def drawSave(h, updatePaletteStyle=False):
h.draw()
if updatePaletteStyle:
histograms.updatePaletteStyle(h.histoMgr.getHistos()[0].getRootHisto())
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.save()
def tauPhi(h, step="", rebin=5):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "#phi^{#tau jet}"
ylabel = "Events / %.1f" % h.binWidth()
opts = {"ymaxfactor": 2}
h.stackMCHistograms()
h.addMCUncertainty()
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.001
name = "tauPhiSimulateEmbedded_%s_log" % step
h.createFrameFraction(name, opts=opts)
#h.createFrame(name, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
def jetPt(h, prefix="", rebin=10):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "Jet p_{T} (GeV/c)"
ylabel = "Number of jets / %.0f GeV/c" % h.binWidth()
ptcut = 30
ymin = 1e-1
xmax = 400
h.stackMCHistograms()
h.addMCUncertainty()
h.createFrame(prefix+"jet_pt_log", xmax=xmax, ymin=ymin, yfactor=2)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
h.createFrame(prefix+"jet_pt_log_cut%d"%ptcut, xmin=ptcut, xmax=xmax, ymin=ymin, yfactor=2)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
def drawSave(h, updatePaletteStyle=False):
h.draw()
if updatePaletteStyle:
histograms.updatePaletteStyle(h.histoMgr.getHistos()[0].getRootHisto())
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.save()
def tauCandPhi(h, step="", rebin=5):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "#phi^{#tau candidate}"
ylabel = "Events / %.1f" % h.binWidth()
opts = {"ymaxfactor": 2}
h.stackMCHistograms()
h.addMCUncertainty()
scaleMCfromWmunu(h)
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.01
name = "tauCandidatePhi_%s_log" % step
h.createFrameFraction(name, opts=opts)
#h.createFrame(name, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
def deltaPhi(h, step="", rebin=5):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "R_{#tau}"
ylabel = "Events / %.2f" % h.binWidth()
opts = {"ymaxfactor": 2}
h.stackMCHistograms()
h.addMCUncertainty()
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.001
name = "deltaPhiSimulateEmbedded_%s_log" % step
h.createFrameFraction(name, opts=opts)
#h.createFrame(name, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend(0.5, 0.3, 0.7, 0.6))
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
def transverseMass(h, rebin=5, ratio=False, particle="#tau"):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "m_{T}(%s, MET) (GeV/c^{2})" % particle
ylabel = "Events / %.2f GeV/c^{2}" % h.binWidth()
opts = {"ymaxfactor": 2}
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.001
opts["xmax"] = 200
name = prefix + "_" + h.name
h.createFrame(name, createRatio=ratio, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
name += "_log"
h.createFrame(name, createRatio=ratio, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
def tauCandPt(h, step="", rebin=1):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
ylabel = "Events /%.0f GeV/c" % h.binWidth()
xlabel = "p_{T}^{#tau candidate} (GeV/c)"
opts = {"ymaxfactor": 2}
h.stackMCHistograms()
h.addMCUncertainty()
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.001
name = "tauCandidatePtSimulatedEmbedded_%s_log" % step
h.createFrameFraction(name, opts=opts)
#h.createFrame(name, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
def selectionFlow(h, name, rebin=1, ratio=False):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "Cut"
ylabel = "Events"
#h.stackMCSignalHistograms()
h.stackMCHistograms()
h.addMCUncertainty()
scaleMCfromWmunu(h)
njets = 5
lastSelection = njets
opts = {"xmax": lastSelection, "ymin": 0.1, "ymaxfactor": 2, "nbins": lastSelection}
opts2 = {"ymin": 0.5, "ymax": 1.5}
h.createFrame(name, opts=opts, createRatio=ratio, opts2=opts2)
xaxis = h.getFrame().GetXaxis()
xaxis.SetBinLabel(1, "Trigger")
xaxis.SetBinLabel(2, "#tau ID+R_{#tau}")
xaxis.SetBinLabel(3, "e veto")
xaxis.SetBinLabel(4, "#mu veto")
xaxis.SetBinLabel(5, "N_{jets}")
h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.addLuminosityText()
addMassBRText(x=0.4, y=0.87)
h.save()
def common(h, xlabel, ylabel, cutLine=None, cutBox=None, function=None):
# Add cut line and/or box
if cutLine != None:
lst = cutLine
if not isinstance(lst, list):
lst = [lst]
for line in lst:
h.addCutBoxAndLine(line, box=False, line=True)
if cutBox != None:
lst = cutBox
if not isinstance(lst, list):
lst = [lst]
for box in lst:
h.addCutBoxAndLine(**box)
if function != None:
function(h)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.addLuminosityText()
h.save()
def common(h, xlabel, ylabel, cutLine=None, cutBox=None, function=None):
# Add cut line and/or box
if cutLine != None:
lst = cutLine
if not isinstance(lst, list):
lst = [lst]
for line in lst:
h.addCutBoxAndLine(line, box=False, line=True)
if cutBox != None:
lst = cutBox
if not isinstance(lst, list):
lst = [lst]
for box in lst:
h.addCutBoxAndLine(**box)
if function != None:
function(h)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.addLuminosityText()
h.save()
def muonPhi(h, prefix="", plotAll=False, rebin=1):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "Muon #phi"
ylabel = "Number of muons / %.1f" % h.binWidth()
h.stackMCHistograms()
if plotAll:
h.createFrame(prefix+"muon_phi", yfactor=1.4)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
h.createFrame(prefix+"muon_phi_log", yfactor=2, ymin=0.1)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
ROOT.gPad.SetLogy()
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
def vertexCount(h, prefix="", postfix=""):
xlabel = "Number of vertices"
ylabel = "Number of events"
h.stackMCHistograms()
h.addMCUncertainty()
h.createFrame(prefix+"vertices"+postfix)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
h.createFrame(prefix+"vertices"+postfix+"_log", ymin=0.1, factor=2)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
ROOT.gPad.SetLogy(True)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
def transverseMass(h, name="transverseMass", rebin=2, log=True):
# name = h.getRootHistoPath()
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "m_{T}(#tau jet, MET) (GeV/c^{2})"
ylabel = "A.u."
