doc/master/df004__cutFlowReport_8py.html

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

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.

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.

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.

print('Cut3 stats:')

filtered3.Report()

print('All stats:')

allCutsReport = d.Report()

allCutsReport.Print()

ROOT::RDataFrame
ROOT's RDataFrame offers a modern, high-level interface for analysis of data stored in TTree ,...
Definition RDataFrame.hxx:41