doc/latest-stable/rf202__extendedmlfit_8py.html

import ROOT


# Set up 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])


# 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])


# Method 1 - Construct extended composite model

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


# Sum the composite signal and background into an extended pdf

# nsig*sig+nbkg*bkg

nsig = ROOT.RooRealVar("nsig", "number of signal events", 500, 0.0, 10000)

nbkg = ROOT.RooRealVar("nbkg", "number of background events", 500, 0, 10000)

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


# Sample, fit and plot extended model

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


# Generate a data sample of expected number events in x from model

# = model.expectedEvents() = nsig+nbkg

data = model.generate({x})


# Fit model to data, ML term automatically included

model.fitTo(data, PrintLevel=-1)


# Plot data and PDF overlaid, expected number of events for pdf projection normalization

# rather than observed number of events (==data.numEntries())

xframe = x.frame(Title="extended ML fit example")

data.plotOn(xframe)

model.plotOn(xframe, Normalization=dict(scaleFactor=1.0, scaleType=ROOT.RooAbsReal.RelativeExpected))


# Overlay the background component of model with a dashed line

model.plotOn(

    xframe,

    Components={bkg},

    LineStyle=":",

    Normalization=dict(scaleFactor=1.0, scaleType=ROOT.RooAbsReal.RelativeExpected),

)


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

ras_bkg_sig2 = {bkg, sig2}

model.plotOn(

    xframe,

    Components=ras_bkg_sig2,

    LineStyle=":",

    Normalization=dict(scaleFactor=1.0, scaleType=ROOT.RooAbsReal.RelativeExpected),

)


# Print structure of composite pdf

model.Print("t")


# Method 2 - Construct extended components first

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


# Associated nsig/nbkg as expected number of events with sig/bkg

esig = ROOT.RooExtendPdf("esig", "extended signal pdf", sig, nsig)

ebkg = ROOT.RooExtendPdf("ebkg", "extended background pdf", bkg, nbkg)


# Sum extended components without coefs

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


# Construct sum of two extended pdf (no coefficients required)

model2 = ROOT.RooAddPdf("model2", "(g1+g2)+a", [ebkg, esig])


# Draw the frame on the canvas

c = ROOT.TCanvas("rf202_extendedmlfit", "rf202_extendedmlfit", 600, 600)

ROOT.gPad.SetLeftMargin(0.15)

xframe.GetYaxis().SetTitleOffset(1.4)

xframe.Draw()


c.SaveAs("rf202_extendedmlfit.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