# h.stackMCHistograms()
# h.addMCUncertainty()
opts = {"xmax": 200}
# opts = {"ymin": 0.0001,"xmax": 200, "ymaxfactor": 1.3}
# opts2 = {"ymin": 0.5, "ymax": 1.5}
# opts = {"xmax": 200}
if log:
name += "_log"
opts["ymin"] = 1e-4
opts["ymaxfactor"] = 2
#h.createFrameFraction(name, opts=opts)
h.createFrame(name, opts=opts)
legend = histograms.createLegend()
if log:
h.getPad().SetLogy(True)
histograms.moveLegend(legend, dx=-0.5, dy=-0.3)
h.setLegend(legend)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
# h.addLuminosityText()
h.save()
def transverseMass(h, rebin=5, ratio=False, particle="#tau"):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "m_{T}(%s, MET) (GeV/c^{2})" % particle
ylabel = "Events / %.2f GeV/c^{2}" % h.binWidth()
opts = {"ymaxfactor": 2}
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.001
opts["xmax"] = 200
name = prefix+"_"+h.name
h.createFrame(name, createRatio=ratio, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
name += "_log"
h.createFrame(name, createRatio=ratio, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
def plotCommon(h, rebin, xlabel, log, opts={}, opts2={}, coverPadOpts={}):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
#h.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineWidth(5))
#h.histoMgr.getHistos()[0].getRootHisto().SetLineStyle(3)
ylabel = "A.u. / %.2f" % h.binWidth()
xlabel = xlabel
opts_ = {}
opts_.update(opts)
coverPadOpts_ = {}
coverPadOpts_.update(coverPadOpts)
if log:
if not "ymaxfactor" in opts_:
opts_["ymaxfactor"] = 2
if not "yminfactor" in opts_ and not "ymin" in opts_:
opts_["yminfactor"] = 1e-4
if not "ymin" in coverPadOpts_:
coverPadOpts_["ymin"] = 0.278
name = "TauEmbeddingTaus_%s" % (h.plotName)
if log:
name += "_log"
h.createFrame(name, createRatio=True, opts=opts_, opts2=opts2, coverPadOpts=coverPadOpts_)
h.getFrame2().GetYaxis().SetTitle(h.ratioLabel)
h.getFrame2().GetYaxis().SetTitleSize(27) # Absolute size
h.getFrame2().GetYaxis().SetTitleOffset(2)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(createLegend())
if log:
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText(x=0.5, y=0.3)
h.save()
def tauCandEta(h, step="", rebin=5):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "#eta^{#tau candidate}"
ylabel = "Events / %.1f" % h.binWidth()
opts = {"ymaxfactor": 2}
h.stackMCHistograms()
h.addMCUncertainty()
scaleMCfromWmunu(h)
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.001
# opts = {"xmax": 2.5,"xmin":-2.5}
# opts["xmin"] = -2.7
# opts["xmax"] = 2.7
name = "tauCandidateEta_%s_log" % step
# h.createFrameFraction(name, opts=opts)
h.createFrame(name, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend(0.5, 0.2, 0.7, 0.5))
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
def tauCandPt(h, step="", rebin=2):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
ylabel = "Events /%.0f GeV/c" % h.binWidth()
xlabel = "p_{T}^{#tau candidate} (GeV/c)"
opts = {"ymaxfactor": 2}
h.stackMCHistograms()
h.addMCUncertainty()
scaleMCfromWmunu(h)
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.001
name = "tauCandidatePt_%s_log" % step
h.createFrameFraction(name, opts=opts)
#h.createFrame(name, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.setLegend(histograms.createLegend(0.7, 0.6, 0.9, 0.9))
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
def transverseMass(h, prefix="", rebin=1, ratio=False):
xlabel = "m_{T}(#mu, E_{T}^{miss}) (GeV)"
ylabel = "Events / %.0f GeV"
_optsLin = {}
_optsLog = {"ymin": 0.1, "ymaxfactor": 2}
_opts2 = {"ymin": 0, "ymax": 2}
if rebin > 1:
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
ylabel = ylabel % h.binWidth()
h.stackMCHistograms()
h.addMCUncertainty()
h.createFrame(prefix+"mt", opts=_optsLin)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
h.createFrame(prefix+"mt_log", createRatio=ratio, opts=_optsLog, opts2=_opts2)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.moveLegend(histograms.createLegend()))
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.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(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 vertexCount(h, prefix="", postfix="", ratio=True):
xlabel = "Number of good vertices"
ylabel = "Number of events"
if h.normalizeToOne:
ylabel = "Arbitrary units."
