This tutorial shows how VecOps can be used to slim down the programming model typically adopted in HEP for analysis. In this case we have a dataset containing the kinematic properties of particles stored in individual arrays. We want to plot the transverse momentum of these particles if the energy is greater than 100.
auto filename =
gROOT->GetTutorialDir() +
"/dataframe/df017_vecOpsHEP.root";
auto treename = "myDataset";
void WithTTreeReader()
{
TH1F h(
"pt",
"pt", 16, 0, 4);
while (tr.Next()) {
for (auto i=0U;i < px.GetSize(); ++i) {
if (E[i] > 100)
h.Fill(
sqrt(px[i]*px[i] + py[i]*py[i]));
}
}
}
void WithRDataFrame()
{
RDF
f(treename, filename.Data());
auto CalcPt = [](doubles &px, doubles &py, doubles &
E) {
for (auto i=0U;i < px.size(); ++i) {
if (E[i] > 100) {
v.emplace_back(
sqrt(px[i]*px[i] + py[i]*py[i]));
}
}
};
f.Define(
"pt", CalcPt, {
"px",
"py",
"E"})
.Histo1D<doubles>({"pt", "pt", 16, 0, 4}, "pt")->DrawCopy();
}
void WithRDataFrameVecOps()
{
RDF
f(treename, filename.Data());
auto CalcPt = [](doubles &px, doubles &py, doubles &
E) {
auto pt =
sqrt(px*px + py*py);
};
f.Define(
"good_pt", CalcPt, {
"px",
"py",
"E"})
.Histo1D<doubles>({"pt", "pt", 16, 0, 4}, "good_pt")->DrawCopy();
}
void WithRDataFrameVecOpsJit()
{
RDF
f(treename, filename.Data());
f.Define(
"good_pt",
"sqrt(px*px + py*py)[E>100]")
.Histo1D({"pt", "pt", 16, 0, 4}, "good_pt")->DrawCopy();
}
{
WithTTreeReader();
WithRDataFrame();
WithRDataFrameVecOps();
WithRDataFrameVecOpsJit();
}
ROOT's RDataFrame offers a high level interface for analyses of data stored in TTrees,...
A "std::vector"-like collection of values implementing handy operation to analyse them.
A ROOT file is a suite of consecutive data records (TKey instances) with a well defined format.
1-D histogram with a float per channel (see TH1 documentation)}
An interface for reading collections stored in ROOT columnar datasets.
A simple, robust and fast interface to read values from ROOT columnar datasets such as TTree,...
constexpr Double_t E()
Base of natural log: