def __init__(self, datasets, name, lumi=None):
self.histos = HistoManager(datasets, name)
#print "\n".join(histos.getDatasetNames())
if lumi == None:
self.histos.normalizeMCByLuminosity()
else:
self.histos.normalizeMCToLuminosity(lumi)
hasData = self.histos.hasHisto("Data")
styleGenerator.reset()
self.histos.forEachMCHisto(styleGenerator)
if hasData:
styles.getDataStyle()(self.histos.getHisto("Data"))
self.histos.setHistoDrawStyle("Data", "EP")
def __init__(self, datasets, name, lumi=None):
self.histos = HistoManager(datasets, name)
#print "\n".join(histos.getDatasetNames())
if lumi == None:
self.histos.normalizeMCByLuminosity()
else:
self.histos.normalizeMCToLuminosity(lumi)
hasData = self.histos.hasHisto("Data")
styleGenerator.reset()
self.histos.forEachMCHisto(styleGenerator)
if hasData:
styles.getDataStyle()(self.histos.getHisto("Data"))
self.histos.setHistoDrawStyle("Data", "EP")
def distComparison(datasets):
# Create a comparison plot of two distributions (must have the same binning)
# Set the names of DatasetRootHisto objects in order to be able easily reference them later
drh1 = datasets.getDataset("Data").getDatasetRootHisto(analysis+"/MET_BaseLineTauId")
drh1.setName("Base")
drh1.normalizeToOne()
drh2 = datasets.getDataset("Data").getDatasetRootHisto(analysis+"/MET_InvertedTauId")
drh2.setName("Inv")
drh2.normalizeToOne()
plot = plots.ComparisonPlot(drh1, drh2)
# Set the styles
st1 = styles.getDataStyle().clone()
st2 = st1.clone()
st2.append(styles.StyleLine(lineColor=ROOT.kRed))
plot.histoMgr.forHisto("Base", st1)
plot.histoMgr.forHisto("Inv", st2)
# Set the legend labels
plot.histoMgr.setHistoLegendLabelMany({"Base": "Baseline Tau ID",
"Inv": "Inverted Tau ID"})
# Set the legend styles
plot.histoMgr.setHistoLegendStyleAll("L")
plot.histoMgr.setHistoLegendStyle("Base", "P") # exception to the general rule
# Set the drawing styles
plot.histoMgr.setHistoDrawStyleAll("HIST")
plot.histoMgr.setHistoDrawStyle("Base", "EP") # exception to the general rule
# Rebin, if necessary
plot.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
# Create frame with a ratio pad
plot.createFrame("METbaseVSinvertedTauID", opts={"ymin":1e-5, "ymaxfactor": 1.5},
createRatio=True, opts2={"ymin": -10, "ymax": 50}, # bounds of the ratio plot
)
# Set Y axis of the upper pad to logarithmic
plot.getPad1().SetLogy(True)
# Create legend to the default position
plot.setLegend(histograms.createLegend())
# Set the X/Y axis labels
plot.frame.GetXaxis().SetTitle("MET (GeV)")
plot.frame.GetYaxis().SetTitle("Arbitrary units")
# Draw the plot
plot.draw()
# Add the various texts to
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=datasets.getDataset("Data").getLuminosity())
# Save the plot to files
plot.save()
def distComparison(datasets):
# Create a comparison plot of two distributions (must have the same binning)
# Set the names of DatasetRootHisto objects in order to be able easily reference them later
drh1 = datasets.getDataset("Data").getDatasetRootHisto("ForDataDrivenCtrlPlots/SelectedTau_pT_AfterStandardSelections")
drh1.setName("AfterStandardSelections")
drh2 = datasets.getDataset("Data").getDatasetRootHisto("ForDataDrivenCtrlPlots/SelectedTau_pT_AfterMtSelections")
drh2.setName("AfterMtSelections")
plot = plots.ComparisonPlot(drh1, drh2)
# Set the styles
st1 = styles.getDataStyle().clone()
st2 = st1.clone()
st2.append(styles.StyleLine(lineColor=ROOT.kRed))
plot.histoMgr.forHisto("AfterStandardSelections", st1)
plot.histoMgr.forHisto("AfterMtSelections", st2)
# Set the legend labels
plot.histoMgr.setHistoLegendLabelMany({"AfterStandardSelections": "After standard selections",
"AfterMtSelections": "After all selections"})
# Set the legend styles
plot.histoMgr.setHistoLegendStyleAll("L")
#plot.histoMgr.setHistoLegendStyle("afterTauID", "P") # exception to the general rule
# Set the drawing styles
plot.histoMgr.setHistoDrawStyleAll("HIST")
#plot.histoMgr.setHistoDrawStyleAll("afterTauID", "EP") # exception to the general rule
# Rebin, if necessary
plot.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
# Create frame with a ratio pad
plot.createFrame("tauPtComparison", opts={"ymin":1e-1, "ymaxfactor": 10},
createRatio=True, opts2={"ymin": 0, "ymax": 150}, # bounds of the ratio plot
)
# Set Y axis of the upper pad to logarithmic
plot.getPad1().SetLogy(True)
# Create legend to the default position
plot.setLegend(histograms.createLegend())
# Set the X/Y axis labels
plot.frame.GetXaxis().SetTitle("Tau p_{T} (GeV/c)")
plot.frame.GetYaxis().SetTitle("Number of events")
# Draw the plot
plot.draw()
# Add the various texts to
histograms.addStandardTexts(lumi=datasets.getDataset("Data").getLuminosity())
# Save the plot to files
plot.save()
def plotIntegral(self, plot_orig, objectName, canvasName="Integral"):
# plot = copy.deepcopy(plot_orig)
plot = plot_orig
st = styles.getDataStyle().clone()
st.append(styles.StyleFill(fillColor=ROOT.kYellow))
plot.histoMgr.forHisto(objectName, st)
plot.setFrameName(plot.cf.canvas.GetName() + canvasName)
plot.draw()
plot.save()
st.append(styles.StyleFill(fillColor=0))
plot.histoMgr.forHisto(objectName, st)
def plotIntegral(self, plot_orig, objectName, canvasName = "Integral"):
# plot = copy.deepcopy(plot_orig)
plot = plot_orig
st = styles.getDataStyle().clone()
st.append(styles.StyleFill(fillColor=ROOT.kYellow))
plot.histoMgr.forHisto(objectName, st)
plot.setFrameName(plot.cf.canvas.GetName()+canvasName)
plot.draw()
plot.save()
st.append(styles.StyleFill(fillColor=0))
plot.histoMgr.forHisto(objectName, st)
### Normalize the area of *all* histograms to 1
#tauNProngs.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
tauNProngs.setHistoLegendLabelMany(legendLabels) # many datasets, with dict
### Example how to modify legend styles
tauNProngs.setHistoLegendStyleAll("F")
tauNProngs.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
tauNProngs.forEachMCHisto(styles.generator(
fill=True)) # Apply SetFillColor too, needed for histogram stacking
tauNProngs.forHisto("Data", styles.getDataStyle())
#tauNProngs.setHistoDrawStyle("Data", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
#tauNProngs.stackMCDatasets()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(tauNProngs,
"tauNProngs",
ymin=0.01,
ymax=None,
xmin=0.0,
xmax=15.0)
### Set the frame options, e.g. axis labels
cf.frame.GetXaxis().SetTitle("#tau-jet nProngs")
def doPlotsWTauMu(datasetsEmb, name, btag=True):
if btag:
selection = And(metCut, bTaggingCut, deltaPhi130Cut)
treeDraw = dataset.TreeDraw(analysisEmb+"/tree", weight="weightPileup*weightTrigger*weightBTagging")
else:
selection = And(metCut, deltaPhi130Cut)
treeDraw = dataset.TreeDraw(analysisEmb+"/tree", weight="weightPileup*weightTrigger")
tdMt = treeDraw.clone(varexp="sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi()))) >>tmp(20,0,400)")
(hall, tmp) = datasetsEmb.getHistogram(name, tdMt.clone(selection=selection))
(hpure, tmp) = datasetsEmb.getHistogram(name, tdMt.clone(selection=And(selection, "abs(temuon_mother_pdgid) == 24")))
tdEff = tdMt.clone(weight="", varexp=tdMt.varexp.replace("tmp", "tmpeff"))
heff = datasetsEmb.getEfficiency(name, tdEff.clone(selection=And(selection, "abs(temuon_mother_pdgid) == 24")), tdEff.clone(selection=selection))
hall.SetName("All")
hpure.SetName("Pure")
nall = hall.Integral(0, hall.GetNbinsX())
npure = hpure.Integral(0, hall.GetNbinsX())
print {True: "Btagging", False: "NoBTag"}[btag], npure/nall, (1-npure/nall)*100
p = plots.ComparisonPlot(hall, hpure)
p.histoMgr.setHistoLegendLabelMany({
"All": "All muons",
"Pure": "W#rightarrow#mu"
})
p.histoMgr.forEachHisto(styles.generator())
if btag:
fname = "transverseMass_4AfterDeltaPhi160_wtaumu_"+name
else:
fname = "transverseMass_4AfterDeltaPhi160NoBTag_wtaumu_"+name
hallErr = hall.Clone("AllError")
hallErr.SetFillColor(ROOT.kBlue-7)
hallErr.SetFillStyle(3004)
hallErr.SetMarkerSize(0)
p.prependPlotObject(hallErr, "E2")
hpureErr = hpure.Clone("PureErr")
hpureErr.SetFillColor(ROOT.kRed-7)
hpureErr.SetFillStyle(3005)
hpureErr.SetMarkerSize(0)
p.prependPlotObject(hpureErr, "E2")
p.createFrame(fname, createRatio=True, opts2={"ymin": 0.9, "ymax": 1.05})
styles.getDataStyle().apply(heff)
p.setRatios([heff])
xmin = p.frame.GetXaxis().GetXmin()
xmax = p.frame.GetXaxis().GetXmax()
val = 1-0.038479
l = ROOT.TLine(xmin, val, xmax, val)
l.SetLineWidth(2)
l.SetLineColor(ROOT.kBlue)
l.SetLineStyle(4)
p.prependPlotObjectToRatio(l)
p.appendPlotObjectToRatio(histograms.PlotText(0.65, 0.61, "1-0.038", size=18, color=ROOT.kBlue))
p.getFrame2().GetYaxis().SetTitle("W#rightarrow#mu fraction")
p.setLegend(histograms.moveLegend(histograms.createLegend()))
tmp = hpureErr.Clone("tmp")
tmp.SetFillColor(ROOT.kBlack)
tmp.SetFillStyle(3013)
tmp.SetLineColor(ROOT.kWhite)
p.legend.AddEntry(tmp, "Stat. unc.", "F")
p.frame.GetXaxis().SetTitle("m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})")
p.frame.GetYaxis().SetTitle("Events / %.0f GeV/c^{2}" % p.binWidth())
p.appendPlotObject(histograms.PlotText(0.5, 0.9, plots._legendLabels.get(name, name), size=18))
p.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
p.save()
### Normalize the area of *all* histograms to 1
#TauLeadTrk.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
TauLeadTrk.setHistoLegendLabelMany(legendLabels) # many datasets, with dict
### Example how to modify legend styles
TauLeadTrk.setHistoLegendStyleAll("F")
TauLeadTrk.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
TauLeadTrk.forEachMCHisto(styles.generator(
fill=True)) # Apply SetFillColor too, needed for histogram stacking
TauLeadTrk.forHisto("Data", styles.getDataStyle())
#TauLeadTrk.setHistoDrawStyle("Data", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
TauLeadTrk.stackMCHistograms()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(TauLeadTrk,
"TauLeadTrk",
ymin=0.01,
ymax=None,
xmin=0.0,
xmax=100.0)
### Set the frame options, e.g. axis labels
cf.frame.GetXaxis().SetTitle("#tau-jet, Ldg Trk p_{T} (GeV/c)")
### Normalize the area of *all* histograms to 1
#Rtau.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
Rtau.setHistoLegendLabelMany(legendLabels) # many datasets, with dict
### Example how to modify legend styles
Rtau.setHistoLegendStyleAll("F")
Rtau.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
Rtau.forEachMCHisto(styles.generator(
fill=True)) # Apply SetFillColor too, needed for histogram stacking
Rtau.forHisto("Data", styles.getDataStyle())
#Rtau.setHistoDrawStyle("Data", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
#Rtau.stackMCDatasets()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(Rtau, "Rtau", ymin=0.01, ymax=None, xmin=0.0, xmax=1.05)
### Set the frame options, e.g. axis labels
cf.frame.GetXaxis().SetTitle("R_{#tau}")
cf.frame.GetYaxis().SetTitle(ylabel)
### Legend
legend = createLegend(0.7, 0.5, 0.9, 0.8)
Rtau.addToLegend(legend)
def doPlotsWTauMu(datasets,
name,
datasetName,
ntupleCache,
