def rtauGen(h, name, rebin=2, ratio=False, defaultStyles=True):
if defaultStyles:
h.setDefaultStyles()
h.histoMgr.forEachHisto(styles.generator())
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
xlabel = "PF E_{T}^{miss} (GeV)"
ylabel = "Events / %.2f" % h.binWidth()
if "LeptonsInMt" in name:
xlabel = "m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})"
ylabel = "Events / %.0f GeV/c^{2}" % h.binWidth()
if "NoLeptonsRealTau" in name:
xlabel = "m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})"
ylabel = "Events / %.0f GeV/c^{2}" % h.binWidth()
if "Mass" in name:
xlabel = "m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})"
ylabel = "Events / %.0f GeV/c^{2}" % h.binWidth()
kwargs = {"ymin": 0.1, "ymax": 1000}
if "LeptonsInMt" in name:
kwargs = {"ymin": 0., "xmax": 300}
if "NoLeptonsRealTau" in name:
kwargs = {"ymin": 0., "xmax": 300}
if "Rtau" in name:
kwargs = {"ymin": 0.0001, "xmax": 1.1}
kwargs = {"ymin": 0.1, "xmax": 1.1}
h.getPad().SetLogy(True)
# kwargs["opts"] = {"ymin": 0, "xmax": 14, "ymaxfactor": 1.1}}
if ratio:
kwargs["opts2"] = {"ymin": 0.5, "ymax": 1.5}
kwargs["createRatio"] = True
# name = name+"_log"
h.createFrame(name, **kwargs)
# histograms.addText(0.65, 0.7, "BR(t #rightarrow bH^{#pm})=0.05", 20)
h.getPad().SetLogy(True)
leg = histograms.createLegend(0.6, 0.75, 0.8, 0.9)
if "LeptonsInMt" in name:
h.getPad().SetLogy(False)
leg = histograms.moveLegend(leg, dx=-0.18)
histograms.addText(0.5, 0.65, "TailKiller cut: Tight", 20)
h.setLegend(leg)
plots._legendLabels["MetNoJetInHole"] = "Jets outside dead cells"
plots._legendLabels["MetJetInHole"] = "Jets within dead cells"
histograms.addText(300, 300, "p_{T}^{jet} > 50 GeV/c", 20)
kwargs["opts2"] = {"ymin": 0.5, "ymax": 1.5}
kwargs["createRatio"] = True
# if ratio:
# h.createFrameFraction(name, opts=opts, opts2=opts2)
# h.setLegend(leg)
common(h, xlabel, ylabel)
def PlotHistos(datasetName, hPath):
_kwargs = GetHistoKwargs(hPath, opts)
datasetsMgr_list = []
j = 0
histoList = []
# For-loop: All pseudo-multicrabs
while j < len(opts.mcrabs):
textweight = opts.mcrabs[j].split("_")[-1]
dMgr = GetDatasetsFromDir(opts, j)
dMgr.updateNAllEventsToPUWeighted()
dMgr.loadLuminosities() # from lumi.json
# Set/Overwrite cross-sections
if "ChargedHiggs" in datasetName:
dMgr.getDataset(datasetName).setCrossSection(1.0)
# Get dataset
dataset = dMgr.getDataset(datasetName)
# Get Histogram from dataset
histo = dataset.getDatasetRootHisto(hPath).getHistogram()
# Set style
styles.styles[j].apply(histo)
if (j == 0):
# refHisto = histograms.Histo(histo, "Nominal", legendStyle = "F", drawStyle="HIST")
# refHisto.getRootHisto().SetFillStyle(1001)
# refHisto.getRootHisto().SetFillColor(ROOT.kBlue)
refHisto = histograms.Histo(histo, "Nominal", legendStyle = "L", drawStyle="HIST")
refHisto.getRootHisto().SetLineStyle(ROOT.kSolid)
refHisto.getRootHisto().SetLineWidth(3)
else:
text = str(j)
histoList.append(histograms.Histo(histo, "Weight " + textweight, legendStyle = "LP", drawStyle="AP"))
j = j + 1
# Create the plotter object
p = plots.ComparisonManyPlot(refHisto, histoList, saveFormats=[])
p.setLuminosity(opts.intLumi)
p.setLegendHeader(plots._legendLabels[datasetName])
# Draw the plots
plots.drawPlot(p, opts.saveDir, **_kwargs)
# Add text
if 0:
histograms.addText(0.65, 0.1, "#\pm %.1f %% band" % (opts.bandValue), 20)
# Save plot in all formats
saveName = hPath.split("/")[-1] + "_" + datasetName
SavePlot(p, saveName, opts.saveDir, saveFormats = [".png", ".pdf", ".C"])
return
def MCPlot(datasetsMgr, json):
Verbose("Creating MC plot")
# Create the MC Plot with selected normalization ("normalizeToOne", "normalizeByCrossSection", "normalizeToLumi")
#if json["normalization"]=="normalizeToLumi":
if 0:
kwargs = {}
p = plots.MCPlot(datasetsMgr, json["histogram"], normalizeToLumi=float(json["luminosity"]), **kwargs)
else:
#kwargs = {json["normalization"]: True}
kwargs = {"normalizeToOne": True}
p = plots.MCPlot(datasetsMgr, json["histogram"], **kwargs)
# Label size (optional. Commonly Used in counters)
xlabelSize = None
if "xlabelsize" in json:
xlabelSize = json["xlabelsize"]
ylabelSize = None
if "ylabelsize" in json:
ylabelSize = json["ylabelsize"]
# Draw a customised plot
saveName = os.path.join(json["saveDir"], json["title"])
plots.drawPlot(p,
saveName,
xlabel = json["xlabel"],
ylabel = json["ylabel"],
rebinX = json["rebinX"],
rebinY = json["rebinY"],
stackMCHistograms = json["stackMCHistograms"]=="True",
addMCUncertainty = False, #json["addMCUncertainty"]=="True" and json["normalization"]!="normalizeToOne",
addLuminosityText = False,
addCmsText = json["addCmsText"]=="True",
cmsExtraText = json["cmsExtraText"],
opts = json["opts"],
log = json["logY"]=="True",
errorBarsX = json["errorBarsX"]=="True",
moveLegend = json["moveLegend"],
# cutLine = json["cutValue"], #cannot have this and "cutBox" defined
cutBox = {"cutValue": json["cutValue"], "fillColor": json["cutFillColour"], "box": json["cutBox"]=="True", "line": json["cutLine"]=="True", "greaterThan": json["cutGreaterThan"]=="True"},
xlabelsize = xlabelSize,
ylabelsize = ylabelSize,
)
# Remove legend?
if json["removeLegend"] == "True":
p.removeLegend()
# Additional text
histograms.addText(json["extraText"].get("x"), json["extraText"].get("y"), json["extraText"].get("text"), json["extraText"].get("size") )
# Save in all formats chosen by user
saveFormats = json["saveFormats"]
for i, ext in enumerate(saveFormats):
Print("%s" % saveName + ext, i==0)
p.saveAs(saveName, formats=saveFormats)
return
def MCPlot(datasetsMgr, json):
Verbose("Creating MC plot")
# Create the MC Plot with selected normalization ("normalizeToOne", "normalizeByCrossSection", "normalizeToLumi")
if json["normalization"]=="normalizeToLumi":
kwargs = {}
p = plots.MCPlot(datasetsMgr, json["histogram"], normalizeToLumi=float(json["luminosity"]), **kwargs)
else:
kwargs = {json["normalization"]: True}
p = plots.MCPlot(datasetsMgr, json["histogram"], **kwargs)
# Label size (optional. Commonly Used in counters)
xlabelSize = None
if "xlabelsize" in json:
xlabelSize = json["xlabelsize"]
ylabelSize = None
if "ylabelsize" in json:
ylabelSize = json["ylabelsize"]
# Draw a customised plot
saveName = os.path.join(json["saveDir"], json["title"])
plots.drawPlot(p,
saveName,
xlabel = json["xlabel"],
ylabel = json["ylabel"],
rebinX = json["rebinX"],
rebinY = json["rebinY"],
stackMCHistograms = json["stackMCHistograms"]=="True",
addMCUncertainty = json["addMCUncertainty"]=="True" and json["normalization"]!="normalizeToOne",
addLuminosityText = json["normalization"]=="normalizeToLumi",
addCmsText = json["addCmsText"]=="True",
cmsExtraText = json["cmsExtraText"],
opts = json["opts"],
log = json["logY"]=="True",
errorBarsX = json["errorBarsX"]=="True",
moveLegend = json["moveLegend"],
# cutLine = json["cutValue"], #cannot have this and "cutBox" defined
cutBox = {"cutValue": json["cutValue"], "fillColor": json["cutFillColour"], "box": json["cutBox"]=="True", "line": json["cutLine"]=="True", "greaterThan": json["cutGreaterThan"]=="True"},
xlabelsize = xlabelSize,
ylabelsize = ylabelSize,
)
# Remove legend?
if json["removeLegend"] == "True":
p.removeLegend()
# Additional text
histograms.addText(json["extraText"].get("x"), json["extraText"].get("y"), json["extraText"].get("text"), json["extraText"].get("size") )
# Save in all formats chosen by user
saveFormats = json["saveFormats"]
for i, ext in enumerate(saveFormats):
Print("%s" % saveName + ext, i==0)
p.saveAs(saveName, formats=saveFormats)
return
def DataMCPlot(datasetsMgr, json):
Verbose("Creating Data-MC plot")
# Create the Data-MC Plot
p = plots.DataMCPlot(datasetsMgr, json["histogram"])
# Label size (optional. Commonly Used in counters)
xlabelSize = None
if "xlabelsize" in json:
xlabelSize = json["xlabelsize"]
ylabelSize = None
if "ylabelsize" in json:
ylabelSize = json["ylabelsize"]
# Draw a customised plot
saveName = os.path.join(json["saveDir"], json["title"])
plots.drawPlot(p,
saveName,
xlabel = json["xlabel"],
ylabel = json["ylabel"],
rebinX = json["rebinX"],
rebinY = json["rebinY"],
ratioYlabel = json["ratioYlabel"],
ratio = json["ratio"]=="True",
stackMCHistograms = json["stackMCHistograms"]=="True",
ratioInvert = json["ratioInvert"]=="True",
addMCUncertainty = json["addMCUncertainty"]=="True",
addLuminosityText = json["addLuminosityText"]=="True",
addCmsText = json["addCmsText"]=="True",
cmsExtraText = json["cmsExtraText"],
opts = json["opts"],
opts2 = json["ratioOpts"],
log = json["logY"]=="True",
errorBarsX = json["errorBarsX"]=="True",
moveLegend = json["moveLegend"],
# cutLine = json["cutValue"], #cannot have this and "cutBox" defined
cutBox = {"cutValue": json["cutValue"], "fillColor": json["cutFillColour"], "box": json["cutBox"]=="True", "line": json["cutLine"]=="True", "greaterThan": json["cutGreaterThan"]=="True"},
xlabelsize = xlabelSize,
ylabelsize = ylabelSize,
)
# Remove legend?
if json["removeLegend"] == "True":
p.removeLegend()
# Additional text
histograms.addText(json["extraText"].get("x"), json["extraText"].get("y"), json["extraText"].get("text"), json["extraText"].get("size") )
# Save in all formats chosen by user
saveFormats = json["saveFormats"]
for i, ext in enumerate(saveFormats):
Print("%s" % saveName + ext, i==0)
p.saveAs(saveName, formats=saveFormats)
return
def doPlotMu(name, graphs, styleList, limits, xlabel):
objs = []
ll = {}
for gr, mu in graphs:
objs.append(histograms.HistoGraph(gr, "Obs%d"%mu, drawStyle="LP", legendStyle="lp"))
ll["Obs%d"%mu] = "Observed, #mu=%d %s" % (mu, limit.massUnit())
plot = plots.PlotBase(objs)
plot.histoMgr.forEachHisto(styles.Generator(styleList))
plot.histoMgr.setHistoLegendLabelMany(ll)
plot.setLegend(histograms.moveLegend(histograms.createLegend(0.57, 0.155, 0.87, 0.355), dx=-0.1))
plot.createFrame(name, opts={"ymin": 0, "ymax": tanbMax})
plot.frame.GetXaxis().SetTitle(xlabel)
plot.frame.GetYaxis().SetTitle(limit.tanblimit)
plot.draw()
plot.setLuminosity(limits.getLuminosity())
plot.addStandardTexts()
size = 20
x = 0.2
histograms.addText(x, 0.9, limit.process, size=size)
histograms.addText(x, 0.863, limits.getFinalstateText(), size=size)
histograms.addText(x, 0.815, "MSSM m_{h}^{max}", size=size)
histograms.addText(x, 0.775, limit.BRassumption, size=size)
plot.save()
def plotClosure(mt_nom, mts_var, name, optMode):
#for mt_var in mts_var:
# printSummedDifference(mt_nom, mt_var)
style = tdrstyle.TDRStyle()
plot = plots.ComparisonManyPlot(mt_nom, mts_var)
plot.createFrame(optMode.replace("Opt","mT_Closure_"+ name +"_"), createRatio=True)
moveLegend={"dx": -0.325,"dy": 0.02,"dw":-0.14,"dh":-0.12}
plot.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
histograms.addText(0.65, 0.3, optMode.replace("OptQCDTailKiller","R_{BB} ").replace("Plus",""), 25)
histograms.addStandardTexts()
plot.draw()
plot.save()
def plotClosure(mt_nom, mts_var, name, optMode):
#for mt_var in mts_var:
# printSummedDifference(mt_nom, mt_var)
style = tdrstyle.TDRStyle()
plot = plots.ComparisonManyPlot(mt_nom, mts_var)
plot.createFrame(optMode.replace("Opt", "mT_Closure_" + name + "_"),
createRatio=True)
moveLegend = {"dx": -0.325, "dy": 0.02, "dw": -0.14, "dh": -0.12}
plot.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
histograms.addText(
0.65, 0.3,
optMode.replace("OptQCDTailKiller", "R_{BB} ").replace("Plus", ""), 25)
histograms.addStandardTexts()
plot.draw()
plot.save()
def doPlot(mass, name, nuisance, thetas):
f = ROOT.TFile.Open("lands_histograms_hplushadronic_m%d.root" % mass)
h = f.Get(name)
hUp = f.Get("%s_%dUp" % (name, nuisance))
hDown = f.Get("%s_%dDown" % (name, nuisance))
shapes = []
ll = {}
for i, theta in enumerate(thetas):
morphed = doShape(h, hUp, hDown, theta)
shapes.append(histograms.Histo(morphed, "Theta%d" % i, drawStyle="HIST", legendStyle="l"))
ll["Theta%d" % i] = "#theta=%.2f (%.1f)" % (theta, _integral(morphed))
plot = plots.PlotBase(shapes)
plot.histoMgr.forEachHisto(styles.generator())
plot.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineWidth(3))
plot.histoMgr.setHistoLegendLabelMany(ll)
plot.setLegend(histograms.createLegend())
plot.createFrame("shape_theta_%s_syst%d_m%d" % (name, nuisance, mass))
plot.frame.GetXaxis().SetTitle("m_{T} (GeV)")
plot.frame.GetYaxis().SetTitle("Rate")
plot.draw()
x = 0.6
size = 20
histograms.addText(x, 0.70, "Sample %s" % name, size=size)
histograms.addText(x, 0.65, "Nuisance %d" % nuisance, size=size)
histograms.addText(x, 0.60, "m_{H^{+}}=%d GeV" % mass, size=size)
plot.save()
f.Close()
def doPlot(limits, legendLabels, graphs, name, ylabel, opts={}, plotLabel=None, moveLegend={}, log=False):
hg = []
ll = {}
for i in xrange(len(graphs)):
hg.append(histograms.HistoGraph(graphs[i], "Graph%d"%i, drawStyle="PL", legendStyle="lp"))
ll["Graph%d"%i] = legendLabels[i]
plot = plots.PlotBase(hg)
plot.histoMgr.forEachHisto(styles.Generator(styleList[0:len(limits)]))
def sty(h):
r = h.getRootHisto()
r.SetLineWidth(3)
r.SetLineStyle(1)
plot.histoMgr.forEachHisto(sty)
plot.histoMgr.setHistoLegendLabelMany(ll)
legend = histograms.createLegend(0.48, 0.65, 0.85, 0.92)
if len(limits[0].getFinalstates()) > 1:
legend = histograms.moveLegend(legend, dy=-0.1)
if plotLabel:
legend = histograms.moveLegend(legend, dy=-0.04)
legend = histograms.moveLegend(legend, **moveLegend)
plot.setLegend(legend)
opts_ = {"ymin": 0}
opts_.update(opts)
plot.createFrame(name, opts=opts_)
plot.frame.GetXaxis().SetTitle(limit.mHplus())
plot.frame.GetYaxis().SetTitle(ylabel)
ROOT.gPad.SetLogy(log)
plot.draw()
plot.setLuminosity(limits[0].getLuminosity())
plot.addStandardTexts(cmsTextPosition="outframe")
size = 20
x = 0.18
histograms.addText(x, 0.88, limit.process, size=size)
histograms.addText(x, 0.84, limits[0].getFinalstateText(), size=size)
histograms.addText(x, 0.79, limit.BRassumption, size=size)
if plotLabel:
histograms.addText(legend.GetX1()+0.01, legend.GetY2(), plotLabel, size=size)
plot.save()
def addPhysicsText(histograms, limit, x=0.45, y=0.84, size=20):
'''
Add physics-process text on canvas
'''
# Add process text on canvas
histograms.addText(x, y + 0.04, limit.processHeavyHtb, size=size)
# Add final-state text
histograms.addText(x, y, "fully hadronic final state", size=size)
if limit.BRassumption != "":
histograms.addText(x, y - 0.05, limit.BRassumption, size=size)
return
def doPlotMu(name, graphs, styleList, limits, xlabel):
objs = []
ll = {}
for gr, mu in graphs:
objs.append(
histograms.HistoGraph(gr,
"Obs%d" % mu,
drawStyle="LP",
legendStyle="lp"))
ll["Obs%d" % mu] = "Observed, #mu=%d %s" % (mu, limit.massUnit())
plot = plots.PlotBase(objs)
plot.histoMgr.forEachHisto(styles.Generator(styleList))
plot.histoMgr.setHistoLegendLabelMany(ll)
plot.setLegend(
histograms.moveLegend(histograms.createLegend(0.57, 0.155, 0.87,
0.355),
dx=-0.1))
plot.createFrame(name, opts={"ymin": 0, "ymax": tanbMax})
plot.frame.GetXaxis().SetTitle(xlabel)
plot.frame.GetYaxis().SetTitle(limit.tanblimit)
plot.draw()
plot.setLuminosity(limits.getLuminosity())
plot.addStandardTexts()
size = 20
x = 0.2
histograms.addText(x, 0.9, limit.process, size=size)
histograms.addText(x, 0.863, limits.getFinalstateText(), size=size)
histograms.addText(x, 0.815, "MSSM m_{h}^{max}", size=size)
histograms.addText(x, 0.775, limit.BRassumption, size=size)
plot.save()
def doPlot2(limits, legendLabels, name):
graphs = [
histograms.HistoGraph(limits[0].expectedGraph(), "Expected", drawStyle="L"),
histograms.HistoGraph(limits[0].expectedBandGraph(sigma=1), "Expected1", drawStyle="F", legendStyle="fl"),
histograms.HistoGraph(limits[0].expectedBandGraph(sigma=2), "Expected2", drawStyle="F", legendStyle="fl"),
]
graphs[0].getRootHisto().SetLineStyle(1)
plot = plots.PlotBase(graphs)
ll = {
"Expected": None,
"Expected1": "%s exp. median #pm 1#sigma" % legendLabels[0],
"Expected2": "%s exp. median #pm 2#sigma" % legendLabels[0],
}
stGen = styles.generator()
for i in xrange(1, len(limits)):
gr = histograms.HistoGraph(limits[i].expectedGraph(), "Exp%d"%i, drawStyle="L")
stGen(gr)
gr.getRootHisto().SetLineWidth(3)
gr.getRootHisto().SetLineStyle(1)
plot.histoMgr.insertHisto(len(plot.histoMgr)-2, gr, legendIndex=len(plot.histoMgr))
ll["Exp%d"%i] = "%s exp. median" % legendLabels[i]
plot.histoMgr.setHistoLegendLabelMany(ll)
legend = histograms.moveLegend(histograms.createLegend(0.48, 0.75, 0.85, 0.92), dx=-0.02)
if len(limits[0].getFinalstates()) > 1:
legend = histograms.moveLegend(legend, dy=-0.1)
plot.setLegend(legend)
plot.createFrame(name+"_limits", opts={"ymin": 0, "ymax": limits[0].getFinalstateYmaxBR()})
plot.frame.GetXaxis().SetTitle(limit.mHplus())
plot.frame.GetYaxis().SetTitle(limit.BRlimit)
plot.draw()
plot.setLuminosity(limits[0].getLuminosity())
plot.addStandardTexts(cmsTextPosition="outframe")
size = 20
x = 0.2
histograms.addText(x, 0.88, limit.process, size=size)
histograms.addText(x, 0.84, limits[0].getFinalstateText(), size=size)
histograms.addText(x, 0.79, limit.BRassumption, size=size)
plot.save()
def doPlot(mass, name, nuisance, thetas):
f = ROOT.TFile.Open("lands_histograms_hplushadronic_m%d.root" % mass)
h = f.Get(name)
hUp = f.Get("%s_%dUp" % (name, nuisance))
hDown = f.Get("%s_%dDown" % (name, nuisance))
shapes = []
ll = {}
for i, theta in enumerate(thetas):
morphed = doShape(h, hUp, hDown, theta)
shapes.append(
histograms.Histo(morphed,
"Theta%d" % i,
drawStyle="HIST",
legendStyle="l"))
ll["Theta%d" % i] = "#theta=%.2f (%.1f)" % (theta, _integral(morphed))
plot = plots.PlotBase(shapes)
plot.histoMgr.forEachHisto(styles.generator())
plot.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineWidth(3))
plot.histoMgr.setHistoLegendLabelMany(ll)
plot.setLegend(histograms.createLegend())
plot.createFrame("shape_theta_%s_syst%d_m%d" % (name, nuisance, mass))
plot.frame.GetXaxis().SetTitle("m_{T} (GeV)")
plot.frame.GetYaxis().SetTitle("Rate")
plot.draw()
x = 0.6
size = 20
histograms.addText(x, 0.70, "Sample %s" % name, size=size)
histograms.addText(x, 0.65, "Nuisance %d" % nuisance, size=size)
histograms.addText(x, 0.60, "m_{H^{+}}=%d GeV" % mass, size=size)
plot.save()
f.Close()
dy = 0.12
legend = ROOT.TLegend(x-0.01, 0.50+dy, x+0.40+0.30*(len(myScenarios)>1), 0.80+dy)
if len(myScenarios) == 1:
legend.AddEntry("", "MSSM %s"%scenToLabel(myScenarios[0]), "")
for j in range(0, len(myFinalStates)):
if len(myScenarios) == 1:
# One scenario in one plot
legend.AddEntry(myObjects[j], myFinalStates[j].replace(":",""), "lp")
else:
# Multiple scenarios in one plot
for i in range(0, len(myScenarios)):
if i == 0:
legend.AddEntry("", myFinalStates[j], "")
else:
legend.AddEntry("","","")
for i in range(0, len(myScenarios)):
legend.AddEntry(myObjects[j*len(myScenarios)+i], "MSSM %s"%scenToLabel(myScenarios[i]), "lp")
legend.SetNColumns(len(myScenarios))
legend.SetFillStyle(0)
legend.SetBorderSize(0)
#legend.SetTextSize(20)
legend.Draw()
#plot.setLegend(legend)
#plot.draw()
# Save plot
histograms.addText(0.45, 0.23, "Values from LHCHXSWG used", size=20)
histograms.addText(0.45, 0.18, "m_{H^{+}}=%s GeV"%m, size=20)
plot.save()
print "Created plot %s"%name
def main(opts):
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setWide(True, 0.15)
### style = tdrstyle.TDRStyle()
### #style.setWide(True)
### style.setPaletteMy()
### ROOT.gStyle.SetNumberContours(20)
# tdrstyle.setDeepSeaPalette()
# tdrstyle.setRainBowPalette()
# tdrstyle.setDarkBodyRadiatorPalette()
# tdrstyle.setGreyScalePalette()
# tdrstyle.setTwoColorHuePalette()
style.setWide(True, 0.15)
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(opts.batchMode)
# ========================================
# Datasets
# ========================================
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts.mcrab, opts, **kwargs)
intLumi = GetLumi(datasetsMgr)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.PrintCrossSections()
#datasetsMgr.PrintLuminosities()
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
# Merge datasts (Note: Merged MC histograms must be normalized to something)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Remove datasets
if 0:
datasetsMgr.remove("TTJets")
datasetsMgr.remove(
filter(lambda name: not "QCD" in name,
datasetsMgr.getAllDatasetNames()))
# Print dataset information
datasetsMgr.PrintInfo()
# For-loop: All Histogram names
for counter, hName in enumerate(hNames):
savePath, saveName = GetSavePathAndName(hName, **kwargs)
# Get Histos for Plotter
refHisto, otherHistos = GetHistosForPlotter(datasetsMgr, hName,
**kwargs)
# Create a plot
p = plots.PlotBase([refHisto], kwargs.get("saveFormats"))
# Remove negative contributions
#RemoveNegativeBins(datasetsMgr, hName, p)
# Customize
# p.histoMgr.setHistoDrawStyleAll("COL") #"CONT4" "COLZ" "COL"
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().RebinX(kwargs.get("rebinX")))
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().RebinY(kwargs.get("rebinY")))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetXaxis().SetRangeUser(1.0, 5.0))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetYaxis().SetRangeUser(1.0, 5.0))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetZaxis().SetRangeUser(0.0, 0.015))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMinimum(0.00001))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMaximum(0.05))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinX(2))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinY(2))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMinimum(0.0009))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMaximum(0.0041))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMinimum(0.0012))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMaximum(0.00435))
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().GetZaxis().SetTitle("Arbitrary Units"))
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().GetZaxis().SetTitleOffset(2.))
#ROOT.gPad.RedrawAxis()
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMinimum(kwargs.get("zMin")))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMaximum(kwargs.get("zMax")))
# Create a frame
opts = {"ymin": 0.0, "ymaxfactor": 1.0}
p.createFrame(saveName, opts=opts)
# Customise frame
p.getFrame().GetXaxis().SetTitle(getTitleX(refHisto, **kwargs))
#p.getFrame().GetYaxis().SetTitle( getTitleY(refHisto, **kwargs) )
# SetLog
SetLogAndGrid(p, **kwargs)
# Add cut line/box
_kwargs = {"lessThan": kwargs.get("cutLessThan")}
p.addCutBoxAndLine(cutValue=kwargs.get("cutValue"),
fillColor=kwargs.get("cutFillColour"),
box=kwargs.get("cutBox"),
line=kwargs.get("cutLine"),
**_kwargs)
# Customise Legend
moveLegend = {"dx": -0.1, "dy": +0.0, "dh": -0.1}
p.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
p.removeLegend()
# Add MC Uncertainty (Invalid method for a 2-d histogram)
#p.addMCUncertainty()
# Customise histogram (after frame is created)
# Draw plots
p.draw()
# Customise text
#Soti lumitext histograms.addStandardTexts(lumi=intLumi)
# histograms.addText(0.17, 0.95, plots._legendLabels[kwargs.get("refDataset")], 22)
histograms.addText(0.17, 0.88,
plots._legendLabels[kwargs.get("refDataset")], 17)
# Save canvas under custom dir
SaveAs(p, savePath, saveName, kwargs.get("saveFormats"), counter == 0)
return
def main():
style = tdrstyle.TDRStyle()
#style.setWide(True)
style.setPaletteMy()
ROOT.gStyle.SetNumberContours(20)
# tdrstyle.setDeepSeaPalette()
# tdrstyle.setRainBowPalette()
# tdrstyle.setDarkBodyRadiatorPalette()
# tdrstyle.setGreyScalePalette()
# tdrstyle.setTwoColorHuePalette()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(parseOpts.batchMode)
# Get all datasets from the mcrab dir
#datasetsMgr = GetDatasetsFromDir(parseOpts.mcrab, kwargs.get("analysis"))
datasetsMgr = GetDatasetsFromDir(parseOpts.mcrab, parseOpts, **kwargs)
# Determine Integrated Luminosity (If Data datasets present)
intLumi = GetLumi(datasetsMgr)
# Update to PU
datasetsMgr.updateNAllEventsToPUWeighted()
# Remove datasets
#datasetsMgr.remove(kwargs.get("rmDataset"))
datasetsMgr.remove(filter(lambda name: not kwargs.get("refDataset") in name, datasetsMgr.getAllDatasetNames()))
# Set custom XSections
# d.getDataset("TT_ext3").setCrossSection(831.76)
# Default merging & ordering: "Data", "QCD", "SingleTop", "Diboson"
plots.mergeRenameReorderForDataMC(datasetsMgr) #WARNING: Merged MC histograms must be normalized to something!
