def weightedAverage(histosEff={}, histosWeight={}, histoName='', histoTitle='', verbose=False):
getBinIndices, getBinContents, getBinning = rootUtils.getBinIndices, rootUtils.getBinContents, rootUtils.getBinning
assert sorted(histosEff.keys())==sorted(histosWeight.keys()),"effs and weights must have the same keys:\n\teffs %s\n\tweights %s"%(str(histosEff.keys()), str(histosWeight.keys()))
hout = first(histosEff).Clone(histoName if histoName else 'weighted_average_eff')
hout.SetTitle(histoTitle)
hout.Reset()
def validateWeights(h):
bins, values = getBinIndices(h), getBinContents(h)
allValid = all(v>=0.0 and v<=1.0 for v in values)
if not allValid:
if verbose :
print "warning '%s' weights not in [0,1] : [%s]"%(h.GetName(), ', '.join(("%.3f"%v for v in values)))
print "setting them to 0.0 or 1.0"
for b, v in zip(bins, values) : h.SetBinContent(b, 0.0 if v<0.0 else 1.0 if v>1.0 else v)
return allValid
[validateWeights(hw) for hw in histosWeight.values()]
bins, binning = getBinIndices(first(histosEff)), getBinning(first(histosEff))
for h in histosWeight.values()+histosEff.values():
if getBinning(h)!=binning : print "warning: %s has binning %s, expecting %s"%(h.GetName(), str(getBinning(h)), str(binning))
groups = sorted(histosEff.keys())
epsilon = 1.0e-3
binWeightNormalizations = [sum(histosWeight[g].GetBinContent(b) for g in groups) for b in bins]
weightsAreNormalized = all(abs(1.0-norm)<epsilon for norm in binWeightNormalizations)
if not weightsAreNormalized : print "warning, compositions are not normalized : [%s]"%', '.join(("%.3f"%v for v in binWeightNormalizations))
print '-- ',histoName,'-- '
for g in groups:
bws, bcs = getBinContents(histosWeight[g]), getBinContents(histosEff[g])
print "adding %18s : %s"%(g, ' : '.join("%.4f*%.4f"%(bw, bc) for bw, bc in zip(bws, bcs)))
histoEff, histoWeight = histosEff[g], histosWeight[g]
histoEff.Multiply(histoWeight)
hout.Add(histoEff)
print "tot weight : %s"%' '.join(("%.4f"%v for v in (sum(histosWeight[g].GetBinContent(b) for g in groups) for b in bins)))
print "weighted avg : %s"%' '.join(("%.4f"%v for v in getBinContents(hout)))
return hout
def buildRatioAndScaleIt(histoPrefix='', inputFile=None, scaleFactor=1.0, verbose=False) :
ratioHisto = buildRatio(inputFile, histoPrefix)
def lf2s(l) : return ', '.join(["%.3f"%e for e in l])
if verbose: print ratioHisto.GetName()," before scaling: ",lf2s(getBinContents(ratioHisto))
if type(scaleFactor)==float : ratioHisto.Scale(scaleFactor)
elif type(scaleFactor)==r.TH1F : ratioHisto.Multiply(scaleFactor)
elif type(scaleFactor)==r.TH2F : ratioHisto.Multiply(scaleFactor)
else : raise TypeError("unknown SF type %s, %s"%(type(scaleFactor), str(scaleFactor)))
if verbose: print ratioHisto.GetName()," after scaling: ",lf2s(getBinContents(ratioHisto))
return ratioHisto
def buildCorrectionHisto(
hndRealDataCr,
hndFakeDataLo,
hndFakeDataHi,
hndFakeMcLo,
hndFakeMcHi,
histoname="lep_corHFRate",
nIter=1,
verbose=False,
plotdir=None,
):
hRealEff = buildRatioHistogram(hndRealDataCr["num"], hndRealDataCr["den"], "real_eff")
corrected = dict([(nd, hndFakeDataLo[nd].Clone("corrected_" + nd)) for nd in ["num", "den"]])
print "buildCorrectionHisto with nIter ", nIter
can = r.TCanvas("c_" + histoname, "")
can.Draw()
can.cd()
hRealEff.GetYaxis().SetRangeUser(0.0, 1.0)
hRealEff.Draw("axis")
tex = r.TLatex()
tex.SetNDC(True)
tex.DrawLatex(0.15, 0.915, histoname)
can._tex = tex
can._histos = [hRealEff]
can._leg = r.TLegend(0.925, 0.25, 1.0, 0.9, "iter")
can._leg.SetBorderSize(0)
can._leg.SetFillColor(0)
for iteration in range(nIter):
print "iter ", iteration
rate = buildRatioHistogram(corrected["num"], corrected["den"]) # temporary rate (?)
if verbose:
print "Iteration %d, corrected values:" % iteration
print " num %s" % lf2s(getBinContents(corrected["num"]))
print " den %s" % lf2s(getBinContents(corrected["den"]))
print " ratio %s" % lf2s(getBinContents(rate))
dataNum, dataDen = hndFakeDataHi["num"], hndFakeDataHi["den"]
for nd, tl in [("num", "tight"), ("den", "loose")]:
corr, dataLow = corrected[nd], hndFakeDataLo[nd]
mcLow, mcHi = hndFakeMcLo[nd], hndFakeMcHi[nd]
corrFact = getCorrFactors(hRealEff, rate, dataNum, dataDen, mcHi, tl)
corr = correctRate(corr, dataLow, mcLow, corrFact)
rate.SetLineColor(20 + iteration)
rate.SetMarkerColor(20 + iteration)
rate.SetLineWidth(4)
rate.SetMarkerStyle(r.kFullCross)
can._histos.append(rate.DrawClone("el same"))
can._leg.AddEntry(can._histos[-1], "%d" % iteration, "lp")
ratio = buildRatioHistogram(corrected["num"], corrected["den"], histoname)
can._leg.Draw()
can.Update()
if plotdir:
can.SaveAs(plotdir + "/" + histoname + "_iterations.png")
return ratio
def print_scale_factor_dict(sf_dict):
"dict is something like {conv,heavy : {flat,vs_eta : values}}"
print "scale factors:"
for k,v in sf_dict.iteritems():
values = getBinContents(v['vs_eta']) if 'vs_eta' in v and v['vs_eta'] else [v['flat'],]
formatted_values = ', '.join("%.3f"%_ for _ in values)
print "%s : %s"%(k, formatted_values)
def validateWeights(h):
bins, values = getBinIndices(h), getBinContents(h)
allValid = all(v>=0.0 and v<=1.0 for v in values)
if not allValid:
if verbose :
print "warning '%s' weights not in [0,1] : [%s]"%(h.GetName(), ', '.join(("%.3f"%v for v in values)))
print "setting them to 0.0 or 1.0"
for b, v in zip(bins, values) : h.SetBinContent(b, 0.0 if v<0.0 else 1.0 if v>1.0 else v)
return allValid
def histo1dToTxt(h):
"represent a TH1 as a string with name, bin edges, contents, and errors"
bins = getBinIndices(h)
hisName = h.GetName()
binEdge = [h.GetBinLowEdge(b) for b in bins]
binEdge.append(binEdge[-1] + h.GetBinWidth(bins[-1]))
binCont = getBinContents(h)
binErr = [be(h, b) for b in bins]
def lf2s(l):
return ", ".join(["%.3f" % e for e in l])
return "\n".join(
[
"%s : %s" % (n, v)
for n, v in [("hisName", hisName)] + [(l, lf2s(eval(l))) for l in ["binEdge", "binCont", "binErr"]]
]
)
def getBinContents(self, variable, selection) :
return getBinContents(self.getHistogram)
def subtractRealAndComputeScaleFactor(histosPerGroup={}, variable='', outRatiohistoname='',outDataeffhistoname='',
outputDir='./', region='', subtractReal=True, verbose=False):
"efficiency scale factor"
groups = histosPerGroup.keys()
mkdirIfNeeded(outputDir)
histosPerType = dict([(lt,
dict([(g,
histosPerGroup[g][variable][lt])
for g in groups]))
for lt in leptonTypes])
for lt in leptonTypes :
histosPerType[lt]['totSimBkg'] = summedHisto([histo for group,histo in histosPerType[lt].iteritems()
if group not in ['data', 'signal']])
simuTight = histosPerType['fake_tight']['totSimBkg']
simuLoose = histosPerType['fake_loose']['totSimBkg']
dataTight = histosPerType['tight' ]['data' ]
dataLoose = histosPerType['loose' ]['data' ]
