffErrH = eff->getErrorHi();
outTree->Fill();
// //*************************************************************************************
// //Plot Toys
// //*************************************************************************************
// TCanvas *cv = new TCanvas("cv","cv",800,600);
// char pname[50];
// char binlabelx[100];
// char binlabely[100];
// char yield[50];
// char effstr[100];
// char nsigstr[100];
// char nbkgstr[100];
// char chi2str[100];
// //
// // Plot passing probes
// //
// RooPlot *mframeFail_default = m.frame(Bins(Int_t(130-60)/2));
// modelFail_default->plotOn(mframeFail_default);
// modelFail_default->plotOn(mframeFail_default,Components("ebackgroundFail"),LineStyle(kDashed),LineColor(kRed));
// modelFail_default->plotOn(mframeFail_default,Components("bkgexpFail"),LineStyle(kDashed),LineColor(kGreen+2));
// mframeFail_default->Draw();
// cv->SaveAs("DefaultModel.gif");
// RooPlot *mframeFail = m.frame(Bins(Int_t(130-60)/2));
// modelFail->plotOn(mframeFail);
// modelFail->plotOn(mframeFail,Components("ebackgroundFail_shifted"),LineStyle(kDashed),LineColor(kRed));
// modelFail->plotOn(mframeFail,Components("bkgexpFail_shifted"),LineStyle(kDashed),LineColor(kGreen+2));
// sprintf(yield,"%u Events",(Int_t)passTree->GetEntries());
// sprintf(nsigstr,"N_{sig} = %.1f #pm %.1f",NsigPass.getVal(),NsigPass.getPropagatedError(*fitResult));
// plotPass.AddTextBox(yield,0.21,0.76,0.51,0.80,0,kBlack,-1);
// plotPass.AddTextBox(effstr,0.70,0.85,0.94,0.90,0,kBlack,-1);
// plotPass.AddTextBox(0.70,0.73,0.94,0.83,0,kBlack,-1,1,nsigstr);//,chi2str);
// mframeFail->Draw();
// cv->SaveAs(Form("ShiftedModel_%d.gif",Option));
// //
// // Plot failing probes
// //
// sprintf(pname,"fail%s_%i",name.Data(),ibin);
// sprintf(yield,"%u Events",(Int_t)failTree->GetEntries());
// sprintf(nsigstr,"N_{sig} = %.1f #pm %.1f",NsigFail.getVal(),NsigFail.getPropagatedError(*fitResult));
// sprintf(nbkgstr,"N_{bkg} = %.1f #pm %.1f",NbkgFail.getVal(),NbkgFail.getPropagatedError(*fitResult));
// sprintf(chi2str,"#chi^{2}/DOF = %.3f",mframePass->chiSquare(nflfail));
// CPlot plotFail(pname,mframeFail,"Failing probes","tag-probe mass [GeV/c^{2}]","Events / 2.0 GeV/c^{2}");
// plotFail.AddTextBox(binlabelx,0.21,0.85,0.51,0.90,0,kBlack,-1);
// if((name.CompareTo("etapt")==0) || (name.CompareTo("etaphi")==0)) {
// plotFail.AddTextBox(binlabely,0.21,0.80,0.51,0.85,0,kBlack,-1);
// plotFail.AddTextBox(yield,0.21,0.76,0.51,0.80,0,kBlack,-1);
// } else {
// plotFail.AddTextBox(yield,0.21,0.81,0.51,0.85,0,kBlack,-1);
// }
// plotFail.AddTextBox(effstr,0.70,0.85,0.94,0.90,0,kBlack,-1);
// plotFail.AddTextBox(0.70,0.68,0.94,0.83,0,kBlack,-1,2,nsigstr,nbkgstr);//,chi2str);
// plotFail.Draw(cfail,kTRUE,format);
} //for loop over all toys
//*************************************************************************************
//Save To File
//*************************************************************************************
outputfile->WriteTObject(outTree, outTree->GetName(), "WriteDelete");
}
int main(){
TFile *inFile = TFile::Open("../AnalysisScripts/MK_signal_test/CMS-HGG_sig.root");
RooWorkspace *inWS = (RooWorkspace*)inFile->Get("cms_hgg_workspace");
RooRealVar *mass = (RooRealVar*)inWS->var("CMS_hgg_mass");
RooRealVar *intLumi = (RooRealVar*)inWS->var("IntLumi");
RooRealVar *MH = new RooRealVar("MH","MH",110,150);
InitialFit initFitRV(mass,MH,110,150);
initFitRV.setVerbosity(0);
initFitRV.buildSumOfGaussians("ggh_cat0",3);
InitialFit initFitWV(mass,MH,110,150);
initFitWV.setVerbosity(0);
initFitWV.buildSumOfGaussians("ggh_cat0",1);
for (int mh=110; mh<=150; mh+=5){
RooDataSet *dataRV = (RooDataSet*)inWS->data(Form("sig_ggh_mass_m%d_rv_cat0",mh));
initFitRV.addDataset(mh,dataRV);
RooDataSet *dataWV = (RooDataSet*)inWS->data(Form("sig_ggh_mass_m%d_wv_cat0",mh));
initFitWV.addDataset(mh,dataWV);
}
initFitRV.runFits(1);
initFitRV.saveParamsToFileAtMH("dat/in/ggh_cat0_rv.dat",125);
initFitRV.loadPriorConstraints("dat/in/ggh_cat0_rv.dat",0.1);
initFitRV.runFits(1);
initFitRV.plotFits("plots/test/rv");
initFitWV.runFits(1);
initFitWV.saveParamsToFileAtMH("dat/in/ggh_cat0_wv.dat",125);
initFitWV.loadPriorConstraints("dat/in/ggh_cat0_wv.dat",0.1);
initFitWV.runFits(1);
initFitWV.plotFits("plots/test/wv");
map<int,map<string,RooRealVar*> > fitParamsRV = initFitRV.getFitParams();
map<int,map<string,RooRealVar*> > fitParamsWV = initFitWV.getFitParams();
LinearInterp linInterpRV(MH,110,150,fitParamsRV,false);
linInterpRV.setVerbosity(0);
linInterpRV.interpolate(3);
map<string,RooSpline1D*> splinesRV = linInterpRV.getSplines();
LinearInterp linInterpWV(MH,110,150,fitParamsWV,false);
linInterpWV.setVerbosity(0);
linInterpWV.interpolate(1);
map<string,RooSpline1D*> splinesWV = linInterpWV.getSplines();
FinalModelConstruction finalModel(mass,MH,intLumi,110,150,"ggh",0,false,"dat/photonCatSyst.dat",1,false);
finalModel.setRVsplines(splinesRV);
finalModel.setWVsplines(splinesWV);
map<int,RooDataSet*> datasetsRV;
map<int,RooDataSet*> datasetsWV;
map<int,RooDataSet*> datasetsSTD;
for (int mh=110; mh<=150; mh+=5){
RooDataSet *dataRV = (RooDataSet*)inWS->data(Form("sig_ggh_mass_m%d_rv_cat0",mh));
RooDataSet *dataWV = (RooDataSet*)inWS->data(Form("sig_ggh_mass_m%d_wv_cat0",mh));
RooDataSet *dataSTD = (RooDataSet*)inWS->data(Form("sig_ggh_mass_m%d_cat0",mh));
datasetsRV.insert(pair<int,RooDataSet*>(mh,dataRV));
datasetsWV.insert(pair<int,RooDataSet*>(mh,dataWV));
datasetsSTD.insert(pair<int,RooDataSet*>(mh,dataSTD));
}
finalModel.setRVdatasets(datasetsRV);
finalModel.setWVdatasets(datasetsWV);
finalModel.setSTDdatasets(datasetsSTD);
finalModel.buildRvWvPdf("hggpdfsmrel",3,1,false);
finalModel.getNormalization();
finalModel.plotPdf("plots/test/final");
TFile *outFile = new TFile("tempFile.root","RECREATE");
RooWorkspace *outWS = new RooWorkspace("wsig");
finalModel.save(outWS);
outFile->cd();
outWS->Write();
outFile->Close();
inFile->Close();
return 0;
}
void results2tree(
const char* workDirName,
bool isMC=false,
const char* thePoiNames="RFrac2Svs1S,N_Jpsi,f_Jpsi,m_Jpsi,sigma1_Jpsi,alpha_Jpsi,n_Jpsi,sigma2_Jpsi,MassRatio,rSigma21_Jpsi,lambda1_Bkg,lambda2_Bkg,lambda3_Bkg,lambda4_Bkg,lambda5__Bkg,N_Bkg"
) {
// workDirName: usual tag where to look for files in Output
// thePoiNames: comma-separated list of parameters to store ("par1,par2,par3"). Default: all
TFile *f = new TFile(treeFileName(workDirName,isMC),"RECREATE");
TTree *tr = new TTree("fitresults","fit results");
// bin edges
float ptmin, ptmax, ymin, ymax, centmin, centmax;
// model names
Char_t jpsiName[128], psipName[128], bkgName[128];
// collision system
Char_t collSystem[8];
// goodness of fit
float nll, chi2, normchi2; int npar, ndof;
// parameters to store: make it a vector
vector<poi> thePois;
TString thePoiNamesStr(thePoiNames);
TString t; Int_t from = 0;
while (thePoiNamesStr.Tokenize(t, from , ",")) {
poi p; strcpy(p.name, t.Data());
cout << p.name << endl;
thePois.push_back(p);
}
// create tree branches
tr->Branch("ptmin",&ptmin,"ptmin/F");
tr->Branch("ptmax",&ptmax,"ptmax/F");
tr->Branch("ymin",&ymin,"ymin/F");
tr->Branch("ymax",&ymax,"ymax/F");
tr->Branch("centmin",¢min,"centmin/F");
tr->Branch("centmax",¢max,"centmax/F");
tr->Branch("jpsiName",jpsiName,"jpsiName/C");
tr->Branch("psipName",psipName,"psipName/C");
tr->Branch("bkgName",bkgName,"bkgName/C");
tr->Branch("collSystem",collSystem,"collSystem/C");
tr->Branch("nll",&nll,"nll/F");
tr->Branch("chi2",&chi2,"chi2/F");
tr->Branch("normchi2",&normchi2,"normchi2/F");
tr->Branch("npar",&npar,"npar/I");
tr->Branch("ndof",&ndof,"ndof/I");
for (vector<poi>::iterator it=thePois.begin(); it!=thePois.end(); it++) {
tr->Branch(Form("%s_val",it->name),&(it->val),Form("%s_val/F",it->name));
tr->Branch(Form("%s_err",it->name),&(it->err),Form("%s_err/F",it->name));
}
// list of files
vector<TString> theFiles = fileList(workDirName,"",isMC);
int cnt=0;
for (vector<TString>::const_iterator it=theFiles.begin(); it!=theFiles.end(); it++) {
cout << "Parsing file " << cnt << " / " << theFiles.size() << ": " << *it << endl;
// parse the file name to get info
anabin thebin = binFromFile(*it);
ptmin = thebin.ptbin().low();
ptmax = thebin.ptbin().high();
ymin = thebin.rapbin().low();
ymax = thebin.rapbin().high();
centmin = thebin.centbin().low();
centmax = thebin.centbin().high();
strcpy(collSystem, (it->Index("PbPb")>0) ? "PbPb" : "PP");
// get the model names
from = 0;
bool catchjpsi=false, catchpsip=false, catchbkg=false;
while (it->Tokenize(t, from, "_")) {
if (catchjpsi) {strcpy(jpsiName, t.Data()); catchjpsi=false;}
if (catchpsip) {strcpy(psipName, t.Data()); catchpsip=false;}
if (catchbkg) {strcpy(bkgName, t.Data()); catchbkg=false;}
if (t=="Jpsi") catchjpsi=true;
if (t=="Psi2S") catchpsip=true;
if (t=="Bkg") catchbkg=true;
}
TFile *f = new TFile(*it); RooWorkspace *ws = NULL;
if (!f) {
cout << "Error, file " << *it << " does not exist." << endl;
} else {
ws = (RooWorkspace*) f->Get("workspace");
if (!ws) {
cout << "Error, workspace not found in " << *it << "." << endl;
}
}
nll=0; chi2=0; npar=0; ndof=0;
if (f && ws) {
// get the model for nll and npar
RooAbsPdf *model = pdfFromWS(ws, Form("_%s",collSystem), "pdfMASS_Tot");
if (model) {
RooAbsData *dat = dataFromWS(ws, Form("_%s",collSystem), "dOS_DATA");
if (dat) {
RooAbsReal *NLL = model->createNLL(*dat);
if (NLL) nll = NLL->getVal();
npar = model->getParameters(dat)->selectByAttrib("Constant",kFALSE)->getSize();
// compute the chi2 and the ndof
RooPlot* frame = ws->var("invMass")->frame(Bins(nBins));
dat->plotOn(frame);
model->plotOn(frame);
TH1 *hdatact = dat->createHistogram("hdatact", *(ws->var("invMass")), Binning(nBins));
RooHist *hpull = frame->pullHist(0,0, true);
double* ypulls = hpull->GetY();
unsigned int nFullBins = 0;
for (int i = 0; i < nBins; i++) {
if (hdatact->GetBinContent(i+1) > 0.0) {
chi2 += ypulls[i]*ypulls[i];
nFullBins++;
}
}
ndof = nFullBins - npar;
normchi2 = chi2/ndof;
}
}
// get the POIs
for (vector<poi>::iterator itpoi=thePois.begin(); itpoi!=thePois.end(); itpoi++) {
RooRealVar *thevar = poiFromWS(ws, Form("_%s",collSystem), itpoi->name);
itpoi->val = thevar ? thevar->getVal() : 0;
itpoi->err = thevar ? thevar->getError() : 0;
}
f->Close();
delete f;
} else {
for (vector<poi>::iterator itpoi=thePois.begin(); itpoi!=thePois.end(); itpoi++) {
itpoi->val = 0;
itpoi->err = 0;
}
}
// fill the tree
tr->Fill();
cnt++;
} // loop on the files
f->Write();
f->Close();
}
void eregmerge(bool doele) {
TString dirname = "/afs/cern.ch/user/b/bendavid/CMSSWhgg/CMSSW_5_3_11_patch5/src/HiggsAnalysis/GBRLikelihoodEGTools/data/";
gSystem->mkdir(dirname,true);
gSystem->cd(dirname);
TString fnameeb;
TString fnameee;
if (doele) {
fnameeb = "wereg_ele_eb.root";
fnameee = "wereg_ele_ee.root";
}
else if (!doele) {
fnameeb = "wereg_ph_eb.root";
fnameee = "wereg_ph_ee.root";
}
TString infileeb = TString::Format("/afs/cern.ch/work/b/bendavid/bare/eregAug10RCalphafix/%s",fnameeb.Data());
TString infileee = TString::Format("/afs/cern.ch/work/b/bendavid/bare/eregAug10RCalphafix/%s",fnameee.Data());
TFile *fwseb = TFile::Open(infileeb);
TFile *fwsee = TFile::Open(infileee);
RooWorkspace *wseb = (RooWorkspace*)fwseb->Get("wereg");
RooWorkspace *wsee = (RooWorkspace*)fwsee->Get("wereg");
RooAbsPdf *sigpdfeborig = wseb->pdf("sigpdf");
RooAbsPdf *sigpdfeeorig = wsee->pdf("sigpdf");
RooAbsPdf *sigpdfeb = static_cast<RooAbsPdf*>(cloneRecursiveRename(sigpdfeborig,"EB"));
RooAbsPdf *sigpdfee = static_cast<RooAbsPdf*>(cloneRecursiveRename(sigpdfeeorig,"EE"));
RooWorkspace *wsout = new RooWorkspace("EGRegressionWorkspace");
wsout->import(*sigpdfeb);
wsout->import(*sigpdfee);
TString outname;
if (doele) outname = "regweights_v4_ele.root";
else outname = "regweights_v4_ph.root";
wsout->writeToFile(outname);
RooArgList pdfeblist;
RooArgSet *pdfebcomps = sigpdfeb->getComponents();
RooArgSet *pdfebvars = sigpdfeb->getVariables();
pdfeblist.add(*pdfebcomps);
pdfeblist.add(*pdfebvars);
delete pdfebcomps;
delete pdfebvars;
RooArgList pdfeelist;
RooArgSet *pdfeecomps = sigpdfee->getComponents();
RooArgSet *pdfeevars = sigpdfee->getVariables();
pdfeelist.add(*pdfeecomps);
pdfeelist.add(*pdfeevars);
delete pdfeecomps;
delete pdfeevars;
// RooArgList components(ws->components());
// for (int iarg=0; iarg<components.getSize(); ++iarg) {
// components.at(iarg)->SetName(TString::Format("%s_1",components.at(iarg)->GetName()));
// }
RooGBRFunction *funceb = static_cast<RooGBRFunction*>(pdfeblist.find("func_EB"));
RooGBRFunction *funcee = static_cast<RooGBRFunction*>(pdfeelist.find("func_EE"));
// funceb->Vars().Print("V");
// funcee->Vars().Print("V");
for (int ivar=0; ivar<funceb->Vars().getSize(); ++ivar) {
printf("%i: %s, %s\n",ivar,funceb->Vars().at(ivar)->GetName(),funceb->Vars().at(ivar)->GetTitle());
}
for (int ivar=0; ivar<funcee->Vars().getSize(); ++ivar) {
printf("%i: %s, %s\n",ivar,funcee->Vars().at(ivar)->GetName(),funcee->Vars().at(ivar)->GetTitle());
}
TString outnameforest;
if (doele) outnameforest = "regweights_v4_forest_ele.root";
else outnameforest = "regweights_v4_forest_ph.root";
TFile *fforest = new TFile(outnameforest,"RECREATE");
fforest->WriteObject(funceb->Forest(),"EGRegressionForest_EB");
fforest->WriteObject(funcee->Forest(),"EGRegressionForest_EE");
fforest->Close();
}
void runQ(const char* inFileName,
const char* wsName = "combined",
const char* modelConfigName = "ModelConfig",
const char* dataName = "obsData",
const char* asimov0DataName = "asimovData_0",
const char* conditional0Snapshot = "conditionalGlobs_0",
const char* asimov1DataName = "asimovData_1",
const char* conditional1Snapshot = "conditionalGlobs_1",
const char* nominalSnapshot = "nominalGlobs",
string smass = "130",
string folder = "test")
{
double mass;
stringstream massStr;
massStr << smass;
massStr >> mass;
bool errFast = 0;
bool goFast = 1;
bool remakeData = 1;
bool doRightSided = 1;
bool doInj = 0;
bool doObs = 1;
bool doMedian = 1;
TStopwatch timer;
timer.Start();
TFile f(inFileName);
RooWorkspace* ws = (RooWorkspace*)f.Get(wsName);
if (!ws)
{
cout << "ERROR::Workspace: " << wsName << " doesn't exist!" << endl;
return;
}
ModelConfig* mc = (ModelConfig*)ws->obj(modelConfigName);
if (!mc)
{
cout << "ERROR::ModelConfig: " << modelConfigName << " doesn't exist!" << endl;
return;
}
RooDataSet* data = (RooDataSet*)ws->data(dataName);
if (!data)
{
cout << "ERROR::Dataset: " << dataName << " doesn't exist!" << endl;
return;
}
mc->GetNuisanceParameters()->Print("v");