# Remove datasets (for merged names)
# datasetsMgr.remove(kwargs.get("rmDataset"))
# For-loop: All Histogram names
for hName in hNames:
savePath, saveName = GetSavePathAndName(hName, **kwargs)
# Get Histos for Plotter
refHisto, otherHistos = GetHistosForPlotter(datasetsMgr, hName, **kwargs)
# Create a plot
p = plots.PlotBase([refHisto], kwargs.get("saveFormats"))
# Remove negative contributions
#RemoveNegativeBins(datasetsMgr, hName, p)
# Customize
# p.histoMgr.setHistoDrawStyleAll("COL") #"CONT4" "COLZ" "COL"
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinX(kwargs.get("rebinX")))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinY(kwargs.get("rebinY")))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetXaxis().SetRangeUser(1.0, 5.0))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetYaxis().SetRangeUser(1.0, 5.0))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetZaxis().SetRangeUser(0.0, 0.015))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMinimum(kwargs.get("zMin")))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMaximum(kwargs.get("zMax")))
# Create a frame
opts = {"ymin": 0.0, "ymaxfactor": 1.0}
p.createFrame(saveName, opts=opts)
# Customise frame
p.getFrame().GetXaxis().SetTitle( getTitleX(refHisto, **kwargs) )
p.getFrame().GetYaxis().SetTitle( getTitleY(refHisto, **kwargs) )
# p.getFrame().GetZaxis().SetTitle( getTitleZ(refHisto, **kwargs) ) #does not work
# SetLog
SetLogAndGrid(p, **kwargs)
# Add cut line/box
_kwargs = { "lessThan": kwargs.get("cutLessThan")}
p.addCutBoxAndLine(cutValue=kwargs.get("cutValue"), fillColor=kwargs.get("cutFillColour"), box=kwargs.get("cutBox"), line=kwargs.get("cutLine"), **_kwargs)
# Customise Legend
moveLegend = {"dx": -0.1, "dy": +0.0, "dh": -0.1}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
p.removeLegend()
# Add MC Uncertainty (Invalid method for a 2-d histogram)
#p.addMCUncertainty()
# Draw plots
p.draw()
# Customise text
histograms.addStandardTexts(lumi=intLumi)
#histograms.addText(0.16, 0.95, plots._legendLabels[kwargs.get("refDataset")], 22)
histograms.addText(0.20, 0.88, plots._legendLabels[kwargs.get("refDataset")], 17)
# Save canvas under custom dir
if not os.path.exists(savePath):
os.mkdir(savePath)
SaveAs(p, savePath, saveName, kwargs.get("saveFormats"))
return
def __call__(self, x, y):
mass = "m_{H^{#pm}} = %d GeV/c^{2}" % self.mass
br = "BR(t #rightarrow bH^{#pm})=%.2f" % self.br
histograms.addText(x, y, mass, size=self.size)
histograms.addText(x, y-self.separation, br, size=self.size)
def doPlot(name, graphs, limits, xlabel):
if "obs" in graphs.keys():
obs = graphs["obs"]
excluded = ROOT.TGraph(obs)
excluded.SetName("ExcludedArea")
excluded.SetFillColor(ROOT.kGray)
excluded.SetPoint(excluded.GetN(), obs.GetX()[obs.GetN() - 1], tanbMax)
excluded.SetPoint(excluded.GetN(), obs.GetX()[0], tanbMax)
excluded.SetFillColor(ROOT.kGray)
excluded.SetFillStyle(3354)
excluded.SetLineWidth(0)
excluded.SetLineColor(ROOT.kWhite)
plot = None
if "obs" in graphs.keys():
plot = plots.PlotBase([
histograms.HistoGraph(graphs["obs"],
"Observed",
drawStyle="PL",
legendStyle="lp"),
histograms.HistoGraph(graphs["obs_th_plus"],
"ObservedPlus",
drawStyle="L",
legendStyle="l"),
histograms.HistoGraph(graphs["obs_th_minus"],
"ObservedMinus",
drawStyle="L"),
histograms.HistoGraph(excluded,
"Excluded",
drawStyle="F",
legendStyle="f"),
histograms.HistoGraph(graphs["exp"], "Expected", drawStyle="L"),
histograms.HistoGraph(graphs["exp1"],
"Expected1",
drawStyle="F",
legendStyle="fl"),
histograms.HistoGraph(graphs["exp2"],
"Expected2",
drawStyle="F",
legendStyle="fl"),
])
plot.histoMgr.setHistoLegendLabelMany({
"ObservedPlus":
"Observed #pm1#sigma (th.)",
"ObservedMinus":
None,
"Expected":
None,
"Expected1":
"Expected median #pm 1#sigma",
"Expected2":
"Expected median #pm 2#sigma"
})
else:
plot = plots.PlotBase([
histograms.HistoGraph(graphs["exp"], "Expected", drawStyle="L"),
histograms.HistoGraph(graphs["exp1"],
"Expected1",
drawStyle="F",
legendStyle="fl"),
histograms.HistoGraph(graphs["exp2"],
"Expected2",
drawStyle="F",
legendStyle="fl"),
])
plot.histoMgr.setHistoLegendLabelMany({
"Expected":
None,
"Expected1":
"Expected median #pm 1#sigma",
"Expected2":
"Expected median #pm 2#sigma"
})
plot.setLegend(histograms.createLegend(0.57, 0.155, 0.87, 0.355))
plot.createFrame(name, opts={"ymin": 0, "ymax": tanbMax})
plot.frame.GetXaxis().SetTitle(xlabel)
plot.frame.GetYaxis().SetTitle(limit.tanblimit)
plot.draw()
plot.setLuminosity(limits.getLuminosity())
plot.addStandardTexts()
size = 20
x = 0.2
histograms.addText(x, 0.9, limit.process, size=size)
histograms.addText(x, 0.863, limits.getFinalstateText(), size=size)
histograms.addText(x, 0.815, "MSSM m_{h}^{max}", size=size)
histograms.addText(x, 0.775, limit.BRassumption, size=size)
histograms.addText(x,
0.735,
"#mu=%d %s" % (mu, limit.massUnit()),
size=size)
plot.save()
def analyze(analysis=None):
paths = [sys.argv[1]]
if (len(sys.argv) == 3):
howAnalyse = sys.argv[2]
else:
howAnalyse = "--fit"
# howAnalyse = "--bin"
if not analysis == None:
datasets = dataset.getDatasetsFromMulticrabDirs(
paths,
analysisName=analysis,
excludeTasks="Silver|GluGluHToTauTau_M125")
else:
datasets = dataset.getDatasetsFromMulticrabDirs(
paths, excludeTasks="Silver|GluGluHToTauTau_M125")
analysis = datasets.getAllDatasets()[0].getAnalysisName()
# datasetsDY = None
datasetsDY = dataset.getDatasetsFromMulticrabDirs(
paths, analysisName=analysis, includeOnlyTasks="DYJetsToLL")
datasetsDY = dataset.getDatasetsFromMulticrabDirs(
paths, analysisName=analysis, includeOnlyTasks="DYJetsToLL|Zprime")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="GluGluHToTauTau_M125|TTJets")
datasetsH125 = None
# datasetsH125 = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="GluGluHToTauTau_M125",emptyDatasetsAsNone=True)
# datasetsH125 = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="GluGluHToTauTau_M125")
datasets.loadLuminosities()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
dataset2 = dataset1
# dataset2 = datasets.getMCDatasets()
if not datasetsDY == None:
dataset2 = datasetsDY.getMCDatasets()
histeff1 = getEfficiency(dataset1)
histeff2 = getEfficiency(dataset2)
eff1 = convert2TGraph(histeff1)
eff2 = convert2TGraph(histeff2)
if isinstance(datasetsH125, dataset.DatasetManager):
histeff3 = getEfficiency(datasetsH125.getMCDatasets())
eff3 = convert2TGraph(histeff3)
styles.dataStyle.apply(eff1)
styles.mcStyle.apply(eff2)
eff1.SetMarkerSize(1)
# eff2.SetMarkerSize(1.5)
if isinstance(datasetsH125, dataset.DatasetManager):
styles.mcStyle.apply(eff3)
eff3.SetMarkerSize(1.5)
eff3.SetMarkerColor(4)
eff3.SetLineColor(4)
# p = plots.ComparisonPlot(histograms.HistoGraph(eff1, "eff1", "p", "P"),
# histograms.HistoGraph(eff2, "eff2", "p", "P"))
if isinstance(datasetsH125, dataset.DatasetManager):
p = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1, "eff1", "p", "P"), [
histograms.HistoGraph(eff2, "eff2", "p", "P"),
histograms.HistoGraph(eff3, "eff3", "p", "P")
])
elif isinstance(datasetsDY, dataset.DatasetManager):
p = plots.ComparisonPlot(histograms.HistoGraph(eff1, "eff1", "p", "P"),
histograms.HistoGraph(eff2, "eff2", "p", "P"))
else:
p = plots.PlotBase([histograms.HistoGraph(eff1, "eff1", "p", "P")])
## FIT FUNCTIONS: "Sigmoid", "Error", "Gompertz", "Richard","Crystal" ##
## FIT TYPES: binned max likelihood: "ML" , Chi2-fit: "Chi" ##
if (howAnalyse == "--fit"):
datafit = fitType("Data", p, histeff1, eff1, 20, 500, "Crystal", "ML")
mcfit = fitType("MC", p, histeff2, eff2, 20, 500, "Crystal", "ML")
if isinstance(datasetsH125, dataset.DatasetManager):
fit("H125", p, eff3, 20, 200)
opts = {"ymin": 0, "ymax": 1.1}
opts2 = {"ymin": 0.5, "ymax": 1.5}
# moveLegend = {"dx": -0.55, "dy": -0.15, "dh": -0.1}
moveLegend = {"dx": -0.2, "dy": -0.5, "dh": -0.1}
name = "TauMET_" + analysis + "_DataVsMC_PFTauPt"
legend1 = "Data"
# legend2 = "MC (DY)"
legend2 = "Simulation"
legend3 = "MC (H125)"
createRatio = False
p.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
if isinstance(datasetsDY, dataset.DatasetManager):
p.histoMgr.setHistoLegendLabelMany({"eff1": legend1, "eff2": legend2})
createRatio = True
if isinstance(datasetsH125, dataset.DatasetManager):
p.histoMgr.setHistoLegendLabelMany({
"eff1": legend1,
"eff2": legend2,
"eff3": legend3
})
if createRatio:
p.createFrame(os.path.join(plotDir, name),
createRatio=createRatio,
opts=opts,
opts2=opts2)
else:
p.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
p.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
# p.getFrame().GetXaxis().SetTitle("#tau-jet p_{T} (GeV/c)")
p.getFrame().GetXaxis().SetTitle("#tau_{h} p_{T} (GeV/c)")
if createRatio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.5, 0.6, "LooseIsoPFTau50_Trk30_eta2p1", 17)
# histograms.addText(0.5, 0.6, "VLooseIsoPFTau120_Trk50_eta2p1", 17)
# histograms.addText(0.5, 0.6, "VLooseIsoPFTau140_Trk50_eta2p1", 17)
# label = analysis.split("_")[len(analysis.split("_")) -1]
label = "2016"
histograms.addText(0.5, 0.53, label, 17)
runRange = datasets.loadRunRange()
histograms.addText(0.5, 0.46, "Runs " + runRange, 17)
p.draw()
## does the ratio of the fits
if (howAnalyse == "--fit"):
funcRatio = ROOT.TH1F("", "", 480, 20, 500)
for i in range(0, 480):
ratio = datafit.Eval(i + 20 - 1) / mcfit.Eval(i + 20 - 1)
funcRatio.SetBinContent(i, ratio)
p.getPad().GetCanvas().cd(2)
funcRatio.Draw("SAME")
p.getPad().GetCanvas().cd(1)
##
lumi = 0.0
for d in datasets.getDataDatasets():
if (d.getName() != "SingleMuon_Run2016F_03Feb2017_v1_277932_278800"
and d.getName() !=
"SingleMuon_Run2016C_03Feb2017_v1_275656_276283"):
print "luminosity", d.getName(), d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum", lumi
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
if (howAnalyse == "--fit"):
pythonWriter.addParameters(plotDir, label, runRange, lumi, datafit)
pythonWriter.addMCParameters(label, mcfit)
pythonWriter.writeJSON(
os.path.join(plotDir,
"tauLegTriggerEfficiency_" + label + "_fit.json"))
pythonWriter.__init__() #need to clear the Json arrays
# if (howAnalyse == "--bin"):
pythonWriter.addParameters(plotDir, label, runRange, lumi, eff1)
pythonWriter.addMCParameters(label, eff2)
pythonWriter.writeJSON(
os.path.join(plotDir,
"tauLegTriggerEfficiency_" + label + "_bin.json"))
# if not createRatio:
# sys.exit()
#########################################################################
histeff1eta = getEfficiency(dataset1, "NumeratorEta", "DenominatorEta")
histeff2eta = getEfficiency(dataset2, "NumeratorEta", "DenominatorEta")
eff1eta = convert2TGraph(histeff1eta)
eff2eta = convert2TGraph(histeff2eta)
if isinstance(datasetsH125, dataset.DatasetManager):
histeff3eta = getEfficiency(datasetsH125.getMCDatasets(),
"NumeratorEta", "DenominatorEta")
eff3eta = convert2TGraph(histeff3eta)
styles.dataStyle.apply(eff1eta)
styles.mcStyle.apply(eff2eta)
eff1eta.SetMarkerSize(1)
if isinstance(datasetsH125, dataset.DatasetManager):
styles.mcStyle.apply(eff3eta)
eff3eta.SetMarkerSize(1.5)
eff3eta.SetMarkerColor(4)
eff3eta.SetLineColor(4)
if isinstance(datasetsH125, dataset.DatasetManager):
p_eta = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1eta, "eff1eta", "p", "P"), [
histograms.HistoGraph(eff2eta, "eff2eta", "p", "P"),
histograms.HistoGraph(eff3eta, "eff3eta", "p", "P")
])
elif isinstance(datasetsDY, dataset.DatasetManager):
p_eta = plots.ComparisonPlot(
histograms.HistoGraph(eff1eta, "eff1eta", "p", "P"),
histograms.HistoGraph(eff2eta, "eff2eta", "p", "P"))
else:
p_eta = plots.PlotBase(
[histograms.HistoGraph(eff1eta, "eff1eta", "p", "P")])
p_eta.histoMgr.setHistoLegendLabelMany({"eff1eta": legend1})
if isinstance(datasetsDY, dataset.DatasetManager):
p_eta.histoMgr.setHistoLegendLabelMany({
"eff1eta": legend1,
"eff2eta": legend2
})
if isinstance(datasetsH125, dataset.DatasetManager):
p_eta.histoMgr.setHistoLegendLabelMany({
"eff1eta": legend1,
"eff2eta": legend2,
"eff3eta": legend3
})
name = "TauMET_" + analysis + "_DataVsMC_PFTauEta"
if createRatio:
p_eta.createFrame(os.path.join(plotDir, name),
createRatio=createRatio,
opts=opts,
opts2=opts2)
else:
p_eta.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
moveLegendEta = {"dx": -0.5, "dy": -0.65, "dh": -0.1}
p_eta.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegendEta))
p_eta.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
p_eta.getFrame().GetXaxis().SetTitle("#tau-jet #eta")
if createRatio:
p_eta.getFrame2().GetYaxis().SetTitle("Ratio")
p_eta.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.46, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.38, label, 17)
histograms.addText(0.2, 0.31, "Runs " + datasets.loadRunRange(), 17)
p_eta.draw()
histograms.addStandardTexts(lumi=lumi)
p_eta.save(formats)
#########################################################################
histeff1phi = getEfficiency(dataset1, "NumeratorPhi", "DenominatorPhi")
histeff2phi = getEfficiency(dataset2, "NumeratorPhi", "DenominatorPhi")
eff1phi = convert2TGraph(histeff1phi)
eff2phi = convert2TGraph(histeff2phi)
if isinstance(datasetsH125, dataset.DatasetManager):
histeff3phi = getEfficiency(datasetsH125.getMCDatasets(),
"NumeratorPhi", "DenominatorPhi")
eff3phi = convert2TGraph(histeff3phi)
styles.dataStyle.apply(eff1phi)
styles.mcStyle.apply(eff2phi)
eff1phi.SetMarkerSize(1)
if isinstance(datasetsH125, dataset.DatasetManager):
styles.mcStyle.apply(eff3phi)
eff3phi.SetMarkerSize(1.5)
eff3phi.SetMarkerColor(4)
eff3phi.SetLineColor(4)
if isinstance(datasetsH125, dataset.DatasetManager):
p_phi = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1phi, "eff1phi", "p", "P"), [
histograms.HistoGraph(eff2phi, "eff2phi", "p", "P"),
histograms.HistoGraph(eff3phi, "eff3phi", "p", "P")
])
elif isinstance(datasetsDY, dataset.DatasetManager):
p_phi = plots.ComparisonPlot(
histograms.HistoGraph(eff1phi, "eff1phi", "p", "P"),
histograms.HistoGraph(eff2phi, "eff2phi", "p", "P"))
else:
p_phi = plots.PlotBase(
[histograms.HistoGraph(eff1phi, "eff1phi", "p", "P")])
p_phi.histoMgr.setHistoLegendLabelMany({"eff1phi": legend1})
if isinstance(datasetsDY, dataset.DatasetManager):
p_phi.histoMgr.setHistoLegendLabelMany({
"eff1phi": legend1,
"eff2phi": legend2
})
if isinstance(datasetsH125, dataset.DatasetManager):
p_phi.histoMgr.setHistoLegendLabelMany({
"eff1phi": legend1,
"eff2phi": legend2,
"eff3phi": legend3
})
name = "TauMET_" + analysis + "_DataVsMC_PFTauPhi"
if createRatio:
p_phi.createFrame(os.path.join(plotDir, name),
createRatio=createRatio,
opts=opts,
opts2=opts2)
else:
p_phi.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
moveLegendPhi = {"dx": -0.5, "dy": -0.65, "dh": -0.1}
p_phi.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegendPhi))
p_phi.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
p_phi.getFrame().GetXaxis().SetTitle("#tau-jet #phi")
if createRatio:
p_phi.getFrame2().GetYaxis().SetTitle("Ratio")
p_phi.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.46, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.38, label, 17)
histograms.addText(0.2, 0.31, "Runs " + datasets.loadRunRange(), 17)
p_phi.draw()
histograms.addStandardTexts(lumi=lumi)
p_phi.save(formats)
#########################################################################
namePU = "TauMET_" + analysis + "_DataVsMC_nVtx"
histeff1PU = getEfficiency(dataset1, "NumeratorPU", "DenominatorPU")
histeff2PU = getEfficiency(dataset2, "NumeratorPU", "DenominatorPU")
eff1PU = convert2TGraph(histeff1PU)
eff2PU = convert2TGraph(histeff2PU)
styles.dataStyle.apply(eff1PU)
styles.mcStyle.apply(eff2PU)
eff1PU.SetMarkerSize(1)
eff2PU.SetMarkerSize(1.5)
if isinstance(datasetsDY, dataset.DatasetManager):
pPU = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1PU, "eff1", "p", "P"),
[histograms.HistoGraph(eff2PU, "eff2", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({
"eff1": legend1,
"eff2": legend2
})
else:
pPU = plots.PlotBase([histograms.HistoGraph(eff1PU, "eff1", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
optsPU = {"ymin": 0.01, "ymax": 1.0}
createRatio = False
if createRatio:
pPU.createFrame(os.path.join(plotDir, namePU),
createRatio=True,
opts=optsPU,
opts2=opts2)
else:
pPU.createFrame(os.path.join(plotDir, namePU),
opts=optsPU,
opts2=opts2)
moveLegend = {"dx": -0.5, "dy": -0.5, "dh": -0.1}
pPU.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
# if createRatio:
# pPU.getPad1().SetLogy(True)
# else:
# pPU.getPad().SetLogy(True)
pPU.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
pPU.getFrame().GetXaxis().SetTitle("Number of reco vertices")
if createRatio:
pPU.getFrame2().GetYaxis().SetTitle("Ratio")
pPU.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.6, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.53, label, 17)
histograms.addText(0.2, 0.46, "Runs " + datasets.loadRunRange(), 17)
pPU.draw()
histograms.addStandardTexts(lumi=lumi)
pPU.save(formats)
#########################################################################
"""
hName = "Pull"
# hName = "Sub"
namePull = "TauMET_"+analysis+"_DataVsMC_"+hName+"s"
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("MC", ["TT","WJets","DYJetsToLL","SingleTop","QCD"], keepSources=True)
drh1 = datasets.getDataset("Data").getDatasetRootHisto(hName)
drh2 = datasets.getDataset("MC").getDatasetRootHisto(hName)
drh1.normalizeToOne()
drh2.normalizeToOne()
pull1 = drh1.getHistogram()
pull2 = drh2.getHistogram()
if isinstance(datasetsH125,dataset.DatasetManager):
plots.mergeRenameReorderForDataMC(datasetsH125)
drh3 = datasetsH125.getMCDatasets()[0].getDatasetRootHisto(hName)
drh3.normalizeToOne()
pull3 = drh3.getHistogram()
styles.dataStyle.apply(pull1)
styles.mcStyle.apply(pull2)
pull1.SetMarkerSize(1)
if isinstance(datasetsH125,dataset.DatasetManager):
styles.mcStyle.apply(pull3)
pull3.SetMarkerSize(1.5)
pull3.SetMarkerColor(4)
pull3.SetLineColor(4)
if isinstance(datasetsH125,dataset.DatasetManager):
p_pull = plots.ComparisonManyPlot(histograms.Histo(pull1, "pull1", "p", "P"),
[histograms.Histo(pull2, "pull2", "p", "P"),
histograms.Histo(pull3, "pull3", "p", "P")])
else:
p_pull = plots.ComparisonPlot(histograms.Histo(pull1, "pull1", "p", "P"),
histograms.Histo(pull2, "pull2", "p", "P"))
p_pull.histoMgr.setHistoLegendLabelMany({"pull1": legend1, "pull2": legend2})
if isinstance(datasetsH125,dataset.DatasetManager):
p_pull.histoMgr.setHistoLegendLabelMany({"pull1": legend1, "pull2": legend2, "pull3": legend3})
p_pull.createFrame(os.path.join(plotDir, namePull), createRatio=True, opts=opts, opts2=opts2)
moveLegendPull = {"dx": -0.5, "dy": -0.35, "dh": -0.1}
p_pull.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegendPull))
p_pull.getFrame().GetYaxis().SetTitle("Arbitrary units")
# p_pull.getFrame().GetXaxis().SetTitle("HLT #tau p_{T} - #tau-jet p_{T} (GeV/c)")
p_pull.getFrame().GetXaxis().SetTitle("HLT #tau p_{T}/ #tau-jet p_{T} - 1")
p_pull.getFrame2().GetYaxis().SetTitle("Ratio")
p_pull.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.75, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.68, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.2, 0.61, "Runs "+runRange, 17)
p_pull.draw()
histograms.addStandardTexts(lumi=lumi)
p_pull.save(formats)
"""
#########################################################################
print "Output written in", plotDir
def DataMCPlot(datasetsMgr, json):
Verbose("Creating Data-MC plot")
##ATHER
# evtCount = counter.EventCounter(datasetsMgr)
# evtCount.normalizeToOne()
# Print dataset information
# datasetsMgr.PrintInfo()
# #ATHER
# Create the Data-MC Plot
p = plots.DataMCPlot(datasetsMgr, json["histogram"])
# Label size (optional. Commonly Used in counters)
xlabelSize = None
if "xlabelsize" in json:
xlabelSize = json["xlabelsize"]
ylabelSize = None
if "ylabelsize" in json:
ylabelSize = json["ylabelsize"]
# Draw a customised plot
saveName = os.path.join(json["saveDir"], json["title"])
plots.drawPlot(
p,
saveName,
xlabel=json["xlabel"],
ylabel=json["ylabel"],
rebinX=json["rebinX"],
rebinY=json["rebinY"],
ratioYlabel=json["ratioYlabel"],
ratio=json["ratio"] == "True",
stackMCHistograms=json["stackMCHistograms"] == "True",
ratioInvert=json["ratioInvert"] == "True",
addMCUncertainty=json["addMCUncertainty"] == "True",
addLuminosityText=json["addLuminosityText"] == "True",
addCmsText=json["addCmsText"] == "True",
cmsExtraText=json["cmsExtraText"],
opts=json["opts"],
opts2=json["ratioOpts"],
log=json["logY"] == "True",
errorBarsX=json["errorBarsX"] == "True",
moveLegend=json["moveLegend"],
# cutLine = json["cutValue"], #cannot have this and "cutBox" defined
cutBox={
"cutValue": json["cutValue"],
"fillColor": json["cutFillColour"],
"box": json["cutBox"] == "True",
"line": json["cutLine"] == "True",
"greaterThan": json["cutGreaterThan"] == "True"
},
xlabelsize=xlabelSize,
ylabelsize=ylabelSize,
)
# Remove legend?
if json["removeLegend"] == "True":
p.removeLegend()
# Additional text
histograms.addText(json["extraText"].get("x"), json["extraText"].get("y"),
json["extraText"].get("text"),
json["extraText"].get("size"))
# Save in all formats chosen by user
saveFormats = json["saveFormats"]
for i, ext in enumerate(saveFormats):
Print("%s" % saveName + ext, i == 0)
p.saveAs(saveName, formats=saveFormats)
return
def doLimitError(limits, unblindedStatus):
expRelErrors = []
expLabels = {}
obsRelErrors = []
obsLabels = {}
order = [0, 1, -1, 2, -2]
expErrors = [limits.expectedErrorGraph(sigma=s) for s in order]
if expErrors[0] != None:
exps = [limits.expectedGraph(sigma=s) for s in order]
expRelErrors = [(limit.divideGraph(expErrors[i], exps[i]), "ExpRelErr%d"%i) for i in xrange(len(exps))]
expLabels = {
"ExpRelErr0": "Expected median",
"ExpRelErr1": "Expected +1#sigma",
"ExpRelErr2": "Expected -1#sigma",
"ExpRelErr3": "Expected +2#sigma",
"ExpRelErr4": "Expected -2#sigma",
}
if unblindedStatus:
obsErr = limits.observedErrorGraph()
if obsErr != None:
obs = limits.observedGraph()
if obs != None:
obsRelErrors = [(limit.divideGraph(obsErr, obs), "ObsRelErr")]
obsLabels = {"ObsRelErr": "Observed"}
if len(expRelErrors) == 0 and len(obsRelErrors) == 0:
return
# Create the plot
plot = plots.PlotBase()
if len(expRelErrors) > 0:
plot.histoMgr.extendHistos([histograms.HistoGraph(x[0], x[1], drawStyle="PL", legendStyle="lp") for x in expRelErrors])
plot.histoMgr.forEachHisto(styles.generator())
def sty(h):
r = h.getRootHisto()
r.SetLineStyle(1)
r.SetLineWidth(3)
r.SetMarkerSize(1.4)
plot.histoMgr.forEachHisto(sty)
plot.histoMgr.setHistoLegendLabelMany(expLabels)
if unblindedStatus:
if len(obsRelErrors) > 0:
obsRelErrors[0][0].SetMarkerSize(1.4)
obsRelErrors[0][0].SetMarkerStyle(25)
plot.histoMgr.insertHisto(0, histograms.HistoGraph(obsRelErrors[0][0], obsRelErrors[0][1], drawStyle="PL", legendStyle="lp"))
plot.histoMgr.setHistoLegendLabelMany(obsLabels)
plot.setLegend(histograms.moveLegend(histograms.createLegend(0.48, 0.75, 0.85, 0.92), dx=0.1, dy=-0.1))
if len(limits.mass) == 1:
plot.createFrame("limitsBrRelativeUncertainty", opts={"xmin": limits.mass[0]-5.0, "xmax": limits.mass[0]+5.0, "ymin": 0, "ymaxfactor": 1.5})
else:
plot.createFrame("limitsBrRelativeUncertainty", opts={"ymin": 0, "ymaxfactor": 1.5})
plot.frame.GetXaxis().SetTitle(limit.mHplus())
plot.frame.GetYaxis().SetTitle("Uncertainty/limit")
plot.draw()
plot.setLuminosity(limits.getLuminosity())
plot.addStandardTexts()
size = 20
x = 0.2
histograms.addText(x, 0.88, limit.process, size=size)
histograms.addText(x, 0.84, limits.getFinalstateText(), size=size)
histograms.addText(x, 0.79, limit.BRassumption, size=size)
size = 22
x = 0.55
histograms.addText(x, 0.88, "Toy MC relative", size=size)
histograms.addText(x, 0.84, "statistical uncertainty", size=size)
plot.save()
def doPlot():
# Import the auto-generated file containing the transfer factors
sys.path.append(opts.mcrab)
import FakeBTransferFactors_Run2016_80to1000 as tf
QCDNormalization = {
"Inclusive": 0.441958,
"1": 0.524575,
"0": 0.600980,
"3": 0.363374,
"2": 0.457656,
"5": 0.229395,
"4": 0.315185,
}
xval = []
xerrl = []
xerrh = []
yval = []
yerrl = []
yerrh = []
# Fill TGraph arrays
for i, k in enumerate(tf.QCDNormalization):
xval.append(k)
xerrl.append(0.001)
xerrh.append(0.001)
yval.append(QCDNormalization[k])
yerrl.append(0.01 * QCDNormalization[k])
yerrh.append(0.01 * QCDNormalization[k])
# Create TGraph object
tgraph = ROOT.TGraphAsymmErrors(len(xval), array.array("d", xval),
array.array("d", yval),
array.array("d", xerrl),
array.array("d", xerrh),
array.array("d", yerrl),
array.array("d", yerrh))
# Apply styles
setNominalStyle(tgraph)
# Convert the TGraphs to HistoGraphs and append to a list
graphs = []
graphs.extend([
#histograms.HistoGraph(tgraph, "Nominal", drawStyle="F", legendStyle="fl"),
histograms.HistoGraph(tgraph,
"Nominal",
drawStyle="P",
legendStyle="LP"),
])
# Plot the TGraphs
saveFormats = [".png", ".C", ".pdf"]
plot = plots.PlotBase(graphs, saveFormats=saveFormats)
plot.setLuminosity(opts.intLumi)
# Customise legend entries
plot.histoMgr.setHistoLegendLabelMany({
#"Nominal" : "R_{i} #pm #sigma_{R_{i}}",
"Nominal": "Nominal #pm Stat.",
})
# Create legend
xPos = 0.65
legend = getLegend(opts, xPos)
plot.setLegend(legend)
# Create a frame to be able to impose custom x- and y- range
if opts.yMin != -1 and opts.yMax != -1:
plot.createFrame("dumbie", opts={"ymin": opts.yMin, "ymax": opts.yMax})
elif opts.yMin != -1:
plot.createFrame("dumbie", opts={"ymin": opts.yMin})
elif opts.yMax != -1:
plot.createFrame("dumbie", opts={"ymax": opts.yMax})
else:
plot.createFrame("dumbie", saveFormats=[])
# Add cut box?
if opts.cutLine > 0:
kwargs = {"greaterThan": True}
plot.addCutBoxAndLine(cutValue=opts.cutLine,
fillColor=ROOT.kRed,
box=False,
line=True,
**kwargs)
# Set x-axis title
plot.frame.GetXaxis().SetTitle("bin")
plot.frame.GetYaxis().SetTitle("transfer factor (R_{i})")
# Enable/Disable logscale for axes
plot.getPad().SetLogy(opts.logY)
plot.getPad().SetLogx(opts.logX)
