[TDF][VecOps] Add tutorial with a real analysis usecase

tutorials/dataframe/tdf017_vecOpsHEP.C

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+/// \file
+/// \ingroup tutorial_tdataframe
+/// \notebook -draw
+/// This tutorial shows how VecOps can be used to slim down the programming
+/// model typically adopted in HEP for analysis.
+/// In this case we have a dataset containing the kinematic properties of
+/// particles stored in individual arrays.
+/// We want to plot the transverse momentum of these particles if the energy is
+/// greater than 100.
+///
+/// \macro_code
+///
+/// \date March 2018
+/// \author Danilo Piparo, Andr�e Vieira Silva
+
+auto filename = gROOT->GetTutorialDir() + "/dataframe/tdf017_vecOpsHEP.root";
+auto treename = "myDataset";
+using doubles = ROOT::Experimental::VecOps::TVec<double>;
+using TDF = ROOT::Experimental::TDataFrame;
+
+void WithTTreeReader()
+{
+   TFile f(filename);
+   TTreeReader tr(treename, &f);
+   TTreeReaderArray<double> px(tr, "px");
+   TTreeReaderArray<double> py(tr, "py");
+   TTreeReaderArray<double> E(tr, "E");
+
+   TH1F h("pt", "pt", 16, 0, 4);
+
+   while (tr.Next()) {
+      for (auto i=0U;i < px.GetSize(); ++i) {
+         if (E[i] > 100) h.Fill(sqrt(px[i]*px[i] + py[i]*py[i]));
+      }
+   }
+   h.DrawCopy();
+}
+
+void WithTDataFrame()
+{
+  TDF f(treename, filename.Data());
+   auto CalcPt = [](doubles &px, doubles &py, doubles &E) {
+      doubles v;
+      for (auto i=0U;i < px.size(); ++i) {
+         if (E[i] > 100) {
+            v.emplace_back(sqrt(px[i]*px[i] + py[i]*py[i]));
+         }
+      }
+      return v;
+   };
+   f.Define("pt", CalcPt, {"px", "py", "E"})
+    .Histo1D<doubles>({"pt", "pt", 16, 0, 4}, "pt")->DrawCopy();
+}
+
+void WithTDataFrameVecOps()
+{
+   TDF f(treename, filename.Data());
+   auto CalcPt = [](doubles &px, doubles &py, doubles &E) {
+      auto pt = sqrt(px*px + py*py);
+      return pt[E>100];
+   };
+   f.Define("good_pt", CalcPt, {"px", "py", "E"})
+    .Histo1D<doubles>({"pt", "pt", 16, 0, 4}, "good_pt")->DrawCopy();
+}
+
+void WithTDataFrameVecOpsJit()
+{
+   TDF f(treename, filename.Data());
+   f.Define("good_pt", "sqrt(px*px + py*py)[E>100]")
+    .Histo1D({"pt", "pt", 16, 0, 4}, "good_pt")->DrawCopy();
+}
+
+void tdf017_vecOpsHEP()
+{
+   // We plot four times the same quantity, the key is to look into the implementation
+   // of the functions above
+   auto c = new TCanvas();
+   c->Divide(2,2);
+   c->cd(1);
+   WithTTreeReader();
+   c->cd(2);
+   WithTDataFrame();
+   c->cd(3);
+   WithTDataFrameVecOps();
+   c->cd(4);
+   WithTDataFrameVecOpsJit();
+}

tutorials/dataframe/tdf017_vecOpsHEP.py

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+## \file
+## \ingroup tutorial_tdataframe
+## \notebook -draw
+## This tutorial shows how VecOps can be used to slim down the programming
+## model typically adopted in HEP for analysis.
+## \macro_code
+##
+## \date March 2018
+## \author Danilo Piparo, Andre Vieira Silva
+
+import ROOT
+from math import sqrt
+
+filename = ROOT.gROOT.GetTutorialDir().Data() + "/dataframe/tdf017_vecOpsHEP.root"
+treename = "myDataset"
+TDF = ROOT.ROOT.Experimental.TDataFrame
+
+def WithPyROOT():
+    f = ROOT.TFile(filename)
+    h = ROOT.TH1F("pt", "pt", 16, 0, 4)
+    for event in f.myDataset:
+        for E, px, py in zip(event.E, event.px, event.py):
+            if (E > 100):
+               h.Fill(sqrt(px*px + py*py))
+    h.DrawCopy()
+
+def WithTDataFrameVecOpsJit():
+    f = TDF(treename, filename)
+    h = f.Define("good_pt", "sqrt(px*px + py*py)[E>100]")\
+         .Histo1D(("pt", "pt", 16, 0, 4), "good_pt")
+    h.DrawCopy()
+
+## We plot twice the same quantity, the key is to look into the implementation
+## of the functions above
+c = ROOT.TCanvas()
+c.Divide(2,1)
+c.cd(1)
+WithPyROOT()
+c.cd(2)
+WithTDataFrameVecOpsJit()