def PlotHistograms(datasetsMgr, histoName, histos, **kwargs_):
Verbose("Plotting Histograms")
# Set the saveName
saveName = histoName + "_Cumulative"
legendDict = {}
# Create & draw the plot
p = plots.ComparisonManyPlot(histos[0], histos[1:], saveFormats=[])
# Set individual styles
for index, h in enumerate(p.histoMgr.getHistos()):
p.histoMgr.setHistoDrawStyle(h.getName(), "AP")
p.histoMgr.setHistoLegendStyle(h.getName(), "LP")
legendDict[h.getName()] = plots._legendLabels[h.getName()]
plots._plotStyles[h.getName()].apply(
p.histoMgr.getHistos()[index].getRootHisto())
# Set legend labels
p.histoMgr.setHistoLegendLabelMany(legendDict)
# Draw and save the plot
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), [".pdf", ".png"])
return
def PlotRate(datasetsMgr, histoList, bkg, PU):
# Get Histogram name and its kwargs
taus = "SingleTau"
saveName = "Rate_%s_PU%s" % (taus, PU)
kwargs = GetHistoKwargs(saveName, opts)
hList = []
legDict = {}
algos = getAlgos()
# For-loop: All tau algorithms
for i, hName in enumerate(histoList, 0):
algo = hName.split("_")[0]
#if algo == "DiTau":
# taus = "DiTau"
# algo = hName.split("_")[-1]
if "ditau" in hName.lower():
return
#algo = hName.split("_")[-1]
#tau = hName.split("_")[1]
#if tau == "DiTau":
# taus = "DiTau"
# algo = hName.split("_")[-1]
aux.PrintFlushed("Plotting rate (%s-%s)" % (algo, taus), False)
h = datasetsMgr.getDataset(bkg).getDatasetRootHisto(
hName).getHistogram()
h.SetName(hName)
legDict[hName] = algos[i] #algo
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]
p.histoMgr.forHisto(hName, styles.getTauAlgoStyle(algo))
p.histoMgr.setHistoDrawStyle(hName, "HIST")
p.histoMgr.setHistoLegendStyle(hName, "L")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany(legDict)
# Draw and save the plot
saveName = "Rate_%s_PU%s" % (taus, PU)
plots.drawPlot(p, saveName, **kwargs)
# Add additional canvas text
histograms.addPileupText("PU=%s" % (PU))
histograms.addText(0.70, 0.88, taus, 17)
#histograms.addText(0.75, 0.88, taus, 17)
#histograms.addText(0.60, 0.78, plots._legendLabels[bkg], 17)
# Save the plots in custom list of saveFormats
aux.SavePlot(p, opts.saveDir, saveName, opts.saveFormats, True)
#print
return
def PlotHistograms(datasetsMgr, histoName):
Verbose("Plotting Data-MC Histograms")
# Get Histogram name and its kwargs
saveName = histoName.rsplit("/")[-1]
kwargs_ = GetHistoKwargs(saveName, opts)
kwargs ={}
ROOT.gStyle.SetNdivisions(8, "Z")
ROOT.gStyle.SetNdivisions(8, "Y")
# Get the reference histo and the list of histos to compare
datasets0 = datasetsMgr.getAllDatasets()[0].getName()
histoList = [getHisto(datasetsMgr, datasets0, histoName)]
if opts.normToOne:
for h in histoList:
h.normalizeToOne()
p = plots.PlotBase(histoList, saveFormats=[])
# Set universal histo styles
p.histoMgr.setHistoDrawStyleAll("COLZ")
p.histoMgr.setHistoLegendStyleAll("L")
# Customize histo
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetZaxis().SetTitle(kwargs_["zlabel"]))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetZaxis().SetTitleOffset(1.5))
# Get the x and y axis title
binWidthX = p.histoMgr.getHistos()[0].getRootHisto().GetXaxis().GetBinWidth(0)
binWidthY = p.histoMgr.getHistos()[0].getRootHisto().GetYaxis().GetBinWidth(0)
xlabel = p.histoMgr.getHistos()[0].getRootHisto().GetXaxis().GetTitle() + " / %s" % (GetBinwidthDecimals(binWidthX) % (binWidthX))
kwargs_["xlabel"] = xlabel
ylabel = p.histoMgr.getHistos()[0].getRootHisto().GetYaxis().GetTitle() + " / %s" % (GetBinwidthDecimals(binWidthY) % (binWidthY))
kwargs_["ylabel"] = ylabel
#p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMinimum(zmin))
#p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMaximum(zmax))
# Set default dataset style to all histos
for index, h in enumerate(p.histoMgr.getHistos()):
plots._plotStyles[p.histoMgr.getHistos()[index].getDataset().getName()].apply(p.histoMgr.getHistos()[index].getRootHisto())
# Draw and save the plot
plots.drawPlot(p, saveName, **kwargs_) #the "**" unpacks the kwargs_ dictionary
# Remove legend
p.removeLegend()
# Set log-z?
p.getPad().SetLogz(True)
# Additional text
histograms.addText(0.18, 0.89, plots._legendLabels[datasets0], 17)
# Save the plots in custom list of saveFormats
SavePlot(p, saveName, os.path.join(opts.saveDir, opts.optMode, opts.folder), [".pdf"])#, ".png"] )
return
def PlotEfficiency(datasetsMgr, histoList, signal, PU):
# Get Histogram name and its kwargs
taus = "SingleTau"
saveName = "Efficiency_%s_%s" % (taus, signal)
kwargs = GetHistoKwargs(saveName, opts)
hList = []
legDict = {}
# For-loop: All tau algorithms
count = -1
for i, hName in enumerate(histoList, 0):
algo = hName.split("_")[0]
if algo == "DiTau":
if "TT_" in signal or "GluGlu" in signal:
pass
else:
return
taus = "DiTau"
algo = hName.split("_")[-1]
count += 1
aux.PrintFlushed("Plotting efficiency (%s-%s-%s)" %
(algo, taus, signal), False) #count==0)
h = datasetsMgr.getDataset(signal).getDatasetRootHisto(
hName).getHistogram()
h.SetName(hName)
legDict[hName] = algo
hList.append(h)
# Create the rate histograms
p = plots.ComparisonManyPlot(hList[0], hList[1:], saveFormats=[])
# Set legend labels
for h in p.histoMgr.getHistos():
hName = h.getName()
algo = h.getName().split("_")[0]
if algo == "DiTau":
algo = hName.split("_")[-1]
p.histoMgr.forHisto(hName, styles.getTauAlgoStyle(algo))
p.histoMgr.setHistoDrawStyle(hName, "HIST")
p.histoMgr.setHistoLegendStyle(hName, "L")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany(legDict)
# Draw and save the plot
saveName = "Efficiency_%s_%s" % (taus, signal)
plots.drawPlot(p, saveName, **kwargs)
# Add additional canvas text
histograms.addPileupText("PU=%s" % (PU))
# histograms.addText(0.75, 0.88, taus, 17)
histograms.addText(0.60, 0.88, plots._legendLabels[signal], 17)
