class Reweighter:
def __init__(self, sample, sample_manager):
self.sample = sample
self.sample_manager = sample_manager
#lumi weights
self.lumiweighter = LumiWeight(sample, sample_manager)
#pu weights
if not sample.is_data:
self.puReweighting = getReweightingFunction(
sample.chain.year, 'central')
self.puReweightingUp = getReweightingFunction(
sample.chain.year, 'up')
self.puReweightingDown = getReweightingFunction(
sample.chain.year, 'down')
self.fakerate_collection = None
def getLumiWeight(self):
return self.lumiweighter.getLumiWeight()
def getPUWeight(self):
if not self.sample.is_data:
return self.puReweighting(self.sample.chain._nTrueInt)
else:
return 1.
def getFakeRateWeight(self):
try:
return self.fakerate_collection.getFakeWeight()
except:
self.fakerate_collection = returnFakeRateCollection(
self.sample.chain)
return self.fakerate_collection.getFakeWeight()
def getTotalWeight(self, sideband=False):
tot_weight = 1.
tot_weight *= self.getLumiWeight()
tot_weight *= self.getPUWeight()
if sideband:
tot_weight *= self.getFakeRateWeight()
return tot_weight
#
from HNL.Tools.helpers import progress, makeDirIfNeeded
from HNL.ObjectSelection.tauSelector import isGoodGenTau
from HNL.EventSelection.eventSelectionTools import select3GenLeptons
ec = EventCategory(chain)
for entry in event_range:
chain.GetEntry(entry)
progress(entry - event_range[0], len(event_range))
if not select3GenLeptons(chain, chain): continue
chain.tau_pt = chain.l_pt[0]
chain.tau_eta = chain.l_eta[0]
for v in var.keys():
list_of_hist[sample.output][v].fill(chain, lw.getLumiWeight())
# for tau in xrange(chain._gen_nL):
# if not isGoodGenTau(chain, tau): continue
#
# chain.tau_pt = chain._gen_lPt[tau]
# chain.tau_eta = chain._gen_lEta[tau]
# for v in var.keys():
# list_of_hist[sample.output][v].fill(chain, 1.)
#
# Save histograms
#
if args.isTest: continue
subjobAppendix = '_subJob' + args.subJob if args.subJob else ''
if not select3GenLeptons(chain, new_vars): continue
elif not args.oldAnalysisSkim:
# if not select3Leptons(chain, new_vars, light_algo=args.lightLeptonSelection, tau_algo=args.tauSelection): continue
select3Leptons(chain, new_vars, light_algo=args.lightLeptonSelection, tau_algo=args.tauSelection, workingpoint = 'loose')
if len(chain.leptons) < 3: continue
else:
select3Leptons(chain, new_vars, light_algo='cutbased', workingpoint = 'tight', no_tau = True, cutter = cutter)
if len(chain.leptons) < 3: continue
ec = EventCategory(new_vars)
c = ec.returnCategory()
new_vars.event_category = c
print 'calcing'
calculateGeneralVariables(chain, new_vars, is_reco_level=not args.genSkim)
calculateThreeLepVariables(chain, new_vars, is_reco_level=not args.genSkim)
new_vars.lumiweight = lw.getLumiWeight()
output_tree.Fill()
# print output_name.split('.')[-1]+'_cutflow.root'
# cutter.saveCutFlow(output_name.split('.')[-1]+'_cutflow.root')
output_tree.AutoSave()
#if hcounter is not None:
# hcounter.Write()
output_file.Close()
for mu_wp in getMuWPs(algo):
tau_indices = []
for t in base_tau_indices_per_algo[algo]:
if not tau_id_WP[(algo, iso_wp)](chain)[t]:
continue
if not passedElectronDiscr(chain, t, algo, ele_wp):
continue
if not passedMuonDiscr(chain, t, algo, mu_wp):
continue
tau_indices.append(t)
if not passCuts(chain, lepton_indices + tau_indices):
continue
if len(tau_indices) == 2:
list_of_hist[algo][iso_wp][ele_wp][mu_wp][
'Ditau'].Fill(.5, lw.getLumiWeight())
elif len(tau_indices) == 1:
list_of_hist[algo][iso_wp][ele_wp][mu_wp][
'SingleTau'].Fill(.5, lw.getLumiWeight())
#
# Write
#
# if args.isTest: exit(0)
subjobAppendix = '_subJob' + args.subJob if args.subJob else ''
signalOrBkgr = 'Signal' if 'HNL' in sample.name else 'Background'
if args.isTest:
output_name = os.path.join(os.getcwd(), 'data', 'testArea',
__file__.split('.')[0].rsplit('/')[-1],
args.year, signalOrBkgr, sample.output)
#
# Select most isolated tau candidate
#
tau_index = tau_candidates[0]
for tau in tau_candidates[1:]:
if chain._tauDeepTauVsJetsRaw[
tau] > chain._tauDeepTauVsJetsRaw[tau_index]:
