## \file

## \ingroup tutorial_dataframe

## \notebook -draw

## This tutorial shows the potential of the VecOps approach for treating collections

## stored in datasets, a situation very common in HEP data analysis.

##

## \macro_image

## \macro_code

##

## \date February 2018

## \author Danilo Piparo

import ROOT

tdf = 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 = tdf.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()

ring_h.Draw("Colz")