## \file

## \ingroup tutorial_dataframe

## \notebook -draw

## This tutorial illustrates how use the RDataFrame in combination with a

## RDataSource. In this case we use a TRootDS. This data source allows to read

## a ROOT dataset from a RDataFrame in a different way, not based on the

## regular RDataFrame code. This allows to perform all sorts of consistency

## checks and illustrate the usage of the RDataSource in a didactic context.

##

## \macro_code

## \macro_image

##

## \date September 2017

## \author Danilo Piparo

import ROOT

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

def fill_tree(treeName, fileName):

tdf = ROOT.ROOT.RDataFrame(10000)

tdf.Define("b1", "(int) tdfentry_").Snapshot(treeName, fileName)

# We prepare an input tree to run on

fileName = "df011_rootDataSource_py.root"

treeName = "myTree"

fill_tree(treeName, fileName)

# Create the data frame

MakeRootDataFrame = ROOT.ROOT.RDF.MakeRootDataFrame

d = MakeRootDataFrame(treeName, fileName)

# Now we have a regular RDataFrame: the ingestion of data is delegated to

# the RDataSource. At this point everything works as before.

h = d.Define("x", "1./(b1 + 1.)").Histo1D(("h_s", "h_s", 128, 0, .6), "x")

# Now we redo the same with a RDF and we draw the two histograms

c = ROOT.TCanvas()

c.SetLogy()

h.DrawClone()