def PlotTurnOns(datasetsMgr, histoList, signal, PU, saveName=None):
# Get Histogram name and its kwargs
myRegex = "(?:TurnOn)(.*)"
m = re.search(myRegex, histoList[0])
threshold = m.group(1)
if saveName==None:
#saveName = "TurnOns_%sGeV_%s" % (threshold, signal)
saveName = "TurnOns_%s_%s" % (threshold, signal)
kwargs = GetHistoKwargs(saveName, opts)
hList = []
legDict = {}
algos = getAlgos()
if "_all" in saveName:
algos = ["Inclusive", "1-prong", "3-prong", "#geq 1 #pi^{0}'s", "0 #pi^{0}'s"]
# For-loop: All tau algorithms
for l, hName in enumerate(histoList, 0):
algo = hName.split("_")[0]
msg = "Turn-on for \"%s\" algorithm (%s)" % (algo, signal)
aux.PrintFlushed(msg, False)
#aux.Print(msg, True)
h = datasetsMgr.getDataset(signal).getDatasetRootHisto(hName).getHistogram()
h.SetName(hName)
legDict[hName] = algos[l]
hList.append(h)
# Create the rate histograms
p = plots.ComparisonManyPlot(hList[0], hList[1:], saveFormats=[])
# Set legend labels
for i, h in enumerate(p.histoMgr.getHistos(), 0):
hName = h.getName()
algo = h.getName().split("_")[0]
if algo == "DiTau":
algo = hName.split("_")[-1]
if "_all" in saveName:
p.histoMgr.forHisto(hName, styles.getCaloStyle(i))
else:
p.histoMgr.forHisto(hName, styles.getTauAlgoStyle(algo))
p.histoMgr.setHistoDrawStyle(hName, "AP")
p.histoMgr.setHistoLegendStyle(hName, "P")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany(legDict)
# Draw and save the plot
saveName = saveName.replace("TurnOns_", "")
plots.drawPlot(p, saveName, **kwargs)
# Add additional canvas text
histograms.addPileupText("PU=%s" % (PU) )
histograms.addText(0.22, 0.86, plots._legendLabels[signal], 17)
# Save the plots in custom list of saveFormats
aux.SavePlot(p, opts.saveDir, saveName, opts.saveFormats, True)
return
def PlotRateVsEff(datasetsMgr, effHistoList, rateHistoList, signal, bkg, PU):
# Definitions
tgraphs = []
legendDict = {}
aux.PrintFlushed("Plotting ROC (%s-%s)" % (bkg, signal), False) #count==0)
# Get Histogram name and its kwargs
if "ditau" in effHistoList[0].lower():
if "TT_" in signal or "GluGlu" in signal:
pass
else:
return
saveName = "RateVsEff_DiTau_%s_PU%s" % (signal.split("_")[0], PU)
else:
saveName = "RateVsEff_SingleTau_%s_PU%s" % (signal.split("_")[0], PU)
kwargs_ = GetHistoKwargs(saveName, opts)
for i in range(0, len(effHistoList)):
if (i == 0):
g0 = convert2RateVsEffTGraph(datasetsMgr, effHistoList[i],
rateHistoList[i], signal, bkg)
g0.SetName("Calo")
elif (i == 1):
g1 = convert2RateVsEffTGraph(datasetsMgr, effHistoList[i],
rateHistoList[i], signal, bkg)
g1.SetName("Tk")
elif (i == 2):
g2 = convert2RateVsEffTGraph(datasetsMgr, effHistoList[i],
rateHistoList[i], signal, bkg)
g2.SetName("VtxIso")
elif (i == 3):
g3 = convert2RateVsEffTGraph(datasetsMgr, effHistoList[i],
rateHistoList[i], signal, bkg)
g3.SetName("RelIso")
elif (i == 4):
g4 = convert2RateVsEffTGraph(datasetsMgr, effHistoList[i],
rateHistoList[i], signal, bkg)
g4.SetName("Iso")
# Create the Rate Vs Efficiency TGraphs
p = plots.ComparisonManyPlot(g0, [g1, g2, g3, g4], saveFormats=[])
# Set individual styles
for index, h in enumerate(p.histoMgr.getHistos()):
hName = h.getName()
legendDict[hName] = styles.getCaloLegend(index)
p.histoMgr.forHisto(hName, styles.getCaloStyle(index))
p.histoMgr.setHistoDrawStyle(h.getName(),
"LX") # "X" = Do not draw error bars
p.histoMgr.setHistoLegendStyle(h.getName(), "LP")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany(legendDict)
# Draw and save the plot
plots.drawPlot(p, saveName,
**kwargs_) #the "**" unpacks the kwargs_ dictionary
# Draw Error bands
for i, g in enumerate([g0, g1, g2, g3, g4]):
shapes, min, max = DrawErrorBand(g)
for shape in shapes:
shape.SetFillColor(
p.histoMgr.getHistos()[i].getRootHisto().GetFillColor())
shape.SetFillStyle(3002)
shape.Draw("f same")
ROOT.gPad.RedrawAxis()
histograms.addPileupText("PU=%s" % (PU))
histograms.addText(0.60, 0.38, plots._legendLabels[signal], 17)
# Save the plots in custom list of saveFormats
aux.SavePlot(p, opts.saveDir, saveName, opts.saveFormats, True)
return
def PlotHistos(datasetsMgr, histoList, signal, PU, saveName=None):
# Get Histogram name and its kwargs
kwargs = GetHistoKwargs(saveName, opts)
hList = []
legDict = {}
algos = getAlgos()
if "_all" in saveName:
algos = ["Inclusive", "1-prong", "3-prong", "#geq 1 #pi^{0}'s", "0 #pi^{0}'s"]
if "_cif" in saveName:
#algos = ["Inclusive", "Central", "Intermediate", "Forward"]
algos = ["Inclusive", "|#eta| < 0.8 (C)", "0.8 < |#eta| < 1.6 (I)", "|#eta| > 1.6 (F)"]
# For-loop: All tau algorithms
for l, hName in enumerate(histoList, 0):
algo = hName.split("_")[0]
msg = "Resolution for \"%s\" algorithm (%s)" % (algo, signal)
aux.PrintFlushed(msg, False)
h = datasetsMgr.getDataset(signal).getDatasetRootHisto(hName).getHistogram()
h.SetName(hName)
legDict[hName] = algos[l]
hList.append(h)
# Create the rate histograms
if opts.normalizeToOne:
p = plots.ComparisonManyPlot(hList[0], hList[1:], saveFormats=[])
norm = True
for hist in p.histoMgr.getHistos():
if hist.getRootHisto().Integral() == 0:
norm = False
break
if (norm):
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().Scale(1.0/h.getRootHisto().Integral()) )
else:
aux.Print("Cannot normalise empty histo \"%s\" for dataset \"%s\"" % (hName, signal), True)
else:
p = plots.ComparisonManyPlot(hList[0], hList[1:], saveFormats=[])
# Set legend labels
for i, h in enumerate(p.histoMgr.getHistos(), 0):
hName = h.getName()
p.histoMgr.forHisto(hName, styles.getCaloStyle(i))
#p.histoMgr.setHistoDrawStyle(hName, "HIST")
#p.histoMgr.setHistoLegendStyle(hName, "L")
p.histoMgr.setHistoDrawStyle(hName, "AP")
p.histoMgr.setHistoLegendStyle(hName, "P")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany(legDict)
# Draw and save the plot
plots.drawPlot(p, saveName, **kwargs)
# Add additional canvas text
histograms.addPileupText("PU=%s" % (PU) )
histograms.addText(0.22, 0.86, plots._legendLabels[signal], 17)
# Save the plots in custom list of saveFormats
aux.SavePlot(p, opts.saveDir, saveName, opts.saveFormats, True)
return
def ComparisonPlot(datasetsMgr, json):
Verbose("Creating MC plot")
# Create the MC Plot with selected normalization ("normalizeToOne", "normalizeByCrossSection", "normalizeToLumi")
legendDict = {}
kwargs = {}
ylabel_ = json["ylabel"]
cutBox_ = {"cutValue": json["cutValue"] , "fillColor": json["cutFillColour"],
"box": json["cutBox"]=="True", "line": json["cutLine"]=="True",
"greaterThan": json["cutGreaterThan"]=="True"}
normToOne_ = json["normalizationToOne"]=="True"
if normToOne_:
ylabel_ = ylabel_.replace(json["ylabel"].split(" /")[0], "Arbitrary Units")
# Get the reference histo and the list of histos to compare
histoReference = getHisto(datasetsMgr, json['sample'], json['histograms'][0])
histoCompares = getHistos(datasetsMgr, json['sample'], json['histograms'])
p = plots.ComparisonManyPlot(histoReference, histoCompares, saveFormats=[])
# Set universal histo styles
p.histoMgr.setHistoDrawStyleAll(json["drawStyle"])
p.histoMgr.setHistoLegendStyleAll(json["legendStyle"])
# Set individual styles
for i in range(0, len(histoCompares)+1):
hName = "h%s-%s" % (i, json["sample"])
legendDict[hName] = styles.getCaloLegend(i)
p.histoMgr.forHisto(hName, styles.getCaloStyle(i) )
if 0:
p.histoMgr.setHistoDrawStyle(hName, "L")
p.histoMgr.setHistoLegendStyle(hName, "LP")
if json["drawStyle"]=="HIST":
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetFillStyle(0))
#p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerSize(0.0))
# Set legend labels
p.histoMgr.setHistoLegendLabelMany(legendDict)
# Draw a customised plot
saveName = os.path.join(json["saveDir"], json["title"])
# Create the customised plot
plots.drawPlot(p,
saveName,
xlabel = json["xlabel"],
ylabel = ylabel_,
rebinX = json["rebinX"],
stackMCHistograms = json["stackMCHistograms"]=="True",
addCmsText = json["addCmsText"]=="True",
cmsExtraText = json["cmsExtraText"],
opts = json["opts"],
opts2 = json["opts2"],
log = json["logY"]=="True",
moveLegend = json["moveLegend"],
cutBox = cutBox_,
ratio = json["ratio"]=="True",
ratioInvert = json["ratioInvert"]=="True",
ratioYlabel = json["ratioYlabel"],
)
# 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 all formats chosen by user
saveFormats = json["saveFormats"]
for i, ext in enumerate(saveFormats):
Print("%s" % saveName + ext, i==0)
p.saveAs(saveName, formats=saveFormats)
return