df004_cutFlowReport¶

Display cut/Filter efficiencies with RDataFrame.

This tutorial shows how to get information about the efficiency of the filters applied

Author: Danilo Piparo (CERN)
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Monday, March 27, 2023 at 09:45 AM.

In [1]:

using FourVector = ROOT::Math::XYZTVector;
using FourVectors = std::vector<FourVector>;
using CylFourVector = ROOT::Math::RhoEtaPhiVector;

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

In [2]:

%%cpp -d
void fill_tree(const char *treeName, const char *fileName)
{
   ROOT::RDataFrame d(50);
   int i(0);
   d.Define("b1", [&i]() { return (double)i; })
      .Define("b2",
              [&i]() {
                 auto j = i * i;
                 ++i;
                 return j;
              })
      .Snapshot(treeName, fileName);
}

We prepare an input tree to run on

In [3]:

auto fileName = "df004_cutFlowReport.root";
auto treeName = "myTree";
fill_tree(treeName, fileName);

We read the tree from the file and create a RDataFrame

In [4]:

ROOT::RDataFrame d(treeName, fileName, {"b1", "b2"});

Define cuts and create the report¶

Here we define two simple cuts

In [5]:

auto cut1 = [](double b1) { return b1 > 25.; };
auto cut2 = [](int b2) { return 0 == b2 % 2; };

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.

In [6]:

auto filtered1 = d.Filter(cut1, {"b1"}, "Cut1");
auto filtered2 = d.Filter(cut2, {"b2"}, "Cut2");

auto augmented1 = filtered2.Define("b3", [](double b1, int b2) { return b1 / b2; });
auto cut3 = [](double x) { return x < .5; };
auto filtered3 = augmented1.Filter(cut3, {"b3"}, "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.

In [7]:

std::cout << "Cut3 stats:" << std::endl;
filtered3.Report()->Print();

Cut3 stats:
Cut2      : pass=25         all=50         -- eff=50.00 % cumulative eff=50.00 %
Cut3      : pass=23         all=25         -- eff=92.00 % cumulative eff=46.00 %

It is not only possible to print the information about cuts, but also to retrieve it to then use it programmatically.

In [8]:

std::cout << "All stats:" << std::endl;
auto allCutsReport = d.Report();
allCutsReport->Print();

All stats:
Cut1      : pass=24         all=50         -- eff=48.00 % cumulative eff=48.00 %
Cut2      : pass=25         all=50         -- eff=50.00 % cumulative eff=50.00 %
Cut3      : pass=23         all=25         -- eff=92.00 % cumulative eff=46.00 %
Cut3      : pass=23         all=25         -- eff=92.00 % cumulative eff=46.00 %
Cut2      : pass=25         all=50         -- eff=50.00 % cumulative eff=50.00 %

We can now loop on the cuts

In [9]:

std::cout << "Name\tAll\tPass\tEfficiency" << std::endl;
for (auto &&cutInfo : allCutsReport) {
   std::cout << cutInfo.GetName() << "\t" << cutInfo.GetAll() << "\t" << cutInfo.GetPass() << "\t"
             << cutInfo.GetEff() << " %" << std::endl;
}

Name	All	Pass	Efficiency
Cut1	50	24	48 %
Cut2	50	25	50 %
Cut3	25	23	92 %
Cut3	25	23	92 %
Cut2	50	25	50 %

Or get information about them individually

In [10]:

auto cutName = "Cut1";
auto cut = allCutsReport->At("Cut1");
std::cout << cutName << " efficiency is " << cut.GetEff() << " %" << std::endl;

Cut1 efficiency is 48 %

Draw all canvases

In [11]:

gROOT->GetListOfCanvases()->Draw()