void mvaPUPPETEvaluation() {
//output dir
TString dirname = ".";
gSystem->mkdir(dirname,true);
gSystem->cd(dirname);
//read workspace from training
TString fname;
fname = "mvaPUPPET.root";
TString infile = fname.Data();
TFile *fws = TFile::Open(infile);
RooWorkspace *ws = (RooWorkspace*)fws->Get("wereg");
//read variables from workspace
RooGBRTargetFlex *perpwidth = static_cast<RooGBRTargetFlex*>(ws->arg("perpwidth"));
RooGBRTargetFlex *perpmean = static_cast<RooGBRTargetFlex*>(ws->arg("perpmean"));
RooGBRTargetFlex *parpwidth = static_cast<RooGBRTargetFlex*>(ws->arg("parwidth"));
RooGBRTargetFlex *parmean = static_cast<RooGBRTargetFlex*>(ws->arg("parmean"));
RooRealVar *tgtvarX = ws->var("tgtX");
RooRealVar *tgtvarY = ws->var("tgtY");
RooArgList vars;
vars.add(perpwidth->FuncVars());
vars.add(perpmean->FuncVars());
vars.add(perpwidth->FuncVars());
vars.add(parmean->FuncVars());
vars.add(*tgtvarX);
vars.add(*tgtvarY);
//read testing dataset from TTree
RooRealVar weightvar("weightvar","",1.);
TChain *dtree = new TChain("tree");
dtree->Add("root://eoscms.cern.ch//store//group/dpg_ecal/alca_ecalcalib/ecalMIBI/rgerosa/PUPPETAnalysis/DYJetsToLL_M-50_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8_Asympt50ns_MCRUN2_74_V9A_forMVATraining/DYJetsToLL_M-50_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8/crab_20150724_111858/150724_091912/0000/output_mc_1.root/PUPPET/t");
//TFile *fdin = TFile::Open("/data/bendavid/regTreesAug1/hgg-2013Final8TeV_reg_s12-zllm50-v7n_noskim.root");
// TDirectory *ddir = (TDirectory*)fdin->FindObjectAny("");
// dtree = (TTree*)ddir->Get("t");
//selection cuts for testing
TCut selcut;
selcut = "Boson_daughter==13";
TCut selweight = "1";
TCut prescale100alt = "(evt%100==1)";
weightvar.SetTitle(selcut);
//make testing dataset
RooDataSet *hdata = RooTreeConvert::CreateDataSet("hdata",dtree,vars,weightvar);
weightvar.SetTitle(selcut);
//make reduced testing dataset for integration over conditional variables
RooDataSet *hdatasmall = RooTreeConvert::CreateDataSet("hdatasmall",dtree,vars,weightvar);
//retrieve full pdf from workspace
RooAbsPdf *sigpdfX = ws->pdf("sigpdfX");
RooAbsPdf *sigpdfY = ws->pdf("sigpdfY");
//input variable corresponding to sceta
RooRealVar *scetavar = ws->var("var_1");
//regressed output functions
RooAbsReal *perpwidthlim = ws->function("perpwidthlim");
RooAbsReal *perpmeanlim = ws->function("perpmeanlim");
RooAbsReal *parwidthlim = ws->function("parwidthlim");
RooAbsReal *parmeanlim = ws->function("parmeanlim");
//////////////////////////////////////////////////////////////////////////////////////
//
// formula for corrected recoil?
//
/*
//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 *perpwidthvar = (RooRealVar*)hdataclone->addColumn(*perpwidthlim);
RooRealVar *perpmeanvar = (RooRealVar*)hdataclone->addColumn(*perpmeanlim);
RooRealVar *parwidthvar = (RooRealVar*)hdataclone->addColumn(*parwidthlim);
RooRealVar *parmeanvar = (RooRealVar*)hdataclone->addColumn(*parmeanlim);
//plot target variable and weighted regression prediction (using numerical integration over reduced testing dataset)
TCanvas *crawX = new TCanvas;
//RooPlot *plot = tgtvar->frame(0.6,1.2,100);
RooPlot *plot = tgtvarX->frame(-2.0,2.0,100);
hdata->plotOn(plot);
sigpdfX->plotOn(plot,ProjWData(*hdatasmall));
plot->Draw();
crawX->SaveAs("RawX.png");
//plot target variable and weighted regression prediction (using numerical integration over reduced testing dataset)
TCanvas *crawY = new TCanvas;
//RooPlot *plot = tgtvar->frame(0.6,1.2,100);
RooPlot *plot2 = tgtvarY->frame(-2.0,2.0,100);
hdata->plotOn(plot2);
sigpdfY->plotOn(plot2,ProjWData(*hdatasmall));
plot2->Draw();
crawY->SaveAs("RawY.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.eps");
TCanvas *cwidth = new TCanvas;
RooPlot *plotwidth = widthvar->frame(0.,0.05,100);
hdataclone->plotOn(plotwidth);
plotwidth->Draw();
cwidth->SaveAs("width.eps");
TCanvas *cn = new TCanvas;
RooPlot *plotn = nvar->frame(0.,111.,200);
hdataclone->plotOn(plotn);
plotn->Draw();
cn->SaveAs("n.eps");
TCanvas *cn2 = new TCanvas;
RooPlot *plotn2 = n2var->frame(0.,111.,100);
hdataclone->plotOn(plotn2);
plotn2->Draw();
cn2->SaveAs("n2.eps");
TCanvas *ceta = new TCanvas;
RooPlot *ploteta = scetavar->frame(-2.6,2.6,200);
hdataclone->plotOn(ploteta);
ploteta->Draw();
ceta->SaveAs("eta.eps");
//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);
//heold->SetLineColor(kRed);
hecor->SetLineColor(kBlue);
heraw->SetLineColor(kMagenta);
hecor->GetXaxis()->SetRangeUser(0.6,1.2);
//heold->GetXaxis()->SetRangeUser(0.6,1.2);
TCanvas *cresponse = new TCanvas;
hecor->Draw("HIST");
//heold->Draw("HISTSAME");
heraw->Draw("HISTSAME");
cresponse->SaveAs("response.eps");
cresponse->SetLogy();
cresponse->SaveAs("responselog.eps");
printf("make fine histogram\n");
TH1 *hecorfine = hdata->createHistogram("hecorfine",*ecorvar,Binning(20e3,0.,2.));
printf("calc effsigma\n");
double effsigma = effSigma(hecorfine);
printf("effsigma = %5f\n",effsigma);
*/
}
void fitbkgdataCard(TString configCard="template.config",
bool dobands = true, // create baerror bands for BG models
bool dosignal = false, // plot the signal model (needs to be present)
bool blinded = true, // blind the data in the plots?
bool verbose = true ) {
gROOT->Macro("MitStyle.C");
gStyle->SetErrorX(0);
gStyle->SetOptStat(0);
gROOT->ForceStyle();
TString projectDir;
std::vector<TString> catdesc;
std::vector<TString> catnames;
std::vector<int> polorder;
double massmin = -1.;
double massmax = -1.;
double theCMenergy = -1.;
bool readStatus = readFromConfigCard( configCard,
projectDir,
catnames,
catdesc,
polorder,
massmin,
massmax,
theCMenergy
);
if( !readStatus ) {
std::cerr<<" ERROR: Could not read from card > "<<configCard.Data()<<" <."<<std::endl;
return;
}
TFile *fdata = new TFile(TString::Format("%s/CMS-HGG-data.root",projectDir.Data()),"READ");
if( !fdata ) {
std::cerr<<" ERROR: Could not open file "<<projectDir.Data()<<"/CMS-HGG-data.root."<<std::endl;
return;
}
if( !gSystem->cd(TString::Format("%s/databkg/",projectDir.Data())) ) {
std::cerr<<" ERROR: Could not change directory to "<<TString::Format("%s/databkg/",projectDir.Data()).Data()<<"."<<std::endl;
return;
}
// ----------------------------------------------------------------------
// load the input workspace....
RooWorkspace* win = (RooWorkspace*)fdata->Get("cms_hgg_workspace_data");
if( !win ) {
std::cerr<<" ERROR: Could not load workspace > cms_hgg_workspace_data < from file > "<<TString::Format("%s/CMS-HGG-data.root",projectDir.Data()).Data()<<" <."<<std::endl;
return;
}
RooRealVar *intLumi = win->var("IntLumi");
RooRealVar *hmass = win->var("CMS_hgg_mass");
if( !intLumi || !hmass ) {
std::cerr<<" ERROR: Could not load needed variables > IntLumi < or > CMS_hgg_mass < forom input workspace."<<std::endl;
return;
}
//win->Print();
hmass->setRange(massmin,massmax);
hmass->setBins(4*(int)(massmax-massmin));
hmass->SetTitle("m_{#gamma#gamma}");
hmass->setUnit("GeV");
hmass->setRange("fitrange",massmin,massmax);
hmass->setRange("blind1",100.,110.);
hmass->setRange("blind2",150.,180.);
// ----------------------------------------------------------------------
// some auxiliray vectro (don't know the meaning of all of them ... yet...
std::vector<RooAbsData*> data_vec;
std::vector<RooAbsPdf*> pdfShape_vec; // vector to store the NOT-EXTENDED PDFs (aka pdfshape)
std::vector<RooAbsPdf*> pdf_vec; // vector to store the EXTENDED PDFs
std::vector<RooAbsReal*> normu_vec; // this holds the normalization vars for each Cat (needed in bands for combined cat)
RooArgList normList; // list of range-limityed normalizations (needed for error bands on combined category)
//std::vector<RooRealVar*> coeffv;
//std::vector<RooAbsReal*> normu_vecv; // ???
// ----------------------------------------------------------------------
// define output works
RooWorkspace *wOut = new RooWorkspace("wbkg","wbkg") ;
// util;ities for the combined fit
RooCategory finalcat ("finalcat", "finalcat") ;
RooSimultaneous fullbkgpdf("fullbkgpdf","fullbkgpdf",finalcat);
RooDataSet datacomb ("datacomb", "datacomb", RooArgList(*hmass,finalcat)) ;
RooDataSet *datacombcat = new RooDataSet("data_combcat","",RooArgList(*hmass)) ;
// add the 'combcat' to the list...if more than one cat
if( catnames.size() > 1 ) {
catnames.push_back("combcat");
catdesc.push_back("Combined");
}
for (UInt_t icat=0; icat<catnames.size(); ++icat) {
TString catname = catnames.at(icat);
finalcat.defineType(catname);
// check if we're in a sub-cat or the comb-cat
RooDataSet *data = NULL;
RooDataSet *inData = NULL;
if( icat < (catnames.size() - 1) || catnames.size() == 1) { // this is NOT the last cat (which is by construction the combination)
inData = (RooDataSet*)win->data(TString("data_mass_")+catname);
if( !inData ) {
std::cerr<<" ERROR: Could not find dataset > data_mass_"<<catname.Data()<<" < in input workspace."<<std::endl;
return;
}
data = new RooDataSet(TString("data_")+catname,"",*hmass,Import(*inData)); // copy the dataset (why?)
// append the data to the combined data...
RooDataSet *datacat = new RooDataSet(TString("datacat")+catname,"",*hmass,Index(finalcat),Import(catname,*data)) ;
datacomb.append(*datacat);
datacombcat->append(*data);
// normalization for this category
RooRealVar *nbkg = new RooRealVar(TString::Format("CMS_hgg_%s_bkgshape_norm",catname.Data()),"",800.0,0.0,25e3);
// we keep track of the normalizario vars only for N-1 cats, naming convetnions hystoric...
if( catnames.size() > 2 && icat < (catnames.size() - 2) ) {
RooRealVar* cbkg = new RooRealVar(TString::Format("cbkg%s",catname.Data()),"",0.0,0.0,1e3);
cbkg->removeRange();
normu_vec.push_back(cbkg);
normList.add(*cbkg);
}
/// generate the Bernstrin polynomial (FIX-ME: add possibility ro create other models...)
fstBernModel* theBGmodel = new fstBernModel(hmass, polorder[icat], icat, catname); // using my dedicated class...
std::cout<<" model name is "<<theBGmodel->getPdf()->GetName()<<std::endl;
RooAbsPdf* bkgshape = theBGmodel->getPdf(); // the BG shape
RooAbsPdf* bkgpdf = new RooExtendPdf(TString("bkgpdf")+catname,"",*bkgshape,*nbkg); // the extended PDF
// add the extedned PDF to the RooSimultaneous holding all models...
fullbkgpdf.addPdf(*bkgpdf,catname);
// store the NON-EXTENDED PDF for usgae to compute the error bands later..
pdfShape_vec.push_back(bkgshape);
pdf_vec .push_back(bkgpdf);
data_vec .push_back(data);
} else {
data = datacombcat; // we're looking at the last cat (by construction the combination)
data_vec.push_back(data);
// sum up all the cts PDFs for combined PDF
RooArgList subpdfs;
for (int ipdf=0; ipdf<pdf_vec.size(); ++ipdf) {
subpdfs.add(*pdf_vec.at(ipdf));
}
RooAddPdf* bkgpdf = new RooAddPdf(TString("bkgpdf")+catname,"",subpdfs);
pdfShape_vec.push_back(bkgpdf);
pdf_vec .push_back(bkgpdf); // I don't think this is really needed though....
}
// generate the binned dataset (to be put into the workspace... just in case...)
RooDataHist *databinned = new RooDataHist(TString("databinned_")+catname,"",*hmass,*data);
wOut->import(*data);
wOut->import(*databinned);
}
std::cout<<" ***************** "<<std::endl;
// fit the RooSimultaneous to the combined dataset -> (we could also fit each cat separately)
fullbkgpdf.fitTo(datacomb,Strategy(1),Minos(kFALSE),Save(kTRUE));
RooFitResult *fullbkgfitres = fullbkgpdf.fitTo(datacomb,Strategy(2),Minos(kFALSE),Save(kTRUE));
// in principle we're done now, so store the results in the output workspace
wOut->import(datacomb);
wOut->import(fullbkgpdf);
wOut->import(*fullbkgfitres);
std::cout<<" ***************** "<<std::endl;
if( verbose ) wOut->Print();
std::cout<<" ***************** "<<std::endl;
wOut->writeToFile("bkgdatawithfit.root") ;
if( verbose ) {
printf("IntLumi = %5f\n",intLumi->getVal());
printf("ndata:\n");
for (UInt_t icat=0; icat<catnames.size(); ++icat) {
printf("%i ",data_vec.at(icat)->numEntries());
}
printf("\n");
}
// --------------------------------------------------------------------------------------------
// Now comesd the plotting
// chage the Statistics style...
gStyle->SetOptStat(1110);
// we want to plot in 1GeV bins (apparently...)
UInt_t nbins = (UInt_t) (massmax-massmin);
// here we'll store the curves for the bands...
std::vector<RooCurve*> fitcurves;
// loop again over the cats
TCanvas **canbkg = new TCanvas*[catnames.size()];
RooPlot** plot = new RooPlot*[catnames.size()];
TLatex** lat = new TLatex*[catnames.size()];
TLatex** lat2 = new TLatex*[catnames.size()];
std::cout<<" beofre plotting..."<<std::endl;
for (UInt_t icat=0; icat<catnames.size(); ++icat) {
TString catname = catnames.at(icat);
std::cout<<" trying to plot #"<<icat<<std::endl;
// plot the data and the fit
canbkg[icat] = new TCanvas;
plot [icat] = hmass->frame(Bins(nbins),Range("fitrange"));
std::cout<<" trying to plot #"<<icat<<std::endl;
// first plot the data invisibly... and put the fitted BG model on top...
data_vec .at(icat)->plotOn(plot[icat],RooFit::LineColor(kWhite),MarkerColor(kWhite),Invisible());
pdfShape_vec.at(icat)->plotOn(plot[icat],RooFit::LineColor(kRed),Range("fitrange"),NormRange("fitrange"));
std::cout<<" trying to plot #"<<icat<<std::endl;
// if toggled on, plot also the Data visibly
if( !blinded ) {
data_vec.at(icat)->plotOn(plot[icat]);
}
std::cout<<" trying to plot #"<<icat<<std::endl;
// some cosmetics...
plot[icat]->SetTitle("");
plot[icat]->SetMinimum(0.0);
plot[icat]->SetMaximum(1.40*plot[icat]->GetMaximum());
plot[icat]->GetXaxis()->SetTitle("m_{#gamma#gamma} (GeV/c^{2})");
plot[icat]->Draw();
std::cout<<" trying to plot #"<<icat<<std::endl;
// legend....
TLegend *legmc = new TLegend(0.68,0.70,0.97,0.90);
legmc->AddEntry(plot[icat]->getObject(2),"Data","LPE");
legmc->AddEntry(plot[icat]->getObject(1),"Bkg Model","L");
// this part computes the 1/2-sigma bands.
TGraphAsymmErrors *onesigma = NULL;
TGraphAsymmErrors *twosigma = NULL;
std::cout<<" trying *** to plot #"<<icat<<std::endl;
RooAddition* sumcatsnm1 = NULL;
if ( dobands ) { //&& icat == (catnames.size() - 1) ) {
onesigma = new TGraphAsymmErrors();
twosigma = new TGraphAsymmErrors();
// get the PDF for this cat from the vector
RooAbsPdf *thisPdf = pdfShape_vec.at(icat);
// get the nominal fir curve
RooCurve *nomcurve = dynamic_cast<RooCurve*>(plot[icat]->getObject(1));
fitcurves.push_back(nomcurve);
bool iscombcat = ( icat == (catnames.size() - 1) && catnames.size() > 1);
RooAbsData *datanorm = ( iscombcat ? &datacomb : data_vec.at(icat) );
// this si the nornmalization in the 'sliding-window' (i.e. per 'test-bin')
RooRealVar *nlim = new RooRealVar(TString::Format("nlim%s",catnames.at(icat).Data()),"",0.0,0.0,10.0);
nlim->removeRange();
if( iscombcat ) {
// ----------- HISTORIC NAMING ----------------------------------------
sumcatsnm1 = new RooAddition("sumcatsnm1","",normList); // summing all normalizations epect the last Cat
// this is the normlization of the last Cat
RooFormulaVar *nlast = new RooFormulaVar("nlast","","TMath::Max(0.1,@0-@1)",RooArgList(*nlim,*sumcatsnm1));
// ... and adding it ot the list of norms
normu_vec.push_back(nlast);
}
//if (icat == 1 && catnames.size() == 2) continue; // only 1 cat, so don't need combination
for (int i=1; i<(plot[icat]->GetXaxis()->GetNbins()+1); ++i) {
// this defines the 'binning' we use for the error bands
double lowedge = plot[icat]->GetXaxis()->GetBinLowEdge(i);
double upedge = plot[icat]->GetXaxis()->GetBinUpEdge(i);
double center = plot[icat]->GetXaxis()->GetBinCenter(i);
// get the nominal value at the center of the bin
double nombkg = nomcurve->interpolate(center);
nlim->setVal(nombkg);
hmass->setRange("errRange",lowedge,upedge);
// this is the new extended PDF whith the normalization restricted to the bin-area
RooAbsPdf *extLimPdf = NULL;
if( iscombcat ) {
extLimPdf = new RooSimultaneous("epdf","",finalcat);
// loop over the cats and generate temporary extended PDFs
for (int jcat=0; jcat<(catnames.size()-1); ++jcat) {
RooRealVar *rvar = dynamic_cast<RooRealVar*>(normu_vec.at(jcat));
if (rvar) rvar->setVal(fitcurves.at(jcat)->interpolate(center));
RooExtendPdf *ecpdf = new RooExtendPdf(TString::Format("ecpdf%s",catnames.at(jcat).Data()),"",*pdfShape_vec.at(jcat),*normu_vec.at(jcat),"errRange");
static_cast<RooSimultaneous*>(extLimPdf)->addPdf(*ecpdf,catnames.at(jcat));
}
} else
extLimPdf = new RooExtendPdf("extLimPdf","",*thisPdf,*nlim,"errRange");
RooAbsReal *nll = extLimPdf->createNLL(*datanorm,Extended(),NumCPU(1));
RooMinimizer minim(*nll);
minim.setStrategy(0);
double clone = 1.0 - 2.0*RooStats::SignificanceToPValue(1.0);
double cltwo = 1.0 - 2.0*RooStats::SignificanceToPValue(2.0);
if (iscombcat) minim.setStrategy(2);
minim.migrad();
if (!iscombcat) {
minim.minos(*nlim);
}
else {
minim.hesse();
nlim->removeAsymError();
}
if( verbose )
printf("errlo = %5f, errhi = %5f\n",nlim->getErrorLo(),nlim->getErrorHi());
onesigma->SetPoint(i-1,center,nombkg);
onesigma->SetPointError(i-1,0.,0.,-nlim->getErrorLo(),nlim->getErrorHi());
// to get the 2-sigma bands...
minim.setErrorLevel(0.5*pow(ROOT::Math::normal_quantile(1-0.5*(1-cltwo),1.0), 2)); // the 0.5 is because qmu is -2*NLL
// eventually if cl = 0.95 this is the usual 1.92!
if (!iscombcat) {
minim.migrad();
minim.minos(*nlim);
}
else {
nlim->setError(2.0*nlim->getError());
nlim->removeAsymError();
}
twosigma->SetPoint(i-1,center,nombkg);
twosigma->SetPointError(i-1,0.,0.,-nlim->getErrorLo(),nlim->getErrorHi());
// for memory clean-up
delete nll;
delete extLimPdf;
}
hmass->setRange("errRange",massmin,massmax);
if( verbose )
onesigma->Print("V");
// plot[icat] the error bands
twosigma->SetLineColor(kGreen);
twosigma->SetFillColor(kGreen);
twosigma->SetMarkerColor(kGreen);
twosigma->Draw("L3 SAME");
onesigma->SetLineColor(kYellow);
onesigma->SetFillColor(kYellow);
onesigma->SetMarkerColor(kYellow);
onesigma->Draw("L3 SAME");
plot[icat]->Draw("SAME");
// and add the error bands to the legend
legmc->AddEntry(onesigma,"#pm1 #sigma","F");
legmc->AddEntry(twosigma,"#pm2 #sigma","F");
}
std::cout<<" trying ***2 to plot #"<<icat<<std::endl;
// rest of the legend ....
legmc->SetBorderSize(0);
legmc->SetFillStyle(0);
legmc->Draw();
lat[icat] = new TLatex(103.0,0.9*plot[icat]->GetMaximum(),TString::Format("#scale[0.7]{#splitline{CMS preliminary}{#sqrt{s} = %.1f TeV L = %.2f fb^{-1}}}",theCMenergy,intLumi->getVal()));
lat2[icat] = new TLatex(103.0,0.75*plot[icat]->GetMaximum(),catdesc.at(icat));
lat[icat] ->Draw();
lat2[icat]->Draw();
// -------------------------------------------------------
// save canvas in different formats
canbkg[icat]->SaveAs(TString("databkg") + catname + TString(".pdf"));
canbkg[icat]->SaveAs(TString("databkg") + catname + TString(".eps"));
canbkg[icat]->SaveAs(TString("databkg") + catname + TString(".root"));
}
return;
}
void eregtesting_13TeV_Pi0_lessP2(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_PU_lessP_2/%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_PU_lessP_2/%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 {
if(dobarrel)
{
TFile *fdin = TFile::Open("/afs/cern.ch/work/z/zhicaiz/public/ECALpro_MC_TreeForRegression/sum_Pi0Gun_Flat0to50bx25_EB_combine.root");//("root://eoscms.cern.ch///eos/cms/store/cmst3/user/bendavid/idTreesAug1/hgg-2013Final8TeV_ID_s12-h124gg-gf-v7n_noskim.root");
// TDirectory *ddir = (TDirectory*)fdin->FindObjectAny("PhotonTreeWriterPreselNoSmear");
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");
}
}
else
{
TFile *fdin = TFile::Open("/afs/cern.ch/work/z/zhicaiz/public/ECALpro_MC_TreeForRegression/sum_Pi0Gun_Flat0to50bx25_EE_combine.root");//("root://eoscms.cern.ch///eos/cms/store/cmst3/user/bendavid/idTreesAug1/hgg-2013Final8TeV_ID_s12-h124gg-gf-v7n_noskim.root");
// TDirectory *ddir = (TDirectory*)fdin->FindObjectAny("PhotonTreeWriterPreselNoSmear");
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_nocor/cosh(STr2_Eta)>1.0) && (STr2_S4S9 > 0.75) && (STr2_isMerging < 2) && (STr2_DeltaR < 0.05)";
// TCut selcut = "(STr2_enG_nocor/cosh(STr2_Eta)>1.0) && (STr2_S4S9 > 0.75) && (STr2_S4S9 < 0.999) && (STr2_S2S9 < 0.999)";
/*
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)";
if (doele)
weightvar.SetTitle(prescale100alt*selcut);
else
weightvar.SetTitle(Events01_4*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 *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");
//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);
//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.eps");
craw->SetLogy();
plot->SetMinimum(0.1);
craw->SaveAs("RawElog.eps");
//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.eps");
TCanvas *cwidth = new TCanvas;
RooPlot *plotwidth = widthvar->frame(0.,0.05,100);
hdataclone->plotOn(plotwidth);
plotwidth->Draw();
cwidth->SaveAs("width.eps");
TCanvas *cn = new TCanvas;
RooPlot *plotn = nvar->frame(0.,111.,200);
hdataclone->plotOn(plotn);
plotn->Draw();
cn->SaveAs("n.eps");
TCanvas *cn2 = new TCanvas;
RooPlot *plotn2 = n2var->frame(0.,111.,100);
hdataclone->plotOn(plotn2);
plotn2->Draw();
cn2->SaveAs("n2.eps");
////
/*
TCanvas *ceta = new TCanvas;
RooPlot *ploteta = scetavar->frame(-2.6,2.6,200);
hdataclone->plotOn(ploteta);
ploteta->Draw();
ceta->SaveAs("eta.eps");
*/
//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.));
hecor = hdata->createHistogram("hecor",*ecorvar, Binning(800,0.,2.));
}
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);
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.eps");
cresponse->SetLogy();
cresponse->SaveAs("responselog.eps");
// 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.eps");
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.eps");
*/
/*
RooRealVar *scetaiXvar = ws->var("var_1");
RooRealVar *scphiiYvar = ws->var("var_2");
if(EEorEB=="EB")
{
scetaiXvar->setRange(-90,90);
scetaiXvar->setBins(1800);
scphiiYvar->setRange(0,360);
scphiiYvar->setBins(3600);
}
else
{
scetaiXvar->setRange(0,50);
scetaiXvar->setBins(500);
scphiiYvar->setRange(0,50);
scphiiYvar->setBins(500);
}
ecorvar->setRange(0.5,1.5);
ecorvar->setBins(100);
rawvar->setRange(0.5,1.5);
rawvar->setBins(100);
TCanvas *c_cor_eta = new TCanvas;
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.eps");
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.eps");
TCanvas *c_raw_eta = new TCanvas;
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.eps");
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.eps");
// other variables
TCanvas *myC_variables = new TCanvas;
*/
/////
/* RooRealVar *Nxtalvar = ws->var("var_3");
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.eps");
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.eps");
RooRealVar *S4S9var = ws->var("var_4");
S4S9var->setRange(0.6,1.0);
S4S9var->setBins(100);
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.eps");
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.eps");
*/
//////
/*
RooRealVar *S1S9var = ws->var("var_5");
S1S9var->setRange(0.3,1.0);
S1S9var->setBins(100);
TH2F *h_CC_S1S9 = hdata->createHistogram(*S1S9var, *ecorvar, "","cor_vs_S1S9");
h_CC_S1S9->GetXaxis()->SetTitle("S1S9");
h_CC_S1S9->GetYaxis()->SetTitle("E_{cor}/E_{true}");
h_CC_S1S9->Draw("COLZ");
myC_variables->SaveAs("cor_vs_S1S9.eps");
TH2F *h_RC_S1S9 = hdata->createHistogram(*S1S9var, *rawvar, "","raw_vs_S1S9");
h_RC_S1S9->GetXaxis()->SetTitle("S1S9");
h_RC_S1S9->GetYaxis()->SetTitle("E_{raw}/E_{true}");
h_RC_S1S9->Draw("COLZ");
myC_variables->SaveAs("raw_vs_S1S9.eps");
*/
//////
/*
RooRealVar *S2S9var = ws->var("var_5");
S2S9var->setRange(0.5,1.0);
S2S9var->setBins(100);
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.eps");
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.eps");
RooRealVar *DeltaRvar = ws->var("var_6");
DeltaRvar->setRange(0.0,0.1);
DeltaRvar->setBins(100);
TH2F *h_CC_DeltaR = hdata->createHistogram(*DeltaRvar, *ecorvar, "","cor_vs_DeltaR");
h_CC_DeltaR->GetXaxis()->SetTitle("#Delta R");
h_CC_DeltaR->GetYaxis()->SetTitle("E_{cor}/E_{true}");
h_CC_DeltaR->Draw("COLZ");
myC_variables->SaveAs("cor_vs_DeltaR.eps");
TH2F *h_RC_DeltaR = hdata->createHistogram(*DeltaRvar, *rawvar, "","raw_vs_DeltaR");
h_RC_DeltaR->GetXaxis()->SetTitle("#Delta R");
h_RC_DeltaR->GetYaxis()->SetTitle("E_{raw}/E_{true}");
h_RC_DeltaR->Draw("COLZ");
myC_variables->SaveAs("raw_vs_DeltaR.eps");
if(EEorEB=="EE")
{
RooRealVar *Es_e1var = ws->var("var_9");
Es_e1var->setRange(0.0,200.0);
Es_e1var->setBins(1000);
TH2F *h_CC_Es_e1 = hdata->createHistogram(*Es_e1var, *ecorvar, "","cor_vs_Es_e1");
h_CC_Es_e1->GetXaxis()->SetTitle("Es_e1");
h_CC_Es_e1->GetYaxis()->SetTitle("E_{cor}/E_{true}");
h_CC_Es_e1->Draw("COLZ");
myC_variables->SaveAs("cor_vs_Es_e1.eps");
TH2F *h_RC_Es_e1 = hdata->createHistogram(*Es_e1var, *rawvar, "","raw_vs_Es_e1");
h_RC_Es_e1->GetXaxis()->SetTitle("Es_e1");
h_RC_Es_e1->GetYaxis()->SetTitle("E_{raw}/E_{true}");
h_RC_Es_e1->Draw("COLZ");
myC_variables->SaveAs("raw_vs_Es_e1.eps");
RooRealVar *Es_e2var = ws->var("var_10");
Es_e2var->setRange(0.0,200.0);
Es_e2var->setBins(1000);
TH2F *h_CC_Es_e2 = hdata->createHistogram(*Es_e2var, *ecorvar, "","cor_vs_Es_e2");
h_CC_Es_e2->GetXaxis()->SetTitle("Es_e2");
h_CC_Es_e2->GetYaxis()->SetTitle("E_{cor}/E_{true}");
h_CC_Es_e2->Draw("COLZ");
myC_variables->SaveAs("cor_vs_Es_e2.eps");
TH2F *h_RC_Es_e2 = hdata->createHistogram(*Es_e2var, *rawvar, "","raw_vs_Es_e2");
h_RC_Es_e2->GetXaxis()->SetTitle("Es_e2");
h_RC_Es_e2->GetYaxis()->SetTitle("E_{raw}/E_{true}");
h_RC_Es_e2->Draw("COLZ");
myC_variables->SaveAs("raw_vs_Es_e2.eps");
}
*/
/*
TProfile *p_CC_eta = h_CC_eta->ProfileX();
p_CC_eta->GetYaxis()->SetRangeUser(0.5,1.5);
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.eps");
TProfile *p_RC_eta = h_RC_eta->ProfileX();
p_RC_eta->GetYaxis()->SetRangeUser(0.5,1.5);
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.eps");
TProfile *p_CC_phi = h_CC_phi->ProfileX();
p_CC_phi->GetYaxis()->SetRangeUser(0.84,1.06);
if(EEorEB == "EB")
{
p_CC_phi->GetYaxis()->SetRangeUser(0.91,1.00);
}
p_CC_phi->GetYaxis()->SetTitle("E_{cor}/E_{true}");
p_CC_phi->SetTitle("");
p_CC_phi->Draw();
myC_variables->SaveAs("profile_cor_vs_phi.eps");
TProfile *p_RC_phi = h_RC_phi->ProfileX();
p_RC_phi->GetYaxis()->SetRangeUser(0.82,1.04);
if(EEorEB=="EB")
{
p_RC_phi->GetYaxis()->SetRangeUser(0.86,0.95);
}
p_RC_phi->GetYaxis()->SetTitle("E_{raw}/E_{true}");
p_RC_phi->SetTitle("");
p_RC_phi->Draw();
myC_variables->SaveAs("profile_raw_vs_phi.eps");
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(); */
}
/***********************************************************************
***********************************************************************
* CONSTRUCTOR MAKES ALLLLLLLL
*******************************************************************
*************************************************
*****************************
*****
*/
Tbroomfit(double xlow, double xhi, TH1 *h2, int npeak, double *peak, double *sigm, const char *chpol="p0"){
int iq=0;
printf("constructor - %d %ld", iq++, (int64_t)h2 );
h2->Print();
/*
* get global area, ranges for sigma, x
*/
npeaks=npeak; // class defined int
// double areah2=h2->Integral( int(xlow), int(xhi) ); // WRONG - BINS
min= h2->GetXaxis()->GetFirst();
printf("constructor - %d %f", iq++, min );
max= h2->GetXaxis()->GetLast();
printf("constructor - %d %f", iq++, max );
double areah2=h2->Integral( min, max );
printf("constructor - %d %f", iq++, areah2 );
min=xlow;
max=xhi;
double sigmamin=(max-min)/300;
double sigmamax=(max-min)/4;
double areamin=0;
double areamax=2*areah2;
printf("x:(%f,%f) s:(%f,%f) a:(%f,%f) \n", min,max,sigmamin, sigmamax,areamin, areamax );
/*
* definition of variables..............
*
*/
RooRealVar x("x", "x", min, max);
int MAXPEAKS=6; // later from 5 to 6 ???
printf("RooFit: npeaks=%d\n", npeaks );
// ABOVE: RooRealVar *msat[14][5]; // POINTERS TO ALL variables
// 0 m Mean
// 1 s Sigma
// 2 a Area
// 3 t Tail
// 4 [0] nalpha
// 5 [0] n1
for (int ii=0;ii<14;ii++){
for (int jj=0;jj<MAXPEAKS;jj++){
msat[ii][jj]=NULL;
msat_values[ii][jj]=0.0;
} //for for
}// for for
printf("delete fitresult, why crash?\n%s","");
fitresult=NULL;
printf("delete fitresult, no crash?\n%s","");
RooRealVar mean1("mean1", "mean", 1*(max-min)/(npeaks+1)+min, min,max);msat[0][0]=&mean1;
RooRealVar mean2("mean2", "mean", 2*(max-min)/(npeaks+1)+min, min,max);msat[0][1]=&mean2;
RooRealVar mean3("mean3", "mean", 3*(max-min)/(npeaks+1)+min, min,max);msat[0][2]=&mean3;
RooRealVar mean4("mean4", "mean", 4*(max-min)/(npeaks+1)+min, min,max);msat[0][3]=&mean4;
RooRealVar mean5("mean5", "mean", 5*(max-min)/(npeaks+1)+min, min,max);msat[0][4]=&mean5;
RooRealVar mean6("mean6", "mean", 6*(max-min)/(npeaks+1)+min, min,max);msat[0][5]=&mean6;
RooRealVar sigma1("sigma1","sigma", (max-min)/10, sigmamin, sigmamax );msat[1][0]=&sigma1;
RooRealVar sigma2("sigma2","sigma", (max-min)/10, sigmamin, sigmamax );msat[1][1]=&sigma2;
RooRealVar sigma3("sigma3","sigma", (max-min)/10, sigmamin, sigmamax );msat[1][2]=&sigma3;
RooRealVar sigma4("sigma4","sigma", (max-min)/10, sigmamin, sigmamax );msat[1][3]=&sigma4;
RooRealVar sigma5("sigma5","sigma", (max-min)/10, sigmamin, sigmamax );msat[1][4]=&sigma5;
RooRealVar sigma6("sigma6","sigma", (max-min)/10, sigmamin, sigmamax );msat[1][5]=&sigma6;
RooRealVar area1("area1", "area", areah2/npeaks, areamin, areamax );msat[2][0]=&area1;
RooRealVar area2("area2", "area", areah2/npeaks, areamin, areamax );msat[2][1]=&area2;
RooRealVar area3("area3", "area", areah2/npeaks, areamin, areamax );msat[2][2]=&area3;
RooRealVar area4("area4", "area", areah2/npeaks, areamin, areamax );msat[2][3]=&area4;
RooRealVar area5("area5", "area", areah2/npeaks, areamin, areamax );msat[2][4]=&area5;
RooRealVar area6("area6", "area", areah2/npeaks, areamin, areamax );msat[2][5]=&area6;
RooRealVar bgarea("bgarea", "bgarea", areah2/5, 0, 2*areah2);
double tailstart=-1.0;// tune the tails....
double tailmin=-1e+4;
double tailmax=1e+4;
RooRealVar tail1("tail1", "tail", tailstart, tailmin, tailmax );msat[3][0]=&tail1;
RooRealVar tail2("tail2", "tail", tailstart, tailmin, tailmax );msat[3][1]=&tail2;
RooRealVar tail3("tail3", "tail", tailstart, tailmin, tailmax );msat[3][2]=&tail3;
RooRealVar tail4("tail4", "tail", tailstart, tailmin, tailmax );msat[3][3]=&tail4;
RooRealVar tail5("tail5", "tail", tailstart, tailmin, tailmax );msat[3][4]=&tail5;
RooRealVar tail6("tail6", "tail", tailstart, tailmin, tailmax );msat[3][5]=&tail6;
// for CBShape
RooRealVar nalpha1("nalpha1", "nalpha", 1.3, 0, 100 );msat[4][0]=&nalpha1;
RooRealVar n1("n1", "n", 5.1, 0, 100 ); msat[5][0]=&n1;
/*
* initial values for peak positions................
*/
if (npeaks>=1) {mean1=peak[0];sigma1=sigm[0];}
if (npeaks>=2) {mean2=peak[1];sigma2=sigm[1];}
if (npeaks>=3) {mean3=peak[2];sigma3=sigm[2];}
if (npeaks>=4) {mean4=peak[3];sigma4=sigm[3];}
if (npeaks>=5) {mean5=peak[4];sigma5=sigm[4];}
if (npeaks>=6) {mean6=peak[5];sigma6=sigm[5];}
/*
* RooAbsPdf -> RooGaussian
* RooNovosibirsk
* RooLandau
*/
RooAbsPdf *pk[6]; // MAXIMUM PEAKS ==5 6 NOW!!
RooAbsPdf *pk_dicto[14][6]; // ALL DICTIONARY OF PEAKS..........
// Abstract Class.... carrefuly
RooGaussian gauss1("gauss1","gauss(x,mean,sigma)", x, mean1, sigma1);pk_dicto[0][0]=&gauss1;
RooGaussian gauss2("gauss2","gauss(x,mean,sigma)", x, mean2, sigma2);pk_dicto[0][1]=&gauss2;
RooGaussian gauss3("gauss3","gauss(x,mean,sigma)", x, mean3, sigma3);pk_dicto[0][2]=&gauss3;
RooGaussian gauss4("gauss4","gauss(x,mean,sigma)", x, mean4, sigma4);pk_dicto[0][3]=&gauss4;
RooGaussian gauss5("gauss5","gauss(x,mean,sigma)", x, mean5, sigma5);pk_dicto[0][4]=&gauss5;
RooGaussian gauss6("gauss6","gauss(x,mean,sigma)", x, mean6, sigma6);pk_dicto[0][5]=&gauss6;
RooNovosibirsk ns1("ns1","novosib(x,mean,sigma,tail)", x, mean1,sigma1, tail1 );pk_dicto[1][0]=&ns1;
RooNovosibirsk ns2("ns2","novosib(x,mean,sigma,tail)", x, mean2,sigma2, tail2 );pk_dicto[1][1]=&ns2;
RooNovosibirsk ns3("ns3","novosib(x,mean,sigma,tail)", x, mean3,sigma3, tail3 );pk_dicto[1][2]=&ns3;
RooNovosibirsk ns4("ns4","novosib(x,mean,sigma,tail)", x, mean4,sigma4, tail4 );pk_dicto[1][3]=&ns4;
RooNovosibirsk ns5("ns5","novosib(x,mean,sigma,tail)", x, mean5,sigma5, tail5 );pk_dicto[1][4]=&ns5;
// BreitWiegner is Lorentzian...?
RooBreitWigner bw1("bw1","BreitWigner(x,mean,sigma)", x, mean1, sigma1 );pk_dicto[2][0]=&bw1;
RooBreitWigner bw2("bw2","BreitWigner(x,mean,sigma)", x, mean2, sigma2 );pk_dicto[2][1]=&bw2;
RooBreitWigner bw3("bw3","BreitWigner(x,mean,sigma)", x, mean3, sigma3 );pk_dicto[2][2]=&bw3;
RooBreitWigner bw4("bw4","BreitWigner(x,mean,sigma)", x, mean4, sigma4 );pk_dicto[2][3]=&bw4;
RooBreitWigner bw5("bw5","BreitWigner(x,mean,sigma)", x, mean5, sigma5 );pk_dicto[2][4]=&bw5;
RooCBShape cb1("cb1","CBShape(x,mean,sigma)", x, mean1, sigma1, nalpha1, n1 );pk_dicto[3][0]=&cb1;
RooCBShape cb2("cb2","CBShape(x,mean,sigma)", x, mean2, sigma2, nalpha1, n1 );pk_dicto[3][1]=&cb2;
RooCBShape cb3("cb3","CBShape(x,mean,sigma)", x, mean3, sigma3, nalpha1, n1 );pk_dicto[3][2]=&cb3;
RooCBShape cb4("cb4","CBShape(x,mean,sigma)", x, mean4, sigma4, nalpha1, n1 );pk_dicto[3][3]=&cb4;
RooCBShape cb5("cb5","CBShape(x,mean,sigma)", x, mean5, sigma5, nalpha1, n1 );pk_dicto[3][4]=&cb5;
RooCBShape cb6("cb6","CBShape(x,mean,sigma)", x, mean6, sigma6, nalpha1, n1 );pk_dicto[3][5]=&cb6;
/*
* PEAK TYPES BACKGROUND TYPE ......... COMMAND BOX OPTIONS ......
*/
/****************************************************************************
* PLAY WITH THE DEFINITION COMMANDLINE...................... POLYNOM + PEAKS
*/
// CALSS DECLARED TString s;
s=chpol;
/*
* peaks+bg== ALL BEFORE ; or : (after ... it is a conditions/options)
*/
TString command;
int comstart=s.Index(":"); if (comstart<0){ comstart=s.Index(";");}
if (comstart<0){ command="";}else{
command=s(comstart+1, s.Length()-comstart -1 ); // without ;
s=s(0,comstart); // without ;
printf("COMMANDLINE : %s\n", command.Data() );
if (TPRegexp("scom").Match(command)!=0){
}// COMMANDS -
}// there is some command
/*************************************************
* PLAY WITH peaks+bg.................. s
*/
s.Append("+"); s.Prepend("+"); s.ReplaceAll(" ","+");
s.ReplaceAll("++++","+"); s.ReplaceAll("+++","+"); s.ReplaceAll("++","+");s.ReplaceAll("++","+");
printf (" regextp = %s\n", s.Data() );
if (TPRegexp("\\+p[\\dn]\\+").Match(s)==0){ // no match
printf("NO polynomial demanded =>: %s\n", "appending pn command" ); s.Append("pn+");
}
TString spk=s; TString sbg=s;
TPRegexp("\\+p[\\dn]\\+").Substitute(spk,"+"); // remove +p.+
TPRegexp(".+(p[\\dn]).+").Substitute(sbg,"$1"); // remove all but +p+
printf ("PEAKS=%s BG=%s\n", spk.Data() , sbg.Data() );
spk.ReplaceAll("+",""); // VARIANT 1 ------- EACH LETTER MEANS ONE PEAK
/************************************************************************
* PREPARE PEAKS FOLLOWING THE COMMAND BOX................
*/
//default PEAK types
pk[0]=&gauss1;
pk[1]=&gauss2;
pk[2]=&gauss3;
pk[3]=&gauss4;
pk[4]=&gauss5;
pk[5]=&gauss6;
int maxi=spk.Length();
if (maxi>npeaks){maxi=npeaks;}
for (int i=0;i<maxi;i++){
if (spk[i]=='n'){
pk[i]=pk_dicto[1][i];//novosibirsk
printf("PEAK #%d ... Novosibirsk\n", i );
}else if(spk[i]=='b'){
pk[i]=pk_dicto[2][i];//BreitWiegner
printf("PEAK #%d ... BreitWigner\n", i );
}else if(spk[i]=='c'){
pk[i]=pk_dicto[3][i];//CBShape
printf("PEAK #%d ... CBShape\n", i );
}else if(spk[i]=='y'){
}else if(spk[i]=='z'){
}else{
pk[i]=pk_dicto[0][i]; //gauss
printf("PEAK #%d ... Gaussian\n", i );
}// ELSE CHAIN
}//i to maxi
for (int i=0;i<npeaks;i++){ printf("Peak %d at %f s=%f: PRINT:\n " , i, peak[i], sigm[i] );pk[i]->Print();}
/******************************************************** BACKGROUND pn-p4
* a0 == level - also skew
* a1 == p2
* a2 == p3
*/
// Build Chebychev polynomial p.d.f.
