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

# # rf405_realtocatfuncs
# Data and categories: demonstration of real-discrete mapping functions
# 
# 
# 
# 
# **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


# Define pdf in x, sample dataset in x
# ------------------------------------------------------------------------

# Define a dummy PDF in x

# In[2]:


x = ROOT.RooRealVar("x", "x", 0, 10)
a = ROOT.RooArgusBG("a", "argus(x)", x, 10.0, -1.0)


# Generate a dummy dataset

# In[3]:


data = a.generate({x}, 10000)


# Create a threshold real -> cat function
# --------------------------------------------------------------------------

# A RooThresholdCategory is a category function that maps regions in a real-valued
# input observable observables to state names. At construction time a 'default'
# state name must be specified to which all values of x are mapped that are not
# otherwise assigned

# In[4]:


xRegion = ROOT.RooThresholdCategory("xRegion", "region of x", x, "Background")


# Specify thresholds and state assignments one-by-one.
# Each statement specifies that all values _below_ the given value
# (and above any lower specified threshold) are mapped to the
# category state with the given name
# 
# Background | SideBand | Signal | SideBand | Background
#           4.23       5.23     8.23       9.23

# In[5]:


xRegion.addThreshold(4.23, "Background")
xRegion.addThreshold(5.23, "SideBand")
xRegion.addThreshold(8.23, "Signal")
xRegion.addThreshold(9.23, "SideBand")


# Use threshold function to plot data regions
# ----------------------------------------------

# Add values of threshold function to dataset so that it can be used as
# observable

# In[6]:


data.addColumn(xRegion)


# Make plot of data in x

# In[7]:


xframe = x.frame(Title="Demo of threshold and binning mapping functions")
data.plotOn(xframe)


# Use calculated category to select sideband data

# In[8]:


data.plotOn(xframe, Cut="xRegion==xRegion::SideBand", MarkerColor="r", LineColor="r")


# Create a binning real -> cat function
# ----------------------------------------------------------------------

# A RooBinningCategory is a category function that maps bins of a (named) binning definition
# in a real-valued input observable observables to state names. The state names are automatically
# constructed from the variable name, binning name and the bin number. If no binning name
# is specified the default binning is mapped

# In[9]:


x.setBins(10, "coarse")
xBins = ROOT.RooBinningCategory("xBins", "coarse bins in x", x, "coarse")


# Use binning function for tabulation and plotting
# -----------------------------------------------------------------------------------------------

# Print table of xBins state multiplicity. Note that xBins does not need to be an observable in data
# it can be a function of observables in data as well

# In[10]:


xbtable = data.table(xBins)
xbtable.Print("v")


# Add values of xBins function to dataset so that it can be used as
# observable

# In[11]:


xb = data.addColumn(xBins)


# Define range "alt" as including bins 1,3,5,7,9

# In[12]:


xb.setRange("alt", "x_coarse_bin1,x_coarse_bin3,x_coarse_bin5,x_coarse_bin7,x_coarse_bin9")


# Construct subset of data matching range "alt" but only for the first
# 5000 events and plot it on the frame

# In[13]:


dataSel = data.reduce(CutRange="alt", EventRange=(0, 5000))
dataSel.plotOn(xframe, MarkerColor="g", LineColor="g")

c = ROOT.TCanvas("rf405_realtocatfuncs", "rf405_realtocatfuncs", 600, 600)
xframe.SetMinimum(0.01)
ROOT.gPad.SetLeftMargin(0.15)
xframe.GetYaxis().SetTitleOffset(1.4)
xframe.Draw()

c.SaveAs("rf405_realtocatfuncs.png")


# Draw all canvases 

# In[14]:


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