# subtract real contribution from data
# _Note to self_: currently estimating the real contr from MC; in
# the past also used iterative corr, which might be more
# appropriate in cases like here, where the normalization is
# so-so. Todo: investigate the normalization.
dataSubTight = dataTight.Clone(dataTight.GetName().replace('data_tight','data_minus_prompt_tight'))
dataSubLoose = dataLoose.Clone(dataLoose.GetName().replace('data_loose','data_minus_prompt_loose'))
dataSubTight.SetDirectory(0)
dataSubLoose.SetDirectory(0)
dataSubTight.Add(histosPerType['real_tight']['totSimBkg'], -1.0 if subtractReal else 0.0)
dataSubLoose.Add(histosPerType['real_loose']['totSimBkg'], -1.0 if subtractReal else 0.0)
effData = dataSubTight.Clone(outDataeffhistoname)
effData.SetDirectory(0)
effData.Divide(dataSubLoose)
effSimu = simuTight.Clone(simuTight.GetName().replace('fake_tight','fake_eff'))
effSimu.SetDirectory(0)
effSimu.Divide(simuLoose)
print "eff(T|L) vs. ",variable
def formatFloat(floats): return ["%.4f"%f for f in floats]
print "efficiency data : ",formatFloat(getBinContents(effData))
print "efficiency simu : ",formatFloat(getBinContents(effSimu))
ratio = effData.Clone(outRatiohistoname)
ratio.SetDirectory(0)
ratio.Divide(effSimu)
print "sf data/simu : ",formatFloat(getBinContents(ratio))
print " +/- : ",formatFloat(getBinErrors(ratio))
can = r.TCanvas('c_'+outRatiohistoname, outRatiohistoname, 800, 600)
botPad, topPad = rootUtils.buildBotTopPads(can)
can.cd()
topPad.Draw()
topPad.cd()
pm = effData
pm.SetStats(0)
pm.Draw('axis')
xAx, yAx = pm.GetXaxis(), pm.GetYaxis()
xAx.SetTitle('')
xAx.SetLabelSize(0)
yAx.SetRangeUser(0.0, 0.25)
textScaleUp = 1.0/topPad.GetHNDC()
yAx.SetLabelSize(textScaleUp*0.04)
yAx.SetTitleSize(textScaleUp*0.04)
yAx.SetTitle('#epsilon(T|L)')
yAx.SetTitleOffset(yAx.GetTitleOffset()/textScaleUp)
effSimu.SetLineColor(r.kRed)
effSimu.SetMarkerStyle(r.kOpenCross)
effSimu.SetMarkerColor(effSimu.GetLineColor())
effData.Draw('same')
effSimu.Draw('same')
leg = drawLegendWithDictKeys(topPad, {'data':effData, 'simulation':simuTight}, legWidth=0.4)
leg.SetHeader('scale factor '+region+' '+('electron' if '_el_'in outRatiohistoname else
'muon' if '_mu_' in outRatiohistoname else ''))
can.cd()
botPad.Draw()
botPad.cd()
ratio.SetStats(0)
ratio.Draw()
textScaleUp = 1.0/botPad.GetHNDC()
xAx, yAx = ratio.GetXaxis(), ratio.GetYaxis()
yAx.SetRangeUser(0.0, 2.0)
xAx.SetTitle({'pt1':'p_{T}', 'eta1':'|#eta|', 'pt1_eta1':'p_{T}'}[variable])
yAx.SetNdivisions(-202)
yAx.SetTitle('Data/Sim')
yAx.CenterTitle()
xAx.SetLabelSize(textScaleUp*0.04)
xAx.SetTitleSize(textScaleUp*0.04)
yAx.SetLabelSize(textScaleUp*0.04)
yAx.SetTitleSize(textScaleUp*0.04)
refLine = rootUtils.referenceLine(xAx.GetXmin(), xAx.GetXmax())
refLine.Draw()
can.Update()
outFname = os.path.join(outputDir, region+'_'+outRatiohistoname)
for ext in ['.eps','.png']:
utils.rmIfExists(outFname+ext)
can.SaveAs(outFname+ext)
return {outRatiohistoname : ratio,
outDataeffhistoname : effData,
outDataeffhistoname.replace('_fake_rate_data_', '_tight_data_minus_prompt') : dataSubTight,
