This tutorial shows how VecOps can be used to slim down the programming model typically adopted in HEP for analysis.
Author: Danilo Piparo, Andre Vieira Silva
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Saturday, January 18, 2020 at 01:38 AM.
import ROOT filename = ROOT.gROOT.GetTutorialDir().Data() + "/dataframe/df017_vecOpsHEP.root" treename = "myDataset" def WithPyROOT(filename): from math import sqrt 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 WithRDataFrameVecOpsJit(treename, filename): f = ROOT.ROOT.RDataFrame(treename, filename) h = f.Define("good_pt", "sqrt(px*px + py*py)[E>100]")\ .Histo1D(("pt", "pt", 16, 0, 4), "good_pt") h.DrawCopy()
Welcome to JupyROOT 6.19/01
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(filename) c.cd(2) WithRDataFrameVecOpsJit(treename, filename)
Draw all canvases
from ROOT import gROOT gROOT.GetListOfCanvases().Draw()