def main():
if len(sys.argv) < 2:
usage()
paths = [sys.argv[1]]
analysis = "Hplus2tbAnalysis"
hName = "associatedTPt"
plotname = analysis + "_" + hName
datasetsHiggs = dataset.getDatasetsFromMulticrabDirs(
paths,
analysisName=analysis,
includeOnlyTasks="ChargedHiggs_HplusTB_HplusToTauB_M_")
datasetsTT = dataset.getDatasetsFromMulticrabDirs(paths,
analysisName=analysis,
includeOnlyTasks="TT")
datasetsTT.merge("MC", ["TT", "TT_ext"], keepSources=True)
style = tdrstyle.TDRStyle()
dataset1 = datasetsHiggs.getDataset(
"ChargedHiggs_HplusTB_HplusToTauB_M_200").getDatasetRootHisto(hName)
dataset2 = datasetsTT.getDataset("MC").getDatasetRootHisto(hName)
# dataset1.normalizeToOne()
dataset2.normalizeToOne()
histo1 = dataset1.getHistogram()
histo1.SetMarkerColor(2)
histo1.SetMarkerStyle(20)
removeNegatives(histo1)
histo1.Scale(1. / histo1.Integral())
histo2 = dataset2.getHistogram()
histo2.SetMarkerColor(4)
histo2.SetMarkerStyle(21)
p = plots.ComparisonPlot(
histograms.Histo(histo1, "m_{H^{#pm}} = 200 GeV/c^{2}", "p", "P"),
histograms.Histo(histo2, "t#bar{t}", "p", "P"))
opts = {"ymin": 0, "ymax": 0.2}
opts2 = {"ymin": 0.5, "ymax": 1.5}
p.createFrame(os.path.join(plotDir, plotname),
createRatio=True,
opts=opts,
opts2=opts2)
moveLegend = {"dx": -0.2, "dy": -0.1, "dh": -0.1}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
p.getFrame().GetYaxis().SetTitle("Arbitrary units")
p.getFrame().GetXaxis().SetTitle("Top p_{T} (GeV/c)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
def 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 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 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(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 drawPlot(h, name, xlabel, ylabel="Events / %.0f GeV/c", rebin=1, log=True, addMCUncertainty=True, ratio=False, opts={}, opts2={}, moveLegend={}, textFunction=None, cutLine=None, cutBox=None):
if cutLine != None and cutBox != None:
raise Exception("Both cutLine and cutBox were given, only either one can exist")
if rebin > 1:
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
ylab = ylabel
if "%" in ylabel:
ylab = ylabel % h.binWidth()
# scaleMCfromWmunu(h)
# h.stackMCSignalHistograms()
h.stackMCHistograms(stackSignal=True)#stackSignal=True)
# h.stackMCHistograms()
if addMCUncertainty:
h.addMCUncertainty()
_opts = {"ymin": 0.01, "ymaxfactor": 2}
if not log:
_opts["ymin"] = 0
_opts["ymaxfactor"] = 1.1
## _opts2 = {"ymin": 0.5, "ymax": 1.5}
_opts2 = {"ymin": 0.0, "ymax": 2.0}
_opts.update(opts)
_opts2.update(opts2)
#if log:
# name = name + "_log"
h.createFrame(name, createRatio=ratio, opts=_opts, opts2=_opts2)
if log:
h.getPad().SetLogy(log)
h.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Add cut line and/or box
if cutLine != None:
lst = cutLine
if not isinstance(lst, list):
lst = [lst]
for line in lst:
h.addCutBoxAndLine(line, box=False, line=True)
if cutBox != None:
lst = cutBox
if not isinstance(lst, list):
lst = [lst]
for box in lst:
h.addCutBoxAndLine(**box)
common(h, xlabel, ylab, textFunction=textFunction)
def drawPlot(h, name, xlabel, ylabel="Events / %.0f GeV/c", rebin=1, log=True, addMCUncertainty=True, ratio=False, opts={}, opts2={}, moveLegend={}, textFunction=None, cutLine=None, cutBox=None):
if cutLine != None and cutBox != None:
raise Exception("Both cutLine and cutBox were given, only either one can exist")
if rebin > 1:
h.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(rebin))
ylab = ylabel
if "%" in ylabel:
ylab = ylabel % h.binWidth()
scaleMCfromWmunu(h)
# h.stackMCSignalHistograms()
h.stackMCHistograms(stackSignal=True)#stackSignal=True)
# h.stackMCHistograms()
if addMCUncertainty:
h.addMCUncertainty()
_opts = {"ymin": 0.01, "ymaxfactor": 2}
if not log:
_opts["ymin"] = 0
_opts["ymaxfactor"] = 1.1
## _opts2 = {"ymin": 0.5, "ymax": 1.5}
_opts2 = {"ymin": 0.0, "ymax": 2.0}
_opts.update(opts)
_opts2.update(opts2)
#if log:
# name = name + "_log"
h.createFrame(name, createRatio=ratio, opts=_opts, opts2=_opts2)
if log:
h.getPad().SetLogy(log)
h.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Add cut line and/or box
if cutLine != None:
lst = cutLine
if not isinstance(lst, list):
lst = [lst]
for line in lst:
h.addCutBoxAndLine(line, box=False, line=True)
if cutBox != None:
lst = cutBox
if not isinstance(lst, list):
lst = [lst]
for box in lst:
h.addCutBoxAndLine(**box)
common(h, xlabel, ylab, textFunction=textFunction)
def main():
if len(sys.argv) < 2:
usage()
paths = [sys.argv[1]]
analysis = "Hplus2tbAnalysis"
hName = "associatedTPt"
plotname = analysis+"_"+hName
datasetsHiggs = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="ChargedHiggs_HplusTB_HplusToTauB_M_")
datasetsTT = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="TT")
datasetsTT.merge("MC", ["TT","TT_ext"], keepSources=True)
style = tdrstyle.TDRStyle()
dataset1 = datasetsHiggs.getDataset("ChargedHiggs_HplusTB_HplusToTauB_M_200").getDatasetRootHisto(hName)
dataset2 = datasetsTT.getDataset("MC").getDatasetRootHisto(hName)
# dataset1.normalizeToOne()
dataset2.normalizeToOne()
histo1 = dataset1.getHistogram()
histo1.SetMarkerColor(2)
histo1.SetMarkerStyle(20)
removeNegatives(histo1)
histo1.Scale(1./histo1.Integral())
histo2 = dataset2.getHistogram()
histo2.SetMarkerColor(4)
histo2.SetMarkerStyle(21)
p = plots.ComparisonPlot(histograms.Histo(histo1, "m_{H^{#pm}} = 200 GeV/c^{2}", "p", "P"),
histograms.Histo(histo2, "t#bar{t}", "p", "P"))
opts = {"ymin": 0, "ymax": 0.2}
opts2 = {"ymin": 0.5, "ymax": 1.5}
p.createFrame(os.path.join(plotDir, plotname), createRatio=True, opts=opts, opts2=opts2)
moveLegend = {"dx": -0.2, "dy": -0.1, "dh": -0.1}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
p.getFrame().GetYaxis().SetTitle("Arbitrary units")
p.getFrame().GetXaxis().SetTitle("Top p_{T} (GeV/c)")
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
p.draw()
if not os.path.exists(plotDir):
os.mkdir(plotDir)
p.save(formats)
def 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 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 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 main(opts):
# Setup the style
style = tdrstyle.TDRStyle()
# 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 datasets:All JetHT to "Data", QCD_Pt to "QCD",
# QCD_bEnriched to "QCD_b", single-top to "SingleTop", WW, WZ, ZZ to "Diboson"
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()
# ========================================
# Histograms
# ========================================
for counter, hName in enumerate(hNames):
# Get the save path and name, Get Histos for Plotter
savePath, saveName = GetSavePathAndName(hName, **kwargs)
refHisto, otherHistos = GetHistosForPlotter(datasetsMgr, hName,
**kwargs)
# Create a comparison plot
#p = plots.ComparisonManyPlot(refHisto, otherHistos)
p = plots.DataMCPlot(datasetsMgr, hName)
# Remove negative contributions
if 0:
RemoveNegativeBins(datasetsMgr, hName, p)
# Y-axis
ratioOpts = {"ymin": 0.0, "ymax": 2.0}
if kwargs.get("logY") == True:
opts = {"ymin": 1e-2, "ymaxfactor": 100}
else:
opts = {"ymin": 0.0, "ymaxfactor": 1.2}
# ========================================
# Frame
# ========================================
p.createFrame(saveName,
createRatio=kwargs.get("createRatio"),
opts=opts,
opts2=ratioOpts)
# Legend
moveLegend = {"dx": -0.11, "dy": +0.0, "dh": +0.2}
p.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
# 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))
if 0:
p.removeLegend()
# Axes
#p.getFrame().GetYaxis().SetTitle( getTitleY(refHisto, **kwargs) )
#p.getFrame().GetYaxis().SetTitle( getTitleY(p.histoMgr.getHistos()[0], **kwargs) )
if kwargs.get("createRatio"):
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# Set Log and Grid
SetLogAndGrid(p, **kwargs)
# Cut line / Cut 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)
# Draw the final plot
p.draw()
# ========================================
# Add Text
# ========================================
histograms.addStandardTexts(lumi=intLumi)
# histograms.addText(0.4, 0.9, "Alexandros Attikis", 17)
# histograms.addText(0.4, 0.11, "Runs " + datasetsMgr.loadRunRange(), 17)
# Save the canvas to a file
SaveAs(p, savePath, saveName, kwargs.get("saveFormats"), counter == 0)
return
def PlotProb(datasets, numPath, denPath):
EfficiencyList = []
index = 0
for dataset in datasets:
datasetName = dataset.getName()
print "Dataset = ", datasetName
statOption = ROOT.TEfficiency.kFNormal
## n = dataset.getDatasetRootHisto(numPath).getHistogram()
# n.normalizeToOne()
## d = dataset.getDatasetRootHisto(denPath).getHistogram()
nn = dataset.getDatasetRootHisto(numPath)
nn.normalizeToLuminosity(35.8 * (10**3))
n = nn.getHistogram()
dd = dataset.getDatasetRootHisto(denPath)
# dd.normalizeToOne()
# dd.normalizeToLuminosity(36.3*(10**3))
dd.normalizeToLuminosity(35.8 * (10**3))
# dd.normalizeByCrossSection()
d = dd.getHistogram()
# if "TT" in datasetName and ("Higgs" in numPath or "LdgBjetPt_isLdgFreeBjet" in numPath):
# continue
# elif "M_" in datasetName and not ("Higgs" in numPath or "LdgBjetPt_isLdgFreeBjet" in numPath):
# continue
if "Event" in numPath:
n.Rebin(10)
d.Rebin(10)
else:
n.Rebin(5)
d.Rebin(5)
if d.GetEntries() == 0 or n.GetEntries() == 0:
continue
if n.GetEntries() > d.GetEntries():
continue
# Check Negatives
CheckNegatives(n, d, True)
# Remove Negatives
RemoveNegatives(n)
nBins = d.GetNbinsX()
xMin = d.GetXaxis().GetXmin()
xMax = d.GetXaxis().GetXmax()
binwidth = int(n.GetBinWidth(0))
# ----------------------------------------------------------------------------------------- #
# Ugly hack to ignore EMPTY (in the wanted range) histograms with overflows/underflows
# ----------------------------------------------------------------------------------------- #
if (0):
print "\n"
print "=========== getEfficiency:"
print "Dataset = ", dataset.getName()
print "Numerator: entries=", n.GetEntries(
), " Bins=", n.GetNbinsX(), " Low edge=", n.GetBinLowEdge(1)
print "Denominator: entries=", d.GetEntries(
), " Bins=", d.GetNbinsX(), " Low edge=", d.GetBinLowEdge(1)
print "\n"
print ">>>>>> Sanity Check: <<<<<<"
print "Numerator Mean = ", n.GetMean()
print "Numerator RMS = ", n.GetRMS()
print "Numerator Integral = ", n.Integral(1, nBins)
print "Denominator Mean = ", d.GetMean()
print "Denominator RMS = ", d.GetRMS()
print "Denominator Integral = ", d.Integral(1, nBins)
if (n.GetMean() == 0 or d.GetMean() == 0): continue
if (n.GetRMS() == 0 or d.GetRMS() == 0): continue
if (n.Integral(1, nBins) == 0 or d.Integral(1, nBins) == 0): continue
# if not (ROOT.TEfficiency.CheckConsistency(n,d)): continue;
effic = ROOT.TEfficiency(n, d)
effic.SetStatisticOption(statOption)
weight = 1
if dataset.isMC():
weight = dataset.getCrossSection()
effic.SetWeight(weight)
eff = convert2TGraph(effic)
# Apply Styles
if "TT" in datasetName:
if index == 0:
styles.signalStyleHToTB500.apply(eff)
# styles.ttStyle.apply(eff)
eff.SetLineStyle(1)
eff.SetLineWidth(3)
eff.SetLineColor(619)
legend = "Default: t#bar{t}"
index = 1
else:
styles.signalStyleHToTB500.apply(eff)
# styles.ttStyle.apply(eff)
eff.SetLineStyle(1)
eff.SetLineWidth(3)
eff.SetLineColor(417)
legend = "#Delta R(q,q')>0.8: t#bar{t}"
elif "M_500" in datasetName:
styles.signalStyleHToTB500.apply(eff)
legend = "H^{+} m_{H^{+}} = 500 GeV"
elif "M_300" in datasetName:
styles.signalStyleHToTB300.apply(eff)
legend = "H^{+} m_{H^{+}} = 300 GeV"
elif "M_1000" in datasetName:
styles.signalStyleHToTB1000.apply(eff)
legend = "H^{+} m_{H^{+}} = 1000 GeV"
elif "M_800" in datasetName:
styles.signalStyleHToTB800.apply(eff)
legend = "H^{+} m_{H^{+}} = 800 GeV"
elif "M_200" in datasetName:
styles.signalStyleHToTB200.apply(eff)
legend = "H^{+} m_{H^{+}} = 200 GeV"
else:
styles.ttStyle.apply(eff)
legend = "other"
EfficiencyList.append(histograms.HistoGraph(eff, legend, "lp", "P"))
saveName = "Eff_" + numPath.split("/")[-1] + "Over" + denPath.split(
"/")[-1]
if "Pt" in numPath:
xMin = 0.0
# rebinX = 2
xMax = 805.0
# xMax = 555.0 # For topPt < 500GeV
xTitle = "p_{T} (GeV/c)"
units = "GeV/c"
_format = "%0.1f" + units
yTitle = "Efficiency / " + str(binwidth) + " " + units
yMin = 0.0
yMax = 1.1
elif "_Eta" in numPath:
xMin = -3.0
xMax = +3.0
xTitle = "#eta"
yTitle = "Efficiency"
yMin = 0.0
yMax = 1.1
elif "_Mass" in numPath:
xMin = 50.0
xMax = 300
xTitle = "M (GeV/c^{2})"
yTitle = "Efficiency"
yMin = 0.0
yMax = 1.1
elif "_Phi" in numPath:
xMin = -3
xMax = +3
xTitle = "#phi"
yTitle = "Efficiency"
yMin = 0.0
yMax = 1.1
else:
xMin = 0.0
xMax = 250.0
xTitle = "xTitle"
yTitle = "yTitle"
yMin = 0.0
yMax = 1.1
if "Fake" in numPath:
# xMin = 95.0
# rebinX = 4
xMax = 805.0
xTitle = "candidate p_{T} (GeV/c)"
units = "GeV/c"
_format = "%0.1f" + units
yTitle = "Misid rate / " + str(binwidth) + " " + units
yMin = 0.0
yMax = 0.11
if "Event" in numPath:
rebinX = 2
# xMin = 95.0
xMax = 805.0
xTitle = "candidate p_{T} (GeV/c)"
units = "GeV/c"
_format = "%0.1f" + units
yTitle = "Efficiency / " + str(binwidth) + " " + units
yMin = 0.0
yMax = 1.1
if "NonMatched" in numPath:
