def test_merge_sample_sum(self):
"""
Merge two samples with the sum option true. In this case time samples
do no play any role.
"""
this = TQSampleFolder("this")
s1 = TQSample("htautau")
s1.addObject(TQCounter("c", 1.234, 35.1, 1234))
s1.setTag("test.s1", "hello world")
this.addSampleFolder(s1)
other = TQSampleFolder("other")
s2 = TQSample("htautau")
s2.addObject(TQCounter("c", 4.321, 65.7, 4321))
s2.setTag("test.s2", "hello multiverse")
other.addSampleFolder(s2)
this.merge(other, "asv", True)
S = this.getSample("htautau")
self.assertTrue(S)
self.assertIsInstance(S, TQSample)
self.assertEqual(
repr(S),
'TQSample("htautau") @ this:/htautau test.s1 = "hello world", test.s2 = "hello multiverse"'
)
C = S.getObject("c")
self.assertTrue(C)
self.assertIsInstance(C, TQCounter)
self.assertEqual(C.getCounter(), 5.555)
self.assertEqual(C.getRawCounter(), 5555)
self.assertEqual(C.getError(), 74.48825410761083)
def test_merge_sample_nosum(self):
"""
Merge two samples without the sum option, this means merging should
not take place at all, since there are no timestamps.
"""
this = TQSampleFolder("this")
s1 = TQSample("htautau")
s1.addObject(TQCounter("c", 1.234, 35.1, 1234))
s1.setTag("test.s1", "hello world")
this.addSampleFolder(s1)
other = TQSampleFolder("other")
s2 = TQSample("htautau")
s2.addObject(TQCounter("c", 4.321, 65.7, 4321))
s2.setTag("test.s2", "hello multiverse")
other.addSampleFolder(s2)
this.merge(other, False)
S = this.getSample("htautau")
self.assertTrue(S)
self.assertIsInstance(S, TQSample)
C = S.getObject("c")
self.assertTrue(C)
self.assertIsInstance(C, TQCounter)
self.assertEqual(C.getCounter(), 1.234)
self.assertEqual(C.getRawCounter(), 1234)
self.assertEqual(C.getError(), 35.1)
def test_merge_sample_other_newer_nosum(self):
"""
Merge two samples without the sum option, but the other sample folder
is newer. This means this one should be taken.
"""
this = TQSampleFolder("this")
s1 = TQSample("htautau")
s1.addObject(TQCounter("c", 1.234, 35.1, 1234))
s1.setTag("test.s1", "hello world")
s1.setTag(".test.timestamp.machine", self.YESTERDAY)
this.addSampleFolder(s1)
other = TQSampleFolder("other")
s2 = TQSample("htautau")
s2.addObject(TQCounter("c", 4.321, 65.7, 4321))
s2.setTag("test.s2", "hello multiverse")
s2.setTag(".test.timestamp.machine", self.TODAY)
other.addSampleFolder(s2)
this.merge(other, "test", False)
S = this.getSample("htautau")
self.assertTrue(S)
self.assertIsInstance(S, TQSample)
C = S.getObject("c")
self.assertTrue(C)
self.assertIsInstance(C, TQCounter)
self.assertEqual(C.getCounter(), 4.321)
self.assertEqual(C.getRawCounter(), 4321)
self.assertEqual(C.getError(), 65.7)
def test_number_parsing_w_space(self):
samples = TQSampleFolder("samples")
parser = TQXSecParser(samples)
parser.readCSVfile(os.path.join(self.tempdir, "testXS.csv"))
parser.readMappingFromColumn("*path*")
unit = TQXSecParser.unitName(TQXSecParser.unit("pb"))
parser.setTagString("xSectionUnit", unit)
parser.addPathVariant("channel", "em")
parser.addAllSamples(False)
vbf = samples.getSampleFolder("sig/em/vbf/341080")
self.assertTrue(vbf, msg="Failed to get vbf sample folder")
xsVBF = vbf.getTagDoubleDefault(".xsp.xSection", -999.)
self.assertAlmostEqual(xsVBF, 0.8579E-4, delta=1e-10)
def test_simple_mapping(self):
histogram = os.path.join(self.tempdir, "testHistogram.root:mapping")
samples = TQSampleFolder("samples")
testSample = samples.getSampleFolder("test+").getSample("testSample+")
testSample.setTreeLocation(
os.path.join(self.tempdir, "test.root:testTree"))
testSample.setTagBool("usemcweights", True)
#print "Creating test cuts"
baseCutFolder = TQFolder("cuts")
cutText = '+baseCut{<.cutExpression="1.",.weightExpression="1.",title="dummy base cut">\n+weightedCut{<.cutExpression="1.",.weightExpression="TH1Map:' + histogram + '([testVal])">}\n}'
#print "Creating cuts from expression:"
#print cutText
baseCutFolder.importFromText(cutText)
baseCut = TQCut.importFromFolder(baseCutFolder.getFolder("?"))
#print "creating sample visitor"
visitor = TQAnalysisSampleVisitor()
visitor.setBaseCut(baseCut)
#visitor.setVerbose(True)
#print "creating histomaker analysis job"
histoJob = TQHistoMakerAnalysisJob()
if not histoJob.bookHistogram(
'TH1F("histo","",20,-2.,2.) << (testVal:"original distribution")'
):
print("Failed to book histogram")
baseCut.addAnalysisJob(histoJob, "*")
samples.visitMe(visitor)
#samples.writeToFile("testSampleFolder.root")
original = samples.getHistogram("test", "baseCut/histo")
scaled = samples.getHistogram("test", "weightedCut/histo")
ok = True
for b in range(0, original.GetNbinsX()):
exponent = -1 if original.GetBinCenter(b) < 0 else 1
#check if removing the scaling yield the same bin value
ok = ok and (abs(
original.GetBinContent(b) -
scaled.GetBinContent(b) / pow(2, exponent)) < 1e-10)
self.assertTrue(
ok
) #don't check for exact match, we might have some numerical discrepancies
if len(sys.argv) < 2:
print "Usage:"
print " python {} INDEX [SAMPLE]".format(sys.argv[0])
print ""
print " INDEX determines which variation to run"
print " -1 : print cuts and samples and exit"
print " 0 : nominal"
print " 1 : jec_up"
print " 2 : jec_dn"
print " 3 : gen_met"
print ""
sys.exit()
# Create the master TQSampleFolder
samples = TQSampleFolder("samples")
# Connect input baby ntuple
connectNtuples(samples, "../samples.cfg", ntuplepath, "<-2", "<-3")
samples.printContents("trd")
# Add BSM samples
addBSMsamples(samples)
if len(sys.argv) >= 3:
# Run single job
main(samples, str(sys.argv[2]), [int(sys.argv[1])])
else:
# First remove old files
os.system("rm -f .output_-*")
def main():
options = {
# The main root TQSampleFolder name
"master_sample_name": "samples",
# Where the ntuples are located
"ntuple_path": "/nfs-7/userdata/phchang/WWW_babies/WWW_v1.2.3/skim/",
# Path to the config file that defines how the samples should be organized
"sample_config_path": "samples.cfg",
# The samples with "priority" (defined in sample_config_pat) values satisfying the following condition is looped over
"priority_value": ">0",
# The samples with "priority" (defined in sample_config_pat) values satisfying the following condition is NOT looped over
"exclude_priority_value": "<-1",
# N-cores
"ncore": 4,
# TQCuts config file
"cuts": "cuts.cfg",
# Histogram config file
"histo": "histo.cfg",
# Eventlist histogram
"eventlist": "eventlist.cfg",
# Custom observables (dictionary)
"customobservables": {},
# Custom observables (dictionary)
"output_dir": "outputs/"
}
# First generate cuts.cfg file
generate_cuts_config()
# Analyze
loop(options)
# Create plots and tables
samples = TQSampleFolder.loadSampleFolder("outputs/output.root:samples")
bkg_path = [
("WWW", "/sig/www"),
("WHWWW", "/sig/whwww"),
]
sig_path = [("WZ", "/bkg/WZ")]
autoplot(samples,
bkg_path=bkg_path,
sig_path=sig_path,
options={
"remove_underflow": True,
"signal_scale": "auto"
})
autotable(samples,
"yield",
bkg_path=bkg_path,
sig_path=sig_path,
options={"cuts": "cuts.cfg"})
def compute_fake_factor_1d(th1):
for ix in xrange(0, th1.GetNbinsX() + 2):
frnom = th1.GetBinContent(ix)
frerr = th1.GetBinError(ix)
fr = E(frnom, frerr)
if fr.val != 0 and fr.val != 1:
ff = fr / (E(1., 0.) - fr)
else:
ff = E(0., 0.)
th1.SetBinContent(ix, ff.val)
th1.SetBinError(ix, ff.err)
ROOT.gROOT.SetBatch(True)
samples = TQSampleFolder.loadSampleFolder("output.root:samples")
qcdloosemu = samples.getHistogram("/qcd/mu",
"OneMuLoose/lep_ptcorrcoarse_vs_etacoarse")
qcdtightmu = samples.getHistogram("/qcd/mu",
"OneMuTight/lep_ptcorrcoarse_vs_etacoarse")
qcdloosemu.Print("all")
qcdtightmu.Print("all")
qcdlooseel = samples.getHistogram("/qcd/el",
"OneElLoose/lep_ptcorrcoarse_vs_etacoarse")
qcdtightel = samples.getHistogram("/qcd/el",
"OneElTight/lep_ptcorrcoarse_vs_etacoarse")
qcdlooseelEM = samples.getHistogram(
"/qcd/el/EM", "OneElLoose/lep_ptcorrcoarse_vs_etacoarse")
#!/bin/env python
import os
import sys
import ROOT
from QFramework import TQSampleFolder, TQXSecParser, TQCut, TQAnalysisSampleVisitor, TQSampleInitializer, TQCutflowAnalysisJob, TQCutflowPrinter, TQHistoMakerAnalysisJob
from rooutil import plottery_wrapper as p
from plottery import plottery as ply
try:
tqsamplefolderpath = sys.argv[1]
except:
tqsamplefolderpath = "output.root"
ROOT.gROOT.SetBatch(True)
samples = TQSampleFolder.loadSampleFolder(tqsamplefolderpath + ":samples")
samples_up = TQSampleFolder.loadSampleFolder("output_up.root:samples")
samples_dn = TQSampleFolder.loadSampleFolder("output_dn.root:samples")
output_plot_dir = "plots"
doW = False
docombinedqcdel = True
testsample = "/top"
testsamplename = "t#bar{t}"
testsamplelegendname = "t#bar{t}"
if doW:
testsample = "/W/HT"
#testsample = "/W"
testsamplename = "W"
#!/bin/env python
import os
import sys
import ROOT
from QFramework import TQSampleFolder, TQXSecParser, TQCut, TQAnalysisSampleVisitor, TQSampleInitializer, TQCutflowAnalysisJob, TQCutflowPrinter, TQHistoMakerAnalysisJob
from rooutil import plottery_wrapper as p
from plottery import plottery as ply
from rooutil.syncfiles.pyfiles.errors import E
ROOT.gROOT.SetBatch(True)
samples_cn = TQSampleFolder.loadSampleFolder("output.root:samples")
samples_up = TQSampleFolder.loadSampleFolder("output_up.root:samples")
samples_dn = TQSampleFolder.loadSampleFolder("output_dn.root:samples")
output_plot_dir = "plots"
#_________________________________________________________
def compute_fake_factor_1d(th1):
for ix in xrange(0, th1.GetNbinsX()+2):
frnom = th1.GetBinContent(ix)
frerr = th1.GetBinError(ix)
fr = E(frnom, frerr)
if fr.val != 0 and fr.val != 1:
ff = fr / (E(1., 0.) - fr)
else:
ff = E(0., 0.)
th1.SetBinContent(ix, ff.val)
th1.SetBinError(ix, ff.err)
#___________________________________________________________________________
def main(model="", mass=""):
samples = TQSampleFolder.loadSampleFolder("outputs/output.root:samples")
samples_jec_up = TQSampleFolder.loadSampleFolder(
"outputs/output_jec_up.root:samples")
samples_jec_dn = TQSampleFolder.loadSampleFolder(
"outputs/output_jec_dn.root:samples")
options = {
# Signal name and TQSampleFolder path
"sig":
("www", "/sig" if model == "" else "/bsm/{}/{}".format(model, mass)),
# Background names and TQSampelFolder paths
"bkgs": [
("lostlep", "/typebkg/lostlep/[ttZ+WZ+Other]"),
("fake", "/fake"),
("vbsww", "/typebkg/?/VBSWW"),
("ttw", "/typebkg/?/ttW"),
("photon", "/typebkg/photon/[ttZ+WZ+Other]"),
("qflip", "/typebkg/qflip/[ttZ+WZ+Other]"),
("prompt", "/typebkg/prompt/[ttZ+WZ+Other]"
if model == "" else "/typebkg/prompt/[ttZ+WZ+Other]+sig"),
],
# Data TQSampleFolder paths
"data":
"/data",
# Counter names for getting yields
"bins": [
"SRSSeeFull",
"SRSSemFull",
"SRSSmmFull",
"SideSSeeFull",
"SideSSemFull",
"SideSSmmFull",
"SR0SFOSFull",
"SR1SFOSFull",
"SR2SFOSFull",
#"SRNj1SSeeFull",
#"SRNj1SSemFull",
#"SRNj1SSmmFull",
],
# TQSampleFolder object
"nominal_sample":
samples,
# Control regions
# The control regions will normalize the counts
# The systematics
"control_regions": {
("SRSSeeFull", "/typebkg/lostlep/[ttZ+WZ+Other]"):
("WZCRSSeeFull",
"/data-typebkg/qflip-typebkg/photon-typebkg/prompt-typebkg/fakes-typebkg/lostlep/VBSWW-typebkg/lostlep/ttW-sig"
),
("SRSSemFull", "/typebkg/lostlep/[ttZ+WZ+Other]"):
("WZCRSSemFull",
"/data-typebkg/qflip-typebkg/photon-typebkg/prompt-typebkg/fakes-typebkg/lostlep/VBSWW-typebkg/lostlep/ttW-sig"
),
("SRSSmmFull", "/typebkg/lostlep/[ttZ+WZ+Other]"):
("WZCRSSmmFull",
"/data-typebkg/qflip-typebkg/photon-typebkg/prompt-typebkg/fakes-typebkg/lostlep/VBSWW-typebkg/lostlep/ttW-sig"
),
("SideSSeeFull", "/typebkg/lostlep/[ttZ+WZ+Other]"):
("WZCRSSeeFull",
"/data-typebkg/qflip-typebkg/photon-typebkg/prompt-typebkg/fakes-typebkg/lostlep/VBSWW-typebkg/lostlep/ttW-sig"
),
("SideSSemFull", "/typebkg/lostlep/[ttZ+WZ+Other]"):
("WZCRSSemFull",
"/data-typebkg/qflip-typebkg/photon-typebkg/prompt-typebkg/fakes-typebkg/lostlep/VBSWW-typebkg/lostlep/ttW-sig"
),
("SideSSmmFull", "/typebkg/lostlep/[ttZ+WZ+Other]"):
("WZCRSSmmFull",
"/data-typebkg/qflip-typebkg/photon-typebkg/prompt-typebkg/fakes-typebkg/lostlep/VBSWW-typebkg/lostlep/ttW-sig"
),
("SR1SFOSFull", "/typebkg/lostlep/[ttZ+WZ+Other]"):
("WZCR1SFOSFull",
"/data-typebkg/qflip-typebkg/photon-typebkg/prompt-typebkg/fakes-typebkg/lostlep/VBSWW-typebkg/lostlep/ttW-sig"
),
("SR2SFOSFull", "/typebkg/lostlep/[ttZ+WZ+Other]"):
("WZCR2SFOSFull",
"/data-typebkg/qflip-typebkg/photon-typebkg/prompt-typebkg/fakes-typebkg/lostlep/VBSWW-typebkg/lostlep/ttW-sig"
),
#("SRNj1SSeeFull", "/typebkg/lostlep/[ttZ+WZ+Other]") : ("WZCRNj1SSeeFull" , "/data-typebkg/qflip-typebkg/photon-typebkg/prompt-typebkg/fakes-typebkg/lostlep/VBSWW-typebkg/lostlep/ttW-sig"),
#("SRNj1SSemFull", "/typebkg/lostlep/[ttZ+WZ+Other]") : ("WZCRNj1SSemFull" , "/data-typebkg/qflip-typebkg/photon-typebkg/prompt-typebkg/fakes-typebkg/lostlep/VBSWW-typebkg/lostlep/ttW-sig"),
#("SRNj1SSmmFull", "/typebkg/lostlep/[ttZ+WZ+Other]") : ("WZCRNj1SSmmFull" , "/data-typebkg/qflip-typebkg/photon-typebkg/prompt-typebkg/fakes-typebkg/lostlep/VBSWW-typebkg/lostlep/ttW-sig"),
},
# Weight variation systematics that are saved in the "nominal_sample" TQSampleFolder
# The nomenclature of the coutner names must be <BIN_COUNTER><SYSTS>Up and <BIN_COUNTER><SYSTS>Down
# The keyword are the systematics and then the items list the processes to apply the systematics
"systematics": [
("LepSF", {
"procs_to_apply": [
"www", "vbsww", "ttw", "photon", "qflip", "prompt",
"lostlep"
]
}),
("TrigSF", {
"procs_to_apply": [
"www", "vbsww", "ttw", "photon", "qflip", "prompt",
"lostlep"
]
}),
("BTagLF", {
"procs_to_apply": [
"www", "vbsww", "ttw", "photon", "qflip", "prompt",
"lostlep"
]
}),
("BTagHF", {
"procs_to_apply": [
"www", "vbsww", "ttw", "photon", "qflip", "prompt",
"lostlep"
]
}),
("Pileup", {
"procs_to_apply": [
"www", "vbsww", "ttw", "photon", "qflip", "prompt",
"lostlep"
]
}),
("FakeRateEl", {
"procs_to_apply": ["fake"]
}),
("FakeRateMu", {
"procs_to_apply": ["fake"]
}),
("FakeClosureEl", {
"procs_to_apply": ["fake"]
}),
("FakeClosureMu", {
"procs_to_apply": ["fake"]
}),
("PDF", {
"procs_to_apply": ["www"]
}),
("AlphaS", {
"procs_to_apply": ["www"]
}),
("Qsq", {
"procs_to_apply": ["www"]
}),
("JEC", {
"procs_to_apply": [
"www", "vbsww", "ttw", "photon", "qflip", "prompt",
"lostlep"
],
"syst_samples": {
"Up": samples_jec_up,
"Down": samples_jec_dn
}
}),
],
"statistical":
["www", "vbsww", "ttw", "photon", "qflip", "prompt", "fake"],
"flat_systematics": [
("VBSWWXSec", ["vbsww"], "1.2", ""),
("ttWXSec", ["ttw"], "1.2", ""),
("VBSWWVRSyst", ["vbsww"], "1.22", ""),
("ttWVRSyst", ["ttw"], "1.18", ""),
("QFlipVRSyst", ["qflip"], "1.5", ""),
("PhotonVRSyst", ["photon"], "1.5", ""),
("LostLepMll3LModeling", ["lostlep"], "1.082", "SFOS"),
("LostLepMllSSModeling", ["lostlep"], "1.053", "SS"),
("LostLepMjjModeling", ["lostlep"], "1.049", "SS"),
("LumSyst", ["vbsww", "ttw", "photon", "qflip", "prompt",
"www"], "1.025", ""),
],
}
return qutils.make_counting_experiment_statistics_data_card(options)
def main(index, mode, donotrun):
# Determine JEC mode
jecvar = ""
if mode == 1: jecvar = "_up"
if mode == 2: jecvar = "_dn"
#
#
# Create the master TQSampleFolder
#
#
samples = TQSampleFolder("samples")
#
#
# Connect input baby ntuple
#
#
connectNtuples(samples, samplescfgpath, nfspath, ">4", ">5")
#
#
# Define cuts
#
#
PreselCuts = [
["1", "{$(usefakeweight)?1.:evt_scale1fb*35.9}"],
["1", "{$(usefakeweight)?1.:purewgt}"],
["Flag_AllEventFilters", "1"],
["nj30>=1", "1"],
["firstgoodvertex==0", "1"],
["evt_passgoodrunlist", "1"],
["mc_HLT_SingleIsoMu17+mc_HLT_SingleIsoEl17", "1"],
]
PreselCutExpr, PreselWgtExpr = combexpr(PreselCuts)
# Complicated string construction for looes and tight ID muon
mu_loosetemp = "(TMath::Abs(lep_eta[{idx}]<2.4))*(abs(lep_dz[{idx}])<0.1)*(abs(lep_dxy[{idx}])<0.05)*(abs(lep_ip3d[{idx}])<0.015)*(abs(lep_ip3derr[{idx}]/lep_ip3d[{idx}])<4.)*(abs(lep_pterr[{idx}]/lep_trk_pt[{idx}])<0.2)*(lep_isMediumPOG[{idx}])*(lep_relIso03EAv2Lep[{idx}]<0.4)*(lep_pt[{idx}]>20.)"
mu_tighttemp = "({loose})*(lep_relIso03EAv2Lep[{idx}]<0.03)".format(
loose=mu_loosetemp, idx="{idx}")
leadmu_loose = mu_loosetemp.format(idx="0")
leadmu_tight = mu_tighttemp.format(idx="0")
trailmu_loose = mu_loosetemp.format(idx="1")
trailmu_tight = mu_tighttemp.format(idx="1")
bothmu_loose = "({})&&({})".format(leadmu_loose, trailmu_loose)
bothmu_tight = "({})&&({})".format(leadmu_tight, trailmu_tight)
# Complicated string construction for looes and tight ID muon
mu3l_loosetemp = "(TMath::Abs(lep_eta[{idx}]<2.4))*(abs(lep_dz[{idx}])<0.1)*(abs(lep_dxy[{idx}])<0.05)*(abs(lep_ip3d[{idx}])<0.015)*(abs(lep_ip3derr[{idx}]/lep_ip3d[{idx}])<4.)*(abs(lep_pterr[{idx}]/lep_trk_pt[{idx}])<0.2)*(lep_isMediumPOG[{idx}])*(lep_relIso03EAv2Lep[{idx}]<0.4)*(lep_pt[{idx}]>20.)"
mu3l_tighttemp = "({loose})*(lep_relIso03EAv2Lep[{idx}]<0.07)".format(
loose=mu3l_loosetemp, idx="{idx}")
leadmu3l_loose = mu3l_loosetemp.format(idx="0")
leadmu3l_tight = mu3l_tighttemp.format(idx="0")
trailmu3l_loose = mu3l_loosetemp.format(idx="1")
trailmu3l_tight = mu3l_tighttemp.format(idx="1")
bothmu3l_loose = "({})&&({})".format(leadmu3l_loose, trailmu3l_loose)
bothmu3l_tight = "({})&&({})".format(leadmu3l_tight, trailmu3l_tight)
# Complicated string construction for looes and tight ID electron
el_loosetemp = "(TMath::Abs(lep_eta[{idx}]<2.4))*(abs(lep_dz[{idx}])<0.1)*(abs(lep_dxy[{idx}])<0.05)*(abs(lep_ip3d[{idx}])<0.01)*(lep_tightCharge[{idx}]==2)*((abs(lep_etaSC[{idx}])<=1.479)*(lep_MVA[{idx}]>0.941)+(abs(lep_etaSC[{idx}])>1.479)*(lep_MVA[{idx}]>0.925))*(lep_isTriggerSafe_v1[{idx}])*(lep_relIso03EAv2Lep[{idx}]<0.4)*(lep_pt[{idx}]>20.)"
el_tighttemp = "({loose})*(lep_relIso03EAv2Lep[{idx}]<0.03)".format(
loose=el_loosetemp, idx="{idx}")
leadel_loose = el_loosetemp.format(idx="0")
leadel_tight = el_tighttemp.format(idx="0")
trailel_loose = el_loosetemp.format(idx="1")
trailel_tight = el_tighttemp.format(idx="1")
bothel_loose = "({})&&({})".format(leadel_loose, trailel_loose)
bothel_tight = "({})&&({})".format(leadel_tight, trailel_tight)
# Complicated string construction for looes and tight ID electron for three lepton region
el3l_loosetemp = "(TMath::Abs(lep_eta[{idx}]<2.4))*(abs(lep_dz[{idx}])<0.1)*(abs(lep_dxy[{idx}])<0.05)*(abs(lep_ip3d[{idx}])<0.015)*((abs(lep_etaSC[{idx}])<=1.479)*(lep_MVA[{idx}]>0.92)+(abs(lep_etaSC[{idx}])>1.479)*(lep_MVA[{idx}]>0.88))*(lep_isTriggerSafe_v1[{idx}])*(lep_relIso03EAv2Lep[{idx}]<0.4)*(lep_pt[{idx}]>20.)"
el3l_tighttemp = "({loose})*(lep_relIso03EAv2Lep[{idx}]<0.05)".format(
loose=el3l_loosetemp, idx="{idx}")
leadel3l_loose = el3l_loosetemp.format(idx="0")
leadel3l_tight = el3l_tighttemp.format(idx="0")
trailel3l_loose = el3l_loosetemp.format(idx="1")
trailel3l_tight = el3l_tighttemp.format(idx="1")
bothel3l_loose = "({})&&({})".format(leadel3l_loose, trailel3l_loose)
bothel3l_tight = "({})&&({})".format(leadel3l_tight, trailel3l_tight)
# Expressions to divide heavy flavor and !(heavy flavor)
leadhf = "((lep_motherIdSS[0]==-1)+(lep_motherIdSS[0]==-2))"
leadlf = "((lep_motherIdSS[0]!=-1)*(lep_motherIdSS[0]!=-2))"
trailhf = "((lep_motherIdSS[1]==-1)+(lep_motherIdSS[1]==-2))"
traillf = "((lep_motherIdSS[1]!=-1)*(lep_motherIdSS[1]!=-2))"
# MT expression (as I forgot to add a one lepton MT variable in the WWW baby.)
MTexpr = "(TMath::Sqrt(2*met" + jecvar + "_pt*lep_pt[0]*(1.0-TMath::Cos(lep_phi[0]-met" + jecvar + "_phi))))"
# One lepton kinematic selection
onelep_cuts = "(jets" + jecvar + "_p4[0].pt()>40.)"
twolep_cuts = "(lep_pdgId[0]*lep_pdgId[1]>0)*(nj30" + jecvar + ">=2)" # if removing bveto
twolepos_cuts = "(lep_pdgId[0]*lep_pdgId[1]<0)"
# Electroweak control region selection
# TwoMuHLT17/Mll_Z fSumw[1]=155.889, x=90, error=1.35664
# TwoElHLT17/Mll_Z fSumw[1]=650.599, x=90, error=18.1318
# The reason they are not integer is because the prescales are run/lumi dependent and this number is an "effective" prescale value calculated by comparing MC to data in a dilepton z-peak from this trigger
hlt_mu17_prescale = 155.889
hlt_el17_prescale = 650.599
onelepewkcr_cuts = "(jets" + jecvar + "_p4[0].pt()>40.)*(met_pt>30.)"
onelepewkcr2_cuts = "(jets" + jecvar + "_p4[0].pt()>40.)*(lep_pt[0]>30.)*(met_pt<20.)"
onelepewkcr3_cuts = "(jets" + jecvar + "_p4[0].pt()>40.)*(lep_pt[0]>50.)"
onelepmr_cuts = "(met_pt<20.)*(" + MTexpr + "<20.)*(jets" + jecvar + "_p4[0].pt()>40.)"
oneleptrig_cuts = "(abs(lep_pdgId[0])==11)*(mc_HLT_SingleIsoEl17)+(abs(lep_pdgId[0])==13)*(mc_HLT_SingleIsoMu17)"
oneleptrig_wgts = "{$(usefakeweight)?([abs(lep_pdgId[0])==11])*([mc_HLT_SingleIsoEl17])*(" + str(
hlt_el17_prescale
) + ")+([abs(lep_pdgId[0])==13])*([mc_HLT_SingleIsoMu17])*(" + str(
hlt_mu17_prescale) + "):1.}"
onelepnvtx_wgts = "{$(usefakeweight)?1.:([abs(lep_pdgId[0])==11])*([TH1Map:nvtxreweight.root:OneElTightEWKCR3NoNvtxRewgt_nvtx([nVert])])+([abs(lep_pdgId[0])==13])*([TH1Map:nvtxreweight.root:OneMuTightEWKCR3NoNvtxRewgt_nvtx([nVert])])}"
# These weights are for closure tests. The closure tests are performed for same-sign channel only.
weight_elcomb = "([abs(lep_pdgId[0])==11])*([TH2Map:qcd_fakerates.root:qcdel([abs(lep_eta[0])],[lep_pt[0]*(1.0+TMath::Max(0.0, lep_relIso03EAv2Lep[0]-0.03))])])+([abs(lep_pdgId[1])==11])*([TH2Map:qcd_fakerates.root:qcdel([abs(lep_eta[1])],[lep_pt[1]*(1.0+TMath::Max(0.0, lep_relIso03EAv2Lep[1]-0.03))])])"
weight_el = "([abs(lep_pdgId[0])==11])*([TH2Map:qcd_fakerates.root:qcdelbcToE([abs(lep_eta[0])],[lep_pt[0]*(1.0+TMath::Max(0.0, lep_relIso03EAv2Lep[0]-0.03))])])+([abs(lep_pdgId[1])==11])*([TH2Map:qcd_fakerates.root:qcdelbcToE([abs(lep_eta[1])],[lep_pt[1]*(1.0+TMath::Max(0.0, lep_relIso03EAv2Lep[1]-0.03))])])"
weight_mu = "([abs(lep_pdgId[0])==13])*([TH2Map:qcd_fakerates.root:qcdmu([abs(lep_eta[0])],[lep_pt[0]*(1.0+TMath::Max(0.0, lep_relIso03EAv2Lep[0]-0.03))])])+([abs(lep_pdgId[1])==13])*([TH2Map:qcd_fakerates.root:qcdmu([abs(lep_eta[1])],[lep_pt[1]*(1.0+TMath::Max(0.0, lep_relIso03EAv2Lep[1]-0.03))])])"
weight_elEM1D = "([abs(lep_pdgId[0])==11])*([TH1Map:qcd_fakerates.root:qcdelEM1D([lep_pt[0]*(1.0+TMath::Max(0.0, lep_relIso03EAv2Lep[0]-0.03))])])+([abs(lep_pdgId[1])==11])*([TH1Map:qcd_fakerates.root:qcdelEM1D([lep_pt[1]*(1.0+TMath::Max(0.0, lep_relIso03EAv2Lep[1]-0.03))])])"
# TQCut objects
tqcuts = {}
tqcuts["Presel"] = TQCut("Presel", "Presel", PreselCutExpr, PreselWgtExpr)
tqcuts["OneLep"] = TQCut(
"OneLep", "OneLep",
"(nVlep==1)*({})*({})".format(oneleptrig_cuts, onelep_cuts),
"({})*({})".format(oneleptrig_wgts, onelepnvtx_wgts))
tqcuts["OneLepNoNvtxRewgt"] = TQCut(
"OneLepNoNvtxRewgt", "OneLepNoNvtxRewgt",
"(nVlep==1)*({})*({})".format(oneleptrig_cuts, onelep_cuts),
"({})".format(oneleptrig_wgts))
tqcuts["TwoLep"] = TQCut("TwoLep", "TwoLep",
"(nVlep==2)*({})".format(twolep_cuts), "1")
tqcuts["TwoLepOS"] = TQCut("TwoLepOS", "TwoLepOS",
"(nVlep==2)*({})".format(twolepos_cuts), "1")
tqcuts["OneLepMR"] = TQCut("OneLepMR", "OneLepMR",
"(nVlep==1)*({})".format(onelepmr_cuts), "1")
tqcuts["OneLepEWKCR"] = TQCut("OneLepEWKCR", "OneLepEWKCR",
"(nVlep==1)*({})".format(onelepewkcr_cuts),
"1")
tqcuts["OneLepEWKCR2"] = TQCut("OneLepEWKCR2", "OneLepEWKCR2",
"(nVlep==1)*({})".format(onelepewkcr2_cuts),
"1")
tqcuts["OneLepEWKCR3"] = TQCut("OneLepEWKCR3", "OneLepEWKCR3",
"(nVlep==1)*({})".format(onelepewkcr3_cuts),
"1")
tqcuts["OneLepEWKCR3NoNvtxRewgt"] = TQCut(
"OneLepEWKCR3NoNvtxRewgt", "OneLepEWKCR3NoNvtxRewgt",
"(nVlep==1)*({})".format(onelepewkcr3_cuts), "1")
tqcuts["OneMu"] = TQCut("OneMu", "OneMu", "(abs(lep_pdgId[0])==13)", "1")
tqcuts["OneMuLoose"] = TQCut("OneMuLoose", "OneMuLoose", leadmu_loose, "1")
tqcuts["OneMuTight"] = TQCut("OneMuTight", "OneMuTight", leadmu_tight, "1")
tqcuts["OneMu3lLoose"] = TQCut("OneMu3lLoose", "OneMu3lLoose",
leadmu3l_loose, "1")
tqcuts["OneMu3lTight"] = TQCut("OneMu3lTight", "OneMu3lTight",
leadmu3l_tight, "1")
tqcuts["OneEl"] = TQCut("OneEl", "OneEl", "(abs(lep_pdgId[0])==11)", "1")
tqcuts["OneElLoose"] = TQCut("OneElLoose", "OneElLoose", leadel_loose, "1")
tqcuts["OneElTight"] = TQCut("OneElTight", "OneElTight", leadel_tight, "1")
tqcuts["OneEl3lLoose"] = TQCut("OneEl3lLoose", "OneEl3lLoose",
leadel3l_loose, "1")
tqcuts["OneEl3lTight"] = TQCut("OneEl3lTight", "OneEl3lTight",
leadel3l_tight, "1")
tqcuts["OneMuEWKCR"] = TQCut("OneMuEWKCR", "OneMuEWKCR",
"(abs(lep_pdgId[0])==13)", "1")
tqcuts["OneElEWKCR"] = TQCut("OneElEWKCR", "OneElEWKCR",
"(abs(lep_pdgId[0])==11)", "1")
tqcuts["OneMuTightEWKCR"] = TQCut("OneMuTightEWKCR", "OneMuTightEWKCR",
leadmu_tight, "1")
tqcuts["OneElTightEWKCR"] = TQCut("OneElTightEWKCR", "OneElTightEWKCR",
leadel_tight, "1")
tqcuts["OneMu3lTightEWKCR"] = TQCut("OneMu3lTightEWKCR",
"OneMu3lTightEWKCR", leadmu3l_tight,
"1")
tqcuts["OneEl3lTightEWKCR"] = TQCut("OneEl3lTightEWKCR",
"OneEl3lTightEWKCR", leadel3l_tight,
"1")
tqcuts["OneMuEWKCR2"] = TQCut("OneMuEWKCR2", "OneMuEWKCR2",
"(abs(lep_pdgId[0])==13)", "1")
tqcuts["OneElEWKCR2"] = TQCut("OneElEWKCR2", "OneElEWKCR2",
"(abs(lep_pdgId[0])==11)", "1")
tqcuts["OneMuTightEWKCR2"] = TQCut("OneMuTightEWKCR2", "OneMuTightEWKCR2",
leadmu_tight, "1")
tqcuts["OneElTightEWKCR2"] = TQCut("OneElTightEWKCR2", "OneElTightEWKCR2",
leadel_tight, "1")
tqcuts["OneMu3lTightEWKCR2"] = TQCut("OneMu3lTightEWKCR2",
"OneMu3lTightEWKCR2", leadmu3l_tight,
"1")
tqcuts["OneEl3lTightEWKCR2"] = TQCut("OneEl3lTightEWKCR2",
"OneEl3lTightEWKCR2", leadel3l_tight,
"1")
tqcuts["OneMuEWKCR3"] = TQCut("OneMuEWKCR3", "OneMuEWKCR3",
"(abs(lep_pdgId[0])==13)", "1")
tqcuts["OneElEWKCR3"] = TQCut("OneElEWKCR3", "OneElEWKCR3",
"(abs(lep_pdgId[0])==11)", "1")
tqcuts["OneMuTightEWKCR3"] = TQCut("OneMuTightEWKCR3", "OneMuTightEWKCR3",
leadmu_tight, "1")
tqcuts["OneElTightEWKCR3"] = TQCut("OneElTightEWKCR3", "OneElTightEWKCR3",
leadel_tight, "1")
tqcuts["OneMu3lTightEWKCR3"] = TQCut("OneMu3lTightEWKCR3",
"OneMu3lTightEWKCR3", leadmu3l_tight,
"1")
tqcuts["OneEl3lTightEWKCR3"] = TQCut("OneEl3lTightEWKCR3",
"OneEl3lTightEWKCR3", leadel3l_tight,
"1")
tqcuts["OneMuEWKCR3NoNvtxRewgt"] = TQCut("OneMuEWKCR3NoNvtxRewgt",
"OneMuEWKCR3NoNvtxRewgt",
"(abs(lep_pdgId[0])==13)", "1")
tqcuts["OneElEWKCR3NoNvtxRewgt"] = TQCut("OneElEWKCR3NoNvtxRewgt",
"OneElEWKCR3NoNvtxRewgt",
"(abs(lep_pdgId[0])==11)", "1")
tqcuts["OneMuTightEWKCR3NoNvtxRewgt"] = TQCut(
"OneMuTightEWKCR3NoNvtxRewgt", "OneMuTightEWKCR3NoNvtxRewgt",
leadmu_tight, "1")
tqcuts["OneElTightEWKCR3NoNvtxRewgt"] = TQCut(
"OneElTightEWKCR3NoNvtxRewgt", "OneElTightEWKCR3NoNvtxRewgt",
leadel_tight, "1")
tqcuts["OneMu3lTightEWKCR3NoNvtxRewgt"] = TQCut(
"OneMu3lTightEWKCR3NoNvtxRewgt", "OneMu3lTightEWKCR3NoNvtxRewgt",
leadmu3l_tight, "1")
tqcuts["OneEl3lTightEWKCR3NoNvtxRewgt"] = TQCut(
"OneEl3lTightEWKCR3NoNvtxRewgt", "OneEl3lTightEWKCR3NoNvtxRewgt",
leadel3l_tight, "1")
tqcuts["TwoMu"] = TQCut(
"TwoMu", "TwoMu",
"([ClosureEvtType]==0)*[(abs(lep_pdgId[0]*lep_pdgId[1])==143)]*[(abs(lep_pdgId[0])==11)*(lep_pass_VVV_cutbased_tight[0])+(abs(lep_pdgId[1])==11)*(lep_pass_VVV_cutbased_tight[1])]",
"1") # one any muon and one real tight electron with two total leptons
tqcuts["TwoMuLoose"] = TQCut(
"TwoMuLoose", "TwoMuLoose",
"(abs(lep_pdgId[0])==13)*({})+(abs(lep_pdgId[1])==13)*({})".format(
leadmu_loose, trailmu_loose), "1")
tqcuts["TwoMuTight"] = TQCut(
"TwoMuTight", "TwoMuTight",
"(abs(lep_pdgId[0])==13)*({})+(abs(lep_pdgId[1])==13)*({})".format(
leadmu_tight, trailmu_tight), "1")
tqcuts["TwoMuLoosePredict"] = TQCut(
"TwoMuLoosePredict", "TwoMuLoosePredict",
"(abs(lep_pdgId[0])==13)*({})+(abs(lep_pdgId[1])==13)*({})".format(
leadmu_tight, trailmu_tight), "1")
tqcuts["TwoMuTightPredict"] = TQCut(
"TwoMuTightPredict", "TwoMuTightPredict",
"(abs(lep_pdgId[0])==13)*({})*(!({}))+(abs(lep_pdgId[1])==13)*({})*(!({}))"
.format(leadmu_loose, leadmu_tight, trailmu_loose,
trailmu_tight), weight_mu)
tqcuts["TwoMuLoosePredictBVeto"] = TQCut(
"TwoMuLoosePredictBVeto", "TwoMuLoosePredictBVeto", "(nb" + jecvar +
"==0)*((abs(lep_pdgId[0])==13)*({})+(abs(lep_pdgId[1])==13)*({}))".
format(leadmu_tight, trailmu_tight), "1")
tqcuts["TwoMuTightPredictBVeto"] = TQCut(
"TwoMuTightPredictBVeto", "TwoMuTightPredictBVeto", "(nb" + jecvar +
"==0)*((abs(lep_pdgId[0])==13)*({})*(!({}))+(abs(lep_pdgId[1])==13)*({})*(!({})))"
.format(leadmu_loose, leadmu_tight, trailmu_loose, trailmu_tight),
weight_mu)
tqcuts["TwoEl"] = TQCut(
"TwoEl", "TwoEl",
"([ClosureEvtType]==1)*[(abs(lep_pdgId[0]*lep_pdgId[1])==143)]*[(abs(lep_pdgId[0])==13)*(lep_pass_VVV_cutbased_tight[0])+(abs(lep_pdgId[1])==13)*(lep_pass_VVV_cutbased_tight[1])]",
"1") # one any electron and one real tight muon with two total leptons
tqcuts["TwoElLoose"] = TQCut(
"TwoElLoose", "TwoElLoose",
"(abs(lep_pdgId[0])==11)*({})+(abs(lep_pdgId[1])==11)*({})".format(
leadel_loose, trailel_loose), "1")
tqcuts["TwoElTight"] = TQCut(
"TwoElTight", "TwoElTight",
"(abs(lep_pdgId[0])==11)*({})+(abs(lep_pdgId[1])==11)*({})".format(
leadel_tight, trailel_tight), "1")
tqcuts["TwoElLoosePredict"] = TQCut(
"TwoElLoosePredict", "TwoElLoosePredict",
"(abs(lep_pdgId[0])==11)*({})+(abs(lep_pdgId[1])==11)*({})".format(
leadel_tight, trailel_tight), "1")
tqcuts["TwoElTightPredict"] = TQCut(
"TwoElTightPredict", "TwoElTightPredict",
"(abs(lep_pdgId[0])==11)*({})*(!({}))+(abs(lep_pdgId[1])==11)*({})*(!({}))"
.format(leadel_loose, leadel_tight, trailel_loose,
trailel_tight), weight_el)
tqcuts["TwoElLoosePredictHF"] = TQCut(
"TwoElLoosePredictHF", "TwoElLoosePredictHF",
"(abs(lep_pdgId[0])==11)*({})*({})+(abs(lep_pdgId[1])==11)*({})*({})".
format(leadel_tight, leadhf, trailel_tight, trailhf), "1")
tqcuts["TwoElTightPredictHF"] = TQCut(
"TwoElTightPredictHF", "TwoElTightPredictHF",
"(abs(lep_pdgId[0])==11)*({})*(!({}))*({})+(abs(lep_pdgId[1])==11)*({})*(!({}))*({})"
.format(leadel_loose, leadel_tight, leadhf, trailel_loose,
trailel_tight, trailhf), weight_el)
tqcuts["TwoElLoosePredictEM1DLF"] = TQCut(
"TwoElLoosePredictEM1DLF", "TwoElLoosePredictEM1DLF",
"(abs(lep_pdgId[0])==11)*({})*({})+(abs(lep_pdgId[1])==11)*({})*({})".
format(leadel_tight, leadlf, trailel_tight, traillf), "1")
tqcuts["TwoElTightPredictEM1DLF"] = TQCut(
"TwoElTightPredictEM1DLF", "TwoElTightPredictEM1DLF",
"(abs(lep_pdgId[0])==11)*({})*(!({}))*({})+(abs(lep_pdgId[1])==11)*({})*(!({}))*({})"
.format(leadel_loose, leadel_tight, leadlf, trailel_loose,
trailel_tight, traillf), weight_elEM1D)
tqcuts["TwoElLoosePredictComb"] = TQCut(
"TwoElLoosePredictComb", "TwoElLoosePredictComb",
"(abs(lep_pdgId[0])==11)*({})+(abs(lep_pdgId[1])==11)*({})".format(
leadel_tight, trailel_tight), "1")
tqcuts["TwoElTightPredictComb"] = TQCut(
"TwoElTightPredictComb", "TwoElTightPredictComb",
"(abs(lep_pdgId[0])==11)*({})*(!({}))+(abs(lep_pdgId[1])==11)*({})*(!({}))"
.format(leadel_loose, leadel_tight, trailel_loose,
trailel_tight), weight_elcomb)
tqcuts["TwoElLoosePredictBVeto"] = TQCut(
"TwoElLoosePredictBVeto", "TwoElLoosePredictBVeto", "(nb" + jecvar +
"==0)*((abs(lep_pdgId[0])==11)*({})+(abs(lep_pdgId[1])==11)*({}))".
format(leadel_tight, trailel_tight), "1")
tqcuts["TwoElTightPredictBVeto"] = TQCut(
"TwoElTightPredictBVeto", "TwoElTightPredictBVeto", "(nb" + jecvar +
"==0)*((abs(lep_pdgId[0])==11)*({})*(!({}))+(abs(lep_pdgId[1])==11)*({})*(!({})))"
.format(leadel_loose, leadel_tight, trailel_loose, trailel_tight),
weight_el)
tqcuts["TwoElLoosePredictBVetoHF"] = TQCut(
"TwoElLoosePredictBVetoHF", "TwoElLoosePredictBVetoHF",
"(nb" + jecvar +
"==0)*((abs(lep_pdgId[0])==11)*({})*({})+(abs(lep_pdgId[1])==11)*({})*({}))"
.format(leadel_tight, leadhf, trailel_tight, trailhf), "1")
tqcuts["TwoElTightPredictBVetoHF"] = TQCut(
"TwoElTightPredictBVetoHF", "TwoElTightPredictBVetoHF",
"(nb" + jecvar +
"==0)*((abs(lep_pdgId[0])==11)*({})*(!({}))*({})+(abs(lep_pdgId[1])==11)*({})*(!({}))*({}))"
.format(leadel_loose, leadel_tight, leadhf, trailel_loose,
trailel_tight, trailhf), weight_el)
tqcuts["TwoElLoosePredictBVetoEM1DLF"] = TQCut(
"TwoElLoosePredictBVetoEM1DLF", "TwoElLoosePredictBVetoEM1DLF",
"(nb" + jecvar +
"==0)*((abs(lep_pdgId[0])==11)*({})*({})+(abs(lep_pdgId[1])==11)*({})*({}))"
.format(leadel_tight, leadlf, trailel_tight, traillf), "1")
tqcuts["TwoElTightPredictBVetoEM1DLF"] = TQCut(
"TwoElTightPredictBVetoEM1DLF", "TwoElTightPredictBVetoEM1DLF",
"(nb" + jecvar +
"==0)*((abs(lep_pdgId[0])==11)*({})*(!({}))*({})+(abs(lep_pdgId[1])==11)*({})*(!({}))*({}))"
.format(leadel_loose, leadel_tight, leadlf, trailel_loose,
trailel_tight, traillf), weight_elEM1D)
tqcuts["TwoElLoosePredictBVetoComb"] = TQCut(
"TwoElLoosePredictBVetoComb", "TwoElLoosePredictBVetoComb",
"(nb" + jecvar +
"==0)*((abs(lep_pdgId[0])==11)*({})+(abs(lep_pdgId[1])==11)*({}))".
format(leadel_tight, trailel_tight), "1")
tqcuts["TwoElTightPredictBVetoComb"] = TQCut(
"TwoElTightPredictBVetoComb", "TwoElTightPredictBVetoComb",
"(nb" + jecvar +
"==0)*((abs(lep_pdgId[0])==11)*({})*(!({}))+(abs(lep_pdgId[1])==11)*({})*(!({})))"
.format(leadel_loose, leadel_tight, trailel_loose, trailel_tight),
weight_elcomb)
tqcuts["TwoMuHLT8"] = TQCut(
"TwoMuHLT8", "TwoMuHLT8",
"(mc_HLT_SingleIsoMu8)*(MllSS>60.)*(MllSS<120.)", "1")
tqcuts["TwoMuHLT17"] = TQCut(
"TwoMuHLT17", "TwoMuHLT17",
"(mc_HLT_SingleIsoMu17)*(MllSS>60.)*(MllSS<120.)", "1")
tqcuts["TwoElHLT8"] = TQCut(
"TwoElHLT8", "TwoElHLT8",
"(mc_HLT_SingleIsoEl8)*(MllSS>60.)*(MllSS<120.)", "1")
tqcuts["TwoElHLT17"] = TQCut(
"TwoElHLT17", "TwoElHLT17",
"(mc_HLT_SingleIsoEl17)*(MllSS>60.)*(MllSS<120.)", "1")
# Linking TQCut objects
tqcuts["Presel"].addCut(tqcuts["OneLep"])
tqcuts["Presel"].addCut(tqcuts["OneLepNoNvtxRewgt"])
tqcuts["Presel"].addCut(tqcuts["TwoLep"])
tqcuts["Presel"].addCut(tqcuts["TwoLepOS"])
tqcuts["OneLep"].addCut(tqcuts["OneLepMR"])
tqcuts["OneLep"].addCut(tqcuts["OneLepEWKCR"])
tqcuts["OneLep"].addCut(tqcuts["OneLepEWKCR2"])
tqcuts["OneLep"].addCut(tqcuts["OneLepEWKCR3"])
tqcuts["OneLepNoNvtxRewgt"].addCut(tqcuts["OneLepEWKCR3NoNvtxRewgt"])
tqcuts["OneLepMR"].addCut(tqcuts["OneMu"])
tqcuts["OneMu"].addCut(tqcuts["OneMuLoose"])
tqcuts["OneMuLoose"].addCut(tqcuts["OneMuTight"])
tqcuts["OneMu"].addCut(tqcuts["OneMu3lLoose"])
tqcuts["OneMu3lLoose"].addCut(tqcuts["OneMu3lTight"])
tqcuts["OneLepMR"].addCut(tqcuts["OneEl"])
tqcuts["OneEl"].addCut(tqcuts["OneElLoose"])
tqcuts["OneElLoose"].addCut(tqcuts["OneElTight"])
tqcuts["OneEl"].addCut(tqcuts["OneEl3lLoose"])
tqcuts["OneEl3lLoose"].addCut(tqcuts["OneEl3lTight"])
tqcuts["OneLepEWKCR"].addCut(tqcuts["OneMuEWKCR"])
tqcuts["OneLepEWKCR"].addCut(tqcuts["OneElEWKCR"])
tqcuts["OneMuEWKCR"].addCut(tqcuts["OneMuTightEWKCR"])
tqcuts["OneElEWKCR"].addCut(tqcuts["OneElTightEWKCR"])
tqcuts["OneMuEWKCR"].addCut(tqcuts["OneMu3lTightEWKCR"])
tqcuts["OneElEWKCR"].addCut(tqcuts["OneEl3lTightEWKCR"])
tqcuts["OneLepEWKCR2"].addCut(tqcuts["OneMuEWKCR2"])
tqcuts["OneLepEWKCR2"].addCut(tqcuts["OneElEWKCR2"])
tqcuts["OneMuEWKCR2"].addCut(tqcuts["OneMuTightEWKCR2"])
tqcuts["OneElEWKCR2"].addCut(tqcuts["OneElTightEWKCR2"])
tqcuts["OneMuEWKCR2"].addCut(tqcuts["OneMu3lTightEWKCR2"])
tqcuts["OneElEWKCR2"].addCut(tqcuts["OneEl3lTightEWKCR2"])
tqcuts["OneLepEWKCR3"].addCut(tqcuts["OneMuEWKCR3"])
tqcuts["OneLepEWKCR3"].addCut(tqcuts["OneElEWKCR3"])
tqcuts["OneMuEWKCR3"].addCut(tqcuts["OneMuTightEWKCR3"])
tqcuts["OneElEWKCR3"].addCut(tqcuts["OneElTightEWKCR3"])
tqcuts["OneMuEWKCR3"].addCut(tqcuts["OneMu3lTightEWKCR3"])
tqcuts["OneElEWKCR3"].addCut(tqcuts["OneEl3lTightEWKCR3"])
tqcuts["OneLepEWKCR3NoNvtxRewgt"].addCut(tqcuts["OneMuEWKCR3NoNvtxRewgt"])
tqcuts["OneLepEWKCR3NoNvtxRewgt"].addCut(tqcuts["OneElEWKCR3NoNvtxRewgt"])
tqcuts["OneMuEWKCR3NoNvtxRewgt"].addCut(
tqcuts["OneMuTightEWKCR3NoNvtxRewgt"])
tqcuts["OneElEWKCR3NoNvtxRewgt"].addCut(
tqcuts["OneElTightEWKCR3NoNvtxRewgt"])
tqcuts["OneMuEWKCR3NoNvtxRewgt"].addCut(
tqcuts["OneMu3lTightEWKCR3NoNvtxRewgt"])
tqcuts["OneElEWKCR3NoNvtxRewgt"].addCut(
tqcuts["OneEl3lTightEWKCR3NoNvtxRewgt"])
tqcuts["TwoLep"].addCut(tqcuts["TwoMu"])
tqcuts["TwoMu"].addCut(tqcuts["TwoMuLoosePredict"])
tqcuts["TwoMu"].addCut(tqcuts["TwoMuTightPredict"])
tqcuts["TwoMu"].addCut(tqcuts["TwoMuLoosePredictBVeto"])
tqcuts["TwoMu"].addCut(tqcuts["TwoMuTightPredictBVeto"])
tqcuts["TwoMu"].addCut(tqcuts["TwoMuLoose"])
tqcuts["TwoMuLoose"].addCut(tqcuts["TwoMuTight"])
tqcuts["TwoLep"].addCut(tqcuts["TwoEl"])
tqcuts["TwoEl"].addCut(tqcuts["TwoElLoosePredict"])
tqcuts["TwoEl"].addCut(tqcuts["TwoElTightPredict"])
tqcuts["TwoEl"].addCut(tqcuts["TwoElLoosePredictComb"])
tqcuts["TwoEl"].addCut(tqcuts["TwoElTightPredictComb"])
tqcuts["TwoEl"].addCut(tqcuts["TwoElLoosePredictHF"])
tqcuts["TwoEl"].addCut(tqcuts["TwoElTightPredictHF"])
tqcuts["TwoEl"].addCut(tqcuts["TwoElLoosePredictEM1DLF"])
tqcuts["TwoEl"].addCut(tqcuts["TwoElTightPredictEM1DLF"])
tqcuts["TwoEl"].addCut(tqcuts["TwoElLoosePredictBVeto"])
tqcuts["TwoEl"].addCut(tqcuts["TwoElTightPredictBVeto"])
tqcuts["TwoEl"].addCut(tqcuts["TwoElLoosePredictBVetoComb"])
tqcuts["TwoEl"].addCut(tqcuts["TwoElTightPredictBVetoComb"])
tqcuts["TwoEl"].addCut(tqcuts["TwoElLoosePredictBVetoHF"])
tqcuts["TwoEl"].addCut(tqcuts["TwoElTightPredictBVetoHF"])
tqcuts["TwoEl"].addCut(tqcuts["TwoElLoosePredictBVetoEM1DLF"])
tqcuts["TwoEl"].addCut(tqcuts["TwoElTightPredictBVetoEM1DLF"])
tqcuts["TwoEl"].addCut(tqcuts["TwoElLoose"])
tqcuts["TwoElLoose"].addCut(tqcuts["TwoElTight"])
tqcuts["TwoLepOS"].addCut(tqcuts["TwoMuHLT8"])
tqcuts["TwoLepOS"].addCut(tqcuts["TwoMuHLT17"])
tqcuts["TwoLepOS"].addCut(tqcuts["TwoElHLT8"])
tqcuts["TwoLepOS"].addCut(tqcuts["TwoElHLT17"])
# Grand cut
cuts = tqcuts["Presel"]
#
#
# Define histograms
#
#
# N.B. Any 2D histogram must have "_vs_" in the name. This is an important conventino for the makeplot.py script to be able to distinguish the 1D vs. 2D histogram.
filename = ".histo.mr.{}.cfg".format(index)
f = open(filename, "w")
f.write("""
TH2F('lep_pt_vs_eta' , '' , {{0, 0.9, 1.6, 1.9, 2.4}}, {{20, 30, 40, 50, 60, 70, 150, 2000}} ) << (abs(lep_eta[0]) : '|\#eta|', lep_pt[0] : '\#it{{p}}_{{T}} [GeV]');
@OneLep/*: lep_pt_vs_eta;
TH2F('lep_ptcorr_vs_eta' , '' , {{0, 0.9, 1.6, 1.9, 2.4}}, {{0., 5., 10., 15., 20., 25., 30., 35., 40., 45., 60., 80., 120.}} ) << (abs(lep_eta[0]) : '|\#eta|', TMath::Min(lep_pt[0]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[0]-0.03)),119.) : '\#it{{p}}_{{T}} [GeV]');
@OneLep/*: lep_ptcorr_vs_eta;
TH2F('lep_ptcorrcoarse_vs_eta' , '' , {{0, 0.9, 1.6, 1.9, 2.4}}, {{0., 10., 20., 25., 30., 40., 120.}} ) << (abs(lep_eta[0]) : '|\#eta|', TMath::Min(lep_pt[0]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[0]-0.03)),119.) : '\#it{{p}}_{{T}} [GeV]');
@OneLep/*: lep_ptcorrcoarse_vs_eta;
TH2F('lep_ptcorrcoarse_vs_etacoarse' , '' , {{0, 1.6, 2.4}}, {{0., 10., 20., 25., 30., 40., 120.}} ) << (abs(lep_eta[0]) : '|\#eta|', TMath::Min(lep_pt[0]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[0]-0.03)),119.) : '\#it{{p}}_{{T}} [GeV]');
@OneLep/*: lep_ptcorrcoarse_vs_etacoarse;
TH2F('el3l_ptcorrcoarse_vs_etacoarse' , '' , {{0, 1.6, 2.4}}, {{0., 10., 20., 25., 30., 40., 120.}} ) << (abs(lep_eta[0]) : '|\#eta|', TMath::Min(lep_pt[0]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[0]-0.05)),119.) : '\#it{{p}}_{{T}} [GeV]');
@OneLep/*: el3l_ptcorrcoarse_vs_etacoarse;
TH2F('mu3l_ptcorrcoarse_vs_etacoarse' , '' , {{0, 1.6, 2.4}}, {{0., 10., 20., 25., 30., 40., 120.}} ) << (abs(lep_eta[0]) : '|\#eta|', TMath::Min(lep_pt[0]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[0]-0.07)),119.) : '\#it{{p}}_{{T}} [GeV]');
@OneLep/*: mu3l_ptcorrcoarse_vs_etacoarse;
TH1F('lep_pt' , '' , 180 , 0. , 250 ) << (lep_pt[0] : '\#it{{p}}_{{T}} [GeV]');
@OneLep/*: lep_pt;
TH1F('lep_pt' , '' , 180 , 0. , 250 ) << (lep_pt[0] : '\#it{{p}}_{{T}} [GeV]');
@OneLep/*: lep_pt;
TH1F('lep_pdgId' , '' , 40 , -20. , 20 ) << (lep_pdgId[0] : 'Lepton PDG ID');
@OneLep/*: lep_pdgId;
TH1F('lep_ptcorr' , '' , 180 , 0. , 250 ) << (lep_pt[0]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[0]-0.03)) : '\#it{{p}}_{{T, cone-corr}} [GeV]');
@OneLep/*: lep_ptcorr;
TH1F('lep_ptcorrvarbin' , '' , {{0., 5., 10., 15., 20., 25., 30., 35., 40., 45., 60., 80., 120.}}) << (TMath::Min(lep_pt[0]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[0]-0.03)),119.) : '\#it{{p}}_{{T, cone-corr}} [GeV]');
@OneLep/*: lep_ptcorrvarbin;
TH1F('lep_ptcorrvarbincoarse' , '' , {{0., 10., 20., 25., 30., 40., 120.}}) << (TMath::Min(lep_pt[0]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[0]-0.03)),119.) : '\#it{{p}}_{{T, cone-corr}} [GeV]');
@OneLep/*: lep_ptcorrvarbincoarse;
TH1F('el3l_ptcorrvarbincoarse' , '' , {{0., 10., 20., 25., 30., 40., 120.}}) << (TMath::Min(lep_pt[0]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[0]-0.05)),119.) : '\#it{{p}}_{{T, cone-corr}} [GeV]');
@OneLep/*: el3l_ptcorrvarbincoarse;
TH1F('mu3l_ptcorrvarbincoarse' , '' , {{0., 10., 20., 25., 30., 40., 120.}}) << (TMath::Min(lep_pt[0]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[0]-0.07)),119.) : '\#it{{p}}_{{T, cone-corr}} [GeV]');
@OneLep/*: mu3l_ptcorrvarbincoarse;
TH1F('lep_yield' , '' , 1, 0, 1) << (0 : 'yield');
@OneLep/*: lep_yield;
TH1F('lep_eta' , '' , 180 , -2.5 , 2.5 ) << (lep_eta[0] : '\#eta');
@OneLep/*: lep_eta;
TH1F('lep_etavarbin' , '' , {{-2.5, -2.1, -1.6, -1.0, 0.0, 1.0, 1.6, 2.1, 2.5}} ) << (lep_eta[0] : '\#eta');
@OneLep/*: lep_etavarbin;
TH1F('lep_relIso03EAv2Lep' , '' , 180 , 0.0 , 0.6 ) << (lep_relIso03EAv2Lep[0] : 'I_{{R=0.3,EA,Lep}}');
@OneLep/*: lep_relIso03EAv2Lep;
TH1F('mu_ptcorrvarbin' , '' , {{0., 5., 10., 15., 20., 25., 30., 35., 40., 45., 60., 80., 120.}}) << ((TMath::Min(lep_pt[0]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[0]-0.03)),119.))*(abs(lep_pdgId[0])==13)+(TMath::Min(lep_pt[1]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[1]-0.03)),119.))*(abs(lep_pdgId[1])==13) : '\#it{{p}}_{{T, cone-corr, mu}} [GeV]');
@TwoMu/*: mu_ptcorrvarbin;
TH1F('mu_ptcorrvarbincoarse' , '' , {{0., 10., 20., 25., 30., 40., 120.}}) << ((TMath::Min(lep_pt[0]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[0]-0.03)),119.))*(abs(lep_pdgId[0])==13)+(TMath::Min(lep_pt[1]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[1]-0.03)),119.))*(abs(lep_pdgId[1])==13) : '\#it{{p}}_{{T, cone-corr, mu}} [GeV]');
@TwoMu/*: mu_ptcorrvarbincoarse;
TH1F('mu_yield' , '' , 1, 0, 1) << (0 : 'yield');
@TwoMu/*: mu_yield;
TH2F('mu_ptcorr_vs_eta' , '' , {{0, 0.9, 1.6, 1.9, 2.4}}, {{0., 5., 10., 15., 20., 25., 30., 35., 40., 45., 60., 80., 120.}} ) << ((abs(lep_eta[0]))*(abs(lep_pdgId[0])==13)+(abs(lep_eta[1]))*(abs(lep_pdgId[1])==13) : '|\#eta|', (TMath::Min(lep_pt[0]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[0]-0.03)),119.))*(abs(lep_pdgId[0])==13)+(TMath::Min(lep_pt[1]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[1]-0.03)),119.))*(abs(lep_pdgId[1])==13) : '\#it{{p}}_{{T, cone-corr, mu}} [GeV]');
@TwoMu/*: mu_ptcorr_vs_eta;
TH2F('mu_ptcorrcoarse_vs_eta' , '' , {{0, 0.9, 1.6, 1.9, 2.4}}, {{0., 10., 20., 25., 30., 40., 60., 120.}} ) << ((abs(lep_eta[0]))*(abs(lep_pdgId[0])==13)+(abs(lep_eta[1]))*(abs(lep_pdgId[1])==13) : '|\#eta|', (TMath::Min(lep_pt[0]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[0]-0.03)),119.))*(abs(lep_pdgId[0])==13)+(TMath::Min(lep_pt[1]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[1]-0.03)),119.))*(abs(lep_pdgId[1])==13) : '\#it{{p}}_{{T, cone-corr, mu}} [GeV]');
@TwoMu/*: mu_ptcorrcoarse_vs_eta;
TH2F('mu_ptcorrcoarse_vs_etacoarse' , '' , {{0, 1.6, 2.4}}, {{0., 10., 20., 25., 30., 40., 120.}} ) << ((abs(lep_eta[0]))*(abs(lep_pdgId[0])==13)+(abs(lep_eta[1]))*(abs(lep_pdgId[1])==13) : '|\#eta|', (TMath::Min(lep_pt[0]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[0]-0.03)),119.))*(abs(lep_pdgId[0])==13)+(TMath::Min(lep_pt[1]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[1]-0.03)),119.))*(abs(lep_pdgId[1])==13) : '\#it{{p}}_{{T, cone-corr, mu}} [GeV]');
@TwoMu/*: mu_ptcorrcoarse_vs_etacoarse;
TH1F('mu_pt' , '' , 180 , 0., 250) << ((lep_pt[0])*(abs(lep_pdgId[0])==13)+(lep_pt[1])*(abs(lep_pdgId[1])==13) : '\#it{{p}}_{{T, \#mu}} [GeV]');
@TwoMu/*: mu_pt;
TH1F('mu_eta' , '' , 180 , -2.5, 2.5) << ((lep_eta[0])*(abs(lep_pdgId[0])==13)+(lep_eta[1])*(abs(lep_pdgId[1])==13) : '\#eta_{{\#mu}}');
@TwoMu/*: mu_eta;
TH1F('mu_etavarbin' , '' , {{-2.5, -2.1, -1.6, -1.0, 0.0, 1.0, 1.6, 2.1, 2.5}} ) << (lep_eta[0] : '\#eta');
@TwoMu/*: mu_etavarbin;
TH1F('mu_relIso03EAv2Lep' , '' , 180 , 0., 0.6) << ((lep_relIso03EAv2Lep[0])*(abs(lep_pdgId[0])==13)+(lep_relIso03EAv2Lep[1])*(abs(lep_pdgId[1])==13) : 'I_{{R=0.3,EA,Lep,\#mu}}');
@TwoMu/*: mu_relIso03EAv2Lep;
TH2F('el_ptcorr_vs_eta' , '' , {{0, 0.9, 1.6, 1.9, 2.4}}, {{0., 5., 10., 15., 20., 25., 30., 35., 40., 45., 60., 80., 120.}} ) << ((abs(lep_eta[0]))*(abs(lep_pdgId[0])==11)+(abs(lep_eta[1]))*(abs(lep_pdgId[1])==11) : '|\#eta|', (TMath::Min(lep_pt[0]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[0]-0.03)),119.))*(abs(lep_pdgId[0])==11)+(TMath::Min(lep_pt[1]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[1]-0.03)),119.))*(abs(lep_pdgId[1])==11) : '\#it{{p}}_{{T, cone-corr, mu}} [GeV]');
@TwoEl/*: el_ptcorr_vs_eta;
TH2F('el_ptcorrcoarse_vs_eta' , '' , {{0, 0.9, 1.6, 1.9, 2.4}}, {{0., 10., 20., 25., 30., 40., 120.}} ) << ((abs(lep_eta[0]))*(abs(lep_pdgId[0])==11)+(abs(lep_eta[1]))*(abs(lep_pdgId[1])==11) : '|\#eta|', (TMath::Min(lep_pt[0]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[0]-0.03)),119.))*(abs(lep_pdgId[0])==11)+(TMath::Min(lep_pt[1]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[1]-0.03)),119.))*(abs(lep_pdgId[1])==11) : '\#it{{p}}_{{T, cone-corr, mu}} [GeV]');
@TwoEl/*: el_ptcorrcoarse_vs_eta;
TH2F('el_ptcorrcoarse_vs_etacoarse' , '' , {{0, 1.6, 2.4}}, {{0., 10., 20., 25., 30., 40., 120.}} ) << ((abs(lep_eta[0]))*(abs(lep_pdgId[0])==11)+(abs(lep_eta[1]))*(abs(lep_pdgId[1])==11) : '|\#eta|', (TMath::Min(lep_pt[0]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[0]-0.03)),119.))*(abs(lep_pdgId[0])==11)+(TMath::Min(lep_pt[1]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[1]-0.03)),119.))*(abs(lep_pdgId[1])==11) : '\#it{{p}}_{{T, cone-corr, mu}} [GeV]');
@TwoEl/*: el_ptcorrcoarse_vs_etacoarse;
TH1F('el_ptcorrvarbin' , '' , {{0., 5., 10., 15., 20., 25., 30., 35., 40., 45., 60., 80., 120.}}) << ((TMath::Min(lep_pt[0]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[0]-0.03)),119.))*(abs(lep_pdgId[0])==11)+(TMath::Min(lep_pt[1]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[1]-0.03)),119.))*(abs(lep_pdgId[1])==11) : '\#it{{p}}_{{T, cone-corr, el}} [GeV]');
@TwoEl/*: el_ptcorrvarbin;
TH1F('el_ptcorrvarbincoarse' , '' , {{0., 10., 20., 25., 30., 40., 120.}}) << ((TMath::Min(lep_pt[0]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[0]-0.03)),119.))*(abs(lep_pdgId[0])==11)+(TMath::Min(lep_pt[1]*(1.+TMath::Max(0.,lep_relIso03EAv2Lep[1]-0.03)),119.))*(abs(lep_pdgId[1])==11) : '\#it{{p}}_{{T, cone-corr, el}} [GeV]');
@TwoEl/*: el_ptcorrvarbincoarse;
TH1F('el_yield' , '' , 1, 0, 1) << (0 : 'yield');
@TwoEl/*: el_yield;
TH1F('el_pt' , '' , 180 , 0., 250) << ((lep_pt[0])*(abs(lep_pdgId[0])==11)+(lep_pt[1])*(abs(lep_pdgId[1])==11) : '\#it{{p}}_{{T, el}} [GeV]');
@TwoEl/*: el_pt;
TH1F('el_eta' , '' , 180 , -2.5, 2.5) << ((lep_eta[0])*(abs(lep_pdgId[0])==11)+(lep_eta[1])*(abs(lep_pdgId[1])==11) : '\#eta_{{el}}');
@TwoEl/*: el_eta;
TH1F('el_etavarbin' , '' , {{-2.5, -2.1, -1.6, -1.0, 0.0, 1.0, 1.6, 2.1, 2.5}} ) << (lep_eta[0] : '\#eta');
@TwoEl/*: el_etavarbin;
TH1F('el_relIso03EAv2Lep' , '' , 180 , 0., 0.6) << ((lep_relIso03EAv2Lep[0])*(abs(lep_pdgId[0])==11)+(lep_relIso03EAv2Lep[1])*(abs(lep_pdgId[1])==11) : 'I_{{R=0.3,EA,Lep,el}}');
@TwoEl/*: el_relIso03EAv2Lep;
TH1F('Mjj_el' , '' , 180 , 0., 180. ) << ({Mjj} : '\#it{{m}}_{{jj}} [GeV]');
@TwoEl/*: Mjj_el;
TH1F('Mjj_mu' , '' , 180 , 0., 180. ) << ({Mjj} : '\#it{{m}}_{{jj}} [GeV]');
@TwoMu/*: Mjj_mu;
TH1F('Mll_el' , '' , 180 , 0., 180. ) << (MllSS : '\#it{{m}}_{{ll}} [GeV]');
@TwoEl/*: Mll_el;
TH1F('Mll_mu' , '' , 180 , 0., 180. ) << (MllSS : '\#it{{m}}_{{ll}} [GeV]');
@TwoMu/*: Mll_mu;
TH1F('DPhill_el' , '' , 180 , 0., 3.1416 ) << (TMath::Abs(TVector2::Phi_mpi_pi(lep_phi[0]-lep_phi[1])) : '\#it{{m}}_{{ll}} [GeV]');
@TwoEl/*: DPhill_el;
TH1F('DPhill_mu' , '' , 180 , 0., 3.1416 ) << (TMath::Abs(TVector2::Phi_mpi_pi(lep_phi[0]-lep_phi[1])) : '\#it{{m}}_{{ll}} [GeV]');
@TwoMu/*: DPhill_mu;
TH1F('MET_el' , '' , 180 , 0., 180. ) << ({MET} : 'MET [GeV]');
@TwoEl/*: MET_el;
TH1F('MET_mu' , '' , 180 , 0., 180. ) << ({MET} : 'MET [GeV]');
@TwoMu/*: MET_mu;
TH1F('MTmax_el' , '' , 180 , 0., 180. ) << ({MTmax} : '\#it{{m}}_{{T,max}} [GeV]');
@TwoEl/*: MTmax_el;
TH1F('MTmax_mu' , '' , 180 , 0., 180. ) << ({MTmax} : '\#it{{m}}_{{T,max}} [GeV]');
@TwoMu/*: MTmax_mu;
TH1F('nb_el' , '' , 5, 0., 5.) << ({nb} : 'N_{{b-jets}}');
@TwoEl/*: nb_el;
TH1F('nb_mu' , '' , 5, 0., 5.) << ({nb} : 'N_{{b-jets}}');
@TwoMu/*: nb_mu;
TH1F('nj30_el' , '' , 5, 0., 5.) << ({nj30} : 'N_{{jets,30,cent}}');
@TwoEl/*: nj30_el;
TH1F('nj30_mu' , '' , 5, 0., 5.) << ({nj30} : 'N_{{jets,30,cent}}');
@TwoMu/*: nj30_mu;
TH1F('nj_el' , '' , 5, 0., 5.) << ({nj} : 'N_{{jets,all}}');
@TwoEl/*: nj_el;
TH1F('nj_mu' , '' , 5, 0., 5.) << ({nj} : 'N_{{jets,all}}');
@TwoMu/*: nj_mu;
TH1F('Mll_Z' , '' , 180 , 60., 120. ) << (MllSS : '\#it{{m}}_{{ll}} [GeV]');
@TwoLepOS/*: Mll_Z;
TH1F('MTOneLep' , '' , 180 , 0., 180. ) << ({MT} : '\#it{{m}}_{{T}} [GeV]');
@*/*: MTOneLep;
TH1F('MTOneLepFixed' , '' , 20 , 0., 200. ) << ({MT} : '\#it{{m}}_{{T}} [GeV]');
@*/*: MTOneLepFixed;
TH1F('nvtx' , '' , 60 , 0., 60. ) << (nVert : 'N_{{vtx}}');
@*/*: nvtx;
""".format(Mjj="Mjj" + jecvar,
MET="met" + jecvar + "_pt",
nb="nb" + jecvar,
nj30="nj30" + jecvar,
nj="nj" + jecvar,
MT=MTexpr,
MTmax="MTmax" + jecvar))
f.close()
#
#
# Book Analysis Jobs (Histogramming, Cutflow, Event lists)
#
#
histojob = TQHistoMakerAnalysisJob()
histojob.importJobsFromTextFiles(filename, cuts, "*",
True if index < 0 else False)
# Analysis jobs
cutflowjob = TQCutflowAnalysisJob("cutflow")
cuts.addAnalysisJob(cutflowjob, "*")
# Eventlist jobs (use this if we want to print out some event information in a text format e.g. run, lumi, evt or other variables.)
#eventlistjob = TQEventlistAnalysisJob("eventlist")
#eventlistjob.importJobsFromTextFiles("eventlist.cfg", cuts, "*", True)
# Print cuts and numebr of booked analysis jobs for debugging purpose
if index < 0:
samples.printContents("t[*status]dr")
cuts.printCut("trd")
return
#
#
# Add custom tqobservable that can do more than just string based draw statements
#
#
from QFramework import TQWWWMTOneLep, TQWWWClosureEvtType
customobservables = {}
customobservables["MTOneLep"] = TQWWWMTOneLep("MTOneLep")
customobservables["ClosureEvtType"] = TQWWWClosureEvtType("ClosureEvtType")
TQObservable.addObservable(customobservables["MTOneLep"], "MTOneLep")
TQObservable.addObservable(customobservables["ClosureEvtType"],
"ClosureEvtType")
#
#
# Loop over the samples
#
#
# setup a visitor to actually loop over ROOT files
vis = TQAnalysisSampleVisitor(cuts, True)
#vis.setMaxEvents(30000) # to debug by restricting the looping to 30k max events
if index >= 0:
# Get all sample lists
sample_names, sample_full_names = getSampleLists(samples)
# Select the job based on the index
sample_name = sample_names[index]
sample_full_name = sample_full_names[sample_name]
# Run the job!
samples.visitSampleFolders(vis, "/*/{}".format(sample_full_name))
# Write the output histograms and cutflow cut values and etc.
samples.writeToFile(".output_{}.root".format(sample_name), True)
else:
# Run the job!
samples.visitSampleFolders(vis)
# Write the output histograms and cutflow cut values and etc.
samples.writeToFile("output.root", True)
print ""
print "NOTE : Running with default mode of MODE=0!"
print "NOTE : Running with default mode of MODE=0!"
print "NOTE : Running with default mode of MODE=0!"
print "NOTE : Running with default mode of MODE=0!"
mode = 0
donotrun = len(sys.argv) >= 3
# Delete previous remnants
os.system("rm -f .output_*.root")
os.system("rm -f .histo.mr.*.cfg")
import multiprocessing
samples = TQSampleFolder("samples")
connectNtuples(samples, samplescfgpath, nfspath, ">4", ">5")
# Get all sample lists
sample_names, sample_full_names = getSampleLists(samples)
njobs = len(sample_names)
if donotrun:
main(-1, mode, donotrun)
sys.exit()
jobs = []
for i in range(njobs):
p = multiprocessing.Process(target=main, args=(
i,
mode,
donotrun,
#!/bin/env python
import os
import sys
import ROOT
from QFramework import TQSampleFolder, TQEventlistPrinter, TQTaggable
from rooutil import plottery_wrapper as p
ROOT.gROOT.SetBatch(True)
path = "eventlists/"
filename = sys.argv[1]
samples = TQSampleFolder.loadSampleFolder("{}:samples".format(filename))
printer = TQEventlistPrinter(samples)
printer.addCut("SRSSeeFull")
printer.addCut("SRSSemFull")
printer.addCut("SRSSmmFull")
printer.addCut("SideSSeeFull")
printer.addCut("SideSSemFull")
printer.addCut("SideSSmmFull")
printer.addCut("SR0SFOSFull")
printer.addCut("SR1SFOSFull")
printer.addCut("SR2SFOSFull")
printer.addCut("WZCRSSeeFull")
printer.addCut("WZCRSSemFull")
printer.addCut("WZCRSSmmFull")
printer.addCut("WZCR1SFOSFull")
printer.addCut("WZCR2SFOSFull")
#printer.addProcess("/sig/whwww")
printer.addProcess("/typebkg/lostlep/WZ")
def main(transformation, p, batch_index):
ott_parser = OTTParser()
transformation = ott_parser.parse_file(transformation)
transformation.set_root(transformation)
batch_index = str(batch_index) or "all"
transformation.compile({"-batch-index": batch_index})
if p:
print transformation
for input in transformation.get("input"):
sf = False
print "Open: %s" % input.attributes['path']
if "sample-folder" in input.attributes:
input_file = TQSampleFolder.loadSampleFolder(
"%s:%s" %
(input.attributes['path'], input.attributes['sample-folder']))
sf = True
else:
input_file = ROOT.TFile.Open(input.attributes['path'])
for output in transformation.get("output"):
output_file = ROOT.TFile.Open(output.attributes['path'],
"RECREATE")
for TH1F in output.get("TH1F"):
output_file.cd()
min = float(TH1F.get("min")[0].content)
max = float(TH1F.get("max")[0].content)
destination = TH1F.get("destination")[0].content
bins = TH1F.get("bin")
token = destination.rsplit("/", 1)
if len(token) == 1:
token = "", token[0]
if not output_file.Get(token[0]):
output_file.mkdir(token[0])
output_file.cd(token[0])
destination_name = token[1]
print " TH1F(\"%s\", \"\", %d, %g, %g) -> %s %s" % \
(destination_name, len(bins), min, max,
output.attributes['path'], destination)
histogram = ROOT.TH1F(destination_name, "", len(bins), min,
max)
for i, bin in enumerate(bins):
histogram.SetBinContent(i + 1, float(bin.content))
histogram.Write()
for duplicate in output.get("duplicate"):
output_file.cd()
source = duplicate.get("source")[0].content
destination = duplicate.get("destination")[0].content
token = destination.rsplit("/", 1)
if len(token) == 1:
token = "", token[0]
if not output_file.Get(token[0]):
output_file.mkdir(token[0])
output_file.cd(token[0])
destination_name = token[1]
if sf:
cut = duplicate.get("cut")[0].content
histogram_name = duplicate.get("histogram")[0].content
histogram = input_file.getHistogram(
ROOT.TString(source),
ROOT.TString("%s/%s" % (cut, histogram_name)))
if not histogram:
continue
print " %s %s/%s -> %s %s" % \
(source, cut, histogram_name,
output.attributes['path'], destination)
clone = histogram.Clone(destination_name)
clone.Write()
# todo
output_file.Close()
def main(model="sm", mass0=-1, mass1=-1):
# Print the model name and mass points
print model, mass0, mass1
# Suffis that will be attached to output file names for bookkeeping
suffix = make_suffix(model, mass0, mass1)
# Create directory where the outputs will be
makedir("statinputs")
# Open input files
filename = "output_sf_applied.root"
samples = TQSampleFolder.loadSampleFolder("{}:samples".format(filename))
samples_jec_up = TQSampleFolder.loadSampleFolder("output_jec_up.root:samples")
samples_jec_dn = TQSampleFolder.loadSampleFolder("output_jec_dn.root:samples")
samples_gen_met = TQSampleFolder.loadSampleFolder("output_gen_met.root:samples")
