def __init__(self, chain, new_chain, is_reco_level=True, **kwargs):
self.chain = chain
self.new_chain = new_chain
self.chain.strategy = kwargs.get('strategy')
self.chain.region = kwargs.get('region')
self.chain.selection = kwargs.get('selection')
self.additional_options = kwargs.get('additional_options', None)
self.chain.obj_sel = getObjectSelection(self.chain.selection)
self.event_category = EventCategory(self.chain)
self.event_selector = EventSelector(
self.chain.region,
self.chain,
self.new_chain,
is_reco_level=is_reco_level,
event_categorization=self.event_category,
additional_options=self.additional_options)
max_events = 20000
event_range = xrange(max_events) if max_events < len(
sample.getEventRange(args.subJob)) else sample.getEventRange(
args.subJob)
else:
event_range = sample.getEventRange(args.subJob)
#
# Get luminosity weight
#
from HNL.Weights.lumiweight import LumiWeight
lw = LumiWeight(sample, sample_manager)
from HNL.EventSelection.eventCategorization import EventCategory
try:
ec = EventCategory(chain)
except:
ec = None
#
# Define event selection
#
event = Event(chain,
chain,
is_reco_level=True,
selection=args.selection,
strategy='MVA',
region=region_to_select)
#
# Create fake rate objects
eventRange = xrange(20000)
else:
eventRange = sample.getEventRange(int(args.subJob))
from HNL.Tools.helpers import progress
def getSortKey(item):
return item[0]
from HNL.EventSelection.eventSelector import EventSelector
from HNL.EventSelection.eventCategorization import EventCategory, filterSuperCategory
from HNL.EventSelection.signalLeptonMatcher import SignalLeptonMatcher
slm = SignalLeptonMatcher(chain)
ec = EventCategory(chain)
es = EventSelector('baseline',
chain,
chain,
is_reco_level=False,
event_categorization=ec)
#
# Create cutter to provide cut flow
#
from HNL.EventSelection.cutter import Cutter
cutter = Cutter(chain=chain)
#Loop over events
for entry in eventRange:
if args.removeOverlap:
if 'DYJets' in sample.name and chain._zgEventType>=3: continue
if sample.name == 'ZG' and chain._hasInternalConversion: continue
if args.genSkim:
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:
if args.separateTriggers is not None:
output_name += '/'+args.separateTriggers
if args.isChild:
output_name += '/tmp_'+sample.output
if args.useRef: output_name += '/'+ sample.name +'_TrigEff_' +subjobAppendix+ '.root'
else: output_name += '/'+ sample.name +'_TrigEffnoMET_' +subjobAppendix+ '.root'
return output_name
#
# Define histograms
# Could these lists could have been done in a nicer way but since the wanted lepton is different for different var, I was too lazy for now. Redo later if needed
# (channel, dim)
from HNL.EventSelection.eventCategorization import EventCategory, returnCategoryTriggerNames, returnCategoryTriggers
ec = EventCategory(chain)
categories = ec.categories
category_triggers = lambda chain, category : returnCategoryTriggers(chain, category)
#First if keeps old code for reference
# if args.ignoreCategories:
# category_map = {('eee', '1D') : ['integral'],
# ('eee', '2D') : ['integral'],
# ('eemu', '1D') : ('15to30', '30to55'),
# ('eemu', '2D') : ('15to20', '20to25', '25to30', '30to40', '40to50', '55to100'),
# ('emumu', '1D') : ('15to30', '30to55'),
# ('emumu', '2D') : ('15to20', '20to25', '25to30', '30to40', '40to50', '55to100'),
# ('mumumu', '1D') : ['integral'],
# ('mumumu', '2D') : ['integral']}
# var = {('pt', '1D') : (lambda c : c._lPt[c.l3], np.arange(5., 40., 5.), ('p_{T}(trailing) [GeV]', 'Events')),
output_name += '/tmp_'+args.flavor
if args.baselineCut is None:
output_name += '/'+ sample.name +'_signalSelection-noFilter_' +subjobAppendix+ '.root'
else:
output_name += '/'+ sample.name +'_signalSelection-'+args.baselineCut+'_' +subjobAppendix+ '.root'
