def getPerformanceOnMC(
files,
histogramForEstimation="QCDStudy/PFIsolation_WithMETCutAndAsymJetCuts_DR03",
bjetBin="",
function="expo",
fitRange=(0.3, 1.6),
additionFitRanges=[(0.2, 1.6), (0.4, 1.6)],
):
if bjetBin:
histogramForEstimation = histogramForEstimation + "_" + bjetBin
hists = [histogramForEstimation]
hists = getHistsFromFiles(hists, files)
hists = addSampleSum(hists)
histogramForComparison = hists["qcd"][histogramForEstimation]
histogramForEstimation = hists["allMC"][histogramForEstimation]
estimate, absoluteError = getQCDEstimateFor(
histogramForEstimation, function, fitRange=fitRange, additionFitRanges=additionFitRanges
)
qcdInSignalRegion, qcdError = getIntegral(histogramForComparison, (0, 0.1))
N_est = ufloat((estimate, absoluteError))
N_qcd = ufloat((qcdInSignalRegion, qcdError))
relativeDeviation = N_est / N_qcd
result = {}
result["performance"] = (relativeDeviation.nominal_value, relativeDeviation.std_dev())
result["estimate"] = (estimate, absoluteError)
result["qcdInSignalRegion"] = (qcdInSignalRegion, qcdError)
return result
def getQCDEstimate(
datafile,
histogramForEstimation='QCDStudy/PFIsolation_WithMETCutAndAsymJetCuts_DR03',
bjetBin='',
function='expo',
fitRange=(0.3, 1.6),
additionFitRanges=[(0.2, 1.6), (0.4, 1.6)]):
bias = 0.45
bias = 0
reductionFromBias = 1 - bias
if bjetBin:
histogramForEstimation = histogramForEstimation + '_' + bjetBin
files = {'data': datafile}
hists = [histogramForEstimation]
hists = getHistsFromFiles(hists, files)
histogramForEstimation = hists['data'][histogramForEstimation]
estimate, absoluteError = getQCDEstimateFor(
histogramForEstimation,
function,
fitRange=fitRange,
additionFitRanges=additionFitRanges)
estimate = estimate * reductionFromBias
absoluteError = absoluteError * reductionFromBias
absoluteError = sqrt(absoluteError**2 + (estimate * bias)**2)
return estimate, absoluteError
def compareFitFunctions(datafile, histogramForEstimation, functions, fitRange=(0.3, 1.6)):
files = {"data": datafile}
hists = [histogramForEstimation]
hists = getHistsFromFiles(hists, files)
histogramForEstimation = hists["data"][histogramForEstimation]
result = {}
for function in functions:
fit = doFit(histogramForEstimation, function, fitRange)
ndof = fit.GetNDF()
chi2 = fit.GetChisquare()
result[function] = {"NDOF": ndof, "Chi2": chi2}
return result
def compareFitFunctions(datafile,
histogramForEstimation,
functions,
fitRange=(0.3, 1.6)):
files = {'data': datafile}
hists = [histogramForEstimation]
hists = getHistsFromFiles(hists, files)
histogramForEstimation = hists['data'][histogramForEstimation]
result = {}
for function in functions:
fit = doFit(histogramForEstimation, function, fitRange)
ndof = fit.GetNDF()
chi2 = fit.GetChisquare()
result[function] = {'NDOF': ndof, 'Chi2': chi2}
return result
def getShapeErrorHistogram(
files,
histogramForShape="topReconstruction/backgroundShape/mttbar_conversions_withMETAndAsymJets",
histogramForComparison="topReconstruction/backgroundShape/mttbar_antiIsolated_withMETAndAsymJets",
rebin=1,
suffix="",
):
files = {"data": files["data"]}
# histogramForShape = 'topReconstruction/backgroundShape/mttbar_conversions_withMETAndAsymJets'
# histogramForComparison = 'topReconstruction/backgroundShape/mttbar_antiIsolated_withMETAndAsymJets'
# suffixes = allBjetBins
# rebin = 50
errors = None
# for suffix in suffixes:
# if suffix in histname:
if not suffix == "":
histogramForShape = histogramForShape + "_" + suffix
histogramForComparison = histogramForComparison + "_" + suffix
hists = [histogramForShape, histogramForComparison]
hists = getHistsFromFiles(hists, files)
histogramForShape = hists["data"][histogramForShape]
histogramForComparison = hists["data"][histogramForComparison]
histogramForShape.Sumw2()
histogramForComparison.Sumw2()
histogramForShape.Rebin(rebin)
histogramForComparison.Rebin(rebin)
