def getLeptonSelection(mode):
if mode == "mumu":
if year == 2016:
return cutInterpreter.cutString("diMuon16")
else:
# slower trigger turn-on in 2017&2018
return cutInterpreter.cutString("diMuon1718")
for sample in stack.samples:
sample.normalization = 1.
sample.reduceFiles(factor=40)
sample.scale /= sample.normalization
#sample.reduceFiles( to = 3 )
if args.verySmall:
for sample in stack.samples:
sample.normalization = 1.
sample.reduceFiles(to=1)
sample.scale /= sample.normalization
# Use some defaults
dummyWeight_ = lambda event, sample: float(0)
Plot.setDefaults(stack=stack,
weight=staticmethod(dummyWeight_),
selectionString=cutInterpreter.cutString(args.selection),
histo_class=ROOT.TH1D)
# 1D dummy plot
dummyPlot = Plot(
name='ROC',
texX='signal efficiency',
texY='background rejection',
attribute=lambda event, sample: 0.5 +
index, # yields are somehow acting weird
binning=[1, 0, 1],
)
# Use some defaults
Plot2D.setDefaults(weight=weight_,
selectionString=cutInterpreter.cutString(
if args.small:
for sample in stack.samples:
sample.normalization=1.
sample.reduceFiles( factor=40 )
sample.scale /= sample.normalization
#sample.reduceFiles( to = 3 )
if args.verySmall:
for sample in stack.samples:
sample.normalization=1.
sample.reduceFiles( to = 1 )
sample.scale /= sample.normalization
# Use some defaults
dummyWeight_ = lambda event, sample: float(0)
Plot.setDefaults(stack=stack, weight = staticmethod(dummyWeight_), selectionString = cutInterpreter.cutString(args.selection), histo_class=ROOT.TH1D)
# 1D dummy plot
dummyPlot = Plot(
name = 'ROC', texX = 'signal efficiency', texY = 'background rejection',
attribute = lambda event, sample: 0.5 + index, # yields are somehow acting weird
binning=[1, 0, 1],
)
# Use some defaults
Plot2D.setDefaults(weight = weight_, selectionString = cutInterpreter.cutString(args.selection), histo_class=ROOT.TH2D)
### 2D DY plots ###
plots2D = []
plots2D.append(Plot2D(
"HLT_Mu17_TrkIsoVVL_Mu8_TrkIsoVVL",
"HLT_Mu17_TrkIsoVVL_TkMu8_TrkIsoVVL", "HLT_IsoMu24", "HLT_IsoTkMu24"
]
jer = "Summer16_25nsV1_MC"
METPtVar = "MET_pt_nom"
METPhiVar = "MET_phi_nom"
JetCollection = "Jet_pt_nom"
vetoEtaRegion = (10., 10.)
minimize = True
ttbarModifier = 1
maxSig = 25
jetThreshold = 15
pTdepMetSig = False
# calculate setting
preselection = cutInterpreter.cutString(selection)
triggerSel = "(%s)" % "||".join(
["Alt$(%s,0)" % trigg for trigg in trigger])
eventfilter = getFilterCut(2016, isData=False)
sel = "&&".join([preselection, triggerSel, eventfilter])
JR = JetResolution(jer)
version = postProcessing_directory.split("/")[0]
outfile = "results/test_tune_%s_%s_puWeight_sumPt%i_max%i_ttbar%i_%s" % (
jer, selection, jetThreshold, maxSig, ttbarModifier, version)
r = run(samples,
sel,
JR,
outfile=outfile,
maxN=3e5,
METPtVar=METPtVar,
METPtVar = "MET_pt_nom"
METPhiVar = "MET_phi_nom"
JetCollection = "Jet_pt_nom"
vetoEtaRegion = (10., 10.)
if args.runData:
from nanoMET.samples.nanoTuples_Run2018_17Sep2018_postProcessed import *
samples = [DoubleMuon_Run2018]
jer = "Autumn18_V7b_DATA"
else:
from nanoMET.samples.nanoTuples_Autumn18_postProcessed import *
samples = [DY_LO_18, Top_18, diboson_18, rare_18]
jer = "Autumn18_V7b_MC"
# calculate setting
preselection = cutInterpreter.cutString(args.selection)
triggerSel = "(%s)" % "||".join(trigger)
eventfilter = getFilterCut(args.year, isData=args.runData)
sel = "&&".join([preselection, triggerSel, eventfilter])
JR = JetResolution(jer)
version = postProcessing_directory.split("/")[0]
outfile = "results/tune_%s_%i_%s_%s_sumPt%i_max%i" % (
"DATA" if args.runData else "MC", args.year, jer, args.selection,
args.jetThreshold, args.maxSig)
if args.addon:
outfile += "_" + str(args.addon)
if args.pTdependent:
outfile += "_pTdep"
outfile += "_" + version
# run
stackTT = Stack(top)
if args.small:
for sample in stack.samples:
sample.normalization=1.
sample.reduceFiles( factor=10 )
sample.scale /= sample.normalization
#sample.reduceFiles( to = 3 )
if args.verySmall:
for sample in stack.samples:
sample.normalization=1.
sample.reduceFiles( to = 1 )
sample.scale /= sample.normalization
# Use some defaults
Plot.setDefaults(stack = stack, weight = staticmethod(weight_), selectionString = cutInterpreter.cutString(args.selection), addOverFlowBin='upper', histo_class=ROOT.TH1D)
Plot2D.setDefaults(weight = weight_, selectionString = cutInterpreter.cutString(args.selection), histo_class=ROOT.TH2D)
plots = []
plots2D = []
plots.append(Plot(
name = 'yield', texX = 'yield', texY = 'Number of Events',
attribute = lambda event, sample: 0.5 + index, # yields are somehow acting weird
binning=[1, 0, 1],
))
plots.append(Plot(
name = 'nVtxs', texX = 'vertex multiplicity', texY = 'Number of Events',
attribute = TreeVariable.fromString( "PV_npvsGood/I" ),
binning=[80,0,80],
}
plots = []
if args.year == 2016 or args.year == 2018:
## MET_pt
if not args.noData and (args.selectSys == 'None'
or args.selectSys == 'combine'):
met_data = Plot(
name="MET_pt_data",
texX='E_{T}^{miss} (GeV)',
texY='Number of Events'
if args.normalizeBinWidth else "Number of Event / 20 GeV",
stack=stack_data,
attribute=TreeVariable.fromString("MET_pt_nom/F"),
binning=[20, 0, 400],
selectionString=cutInterpreter.cutString(args.selection),
weight=data_weight,
)
plots.append(met_data)
else:
met_data = None
met_mc = {}
for sys in all_systematics:
met_mc[sys] = Plot(
name="MET_pt" if sys is None else "MET_pt_mc_%s" % sys,
texX='E_{T}^{miss} (GeV)',
texY='Number of Events'
if args.normalizeBinWidth else "Number of Event / 20 GeV",
stack=sys_stacks[sys],
attribute=TreeVariable.fromString('MET_pt_nom/F')