#
# Calculate the range for the histograms. These are as a function of the mass of the signal samples.
# This function looks at the names of all samples and returns an array with all values right the middle of those
# It assumes the samples are ordered by mass in the input list
#
mass_range = getMassRange([sample_name for sample_name in sample_manager.sample_names if '-'+args.flavor+'-' in sample_name])
from HNL.EventSelection.eventCategorization import EventCategory
ec = EventCategory(chain)
#
# Define the variables and axis name of the variable to fill and create efficiency objects
#
var = {'HNLmass': (lambda c : c.HNLmass, np.array(mass_range), ('m_{N} [GeV]', 'Events'))}
efficiency = createEfficiencies(output_name, ec.categories, var)
#
# Set event range
#
if args.isTest:
max_events = 20000
event_range = xrange(max_events) if max_events < len(sample.getEventRange(args.subJob)) else sample.getEventRange(args.subJob)
else:
event_range = sample.getEventRange(args.subJob)
sample.getEventRange(args.subJob)) else sample.getEventRange(
args.subJob)
else:
event_range = sample.getEventRange(args.subJob)
chain.HNLmass = sample.getMass()
chain.year = int(args.year)
#
# Get luminosity weight
#
from HNL.Weights.lumiweight import LumiWeight
lw = LumiWeight(sample, sample_manager, skimmed=not args.noskim)
from HNL.EventSelection.eventCategorization import EventCategory
ec = EventCategory(chain)
#
# Object ID param
#
if args.selection == 'AN2017014':
object_selection_param = {
'no_tau': True,
'light_algo': 'cutbased',
'workingpoint': 'tight'
}
else:
object_selection_param = {
'no_tau': False,
'light_algo': 'leptonMVAtop',
'workingpoint': 'medium'
args.subJob)
else:
event_range = xrange(chain.GetEntries())
chain.HNLmass = float(sample.name.rsplit('-', 1)[1]) if is_signal else None
chain.year = int(args.year)
#
# Loop over all events
#
from HNL.Tools.helpers import progress
from HNL.EventSelection.eventSelectionTools import select3Leptons
from HNL.EventSelection.eventCategorization import EventCategory
from HNL.ObjectSelection.tauSelector import isGoodTau
from HNL.ObjectSelection.leptonSelector import isGoodLepton
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.event_supercategory = ec.returnSuperCategory()
#if chain.event_supercategory > 2: continue
#Add here any additional cuts
list_of_numbers['total'] += 1
chain.leptons = [
l for l in xrange(chain._nLight) if isGoodLepton(chain, l, 'tight')
else:
event_range = sample.getEventRange(args.subJob)
chain.HNLmass = sample.getMass()
chain.year = int(args.year)
#
# Get luminosity weight
#
from HNL.Weights.lumiweight import LumiWeight
if args.noskim:
lw = LumiWeight(sample, sample_manager)
from HNL.EventSelection.eventCategorization import EventCategory
ec = EventCategory(chain)
list_of_numbers = {}
for c in listOfCategories():
list_of_numbers[c] = {}
for sr in xrange(1, srm.getNumberOfSearchRegions()+1):
list_of_numbers[c][sr] = Histogram('_'.join([str(c), str(sr)]), lambda c: 0.5, ('', 'Events'), np.arange(0., 2., 1.))
subjobAppendix = 'subJob' + args.subJob if args.subJob else ''
selection_str = 'OldSel' if args.oldAnalysisCuts else 'NewCuts'
mass_str = 'allMass' if args.massRegion is None else args.massRegion
if not args.isTest:
output_name = os.path.join(os.getcwd(), 'data', __file__.split('.')[0], selection_str, mass_str, sample.output)
else:
output_name = os.path.join(os.getcwd(), 'data', 'testArea', __file__.split('.')[0], selection_str, mass_str, sample.output)