# Enable grids in x and y?
plot.getPad().SetGridx(opts.gridX)
plot.getPad().SetGridy(opts.gridY)
# Draw the plot with standard texts
plot.draw()
plot.addStandardTexts()
# Add text on canvas
if 0:
histograms.addText(0.55, 0.84, "fully hadronic final state", size=20)
# Save the canvas
plot.save()
# Save the plots
SavePlot(plot, "transferFactors", os.path.join(opts.saveDir, ""))
return
def _generateDQMPlot(self):
'''
Create a Data Quality Monitor (DQM) style plot
to easily check the error for each transfer factor
and whether it is within an acceptable relative error
'''
# Define error warning/tolerance on relative errors
okay = 1.0/(len(self._BinLabelMap.keys()))
warn = 0.5*okay
NEvts = []
# Check the uncertainties on the normalization factors
for k in self._BinLabelMap:
relErrorUp = abs(self._TF_Up[k])/(self._TF[k])
relErrorDown = abs(self._TF_Down[k])/(self._TF[k])
relError = self._TF_Error[k]/self._TF[k]
if 0:
print "bin = %s , relErrorUp = %s, relErrorDown = %s " % (k, relErrorUp, relErrorDown)
# Add DQM entries
NCR1 = 0
NCR2 = 0
NCR3 = 0
NCR4 = 0
for j in self._NEvtsCR1:
NCR1 += self._NEvtsCR1[j]
NEvts.append(self._NEvtsCR1[j])
for j in self._NEvtsCR2:
NCR2 += self._NEvtsCR2[j]
NEvts.append(self._NEvtsCR2[j])
for j in self._NEvtsCR3:
NCR3 += self._NEvtsCR3[j]
NEvts.append(self._NEvtsCR3[j])
for j in self._NEvtsCR4:
NCR4 += self._NEvtsCR4[j]
NEvts.append(self._NEvtsCR4[j])
if 0:
print "NCR1[%s] = %0.1f, NCR2[%s] = %0.1f, k = %s" % (k, self._NEvtsCR1[k], k, self._NEvtsCR2[k], k)
print "NCR1 = %s, NCR2 = %s, k = %s" % (NCR1, NCR2, k)
print "error/NCR1[%s] = %0.2f, error/NCR2[%s] = %0.2f" % (k, self._NEvtsCR1_Error[k]/self._NEvtsCR1[k], k, self._NEvtsCR2_Error[k]/self._NEvtsCR2[k])
# Add DQM plot entries
self._addDqmEntry(self._BinLabelMap[k], "N_{CR1}", self._NEvtsCR1[k], NCR1*okay, NCR1*warn)
self._addDqmEntry(self._BinLabelMap[k], "N_{CR2}", self._NEvtsCR2[k], NCR2*okay, NCR2*warn)
self._addDqmEntry(self._BinLabelMap[k], "N_{CR3}", self._NEvtsCR3[k], NCR3*okay, NCR3*warn)
self._addDqmEntry(self._BinLabelMap[k], "N_{CR4}", self._NEvtsCR4[k], NCR4*okay, NCR4*warn)
# self._addDqmEntry(self._BinLabelMap[k], "#frac{#sigma_{CR1}}{N_{CR1}}", self._NEvtsCR1_Error[k]/NCR1, 0.05, 0.15)
# self._addDqmEntry(self._BinLabelMap[k], "#frac{#sigma_{CR2}}{N_{CR2}}", self._NEvtsCR2_Error[k]/NCR2, 0.05, 0.15)
# Construct the DQM histogram
nBinsX = len(self._dqmKeys[self._dqmKeys.keys()[0]].keys())
nBinsY = len(self._dqmKeys.keys())
h = ROOT.TH2F("FakeB DQM", "FakeB DQM", nBinsX, 0, nBinsX, nBinsY, 0, nBinsY)
# Customise axes
h.GetXaxis().SetLabelSize(15)
h.GetYaxis().SetLabelSize(10)
# Set Min and Max of z-axis
if 0: # red, yellow, green for DQM
h.SetMinimum(0)
h.SetMaximum(3)
else: # pure entries instead of red, yellow, green
h.SetMinimum(min(NEvts)*0.25)
h.SetMaximum(round(max(NCR1, NCR2, NCR3, NCR4)))
h.SetContour(10)
#h.SetContour(3)
if 0:
h.GetXaxis().LabelsOption("v")
h.GetYaxis().LabelsOption("v")
nWarnings = 0
nErrors = 0
# For-loop: All x-axis bins
for i in range(h.GetNbinsX()):
# For-loop: All y-axis bins
for j in range(h.GetNbinsY()):
ykey = self._dqmKeys.keys()[j]
xkey = self._dqmKeys[ykey].keys()[i]
# Set the bin content
h.SetBinContent(i+1, j+1, self._dqmKeys[ykey][xkey])
h.GetXaxis().SetBinLabel(i+1, xkey)
h.GetYaxis().SetBinLabel(j+1, ykey)
if self._dqmKeys[ykey][xkey] > 2:
nErrors += 1
elif self._dqmKeys[ykey][xkey] > 1:
nWarnings += 1
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(False)
style.setGridY(False)
style.setWide(True, 0.15)
# Set the colour styling (red, yellow, green)
if 0:
palette = array.array("i", [ROOT.kGreen+1, ROOT.kYellow, ROOT.kRed])
ROOT.gStyle.SetPalette(3, palette)
else:
# https://root.cern.ch/doc/master/classTColor.html
ROOT.gStyle.SetPalette(ROOT.kLightTemperature)
# ROOT.gStyle.SetPalette(ROOT.kColorPrintableOnGrey)
#tdrstyle.setRainBowPalette()
#tdrstyle.setDeepSeaPalette()
# Create canvas
c = ROOT.TCanvas()
c.SetLogx(False)
c.SetLogy(False)
c.SetLogz(True)
c.SetGridx()
c.SetGridy()
h.Draw("COLZ") #"COLZ TEXT"
# Add CMS text and text with colour keys
histograms.addStandardTexts(cmsTextPosition="outframe")
if 0:
histograms.addText(0.55, 0.80, "green < %.0f %%" % (okay*100), size=20)
histograms.addText(0.55, 0.84, "yellow < %.0f %%" % (warn*100), size=20)
histograms.addText(0.55, 0.88, "red > %.0f %%" % (warn*100), size=20)
# Save the canvas to a file
backup = ROOT.gErrorIgnoreLevel
ROOT.gErrorIgnoreLevel = ROOT.kWarning
plotName = os.path.join(self._plotDirName, "FakeBNormalisationDQM")
# For-loop: Save formats
for ext in ["png", "C", "pdf"]:
saveName ="%s.%s" % (plotName, ext)
c.Print(saveName)
ROOT.gErrorIgnoreLevel = backup
ROOT.gStyle.SetPalette(1)
msg = "Obtained %d warnings and %d errors for the normalisation" % (nWarnings, nErrors)
self.Verbose(msg)
if nWarnings > 0:
msg = "DQM has %d warnings and %d errors! Please have a look at %s.png." % (nWarnings, nErrors, os.path.basename(plotName))
self.Verbose(ShellStyles.ErrorStyle() + msg + ShellStyles.NormalStyle(), True)
#if nWarnings > 0 or nErrors > 0:
if nErrors > 0:
msg = "DQM has %d warnings and %d errors! Please have a look at %s.png." % (nWarnings, nErrors, os.path.basename(plotName))
self.Verbose(ShellStyles.ErrorStyle() + msg + ShellStyles.NormalStyle(), True)
return
def doPlot(name, graphs, limits, xlabel, scenario, progversion):
obs = graphs["obs"]
# excluded = ROOT.TGraph(obs)
# excluded.SetName("ExcludedArea")
# excluded.SetFillColor(ROOT.kGray)
## excluded.SetPoint(excluded.GetN(), obs.GetX()[obs.GetN()-1], tanbMax)
## excluded.SetPoint(excluded.GetN(), obs.GetX()[0], tanbMax)
# excluded.SetPoint(excluded.GetN(), -1, 1)
# excluded.SetPoint(excluded.GetN(), -1, 100)
# excluded.SetPoint(excluded.GetN(), obs.GetX()[0], 100)
# if not obs.GetY()[0] == 100:
# excluded.SetPoint(excluded.GetN(), obs.GetX()[0], obs.GetY()[0])
"""
excluded.SetPoint(excluded.GetN(), obs.GetX()[0], tanbMax)
excluded.SetPoint(excluded.GetN(), 0, tanbMax)
excluded.SetPoint(excluded.GetN(), 0, 1)
excluded.SetPoint(excluded.GetN(), obs.GetX()[obs.GetN()-1], 1)
"""
# excluded.SetFillColor(ROOT.kGray)
# excluded.SetFillStyle(3354)
# excluded.SetLineWidth(0)
# excluded.SetLineColor(ROOT.kWhite)
expected = graphs["exp"]
expected.SetLineStyle(2)
expected1 = graphs["exp1"]
expected1.SetLineStyle(2)
expected2 = graphs["exp2"]
expected2.SetLineStyle(2)
plot = plots.PlotBase([
histograms.HistoGraph(graphs["obs"],
"Observed",
drawStyle="PL",
legendStyle="lp"),
histograms.HistoGraph(graphs["obs_th_plus"],
"ObservedPlus",
drawStyle="L",
legendStyle="l"),
histograms.HistoGraph(graphs["obs_th_minus"],
"ObservedMinus",
drawStyle="L"),
histograms.HistoGraph(graphs["excluded"],
"Excluded",
drawStyle="F",
legendStyle="f"),
histograms.HistoGraph(expected, "Expected", drawStyle="L"),
#histograms.HistoGraph(graphs["exp"], "Expected", drawStyle="L"),
#### histograms.HistoGraph(graphs["Allowed"], "Allowed by \nm_{H} = 125.9#pm3.0 "+GeVUnit, drawStyle="F", legendStyle="f"),
#### histograms.HistoGraph(graphs["Allowed"], "AllowedCopy", drawStyle="L", legendStyle="f"),
# histograms.HistoGraph(graphs["mintanb"], "MinTanb", drawStyle="L"),
#histograms.HistoGraph(graphs["exp1"], "Expected1", drawStyle="F", legendStyle="fl"),
#histograms.HistoGraph(graphs["exp2"], "Expected2", drawStyle="F", legendStyle="fl"),
histograms.HistoGraph(expected1,
"Expected1",
drawStyle="F",
legendStyle="fl"),
histograms.HistoGraph(expected2,
"Expected2",
drawStyle="F",
legendStyle="fl"),
])
plot.histoMgr.setHistoLegendLabelMany({
"ObservedPlus":
"Observed #pm1#sigma (th.)",
"ObservedMinus":
None,
"Expected":
None,
# "MinTanb": None,
#### "AllowedCopy": None,
"Expected1":
"Expected median #pm 1#sigma",
"Expected2":
"Expected median #pm 2#sigma"
})
# plot.setLegend(histograms.createLegend(0.57, 0.155, 0.87, 0.355))
plot.setLegend(histograms.createLegend(0.19, 0.60, 0.57, 0.80))
plot.legend.SetFillColor(0)
plot.legend.SetFillStyle(1001)
plot.createFrame(name,
opts={
"ymin": 0,
"ymax": tanbMax,
"xmin": 180,
"xmax": 600
})
plot.frame.GetXaxis().SetTitle(xlabel)
plot.frame.GetYaxis().SetTitle(limit.tanblimit)
plot.draw()
histograms.addStandardTexts(lumi="2.3-4.9", addCmsText=False)
size = 20
x = 0.2
histograms.addText(x, 0.9, limit.processHeavy, size=size)
histograms.addText(x, 0.863, limits.getFinalstateText(), size=size)
#### histograms.addText(x, 0.815, "MSSM m_{h}^{max}", size=size)
histograms.addText(x, 0.815, scenario, size=size)
# histograms.addText(x, 0.775, limit.BRassumption, size=size)
# histograms.addText(x, 0.735, "#mu=%d %s"%(mu, limit.massUnit()), size=size)
# histograms.addText(0.7, 0.23, "Min "+limit.BR+"(t#rightarrowH^{+}b)#times"+limit.BR+"(H^{+}#rightarrow#tau#nu)", size=0.5*size)
#Adding a LHC label:
ROOT.LHCHIGGS_LABEL(0.97, 0.72, 1)
histograms.addText(x, 0.55, progversion, size=size)
histograms.addText(x, 0.48, "Derived from", size=size)
histograms.addText(x, 0.43, "CMS HIG-13-XXX", size=size)
# histograms.addText(x, 0.38, "JHEP07(2012)143", size=size)
plot.save()
def main(argv):
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
dirs_signal = ["../../SignalAnalysis_140605_143702/"]
QCDInvertedNormalization = sort(QCDInvertedNormalizationFactors.QCDInvertedNormalization)
labels,QCDInvertedNormalizationFilteredEWKFakeTaus = getSortedLabelsAndFactors(QCDInvertedNormalizationFactorsFilteredEWKFakeTaus.QCDInvertedNormalization)
analysis_inverted = "signalAnalysisInvertedTau"
analysis = "signalAnalysis"
optModes = []
#optModes.append("OptQCDTailKillerZeroPlus")
optModes.append("OptQCDTailKillerLoosePlus")
optModes.append("OptQCDTailKillerMediumPlus")
optModes.append("OptQCDTailKillerTightPlus")
varHistoName = "shapeEWKGenuineTausTransverseMass"
nomHistoName = "shapeTransverseMass"
signalHistoName = "shapeEWKFakeTausTransverseMass"
#Optimal: 0.8, 0.82, 0.9
###w_list = [0.65, 0.7, 0.76] #old baseline ft
w_list = [0.66, 0.67, 0.75]
#w_list = [0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 1]
#defaultBinning = systematics.getBinningForPlot("shapeTransverseMass")
defaultBinning = [0,20,40,60,80,100,120,140,160,200,400]
defaultBinning_array = array.array("d",defaultBinning)
diff_opt = []
for optMode in optModes:
diff_list = []
for w in w_list:
var_values = []
nom_values = []
# baseline fake taus
mt_baseline_faketaus_data = getDataSets(dirs_signal, dataEra, searchMode, analysis, optMode)
mtplot_signalfaketaus = plots.DataMCPlot(mt_baseline_faketaus_data, signalHistoName)
mt_signalfaketaus = mtplot_signalfaketaus.histoMgr.getHisto("EWK").getRootHisto().Clone(signalHistoName)
# inverted fake taus
mt_inverted_faketaus_data = getDataSets(dirs, dataEra, searchMode, analysis_inverted, optMode)
histonames_var = mt_inverted_faketaus_data.getDataset("Data").getDirectoryContent(varHistoName)
histonames_nom = mt_inverted_faketaus_data.getDataset("Data").getDirectoryContent(nomHistoName)
bins_var = getBins(histonames_var, varHistoName)
bins_nom = getBins(histonames_nom, nomHistoName)
normalization_var_qg = getNormalization(bins_var,w,QCDInvertedNormalizationFilteredEWKFakeTaus,True,True)
normalization_var = getNormalization(bins_var,w,QCDInvertedNormalizationFilteredEWKFakeTaus,True,False)
normalization_nom = getNormalization(bins_nom,w,QCDInvertedNormalization,False,False)
mt_var_qg = getMt(mt_inverted_faketaus_data,bins_var,varHistoName,normalization_var_qg)
mt_var = getMt(mt_inverted_faketaus_data,bins_var,varHistoName,normalization_var)
mt_nom = getMt(mt_inverted_faketaus_data,bins_nom,nomHistoName,normalization_nom)
mt_nom.Add(mt_signalfaketaus)
mt_var_qg.SetName("QCD(Data)+EWK+t#bar{t}(Data, mis-ID. #tau), q-g bal.")
mt_var.SetName("QCD(Data)+EWK+t#bar{t}(Data, mis-ID. #tau)")
mt_nom.SetName("QCD(Data)+EWK+t#bar{t}(MC, mis-ID. #tau)")
mt_var_qg.SetLineWidth(4)
mt_var.SetLineColor(14)
mt_nom.SetLineColor(2)
mt_var_qg = mt_var_qg.Rebin(len(defaultBinning)-1,"",defaultBinning_array)
mt_var = mt_var.Rebin(len(defaultBinning)-1,"",defaultBinning_array)
mt_nom = mt_nom.Rebin(len(defaultBinning)-1,"",defaultBinning_array)
for i in range(0,mt_nom.GetSize()):
var_values.append(mt_var_qg.GetBinContent(i))
nom_values.append(mt_nom.GetBinContent(i))
style = tdrstyle.TDRStyle()
varPlots = [mt_var, mt_var_qg]
plot = plots.ComparisonManyPlot(mt_nom,varPlots)
plot.createFrame(optMode.replace("Opt","Mt_DataDrivenVsMC_"+"w="+str(w)+"_"), createRatio=True)
moveLegend={"dx": -0.35,"dy": 0.05}
plot.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
histograms.addText(0.65, 0.3, optMode.replace("OptQCDTailKiller","R_{BB} ").replace("Plus",""), 25)
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
lumi=mt_inverted_faketaus_data.getDataset("Data").getLuminosity()
histograms.addLuminosityText(x=None, y=None, lumi=lumi)
plot.draw()
plot.save()
mt_var_qg.Delete()
mt_var.Delete()
mt_nom.Delete()
#TFile.CurrentFile().Close("R")
mt_baseline_faketaus_data.close()
mt_inverted_faketaus_data.close()
ROOT.gROOT.CloseFiles()
ROOT.gROOT.GetListOfCanvases().Delete()
ROOT.gDirectory.GetList().Delete()
#print var_values
#print nom_values
# difference metrics
num = 0
denom = 0
for i in range(0,len(nom_values)):
num += var_values[i]*(var_values[i]-nom_values[i])**2
denom += var_values[i]
diff = num/denom
diff_list.append(diff)
diff_opt.append(diff_list)
os.system("rm MtOptimal/*")
os.system("mkdir -p MtOptimal")
print "\nWeights:\t",w_list,'\n'
optimalWeights = {}
for i in range(0,len(diff_opt)):
print optModes[i]
print "Differences:\t",diff_opt[i],"- Optimal: w =",w_list[diff_opt[i].index(min(diff_opt[i]))]
optimalWeights[optModes[i]] = w_list[diff_opt[i].index(min(diff_opt[i]))]
command = "cp *" + str(w_list[diff_opt[i].index(min(diff_opt[i]))])+"*"+optModes[i].replace("Opt","") + ".eps MtOptimal"
os.system(command)
print optimalWeights
writeWeightsToFile("OptimalWeights.py",optimalWeights)
writeNormalizationToFile("QCDPlusEWKFakeTauNormalizationFactors.py",normalization_var_qg,labels)
def drawAllInOne(self, myAllShapeNuisances, luminosity):
myMaxSize = len(myAllShapeNuisances)
# Create pads
canvasHeight = _cHeaderHeight + _cBodyHeight * myMaxSize + _cFooterHeight
c = ROOT.TCanvas("","",600,canvasHeight)
c.Divide(1,myMaxSize)
myHeightBefore = canvasHeight
myHeightAfter = canvasHeight
for i in range(0,myMaxSize):
myHeightBefore = myHeightAfter
p = c.cd(i+1)
myTopMargin = 0.0
myBottomMargin = 0.0
if i == 0:
# Top histogram with header
myHeightAfter -= _cHeaderHeight + _cBodyHeight
myTopMargin = float(_cHeaderHeight) / float(_cHeaderHeight+_cBodyHeight)
elif i == myMaxSize-1:
# Bottom histogram with x axis label and title
myHeightAfter -= _cFooterHeight + _cBodyHeight
myBottomMargin = float(_cFooterHeight) / float(_cFooterHeight+_cBodyHeight)
else:
# Middle histogram, only body
myHeightAfter -= _cBodyHeight
(xlow, ylow, xup, yup) = [ROOT.Double(x) for x in [0.0]*4]
p.GetPadPar(xlow, ylow, xup, yup)
p.SetPad(xlow, float(myHeightAfter)/float(canvasHeight), xup, float(myHeightBefore)/float(canvasHeight))
p.SetBorderMode(0)
p.SetFillStyle(4000)
p.SetTopMargin(myTopMargin)
p.SetBottomMargin(myBottomMargin)
# Draw plots
if len(self._ratioPlotList) == 0:
print "No ratioplots in list! Cannot draw all-in-one plot!"
return
o = self._ratioPlotList[0].getFrame2()
myEmptyPlot = aux.Clone(o) # Keep the clone if it is needed to draw the x axis
for i in range(0,myMaxSize):
p = c.cd(i+1)
# Find plot
myPlotIndex = None
for j in range(0,len(self._systNameList)):
if myAllShapeNuisances[i] == self._systNameList[j]:
myPlotIndex = j
plot = None
if myPlotIndex != None:
plot = self._ratioPlotList[myPlotIndex].getFrame2()
else:
if i == myMaxSize-1:
plot = myEmptyPlot # Use this to draw the x axis
else:
plot = self._ratioPlotList[0].getFrame2() # Only the empty frame matters
# Draw plot
if i == myMaxSize-1:
# Bottom histogram
plot.GetXaxis().SetTitleOffset(0.6*myMaxSize+0.6) # 6.6/10, 3.6/5
else:
plot.GetXaxis().SetTitleSize(0)
plot.GetXaxis().SetLabelSize(0)
plot.GetYaxis().SetLabelSize(26)
plot.GetYaxis().SetTitleOffset(0.34*myMaxSize+0.1) # 3.5/10, 1.8/5
# plot.SetMinimum(0.001)
# plot.SetMaximum(1.999)
plot.SetMinimum(0.601)
plot.SetMaximum(1.399)
plot.Draw() # Plot frame for every nuisance
if myPlotIndex != None:
self._ratioPlotList[myPlotIndex].ratioHistoMgr.draw() # Plot content only if affected
self._ratioPlotList[0].ratioLine.Draw("L")
p.RedrawAxis()
# Labels for shape nuisance and dataset
myHeight = 0.82
if i == 0:
myHeight = myHeight*float(_cBodyHeight) / float(_cHeaderHeight+_cBodyHeight)
elif i == myMaxSize-1:
myHeight = (myHeight*float(_cBodyHeight)+float(_cFooterHeight)) / float(_cFooterHeight +_cBodyHeight)
histograms.addText(x=0.18, y=myHeight, text=myAllShapeNuisances[i], size=30)
myHeight = 0.08
if i == 0:
myHeight = myHeight*float(_cBodyHeight) / float(_cHeaderHeight+_cBodyHeight)
elif i == myMaxSize-1:
myHeight = (myHeight*float(_cBodyHeight)+float(_cFooterHeight)) / float(_cFooterHeight +_cBodyHeight)
histograms.addText(x=0.93, y=myHeight, text=self._dsetName, size=30, align="right")
# Header labels
if i == 0:
histograms.addStandardTexts(lumi=luminosity, cmsTextPosition="outframe")
# Labels for non-existing nuisances
if myPlotIndex == None:
myHeight = 0.44
if i == 0:
myHeight = myHeight*float(_cBodyHeight) / float(_cHeaderHeight+_cBodyHeight)
elif i == myMaxSize-1:
myHeight = (myHeight*float(_cBodyHeight)+float(_cFooterHeight)) / float(_cFooterHeight +_cBodyHeight)
histograms.addText(x=0.555, y=myHeight, text="Not affected", size=30, align="center")
# Save plot
myPlotName = "shapeSystRatioOnlyAll_%s"%(self._dsetName)
backup = ROOT.gErrorIgnoreLevel
ROOT.gErrorIgnoreLevel = ROOT.kError
for suffix in [".png",".C",".eps"]:
c.Print("%s/%s%s"%(_dirname,myPlotName,suffix))
ROOT.gErrorIgnoreLevel = backup
def main():
if len(sys.argv) < 2:
usage()
paths = [sys.argv[1]]
analysis = "METLeg_2015D_MET80"
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis)
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="Tau\S+25ns$|TTJets$")
datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="Tau_Run2015C|Tau\S+25ns_Silver$|DYJetsToLL|WJetsToLNu$")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="Tau_Run2015D_PromptReco_v4_246908_260426_25ns$|DYJetsToLL_M_50$")
for d in datasets.getAllDatasets():
print d.getName()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
dataset2 = datasets.getMCDatasets()
eff1_MET80 = getEfficiency(dataset1)
eff2_MET80 = getEfficiency(dataset2)
styles.dataStyle.apply(eff1_MET80)
styles.mcStyle.apply(eff2_MET80)
eff1_MET80.SetMarkerSize(1)
eff2_MET80.SetMarkerSize(1.5)
p = plots.ComparisonPlot(histograms.HistoGraph(eff1_MET80, "eff1_MET80", "p", "P"),
histograms.HistoGraph(eff2_MET80, "eff2_MET80", "p", "P"))
opts = {"ymin": 0, "ymax": 1.1}
opts2 = {"ymin": 0.5, "ymax": 1.5}
moveLegend = {"dx": -0.55, "dy": -0.15}
name = "TauMET_"+analysis+"_DataVsMC_PFMET"
legend1 = "Data"
# legend2 = "MC (TTJets)"
legend2 = "MC"
p.histoMgr.setHistoLegendLabelMany({"eff1_MET80": legend1, "eff2_MET80": legend2})
p.createFrame(os.path.join(plotDir, name), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.6, "LooseIsoPFTau50_Trk30_eta2p1_MET80", 17)
# histograms.addText(0.2, 0.53, analysis.split("_")[len(analysis.split("_")) -1], 17)
label = analysis.split("_")[1]
histograms.addText(0.2, 0.53, label, 17)
runRange = datasets.loadRunRange()
histograms.addText(0.2, 0.46, "Runs "+runRange, 17)
p.draw()
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity",d.getName(),d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum",lumi
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
pythonWriter.addParameters(plotDir,label,runRange,lumi,eff1_MET80)
pythonWriter.addMCParameters(label,eff2_MET80)
pythonWriter.writeJSON(os.path.join(plotDir,"metLegTriggerEfficiency2015.json"))
"""
#### MET120
analysis = "METLeg_2015CD_MET120"
datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis)
for d in datasets.getAllDatasets():
print d.getName()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
dataset2 = datasets.getMCDatasets()
eff1_MET120 = getEfficiency(dataset1)
eff2_MET120 = getEfficiency(dataset2)
styles.dataStyle.apply(eff1_MET120)
styles.mcStyle.apply(eff2_MET120)
eff1_MET120.SetMarkerSize(1)
eff2_MET120.SetMarkerSize(1.5)
p = plots.ComparisonPlot(histograms.HistoGraph(eff1_MET120, "eff1_MET120", "p", "P"),
histograms.HistoGraph(eff2_MET120, "eff2_MET120", "p", "P"))
opts = {"ymin": 0, "ymax": 1.1}
opts2 = {"ymin": 0.5, "ymax": 1.5}
moveLegend = {"dx": -0.55, "dy": -0.15}
name = "DataVsMC_L1HLTMET_PFMET_MET120"
legend1 = "Data"
legend2 = "MC"
p.histoMgr.setHistoLegendLabelMany({"eff1_MET120": legend1, "eff2_MET120": legend2})
p.createFrame(os.path.join(plotDir, name), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
lumi = 0.0
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
"""
# CaloMET
#### MET80
analysisc = "METLeg_2015D_CaloMET_MET80"
datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc)
datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc,excludeTasks="Tau\S+25ns_Silver$")
# datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc,includeOnlyTasks="Tau\S+25ns$|TTJets$")
style = tdrstyle.TDRStyle()
dataset1c = datasetsc.getDataDatasets()
dataset2c = datasetsc.getMCDatasets()
# eff1c_MET80 = getEfficiency(dataset1c)
eff2c_MET80 = getEfficiency(dataset2c)
# styles.dataStyle.apply(eff1c_MET80)
styles.mcStyle.apply(eff2c_MET80)
# eff1c_MET80.SetMarkerSize(1)
eff2c_MET80.SetMarkerSize(1.5)
eff2c_MET80.SetMarkerColor(4)
p = plots.ComparisonPlot(histograms.HistoGraph(eff2_MET80, "eff2_MET80", "p", "P"),
histograms.HistoGraph(eff2c_MET80, "eff2c_MET80", "p", "P"))
namec = "TauMET_"+analysis+"_MC_TrgBitVsCaloMET80_PFMET"
legend1c = "MC, trigger bit"
legend2c = "MC, CaloMET > 80"
p.histoMgr.setHistoLegendLabelMany({"eff2_MET80": legend1c, "eff2c_MET80": legend2c})
p.createFrame(os.path.join(plotDir, namec), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity",d.getName(),d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum",lumi
histograms.addStandardTexts(lumi=lumi)
histograms.addText(0.2, 0.6, "LooseIsoPFTau50_Trk30_eta2p1_MET80", 17)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
"""
#### MET120
analysisc = "METLeg_2015A_CaloMET_MET120"
datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc)
style = tdrstyle.TDRStyle()
dataset1c = datasetsc.getDataDatasets()
dataset2c = datasetsc.getMCDatasets()
eff1c_MET120 = getEfficiency(dataset1c)
eff2c_MET120 = getEfficiency(dataset2c)
styles.dataStyle.apply(eff1c_MET120)
styles.mcStyle.apply(eff1c_MET120)
eff1c_MET120.SetMarkerSize(1)
eff2c_MET120.SetMarkerSize(1.5)
p = plots.ComparisonPlot(histograms.HistoGraph(eff2_MET120, "eff2_MET120", "p", "P"),
histograms.HistoGraph(eff2c_MET120, "eff2c_MET120", "p", "P"))
namec = "MC_TrgBitVsCaloMET120_L1HLTMET_PFMET"
legend1c = "MC, trigger bit"
legend2c = "MC, CaloMET > 120"
p.histoMgr.setHistoLegendLabelMany({"eff2_MET120": legend1c, "eff2c_MET120": legend2c})
p.createFrame(os.path.join(plotDir, namec), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
lumi = 0.0
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
"""
#########################################################################
namePU = "TauMET_"+analysis+"_DataVsMC_nVtx"
eff1PU = getEfficiency(dataset1,"NumeratorPU","DenominatorPU")
eff2PU = getEfficiency(dataset2,"NumeratorPU","DenominatorPU")
styles.dataStyle.apply(eff1PU)
styles.mcStyle.apply(eff2PU)
eff1PU.SetMarkerSize(1)
eff2PU.SetMarkerSize(1.5)
pPU = plots.ComparisonManyPlot(histograms.HistoGraph(eff1PU, "eff1", "p", "P"),
[histograms.HistoGraph(eff2PU, "eff2", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({"eff1": legend1, "eff2": legend2})
opts = {"ymin": 0.001, "ymax": 0.1}
pPU.createFrame(os.path.join(plotDir, namePU), createRatio=True, opts=opts, opts2=opts2)
pPU.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
pPU.getPad1().SetLogy(True)
pPU.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
pPU.getFrame().GetXaxis().SetTitle("Number of reco vertices")
pPU.getFrame2().GetYaxis().SetTitle("Ratio")
pPU.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.4, 0.25, "LooseIsoPFTau50_Trk30_eta2p1_MET80", 17)
histograms.addText(0.4, 0.18, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.4, 0.11, "Runs "+datasets.loadRunRange(), 17)
pPU.draw()
histograms.addStandardTexts(lumi=lumi)
pPU.save(formats)
print "Output written in",plotDir
def main(argv):
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
QCDInvertedNormalization = QCDInvertedNormalizationFactors.QCDInvertedNormalization
QCDInvertedNormalizationFilteredEWKFakeTaus = QCDInvertedNormalizationFactorsFilteredEWKFakeTaus.QCDInvertedNormalization
analysis = "signalAnalysisInvertedTau"
optModes = []
#optModes.append("OptQCDTailKillerZeroPlus")
optModes.append("OptQCDTailKillerLoosePlus")
optModes.append("OptQCDTailKillerMediumPlus")
optModes.append("OptQCDTailKillerTightPlus")
#optModes.append("OptQCDTailKillerVeryTightPlus")
#optModes.append("OnlyGenuineMCTausFalse")
#optModes.append("OnlyGenuineMCTausTrue")
#Optimal: 0.8, 0.82, 0.9
#w1_list = [0.8, 0.82, 0.84, 0.87]
#w1_list = [0.8, 0.82, 0.9, 1]
w1_list = [0.9]
defaultBinning = systematics.getBinningForPlot("shapeTransverseMass")
diff_opt = []
for optMode in optModes:
diff_list = []
for w1 in w1_list:
var_values = []
nom_values = []
w2 = 1 - w1
color = 1
#signal
dirs_signal = ["../../SignalAnalysis_140605_143702/"]
datasets_signal = dataset.getDatasetsFromMulticrabDirs(
dirs_signal,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis.replace("InvertedTau", ""),
optimizationMode=optMode)
datasets_signal.updateNAllEventsToPUWeighted()
datasets_signal.loadLuminosities()
datasets_signal.remove(
filter(lambda name: "TTToHplus" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "HplusTB" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "Hplus_taunu_t-channel" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "Hplus_taunu_tW-channel" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "TTJets_SemiLept" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "TTJets_FullLept" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "TTJets_Hadronic" in name,
datasets_signal.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets_signal)
datasets_signal.merge(
"EWK",
["TTJets", "WJets", "DYJetsToLL", "SingleTop", "Diboson"])
mtplot_signalfaketaus = plots.DataMCPlot(
datasets_signal, "shapeEWKFakeTausTransverseMass")
mt_signalfaketaus = mtplot_signalfaketaus.histoMgr.getHisto(
"EWK").getRootHisto().Clone("shapeEWKFakeTausTransverseMass")
mt_signalfaketaus.SetName("BaselineFakeTaus")
myBinning = [0, 20, 40, 60, 80, 100, 120, 140, 160, 200, 400]
myArray = array.array("d", myBinning)
fitBinning = []
for i in range(0, 45):
fitBinning.append(i * 10)
fitArray = array.array("d", fitBinning)
mt_baseline = mt_signalfaketaus
#rangeMin = mt_signalfaketaus.GetXaxis().GetXmin()
#rangeMax = mt_signalfaketaus.GetXaxis().GetXmax()
#theFit = TF1('theFit',FitFunction(),rangeMin,rangeMax,4)
#theFit.SetParLimits(0,0.5,10000)
#theFit.SetParLimits(1,90,10000)
#theFit.SetParLimits(2,30,10000)
#theFit.SetParLimits(3,0.001,10000)
#mt_signalfaketaus.Fit(theFit,"R")
#theFit.SetRange(mt_signalfaketaus.GetXaxis().GetXmin(),mt_signalfaketaus.GetXaxis().GetXmax())
#theFit.SetLineStyle(2)
#theFit.SetLineColor(4)
#theFit.SetLineWidth(3)
#theFit.Draw()
#mt_corr = theFit.GetHistogram()
#mt_corr = mt_corr.Rebin(len(fitBinning)-1,"",fitArray)
#mt_corr.Scale(mt_baseline.GetMaximum()/mt_corr.GetMaximum())
for HISTONAME in histoNameList:
var = False
if HISTONAME == "shapeEWKGenuineTausTransverseMass":
var = True
datasets = dataset.getDatasetsFromMulticrabDirs(
dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis,
optimizationMode=optMode)
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge(
"EWK",
["TTJets", "WJets", "DYJetsToLL", "SingleTop", "Diboson"])
histonames = datasets.getDataset("Data").getDirectoryContent(
HISTONAME)
bins = []
for histoname in histonames:
binname = histoname.replace(HISTONAME, "")
if not binname == "Inclusive":
bins.append(binname)
for i, bin in enumerate(bins):
mtplot = plots.DataMCPlot(
datasets, HISTONAME + "/" + HISTONAME + bin)
if i == 0:
mt = mtplot.histoMgr.getHisto(
"Data").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
mt_ewk = mtplot.histoMgr.getHisto(
"EWK").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
mtn = mtplot.histoMgr.getHisto(
"Data").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
mtn_ewk = mtplot.histoMgr.getHisto(
"EWK").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
if var:
legendName = "QCD(Data)+EWK+t#bar{t}(Data, mis-ID. #tau)"
else:
legendName = "QCD(Data)+EWK+t#bar{t}(MC, mis-ID. #tau)"
legendName = legendName.replace("Plus", "")
mt.SetName(legendName)
mt.SetLineColor(color)
mt.Add(mt_ewk, -1)
mtn.Add(mtn_ewk, -1)
mtn.Scale(QCDInvertedNormalization[str(i)])
if var:
scale = w1 * QCDInvertedNormalizationFilteredEWKFakeTaus[
str(
i
)] + w2 * QCDInvertedNormalizationFilteredEWKFakeTaus[
str(i) + "EWK_FakeTaus"]
mt.Scale(scale)
else:
mt.Scale(QCDInvertedNormalization[str(i)])
color += 1
if color == 5:
color += 1
else:
h = mtplot.histoMgr.getHisto(
"Data").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
mt_ewk = mtplot.histoMgr.getHisto(
"EWK").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
hn = mtplot.histoMgr.getHisto(
"Data").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
mtn_ewk = mtplot.histoMgr.getHisto(
"EWK").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
h.Add(mt_ewk, -1)
hn.Add(mtn_ewk, -1)
hn.Scale(QCDInvertedNormalization[str(i)])
if var:
scale = w1 * QCDInvertedNormalizationFilteredEWKFakeTaus[
str(
i
)] + w2 * QCDInvertedNormalizationFilteredEWKFakeTaus[
str(i) + "EWK_FakeTaus"]
h.Scale(scale)
else:
h.Scale(QCDInvertedNormalization[str(i)])
mt.Add(h)
mtn.Add(hn)
#mt = mt.Rebin(len(myBinning)-1,"",myArray)
#mt_corr = mt_corr.Rebin(len(myBinning)-1,"",myArray)
if not var:
mt.Add(mt_baseline)
#mt.Add(mt_corr)
#myBinning = []
#for i in range(0,11):
# myBinning.append(20*i)
#myBinning.append(400)
#myArray = array.array("d",defaultBinning)
mt = mt.Rebin(len(myBinning) - 1, "", myArray)
for i in range(0, mt.GetSize()):
if var:
var_values.append(mt.GetBinContent(i))
else:
nom_values.append(mt.GetBinContent(i))
if var:
#mt.SetLineStyle(2)
var_hist = mt
else:
#mt.SetLineStyle(2)
nom_hist = mt
style = tdrstyle.TDRStyle()
#gStyle.SetOptStat(1101)
#mt_data.SetStats(1)
#gPad.Update()
bins = [0, 390, 400]
arr = array.array("d", bins)
mtn = mtn.Rebin(len(bins) - 1, "", arr)
plot_data = plots.PlotBase()
plot_data.histoMgr.appendHisto(histograms.Histo(mtn, "Data"))
plot_data.createFrame("Data_" + HISTONAME + "_" + optMode +
"_" + str(w1))
plot_data.draw()
plot_data.save()
plot = plots.ComparisonPlot(nom_hist, var_hist)
plot.createFrame(optMode.replace(
"Opt", "Mt_" + "w1=" + str(w1) + "_w2=" + str(w2) +
"_DataDrivenVsMC_"),
createRatio=True)
moveLegend = {"dx": -0.295, "dy": 0.05}
plot.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
histograms.addText(
0.65, 0.20,
optMode.replace("OptQCDTailKiller",
"R_{BB} ").replace("Plus", ""), 25)
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
lumi = datasets.getDataset("Data").getLuminosity()
histograms.addLuminosityText(x=None, y=None, lumi=lumi)
plot.draw()
plot.save()
num = 0
denom = 0
#print var_values
for i in range(0, len(nom_values)):
num += var_values[i] * (var_values[i] - nom_values[i])**2
denom += var_values[i]
diff = num / denom
diff_list.append(diff)
diff_opt.append(diff_list)
print w1_list, '\n'
for i in range(0, len(diff_opt)):
print diff_opt[i]
print w1_list[diff_opt[i].index(min(diff_opt[i]))]
mt_baseline = mt_baseline.Rebin(len(bins) - 1, "", arr)
plot_bft = plots.PlotBase()
plot_bft.histoMgr.appendHisto(histograms.Histo(mt_baseline, "baseline"))
#mt_corr.Scale(2)
#plot_bft.histoMgr.appendHisto(histograms.Histo(mt_corr,"test"))
#rangeMin = mt_signalfaketaus.GetXaxis().GetXmin()
#rangeMax = mt_signalfaketaus.GetXaxis().GetXmax()
#theFit = TF1('theFit',FitFunction(),rangeMin,rangeMax,4)
#theFit.SetParLimits(0,0.5,10000)
#theFit.SetParLimits(1,90,10000)
#theFit.SetParLimits(2,30,10000)
#theFit.SetParLimits(3,0.001,10000)
#mt_signalfaketaus.Fit(theFit,"R")
#theFit.SetRange(mt_signalfaketaus.GetXaxis().GetXmin(),mt_signalfaketaus.GetXaxis().GetXmax())
#theFit.SetLineStyle(2)
#theFit.SetLineColor(4)
#theFit.SetLineWidth(3)
#theFit.Draw()
#mt_corr = theFit.GetHistogram()
#mt_corr = mt_corr.Rebin(len(fitBinning)-1,"",fitArray)
#mt_corr.Scale(mt_baseline.GetMaximum()/mt_corr.GetMaximum())
#plot_bft.histoMgr.appendHisto(histograms.Histo(mt_corr,"test"))
#plot_bft.histoMgr.appendHisto(histograms.Histo(theFit,"theFit"))
plot_bft.createFrame('BaselineFakeTaus')
plot_bft.draw()
plot_bft.save()
def main(opts):
style = tdrstyle.TDRStyle()
#style.setWide(True)
style.setPaletteMy()
ROOT.gStyle.SetNumberContours(20)
# tdrstyle.setDeepSeaPalette()
# tdrstyle.setRainBowPalette()
# tdrstyle.setDarkBodyRadiatorPalette()
# tdrstyle.setGreyScalePalette()
# tdrstyle.setTwoColorHuePalette()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(opts.batchMode)
# ========================================
# Datasets
# ========================================
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts.mcrab, opts, **kwargs)
intLumi = GetLumi(datasetsMgr)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.PrintCrossSections()
#datasetsMgr.PrintLuminosities()
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
# Merge datasts (Note: Merged MC histograms must be normalized to something)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Remove datasets
if 0:
datasetsMgr.remove("TTJets")
datasetsMgr.remove(filter(lambda name: not "QCD" in name, datasetsMgr.getAllDatasetNames()))
# Print dataset information
datasetsMgr.PrintInfo()
# For-loop: All Histogram names
for counter, hName in enumerate(hNames):
savePath, saveName = GetSavePathAndName(hName, **kwargs)
# Get Histos for Plotter
refHisto, otherHistos = GetHistosForPlotter(datasetsMgr, hName, **kwargs)
# Create a plot
p = plots.PlotBase([refHisto], kwargs.get("saveFormats"))
# Remove negative contributions
#RemoveNegativeBins(datasetsMgr, hName, p)
# Customize
# p.histoMgr.setHistoDrawStyleAll("COL") #"CONT4" "COLZ" "COL"
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinX(kwargs.get("rebinX")))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinY(kwargs.get("rebinY")))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetXaxis().SetRangeUser(1.0, 5.0))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetYaxis().SetRangeUser(1.0, 5.0))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetZaxis().SetRangeUser(0.0, 0.015))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMinimum(kwargs.get("zMin")))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMaximum(kwargs.get("zMax")))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinX(2))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinY(2))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMinimum(0.0015))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMaximum(0.0045))
# Create a frame
opts = {"ymin": 0.0, "ymaxfactor": 1.0}
p.createFrame(saveName, opts=opts)
# Customise frame
p.getFrame().GetXaxis().SetTitle( getTitleX(refHisto, **kwargs) )
#p.getFrame().GetYaxis().SetTitle( getTitleY(refHisto, **kwargs) )
# SetLog
SetLogAndGrid(p, **kwargs)
# Add cut line/box
_kwargs = { "lessThan": kwargs.get("cutLessThan")}
p.addCutBoxAndLine(cutValue=kwargs.get("cutValue"), fillColor=kwargs.get("cutFillColour"), box=kwargs.get("cutBox"), line=kwargs.get("cutLine"), **_kwargs)
# Customise Legend
moveLegend = {"dx": -0.1, "dy": +0.0, "dh": -0.1}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
p.removeLegend()
# Add MC Uncertainty (Invalid method for a 2-d histogram)
#p.addMCUncertainty()
# Draw plots
p.draw()
# Customise text
histograms.addStandardTexts(lumi=intLumi)
# histograms.addText(0.17, 0.95, plots._legendLabels[kwargs.get("refDataset")], 22)
histograms.addText(0.17, 0.88, plots._legendLabels[kwargs.get("refDataset")], 17)
# Save canvas under custom dir
SaveAs(p, savePath, saveName, kwargs.get("saveFormats"), counter==0)
return
def doPlot(name, graphs, limits, xlabel, scenario, isMA=False):
blinded = True
if "obs" in graphs.keys():
blinded = False
higgs = "h"
if scenario == "lowMH-LHCHXSWG":
higgs = "H"
if not blinded:
obs = graphs["obs"]
excluded = ROOT.TGraph(obs)
excluded.SetName("ExcludedArea")
if "_mA_" in name:
rightX = obs.GetX()[obs.GetN()-1]+100
rightY = obs.GetY()[obs.GetN()-1]
excluded.SetPoint(excluded.GetN(), rightX, rightY)
excluded.SetPoint(excluded.GetN(), rightX, 1)
else:
excluded.SetPoint(excluded.GetN(), obs.GetX()[obs.GetN()-1], 0.5)
excluded.SetPoint(excluded.GetN(), 0, 0.5)
excluded.SetPoint(excluded.GetN(), 0, tanbMax)
excluded.SetPoint(excluded.GetN(), obs.GetX()[0], tanbMax)
excluded.SetPoint(excluded.GetN(), obs.GetX()[0], obs.GetY()[0])
limit.setExcludedStyle(excluded)
excluded.SetLineWidth(0)
excluded.SetLineColor(ROOT.kWhite)
expected = graphs["exp"]
expected.SetLineStyle(2)
expected1 = graphs["exp1"]
expected1.SetLineStyle(2)
expected2 = graphs["exp2"]
expected2.SetLineStyle(2)
# allowed = graphs["Allowed"]
# if not allowed == None:
# allowed.SetFillStyle(3005)
# allowed.SetFillColor(ROOT.kRed)
# allowed.SetLineWidth(-302)
# allowed.SetLineColor(ROOT.kRed)
# allowed.SetLineStyle(1)
if not blinded:
graphs["obs_th_plus"].SetLineStyle(9)
# graphs["obs_th_minus"].SetLineStyle(9)
excludedCopy = excluded.Clone()
# excludedCopy.SetFillColorAlpha(ROOT.kWhite, 0.0) # actual color doesn't matter, want fully transparent
plot = plots.PlotBase([
histograms.HistoGraph(graphs["obs"], "Observed", drawStyle="PL", legendStyle="lp"),
histograms.HistoGraph(graphs["obs_th_plus"], "ObservedPlus", drawStyle="L", legendStyle="l"),
histograms.HistoGraph(graphs["obs_th_minus"], "ObservedMinus", drawStyle="L"),
# histograms.HistoGraph(graphs["isomass"], "IsoMass", drawStyle="L"),
# histograms.HistoGraph(graphs["isomass"], "IsoMassCopy", drawStyle="F"),
histograms.HistoGraph(excluded, "Excluded", drawStyle="F", legendStyle=None),
histograms.HistoGraph(excludedCopy, "ExcludedCopy", drawStyle=None, legendStyle="f"),
histograms.HistoGraph(expected, "Expected", drawStyle="L"),
# histograms.HistoGraph(graphs["exp"], "Expected", drawStyle="L"),
# histograms.HistoGraph(graphs["Allowed"], "Allowed", drawStyle="L", legendStyle="lf"),
# histograms.HistoGraph(graphs["Allowed"], "AllowedCopy", drawStyle="L", legendStyle="f"),
histograms.HistoGraph(graphs["mintanb"], "MinTanb", drawStyle="L"),
# histograms.HistoGraph(graphs["exp1"], "Expected1", drawStyle="F", legendStyle="fl"),
# histograms.HistoGraph(graphs["exp2"], "Expected2", drawStyle="F", legendStyle="fl"),
histograms.HistoGraph(expected1, "Expected1", drawStyle="F", legendStyle="fl"),
histograms.HistoGraph(expected2, "Expected2", drawStyle="F", legendStyle="fl")
],
saveFormats=[".png", ".pdf", ".C"]
)
plot.histoMgr.setHistoLegendLabelMany({
"ObservedPlus": "Observed #pm1#sigma (th.)",
"ObservedMinus": None,
"Excluded": None,
"ExcludedCopy": "Excluded",
"Expected": None,
"MinTanb": None,
# "Allowed": "m_{"+higgs+"}^{MSSM} #neq 125#pm3 GeV",
"Expected1": "Expected median #pm 1#sigma",
"Expected2": "Expected median #pm 2#sigma",
# "IsoMass": None,
# "IsoMassCopy": None
})
else:
if not graphs["isomass"] == None:
graphs["isomass"].SetFillColor(0)
graphs["isomass"].SetFillStyle(1)
plot = plots.PlotBase([
histograms.HistoGraph(expected, "Expected", drawStyle="L"),
histograms.HistoGraph(graphs["isomass"], "IsoMass", drawStyle="L"),
histograms.HistoGraph(graphs["isomass"], "IsoMassCopy", drawStyle="F"),
# histograms.HistoGraph(graphs["Allowed"], "Allowed", drawStyle="L", legendStyle="lf"),
# histograms.HistoGraph(graphs["Allowed"], "AllowedCopy", drawStyle="L", legendStyle="f"),
histograms.HistoGraph(graphs["mintanb"], "MinTanb", drawStyle="L"),
histograms.HistoGraph(expected1, "Expected1", drawStyle="F", legendStyle="fl"),
histograms.HistoGraph(expected2, "Expected2", drawStyle="F", legendStyle="fl"),
],
saveFormats=[".png", ".pdf", ".C"]
)
plot.histoMgr.setHistoLegendLabelMany({
"Expected": None,
"MinTanb": None,
# "Allowed": "m_{"+higgs+"}^{MSSM} #neq 125#pm3 GeV",
"Expected1": "Expected median #pm 1#sigma",
"Expected2": "Expected median #pm 2#sigma",
"IsoMass": None,
"IsoMassCopy": None
})
# Move the m_h,H allowed region to the last in the legend
histoNames = [h.getName() for h in plot.histoMgr.getHistos()]
plot.histoMgr.reorderLegend(filter(lambda n: "Allowed" not in n, histoNames))
size = 20
x = 0.2
# dy = -0.15
dy = -0.13
plot.setLegend(histograms.createLegend(x-0.01, 0.50+dy, x+0.45, 0.80+dy))
plot.legend.SetMargin(0.17)
# plot.legend.SetFillColor(0)
#plot.legend.SetFillColorAlpha(ROOT.kWhite, 50)
# plot.legend.SetFillStyle(1001)
if blinded:
name += "_blinded"
name = os.path.basename(name)
name = name.replace("-","_")
frameXmax = 160
if "_mA_" in name:
frameXmax = 145
plot.createFrame(name, opts={"ymin": 0, "ymax": tanbMax, "xmin": 90, "xmax": frameXmax})
plot.frame.GetXaxis().SetTitle(xlabel)
plot.frame.GetYaxis().SetTitle(limit.tanblimit)
plot.draw()
plot.setLuminosity(limits.getLuminosity())
plot.addStandardTexts()
histograms.addText(x, 0.9+dy, "t #rightarrow H^{+}b, H^{+} #rightarrow c#bar{s}", size=size)
# histograms.addText(x, 0.863+dy, limits.getFinalstateText(), size=size)
histograms.addText(x, 0.815+dy, limit.getTypesetScenarioName(scenario), size=size)
#histograms.addText(0.2, 0.231, "Min "+limit.BR+"(t#rightarrowH^{+}b)#times"+limit.BR+"(H^{+}#rightarrow#tau#nu)", size=0.5*size)
# Too small to be visible
# if not graphs["isomass"] == None:
# histograms.addText(0.8, 0.15, "m_{H^{#pm}} = 160 GeV", size=0.5*size)
#Adding a LHC label:
# ROOT.LHCHIGGS_LABEL(0.97,0.72,1)
#FH_version = db.getVersion("FeynHiggs")
#histograms.addText(x, 0.55+dy, FH_version, size=size)
# HD_version = db.getVersion("HDECAY")
# histograms.addText(x, 0.55, FH_version+" and "+HD_version, size=size)
# histograms.addText(x, 0.48, "Derived from", size=size)
# histograms.addText(x, 0.43, "CMS HIG-12-052", size=size)
plot.save()
print "Created",name
def main():
if len(sys.argv) == 1:
usage()
rootfile = ""
root_re = re.compile("(?P<rootfile>(\S*\.root))")
match = root_re.search(sys.argv[1])
if match:
rootfile = match.group(0)
limits = limit.BRLimits()
# Apply TDR style
style = tdrstyle.TDRStyle()
if limit.forPaper:
histograms.cmsTextMode = histograms.CMSMode.PAPER
xvar = "mHp"
yvars = []
yvars.append("BR_Hp_taunu")
yvars.append("BR_Hp_tb")
yvars.append("BR_Hp_h0W")
yvars.append("BR_Hp_A0W")
yvars.append("BR_Hp_HW")
# yvars.append("BR_Hp_Neu1Cha1")
# yvars.append("BR_Hp_Neu2Cha1")
susyvars = []
susyvars.append("BR_Hp_Neu1Cha1")
susyvars.append("BR_Hp_Neu2Cha1")
# susyvars.append("BR_Hp_Neu3Cha1")
# susyvars.append("BR_Hp_Neu4Cha1")
# susyvars.append("BR_Hp_Neu1Cha2")
# susyvars.append("BR_Hp_Neu2Cha2")
# susyvars.append("BR_Hp_Neu3Cha2")
# susyvars.append("BR_Hp_Neu4Cha2")
# db = BRXSDB.BRXSDatabaseInterface(rootfile,heavy=True,program="2HDMC")
db = BRXSDB.BRXSDatabaseInterface(rootfile,
heavy=True,
program="FeynHiggs")
progversion = db.GetProgram() + " v" + db.GetVersion()
selection = "tanb==10"
# scenario = "2HDM Type II"
scenario = "mhmax"
graphs = {}
for icolor, yvar in enumerate(yvars):
tmpsel = selection
br = db.getGraph(xvar, yvar, tmpsel)
xarray = array.array('d')
yarray = array.array('d')
for i in range(0, br.GetN()):
x = ROOT.Double()
y = ROOT.Double()
br.GetPoint(i, x, y)
xarray.append(x)
yarray.append(y)
newx, newy = sort(xarray, yarray)
newGraph = ROOT.TGraph(len(newx), newx, newy)
newGraph.SetLineWidth(3)
newGraph.SetLineColor(1 + icolor)
graphs[yvar] = newGraph
xsusy = array.array('d')
ysusy = array.array('d')
for yvar in susyvars:
tmpsel = selection
br = db.getGraph(xvar, yvar, tmpsel)
xarray = array.array('d')
yarray = array.array('d')
for i in range(0, br.GetN()):
x = ROOT.Double()
y = ROOT.Double()
br.GetPoint(i, x, y)
xarray.append(x)
yarray.append(y)
print "check x,y", x, y
xsusy, ysusy = add(xsusy, ysusy, xarray, yarray)
print "check len", len(xsusy)
newx, newy = sort(xsusy, ysusy)
for i in range(len(newx)):
print newx[i], newy[i]
susyGraph = ROOT.TGraph(len(newx), newx, newy)
susyGraph.SetLineWidth(3)
susyGraph.SetLineColor(1 + len(graphs))
graphs["BR_Hp_SUSY"] = susyGraph
plot = plots.PlotBase([
histograms.HistoGraph(graphs["BR_Hp_taunu"],
"BR(H^{+}#rightarrow#tau#nu)",
drawStyle="L",
legendStyle="l"),
histograms.HistoGraph(graphs["BR_Hp_tb"],
"BR(H^{+}#rightarrow tb)",
drawStyle="L",
legendStyle="l"),
histograms.HistoGraph(graphs["BR_Hp_h0W"],
"BR(H^{+}#rightarrow hW)",
drawStyle="L",
legendStyle="l"),
histograms.HistoGraph(graphs["BR_Hp_A0W"],
"BR(H^{+}#rightarrow AW)",
drawStyle="L",
legendStyle="l"),
histograms.HistoGraph(graphs["BR_Hp_HW"],
"BR(H^{+}#rightarrow HW)",
drawStyle="L",
legendStyle="l"),
histograms.HistoGraph(graphs["BR_Hp_SUSY"],
"BR(H^{+}#rightarrow#chi_{i}^{0}#chi_{j}^{+})",
drawStyle="L",
legendStyle="l"),
# histograms.HistoGraph(graphs["BR_Hp_Neu1Cha1"], "BR(H^{+}#rightarrow#chi_{1}^{0}#chi_{1}^{+})", drawStyle="L", legendStyle="l"),
# histograms.HistoGraph(graphs["BR_Hp_Neu2Cha1"], "BR(H^{+}#rightarrow#chi_{2}^{0}#chi_{1}^{+})", drawStyle="L", legendStyle="l"),
])
plot.setLegend(histograms.createLegend(0.57, 0.60, 0.87, 0.80))
plot.legend.SetFillColor(0)
plot.legend.SetFillStyle(1001)
plot.createFrame("br",
opts={
"ymin": 0.0001,
"ymax": 1,
"xmin": 180,
"xmax": 600
})
plot.frame.GetXaxis().SetTitle("m_{H^{+}} (" + GeVUnit + ")")
plot.frame.GetYaxis().SetTitle("BR")
ROOT.gPad.SetLogy(True)
plot.draw()
size = 20
x = 0.57
# histograms.addText(x, 0.9, limit.processHeavy, size=size)
# histograms.addText(x, 0.863, limits.getFinalstateText(), size=size)
#### histograms.addText(x, 0.815, "MSSM m_{h}^{max}", size=size)
histograms.addText(x, 0.863, scenario, size=size)
selectiontxt = selection.replace("==", "=")
histograms.addText(x, 0.815, selectiontxt, size=size)
#Adding a LHC label:
ROOT.LHCHIGGS_LABEL(0.97, 0.72, 1)
# histograms.addText(x, 0.55, progversion, size=size)
plot.save()
def main():
if len(sys.argv) < 2:
usage()
dirs = []
dirs.append(sys.argv[1])
#datasets = dataset.getDatasetsFromMulticrabDirs(dirs)
datasets = dataset.getDatasetsFromMulticrabDirs(dirs,dataEra=dataEra, searchMode=searchMode, analysisName=analysis)
# consistencyCheck.checkConsistencyStandalone(dirs[0],datasets,name="QCD inverted")
datasets.loadLuminosities()
datasets.updateNAllEventsToPUWeighted()
# erik
# datasets.remove(filter(lambda name: "TTJets_SemiLept" in name, datasets.getAllDatasetNames()))
# datasets.remove(filter(lambda name: "TTJets_FullLept" in name, datasets.getAllDatasetNames()))
# datasets.remove(filter(lambda name: "TTJets_Hadronic" in name, datasets.getAllDatasetNames()))
datasets.remove(filter(lambda name: "WJetsToLNu" in name and not "WJetsToLNu_HT" in name, datasets.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("EWK", [
"TT",
"WJetsHT",
"DYJetsToLLHT",
"SingleTop",
# "Diboson"
])
style = tdrstyle.TDRStyle()
plot = plots.PlotBase()
legends = {}
name_re = re.compile("SelectedTau_pT_(?P<name>\S+)")
for i,histo in enumerate(HISTONAMES):
plot.histoMgr.appendHisto(purityGraph(i,datasets,histo))
name = histo
match = name_re.search(histo)
if match:
name = match.group("name")
legends["Purity%s"%i] = name
# if "AfterMetCut" in name:
# legends["Purity%s"%i] = "MET > 60 GeV"
if "SelectedTau_pT_CollinearCuts" in name:
legends["Purity%s"%i] = "Collinear cuts"
if "AfterBtagging" in name:
legends["Purity%s"%i] = "B tagging"
if "AfterBveto" in name:
legends["Purity%s"%i] = "B-jet veto"
if "AfterBvetoPhiCuts" in name:
legends["Purity%s"%i] = "B-jet veto, TailKiller"
if "SelectedTau_pT_BackToBackCuts" in name:
legends["Purity%s"%i] = "BackToBack cuts"
# plot.createFrame("purity_QCD_only", opts={"xmin": 40, "xmax": 160, "ymin": 0., "ymax": 1.05})
plot.createFrame("purity_QCD_only", opts={"xmin": 40, "xmax": 400, "ymin": 0., "ymax":1})
plot.frame.GetXaxis().SetTitle("p_{T}^{#tau jet} (GeV/c)")
plot.frame.GetYaxis().SetTitle("QCD purity")
# plot.setEnergy(datasets.getEnergies())
# plot.histoMgr.appendHisto(histograms.Histo(histo,"InvertedTauPtAfterAllSelections"))
plot.histoMgr.setHistoLegendLabelMany(legends)
### plot.setLegend(histograms.createLegend(0.2, 0.35, 0.6, 0.5))
# histograms.addText(0.2, 0.3, "TailKiller: MediumPlus", 18)
# histograms.addText(0.35, 0.28, "BackToBack cuts: TightPlus", 20)
# histograms.addText(0.35, 0.22, "2011B", 20)
histograms.addText(0.3, 0.35, "QCD only", 20)
histograms.addText(0.3, 0.3, "After all selection cuts", 20)
# histograms.addText(0.2, 0.3, "QCD only", 18)
histograms.addCmsPreliminaryText()
histograms.addEnergyText(s="%s TeV"%(datasets.getEnergies()[0]))
histograms.addLuminosityText(x=None, y=None, lumi=datasets.getDataset("Data").getLuminosity())
plot.draw()
plot.save()
def PlotTemplates(datasetsMgr, histoName, analysisType="Inverted"):
Verbose("Plotting EWK Vs QCD unity-normalised histograms")
# Create comparison plot
p1 = plots.ComparisonPlot(
getHisto(datasetsMgr, "Data", histoName, "Baseline"),
getHisto(datasetsMgr, "Data", histoName, "Inverted")
)
p1.histoMgr.normalizeMCToLuminosity(datasetsMgr.getDataset("Data").getLuminosity())
defaultFolder = "ForFakeBMeasurement"
genuineBFolder = defaultFolder + "EWKGenuineB"
fakeBFolder = defaultFolder + "EWKFakeB"
histoNameNew = histoName.replace( defaultFolder, genuineBFolder)
p2 = plots.ComparisonPlot(
getHisto(datasetsMgr, "EWK", histoNameNew, "Baseline"),
getHisto(datasetsMgr, "EWK", histoNameNew, "Inverted")
)
p2.histoMgr.normalizeMCToLuminosity(datasetsMgr.getDataset("Data").getLuminosity())
# Get EWKGenuineB histos
EWKGenuineB_baseline = p2.histoMgr.getHisto("Baseline-EWK").getRootHisto().Clone("Baseline-EWKGenuineB")
EWKGenuineB_inverted = p2.histoMgr.getHisto("Inverted-EWK").getRootHisto().Clone("Inverted-EWKGenuineB")
# Get FakeB histos
FakeB_baseline = p1.histoMgr.getHisto("Baseline-Data").getRootHisto().Clone("Baseline-FakeB")
FakeB_baseline.Add(EWKGenuineB_baseline, -1)
FakeB_inverted = p1.histoMgr.getHisto("Inverted-Data").getRootHisto().Clone("Inverted-FakeB")
FakeB_inverted.Add(EWKGenuineB_inverted, -1)
# Normalize histograms to unit area
EWKGenuineB_baseline.Scale(1.0/EWKGenuineB_baseline.Integral())
EWKGenuineB_inverted.Scale(1.0/EWKGenuineB_inverted.Integral())
FakeB_baseline.Scale(1.0/FakeB_baseline.Integral())
FakeB_inverted.Scale(1.0/FakeB_inverted.Integral())
# Create the final plot object
comparisonList = [EWKGenuineB_baseline]
p = plots.ComparisonManyPlot(FakeB_inverted, comparisonList, saveFormats=[])
p.setLuminosity(GetLumi(datasetsMgr))
# Apply styles
p.histoMgr.forHisto("Baseline-EWKGenuineB", styles.getBaselineStyle() )
p.histoMgr.forHisto("Inverted-FakeB" , styles.getInvertedStyle() )
# Set draw style
p.histoMgr.setHistoDrawStyle("Baseline-EWKGenuineB", "AP")
p.histoMgr.setHistoDrawStyle("Inverted-FakeB" , "HIST")
# Set legend style
p.histoMgr.setHistoLegendStyle("Baseline-EWKGenuineB", "LP")
p.histoMgr.setHistoLegendStyle("Inverted-FakeB" , "LP")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Baseline-EWKGenuineB": "GenuineB (EWK)", # (Baseline)
"Inverted-FakeB" : "FakeB", # (Inverted)
})
# Append analysisType to histogram name
saveName = histoName
# Draw the histograms #alex
plots.drawPlot(p, saveName, **GetHistoKwargs(histoName) ) #the "**" unpacks the kwargs_
# _kwargs = {"lessThan": True}
# p.addCutBoxAndLine(cutValue=200, fillColor=ROOT.kRed, box=False, line=True, ***_kwargs)
# Add text
text = opts.optMode.replace("OptChiSqrCutValue", "#chi^{2} #leq ")
histograms.addText(0.21, 0.85, text)
# Save plot in all formats
saveDir = os.path.join(opts.saveDir, "Templates", opts.optMode)
SavePlot(p, saveName, saveDir, saveFormats = [".C", ".png", ".pdf"])
return
def doPlot(name, graphs, limits, xlabel, scenario,progversion):
obs = graphs["obs"]
# excluded = ROOT.TGraph(obs)
# excluded.SetName("ExcludedArea")
# excluded.SetFillColor(ROOT.kGray)
## excluded.SetPoint(excluded.GetN(), obs.GetX()[obs.GetN()-1], tanbMax)
## excluded.SetPoint(excluded.GetN(), obs.GetX()[0], tanbMax)
# excluded.SetPoint(excluded.GetN(), -1, 1)
# excluded.SetPoint(excluded.GetN(), -1, 100)
# excluded.SetPoint(excluded.GetN(), obs.GetX()[0], 100)
# if not obs.GetY()[0] == 100:
# excluded.SetPoint(excluded.GetN(), obs.GetX()[0], obs.GetY()[0])
"""
excluded.SetPoint(excluded.GetN(), obs.GetX()[0], tanbMax)
excluded.SetPoint(excluded.GetN(), 0, tanbMax)
excluded.SetPoint(excluded.GetN(), 0, 1)
excluded.SetPoint(excluded.GetN(), obs.GetX()[obs.GetN()-1], 1)
"""
# excluded.SetFillColor(ROOT.kGray)
# excluded.SetFillStyle(3354)
# excluded.SetLineWidth(0)
# excluded.SetLineColor(ROOT.kWhite)
expected = graphs["exp"]
expected.SetLineStyle(2)
expected1 = graphs["exp1"]
expected1.SetLineStyle(2)
expected2 = graphs["exp2"]
expected2.SetLineStyle(2)
plot = plots.PlotBase([
histograms.HistoGraph(graphs["obs"], "Observed", drawStyle="PL", legendStyle="lp"),
histograms.HistoGraph(graphs["obs_th_plus"], "ObservedPlus", drawStyle="L", legendStyle="l"),
histograms.HistoGraph(graphs["obs_th_minus"], "ObservedMinus", drawStyle="L"),
histograms.HistoGraph(graphs["excluded"], "Excluded", drawStyle="F", legendStyle="f"),
histograms.HistoGraph(expected, "Expected", drawStyle="L"),
#histograms.HistoGraph(graphs["exp"], "Expected", drawStyle="L"),
#### histograms.HistoGraph(graphs["Allowed"], "Allowed by \nm_{H} = 125.9#pm3.0 "+GeVUnit, drawStyle="F", legendStyle="f"),
#### histograms.HistoGraph(graphs["Allowed"], "AllowedCopy", drawStyle="L", legendStyle="f"),
# histograms.HistoGraph(graphs["mintanb"], "MinTanb", drawStyle="L"),
#histograms.HistoGraph(graphs["exp1"], "Expected1", drawStyle="F", legendStyle="fl"),
#histograms.HistoGraph(graphs["exp2"], "Expected2", drawStyle="F", legendStyle="fl"),
histograms.HistoGraph(expected1, "Expected1", drawStyle="F", legendStyle="fl"),
histograms.HistoGraph(expected2, "Expected2", drawStyle="F", legendStyle="fl"),
])
plot.histoMgr.setHistoLegendLabelMany({
"ObservedPlus": "Observed #pm1#sigma (th.)",
"ObservedMinus": None,
"Expected": None,
# "MinTanb": None,
#### "AllowedCopy": None,
"Expected1": "Expected median #pm 1#sigma",
"Expected2": "Expected median #pm 2#sigma"
})
# plot.setLegend(histograms.createLegend(0.57, 0.155, 0.87, 0.355))
plot.setLegend(histograms.createLegend(0.19, 0.60, 0.57, 0.80))
plot.legend.SetFillColor(0)
plot.legend.SetFillStyle(1001)
plot.createFrame(name, opts={"ymin": 0, "ymax": tanbMax, "xmin": 180, "xmax": 600})
plot.frame.GetXaxis().SetTitle(xlabel)
plot.frame.GetYaxis().SetTitle(limit.tanblimit)
plot.draw()
histograms.addStandardTexts(lumi="2.3-4.9", addCmsText=False)
size = 20
x = 0.2
histograms.addText(x, 0.9, limit.processHeavy, size=size)
