argParser.add_argument('--plot_directory', action='store', default='NLL_plots')
argParser.add_argument('--useBestFit',
action='store_true',
help="Use best fit value? Default is r=1")
args = argParser.parse_args()
def Eval(obj, x, params):
return obj.Eval(x[0])
fitKey = "dNLL_postfit_r1" if not args.useBestFit else "dNLL_bestfit"
# get the absolute post fit NLL value of pure ttZ
#ttZ_res = getResult(ewkDM_ttZ_ll_noH)
ttZ_res = getResult(dim6top_LO_ttZ_ll_ctZ_0p00_ctZI_0p00)
ttZ_NLL_abs = float(ttZ_res["NLL_prefit"]) + float(ttZ_res[fitKey])
print "Max Likelihood ttZ SM"
print ttZ_NLL_abs
#signals = [ewkDM_ttZ_ll_DC1A_0p60_DC1V_m0p24_DC2A_m0p1767_DC2V_m0p1767, ewkDM_ttZ_ll_DC1A_0p60_DC1V_m0p24_DC2A_m0p1767_DC2V_0p1767]
#signals = [ewkDM_ttZ_ll_noH_DC2V_m0p25, ewkDM_ttZ_ll_noH_DC2V_m0p15, ewkDM_ttZ_ll_noH, ewkDM_ttZ_ll_noH_DC2V_0p05, ewkDM_ttZ_ll_noH_DC2V_0p10, ewkDM_ttZ_ll_noH_DC2V_0p20, ewkDM_ttZ_ll_noH_DC2V_0p30]
signals = [
x for x in allSamples_dim6top
if x.name.startswith("dim6top_LO_ttZ_ll_ctZ_0p00")
]
signals = [
dim6top_LO_ttZ_ll_ctZ_0p00_ctZI_m2p00,
def wrapper(s):
logger.info("Now working on %s", s.name)
xSecScale = 1
c = cardFileWriter.cardFileWriter()
c.releaseLocation = combineReleaseLocation
for coup in nonZeroCouplings:
modification_dict[coup] = getCouplingFromName(s.name, coup)
logger.info("The following coupling is set to non-zero value: %s: %s", coup, modification_dict[coup])
xsec = p.xsecDB.get(modification_dict)
logger.info("Found modified x-sec of %s", xsec)
cardFileName = os.path.join(limitDir, s.name+'.txt')
if not os.path.exists(cardFileName) or overWrite:
counter=0
c.reset()
c.addUncertainty('PU', 'lnN')
c.addUncertainty('JEC', 'lnN')
c.addUncertainty('btag', 'lnN')
c.addUncertainty('trigger', 'lnN')
c.addUncertainty('leptonSF', 'lnN')
c.addUncertainty('scale', 'lnN')
c.addUncertainty('scale_sig', 'lnN')
c.addUncertainty('PDF', 'lnN')
c.addUncertainty('nonprompt', 'lnN')
c.addUncertainty('WZ_xsec', 'lnN')
c.addUncertainty('ZZ_xsec', 'lnN')
c.addUncertainty('rare', 'lnN')
c.addUncertainty('ttX', 'lnN')
c.addUncertainty('tZq', 'lnN')
for setup in setups:
signal = MCBasedEstimate(name="TTZ", sample=setup.samples["TTZ"], cacheDir=setup.defaultCacheDir())
observation = MCBasedEstimate(name="observation", sample=setup.samples["pseudoData"], cacheDir=setup.defaultCacheDir())
#observation = DataObservation(name='Data', sample=setup.sample['pseudoData'], cacheDir=setup.defaultCacheDir())
for e in setup.estimators: e.initCache(setup.defaultCacheDir())
for r in setup.regions:
for channel in setup.channels:
niceName = ' '.join([channel, r.__str__()])
binname = 'Bin'+str(counter)
counter += 1
c.addBin(binname, [e.name.split('-')[0] for e in setup.estimators], niceName)
for e in setup.estimators:
name = e.name.split('-')[0]
expected = e.cachedEstimate(r, channel, setup)
c.specifyExpectation(binname, name, round(expected.val,3) if expected.val > 0 else 0.01)
if expected.val>0:
if not args.statOnly:
c.specifyUncertainty('PU', binname, name, 1.01)
c.specifyUncertainty('JEC', binname, name, 1.03) #1.05
c.specifyUncertainty('btag', binname, name, 1.03) #1.05
c.specifyUncertainty('trigger', binname, name, 1.03) #1.04
c.specifyUncertainty('leptonSF', binname, name, 1.05) #1.07
c.specifyUncertainty('scale', binname, name, 1.01)
c.specifyUncertainty('PDF', binname, name, 1.01)
if name.count('ZZ'): c.specifyUncertainty('ZZ_xsec', binname, name, 1.20) #1.20
if name.count('WZ'): c.specifyUncertainty('WZ_xsec', binname, name, 1.10) #1.20
if name.count('nonprompt'): c.specifyUncertainty('nonprompt', binname, name, 1.30)
if name.count('rare'): c.specifyUncertainty('rare', binname, name, 1.50)
if name.count('TTX'): c.specifyUncertainty('ttX', binname, name, 1.10) #1.15
if name.count('TZQ'): c.specifyUncertainty('tZq', binname, name, 1.10) #1.15
#MC bkg stat (some condition to neglect the smaller ones?)
uname = 'Stat_'+binname+'_'+name
c.addUncertainty(uname, 'lnN')
c.specifyUncertainty(uname, binname, name, round(1+expected.sigma/expected.val,3) )
obs = observation.cachedEstimate(r, channel, setup)
c.specifyObservation(binname, int(round(obs.val,0)))
if args.useShape:
logger.info("Using 2D reweighting method for shapes")
if args.model == "dim6top_LO":
source_gen = dim6top_LO_ttZ_ll_ctZ_0p00_ctZI_0p00
elif args.model == "ewkDM":
source_gen = ewkDM_central
target_gen = s
signalReweighting = SignalReweighting( source_sample = source_gen, target_sample = s, cacheDir = reweightCache)
f = signalReweighting.cachedReweightingFunc( setup.genSelection )
sig = signal.reweight2D(r, channel, setup, f)
else:
sig = signal.cachedEstimate(r, channel, setup)
xSecMod = 1
if args.useXSec:
xSecMod = xsec.val/xsec_central.val
logger.info("x-sec is multiplied by %s",xSecMod)
c.specifyExpectation(binname, 'signal', round(sig.val*xSecScale * xSecMod, 3) )
if sig.val>0:
if not args.statOnly:
c.specifyUncertainty('PU', binname, "signal", 1.01)
c.specifyUncertainty('JEC', binname, "signal", 1.03) #1.05
c.specifyUncertainty('btag', binname, "signal", 1.03) #1.05
c.specifyUncertainty('trigger', binname, "signal", 1.03) #1.04
c.specifyUncertainty('leptonSF', binname, "signal", 1.05) #1.07
c.specifyUncertainty('scale_sig', binname, "signal", 1.10) #1.30
c.specifyUncertainty('PDF', binname, "signal", 1.05) #1.15
uname = 'Stat_'+binname+'_signal'
c.addUncertainty(uname, 'lnN')
c.specifyUncertainty(uname, binname, 'signal', round(1 + sig.sigma/sig.val,3) )
else:
uname = 'Stat_'+binname+'_signal'
c.addUncertainty(uname, 'lnN')
c.specifyUncertainty(uname, binname, 'signal', 1 )
c.addUncertainty('Lumi', 'lnN')
c.specifyFlatUncertainty('Lumi', 1.026)
cardFileName = c.writeToFile(cardFileName)
else:
logger.info("File %s found. Reusing.",cardFileName)
res = {}
if getResult(s) and not overWrite:
res = getResult(s)
logger.info("Found result for %s, reusing", s.name)
else:
# calculate the limit
#limit = c.calcLimit(cardFileName)#, options="--run blind")
#res.update({"exp":limit['0.500'], "obs":limit['-1.000'], "exp1up":limit['0.840'], "exp2up":limit['0.975'], "exp1down":limit['0.160'], "exp2down":limit['0.025']})
res.update({"exp":0, "obs":0, "exp1up":0, "exp2up":0, "exp1down":0, "exp2down":0})
# run the checks
c.calcNuisances(cardFileName)
# extract the NLL
nll = c.calcNLL(cardFileName, options="--fastScan")
if nll["nll0"] > 0:
res.update({"dNLL_postfit_r1":nll["nll"], "dNLL_bestfit":nll["bestfit"], "NLL_prefit":nll["nll0"]})
else:
res.update({"dNLL_postfit_r1":-999, "dNLL_bestfit":-999, "NLL_prefit":-999})
logger.info("Fits failed, adding values -999 as results")
logger.info("Adding results to database")
addResult(s, res, nll['nll_abs'], overwrite=True)
print
print "NLL results:"
print "{:>15}{:>15}{:>15}".format("Pre-fit", "Post-fit r=1", "Best fit")
print "{:15.2f}{:15.2f}{:15.2f}".format(float(res["NLL_prefit"]), float(res["NLL_prefit"])+float(res["dNLL_postfit_r1"]), float(res["NLL_prefit"])+float(res["dNLL_bestfit"]))
if xSecScale != 1:
for k in res:
res[k] *= xSecScale
def wrapper(s):
xSecScale = 1
c = cardFileWriter.cardFileWriter()
c.releaseLocation = combineReleaseLocation
cardFileName = os.path.join(limitDir, s.name + '.txt')
if not os.path.exists(cardFileName) or overWrite:
counter = 0
c.reset()
c.addUncertainty('PU', 'lnN')
c.addUncertainty('JEC', 'lnN')
c.addUncertainty('btag', 'lnN')
c.addUncertainty('trigger', 'lnN')
c.addUncertainty('leptonSF', 'lnN')
c.addUncertainty('scale', 'lnN')
c.addUncertainty('scale_sig', 'lnN')
c.addUncertainty('PDF', 'lnN')
c.addUncertainty('nonprompt', 'lnN')
c.addUncertainty('WZ_xsec', 'lnN')
c.addUncertainty('ZZ_xsec', 'lnN')
c.addUncertainty('rare', 'lnN')
c.addUncertainty('ttX', 'lnN')
c.addUncertainty('tZq', 'lnN')
for setup in setups:
for r in setup.regions:
for channel in setup.channels:
niceName = ' '.join([channel, r.__str__()])
binname = 'Bin' + str(counter)
counter += 1
c.addBin(binname, [p.name for p in processes], niceName)
for p in processes:
name = p.name
expected = getEstimate(
p, r, channel
) #{"process":nam, "region":r, "lumi":35.9, "presel":"nLep==3&&isTTZ&&nJetGodd>2&&nBTag>0", "weightString":"weight", "channel":channel})
c.specifyExpectation(binname, name, expected.val)
if expected.val > 0:
c.specifyUncertainty('PU', binname, name, 1.01)
c.specifyUncertainty('JEC', binname, name, 1.05)
c.specifyUncertainty('btag', binname, name, 1.05)
c.specifyUncertainty('trigger', binname, name,
1.04)
c.specifyUncertainty('leptonSF', binname, name,
1.07)
c.specifyUncertainty('scale', binname, name, 1.01)
c.specifyUncertainty('PDF', binname, name, 1.01)
if name.count('ZZ'):
c.specifyUncertainty('ZZ_xsec', binname, name,
1.20)
if name.count('WZ'):
c.specifyUncertainty('WZ_xsec', binname, name,
1.20)
if name.count('nonprompt'):
c.specifyUncertainty('nonprompt', binname,
name, 1.30)
if name.count('rare'):
c.specifyUncertainty('rare', binname, name,
1.50)
if name.count('TTX'):
c.specifyUncertainty('ttX', binname, name,
1.15)
if name.count('TZQ'):
c.specifyUncertainty('tZq', binname, name,
1.15)
#MC bkg stat (some condition to neglect the smaller ones?)
uname = 'Stat_' + binname + '_' + name
c.addUncertainty(uname, 'lnN')
c.specifyUncertainty(
uname, binname, name,
1 + expected.sigma / expected.val)
c.specifyObservation(
binname,
int(round((getEstimate(
pseudoData, r,
channel).val)))) #can also be pseudoDataPriv
#signalSetup = setup.systematicClone()
signal = getEstimate(
s, r, channel
) #resultsCache.get({"process":s.name, "region":r, "lumi":35.9, "presel":"nLep==3&&isTTZ&&nJetGodd>2&&nBTag>0", "weightString":"weight", "channel":channel})
c.specifyExpectation(binname, 'signal',
signal.val * xSecScale)
if signal.val > 0:
c.specifyUncertainty('PU', binname, "signal", 1.01)
c.specifyUncertainty('JEC', binname, "signal", 1.05)
c.specifyUncertainty('btag', binname, "signal", 1.05)
c.specifyUncertainty('trigger', binname, "signal",
1.04)
c.specifyUncertainty('leptonSF', binname, "signal",
1.07)
c.specifyUncertainty('scale_sig', binname, "signal",
1.30)
c.specifyUncertainty('PDF', binname, "signal", 1.15)
uname = 'Stat_' + binname + '_signal'
c.addUncertainty(uname, 'lnN')
c.specifyUncertainty(uname, binname, 'signal',
1 + signal.sigma / signal.val)
else:
uname = 'Stat_' + binname + '_signal'
c.addUncertainty(uname, 'lnN')
c.specifyUncertainty(uname, binname, 'signal', 1)
## alternative signal model
#signal_ALT = getEstimate(s, r, channel)
#c.specifyExpectation(binname, 'signal_ALT', signal_ALT.val*xSecScale )
#
#if signal_ALT.val>0:
# c.specifyUncertainty('PU', binname, "signal_ALT", 1.01)
# c.specifyUncertainty('JEC', binname, "signal_ALT", 1.05)
# c.specifyUncertainty('btag', binname, "signal_ALT", 1.05)
# c.specifyUncertainty('trigger', binname, "signal_ALT", 1.04)
# c.specifyUncertainty('leptonSF', binname, "signal_ALT", 1.07)
# c.specifyUncertainty('scale_sig', binname, "signal_ALT", 1.30)
# c.specifyUncertainty('PDF', binname, "signal_ALT", 1.15)
# uname = 'Stat_'+binname+'_signal_ALT'
# c.addUncertainty(uname, 'lnN')
# c.specifyUncertainty(uname, binname, 'signal_ALT', 1 + signal_ALT.sigma/signal_ALT.val )
#else:
# uname = 'Stat_'+binname+'_signal_ALT'
# c.addUncertainty(uname, 'lnN')
# c.specifyUncertainty(uname, binname, 'signal_ALT', 1 )
c.addUncertainty('Lumi', 'lnN')
c.specifyFlatUncertainty('Lumi', 1.026)
cardFileName = c.writeToFile(cardFileName)
else:
print "File %s found. Reusing." % cardFileName
res = {}
if getResult(s) and not overWrite:
res = getResult(s)
print "Found result for %s, reusing" % s.name
else:
# calculate the limit
limit = c.calcLimit(cardFileName) #, options="--run blind")
res.update({
"exp": limit['0.500'],
"obs": limit['-1.000'],
"exp1up": limit['0.840'],
"exp2up": limit['0.975'],
"exp1down": limit['0.160'],
"exp2down": limit['0.025']
})
# run the checks
c.calcNuisances(cardFileName)
# extract the NLL
nll = c.calcNLL(cardFileName)
res.update({
"dNLL_postfit_r1": nll["nll"],
"dNLL_bestfit": nll["bestfit"],
"NLL_prefit": nll["nll0"]
})
addResult(s, res, nll['nll_abs'], overwrite=True)
print "NLL results:"
print "{:>15}{:>15}{:>15}".format("Pre-fit", "Post-fit r=1", "Best fit")
print "{:15.2f}{:15.2f}{:15.2f}".format(
float(res["NLL_prefit"]),
float(res["NLL_prefit"]) + float(res["dNLL_postfit_r1"]),
float(res["NLL_prefit"]) + float(res["dNLL_bestfit"]))
if xSecScale != 1:
for k in res:
res[k] *= xSecScale
result.SetPoint(i, x[i], y[i], z[i])
h = result.GetHistogram()
h.SetMinimum(min(z))
h.SetMaximum(max(z))
c = ROOT.TCanvas()
result.Draw()
del c
#res = ROOT.TGraphDelaunay(result)
return result
fitKey = "dNLL_postfit_r1" if not args.useBestFit else "dNLL_bestfit"
# get the absolute post fit NLL value of pure ttZ
#ttZ_res = getResult(ewkDM_ttZ_ll_noH)
ttZ_res = getResult(dim6top_LO_ttZ_ll_cpQM_0p00_cpt_0p00)
ttZ_NLL_abs = float(ttZ_res["NLL_prefit"]) + float(ttZ_res[fitKey])
print "Max Likelihood ttZ SM"
print ttZ_NLL_abs
# load all samples, omit the 1/1 point
#signals = [ x for x in allSamples_dim6top if not x.name.startswith('dim6top_LO_ttZ_ll_ctZ_1p00_ctZI_1p00') ]
signals = [x for x in dim6top_currents]
ctZ_values = []
ctZi_values = []
for s in signals:
s.ctZ = float(s.name.split('_')[5].replace('p', '.').replace('m', '-'))
if not (s.ctZ in ctZ_values):
result.SetTitle(title)
for i in range(length):
result.SetPoint(i, x[i], y[i], z[i])
debug.SetPoint(i, x[i], y[i])
c = ROOT.TCanvas()
result.Draw()
debug.Draw()
del c
#res = ROOT.TGraphDelaunay(result)
return result, debug
fitKey = "dNLL_postfit_r1" if not args.useBestFit else "dNLL_bestfit"
# get the absolute post fit NLL value of pure ttZ
ttZ_res = getResult(dim6top_central)
ttZ_NLL_abs = float(ttZ_res["NLL_prefit"]) + float(ttZ_res[fitKey])
print "Max Likelihood ttZ SM"
print "{:10.2f}".format(ttZ_NLL_abs)
# load all samples, omit the 1/1 point
#signals = [ x for x in allSamples_dim6top if not x.name.startswith('dim6top_LO_ttZ_ll_ctZ_1p00_ctZI_1p00') ]
if args.plane == "current":
signals = [dim6top_central] + [x for x in dim6top_currents]
x_var = 'cpQM'
y_var = 'cpt'
x_shift = 0.
y_shift = 0.
result.SetPoint(i, x[i], y[i], z[i])
#h = result.GetHistogram()
#h.SetMinimum(min(z))
#h.SetMaximum(max(z))
c = ROOT.TCanvas()
result.Draw()
#debug.Draw("same")
del c
#res = ROOT.TGraphDelaunay(result)
return result #,debug
fitKey = "dNLL_postfit_r1" if not args.useBestFit else "dNLL_bestfit"
# get the absolute post fit NLL value of pure ttZ
ttZ_res = getResult(ewkDM_central)
#ttZ_res = getResult(dim6top_LO_ttZ_ll_cpQM_0p00_cpt_0p00)
ttZ_NLL_abs = float(ttZ_res["NLL_prefit"]) + float(ttZ_res[fitKey])
print "Max Likelihood ttZ SM"
print "{:10.2f}".format(ttZ_NLL_abs)
# load all samples, omit the 1/1 point
#signals = [ x for x in allSamples_dim6top if not x.name.startswith('dim6top_LO_ttZ_ll_ctZ_1p00_ctZI_1p00') ]
if args.plane == "current":
signals = [ewkDM_central] + [x for x in ewkDM_currents]
x_var = 'DC1V'
y_var = 'DC1A'
x_shift = 0.24
y_shift = -0.60