h.stackMCHistograms()
stack = h.histoMgr.getHisto("StackedMC")
#hsum = stack.getSumRootHisto()
#total = hsum.Integral(0, hsum.GetNbinsX()+1)
#for rh in stack.getAllRootHistos():
# dataset._normalizeToFactor(rh, 1/total)
#dataset._normalizeToOne(h.histoMgr.getHisto("Data").getRootHisto())
h.addMCUncertainty()
opts = {}
opts_log = {"ymin": 1e-10, "ymaxfactor": 10, "xmax": 30}
opts_log.update(opts)
opts2 = {"ymin": 0.5, "ymax": 3}
opts2_log = opts2
#opts2_log = {"ymin": 5e-2, "ymax": 5e2}
h.createFrame(prefix + "vertices" + postfix,
opts=opts,
createRatio=ratio,
opts2=opts2)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
# histograms.addLuminosityText(x=None, y=None, lumi=191.)
h.histoMgr.addLuminosityText()
if h.normalizeToOne:
histograms.addText(0.35, 0.9, "Normalized to unit area", 17)
h.save()
h.createFrame(prefix + "vertices" + postfix + "_log",
opts=opts_log,
createRatio=ratio,
opts2=opts2_log)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.getPad1().SetLogy(True)
#h.getPad2().SetLogy(True)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
# histograms.addLuminosityText(x=None, y=None, lumi=191.)
h.histoMgr.addLuminosityText()
if h.normalizeToOne:
histograms.addText(0.35, 0.9, "Normalized to unit area", 17)
h.save()
def common(h, xlabel, ylabel):
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.addLuminosityText()
h.save()
def _plotLinear(self, h, selection, met):
h.createFrame(selection+"_"+met+self.postfix, ymax=self.ymax, xmax=self.xmax)
h.frame.GetXaxis().SetTitle(self.xlabel(met))
h.frame.GetYaxis().SetTitle(self.ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
def common(h, xlabel, ylabel, addLuminosityText=True, afterDraw=None):
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.draw()
if afterDraw != None:
afterDraw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
if addLuminosityText:
h.addLuminosityText()
h.save()
def common(h, xlabel, ylabel, addLuminosityText=True, textFunction=None):
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
if addLuminosityText:
h.addLuminosityText()
if textFunction != None:
textFunction()
h.save()
def _plotLog(self, h, selection, met):
h.createFrame(selection+"_"+met+"_log"+self.postfix, ymin=0.1, yfactor=2, xmax=self.xmax)
h.frame.GetXaxis().SetTitle(self.xlabel(met))
h.frame.GetYaxis().SetTitle(self.ylabel)
h.setLegend((histograms.createLegend()))
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
def vertexCount(h, prefix="", postfix="", ratio=True):
xlabel = "Number of good vertices"
ylabel = "Number of events"
if h.normalizeToOne:
ylabel = "Arbitrary units."
h.stackMCHistograms()
stack = h.histoMgr.getHisto("StackedMC")
#hsum = stack.getSumRootHisto()
#total = hsum.Integral(0, hsum.GetNbinsX()+1)
#for rh in stack.getAllRootHistos():
# dataset._normalizeToFactor(rh, 1/total)
#dataset._normalizeToOne(h.histoMgr.getHisto("Data").getRootHisto())
h.addMCUncertainty()
opts = {}
opts_log = {"ymin": 1e-10, "ymaxfactor": 10, "xmax": 30}
opts_log.update(opts)
opts2 = {"ymin": 0.5, "ymax": 3}
opts2_log = opts2
#opts2_log = {"ymin": 5e-2, "ymax": 5e2}
h.createFrame(prefix+"vertices"+postfix, opts=opts, createRatio=ratio, opts2=opts2)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
# histograms.addLuminosityText(x=None, y=None, lumi=191.)
h.histoMgr.addLuminosityText()
if h.normalizeToOne:
histograms.addText(0.35, 0.9, "Normalized to unit area", 17)
h.save()
h.createFrame(prefix+"vertices"+postfix+"_log", opts=opts_log, createRatio=ratio, opts2=opts2_log)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.getPad1().SetLogy(True)
#h.getPad2().SetLogy(True)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
# histograms.addLuminosityText(x=None, y=None, lumi=191.)
h.histoMgr.addLuminosityText()
if h.normalizeToOne:
histograms.addText(0.35, 0.9, "Normalized to unit area", 17)
h.save()
def doPlot(datasets, analyses, path, name, rootFile=None, rootHistoName=None):
histos = []
legends = {"Plus": "#tau-jet energy scale variated by +3 %",
"Minus": "#tau-jet energy scale variated by -3 %"}
for aname, analysis in analyses:
p = None
if isinstance(path, basestring):
p = plots.DataMCPlot(datasets, analysis+"/"+path)
else:
p = plots.DataMCPlot(datasets, path.clone(tree=analysis+"/tree"))
if normalize:
tauEmbedding.scaleNormalization(p)
h = p.histoMgr.getHisto("Data")
h.setName(aname)
h.setLegendLabel(legends.get(aname, aname))
histos.append(h)
p = plots.ComparisonManyPlot(histos[0], histos[1:])
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
if rootFile:
baseline = histos[0].getRootHisto().Clone("EWKtau_"+rootHistoName)
baseline.SetDirectory(rootFile)
baseline.Write()
plus = histos[1].getRootHisto().Clone("EWKtau_JESUp_"+rootHistoName)
plus.SetDirectory(rootFile)
plus.Write()
minus = histos[2].getRootHisto().Clone("EWKtau_JESDown_"+rootHistoName)
minus.SetDirectory(rootFile)
minus.Write()
styles.mcStyle(p.histoMgr.getHisto("Plus"))
styles.mcStyle2(p.histoMgr.getHisto("Minus"))
p.histoMgr.getHisto("Minus").getRootHisto().SetMarkerSize(2)
p.setLuminosity(datasets.getDataset("Data").getLuminosity())
p.createFrame(name, createRatio=True, opts2={"ymax": 2}, opts={"ymax": 40})
yaxis = p.getFrame2().GetYaxis()
yaxis.SetTitle("Ratio")
#yaxis.SetTitleSize(yaxis.GetTitleSize()*0.8)
p.setLegend(histograms.moveLegend(histograms.createLegend()))
p.frame.GetXaxis().SetTitle("m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})")
p.frame.GetYaxis().SetTitle("Events / 20 GeV/c^{2}")
p.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
p.addLuminosityText()
p.save()
def jetMultiplicity(h, prefix=""):
h.stackMCHistograms()
h.addMCUncertainty()
h.createFrame(prefix+"njets_log", xmin=3, xmax=10, ymin=0.1, yfactor=2)
h.frame.GetXaxis().SetTitle("Jet multiplicity")
h.frame.GetYaxis().SetTitle("Number of events")
h.setLegend(histograms.createLegend())
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
def muonD0():
# Muon track ip w.r.t. beam spot
h = Plot(datasets, lastSelection+"/muon_trackDB")
h.stackMCHistograms()
h.createFrame(lastSelection+"_muon_trackdb", xmin=0, xmax=0.2, ymin=0.1)
h.frame.GetXaxis().SetTitle("Muon track d_{0}(Bsp) (cm)")
h.frame.GetYaxis().SetTitle("Number of muons")
h.setLegend(histograms.createLegend())
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
def dataMCExample(datasets):
# Create data-MC comparison plot, with the default
# - legend labels (defined in plots._legendLabels)
# - plot styles (defined in plots._plotStyles, and in styles)
# - drawing styles ('HIST' for MC, 'EP' for data)
# - legend styles ('L' for MC, 'P' for data)
plot = plots.DataMCPlot(datasets, analysis + "/SelectedTau_pT_AfterTauID")
# Example of how to rebin all histograms in a histogram manager of a plot
plot.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
# Stack all MC histograms, except signal
# The stacked histograms become filled
plot.stackMCHistograms()
# Add MC statistical uncertainty band, calculated as a total of
# the stacked histograms
plot.addMCUncertainty()
# Create the cancas and the frame, with a file name of taupt
#plot.createFrame("taupt")
# give explicitly some of the boundaries, leave the rest to be the default
plot.createFrame("taupt", opts={"ymin": 1e-1, "ymaxfactor": 10})
# give explicitly the x and y axis boundaries
#plot.createFrame("taupt", opts={"xmin":40, "xmax":100, "ymin":1e-4, "ymaxfactor": 10})
# Set Y axis to logarithmic
plot.getPad().SetLogy(True)
# Create legend to the default position
plot.setLegend(histograms.createLegend())
# to a fixed position
#plot.setLegend(histograms.createLegend(0.7, 0.6, 0.9, 0.9))
# to the default position, move the legend after that, and change the width and height
#plot.setLegend(histograms.moveLegend(histograms.createLegend(), dx=0.1, dy=-0.1, dw=0.1, dh=-0.1)
# Set X/Y axis labels via TAxis
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.addCmsPreliminaryText()
histograms.addEnergyText()
plot.addLuminosityText()
# Save the plot to files
plot.save()
def dataMCExample(datasets):
# Create data-MC comparison plot, with the default
# - legend labels (defined in plots._legendLabels)
# - plot styles (defined in plots._plotStyles, and in styles)
# - drawing styles ('HIST' for MC, 'EP' for data)
# - legend styles ('L' for MC, 'P' for data)
plot = plots.DataMCPlot(datasets, analysis+"/SelectedTau_pT_AfterTauID")
# Example of how to rebin all histograms in a histogram manager of a plot
plot.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
