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 = 5.0 #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 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 PlotRateVsEff(datasetsMgr, effHistoList, rateHistoList, signal, bkg, sPU,
bPU):
# Definitions
tgraphs = []
legDict = {}
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_MBPU%s" % (signal.split("_")[0],
sPU, bPU)
else:
saveName = "RateVsEff_SingleTau_%s_PU%s_MBPU%s" % (
signal.split("_")[0], sPU, bPU)
kwargs_ = GetHistoKwargs(saveName, opts)
for i in range(0, len(effHistoList)):
if (i == 0):
g1 = convert2RateVsEffTGraph(datasetsMgr, effHistoList[i],
rateHistoList[i], signal, bkg)
g1.SetName("Calo")
elif (i == 1):
g2 = convert2RateVsEffTGraph(datasetsMgr, effHistoList[i],
rateHistoList[i], signal, bkg)
g2.SetName("CaloIso")
# Create the Rate Vs Efficiency TGraphs
p = plots.ComparisonManyPlot(g1, [g2], saveFormats=[])
algos = getAlgos()
# Set individual styles
for index, h in enumerate(p.histoMgr.getHistos()):
hName = h.getName()
legDict[hName] = algos[index] #styles.getCaloLegend(index)
p.histoMgr.forHisto(hName, styles.getTauAlgoStyle(
h.getName())) #styles.getCaloStyle(index))
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 and save the plot
saveName = saveName.replace("RateVsEff_", "")
plots.drawPlot(p, saveName,
**kwargs_) #the "**" unpacks the kwargs_ dictionary
# Draw Error bands
if opts.errorBands:
#for i, g in enumerate([g0, g1, g2, g3, g4]):
for i, g in enumerate([g1, g2, g3]):
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" % (bPU))
histograms.addText(0.55, 0.48, plots._legendLabels[signal], 18)
# Save the plots in custom list of saveFormats
aux.SavePlot(p, opts.saveDir, saveName, opts.saveFormats, True)
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 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 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