doc/v620/rf101__basics_8py.html

import ROOT


# Set up model

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

# Declare variables x,mean,sigma with associated name, title, initial

# value and allowed range

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

mean = ROOT.RooRealVar("mean", "mean of gaussian", 1, -10, 10)

sigma = ROOT.RooRealVar("sigma", "width of gaussian", 1, 0.1, 10)


# Build gaussian p.d.f in terms of x,mean and sigma

gauss = ROOT.RooGaussian("gauss", "gaussian PDF", x, mean, sigma)


# Construct plot frame in 'x'

xframe = x.frame(ROOT.RooFit.Title("Gaussian p.d.f."))  # RooPlot


# Plot model and change parameter values

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

# Plot gauss in frame (i.e. in x)

gauss.plotOn(xframe)


# Change the value of sigma to 3

sigma.setVal(3)


# Plot gauss in frame (i.e. in x) and draw frame on canvas

gauss.plotOn(xframe, ROOT.RooFit.LineColor(ROOT.kRed))


# Generate events

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

# Generate a dataset of 1000 events in x from gauss

data = gauss.generate(ROOT.RooArgSet(x), 10000)  # ROOT.RooDataSet


# Make a second plot frame in x and draw both the

# data and the p.d.f in the frame

xframe2 = x.frame(ROOT.RooFit.Title(

    "Gaussian p.d.f. with data"))  # RooPlot

data.plotOn(xframe2)

gauss.plotOn(xframe2)


# Fit model to data

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

# Fit pdf to data

gauss.fitTo(data)


# Print values of mean and sigma (that now reflect fitted values and

# errors)

mean.Print()

sigma.Print()


# Draw all frames on a canvas

c = ROOT.TCanvas("rf101_basics", "rf101_basics", 800, 400)

c.Divide(2)

c.cd(1)

ROOT.gPad.SetLeftMargin(0.15)

xframe.GetYaxis().SetTitleOffset(1.6)

xframe.Draw()

c.cd(2)

ROOT.gPad.SetLeftMargin(0.15)

xframe2.GetYaxis().SetTitleOffset(1.6)

xframe2.Draw()


c.SaveAs("rf101_basics.png")