df016_vecOps.py File Reference

Namespaces
namespace	df016_vecOps

Detailed Description

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::RVecD {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 our hands d, a RDataFrame with two columns, x and y, which
# hold collections of coordinates. The sizes of these collections vary.
# Let's now define radii radii from the x and y coordinates. 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 > .5 && r < 1 && x*y < 0")\
           .Define("yFig", "y[rInFig]")\
           .Define("xFig", "x[rInFig]")\
           .Histo2D(("fig", "Two quarters of a ring", 64, -1.1, 1.1, 64, -1.1, 1.1), "xFig", "yFig")
 
cring = ROOT.TCanvas()
ring_h.Draw("Colz")
cring.SaveAs("df016_ring.png")
 
print("Saved figure to df016_ring.png")

Date

February 2018

Author

Danilo Piparo (CERN)

Definition in file df016_vecOps.py.