import ROOT


# Define observables and decay pdf
# ---------------------------------------------------------------

# Declare observables
t = ROOT.RooRealVar("t", "t", 0, 5)

# Make pdf
tau = ROOT.RooRealVar("tau", "tau", -1.54, -4, -0.1)
model = ROOT.RooExponential("model", "model", t, tau)

# Define efficiency function
# ---------------------------------------------------

# Use error function to simulate turn-on slope
eff = ROOT.RooFormulaVar("eff", "0.5*(TMath::Erf((t-1)/0.5)+1)", [t])

# Define decay pdf with efficiency
# ---------------------------------------------------------------

# Multiply pdf(t) with efficiency in t
modelEff = ROOT.RooEffProd("modelEff", "model with efficiency", model, eff)

# Plot efficiency, pdf
# ----------------------------------------

frame1 = t.frame(Title="Efficiency")
eff.plotOn(frame1, LineColor="r")

frame2 = t.frame(Title="Pdf with and without efficiency")

model.plotOn(frame2, LineStyle="--")
modelEff.plotOn(frame2)

# Generate toy data, fit model eff to data
# ------------------------------------------------------------------------------

# Generate events. If the input pdf has an internal generator, internal generator
# is used and an accept/reject sampling on the efficiency is applied.
data = modelEff.generate({t}, 10000)

# Fit pdf. The normalization integral is calculated numerically.
modelEff.fitTo(data, PrintLevel=-1)

# Plot generated data and overlay fitted pdf
frame3 = t.frame(Title="Fitted pdf with efficiency")
data.plotOn(frame3)
modelEff.plotOn(frame3)

c = ROOT.TCanvas("rf703_effpdfprod", "rf703_effpdfprod", 1200, 400)
c.Divide(3)
c.cd(1)
ROOT.gPad.SetLeftMargin(0.15)
frame1.GetYaxis().SetTitleOffset(1.4)
frame1.Draw()
c.cd(2)
ROOT.gPad.SetLeftMargin(0.15)
frame2.GetYaxis().SetTitleOffset(1.6)
frame2.Draw()
c.cd(3)
ROOT.gPad.SetLeftMargin(0.15)
frame3.GetYaxis().SetTitleOffset(1.6)
frame3.Draw()

c.SaveAs("rf703_effpdfprod.png")