def PlotMC(datasetsMgr, histo, intLumi):
kwargs = {}
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr, histo, normalizeToOne=True, saveFormats=[], **kwargs)
else:
p = plots.MCPlot(datasetsMgr, histo, normalizeToLumi=intLumi, saveFormats=[], **kwargs)
# Get histogram<->kwargs dictionary
kwargs = GetHistoKwargs(histo, opts)
# Customise style
for i, m in enumerate(signalMass):
massNum = m.rsplit("M_")[-1]
if i==len(signalMass)-1:
p.histoMgr.setHistoDrawStyle(m, "HIST")
p.histoMgr.setHistoLegendStyle(m, "F")
p.histoMgr.forHisto(m, styles.getSignalStyleHToTB())
# p.histoMgr.forHisto(m, styles. getSignalfillStyleHToTB())
# p.histoMgr.forHisto(m, datasetsMgr.getDataset(m).getRootHisto().SetMarkerStyle(6))
# h = GetRootHisto(datasetsMgr, m, histo)
# h.SetFillStyle(1001)
# p.histoMgr.setHistoLegendStyle(dName, "LP")
h = datasetsMgr.getDataset(m).getDatasetRootHisto(histo).getHistogram()
styles.qcdFillStyle.apply(h)
else:
p.histoMgr.forHisto(m, styles.getSignalStyleHToTB_M(massNum))
p.histoMgr.setHistoLegendStyle(m, "LP")
# Plot customised histogram
plots.drawPlot(p,
histo,
xlabel = kwargs.get("xlabel"),
ylabel = kwargs.get("ylabel"),
log = kwargs.get("log"),
rebinX = kwargs.get("rebinX"),
cmsExtraText = "Preliminary",
#createLegend = {"x1": 0.62, "y1": 0.75, "x2": 0.92, "y2": 0.92},
moveLegend = kwargs.get("moveLegend"),
opts = kwargs.get("opts"),
opts2 = {"ymin": 0.6, "ymax": 1.4},
cutBox = kwargs.get("cutBox"),
)
# Customise styling
if 0:
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerSize(0))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerStyle(6))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
# Save plot in all formats
saveName = histo.split("/")[-1]
if opts.folder == "":
savePath = os.path.join(opts.saveDir, opts.optMode)
else:
savePath = os.path.join(opts.saveDir, histo.split("/")[0], opts.optMode)
SavePlot(p, saveName, savePath, [".png", ".pdf"])
return
def GetSignificanceHisto(p, datasetsMgr, cutDir=">=", signalDataset="ChargedHiggs_HplusTB_HplusToTB_M_500"):
allowedDirs = [">=", "<="]
if cutDir not in allowedDirs:
raise Exception("Unsupported cut direction \"%s\". Please select from > and <" % (cutDir))
p.setLuminosity(opts.intLumi)
if type(signalDataset) != str:
siganlDataset = str(signalDataset)
hSignal = p.histoMgr.getHisto(signalDataset).getRootHisto().Clone(signalDataset + "_Histo")
hSignif = p.histoMgr.getHisto(signalDataset).getRootHisto().Clone(signalDataset)
hSignif.Reset()
hBkg = p.histoMgr.getHisto(signalDataset).getRootHisto().Clone("Bkg")
hBkg.Reset()
# For-loop: All dataset names
for dset in datasetsMgr.getAllDatasets():
d = dset.getName()
if dset.isData():
continue
if "Charged" in d:
continue
# Add up all bkg MC
hBkg.Add(p.histoMgr.getHisto(d).getRootHisto(), +1)
nBins = hSignif.GetNbinsX()+1
# For-loop: All histo bins
for i in range (1, nBins+1):
sigmaB = ROOT.Double(0)
if cutDir == ">=":
s = hSignal.Integral(i, nBins)
b = hBkg.IntegralAndError(i, nBins, sigmaB)
else:
s = hSignal.Integral(0, i)
b = hBkg.IntegralAndError(0, i, sigmaB)
# Calculate the significance
signif = stat.significance(s, b, sigmaB, option="Asimov")
Verbose("%s, bin %i: Signif = %.3f" % (hSignif.GetName(), i, signif), i==0)
if 0:
Print("%s, bin %i: Signif = %.3f" % (hSignif.GetName(), i, signif), True)
signif = stat.significance(s, b, sigmaB=0.0, option="Simple")
signif = stat.significance(s, b, sigmaB=0.0, option="Asimov")
signif = stat.significance(s, b, sigmaB, option="Simple")
signif = stat.significance(s, b, sigmaB, option="Asimov")
# Set signif for this bin
hSignif.SetBinContent(i, signif)
# Apply style
s = styles.getSignalStyleHToTB_M(hSignif.GetName().split("_")[-1])
s.apply(hSignif)
return hSignif
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 PlotMC(datasetsMgr, histo, intLumi):
kwargs = {}
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr, histo, normalizeToOne=True, saveFormats=[], **kwargs)
else:
p = plots.MCPlot(datasetsMgr, histo, normalizeToLumi=intLumi, saveFormats=[], **kwargs)
# p = plots.MCPlot(datasetsMgr, histo, normalizeToLumi=intLumi, saveFormats=[], **kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.0f"
_xlabel = None
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
if "ChiSqr" in histo:
_rebinX = 1
# logY = True
_units = ""
_format = "%0.1f " + _units
_xlabel = "#chi^{2}"
_cutBox = {"cutValue": 10.0, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 100
elif "trijetmass" in histo.lower():
_rebinX = 2
# logY = False
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 505 #1005
elif "ht" in histo.lower():
_rebinX = 2
# logY = False
_units = "GeV"
_format = "%0.0f " + _units
_xlabel = "H_{T} (%s)" % _units
_cutBox = {"cutValue": 500, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 2000
elif "tetrajetmass" in histo.lower():
_rebinX = 5 #5 #10 #4
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjbb} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 1500 #3500.0
elif "dijetmass" in histo.lower():
_rebinX = 1 #5 #10 #4
# logY = False
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{W} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800 #3500.0
_cutBox = {"cutValue": 80.4, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 300
elif "tetrajetbjetpt" in histo.lower():
_rebinX = 2
# logY = False
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "ldgtrijetpt" in histo.lower():
_rebinX = 2
# logY = False
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "trijetbdt_mass" in histo.lower():
_rebinX = 2
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 505
# elif "topcandmass" in histo.lower():
# _rebinX = 2
# _units = "GeV/c^{2}"
# _format = "%0.0f " + _units
# _xlabel = "m_{jjb} (%s)" % _units
# _cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
# _opts["xmax"] = 505
elif "matched_mass" in histo.lower():
_rebinX = 2
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 505
elif "bdtvalue" in histo.lower():
_format = "%0.1f"
elif "foxwolframmoment" in histo.lower():
_format = "%0.1f"
_cutBox = {"cutValue": 0.5, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
elif "absdeltamvamax_mctruth_sameobjfakes" in histo.lower():
_format = "%0.1f"
_xlabel = "|#Delta BDTG| response"
elif "deltamvamin_mctruth_sameobjfakespassbdt" in histo.lower():
_opts["xmax"] = 1
_opts["xmin"] = -1
elif "bdtmultiplicity" in histo.lower():
_opts["xmax"] = 15
else:
pass
if opts.normaliseToOne:
logY = False
Ylabel = "Arbitrary Units / %s" % (_format)
else:
logY = True
Ylabel = "Events / %s" % (_format)
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
#_opts = {"ymin": 1e-3, "ymaxfactor": yMaxFactor, "xmax": None}
else:
_opts["ymin"] = 1e0
#_opts["ymaxfactor"] = yMaxFactor
#_opts = {"ymin": 1e0, "ymaxfactor": yMaxFactor, "xmax": None}
# Customise styling
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
if "QCD" in datasetsMgr.getAllDatasets():
p.histoMgr.forHisto("QCD", styles.getQCDFillStyle() )
p.histoMgr.setHistoDrawStyle("QCD", "HIST")
p.histoMgr.setHistoLegendStyle("QCD", "F")
if "TT" in datasetsMgr.getAllDatasets():
p.histoMgr.setHistoDrawStyle("TT", "AP")
p.histoMgr.setHistoLegendStyle("TT", "LP")
# Customise style
signalM = []
for m in signalMass:
signalM.append(m.rsplit("M_")[-1])
for m in signalM:
p.histoMgr.forHisto("ChargedHiggs_HplusTB_HplusToTB_M_%s" %m, styles.getSignalStyleHToTB_M(m))
plots.drawPlot(p,
histo,
xlabel = _xlabel,
ylabel = Ylabel,#"Arbitrary Units / %s" % (_format), #"Events / %s" % (_format), #"Arbitrary Units / %s" % (_format),
log = logY,
rebinX = _rebinX, cmsExtraText = "Preliminary",
createLegend = {"x1": 0.58, "y1": 0.65, "x2": 0.92, "y2": 0.92},
# createLegend = {"x1": 0.73, "y1": 0.85, "x2": 0.97, "y2": 0.77},
opts = _opts,
opts2 = {"ymin": 0.6, "ymax": 1.4},
cutBox = _cutBox,
)
# Save plot in all formats
saveName = histo.split("/")[-1]
savePath = os.path.join(opts.saveDir, "HplusMasses", histo.split("/")[0], opts.optMode)
if opts.normaliseToOne:
save_path = savePath + opts.MVAcut
if opts.noQCD:
save_path = savePath + opts.MVAcut + "/noQCD/"
else:
save_path = savePath + opts.MVAcut + "/normToLumi/"
if opts.noQCD:
save_path = savePath + opts.MVAcut + "/noQCD/"
# SavePlot(p, saveName, savePath)
SavePlot(p, saveName, save_path)
return
def PlotMC(datasetsMgr, histo, intLumi):
kwargs = {}
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr,
histo,
normalizeToOne=True,
saveFormats=[],
**kwargs)
else:
p = plots.MCPlot(datasetsMgr,
histo,
normalizeToLumi=intLumi,
saveFormats=[],
**kwargs)
# Get histogram<->kwargs dictionary
kwargs = GetHistoKwargs(histo, opts)
# Customise styling
if 0:
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerSize(0))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerStyle(6))
# Customise signal
for d in datasetsMgr.getAllDatasetNames():
if "Charged" not in d:
continue
else:
#p.histoMgr.setHistoDrawStyle(d, "HIST")
p.histoMgr.setHistoLegendStyle(d, "L")
p.histoMgr.setHistoLegendStyle(d, "L")
# Customise QCD style
p.histoMgr.setHistoDrawStyle("QCD", "P")
p.histoMgr.setHistoLegendStyle("QCD", "P")
p.histoMgr.setHistoLegendLabelMany({
"QCD": "QCD (MC)",
})
# Customise style
signalM = []
for m in signalMass:
signalM.append(m.rsplit("M_")[-1])
for m in signalM:
dName = "ChargedHiggs_HplusTB_HplusToTB_M_%s" % m
if dName in datasetsMgr.getAllDatasetNames():
p.histoMgr.forHisto(dName, styles.getSignalStyleHToTB_M(m))
p.histoMgr.setHistoLegendStyle(dName, "LP")
# Plot customised histogram
plots.drawPlot(
p,
histo,
xlabel=kwargs.get("xlabel"),
ylabel=kwargs.get("ylabel"),
log=kwargs.get("log"),
rebinX=kwargs.get("rebinX"),
cmsExtraText="Preliminary",
#createLegend = {"x1": 0.62, "y1": 0.75, "x2": 0.92, "y2": 0.92},
moveLegend=kwargs.get("moveLegend"),
opts=kwargs.get("opts"),
opts2={
"ymin": 0.6,
"ymax": 1.4
},
cutBox=kwargs.get("cutBox"),
)
# Save plot in all formats
saveName = histo.split("/")[-1]
if opts.folder == "":
savePath = os.path.join(opts.saveDir, opts.optMode)
else:
savePath = os.path.join(opts.saveDir,
histo.split("/")[0], opts.optMode)
SavePlot(p, saveName, savePath, [".png", ".pdf"])
return
def PlotSignificance(h, datasetsMgr, intLumi):
Verbose("Plotting Significance")
significanceList = []
for mass in signalMass:
xValues = []
yValues = []
maxSignificance = 0.0
hSignal = getHisto(datasetsMgr, h, mass, intLumi)
hBackground = getHisto(datasetsMgr, h, "QCD", intLumi)
for i in range(0, hSignal.GetXaxis().GetNbins()):
# Cut value
cut = hSignal.GetBinCenter(i)
s = hSignal.Integral(i, hSignal.GetXaxis().GetNbins())
b = hBackground.Integral(i, hSignal.GetXaxis().GetNbins())
significance = float(s) / math.sqrt(float(b + s))
if (significance > maxSignificance):
maxSignificance = significance
maxSignificanceCut = cut
xValues.append(cut)
yValues.append(significance)
# Create the Significance Plot
tGraph = ROOT.TGraph(len(xValues), array.array("d", xValues),
array.array("d", yValues))
styles.getSignalStyleHToTB_M(mass.split("_")[-1]).apply(tGraph)
drawStyle = "CPE"
legName = plots._legendLabels[mass]
significanceGraph = histograms.HistoGraph(tGraph, legName, "lp",
drawStyle)
significanceList.append(significanceGraph)
saveName = "QGLRSignificance"
# Create the plot with all the significance plots
p = plots.PlotBase(significanceList, saveFormats=["pdf"])
p.createFrame(saveName)
# Customise frame
p.setEnergy("13")
p.getFrame().GetYaxis().SetLabelSize(18)
p.getFrame().GetXaxis().SetLabelSize(20)
p.getFrame().GetYaxis().SetTitle("S/#sqrt{S+B} / 0.01")
p.getFrame().GetXaxis().SetTitle("QGLR Cut")
# Add Standard Texts to plot
histograms.addStandardTexts()
# Customise Legend
moveLegend = {"dx": -0.15, "dy": -0.5, "dh": -0.1}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
p.draw()
savePath = opts.saveDir
if opts.url:
savePath = opts.saveDir.replace("/publicweb/m/mkolosov/",
"http://home.fnal.gov/~mkolosov/")
savePath = os.path.join(opts.saveDir, opts.folder, saveName, opts.optMode)
SavePlot(p, saveName, savePath)
return
def PlotMC(datasetsMgr, histo, intLumi):
kwargs = {}
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr,
histo,
normalizeToOne=True,
saveFormats=[],
**kwargs)
else:
p = plots.MCPlot(datasetsMgr,
histo,
normalizeToLumi=intLumi,
saveFormats=[],
**kwargs)
# p = plots.MCPlot(datasetsMgr, histo, normalizeToLumi=intLumi, saveFormats=[], **kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.1f"
_xlabel = None
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
if "ChiSqr" in histo:
_rebinX = 1
#logY = True
_units = ""
_format = "%0.1f " + _units
_xlabel = "#chi^{2}"
_cutBox = {
"cutValue": 10.0,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 100
elif "trijetjets_deltarmin" in histo.lower():
_xlabel = "#Delta R_{min}"
_rebinX = 2
_opts["xmax"] = 3
_units = ""
_format = "%0.1f "
_cutBox = {
"cutValue": 0.8,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
elif "trijetjets_deltarmax" in histo.lower():
_xlabel = "#Delta R_{max}"
_rebinX = 2
_opts["xmax"] = 3.5
_units = ""
_format = "%0.1f "
_cutBox = {
"cutValue": 0.8,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
elif "trijetdijetdr" in histo.lower():
_rebinX = 1
units = ""
_format = "%0.1f "
_opts["xmax"] = 3.5
_cutBox = {
"cutValue": 0.8,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
elif "trijetdijetpt" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "trijetdijetmass" in histo.lower():
_rebinX = 1
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_opts["xmax"] = 200
_xlabel = "m_{w} (%s)" % _units
_cutBox = {
"cutValue": 80.39,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
if "cevts_ldgtrijetmatchedtofatjet_fatjetpt" in histo.lower():
_rebin = 1
_opts["xmax"] = 3
elif "pt" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "trijetmass" in histo.lower():
_rebinX = 1
# logY = False
_units = "GeV/c^{2}"
# _format = "%0.0f " + _units
_format = "%0.0f" + (_units)
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {
"cutValue": 173.21,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 305 #1005
_opts["xmin"] = 55 #1005
elif "topcandmass" in histo.lower():
_rebinX = 1
# logY = False
_units = "GeV/c^{2}"
# _format = "%0.0f " + _units
_format = "%0.0f" + (_units)
_xlabel = "m_{jjb}^{BDTG} (%s)" % _units