selectorName,
unweighted,
fitFunction,
rebin=None):
ds = datasets.getDataset(datasetName)
# Take first unweighted histograms for the fraction plot
drh_all = ds.getDatasetRootHisto(
ntupleCache.histogram("selectedMuonPt_AfterJetSelection" + unweighted,
selectorName))
drh_pure = ds.getDatasetRootHisto(
ntupleCache.histogram(
"selectedMuonPt_AfterJetSelection_MuFromW" + unweighted,
selectorName))
def Rebin(th1, div=False):
if rebin is None:
return th1
else:
h = th1.Rebin(
len(rebin) - 1, th1.GetName(), array.array("d", rebin))
if div:
h.Scale(1, "width")
return h
def createTEfficiency(drhAll, drhPure):
hallUn = Rebin(drhAll.getHistogram())
hpureUn = Rebin(drhPure.getHistogram())
teff = ROOT.TEfficiency(hpureUn, hallUn)
teff.SetDirectory(0)
teff.SetWeight(drhAll.getDataset().getCrossSection())
return teff
teffs = drh_all.forEach(createTEfficiency, drh_pure)
#coll = ROOT.TList()
#for o in teffs:
# coll.AddLast(o)
#ratio = ROOT.TEfficiency.Combine(coll)
ratio = teffs[0]
for e in teffs[1:]:
ratio.Add(e)
styles.getDataStyle().apply(ratio)
ratio.SetName(datasetName)
ROOT.gStyle.SetStatY(0.99)
ROOT.gStyle.SetStatX(0.52)
ROOT.gStyle.SetStatW(0.18)
ROOT.gStyle.SetStatH(0.23)
expFit = ROOT.TF1("purityFit", [
"1/(1+[0]*exp(-[1]*x))",
"1-[0]*exp(-[1]*x)",
"1-([0]/(x^[1]))",
][fitFunction], aux.th1Xmin(ratio.GetPassedHistogram()),
aux.th1Xmax(ratio.GetPassedHistogram()))
expFit.SetParameter(0, 0.05)
ratio.Fit(expFit, "N")
expFit.SetLineColor(ROOT.kRed)
expFit.SetLineWidth(2)
# expFit = None
# Then the correctly weighted for the main plot
drh_all = ds.getDatasetRootHisto(
ntupleCache.histogram("selectedMuonPt_AfterJetSelection",
selectorName))
drh_pure = ds.getDatasetRootHisto(
ntupleCache.histogram("selectedMuonPt_AfterJetSelection_MuFromW",
selectorName))
if mcOnly:
lumi = mcLuminosity
else:
lumi = datasets.getDataset("Data").getLuminosity()
drh_all.normalizeToLuminosity(lumi)
drh_pure.normalizeToLuminosity(lumi)
hall = Rebin(drh_all.getHistogram(), div=True)
hpure = Rebin(drh_pure.getHistogram(), div=True)
hall.SetName("All")
hpure.SetName("Pure")
p = plots.ComparisonPlot(hall, hpure)
p.setLuminosity(lumi)
p.setEnergy(ds.getEnergy())
p.histoMgr.setHistoLegendLabelMany({
"All": "All muons",
# "Pure": "W#rightarrow#tau#rightarrow#mu"
"Pure": "W#rightarrow#mu"
})
p.histoMgr.forEachHisto(styles.generator())
hallErr = hall.Clone("AllError")
hallErr.SetFillColor(ROOT.kBlue - 7)
hallErr.SetFillStyle(3004)
hallErr.SetMarkerSize(0)
p.prependPlotObject(hallErr, "E2")
hpureErr = hpure.Clone("PureErr")
hpureErr.SetFillColor(ROOT.kRed - 7)
hpureErr.SetFillStyle(3005)
hpureErr.SetMarkerSize(0)
p.prependPlotObject(hpureErr, "E2")
p.createFrame(era + "_" + name +
"_selectedMuonPt_AFterJetSelection_MuFromW_%s_fit%d" %
(datasetName, fitFunction) + unweighted,
createRatio=True,
opts={
"ymin": 1e-1,
"ymaxfactor": 2
},
opts2={
"ymin": 0.9,
"ymax": 1.05
})
p.setRatios([ratio])
xmin = p.frame.GetXaxis().GetXmin()
xmax = p.frame.GetXaxis().GetXmax()
val = 1 - 0.038479
l = ROOT.TLine(xmin, val, xmax, val)
l.SetLineWidth(2)
l.SetLineColor(ROOT.kBlue)
l.SetLineStyle(4)
p.prependPlotObjectToRatio(l)
#p.appendPlotObjectToRatio(histograms.PlotText(0.18, 0.61, "1-0.038", size=18, color=ROOT.kBlue))
p.appendPlotObjectToRatio(
histograms.PlotText(0.18, 0.61, "0.038", size=18, color=ROOT.kBlue))
if expFit is not None:
p.appendPlotObjectToRatio(expFit)
p.getFrame2().GetYaxis().SetTitle("W#rightarrow#mu fraction")
p.getPad().SetLogy(True)
p.setLegend(histograms.moveLegend(histograms.createLegend()))
tmp = hpureErr.Clone("tmp")
tmp.SetFillColor(ROOT.kBlack)
tmp.SetFillStyle(3013)
tmp.SetLineColor(ROOT.kWhite)
p.legend.AddEntry(tmp, "Stat. unc.", "F")
p.frame.GetXaxis().SetTitle("Muon p_{T} (GeV/c)")
p.frame.GetYaxis().SetTitle("Events / #Deltap_{T} %.0f-%.0f GeV/c" %
(min(p.binWidths()), max(p.binWidths())))
p.appendPlotObject(
histograms.PlotText(0.5,
0.9,
plots._legendLabels.get(datasetName, datasetName),
size=18))
p.draw()
p.addStandardTexts()
p.save()
# Clear list of functions
ratio.GetListOfFunctions().Delete()
return (ratio, expFit)
#TauIsoMaxlTrkPt.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
TauIsoMaxlTrkPt.setHistoLegendLabelMany(
legendLabels) # many datasets, with dict
### Example how to modify legend styles
TauIsoMaxlTrkPt.setHistoLegendStyleAll("F")
TauIsoMaxlTrkPt.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
TauIsoMaxlTrkPt.forEachMCHisto(styles.generator(
fill=True)) # Apply SetFillColor too, needed for histogram stacking
TauIsoMaxlTrkPt.forHisto("Data", styles.getDataStyle())
#TauIsoMaxlTrkPt.setHistoDrawStyle("Data", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
#TauIsoMaxlTrkPt.stackMCDatasets()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(TauIsoMaxlTrkPt,
"TauIsoMaxlTrkPt",
ymin=0.01,
ymax=None,
xmin=0.0,
xmax=20.0)
### Set the frame options, e.g. axis labels
cf.frame.GetXaxis().SetTitle(
### Normalize the area of *all* histograms to 1
#TauLeadTrk.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
TauLeadTrk.setHistoLegendLabelMany(legendLabels) # many datasets, with dict
### Example how to modify legend styles
TauLeadTrk.setHistoLegendStyleAll("F")
TauLeadTrk.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
TauLeadTrk.forEachMCHisto(styles.generator(fill=True)) # Apply SetFillColor too, needed for histogram stacking
TauLeadTrk.forHisto("Data", styles.getDataStyle())
#TauLeadTrk.setHistoDrawStyle("Data", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
TauLeadTrk.stackMCHistograms()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(TauLeadTrk, "TauLeadTrk", ymin=0.01, ymax=None, xmin=0.0, xmax=100.0)
### Set the frame options, e.g. axis labels
cf.frame.GetXaxis().SetTitle("#tau-jet, Ldg Trk p_{T} (GeV/c)")
cf.frame.GetYaxis().SetTitle(ylabel)
### Legend
legend = createLegend(0.7, 0.5, 0.9, 0.8)
TauLeadTrk.addToLegend(legend)
#TauNTrksVSPtcut.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
TauNTrksVSPtcut.setHistoLegendLabelMany(
legendLabels) # many datasets, with dict
### Example how to modify legend styles
TauNTrksVSPtcut.setHistoLegendStyleAll("F")
TauNTrksVSPtcut.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
TauNTrksVSPtcut.forEachMCHisto(styles.generator(
fill=True)) # Apply SetFillColor too, needed for histogram stacking
TauNTrksVSPtcut.forHisto("Data", styles.getDataStyle())
#TauNTrksVSPtcut.setHistoDrawStyle("Data", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
#TauNTrksVSPtcut.stackMCHistograms()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(TauNTrksVSPtcut,
"TauNTrksVSPtcut",
ymin=0.01,
ymax=None,
xmin=0.0,
xmax=20.0)
### Set the frame options, e.g. axis labels
cf.frame.GetXaxis().SetTitle("???")
### Normalize the area of *all* histograms to 1
#TauIsoMaxlTrkPt.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
TauIsoMaxlTrkPt.setHistoLegendLabelMany(legendLabels) # many datasets, with dict
### Example how to modify legend styles
TauIsoMaxlTrkPt.setHistoLegendStyleAll("F")
TauIsoMaxlTrkPt.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
TauIsoMaxlTrkPt.forEachMCHisto(styles.generator(fill=True)) # Apply SetFillColor too, needed for histogram stacking
TauIsoMaxlTrkPt.forHisto("Data", styles.getDataStyle())
#TauIsoMaxlTrkPt.setHistoDrawStyle("Data", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
#TauIsoMaxlTrkPt.stackMCDatasets()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(TauIsoMaxlTrkPt, "TauIsoMaxlTrkPt", ymin=0.01, ymax=None, xmin=0.0, xmax=20.0)
### Set the frame options, e.g. axis labels
cf.frame.GetXaxis().SetTitle("#tau-jet, Max Ldg Trk in Isol Annulus, p_{T} (GeV/c)")
cf.frame.GetYaxis().SetTitle(ylabel)
### Legend
legend = createLegend(0.7, 0.5, 0.9, 0.8)
TauIsoMaxlTrkPt.addToLegend(legend)
### Normalize the area of *all* histograms to unity. Canno be used with mergeDatasets
#transverseMass.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
transverseMass.setHistoLegendLabelMany(legendLabels) # many datasets, with dict
### Example how to modify legend styles
transverseMass.setHistoLegendStyleAll("F")
transverseMass.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
transverseMass.forEachMCHisto(styles.generator(fill=True))
transverseMass.forHisto("Data", styles.getDataStyle())
#transverseMass.setHistoDrawStyle("Data", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
#transverseMass.stackMCHistograms()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(transverseMass, "transverseMass", ymin=0.01, ymax=None, xmin=0.0, xmax=300.0)
### Set the frame options, e.g. axis labels
cf.frame.GetXaxis().SetTitle("M_{T} GeV/c^{2}")
cf.frame.GetYaxis().SetTitle(ylabel)
### Legend
legend = createLegend(0.7, 0.5, 0.9, 0.8)
transverseMass.addToLegend(legend)
def cutefficiency(self,histo1,histo2):
h1 = histo1.Clone("h1")
h2 = histo2.Clone("h2")
h1.Scale(1/h1.GetMaximum())
h2.Scale(1/h2.GetMaximum())
# check that no bin has negative value, negative values possible after subtracting EWK from data
iBin = 1
nBins = h1.GetNbinsX()
while iBin < nBins:
value1 = h1.GetBinContent(iBin)
value2 = h2.GetBinContent(iBin)
if value1 < 0:
h1.SetBinContent(iBin,0)
if value2 < 0:
h2.SetBinContent(iBin,0)
iBin = iBin + 1
h1cut = h1.Clone("h1cut")
h1cut.Reset()
h1cut.GetYaxis().SetTitle("Efficiency")
h1cut.GetXaxis().SetTitle("PF MET cut (GeV)")
h2cut = h2.Clone("h2cut")
h2cut.Reset()
h2cut.SetLineColor(2)
integralError = ROOT.Double(0.0)
integralValue = h1.IntegralAndError(1,h1cut.GetNbinsX(),integralError)
h1_integral = h1.Integral(0,h1.GetNbinsX())
h2_integral = h2.Integral(0,h2.GetNbinsX())
iBin = 1
nBins = h1cut.GetNbinsX()
while iBin < nBins:
error = ROOT.Double(0.0)
selected1 = h1.IntegralAndError(iBin,nBins,error)
if selected1 > 0:
error = error/selected1
else:
error = integralError/integralValue
efficiency1 = selected1/h1_integral
h1cut.SetBinContent(iBin,efficiency1)
if self.errorBars:
h1cut.SetBinError(iBin,error)
error = ROOT.Double(0.0)
selected2 = h2.IntegralAndError(iBin,nBins,error)
if selected2 > 0:
error = error/selected2
else:
error = integralError/integralValue
efficiency2 = selected2/h2_integral
h2cut.SetBinContent(iBin,efficiency2)
if self.errorBars:
h2cut.SetBinError(iBin,error)
iBin = iBin + 1
plot = plots.ComparisonPlot(
histograms.Histo(h1cut, "Inv"),
histograms.Histo(h2cut, "Base"),
)
# Set the styles
st1 = styles.getDataStyle().clone()
st2 = st1.clone()
st2.append(styles.StyleLine(lineColor=ROOT.kRed))
st2.append(styles.StyleMarker(markerColor=ROOT.kRed))
plot.histoMgr.forHisto("Base", st1)
plot.histoMgr.forHisto("Inv", st2)
# Set the legend labels
plot.histoMgr.setHistoLegendLabelMany({"Inv": h1.GetTitle(),
"Base": h2.GetTitle()})
# Set the legend styles
#plot.histoMgr.setHistoLegendStyleAll("L")
plot.histoMgr.setHistoLegendStyleAll("P")
# Set the drawing styles
#plot.histoMgr.setHistoDrawStyleAll("HIST")
plot.histoMgr.setHistoDrawStyleAll("EP")