xMin = 90.0
rebinX = 4
xMax = 700.0
xMax = 555.0 # For topPt < 500GeV
xTitle = "p_{T} (GeV)"
yTitle = "Efficiency"
yMin = 0.0
yMax = 0.15
if "AllTopQuarkPt_MatchedBDT" in numPath and "TopQuarkPt" in denPath:
xMin = 0.0
# rebinX = 4
xMax = 805.0 #705
units = "GeV/c"
xTitle = "generated top p_{T} (GeV/c)"
yTitle = "Efficiency / " + str(binwidth) + " " + units
yMin = 0.0
yMax = 1.1
if "SameFake" in numPath:
xMin = 95.0
rebinX = 4
xMax = 705.0
xMax = 555.0 # For topPt < 500GeV
xTitle = "p_{T} [GeV]"
yTitle = "Efficiency"
yMin = 0.0
yMax = 1.1
options = {"ymin": yMin, "ymax": yMax, "xmin": xMin, "xMax": xMax}
# if "TT" in datasetName and ("Higgs" in numPath or "LdgBjetPt_isLdgFreeBjet" in numPath):
# return
# if "M_" in datasetName and not ("Higgs" in numPath or "LdgBjetPt_isLdgFreeBjet" in numPath):
# return
p = plots.PlotBase(datasetRootHistos=EfficiencyList,
saveFormats=kwargs.get("saveFormats"))
#p = plots.ComparisonManyPlot(refEff, EfficiencyList, saveFormats=[])
p.createFrame(saveName, opts=options)
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinX(kwargs.get("rebinX")))
# Set Titles
# p.getFrame().GetYaxis().SetTitle(kwargs.get("ylabel")) #"ylabel"
p.getFrame().GetXaxis().SetTitle(xTitle)
p.getFrame().GetYaxis().SetTitle(yTitle)
# Set range
p.getFrame().GetXaxis().SetRangeUser(xMin, xMax)
moveLegend = {"dx": -0.55, "dy": -0.02, "dh": -0.2}
# moveLegend = {"dx": -0.55, "dy": -0.01, "dh": -0.1}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Add Standard Texts to plot
histograms.addStandardTexts()
p.draw()
# Save plot in all formats
savePath = os.path.join(opts.saveDir, "HplusMasses",
numPath.split("/")[0], opts.optMode)
save_path = savePath + opts.MVAcut
# SavePlot(p, saveName, savePath)
SavePlot(p, saveName, save_path)
return
def analyze(analysis=None):
paths = [sys.argv[1]]
datasets = dataset.getDatasetsFromMulticrabDirs(paths)
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="SingleNeutrino")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="QCD")
# analysis = datasets.getAllDatasets()[0].getAnalysisName()
#datasetsMC = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="ZeroBias")
createRatio = False
# for d in datasets.getAllDatasets():
# print d.getName()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
#### dataset1 = datasets.getMCDatasets()
rateETM120 = 5521.35 # Hz
#effETM120 = 0.000611208781402 #8.75017364672e-05
#effETM120 = 0.000619219298648
effETM120 = 0.000203698623826
#### effETM120 = 0.186701136914 # QCD
scale = rateETM120 / effETM120 * 0.001 #(Hz->kHz)
# for d in dataset1:
# d.scale(scale)
dataset2 = dataset1
createRatio = False
#if isinstance(datasetsMC,dataset.DatasetManager):
# dataset2 = datasetsMC.getMCDatasets()
# createRatio = True
eff1PU = getEfficiency(dataset1, "NumeratorPU", "DenominatorPU")
scaleGraph(eff1PU, scale)
namePU = "TauMET_" + analysis + "_nVtx"
legend1 = "Data"
legend2 = "Simulation"
styles.dataStyle.apply(eff1PU)
eff1PU.SetMarkerSize(1)
#eff2PU.SetMarkerSize(1.5)
pPU = plots.PlotBase([histograms.HistoGraph(eff1PU, "eff1", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
# Fit
# yval = fit("Data",pPU,eff1PU,30,59)
yval = fit("Data", pPU, eff1PU, 5, 59)
#### opts = {"ymin": 0, "ymax": 6, "xmax": 60}
opts = {"ymin": 0, "ymax": 20, "xmax": 60}
#### opts = {"ymin": 0, "ymax": 300, "xmax": 60}
opts2 = {"ymin": 0.5, "ymax": 1.5}
moveLegend = {"dx": -0.5, "dy": -0.1, "dh": -0.1}
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))
pPU.getFrame().GetYaxis().SetTitle("L1 rate (kHz)")
pPU.getFrame().GetXaxis().SetTitle("n vertices")
if createRatio:
pPU.getFrame2().GetYaxis().SetTitle("Ratio")
pPU.getFrame2().GetYaxis().SetTitleOffset(1.6)
pPU.draw()
print "check frame min,max", pPU.getFrame().GetYaxis().GetXmin(
), pPU.getFrame().GetYaxis().GetXmax()
x = array.array('d', [55, 55, 0])
y = array.array('d', [0, yval, yval])
n = 3
vert = ROOT.TGraph(n, x, y)
vert.SetLineStyle(2)
vert.SetLineColor(2)
vert.SetLineWidth(2)
vert.Draw("L")
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity", d.getName(), d.getLuminosity()
lumi += d.getLuminosity()
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
pPU.save(formats)
print "Output written in", plotDir
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 main():
style = tdrstyle.TDRStyle()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(parseOpts.batchMode)
ROOT.gErrorIgnoreLevel = 3000
# Get all datasets from the mcrab dir
datasetsMgr = GetDatasetsFromDir(parseOpts.mcrab, kwargs.get("analysis"))
# datasetsMgr = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=kwargs.get("analysis"), includeOnlyTasks="ChargedHiggs_HplusTB_HplusToTB_M_200")
# datasetsMgr = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=kwargs.get("analysis"), excludeTasks="M_200")
# Determine Integrated Luminosity
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("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"))
# datasetsMgr.remove(filter(lambda name: not "QCD" in name, datasetsMgr.getAllDatasetNames()))
# datasetsMgr.remove(filter(lambda name: "QCD" in name in name, datasetsMgr.getAllDatasetNames()))
# For-loop: All Histogram names
for hName in hNames:
savePath, saveName = GetSavePathAndName(hName, **kwargs)
# Get efficiency histos
refEff, otherEff = GetCutEfficiencyTGraphs(datasetsMgr, hName, "kFCP",
**kwargs)
# refEff, otherEff = GetCutEfficiencyTGraphs(datasetsMgr, hName, "kFFC", **kwargs)
# refEff, otherEff = GetCutEfficiencyTGraphs(datasetsMgr, hName, "kBJeffrey", **kwargs)
# refEff, otherEff = GetCutEfficiencyTGraphs(datasetsMgr, hName, "kFNormal", **kwargs)
# Plot the efficiencies
p = plots.ComparisonManyPlot(refEff, otherEff)
# Create a frame
opts = {"ymin": 0.0, "ymaxfactor": 1.2} #"ymax": 5e-1}
ratioOpts = {"ymin": 0.0, "ymaxfactor": 1.2}
p.createFrame(saveName,
createRatio=kwargs.get("createRatio"),
opts=opts,
opts2=ratioOpts)
# Customise Legend
moveLegend = {"dx": -0.1, "dy": +0.0, "dh": -0.1}
p.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
#p.removeLegend()
# Customise frame
# p.setEnergy("13")
if kwargs.get("createRatio"):
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# 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(0.4, 0.11, "Runs " + datasetsMgr.loadRunRange(), 17)
# Save canvas under custom dir
SaveAs(p, savePath, saveName, kwargs.get("saveFormats"))
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 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 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 main():
style = tdrstyle.TDRStyle()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(parseOpts.batchMode)
# Get all datasets from the mcrab dir
datasetsMgr = GetDatasetsFromDir(parseOpts.mcrab, 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
# 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 counter, hName in enumerate(hNames):
# Get the save path and name
savePath, saveName = GetSavePathAndName(hName, **kwargs)
# Get Histos for Plotter
refHisto, otherHistos = GetHistosForPlotter(datasetsMgr, hName,
**kwargs)
# Create a comparison plot
p = plots.ComparisonManyPlot(refHisto, otherHistos)
# Remove negative contributions
#RemoveNegativeBins(datasetsMgr, hName, p)
# Create a frame
opts = {"ymin": 0.0, "ymaxfactor": 1.2}
ratioOpts = {"ymin": 0.0, "ymax": 2.0}
p.createFrame(saveName,
createRatio=kwargs.get("createRatio"),
opts=opts,
opts2=ratioOpts)
# Customise Legend
moveLegend = {"dx": -0.1, "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(1.6)
# 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(0.4, 0.11, "Runs " + datasetsMgr.loadRunRange(), 17)
# Save canvas under custom dir
SaveAs(p, savePath, saveName, kwargs.get("saveFormats"))
return
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 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")
for optMode in optModes:
plot = plots.PlotBase()
color = 1
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")
for HISTONAME in histoNameList:
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)
invjet_name = "MTInvertedTauIdAfterAllSelectionsPlusJetFakeTau"
basejet_name = "MTBaselineTauIdAfterAllSelectionsPlusJetFakeTau"
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)
if HISTONAME == "shapeEWKGenuineTausTransverseMass":
legendName = "Data-driven Fake Taus"
else:
legendName = "Simulated Fake Taus"
legendName = legendName.replace("Plus", "")
mt.SetName(legendName)
mt.SetLineColor(color)
mt.Add(mt_ewk, -1)
if HISTONAME == "shapeEWKGenuineTausTransverseMass":
mt.Scale(QCDInvertedNormalizationFilteredEWKFakeTaus[
str(i)])
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)
h.Add(mt_ewk, -1)
if HISTONAME == "shapeEWKGenuineTausTransverseMass":
h.Scale(QCDInvertedNormalizationFilteredEWKFakeTaus[
str(i)])
else:
h.Scale(QCDInvertedNormalization[str(i)])
mt.Add(h)
if HISTONAME == "shapeTransverseMass":
mt.Add(mt_signalfaketaus)
plot.histoMgr.appendHisto(histograms.Histo(mt, mt.GetName()))
style = tdrstyle.TDRStyle()
#plot.createFrame("mt")
#plot.createFrame(HISTONAME.replace("shape","final"))
plot.createFrame(optMode.replace("Opt", "Mt_DataDrivenVsMC_"))
moveLegend = {"dx": -0.3, "dy": 0.}
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()
def PlotHistosAndCalculateTF(datasetsMgr, histoList, binLabels, opts):
# Get the histogram customisations (keyword arguments)
_kwargs = GetHistoKwargs(histoList[0])
# Get the root histos for all datasets and Control Regions (CRs)
regions = ["SR", "VR", "CRone", "CRtwo"]
rhDict = GetRootHistos(datasetsMgr, histoList, regions, binLabels)
#=========================================================================================
# Calculate the Transfer Factor (TF) and save to file
#=========================================================================================
manager = FakeBNormalization.FakeBNormalizationManager(binLabels, opts.mcrab, opts.optMode, verbose=False)
if opts.inclusiveOnly:
#manager.CalculateTransferFactor(binLabels[0], rhDict["CRone-FakeB"], rhDict["CRtwo-FakeB"])
binLabel = "Inclusive"
manager.CalculateTransferFactor("Inclusive", rhDict["FakeB-CRone-Inclusive"], rhDict["FakeB-CRtwo-Inclusive"])
else:
for bin in binLabels:
manager.CalculateTransferFactor(bin, rhDict["FakeB-CRone-%s" % bin], rhDict["FakeB-CRtwo-%s" % bin])
# Get unique a style for each region
for k in rhDict:
dataset = k.split("-")[0]
region = k.split("-")[1]
styles.getABCDStyle(region).apply(rhDict[k])
if "FakeB" in k:
styles.getFakeBStyle().apply(rhDict[k])
# sr.apply(rhDict[k])
# =========================================================================================
# Create the final plot object
# =========================================================================================
rData_SR = rhDict["Data-SR-Inclusive"]
rEWKGenuineB_SR = rhDict["EWK-SR-Inclusive-EWKGenuineB"]
rBkgSum_SR = rhDict["FakeB-VR-Inclusive"].Clone("BkgSum-SR-Inclusive")
rBkgSum_SR.Reset()
if opts.inclusiveOnly:
bin = "Inclusive"
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
binHisto_VR = rhDict["FakeB-VR-%s" % (bin)]
VRtoSR_TF = manager.GetTransferFactor(bin)
Print("Applying TF = %s%0.6f%s to VR shape" % (ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()), True)
binHisto_VR.Scale(VRtoSR_TF)
# Add the normalised histogram to the final Inclusive SR (predicted) histo
rBkgSum_SR.Add(binHisto_VR, +1)
else:
# For-loop: All bins
for i, bin in enumerate(binLabels, 1):
if bin == "Inclusive":
continue
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
binHisto_VR = rhDict["FakeB-VR-%s" % (bin)]
VRtoSR_TF = manager.GetTransferFactor(bin)
Print("Applying TF = %s%0.6f%s to VR shape" % (ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()), i==1)
binHisto_VR.Scale(VRtoSR_TF)
# Add the normalised histogram to the final Inclusive SR (predicted) histo
rBkgSum_SR.Add(binHisto_VR, +1)
#Print("Got Verification Region (VR) shape %s%s%s" % (ShellStyles.NoteStyle(), rFakeB_VR.GetName(), ShellStyles.NormalStyle()), True)
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
#VRtoSR_TF = manager.GetTransferFactor("Inclusive")
#Print("Applying TF = %s%0.6f%s to VR shape" % (ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()), True)
#rBkgSum_SR.Scale(VRtoSR_TF)
# Plot histograms
if opts.altPlot:
# Add the SR EWK Genuine-b to the SR FakeB ( BkgSum = [FakeB] + [GenuineB-MC] = [VR * (CR1/CR2)] + [GenuineB-MC] )
rBkgSum_SR.Add(rEWKGenuineB_SR, +1)
# Change style
styles.getGenuineBStyle().apply(rBkgSum_SR)
# Remove unsupported settings of kwargs
_kwargs["stackMCHistograms"] = False
_kwargs["addLuminosityText"] = False
# Create the plot
p = plots.ComparisonManyPlot(rData_SR, [rBkgSum_SR], saveFormats=[])
# Set draw / legend style
p.histoMgr.setHistoDrawStyle("Data-SR-Inclusive", "P")
p.histoMgr.setHistoLegendStyle("Data-SR-Inclusive" , "LP")
p.histoMgr.setHistoDrawStyle("BkgSum-SR-Inclusive", "HIST")
p.histoMgr.setHistoLegendStyle("BkgSum-SR-Inclusive" , "F")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Data-SR" : "Data",
"BkgSum-SR" : "Fake-b + Gen-b",
})
else:
# Create empty histogram stack list
myStackList = []
# Signal
p2 = plots.DataMCPlot(datasetsMgr, "ForTestQGLR/QGLR_SR/QGLR_SRInclusive", saveFormats=[])
hSignal_800 = p2.histoMgr.getHisto('ChargedHiggs_HplusTB_HplusToTB_M_800').getRootHisto()
hhSignal_800= histograms.Histo(hSignal_800, 'ChargedHiggs_HplusTB_HplusToTB_M_800', "H^{+} m_{H^+}=800 GeV")
hhSignal_800.setIsDataMC(isData=False, isMC=True)
myStackList.append(hhSignal_800)
hSignal_250 = p2.histoMgr.getHisto('ChargedHiggs_HplusTB_HplusToTB_M_250').getRootHisto()
hhSignal_250= histograms.Histo(hSignal_250, 'ChargedHiggs_HplusTB_HplusToTB_M_250', "H^{+} m_{H^+}=250 GeV")#plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_250'])
hhSignal_250.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_250)
hSignal_500 = p2.histoMgr.getHisto('ChargedHiggs_HplusTB_HplusToTB_M_500').getRootHisto()
hhSignal_500= histograms.Histo(hSignal_500, 'ChargedHiggs_HplusTB_HplusToTB_M_500', "H^{+} m_{H^+}=500 GeV")#plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_500'])
hhSignal_500.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_500)
hSignal_1000 = p2.histoMgr.getHisto('ChargedHiggs_HplusTB_HplusToTB_M_1000').getRootHisto()
hhSignal_1000= histograms.Histo(hSignal_1000, 'ChargedHiggs_HplusTB_HplusToTB_M_1000', "H^{+} m_{H^+}=1000 GeV")#plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_500'])
hhSignal_1000.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_1000)
# Add the FakeB data-driven background to the histogram list
hFakeB = histograms.Histo(rBkgSum_SR, "FakeB", "Fake-b")
hFakeB.setIsDataMC(isData=False, isMC=True)
myStackList.append(hFakeB)
# Add the EWKGenuineB MC background to the histogram list
hGenuineB = histograms.Histo(rEWKGenuineB_SR, "GenuineB", "EWK Genuine-b")
hGenuineB.setIsDataMC(isData=False, isMC=True)
myStackList.append(hGenuineB)
# Add the collision datato the histogram list
hData = histograms.Histo(rData_SR, "Data", "Data")
hData.setIsDataMC(isData=True, isMC=False)
myStackList.insert(0, hData)
p = plots.DataMCPlot2( myStackList, saveFormats=[])
p.setLuminosity(opts.intLumi)
p.setDefaultStyles()
# Draw the plot and save it
hName = "test"
plots.drawPlot(p, hName, **_kwargs)
SavePlot(p, hName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png", ".pdf"])
#==========================================================================================
# Calculate Cut-Flow Efficiency
#==========================================================================================
kwargs = {
"rebinX" : 1,
"xlabel" : "QGLR",
"ylabel" : "Significance / %.02f ",
"opts" : {"ymin": 0.0, "ymaxfactor": 1.3},
"createLegend": {"x1": 0.55, "y1": 0.70, "x2": 0.92, "y2": 0.92},
# "cutBox" : {"cutValue": 0.0, "fillColor" : 16, "box": False, "line": False, "greaterThan": True},
}
efficiencyList = []
xValues = []
yValues_250 = []
yValues_500 = []
yValues_800 = []
yValues_1000 = []
yValues_Bkg = []
nBins = hSignal_250.GetNbinsX()+1
hBkg = p.histoMgr.getHisto("ChargedHiggs_HplusTB_HplusToTB_M_800").getRootHisto().Clone("Bkg")
hBkg.Reset()
# Bkg: FakeB + Genuine B
hBkg.Add(hFakeB.getRootHisto(), +1)
hBkg.Add(hGenuineB.getRootHisto(), +1)
for i in range (0, nBins):
# Cut value
cut = hSignal_250.GetBinCenter(i)
passed_250 = hSignal_250.Integral(i, hSignal_250.GetXaxis().GetNbins())
passed_500 = hSignal_500.Integral(i, hSignal_500.GetXaxis().GetNbins())
passed_800 = hSignal_800.Integral(i, hSignal_800.GetXaxis().GetNbins())
passed_1000 = hSignal_1000.Integral(i, hSignal_1000.GetXaxis().GetNbins())
passed_Bkg = hBkg.Integral(i, hBkg.GetXaxis().GetNbins())
total_250 = hSignal_250.Integral()
total_500 = hSignal_500.Integral()
total_800 = hSignal_800.Integral()
total_1000 = hSignal_1000.Integral()
total_Bkg = hBkg.Integral()
eff_250 = float(passed_250)/total_250
eff_500 = float(passed_500)/total_500
eff_800 = float(passed_800)/total_800
eff_1000 = float(passed_1000)/total_1000
eff_Bkg = float(passed_Bkg)/total_Bkg
xValues.append(cut)
yValues_250.append(eff_250)
yValues_500.append(eff_500)
yValues_800.append(eff_800)
yValues_1000.append(eff_1000)
yValues_Bkg.append(eff_Bkg)
# Create the Efficiency Plot
tGraph_250 = ROOT.TGraph(len(xValues), array.array("d", xValues), array.array("d", yValues_250))
tGraph_500 = ROOT.TGraph(len(xValues), array.array("d", xValues), array.array("d", yValues_500))
tGraph_800 = ROOT.TGraph(len(xValues), array.array("d", xValues), array.array("d", yValues_800))
tGraph_1000 = ROOT.TGraph(len(xValues), array.array("d", xValues), array.array("d", yValues_1000))
tGraph_Bkg = ROOT.TGraph(len(xValues), array.array("d", xValues), array.array("d", yValues_Bkg))
styles.getSignalStyleHToTB_M("200").apply(tGraph_250)
styles.getSignalStyleHToTB_M("500").apply(tGraph_500)
styles.getSignalStyleHToTB_M("800").apply(tGraph_800)
styles.getSignalStyleHToTB_M("1000").apply(tGraph_1000)
styles.getQCDLineStyle().apply(tGraph_Bkg)
drawStyle = "CPE"
effGraph_250 = histograms.HistoGraph(tGraph_250, "H^{+} m_{H^{+}} = 250 GeV", "lp", drawStyle)
effGraph_500 = histograms.HistoGraph(tGraph_500, "H^{+} m_{H^{+}} = 500 GeV", "lp", drawStyle)
effGraph_800 = histograms.HistoGraph(tGraph_800, "H^{+} m_{H^{+}} = 800 GeV", "lp", drawStyle)
effGraph_1000 = histograms.HistoGraph(tGraph_1000, "H^{+} m_{H^{+}} = 1000 GeV", "lp", drawStyle)
effGraph_Bkg = histograms.HistoGraph(tGraph_Bkg, "Bkg", "lp", drawStyle)
efficiencyList.append(effGraph_250)
efficiencyList.append(effGraph_500)
efficiencyList.append(effGraph_800)
efficiencyList.append(effGraph_1000)
efficiencyList.append(effGraph_Bkg)
# Efficiency plot
pE = plots.PlotBase(efficiencyList, saveFormats=["pdf"])
pE.createFrame("QGLR_Efficiency")
pE.setEnergy("13")
pE.getFrame().GetYaxis().SetLabelSize(18)
pE.getFrame().GetXaxis().SetLabelSize(20)
pE.getFrame().GetYaxis().SetTitle("Efficiency / 0.01")
pE.getFrame().GetXaxis().SetTitle("QGLR Cut")
# Add Standard Texts to plot
histograms.addStandardTexts()
# Customise Legend
moveLegend = {"dx": -0.50, "dy": -0.5, "dh": -0.1}
pE.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
pE.draw()
# plots.drawPlot(pE, "QGLR_Efficiency", **kwargs)
SavePlot(pE, "QGLR_Efficiency", os.path.join(opts.saveDir, opts.optMode), saveFormats=[".png", ".pdf"] )
#==========================================================================================
# Calculate Significance
#==========================================================================================
SignalName = "ChargedHiggs_HplusTB_HplusToTB_M_800"
hSignif_250 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(SignalName)
hSignif_500 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(SignalName)
hSignif_800 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(SignalName)
hSignif_1000 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(SignalName)
hSignif_250.Reset()
hSignif_500.Reset()
hSignif_800.Reset()
hSignif_1000.Reset()
hBkg = p.histoMgr.getHisto(SignalName).getRootHisto().Clone("Bkg")
hBkg.Reset()
# Bkg: FakeB + Genuine B
hBkg.Add(hFakeB.getRootHisto(), +1)
hBkg.Add(hGenuineB.getRootHisto(), +1)
nBins = hSignif_250.GetNbinsX()+1
# For-loop: All histo bins
for i in range (1, nBins+1):
sigmaB = ROOT.Double(0)
b = hBkg.IntegralAndError(i, nBins, sigmaB)
s_250 = hSignal_250.Integral(i, nBins)
s_500 = hSignal_500.Integral(i, nBins)
s_800 = hSignal_800.Integral(i, nBins)
s_1000 = hSignal_1000.Integral(i, nBins)
# Calculate significance
signif_250 = stat.significance(s_250, b, sigmaB, option="Simple")#Asimov")
signif_500 = stat.significance(s_500, b, sigmaB, option="Simple")#Asimov")
signif_800 = stat.significance(s_800, b, sigmaB, option="Simple")#Asimov")
signif_1000 = stat.significance(s_1000, b, sigmaB, option="Simple")#"Asimov")
# Set signif for this bin
hSignif_250.SetBinContent(i, signif_250)
hSignif_500.SetBinContent(i, signif_500)
hSignif_800.SetBinContent(i, signif_800)
hSignif_1000.SetBinContent(i, signif_1000)
# Apply style
s_250 = styles.getSignalStyleHToTB_M("200")
s_250.apply(hSignif_250)
s_500 = styles.getSignalStyleHToTB_M("500")
s_500.apply(hSignif_500)
s_800 = styles.getSignalStyleHToTB_M("800")
s_800.apply(hSignif_800)
s_1000 = styles.getSignalStyleHToTB_M("1000")
s_1000.apply(hSignif_1000)
hList = []
hList.append(hSignif_250)
hList.append(hSignif_500)
hList.append(hSignif_800)
hList.append(hSignif_1000)
hSignif_250.SetName("H^{+} m_{H^{+}} = 250 GeV")
hSignif_500.SetName("H^{+} m_{H^{+}} = 500 GeV")
hSignif_800.SetName("H^{+} m_{H^{+}} = 800 GeV")
hSignif_1000.SetName("H^{+} m_{H^{+}} = 1000 GeV")
pS = plots.PlotBase(hList, saveFormats=["png", "pdf"])
pS.setLuminosity(opts.intLumi)
# Drawing style
pS.histoMgr.setHistoDrawStyleAll("HIST")
pS.histoMgr.setHistoLegendStyleAll("L")
pS.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerSize(1.0))
pS.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
pS.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerStyle(ROOT.kFullCircle))
# Draw the plot
name = "QGLR_Signif" + "GE"
plots.drawPlot(pS, name, **kwargs)
SavePlot(pS, name, os.path.join(opts.saveDir, opts.optMode), saveFormats=[".png", ".pdf"] )
#=========================================================================================
# Calculate the Transfer Factor (TF) and save to file
#=========================================================================================
Verbose("Write the normalisation factors to a python file", True)
fileName = os.path.join(opts.mcrab, "FakeBTransferFactors%s.py"% ( getModuleInfoString(opts) ) )
manager.writeNormFactorFile(fileName, opts)
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"))
datasetsMgr_fixed = GetDatasetsFromDir(opts.mcrab, opts, **kwargs_fixed)
# Determine Integrated Luminosity (If Data datasets present)
intLumi = GetLumi(datasetsMgr)
# Update to PU
datasetsMgr.updateNAllEventsToPUWeighted()
# Remove datasets
datasetsMgr.remove(kwargs.get("rmDataset"))
datasetsMgr_fixed.remove(kwargs_fixed.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)
plots.mergeRenameReorderForDataMC(datasetsMgr_fixed)
# Remove datasets (for merged names)
datasetsMgr.remove(kwargs.get("rmDataset"))
datasetsMgr_fixed.remove(kwargs_fixed.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_fixed, h_prefix + "MHT", **kwargs_fixed)
inclusiveHisto.setName(inclusiveHisto.getName() + " (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, **kwargs)
# Get Histos for Plotter
refHisto_, otherHistos_ = GetHistosForPlotter(
datasetsMgr, h_prefix + hName, **kwargs)
refHisto = refHisto_
otherHistos = otherHistos_ + fixedHistos
#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": 0.0, "ymax": 2.0}
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(1.6)
# 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(0.4, 0.11, "Runs " + datasetsMgr.loadRunRange(), 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, 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"),
# histograms.HistoGraph(graphs["IsoMass120"], "IsoMass120", drawStyle="L", legendStyle="lf"),
# histograms.HistoGraph(graphs["IsoMass160"], "IsoMass160", drawStyle="L", legendStyle="lf"),
# histograms.HistoGraph(graphs["IsoMass165"], "IsoMass165", drawStyle="L", legendStyle="lf"),
histograms.HistoGraph(graphs["InAccessible"], "InAccessible", drawStyle="F", 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,
# "IsoMass120": None,
# "IsoMass160": None,
# "IsoMass165": None,
"InAccessible": "Inaccessible",
})
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": 0.5, "ymax": tanbMax, "xmin": 200, "xmax": 3300})
plot.frame.GetXaxis().SetTitle(xlabel)
plot.frame.GetYaxis().SetTitle(limit.tanblimit)
ROOT.gPad.SetLogy(True)
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_{A}=%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 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(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():
style = tdrstyle.TDRStyle()
hNames = getHistoNames(kinVar)
# hName = hNames[0]
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(parseOpts.batchMode)
# Get all datasets from the mcrab dir
datasets = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=analysis)
# datasets = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=analysis, includeOnlyTasks="ChargedHiggs_HplusTB_HplusToTB_M_")
# datasets = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=analysis, includeOnlyTasks="ChargedHiggs_HplusTB_HplusToTB_M_")
# datasets = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=analysis, excludeTasks="Tau_Run2015C|Tau\S+25ns_Silver$|DYJetsToLL|WJetsToLNu$")
# Inform user of datasets retrieved
Print("Got following datasets from multicrab dir \"%s\"" % parseOpts.mcrab)
for d in datasets.getAllDatasets():
print "\t", d.getName()
# Determine Integrated Luminosity (If Data datasets present)
intLumi = 0.0
if len(datasets.getDataDatasets()) != 0:
# Load Luminosity JSON file
datasets.loadLuminosities(fname="lumi.json")
# Load RUN range
# runRange = datasets.loadRunRange(fname="runrange.json")
# Calculate Integrated Luminosity
intLumi = GetLumi(datasets)
# For-loop: All Histogram names
for counter, hName in enumerate(hNames):
plotName = hName #analysis + "_" + hName
saveName = os.path.join(savePath, plotName)
# Get Data or MC datasets
# dataDatasets = datasets.getDataDatasets()
# mcDatasets = datasets.getMCDatasets()
# Build ROOT histos from individual datasets
dataset1 = datasets.getDataset("ChargedHiggs_HplusTB_HplusToTB_M_400").getDatasetRootHisto(hName)
dataset2 = datasets.getDataset("ChargedHiggs_HplusTB_HplusToTB_M_300").getDatasetRootHisto(hName)
# dataset2 = datasets.getDataset("TT_ext3").getDatasetRootHisto(hName)
# datasets.getDataset("TT_ext3").setCrossSection(831.76)
# Normalise datasets
if normalizeTo == "One":
dataset1.normalizeToOne()
dataset2.normalizeToOne()
elif normalizeTo == "XSection":
dataset1.normalizeByCrossSection()
dataset2.normalizeByCrossSection()
elif normalizeTo == "Luminosity":
dataset1.normalizeToLumi(intLumi)
dataset2.normalizeToLumi(intLumi)
else:
isValidNorm(normalizeTo)
# Customise histos
histo1 = dataset1.getHistogram()
styles.signal200Style.apply(histo1)
# histo1.SetMarkerStyle(ROOT.kFullCircle)
# histo1.SetFillStyle(3001)
# histo1.SetFillColor(histo2.GetMarkerColor())
# removeNegatives(histo1)
# removeErrorBars(histo1)
histo1.Rebin(rebinFactor)
# Customise histos
histo2 = dataset2.getHistogram()
styles.ttStyle.apply(histo2)
# histo2.SetMarkerStyle(ROOT.kFullCross)
histo2.SetFillStyle(3001)
histo2.SetFillColor(styles.ttStyle.color)
# removeNegatives(histo2)
# removeErrorBars(histo2)
histo2.Rebin(rebinFactor)
# Create a comparison plot
p = plots.ComparisonPlot(histograms.Histo(histo1, "m_{H^{#pm}} = 400 GeV/c^{2}", "p", "P"),
histograms.Histo(histo2, "m_{H^{#pm}} = 300 GeV/c^{2}", "F", "HIST,E,9"))
# histograms.Histo(histo2, "t#bar{t}", "F", "HIST,E,9"))
# Create a comparison plot (One histogram is treated as a reference histogram, and all other histograms are compared with respect to that)
# p = plots.ComparisonManyPlot(histograms.Histo(histo1, "m_{H^{#pm}} = 200 GeV/c^{2}", "p", "P"),
# [histograms.Histo(histo2, "m_{H^{#pm}} = 300 GeV/c^{2}", "F", "HIST9"),
# histograms.Histo(histo3, "t#bar{t}", "F", "HIST9")])
# Customise plots
opts = {"ymin": 0.0, "binWidthX": histo1.GetXaxis().GetBinWidth(0), "xUnits": getUnitsX(kinVar)}
ratioOpts = {"ymin": 0.0, "ymax": 2.0 , "binWidthX": histo1.GetXaxis().GetBinWidth(0), "xUnits": getUnitsX(kinVar)}
p.createFrame(os.path.join(savePath, plotName), createRatio=ratio, opts=opts, opts2=ratioOpts)
# Customise Legend
moveLegend = {"dx": -0.1, "dy": +0.0, "dh": -0.2}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Customise text
if intLumi > 0.0:
histograms.addStandardTexts(lumi=intLumi)
else:
histograms.addStandardTexts()
# histograms.addText(0.4, 0.9, "Alexandros Attikis", 17)
# histograms.addText(0.4, 0.11, "Runs " + datasets.loadRunRange(), 17)
# Customise frame
p.setEnergy("13")
p.getFrame().GetYaxis().SetTitle( getTitleY(normalizeTo, kinVar, opts) )
p.getFrame().GetXaxis().SetTitle( getTitleX(kinVar, opts) )
if ratio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# Draw plots
p.draw()
# Save canvas under custom dir
if counter == 0:
Print("Saving plots in %s format(s)" % (len(saveFormats)) )
SavePlotterCanvas(p, savePath, saveName, saveFormats)
return
def main(opts):
global RunEra
mcrabName = opts.mcrab
print("mcrabNAME ISSSSSS ", mcrabName)
RunEra = mcrabName.split("_")[1]
print " Run Era is: ", RunEra
print " Plotting efficiency for trigger:", kwargs.get("trigger")
# Setup the style
style = tdrstyle.TDRStyle()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(opts.batchMode)
# Setup & configure the dataset manager
#datasetsMgr = GetSpecificDatasetsFromDir(opts.mcrab, opts, RunEra, reHLT_samples, **kwargs)
datasetsMgr = GetDatasetsFromDir(opts.mcrab, opts, **kwargs)
minRunRange = None
maxRunRange = None
Counter = 0
nDataDatasets = len(datasetsMgr.getDataDatasets())
for d in datasetsMgr.getDataDatasets():
Counter = Counter + 1
if Counter == 1:
minRunRange = d.getName().split("_")[-2]
if Counter == nDataDatasets:
maxRunRange = d.getName().split("_")[-1]
print "Data Name==> ", d.getName()
print "RunRange ==> ", d.getName().split(
"_")[-2], " - ", d.getName().split("_")[-1]
print "Run Range = ", minRunRange, " - ", maxRunRange
runRange = minRunRange + " - " + maxRunRange
intLumi = GetLumi(datasetsMgr)
print "intLumi=", intLumi
# Update to PU
datasetsMgr.updateNAllEventsToPUWeighted()
# Load Luminosities
datasetsMgr.loadLuminosities()
datasetsMgr.PrintLuminosities()
# Default merging & ordering: "Data", "QCD", "SingleTop", "Diboson"
#plots.mergeRenameReorderForDataMC(datasetsMgr) #WARNING: Merged MC histograms must be normalized to something!
#dataset_MC.normalizeMCByLuminosity()
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
plots.mergeRenameReorderForDataMC(datasetsMgr)
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
dataset_Data = datasetsMgr.getDataDatasets()
datasetsMgr.mergeMC() #ATHER
dataset_MC = None
dataset_MC = datasetsMgr.getMCDatasets()
#datasetsMgr.normalizeMCByLuminosity()
#datasetsMgr.mergeMC()
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
lumi = 0.0
for d in datasetsMgr.getDataDatasets():
lumi += d.getLuminosity()
SigSel = ["1BTag", "2BTag", "OR", "OR_PFJet450"]
for s in SigSel:
for xVar in xVariables:
print s, xVar
#if xVar == "pt6thJet":
# cutValue = 40
# cutLine = True
#for den,num
num_name = "hNum_" + xVar + "_RefTrg_OfflineSel_Signal" + s
den_name = "hDen_" + xVar + "_RefTrg_OfflineSel"
# Get the save path and name
plotName = "Eff" + "_" + xVar + "_" + s
savePath, saveName = GetSavePathAndName(plotName, **kwargs)
# Get Efficiency Plots
eff_Data = getEfficiency(datasetsMgr, dataset_Data, num_name,
den_name, **kwargs)
eff_MC = getEfficiency(datasetsMgr, dataset_MC, num_name, den_name,
**kwargs)
if xVar == "pt6thJet":
xMin = 29.0
xMax = 125.0
elif xVar == "eta6thJet":
xMin = -2.5
xMax = 2.5
elif xVar == "phi6thJet":
xMin = -3.2
xMax = 3.2
elif xVar == "Ht":
xMin = 350
xMax = 2000
elif xVar == "nBTagJets":
xMin = 0
xMax = 10
elif xVar == "pu":
xMin = 0
xMax = 50
elif xVar == "CSV":
xMin = 0
xMax = 1
elif xVar == "JetMulti":
xMin = 4
xMax = 15
elif xVar == "BJetMulti":
xMin = 0
xMax = 8
# Apply Styles
styles.dataStyle.apply(eff_Data)
styles.mcStyle.apply(eff_MC)
# Marker Style
eff_Data.SetMarkerSize(1)
# Create Comparison Plot
p = plots.ComparisonPlot(
histograms.HistoGraph(eff_Data, "eff_Data", "p", "P"),
histograms.HistoGraph(eff_MC, "eff_MC", "p", "P"))
opts = {"ymin": 0, "ymax": 1.1, "xmin": xMin, "xmax": xMax}
opts2 = {"ymin": 0.8, "ymax": 1.05}
if xVar == "pu":
moveLegend = {"dx": -0.44, "dy": -0.62, "dh": -0.2}
else:
moveLegend = {"dx": -0.44, "dy": -0.57, "dh": -0.2}
p.histoMgr.setHistoLegendLabelMany({
"eff_Data": "Data",
"eff_MC": "Simulation"
})
createRatio = True #kwargs.get("ratio")
p.createFrame(saveName,
createRatio=createRatio,
opts=opts,
opts2=opts2)
# Set Legend
p.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Set Log & Grid
SetLogAndGrid(p, **kwargs)
# Set Titles
p.getFrame().GetYaxis().SetTitle(kwargs.get("ylabel"))
if xVar == "pt6thJet":
p.getFrame().GetXaxis().SetTitle("p_{T,6thJet} (Gev/c)")
elif xVar == "eta6thJet":
p.getFrame().GetXaxis().SetTitle("#eta_{6thJet}")
elif xVar == "phi6thJet":
p.getFrame().GetXaxis().SetTitle("#phi_{6thJet}")
elif xVar == "Ht":
p.getFrame().GetXaxis().SetTitle("H_{T} (Gev/c)")
elif xVar == "nBTagJets":
p.getFrame().GetXaxis().SetTitle("nB-Jet")
elif xVar == "pu":
p.getFrame().GetXaxis().SetTitle("nPU")
elif xVar == "CSV":
p.getFrame().GetXaxis().SetTitle("CSV")
elif xVar == "JetMulti":
p.getFrame().GetXaxis().SetTitle("Jet Multiplicity")
elif xVar == "BJetMulti":
p.getFrame().GetXaxis().SetTitle("B-Jet Multiplicity")
if createRatio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
#if xVar == "pt6thJet" and s == "OR":
# plist = [0.7, 0.99, 1000, 0.16, 28.0]
# Fit_Richards(30.0, 120.0, p, eff_Data, plist)
# plist = [0.72, 0.91, 0.212, 0.15, 50.0]
# Fit_Richards(30.0, 120.0, p, eff_MC, plist)
#elif xVar == "Ht" and s == "OR":
# plist = [0.00005, 0.988, 0.000000109, 0.15, 29.0]
# Fit_Richards(350.0, 2000.0, p, eff_Data, plist)
#plist = [0.0003, 0.97, 0.45, 0.24, 43.0]
#Fit_Richards(500.0, 2000.0, p, eff_MC, plist)
#elif xVar == "nBTagJets":
#plist = [0.07, 0.984, 0.45, 0.24, 43.0]
#Fit_Richards(30.0, 120.0, p, eff_Data, plist)
#elif xVar == "pu":
#plist = [0.07, 0.984, 0.45, 0.24, 43.0]
#Fit_Richards(30.0, 120.0, p, eff_Data, plist)
# Draw
histograms.addText(0.30, 0.10, "Runs " + runRange, 17)
histograms.addText(0.30, 0.15, "2016", 17)
histograms.addStandardTexts(lumi=lumi)
p.draw()
# Save the canvas to a file
SaveAs(p, kwargs.get("savePath"), saveName,
kwargs.get("saveFormats"), True)
'''
# IN SLICES OF HT
HTSlices = ["450ht600","600ht800","800ht1000", "1000ht1250", "1250ht1500", "1500ht2000"]
for s in SigSel:
for hsl in HTSlices:
num_name = "Num_pt6thJet_Vs_"+hsl+"_RefTrg_OfflineSel_"+s
den_name = "Den_pt6thJet_Vs_"+hsl+"_RefTrg_OfflineSel"
# Get the save path and name
plotName = "Eff_pt6thJet_Vs_"+hsl+"_"+s
savePath, saveName = GetSavePathAndName(plotName, **kwargs)
# Get Efficiency Plots
eff_Data = getEfficiency(datasetsMgr, dataset_Data, num_name, den_name , **kwargs)
eff_MC = getEfficiency(datasetsMgr, dataset_MC, num_name, den_name, **kwargs)
xMin = 29.0
xMax = 125.0
# Apply Styles
styles.dataStyle.apply(eff_Data)
styles.mcStyle.apply(eff_MC)
# Marker Style
eff_Data.SetMarkerSize(1)
# Create Comparison Plot
p = plots.ComparisonPlot(histograms.HistoGraph(eff_Data, "eff_Data","p","P"),
histograms.HistoGraph(eff_MC, "eff_MC", "p", "P"))
opts = {"ymin": 0 , "ymax": 1.1, "xmin":xMin, "xMax":xMax}
opts2 = {"ymin": 0.7, "ymax": 1.3}
moveLegend = {"dx": -0.44, "dy": -0.57, "dh": -0.2 }
p.histoMgr.setHistoLegendLabelMany({"eff_Data": "Data", "eff_MC": "Simulation"})
createRatio = True #kwargs.get("ratio")
p.createFrame(saveName, createRatio=createRatio, opts=opts, opts2=opts2)
# Set Legend
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Set Log & Grid
SetLogAndGrid(p, **kwargs)
# Set Titles
p.getFrame().GetYaxis().SetTitle(kwargs.get("ylabel"))
p.getFrame().GetXaxis().SetTitle("p_{T} (Gev/c)")
if createRatio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# Draw
histograms.addText(0.30, 0.10, "Runs "+runRange, 17)
histograms.addText(0.30, 0.15, "2016", 17)
histograms.addStandardTexts(lumi=lumi)
p.draw()
# Save the canvas to a file
SaveAs(p, kwargs.get("savePath"), saveName, kwargs.get("saveFormats"), True)
PTSlices = ["40pt50","50pt60", "60pt70", "70pt90", "90pt120"]
for s in SigSel:
for psl in PTSlices:
num_name = "Num_ht_Vs_"+psl+"_HLT_PFHT350_"+s
den_name = "Den_ht_Vs_"+psl+"_HLT_PFHT350"
# Get the save path and name
plotName = "Eff_ht_Vs_"+psl+"_"+s
savePath, saveName = GetSavePathAndName(plotName, **kwargs)
# Get Efficiency Plots
eff_Data = getEfficiency(datasetsMgr, dataset_Data, num_name, den_name , **kwargs)
eff_MC = getEfficiency(datasetsMgr, dataset_MC, num_name, den_name, **kwargs)
xMin = 450
xMax = 2000
# Apply Styles
styles.dataStyle.apply(eff_Data)
styles.mcStyle.apply(eff_MC)
# Marker Style
eff_Data.SetMarkerSize(1)
# Create Comparison Plot
p = plots.ComparisonPlot(histograms.HistoGraph(eff_Data, "eff_Data","p","P"),
histograms.HistoGraph(eff_MC, "eff_MC", "p", "P"))
opts = {"ymin": 0 , "ymax": 1.1, "xmin":xMin, "xMax":xMax}
opts2 = {"ymin": 0.7, "ymax": 1.3}
moveLegend = {"dx": -0.44, "dy": -0.57, "dh": -0.2 }
p.histoMgr.setHistoLegendLabelMany({"eff_Data": "Data", "eff_MC": "Simulation"})
createRatio = True #kwargs.get("ratio")
#if eff_Data != None and eff_MC != None:
p.createFrame(saveName, createRatio=createRatio, opts=opts, opts2=opts2)
# Set Legend
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Set Log & Grid
SetLogAndGrid(p, **kwargs)
# Set Titles
p.getFrame().GetYaxis().SetTitle(kwargs.get("ylabel"))
p.getFrame().GetXaxis().SetTitle("H_{T} (Gev/c)")
if createRatio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# Draw
histograms.addText(0.30, 0.10, "Runs "+runRange, 17)
histograms.addText(0.30, 0.15, "2016", 17)
histograms.addStandardTexts(lumi=lumi)
p.draw()
# Save the canvas to a file
SaveAs(p, kwargs.get("savePath"), saveName, kwargs.get("saveFormats"), True)
'''
return
def Fit (datasets, histo, function):
FitList = []
for dataset in datasets:
datasetName = dataset.getName()
print "Dataset = ", datasetName
hh = dataset.getDatasetRootHisto(histo)
hh.normalizeToOne()
h = hh.getHistogram()
#h = dataset.getDatasetRootHisto(histo).getHistogram()
xMin = h.GetXaxis().GetXmin()
xMax = h.GetXaxis().GetXmax()
yMin = 0
yMax = 1.2
#statOption = ROOT.TEfficiency.kFNormal
if "TT" in datasetName:
if function == "gaus":
fitGauss = ROOT.TF1("fitGauss", "gaus", -2.5, 2.5)
# TF1 *fitBoFreq = new TF1("fitBoFreq","[0]*x+[1]",0,20);
# h.Fit("gaus")
#fitTest = ROOT.TF1("fitTest", "0.01", -2.5, 2.5)
h.Fit("fitGauss","SRBM")
#h.GetListOfFunctions().Add(fitTest)
legend = "TT"
legend = "a legend"
print "Legend", legend
saveName = histo.split("/")[-1]+"_Fit"
print saveName
xTitle = "fixXTitle"
yTitle = "fixYTitle"
yMin = 0.