_cutBox = {
"cutValue": 173.21,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 505 #1005
_opts["xmin"] = 0 #1005
# elif "ht" in histo.lower():
# _rebinX = 2
## logY = False
# _units = "GeV"
# _format = "%0.0f " + _units
# _xlabel = "H_{T} (%s)" % _units
# _cutBox = {"cutValue": 500, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
# _opts["xmax"] = 2000
elif "tetrajetptd" in histo.lower():
_rebinX = 2 #5 #10 #4
_units = "GeV/c^{2}"
# _xlabel = "m_{jjbb} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 2000 #3500.0
_cutBox = {
"cutValue": 400,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
elif "tetrajetmass" in histo.lower() or "tetrajetmass_" in histo.lower():
#ROOT.gStyle.SetNdivisions(10, "X")
# h = dataset.getDatasetRootHisto(histo).getHistogram()
# h.SetTickLength(100, "X")
_rebinX = 5 #5 #10 #4
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjbb} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 1500 #3500.0
_cutBox = {
"cutValue": 500,
"fillColor": 16,
"box": False,
"line": False,
"greaterThan": True
}
elif "dijetmass" in histo.lower():
_rebinX = 1 #5 #10 #4
# logY = False
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{W} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800 #3500.0
_cutBox = {
"cutValue": 80.4,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 205
elif "trijetmultiplicitypassbdt" in histo.lower():
_rebinX = 1
_opts["xmax"] = 5
_format = "%0.0f "
elif "bdtg" or "bdtvalue" in histo.lower():
_rebinX = 1
_format = "%0.2f "
elif "trijetbdt_mass" in histo.lower():
_rebinX = 2
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_opts["xmax"] = 800
# if "eta" or "phi" or "delta" in histo.lower():
#_rebinX = 1 #5 #10 #4
# _units = ""
# _format = "%0.1f "
# if "eta" in histo.lower():
# _xlabel = "#eta"
# if "phi" in histo.lower():
# _xlabel = "#phi"
# if "pt" in histo.lower():
# _opts["xmax"] = 800
elif "boosted" in histo.lower():
_xlabel = "Trijet Counter"
elif "matched_dijetpt" in histo.lower():
_units = "(GeV/c)"
_xlabel = "p_{T}" + _units
elif "deltar_bdttrijets_tetrajetbjet" in histo.lower():
_xlabel = "#Delta R(Trijet,b_{free})"
elif "tetrajetbjetbdisc" in histo.lower():
_rebinX = 2
_opts["xmax"] = 1.05
# if "matched_dijetmass" in histo.lower():
# _rebinX = 2
# if "higgstop_dijetmass" in histo.lower():
# _rebinX = 2
elif "trijet_deltaeta_trijet_tetrajetbjet" in histo.lower():
_rebinX = 2
_xlabel = "#Delta #eta (Trijet, b_{free})"
elif "trijet_deltaphi_trijet_tetrajetbjet" in histo.lower():
_xlabel = "#Delta #phi (Trijet, b_{free})"
elif "cevts_closejettotetrajetbjet_isbtagged" in histo.lower():
_units = ""
_format = "%0.0f " + _units
# if "higgstop_" in histo.lower():
# _rebinX = 2
if "eventtrijetpt2t" in histo.lower():
_rebinX = 2
if "ldgfatjetpt" in histo.lower():
_opts["xmax"] = 1000
if "deltar_w" in histo.lower():
_rebinX = 2
_xlabel = "#Delta R"
_format = "%0.1f "
_opts["xmax"] = 5
logY = True
if "ldgtrijet_deltar" in histo.lower():
_rebinX = 2
_xlabel = "#Delta R"
_format = "%0.1f "
_opts["xmax"] = 5
logY = True
if "higgstop_deltar" in histo.lower():
_rebinX = 2
_xlabel = "#Delta R"
_format = "%0.1f "
_opts["xmax"] = 5
logY = True
if "allfatjet" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_opts["xmax"] = 800
else:
pass
if opts.normaliseToOne:
logY = True
Ylabel = "Arbitrary Units / %s" % (_format)
else:
logY = True
Ylabel = "Events / %s" % (_format)
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
#_opts = {"ymin": 1e-3, "ymaxfactor": yMaxFactor, "xmax": None}
else:
_opts["ymin"] = 1e0
#_opts["ymaxfactor"] = yMaxFactor
#_opts = {"ymin": 1e0, "ymaxfactor": yMaxFactor, "xmax": None}
# Customise styling
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
if "QCD" in datasetsMgr.getAllDatasets():
p.histoMgr.forHisto("QCD", styles.getQCDFillStyle())
p.histoMgr.setHistoDrawStyle("QCD", "HIST")
p.histoMgr.setHistoLegendStyle("QCD", "F")
if "TT" in datasetsMgr.getAllDatasets():
p.histoMgr.setHistoDrawStyle("TT", "HIST")
p.histoMgr.setHistoLegendStyle("TT", "LP")
# if "M_200" in datasetsMgr.getAllDatasets() or "M_300" in datasetsMgr.getAllDatasets():
# p.histoMgr.forHisto("QCD", styles.getQCDFillStyle() )
# p.histoMgr.setHistoDrawStyle("QCD", "P")
# p.histoMgr.setHistoLegendStyle("QCD", "F")
# elif d.getName() == "TT" or d.getName() == "QCD" or d.getName() == "Data":
# otherHisto = histograms.Histo(histo, legName, "LP", "P")
# otherHistos.append(otherHisto)
# Customise style
signalM = []
for m in signalMass:
signalM.append(m.rsplit("M_")[-1])
for m in signalM:
p.histoMgr.forHisto("ChargedHiggs_HplusTB_HplusToTB_M_%s" % m,
styles.getSignalStyleHToTB_M(m))
#soti
# p.histoMgr.setHistoDrawStyle("ChargedHiggs_HplusTB_HplusToTB_M_%s" %m, "LP")
# p.histoMgr.setHistoLegendStyle("ChargedHiggs_HplusTB_HplusToTB_M_%s" %m, "P")
plots.drawPlot(
p,
histo,
xlabel=_xlabel,
ylabel=
Ylabel, #"Arbitrary Units / %s" % (_format), #"Events / %s" % (_format), #"Arbitrary Units / %s" % (_format),
# ylabel = "Arbitrary Units / %s" % (_format), #"Events / %s" % (_format), #"Arbitrary Units / %s" % (_format),
log=logY,
rebinX=_rebinX,
cmsExtraText="Preliminary", #_rebinX
#createLegend = {"x1": 0.48, "y1": 0.45, "x2": 0.92, "y2": 0.92}, #All datasets
# createLegend = {"x1": 0.58, "y1": 0.7, "x2": 0.92, "y2": 0.92},
createLegend={
"x1": 0.58,
"y1": 0.65,
"x2": 0.92,
"y2": 0.87
},
#createLegend = {"x1": 0.73, "y1": 0.85, "x2": 0.97, "y2": 0.77}, #One dataset
opts=_opts,
opts2={
"ymin": 0.6,
"ymax": 1.4
},
cutBox=_cutBox,
)
# Save plot in all formats
saveName = histo.split("/")[-1]
savePath = os.path.join(opts.saveDir, "HplusMasses",
histo.split("/")[0], opts.optMode)
if opts.normaliseToOne:
save_path = savePath + opts.MVAcut
if opts.noQCD:
save_path = savePath + opts.MVAcut + "/noQCD/"
else:
save_path = savePath + opts.MVAcut + "/normToLumi/TT/"
if opts.noQCD:
save_path = savePath + opts.MVAcut + "/noQCD/"
# SavePlot(p, saveName, savePath)
SavePlot(p, saveName, save_path)
return
def PlotMC(datasetsMgr, histo, intLumi):
kwargs = {}
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr, histo, normalizeToOne=True, saveFormats=[], **kwargs)
else:
p = plots.MCPlot(datasetsMgr, histo, normalizeToLumi=intLumi, saveFormats=[], **kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.0f"
_xlabel = None
logY = False
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
if "ChiSqr" in histo:
_rebinX = 1
logY = True
_units = ""
_format = "%0.1f " + _units
_xlabel = "#chi^{2}"
_cutBox = {"cutValue": 10.0, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 100
elif "trijetmass" in histo.lower():
_rebinX = 4
logY = False
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 805 #1005
elif "ht" in histo.lower():
_rebinX = 2
logY = False
_units = "GeV"
_format = "%0.0f " + _units
_xlabel = "H_{T} (%s)" % _units
_cutBox = {"cutValue": 500, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
#_opts["xmin"] = 500
_opts["xmax"] = 2000
elif "tetrajetmass" in histo.lower():
_rebinX = 5 #5 #10 #4
logY = False
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjbb} (%s)" % (_units)
_format = "%0.0f " + _units
_cutBox = {"cutValue": 500.0, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 1500 #3500.0
#_rebinX = 10
#_opts["xmax"] = 3500
elif "tetrajetbjetpt" in histo.lower():
_rebinX = 2
logY = False
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 600
elif "foxwolframmoment" in histo.lower():
_format = "%0.1f"
_cutBox = {"cutValue": 0.5, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
else:
pass
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
else:
_opts["ymin"] = 1e0
# Customise styling
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
if "QCD" in datasetsMgr.getAllDatasets():
p.histoMgr.forHisto("QCD", styles.getQCDFillStyle() )
p.histoMgr.setHistoDrawStyle("QCD", "HIST")
p.histoMgr.setHistoLegendStyle("QCD", "F")
if "TT" in datasetsMgr.getAllDatasets():
p.histoMgr.setHistoDrawStyle("TT", "AP")
p.histoMgr.setHistoLegendStyle("TT", "LP")
# Customise style
signalM = []
for m in signalMass:
signalM.append(m.rsplit("M_")[-1])
for m in signalM:
p.histoMgr.forHisto("ChargedHiggs_HplusTB_HplusToTB_M_%s" %m, styles.getSignalStyleHToTB_M(m))
plots.drawPlot(p,
histo,
xlabel = _xlabel,
ylabel = "Arbitrary Units / %s" % (_format),
log = logY,
rebinX = _rebinX, cmsExtraText = "Preliminary",
createLegend = {"x1": 0.58, "y1": 0.65, "x2": 0.92, "y2": 0.92},
opts = _opts,
opts2 = {"ymin": 0.6, "ymax": 1.4},
cutBox = _cutBox,
)
# Save plot in all formats
saveName = histo.split("/")[-1]
savePath = os.path.join(opts.saveDir, "HplusMasses", histo.split("/")[0], opts.optMode)
SavePlot(p, saveName, savePath)
return
def PlotMC(datasetsMgr, histo, intLumi):
kwargs = {}
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr, histo, normalizeToOne=True, saveFormats=[], **kwargs)
else:
p = plots.MCPlot(datasetsMgr, histo, normalizeToLumi=intLumi, saveFormats=[], **kwargs)
# Get histogram<->kwargs dictionary
kwargs = GetHistoKwargs(histo, opts)
# Customise styling
if 0:
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerSize(0))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerStyle(6))
# Customise signal
for d in datasetsMgr.getAllDatasetNames():
if "Charged" not in d:
continue
else:
#p.histoMgr.setHistoDrawStyle(d, "HIST")
p.histoMgr.setHistoLegendStyle(d, "L")
p.histoMgr.setHistoLegendStyle(d, "L")
# Customise QCD style
p.histoMgr.setHistoDrawStyle("QCD", "P")
p.histoMgr.setHistoLegendStyle("QCD", "P")
p.histoMgr.setHistoLegendLabelMany({
"QCD": "QCD (MC)",
})
# Customise style
signalM = []
for m in signalMass:
signalM.append(m.rsplit("M_")[-1])
for m in signalM:
dName = "ChargedHiggs_HplusTB_HplusToTB_M_%s" %m
if dName in datasetsMgr.getAllDatasetNames():
p.histoMgr.forHisto(dName, styles.getSignalStyleHToTB_M(m))
p.histoMgr.setHistoLegendStyle(dName, "LP")
# Plot customised histogram
plots.drawPlot(p,
histo,
xlabel = kwargs.get("xlabel"),
ylabel = kwargs.get("ylabel"),
log = kwargs.get("log"),
rebinX = kwargs.get("rebinX"),
cmsExtraText = "Preliminary",
#createLegend = {"x1": 0.62, "y1": 0.75, "x2": 0.92, "y2": 0.92},
moveLegend = kwargs.get("moveLegend"),
opts = kwargs.get("opts"),
opts2 = {"ymin": 0.6, "ymax": 1.4},
cutBox = kwargs.get("cutBox"),
)
# Save plot in all formats
saveName = histo.split("/")[-1]
if opts.folder == "":
savePath = os.path.join(opts.saveDir, opts.optMode)
else:
savePath = os.path.join(opts.saveDir, histo.split("/")[0], opts.optMode)
SavePlot(p, saveName, savePath, [".png", ".pdf"])
return
def GetHistoGraphs(datasetsMgr, folder, hName):
# Get histogram customisations
_kwargs = GetHistoKwargs(opts)
# Get histos (Data, EWK) for Inclusive
p1 = plots.DataMCPlot(datasetsMgr, hName, saveFormats=[])
kwargs = copy.deepcopy(_kwargs)
kwargs["opts"] = {"ymin": 1.0, "ymaxfactor": 10.0}
kwargs["xlabelsize"] = 10
kwargs["ratio"] = False
plots.drawPlot(p1, hName, **kwargs)
SavePlot(p1, hName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".C", ".png", ".pdf"] )
# Clone histograms
histoList = []
graphList = []
hgList = []
# Append some bkg samples as well
opts.datasets = opts.signal
if not opts.acceptance:
opts.datasets.extend(opts.backgrounds)
for d in opts.datasets:
h = p1.histoMgr.getHisto(d).getRootHisto().Clone(d)
histoList.append(h)
# Create the Efficiency histos
for i, h in enumerate(histoList, 1):
if opts.efficiency:
histo = GetEfficiencyHisto(histoList[i-1], opts.refCounter, _kwargs, printValues=True, hideZeros=True)
histo.SetName("Efficiency_%d" % (i))
elif opts.events:
histo = GetEventsHisto(histoList[i-1], opts.refCounter, _kwargs, printValues=True, hideZeros=True)
histo.SetName("Events_%d" % (i))
elif opts.acceptance:
histo = GetAcceptanceHisto(histoList[i-1], _kwargs, printValues=True, hideZeros=True)
histo.SetName("Acceptance_%d" % (i))
# Convert histos to TGraph (don't do it for acceptance to get better positioning of markers wrt x-axis!)
if not opts.acceptance:
tgraph = convertHisto2TGraph(histo, printValues=False)
else:
tgraph = histo
# Apply random histo styles and append
if "charged" in h.GetName().lower():
s = styles.getSignalStyleHToTB_M(h.GetName().split("M_")[-1])
s.apply(tgraph)
if "QCD" in h.GetName():
styles.fakeBStyle.apply(tgraph)
if "EWK" in h.GetName():
s = styles.ttStyle.apply(tgraph)
# Append to list
graphList.append(tgraph)
# Create histoGraph object
htgraph = histograms.HistoGraph( tgraph, plots._legendLabels[opts.signal[i-1]], "LP", "LP")
# Append to list
hgList.append(htgraph)
return hgList, _kwargs
def DataMCPlot(datasetsMgr, json, opts):
Verbose("Creating Data-MC plot")
if opts.saveDir == None:
saveDir = json["saveDir"]
else:
saveDir = json["saveDir"]
# Create the Data-MC Plot
p = plots.DataMCPlot(datasetsMgr, json["histogram"], saveFormats=[])
# Label size (optional. Commonly Used in counters)
xlabelSize = None
if "xlabelsize" in json:
xlabelSize = json["xlabelsize"]
ylabelSize = None
if "ylabelsize" in json:
ylabelSize = json["ylabelsize"]
# Customise style
if opts.signal != None:
p.histoMgr.forHisto(
"ChargedHiggs_HplusTB_HplusToTB_M_%.0f" % (opts.signal),
styles.getSignalStyleHToTB_M("%.0f" % opts.signal))
# Draw a customised plot, os.path.join(opts.saveDir, "Fit")
plots.drawPlot(
p,
json["title"], #os.path.join(saveDir, json["title"])
xlabel=json["xlabel"],
ylabel=json["ylabel"],
rebinX=json["rebinX"],
rebinY=json["rebinY"],
ratioYlabel=json["ratioYlabel"],
ratio=json["ratio"] == "True",
stackMCHistograms=json["stackMCHistograms"] == "True",
ratioInvert=json["ratioInvert"] == "True",
addMCUncertainty=json["addMCUncertainty"] == "True",
addLuminosityText=json["addLuminosityText"] == "True",
addCmsText=json["addCmsText"] == "True",
cmsExtraText=json["cmsExtraText"],
opts=json["opts"],
opts2=json["ratioOpts"],
log=json["logY"] == "True",
errorBarsX=json["errorBarsX"] == "True",
moveLegend=json["moveLegend"],
# cutLine = json["cutValue"], #cannot have this and "cutBox" defined
cutBox={
"cutValue": json["cutValue"],
"fillColor": json["cutFillColour"],
"box": json["cutBox"] == "True",
"line": json["cutLine"] == "True",
"greaterThan": json["cutGreaterThan"] == "True"
},
xlabelsize=xlabelSize,
ylabelsize=ylabelSize,
)
