void
sobWeightedPlot(TString filename,const char* dataset , const char* channel,const char* cat)
{
TFile F((TString("Plot_")+filename+".root").Data(),"READ");
gROOT->cd();
TH1F* data=(TH1F*)F.Get("data_obs");
TH1F* Ztt=(TH1F*)F.Get("Ztt");
TH1F* ggH=(TH1F*)F.Get("ggH");
TH1F* sig=(TH1F*)F.Get("signal");
TH1F* tt=(TH1F*)F.Get("ttbar");
TH1F* ewk=(TH1F*)F.Get("EWK");
TH1F* fakes=(TH1F*)F.Get("Fakes");
if(!sig){cout<<"No input histograms in file: "<<filename.Data()<<endl; return;}
float xmininset=60; float xmaxinset=180;
//float xmininset=0; float xmaxinset=340;//full range
////Format the histograms
Ztt->GetYaxis()->SetRangeUser(0.,1.3*findMaxY(data,0));
Ztt->GetXaxis()->SetTitle("#bf{m_{#tau#tau} [GeV]}");
Ztt->GetYaxis()->SetTitle("#bf{S/B Weighted dN/dm_{#tau#tau} [1/GeV]}");
Ztt->SetTitleOffset(1.300, "Y");
Ztt->SetTitleOffset(1.000, "X");
Ztt->SetNdivisions(505);
for(Int_t b=0;b<=sig->GetNbinsX()+1;b++){
//remove red line on top of y axis in plot
if(sig->GetBinCenter(b)<xmininset||xmaxinset<sig->GetBinCenter(b)){
sig->SetBinContent(b,0);
sig->SetBinError(b,0);
}
}
sig->SetName("sig");
sig->SetFillStyle(3353);//1001=solid , 3004,3005=diagonal
sig->SetFillColor(2);
sig->SetLineColor(2);
sig->SetLineStyle(1);
sig->SetLineWidth(0.);
ggH->SetBinContent(0,0);//remove red line on top of y axis in plot
ggH->SetBinContent(ggH->GetNbinsX()+1,0);
ggH->SetBinError(0,0);
ggH->SetBinError(ggH->GetNbinsX()+1,0);
ggH->SetName("ggH");
ggH->SetFillStyle(3353);//1001=solid , 3004,3005=diagonal
ggH->SetFillColor(2);
ggH->SetLineColor(2);
ggH->SetLineStyle(1);
ggH->SetLineWidth(0.);
TH1F* errorBand = (TH1F*)Ztt->Clone("errorBand");
errorBand->SetMarkerSize(0);
errorBand->SetFillColor(1);
errorBand->SetFillStyle(3013);
errorBand->SetLineWidth(1);
TLegend legend;
TString higgslabel="H(125 GeV)#rightarrow#tau#tau";
legend.SetFillStyle(0);
legend.SetFillColor(0);
legend.SetBorderSize(0);
legend.AddEntry(ggH,higgslabel,"F");
legend.AddEntry(data,"observed","LP");
legend.AddEntry(Ztt,"Z#rightarrow#tau#tau","F");
legend.AddEntry(tt,"t#bar{t}","F");
legend.AddEntry(ewk,"electroweak","F");
legend.AddEntry(fakes,"QCD","F");
legend.SetX1NDC(0.63);
legend.SetX2NDC(1.05);
legend.SetY1NDC(0.27);
legend.SetY2NDC(0.48);
legend.SetTextSize(.028);
legend.SetTextAlign( 12 );
//TH1F* dataDiff=diffPlot(data,Ztt,2);
TH1F* dataDiff=diffPlot(data,Ztt,1);
TH1F* errBand=getErrorBand(Ztt);
errBand->SetFillStyle(3013);//1001=solid , 3004,3005=diagonal, 3013=hatched official for H->tau tau
errBand->SetFillColor(1);
errBand->SetLineStyle(1);
errBand->SetLineColor(1);
errBand->SetLineWidth(1);
TH1F errBandFrame("errBandFrame","",(xmaxinset-xmininset)/dataDiff->GetBinWidth(1),xmininset,xmaxinset);
errBandFrame.GetYaxis()->SetTitle("");
errBandFrame.GetYaxis()->SetRangeUser(-1.1*findMinY(dataDiff,0,xmininset,xmaxinset),2.0*findMaxY(dataDiff,0,xmininset,xmaxinset));
errBandFrame.GetYaxis()->SetNdivisions(5);
errBandFrame.GetYaxis()->SetLabelSize(0.06);
errBandFrame.GetXaxis()->SetTitle("#bf{m_{#tau#tau} [GeV]} ");
errBandFrame.GetXaxis()->SetTitleColor(kBlack);
errBandFrame.GetXaxis()->SetTitleSize(0.07);
errBandFrame.GetXaxis()->SetTitleOffset(0.95);
errBandFrame.GetXaxis()->SetLabelSize(0.06);
errBandFrame.SetNdivisions(505);
TLegend legendDiff;
legendDiff.SetFillStyle(0);
legendDiff.SetFillColor(0);
legendDiff.SetBorderSize(0);
legendDiff.AddEntry(sig,higgslabel,"F");
legendDiff.AddEntry(dataDiff,"Data - Background","LP");
legendDiff.AddEntry(errBand,"Bkg. Uncertainty","F");
legendDiff.SetX1NDC(0.45);
legendDiff.SetX2NDC(0.88);
legendDiff.SetY1NDC(0.67);
legendDiff.SetY2NDC(0.88);
legendDiff.SetTextSize(.045);
legendDiff.SetTextAlign(12);
TCanvas C(filename,"",600,600);
TPad padBack("padBack","padBack",0.57,0.58,0.975,0.956);//TPad must be created after TCanvas otherwise ROOT crashes
padBack.SetFillColor(0);
TPad pad("diff","diff",0.45,0.5,0.9765,0.957);//TPad must be created after TCanvas otherwise ROOT crashes
pad.cd();
pad.SetFillColor(0);
pad.SetFillStyle(0);
errBandFrame.Draw();
errBand->Draw("e2lsame");
sig->Draw("histsame");
TLine line;
line.DrawLine(xmininset,0,xmaxinset,0);
dataDiff->Draw("pesame");
legendDiff.Draw();
pad.RedrawAxis();
C.cd();
Ztt->Draw("hist");
ggH->Draw("histsame");
Ztt->Draw("histsame");
errorBand->Draw("e2same");
tt->Draw("histsame");
ewk->Draw("histsame");
fakes->Draw("histsame");
data->Draw("pesame");
legend.Draw();
C.RedrawAxis();
padBack.Draw();//clear the background axes
pad.Draw();
CMSPrelim(dataset,channel,cat);
C.Print(TString("Plot_")+filename+".eps");
C.Print(TString("Plot_")+filename+".png");
C.Print(TString("Plot_")+filename+".pdf");
delete errorBand;
delete dataDiff;
delete errBand;
}
//------------------------------------------------------------------------------
void PlotAlignmentValidation::plotChi2(const char *inputFile)
{
// Opens the file (it should be OfflineValidation(Parallel)_result.root)
// and reads and plots the norm_chi^2 and h_chi2Prob -distributions.
// First set default style: plots are already formatted
TStyle defStyle("Default","Default Style");
defStyle.cd();
gStyle->SetOptStat(1);
TGaxis::SetMaxDigits(3);
Bool_t errorflag = kTRUE;
TFile* fi1 = new TFile(inputFile,"read");
TDirectoryFile* mta1 = NULL;
TDirectoryFile* mtb1 = NULL;
TCanvas* normchi = NULL;
TCanvas* chiprob = NULL;
if (fi1 != NULL) {
mta1 = (TDirectoryFile*) fi1->Get("TrackerOfflineValidationStandalone");
if(mta1 != NULL) {
mtb1 = (TDirectoryFile*) mta1->Get("GlobalTrackVariables");
if(mtb1 != NULL) {
normchi = (TCanvas*) mtb1->Get("h_normchi2");
chiprob = (TCanvas*) mtb1->Get("h_chi2Prob");
if (normchi != NULL && chiprob != NULL) {
errorflag = kFALSE;
}
}
}
}
if(errorflag)
{
std::cout << "PlotAlignmentValidation::plotChi2: Can't find data from given file,"
<< " no chi^2-plots produced" << std::endl;
return;
}
// Small adjustments: move the legend right so that it doesn't block
// the exponent of the y-axis scale
TLegend* l = (TLegend*)findObjectFromCanvas(normchi, "TLegend");
if (l != 0) {
l->SetX1NDC(0.25);
}
l = (TLegend*)findObjectFromCanvas(chiprob, "TLegend");
if (l != 0) {
l->SetX1NDC(0.25);
}
chiprob->Draw();
normchi->Draw();
// EPS-files
normchi->Print((outputDir + "/h_normchi2.eps").c_str());
chiprob->Print((outputDir + "/h_chi2Prob.eps").c_str());
// ROOT-files
TFile fi2((outputDir + "/h_normchi2.root").c_str(), "recreate");
normchi->Write();
fi2.Close();
TFile fi3((outputDir + "/h_chi2Prob.root").c_str(), "recreate");
chiprob->Write();
fi3.Close();
fi1->Close();
TGaxis::SetMaxDigits(4);
}
void plotNLLMassToys(long injmass){
TString filenamesb=TString("nllsb_")+injmass;
TString filenameb=TString("nllb_")+injmass;
TH1F * H[8];
TH1F * HDiff[8];
TH1F * HDiffFine[8];
TH1F * HSigma[8];
TF1* GScanToy[500];
for(Int_t m=0;m<8;m++){
H[m]=new TH1F(TString("H")+(long)(110+5*m),TString("mass=")+(long)(110+5*m),50,-200,-25);
HDiff[m]=new TH1F(TString("HDiff")+(long)(110+5*m),TString("mass=")+(long)(110+5*m),20,0.0,8);
HDiffFine[m]=new TH1F(TString("HDiffFine")+(long)(110+5*m),TString("mass=")+(long)(110+5*m),1000,0.0,8);
HSigma[m]=new TH1F(TString("HSigma")+(long)(110+5*m),TString("mass=")+(long)(110+5*m),50,10,10);
}
TH1F HMass("HMass","",8,110-2.5,150-2.5);
TH1F HPull("HPull","",11,-5.5,5.5);
TH1F HSigmaLow("HSigmaLow","",8,0,30);
HSigmaLow.GetXaxis()->SetTitle("#sigma_{low}");
TH1F HSigmaHigh("HSigmaHigh","",8,0,30);
HSigmaHigh.GetXaxis()->SetTitle("#sigma_{high}");
TH2F HSigma2D("HSigma2D","",8,0,20,8,0,20);
HSigma2D.GetYaxis()->SetTitle("#sigma_{high}");
HSigma2D.GetXaxis()->SetTitle("#sigma_{low}");
TH2F HBestFitvsSigmaHigh("HBestFitvsSigmaHigh","",8,110-2.5,145+2.5,8,110-2.5,145+2.5);
HBestFitvsSigmaHigh.GetYaxis()->SetTitle("#sigma_{high}");
HBestFitvsSigmaHigh.GetXaxis()->SetTitle("m_{bestfit}");
TH1F HSigmaFull("HSigmaFull","",20,0,50);
HSigmaFull.GetXaxis()->SetTitle("#sigma_{low}+#sigma_{high}");
ifstream file;
file.open((filenamesb+".txt").Data());
ifstream fileb;
fileb.open((filenameb+".txt").Data());
bool GoodToy[500];
int n;
float nll[8];
float nllb[8];
int ngood=0;
while(!file.eof()){
file>>n>>nll[0]>>nll[1]>>nll[2]>>nll[3]>>nll[4]>>nll[5]>>nll[6]>>nll[7];
fileb>>n>>nllb[0]>>nllb[1]>>nllb[2]>>nllb[3]>>nllb[4]>>nllb[5]>>nllb[6]>>nllb[7];
//cout<<n<<nll[0]<<nll[1]<<nll[2]<<nll[3]<<nll[4]<<nll[5]<<nll[6]<<nll[7]<<endl;
GoodToy[n]=1;
for(Int_t m=0;m<8;m++)
if(nll[m]==0.) GoodToy[n]=0;
if(!GoodToy[n])continue;
float minnll=100.;
int minm=0;
float minmass=0.;
for(Int_t m=0;m<8;m++)
if(minnll>nll[m]){
minnll = nll[m];
minm = m;
}
minmass=110+minm*5;
for(Int_t m=0;m<8;m++){
H[m]->Fill(nll[m]);
HDiff[m]->Fill(nll[m]-minnll);
HDiffFine[m]->Fill(nll[m]-minnll);
}
//////scan of each toy
GScanToy[n]=new TF1(TString("GScanToy")+(long)(110+5*m),"(110<=x&&x<115)*([0]+([1]-[0])*(x-110)/5) + (115<=x&&x<120)*([1]+([2]-[1])*(x-115)/5) + (120<=x&&x<125)*([2]+([3]-[2])*(x-120)/5) + (125<=x&&x<130)*([3]+([4]-[3])*(x-125)/5) + (130<=x&&x<135)*([4]+([5]-[4])*(x-130)/5) + (135<=x&&x<140)*([5]+([6]-[5])*(x-135)/5) + (140<=x&&x<145)*([6]+([7]-[6])*(x-140)/5)",110,145);
for(Int_t m=0;m<8;m++){
GScanToy[n]->SetParameter(m,(nll[m]-minnll));
}
//for this toy find the uncertainty
float x1sigmalow=0;