histograms.addText(x, 0.863, limits.getFinalstateText(), size=size)
#### histograms.addText(x, 0.815, "MSSM m_{h}^{max}", size=size)
histograms.addText(x, 0.815,scenario, size=size)
# histograms.addText(x, 0.775, limit.BRassumption, size=size)
# histograms.addText(x, 0.735, "#mu=%d %s"%(mu, limit.massUnit()), size=size)
# histograms.addText(0.7, 0.23, "Min "+limit.BR+"(t#rightarrowH^{+}b)#times"+limit.BR+"(H^{+}#rightarrow#tau#nu)", size=0.5*size)
#Adding a LHC label:
ROOT.LHCHIGGS_LABEL(0.97,0.72,1)
histograms.addText(x, 0.55, progversion, size=size)
histograms.addText(x, 0.48, "Derived from", size=size)
histograms.addText(x, 0.43, "CMS HIG-13-XXX", size=size)
# histograms.addText(x, 0.38, "JHEP07(2012)143", size=size)
plot.save()
def doBRlimit(limits, unblindedStatus, opts, log=False):
leptonicFS = False
graphs = []
if unblindedStatus:
gr = limits.observedGraph()
if gr != None:
gr.SetPoint(gr.GetN() - 1,
gr.GetX()[gr.GetN() - 1] - 1e-10,
gr.GetY()[gr.GetN() - 1])
if opts.excludedArea:
graphs.append(
histograms.HistoGraph(gr,
"Observed",
drawStyle="PL",
legendStyle=None))
excluded = gr.Clone()
excluded.SetPoint(excluded.GetN(),
excluded.GetX()[excluded.GetN() - 1], 0.05)
excluded.SetPoint(excluded.GetN(), excluded.GetX()[0], 0.05)
limit.setExcludedStyle(excluded)
graphs.append(
histograms.HistoGraph(excluded,
"Excluded",
drawStyle="F",
legendStyle="lpf",
legendLabel="Observed"))
else:
graphs.append(
histograms.HistoGraph(gr,
"Observed",
drawStyle="PL",
legendStyle="lp"))
graphs.extend([
histograms.HistoGraph(limits.expectedGraph(),
"Expected",
drawStyle="L"),
histograms.HistoGraph(limits.expectedBandGraph(sigma=1),
"Expected1",
drawStyle="F",
legendStyle="fl"),
histograms.HistoGraph(limits.expectedBandGraph(sigma=2),
"Expected2",
drawStyle="F",
legendStyle="fl"),
])
saveFormats = [".png", ".C", ".pdf"]
if not opts.excludedArea:
saveFormats.append(".eps")
plot = plots.PlotBase(graphs, saveFormats=saveFormats)
plot.setLuminosity(limits.getLuminosity())
plot.histoMgr.setHistoLegendLabelMany({
"Expected":
None,
"Expected1":
"Expected median #pm 1#sigma",
"Expected2":
"Expected median #pm 2#sigma"
})
dy = -0.1
limit.BRassumption = ""
#limit.BRassumption = "Assuming B(H^{+}#rightarrow#tau^{+}#nu_{#tau}) = 1"
#limit.BRassumption = "Assuming B(H^{+}#rightarrowt#bar{b}) = 1"
if limit.BRassumption != "":
dy -= 0.05
#if len(limits.getFinalstates()) > 1:
# dy -= 0.1
# Create legend
x = 0.51
x = 0.45
legend = histograms.createLegend(x, 0.78 + dy, x + 0.4, 0.92 + dy)
legend.SetMargin(0.17)
# Make room for the final state text
if opts.excludedArea:
legend.SetFillStyle(1001)
plot.setLegend(legend)
name = "limitsBr"
ymin = 0
ymax = limits.getFinalstateYmaxBR() #fixme: alexandros
if opts.logx:
name += "_logx"
if log:
name += "_log"
if limits.isHeavyStatus:
ymin = 1e-3
ymax = 10.0
if limit.BRassumption != "":
ymax = 10.0
else:
ymin = 1e-3
ymax = 4e-2
if leptonicFS:
ymax = 10
if len(limits.mass) == 1:
plot.createFrame(name,
opts={
"xmin": limits.mass[0] - 5.0,
"xmax": limits.mass[0] + 5.0,
"ymin": ymin,
"ymax": ymax
})
else:
plot.createFrame(name, opts={"ymin": ymin, "ymax": ymax})
# Set x-axis title
plot.frame.GetXaxis().SetTitle(limit.mHplus())
if limits.isHeavyStatus:
if limit.BRassumption != "":
plot.frame.GetYaxis().SetTitle(
"95% CL limit for #sigma_{H^{+}} (pb)")
else:
plot.frame.GetYaxis().SetTitle(limit.sigmaBRlimit)
else:
plot.frame.GetYaxis().SetTitle(limit.BRlimit)
# Enable/Disable logscale for axes
if log:
plot.getPad().SetLogy(log)
if opts.logx:
plot.getPad().SetLogx(log)
# Draw the plot with standard texts
plot.draw()
plot.addStandardTexts()
# Add physics-process text
size = 20
x = 0.51
x = 0.45
process = limit.process
if limits.isHeavyStatus:
process = limit.processHeavy
histograms.addText(x, 0.88, process, size=size)
# Add final-state text
# histograms.addText(x, 0.84, limits.getFinalstateText(), size=size) #fixme: alexandros
histograms.addText(x, 0.84, "fully hadronic final state",
size=size) #fixme: alexandros
# histograms.addText(x, 0.84, "#tau_{h}+jets and #mu#tau_{h} final states", size=size)
# histograms.addText(x, 0.84, "#tau_{h}+jets final state", size=size)
# histograms.addText(x, 0.84, "#tau_{h}+jets, #mu#tau_{h}, ee, e#mu, #mu#mu final states", size=size)
if leptonicFS:
histograms.addText(x,
0.84,
"#mu#tau_{h}, ee, e#mu, #mu#mu final states",
size=size)
if limit.BRassumption != "":
histograms.addText(x, 0.79, limit.BRassumption, size=size)
plot.save()
return
def analyze(analysis=None):
paths = [sys.argv[1]]
if not analysis == None:
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis, includeOnlyTasks="Tau|TT")
datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis)
else:
datasets = dataset.getDatasetsFromMulticrabDirs(paths)
analysis = datasets.getAllDatasets()[0].getAnalysisName()
analysisList = []
# analysisList.append("METLeg_2016MET80_MET80")
# analysisList.append("METLeg_2016ICHEP_MET90")
# analysisList.append("METLeg_2016_MET90")
met_re = re.compile("_(?P<met>MET\d+)$")
if True:
#for analysis in analysisList:
met = "METX"
met_match = met_re.search(analysis)
if met_match:
met = met_match.group("met")
print "Analysis",analysis
datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis)
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="Tau\S+25ns$|TTJets$")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="Tau_Run2015C|Tau\S+25ns_Silver$|DYJetsToLL|WJetsToLNu$")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="Tau_Run2015D_PromptReco_v4_246908_260426_25ns$|DYJetsToLL_M_50$")
# datasetsMC = None
datasetsMC = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="Tau_")
for d in datasets.getAllDatasets():
print d.getName()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
dataset2 = dataset1
createRatio = False
if isinstance(datasetsMC,dataset.DatasetManager):
dataset2 = datasetsMC.getMCDatasets()
createRatio = True
eff1_MET80 = getEfficiency(dataset1)
eff2_MET80 = getEfficiency(dataset2)
styles.dataStyle.apply(eff1_MET80)
eff1_MET80.SetMarkerSize(1)
if createRatio:
styles.mcStyle.apply(eff2_MET80)
eff2_MET80.SetMarkerSize(1.5)
if createRatio:
p = plots.ComparisonPlot(histograms.HistoGraph(eff1_MET80, "eff1_MET80", "p", "P"),
histograms.HistoGraph(eff2_MET80, "eff2_MET80", "p", "P"))
else:
p = plots.PlotBase([histograms.HistoGraph(eff1_MET80, "eff1_MET80", "p", "P")])
#from plotTauLegEfficiency import fit
#fit("Data",p,eff1_MET80,20,300)
#fit("MC",p,eff2_MET80,20,300)
opts = {"ymin": 0, "ymax": 1.1}
opts2 = {"ymin": 0.5, "ymax": 1.5}
moveLegend = {"dx": -0.55, "dy": -0.15}
name = "TauMET_"+analysis+"_DataVsMC_PFMET"
legend1 = "Data"
# legend2 = "MC (TTJets)"
#legend2 = "MC"
legend2 = "Simulation"
if createRatio:
p.histoMgr.setHistoLegendLabelMany({"eff1_MET80": legend1, "eff2_MET80": legend2})
p.createFrame(os.path.join(plotDir, name), createRatio=True, opts=opts, opts2=opts2)
else:
p.histoMgr.setHistoLegendLabelMany({"eff1_MET80": legend1})
p.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
if createRatio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# histograms.addText(0.2, 0.6, "LooseIsoPFTau50_Trk30_eta2p1_"+met, 17)
histograms.addText(0.4, 0.87, "LooseIsoPFTau50_Trk30_eta2p1_"+met, 17)
# histograms.addText(0.2, 0.53, analysis.split("_")[len(analysis.split("_")) -1], 17)
# label = analysis.split("_")[1]
# label = "2016"
label = string.replace(analysis,"METLeg_","")
histograms.addText(0.2, 0.53, label, 17)
runRange = datasets.loadRunRange()
histograms.addText(0.2, 0.46, "Runs "+runRange, 17)
p.draw()
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity",d.getName(),d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum",lumi
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
pythonWriter.addParameters(plotDir,label,runRange,lumi,eff1_MET80)
pythonWriter.addMCParameters(label,eff2_MET80)
pythonWriter.writeJSON(os.path.join(plotDir,"metLegTriggerEfficiency_"+label+".json"))
#########################################################################
eff1phi = getEfficiency(dataset1,"NumeratorPhi","DenominatorPhi")
eff2phi = getEfficiency(dataset2,"NumeratorPhi","DenominatorPhi")
styles.dataStyle.apply(eff1phi)
styles.mcStyle.apply(eff2phi)
eff1phi.SetMarkerSize(1)
if isinstance(datasetsMC,dataset.DatasetManager):
p_phi = plots.ComparisonPlot(histograms.HistoGraph(eff1phi, "eff1phi", "p", "P"),
histograms.HistoGraph(eff2phi, "eff2phi", "p", "P"))
else:
p_phi = plots.PlotBase([histograms.HistoGraph(eff1phi, "eff1phi", "p", "P")])
p_phi.histoMgr.setHistoLegendLabelMany({"eff1phi": legend1})
if isinstance(datasetsMC,dataset.DatasetManager):
p_phi.histoMgr.setHistoLegendLabelMany({"eff1phi": legend1, "eff2phi": legend2})
opts = {"ymin": 0, "ymax": 0.3}
name = "TauMET_"+analysis+"_DataVsMC_PFMETPhi"
if createRatio:
p_phi.createFrame(os.path.join(plotDir, name), createRatio=createRatio, opts=opts, opts2=opts2)
else:
p_phi.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
# moveLegendPhi = {"dx": -0.5, "dy": -0.6, "dh": -0.1}
moveLegendPhi = {"dx": -0.55, "dy": -0.15, "dh": -0.1}
p_phi.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegendPhi))
p_phi.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p_phi.getFrame().GetXaxis().SetTitle("MET Type 1 #phi")
if createRatio:
p_phi.getFrame2().GetYaxis().SetTitle("Ratio")
p_phi.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.4, 0.86, "LooseIsoPFTau50_Trk30_eta2p1_"+met, 17)
# histograms.addText(0.4, 0.78, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.4, 0.78, "2016", 17)
histograms.addText(0.4, 0.71, "Runs "+datasets.loadRunRange(), 17)
p_phi.draw()
histograms.addStandardTexts(lumi=lumi)
p_phi.save(formats)
#########################################################################
"""
#### MET120
analysis = "METLeg_2015CD_MET120"
datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis)
for d in datasets.getAllDatasets():
print d.getName()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
dataset2 = datasets.getMCDatasets()
eff1_MET120 = getEfficiency(dataset1)
eff2_MET120 = getEfficiency(dataset2)
styles.dataStyle.apply(eff1_MET120)
styles.mcStyle.apply(eff2_MET120)
eff1_MET120.SetMarkerSize(1)
eff2_MET120.SetMarkerSize(1.5)
p = plots.ComparisonPlot(histograms.HistoGraph(eff1_MET120, "eff1_MET120", "p", "P"),
histograms.HistoGraph(eff2_MET120, "eff2_MET120", "p", "P"))
opts = {"ymin": 0, "ymax": 1.1}
opts2 = {"ymin": 0.5, "ymax": 1.5}
moveLegend = {"dx": -0.55, "dy": -0.15}
name = "DataVsMC_L1HLTMET_PFMET_MET120"
legend1 = "Data"
legend2 = "MC"
p.histoMgr.setHistoLegendLabelMany({"eff1_MET120": legend1, "eff2_MET120": legend2})
p.createFrame(os.path.join(plotDir, name), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
lumi = 0.0
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
"""
# CaloMET
"""
#### MET80
analysisc = "METLeg_2016B_CaloMET_MET80"
datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc)
datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc,excludeTasks="Tau\S+25ns_Silver$")
# datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc,includeOnlyTasks="Tau\S+25ns$|TTJets$")
style = tdrstyle.TDRStyle()
dataset1c = datasetsc.getDataDatasets()
dataset2c = datasetsc.getMCDatasets()
# eff1c_MET80 = getEfficiency(dataset1c)
eff2c_MET80 = getEfficiency(dataset2c)
# styles.dataStyle.apply(eff1c_MET80)
styles.mcStyle.apply(eff2c_MET80)
# eff1c_MET80.SetMarkerSize(1)
eff2c_MET80.SetMarkerSize(1.5)
eff2c_MET80.SetMarkerColor(4)
p = plots.ComparisonPlot(histograms.HistoGraph(eff2_MET80, "eff2_MET80", "p", "P"),
histograms.HistoGraph(eff2c_MET80, "eff2c_MET80", "p", "P"))
namec = "TauMET_"+analysis+"_MC_TrgBitVsCaloMET80_PFMET"
legend1c = "MC, trigger bit"
legend2c = "MC, CaloMET > 80"
p.histoMgr.setHistoLegendLabelMany({"eff2_MET80": legend1c, "eff2c_MET80": legend2c})
p.createFrame(os.path.join(plotDir, namec), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity",d.getName(),d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum",lumi
histograms.addStandardTexts(lumi=lumi)
histograms.addText(0.2, 0.6, "LooseIsoPFTau50_Trk30_eta2p1_MET80", 17)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
"""
"""
#### MET120
analysisc = "METLeg_2015A_CaloMET_MET120"
datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc)
style = tdrstyle.TDRStyle()
dataset1c = datasetsc.getDataDatasets()
dataset2c = datasetsc.getMCDatasets()
eff1c_MET120 = getEfficiency(dataset1c)
eff2c_MET120 = getEfficiency(dataset2c)
styles.dataStyle.apply(eff1c_MET120)
styles.mcStyle.apply(eff1c_MET120)
eff1c_MET120.SetMarkerSize(1)
eff2c_MET120.SetMarkerSize(1.5)
p = plots.ComparisonPlot(histograms.HistoGraph(eff2_MET120, "eff2_MET120", "p", "P"),
histograms.HistoGraph(eff2c_MET120, "eff2c_MET120", "p", "P"))
namec = "MC_TrgBitVsCaloMET120_L1HLTMET_PFMET"
legend1c = "MC, trigger bit"
legend2c = "MC, CaloMET > 120"
p.histoMgr.setHistoLegendLabelMany({"eff2_MET120": legend1c, "eff2c_MET120": legend2c})
p.createFrame(os.path.join(plotDir, namec), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
lumi = 0.0
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
"""
#########################################################################
namePU = "TauMET_"+analysis+"_DataVsMC_nVtx"
eff1PU = getEfficiency(dataset1,"NumeratorPU","DenominatorPU")
eff2PU = getEfficiency(dataset2,"NumeratorPU","DenominatorPU")
styles.dataStyle.apply(eff1PU)
styles.mcStyle.apply(eff2PU)
eff1PU.SetMarkerSize(1)
eff2PU.SetMarkerSize(1.5)
if isinstance(datasetsMC,dataset.DatasetManager):
pPU = plots.ComparisonManyPlot(histograms.HistoGraph(eff1PU, "eff1", "p", "P"),
[histograms.HistoGraph(eff2PU, "eff2", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({"eff1": legend1, "eff2": legend2})
else:
pPU = plots.PlotBase([histograms.HistoGraph(eff1PU, "eff1", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
opts = {"ymin": 0.001, "ymax": 1.0}
moveLegend = {"dx": -0.33, "dy": -0.25, "dh": -0.1}
createRatio = False
if createRatio:
pPU.createFrame(os.path.join(plotDir, namePU), createRatio=True, opts=opts, opts2=opts2)
else:
pPU.createFrame(os.path.join(plotDir, namePU), opts=opts, opts2=opts2)
pPU.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
# if createRatio:
# pPU.getPad1().SetLogy(True)
# else:
# pPU.getPad().SetLogy(True)
pPU.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
pPU.getFrame().GetXaxis().SetTitle("Number of reco vertices")
if createRatio:
pPU.getFrame2().GetYaxis().SetTitle("Ratio")
pPU.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.4, 0.85, "LooseIsoPFTau50_Trk30_eta2p1_MET90", 17)
# histograms.addText(0.4, 0.78, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.4, 0.78, "2016", 17)
histograms.addText(0.4, 0.71, "Runs "+datasets.loadRunRange(), 17)
pPU.draw()
histograms.addStandardTexts(lumi=lumi)
pPU.save(formats)
print "Output written in",plotDir
def drawAllInOne(self, myAllShapeNuisances, luminosity):
myMaxSize = len(myAllShapeNuisances)
# Create pads
canvasHeight = _cHeaderHeight + _cBodyHeight * myMaxSize + _cFooterHeight
c = ROOT.TCanvas("","",600,canvasHeight)
c.Divide(1,myMaxSize)
myHeightBefore = canvasHeight
myHeightAfter = canvasHeight
for i in range(0,myMaxSize):
myHeightBefore = myHeightAfter
p = c.cd(i+1)
myTopMargin = 0.0
myBottomMargin = 0.0
if i == 0:
# Top histogram with header
myHeightAfter -= _cHeaderHeight + _cBodyHeight
myTopMargin = float(_cHeaderHeight) / float(_cHeaderHeight+_cBodyHeight)
elif i == myMaxSize-1:
# Bottom histogram with x axis label and title
myHeightAfter -= _cFooterHeight + _cBodyHeight
myBottomMargin = float(_cFooterHeight) / float(_cFooterHeight+_cBodyHeight)
else:
# Middle histogram, only body
myHeightAfter -= _cBodyHeight
(xlow, ylow, xup, yup) = [ROOT.Double(x) for x in [0.0]*4]
p.GetPadPar(xlow, ylow, xup, yup)
p.SetPad(xlow, float(myHeightAfter)/float(canvasHeight), xup, float(myHeightBefore)/float(canvasHeight))
p.SetBorderMode(0)
p.SetFillStyle(4000)
p.SetTopMargin(myTopMargin)
p.SetBottomMargin(myBottomMargin)
# Draw plots
if len(self._ratioPlotList) == 0:
print "No ratioplots in list! Cannot draw all-in-one plot!"
return
o = self._ratioPlotList[0].getFrame2()
myEmptyPlot = aux.Clone(o) # Keep the clone if it is needed to draw the x axis
for i in range(0,myMaxSize):
p = c.cd(i+1)
# Find plot
myPlotIndex = None
for j in range(0,len(self._systNameList)):
if myAllShapeNuisances[i] == self._systNameList[j]:
myPlotIndex = j
plot = None
if myPlotIndex != None:
plot = self._ratioPlotList[myPlotIndex].getFrame2()
else:
if i == myMaxSize-1:
plot = myEmptyPlot # Use this to draw the x axis
else:
plot = self._ratioPlotList[0].getFrame2() # Only the empty frame matters
# Draw plot
if i == myMaxSize-1:
# Bottom histogram
plot.GetXaxis().SetTitleOffset(0.6*myMaxSize+0.6) # 6.6/10, 3.6/5
else:
plot.GetXaxis().SetTitleSize(0)
plot.GetXaxis().SetLabelSize(0)
plot.GetYaxis().SetLabelSize(26)
plot.GetYaxis().SetTitleOffset(0.34*myMaxSize+0.1) # 3.5/10, 1.8/5
# plot.SetMinimum(0.001)
# plot.SetMaximum(1.999)
plot.SetMinimum(0.601)
plot.SetMaximum(1.399)
plot.Draw() # Plot frame for every nuisance
if myPlotIndex != None:
self._ratioPlotList[myPlotIndex].ratioHistoMgr.draw() # Plot content only if affected
self._ratioPlotList[0].ratioLine.Draw("L")
p.RedrawAxis()
# Labels for shape nuisance and dataset
myHeight = 0.82
if i == 0:
myHeight = myHeight*float(_cBodyHeight) / float(_cHeaderHeight+_cBodyHeight)
elif i == myMaxSize-1:
myHeight = (myHeight*float(_cBodyHeight)+float(_cFooterHeight)) / float(_cFooterHeight +_cBodyHeight)
histograms.addText(x=0.18, y=myHeight, text=myAllShapeNuisances[i], size=30)
myHeight = 0.08
if i == 0:
myHeight = myHeight*float(_cBodyHeight) / float(_cHeaderHeight+_cBodyHeight)
elif i == myMaxSize-1:
myHeight = (myHeight*float(_cBodyHeight)+float(_cFooterHeight)) / float(_cFooterHeight +_cBodyHeight)
histograms.addText(x=0.93, y=myHeight, text=self._dsetName, size=30, align="right")
# Header labels
if i == 0:
histograms.addStandardTexts(lumi=luminosity, cmsTextPosition="outframe")
# Labels for non-existing nuisances
if myPlotIndex == None:
myHeight = 0.44
if i == 0:
myHeight = myHeight*float(_cBodyHeight) / float(_cHeaderHeight+_cBodyHeight)
elif i == myMaxSize-1:
myHeight = (myHeight*float(_cBodyHeight)+float(_cFooterHeight)) / float(_cFooterHeight +_cBodyHeight)
histograms.addText(x=0.555, y=myHeight, text="Not affected", size=30, align="center")
# Save plot
myPlotName = "shapeSystRatioOnlyAll_%s"%(self._dsetName)
backup = ROOT.gErrorIgnoreLevel
ROOT.gErrorIgnoreLevel = ROOT.kError
for suffix in [".png",".C",".eps"]:
c.Print("%s/%s%s"%(_dirname,myPlotName,suffix))
ROOT.gErrorIgnoreLevel = backup
def doPlot(rootfile):
f = ROOT.TFile.Open(rootfile)
basename = rootfile.replace(".root", "")
match = name_re.search(rootfile)
if not match:
raise Exception("Assert: the file name regex did not match")
mass = match.group("mass")
name = match.group("name")
canvas = f.Get("c1")
primitives = canvas.GetListOfPrimitives()
# Suppress the warning message of missing dictionary for some iterator
backup = ROOT.gErrorIgnoreLevel
ROOT.gErrorIgnoreLevel = ROOT.kError
primiter = primitives.MakeIterator()
ROOT.gErrorIgnoreLevel = backup
graph = None
expoFit = None
lines = []
obj = primiter.Next()
while obj:
# print obj, obj.GetName()
if isinstance(obj, ROOT.TGraph):
if graph != None:
raise Exception("Assert: graph was already found")
graph = obj
elif isinstance(obj, ROOT.TF1):
if expoFit != None:
raise Exception("Assert: expoFit was already found")
expoFit = obj
elif isinstance(obj, ROOT.TLine):
lines.append(obj)
obj = primiter.Next()
if graph == None:
raise Exception("Assert: did not find any TGraph")
if expoFit == None:
raise Exception("Assert: did not find any TF1")
plot = plots.PlotBase([
histograms.HistoGraph(graph, "CLs", drawStyle="PE")
])
plot.createFrame(basename, opts={"xmin": 0, "ymin": 0, "ymaxfactor": 1.1,
"xmaxlist": [0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.15, 0.2]
})
plot.frame.GetXaxis().SetTitle("#mu")
plot.frame.GetXaxis().SetNdivisions(1005)
plot.frame.GetYaxis().SetTitle("CL_{s}")
expoFit.SetLineColor(ROOT.kBlue-9)
# skip the vertical non-solid lines
lines = filter(lambda l: l.GetLineStyle() == 1 or l.GetX1() != l.GetX2(), lines)
plot.prependPlotObject(expoFit)
for l in lines:
plot.appendPlotObject(l)
plot.draw()
labelName = {"observed": "Observed",
"median": "Expected median",
"1sigma": "Expected +1#sigma",
"2sigma": "Expected +2#sigma",
"-1sigma": "Expected .1#sigma",
"-2sigma": "Expected -2#sigma"}[name]
size = 20
x = 0.62
histograms.addText(x, 0.85, "m_{H^{+}} = %s %s" % (mass, limit.massUnit()), size=size)
histograms.addText(x, 0.81, labelName, size=size)
plot.save()
f.Close()
f.Delete()
def doPlot(mass, mu, mul=None):
f = openFile(mass)
tree_obs = f.Get("TESTED_R%s"%mu)
if not tree_obs:
print "Did not find TESTED_R%s TTree in file %s" % (mu, f.GetName())
listMu(mass, f)
f.Close()
return
tree_sb = f.Get("SAMPLING_SB_TESTED_R%s" % mu)
if not tree_sb:
raise Exception("Did not find SAMPLING_SB_TESTED_R%s from file %s" % (mu, f.GetName()))
tree_b = f.Get("SAMPLING_B_TESTED_R%s" % mu)
if not tree_b:
raise Exception("Did not find SAMPLING_B_TESTED_R%s from file %s" % (mu, f.GetName()))
xmin = min(tree_sb.GetMinimum("brT"), tree_b.GetMinimum("brT"))
xmax = max(tree_sb.GetMaximum("brT"), tree_b.GetMaximum("brT"))
obsval = tree_obs.GetMinimum("brT")
# Get the distributions
tree_sb.Draw("brT >>hsb(%d, %f, %f)" % (nbins, xmin, xmax), "", "goff")
h_sb = tree_sb.GetHistogram()
tree_b.Draw("brT >>hb(%d, %f, %f)" % (nbins, xmin, xmax), "", "goff")
h_b = tree_b.GetHistogram()
# Set obsval to bin boundary
obsval_bin = obsval
for bin in xrange(0, h_sb.GetNbinsX()+1):
edge = h_sb.GetBinLowEdge(bin)
if obsval > edge:
obsval_bin = edge
# Normalize the distributions to unit area
dataset._normalizeToOne(h_sb)
dataset._normalizeToOne(h_b)
plot = plots.PlotBase([
histograms.Histo(h_b, "B"),
histograms.Histo(h_sb, "SB")
])
mulegend = mul
if mul == None:
mulegend = mu
plot.histoMgr.setHistoLegendLabelMany({
"B": "f(#tilde{q}_{#mu=%s} | #mu=0, #hat{#bf{#theta}}_{0}^{obs})" % mulegend,
"SB": "f(#tilde{q}_{#mu=%s} | #mu=%s, #hat{#bf{#theta}}_{#mu=%s}^{obs})" % (mulegend, mulegend, mulegend),
})
plot.setLegend(histograms.moveLegend(histograms.createLegend(0.6, 0.8, 0.9, 0.9), dy=0.02, dx=-0.05))
plot.histoMgr.forEachHisto(styles.generator())
fname = "teststat_m%s_mu%s" % (mass, mu)
plot.createFrame(fname.replace(".", "_"), opts={"ymin": 1e-6, "ymaxfactor": 2})
plot.frame.GetXaxis().SetTitle("Test statistic #tilde{q}_{#mu}")
plot.frame.GetYaxis().SetTitle("Arbitrary units")
plot.getPad().SetLogy(True)
area_sb = h_sb.Clone("AreaSB")
area_sb.SetFillColor(area_sb.GetLineColor())
area_sb.SetFillStyle(3005)
area_sb.SetLineWidth(0)
area_b = h_b.Clone("AreaB")
area_b.SetFillColor(area_b.GetLineColor())
area_b.SetFillStyle(3004)
area_b.SetLineWidth(0)
for bin in xrange(0, area_sb.GetNbinsX()+1):
if area_sb.GetBinLowEdge(bin) < obsval_bin:
area_sb.SetBinContent(bin, 0)
if area_b.GetBinLowEdge(bin) >= obsval_bin:
area_b.SetBinContent(bin, 0)
plot.appendPlotObject(area_b, "HIST ][")
plot.appendPlotObject(area_sb, "HIST ][")
plot.addCutBoxAndLine(box=False, cutValue=obsval_bin)
plot.draw()
size = 26
histograms.addText(0.35, 0.4, "CL_{s+b}", size=size)
histograms.addText(0.18, 0.4, "1-CL_{b}", size=size)
size = 20
# x = obsval_bin/(plot.frame.GetXaxis().GetXmax() - plot.frame.GetXaxis().GetXmin())+0.02
x = (obsval_bin - plot.getPad().GetX1()) / (plot.getPad().GetX2() - plot.getPad().GetX1())
x += 0.01
histograms.addText(x, 0.88, "Observed", size=size)
histograms.addText(x, 0.85, "value", size=size)
histograms.addText(0.62, 0.78, "m_{H^{+}} = %s %s" % (mass, limit.massUnit()), size=size)
# size = 20
# x = 0.2
# histograms.addText(x, 0.88, brlimit.process, size=size)
# histograms.addText(x, 0.84, brlimit._finalstateLabels["taujets"], size=size)
# histograms.addText(x, 0.79, brlimit.BRassumption, size=size)
plot.save()
f.Close()
def PlotBaselineVsInvertedTemplates(datasetsMgr,
histoName,
addQcdBaseline=False):
Verbose("Plotting EWK Vs QCD unity-normalised histograms")
# Create comparison plot
p1 = plots.ComparisonPlot(
getHisto(datasetsMgr, "Data", "topSelection_Baseline/%s" % histoName,
"Baseline"),
getHisto(datasetsMgr, "EWK", "topSelection_Baseline/%s" % histoName,
"Baseline"))
p1.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
p2 = plots.ComparisonPlot(
getHisto(datasetsMgr, "Data", "topSelection_Inverted/%s" % histoName,
"Inverted"),
getHisto(datasetsMgr, "EWK", "topSelection_Inverted/%s" % histoName,
"Inverted"))
p2.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
# Get EWK histos
EWK_baseline = p1.histoMgr.getHisto("Baseline-EWK").getRootHisto().Clone(
"Baseline-EWK")
EWK_inverted = p2.histoMgr.getHisto("Inverted-EWK").getRootHisto().Clone(
"Inverted-EWK")
# Get QCD histos
QCD_baseline = p1.histoMgr.getHisto("Baseline-Data").getRootHisto().Clone(
"Baseline-QCD")
QCD_inverted = p2.histoMgr.getHisto("Inverted-Data").getRootHisto().Clone(
"Inverted-QCD")
# Create QCD histos: QCD = Data-EWK
QCD_baseline.Add(EWK_baseline, -1)
QCD_inverted.Add(EWK_inverted, -1)
# Normalize histograms to unit area
EWK_baseline.Scale(1.0 / EWK_baseline.Integral())
EWK_inverted.Scale(1.0 / EWK_inverted.Integral())
QCD_baseline.Scale(1.0 / QCD_baseline.Integral())
QCD_inverted.Scale(1.0 / QCD_inverted.Integral())
# Create the final plot object
if addQcdBaseline:
compareHistos = [EWK_baseline, QCD_baseline]
else:
compareHistos = [EWK_baseline]
p = plots.ComparisonManyPlot(QCD_inverted, compareHistos, saveFormats=[])
p.setLuminosity(GetLumi(datasetsMgr))
# Apply styles
p.histoMgr.forHisto("Baseline-EWK", styles.getBaselineStyle())
if addQcdBaseline:
p.histoMgr.forHisto("Baseline-QCD", styles.getInvertedLineStyle())
p.histoMgr.forHisto("Inverted-QCD", styles.getInvertedStyle())
# Set draw style
p.histoMgr.setHistoDrawStyle("Baseline-EWK", "AP")
p.histoMgr.setHistoLegendStyle("Baseline-EWK", "LP")
if addQcdBaseline:
p.histoMgr.setHistoDrawStyle("Baseline-QCD", "HIST")
p.histoMgr.setHistoLegendStyle("Baseline-QCD", "FL")
p.histoMgr.setHistoDrawStyle("Inverted-QCD", "AP")
p.histoMgr.setHistoLegendStyle("Inverted-QCD", "LP")
# Set legend labels
if addQcdBaseline:
p.histoMgr.setHistoLegendLabelMany({
"Baseline-EWK": "EWK (Baseline)",
"Baseline-QCD": "QCD (Baseline)",
"Inverted-QCD": "QCD (Inverted)",
})
else:
p.histoMgr.setHistoLegendLabelMany({
"Baseline-EWK": "EWK (Baseline)",
"Inverted-QCD": "QCD (Inverted)",
})
# Append analysisType to histogram name
saveName = histoName
# Draw the histograms #alex
plots.drawPlot(p, saveName,
**GetHistoKwargs(histoName)) #the "**" unpacks the kwargs_
# _kwargs = {"lessThan": True}
# p.addCutBoxAndLine(cutValue=200, fillColor=ROOT.kRed, box=False, line=True, ***_kwargs)
# Add text
text = opts.optMode.replace("OptChiSqrCutValue", "#chi^{2} #leq ")
histograms.addText(0.21, 0.85, text)
# Save plot in all formats
saveDir = os.path.join(opts.saveDir, "Templates", opts.optMode)
SavePlot(p, saveName, saveDir, saveFormats=[".C", ".png", ".pdf"])
return
def main(argv):
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
dirs_signal = ["../../SignalAnalysis_140605_143702/"]
QCDInvertedNormalization = sort(
QCDInvertedNormalizationFactors.QCDInvertedNormalization)
labels, QCDInvertedNormalizationFilteredEWKFakeTaus = getSortedLabelsAndFactors(
QCDInvertedNormalizationFactorsFilteredEWKFakeTaus.
