doc/hackathon/rf408__RDataFrameToRooFit_8py.html

import ROOT

import math


# Set up

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


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

df = ROOT.RDataFrame(2000000).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 RooDataSetHelper 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*!

#

# The returned object is not yet a RooDataSet, but an RResultPtr that will be

# lazy-evaluated once you call GetValue() on it. We will only evaluate the

# RResultPtr once all other RDataFrame related actions are declared. This way

# we trigger the event loop computation only once, which will improve the

# runtime significantly.

#

# To learn more about lazy actions, see:

#     https://root.cern/doc/master/classROOT_1_1RDataFrame.html#actions

roo_data_set_result = df.Book(

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

)


# Method 2:

# ---------

# We first declare the RooDataHistHelper

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


# Then, we move it into an RDataFrame action:

roo_data_hist_result = df.Book(ROOT.std.move(rdhMaker), ("x", "y"))


# Run it and inspect the results

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


# At this point, all RDF actions were defined (namely, the `Book` operations),

# so we can get values from the RResultPtr objects, triggering the event loop

# and getting the actual RooFit data objects.

roo_data_set = roo_data_set_result.GetValue()

roo_data_hist = roo_data_hist_result.GetValue()


# Let's inspect the dataset / datahist.


def print_data(data):

    print("")

    data.Print()

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

        print(

            "("

            + ", ".join(["{0:8.3f}".format(var.getVal()) for var in data.get(i)])

            + ", )  weight={0:10.3f}".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))))


print_data(roo_data_set)

print_data(roo_data_hist)

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