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#include "TH1D.h" |
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#include "TTree.h" |
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using namespace RooFit; |
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// .493/.2114 j1 |
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//.773/.3257 j2 |
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// .863/.353 j3 |
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//.808/.294 j4 |
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void fitNewRelIso() |
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{ |
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< |
TString fileName = "skimmed/QCDbin2_005.root"; |
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> |
TString fileName = "skimmed226/QCDbin4_005.root"; |
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// TString fileName = "skimmed/QCDbinall_NewRelIso.root"; |
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// TString fileName = "skimmed/QCDbin4_NewRelIso_CD30_rebin2.root"; |
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// TString fileName = "skimmed/QCDbin3_NewRelIso_CD.root"; |
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//TString fileName = "skimmed/QCDbin4_rebin.root"; |
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//TString fileName = "skimmed/QCDbin4_NewRelIso_FastSim.root"; |
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> |
// TString fileName = "skimmed/QCDbin4_rebin.root"; |
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> |
// TString fileName = "skimmed/QCDbin4_NewRelIso_FastSim.root"; |
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|
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// Dynamic(true) or static(false) range |
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bool dynamic = true; |
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double epslon = 1.e-5; |
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double epslon1 = 1.e-6; |
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// Dynamic fit region weight |
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double wxmin = 1.7; // 1.81 1.7 |
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double wxmax = .94; // 0.9 .95 |
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> |
double wxmin = 2.; // 1.81 1.7 1.81875 for fastsim |
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> |
double wxmax = wxmin/2.5; // 0.9 .95 |
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> |
// Select fitting function: |
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> |
// TString s0 = "landau"; |
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> |
// Select New or Old reliso: |
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> |
// TString isoname = "New"; |
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|
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Double_t ax0=0.; |
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Double_t axf=0.05; |
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tree->SetBranchAddress("jbin",&jbin); |
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tree->SetBranchAddress("na",&na); |
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tree->GetEntry(0); |
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|
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TH1D *h_all = (TH1D*)h0->Clone(); |
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TH1D *h_qcd = (TH1D*)h1->Clone(); |
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Int_t nbxMax = h_all->GetXaxis()->FindBin(2.0); |
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cout << "=========> bwidth = " << bwidth << endl; |
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|
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cout << "\n>>>>> Iteration step: "<< niter << endl; |
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< |
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|
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RooRealVar x("x","x",0.,2.); |
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RooRealVar y("y","y",0.,2.); |
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RooDataHist *dh0 = new RooDataHist("dh0","dh0",x,Import(*h0)); |
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// --------------------- |
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|
