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

# # df010_trivialDataSource
# Use the "trivial data source", an example data source implementation.
# 
# This tutorial illustrates how use the RDataFrame in combination with a
# RDataSource. In this case we use a RTrivialDS, which is nothing more
# than a simple generator: it does not interface to any existing dataset.
# The RTrivialDS has a single column, col0, which has value n for entry n.
# 
# Note that RTrivialDS is only a demo data source implementation and superior alternatives
# typically exist for production use (e.g. constructing an empty RDataFrame as `RDataFrame(nEntries)`).
# 
# 
# 
# 
# **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


# Create the data frame

# In[2]:


MakeTrivialDataFrame = ROOT.RDF.MakeTrivialDataFrame

nEvents = 128

d_s = MakeTrivialDataFrame(nEvents)


# Now we have a regular RDataFrame: the ingestion of data is delegated to
# the RDataSource. At this point everything works as before.

# In[3]:


h_s = d_s.Define("x", "1./(1. + col0)").Histo1D(("h_s", "h_s", 128, 0, .6), "x")

c = ROOT.TCanvas()
c.SetLogy()
h_s.Draw()
c.SaveAs("df010_trivialDataSource.png")

print("Saved figure to df010_trivialDataSource.png")


# Draw all canvases 

# In[4]:


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