void labelBins( TH1F* hist ) {
TAxis* xaxis = hist->GetXaxis() ;
for ( int mbi=0; mbi<hist->GetNbinsX(); mbi++ ) {
char label[1000] ;
sprintf( label, "MET%d", mbi+1 ) ;
xaxis->SetBinLabel( mbi+1, label ) ;
} // mbi.
}
// Report cut flow findings to screen
void DileptonAnalyzer::reportCutFlow( TFile & outputFile ) {
// Store in a TProfile
TProfile *cutFlow = new TProfile("cutFlow","Cut flow for final set of analysis cuts",20,0.5,20.5,-9.9e9,9.9e9);
TAxis *cutFlowAxis = cutFlow->GetXaxis();
for ( unsigned int iCut = 0; iCut < cutNamesInOrder_.size(); iCut++ ) {
cutFlow->Fill( iCut+1, cutFlowMap_[cutNamesInOrder_[iCut]]);
cutFlowAxis->SetBinLabel( iCut+1, cutNamesInOrder_[iCut]);
}
outputFile.cd();
cutFlow->Write();
}
AndSelection::AndSelection(const ptree & cfg, InputManager & in, OutputManager & out): cutflow(0), cutflow_raw(0){
// get selections as string:
std::string ids = cfg.get<string>("selections");
vector<string> vids;
boost::split(vids, ids, boost::algorithm::is_space(), boost::algorithm::token_compress_on);
for(const string & id : vids){
sel_handles.push_back(in.get_handle<bool>(id));
}
string hname = cfg.get<string>("cutflow_hname", "");
if(!hname.empty()){
cutflow = new TH1D(hname.c_str(), hname.c_str(), vids.size()+1, 0, vids.size()+1);
cutflow_raw = new TH1D((hname + "_raw").c_str(), (hname + "_raw").c_str(), vids.size()+1, 0, vids.size()+1);
for(TH1D * h : {cutflow, cutflow_raw}){
h->Sumw2();
TAxis * x = h->GetXaxis();
x->SetBinLabel(1, "before cuts");
for(auto i=0ul; i < vids.size(); ++i){
x->SetBinLabel(i+2, vids[i].c_str());
}
out.put(h->GetName(), h);
}
}
weight_handle = in.get_handle<double>("weight");
}
TH1F* bookHist(const char* hname, const char* htitle, int nBinsMET, int nBinsHT, int sampleIndex ) {
int nbins = nBinsMET*(nBinsHT+1) + 1 ;
TH1F* retVal = new TH1F( hname, htitle, nbins, 0.5+0.05*(sampleIndex-5), nbins+0.5+0.05*(sampleIndex-5) ) ;
TAxis* xaxis = retVal->GetXaxis() ;
for ( int mbi=0; mbi<nBinsMET; mbi++ ) {
for ( int hbi=0; hbi<nBinsHT; hbi++ ) {
int histbin = 1 + (nBinsHT+1)*mbi + hbi + 1 ;
char binlabel[1000] ;
sprintf( binlabel, "M%d_H%d", mbi+1, hbi+1 ) ;
xaxis->SetBinLabel( histbin, binlabel ) ;
} // hbi.
} // mbi.
retVal->SetLabelSize(0.055,"x") ;
xaxis->LabelsOption("v") ;
return retVal ;
}
TH1F* bookHist(const char* hname, const char* htitle, const char* selstring, int nbjet, int nBinsMET, int nBinsHT ) {
int nbins = nBinsMET*(nBinsHT+1) + 1 ;
TH1F* retVal = new TH1F( hname, htitle, nbins, 0.5 + 0.1*(nbjet-2), nbins+0.5 + 0.1*(nbjet-2) ) ;
TAxis* xaxis = retVal->GetXaxis() ;
for ( int mbi=0; mbi<nBinsMET; mbi++ ) {
for ( int hbi=0; hbi<nBinsHT; hbi++ ) {
int histbin = 1 + (nBinsHT+1)*mbi + hbi + 1 ;
char binlabel[1000] ;
sprintf( binlabel, "%s_M%d_H%d_%db", selstring, mbi+1, hbi+1, nbjet ) ;
xaxis->SetBinLabel( histbin, binlabel ) ;
} // hbi.
} // mbi.
retVal->SetLabelSize(0.055,"x") ;
xaxis->LabelsOption("v") ;
return retVal ;
}
void DileptonAnalyzer::storeSignalEfficiencies( TFile & outputFile ) {
// Store in a TProfile
TProfile *sigEffOne = new TProfile("SignalEfficOneDecay","Signal efficiencies when one exotic decays in this channel",10,0.5,10.5,-9.9e9,9.9e9);
TAxis *sigEffOneAxis = sigEffOne->GetXaxis();
TProfile *sigEffTwo = new TProfile("SignalEfficTwoDecay","Signal efficiencies when two exotic decays in this channel",10,0.5,10.5,-9.9e9,9.9e9);
TAxis *sigEffTwoAxis = sigEffTwo->GetXaxis();
// Fill set of 3 bins in each TProfile for each exotic
for ( int iExotic = 0; iExotic < nSignalParticles_; iExotic++ ) {
TString axisTitle;
sigEffOne->Fill( iExotic*nSignalParticles_+1, numEvents_oneSensitiveDecay_[iExotic] );
axisTitle.Form("Number of events coming into macro for exotic %d",signalPdgId_[iExotic]);
sigEffOneAxis->SetBinLabel( iExotic*nSignalParticles_+1, axisTitle.Data());
sigEffOne->Fill( iExotic*nSignalParticles_+2, numExoticsRECO_oneSensitiveDecay_[iExotic] );
axisTitle.Form("Number of candidates RECO'd for exotic %d",signalPdgId_[iExotic]);
sigEffOneAxis->SetBinLabel( iExotic*nSignalParticles_+2, axisTitle.Data());
sigEffOne->Fill( iExotic*nSignalParticles_+3, numExoticsCorrectRECO_oneSensitiveDecay_[iExotic] );
axisTitle.Form("Number of exotics correctly RECO'd for exotic %d",signalPdgId_[iExotic]);
sigEffOneAxis->SetBinLabel( iExotic*nSignalParticles_+3, axisTitle.Data());
sigEffTwo->Fill( iExotic*nSignalParticles_+1, numEvents_twoSensitiveDecay_[iExotic] );
axisTitle.Form("Number of events coming into macro for exotic %d",signalPdgId_[iExotic]);
sigEffTwoAxis->SetBinLabel( iExotic*nSignalParticles_+1, axisTitle.Data());
sigEffTwo->Fill( iExotic*nSignalParticles_+2, numExoticsRECO_twoSensitiveDecay_[iExotic] );
axisTitle.Form("Number of candidates RECO'd for exotic %d",signalPdgId_[iExotic]);
sigEffTwoAxis->SetBinLabel( iExotic*nSignalParticles_+2, axisTitle.Data());
sigEffTwo->Fill( iExotic*nSignalParticles_+3, numExoticsCorrectRECO_twoSensitiveDecay_[iExotic] );
axisTitle.Form("Number of exotics correctly RECO'd for exotic %d",signalPdgId_[iExotic]);
sigEffTwoAxis->SetBinLabel( iExotic*nSignalParticles_+3, axisTitle.Data());
}
outputFile.cd();
sigEffOne->Write();
sigEffTwo->Write();
}
int looperCR2lep( analysis* myAnalysis, sample* mySample, int nEvents = -1, bool fast = true) {
// Benchmark
TBenchmark *bmark = new TBenchmark();
bmark->Start("benchmark");
// Setup
TChain *chain = mySample->GetChain();
TString sampleName = mySample->GetLabel();
const int nSigRegs = myAnalysis->GetSigRegionsAll().size();
const int nVariations = mySample->IsData() ? 0 : myAnalysis->GetSystematics(false).size();