# Remove legend?
if json["removeLegend"] == "True":
p.removeLegend()
# Additional text
histograms.addText(json["extraText"].get("x"), json["extraText"].get("y"),
json["extraText"].get("text"),
json["extraText"].get("size"))
# Save in custom formats
SavePlot(p, json["title"], os.path.join(saveDir, opts.optMode),
json["saveFormats"])
return
def PlotMC(datasetsMgr, datasetsMgr1, histo, intLumi):
kwargs = {}
print "here1"
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr, histo, normalizeToOne=True, saveFormats=[".pdf", "png"], **kwargs)
p = plots.MCPlot(datasetsMgr1, histo, normalizeToOne=True, saveFormats=[".pdf", ".png"], **kwargs)
else:
p = plots.MCPlot(datasetsMgr, histo, normalizeToLumi=intLumi, saveFormats=[".pdf", ".png"], **kwargs)
p = plots.MCPlot(datasetsMgr1, histo, normalizeToOne=True, saveFormats=[".pdf", "png"], **kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.0f"
_xlabel = None
logY = False
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
print "here2"
if "Pt" in histo:
_rebinX = 2
xMin = 0.0
# rebinX = 2
xMax = 805.0
# xMax = 555.0 # For topPt < 500GeV
xTitle = "p_{T} (GeV/c)"
units = "GeV/c"
_format = "%0.0f" + units
# yTitle = "Efficiency / " + str(binwidth) + " "+units
# yMin = 0.0
# yMax = 1.1
else:
pass
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
#_opts = {"ymin": 1e-3, "ymaxfactor": yMaxFactor, "xmax": None}
else:
_opts["ymin"] = 1e0
#_opts["ymaxfactor"] = yMaxFactor
#_opts = {"ymin": 1e0, "ymaxfactor": yMaxFactor, "xmax": None}
# Customise styling
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
print "here3"
histoDef = datasetsMgr.getDataset("TT").getDatasetRootHisto(histo)
histoDR = datasetsMgr1.getDataset("TT").getDatasetRootHisto(histo)
histoDef.normalizeToOne()
histoDR.normalizeToOne()
hDef = histoDef.getHistogram()
hDR = histoDR.getHistogram()
p = plots.ComparisonPlot(histograms.Histo(hDR,"DeltaR08", "l", "L"),
histograms.Histo(hDef,"Default", "l", "L"))
p.histoMgr.setHistoLegendLabelMany({"DeltaR08": "#DeltaR(q,q')>0.8",
"Default": "Default"})
p.histoMgr.forHisto("DeltaR08", styles.getQCDLineStyle() )
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
p.histoMgr.setHistoDrawStyle("DeltaR08", "HIST") #HIST
p.histoMgr.setHistoLegendStyle("DeltaR08", "L") #F
p.histoMgr.setHistoDrawStyle("Default", "HIST") #HIST
p.histoMgr.setHistoLegendStyle("Default", "L") #F
p.histoMgr.forHisto("Default", styles.ttStyle)
print "here4"
# Customise style
signalM = []
for m in signalMass:
signalM.append(m.rsplit("M_")[-1])
for m in signalM:
p.histoMgr.forHisto("ChargedHiggs_HplusTB_HplusToTB_M_%s" %m, styles.getSignalStyleHToTB_M(m))
print "here5"
plots.drawPlot(p,
histo,
xlabel = _xlabel,
ylabel = "Arbitrary Units / %s" % (_format),
log = logY,
rebinX = _rebinX, cmsExtraText = "Preliminary",
createLegend = {"x1": 0.58, "y1": 0.75, "x2": 0.92, "y2": 0.92},
# createLegend = {"x1": 0.58, "y1": 0.65, "x2": 0.92, "y2": 0.92},
opts = _opts,
opts2 = {"ymin": 0.6, "ymax": 1.4},
cutBox = _cutBox,
)
print "here6"
# Save plot in all formats
saveName = histo.split("/")[-1]
savePath = os.path.join(opts.saveDir, "HplusMasses", histo.split("/")[0], opts.optMode)
SavePlot(p, saveName, savePath)
print "here7"
return
def PlotMC(datasetsMgr, histo, intLumi):
kwargs = {}
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr, histo, normalizeToOne=True, saveFormats=[], **kwargs)
else:
p = plots.MCPlot(datasetsMgr, histo, normalizeToLumi=intLumi, saveFormats=[], **kwargs)
# p = plots.MCPlot(datasetsMgr, histo, normalizeToLumi=intLumi, saveFormats=[], **kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.0f"
_xlabel = None
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
if "ChiSqr" in histo:
_rebinX = 1
# logY = True
_units = ""
_format = "%0.1f " + _units
_xlabel = "#chi^{2}"
_cutBox = {"cutValue": 10.0, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 100
elif "trijetmass" in histo.lower():
_rebinX = 2
# logY = False
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 505 #1005
elif "ht" in histo.lower():
_rebinX = 2
# logY = False
_units = "GeV"
_format = "%0.0f " + _units
_xlabel = "H_{T} (%s)" % _units
_cutBox = {"cutValue": 500, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 2000
elif "tetrajetmass" in histo.lower():
_rebinX = 5 #5 #10 #4
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjbb} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 1500 #3500.0
elif "dijetmass" in histo.lower():
_rebinX = 1 #5 #10 #4
# logY = False
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{W} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800 #3500.0
_cutBox = {"cutValue": 80.4, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 300
elif "tetrajetbjetpt" in histo.lower():
_rebinX = 2
# logY = False
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "ldgtrijetpt" in histo.lower():
_rebinX = 2
# logY = False
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "trijetbdt_mass" in histo.lower():
_rebinX = 2
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 505
# elif "topcandmass" in histo.lower():
# _rebinX = 2
# _units = "GeV/c^{2}"
# _format = "%0.0f " + _units
# _xlabel = "m_{jjb} (%s)" % _units
# _cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
# _opts["xmax"] = 505
elif "matched_mass" in histo.lower():
_rebinX = 2
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 505
elif "bdtvalue" in histo.lower():
_format = "%0.1f"
elif "foxwolframmoment" in histo.lower():
_format = "%0.1f"
_cutBox = {"cutValue": 0.5, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
elif "absdeltamvamax_mctruth_sameobjfakes" in histo.lower():
_format = "%0.1f"
_xlabel = "|#Delta BDTG| response"
elif "deltamvamin_mctruth_sameobjfakespassbdt" in histo.lower():
_opts["xmax"] = 1
_opts["xmin"] = -1
elif "bdtmultiplicity" in histo.lower():
_opts["xmax"] = 15
else:
pass
if opts.normaliseToOne:
logY = False
Ylabel = "Arbitrary Units / %s" % (_format)
else:
logY = True
Ylabel = "Events / %s" % (_format)
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
#_opts = {"ymin": 1e-3, "ymaxfactor": yMaxFactor, "xmax": None}
else:
_opts["ymin"] = 1e0
#_opts["ymaxfactor"] = yMaxFactor
#_opts = {"ymin": 1e0, "ymaxfactor": yMaxFactor, "xmax": None}
# Customise styling
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
if "QCD" in datasetsMgr.getAllDatasets():
p.histoMgr.forHisto("QCD", styles.getQCDFillStyle() )
p.histoMgr.setHistoDrawStyle("QCD", "HIST")
p.histoMgr.setHistoLegendStyle("QCD", "F")
if "TT" in datasetsMgr.getAllDatasets():
p.histoMgr.setHistoDrawStyle("TT", "AP")
p.histoMgr.setHistoLegendStyle("TT", "LP")
# Customise style
signalM = []
for m in signalMass:
signalM.append(m.rsplit("M_")[-1])
for m in signalM:
p.histoMgr.forHisto("ChargedHiggs_HplusTB_HplusToTB_M_%s" %m, styles.getSignalStyleHToTB_M(m))
plots.drawPlot(p,
histo,
xlabel = _xlabel,
ylabel = Ylabel,#"Arbitrary Units / %s" % (_format), #"Events / %s" % (_format), #"Arbitrary Units / %s" % (_format),
log = logY,
rebinX = _rebinX, cmsExtraText = "Preliminary",
createLegend = {"x1": 0.58, "y1": 0.65, "x2": 0.92, "y2": 0.92},
# createLegend = {"x1": 0.73, "y1": 0.85, "x2": 0.97, "y2": 0.77},
opts = _opts,
opts2 = {"ymin": 0.6, "ymax": 1.4},
cutBox = _cutBox,
)
# Save plot in all formats
saveName = histo.split("/")[-1]
savePath = os.path.join(opts.saveDir, "HplusMasses", histo.split("/")[0], opts.optMode)
if opts.normaliseToOne:
save_path = savePath + opts.MVAcut
if opts.noQCD:
save_path = savePath + opts.MVAcut + "/noQCD/"
else:
save_path = savePath + opts.MVAcut + "/normToLumi/"
if opts.noQCD:
save_path = savePath + opts.MVAcut + "/noQCD/"
# SavePlot(p, saveName, savePath)
SavePlot(p, saveName, save_path)
return
def CalcEfficiency(datasetsMgr, numPath, denPath, intLumi):
# Definitions
myList = []
index = 0
_kwargs = GetHistoKwargs(numPath, opts)
# For-loop: All datasets
for dataset in datasetsMgr.getAllDatasets():
x = []
y = []
n = dataset.getDatasetRootHisto(numPath)
n.normalizeToLuminosity(intLumi)
num = n.getHistogram()
d = dataset.getDatasetRootHisto(denPath)
d.normalizeToLuminosity(intLumi)
den = d.getHistogram()
if "binList" in _kwargs:
xBins = _kwargs["binList"]
nx = len(xBins)-1
num = num.Rebin(nx, "", xBins)
den = den.Rebin(nx, "", xBins)
for i in range(1, num.GetNbinsX()+1):
nbin = num.GetBinContent(i)
dbin = den.GetBinContent(i)
if nbin < 0:
nbin = 0
if dbin < 0:
nbin = 0
dbin = 1
if nbin > dbin:
nbin = dbin
x.append(num.GetBinLowEdge(i)+0.5*num.GetBinWidth(i))
y.append(nbin/dbin)
n = num.GetNbinsX()
eff = ROOT.TGraph(n, array.array("d",x), array.array("d",y))
# Apply default style (according to dataset name)
plots._plotStyles[dataset.getName()].apply(eff)
# Apply random histo styles and append
if "charged" in dataset.getName().lower():
mass = dataset.getName().split("M_")[-1]
s = styles.getSignalStyleHToTB_M(mass)
s.apply(eff)
# Append in list
myList.append(histograms.HistoGraph(eff, plots._legendLabels[dataset.getName()], "lp", "P"))
# Define save name
saveName = "Eff_" + numPath.split("/")[-1] + "Over" + denPath.split("/")[-1]
# Plot the efficiency
p = plots.PlotBase(datasetRootHistos=myList, saveFormats=[])
plots.drawPlot(p, saveName, **_kwargs)
# Save plot in all formats
savePath = os.path.join(opts.saveDir, numPath.split("/")[0], opts.optMode)
SavePlot(p, saveName, savePath, saveFormats = [".png"])#, ".pdf"])
return
def PlotEfficiency(datasetsMgr, numPath, denPath, intLumi):
# Definitions
myList = []
myBckList = []
index = 0
_kwargs = GetHistoKwargs(denPath, opts)
counter = 0
# For-loop: All datasets
for dataset in datasetsMgr.getAllDatasets():
name_N = numPath
name_D = denPath
# Get the histograms
#num = dataset.getDatasetRootHisto(numPath).getHistogram()
#den = dataset.getDatasetRootHisto(denPath).getHistogram()
#if "TT" in dataset.getName():
# numPath = numPath.replace("HiggsTop", "AllTop")
# denPath = denPath.replace("HiggsTop", "AllTop")
# numPath = numPath.replace("AssocTop", "AllTop")
# denPath = denPath.replace("AssocTop", "AllTop")
n = dataset.getDatasetRootHisto(numPath)
n.normalizeToLuminosity(intLumi)
num = n.getHistogram()
d = dataset.getDatasetRootHisto(denPath)
d.normalizeToLuminosity(intLumi)
den = d.getHistogram()
if "binList" in _kwargs:
xBins = _kwargs["binList"]
nx = len(xBins)-1
num = num.Rebin(nx, "", xBins)
den = den.Rebin(nx, "", xBins)
for i in range(1, num.GetNbinsX()+1):
nbin = num.GetBinContent(i)
dbin = den.GetBinContent(i)
#print dataset.getName(), nbin, dbin
if (nbin > dbin):
print "error"
# Sanity checks
if den.GetEntries() == 0 or num.GetEntries() == 0:
continue
if num.GetEntries() > den.GetEntries():
continue
# Remove negative bins and ensure numerator bin <= denominator bin
#CheckNegatives(num, den, False)
#CheckNegatives(num, den, True)
#RemoveNegatives(num)
#RemoveNegatives(den)
# Sanity check (Histograms are valid and consistent) - Always false!
# if not ROOT.TEfficiency.CheckConsistency(num, den):
# continue
# Create Efficiency plots with Clopper-Pearson stats
eff = ROOT.TEfficiency(num, den) # fixme: investigate warnings
eff.SetStatisticOption(ROOT.TEfficiency.kFCP) #
# Set the weights - Why is this needed?
if 0:
weight = 1
if dataset.isMC():
weight = dataset.getCrossSection()
eff.SetWeight(weight)
# Convert to TGraph
eff = convert2TGraph(eff)
# Apply default style (according to dataset name)
plots._plotStyles[dataset.getName()].apply(eff)
# Apply random histo styles and append
if "charged" in dataset.getName().lower():
counter +=1
mass = dataset.getName().split("M_")[-1]
styles.markerStyles[counter].apply(eff)
if "300" in mass or "650" in mass:
s = styles.getSignalStyleHToTB_M(mass)
s.apply(eff)
eff.SetLineStyle(ROOT.kSolid)
eff.SetLineWidth(3)
eff.SetMarkerSize(1.2)
'''
mass = dataset.getName().split("M_")[-1]
mass = mass.replace("650", "1000")
s = styles.getSignalStyleHToTB_M(mass)
s.apply(eff)
'''
'''
ttStyle = styles.getEWKLineStyle()
if "tt" in dataset.getName().lower():
ttStyle.apply(eff)
'''
# Append in list
#if "charged" in dataset.getName().lower():
# if "m_500" in dataset.getName().lower():
if 1:
#if "tt" in dataset.getName().lower():
if "m_500" in dataset.getName().lower():
eff_ref = histograms.HistoGraph(eff, plots._legendLabels[dataset.getName()], "lp", "P")
else:
myList.append(histograms.HistoGraph(eff, plots._legendLabels[dataset.getName()], "lp", "P"))
#elif "tt" in dataset.getName().lower():
# eff_ref = histograms.HistoGraph(eff, plots._legendLabels[dataset.getName()], "lp", "P")
# Define save name
saveName = "Eff_" + name_N.split("/")[-1] + "Over"+ name_D.split("/")[-1]
# Plot the efficiency
#p = plots.PlotBase(datasetRootHistos=myList, saveFormats=[])
p = plots.ComparisonManyPlot(eff_ref, myList, saveFormats=[])
plots.drawPlot(p, saveName, **_kwargs)
# Save plot in all formats
savePath = os.path.join(opts.saveDir, name_N.split("/")[0], opts.optMode)
SavePlot(p, saveName, savePath, saveFormats = [".png", ".C", ".pdf"])#, ".pdf"])
return
def PlotMC(datasetsMgr, histo, intLumi):
kwargs = {}
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr,
histo,
normalizeToOne=True,
saveFormats=[],
**kwargs)
else:
p = plots.MCPlot(datasetsMgr,
histo,
normalizeToLumi=intLumi,
saveFormats=[],
**kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.0f"
_xlabel = None
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
if "Gluon" in histo:
_rebinX = 2
_units = ""
_xlabel = "Gluon Jets QGL"
_format = 0.02
if "Light" in histo:
_rebinX = 2
_xlabel = "Quark Jets QGL"
_format = 0.02
if "QGLR" in histo:
_rebinX = 2
_units = ""
_xlabel = "QGLR"
logY = True
_format = 0.02
if opts.normaliseToOne:
logY = False
Ylabel = "Arbitrary Units / %s" % (_format)
else:
logY = True
Ylabel = "Events / %s" % (_format)
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
else:
_opts["ymin"] = 1e0
# Customise styling
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
# QCD
p.histoMgr.forHisto("QCD", styles.getAltQCDStyle()) #getQCDFillStyle() )
p.histoMgr.setHistoDrawStyle("QCD", "HIST")
p.histoMgr.setHistoLegendStyle("QCD", "F")
# TT
#p.histoMgr.forHisto("TT", styles.getAltTTStyle())
#p.histoMgr.setHistoDrawStyle("TT", "HIST")
#p.histoMgr.setHistoLegendStyle("TT", "F")
# Customise style
signalM = []
for m in signalMass:
signalM.append(m.rsplit("M_")[-1])
for m in signalM:
p.histoMgr.forHisto("ChargedHiggs_HplusTB_HplusToTB_M_%s" % m,
styles.getSignalStyleHToTB_M(m))
plots.drawPlot(
p,
histo,
xlabel=_xlabel,
ylabel=
Ylabel, #"Arbitrary Units / %s" % (_format), #"Events / %s" % (_format), #"Arbitrary Units / %s" % (_format),
log=logY,
rebinX=_rebinX,
cmsExtraText="Preliminary",
createLegend={
"x1": 0.58,
"y1": 0.65,
"x2": 0.92,
"y2": 0.92
},
opts=_opts,
opts2={
"ymin": 0.6,
"ymax": 1.4
},
cutBox=_cutBox,
)
# Save plot in all formats
saveName = histo.split("/")[-1]
if "False" in opts.folder:
saveName += "_False"
if "True" in opts.folder:
saveName += "_True"
savePath = os.path.join(opts.saveDir, "",
histo.split("/")[0], opts.optMode)
SavePlot(p, saveName, savePath)
return
def PlotMC(datasetsMgr, histo, intLumi):
kwargs = {}
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr, histo, normalizeToOne=True, saveFormats=[], **kwargs)
else:
p = plots.MCPlot(datasetsMgr, histo, normalizeToLumi=intLumi, saveFormats=[], **kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.0f"
_xlabel = None
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
#_opts = {"ymin": 1e-3, "ymax": 60}
if "pt" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": False, "greaterThan": True}
#_opts["xmax"] = 505 #1005
if "ht" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "H_{T} (%s)" % _units
_opts["xmax"] = 2000
#_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": False, "greaterThan": True}
#_opts["xmax"] = 505 #1005
if "eta" in histo.lower():
_units = ""
_format = "%0.1f " + _units
_xlabel = "#eta %s" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": False, "greaterThan": True}
_opts["xmax"] = 2.5
_opts["xmin"] = -2.5
if "trijetmass" in histo.lower():
_rebinX = 2
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 505 #1005
if "ldg" in histo.lower():
_xlabel = "m_{jjb}^{ldg} (%s)" % _units
elif "tetrajetmass" in histo.lower():
