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

# # rf607_fitresult
# Likelihood and minimization: demonstration of options of the RooFitResult class
# 
# 
# 
# 
# **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 11:19 AM.</small></i>

# In[1]:


from __future__ import print_function
import ROOT


# Create pdf, data
# --------------------------------

# 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, -10, 10)
sigma1 = ROOT.RooRealVar("sigma1", "width of gaussians", 0.5, 0.1, 10)
sigma2 = ROOT.RooRealVar("sigma2", "width of gaussians", 1, 0.1, 10)

sig1 = ROOT.RooGaussian("sig1", "Signal component 1", x, mean, sigma1)
sig2 = ROOT.RooGaussian("sig2", "Signal component 2", x, mean, sigma2)


# Build Chebychev polynomial pdf

# In[4]:


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


# Sum the signal components into a composite signal pdf

# In[5]:


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


# Sum the composite signal and background

# In[6]:


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


# Generate 1000 events

# In[7]:


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


# Fit pdf to data, save fit result
# -------------------------------------------------------------

# Perform fit and save result

# In[8]:


r = model.fitTo(data, Save=True, PrintLevel=-1)


# Print fit results
# ---------------------------------

# Summary printing: Basic info plus final values of floating fit parameters

# In[9]:


r.Print()


# Verbose printing: Basic info, of constant parameters, and
# final values of floating parameters, correlations

# In[10]:


r.Print("v")


# Visualize correlation matrix
# -------------------------------------------------------

# Construct 2D color plot of correlation matrix

# In[11]:


ROOT.gStyle.SetOptStat(0)
ROOT.gStyle.SetPalette(1)
hcorr = r.correlationHist()


# Visualize ellipse corresponding to single correlation matrix element

# In[12]:


frame = ROOT.RooPlot(sigma1, sig1frac, 0.45, 0.60, 0.65, 0.90)
frame.SetTitle("Covariance between sigma1 and sig1frac")
r.plotOn(frame, sigma1, sig1frac, "ME12ABHV")


# Access fit result information
# ---------------------------------------------------------

# Access basic information

# In[13]:


print("EDM = ", r.edm())
print("-log(L) minimum = ", r.minNll())


# Access list of final fit parameter values

# In[14]:


print("final value of floating parameters")
r.floatParsFinal().Print("s")


# Access correlation matrix elements

# In[15]:


print("correlation between sig1frac and a0 is  ", r.correlation(sig1frac, a0))
print("correlation between bkgfrac and mean is ", r.correlation("bkgfrac", "mean"))


# Extract covariance and correlation matrix as ROOT.TMatrixDSym

# In[16]:


cor = r.correlationMatrix()
cov = r.covarianceMatrix()


# Print correlation, matrix

# In[17]:


print("correlation matrix")
cor.Print()
print("covariance matrix")
cov.Print()


# Persist fit result in root file
# -------------------------------------------------------------

# Open ROOT file save save result

# In[18]:


f = ROOT.TFile("rf607_fitresult.root", "RECREATE")
r.Write("rf607")
f.Close()


# In a clean ROOT session retrieve the persisted fit result as follows:
# r = gDirectory.Get("rf607")

# In[19]:


c = ROOT.TCanvas("rf607_fitresult", "rf607_fitresult", 800, 400)
c.Divide(2)
c.cd(1)
ROOT.gPad.SetLeftMargin(0.15)
hcorr.GetYaxis().SetTitleOffset(1.4)
hcorr.Draw("colz")
c.cd(2)
ROOT.gPad.SetLeftMargin(0.15)
frame.GetYaxis().SetTitleOffset(1.6)
frame.Draw()

c.SaveAs("rf607_fitresult.png")


# Draw all canvases 

# In[20]:


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