// RooRealVar a0("a0","a0", 0.) ;
RooRealVar a0("a0","a0", 0., -10, 10) ;
RooRealVar a1("a1","a1", 0., -10, 10) ;
RooRealVar a2("a2","a2", 0., -10, 10) ;
RooRealVar a3("a3","a3", 0., -10, 10) ;
RooArgSet setcheb;
if ( sbg=="pn" ){ setcheb.add(a0); a0=0.; a0.setConstant(kTRUE);bgarea=0.; bgarea.setConstant(kTRUE);}
if ( sbg=="p0" ){ setcheb.add(a0); a0=0.; a0.setConstant(kTRUE); }
if ( sbg=="p1" ){ setcheb.add(a0); }
if ( sbg=="p2" ){ setcheb.add(a1); setcheb.add(a0); }
if ( sbg=="p3" ){ setcheb.add(a2); setcheb.add(a1); setcheb.add(a0); }
if ( sbg=="p4" ){ setcheb.add(a3);setcheb.add(a2); setcheb.add(a1); setcheb.add(a0); }
// RooChebychev bkg("bkg","Background",x,RooArgSet(a0,a1,a2,a3) ) ;
RooChebychev bkg("bkg","Background",x, setcheb ) ;
/**********************************************************************
* MODEL
*/
RooArgList rl;
if (npeaks>0)rl.add( *pk[0] );
if (npeaks>1)rl.add( *pk[1] );
if (npeaks>2)rl.add( *pk[2] );
if (npeaks>3)rl.add( *pk[3] );
if (npeaks>4)rl.add( *pk[4] );
if (npeaks>5)rl.add( *pk[5] );
rl.add( bkg );
RooArgSet rs;
if (npeaks>0)rs.add( area1 );
if (npeaks>1)rs.add( area2 );
if (npeaks>2)rs.add( area3 );
if (npeaks>3)rs.add( area4 );
if (npeaks>4)rs.add( area5 );
if (npeaks>5)rs.add( area6 );
rs.add( bgarea );
RooAddPdf modelV("model","model", rl, rs );
/*
* WITH CUSTOMIZER - I can change parameters inside. But
* - then all is a clone and I dont know how to reach it
*/
RooCustomizer cust( modelV ,"cust");
/*
* Possibility to fix all sigma or tails....
*/
if (TPRegexp("scom").Match(command)!=0){//----------------------SCOM
printf("all sigma have common values.....\n%s", "");
if (npeaks>1)cust.replaceArg(sigma2,sigma1) ;
if (npeaks>2)cust.replaceArg(sigma3,sigma1) ;
if (npeaks>3)cust.replaceArg(sigma4,sigma1) ;
if (npeaks>4)cust.replaceArg(sigma5,sigma1) ;
if (npeaks>5)cust.replaceArg(sigma6,sigma1) ;
}
if (TPRegexp("tcom").Match(command)!=0){//----------------------TCOM
printf("all tails have common values.....\n%s", "");
if (npeaks>1)cust.replaceArg(tail2,tail1) ;
if (npeaks>2)cust.replaceArg(tail3,tail1) ;
if (npeaks>3)cust.replaceArg(tail4,tail1) ;
if (npeaks>4)cust.replaceArg(tail5,tail1) ;
if (npeaks>5)cust.replaceArg(tail6,tail1) ;
}
/* if (TPRegexp("tcom").Match(command)!=0){//----------------------TCOM Neni dalsi ACOM,NCOM pro CB...
printf("all tails have common values.....\n%s", "");
if (npeaks>1)cust.replaceArg(tail2,tail1) ;
if (npeaks>2)cust.replaceArg(tail3,tail1) ;
if (npeaks>3)cust.replaceArg(tail4,tail1) ;
if (npeaks>4)cust.replaceArg(tail5,tail1) ;
}
*/
if (TPRegexp("p1fix").Match(command)!=0){//----------------------
mean1.setConstant();printf("position 1 set constant%s\n","");
}
if (TPRegexp("p2fix").Match(command)!=0){//----------------------
mean2.setConstant();printf("position 2 set constant%s\n","");
}
if (TPRegexp("p3fix").Match(command)!=0){//----------------------
mean3.setConstant();printf("position 3 set constant%s\n","");
}
if (TPRegexp("p4fix").Match(command)!=0){//----------------------
mean4.setConstant();printf("position 4 set constant%s\n","");
}
if (TPRegexp("p5fix").Match(command)!=0){//----------------------
mean5.setConstant();printf("position 5 set constant%s\n","");
}
if (TPRegexp("p6fix").Match(command)!=0){//----------------------
mean6.setConstant();printf("position 6 set constant%s\n","");
}
if (TPRegexp("s1fix").Match(command)!=0){//----------------------
sigma1.setConstant();printf("sigma 1 set constant%s\n","");
}
if (TPRegexp("s2fix").Match(command)!=0){//----------------------
sigma2.setConstant();printf("sigma 2 set constant%s\n","");
}
if (TPRegexp("s3fix").Match(command)!=0){//----------------------
sigma3.setConstant();printf("sigma 3 set constant%s\n","");
}
if (TPRegexp("s4fix").Match(command)!=0){//----------------------
sigma4.setConstant();printf("sigma 4 set constant%s\n","");
}
if (TPRegexp("s5fix").Match(command)!=0){//----------------------
sigma5.setConstant();printf("sigma 5 set constant%s\n","");
}
if (TPRegexp("s6fix").Match(command)!=0){//----------------------
sigma6.setConstant();printf("sigma 6 set constant%s\n","");
}
RooAbsPdf* model = (RooAbsPdf*) cust.build(kTRUE) ; //build a clone...comment on changes...
// model->Print("t") ;
//delete model ; // eventualy delete the model...
/*
* DISPLAY RESULTS >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
*/
TPad *orig_gpad=(TPad*)gPad;
TCanvas *c;
c=(TCanvas*)gROOT->GetListOfCanvases()->FindObject("fitresult");
if (c==NULL){
printf("making new canvas\n%s","");
c=new TCanvas("fitresult",h2->GetName(),1000,700);
}else{
printf("using old canvas\n%s","");
c->SetTitle( h2->GetName() );
}
c->Clear();
printf(" canvas cleared\n%s","");
c->Divide(1,2) ;
printf(" canvas divided\n%s","");
c->Modified();c->Update();
RooDataHist datah("datah","datah with x",x,h2);
RooPlot* xframe = x.frame();
datah.plotOn(xframe, DrawOption("logy") );
// return;
if (TPRegexp("chi2").Match(command)!=0){//----------------------CHI2
//from lorenzo moneta
// TH1 * h1 = datah.createHistogram(x);
// TF1 * f = model->asTF(RooArgList(x) , parameters ); //???
// h2->Fit(f);
//It will work but you need to create a THNSparse and fit it
//or use directly the ROOT::Fit::BinData class to create a ROOT::Fit::Chi2Function to minimize.
// THIS CANNOT DO ZERO BINS
fitresult = model->chi2FitTo( datah , Save() );
}else{
// FIT FIT FIT FIT FIT FIT FIT FIT FIT FIT
fitresult = model->fitTo( datah , Save() );
}
fitresult->SetTitle( h2->GetName() ); // I PUT histogram name to global fitresult
// will be done by printResult ... fitresult->Print("v") ;
//duplicite fitresult->floatParsFinal().Print("s") ;
// later - after parsfinale .... : printResult();
// model->Print(); // not interesting........
model->plotOn(xframe, LineColor(kRed), DrawOption("l0z") );
//,Minos(kFALSE)
/*
* Posledni nakreslena vec je vychodiskem pro xframe->resid...?
* NA PORADI ZALEZI....
*/
//unused RooHist* hresid = xframe->residHist() ;
RooHist* hpull = xframe->pullHist() ;
// RooPlot* xframe2 = x.frame(Title("Residual Distribution")) ;
// xframe2->addPlotable(hresid,"P") ;
// Construct a histogram with the pulls of the data w.r.t the curve
RooPlot* xframe3 = x.frame(Title("Pull Distribution")) ;
xframe3->addPlotable(hpull,"P") ;
/*
* plot components at the end.... PLOT >>>>>>>>>>>>>>>>
*/
int colorseq[10]={kRed,kGreen,kBlue,kYellow,kCyan,kMagenta,kViolet,kAzure,kGray,kOrange};
// RooArgSet* model_params = model->getParameters(x); // this returns all parameters
RooArgSet* model_params = model->getComponents();
TIterator* iter = model_params->createIterator() ;
RooAbsArg* arg ; int icomp=0, ipeak=0;
// printf("ENTERING COMPONENT ITERATOR x%dx.....................\n", icomp );
while((arg=(RooAbsArg*)iter->Next())) {
// printf("printing COMPONENT %d\n", icomp );
// arg->Print();
// printf("NAME==%s\n", arg->Class_Name() ); //This returns only RooAbsArg
// printf("NAME==%s\n", arg->ClassName() ); //This RooGaussian RooChebychev
if ( IsPeak( arg->ClassName() )==1 ){
pk[ipeak]=(RooAbsPdf*)arg; //?
// pk[ipeak]->Print();
ipeak++;
// printf("adresses ... %d - %d - %d\n", pk[0], pk[1], pk[2] );
}// yes peak.
icomp++;
}//iterations over all components
model->plotOn(xframe, Components(bkg), LineColor(kRed), LineStyle(kDashed), DrawOption("l0z") );
for (int i=0;i<npeaks;i++){
// printf("plotting %d. peak, color %d\n", i, colorseq[i+1] );
// printf("adresses ... %d - %d - %d\n", pk[0], pk[1], pk[2] );
// pk[i]->Print();
model->plotOn(xframe, Components( RooArgSet(*pk[i],bkg) ), LineColor(colorseq[i+1]), LineStyle(kDashed),
DrawOption("l0z") );
// DrawOption("pz"),DataError(RooAbsData::SumW2) );??? pz removes complains...warnings
// model.plotOn(xframe, Components( RooArgSet(*pk[i],bkg) ), LineColor(colorseq[i+1]), LineStyle(kDashed));
}
// WE SET THE 1st PAD in "fitresult" to LOGY.... 1
// ..... if the original window is LOGY..... :)
//
// printf("########### ORIGPAD LOGY==%d #########3\n", orig_gpad->GetLogy() );
c->cd(1); xframe->Draw(); gPad->SetLogy( orig_gpad->GetLogy() );
// c->cd(2); xframe2->Draw();
c->cd(2); xframe3->Draw();
c->Modified();c->Update();
orig_gpad->cd();
// printf("msat reference to peak 0 0 = %d, (%f)\n", msat[0][0] , msat[0][0]->getVal() );
for (int ii=0;ii<14;ii++){
for (int jj=0;jj<MAXPEAKS;jj++){
if ( msat[ii][jj]!=NULL){
msat_values[ii][jj]=msat[ii][jj]->getVal();
}//if
} //for for
}// for for
printf("at the total end of the constructor....%s\n","");
// done in pirntResult .. fitresult->floatParsFinal().Print("s") ;
printResult();
}; // constructor
void fit2015(
TString FileName ="/afs/cern.ch/user/a/anstahll/work/public/ExpressStream2015/ppData/OniaTree_262163_262328.root",
int oniamode = 2, // oniamode-> 3: Z, 2: Upsilon and 1: J/Psi
bool isData = true, // isData = false for MC, true for Data
bool isPbPb = false, // isPbPb = false for pp, true for PbPb
bool doFit = false ,
bool inExcStat = true // if inExcStat is true, then the excited states are fitted
) {
InputOpt opt;
SetOptions(&opt, isData, isPbPb, oniamode,inExcStat);
if (isPbPb) {
FileName = "/afs/cern.ch/user/a/anstahll/work/public/ExpressStream2015/PbPbData/OniaTree_262548_262893.root";
} else {
FileName = "/afs/cern.ch/user/a/anstahll/work/public/ExpressStream2015/ppData/OniaTree_262163_262328.root";
}
int nbins = 1; //ceil((opt.dMuon->M->Max - opt.dMuon->M->Min)/binw);
if (oniamode==1){
nbins = 140;
} else if (oniamode==2) {
nbins = 70;
} else if (oniamode==3) {
nbins = 40;
}
RooWorkspace myws;
TH1F* hDataOS = new TH1F("hDataOS","hDataOS", nbins, opt.dMuon.M.Min, opt.dMuon.M.Max);
makeWorkspace2015(myws, FileName, opt, hDataOS);
RooRealVar* mass = (RooRealVar*) myws.var("invariantMass");
RooDataSet* dataOS_fit = (RooDataSet*) myws.data("dataOS");
RooDataSet* dataSS_fit = (RooDataSet*) myws.data("dataSS");
RooAbsPdf* pdf = NULL;
if (oniamode==3) { doFit=false; }
if (doFit) {
int sigModel=0, bkgModel=0;
if (isData) {
if (oniamode==1){
sigModel = inExcStat ? 2 : 3;
bkgModel = 1;
} else {
sigModel = inExcStat ? 1 : 3; // gaussian
bkgModel = 2;
}
} else {
if (oniamode==1){
sigModel = inExcStat ? 2 : 3; // gaussian
bkgModel = 2;
} else {
sigModel = inExcStat ? 2 : 3; // gaussian
bkgModel = 3;
}
}
if (opt.oniaMode==1) buildModelJpsi2015(myws, sigModel, bkgModel,inExcStat);
else if (opt.oniaMode==2) buildModelUpsi2015(myws, sigModel, bkgModel,inExcStat);
pdf =(RooAbsPdf*) myws.pdf("pdf");
RooFitResult* fitObject = pdf->fitTo(*dataOS_fit,Save(),Hesse(kTRUE),Extended(kTRUE)); // Fit
}
RooPlot* frame = mass->frame(Bins(nbins),Range(opt.dMuon.M.Min, opt.dMuon.M.Max));
RooPlot* frame2 = NULL;
dataSS_fit->plotOn(frame, Name("dataSS_FIT"), MarkerColor(kRed), LineColor(kRed), MarkerSize(1.2));
dataOS_fit->plotOn(frame, Name("dataOS_FIT"), MarkerColor(kBlue), LineColor(kBlue), MarkerSize(1.2));
if (doFit) {
pdf->plotOn(frame,Name("thePdf"),Normalization(dataOS_fit->sumEntries(),RooAbsReal::NumEvent));
RooHist *hpull = frame -> pullHist(0,0,true);
hpull -> SetName("hpull");
frame2 = mass->frame(Title("Pull Distribution"),Bins(nbins),Range(opt.dMuon.M.Min,opt.dMuon.M.Max));
frame2 -> addPlotable(hpull,"PX");
}
drawPlot(frame,frame2, pdf, opt, doFit,inExcStat);
TString OutputFileName = "";
if (isPbPb) {
FileName = "/afs/cern.ch/user/a/anstahll/work/public/ExpressStream2015/PbPbData/OniaTree_262548_262893.root";
opt.RunNb.Start=262548;
opt.RunNb.End=262893;
if (oniamode==1) {OutputFileName = (TString)("JPSIPbPbDataset.root");}
if (oniamode==2) {OutputFileName = (TString)("YPbPbDataset.root");}
if (oniamode==3) {OutputFileName = (TString)("ZPbPbDataset.root");}
} else {
FileName = "/afs/cern.ch/user/a/anstahll/work/public/ExpressStream2015/ppData/OniaTree_262163_262328.root";
opt.RunNb.Start=262163;
opt.RunNb.End=262328;
if (oniamode==1) {OutputFileName = (TString)("JPSIppDataset.root");}
if (oniamode==2) {OutputFileName = (TString)("YppDataset.root");}
if (oniamode==3) {OutputFileName = (TString)("ZppDataset.root");}
}
TFile* oFile = new TFile(OutputFileName,"RECREATE");
oFile->cd();
hDataOS->Write("hDataOS");
dataOS_fit->Write("dataOS_FIT");
oFile->Write();
oFile->Close();
}
void LeptonPreselectionCMG( PreselType type, RooWorkspace * w ) {
const Options & opt = Options::getInstance();
if (type == ELE)
cout << "Running Electron Preselection :" << endl;
else if (type == MU)
cout << "Running Muon Preselection :" << endl;
else if (type == EMU)
cout << "Running Electron-Muon Preselection() ..." << endl;
else if (type == PHOT)
cout << "Running Photon Preselection :" << endl;
string systVar;
try {
systVar = opt.checkStringOption("SYSTEMATIC_VAR");
} catch (const std::string & exc) {
cout << exc << endl;
}
if (systVar == "NONE")
systVar.clear();
#ifdef CMSSWENV
JetCorrectionUncertainty jecUnc("Summer13_V4_MC_Uncertainty_AK5PFchs.txt");
#endif
string inputDir = opt.checkStringOption("INPUT_DIR");
string outputDir = opt.checkStringOption("OUTPUT_DIR");
string sampleName = opt.checkStringOption("SAMPLE_NAME");
string inputFile = inputDir + '/' + sampleName + ".root";
cout << "\tInput file: " << inputFile << endl;
bool isSignal = opt.checkBoolOption("SIGNAL");
TGraph * higgsW = 0;
TGraph * higgsI = 0;
if (isSignal) {
double higgsM = opt.checkDoubleOption("HIGGS_MASS");
if (higgsM >= 400) {
string dirName = "H" + double2string(higgsM);
bool isVBF = opt.checkBoolOption("VBF");
string lshapeHistName = "cps";
string intHistName = "nominal";
if (systVar == "LSHAPE_UP") {
intHistName = "up";
} else if (systVar == "LSHAPE_DOWN") {
intHistName = "down";
}
if (isVBF) {
TFile weightFile("VBF_LineShapes.root");
higgsW = (TGraph *) ( (TDirectory *) weightFile.Get(dirName.c_str()))->Get( lshapeHistName.c_str() )->Clone();
} else {
TFile weightFile("GG_LineShapes.root");
higgsW = (TGraph *) ( (TDirectory *) weightFile.Get(dirName.c_str()))->Get( lshapeHistName.c_str() )->Clone();
TFile interfFile("newwgts_interf.root");
higgsI = (TGraph *) ( (TDirectory *) interfFile.Get(dirName.c_str()))->Get( intHistName.c_str() )->Clone();
}
}
}
TFile * file = new TFile( inputFile.c_str() );
if (!file->IsOpen())
throw string("ERROR: Can't open the file: " + inputFile + "!");
TDirectory * dir = (TDirectory *) file->Get("dataAnalyzer");
TH1D * nEvHisto = (TH1D *) dir->Get("cutflow");
TH1D * puHisto = (TH1D *) dir->Get("pileup");
TTree * tree = ( TTree * ) dir->Get( "data" );
Event ev( tree );
const int * runP = ev.getSVA<int>("run");
const int * lumiP = ev.getSVA<int>("lumi");
const int * eventP = ev.getSVA<int>("event");
const bool * trigBits = ev.getAVA<bool>("t_bits");
const int * trigPres = ev.getAVA<int>("t_prescale");
const float * metPtA = ev.getAVA<float>("met_pt");
const float * metPhiA = ev.getAVA<float>("met_phi");
const float * rhoP = ev.getSVA<float>("rho");
const float * rho25P = ev.getSVA<float>("rho25");
const int * nvtxP = ev.getSVA<int>("nvtx");
const int * niP = ev.getSVA<int>("ngenITpu");
#ifdef PRINTEVENTS
string eventFileName;
if (type == ELE)
eventFileName = "events_ele.txt";
else if (type == MU)
eventFileName = "events_mu.txt";
else if (type == EMU)
eventFileName = "events_emu.txt";
EventPrinter evPrint(ev, type, eventFileName);
evPrint.readInEvents("diff.txt");
evPrint.printElectrons();
evPrint.printMuons();
evPrint.printZboson();
evPrint.printJets();
evPrint.printHeader();
#endif
string outputFile = outputDir + '/' + sampleName;
if (systVar.size())
outputFile += ('_' + systVar);
if (type == ELE)
outputFile += "_elePresel.root";
else if (type == MU)
outputFile += "_muPresel.root";
else if (type == EMU)
outputFile += "_emuPresel.root";
else if (type == PHOT)
outputFile += "_phPresel.root";
cout << "\tOutput file: " << outputFile << endl;
TFile * out = new TFile( outputFile.c_str(), "recreate" );
TH1D * outNEvHisto = new TH1D("nevt", "nevt", 1, 0, 1);
outNEvHisto->SetBinContent(1, nEvHisto->GetBinContent(1));
outNEvHisto->Write("nevt");
TH1D * outPuHisto = new TH1D( *puHisto );
outPuHisto->Write("pileup");
std::vector< std::tuple<std::string, std::string> > eleVars;
eleVars.push_back( std::make_tuple("ln_px", "F") );
eleVars.push_back( std::make_tuple("ln_py", "F") );
eleVars.push_back( std::make_tuple("ln_pz", "F") );
eleVars.push_back( std::make_tuple("ln_en", "F") );
eleVars.push_back( std::make_tuple("ln_idbits", "I") );
eleVars.push_back( std::make_tuple("ln_d0", "F") );
eleVars.push_back( std::make_tuple("ln_dZ", "F") );
eleVars.push_back( std::make_tuple("ln_nhIso03", "F") );
eleVars.push_back( std::make_tuple("ln_gIso03", "F") );
eleVars.push_back( std::make_tuple("ln_chIso03", "F") );
eleVars.push_back( std::make_tuple("ln_trkLostInnerHits", "F") );
std::vector< std::tuple<std::string, std::string> > addEleVars;
addEleVars.push_back( std::make_tuple("egn_sceta", "F") );
addEleVars.push_back( std::make_tuple("egn_detain", "F") );
addEleVars.push_back( std::make_tuple("egn_dphiin", "F") );
addEleVars.push_back( std::make_tuple("egn_sihih", "F") );
addEleVars.push_back( std::make_tuple("egn_hoe", "F") );
addEleVars.push_back( std::make_tuple("egn_ooemoop", "F") );
addEleVars.push_back( std::make_tuple("egn_isConv", "B") );
std::vector< std::tuple<std::string, std::string> > muVars;
muVars.push_back( std::make_tuple("ln_px", "F") );
muVars.push_back( std::make_tuple("ln_py", "F") );
muVars.push_back( std::make_tuple("ln_pz", "F") );
muVars.push_back( std::make_tuple("ln_en", "F") );
muVars.push_back( std::make_tuple("ln_idbits", "I") );
muVars.push_back( std::make_tuple("ln_d0", "F") );
muVars.push_back( std::make_tuple("ln_dZ", "F") );
muVars.push_back( std::make_tuple("ln_nhIso04", "F") );
muVars.push_back( std::make_tuple("ln_gIso04", "F") );
muVars.push_back( std::make_tuple("ln_chIso04", "F") );
muVars.push_back( std::make_tuple("ln_puchIso04", "F") );
muVars.push_back( std::make_tuple("ln_trkchi2", "F") );
muVars.push_back( std::make_tuple("ln_trkValidPixelHits", "F") );
std::vector< std::tuple<std::string, std::string> > addMuVars;
addMuVars.push_back( std::make_tuple("mn_trkLayersWithMeasurement", "F") );
addMuVars.push_back( std::make_tuple("mn_pixelLayersWithMeasurement", "F") );
addMuVars.push_back( std::make_tuple("mn_innerTrackChi2", "F") );
addMuVars.push_back( std::make_tuple("mn_validMuonHits", "F") );
addMuVars.push_back( std::make_tuple("mn_nMatchedStations", "F") );
unsigned run;
unsigned lumi;
unsigned event;
double pfmet;
int nele;
int nmu;
int nsoftmu;
double l1pt;
double l1eta;
double l1phi;
double l2pt;
double l2eta;
double l2phi;
double zmass;
double zpt;
double zeta;
double mt;
int nsoftjet;
int nhardjet;
double maxJetBTag;
double minDeltaPhiJetMet;
double detajj;
double mjj;
int nvtx;
int ni;
int category;
double weight;
double hmass;
double hweight;
TTree * smallTree = new TTree("HZZ2l2nuAnalysis", "HZZ2l2nu Analysis Tree");
smallTree->Branch( "Run", &run, "Run/i" );
smallTree->Branch( "Lumi", &lumi, "Lumi/i" );
smallTree->Branch( "Event", &event, "Event/i" );
smallTree->Branch( "PFMET", &pfmet, "PFMET/D" );
smallTree->Branch( "NELE", &nele, "NELE/I" );
smallTree->Branch( "NMU", &nmu, "NMU/I" );
smallTree->Branch( "NSOFTMU", &nsoftmu, "NSOFTMU/I" );
smallTree->Branch( "L1PT", &l1pt, "L1PT/D" );
smallTree->Branch( "L1ETA", &l1eta, "L1ETA/D" );
smallTree->Branch( "L1PHI", &l1phi, "L1PHI/D" );
smallTree->Branch( "L2PT", &l2pt, "L2PT/D" );
smallTree->Branch( "L2ETA", &l2eta, "L2ETA/D" );
smallTree->Branch( "L2PHI", &l2phi, "L2PHI/D" );
smallTree->Branch( "ZMASS", &zmass, "ZMASS/D" );
smallTree->Branch( "ZPT", &zpt, "ZPT/D" );
smallTree->Branch( "ZETA", &zeta, "ZETA/D" );
smallTree->Branch( "MT", &mt, "MT/D" );
smallTree->Branch( "NSOFTJET", &nsoftjet, "NSOFTJET/I" );
smallTree->Branch( "NHARDJET", &nhardjet, "NHARDJET/I" );
smallTree->Branch( "MAXJETBTAG", &maxJetBTag, "MAXJETBTAG/D" );
smallTree->Branch( "MINDPJETMET", &minDeltaPhiJetMet, "MINDPJETMET/D" );
smallTree->Branch( "DETAJJ", &detajj, "DETAJJ/D" );
smallTree->Branch( "MJJ", &mjj, "MJJ/D" );
smallTree->Branch( "NVTX", &nvtx, "NVTX/I" );
smallTree->Branch( "nInter" , &ni, "nInter/I" );
smallTree->Branch( "CATEGORY", &category, "CATEGORY/I" );
smallTree->Branch( "Weight" , &weight, "Weight/D" );
smallTree->Branch( "HMASS", &hmass, "HMASS/D" );
smallTree->Branch( "HWEIGHT", &hweight, "HWEIGHT/D" );
bool isData = opt.checkBoolOption("DATA");
unsigned long nentries = tree->GetEntries();
RooDataSet * events = nullptr;
PhotonPrescale photonPrescales;
vector<int> thresholds;
if (type == PHOT) {
if (w == nullptr)
throw string("ERROR: No mass peak pdf!");
RooRealVar * zmass = w->var("mass");
zmass->setRange(76.0, 106.0);
RooAbsPdf * pdf = w->pdf("massPDF");
events = pdf->generate(*zmass, nentries);
photonPrescales.addTrigger("HLT_Photon36_R9Id90_HE10_Iso40_EBOnly", 36, 3, 7);
photonPrescales.addTrigger("HLT_Photon50_R9Id90_HE10_Iso40_EBOnly", 50, 5, 8);
photonPrescales.addTrigger("HLT_Photon75_R9Id90_HE10_Iso40_EBOnly", 75, 7, 9);
photonPrescales.addTrigger("HLT_Photon90_R9Id90_HE10_Iso40_EBOnly", 90, 10, 10);
}
TH1D ptSpectrum("ptSpectrum", "ptSpectrum", 200, 55, 755);
ptSpectrum.Sumw2();
unordered_set<EventAdr> eventsSet;
for ( unsigned long iEvent = 0; iEvent < nentries; iEvent++ ) {
// if (iEvent < 6060000)
// continue;
if ( iEvent % 10000 == 0) {
cout << string(40, '\b');
cout << setw(10) << iEvent << " / " << setw(10) << nentries << " done ..." << std::flush;
}
tree->GetEntry( iEvent );
run = -999;
lumi = -999;
event = -999;
pfmet = -999;
nele = -999;
nmu = -999;
nsoftmu = -999;
l1pt = -999;
l1eta = -999;
l1phi = -999;
l2pt = -999;
l2eta = -999;
l2phi = -999;
zmass = -999;
zpt = -999;
zeta = -999;
mt = -999;
nsoftjet = -999;
nhardjet = -999;
maxJetBTag = -999;
minDeltaPhiJetMet = -999;
detajj = -999;
mjj = -999;
nvtx = -999;
ni = -999;
weight = -999;
category = -1;
hmass = -999;
hweight = -999;
run = *runP;
lumi = *lumiP;
event = *eventP;
EventAdr tmp(run, lumi, event);
if (eventsSet.find( tmp ) != eventsSet.end()) {
continue;
}
eventsSet.insert( tmp );
if (type == ELE && isData) {
if (trigBits[0] != 1 || trigPres[0] != 1)
continue;
}
if (type == MU && isData) {
if ( (trigBits[2] != 1 || trigPres[2] != 1)
&& (trigBits[3] != 1 || trigPres[3] != 1)
&& (trigBits[6] != 1 || trigPres[6] != 1)
)
continue;
}
if (type == EMU && isData) {
if ( (trigBits[4] != 1 || trigPres[4] != 1)
&& (trigBits[5] != 1 || trigPres[5] != 1)
)
continue;
}
vector<Electron> electrons = buildLeptonCollection<Electron, 11>(ev, eleVars, addEleVars);
vector<Muon> muons = buildLeptonCollection<Muon, 13>(ev, muVars, addMuVars);
float rho = *rhoP;
float rho25 = *rho25P;
vector<Electron> looseElectrons;
vector<Electron> selectedElectrons;
for (unsigned j = 0; j < electrons.size(); ++j) {
try {
TLorentzVector lv = electrons[j].lorentzVector();
if (
lv.Pt() > 10 &&
fabs(lv.Eta()) < 2.5 &&
!electrons[j].isInCrack() &&
electrons[j].passesVetoID() &&
electrons[j].isPFIsolatedLoose(rho25)
) {
looseElectrons.push_back(electrons[j]);
}
if (
lv.Pt() > 20 &&
fabs(lv.Eta()) < 2.5 &&
!electrons[j].isInCrack() &&
electrons[j].passesMediumID() &&
electrons[j].isPFIsolatedMedium(rho25)
) {
selectedElectrons.push_back(electrons[j]);
}
} catch (const string & exc) {
cout << exc << endl;
cout << "run = " << run << endl;
cout << "lumi = " << lumi << endl;
cout << "event = " << event << endl;
}
}
vector<Muon> looseMuons;
vector<Muon> softMuons;
vector<Muon> selectedMuons;
for (unsigned j = 0; j < muons.size(); ++j) {
TLorentzVector lv = muons[j].lorentzVector();
if (
lv.Pt() > 10 &&
fabs(lv.Eta()) < 2.4 &&
muons[j].isLooseMuon() &&
muons[j].isPFIsolatedLoose()
) {
looseMuons.push_back(muons[j]);
} else if (
lv.Pt() > 3 &&
fabs(lv.Eta()) < 2.4 &&
muons[j].isSoftMuon()
) {
softMuons.push_back(muons[j]);
}
if (
lv.Pt() > 20 &&
fabs(lv.Eta()) < 2.4 &&
muons[j].isTightMuon() &&
muons[j].isPFIsolatedTight()
) {
selectedMuons.push_back(muons[j]);
}
}
vector<Lepton> looseLeptons;
for (unsigned i = 0; i < looseElectrons.size(); ++i)
looseLeptons.push_back(looseElectrons[i]);
for (unsigned i = 0; i < looseMuons.size(); ++i)
looseLeptons.push_back(looseMuons[i]);
for (unsigned i = 0; i < softMuons.size(); ++i)
looseLeptons.push_back(softMuons[i]);
#ifdef PRINTEVENTS
evPrint.setElectronCollection(selectedElectrons);
evPrint.setMuonCollection(selectedMuons);
#endif
vector<Photon> photons = selectPhotonsCMG( ev );
vector<Photon> selectedPhotons;
for (unsigned i = 0; i < photons.size(); ++i) {
if (photons[i].isSelected(rho) && photons[i].lorentzVector().Pt() > 55)
selectedPhotons.push_back( photons[i] );
}
if (type == PHOT) {
vector<Electron> tmpElectrons;
for (unsigned i = 0; i < selectedPhotons.size(); ++i) {
TLorentzVector phVec = selectedPhotons[i].lorentzVector();
for (unsigned j = 0; j < looseElectrons.size(); ++j) {
TLorentzVector elVec = looseElectrons[j].lorentzVector();
double dR = deltaR(phVec.Eta(), phVec.Phi(), elVec.Eta(), elVec.Phi());
if ( dR > 0.05 )
tmpElectrons.push_back( looseElectrons[j] );
}
}
looseElectrons = tmpElectrons;
}
string leptonsType;
Lepton * selectedLeptons[2] = {0};
if (type == ELE) {
if (selectedElectrons.size() < 2) {
continue;
} else {
selectedLeptons[0] = &selectedElectrons[0];
selectedLeptons[1] = &selectedElectrons[1];
}
} else if (type == MU) {
if (selectedMuons.size() < 2) {
continue;
} else {
selectedLeptons[0] = &selectedMuons[0];
selectedLeptons[1] = &selectedMuons[1];
}
} else if (type == EMU) {
if (selectedElectrons.size() < 1 || selectedMuons.size() < 1) {
continue;
} else {
selectedLeptons[0] = &selectedElectrons[0];
selectedLeptons[1] = &selectedMuons[0];
}
} else if (type == PHOT) {
if (selectedPhotons.size() != 1) {
continue;
}
}
nele = looseElectrons.size();
nmu = looseMuons.size();
nsoftmu = softMuons.size();
TLorentzVector Zcand;
if (type == ELE || type == MU || type == EMU) {
TLorentzVector lep1 = selectedLeptons[0]->lorentzVector();
TLorentzVector lep2 = selectedLeptons[1]->lorentzVector();
if (lep2.Pt() > lep1.Pt() && type != EMU) {
TLorentzVector temp = lep1;
lep1 = lep2;
lep2 = temp;
}
l1pt = lep1.Pt();
l1eta = lep1.Eta();
l1phi = lep1.Phi();
l2pt = lep2.Pt();
l2eta = lep2.Eta();
l2phi = lep2.Phi();
Zcand = lep1 + lep2;
zmass = Zcand.M();
} else if (type == PHOT) {
Zcand = selectedPhotons[0].lorentzVector();
zmass = events->get(iEvent)->getRealValue("mass");
}
zpt = Zcand.Pt();
zeta = Zcand.Eta();
if (type == PHOT) {
unsigned idx = photonPrescales.getIndex(zpt);
if (trigBits[idx])
weight = trigPres[idx];
else
continue;
ptSpectrum.Fill(zpt, weight);
}
TLorentzVector met;
met.SetPtEtaPhiM(metPtA[0], 0.0, metPhiA[0], 0.0);
TLorentzVector clusteredFlux;
unsigned mode = 0;
if (systVar == "JES_UP")
mode = 1;
else if (systVar == "JES_DOWN")
mode = 2;
TLorentzVector jecCorr;
#ifdef CMSSWENV
vector<Jet> jetsAll = selectJetsCMG( ev, looseLeptons, jecUnc, &jecCorr, mode );
#else
vector<Jet> jetsAll = selectJetsCMG( ev, looseLeptons, &jecCorr, mode );
#endif
met -= jecCorr;
mode = 0;
if (systVar == "JER_UP")
mode = 1;
else if (systVar == "JER_DOWN")
mode = 2;
TLorentzVector smearCorr = smearJets( jetsAll, mode );
if (isData && smearCorr != TLorentzVector())
throw std::string("Jet smearing corrections different from zero in DATA!");
met -= smearCorr;
vector<Jet> selectedJets;
for (unsigned i = 0; i < jetsAll.size(); ++i) {
if (
jetsAll[i].lorentzVector().Pt() > 10
&& fabs(jetsAll[i].lorentzVector().Eta()) < 4.7
&& jetsAll[i].passesPUID() &&
jetsAll[i].passesPFLooseID()
)
selectedJets.push_back( jetsAll[i] );
}
if (type == PHOT) {
vector<Jet> tmpJets;
for (unsigned i = 0; i < selectedPhotons.size(); ++i) {
TLorentzVector phVec = selectedPhotons[i].lorentzVector();
for (unsigned j = 0; j < selectedJets.size(); ++j) {
TLorentzVector jVec = selectedJets[j].lorentzVector();
double dR = deltaR(phVec.Eta(), phVec.Phi(), jVec.Eta(), jVec.Phi());
if ( dR > 0.4 )
tmpJets.push_back( selectedJets[j] );
}
}
selectedJets = tmpJets;
}
if (systVar == "UMET_UP" || systVar == "UMET_DOWN") {
for (unsigned i = 0; i < jetsAll.size(); ++i)
clusteredFlux += jetsAll[i].lorentzVector();
for (unsigned i = 0; i < looseElectrons.size(); ++i)
clusteredFlux += looseElectrons[i].lorentzVector();
for (unsigned i = 0; i < looseMuons.size(); ++i)
clusteredFlux += looseMuons[i].lorentzVector();
TLorentzVector unclusteredFlux = -(met + clusteredFlux);
if (systVar == "UMET_UP")
unclusteredFlux *= 1.1;
else
unclusteredFlux *= 0.9;
met = -(clusteredFlux + unclusteredFlux);
}
if (systVar == "LES_UP" || systVar == "LES_DOWN") {
TLorentzVector diff;
double sign = 1.0;
if (systVar == "LES_DOWN")
sign = -1.0;
for (unsigned i = 0; i < looseElectrons.size(); ++i) {
TLorentzVector tempEle = looseElectrons[i].lorentzVector();
if (looseElectrons[i].isEB())
diff += sign * 0.02 * tempEle;
else
diff += sign * 0.05 * tempEle;
}
for (unsigned i = 0; i < looseMuons.size(); ++i)
diff += sign * 0.01 * looseMuons[i].lorentzVector();
met -= diff;
}
pfmet = met.Pt();
double px = met.Px() + Zcand.Px();
double py = met.Py() + Zcand.Py();
double pt2 = px * px + py * py;
double e = sqrt(zpt * zpt + zmass * zmass) + sqrt(pfmet * pfmet + zmass * zmass);
double mt2 = e * e - pt2;
mt = (mt2 > 0) ? sqrt(mt2) : 0;
vector<Jet> hardjets;
vector<Jet> softjets;
maxJetBTag = -999;
minDeltaPhiJetMet = 999;
for ( unsigned j = 0; j < selectedJets.size(); ++j ) {
TLorentzVector jet = selectedJets[j].lorentzVector();
if ( jet.Pt() > 30 ) {
hardjets.push_back( selectedJets[j] );
}
if ( jet.Pt() > 15 )
softjets.push_back( selectedJets[j] );
}
nhardjet = hardjets.size();
nsoftjet = softjets.size();
// if ( type == PHOT && nsoftjet == 0 )
// continue;
if (nhardjet > 1) {
sort(hardjets.begin(), hardjets.end(), [](const Jet & a, const Jet & b) {
return a.lorentzVector().Pt() > b.lorentzVector().Pt();
});
TLorentzVector jet1 = hardjets[0].lorentzVector();
TLorentzVector jet2 = hardjets[1].lorentzVector();
const double maxEta = max( jet1.Eta(), jet2.Eta() );
const double minEta = min( jet1.Eta(), jet2.Eta() );
bool passCJV = true;
for (unsigned j = 2; j < hardjets.size(); ++j) {
double tmpEta = hardjets[j].lorentzVector().Eta();
if ( tmpEta > minEta && tmpEta < maxEta )
passCJV = false;
}
const double tmpDelEta = std::fabs(jet2.Eta() - jet1.Eta());
TLorentzVector diJetSystem = jet1 + jet2;
const double tmpMass = diJetSystem.M();
if ( type == PHOT) {
if (passCJV && tmpDelEta > 4.0 && tmpMass > 500 && zeta > minEta && maxEta > zeta) {
detajj = tmpDelEta;
mjj = tmpMass;
}
} else {
if (passCJV && tmpDelEta > 4.0 && tmpMass > 500 && l1eta > minEta && l2eta > minEta && maxEta > l1eta && maxEta > l2eta) {
detajj = tmpDelEta;
mjj = tmpMass;
}
}
}
category = evCategory(nhardjet, nsoftjet, detajj, mjj, type == PHOT);
minDeltaPhiJetMet = 10;
for ( unsigned j = 0; j < hardjets.size(); ++j ) {
TLorentzVector jet = hardjets[j].lorentzVector();
if ( hardjets[j].getVarF("jn_jp") > maxJetBTag && fabs(jet.Eta()) < 2.5 )
maxJetBTag = hardjets[j].getVarF("jn_jp");
double tempDelPhiJetMet = deltaPhi(met.Phi(), jet.Phi());
if ( tempDelPhiJetMet < minDeltaPhiJetMet )
minDeltaPhiJetMet = tempDelPhiJetMet;
}
nvtx = *nvtxP;
if (isData)
ni = -1;
else
ni = *niP;
if (isSignal) {
const int nMC = ev.getSVV<int>("mcn");
const int * mcID = ev.getAVA<int>("mc_id");
int hIdx = 0;
for (; hIdx < nMC; ++hIdx)
if (fabs(mcID[hIdx]) == 25)
break;
if (hIdx == nMC)
throw string("ERROR: Higgs not found in signal sample!");
float Hpx = ev.getAVV<float>("mc_px", hIdx);
float Hpy = ev.getAVV<float>("mc_py", hIdx);
float Hpz = ev.getAVV<float>("mc_pz", hIdx);
float Hen = ev.getAVV<float>("mc_en", hIdx);
TLorentzVector higgs;
higgs.SetPxPyPzE( Hpx, Hpy, Hpz, Hen );
hmass = higgs.M();
if (higgsW) {
hweight = higgsW->Eval(hmass);
if (higgsI)
hweight *= higgsI->Eval(hmass);
} else
hweight = 1;
}
if ( opt.checkBoolOption("ADDITIONAL_LEPTON_VETO") && (type == ELE || type == MU || type == EMU) && ((nele + nmu + nsoftmu) > 2) )
continue;
if ( opt.checkBoolOption("ADDITIONAL_LEPTON_VETO") && (type == PHOT) && ((nele + nmu + nsoftmu) > 0) )
continue;
if ( opt.checkBoolOption("ZPT_CUT") && zpt < 55 )
continue;
// for different background estimation methods different window should be applied:
// * sample for photons should have 76.0 < zmass < 106.0
// * sample for non-resonant background should not have this cut applied
if ( opt.checkBoolOption("TIGHT_ZMASS_CUT") && (type == ELE || type == MU) && (zmass < 76.0 || zmass > 106.0))
continue;
if ( opt.checkBoolOption("WIDE_ZMASS_CUT") && (type == ELE || type == MU) && (zmass < 76.0 || zmass > 106.0))
continue;
if ( opt.checkBoolOption("BTAG_CUT") && ( maxJetBTag > 0.264) )
continue;
if ( opt.checkBoolOption("DPHI_CUT") && ( minDeltaPhiJetMet < 0.5) )
continue;
#ifdef PRINTEVENTS
evPrint.setJetCollection(hardjets);
evPrint.setMET(met);
evPrint.setMT(mt);
string channelType;
if (type == ELE)
channelType = "ee";
else if (type == MU)
channelType = "mumu";
else if (type == EMU)
channelType = "emu";
if (category == 1)
channelType += "eq0jets";
else if (category == 2)
channelType += "geq1jets";
else
channelType += "vbf";
evPrint.setChannel(channelType);
unsigned bits = 0;
bits |= (0x7);
bits |= ((zmass > 76.0 && zmass < 106.0) << 3);
bits |= ((zpt > 55) << 4);
bits |= (((nele + nmu + nsoftmu) == 2) << 5);
bits |= ((maxJetBTag < 0.275) << 6);
bits |= ((minDeltaPhiJetMet > 0.5) << 7);
evPrint.setBits(bits);
evPrint.print();
#endif
smallTree->Fill();
}
cout << endl;
TCanvas canv("canv", "canv", 800, 600);
//effNum.Sumw2();
//effDen.Sumw2();
//effNum.Divide(&effDen);
//effNum.Draw();
canv.SetGridy();
canv.SetGridx();
//canv.SaveAs("triggEff.ps");
//canv.Clear();
ptSpectrum.SetMarkerStyle(20);
ptSpectrum.SetMarkerSize(0.5);
ptSpectrum.Draw("P0E");
//ptSpectrum.Draw("COLZ");
canv.SetLogy();
canv.SaveAs("ptSpectrum.ps");
delete file;
smallTree->Write("", TObject::kOverwrite);
delete smallTree;
delete out;
}
// internal routine to run the inverter
HypoTestInverterResult *
RooStats::HypoTestInvTool::RunInverter(RooWorkspace * w,
const char * modelSBName, const char * modelBName,
const char * dataName, int type, int testStatType,
bool useCLs, int npoints, double poimin, double poimax,
int ntoys,
bool useNumberCounting,
const char * nuisPriorName ){
std::cout << "Running HypoTestInverter on the workspace " << w->GetName() << std::endl;
w->Print();
RooAbsData * data = w->data(dataName);
if (!data) {
Error("StandardHypoTestDemo","Not existing data %s",dataName);
return 0;
}
else
std::cout << "Using data set " << dataName << std::endl;
if (mUseVectorStore) {
RooAbsData::setDefaultStorageType(RooAbsData::Vector);
data->convertToVectorStore() ;
}
// get models from WS
// get the modelConfig out of the file
ModelConfig* bModel = (ModelConfig*) w->obj(modelBName);
ModelConfig* sbModel = (ModelConfig*) w->obj(modelSBName);
if (!sbModel) {
Error("StandardHypoTestDemo","Not existing ModelConfig %s",modelSBName);
return 0;
}
// check the model
if (!sbModel->GetPdf()) {
Error("StandardHypoTestDemo","Model %s has no pdf ",modelSBName);
return 0;
}
if (!sbModel->GetParametersOfInterest()) {
Error("StandardHypoTestDemo","Model %s has no poi ",modelSBName);
return 0;
}
if (!sbModel->GetObservables()) {
Error("StandardHypoTestInvDemo","Model %s has no observables ",modelSBName);
return 0;
}
if (!sbModel->GetSnapshot() ) {
Info("StandardHypoTestInvDemo","Model %s has no snapshot - make one using model poi",modelSBName);
sbModel->SetSnapshot( *sbModel->GetParametersOfInterest() );
}
// case of no systematics
// remove nuisance parameters from model
if (noSystematics) {
const RooArgSet * nuisPar = sbModel->GetNuisanceParameters();
if (nuisPar && nuisPar->getSize() > 0) {
std::cout << "StandardHypoTestInvDemo" << " - Switch off all systematics by setting them constant to their initial values" << std::endl;
RooStats::SetAllConstant(*nuisPar);
}
if (bModel) {
const RooArgSet * bnuisPar = bModel->GetNuisanceParameters();
if (bnuisPar)
RooStats::SetAllConstant(*bnuisPar);
}
}
if (!bModel || bModel == sbModel) {
Info("StandardHypoTestInvDemo","The background model %s does not exist",modelBName);
Info("StandardHypoTestInvDemo","Copy it from ModelConfig %s and set POI to zero",modelSBName);
bModel = (ModelConfig*) sbModel->Clone();
bModel->SetName(TString(modelSBName)+TString("_with_poi_0"));
RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
if (!var) return 0;
double oldval = var->getVal();
var->setVal(0);
bModel->SetSnapshot( RooArgSet(*var) );
var->setVal(oldval);
}
else {
if (!bModel->GetSnapshot() ) {
Info("StandardHypoTestInvDemo","Model %s has no snapshot - make one using model poi and 0 values ",modelBName);
RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
if (var) {
double oldval = var->getVal();
var->setVal(0);
bModel->SetSnapshot( RooArgSet(*var) );
var->setVal(oldval);
}
else {
Error("StandardHypoTestInvDemo","Model %s has no valid poi",modelBName);
return 0;
}
}
}
// check model has global observables when there are nuisance pdf
// for the hybrid case the globobs are not needed
if (type != 1 ) {
bool hasNuisParam = (sbModel->GetNuisanceParameters() && sbModel->GetNuisanceParameters()->getSize() > 0);
bool hasGlobalObs = (sbModel->GetGlobalObservables() && sbModel->GetGlobalObservables()->getSize() > 0);
if (hasNuisParam && !hasGlobalObs ) {
// try to see if model has nuisance parameters first
RooAbsPdf * constrPdf = RooStats::MakeNuisancePdf(*sbModel,"nuisanceConstraintPdf_sbmodel");
if (constrPdf) {
Warning("StandardHypoTestInvDemo","Model %s has nuisance parameters but no global observables associated",sbModel->GetName());
Warning("StandardHypoTestInvDemo","\tThe effect of the nuisance parameters will not be treated correctly ");
}
}
}
// save all initial parameters of the model including the global observables
RooArgSet initialParameters;
RooArgSet * allParams = sbModel->GetPdf()->getParameters(*data);
allParams->snapshot(initialParameters);
delete allParams;
// run first a data fit
const RooArgSet * poiSet = sbModel->GetParametersOfInterest();
RooRealVar *poi = (RooRealVar*)poiSet->first();
std::cout << "StandardHypoTestInvDemo : POI initial value: " << poi->GetName() << " = " << poi->getVal() << std::endl;
// fit the data first (need to use constraint )
TStopwatch tw;
bool doFit = initialFit;
if (testStatType == 0 && initialFit == -1) doFit = false; // case of LEP test statistic
if (type == 3 && initialFit == -1) doFit = false; // case of Asymptoticcalculator with nominal Asimov
double poihat = 0;
if (minimizerType.size()==0) minimizerType = ROOT::Math::MinimizerOptions::DefaultMinimizerType();
else
ROOT::Math::MinimizerOptions::SetDefaultMinimizer(minimizerType.c_str());
Info("StandardHypoTestInvDemo","Using %s as minimizer for computing the test statistic",
ROOT::Math::MinimizerOptions::DefaultMinimizerType().c_str() );
if (doFit) {
// do the fit : By doing a fit the POI snapshot (for S+B) is set to the fit value
// and the nuisance parameters nominal values will be set to the fit value.