# Set the histogram name to perform the fit on (we use the 9 bin histogram
histname = "{SRSSeeFull,SRSSemFull,SRSSmmFull,SideSSeeFull,SideSSemFull,SideSSmmFull,SR0SFOSFull,SR1SFOSFull,SR2SFOSFull}"
# We have 8 categories for the fit
processes = [ "vbsww" , "ttw" , "lostlep" , "photon" , "qflip" , "prompt" , "fake" , "www" ,]
#######
# NOTE "www" means "signal" - i.e. for whsusy model www = whsusy and www is included in prompt
#######
# Set the diectionary of the paths where we will retrieve the histograms from
sampledirpaths = {
"vbsww" : "/typebkg/?/VBSWW",
"ttw" : "/typebkg/?/ttW",
"lostlep" : "/typebkg/lostlep/[ttZ+WZ+Other]",
"photon" : "/typebkg/photon/[ttZ+WZ+Other]",
"qflip" : "/typebkg/qflip/[ttZ+WZ+Other]",
"prompt" : "/typebkg/prompt/[ttZ+WZ+Other]" if model == "sm" else "/typebkg/prompt/[ttZ+WZ+Other]+sig" if model != "whwww" else "/typebkg/prompt/[ttZ+WZ+Other]+sig/www",
"fake" : "/fake",
"www" : get_sigmodel_path(model, mass0, mass1),
}
# Create output file
ofile = ROOT.TFile("statinputs/hist_{}.root".format(suffix), "recreate")
ofile.cd()
# Array of numbers where we will aggregate some results for nice tables
rates = {}
# Write histograms
for process in processes:
#print process, histname
# Get nominal histogram
h_nom = samples.getHistogram(sampledirpaths[process], histname).Clone(process)
# If lost lepton get the nominal number directly from the AN Table 13
if process == "lostlep": h_nom = set_to_lostlep_nominal_hist(h_nom)
# If whsusy model with signal then get the average of the two histogram
#if model == "whsusy" and process == "www": set_to_average_and_write_genmet_syst_hist(h_nom, samples_gen_met.getHistogram(sampledirpaths[process], histname).Clone(process))
# Write nominal histogram
#h_nom.Write()
mask_bins(h_nom).Write()
# Save the total number that will be used to output to datacards
rates[process] = h_nom.Integral()
# Nominal histogram errors are needed to be varied one by one to create an effective uncorrelated histograms
if process != "lostlep" and process != "fake": write_nominal_stat_variations(h_nom, process)
# Write systematic histograms that are from weight variations
for systvar in systvars:
# Some process or some variations do not need to be written
if do_not_write_syst_hist(process, systvar, model):
continue
# Write the systvariation histograms
#samples.getHistogram(sampledirpaths[process], histname.replace("Full", "Full" + systvar)).Clone(process + "_" + systvar).Write()
mask_bins(samples.getHistogram(sampledirpaths[process], histname.replace("Full", "Full" + systvar)).Clone(process + "_" + systvar)).Write()
# JEC systematic histograms need to be called from a different sample output
if process != "fake" and process != "lostlep":
#samples_jec_up.getHistogram(sampledirpaths[process], histname).Clone(process + "_JECUp").Write()
#samples_jec_dn.getHistogram(sampledirpaths[process], histname).Clone(process + "_JECDown").Write()
mask_bins(samples_jec_up.getHistogram(sampledirpaths[process], histname).Clone(process + "_JECUp")).Write()
mask_bins(samples_jec_dn.getHistogram(sampledirpaths[process], histname).Clone(process + "_JECDown")).Write()
# Lost lepton has special treatment
if process == "lostlep":
#write_lostlep_stat_variations(h_nom)
#write_lostlep_syst_variations(h_nom)
write_lostlep_CRstat_variations(h_nom)
write_lostlep_TFstat_variations(h_nom)
write_lostlep_TFsyst_variations(h_nom)
write_lostlep_Mjjsyst_variations(h_nom)
write_lostlep_MllSSsyst_variations(h_nom)
write_lostlep_Mll3lsyst_variations(h_nom)
# WWW signal theory systematics
if model == "sm":
if process == "www":
write_www_theory_syst_variations(h_nom)
# Fake has AR statistics
if process == "fake":
write_fake_ARstat_variations(h_nom)
# Write data histogram
h_data = samples.getHistogram("/typebkg", histname).Clone("data_obs")
for i in xrange(0,h_data.GetNbinsX()+2):
h_data.SetBinContent(i, int(h_data.GetBinContent(i)))
#h_data.Write()
mask_bins(h_data).Write()
datacard="""imax 1 number of bins
jmax * number of processes
kmax * number of nuisance parameters
----------------------------------------------------------------------------------------------------------------------------------
shapes * * statinputs/hist_{}.root $PROCESS $PROCESS_$SYSTEMATIC
----------------------------------------------------------------------------------------------------------------------------------
bin SR
observation {:.1f}
----------------------------------------------------------------------------------------------------------------------------------
bin SR SR SR SR SR SR SR SR
process 0 1 2 3 4 5 6 7
process www fake photon lostlep qflip prompt ttw vbsww
rate {:<6.3f} {:<6.3f} {:<6.3f} {:<6.3f} {:<6.3f} {:<6.3f} {:<6.3f} {:<6.3f}
----------------------------------------------------------------------------------------------------------------------------------
JEC shape 1 - 1 - - 1 1 1
LepSF shape 1 - 1 - - 1 1 1
TrigSF shape 1 - 1 - - 1 1 1
BTagHF shape 1 - 1 - - 1 1 1
BTagLF shape 1 - 1 - - 1 1 1
Pileup shape 1 - 1 - - 1 1 1
FakeRateEl shape - 1 - - - - - -
FakeRateMu shape - 1 - - - - - -
FakeClosureEl shape - 1 - - - - - -
FakeClosureMu shape - 1 - - - - - -
LostLepSyst shape - - - 1 - - - -
MjjModeling shape - - - 1 - - - -
MllSSModeling shape - - - 1 - - - -
Mll3lModeling shape - - - 1 - - - -
SigXSec lnN 1.06 - - - - - - -
LumSyst lnN 1.025 - 1.025 - 1.025 1.025 1.025 1.025
vbsww_xsec lnN - - - - - - - 1.20
vbsww_validation lnN - - - - - - - 1.22
ttw_xsec lnN - - - - - - 1.20 -
ttw_validation lnN - - - - - - 1.18 -
photon_syst lnN - - 1.50 - - - - -
qflip_syst lnN - - - - 1.50 - - -
www_stat_in_ee shape 1 - - - - - - -
www_stat_in_em shape 1 - - - - - - -
www_stat_in_mm shape 1 - - - - - - -
www_stat_out_ee shape 1 - - - - - - -
www_stat_out_em shape 1 - - - - - - -
www_stat_out_mm shape 1 - - - - - - -
www_stat_0sfos shape 1 - - - - - - -
www_stat_1sfos shape 1 - - - - - - -
www_stat_2sfos shape 1 - - - - - - -
fake_ARstat_in_ee shape - 1 - - - - - -
fake_ARstat_in_em shape - 1 - - - - - -
fake_ARstat_in_mm shape - 1 - - - - - -
fake_ARstat_out_ee shape - 1 - - - - - -
fake_ARstat_out_em shape - 1 - - - - - -
fake_ARstat_out_mm shape - 1 - - - - - -
fake_ARstat_0sfos shape - 1 - - - - - -
fake_ARstat_1sfos shape - 1 - - - - - -
fake_ARstat_2sfos shape - 1 - - - - - -
photon_stat_in_ee shape - - 1 - - - - -
photon_stat_in_em shape - - 1 - - - - -
photon_stat_in_mm shape - - 1 - - - - -
photon_stat_out_ee shape - - 1 - - - - -
photon_stat_out_em shape - - 1 - - - - -
photon_stat_out_mm shape - - 1 - - - - -
photon_stat_0sfos shape - - 1 - - - - -
photon_stat_1sfos shape - - 1 - - - - -
photon_stat_2sfos shape - - 1 - - - - -
lostlep_stat_in_ee shape - - - 1 - - - -
lostlep_stat_in_em shape - - - 1 - - - -
lostlep_stat_in_mm shape - - - 1 - - - -
lostlep_stat_out_ee shape - - - 1 - - - -
lostlep_stat_out_em shape - - - 1 - - - -
lostlep_stat_out_mm shape - - - 1 - - - -
lostlep_stat_0sfos shape - - - 1 - - - -
lostlep_stat_1sfos shape - - - 1 - - - -
lostlep_stat_2sfos shape - - - 1 - - - -
qflip_stat_in_ee shape - - - - 1 - - -
qflip_stat_in_em shape - - - - 1 - - -
qflip_stat_in_mm shape - - - - 1 - - -
qflip_stat_out_ee shape - - - - 1 - - -
qflip_stat_out_em shape - - - - 1 - - -
qflip_stat_out_mm shape - - - - 1 - - -
qflip_stat_0sfos shape - - - - 1 - - -
qflip_stat_1sfos shape - - - - 1 - - -
qflip_stat_2sfos shape - - - - 1 - - -
prompt_stat_in_ee shape - - - - - 1 - -
prompt_stat_in_em shape - - - - - 1 - -
prompt_stat_in_mm shape - - - - - 1 - -
prompt_stat_out_ee shape - - - - - 1 - -
prompt_stat_out_em shape - - - - - 1 - -
prompt_stat_out_mm shape - - - - - 1 - -
prompt_stat_0sfos shape - - - - - 1 - -
prompt_stat_1sfos shape - - - - - 1 - -
prompt_stat_2sfos shape - - - - - 1 - -
ttw_stat_in_ee shape - - - - - - 1 -
ttw_stat_in_em shape - - - - - - 1 -
ttw_stat_in_mm shape - - - - - - 1 -
ttw_stat_out_ee shape - - - - - - 1 -
ttw_stat_out_em shape - - - - - - 1 -
ttw_stat_out_mm shape - - - - - - 1 -
ttw_stat_0sfos shape - - - - - - 1 -
ttw_stat_1sfos shape - - - - - - 1 -
ttw_stat_2sfos shape - - - - - - 1 -
vbsww_stat_in_ee shape - - - - - - - 1
vbsww_stat_in_em shape - - - - - - - 1
vbsww_stat_in_mm shape - - - - - - - 1
vbsww_stat_out_ee shape - - - - - - - 1
vbsww_stat_out_em shape - - - - - - - 1
vbsww_stat_out_mm shape - - - - - - - 1
vbsww_stat_0sfos shape - - - - - - - 1
vbsww_stat_1sfos shape - - - - - - - 1
vbsww_stat_2sfos shape - - - - - - - 1
lostlep_CRstat_ee shape - - - 1 - - - -
lostlep_CRstat_em shape - - - 1 - - - -
lostlep_CRstat_mm shape - - - 1 - - - -
lostlep_CRstat_0sfos shape - - - 1 - - - -
lostlep_CRstat_1sfos shape - - - 1 - - - -
lostlep_CRstat_2sfos shape - - - 1 - - - -
""".format(suffix, h_data.Integral(), rates["www"], rates["fake"], rates["photon"], rates["lostlep"], rates["qflip"], rates["prompt"], rates["ttw"], rates["vbsww"])
if model == "sm":
datacard += """SigPDF shape 1 - - - - - - -
SigQsq shape 1 - - - - - - -
SigAlpha shape 1 - - - - - - -
"""
if model == "whsusy":
datacard += """ISR shape 1 - - - - - - -
Qsq shape 1 - - - - - - -
PDF shape 1 - - - - - - -
AlphaS shape 1 - - - - - - -
GenMET shape 1 - - - - - - -
"""
f = open('statinputs/datacard_{}.txt'.format(suffix), 'w')
f.write(datacard)
f.close()
ofile.Close()