TProfile* overlayTProfiles(TH2F* hdata, TH2F* hdy, float ymin, float ymax, TString savename)
{
// See how the mean of the dimumass changes vs some variable by making TProfiles of a 2D histogram with the Dimu mass as the y axis.
// Compare data to MC and save the results as png files.
TString dataname = TString("Golden_JSON_DoubleMuon_Data");
TString dyname = TString("Drell_Yan_Monte_Carlo_2015");
// Make TProfiles from them to see how the mean changes vs the x variable.
TProfile* pdata = hdata->ProfileX();
pdata->SetLineColor(1);
pdata->SetLineWidth(3);
pdata->SetTitle(hdata->GetTitle());
pdata->GetYaxis()->SetTitle(hdata->GetYaxis()->GetTitle());
TProfile* pdy = hdy->ProfileX();
pdy->SetLineColor(2);
pdy->SetLineWidth(3);
pdy->SetTitle(hdy->GetTitle());
pdy->GetYaxis()->SetTitle(hdy->GetYaxis()->GetTitle());
std::cout<< "hdata: " << hdata << std::endl;
std::cout<< "hdy: " << hdy << std::endl;
std::cout<< "pdata: " << pdata << std::endl;
std::cout<< "pdy: " << pdy << std::endl;
TCanvas* c = new TCanvas();
c->SetGridx(kTRUE);
c->SetGridy(kTRUE);
// Draw data and MC on the same plot
// Have to draw the same plots twice to get error bars and a curve through the error bars.
c->cd();
// hdata->Draw("colz");
pdata->SetAxisRange(ymin,ymax,"Y");
pdata->Draw("hist c");
pdata->Draw("E X0 same");
pdy->Draw("hist c same");
pdy->Draw("E X0 same");
// Stat box alignment
DiMuPlottingSystem* dps = new DiMuPlottingSystem();
dps->arrangeStatBox(c);
// Legend
TLegend* l = new TLegend(0.15, 0.15, 0.7, 0.25, "", "brNDC");
l->AddEntry(pdata, dataname, "l");
l->AddEntry(pdy, dyname, "l");
l->Draw("same");
c->Print(savename);
return pdata;
}
// Draw a 2-D plot within the specified Y range and superimpose its X profile,
// setting as sigmas that of the fit (and not the error of the mean)
void plotAndProfileXSpread (TH2* h2, float min, float max, bool profile=false, float ymin=-5., float ymax=5.) {
setStyle(h2);
gPad->SetGrid(1,1);
gStyle->SetGridColor(15);
gStyle->SetOptStat(0);
// h2->RebinX(3);
// h2->RebinY(2);
// h2->SetXTitle("distance from anode (cm)");
// h2->SetYTitle("(d_{reco}-d_{sim})/#sigma_{reco}");
h2->SetMarkerColor(2);
h2->SetLineColor(2);
h2->GetYaxis()->SetTitleOffset(1.4);
h2->GetXaxis()->SetRangeUser(min,max);
h2->GetYaxis()->SetRangeUser(ymin,ymax);
h2->DrawCopy("box");
if (profile) {
TProfile* prof = h2->ProfileX("profile",-1,-1,"s");
prof->SetMarkerStyle(20);
prof->SetMarkerSize(1.2);
prof->SetMarkerColor(1);
prof->SetLineColor(1);
prof->SetLineWidth(2);
prof->DrawCopy("same e1");
delete prof;
}
TLine * l = new TLine(h2->GetXaxis()->GetXmin(),0,h2->GetXaxis()->GetXmax(),0);
l->SetLineColor(3);
l->Draw();
}
void QAoccupancy(const Char_t *fdata, const Char_t *fmc)
{
style();
TFile *fdtin = TFile::Open(fdata);
TList *ldtin = (TList *)fdtin->Get("clist");
TH2 *hdtin = (TH2 *)ldtin->FindObject("NClustersSPD2");
TProfile *pdtin = hdtin->ProfileY("pdtin_clusters");