nShape = histogramForShape.Integral()
nCompare = histogramForComparison.Integral()
if nShape > 0 and nCompare > 0:
histogramForShape.Scale(1 / nShape)
histogramForComparison.Scale(1 / nCompare)
errors = histogramForShape.Clone("ShapeErrors")
errors.Add(histogramForComparison, -1) # subtraction
for bin in range(1, errors.GetNbinsX()):
errors.SetBinContent(bin, fabs(errors.GetBinContent(bin)))
errors.Divide(histogramForShape)
return errors
def getShapeErrorHistogram(
files,
histogramForShape='topReconstruction/backgroundShape/mttbar_conversions_withMETAndAsymJets',
histogramForComparison='topReconstruction/backgroundShape/mttbar_antiIsolated_withMETAndAsymJets',
rebin=1,
suffix=''):
files = {'data': files['data']}
# histogramForShape = 'topReconstruction/backgroundShape/mttbar_conversions_withMETAndAsymJets'
# histogramForComparison = 'topReconstruction/backgroundShape/mttbar_antiIsolated_withMETAndAsymJets'
# suffixes = allBjetBins
# rebin = 50
errors = None
# for suffix in suffixes:
# if suffix in histname:
if not suffix == '':
histogramForShape = histogramForShape + '_' + suffix
histogramForComparison = histogramForComparison + '_' + suffix
hists = [histogramForShape, histogramForComparison]
hists = getHistsFromFiles(hists, files)
histogramForShape = hists['data'][histogramForShape]
histogramForComparison = hists['data'][histogramForComparison]
histogramForShape.Sumw2()
histogramForComparison.Sumw2()
histogramForShape.Rebin(rebin)
histogramForComparison.Rebin(rebin)
nShape = histogramForShape.Integral()
nCompare = histogramForComparison.Integral()
if nShape > 0 and nCompare > 0:
histogramForShape.Scale(1 / nShape)
histogramForComparison.Scale(1 / nCompare)
errors = histogramForShape.Clone('ShapeErrors')
errors.Add(histogramForComparison, -1) #subtraction
for bin in range(1, errors.GetNbinsX()):
errors.SetBinContent(bin, fabs(errors.GetBinContent(bin)))
errors.Divide(histogramForShape)
return errors
def getPerformanceOnMC(
files,
histogramForEstimation='QCDStudy/PFIsolation_WithMETCutAndAsymJetCuts_DR03',
bjetBin='',
function='expo',
fitRange=(0.3, 1.6),
additionFitRanges=[(0.2, 1.6), (0.4, 1.6)]):
if bjetBin:
histogramForEstimation = histogramForEstimation + '_' + bjetBin
hists = [histogramForEstimation]
hists = getHistsFromFiles(hists, files)
hists = addSampleSum(hists)
histogramForComparison = hists['qcd'][histogramForEstimation]
histogramForEstimation = hists['allMC'][histogramForEstimation]
estimate, absoluteError = getQCDEstimateFor(
histogramForEstimation,
function,
fitRange=fitRange,
additionFitRanges=additionFitRanges)
qcdInSignalRegion, qcdError = getIntegral(histogramForComparison, (0, 0.1))
N_est = ufloat((estimate, absoluteError))
N_qcd = ufloat((qcdInSignalRegion, qcdError))
relativeDeviation = N_est / N_qcd
result = {}
result['performance'] = (relativeDeviation.nominal_value,
relativeDeviation.std_dev())
result['estimate'] = (estimate, absoluteError)
result['qcdInSignalRegion'] = (qcdInSignalRegion, qcdError)
return result
def getQCDEstimate(
datafile,
histogramForEstimation="QCDStudy/PFIsolation_WithMETCutAndAsymJetCuts_DR03",
bjetBin="",
function="expo",
fitRange=(0.3, 1.6),
additionFitRanges=[(0.2, 1.6), (0.4, 1.6)],
):
bias = 0.45
bias = 0
reductionFromBias = 1 - bias
if bjetBin:
histogramForEstimation = histogramForEstimation + "_" + bjetBin
files = {"data": datafile}
hists = [histogramForEstimation]
hists = getHistsFromFiles(hists, files)
histogramForEstimation = hists["data"][histogramForEstimation]
estimate, absoluteError = getQCDEstimateFor(
histogramForEstimation, function, fitRange=fitRange, additionFitRanges=additionFitRanges
)
estimate = estimate * reductionFromBias
absoluteError = absoluteError * reductionFromBias
absoluteError = sqrt(absoluteError ** 2 + (estimate * bias) ** 2)
return estimate, absoluteError