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// Declare variables x,mean,sigma with associated name, title, initial value and allowed range |
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// Gaussian |
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// Exponential parameters |
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RooRealVar lamda("lamda","decay constant of exponential",1,-100.,100.); |
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// Gaussian parameters |
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RooRealVar mean("mean","mean of gaussian",1.5,0.,2.); |
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RooRealVar sigma("sigma","width of gaussian",1,0.,100.); |
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// Landau |
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// Landau parameters |
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RooRealVar MPV("MPV","mean of landau",1,0,2); |
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RooRealVar Sig("Sig","sigma of landau",1,0,2); |
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// Build gaussian p.d.f in terms of x,mean and sigma |
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RooExponential expo("expo","exponential PDF",x,lamda); |
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RooGaussian gauss("gauss","gaussian PDF",x,mean,sigma); |
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RooLandau landau("landau","landau PDF",x,MPV,Sig); |
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// Build model |
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// RooRealVar elfrac("elfrac","fraction of Exponential and Landau",0.05,0.,1.); |
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// RooRealVar glfrac("glfrac","fraction of Gaussian and Landau",0.,0.,1.); |
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// RooAddPdf model("model","g+l",RooArgList(gauss,landau),glfrac); |
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|
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// Construct plot frame in 'x' |
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RooPlot* xframe = x.frame(Title("Landau p.d.f. with data")); |
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// P l o t m o d e l a n d c h a n g e p a r a m e t e r v a l u e s |
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// --------------------------------------------------------------------------- |
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// Change the value of Parameters |
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// sigma.setVal(2.); |
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// MPV.setVal(0.8412); |
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// Sig.setVal(0.314); |
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// Plot gauss in frame (i.e. in x) and draw frame on canvas |
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// gauss.plotOn(xframe); |
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// landau.plotOn(xframe,LineColor(kRed)); |
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|
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// G e n e r a t e e v e n t s |
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// ----------------------------- |
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|
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// Make a second plot frame in x and draw both the |
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// data and the p.d.f in the frame |
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< |
dh0->plotOn(xframe); |
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< |
dh1->plotOn(xframe,MarkerColor(9)); |
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< |
dhy0->plotOn(yframe); |
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< |
dhy1->plotOn(yframe,MarkerColor(9)); |
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> |
dh0->plotOn(xframe,MarkerStyle(23),MarkerColor(2),LineColor(2),XErrorSize(0)); |
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> |
dh1->plotOn(xframe,MarkerStyle(22),MarkerColor(9),LineColor(9),XErrorSize(0)); |
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> |
dhy0->plotOn(yframe,MarkerStyle(23),MarkerColor(2),LineColor(2),XErrorSize(0)); |
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dhy1->plotOn(yframe,MarkerStyle(22),MarkerColor(9),LineColor(9),XErrorSize(0)); |
