"marker": 22,
"color": ROOT.kGreen + 2
}
}
processes = ["ttZ", "ttH", "ttW"]
SM_xsec = {}
modified_couplings = {args.coupling: 0.0}
for proc in processes:
logger.info("Checking SM x-sec:")
config = Configuration(model_name=model_name,
modified_couplings=modified_couplings)
p = Process(process=proc, nEvents=50000, config=config)
SM_xsec[proc] = p.xsec()
logger.info("SM x-sec for %s is %s", proc, SM_xsec[proc])
if SM_xsec[proc].val == 0.: SM_xsec[proc] = u_float(1)
del config
hists = []
fits = []
m = 0
if args.scale:
scale = lambdaSqInv[args.coupling]
else:
scale = 1
if args.signal == "dipoles":
nonZeroCouplings = ["DC2A","DC2V"]
elif args.signal == "currents":
nonZeroCouplings = ["DC1A", "DC1V"]
# for safety, set all couplings to 0.
modification_dict["DC1A"] = 0.
modification_dict["DC1V"] = 0.
modification_dict["DC2A"] = 0.
modification_dict["DC2V"] = 0.
logger.info("Using model %s in plane: %s", args.model, args.signal)
logger.info("Will scan the following coupling values: %s and %s", nonZeroCouplings[0], nonZeroCouplings[1])
p = Process(process = "ttZ_ll", nEvents = 5000, config = config, xsec_cache=xsecDB)
xsec_central = p.xsecDB.get(modification_dict)
if year == 2016:
PDFset = "NNPDF30"
TTZ_sample = "TTZ_NLO"
elif year == 2017:
PDFset = "NNPDF30"
TTZ_sample = "TTZ_NLO_17"
elif year == 20167:
PDFset = "NNPDF30"
TTZ_sample = "TTZ_NLO"
logger.info("Will use PDF and scale uncertainties based on 2016.")
# always use PDF4LHC set for now, only included in Fall17
def wrapper(s):
logger.info("Now working on %s", s.name)
xSecScale = 1
c = cardFileWriter.cardFileWriter()
c.releaseLocation = combineReleaseLocation
for coup in nonZeroCouplings:
try:
modification_dict[coup] = getCouplingFromName(s.name, coup)
logger.info("The following coupling is set to non-zero value: %s: %s", coup, modification_dict[coup])
except ValueError:
logger.info("The following coupling is kept at zero: %s: %s", coup, modification_dict[coup])
continue
try:
p = Process(process = "ttZ_ll", nEvents = 5000, config = config, xsec_cache=xsecDB)
xsec = p.xsecDB.get(modification_dict)
except IndexError:
logger.info("Looking into backup DB for x-sec")
p = Process(process = "ttZ_ll", nEvents = 5000, config = config, xsec_cache=xsecDB_Backup)
xsec = p.xsecDB.get(modification_dict)
if not xsec:
try:
p = Process(process = "ttZ_ll", nEvents = 5000, config = config, xsec_cache=xsecDB_Backup)
xsec = p.xsecDB.get(modification_dict)
except IndexError:
logger.info("No x-sec found.")
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.setPrecision(3)
postfix = '_%s'%args.year
c.addUncertainty('PU', 'lnN') # correlated
c.addUncertainty('JEC'+postfix, 'lnN') # uncorrelated, for now!
c.addUncertainty('btag_heavy'+postfix, 'lnN') # uncorrelated, wait for offical recommendation
c.addUncertainty('btag_light'+postfix, 'lnN') # uncorrelated, wait for offical recommendation
c.addUncertainty('trigger'+postfix, 'lnN') # uncorrelated, statistics dominated
c.addUncertainty('leptonSF', 'lnN') # correlated
c.addUncertainty('scale', 'lnN') # correlated.
c.addUncertainty('scale_sig', 'lnN') # correlated.
c.addUncertainty('PDF', 'lnN') # correlated.
c.addUncertainty('PartonShower', 'lnN') # correlated.
c.addUncertainty('nonprompt', 'lnN') # correlated?!