# Stack all MC histograms, except signal
# The stacked histograms become filled
plot.stackMCHistograms()
# Add MC statistical uncertainty band, calculated as a total of
# the stacked histograms
plot.addMCUncertainty()
# Create the cancas and the frame, with a file name of taupt
#plot.createFrame("taupt")
# give explicitly some of the boundaries, leave the rest to be the default
plot.createFrame("taupt", opts={"ymin": 1e-1, "ymaxfactor": 10})
# give explicitly the x and y axis boundaries
#plot.createFrame("taupt", opts={"xmin":40, "xmax":100, "ymin":1e-4, "ymaxfactor": 10})
# Set Y axis to logarithmic
plot.getPad().SetLogy(True)
# Create legend to the default position
plot.setLegend(histograms.createLegend())
# to a fixed position
#plot.setLegend(histograms.createLegend(0.7, 0.6, 0.9, 0.9))
# to the default position, move the legend after that, and change the width and height
#plot.setLegend(histograms.moveLegend(histograms.createLegend(), dx=0.1, dy=-0.1, dw=0.1, dh=-0.1)
# Set X/Y axis labels via TAxis
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.addCmsPreliminaryText()
histograms.addEnergyText()
plot.addLuminosityText()
# Save the plot to files
plot.save()
def transverseMass(plot,name):
plot.histoMgr.forHisto("EWK", styles.StyleFill(styles.ttStyle))
plot.stackMCHistograms()
plot.setLegend(histograms.createLegend(0.7, 0.68, 0.9, 0.93))
plot.createFrame(name, opts={"ymin": 1e-1, "ymaxfactor": 10})
# Set Y axis to logarithmic
plot.getPad().SetLogy(True)
plot.frame.GetXaxis().SetTitle("MET (GeV)")
plot.frame.GetYaxis().SetTitle("Events / %.0f GeV" % plot.binWidth())
plot.draw()
# Add the various texts to
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
plot.addLuminosityText()
plot.save()
def plotDataEwkDiff(mT, name):
data = dataEwkDiff(mT, name)
data.SetName("Data-EWK")
# Draw the subtracted plot
plot = plots.PlotBase([data])
plot.createFrame(name, opts={"ymin": 1e-1, "ymaxfactor": 10})
plot.frame.GetXaxis().SetTitle("MET (GeV)")
plot.frame.GetYaxis().SetTitle("Data - EWK")
# Set Y axis to logarithmic
plot.getPad().SetLogy(True)
plot.draw()
# Add the various texts to
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
# plot.addLuminosityText()
plot.save()
def muonPt(h, prefix="", rebin=1, ratio=False, cutBox=None):
xlabel = "Muon p_{T} (GeV/c)"
ylabel = "Events / %.0f GeV/c"
#ylabel = "Number of events / 5.0 GeV/c"
_optsLin = {}
_optsLog = {"ymin": 0.1, "ymaxfactor": 2}
_opts2 = {"ymin": 0, "ymax": 2}
if rebin > 1:
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
ylabel = ylabel % h.binWidth()
h.stackMCHistograms()
h.addMCUncertainty()
# tmp = h.histoMgr.getHisto("Data").getRootHisto()
# dataEvents = tmp.Integral(0, tmp.GetNbinsX()+1)
# tmp = h.histoMgr.getHisto("StackedMC").getSumRootHisto()
# mcEvents = tmp.Integral(0, tmp.GetNbinsX()+1)
# print "Muon pt Data/MC = %f/%f = %f" % (dataEvents, mcEvents, dataEvents/mcEvents)
h.createFrame(prefix+"muon_pt", opts=_optsLin)
if cutBox != None:
h.addCutBoxAndLine(**cutBox)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
h.createFrame(prefix+"muon_pt_log", createRatio=ratio, opts=_optsLog, opts2=_opts2)
if cutBox != None:
h.addCutBoxAndLine(**cutBox)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.moveLegend(histograms.createLegend()))
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
def wTransMass(h, prefix="", rebin=10):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(10))
xlabel = "m_{T}(#mu, MET) (GeV/c)"
ylabel = "Number of events / %.0f GeV/c^{2}" % h.binWidth()
h.stackMCHistograms()
h.addMCUncertainty()
h.createFrame(prefix+"wtmass_log", ymin=0.1, xmax=350, yfactor=2)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.histoMgr.addLuminosityText()
h.save()
def vertexCount(h, prefix="", postfix=""):
xlabel = "Number of vertices"
ylabel = "A.u."
h.stackMCHistograms()
stack = h.histoMgr.getHisto("StackedMC")
#hsum = stack.getSumRootHisto()
#total = hsum.Integral(0, hsum.GetNbinsX()+1)
#for rh in stack.getAllRootHistos():
# dataset._normalizeToFactor(rh, 1/total)
#dataset._normalizeToOne(h.histoMgr.getHisto("Data").getRootHisto())
h.addMCUncertainty()
opts = {"xmax": 16}
opts_log = {"ymin": 1e-10, "ymaxfactor": 10}
opts_log.update(opts)
h.createFrame(prefix+"vertices"+postfix, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
# histograms.addLuminosityText(x=None, y=None, lumi=191.)
h.histoMgr.addLuminosityText()
h.save()
h.createFrame(prefix+"vertices"+postfix+"_log", opts=opts_log)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
ROOT.gPad.SetLogy(True)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
# histograms.addLuminosityText(x=None, y=None, lumi=191.)
h.histoMgr.addLuminosityText()
h.save()
def vertexCount(h, prefix="", postfix=""):
xlabel = "Number of vertices"
ylabel = "A.u."