_rebinX = 10 #5 #10 #4
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjbb} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 2500 #3500.0
elif "tetrajetpt" in histo.lower():
_rebinX = 2 #5 #10 #4
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T,jjbb} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 1000 #3500.0
elif "dijetmass" in histo.lower():
_rebinX = 2 #5 #10 #4
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jj} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800 #3500.0
_cutBox = {"cutValue": 80.4, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 300
if "ldg" in histo.lower():
_xlabel = "m_{jj}^{ldg} (%s)" % (_units)
elif "tetrajetbjetpt" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T,bjet} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "ldgtrijetpt" in histo.lower():
_rebinX = 2
# logY = False
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T,jjb}^{ldg} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "ldgtrijetdijetpt" in histo.lower():
_rebinX = 2
# logY = False
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T,jj}^{ldg} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "topbdt_" in histo.lower():
_format = "%0.1f"
_cutBox = {"cutValue": +0.40, "fillColor": 16, "box": False, "line": False, "greaterThan": True}
_xlabel = "BDTG discriminant"
if "gentop_pt" in histo.lower():
_rebinX = 1
if "genquark_pt" in histo.lower():
_rebinX = 1
_opts["xmax"] = 500
else:
pass
if "delta" in histo.lower():
_format = "%0.1f "
if "phi" in histo.lower():
_xlabel = "#Delta #phi"
if "deltar" in histo.lower():
_rebinX = 2
if "DiBjetMaxMass_Mass" in histo:
_units = "GeV"
_rebinX = 4
_xlabel = "m_{max}(bb) (%s)" % (_units)
_format = "%0.0f " + _units
if "DiJetDeltaRmin_Mass" in histo:
_units = "GeV"
_format = "%0.0f " + _units
_xlabel = "m_{#DeltaR_{min}(jj)}(jj) (%s)" % (_units)
_opts["xmax"] = 200
if "DiBjetDeltaRmin_Mass" in histo:
_units = "GeV"
_format = "%0.0f " + _units
_rebinX= 2
_xlabel = "m_{#DeltaR_{min}(bb)}(bb) (%s)" % (_units)
_opts["xmax"] = 600
if "LdgBjet_SubldgBjet_Mass" in histo:
_units = "GeV"
_format = "%0.0f " + _units
_rebinX= 4
_xlabel = "m(b_{ldg}b_{sldg}) (%s)" % (_units)
if "DeltaR_LdgTop_DiBjetDeltaRmin" in histo:
_rebinX= 2
_format = "%0.1f "
_xlabel = "#DeltaR (top_{ldg}, bb_{#Delta Rmin})"
if "DeltaR_SubldgTop_DiBjetDeltaRmin" in histo:
_rebinX= 2
_format = "%0.1f "
_xlabel = "#DeltaR (top_{sldg}, bb_{#Delta Rmin})"
if "over" in histo.lower():
_opts["xmax"] = 15
_opts["xmax"] = +0.8
_opts["xmin"] = -0.8
_xlabel = "#Delta p_{T}(j-q)/p_{T,q}"
_cutBox = {"cutValue": +0.32, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
if "within" in histo.lower():
_opts["xmax"] = +0.8
_opts["xmin"] = -0.8
_xlabel = "#Delta p_{T}(j-q)/p_{T,q}"
_cutBox = {"cutValue": +0.32, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
if "axis2" in histo.lower():
_opts["xmax"] = +0.2
_opts["xmin"] = 0.0
_xlabel = "axis2"
_units = ""
_format = "%0.1f "+_units
if "axis2" in histo.lower():
_opts["xmax"] = +0.2
_opts["xmin"] = 0.0
_xlabel = "axis2"
_units = ""
_format = "%0.2f "+_units
if "mass" in histo.lower():
_xlabel = "M (GeV/c^{2})"
_units = "GeV/c^{2}"
_format = "%0.0f "+_units
if "mult" in histo.lower():
_xlabel = "mult"
_units = ""
_format = "%0.0f "+_units
if "BQuarkFromH_Pt" in histo:
_opts["xmax"] = 200
_rebinX= 1
if "order" in histo.lower():
_opts["xmax"] = 15
_rebinX= 1
if "pt" in histo.lower():
_xlabel = "indx_{p_{T}}"
if "csv" in histo.lower():
_xlabel = "indx_{csv}"
if "phi_alpha" in histo.lower() or "phi_beta" in histo.lower() or "r_alpha" in histo.lower() or "r_beta" in histo.lower():
_format = "%0.1f "
_opts["xmin"] = 0.0
if "phi" in histo.lower():
_xlabel = "#phi"
_opts["xmax"] = 5.5
else:
_xlabel = "r"
_opts["xmax"] = 6.5
if "alpha" in histo.lower():
_xlabel = _xlabel+"_{#alpha}"
else:
_xlabel = _xlabel+"_{#beta}"
if opts.normaliseToOne:
logY = False
Ylabel = "Arbitrary Units / %s" % (_format)
else:
logY = True
Ylabel = "Events / %s" % (_format)
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
#_opts = {"ymin": 1e-3, "ymaxfactor": yMaxFactor, "xmax": None}
else:
_opts["ymin"] = 1e0
if "order" in histo.lower():
_opts["ymin"] = 1e-3
#_opts["ymaxfactor"] = yMaxFactor
#_opts = {"ymin": 1e0, "ymaxfactor": yMaxFactor, "xmax": None}
# Customise styling
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
if "QCD" in datasetsMgr.getAllDatasetNames():
p.histoMgr.forHisto("QCD", styles.getQCDLineStyle()) #getQCDFillStyle() )
p.histoMgr.setHistoDrawStyle("QCD", "HIST")
p.histoMgr.setHistoLegendStyle("QCD", "F")
if "TT" in datasetsMgr.getAllDatasetNames():
TTStyle = styles.StyleCompound([styles.StyleFill(fillColor=ROOT.kMagenta-2), styles.StyleLine(lineColor=ROOT.kMagenta-2, lineStyle=ROOT.kSolid, lineWidth=3),
styles.StyleMarker(markerSize=1.2, markerColor=ROOT.kMagenta-2, markerSizes=None, markerStyle=ROOT.kFullTriangleUp)])
p.histoMgr.forHisto("TT", TTStyle)
p.histoMgr.setHistoDrawStyle("TT", "HIST") #AP
p.histoMgr.setHistoLegendStyle("TT", "F") #LP
# Customise style
signalM = []
for m in signalMass:
signalM.append(m.rsplit("M_")[-1])
for m in signalM:
p.histoMgr.forHisto("ChargedHiggs_HplusTB_HplusToTB_M_%s" %m, styles.getSignalStyleHToTB_M(m))
plots.drawPlot(p,
histo,
xlabel = _xlabel,
ylabel = Ylabel,
log = logY,
rebinX = _rebinX, cmsExtraText = "Preliminary",
#createLegend = {"x1": 0.59, "y1": 0.65, "x2": 0.92, "y2": 0.92},
createLegend = {"x1": 0.59, "y1": 0.70, "x2": 0.92, "y2": 0.92},
#createLegend = {"x1": 0.73, "y1": 0.85, "x2": 0.97, "y2": 0.77},
opts = _opts,
opts2 = {"ymin": 0.6, "ymax": 1.4},
cutBox = _cutBox,
)
# Save plot in all formats
saveName = histo.split("/")[-1]
savePath = os.path.join(opts.saveDir, histo.split("/")[0], opts.optMode)
'''
if opts.normaliseToOne:
save_path = savePath + opts.MVAcut
if opts.noQCD:
save_path = savePath + opts.MVAcut + "/noQCD/"
else:
save_path = savePath + opts.MVAcut + "/normToLumi/"
if opts.noQCD:
save_path = savePath + opts.MVAcut + "/noQCD/"
'''
SavePlot(p, saveName, savePath)
return
def PlotMC(datasetsMgr, histo, intLumi):
kwargs = {}
p = plots.MCPlot(datasetsMgr, histo, normalizeToOne=True, saveFormats=[], **kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.01f"
_xlabel = None
logY = False
_opts = {"ymin": 1e-3, "ymaxfactor": 1.2}
if "Pt" in histo:
if "Gluon" in histo:
_xlabel = "Gluon Jets p_{T} (GeV)"
elif "Light" in histo:
_xlabel = "Light Jets p_{T} (GeV)"
if "JetsN" in histo:
if "Gluon" in histo:
_xlabel = "Gluon Jets Multiplicity"
elif "Light" in histo:
_xlabel = "Light Jets Multiplicity"
if "QGL" in histo:
_opts["xmin"] = 0.0
_opts["xmax"] = 1.0
_units = ""
_format = "%0.01f " + _units
if "Gluon" in histo:
_xlabel = "Gluon Jets QGL"
elif "Light" in histo:
_xlabel = "Light Jets QGL"
# Customise styling
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
# Customise style
signalM = []
for m in signalMass:
signalM.append(m.rsplit("M_")[-1])
for m in signalM:
p.histoMgr.forHisto("ChargedHiggs_HplusTB_HplusToTB_M_%s" %m, styles.getSignalStyleHToTB_M(m))
plots.drawPlot(p,
histo,
xlabel = _xlabel,
ylabel = "Arbitrary Units",# / %s" % (_format),
log = logY,
rebinX = _rebinX, cmsExtraText = "Preliminary",
createLegend = {"x1": 0.70, "y1": 0.65, "x2": 0.98, "y2": 0.92},
opts = _opts,
opts2 = {"ymin": 0.6, "ymax": 1.4},
cutBox = _cutBox,
)
# Save plot in all formats
saveName = histo.split("/")[-1]
savePath = os.path.join(opts.saveDir, "", "", opts.optMode)
SavePlot(p, saveName, savePath)
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", "METSelection", "NvtxAllSelections"] #latter is empty!
if opts.folder == "jetSelection_":
skipStrings = ["JetMatching", "SeventhJet"]
if opts.folder == "bjetSelection_":
skipStrings = ["MatchDeltaR", "btagSFRelUncert", "_dEta", "_dPhi", "_dPt", "_dR"]
if opts.folder == "metSelection_":
skipStrings = [""]
if opts.folder == "topologySelection_":
skipStrings = ["_Vs_"]
if opts.folder == "topSelectionBDT_":
skipStrings = ["RelUncert"]
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 PlotCutFlowEfficiency(h, datasetsMgr, intLumi):
Verbose("Plotting Cut Flow Efficiency")
efficiencyList = []
signalMass.append("QCD")
for mass in signalMass:
xValues = []
yValues = []
hSignal = getHisto(datasetsMgr, h, mass, intLumi)
for i in range (0, hSignal.GetXaxis().GetNbins()):
# Cut value
cut = hSignal.GetBinCenter(i)
passed = hSignal.Integral(i, hSignal.GetXaxis().GetNbins())
total = hSignal.Integral()
eff = float(passed)/total
xValues.append(cut)
yValues.append(eff)
# Create the Significance Plot
tGraph = ROOT.TGraph(len(xValues), array.array("d", xValues), array.array("d", yValues))
if "M_" in mass:
styles.getSignalStyleHToTB_M(mass.split("_")[-1]).apply(tGraph)
else:
styles.getQCDStyle().apply(tGraph)
drawStyle = "CPE"
legName = plots._legendLabels[mass]
effGraph = histograms.HistoGraph(tGraph, legName, "lp", drawStyle)
efficiencyList.append(effGraph)
saveName = "QGLREfficiency"
# Create the plot with all the significance plots
p = plots.PlotBase(efficiencyList, saveFormats=["pdf"])
p.createFrame(saveName)
# Customise frame
p.setEnergy("13")
p.getFrame().GetYaxis().SetLabelSize(18)
p.getFrame().GetXaxis().SetLabelSize(20)
p.getFrame().GetYaxis().SetTitle("Efficiency / 0.01")
p.getFrame().GetXaxis().SetTitle("QGLR Cut")
# Add Standard Texts to plot
histograms.addStandardTexts()
# Customise Legend
moveLegend = {"dx": -0.50, "dy": -0.5, "dh": -0.1}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
p.draw()
savePath = opts.saveDir
if opts.url:
savePath = opts.saveDir.replace("/publicweb/m/mkolosov/", "http://home.fnal.gov/~mkolosov/")
savePath = os.path.join(opts.saveDir, opts.folder, saveName, opts.optMode)
SavePlot(p, saveName, savePath)
return
def PlotMC(datasetsMgr, histo):
kwargs = {}
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr,
histo,
normalizeToOne=True,
saveFormats=[],
**kwargs)
else:
p = plots.MCPlot(datasetsMgr,
histo,
normalizeToLumi=opts.intLumi,
saveFormats=[],
**kwargs)
p.setLuminosity(opts.intLumi)
p1 = "a"
p2 = "b"
if "Htb_tbW_WBoson_Quark_Htb_tbW_WBoson_AntiQuark" in histo:
p1 = "q_{1}"
p2 = "q_{2}"
if "Htb_tbW_BQuark_Htb_tbW_Wqq_Quark" in histo:
p1 = "b_{2}"
p2 = "q_{1}"
if "Htb_tbW_BQuark_Htb_tbW_Wqq_AntiQuark" in histo:
p1 = "b_{2}"
p2 = "q_{2}"
if "Htb_tbW_WBoson_Htb_tbW_BQuark_dR" in histo:
p1 = "W^{+}"
p2 = "b_{2}"
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.0f"
_xlabel = None
_format = "%0.2f"
_logY = True
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
if "pt" in histo.lower():
_format = "%0.0f"
_rebinX = 2
_format = "%0.0f GeV/c"
if "eta" in histo.lower():
_cutBox = {
"cutValue": 0.0,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmin"] = -3.0
_opts["xmax"] = +3.0
if "dPhi" in histo:
_format = "%0.2f"
_opts["xmin"] = 0.0
_opts["xmax"] = +3.2
_xlabel = "#Delta#phi(%s, %s)" % (p1, p2)
if "dEta" in histo:
_format = "%0.2f"
_opts["xmin"] = 0.0
_opts["xmax"] = 3.2
_xlabel = "#Delta#eta(%s, %s)" % (p1, p2)
if "dR" in histo:
_format = "%0.2f"
_rebinX = 2
_opts["xmin"] = 0.0
_opts["xmax"] = 5.0
#_cutBox = {"cutValue": 0.8, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_xlabel = "#DeltaR(%s, %s)" % (p1, p2)
if _logY:
_opts["ymaxfactor"] = 2.0
else:
_opts["ymaxfactor"] = 1.2
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
else:
_opts["ymin"] = 1e0
# Customise Dataset styling
for signal in opts.signalMass:
m = signal.split("M_")[-1]
p.histoMgr.forHisto(signal, styles.getSignalStyleHToTB_M(m))
# Draw the customised plot
plots.drawPlot(
p,
histo,
xlabel=_xlabel,
ylabel="Arbitrary Units / %s" % (_format),
log=_logY,
rebinX=_rebinX,
cmsExtraText="Preliminary",
createLegend={
"x1": 0.60,
"y1": 0.72,
"x2": 0.92,
"y2": 0.92
},
opts=_opts,
addLuminosityText=(opts.intLumi != -1),
ratio=False,
opts2={
"ymin": 0.6,
"ymax": 1.4
},
cutBox=_cutBox,
)
if 0:
histograms.addText(0.20, 0.88, "test", 27)
# Save plot in all formats
saveName = histo.split("/")[-1]
saveDict = {}
saveDict["Htb_tbW_WBoson_Htb_tbW_BQuark_dR"] = "dR_W_b2"
saveDict["Htb_tbW_WBoson_Quark_Htb_tbW_WBoson_AntiQuark_dR"] = "dR_q1_q2"
saveDict["Htb_tbW_BQuark_Htb_tbW_Wqq_Quark_dR"] = "dR_b2_q1"
saveDict["Htb_tbW_BQuark_Htb_tbW_Wqq_AntiQuark_dR"] = "dR_b2_q2"
savePath = os.path.join(opts.saveDir,
histo.split("/")[0], "", opts.optMode)
if saveName in saveDict.keys():
SavePlot(p, saveDict[saveName], savePath)
else:
SavePlot(p, saveName, savePath)
return
def PlotHistosAndCalculateTF(datasetsMgr, histoList, binLabels, opts):
# Get the histogram customisations (keyword arguments)
_kwargs = GetHistoKwargs(histoList[0])
# Get the root histos for all datasets and Control Regions (CRs)
regions = ["SR", "VR", "CRone", "CRtwo"]
rhDict = GetRootHistos(datasetsMgr, histoList, regions, binLabels)
#=========================================================================================
# Calculate the Transfer Factor (TF) and save to file
#=========================================================================================
manager = FakeBNormalization.FakeBNormalizationManager(binLabels,
opts.mcrab,
opts.optMode,
verbose=False)
if opts.inclusiveOnly:
#manager.CalculateTransferFactor(binLabels[0], rhDict["CRone-FakeB"], rhDict["CRtwo-FakeB"])
binLabel = "Inclusive"
manager.CalculateTransferFactor("Inclusive",
rhDict["FakeB-CRone-Inclusive"],
rhDict["FakeB-CRtwo-Inclusive"])
else:
for bin in binLabels:
manager.CalculateTransferFactor(bin,
rhDict["FakeB-CRone-%s" % bin],
rhDict["FakeB-CRtwo-%s" % bin])
# Get unique a style for each region
for k in rhDict:
dataset = k.split("-")[0]
region = k.split("-")[1]
styles.getABCDStyle(region).apply(rhDict[k])
if "FakeB" in k:
styles.getFakeBStyle().apply(rhDict[k])
# sr.apply(rhDict[k])
# =========================================================================================
# Create the final plot object
# =========================================================================================
rData_SR = rhDict["Data-SR-Inclusive"]
rEWKGenuineB_SR = rhDict["EWK-SR-Inclusive-EWKGenuineB"]
rBkgSum_SR = rhDict["FakeB-VR-Inclusive"].Clone("BkgSum-SR-Inclusive")
rBkgSum_SR.Reset()
if opts.inclusiveOnly:
bin = "Inclusive"
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
binHisto_VR = rhDict["FakeB-VR-%s" % (bin)]
VRtoSR_TF = manager.GetTransferFactor(bin)
Print(
"Applying TF = %s%0.6f%s to VR shape" %
(ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()),
True)
binHisto_VR.Scale(VRtoSR_TF)
# Add the normalised histogram to the final Inclusive SR (predicted) histo
rBkgSum_SR.Add(binHisto_VR, +1)
else:
# For-loop: All bins
for i, bin in enumerate(binLabels, 1):
if bin == "Inclusive":
continue
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
binHisto_VR = rhDict["FakeB-VR-%s" % (bin)]
VRtoSR_TF = manager.GetTransferFactor(bin)
Print(
"Applying TF = %s%0.6f%s to VR shape" %
(ShellStyles.NoteStyle(), VRtoSR_TF,
ShellStyles.NormalStyle()), i == 1)
binHisto_VR.Scale(VRtoSR_TF)
# Add the normalised histogram to the final Inclusive SR (predicted) histo
rBkgSum_SR.Add(binHisto_VR, +1)
#Print("Got Verification Region (VR) shape %s%s%s" % (ShellStyles.NoteStyle(), rFakeB_VR.GetName(), ShellStyles.NormalStyle()), True)
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
#VRtoSR_TF = manager.GetTransferFactor("Inclusive")
#Print("Applying TF = %s%0.6f%s to VR shape" % (ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()), True)
#rBkgSum_SR.Scale(VRtoSR_TF)
# Plot histograms
if opts.altPlot:
# Add the SR EWK Genuine-b to the SR FakeB ( BkgSum = [FakeB] + [GenuineB-MC] = [VR * (CR1/CR2)] + [GenuineB-MC] )
rBkgSum_SR.Add(rEWKGenuineB_SR, +1)
# Change style
styles.getGenuineBStyle().apply(rBkgSum_SR)
# Remove unsupported settings of kwargs
_kwargs["stackMCHistograms"] = False
_kwargs["addLuminosityText"] = False
# Create the plot
p = plots.ComparisonManyPlot(rData_SR, [rBkgSum_SR], saveFormats=[])
# Set draw / legend style
p.histoMgr.setHistoDrawStyle("Data-SR-Inclusive", "P")
p.histoMgr.setHistoLegendStyle("Data-SR-Inclusive", "LP")
p.histoMgr.setHistoDrawStyle("BkgSum-SR-Inclusive", "HIST")
p.histoMgr.setHistoLegendStyle("BkgSum-SR-Inclusive", "F")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Data-SR": "Data",