bool isFastsim = mySample->IsSignal();
cout << "\nSample: " << sampleName.Data() << " (CR2L";
if( myContext.GetJesDir() == contextVars::kUp ) cout << ", JES up";
else if( myContext.GetJesDir() == contextVars::kDown ) cout << ", JES down";
cout << ")" << endl;
myContext.SetUseRl( true );
/////////////////////////////////////////////////////////
// Histograms
TDirectory *rootdir = gDirectory->GetDirectory("Rint:"); // Use TDirectories to assist in memory management
TDirectory *histdir = new TDirectory( "histdir", "histdir", "", rootdir );
TDirectory *systdir = new TDirectory( "systdir", "systdir", "", rootdir );
TDirectory *zerodir = new TDirectory( "zerodir", "zerodir", "", rootdir );
TH1::SetDefaultSumw2();
TH1D* h_bkgtype_sum[nSigRegs][nVariations+1];
TH1D* h_evttype_sum[nSigRegs][nVariations+1];
TH2D* h_sigyields[nSigRegs][nVariations+1];
TH1D* h_bkgtype[nSigRegs][nVariations+1]; // per-file versions for zeroing
TH1D* h_evttype[nSigRegs][nVariations+1];
TH1D *h_mt[nSigRegs];
TH1D *h_met[nSigRegs];
TH1D *h_mt2w[nSigRegs];
TH1D *h_chi2[nSigRegs];
TH1D *h_htratio[nSigRegs];
TH1D *h_mindphi[nSigRegs];
TH1D *h_ptb1[nSigRegs];
TH1D *h_drlb1[nSigRegs];
TH1D *h_ptlep[nSigRegs];
TH1D *h_metht[nSigRegs];
TH1D *h_dphilw[nSigRegs];
TH1D *h_njets[nSigRegs];
TH1D *h_nbtags[nSigRegs];
TH1D *h_ptj1[nSigRegs];
TH1D *h_j1btag[nSigRegs];
TH1D *h_modtop[nSigRegs];
TH1D *h_dphilmet[nSigRegs];
TH1D *h_mlb[nSigRegs];
vector<TString> regNames = myAnalysis->GetSigRegionLabelsAll();
vector<sigRegion*> sigRegions = myAnalysis->GetSigRegionsAll();
vector<systematic*> variations = myAnalysis->GetSystematics(false);
for( int i=0; i<nSigRegs; i++ ) {
TString plotLabel = sampleName + "_" + regNames.at(i);
systdir->cd();
for( int j=1; j<=nVariations; j++ ) {
TString varName = variations.at(j-1)->GetNameLong();
h_bkgtype_sum[i][j] = new TH1D( "bkgtype_" + plotLabel + "_" + varName, "Yield by background type", 5, 0.5, 5.5);
h_evttype_sum[i][j] = new TH1D( "evttype_" + regNames.at(i) + "_" + varName, "Yield by event type", 6, 0.5, 6.5);
h_sigyields[i][j] = new TH2D( "sigyields_" + regNames.at(i) + "_" + varName, "Signal yields by mass point", 37,99,1024, 19,-1,474 );
}
histdir->cd();
h_bkgtype_sum[i][0] = new TH1D( "bkgtype_" + plotLabel, "Yield by background type", 5, 0.5, 5.5);
h_evttype_sum[i][0] = new TH1D( "evttype_" + regNames.at(i), "Yield by event type", 6, 0.5, 6.5);
h_sigyields[i][0] = new TH2D( "sigyields_" + regNames.at(i), "Signal yields by mass point", 37,99,1024, 19,-1,474 );
h_mt[i] = new TH1D( "mt_" + plotLabel, "Transverse mass", 80, 0, 800);
h_met[i] = new TH1D( "met_" + plotLabel, "MET", 40, 0, 1000);
h_mt2w[i] = new TH1D( "mt2w_" + plotLabel, "MT2W", 50, 0, 500);
h_chi2[i] = new TH1D( "chi2_" + plotLabel, "Hadronic #chi^{2}", 50, 0, 15);
h_htratio[i] = new TH1D( "htratio_" + plotLabel, "H_{T} ratio", 50, 0, 1);
h_mindphi[i] = new TH1D( "mindphi_" + plotLabel, "min #Delta#phi(j12,MET)", 63, 0, 3.15);
h_ptb1[i] = new TH1D( "ptb1_" + plotLabel, "p_{T} (b1)", 50, 0, 500);
h_drlb1[i] = new TH1D( "drlb1_" + plotLabel, "#DeltaR (lep, b1)", 50, 0, 5);
h_ptlep[i] = new TH1D( "ptlep_" + plotLabel, "p_{T} (lep)", 50, 0, 500);
h_metht[i] = new TH1D( "metht_" + plotLabel, "MET/sqrt(HT)", 50, 0, 100);
h_dphilw[i] = new TH1D( "dphilw_" + plotLabel, "#Delta#phi (lep,W)", 63, 0, 3.15);
h_njets[i] = new TH1D( "njets_" + plotLabel, "Number of jets", 16, -0.5, 15.5);
h_nbtags[i] = new TH1D( "nbtags_" + plotLabel, "Number of b-tags", 7, -0.5, 6.5);
h_ptj1[i] = new TH1D( "ptj1_" + plotLabel, "Leading jet p_{T}", 40, 0, 1000);
h_j1btag[i] = new TH1D( "j1btag_" + plotLabel, "Is leading jet b-tagged?", 2, -0.5, 1.5);
h_modtop[i] = new TH1D( "modtop_" + plotLabel, "Modified topness", 30, -15., 15.);
h_dphilmet[i] = new TH1D( "dphilmet_"+ plotLabel, "#Delta#phi (lep1, MET)", 63, 0., 3.15);
h_mlb[i] = new TH1D( "mlb_" + plotLabel, "M_{lb}", 50, 0., 500.);
for( int j=0; j<=nVariations; j++ ) {
TAxis* axis = h_bkgtype_sum[i][j]->GetXaxis();
axis->SetBinLabel( 1, "2+lep" );
axis->SetBinLabel( 2, "1lepW" );
axis->SetBinLabel( 3, "1lepTop" );
axis->SetBinLabel( 4, "ZtoNuNu" );
axis->SetBinLabel( 5, "Other" );
axis = h_evttype_sum[i][j]->GetXaxis();
axis->SetBinLabel( 1, "Data" );
axis->SetBinLabel( 2, "Signals" );
axis->SetBinLabel( 3, "2+lep" );
axis->SetBinLabel( 4, "1lepW" );
axis->SetBinLabel( 5, "1lepTop" );
axis->SetBinLabel( 6, "ZtoNuNu" );
}
}
TH1D *h_yields_sum = new TH1D( Form("srYields_%s", sampleName.Data()), "Yield by signal region", nSigRegs, 0.5, float(nSigRegs)+0.5);
for( int i=0; i<nSigRegs; i++ ) h_yields_sum->GetXaxis()->SetBinLabel( i+1, regNames.at(i) );
// Set up copies of histograms, in order to zero out negative yields
zerodir->cd();
TH1D* h_yields = (TH1D*)h_yields_sum->Clone( "tmp_" + TString(h_yields_sum->GetName()) );
for( int i=0; i<nSigRegs; i++ ) {
for( int j=0; j<=nVariations; j++ ) {
h_bkgtype[i][j] = (TH1D*)h_bkgtype_sum[i][j]->Clone( "tmp_" + TString(h_bkgtype_sum[i][j]->GetName()) );
h_evttype[i][j] = (TH1D*)h_evttype_sum[i][j]->Clone( "tmp_" + TString(h_evttype_sum[i][j]->GetName()) );
}
}
// Set up cutflow variables
double yield_total = 0;
double yield_unique = 0;
double yield_filter = 0;
double yield_vtx = 0;
double yield_1goodlep = 0;
double yield_lepSel = 0;
double yield_2lepveto = 0;
double yield_trkVeto = 0;
double yield_2lepCR = 0;
double yield_tauVeto = 0;
double yield_njets = 0;
double yield_1bjet = 0;
double yield_METcut = 0;
double yield_MTcut = 0;
double yield_dPhi = 0;
double yield_chi2 = 0;
int yGen_total = 0;
