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1/// \file
2/// \ingroup tutorial_roofit
3/// \notebook -js
4/// Likelihood and minimization: setting up a multi-core parallelized unbinned maximum likelihood fit
6/// \macro_image
7/// \macro_output
8/// \macro_code
10/// \date July 2008
11/// \author Wouter Verkerke
13#include "RooRealVar.h"
14#include "RooDataSet.h"
15#include "RooGaussian.h"
16#include "RooConstVar.h"
17#include "RooPolynomial.h"
18#include "RooAddPdf.h"
19#include "RooProdPdf.h"
20#include "TCanvas.h"
21#include "TAxis.h"
22#include "RooPlot.h"
23using namespace RooFit;
25void rf603_multicpu()
28 // C r e a t e 3 D p d f a n d d a t a
29 // -------------------------------------------
31 // Create observables
32 RooRealVar x("x", "x", -5, 5);
33 RooRealVar y("y", "y", -5, 5);
34 RooRealVar z("z", "z", -5, 5);
36 // Create signal pdf gauss(x)*gauss(y)*gauss(z)
37 RooGaussian gx("gx", "gx", x, RooConst(0), RooConst(1));
38 RooGaussian gy("gy", "gy", y, RooConst(0), RooConst(1));
39 RooGaussian gz("gz", "gz", z, RooConst(0), RooConst(1));
40 RooProdPdf sig("sig", "sig", RooArgSet(gx, gy, gz));
42 // Create background pdf poly(x)*poly(y)*poly(z)
43 RooPolynomial px("px", "px", x, RooArgSet(-0.1, 0.004));
44 RooPolynomial py("py", "py", y, RooArgSet(0.1, -0.004));
45 RooPolynomial pz("pz", "pz", z);
46 RooProdPdf bkg("bkg", "bkg", RooArgSet(px, py, pz));
48 // Create composite pdf sig+bkg
49 RooRealVar fsig("fsig", "signal fraction", 0.1, 0., 1.);
50 RooAddPdf model("model", "model", RooArgList(sig, bkg), fsig);
52 // Generate large dataset
53 RooDataSet *data = model.generate(RooArgSet(x, y, z), 200000);
55 // P a r a l l e l f i t t i n g
56 // -------------------------------
58 // In parallel mode the likelihood calculation is split in N pieces,
59 // that are calculated in parallel and added a posteriori before passing
60 // it back to MINUIT.
62 // Use four processes and time results both in wall time and CPU time
63 model.fitTo(*data, NumCPU(4), Timer(true));
65 // P a r a l l e l M C p r o j e c t i o n s
66 // ----------------------------------------------
68 // Construct signal, total likelihood projection on (y,z) observables and likelihood ratio
69 RooAbsPdf *sigyz = sig.createProjection(x);
70 RooAbsPdf *totyz = model.createProjection(x);
71 RooFormulaVar llratio_func("llratio", "log10(@0)-log10(@1)", RooArgList(*sigyz, *totyz));
73 // Calculate likelihood ratio for each event, define subset of events with high signal likelihood
74 data->addColumn(llratio_func);
75 RooDataSet *dataSel = (RooDataSet *)data->reduce(Cut("llratio>0.7"));
77 // Make plot frame and plot data
78 RooPlot *frame = x.frame(Title("Projection on X with LLratio(y,z)>0.7"), Bins(40));
79 dataSel->plotOn(frame);
81 // Perform parallel projection using MC integration of pdf using given input dataSet.
82 // In this mode the data-weighted average of the pdf is calculated by splitting the
83 // input dataset in N equal pieces and calculating in parallel the weighted average
84 // one each subset. The N results of those calculations are then weighted into the
85 // final result
87 // Use four processes
88 model.plotOn(frame, ProjWData(*dataSel), NumCPU(4));
90 new TCanvas("rf603_multicpu", "rf603_multicpu", 600, 600);
91 gPad->SetLeftMargin(0.15);
92 frame->GetYaxis()->SetTitleOffset(1.6);
93 frame->Draw();
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void data
#define gPad
Definition: TVirtualPad.h:288
virtual RooPlot * plotOn(RooPlot *frame, const RooCmdArg &arg1=RooCmdArg::none(), const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none()) const
Definition: RooAbsData.cxx:598
virtual RooAbsPdf * createProjection(const RooArgSet &iset)
Return a p.d.f that represent a projection of this p.d.f integrated over given observables.
Definition: RooAbsPdf.cxx:3291
RooAddPdf is an efficient implementation of a sum of PDFs of the form.
Definition: RooAddPdf.h:34
RooArgList is a container object that can hold multiple RooAbsArg objects.
Definition: RooArgList.h:22
RooArgSet is a container object that can hold multiple RooAbsArg objects.
Definition: RooArgSet.h:56
RooDataSet is a container class to hold unbinned data.
Definition: RooDataSet.h:55
A RooFormulaVar is a generic implementation of a real-valued object, which takes a RooArgList of serv...
Definition: RooFormulaVar.h:30
Plain Gaussian p.d.f.
Definition: RooGaussian.h:24
A RooPlot is a plot frame and a container for graphics objects within that frame.
Definition: RooPlot.h:43
TAxis * GetYaxis() const
Definition: RooPlot.cxx:1276
void Draw(Option_t *options=nullptr) override
Draw this plot and all of the elements it contains.
Definition: RooPlot.cxx:649
RooPolynomial implements a polynomial p.d.f of the form.
Definition: RooPolynomial.h:28
RooProdPdf is an efficient implementation of a product of PDFs of the form.
Definition: RooProdPdf.h:33
RooRealVar represents a variable that can be changed from the outside.
Definition: RooRealVar.h:40
virtual void SetTitleOffset(Float_t offset=1)
Set distance between the axis and the axis title.
Definition: TAttAxis.cxx:301
The Canvas class.
Definition: TCanvas.h:23
RooConstVar & RooConst(double val)
RooCmdArg Bins(Int_t nbin)
RooCmdArg NumCPU(Int_t nCPU, Int_t interleave=0)
RooCmdArg Timer(bool flag=true)
RooCmdArg ProjWData(const RooAbsData &projData, bool binData=false)
RooCmdArg Cut(const char *cutSpec)
Double_t y[n]
Definition: legend1.C:17
Double_t x[n]
Definition: legend1.C:17
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...
Definition: Common.h:18
const char * Title
Definition: TXMLSetup.cxx:68