# Create frame with a ratio pad
plot.createFrame("cuteff"+self.label, opts={"ymin":1e-5, "ymaxfactor": 2},
createRatio=True, opts2={"ymin": 0, "ymax": 2}, # bounds of the ratio plot
)
# Set Y axis of the upper pad to logarithmic
plot.getPad().SetLogy(True)
plot.setLegend(histograms.createLegend(0.4,0.82,0.9,0.93))
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
plot.draw()
plot.save()
######
hError = h1cut.Clone("hError")
hError.Divide(h2cut)
iBin = 1
nBins = hError.GetNbinsX()
while iBin < nBins:
hError.SetBinContent(iBin,abs(hError.GetBinContent(iBin) - 1))
iBin = iBin + 1
hError.GetYaxis().SetTitle("abs( (#varepsilon^{Inverted} - #varepsilon^{Baseline})/#varepsilon^{Baseline} )")
hError.GetXaxis().SetTitle("PF MET cut (GeV)")
plot2 = plots.PlotBase()
plot2.histoMgr.appendHisto(histograms.Histo(hError,"ShapeUncertainty"))
plot2.histoMgr.forHisto("ShapeUncertainty", st1)
plot2.histoMgr.setHistoDrawStyleAll("EP")
# plot2.createFrame("shapeUncertainty"+self.label, opts={"ymin":-1, "ymax": 1})
plot2.createFrame("shapeUncertainty"+self.label, opts={"ymin":-0.1, "ymax": 1.1})
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
rangeMin = hError.GetXaxis().GetXmin()
rangeMax = hError.GetXaxis().GetXmax()
# rangeMax = 80
rangeMax = 120
# rangeMax = 380
numberOfParameters = 2
class FitFunction:
def __call__( self, x, par ):
# return Linear(x,par)
return ErrorFunction(x,par)
theFit = TF1('theFit',FitFunction(),rangeMin,rangeMax,numberOfParameters)
theFit.SetParLimits(0,0.01,0.03)
theFit.SetParLimits(1,50,150)
# theFit.FixParameter(0,0.02)
# theFit.FixParameter(1,100)
hError.Fit(theFit,"LRN")
print "Error MET > 40",theFit.Eval(40)
print "Error MET > 50",theFit.Eval(50)
print "Error MET > 70",theFit.Eval(70)
plot2.histoMgr.appendHisto(histograms.Histo(theFit,"Fit"))
plot2.draw()
plot2.save()
def Save(self, name):
if len(self.histogramsNotFound) > 0:
for name in self.histogramsNotFound:
print "Histo",name,"not found"
self.histogramsNotFound = []
return
hObserved = self.histograms[self.FindHistoIndex(self.dataHistos[0].name)].Clone("Data")
hObserved.Reset()
for histo in self.dataHistos:
hObserved.Add(self.histograms[self.FindHistoIndex(histo.label)])
print " Data: ", hObserved.Integral(0,hObserved.GetNbinsX()),"events"
hEstimatedEWKfake = self.histograms[self.FindHistoIndex("EWKfake")].Clone("hEstimatedEWKfake")
print " EWKfake: ",hEstimatedEWKfake.Integral(0,hEstimatedEWKfake.GetNbinsX()),"events"
hEstimatedEWK = self.histograms[self.FindHistoIndex("EWK")].Clone("hEstimatedEWK")
print " EWK: ",hEstimatedEWK.Integral(0,hEstimatedEWK.GetNbinsX()),"events"
hEstimatedEWK.Add(hEstimatedEWKfake)
hEstimatedQCD = self.histograms[self.FindHistoIndex("QCD")].Clone("hEstimatedQCD")
print " QCD: ",hEstimatedQCD.Integral(0,hEstimatedQCD.GetNbinsX()),"events"
hEstimatedQCD.Add(hEstimatedEWK)
hUncertainty = hEstimatedQCD.Clone("BackgrUncertainty")
hUncertainty.SetFillColor(1)
hUncertainty.SetFillStyle(3354)
hUncertainty.SetLineColor(0)
hUncertainty.SetLineStyle(0)
hUncertainty.SetLineWidth(0)
hUncertainty.SetMarkerColor(0)
hUncertainty.SetMarkerSize(0)
hSignal = self.histograms[self.FindHistoIndex("Signal")].Clone("hSignal")
print " Signal: ",hSignal.Integral(0,hSignal.GetNbinsX()),"events"
hSignal.Add(hEstimatedQCD)
style = tdrstyle.TDRStyle()
plot = plots.ComparisonManyPlot(
histograms.Histo(hObserved, "Data"),
[
histograms.Histo(hUncertainty, "Backgr.Uncertainty"),
histograms.Histo(hEstimatedEWKfake, "EWKfake"),
histograms.Histo(hEstimatedEWK, "EWK"),
histograms.Histo(hEstimatedQCD, "QCD"),
histograms.Histo(hSignal, "Signal")
]
)
plot.histoMgr.forHisto("Data", styles.getDataStyle())
plot.histoMgr.forHisto("EWK",styles.getEWKStyle())
plot.histoMgr.forHisto("EWKfake",styles.getEWKFakeStyle())
plot.histoMgr.forHisto("QCD",styles.getQCDStyle())
plot.histoMgr.forHisto("Signal",styles.getSignalStyle())
plot.histoMgr.forHisto("Backgr.Uncertainty",styles.getErrorStyle())
plot.histoMgr.setHistoDrawStyleAll("HIST")
plot.histoMgr.setHistoDrawStyle("Data", "EP")
plot.histoMgr.setHistoDrawStyle("Backgr.Uncertainty", "E2")
plot.histoMgr.setHistoLegendStyleAll("F")
plot.histoMgr.setHistoLegendStyle("Data", "P")
plot.histoMgr.setHistoLegendStyle("Signal", "L")
plot.createFrame(name, opts={"ymin":0, "ymaxfactor": 1.2})
plot.frame.GetXaxis().SetTitle(self.xtitle)
plot.frame.GetYaxis().SetTitle(self.ytitle)
plot.histoMgr.reorderLegend(["Data", "Signal", "QCD", "EWK", "EWKfake", "Backgr.Uncertainty"])
self.setLegendLabels(plot)
plot.setLegend(histograms.createLegend(0.55,0.68,0.9,0.93))
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
plot.draw()
plot.save()
### Normalize the area of *all* histograms to 1
#tauInvMass.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
tauInvMass.setHistoLegendLabelMany(legendLabels) # many datasets, with dict
### Example how to modify legend styles
tauInvMass.setHistoLegendStyleAll("F")
tauInvMass.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
tauInvMass.forEachMCHisto(styles.generator(fill=True)) # Apply SetFillColor too, needed for histogram stacking
tauInvMass.forHisto("Data", styles.getDataStyle())
#tauInvMass.setHistoDrawStyle("Data", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
#tauInvMass.stackMCDatasets()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(tauInvMass, "tau_InvMass", ymin=0.01, ymax=None, xmin=0.0, xmax=2.0)
### Set the frame options, e.g. axis labels
cf.frame.GetXaxis().SetTitle("Inv-Mass (GeV/c^{2})")
cf.frame.GetYaxis().SetTitle(ylabel)
### Legend
legend = createLegend(0.7, 0.5, 0.9, 0.8)
tauInvMass.addToLegend(legend)
import ROOT
ROOT.gROOT.SetBatch(True)
ROOT.PyConfig.IgnoreCommandLineOptions = True
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.dataset as dataset
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.histograms as histograms
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.plots as plots
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.counter as counter
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.tdrstyle as tdrstyle
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.styles as styles
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.tauEmbedding as tauEmbedding
analysisEmb = "signalAnalysis"
dataEra = "Run2012ABCD"
myStyles = [styles.getDataStyle()]
for st in styles.getStyles():
myStyles.append(st)
#myStyles.append(styles.StyleCompound([st, styles.StyleLine(lineStyle=2)]))
def main():
dirAve = "."
# dirSeeds = []
# inputInfoPath = os.path.join(dirAve, "inputInfo.txt")
# f = open(inputInfoPath)
# input_re = re.compile("Embedded input directory: (?P<dir>\S+)")
# for line in f:
# m = input_re.search(line)
# if m:
# dirSeeds.append(os.path.join("..", m.group("dir")))
def distComparison(datasets):
# Create a comparison plot of two distributions (must have the same binning)
# Set the names of DatasetRootHisto objects in order to be able easily reference them later
drh1 = datasets.getDataset("Data").getDatasetRootHisto(
analysis + "/MET_BaseLineTauId")
drh1.setName("Base")
drh1.normalizeToOne()
drh2 = datasets.getDataset("Data").getDatasetRootHisto(
analysis + "/MET_InvertedTauId")
drh2.setName("Inv")
drh2.normalizeToOne()
plot = plots.ComparisonPlot(drh1, drh2)
# Set the styles
st1 = styles.getDataStyle().clone()
st2 = st1.clone()
st2.append(styles.StyleLine(lineColor=ROOT.kRed))
plot.histoMgr.forHisto("Base", st1)
plot.histoMgr.forHisto("Inv", st2)
# Set the legend labels
plot.histoMgr.setHistoLegendLabelMany({
"Base": "Baseline Tau ID",
"Inv": "Inverted Tau ID"
})
# Set the legend styles
plot.histoMgr.setHistoLegendStyleAll("L")
plot.histoMgr.setHistoLegendStyle("Base",
"P") # exception to the general rule
# Set the drawing styles
plot.histoMgr.setHistoDrawStyleAll("HIST")
plot.histoMgr.setHistoDrawStyle("Base",
"EP") # exception to the general rule
# Rebin, if necessary
plot.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
# Create frame with a ratio pad
plot.createFrame(
"METbaseVSinvertedTauID",
opts={
"ymin": 1e-5,
"ymaxfactor": 1.5
},
createRatio=True,
opts2={
"ymin": -10,
"ymax": 50
}, # bounds of the ratio plot
)
# Set Y axis of the upper pad to logarithmic
plot.getPad1().SetLogy(True)
# Create legend to the default position
plot.setLegend(histograms.createLegend())
# Set the X/Y axis labels
plot.frame.GetXaxis().SetTitle("MET (GeV)")
plot.frame.GetYaxis().SetTitle("Arbitrary units")
# Draw the plot
plot.draw()
# Add the various texts to
plot.setLuminosity(datasets.getDataset("Data").getLuminosity())
plot.addStandardTexts()
# Save the plot to files
plot.save()
### Normalize the area of *all* histograms to 1
#tauNProngs.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
tauNProngs.setHistoLegendLabelMany(legendLabels) # many datasets, with dict
### Example how to modify legend styles
tauNProngs.setHistoLegendStyleAll("F")
tauNProngs.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
tauNProngs.forEachMCHisto(styles.generator(fill=True)) # Apply SetFillColor too, needed for histogram stacking
tauNProngs.forHisto("Data", styles.getDataStyle())
#tauNProngs.setHistoDrawStyle("Data", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
#tauNProngs.stackMCDatasets()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(tauNProngs, "tauNProngs", ymin=0.01, ymax=None, xmin=0.0, xmax=15.0)
### Set the frame options, e.g. axis labels
cf.frame.GetXaxis().SetTitle("#tau-jet nProngs")
cf.frame.GetYaxis().SetTitle(ylabel)
### Legend
legend = createLegend(0.7, 0.5, 0.9, 0.8)
tauNProngs.addToLegend(legend)
def comparison(self,histo1,histo2,norm=1):
h1 = histo1.Clone("h1")
h2 = histo2.Clone("h2")
if norm == 1:
h1.Scale(1/h1.GetMaximum())
h2.Scale(1/h2.GetMaximum())
# check that no bin has negative value, negative values possible after subtracting EWK from data
iBin = 1
nBins = h1.GetNbinsX()
while iBin < nBins:
value1 = h1.GetBinContent(iBin)
value2 = h2.GetBinContent(iBin)
if value1 < 0:
h1.SetBinContent(iBin,0)
if value2 < 0:
h2.SetBinContent(iBin,0)
iBin = iBin + 1
if norm > 0:
h1.GetYaxis().SetTitle("Arbitrary units")
plot = plots.ComparisonPlot(
histograms.Histo(h1, "Inv"),
histograms.Histo(h2, "Base"),
)
# Set the styles
st1 = styles.getDataStyle().clone()
st2 = st1.clone()
st2.append(styles.StyleMarker(markerColor=ROOT.kRed))
plot.histoMgr.forHisto("Base", st1)
plot.histoMgr.forHisto("Inv", st2)
# Set the legend labels
plot.histoMgr.setHistoLegendLabelMany({"Inv": h1.GetTitle(),
"Base": h2.GetTitle()})
# Set the legend styles
plot.histoMgr.setHistoLegendStyleAll("P")
# Set the drawing styles
plot.histoMgr.setHistoDrawStyleAll("EP")
# Create frame with a ratio pad
plot.createFrame("comparison"+self.label, opts={"ymin":1e-5, "ymaxfactor": 2},
createRatio=True, opts2={"ymin": 0, "ymax": 2}, # bounds of the ratio plot
)
# Set Y axis of the upper pad to logarithmic
plot.getPad1().SetLogy(True)
plot.setLegend(histograms.createLegend(0.4,0.82,0.9,0.93))
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
