doc/latest-stable/rf201__composite_8py.html

import ROOT


# Setup component pdfs

# ---------------------------------------


# Declare observable x

x = ROOT.RooRealVar("x", "x", 0, 10)


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

# their parameters

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)


# Build Chebychev polynomial pdf

a0 = ROOT.RooRealVar("a0", "a0", 0.5, 0.0, 1.0)

a1 = ROOT.RooRealVar("a1", "a1", -0.2, 0.0, 1.0)

bkg = ROOT.RooChebychev("bkg", "Background", x, [a0, a1])


# Method 1 - Two RooAddPdfs

# ------------------------------------------

# Add signal components


# Sum the signal components into a composite signal pdf

sig1frac = ROOT.RooRealVar("sig1frac", "fraction of component 1 in signal", 0.8, 0.0, 1.0)

sig = ROOT.RooAddPdf("sig", "Signal", [sig1, sig2], [sig1frac])


# Add signal and background

# ------------------------------------------------


# Sum the composite signal and background

bkgfrac = ROOT.RooRealVar("bkgfrac", "fraction of background", 0.5, 0.0, 1.0)

model = ROOT.RooAddPdf("model", "g1+g2+a", [bkg, sig], [bkgfrac])


# Sample, fit and plot model

# ---------------------------------------------------


# Generate a data sample of 1000 events in x from model

data = model.generate({x}, 1000)


# Fit model to data

model.fitTo(data, PrintLevel=-1)


# Plot data and PDF overlaid

xframe = x.frame(Title="Example of composite pdf=(sig1+sig2)+bkg")

data.plotOn(xframe)

model.plotOn(xframe)


# Overlay the background component of model with a dashed line

model.plotOn(xframe, Components={bkg}, LineStyle="--")


# Overlay the background+sig2 components of model with a dotted line

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


# Print structure of composite pdf

model.Print("t")


# Method 2 - One RooAddPdf with recursive fractions

# ---------------------------------------------------


# Construct sum of models on one go using recursive fraction interpretations

#

#   model2 = bkg + (sig1 + sig2)

#

model2 = ROOT.RooAddPdf("model", "g1+g2+a", [bkg, sig1, sig2], [bkgfrac, sig1frac], True)


# NB: Each coefficient is interpreted as the fraction of the

# left-hand component of the i-th recursive sum, i.e.

#

#   sum4 = A + ( B + ( C + D)  with fraction fA, and fC expands to

#

#   sum4 = fA*A + (1-fA)*(fB*B + (1-fB)*(fC*C + (1-fC)*D))


# Plot recursive addition model

# ---------------------------------------------------------

model2.plotOn(xframe, LineColor="r", LineStyle="--")

model2.plotOn(xframe, Components={bkg, sig2}, LineColor="r", LineStyle="--")

model2.Print("t")


# Draw the frame on the canvas

c = ROOT.TCanvas("rf201_composite", "rf201_composite", 600, 600)

ROOT.gPad.SetLeftMargin(0.15)

xframe.GetYaxis().SetTitleOffset(1.4)

xframe.Draw()


c.SaveAs("rf201_composite.png")

TRangeDynCast
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Definition TCollection.h:358