We reduced the initial datasets by filtering suitable events and selecting interesting observables. You can find working code examples explaining these helpers in the VecOps tutorials! Try it by yourself!įeel free to open the ROOT prompt and try ROOT::RVec by yourself! The prompt is well suited to try some of the features shown above because you can print the content of the vectors just by leaving out the semicolon at the end of the line. ROOT::VecOps also offers helpers typical to HEP such as DeltaPhi and InvariantMass. using namespace ROOT :: VecOps RVec x = (the sorted vector) auto allEqual = All ( Reverse ( z ) = y ) // true (compares all elements) Sorting, index manipulation, comparison.

You can adopt memory just by passing the pointer and the length of the vector! This may improve the runtime of your program greatly because copies are costly operations. You can use all features shown below in strings passed to RDataFrame! Adopting memory Note that all vectors in RDataFrame are automatically treated as RVecs. You can find the full documentation here! Because RVec has the same interface as a std::vector you can use them interchangeably! However, following additional features simplifies typical tasks in analysis. ROOT::RVec is an extended std::vector with additional features to deal with collections, similar to NumPy arrays. Also the results of this step are files in the same directory in which you have run the executable and have the filenames *Skim.root. Run it! Note that the program picks up the files from the same directory in which you run it. G++ -O3 -o skim skim.cxx $(root-config -cflags -libs ) Run the C++ program and investigate the output!

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