def main():
# Read the datasets
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
#datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Set the signal cross sections to a value from MSSM
# xsect.setHplusCrossSectionsToMSSM(datasets, tanbeta=20, mu=200)
plots.mergeWHandHH(datasets) # merging of WH and HH signals must be done after setting the cross section
# Create counter
eventCounter = counter.EventCounter(datasets)
#eventCounter.normalizeMCByLuminosity()
eventCounter.normalizeMCToLuminosity(1000) # in pb^-1
print "============================================================"
print "Main counter (MC normalized by collision data luminosity)"
print eventCounter.getMainCounterTable().format()
triggerCounter = eventCounter.getSubCounter("Trigger")
triggerCounter.forEachDataset(printTriggerEfficiency)
def main():
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
# datasets.loadLuminosities()
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
plots.mergeWHandHH(datasets)
style = tdrstyle.TDRStyle()
td = dataset.TreeDraw(analysis+"/tree", weight="weightPileup*weightTrigger*weightPrescale",
selection="genMet_p4.Et() > 20"
# selection="genMet_p4.Et() > 20 && [email protected]() >= 1"
# selection="genMet_p4.Et() > 20 && [email protected]() >= 2"
# selection="genMet_p4.Et() > 20 && [email protected]() >= 3"
)
kwargs = {}
kwargs["normalizeToLumi"] = 1150
dist=">>dist(25,0,5)"
for q in ["Et", "X", "Y", "Phi"]:
rawRes = plots.DataMCPlot(datasets, td.clone(varexp="met_p4.%s()/genMet_p4.%s() %s" % (q, q, dist)), **kwargs)
type1Res = plots.DataMCPlot(datasets, td.clone(varexp="metType1_p4.%s()/genMet_p4.%s() %s" % (q, q, dist)), **kwargs)
#type1Res = plots.DataMCPlot(datasets, td.clone(varexp="tcMet_p4.%s()/genMet_p4.%s() %s" % (q, q, dist)), normalizeToLumi=1150)
compare(rawRes, type1Res, q, "TTJets")
def manageDatasets(datasets):
'''
def manageDatasets(datasets):
Handles the PU weighting, luminosity loading and signal merging of the datatasets.
'''
### Since (by default) we use weighted counters, and the analysis job inputs are
### normally skims (as are "v44_4" and "v53_1"), need to update events to PU weighted
print "\n*** Updating events to PU weighted:"
datasets.updateNAllEventsToPUWeighted()
if bMcOnly:
datasets.remove(datasets.getDataDatasetNames())
histograms.cmsTextMode = histograms.CMSMode.SIMULATION
else:
datasets.loadLuminosities()
print "\n*** Default merging of dataset components:"
plots.mergeRenameReorderForDataMC(datasets)
print "\n*** Removing all signal samples, except m=%s" % (signalMass)
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M"+signalMass in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
### Setup style
styleGenerator = styles.generator(fill=False)
style = tdrstyle.TDRStyle()
histograms.createLegend.moveDefaults(dx=-0.02, dy=+0.00)
print "*** Setting signal cross sections, using BR(t->bH+)=%s and BR(H+ -> tau+ nu)=%s" % (BR_tH, BR_Htaunu)
xsect.setHplusCrossSectionsToBR(datasets, BR_tH, BR_Htaunu)
print "*** Merging WH and HH signals"
plots.mergeWHandHH(datasets) # merging of WH and HH signals must be done after setting the cross section
return datasets
def main():
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets.loadLuminosities()
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
plots.mergeWHandHH(datasets)
style = tdrstyle.TDRStyle()
plot = plots.DataMCPlot(datasets, analysis+"/MET/met")
plot.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
plot.createFrame("MET", opts={"ymin": 1e-4, "ymaxfactor": 10})
plot.getPad().SetLogy(True)
plot.setLegend(histograms.createLegend())
plot.frame.GetXaxis().SetTitle("MET (GeV)")
plot.frame.GetYaxis().SetTitle("Number of events")
plot.draw()
plot.addLuminosityText()
plot.save()
def main():
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.2, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
# Apply TDR style
style = tdrstyle.TDRStyle()
dataMCExample(datasets)
distComparison(datasets)
# Script execution can be paused like this, it will continue after
# user has given some input (which must include enter)
if drawToScreen:
raw_input("Hit enter to continue")
def main():
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
# datasets.loadLuminosities()
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
datasets.updateNAllEventsToPUWeighted()
plots.mergeRenameReorderForDataMC(datasets)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
plots.mergeWHandHH(datasets)
style = tdrstyle.TDRStyle()
td = dataset.TreeDraw(analysis+"/tree", weight="weightPileup*weightTrigger*weightPrescale",
selection="genMet_p4.Et() > 20"
# selection="genMet_p4.Et() > 20 && [email protected]() >= 1"
# selection="genMet_p4.Et() > 20 && [email protected]() >= 2"
# selection="genMet_p4.Et() > 20 && [email protected]() >= 3"
)
kwargs = {}
kwargs["normalizeToLumi"] = 1150
dist=">>dist(25,0,5)"
for q in ["Et", "X", "Y", "Phi"]:
rawRes = plots.DataMCPlot(datasets, td.clone(varexp="met_p4.%s()/genMet_p4.%s() %s" % (q, q, dist)), **kwargs)
type1Res = plots.DataMCPlot(datasets, td.clone(varexp="metType1_p4.%s()/genMet_p4.%s() %s" % (q, q, dist)), **kwargs)
#type1Res = plots.DataMCPlot(datasets, td.clone(varexp="tcMet_p4.%s()/genMet_p4.%s() %s" % (q, q, dist)), normalizeToLumi=1150)
compare(rawRes, type1Res, q, "TTJets")
def main():
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets.loadLuminosities()
datasets.remove(
filter(lambda name: "TTToHplus" in name and not "M120" in name,
datasets.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
plots.mergeWHandHH(datasets)
style = tdrstyle.TDRStyle()
plot = plots.DataMCPlot(datasets, analysis + "/MET/met")
plot.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
plot.createFrame("MET", opts={"ymin": 1e-4, "ymaxfactor": 10})
plot.getPad().SetLogy(True)
plot.setLegend(histograms.createLegend())
plot.frame.GetXaxis().SetTitle("MET (GeV)")
plot.frame.GetYaxis().SetTitle("Number of events")
plot.draw()
plot.addLuminosityText()
plot.save()
def main():
# Read the datasets
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets.updateNAllEventsToPUWeighted()
#datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Set the signal cross sections to a value from MSSM
# xsect.setHplusCrossSectionsToMSSM(datasets, tanbeta=20, mu=200)
plots.mergeWHandHH(
datasets
) # merging of WH and HH signals must be done after setting the cross section
# Create counter
eventCounter = counter.EventCounter(datasets)
#eventCounter.normalizeMCByLuminosity()
eventCounter.normalizeMCToLuminosity(1000) # in pb^-1
print "============================================================"
print "Main counter (MC normalized by collision data luminosity)"
print eventCounter.getMainCounterTable().format()
triggerCounter = eventCounter.getSubCounter("Trigger")
triggerCounter.forEachDataset(printTriggerEfficiency)
def main(argv):
interactive = True
interactive = False
# HISTONAME = "TauIdJets"
# HISTONAME = "TauIdBtag"
HISTONAME = "TauIdBveto"
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
# Disable batch mode here to have the interactivity (see also the line with 'raw_input') below
if interactive:
ROOT.gROOT.SetBatch(False)
# Create all datasets from a multicrab task
# datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra)
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra, analysisbaseName = "signalAnalysisInvertedTau")
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.2, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
# Apply TDR style
style = tdrstyle.TDRStyle()
#dataMCExample(datasets)
distComparison(datasets)
# Script execution can be paused like this, it will continue after
# user has given some input (which must include enter)
if interactive:
raw_input("Hit enter to continue")
def main():
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabCfg(analysisName=analysis)
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.2, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
# Merge EWK datasets
datasets.merge("EWK", [
"WJets",
"TTJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create the normalized plot of transverse mass
# Read the histogram from the file
#mT = plots.DataMCPlot(datasets, "transverseMass")
# Create the histogram from the tree (and see the selections explicitly)
td = dataset.TreeDraw("tree", weight="weightPileup*weightTrigger*weightPrescale",
selection="met_p4.Et() > 70 && Max$(jets_btag) > 1.7")
mT = plots.DataMCPlot(datasets, td.clone(varexp="sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi())))>>dist(400, 0, 400)"))
# Rebin before subtracting
mT.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
# Create the data-EWK histogram and draw it
dataEwkDiff(mT)
# Draw the mT distribution
transverseMass(mT)
def main():
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets.updateNAllEventsToPUWeighted()
# remove data datasets
datasets.remove(filter(lambda name: "Tau_" in name, datasets.getAllDatasetNames()))
# remove heavy H+ datasets
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
plots.mergeWHandHH(datasets) # merging of WH and HH signals must be done after setting the cross section
# Apply TDR style
style = tdrstyle.TDRStyle()
# Define variables and cuts
tauPt = "tau_p4.Pt()"; tauPtCut = tauPt+" > 40"
tauLeadingCandPt = "tau_leadPFChargedHadrCand_p4.Pt()"; tauLeadingCandPtCut = tauLeadingCandPt+" > 20"
met = "met_p4.Et()"; metCut = met+" > 70"
btagMax = "Max$(jets_btag)"; btagCut = btagMax+" > 1.7"
btagJetNum17 = "Sum$(jets_btag > 1.7"
btag2ndMax = "MaxIf(kets_btag, jets_btag < Max$(jets_btag))"
rtau = "tau_leadPFChargedHadrCand_p4.P()/tau_p4.P()"; rtauCut = rtau+" > 0.65"
mt = "sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi())))"; mtCut = mt+" > 80"
deltaPhi = "acos((tau_p4.Px()*met_p4.Px()+tau_p4.Py()*met_p4.Py())/tau_p4.Pt()/met_p4.Et())*57.2958"; deltaPhiCut = deltaPhi+" < 160"
npv = "goodPrimaryVertices_n";
td = dataset.TreeDraw(analysis+"/tree", weight="weightPileup*weightTrigger*weightPrescale")
lumi = 1145
def createResult(varexp, selection, weight=None, **kwargs):
args = {"varexp": varexp, "selection": selection}
if weight != None:
args["weight"] = weight
return Result(datasets, td.clone(**args), normalizeToLumi=lumi, **kwargs)
# metRes = createResult(met+">>dist(100,0,200)", btagCut, greaterThan=True)
# metRes.save("met", "MET (GeV)", rebin=10, logy=True, opts={"ymin": 0.01, "ymaxfactor": 10})
# btagRes = createResult(btagMax+">>dist(80, 0, 8)", metCut, greaterThan=True)
# btagRes.save("btag", "TCHE", rebin=5, logy=True, opts={"ymin": 0.01, "ymaxfactor": 10})
# rtauRes = createResult(rtau+">>dist(110,0,1.1)", metCut+"&&"+btagCut, greaterThan=True)
# rtauRes.save("rtau", "R_{#tau}", rebin=10, logy=True, opts={"ymin": 0.01, "ymaxfactor": 10})
# mtRes = createResult(mt+">>dist(50,0,200)", metCut+"&&"+btagCut, greaterThan=True)
# mtRes.save("mt", "M_{T}(#tau, MET) (GeV)", rebin=2, logy=True, opts={"ymin": 0.01, "ymaxfactor": 10})
# deltaPhiRes = createResult(deltaPhi+">>dist(90,0,180)", metCut+"&&"+btagCut, lessThan=True)
# deltaPhiRes.save("deltaPhi", "#Delta#Phi(#tau, MET) (#circ)", rebin=5, logy=True, opts={"ymin": 0.01, "ymaxfactor": 10})
pileupRes = createResult(npv+">>dist(4,1,17)", metCut+"&&"+btagCut, weight="", doPassed=False, lessThan=True)
pileupRes.save("goodPV", "N(good primary vertices)")
def main():
### Get the ROOT files for all datasets, merge datasets and reorder them
print "*** Obtaining ROOT files from:\n %s" % (multicrabPath)
datasets = dataset.getDatasetsFromMulticrabCfg(directory=multicrabPath, dataEra="Run2011A")
printPSet(bPrintPSet, folderName="signalAnalysisRun2011A")
print "*** Calling datasets.updateNAllEventsToPUWeighted():"
datasets.updateNAllEventsToPUWeighted()
print "*** Loading luminosities"
datasets.loadLuminosities()
print "*** Calling plots.mergeRenameReorderForDataMC(datasets):"
plots.mergeRenameReorderForDataMC(datasets)
if mcOnly:
datasets.remove(datasets.getDataDatasetNames())
histograms.cmsTextMode = histograms.CMSMode.SIMULATION
### Merge desirable datasets
if bMergeEwk:
print "*** Merging EWK MC"
datasets.merge("EWK MC", ["WJets", "TTJets", "DYJetsToLL", "SingleTop", "Diboson"])
plots._plotStyles["EWK MC"] = styles.ttStyle #plots._plotStyles["EWK"] = styles.getEWKStyle()
### Remove signals other than M120
print "*** Removing all signal except M120"
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
### Setup style
print "*** Setting up style"
styleGenerator = styles.generator(fill=True)
style = tdrstyle.TDRStyle()
histograms.createLegend.moveDefaults(dx=-0.0, dy=+0.0)
### Merge signals into one histo
print "*** Merging WH and HH signals into one histogram"
plots.mergeWHandHH(datasets) # merging of WH and HH signals must be done after setting the cross section
### Print desirable information here
print "*** Available datasets:\n %s" % (datasets.getAllDatasetNames())
if mcOnly:
print "*** Integrated Luminosity:\n %s (pb)" % (mcOnlyLumi)
else:
print "*** Integrated Luminosity:\n %s (pb)" % (datasets.getDataset("Data").getLuminosity())
### Do all plots defined in histoDict function
doPlots(datasets, histoDict, JetSelectionCuts, SaveExtension = "JetSelection")
doPlots(datasets, histoDict, MtCut, SaveExtension = "Mt")
doPlots(datasets, histoDict, MetBtagDeltaPhiCuts, SaveExtension = "MetBtagDeltaPhi")
doPlots(datasets, histoDict, MetBtagDeltaPhiMtCuts, SaveExtension = "MetBtagDeltaPhiMt")
### Keep session alive (otherwise canvases close automatically)
if bBatchMode == False:
raw_input("*** Press \"any\" key to exit pyROOT: ")
def main():
# Get the ROOT files for all datasets, merge datasets and reorder them
datasets = dataset.getDatasetsFromMulticrabCfg(directory="/Volumes/disk/attikis/HIG-12-037/TreeAnalysis_v44_4_130113_105229/", dataEra="Run2011A")
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
# Merge desirable datasets
datasets.merge("EWK MC", ["WJets", "TTJets", "DYJetsToLL", "SingleTop", "Diboson"])
plots._plotStyles["EWK MC"] = styles.ttStyle #plots._plotStyles["EWK"] = styles.getEWKStyle()
# Remove signals other than M120
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
# Setup style
styleGenerator = styles.generator(fill=True)
style = tdrstyle.TDRStyle()
histograms.createLegend.moveDefaults(dx=-0.0, dy=+0.0)
# Merge signals into one histo
plots.mergeWHandHH(datasets) # merging of WH and HH signals must be done after setting the cross section
# Print desirable information here
print "*** Event weight applied: %s" % (EvtWeight)
print "*** Available datasets: %s" % (datasets.getAllDatasetNames())
print "*** WARNING! What about btag scale factor?"
print "*** Integrated Luminosity: %s (pb)" % (datasets.getDataset("Data").getLuminosity())
# Define histogram name, expression, and binning here:
# { hName": "expression >> hNameTmp(nBins, xMin, xMax) }
histoDict1 = {
"sphericity": "sphericity >> sphericity(20, 0.0, 1.0)",
"aplanarity": "aplanarity >> aplanarity(20, 0.0, 0.5)",
"planarity": "planarity >> planarity(20, 0.0, 0.5)",
"circularity": "circularity >> circularity(20, 0.0, 1.0)",
"alphaT": "alphaT >> alphaT(20, 0.0, 1.0)",
"mT": "%s >> mT(30, 0.0, 600.0)" % (Mt)
}
# Do all plots defined in doPlots(datasets) function
# doPlots(datasets, histoDict1, "", SaveExtension = "JetSelection")
# doPlots(datasets, histoDict1, MetBtagDeltaPhiCuts, SaveExtension = "Met_Btag_DeltaPhi")
# doPlots(datasets, histoDict1, MetBtagDeltaPhiCircularityCuts, SaveExtension = "Met_Btag_DeltaPhi_Circularity")
# doPlots(datasets, histoDict1, TestCuts, SaveExtension = "Test")
# Do just mT plots
histoDict2 = {
"mT": "%s >> mT(30, 0.0, 600.0)" % (Mt)
}
for i in range(1, 8):
cutValue = (i+0.0)/(10.0)
doPlots(datasets, histoDict2, "circularity <= %f" % (cutValue), SaveExtension = "Circularity%s" % (i))
def main():
creator = dataset.readFromMulticrabCfg()
datasets = creator.createDatasetManager(dataEra=dataEra)
if not mcOnly:
datasets.loadLuminosities()
datasets.updateNAllEventsToPUWeighted()
plots.mergeRenameReorderForDataMC(datasets)
if not mcOnly:
mcOnlyLumi = datasets.getDataset("Data").getLuminosity()
datasets.remove(datasets.getDataDatasetNames())
histograms.cmsTextMode = histograms.CMSMode.SIMULATION
histograms.createLegend.moveDefaults(dx=-0.05, dh=-0.15)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.01, br_Htaunu=1)
for dset in datasets.getAllDatasets():
if "HplusTB_" in dset.getName():
dset.setCrossSection(1.0)
plots.mergeWHandHH(datasets)
datasets.merge("EWK", ["TTJets",
"WJets",
"DYJetsToLL",
"SingleTop", "Diboson"
], keepSources=True)
style = tdrstyle.TDRStyle()
# Setup the systematics recipe object
systematics = dataset.Systematics(
allShapes=True,
#shapes=["SystVarJER", "SystVarJES", "SystVarMET", "SystVarTES"],
#verbose=True,
#additionalNormalizations={"Foo": 0.2, "Bar": 0.05},
)
histograms.uncertaintyMode.set(histograms.Uncertainty.StatOnly)
def dop(datasetName):
doPlots(datasets, datasetName, systematics)
doCounters(datasets, datasetName, creator.getSystematicVariationSources())
# dop("TTToHplus_M80")
dop("TTToHplus_M120")
# dop("TTToHplus_M160")
# dop("HplusTB_M180")
dop("HplusTB_M200")
# dop("HplusTB_M300")
dop("TTJets")
# dop("DYJetsToLL")
dop("WJets")
dop("EWK")
def main():
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.2, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
# Merge EWK datasets
datasets.merge("EWK", [
"WJets",
"TTJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create the normalized plot of transverse mass
# Read the histogram from the file
#mT = plots.DataMCPlot(datasets, analysis+"/transverseMass")
# Create the histogram from the tree (and see the selections explicitly)
td = dataset.TreeDraw(analysis+"/tree", weight="weightPileup*weightTrigger*weightPrescale",
selection="met_p4.Et() > 70 && Max$(jets_btag) > 1.7")
mT = plots.DataMCPlot(datasets, td.clone(varexp="sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi())))>>dist(400, 0, 400)"))
# Rebin before subtracting
mT.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
# Create the data-EWK histogram and draw it
dataEwkDiff(mT)
# Draw the mT distribution
transverseMass(mT)
def addSignal(datasetMgr):
xsect.setHplusCrossSectionsToBR(datasetMgr, br_tH=signal_br_tH, br_Htaunu=1)
plots.mergeWHandHH(datasetMgr)
ttjets2 = datasetMgr.getDataset("TTJets").deepCopy()
ttjets2.setName("TTJets2")
ttjets2.setCrossSection(ttjets2.getCrossSection() - datasetMgr.getDataset("TTToHplus_M120").getCrossSection())
datasetMgr.append(ttjets2)
datasetMgr.merge("EWKnoTT", ["WJets", "DYJetsToLL", "SingleTop", "Diboson"], keepSources=True)
datasetMgr.merge("EWKScaled", ["EWKnoTT", "TTJets2"])
datasetMgr.merge("EWKMC", ["WJets", "TTJets", "DYJetsToLL", "SingleTop", "Diboson"])
def manageDatasets(datasets):
'''
def manageDatasets(datasets):
Handles the PU weighting, luminosity loading and signal merging of the datatasets.
'''
### Since (by default) we use weighted counters, and the analysis job inputs are
### normally skims (as are "v44_4" and "v53_1"), need to update events to PU weighted
print "\n*** Updating events to PU weighted:"
datasets.updateNAllEventsToPUWeighted()
if bMcOnly==True:
datasets.remove(datasets.getDataDatasetNames())
histograms.cmsTextMode = histograms.CMSMode.SIMULATION
else:
datasets.loadLuminosities()
print "\n*** Default merging of dataset components:"
plots.mergeRenameReorderForDataMC(datasets)
print "\n*** Removing all signal samples, except m=%s GeV/cc" % (signalMass)
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M"+signalMass in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
### Setup style
styleGenerator = styles.generator(fill=False)
plots._legendLabels["TTToHplus_M"+signalMass] = "m_{H^{+}} = " + signalMass + " GeV/c^{2}"
style = tdrstyle.TDRStyle()
print "*** Setting signal cross sections, using BR(t->bH+)=%s and BR(H+ -> tau+ nu)=%s" % (BR_tH, BR_Htaunu)
xsect.setHplusCrossSectionsToBR(datasets, BR_tH, BR_Htaunu)
print "*** Merging WH and HH signals"
plots.mergeWHandHH(datasets) # merging of WH and HH signals must be done after setting the cross section
### Now optionally remove datasets
if bRemoveEwk:
mergeEwkMc(datasets)
datasets.remove(filter(lambda name: "EWK MC" in name, datasets.getAllDatasetNames()))
else:
if bMergeEwk==True:
mergeEwkMc(datasets)
if bRemoveQcd:
datasets.remove(filter(lambda name: "QCD" in name, datasets.getAllDatasetNames()))
#if bRemoveSignal: FIXME
# datasets.remove(filter(lambda name: "TTToHplus" in name, datasets.getAllDatasetNames()))
# datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
print "*** Available datasets: %s" % (datasets.getAllDatasetNames())
return datasets
def manageDatasets(datasets):
'''
def manageDatasets(datasets):
Handles the PU weighting, luminosity loading and signal merging of the datatasets.
'''
### Since (by default) we use weighted counters, and the analysis job inputs are
### normally skims (as are "v44_4" and "v53_1"), need to update events to PU weighted
print "\n*** Updating events to PU weighted:"
datasets.updateNAllEventsToPUWeighted()
if getBool("bRemoveData"):
datasets.remove(datasets.getDataDatasetNames())
histograms.cmsTextMode = histograms.CMSMode.SIMULATION
else:
datasets.loadLuminosities()
print "\n*** Default merging of dataset components:"
plots.mergeRenameReorderForDataMC(datasets)
if getBool("bRemoveSignal"):
print "\n*** Removing all signal samples"
datasets.remove(filter(lambda name: "TTToHplus" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
else:
print "\n*** Removing all signal samples, except m=%s GeV/cc" % (signalMass)
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M"+signalMass in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
plots._legendLabels["TTToHplus_M"+signalMass] = "m_{H^{#pm}} = " + signalMass + " GeV/c^{2}"
### Setup style
styleGenerator = styles.generator(fill=False)
style = tdrstyle.TDRStyle()
print "*** Setting signal cross sections, using BR(t->bH+)=%s and BR(H+ -> tau+ nu)=%s" % (BR_tH, BR_Htaunu)
xsect.setHplusCrossSectionsToBR(datasets, BR_tH, BR_Htaunu)
print "*** Merging WH and HH signals"
plots.mergeWHandHH(datasets) # merging of WH and HH signals must be done after setting the cross section
### Now optionally remove datasets
if getBool("bRemoveEwk"):
mergeEwkMc(datasets)
datasets.remove(filter(lambda name: "EWK MC" in name, datasets.getAllDatasetNames()))
else:
if getBool("bMergeEwk"):
mergeEwkMc(datasets)
if getBool("bRemoveQcd"):
datasets.remove(filter(lambda name: "QCD" in name, datasets.getAllDatasetNames()))
print "*** Available datasets: %s" % (datasets.getAllDatasetNames())
return datasets
def addSignal(datasetMgr):
xsect.setHplusCrossSectionsToBR(datasetMgr, br_tH=0.03, br_Htaunu=1) # agreed to use 3 % as with QCD
plots.mergeWHandHH(datasetMgr)
ttjets2 = datasetMgr.getDataset("TTJets").deepCopy()
ttjets2.setName("TTJets2")
ttjets2.setCrossSection(ttjets2.getCrossSection() - datasetMgr.getDataset("TTToHplus_M120").getCrossSection())
datasetMgr.append(ttjets2)
datasetMgr.merge("EWKnoTT", ["WJets", "DYJetsToLL", "SingleTop", "Diboson"], keepSources=True)
datasetMgr.merge("EWKScaled", ["EWKnoTT", "TTJets2"])
# for mass in [80, 100]:
for mass in [80, 90, 100, 120, 140, 150, 155, 160]:
datasetMgr.merge("EWKSignal_M%d"%mass, ["TTToHplus_M%d"%mass, "EWKScaled"], keepSources=True)
def main():
# Read the datasets
datasets = dataset.getDatasetsFromMulticrabCfg(counters=analysis+"Counters")
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
print "Int.Lumi",datasets.getDataset("Data").getLuminosity()
# Take a deep copy of DatasetManager for all signals
datasetsAllSignals = datasets.deepCopy()
# Remove signals other than M120
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
# Remove QCD
datasets.remove(filter(lambda name: "QCD" in name, datasets.getAllDatasetNames()))
histograms.createLegend.moveDefaults(dx=-0.02)
histograms.createLegend.moveDefaults(dh=-0.03)
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
xsect.setHplusCrossSectionsToBR(datasetsAllSignals, br_tH=0.05, br_Htaunu=1)
plots.mergeWHandHH(datasets) # merging of WH and HH signals must be done after setting the cross section
# Replace signal dataset with EWK+signal
# This produces the "bkg+signal" histogram
# The ttbar cross section is adjusted for the BR(t->H+)
ttjets2 = datasets.getDataset("TTJets").deepCopy()
ttjets2.setName("TTJets2")
ttjets2.setCrossSection(ttjets2.getCrossSection() - datasets.getDataset("TTToHplus_M120").getCrossSection())
datasets.append(ttjets2)
datasets.merge("EWKnoTT", ["WJets", "DYJetsToLL", "SingleTop", "Diboson"], keepSources=True)
datasets.merge("TTToHplus_M120", ["TTToHplus_M120", "EWKnoTT", "TTJets2"])
plots._legendLabels["TTToHplus_M120"] = "with H^{#pm}#rightarrow#tau^{#pm}#nu"
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create plots
doPlots(datasets)
# Print counters
massPoints = [80, 90, 100, 120, 140, 150, 155, 160]
doCounters(datasetsAllSignals, massPoints)
def main():
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabCfg(dataEra="Run2012ABCD")
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(
filter(lambda name: "TTToHplus" in name and not "M120" in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "Hplus_taunu_" in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "HplusTB_M" in name,
datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.2, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
# Apply TDR style
style = tdrstyle.TDRStyle()
dataMCExample(datasets)
distComparison(datasets)
# Script execution can be paused like this, it will continue after
# user has given some input (which must include enter)
if drawToScreen:
raw_input("Hit enter to continue")
def main():
interactive = True
interactive = False
# Disable batch mode here to have the interactivity (see also the line with 'raw_input') below
if interactive:
ROOT.gROOT.SetBatch(False)
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(
filter(lambda name: "TTToHplus" in name and not "M120" in name,
datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.2, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
# Apply TDR style
style = tdrstyle.TDRStyle()
#dataMCExample(datasets)
distComparison(datasets)
# Script execution can be paused like this, it will continue after
# user has given some input (which must include enter)
if interactive:
raw_input("Hit enter to continue")
def manageDatasets(datasets):
'''
def manageDatasets(datasets):
Handles the PU weighting, luminosity loading and signal merging of the datatasets.
'''
### Since (by default) we use weighted counters, and the analysis job inputs are
### normally skims (as are "v44_4" and "v53_1"), need to update events to PU weighted
print "\n*** Updating events to PU weighted:"
datasets.updateNAllEventsToPUWeighted()
if bMcOnly:
datasets.remove(datasets.getDataDatasetNames())
histograms.cmsTextMode = histograms.CMSMode.SIMULATION
else:
datasets.loadLuminosities()
print "\n*** Default merging of dataset components:"
plots.mergeRenameReorderForDataMC(datasets)
print "\n*** Removing all signal samples, except m=%s" % (signalMass)
datasets.remove(
filter(
lambda name: "TTToHplus" in name and not "M" + signalMass in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
### Setup style
styleGenerator = styles.generator(fill=False)
style = tdrstyle.TDRStyle()
histograms.createLegend.moveDefaults(dx=-0.02, dy=+0.00)
print "*** Setting signal cross sections, using BR(t->bH+)=%s and BR(H+ -> tau+ nu)=%s" % (
BR_tH, BR_Htaunu)
xsect.setHplusCrossSectionsToBR(datasets, BR_tH, BR_Htaunu)
print "*** Merging WH and HH signals"
plots.mergeWHandHH(
datasets
) # merging of WH and HH signals must be done after setting the cross section
return datasets
def main():
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabCfg(dataEra="Run2012ABCD")
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB_M" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.2, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
# Apply TDR style
style = tdrstyle.TDRStyle()
dataMCExample(datasets)
distComparison(datasets)
# Script execution can be paused like this, it will continue after
# user has given some input (which must include enter)
if drawToScreen:
raw_input("Hit enter to continue")
def addSignal(datasetMgr):
xsect.setHplusCrossSectionsToBR(
datasetMgr, br_tH=0.03,
br_Htaunu=1) # agreed to use 3 % as with QCD
plots.mergeWHandHH(datasetMgr)
ttjets2 = datasetMgr.getDataset("TTJets").deepCopy()
ttjets2.setName("TTJets2")
ttjets2.setCrossSection(
ttjets2.getCrossSection() -
datasetMgr.getDataset("TTToHplus_M120").getCrossSection())
datasetMgr.append(ttjets2)
datasetMgr.merge("EWKnoTT",
["WJets", "DYJetsToLL", "SingleTop", "Diboson"],
keepSources=True)
datasetMgr.merge("EWKScaled", ["EWKnoTT", "TTJets2"])
# for mass in [80, 100]:
for mass in [80, 90, 100, 120, 140, 150, 155, 160]:
datasetMgr.merge("EWKSignal_M%d" % mass,
["TTToHplus_M%d" % mass, "EWKScaled"],
keepSources=True)
def manageDatasets(datasets):
# Since (by default) we use weighted counters, and the analysis job inputs are normally skims (as are "v44_4" and "v53_1"), need to update events to PU weighted
print "%sUpdating events to PU weighted:" % (" " * 0)
datasets.updateNAllEventsToPUWeighted()
# Determine number of Data/MC datasets present
nDataDatasets = len(datasets.getDataDatasets())
nMCDatasets = len(datasets.getMCDatasets())
nDatasetsToKeep = len(datasetsToKeep)
# Sanity check
if nDatasetsToKeep < 1:
print "%sERROR! There are zero (0) datasets." % (" " * 0)
sys.exit()
# Determine luminosity and between canvas "Lumi" or "Simulation" text
if nDataDatasets < 1:
histograms.cmsTextMode = histograms.CMSMode.SIMULATION
else:
datasets.loadLuminosities()
# Merge and rename datasets
#print "%sDefault merging of dataset components:" % (" "*0)
plots.mergeRenameReorderForDataMC(datasets)
# Set signal xSectio and BR, and merge WH and HH
print "%sSetting signal cross sections, using BR(t->bH+)=%s and BR(H+ -> tau+ nu)=%s" % (
" " * 0, BR_tH, BR_Htaunu)
xsect.setHplusCrossSectionsToBR(datasets, BR_tH, BR_Htaunu)
plots.mergeWHandHH(
datasets) # Must be done after setting the cross section
# Remove all datasets except those passed as a list
datasets = getCustomDatasets(datasets, datasetsToKeep)
# Finally, set TDR as the default style
style = tdrstyle.TDRStyle()
return datasets
def manageDatasets(datasets):
# Since (by default) we use weighted counters, and the analysis job inputs are normally skims (as are "v44_4" and "v53_1"), need to update events to PU weighted
print "%sUpdating events to PU weighted:" % (" "*0)
datasets.updateNAllEventsToPUWeighted()
# Determine number of Data/MC datasets present
nDataDatasets = len(datasets.getDataDatasets())
nMCDatasets = len(datasets.getMCDatasets())
nDatasetsToKeep = len(datasetsToKeep)
# Sanity check
if nDatasetsToKeep < 1:
print "%sERROR! There are zero (0) datasets." % (" "*0)
sys.exit()
# Determine luminosity and between canvas "Lumi" or "Simulation" text
if nDataDatasets < 1:
histograms.cmsTextMode = histograms.CMSMode.SIMULATION
else:
datasets.loadLuminosities()
# Merge and rename datasets
#print "%sDefault merging of dataset components:" % (" "*0)
plots.mergeRenameReorderForDataMC(datasets)
# Set signal xSectio and BR, and merge WH and HH
print "%sSetting signal cross sections, using BR(t->bH+)=%s and BR(H+ -> tau+ nu)=%s" % (" "*0, BR_tH, BR_Htaunu)
xsect.setHplusCrossSectionsToBR(datasets, BR_tH, BR_Htaunu)
plots.mergeWHandHH(datasets) # Must be done after setting the cross section
# Remove all datasets except those passed as a list
datasets = getCustomDatasets(datasets, datasetsToKeep)
# Finally, set TDR as the default style
style = tdrstyle.TDRStyle()
return datasets
def main():
# Read the datasets
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets.remove([
"WJets_TuneD6T_Winter10",
"TTJets_TuneD6T_Winter10",
"TTToHplusBWB_M140_Spring11",
"TTToHplusBWB_M80_Spring11",
"TTToHplusBWB_M90_Spring11",
"TTToHplusBWB_M155_Spring11",
"TTToHplusBWB_M150_Spring11",
"TTToHplusBWB_M160_Spring11",
"TTToHplusBWB_M100_Spring11",
"TTToHplusBHminusB_M80_Spring11",
"TTToHplusBHminusB_M100_Spring11",
"TTToHplusBHminusB_M160_Spring11",
"TTToHplusBHminusB_M150_Spring11",
"TTToHplusBHminusB_M140_Spring11",
"TTToHplusBHminusB_M155_Spring11",
"TauPlusX_160431-161016_Prompt",
"TauPlusX_162803-162828_Prompt",
"QCD_Pt30to50_TuneZ2_Spring11",
"QCD_Pt50to80_TuneZ2_Spring11",
"QCD_Pt80to120_TuneZ2_Spring11",
"QCD_Pt120to170_TuneZ2_Spring11",
"QCD_Pt170to300_TuneZ2_Spring11",
"QCD_Pt300to470_TuneZ2_Spring11",
"HplusTB_M180_Summer11",
"HplusTB_M190_Summer11",
"HplusTB_M250_Summer11",
"HplusTB_M300_Summer11",
"HplusTB_M220_Summer11",
"HplusTB_M200_Summer11",
# "Tau_165970-166164_Prompt", "Tau_166374-167043_Prompt", "Tau_167078-167784_Prompt", "Tau_165088-165633_Prompt"
# "Tau_163270-163869_May10","Tau_161217-163261_May10", "Tau_160431-161176_May10"
])
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
# Take signals from 42X
datasets.remove(
filter(lambda name: "TTToHplus" in name and not "M120" in name,
datasets.getAllDatasetNames()))
# datasetsSignal = dataset.getDatasetsFromMulticrabCfg(cfgfile="/home/rkinnune/signalAnalysis/CMSSW_4_2_4_patch1/src/HiggsAnalysis/HeavyChHiggsToTauNu/test/multicrab_110621_150040/multicrab.cfg", counters=counters)
#Rtau =0
# datasetsSignal = dataset.getDatasetsFromMulticrabCfg(cfgfile="/home/rkinnune/signalAnalysis/CMSSW_4_2_5/src/HiggsAnalysis/HeavyChHiggsToTauNu/test/multicrab_110804_104313/multicrab.cfg", counters=counters)
# datasetsSignal.selectAndReorder(["HplusTB_M200_Summer11"])
# datasetsSignal = dataset.getDatasetsFromMulticrabCfg(cfgfile="/home/rkinnune/signalAnalysis/CMSSW_4_2_4_patch1/src/HiggsAnalysis/HeavyChHiggsToTauNu/test/multicrab_110622_112321/multicrab.cfg", counters=counters)
#datasetsSignal = dataset.getDatasetsFromMulticrabCfg(cfgfile="/home/rkinnune/signalAnalysis/CMSSW_4_1_5/src/HiggsAnalysis/HeavyChHiggsToTauNu/test/Signal_v11f_scaledb_424/multicrab.cfg", counters=counters)
#datasetsSignal.selectAndReorder(["TTToHplusBWB_M120_Summer11", "TTToHplusBHminusB_M120_Summer11"])
#datasetsSignal.renameMany({"TTToHplusBWB_M120_Summer11" :"TTToHplusBWB_M120_Spring11",
# "TTToHplusBHminusB_M120_Summer11": "TTToHplusBHminusB_M120_Spring11"})
#datasets.extend(datasetsSignal)
plots.mergeRenameReorderForDataMC(datasets)
# Set the signal cross sections to the ttbar
# xsect.setHplusCrossSectionsToTop(datasets)
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.2, br_Htaunu=1)
# Set the signal cross sections to a value from MSSM
# xsect.setHplusCrossSectionsToMSSM(datasets, tanbeta=20, mu=200)
plots.mergeWHandHH(
datasets
) # merging of WH and HH signals must be done after setting the cross section
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create the plot objects and pass them to the formatting
# functions to be formatted, drawn and saved to files
# selectionFlow(plots.DataMCPlot(datasets, analysis+"/SignalSelectionFlow"), "SignalSelectionFlow")
# met(plots.DataMCPlot(datasets, analysis+"/TauEmbeddingAnalysis_afterTauId_embeddingMet"), ratio=True)
# met(plots.DataMCPlot(datasets, analysis+"/TauEmbeddingAnalysis_begin_embeddingMet"), ratio=True)
# met2(plots.DataMCPlot(datasets, analysis+"/Met_BeforeTauId"), "MetBeforeTauId", rebin=40)
met2(plots.DataMCPlot(datasets, analysis + "/QCD_MET_AfterJetSelection"),
"QCD_MET_AfterJetSelection",
rebin=1)
met2(plots.DataMCPlot(datasets, analysis + "/QCD_MET_AfterJetsBtagging"),
"QCD_MET_AfterJetsBtagging",
rebin=1)
met2(plots.DataMCPlot(datasets,
analysis + "/QCD_MET_AfterJetsBtagging4050"),
"QCD_MET_AfterJetsBtagging4050",
rebin=1)
met2(plots.DataMCPlot(datasets,
analysis + "/QCD_MET_AfterJetsBtagging5060"),
"QCD_MET_AfterJetsBtagging5060",
rebin=1)
met2(plots.DataMCPlot(datasets,
analysis + "/QCD_MET_AfterJetsBtagging6070"),
"QCD_MET_AfterJetsBtagging6070",
rebin=1)
met2(plots.DataMCPlot(datasets,
analysis + "/QCD_MET_AfterJetsBtagging7080"),
"QCD_MET_AfterJetsBtagging7080",
rebin=1)
met2(plots.DataMCPlot(datasets,
analysis + "/QCD_MET_AfterJetsBtagging80100"),
"QCD_MET_AfterJetsBtagging80100",
rebin=1)
met2(plots.DataMCPlot(datasets,
analysis + "/QCD_MET_AfterJetsBtagging100120"),
"QCD_MET_AfterJetsBtagging100120",
rebin=1)
met2(plots.DataMCPlot(datasets,
analysis + "/QCD_MET_AfterJetsBtagging120150"),
"QCD_MET_AfterJetsBtagging120150",
rebin=1)
met2(plots.DataMCPlot(datasets,
analysis + "/QCD_MET_AfterJetsBtagging150"),
"QCD_MET_AfterJetsBtagging150",
rebin=1)
# Set temporarily the signal cross sections to a value from MSSM
# xsect.setHplusCrossSections(datasets, tanbeta=20, mu=200)
# datasets.getDataset("TTToHplusBHminusB_M120").setCrossSection(0.2*165)
# datasets.getDataset("TTToHplusBWB_M120").setCrossSection(0.2*165)
####################
datasets_tm = datasets
# datasets_tm = datasets.deepCopy()
# xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.2, br_Htaunu=1)
# xsect.setHplusCrossSectionsToBR(datasets_tm, br_tH=0.2, br_Htaunu=1)
# datasets_tm.merge("TTToHplus_M120", ["TTToHplusBWB_M120", "TTToHplusBHminusB_M120"])
transverseMass2(plots.DataMCPlot(
datasets, analysis + "/QCD_TransverseMass_AfterJetSelection"),
"QCD_TransverseMass_AfterJetSelection",
rebin=1)
transverseMass2(plots.DataMCPlot(
datasets, analysis + "/QCD_TransverseMass_AfterJetSelectionMetCut"),
"QCD_TransverseMass_AfterJetSelectionMetCut",
rebin=1)
transverseMass2(plots.DataMCPlot(
datasets, analysis + "/QCD_TransverseMass_AfterJetsBtagging"),
"QCD_TransverseMass_AfterJetsBtagging",
rebin=1)
transverseMass2(plots.DataMCPlot(
datasets, analysis + "/QCD_TransverseMass_AfterBigBox"),
"QCD_TransverseMass_AfterBigBox",
rebin=1)
transverseMass2(plots.DataMCPlot(
datasets, analysis + "/QCD_TransverseMass_AfterBigBoxAndMet"),
"QCD_TransverseMass_AfterBigBoxAndMet",
rebin=1)
transverseMass2(plots.DataMCPlot(
datasets, analysis + "/QCD_TransverseMass_AfterBigBoxAndBtag"),
"QCD_TransverseMass_AfterBigBoxAndBtag",
rebin=1)
transverseMass2(plots.DataMCPlot(
datasets, analysis + "/QCD_TransverseMass_AfterBigBoxAndTauID"),
"QCD_TransverseMass_AfterBigBoxAndTauID",
rebin=1)
# xsect.setHplusCrossSections(datasets, toTop=True)
# genComparison(datasets)
# zMassComparison(datasets)
# topMassComparison(datasets)
# topPtComparison(datasets)
# vertexComparison(datasets)
eventCounter = counter.EventCounter(datasets)
eventCounter.normalizeMCByLuminosity()
# eventCounter.normalizeMCToLuminosity(73)
print "============================================================"
print "Main counter (MC normalized by collision data luminosity)"
print eventCounter.getMainCounterTable().format()
# print eventCounter.getSubCounterTable("GlobalMuon_ID").format()
# print eventCounter.getSubCounterTable("tauIDTauSelection").format()
# print eventCounter.getSubCounterTable("TauIDPassedEvt::tauID_HPSTight").format()
# print eventCounter.getSubCounterTable("TauIDPassedJets::tauID_HPSTight").format()
print eventCounter.getSubCounterTable("b-tagging").format()
print eventCounter.getSubCounterTable("Jet selection").format()
print eventCounter.getSubCounterTable("Jet main").format()
drh1 = datasets.getDataset("Data").getDatasetRootHisto(
analysis + "/QCD_MET_AfterJetSelection")
drh2 = datasets.getDataset("Data").getDatasetRootHisto(
analysis + "/QCD_MET_AfterJetsBtagging7080")
drh1.setName("Base")
drh1.normalizeToOne()
drh2.normalizeToOne()
# drh1.normalizeMCByLuminosity()
plot = plots.ComparisonPlot(drh1, drh2)
# plot = plots.PlotBase(drh1)
# plot = plots.MCPlot(drh1)
# Rebin, if necessary
plot.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
# drh1.normalizeMCByLuminosity()
# plot = plots.PlotBase(drh1)
# plot = plots.MCPlot(drh
# Create frame with a ratio pad
plot.createFrame("distribution", opts={"ymin": 1e-5, "ymaxfactor": 1.5})
# createRatio=True, opts2={"ymin": -10, "ymax": 50}, # bounds of the ratio plot
# )
plot.draw()
plot.save()
mt = plots.DataMCPlot(datasets, analysis + "/QCD_MET_AfterJetSelection")
mt.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
mt_data = mt.histoMgr.getHisto("Data").getRootHisto().Clone("mt_data")
dataEra=era, searchMode=searchMode, optimizationMode=optimizationMode)
# Do the usual normalisation
dsetMgr.updateNAllEventsToPUWeighted()
dsetMgr.loadLuminosities()
plots.mergeRenameReorderForDataMC(dsetMgr)
dsetMgr.merge("EWK",
["TTJets", "WJets", "DYJetsToLL", "SingleTop", "Diboson"],
keepSources=True) # Keep sources needed to keep open TTJets
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
xsect.setHplusCrossSectionsToBR(dsetMgr,
br_tH=optionBrTopToHplusb,
br_Htaunu=1)
# Set the signal cross sections to a value from MSSM
# xsect.setHplusCrossSectionsToMSSM(dsetMgr, tanbeta=20, mu=200)
plots.mergeWHandHH(
dsetMgr
) # merging of WH and HH signals must be done after setting the cross section
# Make a directory for output
myDir = "invertedControlPlots_%s" % myModuleInfoString
# Remove the directory with its contents if it exists
if os.path.exists(myDir):
shutil.rmtree(myDir)
os.mkdir(myDir)
# Obtain luminosity
myLuminosity = dsetMgr.getDataset("Data").getLuminosity()
# Print info so that user can check that merge went correct
if myDisplayStatus:
dsetMgr.printDatasetTree()
print "Luminosity = %f 1/fb" % (myLuminosity / 1000.0)
print
def main(opts, signalDsetCreator, era, searchMode, optimizationMode):
# Make directory for output
mySuffix = "normalisationPlots_%s_%s_%s" % (era, searchMode,
optimizationMode)
if os.path.exists(mySuffix):
if os.path.exists("%s_old" % mySuffix):
shutil.rmtree("%s_old" % mySuffix)
os.rename(mySuffix, "%s_old" % mySuffix)
os.mkdir(mySuffix)
# Create dataset manager
myDsetMgr = signalDsetCreator.createDatasetManager(
dataEra=era, searchMode=searchMode, optimizationMode=optimizationMode)
if mcOnly:
myDsetMgr.remove(myDsetMgr.getDataDatasetNames())
histograms.cmsTextMode = histograms.CMSMode.SIMULATION
else:
myDsetMgr.loadLuminosities()
myDsetMgr.updateNAllEventsToPUWeighted()
# Take QCD from data
if opts.noMCQCD:
myDsetMgr.remove(
filter(lambda name: "QCD" in name, myDsetMgr.getAllDatasetNames()))
# Remove signal
if opts.noSignal:
myDsetMgr.remove(
filter(lambda name: "TTToHplus" in name,
myDsetMgr.getAllDatasetNames()))
myDsetMgr.remove(
filter(lambda name: "Hplus_taunu" in name,
myDsetMgr.getAllDatasetNames()))
myDsetMgr.remove(
filter(lambda name: "HplusTB" in name,
myDsetMgr.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(myDsetMgr)
if mcOnly:
print "Int.Lumi (manually set)", mcOnlyLumi
else:
print "Int.Lumi", myDsetMgr.getDataset("Data").getLuminosity()
if not opts.noSignal:
print "norm=", myDsetMgr.getDataset(
"TTToHplusBWB_M120").getNormFactor()
myDsetMgr.remove(
filter(lambda name: "QCD_Pt20_MuEnriched" in name,
myDsetMgr.getAllDatasetNames()))
# Remove signals other than M120
myDsetMgr.remove(
filter(lambda name: "TTToHplus" in name and not "M120" in name,
myDsetMgr.getAllDatasetNames()))
myDsetMgr.remove(
filter(lambda name: "Hplus_taunu" in name,
myDsetMgr.getAllDatasetNames()))
myDsetMgr.remove(
filter(lambda name: "HplusTB" in name, myDsetMgr.getAllDatasetNames()))
histograms.createLegend.moveDefaults(dx=-0.05)
histograms.createSignalText.set(xmin=0.4, ymax=0.93, mass=120)
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
xsect.setHplusCrossSectionsToBR(myDsetMgr, br_tH=optionBr, br_Htaunu=1)
# Set the signal cross sections to a value from MSSM
# xsect.setHplusCrossSectionsToMSSM(myDsetMgr, tanbeta=20, mu=200)
plots.mergeWHandHH(
myDsetMgr
) # merging of WH and HH signals must be done after setting the cross section
# Replace signal dataset with a signal+background dataset, where
# the BR(t->H+) is taken into account for SM ttbar
#plots.replaceLightHplusWithSignalPlusBackground(myDsetMgr)
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create plots
doPlots(myDsetMgr, opts, mySuffix)
# Print counters
#doCounters(myDsetMgr)
print "Results saved in directory: %s" % mySuffix
def main():
# Read the datasets
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
if era == "Run2011A":
datasets.remove(
filter(lambda name: "2011B_" in name,
datasets.getDataDatasetNames()))
elif era == "Run2011B":
datasets.remove(
filter(lambda name: "2011A_" in name,
datasets.getDataDatasetNames()))
elif era == "Run2011AB":
pass
else:
raise Exception("Unsupported era " + era)
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
#print datasets.getAllDatasetNames()
#return
# apply_v13_1_bugfix(datasets)
plots.mergeRenameReorderForDataMC(datasets)
# datasets.remove(["W3Jets"])
# datasets.remove(["DYJetsToLL", "SingleTop", "Diboson", "QCD_Pt20_MuEnriched"])
# Signal
#keepSignal = "M80"
#keepSignal = "M90"
#keepSignal = "M100"
#keepSignal = "M120"
#keepSignal = "M140"
#keepSignal = "M150"
keepSignal = "M155"
#keepSignal = "M160"
datasets.remove(
filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
# Replace signal dataset with EWK+signal
if False:
datasets.remove(
filter(lambda name: "TTToHplus" in name and not keepSignal in name,
datasets.getAllDatasetNames()))
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
#xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.1, br_Htaunu=1)
plots.mergeWHandHH(
datasets
) # merging of WH and HH signals must be done after setting the cross section
ttjets2 = datasets.getDataset("TTJets").deepCopy()
ttjets2.setName("TTJets2")
ttjets2.setCrossSection(
ttjets2.getCrossSection() -
datasets.getDataset("TTToHplus_" + keepSignal).getCrossSection())
datasets.append(ttjets2)
datasets.merge("EWKnoTT",
["WJets", "DYJetsToLL", "SingleTop", "Diboson"],
keepSources=True)
#datasets.merge("TTToHplus_"+keepSignal, ["TTToHplus_"+keepSignal, "EWKnoTT", "TTJets2"])
datasets.merge("EWKScaled", ["EWKnoTT", "TTJets2"])
datasets.merge("EWKSignal", ["TTToHplus_" + keepSignal, "EWKScaled"],
keepSources=True)
plots._legendLabels[
"TTToHplus_" + keepSignal] = "with H^{#pm}#rightarrow#tau^{#pm}#nu"
else:
datasets.remove(
filter(lambda name: "TTToHplus" in name,
datasets.getAllDatasetNames()))
#datasets.remove(filter(lambda name: "TTJets" not in name, datasets.getAllDatasetNames()))
mcLumi = None
if not datasets.hasDataset("Data"):
mcLumi = 5000
# scaleLumi.signalLumi = 43.4024599650000037
# scaleLumi.ewkLumi = datasets.getDataset("Data").getLuminosity()
# Apply TDR style
style = tdrstyle.TDRStyle()
histograms.createLegend.moveDefaults(dx=-0.04)
#datasets.remove(["QCD_Pt20_MuEnriched"])
plots._legendLabels["QCD_Pt20_MuEnriched"] = "QCD"
histograms.createLegend.moveDefaults(dh=-0.05)
#histograms.createLegend.moveDefaults(dx=-0.18, dy=0.05, dh=-0.05)
# doPlots(datasets, mcLumi)
doCounters(datasets, mcLumi)
def main():
# HISTONAME = "TauIdJets"
HISTONAME = "TauIdBtag"
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
bins = ["inclusive"]
# bins = ["4050","5060","6070","7080","80100","100120","120150","150"]
# bins = ["4050"]
for bin in bins:
invertedQCD.setLabel(bin)
if bin == "inclusive":
bin = ""
metBase = plots.DataMCPlot(datasets, analysis+"/MET_BaseLine"+HISTONAME+bin)
metInver = plots.DataMCPlot(datasets, analysis+"/MET_Inverted"+HISTONAME+bin)
# Rebin before subtracting
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone(analysis+"/MET_Inverted"+HISTONAME+bin)
metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone(analysis+"/MET_Inverted"+HISTONAME+bin)
metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone(analysis+"/MET_Baseline"+HISTONAME+bin)
metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto().Clone(analysis+"/MET_Baseline"+HISTONAME+bin)
metBase_QCD = metBase_data.Clone("QCD")
metBase_QCD.Add(metBase_EWK,-1)
invertedQCD.plotHisto(metInverted_data,"inverted")
invertedQCD.plotHisto(metBase_data,"baseline")
invertedQCD.fitQCD(metInverted_data)
invertedQCD.fitEWK(metBase_EWK,"LR")
invertedQCD.fitData(metBase_data)
invertedQCD.getNormalization()
invertedQCD.Summary()
def main():
if len(sys.argv) < 2:
usage()
dirs = []
dirs.append(sys.argv[1])
# Read the datasets
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra, analysisBaseName="signalAnalysisInvertedTau")
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis,
optimizationMode=optMode)
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters)
# datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters, dataEra=dataEra)
# datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
datasets.updateNAllEventsToPUWeighted()
# Take QCD from data
datasetsQCD = None
if QCDfromData:
datasetsQCD = dataset.getDatasetsFromMulticrabCfg(
cfgfile=
"/home/rkinnune/signalAnalysis/CMSSW_4_2_8_patch2/src/HiggsAnalysis/HeavyChHiggsToTauNu/test/multicrab_111123_132128/multicrab.cfg",
counters=counters)
datasetsQCD.loadLuminosities()
print "QCDfromData", QCDfromData
datasetsQCD.mergeData()
datasetsQCD.remove(datasetsQCD.getMCDatasetNames())
datasetsQCD.rename("Data", "QCD")
plots.mergeRenameReorderForDataMC(datasets)
print "Int.Lumi", datasets.getDataset("Data").getLuminosity()
# Remove signals other than M120
datasets.remove(
filter(lambda name: "TTToHplus" in name and not "M120" in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.merge("EWK",
["WJets", "DYJetsToLL", "SingleTop", "Diboson", "TTJets"],
keepSources=True)
datasets.remove(
filter(lambda name: "W2Jets" in name, datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "W3Jets" in name, datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "W4Jets" in name, datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "Hplus_taunu_s-channel" in name,
datasets.getAllDatasetNames()))
datasets_lands = datasets.deepCopy()
# Set the signal cross sections to the ttbar for datasets for lands
# xsect.setHplusCrossSectionsToTop(datasets_lands)
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.01, br_Htaunu=1)
# Set the signal cross sections to a value from MSSM
# xsect.setHplusCrossSectionsToMSSM(datasets, tanbeta=20, mu=200)
plots.mergeWHandHH(
datasets
) # merging of WH and HH signals must be done after setting the cross section
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create plots
doPlots(datasets)
# Write mt histograms to ROOT file
# writeTransverseMass(datasets_lands)
# Print counters
doCounters(datasets)
def main():
# Read the datasets
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets.remove(["WJets_TuneD6T_Winter10", "TTJets_TuneD6T_Winter10",
"TTToHplusBWB_M140_Spring11","TTToHplusBWB_M80_Spring11","TTToHplusBWB_M90_Spring11",
"TTToHplusBWB_M155_Spring11","TTToHplusBWB_M150_Spring11","TTToHplusBWB_M160_Spring11","TTToHplusBWB_M100_Spring11",
"TTToHplusBHminusB_M80_Spring11","TTToHplusBHminusB_M100_Spring11","TTToHplusBHminusB_M160_Spring11",
"TTToHplusBHminusB_M150_Spring11","TTToHplusBHminusB_M140_Spring11","TTToHplusBHminusB_M155_Spring11", "TauPlusX_160431-161016_Prompt","TauPlusX_162803-162828_Prompt",
"QCD_Pt30to50_TuneZ2_Spring11","QCD_Pt50to80_TuneZ2_Spring11","QCD_Pt80to120_TuneZ2_Spring11",
"QCD_Pt120to170_TuneZ2_Spring11","QCD_Pt170to300_TuneZ2_Spring11","QCD_Pt300to470_TuneZ2_Spring11",
"HplusTB_M180_Summer11", "HplusTB_M190_Summer11","HplusTB_M250_Summer11","HplusTB_M300_Summer11","HplusTB_M220_Summer11","HplusTB_M200_Summer11",
# "Tau_165970-166164_Prompt", "Tau_166374-167043_Prompt", "Tau_167078-167784_Prompt", "Tau_165088-165633_Prompt"
# "Tau_163270-163869_May10","Tau_161217-163261_May10", "Tau_160431-161176_May10"
])
datasets.loadLuminosities()
# Take signals from 42X
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
# datasetsSignal = dataset.getDatasetsFromMulticrabCfg(cfgfile="/home/rkinnune/signalAnalysis/CMSSW_4_2_4_patch1/src/HiggsAnalysis/HeavyChHiggsToTauNu/test/multicrab_110621_150040/multicrab.cfg", counters=counters)
#Rtau =0
# datasetsSignal = dataset.getDatasetsFromMulticrabCfg(cfgfile="/home/rkinnune/signalAnalysis/CMSSW_4_2_5/src/HiggsAnalysis/HeavyChHiggsToTauNu/test/multicrab_110804_104313/multicrab.cfg", counters=counters)
# datasetsSignal.selectAndReorder(["HplusTB_M200_Summer11"])
# datasetsSignal = dataset.getDatasetsFromMulticrabCfg(cfgfile="/home/rkinnune/signalAnalysis/CMSSW_4_2_4_patch1/src/HiggsAnalysis/HeavyChHiggsToTauNu/test/multicrab_110622_112321/multicrab.cfg", counters=counters)
#datasetsSignal = dataset.getDatasetsFromMulticrabCfg(cfgfile="/home/rkinnune/signalAnalysis/CMSSW_4_1_5/src/HiggsAnalysis/HeavyChHiggsToTauNu/test/Signal_v11f_scaledb_424/multicrab.cfg", counters=counters)
#datasetsSignal.selectAndReorder(["TTToHplusBWB_M120_Summer11", "TTToHplusBHminusB_M120_Summer11"])
#datasetsSignal.renameMany({"TTToHplusBWB_M120_Summer11" :"TTToHplusBWB_M120_Spring11",
# "TTToHplusBHminusB_M120_Summer11": "TTToHplusBHminusB_M120_Spring11"})
#datasets.extend(datasetsSignal)
plots.mergeRenameReorderForDataMC(datasets)
# Set the signal cross sections to the ttbar
# xsect.setHplusCrossSectionsToTop(datasets)
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.2, br_Htaunu=1)
# Set the signal cross sections to a value from MSSM
# xsect.setHplusCrossSectionsToMSSM(datasets, tanbeta=20, mu=200)
plots.mergeWHandHH(datasets) # merging of WH and HH signals must be done after setting the cross section
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create the plot objects and pass them to the formatting
# functions to be formatted, drawn and saved to files
# selectionFlow(plots.DataMCPlot(datasets, analysis+"/SignalSelectionFlow"), "SignalSelectionFlow")
# met(plots.DataMCPlot(datasets, analysis+"/TauEmbeddingAnalysis_afterTauId_embeddingMet"), ratio=True)
# met(plots.DataMCPlot(datasets, analysis+"/TauEmbeddingAnalysis_begin_embeddingMet"), ratio=True)
# met2(plots.DataMCPlot(datasets, analysis+"/Met_BeforeTauId"), "MetBeforeTauId", rebin=40)
met2(plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetSelection"), "QCD_MET_AfterJetSelection", rebin=1)
met2(plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging"), "QCD_MET_AfterJetsBtagging", rebin=1)
met2(plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging4050"), "QCD_MET_AfterJetsBtagging4050", rebin=1)
met2(plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging5060"), "QCD_MET_AfterJetsBtagging5060", rebin=1)
met2(plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging6070"), "QCD_MET_AfterJetsBtagging6070", rebin=1)
met2(plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging7080"), "QCD_MET_AfterJetsBtagging7080", rebin=1)
met2(plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging80100"), "QCD_MET_AfterJetsBtagging80100", rebin=1)
met2(plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging100120"), "QCD_MET_AfterJetsBtagging100120", rebin=1)
met2(plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging120150"), "QCD_MET_AfterJetsBtagging120150", rebin=1)
met2(plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging150"), "QCD_MET_AfterJetsBtagging150", rebin=1)
# Set temporarily the signal cross sections to a value from MSSM
# xsect.setHplusCrossSections(datasets, tanbeta=20, mu=200)
# datasets.getDataset("TTToHplusBHminusB_M120").setCrossSection(0.2*165)
# datasets.getDataset("TTToHplusBWB_M120").setCrossSection(0.2*165)
####################
datasets_tm = datasets
# datasets_tm = datasets.deepCopy()
# xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.2, br_Htaunu=1)
# xsect.setHplusCrossSectionsToBR(datasets_tm, br_tH=0.2, br_Htaunu=1)
# datasets_tm.merge("TTToHplus_M120", ["TTToHplusBWB_M120", "TTToHplusBHminusB_M120"])
transverseMass2(plots.DataMCPlot(datasets, analysis+"/QCD_TransverseMass_AfterJetSelection"), "QCD_TransverseMass_AfterJetSelection", rebin=1)
transverseMass2(plots.DataMCPlot(datasets, analysis+"/QCD_TransverseMass_AfterJetSelectionMetCut"), "QCD_TransverseMass_AfterJetSelectionMetCut", rebin=1)
transverseMass2(plots.DataMCPlot(datasets, analysis+"/QCD_TransverseMass_AfterJetsBtagging"), "QCD_TransverseMass_AfterJetsBtagging", rebin=1)
transverseMass2(plots.DataMCPlot(datasets, analysis+"/QCD_TransverseMass_AfterBigBox"), "QCD_TransverseMass_AfterBigBox", rebin=1)
transverseMass2(plots.DataMCPlot(datasets, analysis+"/QCD_TransverseMass_AfterBigBoxAndMet"), "QCD_TransverseMass_AfterBigBoxAndMet", rebin=1)
transverseMass2(plots.DataMCPlot(datasets, analysis+"/QCD_TransverseMass_AfterBigBoxAndBtag"), "QCD_TransverseMass_AfterBigBoxAndBtag", rebin=1)
transverseMass2(plots.DataMCPlot(datasets, analysis+"/QCD_TransverseMass_AfterBigBoxAndTauID"), "QCD_TransverseMass_AfterBigBoxAndTauID", rebin=1)
# xsect.setHplusCrossSections(datasets, toTop=True)
# genComparison(datasets)
# zMassComparison(datasets)
# topMassComparison(datasets)
# topPtComparison(datasets)
# vertexComparison(datasets)
eventCounter = counter.EventCounter(datasets, counters=countersWeighted)
eventCounter.normalizeMCByLuminosity()
# eventCounter.normalizeMCToLuminosity(73)
print "============================================================"
print "Main counter (MC normalized by collision data luminosity)"
print eventCounter.getMainCounterTable().format()
# print eventCounter.getSubCounterTable("GlobalMuon_ID").format()
# print eventCounter.getSubCounterTable("tauIDTauSelection").format()
# print eventCounter.getSubCounterTable("TauIDPassedEvt::tauID_HPSTight").format()
# print eventCounter.getSubCounterTable("TauIDPassedJets::tauID_HPSTight").format()
print eventCounter.getSubCounterTable("b-tagging").format()
print eventCounter.getSubCounterTable("Jet selection").format()
print eventCounter.getSubCounterTable("Jet main").format()
drh1 = datasets.getDataset("Data").getDatasetRootHisto(analysis+"/QCD_MET_AfterJetSelection")
drh2 = datasets.getDataset("Data").getDatasetRootHisto(analysis+"/QCD_MET_AfterJetsBtagging7080")
drh1.setName("Base")
drh1.normalizeToOne()
drh2.normalizeToOne()
# drh1.normalizeMCByLuminosity()
plot = plots.ComparisonPlot(drh1, drh2)
# plot = plots.PlotBase(drh1)
# plot = plots.MCPlot(drh1)
# Rebin, if necessary
plot.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
# drh1.normalizeMCByLuminosity()
# plot = plots.PlotBase(drh1)
# plot = plots.MCPlot(drh
# Create frame with a ratio pad
plot.createFrame("distribution", opts={"ymin":1e-5, "ymaxfactor": 1.5})
# createRatio=True, opts2={"ymin": -10, "ymax": 50}, # bounds of the ratio plot
# )
plot.draw()
plot.save()
mt = plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetSelection")
mt.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
mt_data = mt.histoMgr.getHisto("Data").getRootHisto().Clone("mt_data")
def main():
# Read the datasets, see twiki page for more examples
# https://twiki.cern.ch/twiki/bin/view/CMS/HiggsChToTauNuFullyHadronicSoftware#Construct_datasets
#
# This one constructs datasets assuming that the multicrab
# directory is the working directory
datasets = dataset.getDatasetsFromMulticrabCfg(analysisName=analysis, dataEra=dataEra)
# This one constructs from some other multicrab directory, the
# path can be absolute or relative
#datasets = dataset.getDatasetsFromMulticrabCfg(directory="/path/to/your/multicrab/directory", analysisName=analysis, dataEra=dataEra)
# Usually you can also omit the analysisName, as it can be detected automatically
# datasets = dataset.getDatasetsFromMulticrabCfg(dataEra=dataEra)
# If you have both light and heavy analysis and/or optimization
# enabled, you have to specify searchMode and/or optimizationMode
# explicitly
# datasets = dataset.getDatasetsFromMulticrabCfg(searchMode="Light", dataEra=dataEra, optimizationMode="Opt...")
if mcOnly:
datasets.remove(datasets.getDataDatasetNames())
histograms.cmsTextMode = histograms.CMSMode.SIMULATION
else:
datasets.loadLuminosities()
datasets.updateNAllEventsToPUWeighted()
plots.mergeRenameReorderForDataMC(datasets)
if mcOnly:
print "*** Int.Lumi (manually set)", mcOnlyLumi
else:
print "*** Int.Lumi",datasets.getDataset("Data").getLuminosity()
print "*** norm=",datasets.getDataset("TTToHplusBWB_M120").getNormFactor()
# Remove signals other than M120
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M%d"%lightHplusMassPoint in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_" in name, datasets.getAllDatasetNames()))
# Change legend creator defaults
histograms.createLegend.moveDefaults(dx=-0.05)
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=lightHplusTopBR, br_Htaunu=1)
# Example how to set cross section to a specific MSSM point
#xsect.setHplusCrossSectionsToMSSM(datasets, tanbeta=20, mu=200)
histograms.createSignalText.set(mass=lightHplusMassPoint)
# Merge TT->WH+ and TT->H+H- signals into one dataset (per mass point)
plots.mergeWHandHH(datasets) # merging of WH and HH signals must be done after setting the cross section
# Merge T->H+ signals into one dataset (per mass point)
#plots.mergeSingleTopHplus(datasets)
# Merge TT->WH+, TT->H+H-, and T->H+ signals into one dataset (per mass point)
#plots.mergeLightHplus(datasets)
# Replace signal dataset with a signal+background dataset, where
# the BR(t->H+) is taken into account for SM ttbar
plots.replaceLightHplusWithSignalPlusBackground(datasets)
# You can print a tree of all the merged datasets with this
#datasets.printDatasetTree()
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create plots
doPlots(datasets)
# Create counters
doCounters(datasets)
if interactiveMode:
raw_input("*** Press \"Enter\" to exit pyROOT: ")
def main(argv):
# HISTONAME = "TauIdJets"
# HISTONAME = "TauIdJetsCollinear"
# HISTONAME = "TauIdBtag"
# HISTONAME = "TauIdBvetoCollinear"
# HISTONAME = "TauIdBveto"
HISTONAME = "TauIdAfterCollinearCuts"
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
# Create all datasets from a multicrab task
# datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters, dataEra=dataEra, analysisBaseName="signalAnalysisInvertedTau")
#datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra, analysisBaseName="signalAnalysisInvertedTau")
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis, optimizationMode=optimizationMode)
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra)
# Check multicrab consistency
consistencyCheck.checkConsistencyStandalone(dirs[0],datasets,name="QCD inverted")
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_t-channel" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_tW-channel" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
invertedQCD.setInfo([dataEra,searchMode,HISTONAME])
histonames = datasets.getDataset("Data").getDirectoryContent("baseline/METBaseline"+HISTONAME)
bins = []
binLabels = []
for histoname in histonames:
bins.append(histoname.replace("METBaseline"+HISTONAME,""))
title = datasets.getDataset("Data").getDatasetRootHisto("baseline/METBaseline"+HISTONAME+"/"+histoname).getHistogram().GetTitle()
title = title.replace("METBaseline"+HISTONAME,"")
title = title.replace("#tau p_{T}","taup_T")
title = title.replace("#tau eta","taueta")
title = title.replace("<","lt")
title = title.replace(">","gt")
title = title.replace("=","eq")
title = title.replace("..","to")
title = title.replace(".","p")
title = title.replace("/","_")
binLabels.append(title)
print
print "Histogram bins available",bins
# bins = ["Inclusive"]
# bins = ["taup_Tleq50","taup_Teq50to60"]
print "Using bins ",bins
print
print "Bin labels"
for i in range(len(binLabels)):
line = bins[i]
while len(line) < 10:
line += " "
line += ": "+binLabels[i]
print line
print
for i,bin in enumerate(bins):
invertedQCD.setLabel(binLabels[i])
metBase = plots.DataMCPlot(datasets, "baseline/METBaseline"+HISTONAME+"/METBaseline"+HISTONAME+bin)
metInver = plots.DataMCPlot(datasets, "Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
# Rebin before subtracting
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone("Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone("Baseline/METBaseLine"+HISTONAME+"/METBaseline"+HISTONAME+bin)
metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto().Clone("Baseline/METBaseLine"+HISTONAME+"/METBaseline"+HISTONAME+bin)
metBase_QCD = metBase_data.Clone("QCD")
metBase_QCD.Add(metBase_EWK,-1)
metInverted_QCD = metInverted_data.Clone("QCD")
metInverted_QCD.Add(metInverted_EWK,-1)
metInverted_data = addlabels(metInverted_data)
metInverted_EWK = addlabels(metInverted_EWK)
metBase_data = addlabels(metBase_data)
metBase_EWK = addlabels(metBase_EWK)
metInverted_QCD = addlabels(metInverted_QCD)
invertedQCD.plotHisto(metInverted_data,"inverted")
invertedQCD.plotHisto(metInverted_EWK,"invertedEWK")
invertedQCD.plotHisto(metBase_data,"baseline")
invertedQCD.plotHisto(metBase_EWK,"baselineEWK")
fitOptions = "LRB"
# fitOptions = "RB"
# fitOptions = "IEB"
# ROOT.TVirtualFitter.SetDefaultFitter("Minuit")
# ROOT.TVirtualFitter.SetDefaultFitter("Minuit2")
# fitOptions = "Q" # Chi2 Fit
# fitOptions = "VEB" # Chi2 Fit with Minos Error
# fitOptions = "IEB" # Chi2 Fit with Integral and Minos
# fitOptions = "VLEB" # Likelihood Fit with Minos Error
# ROOT.TVirtualFitter.SetDefaultFitter("Fumili")
# ROOT.TVirtualFitter.SetDefaultFitter("Fumili2")
# fitOptions = "Q" # Chi2 Fit
# fitOptions = "VE" # Chi2 Fit with Minos Error
# fitOptions = "IE" # Chi2 Fit with Integral and Minos
# fitOptions = "VLE" # Likelihood Fit with Minos Error
invertedQCD.fitEWK(metInverted_EWK,fitOptions)
invertedQCD.fitEWK(metBase_EWK,fitOptions)
invertedQCD.fitQCD(metInverted_QCD,fitOptions)
invertedQCD.fitData(metBase_data)
invertedQCD.getNormalization()
invertedQCD.Summary()
invertedQCD.WriteNormalizationToFile("QCDInvertedNormalizationFactors.py")
invertedQCD.WriteLatexOutput("fits.tex")
def main():
if len(sys.argv) < 2:
usage()
dirs = []
dirs.append(sys.argv[1])
# Read the datasets
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra, analysisBaseName="signalAnalysisInvertedTau")
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis)
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters)
# datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters, dataEra=dataEra)
# datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
datasets.updateNAllEventsToPUWeighted()
# Take QCD from data
datasetsQCD = None
if QCDfromData:
datasetsQCD = dataset.getDatasetsFromMulticrabCfg(cfgfile="/home/rkinnune/signalAnalysis/CMSSW_4_2_8_patch2/src/HiggsAnalysis/HeavyChHiggsToTauNu/test/multicrab_111123_132128/multicrab.cfg", counters=counters)
datasetsQCD.loadLuminosities()
print "QCDfromData", QCDfromData
datasetsQCD.mergeData()
datasetsQCD.remove(datasetsQCD.getMCDatasetNames())
datasetsQCD.rename("Data", "QCD")
plots.mergeRenameReorderForDataMC(datasets)
print "Int.Lumi",datasets.getDataset("Data").getLuminosity()
# Remove signals other than M120
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.merge("EWK", ["WJets", "DYJetsToLL", "SingleTop", "Diboson","TTJets"], keepSources=True)
datasets.remove(filter(lambda name: "W2Jets" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "W3Jets" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "W4Jets" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_s-channel" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_t-channel" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_tW-channel" in name, datasets.getAllDatasetNames()))
datasets_lands = datasets.deepCopy()
# Set the signal cross sections to the ttbar for datasets for lands
# xsect.setHplusCrossSectionsToTop(datasets_lands)
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.01, br_Htaunu=1)
# Set the signal cross sections to a value from MSSM
# xsect.setHplusCrossSectionsToMSSM(datasets, tanbeta=20, mu=200)
plots.mergeWHandHH(datasets) # merging of WH and HH signals must be done after setting the cross section
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create plots
doPlots(datasets)
# Write mt histograms to ROOT file
# writeTransverseMass(datasets_lands)
# Print counters
doCounters(datasets)
def main():
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.2, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
# Merge EWK datasets
datasets.merge("EWK", [
"WJets",
"TTJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create the normalized plot of transverse mass
# Read the histogram from the file
#mT = plots.DataMCPlot(datasets, analysis+"/transverseMass")
# Create the histogram from the tree (and see the selections explicitly)
td = dataset.TreeDraw(analysis+"/tree", weight="weightPileup*weightTrigger*weightPrescale",
selection="met_p4.Et() > 70 && Max$(jets_btag) > 1.7")
# mT = plots.DataMCPlot(datasets, td.clone(varexp="sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi())))>>dist(400, 0, 400)"))
# met = plots.DataMCPlot(datasets, td.clone(varexp="met_p4.Et()>>dist(400, 0, 400)"))
met5060 = plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging5060")
met6070 = plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging6070")
met7080 = plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging7080")
met80100 = plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging80100")
met100120 = plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging100120")
met120150 = plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging120150")
met150 = plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging150")
# Rebin before subtracting
met5060.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met6070.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met7080.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met80100.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met100120.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met120150.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met150.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
data5060 = met5060.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
data6070 = met6070.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
data7080 = met7080.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
data80100 = met80100.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
data100120 = met100120.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
data120150 = met120150.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
data150 = met150.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
# Create the data-EWK histogram and draw it
# diffBase = dataEwkDiff(metBase, "MET_base_data-ewk")
# diffInverted = dataEwkDiff(metInver,"MET_inverted_data-ewk")
# Draw the MET distribution
# transverseMass(metBase,"MET_base")
# transverseMass(metInver,"MET_inverted")
# Set the styles
# dataset._normalizeToOne(diffBase)
# dataset._normalizeToOne(diffInverted)
plot = plots.PlotBase()
plot.histoMgr.appendHisto(histograms.Histo(data5060, "50 < p_{T}^{#tau jet} < 60 GeV"))
plot.histoMgr.appendHisto(histograms.Histo(data6070, "60 < p_{T}^{#tau jet} < 70 GeV"))
plot.histoMgr.appendHisto(histograms.Histo(data7080, "70 < p_{T}^{#tau jet} < 80 GeV"))
plot.histoMgr.appendHisto(histograms.Histo(data80100, "80 < p_{T}^{#tau jet} < 100 GeV"))
plot.histoMgr.appendHisto(histograms.Histo(data100120, "100 < p_{T}^{#tau jet} < 120 GeV"))
plot.histoMgr.appendHisto(histograms.Histo(data120150, "120 < p_{T}^{#tau jet} < 150 GeV"))
plot.histoMgr.appendHisto(histograms.Histo(data150, "p_{T}^{#tau jet} > 150 GeV"))
st1 = styles.getDataStyle().clone()
st2 = st1.clone()
st2.append(styles.StyleLine(lineColor=ROOT.kRed))
st3 = st1.clone()
st3.append(styles.StyleLine(lineColor=ROOT.kBlue))
st4 = st1.clone()
st4.append(styles.StyleLine(lineColor=ROOT.kGreen))
st5 = st1.clone()
st5.append(styles.StyleLine(lineColor=ROOT.kMagenta, lineStyle=2))
st6 = st1.clone()
st6.append(styles.StyleLine(lineColor=ROOT.kPink, lineStyle=8)) # lineWidth=6))
st7 = st1.clone()
st7.append(styles.StyleLine(lineColor=ROOT.kBlue-2, lineStyle=3))
plot.histoMgr.forHisto("50 < p_{T}^{#tau jet} < 60 GeV", st1)
plot.histoMgr.forHisto("60 < p_{T}^{#tau jet} < 70 GeV", st2)
plot.histoMgr.forHisto("70 < p_{T}^{#tau jet} < 80 GeV", st3)
plot.histoMgr.forHisto("80 < p_{T}^{#tau jet} < 100 GeV", st4)
plot.histoMgr.forHisto("100 < p_{T}^{#tau jet} < 120 GeV", st5)
plot.histoMgr.forHisto("120 < p_{T}^{#tau jet} < 150 GeV", st6)
plot.histoMgr.forHisto("p_{T}^{#tau jet} > 150 GeV", st7)
plot.createFrame("METinBins", opts={"xmax": 200, "ymin":1e-1, "ymaxfactor": 1.5},
# createRatio=True, opts2={"ymin": -5 , "ymax": 6 }, # bounds of the ratio plot
)
plot.getPad().SetLogy(True)
plot.setLegend(histograms.createLegend(0.6, 0.68, 0.8, 0.93))
plot.frame.GetXaxis().SetTitle("MET (GeV)")
plot.frame.GetYaxis().SetTitle("Data")
# Draw the plot
plot.draw()
plot.save()
def main():
if len(sys.argv) < 2:
usage()
# HISTONAME = "METInvertedTauIdAfterJets"
HISTONAME = "METInvertedTauIdAfterCollinearCuts"
# HISTONAME = "MET_InvertedTauIdBveto"
# HISTONAME = "METInvertedTauIdAfterCollinearCutsPlusBtag"
# HISTONAME = "METInvertedTauIdAfterCollinearCutsPlusBtag"
# HISTONAME = "MTInvertedTauIdJet"
# HISTONAME = "MTInvertedTauIdPhi"
invertedhisto = HISTONAME
baselinehisto = HISTONAME.replace("Inverted","BaseLine")
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
# Create all datasets from a multicrab task
#datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis)
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra, analysisBaseName="signalAnalysisInvertedTau" )
# As we use weighted counters for MC normalisation, we have to
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
if ReBinning:
rebinfactor = 1.3
histobins = []
histobins.append(0)
histobins.append(1)
i = 1
while histobins[len(histobins)-1] < 400:
edge = histobins[i] + (histobins[i]-histobins[i-1])*rebinfactor
histobins.append(edge)
i += 1
print histobins
# metBase_data = metBase_data.Rebin(len(histobins)-1, metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone(analysis+"/"+invertedhisto) "",array.array("d", histobins))
metInverted_data = metInverted_data.Rebin(len(histobins)-1,"",array.array("d", histobins))
metBase_EWK = metBase_EWK.Rebin(len(histobins)-1,"",array.array("d", histobins))
metInverted_EWK = metInverted_EWK.Rebin(len(histobins)-1,"",array.array("d", histobins))
normData,normEWK=normalisation()
metInverted = []
metBaseline = []
metBaselineQCD = []
for ptbin in ptbins:
## inverted
met_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/METInvertedTauIdAfterCollinearCuts/METInvertedTauIdAfterCollinearCuts"+ptbin)])
#met_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/"+invertedhisto+ptbin)])
met_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
met = met_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
met.Scale(normData[ptbin])
metEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/METInvertedTauIdAfterCollinearCuts/METInvertedTauIdAfterCollinearCuts"+ptbin)])
#metEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/"+invertedhisto+ptbin)])
metEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
metEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
metEWK = metEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
metEWK.Scale(normEWK[ptbin])
met.Add(metEWK, -1)
metInverted.append(met)
## baseline
met_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("baseline/METBaselineTauIdAfterCollinearCuts/METBaselineTauIdAfterCollinearCuts"+ptbin)])
#met_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/"+invertedhisto+ptbin)])
met_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
metbaseline = met_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
metEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("baseline/METBaselineTauIdAfterCollinearCuts/METBaselineTauIdAfterCollinearCuts"+ptbin)])
#metEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/"+invertedhisto+ptbin)])
metEWK_tmp.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
metEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
metEWKbaseline = metEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
metBaseline.append(metbaseline)
metbaseline.Add(metEWKbaseline, -1)
metBaselineQCD.append(metbaseline)
metInverted_data = metInverted[0].Clone("met")
metInverted_data.SetName("met")
metInverted_data.SetTitle("Inverted tau ID")
metInverted_data.Reset()
for histo in metInverted:
metInverted_data.Add(histo)
metBaseline_data = metBaseline[0].Clone("met")
metBaseline_data.SetName("met")
metBaseline_data.SetTitle("baseline tau ID")
metBaseline_data.Reset()
for histo in metBaseline:
metBaseline_data.Add(histo)
metBaseline_QCD = metBaselineQCD[0].Clone("met")
metBaseline_QCD .SetName("met")
metBaseline_QCD.SetTitle("baseline tau ID")
metBaseline_QCD.Reset()
for histo in metBaselineQCD:
metBaseline_QCD.Add(histo)
#metBase_data.SetTitle("Data: BaseLine TauID")
#metInverted_data.SetTitle("Data: Inverted TauID")
#metBase_QCD = metBase_data.Clone("QCD")
#metBase_QCD.Add(metBase_EWK,-1)
#metBase_QCD.SetTitle("Data - EWK MC: BaseLine TauID")
invertedQCD.setLabel("BaseVsInverted")
invertedQCD.comparison(metInverted_data,metBaseline_data)
invertedQCD.setLabel("BaseMinusEWKVsInverted")
invertedQCD.comparison(metInverted_data,metBaseline_QCD)
invertedQCD.setLabel("McVsInverted")
# invertedQCD.comparison(metInverted_data,metInverted_MC)
invertedQCD.setLabel("EfficiencyBaseMinusEWKVsInverted")
invertedQCD.cutefficiency(metInverted_data,metBaseline_QCD )
def main():
errorBars = False
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis)
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs, counters=counters, dataEra=dataEra, analysisBaseName="signalAnalysisInvertedTau")
## datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters, dataEra=dataEra)
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
invertedQCD.useErrorBars(errorBars)
metBase = plots.DataMCPlot(datasets, "BaseLine/MET_BaseLineTauIdBvetoCollinear")
metInver = plots.DataMCPlot(datasets, "Inverted/MET_InvertedTauIdBvetoCollinear")
# metBase = plots.DataMCPlot(datasets, "BaseLine/MET_BaseLineTauIdJetsCollinear")
# metInver = plots.DataMCPlot(datasets, "Inverted/MET_InvertedTauIdJetsCollinear")
# metBase = plots.DataMCPlot(datasets, "BaseLine/MET_BaseLineTauIdBveto")
# metInver = plots.DataMCPlot(datasets, "Inverted/MET_InvertedTauIdBveto")
# Rebin before subtracting
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
# metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone("Inverted/MET_InvertedTauIdBveto")
# metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted/MET_InvertedTauIdBveto")
# metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone("BaseLine/MET_BaselineTauIdBveto")
# metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto().Clone("BaseLine/MET_BaselineTauIdBveto")
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone("Inverted/MET_InvertedTauIdBvetoCollinear")
metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted/MET_InvertedTauIdBvetoCollinear")
metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone("BaseLine/MET_BaselineTauIdBvetoCollinear")
metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto().Clone("BaseLine/MET_BaselineTauIdBvetoCollinear")
# metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone("Inverted/MET_InvertedTauIdJetsCollinear")
# metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted/MET_InvertedTauIdJetsCollinear")
# metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone("BaseLine/MET_BaselineTauIdJetsCollinear")
# metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto().Clone("BaseLine/MET_BaselineTauIdJetsCollinear")
metBase_data.SetTitle("Data: BaseLine TauID")
metInverted_data.SetTitle("Data: Inverted TauID")
metBase_QCD = metBase_data.Clone("QCD")
metBase_QCD.Add(metBase_EWK,-1)
metBase_QCD.SetTitle("Data - EWK MC: BaseLine TauID")
metInverted_data.GetXaxis().SetTitle("PFMET (GeV)")
invertedQCD.setLabel("BaseVsInverted")
# invertedQCD.comparison(metInverted_data,metBase_data)
invertedQCD.setLabel("BaseMinusEWKVsInverted")
# invertedQCD.comparison(metInverted_data,metBase_QCD)
invertedQCD.cutefficiency(metInverted_data,metBase_QCD)
def main():
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(
filter(lambda name: "TTToHplus" in name and not "M120" in name,
datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.2, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
# Merge EWK datasets
datasets.merge("EWK",
["WJets", "TTJets", "DYJetsToLL", "SingleTop", "Diboson"])
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create the normalized plot of transverse mass
# Read the histogram from the file
#mT = plots.DataMCPlot(datasets, analysis+"/transverseMass")
# Create the histogram from the tree (and see the selections explicitly)
td = dataset.TreeDraw(
analysis + "/tree",
weight="weightPileup*weightTrigger*weightPrescale",
selection="met_p4.Et() > 70 && Max$(jets_btag) > 1.7")
# mT = plots.DataMCPlot(datasets, td.clone(varexp="sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi())))>>dist(400, 0, 400)"))
# met = plots.DataMCPlot(datasets, td.clone(varexp="met_p4.Et()>>dist(400, 0, 400)"))
met5060 = plots.DataMCPlot(datasets,
analysis + "/QCD_MET_AfterJetsBtagging5060")
met6070 = plots.DataMCPlot(datasets,
analysis + "/QCD_MET_AfterJetsBtagging6070")
met7080 = plots.DataMCPlot(datasets,
analysis + "/QCD_MET_AfterJetsBtagging7080")
met80100 = plots.DataMCPlot(datasets,
analysis + "/QCD_MET_AfterJetsBtagging80100")
met100120 = plots.DataMCPlot(datasets,
analysis + "/QCD_MET_AfterJetsBtagging100120")
met120150 = plots.DataMCPlot(datasets,
analysis + "/QCD_MET_AfterJetsBtagging120150")
met150 = plots.DataMCPlot(datasets,
analysis + "/QCD_MET_AfterJetsBtagging150")
# Rebin before subtracting
met5060.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met6070.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met7080.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met80100.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met100120.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met120150.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met150.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
data5060 = met5060.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
data6070 = met6070.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
data7080 = met7080.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
data80100 = met80100.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
data100120 = met100120.histoMgr.getHisto("Data").getRootHisto().Clone(
"Data")
data120150 = met120150.histoMgr.getHisto("Data").getRootHisto().Clone(
"Data")
data150 = met150.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
# Create the data-EWK histogram and draw it
# diffBase = dataEwkDiff(metBase, "MET_base_data-ewk")
# diffInverted = dataEwkDiff(metInver,"MET_inverted_data-ewk")
# Draw the MET distribution
# transverseMass(metBase,"MET_base")
# transverseMass(metInver,"MET_inverted")
# Set the styles
# dataset._normalizeToOne(diffBase)
# dataset._normalizeToOne(diffInverted)
plot = plots.PlotBase()
plot.histoMgr.appendHisto(
histograms.Histo(data5060, "50 < p_{T}^{#tau jet} < 60 GeV"))
plot.histoMgr.appendHisto(
histograms.Histo(data6070, "60 < p_{T}^{#tau jet} < 70 GeV"))
plot.histoMgr.appendHisto(
histograms.Histo(data7080, "70 < p_{T}^{#tau jet} < 80 GeV"))
plot.histoMgr.appendHisto(
histograms.Histo(data80100, "80 < p_{T}^{#tau jet} < 100 GeV"))
plot.histoMgr.appendHisto(
histograms.Histo(data100120, "100 < p_{T}^{#tau jet} < 120 GeV"))
plot.histoMgr.appendHisto(
histograms.Histo(data120150, "120 < p_{T}^{#tau jet} < 150 GeV"))
plot.histoMgr.appendHisto(
histograms.Histo(data150, "p_{T}^{#tau jet} > 150 GeV"))
st1 = styles.getDataStyle().clone()
st2 = st1.clone()
st2.append(styles.StyleLine(lineColor=ROOT.kRed))
st3 = st1.clone()
st3.append(styles.StyleLine(lineColor=ROOT.kBlue))
st4 = st1.clone()
st4.append(styles.StyleLine(lineColor=ROOT.kGreen))
st5 = st1.clone()
st5.append(styles.StyleLine(lineColor=ROOT.kMagenta, lineStyle=2))
st6 = st1.clone()
st6.append(styles.StyleLine(lineColor=ROOT.kPink,
lineStyle=8)) # lineWidth=6))
st7 = st1.clone()
st7.append(styles.StyleLine(lineColor=ROOT.kBlue - 2, lineStyle=3))
plot.histoMgr.forHisto("50 < p_{T}^{#tau jet} < 60 GeV", st1)
plot.histoMgr.forHisto("60 < p_{T}^{#tau jet} < 70 GeV", st2)
plot.histoMgr.forHisto("70 < p_{T}^{#tau jet} < 80 GeV", st3)
plot.histoMgr.forHisto("80 < p_{T}^{#tau jet} < 100 GeV", st4)
plot.histoMgr.forHisto("100 < p_{T}^{#tau jet} < 120 GeV", st5)
plot.histoMgr.forHisto("120 < p_{T}^{#tau jet} < 150 GeV", st6)
plot.histoMgr.forHisto("p_{T}^{#tau jet} > 150 GeV", st7)
plot.createFrame(
"METinBins",
opts={
"xmax": 200,
"ymin": 1e-1,
"ymaxfactor": 1.5
},
# createRatio=True, opts2={"ymin": -5 , "ymax": 6 }, # bounds of the ratio plot
)
plot.getPad().SetLogy(True)
plot.setLegend(histograms.createLegend(0.6, 0.68, 0.8, 0.93))
plot.frame.GetXaxis().SetTitle("MET (GeV)")
plot.frame.GetYaxis().SetTitle("Data")
# Draw the plot
plot.draw()
plot.save()
def main():
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(
filter(lambda name: "TTToHplus" in name and not "M120" in name,
datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
ttjets2 = datasets.getDataset("TTJets").deepCopy()
ttjets2.setCrossSection(
ttjets2.getCrossSection() -
datasets.getDataset("TTToHplus_M120").getCrossSection())
print "Set TTJets2 cross section to %f" % ttjets2.getCrossSection()
ttjets2.setName("TTJets2")
datasets.append(ttjets2)
datasets.merge("EWKnott", ["WJets", "DYJetsToLL", "SingleTop", "Diboson"])
tmp = datasets.getDataset("EWKnott").deepCopy()
tmp.setName("EWKnott2")
datasets.append(tmp)
datasets.merge("EWK", ["EWKnott", "TTJets"])
# Apply TDR style
style = tdrstyle.TDRStyle()
invertedQCD = InvertedTauID()
metBase = plots.DataMCPlot(datasets, analysis + "/MET_BaseLineTauIdJets")
metInver = plots.DataMCPlot(datasets, analysis + "/MET_InvertedTauIdJets")
# Rebin before subtracting
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone(
analysis + "/MET_InvertedTauIdJets")
metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone(
analysis + "/MET_InvertedTauIdJets")
metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone(
analysis + "/MET_BaselineTauIdJets")
metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto().Clone(
analysis + "/MET_BaselineTauIdJets")
metBase_QCD = metBase_data.Clone("QCD")
metBase_QCD.Add(metBase_EWK, -1)
metBase_EWK.SetTitle("MC EWK")
invertedQCD.setLabel("InvData")
invertedQCD.fitQCD(metInverted_data)
invertedQCD.setLabel("ChiSq")
invertedQCD.fitEWK(metBase_EWK, "R")
invertedQCD.fitData(metBase_data)
normalizationWithChiSq = invertedQCD.getNormalization()
invertedQCD.setLabel("LogLikelihood")
invertedQCD.fitEWK(metBase_EWK, "LR")
invertedQCD.fitData(metBase_data)
normalizationWithLogLL = invertedQCD.getNormalization()
print "Difference Signal vs no signal in EWK fit", (
normalizationWithLogLL -
normalizationWithChiSq) / normalizationWithChiSq
def main():
if len(sys.argv) < 2:
usage()
# HISTONAME = "METInvertedTauIdAfterJets"
HISTONAME = "METInvertedTauIdAfterCollinearCuts"
# HISTONAME = "MET_InvertedTauIdBveto"
# HISTONAME = "METInvertedTauIdAfterCollinearCutsPlusBtag"
# HISTONAME = "METInvertedTauIdAfterCollinearCutsPlusBtag"
# HISTONAME = "MTInvertedTauIdJet"
# HISTONAME = "MTInvertedTauIdPhi"
invertedhisto = HISTONAME
baselinehisto = HISTONAME.replace("Inverted", "BaseLine")
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
# Create all datasets from a multicrab task
#datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis)
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra, analysisBaseName="signalAnalysisInvertedTau" )
# As we use weighted counters for MC normalisation, we have to
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(
filter(lambda name: "TTToHplus" in name and not "M120" in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
datasets.merge("EWK",
["TTJets", "WJets", "DYJetsToLL", "SingleTop", "Diboson"])
# Apply TDR style
style = tdrstyle.TDRStyle()
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
if ReBinning:
rebinfactor = 1.3
histobins = []
histobins.append(0)
histobins.append(1)
i = 1
while histobins[len(histobins) - 1] < 400:
edge = histobins[i] + (histobins[i] -
histobins[i - 1]) * rebinfactor
histobins.append(edge)
i += 1
print histobins
# metBase_data = metBase_data.Rebin(len(histobins)-1, metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone(analysis+"/"+invertedhisto) "",array.array("d", histobins))
metInverted_data = metInverted_data.Rebin(
len(histobins) - 1, "", array.array("d", histobins))
metBase_EWK = metBase_EWK.Rebin(
len(histobins) - 1, "", array.array("d", histobins))
metInverted_EWK = metInverted_EWK.Rebin(
len(histobins) - 1, "", array.array("d", histobins))
normData, normEWK = normalisation()
metInverted = []
metBaseline = []
metBaselineQCD = []
for ptbin in ptbins:
## inverted
met_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"Inverted/METInvertedTauIdAfterCollinearCuts/METInvertedTauIdAfterCollinearCuts"
+ ptbin)
])
#met_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/"+invertedhisto+ptbin)])
met_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
met = met_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
met.Scale(normData[ptbin])
metEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"Inverted/METInvertedTauIdAfterCollinearCuts/METInvertedTauIdAfterCollinearCuts"
+ ptbin)
])
#metEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/"+invertedhisto+ptbin)])
metEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
metEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
metEWK = metEWK_tmp.histoMgr.getHisto("EWK").getRootHisto().Clone()
metEWK.Scale(normEWK[ptbin])
met.Add(metEWK, -1)
metInverted.append(met)
## baseline
met_tmp = plots.PlotBase([
datasets.getDataset("Data").getDatasetRootHisto(
"baseline/METBaselineTauIdAfterCollinearCuts/METBaselineTauIdAfterCollinearCuts"
+ ptbin)
])
#met_tmp = plots.PlotBase([datasets.getDataset("Data").getDatasetRootHisto("Inverted/"+invertedhisto+ptbin)])
met_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
metbaseline = met_tmp.histoMgr.getHisto("Data").getRootHisto().Clone()
metEWK_tmp = plots.PlotBase([
datasets.getDataset("EWK").getDatasetRootHisto(
"baseline/METBaselineTauIdAfterCollinearCuts/METBaselineTauIdAfterCollinearCuts"
+ ptbin)
])
#metEWK_tmp = plots.PlotBase([datasets.getDataset("EWK").getDatasetRootHisto("Inverted/"+invertedhisto+ptbin)])
metEWK_tmp.histoMgr.normalizeMCToLuminosity(
datasets.getDataset("Data").getLuminosity())
metEWK_tmp.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(5))
metEWKbaseline = metEWK_tmp.histoMgr.getHisto(
"EWK").getRootHisto().Clone()
metBaseline.append(metbaseline)
metbaseline.Add(metEWKbaseline, -1)
metBaselineQCD.append(metbaseline)
metInverted_data = metInverted[0].Clone("met")
metInverted_data.SetName("met")
metInverted_data.SetTitle("Inverted tau ID")
metInverted_data.Reset()
for histo in metInverted:
metInverted_data.Add(histo)
metBaseline_data = metBaseline[0].Clone("met")
metBaseline_data.SetName("met")
metBaseline_data.SetTitle("baseline tau ID")
metBaseline_data.Reset()
for histo in metBaseline:
metBaseline_data.Add(histo)
metBaseline_QCD = metBaselineQCD[0].Clone("met")
metBaseline_QCD.SetName("met")
metBaseline_QCD.SetTitle("baseline tau ID")
metBaseline_QCD.Reset()
for histo in metBaselineQCD:
metBaseline_QCD.Add(histo)
#metBase_data.SetTitle("Data: BaseLine TauID")
#metInverted_data.SetTitle("Data: Inverted TauID")
#metBase_QCD = metBase_data.Clone("QCD")
#metBase_QCD.Add(metBase_EWK,-1)
#metBase_QCD.SetTitle("Data - EWK MC: BaseLine TauID")
invertedQCD.setLabel("BaseVsInverted")
invertedQCD.comparison(metInverted_data, metBaseline_data)
invertedQCD.setLabel("BaseMinusEWKVsInverted")
invertedQCD.comparison(metInverted_data, metBaseline_QCD)
invertedQCD.setLabel("McVsInverted")
# invertedQCD.comparison(metInverted_data,metInverted_MC)
invertedQCD.setLabel("EfficiencyBaseMinusEWKVsInverted")
invertedQCD.cutefficiency(metInverted_data, metBaseline_QCD)
def main():
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
ttjets2 = datasets.getDataset("TTJets").deepCopy()
ttjets2.setCrossSection(ttjets2.getCrossSection()-datasets.getDataset("TTToHplus_M120").getCrossSection())
print "Set TTJets2 cross section to %f" % ttjets2.getCrossSection()
ttjets2.setName("TTJets2")
datasets.append(ttjets2)
datasets.merge("EWKnott", [
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
tmp = datasets.getDataset("EWKnott").deepCopy()
tmp.setName("EWKnott2")
datasets.append(tmp)
datasets.merge("EWK", [
"EWKnott",
"TTJets"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
invertedQCD = InvertedTauID()
metBase = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJets")
metInver = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets")
# Rebin before subtracting
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone(analysis+"/MET_InvertedTauIdJets")
metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone(analysis+"/MET_InvertedTauIdJets")
metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone(analysis+"/MET_BaselineTauIdJets")
metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto().Clone(analysis+"/MET_BaselineTauIdJets")
metBase_QCD = metBase_data.Clone("QCD")
metBase_QCD.Add(metBase_EWK,-1)
metBase_EWK.SetTitle("MC EWK")
invertedQCD.setLabel("InvData")
invertedQCD.fitQCD(metInverted_data)
invertedQCD.setLabel("ChiSq")
invertedQCD.fitEWK(metBase_EWK,"R")
invertedQCD.fitData(metBase_data)
normalizationWithChiSq = invertedQCD.getNormalization()
invertedQCD.setLabel("LogLikelihood")
invertedQCD.fitEWK(metBase_EWK,"LR")
invertedQCD.fitData(metBase_data)
normalizationWithLogLL = invertedQCD.getNormalization()
print "Difference Signal vs no signal in EWK fit",(normalizationWithLogLL - normalizationWithChiSq)/normalizationWithChiSq
def main(argv):
# HISTONAME = "TauIdJets"
# HISTONAME = "TauIdJetsCollinear"
# HISTONAME = "TauIdBtag"
# HISTONAME = "TauIdBvetoCollinear"
# HISTONAME = "TauIdBveto"
HISTONAME = "TauIdAfterCollinearCuts"
FAKEHISTO = "OnlyEWKFakeTaus"
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName="signalAnalysisInvertedTau",optimizationMode="") #no collinear
#datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName="signalAnalysisInvertedTau",optimizationMode="OptQCDTailKillerLoosePlus") #collinear
# Check multicrab consistency
consistencyCheck.checkConsistencyStandalone(dirs[0],datasets,name="QCD inverted")
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_t-channel" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "Hplus_taunu_tW-channel" in name, datasets.getAllDatasetNames()))
#datasets.remove(filter(lambda name: "TTJets_SemiLept" in name, datasets.getAllDatasetNames()))
#datasets.remove(filter(lambda name: "TTJets_FullLept" in name, datasets.getAllDatasetNames()))
#datasets.remove(filter(lambda name: "TTJets_Hadronic" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
invertedQCD.setInfo([dataEra,searchMode,HISTONAME])
histonames = datasets.getDataset("Data").getDirectoryContent("baseline/METBaseline"+HISTONAME)
bins = []
binLabels = []
for histoname in histonames:
bins.append(histoname.replace("METBaseline"+HISTONAME,""))
title = datasets.getDataset("Data").getDatasetRootHisto("baseline/METBaseline"+HISTONAME+"/"+histoname).getHistogram().GetTitle()
title = title.replace("METBaseline"+HISTONAME,"")
title = title.replace("#tau p_{T}","taup_T")
title = title.replace("#tau eta","taueta")
title = title.replace("<","lt")
title = title.replace(">","gt")
title = title.replace("=","eq")
title = title.replace("..","to")
title = title.replace(".","p")
title = title.replace("/","_")
binLabels.append(title)
#binLabels = bins # for this data set
print
print "Histogram bins available",bins
# bins = ["Inclusive"]
# bins = ["taup_Tleq50","taup_Teq50to60"]
print "Using bins ",bins
print
print "Bin labels"
for i in range(len(binLabels)):
line = bins[i]
while len(line) < 10:
line += " "
line += ": "+binLabels[i]
print line
print
for i,bin in enumerate(bins):
invertedQCD.setLabel(binLabels[i])
metBase = plots.DataMCPlot(datasets, "baseline/METBaseline"+HISTONAME+"/METBaseline"+HISTONAME+bin)
metInver = plots.DataMCPlot(datasets, "Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
metBase_FakeTaus = plots.DataMCPlot(datasets, "baseline/METBaseline"+HISTONAME+FAKEHISTO+"/METBaseline"+HISTONAME+FAKEHISTO+bin)
metInver_FakeTaus = plots.DataMCPlot(datasets, "Inverted/METInverted"+HISTONAME+FAKEHISTO+"/METInverted"+HISTONAME+FAKEHISTO+bin)
# Rebin before subtracting
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
metBase_FakeTaus.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
metInver_FakeTaus.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10)) #5
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone("Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone("baseline/METBaseLine"+HISTONAME+"/METBaseline"+HISTONAME+bin)
metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted/METInverted"+HISTONAME+"/METInverted"+HISTONAME+bin)
metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto().Clone("baseline/METBaseLine"+HISTONAME+"/METBaseline"+HISTONAME+bin)
metInverted_EWK_FakeTaus = metInver_FakeTaus.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted/METInverted"+HISTONAME+FAKEHISTO+"/METInverted"+HISTONAME+FAKEHISTO+bin)
metBase_EWK_FakeTaus = metBase_FakeTaus.histoMgr.getHisto("EWK").getRootHisto().Clone("baseline/METBaseLine"+HISTONAME+FAKEHISTO+"/METBaseline"+HISTONAME+FAKEHISTO+bin)
metBase_QCD = metBase_data.Clone("QCD")
metBase_QCD.Add(metBase_EWK,-1)
metInverted_QCD = metInverted_data.Clone("QCD")
metInverted_QCD.Add(metInverted_EWK,-1)
metInverted_data = addlabels(metInverted_data)
metInverted_EWK = addlabels(metInverted_EWK)
metInverted_EWK_FakeTaus = addlabels(metInverted_EWK_FakeTaus)
metBase_data = addlabels(metBase_data)
metBase_EWK = addlabels(metBase_EWK)
metBase_EWK_FakeTaus = addlabels(metBase_EWK_FakeTaus)
metInverted_QCD = addlabels(metInverted_QCD)
invertedQCD.plotHisto(metInverted_data,"inverted")
invertedQCD.plotHisto(metInverted_EWK,"invertedEWK")
invertedQCD.plotHisto(metBase_data,"baseline")
invertedQCD.plotHisto(metBase_EWK,"baselineEWK")
invertedQCD.plotHisto(metInverted_EWK_FakeTaus,"invertedEWK_FakeTaus")
invertedQCD.plotHisto(metBase_EWK_FakeTaus,"baselineEWK_FakeTaus")
fitOptions = "LRB"
#fitOptions = "RB"
#fitOptions = "IEB"
invertedQCD.fitEWK(metInverted_EWK,fitOptions)
invertedQCD.fitEWK(metBase_EWK,fitOptions)
invertedQCD.fitQCD(metInverted_QCD,fitOptions)
invertedQCD.fitData(metBase_data)
invertedQCD.fitEWK_FakeTaus(metInverted_EWK_FakeTaus,fitOptions)
invertedQCD.fitEWK_FakeTaus(metBase_EWK_FakeTaus,fitOptions)
invertedQCD.getNormalization()
invertedQCD.Summary()
invertedQCD.WriteNormalizationToFile("QCDInvertedNormalizationFactors.py")
invertedQCD.WriteLatexOutput("fits.tex")
def main(opts,signalDsetCreator,era,searchMode,optimizationMode):
# Make directory for output
mySuffix = "normalisationPlots_%s_%s_%s"%(era,searchMode,optimizationMode)
if os.path.exists(mySuffix):
if os.path.exists("%s_old"%mySuffix):
shutil.rmtree("%s_old"%mySuffix)
os.rename(mySuffix, "%s_old"%mySuffix)
os.mkdir(mySuffix)
# Create dataset manager
myDsetMgr = signalDsetCreator.createDatasetManager(dataEra=era,searchMode=searchMode,optimizationMode=optimizationMode)
if mcOnly:
myDsetMgr.remove(myDsetMgr.getDataDatasetNames())
histograms.cmsTextMode = histograms.CMSMode.SIMULATION
else:
myDsetMgr.loadLuminosities()
myDsetMgr.updateNAllEventsToPUWeighted()
# Take QCD from data
if opts.noMCQCD:
myDsetMgr.remove(filter(lambda name: "QCD" in name, myDsetMgr.getAllDatasetNames()))
# Remove signal
if opts.noSignal:
myDsetMgr.remove(filter(lambda name: "TTToHplus" in name, myDsetMgr.getAllDatasetNames()))
myDsetMgr.remove(filter(lambda name: "Hplus_taunu" in name, myDsetMgr.getAllDatasetNames()))
myDsetMgr.remove(filter(lambda name: "HplusTB" in name, myDsetMgr.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(myDsetMgr)
if mcOnly:
print "Int.Lumi (manually set)",mcOnlyLumi
else:
print "Int.Lumi",myDsetMgr.getDataset("Data").getLuminosity()
if not opts.noSignal:
print "norm=",myDsetMgr.getDataset("TTToHplusBWB_M120").getNormFactor()
myDsetMgr.remove(filter(lambda name: "QCD_Pt20_MuEnriched" in name, myDsetMgr.getAllDatasetNames()))
# Remove signals other than M120
myDsetMgr.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, myDsetMgr.getAllDatasetNames()))
myDsetMgr.remove(filter(lambda name: "Hplus_taunu" in name, myDsetMgr.getAllDatasetNames()))
myDsetMgr.remove(filter(lambda name: "HplusTB" in name, myDsetMgr.getAllDatasetNames()))
histograms.createLegend.moveDefaults(dx=-0.05)
histograms.createSignalText.set(xmin=0.4, ymax=0.93, mass=120)
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
xsect.setHplusCrossSectionsToBR(myDsetMgr, br_tH=optionBr, br_Htaunu=1)
# Set the signal cross sections to a value from MSSM
# xsect.setHplusCrossSectionsToMSSM(myDsetMgr, tanbeta=20, mu=200)
plots.mergeWHandHH(myDsetMgr) # merging of WH and HH signals must be done after setting the cross section
# Replace signal dataset with a signal+background dataset, where
# the BR(t->H+) is taken into account for SM ttbar
#plots.replaceLightHplusWithSignalPlusBackground(myDsetMgr)
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create plots
doPlots(myDsetMgr, opts, mySuffix)
# Print counters
#doCounters(myDsetMgr)
print "Results saved in directory: %s"%mySuffix
def main():
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.2, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
# Merge EWK datasets
datasets.merge("EWK", [
"WJets",
"TTJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create the normalized plot of transverse mass
# Read the histogram from the file
#mT = plots.DataMCPlot(datasets, analysis+"/transverseMass")
# Create the histogram from the tree (and see the selections explicitly)
td = dataset.TreeDraw(analysis+"/tree", weight="weightPileup*weightTrigger*weightPrescale",
selection="met_p4.Et() > 70 && Max$(jets_btag) > 1.7")
# mT = plots.DataMCPlot(datasets, td.clone(varexp="sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi())))>>dist(400, 0, 400)"))
# met = plots.DataMCPlot(datasets, td.clone(varexp="met_p4.Et()>>dist(400, 0, 400)"))
metBase = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJets")
metInver = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets")
metBase4050 = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJet4070")
metInver4050 = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets4070")
metBase5060 = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJets5060")
metInver5060 = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets5060")
metBase6070 = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJets6070")
metInver6070 = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets6070")
metBase7080 = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJets7080")
metInver7080 = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets7080")
metBase80100 = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJets80100")
metInver80100 = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets80100")
metBase100120 = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJets100120")
metInver100120 = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets100120")
metBase120150 = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJets120150")
metInver120150 = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets120150")
metBase150 = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJets150")
metInver150 = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets150")
# metInver = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdLoose")
# Rebin before subtracting
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metBase4050.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metInver4050.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone(analysis+"/MET_InvertedTauIdJets")
metInverted_data4050 = metInver.histoMgr.getHisto("Data").getRootHisto().Clone(analysis+"/MET_InvertedTauIdJets4050")
print "print inverted met"
print metInverted_data.GetEntries()
# Create the data-EWK histogram and draw it
diffBase = dataEwkDiff(metBase, "MET_base_data-ewk")
# diffInverted = dataEwkDiff(metInver,"MET_inverted_data-ewk")
diffInverted = dataEwkNoDiff(metInver,"MET_inverted_data-ewk")
diffBase4050 = dataEwkDiff(metBase, "MET_base_data-ewk-4050")
# diffInverted4070 = dataEwkDiff(metInver,"MET_inverted_data-ewk-4070")
diffInverted4050 = dataEwkNoDiff(metInver,"MET_inverted_data-ewk-4050")
# Draw the MET distribution
transverseMass(metBase,"MET_base")
transverseMass(metInver,"MET_inverted")
# Draw the MET distribution
transverseMass(metBase4050,"MET_base4050")
transverseMass(metInver4050,"MET_inverted4050")
# Set the styles
dataset._normalizeToOne(diffBase)
dataset._normalizeToOne(diffInverted)
plot = plots.ComparisonPlot(
histograms.Histo(diffBase, "Baseline"),
histograms.Histo(diffInverted, "Inverted"))
dataset._normalizeToOne(diffBase4050)
dataset._normalizeToOne(diffInverted4050)
plot2 = plots.ComparisonPlot(
histograms.Histo(diffBase4050, "Baseline4050"),
histograms.Histo(diffInverted4050, "Inverted4050"))
st1 = styles.getDataStyle().clone()
st2 = st1.clone()
st2.append(styles.StyleLine(lineColor=ROOT.kRed))
plot.histoMgr.forHisto("Baseline", st1)
plot.histoMgr.forHisto("Inverted", st2)
plot.createFrame("METbaseVSinverted-ewk", opts={"xmax": 400, "ymin":1e-5, "ymaxfactor": 1.5},
createRatio=True, opts2={"ymin": -5 , "ymax": 6 }, # bounds of the ratio plot
)
plot.getPad().SetLogy(True)
plot.setLegend(histograms.createLegend(0.7, 0.68, 0.9, 0.93))
plot.frame.GetXaxis().SetTitle("MET (GeV)")
plot.frame.GetYaxis().SetTitle("Data - EWK")
# Draw the plot
plot.draw()
plot.save()
plot2.createFrame("METbaseVSinverted-ewk-4070", opts={"xmax": 400, "ymin":1e-5, "ymaxfactor": 1.5},
createRatio=True, opts2={"ymin": -5 , "ymax": 6 }, # bounds of the ratio plot
)
plot2.getPad().SetLogy(True)
plot2.setLegend(histograms.createLegend(0.7, 0.68, 0.9, 0.93))
plot2.frame.GetXaxis().SetTitle("MET (GeV)")
plot2.frame.GetYaxis().SetTitle("Data - EWK")
# Draw the plot
plot2.draw()
plot2.save()
def main():
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets.updateNAllEventsToPUWeighted()
# remove data datasets
datasets.remove(
filter(lambda name: "Tau_" in name, datasets.getAllDatasetNames()))
# remove heavy H+ datasets
datasets.remove(
filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
plots.mergeWHandHH(
datasets
) # merging of WH and HH signals must be done after setting the cross section
# Apply TDR style
style = tdrstyle.TDRStyle()
# Define variables and cuts
tauPt = "tau_p4.Pt()"
tauPtCut = tauPt + " > 40"
tauLeadingCandPt = "tau_leadPFChargedHadrCand_p4.Pt()"
tauLeadingCandPtCut = tauLeadingCandPt + " > 20"
met = "met_p4.Et()"
metCut = met + " > 70"
btagMax = "Max$(jets_btag)"
btagCut = btagMax + " > 1.7"
btagJetNum17 = "Sum$(jets_btag > 1.7"
btag2ndMax = "MaxIf(kets_btag, jets_btag < Max$(jets_btag))"
rtau = "tau_leadPFChargedHadrCand_p4.P()/tau_p4.P()"
rtauCut = rtau + " > 0.65"
mt = "sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi())))"
mtCut = mt + " > 80"
deltaPhi = "acos((tau_p4.Px()*met_p4.Px()+tau_p4.Py()*met_p4.Py())/tau_p4.Pt()/met_p4.Et())*57.2958"
deltaPhiCut = deltaPhi + " < 160"
npv = "goodPrimaryVertices_n"
td = dataset.TreeDraw(analysis + "/tree",
weight="weightPileup*weightTrigger*weightPrescale")
lumi = 1145
def createResult(varexp, selection, weight=None, **kwargs):
args = {"varexp": varexp, "selection": selection}
if weight != None:
args["weight"] = weight
return Result(datasets,
td.clone(**args),
normalizeToLumi=lumi,
**kwargs)
# metRes = createResult(met+">>dist(100,0,200)", btagCut, greaterThan=True)
# metRes.save("met", "MET (GeV)", rebin=10, logy=True, opts={"ymin": 0.01, "ymaxfactor": 10})
# btagRes = createResult(btagMax+">>dist(80, 0, 8)", metCut, greaterThan=True)
# btagRes.save("btag", "TCHE", rebin=5, logy=True, opts={"ymin": 0.01, "ymaxfactor": 10})
# rtauRes = createResult(rtau+">>dist(110,0,1.1)", metCut+"&&"+btagCut, greaterThan=True)
# rtauRes.save("rtau", "R_{#tau}", rebin=10, logy=True, opts={"ymin": 0.01, "ymaxfactor": 10})
# mtRes = createResult(mt+">>dist(50,0,200)", metCut+"&&"+btagCut, greaterThan=True)
# mtRes.save("mt", "M_{T}(#tau, MET) (GeV)", rebin=2, logy=True, opts={"ymin": 0.01, "ymaxfactor": 10})
# deltaPhiRes = createResult(deltaPhi+">>dist(90,0,180)", metCut+"&&"+btagCut, lessThan=True)
# deltaPhiRes.save("deltaPhi", "#Delta#Phi(#tau, MET) (#circ)", rebin=5, logy=True, opts={"ymin": 0.01, "ymaxfactor": 10})
pileupRes = createResult(npv + ">>dist(4,1,17)",
metCut + "&&" + btagCut,
weight="",
doPassed=False,
lessThan=True)
pileupRes.save("goodPV", "N(good primary vertices)")
def main():
errorBars = False
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.05, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
datasets.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
invertedQCD = InvertedTauID()
invertedQCD.setLumi(datasets.getDataset("Data").getLuminosity())
invertedQCD.useErrorBars(errorBars)
metBase = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJets")
metInver = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets")
# Rebin before subtracting
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone(analysis+"/MET_InvertedTauIdJets")
metInverted_EWK = metInver.histoMgr.getHisto("EWK").getRootHisto().Clone(analysis+"/MET_InvertedTauIdJets")
metBase_data = metBase.histoMgr.getHisto("Data").getRootHisto().Clone(analysis+"/MET_BaselineTauIdJets")
metBase_EWK = metBase.histoMgr.getHisto("EWK").getRootHisto().Clone(analysis+"/MET_BaselineTauIdJets")
metBase_data.SetTitle("Data: BaseLine TauID")
metInverted_data.SetTitle("Data: Inverted TauID")
metBase_QCD = metBase_data.Clone("QCD")
metBase_QCD.Add(metBase_EWK,-1)
metBase_QCD.SetTitle("Data - EWK MC: BaseLine TauID")
metInverted_data.GetXaxis().SetTitle("PFMET (GeV)")
invertedQCD.setLabel("BaseVsInverted")
# invertedQCD.comparison(metInverted_data,metBase_data)
invertedQCD.setLabel("BaseMinusEWKVsInverted")
# invertedQCD.comparison(metInverted_data,metBase_QCD)
invertedQCD.cutefficiency(metInverted_data,metBase_QCD)
# Do the usual normalisation
dsetMgr.updateNAllEventsToPUWeighted()
dsetMgr.loadLuminosities()
plots.mergeRenameReorderForDataMC(dsetMgr)
dsetMgr.merge("EWK", [
"TTJets",
"WJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
],keepSources=True) # Keep sources needed to keep open TTJets
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
xsect.setHplusCrossSectionsToBR(dsetMgr, br_tH=optionBrTopToHplusb, br_Htaunu=1)
# Set the signal cross sections to a value from MSSM
# xsect.setHplusCrossSectionsToMSSM(dsetMgr, tanbeta=20, mu=200)
plots.mergeWHandHH(dsetMgr) # merging of WH and HH signals must be done after setting the cross section
# Make a directory for output
myDir = "invertedControlPlots_%s"%myModuleInfoString
# Remove the directory with its contents if it exists
if os.path.exists(myDir):
shutil.rmtree(myDir)
os.mkdir(myDir)
# Obtain luminosity
myLuminosity = dsetMgr.getDataset("Data").getLuminosity()
# Print info so that user can check that merge went correct
if myDisplayStatus:
dsetMgr.printDatasetTree()
print "Luminosity = %f 1/fb"%(myLuminosity / 1000.0)
print
myDisplayStatus = False
def main(argv):
interactive = True
interactive = False
# HISTONAME = "TauIdJets"
# HISTONAME = "TauIdBtag"
HISTONAME = "TauIdBveto"
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
# Disable batch mode here to have the interactivity (see also the line with 'raw_input') below
if interactive:
ROOT.gROOT.SetBatch(False)
# Create all datasets from a multicrab task
# datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
# datasets = dataset.getDatasetsFromMulticrabDirs(dirs,counters=counters, dataEra=dataEra)
datasets = dataset.getDatasetsFromMulticrabDirs(
dirs,
counters=counters,
dataEra=dataEra,
analysisbaseName="signalAnalysisInvertedTau")
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(
filter(lambda name: "TTToHplus" in name and not "M120" in name,
datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.2, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
# Apply TDR style
style = tdrstyle.TDRStyle()
#dataMCExample(datasets)
distComparison(datasets)
# Script execution can be paused like this, it will continue after
# user has given some input (which must include enter)
if interactive:
raw_input("Hit enter to continue")
def main():
# Read the datasets
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets.loadLuminosities()
# Take QCD from data
datasetsQCD = None
if QCDfromData:
datasetsQCD = dataset.getDatasetsFromMulticrabCfg(
cfgfile=
"/home/rkinnune/signalAnalysis/CMSSW_4_2_8_patch2/src/HiggsAnalysis/HeavyChHiggsToTauNu/test/multicrab_111123_132128/multicrab.cfg",
counters=counters)
datasetsQCD.loadLuminosities()
datasetsQCD.mergeData()
datasetsQCD.remove(datasetsQCD.getMCDatasetNames())
datasetsQCD.rename("Data", "QCD")
#Rtau =0
# datasetsSignal = dataset.getDatasetsFromMulticrabCfg(cfgfile="/home/rkinnune/signalAnalysis/CMSSW_4_2_5/src/HiggsAnalysis/HeavyChHiggsToTauNu/test/multicrab_110804_104313/multicrab.cfg", counters=counters)
# datasetsSignal.selectAndReorder(["HplusTB_M200_Summer11"])
# datasetsSignal = dataset.getDatasetsFromMulticrabCfg(cfgfile="/home/rkinnune/signalAnalysis/CMSSW_4_2_4_patch1/src/HiggsAnalysis/HeavyChHiggsToTauNu/test/multicrab_110622_112321/multicrab.cfg", counters=counters)
#datasetsSignal = dataset.getDatasetsFromMulticrabCfg(cfgfile="/home/rkinnune/signalAnalysis/CMSSW_4_1_5/src/HiggsAnalysis/HeavyChHiggsToTauNu/test/Signal_v11f_scaledb_424/multicrab.cfg", counters=counters)
#datasetsSignal.selectAndReorder(["TTToHplusBWB_M120_Summer11", "TTToHplusBHminusB_M120_Summer11"])
#datasetsSignal.renameMany({"TTToHplusBWB_M120_Summer11" :"TTToHplusBWB_M120_Spring11",
# "TTToHplusBHminusB_M120_Summer11": "TTToHplusBHminusB_M120_Spring11"})
#datasets.extend(datasetsSignal)
plots.mergeRenameReorderForDataMC(datasets)
print "Int.Lumi", datasets.getDataset("Data").getLuminosity()
# Remove signals other than M120
#datasets.remove(filter(lambda name: "TTToHplus" in name, datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "TTToHplus" in name and not "M120" in name,
datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
# Remove QCD
datasets.remove(
filter(lambda name: "QCD" in name, datasets.getAllDatasetNames()))
histograms.createLegend.moveDefaults(dx=-0.02)
histograms.createLegend.moveDefaults(dh=-0.03)
datasets_lands = datasets.deepCopy()
# Set the signal cross sections to the ttbar for datasets for lands
xsect.setHplusCrossSectionsToTop(datasets_lands)
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Set the signal cross sections to a value from MSSM
# xsect.setHplusCrossSectionsToMSSM(datasets, tanbeta=20, mu=200)
plots.mergeWHandHH(
datasets
) # merging of WH and HH signals must be done after setting the cross section
# Replace signal dataset with EWK+signal
if True:
ttjets2 = datasets.getDataset("TTJets").deepCopy()
ttjets2.setName("TTJets2")
ttjets2.setCrossSection(
ttjets2.getCrossSection() -
datasets.getDataset("TTToHplus_M120").getCrossSection())
datasets.append(ttjets2)
datasets.merge("EWKnoTT",
["WJets", "DYJetsToLL", "SingleTop", "Diboson"],
keepSources=True)
datasets.merge("TTToHplus_M120",
["TTToHplus_M120", "EWKnoTT", "TTJets2"])
plots._legendLabels[
"TTToHplus_M120"] = "with H^{#pm}#rightarrow#tau^{#pm}#nu"
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create plots
doPlots(datasets)
# Write mt histograms to ROOT file
#writeTransverseMass(datasets_lands)
# Print counters
doCounters(datasets)
def main():
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(
filter(lambda name: "TTToHplus" in name and not "M120" in name,
datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.2, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
# Merge EWK datasets
datasets.merge("EWK",
["WJets", "TTJets", "DYJetsToLL", "SingleTop", "Diboson"])
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create the normalized plot of transverse mass
# Read the histogram from the file
#mT = plots.DataMCPlot(datasets, analysis+"/transverseMass")
# Create the histogram from the tree (and see the selections explicitly)
td = dataset.TreeDraw(
analysis + "/tree",
weight="weightPileup*weightTrigger*weightPrescale",
selection="met_p4.Et() > 70 && Max$(jets_btag) > 1.7")
# mT = plots.DataMCPlot(datasets, td.clone(varexp="sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi())))>>dist(400, 0, 400)"))
# met = plots.DataMCPlot(datasets, td.clone(varexp="met_p4.Et()>>dist(400, 0, 400)"))
metBase = plots.DataMCPlot(datasets, analysis + "/MET_BaseLineTauIdJets")
metInver = plots.DataMCPlot(datasets, analysis + "/MET_InvertedTauIdJets")
metBase4050 = plots.DataMCPlot(datasets,
analysis + "/MET_BaseLineTauIdJet4070")
metInver4050 = plots.DataMCPlot(datasets,
analysis + "/MET_InvertedTauIdJets4070")
metBase5060 = plots.DataMCPlot(datasets,
analysis + "/MET_BaseLineTauIdJets5060")
metInver5060 = plots.DataMCPlot(datasets,
analysis + "/MET_InvertedTauIdJets5060")
metBase6070 = plots.DataMCPlot(datasets,
analysis + "/MET_BaseLineTauIdJets6070")
metInver6070 = plots.DataMCPlot(datasets,
analysis + "/MET_InvertedTauIdJets6070")
metBase7080 = plots.DataMCPlot(datasets,
analysis + "/MET_BaseLineTauIdJets7080")
metInver7080 = plots.DataMCPlot(datasets,
analysis + "/MET_InvertedTauIdJets7080")
metBase80100 = plots.DataMCPlot(datasets,
analysis + "/MET_BaseLineTauIdJets80100")
metInver80100 = plots.DataMCPlot(datasets,
analysis + "/MET_InvertedTauIdJets80100")
metBase100120 = plots.DataMCPlot(datasets,
analysis + "/MET_BaseLineTauIdJets100120")
metInver100120 = plots.DataMCPlot(
datasets, analysis + "/MET_InvertedTauIdJets100120")
metBase120150 = plots.DataMCPlot(datasets,
analysis + "/MET_BaseLineTauIdJets120150")
metInver120150 = plots.DataMCPlot(
datasets, analysis + "/MET_InvertedTauIdJets120150")
metBase150 = plots.DataMCPlot(datasets,
analysis + "/MET_BaseLineTauIdJets150")
metInver150 = plots.DataMCPlot(datasets,
analysis + "/MET_InvertedTauIdJets150")
# metInver = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdLoose")
# Rebin before subtracting
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metBase4050.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metInver4050.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone(
analysis + "/MET_InvertedTauIdJets")
metInverted_data4050 = metInver.histoMgr.getHisto(
"Data").getRootHisto().Clone(analysis + "/MET_InvertedTauIdJets4050")
print "print inverted met"
print metInverted_data.GetEntries()
# Create the data-EWK histogram and draw it
diffBase = dataEwkDiff(metBase, "MET_base_data-ewk")
# diffInverted = dataEwkDiff(metInver,"MET_inverted_data-ewk")
diffInverted = dataEwkNoDiff(metInver, "MET_inverted_data-ewk")
diffBase4050 = dataEwkDiff(metBase, "MET_base_data-ewk-4050")
# diffInverted4070 = dataEwkDiff(metInver,"MET_inverted_data-ewk-4070")
diffInverted4050 = dataEwkNoDiff(metInver, "MET_inverted_data-ewk-4050")
# Draw the MET distribution
transverseMass(metBase, "MET_base")
transverseMass(metInver, "MET_inverted")
# Draw the MET distribution
transverseMass(metBase4050, "MET_base4050")
transverseMass(metInver4050, "MET_inverted4050")
# Set the styles
dataset._normalizeToOne(diffBase)
dataset._normalizeToOne(diffInverted)
plot = plots.ComparisonPlot(histograms.Histo(diffBase, "Baseline"),
histograms.Histo(diffInverted, "Inverted"))
dataset._normalizeToOne(diffBase4050)
dataset._normalizeToOne(diffInverted4050)
plot2 = plots.ComparisonPlot(
histograms.Histo(diffBase4050, "Baseline4050"),
histograms.Histo(diffInverted4050, "Inverted4050"))
st1 = styles.getDataStyle().clone()
st2 = st1.clone()
st2.append(styles.StyleLine(lineColor=ROOT.kRed))
plot.histoMgr.forHisto("Baseline", st1)
plot.histoMgr.forHisto("Inverted", st2)
plot.createFrame(
"METbaseVSinverted-ewk",
opts={
"xmax": 400,
"ymin": 1e-5,
"ymaxfactor": 1.5
},
createRatio=True,
opts2={
"ymin": -5,
"ymax": 6
}, # bounds of the ratio plot
)
plot.getPad().SetLogy(True)
plot.setLegend(histograms.createLegend(0.7, 0.68, 0.9, 0.93))
plot.frame.GetXaxis().SetTitle("MET (GeV)")
plot.frame.GetYaxis().SetTitle("Data - EWK")
# Draw the plot
plot.draw()
plot.save()
plot2.createFrame(
"METbaseVSinverted-ewk-4070",
opts={
"xmax": 400,
"ymin": 1e-5,
"ymaxfactor": 1.5
},
createRatio=True,
opts2={
"ymin": -5,
"ymax": 6
}, # bounds of the ratio plot
)
plot2.getPad().SetLogy(True)
plot2.setLegend(histograms.createLegend(0.7, 0.68, 0.9, 0.93))
plot2.frame.GetXaxis().SetTitle("MET (GeV)")
plot2.frame.GetYaxis().SetTitle("Data - EWK")
# Draw the plot
plot2.draw()
plot2.save()
def main():
# Read the datasets, see twiki page for more examples
# https://twiki.cern.ch/twiki/bin/view/CMS/HiggsChToTauNuFullyHadronicSoftware#Construct_datasets
# Usually you can also omit the analysisName, as it can be detected automatically
# If you have optimization enabled, you have to specify optimizationMode explicitly (optimizationMode="Opt...")
datasets = dataset.getDatasetsFromMulticrabCfg(directory=multicrabDir,
analysisName=analysis,
dataEra=dataEra)
if mcOnly:
datasets.remove(datasets.getDataDatasetNames())
histograms.cmsTextMode = histograms.CMSMode.SIMULATION
else:
datasets.loadLuminosities()
datasets.updateNAllEventsToPUWeighted()
plots.mergeRenameReorderForDataMC(datasets)
if mcOnly:
myIntegratedLuminosity = mcOnlyLumi
else:
myIntegratedLuminosity = datasets.getDataset("Data").getLuminosity()
print "*** Integrated luminosity", myIntegratedLuminosity, "/pb"
# print "*** Norm =",datasets.getDataset("TTToHplusBWB_M120").getNormFactor()
# Remove signals other than M120
datasets.remove(
filter(
lambda name: "TTToHplus" in name and not "M%d" %
lightHplusMassPoint in name, datasets.getAllDatasetNames()))
datasets.remove(
filter(lambda name: "HplusTB" in name, datasets.getAllDatasetNames()))
# Remove QCD datasets (because the simulated QCD background is negligible but has huge artefacts)
if removeQCD:
datasets.remove(
filter(lambda name: "QCD" in name, datasets.getAllDatasetNames()))
# Change legend creator defaults
histograms.createLegend.moveDefaults(dx=-0.05)
# Set the signal cross sections to a given BR(t->H), BR(h->taunu)
xsect.setHplusCrossSectionsToBR(datasets,
br_tH=lightHplusTopBR,
br_Htaunu=1)
# Example how to set cross section to a specific MSSM point
#xsect.setHplusCrossSectionsToMSSM(datasets, tanbeta=20, mu=200)
myXSect = datasets.getDataset("TTToHplusBWB_M120").getCrossSection()
myNormFactor = datasets.getDataset("TTToHplusBWB_M120").getNormFactor()
myNAll = datasets.getDataset("TTToHplusBWB_M120").getNAllEvents()
print "### HW cross section =", myXSect
# Merge signals into one dataset (per mass point)
plots.mergeWHandHH(
datasets
) # merging of WH and HH signals must be done after setting the cross section
# Replace signal dataset with a signal+background dataset, where
# the BR(t->H+) is taken into account for SM ttbar
plots.replaceLightHplusWithSignalPlusBackground(datasets)
# You can print a tree of all the merged datasets with this
#datasets.printDatasetTree()
# Apply TDR style
style = tdrstyle.TDRStyle()
# Calculate N_signal/N_background and Poisson significance of signal
#numberOfSignalEvents = datasets.getDataset("TTToHplus_M120").getCrossSection() * myIntegratedLuminosity
numberOfSignalEvents = myXSect * myIntegratedLuminosity
print "###", datasets.getDataset("TTToHplus_M120").getCrossSection()
print "###", myIntegratedLuminosity
print "*** Number of signal events:", numberOfSignalEvents
print "*** ...or:", myNormFactor * myNAll * myIntegratedLuminosity
# names = datasetMgr.getAllDatasetNames()
#d_ttjets = datasets.getDataset("TTJets")
#print d_ttjets.getNAllEvents()
# Create plots
doPlots(datasets)
if interactiveMode:
raw_input("*** Press \"Enter\" to exit pyROOT: ")
