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

# # rf307_fullpereventerrors
# Multidimensional models: usage of full pdf with per-event errors
# 
# 
# 
# 
# **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


# B-physics pdf with per-event Gaussian resolution
# ----------------------------------------------------------------------------------------------

# Observables

# In[2]:


dt = ROOT.RooRealVar("dt", "dt", -10, 10)
dterr = ROOT.RooRealVar("dterr", "per-event error on dt", 0.01, 10)


# Build a gaussian resolution model scaled by the per-error =
# gauss(dt,bias,sigma*dterr)

# In[3]:


bias = ROOT.RooRealVar("bias", "bias", 0, -10, 10)
sigma = ROOT.RooRealVar("sigma", "per-event error scale factor", 1, 0.1, 10)
gm = ROOT.RooGaussModel("gm1", "gauss model scaled bt per-event error", dt, bias, sigma, dterr)


# Construct decay(dt) (x) gauss1(dt|dterr)

# In[4]:


tau = ROOT.RooRealVar("tau", "tau", 1.548)
decay_gm = ROOT.RooDecay("decay_gm", "decay", dt, tau, gm, type="DoubleSided")


# Construct empirical pdf for per-event error
# -----------------------------------------------------------------

# Use landau pdf to get empirical distribution with long tail

# In[5]:


pdfDtErr = ROOT.RooLandau("pdfDtErr", "pdfDtErr", dterr, 1.0, 0.25)
expDataDterr = pdfDtErr.generate({dterr}, 10000)


# Construct a histogram pdf to describe the shape of the dtErr distribution

# In[6]:


expHistDterr = expDataDterr.binnedClone()
pdfErr = ROOT.RooHistPdf("pdfErr", "pdfErr", {dterr}, expHistDterr)


# Construct conditional product decay_dm(dt|dterr)*pdf(dterr)
# ----------------------------------------------------------------------------------------------------------------------

# Construct production of conditional decay_dm(dt|dterr) with empirical
# pdfErr(dterr)

# In[7]:


model = ROOT.RooProdPdf("model", "model", {pdfErr}, Conditional=({decay_gm}, {dt}))


# (Alternatively you could also use the landau shape pdfDtErr)
# ROOT.RooProdPdf model("model", "model",pdfDtErr,
# ROOT.RooFit.Conditional(decay_gm,dt))

# Sample, fit and plot product model
# ------------------------------------------------------------------

# Specify external dataset with dterr values to use model_dm as
# conditional pdf

# In[8]:


data = model.generate({dt, dterr}, 10000)


# Fit conditional decay_dm(dt|dterr)
# ---------------------------------------------------------------------

# Specify dterr as conditional observable

# In[9]:


model.fitTo(data, PrintLevel=-1)


# Plot conditional decay_dm(dt|dterr)
# ---------------------------------------------------------------------

# Make two-dimensional plot of conditional pdf in (dt,dterr)

# In[10]:


hh_model = model.createHistogram("hh_model", dt, Binning=50, YVar=dict(var=dterr, Binning=50))
hh_model.SetLineColor(ROOT.kBlue)


# Make projection of data an dt

# In[11]:


frame = dt.frame(Title="Projection of model(dt|dterr) on dt")
data.plotOn(frame)
model.plotOn(frame)


# Draw all frames on canvas

# In[12]:


c = ROOT.TCanvas("rf307_fullpereventerrors", "rf307_fullpereventerrors", 800, 400)
c.Divide(2)
c.cd(1)
ROOT.gPad.SetLeftMargin(0.20)
hh_model.GetZaxis().SetTitleOffset(2.5)
hh_model.Draw("surf")
c.cd(2)
ROOT.gPad.SetLeftMargin(0.15)
frame.GetYaxis().SetTitleOffset(1.6)
frame.Draw()

c.SaveAs("rf307_fullpereventerrors.png")


# Draw all canvases 

# In[13]:


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