#!/usr/bin/env python
# coding: utf-8

# # rf205_compplot
# Addition and convolution: options for plotting components of composite pdfs.
# 
# 
# 
# 
# **Author:**  Clemens Lange, Wouter Verkerke (C++ version)  
# <i><small>This notebook tutorial was automatically generated with <a href= "https://github.com/root-project/root/blob/master/documentation/doxygen/converttonotebook.py">ROOTBOOK-izer</a> from the macro found in the ROOT repository  on Wednesday, April 17, 2024 at 11:17 AM.</small></i>

# In[1]:


import ROOT


# Set up composite pdf
# --------------------------------------

# Declare observable x

# In[2]:


x = ROOT.RooRealVar("x", "x", 0, 10)


# Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and
# their parameters

# In[3]:


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)


# Sum the signal components into a composite signal pdf

# In[4]:


sig1frac = ROOT.RooRealVar("sig1frac", "fraction of component 1 in signal", 0.8, 0.0, 1.0)
sig = ROOT.RooAddPdf("sig", "Signal", [sig1, sig2], [sig1frac])


# Build Chebychev polynomial pdf

# In[5]:


a0 = ROOT.RooRealVar("a0", "a0", 0.5, 0.0, 1.0)
a1 = ROOT.RooRealVar("a1", "a1", -0.2, 0.0, 1.0)
bkg1 = ROOT.RooChebychev("bkg1", "Background 1", x, [a0, a1])


# Build expontential pdf

# In[6]:


alpha = ROOT.RooRealVar("alpha", "alpha", -1)
bkg2 = ROOT.RooExponential("bkg2", "Background 2", x, alpha)


# Sum the background components into a composite background pdf

# In[7]:


bkg1frac = ROOT.RooRealVar("bkg1frac", "fraction of component 1 in background", 0.8, 0.0, 1.0)
bkg = ROOT.RooAddPdf("bkg", "Total background", [bkg1, bkg2], [bkg1frac])


# Sum the composite signal and background

# In[8]:


bkgfrac = ROOT.RooRealVar("bkgfrac", "fraction of background", 0.5, 0.0, 1.0)
model = ROOT.RooAddPdf("model", "g1+g2+a", [bkg, sig], [bkgfrac])


# Set up basic plot with data and full pdf
# ------------------------------------------------------------------------------

# Generate a data sample of 1000 events in x from model

# In[9]:


data = model.generate({x}, 1000)


# Plot data and complete PDF overlaid

# In[10]:


xframe = x.frame(Title="Component plotting of pdf=(sig1+sig2)+(bkg1+bkg2)")
data.plotOn(xframe)
model.plotOn(xframe)


# Clone xframe for use below

# In[11]:


xframe2 = xframe.Clone("xframe2")


# Make component by object reference
# --------------------------------------------------------------------

# Plot single background component specified by object reference

# In[12]:


ras_bkg = {bkg}
model.plotOn(xframe, Components=ras_bkg, LineColor="r")


# Plot single background component specified by object reference

# In[13]:


ras_bkg2 = {bkg2}
model.plotOn(xframe, Components=ras_bkg2, LineStyle="--", LineColor="r")


# Plot multiple background components specified by object reference
# Note that specified components may occur at any level in object tree
# (e.g bkg is component of 'model' and 'sig2' is component 'sig')

# In[14]:


ras_bkg_sig2 = {bkg, sig2}
model.plotOn(xframe, Components=ras_bkg_sig2, LineStyle=":")


# Make component by name/regexp
# ------------------------------------------------------------

# Plot single background component specified by name

# In[15]:


model.plotOn(xframe2, Components="bkg", LineColor="c")


# Plot multiple background components specified by name

# In[16]:


model.plotOn(xframe2, Components="bkg1,sig2", LineStyle=":", LineColor="c")


# Plot multiple background components specified by regular expression on
# name

# In[17]:


model.plotOn(xframe2, Components="sig*", LineStyle="--", LineColor="c")


# Plot multiple background components specified by multiple regular
# expressions on name

# In[18]:


model.plotOn(xframe2, Invisible=True, Components="bkg1,sig*", LineStyle="--", LineColor="y")


# Draw the frame on the canvas

# In[19]:


c = ROOT.TCanvas("rf205_compplot", "rf205_compplot", 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)
xframe2.GetYaxis().SetTitleOffset(1.4)
xframe2.Draw()

c.SaveAs("rf205_compplot.png")


# Draw all canvases 

# In[20]:


from ROOT import gROOT 
gROOT.GetListOfCanvases().Draw()