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")