def PlotHistograms(datasetsMgr, histoName):
# Get Histogram name and its kwargs
saveName = histoName.rsplit("/")[-1] # histoName.replace("/", "_")
kwargs_ = GetHistoKwargs(saveName, opts)
# Create the plotting object
if "Data" in datasetsMgr.getAllDatasetNames():
p = plots.DataMCPlot(datasetsMgr, histoName, saveFormats=[])
else:
if opts.normalizeToLumi:
p = plots.MCPlot(datasetsMgr, histoName, normalizeToLumi=opts.intLumi, saveFormats=[])
elif opts.normalizeByCrossSection:
p = plots.MCPlot(datasetsMgr, histoName, normalizeByCrossSection=True, saveFormats=[], **{})
elif opts.normalizeToOne:
p = plots.MCPlot(datasetsMgr, histoName, normalizeToOne=True, saveFormats=[], **{})
else:
raise Exception("One of the options --normalizeToOne, --normalizeByCrossSection, --normalizeToLumi must be enabled (set to \"True\").")
# Overwite signal style?
style = [200, 500, 800, 1000, 2000, 5000]
lstyle = [ROOT.kSolid, ROOT.kDashed, ROOT.kDashDotted, ROOT.kDotted, ROOT.kDotted, ROOT.kSolid]
for i, d in enumerate(datasetsMgr.getAllDatasets(), 0):
p.histoMgr.forHisto(d.getName(), styles.getSignalStyleHToTB_M(style[i]))
if 1:
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
p.histoMgr.setHistoLegendLabelMany({
#"ChargedHiggs_HplusTB_HplusToTB_M_500_MVA0p30": "H^{+} m_{H^{+}} = 500 GeV (BDT #geq 0.3)",
"ChargedHiggs_HplusTB_HplusToTB_M_%s_MVA0p30" % (opts.signalMass): "m_{H^{+}}=%s GeV (BDT #geq 0.3)" % (opts.signalMass),
"ChargedHiggs_HplusTB_HplusToTB_M_%s_MVA0p40" % (opts.signalMass): "m_{H^{+}}=%s GeV (BDT #geq 0.4)" % (opts.signalMass),
"ChargedHiggs_HplusTB_HplusToTB_M_%s_MVA0p50" % (opts.signalMass): "m_{H^{+}}=%s GeV (BDT #geq 0.5)" % (opts.signalMass),
"ChargedHiggs_HplusTB_HplusToTB_M_%s_MVA0p60" % (opts.signalMass): "m_{H^{+}}=%s GeV (BDT #geq 0.6)" % (opts.signalMass),
"ChargedHiggs_HplusTB_HplusToTB_M_%s_MVA0p70" % (opts.signalMass): "m_{H^{+}}=%s GeV (BDT #geq 0.7)" % (opts.signalMass),
})
# Apply blinding of signal region
if "blindingRangeString" in kwargs_:
startBlind = float(kwargs_["blindingRangeString"].split("-")[1])
endBlind = float(kwargs_["blindingRangeString"].split("-")[0])
plots.partiallyBlind(p, maxShownValue=startBlind, minShownValue=endBlind, invert=True, moveBlindedText=kwargs_["moveBlindedText"])
# Draw and save the plot
saveName += "_M%s" % (opts.signalMass)
plots.drawPlot(p, saveName, **kwargs_) #the "**" unpacks the kwargs_ dictionary
# Save the plots in custom list of saveFormats
SavePlot(p, saveName, os.path.join(opts.saveDir, opts.optMode, opts.folder), [".png", ".pdf"] )
return
def DataMCHistograms(datasetsMgr, qcdDatasetName):
Verbose("Plotting Data-MC Histograms")
# Definitions
histoNames = []
saveFormats = [".png"] #[".C", ".png", ".pdf"]
# Get list of histograms
dataPath = "ForDataDrivenCtrlPlots"
allHistos = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(dataPath)
histoList = [h for h in allHistos if "StandardSelections" in h]
histoList.extend([h for h in allHistos if "AllSelections" in h])
histoList = [h for h in histoList if "_MCEWK" not in h]
histoList = [h for h in histoList if "_Purity" not in h]
histoPaths = [dataPath + "/" + h for h in histoList]
# Get histogram<->kwargs dictionary
histoKwargs = GetHistoKwargs(histoPaths, opts)
# For-loop: All histograms in list
for histoName in histoPaths:
#if "Mass" not in histoName:
# continue
if "JetEtaPhi" in histoName:
continue
if opts.signalMass == 0:
if "LdgTrijetMass" in histoName:
continue
if "LdgTetrajetMass" in histoName:
continue
if "Vs" in histoName:
continue
# Not used in this analysis (yet)
if "MHT" in histoName:
continue
# By definition of the Inverted sample the following histos cannot agree!
if "NBjets_" in histoName:
continue
if "BJetPt_" in histoName:
continue
if "_BJetEta_" in histoName:
continue
if "_BtagDiscriminator_" in histoName:
continue
kwargs_ = histoKwargs[histoName]
saveName = histoName.replace("/", "_")
# Create the plotting object
p = plots.DataMCPlot(datasetsMgr, histoName, saveFormats=[])
# Apply QCD data-driven style
if opts.signalMass != 0:
signal = "ChargedHiggs_HplusTB_HplusToTB_M_%.0f" % opts.signalMass
mHPlus = "%s" % int(opts.signalMass)
p.histoMgr.forHisto(signal, styles.getSignalStyleHToTB_M(mHPlus))
#p.histoMgr.forHisto(opts.signalMass, styles.getSignalStyleHToTB())
if opts.mcQCD:
pass
else:
p.histoMgr.forHisto(qcdDatasetName, styles.getAltQCDStyle())
p.histoMgr.setHistoDrawStyle(qcdDatasetName, "HIST")
p.histoMgr.setHistoLegendStyle(qcdDatasetName, "F")
if not opts.mcQCD:
p.histoMgr.setHistoLegendLabelMany({
qcdDatasetName: "QCD (Data)",
})
else:
p.histoMgr.setHistoLegendLabelMany({
"QCD": "QCD (MC)",
})
# Apply blinding of signal region
if "blindingRangeString" in kwargs_:
startBlind = float(kwargs_["blindingRangeString"].split("-")[1])
endBlind = float(kwargs_["blindingRangeString"].split("-")[0])
plots.partiallyBlind(p, maxShownValue=startBlind, minShownValue=endBlind, invert=True, moveBlindedText=kwargs_["moveBlindedText"])
# Draw and save the plot
plots.drawPlot(p, saveName, **kwargs_) #the "**" unpacks the kwargs_ dictionary
SavePlot(p, saveName, os.path.join(opts.saveDir, opts.optMode) )
return
def PlotHistogram(dsetMgr, histoName, opts):
# Get kistogram argumetns
kwargs = GetHistoKwargs(histoName, opts)
saveName = histoName.replace(opts.folder + "/", "")
# Create the plotting object (Data, "FakeB")
p1 = plots.DataMCPlot(dsetMgr, histoName, saveFormats=[])
# Copy dataset manager before changing datasets. Keep only EWK (GenuineB) datasets
datasetMgr = dsetMgr.deepCopy()
datasetMgr.selectAndReorder(aux.GetListOfEwkDatasets())
# Create the MCPlot for the EWKGenuineB histograms
if opts.useMC:
p2 = plots.MCPlot(datasetMgr, histoName, normalizeToLumi=opts.intLumi, saveFormats=[])
else:
histoNameGenuineB = histoName.replace(opts.folder, opts.folder + "EWKGenuineB")
p2 = plots.MCPlot(datasetMgr, histoNameGenuineB, normalizeToLumi=opts.intLumi, saveFormats=[])
# Add the datasets to be included in the plot
myStackList = []
# Data-driven FakeB background
if not opts.useMC:
hFakeB = p1.histoMgr.getHisto("FakeB").getRootHisto()
hhFakeB = histograms.Histo(hFakeB, "FakeB", legendLabel="Fake-b")
