def calculation(variables):
#def calculation( var1, var2 ):
if args.variables[0] == 'cuB' and args.variables[1] == 'cuW':
var1, var2 = variables #cuB cuW
ctZ, ctW = cuBWtoctWZ(var1, var2)
kwargs = {'ctZ': ctZ, 'ctW': ctW}
else:
var1, var2 = variables
kwargs = {args.variables[0]: var1, args.variables[1]: var2}
# uncertainties
c.reset()
c.addUncertainty('lumi', 'lnN')
c.addUncertainty('JEC', 'lnN')
c.addUncertainty('fake', 'lnN')
signal_rate = {}
for i_region, region in enumerate(ttZRegions):
signal_rate[region] = ttZSample.weightInfo.get_weight_yield(
ttZ_coeffList[region], **kwargs)
bin_name = "Region_%i" % i_region
nice_name = region.__str__()
c.addBin(bin_name,
['_'.join(s.name.split('_')[1:3]) for s in ttZBg],
nice_name)
c.specifyObservation(bin_name, observation[region])
# c.specifyFlatUncertainty( 'lumi', 1.05 )
# c.specifyFlatUncertainty( 'lumi', 1.026 )
c.specifyFlatUncertainty('lumi', 1.01)
c.specifyExpectation(bin_name, 'signal', signal_rate[region])
c.specifyUncertainty('JEC', bin_name, 'signal',
signal_jec_uncertainty[region])
c.specifyUncertainty('fake', bin_name, 'signal',
signal_fakerate_uncertainty[region])
#c.specifyExpectation( bin_name, 'ttX_SM', ttX_SM_rate[region] )
#c.specifyUncertainty( 'JEC', bin_name, 'ttX_SM', ttX_SM_jec_uncertainty[region])
#c.specifyUncertainty( 'fake',bin_name, 'ttX_SM', ttX_SM_fakerate_uncertainty[region])
for background in ttZBg:
c.specifyExpectation(bin_name,
'_'.join(background.name.split('_')[1:3]),
background_rate[region][background.name])
c.specifyUncertainty(
'JEC', bin_name, '_'.join(background.name.split('_')[1:3]),
background_jec_uncertainty[region][background.name])
c.specifyUncertainty(
'fake', bin_name,
'_'.join(background.name.split('_')[1:3]),
background_fakerate_uncertainty[region][background.name])
for i_region, region in enumerate(ttgammaRegions):
signal_rate[region] = ttgamma1lSample.weightInfo.get_weight_yield(
ttgamma1l_coeffList[region], **kwargs)
signal_rate[region] += ttgamma2lSample.weightInfo.get_weight_yield(
ttgamma2l_coeffList[region], **kwargs)
bin_name = "Region_%i" % (i_region + len(ttZRegions))
nice_name = region.__str__()
c.addBin(bin_name,
['_'.join(s.name.split('_')[1:3]) for s in ttgammaBg],
nice_name)
c.specifyObservation(bin_name, observation[region])
# c.specifyFlatUncertainty( 'lumi', 1.05 )
# c.specifyFlatUncertainty( 'lumi', 1.026 )
c.specifyFlatUncertainty('lumi', 1.05)
c.specifyExpectation(bin_name, 'signal', signal_rate[region])
c.specifyUncertainty('JEC', bin_name, 'signal',
signal_jec_uncertainty[region])
c.specifyUncertainty('fake', bin_name, 'signal',
signal_fakerate_uncertainty[region])
#c.specifyExpectation( bin_name, 'ttX_SM', ttX_SM_rate[region] )
#c.specifyUncertainty( 'JEC', bin_name, 'ttX_SM', ttX_SM_jec_uncertainty[region])
#c.specifyUncertainty( 'fake',bin_name, 'ttX_SM', ttX_SM_fakerate_uncertainty[region])
for background in ttgammaBg:
c.specifyExpectation(bin_name,
'_'.join(background.name.split('_')[1:3]),
background_rate[region][background.name])
c.specifyUncertainty(
'JEC', bin_name, '_'.join(background.name.split('_')[1:3]),
background_jec_uncertainty[region][background.name])
c.specifyUncertainty(
'fake', bin_name,
'_'.join(background.name.split('_')[1:3]),
background_fakerate_uncertainty[region][background.name])
nameList = ['combined'] + args.variables + args.binning + [
args.level, args.version, args.order, args.luminosity,
'small' if args.small else 'full', var1, var2
]
cardname = '%s_nll_card' % '_'.join(map(str, nameList))
c.writeToFile('./tmp/%s.txt' % cardname)
