df015_LazyDataSource.C

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/// \file
/// \ingroup tutorial_dataframe
/// \notebook -draw
/// This tutorial illustrates how to take advantage of a *lazy data source*
/// creating a data frame from columns of one or multiple parent dataframe(s),
/// delaying the creation of the columns to the actual usage of the daughter
/// data frame.
/// 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).
/// From the ROOT website: https://root.cern.ch/files/tutorials/tdf014_CsvDataSource_MuRun2010B.csv
///
/// \macro_code
/// \macro_image
///
/// \date February 2018
/// \author Danilo Piparo
 
int df015_LazyDataSource()
{
   using namespace ROOT::RDF;
 
   // Let's first create a RDF that will read from the CSV file.
   // See the tutorial relative to CSV data sources for more details!
   auto fileName = "df014_CsvDataSource_MuRun2010B.csv";
   auto csv_tdf = MakeCsvDataFrame(fileName);
 
   // Now we take out four columns: px and py of the first muon in the muon pair
   std::string px1Name = "px1";
   auto px1 = csv_tdf.Take<double>(px1Name);
   std::string py1Name = "py1";
   auto py1 = csv_tdf.Take<double>(py1Name);
 
   // Now we create a new dataframe built on top of the columns above. Note that up to now, no event loop
   // has been carried out!
   auto tdf = MakeLazyDataFrame(std::make_pair(px1Name, px1), std::make_pair(py1Name, py1));
 
   // We build a histogram of the transverse momentum the muons.
   auto ptFormula = [](double px, double py) { return sqrt(px * px + py * py); };
   auto pt_h = tdf.Define("pt", ptFormula, {"px1", "py1"})
                  .Histo1D<double>({"pt", "Muon p_{T};p_{T} [GeV/c];", 128, 0, 128}, "pt");
 
   auto can = new TCanvas();
   can->SetLogy();
   pt_h->DrawCopy();
 
   return 0;
}