def parse_formula(fcn_string, pars_string):
pars = []
formula = fcn_string
for par_num, match in enumerate(re.finditer("(?P<name>\w+)(?P<boundaries>\[[^\]]+\]),? ?", pars_string)):
par = struct()
par.num = par_num
par.name = match.group('name')
par.bounds = eval( match.group('boundaries') )
formula = formula.replace(par.name, '[%i]' % par.num)
pars.append(par)
ret = ROOT.TF1('ret', formula, 0, 200)
for par in pars:
ret.SetParName(par.num, par.name)
if len(par.bounds) == 1:
ret.FixParameter(par.num, par.bounds[0])
else:
ret.SetParameter(par.num, par.bounds[0])
ret.SetParLimits(par.num, par.bounds[1], par.bounds[2])
return ret
def parse_formula(fcn_string, pars_string):
pars = []
formula = fcn_string
for par_num, match in enumerate(re.finditer("(?P<name>\w+)(?P<boundaries>\[[^\]]+\]),? ?", pars_string)):
par = struct()
par.num = par_num
par.name = match.group('name')
par.bounds = eval( match.group('boundaries') )
formula = formula.replace(par.name, '[%i]' % par.num)
pars.append(par)
ret = ROOT.TF1('ret', formula, 0, 200)
for par in pars:
ret.SetParName(par.num, par.name)
if len(par.bounds) == 1:
ret.FixParameter(par.num, par.bounds[0])
else:
ret.SetParameter(par.num, par.bounds[0])
ret.SetParLimits(par.num, par.bounds[1], par.bounds[2])
return ret
def process(self):
logging.debug('Starting processing')
systematics = self.systematics
frw = []
lock = ()
ievt = 0
logging.debug('Starting evt loop')
#pre-compute static things
sys_shifts = systematics['trig'] # + \
# systematics['pu'] + \
# systematics['eid'] + \
# systematics['eiso'] #+ \
jes_dirs = [i.strip('_') for i in systematics['jes']]
#anyway the first is always ""
tes_dirs = [i.strip('_') for i in systematics['tes']][1:]
ees_dirs = [i.strip('_') for i in systematics['ees']][1:]
for row in self.tree:
if (ievt % 100) == 0:
logging.debug('New event')
ievt += 1
#avoid double counting events!
evt_id = (row.run, row.lumi, row.evt)
if evt_id == lock: continue
if lock != () and evt_id == lock:
logging.info('Removing duplicate of event: %d %d %d' % evt_id)
#
#preselection, common to everything and everyone
#
#trigger
if self.is_embedded:
if not bool(row.doubleMuPass): continue
else:
if not bool(row.singleE27WP80Pass): continue
if not bool(row.eMatchesSingleE27WP80): continue
#objects
if not selections.eSelection(row, 'e'): continue
#if row.ePt < 30 : continue
if not selections.tauSelection(row, 't'): continue
if not row.tAntiElectronMVA5Tight: continue
if not row.tAntiMuon2Loose: continue
if not row.tLooseIso3Hits: continue
logging.debug('object selection passed')
#e ID/ISO
if not selections.lepton_id_iso(row, 'e', 'eid13Tight_idiso02'):
continue
logging.debug('Passed preselection')
#
# Compute event weight
#
#event weight
#sys_shifts is precomputed
#set_trace()
weight_map = self.event_weight(row, sys_shifts)
#Fill embedded sample normalization BEFORE the vetoes
# if not row.e_t_SS:
# self.fill_histos('os/gg/ept30', row, weight_map[''])
# it is better vetoing on b-jets after the histo for the DY embedded
#bjet veto
if row.bjetCSVVeto30 != 0: continue
#tau ID, id Tau is tight then go in full selection, otherwise use for fakes
isTauTight = bool(row.tTightIso3Hits)
isETight = bool(
selections.lepton_id_iso(row, 'e', 'eid13Tight_etauiso01'))
# etau_category = ['']
# if (not isETight) and (not isTauTight):
# etau_category = ['etLoose', 'etLoose/Up', 'etLoose/Down']
# elif (not isTauTight):
# etau_category = ['tLoose', 'tLoose/Up', 'tLoose/Down', 'tLooseUnweight']
# elif (not isETight):
# etau_category = ['eLoose', 'eLoose/Up', 'eLoose/Down']
#jet category
central = struct(njets=min(row.jetVeto30, 3),
tPt=row.tPt,
ePt=row.ePt)
jets = [
min(row.jetVeto30, 3),
min(row.jetVeto30jes_plus, 3),
min(row.jetVeto30jes_minus, 3)
]
tpts = [row.tPt_tes_plus, row.tPt_tes_minus]
epts = [row.ePt_ees_plus, row.ePt_ees_minus]
sys_effects = [(name, central.clone(njets=jnum))
for name, jnum in zip(jes_dirs, jets)]
# sys_effects.extend(
# [(name, central.clone(tPt = pt)) for name, pt in zip(tes_dirs, tpts)]
# )
# sys_effects.extend(
# [(name, central.clone(ePt = pt)) for name, pt in zip(ees_dirs, epts)]
# )
#
# Full tight selection
#
passes_full_selection = False
selection_categories = []
# for name, shifted in sys_effects:
#preselection. flat pt values
if row.ePt < 30 and row.tPt < 30:
# selection_categories.append((name, '%i' % shifted.njets, ''))
# else:
continue
# if shifted.njets == 0 :
# if shifted.tPt < 35: continue
# if shifted.ePt < 40: continue
# if deltaPhi(row.ePhi, row.tPhi) < 2.7 : continue
# if row.tMtToPfMet > 50 : continue
# selection_categories.append((name, '0', 'selected'))
# passes_full_selection = True
# elif shifted.njets == 1 :
# if shifted.tPt < 40: continue
# if shifted.ePt < 35: continue
# if row.tMtToPfMet > 35 : continue
# selection_categories.append((name, '1', 'selected'))
# passes_full_selection = True
# elif shifted.njets == 2 :
# if shifted.tPt < 40: continue
# if shifted.ePt < 30: continue
# if row.tMtToPfMet > 35 : continue
# if row.vbfMass < 550 : continue
# if row.vbfDeta < 3.5 : continue
# selection_categories.append((name, '2', 'selected'))
# passes_full_selection = True
#dir_name1 = os.path.join(sys, selection_sys, sign, processtype,
# e_thr, jet_dir, selection_step)
# tvetoes = [row.tauVetoPt20EleTight3MuLoose, row.tauVetoPt20EleTight3MuLoose_tes_plus, row.tauVetoPt20EleTight3MuLoose_tes_minus]
# mvetoes = [row.muVetoPt5IsoIdVtx , row.muVetoPt5IsoIdVtx_mes_plus , row.muVetoPt5IsoIdVtx_mes_minus ]
# evetoes = [row.eVetoCicLooseIso , row.eVetoCicLooseIso_ees_plus , row.eVetoCicLooseIso_ees_minus ]
# tdirs = [ i for i, j in zip( systematics['tvetos'], tvetoes) if not j]
# mdirs = [ i for i, j in zip( systematics['mvetos'], mvetoes) if not j]
# edirs = [ i for i, j in zip( systematics['evetos'], evetoes) if not j]
#if any of the lists is empty
#set_trace()
# if not tdirs or not mdirs or not edirs:
# continue
# logging.debug('Passed Vetoes')
jet0panel = [('tPtcut', 1, 1, 30, 70), ('ePtcut', 1, 1, 30, 70),
('deltaPhicut', 1, 1, 20, 35),
('tMtToPFMETcut', 1, 1, 20, 80)]
jet1panel = [('tPtcut', 1, 1, 30, 70), ('ePtcut', 1, 1, 30, 70),
('tMtToPFMETcut', 1, 1, 20, 80)]
jet2panel = [('tPtcut', 1, 1, 30, 70), ('ePtcut', 1, 1, 30, 70),