profiledLoglikelihoodFit = ProfiledLoglikelihoodFit('./tmp/%s.txt' %
cardname)
profiledLoglikelihoodFit.make_workspace(rmin=rmin, rmax=rmax)
#expected_limit = profiledLoglikelihoodFit.calculate_limit( calculator = "frequentist" )
nll = profiledLoglikelihoodFit.likelihoodTest()
logger.info("NLL: %f", nll)
profiledLoglikelihoodFit.cleanup(removeFiles=True)
del profiledLoglikelihoodFit
ROOT.gDirectory.Clear()
if nll is None or abs(nll) > 10000: nll = 999
return var1, var2, nll
def calculation( c_var ):
sigmaC = getHiggsWeight( c_var )
nameList = [args.sample] + args.binning + [ args.selection, 'small' if args.small else 'full', c_var ]
cardname = '%s_nll_card'%'_'.join( map( str, nameList ) )
cardFilePath = os.path.join( cardfileLocation, cardname + '.txt' )
c = cardFileWriter.cardFileWriter()
if not args.fitOnly:
# print 'run cardfile'
# uncertainties
c.reset()
c.addUncertainty('Luminosity', 'lnN')
c.addUncertainty('JER', 'lnN')
c.addUncertainty('btagging', 'lnN')
c.addUncertainty('mistagging', 'lnN')
c.addUncertainty('LeptonID', 'lnN')
signal_rate = {}
for i_region, region in enumerate(regions):
i_r = i_region % 4
signal_rate[region] = signalPP.getYieldFromDraw( selectionString=region.cutString(), weightString="%f"%(nloXSec*(1-c_var)**2/sigmaC) )['val']
signal_rate[region] += signalGH.getYieldFromDraw( selectionString=region.cutString(), weightString="%f"%(nloXSec*(1-c_var)*c_var/sigmaC) )['val']
signal_rate[region] += signalHG.getYieldFromDraw( selectionString=region.cutString(), weightString="%f"%(nloXSec*(1-c_var)*c_var/sigmaC) )['val']
signal_rate[region] += signalHH.getYieldFromDraw( selectionString=region.cutString(), weightString="%f"%(nloXSec*c_var**2/sigmaC) )['val']
signal_btagging_uncertainty [region] = 1 + .015/(i_r+1.)
signal_mistagging_uncertainty [region] = 1 + .01/(i_r+1.)
signal_leptonId_uncertainty [region] = 1 + .01/(i_r+1.)
signal_jes_uncertainty [region] = 1 + .05/(i_r+1.)
bin_name = "Region_%i" % i_region
nice_name = region.__str__()
c.addBin(bin_name, ['_'.join(s.name.split('_')[1:3]) for s in bg], nice_name)
c.specifyObservation( bin_name, observation[region] )
c.specifyExpectation( bin_name, 'signal', signal_rate[region] )
c.specifyFlatUncertainty( 'Luminosity', 1.026 )
c.specifyUncertainty( 'JER', bin_name, 'signal', signal_jes_uncertainty[region] )
c.specifyUncertainty( 'btagging', bin_name, 'signal', signal_btagging_uncertainty[region] )
c.specifyUncertainty( 'mistagging', bin_name, 'signal', signal_mistagging_uncertainty[region] )
c.specifyUncertainty( 'LeptonID', bin_name, 'signal', signal_leptonId_uncertainty[region] )
for background in bg:
c.specifyExpectation( bin_name, '_'.join( background.name.split('_')[1:3] ), background_rate[region][background.name] )
c.specifyUncertainty( 'JER', bin_name, '_'.join( background.name.split('_')[1:3] ), background_jes_uncertainty[region][background.name])
c.specifyUncertainty( 'btagging', bin_name, '_'.join( background.name.split('_')[1:3] ), background_btagging_uncertainty[region][background.name])
c.specifyUncertainty( 'mistagging', bin_name, '_'.join( background.name.split('_')[1:3] ), background_mistagging_uncertainty[region][background.name])
c.specifyUncertainty( 'LeptonID', bin_name, '_'.join( background.name.split('_')[1:3] ), background_leptonId_uncertainty[region][background.name])
c.writeToFile( cardFilePath )
else:
logger.info( "Running only NLL Fit with given CardFile %s"%cardFilePath)
if not os.path.isfile( cardFilePath ):
raise ValueError('CardFiles not found! Run script without --fitOnly!')
