Df 0 0 2_Data Model

Show how to work with non-flat data models, e.g. vectors of tracks.

This tutorial shows the possibility to use data models which are more complex than flat ntuples with RDataFrame

Author: Danilo Piparo (CERN)
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Tuesday, June 15, 2021 at 07:13 AM.

In [1]:
using FourVector = ROOT::Math::XYZTVector;
using FourVectorVec = std::vector<FourVector>;
using FourVectorRVec = ROOT::VecOps::RVec<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 *filename, const char *treeName)
   const double M = 0.13957; // set pi+ mass
   TRandom3 R(1);

   auto genTracks = [&](){
      FourVectorVec tracks;
      const auto nPart = R.Poisson(15);
      for (int j = 0; j < nPart; ++j) {
         const auto px = R.Gaus(0, 10);
         const auto py = R.Gaus(0, 10);
         const auto pt = sqrt(px * px + py * py);
         const auto eta = R.Uniform(-3, 3);
         const auto phi = R.Uniform(0.0, 2 * TMath::Pi());
         CylFourVector vcyl(pt, eta, phi);
         // set energy
         auto E = sqrt(vcyl.R() * vcyl.R() + M * M);
         // fill track vector
         tracks.emplace_back(vcyl.X(), vcyl.Y(), vcyl.Z(), E);
      return tracks;

   ROOT::RDataFrame d(64);
   d.Define("tracks", genTracks).Snapshot<FourVectorVec>(treeName, filename, {"tracks"});

We prepare an input tree to run on

In [3]:
auto fileName = "df002_dataModel.root";
auto treeName = "myTree";
fill_tree(fileName, treeName);

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.

In [4]:
ROOT::RDataFrame d(treeName, fileName, {"tracks"});

Operating on branches which are collection of objects

Here we deal with the simplest of the cuts: we decide to accept the event only if the number of tracks is greater than 5.

In [5]:
auto n_cut = [](const FourVectorRVec &tracks) { return tracks.size() > 8; };
auto nentries = d.Filter(n_cut, {"tracks"}).Count();

std::cout << *nentries << " passed all filters" << std::endl;
62 passed all filters

Another possibility consists in creating a new column containing the quantity we are interested in. In this example, we will cut on the number of tracks and plot their transverse momentum.

In [6]:
auto getPt = [](const FourVectorRVec &tracks) {
   return ROOT::VecOps::Map(tracks, [](const FourVector& v){return v.Pt();});

We do the same for the weights.

In [7]:
auto getPtWeights = [](const FourVectorRVec &tracks) {
   return ROOT::VecOps::Map(tracks, [](const FourVector& v){ return 1. / v.Pt();});

auto augmented_d = d.Define("tracks_n", [](const FourVectorRVec &tracks) { return (int)tracks.size(); })
                      .Filter([](int tracks_n) { return tracks_n > 2; }, {"tracks_n"})
                      .Define("tracks_pts", getPt)
                      .Define("tracks_pts_weights", getPtWeights);

auto trN = augmented_d.Histo1D({"", "", 40, -.5, 39.5}, "tracks_n");
auto trPts = augmented_d.Histo1D("tracks_pts");
auto trWPts = augmented_d.Histo1D("tracks_pts", "tracks_pts_weights");

auto c1 = new TCanvas();

auto c2 = new TCanvas();

auto c3 = new TCanvas();

return 0;

Draw all canvases

In [8]: