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

# # rf306_condpereventerrors
# Multidimensional models: complete example with use of conditional 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 Thursday, July 03, 2025 at 03:45 PM.</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 fake 'external' data with per-event error
# ------------------------------------------------------------------------------------------------------

# Use landau pdf to get somewhat realistic distribution with long tail

# In[5]:


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


# Sample data from conditional decay_gm(dt|dterr)
# ---------------------------------------------------------------------------------------------

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

# In[6]:


data = decay_gm.generate({dt}, ProtoData=expDataDterr)


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

# Specify dterr as conditional observable

# In[7]:


decay_gm.fitTo(data, ConditionalObservables={dterr}, PrintLevel=-1)


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

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

# In[8]:


hh_decay = decay_gm.createHistogram("hh_decay", dt, Binning=50, YVar=dict(var=dterr, Binning=50))
hh_decay.SetLineColor("kBlue")


# Plot decay_gm(dt|dterr) at various values of dterr

# In[9]:


frame = dt.frame(Title="Slices of decay(dt|dterr) at various dterr")
for ibin in range(0, 100, 20):
    dterr.setBin(ibin)
    decay_gm.plotOn(frame, Normalization=5.0)


# Make projection of data an dt

# In[10]:


frame2 = dt.frame(Title="Projection of decay(dt|dterr) on dt")
data.plotOn(frame2)


# Make projection of decay(dt|dterr) on dt.
# 
# Instead of integrating out dterr, a weighted average of curves
# at values dterr_i as given in the external dataset.
# (The kTRUE argument bins the data before projection to speed up the process)

# In[11]:


decay_gm.plotOn(frame2, ProjWData=(expDataDterr, True))


# Draw all frames on canvas

# In[12]:


c = ROOT.TCanvas("rf306_condpereventerrors", "rf306_condperventerrors", 1200, 400)
c.Divide(3)
c.cd(1)
ROOT.gPad.SetLeftMargin(0.20)
hh_decay.GetZaxis().SetTitleOffset(2.5)
hh_decay.Draw("surf")
c.cd(2)
ROOT.gPad.SetLeftMargin(0.15)
frame.GetYaxis().SetTitleOffset(1.6)
frame.Draw()
c.cd(3)
ROOT.gPad.SetLeftMargin(0.15)
frame2.GetYaxis().SetTitleOffset(1.6)
frame2.Draw()

c.SaveAs("rf306_condpereventerrors.png")


# Draw all canvases 

# In[13]:


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