if args.bestFit: r = (0.99, 1.01)
else: r = (0., 2.)
profiledLoglikelihoodFit = ProfiledLoglikelihoodFit( cardFilePath )
profiledLoglikelihoodFit.make_workspace(rmin=r[0], rmax=r[1])
nll = profiledLoglikelihoodFit.likelihoodTest()
profiledLoglikelihoodFit.cleanup(removeFiles=args.removeCardFiles)
del profiledLoglikelihoodFit
logger.info( "NLL: %f", nll)
ROOT.gDirectory.Clear()
# in very large WC regions, the fit fails, not relevant for the interesting regions
if nll is None or abs(nll) > 10000 or abs(nll) < 1: nll = 999
del c
return c_var, nll
def calculation(variables):
#def calculation( var1, var2 ):
if args.variables[0] == 'cuB' and args.variables[1] == 'cuW':
var1, var2 = variables #cuB cuW
ctZ, ctW = cuBWtoctWZ(var1, var2)
kwargs = {'ctZ': ctZ, 'ctW': ctW}
else:
var1, var2 = variables
kwargs = {args.variables[0]: var1, args.variables[1]: var2}
nameList = args.sample.split(
'_')[1:3] + args.variables + args.binning + [
args.level, args.version, args.order, args.luminosity,
"14TeV" if args.scale14TeV else "13TeV", args.selection,
'small' if args.small else 'full', 'statOnly' if args.statOnly
else 'fullUnc' if not args.noExpUnc else 'noExpUnc', var1, var2
]
cardname = '%s_nll_card' % '_'.join(map(str, nameList))
cardFilePath = os.path.join(cardfileLocation, cardname + '.txt')
c = cardFileWriter.cardFileWriter()
if args.useCombine:
c.releaseLocation = combineReleaseLocation
if not args.fitOnly:
# print 'run cardfile'
# uncertainties
c.reset()
if not args.statOnly:
if not args.noExpUnc:
c.addUncertainty('lumi', 'lnN')
c.addUncertainty('JES', 'lnN')
c.addUncertainty('btagging', 'lnN')
c.addUncertainty('mistagging', 'lnN')
c.addUncertainty('muonId', 'lnN')
c.addUncertainty('electronId', 'lnN')
for unc in args.addUncertainties:
c.addUncertainty(unc, 'lnN')
signal_rate = {}
for i_region, region in enumerate(regions):
signal_rate[region] = ttXSample.weightInfo.get_weight_yield(
ttX_coeffList[region], **kwargs)
if not args.statOnly and not args.noExpUnc:
# signal uncertainties
# btagging
signal_rate_reweighted = ttXSample.weightInfo.get_weight_yield(
ttX_coeffList_reweighted_btagging[region], **kwargs)
signal_btagging_uncertainty[region] = 1 + (
(signal_rate_reweighted - signal_rate[region]) /
signal_rate[region]) if signal_rate[region] > 0 else 1.
# mistagging
signal_rate_reweighted = ttXSample.weightInfo.get_weight_yield(
ttX_coeffList_reweighted_mistagging[region], **kwargs)
signal_mistagging_uncertainty[region] = 1 + (
(signal_rate_reweighted - signal_rate[region]) /
signal_rate[region]) if signal_rate[region] > 0 else 1.