c.addUncertainty('WZ_xsec', 'lnN') # correlated.
c.addUncertainty('WZ_bb', 'lnN') # correlated
c.addUncertainty('WZ_powheg', 'lnN') # correlated
c.addUncertainty('ZZ_xsec', 'lnN') # correlated.
c.addUncertainty('ZG_xsec', 'lnN') # correlated.
c.addUncertainty('rare', 'lnN') # correlated.
c.addUncertainty('ttX', 'lnN') # correlated.
c.addUncertainty('Lumi'+postfix, 'lnN')
uncList = ['PU', 'JEC', 'btag_heavy', 'btag_light', 'leptonSF', 'trigger']
for unc in uncList:
uncertainties[unc] = []
## use rate parameters??
#c.addRateParameter('WZ', 1, '[0,2]')
#c.addRateParameter('ZZ', 1, '[0,2]')
for setupPair in setups:
# extract the nominal and nonprompt setup from the pair
setup, setupNP = setupPair
signal = MCBasedEstimate(name="TTZ", sample=setup.samples["TTZ"], cacheDir=setup.defaultCacheDir())
#nonprompt = FakeEstimate(name="nonPromptDD", sample=setup.samples["Data"], setup=setupNP, cacheDir=setup.defaultCacheDir())
if args.unblind or (setup == setup3l_CR) or (setup == setup4l_CR):
observation = DataObservation(name="Data", sample=setup.samples["Data"], cacheDir=setup.defaultCacheDir())
logger.info("Using data!")
else:
observation = MCBasedEstimate(name="observation", sample=setup.samples["pseudoData"], cacheDir=setup.defaultCacheDir())
logger.info("Using pseudo-data!")
for e in setup.estimators: e.initCache(setup.defaultCacheDir())
for r in setup.regions:
totalBackground = u_float(0)
for channel in setup.channels:
niceName = ' '.join([channel.name, r.__str__()])
binname = 'Bin'+str(counter)
logger.info("Working on %s", binname)
counter += 1
c.addBin(binname, [e.name.split('-')[0] for e in setup.estimators]+["nonPromptDD"], niceName)
#c.addBin(binname, 'nonPromptDD', niceName)
for e in setup.estimators:
name = e.name.split('-')[0]
if name.count('WZ'):
logger.info("Using reweighting to powheg for WZ sample")
wzReweighting = WZReweighting( cacheDir = reweightCacheWZ )
f = wzReweighting.cachedReweightingFunc( setup.WZselection )
powhegExpected = e.reweight1D(r, channel, setup, f)
expected = e.cachedEstimate(r, channel, setup)
print expected
WZ_powheg_unc = (powhegExpected-expected)/expected
else:
expected = e.cachedEstimate(r, channel, setup)
logger.info("Adding expectation %s for process %s", expected.val, name)
c.specifyExpectation(binname, name, expected.val if expected.val > 0.01 else 0.01)
totalBackground += expected
if not args.statOnly:
# uncertainties
pu = 1 + e.PUSystematic( r, channel, setup).val if expected.val>0.01 else 1.1
jec = 1 + e.JECSystematic( r, channel, setup).val if expected.val>0.01 else 1.1
btag_heavy = 1 + e.btaggingSFbSystematic(r, channel, setup).val if expected.val>0.01 else 1.1
btag_light = 1 + e.btaggingSFlSystematic(r, channel, setup).val if expected.val>0.01 else 1.1
trigger = 1 + e.triggerSystematic(r, channel, setup).val if expected.val>0.01 else 1.1
leptonSF = 1 + e.leptonSFSystematic(r, channel, setup).val if expected.val>0.01 else 1.1
if name.count('WZ'):
WZ_powheg = 1 + WZ_powheg_unc.val if expected.val>0.01 else 1.1
c.specifyUncertainty('PU', binname, name, 1 + e.PUSystematic( r, channel, setup).val)
if not name.count('nonprompt'):
c.specifyUncertainty('JEC'+postfix, binname, name, jec)
c.specifyUncertainty('btag_heavy'+postfix, binname, name, btag_heavy)
c.specifyUncertainty('btag_light'+postfix, binname, name, btag_light)
c.specifyUncertainty('trigger'+postfix, binname, name, trigger)
c.specifyUncertainty('leptonSF', binname, name, leptonSF)
c.specifyUncertainty('scale', binname, name, 1.01)
c.specifyUncertainty('PDF', binname, name, 1.01)
c.specifyUncertainty('Lumi'+postfix, binname, name, 1.025 )
if name.count('ZZ'): c.specifyUncertainty('ZZ_xsec', binname, name, 1.10)
if name.count('ZG'): c.specifyUncertainty('ZG_xsec', binname, name, 1.20)
if name.count('WZ'):
c.specifyUncertainty('WZ_xsec', binname, name, 1.10)
if setup == setup3l:
c.specifyUncertainty('WZ_bb', binname, name, 1.08)
c.specifyUncertainty('WZ_powheg', binname, name, WZ_powheg)
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, 1.11)
#MC bkg stat (some condition to neglect the smaller ones?)