RooNLLVar::SetIgnoreZeroEntries(1);
ROOT::Math::MinimizerOptions::SetDefaultMinimizer("Minuit2");
ROOT::Math::MinimizerOptions::SetDefaultStrategy(0);
ROOT::Math::MinimizerOptions::SetDefaultPrintLevel(1);
cout << "Setting max function calls" << endl;
//ROOT::Math::MinimizerOptions::SetDefaultMaxFunctionCalls(20000);
RooMinimizer::SetMaxFunctionCalls(10000);
ws->loadSnapshot("conditionalNuis_0");
RooArgSet nuis(*mc->GetNuisanceParameters());
RooRealVar* mu = (RooRealVar*)mc->GetParametersOfInterest()->first();
if (string(mc->GetPdf()->ClassName()) == "RooSimultaneous" && remakeData)
{
RooSimultaneous* simPdf = (RooSimultaneous*)mc->GetPdf();
double min_mu;
data = makeData(data, simPdf, mc->GetObservables(), mu, mass, min_mu);
}
RooDataSet* asimovData0 = (RooDataSet*)ws->data(asimov0DataName);
if (!asimovData0)
{
cout << "Asimov data doesn't exist! Please, allow me to build one for you..." << endl;
makeAsimovData(mc, true, ws, mc->GetPdf(), data, 1);
ws->Print();
asimovData0 = (RooDataSet*)ws->data("asimovData_0");
}
RooDataSet* asimovData1 = (RooDataSet*)ws->data(asimov1DataName);
if (!asimovData1)
{
cout << "Asimov data doesn't exist! Please, allow me to build one for you..." << endl;
makeAsimovData(mc, true, ws, mc->GetPdf(), data, 0);
ws->Print();
asimovData1 = (RooDataSet*)ws->data("asimovData_1");
}
if (!doRightSided) mu->setRange(0, 40);
else mu->setRange(-40, 40);
bool old = false;
if (old)
{
mu->setVal(0);
RooArgSet poi(*mu);
ProfileLikelihoodTestStat_modified asimov_testStat_sig(*mc->GetPdf());
asimov_testStat_sig.SetRightSided(doRightSided);
asimov_testStat_sig.SetNuis(&nuis);
if (!doInj) asimov_testStat_sig.SetDoAsimov(true, 1);
asimov_testStat_sig.SetWorkspace(ws);
ProfileLikelihoodTestStat_modified testStat(*mc->GetPdf());
testStat.SetRightSided(doRightSided);
testStat.SetNuis(&nuis);
testStat.SetWorkspace(ws);
//RooMinimizerFcn::SetOverrideEverything(true);
double med_sig = 0;
double med_testStat_val = 0;
//gRandom->SetSeed(1);
//RooRandom::randomGenerator()->SetSeed(1);
RooNLLVar::SetIgnoreZeroEntries(1);
if (asimov1DataName != "" && doMedian)
{
mu->setVal(0);
if (!doInj) mu->setRange(0, 2);
ws->loadSnapshot("conditionalNuis_0");
asimov_testStat_sig.SetLoadUncondSnapshot("conditionalNuis_1");
if (string(conditional1Snapshot) != "") ws->loadSnapshot(conditional1Snapshot);
med_testStat_val = 2*asimov_testStat_sig.Evaluate(*asimovData1, poi);
if (med_testStat_val < 0 && !doInj)
{
mu->setVal(0);
med_testStat_val = 2*asimov_testStat_sig.Evaluate(*asimovData1, poi); // just try again
}
int sign = med_testStat_val != 0 ? med_testStat_val/fabs(med_testStat_val) : 0;
med_sig = sign*sqrt(fabs(med_testStat_val));
if (string(nominalSnapshot) != "") ws->loadSnapshot(nominalSnapshot);
if (!doRightSided) mu->setRange(0, 40);
else mu->setRange(-40, 40);
}
RooNLLVar::SetIgnoreZeroEntries(0);
//gRandom->SetSeed(1);
//RooRandom::randomGenerator()->SetSeed(1);
//RooMinimizerFcn::SetOverrideEverything(false);
cout << "med test stat: " << med_testStat_val << endl;
ws->loadSnapshot("nominalGlobs");
ws->loadSnapshot("conditionalNuis_0");
mu->setVal(0);
testStat.SetWorkspace(ws);
testStat.SetLoadUncondSnapshot("ucmles");
double obsTestStat_val = doObs ? 2*testStat.Evaluate(*data, poi) : 0;
cout << "obs test stat: " << obsTestStat_val << endl;
// obsTestStat_val = 2*testStat.Evaluate(*data, poi);
// cout << "obs test stat: " << obsTestStat_val << endl;
// obsTestStat_val = 2*testStat.Evaluate(*data, poi);
// cout << "obs test stat: " << obsTestStat_val << endl;
double obs_sig;
int sign = obsTestStat_val == 0 ? 0 : obsTestStat_val / fabs(obsTestStat_val);
if (!doRightSided && (obsTestStat_val < 0 && obsTestStat_val > -0.1 || mu->getVal() < 0.001)) obs_sig = 0;
else obs_sig = sign*sqrt(fabs(obsTestStat_val));
if (obs_sig != obs_sig) //nan, do by hand
{
cout << "Obs test stat gave nan: try by hand" << endl;
mu->setVal(0);
mu->setConstant(1);
mc->GetPdf()->fitTo(*data, Hesse(0), Minos(0), PrintLevel(-1), Constrain(*mc->GetNuisanceParameters()));
mu->setConstant(0);
double L_0 = mc->GetPdf()->getVal();
//mu->setVal(0);
//mu->setConstant(1);
mc->GetPdf()->fitTo(*data, Hesse(0), Minos(0), PrintLevel(-1), Constrain(*mc->GetNuisanceParameters()));
//mu->setConstant(0);
double L_muhat = mc->GetPdf()->getVal();
cout << "L_0: " << L_0 << ", L_muhat: " << L_muhat << endl;
obs_sig = sqrt(-2*TMath::Log(L_0/L_muhat));
//still nan
if (obs_sig != obs_sig && fabs(L_0 - L_muhat) < 0.000001) obs_sig = 0;
}
cout << "obs: " << obs_sig << endl;
cout << "Observed significance: " << obs_sig << endl;
if (med_sig)
{
cout << "Median test stat val: " << med_testStat_val << endl;
cout << "Median significance: " << med_sig << endl;
}
f.Close();
stringstream fileName;
fileName << "root_files/" << folder << "/" << mass << ".root";
system(("mkdir -vp root_files/" + folder).c_str());
TFile f2(fileName.str().c_str(),"recreate");
// stringstream fileName;
// fileName << "results_sig/" << mass << ".root";
// system("mkdir results_sig");
// TFile f(fileName.str().c_str(),"recreate");
TH1D* h_hypo = new TH1D("hypo","hypo",2,0,2);
h_hypo->SetBinContent(1, obs_sig);
h_hypo->SetBinContent(2, med_sig);
f2.Write();
f2.Close();
//mc->GetPdf()->fitTo(*data, PrintLevel(0));
timer.Stop();
timer.Print();
}
else
{
RooAbsPdf* pdf = mc->GetPdf();
RooArgSet nuis_tmp1 = *mc->GetNuisanceParameters();
RooNLLVar* asimov_nll0 = (RooNLLVar*)pdf->createNLL(*asimovData0, Constrain(nuis_tmp1));
RooArgSet nuis_tmp2 = *mc->GetNuisanceParameters();
RooNLLVar* asimov_nll1 = (RooNLLVar*)pdf->createNLL(*asimovData1, Constrain(nuis_tmp2));
RooArgSet nuis_tmp3 = *mc->GetNuisanceParameters();
RooNLLVar* obs_nll = (RooNLLVar*)pdf->createNLL(*data, Constrain(nuis_tmp3));
//do asimov
int status;
//get sigma_b
ws->loadSnapshot(conditional0Snapshot);
status = ws->loadSnapshot("conditionalNuis_0");
if (status != 0 && goFast) errFast = 1;
mu->setVal(0);
mu->setConstant(1);
status = goFast ? 0 : minimize(asimov_nll0, ws);
if (status < 0)
{
cout << "Retrying" << endl;
//ws->loadSnapshot("conditionalNuis_0");
status = minimize(asimov_nll0, ws);
if (status >= 0) cout << "Success!" << endl;
}
double asimov0_nll0 = asimov_nll0->getVal();
mu->setVal(1);
ws->loadSnapshot("conditionalNuis_1");
status = minimize(asimov_nll0, ws);
if (status < 0)
{
cout << "Retrying" << endl;
//ws->loadSnapshot("conditionalNuis_0");
status = minimize(asimov_nll0, ws);
if (status >= 0) cout << "Success!" << endl;
}
double asimov0_nll1 = asimov_nll0->getVal();
double asimov0_q = 2*(asimov0_nll1 - asimov0_nll0);
double sigma_b = sqrt(1./asimov0_q);
ws->loadSnapshot(nominalSnapshot);
//get sigma_sb
ws->loadSnapshot(conditional1Snapshot);
ws->loadSnapshot("conditionalNuis_0");
mu->setVal(0);
mu->setConstant(1);
status = minimize(asimov_nll1, ws);
if (status < 0)
{
cout << "Retrying" << endl;
//ws->loadSnapshot("conditionalNuis_0");
status = minimize(asimov_nll1, ws);
if (status >= 0) cout << "Success!" << endl;
}
double asimov1_nll0 = asimov_nll1->getVal();
mu->setVal(1);
status = ws->loadSnapshot("conditionalNuis_1");
if (status != 0 && goFast) errFast = 1;
status = goFast ? 0 : minimize(asimov_nll1, ws);
if (status < 0)
{
cout << "Retrying" << endl;
//ws->loadSnapshot("conditionalNuis_0");
status = minimize(asimov_nll1, ws);
if (status >= 0) cout << "Success!" << endl;
}
double asimov1_nll1 = asimov_nll1->getVal();
double asimov1_q = 2*(asimov1_nll1 - asimov1_nll0);
double sigma_sb = sqrt(-1./asimov1_q);
ws->loadSnapshot(nominalSnapshot);
//do obs
mu->setVal(0);
status = ws->loadSnapshot("conditionalNuis_0");
if (status != 0 && goFast) errFast = 1;
mu->setConstant(1);
status = goFast ? 0 : minimize(obs_nll, ws);
if (status < 0)
{
cout << "Retrying with conditional snapshot at mu=1" << endl;
ws->loadSnapshot("conditionalNuis_0");
status = minimize(obs_nll, ws);
if (status >= 0) cout << "Success!" << endl;
}
double obs_nll0 = obs_nll->getVal();
status = ws->loadSnapshot("conditionalNuis_1");
if (status != 0 && goFast) errFast = 1;
mu->setVal(1);
status = goFast ? 0 : minimize(obs_nll, ws);
if (status < 0)
{
cout << "Retrying with conditional snapshot at mu=1" << endl;
ws->loadSnapshot("conditionalNuis_0");
status = minimize(obs_nll, ws);
if (status >= 0) cout << "Success!" << endl;
}
double obs_nll1 = obs_nll->getVal();
double obs_q = 2*(obs_nll1 - obs_nll0);
double Zobs = (1./sigma_b/sigma_b - obs_q) / (2./sigma_b);
double Zexp = (1./sigma_b/sigma_b - asimov1_q) / (2./sigma_b);
double pb_obs = 1-ROOT::Math::gaussian_cdf(Zobs);
double pb_exp = 1-ROOT::Math::gaussian_cdf(Zexp);
cout << "asimov0_q = " << asimov0_q << endl;
cout << "asimov1_q = " << asimov1_q << endl;
cout << "obs_q = " << obs_q << endl;
cout << "sigma_b = " << sigma_b << endl;
cout << "sigma_sb = " << sigma_sb << endl;
cout << "Z obs = " << Zobs << endl;
cout << "Z exp = " << Zexp << endl;
f.Close();
stringstream fileName;
fileName << "root_files/" << folder << "/" << mass << ".root";
system(("mkdir -vp root_files/" + folder).c_str());
TFile f2(fileName.str().c_str(),"recreate");
TH1D* h_hypo = new TH1D("hypo_tev","hypo_tev",2,0,2);
h_hypo->SetBinContent(1, pb_obs);
h_hypo->SetBinContent(2, pb_exp);
f2.Write();
f2.Close();
stringstream fileName3;
fileName3 << "root_files/" << folder << "_llr/" << mass << ".root";
system(("mkdir -vp root_files/" + folder + "_llr").c_str());
TFile f3(fileName3.str().c_str(),"recreate");
TH1D* h_hypo3 = new TH1D("hypo_llr","hypo_llr",7,0,7);
h_hypo3->SetBinContent(1, -obs_q);
h_hypo3->SetBinContent(2, -asimov1_q);
h_hypo3->SetBinContent(3, -asimov0_q);
h_hypo3->SetBinContent(4, -asimov0_q-2*2/sigma_b);
h_hypo3->SetBinContent(5, -asimov0_q-1*2/sigma_b);
h_hypo3->SetBinContent(6, -asimov0_q+1*2/sigma_b);
h_hypo3->SetBinContent(7, -asimov0_q+2*2/sigma_b);
f3.Write();
f3.Close();
timer.Stop();
timer.Print();
}
}
void hggfitmceerr(double nommass=123., double tgtr=1., int ijob=0) {
//gSystem->cd("/scratch/bendavid/root/bare/fitplotsJun10test/");
int seed = 65539+ijob+1;
TString dirname = "/scratch/bendavid/root/bare/hggfiteerrtestall_large2/";
gSystem->mkdir(dirname,true);
gSystem->cd(dirname);
//nommass=150.;
// gSystem->cd("/scratch/bendavid/root/bare/fitplotsJun8_150_2x/");
gRandom->SetSeed(seed);
RooRandom::randomGenerator()->SetSeed(seed);
// TFile *fin = TFile::Open("/home/mingyang/cms/hist_approval/hgg-2013Moriond/merged/hgg-2013Moriond_s12-h150gg-gf-v7a_noskim.root");
// TDirectory *hdir = (TDirectory*)fin->FindObjectAny("PhotonTreeWriterPresel");
// TTree *htree = (TTree*)hdir->Get("hPhotonTree");
// TFile *fdin = TFile::Open("/home/mingyang/cms/hist/hgg-2013Moriond/merged/hgg-2013Moriond_r12_ABCD.root");
// TDirectory *ddir = (TDirectory*)fdin->FindObjectAny("PhotonTreeWriterPresel");
// TTree *dtree = (TTree*)ddir->Get("hPhotonTree");
//TCut selcut = "(ph1.pt > (mass/3.0) && ph2.pt > (mass/4.0) && mass>100. && mass<180. && ph1.idmva>-0.2 && ph2.idmva>-0.2)";
TCut selcut = "(ph1.pt > (mass/3.0) && ph2.pt > (mass/4.0) && mass>100. && mass<180. && ph1.idmva>-0.2 && ph2.idmva>-0.2)";
//TCut selweight = "xsecweight(procidx)*puweight(numPU,procidx)";
TCut selweight = "xsecweight(procidx)*mcweight*kfact(procidx,ph1.ispromptgen,ph2.ispromptgen)";
TCut sigFcut = "(procidx==0 || procidx==3)";
TCut sigVcut = "(procidx==1 || procidx==2)";
TCut bkgPPcut = "(procidx==4)";
TCut bkgPFcut = "(procidx==5 || procidx==6)";
TCut bkgFFcut = "(procidx==7 || procidx==8)";
TCut bkgcut = "(procidx>3)";
TCut bkgcutnoq = "(procidx>3 && procidx<7)";
TCut prescalenone = "(1==1)";
TCut evenevents = "(evt%2==0)";
TCut oddevents = "(evt%2==1)";
TCut prescale10 = "(evt%10==0)";
TCut prescale25 = "(evt%25==0)";
TCut prescale50 = "(evt%50==0)";
TCut prescale100 = "(evt%100==0)";
TCut fcut = prescale50;
float xsecs[50];
//TCut selcutsingle = "ph.pt>25. && ph.isbarrel && ph.ispromptgen";
//TCut selcutsingle = "ph.pt>25.&& ph.ispromptgen";
TCut selcutsingle = "ph.genpt>16.&& ph.ispromptgen";
TCut selweightsingle = "xsecweight(procidx)";
// TChain *tree = new TChain("RunLumiSelectionMod/MCProcessSelectionMod/HLTModP/GoodPVFilterMod/PhotonMvaMod/JetPub/JetCorrectionMod/SeparatePileUpMod/ElectronIDMod/MuonIDMod/PhotonPairSelectorPresel/PhotonTreeWriterPresel/hPhotonTreeSingle");
// tree->Add("/home/mingyang/cms/hist/hgg-2013Final8TeV/merged/hgg-2013Final8TeV_s12-diphoj-v7n_noskim.root");
TChain *tree = new TChain("RunLumiSelectionMod/MCProcessSelectionMod/HLTModP/GoodPVFilterMod/PhotonMvaMod/PhotonIDModPresel/PhotonTreeWriterSingle/hPhotonTreeSingle");
tree->Add("/home/mingyang/cms/hist/hgg-2013Final8TeV_reg_trans/merged/hgg-2013Final8TeV_reg_trans_s12-pj20_40-2em-v7n_noskim.root");
tree->Add("/home/mingyang/cms/hist/hgg-2013Final8TeV_reg_trans/merged/hgg-2013Final8TeV_reg_trans_s12-pj40-2em-v7n_noskim.root");
xsecs[0] = 0.001835*81930.0;
xsecs[1] = 0.05387*8884.0;
initweights(tree,xsecs,1.);
double weightscale = xsecweights[1];
xsecweights[0] /= weightscale;
xsecweights[1] /= weightscale;
tree->SetCacheSize(64*1024*1024);
RooRealVar energy("energy","ph.e",0);
RooRealVar sceta("sceta","ph.sceta",0.);
RooRealVar idmva("idmva","ph.idmva",0.,-1.,1.);
RooRealVar eerr("eerr","(ph.isbarrel + 0.5*!ph.isbarrel)*ph.eerr/ph.e",0.);
RooRealVar evt("evt","evt",0.);
RooArgList vars;
vars.add(energy);
vars.add(sceta);
//vars.add(idmva);
RooArgList condvars(vars);
vars.add(eerr);
RooArgList condvarsid(vars);
vars.add(idmva);
vars.add(evt);
// RooPowerLaw("testpow","",pt1,pt2);
// return;
// new TCanvas;
// tree->Draw("mass>>htmpall(80,100.,180.)",bkgcut*selcut*selweight,"HIST");
//
// new TCanvas;
// tree->Draw("mass>>htmpallid(80,100.,180.)",idcut*bkgcut*selcut*selweight,"HIST");
//
// new TCanvas;
// tree->Draw("mass>>htmp(80,100.,180.)",bkgcutnoq*selcut*selweight,"HIST");
// //
// return;
//RooRealVar weightvar("weightvar","(ph1.pt > (mass/3.0) && ph2.pt > (mass/4.0) && mass>100. && mass<180. && ph1.idmva>-0.2 && ph2.idmva>-0.2 && evt%100!=0)",1.);
RooRealVar weightvar("weightvar","",1.);
//RooRealVar weightvar("weightvar","(ph1.pt > (mass/3.0) && ph2.pt > (mass/4.0) && mass>100. && mass<180. && ph1.idmva>-0.2 && ph2.idmva>-0.2)",1.);
weightvar.SetTitle(selcutsingle*selweightsingle);
RooDataSet *hdataSingle = RooTreeConvert::CreateDataSet("hdataSingle",tree,vars,weightvar);
int ngauseerr = 4;
//int nparmseerr = 3*ngauseerr + 2;
//int nparmseerr = 3*ngauseerr + 2;
int nparmseerr = 5*ngauseerr;
RooArgList tgtseerr;
RooGBRFunction funceerr("funceerr","",condvars,nparmseerr);
int iparmeerr = 0;
RooArgList eerrgauspdfs;
