df014_CSVDataSource.py File Reference

Namespaces
	df014_CSVDataSource

Detailed Description

This tutorial illustrates how use the RDataFrame in combination with a RDataSource.

In this case we use a TCsvDS. This data source allows to read a CSV file from a RDataFrame. As a result of running this tutorial, we will produce plots of the dimuon spectrum starting from a subset of the CMS collision events of Run2010B. Dataset Reference: McCauley, T. (2014). Dimuon event information derived from the Run2010B public Mu dataset. CERN Open Data Portal. DOI: 10.7483/OPENDATA.CMS.CB8H.MFFA.

import ROOT

# Let's first create a RDF that will read from the CSV file.
# The types of the columns will be automatically inferred.
fileName = "df014_CsvDataSource_MuRun2010B.csv"
MakeCsvDataFrame = ROOT.ROOT.RDF.MakeCsvDataFrame
tdf = MakeCsvDataFrame(fileName)

# Now we will apply a first filter based on two columns of the CSV,
# and we will define a new column that will contain the invariant mass.
# Note how the new invariant mass column is defined from several other
# columns that already existed in the CSV file.
filteredEvents = tdf.Filter("Q1 * Q2 == -1") \
                    .Define("m", "sqrt(pow(E1 + E2, 2) - (pow(px1 + px2, 2) + pow(py1 + py2, 2) + pow(pz1 + pz2, 2)))")

# Next we create a histogram to hold the invariant mass values and we draw it.
invMass = filteredEvents.Histo1D(("invMass", "CMS Opendata: #mu#mu mass;#mu#mu mass [GeV];Events", 512, 2, 110), "m")

c = ROOT.TCanvas()
c.SetLogx()
c.SetLogy()
invMass.Draw()

# We will now produce a plot also for the J/Psi particle. We will plot
# on the same canvas the full spectrum and the zoom in the J/psi particle.
# First we will create the full spectrum histogram from the invariant mass
# column, using a different histogram model than before.
fullSpectrum = filteredEvents.Histo1D(("Spectrum", "Subset of CMS Run 2010B;#mu#mu mass [GeV];Events", 1024, 2, 110), "m")

# Next we will create the histogram for the J/psi particle, applying first
# the corresponding cut.
jpsiLow = 2.95
jpsiHigh = 3.25
jpsiCut = 'm < %s && m > %s' % (jpsiHigh, jpsiLow)
jpsi = filteredEvents.Filter(jpsiCut) \
                     .Histo1D(("jpsi", "Subset of CMS Run 2010B: J/#psi window;#mu#mu mass [GeV];Events", 128, jpsiLow, jpsiHigh), "m")

# Finally we draw the two histograms side by side.
dualCanvas = ROOT.TCanvas("DualCanvas", "DualCanvas", 800, 512)
dualCanvas.Divide(2, 1)
leftPad = dualCanvas.cd(1)
leftPad.SetLogx()
leftPad.SetLogy()
fullSpectrum.Draw("Hist")
dualCanvas.cd(2)
jpsi.Draw("HistP")

Date

October 2017

Author

Enric Tejedor

Definition in file df014_CSVDataSource.py.