def MtComparisonBaseline(datasets):
mt = plots.PlotBase(getHistos(datasets,"ForQCDNormalization/NormalizationMETBaselineTauAfterStdSelections/NormalizationMETBaselineTauAfterStdSelectionsInclusive", "ForQCDNormalizationEWKFakeTaus/NormalizationMETBaselineTauAfterStdSelections/NormalizationMETBaselineTauAfterStdSelectionsInclusive", "ForQCDNormalizationEWKGenuineTaus/NormalizationMETBaselineTauAfterStdSelections/NormalizationMETBaselineTauAfterStdSelectionsInclusive"))
# mt.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mt._setLegendStyles()
st1 = styles.StyleCompound([styles.styles[2]])
st2 = styles.StyleCompound([styles.styles[1]])
st3 = styles.StyleCompound([styles.styles[3]])
st1.append(styles.StyleLine(lineWidth=3))
st2.append(styles.StyleLine(lineStyle=2, lineWidth=3))
st2.append(styles.StyleLine(lineStyle=3, lineWidth=3))
mt.histoMgr.forHisto("transverseMass", st1)
mt.histoMgr.forHisto("transverseMassTriangleCut", st2)
mt.histoMgr.forHisto("transverseMass3JetCut", st3)
mt.histoMgr.setHistoLegendLabelMany({
"transverseMass": "All baseline Taus",
"transverseMassTriangleCut": "Baseline Fake Taus",
"transverseMass3JetCut": "Baseline Genuine Taus"
})
# mt.histoMgr.setHistoDrawStyleAll("P")
mt.appendPlotObject(histograms.PlotText(50, 1, "3-prong Taus", size=20))
xlabel = "E_{T}^{miss} (GeV)"
ylabel = "Events / %.2f"
plots.drawPlot(mt, "MtComparisonBaseline", xlabel=xlabel, ylabel=ylabel, rebinX=1, log=True,
createLegend={"x1": 0.4, "y1": 0.75, "x2": 0.8, "y2": 0.9},
ratio=False, opts2={"ymin": 0.5, "ymax": 1.5})
def MtComparison(datasets):
mt = plots.PlotBase(
getHistos(datasets, "transverseMass", "transverseMassTriangleCut",
"transverseMass3JetCut"))
# mt.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mt._setLegendStyles()
st1 = styles.StyleCompound([styles.styles[2]])
st2 = styles.StyleCompound([styles.styles[1]])
st3 = styles.StyleCompound([styles.styles[3]])
st1.append(styles.StyleLine(lineWidth=3))
st2.append(styles.StyleLine(lineStyle=2, lineWidth=3))
st2.append(styles.StyleLine(lineStyle=3, lineWidth=3))
mt.histoMgr.forHisto("transverseMass", st1)
mt.histoMgr.forHisto("transverseMassTriangleCut", st2)
mt.histoMgr.forHisto("transverseMass3JetCut", st3)
mt.histoMgr.setHistoLegendLabelMany({
"transverseMass":
"m_{T}(#tau jet, E_{T}^{miss})",
"transverseMassTriangleCut":
"m_{T}(#tau jet, E_{T}^{miss}) with Triangle Cut",
"transverseMass3JetCut":
"m_{T}(#tau jet, E_{T}^{miss}) with 3-jet Cut"
})
# mt.histoMgr.setHistoDrawStyleAll("P")
mt.appendPlotObject(
histograms.PlotText(300, 50, "p_{T}^{jet} > 50 GeV/c", size=20))
xlabel = "m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})"
ylabel = "Events / %.2f"
plots.drawPlot(mt,
"MtComparison",
xlabel=xlabel,
ylabel=ylabel,
rebinX=2,
log=False,
createLegend={
"x1": 0.4,
"y1": 0.75,
"x2": 0.8,
"y2": 0.9
},
ratio=False,
opts2={
"ymin": 0.5,
"ymax": 1.5
})
def TauPtComparison(datasets):
mt = plots.ComparisonPlot(*getHistos3(datasets, "tauPt", "tauPt"))
mt._setLegendStyles()
st1 = styles.StyleCompound([styles.styles[2]])
st2 = styles.StyleCompound([styles.styles[1]])
st1.append(styles.StyleLine(lineWidth=3))
st2.append(styles.StyleLine(lineStyle=2, lineWidth=3))
mt.histoMgr.forHisto("Taupt_madgraph", st1)
mt.histoMgr.forHisto("Taupt_pythia8", st2)
mt.histoMgr.setHistoLegendLabelMany({
"Taupt_madgraph": "Tau pt from Madgraph",
"Taupt_pythia8": "Tau pt from Pythia8"
})
mt.histoMgr.setHistoDrawStyleAll("PE")
mt.appendPlotObject(histograms.PlotText(100, 0.01, "tt events", size=20))
xlabel = "p_{T}^{#tau jet} (GeV)"
ylabel = "Events / %.2f"
plots.drawPlot(mt,
"TauPtComparison",
xlabel=xlabel,
ylabel=ylabel,
rebinX=2,
log=True,
createLegend={
"x1": 0.6,
"y1": 0.75,
"x2": 0.8,
"y2": 0.9
},
ratio=False,
opts2={
"ymin": 0.5,
"ymax": 50
},
opts={
"xmax": 800,
"ymin": 1,
"ymax": 1000000
})
def MetComparison(datasets):
mt = plots.ComparisonPlot(*getHistos2(datasets,"Met","Met"))
# mt.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mt._setLegendStyles()
st1 = styles.StyleCompound([styles.styles[2]])
st2 = styles.StyleCompound([styles.styles[1]])
st1.append(styles.StyleLine(lineWidth=3))
st2.append(styles.StyleLine(lineStyle=2, lineWidth=3))
mt.histoMgr.forHisto("Met_madgraph", st1)
mt.histoMgr.forHisto("Met_pythia8", st2)
mt.histoMgr.setHistoLegendLabelMany({
"Met_madgraph": "Met from Madgraph",
"Met_pythia8": "Met from Pythia8"
})
mt.histoMgr.setHistoDrawStyleAll("PE")
mt.appendPlotObject(histograms.PlotText(100, 0.01, "tt events", size=20))
xlabel = "PF E_{T}^{miss} (GeV)"
ylabel = "Events / %.2f"
plots.drawPlot(mt, "MetComparison", xlabel=xlabel, ylabel=ylabel, rebinX=2, log=True,
createLegend={"x1": 0.6, "y1": 0.75, "x2": 0.8, "y2": 0.9},
ratio=False, opts2={"ymin": 0.5, "ymax": 50}, opts={"xmax": 500})
def PlotSignalBackground(datasetsMgr, hG, hF, intLumi):
kwargs = {}
_kwargs = {}
if opts.normaliseToOne:
pG = plots.MCPlot(datasetsMgr,
hG,
normalizeToOne=True,
saveFormats=[],
**_kwargs)
pF = plots.MCPlot(datasetsMgr,
hF,
normalizeToOne=True,
saveFormats=[],
**_kwargs)
else:
pG = plots.MCPlot(datasetsMgr,
hG,
normalizeToLumi=intLumi,
saveFormats=[],
**_kwargs)
pF = plots.MCPlot(datasetsMgr,
hF,
normalizeToLumi=intLumi,
saveFormats=[],
**_kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.2f"
_xlabel = None
logY = False
_opts = {"ymin": 0, "ymaxfactor": 1.1}
if "mass" in hG.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "M (%s)" % _units
if "trijetmass" in hG.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{top} (%s)" % _units
#_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 805 #1005
_opts = {"xmin": 0.0, "xmax": 805}
if "bjetmass" in hG.lower():
_xlabel = "m_{b-tagged jet} (%s)" % _units
_opts["xmax"] = 50
if "bjetldgjet_mass" in hG.lower():
_xlabel = "m_{b, ldg jet} (%s)" % _units
_opts["xmax"] = 705
if "bjetsubldgjet_mass" in hG.lower():
_xlabel = "m_{b-tagged, subldg jet} (%s)" % _units
_opts["xmax"] = 705
if "jet_mass" in hG.lower():
_opts["xmax"] = 750
if "mult" in hG.lower():
_format = "%0.0f"
if "ldg" in hG.lower():
_xlabel = "Leading jet mult"
if "subldg" in hG.lower():
_xlabel = "Subleading jet mult"
if "avg" in hG.lower():
_xlabel = "avg CvsL"
if "cvsl" in hG.lower():
_format = "%0.2f"
if "ldg" in hG.lower():
_xlabel = "Leading jet CvsL"
if "subldg" in hG.lower():
_xlabel = "Subleading jet CvsL"
if "avg" in hG.lower():
_xlabel = "avg CvsL"
if "axis2" in hG.lower():
_format = "%0.3f"