float x1sigmahigh=0;
float y1sigmalow=1000;
float y1sigmahigh=1000;
for(Int_t m=0;m<1000;m++){
float xlow=110+m*(145-110)/1000.;
float xhigh=145+m*(110-145)/1000.;
float ylow=GScanToy[n]->Eval(xlow);
float yhigh=GScanToy[n]->Eval(xhigh);
if(ylow<y1sigmalow && xlow<110+minm*5 && ylow>0.5){
y1sigmalow=ylow;
x1sigmalow=xlow;
}
if(yhigh<y1sigmahigh && xhigh>110+minm*5 && yhigh>0.5){
y1sigmahigh=yhigh;
x1sigmahigh=xhigh;
}
}
//cout<<110+minm*5<<" "<<x1sigmalow<<" "<<x1sigmahigh<<endl;
float sigmamass=0.;
if( x1sigmalow !=0 && x1sigmahigh ==0)sigmamass = minmass - x1sigmalow;
if( x1sigmahigh!=0 && x1sigmalow ==0)sigmamass = x1sigmahigh - minmass;
if( x1sigmalow !=0 && x1sigmahigh !=0)sigmamass = ((minmass - x1sigmalow) + (x1sigmahigh - minmass))/2.;
if(sigmamass!=0.)HPull.Fill((minmass-injmass)/sigmamass);
if( x1sigmalow !=0 ) {
HSigmaLow.Fill(minmass-x1sigmalow);
}
if( x1sigmahigh !=0 ) {
HSigmaHigh.Fill(x1sigmahigh-minmass);
HBestFitvsSigmaHigh.Fill(minmass,x1sigmahigh);
}
if( x1sigmalow !=0 && x1sigmahigh !=0 ) {
HSigma2D.Fill(minmass-x1sigmalow,x1sigmahigh-minmass);
HSigmaFull.Fill(x1sigmahigh-x1sigmalow);
}
/////Find best fit mass
HMass.Fill(110 + minm*5);
ngood++;
}
file.close();
fileb.close();
cout<<"Number of good toys = "<<ngood<<endl;
float MinNLL=1000;
for(Int_t m=0;m<8;m++)
if(MinNLL>H[m]->GetMean())
MinNLL=H[m]->GetMean();
file.open((filenamesb+".txt").Data());
fileb.open((filenameb+".txt").Data());
while(!file.eof()){
file>>n>>nll[0]>>nll[1]>>nll[2]>>nll[3]>>nll[4]>>nll[5]>>nll[6]>>nll[7];
if(!GoodToy[n])continue;
for(Int_t m=0;m<8;m++)
HSigma[m]->Fill(nll[m]-MinNLL);
}
file.close();
fileb.close();
TCanvas C("C","",600,600);
C.Print(filenamesb+".ps[");
//fill what is effectively the asimov dataset
TGraph GScan;
float minnll=100.;
for(Int_t m=0;m<8;m++)
if(minnll>H[m]->GetMean())
minnll=H[m]->GetMean();
const double quantiles[5]={0.05,0.16,0.5,0.84,0.95};
const double xq[8][5];
TLine QLine;
QLine.SetLineColor(2);
///////////////////
for(Int_t m=0;m<8;m++){
HDiffFine[m]->GetQuantiles(5,xq[m],quantiles);
C.Clear();
//HSigma[m]->Draw("pe");
//H[m]->Draw("pe");
//HDiff[m]->Draw("hist");
HDiffFine[m]->Draw("hist");
for(Int_t q=0;q<5;q++){
QLine.DrawLine(xq[m][q],0,xq[m][q],HDiffFine[m]->GetMaximum());
}
C.Print(filenamesb+".ps");
if(m==7)C.Print(filenamesb+"_DNLLDistribution.png");
GScan.SetPoint(m,110+m*5,H[m]->GetMean()-minnll);
}
TGraph* HBands[5];
for(Int_t q=0;q<5;q++){
HBands[q]=new TGraph();
HBands[q]->SetPoint(0,110,0);
for(Int_t m=0;m<8;m++){
HBands[q]->SetPoint(m+1,110+m*5,xq[m][q]);
}
HBands[q]->SetPoint(9,145,0);
}
HBands[0]->SetFillColor(kYellow); HBands[0]->SetLineColor(kYellow); HBands[0]->SetLineWidth(0);
HBands[1]->SetFillColor(kYellow); HBands[1]->SetLineColor(kYellow); HBands[1]->SetLineWidth(0);
HBands[2]->SetFillColor(0); HBands[2]->SetLineColor(kBlue);
HBands[3]->SetFillColor(kGreen); HBands[3]->SetLineColor(kGreen);
HBands[4]->SetFillColor(kYellow); HBands[4]->SetLineColor(kYellow);
/////////////////////Likelihood scan
TH1F HScanFrame("HScanFrame","",1,110,145);