QCDInvertedNormalization)
analysis_inverted = "signalAnalysisInvertedTau"
analysis = "signalAnalysis"
optModes = []
#optModes.append("OptQCDTailKillerZeroPlus")
optModes.append("OptQCDTailKillerLoosePlus")
optModes.append("OptQCDTailKillerMediumPlus")
optModes.append("OptQCDTailKillerTightPlus")
varHistoName = "shapeEWKGenuineTausTransverseMass"
nomHistoName = "shapeTransverseMass"
signalHistoName = "shapeEWKFakeTausTransverseMass"
#Optimal: 0.8, 0.82, 0.9
###w_list = [0.65, 0.7, 0.76] #old baseline ft
w_list = [0.66, 0.67, 0.75]
#w_list = [0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 1]
#defaultBinning = systematics.getBinningForPlot("shapeTransverseMass")
defaultBinning = [0, 20, 40, 60, 80, 100, 120, 140, 160, 200, 400]
defaultBinning_array = array.array("d", defaultBinning)
diff_opt = []
for optMode in optModes:
diff_list = []
for w in w_list:
var_values = []
nom_values = []
# baseline fake taus
mt_baseline_faketaus_data = getDataSets(dirs_signal, dataEra,
searchMode, analysis,
optMode)
mtplot_signalfaketaus = plots.DataMCPlot(mt_baseline_faketaus_data,
signalHistoName)
mt_signalfaketaus = mtplot_signalfaketaus.histoMgr.getHisto(
"EWK").getRootHisto().Clone(signalHistoName)
# inverted fake taus
mt_inverted_faketaus_data = getDataSets(dirs, dataEra, searchMode,
analysis_inverted, optMode)
histonames_var = mt_inverted_faketaus_data.getDataset(
"Data").getDirectoryContent(varHistoName)
histonames_nom = mt_inverted_faketaus_data.getDataset(
"Data").getDirectoryContent(nomHistoName)
bins_var = getBins(histonames_var, varHistoName)
bins_nom = getBins(histonames_nom, nomHistoName)
normalization_var_qg = getNormalization(
bins_var, w, QCDInvertedNormalizationFilteredEWKFakeTaus, True,
True)
normalization_var = getNormalization(
bins_var, w, QCDInvertedNormalizationFilteredEWKFakeTaus, True,
False)
normalization_nom = getNormalization(bins_nom, w,
QCDInvertedNormalization,
False, False)
mt_var_qg = getMt(mt_inverted_faketaus_data, bins_var,
varHistoName, normalization_var_qg)
mt_var = getMt(mt_inverted_faketaus_data, bins_var, varHistoName,
normalization_var)
mt_nom = getMt(mt_inverted_faketaus_data, bins_nom, nomHistoName,
normalization_nom)
mt_nom.Add(mt_signalfaketaus)
mt_var_qg.SetName(
"QCD(Data)+EWK+t#bar{t}(Data, mis-ID. #tau), q-g bal.")
mt_var.SetName("QCD(Data)+EWK+t#bar{t}(Data, mis-ID. #tau)")
mt_nom.SetName("QCD(Data)+EWK+t#bar{t}(MC, mis-ID. #tau)")
mt_var_qg.SetLineWidth(4)
mt_var.SetLineColor(14)
mt_nom.SetLineColor(2)
mt_var_qg = mt_var_qg.Rebin(
len(defaultBinning) - 1, "", defaultBinning_array)
mt_var = mt_var.Rebin(
len(defaultBinning) - 1, "", defaultBinning_array)
mt_nom = mt_nom.Rebin(
len(defaultBinning) - 1, "", defaultBinning_array)
for i in range(0, mt_nom.GetSize()):
var_values.append(mt_var_qg.GetBinContent(i))
nom_values.append(mt_nom.GetBinContent(i))
style = tdrstyle.TDRStyle()
varPlots = [mt_var, mt_var_qg]
plot = plots.ComparisonManyPlot(mt_nom, varPlots)
plot.createFrame(optMode.replace(
"Opt", "Mt_DataDrivenVsMC_" + "w=" + str(w) + "_"),
createRatio=True)
moveLegend = {"dx": -0.35, "dy": 0.05}
plot.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
histograms.addText(
0.65, 0.3,
optMode.replace("OptQCDTailKiller",
"R_{BB} ").replace("Plus", ""), 25)
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
lumi = mt_inverted_faketaus_data.getDataset("Data").getLuminosity()
histograms.addLuminosityText(x=None, y=None, lumi=lumi)
plot.draw()
plot.save()
mt_var_qg.Delete()
mt_var.Delete()
mt_nom.Delete()
#TFile.CurrentFile().Close("R")
mt_baseline_faketaus_data.close()
mt_inverted_faketaus_data.close()
ROOT.gROOT.CloseFiles()
ROOT.gROOT.GetListOfCanvases().Delete()
ROOT.gDirectory.GetList().Delete()
#print var_values
#print nom_values
# difference metrics
num = 0
denom = 0
for i in range(0, len(nom_values)):
num += var_values[i] * (var_values[i] - nom_values[i])**2
denom += var_values[i]
diff = num / denom
diff_list.append(diff)
diff_opt.append(diff_list)
os.system("rm MtOptimal/*")
os.system("mkdir -p MtOptimal")
print "\nWeights:\t", w_list, '\n'
optimalWeights = {}
for i in range(0, len(diff_opt)):
print optModes[i]
print "Differences:\t", diff_opt[i], "- Optimal: w =", w_list[
diff_opt[i].index(min(diff_opt[i]))]
optimalWeights[optModes[i]] = w_list[diff_opt[i].index(min(
diff_opt[i]))]
command = "cp *" + str(w_list[diff_opt[i].index(min(
diff_opt[i]))]) + "*" + optModes[i].replace("Opt",
"") + ".eps MtOptimal"
os.system(command)
print optimalWeights
writeWeightsToFile("OptimalWeights.py", optimalWeights)
writeNormalizationToFile("QCDPlusEWKFakeTauNormalizationFactors.py",
normalization_var_qg, labels)
def main():
style = tdrstyle.TDRStyle()
#style.setWide(True)
style.setPaletteMy()
ROOT.gStyle.SetNumberContours(20)
# tdrstyle.setDeepSeaPalette()
# tdrstyle.setRainBowPalette()
# tdrstyle.setDarkBodyRadiatorPalette()
# tdrstyle.setGreyScalePalette()
# tdrstyle.setTwoColorHuePalette()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(parseOpts.batchMode)
# Get all datasets from the mcrab dir
#datasetsMgr = GetDatasetsFromDir(parseOpts.mcrab, kwargs.get("analysis"))
datasetsMgr = GetDatasetsFromDir(parseOpts.mcrab, parseOpts, **kwargs)
# Determine Integrated Luminosity (If Data datasets present)
intLumi = GetLumi(datasetsMgr)
# Update to PU
datasetsMgr.updateNAllEventsToPUWeighted()
# Remove datasets
#datasetsMgr.remove(kwargs.get("rmDataset"))
datasetsMgr.remove(
filter(lambda name: not kwargs.get("refDataset") in name,
datasetsMgr.getAllDatasetNames()))
# Set custom XSections
# d.getDataset("TT_ext3").setCrossSection(831.76)
# Default merging & ordering: "Data", "QCD", "SingleTop", "Diboson"
plots.mergeRenameReorderForDataMC(
datasetsMgr
) #WARNING: Merged MC histograms must be normalized to something!
# Remove datasets (for merged names)
# datasetsMgr.remove(kwargs.get("rmDataset"))
# For-loop: All Histogram names
for hName in hNames:
savePath, saveName = GetSavePathAndName(hName, **kwargs)
# Get Histos for Plotter
refHisto, otherHistos = GetHistosForPlotter(datasetsMgr, hName,
**kwargs)
# Create a plot
p = plots.PlotBase([refHisto], kwargs.get("saveFormats"))
# Remove negative contributions
#RemoveNegativeBins(datasetsMgr, hName, p)
# Customize
# p.histoMgr.setHistoDrawStyleAll("COL") #"CONT4" "COLZ" "COL"
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinX(kwargs.get("rebinX")))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinY(kwargs.get("rebinY")))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetXaxis().SetRangeUser(1.0, 5.0))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetYaxis().SetRangeUser(1.0, 5.0))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetZaxis().SetRangeUser(0.0, 0.015))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMinimum(kwargs.get("zMin")))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMaximum(kwargs.get("zMax")))
# Create a frame
opts = {"ymin": 0.0, "ymaxfactor": 1.0}
p.createFrame(saveName, opts=opts)
# Customise frame
p.getFrame().GetXaxis().SetTitle(getTitleX(refHisto, **kwargs))
p.getFrame().GetYaxis().SetTitle(getTitleY(refHisto, **kwargs))
# p.getFrame().GetZaxis().SetTitle( getTitleZ(refHisto, **kwargs) ) #does not work
# SetLog
SetLogAndGrid(p, **kwargs)
# Add cut line/box
_kwargs = {"lessThan": kwargs.get("cutLessThan")}
p.addCutBoxAndLine(cutValue=kwargs.get("cutValue"),
fillColor=kwargs.get("cutFillColour"),
box=kwargs.get("cutBox"),
line=kwargs.get("cutLine"),
**_kwargs)
# Customise Legend
moveLegend = {"dx": -0.1, "dy": +0.0, "dh": -0.1}
p.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
p.removeLegend()
# Add MC Uncertainty (Invalid method for a 2-d histogram)
#p.addMCUncertainty()
# Draw plots
p.draw()
# Customise text
histograms.addStandardTexts(lumi=intLumi)
#histograms.addText(0.16, 0.95, plots._legendLabels[kwargs.get("refDataset")], 22)
histograms.addText(0.20, 0.88,
plots._legendLabels[kwargs.get("refDataset")], 17)
# Save canvas under custom dir
if not os.path.exists(savePath):
os.mkdir(savePath)
SaveAs(p, savePath, saveName, kwargs.get("saveFormats"))
return
def main(opts):
style = tdrstyle.TDRStyle()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(opts.batchMode)
# Get all datasets from the mcrab dir
# def GetDatasetsFromDir(mcrab, opts, **kwargs): #iro
datasetsMgr = GetDatasetsFromDir(opts.mcrab, opts, **kwargs) #kwargs.get("analysis"))
# Determine Integrated Luminosity (If Data datasets present)
intLumi = GetLumi(datasetsMgr)
# Update to PU
datasetsMgr.updateNAllEventsToPUWeighted()
# Remove datasets
datasetsMgr.remove(kwargs.get("rmDataset"))
# datasetsMgr.remove(filter(lambda name: not "QCD" in name, datasetsMgr.getAllDatasetNames()))
# datasetsMgr.remove(filter(lambda name: "QCD" in name in name, datasetsMgr.getAllDatasetNames()))
# Set custom XSections
#datasetsMgr.getDataset("QCD_bEnriched_HT1000to1500").setCrossSection(1.0)
#datasetsMgr.getDataset("QCD_bEnriched_HT1500to2000").setCrossSection(1.0)
#datasetsMgr.getDataset("QCD_bEnriched_HT2000toInf").setCrossSection(1.0)
#datasetsMgr.getDataset("QCD_bEnriched_HT300to500").setCrossSection(1.0)
#datasetsMgr.getDataset("QCD_bEnriched_HT500to700").setCrossSection(1.0)
#datasetsMgr.getDataset("QCD_bEnriched_HT700to1000").setCrossSection(1.0)
# Default merging & ordering: "Data", "QCD", "SingleTop", "Diboson"
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Remove datasets (for merged names)
datasetsMgr.remove(kwargs.get("rmDataset"))
# Print the cross
datasetsMgr.PrintCrossSections()
for h_prefix in ["reco"]:
# Get ref histo here and the fixed histos (TT, QCD, QCD-b
inclusiveHisto, fixedHistos = GetHistosForPlotter(datasetsMgr, h_prefix + "MET_Et", **kwargs)
inclusiveHisto.setName("Inclusive")
inclusiveHisto.setLegendLabel("Inclusive")
#for hi in fixedHistos:
# print(type(hi), hi.getName())
#return
# For-loop: All Histogram names
for counter, hName in enumerate(hNames):
# Get the save path and name
savePath, saveName = GetSavePathAndName(h_prefix + hName[0], **kwargs)
saveName = savePath + h_prefix + hSaveNames[counter]
# Get Histos for Plotter
refHisto_, otherHistos_ = GetHistosForPlotter(datasetsMgr, h_prefix + hName[0], **kwargs)
refHisto1, otherHistos2 = GetHistosForPlotter(datasetsMgr, h_prefix + hName[1], **kwargs)
refHisto2, otherHistos2 = GetHistosForPlotter(datasetsMgr, h_prefix + hName[2], **kwargs)
refHisto3, otherHistos2 = GetHistosForPlotter(datasetsMgr, h_prefix + hName[3], **kwargs)
refHisto4, otherHistos2 = GetHistosForPlotter(datasetsMgr, h_prefix + hName[4], **kwargs)
refHisto5, otherHistos2 = GetHistosForPlotter(datasetsMgr, h_prefix + hName[5], **kwargs)
refHisto = inclusiveHisto
# customize histos
refHisto_.setName(hLegends[counter][0])
refHisto_.setLegendLabel(hLegends[counter][0])
refHisto_.setDrawStyle("P")
refHisto_.setLegendStyle("P")
styleDict[hStyles[0]].apply(refHisto_.getRootHisto())
refHisto1.setName(hLegends[counter][1])
refHisto1.setLegendLabel(hLegends[counter][1])
refHisto1.setDrawStyle("P")
refHisto1.setLegendStyle("P")
styleDict[hStyles[1]].apply(refHisto1.getRootHisto())
refHisto2.setName(hLegends[counter][2])
refHisto2.setLegendLabel(hLegends[counter][2])
refHisto2.setDrawStyle("P")
refHisto2.setLegendStyle("P")
styleDict[hStyles[2]].apply(refHisto2.getRootHisto())
refHisto3.setName(hLegends[counter][3])
refHisto3.setLegendLabel(hLegends[counter][3])
refHisto3.setDrawStyle("P")
refHisto3.setLegendStyle("P")
styleDict[hStyles[3]].apply(refHisto3.getRootHisto())
refHisto4.setName(hLegends[counter][4])
refHisto4.setLegendLabel(hLegends[counter][4])
refHisto4.setDrawStyle("P")
refHisto4.setLegendStyle("P")
styleDict[hStyles[4]].apply(refHisto4.getRootHisto())
refHisto5.setName(hLegends[counter][5])
refHisto5.setLegendLabel(hLegends[counter][5])
refHisto5.setDrawStyle("P")
refHisto5.setLegendStyle("P")
styleDict[hStyles[5]].apply(refHisto5.getRootHisto())
otherHistos = [refHisto_, refHisto1, refHisto2, refHisto3, refHisto4, refHisto5]
#for hi in otherHistos:
# print hi.getName()
#return
# Create a comparison plot
p = plots.ComparisonManyPlot(refHisto, otherHistos)
# Remove negative contributions
#RemoveNegativeBins(datasetsMgr, hName, p)
# Create a frame
if kwargs.get("logY")==True:
opts = {"ymin": 8e-5, "ymax": 2}
#opts = {"ymin": 1e-3, "ymax": 1}
else:
opts = {"ymin": 8.e-5, "ymax": 2}
ratioOpts = {"ymin": 0.1, "ymax": 10.0}
p.createFrame(saveName, createRatio=kwargs.get("createRatio"), opts=opts, opts2=ratioOpts)
# Customise Legend
moveLegend = {"dx": -0.2, "dy": +0.0, "dh": -0.1}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
#p.removeLegend()
# Customise frame
p.getFrame().GetYaxis().SetTitle( getTitleY(refHisto, **kwargs) )
#p.setEnergy("13")
if kwargs.get("createRatio"):
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.7)
# SetLog
SetLogAndGrid(p, **kwargs)
# Add cut line/box
_kwargs = { "lessThan": kwargs.get("cutLessThan")}
p.addCutBoxAndLine(cutValue=kwargs.get("cutValue"), fillColor=kwargs.get("cutFillColour"), box=kwargs.get("cutBox"), line=kwargs.get("cutLine"), **_kwargs)
# Move the refDataset to first in the draw order (back)
histoNames = [h.getName() for h in p.histoMgr.getHistos()]
p.histoMgr.reorder(filter(lambda n: plots._legendLabels[kwargs.get("refDataset") ] not in n, histoNames))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(2))
# Draw plots
p.draw()
# Customise text
histograms.addStandardTexts(lumi=intLumi)
# histograms.addText(0.4, 0.9, "Alexandros Attikis", 17)
histograms.addText(0.2, 0.88, plots._legendLabels[kwargs.get("refDataset")], 17)
# Save canvas under custom dir
if not os.path.exists(savePath):
os.mkdir(savePath)
SaveAs(p, savePath, saveName, kwargs.get("saveFormats"))
return
def doPlot(name, graphs, limits, xlabel):
if "obs" in graphs.keys():
obs = graphs["obs"]
excluded = ROOT.TGraph(obs)
excluded.SetName("ExcludedArea")
excluded.SetFillColor(ROOT.kGray)
excluded.SetPoint(excluded.GetN(), obs.GetX()[obs.GetN()-1], tanbMax)
excluded.SetPoint(excluded.GetN(), obs.GetX()[0], tanbMax)
excluded.SetFillColor(ROOT.kGray)
excluded.SetFillStyle(3354)
excluded.SetLineWidth(0)
excluded.SetLineColor(ROOT.kWhite)
plot = None
if "obs" in graphs.keys():
plot = plots.PlotBase([
histograms.HistoGraph(graphs["obs"], "Observed", drawStyle="PL", legendStyle="lp"),
histograms.HistoGraph(graphs["obs_th_plus"], "ObservedPlus", drawStyle="L", legendStyle="l"),
histograms.HistoGraph(graphs["obs_th_minus"], "ObservedMinus", drawStyle="L"),
histograms.HistoGraph(excluded, "Excluded", drawStyle="F", legendStyle="f"),
histograms.HistoGraph(graphs["exp"], "Expected", drawStyle="L"),
histograms.HistoGraph(graphs["exp1"], "Expected1", drawStyle="F", legendStyle="fl"),
histograms.HistoGraph(graphs["exp2"], "Expected2", drawStyle="F", legendStyle="fl"),
])
plot.histoMgr.setHistoLegendLabelMany({
"ObservedPlus": "Observed #pm1#sigma (th.)",
"ObservedMinus": None,
"Expected": None,
"Expected1": "Expected median #pm 1#sigma",
"Expected2": "Expected median #pm 2#sigma"
})
else:
plot = plots.PlotBase([
histograms.HistoGraph(graphs["exp"], "Expected", drawStyle="L"),
histograms.HistoGraph(graphs["exp1"], "Expected1", drawStyle="F", legendStyle="fl"),
histograms.HistoGraph(graphs["exp2"], "Expected2", drawStyle="F", legendStyle="fl"),
])
plot.histoMgr.setHistoLegendLabelMany({
"Expected": None,
"Expected1": "Expected median #pm 1#sigma",
"Expected2": "Expected median #pm 2#sigma"
})
plot.setLegend(histograms.createLegend(0.57, 0.155, 0.87, 0.355))
plot.createFrame(name, opts={"ymin": 0, "ymax": tanbMax})
plot.frame.GetXaxis().SetTitle(xlabel)
plot.frame.GetYaxis().SetTitle(limit.tanblimit)
plot.draw()
plot.setLuminosity(limits.getLuminosity())
plot.addStandardTexts()
size = 20
x = 0.2
histograms.addText(x, 0.9, limit.process, size=size)
histograms.addText(x, 0.863, limits.getFinalstateText(), size=size)
histograms.addText(x, 0.815, "MSSM m_{h}^{max}", size=size)
histograms.addText(x, 0.775, limit.BRassumption, size=size)
histograms.addText(x, 0.735, "#mu=%d %s"%(mu, limit.massUnit()), size=size)
plot.save()
def doPlotMu(name, graphs, styleList, limits, xlabel, xvarName, legendVariable):
objs = []
ll = {}
cutValue = 100 # for cutting a slice +- cutValue around 0 from the (mu,tanb) plot
for gr, mu in graphs:
if xvarName == "mu":
histo = histograms.HistoGraph(gr, "Obs%d"%mu, drawStyle="LP", legendStyle="lp")
plus = gr.Clone()
minus = gr.Clone()
N = gr.GetN()
for i in range(0,N):
j = N - 1 - i
x = gr.GetX()[j]
if x > -cutValue:
minus.RemovePoint(j)
if x < cutValue:
plus.RemovePoint(0)
objs.append(histograms.HistoGraph(minus, "Obs%d"%mu, drawStyle="LP", legendStyle="lp"))
objs.append(histograms.HistoGraph(plus, "ObsCopy%d"%mu, drawStyle="LP", legendStyle="lp"))
ll["ObsCopy%d"%mu] = None
else:
objs.append(histograms.HistoGraph(gr, "Obs%d"%mu, drawStyle="LP", legendStyle="lp"))
ll["Obs%d"%mu] = "Observed, "+legendVariable+"=%d %s" % (mu, limit.massUnit())
# N = gr.GetN()
# for i in range(0,N):
# j = N - 1 - i
# if gr.GetY()[j] == 1:
# gr.RemovePoint(j)
plot = plots.PlotBase(objs)
if xvarName == "mu":
doubleStyleList = []
for style in styleList:
doubleStyleList.append(style)
doubleStyleList.append(style)
plot.histoMgr.forEachHisto(styles.Generator(doubleStyleList))
else:
plot.histoMgr.forEachHisto(styles.Generator(styleList))
plot.histoMgr.setHistoLegendLabelMany(ll)
plot.setLegend(histograms.moveLegend(histograms.createLegend(0.57, 0.155, 0.87, 0.355), dx=-0.1))
plot.createFrame(name, opts={"ymin": 0, "ymax": tanbMax})
plot.frame.GetXaxis().SetTitle(xlabel)
plot.frame.GetXaxis().SetLabelSize(20)
plot.frame.GetYaxis().SetTitle(limit.tanblimit)
plot.draw()
plot.setLuminosity(limits.getLuminosity())
plot.addStandardTexts()
size = 20
x = 0.2
histograms.addText(x, 0.9, limit.process, size=size)
histograms.addText(x, 0.863, limits.getFinalstateText(), size=size)
histograms.addText(x, 0.815, "MSSM m_{h}^{max}", size=size)
# histograms.addText(x, 0.775, limit.BRassumption, size=size)
histograms.addText(x, 0.72, "FeynHiggs 2.9.4", size=size)
histograms.addText(x, 0.65, "Derived from", size=size)
histograms.addText(x, 0.6, "CMS HIG-12-052", size=size)
# histograms.addText(x, 0.6, "CMS HIG-11-019", size=size)
# histograms.addText(x, 0.55, "JHEP07(2012)143", size=size)
#Adding a LHC label:
ROOT.LHCHIGGS_LABEL(0.97,0.72,1)
plot.save()
print
print "Plotting",name
print
def main(opts):
global RunEra
mcrabName = opts.mcrab
print("mcrabNAME ISSSSSS ", mcrabName)
RunEra = mcrabName.split("_")[1]
print " Run Era is: ", RunEra
print " Plotting efficiency for trigger:", kwargs.get("trigger")
# Setup the style
style = tdrstyle.TDRStyle()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(opts.batchMode)
# Setup & configure the dataset manager
#datasetsMgr = GetSpecificDatasetsFromDir(opts.mcrab, opts, RunEra, reHLT_samples, **kwargs)
datasetsMgr = GetDatasetsFromDir(opts.mcrab, opts, **kwargs)
minRunRange = None
maxRunRange = None
Counter = 0
nDataDatasets = len(datasetsMgr.getDataDatasets())
for d in datasetsMgr.getDataDatasets():
Counter = Counter + 1
if Counter == 1:
minRunRange = d.getName().split("_")[-2]
if Counter == nDataDatasets:
maxRunRange = d.getName().split("_")[-1]
print "Data Name==> ", d.getName()
print "RunRange ==> ", d.getName().split(
"_")[-2], " - ", d.getName().split("_")[-1]
print "Run Range = ", minRunRange, " - ", maxRunRange
runRange = minRunRange + " - " + maxRunRange
intLumi = GetLumi(datasetsMgr)
print "intLumi=", intLumi
# Update to PU
datasetsMgr.updateNAllEventsToPUWeighted()
# Load Luminosities
datasetsMgr.loadLuminosities()
datasetsMgr.PrintLuminosities()
# Default merging & ordering: "Data", "QCD", "SingleTop", "Diboson"
#plots.mergeRenameReorderForDataMC(datasetsMgr) #WARNING: Merged MC histograms must be normalized to something!
