def __init__(self,
process,
nEvents,
config,
xsec_cache='xsec_DBv2.db',
reweight=False):
self.process = process
self.config = config
self.processCardDir = os.path.join(self.config.data_path,
'processCards')
self.processCardFile = process + '.dat'
self.reweight = reweight
# template process card file
self.templateProcessCard = os.path.join(self.processCardDir,
self.processCardFile)
# temporary process card output
self.tmpProcessCard = os.path.join(self.config.uniquePath,
self.processCardFile)
# temporary process output directory
self.processTmpDir = os.path.join(self.config.uniquePath, 'processtmp')
self.nEvents = nEvents
self.GP_outputDir = os.path.join(results_directory, 'gridpacks')
# xsec cache location
columns = ["process", "nEvents"] + self.config.all_model_couplings
self.xsecDB = resultsDB(os.path.join(results_directory, xsec_cache),
self.config.model_name, columns)
def getEstimate(sample, region, channel, overwrite=False):
''' to be extended '''
res = resultsDB(results_directory + "resultsCache_v2.db", "TopEFT",
columns)
key = {
"process": sample.name,
"channel": channel,
"region": region.cutString(),
"lumi": lumi,
"presel": presel,
"weightString": weightString
}
if res.contains(key) and not overwrite:
print "Found estimate for %s in region %s" % (sample.name, region)
return res.get(key)
else:
print "Adding estimate for %s in region %s" % (sample.name, region)
y = u_float(
sample.getYieldFromDraw("&&".join([presel,
region.cutString()]),
weightString))
res.add(key, y, overwrite=True)
return y
else:
variations = ["abs(LHEweight_wgt[%s])" % (options.selectWeight)]
results = {}
scale_systematics = {}
cacheDir = "/afs/hephy.at/data/dspitzbart01/TopEFT/results/PDF_v2_%s/" % (
PDFset)
estimate = MCBasedEstimate(name=sample.name, sample=sample)
estimate.initCache(cacheDir)
## Results DB for scale and PDF uncertainties
PDF_cache = resultsDB(cacheDir + sample.name + '_unc.sq', "PDF",
["region", "channel", "PDFset"])
scale_cache = resultsDB(cacheDir + sample.name + '_unc.sq', "scale",
["region", "channel", "PDFset"])
PS_cache = resultsDB(cacheDir + sample.name + '_unc.sq', "PSscale",
["region", "channel", "PDFset"])
'''
Recommendation from arxiv:1510.03865
for MC sets sort the obtained values e.g. in a list, then calculate
delta(PDF)sigma = (sigma[84] - sigma[16])/2
which gives the 68% CL
'''
def wrapper(args):
r, c, setup = args
res = estimate.cachedEstimate(r,
import os
import argparse
from RootTools.core.Sample import Sample
from TopEFT.Tools.resultsDB import resultsDB
from TopEFT.Tools.user import combineReleaseLocation, analysis_results, results_directory, plot_directory
from TopEFT.Analysis.Setup import Setup
setup = Setup()
cacheFileName = os.path.join(plot_directory, setup.resultsFile)
columns = ['signal', 'exp', 'obs', 'exp1up', 'exp1down', 'exp2up', 'exp2down', 'NLL_prefit', 'dNLL_postfit_r1', 'dNLL_bestfit']
res = resultsDB(cacheFileName, "limits", columns)
def getResult(sample):
''' to be extended '''
key = {"signal":sample.name}
if res.contains(key):
return res.getDicts(key)[0]
def addResult(sample, key, value, overwrite):
print "Adding result for %s to database %s"%(sample.name, cacheFileName)
key.update({"signal":sample.name})
if overwrite:
res.removeObjects({"signal":sample.name})
res.add(key, value, overwrite=True)
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'])
def initCache(self, cacheDir):
self.cache = resultsDB(
os.path.join(cacheDir, 'WZReweightingTemplate.sql'),
"signalWeights", ["selection", "weight", "source", "target"])
cacheDir = "/afs/hephy.at/data/dspitzbart01/TopEFT/results/FEBug/"
#TZQToLL_nom#WZTo3LNu_fxfx_nom#TTZToLLNuNu_amc_nom
sample_nom = WZTo3LNu_fxfx_nom
#TZQToLL_FEBug#WZTo3LNu_fxfx_FEBug#TTZToLLNuNu_amc_FEBug
sample_FEBug = WZTo3LNu_fxfx_FEBug
estimate_nom = MCBasedEstimate(name=sample_nom.name, sample=sample_nom)
estimate_nom.initCache(cacheDir)
estimate_FEBug = MCBasedEstimate(name=sample_FEBug.name, sample=sample_FEBug)
estimate_FEBug.initCache(cacheDir)
## Results DB for scale and PDF uncertainties
FE_cache = resultsDB(cacheDir + sample_nom.name + '_unc.sq', "PDF",
["region", "channel"])
def wrapper(args):
e, r, c, setup = args
res = e.cachedEstimate(r, c, setup, save=True, overwrite=options.overwrite)
return (e.uniqueKey(r, c, setup), res)
jobs = []
# remove all so to avoid unnecessary concurrency. All will be calculated as sum of the individual channels later
seperateChannels3l = allTrilepChannels
allTrilepChannelNames = [c.name for c in allTrilepChannels]