RooArgList eerrgauscoeffs;
double stepeerr = 0.07/double(std::max(1,ngauseerr-1));
// RooRealVar *gmeanvar = new RooRealVar(TString::Format("gmeanvar_eerr_%i",0),"",0.007+stepeerr*0);
// RooRealVar *gsigmavar = new RooRealVar(TString::Format("gsigmavar_eerr_%i",0),"",0.01);
// // //RooRealVar *gsigmaRvar = new RooRealVar(TString::Format("gsigmaRvar_eerr_%i",0),"",0.02);
// //
// // //if (0==0) gmeanvar->setVal(0.007);
// //
// gmeanvar->setConstant(false);
// gsigmavar->setConstant(false);
// // //gsigmaRvar->setConstant(false);
// //
// //
// RooGBRTarget *gmean = new RooGBRTarget(TString::Format("gmean_eerr_%i",0),"",funceerr,iparmeerr++,*gmeanvar);
// RooGBRTarget *gsigma = new RooGBRTarget(TString::Format("gsigma_eerr_%i",0),"",funceerr,iparmeerr++,*gsigmavar);
// // //RooGBRTarget *gsigmaR = new RooGBRTarget(TString::Format("gsigmaR_eerr_%i",0),"",funceerr,iparmeerr++,*gsigmaRvar);
// //
// RooRealConstraint *gmeanlim = new RooRealConstraint(TString::Format("gmeanlim_eerr_%i",0),"",*gmean,0.,0.5);
// RooRealConstraint *gsigmalim = new RooRealConstraint(TString::Format("gsigmalim_eerr_%i",0),"",*gsigma,1e-7,0.5);
// //RooRealConstraint *gsigmaRlim = new RooRealConstraint(TString::Format("gsigmaRlim_eerr_%i",0),"",*gsigmaR,1e-7,0.2);
//
// tgtseerr.add(*gmean);
// tgtseerr.add(*gsigma);
for (int igaus=0; igaus<ngauseerr; ++igaus) {
RooRealVar *gmeanvar = new RooRealVar(TString::Format("gmeanvar_eerr_%i",igaus),"",0.007+stepeerr*igaus);
RooRealVar *gsigmavar = new RooRealVar(TString::Format("gsigmavar_eerr_%i",igaus),"",0.01);
RooRealVar *galphavar = new RooRealVar(TString::Format("galphavar_eerr_%i",igaus),"",1.0);
RooRealVar *gnvar = new RooRealVar(TString::Format("gnvar_eerr_%i",igaus),"",2.);
RooRealVar *gfracvar = new RooRealVar(TString::Format("gfracvar_eerr_%i",igaus),"",1.0);
//if (igaus==0) gmeanvar->setVal(0.007);
gmeanvar->setConstant(false);
gsigmavar->setConstant(false);
galphavar->setConstant(false);
gnvar->setConstant(false);
gfracvar->setConstant(false);
RooGBRTarget *gmean = new RooGBRTarget(TString::Format("gmean_eerr_%i",igaus),"",funceerr,iparmeerr++,*gmeanvar);
RooGBRTarget *gsigma = new RooGBRTarget(TString::Format("gsigma_eerr_%i",igaus),"",funceerr,iparmeerr++,*gsigmavar);
RooGBRTarget *galpha = new RooGBRTarget(TString::Format("galpha_eerr_%i",igaus),"",funceerr,iparmeerr++,*galphavar);
RooGBRTarget *gn = new RooGBRTarget(TString::Format("gn_eerr_%i",igaus),"",funceerr,iparmeerr++,*gnvar);
RooGBRTarget *gfrac = new RooGBRTarget(TString::Format("gfrac_eerr_%i",igaus),"",funceerr,iparmeerr++,*gfracvar);
RooRealConstraint *gmeanlim = new RooRealConstraint(TString::Format("gmeanlim_eerr_%i",igaus),"",*gmean,0.,0.5);
RooRealConstraint *gsigmalim = new RooRealConstraint(TString::Format("gsigmalim_eerr_%i",igaus),"",*gsigma,1e-5,0.1);
RooRealConstraint *galphalim = new RooRealConstraint(TString::Format("galphalim_eerr_%i",igaus),"",*galpha,0.05,8.);
RooRealConstraint *gnlim = new RooRealConstraint(TString::Format("gnlim_eerr_%i",igaus),"",*gn,1.01,5000.);
//RooRealConstraint *gfraclim = new RooRealConstraint(TString::Format("gfraclim_eerr_%i",igaus),"",*gfrac,0.,1.);
RooAbsReal *gfraclim = new RooProduct(TString::Format("gfraclim_eerr_%i",igaus),"",RooArgList(*gfrac,*gfrac));
if (igaus==0) {
gfraclim = new RooConstVar(TString::Format("gfraclimconst_eerr_%i",igaus),"",1.);
}
else {
tgtseerr.add(*gfrac);
}
//RooGaussianFast *gpdf = new RooGaussianFast(TString::Format("gdf_eerr_%i",igaus),"",eerr,*gmeanlim,*gsigmalim);
//RooBifurGauss *gpdf = new RooBifurGauss(TString::Format("gdf_eerr_%i",igaus),"",eerr,*gmeanlim,*gsigmalim,*galphalim);
if (igaus==0) {
RooRevCBFast *gpdf = new RooRevCBFast(TString::Format("gdf_eerr_%i",igaus),"",eerr,*gmeanlim,*gsigmalim,*galphalim, *gnlim);
tgtseerr.add(*gmean);
tgtseerr.add(*gsigma);
tgtseerr.add(*galpha);
tgtseerr.add(*gn);
eerrgauspdfs.add(*gpdf);
}
else {
RooGaussianFast *gpdf = new RooGaussianFast(TString::Format("gdf_eerr_%i",igaus),"",eerr,*gmeanlim,*gsigmalim);
tgtseerr.add(*gmean);
tgtseerr.add(*gsigma);
eerrgauspdfs.add(*gpdf);
}
eerrgauscoeffs.add(*gfraclim);
}
RooCondAddPdf eerrpdf("eerrpdf","",eerrgauspdfs,eerrgauscoeffs);
RooAbsPdf *pdf0 = static_cast<RooAbsPdf*>(eerrgauspdfs.at(0));
int ngaus = 6;
int nparms = 4*ngaus;
RooArgList tgtsid;
RooGBRFunction funcid("funcid","",condvarsid,nparms);
RooArgList gauspdfs;
RooArgList gauscoeffs;
double step = 0.5/double(std::max(1,ngaus-1));
int iparm = 0;
for (int igaus=0; igaus<ngaus; ++igaus) {
RooRealVar *gmeanvar = new RooRealVar(TString::Format("gmeanvar_%i",igaus),"",-0.2+step*igaus);
RooRealVar *gsigmavar = new RooRealVar(TString::Format("gsigmavar_%i",igaus),"",0.1);
RooRealVar *gsigmaRvar = new RooRealVar(TString::Format("gsigmaRvar_%i",igaus),"",0.1);
RooRealVar *gfracvar = new RooRealVar(TString::Format("gfracvar_%i",igaus),"",1.0);
gmeanvar->setConstant(false);
gsigmavar->setConstant(false);
gsigmaRvar->setConstant(false);
gfracvar->setConstant(false);
RooGBRTarget *gmean = new RooGBRTarget(TString::Format("gmean_%i",igaus),"",funcid,iparm++,*gmeanvar);
RooGBRTarget *gsigma = new RooGBRTarget(TString::Format("gsigma_%i",igaus),"",funcid,iparm++,*gsigmavar);
RooGBRTarget *gsigmaR = new RooGBRTarget(TString::Format("gsigmaR_%i",igaus),"",funcid,iparm++,*gsigmaRvar);
RooGBRTarget *gfrac = new RooGBRTarget(TString::Format("gfrac_%i",igaus),"",funcid,iparm++,*gfracvar);
RooRealConstraint *gmeanlim = new RooRealConstraint(TString::Format("gmeanlim_%i",igaus),"",*gmean,-1.,1.);
RooRealConstraint *gsigmalim = new RooRealConstraint(TString::Format("gsigmalim_%i",igaus),"",*gsigma,1e-4,2.);
RooRealConstraint *gsigmaRlim = new RooRealConstraint(TString::Format("gsigmaRlim_%i",igaus),"",*gsigmaR,1e-4,2.);
//RooRealConstraint *gfraclim = new RooRealConstraint(TString::Format("gfraclim_%i",igaus),"",*gfrac,0.,1.);
RooAbsReal *gfraclim = new RooProduct(TString::Format("gfraclim_%i",igaus),"",RooArgList(*gfrac,*gfrac));
if (igaus==0) {
gfraclim = new RooConstVar(TString::Format("gfraclimconst_%i",igaus),"",1.);
}
else {
tgtsid.add(*gfrac);
}
RooGaussianFast *gpdf = new RooGaussianFast(TString::Format("gdf_%i",igaus),"",idmva,*gmeanlim,*gsigmalim);
//RooBifurGauss *gpdf = new RooBifurGauss(TString::Format("gdf_%i",igaus),"",idmva,*gmeanlim,*gsigmalim,*gsigmaRlim);
gauspdfs.add(*gpdf);
gauscoeffs.add(*gfraclim);
tgtsid.add(*gmean);
tgtsid.add(*gsigma);
//tgtsid.add(*gsigmaR);
//tgtsid.add(*gfrac);
}
RooCondAddPdf idpdf("idpdf","",gauspdfs,gauscoeffs);
RooConstVar etermconst("etermconst","",0.);
RooAbsReal &eterm = etermconst;
RooRealVar dummy("dummy","",1.0);
std::vector<RooAbsData*> vdata;
vdata.push_back(hdataSingle);
std::vector<RooAbsReal*> vpdf;
vpdf.push_back(&eerrpdf);
//vpdf.push_back(pdf0);
std::vector<RooAbsReal*> vpdfid;
vpdfid.push_back(&idpdf);
RooHybridBDTAutoPdf bdtpdf("bdtpdf","",funceerr,tgtseerr,eterm,dummy,vdata,vpdf);
bdtpdf.SetPrescaleInit(100);
bdtpdf.SetMinCutSignificance(5.0);
bdtpdf.SetShrinkage(0.1);
bdtpdf.SetMinWeightTotal(200.);
bdtpdf.SetMaxNodes(200);
bdtpdf.TrainForest(1e6);
RooHybridBDTAutoPdf bdtpdfid("bdtpdfid","",funcid,tgtsid,eterm,dummy,vdata,vpdfid);
bdtpdfid.SetPrescaleInit(100);
bdtpdfid.SetMinCutSignificance(5.0);
bdtpdfid.SetShrinkage(0.1);
bdtpdfid.SetMinWeightTotal(200.);
bdtpdfid.SetMaxNodes(200);
bdtpdfid.TrainForest(1e6);
RooAbsReal *finalcdferr = eerrpdf.createCDF(eerr);
RooFormulaVar transerr("transerr","","sqrt(2.)*TMath::ErfInverse(2.*@0-1.)",*finalcdferr);
RooAbsReal *finalcdfid = idpdf.createCDF(idmva);
RooFormulaVar transid("transid","","sqrt(2.)*TMath::ErfInverse(2.*@0-1.)",*finalcdfid);
RooWorkspace *wsout = new RooWorkspace("wsfiteerr");
wsout->import(*hdataSingle);
wsout->import(eerrpdf,RecycleConflictNodes());
wsout->import(idpdf,RecycleConflictNodes());
// wsout->import(transerr,RecycleConflictNodes());
// wsout->import(transid,RecycleConflictNodes());
wsout->defineSet("datavars",vars,true);
wsout->writeToFile("hggfiteerr.root");
RooRealVar *cdfidvar = (RooRealVar*)hdataSingle->addColumn(*finalcdfid);
RooRealVar *transidvar = (RooRealVar*)hdataSingle->addColumn(transid);
RooGaussianFast unormpdfid("unormpdfid","",*transidvar,RooConst(0.),RooConst(1.));
RooRealVar *cdferrvar = (RooRealVar*)hdataSingle->addColumn(*finalcdferr);
RooRealVar *transerrvar = (RooRealVar*)hdataSingle->addColumn(transerr);
RooGaussianFast unormpdferr("unormpdferr","",*transerrvar,RooConst(0.),RooConst(1.));
//RooDataSet *testdata = (RooDataSet*)hdataSingle->reduce("abs(sceta)>1.3 && abs(sceta)<1.4");
RooDataSet *testdata = hdataSingle;
new TCanvas;
RooPlot *eerrplot = eerr.frame(0.,0.1,200);
testdata->plotOn(eerrplot);
eerrpdf.plotOn(eerrplot,ProjWData(*testdata));
eerrplot->Draw();
new TCanvas;
RooPlot *transplot = transerrvar->frame(-5.,5.,100);
hdataSingle->plotOn(transplot);
unormpdferr.plotOn(transplot);
transplot->Draw();
//return;
new TCanvas;
RooPlot *cdfploterr = cdferrvar->frame(0.,1.,100);
hdataSingle->plotOn(cdfploterr);
//unormpdf.plotOn(transplot);
cdfploterr->Draw();
//return;
new TCanvas;
RooPlot *idplot = idmva.frame(-1.,1.,200);
testdata->plotOn(idplot);
idpdf.plotOn(idplot,ProjWData(*testdata));
idplot->Draw();
new TCanvas;
RooPlot *transplotid = transidvar->frame(-5.,5.,100);
testdata->plotOn(transplotid);
unormpdfid.plotOn(transplotid);
transplotid->Draw();
//return;
new TCanvas;
RooPlot *cdfplotid = cdfidvar->frame(0.,1.,100);
testdata->plotOn(cdfplotid);
//unormpdf.plotOn(transplot);
cdfplotid->Draw();
//return;
TH1 *herrid = testdata->createHistogram("herrid",eerr,Binning(30,0.,0.1), YVar(idmva,Binning(30,-0.5,0.6)));
TH1 *herre = testdata->createHistogram("herre",energy,Binning(30,0.,200.), YVar(eerr,Binning(30,0.,0.1)));
TH1 *hideta = testdata->createHistogram("hideta",sceta,Binning(40,-2.5,2.5), YVar(idmva,Binning(30,-0.5,0.6)));
TH1 *herridtrans = testdata->createHistogram("herridtrans",*transerrvar,Binning(30,-5.,5.), YVar(*transidvar,Binning(30,-5.,5.)));
TH1 *herrtranse = testdata->createHistogram("herrtranse",energy,Binning(30,0.,200.), YVar(*transerrvar,Binning(30,-5.,5.)));
TH1 *hidtranseta = testdata->createHistogram("hidtranseta",sceta,Binning(40,-2.5,2.5), YVar(*transidvar,Binning(30,-5.,5.)));
new TCanvas;
herrid->Draw("COLZ");
new TCanvas;
herre->Draw("COLZ");
new TCanvas;
hideta->Draw("COLZ");
new TCanvas;
herridtrans->Draw("COLZ");
new TCanvas;
herrtranse->Draw("COLZ");
new TCanvas;
hidtranseta->Draw("COLZ");
// new TCanvas;
// RooRealVar *meanvar = (RooRealVar*)hdataSingle->addColumn(eerrmeanlim);
// RooPlot *meanplot = meanvar->frame(0.,0.1,200);
// hdataSingle->plotOn(meanplot);
// meanplot->Draw();
return;
}
void runBATCalculator()
{
// Definiton of a RooWorkspace containing the statistics model. Later the
// information for BATCalculator is retrieved from the workspace. This is
// certainly a bit of overhead but better from an educative point of view.
cout << "preparing the RooWorkspace object" << endl;
RooWorkspace* myWS = new RooWorkspace("myWS", true);
// combined prior for signal contribution
myWS->factory("Product::signal({sigma_s[0,20],L[5,15],epsilon[0,1]})");
myWS->factory("N_bkg[0,3]");
// define prior functions
// uniform prior for signal crosssection
myWS->factory("Uniform::prior_sigma_s(sigma_s)");
// (truncated) prior for efficiency
myWS->factory("Gaussian::prior_epsilon(epsilon,0.51,0.0765)");
// (truncated) Gaussian prior for luminosity
myWS->factory("Gaussian::prior_L(L,10,1)");
// (truncated) Gaussian prior for bkg crosssection
myWS->factory("Gaussian::prior_N_bkg(N_bkg,0.52,0.156)");
// Poisson distribution with mean signal+bkg
myWS->factory("Poisson::model(n[0,300],sum(signal,N_bkg))");
// define the global prior function
myWS->factory("PROD::prior(prior_sigma_s,prior_epsilon,prior_L,prior_N_bkg)");
// Definition of observables and parameters of interest
myWS->defineSet("obsSet", "n");
myWS->defineSet("poiSet", "sigma_s");
myWS->defineSet("nuisanceSet", "N_bkg,L,epsilon");
// ->model complete (Additional information can be found in the
// RooStats manual)
// feel free to vary the parameters, but don't forget to choose reasonable ranges for the
// variables. Currently the Bayesian methods will often not work well if the variable ranges
// are either too short (for obvious reasons) or too large (for technical reasons).
// A ModelConfig object is used to associate parts of your workspace with their statistical
// meaning (it is also possible to initialize BATCalculator directly with elements from the
// workspace but if you are sharing your workspace with others or if you want to use several
// different methods the use of ModelConfig will most often turn out to be the better choice.)
// setup the ModelConfig object
cout << "preparing the ModelConfig object" << endl;
ModelConfig modelconfig("modelconfig", "ModelConfig for this example");
modelconfig.SetWorkspace(*myWS);
modelconfig.SetPdf(*(myWS->pdf("model")));
modelconfig.SetParametersOfInterest(*(myWS->set("poiSet")));
modelconfig.SetPriorPdf(*(myWS->pdf("prior")));
modelconfig.SetNuisanceParameters(*(myWS->set("nuisanceSet")));
modelconfig.SetObservables(*(myWS->set("obsSet")));
// use BATCalculator to the derive credibility intervals as a function of the observed number of
// events in the hypothetical experiment
// define vector with tested numbers of events
TVectorD obsEvents;
// define vectors which will be filled with the lower and upper limits for each tested number
// of observed events
TVectorD BATul;
TVectorD BATll;
// fix upper limit of tested observed number of events
int obslimit = 10;
obsEvents.ResizeTo(obslimit);
BATul.ResizeTo(obslimit);
BATll.ResizeTo(obslimit);
cout << "starting the calculation of Bayesian credibility intervals with BATCalculator" << endl;
// loop over observed number of events in the hypothetical experiment
for (int obs = 1; obs <= obslimit; obs++) {
obsEvents[obs - 1] = (static_cast<double>(obs));
// prepare data input for the the observed number of events
// adjust number of observed events in the workspace. This is communicated to ModelConfig!
myWS->var("n")->setVal(obs);
// create data
RooDataSet data("data", "", *(modelconfig.GetObservables()));
data.add( *(modelconfig.GetObservables()));
// prepare BATCalulator
BATCalculator batcalc(data, modelconfig);
// give the BATCalculator a unique name (always a good idea in ROOT)
TString namestring = "mybatc_";
namestring += obs;
batcalc.SetName(namestring);
// fix amount of posterior probability in the calculated interval.
