df014_CSVDataSource.py

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## \file
## \ingroup tutorial_dataframe
## \notebook -draw
## Process a CSV file with RDataFrame and the CSV data source.
##
## 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](http://opendata.cern.ch/record/700).
##
## \macro_code
## \macro_image
##
## \date October 2017
## \author Enric Tejedor (CERN)
 
import ROOT
import os
 
# Let's first create a RDF that will read from the CSV file.
# The types of the columns will be automatically inferred.
fileNameUrl = "http://root.cern/files/tutorials/df014_CsvDataSource_MuRun2010B.csv"
fileName = "df014_CsvDataSource_MuRun2010B_py.csv"
if not os.path.isfile(fileName):
    ROOT.TFile.Cp(fileNameUrl, fileName)
 
df = ROOT.RDF.FromCSV(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 = df.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()
c.SaveAs("df014_invMass.png")
 
# 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 on 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.SetMarkerStyle(20)
jpsi.Draw("HistP")
dualCanvas.SaveAs("df014_jpsi.png")
 
print("Saved figures to df014_*.png")