rs601_HLFactoryexample.py File Reference

Detailed Description

High Level Factory: creation of a simple model

[#1] INFO:Minimization -- p.d.f. provides expected number of events, including extended term in likelihood.
[#1] INFO:Fitting -- RooAbsPdf::fitTo(sum) 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_sum_sumData) Summation contains a RooNLLVar, using its error level
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization
Minuit2Minimizer: Minimize with max-calls 2500 convergence for edm < 1 strategy 1
Minuit2Minimizer : Valid minimum - status = 0
FVAL  = -18178.1839194288223
Edm   = 7.61294712667117464e-05
Nfcn  = 112
argpar     = -22.9054    +/-  3.42143  (limited)
mean    = 5.27987  +/-  0.000215769 (limited)
nbkg    = 1612.74  +/-  44.6723  (limited)
nsig    = 387.37   +/-  27.7674  (limited)
width   = 0.00300965  +/-  0.000199023 (limited)
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization
[#1] INFO:Plotting -- RooAbsPdf::plotOn(sum) directly selected PDF components: (argus)
[#1] INFO:Plotting -- RooAbsPdf::plotOn(sum) indirectly selected PDF components: ()

 
import ROOT
 
# --- Build the datacard and dump to file---
card_name = "HLFavtoryexample.rs"
with open(card_name, "w") as f:
    f.write("// The simplest card\n\n")
    f.write("gauss = Gaussian(mes[5.20,5.30],mean[5.28,5.2,5.3],width[0.0027,0.001,1]);\n")
    f.write("argus = ArgusBG(mes,5.291,argpar[-20,-100,-1]);\n")
    f.write("sum = SUM(nsig[200,0,10000]*gauss,nbkg[800,0,10000]*argus);\n\n")
 
hlf = ROOT.RooStats.HLFactory("HLFavtoryexample", card_name, False)
 
# --- Take elements out of the internal workspace ---
w = hlf.GetWs()
 
mes = w.arg("mes")
sumpdf = w["sum"]
argus = w["argus"]
 
# --- Generate a toyMC sample from composite PDF ---
data = sumpdf.generate(mes, 2000)
 
# --- Perform extended ML fit of composite PDF to toy data ---
sumpdf.fitTo(data)
 
# --- Plot toy data and composite PDF overlaid ---
mesframe = mes.frame()
data.plotOn(mesframe)
sumpdf.plotOn(mesframe)
sumpdf.plotOn(mesframe, Components=argus, LineStyle="--")
 
ROOT.gROOT.SetStyle("Plain")
 
c = ROOT.TCanvas()
mesframe.Draw()
 
c.SaveAs("rs601_HLFactoryexample.png")

Date

July 2022

Authors

Artem Busorgin, Danilo Piparo (C++ version)

Definition in file rs601_HLFactoryexample.py.