"BkgSum-SR": "Fake-b + Gen-b",
})
else:
# Create empty histogram stack list
myStackList = []
# Signal
p2 = plots.DataMCPlot(datasetsMgr,
"ForTestQGLR/QGLR_SR/QGLR_SRInclusive",
saveFormats=[])
hSignal_800 = p2.histoMgr.getHisto(
'ChargedHiggs_HplusTB_HplusToTB_M_800').getRootHisto()
hhSignal_800 = histograms.Histo(
hSignal_800, 'ChargedHiggs_HplusTB_HplusToTB_M_800',
"H^{+} m_{H^+}=800 GeV")
hhSignal_800.setIsDataMC(isData=False, isMC=True)
myStackList.append(hhSignal_800)
hSignal_250 = p2.histoMgr.getHisto(
'ChargedHiggs_HplusTB_HplusToTB_M_250').getRootHisto()
hhSignal_250 = histograms.Histo(
hSignal_250, 'ChargedHiggs_HplusTB_HplusToTB_M_250',
"H^{+} m_{H^+}=250 GeV"
) #plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_250'])
hhSignal_250.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_250)
hSignal_500 = p2.histoMgr.getHisto(
'ChargedHiggs_HplusTB_HplusToTB_M_500').getRootHisto()
hhSignal_500 = histograms.Histo(
hSignal_500, 'ChargedHiggs_HplusTB_HplusToTB_M_500',
"H^{+} m_{H^+}=500 GeV"
) #plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_500'])
hhSignal_500.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_500)
hSignal_1000 = p2.histoMgr.getHisto(
'ChargedHiggs_HplusTB_HplusToTB_M_1000').getRootHisto()
hhSignal_1000 = histograms.Histo(
hSignal_1000, 'ChargedHiggs_HplusTB_HplusToTB_M_1000',
"H^{+} m_{H^+}=1000 GeV"
) #plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_500'])
hhSignal_1000.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_1000)
# Add the FakeB data-driven background to the histogram list
hFakeB = histograms.Histo(rBkgSum_SR, "FakeB", "Fake-b")
hFakeB.setIsDataMC(isData=False, isMC=True)
myStackList.append(hFakeB)
# Add the EWKGenuineB MC background to the histogram list
hGenuineB = histograms.Histo(rEWKGenuineB_SR, "GenuineB",
"EWK Genuine-b")
hGenuineB.setIsDataMC(isData=False, isMC=True)
myStackList.append(hGenuineB)
# Add the collision datato the histogram list
hData = histograms.Histo(rData_SR, "Data", "Data")
hData.setIsDataMC(isData=True, isMC=False)
myStackList.insert(0, hData)
p = plots.DataMCPlot2(myStackList, saveFormats=[])
p.setLuminosity(opts.intLumi)
p.setDefaultStyles()
# Draw the plot and save it
hName = "test"
plots.drawPlot(p, hName, **_kwargs)
SavePlot(p,
hName,
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png", ".pdf"])
#==========================================================================================
# Calculate Cut-Flow Efficiency
#==========================================================================================
kwargs = {
"rebinX": 1,
"xlabel": "QGLR",
"ylabel": "Significance / %.02f ",
"opts": {
"ymin": 0.0,
"ymaxfactor": 1.3
},
"createLegend": {
"x1": 0.55,
"y1": 0.70,
"x2": 0.92,
"y2": 0.92
},
# "cutBox" : {"cutValue": 0.0, "fillColor" : 16, "box": False, "line": False, "greaterThan": True},
}
efficiencyList = []
xValues = []
yValues_250 = []
yValues_500 = []
yValues_800 = []
yValues_1000 = []
yValues_Bkg = []
nBins = hSignal_250.GetNbinsX() + 1
hBkg = p.histoMgr.getHisto(
"ChargedHiggs_HplusTB_HplusToTB_M_800").getRootHisto().Clone("Bkg")
hBkg.Reset()
# Bkg: FakeB + Genuine B
hBkg.Add(hFakeB.getRootHisto(), +1)
hBkg.Add(hGenuineB.getRootHisto(), +1)
for i in range(0, nBins):
# Cut value
cut = hSignal_250.GetBinCenter(i)
passed_250 = hSignal_250.Integral(i, hSignal_250.GetXaxis().GetNbins())
passed_500 = hSignal_500.Integral(i, hSignal_500.GetXaxis().GetNbins())
passed_800 = hSignal_800.Integral(i, hSignal_800.GetXaxis().GetNbins())
passed_1000 = hSignal_1000.Integral(i,
hSignal_1000.GetXaxis().GetNbins())
passed_Bkg = hBkg.Integral(i, hBkg.GetXaxis().GetNbins())
total_250 = hSignal_250.Integral()
total_500 = hSignal_500.Integral()
total_800 = hSignal_800.Integral()
total_1000 = hSignal_1000.Integral()
total_Bkg = hBkg.Integral()
eff_250 = float(passed_250) / total_250
eff_500 = float(passed_500) / total_500
eff_800 = float(passed_800) / total_800
eff_1000 = float(passed_1000) / total_1000
eff_Bkg = float(passed_Bkg) / total_Bkg
xValues.append(cut)
yValues_250.append(eff_250)
yValues_500.append(eff_500)
yValues_800.append(eff_800)
yValues_1000.append(eff_1000)
yValues_Bkg.append(eff_Bkg)
# Create the Efficiency Plot
tGraph_250 = ROOT.TGraph(len(xValues), array.array("d", xValues),
array.array("d", yValues_250))
tGraph_500 = ROOT.TGraph(len(xValues), array.array("d", xValues),
array.array("d", yValues_500))
tGraph_800 = ROOT.TGraph(len(xValues), array.array("d", xValues),
array.array("d", yValues_800))
tGraph_1000 = ROOT.TGraph(len(xValues), array.array("d", xValues),
array.array("d", yValues_1000))
tGraph_Bkg = ROOT.TGraph(len(xValues), array.array("d", xValues),
array.array("d", yValues_Bkg))
styles.getSignalStyleHToTB_M("200").apply(tGraph_250)
styles.getSignalStyleHToTB_M("500").apply(tGraph_500)
styles.getSignalStyleHToTB_M("800").apply(tGraph_800)
styles.getSignalStyleHToTB_M("1000").apply(tGraph_1000)
styles.getQCDLineStyle().apply(tGraph_Bkg)
drawStyle = "CPE"
effGraph_250 = histograms.HistoGraph(tGraph_250,
"H^{+} m_{H^{+}} = 250 GeV", "lp",
drawStyle)
effGraph_500 = histograms.HistoGraph(tGraph_500,
"H^{+} m_{H^{+}} = 500 GeV", "lp",
drawStyle)
effGraph_800 = histograms.HistoGraph(tGraph_800,
"H^{+} m_{H^{+}} = 800 GeV", "lp",
drawStyle)
effGraph_1000 = histograms.HistoGraph(tGraph_1000,
"H^{+} m_{H^{+}} = 1000 GeV", "lp",
drawStyle)
effGraph_Bkg = histograms.HistoGraph(tGraph_Bkg, "Bkg", "lp", drawStyle)
efficiencyList.append(effGraph_250)
efficiencyList.append(effGraph_500)
efficiencyList.append(effGraph_800)
efficiencyList.append(effGraph_1000)
efficiencyList.append(effGraph_Bkg)
# Efficiency plot
pE = plots.PlotBase(efficiencyList, saveFormats=["pdf"])
pE.createFrame("QGLR_Efficiency")
pE.setEnergy("13")
pE.getFrame().GetYaxis().SetLabelSize(18)
pE.getFrame().GetXaxis().SetLabelSize(20)
pE.getFrame().GetYaxis().SetTitle("Efficiency / 0.01")
pE.getFrame().GetXaxis().SetTitle("QGLR Cut")
# Add Standard Texts to plot
histograms.addStandardTexts()
# Customise Legend
moveLegend = {"dx": -0.50, "dy": -0.5, "dh": -0.1}
pE.setLegend(histograms.moveLegend(histograms.createLegend(),
**moveLegend))
pE.draw()
# plots.drawPlot(pE, "QGLR_Efficiency", **kwargs)
SavePlot(pE,
"QGLR_Efficiency",
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png", ".pdf"])
#==========================================================================================
# Calculate Significance
#==========================================================================================
SignalName = "ChargedHiggs_HplusTB_HplusToTB_M_800"
hSignif_250 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(
SignalName)
hSignif_500 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(
SignalName)
hSignif_800 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(
SignalName)
hSignif_1000 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(
SignalName)
hSignif_250.Reset()
hSignif_500.Reset()
hSignif_800.Reset()
hSignif_1000.Reset()
hBkg = p.histoMgr.getHisto(SignalName).getRootHisto().Clone("Bkg")
hBkg.Reset()
# Bkg: FakeB + Genuine B
hBkg.Add(hFakeB.getRootHisto(), +1)
hBkg.Add(hGenuineB.getRootHisto(), +1)
nBins = hSignif_250.GetNbinsX() + 1
# For-loop: All histo bins
for i in range(1, nBins + 1):
sigmaB = ROOT.Double(0)
b = hBkg.IntegralAndError(i, nBins, sigmaB)
s_250 = hSignal_250.Integral(i, nBins)
s_500 = hSignal_500.Integral(i, nBins)
s_800 = hSignal_800.Integral(i, nBins)
s_1000 = hSignal_1000.Integral(i, nBins)
# Calculate significance
signif_250 = stat.significance(s_250, b, sigmaB,
option="Simple") #Asimov")
signif_500 = stat.significance(s_500, b, sigmaB,
option="Simple") #Asimov")
signif_800 = stat.significance(s_800, b, sigmaB,
option="Simple") #Asimov")
signif_1000 = stat.significance(s_1000, b, sigmaB,
option="Simple") #"Asimov")
# Set signif for this bin
hSignif_250.SetBinContent(i, signif_250)
hSignif_500.SetBinContent(i, signif_500)
hSignif_800.SetBinContent(i, signif_800)
hSignif_1000.SetBinContent(i, signif_1000)
# Apply style
s_250 = styles.getSignalStyleHToTB_M("200")
s_250.apply(hSignif_250)
s_500 = styles.getSignalStyleHToTB_M("500")
s_500.apply(hSignif_500)
s_800 = styles.getSignalStyleHToTB_M("800")
s_800.apply(hSignif_800)
s_1000 = styles.getSignalStyleHToTB_M("1000")
s_1000.apply(hSignif_1000)
hList = []
hList.append(hSignif_250)
hList.append(hSignif_500)
hList.append(hSignif_800)
hList.append(hSignif_1000)
hSignif_250.SetName("H^{+} m_{H^{+}} = 250 GeV")
hSignif_500.SetName("H^{+} m_{H^{+}} = 500 GeV")
hSignif_800.SetName("H^{+} m_{H^{+}} = 800 GeV")
hSignif_1000.SetName("H^{+} m_{H^{+}} = 1000 GeV")
pS = plots.PlotBase(hList, saveFormats=["png", "pdf"])
pS.setLuminosity(opts.intLumi)
# Drawing style
pS.histoMgr.setHistoDrawStyleAll("HIST")
pS.histoMgr.setHistoLegendStyleAll("L")
pS.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerSize(1.0))
pS.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
pS.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetMarkerStyle(ROOT.kFullCircle))
# Draw the plot
name = "QGLR_Signif" + "GE"
plots.drawPlot(pS, name, **kwargs)
SavePlot(pS,
name,
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png", ".pdf"])
#=========================================================================================
# Calculate the Transfer Factor (TF) and save to file
#=========================================================================================
Verbose("Write the normalisation factors to a python file", True)
fileName = os.path.join(
opts.mcrab, "FakeBTransferFactors%s.py" % (getModuleInfoString(opts)))
manager.writeNormFactorFile(fileName, opts)
return
def GetHistoGraphs(datasetsMgr, folder, hName):
# Get histogram customisations
_kwargs = GetHistoKwargs(opts)
# Get histos (Data, EWK) for Inclusive
p1 = plots.DataMCPlot(datasetsMgr, hName, saveFormats=[])
kwargs = copy.deepcopy(_kwargs)
kwargs["opts"] = {"ymin": 1.0, "ymaxfactor": 10.0}
kwargs["xlabelsize"] = 10
kwargs["ratio"] = False
plots.drawPlot(p1, hName, **kwargs)
SavePlot(p1,
hName,
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".C", ".png", ".pdf"])
# Clone histograms
histoList = []
graphList = []
hgList = []
# Append some bkg samples as well
opts.datasets = opts.signal
if not opts.acceptance:
opts.datasets.extend(opts.backgrounds)
for d in opts.datasets:
h = p1.histoMgr.getHisto(d).getRootHisto().Clone(d)
histoList.append(h)
# Create the Efficiency histos
for i, h in enumerate(histoList, 1):
if opts.efficiency:
histo = GetEfficiencyHisto(histoList[i - 1],
opts.refCounter,
_kwargs,
printValues=True,
hideZeros=True)
histo.SetName("Efficiency_%d" % (i))
elif opts.events:
histo = GetEventsHisto(histoList[i - 1],
opts.refCounter,
_kwargs,
printValues=True,
hideZeros=True)
histo.SetName("Events_%d" % (i))
elif opts.acceptance:
histo = GetAcceptanceHisto(histoList[i - 1],
_kwargs,
printValues=True,
hideZeros=True)
histo.SetName("Acceptance_%d" % (i))
# Convert histos to TGraph (don't do it for acceptance to get better positioning of markers wrt x-axis!)
if not opts.acceptance:
tgraph = convertHisto2TGraph(histo, printValues=False)
else:
tgraph = histo
# Apply random histo styles and append
if "charged" in h.GetName().lower():
s = styles.getSignalStyleHToTB_M(h.GetName().split("M_")[-1])
s.apply(tgraph)
if "QCD" in h.GetName():
styles.fakeBStyle.apply(tgraph)
if "EWK" in h.GetName():
s = styles.ttStyle.apply(tgraph)
# Append to list
graphList.append(tgraph)
# Create histoGraph object
htgraph = histograms.HistoGraph(
tgraph, plots._legendLabels[opts.signal[i - 1]], "LP", "LP")
# Append to list
hgList.append(htgraph)
return hgList, _kwargs
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 PlotMC(datasetsMgr, histo, intLumi):
kwargs = {}
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr,
histo,
normalizeToOne=True,
saveFormats=[],
**kwargs)
else:
p = plots.MCPlot(datasetsMgr,
histo,
normalizeToLumi=intLumi,
saveFormats=[],
**kwargs)
# Get histogram<->kwargs dictionary
kwargs = GetHistoKwargs(histo, opts)
# Customise style
for i, m in enumerate(signalMass):
massNum = m.rsplit("M_")[-1]
if i == len(signalMass) - 1:
p.histoMgr.setHistoDrawStyle(m, "HIST")
p.histoMgr.setHistoLegendStyle(m, "F")
p.histoMgr.forHisto(m, styles.getSignalStyleHToTB())
# p.histoMgr.forHisto(m, styles. getSignalfillStyleHToTB())
# p.histoMgr.forHisto(m, datasetsMgr.getDataset(m).getRootHisto().SetMarkerStyle(6))
# h = GetRootHisto(datasetsMgr, m, histo)
# h.SetFillStyle(1001)
# p.histoMgr.setHistoLegendStyle(dName, "LP")
h = datasetsMgr.getDataset(m).getDatasetRootHisto(
histo).getHistogram()
styles.qcdFillStyle.apply(h)
else:
p.histoMgr.forHisto(m, styles.getSignalStyleHToTB_M(massNum))
p.histoMgr.setHistoLegendStyle(m, "LP")
# Plot customised histogram
plots.drawPlot(
p,
histo,
xlabel=kwargs.get("xlabel"),
ylabel=kwargs.get("ylabel"),
log=kwargs.get("log"),
rebinX=kwargs.get("rebinX"),
cmsExtraText="Preliminary",
#createLegend = {"x1": 0.62, "y1": 0.75, "x2": 0.92, "y2": 0.92},
moveLegend=kwargs.get("moveLegend"),
opts=kwargs.get("opts"),
opts2={
"ymin": 0.6,
"ymax": 1.4
},
cutBox=kwargs.get("cutBox"),
)
# Customise styling
if 0:
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerSize(0))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerStyle(6))
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
# Save plot in all formats
saveName = histo.split("/")[-1]
if opts.folder == "":
savePath = os.path.join(opts.saveDir, opts.optMode)
else:
savePath = os.path.join(opts.saveDir,
histo.split("/")[0], opts.optMode)
SavePlot(p, saveName, savePath, [".png", ".pdf"])
return
def plot(self):
style = tdrstyle.TDRStyle()
ROOT.gStyle.SetErrorX(
0.5
) #required for x-axis error bars! (must be called AFTER tdrstyle.TDRStyle())
histolist = []
#styles.dataStyle.apply(self.h_data)
hhd = histograms.Histo(self.h_data,
"Data",
legendStyle="PL",
drawStyle="E1P")
hhd.setIsDataMC(isData=True, isMC=False)
histolist.append(hhd)
if 0:
aux.PrintTH1Info(hhd.getRootHisto())
# For-loop: All signal histo
for i, hsignal in enumerate(self.h_signal, 1):
mass = hsignal.GetName().replace("Hp", "")
Verbose("Customing signal histogram for Mass = \"%s\"" % (mass),
i == 1)
hhs = histograms.Histo(hsignal,
hsignal.GetTitle(),
legendStyle="L",
drawStyle="HIST")
hhs.setIsDataMC(isData=False, isMC=True)
signalStyle = styles.getSignalStyleHToTB_M(mass)
signalStyle.apply(hhs.getRootHisto())
histolist.append(hhs)
# For-loop: All bkg histos
for i, hname in enumerate(self.histonames, 1):
# Safety net for empty histos
if hname in opts.empty:
msg = "Skipping %s. Not a histogram!" % (hname)
Print(
ShellStyles.ErrorStyle() + msg + ShellStyles.NormalStyle(),
True)
continue
else:
#print hname
pass
myLabel = self.labels[hname]
myHisto = self.histograms[hname]
Verbose(
"Creating histogram \"%s\" from ROOT file \"%s\" (Integral = %.1f)"
% (hname, myHisto.GetName(), myHisto.Integral()), i == 1)
hhp = histograms.Histo(myHisto,
hname,
legendStyle="F",
drawStyle="HIST",
legendLabel=myLabel)
hhp.setIsDataMC(isData=False, isMC=True)
histolist.append(hhp)
if 0:
aux.PrintTH1Info(hhp.getRootHisto())
# Sanity check
for i, h in enumerate(histolist, 1):
hName = h.getRootHisto().GetName()
Verbose(hName, i == 1)
# Do the plot
p = plots.DataMCPlot2(histolist)
p.setDefaultStyles()
p.stackMCHistograms()
p.setLuminosity(opts.lumi)
if opts.fitUncert:
p.addMCUncertainty(
postfit=not opts.prefit) # boolean changes only the legend
if opts.prefit:
p.setLegendHeader("Pre-Fit")
else:
p.setLegendHeader("Post-Fit")
# Customise histogram
units = "GeV" #(GeV/c^{2})
myParams = {}
myParams["xlabel"] = "m_{jjbb} (%s)" % (units)
myParams["ylabel"] = "< Events / " + units + " >"
myParams["ratio"] = True
myParams["ratioYlabel"] = "Data/Bkg. "
myParams["logx"] = self.gOpts.logX
myParams["log"] = self.gOpts.logY
myParams["ratioType"] = "errorScale"
myParams["ratioErrorOptions"] = {
"numeratorStatSyst": False,
"denominatorStatSyst": True
}
myParams["opts"] = self.opts
myParams["optsLogx"] = self.optsLogx
myParams["opts2"] = self.opts2
myParams[
"divideByBinWidth"] = True #not opts.fitUncert # Error when used for "TGraphAsymmErrors" (uncert.)
myParams["errorBarsX"] = True
myParams["xlabelsize"] = 25
myParams["ylabelsize"] = 25
myParams["addMCUncertainty"] = True
myParams["addLuminosityText"] = True
myParams["moveLegend"] = self.moveLegend
#myParams["saveFormats"] = []
# Draw the plot
if not os.path.exists(opts.saveDir):
os.makedirs(opts.saveDir)
plots.drawPlot(p, os.path.join(opts.saveDir, self.gOpts.saveName),
**myParams)
# Save the plot (not needed - drawPlot saves the canvas already)
SavePlot(p,
self.gOpts.saveName,
opts.saveDir,
saveFormats=[".png", ".pdf", ".C"])
return
p.histoMgr.setHistoDrawStyle("QCD", "HIST")
p.histoMgr.setHistoLegendStyle("QCD", "F")
for d in datasetsMgr.getAllDatasets():
if "TT" in d.getName():
p.histoMgr.forHisto("TT", styles.getTTFillStyle() )
#p.histoMgr.setHistoDrawStyle("TT", "AP")
p.histoMgr.setHistoLegendStyle("TT", "F")
# Customise style
signalM = []
for m in signalMass:
signalM.append(m.rsplit("M_")[-1])
for m in signalM:
p.histoMgr.forHisto("ChargedHiggs_HplusTB_HplusToTB_M_%s" %m, styles.getSignalStyleHToTB_M(m))
plots.drawPlot(p,
histo,
xlabel = _xlabel,
ylabel = Ylabel,#"Arbitrary Units / %s" % (_format), #"Events / %s" % (_format), #"Arbitrary Units / %s" % (_format),
log = logY,
rebinX = _rebinX, cmsExtraText = "Preliminary",
createLegend = {"x1": 0.58, "y1": 0.65, "x2": 0.92, "y2": 0.92},
# createLegend = {"x1": 0.73, "y1": 0.85, "x2": 0.97, "y2": 0.77},
opts = _opts,
opts2 = {"ymin": 0.6, "ymax": 1.4},
cutBox = _cutBox,
)
def PlotMC(datasetsMgr, histo, intLumi):
kwargs = {}
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr, histo, normalizeToOne=True, saveFormats=[], **kwargs)
else:
p = plots.MCPlot(datasetsMgr, histo, normalizeToLumi=intLumi, saveFormats=[], **kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.0f"
_xlabel = None
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
#_opts = {"ymin": 1e-3, "ymax": 60}
if "pt" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": False, "greaterThan": True}
#_opts["xmax"] = 505 #1005
if "eta" in histo.lower():
_units = ""
_format = "%0.1f " + _units
_xlabel = "#eta %s" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": False, "greaterThan": True}
_opts["xmax"] = 2.5
_opts["xmin"] = -2.5
if "trijetmass" in histo.lower():
_rebinX = 2
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 505 #1005
if "ldg" in histo.lower():
_xlabel = "m_{jjb}^{ldg} (%s)" % _units
elif "tetrajetmass" in histo.lower():
_rebinX = 5 #5 #10 #4
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjbb} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 2500 #3500.0
elif "tetrajetpt" in histo.lower():
_rebinX = 2 #5 #10 #4
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T,jjbb} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 1000 #3500.0
elif "dijetmass" in histo.lower():
_rebinX = 2 #5 #10 #4
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jj} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800 #3500.0
_cutBox = {"cutValue": 80.4, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 300
if "ldg" in histo.lower():
_xlabel = "m_{jj}^{ldg} (%s)" % (_units)
elif "tetrajetbjetpt" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T,bjet} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "ldgtrijetpt" in histo.lower():
_rebinX = 2
# logY = False
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T,jjb}^{ldg} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "ldgtrijetdijetpt" in histo.lower():
_rebinX = 2
# logY = False
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T,jj}^{ldg} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "topbdt_" in histo.lower():
_format = "%0.1f"
_cutBox = {"cutValue": +0.40, "fillColor": 16, "box": False, "line": False, "greaterThan": True}
_xlabel = "BDTG discriminant"
if "gentop_pt" in histo.lower():
_rebinX = 1
if "genquark_pt" in histo.lower():
_rebinX = 1
_opts["xmax"] = 500
else:
pass
if "delta" in histo.lower():
_format = "%0.1f "
if "phi" in histo.lower():
_xlabel = "#Delta #phi"
if "deltar" in histo.lower():
_rebinX = 2
if "DiBjetMaxMass_Mass" in histo:
_units = "GeV"
_rebinX = 4
_xlabel = "m_{max}(bb) (%s)" % (_units)
_format = "%0.0f " + _units
if "DiJetDeltaRmin_Mass" in histo:
_units = "GeV"
_format = "%0.0f " + _units
_xlabel = "m_{#DeltaR_{min}(jj)}(jj) (%s)" % (_units)
_opts["xmax"] = 200
if "DiBjetDeltaRmin_Mass" in histo:
_units = "GeV"
_format = "%0.0f " + _units
_rebinX= 2
_xlabel = "m_{#DeltaR_{min}(bb)}(bb) (%s)" % (_units)
_opts["xmax"] = 600
if "LdgBjet_SubldgBjet_Mass" in histo:
_units = "GeV"
_format = "%0.0f " + _units
_rebinX= 4
_xlabel = "m(b_{ldg}b_{sldg}) (%s)" % (_units)
if "DeltaR_LdgTop_DiBjetDeltaRmin" in histo:
_rebinX= 2
_format = "%0.1f "
_xlabel = "#DeltaR (top_{ldg}, bb_{#Delta Rmin})"
if "DeltaR_SubldgTop_DiBjetDeltaRmin" in histo:
_rebinX= 2
_format = "%0.1f "
_xlabel = "#DeltaR (top_{sldg}, bb_{#Delta Rmin})"
if "phi_alpha" in histo.lower() or "phi_beta" in histo.lower() or "r_alpha" in histo.lower() or "r_beta" in histo.lower():
_format = "%0.1f "
_opts["xmin"] = 0.0
if "phi" in histo.lower():
_xlabel = "#phi"
_opts["xmax"] = 5.5
else:
_xlabel = "r"
_opts["xmax"] = 6.5
if "alpha" in histo.lower():
_xlabel = _xlabel+"_{#alpha}"
else:
_xlabel = _xlabel+"_{#beta}"
if opts.normaliseToOne:
logY = False
Ylabel = "Arbitrary Units / %s" % (_format)
else:
logY = True
Ylabel = "Events / %s" % (_format)
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
#_opts = {"ymin": 1e-3, "ymaxfactor": yMaxFactor, "xmax": None}
else:
_opts["ymin"] = 1e0
#_opts["ymaxfactor"] = yMaxFactor
#_opts = {"ymin": 1e0, "ymaxfactor": yMaxFactor, "xmax": None}
# Customise styling
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
if "QCD" in datasetsMgr.getAllDatasetNames():
p.histoMgr.forHisto("QCD", styles.getQCDLineStyle()) #getQCDFillStyle() )
p.histoMgr.setHistoDrawStyle("QCD", "HIST")
p.histoMgr.setHistoLegendStyle("QCD", "F")
if "TT" in datasetsMgr.getAllDatasetNames():
TTStyle = styles.StyleCompound([styles.StyleFill(fillColor=ROOT.kMagenta-2), styles.StyleLine(lineColor=ROOT.kMagenta-2, lineStyle=ROOT.kSolid, lineWidth=3),
styles.StyleMarker(markerSize=1.2, markerColor=ROOT.kMagenta-2, markerSizes=None, markerStyle=ROOT.kFullTriangleUp)])
p.histoMgr.forHisto("TT", TTStyle)
p.histoMgr.setHistoDrawStyle("TT", "HIST") #AP
p.histoMgr.setHistoLegendStyle("TT", "F") #LP
# Customise style
signalM = []
for m in signalMass:
signalM.append(m.rsplit("M_")[-1])
for m in signalM:
p.histoMgr.forHisto("ChargedHiggs_HplusTB_HplusToTB_M_%s" %m, styles.getSignalStyleHToTB_M(m))
plots.drawPlot(p,
histo,
xlabel = _xlabel,
ylabel = Ylabel,
log = logY,
rebinX = _rebinX, cmsExtraText = "Preliminary",
#createLegend = {"x1": 0.59, "y1": 0.65, "x2": 0.92, "y2": 0.92},
createLegend = {"x1": 0.59, "y1": 0.70, "x2": 0.92, "y2": 0.92},
#createLegend = {"x1": 0.73, "y1": 0.85, "x2": 0.97, "y2": 0.77},
opts = _opts,
opts2 = {"ymin": 0.6, "ymax": 1.4},
cutBox = _cutBox,
)
# Save plot in all formats
saveName = histo.split("/")[-1]
savePath = os.path.join(opts.saveDir, histo.split("/")[0], opts.optMode)
'''
if opts.normaliseToOne:
save_path = savePath + opts.MVAcut
if opts.noQCD:
save_path = savePath + opts.MVAcut + "/noQCD/"
else:
save_path = savePath + opts.MVAcut + "/normToLumi/"
if opts.noQCD:
save_path = savePath + opts.MVAcut + "/noQCD/"
'''
SavePlot(p, saveName, savePath)
return
def PlotMC(datasetsMgr, histo):
kwargs = {}
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr, histo, normalizeToOne=True, saveFormats=[], **kwargs)
else:
p = plots.MCPlot(datasetsMgr, histo, normalizeToLumi=opts.intLumi, saveFormats=[], **kwargs)
p.setLuminosity(opts.intLumi)
p1 = "a"
p2 = "b"
if "Htb_tbW_WBoson_Quark_Htb_tbW_WBoson_AntiQuark" in histo:
p1 = "q_{1}"
p2 = "q_{2}"
if "Htb_tbW_BQuark_Htb_tbW_Wqq_Quark" in histo:
p1 = "b_{2}"
p2 = "q_{1}"
if "Htb_tbW_BQuark_Htb_tbW_Wqq_AntiQuark" in histo:
p1 = "b_{2}"
p2 = "q_{2}"
if "Htb_tbW_WBoson_Htb_tbW_BQuark_dR" in histo:
p1 = "W^{+}"
p2 = "b_{2}"
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.0f"
_xlabel = None
_format = "%0.2f"
_logY = True
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
if "pt" in histo.lower():
_format = "%0.0f"
_rebinX = 2
_format = "%0.0f GeV/c"
if "eta" in histo.lower():
_cutBox = {"cutValue": 0.0, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmin"] = -3.0
_opts["xmax"] = +3.0
if "dPhi" in histo:
_format = "%0.2f"
_opts["xmin"] = 0.0
_opts["xmax"] = +3.2
_xlabel = "#Delta#phi(%s, %s)" % (p1, p2)
if "dEta" in histo:
_format = "%0.2f"
_opts["xmin"] = 0.0
_opts["xmax"] = 3.2
_xlabel = "#Delta#eta(%s, %s)" % (p1, p2)
if "dR" in histo:
_format = "%0.2f"
_rebinX = 2
_opts["xmin"] = 0.0
_opts["xmax"] = 5.0
#_cutBox = {"cutValue": 0.8, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_xlabel = "#DeltaR(%s, %s)" % (p1, p2)
if _logY:
_opts["ymaxfactor"] = 2.0
else:
_opts["ymaxfactor"] = 1.2
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
else:
_opts["ymin"] = 1e0
# Customise Dataset styling
for signal in opts.signalMass:
m = signal.split("M_")[-1]
p.histoMgr.forHisto(signal, styles.getSignalStyleHToTB_M(m))
# Draw the customised plot
plots.drawPlot(p,
histo,
xlabel = _xlabel,
ylabel = "Arbitrary Units / %s" % (_format),
log = _logY,
rebinX = _rebinX, cmsExtraText = "Preliminary",
createLegend = {"x1": 0.60, "y1": 0.72, "x2": 0.92, "y2": 0.92},
opts = _opts,
addLuminosityText = (opts.intLumi!=-1),
ratio = False,
opts2 = {"ymin": 0.6, "ymax": 1.4},
cutBox = _cutBox,
)
if 0:
histograms.addText(0.20, 0.88, "test", 27)
# Save plot in all formats
saveName = histo.split("/")[-1]
saveDict = {}
saveDict["Htb_tbW_WBoson_Htb_tbW_BQuark_dR"] = "dR_W_b2"
saveDict["Htb_tbW_WBoson_Quark_Htb_tbW_WBoson_AntiQuark_dR"] = "dR_q1_q2"
saveDict["Htb_tbW_BQuark_Htb_tbW_Wqq_Quark_dR"] = "dR_b2_q1"
saveDict["Htb_tbW_BQuark_Htb_tbW_Wqq_AntiQuark_dR"] = "dR_b2_q2"
savePath = os.path.join(opts.saveDir, histo.split("/")[0], "", opts.optMode)
if saveName in saveDict.keys():
SavePlot(p, saveDict[saveName], savePath)
else:
SavePlot(p, saveName, savePath)
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 PlotMC(datasetsMgr, datasetsMgr1, histo, intLumi):
kwargs = {}
print "here1"
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr,
histo,
normalizeToOne=True,
saveFormats=[".pdf", "png"],
**kwargs)
p = plots.MCPlot(datasetsMgr1,
histo,
normalizeToOne=True,
saveFormats=[".pdf", ".png"],
**kwargs)
else:
p = plots.MCPlot(datasetsMgr,
histo,
normalizeToLumi=intLumi,
saveFormats=[".pdf", ".png"],
**kwargs)
p = plots.MCPlot(datasetsMgr1,
histo,
normalizeToOne=True,
saveFormats=[".pdf", "png"],
**kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.0f"
_xlabel = None
logY = False
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
print "here2"
if "Pt" in histo:
_rebinX = 2
xMin = 0.0
# rebinX = 2
xMax = 805.0
# xMax = 555.0 # For topPt < 500GeV
xTitle = "p_{T} (GeV/c)"
units = "GeV/c"
_format = "%0.0f" + units
# yTitle = "Efficiency / " + str(binwidth) + " "+units
# yMin = 0.0
# yMax = 1.1
else:
pass
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
#_opts = {"ymin": 1e-3, "ymaxfactor": yMaxFactor, "xmax": None}
else:
_opts["ymin"] = 1e0
#_opts["ymaxfactor"] = yMaxFactor
#_opts = {"ymin": 1e0, "ymaxfactor": yMaxFactor, "xmax": None}
# Customise styling
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
print "here3"
histoDef = datasetsMgr.getDataset("TT").getDatasetRootHisto(histo)
histoDR = datasetsMgr1.getDataset("TT").getDatasetRootHisto(histo)
histoDef.normalizeToOne()
histoDR.normalizeToOne()
hDef = histoDef.getHistogram()
hDR = histoDR.getHistogram()
p = plots.ComparisonPlot(histograms.Histo(hDR, "DeltaR08", "l", "L"),
histograms.Histo(hDef, "Default", "l", "L"))
p.histoMgr.setHistoLegendLabelMany({
"DeltaR08": "#DeltaR(q,q')>0.8",
"Default": "Default"
})
p.histoMgr.forHisto("DeltaR08", styles.getQCDLineStyle())
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
p.histoMgr.setHistoDrawStyle("DeltaR08", "HIST") #HIST
p.histoMgr.setHistoLegendStyle("DeltaR08", "L") #F
p.histoMgr.setHistoDrawStyle("Default", "HIST") #HIST
p.histoMgr.setHistoLegendStyle("Default", "L") #F
p.histoMgr.forHisto("Default", styles.ttStyle)
print "here4"
# Customise style
signalM = []
for m in signalMass:
signalM.append(m.rsplit("M_")[-1])
for m in signalM:
p.histoMgr.forHisto("ChargedHiggs_HplusTB_HplusToTB_M_%s" % m,
styles.getSignalStyleHToTB_M(m))
print "here5"
plots.drawPlot(
p,
histo,
xlabel=_xlabel,
ylabel="Arbitrary Units / %s" % (_format),
log=logY,
rebinX=_rebinX,
cmsExtraText="Preliminary",
createLegend={
"x1": 0.58,
"y1": 0.75,
"x2": 0.92,
"y2": 0.92
},
# createLegend = {"x1": 0.58, "y1": 0.65, "x2": 0.92, "y2": 0.92},
opts=_opts,
opts2={
"ymin": 0.6,
"ymax": 1.4
},
cutBox=_cutBox,
)
print "here6"
# Save plot in all formats
saveName = histo.split("/")[-1]
savePath = os.path.join(opts.saveDir, "HplusMasses",
histo.split("/")[0], opts.optMode)
SavePlot(p, saveName, savePath)
print "here7"
return
def plot(self):
style = tdrstyle.TDRStyle()
ROOT.gStyle.SetErrorX(0.5) #required for x-axis error bars! (must be called AFTER tdrstyle.TDRStyle())
histolist = []
#styles.dataStyle.apply(self.h_data)
hhd = histograms.Histo(self.h_data,"Data", legendStyle="PL", drawStyle="E1P")
hhd.setIsDataMC(isData=True, isMC=False)
histolist.append(hhd)
# For-loop: All signal histo
for i, hsignal in enumerate(self.h_signal, 1):
mass = hsignal.GetName().replace("Hp", "")
Verbose( "Customing signal histogram for Mass = \"%s\"" % (mass), i==1)
hhs = histograms.Histo(hsignal,hsignal.GetTitle(), legendStyle="L", drawStyle="HIST")
hhs.setIsDataMC(isData=False, isMC=True)
signalStyle = styles.getSignalStyleHToTB_M(mass)
signalStyle.apply(hhs.getRootHisto())
histolist.append(hhs)
# For-loop: All bkg histos
for hname in self.histonames:
hhp = histograms.Histo(self.histograms[hname],hname,legendStyle="F", drawStyle="HIST",legendLabel=self.labels[hname])
hhp.setIsDataMC(isData=False,isMC=True)
histolist.append(hhp)
# Sanity check
for i, h in enumerate(histolist, 1):
hName = h.getRootHisto().GetName()
Verbose(hName, i==1)
# Do the plot
p = plots.DataMCPlot2(histolist)
p.setDefaultStyles()
p.stackMCHistograms()
p.setLuminosity(opts.lumi)
if opts.fitUncert:
p.addMCUncertainty(postfit=not opts.prefit) # boolean changes only the legend
if opts.prefit:
p.setLegendHeader("Pre-Fit")
else:
p.setLegendHeader("Post-Fit")
# Customise histogram
units = "GeV" #(GeV/c^{2})
myParams = {}
myParams["xlabel"] = "m_{jjbb} (%s)" % (units)
myParams["ylabel"] = "< Events / " + units + " >"
myParams["ratio"] = True
myParams["ratioYlabel"] = "Data/Bkg. "
myParams["logx"] = self.gOpts.logX
myParams["log"] = self.gOpts.logY
myParams["ratioType"] = "errorScale"
myParams["ratioErrorOptions"] = {"numeratorStatSyst": False, "denominatorStatSyst": True}
myParams["opts"] = self.opts
myParams["optsLogx"] = self.optsLogx
myParams["opts2"] = self.opts2
myParams["divideByBinWidth"] = True #not opts.fitUncert # Error when used for "TGraphAsymmErrors" (uncert.)
myParams["errorBarsX"] = True
myParams["xlabelsize"] = 25
myParams["ylabelsize"] = 25
myParams["addMCUncertainty"] = True
myParams["addLuminosityText"] = True
myParams["moveLegend"] = self.moveLegend
#myParams["saveFormats"] = []
# Draw the plot
if not os.path.exists(opts.saveDir):
os.makedirs(opts.saveDir)
plots.drawPlot(p, os.path.join(opts.saveDir, self.gOpts.saveName), **myParams)
# Save the plot (not needed - drawPlot saves the canvas already)
SavePlot(p, self.gOpts.saveName, opts.saveDir, saveFormats = [".png", ".pdf", ".C"])
return
def PlotHistosAndCalculateTF(datasetsMgr, histoList, binLabels, opts):
# Get the histogram customisations (keyword arguments)
_kwargs = GetHistoKwargs(histoList[0])
# Get the root histos for all datasets and Control Regions (CRs)
regions = ["SR", "VR", "CRone", "CRtwo"]
rhDict = GetRootHistos(datasetsMgr, histoList, regions, binLabels)
#=========================================================================================
# Calculate the Transfer Factor (TF) and save to file
#=========================================================================================
manager = FakeBNormalization.FakeBNormalizationManager(binLabels, opts.mcrab, opts.optMode, verbose=False)
if opts.inclusiveOnly:
#manager.CalculateTransferFactor(binLabels[0], rhDict["CRone-FakeB"], rhDict["CRtwo-FakeB"])
binLabel = "Inclusive"
manager.CalculateTransferFactor("Inclusive", rhDict["FakeB-CRone-Inclusive"], rhDict["FakeB-CRtwo-Inclusive"])
else:
for bin in binLabels:
manager.CalculateTransferFactor(bin, rhDict["FakeB-CRone-%s" % bin], rhDict["FakeB-CRtwo-%s" % bin])
# Get unique a style for each region
for k in rhDict:
dataset = k.split("-")[0]
region = k.split("-")[1]
styles.getABCDStyle(region).apply(rhDict[k])
if "FakeB" in k:
styles.getFakeBStyle().apply(rhDict[k])
# sr.apply(rhDict[k])
# =========================================================================================
# Create the final plot object
# =========================================================================================
rData_SR = rhDict["Data-SR-Inclusive"]
rEWKGenuineB_SR = rhDict["EWK-SR-Inclusive-EWKGenuineB"]
rBkgSum_SR = rhDict["FakeB-VR-Inclusive"].Clone("BkgSum-SR-Inclusive")
rBkgSum_SR.Reset()
if opts.inclusiveOnly:
bin = "Inclusive"
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
binHisto_VR = rhDict["FakeB-VR-%s" % (bin)]
VRtoSR_TF = manager.GetTransferFactor(bin)
Print("Applying TF = %s%0.6f%s to VR shape" % (ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()), True)
binHisto_VR.Scale(VRtoSR_TF)
# Add the normalised histogram to the final Inclusive SR (predicted) histo
rBkgSum_SR.Add(binHisto_VR, +1)
else:
# For-loop: All bins
for i, bin in enumerate(binLabels, 1):
if bin == "Inclusive":
continue
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
binHisto_VR = rhDict["FakeB-VR-%s" % (bin)]
VRtoSR_TF = manager.GetTransferFactor(bin)
Print("Applying TF = %s%0.6f%s to VR shape" % (ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()), i==1)
binHisto_VR.Scale(VRtoSR_TF)
# Add the normalised histogram to the final Inclusive SR (predicted) histo
rBkgSum_SR.Add(binHisto_VR, +1)
#Print("Got Verification Region (VR) shape %s%s%s" % (ShellStyles.NoteStyle(), rFakeB_VR.GetName(), ShellStyles.NormalStyle()), True)
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
#VRtoSR_TF = manager.GetTransferFactor("Inclusive")
#Print("Applying TF = %s%0.6f%s to VR shape" % (ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()), True)
#rBkgSum_SR.Scale(VRtoSR_TF)
# Plot histograms
if opts.altPlot:
# Add the SR EWK Genuine-b to the SR FakeB ( BkgSum = [FakeB] + [GenuineB-MC] = [VR * (CR1/CR2)] + [GenuineB-MC] )
rBkgSum_SR.Add(rEWKGenuineB_SR, +1)
# Change style
styles.getGenuineBStyle().apply(rBkgSum_SR)
# Remove unsupported settings of kwargs
_kwargs["stackMCHistograms"] = False
_kwargs["addLuminosityText"] = False
# Create the plot
p = plots.ComparisonManyPlot(rData_SR, [rBkgSum_SR], saveFormats=[])
# Set draw / legend style
p.histoMgr.setHistoDrawStyle("Data-SR-Inclusive", "P")
p.histoMgr.setHistoLegendStyle("Data-SR-Inclusive" , "LP")
p.histoMgr.setHistoDrawStyle("BkgSum-SR-Inclusive", "HIST")
p.histoMgr.setHistoLegendStyle("BkgSum-SR-Inclusive" , "F")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Data-SR" : "Data",
"BkgSum-SR" : "Fake-b + Gen-b",
})
else:
# Create empty histogram stack list
myStackList = []
# Signal
p2 = plots.DataMCPlot(datasetsMgr, "ForTestQGLR/QGLR_SR/QGLR_SRInclusive", saveFormats=[])
hSignal_800 = p2.histoMgr.getHisto('ChargedHiggs_HplusTB_HplusToTB_M_800').getRootHisto()
hhSignal_800= histograms.Histo(hSignal_800, 'ChargedHiggs_HplusTB_HplusToTB_M_800', "H^{+} m_{H^+}=800 GeV")
hhSignal_800.setIsDataMC(isData=False, isMC=True)
myStackList.append(hhSignal_800)
hSignal_250 = p2.histoMgr.getHisto('ChargedHiggs_HplusTB_HplusToTB_M_250').getRootHisto()
hhSignal_250= histograms.Histo(hSignal_250, 'ChargedHiggs_HplusTB_HplusToTB_M_250', "H^{+} m_{H^+}=250 GeV")#plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_250'])
hhSignal_250.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_250)
hSignal_500 = p2.histoMgr.getHisto('ChargedHiggs_HplusTB_HplusToTB_M_500').getRootHisto()
hhSignal_500= histograms.Histo(hSignal_500, 'ChargedHiggs_HplusTB_HplusToTB_M_500', "H^{+} m_{H^+}=500 GeV")#plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_500'])
hhSignal_500.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_500)
hSignal_1000 = p2.histoMgr.getHisto('ChargedHiggs_HplusTB_HplusToTB_M_1000').getRootHisto()
hhSignal_1000= histograms.Histo(hSignal_1000, 'ChargedHiggs_HplusTB_HplusToTB_M_1000', "H^{+} m_{H^+}=1000 GeV")#plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_500'])
hhSignal_1000.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_1000)
# Add the FakeB data-driven background to the histogram list
hFakeB = histograms.Histo(rBkgSum_SR, "FakeB", "Fake-b")
hFakeB.setIsDataMC(isData=False, isMC=True)
myStackList.append(hFakeB)
# Add the EWKGenuineB MC background to the histogram list
hGenuineB = histograms.Histo(rEWKGenuineB_SR, "GenuineB", "EWK Genuine-b")
hGenuineB.setIsDataMC(isData=False, isMC=True)
myStackList.append(hGenuineB)
# Add the collision datato the histogram list
hData = histograms.Histo(rData_SR, "Data", "Data")
hData.setIsDataMC(isData=True, isMC=False)
myStackList.insert(0, hData)
p = plots.DataMCPlot2( myStackList, saveFormats=[])
p.setLuminosity(opts.intLumi)
p.setDefaultStyles()
# Draw the plot and save it
hName = "test"
plots.drawPlot(p, hName, **_kwargs)
SavePlot(p, hName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png", ".pdf"])
#==========================================================================================
# Calculate Cut-Flow Efficiency
#==========================================================================================
kwargs = {
"rebinX" : 1,
"xlabel" : "QGLR",
"ylabel" : "Significance / %.02f ",
"opts" : {"ymin": 0.0, "ymaxfactor": 1.3},
"createLegend": {"x1": 0.55, "y1": 0.70, "x2": 0.92, "y2": 0.92},
# "cutBox" : {"cutValue": 0.0, "fillColor" : 16, "box": False, "line": False, "greaterThan": True},
}
efficiencyList = []
xValues = []
yValues_250 = []
yValues_500 = []
yValues_800 = []
yValues_1000 = []
yValues_Bkg = []
nBins = hSignal_250.GetNbinsX()+1
hBkg = p.histoMgr.getHisto("ChargedHiggs_HplusTB_HplusToTB_M_800").getRootHisto().Clone("Bkg")
hBkg.Reset()
# Bkg: FakeB + Genuine B
hBkg.Add(hFakeB.getRootHisto(), +1)
hBkg.Add(hGenuineB.getRootHisto(), +1)
for i in range (0, nBins):
# Cut value
cut = hSignal_250.GetBinCenter(i)
passed_250 = hSignal_250.Integral(i, hSignal_250.GetXaxis().GetNbins())
passed_500 = hSignal_500.Integral(i, hSignal_500.GetXaxis().GetNbins())
passed_800 = hSignal_800.Integral(i, hSignal_800.GetXaxis().GetNbins())
passed_1000 = hSignal_1000.Integral(i, hSignal_1000.GetXaxis().GetNbins())
passed_Bkg = hBkg.Integral(i, hBkg.GetXaxis().GetNbins())
total_250 = hSignal_250.Integral()
total_500 = hSignal_500.Integral()
total_800 = hSignal_800.Integral()
total_1000 = hSignal_1000.Integral()
total_Bkg = hBkg.Integral()
eff_250 = float(passed_250)/total_250
eff_500 = float(passed_500)/total_500
eff_800 = float(passed_800)/total_800
eff_1000 = float(passed_1000)/total_1000
eff_Bkg = float(passed_Bkg)/total_Bkg
xValues.append(cut)
yValues_250.append(eff_250)
yValues_500.append(eff_500)
yValues_800.append(eff_800)
yValues_1000.append(eff_1000)
yValues_Bkg.append(eff_Bkg)
# Create the Efficiency Plot
tGraph_250 = ROOT.TGraph(len(xValues), array.array("d", xValues), array.array("d", yValues_250))
tGraph_500 = ROOT.TGraph(len(xValues), array.array("d", xValues), array.array("d", yValues_500))
tGraph_800 = ROOT.TGraph(len(xValues), array.array("d", xValues), array.array("d", yValues_800))
tGraph_1000 = ROOT.TGraph(len(xValues), array.array("d", xValues), array.array("d", yValues_1000))
tGraph_Bkg = ROOT.TGraph(len(xValues), array.array("d", xValues), array.array("d", yValues_Bkg))
styles.getSignalStyleHToTB_M("200").apply(tGraph_250)
styles.getSignalStyleHToTB_M("500").apply(tGraph_500)
styles.getSignalStyleHToTB_M("800").apply(tGraph_800)
styles.getSignalStyleHToTB_M("1000").apply(tGraph_1000)
styles.getQCDLineStyle().apply(tGraph_Bkg)
drawStyle = "CPE"
effGraph_250 = histograms.HistoGraph(tGraph_250, "H^{+} m_{H^{+}} = 250 GeV", "lp", drawStyle)
effGraph_500 = histograms.HistoGraph(tGraph_500, "H^{+} m_{H^{+}} = 500 GeV", "lp", drawStyle)
effGraph_800 = histograms.HistoGraph(tGraph_800, "H^{+} m_{H^{+}} = 800 GeV", "lp", drawStyle)
effGraph_1000 = histograms.HistoGraph(tGraph_1000, "H^{+} m_{H^{+}} = 1000 GeV", "lp", drawStyle)
effGraph_Bkg = histograms.HistoGraph(tGraph_Bkg, "Bkg", "lp", drawStyle)
efficiencyList.append(effGraph_250)
efficiencyList.append(effGraph_500)
efficiencyList.append(effGraph_800)
efficiencyList.append(effGraph_1000)
efficiencyList.append(effGraph_Bkg)
# Efficiency plot
pE = plots.PlotBase(efficiencyList, saveFormats=["pdf"])
pE.createFrame("QGLR_Efficiency")
pE.setEnergy("13")
pE.getFrame().GetYaxis().SetLabelSize(18)
pE.getFrame().GetXaxis().SetLabelSize(20)
pE.getFrame().GetYaxis().SetTitle("Efficiency / 0.01")
pE.getFrame().GetXaxis().SetTitle("QGLR Cut")
# Add Standard Texts to plot
histograms.addStandardTexts()
# Customise Legend
moveLegend = {"dx": -0.50, "dy": -0.5, "dh": -0.1}
pE.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
pE.draw()
# plots.drawPlot(pE, "QGLR_Efficiency", **kwargs)
SavePlot(pE, "QGLR_Efficiency", os.path.join(opts.saveDir, opts.optMode), saveFormats=[".png", ".pdf"] )
#==========================================================================================
# Calculate Significance
#==========================================================================================
SignalName = "ChargedHiggs_HplusTB_HplusToTB_M_800"
hSignif_250 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(SignalName)
hSignif_500 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(SignalName)
hSignif_800 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(SignalName)
hSignif_1000 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(SignalName)
hSignif_250.Reset()
hSignif_500.Reset()
hSignif_800.Reset()
hSignif_1000.Reset()
hBkg = p.histoMgr.getHisto(SignalName).getRootHisto().Clone("Bkg")
hBkg.Reset()
# Bkg: FakeB + Genuine B
hBkg.Add(hFakeB.getRootHisto(), +1)
hBkg.Add(hGenuineB.getRootHisto(), +1)
nBins = hSignif_250.GetNbinsX()+1
# For-loop: All histo bins
for i in range (1, nBins+1):
sigmaB = ROOT.Double(0)
b = hBkg.IntegralAndError(i, nBins, sigmaB)
s_250 = hSignal_250.Integral(i, nBins)
s_500 = hSignal_500.Integral(i, nBins)
s_800 = hSignal_800.Integral(i, nBins)
s_1000 = hSignal_1000.Integral(i, nBins)
# Calculate significance
signif_250 = stat.significance(s_250, b, sigmaB, option="Simple")#Asimov")
signif_500 = stat.significance(s_500, b, sigmaB, option="Simple")#Asimov")
signif_800 = stat.significance(s_800, b, sigmaB, option="Simple")#Asimov")
signif_1000 = stat.significance(s_1000, b, sigmaB, option="Simple")#"Asimov")
# Set signif for this bin
hSignif_250.SetBinContent(i, signif_250)
hSignif_500.SetBinContent(i, signif_500)
hSignif_800.SetBinContent(i, signif_800)
hSignif_1000.SetBinContent(i, signif_1000)
# Apply style
s_250 = styles.getSignalStyleHToTB_M("200")
s_250.apply(hSignif_250)
s_500 = styles.getSignalStyleHToTB_M("500")
s_500.apply(hSignif_500)
s_800 = styles.getSignalStyleHToTB_M("800")
s_800.apply(hSignif_800)
s_1000 = styles.getSignalStyleHToTB_M("1000")
s_1000.apply(hSignif_1000)
hList = []
hList.append(hSignif_250)
hList.append(hSignif_500)
hList.append(hSignif_800)
hList.append(hSignif_1000)
hSignif_250.SetName("H^{+} m_{H^{+}} = 250 GeV")
hSignif_500.SetName("H^{+} m_{H^{+}} = 500 GeV")
hSignif_800.SetName("H^{+} m_{H^{+}} = 800 GeV")
hSignif_1000.SetName("H^{+} m_{H^{+}} = 1000 GeV")
pS = plots.PlotBase(hList, saveFormats=["png", "pdf"])
pS.setLuminosity(opts.intLumi)
# Drawing style
pS.histoMgr.setHistoDrawStyleAll("HIST")
pS.histoMgr.setHistoLegendStyleAll("L")
pS.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerSize(1.0))
pS.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
pS.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerStyle(ROOT.kFullCircle))
# Draw the plot
name = "QGLR_Signif" + "GE"
plots.drawPlot(pS, name, **kwargs)
SavePlot(pS, name, os.path.join(opts.saveDir, opts.optMode), saveFormats=[".png", ".pdf"] )
#=========================================================================================
# Calculate the Transfer Factor (TF) and save to file
#=========================================================================================
Verbose("Write the normalisation factors to a python file", True)
fileName = os.path.join(opts.mcrab, "FakeBTransferFactors%s.py"% ( getModuleInfoString(opts) ) )
manager.writeNormFactorFile(fileName, opts)
return
def PlotMC(datasetsMgr, histo, intLumi):
kwargs = {}
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr, histo, normalizeToOne=True, saveFormats=[], **kwargs)
else:
p = plots.MCPlot(datasetsMgr, histo, normalizeToLumi=intLumi, saveFormats=[], **kwargs)
# p = plots.MCPlot(datasetsMgr, histo, normalizeToLumi=intLumi, saveFormats=[], **kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.1f"
_xlabel = None
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
if "ChiSqr" in histo:
_rebinX = 1
#logY = True
_units = ""
_format = "%0.1f " + _units
_xlabel = "#chi^{2}"
_cutBox = {"cutValue": 10.0, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 100
elif "trijetjets_deltarmin" in histo.lower():
_xlabel = "#Delta R_{min}"
_rebinX = 2
_opts["xmax"] = 3
_units = ""
_format = "%0.1f "
_cutBox = {"cutValue": 0.8, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
elif "trijetjets_deltarmax" in histo.lower():
_xlabel = "#Delta R_{max}"
_rebinX = 2
_opts["xmax"] = 3.5
_units = ""
_format = "%0.1f "
_cutBox = {"cutValue": 0.8, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
elif "trijetdijetdr" in histo.lower():
_rebinX = 1
units = ""
_format = "%0.1f "
_opts["xmax"] = 3.5
_cutBox = {"cutValue": 0.8, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
elif "trijetdijetpt" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "trijetdijetmass" in histo.lower():
_rebinX = 1
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_opts["xmax"] = 200