outDataeffhistoname.replace('_fake_rate_data_', '_loose_data_minus_prompt') : dataSubLoose
}
def main():
parser = optparse.OptionParser(usage=usage)
parser.add_option("-n", "--n_iter", type="int", default=8)
parser.add_option("-m", "--input_mc")
parser.add_option("-d", "--input_data")
parser.add_option("-o", "--output")
parser.add_option("-p", "--plot", help="plot inputs") # todo: implement sanity plot vs. n_iter
parser.add_option("-v", "--verbose", action="store_true", default=False)
(opts, args) = parser.parse_args()
requiredOptions = ["n_iter", "input_mc", "input_data", "output"]
otherOptions = ["plot", "verbose"]
allOptions = requiredOptions + otherOptions
def optIsNotSpecified(o):
return not hasattr(opts, o) or getattr(opts, o) is None
if any(optIsNotSpecified(o) for o in requiredOptions):
parser.error("Missing required option")
nIter = opts.n_iter
fnameInputMc = opts.input_mc
fnameInputDa = opts.input_data
fnameOutput = opts.output
plotdir = opts.plot
verbose = opts.verbose
if verbose:
print (
"\nUsing the following options:\n" + "\n".join("%s : %s" % (o, str(getattr(opts, o))) for o in allOptions)
)
fileData = r.TFile.Open(fnameInputDa)
fileMc = r.TFile.Open(fnameInputMc)
if plotdir:
mkdirIfNeeded(plotdir)
assert fileData and fileMc, "Missing input files: data %s, mc %s" % (str(fileData), str(fileMc))
correctionHistos = {}
for lep in ["muon", "elec"]:
if verbose:
print "Lepton: %s" % lep
hRealDataCr = getNumDenHistos(fileData, lep + "_realCR_all_l_pt")
hFakeDataLo = getNumDenHistos(fileData, lep + "_fakeHF_all_l_pt")
hFakeDataHi = getNumDenHistos(fileData, lep + "_fakeHF_high_all_l_pt")
hFakeMcLo = getNumDenHistos(fileMc, lep + "_fakeHF_all_l_pt")
hFakeMcHi = getNumDenHistos(fileMc, lep + "_fakeHF_high_all_l_pt")
if plotdir:
hNumDen = [hFakeDataLo, hFakeDataHi, hFakeMcLo, hFakeMcHi]
for nd in ["num", "den"]:
plotHistos([h[nd] for h in hNumDen], "c_" + lep + "_" + nd, plotdir)
plotHistosRatio(hNumDen, "c_" + lep + "_ratio", plotdir)
h2dRealDataCr = getNumDenHistos(fileData, lep + "_realCR_all_l_pt_eta")
h2dFakeDataLo = getNumDenHistos(fileData, lep + "_fakeHF_all_l_pt_eta")
h2dFakeDataHi = getNumDenHistos(fileData, lep + "_fakeHF_high_all_l_pt_eta")
h2dFakeMcLo = getNumDenHistos(fileMc, lep + "_fakeHF_all_l_pt_eta")
h2dFakeMcHi = getNumDenHistos(fileMc, lep + "_fakeHF_high_all_l_pt_eta")
def missingInputHisto(ndHistos):
return any(not h for h in ndHistos.values())
histoCollToBeChecked = ["hRealDataCr", "hFakeDataLo", "hFakeDataHi", "hFakeMcLo", "hFakeMcHi"]
missingHistos = dict(
[(nhc, hp) for nhc, hp in [(hc, eval(hc)) for hc in histoCollToBeChecked] if missingInputHisto(hp)]
)
for v in histoCollToBeChecked:
print "entries 1d %s : num %d den %d (%s)" % (
v,
eval(v)["num"].GetEntries(),
eval(v)["den"].GetEntries(),
str(eval(v)["den"]),
)
histoCollToBeChecked = ["h2dRealDataCr", "h2dFakeDataLo", "h2dFakeDataHi", "h2dFakeMcLo", "h2dFakeMcHi"]
missingHistos = dict(
[(nhc, hp) for nhc, hp in [(hc, eval(hc)) for hc in histoCollToBeChecked] if missingInputHisto(hp)]
)
for v in histoCollToBeChecked:
print "entries 2d %s : num %d den %d (%s)" % (
v,
eval(v)["num"].GetEntries(),
eval(v)["den"].GetEntries(),
str(eval(v)["den"]),
)
print histoCollToBeChecked
print missingHistos
if len(missingHistos):
print (
lep
+ " : missing histograms: \n"
+ "\n".join(["%s: num %s den %s" % (k, v["num"], v["den"]) for k, v in missingHistos.iteritems()])
)
continue
correctionHistos[lep] = buildCorrectionHisto(
hRealDataCr,
hFakeDataLo,
hFakeDataHi,
hFakeMcLo,
hFakeMcHi,
nIter=nIter,
verbose=verbose,
histoname=lep + "_corHFRate",
plotdir=plotdir,
)
# here do the 2d ones
print 10 * "--", " now doing the 2d ones ", 10 * "--"
dummy = h2dRealDataCr["num"]
xAx, yAx = dummy.GetXaxis(), dummy.GetYaxis()
print dummy.GetName(), ": bins (%d, %d)" % (dummy.GetNbinsX(), dummy.GetNbinsY())
nEtaBins = yAx.GetNbins()
print "nEtaBins: ", nEtaBins
xMin, xMax = xAx.GetXmin(), xAx.GetXmax()
etaBins = range(1, 1 + nEtaBins)
for eb in etaBins:
def etaSlice(h, b, p):
return h.ProjectionX(p + h.GetName() + "_eta%d" % b, b, b) # prefix needed to avoid overwriting
hRealDataCr = dict((k, etaSlice(h, eb, "rdc")) for k, h in h2dRealDataCr.iteritems())
hFakeDataLo = dict((k, etaSlice(h, eb, "fdl")) for k, h in h2dFakeDataLo.iteritems())
hFakeDataHi = dict((k, etaSlice(h, eb, "fdh")) for k, h in h2dFakeDataHi.iteritems())
hFakeMcLo = dict((k, etaSlice(h, eb, "fml")) for k, h in h2dFakeMcLo.iteritems())
hFakeMcHi = dict((k, etaSlice(h, eb, "fmh")) for k, h in h2dFakeMcHi.iteritems())
print "eta bin ", eb
for k, h in hFakeDataLo.iteritems():
print "fakeDataLo %s : %s" % (k, lf2s(getBinContents(h)))
correctionHistos[lep + "_eta%d" % eb] = buildCorrectionHisto(
hRealDataCr,
hFakeDataLo,
hFakeDataHi,
hFakeMcLo,
hFakeMcHi,
nIter=nIter,
verbose=verbose,
histoname=lep + "_corHFRate" + "_eta_bin%d" % eb,
)
correctionHistos[lep + "_eta"] = combineEtaSlices(
template2d=h2dRealDataCr["num"],
etaSlicedRates=dict((k, h) for k, h in correctionHistos.iteritems() if (lep + "_eta") in k),
histoname=lep + "_corHFRate_eta",
)
print 10 * "--", " done ", 10 * "--"
if verbose:
print "saving output to ", fnameOutput
fileOut = r.TFile.Open(fnameOutput, "recreate")
fileOut.cd()
print "keys ", correctionHistos.keys()
for l, h in correctionHistos.iteritems():
if verbose:
print "%s : writing %s\n%s" % (l, h.GetName(), histo1dToTxt(h))
h.Write()
fileOut.Close()
def subtractRealAndComputeScaleFactor(histosPerGroup={},
variable='',
outRatiohistoname='',
outDataeffhistoname='',
outputDir='./',
region='',
subtractReal=True,
verbose=False):
"efficiency scale factor"
groups = histosPerGroup.keys()
mkdirIfNeeded(outputDir)
histosPerType = dict([(lt,
dict([(g, histosPerGroup[g][variable][lt])
for g in groups])) for lt in leptonTypes])
for lt in leptonTypes:
histosPerType[lt]['totSimBkg'] = summedHisto([
histo for group, histo in histosPerType[lt].iteritems()
if group not in ['data', 'signal']
])
simuTight = histosPerType['fake_tight']['totSimBkg']
simuLoose = histosPerType['fake_loose']['totSimBkg']
dataTight = histosPerType['tight']['data']
dataLoose = histosPerType['loose']['data']