seperateChannels3l.pop(allTrilepChannelNames.index('all'))
key = {"channel":"all", "lumi":lumi, "presel":presel, "weightString":weightString}
r0 = regionsA[0].cutString()
r1 = regionsA[1].cutString()
r2 = regionsA[2].cutString()
#ttZ = Sample("ttZ", "Events", ["dummy.root"])
def fillResults(process, values, key):
tmp = copy.deepcopy(key)
for i in range(3):
r = regionsA[i].cutString()
k = {"channel":"all", "lumi":lumi, "presel":presel, "weightString":weightString, "process":process, "region":r}
resultsCache.add(k, values[i], overwrite=True)
resultsCache = resultsDB( os.path.join(results_directory, "resultsCache.db"), "TopEFT", columns )
values = [u(102.2091, 102.2091*0.0075), u(31.6185, 31.6185*0.0143), u(5.3084, 5.3084*0.0352)]
fillResults("ttZ", values, key)
values = [u(30.8514, 30.8514*0.1178), u(2.4059, 2.4059*0.4277), u(0.1, 0.1*0.7393)]
fillResults("fake", values, key)
values = [u(23.1666, 23.1666*0.0438), u(7.4121, 7.4121*0.0758), u(1.8730, 1.8730*0.1537)]
fillResults("WZ", values, key)
values = [u(2.4804, 2.4804*0.0511), u(0.1074, 0.1074*0.2437), u(0.0020,0.0020*2.0613)]
fillResults("ttH", values, key)
values = [u(3.9295, 3.9295*0.0228), u(0.2330, 0.2330*0.0935), u(0.0105, 0.0105*0.4897)]
fillResults("ttW", values, key)
#cardDir += "_lowUnc%s"%exp
cardDir += "_lowUnc%s_SRandCR" % exp
#regionsE_COMBINED_xsec_shape_lowUnc_expected_SRandCR
#regionsE_2017_xsec_shape_lowUnc_expected_SRandCR
#/afs/hephy.at/data/dspitzbart01/TopEFT/results/cardFiles/regionsE_2016_xsec_shape_lowUnc_WZreweight_SRandCR/
plane = "currents" if args.parameter in ["DC1V", "DC1A", "cpt", "cpQM"
] else "dipoles"
limitDir = os.path.join(baseDir, 'cardFiles', cardDir, subDir,
'_'.join([args.model, plane]))
print limitDir
resDB = resultsDB(limitDir + '/results.sq', "results", setup.resultsColumns)
fitKey = "dNLL_postfit_r1" if not args.useBestFit else "dNLL_bestfit"
# get the absolute post fit NLL value of pure ttZ
if args.model == "ewkDM":
ttZ_res = resDB.getDicts({"signal": ewkDM_central.name})[-1]
elif args.model == "dim6top_LO":
ttZ_res = resDB.getDicts({"signal": dim6top_central.name})[-1]
if args.prefit:
ttZ_NLL_abs = float(ttZ_res["NLL_prefit"])
else:
ttZ_NLL_abs = float(ttZ_res["NLL_prefit"]) + float(ttZ_res[fitKey])
print "Max Likelihood ttZ SM"
def wrapper(s):
logger.info("Now working on %s", s.name)
c = cardFileWriter.cardFileWriter()
c.releaseLocation = combineReleaseLocation
cards = {}
# get the seperated cards
for year in [2016,2017]:
subDir = ''
cardDir = "regionsE_%s"%(year)
if args.useXSec: cardDir += "_xsec"
if args.useShape: cardDir += "_shape"
exp = "_expected" if args.expected else ''
cardDir += "_lowUnc%s"%exp
if args.includeCR: cardDir += "_SRandCR"
baseDir = os.path.join(analysis_results)
limitDir = os.path.join(baseDir, 'cardFiles', cardDir, subDir, '_'.join([args.model, args.signal]))
#resDB = resultsDB(limitDir+'/results.sq', "results", setup.resultsColumns)
cardFileName = os.path.join(limitDir, s.name+'.txt')
if not os.path.isfile(cardFileName):
raise IOError("File %s doesn't exist!"%cardFileName)
cards[year] = cardFileName
limitDir = limitDir.replace('2017','COMBINED')
# run combine and store results in sqlite database
if not os.path.isdir(limitDir):
os.makedirs(limitDir)
resDB = resultsDB(limitDir+'/results.sq', "results", setup.resultsColumns)
res = {"signal":s.name}
overWrite = True
if not overWrite and res.DB.contains(key):
res = resDB.getDicts(key)[0]
logger.info("Found result for %s, reusing", s.name)
else:
signalRegions = range(15,30)
masks_2016 = ['mask_ch1_dc_2016_Bin'+str(i)+'=1' for i in signalRegions]
masks_2017 = ['mask_ch1_dc_2017_Bin'+str(i)+'=1' for i in signalRegions]
masks = ','.join(masks_2016+masks_2017)
combinedCard = c.combineCards( cards )
# 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
if args.calcNuisances:
c.calcNuisances(combinedCard, masks=masks)
#nll = c.physicsModel(combinedCard, options="", normList=["WZ_norm","ZZ_norm"], masks=masks) # fastScan turns of profiling
nll = c.calcNLL(combinedCard)
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)
logger.info("Results stored in %s", limitDir)
def initCache(self, cacheDir):
self.cache = resultsDB(os.path.join(cacheDir, 'puProfiles_v2.sql'),
"puProfile", ["selection", "weight", "source"])
def initCache(self, filename):
self.filename = filename
self.columns = ["region", "channel", "weights", "modification", "lumi"]
self.DB = resultsDB(filename, "Cache", self.columns)