// the name confidence level is incorrect here
batcalc.SetConfidenceLevel(0.90);
// fix length of the Markov chain. (in general: the longer the Markov chain the more
// precise will be the results)
batcalc.SetnMCMC(20000);
// retrieve SimpleInterval object containing the information about the interval (this
// triggers the actual calculations)
SimpleInterval* interval = batcalc.GetInterval1D("sigma_s");
std::cout << "BATCalculator: 90% credibility interval: [ " << interval->LowerLimit() << " - " << interval->UpperLimit() << " ] or 95% credibility upper limit\n";
// add the interval borders for the current number of observed events to the vectors
// containing the lower and upper limits
BATll[obs - 1] = interval->LowerLimit();
BATul[obs - 1] = interval->UpperLimit();
// clean up for next loop element
batcalc.CleanCalculatorForNewData();
delete interval;
}
cout << "all limits calculated" << endl;
// summarize the results in a plot
TGraph* grBATll = new TGraph(obsEvents, BATll);
grBATll->SetLineColor(kGreen);
grBATll->SetLineWidth(200);
grBATll->SetFillStyle(3001);
grBATll->SetFillColor(kGreen);
TGraph* grBATul = new TGraph(obsEvents, BATul);
grBATul->SetLineColor(kGreen);
grBATul->SetLineWidth(-200);
grBATul->SetFillStyle(3001);
grBATul->SetFillColor(kGreen);
// create and draw multigraph
TMultiGraph* mg = new TMultiGraph("BayesianLimitsBATCalculator", "BayesianLimitsBATCalculator");
mg->SetTitle("example of Bayesian credibility intervals derived with BATCAlculator ");
mg->Add(grBATll);
mg->Add(grBATul);
mg->Draw("AC");
mg->GetXaxis()->SetTitle ("# observed events");
mg->GetYaxis()->SetTitle("limits on signal S (size of test: 0.1)");
mg->Draw("AC");
}
void FitScenariosTwoD_RequiredPerformance_card(const string inputfilePho = "/afs/cern.ch/work/d/daan/public/releases/CMSSW_5_3_9_patch3/src/CMSAna/HHToBBGG/data/HHToBBGG_SignalBkgd_AfterCuts_diphotonMass.root", const string inputfileBjet = "/afs/cern.ch/work/d/daan/public/releases/CMSSW_5_3_9_patch3/src/CMSAna/HHToBBGG/data/HHToBBGG_SignalBkgd_AfterCuts_dibjetMass.root", const string inputfileTwoD = "/afs/cern.ch/work/d/daan/public/releases/CMSSW_5_3_9_patch3/src/CMSAna/HHToBBGG/data/HHToBBGG_SignalBkgd_AfterCuts_twoDMass.root", const string scanOption = "lum", Int_t NToys = 500, Int_t s = 5) {
TRandom3 *randomNumber = new TRandom3(1200);
TFile *file = new TFile(Form("HHToBBGGWorkspace_%s_%d.root",scanOption.c_str(),s),"RECREATE");
RooWorkspace* ws = new RooWorkspace("CMSAna/HHToBBGG/fits/Workspace");
AddModels(ws, inputfilePho, inputfileBjet, inputfileTwoD, scanOption, s);
//Import variables from workspace
RooAbsPdf *model2Dpdf = ws->pdf("model2Dpdf");
RooAbsPdf *model2Dpdf_cat0 = ws->pdf("model2Dpdf_cat0");
RooAbsPdf *model2Dpdf_cat1 = ws->pdf("model2Dpdf_cat1");
RooRealVar *massBjet = ws->var("massBjet");
RooRealVar *massPho = ws->var("massPho");
RooCategory *sampleCategory = ws->cat("sampleCategory");
RooRealVar *nsig = ws->var("nsig"); RooRealVar constNsig(*nsig);
RooRealVar *nres_cat0 = ws->var("nres_cat0"); RooRealVar constNres_cat0(*nres_cat0);
RooRealVar *nnonres_cat0 = ws->var("nnonres_cat0"); RooRealVar constNnonres_cat0(*nnonres_cat0);
RooRealVar *nres_cat1 = ws->var("nres_cat1"); RooRealVar constNres_cat1(*nres_cat1);
RooRealVar *nnonres_cat1 = ws->var("nnonres_cat1"); RooRealVar constNnonres_cat1(*nnonres_cat1);
//Create TTree to store the resulting yield data
TFile *f = new TFile(Form(("CMSAna/HHToBBGG/data/MassFitResults/ResolutionAnalysis/OptionB_Categories/tmp/"+scanOption+"FitScenario%d.root").c_str(), s), "RECREATE");
TTree *resultTree = new TTree("resultTree", "Parameter results from fitting");
Float_t nsigOut, nresOut, nnonresOut;
Float_t nsigStd, nresStd, nnonresStd;
resultTree->Branch("nsigOut",&nsigOut, "nsigOut/F");
resultTree->Branch("nresOut",&nresOut, "nresOut/F");
resultTree->Branch("nnonresOut",&nnonresOut, "nnonresOut/F");
resultTree->Branch("nsigStd",&nsigStd, "nsigStd/F");
resultTree->Branch("nresStd",&nresStd, "nresStd/F");
resultTree->Branch("nnonresStd",&nnonresStd, "nnonresStd/F");
//-------------------------------------------------------------
//Yield Information
//=============================================================
double myPhoEffRatio = 1.00;
double myBtagEffRatio = 1.00;
double myEndcapPhotonFakerateRatio = 1.0;
if (scanOption == "phoEff") {
myPhoEffRatio = phoEffRatio[s];
myBtagEffRatio = 1.25;
}
if (scanOption == "btagEff") {
myBtagEffRatio = btagEffRatio[s];
myPhoEffRatio = 1.25;
}
if (scanOption == "endcapPhotonFakerate") myEndcapPhotonFakerateRatio = endcapPhotonFakerate[s];
double totalYield =
6.11*myPhoEffRatio*myPhoEffRatio*myBtagEffRatio*myBtagEffRatio +
7.88*myPhoEffRatio*myPhoEffRatio*myBtagEffRatio*myBtagEffRatio +
31.1*myPhoEffRatio*myPhoEffRatio*myBtagEffRatio*myBtagEffRatio
+ 26.4*myPhoEffRatio*myPhoEffRatio*myBtagEffRatio*myBtagEffRatio
+ 48.7*myBtagEffRatio*myBtagEffRatio*myPhoEffRatio
+ 1.8*myBtagEffRatio*myBtagEffRatio
+
2.41*myPhoEffRatio*myPhoEffRatio*myBtagEffRatio*myBtagEffRatio +
17.4*myPhoEffRatio*myPhoEffRatio*myBtagEffRatio*myBtagEffRatio
+ 17.2*myPhoEffRatio*myPhoEffRatio*myBtagEffRatio*myBtagEffRatio
+ 206.0*myBtagEffRatio*myBtagEffRatio*myPhoEffRatio*myEndcapPhotonFakerateRatio
+ 1.7*myBtagEffRatio*myBtagEffRatio
;
double cat0Yield = 6.11*myPhoEffRatio*myPhoEffRatio*myBtagEffRatio*myBtagEffRatio +
7.88*myPhoEffRatio*myPhoEffRatio*myBtagEffRatio*myBtagEffRatio +
31.1*myPhoEffRatio*myPhoEffRatio*myBtagEffRatio*myBtagEffRatio
+ 26.4*myPhoEffRatio*myPhoEffRatio*myBtagEffRatio*myBtagEffRatio
+ 48.7*myBtagEffRatio*myBtagEffRatio*myPhoEffRatio
+ 1.8*myBtagEffRatio*myBtagEffRatio;
double cat1Yield = 2.41*myPhoEffRatio*myPhoEffRatio*myBtagEffRatio*myBtagEffRatio +
17.4*myPhoEffRatio*myPhoEffRatio*myBtagEffRatio*myBtagEffRatio
+ 17.2*myPhoEffRatio*myPhoEffRatio*myBtagEffRatio*myBtagEffRatio
+ 206.0*myBtagEffRatio*myBtagEffRatio*myPhoEffRatio*myEndcapPhotonFakerateRatio
+ 1.7*myBtagEffRatio*myBtagEffRatio;
double cat0Fraction = cat0Yield / totalYield;
double cat1Fraction = cat1Yield / totalYield;
//-------------------------------------------------------------
//Generate Toy MC experiment data and fit
//=============================================================
for(UInt_t t=0; t < NToys; ++t) {
cout << t << "|" << s << endl;
nsig->setVal(constNsig.getVal());
nres_cat0->setVal(constNres_cat0.getVal());
nnonres_cat0->setVal(constNnonres_cat0.getVal());
nres_cat1->setVal(constNres_cat1.getVal());
nnonres_cat1->setVal(constNnonres_cat1.getVal());
Float_t ran;
//if (scanOption == "lum") ran = randomNumber->Poisson(totalYield*luminosity[s]);
//else ran = randomNumber->Poisson(totalYield);
if (scanOption == "lum") ran = totalYield*luminosity[s];
else ran = totalYield;
RooDataSet *pseudoData2D = model2Dpdf->generate(RooArgList(*massBjet,*massPho, *sampleCategory), ran);
RooFitResult *fitResult2D = model2Dpdf->fitTo(*pseudoData2D, RooFit::Save(), RooFit::Extended(kTRUE), RooFit::Strategy(2));
ws->import(*pseudoData2D, kTRUE);
ws->import(*pseudoData2D, Rename("pseudoData2D"));
RooDataSet *pseudoData2D_cat0 = model2Dpdf_cat0->generate(RooArgList(*massBjet,*massPho), ran*cat0Fraction);
RooDataSet *pseudoData2D_cat1 = model2Dpdf_cat1->generate(RooArgList(*massBjet,*massPho), ran*cat1Fraction);
ws->import(*pseudoData2D_cat0, kTRUE);
ws->import(*pseudoData2D_cat0, Rename("pseudoData2D_cat0"));
ws->import(*pseudoData2D_cat1, kTRUE);
ws->import(*pseudoData2D_cat1, Rename("pseudoData2D_cat1"));
//Save variables into separate branches of root tree
nsigOut = nsig->getVal();
nresOut = nres_cat0->getVal();
nnonresOut = nnonres_cat0->getVal();
nsigStd = nsig->getPropagatedError(*fitResult2D);
nresStd = nres_cat0->getPropagatedError(*fitResult2D);
nnonresStd = nnonres_cat0->getPropagatedError(*fitResult2D);
resultTree->Fill();
}
//Write to the TTree and close it
resultTree->Write();
file->WriteTObject(ws, Form("HHToBBGGWorkspace",scanOption.c_str(),s), "WriteDelete");
file->Close();
delete file;
}
void makeSystPlot( TFile * f, TString oldFolder, RooWorkspace *WS, string channel, string syst, int toMassNo, int fromMassNo) //massNo 0-51, see xSec7TeV.h
{
RooArgList * hobs = new RooArgList("hobs");
RooRealVar BDT("CMS_vhbb_BDT_Zll", "CMS_vhbb_BDT_Zll", -1, 1);///OLD VARIABLE NAME HERE
hobs->add(*WS->var("CMS_vhbb_BDT_Zll")); ///NEW VARIABLE NAME HERE
RooWorkspace *tempWS = (RooWorkspace*) f->Get(oldFolder.Data());
TString systT(syst);
TString chanT(channel);
bool writeIt = 1;
if(chanT.Contains("QCD") || chanT.Contains("Wj"))
if(!(systT.Contains("stat"))) writeIt = 0;
if((kount < 3) && (channel=="data_obs"))
{
kount++;
std::string namen = channel;
std::cout << "reading WS "<< oldFolder.Data() << std::endl;
std::cout << namen << std::endl;
RooDataHist* tempRooDataHistNom = (RooDataHist*) tempWS->data(namen.c_str());
TH1 *tempHistNom = tempRooDataHistNom->createHistogram(namen.c_str(),BDT,Binning(bins));
std::cout << namen << std::endl;
RooDataHist *DHnom = new RooDataHist(channel.c_str(),"",*hobs,tempHistNom);
WS->import(*(new RooHistPdf(channel.c_str(),"",*hobs,*DHnom)));
}
if (channel!="data_obs")
{
std::string nameUp;
std::string namen;
std::string nameDown;
if((syst == "stat"))
{
if(IFILE.Contains("7TeV"))
{
nameUp = channel + "CMS_vhbb_stats_" + channel + "_" + oldFolder.Data() + "Up";
namen = channel;
nameDown = channel + "CMS_vhbb_stats_" + channel + "_" + oldFolder.Data() + "Down";
}
if(IFILE.Contains("8TeV"))
{
nameUp = channel + "CMS_vhbb_stats_" + channel + "_" + oldFolder.Data() + "Up";
namen = channel;
nameDown = channel + "CMS_vhbb_stats_" + channel + "_" + oldFolder.Data() + "Down";
}
}
else
{
nameUp = channel + "CMS_" + syst + "Up";
namen = channel;
nameDown = channel + "CMS_" + syst + "Down";
}
if((syst == "ZJModel"))
{
if(IFILE.Contains("7TeV"))
{
nameUp = channel + "CMS_vhbb_ZJModel_" + oldFolder.Data() + "_7TeVUp";
namen = channel;
nameDown = channel + "CMS_vhbb_ZJModel_" + oldFolder.Data() + "_7TeVDown";
}
if(IFILE.Contains("8TeV"))
{
nameUp = channel + "CMS_vhbb_ZJModel_" + oldFolder.Data() + "_8TeVUp";
namen = channel;
nameDown = channel + "CMS_vhbb_ZJModel_" + oldFolder.Data() + "_8TeVDown";
}
}
if(writeIt)
{
RooDataHist* tempRooDataHistUp = (RooDataHist*) tempWS->data(nameUp.c_str());
RooDataHist* tempRooDataHistDown = (RooDataHist*) tempWS->data(nameDown.c_str());
RooDataHist* tempRooDataHistNom = (RooDataHist*) tempWS->data(namen.c_str());
std::cout << oldFolder.Data() << std::endl;
std::cout << nameUp.c_str() << std::endl;
TH1 *tempHistUp = tempRooDataHistUp->createHistogram(nameUp.c_str(),BDT,Binning(bins));
TH1 *tempHistDown = tempRooDataHistDown->createHistogram(nameDown.c_str(),BDT,Binning(bins));
std::cout << namen.c_str() << std::endl;
TH1 *tempHistNom = tempRooDataHistNom->createHistogram(namen.c_str(),BDT,Binning(bins));
if(chanT.Contains("VH") && IFILE.Contains("7TeV"))
{
tempHistUp->Scale(xSec7ZH[toMassNo]/xSec7ZH[fromMassNo]);
tempHistDown->Scale(xSec7ZH[toMassNo]/xSec7ZH[fromMassNo]);
tempHistNom->Scale(xSec7ZH[toMassNo]/xSec7ZH[fromMassNo]);
}
if(chanT.Contains("VH") && IFILE.Contains("8TeV"))
{
tempHistUp->Scale(xSec8ZH[toMassNo]/xSec8ZH[fromMassNo]);
tempHistDown->Scale(xSec8ZH[toMassNo]/xSec8ZH[fromMassNo]);
tempHistNom->Scale(xSec8ZH[toMassNo]/xSec8ZH[fromMassNo]);
}
std::cout<< "channel--> " << channel << std::endl;
tempHistUp->SetLineColor(kRed);
tempHistUp->SetLineWidth(3);
tempHistUp->SetFillColor(0);
tempHistDown->SetLineColor(kBlue);
tempHistDown->SetFillColor(0);
tempHistDown->SetLineWidth(3);
tempHistNom->SetFillColor(0);
tempHistNom->SetMarkerStyle(20);
tempHistUp->SetTitle((channel + syst).c_str());
RooDataHist *DHnom;
RooDataHist *DHup = new RooDataHist(nameUp.c_str(),"",*hobs,tempHistUp);
if(kount2 < 3) DHnom = new RooDataHist(namen.c_str(),"",*hobs,tempHistNom);
RooDataHist *DHdown = new RooDataHist(nameDown.c_str(),"",*hobs,tempHistDown);
WS->import(*(new RooHistPdf(nameUp.c_str(),"",*hobs,*DHup)));
WS->import(*(new RooHistPdf(nameDown.c_str(),"",*hobs,*DHdown)));
if(kount2 < 3){ WS->import(*(new RooHistPdf(namen.c_str(),"",*hobs,*DHnom))); kount2++;}
}
}
}
void FitLeptonResponseModels(const string Label = "ZZ", Int_t Option = 0, Int_t PtBin = -1, Int_t EtaBin = -1) {
string label = Label;
if (Label != "") label = "_" + Label;
TFile *fileInput = new TFile(("FakeRate" + label + ".root").c_str(), "READ");
//********************************************************
// Bins
//********************************************************
const UInt_t NPtBins = 4;
const UInt_t NEtaBins = 3;
double ptBins[NPtBins+1] = { 5, 7, 10, 15, 25 };
double etaBins[NEtaBins+1] = { 0.0, 1.2, 2.2, 2.4 };
//********************************************************
// Output
//********************************************************
TFile *fileOutput = new TFile(("FakeMuonPtResolutionModel" + label + ".root").c_str(), "UPDATE");
TH2F* GaussParamArray_Muons_mean = (TH2F*)fileOutput->Get("GaussParamArray_Muons_mean");
TH2F* GaussParamArray_Muons_sigma = (TH2F*)fileOutput->Get("GaussParamArray_Muons_sigma");
if (!GaussParamArray_Muons_mean) {
GaussParamArray_Muons_mean = new TH2F( "GaussParamArray_Muons_mean", "", NPtBins, 0, NPtBins, NEtaBins, 0, NEtaBins);
for (uint i=0; i < NPtBins+2; ++i) {
for (uint j=0; j < NEtaBins+2; ++j) {
GaussParamArray_Muons_mean->SetBinContent(i,j,0.0);
}
}
}
if (!GaussParamArray_Muons_sigma) {
GaussParamArray_Muons_sigma = new TH2F( "GaussParamArray_Muons_sigma", "", NPtBins, 0, NPtBins, NEtaBins, 0, NEtaBins);
for (uint i=0; i < NPtBins+2; ++i) {
for (uint j=0; j < NEtaBins+2; ++j) {
GaussParamArray_Muons_sigma->SetBinContent(i,j,0.0);
}
}
}
for (uint i=0; i < NPtBins+2; ++i) {
for (uint j=0; j < NEtaBins+2; ++j) {
if (i >= 1 && ( j >= 1 && j <= NEtaBins )) {
} else {
GaussParamArray_Muons_mean->SetBinContent(i,j,0);
GaussParamArray_Muons_sigma->SetBinContent(i,j,0);
}
}
}
//********************************************************
// Fit for resolution function
//********************************************************
for (uint i=0; i < NPtBins+2; ++i) {
for (uint j=0; j < NEtaBins+2; ++j) {
if (PtBin >= 0 && EtaBin >= 0) {
if (!(i==PtBin && j==EtaBin)) continue;
}
TH1F* hist = (TH1F*)fileInput->Get(Form("LeptonPtResolution_Muons_PtBin%d_EtaBin%d",i,j));
assert(hist);
RooRealVar leptonPtRes("leptonPtRes","leptonPtRes",-1.0,0.25);
leptonPtRes.setRange("range",-1.00,0.25);
leptonPtRes.setBins(100);
RooDataHist *data=0;
data = new RooDataHist("data","data",RooArgSet(leptonPtRes),hist);
RooRealVar *mean = new RooRealVar("mean","mean",-0.25,-10,10);
RooRealVar *sigma = new RooRealVar("sigma","sigma",0.05,0.00001,0.5);
RooGaussian *model = new RooGaussian("LeptonPtResModel","LeptonPtResModel",leptonPtRes,*mean,*sigma);
RooFitResult *fitResult=0;
fitResult = model->fitTo(*data,
RooFit::Binned(true),
RooFit::Strategy(1),
RooFit::Save());
cout << "Fitted parameters\n";
cout << mean->getVal() << endl;
cout << sigma->getVal() << endl;
if (i >= 1 && ( j >= 1 && j <= NEtaBins )) {
GaussParamArray_Muons_mean->SetBinContent(i,j,mean->getVal());
GaussParamArray_Muons_sigma->SetBinContent(i,j,sigma->getVal());
} else {
GaussParamArray_Muons_mean->SetBinContent(i,j,0);
GaussParamArray_Muons_sigma->SetBinContent(i,j,0);
}
//Save Workspace
RooWorkspace *w = new RooWorkspace(Form("LeptonPtResolutionModel_Muons_PtBin%d_EtaBin%d",i,j),Form("LeptonPtResolutionModel_Muons_PtBin%d_EtaBin%d",i,j));
w->import(*model);
w->import(*data);
//w->Print();
//Make Plot
RooPlot *frame = leptonPtRes.frame(RooFit::Bins(100));
data->plotOn(frame,RooFit::MarkerStyle(kFullCircle),RooFit::MarkerSize(0.8),RooFit::DrawOption("ZP"));
model->plotOn(frame);
TCanvas *cv = new TCanvas("cv","cv",800,600);
frame->Draw();
cv->SaveAs(Form("LeptonPtResolution_Muons%s_PtBin%d_EtaBin%d.gif",label.c_str(),i,j));
fileOutput->WriteTObject(model, Form("LeptonPtResolutionModel_Muons_PtBin%d_EtaBin%d",i,j), "WriteDelete");
fileOutput->WriteTObject(cv, Form("LeptonPtResolutionFit_Muons_PtBin%d_EtaBin%d",i,j), "WriteDelete");
fileOutput->WriteTObject(w, w->GetName(), "WriteDelete");
fileOutput->WriteTObject(GaussParamArray_Muons_mean, "GaussParamArray_Muons_mean", "WriteDelete");
fileOutput->WriteTObject(GaussParamArray_Muons_sigma, "GaussParamArray_Muons_sigma", "WriteDelete");
}
}
//********************************************************
// Produce output lookup table
//********************************************************
ofstream outf_e("FakeMuonResponseMap.h");
outf_e << "UInt_t FindFakeMuonResponseBin( double value, double bins[], UInt_t nbins) {" << endl;
outf_e << " UInt_t nbinboundaries = nbins+1;" << endl;
outf_e << " UInt_t bin = 0;" << endl;
outf_e << " for (uint i=0; i < nbinboundaries; ++i) {" << endl;
outf_e << " if (i < nbinboundaries-1) {" << endl;
outf_e << " if (value >= bins[i] && value < bins[i+1]) {" << endl;
outf_e << " bin = i+1;" << endl;
outf_e << " break;" << endl;
outf_e << " }" << endl;
outf_e << " } else if (i == nbinboundaries-1) {" << endl;
outf_e << " if (value >= bins[i]) {" << endl;
outf_e << " bin = nbinboundaries;" << endl;
outf_e << " break;" << endl;
outf_e << " }" << endl;
outf_e << " } " << endl;
outf_e << " }" << endl;
outf_e << " return bin;" << endl;
outf_e << "}" << endl;
outf_e << endl;
outf_e << endl;
outf_e << "Double_t GetMuonResponseMeanPtEta(Double_t Pt, Double_t Eta) {" << endl;
outf_e << endl;
outf_e << " Double_t ptBins[" << NPtBins+1 << "] = {";
for (uint i=0; i < NPtBins+1; ++i) {
outf_e << ptBins[i];
if (i < NPtBins) {
outf_e << ",";
}
}
outf_e << "};\n";
outf_e << " Double_t etaBins[" << NEtaBins+1 << "] = {";
for (uint i=0; i < NEtaBins+1; ++i) {
outf_e << etaBins[i];
if (i < NEtaBins) {
outf_e << ",";
}
}
outf_e << "};\n";
outf_e << endl;
outf_e << endl;
outf_e << " Double_t ResponseMean[" << NPtBins+2 << "][" << NEtaBins+2 << "] = {";
outf_e << endl;
for (uint i=0; i < NPtBins+2; ++i) {
outf_e << " {";
for (uint j=0; j < NEtaBins+2; ++j) {
outf_e << GaussParamArray_Muons_mean->GetBinContent(i,j);
if (j< NEtaBins+1) {
outf_e << ",";
}
}
if (i< NPtBins+1) {
outf_e << " },";
} else {
outf_e << "}";
}
outf_e << endl;
}
outf_e << " };" << endl;
outf_e << endl;
outf_e << endl;
outf_e << " Int_t tmpPtBin = FindFakeMuonResponseBin( Pt , ptBins, " << NPtBins << ");" << endl;
outf_e << " Int_t tmpEtaBin = FindFakeMuonResponseBin( Eta , etaBins, " << NEtaBins << ");" << endl;
outf_e << " return ResponseMean[tmpPtBin][tmpEtaBin];" << endl;
outf_e << "}" << endl;
outf_e << endl;
outf_e << endl;
outf_e << "Double_t GetMuonResponseSigmaPtEta(Double_t Pt, Double_t Eta) {" << endl;
outf_e << endl;
outf_e << " Double_t ptBins[" << NPtBins+1 << "] = {";
for (uint i=0; i < NPtBins+1; ++i) {
outf_e << ptBins[i];
if (i < NPtBins) {
outf_e << ",";
}
}
outf_e << "};\n";
outf_e << " Double_t etaBins[" << NEtaBins+1 << "] = {";
for (uint i=0; i < NEtaBins+1; ++i) {
outf_e << etaBins[i];
if (i < NEtaBins) {
outf_e << ",";
}
}
outf_e << "};\n";
outf_e << endl;
outf_e << endl;
outf_e << " Double_t ResponseSigma[" << NPtBins+2 << "][" << NEtaBins+2 << "] = {";
outf_e << endl;
for (uint i=0; i < NPtBins+2; ++i) {
outf_e << " {";
for (uint j=0; j < NEtaBins+2; ++j) {
outf_e << GaussParamArray_Muons_sigma->GetBinContent(i,j);
if (j< NEtaBins+1) {
outf_e << ",";
}
}
if (i< NPtBins+1) {
outf_e << " },";
} else {
outf_e << "}";
}
outf_e << endl;
}
outf_e << " };" << endl;
outf_e << endl;
outf_e << endl;
outf_e << " Int_t tmpPtBin = FindFakeMuonResponseBin( Pt , ptBins, " << NPtBins << ");" << endl;
outf_e << " Int_t tmpEtaBin = FindFakeMuonResponseBin( Eta , etaBins, " << NEtaBins << ");" << endl;
outf_e << " return ResponseSigma[tmpPtBin][tmpEtaBin];" << endl;
outf_e << "}" << endl;
outf_e.close();
fileInput->Close();
delete fileInput;
fileOutput->Close();
gBenchmark->Show("WWTemplate");
}
void eregtesting_13TeV_Eta(bool dobarrel=true, bool doele=false,int gammaID=0) {
//output dir
TString EEorEB = "EE";
if(dobarrel)
{
EEorEB = "EB";
}
TString gammaDir = "bothGammas";
if(gammaID==1)
{
gammaDir = "gamma1";
}
else if(gammaID==2)
{
gammaDir = "gamma2";
}
TString dirname = TString::Format("ereg_test_plots_Eta/%s_%s",gammaDir.Data(),EEorEB.Data());
gSystem->mkdir(dirname,true);
gSystem->cd(dirname);
//read workspace from training
TString fname;
if (doele && dobarrel)
fname = "wereg_ele_eb.root";
else if (doele && !dobarrel)
fname = "wereg_ele_ee.root";
else if (!doele && dobarrel)
fname = "wereg_ph_eb.root";
else if (!doele && !dobarrel)
fname = "wereg_ph_ee.root";
TString infile = TString::Format("../../ereg_ws_Eta/%s/%s",gammaDir.Data(),fname.Data());
TFile *fws = TFile::Open(infile);
RooWorkspace *ws = (RooWorkspace*)fws->Get("wereg");
//read variables from workspace
RooGBRTargetFlex *meantgt = static_cast<RooGBRTargetFlex*>(ws->arg("sigmeant"));
RooRealVar *tgtvar = ws->var("tgtvar");
RooArgList vars;
vars.add(meantgt->FuncVars());
vars.add(*tgtvar);
//read testing dataset from TTree
RooRealVar weightvar("weightvar","",1.);
TTree *dtree;
if (doele) {
//TFile *fdin = TFile::Open("root://eoscms.cern.ch//eos/cms/store/cmst3/user/bendavid/regTreesAug1/hgg-2013Final8TeV_reg_s12-zllm50-v7n_noskim.root");
TFile *fdin = TFile::Open("/data/bendavid/regTreesAug1/hgg-2013Final8TeV_reg_s12-zllm50-v7n_noskim.root");
TDirectory *ddir = (TDirectory*)fdin->FindObjectAny("PhotonTreeWriterSingleInvert");
dtree = (TTree*)ddir->Get("hPhotonTreeSingle");
}
else {
//TFile *fdin = TFile::Open("/eos/cms/store/group/dpg_ecal/alca_ecalcalib/piZero2017/zhicaiz/Gun_MultiPion_FlatPt-1To15/Gun_FlatPt1to15_MultiPion_withPhotonPtFilter_pythia8/photons_0_half2.root");
//TFile *fdin = TFile::Open("/eos/cms/store/group/dpg_ecal/alca_ecalcalib/piZero2017/zhicaiz/Gun_MultiEta_FlatPt-1To15/Gun_FlatPt1to15_MultiEta_withPhotonPtFilter_pythia8/photons_22Aug2017_V3_half2.root");
TFile *fdin = TFile::Open("/eos/cms/store/group/dpg_ecal/alca_ecalcalib/piZero2017/zhicaiz/Gun_MultiEta_FlatPt-1To15/Gun_FlatPt1to15_MultiEtaToGG_withPhotonPtFilter_pythia8/photons_20171008_half2.root");
if(gammaID==0)
{
dtree = (TTree*)fdin->Get("Tree_Optim_gamma");
}
else if(gammaID==1)
{
dtree = (TTree*)fdin->Get("Tree_Optim_gamma1");
}
else if(gammaID==2)
{
dtree = (TTree*)fdin->Get("Tree_Optim_gamma2");
}
}
//selection cuts for testing
//TCut selcut = "(STr2_enG1_true/cosh(STr2_Eta_1)>1.0) && (STr2_S4S9_1>0.75)";
//TCut selcut = "(STr2_enG_rec/cosh(STr2_Eta)>1.0) && (STr2_S4S9 > 0.75) && (STr2_isMerging < 2) && (STr2_DeltaR < 0.03) && (STr2_enG_true/STr2_enG_rec)<3.0 && STr2_EOverEOther < 10.0 && STr2_EOverEOther > 0.1";
//TCut selcut = "(STr2_enG_rec/cosh(STr2_Eta)>0) && (STr2_S4S9 > 0.75) && (STr2_isMerging < 2) && (STr2_DeltaR < 0.03) && (STr2_mPi0_nocor>0.1)";
//TCut selcut = "(STr2_enG_rec/cosh(STr2_Eta)>1.0) && (STr2_S4S9 > 0.75) && (STr2_Nxtal > 6) && (STr2_mPi0_nocor>0.1) && (STr2_mPi0_nocor < 0.2)";
TCut selcut = "";
if(dobarrel) selcut = "(STr2_enG_rec/cosh(STr2_Eta)>1.0) && (STr2_S4S9 > 0.75) && (STr2_Nxtal > 6) && (STr2_mPi0_nocor>0.2) && (STr2_mPi0_nocor < 1.0) && (STr2_ptPi0_nocor > 2.0) && abs(STr2_Eta)<1.479 && (!STr2_fromPi0)";
//if(dobarrel) selcut = "(STr2_enG_rec/cosh(STr2_Eta)>1.0) && (STr2_S4S9 > 0.75) && (STr2_Nxtal > 6) && (STr2_mPi0_nocor>0.1) && (STr2_mPi0_nocor < 0.2) && (STr2_ptPi0_nocor > 2.0) && abs(STr2_Eta)<1.479";
else selcut = "(STr2_enG_rec/cosh(STr2_Eta)>1.0) && (STr2_S4S9 > 0.75) && (STr2_Nxtal > 6) && (STr2_mPi0_nocor>0.2) && (STr2_mPi0_nocor < 1.0) && (STr2_ptPi0_nocor > 2.0) && abs(STr2_Eta)>1.479 && (!STr2_fromPi0)";
//else selcut = "(STr2_enG_rec/cosh(STr2_Eta)>1.0) && (STr2_S4S9 > 0.75) && (STr2_Nxtal > 6) && (STr2_mPi0_nocor>0.1) && (STr2_mPi0_nocor < 0.2) && (STr2_ptPi0_nocor > 2.0) && abs(STr2_Eta)>1.479";
//TCut selcut = "(STr2_enG_rec/cosh(STr2_Eta)>1.0) && (STr2_S4S9 > 0.75) && (STr2_isMerging < 2) && (STr2_DeltaR < 0.03) && (STr2_iEta_on2520==0 || STr2_iPhi_on20==0) ";
//TCut selcut = "(STr2_enG_rec/cosh(STr2_Eta)>1.0) && (STr2_S4S9 > 0.75) && (STr2_isMerging < 2) && (STr2_DeltaR < 0.03) && (abs(STr2_iEtaiX)<60)";
//TCut selcut = "(STr2_enG_rec/cosh(STr2_Eta)>1.0) && (STr2_S4S9 > 0.75) && (STr2_isMerging < 2) && (STr2_DeltaR < 0.03) && (abs(STr2_iEtaiX)>60)";
//TCut selcut = "(STr2_enG_rec/cosh(STr2_Eta)>1.0) && (STr2_S4S9 > 0.9) && (STr2_S2S9>0.85)&& (STr2_isMerging < 2) && (STr2_DeltaR < 0.03) && (abs(STr2_iEtaiX)<60)";
//TCut selcut = "(STr2_enG_rec/cosh(STr2_Eta)>1.0) && (STr2_S4S9 > 0.9) && (STr2_S2S9>0.85)&& (STr2_isMerging < 2) && (STr2_DeltaR < 0.03)";
/*
TCut selcut;
if (dobarrel)
selcut = "ph.genpt>25. && ph.isbarrel && ph.ispromptgen";
else
selcut = "ph.genpt>25. && !ph.isbarrel && ph.ispromptgen";
*/
TCut selweight = "xsecweight(procidx)*puweight(numPU,procidx)";
TCut prescale10 = "(Entry$%10==0)";
TCut prescale10alt = "(Entry$%10==1)";
TCut prescale25 = "(Entry$%25==0)";
TCut prescale100 = "(Entry$%100==0)";
TCut prescale1000 = "(Entry$%1000==0)";
TCut evenevents = "(Entry$%2==0)";
TCut oddevents = "(Entry$%2==1)";
TCut prescale100alt = "(Entry$%100==1)";
TCut prescale1000alt = "(Entry$%1000==1)";
TCut prescale50alt = "(Entry$%50==1)";
TCut Events3_4 = "(Entry$%4==3)";
TCut Events1_4 = "(Entry$%4==1)";
TCut Events2_4 = "(Entry$%4==2)";
TCut Events0_4 = "(Entry$%4==0)";
TCut Events01_4 = "(Entry$%4<2)";
TCut Events23_4 = "(Entry$%4>1)";
TCut EventsTest = "(Entry$%2==1)";
//weightvar.SetTitle(EventsTest*selcut);
weightvar.SetTitle(selcut);
/*
if (doele)
weightvar.SetTitle(prescale100alt*selcut);
else
weightvar.SetTitle(selcut);
*/
//make testing dataset
RooDataSet *hdata = RooTreeConvert::CreateDataSet("hdata",dtree,vars,weightvar);
if (doele)
weightvar.SetTitle(prescale1000alt*selcut);
else
weightvar.SetTitle(prescale10alt*selcut);
//make reduced testing dataset for integration over conditional variables
RooDataSet *hdatasmall = RooTreeConvert::CreateDataSet("hdatasmall",dtree,vars,weightvar);
//retrieve full pdf from workspace
RooAbsPdf *sigpdf = ws->pdf("sigpdf");
//input variable corresponding to sceta
RooRealVar *scEraw = ws->var("var_0");
scEraw->setRange(1.,2.);
scEraw->setBins(100);
// RooRealVar *scetavar = ws->var("var_1");
// RooRealVar *scphivar = ws->var("var_2");
//regressed output functions
RooAbsReal *sigmeanlim = ws->function("sigmeanlim");
RooAbsReal *sigwidthlim = ws->function("sigwidthlim");
RooAbsReal *signlim = ws->function("signlim");
RooAbsReal *sign2lim = ws->function("sign2lim");
// RooAbsReal *sigalphalim = ws->function("sigalphalim");
//RooAbsReal *sigalpha2lim = ws->function("sigalpha2lim");
//formula for corrected energy/true energy ( 1.0/(etrue/eraw) * regression mean)
RooFormulaVar ecor("ecor","","1./(@0)*@1",RooArgList(*tgtvar,*sigmeanlim));
RooRealVar *ecorvar = (RooRealVar*)hdata->addColumn(ecor);
ecorvar->setRange(0.,2.);
ecorvar->setBins(800);
//formula for raw energy/true energy (1.0/(etrue/eraw))
RooFormulaVar raw("raw","","1./@0",RooArgList(*tgtvar));
RooRealVar *rawvar = (RooRealVar*)hdata->addColumn(raw);
rawvar->setRange(0.,2.);
rawvar->setBins(800);
//clone data and add regression outputs for plotting
RooDataSet *hdataclone = new RooDataSet(*hdata,"hdataclone");
RooRealVar *meanvar = (RooRealVar*)hdataclone->addColumn(*sigmeanlim);
RooRealVar *widthvar = (RooRealVar*)hdataclone->addColumn(*sigwidthlim);
RooRealVar *nvar = (RooRealVar*)hdataclone->addColumn(*signlim);
RooRealVar *n2var = (RooRealVar*)hdataclone->addColumn(*sign2lim);
// RooRealVar *alphavar = (RooRealVar*)hdataclone->addColumn(*sigalphalim);
// RooRealVar *alpha2var = (RooRealVar*)hdataclone->addColumn(*sigalpha2lim);
//plot target variable and weighted regression prediction (using numerical integration over reduced testing dataset)
TCanvas *craw = new TCanvas;
//RooPlot *plot = tgtvar->frame(0.6,1.2,100);
RooPlot *plot = tgtvar->frame(0.6,2.0,100);
hdata->plotOn(plot);
sigpdf->plotOn(plot,ProjWData(*hdatasmall));
plot->Draw();
craw->SaveAs("RawE.pdf");
craw->SaveAs("RawE.png");
craw->SetLogy();
plot->SetMinimum(0.1);
craw->SaveAs("RawElog.pdf");
craw->SaveAs("RawElog.png");
//plot distribution of regressed functions over testing dataset
TCanvas *cmean = new TCanvas;
RooPlot *plotmean = meanvar->frame(0.8,2.0,100);
hdataclone->plotOn(plotmean);
plotmean->Draw();
cmean->SaveAs("mean.pdf");
cmean->SaveAs("mean.png");
TCanvas *cwidth = new TCanvas;
RooPlot *plotwidth = widthvar->frame(0.,0.05,100);
hdataclone->plotOn(plotwidth);
plotwidth->Draw();
cwidth->SaveAs("width.pdf");
cwidth->SaveAs("width.png");
TCanvas *cn = new TCanvas;
RooPlot *plotn = nvar->frame(0.,111.,200);
hdataclone->plotOn(plotn);
plotn->Draw();
cn->SaveAs("n.pdf");
cn->SaveAs("n.png");
TCanvas *cn2 = new TCanvas;
RooPlot *plotn2 = n2var->frame(0.,111.,100);
hdataclone->plotOn(plotn2);
plotn2->Draw();
cn2->SaveAs("n2.pdf");
cn2->SaveAs("n2.png");
/*
TCanvas *calpha = new TCanvas;
RooPlot *plotalpha = alphavar->frame(0.,5.,200);
hdataclone->plotOn(plotalpha);
plotalpha->Draw();
calpha->SaveAs("alpha.pdf");
calpha->SaveAs("alpha.png");
TCanvas *calpha2 = new TCanvas;
RooPlot *plotalpha2 = alpha2var->frame(0.,5.,200);
hdataclone->plotOn(plotalpha2);
plotalpha2->Draw();
calpha2->SaveAs("alpha2.pdf");
calpha2->SaveAs("alpha2.png");
*/
/*
TCanvas *ceta = new TCanvas;
RooPlot *ploteta = scetavar->frame(-2.6,2.6,200);
hdataclone->plotOn(ploteta);
ploteta->Draw();
ceta->SaveAs("eta.pdf");
ceta->SaveAs("eta.png");
*/
//create histograms for eraw/etrue and ecor/etrue to quantify regression performance
TH1 *heraw;// = hdata->createHistogram("hraw",*rawvar,Binning(800,0.,2.));
TH1 *hecor;// = hdata->createHistogram("hecor",*ecorvar);
if (EEorEB == "EB")
{
heraw = hdata->createHistogram("hraw",*rawvar,Binning(800,0.,2.0));
hecor = hdata->createHistogram("hecor",*ecorvar, Binning(800,0.,2.0));
}
else
{
heraw = hdata->createHistogram("hraw",*rawvar,Binning(200,0.,2.));
hecor = hdata->createHistogram("hecor",*ecorvar, Binning(200,0.,2.));
}
//heold->SetLineColor(kRed);
hecor->SetLineColor(kBlue);
heraw->SetLineColor(kMagenta);
hecor->GetYaxis()->SetRangeUser(1.0,1.3*hecor->GetMaximum());
heraw->GetYaxis()->SetRangeUser(1.0,1.3*hecor->GetMaximum());
hecor->GetXaxis()->SetRangeUser(0.0,1.5);
heraw->GetXaxis()->SetRangeUser(0.0,1.5);
/*if(EEorEB == "EE")
{
heraw->GetYaxis()->SetRangeUser(10.0,200.0);
hecor->GetYaxis()->SetRangeUser(10.0,200.0);
}
*/
//heold->GetXaxis()->SetRangeUser(0.6,1.2);
double effsigma_cor, effsigma_raw, fwhm_cor, fwhm_raw;
if(EEorEB == "EB")
{
TH1 *hecorfine = hdata->createHistogram("hecorfine",*ecorvar,Binning(800,0.,2.));
effsigma_cor = effSigma(hecorfine);
fwhm_cor = FWHM(hecorfine);
TH1 *herawfine = hdata->createHistogram("herawfine",*rawvar,Binning(800,0.,2.));
effsigma_raw = effSigma(herawfine);
fwhm_raw = FWHM(herawfine);
}
else
{
TH1 *hecorfine = hdata->createHistogram("hecorfine",*ecorvar,Binning(200,0.,2.));
effsigma_cor = effSigma(hecorfine);
fwhm_cor = FWHM(hecorfine);