yMax = 0.03
xMin = -2.3
xMax = 2.3
kwargs = {}
options = {"ymin": yMin , "ymax": yMax, "xmin":xMin, "xMax":xMax}
FitList.append(h)
#p = plots.MCPlot(dataset, h, normalizeToLumi=0, saveFormats=[], **kwargs)
p = plots.PlotBase(datasetRootHistos=FitList, saveFormats=kwargs.get("saveFormats"))
p.createFrame(saveName, opts=options)
p.getFrame().GetXaxis().SetTitle(xTitle)
p.getFrame().GetYaxis().SetTitle(yTitle)
#p.histoMgr.setHistoDrawStyle(datasetName, "AP")
# Set range
p.getFrame().GetXaxis().SetRangeUser(xMin, xMax)
moveLegend = {"dx": -0.55, "dy": -0.01, "dh": -0.1}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Add Standard Texts to plot
histograms.addStandardTexts()
p.draw()
# Save plot in all formats
savePath = os.path.join(opts.saveDir, "HplusMasses", histo.split("/")[0], opts.optMode)
save_path = savePath
SavePlot(p, saveName, save_path)
return
def main():
style = tdrstyle.TDRStyle()
hNames = getHistoNames(kinVar)
# hName = hNames[0]
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(parseOpts.batchMode)
# Get all datasets from the mcrab dir
datasets = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab],
analysisName=analysis)
# datasets = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=analysis, includeOnlyTasks="ChargedHiggs_HplusTB_HplusToTB_M_")
# datasets = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=analysis, includeOnlyTasks="ChargedHiggs_HplusTB_HplusToTB_M_")
# datasets = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=analysis, excludeTasks="Tau_Run2015C|Tau\S+25ns_Silver$|DYJetsToLL|WJetsToLNu$")
# Inform user of datasets retrieved
Print("Got following datasets from multicrab dir \"%s\"" % parseOpts.mcrab)
for d in datasets.getAllDatasets():
print "\t", d.getName()
# Determine Integrated Luminosity (If Data datasets present)
intLumi = 0.0
if len(datasets.getDataDatasets()) != 0:
# Load Luminosity JSON file
datasets.loadLuminosities(fname="lumi.json")
# Load RUN range
# runRange = datasets.loadRunRange(fname="runrange.json")
# Calculate Integrated Luminosity
intLumi = GetLumi(datasets)
# For-loop: All Histogram names
for counter, hName in enumerate(hNames):
plotName = hName #analysis + "_" + hName
saveName = os.path.join(savePath, plotName)
# Get Data or MC datasets
# dataDatasets = datasets.getDataDatasets()
# mcDatasets = datasets.getMCDatasets()
# Build ROOT histos from individual datasets
dataset1 = datasets.getDataset(
"ChargedHiggs_HplusTB_HplusToTB_M_400").getDatasetRootHisto(hName)
dataset2 = datasets.getDataset(
"ChargedHiggs_HplusTB_HplusToTB_M_300").getDatasetRootHisto(hName)
# dataset2 = datasets.getDataset("TT_ext3").getDatasetRootHisto(hName)
# datasets.getDataset("TT_ext3").setCrossSection(831.76)
# Normalise datasets
if normalizeTo == "One":
dataset1.normalizeToOne()
dataset2.normalizeToOne()
elif normalizeTo == "XSection":
dataset1.normalizeByCrossSection()
dataset2.normalizeByCrossSection()
elif normalizeTo == "Luminosity":
dataset1.normalizeToLumi(intLumi)
dataset2.normalizeToLumi(intLumi)
else:
isValidNorm(normalizeTo)
# Customise histos
histo1 = dataset1.getHistogram()
styles.signal200Style.apply(histo1)
# histo1.SetMarkerStyle(ROOT.kFullCircle)
# histo1.SetFillStyle(3001)
# histo1.SetFillColor(histo2.GetMarkerColor())
# removeNegatives(histo1)
# removeErrorBars(histo1)
histo1.Rebin(rebinFactor)
# Customise histos
histo2 = dataset2.getHistogram()
styles.ttStyle.apply(histo2)
# histo2.SetMarkerStyle(ROOT.kFullCross)
histo2.SetFillStyle(3001)
histo2.SetFillColor(styles.ttStyle.color)
# removeNegatives(histo2)
# removeErrorBars(histo2)
histo2.Rebin(rebinFactor)
# Create a comparison plot
p = plots.ComparisonPlot(
histograms.Histo(histo1, "m_{H^{#pm}} = 400 GeV/c^{2}", "p", "P"),
histograms.Histo(histo2, "m_{H^{#pm}} = 300 GeV/c^{2}", "F",
"HIST,E,9"))
# histograms.Histo(histo2, "t#bar{t}", "F", "HIST,E,9"))
# Create a comparison plot (One histogram is treated as a reference histogram, and all other histograms are compared with respect to that)
# p = plots.ComparisonManyPlot(histograms.Histo(histo1, "m_{H^{#pm}} = 200 GeV/c^{2}", "p", "P"),
# [histograms.Histo(histo2, "m_{H^{#pm}} = 300 GeV/c^{2}", "F", "HIST9"),
# histograms.Histo(histo3, "t#bar{t}", "F", "HIST9")])
# Customise plots
opts = {
"ymin": 0.0,
"binWidthX": histo1.GetXaxis().GetBinWidth(0),
"xUnits": getUnitsX(kinVar)
}
ratioOpts = {
"ymin": 0.0,
"ymax": 2.0,
"binWidthX": histo1.GetXaxis().GetBinWidth(0),
"xUnits": getUnitsX(kinVar)
}
p.createFrame(os.path.join(savePath, plotName),
createRatio=ratio,
opts=opts,
opts2=ratioOpts)
# Customise Legend
moveLegend = {"dx": -0.1, "dy": +0.0, "dh": -0.2}
p.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Customise text
if intLumi > 0.0:
histograms.addStandardTexts(lumi=intLumi)
else:
histograms.addStandardTexts()
# histograms.addText(0.4, 0.9, "Alexandros Attikis", 17)
# histograms.addText(0.4, 0.11, "Runs " + datasets.loadRunRange(), 17)
# Customise frame
p.setEnergy("13")
p.getFrame().GetYaxis().SetTitle(getTitleY(normalizeTo, kinVar, opts))
p.getFrame().GetXaxis().SetTitle(getTitleX(kinVar, opts))
if ratio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# Draw plots
p.draw()
# Save canvas under custom dir
if counter == 0:
Print("Saving plots in %s format(s)" % (len(saveFormats)))
SavePlotterCanvas(p, savePath, saveName, saveFormats)
return
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 PlotCutFlowEfficiency(h, datasetsMgr, intLumi):
Verbose("Plotting Cut Flow Efficiency")
efficiencyList = []
signalMass.append("QCD")
for mass in signalMass:
xValues = []
yValues = []
hSignal = getHisto(datasetsMgr, h, mass, intLumi)
for i in range (0, hSignal.GetXaxis().GetNbins()):
# Cut value
cut = hSignal.GetBinCenter(i)
passed = hSignal.Integral(i, hSignal.GetXaxis().GetNbins())
total = hSignal.Integral()
eff = float(passed)/total
xValues.append(cut)
yValues.append(eff)
# Create the Significance Plot
tGraph = ROOT.TGraph(len(xValues), array.array("d", xValues), array.array("d", yValues))
if "M_" in mass:
styles.getSignalStyleHToTB_M(mass.split("_")[-1]).apply(tGraph)
else:
styles.getQCDStyle().apply(tGraph)
drawStyle = "CPE"
legName = plots._legendLabels[mass]
effGraph = histograms.HistoGraph(tGraph, legName, "lp", drawStyle)
efficiencyList.append(effGraph)
saveName = "QGLREfficiency"
# Create the plot with all the significance plots
p = plots.PlotBase(efficiencyList, saveFormats=["pdf"])
p.createFrame(saveName)
# Customise frame
p.setEnergy("13")
p.getFrame().GetYaxis().SetLabelSize(18)
p.getFrame().GetXaxis().SetLabelSize(20)
p.getFrame().GetYaxis().SetTitle("Efficiency / 0.01")
p.getFrame().GetXaxis().SetTitle("QGLR Cut")
# Add Standard Texts to plot
histograms.addStandardTexts()
# Customise Legend
moveLegend = {"dx": -0.50, "dy": -0.5, "dh": -0.1}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
p.draw()
savePath = opts.saveDir
if opts.url:
savePath = opts.saveDir.replace("/publicweb/m/mkolosov/", "http://home.fnal.gov/~mkolosov/")
savePath = os.path.join(opts.saveDir, opts.folder, saveName, opts.optMode)
SavePlot(p, saveName, savePath)
return
def PlotProb(datasets, numPath, denPath):
EfficiencyList = []
index = 0
for dataset in datasets:
datasetName = dataset.getName()
print "Dataset = ", datasetName
statOption = ROOT.TEfficiency.kFNormal
## n = dataset.getDatasetRootHisto(numPath).getHistogram()
# n.normalizeToOne()
## d = dataset.getDatasetRootHisto(denPath).getHistogram()
nn = dataset.getDatasetRootHisto(numPath)
nn.normalizeToLuminosity(35.8*(10**3))
n = nn.getHistogram()
dd = dataset.getDatasetRootHisto(denPath)
# dd.normalizeToOne()
# dd.normalizeToLuminosity(36.3*(10**3))
dd.normalizeToLuminosity(35.8*(10**3))
# dd.normalizeByCrossSection()
d = dd.getHistogram()
# if "TT" in datasetName and ("Higgs" in numPath or "LdgBjetPt_isLdgFreeBjet" in numPath):
# continue
# elif "M_" in datasetName and not ("Higgs" in numPath or "LdgBjetPt_isLdgFreeBjet" in numPath):
# continue
if "Event" in numPath:
n.Rebin(10)
d.Rebin(10)
else:
n.Rebin(5)
d.Rebin(5)
if d.GetEntries() == 0 or n.GetEntries() == 0:
continue
if n.GetEntries() > d.GetEntries():
continue
# Check Negatives
CheckNegatives(n, d, True)
# Remove Negatives
RemoveNegatives(n)
nBins = d.GetNbinsX()
xMin = d.GetXaxis().GetXmin()
xMax = d.GetXaxis().GetXmax()
binwidth = int(n.GetBinWidth(0))
# ----------------------------------------------------------------------------------------- #
# Ugly hack to ignore EMPTY (in the wanted range) histograms with overflows/underflows
# ----------------------------------------------------------------------------------------- #
if (0):
print "\n"
print "=========== getEfficiency:"
print "Dataset = ", dataset.getName()
print "Numerator: entries=", n.GetEntries(), " Bins=", n.GetNbinsX(), " Low edge=", n.GetBinLowEdge(1)
print "Denominator: entries=", d.GetEntries(), " Bins=", d.GetNbinsX(), " Low edge=", d.GetBinLowEdge(1)
print "\n"
print ">>>>>> Sanity Check: <<<<<<"
print "Numerator Mean = ", n.GetMean()
print "Numerator RMS = ", n.GetRMS()
print "Numerator Integral = ", n.Integral(1, nBins)
print "Denominator Mean = ", d.GetMean()
print "Denominator RMS = ", d.GetRMS()
print "Denominator Integral = ", d.Integral(1, nBins)
if (n.GetMean() == 0 or d.GetMean() == 0): continue
if (n.GetRMS() == 0 or d.GetRMS() == 0): continue
if (n.Integral(1,nBins) == 0 or d.Integral(1,nBins) == 0): continue
# if not (ROOT.TEfficiency.CheckConsistency(n,d)): continue;
effic = ROOT.TEfficiency(n,d)
effic.SetStatisticOption(statOption)
weight = 1
if dataset.isMC():
weight = dataset.getCrossSection()
effic.SetWeight(weight)
eff = convert2TGraph(effic)
# Apply Styles
if "TT" in datasetName:
if index == 0:
styles.signalStyleHToTB500.apply(eff)
# styles.ttStyle.apply(eff)
eff.SetLineStyle(1)
eff.SetLineWidth(3)
eff.SetLineColor(619)
legend = "Default: t#bar{t}"
index = 1
else:
styles.signalStyleHToTB500.apply(eff)
# styles.ttStyle.apply(eff)
eff.SetLineStyle(1)
eff.SetLineWidth(3)
eff.SetLineColor(417)
legend = "#Delta R(q,q')>0.8: t#bar{t}"
elif "M_500" in datasetName:
styles.signalStyleHToTB500.apply(eff)
legend = "H^{+} m_{H^{+}} = 500 GeV"
elif "M_300" in datasetName:
styles.signalStyleHToTB300.apply(eff)
legend = "H^{+} m_{H^{+}} = 300 GeV"
elif "M_1000" in datasetName:
styles.signalStyleHToTB1000.apply(eff)
legend = "H^{+} m_{H^{+}} = 1000 GeV"
elif "M_800" in datasetName:
styles.signalStyleHToTB800.apply(eff)
legend = "H^{+} m_{H^{+}} = 800 GeV"
elif "M_200" in datasetName:
styles.signalStyleHToTB200.apply(eff)
legend = "H^{+} m_{H^{+}} = 200 GeV"
else:
styles.ttStyle.apply(eff)
legend = "other"
EfficiencyList.append(histograms.HistoGraph(eff, legend, "lp", "P"))
saveName = "Eff_"+numPath.split("/")[-1]+"Over"+denPath.split("/")[-1]
if "Pt" in numPath:
xMin = 0.0
# rebinX = 2
xMax = 805.0
# xMax = 555.0 # For topPt < 500GeV
xTitle = "p_{T} (GeV/c)"
units = "GeV/c"
_format = "%0.1f" + units
yTitle = "Efficiency / " + str(binwidth) + " "+units
yMin = 0.0
yMax = 1.1
elif "_Eta" in numPath:
xMin = -3.0
xMax = +3.0
xTitle = "#eta"
yTitle = "Efficiency"
yMin = 0.0
yMax = 1.1
elif "_Mass" in numPath:
xMin = 50.0
xMax = 300
xTitle = "M (GeV/c^{2})"
yTitle = "Efficiency"
yMin = 0.0
yMax = 1.1
elif "_Phi" in numPath:
xMin = -3
xMax = +3
xTitle = "#phi"
yTitle = "Efficiency"
yMin = 0.0
yMax = 1.1
else:
xMin = 0.0
xMax = 250.0
xTitle = "xTitle"
yTitle = "yTitle"
yMin = 0.0
yMax = 1.1
if "Fake" in numPath:
# xMin = 95.0
# rebinX = 4
xMax = 805.0
xTitle = "candidate p_{T} (GeV/c)"
units = "GeV/c"
_format = "%0.1f" + units
yTitle = "Misid rate / " + str(binwidth) + " " +units
yMin = 0.0
yMax = 0.11
if "Event" in numPath:
rebinX = 2
# xMin = 95.0
xMax = 805.0
xTitle = "candidate p_{T} (GeV/c)"
units = "GeV/c"
_format = "%0.1f" + units
yTitle = "Efficiency / " + str(binwidth) + " "+ units
yMin = 0.0
yMax = 1.1
if "NonMatched" in numPath:
xMin = 90.0
rebinX = 4
xMax = 700.0
xMax = 555.0 # For topPt < 500GeV
xTitle = "p_{T} (GeV)"
yTitle = "Efficiency"
yMin = 0.0
yMax = 0.15
if "AllTopQuarkPt_MatchedBDT" in numPath and "TopQuarkPt" in denPath:
xMin = 0.0
# rebinX = 4
xMax = 805.0 #705
units = "GeV/c"
xTitle = "generated top p_{T} (GeV/c)"
yTitle = "Efficiency / " + str(binwidth) + " " + units
yMin = 0.0
yMax = 1.1
if "SameFake" in numPath:
xMin = 95.0
rebinX = 4
xMax = 705.0
xMax = 555.0 # For topPt < 500GeV
xTitle = "p_{T} [GeV]"
yTitle = "Efficiency"
yMin = 0.0
yMax = 1.1
options = {"ymin": yMin , "ymax": yMax, "xmin":xMin, "xMax":xMax}