plot.draw()
plot.save()
def comparison(self, histo1, histo2, norm=1):
h1 = histo1.Clone("h1")
h2 = histo2.Clone("h2")
if norm == 1:
h1.Scale(1 / h1.GetMaximum())
h2.Scale(1 / h2.GetMaximum())
# check that no bin has negative value, negative values possible after subtracting EWK from data
iBin = 1
nBins = h1.GetNbinsX()
while iBin < nBins:
value1 = h1.GetBinContent(iBin)
value2 = h2.GetBinContent(iBin)
if value1 < 0:
h1.SetBinContent(iBin, 0)
if value2 < 0:
h2.SetBinContent(iBin, 0)
iBin = iBin + 1
if norm > 0:
h1.GetYaxis().SetTitle("Arbitrary units")
plot = plots.ComparisonPlot(
histograms.Histo(h1, "Inv"),
histograms.Histo(h2, "Base"),
)
# Set the styles
st1 = styles.getDataStyle().clone()
st2 = st1.clone()
st2.append(styles.StyleMarker(markerColor=ROOT.kRed))
plot.histoMgr.forHisto("Base", st1)
plot.histoMgr.forHisto("Inv", st2)
# Set the legend labels
plot.histoMgr.setHistoLegendLabelMany({
"Inv": h1.GetTitle(),
"Base": h2.GetTitle()
})
# Set the legend styles
plot.histoMgr.setHistoLegendStyleAll("P")
# Set the drawing styles
plot.histoMgr.setHistoDrawStyleAll("EP")
# Create frame with a ratio pad
plot.createFrame(
"comparison" + self.label,
opts={
"ymin": 1e-5,
"ymaxfactor": 2
},
createRatio=True,
opts2={
"ymin": 0,
"ymax": 2
}, # bounds of the ratio plot
)
# Set Y axis of the upper pad to logarithmic
plot.getPad1().SetLogy(True)
plot.setLegend(histograms.createLegend(0.4, 0.82, 0.9, 0.93))
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
plot.draw()
plot.save()
def doPlotsWTauMu(datasetsEmb, name, btag=True):
if btag:
selection = And(metCut, bTaggingCut, deltaPhi130Cut)
treeDraw = dataset.TreeDraw(
analysisEmb + "/tree",
weight="weightPileup*weightTrigger*weightBTagging")
else:
selection = And(metCut, deltaPhi130Cut)
treeDraw = dataset.TreeDraw(analysisEmb + "/tree",
weight="weightPileup*weightTrigger")
tdMt = treeDraw.clone(
varexp=
"sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi()))) >>tmp(20,0,400)"
)
(hall, tmp) = datasetsEmb.getHistogram(name,
tdMt.clone(selection=selection))
(hpure, tmp) = datasetsEmb.getHistogram(
name,
tdMt.clone(selection=And(selection, "abs(temuon_mother_pdgid) == 24")))
tdEff = tdMt.clone(weight="", varexp=tdMt.varexp.replace("tmp", "tmpeff"))
heff = datasetsEmb.getEfficiency(
name,
tdEff.clone(
selection=And(selection, "abs(temuon_mother_pdgid) == 24")),
tdEff.clone(selection=selection))
hall.SetName("All")
hpure.SetName("Pure")
nall = hall.Integral(0, hall.GetNbinsX())
npure = hpure.Integral(0, hall.GetNbinsX())
print {
True: "Btagging",
False: "NoBTag"
}[btag], npure / nall, (1 - npure / nall) * 100
p = plots.ComparisonPlot(hall, hpure)
p.histoMgr.setHistoLegendLabelMany({
"All": "All muons",
"Pure": "W#rightarrow#mu"
})
p.histoMgr.forEachHisto(styles.generator())
if btag:
fname = "transverseMass_4AfterDeltaPhi160_wtaumu_" + name
else:
fname = "transverseMass_4AfterDeltaPhi160NoBTag_wtaumu_" + name
hallErr = hall.Clone("AllError")
hallErr.SetFillColor(ROOT.kBlue - 7)
hallErr.SetFillStyle(3004)
hallErr.SetMarkerSize(0)
p.prependPlotObject(hallErr, "E2")
hpureErr = hpure.Clone("PureErr")
hpureErr.SetFillColor(ROOT.kRed - 7)
hpureErr.SetFillStyle(3005)
hpureErr.SetMarkerSize(0)
p.prependPlotObject(hpureErr, "E2")
p.createFrame(fname, createRatio=True, opts2={"ymin": 0.9, "ymax": 1.05})
styles.getDataStyle().apply(heff)
p.setRatios([heff])
xmin = p.frame.GetXaxis().GetXmin()
xmax = p.frame.GetXaxis().GetXmax()
val = 1 - 0.038479
l = ROOT.TLine(xmin, val, xmax, val)
l.SetLineWidth(2)
l.SetLineColor(ROOT.kBlue)
l.SetLineStyle(4)
p.prependPlotObjectToRatio(l)
p.appendPlotObjectToRatio(
histograms.PlotText(0.65, 0.61, "1-0.038", size=18, color=ROOT.kBlue))
p.getFrame2().GetYaxis().SetTitle("W#rightarrow#mu fraction")
p.setLegend(histograms.moveLegend(histograms.createLegend()))
tmp = hpureErr.Clone("tmp")
tmp.SetFillColor(ROOT.kBlack)
tmp.SetFillStyle(3013)
tmp.SetLineColor(ROOT.kWhite)
p.legend.AddEntry(tmp, "Stat. unc.", "F")
p.frame.GetXaxis().SetTitle("m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})")
p.frame.GetYaxis().SetTitle("Events / %.0f GeV/c^{2}" % p.binWidth())
p.appendPlotObject(
histograms.PlotText(0.5,
0.9,
plots._legendLabels.get(name, name),
size=18))
p.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
p.save()
### Normalize the area of *all* histograms to 1
#alphaT.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
alphaT.setHistoLegendLabelMany(legendLabels) # many datasets, with dict
### Example how to modify legend styles
alphaT.setHistoLegendStyleAll("F")
alphaT.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
alphaT.forEachMCHisto(styles.generator(fill=True)) # Apply SetFillColor too, needed for histogram stacking
alphaT.forHisto("Data", styles.getDataStyle())
#alphaT.setHistoDrawStyle("Data", "EP")
#alphaT.forHisto("BTau_146240-147116", styles.getDataStyle())
#alphaT.setHistoDrawStyle("BTau_146240-147116", "EP")
#alphaT.forHisto("BTau_141950-144114", styles.getDataStyle())
#alphaT.setHistoDrawStyle("BTau_141950-144114", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
alphaT.stackMCHistograms()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(alphaT, "alphaT", ymin=0.01, ymax=None, xmin=0.0, xmax=1.2)
### Set the frame options, e.g. axis labels
cf.frame.GetXaxis().SetTitle("#alpha_{T}")
cf.frame.GetYaxis().SetTitle(ylabel)
def main():
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.2, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
# Merge EWK datasets
datasets.merge("EWK", [
"WJets",
"TTJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create the normalized plot of transverse mass
# Read the histogram from the file
#mT = plots.DataMCPlot(datasets, analysis+"/transverseMass")
# Create the histogram from the tree (and see the selections explicitly)
td = dataset.TreeDraw(analysis+"/tree", weight="weightPileup*weightTrigger*weightPrescale",
selection="met_p4.Et() > 70 && Max$(jets_btag) > 1.7")
# mT = plots.DataMCPlot(datasets, td.clone(varexp="sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi())))>>dist(400, 0, 400)"))
# met = plots.DataMCPlot(datasets, td.clone(varexp="met_p4.Et()>>dist(400, 0, 400)"))
met5060 = plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging5060")
met6070 = plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging6070")
met7080 = plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging7080")
met80100 = plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging80100")
met100120 = plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging100120")
met120150 = plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging120150")
met150 = plots.DataMCPlot(datasets, analysis+"/QCD_MET_AfterJetsBtagging150")
# Rebin before subtracting
met5060.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met6070.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met7080.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met80100.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met100120.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met120150.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met150.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
data5060 = met5060.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
data6070 = met6070.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
data7080 = met7080.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
data80100 = met80100.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
data100120 = met100120.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
data120150 = met120150.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
data150 = met150.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
# Create the data-EWK histogram and draw it
# diffBase = dataEwkDiff(metBase, "MET_base_data-ewk")
# diffInverted = dataEwkDiff(metInver,"MET_inverted_data-ewk")
# Draw the MET distribution
# transverseMass(metBase,"MET_base")
# transverseMass(metInver,"MET_inverted")
# Set the styles
# dataset._normalizeToOne(diffBase)
# dataset._normalizeToOne(diffInverted)
plot = plots.PlotBase()
plot.histoMgr.appendHisto(histograms.Histo(data5060, "50 < p_{T}^{#tau jet} < 60 GeV"))
plot.histoMgr.appendHisto(histograms.Histo(data6070, "60 < p_{T}^{#tau jet} < 70 GeV"))
plot.histoMgr.appendHisto(histograms.Histo(data7080, "70 < p_{T}^{#tau jet} < 80 GeV"))
plot.histoMgr.appendHisto(histograms.Histo(data80100, "80 < p_{T}^{#tau jet} < 100 GeV"))
plot.histoMgr.appendHisto(histograms.Histo(data100120, "100 < p_{T}^{#tau jet} < 120 GeV"))
plot.histoMgr.appendHisto(histograms.Histo(data120150, "120 < p_{T}^{#tau jet} < 150 GeV"))
plot.histoMgr.appendHisto(histograms.Histo(data150, "p_{T}^{#tau jet} > 150 GeV"))
st1 = styles.getDataStyle().clone()
st2 = st1.clone()
st2.append(styles.StyleLine(lineColor=ROOT.kRed))
st3 = st1.clone()
st3.append(styles.StyleLine(lineColor=ROOT.kBlue))
st4 = st1.clone()
st4.append(styles.StyleLine(lineColor=ROOT.kGreen))
st5 = st1.clone()
st5.append(styles.StyleLine(lineColor=ROOT.kMagenta, lineStyle=2))
st6 = st1.clone()
st6.append(styles.StyleLine(lineColor=ROOT.kPink, lineStyle=8)) # lineWidth=6))
st7 = st1.clone()
st7.append(styles.StyleLine(lineColor=ROOT.kBlue-2, lineStyle=3))
plot.histoMgr.forHisto("50 < p_{T}^{#tau jet} < 60 GeV", st1)
plot.histoMgr.forHisto("60 < p_{T}^{#tau jet} < 70 GeV", st2)
plot.histoMgr.forHisto("70 < p_{T}^{#tau jet} < 80 GeV", st3)
plot.histoMgr.forHisto("80 < p_{T}^{#tau jet} < 100 GeV", st4)
plot.histoMgr.forHisto("100 < p_{T}^{#tau jet} < 120 GeV", st5)
plot.histoMgr.forHisto("120 < p_{T}^{#tau jet} < 150 GeV", st6)
plot.histoMgr.forHisto("p_{T}^{#tau jet} > 150 GeV", st7)
plot.createFrame("METinBins", opts={"xmax": 200, "ymin":1e-1, "ymaxfactor": 1.5},
# createRatio=True, opts2={"ymin": -5 , "ymax": 6 }, # bounds of the ratio plot
)
plot.getPad().SetLogy(True)
plot.setLegend(histograms.createLegend(0.6, 0.68, 0.8, 0.93))
plot.frame.GetXaxis().SetTitle("MET (GeV)")
plot.frame.GetYaxis().SetTitle("Data")
# Draw the plot
plot.draw()
plot.save()
### Normalize the area of *all* histograms to 1
#TauNTrksVSPtcut.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
TauNTrksVSPtcut.setHistoLegendLabelMany(legendLabels) # many datasets, with dict
### Example how to modify legend styles
TauNTrksVSPtcut.setHistoLegendStyleAll("F")
TauNTrksVSPtcut.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
TauNTrksVSPtcut.forEachMCHisto(styles.generator(fill=True)) # Apply SetFillColor too, needed for histogram stacking
TauNTrksVSPtcut.forHisto("Data", styles.getDataStyle())
#TauNTrksVSPtcut.setHistoDrawStyle("Data", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
#TauNTrksVSPtcut.stackMCHistograms()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(TauNTrksVSPtcut, "TauNTrksVSPtcut", ymin=0.01, ymax=None, xmin=0.0, xmax=20.0)
### Set the frame options, e.g. axis labels
cf.frame.GetXaxis().SetTitle("???")