# muonId
signal_rate_reweighted = ttXSample.weightInfo.get_weight_yield(
ttX_coeffList_reweighted_muonId[region], **kwargs)
signal_muonId_uncertainty[region] = 1 + (
(signal_rate_reweighted - signal_rate[region]) /
signal_rate[region]) if signal_rate[region] > 0 else 1.
# electronId
signal_rate_reweighted = ttXSample.weightInfo.get_weight_yield(
ttX_coeffList_reweighted_electronId[region], **kwargs)
signal_electronId_uncertainty[region] = 1 + (
(signal_rate_reweighted - signal_rate[region]) /
signal_rate[region]) if signal_rate[region] > 0 else 1.
# JES
signal_rate_reweighted_JES_up = ttXSample.weightInfo.get_weight_yield(
ttX_coeffList_reweighted_jes_up[region], **kwargs)
signal_rate_reweighted_JES_down = ttXSample.weightInfo.get_weight_yield(
ttX_coeffList_reweighted_jes_down[region], **kwargs)
signal_jes_uncertainty[region] = 1 + (
(signal_rate_reweighted_JES_up -
signal_rate_reweighted_JES_down) /
(2 * signal_rate[region])
) if signal_rate[region] > 0 else 1.
bin_name = "Region_%i" % i_region
nice_name = region.__str__()
c.addBin(bin_name,
['_'.join(s.name.split('_')[1:3])
for s in bg] + ['nonPrompt'] if args.addNonPrompt
else ['_'.join(s.name.split('_')[1:3])
for s in bg], nice_name)
c.specifyObservation(bin_name, observation[region])
c.specifyExpectation(bin_name, 'signal', signal_rate[region])
if not args.statOnly:
if not args.noExpUnc:
c.specifyFlatUncertainty('lumi', 1.01)
c.specifyUncertainty('JES', bin_name, 'signal',
signal_jes_uncertainty[region])
c.specifyUncertainty(
'btagging', bin_name, 'signal',
signal_btagging_uncertainty[region])
c.specifyUncertainty(
'mistagging', bin_name, 'signal',
signal_mistagging_uncertainty[region])
c.specifyUncertainty('muonId', bin_name, 'signal',
signal_muonId_uncertainty[region])
c.specifyUncertainty(
'electronId', bin_name, 'signal',
signal_electronId_uncertainty[region])
for unc in args.addUncertainties:
c.specifyUncertainty(
unc, bin_name, 'signal', 1 + (getUncertaintyValue(
args.additionalCardFile, args.addBinNumberShift
+ i_region, 'signal', unc) - 1) *
args.uncertaintyScale)
if args.addNonPrompt:
# for nonpromt only nonpromt uncertainty is important
c.specifyExpectation(bin_name, 'nonPrompt',
nonPromptObservation[region])
if not args.statOnly:
c.specifyUncertainty(
'nonprompt', bin_name, 'nonPrompt',
1 + (getUncertaintyValue(
args.additionalCardFile, args.addBinNumberShift
+ i_region, 'nonPromptDD', 'nonprompt') - 1) *
args.uncertaintyScale)
#c.specifyExpectation( bin_name, 'ttX_SM', ttX_SM_rate[region] )
#c.specifyUncertainty( 'JES', bin_name, 'ttX_SM', ttX_SM_jes_uncertainty[region])
#c.specifyUncertainty( 'btagging',bin_name, 'ttX_SM', ttX_SM_btagging_uncertainty[region])
for background in bg:
c.specifyExpectation(
bin_name, '_'.join(background.name.split('_')[1:3]),
background_rate[region][background.name])
if not args.statOnly:
if not args.noExpUnc:
c.specifyUncertainty(
'JES', bin_name,
'_'.join(background.name.split('_')[1:3]),
background_jes_uncertainty[region][
background.name])
c.specifyUncertainty(
'btagging', bin_name,
'_'.join(background.name.split('_')[1:3]),
background_btagging_uncertainty[region][
background.name])
c.specifyUncertainty(
'mistagging', bin_name,
'_'.join(background.name.split('_')[1:3]),
background_mistagging_uncertainty[region][
background.name])
c.specifyUncertainty(
'muonId', bin_name,
'_'.join(background.name.split('_')[1:3]),
background_muonId_uncertainty[region][
background.name])
c.specifyUncertainty(
'electronId', bin_name,
'_'.join(background.name.split('_')[1:3]),
background_electronId_uncertainty[region][
background.name])
for unc in args.addUncertainties:
if 'tZq' in background.name.split(
'_') or 'ttgamma' in background.name.split(
'_') or 'tWZ' in background.name.split(
'_'):
proc = 'TTX'
elif 'WZ' in background.name.split('_'):
proc = 'WZ'
else:
raise ValueError('Background not found: %s' %
background.name)
c.specifyUncertainty(
unc, bin_name,
'_'.join(background.name.split('_')[1:3]),
1 + (getUncertaintyValue(
args.additionalCardFile,
args.addBinNumberShift + i_region, proc,
unc) - 1) * args.uncertaintyScale)
c.writeToFile(cardFilePath)
else:
logger.info("Running only NLL Fit with given CardFile %s" %
cardFilePath)
if not os.path.isfile(cardFilePath):
raise ValueError(
'CardFiles not found! Run script without --fitOnly!')
if args.useCombine:
# use the official cms combine tool
# c.calcNuisances( cardFilePath, bestFit=args.bestFit )
nll = c.calcNLL(cardFilePath, bestFit=args.bestFit)
# nll = nll['nll0'] #pre-fit
nll = nll['nll_abs'] #post-fit
if args.removeCardFiles:
for file in os.listdir(cardfileLocation):
if file.startswith(cardname):
os.remove(os.path.join(cardfileLocation, file))
else:
if args.bestFit: r = (0.99, 1.01)
else: r = (0., 2.)
profiledLoglikelihoodFit = ProfiledLoglikelihoodFit(cardFilePath)
profiledLoglikelihoodFit.make_workspace(rmin=r[0], rmax=r[1])
nll = profiledLoglikelihoodFit.likelihoodTest()
profiledLoglikelihoodFit.cleanup(removeFiles=args.removeCardFiles)
del profiledLoglikelihoodFit
logger.info("NLL: %f", nll)
ROOT.gDirectory.Clear()
# in very large WC regions, the fit fails, not relevant for the interesting regions
if nll is None or abs(nll) > 10000 or abs(nll) < 1: nll = 999
del c
return var1, var2, nll
tot_background = sum([
background_rate[region][background.name]
for background in backgrounds
])
exp_tot_sigmas += abs(signal_rate[region]) / sqrt(tot_background)
# avoid total neg. yield
if signal_rate[region] < 0:
max_r = -tot_background / signal_rate[region]
if max_r < max_rmax:
max_rmax = max_r
rmax_est = 400. / exp_tot_sigmas if exp_tot_sigmas > 0 else 1.
if max_rmax < rmax_est:
rmax_est = 0.9 * max_rmax # safety margin such that at least +10% total yield survives in the smallest SR
profiledLoglikelihoodFit = ProfiledLoglikelihoodFit(
'./tmp_limit_card.txt')
profiledLoglikelihoodFit.make_workspace(rmin=0, rmax=rmax_est)
#expected_limit = profiledLoglikelihoodFit.calculate_limit( calculator = "frequentist" )
expected_limit = profiledLoglikelihoodFit.calculate_limit(
calculator="asymptotic")
logger.info("Expected Limit: %f", expected_limit[0])
limit.SetBinContent(limit.FindBin(cpt, cpQM), expected_limit[0])
profiledLoglikelihoodFit.cleanup()
limitPlot = Plot2D.fromHisto("2D_limit_3",
texX="cpt",
texY="cpQM",
histos=[[limit]])
limitPlot.drawOption = "colz"
ROOT.gStyle.SetPaintTextFormat("2.2f")
plotting.draw2D(limitPlot,
def calculation( variables ):
if args.variables[0] == 'cuB' and args.variables[1] == 'cuW':
var1, var2 = variables #cuB cuW
ctZ, ctW = cuBWtoctWZ( var1, var2 )
kwargs = { 'ctZ':ctZ, 'ctW':ctW }
else:
var1, var2 = variables
kwargs = { args.variables[0]:var1, args.variables[1]:var2 }
# uncertainties
c.reset()
c.addUncertainty('lumi', 'lnN')
c.addUncertainty('JEC', 'lnN')
c.addUncertainty('fake', 'lnN')
signal_rate = {}
for i_region, region in enumerate(ttZRegions):
signal_rate[region] = ttZSample.weightInfo.get_weight_yield( ttZ_coeffList[region], **kwargs) - ttZ_SM_rate[region]
bin_name = "Region_%i" % i_region
nice_name = region.__str__()
c.addBin(bin_name,['ttX_SM'] + ['_'.join(s.name.split('_')[1:3]) for s in ttZBg], nice_name)
c.specifyObservation( bin_name, observation[region] )
c.specifyFlatUncertainty( 'lumi', 1.05 )
c.specifyExpectation( bin_name, 'signal', signal_rate[region] )
