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 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 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 "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):
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