// This is relevant when using LEP test statistics
Info( "StandardHypoTestInvDemo"," Doing a first fit to the observed data ");
RooArgSet constrainParams;
if (sbModel->GetNuisanceParameters() ) constrainParams.add(*sbModel->GetNuisanceParameters());
RooStats::RemoveConstantParameters(&constrainParams);
tw.Start();
RooFitResult * fitres = sbModel->GetPdf()->fitTo(*data,InitialHesse(false), Hesse(false),
Minimizer(minimizerType.c_str(),"Migrad"), Strategy(0), PrintLevel(mPrintLevel), Constrain(constrainParams), Save(true), Offset(RooStats::IsNLLOffset()) );
if (fitres->status() != 0) {
Warning("StandardHypoTestInvDemo","Fit to the model failed - try with strategy 1 and perform first an Hesse computation");
fitres = sbModel->GetPdf()->fitTo(*data,InitialHesse(true), Hesse(false),Minimizer(minimizerType.c_str(),"Migrad"), Strategy(1), PrintLevel(mPrintLevel+1), Constrain(constrainParams),
Save(true), Offset(RooStats::IsNLLOffset()) );
}
if (fitres->status() != 0)
Warning("StandardHypoTestInvDemo"," Fit still failed - continue anyway.....");
poihat = poi->getVal();
std::cout << "StandardHypoTestInvDemo - Best Fit value : " << poi->GetName() << " = "
<< poihat << " +/- " << poi->getError() << std::endl;
std::cout << "Time for fitting : "; tw.Print();
//save best fit value in the poi snapshot
sbModel->SetSnapshot(*sbModel->GetParametersOfInterest());
std::cout << "StandardHypoTestInvo: snapshot of S+B Model " << sbModel->GetName()
<< " is set to the best fit value" << std::endl;
}
// print a message in case of LEP test statistics because it affects result by doing or not doing a fit
if (testStatType == 0) {
if (!doFit)
Info("StandardHypoTestInvDemo","Using LEP test statistic - an initial fit is not done and the TS will use the nuisances at the model value");
else
Info("StandardHypoTestInvDemo","Using LEP test statistic - an initial fit has been done and the TS will use the nuisances at the best fit value");
}
// build test statistics and hypotest calculators for running the inverter
SimpleLikelihoodRatioTestStat slrts(*sbModel->GetPdf(),*bModel->GetPdf());
// null parameters must includes snapshot of poi plus the nuisance values
RooArgSet nullParams(*sbModel->GetSnapshot());
if (sbModel->GetNuisanceParameters()) nullParams.add(*sbModel->GetNuisanceParameters());
if (sbModel->GetSnapshot()) slrts.SetNullParameters(nullParams);
RooArgSet altParams(*bModel->GetSnapshot());
if (bModel->GetNuisanceParameters()) altParams.add(*bModel->GetNuisanceParameters());
if (bModel->GetSnapshot()) slrts.SetAltParameters(altParams);
if (mEnableDetOutput) slrts.EnableDetailedOutput();
// ratio of profile likelihood - need to pass snapshot for the alt
RatioOfProfiledLikelihoodsTestStat
ropl(*sbModel->GetPdf(), *bModel->GetPdf(), bModel->GetSnapshot());
ropl.SetSubtractMLE(false);
if (testStatType == 11) ropl.SetSubtractMLE(true);
ropl.SetPrintLevel(mPrintLevel);
ropl.SetMinimizer(minimizerType.c_str());
if (mEnableDetOutput) ropl.EnableDetailedOutput();
ProfileLikelihoodTestStat profll(*sbModel->GetPdf());
if (testStatType == 3) profll.SetOneSided(true);
if (testStatType == 4) profll.SetSigned(true);
profll.SetMinimizer(minimizerType.c_str());
profll.SetPrintLevel(mPrintLevel);
if (mEnableDetOutput) profll.EnableDetailedOutput();
profll.SetReuseNLL(mOptimize);
slrts.SetReuseNLL(mOptimize);
ropl.SetReuseNLL(mOptimize);
if (mOptimize) {
profll.SetStrategy(0);
ropl.SetStrategy(0);
ROOT::Math::MinimizerOptions::SetDefaultStrategy(0);
}
if (mMaxPoi > 0) poi->setMax(mMaxPoi); // increase limit
MaxLikelihoodEstimateTestStat maxll(*sbModel->GetPdf(),*poi);
NumEventsTestStat nevtts;
AsymptoticCalculator::SetPrintLevel(mPrintLevel);
// create the HypoTest calculator class
HypoTestCalculatorGeneric * hc = 0;
if (type == 0) hc = new FrequentistCalculator(*data, *bModel, *sbModel);
else if (type == 1) hc = new HybridCalculator(*data, *bModel, *sbModel);
// else if (type == 2 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, false, mAsimovBins);
// else if (type == 3 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, true, mAsimovBins); // for using Asimov data generated with nominal values
else if (type == 2 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, false );
else if (type == 3 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, true ); // for using Asimov data generated with nominal values
else {
Error("StandardHypoTestInvDemo","Invalid - calculator type = %d supported values are only :\n\t\t\t 0 (Frequentist) , 1 (Hybrid) , 2 (Asymptotic) ",type);
return 0;
}
// set the test statistic
TestStatistic * testStat = 0;
if (testStatType == 0) testStat = &slrts;
if (testStatType == 1 || testStatType == 11) testStat = &ropl;
if (testStatType == 2 || testStatType == 3 || testStatType == 4) testStat = &profll;
if (testStatType == 5) testStat = &maxll;
if (testStatType == 6) testStat = &nevtts;
if (testStat == 0) {
Error("StandardHypoTestInvDemo","Invalid - test statistic type = %d supported values are only :\n\t\t\t 0 (SLR) , 1 (Tevatron) , 2 (PLR), 3 (PLR1), 4(MLE)",testStatType);
return 0;
}
ToyMCSampler *toymcs = (ToyMCSampler*)hc->GetTestStatSampler();
if (toymcs && (type == 0 || type == 1) ) {
// look if pdf is number counting or extended
if (sbModel->GetPdf()->canBeExtended() ) {
if (useNumberCounting) Warning("StandardHypoTestInvDemo","Pdf is extended: but number counting flag is set: ignore it ");
}
else {
// for not extended pdf
if (!useNumberCounting ) {
int nEvents = data->numEntries();
Info("StandardHypoTestInvDemo","Pdf is not extended: number of events to generate taken from observed data set is %d",nEvents);
toymcs->SetNEventsPerToy(nEvents);
}
else {
Info("StandardHypoTestInvDemo","using a number counting pdf");
toymcs->SetNEventsPerToy(1);
}
}
toymcs->SetTestStatistic(testStat);
if (data->isWeighted() && !mGenerateBinned) {
Info("StandardHypoTestInvDemo","Data set is weighted, nentries = %d and sum of weights = %8.1f but toy generation is unbinned - it would be faster to set mGenerateBinned to true\n",data->numEntries(), data->sumEntries());
}
toymcs->SetGenerateBinned(mGenerateBinned);
toymcs->SetUseMultiGen(mOptimize);
if (mGenerateBinned && sbModel->GetObservables()->getSize() > 2) {
Warning("StandardHypoTestInvDemo","generate binned is activated but the number of ovservable is %d. Too much memory could be needed for allocating all the bins",sbModel->GetObservables()->getSize() );
}
// set the random seed if needed
if (mRandomSeed >= 0) RooRandom::randomGenerator()->SetSeed(mRandomSeed);
}
// specify if need to re-use same toys
if (reuseAltToys) {
hc->UseSameAltToys();
}
if (type == 1) {
HybridCalculator *hhc = dynamic_cast<HybridCalculator*> (hc);
assert(hhc);
hhc->SetToys(ntoys,ntoys/mNToysRatio); // can use less ntoys for b hypothesis
// remove global observables from ModelConfig (this is probably not needed anymore in 5.32)
bModel->SetGlobalObservables(RooArgSet() );
sbModel->SetGlobalObservables(RooArgSet() );
// check for nuisance prior pdf in case of nuisance parameters
if (bModel->GetNuisanceParameters() || sbModel->GetNuisanceParameters() ) {
// fix for using multigen (does not work in this case)
toymcs->SetUseMultiGen(false);
ToyMCSampler::SetAlwaysUseMultiGen(false);
RooAbsPdf * nuisPdf = 0;
if (nuisPriorName) nuisPdf = w->pdf(nuisPriorName);
// use prior defined first in bModel (then in SbModel)
if (!nuisPdf) {
Info("StandardHypoTestInvDemo","No nuisance pdf given for the HybridCalculator - try to deduce pdf from the model");
if (bModel->GetPdf() && bModel->GetObservables() )
nuisPdf = RooStats::MakeNuisancePdf(*bModel,"nuisancePdf_bmodel");
else
nuisPdf = RooStats::MakeNuisancePdf(*sbModel,"nuisancePdf_sbmodel");
}
if (!nuisPdf ) {
if (bModel->GetPriorPdf()) {
nuisPdf = bModel->GetPriorPdf();
Info("StandardHypoTestInvDemo","No nuisance pdf given - try to use %s that is defined as a prior pdf in the B model",nuisPdf->GetName());
}
else {
Error("StandardHypoTestInvDemo","Cannnot run Hybrid calculator because no prior on the nuisance parameter is specified or can be derived");
return 0;
}
}
assert(nuisPdf);
Info("StandardHypoTestInvDemo","Using as nuisance Pdf ... " );
nuisPdf->Print();
const RooArgSet * nuisParams = (bModel->GetNuisanceParameters() ) ? bModel->GetNuisanceParameters() : sbModel->GetNuisanceParameters();
RooArgSet * np = nuisPdf->getObservables(*nuisParams);
if (np->getSize() == 0) {
Warning("StandardHypoTestInvDemo","Prior nuisance does not depend on nuisance parameters. They will be smeared in their full range");
}
delete np;
hhc->ForcePriorNuisanceAlt(*nuisPdf);
hhc->ForcePriorNuisanceNull(*nuisPdf);
}
}
else if (type == 2 || type == 3) {
if (testStatType == 3) ((AsymptoticCalculator*) hc)->SetOneSided(true);
if (testStatType != 2 && testStatType != 3)
Warning("StandardHypoTestInvDemo","Only the PL test statistic can be used with AsymptoticCalculator - use by default a two-sided PL");
}
else if (type == 0 || type == 1) {
((FrequentistCalculator*) hc)->SetToys(ntoys,ntoys/mNToysRatio);
// store also the fit information for each poi point used by calculator based on toys
if (mEnableDetOutput) ((FrequentistCalculator*) hc)->StoreFitInfo(true);
}
// Get the result
RooMsgService::instance().getStream(1).removeTopic(RooFit::NumIntegration);
HypoTestInverter calc(*hc);
calc.SetConfidenceLevel(confidenceLevel);
calc.UseCLs(useCLs);
calc.SetVerbose(true);
// can speed up using proof-lite
if (mUseProof) {
ProofConfig pc(*w, mNWorkers, "", kFALSE);
toymcs->SetProofConfig(&pc); // enable proof
}
if (npoints > 0) {
if (poimin > poimax) {
// if no min/max given scan between MLE and +4 sigma
poimin = int(poihat);
poimax = int(poihat + 4 * poi->getError());
}
std::cout << "Doing a fixed scan in interval : " << poimin << " , " << poimax << std::endl;
calc.SetFixedScan(npoints,poimin,poimax);
}
else {
//poi->setMax(10*int( (poihat+ 10 *poi->getError() )/10 ) );
std::cout << "Doing an automatic scan in interval : " << poi->getMin() << " , " << poi->getMax() << std::endl;
}
tw.Start();
HypoTestInverterResult * r = calc.GetInterval();
std::cout << "Time to perform limit scan \n";
tw.Print();
if (mRebuild) {
std::cout << "\n***************************************************************\n";
std::cout << "Rebuild the upper limit distribution by re-generating new set of pseudo-experiment and re-compute for each of them a new upper limit\n\n";
allParams = sbModel->GetPdf()->getParameters(*data);
// define on which value of nuisance parameters to do the rebuild
// default is best fit value for bmodel snapshot
if (mRebuildParamValues != 0) {
// set all parameters to their initial workspace values
*allParams = initialParameters;
}
if (mRebuildParamValues == 0 || mRebuildParamValues == 1 ) {
RooArgSet constrainParams;
if (sbModel->GetNuisanceParameters() ) constrainParams.add(*sbModel->GetNuisanceParameters());
RooStats::RemoveConstantParameters(&constrainParams);
const RooArgSet * poiModel = sbModel->GetParametersOfInterest();
bModel->LoadSnapshot();
// do a profile using the B model snapshot
if (mRebuildParamValues == 0 ) {
RooStats::SetAllConstant(*poiModel,true);
sbModel->GetPdf()->fitTo(*data,InitialHesse(false), Hesse(false),
Minimizer(minimizerType.c_str(),"Migrad"), Strategy(0), PrintLevel(mPrintLevel), Constrain(constrainParams), Offset(RooStats::IsNLLOffset()) );
std::cout << "rebuild using fitted parameter value for B-model snapshot" << std::endl;
constrainParams.Print("v");
RooStats::SetAllConstant(*poiModel,false);
}
}
std::cout << "StandardHypoTestInvDemo: Initial parameters used for rebuilding: ";
RooStats::PrintListContent(*allParams, std::cout);
delete allParams;
calc.SetCloseProof(1);
tw.Start();
SamplingDistribution * limDist = calc.GetUpperLimitDistribution(true,mNToyToRebuild);
std::cout << "Time to rebuild distributions " << std::endl;
tw.Print();
if (limDist) {
std::cout << "Expected limits after rebuild distribution " << std::endl;
std::cout << "expected upper limit (median of limit distribution) " << limDist->InverseCDF(0.5) << std::endl;
std::cout << "expected -1 sig limit (0.16% quantile of limit dist) " << limDist->InverseCDF(ROOT::Math::normal_cdf(-1)) << std::endl;
std::cout << "expected +1 sig limit (0.84% quantile of limit dist) " << limDist->InverseCDF(ROOT::Math::normal_cdf(1)) << std::endl;
std::cout << "expected -2 sig limit (.025% quantile of limit dist) " << limDist->InverseCDF(ROOT::Math::normal_cdf(-2)) << std::endl;
std::cout << "expected +2 sig limit (.975% quantile of limit dist) " << limDist->InverseCDF(ROOT::Math::normal_cdf(2)) << std::endl;
// Plot the upper limit distribution
SamplingDistPlot limPlot( (mNToyToRebuild < 200) ? 50 : 100);
limPlot.AddSamplingDistribution(limDist);
limPlot.GetTH1F()->SetStats(true); // display statistics
limPlot.SetLineColor(kBlue);
new TCanvas("limPlot","Upper Limit Distribution");
limPlot.Draw();
/// save result in a file
limDist->SetName("RULDist");
TFile * fileOut = new TFile("RULDist.root","RECREATE");
limDist->Write();
fileOut->Close();
//update r to a new updated result object containing the rebuilt expected p-values distributions
// (it will not recompute the expected limit)
if (r) delete r; // need to delete previous object since GetInterval will return a cloned copy
r = calc.GetInterval();
}
else
std::cout << "ERROR : failed to re-build distributions " << std::endl;
}
return r;
}
void MakePlots(RooWorkspace* ws){
std::cout << "make plots" << std::endl;
RooAbsPdf* model = ws->pdf("model");
RooAbsPdf* model_dY = ws->pdf("model_dY");
RooRealVar* nsig = ws->var("nsig");
RooRealVar* nsigDPS = ws->var("nsigDPS");
RooRealVar* nsigSPS = ws->var("nsigSPS");
RooRealVar* nBbkg = ws->var("nBbkg");
RooRealVar* nbkg = ws->var("nbkg");
RooRealVar* nbkg2 = ws->var("nbkg2");
RooRealVar* FourMu_Mass = ws->var("FourMu_Mass");
RooRealVar* Psi1_Mass = ws->var("Psi1_Mass");
RooRealVar* Psi2_Mass = ws->var("Psi2_Mass");
RooRealVar* Psi1_CTxy = ws->var("Psi1_CTxy");
RooRealVar* Psi2_CTxy = ws->var("Psi2_CTxy");
RooRealVar* Psi1To2_dY = ws->var("Psi1To2_dY");
RooRealVar* Psi1To2Significance = ws->var("Psi1To2Significance");
// note, we get the dataset with sWeights
RooDataSet* data = (RooDataSet*) ws->data("dataWithSWeights");
model->fitTo(*data, Extended() );
// make TTree with efficiency variation info
//TFile *fFile = new TFile("Fit_Results_Eff_Cut.root","recreate");
setTDRStyle();
// make our canvas
TCanvas* cmass1 = new TCanvas("sPlotMass1","sPlotMass1", 600, 600);
cmass1->cd();
cmass1->SetFillColor(kWhite);
RooPlot* Mass1Plot = Psi1_Mass->frame(20);
data->plotOn(Mass1Plot,Name("data"), DataError(RooAbsData::SumW2));
model->plotOn(Mass1Plot,Name("all"));
model->plotOn(Mass1Plot,Name("sig"),RooFit::Components("Sig,sig_*"),RooFit::LineColor(kRed), RooFit::LineStyle(2));
model->plotOn(Mass1Plot,Name("bkg"),RooFit::Components("bkg_model"),RooFit::LineColor(kGreen), RooFit::LineStyle(3));
model->plotOn(Mass1Plot,Name("bkg2"),RooFit::Components("bkg2_model"),RooFit::LineColor(kBlack), RooFit::LineStyle(4));
model->plotOn(Mass1Plot,Name("Bbkg"),RooFit::Components("BBkg,Bbkg_*"),RooFit::LineColor(6), RooFit::LineStyle(7));
Mass1Plot->SetTitle("");
Mass1Plot->SetXTitle("#mu^{+}#mu^{-} 1 Invariant Mass (GeV/c^{2})");
Mass1Plot->SetYTitle("Events / 0.025 GeV/c^{2}");
Mass1Plot->SetLabelOffset(0.012);
//Mass1Plot->SetTitleOffset(0.95);
Mass1Plot->Draw();
//cmass1->SaveAs("pic/Psi1_mass.pdf");
//cmass1->Close();
TCanvas* cmass2 = new TCanvas("sPlotMass2","sPlotMass2", 600, 600);
cmass2->cd();
cmass2->SetFillColor(kWhite);
RooPlot* Mass2Plot = Psi2_Mass->frame(20);
data->plotOn(Mass2Plot,Name("data"), DataError(RooAbsData::SumW2));
model->plotOn(Mass2Plot,Name("all"));
model->plotOn(Mass2Plot,Name("sig"),RooFit::Components("Sig,sig_*"),RooFit::LineColor(kRed), RooFit::LineStyle(2));
model->plotOn(Mass2Plot,Name("bkg"),RooFit::Components("bkg_model"),RooFit::LineColor(kGreen), RooFit::LineStyle(3));
model->plotOn(Mass2Plot,Name("bkg2"),RooFit::Components("bkg2_model"),RooFit::LineColor(kBlack), RooFit::LineStyle(4));
model->plotOn(Mass2Plot,Name("Bbkg"),RooFit::Components("BBkg,Bbkg_*"),RooFit::LineColor(6), RooFit::LineStyle(7));
Mass2Plot->SetTitle("");
Mass2Plot->SetXTitle("#mu^{+}#mu^{-} 2 Invariant Mass (GeV/c^{2})");
Mass2Plot->SetYTitle("Events / 0.025 GeV/c^{2}");
Mass2Plot->SetLabelOffset(0.012);
//Mass2Plot->SetTitleOffset(0.95);
Mass2Plot->Draw();
//cmass2->SaveAs("pic/Psi2_mass.pdf");
//cmass2->Close();
TCanvas* cctxy1 = new TCanvas("sPlotCTxy1","sPlotCTxy1", 600, 600);
cctxy1->cd();
cctxy1->SetFillColor(kWhite);
RooPlot* CTxy1Plot = Psi1_CTxy->frame(30);
data->plotOn(CTxy1Plot,Name("data"), DataError(RooAbsData::SumW2));
model->plotOn(CTxy1Plot,Name("all"));
model->plotOn(CTxy1Plot,Name("sig"),RooFit::Components("Sig,sig_*"),RooFit::LineColor(kRed), RooFit::LineStyle(2));
model->plotOn(CTxy1Plot,Name("bkg"),RooFit::Components("bkg_model"),RooFit::LineColor(kGreen), RooFit::LineStyle(3));
model->plotOn(CTxy1Plot,Name("bkg2"),RooFit::Components("bkg2_model"),RooFit::LineColor(kBlack), RooFit::LineStyle(4));
model->plotOn(CTxy1Plot,Name("Bbkg"),RooFit::Components("BBkg,Bbkg_*"),RooFit::LineColor(6), RooFit::LineStyle(7));
CTxy1Plot->SetTitle("");
CTxy1Plot->SetXTitle("J/#psi^{1} ct_{xy} (cm)");
CTxy1Plot->SetYTitle("Events / 0.005 cm");
CTxy1Plot->SetMaximum(2000);
CTxy1Plot->SetMinimum(0.1);
CTxy1Plot->Draw();
cctxy1->SetLogy();
//cctxy1->SaveAs("pic/Psi1_CTxy.pdf");
//cctxy1->Close();
TCanvas* csig = new TCanvas("sPlotSig","sPlotSig", 600, 600);
csig->cd();
RooPlot* SigPlot = Psi1To2Significance->frame(20);
data->plotOn(SigPlot,Name("data"), DataError(RooAbsData::SumW2));
model->plotOn(SigPlot,Name("all"));
model->plotOn(SigPlot,Name("sig"),RooFit::Components("Sig,sig_*"),RooFit::LineColor(kRed), RooFit::LineStyle(2));
model->plotOn(SigPlot,Name("bkg"),RooFit::Components("bkg_model"),RooFit::LineColor(kGreen), RooFit::LineStyle(3));
model->plotOn(SigPlot,Name("bkg2"),RooFit::Components("bkg2_model"),RooFit::LineColor(kBlack), RooFit::LineStyle(4));
model->plotOn(SigPlot,Name("Bbkg"),RooFit::Components("BBkg,Bbkg_*"),RooFit::LineColor(6), RooFit::LineStyle(7));
SigPlot->SetTitle("");
SigPlot->SetYTitle("Events / 0.4");
SigPlot->SetXTitle("J/#psi Distance Significance");
SigPlot->Draw();
//csig->SaveAs("pic/Psi1To2Significance.pdf");
//csig->Close();
// create weighted data set (signal-weighted)
//RooDataSet * dataw_sig = new RooDataSet(data->GetName(),data->GetTitle(),data,*data->get(),0,"nsig_sw");
// model_dY->fitTo(*data);
//TCanvas* cdata2 = new TCanvas("sPlot2","sPlots2", 700, 500);
//cdata2->cd();
//RooPlot* frame2 = Psi1To2_dY->frame(20);
//dataw_sig->plotOn(frame2, DataError(RooAbsData::SumW2) );
/*
model_dY->fitTo(*dataw_sig, SumW2Error(kTRUE), Extended());
model_dY->plotOn(frame2);
model_dY->plotOn(frame2,RooFit::Components("*DPS"),RooFit::LineColor(kRed), RooFit::LineStyle(kDashed));
model_dY->plotOn(frame2,RooFit::Components("*SPS"),RooFit::LineColor(kGreen), RooFit::LineStyle(kDashed));
*/
//frame2->SetTitle("DeltaY distribution for sig");
//frame2->SetMinimum(1e-03);
//frame2->Draw();
//TCanvas* cdata3 = new TCanvas("sPlot3","sPlots3", 700, 500);
//cdata3->cd();
//RooPlot* frame3 = FourMu_Mass->frame(Bins(14),Range(6.,20.));
//dataw_sig->plotOn(frame3, DataError(RooAbsData::SumW2));
//frame3->SetTitle("FourMu_Mass distribution for sig");
//frame3->SetMinimum(1e-03);
//frame3->Draw();
//fFile->Write();
//fFile->Close();
//delete fFile;
}
float ComputeTestStat(TString wsfile, double mu_susy_sig_val) {
gROOT->Reset();
TFile* wstf = new TFile( wsfile ) ;
RooWorkspace* ws = dynamic_cast<RooWorkspace*>( wstf->Get("ws") );
ws->Print() ;
ModelConfig* modelConfig = (ModelConfig*) ws->obj( "SbModel" ) ;
modelConfig->Print() ;
RooDataSet* rds = (RooDataSet*) ws->obj( "ra2b_observed_rds" ) ;
rds->Print() ;
rds->printMultiline(cout, 1, kTRUE, "") ;
RooAbsPdf* likelihood = modelConfig->GetPdf() ;
RooRealVar* rrv_mu_susy_sig = ws->var("mu_susy_all0lep") ;
if ( rrv_mu_susy_sig == 0x0 ) {
printf("\n\n\n *** can't find mu_susy_all0lep in workspace. Quitting.\n\n\n") ;
return ;
} else {
printf(" current value is : %8.3f\n", rrv_mu_susy_sig->getVal() ) ; cout << flush ;
rrv_mu_susy_sig->setConstant(kFALSE) ;
}
/*
// check the impact of varying the qcd normalization:
RooRealVar *rrv_qcd_0lepLDP_ratioH1 = ws->var("qcd_0lepLDP_ratio_H1");
RooRealVar *rrv_qcd_0lepLDP_ratioH2 = ws->var("qcd_0lepLDP_ratio_H2");
RooRealVar *rrv_qcd_0lepLDP_ratioH3 = ws->var("qcd_0lepLDP_ratio_H3");
rrv_qcd_0lepLDP_ratioH1->setVal(0.3);
rrv_qcd_0lepLDP_ratioH2->setVal(0.3);
rrv_qcd_0lepLDP_ratioH3->setVal(0.3);
rrv_qcd_0lepLDP_ratioH1->setConstant(kTRUE);
rrv_qcd_0lepLDP_ratioH2->setConstant(kTRUE);
rrv_qcd_0lepLDP_ratioH3->setConstant(kTRUE);
*/
printf("\n\n\n ===== Doing a fit with SUSY component floating ====================\n\n") ;
RooFitResult* fitResult = likelihood->fitTo( *rds, Save(true), PrintLevel(0) ) ;
double logLikelihoodSusyFloat = fitResult->minNll() ;
double logLikelihoodSusyFixed(0.) ;
double testStatVal(-1.) ;
if ( mu_susy_sig_val >= 0. ) {
printf("\n\n\n ===== Doing a fit with SUSY fixed ====================\n\n") ;
printf(" fixing mu_susy_sig to %8.2f.\n", mu_susy_sig_val ) ;
rrv_mu_susy_sig->setVal( mu_susy_sig_val ) ;
rrv_mu_susy_sig->setConstant(kTRUE) ;
fitResult = likelihood->fitTo( *rds, Save(true), PrintLevel(0) ) ;
logLikelihoodSusyFixed = fitResult->minNll() ;
testStatVal = 2.*(logLikelihoodSusyFixed - logLikelihoodSusyFloat) ;
printf("\n\n\n ======= test statistic : -2 * ln (L_fixed / ln L_max) = %8.3f\n\n\n", testStatVal ) ;
}
return testStatVal ;
}
void drawSidebandsWithCurve( DrawBase* db, const std::string& data_dataset, const std::string& PUType, const std::string& data_mc, const std::string& flags, int nbtags, const std::string& leptType, std::string suffix ) {
if( suffix!="" ) suffix = "_" + suffix;
TH1F::AddDirectory(kTRUE);
// get histograms:
std::vector< TH1D* > lastHistos_data = db->get_lastHistos_data();
TH1D* h1_data = new TH1D(*(lastHistos_data[0]));
float xMin = (db->get_xAxisMin()!=9999.) ? db->get_xAxisMin() : h1_data->GetXaxis()->GetXmin();
float xMax = (db->get_xAxisMax()!=9999.) ? db->get_xAxisMax() : h1_data->GetXaxis()->GetXmax();
int nBins = (int)(xMax-xMin)/h1_data->GetXaxis()->GetBinWidth(1);
std::string sherpa_suffix = (use_sherpa) ? "_sherpa" : "";
// open fit results file:
char fitResultsFileName[200];
sprintf( fitResultsFileName, "fitResultsFile_%s_%dbtag_ALL_PU%s_fit%s%s%s.root", data_dataset.c_str(), nbtags, PUType.c_str(), data_mc.c_str(), sherpa_suffix.c_str(), flags.c_str() );
TFile* fitResultsFile = TFile::Open(fitResultsFileName);
// get bg workspace:
char workspaceName[200];
sprintf( workspaceName, "fitWorkspace_%dbtag", nbtags );
RooWorkspace* bgws = (RooWorkspace*)fitResultsFile->Get(workspaceName);
char fitResultsName[200];
sprintf( fitResultsName, "fitResults_%dbtag_decorr", nbtags );
RooFitResult* bgfr = (RooFitResult*)fitResultsFile->Get(fitResultsName);
// get mZZ variable:
RooRealVar* CMS_hzz2l2q_mZZ = (RooRealVar*)bgws->var("CMS_hzz2l2q_mZZ");
// get bg shape:
RooAbsPdf* background = (RooAbsPdf*)bgws->pdf("background_decorr");
// get data sidebands:
TTree* tree_sidebands = (TTree*)fitResultsFile->Get("sidebandsDATA_alpha");
TH1D* h1_dataSidebands = new TH1D("dataSidebands", "", nBins, xMin, xMax);
h1_dataSidebands->Sumw2();
char selection[900];
if( leptType=="ALL" )
sprintf( selection, "eventWeight_alpha*(((mZjj>60.&&mZjj<75.)||(mZjj>105.&&mZjj<130.)) && nBTags==%d && CMS_hzz2l2q_mZZ>183. && CMS_hzz2l2q_mZZ<800.)", nbtags);
else {
int leptType_int = SidebandFitter::convert_leptType(leptType);
sprintf( selection, "eventWeight_alpha*(((mZjj>60.&&mZjj<75.)||(mZjj>105.&&mZjj<130.)) && nBTags==%d && leptType==%d && CMS_hzz2l2q_mZZ>183. && CMS_hzz2l2q_mZZ<800.)", nbtags, leptType_int);
}
tree_sidebands->Project("dataSidebands", "CMS_hzz2l2q_mZZ", selection);
// create data graph (poisson asymm errors):
TGraphAsymmErrors* graph_data_poisson = new TGraphAsymmErrors(0);
graph_data_poisson = fitTools::getGraphPoissonErrors(h1_dataSidebands);
graph_data_poisson->SetMarkerStyle(20);
std::string leptType_cut;
if( leptType=="MU" ) leptType_cut = "&& leptType==0";
if( leptType=="ELE" ) leptType_cut = "&& leptType==1";
SidebandFitter* sf = new SidebandFitter( data_dataset, PUType, data_mc, flags );
// get bg normalization:
//float expBkg = sf->get_backgroundNormalization( nbtags, leptType, "DATA" );
std::pair<Double_t, Double_t> bgNormAndError = sf->get_backgroundNormalizationAndError( nbtags, leptType, "DATA" );
float expBkg = bgNormAndError.first;
float expBkg_err = bgNormAndError.second;
//TTree* treeSidebandsDATA_alphaCorr = (TTree*)fitResultsFile->Get("sidebandsDATA_alpha");
//TH1D* h1_mZZ_sidebands_alpha = new TH1D("mZZ_sidebands_alpha", "", 65, xMin, xMax);
//char sidebandsCut_alpha[500];
//sprintf(sidebandsCut_alpha, "eventWeight_alpha*(isSidebands && nBTags==%d %s)", nbtags, leptType_cut.c_str());
//treeSidebandsDATA_alphaCorr->Project("mZZ_sidebands_alpha", "CMS_hzz2l2q_mZZ", sidebandsCut_alpha);
//float expBkg = h1_mZZ_sidebands_alpha->Integral();
RooPlot *plot_MCbkg = CMS_hzz2l2q_mZZ->frame(xMin,xMax,nBins);
background->plotOn(plot_MCbkg,RooFit::Normalization(expBkg));
if( !QUICK_ ) {
background->plotOn(plot_MCbkg,RooFit::VisualizeError(*bgfr,2.0,kFALSE),RooFit::FillColor(kYellow), RooFit::Normalization(expBkg));
background->plotOn(plot_MCbkg,RooFit::VisualizeError(*bgfr,1.0,kFALSE),RooFit::FillColor(kGreen), RooFit::Normalization(expBkg));
background->plotOn(plot_MCbkg,RooFit::Normalization(expBkg));
}
TF1* f1_bgForLegend = new TF1("bgForLegend", "[0]");
f1_bgForLegend->SetLineColor(kBlue);
f1_bgForLegend->SetLineWidth(3);
TH2D* h2_axes = new TH2D("axes", "", 10, xMin, xMax, 10, 0., 1.3*h1_data->GetMaximum());
char yTitle[200];
sprintf( yTitle, "Events / (%.0f GeV)", h1_data->GetXaxis()->GetBinWidth(1) );
h2_axes->SetYTitle(yTitle);
if( leptType=="MU" )
h2_axes->SetXTitle("m_{#mu#mujj} [GeV]");
else if( leptType=="ELE" )
h2_axes->SetXTitle("m_{eejj} [GeV]");
else
h2_axes->SetXTitle("m_{ZZ} [GeV]");
float legend_xMin = 0.5;
float legend_yMax = 0.85;
float legend_yMin = legend_yMax - 0.12*2.;
float legend_xMax = 0.92;
char legendTitle[300];
sprintf( legendTitle, "%d b-tag sidebands", nbtags );
TLegend* legend = new TLegend(legend_xMin, legend_yMin, legend_xMax, legend_yMax, legendTitle);
legend->SetTextSize(0.04);
legend->SetFillColor(0);
legend->AddEntry( graph_data_poisson, "Data", "P");
legend->AddEntry( f1_bgForLegend, "Fit", "L");
TPaveText* cmsLabel = db->get_labelCMS();
TPaveText* sqrtLabel = db->get_labelSqrt();
TCanvas* c1 = new TCanvas( "c1", "", 600, 600);
c1->cd();
h2_axes->Draw();
cmsLabel->Draw("same");
sqrtLabel->Draw("same");
if( suffix != "_turnOnZOOM" )
legend->Draw("same");
plot_MCbkg->Draw("same");
graph_data_poisson->Draw("P same");
gPad->RedrawAxis();
char canvasName[1000];
if( leptType=="ALL" )
sprintf( canvasName, "%s/mZZsidebands_%dbtag_withCurve%s", (db->get_outputdir()).c_str(), nbtags, suffix.c_str() );
else
sprintf( canvasName, "%s/mZZsidebands_%dbtag_withCurve%s_%s", (db->get_outputdir()).c_str(), nbtags, suffix.c_str(), leptType.c_str() );
std::string canvasName_str(canvasName);
std::string canvasName_eps = canvasName_str+".eps";
c1->SaveAs(canvasName_eps.c_str());
c1->Clear();
c1->SetLogy();
TLegend* legend_log = new TLegend(0.6, legend_yMin, legend_xMax, legend_yMax, (db->get_legendTitle()).c_str());
legend_log->SetTextSize(0.04);
legend_log->SetFillColor(0);
legend_log->AddEntry( graph_data_poisson, "Data", "P");
legend_log->AddEntry( f1_bgForLegend, "Exp. BG", "L");
TH2D* h2_axes_log = new TH2D("axes_log", "", 10, xMin, xMax, 10, 0.05, 200.*h1_data->GetMaximum());
h2_axes_log->SetYTitle(yTitle);
h2_axes_log->SetXTitle("m_{ZZ} [GeV]");
h2_axes_log->Draw();
cmsLabel->Draw("same");
sqrtLabel->Draw("same");
//legend_log->Draw("same");
legend->Draw("same");
plot_MCbkg->Draw("same");
graph_data_poisson->Draw("P same");
gPad->RedrawAxis();
std::string canvasName_log_eps = canvasName_str+"_log.eps";
c1->SaveAs(canvasName_log_eps.c_str());
delete sf;
delete h2_axes;
delete h2_axes_log;
delete c1;
}
//____________________________________
void DoSPlot(RooWorkspace* ws){
std::cout << "Calculate sWeights" << std::endl;
RooAbsPdf* model = ws->pdf("model");
RooRealVar* nsig = ws->var("nsig");
RooRealVar* nBbkg = ws->var("nBbkg");
RooRealVar* nbkg = ws->var("nbkg");
RooRealVar* nbkg2 = ws->var("nbkg2");
RooDataSet* data = (RooDataSet*) ws->data("data");
// fit the model to the data.