c.specifyUncertainty( 'JEC', bin_name, 'signal', signal_jec_uncertainty[region])
c.specifyUncertainty( 'fake',bin_name, 'signal', signal_fakerate_uncertainty[region])
c.specifyExpectation( bin_name, 'ttX_SM', ttZ_SM_rate[region] )
c.specifyUncertainty( 'JEC', bin_name, 'ttX_SM', ttX_SM_jec_uncertainty[region])
c.specifyUncertainty( 'fake',bin_name, 'ttX_SM', ttX_SM_fakerate_uncertainty[region])
for background in ttZBg:
c.specifyExpectation( bin_name, '_'.join( background.name.split('_')[1:3] ), background_rate[region][background.name] )
c.specifyUncertainty( 'JEC', bin_name, '_'.join( background.name.split('_')[1:3] ), background_jec_uncertainty[region][background.name])
c.specifyUncertainty( 'fake',bin_name, '_'.join( background.name.split('_')[1:3] ), background_fakerate_uncertainty[region][background.name])
for i_region, region in enumerate(ttgammaRegions):
signal_rate[region] = ttgamma1lSample.weightInfo.get_weight_yield( ttgamma1l_coeffList[region], **kwargs) - ttgamma1l_SM_rate[region]
signal_rate[region] += ttgamma2lSample.weightInfo.get_weight_yield( ttgamma2l_coeffList[region], **kwargs) - ttgamma2l_SM_rate[region]
bin_name = "Region_%i" % (i_region + len(ttZRegions))
nice_name = region.__str__()
c.addBin(bin_name,['ttX_SM'] + ['_'.join(s.name.split('_')[1:3]) for s in ttgammaBg], nice_name)
c.specifyObservation( bin_name, observation[region] )
# c.specifyFlatUncertainty( 'lumi', 1.05 )
# c.specifyFlatUncertainty( 'lumi', 1.026 )
c.specifyFlatUncertainty( 'lumi', 1.05 )
c.specifyExpectation( bin_name, 'signal', signal_rate[region] )
c.specifyUncertainty( 'JEC', bin_name, 'signal', signal_jec_uncertainty[region])
c.specifyUncertainty( 'fake',bin_name, 'signal', signal_fakerate_uncertainty[region])
c.specifyExpectation( bin_name, 'ttX_SM', ttgamma1l_SM_rate[region] + ttgamma2l_SM_rate[region] )
c.specifyUncertainty( 'JEC', bin_name, 'ttX_SM', ttX_SM_jec_uncertainty[region])
c.specifyUncertainty( 'fake',bin_name, 'ttX_SM', ttX_SM_fakerate_uncertainty[region])
for background in ttgammaBg:
c.specifyExpectation( bin_name, '_'.join( background.name.split('_')[1:3] ), background_rate[region][background.name] )
c.specifyUncertainty( 'JEC', bin_name, '_'.join( background.name.split('_')[1:3] ), background_jec_uncertainty[region][background.name])
c.specifyUncertainty( 'fake',bin_name, '_'.join( background.name.split('_')[1:3] ), background_fakerate_uncertainty[region][background.name])
nameList = ['combined'] + args.variables + args.binning + [ args.level, args.version, args.order, args.luminosity, 'small' if args.small else 'full', var1, var2 ]
cardname = '%s_limit_card'%'_'.join( map( str, nameList ) )
c.writeToFile( './tmp/%s.txt'%cardname )
# try to adjust rmax with some margin
exp_tot_sigmas = 0
max_rmax = float('inf')
for region in ttZRegions:
tot_background = sum( [ background_rate[region][background.name] for background in ttZBg ] )
exp_tot_sigmas += abs(signal_rate[region]) / sqrt( tot_background ) if tot_background > 0 else 1. #float('inf')
print 'region', region
print 'exp_sigma', exp_tot_sigmas
# avoid total neg. yield
if signal_rate[region] < 0:
max_r = -tot_background / signal_rate[region]
if max_r < max_rmax:
max_rmax = max_r
print 'max_rmax', max_rmax
print
for region in ttgammaRegions:
tot_background = sum( [ background_rate[region][background.name] for background in ttgammaBg ] )
exp_tot_sigmas += abs(signal_rate[region]) / sqrt( tot_background ) if tot_background > 0 else 100. #float('inf')
print 'region', region
print 'exp_sigma', exp_tot_sigmas
# avoid total neg. yield
if signal_rate[region] < 0:
max_r = -tot_background / signal_rate[region]
if max_r < max_rmax:
max_rmax = max_r
print 'max_rmax', max_rmax
print
if exp_tot_sigmas is float('inf'): rmax_est = 0.5 #float('inf')
elif exp_tot_sigmas == 0: rmax_est = 200 #float('inf')
else: rmax_est = 400. / exp_tot_sigmas
print
print 'rmax_est', rmax_est
if max_rmax < rmax_est:
rmax_est = 0.9*max_rmax # safety margin such that at least +10% total yield survives in the smallest SR
print 'rmax_est', rmax_est
print
profiledLoglikelihoodFit = ProfiledLoglikelihoodFit( './tmp/%s.txt'%cardname )
profiledLoglikelihoodFit.make_workspace(rmin=0, rmax=rmax_est)
#expected_limit = profiledLoglikelihoodFit.calculate_limit( calculator = "frequentist" )
expected_limit = profiledLoglikelihoodFit.calculate_limit( calculator = "asymptotic", plotLimit = False )
logger.info( "Expected Limit: %f", expected_limit[0] )
profiledLoglikelihoodFit.cleanup( removeFiles = True )
del profiledLoglikelihoodFit
ROOT.gDirectory.Clear()
return var1, var2, [ expected_limit[i] for i in range(-2,3) ]
#c.specifyUncertainty( 'JEC', bin_name, 'ttZ_SM', ttZ_SM_jec_uncertainty[region])
#c.specifyUncertainty( 'fake',bin_name, 'ttZ_SM', ttZ_SM_fakerate_uncertainty[region])
for background in backgrounds:
c.specifyExpectation(bin_name, background.name,
background_rate[region][background.name])
c.specifyUncertainty(
'JEC', bin_name, background.name,
background_jec_uncertainty[region][background.name])
c.specifyUncertainty(
'fake', bin_name, background.name,
background_fakerate_uncertainty[region][background.name])
c.writeToFile('./tmp_nll_card.txt')
profiledLoglikelihoodFit = ProfiledLoglikelihoodFit(
'./tmp_nll_card.txt')
profiledLoglikelihoodFit.make_workspace(rmin=0, rmax=1)
#expected_limit = profiledLoglikelihoodFit.calculate_limit( calculator = "frequentist" )
nll = profiledLoglikelihoodFit.likelihoodTest()
logger.info("NLL: %f", nll)
nll_plot.SetBinContent(nll_plot.FindBin(cpt, cpQM), nll)
profiledLoglikelihoodFit.cleanup()
nll_SM = nll_plot.GetBinContent(nll_plot.FindBin(0, 0))
for bin_x in range(nll_plot.GetNbinsX() + 1):
for bin_y in range(nll_plot.GetNbinsY() + 1):
nll_plot.SetBinContent(
bin_x, bin_y,
nll_plot.GetBinContent(nll_plot.GetBin(bin_x, bin_y)) - nll_SM)
nllPlot = Plot2D.fromHisto("2D_nll_2",