tau_index = tau
mu_vec = getLeptonFourVecFromChain(chain, muon_index)
tau_vec = getLeptonFourVecFromChain(chain, tau_index)
chain.m_vis = (mu_vec + tau_vec).M()
for v in var.keys():
list_of_hist[sample.name][v].fill(chain, lw.getLumiWeight())
#
# Save histograms
#
subjobAppendix = '_subJob' + args.subJob if args.subJob else '_0'
if not args.isTest:
output_name = os.path.join(os.getcwd(), 'data',
__file__.split('.')[0], args.year,
sample.output)
else:
output_name = os.path.join(os.getcwd(), 'data', 'testArea',
__file__.split('.')[0], args.year,
sample.output)
tmp_tau = [l for l in xrange(chain._gen_nL) if chain._gen_lFlavor[l] == 2 and chain._gen_lVisPt[l] > 18]
tmp_light = [l for l in xrange(chain._gen_nL) if chain._gen_lFlavor[l] in (0, 1) and chain._gen_lPt[l] > 15]
if len(tmp_tau) < 1 or len(tmp_light) < 1: passed_baseline_cut = False
if args.baselineCut is not None and 'Inverted' in args.baselineCut: passed_baseline_cut = not passed_baseline_cut #Invert selection if needed
if not args.divideByCategory == 'gendenom' and not passed_baseline_cut: continue
event.initEvent()
if not args.genLevel:
passed = event.passedFilter(cutter, sample.output)
elif args.divideByCategory == 'gendenom':
passed = passed_baseline_cut
else:
passed = True
weight = lw.getLumiWeight()
if args.divideByCategory is not None:
cat_to_use = ec.returnCategory() if args.divideByCategory == 'reco' else true_cat
for category in CATEGORIES:
if not args.genLevel and args.divideByCategory == 'both' and category != true_cat: continue
if not args.genLevel and args.divideByCategory == 'both' and ec.returnCategory() != true_cat: continue
if args.divideByCategory == 'gendenom' and category != true_cat: continue
full_pass = lambda passes : passes if category == cat_to_use else False
if args.compareTriggerCuts is None:
efficiency[category]['regularRun'].fill(chain, weight, full_pass(passed))
elif args.compareTriggerCuts == 'single':
'pass_triggers'):
continue
#Event selection
if not es.passedFilter(chain, chain, cutter): continue
nprompt = 0
for index in [chain.l1, chain.l2, chain.l3]:
if chain._lIsPrompt[index]: nprompt += 1
prompt_str = 'prompt' if nprompt == nl else 'nonprompt'
# print nprompt, prompt_str, chain.M3l, chain.minMossf
chain.search_region = srm[args.region].getSearchRegion(chain)
list_of_numbers[ec.returnCategory()][prompt_str].fill(
chain, lw.getLumiWeight())
list_of_numbers[ec.returnCategory()]['total'].fill(
chain, lw.getLumiWeight())
for prompt_str in ['prompt', 'nonprompt', 'total']:
for i, c_h in enumerate(list_of_numbers.keys()):
output_name = getOutputName(prompt_str)
if i == 0:
list_of_numbers[c_h][prompt_str].write(output_name)
else:
list_of_numbers[c_h][prompt_str].write(output_name,
append=True)
cutter.saveCutFlow(getOutputName('total'))
#
#
# Loop over all events
#
from HNL.Tools.helpers import progress
from HNL.EventSelection.eventSelectionTools import select3Leptons, select3GenLeptons, passBaseCutsAN2017014, passBaseCuts, lowMassCuts, highMassCuts, calculateKinematicVariables, containsOSSF
from HNL.EventSelection.signalLeptonMatcher import SignalLeptonMatcher
from HNL.Triggers.triggerSelection import applyCustomTriggers, listOfTriggersAN2017014
from HNL.ObjectSelection.leptonSelector import isFOLepton, isTightLepton
print 'tot_range', len(event_range)
for entry in event_range:
chain.GetEntry(entry)
progress(entry - event_range[0], len(event_range))
if args.noskim: chain.weight = lw.getLumiWeight()
else: chain.weight = chain.lumiweight
cutter.cut(True, 'total')
#Triggers
if args.oldAnalysisCuts:
if not cutter.cut(applyCustomTriggers(listOfTriggersAN2017014(chain)), 'pass_triggers'): continue
#Selecting 3 leptons
if not args.oldAnalysisCuts:
if not cutter.cut(select3Leptons(chain, chain, light_algo = 'leptonMVAtZq', cutter=cutter), '3_tight_leptons'): continue
else:
if not cutter.cut(select3Leptons(chain, chain, no_tau=True, light_algo = 'cutbased', cutter=cutter), '3_tight_leptons'): continue
# chain.category will be needed in passBaseCutsAN2017014