rf108_plotbinning.py

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## \file
## \ingroup tutorial_roofit
## \notebook
## Basic functionality: plotting unbinned data with alternate and variable binnings
##
## \macro_image
## \macro_code
## \macro_output
##
## \date February 2018
## \authors Clemens Lange, Wouter Verkerke (C++ version)
 
import ROOT
 
# Set up model
# ---------------------
 
# Build a B decay pdf with mixing
dt = ROOT.RooRealVar("dt", "dt", -20, 20)
dm = ROOT.RooRealVar("dm", "dm", 0.472)
tau = ROOT.RooRealVar("tau", "tau", 1.547)
w = ROOT.RooRealVar("w", "mistag rate", 0.1)
dw = ROOT.RooRealVar("dw", "delta mistag rate", 0.0)
 
mixState = ROOT.RooCategory("mixState", "B0/B0bar mixing state", {"mixed": -1, "unmixed": 1})
tagFlav = ROOT.RooCategory("tagFlav", "Flavour of the tagged B0", {"B0": 1, "B0bar": -1})
 
# Build a gaussian resolution model
dterr = ROOT.RooRealVar("dterr", "dterr", 0.1, 1.0)
bias1 = ROOT.RooRealVar("bias1", "bias1", 0)
sigma1 = ROOT.RooRealVar("sigma1", "sigma1", 0.1)
gm1 = ROOT.RooGaussModel("gm1", "gauss model 1", dt, bias1, sigma1)
 
# Construct Bdecay (x) gauss
bmix = ROOT.RooBMixDecay("bmix", "decay", dt, mixState, tagFlav, tau, dm, w, dw, gm1, type="DoubleSided")
 
# Sample data from model
# --------------------------------------------
 
# Sample 2000 events in (dt,mixState,tagFlav) from bmix
data = bmix.generate({dt, mixState, tagFlav}, 2000)
 
# Show dt distribution with custom binning
# -------------------------------------------------------------------------------
 
# Make plot of dt distribution of data in range (-15,15) with fine binning
# for dt>0 and coarse binning for dt<0
 
# Create binning object with range (-15,15)
tbins = ROOT.RooBinning(-15, 15)
 
# Add 60 bins with uniform spacing in range (-15,0)
tbins.addUniform(60, -15, 0)
 
# Add 15 bins with uniform spacing in range (0,15)
tbins.addUniform(15, 0, 15)
 
# Make plot with specified binning
dtframe = dt.frame(Range=(-15, 15), Title="dt distribution with custom binning")
data.plotOn(dtframe, Binning=tbins)
bmix.plotOn(dtframe)
 
# NB: Note that bin density for each bin is adjusted to that of default frame binning as shown
# in Y axis label (100 bins -. Events/0.4*Xaxis-dim) so that all bins
# represent a consistent density distribution
 
# Show mixstate asymmetry with custom binning
# ------------------------------------------------------------------------------------
 
# Make plot of dt distribution of data asymmetry in 'mixState' with
# variable binning
 
# Create binning object with range (-10,10)
abins = ROOT.RooBinning(-10, 10)
 
# Add boundaries at 0, (-1,1), (-2,2), (-3,3), (-4,4) and (-6,6)
abins.addBoundary(0)
abins.addBoundaryPair(1)
abins.addBoundaryPair(2)
abins.addBoundaryPair(3)
abins.addBoundaryPair(4)
abins.addBoundaryPair(6)
 
# Create plot frame in dt
aframe = dt.frame(Range=(-10, 10), Title="mixState asymmetry distribution with custom binning")
 
# Plot mixState asymmetry of data with specified customg binning
data.plotOn(aframe, Asymmetry=mixState, Binning=abins)
 
# Plot corresponding property of pdf
bmix.plotOn(aframe, Asymmetry=mixState)
 
# Adjust vertical range of plot to sensible values for an asymmetry
aframe.SetMinimum(-1.1)
aframe.SetMaximum(1.1)
 
# NB: For asymmetry distributions no density corrects are needed (and are
# thus not applied)
 
# Draw plots on canvas
c = ROOT.TCanvas("rf108_plotbinning", "rf108_plotbinning", 800, 400)
c.Divide(2)
c.cd(1)
ROOT.gPad.SetLeftMargin(0.15)
dtframe.GetYaxis().SetTitleOffset(1.6)
dtframe.Draw()
c.cd(2)
ROOT.gPad.SetLeftMargin(0.15)
aframe.GetYaxis().SetTitleOffset(1.6)
aframe.Draw()
 
c.SaveAs("rf108_plotbinning.png")