uname = 'Stat_'+binname+'_'+name+postfix
c.addUncertainty(uname, 'lnN')
if expected.val > 0:
c.specifyUncertainty(uname, binname, name, 1 + expected.sigma/expected.val )
else:
c.specifyUncertainty(uname, binname, name, 1.01 )
uname = 'Stat_'+binname+'_nonprompt'+postfix
c.addUncertainty(uname, 'lnN')
if setup.nLeptons == 3 and setupNP:
nonprompt = FakeEstimate(name="nonPromptDD", sample=setup.samples["Data"], setup=setupNP, cacheDir=setup.defaultCacheDir())
np = nonprompt.cachedEstimate(r, channel, setupNP)
if np.val < 0.01:
np = u_float(0.01,0.)
c.specifyExpectation(binname, 'nonPromptDD', np.val )
c.specifyUncertainty(uname, binname, "nonPromptDD", 1 + np.sigma/np.val )
c.specifyUncertainty('nonprompt', binname, "nonPromptDD", 1.30)
else:
np = u_float(0)
c.specifyExpectation(binname, 'nonPromptDD', np.val)
if args.expected:
sig = signal.cachedEstimate(r, channel, setup)
obs = totalBackground + sig + np
elif args.unblind or (setup == setup3l_CR) or (setup == setup4l_CR):
obs = observation.cachedObservation(r, channel, setup)
else:
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_central
elif args.model == "ewkDM":
source_gen = ewkDM_central
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', sig.val * xSecScale * xSecMod )
logger.info('Adding signal %s'%(sig.val * xSecScale * xSecMod))
if sig.val>0:
c.specifyUncertainty('Lumi'+postfix, binname, 'signal', 1.025 )
if not args.statOnly:
# uncertainties
pu = 1 + e.PUSystematic( r, channel, setup).val
jec = 1 + e.JECSystematic( r, channel, setup).val
btag_heavy = 1 + e.btaggingSFbSystematic(r, channel, setup).val
btag_light = 1 + e.btaggingSFlSystematic(r, channel, setup).val
trigger = 1 + e.triggerSystematic(r, channel, setup).val
leptonSF = 1 + e.leptonSFSystematic(r, channel, setup).val
if sig.sigma/sig.val < 0.05:
uncertainties['PU'] += [pu]
uncertainties['JEC'] += [jec]
uncertainties['btag_heavy'] += [btag_heavy]
uncertainties['btag_light'] += [btag_light]
uncertainties['trigger'] += [trigger]
uncertainties['leptonSF'] += [leptonSF]
c.specifyUncertainty('PU', binname, "signal", pu)
c.specifyUncertainty('JEC'+postfix, binname, "signal", jec)
c.specifyUncertainty('btag_heavy'+postfix, binname, "signal", btag_heavy)
c.specifyUncertainty('btag_light'+postfix, binname, "signal", btag_light)
c.specifyUncertainty('trigger'+postfix, binname, "signal", trigger)
c.specifyUncertainty('leptonSF', binname, "signal", leptonSF)
# This doesn't get the right uncertainty in CRs. However, signal doesn't matter there anyway.
if setup in [setup3l, setup4l]:
c.specifyUncertainty('scale_sig', binname, "signal", 1 + scale_cache.get({"region":r, "channel":channel.name, "PDFset":"scale"}).val)
c.specifyUncertainty('PDF', binname, "signal", 1 + PDF_cache.get({"region":r, "channel":channel.name, "PDFset":PDFset}).val)
c.specifyUncertainty('PartonShower',binname, "signal", PS_cache.get({"region":r, "channel":channel.name, "PDFset":"PSscale"}).val) #something wrong here?
#c.specifyUncertainty('scale_sig', binname, "signal", 1.05) #1.30
#c.specifyUncertainty('PDF', binname, "signal", 1.04) #1.15
uname = 'Stat_'+binname+'_signal'+postfix
c.addUncertainty(uname, 'lnN')
c.specifyUncertainty(uname, binname, 'signal', 1 + sig.sigma/sig.val )
else:
uname = 'Stat_'+binname+'_signal'+postfix
c.addUncertainty(uname, 'lnN')
c.specifyUncertainty(uname, binname, 'signal', 1 )
#c.addUncertainty('Lumi'+postfix, 'lnN')
#c.specifyFlatUncertainty('Lumi'+postfix, 1.026)
cardFileName = c.writeToFile(cardFileName)
else:
logger.info("File %s found. Reusing.",cardFileName)
res = {}
if not os.path.isdir(limitDir):
os.makedirs(limitDir)
resDB = resultsDB(limitDir+'/results.sq', "results", setup.resultsColumns)
res = {"signal":s.name}
if not overWrite and res.DB.contains(key):
res = resDB.getDicts(key)[0]
logger.info("Found result for %s, reusing", s.name)
else:
# We don't calculate limits here, but just in case we find a way how to do it, put placeholders here
res.update({"exp":0, "obs":0, "exp1up":0, "exp2up":0, "exp1down":0, "exp2down":0})
# Don't extract all the nuisances by default
signalRegions = range(15,30) ## shouldn't be hardcoded
masks = ['mask_ch1_Bin'+str(i)+'=1' for i in signalRegions]
masks = ','.join(masks)
if args.calcNuisances:
c.calcNuisances(cardFileName, masks=masks)
# extract the NLL
#nll = c.calcNLL(cardFileName, options="")
nll = c.physicsModel(cardFileName, options="", normList=["WZ_norm","ZZ_norm"], masks=masks) # fastScan turns of profiling
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")
resDB.add(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"]))
print 'PU', min(uncertainties['PU']), max(uncertainties['PU'])
print 'JEC', min(uncertainties['JEC']), max(uncertainties['JEC'])
print 'btag_heavy', min(uncertainties['btag_heavy']), max(uncertainties['btag_heavy'])
print 'btag_light', min(uncertainties['btag_light']), max(uncertainties['btag_light'])
print 'trigger', min(uncertainties['trigger']), max(uncertainties['trigger'])
print 'leptonSF', min(uncertainties['leptonSF']), max(uncertainties['leptonSF'])
# Check that we have an even number of arguments
if not len(args.couplings) % 2 == 0:
logger.error(
"Need an even number of coupling arguments of the format coupling1, value1, coupling2, value2, ... . Got %r",
args.couplings)
# Interpret coupling argument list
coupling_names = args.couplings[::2]
coupling_values = map(float, args.couplings[1::2])
modified_couplings = {c: v for c, v in zip(coupling_names, coupling_values)}
# Let's not leave the user in the dark
logger.info("Model: %s", args.model)
logger.info("Process: %s", args.process)
logger.info("Couplings: %s",
", ".join(["%s=%5.4f" % c for c in modified_couplings.items()]))
# Create configuration class
config = Configuration(model_name=args.model)
#p = Process(process = args.process, nEvents = 1, config = config, modified_couplings = modified_couplings )
p = Process(process=args.process, nEvents=1, config=config)
xsec_val = p.diagrams(plot_dir=plot_directory,
modified_couplings=modified_couplings)
config.cleanup()
logger.info("Done! Calculated xsec: %s ", repr(xsec_val))
logger = logger.get_logger(args.logLevel, logFile=None)
if len(args.couplings) % 2 == 0:
param_points = args.couplings
else:
logger.error(
"Need an even number of coupling arguments of the format coupling1, value1, coupling2, value2, ... . Got %r",
args.couplings)
raise ValueError
# Create configuration class
config = Configuration(model_name=args.model)
if args.dbfile:
p = Process(process=args.process,
nEvents=args.nEvents,
config=config,
xsec_cache=args.dbfile)
else:
p = Process(process=args.process, nEvents=args.nEvents, config=config)
names = param_points[::2]
values = map(float, param_points[1::2])
print values
modification_dict = {c: v for c, v in zip(names, values)}
modification_dict["process"] = args.process
modification_dict["nEvents"] = args.nEvents
p.xsecDB.getTable(modification_dict)
len(param_points))
# Interpret coupling argument list
names = param_point[::2]
values = map(float, param_point[1::2])
modification_dict = {c: v for c, v in zip(names, values)}
# Let's not leave the user in the dark
logger.info("Model: %s", args.model)
logger.info("Process: %s", args.process)
logger.info("Couplings: %s",
", ".join(["%s=%5.4f" % c for c in modification_dict.items()]))
# make process
p = Process(process=args.process, nEvents=args.nEvents, config=config)
# Make grid pack
if args.makeGridpack:
gridpack = p.makeGridpack(modified_couplings=modification_dict,
overwrite=args.overwrite)
# calculate x-sec
if args.calcXSec:
xsec = p.xsec(modified_couplings=modification_dict,
overwrite=args.overwrite)
logger.info("xsec: %s ", repr(xsec))
if not args.keepWorkspace:
config.cleanup()
subDir = ''
baseDir = os.path.join(analysis_results, subDir)
limitDir = os.path.join(baseDir, 'cardFiles', setup.name, args.signal + args.extension)
overWrite = (args.only is not None) or args.overwrite
reweightCache = os.path.join( results_directory, 'SignalReweightingTemplate' )
from TopEFT.Generation.Configuration import Configuration
from TopEFT.Generation.Process import Process
from TopEFT.Tools.u_float import u_float
config = Configuration( model_name = "dim6top_LO" )
p = Process(process = "ttZ_ll", nEvents = 5000, config = config)
modification_dict = {"process":"ttZ_ll"}
modification_dict["ctZ"] = 0.
modification_dict["ctZi"] = 0.
modification_dict["cpQM"] = 0.
modification_dict["cpt"] = 0.
xsec_central = p.xsecDB.get(modification_dict)
def wrapper(s):
logger.info("Now working on %s", s.name)
xSecScale = 1
c = cardFileWriter.cardFileWriter()
c.releaseLocation = combineReleaseLocation
hists = []
cans = []
pads = []
SM_xsec = {}
latex1 = ROOT.TLatex()
latex1.SetNDC()
latex1.SetTextSize(0.04)
latex1.SetTextAlign(11)
logger.info("Model: %s", model_name)
for proc in processes[:1]:
logger.info("Starting with process %s", proc)
config = Configuration(model_name=model_name)
p = Process(process=proc, nEvents=50000, config=config)
SM_xsec[proc] = p.xsec(modified_couplings={})
logger.info("SM x-sec: %s", SM_xsec[proc])
for comb in combinations:
logger.info("Making 2D plots for %s", comb)
fixedPoints = list(nonZeroCouplings)
subDir = "{}_vs_{}".format(comb[0], comb[1])
if interpolate:
subDir += "_interpolated"
for c in comb:
fixedPoints.remove(c)
for v in points[fixedPoints[0]]:
for w in points[fixedPoints[1]]: