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Reference Guide
df016_vecOps.py File Reference



Detailed Description

View in nbviewer Open in SWAN Process collections in RDataFrame with the help of RVec.

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

import ROOT
df = ROOT.RDataFrame(1024)
coordDefineCode = '''ROOT::VecOps::RVec<double> {0}(len);
std::transform({0}.begin(), {0}.end(), {0}.begin(), [](double){{return gRandom->Uniform(-1.0, 1.0);}});
return {0};'''
d = df.Define("len", "gRandom->Uniform(0, 16)")\
.Define("x", coordDefineCode.format("x"))\
.Define("y", coordDefineCode.format("y"))
# 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.
d1 = d.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
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")
cring = ROOT.TCanvas()
print("Saved figure to df016_ring.png")
February 2018
Danilo Piparo (CERN)

Definition in file df016_vecOps.py.

ROOT's RDataFrame offers a high level interface for analyses of data stored in TTree,...
Definition: RDataFrame.hxx:42