model->fitTo(*data, Extended() );
RooMsgService::instance().setSilentMode(true);
// Now we use the SPlot class to add SWeights to our data set
// based on our model and our yield variables
RooStats::SPlot* sData = new RooStats::SPlot("sData","An SPlot",
*data, model, RooArgList(*nsig,*nBbkg,*nbkg,*nbkg2) );
// Check that our weights have the desired properties
std::cout << "Check SWeights:" << std::endl;
std::cout << std::endl << "Yield of sig is "
<< nsig->getVal() << ". From sWeights it is "
<< sData->GetYieldFromSWeight("nsig") << std::endl;
std::cout << std::endl << "Yield of Bbkg is "
<< nBbkg->getVal() << ". From sWeights it is "
<< sData->GetYieldFromSWeight("nBbkg") << std::endl;
std::cout << std::endl << "Yield of bkg is "
<< nbkg->getVal() << ". From sWeights it is "
<< sData->GetYieldFromSWeight("nbkg") << std::endl;
std::cout << std::endl << "Yield of bkg2 is "
<< nbkg2->getVal() << ". From sWeights it is "
<< sData->GetYieldFromSWeight("nbkg2") << std::endl;
cout << endl; cout << endl; cout << endl;
float sum20=0;
float sum50=0;
float sum100=0;
float sum200=0;
float sum300=0;
float sum600=0;
float sum900=0;
float sum1200=0;
float total=0;
// saving weights into a file
ofstream myfile;
myfile.open ("weights.txt");
// plot the weight event by event with the Sum of events values as cross-check
for(Int_t i=0; i < data->numEntries(); i++) {
//myfile << sData->GetSWeight(i,"nsig") << " " << sData->GetSWeight(i,"nBbkg") << " " << sData->GetSWeight(i,"nbkg") << " " << sData->GetSWeight(i,"nbkg2") << endl;
//myfile << sData->GetSWeight(i,"nsig") <<endl;
myfile << (unsigned int) data->get(i)->getRealValue("run")
<< " " << (unsigned int) data->get(i)->getRealValue("event")
<< " " << (float) data->get(i)->getRealValue("FourMu_Mass")
<< " " << sData->GetSWeight(i,"nsig")
<< endl;
// std::cout << "nsig Weight " << sData->GetSWeight(i,"nsig")
// << " nBbkg Weight " << sData->GetSWeight(i,"nBbkg")
// << " nbkg Weight " << sData->GetSWeight(i,"nbkg")
// << " nbkg2 Weight " << sData->GetSWeight(i,"nbkg2")
// << " Total Weight " << sData->GetSumOfEventSWeight(i)
// << std::endl;
total+=sData->GetSWeight(i,"nsig");
if(i<20) sum20+=sData->GetSWeight(i,"nsig");
if(i<50) sum50+=sData->GetSWeight(i,"nsig");
if(i<100) sum100+=sData->GetSWeight(i,"nsig");
if(i<200) sum200+=sData->GetSWeight(i,"nsig");
if(i<300) sum300+=sData->GetSWeight(i,"nsig");
if(i<600) sum600+=sData->GetSWeight(i,"nsig");
if(i<900) sum900+=sData->GetSWeight(i,"nsig");
if(i<1200) sum1200+=sData->GetSWeight(i,"nsig");
}
myfile.close();
std::cout << std::endl;
std::cout<<"Sum of the sWeights is: "<<total<<std::endl;
std::cout<<"Sum of the first 20 sWeights is: "<<sum20<<std::endl;
std::cout<<"Sum of the first 50 sWeights is: "<<sum50<<std::endl;
std::cout<<"Sum of the first 100 sWeights is: "<<sum100<<std::endl;
std::cout<<"Sum of the first 200 sWeights is: "<<sum200<<std::endl;
std::cout<<"Sum of the first 300 sWeights is: "<<sum300<<std::endl;
std::cout<<"Sum of the first 600 sWeights is: "<<sum600<<std::endl;
std::cout<<"Sum of the first 900 sWeights is: "<<sum900<<std::endl;
std::cout<<"Sum of the first 1200 sWeights is: "<<sum1200<<std::endl;
std::cout<<"Total # of events: "<<data->numEntries()<<std::endl;
// import this new dataset with sWeights
std::cout << "import new dataset with sWeights" << std::endl;
ws->import(*data, Rename("dataWithSWeights"));
}
void drawHistoWithCurve( DrawBase* db, const std::string& data_dataset, const std::string& PUType, const std::string& data_mc, const std::string& flags, int nbtags, const std::string& leptType, std::string suffix ) {
if( suffix!="" ) suffix = "_" + suffix;
TH1F::AddDirectory(kTRUE);
// get histograms:
std::vector< TH1D* > lastHistos_data = db->get_lastHistos_data();
std::vector< TH1D* > lastHistos_mc = db->get_lastHistos_mc();
std::vector< TH1D* > lastHistos_mc_superimp = db->get_lastHistos_mc_superimp();
TH1D* h1_data = new TH1D(*(lastHistos_data[0]));
float xMin = (db->get_xAxisMin()!=9999.) ? db->get_xAxisMin() : h1_data->GetXaxis()->GetXmin();
float xMax = (db->get_xAxisMax()!=9999.) ? db->get_xAxisMax() : h1_data->GetXaxis()->GetXmax();
// create data graph (poisson asymm errors):
TGraphAsymmErrors* graph_data_poisson = new TGraphAsymmErrors(0);
graph_data_poisson = fitTools::getGraphPoissonErrors(h1_data);
graph_data_poisson->SetMarkerStyle(20);
THStack* mc_stack = new THStack();
for( unsigned ihisto=0; ihisto<lastHistos_mc.size(); ++ihisto )
mc_stack->Add(lastHistos_mc[lastHistos_mc.size()-ihisto-1]);
std::string sherpa_suffix = (use_sherpa) ? "_sherpa" : "";
// open fit results file:
char fitResultsFileName[200];
sprintf( fitResultsFileName, "fitResultsFile_%s_%dbtag_ALL_PU%s_fit%s%s%s.root", data_dataset.c_str(), nbtags, PUType.c_str(), data_mc.c_str(), sherpa_suffix.c_str(), flags.c_str() );
TFile* fitResultsFile = TFile::Open(fitResultsFileName);
// get bg workspace:
char workspaceName[200];
sprintf( workspaceName, "fitWorkspace_%dbtag", nbtags );
RooWorkspace* bgws = (RooWorkspace*)fitResultsFile->Get(workspaceName);
char fitResultsName[200];
sprintf( fitResultsName, "fitResults_%dbtag_decorr", nbtags );
RooFitResult* bgfr = (RooFitResult*)fitResultsFile->Get(fitResultsName);
// get mZZ variable:
RooRealVar* CMS_hzz2l2q_mZZ = (RooRealVar*)bgws->var("CMS_hzz2l2q_mZZ");
// get bg shape:
RooAbsPdf* background = (RooAbsPdf*)bgws->pdf("background_decorr");
std::string leptType_cut;
if( leptType=="MU" ) leptType_cut = "&& leptType==0";
if( leptType=="ELE" ) leptType_cut = "&& leptType==1";
SidebandFitter* sf = new SidebandFitter( data_dataset, PUType, data_mc, flags );
// get bg normalization:
//float expBkg = sf->get_backgroundNormalization( nbtags, leptType, "DATA" );
std::pair<Double_t, Double_t> bgNormAndError = sf->get_backgroundNormalizationAndError( nbtags, leptType, "DATA" );
float expBkg = bgNormAndError.first;
float expBkg_err = bgNormAndError.second;
//TTree* treeSidebandsDATA_alphaCorr = (TTree*)fitResultsFile->Get("sidebandsDATA_alpha");
//TH1D* h1_mZZ_sidebands_alpha = new TH1D("mZZ_sidebands_alpha", "", 65, xMin, xMax);
//char sidebandsCut_alpha[500];
//sprintf(sidebandsCut_alpha, "eventWeight_alpha*(isSidebands && nBTags==%d %s)", nbtags, leptType_cut.c_str());
//treeSidebandsDATA_alphaCorr->Project("mZZ_sidebands_alpha", "CMS_hzz2l2q_mZZ", sidebandsCut_alpha);
//float expBkg = h1_mZZ_sidebands_alpha->Integral();
RooPlot *plot_MCbkg = CMS_hzz2l2q_mZZ->frame(xMin,xMax,(int)(xMax-xMin)/h1_data->GetXaxis()->GetBinWidth(1));
background->plotOn(plot_MCbkg,RooFit::Normalization(expBkg));
if( suffix == "_turnOnZOOM" && !QUICK_ ) {
background->plotOn(plot_MCbkg,RooFit::VisualizeError(*bgfr,2.0,kFALSE),RooFit::FillColor(kYellow), RooFit::Normalization(expBkg));
background->plotOn(plot_MCbkg,RooFit::VisualizeError(*bgfr,1.0,kFALSE),RooFit::FillColor(kGreen), RooFit::Normalization(expBkg));
background->plotOn(plot_MCbkg,RooFit::Normalization(expBkg));
background->plotOn(plot_MCbkg,RooFit::LineStyle(2),RooFit::Normalization(expBkg+expBkg_err));
background->plotOn(plot_MCbkg,RooFit::LineStyle(2),RooFit::Normalization(expBkg-expBkg_err));
}
TF1* f1_bgForLegend = new TF1("bgForLegend", "[0]");
f1_bgForLegend->SetLineColor(kBlue);
f1_bgForLegend->SetLineWidth(3);
TH2D* h2_axes = new TH2D("axes", "", 10, xMin, xMax, 10, 0., 1.3*h1_data->GetMaximum());
char yTitle[200];
sprintf( yTitle, "Events / (%.0f GeV)", h1_data->GetXaxis()->GetBinWidth(1) );
h2_axes->SetYTitle(yTitle);
if( leptType=="MU" )
h2_axes->SetXTitle("m_{#mu#mujj} [GeV]");
else if( leptType=="ELE" )
h2_axes->SetXTitle("m_{eejj} [GeV]");
else
h2_axes->SetXTitle("m_{ZZ} [GeV]");
float legend_xMin = 0.38;
float legend_yMax = 0.91;
float legend_yMin = legend_yMax - 0.07*6.;
float legend_xMax = 0.92;
TLegend* legend = new TLegend(legend_xMin, legend_yMin, legend_xMax, legend_yMax, (db->get_legendTitle()).c_str());
legend->SetTextSize(0.04);
legend->SetFillColor(0);
legend->AddEntry( graph_data_poisson, "Data", "P");
legend->AddEntry( f1_bgForLegend, "Expected background", "L");
for( unsigned imc=0; imc<lastHistos_mc.size(); ++imc )
legend->AddEntry( lastHistos_mc[imc], (db->get_mcFile(imc).legendName).c_str(), "F");
TPaveText* cmsLabel = db->get_labelCMS();
TPaveText* sqrtLabel = db->get_labelSqrt();
TCanvas* c1 = new TCanvas( "c1", "", 600, 600);
c1->cd();
h2_axes->Draw();
cmsLabel->Draw("same");
sqrtLabel->Draw("same");
if( suffix != "_turnOnZOOM" )
legend->Draw("same");
mc_stack->Draw("histo same");
for( unsigned imc=0; imc<lastHistos_mc_superimp.size(); ++imc )
lastHistos_mc_superimp[imc]->Draw("same");
plot_MCbkg->Draw("same");
graph_data_poisson->Draw("P same");
gPad->RedrawAxis();
char canvasName[1000];
if( leptType=="ALL" )
sprintf( canvasName, "%s/mZZ_%dbtag_withCurve%s", (db->get_outputdir()).c_str(), nbtags, suffix.c_str() );
else
sprintf( canvasName, "%s/mZZ_%dbtag_withCurve%s_%s", (db->get_outputdir()).c_str(), nbtags, suffix.c_str(), leptType.c_str() );
std::string canvasName_str(canvasName);
std::string canvasName_eps = canvasName_str+".eps";
c1->SaveAs(canvasName_eps.c_str());
std::string canvasName_root = canvasName_str+".root";
if( root_aussi_ ) {
c1->SetFixedAspectRatio(true);
c1->SaveAs(canvasName_root.c_str());
}
c1->Clear();
legend_yMin = legend_yMax - 0.07*4.;
TLegend* legend2 = new TLegend(legend_xMin, legend_yMin, legend_xMax, legend_yMax, (db->get_legendTitle()).c_str());
legend2->SetTextSize(0.04);
legend2->SetFillColor(0);
legend2->AddEntry( graph_data_poisson, "Data", "P");
legend2->AddEntry( f1_bgForLegend, "Expected background", "L");
for( unsigned imc=0; imc<lastHistos_mc.size(); ++imc ) {
if( db->get_mcFile(imc).fillColor==kYellow ) //signal only
legend2->AddEntry( lastHistos_mc[imc], (db->get_mcFile(imc).legendName).c_str(), "F");
}
h2_axes->Draw();
cmsLabel->Draw("same");
sqrtLabel->Draw("same");
legend2->Draw("same");
for( unsigned imc=0; imc<lastHistos_mc.size(); ++imc ) {
if( db->get_mcFile(imc).fillColor==kYellow ) //signal only
lastHistos_mc[imc]->Draw("same");
}
plot_MCbkg->Draw("same");
graph_data_poisson->Draw("P same");
gPad->RedrawAxis();
if( leptType=="ALL" )
sprintf( canvasName, "%s/mZZ_%dbtag_withCurve_noMC%s", (db->get_outputdir()).c_str(), nbtags, suffix.c_str() );
else
sprintf( canvasName, "%s/mZZ_%dbtag_withCurve_noMC%s_%s", (db->get_outputdir()).c_str(), nbtags, suffix.c_str(), leptType.c_str() );
std::string canvasName2_str(canvasName);
std::string canvasName2_eps = canvasName2_str+".eps";
c1->SaveAs(canvasName2_eps.c_str());
c1->Clear();
c1->SetLogy();
TLegend* legend_log = new TLegend(0.6, legend_yMin, legend_xMax, legend_yMax, (db->get_legendTitle()).c_str());
legend_log->SetTextSize(0.04);
legend_log->SetFillColor(0);
legend_log->AddEntry( graph_data_poisson, "Data", "P");
legend_log->AddEntry( f1_bgForLegend, "Exp. BG", "L");
for( unsigned imc=0; imc<lastHistos_mc.size(); ++imc )
legend_log->AddEntry( lastHistos_mc[imc], (db->get_mcFile(imc).legendName).c_str(), "F");
TH2D* h2_axes_log = new TH2D("axes_log", "", 10, xMin, xMax, 10, 0.05, 200.*h1_data->GetMaximum());
h2_axes_log->SetYTitle(yTitle);
h2_axes_log->SetXTitle("m_{ZZ} [GeV]");
h2_axes_log->GetYaxis()->SetNoExponent();
h2_axes_log->Draw();
cmsLabel->Draw("same");
sqrtLabel->Draw("same");
legend_log->Draw("same");
mc_stack->Draw("histo same");
plot_MCbkg->Draw("same");
graph_data_poisson->Draw("P same");
gPad->RedrawAxis();
std::string canvasName_log_eps = canvasName_str+"_log.eps";
c1->SaveAs(canvasName_log_eps.c_str());
delete sf;
delete h2_axes;
delete h2_axes_log;
delete c1;
}
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
bool newHypoTest = true,
int ntoys = 5000,
const char* hypoTestGraphFile = "hypoTestGraph.root",
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";
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;
#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;
}
/////////////////////////////////////////////////////////////
// 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()); //Obtains parameters of the null Hypothesis
if (bModel->GetNuisanceParameters()) nullParams.add(*bModel->GetNuisanceParameters()); //Add nuisance parameters to the null hypothesis
slrts->SetNullParameters(nullParams);
RooArgSet altParams(*sbModel->GetSnapshot()); //Obtains parameters of the alternate Hypothesis
if (sbModel->GetNuisanceParameters()) altParams.add(*sbModel->GetNuisanceParameters());//Add nuisance parameters to the alternate hypothesis
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();
plot->SamplingDistPlot::DumpToFile(hypoTestGraphFile,"RECREATE");
}
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;
}
}
////////////////////////////////////////////////////////////////////////////////////////////////
// FROM HERE ON IT HAS BEEN MODIFIED TO SAVE THE RESULTS IN TREES IN A .ROOT FILE
//Declare the variable in which the hypothesis test results will be stored
Double_t p_value, significance_t, cl_b, cl_sb, cl_s ;
if(newHypoTest){
TNtuple *resultsHypoTest = new TNtuple("resultsHypoTest", "resultsHypoTest", "p_value:significance_t:cl_b:cl_sb:cl_s");
//Store the current results
resultsHypoTest->Fill(htr->NullPValue(),htr->Significance(),htr->CLb(),htr->CLsplusb(),htr->CLs()) ;
// Save the NTuple to a .root file
TFile* f_hypoTestResults = new TFile("resultsHypoTestDisc.root","RECREATE") ;
resultsHypoTest->Write() ;
f_hypoTestResults->Close() ;
}
else{
// Open the .root that contains the NTuple and add the newly calculated results
TFile* f_hypoTestResults = new TFile("resultsHypoTestDisc.root","UPDATE") ;
//Get the NTuple from the file
TNtuple *resultsHypoTest = (TNtuple*)f_hypoTestResults->Get("resultsHypoTest");
resultsHypoTest->Fill(htr->NullPValue(),htr->Significance(),htr->CLb(),htr->CLsplusb(),htr->CLs()) ;
//IF YOU WANT A DIFFERENT BRANCH FOR EACH TEST
resultsHypoTest->Write();
f_hypoTestResults->Close();
//*/
/*// IF YOU ONLY WNAT ONE BRANCH WITH ALL VALUES INSIDE IT. keep the latest ntuple header only
resultsHypoTest->Write("",TObject::kOverwrite);
f_hypoTestResults->Close();
//*/
}
}
void fitpeaks(int bin){
switch (bin)
{
case 0:
cut_="abs(upsRapidity)<2.4";
//cut_="( (muPlusPt>3.5 && abs(muPlusEta)<1.6) || (muPlusPt>2.5 && abs(muPlusEta)>=1.6 && abs(muPlusEta)<2.4) ) && ( (muMinusPt>3.5 && abs(muMinusEta)<1.6) || (muMinusPt>2.5 && abs(muMinusEta)>=1.6 && abs(muMinusEta)<2.4) ) && abs(upsRapidity)<2.0"; //pp acceptance for Upsilon
suffix_="";
f2Svs1S_pp->setVal(0.5569);
//f2Svs1S_pp->setVal(0);
f3Svs1S_pp->setVal(0.4140);
//f3Svs1S_pp->setVal(0);
break;
case 1:
cut_="abs(upsRapidity)>=0.0 && abs(upsRapidity)<1.2";
suffix_="_eta0-12"; binw_=0.14;
break;
case 2:
cut_="abs(upsRapidity)>=1.2 && abs(upsRapidity)<2.4";
suffix_="_eta12-24"; binw_=0.14;
break;
case 3:
cut_="Centrality>=0 && Centrality<2";
suffix_="_cntr0-5"; binw_=0.14;
break;
case 4:
cut_="Centrality>=2 && Centrality<4";
suffix_="_cntr5-10"; binw_=0.14;
break;
case 5:
cut_="Centrality>=4 && Centrality<8";
suffix_="_cntr10-20"; binw_=0.14;
break;
case 6:
cut_="Centrality>=8 && Centrality<12";
suffix_="_cntr20-30"; binw_=0.14;
break;
case 7:
cut_="Centrality>=12 && Centrality<16";
suffix_="_cntr30-40"; binw_=0.14;
break;
case 8:
cut_="Centrality>=16 && Centrality<20";
suffix_="_cntr40-50"; binw_=0.14;
break;
case 9:
cut_="Centrality>=20 && Centrality<50";
suffix_="_cntr50-100"; binw_=0.14;
break;
case 10:
cut_="Centrality>=20 && Centrality<24";
suffix_="_cntr50-60"; binw_=0.14;
break;
case 11:
cut_="Centrality>=0 && Centrality<8";
suffix_="_cntr0-20"; binw_=0.1;
break;
case 12:
cut_="Centrality>=16 && Centrality<50";
suffix_="_cntr40-100"; binw_=0.14;
break;
case 13:
cut_="Centrality>=8 && Centrality<50";
suffix_="_cntr20-100"; binw_=0.1;
break;
default:
cout<<"error in binning"<<endl;
break;
}
cout << "oniafitter processing"
<< "\n\tInput: \t" << finput
<< "\n\tresults:\t" << figs_
<< endl;
ofstream outfile("fitresults.out", ios_base::app);
outfile<<endl<<"**********"<<suffix_<<"**********"<<endl<<endl;
//read the data
TFile f(finput,"read");
gDirectory->Cd(finput+":/"+dirname_);
TTree* theTree = (TTree*)gROOT->FindObject("UpsilonTree");
TTree* allsignTree = (TTree*)gROOT->FindObject("UpsilonTree_allsign");
if (PR_plot) {TRKROT = 1; PbPb=1;}
if (TRKROT) TTree* trkRotTree = (TTree*)gROOT->FindObject("UpsilonTree_trkRot");
RooRealVar* mass = new RooRealVar("invariantMass","#mu#mu mass",mmin_,mmax_,"GeV/c^{2}");
RooRealVar* upsPt = new RooRealVar("upsPt","p_{T}(#Upsilon)",0,60,"GeV");
RooRealVar* upsEta = new RooRealVar("upsEta", "upsEta" ,-7,7);
RooRealVar* upsRapidity = new RooRealVar("upsRapidity", "upsRapidity" ,-2.4,2.4);
RooRealVar* vProb = new RooRealVar("vProb", "vProb" ,0.05,1.00);
RooRealVar* QQsign = new RooRealVar("QQsign", "QQsign" ,-1,5);
RooRealVar* weight = new RooRealVar("weight", "weight" ,-2,2);
if (PbPb) RooRealVar* Centrality = new RooRealVar("Centrality", "Centrality" ,0,40);
RooRealVar* muPlusPt = new RooRealVar("muPlusPt","muPlusPt",muonpTcut,50);
RooRealVar* muPlusEta = new RooRealVar("muPlusEta","muPlusEta",-2.5,2.5);
RooRealVar* muMinusPt = new RooRealVar("muMinusPt","muMinusPt",muonpTcut,50);
RooRealVar* muMinusEta = new RooRealVar("muMinusEta","muMinusEta",-2.5,2.5);
//import unlike-sign data set
RooDataSet* data0, *data, *likesignData0, *likesignData, *TrkRotData0, *TrkRotData;
if (PbPb) data0 = new RooDataSet("data","data",theTree,RooArgSet(*mass,*upsRapidity,*vProb,*upsPt,*Centrality,*muPlusPt,*muMinusPt));
//data0 = new RooDataSet("data","data",theTree,RooArgSet(*mass,*upsRapidity,*upsPt,*muPlusPt,*muMinusPt,*QQsign,*weight));
else data0 = new RooDataSet("data","data",theTree,RooArgSet(*mass,*upsRapidity,*vProb,*upsPt,*muPlusPt,*muMinusPt,*muPlusEta,*muMinusEta));
data0->Print();
data = ( RooDataSet*) data0->reduce(Cut(cut_));
data->Print();
//import like-sign data set
if (PbPb) likesignData0 = new RooDataSet("likesignData","likesignData",allsignTree,RooArgSet(*mass,*upsRapidity,*vProb,*upsPt,*Centrality,*muPlusPt,*muMinusPt,*QQsign));
else likesignData0 = new RooDataSet("likesignData","likesignData",allsignTree,RooArgSet(*mass,*upsRapidity,*vProb,*upsPt,*muPlusPt,*muMinusPt,*QQsign));
likesignData0->Print();
likesignData = ( RooDataSet*) likesignData0->reduce(Cut(cut_+" && QQsign != 0"));
likesignData->Print();
//import track-rotation data set
if (TRKROT) {
if (PbPb) TrkRotData0 = new RooDataSet("TrkRotData","TrkRotData",trkRotTree,RooArgSet(*mass,*upsRapidity,*vProb,*upsPt,*Centrality,*muPlusPt,*muMinusPt,*QQsign));
else TrkRotData0 = new RooDataSet("TrkRotData","TrkRotData",trkRotTree,RooArgSet(*mass,*upsRapidity,*upsPt,*vProb,*muPlusPt,*muMinusPt,*QQsign));
TrkRotData0->Print();
if (PR_plot && RAA) TrkRotData = ( RooDataSet*) TrkRotData0->reduce(Cut(cut_+" && upsPt < 8.1"));
else if (PR_plot && !RAA) TrkRotData = ( RooDataSet*) TrkRotData0->reduce(Cut(cut_+" && upsPt < 7.07"));
else TrkRotData = ( RooDataSet*) TrkRotData0->reduce(Cut(cut_+" && QQsign != 0"));
TrkRotData->Print();
}
mass->setRange("R1",7.0,10.2);
mass->setRange("R2",7,14);
mass->setRange("R3",10.8,14);
const double M1S = 9.46; //upsilon 1S pgd mass value
const double M2S = 10.02; //upsilon 2S pgd mass value
const double M3S = 10.35; //upsilon 3S pgd mass value
RooRealVar *mean = new RooRealVar("#mu_{#Upsilon(1S)}","#Upsilon mean",M1S,M1S-0.1,M1S+0.1);
RooRealVar *shift21 = new RooRealVar("shift2","mass diff #Upsilon(1,2S)",M2S-M1S);
RooRealVar *shift31 = new RooRealVar("shift3","mass diff #Upsilon(1,3S)",M3S-M1S);
RooRealVar *mscale = new RooRealVar("mscale","mass scale factor",1.,0.7,1.3);
mscale->setConstant(kTRUE); /* the def. parameter value is fixed in the fit */
RooFormulaVar *mean1S = new RooFormulaVar("mean1S","@0",
RooArgList(*mean));
RooFormulaVar *mean2S = new RooFormulaVar("mean2S","@0+@1*@2",
RooArgList(*mean,*mscale,*shift21));
RooFormulaVar *mean3S = new RooFormulaVar("mean3S","@0+@1*@2",
RooArgList(*mean,*mscale,*shift31));
RooRealVar *sigma1 = new RooRealVar("sigma","Sigma_1",0.10,0.01,0.30); //detector resolution
RooRealVar *sigma2 = new RooRealVar("#sigma_{#Upsilon(1S)}","Sigma_1S",0.08,0.01,0.30); //Y(1S) resolution
RooFormulaVar *reso1S = new RooFormulaVar("reso1S","@0" ,RooArgList(*sigma2));
RooFormulaVar *reso2S = new RooFormulaVar("reso2S","@0*10.023/9.460",RooArgList(*sigma2));
RooFormulaVar *reso3S = new RooFormulaVar("reso3S","@0*10.355/9.460",RooArgList(*sigma2));
/// to describe final state radiation tail on the left of the peaks
RooRealVar *alpha = new RooRealVar("alpha","tail shift",0.982,0,2.4); // minbias fit value
//RooRealVar *alpha = new RooRealVar("alpha","tail shift",1.6,0.2,4); // MC value
RooRealVar *npow = new RooRealVar("npow","power order",2.3,1,3); // MC value
npow ->setConstant(kTRUE);
if (!fitMB) alpha->setConstant(kTRUE);
// relative fraction of the two peak components
RooRealVar *sigmaFraction = new RooRealVar("sigmaFraction","Sigma Fraction",0.3,0.,1.);
sigmaFraction->setVal(0);
sigmaFraction->setConstant(kTRUE);
/// Upsilon 1S
//RooCBShape *gauss1S1 = new RooCBShape ("gauss1S1", "FSR cb 1s",
// *mass,*mean1S,*sigma1,*alpha,*npow);
RooCBShape *gauss1S2 = new RooCBShape ("gauss1S2", "FSR cb 1s",
*mass,*mean1S,*reso1S,*alpha,*npow);
//RooAddPdf *sig1S = new RooAddPdf ("sig1S","1S mass pdf",
// RooArgList(*gauss1S1,*gauss1S2),*sigmaFraction);
//mean->setVal(9.46);
//mean->setConstant(kTRUE);
sigma1->setVal(0);
sigma1->setConstant(kTRUE);
if (!fitMB) {
sigma2->setVal(width_); //fix the resolution
sigma2->setConstant(kTRUE);
}
/// Upsilon 2S
RooCBShape *gauss2S1 = new RooCBShape ("gauss2S1", "FSR cb 2s",
*mass,*mean2S,*sigma1,*alpha,*npow);
RooCBShape *gauss2S2 = new RooCBShape ("gauss2S2", "FSR cb 2s",
*mass,*mean2S,*reso2S,*alpha,*npow);
RooAddPdf *sig2S = new RooAddPdf ("sig2S","2S mass pdf",
RooArgList(*gauss2S1,*gauss2S2),*sigmaFraction);
/// Upsilon 3S
RooCBShape *gauss3S1 = new RooCBShape ("gauss3S1", "FSR cb 3s",
*mass,*mean3S,*sigma1,*alpha,*npow);
RooCBShape *gauss3S2 = new RooCBShape ("gauss3S2", "FSR cb 3s",
*mass,*mean3S,*reso3S,*alpha,*npow);
RooAddPdf *sig3S = new RooAddPdf ("sig3S","3S mass pdf",
RooArgList(*gauss3S1,*gauss3S2),*sigmaFraction);
/// Background
RooRealVar *bkg_a1 = new RooRealVar("bkg_{a1}", "background a1", 0, -2, 2);
RooRealVar *bkg_a2 = new RooRealVar("bkg_{a2}", "background a2", 0, -1, 1);
//RooRealVar *bkg_a3 = new RooRealVar("bkg_{a3}", "background a3", 0, -1, 1);
RooAbsPdf *bkgPdf = new RooChebychev("bkg","background",
*mass, RooArgList(*bkg_a1,*bkg_a2));
//bkg_a1->setVal(0);
//bkg_a1->setConstant(kTRUE);
//bkg_a2->setVal(0);
//bkg_a2->setConstant(kTRUE); //set constant for liner background
// only sideband region pdf, using RooPolynomial instead of RooChebychev for multiple ranges fit
RooRealVar *SB_bkg_a1 = new RooRealVar("SB bkg_{a1}", "background a1", 0, -1, 1);
RooRealVar *SB_bkg_a2 = new RooRealVar("SB bkg_{a2}", "background a2", 0, -1, 1);
RooAbsPdf *SB_bkgPdf = new RooPolynomial("SB_bkg","side-band background",
*mass, RooArgList(*SB_bkg_a1,*SB_bkg_a2));
//SB_bkg_a1->setVal(0);
//SB_bkg_a1->setConstant(kTRUE);
//SB_bkg_a2->setVal(0);
//SB_bkg_a2->setConstant(kTRUE);
/// Combined pdf
int nt = 100000;
//bool fitfraction = true;
RooRealVar *nbkgd = new RooRealVar("N_{bkg}","nbkgd",nt*0.75,0,10*nt);
RooRealVar *SB_nbkgd = new RooRealVar("SB N_{bkg}","SB_nbkgd",nt*0.75,0,10*nt);
RooRealVar *nsig1f = new RooRealVar("N_{#Upsilon(1S)}","nsig1S",nt*0.25,0,10*nt);
/*
//use the YIELDs of 2S and 3S as free parameters
RooRealVar *nsig2f = new RooRealVar("N_{#Upsilon(2S)}","nsig2S", nt*0.25,-1*nt,10*nt);
RooRealVar *nsig3f = new RooRealVar("N_{#Upsilon(3S)}","nsig3S", nt*0.25,-1*nt,10*nt);
*/
//use the RATIOs of 2S and 3S as free parameters
RooRealVar *f2Svs1S = new RooRealVar("N_{2S}/N_{1S}","f2Svs1S",0.21,-0.1,1);
//RooRealVar *f3Svs1S = new RooRealVar("N_{3S}/N_{1S}","f3Svs1S",0.0,-0.1,0.5);
RooRealVar *f23vs1S = new RooRealVar("N_{2S+3S}/N_{1S}","f23vs1S",0.45,-0.1,1);
RooFormulaVar *nsig2f = new RooFormulaVar("nsig2S","@0*@1", RooArgList(*nsig1f,*f2Svs1S));
//RooFormulaVar *nsig3f = new RooFormulaVar("nsig3S","@0*@1", RooArgList(*nsig1f,*f3Svs1S));
RooFormulaVar *nsig3f = new RooFormulaVar("nsig3S","@0*@2-@0*@1", RooArgList(*nsig1f,*f2Svs1S,*f23vs1S));
//f3Svs1S->setConstant(kTRUE);
//force the ratio to the pp value
f2Svs1S_pp->setConstant(kTRUE);
f3Svs1S_pp->setConstant(kTRUE);
RooFormulaVar *nsig2f_ = new RooFormulaVar("nsig2S_pp","@0*@1", RooArgList(*nsig1f,*f2Svs1S_pp));
RooFormulaVar *nsig3f_ = new RooFormulaVar("nsig3S_pp","@0*@1", RooArgList(*nsig1f,*f3Svs1S_pp));
//only sideband region pdf, using RooPolynomial instead of RooChebychev for multiple ranges fit
RooAbsPdf *SB_pdf = new RooAddPdf ("SB_pdf","sideband background pdf",
RooArgList(*SB_bkgPdf),
RooArgList(*SB_nbkgd));
//only signal region pdf, using RooPolynomial instead of RooChebychev for multiple ranges fit
RooAbsPdf *S_pdf = new RooAddPdf ("S_pdf","total signal+background pdf",
RooArgList(*gauss1S2,*sig2S,*sig3S,*SB_bkgPdf),
RooArgList(*nsig1f,*nsig2f,*nsig3f,*SB_nbkgd));
//parameters for likesign
RooRealVar m0shift("turnOn","turnOn",8.6,0,20.) ;
RooRealVar width("width","width",2.36,0,20.) ;
RooRealVar par3("decay","decay",6.8, 0, 20.) ;
RooGaussian* m0shift_constr;
RooGaussian* width_constr;
RooGaussian* par3_constr;
RooRealVar *nLikesignbkgd = new RooRealVar("NLikesign_{bkg}","nlikesignbkgd",nt*0.75,0,10*nt);
if (TRKROT) {
nLikesignbkgd->setVal(TrkRotData->sumEntries());
nLikesignbkgd->setError(sqrt(TrkRotData->sumEntries()));
}
else {
nLikesignbkgd->setVal(likesignData->sumEntries());
nLikesignbkgd->setError(sqrt(likesignData->sumEntries()));
}
if (LS_constrain) {
RooGaussian* nLikesignbkgd_constr = new RooGaussian("nLikesignbkgd_constr","nLikesignbkgd_constr",*nLikesignbkgd,RooConst(nLikesignbkgd->getVal()),RooConst(nLikesignbkgd->getError()));
}
else nLikesignbkgd->setConstant(kTRUE);
RooFormulaVar *nResidualbkgd = new RooFormulaVar("NResidual_{bkg}","@0-@1",RooArgList(*nbkgd,*nLikesignbkgd));
switch (bkgdModel) {
case 1 : //use error function to fit the like-sign, then fix the shape and normailization,
RooGenericPdf *LikeSignPdf = new RooGenericPdf("Like-sign","likesign","exp(-@0/decay)*(TMath::Erf((@0-turnOn)/width)+1)",RooArgList(*mass,m0shift,width,par3));
if (TRKROT) RooFitResult* fit_1st = LikeSignPdf->fitTo(*TrkRotData,Save()) ;
else RooFitResult* fit_1st = LikeSignPdf->fitTo(*likesignData,Save()) ; // likesign data
//LikeSignPdf.fitTo(*data) ; // unlikesign data
//fit_1st->Print();
if (LS_constrain) {
m0shift_constr = new RooGaussian("m0shift_constr","m0shift_constr",m0shift,RooConst(m0shift.getVal()),RooConst(m0shift.getError()));
width_constr = new RooGaussian("width_constr","width_constr",width,RooConst(width.getVal()),RooConst(width.getError()));
par3_constr = new RooGaussian("par3_constr","par3_constr",par3,RooConst(par3.getVal()),RooConst(par3.getError()));
//m0shift_constr = new RooGaussian("m0shift_constr","m0shift_constr",m0shift,RooConst(7.9),RooConst(0.34*2));
//width_constr = new RooGaussian("width_constr","width_constr",width,RooConst(2.77),RooConst(0.38*2));
//par3_constr = new RooGaussian("par3_constr","par3_constr",par3,RooConst(6.3),RooConst(1.0*2));
}
else {
m0shift.setConstant(kTRUE);
width.setConstant(kTRUE);
par3.setConstant(kTRUE);
}
RooAbsPdf *pdf_combinedbkgd = new RooAddPdf ("pdf_combinedbkgd","total combined background pdf",
RooArgList(*bkgPdf,*LikeSignPdf),
RooArgList(*nResidualbkgd,*nLikesignbkgd));
//RooArgList(*LikeSignPdf),
//RooArgList(*nbkgd));
break;
case 2 : //use RooKeysPdf to smooth the like-sign, then fix the shape and normailization
if (TRKROT) RooKeysPdf *LikeSignPdf = new RooKeysPdf("Like-sign","likesign",*mass,*TrkRotData,3,1.5);
else RooKeysPdf *LikeSignPdf = new RooKeysPdf("Like-sign","likesign",*mass,*likesignData,3,1.7);
RooAbsPdf *pdf_combinedbkgd = new RooAddPdf ("pdf_combinedbkgd","total combined background pdf",
RooArgList(*bkgPdf,*LikeSignPdf),
RooArgList(*nResidualbkgd,*nLikesignbkgd));
break;
case 3 : //use error function to fit the unlike-sign directly
RooGenericPdf *LikeSignPdf = new RooGenericPdf("Like-sign","likesign","exp(-@0/decay)*(TMath::Erf((@0-turnOn)/width)+1)",RooArgList(*mass,m0shift,width,par3));
RooAbsPdf *pdf_combinedbkgd = new RooAddPdf ("pdf_combinedbkgd","total combined background pdf",
RooArgList(*LikeSignPdf),
RooArgList(*nbkgd));
break;
case 4 : //use polynomial to fit the unlike-sign directly
RooAbsPdf *pdf_combinedbkgd = new RooAddPdf ("pdf_combinedbkgd","total combined background pdf",
RooArgList(*bkgPdf),
RooArgList(*nbkgd));
break;
case 5 : //use ( error function + polynomial ) to fit the unlike-sign directly
RooGenericPdf *LikeSignPdf = new RooGenericPdf("Like-sign","likesign","exp(-@0/decay)*(TMath::Erf((@0-turnOn)/width)+1)",RooArgList(*mass,m0shift,width,par3));
RooAbsPdf *pdf_combinedbkgd = new RooAddPdf ("pdf_combinedbkgd","total combined background pdf",
RooArgList(*bkgPdf,*LikeSignPdf),
RooArgList(*nResidualbkgd,*nLikesignbkgd));
break;
default :
break;
}
//pdf with fixed ratio of the pp ratio
RooAbsPdf *pdf_pp = new RooAddPdf ("pdf_pp","total signal+background pdf",
RooArgList(*gauss1S2,*sig2S,*sig3S,*pdf_combinedbkgd),
RooArgList(*nsig1f,*nsig2f_,*nsig3f_,*nbkgd));
//the nominal fit with default pdf
if (LS_constrain) {
RooAbsPdf *pdf_unconstr = new RooAddPdf ("pdf_unconstr","total signal+background pdf",
RooArgList(*gauss1S2,*sig2S,*sig3S,*pdf_combinedbkgd),
RooArgList(*nsig1f,*nsig2f,*nsig3f,*nbkgd));
RooProdPdf *pdf = new RooProdPdf ("pdf","total constr pdf",
RooArgSet(*pdf_unconstr,*m0shift_constr,*width_constr,*par3_constr,*nLikesignbkgd_constr));
RooFitResult* fit_2nd = pdf->fitTo(*data,Constrained(),Save(kTRUE),Extended(kTRUE),Minos(doMinos));
}
else {
RooAbsPdf *pdf = new RooAddPdf ("pdf","total signal+background pdf",
RooArgList(*gauss1S2,*sig2S,*sig3S,*pdf_combinedbkgd),
RooArgList(*nsig1f,*nsig2f,*nsig3f,*nbkgd));
RooFitResult* fit_2nd = pdf->fitTo(*data,Save(kTRUE),Extended(kTRUE),Minos(doMinos));
}
//plot
TCanvas c; c.cd();
int nbins = ceil((mmax_-mmin_)/binw_);
RooPlot* frame = mass->frame(Bins(nbins),Range(mmin_,mmax_));
data->plotOn(frame,Name("theData"),MarkerSize(0.8));
pdf->plotOn(frame,Name("thePdf"));
if (plotLikeSign) {
if (TRKROT) TrkRotData->plotOn(frame,Name("theLikeSignData"),MarkerSize(0.8),MarkerColor(kMagenta),MarkerStyle(22));
else likesignData->plotOn(frame,Name("theLikeSignData"),MarkerSize(0.8),MarkerColor(kRed),MarkerStyle(24));
//LikeSignPdf->plotOn(frame,Name("theLikeSign"),VisualizeError(*fit_1st,1),FillColor(kOrange));
//LikeSignPdf->plotOn(frame,Name("theLikeSign"),LineColor(kRed));
}
RooArgSet * pars = pdf->getParameters(data);
//RooArgSet * pars = LikeSignPdf->getParameters(likesignData);
//calculate chi2 in a mass range
float bin_Min = (8.2-mmin_)/binw_;
float bin_Max = (10.8-mmin_)/binw_;
int binMin = ceil(bin_Min);
int binMax = ceil(bin_Max);
int nfloatpars = pars->selectByAttrib("Constant",kFALSE)->getSize();
float myndof = ceil((10.8-8.2)/binw_) - nfloatpars;
cout<<binMin<<" "<<binMax<<" "<<nfloatpars<<" "<<myndof<<endl;
double mychsq = frame->mychiSquare("thePdf","theData",nfloatpars,true,binMin,binMax)*myndof;
//double mychsq = frame->mychiSquare("theLikeSign","theLikeSignData",nfloatpars,true,binMin,binMax)*myndof;
/*
int nfloatpars = pars->selectByAttrib("Constant",kFALSE)->getSize();
float myndof = frame->GetNbinsX() - nfloatpars;
double mychsq = frame->chiSquare("theLikeSign","theLikeSignData",nfloatpars)*myndof;
*/
//plot parameters
if(plotpars) {
paramOn_ = "_paramOn";
pdf->paramOn(frame,Layout(0.15,0.6,0.4),Layout(0.5,0.935,0.97),Label(Form("#chi^{2}/ndf = %2.1f/%2.0f", mychsq,myndof)));
}
/*
mass->setRange("R1S",8.8,9.7);
mass->setRange("R2S",9.8,10.2);
//pdf_combinedbkgd->fitTo(*data,Range("R1,R3"),Constrained(),Save(kTRUE),Extended(kTRUE),Minos(doMinos));
RooAbsReal* integral_1S = pdf_combinedbkgd->createIntegral(*mass,NormSet(*mass),Range("R1S")) ;
cout << "1S bkgd integral = " << integral_1S->getVal() * (nbkgd->getVal()) << endl ;
RooAbsReal* integral_2S = pdf_combinedbkgd->createIntegral(*mass,NormSet(*mass),Range("R2S")) ;
cout << "2S bkgd integral = " << integral_2S->getVal() * (nbkgd->getVal()) << endl ;
cout << "1S range count: " << data->sumEntries("invariantMass","R1S") <<endl;
cout << "2S range count: " << data->sumEntries("invariantMass","R2S") <<endl;
cout << "1S signal yield: " << data->sumEntries("invariantMass","R1S") - integral_1S->getVal() * (nbkgd->getVal()) << endl;
cout << "2S signal yield: " << data->sumEntries("invariantMass","R2S") - integral_2S->getVal() * (nbkgd->getVal()) << endl;
*/
outfile<<"Y(1S) yield : = "<<nsig1f->getVal()<<" +/- "<<nsig1f->getError()<<endl<<endl;
outfile<<"free parameter = "<< nfloatpars << ", mychi2 = " << mychsq << ", ndof = " << myndof << endl << endl;
//draw the fit lines and save plots
data->plotOn(frame,Name("theData"),MarkerSize(0.8));
pdf->plotOn(frame,Components("bkg"),Name("theBkg"),LineStyle(5),LineColor(kGreen));
pdf->plotOn(frame,Components("pdf_combinedbkgd"),LineStyle(kDashed));
if (plotLikeSign) {
if (TRKROT) pdf->plotOn(frame,Components("Like-sign"),Name("theLikeSign"),LineStyle(9),LineColor(kMagenta));
else pdf->plotOn(frame,Components("Like-sign"),Name("theLikeSign"),LineStyle(9),LineColor(kRed));
}
pdf->plotOn(frame,Name("thePdf"));
data->plotOn(frame,MarkerSize(0.8));
if (plotLikeSign) {
if (TRKROT) TrkRotData->plotOn(frame,Name("theTrkRotData"),MarkerSize(0.8),MarkerColor(kMagenta),MarkerStyle(22));
else likesignData->plotOn(frame,Name("theLikeSignData"),MarkerSize(0.8),MarkerColor(kRed),MarkerStyle(24));
}
frame->SetTitle( "" );
frame->GetXaxis()->SetTitle("m_{#mu^{+}#mu^{-}} (GeV/c^{2})");
frame->GetXaxis()->CenterTitle(kTRUE);
frame->GetYaxis()->SetTitleOffset(1.3);
if (PR_plot && RAA) frame->GetYaxis()->SetRangeUser(0,1200);
//frame->GetYaxis()->SetLabelSize(0.05);
frame->Draw();
//plot parameters
if(!plotpars) {
paramOn_ = "";
TLatex latex1;
latex1.SetNDC();
if (PbPb) {
latex1.DrawLatex(0.46,1.-0.05*3,"CMS PbPb #sqrt{s_{NN}} = 2.76 TeV");
latex1.DrawLatex(0.5,1.-0.05*4.9,"L_{int} = 150 #mub^{-1}");
switch (bin) {
case 0: latex1.DrawLatex(0.5,1.-0.05*6.2,"Cent. 0-100%, |y| < 2.4"); break;
case 3: latex1.DrawLatex(0.5,1.-0.05*6.2,"Cent. 0-5%, |y| < 2.4"); break;
case 4: latex1.DrawLatex(0.5,1.-0.05*6.2,"Cent. 5-10%, |y| < 2.4"); break;
case 5: latex1.DrawLatex(0.5,1.-0.05*6.2,"Cent. 10-20%, |y| < 2.4"); break;
case 6: latex1.DrawLatex(0.5,1.-0.05*6.2,"Cent. 20-30%, |y| < 2.4"); break;
case 7: latex1.DrawLatex(0.5,1.-0.05*6.2,"Cent. 30-40%, |y| < 2.4"); break;
case 8: latex1.DrawLatex(0.5,1.-0.05*6.2,"Cent. 40-50%, |y| < 2.4"); break;
case 9: latex1.DrawLatex(0.5,1.-0.05*6.2,"Cent. 50-100%, |y| < 2.4"); break;
default; break;
}
}
else {
void buildAndFitModels(TDirectory *fout, RooWorkspace *wspace, RooRealVar &x, std::string proc="Zvv"){
// Build and fit the model for the Zvv/Wlv background
RooAbsPdf *pdfZvv = wspace->pdf(doubleexp(wspace,x,Form("%s_control",proc.c_str())));
RooAbsPdf *pdfZvv_mc = wspace->pdf(doubleexp(wspace,x,Form("%s_control_mc",proc.c_str())));
RooAbsPdf *pdfZvv_background_mc = wspace->pdf(doubleexp(wspace,x,Form("%s_control_bkg_mc",proc.c_str())));
pdfZvv_mc->Print("v");
// Fit control region MC
std::cout << " Fit for control MC " << Form("%s_control_mc",proc.c_str())<< std::endl;
RooFitResult *fit_res_control_mc = pdfZvv_mc->fitTo(*(wspace->data(Form("%s_control_mc",proc.c_str()))),RooFit::Save(1),RooFit::SumW2Error(false));
fout->cd(); fit_res_control_mc->SetName(Form("fitResult_%s_control_mc",proc.c_str())); fit_res_control_mc->Write();
std::cout << " Fit for background MC " << Form("%s_control_bkg_mc",proc.c_str()) << std::endl;
// Fit background MC and then fix it
pdfZvv_background_mc->fitTo(*(wspace->data(Form("%s_control_bkg_mc",proc.c_str()))),RooFit::SumW2Error(true));
freezeParameters(pdfZvv_background_mc->getParameters(RooArgSet(x)));
// Now fit the Zvv Data
//RooRealVar frac_contamination_Zvv(Form("frac_contamination_%s",proc.c_str()),Form("frac_contamination_%s",proc.c_str()),0,1);
double nbkgcont = wspace->data(Form("%s_control_bkg_mc",proc.c_str()))->sumEntries();
double ncont = wspace->data(Form("%s_control",proc.c_str()))->sumEntries()-nbkgcont;
RooRealVar num_contamination_Zvv(Form("num_contamination_%s",proc.c_str()),Form("num_contamination_%s",proc.c_str()),nbkgcont,0,10E10);
num_contamination_Zvv.setConstant();
RooRealVar num_Zvv(Form("num_%s",proc.c_str()),Form("num_%s",proc.c_str()),ncont,0,10E10);
num_Zvv.setConstant(true);// freeze the n_data now
RooAddPdf modelZvv(Form("model_%s_control",proc.c_str()),Form("model_%s_control",proc.c_str()),RooArgList(*pdfZvv_background_mc,*pdfZvv),RooArgList(num_contamination_Zvv,num_Zvv));
std::cout << " Fit for control Data " << Form("%s_control",proc.c_str()) << std::endl;
RooFitResult *fit_res_control = modelZvv.fitTo(*(wspace->data(Form("%s_control",proc.c_str()))),RooFit::Save(1));
fout->cd(); fit_res_control->SetName(Form("fitResult_%s_control",proc.c_str())); fit_res_control->Write();
// Find the scale of ndata/nmc to normalize the yields
double nmontecarlo = wspace->data(Form("%s_control_mc",proc.c_str()))->sumEntries();
double ndata = num_Zvv.getVal();
RooRealVar scalef(Form("scalef_%s",proc.c_str()),"scalef",ndata/nmontecarlo);
// uncomment make this ONLY a shape correction!
// scalef.setVal(1);
scalef.setConstant(true);
std::cout << proc.c_str() << " N Control Data == " << ndata << std::endl;
std::cout << proc.c_str() << " N Control MC == " << nmontecarlo << std::endl;
// Plot the fits
TCanvas can_datafit(Form("%s_datafit",proc.c_str()),"Data Fit",800,600);
RooPlot *pl = x.frame();
(wspace->data(Form("%s_control_bkg_mc",proc.c_str())))->plotOn(pl,RooFit::MarkerStyle(kOpenCircle));
(wspace->data(Form("%s_control",proc.c_str())))->plotOn(pl);
modelZvv.plotOn(pl);
modelZvv.paramOn(pl);
//pdfZvv_background_mc->plotOn(pl,RooFit::LineStyle(2));
pl->Draw();
fout->cd();can_datafit.Write();
TCanvas can_mcfit(Form("%s_mcfit",proc.c_str()),"MC Fit",800,600);
RooPlot *plmc = x.frame();
(wspace->data(Form("%s_control_mc",proc.c_str())))->plotOn(plmc,RooFit::MarkerColor(kBlack));
pdfZvv_mc->plotOn(plmc,RooFit::LineStyle(1),RooFit::LineColor(2));
pdfZvv_mc->paramOn(plmc);
plmc->Draw();
fout->cd();can_mcfit.Write();
TCanvas can_mcdatafit(Form("%s_mcdatafit",proc.c_str()),"MC and Data Fits",800,600);
RooPlot *plmcdata = x.frame();
pdfZvv_mc->plotOn(plmcdata,RooFit::LineColor(2),RooFit::Normalization(nmontecarlo));
pdfZvv->plotOn(plmcdata,RooFit::Normalization(ndata));
plmcdata->Draw();
fout->cd();can_mcdatafit.Write();
// Import the correction and the models
RooFormulaVar ratio(Form("ratio_%s",proc.c_str()),"@0*@1/@2",RooArgList(scalef,*pdfZvv,*pdfZvv_mc));
wspace->import(ratio);
wspace->import(num_Zvv);
TCanvas can_ra(Form("%s_ratio",proc.c_str()),"MC Fit",800,600);
RooPlot *plra = x.frame();
ratio.plotOn(plra,RooFit::LineStyle(1));
plra->Draw();
fout->cd();can_ra.Write();
}
void rf104_classfactory()
{
// W r i t e c l a s s s k e l e t o n c o d e
// --------------------------------------------------
// Write skeleton p.d.f class with variable x,a,b
// To use this class,
// - Edit the file MyPdfV1.cxx and implement the evaluate() method in terms of x,a and b
// - Compile and link class with '.x MyPdfV1.cxx+'
//
RooClassFactory::makePdf("MyPdfV1","x,A,B") ;
// W i t h a d d e d i n i t i a l v a l u e e x p r e s s i o n
// ---------------------------------------------------------------------
// Write skeleton p.d.f class with variable x,a,b and given formula expression
// To use this class,
// - Compile and link class with '.x MyPdfV2.cxx+'
//
RooClassFactory::makePdf("MyPdfV2","x,A,B","","A*fabs(x)+pow(x-B,2)") ;
// W i t h a d d e d a n a l y t i c a l i n t e g r a l e x p r e s s i o n
// ---------------------------------------------------------------------------------
// Write skeleton p.d.f class with variable x,a,b, given formula expression _and_
// given expression for analytical integral over x
// To use this class,
// - Compile and link class with '.x MyPdfV3.cxx+'
//
RooClassFactory::makePdf("MyPdfV3","x,A,B","","A*fabs(x)+pow(x-B,2)",kTRUE,kFALSE,
"x:(A/2)*(pow(x.max(rangeName),2)+pow(x.min(rangeName),2))+(1./3)*(pow(x.max(rangeName)-B,3)-pow(x.min(rangeName)-B,3))") ;
// U s e i n s t a n c e o f c r e a t e d c l a s s
// ---------------------------------------------------------
// Compile MyPdfV3 class (only when running in CINT)
#ifdef __CINT__
gROOT->ProcessLineSync(".x MyPdfV3.cxx+") ;
#endif
// Creat instance of MyPdfV3 class
RooRealVar a("a","a",1) ;
RooRealVar b("b","b",2,-10,10) ;
RooRealVar y("y","y",-10,10);
MyPdfV3 pdf("pdf","pdf",y,a,b) ;
// Generate toy data from pdf and plot data and p.d.f on frame
RooPlot* frame1 = y.frame(Title("Compiled class MyPdfV3")) ;
RooDataSet* data = pdf.generate(y,1000) ;
pdf.fitTo(*data) ;
data->plotOn(frame1) ;
pdf.plotOn(frame1) ;
///////////////////////////////////////////////////////////////////////
// C o m p i l e d v e r s i o n o f e x a m p l e r f 1 0 3 //
///////////////////////////////////////////////////////////////////////
// Declare observable x
RooRealVar x("x","x",-20,20) ;
// The RooClassFactory::makePdfInstance() function performs code writing, compiling, linking
// and object instantiation in one go and can serve as a straight replacement of RooGenericPdf
RooRealVar alpha("alpha","alpha",5,0.1,10) ;
RooAbsPdf* genpdf = RooClassFactory::makePdfInstance("GenPdf","(1+0.1*fabs(x)+sin(sqrt(fabs(x*alpha+0.1))))",RooArgSet(x,alpha)) ;
// Generate a toy dataset from the interpreted p.d.f
RooDataSet* data2 = genpdf->generate(x,50000) ;
// Fit the interpreted p.d.f to the generated data
genpdf->fitTo(*data2) ;
// Make a plot of the data and the p.d.f overlaid
RooPlot* frame2 = x.frame(Title("Compiled version of pdf of rf103")) ;
data2->plotOn(frame2) ;
genpdf->plotOn(frame2) ;
// Draw all frames on a canvas
TCanvas* c = new TCanvas("rf104_classfactory","rf104_classfactory",800,400) ;
c->Divide(2) ;
c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;
}
void fitqual_plots( const char* wsfile = "outputfiles/ws.root", const char* plottitle="" ) {
TText* tt_title = new TText() ;
tt_title -> SetTextAlign(33) ;
gStyle -> SetOptStat(0) ;
gStyle -> SetLabelSize( 0.06, "y" ) ;
gStyle -> SetLabelSize( 0.08, "x" ) ;
gStyle -> SetLabelOffset( 0.010, "y" ) ;
gStyle -> SetLabelOffset( 0.010, "x" ) ;
gStyle -> SetTitleSize( 0.07, "y" ) ;
gStyle -> SetTitleSize( 0.05, "x" ) ;
gStyle -> SetTitleOffset( 1.50, "x" ) ;
gStyle -> SetTitleH( 0.07 ) ;
gStyle -> SetPadLeftMargin( 0.15 ) ;
gStyle -> SetPadBottomMargin( 0.15 ) ;
gStyle -> SetTitleX( 0.10 ) ;
gDirectory->Delete("h*") ;
TFile* wstf = new TFile( wsfile ) ;
RooWorkspace* ws = dynamic_cast<RooWorkspace*>( wstf->Get("ws") );
ws->Print() ;
int bins_of_met = TMath::Nint( ws->var("bins_of_met")->getVal() ) ;
printf("\n\n Bins of MET : %d\n\n", bins_of_met ) ;
int bins_of_nb = TMath::Nint( ws->var("bins_of_nb")->getVal() ) ;
printf("\n\n Bins of nb : %d\n\n", bins_of_nb ) ;
int nb_lookup[10] ;
if ( bins_of_nb == 2 ) {
nb_lookup[0] = 2 ;
nb_lookup[1] = 4 ;
} else if ( bins_of_nb == 3 ) {
nb_lookup[0] = 2 ;
nb_lookup[1] = 3 ;
nb_lookup[2] = 4 ;
}
TCanvas* cfq1 = (TCanvas*) gDirectory->FindObject("cfq1") ;
if ( cfq1 == 0x0 ) {
if ( bins_of_nb == 3 ) {
cfq1 = new TCanvas("cfq1","hbb fit", 700, 1000 ) ;
} else if ( bins_of_nb == 2 ) {
cfq1 = new TCanvas("cfq1","hbb fit", 700, 750 ) ;
} else {
return ;
}
}
RooRealVar* rv_sig_strength = ws->var("sig_strength") ;
if ( rv_sig_strength == 0x0 ) { printf("\n\n *** can't find sig_strength in workspace.\n\n" ) ; return ; }
ModelConfig* modelConfig = (ModelConfig*) ws->obj( "SbModel" ) ;
RooDataSet* rds = (RooDataSet*) ws->obj( "hbb_observed_rds" ) ;
rds->Print() ;
rds->printMultiline(cout, 1, kTRUE, "") ;
RooAbsPdf* likelihood = modelConfig->GetPdf() ;
///RooFitResult* fitResult = likelihood->fitTo( *rds, Save(true), PrintLevel(0) ) ;
RooFitResult* fitResult = likelihood->fitTo( *rds, Save(true), PrintLevel(3) ) ;
fitResult->Print() ;
char hname[1000] ;
char htitle[1000] ;
char pname[1000] ;
//-- unpack observables.
int obs_N_msig[10][50] ; // first index is n btags, second is met bin.
int obs_N_msb[10][50] ; // first index is n btags, second is met bin.
const RooArgSet* dsras = rds->get() ;
TIterator* obsIter = dsras->createIterator() ;
while ( RooRealVar* obs = (RooRealVar*) obsIter->Next() ) {
for ( int nbi=0; nbi<bins_of_nb; nbi++ ) {
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
sprintf( pname, "N_%db_msig_met%d", nb_lookup[nbi], mbi+1 ) ;
if ( strcmp( obs->GetName(), pname ) == 0 ) { obs_N_msig[nbi][mbi] = TMath::Nint( obs -> getVal() ) ; }
sprintf( pname, "N_%db_msb_met%d", nb_lookup[nbi], mbi+1 ) ;
if ( strcmp( obs->GetName(), pname ) == 0 ) { obs_N_msb[nbi][mbi] = TMath::Nint( obs -> getVal() ) ; }
} // mbi.
} // nbi.
} // obs iterator.
printf("\n\n") ;
for ( int nbi=0; nbi<bins_of_nb; nbi++ ) {
printf(" nb=%d : ", nb_lookup[nbi] ) ;
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
printf(" sig=%3d, sb=%3d |", obs_N_msig[nbi][mbi], obs_N_msb[nbi][mbi] ) ;
} // mbi.
printf("\n") ;
} // nbi.
printf("\n\n") ;
int pad(1) ;
cfq1->Clear() ;
cfq1->Divide( 2, bins_of_nb+1 ) ;
for ( int nbi=0; nbi<bins_of_nb; nbi++ ) {
sprintf( hname, "h_bg_%db_msig_met", nb_lookup[nbi] ) ;
sprintf( htitle, "mass sig, %db, MET", nb_lookup[nbi] ) ;
TH1F* hist_bg_msig = new TH1F( hname, htitle, bins_of_met, 0.5, bins_of_met+0.5 ) ;
hist_bg_msig -> SetFillColor( kBlue-9 ) ;
labelBins( hist_bg_msig ) ;
sprintf( hname, "h_bg_%db_msb_met", nb_lookup[nbi] ) ;
sprintf( htitle, "mass sb, %db, MET", nb_lookup[nbi] ) ;
TH1F* hist_bg_msb = new TH1F( hname, htitle, bins_of_met, 0.5, bins_of_met+0.5 ) ;
hist_bg_msb -> SetFillColor( kBlue-9 ) ;
labelBins( hist_bg_msb ) ;
sprintf( hname, "h_sig_%db_msig_met", nb_lookup[nbi] ) ;
sprintf( htitle, "mass sig, %db, MET", nb_lookup[nbi] ) ;
TH1F* hist_sig_msig = new TH1F( hname, htitle, bins_of_met, 0.5, bins_of_met+0.5 ) ;
hist_sig_msig -> SetFillColor( kMagenta+2 ) ;
labelBins( hist_sig_msig ) ;
sprintf( hname, "h_sig_%db_msb_met", nb_lookup[nbi] ) ;
sprintf( htitle, "mass sb, %db, MET", nb_lookup[nbi] ) ;
TH1F* hist_sig_msb = new TH1F( hname, htitle, bins_of_met, 0.5, bins_of_met+0.5 ) ;
hist_sig_msb -> SetFillColor( kMagenta+2 ) ;
labelBins( hist_sig_msb ) ;
sprintf( hname, "h_all_%db_msig_met", nb_lookup[nbi] ) ;
sprintf( htitle, "mass sig, %db, MET", nb_lookup[nbi] ) ;
TH1F* hist_all_msig = new TH1F( hname, htitle, bins_of_met, 0.5, bins_of_met+0.5 ) ;
sprintf( hname, "h_all_%db_msb_met", nb_lookup[nbi] ) ;
sprintf( htitle, "mass sb, %db, MET", nb_lookup[nbi] ) ;
TH1F* hist_all_msb = new TH1F( hname, htitle, bins_of_met, 0.5, bins_of_met+0.5 ) ;
sprintf( hname, "h_data_%db_msig_met", nb_lookup[nbi] ) ;
sprintf( htitle, "mass sig, %db, MET", nb_lookup[nbi] ) ;
TH1F* hist_data_msig = new TH1F( hname, htitle, bins_of_met, 0.5, bins_of_met+0.5 ) ;
hist_data_msig -> SetLineWidth(2) ;
hist_data_msig -> SetMarkerStyle(20) ;
labelBins( hist_data_msig ) ;
sprintf( hname, "h_data_%db_msb_met", nb_lookup[nbi] ) ;
sprintf( htitle, "mass sb, %db, MET", nb_lookup[nbi] ) ;
TH1F* hist_data_msb = new TH1F( hname, htitle, bins_of_met, 0.5, bins_of_met+0.5 ) ;
hist_data_msb -> SetLineWidth(2) ;
hist_data_msb -> SetMarkerStyle(20) ;
labelBins( hist_data_msb ) ;
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
sprintf( pname, "mu_bg_%db_msig_met%d", nb_lookup[nbi], mbi+1 ) ;
RooAbsReal* mu_bg_msig = ws->function( pname ) ;
if ( mu_bg_msig == 0x0 ) { printf("\n\n *** ws missing %s\n\n", pname ) ; return ; }
hist_bg_msig -> SetBinContent( mbi+1, mu_bg_msig->getVal() ) ;
sprintf( pname, "mu_sig_%db_msig_met%d", nb_lookup[nbi], mbi+1 ) ;
RooAbsReal* mu_sig_msig = ws->function( pname ) ;
if ( mu_sig_msig == 0x0 ) { printf("\n\n *** ws missing %s\n\n", pname ) ; return ; }
hist_sig_msig -> SetBinContent( mbi+1, mu_sig_msig->getVal() ) ;
hist_all_msig -> SetBinContent( mbi+1, mu_bg_msig->getVal() + mu_sig_msig->getVal() ) ;
hist_data_msig -> SetBinContent( mbi+1, obs_N_msig[nbi][mbi] ) ;
sprintf( pname, "mu_bg_%db_msb_met%d", nb_lookup[nbi], mbi+1 ) ;
RooAbsReal* mu_bg_msb = ws->function( pname ) ;
if ( mu_bg_msb == 0x0 ) { printf("\n\n *** ws missing %s\n\n", pname ) ; return ; }
hist_bg_msb -> SetBinContent( mbi+1, mu_bg_msb->getVal() ) ;
sprintf( pname, "mu_sig_%db_msb_met%d", nb_lookup[nbi], mbi+1 ) ;
RooAbsReal* mu_sig_msb = ws->function( pname ) ;
if ( mu_sig_msb == 0x0 ) { printf("\n\n *** ws missing %s\n\n", pname ) ; return ; }
hist_sig_msb -> SetBinContent( mbi+1, mu_sig_msb->getVal() ) ;
hist_all_msb -> SetBinContent( mbi+1, mu_bg_msb->getVal() + mu_sig_msb->getVal() ) ;
hist_data_msb -> SetBinContent( mbi+1, obs_N_msb[nbi][mbi] ) ;
} // mbi.
cfq1->cd( pad ) ;
sprintf( hname, "h_stack_%db_msig_met", nb_lookup[nbi] ) ;
sprintf( htitle, "mass sig, %db, MET", nb_lookup[nbi] ) ;
THStack* hstack_msig = new THStack( hname, htitle ) ;
hstack_msig -> Add( hist_bg_msig ) ;
hstack_msig -> Add( hist_sig_msig ) ;
hist_data_msig -> Draw("e") ;
hstack_msig -> Draw("same") ;
hist_data_msig -> Draw("same e") ;
hist_data_msig -> Draw("same axis") ;
tt_title -> DrawTextNDC( 0.85, 0.85, plottitle ) ;
pad++ ;
cfq1->cd( pad ) ;
sprintf( hname, "h_stack_%db_msb_met", nb_lookup[nbi] ) ;
sprintf( htitle, "mass sig, %db, MET", nb_lookup[nbi] ) ;
THStack* hstack_msb = new THStack( hname, htitle ) ;
hstack_msb -> Add( hist_bg_msb ) ;
hstack_msb -> Add( hist_sig_msb ) ;
hist_data_msb -> Draw("e") ;
hstack_msb -> Draw("same") ;
hist_data_msb -> Draw("same e") ;
hist_data_msb -> Draw("same axis") ;
tt_title -> DrawTextNDC( 0.85, 0.85, plottitle ) ;
pad++ ;
} // nbi.
TH1F* hist_R_msigmsb = new TH1F( "h_R_msigmsb", "R msig/msb vs met bin", bins_of_met, 0.5, 0.5+bins_of_met ) ;
hist_R_msigmsb -> SetLineWidth(2) ;
hist_R_msigmsb -> SetMarkerStyle(20) ;
hist_R_msigmsb -> SetYTitle("R msig/msb") ;
labelBins( hist_R_msigmsb ) ;
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
sprintf( pname, "R_msigmsb_met%d", mbi+1 ) ;
RooRealVar* rrv_R = ws->var( pname ) ;
if ( rrv_R == 0x0 ) { printf("\n\n *** Can't find %s in ws.\n\n", pname ) ; return ; }
hist_R_msigmsb -> SetBinContent( mbi+1, rrv_R -> getVal() ) ;
hist_R_msigmsb -> SetBinError( mbi+1, rrv_R -> getError() ) ;
} // mbi.
cfq1->cd( pad ) ;
gPad->SetGridy(1) ;
hist_R_msigmsb -> SetMaximum(0.35) ;
hist_R_msigmsb -> Draw("e") ;
tt_title -> DrawTextNDC( 0.85, 0.85, plottitle ) ;
pad++ ;
cfq1->cd( pad ) ;
scan_sigstrength( wsfile ) ;
tt_title -> DrawTextNDC( 0.85, 0.25, plottitle ) ;
TString pdffile( wsfile ) ;
pdffile.ReplaceAll("ws-","fitqual-") ;
pdffile.ReplaceAll("root","pdf") ;
cfq1->SaveAs( pdffile ) ;
TString histfile( wsfile ) ;
histfile.ReplaceAll("ws-","fitqual-") ;
saveHist( histfile, "h*" ) ;
} // fitqual_plots
void Raa3S_Workspace(const char* filename="fitresult_combo_nofixed.root"){
TFile File(filename);
RooWorkspace * ws;
File.GetObject("wcombo", ws);
// ws->Print();
RooAbsData * data = ws->data("data");
// RooDataSet * US_data = (RooDataSet*) data->reduce( "QQsign == QQsign::PlusMinus");
// US_data->SetName("US_data");
// ws->import(* US_data);
// RooDataSet * hi_data = (RooDataSet*) US_data->reduce("dataCat == dataCat::hi");
// hi_data->SetName("hi_data");
// ws->import(* hi_data);
// hi_data->Print();
RooRealVar* raa3 = new RooRealVar("raa3","R_{AA}(#Upsilon (3S))",0.5,0,1);
RooRealVar* leftEdge = new RooRealVar("leftEdge","leftEdge",0);
RooRealVar* rightEdge = new RooRealVar("rightEdge","rightEdge",1);
RooGenericPdf step("step", "step", "(@0 >= @1) && (@0 < @2)", RooArgList(*raa3, *leftEdge, *rightEdge));
ws->import(step);
ws->factory( "Uniform::flat(raa3)" );
//pp Luminosities, Taa and efficiency ratios Systematics
ws->factory( "Taa_hi[5.662e-9]" );
ws->factory( "Taa_kappa[1.057]" );
ws->factory( "expr::alpha_Taa('pow(Taa_kappa,beta_Taa)',Taa_kappa,beta_Taa[0,-5,5])" );
ws->factory( "prod::Taa_nom(Taa_hi,alpha_Taa)" );
ws->factory( "Gaussian::constr_Taa(beta_Taa,glob_Taa[0,-5,5],1)" );
ws->factory( "lumipp_hi[5.4]" );
ws->factory( "lumipp_kappa[1.06]" );
ws->factory( "expr::alpha_lumipp('pow(lumipp_kappa,beta_lumipp)',lumipp_kappa,beta_lumipp[0,-5,5])" );
ws->factory( "prod::lumipp_nom(lumipp_hi,alpha_lumipp)" );
ws->factory( "Gaussian::constr_lumipp(beta_lumipp,glob_lumipp[0,-5,5],1)" );
// ws->factory( "effRat1[1]" );
// ws->factory( "effRat2[1]" );
ws->factory( "effRat3_hi[0.95]" );
ws->factory( "effRat_kappa[1.054]" );
ws->factory( "expr::alpha_effRat('pow(effRat_kappa,beta_effRat)',effRat_kappa,beta_effRat[0,-5,5])" );
// ws->factory( "prod::effRat1_nom(effRat1_hi,alpha_effRat)" );
ws->factory( "Gaussian::constr_effRat(beta_effRat,glob_effRat[0,-5,5],1)" );
// ws->factory( "prod::effRat2_nom(effRat2_hi,alpha_effRat)" );
ws->factory( "prod::effRat3_nom(effRat3_hi,alpha_effRat)" );
//
ws->factory("Nmb_hi[1.161e9]");
ws->factory("prod::denominator(Taa_nom,Nmb_hi)");
ws->factory( "expr::lumiOverTaaNmbmodified('lumipp_nom/denominator',lumipp_nom,denominator)");
RooFormulaVar *lumiOverTaaNmbmodified = ws->function("lumiOverTaaNmbmodified");
//
// RooRealVar *raa1 = ws->var("raa1");
// RooRealVar* nsig1_pp = ws->var("nsig1_pp");
// RooRealVar* effRat1 = ws->function("effRat1_nom");
// RooRealVar *raa2 = ws->var("raa2");
// RooRealVar* nsig2_pp = ws->var("nsig2_pp");
// RooRealVar* effRat2 = ws->function("effRat2_nom");
RooRealVar* nsig3_pp = ws->var("N_{#Upsilon(3S)}_pp");
cout << nsig3_pp << endl;
RooRealVar* effRat3 = ws->function("effRat3_nom");
//
// RooFormulaVar nsig1_hi_modified("nsig1_hi_modified", "@0*@1*@3/@2", RooArgList(*raa1, *nsig1_pp, *lumiOverTaaNmbmodified, *effRat1));
// ws->import(nsig1_hi_modified);
// RooFormulaVar nsig2_hi_modified("nsig2_hi_modified", "@0*@1*@3/@2", RooArgList(*raa2, *nsig2_pp, *lumiOverTaaNmbmodified, *effRat2));
// ws->import(nsig2_hi_modified);
RooFormulaVar nsig3_hi_modified("nsig3_hi_modified", "@0*@1*@3/@2", RooArgList(*raa3, *nsig3_pp, *lumiOverTaaNmbmodified, *effRat3));
ws->import(nsig3_hi_modified);
// // background yield with systematics
ws->factory( "nbkg_hi_kappa[1.10]" );
ws->factory( "expr::alpha_nbkg_hi('pow(nbkg_hi_kappa,beta_nbkg_hi)',nbkg_hi_kappa,beta_nbkg_hi[0,-5,5])" );
ws->factory( "SUM::nbkg_hi_nom(alpha_nbkg_hi*bkgPdf_hi)" );
ws->factory( "Gaussian::constr_nbkg_hi(beta_nbkg_hi,glob_nbkg_hi[0,-5,5],1)" );
RooAbsPdf* sig1S_hi = ws->pdf("cbcb_hi");
RooAbsPdf* sig2S_hi = ws->pdf("sig2S_hi");
RooAbsPdf* sig3S_hi = ws->pdf("sig3S_hi");
RooAbsPdf* LSBackground_hi = ws->pdf("nbkg_hi_nom");
RooRealVar* nsig1_hi = ws->var("N_{#Upsilon(1S)}_hi");
RooRealVar* nsig2_hi = ws->var("N_{#Upsilon(2S)}_hi");
cout << nsig1_hi << " " << nsig2_hi << " " << nsig3_pp << endl;
RooFormulaVar* nsig3_hi = ws->function("nsig3_hi_modified");
RooRealVar* norm_nbkg_hi = ws->var("n_{Bkgd}_hi");
RooArgList pdfs_hi( *sig1S_hi,*sig2S_hi,*sig3S_hi, *LSBackground_hi);
RooArgList norms_hi(*nsig1_hi,*nsig2_hi,*nsig3_hi, *norm_nbkg_hi);
////////////////////////////////////////////////////////////////////////////////
ws->factory( "nbkg_pp_kappa[1.03]" );
ws->factory( "expr::alpha_nbkg_pp('pow(nbkg_pp_kappa,beta_nbkg_pp)',nbkg_pp_kappa,beta_nbkg_pp[0,-5,5])" );
ws->factory( "SUM::nbkg_pp_nom(alpha_nbkg_pp*bkgPdf_pp)" );
ws->factory( "Gaussian::constr_nbkg_pp(beta_nbkg_pp,glob_nbkg_pp[0,-5,5],1)" );
RooAbsPdf* sig1S_pp = ws->pdf("cbcb_pp");
RooAbsPdf* sig2S_pp = ws->pdf("sig2S_pp");
RooAbsPdf* sig3S_pp = ws->pdf("sig3S_pp");
RooAbsPdf* LSBackground_pp = ws->pdf("nbkg_pp_nom");
RooRealVar* nsig1_pp = ws->var("N_{#Upsilon(1S)}_pp");
RooRealVar* nsig2_pp = ws->var("N_{#Upsilon(2S)}_pp");
RooRealVar* nsig3_pp = ws->var("N_{#Upsilon(3S)}_pp");
RooRealVar* norm_nbkg_pp = ws->var("n_{Bkgd}_pp");
RooArgList pdfs_pp( *sig1S_pp,*sig2S_pp,*sig3S_pp, *LSBackground_pp);
RooArgList norms_pp( *nsig1_pp,*nsig2_pp,*nsig3_pp,*norm_nbkg_pp);
RooAddPdf model_num("model_num", "model_num", pdfs_hi,norms_hi);
ws->import(model_num);
ws->factory("PROD::model_hi(model_num, constr_nbkg_hi,constr_lumipp,constr_Taa,constr_effRat)");
RooAddPdf model_den("model_den", "model_den", pdfs_pp,norms_pp);
ws->import(model_den);
ws->factory("PROD::model_pp(model_den, constr_nbkg_pp)");
ws->factory("SIMUL::joint(dataCat,hi=model_hi,pp=model_pp)");
/////////////////////////////////////////////////////////////////////
RooRealVar * pObs = ws->var("invariantMass"); // get the pointer to the observable
RooArgSet obs("observables");
obs.add(*pObs);
obs.add( *ws->cat("dataCat"));
// /////////////////////////////////////////////////////////////////////
ws->var("glob_lumipp")->setConstant(true);
ws->var("glob_Taa")->setConstant(true);
ws->var("glob_effRat")->setConstant(true);
ws->var("glob_nbkg_pp")->setConstant(true);
ws->var("glob_nbkg_hi")->setConstant(true);
RooArgSet globalObs("global_obs");
globalObs.add( *ws->var("glob_lumipp") );
globalObs.add( *ws->var("glob_Taa") );
globalObs.add( *ws->var("glob_effRat") );
globalObs.add( *ws->var("glob_nbkg_hi") );
globalObs.add( *ws->var("glob_nbkg_pp") );
// ws->Print();
RooArgSet poi("poi");
poi.add( *ws->var("raa3") );
// create set of nuisance parameters
RooArgSet nuis("nuis");
nuis.add( *ws->var("beta_lumipp") );
nuis.add( *ws->var("beta_nbkg_hi") );
nuis.add( *ws->var("beta_nbkg_pp") );
nuis.add( *ws->var("beta_Taa") );
nuis.add( *ws->var("beta_effRat") );
ws->var("#alpha_{CB}_hi")->setConstant(true);
ws->var("#alpha_{CB}_pp")->setConstant(true);
ws->var("#sigma_{CB1}_hi")->setConstant(true);
ws->var("#sigma_{CB1}_pp")->setConstant(true);
ws->var("#sigma_{CB2}/#sigma_{CB1}_hi")->setConstant(true);
ws->var("#sigma_{CB2}/#sigma_{CB1}_pp")->setConstant(true);
ws->var("Centrality")->setConstant(true);
ws->var("N_{#Upsilon(1S)}_hi")->setConstant(true);
ws->var("N_{#Upsilon(1S)}_pp")->setConstant(true);
ws->var("N_{#Upsilon(2S)}_hi")->setConstant(true);
ws->var("N_{#Upsilon(2S)}_pp")->setConstant(true);
ws->var("N_{#Upsilon(3S)}_pp")->setConstant(true);
ws->var("Nmb_hi")->setConstant(true);
// ws->var("QQsign")->setConstant(true);
ws->var("Taa_hi")->setConstant(true);
ws->var("Taa_kappa")->setConstant(true);
// ws->var("beta_Taa")->setConstant(true);
// ws->var("beta_effRat")->setConstant(true);
// ws->var("beta_lumipp")->setConstant(true);
// ws->var("beta_nbkg_hi")->setConstant(true);
// ws->var("beta_nbkg_pp")->setConstant(true);
// ws->var("dataCat")->setConstant(true);
ws->var("decay_hi")->setConstant(true);
ws->var("decay_pp")->setConstant(true);
ws->var("effRat3_hi")->setConstant(true);
ws->var("effRat_kappa")->setConstant(true);
// ws->var("glob_Taa")->setConstant(true);
// ws->var("glob_effRat")->setConstant(true);
// ws->var("glob_lumipp")->setConstant(true);
// ws->var("glob_nbkg_hi")->setConstant(true);
// ws->var("glob_nbkg_pp")->setConstant(true);
// ws->var("invariantMass")->setConstant(true);
ws->var("leftEdge")->setConstant(true);
ws->var("lumipp_hi")->setConstant(true);
ws->var("lumipp_kappa")->setConstant(true);
ws->var("mass1S_hi")->setConstant(true);
ws->var("mass1S_pp")->setConstant(true);
ws->var("muMinusPt")->setConstant(true);
ws->var("muPlusPt")->setConstant(true);
ws->var("n_{Bkgd}_hi")->setConstant(true);
ws->var("n_{Bkgd}_pp")->setConstant(true);
ws->var("nbkg_hi_kappa")->setConstant(true);
ws->var("nbkg_pp_kappa")->setConstant(true);
ws->var("npow")->setConstant(true);
ws->var("N_{#Upsilon(3S)}_pp")->setConstant(true);
// ws->var("raa3")->setConstant(true);
ws->var("rightEdge")->setConstant(true);
ws->var("sigmaFraction_hi")->setConstant(true);
ws->var("sigmaFraction_pp")->setConstant(true);
ws->var("turnOn_hi")->setConstant(true);
ws->var("turnOn_pp")->setConstant(true);
ws->var("upsPt")->setConstant(true);
ws->var("upsRapidity")->setConstant(true);
ws->var("vProb")->setConstant(true);
ws->var("width_hi")->setConstant(true);
ws->var("width_pp")->setConstant(true);
ws->var("x3raw")->setConstant(true);
// RooArgSet fixed_again("fixed_again");
// fixed_again.add( *ws->var("leftEdge") );
// fixed_again.add( *ws->var("rightEdge") );
// fixed_again.add( *ws->var("Taa_hi") );
// fixed_again.add( *ws->var("Nmb_hi") );
// fixed_again.add( *ws->var("lumipp_hi") );
// fixed_again.add( *ws->var("effRat1_hi") );
// fixed_again.add( *ws->var("effRat2_hi") );
// fixed_again.add( *ws->var("effRat3_hi") );
// fixed_again.add( *ws->var("nsig3_pp") );
// fixed_again.add( *ws->var("nsig1_pp") );
// fixed_again.add( *ws->var("nbkg_hi") );
// fixed_again.add( *ws->var("alpha") );
// fixed_again.add( *ws->var("nbkg_kappa") );
// fixed_again.add( *ws->var("Taa_kappa") );
// fixed_again.add( *ws->var("lumipp_kappa") );
// fixed_again.add( *ws->var("mean_hi") );
// fixed_again.add( *ws->var("mean_pp") );
// fixed_again.add( *ws->var("width_hi") );
// fixed_again.add( *ws->var("turnOn_hi") );
// fixed_again.add( *ws->var("bkg_a1_pp") );
// fixed_again.add( *ws->var("bkg_a2_pp") );
// fixed_again.add( *ws->var("decay_hi") );
// fixed_again.add( *ws->var("raa1") );
// fixed_again.add( *ws->var("raa2") );
// fixed_again.add( *ws->var("nsig2_pp") );
// fixed_again.add( *ws->var("sigma1") );
// fixed_again.add( *ws->var("nbkg_pp") );
// fixed_again.add( *ws->var("npow") );
// fixed_again.add( *ws->var("muPlusPt") );
// fixed_again.add( *ws->var("muMinusPt") );
// fixed_again.add( *ws->var("mscale_hi") );
// fixed_again.add( *ws->var("mscale_pp") );
//
// ws->Print();
// create signal+background Model Config
RooStats::ModelConfig sbHypo("SbHypo");
sbHypo.SetWorkspace( *ws );
sbHypo.SetPdf( *ws->pdf("joint") );
sbHypo.SetObservables( obs );
sbHypo.SetGlobalObservables( globalObs );
sbHypo.SetParametersOfInterest( poi );
sbHypo.SetNuisanceParameters( nuis );
sbHypo.SetPriorPdf( *ws->pdf("step") ); // this is optional
// ws->Print();
/////////////////////////////////////////////////////////////////////
RooAbsReal * pNll = sbHypo.GetPdf()->createNLL( *data,NumCPU(2) );
RooMinuit(*pNll).migrad(); // minimize likelihood wrt all parameters before making plots
RooPlot *framepoi = ((RooRealVar *)poi.first())->frame(Bins(10),Range(0.,0.2),Title("LL and profileLL in raa3"));
pNll->plotOn(framepoi,ShiftToZero());
RooAbsReal * pProfile = pNll->createProfile( globalObs ); // do not profile global observables
pProfile->getVal(); // this will do fit and set POI and nuisance parameters to fitted values
pProfile->plotOn(framepoi,LineColor(kRed));
framepoi->SetMinimum(0);
framepoi->SetMaximum(3);
TCanvas *cpoi = new TCanvas();
cpoi->cd(); framepoi->Draw();
cpoi->SaveAs("cpoi.pdf");
RooArgSet * pPoiAndNuisance = new RooArgSet("poiAndNuisance");
// pPoiAndNuisance->add(*sbHypo.GetNuisanceParameters());
// pPoiAndNuisance->add(*sbHypo.GetParametersOfInterest());
pPoiAndNuisance->add( nuis );
pPoiAndNuisance->add( poi );
sbHypo.SetSnapshot(*pPoiAndNuisance);
RooPlot* xframeSB = pObs->frame(Title("SBhypo"));
data->plotOn(xframeSB,Cut("dataCat==dataCat::hi"));
RooAbsPdf *pdfSB = sbHypo.GetPdf();
RooCategory *dataCat = ws->cat("dataCat");
pdfSB->plotOn(xframeSB,Slice(*dataCat,"hi"),ProjWData(*dataCat,*data));
TCanvas *c1 = new TCanvas();
c1->cd(); xframeSB->Draw();
c1->SaveAs("c1.pdf");
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
ws->import( sbHypo );
/////////////////////////////////////////////////////////////////////
RooStats::ModelConfig bHypo = sbHypo;
bHypo.SetName("BHypo");
bHypo.SetWorkspace(*ws);
pNll = bHypo.GetPdf()->createNLL( *data,NumCPU(2) );
RooArgSet poiAndGlobalObs("poiAndGlobalObs");
poiAndGlobalObs.add( poi );
poiAndGlobalObs.add( globalObs );
pProfile = pNll->createProfile( poiAndGlobalObs ); // do not profile POI and global observables
double oldval = ((RooRealVar *)poi.first())->getVal( );
((RooRealVar *)poi.first())->setVal( 0 ); // set raa3=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);
RooPlot* xframeB = pObs->frame(Title("Bhypo"));
data->plotOn(xframeB,Cut("dataCat==dataCat::hi"));
RooAbsPdf *pdfB = bHypo.GetPdf();
pdfB->plotOn(xframeB,Slice(*dataCat,"hi"),ProjWData(*dataCat,*data));
TCanvas *c2 = new TCanvas();
c2->cd(); xframeB->Draw();
c2->SaveAs("c2.pdf");
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// import model config into workspace
bHypo.SetWorkspace(*ws);
ws->import( bHypo );
/////////////////////////////////////////////////////////////////////
ws->Print();
bHypo.Print();
sbHypo.Print();
((RooRealVar *)poi.first())->setVal( oldval ); // set raa3=oldval here
// save workspace to file
ws -> SaveAs("TRIAL.root");
return;
}
// internal routine to run the inverter
HypoTestInverterResult *
RooStats::HypoTestInvTool::RunInverter(RooWorkspace * w,
const char * modelSBName, const char * modelBName,
const char * dataName, int type, int testStatType,
bool useCLs, int npoints, double poimin, double poimax,
int ntoys,
bool useNumberCounting,
const char * nuisPriorName ){
std::cout << "Running HypoTestInverter on the workspace " << w->GetName() << std::endl;
w->Print();
RooAbsData * data = w->data(dataName);
if (!data) {
Error("StandardHypoTestDemo","Not existing data %s",dataName);
return 0;
}
else
std::cout << "Using data set " << dataName << std::endl;
if (mUseVectorStore) {
RooAbsData::defaultStorageType = RooAbsData::Vector;
data->convertToVectorStore() ;
}
// get models from WS
// get the modelConfig out of the file
ModelConfig* bModel = (ModelConfig*) w->obj(modelBName);
ModelConfig* sbModel = (ModelConfig*) w->obj(modelSBName);
if (!sbModel) {
Error("StandardHypoTestDemo","Not existing ModelConfig %s",modelSBName);
return 0;
}
// check the model
if (!sbModel->GetPdf()) {
Error("StandardHypoTestDemo","Model %s has no pdf ",modelSBName);
return 0;
}
if (!sbModel->GetParametersOfInterest()) {
Error("StandardHypoTestDemo","Model %s has no poi ",modelSBName);
return 0;
}
if (!sbModel->GetObservables()) {
Error("StandardHypoTestInvDemo","Model %s has no observables ",modelSBName);
return 0;
}
if (!sbModel->GetSnapshot() ) {
Info("StandardHypoTestInvDemo","Model %s has no snapshot - make one using model poi",modelSBName);
sbModel->SetSnapshot( *sbModel->GetParametersOfInterest() );
}
// case of no systematics
// remove nuisance parameters from model
if (noSystematics) {
const RooArgSet * nuisPar = sbModel->GetNuisanceParameters();
if (nuisPar && nuisPar->getSize() > 0) {
std::cout << "StandardHypoTestInvDemo" << " - Switch off all systematics by setting them constant to their initial values" << std::endl;
RooStats::SetAllConstant(*nuisPar);
}
if (bModel) {
const RooArgSet * bnuisPar = bModel->GetNuisanceParameters();
if (bnuisPar)
RooStats::SetAllConstant(*bnuisPar);
}
}
if (!bModel || bModel == sbModel) {
Info("StandardHypoTestInvDemo","The background model %s does not exist",modelBName);
Info("StandardHypoTestInvDemo","Copy it from ModelConfig %s and set POI to zero",modelSBName);
bModel = (ModelConfig*) sbModel->Clone();
bModel->SetName(TString(modelSBName)+TString("_with_poi_0"));
RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
if (!var) return 0;
double oldval = var->getVal();
var->setVal(0);
bModel->SetSnapshot( RooArgSet(*var) );
var->setVal(oldval);
}
else {
if (!bModel->GetSnapshot() ) {
Info("StandardHypoTestInvDemo","Model %s has no snapshot - make one using model poi and 0 values ",modelBName);
RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
if (var) {
double oldval = var->getVal();
var->setVal(0);
bModel->SetSnapshot( RooArgSet(*var) );
var->setVal(oldval);
}
else {
Error("StandardHypoTestInvDemo","Model %s has no valid poi",modelBName);
return 0;
}
}
}
// run first a data fit
const RooArgSet * poiSet = sbModel->GetParametersOfInterest();
RooRealVar *poi = (RooRealVar*)poiSet->first();
std::cout << "StandardHypoTestInvDemo : POI initial value: " << poi->GetName() << " = " << poi->getVal() << std::endl;
// fit the data first (need to use constraint )
Info( "StandardHypoTestInvDemo"," Doing a first fit to the observed data ");
if (minimizerType.size()==0) minimizerType = ROOT::Math::MinimizerOptions::DefaultMinimizerType();
else
ROOT::Math::MinimizerOptions::SetDefaultMinimizer(minimizerType.c_str());
Info("StandardHypoTestInvDemo","Using %s as minimizer for computing the test statistic",
ROOT::Math::MinimizerOptions::DefaultMinimizerType().c_str() );
RooArgSet constrainParams;
if (sbModel->GetNuisanceParameters() ) constrainParams.add(*sbModel->GetNuisanceParameters());
RooStats::RemoveConstantParameters(&constrainParams);
TStopwatch tw;
tw.Start();
RooFitResult * fitres = sbModel->GetPdf()->fitTo(*data,InitialHesse(false), Hesse(false),
Minimizer(minimizerType.c_str(),"Migrad"), Strategy(0), PrintLevel(mPrintLevel+1), Constrain(constrainParams), Save(true) );
if (fitres->status() != 0) {
Warning("StandardHypoTestInvDemo","Fit to the model failed - try with strategy 1 and perform first an Hesse computation");
fitres = sbModel->GetPdf()->fitTo(*data,InitialHesse(true), Hesse(false),Minimizer(minimizerType.c_str(),"Migrad"), Strategy(1), PrintLevel(mPrintLevel+1), Constrain(constrainParams), Save(true) );
}
if (fitres->status() != 0)
Warning("StandardHypoTestInvDemo"," Fit still failed - continue anyway.....");
double poihat = poi->getVal();
std::cout << "StandardHypoTestInvDemo - Best Fit value : " << poi->GetName() << " = "
<< poihat << " +/- " << poi->getError() << std::endl;
std::cout << "Time for fitting : "; tw.Print();
//save best fit value in the poi snapshot
sbModel->SetSnapshot(*sbModel->GetParametersOfInterest());
std::cout << "StandardHypoTestInvo: snapshot of S+B Model " << sbModel->GetName()
<< " is set to the best fit value" << std::endl;
// build test statistics and hypotest calculators for running the inverter
SimpleLikelihoodRatioTestStat slrts(*sbModel->GetPdf(),*bModel->GetPdf());
if (sbModel->GetSnapshot()) slrts.SetNullParameters(*sbModel->GetSnapshot());
if (bModel->GetSnapshot()) slrts.SetAltParameters(*bModel->GetSnapshot());
// ratio of profile likelihood - need to pass snapshot for the alt
RatioOfProfiledLikelihoodsTestStat
ropl(*sbModel->GetPdf(), *bModel->GetPdf(), bModel->GetSnapshot());
ropl.SetSubtractMLE(false);
ropl.SetPrintLevel(mPrintLevel);
ropl.SetMinimizer(minimizerType.c_str());
ProfileLikelihoodTestStat profll(*sbModel->GetPdf());
if (testStatType == 3) profll.SetOneSided(1);
profll.SetMinimizer(minimizerType.c_str());
profll.SetPrintLevel(mPrintLevel);
profll.SetReuseNLL(mOptimize);
slrts.SetReuseNLL(mOptimize);
ropl.SetReuseNLL(mOptimize);
if (mOptimize) {
profll.SetStrategy(0);
ropl.SetStrategy(0);
}
if (mMaxPoi > 0) poi->setMax(mMaxPoi); // increase limit
MaxLikelihoodEstimateTestStat maxll(*sbModel->GetPdf(),*poi);
// create the HypoTest calculator class
HypoTestCalculatorGeneric * hc = 0;
if (type == 0) hc = new FrequentistCalculator(*data, *bModel, *sbModel);
else if (type == 1) hc = new HybridCalculator(*data, *bModel, *sbModel);
else if (type == 2) hc = new AsymptoticCalculator(*data, *bModel, *sbModel);
else {
Error("StandardHypoTestInvDemo","Invalid - calculator type = %d supported values are only :\n\t\t\t 0 (Frequentist) , 1 (Hybrid) , 2 (Asymptotic) ",type);
return 0;
}
// set the test statistic
TestStatistic * testStat = 0;
if (testStatType == 0) testStat = &slrts;
if (testStatType == 1) testStat = &ropl;
if (testStatType == 2 || testStatType == 3) testStat = &profll;
if (testStatType == 4) testStat = &maxll;
if (testStat == 0) {
Error("StandardHypoTestInvDemo","Invalid - test statistic type = %d supported values are only :\n\t\t\t 0 (SLR) , 1 (Tevatron) , 2 (PLR), 3 (PLR1), 4(MLE)",testStatType);
return 0;
}
ToyMCSampler *toymcs = (ToyMCSampler*)hc->GetTestStatSampler();
if (toymcs) {
if (useNumberCounting) toymcs->SetNEventsPerToy(1);
toymcs->SetTestStatistic(testStat);
if (data->isWeighted() && !mGenerateBinned) {
Info("StandardHypoTestInvDemo","Data set is weighted, nentries = %d and sum of weights = %8.1f but toy generation is unbinned - it would be faster to set mGenerateBinned to true\n",data->numEntries(), data->sumEntries());
}
toymcs->SetGenerateBinned(mGenerateBinned);
toymcs->SetUseMultiGen(mOptimize);
if (mGenerateBinned && sbModel->GetObservables()->getSize() > 2) {
Warning("StandardHypoTestInvDemo","generate binned is activated but the number of ovservable is %d. Too much memory could be needed for allocating all the bins",sbModel->GetObservables()->getSize() );
}
}
if (type == 1) {
HybridCalculator *hhc = dynamic_cast<HybridCalculator*> (hc);
assert(hhc);
hhc->SetToys(ntoys,ntoys/mNToysRatio); // can use less ntoys for b hypothesis
// remove global observables from ModelConfig (this is probably not needed anymore in 5.32)
bModel->SetGlobalObservables(RooArgSet() );
sbModel->SetGlobalObservables(RooArgSet() );
// check for nuisance prior pdf in case of nuisance parameters
if (bModel->GetNuisanceParameters() || sbModel->GetNuisanceParameters() ) {
RooAbsPdf * nuisPdf = 0;
if (nuisPriorName) nuisPdf = w->pdf(nuisPriorName);
// use prior defined first in bModel (then in SbModel)
if (!nuisPdf) {
Info("StandardHypoTestInvDemo","No nuisance pdf given for the HybridCalculator - try to deduce pdf from the model");
if (bModel->GetPdf() && bModel->GetObservables() )
nuisPdf = RooStats::MakeNuisancePdf(*bModel,"nuisancePdf_bmodel");
else
nuisPdf = RooStats::MakeNuisancePdf(*sbModel,"nuisancePdf_sbmodel");
}
if (!nuisPdf ) {
if (bModel->GetPriorPdf()) {
nuisPdf = bModel->GetPriorPdf();
Info("StandardHypoTestInvDemo","No nuisance pdf given - try to use %s that is defined as a prior pdf in the B model",nuisPdf->GetName());
}
else {
Error("StandardHypoTestInvDemo","Cannnot run Hybrid calculator because no prior on the nuisance parameter is specified or can be derived");
return 0;
}
}
assert(nuisPdf);
Info("StandardHypoTestInvDemo","Using as nuisance Pdf ... " );
nuisPdf->Print();
const RooArgSet * nuisParams = (bModel->GetNuisanceParameters() ) ? bModel->GetNuisanceParameters() : sbModel->GetNuisanceParameters();
RooArgSet * np = nuisPdf->getObservables(*nuisParams);
if (np->getSize() == 0) {
Warning("StandardHypoTestInvDemo","Prior nuisance does not depend on nuisance parameters. They will be smeared in their full range");
}
delete np;
hhc->ForcePriorNuisanceAlt(*nuisPdf);
hhc->ForcePriorNuisanceNull(*nuisPdf);
}
}
else if (type == 2) {
((AsymptoticCalculator*) hc)->SetOneSided(true);
// ((AsymptoticCalculator*) hc)->SetQTilde(true); // not needed should be done automatically now
((AsymptoticCalculator*) hc)->SetPrintLevel(mPrintLevel+1);
}
else if (type != 2)
((FrequentistCalculator*) hc)->SetToys(ntoys,ntoys/mNToysRatio);
// Get the result
RooMsgService::instance().getStream(1).removeTopic(RooFit::NumIntegration);
HypoTestInverter calc(*hc);
calc.SetConfidenceLevel(0.95);
calc.UseCLs(useCLs);
calc.SetVerbose(true);
// can speed up using proof-lite
if (mUseProof && mNWorkers > 1) {
ProofConfig pc(*w, mNWorkers, "", kFALSE);
toymcs->SetProofConfig(&pc); // enable proof
}
if (npoints > 0) {
if (poimin > poimax) {
// if no min/max given scan between MLE and +4 sigma
poimin = int(poihat);
poimax = int(poihat + 4 * poi->getError());
}
std::cout << "Doing a fixed scan in interval : " << poimin << " , " << poimax << std::endl;
calc.SetFixedScan(npoints,poimin,poimax);
}
else {
//poi->setMax(10*int( (poihat+ 10 *poi->getError() )/10 ) );
std::cout << "Doing an automatic scan in interval : " << poi->getMin() << " , " << poi->getMax() << std::endl;
}
tw.Start();
HypoTestInverterResult * r = calc.GetInterval();
std::cout << "Time to perform limit scan \n";
tw.Print();
if (mRebuild) {
calc.SetCloseProof(1);
tw.Start();
SamplingDistribution * limDist = calc.GetUpperLimitDistribution(true,mNToyToRebuild);
std::cout << "Time to rebuild distributions " << std::endl;
tw.Print();
if (limDist) {
std::cout << "expected up limit " << limDist->InverseCDF(0.5) << " +/- "
<< limDist->InverseCDF(0.16) << " "
<< limDist->InverseCDF(0.84) << "\n";
//update r to a new updated result object containing the rebuilt expected p-values distributions
// (it will not recompute the expected limit)
if (r) delete r; // need to delete previous object since GetInterval will return a cloned copy
r = calc.GetInterval();
}
else
std::cout << "ERROR : failed to re-build distributions " << std::endl;
}
return r;
}
void cutChecker()
{
int kCut =2 ;//1:vProb, 2:dca, 3:MatchedStations, 4:ctau/ctauErr
int pbpb=false;
gROOT->Macro("./cm/logon.C+");//it all looks much nicer with this.
// TString fname2011="../dimuonTree_HI2011_fulldataset_trkRot.root";
// TFile *_file0 = TFile::Open(fname2011);
// TTree *upsi2011 = (TTree*)_file0->Get("UpsilonTree");
if(pbpb) { TString fname2013=" ../dimuonTree_upsiMiniTree_AA2p76tev_WithIDCuts_RunHIN-15-001_trigBit1_allTriggers0.root";//../dimuonTree_upsiMiniTree_aa276tev_regitreco_glbglb_Runa_trigBit1_allTriggers0_pt4.root";
}else if(!pbpb){
TString fname2013=" ../dimuonTree_upsiMiniTree_pp2p76tev_noIDVars_GlbGlb_RunHIN-15-001_trigBit2_allTriggers0.root";
}
//TString fname2013="../upsiMiniTree_pyquen1S_noMuonPtCuts_QQtrigbit1_Trig_analysisOK_20140729_cuts10-006.root";
TFile *_file1 = TFile::Open(fname2013);
TTree *upsi2013 = (TTree*)_file1->Get("UpsilonTree");
RooRealVar* upsPt = new RooRealVar("upsPt","p_{T}(#Upsilon)",0,60,"GeV");
// RooRealVar* upsEta = new RooRealVar("upsEta", "upsEta" ,-10,10);
RooRealVar* upsRapidity= new RooRealVar("upsRapidity", "upsRapidity",-1000, 1000);
RooRealVar* vProb = new RooRealVar("vProb", "vProb" ,0.01,1.00);
RooRealVar* _dca = new RooRealVar("_dca", "_dca" ,0.0,5.00);
RooRealVar* _ctau = new RooRealVar("_ctau", "_ctau" ,-100,100,"cm");
RooRealVar* _ctauErr = new RooRealVar("_ctauErr", "_ctauErr" ,-100,100,"cm");
RooRealVar* muPlusPt = new RooRealVar("muPlusPt","muPlusPt",3.5,100);
RooRealVar* muMinusPt = new RooRealVar("muMinusPt","muMinusPt",3.5,100);
RooRealVar* muPlusEta = new RooRealVar("muPlusEta","muPlusEta", -2.4,2.4);
RooRealVar* muMinusEta = new RooRealVar("muMinusEta","muMinusEta",-2.4,2.4);
RooRealVar* _mupl_StationsMatched = new RooRealVar("_mupl_StationsMatched","_mupl_StationsMatched",0,5);
RooRealVar* _mumi_StationsMatched = new RooRealVar("_mumi_StationsMatched","_mumi_StationsMatched",0,5);
RooRealVar* muMinusEta = new RooRealVar("muMinusEta","muMinusEta",-2.4,2.4);
RooRealVar* mass = new RooRealVar("invariantMass","#mu#mu mass",7,14,"GeV/c^{2}");
TCut cut_acc = " ((muPlusPt >3.5 && muMinusPt>4)||(muPlusPt>4 &&muMinusPt>3.5)) && abs(upsRapidity)<2.4 && (invariantMass<14 && invariantMass>7)"; //
cout << "cut: "<< cut_acc.Print() << endl;
// TCut cut_add(cutList(1,1));
//cout << "cut: "<< cut_add.Print() << endl;
switch (kCut){
case 1: //vProb]
RooDataSet *data0 = new RooDataSet("data0","data0",upsi2013,
RooArgSet(*mass,*muPlusPt,*muMinusPt,*upsRapidity,*vProb));
string cut[4]={"vProb>0.01",// very loose
"vProb>0.05",
"vProb>0.1",
"vProb>0.2"};//very tight
// for plotting purposes...
break;
case 2: //dca
RooDataSet *data0 = new RooDataSet("data0","data0",upsi2013,
RooArgSet(*mass,*muPlusPt,*muMinusPt,*upsRapidity,*_dca));
string cut[4]={"_dca<0.004", //very tight
"_dca<0.006",
"_dca<0.008",
"_dca<0.01"}; // very loose
break;
case 3: // number of matched Stations
RooDataSet *data0 = new RooDataSet("data0","data0",upsi2013,
RooArgSet(*mass,*muPlusPt,*muMinusPt,*upsRapidity,*_mupl_StationsMatched,*_mumi_StationsMatched));
string cut[4]={ "_mumi_StationsMatched>0&&_mupl_StationsMatched>0",
"_mumi_StationsMatched>1&&_mupl_StationsMatched>1",
"_mumi_StationsMatched>2&&_mupl_StationsMatched>2",
"_mumi_StationsMatched>3&&_mupl_StationsMatched>3"};
string cutname[4]={ "at least one",
"more than 1",
"more than 2",
"more than 3"};
break;
case 4: ///fabs(ctau/ctau_err)
RooDataSet *data0 = new RooDataSet("data0","data0",upsi2013,
RooArgSet(*mass,*muPlusPt,*muMinusPt,*upsRapidity,*_ctau,*_ctauErr));
string cut[4]={"abs(_ctau/_ctauErr)<5",//very loose
"abs(_ctau/_ctauErr)<4" ,
"abs(_ctau/_ctauErr)<3" ,
"abs(_ctau/_ctauErr)<2" }; //tighter
string cutname[4]={"|c#tau/#sigma(c#tau)| < 5",
"|c#tau/#sigma(c#tau)| < 4",
"|c#tau/#sigma(c#tau)| < 3",
"|c#tau/#sigma(c#tau)| < 2"};
break;
default:
cout<< "no Cut Variable specified!"<<endl; break;
}
TCut cut_add1((cut[0]).c_str());
TCut cut_add2((cut[1]).c_str());
TCut cut_add3((cut[2]).c_str());
TCut cut_add4((cut[3]).c_str());
TString figName_(Form("%s",(cut[0]).c_str()));
figName_.ReplaceAll(">","_gt");
figName_.ReplaceAll("<","_lt");
figName_.ReplaceAll(".","p");
figName_.ReplaceAll("&&","_AND_");
figName_.ReplaceAll("||","_OR_");
figName_.ReplaceAll("(","");
figName_.ReplaceAll("/","-");
figName_.ReplaceAll(")","");
cout << "hello"<< endl;
// cut_add.Print();
int nt = data0->sumEntries();
redData1 = ( RooDataSet*) data0->reduce(Cut(cut_acc+cut_add1));
redData1->Print();
TH1D *MReco1;
MReco1 = new TH1D("MReco1","Reco di-muon mass",70,7,14);
MReco1 = (TH1D*) redData1->createHistogram("invariantMass",*mass);
redData2 = ( RooDataSet*) data0->reduce(Cut(cut_acc+cut_add2));
redData2->Print();
TH1D *MReco2;
MReco2 = new TH1D("MReco2","Reco di-muon mass",70,7,14);
MReco2 = (TH1D*) redData2->createHistogram("invariantMass",*mass);
redData3 = ( RooDataSet*) data0->reduce(Cut(cut_acc+cut_add3));
redData3->Print();
TH1D *MReco3;
MReco3 = new TH1D("MReco3","Reco di-muon mass",70,7,14);
MReco3 = (TH1D*) redData3->createHistogram("invariantMass",*mass);
redData4 = ( RooDataSet*) data0->reduce(Cut(cut_acc+cut_add4));
redData4->Print();
TH1D *MReco4;
MReco4 = new TH1D("MReco4","Reco di-muon mass",70,7,14);
MReco4 = (TH1D*) redData4->createHistogram("invariantMass",*mass);
const double M1S = 9.46; //upsilon 1S pgd mass value
const double M2S = 10.023; //upsilon 2S pgd mass value
const double M3S = 10.355; //upsilon 3S pgd mass value
RooRealVar *nsig1f = new RooRealVar("N_{#Upsilon(1S)}","nsig1S",0,nt*10);
RooRealVar *nsig2f = new RooRealVar("N_{#Upsilon(2S)}","nsig2S", nt*0.25,-1*nt,10*nt);
RooRealVar *nsig3f = new RooRealVar("N_{#Upsilon(3S)}","nsig3S", nt*0.25,-1*nt,10*nt);
RooRealVar *mean = new RooRealVar("mass1S","#Upsilon mean",M1S,M1S-0.1,M1S+0.1);
RooConstVar *rat2 = new RooConstVar("rat2", "rat2", M2S/M1S);
RooConstVar *rat3 = new RooConstVar("rat3", "rat3", M3S/M1S);
// scale mean and resolution by mass ratio
RooFormulaVar *mean1S = new RooFormulaVar("mean1S","@0",RooArgList(*mean));
RooFormulaVar *mean2S = new RooFormulaVar("mean2S","@0*@1", RooArgList(*mean,*rat2));
RooFormulaVar *mean3S = new RooFormulaVar("mean3S","@0*@1", RooArgList(*mean,*rat3));
//detector resolution ?? where is this coming from?
RooRealVar *sigma1 = new RooRealVar("#sigma_{CB1}","#sigma_{CB1}",0,0.5); //
RooFormulaVar *sigma1S = new RooFormulaVar("sigma1S","@0" ,RooArgList(*sigma1));
RooFormulaVar *sigma2S = new RooFormulaVar("sigma2S","@0*@1",RooArgList(*sigma1,*rat2));
RooFormulaVar *sigma3S = new RooFormulaVar("sigma3S","@0*@1",RooArgList(*sigma1,*rat3));
RooRealVar *alpha = new RooRealVar("#alpha_{CB}","tail shift",0.01,8); // MC 5tev 1S pol2
RooRealVar *npow = new RooRealVar("npow","power order",1,60); // MC 5tev 1S pol2
RooRealVar *sigmaFraction = new RooRealVar("sigmaFraction","Sigma Fraction",0.,1.);
// scale the sigmaGaus with sigma1S*scale=sigmaGaus now.
RooRealVar *scaleWidth = new RooRealVar("#sigma_{CB2}/#sigma_{CB1}","scaleWidth",1.,2.7);
RooFormulaVar *sigmaGaus = new RooFormulaVar("sigmaGaus","@0*@1", RooArgList(*sigma1,*scaleWidth));
RooFormulaVar *sigmaGaus2 = new RooFormulaVar("sigmaGaus","@0*@1*@2", RooArgList(*sigma1,*scaleWidth,*rat2));
RooFormulaVar *sigmaGaus3 = new RooFormulaVar("sigmaGaus","@0*@1*@2", RooArgList(*sigma1,*scaleWidth,*rat3));
RooCBShape *cb1S_1 = new RooCBShape ("cb1S_1", "FSR cb 1s",
*mass,*mean1S,*sigma1,*alpha,*npow);
RooCBShape *cb1S_2 = new RooCBShape ("cb1S_2", "FSR cb 1s",
*mass,*mean1S,*sigmaGaus,*alpha,*npow);
RooAddPdf *sig1S = new RooAddPdf ("cbcb","1S mass pdf",
RooArgList(*cb1S_1,*cb1S_2),*sigmaFraction);
// /// Upsilon 2S
RooCBShape *cb2S_1 = new RooCBShape ("cb2S_1", "FSR cb 2s",
*mass,*mean2S,*sigma2S,*alpha,*npow);
RooCBShape *cb2S_2 = new RooCBShape ("cb2S_2", "FSR cb 2s",
*mass,*mean2S,*sigmaGaus2,*alpha,*npow);
RooAddPdf *sig2S = new RooAddPdf ("sig2S","2S mass pdf",
RooArgList(*cb2S_1,*cb2S_2),*sigmaFraction);
// /// Upsilon 3S
RooCBShape *cb3S_1 = new RooCBShape ("cb3S_1", "FSR cb 3s",
*mass,*mean3S,*sigma3S,*alpha,*npow);
RooCBShape *cb3S_2 = new RooCBShape ("cb3S_2", "FSR cb 3s",
*mass,*mean3S,*sigmaGaus3,*alpha,*npow);
RooAddPdf *sig3S = new RooAddPdf ("sig3S","3S mass pdf",
RooArgList(*cb3S_1,*cb3S_2),*sigmaFraction);
// bkg Chebychev
RooRealVar *nbkgd = new RooRealVar("n_{Bkgd}","nbkgd",0,nt);
RooRealVar *bkg_a1 = new RooRealVar("a1_bkg", "bkg_{a1}", 0, -5, 5);
RooRealVar *bkg_a2 = new RooRealVar("a2_Bkg", "bkg_{a2}", 0, -5, 5);
RooRealVar *bkg_a3 = new RooRealVar("a3_Bkg", "bkg_{a3}", 0, -2, 2);
RooAbsPdf *pdf_combinedbkgd = new RooChebychev("bkgPdf","bkgPdf",
*mass, RooArgList(*bkg_a1,*bkg_a2));
RooRealVar turnOn("turnOn","turnOn",2.,8.6);
RooRealVar width("width","width",0.3,8.5);// MB 2.63
RooRealVar decay("decay","decay",1,18);// MB: 3.39
RooGenericPdf *ErrPdf = new RooGenericPdf("ErrPdf","ErrPdf",
"exp(-@0/decay)*(TMath::Erf((@0-turnOn)/width)+1)",
RooArgList(*mass,turnOn,width,decay));
// bkg_a2->setVal(0);
// bkg_a2->setConstant();
RooDataHist binnedData1 ("binnedData1","binnedData1",*mass,Import(*MReco1));
RooDataHist binnedData2 ("binnedData2","binnedData2",*mass,Import(*MReco2));
RooDataHist binnedData3 ("binnedData3","binnedData3",*mass,Import(*MReco3));
RooDataHist binnedData4 ("binnedData4","binnedData4",*mass,Import(*MReco4));
RooAbsPdf *pdf = new RooAddPdf ("pdf","total p.d.f.",
RooArgList(*sig1S,*sig2S,*sig3S,*ErrPdf),
RooArgList(*nsig1f,*nsig2f,*nsig3f,*nbkgd));
npow->setVal(2);
npow->setConstant();
//for the plots!
TCanvas c; c.cd();
TPad phead("phead","phead",0.05,0.9,1.,1.,0,0,0);
phead.Draw(); phead.cd();
TLatex *cms = new TLatex (0.1,0.1,"CMS Internal");
cms->SetTextFont(40);
cms->SetTextSize(0.4);
cms->SetTextColor(kBlack);
cms->Draw();
if(pbpb){ TLatex *pbpb = new TLatex (0.6,0.1,"PbPb #sqrt{s_{NN}} = 2.76 TeV");
pbpb->SetTextFont(42);
pbpb->SetTextSize(0.35);
pbpb->SetTextColor(kBlack);
pbpb->Draw();
}else if(!pbpb){
TLatex *pp = new TLatex (0.6,0.1,"pp #sqrt{s} = 2.76 TeV");
pp->SetTextFont(42);
pp->SetTextSize(0.35);
pp->SetTextColor(kBlack);
pp->Draw();
}
TPad pbody("pbody","pbody",0.0,0.0,1.,0.9,0,0,0);
c.cd();
pbody.SetLeftMargin(0.15);
pbody.Draw(); pbody.cd();
RooPlot* frame = mass->frame(Bins(70),Range(7,14));
// 1st round
RooAbsReal* nll1 = pdf->createNLL(binnedData1,NumCPU(4)) ;
RooMinuit(*nll1).migrad();
RooMinuit(*nll1).hesse();
binnedData1.plotOn(frame,Name("theData"),MarkerSize(0.6),MarkerStyle(20),MarkerColor(kBlue));
pdf->plotOn(frame,Name("thePdf"),LineColor(kBlue));
double signif1 = nsig1f->getVal()/nsig1f->getError();
double signif1_2s = nsig2f->getVal()/nsig2f->getError();
double signif1_3s = nsig3f->getVal()/nsig3f->getError();
MReco1->SetMarkerSize(1.0);
MReco1->SetMarkerStyle(20);
MReco1->SetMarkerColor(kBlue);
MReco1->Draw("esame");
// 2nd round
RooAbsReal* nll2 = pdf->createNLL(binnedData2,NumCPU(4)) ;
RooMinuit(*nll2).migrad();
RooMinuit(*nll2).hesse();
binnedData2.plotOn(frame,Name("theData"),MarkerSize(0.6),MarkerStyle(20),MarkerColor(kRed));
pdf->plotOn(frame,Name("thePdf"),LineColor(kRed));
double signif2 = nsig1f->getVal()/nsig1f->getError();
double signif2_2s = nsig2f->getVal()/nsig2f->getError();
double signif2_3s = nsig3f->getVal()/nsig3f->getError();
MReco2->SetMarkerSize(1.0);
MReco2->SetMarkerStyle(20);
MReco2->SetMarkerColor(kRed);
MReco2->Draw("esame");
// 3rd round
RooAbsReal* nll3 = pdf->createNLL(binnedData3,NumCPU(4)) ;
RooMinuit(*nll3).migrad();
RooMinuit(*nll3).hesse();
binnedData3.plotOn(frame,Name("theData"),MarkerSize(0.6),MarkerStyle(20),MarkerColor(8));
pdf->plotOn(frame,Name("thePdf"),LineColor(8));
double signif3 = nsig1f->getVal()/nsig1f->getError();
double signif3_2s = nsig2f->getVal()/nsig2f->getError();
double signif3_3s = nsig3f->getVal()/nsig3f->getError();
MReco3->SetMarkerSize(1.0);
MReco3->SetMarkerStyle(20);
MReco3->SetMarkerColor(8);
MReco3->Draw("esame");
// 4th round
RooAbsReal* nll4 = pdf->createNLL(binnedData4,NumCPU(4)) ;
RooMinuit(*nll4).migrad();
RooMinuit(*nll4).hesse();
binnedData4.plotOn(frame,Name("theData"),MarkerSize(0.6),MarkerStyle(20),MarkerColor(28));
pdf->plotOn(frame,Name("thePdf"),LineColor(28));
double signif4 = nsig1f->getVal()/nsig1f->getError();
double signif4_2s = nsig2f->getVal()/nsig2f->getError();
double signif4_3s = nsig3f->getVal()/nsig3f->getError();
// pdf->paramOn(frame,Layout(0.5,0.95,0.9),Parameters(RooArgSet(signif)),Format("N",AutoPrecision(1)));
MReco4->SetMarkerSize(1.0);
MReco4->SetMarkerStyle(20);
MReco4->SetMarkerColor(28);
MReco4->Draw("esame");
// and all that.
frame->SetTitle("");
frame->GetXaxis()->SetTitle("m_{#mu^{+}#mu^{-}} (GeV/c^{2})");
frame->GetXaxis()->CenterTitle(kTRUE);
frame->GetYaxis()->SetTitleOffset(2);
frame->GetXaxis()->SetTitleOffset(1.5);
frame->Draw();
TLegend *legend = new TLegend(0.5,0.6,0.95,0.9);
legend->SetTextSize(0.034);
legend->SetFillStyle(0);
legend->SetFillColor(0);
legend->SetBorderSize(0);
legend->SetTextFont(42);
legend->AddEntry(MReco1,"1S significance, #Sigma","");
switch (kCut){
case 1: //vProb]
legend->AddEntry(MReco1,"Vertex Probability","");
legend->AddEntry(MReco1,Form("%s, #Sigma = %0.2f",cut[0].c_str(),signif1),"p");
legend->AddEntry(MReco2,Form("%s, #Sigma = %0.2f",cut[1].c_str(),signif2),"p");
legend->AddEntry(MReco3,Form("%s, #Sigma = %0.2f",cut[2].c_str(),signif3),"p");
legend->AddEntry(MReco4,Form("%s, #Sigma = %0.2f",cut[3].c_str(),signif4),"p");
break;
case 2:
legend->AddEntry(MReco1,"Dist. of closest approach","");
legend->AddEntry(MReco1,Form("%s, #Sigma = %0.2f",cut[0].c_str(),signif1),"p");
legend->AddEntry(MReco2,Form("%s, #Sigma = %0.2f",cut[1].c_str(),signif2),"p");
legend->AddEntry(MReco3,Form("%s, #Sigma = %0.2f",cut[2].c_str(),signif3),"p");
legend->AddEntry(MReco4,Form("%s, #Sigma = %0.2f",cut[3].c_str(),signif4),"p");
break;
case 3:
legend->AddEntry(MReco1,"Stations matched to each track","");
legend->AddEntry(MReco1,Form("%s, #Sigma = %0.2f",cutname[0].c_str(),signif1),"p");
legend->AddEntry(MReco2,Form("%s, #Sigma = %0.2f",cutname[1].c_str(),signif2),"p");
legend->AddEntry(MReco3,Form("%s, #Sigma = %0.2f",cutname[2].c_str(),signif3),"p");
legend->AddEntry(MReco4,Form("%s, #Sigma = %0.2f",cutname[3].c_str(),signif4),"p");
break;
case 4:
legend->AddEntry(MReco1,"Pseudo-proper decay length","");
legend->AddEntry(MReco1,Form("%s, #Sigma = %0.2f",cutname[0].c_str(),signif1),"p");
legend->AddEntry(MReco2,Form("%s, #Sigma = %0.2f",cutname[1].c_str(),signif2),"p");
legend->AddEntry(MReco3,Form("%s, #Sigma = %0.2f",cutname[2].c_str(),signif3),"p");
legend->AddEntry(MReco4,Form("%s, #Sigma = %0.2f",cutname[3].c_str(),signif4),"p");
break;
default: break;
}
legend->Draw();
// legend->AddEntry(MReco1,Form(",),"f");
// TLatex latex1;
// latex1.SetNDC();
// latex1.SetTextSize(0.032);
// latex1.DrawLatex(0.35,1.-0.05*2.,Form("significance: #Sigma vs %s",cut[0].c_str()));
// latex1.DrawLatex(0.55,1.-0.05*3.,Form(" #Sigma = %f",signif1));
// latex1.DrawLatex(0.55,1.-0.05*4.,Form(" #Sigma = %f",signif2));
// latex1.DrawLatex(0.55,1.-0.05*5.,Form(" #Sigma = %f",signif3));
// latex1.DrawLatex(0.55,1.-0.05*6.,Form(" #Sigma = %f",signif4));
c.Draw();
if(pbpb){
c.SaveAs("~/Desktop/Grenelle/"+figName_+".pdf");
}
else if(!pbpb){
c.SaveAs("~/Desktop/Grenelle/"+figName_+"_pp.pdf");
}
cout <<" SIGNIFICANCES \\Sigma OF ALL STATES:" << endl;
cout << "xxxx - \\Sigma(1S) \& \\Sigma(2S) \& \\Sigma(3S) " <<endl;
cout << cut[0].c_str() <<" & "<< signif1 << " &"<< signif1_2s << " & "<< signif1_3s << endl;
cout << cut[1].c_str() <<" & "<< signif2 << " &"<< signif2_2s << " & "<< signif2_3s << endl;
cout << cut[2].c_str() <<" & "<< signif3 << " &"<< signif3_2s << " & "<< signif3_3s << endl;
cout << cut[3].c_str() <<" & "<< signif4 << " &"<< signif4_2s << " & "<< signif4_3s << endl;
}
void ws_constrained_profile3D( const char* wsfile = "rootfiles/ws-data-unblind.root",
const char* new_poi_name = "n_M234_H4_3b",
int npoiPoints = 20,
double poiMinVal = 0.,
double poiMaxVal = 20.,
double constraintWidth = 1.5,
double ymax = 10.,
int verbLevel=0 ) {
gStyle->SetOptStat(0) ;
//--- make output directory.
char command[10000] ;
sprintf( command, "basename %s", wsfile ) ;
TString wsfilenopath = gSystem->GetFromPipe( command ) ;
wsfilenopath.ReplaceAll(".root","") ;
char outputdirstr[1000] ;
sprintf( outputdirstr, "outputfiles/scans-%s", wsfilenopath.Data() ) ;
TString outputdir( outputdirstr ) ;
printf("\n\n Creating output directory: %s\n\n", outputdir.Data() ) ;
sprintf(command, "mkdir -p %s", outputdir.Data() ) ;
gSystem->Exec( command ) ;
//--- Tell RooFit to shut up about anything less important than an ERROR.
RooMsgService::instance().setGlobalKillBelow(RooFit::ERROR) ;
if ( verbLevel > 0 ) { printf("\n\n Verbose level : %d\n\n", verbLevel) ; }
TFile* wstf = new TFile( wsfile ) ;
RooWorkspace* ws = dynamic_cast<RooWorkspace*>( wstf->Get("ws") );
if ( verbLevel > 0 ) { ws->Print() ; }
RooDataSet* rds = (RooDataSet*) ws->obj( "ra2b_observed_rds" ) ;
if ( verbLevel > 0 ) {
printf("\n\n\n ===== RooDataSet ====================\n\n") ;
rds->Print() ;
rds->printMultiline(cout, 1, kTRUE, "") ;
}
ModelConfig* modelConfig = (ModelConfig*) ws->obj( "SbModel" ) ;
RooAbsPdf* likelihood = modelConfig->GetPdf() ;
RooRealVar* rrv_mu_susy_all0lep = ws->var("mu_susy_all0lep") ;
if ( rrv_mu_susy_all0lep == 0x0 ) {
printf("\n\n\n *** can't find mu_susy_all0lep in workspace. Quitting.\n\n\n") ;
return ;
}
//-- do BG only.
rrv_mu_susy_all0lep->setVal(0.) ;
rrv_mu_susy_all0lep->setConstant( kTRUE ) ;
//-- do a prefit.
printf("\n\n\n ====== Pre fit with unmodified nll var.\n\n") ;
RooFitResult* dataFitResultSusyFixed = likelihood->fitTo(*rds, Save(true),Hesse(false),Minos(false),Strategy(1),PrintLevel(verbLevel));
int dataSusyFixedFitCovQual = dataFitResultSusyFixed->covQual() ;
if ( dataSusyFixedFitCovQual < 2 ) { printf("\n\n\n *** Failed fit! Cov qual %d. Quitting.\n\n", dataSusyFixedFitCovQual ) ; return ; }
double dataFitSusyFixedNll = dataFitResultSusyFixed->minNll() ;
if ( verbLevel > 0 ) {
dataFitResultSusyFixed->Print("v") ;
}
printf("\n\n Nll value, from fit result : %.3f\n\n", dataFitSusyFixedNll ) ;
delete dataFitResultSusyFixed ;
//-- Construct the new POI parameter.
RooAbsReal* new_poi_rar(0x0) ;
new_poi_rar = ws->var( new_poi_name ) ;
if ( new_poi_rar == 0x0 ) {
printf("\n\n New POI %s is not a variable. Trying function.\n\n", new_poi_name ) ;
new_poi_rar = ws->function( new_poi_name ) ;
if ( new_poi_rar == 0x0 ) {
printf("\n\n New POI %s is not a function. I quit.\n\n", new_poi_name ) ;
return ;
}
} else {
printf("\n\n New POI %s is a variable with current value %.1f.\n\n", new_poi_name, new_poi_rar->getVal() ) ;
}
if ( npoiPoints <=0 ) {
printf("\n\n Quitting now.\n\n" ) ;
return ;
}
double startPoiVal = new_poi_rar->getVal() ;
//--- The RooNLLVar is NOT equivalent to what minuit uses.
// RooNLLVar* nll = new RooNLLVar("nll","nll", *likelihood, *rds ) ;
// printf("\n\n Nll value, from construction : %.3f\n\n", nll->getVal() ) ;
//--- output of createNLL IS what minuit uses, so use that.
RooAbsReal* nll = likelihood -> createNLL( *rds, Verbose(true) ) ;
RooRealVar* rrv_poiValue = new RooRealVar( "poiValue", "poiValue", 0., -10000., 10000. ) ;
/// rrv_poiValue->setVal( poiMinVal ) ;
/// rrv_poiValue->setConstant(kTRUE) ;
RooRealVar* rrv_constraintWidth = new RooRealVar("constraintWidth","constraintWidth", 0.1, 0.1, 1000. ) ;
rrv_constraintWidth -> setVal( constraintWidth ) ;
rrv_constraintWidth -> setConstant(kTRUE) ;
if ( verbLevel > 0 ) {
printf("\n\n ======= debug likelihood print\n\n") ;
likelihood->Print("v") ;
printf("\n\n ======= debug nll print\n\n") ;
nll->Print("v") ;
}
//----------------------------------------------------------------------------------------------
RooMinuit* rminuit( 0x0 ) ;
RooMinuit* rminuit_uc = new RooMinuit( *nll ) ;
rminuit_uc->setPrintLevel(verbLevel-1) ;
rminuit_uc->setNoWarn() ;
rminuit_uc->migrad() ;
rminuit_uc->hesse() ;
RooFitResult* rfr_uc = rminuit_uc->fit("mr") ;
double floatParInitVal[10000] ;
char floatParName[10000][100] ;
int nFloatParInitVal(0) ;
RooArgList ral_floats = rfr_uc->floatParsFinal() ;
TIterator* floatParIter = ral_floats.createIterator() ;
{
RooRealVar* par ;
while ( (par = (RooRealVar*) floatParIter->Next()) ) {
sprintf( floatParName[nFloatParInitVal], "%s", par->GetName() ) ;
floatParInitVal[nFloatParInitVal] = par->getVal() ;
nFloatParInitVal++ ;
}
}
//-------
printf("\n\n Unbiased best value for new POI %s is : %7.1f\n\n", new_poi_rar->GetName(), new_poi_rar->getVal() ) ;
double best_poi_val = new_poi_rar->getVal() ;
char minuit_formula[10000] ;
sprintf( minuit_formula, "%s+%s*(%s-%s)*(%s-%s)",
nll->GetName(),
rrv_constraintWidth->GetName(),
new_poi_rar->GetName(), rrv_poiValue->GetName(),
new_poi_rar->GetName(), rrv_poiValue->GetName()
) ;
printf("\n\n Creating new minuit variable with formula: %s\n\n", minuit_formula ) ;
RooFormulaVar* new_minuit_var = new RooFormulaVar("new_minuit_var", minuit_formula,
RooArgList( *nll,
*rrv_constraintWidth,
*new_poi_rar, *rrv_poiValue,
*new_poi_rar, *rrv_poiValue
) ) ;
printf("\n\n Current value is %.2f\n\n",
new_minuit_var->getVal() ) ;
rminuit = new RooMinuit( *new_minuit_var ) ;
RooAbsReal* plot_var = nll ;
printf("\n\n Current value is %.2f\n\n",
plot_var->getVal() ) ;
rminuit->setPrintLevel(verbLevel-1) ;
if ( verbLevel <=0 ) { rminuit->setNoWarn() ; }
//----------------------------------------------------------------------------------------------
//-- If POI range is -1 to -1, automatically determine the range using the set value.
if ( poiMinVal < 0. && poiMaxVal < 0. ) {
printf("\n\n Automatic determination of scan range.\n\n") ;
if ( startPoiVal <= 0. ) {
printf("\n\n *** POI starting value zero or negative %g. Quit.\n\n\n", startPoiVal ) ;
return ;
}
poiMinVal = startPoiVal - 3.5 * sqrt(startPoiVal) ;
poiMaxVal = startPoiVal + 6.0 * sqrt(startPoiVal) ;
if ( poiMinVal < 0. ) { poiMinVal = 0. ; }
printf(" Start val = %g. Scan range: %g to %g\n\n", startPoiVal, poiMinVal, poiMaxVal ) ;
}
//----------------------------------------------------------------------------------------------
double poiVals_scanDown[1000] ;
double nllVals_scanDown[1000] ;
//-- Do scan down from best value.
printf("\n\n +++++ Starting scan down from best value.\n\n") ;
double minNllVal(1.e9) ;
for ( int poivi=0; poivi < npoiPoints/2 ; poivi++ ) {
////double poiValue = poiMinVal + poivi*(poiMaxVal-poiMinVal)/(1.*(npoiPoints-1)) ;
double poiValue = best_poi_val - poivi*(best_poi_val-poiMinVal)/(1.*(npoiPoints/2-1)) ;
rrv_poiValue -> setVal( poiValue ) ;
rrv_poiValue -> setConstant( kTRUE ) ;
//+++++++++++++++++++++++++++++++++++
rminuit->migrad() ;
rminuit->hesse() ;
RooFitResult* rfr = rminuit->save() ;
//+++++++++++++++++++++++++++++++++++
if ( verbLevel > 0 ) { rfr->Print("v") ; }
float fit_minuit_var_val = rfr->minNll() ;
printf(" %02d : poi constraint = %.2f : allvars : MinuitVar, createNLL, PV, POI : %.5f %.5f %.5f %.5f\n",
poivi, rrv_poiValue->getVal(), fit_minuit_var_val, nll->getVal(), plot_var->getVal(), new_poi_rar->getVal() ) ;
cout << flush ;
poiVals_scanDown[poivi] = new_poi_rar->getVal() ;
nllVals_scanDown[poivi] = plot_var->getVal() ;
if ( nllVals_scanDown[poivi] < minNllVal ) { minNllVal = nllVals_scanDown[poivi] ; }
delete rfr ;
} // poivi
printf("\n\n +++++ Resetting floats to best fit values.\n\n") ;
for ( int pi=0; pi<nFloatParInitVal; pi++ ) {
RooRealVar* par = ws->var( floatParName[pi] ) ;
par->setVal( floatParInitVal[pi] ) ;
} // pi.
printf("\n\n +++++ Starting scan up from best value.\n\n") ;
//-- Now do scan up.
