## \file
## \ingroup tutorial_roofit_main
## \notebook
## Special pdf's: histogram based pdfs and functions
##
## \macro_image
## \macro_code
## \macro_output
##
## \date February 2018
## \authors Clemens Lange, Wouter Verkerke (C++ version)

import ROOT


# Create pdf for sampling
# ---------------------------------------------

x = ROOT.RooRealVar("x", "x", 0, 20)
p = ROOT.RooPolynomial("p", "p", x, [0.01, -0.01, 0.0004])

# Create low stats histogram
# ---------------------------------------------------

# Sample 500 events from p
x.setBins(20)
data1 = p.generate({x}, 500)

# Create a binned dataset with 20 bins and 500 events
hist1 = data1.binnedClone()

# Represent data in dh as pdf in x
histpdf1 = ROOT.RooHistPdf("histpdf1", "histpdf1", {x}, hist1, 0)

# Plot unbinned data and histogram pdf overlaid
frame1 = x.frame(Title="Low statistics histogram pdf", Bins=100)
data1.plotOn(frame1)
histpdf1.plotOn(frame1)

# Create high stats histogram
# -----------------------------------------------------

# Sample 100000 events from p
x.setBins(10)
data2 = p.generate({x}, 100000)

# Create a binned dataset with 10 bins and 100K events
hist2 = data2.binnedClone()

# Represent data in dh as pdf in x, 2nd order interpolation
histpdf2 = ROOT.RooHistPdf("histpdf2", "histpdf2", {x}, hist2, 2)

# Plot unbinned data and histogram pdf overlaid
frame2 = x.frame(Title="High stats histogram pdf with interpolation", Bins=100)
data2.plotOn(frame2)
histpdf2.plotOn(frame2)

c = ROOT.TCanvas("rf706_histpdf", "rf706_histpdf", 800, 400)
c.Divide(2)
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(1.8)
frame2.Draw()

c.SaveAs("rf706_histpdf.png")