TH1 *herawfine = hdata->createHistogram("herawfine",*rawvar,Binning(200,0.,2.));
effsigma_raw = effSigma(herawfine);
fwhm_raw = FWHM(herawfine);
}
TCanvas *cresponse = new TCanvas;
gStyle->SetOptStat(0);
gStyle->SetPalette(107);
hecor->SetTitle("");
heraw->SetTitle("");
hecor->Draw("HIST");
//heold->Draw("HISTSAME");
heraw->Draw("HISTSAME");
//show errSigma in the plot
TLegend *leg = new TLegend(0.1, 0.75, 0.7, 0.9);
leg->AddEntry(hecor,Form("E_{cor}/E_{true}, #sigma_{eff}=%4.3f, FWHM=%4.3f", effsigma_cor, fwhm_cor),"l");
leg->AddEntry(heraw,Form("E_{raw}/E_{true}, #sigma_{eff}=%4.3f, FWHM=%4.3f", effsigma_raw, fwhm_raw),"l");
leg->SetFillStyle(0);
leg->SetBorderSize(0);
// leg->SetTextColor(kRed);
leg->Draw();
cresponse->SaveAs("response.pdf");
cresponse->SaveAs("response.png");
cresponse->SetLogy();
cresponse->SaveAs("responselog.pdf");
cresponse->SaveAs("responselog.png");
// draw CCs vs eta and phi
/*
TCanvas *c_eta = new TCanvas;
TH1 *h_eta = hdata->createHistogram("h_eta",*scetavar,Binning(100,-3.2,3.2));
h_eta->Draw("HIST");
c_eta->SaveAs("heta.pdf");
c_eta->SaveAs("heta.png");
TCanvas *c_phi = new TCanvas;
TH1 *h_phi = hdata->createHistogram("h_phi",*scphivar,Binning(100,-3.2,3.2));
h_phi->Draw("HIST");
c_phi->SaveAs("hphi.pdf");
c_phi->SaveAs("hphi.png");
*/
RooRealVar *scetaiXvar = ws->var("var_4");
RooRealVar *scphiiYvar = ws->var("var_5");
if(EEorEB=="EB")
{
scetaiXvar->setRange(-90,90);
scetaiXvar->setBins(180);
scphiiYvar->setRange(0,360);
scphiiYvar->setBins(360);
}
else
{
scetaiXvar->setRange(0,50);
scetaiXvar->setBins(50);
scphiiYvar->setRange(0,50);
scphiiYvar->setBins(50);
}
ecorvar->setRange(0.5,1.5);
ecorvar->setBins(800);
rawvar->setRange(0.5,1.5);
rawvar->setBins(800);
TCanvas *c_cor_eta = new TCanvas;
TH3F *h3_CC_eta_phi = (TH3F*) hdata->createHistogram("var_5,var_4,ecor",(EEorEB=="EB") ? 170 : 100, (EEorEB=="EB") ? 360 : 100,25);
TProfile2D *h_CC_eta_phi = h3_CC_eta_phi->Project3DProfile();
h_CC_eta_phi->SetTitle("E_{cor}/E_{true}");
if(EEorEB=="EB")
{
h_CC_eta_phi->GetXaxis()->SetTitle("i#eta");
h_CC_eta_phi->GetYaxis()->SetTitle("i#phi");
h_CC_eta_phi->GetXaxis()->SetRangeUser(-85,85);
h_CC_eta_phi->GetYaxis()->SetRangeUser(0,360);
}
else
{
h_CC_eta_phi->GetXaxis()->SetTitle("iX");
h_CC_eta_phi->GetYaxis()->SetTitle("iY");
}
h_CC_eta_phi->SetMinimum(0.5);
h_CC_eta_phi->SetMaximum(1.5);
h_CC_eta_phi->Draw("COLZ");
c_cor_eta->SaveAs("cor_vs_eta_phi.pdf");
c_cor_eta->SaveAs("cor_vs_eta_phi.png");
TH2F *h_CC_eta = hdata->createHistogram(*scetaiXvar, *ecorvar, "","cor_vs_eta");
if(EEorEB=="EB")
{
h_CC_eta->GetXaxis()->SetTitle("i#eta");
}
else
{
h_CC_eta->GetXaxis()->SetTitle("iX");
}
h_CC_eta->GetYaxis()->SetTitle("E_{cor}/E_{true}");
h_CC_eta->Draw("COLZ");
c_cor_eta->SaveAs("cor_vs_eta.pdf");
c_cor_eta->SaveAs("cor_vs_eta.png");
TCanvas *c_cor_scEraw = new TCanvas;
TH2F *h_CC_scEraw = hdata->createHistogram(*scEraw, *ecorvar, "","cor_vs_scEraw");
h_CC_scEraw->GetXaxis()->SetTitle("E_{raw}");
h_CC_scEraw->GetYaxis()->SetTitle("E_{cor}/E_{true}");
h_CC_scEraw->Draw("COLZ");
c_cor_scEraw->SaveAs("cor_vs_scEraw.pdf");
c_cor_scEraw->SaveAs("cor_vs_scEraw.png");
TCanvas *c_raw_scEraw = new TCanvas;
TH2F *h_RC_scEraw = hdata->createHistogram(*scEraw, *rawvar, "","raw_vs_scEraw");
h_RC_scEraw->GetXaxis()->SetTitle("E_{raw}");
h_RC_scEraw->GetYaxis()->SetTitle("E_{raw}/E_{true}");
h_RC_scEraw->Draw("COLZ");
c_raw_scEraw->SaveAs("raw_vs_scEraw.pdf");
c_raw_scEraw->SaveAs("raw_vs_scEraw.png");
TCanvas *c_cor_phi = new TCanvas;
TH2F *h_CC_phi = hdata->createHistogram(*scphiiYvar, *ecorvar, "","cor_vs_phi");
if(EEorEB=="EB")
{
h_CC_phi->GetXaxis()->SetTitle("i#phi");
}
else
{
h_CC_phi->GetXaxis()->SetTitle("iY");
}
h_CC_phi->GetYaxis()->SetTitle("E_{cor}/E_{true}");
h_CC_phi->Draw("COLZ");
c_cor_phi->SaveAs("cor_vs_phi.pdf");
c_cor_phi->SaveAs("cor_vs_phi.png");
TCanvas *c_raw_eta = new TCanvas;
TH3F *h3_RC_eta_phi = (TH3F*) hdata->createHistogram("var_5,var_4,raw",(EEorEB=="EB") ? 170 : 100, (EEorEB=="EB") ? 360 : 100,25);
TProfile2D *h_RC_eta_phi = h3_RC_eta_phi->Project3DProfile();
h_RC_eta_phi->SetTitle("E_{raw}/E_{true}");
if(EEorEB=="EB")
{
h_RC_eta_phi->GetXaxis()->SetTitle("i#eta");
h_RC_eta_phi->GetYaxis()->SetTitle("i#phi");
h_RC_eta_phi->GetXaxis()->SetRangeUser(-85,85);
h_RC_eta_phi->GetYaxis()->SetRangeUser(0,360);
}
else
{
h_RC_eta_phi->GetXaxis()->SetTitle("iX");
h_RC_eta_phi->GetYaxis()->SetTitle("iY");
}
h_RC_eta_phi->SetMinimum(0.5);
h_RC_eta_phi->SetMaximum(1.5);
h_RC_eta_phi->Draw("COLZ");
c_raw_eta->SaveAs("raw_vs_eta_phi.pdf");
c_raw_eta->SaveAs("raw_vs_eta_phi.png");
TH2F *h_RC_eta = hdata->createHistogram(*scetaiXvar, *rawvar, "","raw_vs_eta");
if(EEorEB=="EB")
{
h_RC_eta->GetXaxis()->SetTitle("i#eta");
}
else
{
h_RC_eta->GetXaxis()->SetTitle("iX");
}
h_RC_eta->GetYaxis()->SetTitle("E_{raw}/E_{true}");
h_RC_eta->Draw("COLZ");
c_raw_eta->SaveAs("raw_vs_eta.pdf");
c_raw_eta->SaveAs("raw_vs_eta.png");
TCanvas *c_raw_phi = new TCanvas;
TH2F *h_RC_phi = hdata->createHistogram(*scphiiYvar, *rawvar, "","raw_vs_phi");
if(EEorEB=="EB")
{
h_RC_phi->GetXaxis()->SetTitle("i#phi");
}
else
{
h_RC_phi->GetXaxis()->SetTitle("iY");
}
h_RC_phi->GetYaxis()->SetTitle("E_{raw}/E_{true}");
h_RC_phi->Draw("COLZ");
c_raw_phi->SaveAs("raw_vs_phi.pdf");
c_raw_phi->SaveAs("raw_vs_phi.png");
//on2,5,20, etc
if(EEorEB == "EB")
{
TCanvas *myC_iCrystal_mod = new TCanvas;
RooRealVar *SM_distvar = ws->var("var_6");
SM_distvar->setRange(0,10);
SM_distvar->setBins(10);
TH2F *h_CC_SM_dist = hdata->createHistogram(*SM_distvar, *ecorvar, "","cor_vs_SM_dist");
h_CC_SM_dist->GetXaxis()->SetTitle("SM_dist");
h_CC_SM_dist->GetYaxis()->SetTitle("E_{cor}/E_{true}");
h_CC_SM_dist->Draw("COLZ");
myC_iCrystal_mod->SaveAs("cor_vs_SM_dist.pdf");
myC_iCrystal_mod->SaveAs("cor_vs_SM_dist.png");
TH2F *h_RC_SM_dist = hdata->createHistogram(*SM_distvar, *rawvar, "","raw_vs_SM_dist");
h_RC_SM_dist->GetXaxis()->SetTitle("distance to SM gap");
h_RC_SM_dist->GetYaxis()->SetTitle("E_{raw}/E_{true}");
h_RC_SM_dist->Draw("COLZ");
myC_iCrystal_mod->SaveAs("raw_vs_SM_dist.pdf");
myC_iCrystal_mod->SaveAs("raw_vs_SM_dist.png");
RooRealVar *M_distvar = ws->var("var_7");
M_distvar->setRange(0,13);
M_distvar->setBins(10);
TH2F *h_CC_M_dist = hdata->createHistogram(*M_distvar, *ecorvar, "","cor_vs_M_dist");
h_CC_M_dist->GetXaxis()->SetTitle("M_dist");
h_CC_M_dist->GetYaxis()->SetTitle("E_{cor}/E_{true}");
h_CC_M_dist->Draw("COLZ");
myC_iCrystal_mod->SaveAs("cor_vs_M_dist.pdf");
myC_iCrystal_mod->SaveAs("cor_vs_M_dist.png");
TH2F *h_RC_M_dist = hdata->createHistogram(*M_distvar, *rawvar, "","raw_vs_M_dist");
h_RC_M_dist->GetXaxis()->SetTitle("distance to module gap");
h_RC_M_dist->GetYaxis()->SetTitle("E_{raw}/E_{true}");
h_RC_M_dist->Draw("COLZ");
myC_iCrystal_mod->SaveAs("raw_vs_M_dist.pdf");
myC_iCrystal_mod->SaveAs("raw_vs_M_dist.png");
/*
RooRealVar *DeltaRG1G2var = ws->var("var_8");
DeltaRG1G2var->setRange(0,0.2);
DeltaRG1G2var->setBins(100);
TH2F *h_CC_DeltaRG1G2 = hdata->createHistogram(*DeltaRG1G2var, *ecorvar, "","cor_vs_DeltaRG1G2");
h_CC_DeltaRG1G2->GetXaxis()->SetTitle("DeltaRG1G2");
h_CC_DeltaRG1G2->GetYaxis()->SetTitle("E_{cor}/E_{true}");
h_CC_DeltaRG1G2->Draw("COLZ");
myC_iCrystal_mod->SaveAs("cor_vs_DeltaRG1G2.pdf");
myC_iCrystal_mod->SaveAs("cor_vs_DeltaRG1G2.png");
TH2F *h_RC_DeltaRG1G2 = hdata->createHistogram(*DeltaRG1G2var, *rawvar, "","raw_vs_DeltaRG1G2");
h_RC_DeltaRG1G2->GetXaxis()->SetTitle("distance to module gap");
h_RC_DeltaRG1G2->GetYaxis()->SetTitle("E_{raw}/E_{true}");
h_RC_DeltaRG1G2->Draw("COLZ");
myC_iCrystal_mod->SaveAs("raw_vs_DeltaRG1G2.pdf");
myC_iCrystal_mod->SaveAs("raw_vs_DeltaRG1G2.png");
*/
}
// other variables
TCanvas *myC_variables = new TCanvas;
RooRealVar *Nxtalvar = ws->var("var_1");
Nxtalvar->setRange(0,10);
Nxtalvar->setBins(10);
TH2F *h_CC_Nxtal = hdata->createHistogram(*Nxtalvar, *ecorvar, "","cor_vs_Nxtal");
h_CC_Nxtal->GetXaxis()->SetTitle("Nxtal");
h_CC_Nxtal->GetYaxis()->SetTitle("E_{cor}/E_{true}");
h_CC_Nxtal->Draw("COLZ");
myC_variables->SaveAs("cor_vs_Nxtal.pdf");
myC_variables->SaveAs("cor_vs_Nxtal.png");
TH2F *h_RC_Nxtal = hdata->createHistogram(*Nxtalvar, *rawvar, "","raw_vs_Nxtal");
h_RC_Nxtal->GetXaxis()->SetTitle("Nxtal");
h_RC_Nxtal->GetYaxis()->SetTitle("E_{raw}/E_{true}");
h_RC_Nxtal->Draw("COLZ");
myC_variables->SaveAs("raw_vs_Nxtal.pdf");
myC_variables->SaveAs("raw_vs_Nxtal.png");
RooRealVar *S4S9var = ws->var("var_2");
int Nbins_S4S9 = 100;
double Low_S4S9 = 0.6;
double High_S4S9 = 1.0;
S4S9var->setRange(Low_S4S9,High_S4S9);
S4S9var->setBins(Nbins_S4S9);
TH2F *h_CC_S4S9 = hdata->createHistogram(*S4S9var, *ecorvar, "","cor_vs_S4S9");
h_CC_S4S9->GetXaxis()->SetTitle("S4S9");
h_CC_S4S9->GetYaxis()->SetTitle("E_{cor}/E_{true}");
h_CC_S4S9->Draw("COLZ");
myC_variables->SaveAs("cor_vs_S4S9.pdf");
myC_variables->SaveAs("cor_vs_S4S9.png");
TH2F *h_RC_S4S9 = hdata->createHistogram(*S4S9var, *rawvar, "","raw_vs_S4S9");
h_RC_S4S9->GetXaxis()->SetTitle("S4S9");
h_RC_S4S9->GetYaxis()->SetTitle("E_{raw}/E_{true}");
h_RC_S4S9->Draw("COLZ");
myC_variables->SaveAs("raw_vs_S4S9.pdf");
myC_variables->SaveAs("raw_vs_S4S9.png");
RooRealVar *S2S9var = ws->var("var_3");
int Nbins_S2S9 = 100;
double Low_S2S9 = 0.5;
double High_S2S9 = 1.0;
S2S9var->setRange(Low_S2S9,High_S2S9);
S2S9var->setBins(Nbins_S2S9);
TH2F *h_CC_S2S9 = hdata->createHistogram(*S2S9var, *ecorvar, "","cor_vs_S2S9");
h_CC_S2S9->GetXaxis()->SetTitle("S2S9");
h_CC_S2S9->GetYaxis()->SetTitle("E_{cor}/E_{true}");
h_CC_S2S9->Draw("COLZ");
myC_variables->SaveAs("cor_vs_S2S9.pdf");
myC_variables->SaveAs("cor_vs_S2S9.png");
TH2F *h_RC_S2S9 = hdata->createHistogram(*S2S9var, *rawvar, "","raw_vs_S2S9");
h_RC_S2S9->GetXaxis()->SetTitle("S2S9");
h_RC_S2S9->GetYaxis()->SetTitle("E_{raw}/E_{true}");
h_RC_S2S9->Draw("COLZ");
myC_variables->SaveAs("raw_vs_S2S9.pdf");
myC_variables->SaveAs("raw_vs_S2S9.png");
TH2F *h_S2S9_eta = hdata->createHistogram(*scetaiXvar, *S2S9var, "","S2S9_vs_eta");
h_S2S9_eta->GetYaxis()->SetTitle("S2S9");
if(EEorEB=="EB")
{
h_CC_eta->GetYaxis()->SetTitle("i#eta");
}
else
{
h_CC_eta->GetYaxis()->SetTitle("iX");
}
h_S2S9_eta->Draw("COLZ");
myC_variables->SaveAs("S2S9_vs_eta.pdf");
myC_variables->SaveAs("S2S9_vs_eta.png");
TH2F *h_S4S9_eta = hdata->createHistogram(*scetaiXvar, *S4S9var, "","S4S9_vs_eta");
h_S4S9_eta->GetYaxis()->SetTitle("S4S9");
if(EEorEB=="EB")
{
h_CC_eta->GetYaxis()->SetTitle("i#eta");
}
else
{
h_CC_eta->GetYaxis()->SetTitle("iX");
}
h_S4S9_eta->Draw("COLZ");
myC_variables->SaveAs("S4S9_vs_eta.pdf");
myC_variables->SaveAs("S4S9_vs_eta.png");
TH2F *h_S2S9_phi = hdata->createHistogram(*scphiiYvar, *S2S9var, "","S2S9_vs_phi");
h_S2S9_phi->GetYaxis()->SetTitle("S2S9");
if(EEorEB=="EB")
{
h_CC_phi->GetYaxis()->SetTitle("i#phi");
}
else
{
h_CC_phi->GetYaxis()->SetTitle("iY");
}
h_S2S9_phi->Draw("COLZ");
myC_variables->SaveAs("S2S9_vs_phi.pdf");
myC_variables->SaveAs("S2S9_vs_phi.png");
TH2F *h_S4S9_phi = hdata->createHistogram(*scphiiYvar, *S4S9var, "","S4S9_vs_phi");
h_S4S9_phi->GetYaxis()->SetTitle("S4S9");
if(EEorEB=="EB")
{
h_CC_phi->GetYaxis()->SetTitle("i#phi");
}
else
{
h_CC_phi->GetYaxis()->SetTitle("iY");
}
h_S4S9_phi->Draw("COLZ");
myC_variables->SaveAs("S4S9_vs_phi.pdf");
myC_variables->SaveAs("S4S9_vs_phi.png");
if(EEorEB=="EE")
{
}
TProfile *p_CC_eta = h_CC_eta->ProfileX("p_CC_eta");//,1,-1,"s");
p_CC_eta->GetYaxis()->SetRangeUser(0.8,1.05);
if(EEorEB == "EB")
{
// p_CC_eta->GetYaxis()->SetRangeUser(0.85,1.0);
// p_CC_eta->GetXaxis()->SetRangeUser(-1.5,1.5);
}
p_CC_eta->GetYaxis()->SetTitle("E_{cor}/E_{true}");
p_CC_eta->SetTitle("");
p_CC_eta->Draw();
myC_variables->SaveAs("profile_cor_vs_eta.pdf");
myC_variables->SaveAs("profile_cor_vs_eta.png");
gStyle->SetOptStat(111);
gStyle->SetOptFit(1);
TH1F *h1_fit_CC_eta = new TH1F("h1_fit_CC_eta","h1_fit_CC_eta",(EEorEB=="EB") ? 180 : 50,(EEorEB=="EB") ? -90 : 0, (EEorEB=="EB") ? 90 : 50);
for(int ix = 1;ix <= h_CC_eta->GetNbinsX(); ix++)
{
stringstream os_iEta;
os_iEta << ((EEorEB=="EB") ? (-90 + ix -1) : (0 + ix -1));
string ss_iEta = os_iEta.str();
TH1D * h_temp = h_CC_eta->ProjectionY("h_temp",ix,ix);
h_temp->Rebin(4);
TF1 *f_temp = new TF1("f_temp","gaus(0)",0.95,1.07);
h_temp->Fit("f_temp","R");
h1_fit_CC_eta->SetBinContent(ix, f_temp->GetParameter(1));
h1_fit_CC_eta->SetBinError(ix, f_temp->GetParError(1));
h_temp->GetXaxis()->SetTitle("E_{cor}/E_{true}");
h_temp->SetTitle("");
h_temp->Draw();
myC_variables->SaveAs(("fits/CC_iEta_"+ss_iEta+".pdf").c_str());
myC_variables->SaveAs(("fits/CC_iEta_"+ss_iEta+".png").c_str());
myC_variables->SaveAs(("fits/CC_iEta_"+ss_iEta+".C").c_str());
}
gStyle->SetOptStat(0);
gStyle->SetOptFit(0);
h1_fit_CC_eta->GetYaxis()->SetRangeUser(0.95,1.05);
h1_fit_CC_eta->GetYaxis()->SetTitle("E_{cor}/E_{true}");
h1_fit_CC_eta->GetXaxis()->SetTitle((EEorEB=="EB") ? "i#eta" : "iX");
h1_fit_CC_eta->SetTitle("");
h1_fit_CC_eta->Draw();
myC_variables->SaveAs("profile_fit_cor_vs_eta.pdf");
myC_variables->SaveAs("profile_fit_cor_vs_eta.png");
myC_variables->SaveAs("profile_fit_cor_vs_eta.C");
TProfile *p_RC_eta = h_RC_eta->ProfileX("p_RC_eta");//,1,-1,"s");
p_RC_eta->GetYaxis()->SetRangeUser(0.8,1.05);
if(EEorEB=="EB")
{
// p_RC_eta->GetYaxis()->SetRangeUser(0.80,0.95);
// p_RC_eta->GetXaxis()->SetRangeUser(-1.5,1.5);
}
p_RC_eta->GetYaxis()->SetTitle("E_{raw}/E_{true}");
p_RC_eta->SetTitle("");
p_RC_eta->Draw();
myC_variables->SaveAs("profile_raw_vs_eta.pdf");
myC_variables->SaveAs("profile_raw_vs_eta.png");
gStyle->SetOptStat(111);
gStyle->SetOptFit(1);
TH1F *h1_fit_RC_eta = new TH1F("h1_fit_RC_eta","h1_fit_RC_eta",(EEorEB=="EB") ? 180 : 50,(EEorEB=="EB") ? -90 : 0, (EEorEB=="EB") ? 90 : 50);
for(int ix = 1;ix <= h_RC_eta->GetNbinsX(); ix++)
{
stringstream os_iEta;
os_iEta << ((EEorEB=="EB") ? (-90 + ix -1) : (0 + ix -1));
string ss_iEta = os_iEta.str();
TH1D * h_temp = h_RC_eta->ProjectionY("h_temp",ix,ix);
h_temp->Rebin(4);
TF1 *f_temp = new TF1("f_temp","gaus(0)",0.87,1.05);
h_temp->Fit("f_temp","R");
h1_fit_RC_eta->SetBinContent(ix, f_temp->GetParameter(1));
h1_fit_RC_eta->SetBinError(ix, f_temp->GetParError(1));
h_temp->GetXaxis()->SetTitle("E_{raw}/E_{true}");
h_temp->SetTitle("");
h_temp->Draw();
myC_variables->SaveAs(("fits/RC_iEta_"+ss_iEta+".pdf").c_str());
myC_variables->SaveAs(("fits/RC_iEta_"+ss_iEta+".png").c_str());
myC_variables->SaveAs(("fits/RC_iEta_"+ss_iEta+".C").c_str());
}
gStyle->SetOptStat(0);
gStyle->SetOptFit(0);
h1_fit_RC_eta->GetYaxis()->SetRangeUser(0.9,1.0);
h1_fit_RC_eta->GetYaxis()->SetTitle("E_{raw}/E_{true}");
h1_fit_RC_eta->GetXaxis()->SetTitle((EEorEB=="EB") ? "i#eta" : "iX");
h1_fit_RC_eta->SetTitle("");
h1_fit_RC_eta->Draw();
myC_variables->SaveAs("profile_fit_raw_vs_eta.pdf");
myC_variables->SaveAs("profile_fit_raw_vs_eta.png");
myC_variables->SaveAs("profile_fit_raw_vs_eta.C");
int Nbins_iEta = EEorEB=="EB" ? 180 : 50;
int nLow_iEta = EEorEB=="EB" ? -90 : 0;
int nHigh_iEta = EEorEB=="EB" ? 90 : 50;
TH1F *h1_RC_eta = new TH1F("h1_RC_eta","h1_RC_eta",Nbins_iEta,nLow_iEta,nHigh_iEta);
for(int i=1;i<=Nbins_iEta;i++)
{
h1_RC_eta->SetBinContent(i,p_RC_eta->GetBinError(i));
}
h1_RC_eta->GetXaxis()->SetTitle("i#eta");
h1_RC_eta->GetYaxis()->SetTitle("#sigma_{E_{raw}/E_{true}}");
h1_RC_eta->SetTitle("");
h1_RC_eta->Draw();
myC_variables->SaveAs("sigma_Eraw_Etrue_vs_eta.pdf");
myC_variables->SaveAs("sigma_Eraw_Etrue_vs_eta.png");