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|
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// F i t m o d e l t o d a t a |
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// ----------------------------- |
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|
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// Fit pdf to data |
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// MPV.setRange(0.4,1.); |
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– |
// Sig.setRange(0.1,0.5); |
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// Sig.setConstant(kFALSE); |
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RooFitResult*rest = landau.fitTo(*dh0,Range("ctrlregion"),PrintLevel(-1),Save()); |
| 140 |
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rest->Print("v"); |
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niter++; |
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// Print values of mean and sigma (that now reflect fitted values and errors) |
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|
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Double_t xb0 = MPV.getVal()-wxmin*Sig.getVal(); |
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Double_t xbf = MPV.getVal()+wxmax*Sig.getVal(); |
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– |
|
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RooAbsReal* nQCDsigw = landau.createIntegral(x,Range("signalregion")); |
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RooAbsReal* nQCDbkgw = landau.createIntegral(x,Range("ctrlregion")); |
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// Draw all frames on a canvas |
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< |
TCanvas* c = new TCanvas("fitNewRelIso","fitNewRelIso",1000,600); |
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< |
c->Divide(2,1); |
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c->cd(1); |
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> |
Double_t xb0 = bx0; |
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> |
Double_t xbf = bxf; |
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|
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< |
for (;niter <= np && nsiter < 2; niter++){ |
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> |
if (dynamic) { |
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> |
TCanvas* c = new TCanvas("fitNewRelIso","fitNewRelIso",1000,600); |
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> |
c->Divide(2,1); |
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> |
c->cd(1); |
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> |
|
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xb0 = MPV.getVal()-wxmin*Sig.getVal(); |
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xbf = MPV.getVal()+wxmax*Sig.getVal(); |
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< |
cout << "\n>>>>> Iteration step: "<< niter << endl; |
| 156 |
< |
cout << "=========> xb0, xbf = " << xb0 << ", " << xbf << endl; |
| 157 |
< |
x.setRange("ctrlregion",xb0,xbf); |
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< |
|
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< |
// Construct a chi^2 of the data and the model, |
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< |
// which is the input probability density scaled |
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< |
// by the number of events in the dataset |
| 162 |
< |
RooChi2Var chi2("chi2","chi2",landau,*dh0,Range("ctrlregion")); |
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< |
// Use RooMinuit interface to minimize chi^2 |
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< |
RooMinuit m(chi2); |
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< |
m.migrad(); |
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< |
m.hesse(); |
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< |
|
| 168 |
< |
if ((fabs(chi2.getVal() - bound[0]) <= epslon) || |
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< |
(fabs(chi2.getVal() - temp) <= epslon) || |
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< |
(fabs(xb0 - tempb) < = epslon1 ) |
| 171 |
< |
){ |
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< |
nsiter++; |
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< |
cout << "=========> Delta chi2 converges for " << nsiter << "time(s)" << endl; |
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< |
} |
| 175 |
< |
if (chi2.getVal() < bound[0]){ |
| 176 |
< |
bound[0] = chi2.getVal(); |
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< |
bound[1] = xb0; |
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< |
bound[2] = xbf; |
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< |
pass[1] = MPV.getVal(); |
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< |
pass[2] = Sig.getVal(); |
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< |
nQCDsigw = landau.createIntegral(x,NormSet(x),Range("signalregion")); |
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< |
nQCDbkgw = landau.createIntegral(x,NormSet(x),Range("ctrlregion")); |
| 183 |
< |
cout << "=========> New Min chi2 = " << chi2.getVal() <<endl; |
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< |
cout << "=========> Fitting range = " << bound[1] << " to " << bound[2] << endl; |
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< |
landau.plotOn(xframe,NormRange("ctrlregion"),Range("ctrlregion"),LineColor(niter)); |
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< |
landau.paramOn(xframe,Layout(0.28,0.75,0.42)); |
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< |
landau.plotOn(xframe,NormRange("ctrlregion"),Range("signalregion"),LineColor(niter)); |
| 188 |
< |
xframe->Draw(); |
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< |
nQCDsigw->Print(); |
| 190 |
< |
nQCDbkgw->Print(); |
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> |
|
| 156 |
> |
for (;niter <= np && nsiter < 2; niter++){ |
| 157 |
> |
xb0 = MPV.getVal()-wxmin*Sig.getVal(); |
| 158 |
> |
xbf = MPV.getVal()+wxmax*Sig.getVal(); |
| 159 |
> |
cout << "\n>>>>> Iteration step: "<< niter << endl; |
| 160 |
> |
cout << "=========> xb0, xbf = " << xb0 << ", " << xbf << endl; |
| 161 |
> |
// x.setRange("ctrlregion",xb0.getVal(),xbf.getVal()); |
| 162 |
> |
x.setRange("ctrlregion",xb0,xbf); |
| 163 |
> |
|
| 164 |
> |
// Construct a chi^2 of the data and the model, |
| 165 |
> |
// which is the input probability density scaled |
| 166 |
> |
// by the number of events in the dataset |
| 167 |
> |
// RooChi2Var chi2("chi2","chi2",landau,*dh0,Range(xb0.getVal(),xbf.getVal())); |
| 168 |
> |
RooChi2Var chi2("chi2","chi2",landau,*dh0,Range("ctrlregion")); |
| 169 |
> |
// Use RooMinuit interface to minimize chi^2 |
| 170 |
> |
RooMinuit m(chi2); |
| 171 |
> |
m.migrad(); |
| 172 |
> |
m.hesse(); |
| 173 |
> |
|
| 174 |
> |
if ((fabs(chi2.getVal() - bound[0]) <= epslon) || |
| 175 |
> |
(fabs(chi2.getVal() - temp) <= epslon) || |
| 176 |
> |
(fabs(xb0 - tempb) < = epslon1 ) |
| 177 |
> |
){ |
| 178 |
> |
nsiter++; |
| 179 |
> |
cout << "=========> Delta chi2 converges for " << nsiter << "time(s)" << endl; |
| 180 |
> |
} |
| 181 |
> |
if (chi2.getVal() < bound[0]){ |
| 182 |
> |
bound[0] = chi2.getVal(); |
| 183 |
> |
bound[1] = xb0; |
| 184 |
> |
bound[2] = xbf; |
| 185 |
> |
pass[1] = MPV.getVal(); |
| 186 |
> |
pass[2] = Sig.getVal(); |
| 187 |
> |
nQCDsigw = landau.createIntegral(x,NormSet(x),Range("signalregion")); |
| 188 |
> |
nQCDbkgw = landau.createIntegral(x,NormSet(x),Range("ctrlregion")); |
| 189 |
> |
cout << "=========> New Min chi2 = " << chi2.getVal() <<endl; |
| 190 |
> |
cout << "=========> Fitting range = " << bound[1] << " to " << bound[2] << endl; |
| 191 |
> |
landau.plotOn(xframe,NormRange("ctrlregion"),Range("ctrlregion"),LineColor(niter)); |
| 192 |
> |
landau.paramOn(xframe,Layout(0.28,0.75,0.42)); |
| 193 |
> |
landau.plotOn(xframe,NormRange("ctrlregion"),Range("signalregion"),LineColor(niter)); |
| 194 |
> |
xframe->Draw(); |
| 195 |
> |
nQCDsigw->Print(); |
| 196 |
> |
nQCDbkgw->Print(); |
| 197 |
> |
} |
| 198 |
> |
temp = chi2.getVal(); |
| 199 |
> |
tempb = xb0; |
| 200 |
|
} |
| 201 |
< |
temp = chi2.getVal(); |
| 202 |
< |
tempb = xb0; |
| 201 |
> |
// plot |
| 202 |
> |
Double_t mpv1 = pass[1]; |
| 203 |
> |
Double_t sig1 = pass[2]; |
| 204 |
> |
RooRealVar MPV1("MPV1","MPV1",mpv1); |
| 205 |
> |
RooRealVar Sig1("Sig1","Sig1",sig1); |
| 206 |
> |
RooLandau landau1("landau1","landau1",y,MPV1,Sig1); |
| 207 |
> |
|
| 208 |
> |
y.setRange("signalregion",ax0,axf); |
| 209 |
> |
y.setRange("centralregion",axf,bound[1]); |
| 210 |
> |
y.setRange("ctrlregion",bound[1],bound[2]); |
| 211 |
> |
|
| 212 |
> |
landau1.plotOn(yframe,NormRange("ctrlregion"),Range("ctrlregion")); |
| 213 |
> |
landau1.plotOn(yframe,NormRange("ctrlregion"),Range("centralregion"), |
| 214 |
> |
LineColor(3),LineStyle(kDashed)); |
| 215 |
> |
landau1.plotOn(yframe,NormRange("ctrlregion"),Range("signalregion"),LineColor(2)); |
| 216 |
> |
c->cd(2); |
| 217 |
> |
yframe->Draw(); |
| 218 |
> |
|
| 219 |
> |
} |
| 220 |
> |
else { // Fixed range |
| 221 |
> |
|
| 222 |
> |
TCanvas* c = new TCanvas("fitNewRelIso","fitNewRelIso",800,600); |
| 223 |
> |
pass[1] = MPV.getVal(); |
| 224 |
> |
pass[2] = Sig.getVal(); |
| 225 |
> |
bound[1] = bx0; |
| 226 |
> |
bound[2] = bxf; |
| 227 |
> |
x.setRange("signalregion",ax0,axf); |
| 228 |
> |
x.setRange("centralregion",axf,bound[1]); |
| 229 |
> |
x.setRange("ctrlregion",bound[1],bound[2]); |
| 230 |
> |
|
| 231 |
> |
landau.plotOn(xframe,NormRange("ctrlregion"),Range("ctrlregion")); |
| 232 |
> |
landau.plotOn(xframe,NormRange("ctrlregion"),Range("centralregion"), |
| 233 |
> |
LineColor(3),LineStyle(kDashed)); |
| 234 |
> |
landau.plotOn(xframe,NormRange("ctrlregion"),Range("signalregion"),LineColor(2)); |
| 235 |
> |
xframe->Draw(); |
| 236 |
> |
|
| 237 |
> |
} |
| 238 |
> |
// h0->SetLineColor(9); |
| 239 |
> |
// h0->SetLineStyle(2); |
| 240 |
> |
// h0->SetLineWidth(2); |
| 241 |
> |
h0->Draw("same"); |
| 242 |
> |
// h1->SetLineStyle(3); |
| 243 |
> |
// h1->SetLineWidth(2); |
| 244 |
> |
h1->SetLineColor(9); //40 |
| 245 |
> |
// h1->SetFillStyle(3020); // 3018 |
| 246 |
> |
// h1->SetFillColor(41); //38 |
| 247 |
> |
h1->Draw("same"); |
| 248 |
> |
|
| 249 |
> |
if (dynamic){ |
| 250 |
> |
cout << "=========> chi2 of the last iteration = " << chi2.getVal() << endl; |
| 251 |
> |
cout << "=========> Fit range of the last iteration = " << xb0 << " to " << xbf << endl; |
| 252 |
> |
cout << "================================================================" << endl; |
| 253 |
> |
cout << "=========> Minimum chi2 = " << bound[0] << endl; |
| 254 |
|
} |
| 184 |
– |
// plot |
| 185 |
– |
Double_t mpv1 = pass[1]; |
| 186 |
– |
Double_t sig1 = pass[2]; |
| 187 |
– |
RooRealVar MPV1("MPV1","MPV1",mpv1); |
| 188 |
– |
RooRealVar Sig1("Sig1","Sig1",sig1); |
| 189 |
– |
RooLandau landau1("landau1","landau1",y,MPV1,Sig1); |
| 190 |
– |
|
| 191 |
– |
y.setRange("signalregion",ax0,axf); |
| 192 |
– |
y.setRange("centralregion",axf,bound[1]); |
| 193 |
– |
y.setRange("ctrlregion",bound[1],bound[2]); |
| 194 |
– |
|
| 195 |
– |
landau1.plotOn(yframe,NormRange("ctrlregion"),Range("ctrlregion")); |
| 196 |
– |
landau1.plotOn(yframe,NormRange("ctrlregion"),Range("centralregion"), |
| 197 |
– |
LineColor(3),LineStyle(kDashed)); |
| 198 |
– |
landau1.plotOn(yframe,NormRange("ctrlregion"),Range("signalregion"),LineColor(2)); |
| 199 |
– |
c->cd(2); |
| 200 |
– |
yframe->Draw(); |
| 201 |
– |
|
| 202 |
– |
|
| 203 |
– |
cout << "=========> chi2 of the last iteration = " << chi2.getVal() << endl; |
| 204 |
– |
cout << "=========> Fit range of the last iteration = " << xb0 << " to " << xbf << endl; |
| 205 |
– |
cout << "================================================================" << endl; |
| 206 |
– |
cout << "=========> Minimum chi2 = " << bound[0] << endl; |
| 255 |
|
cout << "=========> Final fit range = " << bound[1] << " to " << bound[2] << endl; |
| 256 |
|
cout << "=========> MPV = " << pass[1] << "; Sig = " << pass[2] << endl; |
| 209 |
– |
|
| 257 |
|
// Print values of mean and sigma (that now reflect fitted values and errors) |
| 211 |
– |
// mean.Print(); |
| 212 |
– |
// sigma.Print(); |
| 213 |
– |
// MPV.Print(); |
| 214 |
– |
// Sig.Print(); |
| 258 |
|
cout << "=========> Final MPV = pass[1] = " << pass[1] << endl; |
| 259 |
< |
cout << "=========> Final MPV = pass[2] = " << pass[2] << endl; |
| 259 |
> |
cout << "=========> Final SIG = pass[2] = " << pass[2] << endl; |
| 260 |
|
|
| 261 |
|
// Get number of events within fitted region ==> cross check |
| 262 |
|
TAxis *axis = h0->GetXaxis(); |
| 266 |
|
nfac -= (h0->GetBinContent(bmin))*(xb0-axis->GetBinLowEdge(bmin))/axis->GetBinWidth(bmin); |
| 267 |
|
nfac -= (h0->GetBinContent(bmax))*(axis->GetBinUpEdge(bmax)-xbf)/axis->GetBinWidth(bmax); |
| 268 |
|
cout << "=========> Final Nfac = " << nfac << endl; |
| 226 |
– |
|
| 269 |
|
nQCDsigw->Print(); |
| 270 |
|
nQCDbkgw->Print(); |
| 271 |
|
Double_t nQCDsig = nfac * nQCDsigw->getVal()/nQCDbkgw->getVal(); |
| 276 |
|
cout << ">>>>> Observed Ns = " << nQCDsig << endl; |
| 277 |
|
cout << ">>>>> Expected Na = " << na << endl; |
| 278 |
|
cout << "Deviation = " << (nQCDsig-na)/na*100.0 << " %" <<endl; |
| 279 |
< |
|
| 279 |
> |
|
| 280 |
|
} |