pdtin->SetMarkerStyle(20);
pdtin->SetMarkerSize(2);
pdtin->SetMarkerColor(kAzure-3);
TFile *fmcin = TFile::Open(fmc);
TList *lmcin = (TList *)fmcin->Get("clist");
TH2 *hmcin = (TH2 *)lmcin->FindObject("NClustersSPD2");
TProfile *pmcin = hmcin->ProfileY("pmcin_clusters");
pmcin->SetMarkerStyle(25);
pmcin->SetMarkerSize(2);
pmcin->SetMarkerColor(kRed+1);
TCanvas *c = new TCanvas("cOccupancy", "cOccupancy", 800, 800);
c->SetLogy();
TH1 * hfr = c->DrawFrame(-0.5, 2., 10.5, 500.);
DrawBinLabelsX(hfr, kTRUE);
hfr->SetTitle(";;#LT#it{N}_{clusters,SPD-1}#GT");
pdtin->DrawCopy("same");
pmcin->DrawCopy("same");
TLegend *legend = new TLegend(0.20, 0.18, 0.50, 0.30);
legend->SetFillColor(0);
legend->SetBorderSize(0);
legend->SetTextFont(42);
legend->SetTextSize(0.04);
legend->AddEntry(pdtin, "data", "pl");
legend->AddEntry(pmcin, "Monte Carlo", "pl");
legend->Draw("same");
c->SaveAs(canvasPrefix+"occupancy.pdf");
return;
TCanvas *cr = new TCanvas("cOccupancyr", "cOccupancyr", 800, 800);
// hfr = cr->DrawFrame(-0.5, 0.75, 10.5, 1.25);
// DrawBinLabelsX(hfr, kTRUE);
// hfr->SetTitle(";;#LT#it{N}_{clusters,SPD-1}#GT ratio");
pdtin->SetLineColor(kAzure-3);
pdtin->SetLineWidth(3);
pdtin->Divide(pmcin);
pdtin->Draw("same,histo");
legend = new TLegend(0.505025, 0.760673, 0.805276, 0.930142);
legend->SetFillColor(0);
legend->SetBorderSize(0);
legend->SetTextFont(42);
legend->SetTextSize(0.04);
legend->AddEntry(pdtin, "data / Monte Carlo", "l");
legend->Draw("same");
cr->SaveAs(canvasPrefix+"occupancyr.pdf");
}
TProfile* plotIsoPerformance( TFile* ftt,
const char* signal, // histogram name
const char* background, // histogram name
const char* name, // unique name
bool reverse = false, // normally signal near zero bin, reverse means signal is around max bin
double bkg_eff_min = 0,
double bkg_eff_max = 1,
double sig_eff_min = 0,
double sig_eff_max = 0
)
{
TH1F* S = dynamic_cast<TH1F*>(ftt->Get(signal));
if ( ! S ) {
std::cout << "Error: histogram not found " << signal << std::endl;
return 0;
}
TH1F* B = dynamic_cast<TH1F*>(ftt->Get(background));
if ( ! B ) {
std::cout << "Error: histogram not found " << background << std::endl;
return 0;
}
char buf[1024];
sprintf(buf,"c_%s",name);
// TCanvas* c = new TCanvas(buf,buf,500,500);
sprintf(buf,"p_%s",name);
TProfile* p = new TProfile(buf,buf,50,bkg_eff_min,bkg_eff_max,sig_eff_min,sig_eff_max);
p->SetLineColor(kBlue);
p->SetLineWidth(2);
p->SetMarkerStyle(20);
p->SetMarkerSize(1);
p->GetXaxis()->SetTitle("Background Efficiency");
p->GetYaxis()->SetTitle("Signal Efficiency");
p->SetStats(kFALSE);
for( int i=0;i<=S->GetNbinsX()+1; ++i )
if ( reverse )
p->Fill(B->Integral(i,B->GetNbinsX()+1)/B->Integral(0,B->GetNbinsX()+1),
S->Integral(i,S->GetNbinsX()+1)/S->Integral(0,S->GetNbinsX()+1));
else
p->Fill(B->Integral(0,i)/B->Integral(0,B->GetNbinsX()+1),
S->Integral(0,i)/S->Integral(0,S->GetNbinsX()+1));
// p->Draw();
return p;
}