('tMtToPFMETcut', 1, 1, 20, 80),
('vbfMasscut', 1, 4, 300, 700),
('vbfDetacut', 1, 1, 20, 60)]
jet0refer = (30, 30, 2, 80)
jet1refer = (30, 30, 80)
jet2refer = (30, 30, 80, 300, 2)
if (not isETight) or (not isTauTight):
continue
jn = row.jetVeto30
if jn > 3: jn = 3
jetlist = [(int(jn), str(int(jn)))]
for jet in jetlist:
foldertofill = 'os/gg/ept30/' + jet[1] + '/selected'
if jets[0] == 0:
for nbjet0, jetwhole0 in enumerate(jet0panel):
if nbjet0 == 0 and jetwhole0[1] == 1:
for j in range(jetwhole0[3], jetwhole0[4],
jetwhole0[2]):
if row.tPt < j: continue
if row.ePt < jet0refer[1]: continue
if deltaPhi(row.ePhi, row.tPhi) < jet0refer[2]:
continue
if row.tMtToPfMet > jet0refer[3]: continue
passes_full_selection = True
self.fill_histos3(jetwhole0[0], j,
foldertofill, row,
weight_map[''])
if nbjet0 == 1 and jetwhole0[1] == 1:
for j in range(jetwhole0[3], jetwhole0[4],
jetwhole0[2]):
if row.tPt < jet0refer[0]: continue
if row.ePt < j: continue
if deltaPhi(row.ePhi, row.tPhi) < jet0refer[2]:
continue
if row.tMtToPfMet > jet0refer[3]: continue
passes_full_selection = True
self.fill_histos3(jetwhole0[0], j,
foldertofill, row,
weight_map[''])
if nbjet0 == 2 and jetwhole0[1] == 1:
for j in range(jetwhole0[3], jetwhole0[4],
jetwhole0[2]):
if row.tPt < jet0refer[0]: continue
if row.ePt < jet0refer[1]: continue
if deltaPhi(row.ePhi, row.tPhi) < j / 10.0:
continue
if row.tMtToPfMet > jet0refer[3]: continue
passes_full_selection = True
self.fill_histos3(jetwhole0[0], j / 10.0,
foldertofill, row,
weight_map[''])
if nbjet0 == 3 and jetwhole0[1] == 1:
for j in range(jetwhole0[3], jetwhole0[4],
jetwhole0[2]):
if row.tPt < jet0refer[0]: continue
if row.ePt < jet0refer[1]: continue
if deltaPhi(row.ePhi, row.tPhi) < jet0refer[2]:
continue
if row.tMtToPfMet > j: continue
passes_full_selection = True
self.fill_histos3(jetwhole0[0], j,
foldertofill, row,
weight_map[''])
elif jets[0] == 1:
for nbjet1, jetwhole1 in enumerate(jet1panel):
if nbjet1 == 0 and jetwhole1[1] == 1:
for j in range(jetwhole1[3], jetwhole1[4],
jetwhole1[2]):
if row.tPt < j: continue
if row.ePt < jet1refer[1]: continue
if row.tMtToPfMet > jet1refer[2]: continue
# selection_categories.append((name, '1', 'selected'))
passes_full_selection = True
self.fill_histos3(jetwhole1[0], j,
foldertofill, row,
weight_map[''])
if nbjet1 == 1 and jetwhole1[1] == 1:
for j in range(jetwhole1[3], jetwhole1[4],
jetwhole1[2]):
if row.tPt < jet1refer[0]: continue
if row.ePt < j: continue
if row.tMtToPfMet > jet1refer[2]: continue
# selection_categories.append((name, '1', 'selected'))
passes_full_selection = True
self.fill_histos3(jetwhole1[0], j,
foldertofill, row,
weight_map[''])
if nbjet1 == 2 and jetwhole1[1] == 1:
for j in range(jetwhole1[3], jetwhole1[4],
jetwhole1[2]):
if row.tPt < jet1refer[0]: continue
if row.ePt < jet1refer[1]: continue
if row.tMtToPfMet > j: continue
# selection_categories.append((name, '1', 'selected'))
passes_full_selection = True
self.fill_histos3(jetwhole1[0], j,
foldertofill, row,
weight_map[''])
elif jets[0] == 2:
for nbjet2, jetwhole2 in enumerate(jet2panel):
if nbjet2 == 0 and jetwhole2[1] == 1:
for j in range(jetwhole2[3], jetwhole2[4],
jetwhole2[2]):
if row.tPt < j: continue
if row.ePt < jet2refer[1]: continue
if row.tMtToPfMet > jet2refer[2]: continue
if row.vbfMass < jet2refer[3]: continue
if row.vbfDeta < jet2refer[4]: continue
passes_full_selection = True
self.fill_histos3(jetwhole2[0], j,
foldertofill, row,
weight_map[''])
if nbjet2 == 1 and jetwhole2[1] == 1:
for j in range(jetwhole2[3], jetwhole2[4],
jetwhole2[2]):
if row.tPt < jet2refer[0]: continue
if row.ePt < j: continue
if row.tMtToPfMet > jet2refer[2]: continue
if row.vbfMass < jet2refer[3]: continue
if row.vbfDeta < jet2refer[4]: continue
passes_full_selection = True
self.fill_histos3(jetwhole2[0], j,
foldertofill, row,
weight_map[''])
if nbjet2 == 2 and jetwhole2[1] == 1:
for j in range(jetwhole2[3], jetwhole2[4],
jetwhole2[2]):
if row.tPt < jet2refer[0]: continue
if row.ePt < jet2refer[1]: continue
if row.tMtToPfMet > j: continue
if row.vbfMass < jet2refer[3]: continue
if row.vbfDeta < jet2refer[4]: continue
passes_full_selection = True
self.fill_histos3(jetwhole2[0], j,
foldertofill, row,
weight_map[''])
if nbjet2 == 3 and jetwhole2[1] == 1:
for j in range(jetwhole2[3], jetwhole2[4],
jetwhole2[2]):
if row.tPt < jet2refer[0]: continue
if row.ePt < jet2refer[1]: continue
if row.tMtToPfMet > jet2refer[2]: continue
if row.vbfMass < j: continue
if row.vbfDeta < jet2refer[4]: continue
passes_full_selection = True
self.fill_histos3(jetwhole2[0], j,
foldertofill, row,
weight_map[''])
if nbjet2 == 4 and jetwhole2[1] == 1:
for j in range(jetwhole2[3], jetwhole2[4],
jetwhole2[2]):
if row.tPt < jet2refer[0]: continue
if row.ePt < jet2refer[1]: continue
if row.tMtToPfMet > jet2refer[2]: continue
if row.vbfMass < jet2refer[3]: continue
if row.vbfDeta < j / 10.0: continue
passes_full_selection = True
self.fill_histos3(jetwhole2[0], j / 10.0,
foldertofill, row,
weight_map[''])
# selection_categories.append((name, '2', 'selected'))
if passes_full_selection:
logging.debug('Passed full selection')
def __init__(self, tree, outfile, **kwargs):
logging.debug('LFVHETauAnalyzerMVA3 constructor')
self.channel = 'ET'
super(LFVHETauAnalyzerMVA3, self).__init__(tree, outfile, **kwargs)
self.tree = ETauTree(tree)
self.out = outfile
self.histograms = {}
#understand what we are running
target = os.path.basename(os.environ['megatarget'])
self.is_data = target.startswith('data_')
self.is_embedded = ('Embedded' in target)
self.is_mc = not (self.is_data or self.is_embedded)
self.efake = e_fake_rate(0.2)
self.efakeup = e_fake_rate_up(0.2)
self.efakedw = e_fake_rate_dw(0.2)
#systematics used
self.systematics = {
'trig': (['', 'trp1s', 'trm1s'] if not self.is_data else []),
'pu': (['', 'p1s', 'm1s'] if self.is_mc else []),
'eid': (['', 'eidp1s', 'eidm1s'] if not self.is_data else []),
'eiso': (['', 'eisop1s', 'eisom1s'] if not self.is_data else []),
'jes': (['', '_jes_plus', '_jes_minus'] if self.is_mc else ['']),
'mvetos': (['', 'mVetoUp', 'mVetoDown'] if self.is_mc else ['']),
'tvetos': (['', 'tVetoUp', 'tVetoDown'] if self.is_mc else ['']),
'evetos': (['', 'eVetoUp', 'eVetoDown'] if self.is_mc else ['']),
