本文整理汇总了C++中RooDataSet::reduce方法的典型用法代码示例。如果您正苦于以下问题：C++ RooDataSet::reduce方法的具体用法？C++ RooDataSet::reduce怎么用？C++ RooDataSet::reduce使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类RooDataSet的用法示例。
在下文中一共展示了RooDataSet::reduce方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: rf405_realtocatfuncs
void rf405_realtocatfuncs()
{
// D e f i n e p d f i n x , s a m p l e d a t a s e t i n x
// ------------------------------------------------------------------------
// Define a dummy PDF in x
RooRealVar x("x","x",0,10) ;
RooArgusBG a("a","argus(x)",x,RooConst(10),RooConst(-1)) ;
// Generate a dummy dataset
RooDataSet *data = a.generate(x,10000) ;
// C r e a t e a t h r e s h o l d r e a l - > c a t f u n c t i o n
// --------------------------------------------------------------------------
// A RooThresholdCategory is a category function that maps regions in a real-valued
// input observable observables to state names. At construction time a 'default'
// state name must be specified to which all values of x are mapped that are not
// otherwise assigned
RooThresholdCategory xRegion("xRegion","region of x",x,"Background") ;
// Specify thresholds and state assignments one-by-one.
// Each statement specifies that all values _below_ the given value
// (and above any lower specified threshold) are mapped to the
// category state with the given name
//
// Background | SideBand | Signal | SideBand | Background
// 4.23 5.23 8.23 9.23
xRegion.addThreshold(4.23,"Background") ;
xRegion.addThreshold(5.23,"SideBand") ;
xRegion.addThreshold(8.23,"Signal") ;
xRegion.addThreshold(9.23,"SideBand") ;
// U s e t h r e s h o l d f u n c t i o n t o p l o t d a t a r e g i o n s
// -------------------------------------------------------------------------------------
// Add values of threshold function to dataset so that it can be used as observable
data->addColumn(xRegion) ;
// Make plot of data in x
RooPlot* xframe = x.frame(Title("Demo of threshold and binning mapping functions")) ;
data->plotOn(xframe) ;
// Use calculated category to select sideband data
data->plotOn(xframe,Cut("xRegion==xRegion::SideBand"),MarkerColor(kRed),LineColor(kRed)) ;
// C r e a t e a b i n n i n g r e a l - > c a t f u n c t i o n
// ----------------------------------------------------------------------
// A RooBinningCategory is a category function that maps bins of a (named) binning definition
// in a real-valued input observable observables to state names. The state names are automatically
// constructed from the variable name, the binning name and the bin number. If no binning name
// is specified the default binning is mapped
x.setBins(10,"coarse") ;
RooBinningCategory xBins("xBins","coarse bins in x",x,"coarse") ;
// U s e b i n n i n g f u n c t i o n f o r t a b u l a t i o n a n d p l o t t i n g
// -----------------------------------------------------------------------------------------------
// Print table of xBins state multiplicity. Note that xBins does not need to be an observable in data
// it can be a function of observables in data as well
Roo1DTable* xbtable = data->table(xBins) ;
xbtable->Print("v") ;
// Add values of xBins function to dataset so that it can be used as observable
RooCategory* xb = (RooCategory*) data->addColumn(xBins) ;
// Define range "alt" as including bins 1,3,5,7,9
xb->setRange("alt","x_coarse_bin1,x_coarse_bin3,x_coarse_bin5,x_coarse_bin7,x_coarse_bin9") ;
// Construct subset of data matching range "alt" but only for the first 5000 events and plot it on the frame
RooDataSet* dataSel = (RooDataSet*) data->reduce(CutRange("alt"),EventRange(0,5000)) ;
dataSel->plotOn(xframe,MarkerColor(kGreen),LineColor(kGreen)) ;
new TCanvas("rf405_realtocatfuncs","rf405_realtocatfuncs",600,600) ;
xframe->SetMinimum(0.01) ;
gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.4) ; xframe->Draw() ;
}示例2: rf303_conditional
void rf303_conditional()
{
// S e t u p c o m p o s e d m o d e l g a u s s ( x , m ( y ) , s )
// -----------------------------------------------------------------------
// Create observables