HScanFrame.GetYaxis()->SetRangeUser(0,4);
//HScanFrame.GetYaxis()->SetTitle("#scale[1]{#Delta} -ln(#font[132]{L})");
HScanFrame.GetYaxis()->SetTitle("#scale[1]{#Delta}( -ln L )");
HScanFrame.GetXaxis()->SetTitle("#bf{m_{H} [GeV]}");
TLine line;
line.SetLineColor(2);
C.Clear();
HScanFrame.Draw();
for(Int_t q=4;q>=0;q--){
if(q!=2) HBands[q]->Draw("Fsame");
}
GScan.SetLineColor(kBlue);
GScan.Draw("lsame");
TFile FData("likelihood-mass-scan.root","READ");
TGraph*DataInput=(TGraph*)FData.Get("b+_All_final_1nd/mass_scan");
float mindata=100.;
float minmassdata=0.;
for(Int_t m=0;m<8;m++){
double x; double y;
DataInput->GetPoint(m*5,x,y);
if(mindata>y){
mindata=y;
minmassdata=110+m*5;
}
}
TGraph GData;
for(Int_t m=0;m<8;m++){
double x; double y;
DataInput->GetPoint(m*5,x,y);
GData.SetPoint(m,x,y-mindata);
}
GData.Draw("plsame");
//DataInput->Draw("plsame");
//GScanToy[0]->Draw("lsame");
C.RedrawAxis();
line.DrawLine(110,0.5,145,0.5);
line.DrawLine(110,1.92,145,1.92);
float datasigmahigh=8.55;
float datasigmalow=5.85;
TLine verticalline;
verticalline.SetLineStyle(2);
verticalline.DrawLine(minmassdata-datasigmalow,0,minmassdata-datasigmalow,0.5);
verticalline.DrawLine(minmassdata+datasigmahigh,0,minmassdata+datasigmahigh,0.5);
TLegend legend;
legend.SetFillStyle(0);
legend.SetFillColor(0);
legend.SetBorderSize(0);
legend.AddEntry(&GData,"observed","LP");
legend.AddEntry(&GScan,"H(125 GeV) expected","L");
legend.AddEntry(HBands[3],"#pm 1#sigma expected","F");
legend.AddEntry(HBands[4],"#pm 2#sigma expected","F");
legend.SetX1NDC(0.37);
legend.SetX2NDC(0.85);
legend.SetY1NDC(0.67);
legend.SetY2NDC(0.88);
legend.SetTextSize(.04);
legend.SetTextAlign(12);
legend.Draw();
TLatex text;
text.SetTextColor(2);
text.SetTextSize(0.04);
text.DrawLatex(142,0.5+0.05,"1#sigma");
text.DrawLatex(142,1.92+0.05,"2#sigma");
//CMSPrelim("CMS Preliminary, H#rightarrow#tau#tau, L = 24.3 fb^{-1}"); //#sqrt{s} = 7 - 8 TeV , L = 24.3 fb^{-1}
//CMSPrelim("CMS Prelim., H#rightarrow#tau#tau, #sqrt{s} = 7 - 8 TeV, L = 24.3 fb^{-1}");
//CMSPrelim("");
//CMSPrelim("CMS Preliminary, #sqrt{s}=7 TeV, L=4.9 fb^{-1}, #sqrt{s}=8 TeV, L=19.3 fb^{-1}, H#rightarrow#tau#tau");
CMSPrelim();
C.Print(filenamesb+".ps");
C.Print(filenamesb+"_DNLL.png");
C.Print(filenamesb+"_DNLL.pdf");
//////////////////////Pull distribution
TH1F HMassFrame("HMassFrame","",1,100,155);
HMassFrame.GetYaxis()->SetRangeUser(0,HMass.GetMaximum()*1.3);
HMassFrame.GetYaxis()->SetTitle("# of toys");
HMassFrame.GetXaxis()->SetTitle("m_{H} [GeV]");
cout<<" Fit with 3 gaussians "<<endl;
TF1 Gaus("Gaus","[0]*exp(-0.5*(x-[1])**2/([2]*[2])) + [3]*exp(-0.5*(x-[5])**2/([2]*[2])) + [4]*exp(-0.5*(x-[6])**2/([2]*[2]))",110,145);
Gaus.SetParLimits(0,0,10000);
Gaus.SetParLimits(1,110,145);
Gaus.SetParLimits(2,1,20);
Gaus.SetParLimits(3,0,10000);
Gaus.SetParLimits(4,0,10000);
Gaus.FixParameter(5,110-(injmass-110));
Gaus.FixParameter(6,145+(145-injmass));
C.Clear();
HMass.Fit(&Gaus);
char title[100];
sprintf(title,"Mean = %.1f +- %.1f Sigma = %.1f +- %.1f",Gaus.GetParameter(1),Gaus.GetParError(1),Gaus.GetParameter(2),Gaus.GetParError(2));
HMassFrame.SetTitle(title);
HMassFrame.Draw("hist");
HMass.Draw("histpesame");
Gaus.SetLineColor(4);
Gaus.Draw("lsame");
TF1 GausSig("GausSig","[0]*exp(-0.5*(x-[1])**2/([2]*[2]))",90,160);
GausSig.SetParameter(0,Gaus.GetParameter(0));
GausSig.SetParameter(1,Gaus.GetParameter(1));
GausSig.SetParameter(2,Gaus.GetParameter(2));
GausSig.SetLineColor(2);
GausSig.Draw("lsame");
C.Print(filenamesb+".ps");
C.Print(filenamesb+"_MassUnc.png");
C.Print(filenamesb+"_MassUnc.pdf");
cout<<" Fit with 1 gaussian "<<endl;
C.Clear();
TF1 GausCore("GausCore","[0]*exp(-0.5*(x-[1])**2/([2]*[2]))",110,145);
GausCore.SetParLimits(0,0.1,10000);
GausCore.SetParLimits(1,injmass-5.0,injmass+5.0);
GausCore.SetParLimits(2,1,20);
//HMass.Fit(&GausCore,"","",injmass-5.5,injmass+5.5);
HMass.Fit(&GausCore,"","LL",injmass-10.0,injmass+10.0);
sprintf(title,"Mean = %.1f +- %.1f Sigma = %.1f +- %.1f",GausCore.GetParameter(1),GausCore.GetParError(1),GausCore.GetParameter(2),GausCore.GetParError(2));
HMassFrame.SetTitle(title);
HMassFrame.Draw("hist");
HMass.Draw("histpesame");
GausCore.SetLineColor(2);
GausCore.Draw("lsame");
C.Print(filenamesb+".ps");
C.Print(filenamesb+"_MassUncCore.png");
C.Print(filenamesb+"_MassUncCore.pdf");
C.Clear();
cout<<"# of Pull Toys = "<<HPull.Integral()<<endl;
TF1 GausPull("GausPull","[0]*exp(-0.5*(x-[1])**2/([2]*[2]))",-5,5);
GausPull.SetParLimits(0,0.,1000);
GausPull.SetParLimits(1,-1,1.1);
GausPull.SetParLimits(2,0.2,3);
GausPull.SetLineColor(4);
HPull.Fit(&GausPull,"","I",-2,2);
sprintf(title,"Mean = %.2f +- %.2f Sigma = %.2f +- %.2f",GausPull.GetParameter(1),GausPull.GetParError(1),GausPull.GetParameter(2),GausPull.GetParError(2));
HPull.SetTitle(title);
HPull.GetXaxis()->SetTitle("( m_{best-fit} - m_{injected} )/#sigma_{m}");
HPull.GetYaxis()->SetTitle("# of toys");
HPull.Draw("pe");
GausPull.Draw("lsame");
C.Print(filenamesb+".ps");
C.Print(filenamesb+"_MassPull.png");
C.Print(filenamesb+"_MassPull.pdf");
C.Clear();
TLine sigmaline;
cout<<" # of toys for 2D uncertainty plot "<<HSigma2D.Integral()<<endl;
TF1 SigmaPol("SigmaPol","[0]+[1]*x",0,20);
//HSigma2D.Fit(&SigmaPol);
HSigma2D.Draw("colz");
//SigmaPol.Draw("lsame");
C.Print(filenamesb+".ps");
C.Print(filenamesb+"_sigma2D.png");
C.Clear();
cout<<" # of toys for sigmalow "<<HSigmaLow.Integral()<<endl;
HSigmaLow.Draw();
sigmaline.DrawLine(datasigmalow,0,datasigmalow,HSigmaLow.GetMaximum());
C.Print(filenamesb+".ps");
C.Print(filenamesb+"_sigmaLow.png");
C.Clear();
cout<<" # of toys for sigmahigh "<<HSigmaHigh.Integral()<<endl;
HSigmaHigh.Draw();
sigmaline.DrawLine(datasigmahigh,0,datasigmahigh,HSigmaHigh.GetMaximum());
C.Print(filenamesb+".ps");
C.Print(filenamesb+"_sigmaHigh.png");
C.Clear();
cout<<" # of toys for sigmafull "<<HSigmaFull.Integral()<<endl;
HSigmaFull.Draw();
sigmaline.DrawLine(datasigmalow+datasigmahigh,0,datasigmalow+datasigmahigh,HSigmaFull.GetMaximum());
C.Print(filenamesb+".ps");
C.Print(filenamesb+"_sigmafull.png");
C.Clear();
TF1 SigmaBFitvsSigma("SigmaBFitvsSigma","[0]+[1]*x",0,20);
//HBestFitvsSigmaHigh.Fit(&SigmaBFitvsSigma);
HBestFitvsSigmaHigh.Draw("colz");
//SigmaBFitvsSigma.Draw("lsame");
C.Print(filenamesb+".ps");
C.Print(filenamesb+".ps]");
gROOT->ProcessLine(".q");
}
void compareThreeVersions(TimeInfoAllSteps TIAS_1,TimeInfoAllSteps TIAS_2,TimeInfoAllSteps TIAS_3,TString file_postfix) {
gStyle->SetOptStat(0);
TPaveText* labelcms = new TPaveText(0.20,0.79,0.54,0.92,"NDCBR");
labelcms->SetTextAlign(12);
labelcms->SetTextSize(0.04);
labelcms->SetFillColor(kWhite);
labelcms->AddText("CMS Preliminary Simulation");
//labelcms->AddText("CMS Preliminary");
//labelcms->AddText("Simulation");
labelcms->AddText("#sqrt{s} = 8 TeV, t#bar{t}+PU");
labelcms->SetBorderSize(0);
labelcms->SetTextFont(42);
labelcms->SetLineWidth(2);
TH1F *htime_iter_1 = new TH1F("time_iter_1"+file_postfix,"time_iter_1",8,0,8);
timingTestPerIter(TIAS_1,htime_iter_1);
htime_iter_1->SetLineStyle(TIAS_1.style());
htime_iter_1->SetLineColor(TIAS_1.color());
TH1F *htime_iter_2 = new TH1F("time_iter_2"+file_postfix,"time_iter_2",8,0,8);
timingTestPerIter(TIAS_2,htime_iter_2);
htime_iter_2->SetLineStyle(TIAS_2.style());
htime_iter_2->SetLineColor(TIAS_2.color());
TH1F *htime_iter_3 = new TH1F("time_iter_3"+file_postfix,"time_iter_3",8,0,8);
timingTestPerIter(TIAS_3,htime_iter_3);
htime_iter_3->SetLineStyle(TIAS_3.style());
htime_iter_3->SetLineColor(TIAS_3.color());
float iter_Iter0PU20 = htime_iter_1->GetBinContent(1);
htime_iter_1->Scale(1./iter_Iter0PU20);
htime_iter_2->Scale(1./iter_Iter0PU20);
htime_iter_3->Scale(1./iter_Iter0PU20);
htime_iter_1->GetYaxis()->SetRangeUser(0,1.1*TMath::Max(htime_iter_1->GetBinContent(htime_iter_1->GetMaximumBin()),TMath::Max(htime_iter_2->GetBinContent(htime_iter_2->GetMaximumBin()),htime_iter_3->GetBinContent(htime_iter_3->GetMaximumBin()))));
TLegend* leg = new TLegend(0.2,0.59,0.45,0.79);
leg->SetNColumns(1);
leg->SetFillColor(kWhite);
leg->SetLineColor(kWhite);
leg->SetBorderSize(0);
leg->SetTextSize(0.04);
leg->SetTextFont(42);
leg->AddEntry(htime_iter_1,TIAS_1.legend(),"L");
leg->AddEntry(htime_iter_2,TIAS_2.legend(),"L");
leg->AddEntry(htime_iter_3,TIAS_3.legend(),"L");
htime_iter_1->GetYaxis()->SetTitleOffset(1.2);
htime_iter_1->GetXaxis()->SetRangeUser(0,7);
htime_iter_1->GetYaxis()->SetTitle("time [1/iter0@<PU>=20]");
htime_iter_1->GetYaxis()->SetTitleSize(0.04);
htime_iter_1->GetXaxis()->SetTitleSize(0.04);
htime_iter_1->GetYaxis()->SetLabelSize(0.04);
TCanvas c0;
c0.SetTicks(1,1);
htime_iter_1->Draw("H");
htime_iter_2->Draw("H,same");
htime_iter_3->Draw("H,same");
leg->Draw();
labelcms->Draw();
c0.SetGridy();
c0.SaveAs("timingNew_iter_"+file_postfix+".png");
TH1F *htime_step_1 = new TH1F("time_step_1"+file_postfix,"time_step_1",5,0,5);
timingTestPerStep(TIAS_1,htime_step_1);
htime_step_1->SetLineStyle(TIAS_1.style());
htime_step_1->SetLineColor(TIAS_1.color());
htime_step_1->GetYaxis()->SetTitle("time [1/building@<PU>=20]");
TH1F *htime_step_2 = new TH1F("time_step_2"+file_postfix,"time_step_2",5,0,5);
timingTestPerStep(TIAS_2,htime_step_2);
htime_step_2->SetLineStyle(TIAS_2.style());
htime_step_2->SetLineColor(TIAS_2.color());
TH1F *htime_step_3 = new TH1F("time_step_3"+file_postfix,"time_step_3",5,0,5);
timingTestPerStep(TIAS_3,htime_step_3);
htime_step_3->SetLineStyle(TIAS_3.style());
htime_step_3->SetLineColor(TIAS_3.color());
float step_BuildPU20 = htime_step_1->GetBinContent(3);
htime_step_1->Scale(1./step_BuildPU20);
htime_step_2->Scale(1./step_BuildPU20);
htime_step_3->Scale(1./step_BuildPU20);
htime_step_1->GetYaxis()->SetTitleOffset(1.2);
htime_step_1->GetXaxis()->SetRangeUser(0,7);
htime_step_1->GetYaxis()->SetTitleSize(0.04);
htime_step_1->GetXaxis()->SetTitleSize(0.04);
htime_step_1->GetYaxis()->SetLabelSize(0.04);
TCanvas c1;
c1.SetTicks(1,1);
//htime_step_1->Scale(1./htime_step_1->Integral());
//htime_step_2->Scale(1./htime_step_2->Integral());
//htime_step_3->Scale(1./htime_step_3->Integral());
htime_step_1->GetYaxis()->SetRangeUser(0,1.1*TMath::Max(htime_step_1->GetBinContent(htime_step_1->GetMaximumBin()),TMath::Max(htime_step_2->GetBinContent(htime_step_2->GetMaximumBin()),htime_step_3->GetBinContent(htime_step_3->GetMaximumBin()))));
htime_step_1->GetXaxis()->SetRangeUser(1,5);
htime_step_1->Draw("H");
htime_step_2->Draw("H,same");
htime_step_3->Draw("H,same");
leg->SetX1NDC(0.60);
leg->SetX2NDC(0.85);
labelcms->SetX1NDC(0.60);
labelcms->SetX2NDC(0.94);
leg->Draw();
labelcms->Draw();
c1.SetGridy();
c1.SaveAs("timingNew_step_"+file_postfix+".png");
TH1F *htime_track_1 = (TH1F*) htime_iter_1->Clone("time_track_1"+file_postfix);
htime_track_1->Reset();
makeTimePerTrackPlot(htime_track_1,htime_iter_1,TIAS_1.mtvfile());
TH1F *htime_track_2 = (TH1F*) htime_iter_2->Clone("time_track_2"+file_postfix);
htime_track_2->Reset();
makeTimePerTrackPlot(htime_track_2,htime_iter_2,TIAS_2.mtvfile());
TH1F *htime_track_3 = (TH1F*) htime_iter_3->Clone("time_track_3"+file_postfix);
htime_track_3->Reset();
makeTimePerTrackPlot(htime_track_3,htime_iter_3,TIAS_3.mtvfile());
float track_Iter0PU20 = htime_track_1->GetBinContent(1);
htime_track_1->Scale(1./track_Iter0PU20);
htime_track_2->Scale(1./track_Iter0PU20);
htime_track_3->Scale(1./track_Iter0PU20);
htime_track_1->GetYaxis()->SetRangeUser(0,1.1*TMath::Max(htime_track_1->GetBinContent(htime_track_1->GetMaximumBin()),TMath::Max(htime_track_2->GetBinContent(htime_track_2->GetMaximumBin()),htime_track_3->GetBinContent(htime_track_3->GetMaximumBin()))));
TCanvas c2;
c2.SetTicks(1,1);
htime_track_1->GetYaxis()->SetTitleOffset(1.2);
htime_track_1->GetXaxis()->SetRangeUser(0,7);
htime_track_1->GetYaxis()->SetTitle("time/track [1/iter0@<PU>=20]");
htime_track_1->GetYaxis()->SetTitleSize(0.04);
htime_track_1->GetXaxis()->SetTitleSize(0.04);
htime_track_1->GetYaxis()->SetLabelSize(0.04);
htime_track_1->Draw("H");
htime_track_2->Draw("H,same");
htime_track_3->Draw("H,same");
leg->SetX1NDC(0.2);
leg->SetX2NDC(0.45);
labelcms->SetX1NDC(0.2);
labelcms->SetX2NDC(0.54);
leg->Draw();
labelcms->Draw();
c2.SetGridy();
c2.SaveAs("timingNew_track_"+file_postfix+".png");
}