h.stackMCHistograms()
stack = h.histoMgr.getHisto("StackedMC")
#hsum = stack.getSumRootHisto()
#total = hsum.Integral(0, hsum.GetNbinsX()+1)
#for rh in stack.getAllRootHistos():
# dataset._normalizeToFactor(rh, 1/total)
#dataset._normalizeToOne(h.histoMgr.getHisto("Data").getRootHisto())
h.addMCUncertainty()
opts = {"xmax": 16}
opts_log = {"ymin": 1e-10, "ymaxfactor": 10}
opts_log.update(opts)
h.createFrame(prefix + "vertices" + postfix, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
# histograms.addLuminosityText(x=None, y=None, lumi=191.)
h.histoMgr.addLuminosityText()
h.save()
h.createFrame(prefix + "vertices" + postfix + "_log", opts=opts_log)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
ROOT.gPad.SetLogy(True)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
# histograms.addLuminosityText(x=None, y=None, lumi=191.)
h.histoMgr.addLuminosityText()
h.save()
def transverseMass(h, step="", rebin=5, particle="#tau"):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "m_{T}(%s, MET) (GeV/c^{2})" % particle
ylabel = "Events / %.2f GeV/c^{2}" % h.binWidth()
opts = {"ymaxfactor": 2}
h.stackMCHistograms()
h.addMCUncertainty()
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.001
opts["xmax"] = 200
name = "transverseMassSimulateEmbedded_%s_log" % step
h.createFrameFraction(name, opts=opts)
#h.createFrame(name, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
name = "transverseMassSimulateEmbedded_%s" % step
h.createFrameFraction(name, opts=opts)
#h.createFrame(name, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
def transverseMass(h, step="", rebin=5, particle="#tau"):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "m_{T}(%s, MET) (GeV/c^{2})" % particle
ylabel = "Events / %.2f GeV/c^{2}" % h.binWidth()
opts = {"ymaxfactor": 2}
h.stackMCHistograms()
h.addMCUncertainty()
if h.normalizeToOne:
ylabel = "A.u."
opts["yminfactor"] = 1e-5
else:
opts["ymin"] = 0.001
opts["xmax"] = 200
name = "transverseMassSimulateEmbedded_%s_log" % step
h.createFrameFraction(name, opts=opts)
#h.createFrame(name, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
name = "transverseMassSimulateEmbedded_%s" % step
h.createFrameFraction(name, opts=opts)
#h.createFrame(name, opts=opts)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
def plotCommon(h, rebin, xlabel, log, opts={}, opts2={}, coverPadOpts={}):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
#h.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineWidth(5))
#h.histoMgr.getHistos()[0].getRootHisto().SetLineStyle(3)
ylabel = "A.u. / %.2f" % h.binWidth()
xlabel = xlabel
opts_ = {}
opts_.update(opts)
coverPadOpts_ = {}
coverPadOpts_.update(coverPadOpts)
if log:
if not "ymaxfactor" in opts_:
opts_["ymaxfactor"] = 2
if not "yminfactor" in opts_ and not "ymin" in opts_:
opts_["yminfactor"] = 1e-4
if not "ymin" in coverPadOpts_:
coverPadOpts_["ymin"] = 0.278
name = "TauEmbeddingTaus_%s" % (h.plotName)
if log:
name += "_log"
h.createFrame(name,
createRatio=True,
opts=opts_,
opts2=opts2,
coverPadOpts=coverPadOpts_)
h.getFrame2().GetYaxis().SetTitle(h.ratioLabel)
h.getFrame2().GetYaxis().SetTitleSize(27) # Absolute size
h.getFrame2().GetYaxis().SetTitleOffset(2)
h.frame.GetXaxis().SetTitle(xlabel)
h.frame.GetYaxis().SetTitle(ylabel)
h.setLegend(createLegend())
if log:
ROOT.gPad.SetLogy(True)
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText(x=0.5, y=0.3)
h.save()
def doPlot(datasetsEmb, analyses, path, name, text):
histos = []
legends = {
"Plus": "#tau-jet energy scale variated by +3 %",
"Minus": "#tau-jet energy scale variated by -3 %"
}
for aname, analysis in analyses:
(rootHisto, tmp) = datasetsEmb.getHistogram("Data",
analysis + "/" + path)
h = histograms.Histo(rootHisto, aname)
h.setLegendLabel(legends.get(aname, aname))
h.setDrawStyle("EP")
h.setLegendStyle("p")
histos.append(h)
p = plots.ComparisonManyPlot(histos[0], histos[1:])
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
styles.dataStyle(p.histoMgr.getHisto("Baseline"))
styles.mcStyle(p.histoMgr.getHisto("Plus"))
styles.mcStyle2(p.histoMgr.getHisto("Minus"))
p.histoMgr.getHisto("Minus").getRootHisto().SetMarkerSize(2)
p.setLuminosity(datasetsEmb.getLuminosity())
p.createFrame(name, createRatio=True, opts2={"ymax": 2}, opts={"ymax": 35})
yaxis = p.getFrame2().GetYaxis()
yaxis.SetTitle("Ratio")
#yaxis.SetTitleSize(yaxis.GetTitleSize()*0.8)
p.setLegend(histograms.moveLegend(histograms.createLegend()))
p.appendPlotObject(histograms.PlotText(0.5, 0.55, text, size=20))
p.frame.GetXaxis().SetTitle("m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})")
p.frame.GetYaxis().SetTitle("Events / 20 GeV/c^{2}")
p.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
p.addLuminosityText()
p.save()
def plotHisto(self,histo,canvasName):
plot = plots.PlotBase()
plot.histoMgr.appendHisto(histograms.Histo(histo,histo.GetName()))
plot.createFrame(canvasName+self.label, opts={"ymin": 0.1, "ymaxfactor": 2.})
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
plot.getPad().SetLogy(True)
integralValue = int(0.5 + histo.Integral(0,histo.GetNbinsX(),"width"))
print histo.GetName(),"Integral",histo.Integral(0,histo.GetNbinsX(),"width")
histograms.addText(0.4,0.7,"Integral = %s ev"% integralValue)
match = re.search("aseline",histo.GetName())
if match:
self.nBaseQCD = integralValue
match = re.search("nverted",histo.GetName())
if match:
self.nInvQCD = integralValue
self.plotIntegral(plot, histo.GetName())
def selectionFlow(h, name, rebin=1, ratio=False):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "Cut"
ylabel = "Events"
#h.stackMCSignalHistograms()
h.stackMCHistograms()
h.addMCUncertainty()
scaleMCfromWmunu(h)
njets = 5
lastSelection = njets
opts = {
"xmax": lastSelection,
"ymin": 0.1,
"ymaxfactor": 2,
"nbins": lastSelection
}
opts2 = {"ymin": 0.5, "ymax": 1.5}
h.createFrame(name, opts=opts, createRatio=ratio, opts2=opts2)
xaxis = h.getFrame().GetXaxis()
xaxis.SetBinLabel(1, "Trigger")
xaxis.SetBinLabel(2, "#tau ID+R_{#tau}")
xaxis.SetBinLabel(3, "e veto")
xaxis.SetBinLabel(4, "#mu veto")
xaxis.SetBinLabel(5, "N_{jets}")
h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend())
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.addLuminosityText()
addMassBRText(x=0.4, y=0.87)
h.save()
def selectionFlow(h, name, rebin=1, ratio=False):
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "Cut"
ylabel = "Events"
h.stackMCHistograms()
h.addMCUncertainty()
scaleMCfromWmunu(h)
opts = {"xmax": 7, "ymin": 0.01, "ymaxfactor": 2}
opts2 = {"ymin": 0.5, "ymax": 1.5}
if ratio:
h.createFrameFraction(name, opts=opts, opts2=opts2)
else:
h.createFrame(name, opts=opts)
h.getPad().SetLogy(True)
h.setLegend(histograms.createLegend(0.7, 0.6, 0.9, 0.9))
h.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
#h.addLuminosityText()
h.save()
h.addMCUncertainty()
# Create canvas and frame for only the distributions
h.createFrame("taupt_new")
h.frame.GetXaxis().SetTitle("#tau p_T (GeV/c)")
h.frame.GetYaxis().SetTitle("#tau cands / 1 GeV/c")
# Create legend
h.setLegend(histograms.createLegend())
# Draw the histograms and the legend
h.draw()
# Add the necessary pieces of text
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
h.addLuminosityText()
# Save to .png, .eps and .C file
h.save()
# counters = "qcdMeasurementMethod2Part1Counters/weighted"
# Same, but create two pads, one for the distributions and another for
# the data/MC
h = plots.DataMCPlot(
datasets,
"qcdMeasurementMethod2Part1Counters/TauSelection_all_tau_candidates_pt")
h.stackMCHistograms()
h.addMCUncertainty()
h.createFrameFraction("taupt_new2", )
h.frame.GetXaxis().SetTitle("#tau p_{T} (GeV/c)")
def doPlots(table, onlyWjets, mcEvents, normalize, lumi):
nrows = table.getNrows()
function = ROOT.TF1("fitFunction", "[0]")
function.SetParameter(0, 0)
f2 = ROOT.TF1("fitG", "gaus")
f2.SetLineColor(ROOT.kRed)
f2.SetLineWidth(2)
binning = {
"Data": (8, 60, 100),
"Diboson": (8, 0, 2),
"DYJetsToLL": (8, 1, 5),
"EWKMCsum": (8, 40, 120),
"SingleTop": (8, 3, 6),
"TTJets": (10, 25, 35),
"W3Jets": (10, 6, 11),
"WJets": (14, 10, 80),
}
if onlyWjets:
binning["WJets"] = (24, 10, 90)
if not normalize:
binning["Data"] = (10, 70, 120)
binning["EWKMCsum"] = (6, 60, 120)
binning["SingleTop"] = (8, 4, 6)
binning["TTJets"] = (10, 32, 42)
binning["W3Jets"] = (12, 6, 12)
if onlyWjets:
binning["WJets"] = (10, 20, 60)
if mcEvents:
binning["TTJets"] = (12, 320, 440)
binning["WJets"] = (24, 30, 90)
for icol in xrange(table.getNcolumns()):
name = table.getColumnNames()[icol]
label = plots._legendLabels.get(name, name)
