def main():
if len(sys.argv) < 2:
usage()
paths = [sys.argv[1]]
analysis = "Hplus2tbAnalysis"
hName = "associatedTPt"
plotname = analysis + "_" + hName
datasetsHiggs = dataset.getDatasetsFromMulticrabDirs(
paths,
analysisName=analysis,
includeOnlyTasks="ChargedHiggs_HplusTB_HplusToTauB_M_")
datasetsTT = dataset.getDatasetsFromMulticrabDirs(paths,
analysisName=analysis,
includeOnlyTasks="TT")
datasetsTT.merge("MC", ["TT", "TT_ext"], keepSources=True)
style = tdrstyle.TDRStyle()
dataset1 = datasetsHiggs.getDataset(
"ChargedHiggs_HplusTB_HplusToTauB_M_200").getDatasetRootHisto(hName)
dataset2 = datasetsTT.getDataset("MC").getDatasetRootHisto(hName)
# dataset1.normalizeToOne()
dataset2.normalizeToOne()
histo1 = dataset1.getHistogram()
histo1.SetMarkerColor(2)
histo1.SetMarkerStyle(20)
removeNegatives(histo1)
histo1.Scale(1. / histo1.Integral())
histo2 = dataset2.getHistogram()
histo2.SetMarkerColor(4)
histo2.SetMarkerStyle(21)
p = plots.ComparisonPlot(
histograms.Histo(histo1, "m_{H^{#pm}} = 200 GeV/c^{2}", "p", "P"),
histograms.Histo(histo2, "t#bar{t}", "p", "P"))
opts = {"ymin": 0, "ymax": 0.2}
opts2 = {"ymin": 0.5, "ymax": 1.5}
p.createFrame(os.path.join(plotDir, plotname),
createRatio=True,
opts=opts,
opts2=opts2)
moveLegend = {"dx": -0.2, "dy": -0.1, "dh": -0.1}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
p.getFrame().GetYaxis().SetTitle("Arbitrary units")
p.getFrame().GetXaxis().SetTitle("Top p_{T} (GeV/c)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
def doSystematicsPlots(self):
if self._mySystObject == None:
return
# Make sure results have been obtained
if self._hSystematicsUp == None:
self.doSystematicsForMetShapeDifference()
# Create output directory
if os.path.exists("%s/Systematics_%s"%(self._myDir, self._systName)):
shutils.rmtree("%s/Systematics_%s"%(self._myDir, self._systName))
os.mkdir("%s/Systematics_%s"%(self._myDir, self._systName))
# Make plot for systematics (phase-space bins integrated)
if self._hFinalShape != None:
self._hFinalShape.SetLineColor(ROOT.kBlack)
self._hSystematicsUp.SetLineColor(ROOT.kBlue)
self._hSystematicsDown.SetLineColor(ROOT.kRed)
myYmax = 15
if "2012" in self._moduleInfoString:
myYmax = 30
plot = plots.ComparisonManyPlot(histograms.Histo(self._hFinalShape, "Nominal", drawStyle="E"),
[histograms.Histo(self._hSystematicsUp, "Up", drawStyle="E"), histograms.Histo(self._hSystematicsDown, "Down", drawStyle="E")])
plot.createFrame("%s/Systematics_%s/QCDInvertedShapeWithMetSyst_final_%s"%(self._myDir, self._systName, self._moduleInfoString), createRatio=True, opts2={"ymin": 0, "ymax": 2}, opts={"addMCUncertainty": True, "ymin": -5, "ymax": myYmax})
plot.frame.GetXaxis().SetTitle(self._histoSpecs["xtitle"])
plot.frame.GetYaxis().SetTitle(self._histoSpecs["ytitle"])
plot.setLegend(histograms.createLegend(0.59, 0.70, 0.87, 0.90))
plot.legend.SetFillColor(0)
plot.legend.SetFillStyle(1001)
styles.mcStyle(plot.histoMgr.getHisto("Up"))
#plot.histoMgr.getHisto("Up").getRootHisto().SetMarkerSize(0)
styles.mcStyle2(plot.histoMgr.getHisto("Down"))
styles.dataStyle(plot.histoMgr.getHisto("Nominal"))
self._drawPlot(plot)
# Make plots for systematics (for each phase-space bin separately)
nSplitBins = self._myCtrlRegion.getNumberOfPhaseSpaceSplitBins()
myCtrlHistograms = self._mySystObject.getHistogramsForCtrlRegion()
mySignalHistograms = self._mySystObject.getHistogramsForSignalRegion()
myMinIndex = 0
if myCtrlHistograms[0] == None:
myMinIndex = 1 # Skip empty first bin
for i in range(myMinIndex, nSplitBins):
plot = plots.ComparisonPlot(histograms.Histo(mySignalHistograms[i].Clone(), "Signal region", drawStyle="E"),
histograms.Histo(myCtrlHistograms[i].Clone(), "Ctrl. region", drawStyle="E"))
plot.createFrame("%s/Systematics_%s/QCDInvertedShapeWithMetSyst_%s_%s"%(self._myDir, self._systName, self._myCtrlRegion.getPhaseSpaceBinFileFriendlyTitle(i), self._moduleInfoString), createRatio=True, opts2={"ymin": 0, "ymax": 2}, opts={"addMCUncertainty": True, "ymin": -0.05, "ymax": 0.3})
plot.frame.GetXaxis().SetTitle(self._histoSpecs["xtitle"])
plot.frame.GetYaxis().SetTitle("Arb. units. (Normalised to 1)")
styles.mcStyle(plot.histoMgr.getHisto("Ctrl. region"))
styles.dataStyle(plot.histoMgr.getHisto("Signal region"))
plot.setLegend(histograms.createLegend(0.59, 0.77, 0.87, 0.90))
plot.legend.SetFillColor(0)
plot.legend.SetFillStyle(1001)
#plot.histoMgr.getHisto("Up").getRootHisto().SetMarkerSize(0)
self._drawPlot(plot)
print "Saved MET shape systematics plot for bin %s"%self._myCtrlRegion.getPhaseSpaceBinFileFriendlyTitle(i)
def TauPtComparison(datasets):
mt = plots.ComparisonPlot(*getHistos3(datasets, "tauPt", "tauPt"))
mt._setLegendStyles()
st1 = styles.StyleCompound([styles.styles[2]])
st2 = styles.StyleCompound([styles.styles[1]])
st1.append(styles.StyleLine(lineWidth=3))
st2.append(styles.StyleLine(lineStyle=2, lineWidth=3))
mt.histoMgr.forHisto("Taupt_madgraph", st1)
mt.histoMgr.forHisto("Taupt_pythia8", st2)
mt.histoMgr.setHistoLegendLabelMany({
"Taupt_madgraph": "Tau pt from Madgraph",
"Taupt_pythia8": "Tau pt from Pythia8"
})
mt.histoMgr.setHistoDrawStyleAll("PE")
mt.appendPlotObject(histograms.PlotText(100, 0.01, "tt events", size=20))
xlabel = "p_{T}^{#tau jet} (GeV)"
ylabel = "Events / %.2f"
plots.drawPlot(mt,
"TauPtComparison",
xlabel=xlabel,
ylabel=ylabel,
rebinX=2,
log=True,
createLegend={
"x1": 0.6,
"y1": 0.75,
"x2": 0.8,
"y2": 0.9
},
ratio=False,
opts2={
"ymin": 0.5,
"ymax": 50
},
opts={
"xmax": 800,
"ymin": 1,
"ymax": 1000000
})
def MetComparison(datasets):
mt = plots.ComparisonPlot(*getHistos2(datasets,"Met","Met"))
# mt.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mt._setLegendStyles()
st1 = styles.StyleCompound([styles.styles[2]])
st2 = styles.StyleCompound([styles.styles[1]])
st1.append(styles.StyleLine(lineWidth=3))
st2.append(styles.StyleLine(lineStyle=2, lineWidth=3))
mt.histoMgr.forHisto("Met_madgraph", st1)
mt.histoMgr.forHisto("Met_pythia8", st2)
mt.histoMgr.setHistoLegendLabelMany({
"Met_madgraph": "Met from Madgraph",
"Met_pythia8": "Met from Pythia8"
})
mt.histoMgr.setHistoDrawStyleAll("PE")
mt.appendPlotObject(histograms.PlotText(100, 0.01, "tt events", size=20))
xlabel = "PF E_{T}^{miss} (GeV)"
ylabel = "Events / %.2f"
plots.drawPlot(mt, "MetComparison", xlabel=xlabel, ylabel=ylabel, rebinX=2, log=True,
createLegend={"x1": 0.6, "y1": 0.75, "x2": 0.8, "y2": 0.9},
ratio=False, opts2={"ymin": 0.5, "ymax": 50}, opts={"xmax": 500})
def PurityPlots(datasetsMgr, histoName, analysisType="Inverted"):
'''
Create plots with "FakeB=Data-EWKGenuineB"
'''
Verbose("Plotting histogram %s for Data, EWK, QCD for %s" % (histoName, analysisType), True)
# Sanity check
IsBaselineOrInverted(analysisType)
# Which folder to use (redundant)
defaultFolder = "FakeBPurity"
genuineBFolder = defaultFolder + "EWKGenuineB"
fakeBFolder = defaultFolder + "EWKFakeB"
# Get histos (Data, EWK) for Inclusive
p1 = plots.ComparisonPlot(*getHistos(datasetsMgr, histoName, analysisType) )
p1.histoMgr.normalizeMCToLuminosity(datasetsMgr.getDataset("Data").getLuminosity())
# Clone histograms
Data = p1.histoMgr.getHisto("Data").getRootHisto().Clone(analysisType + "-Data")
EWK = p1.histoMgr.getHisto("EWK").getRootHisto().Clone(analysisType + "-EWK")
QCD= p1.histoMgr.getHisto("Data").getRootHisto().Clone(analysisType + "-QCD")
# Get QCD = Data-EWK
QCD.Add(EWK, -1)
# Comparison plot. The first argument is the reference histo. All other histograms are compared with respect to that.
QCD_Purity, xMin, xMax, binList, valueDict, upDict, downDict = getPurityHisto(QCD, Data, inclusiveBins=False, printValues=False)
EWK_Purity , xMin, xMax, binList, valueDict, upDict, downDict = getPurityHisto(EWK , Data, inclusiveBins=False, printValues=False)
# Create TGraphs
if 0:
gQCD_Purity = MakeGraph(ROOT.kFullTriangleUp, ROOT.kOrange, binList, valueDict, upDict, downDict)
gEWK_Purity = MakeGraph(ROOT.kFullTriangleDown, ROOT.kPurple, binList, valueDict, upDict, downDict)
# Make the plots
p = plots.ComparisonManyPlot(QCD_Purity, [EWK_Purity], saveFormats=[])
if 0:
p = plots.PlotBase([gQCD_Purity, gEWK_Purity], saveFormats=[]) #legend/styles all wrog
# Apply histo styles
p.histoMgr.forHisto(analysisType + "-QCD", styles.getQCDLineStyle() )
p.histoMgr.forHisto(analysisType + "-EWK" , styles.getAltEWKLineStyle() )
# Set draw style
p.histoMgr.setHistoDrawStyle(analysisType + "-QCD", "P")
p.histoMgr.setHistoDrawStyle(analysisType + "-EWK", "P")
# Set legend style
p.histoMgr.setHistoLegendStyle(analysisType + "-QCD", "P")
p.histoMgr.setHistoLegendStyle(analysisType + "-EWK", "P")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
analysisType + "-QCD" : "QCD",
analysisType + "-EWK" : "EWK",
})
# Draw the histograms
_cutBox = None
_rebinX = 1
_opts = {"ymin": 1e-3, "ymax": 1.0} #"ymaxfactor": 1.2}
_format = "%0.0f"
_opts["xmax"] = xMax
_xlabel = None
if "dijetm" in histoName.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jj} (%s)" % (_units)
_cutBox = {"cutValue": 80.399, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
if "trijetm" in histoName.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 1000.0
if "pt" in histoName.lower():
_format = "%0.0f GeV/c"
if "eta" in histoName.lower():
_format = "%0.2f"
_cutBox = {"cutValue": 0., "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmin"] = -3.0
_opts["xmax"] = +3.0
if "deltaeta" in histoName.lower():
_format = "%0.2f"
_opts["xmin"] = 0.0
_opts["xmax"] = 6.0
if "bdisc" in histoName.lower():
_format = "%0.2f"
if "tetrajetm" in histoName.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjjb} (%s)" % (_units)
_opts["xmax"] = 2500.0
# Do the plot
plots.drawPlot(p,
histoName,
xlabel = _xlabel,
ylabel = "Purity",
log = False,
cmsExtraText = "Preliminary",
createLegend = {"x1": 0.80, "y1": 0.80, "x2": 0.92, "y2": 0.92},
opts = _opts,
opts2 = {"ymin": 1e-5, "ymax": 1e0},
ratio = False,
ratioInvert = False,
ratioYlabel = "Ratio",
cutBox = _cutBox,
)
# Save plot in all formats
SavePlot(p, histoName, os.path.join(opts.saveDir, "Purity", opts.optMode))#, saveFormats = [".png"] )
return
def GetPurityHistoGraph(datasetsMgr, folder, hName):
# Which folder to use
genuineBFolder = folder + "EWKGenuineB"
fakeBFolder = folder + "EWKFakeB"
histoName = os.path.join(folder, hName)
hNameGenuineB = os.path.join(genuineBFolder, hName)
Verbose("Creating purity plot for %s" % (histoName), True)
# Get histogram customisations
_kwargs = GetHistoKwargs(histoName, opts)
# Get histos (Data, EWK) for Inclusive
p1 = plots.ComparisonPlot(*getHistos(datasetsMgr, histoName))
if opts.doQCD:
p2 = plots.ComparisonPlot(*getHistos(datasetsMgr, histoName))
else:
p2 = plots.ComparisonPlot(*getHistos(datasetsMgr, hNameGenuineB))
p1.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
p2.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
# Clone histograms
Data = p1.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
EWK = p2.histoMgr.getHisto("EWK").getRootHisto().Clone(
"EWK") # EWKGenuineB
QCD = p1.histoMgr.getHisto("Data").getRootHisto().Clone("QCD")
# Rebin histograms (Before calculating Purity)
if "binList" in _kwargs:
xBins = _kwargs["binList"]
nx = len(xBins) - 1
newName = ""
Verbose("Setting customly-selected bins %s" % (xBins), True)
Data = Data.Rebin(nx, "", xBins)
EWK = EWK.Rebin(nx, "", xBins)
QCD = QCD.Rebin(nx, "", xBins)
# Get QCD = Data-EWK
QCD.Add(EWK, -1)
# Create the Purity histos
hPurity = GetPurityHisto(Data,
EWK,
_kwargs,
printValues=False,
hideZeros=True)
if opts.doEWK:
hPurity = GetPurityHisto(Data,
QCD,
_kwargs,
printValues=False,
hideZeros=True)
# Convert histos to TGraph
gPurity = convertHisto2TGraph(hPurity, printValues=False)
# Set legend labels
if opts.doQCD:
qcdLabel = "QCD (%s)" % GetControlRegionLabel(histoName)
ewkLabel = "EWK (%s)" % GetControlRegionLabel(histoName)
else:
qcdLabel = "Fake-b (%s)" % GetControlRegionLabel(histoName)
ewkLabel = "Genuine-b (%s)" % GetControlRegionLabel(histoName)
# Apply random histo styles
s = styles.getABCDStyle(GetControlRegionLabel(histoName))
s.apply(gPurity)
# Create histoGraph object
if opts.doEWK:
hgPurity = histograms.HistoGraph(gPurity, ewkLabel, "p", "P")
else:
hgPurity = histograms.HistoGraph(gPurity, qcdLabel, "p", "P")
# Save for future use
_kwargs["histoName"] = histoName
return hgPurity, _kwargs
def BaselineVsInvertedPlots(datasetsMgr, histoName):
# Get the Inclusive (Data, EWK)
p1 = plots.ComparisonPlot(*getHistos(datasetsMgr, histoName, "Baseline"))
p1.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
p2 = plots.ComparisonPlot(*getHistos(datasetsMgr, histoName, "Inverted"))
p2.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
# Get Baseline histos
baseline_Data = p1.histoMgr.getHisto("Data").getRootHisto().Clone(
"Baseline-Data")
baseline_QCD = p1.histoMgr.getHisto("Data").getRootHisto().Clone(
"Baseline-QCD")
baseline_EWK = p1.histoMgr.getHisto("EWK").getRootHisto().Clone(
"Baseline-EWK")
# Get Inverted histos
inverted_Data = p2.histoMgr.getHisto("Data").getRootHisto().Clone(
"Inverted-Data")
inverted_QCD = p2.histoMgr.getHisto("Data").getRootHisto().Clone(
"Inverted-QCD")
inverted_EWK = p2.histoMgr.getHisto("EWK").getRootHisto().Clone(
"Inverted-EWK")
# Subtract EWK from Data to get QCD
baseline_QCD.Add(baseline_EWK, -1)
inverted_QCD.Add(inverted_EWK, -1)
# Normalize histograms to unit area
baseline_Data.Scale(1.0 / baseline_Data.Integral())
baseline_QCD.Scale(1.0 / baseline_QCD.Integral())
baseline_EWK.Scale(1.0 / baseline_EWK.Integral())
inverted_Data.Scale(1.0 / inverted_Data.Integral())
inverted_QCD.Scale(1.0 / inverted_QCD.Integral())
inverted_EWK.Scale(1.0 / inverted_EWK.Integral())
# Create the final plot object
p = plots.ComparisonManyPlot(baseline_QCD, [inverted_QCD], saveFormats=[])
#p.setLuminosity(GetLumi(datasetsMgr))
# Apply styles
p.histoMgr.forHisto("Baseline-QCD", styles.getBaselineStyle())
p.histoMgr.forHisto("Inverted-QCD", styles.getInvertedStyle())
# Set draw style
p.histoMgr.setHistoDrawStyle("Baseline-QCD", "AP")
p.histoMgr.setHistoDrawStyle("Inverted-QCD", "HIST")
# Set legend style
p.histoMgr.setHistoLegendStyle("Baseline-QCD", "LP")
p.histoMgr.setHistoLegendStyle("Inverted-QCD", "F")
# p.histoMgr.setHistoLegendStyleAll("LP")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Baseline-QCD": "QCD (Baseline)",
"Inverted-QCD": "QCD (Inverted)",
})
# Draw the histograms
_cutBox = None
_rebinX = 1
_opts = {"ymin": 1e-4, "ymaxfactor": 2.0}
_format = "%0.0f"
_xlabel = None
if "dijetm" in histoName.lower():
_rebinX = 2
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jj} (%s)" % (_units)
_cutBox = {
"cutValue": 80.399,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 400.0
if "trijetm" in histoName.lower():
_rebinX = 5
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {
"cutValue": 173.21,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 1500.0
if "pt" in histoName.lower():
_rebinX = 2
_format = "%0.0f GeV/c"
if "eta" in histoName.lower():
_format = "%0.2f"
_cutBox = {
"cutValue": 0.,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmin"] = -3.0
_opts["xmax"] = +3.0
if "deltaeta" in histoName.lower():
_format = "%0.2f"
_opts["xmin"] = 0.0
_opts["xmax"] = 6.0
if "bdisc" in histoName.lower():
_format = "%0.2f"
if "tetrajetm" in histoName.lower():
_rebinX = 10
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjjb} (%s)" % (_units)
_opts["xmax"] = 3500.0
plots.drawPlot(
p,
histoName,
xlabel=_xlabel,
ylabel="Arbitrary Units / %s" % (_format),
log=True,
rebinX=_rebinX,
cmsExtraText="Preliminary",
createLegend={
"x1": 0.62,
"y1": 0.78,
"x2": 0.92,
"y2": 0.92
},
opts=_opts,
opts2={
"ymin": 0.6,
"ymax": 1.4
},
ratio=True,
ratioInvert=False,
ratioYlabel="Ratio",
cutBox=_cutBox,
)
# Save plot in all formats
SavePlot(p, histoName.replace("<Analysis>_", ""),
os.path.join(opts.saveDir, "BaselineVsInverted", opts.optMode))
return
def Plot_Comparisons(datasetsMgr, datasetsMgrRef, hT, hR, intLumi):
#kwargs = {}
_kwargs = {}
#kwargs = GetHistoKwargs(hG, opts)
print
if opts.normaliseToOne:
pT = plots.MCPlot(datasetsMgr,
hT,
normalizeToOne=True,
saveFormats=[],
**_kwargs)
pR = plots.MCPlot(datasetsMgrRef,
hR,
normalizeToOne=True,
saveFormats=[],
**_kwargs)
else:
pT = plots.MCPlot(datasetsMgr,
hT,
normalizeToLumi=intLumi,
saveFormats=[],
**_kwargs)
pR = plots.MCPlot(datasetsMgrRef,
hR,
normalizeToLumi=intLumi,
saveFormats=[],
**_kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.2f"
_xlabel = None
logY = False
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
print "HT", hT
xMax = 200
_kwargs = {
"xlabel": _xlabel,
"ylabel": "Events / %s" % (_format),
"ratioYlabel": "Ratio ",
"ratio": False,
"ratioInvert": False,
"stackMCHistograms": False,
"addMCUncertainty": False,
"addLuminosityText": False,
"addCmsText": True,
"cmsExtraText": "Preliminary",
#"opts" : {"ymin": 0.1, "ymaxfactor": 1.2},
"opts": {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": xMax
},
"opts2": {
"ymin": 0.6,
"ymax": 1.5
},
"log": True,
#"createLegend" : {"x1": 0.5, "y1": 0.75, "x2": 0.9, "y2": 0.9},
"createLegend": {
"x1": 0.58,
"y1": 0.65,
"x2": 0.92,
"y2": 0.82
},
"rebinX": 1,
}
if "counters" in hT.lower():
print "HERE"
ROOT.gStyle.SetLabelSize(16.0, "X")
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 6
}
_kwargs["createLegend"] = {
"x1": 0.65,
"y1": 0.6,
"x2": 0.90,
"y2": 0.77
},
if "pt" in hT.lower():
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T} (%s)" % _units
_kwargs["xmax"] = 800
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 800
}
if "mass" in hT.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "M (%s)" % _units
_kwargs["xmax"] = 800
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 800
}
if "tetrajet" in hT.lower():
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 2000
}
_kwargs["rebinX"] = 8
if "trijetmass" in hT.lower():
print "mass"
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{top} (%s)" % _units
#_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_kwargs["xmax"] = 400 #1005
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 400
}
if "eta" in hT.lower():
_units = ""
_format = "%0.1f " + _units
_xlabel = "#eta (%s)" % _units
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": -5.0,
"xmax": +5.0
}
if "mult" in hT.lower():
_format = "%0.0f"
if "cleaned" in hT.lower():
_kwargs["xmax"] = 10
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 10
}
else:
_kwargs["xmax"] = 60
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 60
}
if "bdisc" in hT.lower():
_format = "%0.2f"
_kwargs["xmax"] = 1
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 1
}
if "topbdt" in hT.lower():
_format = "%0.2f"
_kwargs["xmax"] = 1
_kwargs["xmin"] = 0.3
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0.3,
"xmax": 1.0
}
if "dijetmass" in hT.lower():
print "dijet-mass"
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{W} (%s)" % _units
_opts["xmax"] = 600
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 300
}
else:
pass
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.5
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
else:
_opts["ymin"] = 1e0
myList = []
# Customise styling
pT.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
pR.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
datasetTarg = datasetsMgr.getDataset(
"QCD") #ChargedHiggs_HplusTB_HplusToTB_M_500
datasetRef = datasetsMgrRef.getDataset(
"QCD") #ChargedHiggs_HplusTB_HplusToTB_M_500
h = datasetTarg.getDatasetRootHisto(hT)
#h.normalizeToOne()
h.normalizeToLuminosity(intLumi)
HT = h.getHistogram()
h = datasetRef.getDatasetRootHisto(hR)
#h.normalizeToOne()
h.normalizeToLuminosity(intLumi)
HR = h.getHistogram()
altSignalBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kAzure + 9,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kAzure + 9,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kAzure + 9, fillStyle=3001)
])
altBackgroundBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kRed - 4,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kRed - 4,
lineStyle=ROOT.kSolid,
lineWidth=3),
#styles.StyleFill(fillColor=ROOT.kRed-4)
])
signalStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kTeal + 2,
markerSizes=None,
markerStyle=ROOT.kFullTriangleUp),
styles.StyleLine(lineColor=ROOT.kTeal + 2,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kTeal + 2, fillStyle=3001)
])
p = plots.ComparisonPlot(
histograms.Histo(HT, "Target", "pl", "PL"),
histograms.Histo(HR, "Reference", "pl", "PL"),
)
p.histoMgr.setHistoLegendLabelMany({
"Target": "bug fixed",
"Reference": "bug"
})
if ("TT" in datasetTarg.getName()):
p.histoMgr.forHisto("Target", altBackgroundBDTGStyle)
p.histoMgr.forHisto("Reference", altSignalBDTGStyle)
elif ("QCD" in datasetTarg.getName()):
p.histoMgr.forHisto(
"Target", altBackgroundBDTGStyle) #styles.getABCDStyle("VR"))
p.histoMgr.forHisto("Reference", styles.qcdFillStyle)
elif ("Charged" in datasetTarg.getName()):
p.histoMgr.forHisto("Target", altBackgroundBDTGStyle)
p.histoMgr.forHisto("Reference", signalStyle)
p.histoMgr.setHistoDrawStyle("Target", "HIST")
p.histoMgr.setHistoLegendStyle("Target", "LP") #F
p.histoMgr.setHistoDrawStyle("Reference", "HIST")
p.histoMgr.setHistoLegendStyle("Reference", "F") #LP
'''
histoG = histograms.Histo(HT, "TT", "Signal")
histoG.setIsDataMC(isData=False, isMC=True)
histoF = histograms.Histo(HR, "QCD", "Signal")
histoF.setIsDataMC(isData=False, isMC=True)
'''
#styleT = styles.ttStyle
#styleR = styles.signalStyleHToTB1000
#styleT.apply(HT)
#styleR.apply(HR)
'''
myList.append(histoT)
myList.append(histoR)
'''
# Save plot in all formats
#saveName = hT.split("/")[-1]
#plots.drawPlot(p, saveName, **_kwargs)
#savePath = os.path.join(opts.saveDir, "HplusMasses", hT.split("/")[0], opts.optMode)
#plots.drawPlot(p, savePath, **_kwargs)
#SavePlot(p, saveName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png", ".pdf", ".C"])
dName = datasetTarg.getName()
dName = dName.replace("ChargedHiggs_HplusTB_HplusToTB_", "")
saveName = hT.split("/")[-1] + "_" + dName
#print saveName, hT, opts.saveDir
savePath = os.path.join(opts.saveDir, hT.split("/")[0], opts.optMode)
print "TYPE", type(hT), type(savePath), type(p)
plots.drawPlot(p, savePath, **_kwargs)
#leg = ROOT.TLegend(0.2, 0.8, 0.81, 0.87)
leg = ROOT.TLegend(0.2, 0.8, 0.51, 0.87)
leg.SetFillStyle(0)
leg.SetFillColor(0)
leg.SetBorderSize(0)
#{"dx": -0.55, "dy": -0.55, "dh": -0.08}
datasetName = datasetTarg.getName()
datasetName = datasetName.replace("TT", "t#bar{t}")
if "ChargedHiggs" in datasetName:
datasetName = datasetName.replace("ChargedHiggs_HplusTB_HplusToTB_M_",
"m_{H+} = ")
datasetName = datasetName + "GeV"
#leg.SetHeader("t#bar{t}")
leg.SetHeader(datasetName)
leg.Draw()
#print savePath
#savePath = os.path.join(opts.saveDir, opts.optMode)
SavePlot(p, saveName, savePath)
return
def PlotSignalBackground(datasetsMgr, hG, hF, intLumi):
kwargs = {}
_kwargs = {}
if opts.normaliseToOne:
pG = plots.MCPlot(datasetsMgr,
hG,
normalizeToOne=True,
saveFormats=[],
**_kwargs)
pF = plots.MCPlot(datasetsMgr,
hF,
normalizeToOne=True,
saveFormats=[],
**_kwargs)
else:
pG = plots.MCPlot(datasetsMgr,
hG,
normalizeToLumi=intLumi,
saveFormats=[],
**_kwargs)
pF = plots.MCPlot(datasetsMgr,
hF,
normalizeToLumi=intLumi,
saveFormats=[],
**_kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.2f"
_xlabel = None
logY = False
_opts = {"ymin": 0, "ymaxfactor": 1.1}
if "mass" in hG.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "M (%s)" % _units
if "trijetmass" in hG.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{top} (%s)" % _units
#_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 805 #1005
_opts = {"xmin": 0.0, "xmax": 805}
if "bjetmass" in hG.lower():
_xlabel = "m_{b-tagged jet} (%s)" % _units
_opts["xmax"] = 50
if "bjetldgjet_mass" in hG.lower():
_xlabel = "m_{b, ldg jet} (%s)" % _units
_opts["xmax"] = 705
if "bjetsubldgjet_mass" in hG.lower():
_xlabel = "m_{b-tagged, subldg jet} (%s)" % _units
_opts["xmax"] = 705
if "jet_mass" in hG.lower():
_opts["xmax"] = 750
if "mult" in hG.lower():
_format = "%0.0f"
if "ldg" in hG.lower():
_xlabel = "Leading jet mult"
if "subldg" in hG.lower():
_xlabel = "Subleading jet mult"
if "avg" in hG.lower():
_xlabel = "avg CvsL"
if "cvsl" in hG.lower():
_format = "%0.2f"
if "ldg" in hG.lower():
_xlabel = "Leading jet CvsL"
if "subldg" in hG.lower():
_xlabel = "Subleading jet CvsL"
if "avg" in hG.lower():
_xlabel = "avg CvsL"
if "axis2" in hG.lower():
_format = "%0.3f"
if "ldg" in hG.lower():
_xlabel = "Leading jet axis2"
if "subldg" in hG.lower():
_xlabel = "Subleading jet axis2"
if "avg" in hG.lower():
_xlabel = "avg axis2"
if "dijetmass" in hG.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{W} (%s)" % _units
_opts["xmax"] = 600
_opts = {"xmin": 0.0, "xmax": 605, "ymin": 1e-3, "ymaxfactor": 1.0}
if "trijetptdr" in hG.lower():
_opts["xmax"] = 800
_format = "%0.0f"
_xlabel = "p_{T}#Delta R_{t}"
if "dijetptdr" in hG.lower():
_opts["xmax"] = 800
_format = "%0.0f"
_xlabel = "p_{T}#Delta R_{W}"
if "dgjetptd" in hG.lower():
_format = "%0.2f"
_xlabel = "Leading jet p_{T}D"
if "subldg" in hG.lower():
_xlabel = "Subleading jet p_{T}D"
if "bdisc" in hG.lower():
_format = "%0.2f"
if "subldg" in hG.lower():
_xlabel = "Subleading jet CSV"
_opts = {"ymin": 1e-3, "ymax": 0.06}
elif "ldg" in hG.lower():
_xlabel = "Leading jet CSV"
_opts = {"ymin": 1e-3, "ymax": 0.06}
else:
_xlabel = "b-tagged jet CSV"
_opts = {"ymin": 1e-3, "ymax": 0.35}
if "over" in hG.lower():
_format = "%0.2f "
_xlabel = "m_{W}/m_{t}"
_opts["xmax"] = 1
_opts["xmin"] = 0
if "likelihood" in hG.lower():
_format = "%0.2f"
if "ldg" in hG.lower():
_xlabel = "Leading jet QGL"
if "subldg" in hG.lower():
_xlabel = "Subleading jet QGL"
if "avg" in hG.lower():
_xlabel = "avg QGL"
else:
pass
'''
if "bdisc" in hG.lower():
_format = "%0.2f"
if "subldg" in hG.lower():
_xlabel = "Subleading jet CSV"
elif "ldg" in hG.lower():
_xlabel = "Leading jet CSV"
else:
_xlabel = "b-tagged jet CSV"
'''
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
else:
_opts["ymin"] = 1e0
myList = []
# Customise styling
pG.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
pF.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
#Dataset: ttbar
dataset = datasetsMgr.getDataset(opts.dataset) #soti
#dataset = datasetsMgr.getDataset("ChargedHiggs_HplusTB_HplusToTB_M_1000")
#Get Genuine-top histogram
h = dataset.getDatasetRootHisto(hG)
h.normalizeToOne()
HG = h.getHistogram()
#Get Fake-top histogram
h = dataset.getDatasetRootHisto(hF)
h.normalizeToOne()
HF = h.getHistogram()
#Define Signal style
altSignalBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kAzure + 9,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kAzure + 9,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kAzure + 9)
])
#Define Background style
altBackgroundBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kRed - 4,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kRed - 4,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kRed - 4, fillStyle=3001)
])
signalBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kTeal + 2,
markerSizes=None,
markerStyle=ROOT.kFullTriangleUp),
styles.StyleLine(lineColor=ROOT.kTeal + 2,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kTeal + 2)
]) #, fillStyle=3001)])
signalGrayStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kGray + 1,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kGray + 1,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kGray + 1)
])
backgroundGrayStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kGray + 3,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kGray + 3,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kGray + 3, fillStyle=3001)
])
#Comparison Plot
p = plots.ComparisonPlot(
histograms.Histo(HF, "Fake", "p", "P"),
histograms.Histo(HG, "Genuine", "pl", "PL"),
)
#Set labels
p.histoMgr.setHistoLegendLabelMany({
"Fake": "Unmatched",
"Genuine": "Truth-matched"
})
#Set Draw style
p.histoMgr.forHisto("Fake", altBackgroundBDTGStyle)
p.histoMgr.setHistoDrawStyle("Fake", "LP")
p.histoMgr.setHistoLegendStyle("Fake", "LP") #F
p.histoMgr.forHisto("Genuine", altSignalBDTGStyle)
p.histoMgr.setHistoDrawStyle("Genuine", "HIST")
p.histoMgr.setHistoLegendStyle("Genuine", "LP") #LP
if "avg" in hG.lower() or "likelihood" in hG.lower() or "over" in hG.lower(
):
p.histoMgr.forHisto("Genuine", signalBDTGStyle)
p.histoMgr.setHistoDrawStyle("Genuine", "HIST")
p.histoMgr.setHistoLegendStyle("Genuine", "LP") #LP
if "Gray" in opts.mcrab:
p.histoMgr.forHisto("Fake", backgroundGrayStyle)
p.histoMgr.forHisto("Genuine", signalGrayStyle)
histoG = histograms.Histo(HG, "TT", "Signal")
histoG.setIsDataMC(isData=False, isMC=True)
histoF = histograms.Histo(HF, "QCD", "Signal")
histoF.setIsDataMC(isData=False, isMC=True)
styleG = styles.ttStyle
styleF = styles.signalStyleHToTB1000
styleG.apply(HG)
styleF.apply(HF)
myList.append(histoG)
myList.append(histoF)
_kwargs = {
"xlabel": _xlabel,
"ylabel": "Arbitrary Units / %s" % (_format),
"ratioYlabel": "Ratio ",
"ratio": False,
"ratioInvert": False,
"stackMCHistograms": False,
"addMCUncertainty": False,
"addLuminosityText": False,
"addCmsText": True,
"cmsExtraText": "Preliminary",
#"opts" : {"ymin": 0.0, "ymaxfactor": 1.1},
"opts": _opts,
"opts2": {
"ymin": 0.6,
"ymax": 1.5
},
"log": False,
#"createLegend" : {"x1": 0.5, "y1": 0.75, "x2": 0.9, "y2": 0.9},
"createLegend": {
"x1": 0.58,
"y1": 0.65,
"x2": 0.92,
"y2": 0.82
},
}
# Save plot in all formats
saveName = hG.split("/")[-1]
#plots.drawPlot(p, saveName, **_kwargs)
savePath = os.path.join(opts.saveDir + ANALYSISNAME, "HplusMasses",
hG.split("/")[0], opts.optMode)
plots.drawPlot(p, savePath, **_kwargs)
SavePlot(p,
saveName,
os.path.join(opts.saveDir + opts.mcrab, opts.optMode),
saveFormats=[".png"])
return
def PlotPurity(datasetsMgr, histoName):
'''
Create plots with "FakeB=Data-EWKGenuineB"
'''
Verbose("Plotting histogram %s for Data, EWK, QCD " % (histoName), True)
# Which folder to use (redundant)
defaultFolder = "FakeBPurity"
genuineBFolder = defaultFolder + "EWKGenuineB"
fakeBFolder = defaultFolder + "EWKFakeB"
# Customize the histograms (BEFORE calculating purity obviously otherwise numbers are nonsense)
_cutBox = None
_rebinX = 1
_opts = {"ymin": 1e-3, "ymax": 1.0} #"ymaxfactor": 1.2}
_format = "%0.0f"
#_opts["xmax"] = xMax
_xlabel = None
_ylabel = "Purity / "
_format = "/ %.0f "
h = histoName.split("/")[-1]
if "dijetm" in h.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jj} (%s)" % (_units)
_cutBox = {
"cutValue": 80.399,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
if "trijetm" in h.lower():
_rebinX = 2
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {
"cutValue": 173.21,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 1000.0
if "pt" in h.lower():
_format = "%0.0f GeV/c"
if "chisqr" in h.lower():
_opts["xmax"] = 100.0
if "allselections" in h.lower():
_opts["xmax"] = 10.0
#if histo.lower().endswith("met_et"):
if h.lower().startswith("ht_"):
_rebinX = 5
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "H_{T} (%s)" % _units
_cutBox = {
"cutValue": 500.0,
"fillColor": 16,
"box": False,
"line": False,
"greaterThan": True
}
_opts["xmin"] = 500.0
_opts["xmax"] = 3500.0
if "eta" in h.lower():
_rebinX = 1
_format = "%0.2f"
_cutBox = {
"cutValue": 0.,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmin"] = -3.0
_opts["xmax"] = +3.0
if "deltaeta" in h.lower():
_format = "%0.2f"
_opts["xmin"] = 0.0
_opts["xmax"] = 6.0
if "bdisc" in h.lower():
_format = "%0.2f"
if "tetrajetm" in h.lower():
_rebinX = 4
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjbb} (%s)" % (_units)
_opts["xmax"] = 2500.0
if "pt_" in h.lower():
_rebinX = 2
_ylabel += _format
# Get histos (Data, EWK) for Inclusive
p1 = plots.ComparisonPlot(*getHistos(datasetsMgr, histoName))
p1.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
# Clone histograms
Data = p1.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
EWK = p1.histoMgr.getHisto("EWK").getRootHisto().Clone("EWK")
QCD = p1.histoMgr.getHisto("Data").getRootHisto().Clone("QCD")
# Rebin histograms (Before calculating Purity)
Data.Rebin(_rebinX)
EWK.Rebin(_rebinX)
QCD.Rebin(_rebinX)
# Get QCD = Data-EWK
QCD.Add(EWK, -1)
# Comparison plot. The first argument is the reference histo. All other histograms are compared with respect to that.
QCD_Purity, xMin, xMax, binList, valueDict, upDict, downDict = getPurityHisto(
QCD, Data, inclusiveBins=False, printValues=False)
EWK_Purity, xMin, xMax, binList, valueDict, upDict, downDict = getPurityHisto(
EWK, Data, inclusiveBins=False, printValues=False)
# Create TGraphs
if 0:
gQCD_Purity = MakeGraph(ROOT.kFullTriangleUp, ROOT.kOrange, binList,
valueDict, upDict, downDict)
gEWK_Purity = MakeGraph(ROOT.kFullTriangleDown, ROOT.kPurple, binList,
valueDict, upDict, downDict)
# Make the plots
if opts.plotEWK:
p = plots.ComparisonManyPlot(QCD_Purity, [EWK_Purity], saveFormats=[])
else:
p = plots.PlotBase([QCD_Purity], saveFormats=[])
# Apply histo styles
p.histoMgr.forHisto("QCD", styles.getQCDLineStyle())
if opts.plotEWK:
p.histoMgr.forHisto("EWK", styles.getAltEWKLineStyle())
# Set draw style
p.histoMgr.setHistoDrawStyle("QCD", "P")
if opts.plotEWK:
p.histoMgr.setHistoDrawStyle("EWK", "HIST")
# Set legend style
p.histoMgr.setHistoLegendStyle("QCD", "P")
if opts.plotEWK:
p.histoMgr.setHistoLegendStyle("EWK", "F")
# Set legend labels
if opts.plotEWK:
p.histoMgr.setHistoLegendLabelMany({
"QCD": "QCD",
"EWK": "EWK",
})
else:
p.histoMgr.setHistoLegendLabelMany({
"QCD": "QCD",
})
# Do the plot
plots.drawPlot(
p,
histoName,
xlabel=_xlabel,
ylabel=_ylabel,
log=False,
rebinX=1, # must be done BEFORE calculating purity
cmsExtraText="Preliminary",
createLegend={
"x1": 0.76,
"y1": 0.80,
"x2": 0.92,
"y2": 0.92
},
opts=_opts,
opts2={
"ymin": 1e-5,
"ymax": 1e0
},
ratio=False,
ratioInvert=False,
ratioYlabel="Ratio",
cutBox=_cutBox,
)
# Save plot in all formats
SavePlot(p, histoName,
os.path.join(opts.saveDir, "Purity",
opts.optMode)) #, saveFormats = [".png"] )
return
def PurityPlots(datasetsMgr, histoName, analysisType="Inverted"):
'''
Create plots with "FakeB=Data-EWKGenuineB"
'''
Verbose(
"Plotting histogram %s for Data, EWK, QCD for %s" %
(histoName, analysisType), True)
# Sanity check
IsBaselineOrInverted(analysisType)
defaultFolder = "FakeBPurity"
genuineBFolder = defaultFolder + "EWKGenuineB"
fakeBFolder = defaultFolder + "EWKFakeB"
# Get histos (Data, EWK) for Inclusive
p1 = plots.ComparisonPlot(*getHistos(datasetsMgr, histoName, analysisType))
p1.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
# Get histos (Data, EWK) for GenuineB
p2 = plots.ComparisonPlot(
*getHistos(datasetsMgr, histoName.replace(
defaultFolder, genuineBFolder), analysisType))
p2.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
# Clone histograms
Data = p1.histoMgr.getHisto(analysisType +
"-Data").getRootHisto().Clone(analysisType +
"-Data")
EWK = p1.histoMgr.getHisto(analysisType +
"-EWK").getRootHisto().Clone(analysisType +
"-EWK")
EWKGenuineB = p2.histoMgr.getHisto(
analysisType + "-EWK").getRootHisto().Clone(analysisType + "-EWK")
FakeB = p1.histoMgr.getHisto(analysisType +
"-Data").getRootHisto().Clone(analysisType +
"-FakeB")
# Subtract EWKGEnuineB from Data to get FakeB
FakeB.Add(EWKGenuineB, -1)
#FakeB.Add(EWK, -1)
# Dos not work
# p1.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(2))
# p2.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(2))
# Comparison plot. The first argument is the reference histo. All other histograms are compared with respect to that.
FakeB_Purity, xMin, xMax = getPurityHisto(FakeB,
Data,
inclusiveBins=False,
printValues=False)
EWKGenuineB_Purity, xMin, xMax = getPurityHisto(EWKGenuineB,
Data,
inclusiveBins=False,
printValues=False)
p = plots.ComparisonManyPlot(FakeB_Purity, [EWKGenuineB_Purity],
saveFormats=[])
# Apply histo styles
p.histoMgr.forHisto(
analysisType + "-FakeB",
styles.getInvertedLineStyle()) #styles.getFakeBLineStyle() )
p.histoMgr.forHisto(
analysisType + "-EWK",
styles.getAltEWKLineStyle()) #styles.getAltEWKStyle() )
# Set draw style
p.histoMgr.setHistoDrawStyle(analysisType + "-FakeB", "HIST")
p.histoMgr.setHistoDrawStyle(analysisType + "-EWK", "HIST")
# Set legend style
p.histoMgr.setHistoLegendStyle(analysisType + "-FakeB", "L")
p.histoMgr.setHistoLegendStyle(analysisType + "-EWK", "L")
# p.histoMgr.setHistoLegendStyleAll("LP")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
analysisType + "-FakeB": "FakeB",
analysisType + "-EWK": "GenuineB (EWK)",
})
# Draw the histograms
_cutBox = None
_rebinX = 1
_opts = {"ymin": 1e-3, "ymaxfactor": 1.2}
_format = "%0.0f"
_opts["xmax"] = xMax
_xlabel = None
if "dijetm" in histoName.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jj} (%s)" % (_units)
_cutBox = {
"cutValue": 80.399,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
if "trijetm" in histoName.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {
"cutValue": 173.21,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 1500.0
if "pt" in histoName.lower():
_format = "%0.0f GeV/c"
if "eta" in histoName.lower():
_format = "%0.2f"
_cutBox = {
"cutValue": 0.,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmin"] = -3.0
_opts["xmax"] = +3.0
if "deltaeta" in histoName.lower():
_format = "%0.2f"
_opts["xmin"] = 0.0
_opts["xmax"] = 6.0
if "bdisc" in histoName.lower():
_format = "%0.2f"
if "tetrajetm" in histoName.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjjb} (%s)" % (_units)
_opts["xmax"] = 3500.0
# Do the plot
plots.drawPlot(
p,
histoName,
xlabel=_xlabel,
ylabel="Purity / {0}".format(_format),
log=False,
rebinX=_rebinX, # Can only Rebin BEFORE calculating purity
cmsExtraText="Preliminary",
createLegend={
"x1": 0.60,
"y1": 0.80,
"x2": 0.92,
"y2": 0.92
},
opts=_opts,
opts2={
"ymin": 1e-5,
"ymax": 1e0
},
ratio=False,
ratioInvert=False,
ratioYlabel="Ratio",
cutBox=_cutBox,
)
# Save plot in all formats
SavePlot(p, histoName,
os.path.join(opts.saveDir, "Purity",
opts.optMode)) #, saveFormats = [".png"] )
return
def main(opts):
global RunEra
mcrabName = opts.mcrab
print("mcrabNAME ISSSSSS ", mcrabName)
RunEra = mcrabName.split("_")[1]
print " Run Era is: ", RunEra
print " Plotting efficiency for trigger:", kwargs.get("trigger")
# Setup the style
style = tdrstyle.TDRStyle()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(opts.batchMode)
# Setup & configure the dataset manager
#datasetsMgr = GetSpecificDatasetsFromDir(opts.mcrab, opts, RunEra, reHLT_samples, **kwargs)
datasetsMgr = GetDatasetsFromDir(opts.mcrab, opts, **kwargs)
minRunRange = None
maxRunRange = None
Counter = 0
nDataDatasets = len(datasetsMgr.getDataDatasets())
for d in datasetsMgr.getDataDatasets():
Counter = Counter + 1
if Counter == 1:
minRunRange = d.getName().split("_")[-2]
if Counter == nDataDatasets:
maxRunRange = d.getName().split("_")[-1]
print "Data Name==> ", d.getName()
print "RunRange ==> ", d.getName().split(
"_")[-2], " - ", d.getName().split("_")[-1]
print "Run Range = ", minRunRange, " - ", maxRunRange
runRange = minRunRange + " - " + maxRunRange
intLumi = GetLumi(datasetsMgr)
print "intLumi=", intLumi
# Update to PU
datasetsMgr.updateNAllEventsToPUWeighted()
# Load Luminosities
datasetsMgr.loadLuminosities()
datasetsMgr.PrintLuminosities()
# Default merging & ordering: "Data", "QCD", "SingleTop", "Diboson"
#plots.mergeRenameReorderForDataMC(datasetsMgr) #WARNING: Merged MC histograms must be normalized to something!
