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Reference Guide
rf702_efficiencyfit_2D.C
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1 /// \file
2 /// \ingroup tutorial_roofit
3 /// \notebook
4 ///
5 /// Special p.d.f.'s: unbinned maximum likelihood fit of an efficiency eff(x) function
6 /// to a dataset D(x,cut), cut is a category encoding a selection whose efficiency as function
7 /// of x should be described by eff(x)
8 ///
9 /// \macro_image
10 /// \macro_output
11 /// \macro_code
12 ///
13 /// \date February 2018
14 /// \authors Clemens Lange, Wouter Verkerke (C++ version)
15 
16 #include "RooRealVar.h"
17 #include "RooDataSet.h"
18 #include "RooGaussian.h"
19 #include "RooConstVar.h"
20 #include "RooCategory.h"
21 #include "RooEfficiency.h"
22 #include "RooPolynomial.h"
23 #include "RooProdPdf.h"
24 #include "RooFormulaVar.h"
25 #include "TCanvas.h"
26 #include "TAxis.h"
27 #include "TH1.h"
28 #include "RooPlot.h"
29 using namespace RooFit;
30 
32 {
33  // C o n s t r u c t e f f i c i e n c y f u n c t i o n e ( x , y )
34  // -----------------------------------------------------------------------
35 
36  // Declare variables x,mean,sigma with associated name, title, initial value and allowed range
37  RooRealVar x("x", "x", -10, 10);
38  RooRealVar y("y", "y", -10, 10);
39 
40  // Efficiency function eff(x;a,b)
41  RooRealVar ax("ax", "ay", 0.6, 0, 1);
42  RooRealVar bx("bx", "by", 5);
43  RooRealVar cx("cx", "cy", -1, -10, 10);
44 
45  RooRealVar ay("ay", "ay", 0.2, 0, 1);
46  RooRealVar by("by", "by", 5);
47  RooRealVar cy("cy", "cy", -1, -10, 10);
48 
49  RooFormulaVar effFunc("effFunc", "((1-ax)+ax*cos((x-cx)/bx))*((1-ay)+ay*cos((y-cy)/by))",
50  RooArgList(ax, bx, cx, x, ay, by, cy, y));
51 
52  // Acceptance state cut (1 or 0)
53  RooCategory cut("cut", "cutr", { {"accept", 1}, {"reject", 0} });
54 
55  // C o n s t r u c t c o n d i t i o n a l e f f i c i e n c y p d f E ( c u t | x , y )
56  // ---------------------------------------------------------------------------------------------
57 
58  // Construct efficiency p.d.f eff(cut|x)
59  RooEfficiency effPdf("effPdf", "effPdf", effFunc, cut, "accept");
60 
61  // G e n e r a t e d a t a ( x , y , c u t ) f r o m a t o y m o d e l
62  // -------------------------------------------------------------------------------
63 
64  // Construct global shape p.d.f shape(x) and product model(x,cut) = eff(cut|x)*shape(x)
65  // (These are _only_ needed to generate some toy MC here to be used later)
66  RooPolynomial shapePdfX("shapePdfX", "shapePdfX", x, RooConst(flat ? 0 : -0.095));
67  RooPolynomial shapePdfY("shapePdfY", "shapePdfY", y, RooConst(flat ? 0 : +0.095));
68  RooProdPdf shapePdf("shapePdf", "shapePdf", RooArgSet(shapePdfX, shapePdfY));
69  RooProdPdf model("model", "model", shapePdf, Conditional(effPdf, cut));
70 
71  // Generate some toy data from model
72  RooDataSet *data = model.generate(RooArgSet(x, y, cut), 10000);
73 
74  // F i t c o n d i t i o n a l e f f i c i e n c y p d f t o d a t a
75  // --------------------------------------------------------------------------
76 
77  // Fit conditional efficiency p.d.f to data
78  effPdf.fitTo(*data, ConditionalObservables(RooArgSet(x, y)));
79 
80  // P l o t f i t t e d , d a t a e f f i c i e n c y
81  // --------------------------------------------------------
82 
83  // Make 2D histograms of all data, selected data and efficiency function
84  TH1 *hh_data_all = data->createHistogram("hh_data_all", x, Binning(8), YVar(y, Binning(8)));
85  TH1 *hh_data_sel = data->createHistogram("hh_data_sel", x, Binning(8), YVar(y, Binning(8)), Cut("cut==cut::accept"));
86  TH1 *hh_eff = effFunc.createHistogram("hh_eff", x, Binning(50), YVar(y, Binning(50)));
87 
88  // Some adjustment for good visualization
89  hh_data_all->SetMinimum(0);
90  hh_data_sel->SetMinimum(0);
91  hh_eff->SetMinimum(0);
92  hh_eff->SetLineColor(kBlue);
93 
94  // Draw all frames on a canvas
95  TCanvas *ca = new TCanvas("rf702_efficiency_2D", "rf702_efficiency_2D", 1200, 400);
96  ca->Divide(3);
97  ca->cd(1);
98  gPad->SetLeftMargin(0.15);
99  hh_data_all->GetZaxis()->SetTitleOffset(1.8);
100  hh_data_all->Draw("lego");
101  ca->cd(2);
102  gPad->SetLeftMargin(0.15);
103  hh_data_sel->GetZaxis()->SetTitleOffset(1.8);
104  hh_data_sel->Draw("lego");
105  ca->cd(3);
106  gPad->SetLeftMargin(0.15);
107  hh_eff->GetZaxis()->SetTitleOffset(1.8);
108  hh_eff->Draw("surf");
109 
110  return;
111 }
RooFormulaVar.h
RooDataSet::createHistogram
TH2F * createHistogram(const RooAbsRealLValue &var1, const RooAbsRealLValue &var2, const char *cuts="", const char *name="hist") const
Create a TH2F histogram of the distribution of the specified variable using this dataset.
Definition: RooDataSet.cxx:1419
TH1::SetMinimum
virtual void SetMinimum(Double_t minimum=-1111)
Definition: TH1.h:396
RooArgList
Definition: RooArgList.h:21
RooGaussian.h
x
Double_t x[n]
Definition: legend1.C:17
TPad::Divide
virtual void Divide(Int_t nx=1, Int_t ny=1, Float_t xmargin=0.01, Float_t ymargin=0.01, Int_t color=0)
Automatic pad generation by division.
Definition: TPad.cxx:1166
TCanvas.h
RooFit::YVar
RooCmdArg YVar(const RooAbsRealLValue &var, const RooCmdArg &arg=RooCmdArg::none())
Definition: RooGlobalFunc.cxx:240
RooFit::Binning
RooCmdArg Binning(const RooAbsBinning &binning)
Definition: RooGlobalFunc.cxx:82
TH1::GetZaxis
TAxis * GetZaxis()
Definition: TH1.h:319
RooDataSet.h
bool
RooPolynomial.h
rf702_efficiencyfit_2D
Definition: rf702_efficiencyfit_2D.py:1
RooFit::Cut
RooCmdArg Cut(const char *cutSpec)
Definition: RooGlobalFunc.cxx:80
RooFormulaVar
Definition: RooFormulaVar.h:30
RooProdPdf.h
RooFit
Definition: RooCFunction1Binding.h:29
TCanvas::cd
TVirtualPad * cd(Int_t subpadnumber=0)
Set current canvas & pad.
Definition: TCanvas.cxx:704
RooPolynomial
Definition: RooPolynomial.h:28
kFALSE
const Bool_t kFALSE
Definition: RtypesCore.h:92
RooPlot.h
RooEfficiency.h
RooCategory.h
y
Double_t y[n]
Definition: legend1.C:17
RooRealVar.h
RooFit::ConditionalObservables
RooCmdArg ConditionalObservables(const RooArgSet &set)
Definition: RooGlobalFunc.cxx:196
RooConstVar.h
RooFit::Conditional
RooCmdArg Conditional(const RooArgSet &pdfSet, const RooArgSet &depSet, Bool_t depsAreCond=kFALSE)
Definition: RooGlobalFunc.cxx:225
TCanvas
Definition: TCanvas.h:23
RooCategory
Definition: RooCategory.h:27
TAxis.h
TH1
Definition: TH1.h:57
RooEfficiency
Definition: RooEfficiency.h:27
kBlue
@ kBlue
Definition: Rtypes.h:66
gPad
#define gPad
Definition: TVirtualPad.h:287
RooDataSet
Definition: RooDataSet.h:33
make_cnn_model.model
model
Definition: make_cnn_model.py:6
TAttAxis::SetTitleOffset
virtual void SetTitleOffset(Float_t offset=1)
Set distance between the axis and the axis title.
Definition: TAttAxis.cxx:293
RooRealVar
Definition: RooRealVar.h:35
RooProdPdf
Definition: RooProdPdf.h:33
TH1.h
RooArgSet
Definition: RooArgSet.h:28
TH1::Draw
virtual void Draw(Option_t *option="")
Draw this histogram with options.
Definition: TH1.cxx:2997
RooFit::RooConst
RooConstVar & RooConst(Double_t val)
Definition: RooGlobalFunc.cxx:341