def doPerformanceStudyOnMCOnly(inputFiles,
histogramForEstimation=defaultHistogram,
function='expo',
fitRanges=[(0.2, 1.6), (0.3, 1.6), (0.4, 1.6)]):
if DEBUG:
print '*' * 120
print "Estimating QCD using a fit to RelIso"
print 'Histogram = ', histogramForEstimation
print 'Fit functions = ', function
print 'Fit ranges = ', fitRanges
print '*' * 120
#get histograms
histograms = FileReader.getHistogramDictionary(histogramForEstimation,
inputFiles)
global allMC, qcd
histograms['SumMC'] = plotting.sumSamples(histograms, allMC)
histograms['QCD'] = plotting.sumSamples(histograms, qcd)
# qcdInSignalRegion = histograms['QCD'].Integral()
# qcdError = 0
# if not qcdInSignalRegion == 0:
# qcdError = qcdInSignalRegion / sqrt(qcdInSignalRegion)
import copy
results = {}
qcdInSignalRegion, qcdError = getIntegral(histograms['QCD'], (0, 0.1))
# getRelIsoCalibrationCurve(inputFiles, histogramForEstimation, function, fitRanges)
for fitRange in fitRanges:
#take all other fit ranges as systematics
fitRangesForSystematics = copy.deepcopy(fitRanges)
fitRangesForSystematics.remove(fitRange)
#instead of data use sum MC
resultFromMethod = relIsoMethodWithSystematics(
histograms['SumMC'], function, fitRange, fitRangesForSystematics,
False)
estimate, absoluteError = resultFromMethod[
'estimate'], resultFromMethod['absoluteError']
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)
result['fitfunction'] = function
result['fitRange'] = fitRange
result['fitRangesForSystematics'] = fitRangesForSystematics
result['fit'] = resultFromMethod['fit']
results[str(fitRange)] = result
return results
def doPerformanceStudyOnMCOnly(inputFiles,
histogramForEstimation=defaultHistogram,
function='expo',
fitRanges=[(0.2, 1.6), (0.3, 1.6), (0.4, 1.6)]):
if DEBUG:
print '*' * 120
print "Estimating QCD using a fit to RelIso"
print 'Histogram = ', histogramForEstimation
print 'Fit functions = ', function
print 'Fit ranges = ', fitRanges
print '*' * 120
#get histograms
histograms = FileReader.getHistogramDictionary(histogramForEstimation, inputFiles)
global allMC, qcd
histograms['SumMC'] = plotting.sumSamples(histograms, allMC)
histograms['QCD'] = plotting.sumSamples(histograms, qcd)
# qcdInSignalRegion = histograms['QCD'].Integral()
# qcdError = 0
# if not qcdInSignalRegion == 0:
# qcdError = qcdInSignalRegion / sqrt(qcdInSignalRegion)
import copy
results = {}
qcdInSignalRegion, qcdError = getIntegral(histograms['QCD'], (0, 0.1))
# getRelIsoCalibrationCurve(inputFiles, histogramForEstimation, function, fitRanges)
for fitRange in fitRanges:
#take all other fit ranges as systematics
fitRangesForSystematics = copy.deepcopy(fitRanges)
fitRangesForSystematics.remove(fitRange)
#instead of data use sum MC
resultFromMethod = relIsoMethodWithSystematics(histograms['SumMC'], function, fitRange, fitRangesForSystematics, False)
estimate, absoluteError = resultFromMethod['estimate'], resultFromMethod['absoluteError']
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)
result['fitfunction'] = function
result['fitRange'] = fitRange
result['fitRangesForSystematics'] = fitRangesForSystematics
result['fit'] = resultFromMethod['fit']
results[str(fitRange)] = result
return results
def getStuff(histogramForEstimation, inputFiles):
histograms = FileReader.getHistogramDictionary(histogramForEstimation, inputFiles)
global allMC, qcd
histograms['SumMC'] = plotting.sumSamples(histograms, allMC)
histograms['QCD'] = plotting.sumSamples(histograms, qcd)
qcdInSignalRegion, qcdError = getIntegral(histograms['QCD'], (0, 0.1))
data, dataError = getIntegral(histograms['SingleElectron'], (0, 0.1))
sumMC, sumMCError = getIntegral(histograms['SumMC'], (0, 0.1))
result = {
'N_data': data,
'N_QCD': qcdInSignalRegion,
'N_QCD_Error': qcdError,
'N_SumMC': sumMC
}
return result
def getStuff(histogramForEstimation, inputFiles):
histograms = FileReader.getHistogramDictionary(histogramForEstimation,
inputFiles)
global allMC, qcd
histograms['SumMC'] = plotting.sumSamples(histograms, allMC)
histograms['QCD'] = plotting.sumSamples(histograms, qcd)
qcdInSignalRegion, qcdError = getIntegral(histograms['QCD'], (0, 0.1))
data, dataError = getIntegral(histograms['SingleElectron'], (0, 0.1))
sumMC, sumMCError = getIntegral(histograms['SumMC'], (0, 0.1))
result = {
'N_data': data,
'N_QCD': qcdInSignalRegion,
'N_QCD_Error': qcdError,
'N_SumMC': sumMC
}
return result
