void rf103_interprfuncs()
{
   
   
   
 
   
 
   
   
 
   
   
   
 
   
   
 
   
   std::unique_ptr<RooDataSet> 
data{
genpdf.generate(
x, 10000)};
 
 
   
 
   
 
   
   
   
   
 
   
   
 
   
 
   
 
   
 
   
   
 
   
   std::unique_ptr<RooDataSet> 
data2{
g1.generate(
x, 1000)};
 
 
   
   
 
   
   fitResult->Print();
 
   
 
   
   TCanvas *
c = 
new TCanvas(
"rf103_interprfuncs", 
"rf103_interprfuncs", 800, 400);
 
   gPad->SetLeftMargin(0.15);
 
   xframe->GetYaxis()->SetTitleOffset(1.4);
 
   gPad->SetLeftMargin(0.15);
 
   xframe2->GetYaxis()->SetTitleOffset(1.4);
 
}
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
 
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void data
 
RooArgSet is a container object that can hold multiple RooAbsArg objects.
 
Implementation of a probability density function that takes a RooArgList of servers and a C++ express...
 
Plot frame and a container for graphics objects within that frame.
 
Variable that can be changed from the outside.
 
RooCmdArg Save(bool flag=true)
 
RooCmdArg PrintLevel(Int_t code)
 
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...
 
   
[#1] INFO:NumericIntegration -- RooRealIntegral::init(genpdf_Int[x]) using numeric integrator RooIntegrator1D to calculate Int(x)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(genpdf_Int[x]) using numeric integrator RooIntegrator1D to calculate Int(x)
[#1] INFO:Fitting -- RooAbsPdf::fitTo(genpdf_over_genpdf_Int[x]) fixing normalization set for coefficient determination to observables in data
[#1] INFO:Fitting -- using generic CPU library compiled with no vectorizations
[#1] INFO:Fitting -- Creation of NLL object took 708.161 μs
[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_genpdf_over_genpdf_Int[x]_genpdfData) Summation contains a RooNLLVar, using its error level
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization
[#1] INFO:NumericIntegration -- RooRealIntegral::init(genpdf_Int[x]) using numeric integrator RooIntegrator1D to calculate Int(x)
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization
[#1] INFO:NumericIntegration -- RooRealIntegral::init(genpdf_Int[x]) using numeric integrator RooIntegrator1D to calculate Int(x)
[#1] INFO:Fitting -- RooAbsPdf::fitTo(g2_over_g2_Int[x]) fixing normalization set for coefficient determination to observables in data
[#1] INFO:Fitting -- Creation of NLL object took 115.801 μs
[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_g2_over_g2_Int[x]_g1Data) Summation contains a RooNLLVar, using its error level
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization
 
  RooFitResult: minimized FCN value: 2551.39, estimated distance to minimum: 4.39288e-06
                covariance matrix quality: Full, accurate covariance matrix
                Status : MINIMIZE=0 HESSE=0 
 
    Floating Parameter    FinalValue +/-  Error   
  --------------------  --------------------------
                 mean2    1.0010e+02 +/-  1.98e+00
                 sigma    3.1172e+00 +/-  7.12e-02