if name != "Data":
label += " simulation"
h = ROOT.TH1F(name, name, nrows, 0, nrows)
h2 = ROOT.TH1F(name+"_dist", name, *(binning.get(name, (10, 0, 100))))
mean = dataset.Count(0, 0)
for irow in xrange(nrows):
count = table.getCount(irow, icol)
h.SetBinContent(irow+1, count.value())
h.SetBinError(irow+1, count.uncertainty())
h2.Fill(count.value())
mean.add(count)
mean = dataset.Count(mean.value()/nrows, mean.uncertainty()/nrows)
h.Fit("fitFunction")
value = function.GetParameter(0)
error = function.GetParError(0)
# function.SetParameters(1., 40., 1.);
# function.SetParLimits(0, 0.0, 1.0);
# fitResult = graph.Fit(function, "NRSE+EX0");
# print "Fit status", fitResult.Status()
# #fitResult.Print("V");
# #fitResult.GetCovarianceMatrix().Print();
# function.SetLineColor(graph.GetMarkerColor());
# function.SetLineWidth(2);
function.Draw("same")
ROOT.gPad.Update()
stat = h.FindObject("stats")
if stat:
stat.SetX1NDC(0.2)
stat.SetX2NDC(0.44)
stat.SetY1NDC(0.2)
stat.SetY2NDC(0.3)
stat.SetTextColor(ROOT.kRed)
stat.SetLineColor(ROOT.kRed)
# return (function, fitResult)
styles.dataStyle.apply(h)
p = plots.PlotBase([h])
p.histoMgr.setHistoDrawStyle(name, "EP")
p.createFrame("fluctuation_"+name, opts={"ymin": 0, "ymaxfactor": 1.2, "nbins": nrows})
p.frame.GetXaxis().SetTitle("Embedding trial number")
ylabel = "Simulation"
if name == "Data":
ylabel = "Data"
ylabel += " events"
p.frame.GetYaxis().SetTitle(ylabel)
step = 1
start = 0
if onlyWjets:
start = 4
step = 5
for irow in xrange(start, nrows, step):
p.frame.GetXaxis().SetBinLabel(irow+1, "%d"%(irow+1))
xmin = p.frame.GetXaxis().GetXmin()
xmax = p.frame.GetXaxis().GetXmax()
leg = histograms.moveLegend(histograms.createLegend(), dx=-0.07, dy=-0.6, dh=-0.15)
leg.AddEntry(h, "Trial values", "P")
def createLine(val, st=1, col=ROOT.kRed):
l = ROOT.TLine(xmin, val, xmax, val)
l.SetLineWidth(2)
l.SetLineStyle(st)
l.SetLineColor(col)
return l
fv = createLine(value)
leg.AddEntry(fv, "Fitted value", "L")
p.appendPlotObject(fv)
# fe = createLine(value+error, ROOT.kDashed)
# leg.AddEntry(fe, "Fit uncertainty", "L")
# p.appendPlotObject(fe)
# p.appendPlotObject(createLine(value-error, ROOT.kDashed))
v = createLine(mean.value(), col=ROOT.kBlue)
leg.AddEntry(v, "Mean", "L")
p.appendPlotObject(v)
ve = createLine(mean.value()+mean.uncertainty(), st=ROOT.kDashed, col=ROOT.kBlue)
leg.AddEntry(ve, "Mean uncertainty", "L")
p.appendPlotObject(ve)
p.appendPlotObject(createLine(mean.value()-mean.uncertainty(), st=ROOT.kDashed, col=ROOT.kBlue))
p.legend = leg
p.appendPlotObject(histograms.PlotText(0.65, 0.33, label, size=20))
p.draw()
if name != "Data":
histograms.addCmsPreliminaryText(text="Simulation")
histograms.addEnergyText()
histograms.addLuminosityText(None, None, lumi)
p.save()
###############
f2.SetParameter(1, value)
h2.Fit("fitG")
# f2.Draw("same")
ROOT.gPad.Update()
stat = h2.FindObject("stats")
if stat:
stat.SetX1NDC(0.62)
stat.SetX2NDC(0.9)
stat.SetY1NDC(0.7)
stat.SetY2NDC(0.85)
stat.SetTextColor(ROOT.kRed)
stat.SetLineColor(ROOT.kRed)
styles.dataStyle.apply(h2)
p = plots.PlotBase([h2])
p.histoMgr.setHistoDrawStyle(name+"_dist", "HIST")
p.createFrame("fluctuation_"+name+"_dist", opts={"ymin": 0, "ymaxfactor": 1.4, "nbins": nrows})
p.frame.GetXaxis().SetTitle(ylabel)
p.frame.GetYaxis().SetTitle("Occurrances")
ymin = p.frame.GetYaxis().GetXmin()
ymax = p.frame.GetYaxis().GetXmax()
leg = histograms.moveLegend(histograms.createLegend(), dx=-0.07, dy=-0.25, dh=-0.15)
leg.AddEntry(h2, "Trials", "F")
leg.AddEntry(f2, "Gaussian fit", "L")
def createLine2(val, st=1):
l = ROOT.TLine(val, ymin, val, ymax)
l.SetLineWidth(1)
l.SetLineColor(ROOT.kBlue)
l.SetLineStyle(st)
return l
p.appendPlotObject(h2, "FUNC")
p.appendPlotObject(stat)
p.appendPlotObject(histograms.PlotText(0.65, 0.88, label, size=20))
# fv = createLine2(value)
# leg.AddEntry(fv, "Fit of values", "L")
# p.appendPlotObject(fv)
# fe = createLine2(value+error, ROOT.kDashed)
# leg.AddEntry(fe, "Fit of values unc.", "L")
# p.appendPlotObject(fe)
# p.appendPlotObject(createLine2(value-error, ROOT.kDashed))
p.legend = leg
p.draw()
if name != "Data":
histograms.addCmsPreliminaryText(text="Simulation")
histograms.addEnergyText()
histograms.addLuminosityText(None, None, lumi)
p.save()
