Rf 4 0 8_ R Data Frame To Roo Fit

Fill RooDataSet/RooDataHist in RDataFrame.

This tutorial shows how to fill RooFit data classes directly from RDataFrame. Using two small helpers, we tell RDataFrame where the data has to go.

Author: Harshal Shende, Stephan Hageboeck (C++ version)
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Monday, January 17, 2022 at 10:03 AM.

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import ROOT
import math

Set up

We enable implicit parallelism, so RDataFrame runs in parallel.

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

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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.

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x = ROOT.RooRealVar("x", "x", -5.0, 5.0)
y = ROOT.RooRealVar("y", "y", -50.0, 50.0)

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!

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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

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

Then, we move it into the RDataFrame action:

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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.

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for data in [rooDataSet, rooDataHist]:
    for i in range(data.numEntries(), 20):
        for var in data.get(i):
        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))))

Draw all canvases

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from ROOT import gROOT