double poiVals_scanUp[1000] ;
double nllVals_scanUp[1000] ;
for ( int poivi=0; poivi < npoiPoints/2 ; poivi++ ) {
double poiValue = best_poi_val + poivi*(poiMaxVal-best_poi_val)/(1.*(npoiPoints/2-1)) ;
rrv_poiValue -> setVal( poiValue ) ;
rrv_poiValue -> setConstant( kTRUE ) ;
//+++++++++++++++++++++++++++++++++++
rminuit->migrad() ;
rminuit->hesse() ;
RooFitResult* rfr = rminuit->save() ;
//+++++++++++++++++++++++++++++++++++
if ( verbLevel > 0 ) { rfr->Print("v") ; }
float fit_minuit_var_val = rfr->minNll() ;
printf(" %02d : poi constraint = %.2f : allvars : MinuitVar, createNLL, PV, POI : %.5f %.5f %.5f %.5f\n",
poivi, rrv_poiValue->getVal(), fit_minuit_var_val, nll->getVal(), plot_var->getVal(), new_poi_rar->getVal() ) ;
cout << flush ;
poiVals_scanUp[poivi] = new_poi_rar->getVal() ;
nllVals_scanUp[poivi] = plot_var->getVal() ;
if ( nllVals_scanUp[poivi] < minNllVal ) { minNllVal = nllVals_scanUp[poivi] ; }
delete rfr ;
} // poivi
double poiVals[1000] ;
double nllVals[1000] ;
int pointCount(0) ;
for ( int pi=0; pi<npoiPoints/2; pi++ ) {
poiVals[pi] = poiVals_scanDown[(npoiPoints/2-1)-pi] ;
nllVals[pi] = nllVals_scanDown[(npoiPoints/2-1)-pi] ;
pointCount++ ;
}
for ( int pi=1; pi<npoiPoints/2; pi++ ) {
poiVals[pointCount] = poiVals_scanUp[pi] ;
nllVals[pointCount] = nllVals_scanUp[pi] ;
pointCount++ ;
}
npoiPoints = pointCount ;
printf("\n\n --- TGraph arrays:\n") ;
for ( int i=0; i<npoiPoints; i++ ) {
printf(" %2d : poi = %6.1f, nll = %g\n", i, poiVals[i], nllVals[i] ) ;
}
printf("\n\n") ;
double nllDiffVals[1000] ;
double poiAtMinlnL(-1.) ;
double poiAtMinusDelta2(-1.) ;
double poiAtPlusDelta2(-1.) ;
for ( int poivi=0; poivi < npoiPoints ; poivi++ ) {
nllDiffVals[poivi] = 2.*(nllVals[poivi] - minNllVal) ;
double poiValue = poiMinVal + poivi*(poiMaxVal-poiMinVal)/(1.*npoiPoints) ;
if ( nllDiffVals[poivi] < 0.01 ) { poiAtMinlnL = poiValue ; }
if ( poiAtMinusDelta2 < 0. && nllDiffVals[poivi] < 2.5 ) { poiAtMinusDelta2 = poiValue ; }
if ( poiAtMinlnL > 0. && poiAtPlusDelta2 < 0. && nllDiffVals[poivi] > 2.0 ) { poiAtPlusDelta2 = poiValue ; }
} // poivi
printf("\n\n Estimates for poi at delta ln L = -2, 0, +2: %g , %g , %g\n\n", poiAtMinusDelta2, poiAtMinlnL, poiAtPlusDelta2 ) ;
//--- Main canvas
TCanvas* cscan = (TCanvas*) gDirectory->FindObject("cscan") ;
if ( cscan == 0x0 ) {
printf("\n Creating canvas.\n\n") ;
cscan = new TCanvas("cscan","Delta nll") ;
}
char gname[1000] ;
TGraph* graph = new TGraph( npoiPoints, poiVals, nllDiffVals ) ;
sprintf( gname, "scan_%s", new_poi_name ) ;
graph->SetName( gname ) ;
double poiBest(-1.) ;
double poiMinus1stdv(-1.) ;
double poiPlus1stdv(-1.) ;
double poiMinus2stdv(-1.) ;
double poiPlus2stdv(-1.) ;
double twoDeltalnLMin(1e9) ;
int nscan(1000) ;
for ( int xi=0; xi<nscan; xi++ ) {
double x = poiVals[0] + xi*(poiVals[npoiPoints-1]-poiVals[0])/(nscan-1) ;
double twoDeltalnL = graph -> Eval( x, 0, "S" ) ;
if ( poiMinus1stdv < 0. && twoDeltalnL < 1.0 ) { poiMinus1stdv = x ; printf(" set m1 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( poiMinus2stdv < 0. && twoDeltalnL < 4.0 ) { poiMinus2stdv = x ; printf(" set m2 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( twoDeltalnL < twoDeltalnLMin ) { poiBest = x ; twoDeltalnLMin = twoDeltalnL ; }
if ( twoDeltalnLMin < 0.3 && poiPlus1stdv < 0. && twoDeltalnL > 1.0 ) { poiPlus1stdv = x ; printf(" set p1 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( twoDeltalnLMin < 0.3 && poiPlus2stdv < 0. && twoDeltalnL > 4.0 ) { poiPlus2stdv = x ; printf(" set p2 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( xi%100 == 0 ) { printf( " %4d : poi=%6.2f, 2DeltalnL = %6.2f\n", xi, x, twoDeltalnL ) ; }
}
printf("\n\n POI estimate : %g +%g -%g [%g,%g], two sigma errors: +%g -%g [%g,%g]\n\n",
poiBest,
(poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv), poiMinus1stdv, poiPlus1stdv,
(poiPlus2stdv-poiBest), (poiBest-poiMinus2stdv), poiMinus2stdv, poiPlus2stdv
) ;
printf(" %s val,pm1sig,pm2sig: %7.2f %7.2f %7.2f %7.2f %7.2f\n",
new_poi_name, poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv), (poiPlus2stdv-poiBest), (poiBest-poiMinus2stdv) ) ;
char htitle[1000] ;
sprintf(htitle, "%s profile likelihood scan: -2ln(L/Lm)", new_poi_name ) ;
TH1F* hscan = new TH1F("hscan", htitle, 10, poiMinVal, poiMaxVal ) ;
hscan->SetMinimum(0.) ;
hscan->SetMaximum(ymax) ;
hscan->DrawCopy() ;
graph->SetLineColor(4) ;
graph->SetLineWidth(3) ;
graph->Draw("CP") ;
gPad->SetGridx(1) ;
gPad->SetGridy(1) ;
cscan->Update() ;
TLine* line = new TLine() ;
line->SetLineColor(2) ;
line->DrawLine(poiMinVal, 1., poiPlus1stdv, 1.) ;
line->DrawLine(poiMinus1stdv,0., poiMinus1stdv, 1.) ;
line->DrawLine(poiPlus1stdv ,0., poiPlus1stdv , 1.) ;
TText* text = new TText() ;
text->SetTextSize(0.04) ;
char tstring[1000] ;
sprintf( tstring, "%s = %.1f +%.1f -%.1f", new_poi_name, poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv) ) ;
text -> DrawTextNDC( 0.15, 0.85, tstring ) ;
sprintf( tstring, "68%% interval [%.1f, %.1f]", poiMinus1stdv, poiPlus1stdv ) ;
text -> DrawTextNDC( 0.15, 0.78, tstring ) ;
char hname[1000] ;
sprintf( hname, "hscanout_%s", new_poi_name ) ;
TH1F* hsout = new TH1F( hname,"scan results",4,0.,4.) ;
double obsVal(-1.) ;
hsout->SetBinContent(1, obsVal ) ;
hsout->SetBinContent(2, poiPlus1stdv ) ;
hsout->SetBinContent(3, poiBest ) ;
hsout->SetBinContent(4, poiMinus1stdv ) ;
TAxis* xaxis = hsout->GetXaxis() ;
xaxis->SetBinLabel(1,"Observed val.") ;
xaxis->SetBinLabel(2,"Model+1sd") ;
xaxis->SetBinLabel(3,"Model") ;
xaxis->SetBinLabel(4,"Model-1sd") ;
char outrootfile[10000] ;
sprintf( outrootfile, "%s/scan-ff-%s.root", outputdir.Data(), new_poi_name ) ;
char outpdffile[10000] ;
sprintf( outpdffile, "%s/scan-ff-%s.pdf", outputdir.Data(), new_poi_name ) ;
cscan->Update() ; cscan->Draw() ;
printf("\n Saving %s\n", outpdffile ) ;
cscan->SaveAs( outpdffile ) ;
//--- save in root file
printf("\n Saving %s\n", outrootfile ) ;
TFile fout(outrootfile,"recreate") ;
graph->Write() ;
hsout->Write() ;
fout.Close() ;
delete ws ;
wstf->Close() ;
}
void eregtestingExample(bool dobarrel=true, bool doele=true) {
//output dir
TString dirname = "/data/bendavid/eregexampletest/eregexampletest_test/";
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("/data/bendavid/eregexampletest/%s",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("root://eoscms.cern.ch///eos/cms/store/cmst3/user/bendavid/idTreesAug1/hgg-2013Final8TeV_ID_s12-h124gg-gf-v7n_noskim.root");
TDirectory *ddir = (TDirectory*)fdin->FindObjectAny("PhotonTreeWriterPreselNoSmear");
dtree = (TTree*)ddir->Get("hPhotonTreeSingle");
}
//selection cuts for testing
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 = "(evt%10==0)";
TCut prescale10alt = "(evt%10==1)";
TCut prescale25 = "(evt%25==0)";
TCut prescale100 = "(evt%100==0)";
TCut prescale1000 = "(evt%1000==0)";
TCut evenevents = "(evt%2==0)";
TCut oddevents = "(evt%2==1)";
TCut prescale100alt = "(evt%100==1)";
TCut prescale1000alt = "(evt%1000==1)";
TCut prescale50alt = "(evt%50==1)";
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 *scetavar = ws->var("var_1");
//regressed output functions
RooAbsReal *sigmeanlim = ws->function("sigmeanlim");
RooAbsReal *sigwidthlim = ws->function("sigwidthlim");
RooAbsReal *signlim = ws->function("signlim");
RooAbsReal *sign2lim = ws->function("sign2lim");
//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);
//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.eps");
craw->SetLogy();
plot->SetMinimum(0.1);
craw->SaveAs("RawElog.eps");
//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.eps");
TCanvas *cwidth = new TCanvas;
RooPlot *plotwidth = widthvar->frame(0.,0.05,100);
hdataclone->plotOn(plotwidth);
plotwidth->Draw();
cwidth->SaveAs("width.eps");
TCanvas *cn = new TCanvas;
RooPlot *plotn = nvar->frame(0.,111.,200);
hdataclone->plotOn(plotn);
plotn->Draw();
cn->SaveAs("n.eps");
TCanvas *cn2 = new TCanvas;
RooPlot *plotn2 = n2var->frame(0.,111.,100);
hdataclone->plotOn(plotn2);
plotn2->Draw();
cn2->SaveAs("n2.eps");
TCanvas *ceta = new TCanvas;
RooPlot *ploteta = scetavar->frame(-2.6,2.6,200);
hdataclone->plotOn(ploteta);
ploteta->Draw();
ceta->SaveAs("eta.eps");
//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);
//heold->SetLineColor(kRed);
hecor->SetLineColor(kBlue);
heraw->SetLineColor(kMagenta);
hecor->GetXaxis()->SetRangeUser(0.6,1.2);
//heold->GetXaxis()->SetRangeUser(0.6,1.2);
TCanvas *cresponse = new TCanvas;
hecor->Draw("HIST");
//heold->Draw("HISTSAME");
heraw->Draw("HISTSAME");
cresponse->SaveAs("response.eps");
cresponse->SetLogy();
cresponse->SaveAs("responselog.eps");
printf("make fine histogram\n");
TH1 *hecorfine = hdata->createHistogram("hecorfine",*ecorvar,Binning(20e3,0.,2.));
printf("calc effsigma\n");
double effsigma = effSigma(hecorfine);
printf("effsigma = %5f\n",effsigma);
/* 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(); */
}
int main(int argc, char** argv)
{
RooMsgService::instance().deleteStream(0);
RooMsgService::instance().deleteStream(1);
//Check if all nedeed arguments to parse are there
if(argc != 2)
{
std::cerr << ">>>>> drawWorkspace::usage: " << argv[0] << " configFileName" << std::endl ;
return 1;
}
// Parse the config file
parseConfigFile (argv[1]);
//[Input]
std::string inputDir = gConfigParser -> readStringOption("Input::inputDir");
std::string analysisMethod = gConfigParser -> readStringOption("Input::analysisMethod");
std::string fitMethod = gConfigParser -> readStringOption("Input::fitMethod");
//[Output]
std::string outputDir = gConfigParser -> readStringOption("Output::outputDir");
//[Options]
float mass = gConfigParser -> readIntOption("Options::mH");
float xWidth = gConfigParser -> readFloatOption("Options::xWidth");
char xWidthChar[50];
sprintf(xWidthChar,"%d",int(xWidth));
int step = gConfigParser -> readIntOption("Options::step");
char stepChar[50];
sprintf(stepChar,"%d",step);
std::string additionalCuts = gConfigParser -> readStringOption("Options::additionalCuts");
std::string flavour = gConfigParser -> readStringOption("Options::flavour");
float sigStrength = gConfigParser -> readFloatOption("Options::sigStrength");
int nToys = gConfigParser -> readIntOption("Options::nToys");
if( additionalCuts == "none" )
{
inputDir += "/combine_signal/binWidth" + std::string(xWidthChar) + "/step" + std::string(stepChar) + "/";
}
else
{
inputDir += "/coumbine_signal/binWidth" + std::string(xWidthChar) + "/step" + std::string(stepChar) + "_" + additionalCuts + "/";
}
// define infile
std::stringstream inFileName;
if( analysisMethod != "sidebands" )
inFileName << inputDir << "/shapes_" << analysisMethod << "_" << fitMethod << "_" << mass << "_" << flavour << ".root";
else
inFileName << inputDir << "/shapes_" << analysisMethod << "_" << mass << "_" << flavour << ".root";
std::stringstream outFileName;
if( analysisMethod != "sidebands" )
outFileName << outputDir << "/drawWorkspace_" << analysisMethod << "_" << fitMethod << "_" << mass << "_" << flavour << ".root";
else
outFileName << outputDir << "/drawWorkspace_" << analysisMethod << "_" << mass << "_" << flavour << ".root";
//------------------------------------
// open the file and get the workspace
std::cout << ">>> drawWorkspace::open file " << inFileName.str() << std::endl;
TFile* inFile = new TFile((inFileName.str()).c_str(), "READ");
TFile* outFile = new TFile((outFileName.str()).c_str(),"RECREATE");
inFile -> cd();
RooWorkspace* workspace = (RooWorkspace*)( inFile->Get("workspace") );
workspace -> Print();
//-------------------
// get the x variable
RooRealVar* x = (RooRealVar*)( workspace->var("x"));
RooRealVar* rooXMin = (RooRealVar*)( workspace->var("rooXMin"));
RooRealVar* rooXMax = (RooRealVar*)( workspace->var("rooXMax"));
RooRealVar* rooXWidth = (RooRealVar*)( workspace->var("rooXWidth"));
x -> setMin(rooXMin->getVal());
x -> setMax(rooXMax->getVal());
x -> setBins(int((rooXMax->getVal()-rooXMin->getVal())/rooXWidth->getVal()));
x -> setRange("signal",GetLepNuWMMIN(mass),GetLepNuWMMAX(mass));
//-------------------------
// get the number of events
RooRealVar* rooN_data_obs = (RooRealVar*)( workspace->var("rooN_data_obs") );
RooRealVar* rooN_ggH = (RooRealVar*)( workspace->var("rooN_ggH") );
RooRealVar* rooN_qqH = (RooRealVar*)( workspace->var("rooN_qqH") );
double n_data_obs = rooN_data_obs -> getVal();
double n_ggH = sigStrength * (rooN_ggH -> getVal());
double n_qqH = sigStrength * (rooN_qqH -> getVal());
double n_H = n_ggH + n_qqH;
//------------
// get the pdf
RooDataHist* data_obs = (RooDataHist*)( workspace->data("data_obs") );
RooAbsPdf* ggH = (RooAbsPdf*)( workspace->pdf("ggH") );
RooAbsPdf* qqH = (RooAbsPdf*)( workspace->pdf("qqH") );
RooGenericPdf* bkg = (RooGenericPdf*)( workspace->pdf("bkg") );
//-------------------
// get the parameters
int nPars = 0;
if( fitMethod == "exponential" ) nPars = 1;
if( fitMethod == "attenuatedExponential" ) nPars = 3;
if( fitMethod == "doubleExponential" ) nPars = 3;
if( fitMethod == "attenuatedDoubleExponential" ) nPars = 5;
float* initPars = new float[nPars];
std::string* initParNames = new std::string[nPars];
if( fitMethod == "exponential")
{
RooRealVar* roo_L1 = (RooRealVar*)( workspace->var(("CMS_HWWlvjj_"+flavour+"_L1").c_str()) );
initPars[0] = roo_L1 -> getVal();
initParNames[0] == "L1";
}
if( fitMethod == "attenuatedExponential")
{
RooRealVar* roo_mu = (RooRealVar*)( workspace->var(("CMS_HWWlvjj_"+flavour+"_mu").c_str()) );
initPars[0] = roo_mu -> getVal();
initParNames[0] = "mu";
RooRealVar* roo_kT = (RooRealVar*)( workspace->var(("CMS_HWWlvjj_"+flavour+"_kT").c_str()) );
initPars[1] = roo_kT -> getVal();
initParNames[1] = "kT";
RooRealVar* roo_L1 = (RooRealVar*)( workspace->var(("CMS_HWWlvjj_"+flavour+"_L1").c_str()) );
initPars[2] = roo_L1 -> getVal();
initParNames[2] = "L1";
}
if( fitMethod == "doubleExponential")
{
RooRealVar* roo_N = (RooRealVar*)( workspace->var(("CMS_HWWlvjj_"+flavour+"_N").c_str()) );
initPars[0] = roo_N -> getVal();
initParNames[0] = "N";
RooRealVar* roo_L1 = (RooRealVar*)( workspace->var(("CMS_HWWlvjj_"+flavour+"_L1").c_str()) );
initPars[1] = roo_L1 -> getVal();
initParNames[1] = "L1";
RooRealVar* roo_L2 = (RooRealVar*)( workspace->var(("CMS_HWWlvjj_"+flavour+"_L2").c_str()) );
initPars[2] = roo_L2 -> getVal();
initParNames[2] = "L2";
}
if( fitMethod == "attenuatedDoubleExponential")
{
RooRealVar* roo_mu = (RooRealVar*)( workspace->var(("CMS_HWWlvjj_"+flavour+"_mu").c_str()) );
initPars[0] = roo_mu -> getVal();
initParNames[0] = "mu";
RooRealVar* roo_kT = (RooRealVar*)( workspace->var(("CMS_HWWlvjj_"+flavour+"_kT").c_str()) );
initPars[1] = roo_kT -> getVal();
initParNames[1] = "kT";
RooRealVar* roo_N = (RooRealVar*)( workspace->var(("CMS_HWWlvjj_"+flavour+"_N").c_str()) );
initPars[2] = roo_N -> getVal();
initParNames[2] = "N";
RooRealVar* roo_L1 = (RooRealVar*)( workspace->var(("CMS_HWWlvjj_"+flavour+"_L1").c_str()) );
initPars[3] = roo_L1 -> getVal();
initParNames[3] = "L1";
RooRealVar* roo_L2 = (RooRealVar*)( workspace->var(("CMS_HWWlvjj_"+flavour+"_L2").c_str()) );
initPars[4] = roo_L2 -> getVal();
initParNames[4] = "L2";
}
std::cout << "\n\n\n***********************************************************" << std::endl;
std::cout << "*** VARIABLES ***" << std::endl;
std::cout << "***********************************************************" << std::endl;
std::cout << "x: " << x->getVal() << std::endl;
std::cout << "xMin: " << rooXMin->getVal() << std::endl;
std::cout << "xMax: " << rooXMax->getVal() << std::endl;
std::cout << "xWidth: " << rooXWidth->getVal() << std::endl;
std::cout << "n_data_obs: " << n_data_obs << std::endl;
std::cout << "n_ggH: " << n_ggH << std::endl;
std::cout << "n_qqH: " << n_qqH << std::endl;
std::cout << "\n\n\n***********************************************************" << std::endl;
std::cout << "*** PARAMETERS ***" << std::endl;
std::cout << "***********************************************************" << std::endl;
for(int parIt = 0; parIt < nPars; ++parIt)
{
std::cout << initParNames[parIt] << ": " << initPars[parIt] << std::endl;
}
std::cout << "\n\n\n***********************************************************" << std::endl;
std::cout << "*** PRINT HISTOGRAMS ***" << std::endl;
std::cout << "***********************************************************" << std::endl;
outFile -> cd();
TCanvas* c_data_obs = new TCanvas("c_data_obs","c_data_obs");
RooPlot* plot_data_obs = x->frame();
data_obs -> plotOn(plot_data_obs);
plot_data_obs -> Draw();
c_data_obs -> Write();
delete plot_data_obs;
delete c_data_obs;
TCanvas* c_H = new TCanvas("c_H","c_H");
RooPlot* plot_H = x->frame();
ggH -> plotOn(plot_H,LineColor(kRed));
qqH -> plotOn(plot_H,LineColor(kBlue));
plot_H -> Draw();
c_H -> Write();
delete plot_H;
delete c_H;
TCanvas* c_bkg = new TCanvas("c_bkg","c_bkg");
RooPlot* plot_bkg = x->frame();
bkg -> plotOn(plot_bkg,LineColor(kRed));
plot_bkg -> Draw();
c_bkg -> Write();
delete plot_bkg;
delete c_bkg;
std::cout << "\n\n\n***********************************************************" << std::endl;
std::cout << "*** FIT B ***" << std::endl;
std::cout << "***********************************************************" << std::endl;
inFile -> cd();
RooRealVar* B = new RooRealVar("B","",n_data_obs,0.,2.*n_data_obs);
RooAddPdf* rooTotPdf_B = new RooAddPdf("rooTotPdf_B","",RooArgList(*bkg),RooArgList(*B));
rooTotPdf_B -> fitTo(*data_obs,Extended(kTRUE),Save(),PrintLevel(-1));
std::cout << ">>> B: " << B -> getVal() << std::endl;
outFile -> cd();
TCanvas* c_fit_B = new TCanvas("c_fit_B","c_fit_B");
RooPlot* plot_fit_B = x->frame();
data_obs -> plotOn(plot_fit_B);
rooTotPdf_B -> plotOn(plot_fit_B);
plot_fit_B -> Draw();
c_fit_B -> Write();
delete plot_fit_B;
delete c_fit_B;
std::cout << "\n\n\n***********************************************************" << std::endl;
std::cout << "*** TOY EXPERIMENTS ***" << std::endl;
std::cout << "***********************************************************" << std::endl;
inFile -> cd();
TH1F* h_diffB_parentB_fitB = new TH1F("h_diffB_parentB_fitB", "",400,-1.,1.);
TH1F* h_diffB_parentBS_fitB = new TH1F("h_diffB_parentBS_fitB", "",400,-1.,1.);
TH1F* h_diffB_parentBS_fitBS = new TH1F("h_diffB_parentBS_fitBS","",400,-1.,1.);
TH1F* h_diffS_parentBS_fitBS = new TH1F("h_diffS_parentBS_fitBS","",400,-1.,1.);
//--------------------------------------1.
// define background parent distribution
RooRealVar** pars = new RooRealVar*[nPars];
RooGenericPdf* rooParentPdf_B;
if( fitMethod == "exponential" )
{
pars[0] = new RooRealVar("parent_L1","",initPars[0],initPars[0],initPars[0]);
rooParentPdf_B = new RooGenericPdf("rooParentPdf_B","","exp(-1*@1*@0)",RooArgSet(*x,*pars[0]));
}
if( fitMethod == "attenuatedExponential" )
{
pars[0] = new RooRealVar("parent_mu","",initPars[0],initPars[0],initPars[0]);
pars[1] = new RooRealVar("parent_kT","",initPars[1],initPars[1],initPars[1]);
pars[2] = new RooRealVar("parent_L1","",initPars[2],initPars[2],initPars[2]);
rooParentPdf_B = new RooGenericPdf("rooParentPdf_B","","1./(exp(-1.*(@0-@1)/@2)+1.) * exp(-1*@3*@0)",RooArgSet(*x,*pars[0],*pars[1],*pars[2]));
}
if( fitMethod == "doubleExponential" )
{
pars[0] = new RooRealVar("parent_N","", initPars[0],initPars[0],initPars[0]);
pars[1] = new RooRealVar("parent_L1","",initPars[1],initPars[1],initPars[1]);
pars[2] = new RooRealVar("parent_L2","",initPars[2],initPars[2],initPars[2]);
rooParentPdf_B = new RooGenericPdf("rooParentPdf_B","","(exp(-1*@2*@0) + @1*exp(-1*@3*@0))",RooArgSet(*x,*pars[0],*pars[1],*pars[2]));
}
if( fitMethod == "attenuatedDoubleExponential" )
{
pars[0] = new RooRealVar("parent_mu","",initPars[0],initPars[0],initPars[0]);
pars[1] = new RooRealVar("parent_kT","",initPars[1],initPars[1],initPars[1]);
pars[2] = new RooRealVar("parent_N","", initPars[2],initPars[2],initPars[2]);
pars[3] = new RooRealVar("parent_L1","",initPars[3],initPars[3],initPars[3]);
pars[4] = new RooRealVar("parent_L2","",initPars[4],initPars[4],initPars[4]);
rooParentPdf_B = new RooGenericPdf("bkg","","1./(exp(-1.*(@0-@1)/@2)+1.) * (exp(-1*@4*@0) + @3*exp(-1*@5*@0))",RooArgSet(*x,*pars[0],*pars[1],*pars[2],*pars[3],*pars[4]));
}
RooAbsReal* integral_parent_B = rooParentPdf_B -> createIntegral(*x,NormSet(*x),Range("signal"));
double n_parent_B = integral_parent_B->getVal() * int(n_data_obs);
std::cout << ">>> n_parent_B: " << n_parent_B << std::endl;
//----------------------------------
// define signal parent distribution
RooGenericPdf* rooParentPdf_ggS = (RooGenericPdf*)( ggH->Clone("rooParentPdf_ggS") );
RooGenericPdf* rooParentPdf_qqS = (RooGenericPdf*)( qqH->Clone("rooParentPdf_qqS") );
RooAddPdf* rooParentPdf_S = new RooAddPdf("rooParentPdf_S","",RooArgList(*rooParentPdf_ggS,*rooParentPdf_qqS),RooArgList(*rooN_ggH,*rooN_qqH));
RooAbsReal* integral_parent_S = rooParentPdf_S -> createIntegral(*x,NormSet(*x),Range("signal"));
double n_parent_S = integral_parent_S->getVal() * (n_H);
std::cout << ">>> n_parent_S: " << n_parent_S << std::endl;
//------------
// create toys
for(int toyIt = 0; toyIt < nToys; ++toyIt)
{
if(toyIt%100 == 0) std::cout << ">>> generating toy " << toyIt << " / " << nToys << "\r" << std::flush;
RooDataSet* ds_B_toy = rooParentPdf_B -> generate(*x,int(n_data_obs));
RooDataHist* dh_B_toy = ds_B_toy -> binnedClone();
RooDataSet* ds_BS_toy = rooParentPdf_S -> generate(*x,int(n_H));
ds_BS_toy -> append(*ds_B_toy);
RooDataHist* dh_BS_toy = ds_BS_toy -> binnedClone();
// generate B - fit B
RooRealVar* B_toy = new RooRealVar("B_toy","",n_data_obs,0.,2.*n_data_obs);
RooRealVar** pars_toy = new RooRealVar*[nPars];
RooGenericPdf* bkg_toy;
InitializeBkgPdf(x,&bkg_toy,pars_toy,initPars,fitMethod,nPars);
RooAddPdf* rooTotPdf_B_toy = new RooAddPdf("rooTotPdf_B_toy","",RooArgList(*bkg_toy),RooArgList(*B_toy));
rooTotPdf_B_toy -> fitTo(*dh_B_toy,Extended(kTRUE),Save(),PrintLevel(-10));
RooAbsReal* integral_B_toy = rooTotPdf_B_toy -> createIntegral(*x,NormSet(*x),Range("signal"));
double n_B_toy = integral_B_toy->getVal() * B_toy->getVal();
h_diffB_parentB_fitB -> Fill(n_B_toy/n_parent_B - 1.);
// generate BS - fit B
RooRealVar* B2_toy = new RooRealVar("B2_toy","",n_data_obs,0.,2.*n_data_obs);
RooRealVar** pars2_toy = new RooRealVar*[nPars];
RooGenericPdf* bkg2_toy;
InitializeBkgPdf(x,&bkg2_toy,pars2_toy,initPars,fitMethod,nPars);
RooAddPdf* rooTotPdf_B2_toy = new RooAddPdf("rooTotPdf_B2_toy","",RooArgList(*bkg2_toy),RooArgList(*B2_toy));
rooTotPdf_B2_toy -> fitTo(*dh_BS_toy,Extended(kTRUE),Save(),PrintLevel(-10));
RooAbsReal* integral_B2_toy = rooTotPdf_B2_toy -> createIntegral(*x,NormSet(*x),Range("signal"));
double n_B2_toy = integral_B2_toy->getVal() * B2_toy->getVal();
h_diffB_parentBS_fitB -> Fill(n_B2_toy/n_parent_B - 1.);
// generate BS - fit BS
RooRealVar* B3_toy = new RooRealVar("B3_toy","",n_data_obs,0.,2.*n_data_obs);
RooRealVar* S3_toy = new RooRealVar("S3_toy","",n_H,0.,2.*n_H);
RooRealVar** pars3_toy = new RooRealVar*[nPars];
RooGenericPdf* bkg3_toy;
InitializeBkgPdf(x,&bkg3_toy,pars3_toy,initPars,fitMethod,nPars);
RooGenericPdf* sig3_toy = (RooGenericPdf*)( rooParentPdf_S -> Clone("sig3_toy") );
RooAddPdf* rooTotPdf_BS3_toy = new RooAddPdf("rooTotPdf_BS3_toy","",RooArgList(*bkg3_toy,*sig3_toy),RooArgList(*B3_toy,*S3_toy));
rooTotPdf_BS3_toy -> fitTo(*dh_BS_toy,Extended(kTRUE),Save(),PrintLevel(-10));
RooAbsReal* integral_B3_toy = bkg3_toy -> createIntegral(*x,NormSet(*x),Range("signal"));
RooAbsReal* integral_S3_toy = sig3_toy -> createIntegral(*x,NormSet(*x),Range("signal"));
double n_B3_toy = integral_B3_toy->getVal() * B3_toy->getVal();
double n_S3_toy = integral_S3_toy->getVal() * S3_toy->getVal();
h_diffB_parentBS_fitBS -> Fill(n_B3_toy/n_parent_B - 1.);
h_diffS_parentBS_fitBS -> Fill(n_S3_toy/n_parent_S - 1.);
if(toyIt < 10)
{
outFile -> cd();
char dirName[50];
sprintf(dirName,"toy%d",toyIt);
outFile -> mkdir(dirName);
outFile -> cd(dirName);
char canvasName[50];
sprintf(canvasName,"parentB_fitB_%d",toyIt);
TCanvas* c_parentB_fitB_toy = new TCanvas(canvasName);
RooPlot* plot_parentB_fitB_toy = x->frame();
dh_B_toy -> plotOn(plot_parentB_fitB_toy);
rooTotPdf_B_toy -> plotOn(plot_parentB_fitB_toy);
plot_parentB_fitB_toy -> Draw();
c_parentB_fitB_toy -> Write();
delete plot_parentB_fitB_toy;
delete c_parentB_fitB_toy;
sprintf(canvasName,"parentBS_fitB_%d",toyIt);
TCanvas* c_parentBS_fitB_toy = new TCanvas(canvasName);
RooPlot* plot_parentBS_fitB_toy = x->frame();
dh_BS_toy -> plotOn(plot_parentBS_fitB_toy);
rooTotPdf_B2_toy -> plotOn(plot_parentBS_fitB_toy);
plot_parentBS_fitB_toy -> Draw();
c_parentBS_fitB_toy -> Write();
delete plot_parentBS_fitB_toy;
delete c_parentBS_fitB_toy;
sprintf(canvasName,"parentBS_fitBS_%d",toyIt);
TCanvas* c_parentBS_fitBS_toy = new TCanvas(canvasName);
RooPlot* plot_parentBS_fitBS_toy = x->frame();
dh_BS_toy -> plotOn(plot_parentBS_fitBS_toy);
rooTotPdf_BS3_toy -> plotOn(plot_parentBS_fitBS_toy);
plot_parentBS_fitBS_toy -> Draw();
c_parentBS_fitBS_toy -> Write();
delete plot_parentBS_fitBS_toy;
delete c_parentBS_fitBS_toy;
}
delete integral_B_toy;
delete rooTotPdf_B_toy;
delete bkg_toy;
for(int parIt = 0; parIt < nPars; ++parIt)
delete pars_toy[parIt];
delete B_toy;
delete integral_B2_toy;
delete rooTotPdf_B2_toy;
delete bkg2_toy;
for(int parIt = 0; parIt < nPars; ++parIt)
delete pars2_toy[parIt];
delete B2_toy;
delete integral_B3_toy;
delete rooTotPdf_BS3_toy;
delete bkg3_toy;
for(int parIt = 0; parIt < nPars; ++parIt)
delete pars3_toy[parIt];
delete B3_toy;
delete integral_S3_toy;
delete sig3_toy;
delete S3_toy;
delete dh_B_toy;
delete ds_B_toy;
delete dh_BS_toy;
delete ds_BS_toy;
}
outFile -> cd();
h_diffB_parentB_fitB -> Write();
h_diffB_parentBS_fitB -> Write();
h_diffB_parentBS_fitBS -> Write();
h_diffS_parentBS_fitBS -> Write();
outFile -> Close();
return 0;
}
void constrained_scan( const char* wsfile = "outputfiles/ws-lhfit3.root",
///////const char* new_poi_name="mu_qcd_hdp_Nj1_HT1",
//////const char* new_poi_name="mu_allnonqcd_ldp_Nj5_HT3",
const char* new_poi_name="mu_allnonqcd_ldp_Nj1_HT1",
double constraintWidth=1.5,
int npoiPoints = 10,
double poiMinVal = 5000.,
double poiMaxVal = 7000.,
double ymax = 9.,
int verbLevel=1 ) {
TString outputdir("outputfiles") ;
gStyle->SetOptStat(0) ;
TFile* wstf = new TFile( wsfile ) ;
RooWorkspace* ws = dynamic_cast<RooWorkspace*>( wstf->Get("ws") );
ws->Print() ;
RooDataSet* rds = (RooDataSet*) ws->obj( "observed_rds" ) ;
cout << "\n\n\n ===== RooDataSet ====================\n\n" << endl ;
rds->Print() ;
rds->printMultiline(cout, 1, kTRUE, "") ;
RooRealVar* rv_sig_strength = ws->var("sig_strength") ;
if ( rv_sig_strength == 0x0 ) { printf("\n\n *** can't find sig_strength in workspace.\n\n" ) ; return ; }
RooAbsPdf* likelihood = ws->pdf("likelihood") ;
if ( likelihood == 0x0 ) { printf("\n\n *** can't find likelihood in workspace.\n\n" ) ; return ; }
printf("\n\n Likelihood:\n") ;
likelihood -> Print() ;
/////rv_sig_strength -> setConstant( kFALSE ) ;
rv_sig_strength -> setVal(0.) ;
rv_sig_strength -> setConstant( kTRUE ) ;
likelihood->fitTo( *rds, Save(false), PrintLevel(0), Hesse(true), Strategy(1) ) ;
//RooFitResult* fitResult = likelihood->fitTo( *rds, Save(true), PrintLevel(0), Hesse(true), Strategy(1) ) ;
//double minNllSusyFloat = fitResult->minNll() ;
//double susy_ss_atMinNll = rv_sig_strength -> getVal() ;
RooMsgService::instance().getStream(1).removeTopic(Minimization) ;
RooMsgService::instance().getStream(1).removeTopic(Fitting) ;
//-- Construct the new POI parameter.
RooAbsReal* new_poi_rar(0x0) ;
new_poi_rar = ws->var( new_poi_name ) ;
if ( new_poi_rar == 0x0 ) {
printf("\n\n New POI %s is not a variable. Trying function.\n\n", new_poi_name ) ;
new_poi_rar = ws->function( new_poi_name ) ;
if ( new_poi_rar == 0x0 ) {
printf("\n\n New POI %s is not a function. I quit.\n\n", new_poi_name ) ;
return ;
} else {
printf("\n Found it.\n\n") ;
}
} else {
printf("\n\n New POI %s is a variable with current value %.1f.\n\n", new_poi_name, new_poi_rar->getVal() ) ;
}
double startPoiVal = new_poi_rar->getVal() ;
RooAbsReal* nll = likelihood -> createNLL( *rds, Verbose(true) ) ;
RooRealVar* rrv_poiValue = new RooRealVar( "poiValue", "poiValue", 0., -10000., 10000. ) ;
RooRealVar* rrv_constraintWidth = new RooRealVar("constraintWidth","constraintWidth", 0.1, 0.1, 1000. ) ;
rrv_constraintWidth -> setVal( constraintWidth ) ;
rrv_constraintWidth -> setConstant(kTRUE) ;
RooMinuit* rminuit( 0x0 ) ;
RooMinuit* rminuit_uc = new RooMinuit( *nll ) ;
rminuit_uc->setPrintLevel(verbLevel-1) ;
rminuit_uc->setNoWarn() ;
rminuit_uc->migrad() ;
rminuit_uc->hesse() ;
RooFitResult* rfr_uc = rminuit_uc->fit("mr") ;
double floatParInitVal[10000] ;
char floatParName[10000][100] ;
int nFloatParInitVal(0) ;
RooArgList ral_floats = rfr_uc->floatParsFinal() ;
TIterator* floatParIter = ral_floats.createIterator() ;
{
RooRealVar* par ;
while ( (par = (RooRealVar*) floatParIter->Next()) ) {
sprintf( floatParName[nFloatParInitVal], "%s", par->GetName() ) ;
floatParInitVal[nFloatParInitVal] = par->getVal() ;
nFloatParInitVal++ ;
}
}
printf("\n\n Unbiased best value for new POI %s is : %7.1f\n\n", new_poi_rar->GetName(), new_poi_rar->getVal() ) ;
double best_poi_val = new_poi_rar->getVal() ;
char minuit_formula[10000] ;
sprintf( minuit_formula, "%s+%s*(%s-%s)*(%s-%s)",
nll->GetName(),
rrv_constraintWidth->GetName(),
new_poi_rar->GetName(), rrv_poiValue->GetName(),
new_poi_rar->GetName(), rrv_poiValue->GetName()
) ;
printf("\n\n Creating new minuit variable with formula: %s\n\n", minuit_formula ) ;
RooFormulaVar* new_minuit_var = new RooFormulaVar("new_minuit_var", minuit_formula,
RooArgList( *nll,
*rrv_constraintWidth,
*new_poi_rar, *rrv_poiValue,
*new_poi_rar, *rrv_poiValue
) ) ;
printf("\n\n Current value is %.2f\n\n",
new_minuit_var->getVal() ) ;
rminuit = new RooMinuit( *new_minuit_var ) ;
RooAbsReal* plot_var = nll ;
printf("\n\n Current value is %.2f\n\n",
plot_var->getVal() ) ;
rminuit->setPrintLevel(verbLevel-1) ;
if ( verbLevel <=0 ) { rminuit->setNoWarn() ; }
if ( poiMinVal < 0. && poiMaxVal < 0. ) {
printf("\n\n Automatic determination of scan range.\n\n") ;
if ( startPoiVal <= 0. ) {
printf("\n\n *** POI starting value zero or negative %g. Quit.\n\n\n", startPoiVal ) ;
return ;
}
poiMinVal = startPoiVal - 3.5 * sqrt(startPoiVal) ;
poiMaxVal = startPoiVal + 6.0 * sqrt(startPoiVal) ;
if ( poiMinVal < 0. ) { poiMinVal = 0. ; }
printf(" Start val = %g. Scan range: %g to %g\n\n", startPoiVal, poiMinVal, poiMaxVal ) ;
}
//----------------------------------------------------------------------------------------------
double poiVals_scanDown[1000] ;
double nllVals_scanDown[1000] ;
//-- Do scan down from best value.
printf("\n\n +++++ Starting scan down from best value.\n\n") ;
double minNllVal(1.e9) ;
for ( int poivi=0; poivi < npoiPoints/2 ; poivi++ ) {
////double poiValue = poiMinVal + poivi*(poiMaxVal-poiMinVal)/(1.*(npoiPoints-1)) ;
double poiValue = best_poi_val - poivi*(best_poi_val-poiMinVal)/(1.*(npoiPoints/2-1)) ;
rrv_poiValue -> setVal( poiValue ) ;
rrv_poiValue -> setConstant( kTRUE ) ;
//+++++++++++++++++++++++++++++++++++
rminuit->migrad() ;
rminuit->hesse() ;
RooFitResult* rfr = rminuit->save() ;
//+++++++++++++++++++++++++++++++++++
if ( verbLevel > 0 ) { rfr->Print("v") ; }
float fit_minuit_var_val = rfr->minNll() ;
printf(" %02d : poi constraint = %.2f : allvars : MinuitVar, createNLL, PV, POI : %.5f %.5f %.5f %.5f\n",
poivi, rrv_poiValue->getVal(), fit_minuit_var_val, nll->getVal(), plot_var->getVal(), new_poi_rar->getVal() ) ;
cout << flush ;
poiVals_scanDown[poivi] = new_poi_rar->getVal() ;
nllVals_scanDown[poivi] = plot_var->getVal() ;
if ( nllVals_scanDown[poivi] < minNllVal ) { minNllVal = nllVals_scanDown[poivi] ; }
delete rfr ;
} // poivi
printf("\n\n +++++ Resetting floats to best fit values.\n\n") ;
for ( int pi=0; pi<nFloatParInitVal; pi++ ) {
RooRealVar* par = ws->var( floatParName[pi] ) ;
par->setVal( floatParInitVal[pi] ) ;
} // pi.
printf("\n\n +++++ Starting scan up from best value.\n\n") ;
//-- Now do scan up.
double poiVals_scanUp[1000] ;
double nllVals_scanUp[1000] ;
for ( int poivi=0; poivi < npoiPoints/2 ; poivi++ ) {
double poiValue = best_poi_val + poivi*(poiMaxVal-best_poi_val)/(1.*(npoiPoints/2-1)) ;
rrv_poiValue -> setVal( poiValue ) ;
rrv_poiValue -> setConstant( kTRUE ) ;
//+++++++++++++++++++++++++++++++++++
rminuit->migrad() ;
rminuit->hesse() ;
RooFitResult* rfr = rminuit->save() ;
//+++++++++++++++++++++++++++++++++++
if ( verbLevel > 0 ) { rfr->Print("v") ; }
float fit_minuit_var_val = rfr->minNll() ;
printf(" %02d : poi constraint = %.2f : allvars : MinuitVar, createNLL, PV, POI : %.5f %.5f %.5f %.5f\n",
poivi, rrv_poiValue->getVal(), fit_minuit_var_val, nll->getVal(), plot_var->getVal(), new_poi_rar->getVal() ) ;
cout << flush ;
poiVals_scanUp[poivi] = new_poi_rar->getVal() ;
nllVals_scanUp[poivi] = plot_var->getVal() ;
if ( nllVals_scanUp[poivi] < minNllVal ) { minNllVal = nllVals_scanUp[poivi] ; }
delete rfr ;
} // poivi
double poiVals[1000] ;
double nllVals[1000] ;
int pointCount(0) ;
for ( int pi=0; pi<npoiPoints/2; pi++ ) {
poiVals[pi] = poiVals_scanDown[(npoiPoints/2-1)-pi] ;
nllVals[pi] = nllVals_scanDown[(npoiPoints/2-1)-pi] ;
pointCount++ ;
}
for ( int pi=1; pi<npoiPoints/2; pi++ ) {
poiVals[pointCount] = poiVals_scanUp[pi] ;
nllVals[pointCount] = nllVals_scanUp[pi] ;
pointCount++ ;
}
npoiPoints = pointCount ;
printf("\n\n --- TGraph arrays:\n") ;
for ( int i=0; i<npoiPoints; i++ ) {
printf(" %2d : poi = %6.1f, nll = %g\n", i, poiVals[i], nllVals[i] ) ;
}
printf("\n\n") ;
double nllDiffVals[1000] ;
double poiAtMinlnL(-1.) ;
double poiAtMinusDelta2(-1.) ;
double poiAtPlusDelta2(-1.) ;
for ( int poivi=0; poivi < npoiPoints ; poivi++ ) {
nllDiffVals[poivi] = 2.*(nllVals[poivi] - minNllVal) ;
double poiValue = poiMinVal + poivi*(poiMaxVal-poiMinVal)/(1.*npoiPoints) ;
if ( nllDiffVals[poivi] < 0.01 ) { poiAtMinlnL = poiValue ; }
if ( poiAtMinusDelta2 < 0. && nllDiffVals[poivi] < 2.5 ) { poiAtMinusDelta2 = poiValue ; }
if ( poiAtMinlnL > 0. && poiAtPlusDelta2 < 0. && nllDiffVals[poivi] > 2.0 ) { poiAtPlusDelta2 = poiValue ; }
} // poivi
printf("\n\n Estimates for poi at delta ln L = -2, 0, +2: %g , %g , %g\n\n", poiAtMinusDelta2, poiAtMinlnL, poiAtPlusDelta2 ) ;
//--- Main canvas
TCanvas* cscan = (TCanvas*) gDirectory->FindObject("cscan") ;
if ( cscan == 0x0 ) {
printf("\n Creating canvas.\n\n") ;
cscan = new TCanvas("cscan","Delta nll") ;
}
char gname[1000] ;
TGraph* graph = new TGraph( npoiPoints, poiVals, nllDiffVals ) ;
sprintf( gname, "scan_%s", new_poi_name ) ;
graph->SetName( gname ) ;
double poiBest(-1.) ;
double poiMinus1stdv(-1.) ;
double poiPlus1stdv(-1.) ;
double poiMinus2stdv(-1.) ;
double poiPlus2stdv(-1.) ;
double twoDeltalnLMin(1e9) ;
int nscan(1000) ;
for ( int xi=0; xi<nscan; xi++ ) {
double x = poiVals[0] + xi*(poiVals[npoiPoints-1]-poiVals[0])/(nscan-1) ;
double twoDeltalnL = graph -> Eval( x, 0, "S" ) ;
if ( poiMinus1stdv < 0. && twoDeltalnL < 1.0 ) { poiMinus1stdv = x ; printf(" set m1 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( poiMinus2stdv < 0. && twoDeltalnL < 4.0 ) { poiMinus2stdv = x ; printf(" set m2 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( twoDeltalnL < twoDeltalnLMin ) { poiBest = x ; twoDeltalnLMin = twoDeltalnL ; }
if ( twoDeltalnLMin < 0.3 && poiPlus1stdv < 0. && twoDeltalnL > 1.0 ) { poiPlus1stdv = x ; printf(" set p1 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( twoDeltalnLMin < 0.3 && poiPlus2stdv < 0. && twoDeltalnL > 4.0 ) { poiPlus2stdv = x ; printf(" set p2 : %d, x=%g, 2dnll=%g\n", xi, x, twoDeltalnL) ;}
if ( xi%100 == 0 ) { printf( " %4d : poi=%6.2f, 2DeltalnL = %6.2f\n", xi, x, twoDeltalnL ) ; }
}
printf("\n\n POI estimate : %g +%g -%g [%g,%g], two sigma errors: +%g -%g [%g,%g]\n\n",
poiBest,
(poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv), poiMinus1stdv, poiPlus1stdv,
(poiPlus2stdv-poiBest), (poiBest-poiMinus2stdv), poiMinus2stdv, poiPlus2stdv
) ;
printf(" %s val,pm1sig,pm2sig: %7.2f %7.2f %7.2f %7.2f %7.2f\n",
new_poi_name, poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv), (poiPlus2stdv-poiBest), (poiBest-poiMinus2stdv) ) ;
char htitle[1000] ;
sprintf(htitle, "%s profile likelihood scan: -2ln(L/Lm)", new_poi_name ) ;
TH1F* hscan = new TH1F("hscan", htitle, 10, poiMinVal, poiMaxVal ) ;
hscan->SetMinimum(0.) ;
hscan->SetMaximum(ymax) ;
hscan->DrawCopy() ;
graph->SetLineColor(4) ;
graph->SetLineWidth(3) ;
graph->Draw("CP") ;
gPad->SetGridx(1) ;
gPad->SetGridy(1) ;
cscan->Update() ;
TLine* line = new TLine() ;
line->SetLineColor(2) ;
line->DrawLine(poiMinVal, 1., poiPlus1stdv, 1.) ;
line->DrawLine(poiMinus1stdv,0., poiMinus1stdv, 1.) ;
line->DrawLine(poiPlus1stdv ,0., poiPlus1stdv , 1.) ;
TText* text = new TText() ;
text->SetTextSize(0.04) ;
char tstring[1000] ;
sprintf( tstring, "%s = %.1f +%.1f -%.1f", new_poi_name, poiBest, (poiPlus1stdv-poiBest), (poiBest-poiMinus1stdv) ) ;
text -> DrawTextNDC( 0.15, 0.85, tstring ) ;
sprintf( tstring, "68%% interval [%.1f, %.1f]", poiMinus1stdv, poiPlus1stdv ) ;
text -> DrawTextNDC( 0.15, 0.78, tstring ) ;
char hname[1000] ;
sprintf( hname, "hscanout_%s", new_poi_name ) ;
TH1F* hsout = new TH1F( hname,"scan results",4,0.,4.) ;
double obsVal(-1.) ;
hsout->SetBinContent(1, obsVal ) ;
hsout->SetBinContent(2, poiPlus1stdv ) ;
hsout->SetBinContent(3, poiBest ) ;
hsout->SetBinContent(4, poiMinus1stdv ) ;
TAxis* xaxis = hsout->GetXaxis() ;
xaxis->SetBinLabel(1,"Observed val.") ;
xaxis->SetBinLabel(2,"Model+1sd") ;
xaxis->SetBinLabel(3,"Model") ;
xaxis->SetBinLabel(4,"Model-1sd") ;
char outrootfile[10000] ;
sprintf( outrootfile, "%s/scan-ff-%s.root", outputdir.Data(), new_poi_name ) ;
char outpdffile[10000] ;
sprintf( outpdffile, "%s/scan-ff-%s.pdf", outputdir.Data(), new_poi_name ) ;
cscan->Update() ; cscan->Draw() ;
printf("\n Saving %s\n", outpdffile ) ;
cscan->SaveAs( outpdffile ) ;
//--- save in root file
printf("\n Saving %s\n", outrootfile ) ;
TFile fout(outrootfile,"recreate") ;
graph->Write() ;
hsout->Write() ;
fout.Close() ;
delete ws ;
wstf->Close() ;
} // constrained_scan.