# if "TT" in datasetName and ("Higgs" in numPath or "LdgBjetPt_isLdgFreeBjet" in numPath):
# return
# if "M_" in datasetName and not ("Higgs" in numPath or "LdgBjetPt_isLdgFreeBjet" in numPath):
# return
p = plots.PlotBase(datasetRootHistos=EfficiencyList, saveFormats=kwargs.get("saveFormats"))
#p = plots.ComparisonManyPlot(refEff, EfficiencyList, saveFormats=[])
p.createFrame(saveName, opts=options)
# p.histoMgr.forEachHisto(lambda h: h.getRootHisto().RebinX(kwargs.get("rebinX")))
# Set Titles
# p.getFrame().GetYaxis().SetTitle(kwargs.get("ylabel")) #"ylabel"
p.getFrame().GetXaxis().SetTitle(xTitle)
p.getFrame().GetYaxis().SetTitle(yTitle)
# Set range
p.getFrame().GetXaxis().SetRangeUser(xMin, xMax)
moveLegend = {"dx": -0.55, "dy": -0.02, "dh": -0.2}
# moveLegend = {"dx": -0.55, "dy": -0.01, "dh": -0.1}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Add Standard Texts to plot
histograms.addStandardTexts()
p.draw()
# Save plot in all formats
savePath = os.path.join(opts.saveDir, "HplusMasses", numPath.split("/")[0], opts.optMode)
save_path = savePath + opts.MVAcut
# SavePlot(p, saveName, savePath)
SavePlot(p, saveName, save_path)
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 main(opts):
# Setup the style
style = tdrstyle.TDRStyle()
# 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 datasets:All JetHT to "Data", QCD_Pt to "QCD",
# QCD_bEnriched to "QCD_b", single-top to "SingleTop", WW, WZ, ZZ to "Diboson"
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()
# ========================================
# Histograms
# ========================================
for counter, hName in enumerate(hNames):
# Get the save path and name, Get Histos for Plotter
savePath, saveName = GetSavePathAndName(hName, **kwargs)
refHisto, otherHistos = GetHistosForPlotter(datasetsMgr, hName, **kwargs)
# Create a comparison plot
#p = plots.ComparisonManyPlot(refHisto, otherHistos)
p = plots.DataMCPlot(datasetsMgr, hName)
# Remove negative contributions
if 0:
RemoveNegativeBins(datasetsMgr, hName, p)
# Y-axis
ratioOpts = {"ymin": 0.0, "ymax": 2.0}
if kwargs.get("logY")==True:
opts = {"ymin": 1e-2, "ymaxfactor": 100}
else:
opts = {"ymin": 0.0, "ymaxfactor": 1.2}
# ========================================
# Frame
# ========================================
p.createFrame(saveName, createRatio=kwargs.get("createRatio"), opts=opts, opts2=ratioOpts)
# Legend
moveLegend = {"dx": -0.11, "dy": +0.0, "dh": +0.2}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
# 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))
if 0:
p.removeLegend()
# Axes
#p.getFrame().GetYaxis().SetTitle( getTitleY(refHisto, **kwargs) )
#p.getFrame().GetYaxis().SetTitle( getTitleY(p.histoMgr.getHistos()[0], **kwargs) )
if kwargs.get("createRatio"):
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# Set Log and Grid
SetLogAndGrid(p, **kwargs)
# Cut line / Cut 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)
# Draw the final plot
p.draw()
# ========================================
# Add Text
# ========================================
histograms.addStandardTexts(lumi=intLumi)
# histograms.addText(0.4, 0.9, "Alexandros Attikis", 17)
# histograms.addText(0.4, 0.11, "Runs " + datasetsMgr.loadRunRange(), 17)
# Save the canvas to a file
SaveAs(p, savePath, saveName, kwargs.get("saveFormats"), counter==0)
return
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 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]]
datasets = dataset.getDatasetsFromMulticrabDirs(paths)
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="SingleNeutrino")
# datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,includeOnlyTasks="QCD")
# analysis = datasets.getAllDatasets()[0].getAnalysisName()
#datasetsMC = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="ZeroBias")
createRatio = False
# for d in datasets.getAllDatasets():
# print d.getName()
style = tdrstyle.TDRStyle()
dataset1 = datasets.getDataDatasets()
#### dataset1 = datasets.getMCDatasets()
rateETM120 = 5521.35 # Hz
#effETM120 = 0.000611208781402 #8.75017364672e-05
#effETM120 = 0.000619219298648
effETM120 = 0.000203698623826
#### effETM120 = 0.186701136914 # QCD
scale = rateETM120/effETM120*0.001 #(Hz->kHz)
# for d in dataset1:
# d.scale(scale)
dataset2 = dataset1
createRatio = False
#if isinstance(datasetsMC,dataset.DatasetManager):
# dataset2 = datasetsMC.getMCDatasets()
# createRatio = True
eff1PU = getEfficiency(dataset1,"NumeratorPU","DenominatorPU")
scaleGraph(eff1PU,scale)
namePU = "TauMET_"+analysis+"_nVtx"
legend1 = "Data"
legend2 = "Simulation"
styles.dataStyle.apply(eff1PU)
eff1PU.SetMarkerSize(1)
#eff2PU.SetMarkerSize(1.5)
pPU = plots.PlotBase([histograms.HistoGraph(eff1PU, "eff1", "p", "P")])
pPU.histoMgr.setHistoLegendLabelMany({"eff1": legend1})
# Fit
# yval = fit("Data",pPU,eff1PU,30,59)
yval = fit("Data",pPU,eff1PU,5,59)
#### opts = {"ymin": 0, "ymax": 6, "xmax": 60}
opts = {"ymin": 0, "ymax": 20, "xmax": 60}
#### opts = {"ymin": 0, "ymax": 300, "xmax": 60}
opts2 = {"ymin": 0.5, "ymax": 1.5}
moveLegend = {"dx": -0.5, "dy": -0.1, "dh": -0.1}
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))
pPU.getFrame().GetYaxis().SetTitle("L1 rate (kHz)")
pPU.getFrame().GetXaxis().SetTitle("n vertices")
if createRatio:
pPU.getFrame2().GetYaxis().SetTitle("Ratio")
pPU.getFrame2().GetYaxis().SetTitleOffset(1.6)
pPU.draw()
print "check frame min,max",pPU.getFrame().GetYaxis().GetXmin(),pPU.getFrame().GetYaxis().GetXmax()
x = array.array('d',[55,55,0])
y = array.array('d',[0,yval,yval])
n = 3
vert = ROOT.TGraph(n,x,y)
vert.SetLineStyle(2)
vert.SetLineColor(2)
vert.SetLineWidth(2)
vert.Draw("L")
lumi = 0.0
for d in datasets.getDataDatasets():
print "luminosity",d.getName(),d.getLuminosity()
lumi += d.getLuminosity()
histograms.addStandardTexts(lumi=lumi)
if not os.path.exists(plotDir):
os.mkdir(plotDir)
pPU.save(formats)
print "Output written in",plotDir
def main():
style = tdrstyle.TDRStyle()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(parseOpts.batchMode)
# Get all datasets from the mcrab dir
datasetsMgr = GetDatasetsFromDir(parseOpts.mcrab, 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
# 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 counter, hName in enumerate(hNames):
# Get the save path and name
savePath, saveName = GetSavePathAndName(hName, **kwargs)
# Get Histos for Plotter
refHisto, otherHistos = GetHistosForPlotter(datasetsMgr, hName, **kwargs)
# Create a comparison plot
p = plots.ComparisonManyPlot(refHisto, otherHistos)
# Remove negative contributions
#RemoveNegativeBins(datasetsMgr, hName, p)
# Create a frame
if kwargs.get("logY"):
opts = {"ymin": 1e-5, "ymaxfactor": 10}
else:
opts = {"ymin": 0.0, "ymaxfactor": 1.2}
ratioOpts = {"ymin": 0.0, "ymax": 2.0}
p.createFrame(saveName, createRatio=kwargs.get("createRatio"), opts=opts, opts2=ratioOpts)
# Customise Legend
moveLegend = {"dx": -0.1, "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(1.6)
# 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(0.4, 0.11, "Runs " + datasetsMgr.loadRunRange(), 17)
# Save canvas under custom dir
SaveAs(p, savePath, saveName, kwargs.get("saveFormats"))
return
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 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 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 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 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(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")
for optMode in optModes:
plot = plots.PlotBase()
color = 1
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")
for HISTONAME in histoNameList:
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)
invjet_name = "MTInvertedTauIdAfterAllSelectionsPlusJetFakeTau"
basejet_name = "MTBaselineTauIdAfterAllSelectionsPlusJetFakeTau"
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)
if HISTONAME == "shapeEWKGenuineTausTransverseMass":
legendName = "Data-driven Fake Taus"
else:
legendName = "Simulated Fake Taus"
legendName = legendName.replace("Plus","")
mt.SetName(legendName)
mt.SetLineColor(color)
mt.Add(mt_ewk,-1)
if HISTONAME == "shapeEWKGenuineTausTransverseMass":
mt.Scale(QCDInvertedNormalizationFilteredEWKFakeTaus[str(i)])
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)
h.Add(mt_ewk,-1)
if HISTONAME == "shapeEWKGenuineTausTransverseMass":
h.Scale(QCDInvertedNormalizationFilteredEWKFakeTaus[str(i)])
else:
h.Scale(QCDInvertedNormalization[str(i)])
mt.Add(h)
if HISTONAME == "shapeTransverseMass":
mt.Add(mt_signalfaketaus)
plot.histoMgr.appendHisto(histograms.Histo(mt,mt.GetName()))
style = tdrstyle.TDRStyle()
#plot.createFrame("mt")
#plot.createFrame(HISTONAME.replace("shape","final"))
plot.createFrame(optMode.replace("Opt","Mt_DataDrivenVsMC_"))
moveLegend={"dx": -0.3,"dy": 0.}
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()
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 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 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(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 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 PlotHistosAndCalculateTF(datasetsMgr, histoList, binLabels, opts):
# Get the histogram customisations (keyword arguments)
_kwargs = GetHistoKwargs(histoList[0])
# Get the root histos for all datasets and Control Regions (CRs)
regions = ["SR", "VR", "CRone", "CRtwo"]
rhDict = GetRootHistos(datasetsMgr, histoList, regions, binLabels)
#=========================================================================================
# Calculate the Transfer Factor (TF) and save to file
#=========================================================================================
manager = FakeBNormalization.FakeBNormalizationManager(binLabels,
opts.mcrab,
opts.optMode,
verbose=False)
if opts.inclusiveOnly:
#manager.CalculateTransferFactor(binLabels[0], rhDict["CRone-FakeB"], rhDict["CRtwo-FakeB"])
binLabel = "Inclusive"
manager.CalculateTransferFactor("Inclusive",
rhDict["FakeB-CRone-Inclusive"],
rhDict["FakeB-CRtwo-Inclusive"])
else:
for bin in binLabels:
manager.CalculateTransferFactor(bin,
rhDict["FakeB-CRone-%s" % bin],
rhDict["FakeB-CRtwo-%s" % bin])
# Get unique a style for each region
for k in rhDict:
dataset = k.split("-")[0]
region = k.split("-")[1]
styles.getABCDStyle(region).apply(rhDict[k])
if "FakeB" in k:
styles.getFakeBStyle().apply(rhDict[k])
# sr.apply(rhDict[k])
# =========================================================================================
# Create the final plot object
# =========================================================================================
rData_SR = rhDict["Data-SR-Inclusive"]
rEWKGenuineB_SR = rhDict["EWK-SR-Inclusive-EWKGenuineB"]
rBkgSum_SR = rhDict["FakeB-VR-Inclusive"].Clone("BkgSum-SR-Inclusive")
rBkgSum_SR.Reset()
if opts.inclusiveOnly:
bin = "Inclusive"
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
binHisto_VR = rhDict["FakeB-VR-%s" % (bin)]
VRtoSR_TF = manager.GetTransferFactor(bin)
Print(
"Applying TF = %s%0.6f%s to VR shape" %
(ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()),
True)
binHisto_VR.Scale(VRtoSR_TF)
# Add the normalised histogram to the final Inclusive SR (predicted) histo
rBkgSum_SR.Add(binHisto_VR, +1)
else:
# For-loop: All bins
for i, bin in enumerate(binLabels, 1):
if bin == "Inclusive":
continue
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
binHisto_VR = rhDict["FakeB-VR-%s" % (bin)]
VRtoSR_TF = manager.GetTransferFactor(bin)
Print(
"Applying TF = %s%0.6f%s to VR shape" %
(ShellStyles.NoteStyle(), VRtoSR_TF,
ShellStyles.NormalStyle()), i == 1)
binHisto_VR.Scale(VRtoSR_TF)
# Add the normalised histogram to the final Inclusive SR (predicted) histo
rBkgSum_SR.Add(binHisto_VR, +1)
#Print("Got Verification Region (VR) shape %s%s%s" % (ShellStyles.NoteStyle(), rFakeB_VR.GetName(), ShellStyles.NormalStyle()), True)
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
#VRtoSR_TF = manager.GetTransferFactor("Inclusive")
#Print("Applying TF = %s%0.6f%s to VR shape" % (ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()), True)
#rBkgSum_SR.Scale(VRtoSR_TF)
# Plot histograms
if opts.altPlot:
# Add the SR EWK Genuine-b to the SR FakeB ( BkgSum = [FakeB] + [GenuineB-MC] = [VR * (CR1/CR2)] + [GenuineB-MC] )
rBkgSum_SR.Add(rEWKGenuineB_SR, +1)
# Change style
styles.getGenuineBStyle().apply(rBkgSum_SR)
# Remove unsupported settings of kwargs
_kwargs["stackMCHistograms"] = False