#dataset_MC.normalizeMCByLuminosity()
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
plots.mergeRenameReorderForDataMC(datasetsMgr)
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
dataset_Data = datasetsMgr.getDataDatasets()
datasetsMgr.mergeMC() #ATHER
dataset_MC = None
dataset_MC = datasetsMgr.getMCDatasets()
#datasetsMgr.normalizeMCByLuminosity()
#datasetsMgr.mergeMC()
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
lumi = 0.0
for d in datasetsMgr.getDataDatasets():
lumi += d.getLuminosity()
SigSel = ["1BTag", "2BTag", "OR", "OR_PFJet450"]
for s in SigSel:
for xVar in xVariables:
print s, xVar
#if xVar == "pt6thJet":
# cutValue = 40
# cutLine = True
#for den,num
num_name = "hNum_" + xVar + "_RefTrg_OfflineSel_Signal" + s
den_name = "hDen_" + xVar + "_RefTrg_OfflineSel"
# Get the save path and name
plotName = "Eff" + "_" + xVar + "_" + s
savePath, saveName = GetSavePathAndName(plotName, **kwargs)
# Get Efficiency Plots
eff_Data = getEfficiency(datasetsMgr, dataset_Data, num_name,
den_name, **kwargs)
eff_MC = getEfficiency(datasetsMgr, dataset_MC, num_name, den_name,
**kwargs)
if xVar == "pt6thJet":
xMin = 29.0
xMax = 125.0
elif xVar == "eta6thJet":
xMin = -2.5
xMax = 2.5
elif xVar == "phi6thJet":
xMin = -3.2
xMax = 3.2
elif xVar == "Ht":
xMin = 350
xMax = 2000
elif xVar == "nBTagJets":
xMin = 0
xMax = 10
elif xVar == "pu":
xMin = 0
xMax = 50
elif xVar == "CSV":
xMin = 0
xMax = 1
elif xVar == "JetMulti":
xMin = 4
xMax = 15
elif xVar == "BJetMulti":
xMin = 0
xMax = 8
# Apply Styles
styles.dataStyle.apply(eff_Data)
styles.mcStyle.apply(eff_MC)
# Marker Style
eff_Data.SetMarkerSize(1)
# Create Comparison Plot
p = plots.ComparisonPlot(
histograms.HistoGraph(eff_Data, "eff_Data", "p", "P"),
histograms.HistoGraph(eff_MC, "eff_MC", "p", "P"))
opts = {"ymin": 0, "ymax": 1.1, "xmin": xMin, "xmax": xMax}
opts2 = {"ymin": 0.8, "ymax": 1.05}
if xVar == "pu":
moveLegend = {"dx": -0.44, "dy": -0.62, "dh": -0.2}
else:
moveLegend = {"dx": -0.44, "dy": -0.57, "dh": -0.2}
p.histoMgr.setHistoLegendLabelMany({
"eff_Data": "Data",
"eff_MC": "Simulation"
})
createRatio = True #kwargs.get("ratio")
p.createFrame(saveName,
createRatio=createRatio,
opts=opts,
opts2=opts2)
# Set Legend
p.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Set Log & Grid
SetLogAndGrid(p, **kwargs)
# Set Titles
p.getFrame().GetYaxis().SetTitle(kwargs.get("ylabel"))
if xVar == "pt6thJet":
p.getFrame().GetXaxis().SetTitle("p_{T,6thJet} (Gev/c)")
elif xVar == "eta6thJet":
p.getFrame().GetXaxis().SetTitle("#eta_{6thJet}")
elif xVar == "phi6thJet":
p.getFrame().GetXaxis().SetTitle("#phi_{6thJet}")
elif xVar == "Ht":
p.getFrame().GetXaxis().SetTitle("H_{T} (Gev/c)")
elif xVar == "nBTagJets":
p.getFrame().GetXaxis().SetTitle("nB-Jet")
elif xVar == "pu":
p.getFrame().GetXaxis().SetTitle("nPU")
elif xVar == "CSV":
p.getFrame().GetXaxis().SetTitle("CSV")
elif xVar == "JetMulti":
p.getFrame().GetXaxis().SetTitle("Jet Multiplicity")
elif xVar == "BJetMulti":
p.getFrame().GetXaxis().SetTitle("B-Jet Multiplicity")
if createRatio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
#if xVar == "pt6thJet" and s == "OR":
# plist = [0.7, 0.99, 1000, 0.16, 28.0]
# Fit_Richards(30.0, 120.0, p, eff_Data, plist)
# plist = [0.72, 0.91, 0.212, 0.15, 50.0]
# Fit_Richards(30.0, 120.0, p, eff_MC, plist)
#elif xVar == "Ht" and s == "OR":
# plist = [0.00005, 0.988, 0.000000109, 0.15, 29.0]
# Fit_Richards(350.0, 2000.0, p, eff_Data, plist)
#plist = [0.0003, 0.97, 0.45, 0.24, 43.0]
#Fit_Richards(500.0, 2000.0, p, eff_MC, plist)
#elif xVar == "nBTagJets":
#plist = [0.07, 0.984, 0.45, 0.24, 43.0]
#Fit_Richards(30.0, 120.0, p, eff_Data, plist)
#elif xVar == "pu":
#plist = [0.07, 0.984, 0.45, 0.24, 43.0]
#Fit_Richards(30.0, 120.0, p, eff_Data, plist)
# Draw
histograms.addText(0.30, 0.10, "Runs " + runRange, 17)
histograms.addText(0.30, 0.15, "2016", 17)
histograms.addStandardTexts(lumi=lumi)
p.draw()
# Save the canvas to a file
SaveAs(p, kwargs.get("savePath"), saveName,
kwargs.get("saveFormats"), True)
'''
# IN SLICES OF HT
HTSlices = ["450ht600","600ht800","800ht1000", "1000ht1250", "1250ht1500", "1500ht2000"]
for s in SigSel:
for hsl in HTSlices:
num_name = "Num_pt6thJet_Vs_"+hsl+"_RefTrg_OfflineSel_"+s
den_name = "Den_pt6thJet_Vs_"+hsl+"_RefTrg_OfflineSel"
# Get the save path and name
plotName = "Eff_pt6thJet_Vs_"+hsl+"_"+s
savePath, saveName = GetSavePathAndName(plotName, **kwargs)
# Get Efficiency Plots
eff_Data = getEfficiency(datasetsMgr, dataset_Data, num_name, den_name , **kwargs)
eff_MC = getEfficiency(datasetsMgr, dataset_MC, num_name, den_name, **kwargs)
xMin = 29.0
xMax = 125.0
# Apply Styles
styles.dataStyle.apply(eff_Data)
styles.mcStyle.apply(eff_MC)
# Marker Style
eff_Data.SetMarkerSize(1)
# Create Comparison Plot
p = plots.ComparisonPlot(histograms.HistoGraph(eff_Data, "eff_Data","p","P"),
histograms.HistoGraph(eff_MC, "eff_MC", "p", "P"))
opts = {"ymin": 0 , "ymax": 1.1, "xmin":xMin, "xMax":xMax}
opts2 = {"ymin": 0.7, "ymax": 1.3}
moveLegend = {"dx": -0.44, "dy": -0.57, "dh": -0.2 }
p.histoMgr.setHistoLegendLabelMany({"eff_Data": "Data", "eff_MC": "Simulation"})
createRatio = True #kwargs.get("ratio")
p.createFrame(saveName, createRatio=createRatio, opts=opts, opts2=opts2)
# Set Legend
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Set Log & Grid
SetLogAndGrid(p, **kwargs)
# Set Titles
p.getFrame().GetYaxis().SetTitle(kwargs.get("ylabel"))
p.getFrame().GetXaxis().SetTitle("p_{T} (Gev/c)")
if createRatio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# Draw
histograms.addText(0.30, 0.10, "Runs "+runRange, 17)
histograms.addText(0.30, 0.15, "2016", 17)
histograms.addStandardTexts(lumi=lumi)
p.draw()
# Save the canvas to a file
SaveAs(p, kwargs.get("savePath"), saveName, kwargs.get("saveFormats"), True)
PTSlices = ["40pt50","50pt60", "60pt70", "70pt90", "90pt120"]
for s in SigSel:
for psl in PTSlices:
num_name = "Num_ht_Vs_"+psl+"_HLT_PFHT350_"+s
den_name = "Den_ht_Vs_"+psl+"_HLT_PFHT350"
# Get the save path and name
plotName = "Eff_ht_Vs_"+psl+"_"+s
savePath, saveName = GetSavePathAndName(plotName, **kwargs)
# Get Efficiency Plots
eff_Data = getEfficiency(datasetsMgr, dataset_Data, num_name, den_name , **kwargs)
eff_MC = getEfficiency(datasetsMgr, dataset_MC, num_name, den_name, **kwargs)
xMin = 450
xMax = 2000
# Apply Styles
styles.dataStyle.apply(eff_Data)
styles.mcStyle.apply(eff_MC)
# Marker Style
eff_Data.SetMarkerSize(1)
# Create Comparison Plot
p = plots.ComparisonPlot(histograms.HistoGraph(eff_Data, "eff_Data","p","P"),
histograms.HistoGraph(eff_MC, "eff_MC", "p", "P"))
opts = {"ymin": 0 , "ymax": 1.1, "xmin":xMin, "xMax":xMax}
opts2 = {"ymin": 0.7, "ymax": 1.3}
moveLegend = {"dx": -0.44, "dy": -0.57, "dh": -0.2 }
p.histoMgr.setHistoLegendLabelMany({"eff_Data": "Data", "eff_MC": "Simulation"})
createRatio = True #kwargs.get("ratio")
#if eff_Data != None and eff_MC != None:
p.createFrame(saveName, createRatio=createRatio, opts=opts, opts2=opts2)
# Set Legend
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Set Log & Grid
SetLogAndGrid(p, **kwargs)
# Set Titles
p.getFrame().GetYaxis().SetTitle(kwargs.get("ylabel"))
p.getFrame().GetXaxis().SetTitle("H_{T} (Gev/c)")
if createRatio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# Draw
histograms.addText(0.30, 0.10, "Runs "+runRange, 17)
histograms.addText(0.30, 0.15, "2016", 17)
histograms.addStandardTexts(lumi=lumi)
p.draw()
# Save the canvas to a file
SaveAs(p, kwargs.get("savePath"), saveName, kwargs.get("saveFormats"), True)
'''
return
def BaselineVsInvertedComparison(datasetsMgr, histoName):
p1 = plots.ComparisonPlot(*getHistos(datasetsMgr, "Data", "topSelection_Baseline/%s" % histoName, "topSelection_Inverted/%s" % histoName))
p1.histoMgr.normalizeMCToLuminosity(datasetsMgr.getDataset("Data").getLuminosity())
p2 = plots.ComparisonPlot(*getHistos(datasetsMgr, "EWK", "topSelection_Baseline/%s" % histoName, "topSelection_Inverted/%s" % histoName) )
p2.histoMgr.normalizeMCToLuminosity(datasetsMgr.getDataset("Data").getLuminosity())
# Get Data histos
baseline_Data = p1.histoMgr.getHisto("Baseline-Data").getRootHisto().Clone("Baseline-Data")
inverted_Data = p1.histoMgr.getHisto("Inverted-Data").getRootHisto().Clone("Inverted-Data")
# Get EWK histos
baseline_EWK = p2.histoMgr.getHisto("Baseline-EWK").getRootHisto().Clone("Baseline-EWK")
inverted_EWK = p2.histoMgr.getHisto("Inverted-EWK").getRootHisto().Clone("Inverted-EWK")
# Create QCD histos: QCD = Data-EWK
baseline_QCD = p1.histoMgr.getHisto("Baseline-Data").getRootHisto().Clone("Baseline-QCD")
baseline_QCD.Add(baseline_EWK, -1)
inverted_QCD = p1.histoMgr.getHisto("Inverted-Data").getRootHisto().Clone("Inverted-QCD")
inverted_QCD.Add(inverted_EWK, -1)
# Normalize histograms to unit area
baseline_QCD.Scale(1.0/baseline_QCD.Integral())
inverted_QCD.Scale(1.0/inverted_QCD.Integral())
# Create the final plot object
p = plots.ComparisonManyPlot(baseline_QCD, [inverted_QCD], saveFormats=[]) #[".C", ".png", ".pdf"])
p.setLuminosity(GetLumi(datasetsMgr))
# Apply styles
p.histoMgr.forHisto("Baseline-QCD" , styles.getBaselineStyle() )
p.histoMgr.forHisto("Inverted-QCD" , styles.getInvertedStyle() )
# Set draw style
p.histoMgr.setHistoDrawStyle("Baseline-QCD", "AP")
p.histoMgr.setHistoLegendStyle("Baseline-QCD", "LP")
p.histoMgr.setHistoDrawStyle("Inverted-QCD", "HIST")
p.histoMgr.setHistoLegendStyle("Inverted-QCD", "F")
# p.histoMgr.setHistoLegendStyleAll("LP")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Baseline-QCD" : "Baseline (QCD)",
"Inverted-QCD" : "Inverted (QCD)",
})
# Draw the histograms
_rebinX = 1
_cutBox = None
_opts = {"ymin": 8e-5, "ymaxfactor": 2.0}
if "Pt_" in histoName:
_format = "%0.f GeV/c"
if "tetrajet" in histoName.lower():
_rebinX = 2
if "ChiSqr" in histoName:
_format = "%0.1f"
_rebinX = 10
if "After" in histoName:
_rebinX = 1
_opts["xmax"] = 20.0
if "Mass" in histoName:
_format = "%0.0f GeV/c^{2}"
_rebinX = 2
if "tetrajet" in histoName.lower():
_rebinX = 5
_opts["xmax"] = 3000.0
if "BDisc" in histoName:
_format = "%0.2f"
_rebinX = 2
_opts["xmax"] = 1.01
if "Eta" in histoName:
_format = "%0.2f"
if "trijet" in histoName.lower():
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
if "dijet" in histoName.lower():
_cutBox = {"cutValue": 80.399, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
if "TetrajetMass" in histoName:
_opts = {"ymin": 8e-5, "ymaxfactor": 2.0, "xmax": 3000.0}
plots.drawPlot(p, histoName,
ylabel = "Arbitrary Units / %s" % (_format),
log = True,
rebinX = _rebinX, cmsExtraText = "Preliminary",
createLegend = {"x1": 0.62, "y1": 0.78, "x2": 0.92, "y2": 0.92},
opts = _opts,
opts2 = {"ymin": 0.6, "ymax": 1.4},
ratio = True,
ratioInvert = False,
ratioYlabel = "Ratio",
cutBox = _cutBox,
)
# Save plot in all formats
SavePlot(p, histoName, os.path.join(opts.saveDir, "BaselineVsInverted") )
return
def GetPurityHistoGraph(datasetsMgr, folder, hName):
# Which folder to use (Inclusive or GenuineB?)
genuineBFolder = folder + "EWKGenuineB"
fakeBFolder = folder + "EWKFakeB"
histoName_Incl = os.path.join(folder, hName)
histoName_Gen = os.path.join(genuineBFolder, hName)
# Get histogram customisations
_kwargs = GetHistoKwargs(histoName_Incl, opts)
# Get histos (Data, EWK) for Inclusive
pIncl = plots.ComparisonPlot(*getHistos(datasetsMgr, histoName_Incl) )
pGen = plots.ComparisonPlot(*getHistos(datasetsMgr, histoName_Gen) )
# Save name for future use
_kwargs["histoName"] = histoName_Incl
# Normalise to luminosity
pIncl.histoMgr.normalizeMCToLuminosity(datasetsMgr.getDataset("Data").getLuminosity())
pGen.histoMgr.normalizeMCToLuminosity(datasetsMgr.getDataset("Data").getLuminosity())
# Clone histograms
Data = pIncl.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
FakeB = pIncl.histoMgr.getHisto("Data").getRootHisto().Clone("FakeB")
QCD = pIncl.histoMgr.getHisto("Data").getRootHisto().Clone("QCD")
EWK = pIncl.histoMgr.getHisto("EWK").getRootHisto().Clone("EWK")
GenB = pGen.histoMgr.getHisto("EWK").getRootHisto().Clone("GenuineB")
# Rebin histograms (Before calculating Purity)
if "binList" in _kwargs:
xBins = _kwargs["binList"]
nx = len(xBins)-1
Data = Data.Rebin(nx , "", xBins)
FakeB = FakeB.Rebin(nx, "", xBins)
QCD = QCD.Rebin(nx , "", xBins)
EWK = EWK.Rebin(nx , "", xBins)
GenB = GenB.Rebin(nx , "", xBins)
# FakeB = Data - EWK_GenuineB ; QCD = Data-EWK
FakeB.Add(GenB, -1)
QCD.Add(EWK, -1)
# Create the Purity histos
if opts.type == "FakeB":
if 1:
hPurity = GetPurityHisto(Data, GenB, _kwargs, subtractFromOne=True, printValues=False, hideZeros=False)
else: #equivalent
hPurity = GetPurityHisto(Data, FakeB, _kwargs, subtractFromOne=False, printValues=False, hideZeros=False)
elif opts.type == "GenuineB":
if 1:
hPurity = GetPurityHisto(Data, GenB, _kwargs, subtractFromOne=False, printValues=False, hideZeros=False)
else:#equivalent
hPurity = GetPurityHisto(Data, FakeB, _kwargs, subtractFromOne=True, printValues=False, hideZeros=False)
elif opts.type == "QCD":
hPurity = GetPurityHisto(Data, EWK, _kwargs, subtractFromOne=True, printValues=False, hideZeros=False)
elif opts.type == "EWK":
hPurity = GetPurityHisto(Data, EWK, _kwargs, subtractFromOne=False, printValues=False, hideZeros=False)
else:
raise Exception("This should never be reached")
# Convert histos to TGraph
gPurity = convertHisto2TGraph(hPurity, _kwargs, printValues=False)
# Set legend label
label = "%s (%s)" % (opts.type, GetControlRegionLabel(histoName_Incl))
label = label.replace("FakeB", "Fake-b").replace("GenuineB", "Genuine-b")
# Apply random histo styles
s = styles.getABCDStyle( GetControlRegionLabel(histoName_Incl) )
s.apply(gPurity)
# Create histoGraph object
hgPurity = histograms.HistoGraph( gPurity, label, "p", "P")
return hgPurity, _kwargs
def main(opts, signalMass):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(False)
style.setGridY(False)
# Do the topSelection histos
folder = ""
#=== Define styles: Fixme: Put in function
#styles_S = styles.StyleCompound([styles.StyleMarker(markerSize=0.4, markerColor=928, markerSizes=None, markerStyle=ROOT.kFullDiamond),
# styles.StyleLine(lineColor=928, lineStyle=ROOT.kSolid, lineWidth=3),
# styles.StyleFill(fillColor=38)])
styles_S = styles.StyleCompound([styles.StyleMarker(markerSize=0.4, markerColor=928, markerSizes=None, markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=928, lineStyle=ROOT.kSolid, lineWidth=3)])
styles_B = styles.StyleCompound([styles.StyleMarker(markerSize=0.4, markerColor=2, markerSizes=None, markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=2, lineStyle=ROOT.kSolid, lineWidth=3),
styles.StyleFill(fillColor=2, fillStyle=3005)])
styles_SEff = styles.StyleCompound([styles.StyleMarker(markerSize=0.4, markerColor=ROOT.kBlue, markerSizes=None, markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kBlue, lineStyle=ROOT.kSolid, lineWidth=3),])
styles_BEff = styles.StyleCompound([styles.StyleMarker(markerSize=0.4, markerColor=ROOT.kRed, markerSizes=None, markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kRed, lineStyle=ROOT.kSolid, lineWidth=3),])
styles_Signif = styles.StyleCompound([styles.StyleMarker(markerSize=0.4, markerColor=ROOT.kGreen+2, markerSizes=None, markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kGreen+2, lineStyle=ROOT.kSolid, lineWidth=3),])
styles_pur = styles.StyleCompound([styles.StyleMarker(markerSize=0.4, markerColor=ROOT.kOrange+7, markerSizes=None, markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kOrange+7, lineStyle=ROOT.kSolid, lineWidth=3),])
styles_pureff = styles.StyleCompound([styles.StyleMarker(markerSize=0.4, markerColor=ROOT.kOrange, markerSizes=None, markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kOrange, lineStyle=ROOT.kSolid, lineWidth=3),])
#=== Histo Names ============================================
hN_SignalEff = "sigEffi"
hN_BgdEff = "bgdEffi"
hN_signif = "significance_BDTG"
hN_purS = "purS_BDTG"
hN_effpurS = "effpurS_BDTG"
hN_BDTs = "MVA_BDTG_S"
hN_BDTb = "MVA_BDTG_B"
#=== Directories ============================================
inpfile_eff = ROOT.TFile(opts.effFile, "R")
inpfile = ROOT.TFile(opts.rootfile, "R") #To be fixed!