# subtract real contribution from data
# _Note to self_: currently estimating the real contr from MC; in
# the past also used iterative corr, which might be more
# appropriate in cases like here, where the normalization is
# so-so. Todo: investigate the normalization.
dataSubTight = dataTight.Clone(dataTight.GetName().replace(
'data_tight', 'data_minus_prompt_tight'))
dataSubLoose = dataLoose.Clone(dataLoose.GetName().replace(
'data_loose', 'data_minus_prompt_loose'))
dataSubTight.SetDirectory(0)
dataSubLoose.SetDirectory(0)
dataSubTight.Add(histosPerType['real_tight']['totSimBkg'],
-1.0 if subtractReal else 0.0)
dataSubLoose.Add(histosPerType['real_loose']['totSimBkg'],
-1.0 if subtractReal else 0.0)
effData = dataSubTight.Clone(outDataeffhistoname)
effData.SetDirectory(0)
effData.Divide(dataSubLoose)
effSimu = simuTight.Clone(simuTight.GetName().replace(
'fake_tight', 'fake_eff'))
effSimu.SetDirectory(0)
effSimu.Divide(simuLoose)
print "eff(T|L) vs. ", variable
def formatFloat(floats):
return ["%.4f" % f for f in floats]
print "efficiency data : ", formatFloat(getBinContents(effData))
print "efficiency simu : ", formatFloat(getBinContents(effSimu))
ratio = effData.Clone(outRatiohistoname)
ratio.SetDirectory(0)
ratio.Divide(effSimu)
print "sf data/simu : ", formatFloat(getBinContents(ratio))
print " +/- : ", formatFloat(getBinErrors(ratio))
can = r.TCanvas('c_' + outRatiohistoname, outRatiohistoname, 800, 600)
botPad, topPad = rootUtils.buildBotTopPads(can)
can.cd()
topPad.Draw()
topPad.cd()
pm = effData
pm.SetStats(0)
pm.Draw('axis')
xAx, yAx = pm.GetXaxis(), pm.GetYaxis()
xAx.SetTitle('')
xAx.SetLabelSize(0)
yAx.SetRangeUser(0.0, 0.25)
textScaleUp = 1.0 / topPad.GetHNDC()
yAx.SetLabelSize(textScaleUp * 0.04)
yAx.SetTitleSize(textScaleUp * 0.04)
yAx.SetTitle('#epsilon(T|L)')
yAx.SetTitleOffset(yAx.GetTitleOffset() / textScaleUp)
effSimu.SetLineColor(r.kRed)
effSimu.SetMarkerStyle(r.kOpenCross)
effSimu.SetMarkerColor(effSimu.GetLineColor())
effData.Draw('same')
effSimu.Draw('same')
leg = drawLegendWithDictKeys(topPad, {
'data': effData,
'simulation': simuTight
},
legWidth=0.4)
leg.SetHeader('scale factor ' + region + ' ' +
('electron' if '_el_' in outRatiohistoname else
'muon' if '_mu_' in outRatiohistoname else ''))
can.cd()
botPad.Draw()
botPad.cd()
ratio.SetStats(0)
ratio.Draw()
textScaleUp = 1.0 / botPad.GetHNDC()
xAx, yAx = ratio.GetXaxis(), ratio.GetYaxis()
yAx.SetRangeUser(0.0, 2.0)
xAx.SetTitle({
'pt1': 'p_{T}',
'eta1': '|#eta|',
'pt1_eta1': 'p_{T}'
}[variable])
yAx.SetNdivisions(-202)
yAx.SetTitle('Data/Sim')
yAx.CenterTitle()
xAx.SetLabelSize(textScaleUp * 0.04)
xAx.SetTitleSize(textScaleUp * 0.04)
yAx.SetLabelSize(textScaleUp * 0.04)
yAx.SetTitleSize(textScaleUp * 0.04)
refLine = rootUtils.referenceLine(xAx.GetXmin(), xAx.GetXmax())
refLine.Draw()
can.Update()
outFname = os.path.join(outputDir, region + '_' + outRatiohistoname)
for ext in ['.eps', '.png']:
utils.rmIfExists(outFname + ext)
can.SaveAs(outFname + ext)
return {
outRatiohistoname:
ratio,
outDataeffhistoname:
effData,
outDataeffhistoname.replace('_fake_rate_data_', '_tight_data_minus_prompt'):
dataSubTight,
outDataeffhistoname.replace('_fake_rate_data_', '_loose_data_minus_prompt'):
dataSubLoose
}
def getBinContents(self, variable, selection):
return getBinContents(self.getHistogram)