#dataset_MC.normalizeMCByLuminosity()
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
plots.mergeRenameReorderForDataMC(datasetsMgr)
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
dataset_Data = datasetsMgr.getDataDatasets()
datasetsMgr.mergeMC() #ATHER
dataset_MC = None
dataset_MC = datasetsMgr.getMCDatasets()
#datasetsMgr.normalizeMCByLuminosity()
#datasetsMgr.mergeMC()
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
lumi = 0.0
for d in datasetsMgr.getDataDatasets():
lumi += d.getLuminosity()
SigSel = ["1BTag", "2BTag", "OR", "OR_PFJet450"]
for s in SigSel:
for xVar in xVariables:
print s, xVar
#if xVar == "pt6thJet":
# cutValue = 40
# cutLine = True
#for den,num
num_name = "hNum_" + xVar + "_RefTrg_OfflineSel_Signal" + s
den_name = "hDen_" + xVar + "_RefTrg_OfflineSel"
# Get the save path and name
plotName = "Eff" + "_" + xVar + "_" + s
savePath, saveName = GetSavePathAndName(plotName, **kwargs)
# Get Efficiency Plots
eff_Data = getEfficiency(datasetsMgr, dataset_Data, num_name,
den_name, **kwargs)
eff_MC = getEfficiency(datasetsMgr, dataset_MC, num_name, den_name,
**kwargs)
if xVar == "pt6thJet":
xMin = 29.0
xMax = 125.0
elif xVar == "eta6thJet":
xMin = -2.5
xMax = 2.5
elif xVar == "phi6thJet":
xMin = -3.2
xMax = 3.2
elif xVar == "Ht":
xMin = 350
xMax = 2000
elif xVar == "nBTagJets":
xMin = 0
xMax = 10
elif xVar == "pu":
xMin = 0
xMax = 50
elif xVar == "CSV":
xMin = 0
xMax = 1
elif xVar == "JetMulti":
xMin = 4
xMax = 15
elif xVar == "BJetMulti":
xMin = 0
xMax = 8
# Apply Styles
styles.dataStyle.apply(eff_Data)
styles.mcStyle.apply(eff_MC)
# Marker Style
eff_Data.SetMarkerSize(1)
# Create Comparison Plot
p = plots.ComparisonPlot(
histograms.HistoGraph(eff_Data, "eff_Data", "p", "P"),
histograms.HistoGraph(eff_MC, "eff_MC", "p", "P"))
opts = {"ymin": 0, "ymax": 1.1, "xmin": xMin, "xmax": xMax}
opts2 = {"ymin": 0.8, "ymax": 1.05}
if xVar == "pu":
moveLegend = {"dx": -0.44, "dy": -0.62, "dh": -0.2}
else:
moveLegend = {"dx": -0.44, "dy": -0.57, "dh": -0.2}
p.histoMgr.setHistoLegendLabelMany({
"eff_Data": "Data",
"eff_MC": "Simulation"
})
createRatio = True #kwargs.get("ratio")
p.createFrame(saveName,
createRatio=createRatio,
opts=opts,
opts2=opts2)
# Set Legend
p.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Set Log & Grid
SetLogAndGrid(p, **kwargs)
# Set Titles
p.getFrame().GetYaxis().SetTitle(kwargs.get("ylabel"))
if xVar == "pt6thJet":
p.getFrame().GetXaxis().SetTitle("p_{T,6thJet} (Gev/c)")
elif xVar == "eta6thJet":
p.getFrame().GetXaxis().SetTitle("#eta_{6thJet}")
elif xVar == "phi6thJet":
p.getFrame().GetXaxis().SetTitle("#phi_{6thJet}")
elif xVar == "Ht":
p.getFrame().GetXaxis().SetTitle("H_{T} (Gev/c)")
elif xVar == "nBTagJets":
p.getFrame().GetXaxis().SetTitle("nB-Jet")
elif xVar == "pu":
p.getFrame().GetXaxis().SetTitle("nPU")
elif xVar == "CSV":
p.getFrame().GetXaxis().SetTitle("CSV")
elif xVar == "JetMulti":
p.getFrame().GetXaxis().SetTitle("Jet Multiplicity")
elif xVar == "BJetMulti":
p.getFrame().GetXaxis().SetTitle("B-Jet Multiplicity")
if createRatio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
#if xVar == "pt6thJet" and s == "OR":
# plist = [0.7, 0.99, 1000, 0.16, 28.0]
# Fit_Richards(30.0, 120.0, p, eff_Data, plist)
# plist = [0.72, 0.91, 0.212, 0.15, 50.0]
# Fit_Richards(30.0, 120.0, p, eff_MC, plist)
#elif xVar == "Ht" and s == "OR":
# plist = [0.00005, 0.988, 0.000000109, 0.15, 29.0]
# Fit_Richards(350.0, 2000.0, p, eff_Data, plist)
#plist = [0.0003, 0.97, 0.45, 0.24, 43.0]
#Fit_Richards(500.0, 2000.0, p, eff_MC, plist)
#elif xVar == "nBTagJets":
#plist = [0.07, 0.984, 0.45, 0.24, 43.0]
#Fit_Richards(30.0, 120.0, p, eff_Data, plist)
#elif xVar == "pu":
#plist = [0.07, 0.984, 0.45, 0.24, 43.0]
#Fit_Richards(30.0, 120.0, p, eff_Data, plist)
# Draw
histograms.addText(0.30, 0.10, "Runs " + runRange, 17)
histograms.addText(0.30, 0.15, "2016", 17)
histograms.addStandardTexts(lumi=lumi)
p.draw()
# Save the canvas to a file
SaveAs(p, kwargs.get("savePath"), saveName,
kwargs.get("saveFormats"), True)
'''
# IN SLICES OF HT
HTSlices = ["450ht600","600ht800","800ht1000", "1000ht1250", "1250ht1500", "1500ht2000"]
for s in SigSel:
for hsl in HTSlices:
num_name = "Num_pt6thJet_Vs_"+hsl+"_RefTrg_OfflineSel_"+s
den_name = "Den_pt6thJet_Vs_"+hsl+"_RefTrg_OfflineSel"
# Get the save path and name
plotName = "Eff_pt6thJet_Vs_"+hsl+"_"+s
savePath, saveName = GetSavePathAndName(plotName, **kwargs)
# Get Efficiency Plots
eff_Data = getEfficiency(datasetsMgr, dataset_Data, num_name, den_name , **kwargs)
eff_MC = getEfficiency(datasetsMgr, dataset_MC, num_name, den_name, **kwargs)
xMin = 29.0
xMax = 125.0
# Apply Styles
styles.dataStyle.apply(eff_Data)
styles.mcStyle.apply(eff_MC)
# Marker Style
eff_Data.SetMarkerSize(1)
# Create Comparison Plot
p = plots.ComparisonPlot(histograms.HistoGraph(eff_Data, "eff_Data","p","P"),
histograms.HistoGraph(eff_MC, "eff_MC", "p", "P"))
opts = {"ymin": 0 , "ymax": 1.1, "xmin":xMin, "xMax":xMax}
opts2 = {"ymin": 0.7, "ymax": 1.3}
moveLegend = {"dx": -0.44, "dy": -0.57, "dh": -0.2 }
p.histoMgr.setHistoLegendLabelMany({"eff_Data": "Data", "eff_MC": "Simulation"})
createRatio = True #kwargs.get("ratio")
p.createFrame(saveName, createRatio=createRatio, opts=opts, opts2=opts2)
# Set Legend
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Set Log & Grid
SetLogAndGrid(p, **kwargs)
# Set Titles
p.getFrame().GetYaxis().SetTitle(kwargs.get("ylabel"))
p.getFrame().GetXaxis().SetTitle("p_{T} (Gev/c)")
if createRatio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# Draw
histograms.addText(0.30, 0.10, "Runs "+runRange, 17)
histograms.addText(0.30, 0.15, "2016", 17)
histograms.addStandardTexts(lumi=lumi)
p.draw()
# Save the canvas to a file
SaveAs(p, kwargs.get("savePath"), saveName, kwargs.get("saveFormats"), True)
PTSlices = ["40pt50","50pt60", "60pt70", "70pt90", "90pt120"]
for s in SigSel:
for psl in PTSlices:
num_name = "Num_ht_Vs_"+psl+"_HLT_PFHT350_"+s
den_name = "Den_ht_Vs_"+psl+"_HLT_PFHT350"
# Get the save path and name
plotName = "Eff_ht_Vs_"+psl+"_"+s
savePath, saveName = GetSavePathAndName(plotName, **kwargs)
# Get Efficiency Plots
eff_Data = getEfficiency(datasetsMgr, dataset_Data, num_name, den_name , **kwargs)
eff_MC = getEfficiency(datasetsMgr, dataset_MC, num_name, den_name, **kwargs)
xMin = 450
xMax = 2000
# Apply Styles
styles.dataStyle.apply(eff_Data)
styles.mcStyle.apply(eff_MC)
# Marker Style
eff_Data.SetMarkerSize(1)
# Create Comparison Plot
p = plots.ComparisonPlot(histograms.HistoGraph(eff_Data, "eff_Data","p","P"),
histograms.HistoGraph(eff_MC, "eff_MC", "p", "P"))
opts = {"ymin": 0 , "ymax": 1.1, "xmin":xMin, "xMax":xMax}
opts2 = {"ymin": 0.7, "ymax": 1.3}
moveLegend = {"dx": -0.44, "dy": -0.57, "dh": -0.2 }
p.histoMgr.setHistoLegendLabelMany({"eff_Data": "Data", "eff_MC": "Simulation"})
createRatio = True #kwargs.get("ratio")
#if eff_Data != None and eff_MC != None:
p.createFrame(saveName, createRatio=createRatio, opts=opts, opts2=opts2)
# Set Legend
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Set Log & Grid
SetLogAndGrid(p, **kwargs)
# Set Titles
p.getFrame().GetYaxis().SetTitle(kwargs.get("ylabel"))
p.getFrame().GetXaxis().SetTitle("H_{T} (Gev/c)")
if createRatio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# Draw
histograms.addText(0.30, 0.10, "Runs "+runRange, 17)
histograms.addText(0.30, 0.15, "2016", 17)
histograms.addStandardTexts(lumi=lumi)
p.draw()
# Save the canvas to a file
SaveAs(p, kwargs.get("savePath"), saveName, kwargs.get("saveFormats"), True)
'''
return
def addPhysicsText(histograms, limit, x=0.45, y=0.84, size=20):
'''
Add physics-process text on canvas
'''
# Add process text on canvas
histograms.addText(x, y+0.04, limit.processHeavyHtb, size=size)
# Add final-state text
histograms.addText(x, y, "fully hadronic final state", size=size)
if 0:
histograms.addText(x, y, "#tau_{h}+jets and #mu#tau_{h} final states", size=size)
histograms.addText(x, y, "#tau_{h}+jets final state", size=size)
histograms.addText(x, y, "#tau_{h}+jets, #mu#tau_{h}, ee, e#mu, #mu#mu final states", size=size)
if limit.BRassumption != "":
histograms.addText(x, y-0.05, limit.BRassumption, size=size)
return
def main(opts):
# Suppress warnings about weight being re-applied
ROOT.gErrorIgnoreLevel = ROOT.kError
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
else:
pass
optModes = optList
else:
optModes = [opts.optMode]
opts.optMode = ""
mcrabName = opts.mcrab
RunEra = mcrabName.split("_")[1]
# Setup ROOT and style
ROOT.gROOT.SetBatch(opts.batchMode)
style = tdrstyle.TDRStyle()
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
if opts.paper:
style.setGridX(False)
style.setGridY(False)
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
# Remove some QCD samples (the cross sections of these samples are not calculated)
if 0:
msg = "Removing following samples:"
Print(ShellStyles.ErrorStyle() + msg + ShellStyles.NormalStyle(), True)
for d in getDatasetsToExclude():
Print(d, False)
datasetsMgr.remove(d)
# Get run-range
minRunRange, maxRunRange, runRange = GetRunRange(datasetsMgr)
# Get int lumi
intLumi = GetLumi(datasetsMgr)
# Update to PU & load luminosities
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities()
#datasetsMgr.normalizeMCByLuminosity()
# Print luminisoties and cross-sections
datasetsMgr.PrintLuminosities()
datasetsMgr.PrintCrossSections()
# Default merging & ordering: "Data", "QCD", "SingleTop", "Diboson"
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get datasets
datasetsMgr.mergeMC()
dataset_Data = datasetsMgr.getDataDatasets()
dataset_MC = datasetsMgr.getMCDatasets()
# Define lists of Triggers to be plotted and Variables
xVars = ["pt6thJet", "eta6thJet", "phi6thJet", "Ht", "nBTagJets", "pu", "JetMulti", "BJetMulti"]
trgList = ["1BTag", "2BTag", "OR", "OR_PFJet450"]
if opts.fast:
trgList = ["OR_PFJet450"]
xVars = ["pt6thJet", "Ht"]
nPlots = len(trgList)*len(xVars)
counter = 0
# For-loop: All signal triggers
for i, trg in enumerate(trgList, 1):
# For-loop: All x-variables
for j, xVar in enumerate(xVars, 1):
counter+=1
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % counter, "/", "%s:" % (nPlots), "%s Vs %s" % (trg, xVar))
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), counter==1)
# Define names
hNumerator = "hNum_%s_RefTrg_OfflineSel_Signal%s" % (xVar, trg)
hDenominator = "hDen_%s_RefTrg_OfflineSel" % (xVar)
plotName = "Eff_%s_%s" % (xVar, trg)
# Get Efficiency Plots
_kwargs = GetHistoKwargs(xVar, opts)
eff_Data = GetEfficiency(datasetsMgr, dataset_Data, hNumerator, hDenominator , **_kwargs)
eff_MC = GetEfficiency(datasetsMgr, dataset_MC, hNumerator, hDenominator, **_kwargs)
# Apply Styles
styles.dataStyle.apply(eff_Data)
styles.mcStyle.apply(eff_MC)
# Create the plot
p = plots.ComparisonPlot(histograms.HistoGraph(eff_Data, "eff_Data", "p", "P"),
histograms.HistoGraph(eff_MC, "eff_MC" , "p", "P"),
saveFormats=[])
# Define the legend entries
p.histoMgr.setHistoLegendLabelMany(
{
"eff_Data": "Data",
"eff_MC" : "Simulation"
}
)
# Draw and save the plot
p.setLuminosity(intLumi)
plots.drawPlot(p, plotName, **_kwargs)
# Draw
histograms.addText(0.65, 0.06, "Runs "+ runRange, 17)
histograms.addText(0.65, 0.10, "2016", 17)
# Save the canvas to a file
SavePlot(p, plotName, os.path.join(opts.saveDir, opts.optMode), saveFormats=[".pdf", ".png", ".C"] )
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def doPlot(rootfile):
f = ROOT.TFile.Open(rootfile)
basename = rootfile.replace(".root", "")
match = name_re.search(rootfile)
if not match:
raise Exception("Assert: the file name regex did not match")
mass = match.group("mass")
name = match.group("name")
canvas = f.Get("c1")
primitives = canvas.GetListOfPrimitives()
# Suppress the warning message of missing dictionary for some iterator
backup = ROOT.gErrorIgnoreLevel
ROOT.gErrorIgnoreLevel = ROOT.kError
primiter = primitives.MakeIterator()
ROOT.gErrorIgnoreLevel = backup
graph = None
expoFit = None
lines = []
obj = primiter.Next()
while obj:
# print obj, obj.GetName()
if isinstance(obj, ROOT.TGraph):
if graph != None:
raise Exception("Assert: graph was already found")
graph = obj
elif isinstance(obj, ROOT.TF1):
if expoFit != None:
raise Exception("Assert: expoFit was already found")
expoFit = obj
elif isinstance(obj, ROOT.TLine):
lines.append(obj)
obj = primiter.Next()
if graph == None:
raise Exception("Assert: did not find any TGraph")
if expoFit == None:
raise Exception("Assert: did not find any TF1")
plot = plots.PlotBase(
[histograms.HistoGraph(graph, "CLs", drawStyle="PE")])
plot.createFrame(basename,
opts={
"xmin": 0,
"ymin": 0,
"ymaxfactor": 1.1,
"xmaxlist":
[0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.15, 0.2]
})
plot.frame.GetXaxis().SetTitle("#mu")
plot.frame.GetXaxis().SetNdivisions(1005)
plot.frame.GetYaxis().SetTitle("CL_{s}")
expoFit.SetLineColor(ROOT.kBlue - 9)
# skip the vertical non-solid lines
lines = filter(lambda l: l.GetLineStyle() == 1 or l.GetX1() != l.GetX2(),
lines)
plot.prependPlotObject(expoFit)
for l in lines:
plot.appendPlotObject(l)
plot.draw()
labelName = {
"observed": "Observed",
"median": "Expected median",
"1sigma": "Expected +1#sigma",
"2sigma": "Expected +2#sigma",
"-1sigma": "Expected .1#sigma",
"-2sigma": "Expected -2#sigma"
}[name]
size = 20
x = 0.62
histograms.addText(x,
0.85,
"m_{H^{+}} = %s %s" % (mass, limit.massUnit()),
size=size)
histograms.addText(x, 0.81, labelName, size=size)
plot.save()
f.Close()
f.Delete()
def PlotHistoGraphs(hGraphList, _kwargs):
# Create & draw the plot
p = plots.ComparisonManyPlot(hGraphList[0], hGraphList[1:], saveFormats=[])
p.setLuminosity(opts.intLumi)
if 0:
p.histoMgr.setHistoLegendStyleAll("P")
p.histoMgr.setHistoDrawStyleAll("LP")
p.histoMgr.setHistoDrawStyle(hGraphList[0].getRootHisto().GetName(),
"HIST")
if opts.saveName == None:
opts.saveName = hGraphList[0].getRootHisto().GetName().split(
"_")[0] + "VsCutflow"
# Draw the plot
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineWidth(3))
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetMarkerSize(1.3)) #1.2
if opts.acceptance:
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetMarkerStyle(ROOT.kFullCircle))
plots.drawPlot(p, opts.saveName, **_kwargs)
if not opts.acceptance:
p.getPad2().SetLogy(True)
# Add some text
if opts.refCounter == "passed PV":
xPos = [0.15, 0.24, 0.37, 0.50, 0.65, 0.77,
0.90] #does not work for every reference counter!
else:
xPos = [
0.15 + float(i) / (float(len(opts.binLabels)))
for i in range(0, len(opts.binLabels))
]
# For-loop: All bin labels
for i, b in enumerate(opts.binLabels, 0):
if opts.acceptance:
break
# Calculate coordinats
dx = 0.10 * (i)
dy = 0.0
if _kwargs["ratio"]:
dy = -0.4
# Basic text replacemenets to get compact bin text
bin = b.replace("passed ", "").replace(")", "").replace(
"selection", "").replace("(", "").replace("Selection",
"").replace("PV", "pv")
label = bin.replace("mu", "#mu").replace("Passed", "").replace(
"tau", "#tau").replace(" Veto",
"-veto").replace("Selected Events",
"selected")
label = label.replace("jet", "jets").replace("-", " ")
if 1:
#histograms.addText(0.16+dx, 0.08+dy, label, 19)
histograms.addText(xPos[i], 0.08 + dy, label, 19)
else:
# Automatic assignment (not perfect, yet!)
x = opts.xValues[i] / opts.xValues[-1]
xlow = opts.xErrLow[i] / opts.xErrLow[-1]
xhigh = opts.xErrHigh[i] / opts.xErrHigh[-1]
if i == 0:
x = 0.15
if i > 2:
x -= 0.05
if i == len(opts.binLabels) - 1:
x = 0.92
if not opts.acceptance:
histograms.addText(x, 0.08 + dy, label, 19)
# Save the plot
SavePlot(p,
opts.saveName,
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".C", ".png", ".pdf"])
return
def analyze(analysis=None):
paths = [sys.argv[1]]
if not analysis == None:
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis, includeOnlyTasks="Tau|TT")
datasets = dataset.getDatasetsFromMulticrabDirs(paths,
analysisName=analysis)
else:
datasets = dataset.getDatasetsFromMulticrabDirs(paths)
analysis = datasets.getAllDatasets()[0].getAnalysisName()
analysisList = []
# analysisList.append("METLeg_2016MET80_MET80")
# analysisList.append("METLeg_2016ICHEP_MET90")
# analysisList.append("METLeg_2016_MET90")
met_re = re.compile("_(?P<met>MET\d+)$")
if True:
#for analysis in analysisList:
met = "METX"
met_match = met_re.search(analysis)
if met_match:
met = met_match.group("met")
print "Analysis", analysis
datasets = dataset.getDatasetsFromMulticrabDirs(paths,
analysisName=analysis)
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="Tau\S+25ns$|TTJets$")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="Tau_Run2015C|Tau\S+25ns_Silver$|DYJetsToLL|WJetsToLNu$")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="Tau_Run2015D_PromptReco_v4_246908_260426_25ns$|DYJetsToLL_M_50$")
# datasetsMC = None
datasetsMC = dataset.getDatasetsFromMulticrabDirs(
paths, analysisName=analysis, excludeTasks="Tau_")
for d in datasets.getAllDatasets():
print d.getName()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
dataset2 = dataset1
createRatio = False
if isinstance(datasetsMC, dataset.DatasetManager):
dataset2 = datasetsMC.getMCDatasets()
createRatio = True
eff1_MET80 = getEfficiency(dataset1)
eff2_MET80 = getEfficiency(dataset2)
styles.dataStyle.apply(eff1_MET80)
eff1_MET80.SetMarkerSize(1)
if createRatio:
styles.mcStyle.apply(eff2_MET80)
eff2_MET80.SetMarkerSize(1.5)
if createRatio:
p = plots.ComparisonPlot(
histograms.HistoGraph(eff1_MET80, "eff1_MET80", "p", "P"),
histograms.HistoGraph(eff2_MET80, "eff2_MET80", "p", "P"))
else:
p = plots.PlotBase(
[histograms.HistoGraph(eff1_MET80, "eff1_MET80", "p", "P")])
#from plotTauLegEfficiency import fit
#fit("Data",p,eff1_MET80,20,300)
#fit("MC",p,eff2_MET80,20,300)
opts = {"ymin": 0, "ymax": 1.1}
opts2 = {"ymin": 0.5, "ymax": 1.5}
moveLegend = {"dx": -0.55, "dy": -0.15}
name = "TauMET_" + analysis + "_DataVsMC_PFMET"
legend1 = "Data"
# legend2 = "MC (TTJets)"
#legend2 = "MC"
legend2 = "Simulation"
if createRatio:
p.histoMgr.setHistoLegendLabelMany({
"eff1_MET80": legend1,
"eff2_MET80": legend2
})
p.createFrame(os.path.join(plotDir, name),
createRatio=True,
opts=opts,
opts2=opts2)
else:
p.histoMgr.setHistoLegendLabelMany({"eff1_MET80": legend1})
p.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
p.setLegend(
histograms.moveLegend(histograms.createLegend(y1=0.8),
**moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
if createRatio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# histograms.addText(0.2, 0.6, "LooseIsoPFTau50_Trk30_eta2p1_"+met, 17)
histograms.addText(0.4, 0.87, "LooseIsoPFTau50_Trk30_eta2p1_" + met,
17)
# histograms.addText(0.2, 0.53, analysis.split("_")[len(analysis.split("_")) -1], 17)
# label = analysis.split("_")[1]
# label = "2016"
label = string.replace(analysis, "METLeg_", "")
histograms.addText(0.2, 0.53, label, 17)
runRange = datasets.loadRunRange()
histograms.addText(0.2, 0.46, "Runs " + runRange, 17)
p.draw()
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity", d.getName(), d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum", lumi
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
pythonWriter.addParameters(plotDir, label, runRange, lumi, eff1_MET80)
pythonWriter.addMCParameters(label, eff2_MET80)
pythonWriter.writeJSON(
os.path.join(plotDir, "metLegTriggerEfficiency_" + label + ".json"))
#########################################################################
eff1phi = getEfficiency(dataset1, "NumeratorPhi", "DenominatorPhi")
eff2phi = getEfficiency(dataset2, "NumeratorPhi", "DenominatorPhi")
styles.dataStyle.apply(eff1phi)
styles.mcStyle.apply(eff2phi)
eff1phi.SetMarkerSize(1)
if isinstance(datasetsMC, dataset.DatasetManager):
p_phi = plots.ComparisonPlot(
histograms.HistoGraph(eff1phi, "eff1phi", "p", "P"),
histograms.HistoGraph(eff2phi, "eff2phi", "p", "P"))
else:
p_phi = plots.PlotBase(
[histograms.HistoGraph(eff1phi, "eff1phi", "p", "P")])
p_phi.histoMgr.setHistoLegendLabelMany({"eff1phi": legend1})
if isinstance(datasetsMC, dataset.DatasetManager):
p_phi.histoMgr.setHistoLegendLabelMany({
"eff1phi": legend1,
"eff2phi": legend2
})
opts = {"ymin": 0, "ymax": 0.3}
name = "TauMET_" + analysis + "_DataVsMC_PFMETPhi"
if createRatio:
p_phi.createFrame(os.path.join(plotDir, name),
createRatio=createRatio,
opts=opts,
opts2=opts2)
else:
p_phi.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
# moveLegendPhi = {"dx": -0.5, "dy": -0.6, "dh": -0.1}
moveLegendPhi = {"dx": -0.55, "dy": -0.15, "dh": -0.1}
p_phi.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegendPhi))
p_phi.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p_phi.getFrame().GetXaxis().SetTitle("MET Type 1 #phi")
if createRatio:
p_phi.getFrame2().GetYaxis().SetTitle("Ratio")
p_phi.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.4, 0.86, "LooseIsoPFTau50_Trk30_eta2p1_" + met, 17)
# histograms.addText(0.4, 0.78, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.4, 0.78, "2016", 17)
histograms.addText(0.4, 0.71, "Runs " + datasets.loadRunRange(), 17)
p_phi.draw()
histograms.addStandardTexts(lumi=lumi)
p_phi.save(formats)
#########################################################################
"""
#### MET120
analysis = "METLeg_2015CD_MET120"
datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis)
for d in datasets.getAllDatasets():
print d.getName()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
dataset2 = datasets.getMCDatasets()
eff1_MET120 = getEfficiency(dataset1)
eff2_MET120 = getEfficiency(dataset2)
styles.dataStyle.apply(eff1_MET120)
styles.mcStyle.apply(eff2_MET120)
eff1_MET120.SetMarkerSize(1)
eff2_MET120.SetMarkerSize(1.5)
p = plots.ComparisonPlot(histograms.HistoGraph(eff1_MET120, "eff1_MET120", "p", "P"),
histograms.HistoGraph(eff2_MET120, "eff2_MET120", "p", "P"))
opts = {"ymin": 0, "ymax": 1.1}
opts2 = {"ymin": 0.5, "ymax": 1.5}
moveLegend = {"dx": -0.55, "dy": -0.15}
name = "DataVsMC_L1HLTMET_PFMET_MET120"
legend1 = "Data"
legend2 = "MC"
p.histoMgr.setHistoLegendLabelMany({"eff1_MET120": legend1, "eff2_MET120": legend2})
p.createFrame(os.path.join(plotDir, name), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
lumi = 0.0
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
"""
# CaloMET
"""
#### MET80
analysisc = "METLeg_2016B_CaloMET_MET80"
datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc)
datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc,excludeTasks="Tau\S+25ns_Silver$")
# datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc,includeOnlyTasks="Tau\S+25ns$|TTJets$")
style = tdrstyle.TDRStyle()
dataset1c = datasetsc.getDataDatasets()
dataset2c = datasetsc.getMCDatasets()
# eff1c_MET80 = getEfficiency(dataset1c)
eff2c_MET80 = getEfficiency(dataset2c)
# styles.dataStyle.apply(eff1c_MET80)
styles.mcStyle.apply(eff2c_MET80)
# eff1c_MET80.SetMarkerSize(1)
eff2c_MET80.SetMarkerSize(1.5)
eff2c_MET80.SetMarkerColor(4)
p = plots.ComparisonPlot(histograms.HistoGraph(eff2_MET80, "eff2_MET80", "p", "P"),
histograms.HistoGraph(eff2c_MET80, "eff2c_MET80", "p", "P"))
namec = "TauMET_"+analysis+"_MC_TrgBitVsCaloMET80_PFMET"
legend1c = "MC, trigger bit"
legend2c = "MC, CaloMET > 80"
p.histoMgr.setHistoLegendLabelMany({"eff2_MET80": legend1c, "eff2c_MET80": legend2c})
p.createFrame(os.path.join(plotDir, namec), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity",d.getName(),d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum",lumi
histograms.addStandardTexts(lumi=lumi)
histograms.addText(0.2, 0.6, "LooseIsoPFTau50_Trk30_eta2p1_MET80", 17)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
"""
"""
#### MET120
analysisc = "METLeg_2015A_CaloMET_MET120"
datasetsc = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysisc)
style = tdrstyle.TDRStyle()
dataset1c = datasetsc.getDataDatasets()
dataset2c = datasetsc.getMCDatasets()
eff1c_MET120 = getEfficiency(dataset1c)
eff2c_MET120 = getEfficiency(dataset2c)
styles.dataStyle.apply(eff1c_MET120)
styles.mcStyle.apply(eff1c_MET120)
eff1c_MET120.SetMarkerSize(1)
eff2c_MET120.SetMarkerSize(1.5)
p = plots.ComparisonPlot(histograms.HistoGraph(eff2_MET120, "eff2_MET120", "p", "P"),
histograms.HistoGraph(eff2c_MET120, "eff2c_MET120", "p", "P"))
namec = "MC_TrgBitVsCaloMET120_L1HLTMET_PFMET"
legend1c = "MC, trigger bit"
legend2c = "MC, CaloMET > 120"
p.histoMgr.setHistoLegendLabelMany({"eff2_MET120": legend1c, "eff2c_MET120": legend2c})
p.createFrame(os.path.join(plotDir, namec), createRatio=True, opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.8), **moveLegend))
p.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
p.getFrame().GetXaxis().SetTitle("MET Type 1 (GeV)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
lumi = 0.0
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
"""
#########################################################################
namePU = "TauMET_" + analysis + "_DataVsMC_nVtx"
eff1PU = getEfficiency(dataset1, "NumeratorPU", "DenominatorPU")
eff2PU = getEfficiency(dataset2, "NumeratorPU", "DenominatorPU")
styles.dataStyle.apply(eff1PU)
styles.mcStyle.apply(eff2PU)
eff1PU.SetMarkerSize(1)
eff2PU.SetMarkerSize(1.5)
if isinstance(datasetsMC, dataset.DatasetManager):
pPU = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1PU, "eff1", "p", "P"),
[histograms.HistoGraph(eff2PU, "eff2", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({
"eff1": legend1,
"eff2": legend2
})
else:
pPU = plots.PlotBase([histograms.HistoGraph(eff1PU, "eff1", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
opts = {"ymin": 0.001, "ymax": 1.0}
moveLegend = {"dx": -0.33, "dy": -0.25, "dh": -0.1}
createRatio = False
if createRatio:
pPU.createFrame(os.path.join(plotDir, namePU),
createRatio=True,
opts=opts,
opts2=opts2)
else:
pPU.createFrame(os.path.join(plotDir, namePU), opts=opts, opts2=opts2)
pPU.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
# if createRatio:
# pPU.getPad1().SetLogy(True)
# else:
# pPU.getPad().SetLogy(True)
pPU.getFrame().GetYaxis().SetTitle("L1+HLT MET efficiency")
pPU.getFrame().GetXaxis().SetTitle("Number of reco vertices")
if createRatio:
pPU.getFrame2().GetYaxis().SetTitle("Ratio")
pPU.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.4, 0.85, "LooseIsoPFTau50_Trk30_eta2p1_MET90", 17)