cf.frame.GetYaxis().SetTitle(ylabel)
### Legend
legend = createLegend(0.7, 0.5, 0.9, 0.8)
TauNTrksVSPtcut.addToLegend(legend)
### Normalize the area of *all* histograms to 1
#alphaTInvMass.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
alphaTInvMass.setHistoLegendLabelMany(legendLabels) # many datasets, with dict
### Example how to modify legend styles
alphaTInvMass.setHistoLegendStyleAll("F")
alphaTInvMass.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
alphaTInvMass.forEachMCHisto(styles.generator(
fill=True)) # Apply SetFillColor too, needed for histogram stacking
alphaTInvMass.forHisto("Data", styles.getDataStyle())
#alphaTInvMass.setHistoDrawStyle("Data", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
#alphaTInvMass.stackMCDatasets()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(alphaTInvMass,
"alphaTInvMass",
ymin=0.01,
ymax=None,
xmin=0.0,
xmax=1000.0)
### Set the frame options, e.g. axis labels
cf.frame.GetXaxis().SetTitle("Di-jet InvMass [GeV/c^{2}]")
### Normalize the area of *all* histograms to 1
#TauIsoltrkPtsumVSPtcut.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
TauIsoltrkPtsumVSPtcut.setHistoLegendLabelMany(legendLabels) # many datasets, with dict
### Example how to modify legend styles
TauIsoltrkPtsumVSPtcut.setHistoLegendStyleAll("F")
TauIsoltrkPtsumVSPtcut.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
TauIsoltrkPtsumVSPtcut.forEachMCHisto(styles.generator(fill=True)) # Apply SetFillColor too, needed for histogram stacking
TauIsoltrkPtsumVSPtcut.forHisto("Data", styles.getDataStyle())
#TauIsoltrkPtsumVSPtcut.setHistoDrawStyle("Data", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
#TauIsoltrkPtsumVSPtcut.stackMCDatasets()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(TauIsoltrkPtsumVSPtcut, "TauIsoltrkPtsumVSPtcut", ymin=0.01, ymax=None, xmin=0.0, xmax=20.0)
### Set the frame options, e.g. axis labels
cf.frame.GetXaxis().SetTitle("???")
cf.frame.GetYaxis().SetTitle(ylabel)
### Legend
legend = createLegend(0.7, 0.5, 0.9, 0.8)
TauIsoltrkPtsumVSPtcut.addToLegend(legend)
def cutefficiency(self, histo1, histo2):
h1 = histo1.Clone("h1")
h2 = histo2.Clone("h2")
h1.Scale(1 / h1.GetMaximum())
h2.Scale(1 / h2.GetMaximum())
# check that no bin has negative value, negative values possible after subtracting EWK from data
iBin = 1
nBins = h1.GetNbinsX()
while iBin < nBins:
value1 = h1.GetBinContent(iBin)
value2 = h2.GetBinContent(iBin)
if value1 < 0:
h1.SetBinContent(iBin, 0)
if value2 < 0:
h2.SetBinContent(iBin, 0)
iBin = iBin + 1
h1cut = h1.Clone("h1cut")
h1cut.Reset()
h1cut.GetYaxis().SetTitle("Efficiency")
h1cut.GetXaxis().SetTitle("PF MET cut (GeV)")
h2cut = h2.Clone("h2cut")
h2cut.Reset()
h2cut.SetLineColor(2)
integralError = ROOT.Double(0.0)
integralValue = h1.IntegralAndError(1, h1cut.GetNbinsX(),
integralError)
h1_integral = h1.Integral(0, h1.GetNbinsX())
h2_integral = h2.Integral(0, h2.GetNbinsX())
iBin = 1
nBins = h1cut.GetNbinsX()
while iBin < nBins:
error = ROOT.Double(0.0)
selected1 = h1.IntegralAndError(iBin, nBins, error)
if selected1 > 0:
error = error / selected1
else:
error = integralError / integralValue
efficiency1 = selected1 / h1_integral
h1cut.SetBinContent(iBin, efficiency1)
if self.errorBars:
h1cut.SetBinError(iBin, error)
error = ROOT.Double(0.0)
selected2 = h2.IntegralAndError(iBin, nBins, error)
if selected2 > 0:
error = error / selected2
else:
error = integralError / integralValue
efficiency2 = selected2 / h2_integral
h2cut.SetBinContent(iBin, efficiency2)
if self.errorBars:
h2cut.SetBinError(iBin, error)
iBin = iBin + 1
plot = plots.ComparisonPlot(
histograms.Histo(h1cut, "Inv"),
histograms.Histo(h2cut, "Base"),
)
# Set the styles
st1 = styles.getDataStyle().clone()
st2 = st1.clone()
st2.append(styles.StyleLine(lineColor=ROOT.kRed))
st2.append(styles.StyleMarker(markerColor=ROOT.kRed))
plot.histoMgr.forHisto("Base", st1)
plot.histoMgr.forHisto("Inv", st2)
# Set the legend labels
plot.histoMgr.setHistoLegendLabelMany({
"Inv": h1.GetTitle(),
"Base": h2.GetTitle()
})
# Set the legend styles
#plot.histoMgr.setHistoLegendStyleAll("L")
plot.histoMgr.setHistoLegendStyleAll("P")
# Set the drawing styles
#plot.histoMgr.setHistoDrawStyleAll("HIST")
plot.histoMgr.setHistoDrawStyleAll("EP")
# Create frame with a ratio pad
plot.createFrame(
"cuteff" + self.label,
opts={
"ymin": 1e-5,
"ymaxfactor": 2
},
createRatio=True,
opts2={
"ymin": 0,
"ymax": 2
}, # bounds of the ratio plot
)
# Set Y axis of the upper pad to logarithmic
plot.getPad().SetLogy(True)
plot.setLegend(histograms.createLegend(0.4, 0.82, 0.9, 0.93))
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
plot.draw()
plot.save()
######
hError = h1cut.Clone("hError")
hError.Divide(h2cut)
iBin = 1
nBins = hError.GetNbinsX()
while iBin < nBins:
hError.SetBinContent(iBin, abs(hError.GetBinContent(iBin) - 1))
iBin = iBin + 1
hError.GetYaxis().SetTitle(
"abs( (#varepsilon^{Inverted} - #varepsilon^{Baseline})/#varepsilon^{Baseline} )"
)
hError.GetXaxis().SetTitle("PF MET cut (GeV)")
plot2 = plots.PlotBase()
plot2.histoMgr.appendHisto(histograms.Histo(hError,
"ShapeUncertainty"))
plot2.histoMgr.forHisto("ShapeUncertainty", st1)
plot2.histoMgr.setHistoDrawStyleAll("EP")
# plot2.createFrame("shapeUncertainty"+self.label, opts={"ymin":-1, "ymax": 1})
plot2.createFrame("shapeUncertainty" + self.label,
opts={
"ymin": -0.1,
"ymax": 1.1
})
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
rangeMin = hError.GetXaxis().GetXmin()
rangeMax = hError.GetXaxis().GetXmax()
# rangeMax = 80
rangeMax = 120
# rangeMax = 380
numberOfParameters = 2
class FitFunction:
def __call__(self, x, par):
# return Linear(x,par)
return ErrorFunction(x, par)
theFit = TF1('theFit', FitFunction(), rangeMin, rangeMax,
numberOfParameters)
theFit.SetParLimits(0, 0.01, 0.03)
theFit.SetParLimits(1, 50, 150)
# theFit.FixParameter(0,0.02)
# theFit.FixParameter(1,100)
hError.Fit(theFit, "LRN")
print "Error MET > 40", theFit.Eval(40)
print "Error MET > 50", theFit.Eval(50)
print "Error MET > 70", theFit.Eval(70)
plot2.histoMgr.appendHisto(histograms.Histo(theFit, "Fit"))
plot2.draw()
plot2.save()
def Save(self, name):
if len(self.histogramsNotFound) > 0:
for name in self.histogramsNotFound:
print "Histo", name, "not found"
self.histogramsNotFound = []
return
hObserved = self.histograms[self.FindHistoIndex(
self.dataHistos[0].name)].Clone("Data")
hObserved.Reset()
for histo in self.dataHistos:
hObserved.Add(self.histograms[self.FindHistoIndex(histo.label)])
print " Data: ", hObserved.Integral(
0, hObserved.GetNbinsX()), "events"
hEstimatedEWKfake = self.histograms[self.FindHistoIndex(
"EWKfake")].Clone("hEstimatedEWKfake")
print " EWKfake: ", hEstimatedEWKfake.Integral(
0, hEstimatedEWKfake.GetNbinsX()), "events"
hEstimatedEWK = self.histograms[self.FindHistoIndex("EWK")].Clone(
"hEstimatedEWK")
print " EWK: ", hEstimatedEWK.Integral(
0, hEstimatedEWK.GetNbinsX()), "events"
hEstimatedEWK.Add(hEstimatedEWKfake)
hEstimatedQCD = self.histograms[self.FindHistoIndex("QCD")].Clone(
"hEstimatedQCD")
print " QCD: ", hEstimatedQCD.Integral(
0, hEstimatedQCD.GetNbinsX()), "events"
hEstimatedQCD.Add(hEstimatedEWK)
hUncertainty = hEstimatedQCD.Clone("BackgrUncertainty")
hUncertainty.SetFillColor(1)
hUncertainty.SetFillStyle(3354)
hUncertainty.SetLineColor(0)
hUncertainty.SetLineStyle(0)
hUncertainty.SetLineWidth(0)
hUncertainty.SetMarkerColor(0)
hUncertainty.SetMarkerSize(0)
hSignal = self.histograms[self.FindHistoIndex("Signal")].Clone(
"hSignal")
print " Signal: ", hSignal.Integral(0,
hSignal.GetNbinsX()), "events"
hSignal.Add(hEstimatedQCD)
style = tdrstyle.TDRStyle()
plot = plots.ComparisonManyPlot(histograms.Histo(hObserved, "Data"), [
histograms.Histo(hUncertainty, "Backgr.Uncertainty"),
histograms.Histo(hEstimatedEWKfake, "EWKfake"),
histograms.Histo(hEstimatedEWK, "EWK"),
histograms.Histo(hEstimatedQCD, "QCD"),
histograms.Histo(hSignal, "Signal")
])
plot.histoMgr.forHisto("Data", styles.getDataStyle())
plot.histoMgr.forHisto("EWK", styles.getEWKStyle())
plot.histoMgr.forHisto("EWKfake", styles.getEWKFakeStyle())
plot.histoMgr.forHisto("QCD", styles.getQCDStyle())
plot.histoMgr.forHisto("Signal", styles.getSignalStyle())
plot.histoMgr.forHisto("Backgr.Uncertainty", styles.getErrorStyle())
plot.histoMgr.setHistoDrawStyleAll("HIST")
plot.histoMgr.setHistoDrawStyle("Data", "EP")
plot.histoMgr.setHistoDrawStyle("Backgr.Uncertainty", "E2")
plot.histoMgr.setHistoLegendStyleAll("F")
plot.histoMgr.setHistoLegendStyle("Data", "P")
plot.histoMgr.setHistoLegendStyle("Signal", "L")
plot.createFrame(name, opts={"ymin": 0, "ymaxfactor": 1.2})
plot.frame.GetXaxis().SetTitle(self.xtitle)
plot.frame.GetYaxis().SetTitle(self.ytitle)
plot.histoMgr.reorderLegend(
["Data", "Signal", "QCD", "EWK", "EWKfake", "Backgr.Uncertainty"])
self.setLegendLabels(plot)
plot.setLegend(histograms.createLegend(0.55, 0.68, 0.9, 0.93))
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
plot.draw()
plot.save()
def distComparison(datasets):
# Create a comparison plot of two distributions (must have the same binning)
# Set the names of DatasetRootHisto objects in order to be able easily reference them later
drh1 = datasets.getDataset("Data").getDatasetRootHisto(
"ForDataDrivenCtrlPlots/SelectedTau_pT_AfterStandardSelections")
drh1.setName("AfterStandardSelections")
drh2 = datasets.getDataset("Data").getDatasetRootHisto(
"ForDataDrivenCtrlPlots/SelectedTau_pT_AfterMtSelections")
drh2.setName("AfterMtSelections")
plot = plots.ComparisonPlot(drh1, drh2)
# Set the styles
st1 = styles.getDataStyle().clone()
st2 = st1.clone()
st2.append(styles.StyleLine(lineColor=ROOT.kRed))
plot.histoMgr.forHisto("AfterStandardSelections", st1)
plot.histoMgr.forHisto("AfterMtSelections", st2)
# Set the legend labels
plot.histoMgr.setHistoLegendLabelMany({
"AfterStandardSelections":
"After standard selections",
"AfterMtSelections":
"After all selections"
})
# Set the legend styles
plot.histoMgr.setHistoLegendStyleAll("L")
#plot.histoMgr.setHistoLegendStyle("afterTauID", "P") # exception to the general rule
# Set the drawing styles
plot.histoMgr.setHistoDrawStyleAll("HIST")
#plot.histoMgr.setHistoDrawStyleAll("afterTauID", "EP") # exception to the general rule
# Rebin, if necessary
plot.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
# Create frame with a ratio pad
plot.createFrame(
"tauPtComparison",
opts={
"ymin": 1e-1,
"ymaxfactor": 10
},
createRatio=True,
opts2={
"ymin": 0,
"ymax": 150
}, # bounds of the ratio plot
)
# Set Y axis of the upper pad to logarithmic
plot.getPad1().SetLogy(True)
# Create legend to the default position
plot.setLegend(histograms.createLegend())
# Set the X/Y axis labels
plot.frame.GetXaxis().SetTitle("Tau p_{T} (GeV/c)")
plot.frame.GetYaxis().SetTitle("Number of events")
# Draw the plot
plot.draw()
# Add the various texts to
histograms.addStandardTexts(
lumi=datasets.getDataset("Data").getLuminosity())
# Save the plot to files
plot.save()
def main():
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(
filter(lambda name: "TTToHplus" in name and not "M120" in name,
datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.2, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
# Merge EWK datasets
datasets.merge("EWK",
["WJets", "TTJets", "DYJetsToLL", "SingleTop", "Diboson"])
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create the normalized plot of transverse mass
# Read the histogram from the file
#mT = plots.DataMCPlot(datasets, analysis+"/transverseMass")
# Create the histogram from the tree (and see the selections explicitly)
td = dataset.TreeDraw(
analysis + "/tree",
weight="weightPileup*weightTrigger*weightPrescale",
selection="met_p4.Et() > 70 && Max$(jets_btag) > 1.7")
# mT = plots.DataMCPlot(datasets, td.clone(varexp="sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi())))>>dist(400, 0, 400)"))
# met = plots.DataMCPlot(datasets, td.clone(varexp="met_p4.Et()>>dist(400, 0, 400)"))
met5060 = plots.DataMCPlot(datasets,
analysis + "/QCD_MET_AfterJetsBtagging5060")
met6070 = plots.DataMCPlot(datasets,
analysis + "/QCD_MET_AfterJetsBtagging6070")
met7080 = plots.DataMCPlot(datasets,
analysis + "/QCD_MET_AfterJetsBtagging7080")
met80100 = plots.DataMCPlot(datasets,
analysis + "/QCD_MET_AfterJetsBtagging80100")
met100120 = plots.DataMCPlot(datasets,
analysis + "/QCD_MET_AfterJetsBtagging100120")
met120150 = plots.DataMCPlot(datasets,
analysis + "/QCD_MET_AfterJetsBtagging120150")
met150 = plots.DataMCPlot(datasets,
analysis + "/QCD_MET_AfterJetsBtagging150")
# Rebin before subtracting
met5060.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met6070.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met7080.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met80100.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met100120.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met120150.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
met150.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(1))
data5060 = met5060.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
data6070 = met6070.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
data7080 = met7080.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
data80100 = met80100.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
data100120 = met100120.histoMgr.getHisto("Data").getRootHisto().Clone(
"Data")
data120150 = met120150.histoMgr.getHisto("Data").getRootHisto().Clone(
"Data")
data150 = met150.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
# Create the data-EWK histogram and draw it
# diffBase = dataEwkDiff(metBase, "MET_base_data-ewk")
# diffInverted = dataEwkDiff(metInver,"MET_inverted_data-ewk")
# Draw the MET distribution
# transverseMass(metBase,"MET_base")
# transverseMass(metInver,"MET_inverted")
# Set the styles
# dataset._normalizeToOne(diffBase)
# dataset._normalizeToOne(diffInverted)
plot = plots.PlotBase()
plot.histoMgr.appendHisto(
histograms.Histo(data5060, "50 < p_{T}^{#tau jet} < 60 GeV"))
plot.histoMgr.appendHisto(
histograms.Histo(data6070, "60 < p_{T}^{#tau jet} < 70 GeV"))
plot.histoMgr.appendHisto(
histograms.Histo(data7080, "70 < p_{T}^{#tau jet} < 80 GeV"))
plot.histoMgr.appendHisto(
histograms.Histo(data80100, "80 < p_{T}^{#tau jet} < 100 GeV"))
plot.histoMgr.appendHisto(
histograms.Histo(data100120, "100 < p_{T}^{#tau jet} < 120 GeV"))
plot.histoMgr.appendHisto(
histograms.Histo(data120150, "120 < p_{T}^{#tau jet} < 150 GeV"))
plot.histoMgr.appendHisto(
histograms.Histo(data150, "p_{T}^{#tau jet} > 150 GeV"))
st1 = styles.getDataStyle().clone()
st2 = st1.clone()
st2.append(styles.StyleLine(lineColor=ROOT.kRed))
st3 = st1.clone()
st3.append(styles.StyleLine(lineColor=ROOT.kBlue))
st4 = st1.clone()
st4.append(styles.StyleLine(lineColor=ROOT.kGreen))
st5 = st1.clone()
st5.append(styles.StyleLine(lineColor=ROOT.kMagenta, lineStyle=2))
st6 = st1.clone()
st6.append(styles.StyleLine(lineColor=ROOT.kPink,
lineStyle=8)) # lineWidth=6))
st7 = st1.clone()
st7.append(styles.StyleLine(lineColor=ROOT.kBlue - 2, lineStyle=3))
plot.histoMgr.forHisto("50 < p_{T}^{#tau jet} < 60 GeV", st1)
plot.histoMgr.forHisto("60 < p_{T}^{#tau jet} < 70 GeV", st2)
plot.histoMgr.forHisto("70 < p_{T}^{#tau jet} < 80 GeV", st3)
plot.histoMgr.forHisto("80 < p_{T}^{#tau jet} < 100 GeV", st4)
plot.histoMgr.forHisto("100 < p_{T}^{#tau jet} < 120 GeV", st5)
plot.histoMgr.forHisto("120 < p_{T}^{#tau jet} < 150 GeV", st6)
plot.histoMgr.forHisto("p_{T}^{#tau jet} > 150 GeV", st7)
plot.createFrame(
"METinBins",
opts={
"xmax": 200,
"ymin": 1e-1,
"ymaxfactor": 1.5
},
# createRatio=True, opts2={"ymin": -5 , "ymax": 6 }, # bounds of the ratio plot
)
plot.getPad().SetLogy(True)
plot.setLegend(histograms.createLegend(0.6, 0.68, 0.8, 0.93))
plot.frame.GetXaxis().SetTitle("MET (GeV)")
plot.frame.GetYaxis().SetTitle("Data")
# Draw the plot
plot.draw()
plot.save()
def Save(self, name):
hObserved = self.CreateGraph("Observed")
hExpected = self.CreateGraph("Expected")
hExpected1s = self.CreateGraph("Expected1")
hExpected1s.SetFillColor(ROOT.kYellow)
hExpected2s = self.CreateGraph("Expected2")
hExpected2s.SetFillColor(ROOT.kOrange)
style = tdrstyle.TDRStyle()
plot = plots.ComparisonManyPlot(
histograms.HistoGraph(hObserved, "Observed"), [
histograms.HistoGraph(hExpected, "Expected"),
histograms.HistoGraph(hExpected1s, "Expected1"),
histograms.HistoGraph(hExpected2s, "Expected2")
])
obsStyle = styles.getDataStyle().clone()
plot.histoMgr.forHisto("Observed", styles.getDataStyle().clone())
plot.histoMgr.setHistoDrawStyle("Observed", "PL")
expStyle = styles.getDataStyle().clone()
expStyle.append(styles.StyleLine(lineStyle=2))
expStyle.append(styles.StyleLine(lineColor=ROOT.kRed))
expStyle.append(styles.StyleMarker(markerStyle=ROOT.kFullSquare))
expStyle.append(styles.StyleMarker(markerColor=ROOT.kRed))
plot.histoMgr.forHisto("Expected", expStyle)
plot.histoMgr.setHistoDrawStyle("Expected", "PL")
plot.histoMgr.setHistoDrawStyle("Expected1", "PL3")
plot.histoMgr.setHistoDrawStyle("Expected2", "PL3")
plot.createFrame(name,
opts={
"xmin": 70.1,
"xmax": 169.9,
"ymin": 0,
"ymax": 0.2
})
plot.frame.GetXaxis().SetTitle(self.xtitle)
plot.frame.GetYaxis().SetTitle(self.ytitle)
plot.histoMgr.setHistoLegendStyle("Observed", "PL")
plot.histoMgr.setHistoLegendStyle("Expected", "PL")
plot.histoMgr.setHistoLegendStyle("Expected1", "F")
plot.histoMgr.setHistoLegendStyle("Expected2", "F")
plot.histoMgr.setHistoLegendLabelMany({
"Expected":
"Expected median",
"Expected1":
"Expected median #pm1#sigma",
"Expected2":
"Expected median #pm2#sigma"
})
plot.setLegend(histograms.createLegend(0.55, 0.68, 0.9, 0.93))
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
textSize = 16
textX = 0.19
textY = 0.8
textDY = 0.038
histograms.addText(textX, textY + 2 * textDY,
"t#rightarrowbH^{#pm}, H^{#pm}#rightarrow#tau#nu",
textSize)
histograms.addText(textX, textY + textDY, "Fully hadronic final state",
textSize)
histograms.addText(textX, textY,
"BR(H^{#pm}#rightarrow#tau^{#pm} #nu) = 1",
textSize)
plot.draw()
plot.save()
### Normalize the area of *all* histograms to unity. Canno be used with mergeDatasets
#transverseMass.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
transverseMass.setHistoLegendLabelMany(
legendLabels) # many datasets, with dict
### Example how to modify legend styles
transverseMass.setHistoLegendStyleAll("F")
transverseMass.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
transverseMass.forEachMCHisto(styles.generator(fill=True))
transverseMass.forHisto("Data", styles.getDataStyle())
#transverseMass.setHistoDrawStyle("Data", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
#transverseMass.stackMCHistograms()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(transverseMass,
"transverseMass",
ymin=0.01,
ymax=None,
xmin=0.0,
xmax=300.0)
### Set the frame options, e.g. axis labels
cf.frame.GetXaxis().SetTitle("M_{T} GeV/c^{2}")
def main():
datasets = dataset.getDatasetsFromMulticrabCfg()
datasets.loadLuminosities()
#mc = "WJets"
mc = "QCD"
data = "2010"
#data = "2011"
# maxVtx = 15
maxVtx = 20
if data == "2010":
datasets.remove(
filter(lambda name: "Prompt" in name,
datasets.getAllDatasetNames()))
elif data == "2011":
datasets.remove(
filter(lambda name: "Dec22" in name,
datasets.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets)
style = tdrstyle.TDRStyle()
if mc == "QCD":
datasets.remove(["WJets"])
elif mc == "WJets":
datasets.remove(["QCD"])
h = histograms.HistoManager(datasets,
"signalAnalysis/verticesBeforeWeight")
h.normalizeToOne()
h.forEachMCHisto(styles.generator())
h.forHisto("Data", styles.getDataStyle())
h.setHistoDrawStyle("Data", "EP")
h.setHistoLegendStyle("Data", "p")
cf = histograms.CanvasFrame(h, "vertex_%s_%s" % (data, mc), xmax=maxVtx)
cf.frame.GetXaxis().SetTitle("N(vtx)")
cf.frame.GetYaxis().SetTitle("A.u.")
legend = histograms.createLegend()
h.addToLegend(legend)
h.draw()
legend.Draw()
cf.canvas.SaveAs(".png")
cf.canvas.SaveAs(".eps")
cf.canvas.SaveAs(".C")
# Weight
dataHisto = h.getHisto("Data").getRootHisto()
mcHisto = h.getHisto(mc).getRootHisto()
# For normalization, see https://twiki.cern.ch/twiki/bin/view/CMS/PileupReweighting
weightHisto = dataHisto.Clone("weights")
weightHisto.Divide(mcHisto)
print "Weight histo integral", weightHisto.Integral()
#weightHisto.Scale(1/dataHisto.Integral())
#weightHisto.Scale(1/weightHisto.Integral())
print "Weight histo integral", weightHisto.Integral()
print "Sum of [weight*prob]", sum([
weightHisto.GetBinContent(bin) * mcHisto.GetBinContent(bin)
for bin in xrange(1, weightHisto.GetNbinsX())
])
print "weights = cms.vdouble(%s)" % ", ".join([
"%.8f" % weightHisto.GetBinContent(bin)
for bin in xrange(1,
min(maxVtx, weightHisto.GetNbinsX()) + 1)
])
h = histograms.HistoManager(datasetRootHistos=[])
h.appendHisto(histograms.Histo(weightHisto, "Weight", "", "HIST"))
h.forEachHisto(styles.generator())
cf = histograms.CanvasFrame(h,
"vertex_weight_%s_%s" % (data, mc),
xmax=maxVtx)
cf.frame.GetXaxis().SetTitle("N(vtx)")
cf.frame.GetYaxis().SetTitle("Weight")
h.draw()
cf.canvas.SaveAs(".png")
cf.canvas.SaveAs(".eps")
cf.canvas.SaveAs(".C")
### Normalize the area of *all* histograms to 1
#NSelJets.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
NSelJets.setHistoLegendLabelMany(legendLabels) # many datasets, with dict
### Example how to modify legend styles
NSelJets.setHistoLegendStyleAll("F")
NSelJets.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
NSelJets.forEachMCHisto(styles.generator(fill=True)) # Apply SetFillColor too, needed for histogram stacking
NSelJets.forHisto("Data", styles.getDataStyle())
#NSelJets.setHistoDrawStyle("Data", "EP")
NSelJets.forHisto("BTau_146240-147116", styles.getDataStyle())
NSelJets.setHistoDrawStyle("BTau_146240-147116", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
NSelJets.stackMCHistograms()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(NSelJets, "NSelJets", ymin=0.01, ymax=None, xmin=0.0, xmax=10.0)
### Set the frame options, e.g. axis labels
cf.frame.GetXaxis().SetTitle("Number of Selected Jets")
cf.frame.GetYaxis().SetTitle(ylabel)
### Legend
def main():
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(filter(lambda name: "TTToHplus" in name and not "M120" in name, datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.2, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
# Merge EWK datasets
datasets.merge("EWK", [
"WJets",
"TTJets",
"DYJetsToLL",
"SingleTop",
"Diboson"
])
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create the normalized plot of transverse mass
# Read the histogram from the file
#mT = plots.DataMCPlot(datasets, analysis+"/transverseMass")
# Create the histogram from the tree (and see the selections explicitly)
td = dataset.TreeDraw(analysis+"/tree", weight="weightPileup*weightTrigger*weightPrescale",
selection="met_p4.Et() > 70 && Max$(jets_btag) > 1.7")
# mT = plots.DataMCPlot(datasets, td.clone(varexp="sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi())))>>dist(400, 0, 400)"))
# met = plots.DataMCPlot(datasets, td.clone(varexp="met_p4.Et()>>dist(400, 0, 400)"))
metBase = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJets")
metInver = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets")
metBase4050 = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJet4070")
metInver4050 = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets4070")
metBase5060 = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJets5060")
metInver5060 = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets5060")
metBase6070 = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJets6070")
metInver6070 = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets6070")
metBase7080 = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJets7080")
metInver7080 = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets7080")
metBase80100 = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJets80100")
metInver80100 = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets80100")
metBase100120 = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJets100120")
metInver100120 = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets100120")
metBase120150 = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJets120150")
metInver120150 = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets120150")
metBase150 = plots.DataMCPlot(datasets, analysis+"/MET_BaseLineTauIdJets150")
metInver150 = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdJets150")
# metInver = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdLoose")
# Rebin before subtracting
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metBase4050.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metInver4050.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone(analysis+"/MET_InvertedTauIdJets")
metInverted_data4050 = metInver.histoMgr.getHisto("Data").getRootHisto().Clone(analysis+"/MET_InvertedTauIdJets4050")
print "print inverted met"
print metInverted_data.GetEntries()
# Create the data-EWK histogram and draw it
diffBase = dataEwkDiff(metBase, "MET_base_data-ewk")
# diffInverted = dataEwkDiff(metInver,"MET_inverted_data-ewk")
diffInverted = dataEwkNoDiff(metInver,"MET_inverted_data-ewk")
diffBase4050 = dataEwkDiff(metBase, "MET_base_data-ewk-4050")
# diffInverted4070 = dataEwkDiff(metInver,"MET_inverted_data-ewk-4070")
diffInverted4050 = dataEwkNoDiff(metInver,"MET_inverted_data-ewk-4050")
# Draw the MET distribution
transverseMass(metBase,"MET_base")
transverseMass(metInver,"MET_inverted")
# Draw the MET distribution
transverseMass(metBase4050,"MET_base4050")
transverseMass(metInver4050,"MET_inverted4050")
# Set the styles
dataset._normalizeToOne(diffBase)
dataset._normalizeToOne(diffInverted)
plot = plots.ComparisonPlot(
histograms.Histo(diffBase, "Baseline"),
histograms.Histo(diffInverted, "Inverted"))
dataset._normalizeToOne(diffBase4050)
dataset._normalizeToOne(diffInverted4050)
plot2 = plots.ComparisonPlot(
histograms.Histo(diffBase4050, "Baseline4050"),
histograms.Histo(diffInverted4050, "Inverted4050"))
st1 = styles.getDataStyle().clone()
st2 = st1.clone()
st2.append(styles.StyleLine(lineColor=ROOT.kRed))
plot.histoMgr.forHisto("Baseline", st1)
plot.histoMgr.forHisto("Inverted", st2)
plot.createFrame("METbaseVSinverted-ewk", opts={"xmax": 400, "ymin":1e-5, "ymaxfactor": 1.5},
createRatio=True, opts2={"ymin": -5 , "ymax": 6 }, # bounds of the ratio plot
)
plot.getPad().SetLogy(True)
plot.setLegend(histograms.createLegend(0.7, 0.68, 0.9, 0.93))
plot.frame.GetXaxis().SetTitle("MET (GeV)")
plot.frame.GetYaxis().SetTitle("Data - EWK")
# Draw the plot
plot.draw()
plot.save()
plot2.createFrame("METbaseVSinverted-ewk-4070", opts={"xmax": 400, "ymin":1e-5, "ymaxfactor": 1.5},
createRatio=True, opts2={"ymin": -5 , "ymax": 6 }, # bounds of the ratio plot
)
plot2.getPad().SetLogy(True)
plot2.setLegend(histograms.createLegend(0.7, 0.68, 0.9, 0.93))
plot2.frame.GetXaxis().SetTitle("MET (GeV)")
plot2.frame.GetYaxis().SetTitle("Data - EWK")
# Draw the plot
plot2.draw()
plot2.save()
import ROOT
ROOT.gROOT.SetBatch(True)
ROOT.PyConfig.IgnoreCommandLineOptions = True
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.dataset as dataset
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.histograms as histograms
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.plots as plots
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.counter as counter
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.tdrstyle as tdrstyle
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.styles as styles
import HiggsAnalysis.HeavyChHiggsToTauNu.tools.tauEmbedding as tauEmbedding
analysisEmb = "signalAnalysis"
dataEra = "Run2012ABCD"
myStyles = [styles.getDataStyle()]
for st in styles.getStyles():
myStyles.append(st)
#myStyles.append(styles.StyleCompound([st, styles.StyleLine(lineStyle=2)]))
def main():
dirAve = "."
# dirSeeds = []
# inputInfoPath = os.path.join(dirAve, "inputInfo.txt")
# f = open(inputInfoPath)
# input_re = re.compile("Embedded input directory: (?P<dir>\S+)")
# for line in f:
# m = input_re.search(line)
# if m:
# dirSeeds.append(os.path.join("..", m.group("dir")))
# f.close()
### Normalize the area of *all* histograms to 1
#NSelJets.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
NSelJets.setHistoLegendLabelMany(legendLabels) # many datasets, with dict
### Example how to modify legend styles
NSelJets.setHistoLegendStyleAll("F")
NSelJets.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
NSelJets.forEachMCHisto(styles.generator(fill=True)) # Apply SetFillColor too, needed for histogram stacking
NSelJets.forHisto("Data", styles.getDataStyle())
#NSelJets.setHistoDrawStyle("Data", "EP")
NSelJets.forHisto("BTau_146240-147116", styles.getDataStyle())
NSelJets.setHistoDrawStyle("BTau_146240-147116", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
NSelJets.stackMCHistograms()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(NSelJets, "NSelJets", ymin=0.01, ymax=None, xmin=0.0, xmax=10.0)
### Set the frame options, e.g. axis labels
cf.frame.GetXaxis().SetTitle("Number of Selected Jets")
cf.frame.GetYaxis().SetTitle(ylabel)
### Legend
def Save(self,name):
hObserved = self.CreateGraph("Observed")
hExpected = self.CreateGraph("Expected")
hExpected1s = self.CreateGraph("Expected1")
hExpected1s.SetFillColor(ROOT.kYellow)
hExpected2s = self.CreateGraph("Expected2")
hExpected2s.SetFillColor(ROOT.kOrange)
style = tdrstyle.TDRStyle()
plot = plots.ComparisonManyPlot(
histograms.HistoGraph(hObserved, "Observed"),
[
histograms.HistoGraph(hExpected, "Expected"),
histograms.HistoGraph(hExpected1s, "Expected1"),
histograms.HistoGraph(hExpected2s, "Expected2")
]
)
obsStyle = styles.getDataStyle().clone()
plot.histoMgr.forHisto("Observed", styles.getDataStyle().clone())
plot.histoMgr.setHistoDrawStyle("Observed", "PL")
expStyle = styles.getDataStyle().clone()
expStyle.append(styles.StyleLine(lineStyle=2))
expStyle.append(styles.StyleLine(lineColor=ROOT.kRed))
expStyle.append(styles.StyleMarker(markerStyle=ROOT.kFullSquare))
expStyle.append(styles.StyleMarker(markerColor=ROOT.kRed))
plot.histoMgr.forHisto("Expected", expStyle)
plot.histoMgr.setHistoDrawStyle("Expected", "PL")
plot.histoMgr.setHistoDrawStyle("Expected1", "PL3")
plot.histoMgr.setHistoDrawStyle("Expected2", "PL3")
plot.createFrame(name, opts={"xmin": 70.1, "xmax": 169.9, "ymin":0, "ymax": 0.2})
plot.frame.GetXaxis().SetTitle(self.xtitle)
plot.frame.GetYaxis().SetTitle(self.ytitle)
plot.histoMgr.setHistoLegendStyle("Observed", "PL")
plot.histoMgr.setHistoLegendStyle("Expected", "PL")
plot.histoMgr.setHistoLegendStyle("Expected1", "F")
plot.histoMgr.setHistoLegendStyle("Expected2", "F")
plot.histoMgr.setHistoLegendLabelMany({
"Expected": "Expected median",
"Expected1": "Expected median #pm1#sigma",
"Expected2": "Expected median #pm2#sigma"
})
plot.setLegend(histograms.createLegend(0.55,0.68,0.9,0.93))
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
textSize = 16
textX = 0.19
textY = 0.8
textDY = 0.038
histograms.addText(textX,textY+2*textDY,"t#rightarrowbH^{#pm}, H^{#pm}#rightarrow#tau#nu",textSize)
histograms.addText(textX,textY+textDY,"Fully hadronic final state",textSize)
histograms.addText(textX,textY,"BR(H^{#pm}#rightarrow#tau^{#pm} #nu) = 1",textSize)
plot.draw()
plot.save()