'met': (["_mes_plus", "_ues_plus", "_mes_minus", "_ues_minus"]
if self.is_mc else []),
'tes':
(["", "_tes_plus", "_tes_minus"] if not self.is_data else ['']),
'ees':
(["", "_ees_plus", '_ees_minus'] if not self.is_data else [''])
}
#self filling histograms
coll_mass = make_collmass_systematics('') #no sys shift
self.histo_locations = {
} #just a mapping of the histograms we have to avoid changing self.histograms indexing an screw other files
self.hfunc = { #maps the name of non-trivial histograms to a function to get the proper value, the function MUST have two args (evt and weight). Used in fill_histos later
'nTruePU':
lambda row, weight: (row.nTruePU, None),
'weight':
lambda row, weight: (weight, None)
if weight is not None else (1., None),
'Event_ID':
lambda row, weight: (array.array("f", [
row.run, row.lumi,
int(row.evt) / 10**5,
int(row.evt) % 10**5
]), None),
'h_collmass_pfmet':
coll_mass,
'tPtcut':
coll_mass,
'ePtcut':
coll_mass,
'deltaPhicut':
coll_mass,
'tMtToPFMETcut':
coll_mass,
'vbfMasscut':
coll_mass,
'vbfDetacut':
coll_mass,
'h_collmass_vs_dPhi_pfmet':
merge_functions(attr_getter('tToMETDPhi'), coll_mass),
'MetEt_vs_dPhi':
merge_functions(
lambda row, weight:
(deltaPhi(row.tPhi, getattr(row, metphi())), weight),
attr_getter('type1_pfMet_Et')),
'ePFMET_DeltaPhi':
lambda row, weight:
(deltaPhi(row.ePhi, getattr(row, metphi())), weight),
'tPFMET_DeltaPhi':
lambda row, weight:
(deltaPhi(row.tPhi, getattr(row, metphi())), weight),
'evtInfo':
lambda row, weight: (struct(
run=row.run, lumi=row.lumi, evt=row.evt, weight=weight), None)
}
for shift in self.systematics['met']:
#patch name
postfix = shift
self.hfunc['h_collmass_pfmet%s' %
postfix] = make_collmass_systematics(shift)
for shift in self.systematics['tes']:
#patch name
postfix = shift
self.hfunc['h_collmass_pfmet%s' %
postfix] = make_collmass_systematics(shift)
for shift in self.systematics['jes']:
#patch name
postfix = shift
self.hfunc['h_collmass_pfmet%s' %
postfix] = make_collmass_systematics(shift)
for shift in self.systematics['ees']:
#patch name
postfix = shift
self.hfunc['h_collmass_pfmet%s' %
postfix] = make_collmass_systematics(shift)
#PU correctors
self.pucorrector = mcCorrections.make_shifted_weights(
mcCorrections.make_puCorrector('singlee'), ['p1s', 'm1s'], [
mcCorrections.make_puCorrectorUp('singlee'),
mcCorrections.make_puCorrectorDown('singlee')
])
self.trig_weight = mcCorrections.trig_efficiency if self.is_embedded else mcCorrections.trig_correction
def process(self):
logging.debug('Starting processing')
systematics = self.systematics
frw = []
lock = ()
ievt = 0
logging.debug('Starting evt loop')
#pre-compute static things
sys_shifts = systematics['trig'] + \
systematics['pu'] + \
systematics['eid'] + \
systematics['eiso'] #+ \
jes_dirs = [i.strip('_') for i in systematics['jes']]
#anyway the first is always ""
tes_dirs = [i.strip('_') for i in systematics['tes']][1:]
ees_dirs = [i.strip('_') for i in systematics['ees']][1:]
for row in self.tree:
if (ievt % 100) == 0:
logging.debug('New event')
ievt += 1
#avoid double counting events!