TH1F *h1_CC_eta = new TH1F("h1_CC_eta","h1_CC_eta",Nbins_iEta,nLow_iEta,nHigh_iEta);
for(int i=1;i<=Nbins_iEta;i++)
{
h1_CC_eta->SetBinContent(i,p_CC_eta->GetBinError(i));
}
h1_CC_eta->GetXaxis()->SetTitle("i#eta");
h1_CC_eta->GetYaxis()->SetTitle("#sigma_{E_{cor}/E_{true}}");
h1_CC_eta->SetTitle("");
h1_CC_eta->Draw();
myC_variables->SaveAs("sigma_Ecor_Etrue_vs_eta.pdf");
myC_variables->SaveAs("sigma_Ecor_Etrue_vs_eta.png");
TProfile *p_CC_phi = h_CC_phi->ProfileX("p_CC_phi");//,1,-1,"s");
p_CC_phi->GetYaxis()->SetRangeUser(0.9,1.0);
if(EEorEB == "EB")
{
// p_CC_phi->GetYaxis()->SetRangeUser(0.94,1.00);
}
p_CC_phi->GetYaxis()->SetTitle("E_{cor}/E_{true}");
p_CC_phi->SetTitle("");
p_CC_phi->Draw();
myC_variables->SaveAs("profile_cor_vs_phi.pdf");
myC_variables->SaveAs("profile_cor_vs_phi.png");
gStyle->SetOptStat(111);
gStyle->SetOptFit(1);
TH1F *h1_fit_CC_phi = new TH1F("h1_fit_CC_phi","h1_fit_CC_phi",(EEorEB=="EB") ? 360 : 50,(EEorEB=="EB") ? 0 : 0, (EEorEB=="EB") ? 360 : 50);
for(int ix = 1;ix <= h_CC_phi->GetNbinsX(); ix++)
{
stringstream os_iPhi;
os_iPhi << ((EEorEB=="EB") ? (0 + ix -1) : (0 + ix -1));
string ss_iPhi = os_iPhi.str();
TH1D * h_temp = h_CC_phi->ProjectionY("h_temp",ix,ix);
h_temp->Rebin(4);
TF1 *f_temp = new TF1("f_temp","gaus(0)",0.95,1.07);
h_temp->Fit("f_temp","R");
h1_fit_CC_phi->SetBinContent(ix, f_temp->GetParameter(1));
h1_fit_CC_phi->SetBinError(ix, f_temp->GetParError(1));
h_temp->GetXaxis()->SetTitle("E_{cor}/E_{true}");
h_temp->SetTitle("");
h_temp->Draw();
myC_variables->SaveAs(("fits/CC_iPhi_"+ss_iPhi+".pdf").c_str());
myC_variables->SaveAs(("fits/CC_iPhi_"+ss_iPhi+".png").c_str());
myC_variables->SaveAs(("fits/CC_iPhi_"+ss_iPhi+".C").c_str());
}
gStyle->SetOptStat(0);
gStyle->SetOptFit(0);
h1_fit_CC_phi->GetYaxis()->SetRangeUser(0.95,1.05);
h1_fit_CC_phi->GetYaxis()->SetTitle("E_{cor}/E_{true}");
h1_fit_CC_phi->GetXaxis()->SetTitle((EEorEB=="EB") ? "i#phi" : "iX");
h1_fit_CC_phi->SetTitle("");
h1_fit_CC_phi->Draw();
myC_variables->SaveAs("profile_fit_cor_vs_phi.pdf");
myC_variables->SaveAs("profile_fit_cor_vs_phi.png");
myC_variables->SaveAs("profile_fit_cor_vs_phi.C");
TProfile *p_RC_phi = h_RC_phi->ProfileX("p_RC_phi");//,1,-1,"s");
p_RC_phi->GetYaxis()->SetRangeUser(0.8,0.9);
if(EEorEB=="EB")
{
// p_RC_phi->GetYaxis()->SetRangeUser(0.89,0.95);
}
p_RC_phi->GetYaxis()->SetTitle("E_{raw}/E_{true}");
p_RC_phi->SetTitle("");
p_RC_phi->Draw();
myC_variables->SaveAs("profile_raw_vs_phi.pdf");
myC_variables->SaveAs("profile_raw_vs_phi.png");
gStyle->SetOptStat(111);
gStyle->SetOptFit(1);
TH1F *h1_fit_RC_phi = new TH1F("h1_fit_RC_phi","h1_fit_RC_phi",(EEorEB=="EB") ? 360 : 50,(EEorEB=="EB") ? 0 : 0, (EEorEB=="EB") ? 360 : 50);
for(int ix = 1;ix <= h_RC_phi->GetNbinsX(); ix++)
{
stringstream os_iPhi;
os_iPhi << ((EEorEB=="EB") ? (0 + ix -1) : (0 + ix -1));
string ss_iPhi = os_iPhi.str();
TH1D * h_temp = h_RC_phi->ProjectionY("h_temp",ix,ix);
h_temp->Rebin(4);
TF1 *f_temp = new TF1("f_temp","gaus(0)",0.87,1.05);
h_temp->Fit("f_temp","R");
h1_fit_RC_phi->SetBinContent(ix, f_temp->GetParameter(1));
h1_fit_RC_phi->SetBinError(ix, f_temp->GetParError(1));
h_temp->GetXaxis()->SetTitle("E_{raw}/E_{true}");
h_temp->SetTitle("");
h_temp->Draw();
myC_variables->SaveAs(("fits/RC_iPhi_"+ss_iPhi+".pdf").c_str());
myC_variables->SaveAs(("fits/RC_iPhi_"+ss_iPhi+".png").c_str());
myC_variables->SaveAs(("fits/RC_iPhi_"+ss_iPhi+".C").c_str());
}
gStyle->SetOptStat(0);
gStyle->SetOptFit(0);
h1_fit_RC_phi->GetYaxis()->SetRangeUser(0.9,1.0);
h1_fit_RC_phi->GetYaxis()->SetTitle("E_{raw}/E_{true}");
h1_fit_RC_phi->GetXaxis()->SetTitle((EEorEB=="EB") ? "i#phi" : "iX");
h1_fit_RC_phi->SetTitle("");
h1_fit_RC_phi->Draw();
myC_variables->SaveAs("profile_fit_raw_vs_phi.pdf");
myC_variables->SaveAs("profile_fit_raw_vs_phi.png");
myC_variables->SaveAs("profile_fit_raw_vs_phi.C");
int Nbins_iPhi = EEorEB=="EB" ? 360 : 50;
int nLow_iPhi = EEorEB=="EB" ? 0 : 0;
int nHigh_iPhi = EEorEB=="EB" ? 360 : 50;
TH1F *h1_RC_phi = new TH1F("h1_RC_phi","h1_RC_phi",Nbins_iPhi,nLow_iPhi,nHigh_iPhi);
for(int i=1;i<=Nbins_iPhi;i++)
{
h1_RC_phi->SetBinContent(i,p_RC_phi->GetBinError(i));
}
h1_RC_phi->GetXaxis()->SetTitle("i#phi");
h1_RC_phi->GetYaxis()->SetTitle("#sigma_{E_{raw}/E_{true}}");
h1_RC_phi->SetTitle("");
h1_RC_phi->Draw();
myC_variables->SaveAs("sigma_Eraw_Etrue_vs_phi.pdf");
myC_variables->SaveAs("sigma_Eraw_Etrue_vs_phi.png");
TH1F *h1_CC_phi = new TH1F("h1_CC_phi","h1_CC_phi",Nbins_iPhi,nLow_iPhi,nHigh_iPhi);
for(int i=1;i<=Nbins_iPhi;i++)
{
h1_CC_phi->SetBinContent(i,p_CC_phi->GetBinError(i));
}
h1_CC_phi->GetXaxis()->SetTitle("i#phi");
h1_CC_phi->GetYaxis()->SetTitle("#sigma_{E_{cor}/E_{true}}");
h1_CC_phi->SetTitle("");
h1_CC_phi->Draw();
myC_variables->SaveAs("sigma_Ecor_Etrue_vs_phi.pdf");
myC_variables->SaveAs("sigma_Ecor_Etrue_vs_phi.png");
// FWHM over sigma_eff vs. eta/phi
TH1F *h1_FoverS_RC_phi = new TH1F("h1_FoverS_RC_phi","h1_FoverS_RC_phi",Nbins_iPhi,nLow_iPhi,nHigh_iPhi);
TH1F *h1_FoverS_CC_phi = new TH1F("h1_FoverS_CC_phi","h1_FoverS_CC_phi",Nbins_iPhi,nLow_iPhi,nHigh_iPhi);
TH1F *h1_FoverS_RC_eta = new TH1F("h1_FoverS_RC_eta","h1_FoverS_RC_eta",Nbins_iEta,nLow_iEta,nHigh_iEta);
TH1F *h1_FoverS_CC_eta = new TH1F("h1_FoverS_CC_eta","h1_FoverS_CC_eta",Nbins_iEta,nLow_iEta,nHigh_iEta);
TH1F *h1_FoverS_CC_S2S9 = new TH1F("h1_FoverS_CC_S2S9","h1_FoverS_CC_S2S9",Nbins_S2S9,Low_S2S9,High_S2S9);
TH1F *h1_FoverS_RC_S2S9 = new TH1F("h1_FoverS_RC_S2S9","h1_FoverS_RC_S2S9",Nbins_S2S9,Low_S2S9,High_S2S9);
TH1F *h1_FoverS_CC_S4S9 = new TH1F("h1_FoverS_CC_S4S9","h1_FoverS_CC_S4S9",Nbins_S4S9,Low_S4S9,High_S4S9);
TH1F *h1_FoverS_RC_S4S9 = new TH1F("h1_FoverS_RC_S4S9","h1_FoverS_RC_S4S9",Nbins_S4S9,Low_S4S9,High_S4S9);
float FWHMoverSigmaEff = 0.0;
TH1F *h_tmp_rawvar = new TH1F("tmp_rawvar","tmp_rawvar",800,0.5,1.5);
TH1F *h_tmp_corvar = new TH1F("tmp_corvar","tmp_corvar",800,0.5,1.5);
for(int i=1;i<=Nbins_iPhi;i++)
{
float FWHM_tmp = 0.0;
float effSigma_tmp = 0.0;
for(int j=1;j<=800;j++)
{
h_tmp_rawvar->SetBinContent(j,h_RC_phi->GetBinContent(i,j));
h_tmp_corvar->SetBinContent(j,h_CC_phi->GetBinContent(i,j));
}
FWHMoverSigmaEff = 0.0;
FWHM_tmp= FWHM(h_tmp_rawvar);
effSigma_tmp = effSigma(h_tmp_rawvar);
if(effSigma_tmp>0.000001) FWHMoverSigmaEff = FWHM_tmp/effSigma_tmp;
h1_FoverS_RC_phi->SetBinContent(i, FWHMoverSigmaEff);
FWHMoverSigmaEff = 0.0;
FWHM_tmp= FWHM(h_tmp_corvar);
effSigma_tmp = effSigma(h_tmp_corvar);
if(effSigma_tmp>0.000001) FWHMoverSigmaEff = FWHM_tmp/effSigma_tmp;
h1_FoverS_CC_phi->SetBinContent(i, FWHMoverSigmaEff);
}
h1_FoverS_CC_phi->GetXaxis()->SetTitle("i#phi");
h1_FoverS_CC_phi->GetYaxis()->SetTitle("FWHM/#sigma_{eff} of E_{cor}/E_{true}");
h1_FoverS_CC_phi->SetTitle("");
h1_FoverS_CC_phi->Draw();
myC_variables->SaveAs("FoverS_Ecor_Etrue_vs_phi.pdf");
myC_variables->SaveAs("FoverS_Ecor_Etrue_vs_phi.png");
h1_FoverS_RC_phi->GetXaxis()->SetTitle("i#phi");
h1_FoverS_RC_phi->GetYaxis()->SetTitle("FWHM/#sigma_{eff} of E_{raw}/E_{true}");
h1_FoverS_RC_phi->SetTitle("");
h1_FoverS_RC_phi->Draw();
myC_variables->SaveAs("FoverS_Eraw_Etrue_vs_phi.pdf");
myC_variables->SaveAs("FoverS_Eraw_Etrue_vs_phi.png");
for(int i=1;i<=Nbins_iEta;i++)
{
float FWHM_tmp = 0.0;
float effSigma_tmp = 0.0;
for(int j=1;j<=800;j++)
{
h_tmp_rawvar->SetBinContent(j,h_RC_eta->GetBinContent(i,j));
h_tmp_corvar->SetBinContent(j,h_CC_eta->GetBinContent(i,j));
}
FWHMoverSigmaEff = 0.0;
FWHM_tmp= FWHM(h_tmp_rawvar);
effSigma_tmp = effSigma(h_tmp_rawvar);
if(effSigma_tmp>0.000001) FWHMoverSigmaEff = FWHM_tmp/effSigma_tmp;
h1_FoverS_RC_eta->SetBinContent(i, FWHMoverSigmaEff);
FWHMoverSigmaEff = 0.0;
FWHM_tmp= FWHM(h_tmp_corvar);
effSigma_tmp = effSigma(h_tmp_corvar);
if(effSigma_tmp>0.000001) FWHMoverSigmaEff = FWHM_tmp/effSigma_tmp;
h1_FoverS_CC_eta->SetBinContent(i, FWHMoverSigmaEff);
}
h1_FoverS_CC_eta->GetXaxis()->SetTitle("i#eta");
h1_FoverS_CC_eta->GetYaxis()->SetTitle("FWHM/#sigma_{eff} of E_{cor}/E_{true}");
h1_FoverS_CC_eta->SetTitle("");
h1_FoverS_CC_eta->Draw();
myC_variables->SaveAs("FoverS_Ecor_Etrue_vs_eta.pdf");
myC_variables->SaveAs("FoverS_Ecor_Etrue_vs_eta.png");
h1_FoverS_RC_eta->GetXaxis()->SetTitle("i#eta");
h1_FoverS_RC_eta->GetYaxis()->SetTitle("FWHM/#sigma_{eff} of E_{raw}/E_{true}");
h1_FoverS_RC_eta->SetTitle("");
h1_FoverS_RC_eta->Draw();
myC_variables->SaveAs("FoverS_Eraw_Etrue_vs_eta.pdf");
myC_variables->SaveAs("FoverS_Eraw_Etrue_vs_eta.png");
for(int i=1;i<=Nbins_S2S9;i++)
{
float FWHM_tmp = 0.0;
float effSigma_tmp = 0.0;
for(int j=1;j<=800;j++)
{
h_tmp_rawvar->SetBinContent(j,h_RC_S2S9->GetBinContent(i,j));
h_tmp_corvar->SetBinContent(j,h_CC_S2S9->GetBinContent(i,j));
}
FWHMoverSigmaEff = 0.0;
FWHM_tmp= FWHM(h_tmp_rawvar);
effSigma_tmp = effSigma(h_tmp_rawvar);
if(effSigma_tmp>0.000001) FWHMoverSigmaEff = FWHM_tmp/effSigma_tmp;
h1_FoverS_RC_S2S9->SetBinContent(i, FWHMoverSigmaEff);
FWHMoverSigmaEff = 0.0;
FWHM_tmp= FWHM(h_tmp_corvar);
effSigma_tmp = effSigma(h_tmp_corvar);
if(effSigma_tmp>0.000001) FWHMoverSigmaEff = FWHM_tmp/effSigma_tmp;
h1_FoverS_CC_S2S9->SetBinContent(i, FWHMoverSigmaEff);
}
h1_FoverS_CC_S2S9->GetXaxis()->SetTitle("S2S9");
h1_FoverS_CC_S2S9->GetYaxis()->SetTitle("FWHM/#sigma_{eff} of E_{cor}/E_{true}");
h1_FoverS_CC_S2S9->GetYaxis()->SetRangeUser(0.0,1.0);
h1_FoverS_CC_S2S9->SetTitle("");
h1_FoverS_CC_S2S9->Draw();
myC_variables->SaveAs("FoverS_Ecor_Etrue_vs_S2S9.pdf");
myC_variables->SaveAs("FoverS_Ecor_Etrue_vs_S2S9.png");
h1_FoverS_RC_S2S9->GetXaxis()->SetTitle("S2S9");
h1_FoverS_RC_S2S9->GetYaxis()->SetTitle("FWHM/#sigma_{eff} of E_{raw}/E_{true}");
h1_FoverS_RC_S2S9->GetYaxis()->SetRangeUser(0.0,2.0);
h1_FoverS_RC_S2S9->SetTitle("");
h1_FoverS_RC_S2S9->Draw();
myC_variables->SaveAs("FoverS_Eraw_Etrue_vs_S2S9.pdf");
myC_variables->SaveAs("FoverS_Eraw_Etrue_vs_S2S9.png");
for(int i=1;i<=Nbins_S4S9;i++)
{
float FWHM_tmp = 0.0;
float effSigma_tmp = 0.0;
for(int j=1;j<=800;j++)
{
h_tmp_rawvar->SetBinContent(j,h_RC_S4S9->GetBinContent(i,j));
h_tmp_corvar->SetBinContent(j,h_CC_S4S9->GetBinContent(i,j));
}
FWHMoverSigmaEff = 0.0;
FWHM_tmp= FWHM(h_tmp_rawvar);
effSigma_tmp = effSigma(h_tmp_rawvar);
if(effSigma_tmp>0.000001) FWHMoverSigmaEff = FWHM_tmp/effSigma_tmp;
h1_FoverS_RC_S4S9->SetBinContent(i, FWHMoverSigmaEff);
FWHMoverSigmaEff = 0.0;
FWHM_tmp= FWHM(h_tmp_corvar);
effSigma_tmp = effSigma(h_tmp_corvar);
if(effSigma_tmp>0.000001) FWHMoverSigmaEff = FWHM_tmp/effSigma_tmp;
h1_FoverS_CC_S4S9->SetBinContent(i, FWHMoverSigmaEff);
}
h1_FoverS_CC_S4S9->GetXaxis()->SetTitle("S4S9");
h1_FoverS_CC_S4S9->GetYaxis()->SetTitle("FWHM/#sigma_{eff} of E_{cor}/E_{true}");
h1_FoverS_CC_S4S9->GetYaxis()->SetRangeUser(0.0,1.0);
h1_FoverS_CC_S4S9->SetTitle("");
h1_FoverS_CC_S4S9->Draw();
myC_variables->SaveAs("FoverS_Ecor_Etrue_vs_S4S9.pdf");
myC_variables->SaveAs("FoverS_Ecor_Etrue_vs_S4S9.png");
h1_FoverS_RC_S4S9->GetXaxis()->SetTitle("S4S9");
h1_FoverS_RC_S4S9->GetYaxis()->SetTitle("FWHM/#sigma_{eff} of E_{raw}/E_{true}");
h1_FoverS_RC_S4S9->GetYaxis()->SetRangeUser(0.0,2.0);
h1_FoverS_RC_S4S9->SetTitle("");
h1_FoverS_RC_S4S9->Draw();
myC_variables->SaveAs("FoverS_Eraw_Etrue_vs_S4S9.pdf");
myC_variables->SaveAs("FoverS_Eraw_Etrue_vs_S4S9.png");
printf("calc effsigma\n");
std::cout<<"_"<<EEorEB<<std::endl;
printf("corrected curve effSigma= %5f, FWHM=%5f \n",effsigma_cor, fwhm_cor);
printf("raw curve effSigma= %5f FWHM=%5f \n",effsigma_raw, fwhm_raw);
/* new TCanvas;
RooPlot *ploteold = testvar.frame(0.6,1.2,100);
hdatasigtest->plotOn(ploteold);
ploteold->Draw();
new TCanvas;
RooPlot *plotecor = ecorvar->frame(0.6,1.2,100);
hdatasig->plotOn(plotecor);
plotecor->Draw(); */
}
void AllInOne_combineLepton(const std::string& dataset,int btag=0, double timesX=1, bool useNewShape=false){
std::string useNewShapeText = (useNewShape) ? "_newshape" : "";
char wsFileName[700];
sprintf(wsFileName,"datacards/400/hzz2l2q_ee%db.input.root",btag);
//sprintf(wsFileName,"PROVA/datacards_%s%s/hzz2l2q_ee%db.input.root",dataset.c_str(), useNewShapeText.c_str(),btag);
gSystem->Load("libRooFit");
gSystem->Load("libFFTW");
string histoName[3];
histoName[0]="mZZ_kinfit_hiMass_0btag";
histoName[1]="mZZ_kinfit_hiMass_1btag";
histoName[2]="mZZ_kinfit_hiMass_2btag";
string btagName[3]={"0b","1b","2b"};
double LumiScale=0.;
if( dataset=="Run2011A_FULL" ) LumiScale = 2100.;
else if( dataset=="LP11" ) LumiScale = 1600.;
else {
std::cout << "Unknown dataset '" << dataset << "'. Exiting." << std::endl;
}
RooDataSet *data_bkg;
RooDataSet *data_temp;
TFile *file;
string cutString[3];
cutString[0]="nBTags==0 && (mZjj>75 && mZjj<105) && mZZ>183";
cutString[1]="nBTags==1 && (mZjj>75 && mZjj<105) && mZZ>183";
cutString[2]="nBTags==2 && (mZjj>75 && mZjj<105) && mZZ>183";
int binWidth=20;
int highBin=750;
int lowBin=150;
//double muonEff[3]={.576,.548,.489};
//double expSig[3];
//expSig[0]=5.65*LumiScale ;
//expSig[1]=4.89*LumiScale ;
//expSig[2]=2.37*LumiScale ;
char alphaFileName[200];
sprintf( alphaFileName, "alphaFile_%s_%dbtag_ALL.root", dataset.c_str(), btag);
TFile* alphaFile = TFile::Open(alphaFileName);
TTree* treeSidebandsDATA_alphaCorr = (TTree*)alphaFile->Get("sidebandsDATA_alpha");
TH1D* h1_mZZ_sidebands_alpha = new TH1D("mZZ_sidebands_alpha", "", 65, 150., 800.);
char sidebandsCut_alpha[500];
sprintf(sidebandsCut_alpha, "eventWeight_alpha*(isSidebands && nBTags==%d)", btag);
treeSidebandsDATA_alphaCorr->Project("mZZ_sidebands_alpha", "mZZ", sidebandsCut_alpha);
float expBkg = h1_mZZ_sidebands_alpha->Integral();
std::cout << "++++ expBkg: " << expBkg << std::endl;
stringstream convert;
// --------------------- measurable (ZZ invariant mass) ----------------
string temp;
temp="m_{ZZ}";
RooRealVar mZZ("mZZ",temp.c_str(),lowBin,highBin);
RooRealVar nBTags("nBTags","nBTags",-1.,3.);
RooRealVar leptType("leptType","leptType",0,1);
RooRealVar mZjj("mZjj","mZjj",0,200.);
// ----------------- get parameters from data cards! -----------------
TFile *wsFile = new TFile(wsFileName);
RooWorkspace *ws = (RooWorkspace*) wsFile->Get("w");
// ==================== defining bkg PDF ==========================
// ------------------------ fermi ------------------------------
RooRealVar cutOff("cutOff","position of fermi",ws->var("cutOff_BKG")->getVal());
cutOff.setConstant(kTRUE);
RooRealVar beta("beta","width of fermi",ws->var("beta_BKG")->getVal());
beta.setConstant(kTRUE);
RooFermi fermi("fermi","fermi function",mZZ,cutOff,beta);
// -------------------- double gauss ---------------------------
temp="CMS_hzz2l2q_bkg"+btagName[btag]+"p1";
RooRealVar m("m","m",ws->var(temp.c_str())->getVal());
m.setConstant(kTRUE);
temp="CMS_hzz2l2q_bkg"+btagName[btag]+"p2";
RooRealVar wdth("wdth","wdth",ws->var(temp.c_str())->getVal());
wdth.setConstant(kTRUE);
temp="CMS_hzz2l2q_bkg"+btagName[btag]+"p3";
RooRealVar n("n","n",ws->var(temp.c_str())->getVal());
n.setConstant(kTRUE);
temp="CMS_hzz2l2q_bkg"+btagName[btag]+"p4";
RooRealVar alpha("alpha","alpha",ws->var(temp.c_str())->getVal());
alpha.setConstant(kTRUE);
temp="CMS_hzz2l2q_bkg"+btagName[btag]+"p5";
RooRealVar theta("theta","theta",ws->var(temp.c_str())->getVal());
theta.setConstant(kTRUE);
RooCB CB("CB","Crystal ball",mZZ,m,wdth,alpha,n,theta);
RooProdPdf background("background","background",RooArgSet(fermi,CB));
// ------------------ get data --------------------------
// for reading sideband extrapolated data...
