Revision 090ccec988dca3bec28755dd9de8472fcbfdfb52 authored by will-cern on 28 September 2023, 12:09:24 UTC, committed by GitHub on 28 September 2023, 12:09:24 UTC
* update xRooFit to latest version * declare ROOT::Experimental::XRooFit namespace for ROOT's copy of xRooFit * fix namespace alias * address PR comments * [RF] Improvements to `xroofit` documentation structure * Add a new `xroofit` group to the RooFit documentation * Add semicolons behind the `NAMESPACE` macros. They have no effect on the C++ compilier, but macros without trailing semicolons confuse doxygen * Add the `xRooNode` and `xRooNLLVar` classes to the `xroofit` documentation group. Other xroofit classes might also go there, but I don't know which ones should really be public. So I only added these two to give and example. * Move RooBrowser documentation to xRooBrowser. Now that xRooFit is considered part as the experimental user interface, this is fine to do. Like this, doxygen can associate the actual xRooBrowser implementation with the docs. * Rename `RooFit::Detail::XRooFit` namespace to `ROOT::Experimental::XRooFit` instad of using namespace alias. Again, this is done to not confuse doxygen. To build the documentation quickly, edit the `documentation/doxygen/makeinput.sh` directory and comment out all subdirectories except for `roofit`. Call `make` in the doxygen directory. Type `y` if it should ask you to overwrite some files in the end. The xRooFit documentation will end up in the `~/rootdoc/html/group__xroofit.html` directory. * address some typos
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df002_dataModel.py
## \file
## \ingroup tutorial_dataframe
## \notebook -draw
## Show how to work with non-flat data models, e.g. vectors of tracks.
##
## This tutorial shows the possibility to use data models which are more
## complex than flat ntuples with RDataFrame.
##
## \macro_code
## \macro_image
##
## \date May 2017
## \author Danilo Piparo (CERN)
import ROOT
# A simple helper function to fill a test tree: this makes the example stand-alone.
fill_tree_code = '''
using FourVector = ROOT::Math::XYZTVector;
using FourVectorVec = std::vector<FourVector>;
using CylFourVector = ROOT::Math::RhoEtaPhiVector;
// A simple helper function to fill a test tree: this makes the example
// stand-alone.
void fill_tree(const char *filename, const char *treeName)
{
const double M = 0.13957; // set pi+ mass
TRandom3 R(1);
auto genTracks = [&](){
FourVectorVec tracks;
const auto nPart = R.Poisson(15);
tracks.reserve(nPart);
for (int j = 0; j < nPart; ++j) {
const auto px = R.Gaus(0, 10);
const auto py = R.Gaus(0, 10);
const auto pt = sqrt(px * px + py * py);
const auto eta = R.Uniform(-3, 3);
const auto phi = R.Uniform(0.0, 2 * TMath::Pi());
CylFourVector vcyl(pt, eta, phi);
// set energy
auto E = sqrt(vcyl.R() * vcyl.R() + M * M);
// fill track vector
tracks.emplace_back(vcyl.X(), vcyl.Y(), vcyl.Z(), E);
}
return tracks;
};
ROOT::RDataFrame d(64);
d.Define("tracks", genTracks).Snapshot<FourVectorVec>(treeName, filename, {"tracks"});
}
'''
# We prepare an input tree to run on
fileName = "df002_dataModel_py.root"
treeName = "myTree"
ROOT.gInterpreter.Declare(fill_tree_code)
ROOT.fill_tree(fileName, treeName)
# We read the tree from the file and create a RDataFrame, a class that
# allows us to interact with the data contained in the tree.
d = ROOT.RDataFrame(treeName, fileName)
# Operating on branches which are collections of objects
# Here we deal with the simplest of the cuts: we decide to accept the event
# only if the number of tracks is greater than 8.
n_cut = 'tracks.size() > 8'
nentries = d.Filter(n_cut).Count();
print("%s events passed all filters" % nentries.GetValue())
# Another possibility consists in creating a new column containing the
# quantity we are interested in.
# In this example, we will cut on the number of tracks and plot their
# transverse momentum.
getPt_code ='''
using namespace ROOT::VecOps;
ROOT::RVecD getPt(const RVec<FourVector> &tracks)
{
auto pt = [](const FourVector &v) { return v.pt(); };
return Map(tracks, pt);
}
'''
ROOT.gInterpreter.Declare(getPt_code)
getPtWeights_code ='''
using namespace ROOT::VecOps;
ROOT::RVecD getPtWeights(const RVec<FourVector> &tracks)
{
auto ptWeight = [](const FourVector &v) { return 1. / v.Pt(); };
return Map(tracks, ptWeight);
};
'''
ROOT.gInterpreter.Declare(getPtWeights_code)
augmented_d = d.Define('tracks_n', '(int)tracks.size()') \
.Filter('tracks_n > 2') \
.Define('tracks_pts', 'getPt( tracks )') \
.Define("tracks_pts_weights", 'getPtWeights( tracks )' )
# The histogram is initialised with a tuple containing the parameters of the
# histogram
trN = augmented_d.Histo1D(("", "", 40, -.5, 39.5), "tracks_n")
trPts = augmented_d.Histo1D("tracks_pts")
trWPts = augmented_d.Histo1D("tracks_pts", "tracks_pts_weights")
c1 = ROOT.TCanvas()
trN.Draw()
c1.SaveAs("df002_trN.png")
c2 = ROOT.TCanvas()
trPts.Draw()
c2.SaveAs("df002_trPts.png")
c3 = ROOT.TCanvas()
trWPts.Draw()
c2.SaveAs("df002_trWPts.png")
print("Saved figures to df002_*.png")
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