def make_puCorrector(dataset, kind=None):
if not kind:
kind = 'S6' if is7TeV else 'S10'
if dataset is 'doublemu':
return PileupWeight.PileupWeight( 'S6' if is7TeV else 'S10', *pu_distributions_doublemu)
elif dataset is 'doublee':
return PileupWeight.PileupWeight( 'S6' if is7TeV else 'S10', *pu_distributions_doublee)
return None
def make_puCorrectorDown(dataset, kind=None):
'makes PU reweighting according to the pu distribution of the reference data and the MC, MC distribution can be forced'
if not kind:
kind = mc_pu_tag
if dataset in pu_distributions:
return PileupWeight.PileupWeight( 'S6' if is7TeV else 'MC_Moriond17', *(pu_distributionsDown[dataset]))
else:
raise KeyError('dataset not present. Please check the spelling or add it to mcCorrectors.py')
def __init__(self, tree, outfile, **kwargs):
super(WHAnalyzeMMT, self).__init__(tree, outfile, MuMuTauTree,
**kwargs)
# Hack to use S6 weights for the one 7TeV sample we use in 8TeV
target = os.environ['megatarget']
if 'HWW3l' in target:
print "HACK using S6 PU weights for HWW3l"
global pu_corrector
pu_corrector = PileupWeight.PileupWeight('S6', *pu_distributions)
def make_puCorrector(dataset, kind=None):
'makes PU reweighting according to the pu distribution of the reference data and the MC, MC distribution can be forced'
if not kind:
kind = mc_pu_tag
weights = []
if dataset in pu_distributions: # and dataset in pu_distributionsUp and dataset in pu_distributionsDown:
return PileupWeight.PileupWeight('S6' if is7TeV else 'S10',
*(pu_distributions[dataset]))
# weights = (PileupWeight.PileupWeight( 'S6' if is7TeV else 'S10', *(pu_distributions[dataset])), PileupWeight.PileupWeight( 'S6' if is7TeV else 'S10', *(pu_distributionsUp[dataset])), PileupWeight.PileupWeight( 'S6' if is7TeV else 'S10', *(pu_distributionsDown[dataset])))
# return weights
else:
raise KeyError(
'dataset not present. Please check the spelling or add it to mcCorrectors.py'
)
'fit_efficiency', # workspace name
'efficiency')
################################################################################
#### MC-DATA and PU corrections ################################################
################################################################################
# Determine MC-DATA corrections
is7TeV = bool('7TeV' in os.environ['jobid'])
print "Is 7TeV:", is7TeV
# Make PU corrector from expected data PU distribution
# PU corrections .root files from pileupCalc.py
pu_distributions = glob.glob(
os.path.join('inputs', os.environ['jobid'], 'data_DoubleMu*pu.root'))
pu_corrector = PileupWeight.PileupWeight('S6' if is7TeV else 'S7',
*pu_distributions)
muon_pog_PFTight_2011 = MuonPOGCorrections.make_muon_pog_PFTight_2011()
muon_pog_PFTight_2012 = MuonPOGCorrections.make_muon_pog_PFTight_2012()
muon_pog_PFRelIsoDB02_2011 = MuonPOGCorrections.make_muon_pog_PFRelIsoDB02_2011(
)
muon_pog_PFRelIsoDB02_2012 = MuonPOGCorrections.make_muon_pog_PFRelIsoDB02_2012(
)
muon_pog_Mu17Mu8_Mu17_2012 = MuonPOGCorrections.make_muon_pog_Mu17Mu8_Mu17_2012(
)
muon_pog_Mu17Mu8_Mu8_2012 = MuonPOGCorrections.make_muon_pog_Mu17Mu8_Mu8_2012()
# takes etas of muons
muon_pog_Mu17Mu8_2011 = MuonPOGCorrections.muon_pog_Mu17Mu8_eta_eta_2011
den = math.sqrt(xth)
mass = row.m_t_Mass / den
#print '%4.2f, %4.2f, %4.2f, %4.2f, %4.2f' %(scaleMass(row), den, xth, METproj,mass)
return mass
pu_distributions = glob.glob(
os.path.join(
# 'inputs', os.environ['jobid'], 'data_TauPlusX*pu.root'))
'inputs',
os.environ['jobidpu'],
'data_SingleMu*pu.root'))
pu_corrector = PileupWeight.PileupWeight('Asympt25ns', *pu_distributions)
def fakeWeight(row):
if row.tDecayMode == 0:
return 0.62 # 0.81
if row.tDecayMode == 1:
return 0.76 #0.80
if row.tDecayMode == 10:
return 0.44 #0.44
class AnalyzeLFVMuTau(MegaBase):
tree = 'mt/final/Ntuple'
#tree = 'New_Tree'