RooRealVar x("x","x",-10,10) ;
RooRealVar y("y","y",-10,10) ;
// Create function f(y) = a0 + a1*y
RooRealVar a0("a0","a0",-0.5,-5,5) ;
RooRealVar a1("a1","a1",-0.5,-1,1) ;
RooPolyVar fy("fy","fy",y,RooArgSet(a0,a1)) ;
// Create gauss(x,f(y),s)
RooRealVar sigma("sigma","width of gaussian",0.5,0.1,2.0) ;
RooGaussian model("model","Gaussian with shifting mean",x,fy,sigma) ;
// Obtain fake external experimental dataset with values for x and y
RooDataSet* expDataXY = makeFakeDataXY() ;
// G e n e r a t e d a t a f r o m c o n d i t i o n a l p . d . f m o d e l ( x | y )
// ---------------------------------------------------------------------------------------------
// Make subset of experimental data with only y values
RooDataSet* expDataY= (RooDataSet*) expDataXY->reduce(y) ;
// Generate 10000 events in x obtained from _conditional_ model(x|y) with y values taken from experimental data
RooDataSet *data = model.generate(x,ProtoData(*expDataY)) ;
data->Print() ;
// F i t c o n d i t i o n a l p . d . f m o d e l ( x | y ) t o d a t a
// ---------------------------------------------------------------------------------------------
model.fitTo(*expDataXY,ConditionalObservables(y)) ;
// P r o j e c t c o n d i t i o n a l p . d . f o n x a n d y d i m e n s i o n s
// ---------------------------------------------------------------------------------------------
// Plot x distribution of data and projection of model on x = 1/Ndata sum(data(y_i)) model(x;y_i)
RooPlot* xframe = x.frame() ;
expDataXY->plotOn(xframe) ;
model.plotOn(xframe,ProjWData(*expDataY)) ;
// Speed up (and approximate) projection by using binned clone of data for projection
RooAbsData* binnedDataY = expDataY->binnedClone() ;
model.plotOn(xframe,ProjWData(*binnedDataY),LineColor(kCyan),LineStyle(kDotted)) ;
// Show effect of projection with too coarse binning
((RooRealVar*)expDataY->get()->find("y"))->setBins(5) ;
RooAbsData* binnedDataY2 = expDataY->binnedClone() ;
model.plotOn(xframe,ProjWData(*binnedDataY2),LineColor(kRed)) ;
// Make canvas and draw RooPlots
new TCanvas("rf303_conditional","rf303_conditional",600, 460);
gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.2) ; xframe->Draw() ;
}示例3: fitKmm_loQ
void fitKmm_loQ(Int_t bin) {
gSystem->Load("libRooFit");
gROOT->SetStyle("Plain");
gStyle->SetOptStat(1111);
TFile * file = TFile::Open("/Disk/ecdf-nfs-ppe/lhcb/gcowan/B2Kll/data/fromAlex/BuKmm.root");
TTree * DecayTree = dynamic_cast<TTree*>(file->Get("DecayTree"));
TString binStr; binStr+=bin;
Double_t minQ(0.), maxQ(0.);
switch(bin) {
case 0:
minQ = TMath::Sqrt(1.1e6);
maxQ = TMath::Sqrt(2.e6);
break;
case 1:
minQ = TMath::Sqrt(2.e6);
maxQ = TMath::Sqrt(3.e6);
break;
case 2:
minQ = TMath::Sqrt(3.e6);
maxQ = TMath::Sqrt(4.e6);
break;
case 3:
minQ = TMath::Sqrt(4.e6);
maxQ = TMath::Sqrt(5.e6);
break;
case 4:
minQ = TMath::Sqrt(5.e6);
maxQ = TMath::Sqrt(6.e6);
break;
default:
return;
}
TString cutStr("Psi_M> "); cutStr += minQ; cutStr += " && Psi_M< "; cutStr += maxQ;
//B_M
RooRealVar B_M("B_M","; m(Kmumu) (MeV/c^{2}); Candidates / 12 MeV/c^{2}",5150,6000);
RooRealVar Psi_M("Psi_M","; m(mumu) (MeV/c^{2}); Candidates / 45 MeV/c^{2}",500,5000);
RooDataSet * data = new RooDataSet("data", "dataset with B_REFITTED_M", DecayTree, RooArgSet(B_M,Psi_M));
RooDataSet * data1 = dynamic_cast<RooDataSet*>(data->reduce(cutStr));
// from J/Psi region
// 1 #sigma_{Lo} 1.59171e+01 9.61516e-02 1.80663e-03 6.11760e-02
// 2 M_{B} 5.28397e+03 3.00802e-02 1.66677e-03 3.66768e-01
// 3 a1 1.57752e+00 1.64484e-02 2.65338e-03 -7.53912e-01
// 4 a2 -2.64268e+00 2.11254e-02 2.51938e-03 4.90950e-01
// 5 frac 6.78672e-01 1.29969e-02 7.03329e-03 3.65422e-01
// 6 n1 4.79832e+00 2.84430e-01 2.61785e-02 -4.03463e-02
// 7 n2 1.08224e+00 2.68180e-02 5.47500e-03 -9.00362e-01
// 8 nbkg 5.56890e+03 1.31433e+02 7.62084e-03 -8.36640e-01
// 9 nsig 6.56230e+05 8.17224e+02 4.15943e-03 6.95832e-01
// 10 p0 -6.44379e-02 2.13769e-03 2.57927e-02 4.41139e-01
// 11 ratio 1.60407e+00 9.46569e-03 3.93086e-03 -7.72555e-01
// B DCB
// start, range to from. plus names and titles.
RooRealVar sigmean("M_{B}","B mass",5281.0,5250.0,5300.0,"MeV/c^{2}");
RooRealVar sigsigma("#sigma_{Lo}","B sigma",15.9,0.0,30.0,"MeV/c^{2}");
RooRealVar a1("a1","a1", 1.57752e+00);
RooRealVar n1("n1","n1", 4.79832e+00);
RooRealVar a2("a2","a2",-2.64268e+00);
RooRealVar n2("n2","n2", 1.08224e+00);
RooRealVar ratio("ratio","Ratio of widths",1.60407e+00);
RooProduct sigsigma2("#sigma_{B}2","B sigma2",RooArgSet(sigsigma,ratio));
RooRealVar frac("frac","fraction of events in each gaussian",6.78672e-01);
RooCBShape BSig_RF( "Bsig_RF", "Signal CB B RF Mass", B_M, sigmean, sigsigma, a1, n1 );
RooCBShape BSig_RF2( "Bsig_RF2", "Signal CB B RF Mass", B_M, sigmean, sigsigma2, a2, n2 );
RooAddPdf B0Sig("B0signal","signal pdf",RooArgList(BSig_RF,BSig_RF2),RooArgList(frac));
RooRealVar p0("p0","",-6.44379e-02,-0.1,0.1);
RooExponential comb_bkg("comb_bkg","",B_M,p0);
// Number of signal & background events
RooRealVar nsig("nsig","#signal events",150,-1000,50000,"Events");
RooRealVar nbkg("nbkg","#signal events",150,-1000,50000,"Events");
RooAddPdf full_RF_PDF("full_RF_PDF","RF PDF of everything",RooArgList(B0Sig,comb_bkg), RooArgList(nsig,nbkg));
//# Do the fit on REFITTED Mass
full_RF_PDF.fitTo(*data1,RooFit::Extended());
TCanvas * can = new TCanvas("can","Mass fits Data",800,600);
B_M_RF_Plot = B_M.frame(100);
B_M_RF_Plot->SetTitle("");
B_M_RF_Plot->GetYaxis()->SetTitle("Candidates / 8.5 MeV/c^{2}");
B_M_RF_Plot->GetXaxis()->SetTitle("m(K#mu#mu) (MeV/c^{2})");
data1->plotOn(B_M_RF_Plot);
full_RF_PDF.plotOn(B_M_RF_Plot);
full_RF_PDF.plotOn(B_M_RF_Plot, RooFit::Components("comb_bkg"), RooFit::LineStyle(kDashed),RooFit::LineColor(kMagenta));
full_RF_PDF.plotOn(B_M_RF_Plot, RooFit::Components("B0signal"), RooFit::LineStyle(kDashed));
B_M_RF_Plot->Draw();
can->SaveAs("plots/Kmm_loQ_"+binStr+".pdf");
can->SetLogy();
//.........这里部分代码省略.........