_kwargs["addLuminosityText"] = False
# Create the plot
p = plots.ComparisonManyPlot(rData_SR, [rBkgSum_SR], saveFormats=[])
# Set draw / legend style
p.histoMgr.setHistoDrawStyle("Data-SR-Inclusive", "P")
p.histoMgr.setHistoLegendStyle("Data-SR-Inclusive", "LP")
p.histoMgr.setHistoDrawStyle("BkgSum-SR-Inclusive", "HIST")
p.histoMgr.setHistoLegendStyle("BkgSum-SR-Inclusive", "F")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Data-SR": "Data",
"BkgSum-SR": "Fake-b + Gen-b",
})
else:
# Create empty histogram stack list
myStackList = []
# Signal
p2 = plots.DataMCPlot(datasetsMgr,
"ForTestQGLR/QGLR_SR/QGLR_SRInclusive",
saveFormats=[])
hSignal_800 = p2.histoMgr.getHisto(
'ChargedHiggs_HplusTB_HplusToTB_M_800').getRootHisto()
hhSignal_800 = histograms.Histo(
hSignal_800, 'ChargedHiggs_HplusTB_HplusToTB_M_800',
"H^{+} m_{H^+}=800 GeV")
hhSignal_800.setIsDataMC(isData=False, isMC=True)
myStackList.append(hhSignal_800)
hSignal_250 = p2.histoMgr.getHisto(
'ChargedHiggs_HplusTB_HplusToTB_M_250').getRootHisto()
hhSignal_250 = histograms.Histo(
hSignal_250, 'ChargedHiggs_HplusTB_HplusToTB_M_250',
"H^{+} m_{H^+}=250 GeV"
) #plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_250'])
hhSignal_250.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_250)
hSignal_500 = p2.histoMgr.getHisto(
'ChargedHiggs_HplusTB_HplusToTB_M_500').getRootHisto()
hhSignal_500 = histograms.Histo(
hSignal_500, 'ChargedHiggs_HplusTB_HplusToTB_M_500',
"H^{+} m_{H^+}=500 GeV"
) #plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_500'])
hhSignal_500.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_500)
hSignal_1000 = p2.histoMgr.getHisto(
'ChargedHiggs_HplusTB_HplusToTB_M_1000').getRootHisto()
hhSignal_1000 = histograms.Histo(
hSignal_1000, 'ChargedHiggs_HplusTB_HplusToTB_M_1000',
"H^{+} m_{H^+}=1000 GeV"
) #plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_500'])
hhSignal_1000.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_1000)
# Add the FakeB data-driven background to the histogram list
hFakeB = histograms.Histo(rBkgSum_SR, "FakeB", "Fake-b")
hFakeB.setIsDataMC(isData=False, isMC=True)
myStackList.append(hFakeB)
# Add the EWKGenuineB MC background to the histogram list
hGenuineB = histograms.Histo(rEWKGenuineB_SR, "GenuineB",
"EWK Genuine-b")
hGenuineB.setIsDataMC(isData=False, isMC=True)
myStackList.append(hGenuineB)
# Add the collision datato the histogram list
hData = histograms.Histo(rData_SR, "Data", "Data")
hData.setIsDataMC(isData=True, isMC=False)
myStackList.insert(0, hData)
p = plots.DataMCPlot2(myStackList, saveFormats=[])
p.setLuminosity(opts.intLumi)
p.setDefaultStyles()
# Draw the plot and save it
hName = "test"
plots.drawPlot(p, hName, **_kwargs)
SavePlot(p,
hName,
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png", ".pdf"])
#==========================================================================================
# Calculate Cut-Flow Efficiency
#==========================================================================================
kwargs = {
"rebinX": 1,
"xlabel": "QGLR",
"ylabel": "Significance / %.02f ",
"opts": {
"ymin": 0.0,
"ymaxfactor": 1.3
},
"createLegend": {
"x1": 0.55,
"y1": 0.70,
"x2": 0.92,
"y2": 0.92
},
# "cutBox" : {"cutValue": 0.0, "fillColor" : 16, "box": False, "line": False, "greaterThan": True},
}
efficiencyList = []
xValues = []
yValues_250 = []
yValues_500 = []
yValues_800 = []
yValues_1000 = []
yValues_Bkg = []
nBins = hSignal_250.GetNbinsX() + 1
hBkg = p.histoMgr.getHisto(
"ChargedHiggs_HplusTB_HplusToTB_M_800").getRootHisto().Clone("Bkg")
hBkg.Reset()
# Bkg: FakeB + Genuine B
hBkg.Add(hFakeB.getRootHisto(), +1)
hBkg.Add(hGenuineB.getRootHisto(), +1)
for i in range(0, nBins):
# Cut value
cut = hSignal_250.GetBinCenter(i)
passed_250 = hSignal_250.Integral(i, hSignal_250.GetXaxis().GetNbins())
passed_500 = hSignal_500.Integral(i, hSignal_500.GetXaxis().GetNbins())
passed_800 = hSignal_800.Integral(i, hSignal_800.GetXaxis().GetNbins())
passed_1000 = hSignal_1000.Integral(i,
hSignal_1000.GetXaxis().GetNbins())
passed_Bkg = hBkg.Integral(i, hBkg.GetXaxis().GetNbins())
total_250 = hSignal_250.Integral()
total_500 = hSignal_500.Integral()
total_800 = hSignal_800.Integral()
total_1000 = hSignal_1000.Integral()
total_Bkg = hBkg.Integral()
eff_250 = float(passed_250) / total_250
eff_500 = float(passed_500) / total_500
eff_800 = float(passed_800) / total_800
eff_1000 = float(passed_1000) / total_1000
eff_Bkg = float(passed_Bkg) / total_Bkg
xValues.append(cut)
yValues_250.append(eff_250)
yValues_500.append(eff_500)
yValues_800.append(eff_800)
yValues_1000.append(eff_1000)
yValues_Bkg.append(eff_Bkg)
# Create the Efficiency Plot
tGraph_250 = ROOT.TGraph(len(xValues), array.array("d", xValues),
array.array("d", yValues_250))
tGraph_500 = ROOT.TGraph(len(xValues), array.array("d", xValues),
array.array("d", yValues_500))
tGraph_800 = ROOT.TGraph(len(xValues), array.array("d", xValues),
array.array("d", yValues_800))
tGraph_1000 = ROOT.TGraph(len(xValues), array.array("d", xValues),
array.array("d", yValues_1000))
tGraph_Bkg = ROOT.TGraph(len(xValues), array.array("d", xValues),
array.array("d", yValues_Bkg))
styles.getSignalStyleHToTB_M("200").apply(tGraph_250)
styles.getSignalStyleHToTB_M("500").apply(tGraph_500)
styles.getSignalStyleHToTB_M("800").apply(tGraph_800)
styles.getSignalStyleHToTB_M("1000").apply(tGraph_1000)
styles.getQCDLineStyle().apply(tGraph_Bkg)
drawStyle = "CPE"
effGraph_250 = histograms.HistoGraph(tGraph_250,
"H^{+} m_{H^{+}} = 250 GeV", "lp",
drawStyle)
effGraph_500 = histograms.HistoGraph(tGraph_500,
"H^{+} m_{H^{+}} = 500 GeV", "lp",
drawStyle)
effGraph_800 = histograms.HistoGraph(tGraph_800,
"H^{+} m_{H^{+}} = 800 GeV", "lp",
drawStyle)
effGraph_1000 = histograms.HistoGraph(tGraph_1000,
"H^{+} m_{H^{+}} = 1000 GeV", "lp",
drawStyle)
effGraph_Bkg = histograms.HistoGraph(tGraph_Bkg, "Bkg", "lp", drawStyle)
efficiencyList.append(effGraph_250)
efficiencyList.append(effGraph_500)
efficiencyList.append(effGraph_800)
efficiencyList.append(effGraph_1000)
efficiencyList.append(effGraph_Bkg)
# Efficiency plot
pE = plots.PlotBase(efficiencyList, saveFormats=["pdf"])
pE.createFrame("QGLR_Efficiency")
pE.setEnergy("13")
pE.getFrame().GetYaxis().SetLabelSize(18)
pE.getFrame().GetXaxis().SetLabelSize(20)
pE.getFrame().GetYaxis().SetTitle("Efficiency / 0.01")
pE.getFrame().GetXaxis().SetTitle("QGLR Cut")
# Add Standard Texts to plot
histograms.addStandardTexts()
# Customise Legend
moveLegend = {"dx": -0.50, "dy": -0.5, "dh": -0.1}
pE.setLegend(histograms.moveLegend(histograms.createLegend(),
**moveLegend))
pE.draw()
# plots.drawPlot(pE, "QGLR_Efficiency", **kwargs)
SavePlot(pE,
"QGLR_Efficiency",
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png", ".pdf"])
#==========================================================================================
# Calculate Significance
#==========================================================================================
SignalName = "ChargedHiggs_HplusTB_HplusToTB_M_800"
hSignif_250 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(
SignalName)
hSignif_500 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(
SignalName)
hSignif_800 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(
SignalName)
hSignif_1000 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(
SignalName)
hSignif_250.Reset()
hSignif_500.Reset()
hSignif_800.Reset()
hSignif_1000.Reset()
hBkg = p.histoMgr.getHisto(SignalName).getRootHisto().Clone("Bkg")
hBkg.Reset()
# Bkg: FakeB + Genuine B
hBkg.Add(hFakeB.getRootHisto(), +1)
hBkg.Add(hGenuineB.getRootHisto(), +1)
nBins = hSignif_250.GetNbinsX() + 1
# For-loop: All histo bins
for i in range(1, nBins + 1):
sigmaB = ROOT.Double(0)
b = hBkg.IntegralAndError(i, nBins, sigmaB)
s_250 = hSignal_250.Integral(i, nBins)
s_500 = hSignal_500.Integral(i, nBins)
s_800 = hSignal_800.Integral(i, nBins)
s_1000 = hSignal_1000.Integral(i, nBins)
# Calculate significance
signif_250 = stat.significance(s_250, b, sigmaB,
option="Simple") #Asimov")
signif_500 = stat.significance(s_500, b, sigmaB,
option="Simple") #Asimov")
signif_800 = stat.significance(s_800, b, sigmaB,
option="Simple") #Asimov")
signif_1000 = stat.significance(s_1000, b, sigmaB,
option="Simple") #"Asimov")
# Set signif for this bin
hSignif_250.SetBinContent(i, signif_250)
hSignif_500.SetBinContent(i, signif_500)
hSignif_800.SetBinContent(i, signif_800)
hSignif_1000.SetBinContent(i, signif_1000)
# Apply style
s_250 = styles.getSignalStyleHToTB_M("200")
s_250.apply(hSignif_250)
s_500 = styles.getSignalStyleHToTB_M("500")
s_500.apply(hSignif_500)
s_800 = styles.getSignalStyleHToTB_M("800")
s_800.apply(hSignif_800)
s_1000 = styles.getSignalStyleHToTB_M("1000")
s_1000.apply(hSignif_1000)
hList = []
hList.append(hSignif_250)
hList.append(hSignif_500)
hList.append(hSignif_800)
hList.append(hSignif_1000)
hSignif_250.SetName("H^{+} m_{H^{+}} = 250 GeV")
hSignif_500.SetName("H^{+} m_{H^{+}} = 500 GeV")
hSignif_800.SetName("H^{+} m_{H^{+}} = 800 GeV")
hSignif_1000.SetName("H^{+} m_{H^{+}} = 1000 GeV")
pS = plots.PlotBase(hList, saveFormats=["png", "pdf"])
pS.setLuminosity(opts.intLumi)
# Drawing style
pS.histoMgr.setHistoDrawStyleAll("HIST")
pS.histoMgr.setHistoLegendStyleAll("L")
pS.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerSize(1.0))
pS.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
pS.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetMarkerStyle(ROOT.kFullCircle))
# Draw the plot
name = "QGLR_Signif" + "GE"
plots.drawPlot(pS, name, **kwargs)
SavePlot(pS,
name,
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png", ".pdf"])
#=========================================================================================
# Calculate the Transfer Factor (TF) and save to file
#=========================================================================================
Verbose("Write the normalisation factors to a python file", True)
fileName = os.path.join(
opts.mcrab, "FakeBTransferFactors%s.py" % (getModuleInfoString(opts)))
manager.writeNormFactorFile(fileName, opts)
return
def main():
style = tdrstyle.TDRStyle()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(parseOpts.batchMode)
ROOT.gErrorIgnoreLevel = 3000
# Get all datasets from the mcrab dir
datasetsMgr = GetDatasetsFromDir(parseOpts.mcrab, kwargs.get("analysis"))
# datasetsMgr = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=kwargs.get("analysis"), includeOnlyTasks="ChargedHiggs_HplusTB_HplusToTB_M_200")
# datasetsMgr = dataset.getDatasetsFromMulticrabDirs([parseOpts.mcrab], analysisName=kwargs.get("analysis"), excludeTasks="M_200")
# Determine Integrated Luminosity
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("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"))
# datasetsMgr.remove(filter(lambda name: not "QCD" in name, datasetsMgr.getAllDatasetNames()))
# datasetsMgr.remove(filter(lambda name: "QCD" in name in name, datasetsMgr.getAllDatasetNames()))
# For-loop: All Histogram names
for hName in hNames:
savePath, saveName = GetSavePathAndName(hName, **kwargs)
# Get efficiency histos
refEff, otherEff = GetCutEfficiencyTGraphs(datasetsMgr, hName, "kFCP", **kwargs)
# refEff, otherEff = GetCutEfficiencyTGraphs(datasetsMgr, hName, "kFFC", **kwargs)
# refEff, otherEff = GetCutEfficiencyTGraphs(datasetsMgr, hName, "kBJeffrey", **kwargs)
# refEff, otherEff = GetCutEfficiencyTGraphs(datasetsMgr, hName, "kFNormal", **kwargs)
# Plot the efficiencies
p = plots.ComparisonManyPlot(refEff, otherEff)
# Create a frame
opts = {"ymin": 0.0, "ymaxfactor": 1.2} #"ymax": 5e-1}
ratioOpts = {"ymin": 0.0, "ymaxfactor": 1.2}
p.createFrame(saveName, createRatio=kwargs.get("createRatio"), opts=opts, opts2=ratioOpts)
# Customise Legend
moveLegend = {"dx": -0.1 , "dy": +0.0, "dh": -0.1}
p.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
#p.removeLegend()
# Customise frame
# p.setEnergy("13")
if kwargs.get("createRatio"):
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.getFrame2().GetYaxis().SetTitleOffset(1.6)
# 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(0.4, 0.11, "Runs " + datasetsMgr.loadRunRange(), 17)
# Save canvas under custom dir
SaveAs(p, savePath, saveName, kwargs.get("saveFormats"))
return
def main(argv):
dirs = []
if len(sys.argv) < 2:
usage()
dirs.append(sys.argv[1])
QCDInvertedNormalization = QCDInvertedNormalizationFactors.QCDInvertedNormalization
QCDInvertedNormalizationFilteredEWKFakeTaus = QCDInvertedNormalizationFactorsFilteredEWKFakeTaus.QCDInvertedNormalization