inpfile_dir = inpfile.Get("Method_BDT/BDTG")
#=== Get histograms from directories=========================
h_SignalEffi = inpfile_eff.Get(hN_SignalEff)
h_BgdEffi = inpfile_eff.Get(hN_BgdEff)
h_signif = inpfile_eff.Get(hN_signif)
h_purS = inpfile_eff.Get(hN_purS)
h_effpurS = inpfile_eff.Get(hN_effpurS)
h_BDTs = inpfile_dir.Get(hN_BDTs)
h_BDTb = inpfile_dir.Get(hN_BDTb)
#=== Scale Significance
h_signifScaled = h_signif.Clone("signif")
maxSignif = h_signif.GetMaximum()
h_signifScaled.Scale(1/maxSignif)
histo_signalEff = histograms.Histo(h_SignalEffi, "sEff", legendStyle = "LP", drawStyle="LP")
histo_bgdEff = histograms.Histo(h_BgdEffi, "bEff", legendStyle = "LP", drawStyle="LP")
histo_signifScaled = histograms.Histo(h_signifScaled, "signif", legendStyle = "LP", drawStyle="LP")
histo_purS = histograms.Histo(h_purS, "purity", legendStyle = "LP", drawStyle="LP")
histo_effpurS = histograms.Histo(h_effpurS, "pureff", legendStyle = "LP", drawStyle="LP")
histo_BDTs = histograms.Histo(h_BDTs, "bdtS", legendStyle = "LP", drawStyle="LP")
histo_BDTb = histograms.Histo(h_BDTb, "bdtB", legendStyle = "LP", drawStyle="LP")
#=== Efficiency - Purity - Significance=================================
histoList_Eff = [histo_bgdEff, histo_signifScaled, histo_purS, histo_effpurS]
p_Eff = plots.ComparisonManyPlot(histo_signalEff, histoList_Eff,
saveFormats=[])
p_Eff.histoMgr.setHistoLegendLabelMany({"sEff": "Signal efficiency",
"bEff": "Bkg efficiency",
"signif": "S/#sqrt{S+B}",
#"signif": "#frac{S}{#sqrt{S+B}}",
"purity": "Signal purity",
"pureff": "Signal eff*purity",})
# Define Right Axis (Significance)
signifColor = ROOT.kGreen+2
#rightAxis = ROOT.TGaxis(0.5, 0.17, 0.97, 0.34, 0, 1.1*maxSignif, 510, "+L")
'''
TGaxis::TGaxis(Double_t xmin, Double_t ymin, Double_t xmax, Double_t ymax,
Double_t wmin, Double_t wmax, Int_t ndiv, Option_t *chopt,
Double_t gridlength)
'''
rightAxis = ROOT.TGaxis(1, 0, 1, 1.1, 0, 1.1*maxSignif, 510, "+L")
rightAxis.SetWmax(1.1*maxSignif)
rightAxis.SetLineColor ( signifColor )
rightAxis.SetLabelColor( signifColor )
rightAxis.SetTitleColor( signifColor )
# rightAxis.SetTitleSize( info->sSig->GetXaxis()->GetTitleSize() )
# rightAxis.SetLabelOffset(0.9)
# This will only be be visible if we use a wide canvas (but we don't want that)
if 0:
style.setWide(True, 0.15) #to make way for significane label
rightAxis.SetTitle( "Significance" )
# rightAxis.SetTitleOffset(1.1)
#=== BDTG output ========================================================
histo_BDTs = histograms.Histo(h_BDTs, "bdtS", legendStyle = "LP", drawStyle="LP")
histo_BDTb = histograms.Histo(h_BDTb, "bdtB", legendStyle = "F", drawStyle="LP")
#p_BDT = plots.ComparisonPlot(histo_BDTb, histo_BDTs, saveFormats=[])
p_BDT = plots.ComparisonPlot(histo_BDTs, histo_BDTb, saveFormats=[])
p_BDT.histoMgr.setHistoLegendLabelMany({"bdtS": "Signal", "bdtB": "Background"})
p_Eff.histoMgr.forHisto("sEff", styles_SEff)
p_Eff.histoMgr.setHistoDrawStyle("sEff", "L")
p_Eff.histoMgr.setHistoLegendStyle("sEff", "L")
p_Eff.histoMgr.forHisto("bEff", styles_BEff)
p_Eff.histoMgr.setHistoDrawStyle("bEff", "L")
p_Eff.histoMgr.setHistoLegendStyle("bEff", "L")
p_Eff.histoMgr.forHisto("signif", styles_Signif)
p_Eff.histoMgr.setHistoDrawStyle("signif", "L")
p_Eff.histoMgr.setHistoLegendStyle("signif", "L")
p_Eff.histoMgr.forHisto("purity", styles_pur)
p_Eff.histoMgr.setHistoDrawStyle("purity", "L")
p_Eff.histoMgr.setHistoLegendStyle("purity", "L")
p_Eff.histoMgr.forHisto("pureff", styles_pureff)
p_Eff.histoMgr.setHistoDrawStyle("pureff", "L")
p_Eff.histoMgr.setHistoLegendStyle("pureff", "L")
p_BDT.histoMgr.forHisto("bdtS", styles_S)
p_BDT.histoMgr.setHistoDrawStyle("bdtS", "HIST")
p_BDT.histoMgr.setHistoLegendStyle("bdtS", "L")
p_BDT.histoMgr.forHisto("bdtB", styles_B)
p_BDT.histoMgr.setHistoDrawStyle("bdtB", "HIST")
p_BDT.histoMgr.setHistoLegendStyle("bdtB", "F")
saveName_eff = "mvaeff"
saveName = "mva"
savePath = os.path.join(opts.saveDir, opts.optMode)
_kwargs = {
"xlabel" : "BDTG", #"Cut value applied on BDTG output",
"ylabel" : "Efficiency (Purity)",
"ratioYlabel" : "Ratio ",
"ratio" : False,
"ratioInvert" : False,
"stackMCHistograms": False,
"addMCUncertainty" : False,
"addLuminosityText": False,
"addCmsText" : True,
"cmsExtraText" : "Preliminary",
"opts" : {"xmin": -1.0, "xmax": 1.01, "ymin": 0.0, "ymax": 1.1},
"opts2" : {"ymin": 0.6, "ymax": 1.5},
"log" : False,
"createLegend" : {"x1": 0.45, "y1": 0.45, "x2": 0.70, "y2": 0.70},
}
ROOT.gStyle.SetNdivisions(6 + 100*5 + 10000*2, "X") #ROOT.gStyle.SetNdivisions(8, "X")
plots.drawPlot(p_Eff, savePath, **_kwargs)
rightAxis.Draw()
saveDir = opts.rootfile.split("/")[-1]
saveDir = saveDir.replace(".root","")
SavePlot(p_Eff, saveName_eff, os.path.join(opts.saveDir, saveDir, opts.optMode), saveFormats = [".png", ".pdf", ".C"])
_kwargs["log"] = True
_kwargs["opts"]["ymin"] = 3e-2
_kwargs["opts"]["ymax"] = 20
_kwargs["xlabel"] = "BDTG"# response"
_kwargs["ylabel"] = "(1/N) dN/dx"
_kwargs["createLegend"] = {"x1": 0.25, "y1": 0.75, "x2": 0.50, "y2": 0.9}
style.setWide(False, 0.15)
ROOT.gStyle.SetNdivisions(6 + 100*5 + 10000*2, "X")
plots.drawPlot(p_BDT, savePath, **_kwargs)
SavePlot(p_BDT, saveName, os.path.join(opts.saveDir, saveDir, opts.optMode), saveFormats = [".png", ".pdf", ".C"])
return
def PlotBaselineVsInverted(datasetsMgr, hBaseline, hInverted):
# Get the Inclusive (Data, EWK)
p1 = plots.ComparisonPlot(
*getHistos(datasetsMgr, "Data", hBaseline, hInverted))
p1.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
p2 = plots.ComparisonPlot(
*getHistos(datasetsMgr, "EWK", hBaseline, hInverted))
p2.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
if opts.useMC:
p3 = plots.ComparisonPlot(
*getHistos(datasetsMgr, "QCD", hBaseline, hInverted))
p3.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
# Get Baseline histos
baseline_Data = p1.histoMgr.getHisto(
"Baseline-Data").getRootHisto().Clone()
baseline_QCD = p1.histoMgr.getHisto("Baseline-Data").getRootHisto().Clone(
"Baseline-QCD")
baseline_EWK = p2.histoMgr.getHisto("Baseline-EWK").getRootHisto().Clone()
if opts.useMC:
baseline_QCDMC = p3.histoMgr.getHisto(
"Baseline-QCD").getRootHisto().Clone("Baseline-QCDMC")
# Get Inverted histos
inverted_Data = p1.histoMgr.getHisto(
"Inverted-Data").getRootHisto().Clone()
inverted_QCD = p1.histoMgr.getHisto("Inverted-Data").getRootHisto().Clone(
"Inverted-QCD")
inverted_EWK = p2.histoMgr.getHisto("Inverted-EWK").getRootHisto().Clone()
if opts.useMC:
inverted_QCDMC = p3.histoMgr.getHisto(
"Inverted-QCD").getRootHisto().Clone("Inverted-QCDMC")
# Subtract EWK from Data to get QCD
baseline_QCD.Add(baseline_EWK, -1)
inverted_QCD.Add(inverted_EWK, -1)
# Option: Use QCD-MC instead of QCD=Data-EWK
if opts.useMC:
baseline_QCD = baseline_QCDMC
inverted_QCD = inverted_QCDMC
# Normalize histograms to unit area
if opts.normaliseToOne:
baseline_Data.Scale(1.0 / baseline_Data.Integral())
baseline_QCD.Scale(1.0 / baseline_QCD.Integral())
baseline_EWK.Scale(1.0 / baseline_EWK.Integral())
inverted_Data.Scale(1.0 / inverted_Data.Integral())
inverted_QCD.Scale(1.0 / inverted_QCD.Integral())
inverted_EWK.Scale(1.0 / inverted_EWK.Integral())
# Create the final plot object
p = plots.ComparisonManyPlot(baseline_QCD, [inverted_QCD], saveFormats=[])
# Apply styles
if opts.useMC:
p.histoMgr.forHisto("Baseline-QCDMC", styles.getBaselineStyle())
p.histoMgr.forHisto("Inverted-QCDMC", styles.getInvertedStyle())
else:
p.histoMgr.forHisto("Baseline-QCD", styles.getBaselineStyle())
p.histoMgr.forHisto("Inverted-QCD", styles.getInvertedStyle())
# Set draw style
if opts.useMC:
p.histoMgr.setHistoDrawStyle("Baseline-QCDMC", "AP")
p.histoMgr.setHistoDrawStyle("Inverted-QCDMC", "HIST")
else:
p.histoMgr.setHistoDrawStyle("Baseline-QCD", "AP")
p.histoMgr.setHistoDrawStyle("Inverted-QCD", "HIST")
# Set legend style
if opts.useMC:
p.histoMgr.setHistoLegendStyle("Baseline-QCDMC", "LP")
p.histoMgr.setHistoLegendStyle("Inverted-QCDMC", "F")
else:
p.histoMgr.setHistoLegendStyle("Baseline-QCD", "LP")
p.histoMgr.setHistoLegendStyle("Inverted-QCD", "F")
# Set legend labels
if opts.useMC:
p.histoMgr.setHistoLegendLabelMany({
"Baseline-QCDMC":
"QCD-MC (Baseline)",
"Inverted-QCDMC":
"QCD-MC (Inverted)",
})
else:
p.histoMgr.setHistoLegendLabelMany({
"Baseline-QCD": "QCD (Baseline)",
"Inverted-QCD": "QCD (Inverted)",
})
# Draw the histograms
_cutBox = None
_rebinX = 1
if opts.normaliseToOne:
_opts = {"ymin": 3e-4, "ymaxfactor": 2.0}
else:
_opts = {"ymin": 1e0, "ymaxfactor": 2.0}
_format = "%0.0f"
_xlabel = None
if "dijetm" in hBaseline.lower():
_rebinX = 2
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jj} (%s)" % (_units)
_cutBox = {
"cutValue": 80.399,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 400.0
if "chisqr" in hBaseline.lower():
_rebinX = 1
_units = ""
_format = "%0.1f " + _units
_xlabel = "#chi^{2}"
_cutBox = {
"cutValue": 10.0,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 100.0
if "ldgdijetpt" in hBaseline.lower():
_rebinX = 1
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T} (%s)" % _units
_cutBox = {
"cutValue": 40.0,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 800.0
if "ldgdijetm" in hBaseline.lower():
_rebinX = 1
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jj} (%s)" % _units
_cutBox = {
"cutValue": 80.385,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 300.0
if "trijetm" in hBaseline.lower():
_rebinX = 4 #5
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {
"cutValue": 173.21,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 1000.0
if "pt" in hBaseline.lower():
_rebinX = 2
_format = "%0.0f GeV/c"
_cutBox = {
"cutValue": 40.,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
ROOT.gStyle.SetNdivisions(8, "X")
_opts["xmax"] = 1000.0
if "eta" in hBaseline.lower():
_format = "%0.2f"
_cutBox = {
"cutValue": 0.,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmin"] = -3.0
_opts["xmax"] = +3.0
if "deltaeta" in hBaseline.lower():
_format = "%0.2f"
_opts["xmin"] = 0.0
_opts["xmax"] = 6.0
if "bdisc" in hBaseline.lower():
_format = "%0.2f"
if "tetrajetm" in hBaseline.lower():
_rebinX = 10 #4
if opts.useMC:
_rebinX = 10
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjbb} (%s)" % (_units)
_opts["xmax"] = 3000.0
#_opts["xmax"] = 3500.0
plots.drawPlot(
p,
hBaseline,
xlabel=_xlabel,
ylabel="Arbitrary Units / %s" % (_format),
log=True,
rebinX=_rebinX,
cmsExtraText="Preliminary",
createLegend={
"x1": 0.62,
"y1": 0.78,
"x2": 0.92,
"y2": 0.92
},
opts=_opts,
opts2={
"ymin": 0.6,
"ymax": 1.4
},
ratio=True,
ratioInvert=False,
ratioYlabel="Ratio",
cutBox=_cutBox,
)
# Save plot in all formats
saveName = hBaseline.replace("/", "_")
saveName = saveName.replace("_Baseline_", "_")
savePath = os.path.join(opts.saveDir, "BaselineVsInverted", opts.optMode)
if opts.useMC:
savePath = os.path.join(opts.saveDir, "BaselineVsInverted", "MC",
opts.optMode)
SavePlot(p, saveName, savePath)
return
def PlotBaselineVsInvertedTemplates(datasetsMgr,
histoName,
addQcdBaseline=False):
Verbose("Plotting EWK Vs QCD unity-normalised histograms")
# Create comparison plot
p1 = plots.ComparisonPlot(
getHisto(datasetsMgr, "Data", "topSelection_Baseline/%s" % histoName,
"Baseline"),
getHisto(datasetsMgr, "EWK", "topSelection_Baseline/%s" % histoName,
"Baseline"))
p1.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
p2 = plots.ComparisonPlot(
getHisto(datasetsMgr, "Data", "topSelection_Inverted/%s" % histoName,
"Inverted"),
getHisto(datasetsMgr, "EWK", "topSelection_Inverted/%s" % histoName,
"Inverted"))
p2.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
# Get EWK histos
EWK_baseline = p1.histoMgr.getHisto("Baseline-EWK").getRootHisto().Clone(
"Baseline-EWK")
EWK_inverted = p2.histoMgr.getHisto("Inverted-EWK").getRootHisto().Clone(
"Inverted-EWK")
# Get QCD histos
QCD_baseline = p1.histoMgr.getHisto("Baseline-Data").getRootHisto().Clone(
"Baseline-QCD")
QCD_inverted = p2.histoMgr.getHisto("Inverted-Data").getRootHisto().Clone(
"Inverted-QCD")
# Create QCD histos: QCD = Data-EWK
QCD_baseline.Add(EWK_baseline, -1)
QCD_inverted.Add(EWK_inverted, -1)
# Normalize histograms to unit area
EWK_baseline.Scale(1.0 / EWK_baseline.Integral())
EWK_inverted.Scale(1.0 / EWK_inverted.Integral())
QCD_baseline.Scale(1.0 / QCD_baseline.Integral())
QCD_inverted.Scale(1.0 / QCD_inverted.Integral())
# Create the final plot object
if addQcdBaseline:
compareHistos = [EWK_baseline, QCD_baseline]
else:
compareHistos = [EWK_baseline]
p = plots.ComparisonManyPlot(QCD_inverted, compareHistos, saveFormats=[])
p.setLuminosity(GetLumi(datasetsMgr))
# Apply styles
p.histoMgr.forHisto("Baseline-EWK", styles.getBaselineStyle())
if addQcdBaseline:
p.histoMgr.forHisto("Baseline-QCD", styles.getInvertedLineStyle())
p.histoMgr.forHisto("Inverted-QCD", styles.getInvertedStyle())
# Set draw style
p.histoMgr.setHistoDrawStyle("Baseline-EWK", "AP")
p.histoMgr.setHistoLegendStyle("Baseline-EWK", "LP")
if addQcdBaseline:
p.histoMgr.setHistoDrawStyle("Baseline-QCD", "HIST")
p.histoMgr.setHistoLegendStyle("Baseline-QCD", "FL")
p.histoMgr.setHistoDrawStyle("Inverted-QCD", "AP")
p.histoMgr.setHistoLegendStyle("Inverted-QCD", "LP")
# Set legend labels
if addQcdBaseline:
p.histoMgr.setHistoLegendLabelMany({
"Baseline-EWK": "EWK (Baseline)",
"Baseline-QCD": "QCD (Baseline)",
"Inverted-QCD": "QCD (Inverted)",
})
else:
p.histoMgr.setHistoLegendLabelMany({
"Baseline-EWK": "EWK (Baseline)",
"Inverted-QCD": "QCD (Inverted)",
})
# Append analysisType to histogram name
saveName = histoName
# Draw the histograms #alex
plots.drawPlot(p, saveName,
**GetHistoKwargs(histoName)) #the "**" unpacks the kwargs_
# _kwargs = {"lessThan": True}
# p.addCutBoxAndLine(cutValue=200, fillColor=ROOT.kRed, box=False, line=True, ***_kwargs)
# Add text
text = opts.optMode.replace("OptChiSqrCutValue", "#chi^{2} #leq ")
histograms.addText(0.21, 0.85, text)
# Save plot in all formats
saveDir = os.path.join(opts.saveDir, "Templates", opts.optMode)
SavePlot(p, saveName, saveDir, saveFormats=[".C", ".png", ".pdf"])
return
def EWKvQCD(datasetsMgr, histoName, analysisType=""):
Verbose("Plotting EWK Vs QCD unity-normalised histograms for %s" %
analysisType)
# Sanity check
IsBaselineOrInverted(analysisType)
p1 = plots.ComparisonPlot(
*getHistos(datasetsMgr, "Data", "topSelection_Baseline/%s" %
histoName, "topSelection_Inverted/%s" % histoName))
p1.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
p2 = plots.ComparisonPlot(
*getHistos(datasetsMgr, "EWK", "topSelection_Baseline/%s" %
histoName, "topSelection_Inverted/%s" % histoName))
p2.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
# Get histos
data = p1.histoMgr.getHisto(analysisType +
"-Data").getRootHisto().Clone(analysisType +
" -Data")
EWK = p2.histoMgr.getHisto(analysisType +
"-EWK").getRootHisto().Clone(analysisType +
"-EWK")
# Create QCD histos: QCD = Data-EWK
QCD = p1.histoMgr.getHisto(analysisType +
"-Data").getRootHisto().Clone(analysisType +
"-QCD")
QCD.Add(EWK, -1)
# Normalize histograms to unit area
QCD.Scale(1.0 / QCD.Integral())
EWK.Scale(1.0 / EWK.Integral())
# Create the final plot object
p = plots.ComparisonManyPlot(QCD, [EWK],
saveFormats=[]) #[".C", ".png", ".pdf"])
p.setLuminosity(GetLumi(datasetsMgr))
# Apply styles
p.histoMgr.forHisto(analysisType + "-QCD", styles.getQCDLineStyle())
p.histoMgr.forHisto(analysisType + "-EWK", styles.getAltEWKStyle())
# Set draw style
p.histoMgr.setHistoDrawStyle(analysisType + "-QCD", "LP")
p.histoMgr.setHistoLegendStyle(analysisType + "-QCD", "LP")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
analysisType + "-QCD":
analysisType + " (QCD)",
analysisType + "-EWK":
analysisType + " (EWK)",
})
# Append analysisType to histogram name
saveName = histoName + "_" + analysisType
# Draw the histograms #alex
plots.drawPlot(p, saveName,
**GetHistoKwargs(histoName)) #the "**" unpacks the kwargs_
# _kwargs = {"lessThan": True}
# p.addCutBoxAndLine(cutValue=200, fillColor=ROOT.kRed, box=False, line=True, ***_kwargs)
# Save plot in all formats
saveDir = os.path.join(opts.saveDir, "EWKvQCD", opts.optMode)
SavePlot(p, saveName, saveDir)
return
def PlotAndFitTemplates(datasetsMgr, histoName, addQcdBaseline, opts):
Verbose("PlotAndFitTemplates()")
# Create comparison plot
p1 = plots.ComparisonPlot(
getHisto(datasetsMgr, "Data", "topSelection_Baseline/%s" % histoName, "Baseline"),
getHisto(datasetsMgr, "EWK" , "topSelection_Baseline/%s" % histoName, "Baseline")
)
p1.histoMgr.normalizeMCToLuminosity(datasetsMgr.getDataset("Data").getLuminosity())
p2 = plots.ComparisonPlot(
getHisto(datasetsMgr, "Data", "topSelection_Inverted/%s" % histoName, "Inverted"),
getHisto(datasetsMgr, "EWK" , "topSelection_Inverted/%s" % histoName, "Inverted")
)
p2.histoMgr.normalizeMCToLuminosity(datasetsMgr.getDataset("Data").getLuminosity())
# Get Baseline histos
Data_baseline = p1.histoMgr.getHisto("Baseline-Data").getRootHisto().Clone("Baseline_Data")
EWK_baseline = p1.histoMgr.getHisto("Baseline-EWK").getRootHisto().Clone("Baseline_EWK")
QCD_baseline = p1.histoMgr.getHisto("Baseline-Data").getRootHisto().Clone("Baseline_QCD")
# Get Inverted histos
Data_inverted = p2.histoMgr.getHisto("Inverted-Data").getRootHisto().Clone("Inverted_Data")
EWK_inverted = p2.histoMgr.getHisto("Inverted-EWK").getRootHisto().Clone("Inverted_EWK")
QCD_inverted = p2.histoMgr.getHisto("Inverted-Data").getRootHisto().Clone("Inverted_QCD")
# Create QCD histos: QCD = Data-EWK
QCD_baseline.Add(EWK_baseline, -1)
QCD_inverted.Add(EWK_inverted, -1)
# Normalize histograms to unit area
if 0:
EWK_baseline.Scale(1.0/EWK_baseline.Integral())
EWK_inverted.Scale(1.0/EWK_inverted.Integral())
QCD_baseline.Scale(1.0/QCD_baseline.Integral())
QCD_inverted.Scale(1.0/QCD_inverted.Integral())
# Create the final plot object
if addQcdBaseline:
compareHistos = [EWK_baseline, QCD_baseline]
else:
compareHistos = [EWK_baseline]
p = plots.ComparisonManyPlot(QCD_inverted, compareHistos, saveFormats=[])
p.setLuminosity(GetLumi(datasetsMgr))
#=========================================================================================
# Start Fit process
#=========================================================================================
binLabels = ["Inclusive"]
moduleInfoString = opts.optMode
FITMIN = 0
FITMAX = 1500
manager = QCDNormalization.QCDNormalizationManagerDefault(binLabels, opts.mcrab, moduleInfoString)
#=========================================================================================
# Create templates (EWK fakes, EWK genuine, QCD; data template is created by manager)
#=========================================================================================
template_EWKFakeB_Baseline = manager.createTemplate("EWKFakeB_Baseline")
template_EWKFakeB_Inverted = manager.createTemplate("EWKFakeB_Inverted")
template_EWKInclusive_Baseline = manager.createTemplate("EWKInclusive_Baseline")
template_EWKInclusive_Inverted = manager.createTemplate("EWKInclusive_Inverted")
template_QCD_Baseline = manager.createTemplate("QCD_Baseline")
template_QCD_Inverted = manager.createTemplate("QCD_Inverted")
#=========================================================================================
# Inclusive EWK
#=========================================================================================
# par[0]*ROOT.Math.crystalball_function(x[0], par[1], par[2], par[3], par[4]) +
# par[5]*ROOT.TMath.BreitWigner(x[0], par[6], par[7]) +
# (1-par[0]-par[5])*ROOT.TMath.Landau(x[0], par[8], par[9])
#template_EWKInclusive_Baseline.setFitter(QCDNormalization.FitFunction("EWKFunction", boundary=200, norm=1, rejectPoints=0), FITMIN, FITMAX)
template_EWKInclusive_Baseline.setFitter(QCDNormalization.FitFunction("EWKFunction", boundary=200, norm=1, rejectPoints=0), FITMIN, 800)
template_EWKInclusive_Baseline.setDefaultFitParam(defaultInitialValue=None,
# p0 p1 p2 p3 p4 p5 p6 p7 p8 p9
#defaultLowerLimit=[0.0, 150.0, 0.0, -5.0, 0.0, 0.0, 150.0, 0.0, 100.0, 0.0],
#defaultUpperLimit=[1.0, 300.0, 50.0, 0.0, 1.0, 1.0, 200.0, 100.0, 200.0, 100.0])
defaultLowerLimit=[0.0, 160.0, 0.0, -1.0, 0.0],
defaultUpperLimit=[1.0, 180.0, 60.0, 0.0, 1e6])
#=========================================================================================
# Note that the same function is used for QCD only and QCD+EWK fakes
#=========================================================================================
#par[0]*ROOT.Math.lognormal_pdf(x[0], par[1], par[2]) + par[3]*ROOT.TMath.Exp(-x[0]*par[4]) + (1-par[0]-par[3])*ROOT.TMath.Gaus(x[0], par[5], part[6])
# 0 = 0.8489 for coeff of lognorm
# 1 = 1.42978 for logshape
# 2 = 238.546 for logmean
#
# 3 = 0.047 for coeff of exp
# 4 = -0.0048 for exp
#
# 5 = 45.486 for gaus sigma
# 6 = 204.6 for gaus mean
#my lognormal is of the form:
#lognorm(x,m0,k) = exp{-[log(x/m0)]^2/[2*log(k)]^2}/sqrt[2pi*log(k)*x]
#and differs than the Math::lognormal_pdf parametrization which takes as arguments lognormal(x,m,s,x0)
#where m = log(m0) and s=log(k)
#TMath::LogNormal return Math::lognormal_pdf
#my exponential is exp(x*a) so the sign comes from the fit
#and my gaussian is exp[-0.5*mean*mean/(sigma*sigma)]
# Double_t LogNormal(x, sigma, theta = 0, m = 1)
#template_QCD_Inverted.setFitter(QCDNormalization.FitFunction("QCDFunction", boundary=350, norm=1), FITMIN, FITMAX)
template_QCD_Inverted.setFitter(QCDNormalization.FitFunction("QCDFunction", boundary=350, norm=1), 100, 800)
template_QCD_Inverted.setDefaultFitParam(defaultInitialValue=None,
defaultLowerLimit=[0.0, 0.0, 200.0, 0.0, -0.1, 200.0, 0.0],
defaultUpperLimit=[1.0, 2.0, 400.0, 0.1, +0.0, 300.0, 60.0])
#=========================================================================================
# Set histograms to the templates
#=========================================================================================
template_EWKFakeB_Baseline.setHistogram(EWK_baseline, "Inclusive")
template_EWKFakeB_Inverted.setHistogram(EWK_inverted, "Inclusive")
template_EWKInclusive_Baseline.setHistogram(EWK_baseline, "Inclusive")
template_EWKInclusive_Inverted.setHistogram(EWK_inverted, "Inclusive")
template_QCD_Baseline.setHistogram(QCD_baseline, "Inclusive")
template_QCD_Inverted.setHistogram(QCD_inverted, "Inclusive")
#=========================================================================================
# Make plots of templates
#=========================================================================================
manager.plotTemplates()
#=========================================================================================
# Fit individual templates to histogram "data_baseline", with custom fit options
#=========================================================================================
fitOptions = "R B L W M Q"
manager.calculateNormalizationCoefficients(Data_baseline, fitOptions, FITMIN, FITMAX)
# Append analysisType to histogram name
saveName = histoName
# Draw the histograms #alex
plots.drawPlot(p, saveName, **GetHistoKwargs(histoName) ) #the "**" unpacks the kwargs_
_kwargs = {"lessThan": True}
p.addCutBoxAndLine(cutValue=173.21, fillColor=ROOT.kRed, box=False, line=True, **_kwargs)
# Save plot in all formats
#SavePlot(p, saveName, os.path.join(opts.saveDir, "Templates") )
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(False)
style.setGridY(False)
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
else:
pass
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
# Print dataset information before removing anything?
datasetsMgr.PrintInfo()
# Determine integrated Lumi before removing data
if "Data" in datasetsMgr.getAllDatasetNames():
intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Remove datasets
filterKeys = ["TTW"]
for key in filterKeys:
datasetsMgr.remove(
filter(lambda name: key in name,
datasetsMgr.getAllDatasetNames()))
else:
intLumi = 35920
for key in filterKeys:
datasetsMgr.remove(
filter(lambda name: key in name,
datasetsMgr.getAllDatasetNames()))
# Re-order datasets
datasetOrder = []
haveQCD = False
for d in datasetsMgr.getAllDatasets():
if "QCD" in d.getName():
haveQCD = True
datasetOrder.append(d.getName())
datasetsMgr.selectAndReorder(datasetOrder)
# Define the mapping histograms in numerator->denominator pairs
VariableList = [
"LdgTop_Pt",
]
minRunRange, maxRunRange, runRange = GetRunRange(datasetsMgr)
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
datasets_ = datasetsMgr.getAllDatasets()
dataset_Data = datasetsMgr.getDataDatasets()
dataset_MC = datasetsMgr.getMCDatasets()
# Print dataset information
datasetsMgr.PrintInfo()
# For-loop: All numerator-denominator pairs
counter = 0
nPlots = len(VariableList)
for var in VariableList:
histoN = "AfterAllSelections_" + var
histoD = "AfterStandardSelections_" + var
numerator = os.path.join(opts.folder, histoN)
denominator = os.path.join(opts.folder, histoD)
counter += 1
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format(
"Histogram", "%i" % counter, "/", "%s:" % (nPlots),
"%s" % (var))
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(),
counter == 1)
for dataset in datasets_:
if dataset.isMC():
n = dataset.getDatasetRootHisto(numerator)
n.normalizeToLuminosity(intLumi)
num = n.getHistogram()
d = dataset.getDatasetRootHisto(denominator)
d.normalizeToLuminosity(intLumi)
den = d.getHistogram()
else:
num = dataset.getDatasetRootHisto(numerator).getHistogram()
den = dataset.getDatasetRootHisto(
denominator).getHistogram()
x_bins = num.GetXaxis().GetNbins()
i = 1
while i < x_bins:
xvalue = num.GetXaxis().GetBinLowEdge(
i) + 0.5 * num.GetXaxis().GetBinWidth(i)
if xvalue < 20:
my_bin = i
i += 1
my_xvalue = num.GetXaxis().GetBinUpEdge(
my_bin) + 0.5 * num.GetXaxis().GetBinWidth(my_bin)
total = den.Integral(0, x_bins) #my_bin
selected = num.Integral(0, x_bins) #my_bin
plotName = "Eff_%s" % (var)
# Get Efficiency Plots
_kwargs = GetHistoKwargs(var, opts)
eff_Data = GetEfficiency(datasetsMgr, dataset_Data, numerator,
denominator, intLumi)
eff_MC = GetEfficiency(datasetsMgr, dataset_MC, numerator,
denominator, intLumi)
# Apply Styles
styles.dataStyle.apply(eff_Data)
styles.qcdStyle.apply(eff_MC)
# Create the plot
p = plots.ComparisonPlot(
histograms.HistoGraph(eff_Data, "eff_Data", "p", "P"),
histograms.HistoGraph(eff_MC, "eff_MC", "p", "P"),
saveFormats=[])
# Define the legend entries
p.histoMgr.setHistoLegendLabelMany({
"eff_Data": "Data",
"eff_MC": "QCD"
})
# Append in list
myList = []
myList.append(
histograms.HistoGraph(eff_Data, plots._legendLabels["Data"],
"lp", "P"))
p.setLuminosity(intLumi)
_kwargs["ratio"] = True
_kwargs["opts"] = {
"xmin": 0.0,
"xmax": 600.0,
"ymin": 0.0,
"ymax": 0.16,
"ymaxfactor": 1.8
}
_kwargs["cutBoxY"] = {
"cutValue": 1.10,
"fillColor": ROOT.kGray + 1,
"fillStyle": 3001,
"box": False,
"line": True,
"greaterThan": True,
"mainCanvas": False,
"ratioCanvas": True,
"mirror": True
}
plots.drawPlot(p, plotName, **_kwargs)
# Draw
savePath = os.path.join(opts.saveDir, opts.optMode)
SavePlot(p, plotName, savePath, saveFormats=[".png", ".pdf", ".C"])
# Save results in JSON
name = opts.mcrab.split("_")[-3]
name = name.replace(opts.analysisName, "")
jsonName = "topMisID_" + name + "_TopMassCut400.json"
analysis = opts.analysisName
label = "2016"
plotDir = os.path.join(opts.folder, jsonName)
pythonWriter.addParameters(plotDir, label, runRange, opts.intLumi,
eff_Data)
pythonWriter.addMCParameters(label, eff_MC)
fileName_json = jsonName
pythonWriter.writeJSON(fileName_json)
return
def PlotSignalBackground(datasetsMgr, hG, hF, intLumi):
kwargs = {}
_kwargs = {}
#kwargs = GetHistoKwargs(hG, opts)
if opts.normaliseToOne:
pG = plots.MCPlot(datasetsMgr,
hG,
normalizeToOne=True,
saveFormats=[],
**_kwargs)
pF = plots.MCPlot(datasetsMgr,
hF,
normalizeToOne=True,
saveFormats=[],
**_kwargs)
else:
pG = plots.MCPlot(datasetsMgr,
hG,
normalizeToLumi=intLumi,
saveFormats=[],
**_kwargs)
pF = plots.MCPlot(datasetsMgr,
hF,
normalizeToLumi=intLumi,
saveFormats=[],
**_kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.2f"
_xlabel = None
logY = False
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
if "mass" in hG.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "M (%s)" % _units
if "trijetmass" in hG.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{top} (%s)" % _units
#_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 805 #1005
if "bjetmass" in hG.lower():
_xlabel = "m_{b-tagged jet} (%s)" % _units
_opts["xmax"] = 50
if "bjetldgjet_mass" in hG.lower():
_xlabel = "m_{b, ldg jet} (%s)" % _units
if "bjetsubldgjet_mass" in hG.lower():
_xlabel = "m_{b-tagged, subldg jet} (%s)" % _units
if "jet_mass" in hG.lower():
_opts["xmax"] = 750
if "mult" in hG.lower():
_format = "%0.0f"
_xlabel = "Leading jet mult"
if "subldg" in hG.lower():
_xlabel = "Subleading jet mult"
if "cvsl" in hG.lower():
_format = "%0.2f"
_xlabel = "Leading jet CvsL"
if "subldg" in hG.lower():
_xlabel = "Subleading jet CvsL"
if "axis2" in hG.lower():
_format = "%0.3f"
_xlabel = "Leading jet axis2"
if "subldg" in hG.lower():
_xlabel = "Subleading jet axis2"
if "dijetmass" in hG.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{W} (%s)" % _units
_opts["xmax"] = 600
if "trijetptdr" in hG.lower():
_opts["xmax"] = 800
_format = "%0.0f"
_xlabel = "p_{T}#Delta R_{t}"
if "dijetptdr" in hG.lower():
_opts["xmax"] = 800
_format = "%0.0f"
_xlabel = "p_{T}#Delta R_{W}"
if "dgjetptd" in hG.lower():
_format = "%0.2f"
_xlabel = "Leading jet p_{T}D"
if "subldg" in hG.lower():
_xlabel = "Subleading jet p_{T}D"
else:
pass
'''
if "bdisc" in hG.lower():
_format = "%0.2f"
if "subldg" in hG.lower():
_xlabel = "Subleading jet CSV"
elif "ldg" in hG.lower():
_xlabel = "Leading jet CSV"
else:
_xlabel = "b-tagged jet CSV"
'''
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
else:
_opts["ymin"] = 1e0
if "bdisc" in hG.lower():
_format = "%0.2f"
if "subldg" in hG.lower():
_xlabel = "Subleading jet CSV"
_opts = {"ymin": 1e-3, "ymax": 0.06}
elif "ldg" in hG.lower():
_xlabel = "Leading jet CSV"
_opts = {"ymin": 1e-3, "ymax": 0.06}
else:
_xlabel = "b-tagged jet CSV"
_opts = {"ymin": 1e-3, "ymax": 0.35}
myList = []
# Customise styling
pG.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
pF.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
'''
plots.drawPlot(pG,
hG,
xlabel = _xlabel,
ylabel = "Arbitrary Units / %s" % (_format),
log = logY,
rebinX = _rebinX, cmsExtraText = "Preliminary",
createLegend = {"x1": 0.58, "y1": 0.65, "x2": 0.92, "y2": 0.92},
opts = _opts,
opts2 = {"ymin": 0.6, "ymax": 1.4},
cutBox = _cutBox,
)
plots.drawPlot(pF,
hF,
xlabel = _xlabel,
ylabel = "Arbitrary Units / %s" % (_format),
log = logY,
rebinX = _rebinX, cmsExtraText = "Preliminary",
createLegend = {"x1": 0.58, "y1": 0.65, "x2": 0.92, "y2": 0.92},
opts = _opts,
opts2 = {"ymin": 0.6, "ymax": 1.4},
cutBox = _cutBox,
)
'''
dataset = datasetsMgr.getDataset("TT")
h = dataset.getDatasetRootHisto(hG)
h.normalizeToOne()
HG = h.getHistogram()
h = dataset.getDatasetRootHisto(hF)
h.normalizeToOne()
HF = h.getHistogram()
altSignalBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kAzure + 9,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kAzure + 9,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kAzure + 9)
])
altBackgroundBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kRed - 4,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kRed - 4,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kRed - 4, fillStyle=3001)
])
p = plots.ComparisonPlot(
histograms.Histo(HF, "Fake", "p", "P"),
histograms.Histo(HG, "Genuine", "pl", "PL"),
)
p.histoMgr.setHistoLegendLabelMany({
"Fake": "Unmatched",
"Genuine": "Truth-matched"
})
p.histoMgr.forHisto("Fake", altBackgroundBDTGStyle)
p.histoMgr.setHistoDrawStyle("Fake", "LP")
p.histoMgr.setHistoLegendStyle("Fake", "LP") #F
p.histoMgr.forHisto("Genuine", altSignalBDTGStyle)
p.histoMgr.setHistoDrawStyle("Genuine", "HIST")
p.histoMgr.setHistoLegendStyle("Genuine", "LP") #LP
#HF = dataset.getDatasetRootHisto(hF).getHistogram()
#HG = dataset.getDatasetRootHisto(hG).getHistogram()
histoG = histograms.Histo(HG, "TT", "Signal")
histoG.setIsDataMC(isData=False, isMC=True)
histoF = histograms.Histo(HF, "QCD", "Signal")
histoF.setIsDataMC(isData=False, isMC=True)
styleG = styles.ttStyle
styleF = styles.signalStyleHToTB1000
#hG = histograms.Histo(histoBkg3, legNameBkg3, "F", "HIST9" )
#background3_histo = histograms.Histo(histoBkg3, legNameBkg3, "F", "HIST9" )
styleG.apply(HG)
styleF.apply(HF)
myList.append(histoG)
myList.append(histoF)
#myList.append(HF)
#myList.append(HG)
# p = plots.PlotBase(datasetRootHistos=myList, saveFormats=[])
_kwargs = {
"xlabel": _xlabel,
"ylabel": "Arbitrary Units / %s" % (_format),
"ratioYlabel": "Ratio ",
"ratio": False,
"ratioInvert": False,
"stackMCHistograms": False,
"addMCUncertainty": False,
"addLuminosityText": False,
"addCmsText": True,
"cmsExtraText": "Preliminary",
"opts": {
"ymin": 0.0,
"ymaxfactor": 1.1
},
"opts2": {
"ymin": 0.6,
"ymax": 1.5
},
"log": False,
#"createLegend" : {"x1": 0.5, "y1": 0.75, "x2": 0.9, "y2": 0.9},
"createLegend": {
"x1": 0.58,
"y1": 0.65,
"x2": 0.92,
"y2": 0.82
},
}
'''
plots.drawPlot(p,
hG,
xlabel = _xlabel,
ylabel = "Arbitrary Units / %s" % (_format),
log = logY,
rebinX = _rebinX, cmsExtraText = "Preliminary",
createLegend = {"x1": 0.58, "y1": 0.65, "x2": 0.92, "y2": 0.82},
opts = _opts,
opts2 = {"ymin": 0.6, "ymaxfactor": 1.1},
cutBox = _cutBox,
)
'''
# Save plot in all formats
saveName = hG.split("/")[-1]
#plots.drawPlot(p, saveName, **_kwargs)
savePath = os.path.join(opts.saveDir, "HplusMasses",
hG.split("/")[0], opts.optMode)
plots.drawPlot(p, savePath, **_kwargs)
SavePlot(p,
saveName,
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png", ".pdf", ".C"])
#SavePlot(p, saveName, savePath)
#p = plots.DataMCPlot2( myList, saveFormats=[])
#p.setLuminosity(opts.intLumi)
#p.setDefaultStyles()
# Draw the plot and save it
#hName = hG
#plots.drawPlot(p, hName, **_kwargs)
#SavePlot(p, hName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png"])
return
def DataEwkQcd(datasetsMgr, histoName, analysisType):
'''
Create plots with "Data", "QCD=Data-EWK", and "EWK" on the same canvas
Mostly for sanity checks and visualisation purposes
'''
Verbose(
"Plotting histogram %s for Data, EWK, QCD for %s" %
(histoName, analysisType), True)
# Sanity check
IsBaselineOrInverted(analysisType)
# Define histogram names (full path)
h1 = "topSelection_Baseline/%s" % (histoName)
h2 = "topSelection_Inverted/%s" % (histoName)
# Create plot object for Data
p1 = plots.ComparisonPlot(*getHistos(datasetsMgr, "Data", h1, h2))
p1.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
# Create plot object for EWK
p2 = plots.ComparisonPlot(*getHistos(datasetsMgr, "EWK", h1, h2))
p2.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
# Get Data and EWK histos
Data = p1.histoMgr.getHisto(analysisType +
"-Data").getRootHisto().Clone(analysisType +
"-Data")
EWK = p2.histoMgr.getHisto(analysisType +
"-EWK").getRootHisto().Clone(analysisType +
"-EWK")
# Create QCD histo: QCD = Data-EWK
QCD = p1.histoMgr.getHisto(analysisType +
"-Data").getRootHisto().Clone(analysisType +
"-QCD")
QCD.Add(EWK, -1)
# Create the final plot object. The Data is treated as the reference histo.
# All other histograms are compared with respect to that.
p = plots.ComparisonManyPlot(Data, [QCD, EWK], saveFormats=[])
p.setLuminosity(GetLumi(datasetsMgr))
# Apply histo styles
p.histoMgr.forHisto(analysisType + "-Data", styles.getDataStyle())
p.histoMgr.forHisto(analysisType + "-QCD", styles.getQCDLineStyle())
p.histoMgr.forHisto(analysisType + "-EWK", styles.getAltEWKStyle())
# Set draw style
p.histoMgr.setHistoDrawStyle(analysisType + "-Data", "P")
p.histoMgr.setHistoLegendStyle(analysisType + "-Data", "P")
p.histoMgr.setHistoDrawStyle(analysisType + "-QCD", "LP")
p.histoMgr.setHistoLegendStyle(analysisType + "-QCD", "LP")
p.histoMgr.setHistoDrawStyle(analysisType + "-EWK", "HIST")
p.histoMgr.setHistoLegendStyle(analysisType + "-EWK", "LFP")
# p.histoMgr.setHistoLegendStyleAll("LP")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
analysisType + "-Data": "Data",
analysisType + "-QCD": "QCD", #=Data-EWK",
analysisType + "-EWK": "EWK",
# analysisType + "-Data": "%s (Data)" % (analysisType),
# analysisType + "-QCD" : "%s (QCD)" % (analysisType),
# analysisType + "-EWK" : "%s (EWK)" % (analysisType),
})
# Draw the histograms
_cutBox = None
_rebinX = 1
_opts = {"ymin": 1e0, "ymaxfactor": 10}
_format = "%0.0f"
if "mass" in histoName.lower():
_rebinX = 2
_format = "%0.0f GeV/c^{2}"
if "TrijetMass" in histoName:
_cutBox = {
"cutValue": 173.21,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
if "DijetMass" in histoName:
_cutBox = {
"cutValue": 80.399,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
if "pt" in histoName.lower():
_rebinX = 2
_format = "%0.0f GeV/c"
if "eta" in histoName.lower():
_rebinX = 2
_format = "%0.2f"
_cutBox = {
"cutValue": 0.,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
if "bdisc" in histoName.lower():
_format = "%0.2f"
_rebinX = 1
if "chisqr" in histoName.lower():
_format = "%0.0f"
_rebinX = 10
if "after" in histoName.lower():
_rebinX = 1
_opts["xmax"] = 20.0
if "tetrajet" in histoName.lower():
if "mass" in histoName.lower():
_rebinX = 10
_opts["xmax"] = 3500.0
else:
pass
histoName += "_" + analysisType
plots.drawPlot(
p,
histoName,
ylabel="Events / %s" % (_format),
log=True,
rebinX=_rebinX,
cmsExtraText="Preliminary",
createLegend={
"x1": 0.75,
"y1": 0.76,
"x2": 0.92,
"y2": 0.92
},
opts=_opts,
opts2={
"ymin": 1e-5,
"ymax": 1e0
},
ratio=True,
ratioInvert=False,
ratioYlabel="Ratio",
cutBox=_cutBox,
)
# Save plot in all formats
SavePlot(p,
histoName,
os.path.join(opts.saveDir, "DataEwkQcd"),
saveFormats=[".png"])
return
def analyze(analysis=None):
paths = [sys.argv[1]]
if not analysis == None:
datasets = dataset.getDatasetsFromMulticrabDirs(
paths,
analysisName=analysis,
excludeTasks="Silver|GluGluHToTauTau_M125")
else:
datasets = dataset.getDatasetsFromMulticrabDirs(
paths, excludeTasks="Silver|GluGluHToTauTau_M125")
analysis = datasets.getAllDatasets()[0].getAnalysisName()
# datasetsDY = None
datasetsDY = dataset.getDatasetsFromMulticrabDirs(
paths, analysisName=analysis, includeOnlyTasks="DYJetsToLL")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="GluGluHToTauTau_M125|TTJets")
datasetsH125 = None
# datasetsH125 = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="GluGluHToTauTau_M125",emptyDatasetsAsNone=True)
# datasetsH125 = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="GluGluHToTauTau_M125")
datasets.loadLuminosities()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
dataset2 = dataset1
# dataset2 = datasets.getMCDatasets()
if not datasetsDY == None:
dataset2 = datasetsDY.getMCDatasets()
eff1 = getEfficiency(dataset1)
eff2 = getEfficiency(dataset2)
if isinstance(datasetsH125, dataset.DatasetManager):
eff3 = getEfficiency(datasetsH125.getMCDatasets())
styles.dataStyle.apply(eff1)
styles.mcStyle.apply(eff2)
eff1.SetMarkerSize(1)
# eff2.SetMarkerSize(1.5)
if isinstance(datasetsH125, dataset.DatasetManager):
styles.mcStyle.apply(eff3)
eff3.SetMarkerSize(1.5)
eff3.SetMarkerColor(4)
eff3.SetLineColor(4)
# p = plots.ComparisonPlot(histograms.HistoGraph(eff1, "eff1", "p", "P"),
# histograms.HistoGraph(eff2, "eff2", "p", "P"))
if isinstance(datasetsH125, dataset.DatasetManager):
p = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1, "eff1", "p", "P"), [
histograms.HistoGraph(eff2, "eff2", "p", "P"),
histograms.HistoGraph(eff3, "eff3", "p", "P")
])
elif isinstance(datasetsDY, dataset.DatasetManager):
p = plots.ComparisonPlot(histograms.HistoGraph(eff1, "eff1", "p", "P"),
histograms.HistoGraph(eff2, "eff2", "p", "P"))
else:
p = plots.PlotBase([histograms.HistoGraph(eff1, "eff1", "p", "P")])
fit("Data", p, eff1, 20, 200)
fit("MC", p, eff2, 20, 200)
if isinstance(datasetsH125, dataset.DatasetManager):
fit("H125", p, eff3, 20, 200)
opts = {"ymin": 0, "ymax": 1.1}
opts2 = {"ymin": 0.5, "ymax": 1.5}
# moveLegend = {"dx": -0.55, "dy": -0.15, "dh": -0.1}
moveLegend = {"dx": -0.2, "dy": -0.5, "dh": -0.1}
name = "TauMET_" + analysis + "_DataVsMC_PFTauPt"
legend1 = "Data"
# legend2 = "MC (DY)"
legend2 = "Simulation"
legend3 = "MC (H125)"
createRatio = False
p.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
if isinstance(datasetsDY, dataset.DatasetManager):
p.histoMgr.setHistoLegendLabelMany({"eff1": legend1, "eff2": legend2})
createRatio = True
if isinstance(datasetsH125, dataset.DatasetManager):
p.histoMgr.setHistoLegendLabelMany({
"eff1": legend1,
"eff2": legend2,
"eff3": legend3
})
if createRatio:
p.createFrame(os.path.join(plotDir, name),
createRatio=createRatio,
opts=opts,
opts2=opts2)
else:
p.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
p.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
# p.getFrame().GetXaxis().SetTitle("#tau-jet p_{T} (GeV/c)")
p.getFrame().GetXaxis().SetTitle("#tau_{h} p_{T} (GeV/c)")
if createRatio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.5, 0.6, "LooseIsoPFTau50_Trk30_eta2p1", 17)
# label = analysis.split("_")[len(analysis.split("_")) -1]
label = "2016"
histograms.addText(0.5, 0.53, label, 17)
runRange = datasets.loadRunRange()
histograms.addText(0.5, 0.46, "Runs " + runRange, 17)
p.draw()
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity", d.getName(), d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum", lumi
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
pythonWriter.addParameters(plotDir, label, runRange, lumi, eff1)
pythonWriter.addMCParameters(label, eff2)
pythonWriter.writeJSON(
os.path.join(plotDir, "tauLegTriggerEfficiency_" + label + ".json"))
# if not createRatio:
# sys.exit()
#########################################################################
eff1eta = getEfficiency(dataset1, "NumeratorEta", "DenominatorEta")
eff2eta = getEfficiency(dataset2, "NumeratorEta", "DenominatorEta")
if isinstance(datasetsH125, dataset.DatasetManager):
eff3eta = getEfficiency(datasetsH125.getMCDatasets(), "NumeratorEta",
"DenominatorEta")
styles.dataStyle.apply(eff1eta)
styles.mcStyle.apply(eff2eta)
eff1eta.SetMarkerSize(1)
if isinstance(datasetsH125, dataset.DatasetManager):
styles.mcStyle.apply(eff3eta)
eff3eta.SetMarkerSize(1.5)
eff3eta.SetMarkerColor(4)
eff3eta.SetLineColor(4)
if isinstance(datasetsH125, dataset.DatasetManager):
p_eta = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1eta, "eff1eta", "p", "P"), [
histograms.HistoGraph(eff2eta, "eff2eta", "p", "P"),
histograms.HistoGraph(eff3eta, "eff3eta", "p", "P")
])
elif isinstance(datasetsDY, dataset.DatasetManager):
p_eta = plots.ComparisonPlot(
histograms.HistoGraph(eff1eta, "eff1eta", "p", "P"),
histograms.HistoGraph(eff2eta, "eff2eta", "p", "P"))
else:
p_eta = plots.PlotBase(
[histograms.HistoGraph(eff1eta, "eff1eta", "p", "P")])
p_eta.histoMgr.setHistoLegendLabelMany({"eff1eta": legend1})
if isinstance(datasetsDY, dataset.DatasetManager):
p_eta.histoMgr.setHistoLegendLabelMany({
"eff1eta": legend1,
"eff2eta": legend2
})
if isinstance(datasetsH125, dataset.DatasetManager):
p_eta.histoMgr.setHistoLegendLabelMany({
"eff1eta": legend1,
"eff2eta": legend2,
"eff3eta": legend3
})
name = "TauMET_" + analysis + "_DataVsMC_PFTauEta"
if createRatio:
p_eta.createFrame(os.path.join(plotDir, name),
createRatio=createRatio,
opts=opts,
opts2=opts2)
else:
p_eta.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
moveLegendEta = {"dx": -0.5, "dy": -0.65, "dh": -0.1}
p_eta.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegendEta))
p_eta.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
p_eta.getFrame().GetXaxis().SetTitle("#tau-jet #eta")
if createRatio:
p_eta.getFrame2().GetYaxis().SetTitle("Ratio")
p_eta.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.46, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.38, label, 17)
histograms.addText(0.2, 0.31, "Runs " + datasets.loadRunRange(), 17)
p_eta.draw()
histograms.addStandardTexts(lumi=lumi)
p_eta.save(formats)
#########################################################################
eff1phi = getEfficiency(dataset1, "NumeratorPhi", "DenominatorPhi")
eff2phi = getEfficiency(dataset2, "NumeratorPhi", "DenominatorPhi")
if isinstance(datasetsH125, dataset.DatasetManager):
eff3phi = getEfficiency(datasetsH125.getMCDatasets(), "NumeratorPhi",
"DenominatorPhi")
styles.dataStyle.apply(eff1phi)
styles.mcStyle.apply(eff2phi)
eff1phi.SetMarkerSize(1)
if isinstance(datasetsH125, dataset.DatasetManager):
styles.mcStyle.apply(eff3phi)
eff3phi.SetMarkerSize(1.5)
eff3phi.SetMarkerColor(4)
eff3phi.SetLineColor(4)
if isinstance(datasetsH125, dataset.DatasetManager):
p_phi = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1phi, "eff1phi", "p", "P"), [
histograms.HistoGraph(eff2phi, "eff2phi", "p", "P"),
histograms.HistoGraph(eff3phi, "eff3phi", "p", "P")
])
elif isinstance(datasetsDY, dataset.DatasetManager):
p_phi = plots.ComparisonPlot(
histograms.HistoGraph(eff1phi, "eff1phi", "p", "P"),
histograms.HistoGraph(eff2phi, "eff2phi", "p", "P"))
else:
p_phi = plots.PlotBase(
[histograms.HistoGraph(eff1phi, "eff1phi", "p", "P")])
p_phi.histoMgr.setHistoLegendLabelMany({"eff1phi": legend1})
if isinstance(datasetsDY, dataset.DatasetManager):
p_phi.histoMgr.setHistoLegendLabelMany({
"eff1phi": legend1,
"eff2phi": legend2
})
if isinstance(datasetsH125, dataset.DatasetManager):
p_phi.histoMgr.setHistoLegendLabelMany({
"eff1phi": legend1,
"eff2phi": legend2,
"eff3phi": legend3
})
name = "TauMET_" + analysis + "_DataVsMC_PFTauPhi"
if createRatio:
p_phi.createFrame(os.path.join(plotDir, name),
createRatio=createRatio,
opts=opts,
opts2=opts2)
else:
p_phi.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
moveLegendPhi = {"dx": -0.5, "dy": -0.65, "dh": -0.1}
p_phi.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegendPhi))
p_phi.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
p_phi.getFrame().GetXaxis().SetTitle("#tau-jet #phi")
if createRatio:
p_phi.getFrame2().GetYaxis().SetTitle("Ratio")
p_phi.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.46, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.38, label, 17)
histograms.addText(0.2, 0.31, "Runs " + datasets.loadRunRange(), 17)
p_phi.draw()
histograms.addStandardTexts(lumi=lumi)
p_phi.save(formats)
#########################################################################
namePU = "TauMET_" + analysis + "_DataVsMC_nVtx"
eff1PU = getEfficiency(dataset1, "NumeratorPU", "DenominatorPU")
eff2PU = getEfficiency(dataset2, "NumeratorPU", "DenominatorPU")
styles.dataStyle.apply(eff1PU)
styles.mcStyle.apply(eff2PU)
eff1PU.SetMarkerSize(1)
eff2PU.SetMarkerSize(1.5)
if isinstance(datasetsDY, dataset.DatasetManager):
pPU = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1PU, "eff1", "p", "P"),
[histograms.HistoGraph(eff2PU, "eff2", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({
"eff1": legend1,
"eff2": legend2
})
else:
pPU = plots.PlotBase([histograms.HistoGraph(eff1PU, "eff1", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
optsPU = {"ymin": 0.01, "ymax": 1.0}
createRatio = False
if createRatio:
pPU.createFrame(os.path.join(plotDir, namePU),
createRatio=True,
opts=optsPU,
opts2=opts2)
else:
pPU.createFrame(os.path.join(plotDir, namePU),
opts=optsPU,
opts2=opts2)
moveLegend = {"dx": -0.5, "dy": -0.5, "dh": -0.1}
pPU.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
# if createRatio:
# pPU.getPad1().SetLogy(True)
# else:
# pPU.getPad().SetLogy(True)
pPU.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
pPU.getFrame().GetXaxis().SetTitle("Number of reco vertices")
if createRatio:
pPU.getFrame2().GetYaxis().SetTitle("Ratio")
pPU.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.6, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.53, label, 17)
histograms.addText(0.2, 0.46, "Runs " + datasets.loadRunRange(), 17)
pPU.draw()
histograms.addStandardTexts(lumi=lumi)
pPU.save(formats)
#########################################################################
"""
hName = "Pull"
# hName = "Sub"
namePull = "TauMET_"+analysis+"_DataVsMC_"+hName+"s"
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("MC", ["TT","WJets","DYJetsToLL","SingleTop","QCD"], keepSources=True)
drh1 = datasets.getDataset("Data").getDatasetRootHisto(hName)
drh2 = datasets.getDataset("MC").getDatasetRootHisto(hName)
drh1.normalizeToOne()
drh2.normalizeToOne()
pull1 = drh1.getHistogram()
pull2 = drh2.getHistogram()
if isinstance(datasetsH125,dataset.DatasetManager):
plots.mergeRenameReorderForDataMC(datasetsH125)
drh3 = datasetsH125.getMCDatasets()[0].getDatasetRootHisto(hName)
drh3.normalizeToOne()
pull3 = drh3.getHistogram()
styles.dataStyle.apply(pull1)
styles.mcStyle.apply(pull2)
pull1.SetMarkerSize(1)
if isinstance(datasetsH125,dataset.DatasetManager):
styles.mcStyle.apply(pull3)
pull3.SetMarkerSize(1.5)
pull3.SetMarkerColor(4)
pull3.SetLineColor(4)
if isinstance(datasetsH125,dataset.DatasetManager):
p_pull = plots.ComparisonManyPlot(histograms.Histo(pull1, "pull1", "p", "P"),
[histograms.Histo(pull2, "pull2", "p", "P"),
histograms.Histo(pull3, "pull3", "p", "P")])
else:
p_pull = plots.ComparisonPlot(histograms.Histo(pull1, "pull1", "p", "P"),
histograms.Histo(pull2, "pull2", "p", "P"))
p_pull.histoMgr.setHistoLegendLabelMany({"pull1": legend1, "pull2": legend2})
if isinstance(datasetsH125,dataset.DatasetManager):
p_pull.histoMgr.setHistoLegendLabelMany({"pull1": legend1, "pull2": legend2, "pull3": legend3})
p_pull.createFrame(os.path.join(plotDir, namePull), createRatio=True, opts=opts, opts2=opts2)
moveLegendPull = {"dx": -0.5, "dy": -0.35, "dh": -0.1}
p_pull.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegendPull))
p_pull.getFrame().GetYaxis().SetTitle("Arbitrary units")
# p_pull.getFrame().GetXaxis().SetTitle("HLT #tau p_{T} - #tau-jet p_{T} (GeV/c)")
p_pull.getFrame().GetXaxis().SetTitle("HLT #tau p_{T}/ #tau-jet p_{T} - 1")
p_pull.getFrame2().GetYaxis().SetTitle("Ratio")
p_pull.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.75, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.68, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.2, 0.61, "Runs "+runRange, 17)
p_pull.draw()
histograms.addStandardTexts(lumi=lumi)
p_pull.save(formats)
"""
#########################################################################
print "Output written in", plotDir
def analyze(analysis=None):
paths = [sys.argv[1]]
if (len(sys.argv) == 3):
howAnalyse = sys.argv[2]
else:
howAnalyse = "--fit"
# howAnalyse = "--bin"
if not analysis == None:
datasets = dataset.getDatasetsFromMulticrabDirs(
paths,
analysisName=analysis,
excludeTasks="Silver|GluGluHToTauTau_M125")
else:
datasets = dataset.getDatasetsFromMulticrabDirs(
paths, excludeTasks="Silver|GluGluHToTauTau_M125")
analysis = datasets.getAllDatasets()[0].getAnalysisName()
# datasetsDY = None
datasetsDY = dataset.getDatasetsFromMulticrabDirs(
paths, analysisName=analysis, includeOnlyTasks="DYJetsToLL")
datasetsDY = dataset.getDatasetsFromMulticrabDirs(
paths, analysisName=analysis, includeOnlyTasks="DYJetsToLL|Zprime")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="GluGluHToTauTau_M125|TTJets")
datasetsH125 = None
# datasetsH125 = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="GluGluHToTauTau_M125",emptyDatasetsAsNone=True)
# datasetsH125 = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="GluGluHToTauTau_M125")
datasets.loadLuminosities()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
dataset2 = dataset1
# dataset2 = datasets.getMCDatasets()
if not datasetsDY == None:
dataset2 = datasetsDY.getMCDatasets()
histeff1 = getEfficiency(dataset1)
histeff2 = getEfficiency(dataset2)
eff1 = convert2TGraph(histeff1)
eff2 = convert2TGraph(histeff2)
if isinstance(datasetsH125, dataset.DatasetManager):
histeff3 = getEfficiency(datasetsH125.getMCDatasets())
eff3 = convert2TGraph(histeff3)
styles.dataStyle.apply(eff1)
styles.mcStyle.apply(eff2)
eff1.SetMarkerSize(1)
# eff2.SetMarkerSize(1.5)
if isinstance(datasetsH125, dataset.DatasetManager):
styles.mcStyle.apply(eff3)
eff3.SetMarkerSize(1.5)
eff3.SetMarkerColor(4)
eff3.SetLineColor(4)
# p = plots.ComparisonPlot(histograms.HistoGraph(eff1, "eff1", "p", "P"),
# histograms.HistoGraph(eff2, "eff2", "p", "P"))
if isinstance(datasetsH125, dataset.DatasetManager):
p = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1, "eff1", "p", "P"), [
histograms.HistoGraph(eff2, "eff2", "p", "P"),
histograms.HistoGraph(eff3, "eff3", "p", "P")
])
elif isinstance(datasetsDY, dataset.DatasetManager):
p = plots.ComparisonPlot(histograms.HistoGraph(eff1, "eff1", "p", "P"),
histograms.HistoGraph(eff2, "eff2", "p", "P"))
else:
p = plots.PlotBase([histograms.HistoGraph(eff1, "eff1", "p", "P")])
## FIT FUNCTIONS: "Sigmoid", "Error", "Gompertz", "Richard","Crystal" ##
## FIT TYPES: binned max likelihood: "ML" , Chi2-fit: "Chi" ##
if (howAnalyse == "--fit"):
datafit = fitType("Data", p, histeff1, eff1, 20, 500, "Crystal", "ML")
mcfit = fitType("MC", p, histeff2, eff2, 20, 500, "Crystal", "ML")
if isinstance(datasetsH125, dataset.DatasetManager):
fit("H125", p, eff3, 20, 200)
opts = {"ymin": 0, "ymax": 1.1}
opts2 = {"ymin": 0.5, "ymax": 1.5}
# moveLegend = {"dx": -0.55, "dy": -0.15, "dh": -0.1}
moveLegend = {"dx": -0.2, "dy": -0.5, "dh": -0.1}
name = "TauMET_" + analysis + "_DataVsMC_PFTauPt"
legend1 = "Data"
# legend2 = "MC (DY)"
legend2 = "Simulation"
legend3 = "MC (H125)"
createRatio = False
p.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
if isinstance(datasetsDY, dataset.DatasetManager):
p.histoMgr.setHistoLegendLabelMany({"eff1": legend1, "eff2": legend2})
createRatio = True
if isinstance(datasetsH125, dataset.DatasetManager):
p.histoMgr.setHistoLegendLabelMany({
"eff1": legend1,
"eff2": legend2,
"eff3": legend3
})
if createRatio:
p.createFrame(os.path.join(plotDir, name),
createRatio=createRatio,
opts=opts,
opts2=opts2)
else:
p.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
p.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
# p.getFrame().GetXaxis().SetTitle("#tau-jet p_{T} (GeV/c)")
p.getFrame().GetXaxis().SetTitle("#tau_{h} p_{T} (GeV/c)")
if createRatio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.5, 0.6, "LooseIsoPFTau50_Trk30_eta2p1", 17)
# histograms.addText(0.5, 0.6, "VLooseIsoPFTau120_Trk50_eta2p1", 17)
# histograms.addText(0.5, 0.6, "VLooseIsoPFTau140_Trk50_eta2p1", 17)
# label = analysis.split("_")[len(analysis.split("_")) -1]
label = "2016"
histograms.addText(0.5, 0.53, label, 17)
runRange = datasets.loadRunRange()
histograms.addText(0.5, 0.46, "Runs " + runRange, 17)
p.draw()
## does the ratio of the fits
if (howAnalyse == "--fit"):
funcRatio = ROOT.TH1F("", "", 480, 20, 500)
for i in range(0, 480):
ratio = datafit.Eval(i + 20 - 1) / mcfit.Eval(i + 20 - 1)
funcRatio.SetBinContent(i, ratio)
p.getPad().GetCanvas().cd(2)
funcRatio.Draw("SAME")
p.getPad().GetCanvas().cd(1)
##
lumi = 0.0
for d in datasets.getDataDatasets():
if (d.getName() != "SingleMuon_Run2016F_03Feb2017_v1_277932_278800"
and d.getName() !=
"SingleMuon_Run2016C_03Feb2017_v1_275656_276283"):
print "luminosity", d.getName(), d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum", lumi
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
if (howAnalyse == "--fit"):
pythonWriter.addParameters(plotDir, label, runRange, lumi, datafit)
pythonWriter.addMCParameters(label, mcfit)
pythonWriter.writeJSON(
os.path.join(plotDir,
"tauLegTriggerEfficiency_" + label + "_fit.json"))
pythonWriter.__init__() #need to clear the Json arrays
# if (howAnalyse == "--bin"):
pythonWriter.addParameters(plotDir, label, runRange, lumi, eff1)
pythonWriter.addMCParameters(label, eff2)
pythonWriter.writeJSON(
os.path.join(plotDir,
"tauLegTriggerEfficiency_" + label + "_bin.json"))
# if not createRatio:
# sys.exit()
#########################################################################
histeff1eta = getEfficiency(dataset1, "NumeratorEta", "DenominatorEta")
histeff2eta = getEfficiency(dataset2, "NumeratorEta", "DenominatorEta")
eff1eta = convert2TGraph(histeff1eta)
eff2eta = convert2TGraph(histeff2eta)
if isinstance(datasetsH125, dataset.DatasetManager):
histeff3eta = getEfficiency(datasetsH125.getMCDatasets(),
"NumeratorEta", "DenominatorEta")
eff3eta = convert2TGraph(histeff3eta)
styles.dataStyle.apply(eff1eta)
styles.mcStyle.apply(eff2eta)
eff1eta.SetMarkerSize(1)
if isinstance(datasetsH125, dataset.DatasetManager):
styles.mcStyle.apply(eff3eta)
eff3eta.SetMarkerSize(1.5)
eff3eta.SetMarkerColor(4)
eff3eta.SetLineColor(4)
if isinstance(datasetsH125, dataset.DatasetManager):
p_eta = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1eta, "eff1eta", "p", "P"), [
histograms.HistoGraph(eff2eta, "eff2eta", "p", "P"),
histograms.HistoGraph(eff3eta, "eff3eta", "p", "P")
])
elif isinstance(datasetsDY, dataset.DatasetManager):
p_eta = plots.ComparisonPlot(
histograms.HistoGraph(eff1eta, "eff1eta", "p", "P"),
histograms.HistoGraph(eff2eta, "eff2eta", "p", "P"))
else:
p_eta = plots.PlotBase(
[histograms.HistoGraph(eff1eta, "eff1eta", "p", "P")])
p_eta.histoMgr.setHistoLegendLabelMany({"eff1eta": legend1})
if isinstance(datasetsDY, dataset.DatasetManager):
p_eta.histoMgr.setHistoLegendLabelMany({
"eff1eta": legend1,
"eff2eta": legend2
})
if isinstance(datasetsH125, dataset.DatasetManager):
p_eta.histoMgr.setHistoLegendLabelMany({
"eff1eta": legend1,
"eff2eta": legend2,
"eff3eta": legend3
})
name = "TauMET_" + analysis + "_DataVsMC_PFTauEta"
if createRatio:
p_eta.createFrame(os.path.join(plotDir, name),
createRatio=createRatio,
opts=opts,
opts2=opts2)
else:
p_eta.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
moveLegendEta = {"dx": -0.5, "dy": -0.65, "dh": -0.1}
p_eta.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegendEta))
p_eta.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
p_eta.getFrame().GetXaxis().SetTitle("#tau-jet #eta")
if createRatio:
p_eta.getFrame2().GetYaxis().SetTitle("Ratio")
p_eta.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.46, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.38, label, 17)
histograms.addText(0.2, 0.31, "Runs " + datasets.loadRunRange(), 17)
p_eta.draw()
histograms.addStandardTexts(lumi=lumi)
p_eta.save(formats)
#########################################################################
histeff1phi = getEfficiency(dataset1, "NumeratorPhi", "DenominatorPhi")
histeff2phi = getEfficiency(dataset2, "NumeratorPhi", "DenominatorPhi")
eff1phi = convert2TGraph(histeff1phi)
eff2phi = convert2TGraph(histeff2phi)
if isinstance(datasetsH125, dataset.DatasetManager):
histeff3phi = getEfficiency(datasetsH125.getMCDatasets(),
"NumeratorPhi", "DenominatorPhi")
eff3phi = convert2TGraph(histeff3phi)
styles.dataStyle.apply(eff1phi)
styles.mcStyle.apply(eff2phi)
eff1phi.SetMarkerSize(1)
if isinstance(datasetsH125, dataset.DatasetManager):
styles.mcStyle.apply(eff3phi)
eff3phi.SetMarkerSize(1.5)
eff3phi.SetMarkerColor(4)
eff3phi.SetLineColor(4)
if isinstance(datasetsH125, dataset.DatasetManager):
p_phi = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1phi, "eff1phi", "p", "P"), [
histograms.HistoGraph(eff2phi, "eff2phi", "p", "P"),
histograms.HistoGraph(eff3phi, "eff3phi", "p", "P")
])
elif isinstance(datasetsDY, dataset.DatasetManager):
p_phi = plots.ComparisonPlot(
histograms.HistoGraph(eff1phi, "eff1phi", "p", "P"),
histograms.HistoGraph(eff2phi, "eff2phi", "p", "P"))
else:
p_phi = plots.PlotBase(
[histograms.HistoGraph(eff1phi, "eff1phi", "p", "P")])
p_phi.histoMgr.setHistoLegendLabelMany({"eff1phi": legend1})
if isinstance(datasetsDY, dataset.DatasetManager):
p_phi.histoMgr.setHistoLegendLabelMany({
"eff1phi": legend1,
"eff2phi": legend2
})
if isinstance(datasetsH125, dataset.DatasetManager):
p_phi.histoMgr.setHistoLegendLabelMany({
"eff1phi": legend1,
"eff2phi": legend2,
"eff3phi": legend3
})
name = "TauMET_" + analysis + "_DataVsMC_PFTauPhi"
if createRatio:
p_phi.createFrame(os.path.join(plotDir, name),
createRatio=createRatio,
opts=opts,
opts2=opts2)
else:
p_phi.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
moveLegendPhi = {"dx": -0.5, "dy": -0.65, "dh": -0.1}
p_phi.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegendPhi))
p_phi.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
p_phi.getFrame().GetXaxis().SetTitle("#tau-jet #phi")
if createRatio:
p_phi.getFrame2().GetYaxis().SetTitle("Ratio")
p_phi.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.46, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.38, label, 17)
histograms.addText(0.2, 0.31, "Runs " + datasets.loadRunRange(), 17)
p_phi.draw()
histograms.addStandardTexts(lumi=lumi)
p_phi.save(formats)
#########################################################################
namePU = "TauMET_" + analysis + "_DataVsMC_nVtx"
histeff1PU = getEfficiency(dataset1, "NumeratorPU", "DenominatorPU")
histeff2PU = getEfficiency(dataset2, "NumeratorPU", "DenominatorPU")
eff1PU = convert2TGraph(histeff1PU)
eff2PU = convert2TGraph(histeff2PU)
styles.dataStyle.apply(eff1PU)
styles.mcStyle.apply(eff2PU)
eff1PU.SetMarkerSize(1)
eff2PU.SetMarkerSize(1.5)
if isinstance(datasetsDY, dataset.DatasetManager):
pPU = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1PU, "eff1", "p", "P"),
[histograms.HistoGraph(eff2PU, "eff2", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({
"eff1": legend1,
"eff2": legend2
})
else:
pPU = plots.PlotBase([histograms.HistoGraph(eff1PU, "eff1", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
optsPU = {"ymin": 0.01, "ymax": 1.0}
createRatio = False
if createRatio:
pPU.createFrame(os.path.join(plotDir, namePU),
createRatio=True,
opts=optsPU,
opts2=opts2)
else:
pPU.createFrame(os.path.join(plotDir, namePU),
opts=optsPU,
opts2=opts2)
moveLegend = {"dx": -0.5, "dy": -0.5, "dh": -0.1}
pPU.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
# if createRatio:
# pPU.getPad1().SetLogy(True)
# else:
# pPU.getPad().SetLogy(True)
pPU.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
pPU.getFrame().GetXaxis().SetTitle("Number of reco vertices")
if createRatio:
pPU.getFrame2().GetYaxis().SetTitle("Ratio")
pPU.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.6, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.53, label, 17)
histograms.addText(0.2, 0.46, "Runs " + datasets.loadRunRange(), 17)
pPU.draw()
histograms.addStandardTexts(lumi=lumi)
pPU.save(formats)
#########################################################################
"""
hName = "Pull"
# hName = "Sub"
namePull = "TauMET_"+analysis+"_DataVsMC_"+hName+"s"
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("MC", ["TT","WJets","DYJetsToLL","SingleTop","QCD"], keepSources=True)
drh1 = datasets.getDataset("Data").getDatasetRootHisto(hName)
drh2 = datasets.getDataset("MC").getDatasetRootHisto(hName)
drh1.normalizeToOne()
drh2.normalizeToOne()
pull1 = drh1.getHistogram()
pull2 = drh2.getHistogram()
if isinstance(datasetsH125,dataset.DatasetManager):
plots.mergeRenameReorderForDataMC(datasetsH125)
drh3 = datasetsH125.getMCDatasets()[0].getDatasetRootHisto(hName)
drh3.normalizeToOne()
pull3 = drh3.getHistogram()
styles.dataStyle.apply(pull1)
styles.mcStyle.apply(pull2)
pull1.SetMarkerSize(1)
if isinstance(datasetsH125,dataset.DatasetManager):
styles.mcStyle.apply(pull3)
pull3.SetMarkerSize(1.5)
pull3.SetMarkerColor(4)
pull3.SetLineColor(4)
if isinstance(datasetsH125,dataset.DatasetManager):
p_pull = plots.ComparisonManyPlot(histograms.Histo(pull1, "pull1", "p", "P"),
[histograms.Histo(pull2, "pull2", "p", "P"),
histograms.Histo(pull3, "pull3", "p", "P")])
else:
p_pull = plots.ComparisonPlot(histograms.Histo(pull1, "pull1", "p", "P"),
histograms.Histo(pull2, "pull2", "p", "P"))
p_pull.histoMgr.setHistoLegendLabelMany({"pull1": legend1, "pull2": legend2})
if isinstance(datasetsH125,dataset.DatasetManager):
p_pull.histoMgr.setHistoLegendLabelMany({"pull1": legend1, "pull2": legend2, "pull3": legend3})
p_pull.createFrame(os.path.join(plotDir, namePull), createRatio=True, opts=opts, opts2=opts2)
moveLegendPull = {"dx": -0.5, "dy": -0.35, "dh": -0.1}
p_pull.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegendPull))
p_pull.getFrame().GetYaxis().SetTitle("Arbitrary units")
# p_pull.getFrame().GetXaxis().SetTitle("HLT #tau p_{T} - #tau-jet p_{T} (GeV/c)")
p_pull.getFrame().GetXaxis().SetTitle("HLT #tau p_{T}/ #tau-jet p_{T} - 1")
p_pull.getFrame2().GetYaxis().SetTitle("Ratio")
p_pull.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.75, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.68, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.2, 0.61, "Runs "+runRange, 17)
p_pull.draw()
histograms.addStandardTexts(lumi=lumi)
p_pull.save(formats)
"""
#########################################################################
print "Output written in", plotDir
def DoPlots(datasetsMgr, histoName):
analysisType = "Inverted"
# Get the Inclusive (Data, EWK)
p1 = plots.ComparisonPlot(*getHistos(datasetsMgr, histoName, analysisType))
p1.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
# Get histos
inverted_Data = p1.histoMgr.getHisto("Data").getRootHisto().Clone(
"Inverted-Data")
inverted_EWK = p1.histoMgr.getHisto("EWK").getRootHisto().Clone(
"Inverted-EWK")
inverted_QCD = p1.histoMgr.getHisto("Data").getRootHisto().Clone(
"Inverted-QCD")
# Normalize histograms to unit area
inverted_Data.Scale(1.0 / inverted_QCD.Integral())
inverted_QCD.Scale(1.0 / inverted_QCD.Integral())
inverted_EWK.Scale(1.0 / inverted_EWK.Integral())
# Create the final plot object (Cannot subtract EWK from Data since the trigger in the MC samples is NOT prescaled. In data it is)
p = plots.ComparisonManyPlot(inverted_Data, [inverted_EWK], saveFormats=[])
#p = plots.PlotBase([inverted_Data], saveFormats=[])
# Apply styles
p.histoMgr.forHisto("Inverted-Data", styles.getDataStyle())
#p.histoMgr.forHisto("Inverted-QCD" , styles.getInvertedLineStyle() )
p.histoMgr.forHisto("Inverted-EWK", styles.getAltEWKStyle())
# Set draw style
p.histoMgr.setHistoDrawStyle("Inverted-Data", "AP")
#p.histoMgr.setHistoDrawStyle("Inverted-QCD" , "HIST")
p.histoMgr.setHistoDrawStyle("Inverted-EWK", "AP")
# Set legend style
p.histoMgr.setHistoLegendStyle("Inverted-Data", "P")
#p.histoMgr.setHistoLegendStyle("Inverted-QCD" , "L")
p.histoMgr.setHistoLegendStyle("Inverted-EWK", "P")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Inverted-Data": "Data",
"Inverted-EWK": "EWK",
#"Inverted-QCD" : "QCD=Data-EWK",
})
# Draw the histograms
_cutBox = None
_rebinX = 1
#_opts = {"ymin": 1e-4, "ymaxfactor": 2.0}
_opts = {"ymin": 1e-4, "ymax": 1e-1}
_xlabel = None
_units = "GeV/c^{2}"
_xlabel = "m_{jjb} (%s)" % _units
_ylabel = "Arbitrary Units / %0.0f %s " % (inverted_QCD.GetBinWidth(0),
_units)
_cutBox = {
"cutValue": 173.21,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 1500.0
# Complains (SysError in <TFile::TFile>: file ...)
if 0:
plots.drawPlot(p,
histoName,
xlabel=_xlabel,
ylabel=_ylabel,
log=True,
rebinX=_rebinX,
cmsExtraText="Preliminary",
createLegend={
"x1": 0.62,
"y1": 0.78,
"x2": 0.92,
"y2": 0.92
},
opts=_opts)
if 1:
plots.drawPlot(
p,
histoName,
xlabel=_xlabel,
ylabel=_ylabel,
log=True,
rebinX=_rebinX,
cmsExtraText="Preliminary",
createLegend={
"x1": 0.62,
"y1": 0.78,
"x2": 0.92,
"y2": 0.92
},
opts=_opts,
opts2={
"ymin": 0.0,
"ymax": 2.0
},
ratio=True,
ratioInvert=False,
ratioYlabel="Ratio",
cutBox=_cutBox,
)
# Save to ROOT file
inverted_QCD.SaveAs("Inverted_QCD.root")
# Save plot in all formats
SavePlot(p, histoName, os.path.join(opts.saveDir, "Test", opts.optMode))
return
def BaselineVsInvertedPlots(datasetsMgr, histoName, analysisType="Inverted"):
# Sanity check
IsBaselineOrInverted(analysisType)
# Get the Data (Inclusive)
p1 = plots.ComparisonPlot(*getHistos(datasetsMgr, "Data", histoName, analysisType))
p1.histoMgr.normalizeMCToLuminosity(datasetsMgr.getDataset("Data").getLuminosity())
# Get the EWK (GenuineB)
defaultFolder = "ForFakeBMeasurement"
genuineBFolder = defaultFolder + "EWKGenuineB"
fakeBFolder = defaultFolder + "EWKFakeB"
histoNameNew = histoName.replace( defaultFolder, genuineBFolder)
p2 = plots.ComparisonPlot(*getHistos(datasetsMgr, "EWK", histoNameNew, analysisType) )
p2.histoMgr.normalizeMCToLuminosity(datasetsMgr.getDataset("Data").getLuminosity())
# Get Data histos
baseline_Data = p1.histoMgr.getHisto("Baseline-Data").getRootHisto().Clone("Baseline-Data")
inverted_Data = p1.histoMgr.getHisto("Inverted-Data").getRootHisto().Clone("Inverted-Data")
# Get EWK histos
baseline_EWKGenuineB = p2.histoMgr.getHisto("Baseline-EWK").getRootHisto().Clone("Baseline-EWKGenuineB")
inverted_EWKGenuineB = p2.histoMgr.getHisto("Inverted-EWK").getRootHisto().Clone("Inverted-EWKGenuineB")
# Get FakeB (Baseline): FakeB = Data-EWKGenuineB
baseline_FakeB = p1.histoMgr.getHisto("Baseline-Data").getRootHisto().Clone("Baseline-FakeB")
baseline_FakeB.Add(baseline_EWKGenuineB, -1)
# Get FakeB (Inverted): FakeB = Data-EWKGenuineB
inverted_FakeB = p1.histoMgr.getHisto("Inverted-Data").getRootHisto().Clone("Inverted-FakeB")
inverted_FakeB.Add(inverted_EWKGenuineB, -1)
# Normalize histograms to unit area
baseline_FakeB.Scale(1.0/baseline_FakeB.Integral())
inverted_FakeB.Scale(1.0/inverted_FakeB.Integral())
# Create the final plot object
p = plots.ComparisonManyPlot(baseline_FakeB, [inverted_FakeB], saveFormats=[]) #[".C", ".png", ".pdf"])
p.setLuminosity(GetLumi(datasetsMgr))
# Apply styles
p.histoMgr.forHisto("Baseline-FakeB" , styles.getBaselineStyle() )
p.histoMgr.forHisto("Inverted-FakeB" , styles.getInvertedStyle() )
# Set draw style
p.histoMgr.setHistoDrawStyle("Baseline-FakeB", "AP")
p.histoMgr.setHistoDrawStyle("Inverted-FakeB", "HIST")
# Set legend style
p.histoMgr.setHistoLegendStyle("Baseline-FakeB", "LP")
p.histoMgr.setHistoLegendStyle("Inverted-FakeB", "F")
# p.histoMgr.setHistoLegendStyleAll("LP")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Baseline-FakeB" : "FakeB (Baseline)",
"Inverted-FakeB" : "FakeB (Inverted)",
})
# Draw the histograms
_cutBox = None
_rebinX = 1
_opts = {"ymin": 1e-4, "ymaxfactor": 2.0}
_format = "%0.0f"
_xlabel = None
if "dijetm" in histoName.lower():
_rebinX = 2
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jj} (%s)" % (_units)
_cutBox = {"cutValue": 80.399, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 400.0
if "trijetm" in histoName.lower():
_rebinX = 5
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 1500.0
if "pt" in histoName.lower():
_rebinX = 2
_format = "%0.0f GeV/c"
if "eta" in histoName.lower():
_format = "%0.2f"
_cutBox = {"cutValue": 0., "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmin"] = -3.0
_opts["xmax"] = +3.0
if "deltaeta" in histoName.lower():
_format = "%0.2f"
_opts["xmin"] = 0.0
_opts["xmax"] = 6.0
if "bdisc" in histoName.lower():
_format = "%0.2f"
if "tetrajetm" in histoName.lower():
_rebinX = 10
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjjb} (%s)" % (_units)
_opts["xmax"] = 3500.0
plots.drawPlot(p, histoName,
xlabel = _xlabel,
ylabel = "Arbitrary Units / %s" % (_format),
log = True,
rebinX = _rebinX, cmsExtraText = "Preliminary",
createLegend = {"x1": 0.62, "y1": 0.78, "x2": 0.92, "y2": 0.92},
opts = _opts,
opts2 = {"ymin": 0.6, "ymax": 1.4},
ratio = True,
ratioInvert = False,
ratioYlabel = "Ratio",
cutBox = _cutBox,
)
# Save plot in all formats
SavePlot(p, histoName, os.path.join(opts.saveDir, "BaselineVsInverted", opts.optMode) )
return
def PlotTemplates(datasetsMgr, histoName, analysisType="Inverted"):
Verbose("Plotting EWK Vs QCD unity-normalised histograms")
# Create comparison plot
p1 = plots.ComparisonPlot(
getHisto(datasetsMgr, "Data", histoName, "Baseline"),
getHisto(datasetsMgr, "Data", histoName, "Inverted")
)
p1.histoMgr.normalizeMCToLuminosity(datasetsMgr.getDataset("Data").getLuminosity())
defaultFolder = "ForFakeBMeasurement"
genuineBFolder = defaultFolder + "EWKGenuineB"
fakeBFolder = defaultFolder + "EWKFakeB"
histoNameNew = histoName.replace( defaultFolder, genuineBFolder)
p2 = plots.ComparisonPlot(
getHisto(datasetsMgr, "EWK", histoNameNew, "Baseline"),
getHisto(datasetsMgr, "EWK", histoNameNew, "Inverted")
)
p2.histoMgr.normalizeMCToLuminosity(datasetsMgr.getDataset("Data").getLuminosity())
# Get EWKGenuineB histos
EWKGenuineB_baseline = p2.histoMgr.getHisto("Baseline-EWK").getRootHisto().Clone("Baseline-EWKGenuineB")
EWKGenuineB_inverted = p2.histoMgr.getHisto("Inverted-EWK").getRootHisto().Clone("Inverted-EWKGenuineB")
# Get FakeB histos
FakeB_baseline = p1.histoMgr.getHisto("Baseline-Data").getRootHisto().Clone("Baseline-FakeB")
FakeB_baseline.Add(EWKGenuineB_baseline, -1)
FakeB_inverted = p1.histoMgr.getHisto("Inverted-Data").getRootHisto().Clone("Inverted-FakeB")
FakeB_inverted.Add(EWKGenuineB_inverted, -1)
# Normalize histograms to unit area
EWKGenuineB_baseline.Scale(1.0/EWKGenuineB_baseline.Integral())
EWKGenuineB_inverted.Scale(1.0/EWKGenuineB_inverted.Integral())
FakeB_baseline.Scale(1.0/FakeB_baseline.Integral())
FakeB_inverted.Scale(1.0/FakeB_inverted.Integral())
# Create the final plot object
comparisonList = [EWKGenuineB_baseline]
p = plots.ComparisonManyPlot(FakeB_inverted, comparisonList, saveFormats=[])
p.setLuminosity(GetLumi(datasetsMgr))
# Apply styles
p.histoMgr.forHisto("Baseline-EWKGenuineB", styles.getBaselineStyle() )
p.histoMgr.forHisto("Inverted-FakeB" , styles.getInvertedStyle() )
# Set draw style
p.histoMgr.setHistoDrawStyle("Baseline-EWKGenuineB", "AP")
p.histoMgr.setHistoDrawStyle("Inverted-FakeB" , "HIST")
# Set legend style
p.histoMgr.setHistoLegendStyle("Baseline-EWKGenuineB", "LP")
p.histoMgr.setHistoLegendStyle("Inverted-FakeB" , "LP")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Baseline-EWKGenuineB": "GenuineB (EWK)", # (Baseline)
"Inverted-FakeB" : "FakeB", # (Inverted)
})
# Append analysisType to histogram name
saveName = histoName
# Draw the histograms #alex
plots.drawPlot(p, saveName, **GetHistoKwargs(histoName) ) #the "**" unpacks the kwargs_
# _kwargs = {"lessThan": True}
# p.addCutBoxAndLine(cutValue=200, fillColor=ROOT.kRed, box=False, line=True, ***_kwargs)
# Add text
text = opts.optMode.replace("OptChiSqrCutValue", "#chi^{2} #leq ")
histograms.addText(0.21, 0.85, text)
# Save plot in all formats
saveDir = os.path.join(opts.saveDir, "Templates", opts.optMode)
SavePlot(p, saveName, saveDir, saveFormats = [".C", ".png", ".pdf"])
return
def main(argv):
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
QCDInvertedNormalization = QCDInvertedNormalizationFactors.QCDInvertedNormalization
QCDInvertedNormalizationFilteredEWKFakeTaus = QCDInvertedNormalizationFactorsFilteredEWKFakeTaus.QCDInvertedNormalization
analysis = "signalAnalysisInvertedTau"
optModes = []
#optModes.append("OptQCDTailKillerZeroPlus")
optModes.append("OptQCDTailKillerLoosePlus")
optModes.append("OptQCDTailKillerMediumPlus")
optModes.append("OptQCDTailKillerTightPlus")
#optModes.append("OptQCDTailKillerVeryTightPlus")
#optModes.append("OnlyGenuineMCTausFalse")
#optModes.append("OnlyGenuineMCTausTrue")
#Optimal: 0.8, 0.82, 0.9
#w1_list = [0.8, 0.82, 0.84, 0.87]
#w1_list = [0.8, 0.82, 0.9, 1]
w1_list = [0.9]
defaultBinning = systematics.getBinningForPlot("shapeTransverseMass")
diff_opt = []
for optMode in optModes:
diff_list = []
for w1 in w1_list:
var_values = []
nom_values = []
w2 = 1 - w1
color = 1
#signal
dirs_signal = ["../../SignalAnalysis_140605_143702/"]
datasets_signal = dataset.getDatasetsFromMulticrabDirs(
dirs_signal,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis.replace("InvertedTau", ""),
optimizationMode=optMode)
datasets_signal.updateNAllEventsToPUWeighted()
datasets_signal.loadLuminosities()
datasets_signal.remove(
filter(lambda name: "TTToHplus" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "HplusTB" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "Hplus_taunu_t-channel" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "Hplus_taunu_tW-channel" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "TTJets_SemiLept" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "TTJets_FullLept" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "TTJets_Hadronic" in name,
datasets_signal.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets_signal)
datasets_signal.merge(
"EWK",
["TTJets", "WJets", "DYJetsToLL", "SingleTop", "Diboson"])
mtplot_signalfaketaus = plots.DataMCPlot(
datasets_signal, "shapeEWKFakeTausTransverseMass")
mt_signalfaketaus = mtplot_signalfaketaus.histoMgr.getHisto(
"EWK").getRootHisto().Clone("shapeEWKFakeTausTransverseMass")
mt_signalfaketaus.SetName("BaselineFakeTaus")
myBinning = [0, 20, 40, 60, 80, 100, 120, 140, 160, 200, 400]
myArray = array.array("d", myBinning)
fitBinning = []
for i in range(0, 45):
fitBinning.append(i * 10)
fitArray = array.array("d", fitBinning)
mt_baseline = mt_signalfaketaus
#rangeMin = mt_signalfaketaus.GetXaxis().GetXmin()
#rangeMax = mt_signalfaketaus.GetXaxis().GetXmax()
#theFit = TF1('theFit',FitFunction(),rangeMin,rangeMax,4)
#theFit.SetParLimits(0,0.5,10000)
#theFit.SetParLimits(1,90,10000)
#theFit.SetParLimits(2,30,10000)
#theFit.SetParLimits(3,0.001,10000)
#mt_signalfaketaus.Fit(theFit,"R")
#theFit.SetRange(mt_signalfaketaus.GetXaxis().GetXmin(),mt_signalfaketaus.GetXaxis().GetXmax())
#theFit.SetLineStyle(2)
#theFit.SetLineColor(4)
#theFit.SetLineWidth(3)
#theFit.Draw()
#mt_corr = theFit.GetHistogram()
#mt_corr = mt_corr.Rebin(len(fitBinning)-1,"",fitArray)
#mt_corr.Scale(mt_baseline.GetMaximum()/mt_corr.GetMaximum())
for HISTONAME in histoNameList:
var = False
if HISTONAME == "shapeEWKGenuineTausTransverseMass":
var = True
datasets = dataset.getDatasetsFromMulticrabDirs(
dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis,
optimizationMode=optMode)
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge(
"EWK",
["TTJets", "WJets", "DYJetsToLL", "SingleTop", "Diboson"])
histonames = datasets.getDataset("Data").getDirectoryContent(
HISTONAME)
bins = []
for histoname in histonames:
binname = histoname.replace(HISTONAME, "")
if not binname == "Inclusive":
bins.append(binname)
for i, bin in enumerate(bins):
mtplot = plots.DataMCPlot(
datasets, HISTONAME + "/" + HISTONAME + bin)
if i == 0:
mt = mtplot.histoMgr.getHisto(
"Data").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
mt_ewk = mtplot.histoMgr.getHisto(
"EWK").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
mtn = mtplot.histoMgr.getHisto(
"Data").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
mtn_ewk = mtplot.histoMgr.getHisto(
"EWK").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
if var:
legendName = "QCD(Data)+EWK+t#bar{t}(Data, mis-ID. #tau)"
else:
legendName = "QCD(Data)+EWK+t#bar{t}(MC, mis-ID. #tau)"
legendName = legendName.replace("Plus", "")
mt.SetName(legendName)
mt.SetLineColor(color)
mt.Add(mt_ewk, -1)
mtn.Add(mtn_ewk, -1)
mtn.Scale(QCDInvertedNormalization[str(i)])
if var:
scale = w1 * QCDInvertedNormalizationFilteredEWKFakeTaus[
str(
i
)] + w2 * QCDInvertedNormalizationFilteredEWKFakeTaus[
str(i) + "EWK_FakeTaus"]
mt.Scale(scale)
else:
mt.Scale(QCDInvertedNormalization[str(i)])
color += 1
if color == 5:
color += 1
else:
h = mtplot.histoMgr.getHisto(
"Data").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
mt_ewk = mtplot.histoMgr.getHisto(
"EWK").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
hn = mtplot.histoMgr.getHisto(
"Data").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
mtn_ewk = mtplot.histoMgr.getHisto(
"EWK").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
h.Add(mt_ewk, -1)
hn.Add(mtn_ewk, -1)
hn.Scale(QCDInvertedNormalization[str(i)])
if var:
scale = w1 * QCDInvertedNormalizationFilteredEWKFakeTaus[
str(
i
)] + w2 * QCDInvertedNormalizationFilteredEWKFakeTaus[
str(i) + "EWK_FakeTaus"]
h.Scale(scale)
else:
h.Scale(QCDInvertedNormalization[str(i)])
mt.Add(h)
mtn.Add(hn)
#mt = mt.Rebin(len(myBinning)-1,"",myArray)
#mt_corr = mt_corr.Rebin(len(myBinning)-1,"",myArray)
if not var:
mt.Add(mt_baseline)
#mt.Add(mt_corr)
#myBinning = []
#for i in range(0,11):
# myBinning.append(20*i)
#myBinning.append(400)
#myArray = array.array("d",defaultBinning)
mt = mt.Rebin(len(myBinning) - 1, "", myArray)
for i in range(0, mt.GetSize()):
if var:
var_values.append(mt.GetBinContent(i))
else:
nom_values.append(mt.GetBinContent(i))
if var:
#mt.SetLineStyle(2)
var_hist = mt
else:
#mt.SetLineStyle(2)
nom_hist = mt
style = tdrstyle.TDRStyle()
#gStyle.SetOptStat(1101)
#mt_data.SetStats(1)
#gPad.Update()
bins = [0, 390, 400]
arr = array.array("d", bins)
mtn = mtn.Rebin(len(bins) - 1, "", arr)
plot_data = plots.PlotBase()
plot_data.histoMgr.appendHisto(histograms.Histo(mtn, "Data"))
plot_data.createFrame("Data_" + HISTONAME + "_" + optMode +
"_" + str(w1))
plot_data.draw()
plot_data.save()
plot = plots.ComparisonPlot(nom_hist, var_hist)
plot.createFrame(optMode.replace(
"Opt", "Mt_" + "w1=" + str(w1) + "_w2=" + str(w2) +
"_DataDrivenVsMC_"),
createRatio=True)
moveLegend = {"dx": -0.295, "dy": 0.05}
plot.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
histograms.addText(
0.65, 0.20,
optMode.replace("OptQCDTailKiller",
"R_{BB} ").replace("Plus", ""), 25)
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
lumi = datasets.getDataset("Data").getLuminosity()
histograms.addLuminosityText(x=None, y=None, lumi=lumi)
plot.draw()
plot.save()
num = 0
denom = 0
#print var_values
for i in range(0, len(nom_values)):
num += var_values[i] * (var_values[i] - nom_values[i])**2
denom += var_values[i]
diff = num / denom
diff_list.append(diff)
diff_opt.append(diff_list)
print w1_list, '\n'
for i in range(0, len(diff_opt)):
print diff_opt[i]
print w1_list[diff_opt[i].index(min(diff_opt[i]))]
mt_baseline = mt_baseline.Rebin(len(bins) - 1, "", arr)
plot_bft = plots.PlotBase()
plot_bft.histoMgr.appendHisto(histograms.Histo(mt_baseline, "baseline"))
#mt_corr.Scale(2)
#plot_bft.histoMgr.appendHisto(histograms.Histo(mt_corr,"test"))
#rangeMin = mt_signalfaketaus.GetXaxis().GetXmin()
#rangeMax = mt_signalfaketaus.GetXaxis().GetXmax()
#theFit = TF1('theFit',FitFunction(),rangeMin,rangeMax,4)
#theFit.SetParLimits(0,0.5,10000)
#theFit.SetParLimits(1,90,10000)
#theFit.SetParLimits(2,30,10000)
#theFit.SetParLimits(3,0.001,10000)
#mt_signalfaketaus.Fit(theFit,"R")
#theFit.SetRange(mt_signalfaketaus.GetXaxis().GetXmin(),mt_signalfaketaus.GetXaxis().GetXmax())
#theFit.SetLineStyle(2)
#theFit.SetLineColor(4)
#theFit.SetLineWidth(3)
#theFit.Draw()
#mt_corr = theFit.GetHistogram()
#mt_corr = mt_corr.Rebin(len(fitBinning)-1,"",fitArray)
#mt_corr.Scale(mt_baseline.GetMaximum()/mt_corr.GetMaximum())
#plot_bft.histoMgr.appendHisto(histograms.Histo(mt_corr,"test"))
#plot_bft.histoMgr.appendHisto(histograms.Histo(theFit,"theFit"))
plot_bft.createFrame('BaselineFakeTaus')
plot_bft.draw()
plot_bft.save()
def doSinglePlot(hbase, hinv, myDir, histoName, luminosity):
def rebin(h, name):
myBinning = []
if name.startswith("MT"):
myBinning = systematics.getBinningForPlot("shapeTransverseMass")
elif name.startswith("INVMASS"):
myBinning = systematics.getBinningForPlot("shapeInvariantMass")
else:
raise Exception("Unknown binning information")
myArray = array.array("d", myBinning)
# Rebin and move under/overflow bins to visible bins
h = h.Rebin(len(myBinning) - 1, "", myArray)
h.SetBinContent(1, h.GetBinContent(0) + h.GetBinContent(1))
h.SetBinError(1,
math.sqrt(h.GetBinContent(0)**2 + h.GetBinContent(1)**2))
h.SetBinContent(
h.GetNbinsX() + 1,
h.GetBinContent(h.GetNbinsX() + 1) +
h.GetBinContent(h.GetNbinsX() + 2))
h.SetBinError(
h.GetNbinsX() + 1,
math.sqrt(
h.GetBinError(h.GetNbinsX() + 1)**2 +
h.GetBinError(h.GetNbinsX() + 2)**2))
h.SetBinContent(0, 0.0)
h.SetBinError(0, 0.0)
h.SetBinContent(h.GetNbinsX() + 2, 0.0)
h.SetBinError(h.GetNbinsX() + 2, 0.0)
return h
hbase.SetLineColor(ROOT.kBlack)
hinv.SetLineColor(ROOT.kRed)
# Rebin
hbase = rebin(hbase, histoName)
hinv = rebin(hinv, histoName)
# Normalize
print "baseline: %.1f events, inverted %.1f events" % (hbase.Integral(),
hinv.Integral())
#hbase.Scale(1.0 / hbase.Integral())
#hinv.Scale(1.0 / hinv.Integral())
# Plot
baseHisto = histograms.Histo(hbase,
"Isolated",
drawStyle="HIST",
legendStyle="l")
invHisto = histograms.Histo(hinv,
"Anti-isolated",
drawStyle="HIST",
legendStyle="l")
plot = plots.ComparisonPlot(baseHisto, invHisto)
plot.setLuminosity(luminosity)
plot.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineWidth(3))
myPlotName = "%s/QCDInv_ClosureTest_%s" % (myDir, histoName)
myParams = {}
myParams["ylabel"] = "Events/#Deltam_{T}, normalized to 1"
myParams["log"] = False
myParams["opts2"] = {"ymin": 0.0, "ymax": 2.0}
myParams["opts"] = {"ymin": 0.0}
myParams["ratio"] = True
myParams["ratioType"] = "errorScale"
myParams["ratioYlabel"] = "Var./Nom."
myParams["cmsText"] = myCMSText
myParams["addLuminosityText"] = True
myParams["divideByBinWidth"] = True
plots.drawPlot(plot, myPlotName, **myParams)
def analyze(analysis=None):
paths = [sys.argv[1]]
if not analysis == None:
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis, includeOnlyTasks="Tau|TT")
datasets = dataset.getDatasetsFromMulticrabDirs(paths,
analysisName=analysis)
else:
datasets = dataset.getDatasetsFromMulticrabDirs(paths)
analysis = datasets.getAllDatasets()[0].getAnalysisName()
analysisList = []
# analysisList.append("METLeg_2016MET80_MET80")
# analysisList.append("METLeg_2016ICHEP_MET90")
# analysisList.append("METLeg_2016_MET90")
met_re = re.compile("_(?P<met>MET\d+)$")
if True:
#for analysis in analysisList:
met = "METX"
met_match = met_re.search(analysis)
if met_match:
met = met_match.group("met")
print "Analysis", analysis
datasets = dataset.getDatasetsFromMulticrabDirs(paths,
analysisName=analysis)
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="Tau\S+25ns$|TTJets$")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="Tau_Run2015C|Tau\S+25ns_Silver$|DYJetsToLL|WJetsToLNu$")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="Tau_Run2015D_PromptReco_v4_246908_260426_25ns$|DYJetsToLL_M_50$")
# datasetsMC = None
datasetsMC = dataset.getDatasetsFromMulticrabDirs(
paths, analysisName=analysis, excludeTasks="Tau_")
for d in datasets.getAllDatasets():
print d.getName()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
dataset2 = dataset1
createRatio = False
if isinstance(datasetsMC, dataset.DatasetManager):
dataset2 = datasetsMC.getMCDatasets()
createRatio = True
eff1_MET80 = getEfficiency(dataset1)
eff2_MET80 = getEfficiency(dataset2)
styles.dataStyle.apply(eff1_MET80)
eff1_MET80.SetMarkerSize(1)
if createRatio:
styles.mcStyle.apply(eff2_MET80)
eff2_MET80.SetMarkerSize(1.5)
if createRatio:
p = plots.ComparisonPlot(
histograms.HistoGraph(eff1_MET80, "eff1_MET80", "p", "P"),
histograms.HistoGraph(eff2_MET80, "eff2_MET80", "p", "P"))
else:
p = plots.PlotBase(
[histograms.HistoGraph(eff1_MET80, "eff1_MET80", "p", "P")])
#from plotTauLegEfficiency import fit
#fit("Data",p,eff1_MET80,20,300)
#fit("MC",p,eff2_MET80,20,300)
opts = {"ymin": 0, "ymax": 1.1}
opts2 = {"ymin": 0.5, "ymax": 1.5}
moveLegend = {"dx": -0.55, "dy": -0.15}
name = "TauMET_" + analysis + "_DataVsMC_PFMET"
legend1 = "Data"
# legend2 = "MC (TTJets)"
#legend2 = "MC"
legend2 = "Simulation"
if createRatio:
p.histoMgr.setHistoLegendLabelMany({
"eff1_MET80": legend1,
"eff2_MET80": legend2
})
p.createFrame(os.path.join(plotDir, name),
createRatio=True,
opts=opts,
opts2=opts2)
else:
p.histoMgr.setHistoLegendLabelMany({"eff1_MET80": legend1})
p.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
p.setLegend(
histograms.moveLegend(histograms.createLegend(y1=0.8),
**moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
if createRatio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# histograms.addText(0.2, 0.6, "LooseIsoPFTau50_Trk30_eta2p1_"+met, 17)
histograms.addText(0.4, 0.87, "LooseIsoPFTau50_Trk30_eta2p1_" + met,
17)
# histograms.addText(0.2, 0.53, analysis.split("_")[len(analysis.split("_")) -1], 17)
# label = analysis.split("_")[1]
# label = "2016"
label = string.replace(analysis, "METLeg_", "")
histograms.addText(0.2, 0.53, label, 17)
runRange = datasets.loadRunRange()
histograms.addText(0.2, 0.46, "Runs " + runRange, 17)
p.draw()
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity", d.getName(), d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum", lumi
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
pythonWriter.addParameters(plotDir, label, runRange, lumi, eff1_MET80)
pythonWriter.addMCParameters(label, eff2_MET80)
pythonWriter.writeJSON(
os.path.join(plotDir, "metLegTriggerEfficiency_" + label + ".json"))
#########################################################################
eff1phi = getEfficiency(dataset1, "NumeratorPhi", "DenominatorPhi")
eff2phi = getEfficiency(dataset2, "NumeratorPhi", "DenominatorPhi")
styles.dataStyle.apply(eff1phi)
styles.mcStyle.apply(eff2phi)
eff1phi.SetMarkerSize(1)
if isinstance(datasetsMC, dataset.DatasetManager):
p_phi = plots.ComparisonPlot(
histograms.HistoGraph(eff1phi, "eff1phi", "p", "P"),
histograms.HistoGraph(eff2phi, "eff2phi", "p", "P"))
else:
p_phi = plots.PlotBase(
[histograms.HistoGraph(eff1phi, "eff1phi", "p", "P")])
p_phi.histoMgr.setHistoLegendLabelMany({"eff1phi": legend1})
if isinstance(datasetsMC, dataset.DatasetManager):
p_phi.histoMgr.setHistoLegendLabelMany({
"eff1phi": legend1,
"eff2phi": legend2
})
opts = {"ymin": 0, "ymax": 0.3}
name = "TauMET_" + analysis + "_DataVsMC_PFMETPhi"
if createRatio:
p_phi.createFrame(os.path.join(plotDir, name),
createRatio=createRatio,
opts=opts,
opts2=opts2)
else:
p_phi.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
# moveLegendPhi = {"dx": -0.5, "dy": -0.6, "dh": -0.1}
moveLegendPhi = {"dx": -0.55, "dy": -0.15, "dh": -0.1}
p_phi.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegendPhi))
p_phi.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p_phi.getFrame().GetXaxis().SetTitle("MET Type 1 #phi")
if createRatio:
p_phi.getFrame2().GetYaxis().SetTitle("Ratio")
p_phi.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.4, 0.86, "LooseIsoPFTau50_Trk30_eta2p1_" + met, 17)
# histograms.addText(0.4, 0.78, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.4, 0.78, "2016", 17)
histograms.addText(0.4, 0.71, "Runs " + datasets.loadRunRange(), 17)
p_phi.draw()
histograms.addStandardTexts(lumi=lumi)
p_phi.save(formats)
#########################################################################
"""
#### MET120
analysis = "METLeg_2015CD_MET120"
datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis)
for d in datasets.getAllDatasets():
print d.getName()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
dataset2 = datasets.getMCDatasets()
eff1_MET120 = getEfficiency(dataset1)
eff2_MET120 = getEfficiency(dataset2)
styles.dataStyle.apply(eff1_MET120)
styles.mcStyle.apply(eff2_MET120)
eff1_MET120.SetMarkerSize(1)
eff2_MET120.SetMarkerSize(1.5)
p = plots.ComparisonPlot(histograms.HistoGraph(eff1_MET120, "eff1_MET120", "p", "P"),
histograms.HistoGraph(eff2_MET120, "eff2_MET120", "p", "P"))
opts = {"ymin": 0, "ymax": 1.1}
opts2 = {"ymin": 0.5, "ymax": 1.5}
moveLegend = {"dx": -0.55, "dy": -0.15}
name = "DataVsMC_L1HLTMET_PFMET_MET120"
legend1 = "Data"
legend2 = "MC"
p.histoMgr.setHistoLegendLabelMany({"eff1_MET120": legend1, "eff2_MET120": legend2})
p.createFrame(os.path.join(plotDir, name), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
lumi = 0.0
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
"""
# CaloMET
"""
#### MET80
analysisc = "METLeg_2016B_CaloMET_MET80"
datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc)
datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc,excludeTasks="Tau\S+25ns_Silver$")
# datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc,includeOnlyTasks="Tau\S+25ns$|TTJets$")
style = tdrstyle.TDRStyle()
dataset1c = datasetsc.getDataDatasets()
dataset2c = datasetsc.getMCDatasets()
# eff1c_MET80 = getEfficiency(dataset1c)
eff2c_MET80 = getEfficiency(dataset2c)
# styles.dataStyle.apply(eff1c_MET80)
styles.mcStyle.apply(eff2c_MET80)
# eff1c_MET80.SetMarkerSize(1)
eff2c_MET80.SetMarkerSize(1.5)
eff2c_MET80.SetMarkerColor(4)
p = plots.ComparisonPlot(histograms.HistoGraph(eff2_MET80, "eff2_MET80", "p", "P"),
histograms.HistoGraph(eff2c_MET80, "eff2c_MET80", "p", "P"))
namec = "TauMET_"+analysis+"_MC_TrgBitVsCaloMET80_PFMET"
legend1c = "MC, trigger bit"
legend2c = "MC, CaloMET > 80"
p.histoMgr.setHistoLegendLabelMany({"eff2_MET80": legend1c, "eff2c_MET80": legend2c})
p.createFrame(os.path.join(plotDir, namec), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity",d.getName(),d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum",lumi
histograms.addStandardTexts(lumi=lumi)
histograms.addText(0.2, 0.6, "LooseIsoPFTau50_Trk30_eta2p1_MET80", 17)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
"""
"""
#### MET120
analysisc = "METLeg_2015A_CaloMET_MET120"
datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc)
style = tdrstyle.TDRStyle()
dataset1c = datasetsc.getDataDatasets()
dataset2c = datasetsc.getMCDatasets()
eff1c_MET120 = getEfficiency(dataset1c)
eff2c_MET120 = getEfficiency(dataset2c)
styles.dataStyle.apply(eff1c_MET120)
styles.mcStyle.apply(eff1c_MET120)
eff1c_MET120.SetMarkerSize(1)
eff2c_MET120.SetMarkerSize(1.5)
p = plots.ComparisonPlot(histograms.HistoGraph(eff2_MET120, "eff2_MET120", "p", "P"),
histograms.HistoGraph(eff2c_MET120, "eff2c_MET120", "p", "P"))
namec = "MC_TrgBitVsCaloMET120_L1HLTMET_PFMET"
legend1c = "MC, trigger bit"
legend2c = "MC, CaloMET > 120"
p.histoMgr.setHistoLegendLabelMany({"eff2_MET120": legend1c, "eff2c_MET120": legend2c})
p.createFrame(os.path.join(plotDir, namec), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
lumi = 0.0
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
"""
#########################################################################
namePU = "TauMET_" + analysis + "_DataVsMC_nVtx"
eff1PU = getEfficiency(dataset1, "NumeratorPU", "DenominatorPU")
eff2PU = getEfficiency(dataset2, "NumeratorPU", "DenominatorPU")
styles.dataStyle.apply(eff1PU)
styles.mcStyle.apply(eff2PU)
eff1PU.SetMarkerSize(1)
eff2PU.SetMarkerSize(1.5)
if isinstance(datasetsMC, dataset.DatasetManager):
pPU = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1PU, "eff1", "p", "P"),
[histograms.HistoGraph(eff2PU, "eff2", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({
"eff1": legend1,
"eff2": legend2
})
else:
pPU = plots.PlotBase([histograms.HistoGraph(eff1PU, "eff1", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
opts = {"ymin": 0.001, "ymax": 1.0}
moveLegend = {"dx": -0.33, "dy": -0.25, "dh": -0.1}
createRatio = False
if createRatio:
pPU.createFrame(os.path.join(plotDir, namePU),
createRatio=True,
opts=opts,
opts2=opts2)
else:
pPU.createFrame(os.path.join(plotDir, namePU), opts=opts, opts2=opts2)
pPU.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
# if createRatio:
# pPU.getPad1().SetLogy(True)
# else:
# pPU.getPad().SetLogy(True)
pPU.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
pPU.getFrame().GetXaxis().SetTitle("Number of reco vertices")
if createRatio:
pPU.getFrame2().GetYaxis().SetTitle("Ratio")
pPU.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.4, 0.85, "LooseIsoPFTau50_Trk30_eta2p1_MET90", 17)
# histograms.addText(0.4, 0.78, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.4, 0.78, "2016", 17)
histograms.addText(0.4, 0.71, "Runs " + datasets.loadRunRange(), 17)
pPU.draw()
histograms.addStandardTexts(lumi=lumi)
pPU.save(formats)
print "Output written in", plotDir