示例4: main
int main(int argc, char* argv[])
{
string name;
for(Int_t i=1;i<argc;i++){
char *pchar = argv[i];
switch(pchar[0]){
case '-':{
switch(pchar[1]){
case 'n':
name = argv[i+1];
cout << "Name of the configuration key " << name << endl;
break;
}
}
}
}
Bool_t doFit = kFALSE;
Bool_t extract = kFALSE;
Bool_t doFrac = kFALSE;
Bool_t doPlots = kTRUE;
Bool_t doChi2 = kFALSE;
BaBarStyle p;
p.SetBaBarStyle();
gROOT->GetStyle("BABAR")->SetPalette(1);
gROOT->GetStyle("BABAR")->SetPadTopMargin(0.04);
gROOT->GetStyle("BABAR")->SetPadLeftMargin(0.17);
gROOT->GetStyle("BABAR")->SetPadBottomMargin(0.19);
gROOT->GetStyle("BABAR")->SetTitleSize(0.08,"xyz"); // set the 3 axes title size
//define DalitzSpace for generation
EvtPDL pdl;
pdl.readPDT("evt.pdl");
EvtDecayMode mode("D0 -> K- pi+ pi0");
EvtDalitzPlot dalitzSpace(mode);
RooRealVar tau("tau","tau",0.4099);
RooRealVar m2Kpi_d0mass("m2Kpi_d0mass","m2Kpi_d0mass",1.,dalitzSpace.qAbsMin(EvtCyclic3::AB),dalitzSpace.qAbsMax(EvtCyclic3::AB));
RooRealVar m2Kpi0_d0mass("m2Kpi0_d0mass","m2Kpi0_d0mass",1.,dalitzSpace.qAbsMin(EvtCyclic3::AC),dalitzSpace.qAbsMax(EvtCyclic3::AC));
RooRealVar m2pipi0_d0mass("m2pipi0_d0mass","m2pipi0_d0mass",1.,dalitzSpace.qAbsMin(EvtCyclic3::BC),dalitzSpace.qAbsMax(EvtCyclic3::BC));
RooRealVar d0Lifetime("d0Lifetime","d0Lifetime",-2.,4.);
RooRealVar d0LifetimeErr("d0LifetimeErr","d0LifetimeErr",0.0000001,0.5);
RooCategory D0flav("D0flav","D0flav");
D0flav.defineType("D0",-1);
D0flav.defineType("antiD0",1);
RooRealVar scalefact1("scalefact1","scalefact1",3.20);
RooRealVar scalefact2("scalefact2","scalefact2",1.42);
RooRealVar scalefact3("scalefact3","scalefact3",0.94);
RooRealVar c1("c1","c1",-2.,2.);
RooRealVar c2("c2","c2",-2.,2.);
RooUnblindOffset c1_unblind("c1_unblind","c1 (unblind)","VaffanculoS",1.,c1) ;
RooUnblindOffset c2_unblind("c2_unblind","c2 (unblind)","VaffanculoS",1.,c2) ;
TFile fWS("DataSet_out_tmp.root");
gROOT->cd();
RooDataSet *data = (RooDataSet*)fWS.Get("fulldata");
RooDataSet *data_clean = (RooDataSet*)data->reduce("d0LifetimeErr < 0.5 && d0Lifetime > -2. && d0Lifetime < 4. && deltaMass > 0.1449 && deltaMass < 0.1459");
RooDataSet *dataWS_2 = (RooDataSet*)data_clean->reduce("isWS == 1");
RooDataSet *dataWS = (RooDataSet*)dataWS_2->reduce("d0Mass > 1.8495 && d0Mass < 1.8795");
RooDataSet *RSdata = (RooDataSet*)data_clean->reduce("isWS == 0 && d0Mass > 1.8495 && d0Mass < 1.8795");
Double_t low12,hig12,low13,hig13,low23,hig23;
Bool_t m12bool = dataWS->getRange(m2Kpi_d0mass,low12,hig12);
Bool_t m13bool = dataWS->getRange(m2Kpi0_d0mass,low13,hig13);
Bool_t m23bool = dataWS->getRange(m2pipi0_d0mass,low23,hig23);
m2Kpi_d0mass.setRange(low12,hig12);
m2Kpi0_d0mass.setRange(low13,hig13);
m2pipi0_d0mass.setRange(low23,hig23);
m2Kpi_d0mass.setBins(10);
m2Kpi0_d0mass.setBins(10);
d0Lifetime.setBins(8);
d0LifetimeErr.setBins(10);
m2pipi0_d0mass.setBins(10);
Float_t total = pow(dalitzSpace.bigM(),2) + pow(dalitzSpace.mA(),2) + pow(dalitzSpace.mB(),2) + pow(dalitzSpace.mC(),2);
RooRealVar totalm("totalm","totalm",total);
RooFormulaVar mass13a("mass13a","@[email protected]@2",RooArgSet(totalm,m2Kpi_d0mass,m2pipi0_d0mass));
//Construct signal pdf
RooRealVar bias("bias","bias",0.0047) ;
RooRealVar one("one","one",1.);
//consider the resolution or the truth model
RooGaussModel gm1("gm1","gauss model 1",d0Lifetime,bias,d0LifetimeErr,one,scalefact1) ;
RooGaussModel gm2("gm2","gauss model 2",d0Lifetime,bias,d0LifetimeErr,one,scalefact2) ;
RooGaussModel gm3("gm3","gauss model 3",d0Lifetime,bias,d0LifetimeErr,one,scalefact3) ;
RooRealVar N1("N1","N1",0.0052);
RooRealVar N2("N2","N2",0.179);
RooFormulaVar f2("f2","f2","([email protected])*@1",RooArgList(N1,N2));
RooFormulaVar f3("f3","f3","([email protected])*([email protected])",RooArgList(N1,N2));
//.........这里部分代码省略.........