mp102_readNtuplesFillHistosAndFit.C
/// \file
/// \ingroup tutorial_multicore
/// \notebook -js
/// Read n-tuples in distinct workers, fill histograms, merge them and fit.
/// We express parallelism with multiprocessing as it is done with multithreading
/// in mt102_readNtuplesFillHistosAndFit.
///
/// \macro_code
///
/// \date January 2016
/// \author Danilo Piparo
Int_t mp102_readNtuplesFillHistosAndFit()
{
// No nuisance for batch execution
gROOT->SetBatch();
//---------------------------------------
// Perform the operation sequentially
TChain inputChain("multiCore");
inputChain.Add("mp101_multiCore_*.root");
if (inputChain.GetNtrees() <= 0) {
Printf(" No files in the TChain: did you run mp101_fillNtuples.C before?");
return 1;
}
TH1F outHisto("outHisto", "Random Numbers", 128, -4, 4);
inputChain.Draw("r >> outHisto");
outHisto.Fit("gaus");
//---------------------------------------
// We now go MP!
// TProcessExecutor offers an interface to directly process trees and chains without
// the need for the user to go through the low level implementation of a
// map-reduce.
// We adapt our parallelisation to the number of input files
const auto nFiles = inputChain.GetListOfFiles()->GetEntries();
// This is the function invoked during the processing of the trees.
auto workItem = [](TTreeReader &reader) {
TTreeReaderValue<Float_t> randomRV(reader, "r");
auto partialHisto = new TH1F("outHistoMP", "Random Numbers", 128, -4, 4);
while (reader.Next()) {
partialHisto->Fill(*randomRV);
}
return partialHisto;
};
// Create the pool of processes
ROOT::TTreeProcessorMP workers(nFiles);
// Process the TChain
auto sumHistogram = workers.Process(inputChain, workItem, "multiCore");
sumHistogram->Fit("gaus", 0);
return 0;
}