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

# # df004_cutFlowReport
# Display cut/Filter efficiencies with RDataFrame.
# 
# This tutorial shows how to get information about the efficiency of the filters
# applied
# 
# 
# 
# 
# **Author:** Danilo Piparo (CERN)  
# <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 11:07 AM.</small></i>

# In[1]:


import ROOT

def fill_tree(treeName, fileName):
    df = ROOT.RDataFrame(50)
    df.Define("b1", "(double) rdfentry_")\
      .Define("b2", "(int) rdfentry_ * rdfentry_").Snapshot(treeName, fileName)


# We prepare an input tree to run on

# In[2]:


fileName = 'df004_cutFlowReport_py.root'
treeName = 'myTree'
fill_tree(treeName, fileName)


# We read the tree from the file and create a RDataFrame, a class that
# allows us to interact with the data contained in the tree.

# In[3]:


d = ROOT.RDataFrame(treeName, fileName)


# ## Define cuts and create the report
# An optional string parameter name can be passed to the Filter method to create a named filter.
# Named filters work as usual, but also keep track of how many entries they accept and reject.

# In[4]:


filtered1 = d.Filter('b1 > 25', 'Cut1')
filtered2 = d.Filter('0 == b2 % 2', 'Cut2')

augmented1 = filtered2.Define('b3', 'b1 / b2')
filtered3 = augmented1.Filter('b3 < .5','Cut3')


# Statistics are retrieved through a call to the Report method:
# when Report is called on the main RDataFrame object, it retrieves stats for
# all named filters declared up to that point. When called on a stored chain
# state (i.e. a chain/graph node), it retrieves stats for all named filters in
# the section of the chain between the main RDataFrame and that node (included).
# Stats are printed in the same order as named filters that have been added to the
# graph, and refer to the latest event-loop that has been running using the relevant
# RDataFrame.

# In[5]:


print('Cut3 stats:')
filtered3.Report()
print('All stats:')
allCutsReport = d.Report()
allCutsReport.Print()


# Draw all canvases 

# In[6]:


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