# since we load all trees, we can compute the factor to scale cross section to luminosity directly (otherwise write it to ntuples
# first and then use it as branch, or compute it with full set of trees before)
scaleXStoLumi = sampleTree.getScale(sample)
# enable only used branches!
# this will speed up processing a lot
sampleTree.enableBranches(
BranchList([
signalRegionSelection, weightExpression_DeepCSV,
weightExpression_DeepJet, taggerExpression_DeepCSV,
taggerExpression_DeepJet
]).getListOfBranches() + ['Jet*'])
# this will create the TTreeformula objects
sampleTree.addFormula(signalRegionSelection)
sampleTree.addFormula(signalRegionSelection_roc)
sampleTree.addFormula(weightExpression_DeepCSV)
sampleTree.addFormula(weightExpression_DeepJet)
sampleTree.addFormula(weightExpression_DeepCSV_nosf)
sampleTree.addFormula(weightExpression_DeepJet_nosf)
sampleTree.addFormula(taggerExpression_DeepCSV)
sampleTree.addFormula(taggerExpression_DeepJet)
isSignal = 1 if sample.name in signalNames else 0
for event in sampleTree:
#bselection = VHbbSelection(channels=["Zll"])
#sortb = bselection.HighestTaggerValueBJets(event, 20, 20, 'Jet_btagDeepFlavB')
from __future__ import print_function
import ROOT
ROOT.gROOT.SetBatch(True)
from myutils.XbbConfig import XbbConfigReader, XbbConfigTools
from myutils.sampleTree import SampleTree as SampleTree
from myutils.BranchList import BranchList
config = XbbConfigTools(XbbConfigReader.read("Zvv2017"))
sampleTree = SampleTree(
{
'name': 'MET',
'folder': config.get('Directories', 'dcSamples')
},
config=config)
variables = ["H_pt", "MET_Pt", "H_pt/MET_Pt"]
# enable only explicitly used branches
sampleTree.enableBranches(BranchList(variables).getListOfBranches())
# create TTReeFormula's
for variable in variables:
sampleTree.addFormula(variable)
# loop over events
for event in sampleTree:
print(
sampleTree.tree.GetReadEntry(),
", ".join([x + "=%1.4f" % sampleTree.evaluate(x) for x in variables]))
if sampleTree.tree.GetReadEntry() > 98:
break