# Save the plots in custom list of saveFormats
aux.SavePlot(p, opts.saveDir, saveName, opts.saveFormats, True)
#print
return
def MCPlot(datasetsMgr, json):
Verbose("Creating MC plot")
# Create the MC Plot with selected normalization ("normalizeToOne", "normalizeByCrossSection", "normalizeToLumi")
ylabel_ = json["ylabel"]
if json["normalization"]=="normalizeToLumi":
kwargs = {}
p = plots.MCPlot(datasetsMgr, json["histogram"], normalizeToLumi=opts.intLumi, **kwargs)
elif json["normalization"]=="normalizeToOne":
ylabel_ = ylabel_.replace(json["ylabel"].split(" /")[0], "Arbitrary Units")
kwargs = {json["normalization"]: True}
p = plots.MCPlot(datasetsMgr, json["histogram"], **kwargs)
else:
raise Exception("Invalid normalization \"%s\"" % (json["normalization"]) )
# 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"]
# Draw a customised plot
saveName = os.path.join(json["saveDir"], json["title"])
plots.drawPlot(p,
saveName,
xlabel = json["xlabel"],
ylabel = ylabel_,
rebinX = json["rebinX"],
rebinY = json["rebinY"],
stackMCHistograms = json["stackMCHistograms"]=="True",
addMCUncertainty = json["addMCUncertainty"]=="True" and json["normalization"]!="normalizeToOne",
addLuminosityText = json["addLuminosityText"]=="True",
addCmsText = json["addCmsText"]=="True",
cmsExtraText = json["cmsExtraText"],
opts = json["opts"],
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 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
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 PlotHisto(dsetsMgr, h, histoType):
kwargs = {}
hList = getHistoList(dsetsMgr, h)
if 1:
p = plots.PlotSameBase(dsetsMgr,
h,
normalizeToOne=False,
saveFormats=[],
**kwargs)
else:
p = plots.MCPlot(dsetsMgr,
h,
normalizeToLumi=opts.intLumi,
saveFormats=[],
**kwargs)
p = plots.PlotSameBase(dsetsMgr, h, saveFormats=[], **kwargs)
p = plots.MCPlot(dsetsMgr,
h,
normalizeToOne=True,
saveFormats=[],
**kwargs)
# Set default styles (Called by default in MCPlot)
p._setLegendStyles()
p._setLegendLabels()
p._setPlotStyles()
# Customise legend
for d in dsetsMgr.getAllDatasetNames():
if "TH2" not in histoType:
if "pCalo" in h:
p.histoMgr.setHistoLegendStyle(d, "LP")
p.histoMgr.setHistoDrawStyle(d, "AP")
# Draw a customised plot
kwargs = GetHistoKwargs(h, opts)
plots.drawPlot(p, h, **kwargs)
# Add text
ptSlice = h.rsplit("_")[-1].replace("GE",
"#geq ").replace("to", " to ").replace(
"Pt", "p_{T} = ")
ptSlice += " GeV"
histograms.addText(0.20, 0.865, ptSlice, 18)
# Remove legend?
if kwargs["removeLegend"]:
p.removeLegend()
# Save in all formats chosen by user
aux.SavePlot(p, opts.saveDir, h, opts.saveFormats, opts.url)
return
def PlotHisto(datasetsMgr, h):
dsetsMgr = datasetsMgr.deepCopy()
opts.normalizeToOne = False
# Create the MC Plot with selected normalization ("normalizeToOne", "normalizeByCrossSection", "normalizeToLumi")
kwargs = {}
if opts.normalizeToOne:
#p = plots.MCPlot(dsetsMgr, h, normalizeToOne=True, saveFormats=[], **kwargs)
p = plots.PlotSameBase(dsetsMgr,
h,
normalizeToOne=True,
saveFormats=[],
**kwargs)
else:
#p = plots.MCPlot(dsetsMgr, h, normalizeToLumi=opts.intLumi, saveFormats=[], **kwargs)
p = plots.PlotSameBase(
dsetsMgr, h, saveFormats=[], **kwargs
) #def __init__(self, datasetMgr, name, normalizeToOne=False, datasetRootHistoArgs={}, **kwargs):
# Set default styles (Called by default in MCPlot)
p._setLegendStyles()
p._setLegendLabels()
p._setPlotStyles()
# Customise legend
# p.histoMgr.setHistoLegendStyleAll("L")
for d in dsetsMgr.getAllDatasetNames():
if "SingleNeutrino" in d:
p.histoMgr.setHistoLegendStyle(d, "F")
else:
p.histoMgr.setHistoLegendStyle(d, "L")
p.setLegend(
histograms.createLegend(
0.68, 0.85 - 0.05 * len(dsetsMgr.getAllDatasetNames()), 0.92,
0.92))
#p.histoMgr.setHistoLegendStyle("histo_" + dataset, "LP")
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().Scale(
1 / h.getRootHisto().GetBinContent(1)))
#
# Draw a customised plot
plots.drawPlot(p, h, **GetHistoKwargs(h, opts))
p.getFrame().GetXaxis().SetLabelSize(15)
p.getFrame().GetXaxis().LabelsOption("u")
# Remove legend?
if 0:
p.removeLegend()
# Save in all formats chosen by user
aux.SavePlot(p, opts.saveDir, h, opts.saveFormats, opts.url)
return
def PlotTurnOns(datasetsMgr, histoList, signal, PU):
# Get Histogram name and its kwargs
myRegex = "(?:TurnOn)(.*)"
m = re.search(myRegex, histoList[0])
threshold = m.group(1)
saveName = "TurnOns_%sGeV_%s" % (threshold, signal)
kwargs = GetHistoKwargs(saveName, opts)
hList = []
legDict = {}
# For-loop: All tau algorithms
for l, hName in enumerate(histoList, 0):
algo = hName.split("_")[0]
aux.PrintFlushed("Turn-on for \"%s\" algorithm (%s)" % (algo, signal),
False) #l==0)
h = datasetsMgr.getDataset(signal).getDatasetRootHisto(
hName).getHistogram()
h.SetName(hName)
legDict[hName] = algo
hList.append(h)
# Create the rate histograms
p = plots.ComparisonManyPlot(hList[0], hList[1:], saveFormats=[])
# Set legend labels
for h in p.histoMgr.getHistos():
hName = h.getName()
algo = h.getName().split("_")[0]
if algo == "DiTau":
algo = hName.split("_")[-1]
p.histoMgr.forHisto(hName, styles.getTauAlgoStyle(algo))
p.histoMgr.setHistoDrawStyle(hName, "AP")
p.histoMgr.setHistoLegendStyle(hName, "LP")
# 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(0.75, 0.88, taus, 17)
histograms.addText(0.60, 0.88 - 0.50, plots._legendLabels[signal], 17)
# Save the plots in custom list of saveFormats
aux.SavePlot(p, opts.saveDir, saveName, opts.saveFormats, True)
#print
return
def PlotRate(datasetsMgr, histoList, bkg, PU, taus):
# Get Histogram name and its kwargs
saveName = "Rate_%s_PU%s" % (
taus, PU
) # "Rate_" here incuded to apply customisations with kwargs. Drop later
kwargs = GetHistoKwargs(saveName, opts)
hList = []
legDict = {}
algos = getAlgos()
# For-loop: All tau algorithms
for i, hName in enumerate(histoList, 0):
algo = hName.split("_")[0]
aux.PrintFlushed("Plotting rate (%s-%s)" % (algo, taus), False)
h = datasetsMgr.getDataset(bkg).getDatasetRootHisto(
hName).getHistogram()
h.SetName(hName)
legDict[hName] = algos[i]
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]
p.histoMgr.forHisto(hName, styles.getTauAlgoStyle(algo))
p.histoMgr.setHistoDrawStyle(hName, "HIST")
p.histoMgr.setHistoLegendStyle(hName, "L")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany(legDict)
# Draw and save the plot
saveName = saveName.replace("Rate_",
"") #drop "Rate_" only AFTER getting kwargs
plots.drawPlot(p, saveName, **kwargs)
# Add additional canvas text
histograms.addPileupText("PU=%s" % (PU))
histograms.addText(0.66, 0.86, taus, 17)
# Save the plots in custom list of saveFormats
aux.SavePlot(p, opts.saveDir, saveName, opts.saveFormats, True)
#print
return
def PlotHisto(datasetsMgr, h):
dsetsMgr = datasetsMgr.deepCopy()
if "eff" in h.lower():
dsetsMgr.remove("SingleNeutrino_L1TPU140", close=False)
dsetsMgr.remove("SingleNeutrino_L1TPU200", close=False)
opts.normalizeToOne = False
elif "resolution" in h.lower(
) or "PoorNeuResol" in h or "match" in h.lower():
dsetsMgr.remove("SingleNeutrino_L1TPU140", close=False)
dsetsMgr.remove("SingleNeutrino_L1TPU200", close=False)
elif "rate" in h.lower():
opts.normalizeToOne = False
for d in dsetsMgr.getAllDatasetNames():
if "SingleNeutrino" in d:
continue
else:
dsetsMgr.remove(d, close=False)
else:
pass
# Create the MC Plot with selected normalization ("normalizeToOne", "normalizeByCrossSection", "normalizeToLumi")
kwargs = {}
'''
hList = getHistoList(dsetsMgr, h)
if opts.normalizeToOne:
if 1:
p = plots.ComparisonManyPlot(hList[0], hList[1:], saveFormats=[], **kwargs)
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().Scale(1.0/h.getRootHisto().Integral()) )
else:
# p = plots.MCPlot(dsetsMgr, h, normalizeToOne=True, saveFormats=[], **kwargs)
p = plots.PlotSameBase(dsetsMgr, h, normalizeToOne=True, saveFormats=[], **kwargs)
else:
if 1:
p = plots.ComparisonManyPlot(hList[0], hList[1:], saveFormats=[], **kwargs) #FIXME
else:
# p = plots.MCPlot(dsetsMgr, h, normalizeToLumi=opts.intLumi, saveFormats=[], **kwargs)
p = plots.PlotSameBase(dsetsMgr, h, saveFormats=[], **kwargs)
'''
if opts.normalizeToOne:
#p = plots.MCPlot(dsetsMgr, h, normalizeToOne=True, saveFormats=[], **kwargs)
p = plots.PlotSameBase(dsetsMgr,
h,
normalizeToOne=True,
saveFormats=[],
**kwargs)
else:
#p = plots.MCPlot(dsetsMgr, h, normalizeToLumi=opts.intLumi, saveFormats=[], **kwargs)
p = plots.PlotSameBase(
dsetsMgr, h, saveFormats=[], **kwargs
) #def __init__(self, datasetMgr, name, normalizeToOne=False, datasetRootHistoArgs={}, **kwargs):
# Set default styles (Called by default in MCPlot)
p._setLegendStyles()
p._setLegendLabels()
p._setPlotStyles()
# Customise legend
# p.histoMgr.setHistoLegendStyleAll("L")
for d in dsetsMgr.getAllDatasetNames():
if "SingleNeutrino" in d:
p.histoMgr.setHistoLegendStyle(d, "F")
else:
p.histoMgr.setHistoLegendStyle(d, "L")
p.setLegend(
histograms.createLegend(
0.68, 0.85 - 0.05 * len(dsetsMgr.getAllDatasetNames()), 0.77,
0.92))
#p.histoMgr.setHistoLegendStyle("histo_" + dataset, "LP")
# Draw a customised plot
plots.drawPlot(p, h, **GetHistoKwargs(h, opts))
if ("photons_egs_matching" in h.lower()):
p.getFrame().GetXaxis().SetLabelSize(14)
p.getFrame().GetXaxis().LabelsOption("u")
# Remove legend?
if 0:
p.removeLegend()
# Save in all formats chosen by user
aux.SavePlot(p, opts.saveDir, h, opts.saveFormats, opts.url)
return
def PlotHisto(datasetsMgr, h):
dsetsMgr = datasetsMgr.deepCopy()
if "_eff" in h.lower():
dsetsMgr.remove("SingleNeutrino_L1TPU140", close=False)
dsetsMgr.remove("SingleNeutrino_L1TPU200", close=False)
opts.normalizeToOne = False
elif "_deltargenp" in h.lower():
dsetsMgr.remove("SingleNeutrino_L1TPU140", close=False)
dsetsMgr.remove("SingleNeutrino_L1TPU200", close=False)
elif "_resolution" in h.lower():
dsetsMgr.remove("SingleNeutrino_L1TPU140", close=False)
dsetsMgr.remove("SingleNeutrino_L1TPU200", close=False)
elif "_rate" in h.lower():
opts.normalizeToOne = False
for d in dsetsMgr.getAllDatasetNames():
if "SingleNeutrino" in d:
continue
else:
dsetsMgr.remove(d, close=False)
else:
pass
# Create the plot with selected normalization ("normalizeToOne", "normalizeByCrossSection", "normalizeToLumi")
kwargs = {}
hList = getHistoList(dsetsMgr, h)
if opts.normalizeToOne:
if 1:
p = plots.ComparisonManyPlot(hList[0],
hList[1:],
saveFormats=[],
**kwargs)
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:
# p = plots.MCPlot(dsetsMgr, h, normalizeToOne=True, saveFormats=[], **kwargs)
p = plots.PlotSameBase(dsetsMgr,
h,
normalizeToOne=True,
saveFormats=[],
**kwargs)
else:
if 1:
p = plots.ComparisonManyPlot(hList[0],
hList[1:],
saveFormats=[],
**kwargs) #FIXME
else:
# p = plots.MCPlot(dsetsMgr, h, normalizeToLumi=opts.intLumi, saveFormats=[], **kwargs)
p = plots.PlotSameBase(dsetsMgr, h, saveFormats=[], **kwargs)
# Set default styles (Called by default in MCPlot)
p._setLegendStyles()
p._setLegendLabels()
p._setPlotStyles()
# Customise legend
for d in dsetsMgr.getAllDatasetNames():
if "SingleNeutrino" in d:
p.histoMgr.setHistoLegendStyle(d, "F")
else:
p.histoMgr.setHistoLegendStyle(d, "L")
p.histoMgr.setHistoDrawStyle(d, "HIST9")
#p.histoMgr.setHistoLegendStyle(d, "P") #"L"
#p.histoMgr.setHistoDrawStyle(d, "AP")
# Create legend
if 0:
p.setLegend(
histograms.createLegend(
0.18, 0.86 - 0.04 * len(dsetsMgr.getAllDatasetNames()), 0.42,
0.92))
else:
p.setLegend(
histograms.createLegend(
0.58, 0.86 - 0.04 * len(dsetsMgr.getAllDatasetNames()), 0.92,
0.92))
# Draw a customised plot
kwargs = GetHistoKwargs(h, opts)
plots.drawPlot(p, h, **kwargs)
# Remove legend?
if kwargs["removeLegend"]:
p.removeLegend()
# Save in all formats chosen by user
aux.SavePlot(p, opts.saveDir, h, opts.saveFormats, opts.url)
return
def PlotHisto(datasetsMgr, h):
dsetsMgr = datasetsMgr.deepCopy()
if "_resolution" in h.lower():
dsetsMgr.remove("SingleNeutrino_L1TPU140", close=False)
dsetsMgr.remove("SingleNeutrino_L1TPU200", close=False)
# Create the plot with selected normalization ("normalizeToOne", "normalizeByCrossSection", "normalizeToLumi")
kwargs = {}
hList = getHistoList(dsetsMgr, h)
if opts.normalizeToOne:
if 1:
p = plots.ComparisonManyPlot(hList[0],
hList[1:],
saveFormats=[],
**kwargs)
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:
# p = plots.MCPlot(dsetsMgr, h, normalizeToOne=True, saveFormats=[], **kwargs)
p = plots.PlotSameBase(dsetsMgr,
h,
normalizeToOne=True,
saveFormats=[],
**kwargs)
else:
if 1:
p = plots.ComparisonManyPlot(hList[0],
hList[1:],
saveFormats=[],
**kwargs) #FIXME
else:
# p = plots.MCPlot(dsetsMgr, h, normalizeToLumi=opts.intLumi, saveFormats=[], **kwargs)
p = plots.PlotSameBase(dsetsMgr, h, saveFormats=[], **kwargs)
# Set default styles (Called by default in MCPlot)
p._setLegendStyles()
p._setLegendLabels()
p._setPlotStyles()
# Overwrite default settings
cNoPU = ROOT.kAzure
cPU140 = ROOT.kRed
cPU200 = ROOT.kOrange #kYellow+2
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetFillColor(cNoPU) if "noPU" in h.getName(
) else h.getRootHisto().SetLineStyle(ROOT.kSolid))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetFillStyle(
3002) if "noPU" in h.getName() else h.getRootHisto().SetFillStyle(0))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineColor(cPU140)
if "PU140" in h.getName() else (h.getRootHisto(
).SetLineColor(cPU200) if "PU200" in h.getName(
) else h.getRootHisto().SetLineColor(cNoPU)))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerColor(cPU140)
if "PU140" in h.getName() else (h.getRootHisto(
).SetMarkerColor(cPU200) if "PU200" in h.getName(
) else h.getRootHisto().SetMarkerColor(cNoPU)))
# Customise legend
dsetName0 = dsetsMgr.getAllDatasetNames()[0].rsplit("_L1T")[0]
for d in dsetsMgr.getAllDatasetNames():
# Sanity check
dsetName = d.rsplit("_L1T")[0]
if dsetName0 != dsetName:
msg = "Different types of datasets detected (\"%s\" and \"%s\"). This script is designed for just one type of dataset for different PU" % (
dsetName, dsetName0)
raise Exception(es + msg + ns)
else:
# print "*"*100
# print dsetName0
# print dsetName
# print "*"*100
pass
if "noPU" in d:
p.histoMgr.setHistoLegendStyle(d, "L")
p.histoMgr.setHistoDrawStyle(d, "HIST")
else:
p.histoMgr.setHistoLegendStyle(d, "AP")
p.histoMgr.setHistoDrawStyle(d, "P")
# Create legend
if 0:
p.setLegend(
histograms.createLegend(
0.18, 0.86 - 0.04 * len(dsetsMgr.getAllDatasetNames()), 0.42,
0.92))
else:
p.setLegend(
histograms.createLegend(
0.68, 0.86 - 0.04 * len(dsetsMgr.getAllDatasetNames()), 0.98,
0.92))
# Draw a customised plot
kwargs = GetHistoKwargs(h, opts)
saveName = h.replace("", "")
#saveName += #iro
plots.drawPlot(p, h, **kwargs)
# Remove legend?
if kwargs["removeLegend"]:
p.removeLegend()
# Save in all formats chosen by user
aux.SavePlot(p, opts.saveDir, h, opts.saveFormats, opts.url)
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 MCPlot(datasetsMgr, json):
Verbose("Creating MC plot")
# Create the MC Plot with selected normalization ("normalizeToOne", "normalizeByCrossSection", "normalizeToLumi")
ylabel_ = json["ylabel"]
if json["normalization"] == "normalizeToLumi":
kwargs = {}
p = plots.MCPlot(datasetsMgr,
json["histogram"],
normalizeToLumi=opts.intLumi,
**kwargs)
elif json["normalization"] == "normalizeToOne":
ylabel_ = ylabel_.replace(json["ylabel"].split(" /")[0],
"Arbitrary Units")
kwargs = {json["normalization"]: True}
p = plots.MCPlot(datasetsMgr, json["histogram"], **kwargs)
else:
raise Exception("Invalid normalization \"%s\"" %
(json["normalization"]))
'''
if json["normalizationToOne"]=="True":
ylabel_ = ylabel_.replace(json["ylabel"].split(" /")[0], "Arbitrary Units")
kwargs = {json["normalizationToOne"]: True}
p = plots.MCPlot(datasetsMgr, json["histogram"], **kwargs)
else:
raise Exception("Invalid normalization \"%s\"" % (json["normalization"]) )
'''
# 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"]
# Set universal histo styles
if ("drawStyle" in json):
p.histoMgr.setHistoDrawStyleAll(json["drawStyle"])
if ("legendStyle" in json):
p.histoMgr.setHistoLegendStyleAll(json["legendStyle"])
# For-loop: All histos
for index, h in enumerate(p.histoMgr.getHistos()):
if index == 0:
continue
else:
p.histoMgr.setHistoDrawStyle(h.getName(), "AP")
p.histoMgr.setHistoLegendStyle(h.getName(), "AP")
# Set default dataset style to all histos
for index, h in enumerate(p.histoMgr.getHistos()):
plots._plotStyles[p.histoMgr.getHistos()
[index].getDataset().getName()].apply(
p.histoMgr.getHistos()[index].getRootHisto())
# Draw a customised plot
saveDir = "/afs/cern.ch/user/m/mtoumazo/public/html/hltaus/TkCalo/"
saveName = os.path.join(saveDir, json["saveName"])
plots.drawPlot(
p,
saveName,
xlabel=json["xlabel"],
ylabel=ylabel_,
rebinX=json["rebinX"],
rebinY=json["rebinY"],
stackMCHistograms=json["stackMCHistograms"] == "True",
addMCUncertainty=json["addMCUncertainty"] == "True"
and json["normalization"] != "normalizeToOne",
addLuminosityText=json["addLuminosityText"] == "True",
addCmsText=json["addCmsText"] == "True",
cmsExtraText=json["cmsExtraText"],
opts=json["opts"],
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 all formats chosen by user
saveFormats = [".pdf"]
for i, ext in enumerate(saveFormats):
saveNameURL = saveName + ext
saveNameURL = saveNameURL.replace(
"/afs/cern.ch/user/m/mtoumazo/public/html/hltaus/",
"https://cmsdoc.cern.ch/~mtoumazo/hltaus/")
if opts.url:
Print(saveNameURL, 1)
else:
Print(saveName + ext, 1)
p.saveAs(saveName, formats=saveFormats)
return
def PlotHisto(datasetsMgr, h):
Verbose("Plotting histogram %s" % (h), True)
dsetsMgr = datasetsMgr.deepCopy()
# Create the plot with selected normalization
kwargs = {}
hList = getHistoList(dsetsMgr, h)
if opts.normalizeToOne:
p = plots.ComparisonManyPlot(hList[0],
hList[1:],
saveFormats=[],
**kwargs)
# For-loop: All histograms in manager
normValue = 1.0
for hist in p.histoMgr.getHistos():
# print "hist.getRootHisto().GetName() = ", hist.getRootHisto().GetName()
# print "histo.getRootHistos().GetBinContent(1) = ", hist.getRootHisto().GetBinContent(1)
normValue = hist.getRootHisto().GetBinContent(1)
hist.getRootHisto().Scale(1.0 / normValue)
# Normalise wrt bin #1
#p.histoMgr.forEachHisto(lambda h: h.getRootHisto().Scale(1.0/normValue) )
else:
p = plots.ComparisonManyPlot(hList[0],
hList[1:],
saveFormats=[],
**kwargs)
#p = plots.PlotSameBase(dsetsMgr, h, saveFormats=[], **kwargs)
# Set default styles (Called by default in MCPlot)
p._setLegendStyles()
p._setLegendLabels()
p._setPlotStyles()
# Customise legend
for d in dsetsMgr.getAllDatasetNames():
if "SingleNeutrino" in d:
p.histoMgr.setHistoLegendStyle(d, "F")
else:
p.histoMgr.setHistoLegendStyle(d, "L")
p.histoMgr.setHistoDrawStyle(d, "HIST9")
# Create legend
if 0:
p.setLegend(
histograms.createLegend(
0.18, 0.86 - 0.04 * len(dsetsMgr.getAllDatasetNames()), 0.42,
0.92))
else:
p.setLegend(
histograms.createLegend(
0.58, 0.86 - 0.04 * len(dsetsMgr.getAllDatasetNames()), 0.92,
0.92))
# Draw a customised plot
kwargs = GetHistoKwargs(h, opts)
plots.drawPlot(p, h, **kwargs)
# Remove legend?
if kwargs["removeLegend"]:
p.removeLegend()
# Save in all formats chosen by user
aux.SavePlot(p, opts.saveDir, h, opts.saveFormats, opts.url)
return
def PlotHistograms(datasetsMgr, histoName):
# Customise histo
kwargs_ = GetHistoKwargs(histoName, opts)
# Get dataset
datasets0 = datasetsMgr.getAllDatasets()[0].getName()
# Get histograms for the ComparisonPlot
if "resVsEta" in histoName:
myHistos = getResolutionHistos(datasetsMgr,
datasets0,
histoName,
regions=["_L", "_M", "_H"])
else:
myHistos = getResolutionHistos(datasetsMgr,
datasets0,
histoName,
regions=["_C", "_I", "_F"])
# Get reference histo and list of comparison histos
hReference = myHistos[0]
hCompares = myHistos[1:]
# Create the plotting object
p = plots.ComparisonManyPlot(hReference, hCompares, saveFormats=[])
# Get legend names
legDict = GetLegendDictionary()
legends = {}
# For-loop: All histograms in manager
for i, h in enumerate(p.histoMgr.getHistos(), 1):
region = h.getName().split("_")[-1]
if region not in legDict.keys():
raise Exception(
"Invalid region \"%s\". Cannot determine legend name for histogram \"%s\""
% (region, h.getName()))
legends[h.getName()] = legDict[region]
p.histoMgr.forHisto(h.getName(), styles.getRegionStyle(region))
p.histoMgr.setHistoDrawStyle(h.getName(), "LP")
p.histoMgr.setHistoLegendStyle(h.getName(), "LP")
# Normalise resolution plots
if "res_" in histoName:
if h.getRootHisto().Integral() > 0:
aux.Verbose("Normalising \"%s\" to unit area" % (histoName), True)
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().Scale(
1.0 / h.getRootHisto().Integral()))
# Set legend labels
p.histoMgr.setHistoLegendLabelMany(legends)
# Draw and save the plot
plots.drawPlot(p, histoName,
**kwargs_) #the "**" unpacks the kwargs_ dictionary
# Additional text on canvas
histograms.addText(
0.65, 0.90, plots._legendLabels[datasetsMgr.getAllDatasetNames()[0]],
17)
# Save the plots in custom list of saveFormats
aux.SavePlot(p, opts.saveDir, histoName, opts.saveFormats, opts.url)
return
def PlotHistos(datasetName, hPath, saveName):
_kwargs = GetHistoKwargs(hPath, opts)
datasetsMgr_list = []
j = 0
histoList = []
legDict = {}
# For-loop: All pseudo-multicrabs
while j < len(opts.mcrabs):
algo = opts.mcrabs[j].split("_")[1]
dMgr = GetDatasetsFromDir(opts, j)
dataset = dMgr.getDataset(datasetName)
dMgr.updateNAllEventsToPUWeighted()
# Get Histogram from dataset
histo = dataset.getDatasetRootHisto(hPath).getHistogram()
# Set style
styles.styles[j].apply(histo)
legDict[algo] = getAlgoLabel(algo)
if (j == 0):
if "turnon" in saveName.lower():
refHisto = histograms.Histo(histo,
algo,
legendStyle="LP",
drawStyle="AP")
else:
refHisto = histograms.Histo(histo,
algo,
legendStyle="L",
drawStyle="HIST")
refHisto.getRootHisto().SetLineStyle(ROOT.kSolid)
refHisto.getRootHisto().SetLineWidth(4)
else:
if "turnon" in saveName.lower():
histoList.append(
histograms.Histo(histo,
algo,
legendStyle="LP",
drawStyle="AP"))
else:
histo.SetLineWidth(4)
histoList.append(
histograms.Histo(histo,
algo,
legendStyle="L",
drawStyle="HIST"))
j = j + 1
# Create the plotter object
p = plots.ComparisonManyPlot(refHisto, histoList, saveFormats=[])
p.setLegendHeader(plots._legendLabels[datasetName])
# Set legend labels
p.histoMgr.setHistoLegendLabelMany(legDict)
# Draw the plots
plots.drawPlot(p, opts.saveDir, **_kwargs)
# Add text
if 0:
histograms.addText(0.65, 0.1, "#\pm %.1f %% band" % (opts.bandValue),
20)
# Save plot in all formats
saveName += datasetName
aux.SavePlot(p, opts.saveDir, saveName, opts.saveFormats, True)
return
def PlotRateVsEff(datasetsMgr, dataset, Type, PU):
# Sanity checks
if PU not in dataset.getName():
return
if "Neutrino" in dataset.getName():
return
# Definitions
_kwargs = GetHistoKwargs("RateVsEff", opts)
j = -1
gList = []
legDict = {}
# For-loop: All pseudo-multicrabs
while j < len(opts.mcrabs) - 1:
j = j + 1
dMgr = GetDatasetsFromDir(opts, j)
dMgr.updateNAllEventsToPUWeighted()
# Get dataset
signal = dataset.getName()
bkg = "SingleNeutrino_L1T%s" % (PU)
# Get Histogram from dataset
hEff = os.path.join(opts.folder, "L1Taus_%s_Eff" % (Type))
hRate = os.path.join(opts.folder, "L1Taus_%s_Rate" % (Type))
graph = convert2RateVsEffTGraph(dMgr, hEff, hRate, signal, bkg)
algo = opts.mcrabs[j].split("_")[1]
graph.SetName(algo)
styles.styles[j].apply(graph)
graph.SetLineWidth(4)
gList.append(graph)
legDict[algo] = getAlgoLabel(algo)
# Create the plotter object
p = plots.ComparisonManyPlot(gList[0], gList[1:], saveFormats=[])
p.setLegendHeader(plots._legendLabels[signal])
# Set individual styles
for index, h in enumerate(p.histoMgr.getHistos(), 1):
hName = h.getName()
legDict[hName] = getAlgoLabel(hName)
p.histoMgr.setHistoDrawStyle(h.getName(),
"LX") # "X" = Do not draw error bars
p.histoMgr.setHistoLegendStyle(h.getName(), "L") #LP
# Set legend labels
p.histoMgr.setHistoLegendLabelMany(legDict)
# Draw the plots
plots.drawPlot(p, opts.saveDir, **_kwargs)
# Add text
if 0:
histograms.addText(0.65, 0.1, "#\pm %.1f %% band" % (opts.bandValue),
20)
# Save plot in all formats
saveName = Type + "_" + dataset.getName()
aux.SavePlot(p, opts.saveDir, saveName, opts.saveFormats, True)
return
def main(opts):
# Apply TDR style
opts.style = tdrstyle.TDRStyle()
opts.style.setGridX(opts.gridX)
opts.style.setGridY(opts.gridY)
# opts.style.setLogX(opts.logX)
# opts.style.setLogY(opts.logY)
opts.style.setOptStat(False)
ROOT.gStyle.SetErrorX(
0.5) #required for x-axis error bars! (overwrites tdrstyle.TDRStyle())
# Create dictionary of algorithms (name -> object) for each dataset (?)
algorithms = {}
for algoName in opts.algos:
algorithms[algoName] = Algorithm(algoName, opts)
# Create dictionary mapping each dataset to the number of merged ROOT files
opts.nFiles = {}
# For-loop: All datasets
for i, dset in enumerate(opts.dsetDict, 1):
# Get the list with the ROOT files
rootFiles = opts.dsetDict[dset]
if opts.verbose:
PrintRootFileNames(rootFiles)
# Get the number of the merged ROOT files
mergedRootFile = ROOT.TFile(rootFiles[0].GetName(), "OLD")
if (mergedRootFile.GetListOfKeys().Contains("nFiles")):
opts.nFiles[dset] = mergedRootFile.Get("nFiles").GetBinContent(1)
else:
msg = "Could not find the histogram with the number of the merged root files!"
raise Exception(es + msg + ns)
# Get all the histogram names
hPathList = GetHistoPaths(rootFiles[0], opts.algos)
# For-loop: All histograms inside the ROOT files
for j, hPath in enumerate(hPathList, 1):
aName = hPath.split("/")[0].replace("Rate", "").replace("Eff", "")
algo = algorithms[aName]
Verbose(
"Fetching histograms for algorithm %s" %
(hs + algo.getName() + ns), j == 1)
algo.fetchHistogram(dset, hPath, rootFiles, opts)
Verbose("Plotting histogram %s" % (hPath), True)
style = algo.plot(dset, hPath)
msg = "{:>10} {:>20} {:<30}".format("%s:" % (algo.getName()),
"%s:" % dset, "%s" % (hPath))
Print(style + msg + ns, j == 1)
# Close all ROOT files for given dataset
CloseRootFiles(rootFiles)
# Declare list of histos to be drawn
turnOns = []
rates = []
effVBF = []
# For-loop: All final algorithm objects
for i, algoName in enumerate(algorithms, 1):
algo = algorithms[algoName]
hDict = algo.getHistos()
for j, hName in enumerate(hDict, 1):
h = hDict[hName]
if "GenEtTurnOn" in h.getRootHisto().GetName():
turnOns.append(h)
elif "EtThreshold" in h.getRootHisto().GetName():
if "Neutrino" not in h.getRootHisto().GetName():
pass
else:
rates.append(h)
elif "EtEfficiency" in h.getRootHisto().GetName():
if "HToTauTau" in h.getRootHisto().GetName():
effVBF.append(h)
else:
pass
# Make the plot
Verbose("Plotting turn-on histograms for comparison", True)
p = plots.ComparisonManyPlot(turnOns[0], turnOns[1:], saveFormats=[])
kwargs = GetHistoKwargs("GenEtTurnOn", "TEST-1")
plots.drawPlot(p, "TurnOns", **kwargs)
SavePlot(p,
"TurnOns",
os.path.join(opts.saveDir, "Algos"),
saveFormats=opts.saveFormats)
# Make the plot
Verbose("Plotting rate histograms for comparison", True)
p = plots.ComparisonManyPlot(rates[0], rates[1:], saveFormats=[])
kwargs = GetHistoKwargs("EtThreshold", "TEST-2")
plots.drawPlot(p, "Rates", **kwargs)
SavePlot(p,
"Rates",
os.path.join(opts.saveDir, "Algos"),
saveFormats=opts.saveFormats)
# Make the plot
Verbose("Plotting efficienciesiciency histograms for comparison", True)
p = plots.ComparisonManyPlot(effVBF[0], effVBF[1:], saveFormats=[])
kwargs = GetHistoKwargs("EtEfficiency", "TEST-3")
plots.drawPlot(p, "Efficiency_VBF", **kwargs)
SavePlot(p,
"Efficiency_VBF",
os.path.join(opts.saveDir, "Algos"),
saveFormats=opts.saveFormats)
# Inform user of location of files
Print("%sPlots saved under director \"%s\"%s" % (hs, opts.saveDir, ns),
True)
return
def ComparisonPlot(datasetsMgr, json):
Verbose("Creating MC plot")
# Create the MC Plot with selected normalization ("normalizeToOne", "normalizeByCrossSection", "normalizeToLumi")
kwargs = {}
ylabel_ = json["ylabel"]
normToOne_ = json["normalizationToOne"] == "True"
if normToOne_:
ylabel_ = ylabel_.replace(json["ylabel"].split(" /")[0],
"Arbitrary Units")
#p = plots.PlotSameBase(datasetsMgr, json["histogram"], normalizeToOne=normToOne_, **kwargs) #works
#p = plots.ComparisonPlot(*getHistos(datasetsMgr, json['samples'][0], json['histogram'], json['histogram'])) #works
p = plots.ComparisonPlot(*getHistos(datasetsMgr, json['samples'][0],
json['histogram'], "Tk_Eff"))
# Customise styling
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetFillStyle(0))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerSize(1.2))
p.histoMgr.setHistoDrawStyleAll(json["drawStyle"])
p.histoMgr.setHistoLegendStyleAll(json["legendStyle"])
# Apply styles
##########################################
p.histoMgr.forHisto("h1-VBF_HToTauTau", styles.getBaselineStyle())
p.histoMgr.forHisto("h2-VBF_HToTauTau", styles.getInvertedStyle())
# Set draw style
p.histoMgr.setHistoDrawStyle("h1-VBF_HToTauTau", "LP")
p.histoMgr.setHistoLegendStyle("h2-VBF_HToTauTau", "LP")
# p.histoMgr.setHistoLegendStyleAll("LP")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"h1-VBF_HToTauTau": "h1",
"h2-VBF_HToTauTau": "h2",
})
##########################################
# 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"]
# Draw a customised plot
saveName = os.path.join(json["saveDir"], json["title"])
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"],
log=json["logY"] == "True",
moveLegend=json["moveLegend"],
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 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
def PlotEfficiency(datasetsMgr, histoList, signal, PU, taus):
# Get Histogram name and its kwargs
saveName = "Efficiency_%s_%s" % (taus, signal)
#saveName = "Efficiency_%s_PU%s" % (taus, PU) # "Rate_" here incuded to apply customisations with kwargs. Drop later
kwargs = GetHistoKwargs(saveName, opts)
hList = []
legDict = {}
algos = getAlgos()
# For-loop: All tau algorithms
count = -1
for i, hName in enumerate(histoList, 0):
algo = hName.split("_")[0]
if algo == "DiTau":
if "TT_" in signal or "GluGlu" in signal:
pass
else:
return
algo = hName.split("_")[-1]
count += 1
aux.PrintFlushed("Plotting efficiency (%s-%s-%s)" %
(algo, taus, signal), False) #count==0)
h = datasetsMgr.getDataset(signal).getDatasetRootHisto(
hName).getHistogram()
h.SetName(hName)
legDict[hName] = algos[i]
hList.append(h)
# Create the rate histograms
p = plots.ComparisonManyPlot(hList[0], hList[1:], saveFormats=[])
# Set legend labels
for h in p.histoMgr.getHistos():
hName = h.getName()
algo = h.getName().split("_")[0]
if algo == "DiTau":
algo = hName.split("_")[-1]
p.histoMgr.forHisto(hName, styles.getTauAlgoStyle(algo))
p.histoMgr.setHistoDrawStyle(hName, "HIST")
p.histoMgr.setHistoLegendStyle(hName, "L")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany(legDict)
# Draw and save the plot
saveName = saveName.replace(
"Efficiency_",
"") # drop "Efficiency_" only AFTER getting histo kwargs
#saveName = "%s_%s_%s" % (taus, algo, signal) # drop "Efficiency_" only AFTER getting histo kwargs
plots.drawPlot(p, saveName, **kwargs)
# Add additional canvas text
histograms.addPileupText("PU=%s" % (PU))
histograms.addText(0.66, 0.86, plots._legendLabels[signal], 17)
# Save the plots in custom list of saveFormats
aux.SavePlot(p, opts.saveDir, saveName, opts.saveFormats, True)
#print
return
def ComparisonPlot(datasetsMgr, json):
Verbose("Creating MC plot")
# Create the MC Plot with selected normalization ("normalizeToOne", "normalizeByCrossSection", "normalizeToLumi")
legendDict = {}
kwargs = {}
ylabel_ = json["ylabel"]
zlabel_ = json["zlabel"]
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_:
zlabel_ = ylabel_.replace(json["ylabel"].split(" /")[0],
"Arbitrary Units")
# Get the reference histo and the list of histos to compare
histoList = [getHisto(datasetsMgr, json['sample'], json['histogram'])]
p = plots.PlotBase(histoList, saveFormats=[])
# Set universal histo styles
p.histoMgr.setHistoDrawStyleAll(json["drawStyle"])
p.histoMgr.setHistoLegendStyleAll(json["legendStyle"])
print json['histogram']
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinX(json["rebinX"]))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinY(json["rebinY"]))
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().GetZaxis().SetTitle(zlabel_))
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().GetZaxis().SetTitleOffset(1.3))
if json["zmin"] != 0 and json["zmax"] != 0:
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetMinimum(json["zmin"]))
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetMaximum(json["zmax"]))
#p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetZaxis().SetRangeUser(json["zmin"], json["zmax"]))
# 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_,
zLabel=zlabel_,
stackMCHistograms=json["stackMCHistograms"] == "True",
addCmsText=json["addCmsText"] == "True",
cmsExtraText=json["cmsExtraText"],
opts=json["opts"],
opts2=json["opts2"],
log=json["logY"] == "True",
moveLegend=json["moveLegend"],
#backgroundColor = ROOT.kAzure,
cutBox=cutBox_,
)
# Remove legend?
if json["removeLegend"] == "True":
p.removeLegend()
# Set log-z?
if json["zlog"] == "True":
p.getPad().SetLogz(True)
# 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
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
def PlotHistograms(datasetsMgr, histoNames):
Verbose("Plotting Data-MC Histograms")
ROOT.gStyle.SetNdivisions(8, "X")
ROOT.gStyle.SetNdivisions(8, "Y")
# Get Histogram saveNname and its kwargs
saveName = histoNames[0].rsplit("/")[-1]
if opts.etaReg:
saveName = saveName.replace(saveName.split("_")[-1], "CIF")
elif opts.ptReg:
saveName = saveName.replace(saveName.split("_")[-1], "LMH")
kwargs_ = GetHistoKwargs(saveName, opts)
kwargs = {}
# Get dataset
datasets0 = datasetsMgr.getAllDatasets()[0].getName()
# Get histograms for the ComparisonPlot
histoReference = getHisto(datasetsMgr, datasets0, histoNames[0])
histoCompares = getHistos(datasetsMgr, datasets0, histoNames)
if ("res_" in histoNames[0]) or ("match_trk_"
in histoNames[0]) or ("match_tp_"
in histoNames[0]):
histoReference.normalizeToOne()
for h in histoCompares:
h.normalizeToOne()
# Create the plotting object
p = plots.ComparisonManyPlot(histoReference, histoCompares, saveFormats=[])
# Get Legend Names depending on the regions & customize plot
legendDict = {}
for index, h in enumerate(p.histoMgr.getHistos()):
hName = h.getName()
if opts.etaReg:
if "_C" in hName:
legendDict[hName] = "|#eta| < 0.8"
elif "_I" in hName:
legendDict[hName] = "0.8 < |#eta| < 1.6"
elif "_F" in hName:
legendDict[hName] = "|#eta| > 1.6"
elif opts.ptReg:
if "_L" in hName:
legendDict[hName] = "p_{T} < 5 GeV/c"
elif "_M" in hName:
legendDict[hName] = "5 < p_{T} < 15 GeV/c"
elif "_H" in hName:
legendDict[hName] = "p_{T} > 15 GeV/c"
p.histoMgr.forHisto(hName, styles.getRegionStyle(index))
p.histoMgr.setHistoDrawStyle(hName, "LP")
p.histoMgr.setHistoLegendStyle(hName, "LP")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany(legendDict)
# Get the y axis title
binWidth = p.histoMgr.getHistos()[0].getRootHisto().GetXaxis().GetBinWidth(
0)
if ("res_" in histoNames[0]) or ("match_trk_"
in histoNames[0]) or ("match_tp_"
in histoNames[0]):
kwargs_["ylabel"] = "Arbitrary Units / %s" % (
GetBinwidthDecimals(binWidth) % (binWidth))
else:
ylabel = p.histoMgr.getHistos()[0].getRootHisto().GetYaxis().GetTitle(
) + " / %s" % (GetBinwidthDecimals(binWidth) % (binWidth))
kwargs_["ylabel"] = ylabel
# Draw and save the plot
plots.drawPlot(p, saveName,
**kwargs_) #the "**" unpacks the kwargs_ dictionary
# Add extra text (dataset name)
#histograms.addText(0.65, 0.88, plots._legendLabels[datasets0], 17)
if ("match_trk_nstub_" in histoNames[0]) or ("match_trk_chi2_"
in histoNames[0]):
histograms.addText(0.23, 0.38, plots._legendLabels[datasets0], 17)
else:
histograms.addText(0.23, 0.88, plots._legendLabels[datasets0], 17)
# Save the plots in custom list of saveFormats
SavePlot(p, saveName, os.path.join(opts.saveDir, opts.optMode,
opts.folder), [".pdf", ".png"])
return
def Plot2dHistograms(datasetsMgr, dsetName, histoName, index):
msg = "%s%s (%s)%s" % (ShellStyles.SuccessStyle(), histoName, dsetName, ShellStyles.NormalStyle() )
aux.PrintFlushed(msg, index==1)
# Custom Filtering of datasets
dsetsMgr = datasetsMgr.deepCopy()
if opts.verbose:
dsetsMgr.PrintInfo()
# Get Histogram name and its kwargs
saveName = histoName.rsplit("/")[-1] + "_" + dsetName.split("_")[0] + dsetName.split("_")[-1]
kwargs_ = GetHistoKwargs(saveName, opts)
for i, d in enumerate(dsetsMgr.getAllDatasetNames(), 0):
if d == dsetName:
continue
else:
# Remove dataset from manager but do NOT close the file!
dsetsMgr.remove(d, close=False)
# Sanity check
nDatasets = len(dsetsMgr.getAllDatasets())
if nDatasets > 1:
raise Exception("More than 1 datasets detected in the dataset manager! Can only support 1 dataset. Please use the -i option to choose exactly 1 dataset'")
# Get the reference histo and the list of histos to compare
datasets0 = dsetsMgr.getAllDatasets()[0].getName()
histoList = [getHisto(dsetsMgr, datasets0, histoName)]
# Create the 2d plot
Verbose("Creating the 2d plot", True)
if opts.normalizeToLumi:
p = plots.MCPlot(dsetsMgr, histoName, normalizeToLumi=opts.intLumi, saveFormats=[])
elif opts.normalizeByCrossSection:
p = plots.MCPlot(dsetsMgr, histoName, normalizeByCrossSection=True, saveFormats=[], **{})
elif opts.normalizeToOne:
p = plots.MCPlot(dsetsMgr, histoName, normalizeToOne=True, saveFormats=[], **{})
else:
raise Exception("One of the options --normalizeToOne, --normalizeByCrossSection, --normalizeToLumi must be enabled (set to \"True\").")
Verbose("Setting universal histo styles", True)
p.histoMgr.setHistoDrawStyleAll("COLZ")
#p.histoMgr.setHistoLegendStyleAll("L")
Verbose("Customising histograms", True)
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetZaxis().SetTitleOffset(1.3)) #fixme
Verbose("Setting plot styles to histograms", True)
for index, h in enumerate(p.histoMgr.getHistos()):
plots._plotStyles[p.histoMgr.getHistos()[index].getDataset().getName()].apply(p.histoMgr.getHistos()[index].getRootHisto())
Verbose("Drawing the plot", True)
plots.drawPlot(p, saveName, **kwargs_) #the "**" unpacks the kwargs_ dictionary
# Add fit line for shrinking cone?
const= 0.0
coeff= 0.0
step = 1
xmin = 0
xmax = 0
if "GenP_VisEt_Vs" in histoName:
const= 3.5
coeff= 1.0
step = 100
xmin = 0
xmax = 30
if "GenP_PtLdg_Vs" in histoName:
const= 2.5 #2.0
coeff= 1.0
step = 100
xmin = 0
xmax = 30
if "VtxIso_Vs_RelIso" in histoName:
const= -0.5
coeff= 0.3 #0.4
step = 100
xmin = 0
xmax = 0#3
if "GenP_VisEt_Vs" in histoName or "GenP_PtLdg_Vs" in histoName:
gr = getCustomTGraph(histoName, const, coeff, xmin, xmax, step)
gr.SetLineWidth(3)
gr.Draw("L same")
Verbose("Removing the legend", True)
p.removeLegend()
Verbose("Adding text on canvas", True)
histograms.addText(0.22, 0.89, plots._legendLabels[datasets0], 18)
#histograms.addText(0.5, 0.89, plots._legendLabels[datasets0], 18)
Verbose("Saving the canvas", True)
aux.SavePlot(p, opts.saveDir, saveName, opts.saveFormats, opts.url)
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 plot(self, dset, hPath):
# Sanity check
if hPath not in self.histoPaths:
msg = "Histogram %s was not stored for plotting. Skipping!" % (
es + hPath + ns)
Print(msg, False)
return
hList = []
myLabel = os.path.basename(hPath)
fullPath = os.path.join(dset, hPath)
myHisto = self.histograms[fullPath]
integral = myHisto.Integral()
# Sanity check (Histogram is not empty)
if integral == 0.0:
msg = "Skipping histogram %s (Integral = %.1f)" % (ls + hPath + ns,
integral)
Verbose(msg, False)
self.histosSkipped.append(hPath)
#return ls
return es
else:
msg = "Creating histogram %s" % (hs + hPath + ns)
Verbose(msg, False)
# Create histogram histo
if "Vs" in hPath:
# opts.style.setWide(onoff=True, percIncrease=0.15)
hh = histograms.Histo(myHisto,
hPath,
legendStyle="L",
drawStyle="COLZ",
legendLabel=myLabel)
elif "genetturnon" in hPath.lower():
hh = histograms.Histo(myHisto,
hPath,
legendStyle="LP",
drawStyle="AP",
legendLabel=myLabel)
else:
hh = histograms.Histo(myHisto,
hPath,
legendStyle="L",
drawStyle="HIST",
legendLabel=myLabel)
# Convert to rate histogram (scale appropriately)
ConvertToRateHisto(hh)
# Convert to turn-on histogram (EtTurnOn/EtGen)
ConvertToTurnOnHisto(hh, self)
# Convert to efficiency histo (requires fix due to use of hadd)
ConvertToEfficiencyHisto(hh, dset, self)
# Skip meaningless histos
if "EtThreshold" in hh.getName() and "neutrino" not in dset.lower():
return es
# Set histogram type (Data or MC)
hh.setIsDataMC(isData=False, isMC=True)
hList.append(hh)
if 0:
aux.PrintTH1Info(hh.getRootHisto())
# Apply histogram style
styles.styles[self.getIndex()].apply(hh.getRootHisto())
self.histosCreated.append(hPath)
# Replace original histo with final version of it
self.histos[fullPath] = hh #iro
if "GenEtTurnOn" in hh.getName():
pass
# aux.PrintTH1Info(hh.getRootHisto())
#sys.exit()
# Sanity check
if len(hList) < 1:
return es
# Get the keyword arguments for this specific histogram
# saveName = hPath.replace("/", "_").replace(self.getName(), self.getName() + "_") #iro
saveName = hPath.replace("/", "_")
kwargs = GetHistoKwargs(saveName, self.getLabel())
# Make the plot
Verbose("Plotting all histograms for comparison", True)
p = plots.ComparisonManyPlot(hList[0], hList[1:], saveFormats=[])
# Apply Univeral style?
if 0:
p.histoMgr.setHistoDrawStyleAll("HIST")
p.histoMgr.setHistoLegendStyleAll("FLP")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({hPath: kwargs["legend"]})
# Set Legend header?
p.setLegendHeader(
GetDatasetLabel(dset)) # p.setLegendHeader(self.getLabel())
# Add MC uncertainty (if not a TH2)
if "Vs" in str(type(hList[0])):
p.addMCUncertainty() # appears to do nothing..
# Draw the plot
plots.drawPlot(p, dset + ":" + hPath, **kwargs)
# Save the plot
SavePlot(p,
saveName,
os.path.join(opts.saveDir, dset),
saveFormats=opts.saveFormats)
return ss
