def loadDDG4():
import ROOT
from ROOT import gSystem
# Try to load libglapi to avoid issues with TLS Static
# Turn off all errors from ROOT about the library missing
if ('libglapi' not in gSystem.GetLibraries()):
orgLevel = ROOT.gErrorIgnoreLevel
ROOT.gErrorIgnoreLevel = 6000
gSystem.Load("libglapi")
ROOT.gErrorIgnoreLevel = orgLevel
import platform
import os
if platform.system() == "Darwin":
gSystem.SetDynamicPath(os.environ['DD4HEP_LIBRARY_PATH'])
os.environ['DYLD_LIBRARY_PATH'] = os.pathsep.join([
os.environ['DD4HEP_LIBRARY_PATH'],
os.environ.get('DYLD_LIBRARY_PATH', '')
]).strip(os.pathsep)
result = gSystem.Load("libDDG4Plugins")
if result < 0:
raise Exception(
'DDG4.py: Failed to load the DDG4 library libDDG4Plugins: ' +
gSystem.GetErrorStr())
from ROOT import dd4hep as module
return module
def __init__(self,
globalTag=None,
jetType="AK4PFchs",
jmr_vals=[1.09, 1.14, 1.04],
year=2017,
systematics=True):
#--------------------------------------------------------------------------------------------
# CV: globalTag and jetType not yet used, as there is no consistent set of txt files for
# JES uncertainties and JER scale factors and uncertainties yet
#--------------------------------------------------------------------------------------------
# GLOBAL TAG
if globalTag == None:
if year == 2016:
globalTag = "Summer16_25nsV1_MC" #Fall17_25nsV1_MC
elif year == 2017:
globalTag = "Fall17_V3_MC"
elif year == 2018:
globalTag = "Autumn18_V1_MC"
# READ JER and JER scale factors and uncertainties
# from https://github.com/cms-jet/JRDatabase/tree/master/textFiles/ )
from JetMETCorrectionTool import ensureJMEFiles
path_JER = ensureJMEFiles(globalTag, JER=True)
filename = ensureFile(path_JER,
"%s_PtResolution_%s.txt" % (globalTag, jetType))
filenameUnc = ensureFile(path_JER,
"%s_SF_%s.txt" % (globalTag, jetType))
# LOAD LIBRARIES for accessing JER scale factors and uncertainties from txt files
for library in [
"libCondFormatsJetMETObjects", "libPhysicsToolsNanoAODTools"
]:
if library not in gSystem.GetLibraries():
print("Load Library '%s'" % library.replace("lib", ""))
gSystem.Load(library)
# INITIALIZE JER scale factors and uncertainties (cf. PhysicsTools/PatUtils/interface/SmearedJetProducerT.h )
print("Loading JER from file '%s'..." % filename)
jer = PyJetResolutionWrapper(filename)
print("Loading JER SFs and uncertainties from file '%s'..." %
filenameUnc)
jerSF_and_Uncertainty = PyJetResolutionScaleFactorWrapper(filenameUnc)
self.path_JER = path_JER
self.filename = filename
self.filenameUnc = filenameUnc
self.params_sf_and_uncertainty = PyJetParametersWrapper()
self.params_resolution = PyJetParametersWrapper()
self.jer = jer
self.jerSF_and_Uncertainty = jerSF_and_Uncertainty
self.jmr_vals = jmr_vals
self.enums_shift = [0, 2, 1] if systematics else [0] # nom, up, down
self.random = TRandom3(12345) # (needed for jet pT smearing)
def createSampleLists(analysis_dir='samples/',
channel='VV',
weight='',
signalSample='',
vJetsKFac=1.,
reweightVJets=False,
reweightTop=False):
# settings and code to reweight V+jets samples (EW and QCD NLO corrections)
# the following two k-factors are from samples_13TeV_RunIISpring16MiniAODv2.py
wJetsKFac = 1.21
dyJetsKFac = 1.23
wJetsQCDCorrections = {}
dyJetsQCDCorrections = {}
# taken from http://cms.cern.ch/iCMS/jsp/db_notes/noteInfo.jsp?cmsnoteid=CMS%20AN-2015/186 (Table 4)
wJetsQCDCorrections["WJetsToLNu_HT100to200"] = 1.459 / wJetsKFac
wJetsQCDCorrections["WJetsToLNu_HT200to400"] = 1.434 / wJetsKFac
wJetsQCDCorrections["WJetsToLNu_HT400to600"] = 1.532 / wJetsKFac
wJetsQCDCorrections["WJetsToLNu_HT600to800"] = 1.004 / wJetsKFac
wJetsQCDCorrections["WJetsToLNu_HT800to1200"] = 1.004 / wJetsKFac
wJetsQCDCorrections["WJetsToLNu_HT1200to2500"] = 1.004 / wJetsKFac
wJetsQCDCorrections["WJetsToLNu_HT2500toInf"] = 1.004 / wJetsKFac
dyJetsQCDCorrections["DYJetsToLL_M50_HT100to200"] = 1.588 / dyJetsKFac
dyJetsQCDCorrections["DYJetsToLL_M50_HT200to400"] = 1.438 / dyJetsKFac
dyJetsQCDCorrections["DYJetsToLL_M50_HT400to600"] = 1.494 / dyJetsKFac
dyJetsQCDCorrections["DYJetsToLL_M50_HT600toInf"] = 1.139 / dyJetsKFac
# explicit list of samples:
wjetsSampleNames = [
"WJetsToLNu_HT1200to2500", "WJetsToLNu_HT2500toInf",
"WJetsToLNu_HT400to600", "WJetsToLNu_HT600to800",
"WJetsToLNu_HT800to1200", 'WJetsToLNu_HT100to200',
'WJetsToLNu_HT200to400'
]
dyjetsSampleNames = [
'DYJetsToLL_M50_HT100to200', 'DYJetsToLL_M50_HT200to400',
'DYJetsToLL_M50_HT400to600', 'DYJetsToLL_M50_HT600toInf'
]
ttjetsSampleNames = ["TT_pow_ext3"]
qcdSampleNames = [
"QCD_HT1000to1500", "QCD_HT1500to2000", "QCD_HT2000toInf",
"QCD_HT500to700", "QCD_HT700to1000"
]
vvSampleNames = ['WWTo1L1Nu2Q', 'WZTo1L1Nu2Q']
singleTopSampleNames = [
'TToLeptons_tch_powheg', 'TBarToLeptons_tch_powheg', 'TToLeptons_sch',
'TBar_tWch', 'T_tWch'
]
jj_SampleNames = qcdSampleNames
lnujj_SampleNames = ttjetsSampleNames + wjetsSampleNames + vvSampleNames + qcdSampleNames + dyjetsSampleNames + singleTopSampleNames
# cuts to split ttbar sample according to W decay
ttjetsWCut = '(lnujj_l2_mergedVTruth==1&&lnujj_l2_nearestBDRTruth>0.8)'
ttjetsNonWCut = '(!(lnujj_l2_mergedVTruth==1&&lnujj_l2_nearestBDRTruth>0.8))'
# add ttbar pT reweighting
if reweightTop:
ttjetsWCut += '*truth_genTop_weight'
ttjetsNonWCut += '*truth_genTop_weight'
tree_prod_name = ''
if (channel == "WV"):
channelSampleNames = lnujj_SampleNames
else:
channelSampleNames = jj_SampleNames
samples_essential = []
if "/sVJetsReweighting_cc.so" not in gSystem.GetLibraries():
ROOT.gROOT.ProcessLine(
".L %s/src/CMGTools/VVResonances/python/plotting/VJetsReweighting.cc+"
% os.environ['CMSSW_BASE'])
from ROOT import getDYWeight, getWWeight
# add QCD, DY+jets, W+jets, DiBosons and SingleTop samples, but not those with _ext, since they are merged with the others
for sample in QCDHT + DYJetsM50HT + WJetsToLNuHT + DiBosons + SingleTop:
vJetsWeight = str(vJetsKFac)
if sample.name in channelSampleNames:
if (sample in DYJetsM50HT) and reweightVJets:
vJetsWeight = 'getDYWeight(truth_genBoson_pt) * {}'.format(
dyJetsQCDCorrections[sample.name])
elif (sample in WJetsToLNuHT) and reweightVJets:
vJetsWeight = 'getWWeight(truth_genBoson_pt) * {}'.format(
wJetsQCDCorrections[sample.name])
samples_essential.append(
SampleCfg(name=sample.name,
dir_name=sample.name,
ana_dir=analysis_dir,
tree_prod_name=tree_prod_name,
xsec=sample.xSection,
sumweights=sample.nGenEvents,
weight_expr=('*'.join([weight, vJetsWeight]))))
# TTJets sample
for sample in [TT_pow]:
if sample.name in channelSampleNames:
# print "Adding", sample.name, sample.xSection, sample.nGenEvents, weight
samples_essential.append(
SampleCfg(name=sample.name + '_W',
dir_name=sample.name,
ana_dir=analysis_dir,
tree_prod_name=tree_prod_name,
xsec=sample.xSection,
sumweights=sample.nGenEvents,
weight_expr=('*'.join([weight, ttjetsWCut]))))
samples_essential.append(
SampleCfg(name=sample.name + '_nonW',
dir_name=sample.name,
ana_dir=analysis_dir,
tree_prod_name=tree_prod_name,
xsec=sample.xSection,
sumweights=sample.nGenEvents,
weight_expr=('*'.join([weight, ttjetsNonWCut]))))
# signal sample (set signal xsec to 5 pb)
samples_signal = []
if (signalSample):
for sample in signalSamples:
if sample.name == signalSample:
samples_signal.append(
SampleCfg(name=sample.name,
dir_name=sample.name,
ana_dir=analysis_dir,
tree_prod_name=tree_prod_name,
xsec=5.,
sumweights=sample.nGenEvents,
weight_expr=('*'.join([weight])),
is_signal=True))
samples_data = []
if channel == 'WV':
samples_data = [
SampleCfg(name='data_SingleMuon',
dir_name='SingleMuon_Run2016B_PromptReco_v2',
ana_dir=analysis_dir,
tree_prod_name=tree_prod_name,
is_data=True),
SampleCfg(name='data_SingleMuon',
dir_name='SingleMuon_Run2016C_PromptReco_v2',
ana_dir=analysis_dir,
tree_prod_name=tree_prod_name,
is_data=True),
SampleCfg(name='data_SingleMuon',
dir_name='SingleMuon_Run2016D_PromptReco_v2',
ana_dir=analysis_dir,
tree_prod_name=tree_prod_name,
is_data=True),
SampleCfg(name='data_SingleElectron',
dir_name='SingleElectron_Run2016B_PromptReco_v2',
ana_dir=analysis_dir,
tree_prod_name=tree_prod_name,
is_data=True),
SampleCfg(name='data_SingleElectron',
dir_name='SingleElectron_Run2016C_PromptReco_v2',
ana_dir=analysis_dir,
tree_prod_name=tree_prod_name,
is_data=True),
SampleCfg(name='data_SingleElectron',
dir_name='SingleElectron_Run2016D_PromptReco_v2',
ana_dir=analysis_dir,
tree_prod_name=tree_prod_name,
is_data=True),
SampleCfg(name='data_MET',
dir_name='MET_Run2016B_PromptReco_v2',
ana_dir=analysis_dir,
tree_prod_name=tree_prod_name,
is_data=True),
SampleCfg(name='data_MET',
dir_name='MET_Run2016C_PromptReco_v2',
ana_dir=analysis_dir,
tree_prod_name=tree_prod_name,
is_data=True),
SampleCfg(name='data_MET',
dir_name='MET_Run2016D_PromptReco_v2',
ana_dir=analysis_dir,
tree_prod_name=tree_prod_name,
is_data=True),
]
else:
samples_data = [
SampleCfg(name='data_JetHT',
dir_name='JetHT_Run2016B_PromptReco_v2',
ana_dir=analysis_dir,
tree_prod_name=tree_prod_name,
is_data=True),
SampleCfg(name='data_JetHT',
dir_name='JetHT_Run2016C_PromptReco_v2',
ana_dir=analysis_dir,
tree_prod_name=tree_prod_name,
is_data=True),
SampleCfg(name='data_JetHT',
dir_name='JetHT_Run2016D_PromptReco_v2',
ana_dir=analysis_dir,
tree_prod_name=tree_prod_name,
is_data=True),
]
# samples_WH = []
# mssm_names = ['HiggsSUSYBB80', 'HiggsSUSYBB90', 'HiggsSUSYBB100', 'HiggsSUSYBB110', 'HiggsSUSYBB120', 'HiggsSUSYBB130', 'HiggsSUSYBB140', 'HiggsSUSYBB160', 'HiggsSUSYBB180', 'HiggsSUSYBB200', 'HiggsSUSYBB250', 'HiggsSUSYBB300', 'HiggsSUSYBB350', 'HiggsSUSYBB400', 'HiggsSUSYBB450', 'HiggsSUSYBB500', 'HiggsSUSYBB600', 'HiggsSUSYBB700', 'HiggsSUSYBB900', 'HiggsSUSYBB1000', 'HiggsSUSYBB1200', 'HiggsSUSYBB1500', 'HiggsSUSYBB1600', 'HiggsSUSYBB1800', 'HiggsSUSYBB2000', 'HiggsSUSYBB2300', 'HiggsSUSYBB2600', 'HiggsSUSYBB2900', 'HiggsSUSYBB3200', 'HiggsSUSYGG80', 'HiggsSUSYGG90',
# 'HiggsSUSYGG100', 'HiggsSUSYGG110', 'HiggsSUSYGG120', 'HiggsSUSYGG130', 'HiggsSUSYGG140', 'HiggsSUSYGG160', 'HiggsSUSYGG180', 'HiggsSUSYGG200', 'HiggsSUSYGG250', 'HiggsSUSYGG300', 'HiggsSUSYGG400', 'HiggsSUSYGG450', 'HiggsSUSYGG500', 'HiggsSUSYGG600', 'HiggsSUSYGG700', 'HiggsSUSYGG800', 'HiggsSUSYGG900', 'HiggsSUSYGG1000', 'HiggsSUSYGG1200', 'HiggsSUSYGG1400', 'HiggsSUSYGG1500', 'HiggsSUSYGG1600', 'HiggsSUSYGG1800', 'HiggsSUSYGG2000', 'HiggsSUSYGG2300', 'HiggsSUSYGG2600', 'HiggsSUSYGG2900', 'HiggsSUSYGG3200'] # HiggsSUSYBB800, HiggsSUSYBB1400, HiggsSUSYGG350
# for name in mssm_names:
# samples_WH.append(SampleCfg(name=name.replace('HiggsSUSYBB', 'bbH').replace('HiggsSUSYGG', 'ggH'), dir_name=name,
# ana_dir=analysis_dir, tree_prod_name=tree_prod_name, xsec=1., sumweights=1., is_signal=True),)
samples_mc = samples_essential + samples_signal
samples = samples_essential + samples_data + samples_signal
all_samples = samples_mc + samples_data
# -> Can add cross sections for samples either explicitly, or from file, or from cfg
# (currently taken from htt_common)
weighted_list = []
for sample in samples_mc:
if sample.name not in weighted_list:
setSumWeights(sample)
# sampleDict = {s.name: s for s in all_samples}
sampleDict = {}
for s in all_samples:
sampleDict[s.name] = s
return samples_mc, samples_data, samples, all_samples, sampleDict
import os
import ROOT
from ROOT import gSystem, gROOT
from CMGTools.H2TauTau.proto.plotter.PlotConfigs import SampleCfg
from CMGTools.H2TauTau.proto.plotter.HistCreator import setSumWeights
from CMGTools.H2TauTau.proto.samples.spring16.sms_xsec import get_xsec
from CMGTools.H2TauTau.proto.samples.spring16.htt_common import TT_pow_ext, DYJetsToLL_M50_LO, DYNJets, WJetsToLNu, WNJets, WWTo2L2Nu, T_tWch, TBar_tWch, VVTo2L2Nu, ZZTo4L, WZTo1L3Nu, WWTo1L1Nu2Q, ZZTo2L2Q, WZTo2L2Q, WZTo1L1Nu2Q, TBarToLeptons_tch_powheg, TToLeptons_tch_powheg, mssm_signals, dy_weight_dict, w_weight_dict
# WJetsToLNu_LO, TToLeptons_tch_amcatnlo, WZTo3LNu_amcatnlo, , WJetsToLNu_HT100to200, WJetsToLNu_HT200to400, WJetsToLNu_HT400to600, WJetsToLNu_HT600toInf, QCD_Mu15, DYJetsToTauTau_M150_LO, DYJetsToLL_M10to50_ext1
if "/sDYReweighting_cc.so" not in gSystem.GetLibraries():
gROOT.ProcessLine(".L %s/src/CMGTools/H2TauTau/python/proto/plotter/DYReweighting.cc+" % os.environ['CMSSW_BASE']);
from ROOT import getDYWeight
splitDY = False
useDYWeight = False
# data2016G = True
if useDYWeight:
dy_exps = []
for njet in xrange(0, 5):
weight = dy_weight_dict[(njet, 0)]
dy_exps.append('(geninfo_nup == {njet})*{weight}'.format(njet=njet, weight=weight))
# dy_exps.append('(geninfo_nup == {njet} && (geninfo_invmass<150. || !(l2_gen_match==5 || l1_gen_lepfromtau)))*{weight}'.format(njet=njet, weight=weight))
# weight = dy_weight_dict[(njet, 150)]
# dy_exps.append('(geninfo_nup == {njet} && (geninfo_invmass>=150. && (l2_gen_match==5 || l1_gen_lepfromtau)))*{weight}'.format(njet=njet, weight=weight))
dy_exp = '*({})'.format(' + '.join(dy_exps))
for key, item in vals:
if key.startswith(name + '__'):
print item, key
if __name__ == '__main__':
mode = 'susy' # 'control' 'mssm' 'mva_train' 'susy' 'sm'
int_lumi = lumi
analysis_dir = '/data1/steggema/Gael3/MC/'
verbose = True
total_weight = 'weight'
import os
from ROOT import gSystem, gROOT
if "/sHTTEfficiencies_cc.so" not in gSystem.GetLibraries():
gROOT.ProcessLine(
".L %s/src/CMGTools/H2TauTau/python/proto/plotter/HTTEfficiencies.cc+"
% os.environ['CMSSW_BASE'])
from ROOT import getTauWeight
total_weight = 'weight*getTauWeight(l1_gen_match, l1_pt, l1_eta, l1_decayMode)*getTauWeight(l2_gen_match, l2_pt, l2_eta, l2_decayMode)'
optimisation = True
make_plots = True
# Check whether friend trees need to be added
friend_func = None
if mode == 'mva':
# friend_func = lambda f: f.replace('MC', 'MCMVAmt200')
friend_func = lambda f: f.replace('MC', 'MCMVAmt200_7Vars')
def MultiDraw(self, Formulae, CommonWeight="1"):
"""Draws many histograms in one loop over a tree.
Instead of:
MyTree.Draw( "nlcts >> a(100, -1, 1)", "weightA" )
MyTree.Draw( "nlcts >> b(100, -1, 1)", "weightB" )
Do:
MyTree.MultiDraw( ( "nlcts >> a(100, -1, 1)", "weightA" ),
( "nlcts >> b(100, -1, 1)", "weightB" ) )
This is significantly faster when there are many histograms to be drawn.
The first parameter, CommonWeight, decides a weight given to all
histograms.
An arbitrary number of additional histograms may be specified. They can
either be specified with just a string containing the formula to be
drawn, the histogram name and bin configuration.
Alternatively it can be a tuple, with said string, and an additional
string specifying the weight to be applied to that histogram only.
"""
if type(CommonWeight) == tuple:
Formulae = (CommonWeight, ) + Formulae
CommonWeight = "1"
results, formulae, weights = [], [], []
lastFormula, lastWeight = None, None
# A weight common to everything being drawn
CommonWeightFormula = TTreeFormula("CommonWeight", CommonWeight, self)
CommonWeightFormula.SetQuickLoad(True)
if not CommonWeightFormula.GetTree():
raise RuntimeError("TTreeFormula didn't compile: " + CommonWeight)
hists = {}
for i, origFormula in enumerate(Formulae):
print "Have an origFormula", origFormula
# Expand out origFormula and weight, otherwise just use weight of 1.
if type(origFormula) == tuple:
origFormula, weight = origFormula
else:
origFormula, weight = origFormula, "1"
# print origFormula, weight
# Pluck out histogram name and arguments
match = re.match(r"^(.*?)\s*>>\s*(.*?)\s*\(\s*(.*?)\s*\)$", origFormula)
if match:
formula, name, arguments = match.groups()
arguments = re.split(",\s*", arguments)
bins, minX, maxX = arguments
bins, minX, maxX = int(bins), float(minX), float(maxX)
# Create histogram with name and arguments
hist = TH1D(name, name, bins, minX, maxX)
hist.Sumw2()
else:
# without arguments
match = re.match(r"^(.*?)\s*>>\s*(.*?)\s*$", origFormula)
if not match:
raise RuntimeError("MultiDraw: Couldn't parse formula: '%s'" % origFormula)
formula, name = match.groups()
# print formula, name
if name.startswith("+") and name[1:] in hists:
# Drawing additionally into a histogram
hist = hists[name[1:]]
else:
# name = name[1:] # JAN: ???
hist = gDirectory.Get(name)
if not hist:
raise RuntimeError("MultiDraw: Couldn't find histogram to fill '%s' in current directory." % name)
if name not in hists:
hists[name] = hist
results.append(hist)
# The following two 'if' clauses check that the next formula is different
# to the previous one. If it is not, we add an ordinary TObject.
# Then, the dynamic cast in MultiDraw.cxx fails, giving 'NULL', and
# The previous value is used. This saves the recomputing of identical values
if formula != lastFormula:
f = TTreeFormula("formula%i" % i, formula, self)
if not f.GetTree():
raise RuntimeError("TTreeFormula didn't compile: " + formula)
f.SetQuickLoad(True)
formulae.append(f)
else:
formulae.append(TObject())
if weight != lastWeight:
f = TTreeFormula("weight%i" % i, weight, self)
if not f.GetTree():
raise RuntimeError("TTreeFormula didn't compile: " + formula)
f.SetQuickLoad(True)
weights.append(f)
else:
weights.append(TObject())
lastFormula, lastWeight = formula, weight
# Only compile MultiDraw once
try:
from ROOT import MultiDraw as _MultiDraw
except ImportError:
# gROOT.ProcessLine(".L %sMultiDraw.cxx+O" % "./")
if "/sMultiDraw_cc.so" not in gSystem.GetLibraries():
gROOT.ProcessLine(".L %s/../SFrameAnalysis_emu/datacard/MultiDraw.cc+" % os.environ['CMSSW_BASE']);
from ROOT import MultiDraw as _MultiDraw
from time import time
start = time()
# Ensure that formulae are told when tree changes
fManager = TTreeFormulaManager()
for formula in formulae + weights + [CommonWeightFormula, ]:
if type(formula) == TTreeFormula:
fManager.Add(formula)
fManager.Sync()
self.SetNotify(fManager)
# Draw everything!
_MultiDraw(self, CommonWeightFormula,
MakeTObjArray(formulae),
MakeTObjArray(weights),
MakeTObjArray(results),
len(Formulae))
print "Took %.2fs" % (time() - start), " "*20
return results
def __init__(self, year, **kwargs):
#--------------------------------------------------------------------------------------------
# CV: globalTag and jetType not yet used, as there is no consistent set of txt files for
# JES uncertainties and JER scale factors and uncertainties yet
#--------------------------------------------------------------------------------------------
globalTag = kwargs.get('globalTag', None)
globalTag_JER = kwargs.get('globalTag_JER', None)
globalTag_JES = kwargs.get('globalTag_JES', globalTag)
jetType = kwargs.get('jet', 'AK4PFchs')
metType = kwargs.get('met', 'MET')
isData = kwargs.get('data', False)
era = kwargs.get('era', "") # for data; A, B, C, D, ...
redoJEC = kwargs.get('redoJEC', True)
doJER = kwargs.get('smear', not isData) and not isData
doSystematics = kwargs.get('systematics', True) and not isData
noGroom = kwargs.get('noGroom', True)
jesUncertainties = kwargs.get('uncertainties',
['Total'] if doSystematics else [])
updateEvent = kwargs.get('updateEvent', False) # unreliable...
correctSeparate = kwargs.get('correctSeparate', False)
jetTypes = ['AK4PFchs', 'AK4PFPuppi', 'AK8PFchs', 'AK8PFPuppi']
assert year in [
2016, 2017, 2018
], "JetMETCorrectionTool: You must choose a year from: 2016, 2017, or 2018."
assert jetType in jetTypes, "JetMETCorrectionTool: You must choose a jet type from: %s" % (
', '.join(jetTypes))
assert all(
u in ['Total', 'All'] for u in jesUncertainties
), "JetMETCorrectionTool: Given uncertainties are %s; must be 'Total' or 'All'!" % (
jesUncertainties)
# TARGET VARIABLES
if "AK4" in jetType:
self.jetBranchName = 'Jet'
self.genJetBranchName = 'GenJet'
self.genSubJetBranchName = None
self.doGroomed = False
self.corrMET = True
elif "AK8" in jetType:
self.jetBranchName = 'FatJet'
self.subJetBranchName = 'SubJet'
self.genJetBranchName = 'GenJetAK8'
self.genSubJetBranchName = 'SubGenJetAK8'
self.doGroomed = not noGroom
self.corrMET = False
else:
raise ValueError("ERROR: Invalid jet type = '%s'!" % jetType)
self.metBranchName = metType
self.rhoBranchName = "fixedGridRhoFastjetAll"
self.jmsVals = [1.00, 0.99, 1.01] # TODO: change to real values
self.unclEnThreshold = 15. # energy threshold below which jets are considered as "unclustered energy"
# cf. JetMETCorrections/Type1MET/python/correctionTermsPfMetType1Type2_cff.py
jetSmearer = None
jmr_vals = []
if isData:
# GLOBAL TAG for JES
if globalTag == None:
if year == 2016:
for eraset in ['BCD', 'EF', 'GH']:
if era in eraset: era = eraset
globalTag = "Summer16_07Aug2017_V11_DATA"
globalTag_JES = "Summer16_07Aug2017%s_V11_DATA" % era
elif year == 2017:
if era in 'DE': era = 'DE'
globalTag = "Fall17_17Nov2017_V32_DATA"
globalTag_JES = "Fall17_17Nov2017%s_V32_DATA" % era
else:
era = "Run" + era
globalTag = "Autumn18_V8_DATA"
globalTag_JES = "Autumn18_%s_V8_DATA" % era
else:
# GLOBAL TAG for JES
if globalTag == None:
if year == 2016:
globalTag = "Summer16_07Aug2017_V11_MC" #"Summer16_23Sep2016V4_MC"
elif year == 2017:
globalTag = "Fall17_17Nov2017_V32_MC"
else:
globalTag = "Autumn18_V8_MC"
globalTag_JES = globalTag
# GLOBAL TAG for JER
if globalTag_JER == None:
if year == 2016:
globalTag_JER = "Summer16_25nsV1_MC"
elif year == 2017:
globalTag_JER = "Fall17_V3_MC"
elif year == 2018:
globalTag_JER = "Autumn18_V1_MC"
# JERs: https://twiki.cern.ch/twiki/bin/view/CMS/JetWtagging
if year == 2016 or year == 2018: #update when 2018 values available
jmr_vals = [1.00, 1.20, 0.80] # nominal, up, down
else:
jmr_vals = [1.09, 1.14, 1.04]
# READ JER uncertainties
###if doJER:
jetSmearer = JetSmearer(globalTag_JER,
jetType,
systematics=doSystematics,
jmr_vals=jmr_vals)
# READ JES
path_JES = ensureJMEFiles(globalTag)
# REDO JECs
if redoJEC:
jetReCalibrator = JetReCalibrator(globalTag_JES,
jetType,
True,
path=path_JES,
correctSeparate=correctSeparate,
correctType1MET=False)
else:
jetReCalibrator = None
# LOAD LIBRARIES for accessing JES scale factors and uncertainties from txt files
for library in [
"libCondFormatsJetMETObjects", "libPhysicsToolsNanoAODTools"
]:
if library not in gSystem.GetLibraries():
print("Load Library '%s'" % library.replace("lib", ""))
gSystem.Load(library)
# READ UNCERTAINTY SOURCE NAMES from the loaded file
jesUncertainty = {}
filename_JES = ""
if doSystematics:
postfix = '' if jesUncertainties == ['Total'] else "Sources"
filename_JES = ensureFile(
path_JES,
"%s_Uncertainty%s_%s.txt" % (globalTag, postfix, jetType))
if jesUncertainties == ['All']:
with open(path_JES + '/' + filename_JES) as file:
lines = file.read().split("\n")
sources = filter(
lambda x: x.startswith("[") and x.endswith("]"), lines)
sources = map(lambda x: x[1:-1], sources)
jesUncertainties = sources
# CREATE JES uncertainties
print("Loading JES uncertainties from file '%s'..." % filename_JES)
#jesUncertainty = JetCorrectionUncertainty(filename_JES)
# implementation didn't seem to work for factorized JEC, try again another way
for uncertainty in jesUncertainties:
unclabel = '' if uncertainty == 'Total' and len(
jesUncertainties) == 1 else uncertainty
pars = JetCorrectorParameters(filename_JES, unclabel)
jesUncertainty[uncertainty] = JetCorrectionUncertainty(pars)
self.year = year
self.globalTag = globalTag
self.jetType = jetType
self.metType = metType
self.isData = isData
self.era = era
self.redoJEC = redoJEC
###self.doJER = doJER
self.doSystematics = doSystematics
self.noGroom = noGroom
self.updateEvent = updateEvent
self.jesUncertainties = jesUncertainties # list
self.jesUncertainty = jesUncertainty # dictionairy
self.path_JES = path_JES
self.filename_JES = filename_JES
self.jmr_vals = jmr_vals
self.jetSmearer = jetSmearer
self.jetReCalibrator = jetReCalibrator
self.correctJetMET = self.correctJetMET_Data if isData else self.correctJetMET_MC
