def getEventCount(config,
sampleIdentifier,
cut="1",
sampleTree=None,
sample=None):
if not sampleTree:
sampleTree = SampleTree(
{
'name': sampleIdentifier,
'folder': config.get('Directories', args.fromFolder).strip()
},
config=config)
h1 = ROOT.TH1D("h1", "h1", 1, 0, 2)
scaleToXs = sampleTree.getScale(sample)
#nEvents = sampleTree.tree.Draw("1>>h1", "(" + cut + ")*genWeight*%1.6f"%scaleToXs, "goff")
nEvents = sampleTree.tree.Draw("1>>h1", cut, "goff")
nEventsWeighted = h1.GetBinContent(1)
#print("DEBUG:", sampleIdentifier, cut, " MC events:", nEvents, " (weighted:", nEventsWeighted, ")")
h1.Delete()
return nEvents
#subsamples = sampleInfo.getSubsamples(sampleIdentifier)
#print('subsample_pirmin', subsamples)
#print('sample_pirmin', sample)
#print("\x1b[41m\x1b[0m")
sampleTree = SampleTree({
'sample': sample,
'folder': directory
},
config=config)
#raw_input()
# 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)