def getHistogram(path,plot,runRange,background):
treesEE = readTrees(path,"EE")
treesMM = readTrees(path,"MuMu")
if "NLL" in path:
plot.cuts = plot.cuts.replace(" && metFilterSummary > 0", "")
eeSF = getTriggerScaleFactor("EE", "Inclusive", runRange)[0]
mmSF = getTriggerScaleFactor("EE", "Inclusive", runRange)[0]
eventCounts = totalNumberOfGeneratedEvents(path)
proc = Process(getattr(Backgrounds[runRange.era],background),eventCounts)
histSF = proc.createCombinedHistogram(runRange.lumi,plot,treesEE,treesMM, 1.0, eeSF, mmSF)
return histSF
def getYields(runRange):
import ratios
import pickle
path = locations[runRange.era].dataSetPathNLL
EE = readTrees(path, "EE")
EM = readTrees(path, "EMu")
MM = readTrees(path, "MuMu")
### Mass bins for Morion 2017 SRs, summed low and high mass regions + legacy regions
massRegions = [
"mass20To60", "mass60To86", "mass86To96", "mass96To150",
"mass150To200", "mass200To300", "mass300To400", "mass400"
]
### Two likelihood bins and MT2 cut
nLLRegions = ["lowNLL", "highNLL"]
MT2Regions = ["highMT2"]
nBJetsRegions = ["zeroBJets", "oneOrMoreBJets"]
signalBins = []
signalCuts = {}
plot = getPlot("mllPlotROutIn")
for massRegion in massRegions:
for nLLRegion in nLLRegions:
for MT2Region in MT2Regions:
for nBJetsRegion in nBJetsRegions:
signalBins.append(
"%s_%s_%s_%s" %
(massRegion, nLLRegion, MT2Region, nBJetsRegion))
signalCuts["%s_%s_%s_%s" %
(massRegion, nLLRegion, MT2Region,
nBJetsRegion)] = "%s && %s && %s && %s" % (
massCuts[massRegion], nLLCuts[nLLRegion],
MT2Cuts[MT2Region],
NBJetsCuts[nBJetsRegion])
selection = getRegion("SignalHighMT2DeltaPhiJetMet")
backgrounds = [
"RareWZOnZ", "RareZZLowMassOnZ", "RareTTZOnZ", "RareRestOnZ"
]
plot.addRegion(selection)
plot.addRunRange(runRange)
plot.cuts = plot.cuts.replace("p4.M()", "mll")
plot.variable = plot.variable.replace("p4.M()", "mll")
if runRange.era == "2018":
plot.cuts = plot.cuts.replace("chargeProduct < 0 &&",
"chargeProduct < 0 && vetoHEM == 1 &&")
counts = {}
eventCounts = totalNumberOfGeneratedEvents(path)
defaultCut = plot.cuts
### loop over signal regions
for signalBin in signalBins:
### Add signal cut and remove those that are renamed or already applied
### on NLL datasets
plot.cuts = defaultCut.replace(
"chargeProduct < 0",
"chargeProduct < 0 && %s" % (signalCuts[signalBin]))
plot.cuts = plot.cuts.replace("metFilterSummary > 0 &&", "")
plot.cuts = plot.cuts.replace("&& metFilterSummary > 0", "")
plot.cuts = plot.cuts.replace("triggerSummary > 0 &&", "")
plot.cuts = plot.cuts.replace("p4.Pt()", "pt")
plot.cuts = "bTagWeight*" + plot.cuts
#print plot.cuts
eventCountSF = 0
eventYieldSF = 0
eventYieldEE = 0
eventYieldMM = 0
eventYieldSFTotErr = 0
eventYieldSFStatErr = 0
eventYieldEEStatErr = 0
eventYieldMMStatErr = 0
eventYieldSFSystErr = 0
eventYieldSFUp = 0
eventYieldEEUp = 0
eventYieldMMUp = 0
eventYieldSFDown = 0
eventYieldEEDown = 0
eventYieldMMDown = 0
eventYieldOF = 0
eventYieldOFTotErr = 0
eventYieldOFStatErr = 0
eventYieldOFSystErr = 0
eventYieldOFUp = 0
eventYieldOFDown = 0
counts["%s_bySample" % signalBin] = {}
processes = []
for background in backgrounds:
proc = Process(getattr(Backgrounds[runRange.era], background),
eventCounts)
processes.append(proc)
picklePath = "/home/home4/institut_1b/teroerde/Doktorand/SUSYFramework/frameWorkBase/shelves/scaleFactor_{runRange}_{background}.pkl"
sf = 1.0
sferr = 0.0
if proc.label in ["WZ", "ZZ", "TTZ"]:
if proc.label == "WZ":
backgroundName = "WZTo3LNu"
elif proc.label == "ZZ":
backgroundName = "ZZTo4L"
elif proc.label == "TTZ":
backgroundName = "ttZToLL"
import pickle
with open(
picklePath.format(runRange=runRange.label,
background=backgroundName),
"r") as fi:
scaleFac = pickle.load(fi)
sf = scaleFac["scaleFac"]
sferr = scaleFac["scaleFacErr"]
else:
sferr = proc.uncertainty
sfErrRel = sferr / sf
saveCut = plot.cuts
histoEE = proc.createCombinedHistogram(
runRange.lumi,
plot,
EE,
"None",
1.0,
getTriggerScaleFactor("EE", "inclusive", runRange)[0] * sf,
1.0,
TopWeightUp=False,
TopWeightDown=False,
signal=False,
doTopReweighting=True,
doPUWeights=False,
normalizeToBinWidth=False,
useTriggerEmulation=False)
histoMM = proc.createCombinedHistogram(
runRange.lumi,
plot,
MM,
"None",
1.0,
getTriggerScaleFactor("MM", "inclusive", runRange)[0] * sf,
1.0,
TopWeightUp=False,
TopWeightDown=False,
signal=False,
doTopReweighting=True,
doPUWeights=False,
normalizeToBinWidth=False,
useTriggerEmulation=False)
# RSFOF weighted EMu sample with rMuE parametrization
rMuEPars = corrections[runRange.era].rMuELeptonPt.inclusive
RT = corrections[runRange.era].rSFOFTrig.inclusive.val
corrMap = {}
corrMap["offset"] = rMuEPars.ptOffsetMC
corrMap["falling"] = rMuEPars.ptFallingMC
corrMap["etaParabolaBase"] = rMuEPars.etaParabolaBaseMC
corrMap["etaParabolaMinus"] = rMuEPars.etaParabolaMinusMC
corrMap["etaParabolaPlus"] = rMuEPars.etaParabolaPlusMC
corrMap["norm"] = rMuEPars.normMC
rMuEDummy = "({norm:.3f}*(({offset:.3f} + {falling:.3f}/{pt})*({etaParabolaBase} + ({eta}<-1.6)*{etaParabolaMinus:.3f}*pow({eta}+1.6, 2)+({eta}>1.6)*{etaParabolaPlus:.3f}*pow({eta}-1.6,2) )))"
rMuE_El = rMuEDummy.format(pt="pt1", eta="eta1", **corrMap)
rMuE_Mu = rMuEDummy.format(pt="pt2", eta="eta2", **corrMap)
rMuEWeight = "(0.5*(%s + pow(%s, -1)))*%.3f" % (rMuE_Mu, rMuE_El,
RT)
#tmpCut = plot.cuts
plot.cuts = "%s*%s" % (rMuEWeight, plot.cuts)
histoEM = proc.createCombinedHistogram(
runRange.lumi,
plot,
EM,
"None",
1.0,
getTriggerScaleFactor("EM", "inclusive", runRange)[0] * sf,
1.0,
TopWeightUp=False,
TopWeightDown=False,
signal=False,
doTopReweighting=True,
doPUWeights=False,
normalizeToBinWidth=False,
useTriggerEmulation=False)
#plot.cuts = tmpCut
plot.cuts = saveCut
statErrEE = ROOT.Double()
statErrMM = ROOT.Double()
statErrEM = ROOT.Double()
eventCountSF += histoEE.GetEntries() + histoMM.GetEntries()
procYield = histoEE.IntegralAndError(
0, -1, statErrEE) + histoMM.IntegralAndError(0, -1, statErrMM)
procYield_EE = histoEE.IntegralAndError(0, -1, statErrEE)
procYield_MM = histoMM.IntegralAndError(0, -1, statErrMM)
#if "mass20To60" in signalBin and proc.label == "ZZ":
#print signalBin, procYield
eventYieldSF += procYield
eventYieldSFUp += (procYield) * (1 + sfErrRel)
eventYieldSFDown += (procYield) * (1 - sfErrRel)
eventYieldEE += procYield_EE
eventYieldEEUp += (procYield_EE) * (1 + sfErrRel)
eventYieldEEDown += (procYield_EE) * (1 - sfErrRel)
eventYieldMM += procYield_MM
eventYieldMMUp += (procYield_MM) * (1 + sfErrRel)
eventYieldMMDown += (procYield_MM) * (1 - sfErrRel)
eventYieldSFStatErr = (eventYieldSFStatErr**2 + statErrEE**2 +
statErrMM**2)**0.5
eventYieldEEStatErr = (eventYieldEEStatErr**2 + statErrEE**2)**0.5
eventYieldMMStatErr = (eventYieldMMStatErr**2 + statErrMM**2)**0.5
eventYieldSFSystErr = (eventYieldSFSystErr**2 +
(eventYieldSF * sfErrRel)**2)**0.5
eventYieldOF += histoEM.IntegralAndError(0, -1, statErrEM)
eventYieldOFUp += histoEM.Integral(0, -1) * (1 + proc.uncertainty)
eventYieldOFDown += histoEM.Integral(0,
-1) * (1 - proc.uncertainty)
eventYieldOFStatErr = (eventYieldOFStatErr**2 + statErrEM**2)**0.5
eventYieldOFSystErr = (eventYieldOFSystErr**2 +
(eventYieldOF * sfErrRel)**2)**0.5
counts["%s_bySample" % signalBin][proc.label] = procYield
if proc.label in ["WZ", "ZZ", "TTZ", "Rare"]:
# uncertainty from just this one sample. Used to treat uncertainties uncorrelated between WZ,ZZ,TTZ,Rare
counts["%s_SingleErr%s" % (signalBin, proc.label)] = (
((procYield) * sfErrRel)**2 + statErrEE**2 +
statErrMM**2)**0.5
#if proc.label == "ZZ" and "highNLL" in signalBin:
#print signalBin, gotten
eventYieldSFTotErr = (eventYieldSFStatErr**2 +
eventYieldSFSystErr**2)**0.5
eventYieldOFTotErr = (eventYieldOFStatErr**2 +
eventYieldOFSystErr**2)**0.5
eventCountSF = histoEE.GetEntries() + histoMM.GetEntries()
counts["%s_SF" % signalBin] = eventYieldSF
counts["%s_EE" % signalBin] = eventYieldEE
counts["%s_MM" % signalBin] = eventYieldMM
counts["%s_SF_Stat" % signalBin] = eventYieldSFStatErr
counts["%s_EE_Stat" % signalBin] = eventYieldEEStatErr
counts["%s_MM_Stat" % signalBin] = eventYieldMMStatErr
counts["%s_SF_Syst" % signalBin] = eventYieldSFSystErr
counts["%s_SF_Up" % signalBin] = eventYieldSFUp
counts["%s_EE_Up" % signalBin] = eventYieldEEUp
counts["%s_MM_Up" % signalBin] = eventYieldMMUp
counts["%s_SF_Down" % signalBin] = eventYieldSFDown
counts["%s_EE_Down" % signalBin] = eventYieldEEDown
counts["%s_MM_Down" % signalBin] = eventYieldMMDown
counts["MCEvents_%s_SF" % signalBin] = eventCountSF
counts["%s_OF" % signalBin] = eventYieldOF
counts["%s_OF_Stat" % signalBin] = eventYieldOFStatErr
counts["%s_OF_Syst" % signalBin] = eventYieldOFSystErr
counts["%s_OF_Err" % signalBin] = eventYieldOFTotErr
counts["%s_OF_Up" % signalBin] = eventYieldOFUp
counts["%s_OF_Down" % signalBin] = eventYieldOFDown
fileName = "RareOnZBG_%s" % (runRange.label)
outFilePkl = open("shelves/%s.pkl" % fileName, "w")
pickle.dump(counts, outFilePkl)
outFilePkl.close()