Rf 2 0 5_Compplot¶

Addition and convolution: options for plotting components of composite pdfs.

Author: Clemens Lange, Wouter Verkerke (C++ version)
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Monday, January 17, 2022 at 09:54 AM.

In [ ]:
import ROOT

Set up composite pdf¶

Declare observable x

In [ ]:
x = ROOT.RooRealVar("x", "x", 0, 10)

Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and their parameters

In [ ]:
mean = ROOT.RooRealVar("mean", "mean of gaussians", 5)
sigma1 = ROOT.RooRealVar("sigma1", "width of gaussians", 0.5)
sigma2 = ROOT.RooRealVar("sigma2", "width of gaussians", 1)
sig1 = ROOT.RooGaussian("sig1", "Signal component 1", x, mean, sigma1)
sig2 = ROOT.RooGaussian("sig2", "Signal component 2", x, mean, sigma2)

Sum the signal components into a composite signal pdf

In [ ]:
sig1frac = ROOT.RooRealVar("sig1frac", "fraction of component 1 in signal", 0.8, 0.0, 1.0)
sig = ROOT.RooAddPdf("sig", "Signal", [sig1, sig2], [sig1frac])

Build Chebychev polynomial pdf

In [ ]:
a0 = ROOT.RooRealVar("a0", "a0", 0.5, 0.0, 1.0)
a1 = ROOT.RooRealVar("a1", "a1", -0.2, 0.0, 1.0)
bkg1 = ROOT.RooChebychev("bkg1", "Background 1", x, [a0, a1])

Build expontential pdf

In [ ]:
alpha = ROOT.RooRealVar("alpha", "alpha", -1)
bkg2 = ROOT.RooExponential("bkg2", "Background 2", x, alpha)

Sum the background components into a composite background pdf

In [ ]:
bkg1frac = ROOT.RooRealVar("sig1frac", "fraction of component 1 in background", 0.2, 0.0, 1.0)
bkg = ROOT.RooAddPdf("bkg", "Signal", [bkg1, bkg2], [sig1frac])

Sum the composite signal and background

In [ ]:
bkgfrac = ROOT.RooRealVar("bkgfrac", "fraction of background", 0.5, 0.0, 1.0)
model = ROOT.RooAddPdf("model", "g1+g2+a", [bkg, sig], [bkgfrac])

Set up basic plot with data and full pdf¶

Generate a data sample of 1000 events in x from model

In [ ]:
data = model.generate({x}, 1000)

Plot data and complete PDF overlaid

In [ ]:
xframe = x.frame(Title="Component plotting of pdf=(sig1+sig2)+(bkg1+bkg2)")
data.plotOn(xframe)
model.plotOn(xframe)

Clone xframe for use below

In [ ]:
xframe2 = xframe.Clone("xframe2")

Make component by object reference¶

Plot single background component specified by object reference

In [ ]:
ras_bkg = {bkg}
model.plotOn(xframe, Components=ras_bkg, LineColor="r")

Plot single background component specified by object reference

In [ ]:
ras_bkg2 = {bkg2}
model.plotOn(xframe, Components=ras_bkg2, LineStyle="--", LineColor="r")

Plot multiple background components specified by object reference Note that specified components may occur at any level in object tree (e.g bkg is component of 'model' and 'sig2' is component 'sig')

In [ ]:
ras_bkg_sig2 = {bkg, sig2}
model.plotOn(xframe, Components=ras_bkg_sig2, LineStyle=":")

Make component by name/regexp¶

Plot single background component specified by name

In [ ]:
model.plotOn(xframe2, Components="bkg", LineColor="c")

Plot multiple background components specified by name

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model.plotOn(xframe2, Components="bkg1,sig2", LineStyle=":", LineColor="c")

Plot multiple background components specified by regular expression on name

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model.plotOn(xframe2, Components="sig*", LineStyle="--", LineColor="c")

Plot multiple background components specified by multiple regular expressions on name

In [ ]:
model.plotOn(xframe2, Invisible=True, Components="bkg1,sig*", LineStyle="--", LineColor="y")

Draw the frame on the canvas

In [ ]:
c = ROOT.TCanvas("rf205_compplot", "rf205_compplot", 800, 400)
c.Divide(2)
c.cd(1)
xframe.GetYaxis().SetTitleOffset(1.4)
xframe.Draw()
c.cd(2)