hhFakeB.setIsDataMC(isData=False, isMC=True)
myStackList.append(hhFakeB)
else:
hQCD = p1.histoMgr.getHisto("QCD").getRootHisto()
hhQCD = histograms.Histo(hQCD, "QCD", legendLabel="QCD")
hhQCD.setIsDataMC(isData=False, isMC=True)
myStackList.append(hhQCD)
# EWK GenuineB background (Replace all EWK histos with GenuineB histos)
ewkHistoList = []
# For-loop: All EWK datasets
for dataset in aux.GetListOfEwkDatasets():
h = p2.histoMgr.getHisto(dataset).getRootHisto()
hh = histograms.Histo(h, dataset, plots._legendLabels[dataset])
hh.setIsDataMC(isData=False, isMC=True)
myStackList.append(hh)
# Collision data
hData = p1.histoMgr.getHisto("Data").getRootHisto()
hhData = histograms.Histo(hData, "Data")
hhData.setIsDataMC(isData=True, isMC=False)
myStackList.insert(0, hhData)
# Signal
hSignal = p1.histoMgr.getHisto(opts.signal).getRootHisto()
hhSignal = histograms.Histo(hSignal, opts.signal, plots._legendLabels[opts.signal])
hhSignal.setIsDataMC(isData=False, isMC=True)
myStackList.insert(1, hhSignal)
# Create the final plot by passing the histogram list
p3 = plots.DataMCPlot2(myStackList, saveFormats=[])
p3.setLuminosity(opts.intLumi)
p3.setDefaultStyles()
# Apply blinding of data in Signal Region (After creating the plot)
if "blindingRangeString" in kwargs:
startBlind = float(kwargs["blindingRangeString"].split("-")[1])
endBlind = float(kwargs["blindingRangeString"].split("-")[0])
plots.partiallyBlind(p3, maxShownValue=startBlind, minShownValue=endBlind, invert=True, moveBlindedText=kwargs["moveBlindedText"])
# Draw and save the plot
plots.drawPlot(p3, saveName, **kwargs)
SavePlot(p3, saveName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png", ".pdf"])
return
def PlotHistograms(datasetsMgr, histoName):
# Get Histogram name and its kwargs
saveName = histoName.rsplit("/")[-1] # histoName.replace("/", "_")
kwargs_ = GetHistoKwargs(saveName, opts)
# Create the plotting object
if "Data" in datasetsMgr.getAllDatasetNames():
p = plots.DataMCPlot(datasetsMgr, histoName, saveFormats=[])
else:
if opts.normalizeToLumi:
p = plots.MCPlot(datasetsMgr,
histoName,
normalizeToLumi=opts.intLumi,
saveFormats=[])
elif opts.normalizeByCrossSection:
p = plots.MCPlot(datasetsMgr,
histoName,
normalizeByCrossSection=True,
saveFormats=[],
**{})
elif opts.normalizeToOne:
p = plots.MCPlot(datasetsMgr,
histoName,
normalizeToOne=True,
saveFormats=[],
**{})
else:
raise Exception(
"One of the options --normalizeToOne, --normalizeByCrossSection, --normalizeToLumi must be enabled (set to \"True\")."
)
# Overwite signal style?
style = [200, 500, 800, 1000, 2000, 3000, 5000]
lstyle = [
ROOT.kSolid, ROOT.kDashed, ROOT.kDashDotted, ROOT.kDotted,
ROOT.kDotted, ROOT.kSolid
]
for i, d in enumerate(datasetsMgr.getAllDatasets(), 0):
p.histoMgr.forHisto(d.getName(),
styles.getSignalStyleHToTB_M(style[i]))
if 1:
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
p.histoMgr.setHistoLegendLabelMany({
#"ChargedHiggs_HplusTB_HplusToTB_M_500_MVA0p30": "H^{+} m_{H^{+}} = 500 GeV (BDT #geq 0.3)",
"ChargedHiggs_HplusTB_HplusToTB_M_%s_MVA0p30" % (opts.signalMass):
"m_{H^{+}}=%s GeV (BDT #geq 0.3)" % (opts.signalMass),
"ChargedHiggs_HplusTB_HplusToTB_M_%s_MVA0p40" % (opts.signalMass):
"m_{H^{+}}=%s GeV (BDT #geq 0.4)" % (opts.signalMass),
"ChargedHiggs_HplusTB_HplusToTB_M_%s_MVA0p50" % (opts.signalMass):
"m_{H^{+}}=%s GeV (BDT #geq 0.5)" % (opts.signalMass),
"ChargedHiggs_HplusTB_HplusToTB_M_%s_MVA0p60" % (opts.signalMass):
"m_{H^{+}}=%s GeV (BDT #geq 0.6)" % (opts.signalMass),
"ChargedHiggs_HplusTB_HplusToTB_M_%s_MVA0p70" % (opts.signalMass):
"m_{H^{+}}=%s GeV (BDT #geq 0.7)" % (opts.signalMass),
})
# Apply blinding of signal region
if "blindingRangeString" in kwargs_:
startBlind = float(kwargs_["blindingRangeString"].split("-")[1])
endBlind = float(kwargs_["blindingRangeString"].split("-")[0])
plots.partiallyBlind(p,
maxShownValue=startBlind,
minShownValue=endBlind,
invert=True,
moveBlindedText=kwargs_["moveBlindedText"])
# Draw and save the plot
saveName += "_M%s" % (opts.signalMass)
plots.drawPlot(p, saveName,
**kwargs_) #the "**" unpacks the kwargs_ dictionary
# Save the plots in custom list of saveFormats
SavePlot(p, saveName, os.path.join(opts.saveDir, opts.optMode,
opts.folder), [".png", ".pdf"])
return
def DataMCHistograms(datasetsMgr, histoName):
Verbose("Plotting Data-MC Histograms")
# Skip 2-D plots
skipStrings = []
if opts.folder == "topbdtSelection_":
skipStrings = ["_Vs_", "Vs", "Matched", "MCtruth", "TopQuark",
"RealSelected", "DeltaMVAgt1", "SelectedTop",
"LdgTrijetFake", "LdgTrijetFakeJJB", "TrijetFake",
"FakeInTopDir", "LdgTrijetFakeJJB_BDT", "LdgTrijetFake_BDT"]
if opts.folder == "counters":
skipStrings = ["weighted"]
if opts.folder == "eSelection_Veto":
skipStrings = ["Resolution"]
if opts.folder == "muSelection_Veto":
skipStrings = ["Resolution"]
if opts.folder == "tauSelection_Veto":
skipStrings = ["riggerMatch", "NprongsMatrix", "Resolution"]
if opts.folder == "PUDependency":
skipStrings = ["WithProbabilisticBtag", "AngularCuts", "AntiIsolatedTau", "NvtxTau"]
if opts.folder == "jetSelection_":
skipStrings = ["JetMatching"]
if opts.folder == "bjetSelection_":
skipStrings = ["MatchDeltaR", "btagSFRelUncert", "_dEta", "_dPhi", "_dPt", "_dR"]
if opts.folder == "metSelection_":
skipStrings = [""]
if opts.folder == "topologySelection_":
skipStrings = ["_Vs_"]
if "ForDataDrivenCtrlPlots" in opts.folder:
skipStrings = ["_Vs_", "JetEtaPhi", "MinDeltaPhiJet", "MaxDeltaPhiJet", "MinDeltaRJet"]
# Skip histograms if they contain a given string
for keyword in skipStrings:
if keyword in histoName:
return
# Get Histogram name and its kwargs
saveName = histoName.rsplit("/")[-1] # histoName.replace("/", "_")
kwargs_ = GetHistoKwargs(saveName, opts)
# Create the plotting object
p = plots.DataMCPlot(datasetsMgr, histoName, saveFormats=[])
# Overwite signal style?
if 0:
if opts.signalMass != 0:
p.histoMgr.forHisto(opts.signal, styles.getSignalStyleHToTB_M(opts.signalMass))
if "QCD" in datasetsMgr.getAllDatasetNames():
p.histoMgr.setHistoLegendLabelMany({
"QCD": "QCD (MC)",
})
# Apply blinding of signal region
if "blindingRangeString" in kwargs_:
startBlind = float(kwargs_["blindingRangeString"].split("-")[1])
endBlind = float(kwargs_["blindingRangeString"].split("-")[0])
plots.partiallyBlind(p, maxShownValue=startBlind, minShownValue=endBlind, invert=True, moveBlindedText=kwargs_["moveBlindedText"])
# Draw and save the plot
plots.drawPlot(p, saveName, **kwargs_) #the "**" unpacks the kwargs_ dictionary
# Replace bin labels
if "counter" in opts.folder and "counter" in histoName.split("/")[-1]:
# p.getFrame().GetXaxis().LabelsOption("v") #vertical orientation of bin labels
replaceBinLabels(p, saveName)
#pass
# Save the plots in custom list of saveFormats
SavePlot(p, saveName, os.path.join(opts.saveDir, opts.optMode, opts.folder), [".png", ".pdf"] )
return
def DataMCHistograms(datasetsMgr, histoName):
Verbose("Plotting Data-MC Histograms")
# Skip 2-D plots
skipStrings = []
if opts.folder == "topbdtSelection_":
skipStrings = ["_Vs_", "Vs", "Matched", "MCtruth", "TopQuark",
"RealSelected", "DeltaMVAgt1", "SelectedTop",
"LdgTrijetFake", "LdgTrijetFakeJJB", "TrijetFake",
"FakeInTopDir", "LdgTrijetFakeJJB_BDT", "LdgTrijetFake_BDT"]
if opts.folder == "counters":
skipStrings = ["weighted"]
if opts.folder == "eSelection_Veto":
skipStrings = ["Resolution"]
if opts.folder == "muSelection_Veto":
skipStrings = ["Resolution"]
if opts.folder == "tauSelection_Veto":
skipStrings = ["riggerMatch", "NprongsMatrix", "Resolution"]
if opts.folder == "PUDependency":
skipStrings = ["WithProbabilisticBtag", "AngularCuts", "AntiIsolatedTau", "NvtxTau"]
if opts.folder == "jetSelection_":
skipStrings = ["JetMatching"]
if opts.folder == "bjetSelection_":
skipStrings = ["MatchDeltaR", "btagSFRelUncert", "_dEta", "_dPhi", "_dPt", "_dR"]
if opts.folder == "metSelection_":
skipStrings = [""]
if opts.folder == "topologySelection_":
skipStrings = ["_Vs_"]
if "ForDataDrivenCtrlPlots" in opts.folder:
skipStrings = ["_Vs_", "JetEtaPhi", "MinDeltaPhiJet", "MaxDeltaPhiJet", "MinDeltaRJet"]
# Skip histograms if they contain a given string
for keyword in skipStrings:
if keyword in histoName:
return
# Get Histogram name and its kwargs
saveName = histoName.rsplit("/")[-1] # histoName.replace("/", "_")
kwargs_ = GetHistoKwargs(saveName, opts)
# Create the plotting object
p = plots.DataMCPlot(datasetsMgr, histoName, saveFormats=[])