# tauPts.normalizeToOne()
# ylabel = "A.u."
# Example how to set legend labels from defaults
# tauPts.setHistoLegendLabel("TTbar_Htaunu_M80", "H^{#pm} M=80") # one dataset at a time
tauPts.setHistoLegendLabelMany(legendLabels) # many datasets, with dict
# Example how to modify legend styles
tauPts.setHistoLegendStyleAll("F")
tauPts.setHistoLegendStyle("Data", "p")
# Apply the default styles (for all histograms, for MC histograms, for a single histogram)
# tauPts.forEachHisto(styles.generator())
# tauPts.forEachMCHisto(styles.generator(fill=True, fillStyle=3002)) # Example how to set non-default fill style
tauPts.forEachMCHisto(styles.generator(fill=True)) # Apply SetFillColor too, needed for histogram stacking
tauPts.forHisto("Data", styles.getDataStyle())
tauPts.setHistoDrawStyle("Data", "EP")
# Example how to stack all MC datasets
# Note: this MUST be done after all legend/style manipulation
tauPts.stackMCHistograms()
# Example how to add MC uncertainty
tauPts.addMCUncertainty(styles.getErrorStyle())
# tauPts.addMCUncertainty(styles.getErrorStyle(), "MC uncertainty")
# Create TCanvas and TH1F such that they cover all histograms
cf = histograms.CanvasFrame(tauPts, "taupt")
# cf = histograms.CanvasFrame(tauPts, "taupt", ymin=10, ymax=1e9) # for logy
def main():
datasets = dataset.getDatasetsFromMulticrabCfg(weightedCounters=False)
datasets.loadLuminosities()
#mc = "WJets"
mc = "QCD"
data = "2010"
#data = "2011"
# maxVtx = 15
maxVtx = 20
if data == "2010":
datasets.remove(filter(lambda name: "Prompt" in name, datasets.getAllDatasetNames()))
elif data == "2011":
datasets.remove(filter(lambda name: "Dec22" in name, datasets.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets)
style = tdrstyle.TDRStyle()
if mc == "QCD":
datasets.remove(["WJets"])
elif mc == "WJets":
datasets.remove(["QCD"])
h = histograms.HistoManager(datasets, "signalAnalysis/verticesBeforeWeight")
h.normalizeToOne()
h.forEachMCHisto(styles.generator())
h.forHisto("Data", styles.getDataStyle())
h.setHistoDrawStyle("Data", "EP")
h.setHistoLegendStyle("Data", "p")
cf = histograms.CanvasFrame(h, "vertex_%s_%s" % (data, mc), xmax=maxVtx)
cf.frame.GetXaxis().SetTitle("N(vtx)")
cf.frame.GetYaxis().SetTitle("A.u.")
legend = histograms.createLegend()
h.addToLegend(legend)
h.draw()
legend.Draw()
cf.canvas.SaveAs(".png")
cf.canvas.SaveAs(".eps")
cf.canvas.SaveAs(".C")
# Weight
dataHisto = h.getHisto("Data").getRootHisto()
mcHisto = h.getHisto(mc).getRootHisto()
# For normalization, see https://twiki.cern.ch/twiki/bin/view/CMS/PileupReweighting
weightHisto = dataHisto.Clone("weights")
weightHisto.Divide(mcHisto)
print "Weight histo integral", weightHisto.Integral()
#weightHisto.Scale(1/dataHisto.Integral())
#weightHisto.Scale(1/weightHisto.Integral())
print "Weight histo integral", weightHisto.Integral()
print "Sum of [weight*prob]", sum([weightHisto.GetBinContent(bin)*mcHisto.GetBinContent(bin) for bin in xrange(1, weightHisto.GetNbinsX())])
print "weights = cms.vdouble(%s)" % ", ".join(["%.8f" % weightHisto.GetBinContent(bin) for bin in xrange(1, min(maxVtx, weightHisto.GetNbinsX())+1)])
h = histograms.HistoManager(datasetRootHistos=[])
h.appendHisto(histograms.Histo(weightHisto, "Weight", "", "HIST"))
h.forEachHisto(styles.generator())
cf = histograms.CanvasFrame(h, "vertex_weight_%s_%s" % (data, mc), xmax=maxVtx)
cf.frame.GetXaxis().SetTitle("N(vtx)")
cf.frame.GetYaxis().SetTitle("Weight")
h.draw()
cf.canvas.SaveAs(".png")
cf.canvas.SaveAs(".eps")
cf.canvas.SaveAs(".C")
def main():
# Create all datasets from a multicrab task
datasets = dataset.getDatasetsFromMulticrabCfg(counters=counters)
# As we use weighted counters for MC normalisation, we have to
# update the all event count to a separately defined value because
# the analysis job uses skimmed pattuple as an input
datasets.updateNAllEventsToPUWeighted()
# Read integrated luminosities of data datasets from lumi.json
datasets.loadLuminosities()
# Include only 120 mass bin of HW and HH datasets
datasets.remove(
filter(lambda name: "TTToHplus" in name and not "M120" in name,
datasets.getAllDatasetNames()))
# Default merging nad ordering of data and MC datasets
# All data datasets to "Data"
# All QCD datasets to "QCD"
# All single top datasets to "SingleTop"
# WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasets)
# Set BR(t->H) to 0.2, keep BR(H->tau) in 1
xsect.setHplusCrossSectionsToBR(datasets, br_tH=0.05, br_Htaunu=1)
# Merge WH and HH datasets to one (for each mass bin)
# TTToHplusBWB_MXXX and TTToHplusBHminusB_MXXX to "TTToHplus_MXXX"
plots.mergeWHandHH(datasets)
# Merge EWK datasets
datasets.merge("EWK",
["WJets", "TTJets", "DYJetsToLL", "SingleTop", "Diboson"])
# Apply TDR style
style = tdrstyle.TDRStyle()
# Create the normalized plot of transverse mass
# Read the histogram from the file
#mT = plots.DataMCPlot(datasets, analysis+"/transverseMass")
# Create the histogram from the tree (and see the selections explicitly)
td = dataset.TreeDraw(
analysis + "/tree",
weight="weightPileup*weightTrigger*weightPrescale",
selection="met_p4.Et() > 70 && Max$(jets_btag) > 1.7")
# mT = plots.DataMCPlot(datasets, td.clone(varexp="sqrt(2 * tau_p4.Pt() * met_p4.Et() * (1-cos(tau_p4.Phi()-met_p4.Phi())))>>dist(400, 0, 400)"))
# met = plots.DataMCPlot(datasets, td.clone(varexp="met_p4.Et()>>dist(400, 0, 400)"))
metBase = plots.DataMCPlot(datasets, analysis + "/MET_BaseLineTauIdJets")
metInver = plots.DataMCPlot(datasets, analysis + "/MET_InvertedTauIdJets")
metBase4050 = plots.DataMCPlot(datasets,
analysis + "/MET_BaseLineTauIdJet4070")
metInver4050 = plots.DataMCPlot(datasets,
analysis + "/MET_InvertedTauIdJets4070")
metBase5060 = plots.DataMCPlot(datasets,
analysis + "/MET_BaseLineTauIdJets5060")
metInver5060 = plots.DataMCPlot(datasets,
analysis + "/MET_InvertedTauIdJets5060")
metBase6070 = plots.DataMCPlot(datasets,
analysis + "/MET_BaseLineTauIdJets6070")
metInver6070 = plots.DataMCPlot(datasets,
analysis + "/MET_InvertedTauIdJets6070")
metBase7080 = plots.DataMCPlot(datasets,
analysis + "/MET_BaseLineTauIdJets7080")
metInver7080 = plots.DataMCPlot(datasets,
analysis + "/MET_InvertedTauIdJets7080")
metBase80100 = plots.DataMCPlot(datasets,
analysis + "/MET_BaseLineTauIdJets80100")
metInver80100 = plots.DataMCPlot(datasets,
analysis + "/MET_InvertedTauIdJets80100")
metBase100120 = plots.DataMCPlot(datasets,
analysis + "/MET_BaseLineTauIdJets100120")
metInver100120 = plots.DataMCPlot(
datasets, analysis + "/MET_InvertedTauIdJets100120")
metBase120150 = plots.DataMCPlot(datasets,
analysis + "/MET_BaseLineTauIdJets120150")
metInver120150 = plots.DataMCPlot(
datasets, analysis + "/MET_InvertedTauIdJets120150")
metBase150 = plots.DataMCPlot(datasets,
analysis + "/MET_BaseLineTauIdJets150")
metInver150 = plots.DataMCPlot(datasets,
analysis + "/MET_InvertedTauIdJets150")
# metInver = plots.DataMCPlot(datasets, analysis+"/MET_InvertedTauIdLoose")
# Rebin before subtracting
metBase.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metInver.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metBase4050.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metInver4050.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
metInverted_data = metInver.histoMgr.getHisto("Data").getRootHisto().Clone(
analysis + "/MET_InvertedTauIdJets")
metInverted_data4050 = metInver.histoMgr.getHisto(
"Data").getRootHisto().Clone(analysis + "/MET_InvertedTauIdJets4050")
print "print inverted met"
print metInverted_data.GetEntries()
# Create the data-EWK histogram and draw it
diffBase = dataEwkDiff(metBase, "MET_base_data-ewk")
# diffInverted = dataEwkDiff(metInver,"MET_inverted_data-ewk")
diffInverted = dataEwkNoDiff(metInver, "MET_inverted_data-ewk")
diffBase4050 = dataEwkDiff(metBase, "MET_base_data-ewk-4050")
# diffInverted4070 = dataEwkDiff(metInver,"MET_inverted_data-ewk-4070")
diffInverted4050 = dataEwkNoDiff(metInver, "MET_inverted_data-ewk-4050")
# Draw the MET distribution
transverseMass(metBase, "MET_base")
transverseMass(metInver, "MET_inverted")
# Draw the MET distribution
transverseMass(metBase4050, "MET_base4050")
transverseMass(metInver4050, "MET_inverted4050")
# Set the styles
dataset._normalizeToOne(diffBase)
dataset._normalizeToOne(diffInverted)
plot = plots.ComparisonPlot(histograms.Histo(diffBase, "Baseline"),
histograms.Histo(diffInverted, "Inverted"))
dataset._normalizeToOne(diffBase4050)
dataset._normalizeToOne(diffInverted4050)
plot2 = plots.ComparisonPlot(
histograms.Histo(diffBase4050, "Baseline4050"),
histograms.Histo(diffInverted4050, "Inverted4050"))
st1 = styles.getDataStyle().clone()
st2 = st1.clone()
st2.append(styles.StyleLine(lineColor=ROOT.kRed))
plot.histoMgr.forHisto("Baseline", st1)
plot.histoMgr.forHisto("Inverted", st2)
plot.createFrame(
"METbaseVSinverted-ewk",
opts={
"xmax": 400,
"ymin": 1e-5,
"ymaxfactor": 1.5
},
createRatio=True,
opts2={
"ymin": -5,
"ymax": 6
}, # bounds of the ratio plot
)
plot.getPad().SetLogy(True)
plot.setLegend(histograms.createLegend(0.7, 0.68, 0.9, 0.93))
plot.frame.GetXaxis().SetTitle("MET (GeV)")
plot.frame.GetYaxis().SetTitle("Data - EWK")
# Draw the plot
plot.draw()
plot.save()
plot2.createFrame(
"METbaseVSinverted-ewk-4070",
opts={
"xmax": 400,
"ymin": 1e-5,
"ymaxfactor": 1.5
},
createRatio=True,
opts2={
"ymin": -5,
"ymax": 6
}, # bounds of the ratio plot
)
plot2.getPad().SetLogy(True)
plot2.setLegend(histograms.createLegend(0.7, 0.68, 0.9, 0.93))
plot2.frame.GetXaxis().SetTitle("MET (GeV)")
plot2.frame.GetYaxis().SetTitle("Data - EWK")
# Draw the plot
plot2.draw()
plot2.save()
### Normalize the area of *all* histograms to 1
#NBtagJets.normalizeToOne()
#ylabel = "a.u"
############################### STYLES ###############################
### Example how to set legend labels from defaults
NBtagJets.setHistoLegendLabelMany(legendLabels) # many datasets, with dict
### Example how to modify legend styles
NBtagJets.setHistoLegendStyleAll("F")
NBtagJets.setHistoLegendStyle("Data", "p")
### Apply the default styles (for all histograms, for MC histograms, for a single histogram)
NBtagJets.forEachMCHisto(styles.generator(fill=True)) # Apply SetFillColor too, needed for histogram stacking
NBtagJets.forHisto("Data", styles.getDataStyle())
#NBtagJets.setHistoDrawStyle("Data", "EP")
### Example how to stack all MC datasets. NOTE: this MUST be done after all legend/style manipulation
#NBtagJets.stackMCDatasets()
### Create TCanvas and TH1F such that they cover all histograms
cf = CanvasFrame(NBtagJets, "NBtagJets", ymin=0.01, ymax=None, xmin=0.0, xmax=8.0)
### Set the frame options, e.g. axis labels
cf.frame.GetXaxis().SetTitle("Number of Btag Jets")
cf.frame.GetYaxis().SetTitle(ylabel)
### Legend
legend = createLegend(0.7, 0.5, 0.9, 0.8)
NBtagJets.addToLegend(legend)