evt_id = (row.run, row.lumi, row.evt)
if evt_id == lock: continue
if lock != () and evt_id == lock:
logging.info('Removing duplicate of event: %d %d %d' % evt_id)
#
#preselection, common to everything and everyone
#
#trigger
if self.is_embedded:
if not bool(row.doubleMuPass): continue
else:
if not bool(row.singleE27WP80Pass): continue
if not bool(row.eMatchesSingleE27WP80): continue
#objects
if not selections.eSelection(row, 'e'): continue
#if row.ePt < 30 : continue
if not selections.tauSelection(row, 't'): continue
if not row.tAntiElectronMVA5Tight: continue
if not row.tAntiMuon2Loose: continue
if not row.tLooseIso3Hits: continue
logging.debug('object selection passed')
#e ID/ISO
if not selections.lepton_id_iso(row, 'e', 'eid13Tight_idiso02'):
continue
logging.debug('Passed preselection')
#
# Compute event weight
#
#event weight
#sys_shifts is precomputed
#set_trace()
weight_map = self.event_weight(row, sys_shifts)
#Fill embedded sample normalization BEFORE the vetoes
if not row.e_t_SS:
self.fill_histos('os/gg/ept30', row, weight_map[''])
# it is better vetoing on b-jets after the histo for the DY embedded
#bjet veto
if row.bjetCSVVeto30 == 0: continue
#tau ID, id Tau is tight then go in full selection, otherwise use for fakes
isTauTight = bool(row.tTightIso3Hits)
isETight = bool(
selections.lepton_id_iso(row, 'e', 'eid13Tight_etauiso01'))
etau_category = ['']
if (not isETight) and (not isTauTight):
etau_category = ['etLoose', 'etLoose/Up', 'etLoose/Down']
elif (not isTauTight):
etau_category = [
'tLoose', 'tLoose/Up', 'tLoose/Down', 'tLooseUnweight'
]
elif (not isETight):
etau_category = ['eLoose', 'eLoose/Up', 'eLoose/Down']
#jet category
central = struct(
njets=2,
#njets = min(row.jetVeto30, 3),
tPt=row.tPt,
ePt=row.ePt)
#jets = [min(row.jetVeto30, 3), min(row.jetVeto30jes_plus, 3), min(row.jetVeto30jes_minus, 3)]
jets = [2, 2, 2]
tpts = [row.tPt_tes_plus, row.tPt_tes_minus]
epts = [row.ePt_ees_plus, row.ePt_ees_minus]
sys_effects = [(name, central.clone(njets=jnum))
for name, jnum in zip(jes_dirs, jets)]
sys_effects.extend([(name, central.clone(tPt=pt))
for name, pt in zip(tes_dirs, tpts)])
sys_effects.extend([(name, central.clone(ePt=pt))
for name, pt in zip(ees_dirs, epts)])
#
# Full tight selection
#
passes_full_selection = False
selection_categories = []
for name, shifted in sys_effects:
#preselection. flat pt values
if shifted.ePt > 30 and shifted.tPt > 30:
selection_categories.append(
(name, '%i' % shifted.njets, ''))
else:
continue
##if shifted.njets == 0 :
## if shifted.tPt < 35: continue
## if shifted.ePt < 40: continue
## if deltaPhi(row.ePhi, row.tPhi) < 2.7 : continue
## if row.tMtToPfMet > 50 : continue
## selection_categories.append((name, '0', 'selected'))
## passes_full_selection = True
##elif shifted.njets == 1 :
## if shifted.tPt < 40: continue
## if shifted.ePt < 35: continue
## if row.tMtToPfMet > 35 : continue
## selection_categories.append((name, '1', 'selected'))
## passes_full_selection = True
##el