analysis = "signalAnalysisInvertedTau"
optModes = []
#optModes.append("OptQCDTailKillerZeroPlus")
optModes.append("OptQCDTailKillerLoosePlus")
optModes.append("OptQCDTailKillerMediumPlus")
optModes.append("OptQCDTailKillerTightPlus")
#optModes.append("OptQCDTailKillerVeryTightPlus")
#optModes.append("OnlyGenuineMCTausFalse")
#optModes.append("OnlyGenuineMCTausTrue")
#Optimal: 0.8, 0.82, 0.9
#w1_list = [0.8, 0.82, 0.84, 0.87]
#w1_list = [0.8, 0.82, 0.9, 1]
w1_list = [0.9]
defaultBinning = systematics.getBinningForPlot("shapeTransverseMass")
diff_opt = []
for optMode in optModes:
diff_list = []
for w1 in w1_list:
var_values = []
nom_values = []
w2 = 1 - w1
color = 1
#signal
dirs_signal = ["../../SignalAnalysis_140605_143702/"]
datasets_signal = dataset.getDatasetsFromMulticrabDirs(
dirs_signal,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis.replace("InvertedTau", ""),
optimizationMode=optMode)
datasets_signal.updateNAllEventsToPUWeighted()
datasets_signal.loadLuminosities()
datasets_signal.remove(
filter(lambda name: "TTToHplus" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "HplusTB" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "Hplus_taunu_t-channel" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "Hplus_taunu_tW-channel" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "TTJets_SemiLept" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "TTJets_FullLept" in name,
datasets_signal.getAllDatasetNames()))
datasets_signal.remove(
filter(lambda name: "TTJets_Hadronic" in name,
datasets_signal.getAllDatasetNames()))
plots.mergeRenameReorderForDataMC(datasets_signal)
datasets_signal.merge(
"EWK",
["TTJets", "WJets", "DYJetsToLL", "SingleTop", "Diboson"])
mtplot_signalfaketaus = plots.DataMCPlot(
datasets_signal, "shapeEWKFakeTausTransverseMass")
mt_signalfaketaus = mtplot_signalfaketaus.histoMgr.getHisto(
"EWK").getRootHisto().Clone("shapeEWKFakeTausTransverseMass")
mt_signalfaketaus.SetName("BaselineFakeTaus")
myBinning = [0, 20, 40, 60, 80, 100, 120, 140, 160, 200, 400]
myArray = array.array("d", myBinning)
fitBinning = []
for i in range(0, 45):
fitBinning.append(i * 10)
fitArray = array.array("d", fitBinning)
mt_baseline = mt_signalfaketaus
#rangeMin = mt_signalfaketaus.GetXaxis().GetXmin()
#rangeMax = mt_signalfaketaus.GetXaxis().GetXmax()
#theFit = TF1('theFit',FitFunction(),rangeMin,rangeMax,4)
#theFit.SetParLimits(0,0.5,10000)
#theFit.SetParLimits(1,90,10000)
#theFit.SetParLimits(2,30,10000)
#theFit.SetParLimits(3,0.001,10000)
#mt_signalfaketaus.Fit(theFit,"R")
#theFit.SetRange(mt_signalfaketaus.GetXaxis().GetXmin(),mt_signalfaketaus.GetXaxis().GetXmax())
#theFit.SetLineStyle(2)
#theFit.SetLineColor(4)
#theFit.SetLineWidth(3)
#theFit.Draw()
#mt_corr = theFit.GetHistogram()
#mt_corr = mt_corr.Rebin(len(fitBinning)-1,"",fitArray)
#mt_corr.Scale(mt_baseline.GetMaximum()/mt_corr.GetMaximum())
for HISTONAME in histoNameList:
var = False
if HISTONAME == "shapeEWKGenuineTausTransverseMass":
var = True
datasets = dataset.getDatasetsFromMulticrabDirs(
dirs,
dataEra=dataEra,
searchMode=searchMode,
analysisName=analysis,
optimizationMode=optMode)
datasets.updateNAllEventsToPUWeighted()
datasets.loadLuminosities()
plots.mergeRenameReorderForDataMC(datasets)
datasets.merge(
"EWK",
["TTJets", "WJets", "DYJetsToLL", "SingleTop", "Diboson"])
histonames = datasets.getDataset("Data").getDirectoryContent(
HISTONAME)
bins = []
for histoname in histonames:
binname = histoname.replace(HISTONAME, "")
if not binname == "Inclusive":
bins.append(binname)
for i, bin in enumerate(bins):
mtplot = plots.DataMCPlot(
datasets, HISTONAME + "/" + HISTONAME + bin)
if i == 0:
mt = mtplot.histoMgr.getHisto(
"Data").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
mt_ewk = mtplot.histoMgr.getHisto(
"EWK").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
mtn = mtplot.histoMgr.getHisto(
"Data").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
mtn_ewk = mtplot.histoMgr.getHisto(
"EWK").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
if var:
legendName = "QCD(Data)+EWK+t#bar{t}(Data, mis-ID. #tau)"
else:
legendName = "QCD(Data)+EWK+t#bar{t}(MC, mis-ID. #tau)"
legendName = legendName.replace("Plus", "")
mt.SetName(legendName)
mt.SetLineColor(color)
mt.Add(mt_ewk, -1)
mtn.Add(mtn_ewk, -1)
mtn.Scale(QCDInvertedNormalization[str(i)])
if var:
scale = w1 * QCDInvertedNormalizationFilteredEWKFakeTaus[
str(
i
)] + w2 * QCDInvertedNormalizationFilteredEWKFakeTaus[
str(i) + "EWK_FakeTaus"]
mt.Scale(scale)
else:
mt.Scale(QCDInvertedNormalization[str(i)])
color += 1
if color == 5:
color += 1
else:
h = mtplot.histoMgr.getHisto(
"Data").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
mt_ewk = mtplot.histoMgr.getHisto(
"EWK").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
hn = mtplot.histoMgr.getHisto(
"Data").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
mtn_ewk = mtplot.histoMgr.getHisto(
"EWK").getRootHisto().Clone(HISTONAME + "/" +
HISTONAME + bin)
h.Add(mt_ewk, -1)
hn.Add(mtn_ewk, -1)
hn.Scale(QCDInvertedNormalization[str(i)])
if var:
scale = w1 * QCDInvertedNormalizationFilteredEWKFakeTaus[
str(
i
)] + w2 * QCDInvertedNormalizationFilteredEWKFakeTaus[
str(i) + "EWK_FakeTaus"]
h.Scale(scale)
else:
h.Scale(QCDInvertedNormalization[str(i)])
mt.Add(h)
mtn.Add(hn)
#mt = mt.Rebin(len(myBinning)-1,"",myArray)
#mt_corr = mt_corr.Rebin(len(myBinning)-1,"",myArray)
if not var:
mt.Add(mt_baseline)
#mt.Add(mt_corr)
#myBinning = []
#for i in range(0,11):
# myBinning.append(20*i)
#myBinning.append(400)
#myArray = array.array("d",defaultBinning)
mt = mt.Rebin(len(myBinning) - 1, "", myArray)
for i in range(0, mt.GetSize()):
if var:
var_values.append(mt.GetBinContent(i))
else:
nom_values.append(mt.GetBinContent(i))
if var:
#mt.SetLineStyle(2)
var_hist = mt
else:
#mt.SetLineStyle(2)
nom_hist = mt
style = tdrstyle.TDRStyle()
#gStyle.SetOptStat(1101)
#mt_data.SetStats(1)
#gPad.Update()
bins = [0, 390, 400]
arr = array.array("d", bins)
mtn = mtn.Rebin(len(bins) - 1, "", arr)
plot_data = plots.PlotBase()
plot_data.histoMgr.appendHisto(histograms.Histo(mtn, "Data"))
plot_data.createFrame("Data_" + HISTONAME + "_" + optMode +
"_" + str(w1))
plot_data.draw()
plot_data.save()
plot = plots.ComparisonPlot(nom_hist, var_hist)
plot.createFrame(optMode.replace(
"Opt", "Mt_" + "w1=" + str(w1) + "_w2=" + str(w2) +
"_DataDrivenVsMC_"),
createRatio=True)
moveLegend = {"dx": -0.295, "dy": 0.05}
plot.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
histograms.addText(
0.65, 0.20,
optMode.replace("OptQCDTailKiller",
"R_{BB} ").replace("Plus", ""), 25)
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
lumi = datasets.getDataset("Data").getLuminosity()
histograms.addLuminosityText(x=None, y=None, lumi=lumi)
plot.draw()
plot.save()
num = 0
denom = 0
#print var_values
for i in range(0, len(nom_values)):
num += var_values[i] * (var_values[i] - nom_values[i])**2
denom += var_values[i]
diff = num / denom
diff_list.append(diff)
diff_opt.append(diff_list)
print w1_list, '\n'
for i in range(0, len(diff_opt)):
print diff_opt[i]
print w1_list[diff_opt[i].index(min(diff_opt[i]))]
mt_baseline = mt_baseline.Rebin(len(bins) - 1, "", arr)
plot_bft = plots.PlotBase()
plot_bft.histoMgr.appendHisto(histograms.Histo(mt_baseline, "baseline"))
#mt_corr.Scale(2)
#plot_bft.histoMgr.appendHisto(histograms.Histo(mt_corr,"test"))
#rangeMin = mt_signalfaketaus.GetXaxis().GetXmin()
#rangeMax = mt_signalfaketaus.GetXaxis().GetXmax()
#theFit = TF1('theFit',FitFunction(),rangeMin,rangeMax,4)
#theFit.SetParLimits(0,0.5,10000)
#theFit.SetParLimits(1,90,10000)
#theFit.SetParLimits(2,30,10000)
#theFit.SetParLimits(3,0.001,10000)
#mt_signalfaketaus.Fit(theFit,"R")
#theFit.SetRange(mt_signalfaketaus.GetXaxis().GetXmin(),mt_signalfaketaus.GetXaxis().GetXmax())
#theFit.SetLineStyle(2)
#theFit.SetLineColor(4)
#theFit.SetLineWidth(3)
#theFit.Draw()
#mt_corr = theFit.GetHistogram()
#mt_corr = mt_corr.Rebin(len(fitBinning)-1,"",fitArray)
#mt_corr.Scale(mt_baseline.GetMaximum()/mt_corr.GetMaximum())
#plot_bft.histoMgr.appendHisto(histograms.Histo(mt_corr,"test"))
#plot_bft.histoMgr.appendHisto(histograms.Histo(theFit,"theFit"))
plot_bft.createFrame('BaselineFakeTaus')
plot_bft.draw()
plot_bft.save()
def Fit(datasets, histo, function):
FitList = []
for dataset in datasets:
datasetName = dataset.getName()
print "Dataset = ", datasetName
hh = dataset.getDatasetRootHisto(histo)
hh.normalizeToOne()
h = hh.getHistogram()
#h = dataset.getDatasetRootHisto(histo).getHistogram()
xMin = h.GetXaxis().GetXmin()
xMax = h.GetXaxis().GetXmax()
yMin = 0
yMax = 1.2
#statOption = ROOT.TEfficiency.kFNormal
if "TT" in datasetName:
if function == "gaus":
fitGauss = ROOT.TF1("fitGauss", "gaus", -2.5, 2.5)
# TF1 *fitBoFreq = new TF1("fitBoFreq","[0]*x+[1]",0,20);
# h.Fit("gaus")
#fitTest = ROOT.TF1("fitTest", "0.01", -2.5, 2.5)
h.Fit("fitGauss", "SRBM")
#h.GetListOfFunctions().Add(fitTest)
legend = "TT"
legend = "a legend"
print "Legend", legend
saveName = histo.split("/")[-1] + "_Fit"
print saveName
xTitle = "fixXTitle"
yTitle = "fixYTitle"
yMin = 0.
yMax = 0.03
xMin = -2.3
xMax = 2.3
kwargs = {}
options = {"ymin": yMin, "ymax": yMax, "xmin": xMin, "xMax": xMax}
FitList.append(h)
#p = plots.MCPlot(dataset, h, normalizeToLumi=0, saveFormats=[], **kwargs)
p = plots.PlotBase(datasetRootHistos=FitList,
saveFormats=kwargs.get("saveFormats"))
p.createFrame(saveName, opts=options)
p.getFrame().GetXaxis().SetTitle(xTitle)
p.getFrame().GetYaxis().SetTitle(yTitle)
#p.histoMgr.setHistoDrawStyle(datasetName, "AP")
# Set range
p.getFrame().GetXaxis().SetRangeUser(xMin, xMax)
moveLegend = {"dx": -0.55, "dy": -0.01, "dh": -0.1}
p.setLegend(
histograms.moveLegend(histograms.createLegend(), **moveLegend))
# Add Standard Texts to plot
histograms.addStandardTexts()
p.draw()
# Save plot in all formats
savePath = os.path.join(opts.saveDir, "HplusMasses",
histo.split("/")[0], opts.optMode)
save_path = savePath
SavePlot(p, saveName, save_path)
return
def analyze(analysis):
paths = [sys.argv[1]]
datasets = dataset.getDatasetsFromMulticrabDirs(paths,analysisName=analysis,excludeTasks="Silver|GluGluHToTauTau_M125")
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 = datasets.getMCDatasets()
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")])
else:
p = plots.ComparisonPlot(histograms.HistoGraph(eff1, "eff1", "p", "P"),
histograms.HistoGraph(eff2, "eff2", "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)"
legend3 = "MC (H125)"
p.histoMgr.setHistoLegendLabelMany({"eff1": legend1, "eff2": legend2})
if isinstance(datasetsH125,dataset.DatasetManager):
p.histoMgr.setHistoLegendLabelMany({"eff1": legend1, "eff2": legend2, "eff3": legend3})
p.createFrame(os.path.join(plotDir, name), createRatio=True, 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.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]
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,"tauLegTriggerEfficiency2015.json"))
#########################################################################
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")])
else:
p_eta = plots.ComparisonPlot(histograms.HistoGraph(eff1eta, "eff1eta", "p", "P"),
histograms.HistoGraph(eff2eta, "eff2eta", "p", "P"))
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"
p_eta.createFrame(os.path.join(plotDir, name), createRatio=True, 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")
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, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.2, 0.31, "Runs "+datasets.loadRunRange(), 17)
p_eta.draw()
histograms.addStandardTexts(lumi=lumi)
p_eta.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})
optsPU = {"ymin": 0.001, "ymax": 0.1}
pPU.createFrame(os.path.join(plotDir, namePU), createRatio=True, opts=optsPU, opts2=opts2)
pPU.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
pPU.getPad1().SetLogy(True)
pPU.getFrame().GetYaxis().SetTitle("HLT tau efficiency")
pPU.getFrame().GetXaxis().SetTitle("Number of reco vertices")
pPU.getFrame2().GetYaxis().SetTitle("Ratio")
pPU.getFrame2().GetYaxis().SetTitleOffset(1.6)
histograms.addText(0.5, 0.6, "LooseIsoPFTau50_Trk30_eta2p1", 17)
histograms.addText(0.5, 0.53, analysis.split("_")[len(analysis.split("_")) -1], 17)
histograms.addText(0.5, 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", ["TTJets","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