# histograms.addText(0.4, 0.78, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.4, 0.78, "2016", 17)
histograms.addText(0.4, 0.71, "Runs " + datasets.loadRunRange(), 17)
pPU.draw()
histograms.addStandardTexts(lumi=lumi)
pPU.save(formats)
print "Output written in", plotDir
def doPlot(name, graphs, limits, xlabel, scenario, mass):
higgs = "h"
if "lowMH" in scenario:
higgs = "H"
excluded = graphs["muexcluded"]
limit.setExcludedStyle(excluded)
excluded.SetFillStyle(1001)
excluded.SetLineWidth(0)
excluded.SetLineStyle(0)
excluded.SetLineColor(ROOT.kWhite)
excludedCopy = excluded.Clone()
if not mass in [90]:
excludedCopy.SetFillColorAlpha(
ROOT.kWhite,
0.0) # actual color doesn't matter, want fully transparent
# else:
# excluded.SetLineColor(ROOT.kBlack)
# Uncomment when we have allowed
for n in ["Allowed", "Allowed2"]:
a = graphs[n]
if a is None:
continue
a.SetFillStyle(3005)
a.SetFillColor(ROOT.kRed)
a.SetLineWidth(-302)
a.SetLineColor(ROOT.kRed)
a.SetLineStyle(1)
legend_dh = 0
grs = []
if "observed" in graphs:
grs.extend([
histograms.HistoGraph(graphs["observed"],
"Observed",
drawStyle="L",
legendStyle="l"),
histograms.HistoGraph(graphs["obs_th_plus"],
"ObservedPlus",
drawStyle="L",
legendStyle="l"),
histograms.HistoGraph(graphs["obs_th_minus"],
"ObservedMinus",
drawStyle="L",
legendStyle=None),
])
legend_dh = 0.1
grs.extend([
histograms.HistoGraph(excluded, "Excluded", drawStyle="F"),
histograms.HistoGraph(excludedCopy,
"ExcludedCopy",
drawStyle=None,
legendStyle="f"),
histograms.HistoGraph(graphs["Allowed"],
"Allowed",
drawStyle="L",
legendStyle="lf"),
])
if graphs["Allowed2"] is not None:
grs.append(
histograms.HistoGraph(graphs["Allowed2"],
"Allowed2",
drawStyle="L",
legendStyle=None))
plot = plots.PlotBase(grs, saveFormats=[".png", ".pdf", ".C"])
plot.histoMgr.setHistoLegendLabelMany({
"ExcludedCopy": "Excluded",
"Allowed": "m_{" + higgs + "}^{MSSM} #neq 125#pm3 GeV",
"Excluded": None,
})
if "observed" in graphs:
plot.histoMgr.setHistoLegendLabelMany({
"ObservedPlus":
"Observed #pm1#sigma (th.)",
})
textPos = "left"
dx = 0
dy = -0.15
if mass in [90, 150]:
textPos = "right"
dx = 0.35
if mass in [155, 160]:
textPos = "right"
dy = -0.02
plot.setLegend(
histograms.createLegend(0.19 + dx, 0.75 + dy - legend_dh, 0.57 + dx,
0.80 + dy))
histograms.moveLegend(plot.legend, dh=0.05, dy=-0.05)
#plot.legend.SetFillColor(0)
#plot.legend.SetFillStyle(1001)
name = name.replace("-", "_")
plot.createFrame(name,
opts={
"ymin": 0,
"ymax": tanbMax,
"xmin": 200,
"xmax": 3300
})
plot.frame.GetXaxis().SetTitle(xlabel)
plot.frame.GetYaxis().SetTitle(limit.tanblimit)
plot.draw()
plot.setLuminosity(limits.getLuminosity())
plot.addStandardTexts(cmsTextPosition=textPos)
size = 20
x = 0.2 + dx
histograms.addText(x, 0.9 + dy, limit.process, size=size)
histograms.addText(x, 0.863 + dy, limits.getFinalstateText(), size=size)
histograms.addText(x,
0.815 + dy,
limit.getTypesetScenarioName(scenario.replace(
"_mu", "")),
size=size)
histograms.addText(x, 0.767 + dy, "m_{H^{+}}=%d GeV" % mass, size=size)
# histograms.addText(0.2, 0.231, "Min "+limit.BR+"(t#rightarrowH^{+}b)#times"+limit.BR+"(H^{+}#rightarrow#tau#nu)", size=0.5*size)
#Adding a LHC label:
# ROOT.LHCHIGGS_LABEL(0.97,0.72,1)
# FH_version = db.getVersion("FeynHiggs")
# histograms.addText(x, 0.55, FH_version)
# HD_version = db.getVersion("HDECAY")
# histograms.addText(x, 0.55, FH_version+" and "+HD_version, size=size)
# histograms.addText(x, 0.48, "Derived from", size=size)
# histograms.addText(x, 0.43, "CMS HIG-12-052", size=size)
plot.save()
print "Created", name
def analyze(analysis=None):
paths = [sys.argv[1]]
if not analysis == None:
datasets = dataset.getDatasetsFromMulticrabDirs(
paths,
analysisName=analysis,
excludeTasks="Silver|GluGluHToTauTau_M125")
else:
datasets = dataset.getDatasetsFromMulticrabDirs(
paths, excludeTasks="Silver|GluGluHToTauTau_M125")
analysis = datasets.getAllDatasets()[0].getAnalysisName()
# datasetsDY = None
datasetsDY = dataset.getDatasetsFromMulticrabDirs(
paths, analysisName=analysis, includeOnlyTasks="DYJetsToLL")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="GluGluHToTauTau_M125|TTJets")
datasetsH125 = None
# datasetsH125 = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="GluGluHToTauTau_M125",emptyDatasetsAsNone=True)
# datasetsH125 = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="GluGluHToTauTau_M125")
datasets.loadLuminosities()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
dataset2 = dataset1
# dataset2 = datasets.getMCDatasets()
if not datasetsDY == None:
dataset2 = datasetsDY.getMCDatasets()
eff1 = getEfficiency(dataset1)
eff2 = getEfficiency(dataset2)
if isinstance(datasetsH125, dataset.DatasetManager):
eff3 = getEfficiency(datasetsH125.getMCDatasets())
styles.dataStyle.apply(eff1)
styles.mcStyle.apply(eff2)
eff1.SetMarkerSize(1)
# eff2.SetMarkerSize(1.5)
if isinstance(datasetsH125, dataset.DatasetManager):
styles.mcStyle.apply(eff3)
eff3.SetMarkerSize(1.5)
eff3.SetMarkerColor(4)
eff3.SetLineColor(4)
# p = plots.ComparisonPlot(histograms.HistoGraph(eff1, "eff1", "p", "P"),
# histograms.HistoGraph(eff2, "eff2", "p", "P"))
if isinstance(datasetsH125, dataset.DatasetManager):
p = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1, "eff1", "p", "P"), [
histograms.HistoGraph(eff2, "eff2", "p", "P"),
histograms.HistoGraph(eff3, "eff3", "p", "P")
])
elif isinstance(datasetsDY, dataset.DatasetManager):
p = plots.ComparisonPlot(histograms.HistoGraph(eff1, "eff1", "p", "P"),
histograms.HistoGraph(eff2, "eff2", "p", "P"))
else:
p = plots.PlotBase([histograms.HistoGraph(eff1, "eff1", "p", "P")])
fit("Data", p, eff1, 20, 200)
fit("MC", p, eff2, 20, 200)
if isinstance(datasetsH125, dataset.DatasetManager):
fit("H125", p, eff3, 20, 200)
opts = {"ymin": 0, "ymax": 1.1}
opts2 = {"ymin": 0.5, "ymax": 1.5}
# moveLegend = {"dx": -0.55, "dy": -0.15, "dh": -0.1}
moveLegend = {"dx": -0.2, "dy": -0.5, "dh": -0.1}
name = "TauMET_" + analysis + "_DataVsMC_PFTauPt"
legend1 = "Data"
# legend2 = "MC (DY)"
legend2 = "Simulation"
legend3 = "MC (H125)"
createRatio = False
p.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
if isinstance(datasetsDY, dataset.DatasetManager):
p.histoMgr.setHistoLegendLabelMany({"eff1": legend1, "eff2": legend2})
createRatio = True
if isinstance(datasetsH125, dataset.DatasetManager):
p.histoMgr.setHistoLegendLabelMany({
"eff1": legend1,
"eff2": legend2,
"eff3": legend3
})
if createRatio:
p.createFrame(os.path.join(plotDir, name),
createRatio=createRatio,
opts=opts,
opts2=opts2)
else:
p.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
p.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
# p.getFrame().GetXaxis().SetTitle("#tau-jet p_{T} (GeV/c)")
p.getFrame().GetXaxis().SetTitle("#tau_{h} p_{T} (GeV/c)")
if createRatio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.5, 0.6, "LooseIsoPFTau50_Trk30_eta2p1", 17)
# label = analysis.split("_")[len(analysis.split("_")) -1]
label = "2016"
histograms.addText(0.5, 0.53, label, 17)
runRange = datasets.loadRunRange()
histograms.addText(0.5, 0.46, "Runs " + runRange, 17)
p.draw()
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity", d.getName(), d.getLuminosity()
lumi += d.getLuminosity()
print "luminosity, sum", lumi
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
pythonWriter.addParameters(plotDir, label, runRange, lumi, eff1)
pythonWriter.addMCParameters(label, eff2)
pythonWriter.writeJSON(
os.path.join(plotDir, "tauLegTriggerEfficiency_" + label + ".json"))
# if not createRatio:
# sys.exit()
#########################################################################
eff1eta = getEfficiency(dataset1, "NumeratorEta", "DenominatorEta")
eff2eta = getEfficiency(dataset2, "NumeratorEta", "DenominatorEta")
if isinstance(datasetsH125, dataset.DatasetManager):
eff3eta = getEfficiency(datasetsH125.getMCDatasets(), "NumeratorEta",
"DenominatorEta")
styles.dataStyle.apply(eff1eta)
styles.mcStyle.apply(eff2eta)
eff1eta.SetMarkerSize(1)
if isinstance(datasetsH125, dataset.DatasetManager):
styles.mcStyle.apply(eff3eta)
eff3eta.SetMarkerSize(1.5)
eff3eta.SetMarkerColor(4)
eff3eta.SetLineColor(4)
if isinstance(datasetsH125, dataset.DatasetManager):
p_eta = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1eta, "eff1eta", "p", "P"), [
histograms.HistoGraph(eff2eta, "eff2eta", "p", "P"),
histograms.HistoGraph(eff3eta, "eff3eta", "p", "P")
])
elif isinstance(datasetsDY, dataset.DatasetManager):
p_eta = plots.ComparisonPlot(
histograms.HistoGraph(eff1eta, "eff1eta", "p", "P"),
histograms.HistoGraph(eff2eta, "eff2eta", "p", "P"))
else:
p_eta = plots.PlotBase(
[histograms.HistoGraph(eff1eta, "eff1eta", "p", "P")])
p_eta.histoMgr.setHistoLegendLabelMany({"eff1eta": legend1})
if isinstance(datasetsDY, dataset.DatasetManager):
p_eta.histoMgr.setHistoLegendLabelMany({
"eff1eta": legend1,
"eff2eta": legend2
})
if isinstance(datasetsH125, dataset.DatasetManager):
p_eta.histoMgr.setHistoLegendLabelMany({
"eff1eta": legend1,
"eff2eta": legend2,
"eff3eta": legend3
})
name = "TauMET_" + analysis + "_DataVsMC_PFTauEta"
if createRatio:
p_eta.createFrame(os.path.join(plotDir, name),
createRatio=createRatio,
opts=opts,
opts2=opts2)
else:
p_eta.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
moveLegendEta = {"dx": -0.5, "dy": -0.65, "dh": -0.1}
p_eta.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegendEta))
p_eta.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
p_eta.getFrame().GetXaxis().SetTitle("#tau-jet #eta")
if createRatio:
p_eta.getFrame2().GetYaxis().SetTitle("Ratio")
p_eta.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.46, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.38, label, 17)
histograms.addText(0.2, 0.31, "Runs " + datasets.loadRunRange(), 17)
p_eta.draw()
histograms.addStandardTexts(lumi=lumi)
p_eta.save(formats)
#########################################################################
eff1phi = getEfficiency(dataset1, "NumeratorPhi", "DenominatorPhi")
eff2phi = getEfficiency(dataset2, "NumeratorPhi", "DenominatorPhi")
if isinstance(datasetsH125, dataset.DatasetManager):
eff3phi = getEfficiency(datasetsH125.getMCDatasets(), "NumeratorPhi",
"DenominatorPhi")
styles.dataStyle.apply(eff1phi)
styles.mcStyle.apply(eff2phi)
eff1phi.SetMarkerSize(1)
if isinstance(datasetsH125, dataset.DatasetManager):
styles.mcStyle.apply(eff3phi)
eff3phi.SetMarkerSize(1.5)
eff3phi.SetMarkerColor(4)
eff3phi.SetLineColor(4)
if isinstance(datasetsH125, dataset.DatasetManager):
p_phi = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1phi, "eff1phi", "p", "P"), [
histograms.HistoGraph(eff2phi, "eff2phi", "p", "P"),
histograms.HistoGraph(eff3phi, "eff3phi", "p", "P")
])
elif isinstance(datasetsDY, dataset.DatasetManager):
p_phi = plots.ComparisonPlot(
histograms.HistoGraph(eff1phi, "eff1phi", "p", "P"),
histograms.HistoGraph(eff2phi, "eff2phi", "p", "P"))
else:
p_phi = plots.PlotBase(
[histograms.HistoGraph(eff1phi, "eff1phi", "p", "P")])
p_phi.histoMgr.setHistoLegendLabelMany({"eff1phi": legend1})
if isinstance(datasetsDY, dataset.DatasetManager):
p_phi.histoMgr.setHistoLegendLabelMany({
"eff1phi": legend1,
"eff2phi": legend2
})
if isinstance(datasetsH125, dataset.DatasetManager):
p_phi.histoMgr.setHistoLegendLabelMany({
"eff1phi": legend1,
"eff2phi": legend2,
"eff3phi": legend3
})
name = "TauMET_" + analysis + "_DataVsMC_PFTauPhi"
if createRatio:
p_phi.createFrame(os.path.join(plotDir, name),
createRatio=createRatio,
opts=opts,
opts2=opts2)
else:
p_phi.createFrame(os.path.join(plotDir, name), opts=opts, opts2=opts2)
moveLegendPhi = {"dx": -0.5, "dy": -0.65, "dh": -0.1}
p_phi.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegendPhi))
p_phi.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
p_phi.getFrame().GetXaxis().SetTitle("#tau-jet #phi")
if createRatio:
p_phi.getFrame2().GetYaxis().SetTitle("Ratio")
p_phi.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.46, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.38, label, 17)
histograms.addText(0.2, 0.31, "Runs " + datasets.loadRunRange(), 17)
p_phi.draw()
histograms.addStandardTexts(lumi=lumi)
p_phi.save(formats)
#########################################################################
namePU = "TauMET_" + analysis + "_DataVsMC_nVtx"
eff1PU = getEfficiency(dataset1, "NumeratorPU", "DenominatorPU")
eff2PU = getEfficiency(dataset2, "NumeratorPU", "DenominatorPU")
styles.dataStyle.apply(eff1PU)
styles.mcStyle.apply(eff2PU)
eff1PU.SetMarkerSize(1)
eff2PU.SetMarkerSize(1.5)
if isinstance(datasetsDY, dataset.DatasetManager):
pPU = plots.ComparisonManyPlot(
histograms.HistoGraph(eff1PU, "eff1", "p", "P"),
[histograms.HistoGraph(eff2PU, "eff2", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({
"eff1": legend1,
"eff2": legend2
})
else:
pPU = plots.PlotBase([histograms.HistoGraph(eff1PU, "eff1", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
optsPU = {"ymin": 0.01, "ymax": 1.0}
createRatio = False
if createRatio:
pPU.createFrame(os.path.join(plotDir, namePU),
createRatio=True,
opts=optsPU,
opts2=opts2)
else:
pPU.createFrame(os.path.join(plotDir, namePU),
opts=optsPU,
opts2=opts2)
moveLegend = {"dx": -0.5, "dy": -0.5, "dh": -0.1}
pPU.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
# if createRatio:
# pPU.getPad1().SetLogy(True)
# else:
# pPU.getPad().SetLogy(True)
pPU.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
pPU.getFrame().GetXaxis().SetTitle("Number of reco vertices")
if createRatio:
pPU.getFrame2().GetYaxis().SetTitle("Ratio")
pPU.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.6, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.53, label, 17)
histograms.addText(0.2, 0.46, "Runs " + datasets.loadRunRange(), 17)
pPU.draw()
histograms.addStandardTexts(lumi=lumi)
pPU.save(formats)
#########################################################################
"""
hName = "Pull"
# hName = "Sub"
namePull = "TauMET_"+analysis+"_DataVsMC_"+hName+"s"
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge("MC", ["TT","WJets","DYJetsToLL","SingleTop","QCD"], keepSources=True)
drh1 = datasets.getDataset("Data").getDatasetRootHisto(hName)
drh2 = datasets.getDataset("MC").getDatasetRootHisto(hName)
drh1.normalizeToOne()
drh2.normalizeToOne()
pull1 = drh1.getHistogram()
pull2 = drh2.getHistogram()
if isinstance(datasetsH125,dataset.DatasetManager):
plots.mergeRenameReorderForDataMC(datasetsH125)
drh3 = datasetsH125.getMCDatasets()[0].getDatasetRootHisto(hName)
drh3.normalizeToOne()
pull3 = drh3.getHistogram()
styles.dataStyle.apply(pull1)
styles.mcStyle.apply(pull2)
pull1.SetMarkerSize(1)
if isinstance(datasetsH125,dataset.DatasetManager):
styles.mcStyle.apply(pull3)
pull3.SetMarkerSize(1.5)
pull3.SetMarkerColor(4)
pull3.SetLineColor(4)
if isinstance(datasetsH125,dataset.DatasetManager):
p_pull = plots.ComparisonManyPlot(histograms.Histo(pull1, "pull1", "p", "P"),
[histograms.Histo(pull2, "pull2", "p", "P"),
histograms.Histo(pull3, "pull3", "p", "P")])
else:
p_pull = plots.ComparisonPlot(histograms.Histo(pull1, "pull1", "p", "P"),
histograms.Histo(pull2, "pull2", "p", "P"))
p_pull.histoMgr.setHistoLegendLabelMany({"pull1": legend1, "pull2": legend2})
if isinstance(datasetsH125,dataset.DatasetManager):
p_pull.histoMgr.setHistoLegendLabelMany({"pull1": legend1, "pull2": legend2, "pull3": legend3})
p_pull.createFrame(os.path.join(plotDir, namePull), createRatio=True, opts=opts, opts2=opts2)
moveLegendPull = {"dx": -0.5, "dy": -0.35, "dh": -0.1}
p_pull.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegendPull))
p_pull.getFrame().GetYaxis().SetTitle("Arbitrary units")
# p_pull.getFrame().GetXaxis().SetTitle("HLT #tau p_{T} - #tau-jet p_{T} (GeV/c)")
p_pull.getFrame().GetXaxis().SetTitle("HLT #tau p_{T}/ #tau-jet p_{T} - 1")
p_pull.getFrame2().GetYaxis().SetTitle("Ratio")
p_pull.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.2, 0.75, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.2, 0.68, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.2, 0.61, "Runs "+runRange, 17)
p_pull.draw()
histograms.addStandardTexts(lumi=lumi)
p_pull.save(formats)
"""
#########################################################################
print "Output written in", plotDir
def doLimitError(limits, unblindedStatus):
expRelErrors = []
expLabels = {}
obsRelErrors = []
obsLabels = {}
order = [0, 1, -1, 2, -2]
expErrors = [limits.expectedErrorGraph(sigma=s) for s in order]
if expErrors[0] != None:
exps = [limits.expectedGraph(sigma=s) for s in order]
expRelErrors = [(limit.divideGraph(expErrors[i],
exps[i]), "ExpRelErr%d" % i)
for i in xrange(len(exps))]
expLabels = {
"ExpRelErr0": "Expected median",
"ExpRelErr1": "Expected +1#sigma",
"ExpRelErr2": "Expected -1#sigma",
"ExpRelErr3": "Expected +2#sigma",
"ExpRelErr4": "Expected -2#sigma",
}
if unblindedStatus:
obsErr = limits.observedErrorGraph()
if obsErr != None:
obs = limits.observedGraph()
if obs != None:
obsRelErrors = [(limit.divideGraph(obsErr, obs), "ObsRelErr")]
obsLabels = {"ObsRelErr": "Observed"}
if len(expRelErrors) == 0 and len(obsRelErrors) == 0:
return
# Create the plot
plot = plots.PlotBase()
if len(expRelErrors) > 0:
plot.histoMgr.extendHistos([
histograms.HistoGraph(x[0], x[1], drawStyle="PL", legendStyle="lp")
for x in expRelErrors
])
plot.histoMgr.forEachHisto(styles.generator())
def sty(h):
r = h.getRootHisto()
r.SetLineStyle(1)
r.SetLineWidth(3)
r.SetMarkerSize(1.4)
plot.histoMgr.forEachHisto(sty)
plot.histoMgr.setHistoLegendLabelMany(expLabels)
if unblindedStatus:
if len(obsRelErrors) > 0:
obsRelErrors[0][0].SetMarkerSize(1.4)
obsRelErrors[0][0].SetMarkerStyle(25)
plot.histoMgr.insertHisto(
0,
histograms.HistoGraph(obsRelErrors[0][0],
obsRelErrors[0][1],
drawStyle="PL",
legendStyle="lp"))
plot.histoMgr.setHistoLegendLabelMany(obsLabels)
plot.setLegend(
histograms.moveLegend(histograms.createLegend(0.48, 0.75, 0.85, 0.92),
dx=0.1,
dy=-0.1))
if len(limits.mass) == 1:
plot.createFrame("limitsBrRelativeUncertainty",
opts={
"xmin": limits.mass[0] - 5.0,
"xmax": limits.mass[0] + 5.0,
"ymin": 0,
"ymaxfactor": 1.5
})
else:
plot.createFrame("limitsBrRelativeUncertainty",
opts={
"ymin": 0,
"ymaxfactor": 1.5
})
plot.frame.GetXaxis().SetTitle(limit.mHplus())
plot.frame.GetYaxis().SetTitle("Uncertainty/limit")
plot.draw()
plot.setLuminosity(limits.getLuminosity())
plot.addStandardTexts()
size = 20
x = 0.2
histograms.addText(x, 0.88, limit.process, size=size)
histograms.addText(x, 0.84, limits.getFinalstateText(), size=size)
histograms.addText(x, 0.79, limit.BRassumption, size=size)
size = 22
x = 0.55
histograms.addText(x, 0.88, "Toy MC relative", size=size)
histograms.addText(x, 0.84, "statistical uncertainty", size=size)
plot.save()
def Plot2d(datasetsMgr, dataset, histoName, kwargs, opts):
'''
'''
# Definitions
saveName = histoName.replace("/", "_")
# Get Histos for the plotter object
refHisto = GetHisto(datasetsMgr, dataset, histoName)
# Create the plotting object
p = plots.PlotBase(datasetRootHistos=[refHisto], saveFormats=kwargs.get("saveFormats"))
if 0:
p.histoMgr.normalizeMCToLuminosity(opts.intLumi)
# Customise axes (before drawing histo)
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinX(kwargs.get("rebinX")))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinY(kwargs.get("rebinY")))
if 0:
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetXaxis().SetTitle(kwargs.get("xlabel")))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetYaxis().SetTitle(kwargs.get("ylabel")))
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetXaxis().SetTitle( h.getRootHisto().GetXaxis().GetTitle() + "%0.1f" ))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetXaxis().SetRangeUser(kwargs.get("xmin"), kwargs.get("xmax")))
#p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetYaxis().SetRangeUser(kwargs.get("ymin"), kwargs.get("ymax")))
#p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetYaxis().SetRangeUser(kwargs.get("ymin"), kwargs.get("ymax")))
#p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetZaxis().SetRangeUser(kwargs.get("zmin"), kwargs.get("zmax")))
# Create a frame
fOpts = {"ymin": 0.0, "ymaxfactor": 1.0}
p.createFrame(saveName, opts=fOpts)
# SetLog
SetLogAndGrid(p, **kwargs)
# Add cut line/box
if 1:
_kwargs = { "lessThan": True}
p.addCutBoxAndLine(cutValue=400.0, fillColor=ROOT.kGray, box=False, line=True, **_kwargs)
p.addCutBoxAndLineY(cutValue=200.0, fillColor=ROOT.kGray, box=False, line=True, **_kwargs)
# Customise Legend
moveLegend = {"dx": -0.1, "dy": +0.0, "dh": -0.1}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **kwargs.get("moveLegend")))
p.removeLegend()
# Customise histogram (after frame is created)
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetZaxis().SetTitle("Events"))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetZaxis().SetTitleOffset(1.4))
#ROOT.gPad.RedrawAxis()
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMinimum(kwargs.get("zmin")))
#p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMaximum(kwargs.get("zmax")))
# Drawing style
p.histoMgr.setHistoDrawStyle(dataset, "COLZ")
p.histoMgr.setHistoDrawStyleAll("COLZ")
# The lines below do nothing since the Legend is disabled
if 0:
p.histoMgr.setHistoLegendStyle(dataset, "F")
if "FakeB" in dataset:
p.histoMgr.setHistoLegendLabelMany({
dataset: "QCD (Data)",
})
if dataset == "QCD":
p.histoMgr.setHistoLegendLabelMany({
dataset: "QCD (MC)",
})
# Draw the plot
p.draw()
# Add canvas text
if kwargs.get("addLuminosityText"):
histograms.addStandardTexts(lumi=opts.intLumi)
else:
histograms.addStandardTexts()
# Add dataset name on the canvas
if dataset == "QCD":
histograms.addText(0.17, 0.88, "QCD (H_{T} binned)", 27)
else:
histograms.addText(0.17, 0.88, plots._legendLabels[dataset], 27)
# Save the plots
SavePlot(p, saveName, os.path.join(opts.saveDir, opts.analysisName, opts.folder, dataset, opts.optMode), [".png", ".pdf"] )
return
def main(opts):
style = tdrstyle.TDRStyle()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(opts.batchMode)
# Get all datasets from the mcrab dir
# def GetDatasetsFromDir(mcrab, opts, **kwargs): #iro
datasetsMgr = GetDatasetsFromDir(opts.mcrab, opts,
**kwargs) #kwargs.get("analysis"))
# Determine Integrated Luminosity (If Data datasets present)
intLumi = GetLumi(datasetsMgr)
# Update to PU
datasetsMgr.updateNAllEventsToPUWeighted()
# Remove datasets
datasetsMgr.remove(kwargs.get("rmDataset"))
# datasetsMgr.remove(filter(lambda name: not "QCD" in name, datasetsMgr.getAllDatasetNames()))
# datasetsMgr.remove(filter(lambda name: "QCD" in name in name, datasetsMgr.getAllDatasetNames()))
# Set custom XSections
#datasetsMgr.getDataset("QCD_bEnriched_HT1000to1500").setCrossSection(1.0)
#datasetsMgr.getDataset("QCD_bEnriched_HT1500to2000").setCrossSection(1.0)
#datasetsMgr.getDataset("QCD_bEnriched_HT2000toInf").setCrossSection(1.0)
#datasetsMgr.getDataset("QCD_bEnriched_HT300to500").setCrossSection(1.0)
#datasetsMgr.getDataset("QCD_bEnriched_HT500to700").setCrossSection(1.0)
#datasetsMgr.getDataset("QCD_bEnriched_HT700to1000").setCrossSection(1.0)
# Default merging & ordering: "Data", "QCD", "SingleTop", "Diboson"
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Remove datasets (for merged names)
datasetsMgr.remove(kwargs.get("rmDataset"))
# Print the cross
datasetsMgr.PrintCrossSections()
for h_prefix in ["reco", "gen"]:
# Get ref histo here and the fixed histos (TT, QCD, QCD-b
inclusiveHisto, fixedHistos = GetHistosForPlotter(
datasetsMgr, h_prefix + "MHT", **kwargs)
inclusiveHisto.setName("Inclusive")
inclusiveHisto.setLegendLabel("Inclusive")
#for hi in fixedHistos:
# print(type(hi), hi.getName())
#return
# For-loop: All Histogram names
for counter, hName in enumerate(hNames):
# Get the save path and name
savePath, saveName = GetSavePathAndName(h_prefix + hName[0],
**kwargs)
saveName = savePath + h_prefix + hSaveNames[counter]
# Get Histos for Plotter
refHisto_, otherHistos_ = GetHistosForPlotter(
datasetsMgr, h_prefix + hName[0], **kwargs)
refHisto2, otherHistos2 = GetHistosForPlotter(
datasetsMgr, h_prefix + hName[1], **kwargs)
refHisto = inclusiveHisto
# customize histos
refHisto_.setName(hLegends[counter][0])
refHisto_.setLegendLabel(hLegends[counter][0])
refHisto_.setDrawStyle("P")
refHisto_.setLegendStyle("P")
styleDict[hStyles[0]].apply(refHisto_.getRootHisto())
refHisto2.setName(hLegends[counter][1])
refHisto2.setLegendLabel(hLegends[counter][1])
refHisto2.setDrawStyle("P")
refHisto2.setLegendStyle("P")
styleDict[hStyles[1]].apply(refHisto2.getRootHisto())
otherHistos = [refHisto_, refHisto2]
#for hi in otherHistos:
# print hi.getName()
#return
# Create a comparison plot
p = plots.ComparisonManyPlot(refHisto, otherHistos)
# Remove negative contributions
#RemoveNegativeBins(datasetsMgr, hName, p)
# Create a frame
if kwargs.get("logY") == True:
opts = {"ymin": 8e-5, "ymax": 2}
#opts = {"ymin": 1e-3, "ymax": 1}
else:
opts = {"ymin": 8.e-5, "ymax": 2}
ratioOpts = {"ymin": 0.1, "ymax": 10.0}
p.createFrame(saveName,
createRatio=kwargs.get("createRatio"),
opts=opts,
opts2=ratioOpts)
# Customise Legend
moveLegend = {"dx": -0.2, "dy": +0.0, "dh": -0.1}
p.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
#p.removeLegend()
# Customise frame
p.getFrame().GetYaxis().SetTitle(getTitleY(refHisto, **kwargs))
#p.setEnergy("13")
if kwargs.get("createRatio"):
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.7)
# SetLog
SetLogAndGrid(p, **kwargs)
# Add cut line/box
_kwargs = {"lessThan": kwargs.get("cutLessThan")}
p.addCutBoxAndLine(cutValue=kwargs.get("cutValue"),
fillColor=kwargs.get("cutFillColour"),
box=kwargs.get("cutBox"),
line=kwargs.get("cutLine"),
**_kwargs)
# Move the refDataset to first in the draw order (back)
histoNames = [h.getName() for h in p.histoMgr.getHistos()]
p.histoMgr.reorder(
filter(
lambda n: plots._legendLabels[kwargs.get("refDataset")]
not in n, histoNames))
# Draw plots
p.draw()
# Customise text
histograms.addStandardTexts(lumi=intLumi)
# histograms.addText(0.4, 0.9, "Alexandros Attikis", 17)
histograms.addText(0.2, 0.88,
plots._legendLabels[kwargs.get("refDataset")],
17)
# Save canvas under custom dir
if not os.path.exists(savePath):
os.mkdir(savePath)
SaveAs(p, savePath, saveName, kwargs.get("saveFormats"))
return
