void Plot(RooRealVar *mass, RooDataSet *data, RooAbsPdf *pdf, pair<double,double> sigRange, vector<double> fwhmRange, string title, string savename){
double semin=sigRange.first;
double semax=sigRange.second;
double fwmin=fwhmRange[0];
double fwmax=fwhmRange[1];
double halfmax=fwhmRange[2];
double binwidth=fwhmRange[3];
RooPlot *plot = mass->frame(Bins(binning_),Range("higgsRange"));
if (data) data->plotOn(plot,Invisible());
pdf->plotOn(plot,NormRange("higgsRange"),Range(semin,semax),FillColor(19),DrawOption("F"),LineWidth(2),FillStyle(1001),VLines(),LineColor(15));
TObject *seffLeg = plot->getObject(int(plot->numItems()-1));
pdf->plotOn(plot,NormRange("higgsRange"),Range(semin,semax),LineColor(15),LineWidth(2),FillStyle(1001),VLines());
pdf->plotOn(plot,NormRange("higgsRange"),Range("higgsRange"),LineColor(kBlue),LineWidth(2),FillStyle(0));
TObject *pdfLeg = plot->getObject(int(plot->numItems()-1));
if (data) data->plotOn(plot,MarkerStyle(kOpenSquare));
TObject *dataLeg = plot->getObject(int(plot->numItems()-1));
TLegend *leg = new TLegend(0.15,0.89,0.5,0.55);
leg->SetFillStyle(0);
leg->SetLineColor(0);
leg->SetTextSize(0.03);
if (data) leg->AddEntry(dataLeg,"Simulation","lep");
leg->AddEntry(pdfLeg,"Parametric model","l");
leg->AddEntry(seffLeg,Form("#sigma_{eff} = %1.2f GeV",0.5*(semax-semin)),"fl");
plot->GetXaxis()->SetNdivisions(509);
halfmax*=(plot->getFitRangeBinW()/binwidth);
TArrow *fwhmArrow = new TArrow(fwmin,halfmax,fwmax,halfmax,0.02,"<>");
fwhmArrow->SetLineWidth(2.);
TPaveText *fwhmText = new TPaveText(0.15,0.45,0.45,0.58,"brNDC");
fwhmText->SetFillColor(0);
fwhmText->SetLineColor(kWhite);
fwhmText->SetTextSize(0.03);
fwhmText->AddText(Form("FWHM = %1.2f GeV",(fwmax-fwmin)));
TLatex lat1(0.65,0.85,"#splitline{CMS Preliminary}{Simulation}");
lat1.SetNDC(1);
lat1.SetTextSize(0.03);
TLatex lat2(0.65,0.75,title.c_str());
lat2.SetNDC(1);
lat2.SetTextSize(0.025);
TCanvas *canv = new TCanvas("c","c",600,600);
plot->SetTitle("");
plot->GetXaxis()->SetTitle("m_{#gamma#gamma} (GeV)");
plot->Draw();
leg->Draw("same");
fwhmArrow->Draw("same <>");
fwhmText->Draw("same");
lat1.Draw("same");
lat2.Draw("same");
canv->Print(Form("%s.pdf",savename.c_str()));
canv->Print(Form("%s.png",savename.c_str()));
string path = savename.substr(0,savename.find('/'));
canv->Print(Form("%s/animation.gif+100",path.c_str()));
delete canv;
}
/////////////////////////////////////////////
// Have a nice plotting functiong
//
/////////////////////////////////////////////
void MethodDatasetsProbScan::plotFitRes(TString fName) {
for (int i=0; i<pdf->getFitObs().size(); i++) {
TString fitVar = pdf->getFitObs()[i];
TCanvas *fitCanv = newNoWarnTCanvas( getUniqueRootName(), Form("S+B and B only fits to the dataset for %s",fitVar.Data()) );
TLegend *leg = new TLegend(0.6,0.7,0.92,0.92);
leg->SetFillColor(0);
leg->SetLineColor(0);
RooPlot *plot = w->var(fitVar)->frame();
// data invisible for norm
w->data(pdf->getDataName())->plotOn( plot, Invisible() );
// bkg pdf
if( pdf->getBkgPdf() ){
if ( !bkgOnlyFitResult ) {
cout << "MethodDatasetsProbScan::plotFitRes() : ERROR : bkgOnlyFitResult is NULL" << endl;
exit(1);
}
setParameters(w, bkgOnlyFitResult);
if ( !w->pdf(pdf->getBkgPdfName()) ) {
cout << "MethodDatasetsProbScan::plotFitRes() : ERROR : No background pdf " << pdf->getBkgPdfName() << " found in workspace" << endl;
exit(1);
}
w->pdf(pdf->getBkgPdfName())->plotOn( plot, LineColor(kRed) );
