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

# # rf407_latextables
# Data and categories: latex printing of lists and sets of RooArgSets
# 
# 
# 
# 
# **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 11:18 AM.</small></i>

# In[1]:


import ROOT


# Setup 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("sig1frac", "fraction of component 1 in background", 0.2, 0.0, 1.0)
bkg = ROOT.RooAddPdf("bkg", "Signal", [bkg1, bkg2], [sig1frac])


# 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])


# Make list of parameters before and after fit
# ----------------------------------------------------------------------------------------

# Make list of model parameters

# In[9]:


params = model.getParameters({x})


# Save snapshot of prefit parameters

# In[10]:


initParams = params.snapshot()


# Do fit to data, obtain error estimates on parameters

# In[11]:


data = model.generate({x}, 1000)
model.fitTo(data, PrintLevel=-1)


# Print LateX table of parameters of pdf
# --------------------------------------------------------------------------

# Print parameter list in LaTeX for (one column with names, column with
# values)

# In[12]:


params.printLatex()


# Print parameter list in LaTeX for (names values|names values)

# In[13]:


params.printLatex(Columns=2)


# Print two parameter lists side by side (name values initvalues)

# In[14]:


params.printLatex(Sibling=initParams)


# Print two parameter lists side by side (name values initvalues|name
# values initvalues)

# In[15]:


params.printLatex(Sibling=initParams, Columns=2)


# Write LaTex table to file

# In[16]:


params.printLatex(Sibling=initParams, OutputFile="rf407_latextables.tex")