std::string fileName = "HZZlljjRM_DATA_" + dataset + "_optLD_looseBTags_v2_ALL.root";
file = new TFile(fileName.c_str());
//file = new TFile("../HZZlljjRM_DATA_Run2011A_FULL_optLD_looseBTags_v2_ALL.root");
TTree* t=(TTree*)file->Get("tree_passedEvents");
data_bkg=new RooDataSet("data_bkg","data_bkg",t,
RooArgSet(mZZ,leptType,nBTags,mZjj),
cutString[btag].c_str());
// --------- draw MC data -------------------
RooPlot *plot_MCbkg = mZZ.frame(lowBin,highBin,(int)(highBin-lowBin)/binWidth);
//-----------------------------------------------------------------------
TCanvas *c2 = new TCanvas("c2","c2",600,600);
TPaveText* cmslabel = new TPaveText( 0.145, 0.953, 0.6, 0.975, "brNDC" );
cmslabel->SetFillColor(kWhite);
cmslabel->SetTextSize(0.038);
cmslabel->SetTextAlign(11);
cmslabel->SetTextFont(62);
cmslabel->SetBorderSize(0);
char lumilabel[500];
sprintf( lumilabel, "CMS Preliminary 2011, %.1f fb^{-1}", LumiScale/1000.);
cmslabel->AddText(lumilabel);
TPaveText* label_sqrt = new TPaveText(0.7,0.953,0.96,0.975, "brNDC");
label_sqrt->SetFillColor(kWhite);
label_sqrt->SetTextSize(0.038);
label_sqrt->SetTextFont(42);
label_sqrt->SetTextAlign(31); // align right
label_sqrt->SetBorderSize(0);
label_sqrt->AddText("#sqrt{s} = 7 TeV");
//-----------------------------------------------------------------------
background.plotOn(plot_MCbkg,Normalization(expBkg));
data_bkg->plotOn(plot_MCbkg,Binning((int)(highBin-lowBin)/binWidth));
// -------------------- get histograms -----------------
TFile *ZjetsFile = new TFile("HZZlljjRM_DYJetsToLL_TuneZ2_M-50_7TeV-madgraph-tauola_Summer11-PU_S4_START42_V11-v1_optLD_looseBTags_v2_ALL.root");
TFile *TTFile = new TFile("HZZlljjRM_TT_TW_TuneZ2_7TeV-powheg-tauola_Summer11-PU_S4_START42_V11-v1_optLD_looseBTags_v2_ALL.root");
TFile *VVFile = new TFile("HZZlljjRM_VV_TuneZ2_7TeV-pythia6-tauola_Summer11-PU_S4_START42_V11-v1_optLD_looseBTags_v2_ALL.root");
TFile *H400File = new TFile("HZZlljjRM_GluGluToHToZZTo2L2Q_M-400_7TeV-powheg-pythia6_Summer11-PU_S4_START42_V11-v1_optLD_looseBTags_v2_ALL.root");
TH1F *hZjets =(TH1F*)ZjetsFile->Get(histoName[btag].c_str());
hZjets->SetName("hZjets");
hZjets->Scale(LumiScale);
hZjets->Rebin(binWidth);
hZjets->SetFillColor(30);
TH1F *hTT =(TH1F*)TTFile->Get(histoName[btag].c_str());
hTT->SetName("hTT");
convert << binWidth;
temp=";m_{ZZ} [GeV]; Events / "+convert.str()+" GeV";
hTT->SetTitle(temp.c_str());
hTT->Scale(LumiScale);
hTT->Rebin(binWidth);
hTT->SetFillColor(39);
gStyle->SetOptStat(0);
//if(btag==0)
// hTT->GetYaxis()->SetRangeUser(0.0001,245);
//if(btag==1)
// hTT->GetYaxis()->SetRangeUser(0.0001,245);
//if(btag==2)
// hTT->GetYaxis()->SetRangeUser(0.0001,25);
//hTT->Draw();
TH1F *hVV =(TH1F*)VVFile->Get(histoName[btag].c_str());
hVV->SetName("hVV");
hVV->Scale(LumiScale);
hVV->Rebin(binWidth);
hVV->SetFillColor(38);
TH1F* hH400 = (TH1F*)H400File->Get(histoName[btag].c_str());
hH400->SetName("hH400");
hH400->Scale(LumiScale*timesX);
cout << "SIGNAL NORMALIZATION: " << hH400->Integral() << endl;
hH400->Rebin(binWidth);
hH400->SetFillColor(kYellow);//kRed+3);
temp = ";m_{ZZ} [GeV];Events / "+convert.str()+" GeV";
THStack *hBkg = new THStack("hBkg",temp.c_str());
convert.str("");
hBkg->Add(hVV);
hBkg->Add(hTT);
hBkg->Add(hZjets);
hBkg->Add(hH400);
float xMin = 150.;
float xMax = 750.;
float yMin = 0.;
float yMax = 1.3*hBkg->GetMaximum();
char yAxisName[500];
sprintf( yAxisName, "Events / %.0f GeV", hVV->GetXaxis()->GetBinWidth(1) );
//std::string yAxisName = "Events / "+convert.str()+" GeV";
convert.str("");
//char xAxisName[400];
//sprintf( xAxisName, "m_{%s%sjj} [GeV]", leptType_forLegend.c_str(), leptType_forLegend.c_str() );
TH2D* h2_axes = new TH2D("axes", "", 10, xMin, xMax, 10, yMin, yMax);
h2_axes->SetXTitle("m_{lljj} [GeV]");
h2_axes->SetYTitle(yAxisName);
h2_axes->Draw();
hBkg->Draw("SAMEHIST");
plot_MCbkg->Draw("SAME");
cmslabel->Draw();
label_sqrt->Draw();
// ---------------legend ---------------------------
TLegend *leg = new TLegend(.4,.5,.8,.9);
leg->SetTextSize(0.036);
leg->SetFillColor(0);
leg->SetBorderSize(0);
convert.str("");
convert << LumiScale/1000;
temp="CMS Preliminary #sqrt{s}=7 TeV "+convert.str()+" fb^{-1}";
//leg->SetHeader(temp.c_str());
leg->AddEntry("background_Norm[mZZ]","Sideband Extrapolated Fit","l");
//leg->AddEntry("model_Norm[mZZ]","Background+10#timesSignal","l");
if(btag==0)
temp="0 b-tag 2l2q data";
if(btag==1)
temp="1 b-tag 2l2q data";
if(btag==2)
temp="2 b-tag 2l2q data";
leg->AddEntry("h_data_bkg",temp.c_str(),"p");
leg->AddEntry("hZjets","Z + Jets","f");
leg->AddEntry("hTT","tt/tW","f");
leg->AddEntry("hVV","ZZ/WZ/WW","f");
convert.str("");
convert << timesX;
temp="400 GeV SM Higgs#times"+convert.str();
leg->AddEntry("hH400",temp.c_str(),"f");
leg->Draw();
string saveFileName="AllInOne_"+dataset + "_"+btagName[btag]+"tag_ll_LIMIT" + useNewShapeText + ".eps";
c2->SaveAs(saveFileName.c_str());
}
void makejpsifit(string inputFilename, string outFilename,
Int_t ptBin, Int_t etaBin,
double minMass, double maxMass,
double mean_bw, double gamma_bw, double cutoff_cb, double power_cb,
const char* plotOpt, const int nbins, Int_t isMC) {
TStyle *mystyle = RooHZZStyle("ZZ");
mystyle->cd();
//Create Data Set
RooRealVar mass("zmass","m(e^{+}e^{-})",minMass,maxMass,"GeV/c^{2}");
// Reading everything from root tree instead
TFile *tfile = TFile::Open(inputFilename.c_str());
TTree *ttree = (TTree*)tfile->Get("zeetree/probe_tree");
hzztree *zeeTree = new hzztree(ttree);
RooArgSet zMassArgSet(mass);
RooDataSet* data = new RooDataSet("data", "ntuple parameters", zMassArgSet);
for (int i = 0; i < zeeTree->fChain->GetEntries(); i++) {
if(i%100000==0) cout << "Processing Event " << i << endl;
zeeTree->fChain->GetEntry(i);
//*************************************************************************
//Electron Selection
//*************************************************************************
// already passed for this tree
//*************************************************************************
//Compute electron four vector;
//*************************************************************************
double ele1pt = zeeTree->l1pt;
double ele2pt = zeeTree->l2pt;
double ELECTRONMASS = 0.51e-3;
TLorentzVector ele1FourVector;
ele1FourVector.SetPtEtaPhiM(zeeTree->l1pt, zeeTree->l1eta, zeeTree->l1phi, ELECTRONMASS);
TLorentzVector ele2FourVector;
ele2FourVector.SetPtEtaPhiM(zeeTree->l2pt, zeeTree->l2eta, zeeTree->l2phi, ELECTRONMASS);
//*************************************************************************
//pt and eta cuts on electron
//*************************************************************************
if (! (ele1pt > 7 && ele2pt > 7
&& fabs( zeeTree->l1eta) < 2.5
&& fabs( zeeTree->l2eta) < 2.5 )) continue;
//*************************************************************************
//pt bins and eta bins
//*************************************************************************
Int_t Ele1PtBin = -1;
Int_t Ele1EtaBin = -1;
Int_t Ele2PtBin = -1;
Int_t Ele2EtaBin = -1;
if (ele1pt > 7 && ele1pt < 10) Ele1PtBin = 0;
else if (ele1pt < 20) Ele1PtBin = 1;
else Ele1PtBin = 2;
if (ele2pt > 7 && ele2pt < 10) Ele2PtBin = 0;
else if (ele2pt < 20) Ele2PtBin = 1;
else Ele2PtBin = 2;
if (fabs(zeeTree->l1sceta) < 1.479) Ele1EtaBin = 0;
else Ele1EtaBin = 1;
if (fabs(zeeTree->l2sceta) < 1.479) Ele2EtaBin = 0;
else Ele2EtaBin = 1;
if (!(Ele1PtBin == ptBin || Ele2PtBin == ptBin)) continue;
if (!(Ele1EtaBin == etaBin && Ele2EtaBin == etaBin)) continue;
//*************************************************************************
// restrict range of mass
//*************************************************************************
double zMass = (ele1FourVector+ele2FourVector).M();
if (zMass < minMass || zMass > maxMass) continue;
//*************************************************************************
//set mass variable
//*************************************************************************
zMassArgSet.setRealValue("zmass", zMass);
data->add(zMassArgSet);
}
// do binned fit to gain time...
mass.setBins(nbins);
RooDataHist *bdata = new RooDataHist("data_binned","data_binned", zMassArgSet, *data);
cout << "dataset size: " << data->numEntries() << endl;
// // Closing file
// treeFile->Close();
//====================== Parameters===========================
//Crystal Ball parameters
// RooRealVar cbBias ("#Deltam_{CB}", "CB Bias", -.01, -10, 10, "GeV/c^{2}");
// RooRealVar cbSigma("sigma_{CB}", "CB Width", 1.7, 0.8, 5.0, "GeV/c^{2}");
// RooRealVar cbCut ("a_{CB}","CB Cut", 1.05, 1.0, 3.0);
// RooRealVar cbPower("n_{CB}","CB Order", 2.45, 0.1, 20.0);
RooRealVar cbBias ("#Deltam_{CB}", "CB Bias", -.01, -10, 10, "GeV/c^{2}");
RooRealVar cbSigma("#sigma_{CB}", "CB Width", 1.5, 0.01, 5.0, "GeV/c^{2}");
RooRealVar cbCut ("a_{CB}","CB Cut", 1.0, 1.0, 3.0);
RooRealVar cbPower("n_{CB}","CB Order", 2.5, 0.1, 20.0);
cbCut.setVal(cutoff_cb);
cbPower.setVal(power_cb);
// Just checking
//cbCut.Print();
//cbPower.Print();
//Breit_Wigner parameters
RooRealVar bwMean("m_{JPsi}","BW Mean", 3.096916, "GeV/c^{2}");
bwMean.setVal(mean_bw);
RooRealVar bwWidth("#Gamma_{JPsi}", "BW Width", 92.9e-6, "GeV/c^{2}");
bwWidth.setVal(gamma_bw);
// Fix the Breit-Wigner parameters to PDG values
bwMean.setConstant(kTRUE);
bwWidth.setConstant(kTRUE);
// Exponential Background parameters
RooRealVar expRate("#lambda_{exp}", "Exponential Rate", -0.064, -1, 1);
RooRealVar c0("c_{0}", "c0", 1., 0., 50.);
//Number of Signal and Background events
RooRealVar nsig("N_{S}", "# signal events", 524, 0.1, 10000000000.);
RooRealVar nbkg("N_{B}", "# background events", 43, 1., 10000000.);
//============================ P.D.F.s=============================
// Mass signal for two decay electrons p.d.f.
RooBreitWigner bw("bw", "bw", mass, bwMean, bwWidth);
RooCBShape cball("cball", "Crystal Ball", mass, cbBias, cbSigma, cbCut, cbPower);
RooFFTConvPdf BWxCB("BWxCB", "bw X crystal ball", mass, bw, cball);
// Mass background p.d.f.
RooExponential bg("bg", "exp. background", mass, expRate);
// Mass model for signal electrons p.d.f.
RooAddPdf model("model", "signal", RooArgList(BWxCB), RooArgList(nsig));
TStopwatch t ;
t.Start() ;
double fitmin, fitmax;
if(isMC) {
fitmin = (etaBin==0) ? 3.00 : 2.7;
fitmax = (etaBin==0) ? 3.20 : 3.4;
} else {
fitmin = (etaBin==0) ? ( (ptBin>=2) ? 3.01 : 3.02 ) : 2.7;
fitmax = (etaBin==0) ? ( (ptBin==3) ? 3.23 : 3.22 ) : 3.4;
}
RooFitResult *fitres = model.fitTo(*bdata,Range(fitmin,fitmax),Hesse(1),Minos(1),Timer(1),Save(1));
fitres->SetName("fitres");
t.Print() ;
TCanvas* c = new TCanvas("c","Unbinned Invariant Mass Fit", 0,0,800,600);
//========================== Plotting ============================
//Create a frame
RooPlot* plot = mass.frame(Range(minMass,maxMass),Bins(nbins));
// Add data and model to canvas
int col = (isMC ? kAzure+4 : kGreen+1);
data->plotOn(plot);
model.plotOn(plot,LineColor(col));
data->plotOn(plot);
model.paramOn(plot, Format(plotOpt, AutoPrecision(1)), Parameters(RooArgSet(cbBias, cbSigma, cbCut, cbPower, bwMean, bwWidth, expRate, nsig, nbkg)), Layout(0.15,0.45,0.80));
plot->getAttText()->SetTextSize(.03);
plot->SetTitle("");
plot->Draw();
// Print Fit Values
TLatex *tex = new TLatex();
tex->SetNDC();
tex->SetTextSize(.1);
tex->SetTextFont(132);
// tex->Draw();
tex->SetTextSize(0.057);
if(isMC) tex->DrawLatex(0.65, 0.75, "J/#psi #rightarrow e^{+}e^{-} MC");
else tex->DrawLatex(0.65, 0.75, "J/#psi #rightarrow e^{+}e^{-} data");
tex->SetTextSize(0.030);
tex->DrawLatex(0.645, 0.65, Form("BW Mean = %.2f GeV/c^{2}", bwMean.getVal()));
tex->DrawLatex(0.645, 0.60, Form("BW #sigma = %.2f GeV/c^{2}", bwWidth.getVal()));
c->Update();
c->SaveAs((outFilename + ".pdf").c_str());
c->SaveAs((outFilename + ".png").c_str());
// tablefile << Form(Outfile + "& $ %f $ & $ %f $ & $ %f $\\ \hline",cbBias.getVal(), cbSigma.getVal(), cbCut.getVal());
// Output workspace with model and data
RooWorkspace *w = new RooWorkspace("JPsieeMassScaleAndResolutionFit");
w->import(model);
w->import(*bdata);
w->writeToFile((outFilename + ".root").c_str());
TFile *tfileo = TFile::Open((outFilename + ".root").c_str(),"update");
fitres->Write();
tfileo->Close();
}
