doc/v630/rf304__uncorrprod_8py.html

import ROOT


# Create component pdfs in x and y

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


# Create two pdfs gaussx(x,meanx,sigmax) gaussy(y,meany,sigmay) and its

# variables

x = ROOT.RooRealVar("x", "x", -5, 5)

y = ROOT.RooRealVar("y", "y", -5, 5)


meanx = ROOT.RooRealVar("mean1", "mean of gaussian x", 2)

meany = ROOT.RooRealVar("mean2", "mean of gaussian y", -2)

sigmax = ROOT.RooRealVar("sigmax", "width of gaussian x", 1)

sigmay = ROOT.RooRealVar("sigmay", "width of gaussian y", 5)


gaussx = ROOT.RooGaussian("gaussx", "gaussian PDF", x, meanx, sigmax)

gaussy = ROOT.RooGaussian("gaussy", "gaussian PDF", y, meany, sigmay)


# Construct uncorrelated product pdf

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


# Multiply gaussx and gaussy into a two-dimensional pdf gaussxy

gaussxy = ROOT.RooProdPdf("gaussxy", "gaussx*gaussy", [gaussx, gaussy])


# Sample pdf, plot projection on x and y

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


# Generate 10000 events in x and y from gaussxy

data = gaussxy.generate({x, y}, 10000)


# Plot x distribution of data and projection of gaussxy x = Int(dy)

# gaussxy(x,y)

xframe = x.frame(Title="X projection of gauss(x)*gauss(y)")

data.plotOn(xframe)

gaussxy.plotOn(xframe)


# Plot x distribution of data and projection of gaussxy y = Int(dx)

# gaussxy(x,y)

yframe = y.frame(Title="Y projection of gauss(x)*gauss(y)")

data.plotOn(yframe)

gaussxy.plotOn(yframe)


# Make canvas and draw ROOT.RooPlots

c = ROOT.TCanvas("rf304_uncorrprod", "rf304_uncorrprod", 800, 400)

c.Divide(2)

c.cd(1)

ROOT.gPad.SetLeftMargin(0.15)

xframe.GetYaxis().SetTitleOffset(1.4)

xframe.Draw()

c.cd(2)

ROOT.gPad.SetLeftMargin(0.15)

yframe.GetYaxis().SetTitleOffset(1.4)

yframe.Draw()


c.SaveAs("rf304_uncorrprod.png")