rf706_histpdf.py

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## \file
## \ingroup tutorial_roofit
## \notebook
## Special pdf's: histogram based pdfs and functions
##
## \macro_code
##
## \date February 2018
## \authors Clemens Lange, Wouter Verkerke (C++ version)
 
import ROOT
 
 
# Create pdf for sampling
# ---------------------------------------------
 
x = ROOT.RooRealVar("x", "x", 0, 20)
p = ROOT.RooPolynomial("p", "p", x, ROOT.RooArgList(ROOT.RooFit.RooConst(
    0.01), ROOT.RooFit.RooConst(-0.01), ROOT.RooFit.RooConst(0.0004)))
 
# Create low stats histogram
# ---------------------------------------------------
 
# Sample 500 events from p
x.setBins(20)
data1 = p.generate(ROOT.RooArgSet(x), 500)
 
# Create a binned dataset with 20 bins and 500 events
hist1 = data1.binnedClone()
 
# Represent data in dh as pdf in x
histpdf1 = ROOT.RooHistPdf("histpdf1", "histpdf1", ROOT.RooArgSet(x), hist1, 0)
 
# Plot unbinned data and histogram pdf overlaid
frame1 = x.frame(ROOT.RooFit.Title(
    "Low statistics histogram pdf"), ROOT.RooFit.Bins(100))
data1.plotOn(frame1)
histpdf1.plotOn(frame1)
 
# Create high stats histogram
# -----------------------------------------------------
 
# Sample 100000 events from p
x.setBins(10)
data2 = p.generate(ROOT.RooArgSet(x), 100000)
 
# Create a binned dataset with 10 bins and 100K events
hist2 = data2.binnedClone()
 
# Represent data in dh as pdf in x, 2nd order interpolation
histpdf2 = ROOT.RooHistPdf("histpdf2", "histpdf2", ROOT.RooArgSet(x), hist2, 2)
 
# Plot unbinned data and histogram pdf overlaid
frame2 = x.frame(ROOT.RooFit.Title(
    "High stats histogram pdf with interpolation"), ROOT.RooFit.Bins(100))
data2.plotOn(frame2)
histpdf2.plotOn(frame2)
 
c = ROOT.TCanvas("rf706_histpdf", "rf706_histpdf", 800, 400)
c.Divide(2)
c.cd(1)
ROOT.gPad.SetLeftMargin(0.15)
frame1.GetYaxis().SetTitleOffset(1.4)
frame1.Draw()
c.cd(2)
ROOT.gPad.SetLeftMargin(0.15)
frame2.GetYaxis().SetTitleOffset(1.8)
frame2.Draw()
 
c.SaveAs("rf706_histpdf.png")