void StandardHistFactoryPlotsWithCategories(const char* infile = "",
const char* workspaceName = "combined",
const char* modelConfigName = "ModelConfig",
const char* dataName = "obsData"){
double nSigmaToVary=5.;
double muVal=0;
bool doFit=false;
// -------------------------------------------------------
// 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";
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;
#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;
}
// -------------------------------------------------------
// 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;
}
// -------------------------------------------------------
// now use the profile inspector
RooRealVar* obs = (RooRealVar*)mc->GetObservables()->first();
TList* list = new TList();
RooRealVar * firstPOI = dynamic_cast<RooRealVar*>(mc->GetParametersOfInterest()->first());
firstPOI->setVal(muVal);
// firstPOI->setConstant();
if(doFit){
mc->GetPdf()->fitTo(*data);
}
// -------------------------------------------------------
mc->GetNuisanceParameters()->Print("v");
int nPlotsMax = 1000;
cout <<" check expectedData by category"<<endl;
RooDataSet* simData=NULL;
RooSimultaneous* simPdf = NULL;
if(strcmp(mc->GetPdf()->ClassName(),"RooSimultaneous")==0){
cout <<"Is a simultaneous PDF"<<endl;
simPdf = (RooSimultaneous *)(mc->GetPdf());
} else {
cout <<"Is not a simultaneous PDF"<<endl;
}
if(doFit) {
RooCategory* channelCat = (RooCategory*) (&simPdf->indexCat());
TIterator* iter = channelCat->typeIterator() ;
RooCatType* tt = NULL;
tt=(RooCatType*) iter->Next();
RooAbsPdf* pdftmp = ((RooSimultaneous*)mc->GetPdf())->getPdf(tt->GetName()) ;
RooArgSet* obstmp = pdftmp->getObservables(*mc->GetObservables()) ;
obs = ((RooRealVar*)obstmp->first());
RooPlot* frame = obs->frame();
cout <<Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())<<endl;
cout << tt->GetName() << " " << channelCat->getLabel() <<endl;
data->plotOn(frame,MarkerSize(1),Cut(Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())),DataError(RooAbsData::None));
Double_t normCount = data->sumEntries(Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())) ;
pdftmp->plotOn(frame,LineWidth(2.),Normalization(normCount,RooAbsReal::NumEvent)) ;
frame->Draw();
cout <<"expected events = " << mc->GetPdf()->expectedEvents(*data->get()) <<endl;
return;
}
int nPlots=0;
if(!simPdf){
TIterator* it = mc->GetNuisanceParameters()->createIterator();
RooRealVar* var = NULL;
while( (var = (RooRealVar*) it->Next()) != NULL){
RooPlot* frame = obs->frame();
frame->SetYTitle(var->GetName());
data->plotOn(frame,MarkerSize(1));
var->setVal(0);
mc->GetPdf()->plotOn(frame,LineWidth(1.));
var->setVal(1);
mc->GetPdf()->plotOn(frame,LineColor(kRed),LineStyle(kDashed),LineWidth(1));
var->setVal(-1);
mc->GetPdf()->plotOn(frame,LineColor(kGreen),LineStyle(kDashed),LineWidth(1));
list->Add(frame);
var->setVal(0);
}
} else {
RooCategory* channelCat = (RooCategory*) (&simPdf->indexCat());
// TIterator* iter = simPdf->indexCat().typeIterator() ;
TIterator* iter = channelCat->typeIterator() ;
RooCatType* tt = NULL;
while(nPlots<nPlotsMax && (tt=(RooCatType*) iter->Next())) {
cout << "on type " << tt->GetName() << " " << endl;
// Get pdf associated with state from simpdf
RooAbsPdf* pdftmp = simPdf->getPdf(tt->GetName()) ;
// Generate observables defined by the pdf associated with this state
RooArgSet* obstmp = pdftmp->getObservables(*mc->GetObservables()) ;
// obstmp->Print();
obs = ((RooRealVar*)obstmp->first());
TIterator* it = mc->GetNuisanceParameters()->createIterator();
RooRealVar* var = NULL;
while(nPlots<nPlotsMax && (var = (RooRealVar*) it->Next())){
TCanvas* c2 = new TCanvas("c2");
RooPlot* frame = obs->frame();
frame->SetName(Form("frame%d",nPlots));
frame->SetYTitle(var->GetName());
cout <<Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())<<endl;
cout << tt->GetName() << " " << channelCat->getLabel() <<endl;
data->plotOn(frame,MarkerSize(1),Cut(Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())),DataError(RooAbsData::None));
Double_t normCount = data->sumEntries(Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())) ;
if(strcmp(var->GetName(),"Lumi")==0){
cout <<"working on lumi"<<endl;
var->setVal(w->var("nominalLumi")->getVal());
var->Print();
} else{
var->setVal(0);
}
// w->allVars().Print("v");
// mc->GetNuisanceParameters()->Print("v");
// pdftmp->plotOn(frame,LineWidth(2.));
// mc->GetPdf()->plotOn(frame,LineWidth(2.),Slice(*channelCat,tt->GetName()),ProjWData(*data));
//pdftmp->plotOn(frame,LineWidth(2.),Slice(*channelCat,tt->GetName()),ProjWData(*data));
normCount = pdftmp->expectedEvents(*obs);
pdftmp->plotOn(frame,LineWidth(2.),Normalization(normCount,RooAbsReal::NumEvent)) ;
if(strcmp(var->GetName(),"Lumi")==0){
cout <<"working on lumi"<<endl;
var->setVal(w->var("nominalLumi")->getVal()+0.05);
var->Print();
} else{
var->setVal(nSigmaToVary);
}
// pdftmp->plotOn(frame,LineColor(kRed),LineStyle(kDashed),LineWidth(2));
// mc->GetPdf()->plotOn(frame,LineColor(kRed),LineStyle(kDashed),LineWidth(2.),Slice(*channelCat,tt->GetName()),ProjWData(*data));
//pdftmp->plotOn(frame,LineColor(kRed),LineStyle(kDashed),LineWidth(2.),Slice(*channelCat,tt->GetName()),ProjWData(*data));
normCount = pdftmp->expectedEvents(*obs);
pdftmp->plotOn(frame,LineWidth(2.),LineColor(kRed),LineStyle(kDashed),Normalization(normCount,RooAbsReal::NumEvent)) ;
if(strcmp(var->GetName(),"Lumi")==0){
cout <<"working on lumi"<<endl;
var->setVal(w->var("nominalLumi")->getVal()-0.05);
var->Print();
} else{
var->setVal(-nSigmaToVary);
}
// pdftmp->plotOn(frame,LineColor(kGreen),LineStyle(kDashed),LineWidth(2));
// mc->GetPdf()->plotOn(frame,LineColor(kGreen),LineStyle(kDashed),LineWidth(2),Slice(*channelCat,tt->GetName()),ProjWData(*data));
//pdftmp->plotOn(frame,LineColor(kGreen),LineStyle(kDashed),LineWidth(2),Slice(*channelCat,tt->GetName()),ProjWData(*data));
normCount = pdftmp->expectedEvents(*obs);
pdftmp->plotOn(frame,LineWidth(2.),LineColor(kGreen),LineStyle(kDashed),Normalization(normCount,RooAbsReal::NumEvent)) ;
// set them back to normal
if(strcmp(var->GetName(),"Lumi")==0){
cout <<"working on lumi"<<endl;
var->setVal(w->var("nominalLumi")->getVal());
var->Print();
} else{
var->setVal(0);
}
list->Add(frame);
// quit making plots
++nPlots;
frame->Draw();
c2->SaveAs(Form("%s_%s_%s.pdf",tt->GetName(),obs->GetName(),var->GetName()));
delete c2;
}
}
}
// -------------------------------------------------------
// now make plots
TCanvas* c1 = new TCanvas("c1","ProfileInspectorDemo",800,200);
if(list->GetSize()>4){
double n = list->GetSize();
int nx = (int)sqrt(n) ;
int ny = TMath::CeilNint(n/nx);
nx = TMath::CeilNint( sqrt(n) );
c1->Divide(ny,nx);
} else
c1->Divide(list->GetSize());
for(int i=0; i<list->GetSize(); ++i){
c1->cd(i+1);
list->At(i)->Draw();
}
}
double RunHypoTest(char *smwwFileName, char *ttbarFileName, char *wp3jetsFileName, char *wp4jetsFileName, char *opsFileName, char *outputFileName, double lambda) {
TFile *smwwFile = new TFile(smwwFileName);
TFile *ttbarFile = new TFile(ttbarFileName);
TFile *wp3jetsFile = new TFile(wp3jetsFileName);
TFile *wp4jetsFile = new TFile(wp4jetsFileName);
TFile *opsFile = new TFile(opsFileName);
TFile *outputFile = new TFile(outputFileName, "UPDATE");
TH1F *smww = (TH1F*)smwwFile->Get(WW_MASS_HISTOGRAM_NAME);
TH1F *ttbar = (TH1F*)ttbarFile->Get(WW_MASS_HISTOGRAM_NAME);
TH1F *wp3jets = (TH1F*)wp3jetsFile->Get(WW_MASS_HISTOGRAM_NAME);
TH1F *wp4jets = (TH1F*)wp4jetsFile->Get(WW_MASS_HISTOGRAM_NAME);
//Histogram of ww-scattering with effective operator contributions
TH1F *ops = (TH1F*)opsFile->Get(WW_MASS_HISTOGRAM_NAME);
RooRealVar *mww = new RooRealVar("mww", "M_{WW}", 600, 2500, "GeV");
RooDataHist smData("smData", "smData", RooArgList(*mww), smww);
RooDataHist opsData("opsData", "opsData", RooArgList(*mww), ops);
RooDataHist ttbarData("ttbarData", "ttbarData", RooArgList(*mww), ttbar);
RooDataHist wp3jetsData("wp3jetsData", "wp3jetsData", RooArgList(*mww), wp3jets);
RooDataHist wp4jetsData("wp4jetsData", "wp4jetsData", RooArgList(*mww), wp4jets);
/*
RooAbsPdf *opsModel;
if (lambda == 400) {
opsModel = SpecialCaseModel(&opsData, mww, (char*)"ops");
}
else {
opsModel = MakeModel(&opsData, mww, (char*)"ops");
}*/
RooAbsPdf *opsModel = MakeModel(&opsData, mww, (char*)"ops");
//RooPlot *xframe = mww->frame();
//opsData.plotOn(xframe);
//opsModel->plotOn(xframe);
//printf("Chi-squared for lambda = %f: = %f\n", lambda, xframe->chiSquare("opsModel", "opsData", 3));
RooAbsPdf *smModel = MakeModelNoSignal(&smData, mww, (char*)"sm");
RooAbsPdf *ttbarModel = MakeModelNoSignal(&ttbarData, mww, (char*)"ttbar");
RooAbsPdf *wp3jetsModel = MakeModelNoSignal(&wp3jetsData, mww, (char*)"wp3jets");
RooAbsPdf *wp4jetsModel = MakeModelNoSignal(&wp4jetsData, mww, (char*)"wp4jets");
TCanvas *canvas = new TCanvas(opsFileName);
RooPlot *frame = mww->frame();
frame->SetTitle("");
//smData.plotOn(frame, RooFit::LineColor(kBlack), RooFit::Name("smData"));
//smModel->plotOn(frame, RooFit::LineColor(kBlue), RooFit::Name("smModel"));
//ttbarModel->plotOn(frame, RooFit::LineColor(kRed), RooFit::Name("ttbarModel"));
//wp3jetsModel->plotOn(frame, RooFit::LineColor(kYellow), RooFit::Name("wpjetsModel"));
opsData.plotOn(frame);
opsModel->plotOn(frame, RooFit::LineColor(kBlue), RooFit::Name("opsModel"));
//leg->AddEntry(frame->findObject("smModel"), "SM Model", "lep");
//leg->AddEntry(frame->findObject("ttbarModel"), "TTBar Model", "lep");
//leg->AddEntry(frame->findObject("wp3jetsModel"), "WP3Jets Model", "lep");
//leg->AddEntry(frame->findObject("opsModel"), "Effective Operator Model", "lep");
frame->Draw();
canvas->Write();
Double_t ww_x = WW_CROSS_SECTION * smww->GetEntries();
Double_t ttbar_x = TTBAR_CROSS_SECTION * ttbar->GetEntries();
Double_t wp3jets_x = WP3JETS_CROSS_SECTION * wp3jets->GetEntries();
Double_t wp4jets_x = WP4JETS_CROSS_SECTION * wp4jets->GetEntries();
Double_t ttbar_weight = ttbar_x/(ttbar_x + wp3jets_x + wp4jets_x + ww_x);
Double_t wp3jets_weight = wp3jets_x/(wp3jets_x + ttbar_x + ww_x);
Double_t wp4jets_weight = wp4jets_x/(wp4jets_x + ttbar_x + ww_x);
RooRealVar *ttbarWeight = new RooRealVar("ttbarWeight", "ttbarWeight", 0.0, 1.0, ttbar_weight);
RooRealVar *wp3jetsWeight = new RooRealVar("wp3jetsWeight", "wp3jetsWeight", 0.0, 1.0, wp3jets_weight);
RooRealVar *wp4jetsWeight = new RooRealVar("wp4jetsWeight", "wp4jetsWeight", 0.0, 1.0, wp4jets_weight);
ttbarWeight->setConstant();
wp3jetsWeight->setConstant();
wp4jetsWeight->setConstant();
RooRealVar *mu = new RooRealVar("mu", "mu", 0.0, 1.0, "");
RooAddPdf *wwModel = new RooAddPdf("wwModel", "u*effective_ww + (1-u)*SM_WW",
RooArgList(*opsModel, *smModel), RooArgList(*mu), kTRUE);
RooAddPdf *model = new RooAddPdf("model", "Full model",
RooArgList(*ttbarModel, *wp3jetsModel, *wp4jetsModel, *wwModel),
RooArgList(*ttbarWeight, *wp3jetsWeight, *wp4jetsWeight), kTRUE);
//Generate data under the alternate hypothesis
mu->setVal(1.0);
int nTestSetEvents = WW_CROSS_SECTION * TOTAL_INTEGRATED_LUMINOSITY;
RooAbsData *generatedData = model->generate(*mww, nTestSetEvents);
TCanvas *canvas2 = new TCanvas("CombinedModels");
RooPlot *frame2 = mww->frame();
//wwModel->plotOn(frame2, RooFit::LineColor(kRed), RooFit::Name("wwModel"));
//generatedData->plotOn(frame2);
mu->setVal(0.0);
model->plotOn(frame2, RooFit::LineColor(kBlue), RooFit::Name("nullModel"));
mu->setVal(1.0);
model->plotOn(frame2, RooFit::LineColor(kRed), RooFit::Name("altModel"));
TLegend *leg2 = new TLegend(0.65,0.73,0.86,0.87);
//leg->AddEntry(frame2->findObject("wwModel"), "SM WW Scattering model with background",
// "lep");
leg2->AddEntry(frame2->findObject("nullModel"),
"SM + Background", "lep");
leg2->AddEntry(frame2->findObject("altModel"),
"Effective Operator + Background", "lep");
frame2->SetTitle("");
frame2->GetXaxis()->SetTitle("M_{WW} (GeV)");
frame2->GetYaxis()->SetTitle("");
frame2->Draw();
leg2->Draw();
canvas2->Write();
outputFile->Close();
RooArgSet poi(*mu);
RooArgSet *nullParams = (RooArgSet*) poi.snapshot();
nullParams->setRealValue("mu", 0.0);
RooStats::ProfileLikelihoodCalculator plc(*generatedData, *model, poi, 0.05, nullParams);
RooStats::HypoTestResult* htr = plc.GetHypoTest();
std::cerr << "P Value = " << htr->NullPValue() << "\n";
return htr->Significance();
}
void eregtesting_13TeV_Pi0(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_Pi0/%s_%s",gammaDir.Data(),EEorEB.Data());
TString dirname_fits = TString::Format("ereg_test_plots_Pi0/%s_%s/fits",gammaDir.Data(),EEorEB.Data());
gSystem->mkdir(dirname,true);
gSystem->mkdir(dirname_fits,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_Pi0/%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_MultiPion_FlatPt-1To15/Gun_FlatPt1to15_MultiPion_withPhotonPtFilter_pythia8/photons_20171008_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");
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");
//2D map of Eraw/Etrue and Ecor/Etrue of ieta and iphi
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");
// fill the E/Etrue vs. eta with each point taken from fits
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((gammaID==1) ? 0.95 : 0.9 , (gammaID==1) ? 1.05 : 1.1);
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");
// fill the E/Etrue vs. eta with each point taken from fits
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((gammaID==1) ? 0.9 : 0.8,1.0);
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( (gammaID==1) ? 0.9 : 0.8,1.0);
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((gammaID==1) ? 0.95 : 0.9,(gammaID==1) ? 1.05 : 1.1);
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((gammaID==1) ? 0.9 : 0.8,1.0);
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((gammaID==1) ? 0.9 : 0.8,1.0);
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()->SetRangeUser(0,2.0);
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()->SetRangeUser(0,2.0);
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()->SetRangeUser(0,2.0);
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()->SetRangeUser(0,2.0);
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(0.0,2.0);
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(0.0,2.0);
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(); */
}
TH1D* runSig(RooWorkspace* ws,
const char* modelConfigName = "ModelConfig",
const char* dataName = "obsData",
const char* asimov1DataName = "asimovData_1",
const char* conditional1Snapshot = "conditionalGlobs_1",
const char* nominalSnapshot = "nominalGlobs")
{
string defaultMinimizer = "Minuit"; // or "Minuit"
int defaultStrategy = 2; // Minimization strategy. 0-2. 0 = fastest, least robust. 2 = slowest, most robust
double mu_profile_value = 1; // mu value to profile the obs data at wbefore generating the expected
bool doUncap = 1; // uncap p0
bool doInj = 0; // setup the poi for injection study (zero is faster if you're not)
bool doMedian = 1; // compute median significance
bool isBlind = 0; // Dont look at observed data
bool doConditional = !isBlind; // do conditional expected data
bool doObs = !isBlind; // compute observed significance
TStopwatch timer;
timer.Start();
if (!ws)
{
cout << "ERROR::Workspace is NULL!" << endl;
return NULL;
}
ModelConfig* mc = (ModelConfig*)ws->obj(modelConfigName);
if (!mc)
{
cout << "ERROR::ModelConfig: " << modelConfigName << " doesn't exist!" << endl;
return NULL;
}
RooDataSet* data = (RooDataSet*)ws->data(dataName);
if (!data)
{
cout << "ERROR::Dataset: " << dataName << " doesn't exist!" << endl;
return NULL;
}
mc->GetNuisanceParameters()->Print("v");
//RooNLLVar::SetIgnoreZeroEntries(1);
ROOT::Math::MinimizerOptions::SetDefaultMinimizer(defaultMinimizer.c_str());
ROOT::Math::MinimizerOptions::SetDefaultStrategy(defaultStrategy);
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();
RooAbsPdf* pdf_temp = mc->GetPdf();
string condSnapshot(conditional1Snapshot);
RooArgSet nuis_tmp2 = *mc->GetNuisanceParameters();
RooNLLVar* obs_nll = doObs ? (RooNLLVar*)pdf_temp->createNLL(*data, Constrain(nuis_tmp2)) : NULL;
RooDataSet* asimovData1 = (RooDataSet*)ws->data(asimov1DataName);
if (!asimovData1)
{
cout << "Asimov data doesn't exist! Please, allow me to build one for you..." << endl;
string mu_str, mu_prof_str;
asimovData1 = makeAsimovData(mc, doConditional, ws, obs_nll, 1, &mu_str, &mu_prof_str, mu_profile_value, true);
condSnapshot="conditionalGlobs"+mu_prof_str;
//makeAsimovData(mc, true, ws, mc->GetPdf(), data, 0);
//ws->Print();
//asimovData1 = (RooDataSet*)ws->data("asimovData_1");
}
if (!doUncap) mu->setRange(0, 40);
else mu->setRange(-40, 40);
RooAbsPdf* pdf = mc->GetPdf();
RooArgSet nuis_tmp1 = *mc->GetNuisanceParameters();
RooNLLVar* asimov_nll = (RooNLLVar*)pdf->createNLL(*asimovData1, Constrain(nuis_tmp1));
//do asimov
mu->setVal(1);
mu->setConstant(0);
if (!doInj) mu->setConstant(1);
int status,sign;
double med_sig=0,obs_sig=0,asimov_q0=0,obs_q0=0;
if (doMedian)
{
ws->loadSnapshot(condSnapshot.c_str());
if (doInj) ws->loadSnapshot("conditionalNuis_inj");
else ws->loadSnapshot("conditionalNuis_1");
mc->GetGlobalObservables()->Print("v");
mu->setVal(0);
mu->setConstant(1);
status = minimize(asimov_nll, ws);
if (status >= 0) cout << "Success!" << endl;
if (status < 0)
{
cout << "Retrying with conditional snapshot at mu=1" << endl;
ws->loadSnapshot("conditionalNuis_0");
status = minimize(asimov_nll, ws);
if (status >= 0) cout << "Success!" << endl;
}
double asimov_nll_cond = asimov_nll->getVal();
mu->setVal(1);
if (doInj) ws->loadSnapshot("conditionalNuis_inj");
else ws->loadSnapshot("conditionalNuis_1");
if (doInj) mu->setConstant(0);
status = minimize(asimov_nll, ws);
if (status >= 0) cout << "Success!" << endl;
if (status < 0)
{
cout << "Retrying with conditional snapshot at mu=1" << endl;
ws->loadSnapshot("conditionalNuis_0");
status = minimize(asimov_nll, ws);
if (status >= 0) cout << "Success!" << endl;
}
double asimov_nll_min = asimov_nll->getVal();
asimov_q0 = 2*(asimov_nll_cond - asimov_nll_min);
if (doUncap && mu->getVal() < 0) asimov_q0 = -asimov_q0;
sign = int(asimov_q0 != 0 ? asimov_q0/fabs(asimov_q0) : 0);
med_sig = sign*sqrt(fabs(asimov_q0));
ws->loadSnapshot(nominalSnapshot);
}
if (doObs)
{
ws->loadSnapshot("conditionalNuis_0");
mu->setVal(0);
mu->setConstant(1);
status = 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_nll_cond = obs_nll->getVal();
//ws->loadSnapshot("ucmles");
mu->setConstant(0);
status = 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_nll_min = obs_nll->getVal();
obs_q0 = 2*(obs_nll_cond - obs_nll_min);
if (doUncap && mu->getVal() < 0) obs_q0 = -obs_q0;
sign = int(obs_q0 == 0 ? 0 : obs_q0 / fabs(obs_q0));
if (!doUncap && ((obs_q0 < 0 && obs_q0 > -0.1) || mu->getVal() < 0.001)) obs_sig = 0;
else obs_sig = sign*sqrt(fabs(obs_q0));
}
cout << "obs: " << obs_sig << endl;
cout << "Observed significance: " << obs_sig << endl;
if (med_sig)
{
cout << "Median test stat val: " << asimov_q0 << endl;
cout << "Median significance: " << med_sig << endl;
}
TH1D* h_hypo = new TH1D("hypo","hypo",2,0,2);
h_hypo->SetBinContent(1, obs_sig);
h_hypo->SetBinContent(2, med_sig);
timer.Stop();
timer.Print();
return h_hypo;
}
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
////////////////////////////////////////////////////////////
const char* filename = "";
if (!strcmp(infile,"")) {
filename = "results/example_combined_GaussExample_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;
#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;
}
/////////////////////////////////////////////////////////////
// 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)
}
// in case of toyMC make a nnuisance pdf
if (integrationType.Contains("TOYMC") ) {
RooAbsPdf * nuisPdf = RooStats::MakeNuisancePdf(*mc, "nuisance_pdf");
cout << "using TOYMC integration: make nuisance pdf from the model " << std::endl;
nuisPdf->Print();
bayesianCalc.ForceNuisancePdf(*nuisPdf);
scanPosterior = true; // for ToyMC the posterior is scanned anyway so used given points
}
// compute interval by scanning the posterior function
if (scanPosterior)
bayesianCalc.SetScanOfPosterior(nScanPoints);
RooRealVar* poi = (RooRealVar*) mc->GetParametersOfInterest()->first();
if (maxPOI != -999 && maxPOI > poi->getMin())
poi->setMax(maxPOI);
SimpleInterval* interval = bayesianCalc.GetInterval();
// print out the iterval on the first Parameter of Interest
cout << "\n95% interval on " << poi->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 ws_cls_hybrid1_ag( const char* wsfile = "output-files/expected-ws-lm9-2BL.root", bool isBgonlyStudy=false, double poiVal = 150.0, int nToys=100, bool makeTtree=true, int verbLevel=0 ) {
TTree* toytt(0x0) ;
TFile* ttfile(0x0) ;
int tt_gen_Nsig ;
int tt_gen_Nsb ;
int tt_gen_Nsig_sl ;
int tt_gen_Nsb_sl ;
int tt_gen_Nsig_ldp ;
int tt_gen_Nsb_ldp ;
int tt_gen_Nsig_ee ;
int tt_gen_Nsb_ee ;
int tt_gen_Nsig_mm ;
int tt_gen_Nsb_mm ;
double tt_testStat ;
double tt_dataTestStat ;
double tt_hypo_mu_susy_sig ;
char ttname[1000] ;
char tttitle[1000] ;
if ( makeTtree ) {
ttfile = gDirectory->GetFile() ;
if ( ttfile == 0x0 ) { printf("\n\n\n *** asked for a ttree but no open file???\n\n") ; return ; }
if ( isBgonlyStudy ) {
sprintf( ttname, "toytt_%.0f_bgo", poiVal ) ;
sprintf( tttitle, "Toy study for background only, mu_susy_sig = %.0f", poiVal ) ;
} else {
sprintf( ttname, "toytt_%.0f_spb", poiVal ) ;
sprintf( tttitle, "Toy study for signal+background, mu_susy_sig = %.0f", poiVal ) ;
}
printf("\n\n Creating TTree : %s : %s\n\n", ttname, tttitle ) ;
gDirectory->pwd() ;
gDirectory->ls() ;
toytt = new TTree( ttname, tttitle ) ;
gDirectory->ls() ;
toytt -> Branch( "gen_Nsig" , &tt_gen_Nsig , "gen_Nsig/I" ) ;
toytt -> Branch( "gen_Nsb" , &tt_gen_Nsb , "gen_Nsb/I" ) ;
toytt -> Branch( "gen_Nsig_sl" , &tt_gen_Nsig_sl , "gen_Nsig_sl/I" ) ;
toytt -> Branch( "gen_Nsb_sl" , &tt_gen_Nsb_sl , "gen_Nsb_sl/I" ) ;
toytt -> Branch( "gen_Nsig_ldp" , &tt_gen_Nsig_ldp , "gen_Nsig_ldp/I" ) ;
toytt -> Branch( "gen_Nsb_ldp" , &tt_gen_Nsb_ldp , "gen_Nsb_ldp/I" ) ;
toytt -> Branch( "gen_Nsig_ee" , &tt_gen_Nsig_ee , "gen_Nsig_ee/I" ) ;
toytt -> Branch( "gen_Nsb_ee" , &tt_gen_Nsb_ee , "gen_Nsb_ee/I" ) ;
toytt -> Branch( "gen_Nsig_mm" , &tt_gen_Nsig_mm , "gen_Nsig_mm/I" ) ;
toytt -> Branch( "gen_Nsb_mm" , &tt_gen_Nsb_mm , "gen_Nsb_mm/I" ) ;
toytt -> Branch( "testStat" , &tt_testStat , "testStat/D" ) ;
toytt -> Branch( "dataTestStat" , &tt_dataTestStat , "dataTestStat/D" ) ;
toytt -> Branch( "hypo_mu_susy_sig" , &tt_hypo_mu_susy_sig , "hypo_mu_susy_sig/D" ) ;
}
//--- Tell RooFit to shut up about anything less important than an ERROR.
RooMsgService::instance().setGlobalKillBelow(RooFit::ERROR) ;
random_ng = new TRandom2(12345) ;
/// char sel[100] ;
/// if ( strstr( wsfile, "1BL" ) != 0 ) {
/// sprintf( sel, "1BL" ) ;
/// } else if ( strstr( wsfile, "2BL" ) != 0 ) {
/// sprintf( sel, "2BL" ) ;
/// } else if ( strstr( wsfile, "3B" ) != 0 ) {
/// sprintf( sel, "3B" ) ;
/// } else if ( strstr( wsfile, "1BT" ) != 0 ) {
/// sprintf( sel, "1BT" ) ;
/// } else if ( strstr( wsfile, "2BT" ) != 0 ) {
/// sprintf( sel, "2BT" ) ;
/// } else {
/// printf("\n\n\n *** can't figure out which selection this is. I quit.\n\n" ) ;
/// return ;
/// }
/// printf("\n\n selection is %s\n\n", sel ) ;
TFile* wstf = new TFile( wsfile ) ;
RooWorkspace* ws = dynamic_cast<RooWorkspace*>( wstf->Get("ws") );
ws->Print() ;
RooDataSet* rds = (RooDataSet*) ws->obj( "ra2b_observed_rds" ) ;
printf("\n\n\n ===== RooDataSet ====================\n\n") ;
rds->Print() ;
rds->printMultiline(cout, 1, kTRUE, "") ;
ModelConfig* modelConfig = (ModelConfig*) ws->obj( "SbModel" ) ;
RooAbsPdf* likelihood = modelConfig->GetPdf() ;
const RooArgSet* nuisanceParameters = modelConfig->GetNuisanceParameters() ;
RooRealVar* rrv_mu_susy_sig = ws->var("mu_susy_sig") ;
if ( rrv_mu_susy_sig == 0x0 ) {
printf("\n\n\n *** can't find mu_susy_sig in workspace. Quitting.\n\n\n") ;
return ;
}
//// printf("\n\n\n ===== Doing a fit ====================\n\n") ;
//// RooFitResult* preFitResult = likelihood->fitTo( *rds, Save(true) ) ;
//// const RooArgList preFitFloatVals = preFitResult->floatParsFinal() ;
//// {
//// TIterator* parIter = preFitFloatVals.createIterator() ;
//// while ( RooRealVar* par = (RooRealVar*) parIter->Next() ) {
//// printf(" %20s : %8.2f\n", par->GetName(), par->getVal() ) ;
//// }
//// }
//--- Get pointers to the model predictions of the observables.
rfv_n_sig = ws->function("n_sig") ;
rfv_n_sb = ws->function("n_sb") ;
rfv_n_sig_sl = ws->function("n_sig_sl") ;
rfv_n_sb_sl = ws->function("n_sb_sl") ;
rfv_n_sig_ldp = ws->function("n_sig_ldp") ;
rfv_n_sb_ldp = ws->function("n_sb_ldp") ;
rfv_n_sig_ee = ws->function("n_sig_ee") ;
rfv_n_sb_ee = ws->function("n_sb_ee") ;
rfv_n_sig_mm = ws->function("n_sig_mm") ;
rfv_n_sb_mm = ws->function("n_sb_mm") ;
if ( rfv_n_sig == 0x0 ) { printf("\n\n\n *** can't find n_sig in workspace. Quitting.\n\n\n") ; return ; }
if ( rfv_n_sb == 0x0 ) { printf("\n\n\n *** can't find n_sb in workspace. Quitting.\n\n\n") ; return ; }
if ( rfv_n_sig_sl == 0x0 ) { printf("\n\n\n *** can't find n_sig_sl in workspace. Quitting.\n\n\n") ; return ; }
if ( rfv_n_sb_sl == 0x0 ) { printf("\n\n\n *** can't find n_sb_sl in workspace. Quitting.\n\n\n") ; return ; }
if ( rfv_n_sig_ldp == 0x0 ) { printf("\n\n\n *** can't find n_sig_ldp in workspace. Quitting.\n\n\n") ; return ; }
if ( rfv_n_sb_ldp == 0x0 ) { printf("\n\n\n *** can't find n_sb_ldp in workspace. Quitting.\n\n\n") ; return ; }
if ( rfv_n_sig_ee == 0x0 ) { printf("\n\n\n *** can't find n_sig_ee in workspace. Quitting.\n\n\n") ; return ; }
if ( rfv_n_sb_ee == 0x0 ) { printf("\n\n\n *** can't find n_sb_ee in workspace. Quitting.\n\n\n") ; return ; }
if ( rfv_n_sig_mm == 0x0 ) { printf("\n\n\n *** can't find n_sig_mm in workspace. Quitting.\n\n\n") ; return ; }
if ( rfv_n_sb_mm == 0x0 ) { printf("\n\n\n *** can't find n_sb_mm in workspace. Quitting.\n\n\n") ; return ; }
//--- Get pointers to the observables.
const RooArgSet* dsras = rds->get() ;
TIterator* obsIter = dsras->createIterator() ;
while ( RooRealVar* obs = (RooRealVar*) obsIter->Next() ) {
if ( strcmp( obs->GetName(), "Nsig" ) == 0 ) { rrv_Nsig = obs ; }
if ( strcmp( obs->GetName(), "Nsb" ) == 0 ) { rrv_Nsb = obs ; }
if ( strcmp( obs->GetName(), "Nsig_sl" ) == 0 ) { rrv_Nsig_sl = obs ; }
if ( strcmp( obs->GetName(), "Nsb_sl" ) == 0 ) { rrv_Nsb_sl = obs ; }
if ( strcmp( obs->GetName(), "Nsig_ldp" ) == 0 ) { rrv_Nsig_ldp = obs ; }
if ( strcmp( obs->GetName(), "Nsb_ldp" ) == 0 ) { rrv_Nsb_ldp = obs ; }
if ( strcmp( obs->GetName(), "Nsig_ee" ) == 0 ) { rrv_Nsig_ee = obs ; }
if ( strcmp( obs->GetName(), "Nsb_ee" ) == 0 ) { rrv_Nsb_ee = obs ; }
if ( strcmp( obs->GetName(), "Nsig_mm" ) == 0 ) { rrv_Nsig_mm = obs ; }
if ( strcmp( obs->GetName(), "Nsb_mm" ) == 0 ) { rrv_Nsb_mm = obs ; }
}
if ( rrv_Nsig == 0x0 ) { printf("\n\n\n *** can't find Nsig in dataset. Quitting.\n\n\n") ; return ; }
if ( rrv_Nsb == 0x0 ) { printf("\n\n\n *** can't find Nsb in dataset. Quitting.\n\n\n") ; return ; }
if ( rrv_Nsig_sl == 0x0 ) { printf("\n\n\n *** can't find Nsig_sl in dataset. Quitting.\n\n\n") ; return ; }
if ( rrv_Nsb_sl == 0x0 ) { printf("\n\n\n *** can't find Nsb_sl in dataset. Quitting.\n\n\n") ; return ; }
if ( rrv_Nsig_ldp == 0x0 ) { printf("\n\n\n *** can't find Nsig_ldp in dataset. Quitting.\n\n\n") ; return ; }
if ( rrv_Nsb_ldp == 0x0 ) { printf("\n\n\n *** can't find Nsb_ldp in dataset. Quitting.\n\n\n") ; return ; }
if ( rrv_Nsig_ee == 0x0 ) { printf("\n\n\n *** can't find Nsig_ee in dataset. Quitting.\n\n\n") ; return ; }
if ( rrv_Nsb_ee == 0x0 ) { printf("\n\n\n *** can't find Nsb_ee in dataset. Quitting.\n\n\n") ; return ; }
if ( rrv_Nsig_mm == 0x0 ) { printf("\n\n\n *** can't find Nsig_mm in dataset. Quitting.\n\n\n") ; return ; }
if ( rrv_Nsb_mm == 0x0 ) { printf("\n\n\n *** can't find Nsb_mm in dataset. Quitting.\n\n\n") ; return ; }
printf("\n\n\n === Model values for observables\n\n") ;
printObservables() ;
//--- save the actual values of the observables.
saveObservables() ;
//--- evaluate the test stat on the data: fit with susy floating.
rrv_mu_susy_sig->setVal( poiVal ) ;
rrv_mu_susy_sig->setConstant( kTRUE ) ;
printf("\n\n\n ====== Fitting the data with susy fixed.\n\n") ;
RooFitResult* dataFitResultSusyFixed = likelihood->fitTo(*rds, Save(true));
int dataSusyFixedFitCovQual = dataFitResultSusyFixed->covQual() ;
if ( dataSusyFixedFitCovQual != 3 ) { printf("\n\n\n *** Failed fit! Cov qual %d. Quitting.\n\n", dataSusyFixedFitCovQual ) ; return ; }
double dataFitSusyFixedNll = dataFitResultSusyFixed->minNll() ;
rrv_mu_susy_sig->setVal( 0.0 ) ;
rrv_mu_susy_sig->setConstant( kFALSE ) ;
printf("\n\n\n ====== Fitting the data with susy floating.\n\n") ;
RooFitResult* dataFitResultSusyFloat = likelihood->fitTo(*rds, Save(true));
int dataSusyFloatFitCovQual = dataFitResultSusyFloat->covQual() ;
if ( dataSusyFloatFitCovQual != 3 ) { printf("\n\n\n *** Failed fit! Cov qual %d. Quitting.\n\n", dataSusyFloatFitCovQual ) ; return ; }
double dataFitSusyFloatNll = dataFitResultSusyFloat->minNll() ;
double dataTestStat = 2.*( dataFitSusyFixedNll - dataFitSusyFloatNll) ;
printf("\n\n\n Data value of test stat : %8.2f\n", dataTestStat ) ;
printf("\n\n\n === Nuisance parameters\n\n") ;
{
int npi(0) ;
TIterator* npIter = nuisanceParameters->createIterator() ;
while ( RooRealVar* np_rrv = (RooRealVar*) npIter->Next() ) {
np_initial_val[npi] = np_rrv->getVal() ; //--- I am assuming that the order of the NPs in the iterator does not change.
TString npname( np_rrv->GetName() ) ;
npname.ReplaceAll("_prim","") ;
RooAbsReal* np_rfv = ws->function( npname ) ;
TString pdfname( np_rrv->GetName() ) ;
pdfname.ReplaceAll("_prim","") ;
pdfname.Prepend("pdf_") ;
RooAbsPdf* np_pdf = ws->pdf( pdfname ) ;
if ( np_pdf == 0x0 ) { printf("\n\n *** Can't find nuisance parameter pdf with name %s.\n\n", pdfname.Data() ) ; }
if ( np_rfv != 0x0 ) {
printf(" %20s : %8.2f , %20s, %8.2f\n", np_rrv->GetName(), np_rrv->getVal(), np_rfv->GetName(), np_rfv->getVal() ) ;
} else {
printf(" %20s : %8.2f\n", np_rrv->GetName(), np_rrv->getVal() ) ;
}
npi++ ;
} // np_rrv iterator.
np_count = npi ;
}
tt_dataTestStat = dataTestStat ;
tt_hypo_mu_susy_sig = poiVal ;
printf("\n\n\n === Doing the toys\n\n") ;
int nToyOK(0) ;
int nToyWorseThanData(0) ;
for ( int ti=0; ti<nToys; ti++ ) {
printf("\n\n\n ======= Toy %4d\n\n\n", ti ) ;
//--- 1) pick values for the nuisance parameters from the PDFs and fix them.
{
TIterator* npIter = nuisanceParameters->createIterator() ;
while ( RooRealVar* np_rrv = (RooRealVar*) npIter->Next() ) {
TString pdfname( np_rrv->GetName() ) ;
pdfname.ReplaceAll("_prim","") ;
pdfname.Prepend("pdf_") ;
RooAbsPdf* np_pdf = ws->pdf( pdfname ) ;
if ( np_pdf == 0x0 ) { printf("\n\n *** Can't find nuisance parameter pdf with name %s.\n\n", pdfname.Data() ) ; return ; }
RooDataSet* nprds = np_pdf->generate( RooArgSet(*np_rrv) ,1) ;
const RooArgSet* npdsras = nprds->get() ;
TIterator* valIter = npdsras->createIterator() ;
RooRealVar* val = (RooRealVar*) valIter->Next() ;
//--- reset the value of the nuisance parameter and fix it for the toy model definition fit.
np_rrv->setVal( val->getVal() ) ;
np_rrv->setConstant( kTRUE ) ;
TString npname( np_rrv->GetName() ) ;
npname.ReplaceAll("_prim","") ;
RooAbsReal* np_rfv = ws->function( npname ) ;
if ( verbLevel > 0 ) {
if ( np_rfv != 0x0 ) {
printf(" %20s : %8.2f , %15s, %8.3f\n", val->GetName(), val->getVal(), np_rfv->GetName(), np_rfv->getVal() ) ;
} else if ( strstr( npname.Data(), "eff_sf" ) != 0 ) {
np_rfv = ws->function( "eff_sf_sig" ) ;
RooAbsReal* np_rfv2 = ws->function( "eff_sf_sb" ) ;
printf(" %20s : %8.2f , %15s, %8.3f , %15s, %8.3f\n", val->GetName(), val->getVal(), np_rfv->GetName(), np_rfv->getVal(), np_rfv2->GetName(), np_rfv2->getVal() ) ;
} else if ( strstr( npname.Data(), "sf_ll" ) != 0 ) {
np_rfv = ws->function( "sf_ee" ) ;
RooAbsReal* np_rfv2 = ws->function( "sf_mm" ) ;
printf(" %20s : %8.2f , %15s, %8.3f , %15s, %8.3f\n", val->GetName(), val->getVal(), np_rfv->GetName(), np_rfv->getVal(), np_rfv2->GetName(), np_rfv2->getVal() ) ;
} else {
printf(" %20s : %8.2f\n", val->GetName(), val->getVal() ) ;
}
}
delete nprds ;
} // np_rrv iterator
}
//--- 2) Fit the dataset with these values for the nuisance parameters.
if ( isBgonlyStudy ) {
//-- fit with susy yield fixed to zero.
rrv_mu_susy_sig -> setVal( 0. ) ;
if ( verbLevel > 0 ) { printf("\n Setting mu_susy_sig to zero.\n\n") ; }
} else {
//-- fit with susy yield fixed to predicted value.
rrv_mu_susy_sig -> setVal( poiVal ) ;
if ( verbLevel > 0 ) { printf("\n Setting mu_susy_sig to %8.1f.\n\n", poiVal) ; }
}
rrv_mu_susy_sig->setConstant( kTRUE ) ;
if ( verbLevel > 0 ) {
printf("\n\n") ;
printf(" Fitting with these values for the observables to define the model for toy generation.\n") ;
rds->printMultiline(cout, 1, kTRUE, "") ;
printf("\n\n") ;
}
RooFitResult* toyModelDefinitionFitResult(0x0) ;
if ( verbLevel < 2 ) {
toyModelDefinitionFitResult = likelihood->fitTo(*rds, Save(true), PrintLevel(-1));
} else {
toyModelDefinitionFitResult = likelihood->fitTo(*rds, Save(true));
}
int toyModelDefFitCovQual = toyModelDefinitionFitResult->covQual() ;
if ( verbLevel > 0 ) { printf("\n fit covariance matrix quality: %d\n\n", toyModelDefFitCovQual ) ; }
if ( toyModelDefFitCovQual != 3 ) {
printf("\n\n\n *** Bad toy model definition fit. Cov qual %d. Aborting this toy.\n\n\n", toyModelDefFitCovQual ) ;
continue ;
}
delete toyModelDefinitionFitResult ;
if ( verbLevel > 0 ) {
printf("\n\n\n === Model values for observables. These will be used to generate the toy dataset.\n\n") ;
printObservables() ;
}
//--- 3) Generate a new set of observables based on this model.
generateObservables() ;
printf("\n\n\n Generated dataset\n") ;
rds->Print() ;
rds->printMultiline(cout, 1, kTRUE, "") ;
//--- Apparently, I need to make a new RooDataSet... Resetting the
// values in the old one doesn't stick. If you do likelihood->fitTo(*rds), it
// uses the original values, not the reset ones, in the fit.
RooArgSet toyFitobservedParametersList ;
toyFitobservedParametersList.add( *rrv_Nsig ) ;
toyFitobservedParametersList.add( *rrv_Nsb ) ;
toyFitobservedParametersList.add( *rrv_Nsig_sl ) ;
toyFitobservedParametersList.add( *rrv_Nsb_sl ) ;
toyFitobservedParametersList.add( *rrv_Nsig_ldp ) ;
toyFitobservedParametersList.add( *rrv_Nsb_ldp ) ;
toyFitobservedParametersList.add( *rrv_Nsig_ee ) ;
toyFitobservedParametersList.add( *rrv_Nsb_ee ) ;
toyFitobservedParametersList.add( *rrv_Nsig_mm ) ;
toyFitobservedParametersList.add( *rrv_Nsb_mm ) ;
RooDataSet* toyFitdsObserved = new RooDataSet("toyfit_ra2b_observed_rds", "RA2b toy observed data values",
toyFitobservedParametersList ) ;
toyFitdsObserved->add( toyFitobservedParametersList ) ;
//--- 4) Reset and free the nuisance parameters.
{
if ( verbLevel > 0 ) { printf("\n\n") ; }
int npi(0) ;
TIterator* npIter = nuisanceParameters->createIterator() ;
while ( RooRealVar* np_rrv = (RooRealVar*) npIter->Next() ) {
np_rrv -> setVal( np_initial_val[npi] ) ; // assuming that the order in the iterator does not change.
np_rrv -> setConstant( kFALSE ) ;
npi++ ;
if ( verbLevel > 0 ) { printf(" reset %20s to %8.2f and freed it.\n", np_rrv->GetName() , np_rrv->getVal() ) ; }
} // np_rrv iterator.
if ( verbLevel > 0 ) { printf("\n\n") ; }
}
//--- 5a) Evaluate the test statistic: Fit with susy yield floating to get the absolute maximum log likelihood.
if ( verbLevel > 0 ) { printf("\n\n Evaluating the test statistic for this toy. Fitting with susy floating.\n\n") ; }
rrv_mu_susy_sig->setVal( 0.0 ) ;
rrv_mu_susy_sig->setConstant( kFALSE ) ;
if ( verbLevel > 0 ) {
printf("\n toy dataset\n\n") ;
toyFitdsObserved->printMultiline(cout, 1, kTRUE, "") ;
}
/////---- nfg. Need to create a new dataset ----------
/////RooFitResult* maxLikelihoodFitResult = likelihood->fitTo(*rds, Save(true), PrintLevel(-1));
/////RooFitResult* maxLikelihoodFitResult = likelihood->fitTo(*rds, Save(true));
/////--------------
RooFitResult* maxLikelihoodFitResult(0x0) ;
if ( verbLevel < 2 ) {
maxLikelihoodFitResult = likelihood->fitTo(*toyFitdsObserved, Save(true), PrintLevel(-1));
} else {
maxLikelihoodFitResult = likelihood->fitTo(*toyFitdsObserved, Save(true));
}
if ( verbLevel > 0 ) { printObservables() ; }
int mlFitCovQual = maxLikelihoodFitResult->covQual() ;
if ( verbLevel > 0 ) { printf("\n fit covariance matrix quality: %d , -log likelihood %f\n\n", mlFitCovQual, maxLikelihoodFitResult->minNll() ) ; }
if ( mlFitCovQual != 3 ) {
printf("\n\n\n *** Bad maximum likelihood fit (susy floating). Cov qual %d. Aborting this toy.\n\n\n", mlFitCovQual ) ;
continue ;
}
double maxL_susyFloat = maxLikelihoodFitResult->minNll() ;
double maxL_mu_susy_sig = rrv_mu_susy_sig->getVal() ;
delete maxLikelihoodFitResult ;
//--- 5b) Evaluate the test statistic: Fit with susy yield fixed to hypothesis value.
// This is only necessary if the maximum likelihood fit value of the susy yield
// is less than the hypothesis value (to get a one-sided limit).
double testStat(0.0) ;
double maxL_susyFixed(0.0) ;
if ( maxL_mu_susy_sig < poiVal ) {
if ( verbLevel > 0 ) { printf("\n\n Evaluating the test statistic for this toy. Fitting with susy fixed to %8.2f.\n\n", poiVal ) ; }
rrv_mu_susy_sig->setVal( poiVal ) ;
rrv_mu_susy_sig->setConstant( kTRUE ) ;
if ( verbLevel > 0 ) {
printf("\n toy dataset\n\n") ;
rds->printMultiline(cout, 1, kTRUE, "") ;
}
////--------- nfg. need to make a new dataset ---------------
////RooFitResult* susyFixedFitResult = likelihood->fitTo(*rds, Save(true), PrintLevel(-1));
////RooFitResult* susyFixedFitResult = likelihood->fitTo(*rds, Save(true));
////-----------------------------
RooFitResult* susyFixedFitResult(0x0) ;
if ( verbLevel < 2 ) {
susyFixedFitResult = likelihood->fitTo(*toyFitdsObserved, Save(true), PrintLevel(-1));
} else {
susyFixedFitResult = likelihood->fitTo(*toyFitdsObserved, Save(true));
}
if ( verbLevel > 0 ) { printObservables() ; }
int susyFixedFitCovQual = susyFixedFitResult->covQual() ;
if ( verbLevel > 0 ) { printf("\n fit covariance matrix quality: %d , -log likelihood %f\n\n", susyFixedFitCovQual, susyFixedFitResult->minNll() ) ; }
if ( susyFixedFitCovQual != 3 ) {
printf("\n\n\n *** Bad maximum likelihood fit (susy fixed). Cov qual %d. Aborting this toy.\n\n\n", susyFixedFitCovQual ) ;
continue ;
}
maxL_susyFixed = susyFixedFitResult->minNll() ;
testStat = 2. * (maxL_susyFixed - maxL_susyFloat) ;
delete susyFixedFitResult ;
} else {
if ( verbLevel > 0 ) { printf("\n\n Floating value of susy yield greater than hypo value (%8.2f > %8.2f). Setting test stat to zero.\n\n", maxL_mu_susy_sig, poiVal ) ; }
testStat = 0.0 ;
}
printf(" --- test stat for toy %4d : %8.2f\n", ti, testStat ) ;
nToyOK++ ;
if ( testStat > dataTestStat ) { nToyWorseThanData++ ; }
if ( makeTtree ) {
tt_testStat = testStat ;
tt_gen_Nsig = rrv_Nsig->getVal() ;
tt_gen_Nsb = rrv_Nsb->getVal() ;
tt_gen_Nsig_sl = rrv_Nsig_sl->getVal() ;
tt_gen_Nsb_sl = rrv_Nsb_sl->getVal() ;
tt_gen_Nsig_ldp = rrv_Nsig_ldp->getVal() ;
tt_gen_Nsb_ldp = rrv_Nsb_ldp->getVal() ;
tt_gen_Nsig_ee = rrv_Nsig_ee->getVal() ;
tt_gen_Nsb_ee = rrv_Nsb_ee->getVal() ;
tt_gen_Nsig_mm = rrv_Nsig_mm->getVal() ;
tt_gen_Nsb_mm = rrv_Nsb_mm->getVal() ;
toytt->Fill() ;
}
//--- *) reset things for the next toy.
resetObservables() ;
delete toyFitdsObserved ;
} // ti.
wstf->Close() ;
printf("\n\n\n") ;
if ( nToyOK == 0 ) { printf("\n\n\n *** All toys bad !?!?!\n\n\n") ; return ; }
double pValue = (1.0*nToyWorseThanData) / (1.0*nToyOK) ;
if ( isBgonlyStudy ) {
printf("\n\n\n p-value result, BG-only , poi=%3.0f : %4d / %4d = %6.3f\n\n\n\n", poiVal, nToyWorseThanData, nToyOK, pValue ) ;
} else {
printf("\n\n\n p-value result, S-plus-B, poi=%3.0f : %4d / %4d = %6.3f\n\n\n\n", poiVal, nToyWorseThanData, nToyOK, pValue ) ;
}
if ( makeTtree ) {
printf("\n\n Writing TTree : %s : %s\n\n", ttname, tttitle ) ;
ttfile->cd() ;
toytt->Write() ;
}
} // ws_cls_hybrid1