def __init__(self,
year=2017,
sigma='central',
sample=None,
buggy=False,
flat=False):
"""Load data and MC pilup profiles."""
assert (year
in [2016, 2017,
2018]), "You must choose a year from: 2016, 2017, or 2018."
assert (
sigma in ['central', 'up', 'down']
), "You must choose a s.d. variation from: 'central', 'up', or 'down'."
minbias = '69p2'
if sigma == 'down':
minbias = '66p0168' # -4.6%
elif sigma == 'up':
minbias = '72p3832' # +4.6%
if year == 2016:
datafilename = path + "Data_PileUp_2016_%s.root" % (minbias)
mcfilename = path + "MC_PileUp_2016_Moriond17.root"
elif year == 2017:
tag = ""
if buggy or sample:
buggy = buggy or hasBuggyPU(sample)
if buggy: tag = "_old_pmx"
else: tag = "_new_pmx"
datafilename = path + "Data_PileUp_2017_%s.root" % (minbias)
mcfilename = path + "MC_PileUp_2017_Winter17_V2%s.root" % (tag)
else:
datafilename = path + "Data_PileUp_2018_%s.root" % (minbias)
mcfilename = path + "MC_PileUp_2018_Autumn18.root"
if flat or (sample and hasFlatPU(sample)):
mcfilename = path + "MC_PileUp_%d_FlatPU0to75.root" % year
print "Loading PileupWeightTool for '%s' and '%s'" % (datafilename,
mcfilename)
self.datafile = ensureTFile(datafilename, 'READ')
self.mcfile = ensureTFile(mcfilename, 'READ')
self.datahist = self.datafile.Get('pileup')
self.mchist = self.mcfile.Get('pileup')
self.datahist.SetDirectory(0)
self.mchist.SetDirectory(0)
self.datahist.Scale(1. / self.datahist.Integral())
self.mchist.Scale(1. / self.mchist.Integral())
self.datafile.Close()
self.mcfile.Close()
def __init__(self, filename_1, filename_2, type, year):
print '>>> ScaleFactor.init(file_1="%s", file_2="%s" ,type="%s")' % (
filename_1, filename_2, type)
self.type = type
self.filename_1 = filename_1
self.filename_2 = filename_2
if type != 'selection':
self.datafile = ensureTFile(filename_1)
self.embfile = ensureTFile(filename_2)
self.datahist = self.datafile.Get('{}_pt_eta_bins'.format(type))
self.embhist = self.embfile.Get('{}_pt_eta_bins'.format(type))
else:
self.file_1 = ensureTFile(filename_1)
self.file_2 = ensureTFile(filename_2)
if year == 2018:
self.file_1_hist = self.file_1.Get('Mu8_pt_eta_bins')
self.file_2_hist = self.file_2.Get('Mu17_pt_eta_bins')
elif year == 2017:
self.file_1_hist = self.file_1.Get('data_trg_eff')
self.file_2_hist = self.file_2.Get('data_trg_eff')
elif year == 2016:
self.file_1_hist = self.file_1.Get('Trg8_pt_eta_bins')
self.file_2_hist = self.file_2.Get('Trg17_pt_eta_bins')
def __init__(self, filename, graphname='ZMass', name="<noname>"):
#print '>>> ScaleFactor.init("%s","%s",name="%s")'%(filename,graphname,name)
self.name = name
self.filename = filename
self.file = ensureTFile(filename)
self.hist_eta = self.file.Get('etaBinsH')
self.hist_eta.SetDirectory(0)
self.effs_data = { }
self.effs_mc = { }
for ieta in range(1,self.hist_eta.GetXaxis().GetNbins()+1):
etalabel = self.hist_eta.GetXaxis().GetBinLabel(ieta)
self.effs_data[etalabel] = self.file.Get(graphname+etalabel+"_Data")
self.effs_mc[etalabel] = self.file.Get(graphname+etalabel+"_MC")
self.file.Close()
def __init__(self, filename, histname, name="<noname>", ptvseta=True):
#print '>>> ScaleFactor.init("%s","%s",name="%s",ptvseta=%r)'%(filename,histname,name,ptvseta)
self.name = name
self.ptvseta = ptvseta
self.filename = filename
self.file = ensureTFile(filename)
self.hist = self.file.Get(histname)
if not self.hist:
print '>>> ScaleFactor(%s).__init__: histogram "%s" does not exist in "%s"'%(self.name,histname,filename)
exit(1)
self.hist.SetDirectory(0)
self.file.Close()
if ptvseta: self.getSF = self.getSF_ptvseta
else: self.getSF = self.getSF_etavspt
def __init__(self, trigger, wp='medium', id='MVAv2', year=2016):
"""Load tau trigger histograms from files."""
print "Loading TauTriggerSFs for %s (%s WP)..."%(trigger,wp)
trigger = trigger.replace('tautau','ditau').replace('eletau','etau')
assert(trigger in ['ditau', 'mutau', 'etau']), "Choose from: 'ditau', 'mutau', 'etau' triggers."
assert(wp in ['vloose', 'loose', 'medium', 'tight', 'vtight', 'vvtight']), "You must choose a WP from: vloose, loose, medium, tight, vtight, or vvtight"
assert(id in ['MVAv2', 'dR0p3']), "Choose from two provided ID types: 'MVAv2', 'dR0p3'. 'MVAv2' uses dR0p5, and 'dR0p3' is also an MVA-based ID."
assert(id=='MVAv2'), "Tau POG is currently only providing efficiencies for MVAv2, sorry."
assert(year in [2016,2017,2018]), "You must choose a year from: 2016, 2017, or 2018."
print "Loading Efficiencies for trigger %s usingTau %s ID WP %s for year %i"%(trigger,id,wp,year)
file = ensureTFile( path+'%d/tauTriggerEfficiencies%i.root'%(year,year), 'r' )
self.fit_data = { }
self.fit_mc = { }
self.fitUnc_data = { }
self.fitUnc_mc = { }
self.effEtaPhi_data = { }
self.effEtaPhi_mc = { }
self.effEtaPhiAvg_data = { }
self.effEtaPhiAvg_mc = { }
# LOOP over decay modes
for dm in [0,1,10]:
# LOAD the TF1s containing the analytic best-fit results
self.fit_data[dm] = extractTH1(file,'%s_%s%s_dm%d_DATA_fit'%(trigger,wp,id,dm))
self.fit_mc[dm] = extractTH1(file,'%s_%s%s_dm%d_MC_fit'%( trigger,wp,id,dm))
# LOAD the TH1s containing the analytic best-fit result in 1 GeV incriments and the associated uncertainty
self.fitUnc_data[dm] = extractTH1(file,'%s_%s%s_dm%d_DATA_errorBand'%(trigger,wp,id,dm))
self.fitUnc_mc[dm] = extractTH1(file,'%s_%s%s_dm%d_MC_errorBand'%( trigger,wp,id,dm))
# LOAD the TH2s containing the eta phi efficiency corrections
self.effEtaPhi_data[dm] = extractTH1(file,'%s_%s%s_dm%d_DATA'%(trigger,wp,id,dm))
self.effEtaPhi_mc[dm] = extractTH1(file,'%s_%s%s_dm%d_MC'%( trigger,wp,id,dm))
# LOAD eta phi Averages
self.effEtaPhiAvg_data[dm] = extractTH1(file,'%s_%s%s_dm%d_DATA_AVG'%(trigger,wp,id,dm))
self.effEtaPhiAvg_mc[dm] = extractTH1(file,'%s_%s%s_dm%d_MC_AVG'%( trigger,wp,id,dm))
file.Close()
self.trigger = trigger
self.year = year
self.wp = wp
self.id = id
def __init__(self, year=2017):
"""Load Z pT weights."""
assert year in [
2016, 2017, 2018
], "ZptCorrectionTool: You must choose a year from: 2016, 2017, or 2018."
if year == 2016:
filename = zptpath + "Zpt_weights_2016.root"
elif year == 2017:
filename = zptpath + "Zpt_weights_2017.root"
else:
filename = zptpath + "Zpt_weights_2018.root"
file = ensureTFile(filename, 'READ')
hist = file.Get('zptmass_weights')
hist.SetDirectory(0)
file.Close()
self.hist = hist
self.filename = filename
def __init__(self, year=2017, sigma='central', sample=None, buggy=False):
"""Load data and MC pilup profiles."""
assert (year
in [2016, 2017,
2018]), "You must choose a year from: 2016, 2017, or 2018."
assert (
sigma in ['central', 'up', 'down']
), "You must choose a s.d. variation from: 'central', 'up', or 'down'."
minbias = '69p2'
if sigma == 'down':
minbias = '66p0168' # -4.6%
elif sigma == 'up':
minbias = '72p3832' # +4.6%
if year == 2016:
self.datafile = ensureTFile(
path + 'Data_PileUp_2016_%s.root' % (minbias), 'READ')
self.mcfile = ensureTFile(path + 'MC_PileUp_2016_Moriond17.root',
'READ')
elif year == 2017:
tag = ""
if buggy or sample:
if buggy or hasBuggyPU(sample): tag = "_old_pmx"
else: tag = "_new_pmx"
self.datafile = ensureTFile(
path + 'Data_PileUp_2017_%s.root' % (minbias), 'READ')
self.mcfile = ensureTFile(
path + 'MC_PileUp_2017_Winter17_V2%s.root' % (tag), 'READ')
else:
self.datafile = ensureTFile(
path + 'Data_PileUp_2018_%s.root' % (minbias), 'READ')
self.mcfile = ensureTFile(path + 'MC_PileUp_2018_Autumn18.root',
'READ')
self.datahist = self.datafile.Get('pileup')
self.mchist = self.mcfile.Get('pileup')
self.datahist.SetDirectory(0)
self.mchist.SetDirectory(0)
self.datahist.Scale(1. / self.datahist.Integral())
self.mchist.Scale(1. / self.mchist.Integral())
self.datafile.Close()
self.mcfile.Close()
def __init__(self,
tagger,
wp='medium',
sigma='central',
channel='mutau',
year=2017,
maxeta=2.4):
"""Load b tag weights from CSV file."""
assert (year
in [2016, 2017,
2018]), "You must choose a year from: 2016, 2017, or 2018."
assert (tagger in [
'CSVv2', 'DeepCSV'
]), "BTagWeightTool: You must choose a tagger from: CSVv2, DeepCSV!"
assert (wp in [
'loose', 'medium', 'tight'
]), "BTagWeightTool: You must choose a WP from: loose, medium, tight!"
assert (sigma in [
'central', 'up', 'down'
]), "BTagWeightTool: You must choose a WP from: central, up, down!"
#assert(channel in ['mutau','eletau','tautau','mumu']), "BTagWeightTool: You must choose a channel from: mutau, eletau, tautau, mumu!"
# FILE
if year == 2016:
if 'deep' in tagger.lower():
csvname = path + 'DeepCSV_Moriond17_B_H.csv'
effname = path + 'DeepCSV_2016_Moriond17_eff.root'
else:
csvname = path + 'CSVv2_Moriond17_B_H.csv'
effname = path + 'CSVv2_2016_Moriond17_eff.root'
elif year == 2017:
if 'deep' in tagger.lower():
csvname = path + 'DeepCSV_94XSF_V3_B_F.csv'
effname = path + 'DeepCSV_2017_12Apr2017_eff.root'
else:
csvname = path + 'CSVv2_94XSF_V2_B_F.csv'
effname = path + 'CSVv2_2017_12Apr2017_eff.root'
elif year == 2018:
if 'deep' in tagger.lower():
csvname = path + 'DeepCSV_94XSF_V3_B_F.csv'
effname = path + 'DeepCSV_2018_Autumn18_eff.root'
else:
csvname = path + 'CSVv2_94XSF_V2_B_F.csv'
effname = path + 'CSVv2_2018_Autumn18_eff.root'
# TAGGING WP
self.wpname = wp
self.wp = getattr(BTagWPs(tagger, year), wp)
if 'deep' in tagger.lower():
tagged = lambda e, i: e.Jet_btagDeepB[i] > self.wp
else:
tagged = lambda e, i: e.Jet_btagCSVV2[i] > self.wp
# CSV READER
print "Loading BTagWeightTool for %s (%s WP)..." % (tagger, wp)
op = OP_LOOSE if wp == 'loose' else OP_MEDIUM if wp == 'medium' else OP_TIGHT if wp == 'tight' else OP_RESHAPING
type_udsg = 'incl'
type_bc = 'comb' # 'mujets' for QCD; 'comb' for QCD+TT
calib = BTagCalibration(tagger, csvname)
reader = BTagCalibrationReader(op, sigma)
reader.load(calib, FLAV_B, type_bc)
reader.load(calib, FLAV_C, type_bc)
reader.load(calib, FLAV_UDSG, type_udsg)
# EFFICIENCIES
hists = {} # histograms to compute the b tagging efficiencies in MC
effmaps = {
} # b tag efficiencies in MC to compute b tagging weight for an event
efffile = ensureTFile(effname)
default = False
if not efffile:
warning(
"File %s with efficiency histograms does not exist! Reverting to default efficiency histogram..."
% (effname),
title="BTagWeightTool")
default = True
for flavor in [0, 4, 5]:
flavor = flavorToString(flavor)
histname = "%s_%s_%s" % (tagger, flavor, wp)
effname = "%s/eff_%s_%s_%s" % (channel, tagger, flavor, wp)
hists[flavor] = createEfficienyMap(
histname) # numerator = b tagged jets
hists[flavor + '_all'] = createEfficienyMap(
histname + '_all') # denominator = all jets
if efffile:
effmaps[flavor] = efffile.Get(effname)
if not effmaps[flavor]:
warning(
"histogram '%s' does not exist in %s! Reverting to default efficiency histogram..."
% (effname, efffile.GetName()),
title="BTagWeightTool")
default = True
effmaps[flavor] = createDefaultEfficiencyMap(
effname, flavor, wp)
else:
effmaps[flavor] = createDefaultEfficiencyMap(
effname, flavor, wp)
effmaps[flavor].SetDirectory(0)
efffile.Close()
if default:
warning("Made use of default efficiency histograms! The b tag weights from this module should be regarded as placeholders only,\n"+\
"and should NOT be used for analyses. B (mis)tag efficiencies in MC are analysis dependent. Please create your own\n"+\
"efficiency histogram with corrections/btag/getBTagEfficiencies.py after running all MC samples with BTagWeightTool.",title="BTagWeightTool")
self.tagged = tagged
self.calib = calib
self.reader = reader
self.hists = hists
self.effmaps = effmaps
self.maxeta = maxeta