if shifted.njets == 2:
if shifted.tPt < 40: continue
if shifted.ePt < 30: continue
if row.tMtToPfMet > 35: continue
if row.vbfMass < 550: continue
if row.vbfDeta < 3.5: continue
selection_categories.append((name, '2', 'selected'))
passes_full_selection = True
if passes_full_selection:
logging.debug('Passed full selection')
#
#different selections
#
sign = 'ss' if row.e_t_SS else 'os'
processtype = 'gg'
ptthreshold = ['ept30']
#
# Lepton vetoes
#
tvetoes = [
row.tauVetoPt20EleTight3MuLoose,
row.tauVetoPt20EleTight3MuLoose_tes_plus,
row.tauVetoPt20EleTight3MuLoose_tes_minus
]
mvetoes = [
row.muVetoPt5IsoIdVtx, row.muVetoPt5IsoIdVtx_mes_plus,
row.muVetoPt5IsoIdVtx_mes_minus
]
evetoes = [
row.eVetoCicLooseIso, row.eVetoCicLooseIso_ees_plus,
row.eVetoCicLooseIso_ees_minus
]
tdirs = [
i for i, j in zip(systematics['tvetos'], tvetoes) if not j
]
mdirs = [
i for i, j in zip(systematics['mvetos'], mvetoes) if not j
]
edirs = [
i for i, j in zip(systematics['evetos'], evetoes) if not j
]
#if any of the lists is empty
#set_trace()
if not tdirs or not mdirs or not edirs:
continue
logging.debug('Passed Vetoes')
#make all possible veto combos...
all_dirs = [
''.join(i) for i in itertools.product(tdirs, mdirs, edirs)
]
#...and choose only the meaningful ones
veto_sys = set(systematics['tvetos'] + systematics['mvetos'] +
systematics['evetos'])
all_dirs = [i for i in all_dirs if i in veto_sys]
sys_directories = all_dirs + sys_shifts
#remove duplicates
sys_directories = list(set(sys_directories))
#at least one loose object
if (not isETight) or (not isTauTight):
#if is a loose tau just compute the fakes!
sys_directories = etau_category
#gather the one and only weight we do care about
mc_weight = weight_map['']
#weights are the fr ones...
weight_map = self.fakerate_weights(row.tEta)
for i in weight_map:
#...times the mc weight (if any)
weight_map[i] *= mc_weight
#Fill histograms in appropriate direcotries
#if passes_full_selection:
#dirs = [os.path.join(sys, sign, processtype, e_thr, jet_dir) for sys, e_thr, jet_dir in itertools.product(sys_directories, ptthreshold, jet_directories)]
#if len(dirs) <> len(set(dirs)):
# set_trace()
for sys, e_thr, selection in itertools.product(
sys_directories, ptthreshold, selection_categories):
selection_sys, jet_dir, selection_step = selection
#if we have multiple systematic shifts applied
#reject the combination
if selection_sys and sys:
continue
#if we fill a histogram, lock the event
lock = evt_id
dir_name = os.path.join(sys, selection_sys, sign, processtype,
e_thr, jet_dir, selection_step)
if dir_name[-1] == '/':
dir_name = dir_name[:-1]
if passes_full_selection:
logging.debug('Filling %s' % dir_name)
#fill them!
weight_to_use = weight_map[
sys] if sys in weight_map else weight_map['']
self.fill_histos(dir_name, row, weight_to_use)