if "ldg" in hG.lower():
_xlabel = "Leading jet axis2"
if "subldg" in hG.lower():
_xlabel = "Subleading jet axis2"
if "avg" in hG.lower():
_xlabel = "avg axis2"
if "dijetmass" in hG.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{W} (%s)" % _units
_opts["xmax"] = 600
_opts = {"xmin": 0.0, "xmax": 605, "ymin": 1e-3, "ymaxfactor": 1.0}
if "trijetptdr" in hG.lower():
_opts["xmax"] = 800
_format = "%0.0f"
_xlabel = "p_{T}#Delta R_{t}"
if "dijetptdr" in hG.lower():
_opts["xmax"] = 800
_format = "%0.0f"
_xlabel = "p_{T}#Delta R_{W}"
if "dgjetptd" in hG.lower():
_format = "%0.2f"
_xlabel = "Leading jet p_{T}D"
if "subldg" in hG.lower():
_xlabel = "Subleading jet p_{T}D"
if "bdisc" in hG.lower():
_format = "%0.2f"
if "subldg" in hG.lower():
_xlabel = "Subleading jet CSV"
_opts = {"ymin": 1e-3, "ymax": 0.06}
elif "ldg" in hG.lower():
_xlabel = "Leading jet CSV"
_opts = {"ymin": 1e-3, "ymax": 0.06}
else:
_xlabel = "b-tagged jet CSV"
_opts = {"ymin": 1e-3, "ymax": 0.35}
if "over" in hG.lower():
_format = "%0.2f "
_xlabel = "m_{W}/m_{t}"
_opts["xmax"] = 1
_opts["xmin"] = 0
if "likelihood" in hG.lower():
_format = "%0.2f"
if "ldg" in hG.lower():
_xlabel = "Leading jet QGL"
if "subldg" in hG.lower():
_xlabel = "Subleading jet QGL"
if "avg" in hG.lower():
_xlabel = "avg QGL"
else:
pass
'''
if "bdisc" in hG.lower():
_format = "%0.2f"
if "subldg" in hG.lower():
_xlabel = "Subleading jet CSV"
elif "ldg" in hG.lower():
_xlabel = "Leading jet CSV"
else:
_xlabel = "b-tagged jet CSV"
'''
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
else:
_opts["ymin"] = 1e0
myList = []
# Customise styling
pG.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
pF.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
#Dataset: ttbar
dataset = datasetsMgr.getDataset(opts.dataset) #soti
#dataset = datasetsMgr.getDataset("ChargedHiggs_HplusTB_HplusToTB_M_1000")
#Get Genuine-top histogram
h = dataset.getDatasetRootHisto(hG)
h.normalizeToOne()
HG = h.getHistogram()
#Get Fake-top histogram
h = dataset.getDatasetRootHisto(hF)
h.normalizeToOne()
HF = h.getHistogram()
#Define Signal style
altSignalBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kAzure + 9,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kAzure + 9,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kAzure + 9)
])
#Define Background style
altBackgroundBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kRed - 4,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kRed - 4,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kRed - 4, fillStyle=3001)
])
signalBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kTeal + 2,
markerSizes=None,
markerStyle=ROOT.kFullTriangleUp),
styles.StyleLine(lineColor=ROOT.kTeal + 2,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kTeal + 2)
]) #, fillStyle=3001)])
signalGrayStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kGray + 1,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kGray + 1,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kGray + 1)
])
backgroundGrayStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kGray + 3,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kGray + 3,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kGray + 3, fillStyle=3001)
])
#Comparison Plot
p = plots.ComparisonPlot(
histograms.Histo(HF, "Fake", "p", "P"),
histograms.Histo(HG, "Genuine", "pl", "PL"),
)
#Set labels
p.histoMgr.setHistoLegendLabelMany({
"Fake": "Unmatched",
"Genuine": "Truth-matched"
})
#Set Draw style
p.histoMgr.forHisto("Fake", altBackgroundBDTGStyle)
p.histoMgr.setHistoDrawStyle("Fake", "LP")
p.histoMgr.setHistoLegendStyle("Fake", "LP") #F
p.histoMgr.forHisto("Genuine", altSignalBDTGStyle)
p.histoMgr.setHistoDrawStyle("Genuine", "HIST")
p.histoMgr.setHistoLegendStyle("Genuine", "LP") #LP
if "avg" in hG.lower() or "likelihood" in hG.lower() or "over" in hG.lower(
):
p.histoMgr.forHisto("Genuine", signalBDTGStyle)
p.histoMgr.setHistoDrawStyle("Genuine", "HIST")
p.histoMgr.setHistoLegendStyle("Genuine", "LP") #LP
if "Gray" in opts.mcrab:
p.histoMgr.forHisto("Fake", backgroundGrayStyle)
p.histoMgr.forHisto("Genuine", signalGrayStyle)
histoG = histograms.Histo(HG, "TT", "Signal")
histoG.setIsDataMC(isData=False, isMC=True)
histoF = histograms.Histo(HF, "QCD", "Signal")
histoF.setIsDataMC(isData=False, isMC=True)
styleG = styles.ttStyle
styleF = styles.signalStyleHToTB1000
styleG.apply(HG)
styleF.apply(HF)
myList.append(histoG)
myList.append(histoF)
_kwargs = {
"xlabel": _xlabel,
"ylabel": "Arbitrary Units / %s" % (_format),
"ratioYlabel": "Ratio ",
"ratio": False,
"ratioInvert": False,
"stackMCHistograms": False,
"addMCUncertainty": False,
"addLuminosityText": False,
"addCmsText": True,
"cmsExtraText": "Preliminary",
#"opts" : {"ymin": 0.0, "ymaxfactor": 1.1},
"opts": _opts,
"opts2": {
"ymin": 0.6,
"ymax": 1.5
},
"log": False,
#"createLegend" : {"x1": 0.5, "y1": 0.75, "x2": 0.9, "y2": 0.9},
"createLegend": {
"x1": 0.58,
"y1": 0.65,
"x2": 0.92,
"y2": 0.82
},
}
# Save plot in all formats
saveName = hG.split("/")[-1]
#plots.drawPlot(p, saveName, **_kwargs)
savePath = os.path.join(opts.saveDir + ANALYSISNAME, "HplusMasses",
hG.split("/")[0], opts.optMode)
plots.drawPlot(p, savePath, **_kwargs)
SavePlot(p,
saveName,
os.path.join(opts.saveDir + opts.mcrab, opts.optMode),
saveFormats=[".png"])
return
def Plot_Comparisons(datasetsMgr, datasetsMgrRef, hT, hR, intLumi):
#kwargs = {}
_kwargs = {}
#kwargs = GetHistoKwargs(hG, opts)
print
if opts.normaliseToOne:
pT = plots.MCPlot(datasetsMgr,
hT,
normalizeToOne=True,
saveFormats=[],
**_kwargs)
pR = plots.MCPlot(datasetsMgrRef,
hR,
normalizeToOne=True,
saveFormats=[],
**_kwargs)
else:
pT = plots.MCPlot(datasetsMgr,
hT,
normalizeToLumi=intLumi,
saveFormats=[],
**_kwargs)
pR = plots.MCPlot(datasetsMgrRef,
hR,
normalizeToLumi=intLumi,
saveFormats=[],
**_kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.2f"
_xlabel = None
logY = False
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
print "HT", hT
xMax = 200
_kwargs = {
"xlabel": _xlabel,
"ylabel": "Events / %s" % (_format),
"ratioYlabel": "Ratio ",
"ratio": False,
"ratioInvert": False,
"stackMCHistograms": False,
"addMCUncertainty": False,
"addLuminosityText": False,
"addCmsText": True,
"cmsExtraText": "Preliminary",
#"opts" : {"ymin": 0.1, "ymaxfactor": 1.2},
"opts": {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": xMax
},
"opts2": {
"ymin": 0.6,
"ymax": 1.5
},
"log": True,
#"createLegend" : {"x1": 0.5, "y1": 0.75, "x2": 0.9, "y2": 0.9},
"createLegend": {
"x1": 0.58,
"y1": 0.65,
"x2": 0.92,
"y2": 0.82
},
"rebinX": 1,
}
if "counters" in hT.lower():
print "HERE"
ROOT.gStyle.SetLabelSize(16.0, "X")
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 6
}
_kwargs["createLegend"] = {
"x1": 0.65,
"y1": 0.6,
"x2": 0.90,
"y2": 0.77
},
if "pt" in hT.lower():
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T} (%s)" % _units
_kwargs["xmax"] = 800
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 800
}
if "mass" in hT.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "M (%s)" % _units
_kwargs["xmax"] = 800
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 800
}
if "tetrajet" in hT.lower():
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 2000
}
_kwargs["rebinX"] = 8
if "trijetmass" in hT.lower():
print "mass"
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{top} (%s)" % _units
#_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_kwargs["xmax"] = 400 #1005
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 400
}
if "eta" in hT.lower():
_units = ""
_format = "%0.1f " + _units
_xlabel = "#eta (%s)" % _units
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": -5.0,
"xmax": +5.0
}
if "mult" in hT.lower():
_format = "%0.0f"
if "cleaned" in hT.lower():
_kwargs["xmax"] = 10
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 10
}
else:
_kwargs["xmax"] = 60
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 60
}
if "bdisc" in hT.lower():
_format = "%0.2f"
_kwargs["xmax"] = 1
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 1
}
if "topbdt" in hT.lower():
_format = "%0.2f"
_kwargs["xmax"] = 1
_kwargs["xmin"] = 0.3
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0.3,
"xmax": 1.0
}
if "dijetmass" in hT.lower():
print "dijet-mass"
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{W} (%s)" % _units
_opts["xmax"] = 600
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 300
}
else:
pass
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.5
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
else:
_opts["ymin"] = 1e0
myList = []
# Customise styling
pT.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
pR.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
datasetTarg = datasetsMgr.getDataset(
"QCD") #ChargedHiggs_HplusTB_HplusToTB_M_500
datasetRef = datasetsMgrRef.getDataset(
"QCD") #ChargedHiggs_HplusTB_HplusToTB_M_500
h = datasetTarg.getDatasetRootHisto(hT)
#h.normalizeToOne()
h.normalizeToLuminosity(intLumi)
HT = h.getHistogram()
h = datasetRef.getDatasetRootHisto(hR)
#h.normalizeToOne()
h.normalizeToLuminosity(intLumi)
HR = h.getHistogram()
altSignalBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kAzure + 9,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kAzure + 9,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kAzure + 9, fillStyle=3001)
])
altBackgroundBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kRed - 4,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kRed - 4,
lineStyle=ROOT.kSolid,
lineWidth=3),
#styles.StyleFill(fillColor=ROOT.kRed-4)
])
signalStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kTeal + 2,
markerSizes=None,
markerStyle=ROOT.kFullTriangleUp),
styles.StyleLine(lineColor=ROOT.kTeal + 2,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kTeal + 2, fillStyle=3001)
])
p = plots.ComparisonPlot(
histograms.Histo(HT, "Target", "pl", "PL"),
histograms.Histo(HR, "Reference", "pl", "PL"),
)
p.histoMgr.setHistoLegendLabelMany({
"Target": "bug fixed",
"Reference": "bug"
})
if ("TT" in datasetTarg.getName()):
p.histoMgr.forHisto("Target", altBackgroundBDTGStyle)
p.histoMgr.forHisto("Reference", altSignalBDTGStyle)
elif ("QCD" in datasetTarg.getName()):
p.histoMgr.forHisto(
"Target", altBackgroundBDTGStyle) #styles.getABCDStyle("VR"))
p.histoMgr.forHisto("Reference", styles.qcdFillStyle)
elif ("Charged" in datasetTarg.getName()):
p.histoMgr.forHisto("Target", altBackgroundBDTGStyle)
p.histoMgr.forHisto("Reference", signalStyle)
p.histoMgr.setHistoDrawStyle("Target", "HIST")
p.histoMgr.setHistoLegendStyle("Target", "LP") #F
p.histoMgr.setHistoDrawStyle("Reference", "HIST")
p.histoMgr.setHistoLegendStyle("Reference", "F") #LP
'''
histoG = histograms.Histo(HT, "TT", "Signal")
histoG.setIsDataMC(isData=False, isMC=True)
histoF = histograms.Histo(HR, "QCD", "Signal")
histoF.setIsDataMC(isData=False, isMC=True)
'''
#styleT = styles.ttStyle
#styleR = styles.signalStyleHToTB1000
#styleT.apply(HT)
#styleR.apply(HR)
'''
myList.append(histoT)
myList.append(histoR)
'''
# Save plot in all formats
#saveName = hT.split("/")[-1]
#plots.drawPlot(p, saveName, **_kwargs)
#savePath = os.path.join(opts.saveDir, "HplusMasses", hT.split("/")[0], opts.optMode)
#plots.drawPlot(p, savePath, **_kwargs)
#SavePlot(p, saveName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png", ".pdf", ".C"])
dName = datasetTarg.getName()
dName = dName.replace("ChargedHiggs_HplusTB_HplusToTB_", "")
saveName = hT.split("/")[-1] + "_" + dName
#print saveName, hT, opts.saveDir
savePath = os.path.join(opts.saveDir, hT.split("/")[0], opts.optMode)
print "TYPE", type(hT), type(savePath), type(p)
plots.drawPlot(p, savePath, **_kwargs)
#leg = ROOT.TLegend(0.2, 0.8, 0.81, 0.87)
leg = ROOT.TLegend(0.2, 0.8, 0.51, 0.87)
leg.SetFillStyle(0)
leg.SetFillColor(0)
leg.SetBorderSize(0)
#{"dx": -0.55, "dy": -0.55, "dh": -0.08}
datasetName = datasetTarg.getName()
datasetName = datasetName.replace("TT", "t#bar{t}")
if "ChargedHiggs" in datasetName:
datasetName = datasetName.replace("ChargedHiggs_HplusTB_HplusToTB_M_",
"m_{H+} = ")
datasetName = datasetName + "GeV"
#leg.SetHeader("t#bar{t}")
leg.SetHeader(datasetName)
leg.Draw()
#print savePath
#savePath = os.path.join(opts.saveDir, opts.optMode)
SavePlot(p, saveName, savePath)
return
def PlotSignalBackground(datasetsMgr, hG, hF, intLumi):
kwargs = {}
_kwargs = {}
#kwargs = GetHistoKwargs(hG, opts)
if opts.normaliseToOne:
pG = plots.MCPlot(datasetsMgr,
hG,
normalizeToOne=True,
saveFormats=[],
**_kwargs)
pF = plots.MCPlot(datasetsMgr,
hF,
normalizeToOne=True,
saveFormats=[],
**_kwargs)
else:
pG = plots.MCPlot(datasetsMgr,
hG,
normalizeToLumi=intLumi,
saveFormats=[],
**_kwargs)
pF = plots.MCPlot(datasetsMgr,
hF,
normalizeToLumi=intLumi,
saveFormats=[],
**_kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.2f"
_xlabel = None
logY = False
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
if "mass" in hG.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "M (%s)" % _units
if "trijetmass" in hG.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{top} (%s)" % _units
#_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 805 #1005
if "bjetmass" in hG.lower():
_xlabel = "m_{b-tagged jet} (%s)" % _units
_opts["xmax"] = 50
if "bjetldgjet_mass" in hG.lower():
_xlabel = "m_{b, ldg jet} (%s)" % _units
if "bjetsubldgjet_mass" in hG.lower():
_xlabel = "m_{b-tagged, subldg jet} (%s)" % _units
if "jet_mass" in hG.lower():
_opts["xmax"] = 750
if "mult" in hG.lower():
_format = "%0.0f"
_xlabel = "Leading jet mult"
if "subldg" in hG.lower():
_xlabel = "Subleading jet mult"
if "cvsl" in hG.lower():
_format = "%0.2f"
_xlabel = "Leading jet CvsL"
if "subldg" in hG.lower():
_xlabel = "Subleading jet CvsL"
if "axis2" in hG.lower():
_format = "%0.3f"
_xlabel = "Leading jet axis2"
if "subldg" in hG.lower():
_xlabel = "Subleading jet axis2"
if "dijetmass" in hG.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{W} (%s)" % _units
_opts["xmax"] = 600
if "trijetptdr" in hG.lower():
_opts["xmax"] = 800
_format = "%0.0f"
_xlabel = "p_{T}#Delta R_{t}"
if "dijetptdr" in hG.lower():
_opts["xmax"] = 800
_format = "%0.0f"
_xlabel = "p_{T}#Delta R_{W}"
if "dgjetptd" in hG.lower():
_format = "%0.2f"
_xlabel = "Leading jet p_{T}D"
if "subldg" in hG.lower():
_xlabel = "Subleading jet p_{T}D"
else:
pass
'''
if "bdisc" in hG.lower():
_format = "%0.2f"
if "subldg" in hG.lower():
_xlabel = "Subleading jet CSV"
elif "ldg" in hG.lower():
_xlabel = "Leading jet CSV"
else:
_xlabel = "b-tagged jet CSV"
'''
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
else:
_opts["ymin"] = 1e0
if "bdisc" in hG.lower():
_format = "%0.2f"
if "subldg" in hG.lower():
_xlabel = "Subleading jet CSV"
_opts = {"ymin": 1e-3, "ymax": 0.06}
elif "ldg" in hG.lower():
_xlabel = "Leading jet CSV"
_opts = {"ymin": 1e-3, "ymax": 0.06}
else:
_xlabel = "b-tagged jet CSV"
_opts = {"ymin": 1e-3, "ymax": 0.35}
myList = []
# Customise styling
pG.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
pF.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
'''
plots.drawPlot(pG,
hG,
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,
)
plots.drawPlot(pF,
hF,
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,
)
'''
dataset = datasetsMgr.getDataset("TT")
h = dataset.getDatasetRootHisto(hG)
h.normalizeToOne()
HG = h.getHistogram()
h = dataset.getDatasetRootHisto(hF)
h.normalizeToOne()
HF = h.getHistogram()
altSignalBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kAzure + 9,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kAzure + 9,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kAzure + 9)
])
altBackgroundBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kRed - 4,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kRed - 4,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kRed - 4, fillStyle=3001)
])
p = plots.ComparisonPlot(
histograms.Histo(HF, "Fake", "p", "P"),
histograms.Histo(HG, "Genuine", "pl", "PL"),
)
p.histoMgr.setHistoLegendLabelMany({
"Fake": "Unmatched",
"Genuine": "Truth-matched"
})
p.histoMgr.forHisto("Fake", altBackgroundBDTGStyle)
p.histoMgr.setHistoDrawStyle("Fake", "LP")
p.histoMgr.setHistoLegendStyle("Fake", "LP") #F
p.histoMgr.forHisto("Genuine", altSignalBDTGStyle)
p.histoMgr.setHistoDrawStyle("Genuine", "HIST")
p.histoMgr.setHistoLegendStyle("Genuine", "LP") #LP
#HF = dataset.getDatasetRootHisto(hF).getHistogram()
#HG = dataset.getDatasetRootHisto(hG).getHistogram()
histoG = histograms.Histo(HG, "TT", "Signal")
histoG.setIsDataMC(isData=False, isMC=True)
histoF = histograms.Histo(HF, "QCD", "Signal")
histoF.setIsDataMC(isData=False, isMC=True)
styleG = styles.ttStyle
styleF = styles.signalStyleHToTB1000
#hG = histograms.Histo(histoBkg3, legNameBkg3, "F", "HIST9" )
#background3_histo = histograms.Histo(histoBkg3, legNameBkg3, "F", "HIST9" )
styleG.apply(HG)
styleF.apply(HF)
myList.append(histoG)
myList.append(histoF)
#myList.append(HF)
#myList.append(HG)
# p = plots.PlotBase(datasetRootHistos=myList, saveFormats=[])
_kwargs = {
"xlabel": _xlabel,
"ylabel": "Arbitrary Units / %s" % (_format),
"ratioYlabel": "Ratio ",
"ratio": False,
"ratioInvert": False,
"stackMCHistograms": False,
"addMCUncertainty": False,
"addLuminosityText": False,
"addCmsText": True,
"cmsExtraText": "Preliminary",
"opts": {
"ymin": 0.0,
"ymaxfactor": 1.1
},
"opts2": {
"ymin": 0.6,
"ymax": 1.5
},
"log": False,
#"createLegend" : {"x1": 0.5, "y1": 0.75, "x2": 0.9, "y2": 0.9},
"createLegend": {
"x1": 0.58,
"y1": 0.65,
"x2": 0.92,
"y2": 0.82
},
}
'''
plots.drawPlot(p,
hG,
xlabel = _xlabel,
ylabel = "Arbitrary Units / %s" % (_format),
log = logY,
rebinX = _rebinX, cmsExtraText = "Preliminary",
createLegend = {"x1": 0.58, "y1": 0.65, "x2": 0.92, "y2": 0.82},
opts = _opts,
opts2 = {"ymin": 0.6, "ymaxfactor": 1.1},
cutBox = _cutBox,
)
'''
# Save plot in all formats
saveName = hG.split("/")[-1]
#plots.drawPlot(p, saveName, **_kwargs)
savePath = os.path.join(opts.saveDir, "HplusMasses",
hG.split("/")[0], opts.optMode)
plots.drawPlot(p, savePath, **_kwargs)
SavePlot(p,
saveName,
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png", ".pdf", ".C"])
#SavePlot(p, saveName, savePath)
#p = plots.DataMCPlot2( myList, saveFormats=[])
#p.setLuminosity(opts.intLumi)
#p.setDefaultStyles()
# Draw the plot and save it
#hName = hG
#plots.drawPlot(p, hName, **_kwargs)
#SavePlot(p, hName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png"])
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
parser.add_option("--individual", dest="individual", action="store_true", default=False, help="Generates individual plots for shape ratios instead of one large plot")
parser.add_option("--cardpattern", dest="cardPattern", action="store", default=None, help="Pattern of datacard with MM denoting mass value")
parser.add_option("--rootfilepattern", dest="rootfilePattern", action="store", default=None, help="Pattern of root file with MM denoting mass value")
(opts, args) = parser.parse_args()
if opts.helpStatus:
parser.print_help()
sys.exit()
# Create directory for plots
if not os.path.exists(_dirname):
os.mkdir(_dirname)
# Apply TDR style
style = tdrstyle.TDRStyle()
histograms.createLegend.moveDefaults(dx=-0.1, dh=-0.15)
histograms.uncertaintyMode.set(histograms.Uncertainty.StatOnly)
styles.ratioLineStyle.append(styles.StyleLine(lineColor=13))
# Find out the mass points
nameList = []
allShapeNuisances = []
signalTable = {}
myDatacardPattern = ""
myRootfilePattern = ""
if opts.cardPattern == None:
mySettings = limitTools.GeneralSettings(".",[])
myDatacardPattern = mySettings.getDatacardPattern(limitTools.LimitProcessType.TAUJETS)
myRootfilePattern = mySettings.getRootfilePattern(limitTools.LimitProcessType.TAUJETS)
else:
myDatacardPattern = opts.cardPattern.replace("MMM","M%s").replace("MM","%s")
myRootfilePattern = opts.rootfilePattern.replace("MMM","M%s").replace("MM","%s")
massPoints = DatacardReader.getMassPointsForDatacardPattern(".", myDatacardPattern)
def main(opts, signalMass):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(False)
style.setGridY(False)
# Do the topSelection histos
folder = ""
#=== Define styles: Fixme: Put in function
#styles_S = styles.StyleCompound([styles.StyleMarker(markerSize=0.4, markerColor=928, markerSizes=None, markerStyle=ROOT.kFullDiamond),
# styles.StyleLine(lineColor=928, lineStyle=ROOT.kSolid, lineWidth=3),
# styles.StyleFill(fillColor=38)])
styles_S = styles.StyleCompound([styles.StyleMarker(markerSize=0.4, markerColor=928, markerSizes=None, markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=928, lineStyle=ROOT.kSolid, lineWidth=3)])
styles_B = styles.StyleCompound([styles.StyleMarker(markerSize=0.4, markerColor=2, markerSizes=None, markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=2, lineStyle=ROOT.kSolid, lineWidth=3),
styles.StyleFill(fillColor=2, fillStyle=3005)])
styles_SEff = styles.StyleCompound([styles.StyleMarker(markerSize=0.4, markerColor=ROOT.kBlue, markerSizes=None, markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kBlue, lineStyle=ROOT.kSolid, lineWidth=3),])
styles_BEff = styles.StyleCompound([styles.StyleMarker(markerSize=0.4, markerColor=ROOT.kRed, markerSizes=None, markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kRed, lineStyle=ROOT.kSolid, lineWidth=3),])
styles_Signif = styles.StyleCompound([styles.StyleMarker(markerSize=0.4, markerColor=ROOT.kGreen+2, markerSizes=None, markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kGreen+2, lineStyle=ROOT.kSolid, lineWidth=3),])
styles_pur = styles.StyleCompound([styles.StyleMarker(markerSize=0.4, markerColor=ROOT.kOrange+7, markerSizes=None, markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kOrange+7, lineStyle=ROOT.kSolid, lineWidth=3),])
styles_pureff = styles.StyleCompound([styles.StyleMarker(markerSize=0.4, markerColor=ROOT.kOrange, markerSizes=None, markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kOrange, lineStyle=ROOT.kSolid, lineWidth=3),])
#=== Histo Names ============================================
hN_SignalEff = "sigEffi"
hN_BgdEff = "bgdEffi"
hN_signif = "significance_BDTG"
hN_purS = "purS_BDTG"
hN_effpurS = "effpurS_BDTG"
hN_BDTs = "MVA_BDTG_S"
hN_BDTb = "MVA_BDTG_B"
#=== Directories ============================================
inpfile_eff = ROOT.TFile(opts.effFile, "R")
inpfile = ROOT.TFile(opts.rootfile, "R") #To be fixed!
inpfile_dir = inpfile.Get("Method_BDT/BDTG")
#=== Get histograms from directories=========================
h_SignalEffi = inpfile_eff.Get(hN_SignalEff)
h_BgdEffi = inpfile_eff.Get(hN_BgdEff)
h_signif = inpfile_eff.Get(hN_signif)
h_purS = inpfile_eff.Get(hN_purS)
h_effpurS = inpfile_eff.Get(hN_effpurS)
h_BDTs = inpfile_dir.Get(hN_BDTs)
h_BDTb = inpfile_dir.Get(hN_BDTb)
#=== Scale Significance
h_signifScaled = h_signif.Clone("signif")
maxSignif = h_signif.GetMaximum()
h_signifScaled.Scale(1/maxSignif)
histo_signalEff = histograms.Histo(h_SignalEffi, "sEff", legendStyle = "LP", drawStyle="LP")
histo_bgdEff = histograms.Histo(h_BgdEffi, "bEff", legendStyle = "LP", drawStyle="LP")
histo_signifScaled = histograms.Histo(h_signifScaled, "signif", legendStyle = "LP", drawStyle="LP")
histo_purS = histograms.Histo(h_purS, "purity", legendStyle = "LP", drawStyle="LP")
histo_effpurS = histograms.Histo(h_effpurS, "pureff", legendStyle = "LP", drawStyle="LP")
histo_BDTs = histograms.Histo(h_BDTs, "bdtS", legendStyle = "LP", drawStyle="LP")
histo_BDTb = histograms.Histo(h_BDTb, "bdtB", legendStyle = "LP", drawStyle="LP")
#=== Efficiency - Purity - Significance=================================
histoList_Eff = [histo_bgdEff, histo_signifScaled, histo_purS, histo_effpurS]
p_Eff = plots.ComparisonManyPlot(histo_signalEff, histoList_Eff,
saveFormats=[])
p_Eff.histoMgr.setHistoLegendLabelMany({"sEff": "Signal efficiency",
"bEff": "Bkg efficiency",
"signif": "S/#sqrt{S+B}",
#"signif": "#frac{S}{#sqrt{S+B}}",
"purity": "Signal purity",
"pureff": "Signal eff*purity",})
# Define Right Axis (Significance)
signifColor = ROOT.kGreen+2
#rightAxis = ROOT.TGaxis(0.5, 0.17, 0.97, 0.34, 0, 1.1*maxSignif, 510, "+L")
'''
TGaxis::TGaxis(Double_t xmin, Double_t ymin, Double_t xmax, Double_t ymax,
Double_t wmin, Double_t wmax, Int_t ndiv, Option_t *chopt,
Double_t gridlength)
'''
rightAxis = ROOT.TGaxis(1, 0, 1, 1.1, 0, 1.1*maxSignif, 510, "+L")
rightAxis.SetWmax(1.1*maxSignif)
rightAxis.SetLineColor ( signifColor )
rightAxis.SetLabelColor( signifColor )
rightAxis.SetTitleColor( signifColor )
# rightAxis.SetTitleSize( info->sSig->GetXaxis()->GetTitleSize() )
# rightAxis.SetLabelOffset(0.9)
# This will only be be visible if we use a wide canvas (but we don't want that)
if 0:
style.setWide(True, 0.15) #to make way for significane label
rightAxis.SetTitle( "Significance" )
# rightAxis.SetTitleOffset(1.1)
#=== BDTG output ========================================================
histo_BDTs = histograms.Histo(h_BDTs, "bdtS", legendStyle = "LP", drawStyle="LP")
histo_BDTb = histograms.Histo(h_BDTb, "bdtB", legendStyle = "F", drawStyle="LP")
#p_BDT = plots.ComparisonPlot(histo_BDTb, histo_BDTs, saveFormats=[])
p_BDT = plots.ComparisonPlot(histo_BDTs, histo_BDTb, saveFormats=[])
p_BDT.histoMgr.setHistoLegendLabelMany({"bdtS": "Signal", "bdtB": "Background"})
p_Eff.histoMgr.forHisto("sEff", styles_SEff)
p_Eff.histoMgr.setHistoDrawStyle("sEff", "L")
p_Eff.histoMgr.setHistoLegendStyle("sEff", "L")
p_Eff.histoMgr.forHisto("bEff", styles_BEff)
p_Eff.histoMgr.setHistoDrawStyle("bEff", "L")
p_Eff.histoMgr.setHistoLegendStyle("bEff", "L")
p_Eff.histoMgr.forHisto("signif", styles_Signif)
p_Eff.histoMgr.setHistoDrawStyle("signif", "L")
p_Eff.histoMgr.setHistoLegendStyle("signif", "L")
p_Eff.histoMgr.forHisto("purity", styles_pur)
p_Eff.histoMgr.setHistoDrawStyle("purity", "L")
p_Eff.histoMgr.setHistoLegendStyle("purity", "L")
p_Eff.histoMgr.forHisto("pureff", styles_pureff)
p_Eff.histoMgr.setHistoDrawStyle("pureff", "L")
p_Eff.histoMgr.setHistoLegendStyle("pureff", "L")
p_BDT.histoMgr.forHisto("bdtS", styles_S)
p_BDT.histoMgr.setHistoDrawStyle("bdtS", "HIST")
p_BDT.histoMgr.setHistoLegendStyle("bdtS", "L")
p_BDT.histoMgr.forHisto("bdtB", styles_B)
p_BDT.histoMgr.setHistoDrawStyle("bdtB", "HIST")
p_BDT.histoMgr.setHistoLegendStyle("bdtB", "F")
saveName_eff = "mvaeff"
saveName = "mva"
savePath = os.path.join(opts.saveDir, opts.optMode)
_kwargs = {
"xlabel" : "BDTG", #"Cut value applied on BDTG output",
"ylabel" : "Efficiency (Purity)",
"ratioYlabel" : "Ratio ",
"ratio" : False,
"ratioInvert" : False,
"stackMCHistograms": False,
"addMCUncertainty" : False,
"addLuminosityText": False,
"addCmsText" : True,
"cmsExtraText" : "Preliminary",
"opts" : {"xmin": -1.0, "xmax": 1.01, "ymin": 0.0, "ymax": 1.1},
"opts2" : {"ymin": 0.6, "ymax": 1.5},
"log" : False,
"createLegend" : {"x1": 0.45, "y1": 0.45, "x2": 0.70, "y2": 0.70},
}
ROOT.gStyle.SetNdivisions(6 + 100*5 + 10000*2, "X") #ROOT.gStyle.SetNdivisions(8, "X")
plots.drawPlot(p_Eff, savePath, **_kwargs)
rightAxis.Draw()
saveDir = opts.rootfile.split("/")[-1]
saveDir = saveDir.replace(".root","")
SavePlot(p_Eff, saveName_eff, os.path.join(opts.saveDir, saveDir, opts.optMode), saveFormats = [".png", ".pdf", ".C"])
_kwargs["log"] = True
_kwargs["opts"]["ymin"] = 3e-2
_kwargs["opts"]["ymax"] = 20
_kwargs["xlabel"] = "BDTG"# response"
_kwargs["ylabel"] = "(1/N) dN/dx"
_kwargs["createLegend"] = {"x1": 0.25, "y1": 0.75, "x2": 0.50, "y2": 0.9}
style.setWide(False, 0.15)
ROOT.gStyle.SetNdivisions(6 + 100*5 + 10000*2, "X")
plots.drawPlot(p_BDT, savePath, **_kwargs)
SavePlot(p_BDT, saveName, os.path.join(opts.saveDir, saveDir, opts.optMode), saveFormats = [".png", ".pdf", ".C"])
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
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(opts.gridx)
style.setGridY(opts.gridy)
style.setLogX(opts.logx)
style.setLogY(opts.logy)
# Create legend and set style
histograms.createLegend.moveDefaults(dx=-0.1, dh=-0.15)
histograms.uncertaintyMode.set(histograms.Uncertainty.StatOnly)
styles.ratioLineStyle.append(styles.StyleLine(lineColor=13))
# Define some variables
nameList = []
allShapeNuisances = []
signalTable = {}
myDatacardPattern = ""
myRootfilePattern = ""
# Find out the mass points
if opts.cardPattern == None:
mySettings = limitTools.GeneralSettings(".", [])
myDatacardPattern = mySettings.getDatacardPattern(
limitTools.LimitProcessType.TAUJETS)
myRootfilePattern = mySettings.getRootfilePattern(
limitTools.LimitProcessType.TAUJETS)
else:
myDatacardPattern = opts.cardPattern.replace("MMM", "M%s").replace(
"MM", "%s")
myRootfilePattern = opts.rootfilePattern.replace("MMM", "M%s").replace(
"MM", "%s")
# Get mass points to consider
massPoints = DatacardReader.getMassPointsForDatacardPattern(
".", myDatacardPattern)
Print(
"The following masses will be considered: %s" %
(ShellStyles.HighlightAltStyle() + ", ".join(massPoints) +
ShellStyles.NormalStyle()), True)
# For-loop: All mass points
for i, m in enumerate(massPoints, 1):
# Obtain luminosity from the datacard
myLuminosity = float(
limitTools.readLuminosityFromDatacard(".", myDatacardPattern % m))
# Do the plots
doPlot(opts, int(m), nameList, allShapeNuisances, myLuminosity,
myDatacardPattern, myRootfilePattern, signalTable)
# Print signal table
Print("Max contracted uncertainty for signal:", True)
table = []
align = "{:>15} {:>15} {:>15}"
hLine = "=" * 50
table.append(hLine)
table.append(align.format("Systematic", "Minimum", "Maximum"))
table.append(hLine)
# For-loop: All signal
for i, k in enumerate(signalTable.keys(), 1):
# Print("Key = %s" % (k), False)
minVal = "%.3f" % (signalTable[k]["min"])
maxVal = "%.3f" % (signalTable[k]["max"])
msg = align.format(k, minVal, maxVal)
table.append(msg)
table.append(hLine)
for row in table:
Print(row, False)
msg = "All results under directory %s" % (
ShellStyles.SuccessStyle() + opts.dirName + ShellStyles.NormalStyle())
Print(msg, True)
return
def Plot_LostEvents(datasetsMgr, datasetsMgrRef, hT, hR, intLumi):
#kwargs = {}
_kwargs = {}
#kwargs = GetHistoKwargs(hG, opts)
if opts.normaliseToOne:
pT = plots.MCPlot(datasetsMgr,
hT,
normalizeToOne=True,
saveFormats=[],
**_kwargs)
pR = plots.MCPlot(datasetsMgrRef,
hR,
normalizeToOne=True,
saveFormats=[],
**_kwargs)
else:
pT = plots.MCPlot(datasetsMgr,
hT,
normalizeToLumi=intLumi,
saveFormats=[],
**_kwargs)
pR = plots.MCPlot(datasetsMgrRef,
hR,
normalizeToLumi=intLumi,
saveFormats=[],
**_kwargs)
#Soti
datasetTarg = datasetsMgr.getDataset(
"TT") #ChargedHiggs_HplusTB_HplusToTB_M_500
datasetRef = datasetsMgrRef.getDataset(
"TT") #ChargedHiggs_HplusTB_HplusToTB_M_500
p0T = plots.MCPlot(datasetsMgr,
hT,
normalizeToLumi=opts.intLumi,
saveFormats=[],
**_kwargs)
HT_ = p0T.histoMgr.getHisto(
datasetTarg.getName()).getRootHisto().Clone("target")
p0R = plots.MCPlot(datasetsMgr,
hR,
normalizeToLumi=opts.intLumi,
saveFormats=[],
**_kwargs)
HR_ = p0R.histoMgr.getHisto(
datasetRef.getName()).getRootHisto().Clone("reference")
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.2f"
_xlabel = None
logY = False
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
print "HT", hT
xMax = 200
_kwargs = {
"xlabel": _xlabel,
"ylabel": "Events / %s" % (_format),
"ratioYlabel": "Ratio ",
"ratio": False,
"ratioInvert": False,
"stackMCHistograms": False,
"addMCUncertainty": False,
"addLuminosityText": False,
"addCmsText": False,
"cmsExtraText": "Preliminary",
#"opts" : {"ymin": 0.1, "ymaxfactor": 1.2},
"opts": {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": xMax
},
"opts2": {
"ymin": 0.6,
"ymax": 1.5
},
"log": True,
#"createLegend" : {"x1": 0.5, "y1": 0.75, "x2": 0.9, "y2": 0.9},
"createLegend": {
"x1": 0.58,
"y1": 0.65,
"x2": 0.92,
"y2": 0.82
},
"rebinX": 1,
}
_units = "units"
if "counters" in hT.lower():
ROOT.gStyle.SetLabelSize(16.0, "X")
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 6
}
_kwargs["createLegend"] = {
"x1": 0.65,
"y1": 0.6,
"x2": 0.90,
"y2": 0.77
},
if "pt" in hT.lower():
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T} (%s)" % _units
_kwargs["xmax"] = 800
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 800
}
if "mass" in hT.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "M (%s)" % _units
_kwargs["xmax"] = 800
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 800
}
if "tetrajet" in hT.lower():
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 2000
}
_kwargs["rebinX"] = 8
if "trijetmass" in hT.lower():
print "mass"
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{top} (%s)" % _units
#_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_kwargs["xmax"] = 400 #1005
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 400
}
if "eta" in hT.lower():
_units = ""
_format = "%0.1f " + _units
_xlabel = "#eta (%s)" % _units
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": -5.0,
"xmax": +5.0
}
if "mult" in hT.lower():
_format = "%0.0f"
if "cleaned" in hT.lower():
_kwargs["xmax"] = 10
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 10
}
else:
_kwargs["xmax"] = 60
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 60
}
if "bdisc" in hT.lower():
_format = "%0.2f"
_kwargs["xmax"] = 1
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 1
}
if "topbdt" in hT.lower():
_format = "%0.2f"
_kwargs["xmax"] = 1
_kwargs["xmin"] = 0.3
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0.3,
"xmax": 1.0
}
if "dijetmass" in hT.lower():
print "dijet-mass"
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{W} (%s)" % _units
_opts["xmax"] = 600
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 300
}
else:
pass
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.5
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
else:
_opts["ymin"] = 1e0
myList = []
# Customise styling
pT.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
pR.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
h = datasetTarg.getDatasetRootHisto(hT)
#h.normalizeToOne()
h.normalizeToLuminosity(intLumi)
HT = h.getHistogram()
h = datasetRef.getDatasetRootHisto(hR)
#h.normalizeToOne()
h.normalizeToLuminosity(intLumi)
HR = h.getHistogram()
myHisto = HT.Clone("h_target-h_ref")
myHisto.Add(HR, -1)
if (HT.Integral() == HR.Integral()):
return
#Define Styles
altSignalBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kAzure + 9,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kAzure + 9,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kAzure + 9, fillStyle=3001)
])
altBackgroundBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kRed - 4,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kRed - 4,
lineStyle=ROOT.kSolid,
lineWidth=3),
#styles.StyleFill(fillColor=ROOT.kRed-4)
])
signalStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kTeal + 2,
markerSizes=None,
markerStyle=ROOT.kFullTriangleUp),
styles.StyleLine(lineColor=ROOT.kTeal + 2,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kTeal + 2, fillStyle=3001)
])
#signalStyle.apply(myHisto)
#altSignalBDTGStyle.apply(myHisto)
myHisto.SetFillStyle(3001)
myHisto.SetLineStyle(ROOT.kSolid)
#p0 = plots.PlotBase([Histo], saveFormats=[".png"])
#histograms.Histo(HT,"Target", "pl", "PL")
print "name", datasetTarg.getName()
if ("TT" in datasetTarg.getName()):
print "here1"
#p0.histoMgr.forHisto("h_target-h_ref", altBackgroundBDTGStyle )
altBackgroundBDTGStyle.apply(myHisto)
elif ("QCD" in datasetTarg.getName()):
print "here2"
#p0.histoMgr.forHisto("h_target-h_ref", styles.qcdFillStyle)
styles.qcdFillStyle.apply(myHisto)
elif ("Charged" in datasetTarg.getName()):
print "here3"
#p0.histoMgr.forHisto("h_target-h_ref", signalStyle)
signalStyle.apply(myHisto)
#p0.histoMgr.setHistoDrawStyle("Target", "HIST")
p0 = plots.PlotBase(
[histograms.Histo(myHisto, "h_target-h_ref", "pl", "PL")],
saveFormats=[])
p0.histoMgr.setHistoLegendStyle("h_target-h_ref", "LP") #F
p0.histoMgr.setHistoDrawStyle("h_target-h_ref", "HIST")
#p0.histoMgr.setHistoDrawStyle("Reference" , "HIST")
#p0.histoMgr.setHistoLegendStyle("Reference", "F") #LP
# Save plot
dName = datasetTarg.getName()
dName = dName.replace("ChargedHiggs_HplusTB_HplusToTB_", "")
saveName = hT.split("/")[-1] + "_" + dName + "_TopBug"
savePath = os.path.join(opts.saveDir, hT.split("/")[0], opts.optMode)
#plots.drawPlot(p0, savePath, **_kwargs)
plots.drawPlot(
p0,
saveName,
xlabel="label (%s)" % (_units),
ylabel="yLabel",
log=True,
rebinX=2, #2, 5
cmsExtraText="Preliminary",
#createLegend = _leg,
createLegend={
"x1": 0.58,
"y1": 0.65,
"x2": 0.92,
"y2": 0.82
},
#opts = {"xmin": 0.0, "xmax": xMax, "ymin": 0.1, "ymaxfactor": 1.3},
opts={
"ymin": 0.1,
"ymaxfactor": 1.3
},
opts2={
"ymin": 0.6,
"ymax": 1.4
},
cutBox={
"cutValue": 0,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
})
leg = ROOT.TLegend(0.2, 0.8, 0.51, 0.87)
leg.SetFillStyle(0)
leg.SetFillColor(0)
leg.SetBorderSize(0)
datasetName = datasetTarg.getName()
datasetName = datasetName.replace("TT", "t#bar{t}")
if "ChargedHiggs" in datasetName:
datasetName = datasetName.replace("ChargedHiggs_HplusTB_HplusToTB_M_",
"m_{H+} = ")
datasetName = datasetName + "GeV"
leg.SetHeader(datasetName)
#leg.Draw()
SavePlot(p0, saveName, savePath)
return
def PlotHistograms(datasetsMgr, dName, var):
# Definitions
kwargs = {}
histos = []
# For-loop: All histos in list
#dName = "ChargedHiggs_HplusTB_HplusToTB_M_%s" % (opts.signalMass)
hPathName = "hplus2tb_/AllCleanedTop_" + var
hPathName_noSF = "hplus2tb_/AllCleanedTop_" + var + "_noSF"
hPathName_noSF = hPathName_noSF.replace("AllCleanedTop_Pt_noSF",
"AllCleanedTop_Pt_noSFt")
# SF applied
p0 = plots.MCPlot(datasetsMgr,
hPathName,
normalizeToLumi=opts.intLumi,
saveFormats=[],
**kwargs)
hTopSF = p0.histoMgr.getHisto(dName).getRootHisto().Clone("wSF")
histos.append(hTopSF)
# no SF applied
p1 = plots.MCPlot(datasetsMgr,
hPathName_noSF,
normalizeToLumi=opts.intLumi,
saveFormats=[],
**kwargs)
hTop_noSF = p1.histoMgr.getHisto(dName).getRootHisto().Clone("noSF")
histos.append(hTop_noSF)
#if opts.normaliseToOne:
# hTopSF = hTopSF.Scale(1/hTopSF.Integral())
# hTop_noSF = hTop_noSF.Scale(1/hTop_noSF.Integral())
# Make comparison plot
'''
nTotal = hInclusive.Integral()
align = "{:>25} {:>25}"
title = align.format("Histogram", "Percentage (%)")
hLine = 50*"="
table = []
table.append(hLine)
table.append(title)
table.append(hLine)
for h in histos:
n = h.Integral()
perc = (n/nTotal)*100
table.append(align.format(h.GetName(), " %.1f" % (perc) ))
if opts.normaliseToOne:
h = h.Scale(1/h.Integral())
# Print table info
table.append(hLine)
for row in table:
print row
'''
# Make comparison plot
#p = plots.ComparisonManyPlot(hTop_noSF, [hTopSF], saveFormats=[])
p = plots.ComparisonManyPlot(hTopSF, [hTop_noSF], saveFormats=[])
p.setLuminosity(opts.intLumi)
# Overwite signal style?
style = [200, 500, 800, 1000, 2000, 5000]
lstyle = [
ROOT.kSolid, ROOT.kDashed, ROOT.kDashDotted, ROOT.kDotted,
ROOT.kDotted, ROOT.kSolid
]
altSignalBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kAzure + 9,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kAzure + 9,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kAzure + 9, fillStyle=3001)
])
altBackgroundBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kRed - 4,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kRed - 4,
lineStyle=ROOT.kSolid,
lineWidth=3),
#styles.StyleFill(fillColor=ROOT.kRed-4)
])
signalStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kTeal + 2,
markerSizes=None,
markerStyle=ROOT.kFullTriangleUp),
styles.StyleLine(lineColor=ROOT.kTeal + 2,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kTeal + 2, fillStyle=3001)
])
if ("TT" in dName):
p.histoMgr.forHisto("wSF", altBackgroundBDTGStyle)
p.histoMgr.forHisto("noSF", altSignalBDTGStyle)
elif ("QCD" in dName):
p.histoMgr.forHisto(
"wSF", altBackgroundBDTGStyle) #styles.getABCDStyle("VR"))
p.histoMgr.forHisto("noSF", styles.qcdFillStyle)
elif ("Charged" in dName):
p.histoMgr.forHisto("wSF", altBackgroundBDTGStyle)
p.histoMgr.forHisto("noSF", signalStyle)
#p.histoMgr.forHisto("wSF" , styles.getFakeBLineStyle()) #styles.getFakeBStyle())
#p.histoMgr.forHisto("noSF" , styles.mcStyle) #getInvertedLineStyle())
# Set draw style
p.histoMgr.setHistoDrawStyle("noSF", "HIST")
p.histoMgr.setHistoDrawStyle("wSF", "HIST")
#p.histoMgr.setHistoDrawStyle("Matched-LdgTrijet", "HIST")
#p.histoMgr.setHistoDrawStyle("Matched-LdgTrijet-Bjet", "HIST")
#p.histoMgr.setHistoDrawStyle("Unmatched", "HIST")
# Set legend style
p.histoMgr.setHistoLegendStyle("noSF", "LP")
p.histoMgr.setHistoLegendStyle("wSF", "LP")
#p.histoMgr.setHistoLegendStyle("Matched-LdgTrijet", "F")
#p.histoMgr.setHistoLegendStyle("Matched-LdgTrijet-Bjet", "L")
#p.histoMgr.setHistoLegendStyle("Unmatched", "F")
p.histoMgr.setHistoLegendLabelMany({
"noSF": "no SF applied",
"wSF": "SF applied",
#"Matched-LdgTrijet" : "top match",
#"Matched-LdgTrijet-Bjet" : "full match", #"top + b-jet match",
#"Unmatched" : "Combinatoric",
})
# Draw customised plot
saveName = "AllCleanedTop_" + var
_units = "GeV/c^{2}"
if opts.signalMass > 500:
_leg = {"x1": 0.70, "y1": 0.75, "x2": 0.5, "y2": 0.97}
#_leg = {"x1": 0.60, "y1": 0.65, "x2": 0.85, "y2": 0.87}
else:
#_leg = {"x1": 0.60, "y1": 0.65, "x2": 0.85, "y2": 0.87}
_leg = {"x1": 0.68, "y1": 0.78, "x2": 0.93, "y2": 0.9}
if opts.normaliseToOne:
yLabel = "Arbitrary Units / %0.1f " + _units
else:
yLabel = "Events / %0.1f " + _units
if opts.logY:
ymin = 1e-3
ymaxf = 5
else:
ymin = 1e-3
ymaxf = 1.2
ROOT.gStyle.SetNdivisions(6 + 100 * 5 + 10000 * 2, "X")
if "AllCleanedTop_M" in hPathName:
plots.drawPlot(
p,
saveName,
#xlabel = "m_{jjbb} (%s)" % (_units),
ylabel=yLabel,
log=opts.logY,
rebinX=2, #2, 5
cmsExtraText="Preliminary",
ratio=True,
ratioType="errorPropagation", #"errorScale", "binomial"
divideByBinWidth=False,
ratioErrorOptions={
"numeratorStatSyst": False,
"denominatorStatSyst": True
},
ratioMoveLegend={
"dx": +0.21,
"dy": 0.03,
"dh": -0.08
},
ratioYlabel="Ratio ",
ratioInvert=True,
createLegend=_leg,
#opts = {"ymin": ymin, "ymaxfactor": ymaxf},
opts={
"xmin": 0.0,
"xmax": 400.0,
"ymin": ymin,
"ymaxfactor": ymaxf
},
opts2={
"ymin": 0.6,
"ymax": 1.4
},
cutBox={
"cutValue": opts.signalMass,
"fillColor": 16,
"box": False,
"line": False,
"greaterThan": True
})
else:
plots.drawPlot(
p,
saveName,
#xlabel = "m_{jjbb} (%s)" % (_units),
ylabel=yLabel,
log=opts.logY,
rebinX=2, #2, 5
cmsExtraText="Preliminary",
ratio=True,
ratioType="errorPropagation", #"errorScale", "binomial"
divideByBinWidth=False,
ratioErrorOptions={
"numeratorStatSyst": False,
"denominatorStatSyst": True
},
ratioMoveLegend={
"dx": +0.21,
"dy": 0.03,
"dh": -0.08
},
ratioYlabel="Ratio ",
ratioInvert=True,
createLegend=_leg,
opts={
"ymin": ymin,
"ymaxfactor": ymaxf
},
#opts = {"xmin": 0.0, "xmax": 1400.0, "ymin": ymin, "ymaxfactor": ymaxf},
opts2={
"ymin": 0.6,
"ymax": 1.4
},
cutBox={
"cutValue": opts.signalMass,
"fillColor": 16,
"box": False,
"line": False,
"greaterThan": True
})
# Save plot in all formats
savePath = os.path.join(opts.saveDir, opts.optMode)
SavePlot(p, saveName + "_" + dName, savePath)
return
