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

# # df001_introduction
# Basic usage of RDataFrame from python.
# 
# This tutorial illustrates the basic features of the RDataFrame class,
# a utility which allows to interact with data stored in TTrees following
# a functional-chain like approach.
# 
# 
# 
# 
# **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


# A simple helper function to fill a test tree: this makes the example stand-alone.

# In[2]:


def fill_tree(treeName, fileName):
    df = ROOT.RDataFrame(10)
    df.Define("b1", "(double) rdfentry_")\
      .Define("b2", "(int) rdfentry_ * rdfentry_").Snapshot(treeName, fileName)


# We prepare an input tree to run on

# In[3]:


fileName = "df001_introduction_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[4]:


d = ROOT.RDataFrame(treeName, fileName)


# Operations on the dataframe
# We now review some *actions* which can be performed on the data frame.
# Actions can be divided into instant actions (e. g. Foreach()) and lazy
# actions (e. g. Count()), depending on whether they trigger the event 
# loop immediately or only when one of the results is accessed for the 
# first time. Actions that return "something" either return their result 
# wrapped in a RResultPtr or in a RDataFrame.
# But first of all, let us we define now our cut-flow with two strings.
# Filters can be expressed as strings. The content must be C++ code. The
# name of the variables must be the name of the branches. The code is
# just-in-time compiled.

# In[5]:


cutb1 = 'b1 < 5.'
cutb1b2 = 'b2 % 2 && b1 < 4.'


# `Count` action
# The `Count` allows to retrieve the number of the entries that passed the
# filters. Here we show how the automatic selection of the column kicks
# in in case the user specifies none.

# In[6]:


entries1 = d.Filter(cutb1) \
            .Filter(cutb1b2) \
            .Count();

print('{} entries passed all filters'.format(entries1.GetValue()))

entries2 = d.Filter("b1 < 5.").Count();
print('{} entries passed all filters'.format(entries2.GetValue()))


# `Min`, `Max` and `Mean` actions
# These actions allow to retrieve statistical information about the entries
# passing the cuts, if any.

# In[7]:


b1b2_cut = d.Filter(cutb1b2)
minVal = b1b2_cut.Min('b1')
maxVal = b1b2_cut.Max('b1')
meanVal = b1b2_cut.Mean('b1')
nonDefmeanVal = b1b2_cut.Mean("b2")
print('The mean is always included between the min and the max: {0} <= {1} <= {2}'.format(minVal.GetValue(), meanVal.GetValue(), maxVal.GetValue()))


# `Histo1D` action
# The `Histo1D` action allows to fill an histogram. It returns a TH1F filled
# with values of the column that passed the filters. For the most common
# types, the type of the values stored in the column is automatically
# guessed.

# In[8]:


hist = d.Filter(cutb1).Histo1D('b1')
print('Filled h {0} times, mean: {1}'.format(hist.GetEntries(), hist.GetMean()))


# Express your chain of operations with clarity!
# We are discussing an example here but it is not hard to imagine much more
# complex pipelines of actions acting on data. Those might require code
# which is well organised, for example allowing to conditionally add filters
# or again to clearly separate filters and actions without the need of
# writing the entire pipeline on one line. This can be easily achieved.
# We'll show this re-working the `Count` example:

# In[9]:


cutb1_result = d.Filter(cutb1);
cutb1b2_result = d.Filter(cutb1b2);
cutb1_cutb1b2_result = cutb1_result.Filter(cutb1b2)


# Now we want to count:

# In[10]:


evts_cutb1_result = cutb1_result.Count()
evts_cutb1b2_result = cutb1b2_result.Count()
evts_cutb1_cutb1b2_result = cutb1_cutb1b2_result.Count()

print('Events passing cutb1: {}'.format(evts_cutb1_result.GetValue()))
print('Events passing cutb1b2: {}'.format(evts_cutb1b2_result.GetValue()))
print('Events passing both: {}'.format(evts_cutb1_cutb1b2_result.GetValue()))


# Calculating quantities starting from existing columns
# Often, operations need to be carried out on quantities calculated starting
# from the ones present in the columns. We'll create in this example a third
# column, the values of which are the sum of the *b1* and *b2* ones, entry by
# entry. The way in which the new quantity is defined is via a callable.
# It is important to note two aspects at this point:
# - The value is created on the fly only if the entry passed the existing
# filters.
# - The newly created column behaves as the one present on the file on disk.
# - The operation creates a new value, without modifying anything. De facto,
# this is like having a general container at disposal able to accommodate
# any value of any type.
# Let's dive in an example:

# In[11]:


entries_sum = d.Define('sum', 'b2 + b1') \
               .Filter('sum > 4.2') \
               .Count()
print(entries_sum.GetValue())