This tutorial illustrates the basic features of RooFit.
import ROOT
xframe =
x.frame(Title=
"Gaussian pdf")
xframe2 =
x.frame(Title=
"Gaussian pdf with data")
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
[#1] INFO:Fitting -- RooAbsPdf::fitTo(gauss_over_gauss_Int[x]) fixing normalization set for coefficient determination to observables in data
[#1] INFO:Fitting -- using CPU computation library compiled with -mavx512
[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_gauss_over_gauss_Int[x]_gaussData) Summation contains a RooNLLVar, using its error level
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization
RooRealVar::mean = 1.01746 +/- 0.0300144 L(-10 - 10)
RooRealVar::sigma = 2.9787 +/- 0.0219217 L(0.1 - 10)
- Date
- February 2018
- Authors
- Clemens Lange, Wouter Verkerke (C++ version)
Definition in file rf101_basics.py.