# Overwite signal style?
if 0:
if opts.signalMass != 0:
p.histoMgr.forHisto(opts.signal, styles.getSignalStyleHToTB_M(opts.signalMass))
if "QCD" in datasetsMgr.getAllDatasetNames():
p.histoMgr.setHistoLegendLabelMany({
"QCD": "QCD (MC)",
})
# Apply blinding of signal region
if "blindingRangeString" in kwargs_:
startBlind = float(kwargs_["blindingRangeString"].split("-")[1])
endBlind = float(kwargs_["blindingRangeString"].split("-")[0])
plots.partiallyBlind(p, maxShownValue=startBlind, minShownValue=endBlind, invert=True, moveBlindedText=kwargs_["moveBlindedText"])
# Draw and save the plot
plots.drawPlot(p, saveName, **kwargs_) #the "**" unpacks the kwargs_ dictionary
# Replace bin labels
if "counter" in opts.folder and "counter" in histoName.split("/")[-1]:
# p.getFrame().GetXaxis().LabelsOption("v") #vertical orientation of bin labels
replaceBinLabels(p, saveName)
#pass
# Save the plots in custom list of saveFormats
SavePlot(p, saveName, os.path.join(opts.saveDir, opts.optMode, opts.folder), [".png", ".pdf"] )
return
def DataMCHistograms(datasetsMgr):
Verbose("Plotting Data-MC Histograms")
# Definitions
histoNames = []
saveFormats = [".png"] #[".C", ".png", ".pdf"]
# Get list of histograms
dataPath = "ForDataDrivenCtrlPlots"
allHistos = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(dataPath)
histoList = [h for h in allHistos if "StandardSelections" in h]
histoList.extend([h for h in allHistos if "AllSelections" in h])
histoList = [h for h in histoList if "_MCEWK" not in h]
histoList = [h for h in histoList if "_Purity" not in h]
histoPaths = [dataPath + "/" + h for h in histoList]
# Get histogram<->kwargs dictionary
histoKwargs = GetHistoKwargs(histoPaths, opts)
# For-loop: All histograms in list
for histoName in histoPaths:
if "Mass" not in histoName:
#if "LdgTetrajetMass" not in histoName:
continue
if "JetEtaPhi" in histoName:
continue
if opts.signalMass == 0:
if "LdgTrijetMass" in histoName:
continue
if "LdgTetrajetMass" in histoName:
continue
if "Vs" in histoName:
continue
# Not used in this analysis (yet)
if "MHT" in histoName:
continue
# By definition of the Inverted sample the following histos cannot agree!
if "NBjets_" in histoName:
continue
if "BJetPt_" in histoName:
continue
if "_BJetEta_" in histoName:
continue
if "_BtagDiscriminator_" in histoName:
continue
kwargs_ = histoKwargs[histoName]
saveName = histoName.replace("/", "_")
# Create the plotting object
p = plots.DataMCPlot(datasetsMgr, histoName, saveFormats=[])
# Apply QCD data-driven style
if opts.signalMass != 0:
signal = "ChargedHiggs_HplusTB_HplusToTB_M_%.0f" % opts.signalMass
mHPlus = "%s" % int(opts.signalMass)
p.histoMgr.forHisto(signal, styles.getSignalStyleHToTB_M(mHPlus))
#p.histoMgr.forHisto(opts.signalMass, styles.getSignalStyleHToTB())
p.histoMgr.setHistoLegendLabelMany({
"QCD": "QCD (MC)",
})
# Apply blinding of signal region
if "blindingRangeString" in kwargs_:
startBlind = float(kwargs_["blindingRangeString"].split("-")[1])
endBlind = float(kwargs_["blindingRangeString"].split("-")[0])
plots.partiallyBlind(p, maxShownValue=startBlind, minShownValue=endBlind, invert=True, moveBlindedText=kwargs_["moveBlindedText"])
# Draw and save the plot
plots.drawPlot(p, saveName, **kwargs_) #the "**" unpacks the kwargs_ dictionary
SavePlot(p, saveName, os.path.join(opts.saveDir, opts.optMode), [".png"] )
return