int yGen_unique = 0;
int yGen_filter = 0;
int yGen_vtx = 0;
int yGen_1goodlep = 0;
int yGen_lepSel = 0;
int yGen_2lepveto = 0;
int yGen_trkVeto = 0;
int yGen_tauVeto = 0;
int yGen_2lepCR = 0;
int yGen_njets = 0;
int yGen_1bjet = 0;
int yGen_METcut = 0;
int yGen_MTcut = 0;
int yGen_dPhi = 0;
int yGen_chi2 = 0;
////////////////////////////////////////////////////////////////////
// Set up data-specific filters
if( mySample->IsData() ) {
set_goodrun_file_json( "reference-files/Cert_271036-284044_13TeV_23Sep2016ReReco_Collisions16_JSON.txt" );
duplicate_removal::clear_list();
}
/////////////////////////////////////////////////////////////////////
// Loop over events to Analyze
unsigned int nEventsTotal = 0;
unsigned int nEventsChain = chain->GetEntries();
if( nEvents >= 0 ) nEventsChain = nEvents;
TObjArray *listOfFiles = chain->GetListOfFiles();
TIter fileIter(listOfFiles);
TFile *currentFile = 0;
// File Loop
while ( (currentFile = (TFile*)fileIter.Next()) ) {
// Get File Content
TFile file( currentFile->GetTitle() );
TString filename = file.GetName();
TTree *tree = (TTree*)file.Get("t");
if(fast) TTreeCache::SetLearnEntries(10);
if(fast) tree->SetCacheSize(128*1024*1024);
cms3.Init(tree);
// Load event weight histograms
TH2F* hNEvts = (TH2F*)file.Get("histNEvts");
TH3D* hCounterSMS = (TH3D*)file.Get("h_counterSMS");
TH1D* hCounter = (TH1D*)file.Get("h_counter");
myHelper.Setup( isFastsim, hCounter, hNEvts, hCounterSMS );
// Reset zeroing histograms
for( int i=0; i<nSigRegs; i++ ) {
for( int j=0; j<=nVariations; j++ ) {
h_bkgtype[i][j]->Reset();
h_evttype[i][j]->Reset();
}
}
h_yields->Reset();
// Loop over Events in current file
if( nEventsTotal >= nEventsChain ) continue;
unsigned int nEventsTree = tree->GetEntriesFast();
for( unsigned int event = 0; event < nEventsTree; ++event) {
// Get Event Content
if( nEventsTotal >= nEventsChain ) continue;
if(fast) tree->LoadTree(event);
cms3.GetEntry(event);
++nEventsTotal;
// Progress
CMS3::progress( nEventsTotal, nEventsChain );
////////////////////////////////////////////////////////////////////////////////////////////////////////
// Analysis Code
// ---------------------------------------------------------------------------------------------------//
///////////////////////////////////////////////////////////////
// Special filters to more finely categorize background events
if( sampleName == "tt2l" && gen_nfromtleps_() != 2 ) continue; //Require 2 leps from top in "tt2l" events
else if( sampleName == "tt1l" && gen_nfromtleps_() != 1 ) continue; //Require 1 lep from top in "tt1l" events
// Stitch W+NJets samples together by removing the MET<200 events from the non-nupT samples
if( sampleName.Contains("wjets") && filename.Contains("JetsToLNu_madgraph") && nupt()>=200. ) continue;
//FastSim anomalous event filter
if( isFastsim && !context::filt_fastsimjets() ) continue;
if( !mySample->PassSelections() ) continue;
/////////////////////////////////
// Set event weight
double evtWeight = 1.;
// Data should have a weight of 1.0
if( is_data() || mySample->IsData() ) evtWeight = 1.;
else {
// Weight background MC using scale1fb
evtWeight = myAnalysis->GetLumi() * scale1fb();
// Weight signal MC using xsec and nEvents
if( mySample->IsSignal() ) {
myHelper.PrepSignal();
double nEvtsSample = hNEvts->GetBinContent( hNEvts->FindBin( mass_stop(), mass_lsp() ) );
evtWeight = myAnalysis->GetLumi() * 1000. * xsec() / nEvtsSample;
}
// Apply scale factors to correct the shape of the MC
evtWeight *= myHelper.TrigEff2l();
evtWeight *= myHelper.LepSF();
evtWeight *= myHelper.BtagSF();
if( isFastsim ) evtWeight *= myHelper.LepSFfastsim();
if( !isFastsim ) evtWeight *= myHelper.PileupSF();
if( mySample->GetLabel() == "tt2l" || filename.Contains("W_5f_powheg_pythia8") ) {
evtWeight *= myHelper.MetResSF();
// evtWeight *= myHelper.TopSystPtSF();
}
else if( mySample->GetLabel() == "tt1l" || mySample->GetLabel() == "wjets" ) evtWeight *= myHelper.MetResSF();
if( mySample->GetLabel() == "tt2l" || mySample->GetLabel() == "tt1l" || mySample->IsSignal() ) evtWeight *= myHelper.ISRnJetsSF();
// Correct event weight when samples are merged together
if( filename.Contains("ttbar_diLept_madgraph_pythia8_ext1_25ns") ) evtWeight *= 23198554./(23198554.+5689986.);
else if( filename.Contains("ttbar_diLept_madgraph_pythia8_25ns") ) evtWeight *= 5689986./(23198554.+5689986.);
else if( filename.Contains("t_tW_5f_powheg_pythia8_noHadDecays_25ns") ) evtWeight *= 4473156./(4473156.+3145334.);
else if( filename.Contains("t_tW_5f_powheg_pythia8_noHadDecays_ext1_25ns") ) evtWeight *= 3145334./(4473156.+3145334.);
else if( filename.Contains("t_tbarW_5f_powheg_pythia8_noHadDecays_25ns") ) evtWeight *= 5029568./(5029568.+3146940.);
else if( filename.Contains("t_tbarW_5f_powheg_pythia8_noHadDecays_ext1_25ns") ) evtWeight *= 3146940./(5029568.+3146940.);
}
// Count the number of events processed
yield_total += evtWeight;
yGen_total++;
// Remove duplicate events in data
if( is_data() ) {
duplicate_removal::DorkyEventIdentifier id( run(), evt(), ls() );
if( is_duplicate(id) ) continue;
yield_unique += evtWeight;
yGen_unique++;
}
// MET filters, bad event filters, and triggers for data
if( is_data() ) {
if( !goodrun( run(), ls() ) ) continue;
if( !filt_met() ) continue;
if( !filt_badChargedCandidateFilter() ) continue;
if( !filt_badMuonFilter() ) continue;
if( !context::filt_jetWithBadMuon() ) continue;
if( !filt_pfovercalomet() ) continue;
if( !HLT_MET() && !HLT_MET110_MHT110() && !HLT_MET120_MHT120() ) {
if( !(HLT_SingleEl() && (abs(lep1_pdgid())==11 || abs(lep2_pdgid())==11) ) &&
!(HLT_SingleMu() && (abs(lep1_pdgid())==13 || abs(lep2_pdgid())==13) ) ) continue;
}
yield_filter += evtWeight;
yGen_filter++;
}
// First vertex must be good
if( nvtxs() < 1 ) continue;
yield_vtx += evtWeight;
yGen_vtx++;
// Must have at least 1 good lepton
if( ngoodleps() < 1 ) continue;
yield_1goodlep += evtWeight;
yGen_1goodlep++;
// Lep 1 must pass lepton selections
// if( abs(lep1_pdgid())==11 ) {
// if( lep1_p4().pt() < 20. ) continue;
// if( fabs(lep1_p4().eta()) > 1.4442 ) continue;
// if( !lep1_passMediumID() ) continue;
// }
// else if( abs(lep1_pdgid())==13 ) {
// if( lep1_p4().pt() < 20. ) continue;
// if( fabs(lep1_p4().eta()) > 2.4 ) continue;
// if( !lep1_passTightID() ) continue;
// }
yield_lepSel += evtWeight;
yGen_lepSel++;
///////////////////
// Make 2-lepton CR
int countGoodLeps = 0;
// Count the number of veto leptons
if( nvetoleps() >= 2 && lep2_p4().pt() > 10. ) countGoodLeps += nvetoleps();
if( countGoodLeps > 1 ) {
yield_2lepveto += evtWeight;
yGen_2lepveto++;
}
// If we fail the track veto, count another good lepton
// if( !PassTrackVeto() ) {
// countGoodLeps++;
// yield_trkVeto += evtWeight;
// yGen_trkVeto++;
// }
// If we fail the tau veto, count another good lepton
// if( !PassTauVeto() ) {
// countGoodLeps++;
// yield_tauVeto += evtWeight;
// yGen_tauVeto++;
// }
if( countGoodLeps < 2 ) continue;
yield_2lepCR += evtWeight;
yGen_2lepCR++;
////////////////////
////////////////////
// N-jet requirement
if( context::ngoodjets() < 2 ) continue;
yield_njets += evtWeight;
yGen_njets++;
j1pt = context::ak4pfjets_p4().at(0).pt();
// B-tag requirement
if( context::ngoodbtags() < 1 ) continue;
yield_1bjet += evtWeight;
yGen_1bjet++;
j1_isBtag = context::ak4pfjets_passMEDbtag().at(0);
// Baseline MET cut (with 2nd lepton pT added to MET)
if( context::Met() < 250. ) continue;
yield_METcut += evtWeight;
yGen_METcut++;
// MT cut (with 2nd lepton pT added to MET)
if( context::MT_met_lep() < 150. ) continue;
yield_MTcut += evtWeight;
yGen_MTcut++;
// Min delta-phi between MET and j1/j2 (with 2nd lepton pT added to MET)
if( context::Mindphi_met_j1_j2() < 0.5 ) continue;
yield_dPhi += evtWeight;
yGen_dPhi++;
// Chi^2 cut
// if( hadronic_top_chi2() >= 10. ) continue;
yield_chi2 += evtWeight;
yGen_chi2++;
//////////////////////////////////////////////////////////
// Classify event based on number of leptons / neutrinos
// Order of evaluation matters, because some events fall into multiple categories
int bkgType = -99;
if( filename.Contains("ZZTo2L2Nu") && isZtoNuNu() ) bkgType = 1; // Force ZZto2L2Nu to be 2lep
else if( isZtoNuNu() ) bkgType = 4; // Z to nu nu
else if( is2lep() ) bkgType = 1; // 2 or more leptons
else if( is1lepFromTop() ) bkgType = 3; // 1 lepton from top quark
else if( is1lepFromW() ) bkgType = 2; // 1 lepton from a W not from top
else bkgType = 5; // Other
int evtType = -99;
if( mySample->IsData() ) evtType = 1;
else if( mySample->IsSignal() ) evtType = 2;
else evtType = 2+bkgType;
// Quickly calculate some variables
double metSqHT = context::Met() / sqrt( context::ak4_HT() );
const TVector3 lepVec( lep1_p4().x(), lep1_p4().y(), lep1_p4().z() );
const TVector3 metVec( context::Met()*cos(context::MetPhi()), context::Met()*sin(context::MetPhi()), 0 );
const TVector3 wVec = lepVec + metVec;
double dPhiLepW = fabs( lepVec.DeltaPhi(wVec) );
double drLepLeadb = ROOT::Math::VectorUtil::DeltaR( lep1_p4(), context::ak4pfjets_leadMEDbjet_p4() );
lep1pt = lep1_p4().Pt();
myMlb = context::Mlb_closestb();
///////////////////////////////////////////
// Signal region cuts and histo filling
// If the event passes the SR cuts, store which background type this event is, and fill histograms
for( int i=0; i<nSigRegs; i++ ) {
if( !sigRegions.at(i)->PassAllCuts() ) continue;
// Make some corrections that depend on the signal region
double fillWeight = evtWeight;
bool is_corridor = sigRegions.at(i)->GetLabel().Contains("corr");
myHelper.SetCorridor( is_corridor );
if( !is_data() && is_corridor ) fillWeight *= sfhelp::MetResCorrectionCorridor();
else if( !is_data() && !is_corridor ) fillWeight *= sfhelp::BtagCorrectionTight();
h_bkgtype[i][0]->Fill( bkgType, fillWeight );
h_evttype[i][0]->Fill( evtType, fillWeight );
if( mySample->IsSignal() ) h_sigyields[i][0]->Fill( mass_stop(), mass_lsp(), fillWeight );
h_mt[i]->Fill( context::MT_met_lep(), fillWeight );
h_met[i]->Fill( context::Met(), fillWeight );
h_mt2w[i]->Fill( context::MT2W(), fillWeight );
h_chi2[i]->Fill( hadronic_top_chi2(), fillWeight );
h_htratio[i]->Fill( context::ak4_htratiom(), fillWeight );
h_mindphi[i]->Fill( context::Mindphi_met_j1_j2(), fillWeight );
h_ptb1[i]->Fill( context::ak4pfjets_leadMEDbjet_p4().pt(), fillWeight );
h_drlb1[i]->Fill( drLepLeadb, fillWeight );
h_ptlep[i]->Fill( lep1_p4().pt(), fillWeight );
h_metht[i]->Fill( metSqHT, fillWeight );
h_dphilw[i]->Fill( dPhiLepW, fillWeight );
h_njets[i]->Fill( context::ngoodjets(), fillWeight );
h_nbtags[i]->Fill( context::ngoodbtags(), fillWeight );
h_ptj1[i]->Fill( j1pt, fillWeight );
h_j1btag[i]->Fill( j1_isBtag, fillWeight );
h_modtop[i]->Fill( context::TopnessMod(), fillWeight );
h_dphilmet[i]->Fill( context::lep1_dphiMET(), fillWeight );
h_mlb[i]->Fill( myMlb, fillWeight );
h_yields->Fill( double(i+1), fillWeight );
// Special systematic variation histograms
for( int j=1; j<=nVariations; j++ ) {
h_bkgtype[i][j]->Fill( bkgType, fillWeight * variations.at(j-1)->GetWeight() );
h_evttype[i][j]->Fill( evtType, fillWeight * variations.at(j-1)->GetWeight() );
if( mySample->IsSignal() ) h_sigyields[i][j]->Fill( mass_stop(), mass_lsp(), fillWeight * variations.at(j-1)->GetWeight() );
}
}
// ---------------------------------------------------------------------------------------------------//
////////////////////////////////////////////////////////////////////////////////////////////////////////
} //End of loop over events in file
// Clean Up
delete tree;
file.Close();
// Zero negative values in each signal region
for( int i=0; i<nSigRegs; i++ ) {
for( int j=0; j<=nVariations; j++ ) {
bool negsFound = false;
// First zero any decay modes with negative yields
for( int k=1; k<= h_bkgtype[i][j]->GetNbinsX(); k++ ) {
if( h_bkgtype[i][j]->GetBinContent(k) < 0.0 ) {
h_bkgtype[i][j]->SetBinContent(k, 0.);
h_bkgtype[i][j]->SetBinError(k, 0.);
negsFound = true;
}
if( h_evttype[i][j]->GetBinContent(k+2) < 0.0 ) {
h_evttype[i][j]->SetBinContent(k+2, 0.);
h_evttype[i][j]->SetBinError(k+2, 0.);
}
}
// If any negative yields were found in any decay mode, recalculate the total yield
if( j==0 && negsFound ) {
double newYield, newErr;
newYield = h_bkgtype[i][0]->IntegralAndError( 0, -1, newErr );
h_yields->SetBinContent(i+1, newYield);
h_yields->SetBinError(i+1, newErr);
}
// Add zeroed histograms to total histograms
h_bkgtype_sum[i][j]->Add( h_bkgtype[i][j] );
h_evttype_sum[i][j]->Add( h_evttype[i][j] );
}
}
h_yields_sum->Add( h_yields );
} // End loop over files in the chain
cout << "Cutflow yields: (yield) (gen evts)" << endl;
printf("Total number of events: %10.2f %9i\n", yield_total , yGen_total );
if( mySample->IsData() ) {
printf("Events passing duplicate removal: %10.2f %9i\n", yield_unique , yGen_unique );
printf("Events passing filters and trigger: %10.2f %9i\n", yield_filter , yGen_filter );
}
printf("Events with 1st vertex good: %10.2f %9i\n", yield_vtx , yGen_vtx );
printf("Events with at least 1 good lepton: %10.2f %9i\n", yield_1goodlep , yGen_1goodlep );
printf("Events passing lepton selection: %10.2f %9i\n", yield_lepSel , yGen_lepSel );
printf("\nEvents passing 2-lep requirement: %10.2f %9i\n", yield_2lepCR , yGen_2lepCR );
printf(" Events with veto lepton: %10.2f %9i\n", yield_2lepveto , yGen_2lepveto );
printf(" Events with isolated track: %10.2f %9i\n", yield_trkVeto , yGen_trkVeto );
printf(" Events with identified tau: %10.2f %9i\n\n", yield_tauVeto , yGen_tauVeto );
printf("Events with at least 2 jets: %10.2f %9i\n", yield_njets , yGen_njets );
printf("Events with at least 1 b-tag: %10.2f %9i\n", yield_1bjet , yGen_1bjet );
printf("Events with MET > 250 GeV: %10.2f %9i\n", yield_METcut , yGen_METcut );
printf("Events with MT > 150 GeV: %10.2f %9i\n", yield_MTcut , yGen_MTcut );
printf("Events with min dPhi > 0.5: %10.2f %9i\n", yield_dPhi , yGen_dPhi );
// printf("Events with chi2 < 10: %10.2f %9i\n", yield_chi2 , yGen_chi2 );
printf("Yield after preselection: %10.2f %9i\n", yield_chi2 , yGen_chi2 );
if ( nEventsChain != nEventsTotal ) {
cout << Form( "ERROR: number of events from files (%d) is not equal to total number of events (%d)", nEventsChain, nEventsTotal ) << endl;
}
///////////////////////////////////////////////////////////////////////////////
// Store histograms and clean them up
TFile* plotfile = new TFile( myAnalysis->GetPlotFileName(), "READ");
TFile* systfile = new TFile( myAnalysis->GetSystFileName(), "READ");
TFile* sourcefile;
// Certain histograms are cumulative across multiple samples. For those histograms, add what the
// looper has just collected to the cumulative version stored in our output files
for( int j=0; j<=nVariations; j++ ) {
if( j==0 ) sourcefile = plotfile;
else sourcefile = systfile;
for( int i=0; i<nSigRegs; i++ ) {
// Build up cumulative histo of SUSY scan yields
TH2D* hTemp2 = (TH2D*)sourcefile->Get( h_sigyields[i][j]->GetName() );
if( hTemp2 != 0 ) h_sigyields[i][j]->Add( hTemp2 );
// Build up cumulative histo of yields by signal/background type
TH1D* hTemp = (TH1D*)sourcefile->Get( h_evttype_sum[i][j]->GetName() );
if( hTemp != 0 ) h_evttype_sum[i][j]->Add( hTemp );
}
}
delete plotfile;
delete systfile;
// Take all histograms in histdir and write them to plotfile
plotfile = new TFile( myAnalysis->GetPlotFileName(), "UPDATE");
plotfile->cd();
histdir->GetList()->Write( "", TObject::kOverwrite );
delete plotfile;
// Take all histograms in systdir and write them to systfile
systfile = new TFile( myAnalysis->GetSystFileName(), "UPDATE");
systfile->cd();
systdir->GetList()->Write( "", TObject::kOverwrite );
delete systfile;
// Cleanup
zerodir->Close();
histdir->Close();
systdir->Close();
// return
bmark->Stop("benchmark");
cout << endl;
cout << nEventsTotal << " Events Processed" << endl;
cout << "------------------------------" << endl;
cout << "CPU Time: " << Form( "%.01f", bmark->GetCpuTime("benchmark") ) << endl;
cout << "Real Time: " << Form( "%.01f", bmark->GetRealTime("benchmark") ) << endl;
cout << endl;
delete bmark;
return 0;
}
void SampleDiagnostics::write() const
{
if (passedRate == 0) {
std::cerr << "WARNING : No accumulated rate for " << name << ". Maybe you just didn't run over it/them?" << std::endl;
return;
}
//.. Histogram output .......................................................
const Int_t numDatasets = static_cast<Int_t>(size());
const UInt_t numBins = numDatasets + 3;
TH2* hCorrelation = new TH2F("h_correlation_" + name, name, numBins, 0, numBins, numBins, 0, numBins);
TH2* hSharedRate = new TH2F("h_shared_rate_" + name, name, numBins, 0, numBins, numBins, 0, numBins);
//...........................................................................
Double_t overhead = 0;
Double_t overheadErr = 0;
for (Int_t iSet = 0, xBin = 1; iSet < numDatasets; ++iSet, ++xBin) {
const Dataset& dataset = at(iSet);
if (!dataset.isNewTrigger) {
overhead += dataset.rate;
overheadErr += dataset.rateUncertainty2; // I think this is over-estimating it because the values are NOT uncorrelated, but oh well
}
if (iSet == firstNewTrigger) ++xBin;
if (dataset.rate == 0) continue;
for (Int_t jSet = 0, yBin = 1; jSet <= numDatasets; ++jSet, ++yBin) {
if (jSet == firstNewTrigger) ++yBin;
if (jSet == numDatasets) ++yBin;
hCorrelation->SetBinContent (xBin, yBin, commonRates[iSet][jSet] / dataset.rate);
hCorrelation->SetBinError (xBin, yBin, ratioError(commonRates[iSet][jSet], commonRateUncertainties2[iSet][jSet], dataset.rate, dataset.rateUncertainty2));
hSharedRate ->SetBinContent (xBin, yBin, commonRates[iSet][jSet]);
hSharedRate ->SetBinError (xBin, yBin, TMath::Sqrt(commonRateUncertainties2[iSet][jSet]));
} // end loop over other datasets
// Rightmost column is the fraction of rate out of the total
hCorrelation->SetBinContent (numBins, xBin, dataset.rate / passedRate);
hCorrelation->SetBinError (numBins, xBin, ratioError(dataset.rate, dataset.rateUncertainty2, passedRate, passedRateUncertainty2));
hSharedRate ->SetBinContent (numBins, xBin, dataset.rate);
hSharedRate ->SetBinError (numBins, xBin, TMath::Sqrt(dataset.rateUncertainty2));
} // end loop over datasets
// Top-right cell is the total overhead for the _current_ datasets (not including new triggers)
hSharedRate ->SetBinContent (numBins, numBins, overhead);
hSharedRate ->SetBinError (numBins, numBins, TMath::Sqrt(overheadErr));
overheadErr = ratioError (overhead, overheadErr, passedRate, passedRateUncertainty2);
overhead /= passedRate; // Can only do this after error is computed
overhead -= 1;
hCorrelation->SetBinContent (numBins, numBins, overhead);
hCorrelation->SetBinError (numBins, numBins, overheadErr);
//...........................................................................
// Histogram format
hCorrelation->SetTitle (TString::Format("%s (overhead = %.3g%% #pm %.3g%%)" , hCorrelation->GetTitle(), 100*overhead, 100*overheadErr));
hSharedRate ->SetTitle (TString::Format("%s (total rate = %.3g #pm %.3g Hz)", hSharedRate ->GetTitle(), passedRate, TMath::Sqrt(passedRateUncertainty2)));
hCorrelation->SetZTitle ("(X #cap Y) / X"); hSharedRate->SetZTitle ("X #cap Y");
hCorrelation->SetOption ("colz"); hSharedRate->SetOption ("colz");
hCorrelation->SetStats (kFALSE); hSharedRate->SetStats (kFALSE);
hCorrelation->SetMinimum(0); hSharedRate->SetMinimum(0);
hCorrelation->SetMaximum(1);
std::vector<TAxis*> axes;
axes.push_back(hCorrelation->GetXaxis()); axes.push_back(hCorrelation->GetYaxis());
axes.push_back(hSharedRate ->GetXaxis()); axes.push_back(hSharedRate ->GetYaxis());
const UInt_t numAxes = axes.size();
for (UInt_t iAxis = 0; iAxis < numAxes; ++iAxis) {
TAxis* axis = axes[iAxis];
for (Int_t iSet = 0, bin = 1; iSet < numDatasets; ++iSet, ++bin) {
if (iSet == firstNewTrigger) ++bin;
axis->SetBinLabel(bin, at(iSet).name);
} // end loop over datasets
axis->SetLabelSize (0.04f);
axis->LabelsOption ("v");
axis->SetTitle (iAxis % 2 == 0 ? "X" : "Y");
} // end loop over axes to format
hCorrelation->GetXaxis()->SetBinLabel(numBins, "rate / total");
hCorrelation->GetYaxis()->SetBinLabel(numBins, "overlap / rate");
hSharedRate ->GetXaxis()->SetBinLabel(numBins, "rate");
hSharedRate ->GetYaxis()->SetBinLabel(numBins, "overlap");
if (gDirectory->GetDirectory("unnormalized") == 0)
gDirectory->mkdir("unnormalized");
gDirectory->cd("unnormalized"); hSharedRate ->Write();
gDirectory->cd("/"); hCorrelation->Write();
//...........................................................................
}
void constrained_scan( const char* wsfile = "outputfiles/ws.root",
const char* new_poi_name="mu_bg_4b_msig_met1",
double constraintWidth=1.5,
int npoiPoints = 20,
double poiMinVal = 0.,
double poiMaxVal = 10.0,
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( "hbb_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.
//------------------------------------------------------------------------------
//
// drawXS
//
//------------------------------------------------------------------------------
void drawXS(UInt_t theCharge = WInclusive)
{
gStyle->SetEndErrorSize(5);
// 7 TeV inclusive
//----------------------------------------------------------------------------
xs_value[WInclusive][EEE] = 23.00;
xs_value[WInclusive][EEM] = 19.67;
xs_value[WInclusive][MME] = 19.81;
xs_value[WInclusive][MMM] = 21.02;
xs_value[WInclusive][all] = 20.76;
xs_stat[WInclusive][EEE] = 3.10;
xs_stat[WInclusive][EEM] = 2.73;
xs_stat[WInclusive][MME] = 2.60;
xs_stat[WInclusive][MMM] = 2.30;
xs_stat[WInclusive][all] = 1.32;
xs_syst[WInclusive][EEE] = 1.39;
xs_syst[WInclusive][EEM] = 1.50;
xs_syst[WInclusive][MME] = 1.55;
xs_syst[WInclusive][MMM] = 1.47;
xs_syst[WInclusive][all] = 1.13;
xs_lumi[WInclusive][EEE] = 0.51;
xs_lumi[WInclusive][EEM] = 0.43;
xs_lumi[WInclusive][MME] = 0.44;
xs_lumi[WInclusive][MMM] = 0.46;
xs_lumi[WInclusive][all] = 0.46;
// 7 TeV W+
//----------------------------------------------------------------------------
xs_value[WPlus][EEE] = 13.39;
xs_value[WPlus][EEM] = 13.18;
xs_value[WPlus][MME] = 14.14;
xs_value[WPlus][MMM] = 11.43;
xs_value[WPlus][all] = 12.73;
xs_stat[WPlus][EEE] = 2.39;
xs_stat[WPlus][EEM] = 2.24;
xs_stat[WPlus][MME] = 2.19;
xs_stat[WPlus][MMM] = 1.71;
xs_stat[WPlus][all] = 1.04;
xs_syst[WPlus][EEE] = 0.75;
xs_syst[WPlus][EEM] = 0.64;
xs_syst[WPlus][MME] = 0.74;
xs_syst[WPlus][MMM] = 0.53;
xs_syst[WPlus][all] = 0.59;
xs_lumi[WPlus][EEE] = 0.29;
xs_lumi[WPlus][EEM] = 0.29;
xs_lumi[WPlus][MME] = 0.31;
xs_lumi[WPlus][MMM] = 0.25;
xs_lumi[WPlus][all] = 0.28;
// 7 TeV W-
//----------------------------------------------------------------------------
xs_value[WMinus][EEE] = 9.49;
xs_value[WMinus][EEM] = 6.51;
xs_value[WMinus][MME] = 5.73;
xs_value[WMinus][MMM] = 9.48;
xs_value[WMinus][all] = 7.46;
xs_stat[WMinus][EEE] = 1.95;
xs_stat[WMinus][EEM] = 1.58;
xs_stat[WMinus][MME] = 1.40;
xs_stat[WMinus][MMM] = 1.52;
xs_stat[WMinus][all] = 0.79;
xs_syst[WMinus][EEE] = 0.60;
xs_syst[WMinus][EEM] = 0.37;
xs_syst[WMinus][MME] = 0.37;
xs_syst[WMinus][MMM] = 0.50;
xs_syst[WMinus][all] = 0.40;
xs_lumi[WMinus][EEE] = 0.21;
xs_lumi[WMinus][EEM] = 0.14;
xs_lumi[WMinus][MME] = 0.13;
xs_lumi[WMinus][MMM] = 0.21;
xs_lumi[WMinus][all] = 0.16;
// Do the work
//----------------------------------------------------------------------------
TGraphErrors* gStat = new TGraphErrors(nChannel);
TGraphErrors* gSyst = new TGraphErrors(nChannel);
TGraphErrors* gLumi = new TGraphErrors(nChannel);
for (UInt_t i=0; i<nChannel; i++)
{
Double_t errorSquared = (xs_stat[theCharge][i] * xs_stat[theCharge][i]);
gStat->SetPointError(i, sqrt(errorSquared) / xs_nlo[theCharge], 0.0);
errorSquared += (xs_syst[theCharge][i] * xs_syst[theCharge][i]);
gSyst->SetPointError(i, sqrt(errorSquared) / xs_nlo[theCharge], 0.0);
errorSquared += (xs_lumi[theCharge][i] * xs_lumi[theCharge][i]);
gLumi->SetPointError(i, sqrt(errorSquared) / xs_nlo[theCharge], 0.0);
gStat->SetPoint(i, xs_value[theCharge][i] / xs_nlo[theCharge], nChannel-i-1);
gSyst->SetPoint(i, xs_value[theCharge][i] / xs_nlo[theCharge], nChannel-i-1);
gLumi->SetPoint(i, xs_value[theCharge][i] / xs_nlo[theCharge], nChannel-i-1);
}
// Cosmetics
//----------------------------------------------------------------------------
gStat->SetLineWidth (2);
gStat->SetMarkerSize (1.3);
gStat->SetMarkerStyle(kFullCircle);
gSyst->SetLineColor (kRed);
gSyst->SetLineWidth (2);
gSyst->SetMarkerSize (1.3);
gSyst->SetMarkerStyle(kFullCircle);
gLumi->SetLineColor (kBlue);
gLumi->SetLineWidth (2);
gLumi->SetMarkerSize (1.3);
gLumi->SetMarkerStyle(kFullCircle);
// Draw
//----------------------------------------------------------------------------
TString suffix = "7TeV_" + sCharge[theCharge];
TCanvas* canvas = new TCanvas("ratioNLO_" + suffix, "ratioNLO_" + suffix);
canvas->SetLeftMargin(canvas->GetRightMargin());
Double_t xmin = 0.0;
Double_t xmax = 2.0;
Double_t ylegend = 1.2;
Double_t ymin = -0.6;
Double_t ymax = nChannel + ymin + ylegend;
TH2F* h2 = new TH2F("h2_" + suffix, "", 100, xmin, xmax, 100, ymin, ymax);
h2->Draw();
// NLO WZ cross-section
//----------------------------------------------------------------------------
TBox* nlo = new TBox(1. - xs_nlo_left [theCharge] / xs_nlo[theCharge], ymin,
1. + xs_nlo_right[theCharge] / xs_nlo[theCharge], ymax - ylegend);
nlo->SetLineColor(0);
nlo->SetFillColor(kGray);
nlo->SetFillStyle(1001);
nlo->Draw("e2,same");
TLine* line = new TLine(1., ymin, 1., ymax - ylegend);
line->SetLineColor(kGray+1);
line->SetLineWidth(2);
line->Draw("same");
// Cross sections
//----------------------------------------------------------------------------
gLumi->Draw("p,same");
gSyst->Draw("p,same");
gStat->Draw("p,same");
// Labels
//----------------------------------------------------------------------------
for (UInt_t i=0; i<nChannel; i++) {
Double_t x = gStat->GetX()[i];
Double_t y = gStat->GetY()[i];
Double_t gStatError = gStat->GetErrorX(i);
Double_t gSystError = gSyst->GetErrorX(i);
Double_t gLumiError = gLumi->GetErrorX(i);
DrawTLatex(42, xmin+0.06, y+0.15, 0.035, 12,
Form("%s %.2f #pm %.2f",
lChannel[i].Data(), x, gLumiError), 0);
gLumiError = sqrt(gLumiError*gLumiError - gSystError*gSystError);
gSystError = sqrt(gSystError*gSystError - gStatError*gStatError);
DrawTLatex(42, xmin+0.06, y-0.15, 0.025, 12,
Form("%.2f #pm %.2f #pm %.2f #pm %.2f",
x, gStatError, gSystError, gLumiError), 0);
}
DrawTLatex(42, 0.050, 0.975, _bigLabelSize, 13, "CMS Preliminary");
DrawTLatex(42, 0.940, 0.983, _bigLabelSize, 33,
Form("#sqrt{s} = 7 TeV, L = %.1f fb^{-1}", luminosity/1e3));
TString swz = "";
if (theCharge == WPlus) swz = "W^{+}Z";
else if (theCharge == WMinus) swz = "W^{-}Z";
else swz = "W^{#pm}Z";
h2->GetXaxis()->CenterTitle();
h2->GetXaxis()->SetTitleOffset(1.4);
h2->GetXaxis()->SetTitle(Form("#sigma_{%s}^{exp} / #sigma_{%s}^{theory}",
swz.Data(),
swz.Data()));
h2->GetYaxis()->SetTitle("");
// Remove y-axis labels
//----------------------------------------------------------------------------
TAxis* yaxis = h2->GetYaxis();
for (Int_t j=1; j<yaxis->GetNbins(); j++) yaxis->SetBinLabel(j, "");
// Additional legend
//----------------------------------------------------------------------------
DrawLegend(0.645, 0.840, gStat, " stat.", "lp");
DrawLegend(0.645, 0.795, nlo, " theory", "f");
DrawLegend(0.800, 0.840, gSyst, " syst.", "l");
DrawLegend(0.800, 0.795, gLumi, " lumi.", "l");
// Save
//----------------------------------------------------------------------------
canvas->Update();
canvas->GetFrame()->DrawClone();
canvas->RedrawAxis();
canvas->SaveAs(Form("pdf/ratioNLO_%s.pdf", suffix.Data()));
canvas->SaveAs(Form("png/ratioNLO_%s.png", suffix.Data()));
}
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() ;
}
int main(int argc, char *argv[]) {
using namespace boost::program_options;
using namespace std;
string programName(argv[0]);
string descString(programName);
descString += " [options] ";
descString += "data_file \nAllowed options";
options_description desc(descString);
desc.add_options()(kHelpCommandOpt, "produce help message")(kAutoLoadCommandOpt,
"automatic library loading (avoid root warnings)")(
kDataFileCommandOpt, value<string>(), "data file")(kAlphabeticOrderCommandOpt,
"sort by alphabetic order (default: sort by size)")(
kPlotCommandOpt, value<string>(), "produce a summary plot")(
kPlotTopCommandOpt, value<int>(), "plot only the <arg> top size branches")(
kSavePlotCommandOpt, value<string>(), "save plot into root file <arg>")(kVerboseCommandOpt, "verbose printout");
positional_options_description p;
p.add(kDataFileOpt, -1);
variables_map vm;
try {
store(command_line_parser(argc, argv).options(desc).positional(p).run(), vm);
notify(vm);
} catch (const error &) {
return 7000;
}
if (vm.count(kHelpOpt)) {
cout << desc << std::endl;
return 0;
}
if (!vm.count(kDataFileOpt)) {
string shortDesc("ConfigFileNotFound");
cerr << programName << ": no data file given" << endl;
return 7001;
}
gROOT->SetBatch();
if (vm.count(kAutoLoadOpt) != 0) {
gSystem->Load("libFWCoreFWLite");
FWLiteEnabler::enable();
}
string fileName = vm[kDataFileOpt].as<string>();
TFile file(fileName.c_str());
if (!file.IsOpen()) {
cerr << programName << ": unable to open data file " << fileName << endl;
return 7002;
}
TObject *o = file.Get("Events");
if (o == 0) {
cerr << programName << ": no object \"Events\" found in file: " << fileName << endl;
return 7003;
}
TTree *events = dynamic_cast<TTree *>(o);
if (events == 0) {
cerr << programName << ": object \"Events\" is not a TTree in file: " << fileName << endl;
return 7004;
}
TObjArray *branches = events->GetListOfBranches();
if (branches == 0) {
cerr << programName << ": tree \"Events\" in file " << fileName << " contains no branches" << endl;
return 7004;
}
bool verbose = vm.count(kVerboseOpt) > 0;
BranchVector v;
const size_t n = branches->GetEntries();
cout << fileName << " has " << n << " branches" << endl;
for (size_t i = 0; i < n; ++i) {
TBranch *b = dynamic_cast<TBranch *>(branches->At(i));
assert(b != 0);
string name(b->GetName());
if (name == "EventAux")
continue;
size_type s = GetTotalSize(b, verbose);
v.push_back(make_pair(b->GetName(), s));
}
if (vm.count(kAlphabeticOrderOpt)) {
sort(v.begin(), v.end(), sortByName());
} else {
sort(v.begin(), v.end(), sortByCompressedSize());
}
bool plot = (vm.count(kPlotOpt) > 0);
bool save = (vm.count(kSavePlotOpt) > 0);
int top = n;
if (vm.count(kPlotTopOpt) > 0)
top = vm[kPlotTopOpt].as<int>();
TH1F uncompressed("uncompressed", "branch sizes", top, -0.5, -0.5 + top);
TH1F compressed("compressed", "branch sizes", top, -0.5, -0.5 + top);
int x = 0;
TAxis *cxAxis = compressed.GetXaxis();
TAxis *uxAxis = uncompressed.GetXaxis();
for (BranchVector::const_iterator b = v.begin(); b != v.end(); ++b) {
const string &name = b->first;
size_type size = b->second;
cout << size << " " << name << endl;
if (x < top) {
cxAxis->SetBinLabel(x + 1, name.c_str());
uxAxis->SetBinLabel(x + 1, name.c_str());
compressed.Fill(x, size.second);
uncompressed.Fill(x, size.first);
x++;
}
}
// size_type branchSize = GetTotalBranchSize( events );
// cout << "total branches size: " << branchSize.first << " bytes (uncompressed), "
// << branchSize.second << " bytes (compressed)"<< endl;
size_type totalSize = GetTotalSize(events);
cout << "total tree size: " << totalSize.first << " bytes (uncompressed), " << totalSize.second
<< " bytes (compressed)" << endl;
double mn = DBL_MAX;
for (int i = 1; i <= top; ++i) {
double cm = compressed.GetMinimum(i), um = uncompressed.GetMinimum(i);
if (cm > 0 && cm < mn)
mn = cm;
if (um > 0 && um < mn)
mn = um;
}
mn *= 0.8;
double mx = max(compressed.GetMaximum(), uncompressed.GetMaximum());
mx *= 1.2;
uncompressed.SetMinimum(mn);
uncompressed.SetMaximum(mx);
compressed.SetMinimum(mn);
// compressed.SetMaximum( mx );
cxAxis->SetLabelOffset(-0.32);
cxAxis->LabelsOption("v");
cxAxis->SetLabelSize(0.03);
uxAxis->SetLabelOffset(-0.32);
uxAxis->LabelsOption("v");
uxAxis->SetLabelSize(0.03);
compressed.GetYaxis()->SetTitle("Bytes");
compressed.SetFillColor(kBlue);
compressed.SetLineWidth(2);
uncompressed.GetYaxis()->SetTitle("Bytes");
uncompressed.SetFillColor(kRed);
uncompressed.SetLineWidth(2);
if (plot) {
string plotName = vm[kPlotOpt].as<string>();
gROOT->SetStyle("Plain");
gStyle->SetOptStat(kFALSE);
gStyle->SetOptLogy();
TCanvas c;
uncompressed.Draw();
compressed.Draw("same");
c.SaveAs(plotName.c_str());
}
if (save) {
string fileName = vm[kSavePlotOpt].as<string>();
TFile f(fileName.c_str(), "RECREATE");
compressed.Write();
uncompressed.Write();
f.Close();
}
return 0;
}