_xlabel = "m_{w} (%s)" % _units
_cutBox = {"cutValue": 80.39, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
if "cevts_ldgtrijetmatchedtofatjet_fatjetpt" in histo.lower():
_rebin = 1
_opts["xmax"] = 3
elif "pt" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "trijetmass" in histo.lower():
_rebinX = 1
# logY = False
_units = "GeV/c^{2}"
# _format = "%0.0f " + _units
_format = "%0.0f" + (_units)
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 305 #1005
_opts["xmin"] = 55 #1005
elif "topcandmass" in histo.lower():
_rebinX = 1
# logY = False
_units = "GeV/c^{2}"
# _format = "%0.0f " + _units
_format = "%0.0f" + (_units)
_xlabel = "m_{jjb}^{BDTG} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 505 #1005
_opts["xmin"] = 0 #1005
# elif "ht" in histo.lower():
# _rebinX = 2
## logY = False
# _units = "GeV"
# _format = "%0.0f " + _units
# _xlabel = "H_{T} (%s)" % _units
# _cutBox = {"cutValue": 500, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
# _opts["xmax"] = 2000
elif "tetrajetptd" in histo.lower():
_rebinX = 2 #5 #10 #4
_units = "GeV/c^{2}"
# _xlabel = "m_{jjbb} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 2000 #3500.0
_cutBox = {"cutValue": 400, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
elif "tetrajetmass" in histo.lower() or "tetrajetmass_" in histo.lower():
#ROOT.gStyle.SetNdivisions(10, "X")
# h = dataset.getDatasetRootHisto(histo).getHistogram()
# h.SetTickLength(100, "X")
_rebinX = 5 #5 #10 #4
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjbb} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 1500 #3500.0
_cutBox = {"cutValue": 500, "fillColor": 16, "box": False, "line": False, "greaterThan": True}
elif "dijetmass" in histo.lower():
_rebinX = 1 #5 #10 #4
# logY = False
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{W} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800 #3500.0
_cutBox = {"cutValue": 80.4, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 205
elif "trijetmultiplicitypassbdt" in histo.lower():
_rebinX = 1
_opts["xmax"] = 5
_format = "%0.0f "
elif "bdtg" or "bdtvalue" in histo.lower():
_rebinX = 1
_format = "%0.2f "
elif "trijetbdt_mass" in histo.lower():
_rebinX = 2
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_opts["xmax"] = 800
# if "eta" or "phi" or "delta" in histo.lower():
#_rebinX = 1 #5 #10 #4
# _units = ""
# _format = "%0.1f "
# if "eta" in histo.lower():
# _xlabel = "#eta"
# if "phi" in histo.lower():
# _xlabel = "#phi"
# if "pt" in histo.lower():
# _opts["xmax"] = 800
elif "boosted" in histo.lower():
_xlabel = "Trijet Counter"
elif "matched_dijetpt" in histo.lower():
_units = "(GeV/c)"
_xlabel = "p_{T}" +_units
elif "deltar_bdttrijets_tetrajetbjet" in histo.lower():
_xlabel = "#Delta R(Trijet,b_{free})"
elif "tetrajetbjetbdisc" in histo.lower():
_rebinX = 2
_opts["xmax"] = 1.05
# if "matched_dijetmass" in histo.lower():
# _rebinX = 2
# if "higgstop_dijetmass" in histo.lower():
# _rebinX = 2
elif "trijet_deltaeta_trijet_tetrajetbjet" in histo.lower():
_rebinX = 2
_xlabel = "#Delta #eta (Trijet, b_{free})"
elif "trijet_deltaphi_trijet_tetrajetbjet" in histo.lower():
_xlabel = "#Delta #phi (Trijet, b_{free})"
elif "cevts_closejettotetrajetbjet_isbtagged" in histo.lower():
_units = ""
_format = "%0.0f " + _units
# if "higgstop_" in histo.lower():
# _rebinX = 2
if "eventtrijetpt2t" in histo.lower():
_rebinX = 2
if "ldgfatjetpt" in histo.lower():
_opts["xmax"] = 1000
if "deltar_w" in histo.lower():
_rebinX = 2
_xlabel = "#Delta R"
_format = "%0.1f "
_opts["xmax"] = 5
logY = True
if "ldgtrijet_deltar" in histo.lower():
_rebinX = 2
_xlabel = "#Delta R"
_format = "%0.1f "
_opts["xmax"] = 5
logY = True
if "higgstop_deltar" in histo.lower():
_rebinX = 2
_xlabel = "#Delta R"
_format = "%0.1f "
_opts["xmax"] = 5
logY = True
if "allfatjet" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_opts["xmax"] = 800
else:
pass
if opts.normaliseToOne:
logY = True
Ylabel = "Arbitrary Units / %s" % (_format)
else:
logY = True
Ylabel = "Events / %s" % (_format)
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
#_opts = {"ymin": 1e-3, "ymaxfactor": yMaxFactor, "xmax": None}
else:
_opts["ymin"] = 1e0
#_opts["ymaxfactor"] = yMaxFactor
#_opts = {"ymin": 1e0, "ymaxfactor": yMaxFactor, "xmax": None}
# Customise styling
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
if "QCD" in datasetsMgr.getAllDatasets():
p.histoMgr.forHisto("QCD", styles.getQCDFillStyle() )
p.histoMgr.setHistoDrawStyle("QCD", "HIST")
p.histoMgr.setHistoLegendStyle("QCD", "F")
if "TT" in datasetsMgr.getAllDatasets():
p.histoMgr.setHistoDrawStyle("TT", "HIST")
p.histoMgr.setHistoLegendStyle("TT", "LP")
# if "M_200" in datasetsMgr.getAllDatasets() or "M_300" in datasetsMgr.getAllDatasets():
# p.histoMgr.forHisto("QCD", styles.getQCDFillStyle() )
# p.histoMgr.setHistoDrawStyle("QCD", "P")
# p.histoMgr.setHistoLegendStyle("QCD", "F")
# elif d.getName() == "TT" or d.getName() == "QCD" or d.getName() == "Data":
# otherHisto = histograms.Histo(histo, legName, "LP", "P")
# otherHistos.append(otherHisto)
# Customise style
signalM = []
for m in signalMass:
signalM.append(m.rsplit("M_")[-1])
for m in signalM:
p.histoMgr.forHisto("ChargedHiggs_HplusTB_HplusToTB_M_%s" %m, styles.getSignalStyleHToTB_M(m))
#soti
# p.histoMgr.setHistoDrawStyle("ChargedHiggs_HplusTB_HplusToTB_M_%s" %m, "LP")
# p.histoMgr.setHistoLegendStyle("ChargedHiggs_HplusTB_HplusToTB_M_%s" %m, "P")
plots.drawPlot(p,
histo,
xlabel = _xlabel,
ylabel = Ylabel,#"Arbitrary Units / %s" % (_format), #"Events / %s" % (_format), #"Arbitrary Units / %s" % (_format),
# ylabel = "Arbitrary Units / %s" % (_format), #"Events / %s" % (_format), #"Arbitrary Units / %s" % (_format),
log = logY,
rebinX = _rebinX, cmsExtraText = "Preliminary", #_rebinX
#createLegend = {"x1": 0.48, "y1": 0.45, "x2": 0.92, "y2": 0.92}, #All datasets
# createLegend = {"x1": 0.58, "y1": 0.7, "x2": 0.92, "y2": 0.92},
createLegend = {"x1": 0.58, "y1": 0.65, "x2": 0.92, "y2": 0.87},
#createLegend = {"x1": 0.73, "y1": 0.85, "x2": 0.97, "y2": 0.77}, #One dataset
opts = _opts,
opts2 = {"ymin": 0.6, "ymax": 1.4},
cutBox = _cutBox,
)
# Save plot in all formats
saveName = histo.split("/")[-1]
savePath = os.path.join(opts.saveDir, "HplusMasses", histo.split("/")[0], opts.optMode)
if opts.normaliseToOne:
save_path = savePath + opts.MVAcut
if opts.noQCD:
save_path = savePath + opts.MVAcut + "/noQCD/"
else:
save_path = savePath + opts.MVAcut + "/normToLumi/TT/"
if opts.noQCD:
save_path = savePath + opts.MVAcut + "/noQCD/"
# SavePlot(p, saveName, savePath)
SavePlot(p, saveName, save_path)
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
def PlotHistograms(datasetsMgr, datasetsMgr_matched, histoName, intLumi):
Verbose("Plotting Data-MC Histograms")
# Skip 2-D plots
skipStrings = []
if "topbdtSelection_" in opts.folder:
skipStrings = ["_Vs_", "Vs", "MCtruth", "TopQuark",
"RealSelected", "DeltaMVAgt1", "SelectedTop",
"LdgTrijetFake", "LdgTrijetFakeJJB", "TrijetFake",
"FakeInTopDir", "LdgTrijetFakeJJB_BDT", "LdgTrijetFake_BDT", "Cevts",] #, "Matched"
if opts.folder == "AnalysisTriplets":
skipStrings = ["Cevts"]
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", "SubldgTetrajet"]
# 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]
kwargs_ = GetHistoKwargs(saveName, opts)
# Create the plotting object
if opts.onlyMC:
if "Matched" in histoName:
p = plots.MCPlot(datasetsMgr_matched, histoName, saveFormats=[], normalizeToLumi=intLumi)
elif opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr, histoName, saveFormats=[], normalizeToOne=True)
else:
p = plots.MCPlot(datasetsMgr, histoName, saveFormats=[], normalizeToLumi=intLumi)
else:
p = plots.DataMCPlot(datasetsMgr, histoName, saveFormats=[])
# Apply style
if opts.signalMass != 0:
p.histoMgr.forHisto(opts.signal, styles.getSignalStyleHToTB_M(opts.signalMass))
# p.histoMgr.forHisto(opts.signalMass, styles.getSignalStyleHToTB())
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:
replaceBinLabels(p, histoName)
if opts.normaliseToOne:
saveDir = opts.saveDir +"/normToOne/logY/"
else:
saveDir = opts.saveDir + "/normToLumi/"
# Save the plots in custom list of saveFormats
SavePlot(p, saveName, os.path.join(saveDir, opts.folder, opts.optMode), [".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 PlotMC(datasetsMgr, histo, intLumi):
kwargs = {}
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr,
histo,
normalizeToOne=True,
saveFormats=[],
**kwargs)
else:
p = plots.MCPlot(datasetsMgr,
histo,
normalizeToLumi=intLumi,
saveFormats=[],
**kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.0f"
_xlabel = None
logY = False
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
if "ChiSqr" in histo:
_rebinX = 1
logY = True
_units = ""
_format = "%0.1f " + _units
_xlabel = "#chi^{2}"
_cutBox = {
"cutValue": 10.0,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 100
elif "trijetmass" in histo.lower():
_rebinX = 4
logY = False
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {
"cutValue": 173.21,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 805 #1005
elif "ht" in histo.lower():
_rebinX = 2
logY = False
_units = "GeV"
_format = "%0.0f " + _units
_xlabel = "H_{T} (%s)" % _units
_cutBox = {
"cutValue": 500,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
#_opts["xmin"] = 500
_opts["xmax"] = 2000
elif "tetrajetmass" in histo.lower():
_rebinX = 5 #5 #10 #4
logY = False
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjbb} (%s)" % (_units)
_format = "%0.0f " + _units
_cutBox = {
"cutValue": 500.0,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 1500 #3500.0
#_rebinX = 10
#_opts["xmax"] = 3500
elif "tetrajetbjetpt" in histo.lower():
_rebinX = 2
logY = False
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 600
elif "foxwolframmoment" in histo.lower():
_format = "%0.1f"
_cutBox = {
"cutValue": 0.5,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
else:
pass
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
#_opts = {"ymin": 1e-3, "ymaxfactor": yMaxFactor, "xmax": None}
else:
_opts["ymin"] = 1e0
#_opts["ymaxfactor"] = yMaxFactor
#_opts = {"ymin": 1e0, "ymaxfactor": yMaxFactor, "xmax": None}
# Customise styling
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
if "QCD" in datasetsMgr.getAllDatasets():
p.histoMgr.forHisto("QCD", styles.getQCDFillStyle())
p.histoMgr.setHistoDrawStyle("QCD", "HIST")
p.histoMgr.setHistoLegendStyle("QCD", "F")
if "TT" in datasetsMgr.getAllDatasets():
p.histoMgr.setHistoDrawStyle("TT", "AP")
p.histoMgr.setHistoLegendStyle("TT", "LP")
# Customise style
signalM = []
for m in signalMass:
signalM.append(m.rsplit("M_")[-1])
for m in signalM:
p.histoMgr.forHisto("ChargedHiggs_HplusTB_HplusToTB_M_%s" % m,
styles.getSignalStyleHToTB_M(m))
plots.drawPlot(
p,
histo,
xlabel=_xlabel,
ylabel="Arbitrary Units / %s" % (_format),
log=logY,
rebinX=_rebinX,
cmsExtraText="Preliminary",
createLegend={
"x1": 0.58,
"y1": 0.65,
"x2": 0.92,
"y2": 0.92
},
opts=_opts,
opts2={
"ymin": 0.6,
"ymax": 1.4
},
cutBox=_cutBox,
)
# Save plot in all formats
saveName = histo.split("/")[-1]
savePath = os.path.join(opts.saveDir, "HplusMasses",
histo.split("/")[0], opts.optMode)
SavePlot(p, saveName, savePath)
return
def PlotMC(datasetsMgr, histo, intLumi):
kwargs = {}
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr, histo, normalizeToOne=True, saveFormats=[], **kwargs)
else:
p = plots.MCPlot(datasetsMgr, histo, normalizeToLumi=intLumi, saveFormats=[], **kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.0f"
_xlabel = None
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
if "pt" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": False, "greaterThan": True}
_opts["xmax"] = 805 #1005
if "bdisc" in histo.lower():
_rebinX = 2
_units = ""
_format = "%0.2f " + _units
_xlabel = "b-discriminator" #(%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": False, "greaterThan": True}
_opts["xmax"] = 1.2
if "ht" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "H_{T} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": False, "greaterThan": True}
_opts["xmax"] = 1505 #1005
if "met" in histo.lower():
_rebinX = 1
_units = "GeV"
_format = "%0.0f " + _units
_xlabel = "E_{T,missing} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": False, "greaterThan": True}
_opts["xmax"] = 205 #1005
if "mult" in histo.lower():
_units = ""
_format = "%0.0f " + _units
_xlabel = "jet multiplicity"
_opts["xmax"] = 10
if "mass" in histo.lower():
_rebinX = 2
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 350 #1005
elif "tetrajetmass" in histo.lower():
_rebinX = 5 #5 #10 #4
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjbb} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 1500 #3500.0
elif "dijetmass" in histo.lower():
_rebinX = 1 #5 #10 #4
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{W} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800 #3500.0
_cutBox = {"cutValue": 80.4, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 300
elif "tetrajetbjetpt" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "ldgtrijetpt" in histo.lower():
_rebinX = 2
# logY = False
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "tau" in histo.lower():
_format = "%0.2f "
elif "deltar" in histo.lower():
_format = "%0.2f "
elif "bdtvalue" in histo.lower():
_format = "%0.1f"
else:
pass
if opts.normaliseToOne:
logY = False
Ylabel = "Arbitrary Units / %s" % (_format)
else:
logY = True
Ylabel = "Events / %s" % (_format)
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
#_opts = {"ymin": 1e-3, "ymaxfactor": yMaxFactor, "xmax": None}
else:
_opts["ymin"] = 1e0
#_opts["ymaxfactor"] = yMaxFactor
#_opts = {"ymin": 1e0, "ymaxfactor": yMaxFactor, "xmax": None}
# Customise styling
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
if "QCD" in datasetsMgr.getAllDatasetNames():
p.histoMgr.forHisto("QCD", styles.getQCDFillStyle() )
p.histoMgr.setHistoDrawStyle("QCD", "HIST")
p.histoMgr.setHistoLegendStyle("QCD", "F")
if "TT" in datasetsMgr.getAllDatasetNames():
TTStyle = styles.StyleCompound([styles.StyleFill(fillColor=ROOT.kMagenta-2), styles.StyleLine(lineColor=ROOT.kMagenta-2, lineStyle=ROOT.kSolid, lineWidth=3),
styles.StyleMarker(markerSize=1.2, markerColor=ROOT.kMagenta-2, markerSizes=None, markerStyle=ROOT.kFullTriangleUp)])
p.histoMgr.forHisto("TT", TTStyle)
p.histoMgr.setHistoDrawStyle("TT", "HIST") #AP
p.histoMgr.setHistoLegendStyle("TT", "F") #LP
# Customise style
signalM = []
for m in signalMass:
signalM.append(m.rsplit("M_")[-1])
for m in signalM:
p.histoMgr.forHisto("ChargedHiggs_HplusTB_HplusToTB_M_%s" %m, styles.getSignalStyleHToTB_M(m))
plots.drawPlot(p,
histo,
xlabel = _xlabel,
ylabel = Ylabel,
log = logY,
rebinX = _rebinX, cmsExtraText = "Preliminary",
createLegend = {"x1": 0.68, "y1": 0.82, "x2": 1.0, "y2": 0.92},
#createLegend = {"x1": 0.73, "y1": 0.85, "x2": 0.97, "y2": 0.77},
opts = _opts,
opts2 = {"ymin": 0.6, "ymax": 1.4},
cutBox = _cutBox,
)
# Save plot in all formats
saveName = histo.split("/")[-1]
savePath = os.path.join(opts.saveDir, histo.split("/")[0], opts.optMode)
'''
if opts.normaliseToOne:
save_path = savePath + opts.MVAcut
if opts.noQCD:
save_path = savePath + opts.MVAcut + "/noQCD/"
else:
save_path = savePath + opts.MVAcut + "/normToLumi/"
if opts.noQCD:
save_path = savePath + opts.MVAcut + "/noQCD/"
'''
SavePlot(p, saveName, savePath)
return
