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

# # rf208_convolution
# 'ADDITION AND CONVOLUTION' RooFit tutorial macro #208
# One-dimensional numeric convolution
# (require ROOT to be compiled with --enable-fftw3)
# 
# pdf = landau(t) (x) gauss(t)
# 
# 
# 
# 
# **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 component pdfs
# ---------------------------------------

# Construct observable

# In[2]:


t = ROOT.RooRealVar("t", "t", -10, 30)


# Construct landau(t,ml,sl)

# In[3]:


ml = ROOT.RooRealVar("ml", "mean landau", 5.0, -20, 20)
sl = ROOT.RooRealVar("sl", "sigma landau", 1, 0.1, 10)
landau = ROOT.RooLandau("lx", "lx", t, ml, sl)


# Construct gauss(t,mg,sg)

# In[4]:


mg = ROOT.RooRealVar("mg", "mg", 0)
sg = ROOT.RooRealVar("sg", "sg", 2, 0.1, 10)
gauss = ROOT.RooGaussian("gauss", "gauss", t, mg, sg)


# Construct convolution pdf
# ---------------------------------------

# Set #bins to be used for FFT sampling to 10000

# In[5]:


t.setBins(10000, "cache")


# Construct landau (x) gauss

# In[6]:


lxg = ROOT.RooFFTConvPdf("lxg", "landau (X) gauss", t, landau, gauss)


# Sample, fit and plot convoluted pdf
# ----------------------------------------------------------------------

# Sample 1000 events in x from gxlx

# In[7]:


data = lxg.generate({t}, 10000)


# Fit gxlx to data

# In[8]:


lxg.fitTo(data, PrintLevel=-1)


# Plot data, pdf, landau (X) gauss pdf

# In[9]:


frame = t.frame(Title="landau (x) gauss convolution")
data.plotOn(frame)
lxg.plotOn(frame)
landau.plotOn(frame, LineStyle="--")


# Draw frame on canvas

# In[10]:


c = ROOT.TCanvas("rf208_convolution", "rf208_convolution", 600, 600)
ROOT.gPad.SetLeftMargin(0.15)
frame.GetYaxis().SetTitleOffset(1.4)
frame.Draw()

c.SaveAs("rf208_convolution.png")


# Draw all canvases 

# In[11]:


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