doc/v626/rf408__RDataFrameToRooFit_8py.html

import ROOT

import math


# Set up

# ------------------------


# We enable implicit parallelism, so RDataFrame runs in parallel.

ROOT.ROOT.EnableImplicitMT()


# We create an RDataFrame with two columns filled with 2 million random numbers.

d = ROOT.RDataFrame(2000000)

dd = d.Define("x", "gRandom->Uniform(-5.,  5.)").Define("y", "gRandom->Gaus(1., 3.)")


# We create RooFit variables that will represent the dataset.

x = ROOT.RooRealVar("x", "x", -5.0, 5.0)

y = ROOT.RooRealVar("y", "y", -50.0, 50.0)

x.setBins(10)

y.setBins(20)


# Booking the creation of RooDataSet / RooDataHist in RDataFrame

# ----------------------------------------------------------------


# Method 1:

# We directly book the RooDataSetMaker action.

# We need to pass

# - the RDataFrame column types as template parameters

# - the constructor arguments for RooDataSet (they follow the same syntax as the usual RooDataSet constructors)

# - the column names that RDataFrame should fill into the dataset

#

# NOTE: RDataFrame columns are matched to RooFit variables by position, *not by name*!

rooDataSet = dd.Book(

    ROOT.std.move(ROOT.RooDataSetHelper("dataset", "Title of dataset", ROOT.RooArgSet(x, y))), ("x", "y")

)


# Method 2:

# We first declare the RooDataHistMaker

rdhMaker = ROOT.RooDataSetHelper("dataset", "Title of dataset", ROOT.RooArgSet(x, y))


# Then, we move it into the RDataFrame action:

rooDataHist = dd.Book(ROOT.std.move(rdhMaker), ("x", "y"))


# Run it and inspect the results

# -------------------------------


# Let's inspect the dataset / datahist.

# Note that the first time we touch one of those objects, the RDataFrame event loop will run.

for data in [rooDataSet, rooDataHist]:

    data.Print()

    for i in range(data.numEntries(), 20):

        print("(")

        for var in data.get(i):

            print("{0:.3f}".format(var.getVal()))

        print(")\tweight= {0:<10}".format(data.weight()))


    print("mean(x) = {0:.3f}".format(data.mean(x)) + "\tsigma(x) = {0:.3f}".format(math.sqrt(data.moment(x, 2.0))))

    print("mean(y) = {0:.3f}".format(data.mean(y)) + "\tsigma(y) = {0:.3f}\n".format(math.sqrt(data.moment(y, 2.0))))

ROOT::RDataFrame
ROOT's RDataFrame offers a high level interface for analyses of data stored in TTree,...
Definition RDataFrame.hxx:50