root-6.10.00/html/tdf004__cutFlowReport_8py.html

import ROOT


fill_tree_code ='''

using FourVector = ROOT::Math::XYZTVector;

using FourVectors = std::vector<FourVector>;

using CylFourVector = ROOT::Math::RhoEtaPhiVector;

void fill_tree(const char *filename, const char *treeName)

{

   TFile f(filename, "RECREATE");

   TTree t(treeName, treeName);

   double b1;

   int b2;

   t.Branch("b1", &b1);

   t.Branch("b2", &b2);

   for (int i = 0; i < 50; ++i) {

      b1 = i;

      b2 = i * i;

      t.Fill();

   }

   t.Write();

   f.Close();

   return;

}

'''


# We prepare an input tree to run on

fileName = 'tdf004_cutFlowReport_py.root'

treeName = 'myTree'

ROOT.gInterpreter.Declare(fill_tree_code)

ROOT.fill_tree(fileName, treeName)


# We read the tree from the file and create a TDataFrame, a class that

# allows us to interact with the data contained in the tree.

TDF = ROOT.ROOT.Experimental.TDataFrame

d = TDF(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 TDataFrame object, it prints stats for all named filters declared up to that

# point when called on a stored chain state (i.e. a chain/graph node), it prints stats for all named filters in the

# section of the chain between the main TDataFrame and that node (included).

# Stats are printed in the same order as named filters have been added to the graph, and refer to the latest

# event-loop that has been run using the relevant TDataFrame.

print('Cut3 stats:')

filtered3.Report()

print('All stats:')

d.Report()