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Reference Guide
df016_vecOps.C File Reference

Detailed Description

View in nbviewer Open in SWAN This tutorial shows the potential of the VecOps approach for treating collections stored in datasets, a situation very common in HEP data analysis.

using namespace ROOT::VecOps;
// We re-create a set of points in a square.
// This is a technical detail, just to create a dataset to play with!
auto unifGen = [](double) { return gRandom->Uniform(-1.0, 1.0); };
auto vGen = [&](int len) {
std::transform(v.begin(), v.end(), v.begin(), unifGen);
return v;
RDataFrame d(1024);
auto d0 = d.Define("len", []() { return (int)gRandom->Uniform(0, 16); })
.Define("x", vGen, {"len"})
.Define("y", vGen, {"len"});
// Now we have in hands d, a RDataFrame with two columns, x and y, which
// hold collections of coordinates. The size of these collections vary.
// Let's now define radii out of x and y. We'll do it treating the collections
// stored in the columns without looping on the individual elements.
auto d1 = d0.Define("r", "sqrt(x*x + y*y)");
// Now we want to plot 2 quarters of a ring with radii .5 and 1
// Note how the cuts are performed on RVecs, comparing them with integers and
// among themselves
auto ring_h = d1.Define("rInFig", "r > .4 && r < .8 && x*y < 0")
.Define("yFig", "y[rInFig]")
.Define("xFig", "x[rInFig]")
.Histo2D({"fig", "Two quarters of a ring", 64, -1, 1, 64, -1, 1}, "xFig", "yFig");
auto cring = new TCanvas();
return 0;
February 2018
Danilo Piparo

Definition in file df016_vecOps.C.

Definition: df016_vecOps.py:1
virtual Double_t Uniform(Double_t x1=1)
Returns a uniform deviate on the interval (0, x1).
Definition: TRandom.cxx:635
ROOT's RDataFrame offers a high level interface for analyses of data stored in TTrees,...
Definition: RDataFrame.hxx:42
@ v
Definition: rootcling_impl.cxx:3635
R__EXTERN TRandom * gRandom
Definition: TRandom.h:62
Definition: Converters.cxx:921
Definition: TCanvas.h:23
#define d(i)
Definition: RSha256.hxx:120
Definition: RVec.hxx:53
ROOT::VecOps::RVec< double >