leg->AddEntry( plot->getObject(plot->numItems()-1), "Background Only Fit", "L");
}
else cout << "MethodDatasetsProbScan::plotFitRes() : WARNING : No background pdf is given. Will only plot S+B hypothesis." << std::endl;
// free fit
if ( !dataFreeFitResult ) {
cout << "MethodDatasetsProbScan::plotFitRes() : ERROR : dataFreeFitResult is NULL" << endl;
exit(1);
}
setParameters(w, dataFreeFitResult);
if ( !w->pdf(pdf->getPdfName()) ) {
cout << "MethodDatasetsProbScan::plotFitRes() : ERROR : No pdf " << pdf->getPdfName() << " found in workspace" << endl;
exit(1);
}
w->pdf(pdf->getPdfName())->plotOn(plot);
leg->AddEntry( plot->getObject(plot->numItems()-1), "Free Fit", "L");
// data unblinded if needed
map<TString,TString> unblindRegs = pdf->getUnblindRegions();
if ( unblindRegs.find( fitVar ) != unblindRegs.end() ) {
w->data(pdf->getDataName())->plotOn( plot, CutRange(pdf->getUnblindRegions()[fitVar]) );
leg->AddEntry( plot->getObject(plot->numItems()-1), "Data", "LEP");
}
plot->Draw();
leg->Draw("same");
savePlot(fitCanv, fName);
}
}
pair<double,double> bkgEvPerGeV(RooWorkspace *work, int m_hyp, int cat, int spin=false){
RooRealVar *mass = (RooRealVar*)work->var("CMS_hgg_mass");
if (spin) mass = (RooRealVar*)work->var("mass");
mass->setRange(100,180);
RooAbsPdf *pdf = (RooAbsPdf*)work->pdf(Form("pdf_data_pol_model_8TeV_cat%d",cat));
RooAbsData *data = (RooDataSet*)work->data(Form("data_mass_cat%d",cat));
RooPlot *tempFrame = mass->frame();
data->plotOn(tempFrame,Binning(80));
pdf->plotOn(tempFrame);
RooCurve *curve = (RooCurve*)tempFrame->getObject(tempFrame->numItems()-1);
double nombkg = curve->Eval(double(m_hyp));
RooRealVar *nlim = new RooRealVar(Form("nlim%d",cat),"",0.,0.,1.e5);
//double lowedge = tempFrame->GetXaxis()->GetBinLowEdge(FindBin(double(m_hyp)));
//double upedge = tempFrame->GetXaxis()->GetBinUpEdge(FindBin(double(m_hyp)));
//double center = tempFrame->GetXaxis()->GetBinUpCenter(FindBin(double(m_hyp)));
nlim->setVal(nombkg);
mass->setRange("errRange",m_hyp-0.5,m_hyp+0.5);
RooAbsPdf *epdf = 0;
epdf = new RooExtendPdf("epdf","",*pdf,*nlim,"errRange");
RooAbsReal *nll = epdf->createNLL(*data,Extended(),NumCPU(4));
RooMinimizer minim(*nll);
minim.setStrategy(0);
minim.setPrintLevel(-1);
minim.migrad();
minim.minos(*nlim);
double error = (nlim->getErrorLo(),nlim->getErrorHi())/2.;
data->Print();
return pair<double,double>(nombkg,error);
}
void MakeSpinPlots::DrawSpinBackground(TString tag, TString mcName,bool signal){
bool drawSM = (smName!="" && smName!=mcName);
TCanvas cv;
double thisN = ws->data(mcName+"_Combined")->reduce(TString("evtcat==evtcat::")+tag)->sumEntries();
float norm = thisN; //607*lumi/12.*thisN/(totEB+totEE);
cout << norm <<endl;
if(signal) norm = ws->data(Form("Data_%s_%s_sigWeight",tag.Data(),mcName.Data()))->sumEntries();
RooPlot *frame = ws->var("cosT")->frame(0,1,5);
RooDataSet* bkgWeight = (RooDataSet*)ws->data(Form("Data_%s_%s_bkgWeight",tag.Data(),mcName.Data()));
RooDataSet* tmp = (RooDataSet*)ws->data("Data_Combined")->reduce(TString("((mass>115 && mass<120) || (mass>130 && mass<135)) && evtcat==evtcat::")+tag);
tmp->plotOn(frame,RooFit::Rescale(norm/tmp->sumEntries()));
cout << "b" <<endl;
ws->pdf(Form("%s_FIT_%s_cosTpdf",mcName.Data(),tag.Data()))->plotOn(frame,RooFit::LineColor(kGreen),RooFit::Normalization(norm/tmp->sumEntries()));
if(drawSM) ws->pdf(Form("%s_FIT_%s_cosTpdf",smName.Data(),tag.Data()))->plotOn(frame,RooFit::LineColor(kRed),RooFit::Normalization(norm/tmp->sumEntries()));
cout << "c " <<bkgWeight <<endl;
bkgWeight->plotOn(frame,RooFit::Rescale(norm/bkgWeight->sumEntries()),RooFit::MarkerColor(kBlue) );
if(signal){
cout << "d" <<endl;
ws->data(Form("Data_%s_%s_sigWeight",tag.Data(),mcName.Data()))->plotOn(frame,RooFit::MarkerStyle(4));
}
cout << "d" <<endl;
frame->SetMaximum(frame->GetMaximum()*(signal?0.8:0.4)*norm/tmp->sumEntries());
frame->SetMinimum(-1*frame->GetMaximum());
TLegend l(0.6,0.2,0.95,0.45);
l.SetFillColor(0);
l.SetBorderSize(0);
l.SetHeader(tag);
l.AddEntry(frame->getObject(0),"Data m#in [115,120]#cup[130,135]","p");
l.AddEntry(frame->getObject(1),mcName,"l");
if(drawSM) l.AddEntry(frame->getObject(2),"SM Higgs","l");
l.AddEntry(frame->getObject(2+drawSM),"background weighted Data","p");
if(signal) l.AddEntry(frame->getObject(3+drawSM),"signal weighted Data","p");
cout << "e" <<endl;
frame->Draw();
l.Draw("SAME");
cv.SaveAs( basePath+Form("/cosThetaPlots/CosThetaDist_%s%s_%s_%s.png",outputTag.Data(),(signal ? "":"_BLIND"),mcName.Data(),tag.Data()) );
cv.SaveAs( basePath+Form("/cosThetaPlots/C/CosThetaDist_%s%s_%s_%s.C",outputTag.Data(),(signal ? "":"_BLIND"),mcName.Data(),tag.Data()) );
cv.SaveAs( basePath+Form("/cosThetaPlots/CosThetaDist_%s%s_%s_%s.pdf",outputTag.Data(),(signal ? "":"_BLIND"),mcName.Data(),tag.Data()) );
}
void MakeSpinPlots::DrawSpinSubTotBackground(TString mcName,bool signal){
bool drawSM = (smName!="" && smName!=mcName);
TCanvas cv;
double thisN = ws->data(mcName+"_Combined")->sumEntries();
float norm = thisN;
if(signal) norm = ws->var(Form("Data_%s_FULLFIT_Nsig",mcName.Data()))->getVal();
RooPlot *frame = ws->var("cosT")->frame(0,1,10);
RooDataSet* tmp = (RooDataSet*)ws->data(Form("Data_Combined"))->reduce("(mass>115 && mass<120) || (mass>130 && mass<135)");
tmp->plotOn(frame,RooFit::Rescale(norm/tmp->sumEntries()));
ws->pdf(Form("%s_FIT_cosTpdf",mcName.Data()))->plotOn(frame,RooFit::LineColor(kGreen),RooFit::Normalization(norm/tmp->sumEntries()));
if(drawSM) ws->pdf(Form("%s_FIT_cosTpdf",smName.Data()))->plotOn(frame,RooFit::LineColor(kRed),RooFit::Normalization(norm/tmp->sumEntries()));
if(signal){
RooDataHist *h = (RooDataHist*)ws->data( Form("Data_%s_Combined_bkgSub_cosT",mcName.Data()) );
h->plotOn(frame,RooFit::MarkerStyle(4));
std::cout << "Nsig: " << h->sumEntries() << std::endl;
}
frame->SetMaximum(frame->GetMaximum()*(signal?2.:1.2)*norm/tmp->sumEntries());
frame->SetMinimum(-1*frame->GetMaximum());
TLegend l(0.6,0.2,0.95,0.45);
l.SetFillColor(0);
l.SetBorderSize(0);
l.SetHeader("Combined");
l.AddEntry(frame->getObject(0),"Data m#in [115,120]#cup[130,135]","p");
l.AddEntry(frame->getObject(1),mcName,"l");
if(drawSM) l.AddEntry(frame->getObject(2),"SM Higgs","l");
if(signal) l.AddEntry(frame->getObject(2+drawSM),"bkg-subtracted Data","p");
frame->Draw();
l.Draw("SAME");
cv.SaveAs( basePath+Form("/cosThetaPlots/CosThetaDist_SimpleSub_%s%s_%s.png",outputTag.Data(),(signal ? "":"_BLIND"),mcName.Data()) );
cv.SaveAs( basePath+Form("/cosThetaPlots/C/CosThetaDist_SimpleSub_%s%s_%s.C",outputTag.Data(),(signal ? "":"_BLIND"),mcName.Data()) );
cv.SaveAs( basePath+Form("/cosThetaPlots/CosThetaDist_SimpleSub_%s%s_%s.pdf",outputTag.Data(),(signal ? "":"_BLIND"),mcName.Data()) );
}
double getMyNLL(RooRealVar *var, RooAbsPdf *pdf, RooDataHist *data){
RooPlot *plot = var->frame();
data->plotOn(plot);
pdf->plotOn(plot);
RooCurve *pdfCurve = (RooCurve*)plot->getObject(plot->numItems()-1);
double sum=0.;
for (int i=0; i<data->numEntries(); i++){
double binCenter = data->get(i)->getRealValue("CMS_hgg_mass");
double weight = data->weight();
sum+=TMath::Log(TMath::Poisson(100.*weight,100.*pdfCurve->Eval(binCenter)));
}
return -1.*sum;
}
void draw_data_mgg(TString folderName,bool blind=true,float min=103,float max=160)
{
TFile inputFile(folderName+"/data.root");
const int nCat = 5;
TString cats[5] = {"HighPt","Hbb","Zbb","HighRes","LowRes"};
TCanvas cv;
for(int iCat=0; iCat < nCat; iCat++) {
RooWorkspace *ws = (RooWorkspace*)inputFile.Get(cats[iCat]+"_mgg_workspace");
RooFitResult* res = (RooFitResult*)ws->obj("fitresult_pdf_data");
RooRealVar * mass = ws->var("mgg");
mass->setRange("all",min,max);
mass->setRange("blind",121,130);
mass->setRange("low",106,121);
mass->setRange("high",130,160);
mass->setUnit("GeV");
mass->SetTitle("m_{#gamma#gamma}");
RooAbsPdf * pdf = ws->pdf("pdf");
RooPlot *plot = mass->frame(min,max,max-min);
plot->SetTitle("");
RooAbsData* data = ws->data("data")->reduce(Form("mgg > %f && mgg < %f",min,max));
double nTot = data->sumEntries();
if(blind) data = data->reduce("mgg < 121 || mgg>130");
double nBlind = data->sumEntries();
double norm = nTot/nBlind; //normalization for the plot
data->plotOn(plot);
pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::Range("Full"),RooFit::LineWidth(0.1) );
plot->Print();
//add the fix error band
RooCurve* c = plot->getCurve("pdf_Norm[mgg]_Range[Full]_NormRange[Full]");
const int Nc = c->GetN();
//TGraphErrors errfix(Nc);
//TGraphErrors errfix2(Nc);
TGraphAsymmErrors errfix(Nc);
TGraphAsymmErrors errfix2(Nc);
Double_t *x = c->GetX();
Double_t *y = c->GetY();
double NtotalFit = ws->var("Nbkg1")->getVal()*ws->var("Nbkg1")->getVal() + ws->var("Nbkg2")->getVal()*ws->var("Nbkg2")->getVal();
for( int i = 0; i < Nc; i++ )
{
errfix.SetPoint(i,x[i],y[i]);
errfix2.SetPoint(i,x[i],y[i]);
mass->setVal(x[i]);
double shapeErr = pdf->getPropagatedError(*res)*NtotalFit;
//double totalErr = TMath::Sqrt( shapeErr*shapeErr + y[i] );
//total normalization error
double totalErr = TMath::Sqrt( shapeErr*shapeErr + y[i]*y[i]/NtotalFit );
if ( y[i] - totalErr > .0 )
{
errfix.SetPointError(i, 0, 0, totalErr, totalErr );
}
else
{
errfix.SetPointError(i, 0, 0, y[i] - 0.01, totalErr );
}
//2sigma
if ( y[i] - 2.*totalErr > .0 )
{
errfix2.SetPointError(i, 0, 0, 2.*totalErr, 2.*totalErr );
}
else
{
errfix2.SetPointError(i, 0, 0, y[i] - 0.01, 2.*totalErr );
}
/*
std::cout << x[i] << " " << y[i] << " "
<< " ,pdf get Val: " << pdf->getVal()
<< " ,pdf get Prop Err: " << pdf->getPropagatedError(*res)*NtotalFit
<< " stat uncertainty: " << TMath::Sqrt(y[i]) << " Ntot: " << NtotalFit << std::endl;
*/
}
errfix.SetFillColor(kYellow);
errfix2.SetFillColor(kGreen);
//pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::FillColor(kGreen),RooFit::Range("Full"), RooFit::VisualizeError(*res,2.0,kFALSE));
//pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::FillColor(kYellow),RooFit::Range("Full"), RooFit::VisualizeError(*res,1.0,kFALSE));
//pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::FillColor(kGreen),RooFit::Range("Full"), RooFit::VisualizeError(*res,2.0,kTRUE));
//pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::FillColor(kYellow),RooFit::Range("Full"), RooFit::VisualizeError(*res,1.0,kTRUE));
plot->addObject(&errfix,"4");
plot->addObject(&errfix2,"4");
plot->addObject(&errfix,"4");
data->plotOn(plot);
TBox blindBox(121,plot->GetMinimum()-(plot->GetMaximum()-plot->GetMinimum())*0.015,130,plot->GetMaximum());
blindBox.SetFillColor(kGray);
if(blind) {
plot->addObject(&blindBox);
pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::FillColor(kGreen),RooFit::Range("Full"), RooFit::VisualizeError(*res,2.0,kTRUE));
pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::FillColor(kYellow),RooFit::Range("Full"), RooFit::VisualizeError(*res,1.0,kTRUE));
}
//plot->addObject(&errfix,"4");
//data->plotOn(plot);
//pdf->plotOn(plot,RooFit::Normalization( norm ) );
//pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::Range("Full"),RooFit::LineWidth(1.5) );
pdf->plotOn(plot,RooFit::NormRange( "low,high" ),RooFit::Range("Full"), RooFit::LineWidth(1));
data->plotOn(plot);
/*
pdf->plotOn(plot,RooFit::Normalization(norm),RooFit::Range("all"),RooFit::LineWidth(0.8) );
//pdf->plotOn(plot,RooFit::Normalization(norm),RooFit::FillColor(kGreen),RooFit::Range("all"), RooFit::VisualizeError(*res,2.0,kFALSE));
//pdf->plotOn(plot,RooFit::Normalization(norm),RooFit::FillColor(kYellow),RooFit::Range("all"), RooFit::VisualizeError(*res,1.0,kFALSE));
pdf->plotOn(plot,RooFit::Normalization(norm),RooFit::FillColor(kGreen),RooFit::Range("all"), RooFit::VisualizeError(*res,2.0,kTRUE));
pdf->plotOn(plot,RooFit::Normalization(norm),RooFit::FillColor(kYellow),RooFit::Range("all"), RooFit::VisualizeError(*res,1.0,kTRUE));
data->plotOn(plot);
pdf->plotOn(plot,RooFit::Normalization(norm),RooFit::Range("all"),RooFit::LineWidth(0.8) );
*/
TLatex lbl0(0.1,0.96,"CMS Preliminary");
lbl0.SetNDC();
lbl0.SetTextSize(0.042);
plot->addObject(&lbl0);
TLatex lbl(0.4,0.96,Form("%s Box",cats[iCat].Data()));
lbl.SetNDC();
lbl.SetTextSize(0.042);
plot->addObject(&lbl);
TLatex lbl2(0.6,0.96,"#sqrt{s}=8 TeV L = 19.78 fb^{-1}");
lbl2.SetNDC();
lbl2.SetTextSize(0.042);
plot->addObject(&lbl2);
int iObj=-1;
TNamed *obj;
while( (obj = (TNamed*)plot->getObject(++iObj)) ) {
obj->SetName(Form("Object_%d",iObj));
}
plot->Draw();
TString tag = (blind ? "_BLIND" : "");
cv.SaveAs(folderName+"/figs/mgg_data_"+cats[iCat]+tag+TString(Form("_%0.0f_%0.0f",min,max))+".png");
cv.SaveAs(folderName+"/figs/mgg_data_"+cats[iCat]+tag+TString(Form("_%0.0f_%0.0f",min,max))+".pdf");
cv.SaveAs(folderName+"/figs/mgg_data_"+cats[iCat]+tag+TString(Form("_%0.0f_%0.0f",min,max))+".C");
}
}
int main() {
float min_logL1 = 5986.94;
float min_logL0 = 5987.16;
string filepath="FINAL_RESULT_AB.root_RESULT__RESULT";
filepath="/shome/buchmann/KillerKoala/CBAF/Development/exchange/RooFit__WorkSpace__Exchange_201417_175622__RNSG_46692.4.root__RESULT__RESULT"; // final MCwS
filepath="/shome/buchmann/KillerKoala/CBAF/Development/exchange/RooFit__WorkSpace__Exchange_201417_175622__RNSG_46692.4.root__RESULT__RESULT";
filepath="/shome/buchmann/KillerKoala/CBAF/Development/exchange/RooFit__WorkSpace__Exchange_201417_141126__RNSG_97048.1.root__RESULT__RESULT";
// *************************************************************************************
setlumi(PlottingSetup::luminosity);
setessentialcut(PlottingSetup::essential); // this sets the essential cut; this one is used in the draw command so it is AUTOMATICALLY applied everywhere. IMPORTANT: Do NOT store weights here!
stringstream resultsummary;
// write_analysis_type(PlottingSetup::RestrictToMassPeak,PlottingSetup::DoBTag);
do_png(true);
do_pdf(true);
do_eps(false);
do_C(true);
do_root(false);
PlottingSetup::directoryname = "pValuePlot";
gROOT->SetStyle("Plain");
bool do_fat_line = false; // if you want to have HistLineWidth=1 and FuncWidth=1 as it was before instead of 2
setTDRStyle(do_fat_line);
gStyle->SetTextFont(42);
bool showList = true;
set_directory(PlottingSetup::directoryname); // Indicate the directory name where you'd like to save the output files in Setup.C
set_treename("events"); // you can set the treename here to be used; options are "events" (for reco) for "PFevents" (for particle flow)
TFile *f = new TFile(filepath.c_str());
if(f->IsZombie()) {
cout << "Seems to be a zombie. goodbye." << endl;
return -1;
}
RooWorkspace *wa = (RooWorkspace*)f->Get("transferSpace");
RooPlot *plot = (RooPlot*) wa->obj("frame_mlledge_109fde50");
// cout << plot << endl;
wa->Print("v");
TCanvas *can = new TCanvas("can","can");
cout << "Address of plot : " << plot << endl;
// plot->Draw();
float pVal_mllmin=35;
float pVal_mllmax=90;
int is_data=PlottingSetup::data;
vector < std::pair < float, float> > loglikelihoods;
string function="";
for(int i=0; i< plot->numItems();i++){
string name = plot->getObject(i)->GetName();
if (plot->getObject(i)->IsA()->InheritsFrom( "RooCurve" ))function=name;
}
RooCurve* curve = (RooCurve*) plot->findObject(function.c_str(),RooCurve::Class()) ;
if (!curve) {
dout << "RooPlot::residHist(" << plot->GetName() << ") cannot find curve" << endl ;
return 0 ;
}
int iMinimum=0;
float min=1e7;
for(int i=0;i<curve->GetN();i++) {
double x,y;
curve->GetPoint(i,x,y);
if(y<min & y>=0) {
min=y;
iMinimum=i;
}
}
double x,y;
curve->GetPoint(iMinimum,x,y);
cout << "Minimum is at " << x << " : " << y << endl;
loglikelihoods.push_back(make_pair(x,y+min_logL1));
//move right starting from the minimum
for(int i=iMinimum+1;i<curve->GetN();i++) {
float yold=y;
curve->GetPoint(i,x,y);
//if(abs((y-yold)/yold)>0.5) continue;
loglikelihoods.push_back(make_pair(x,y+min_logL1));
}
/*
for(int i=0;i<curve->GetN();i++) {
double x,y;
curve->GetPoint(i,x,y);
loglikelihoods.push_back(make_pair(x,y+min_logL1));
}*/
cout << "The whole thing contains " << loglikelihoods.size() << " points " << endl;
ProduceSignificancePlots(min_logL0, loglikelihoods, pVal_mllmin, pVal_mllmax, is_data, "", "");
can->SaveAs("Crap.png");
delete can;
delete plot;
delete wa;
f->Close();
return 0;
}
