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Reference Guide
df004_cutFlowReport.C
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1/// \file
2/// \ingroup tutorial_dataframe
3/// \notebook
4/// This tutorial shows how to get information about the efficiency of the filters
5/// applied
6///
7/// \macro_code
8/// \macro_output
9///
10/// \date December 2016
11/// \author Danilo Piparo
12
13using FourVector = ROOT::Math::XYZTVector;
14using FourVectors = std::vector<FourVector>;
15using CylFourVector = ROOT::Math::RhoEtaPhiVector;
16
17// A simple helper function to fill a test tree: this makes the example
18// stand-alone.
19void fill_tree(const char *treeName, const char *fileName)
20{
22 int i(0);
23 d.Define("b1", [&i]() { return (double)i; })
24 .Define("b2",
25 [&i]() {
26 auto j = i * i;
27 ++i;
28 return j;
29 })
30 .Snapshot(treeName, fileName);
31}
32
34{
35
36 // We prepare an input tree to run on
37 auto fileName = "df004_cutFlowReport.root";
38 auto treeName = "myTree";
39 fill_tree(treeName, fileName);
40
41 // We read the tree from the file and create a RDataFrame
42 ROOT::RDataFrame d(treeName, fileName, {"b1", "b2"});
43
44 // ## Define cuts and create the report
45 // Here we define two simple cuts
46 auto cut1 = [](double b1) { return b1 > 25.; };
47 auto cut2 = [](int b2) { return 0 == b2 % 2; };
48
49 // An optional string parameter name can be passed to the Filter method to create a named filter.
50 // Named filters work as usual, but also keep track of how many entries they accept and reject.
51 auto filtered1 = d.Filter(cut1, {"b1"}, "Cut1");
52 auto filtered2 = d.Filter(cut2, {"b2"}, "Cut2");
53
54 auto augmented1 = filtered2.Define("b3", [](double b1, int b2) { return b1 / b2; });
55 auto cut3 = [](double x) { return x < .5; };
56 auto filtered3 = augmented1.Filter(cut3, {"b3"}, "Cut3");
57
58 // Statistics are retrieved through a call to the Report method:
59 // when Report is called on the main RDataFrame object, it retrieves stats
60 // for all named filters declared up to that point.
61 // When called on a stored chain state (i.e. a chain/graph node), it
62 // retrieves stats for all named filters in the section of the chain between
63 // the main RDataFrame and that node (included).
64 // Stats are printed in the same order as named filters have been added to
65 // the graph, and refer to the latest event-loop that has been run using the
66 // relevant RDataFrame.
67 std::cout << "Cut3 stats:" << std::endl;
68 filtered3.Report()->Print();
69
70 // It is not only possible to print the information about cuts, but also to
71 // retrieve it to then use it programmatically.
72 std::cout << "All stats:" << std::endl;
73 auto allCutsReport = d.Report();
74 allCutsReport->Print();
75
76 // We can now loop on the cuts
77 std::cout << "Name\tAll\tPass\tEfficiency" << std::endl;
78 for (auto &&cutInfo : allCutsReport) {
79 std::cout << cutInfo.GetName() << "\t" << cutInfo.GetAll() << "\t" << cutInfo.GetPass() << "\t"
80 << cutInfo.GetEff() << " %" << std::endl;
81 }
82
83 // Or get information about them individually
84 auto cutName = "Cut1";
85 auto cut = allCutsReport->At("Cut1");
86 std::cout << cutName << " efficiency is " << cut.GetEff() << " %" << std::endl;
87}
#define d(i)
Definition: RSha256.hxx:102
ROOT's RDataFrame offers a high level interface for analyses of data stored in TTrees,...
Definition: RDataFrame.hxx:42
Double_t x[n]
Definition: legend1.C:17
DisplacementVector3D< CylindricalEta3D< double >, DefaultCoordinateSystemTag > RhoEtaPhiVector
3D Vector based on the eta based cylindrical coordinates rho, eta, phi in double precision.
Definition: Vector3Dfwd.h:50
LorentzVector< PxPyPzE4D< double > > XYZTVector
LorentzVector based on x,y,x,t (or px,py,pz,E) coordinates in double precision with metric (-,...
Definition: Vector4Dfwd.h:33