def __init__(self, label, signal, profile, R0_, hackZeroBins=False,
doVis=False, evalSystematics=[],
ensembles=None, ensSlice=(None,None),
calibrations=None, calSlice=(None,None),
outDir='output/', templateID=None):
os.system('mkdir -p %s' % outDir)
self.outNameBase = (outDir +
'_'.join(label.split(',')) +
('_t%03d'%templateID if templateID!=None else ''))
with open(self.outNameBase + 'SM.log', 'w') as log:
pars = systematics.central()
pars['label'] += 'SM'
self.SM = fit(signal=signal, profileVars=profile, R0_=R0_, log=log,
hackZeroBins=hackZeroBins, fixSM=True, **pars)
print >> log, self.SM.model.TTbarComponentsStr()
with open(self.outNameBase + '.log', 'w') as log:
pars = systematics.central()
if templateID!=None: pars['label'] = 'T%03d'%templateID
self.central = fit(signal=signal, profileVars=profile, R0_=R0_, log=log,
hackZeroBins=hackZeroBins, templateID=templateID, **pars)
self.central.model.print_n(log)
self.central.ttreeWrite(self.outNameBase+'.root')
defaults = dict([(v,self.central.model.w.arg(v).getVal())
for v in ['slosh', 'falphaL', 'falphaT','R_ag',
'd_xs_tt', 'd_xs_wj', 'factor_elqcd', 'factor_muqcd']])
if doVis: self.central.model.visualize2D(printName=self.outNameBase+'.pdf')
if ensembles:
pars = systematics.central()
pars.update({'signal':signal, 'profileVars':profile, 'R0_':R0_, 'log':log, 'hackZeroBins':hackZeroBins})
for ensPars in ensemble_specs():
if ensPars['label'] not in ensembles: continue
ensPars.update({'ensSlice':ensSlice})
self.ensembles(pars, **ensPars)
if calibrations:
pars = systematics.central()
pars.update({'signal':signal, 'profileVars':profile, 'R0_':R0_, 'log':log, 'hackZeroBins':hackZeroBins})
for calPars in calibration_specs():
if calPars['which'] not in calibrations: continue
calPars.update({'calSlice':calSlice})
self.calibrations(pars, **calPars)
for sys in systematics.systematics():
if sys['label'] not in evalSystematics: continue
pars = systematics.central()
pars.update(sys)
fname = self.outNameBase +'_sys_'+ sys['label'] + '.log'
with open(fname, 'w') as log:
f = fit(signal=signal, profileVars=profile, R0_=R0_,
quiet=False, hackZeroBins=hackZeroBins,
defaults=defaults, log=log, **pars)
f.ttreeWrite(fname.replace('.log','.root'))
def calibrations(self, pars, which='mn', calSlice=(None,None), N=1000, label='', **kwargs):
sampleList = [c['sample'] for c in calibration_specs() if c['which']==which]
prePre = pars['dirIncrement'] in [0,4,5]
args = {
'signal':pars['signal'],
'sigPrefix':pars['sigPre'],
'dirPrefix':"R%02d" % (pars['R0_'] + pars['dirIncrement']),
'genDirPre':pars['genDirPre'],
'prePre':prePre,
'templateID':None,
'hackZeroBins':pars['hackZeroBins'] and 'QCD'==part,
'sampleList': sampleList
}
alt_channels = dict( [ ((lep,part), inputs.channel_data(lep, part, **args))
for lep in ['el','mu'] for part in ['top','QCD']] )
# get Ac_phi_ttalt and Ac_y_ttalt
filePattern = 'data/stats_top_mu_%s.root'
tag = 'ph_sn_jn_20'
tfile = r.TFile.Open(filePattern%tag)
h = lib.get(tfile,'genTopTanhDeltaAbsY_genTopDPtDPhi/'+ sampleList[0])
Ac_y_ttalt = lib.asymmetry(h.ProjectionX())[0]
Ac_phi_ttalt = lib.asymmetry(h.ProjectionY())[0]
tfile.Close()
model = self.central.model
model.import_alt_model(alt_channels)
wGen = model.w
# bring xs_ttalt to the most consistant value possible
wGen.arg('d_xs_ttalt').setVal((wGen.arg('expect_mu_tt').getVal() + wGen.arg('expect_el_tt').getVal()) /
(wGen.arg('expect_mu_ttalt').getVal() + wGen.arg('expect_el_ttalt').getVal()) - 1)
# not clear how to do the same for factor_*_qcd (equivalent bg representations)
truth = dict([(s,eval(s)) for s in ['Ac_y_ttalt','Ac_phi_ttalt']])
altItems = ['expect_%s_ttalt'%s for s in ['el','mu']]
for item in (set(fit.modelItems()+altItems)-set(fit.altmodelNonItems())): truth[item] = wGen.arg(item).getVal()
mcstudy = r.RooMCStudy(wGen.pdf('altmodel'),
wGen.argSet(','.join(model.observables+['channel'])),
r.RooFit.Binned(True),
r.RooFit.Extended(True)
)
mcstudy.generate(N,0,True)
for i in range(N)[slice(*calSlice)]:
alt = mcstudy.genData(i)
pars['label'] = '%s_cal%s%03d'%(label,which,i)
with open(self.outNameBase + pars['label'] + '.log', 'w') as log:
pars['log']=log
f = fit(altData=alt, **pars)
f.ttreeWrite(self.outNameBase + pars['label'] + '.root', truth)
def old_calibrations(self, pars, which='mn', calSlice=(None,None), N=1000, label='', **kwargs):
sampleList = [c['sample'] for c in calibration_specs() if c['which']==which]
prePre = pars['dirIncrement'] in [0,4,5]
args = {
'signal':pars['signal'],
'sigPrefix':pars['sigPre'],
'dirPrefix':"R%02d" % (pars['R0_'] + pars['dirIncrement']),
'genDirPre':pars['genDirPre'],
'prePre':prePre,
'templateID':None,
'sampleList': sampleList,
'rebin':pars['rebin'],
'no3D':pars['no3D']
}
alt_channels = dict( [ ((lep,part), inputs.channel_data(lep, part, **args))
for lep in ['el','mu'] for part in ['top','QCD']] )
if 'xsfactor' in kwargs:
alt_channels[('el','top')].samples['ttalt'].xs *= kwargs['xsfactor']
alt_channels[('mu','top')].samples['ttalt'].xs *= kwargs['xsfactor']
# get Ac_phi_ttalt and Ac_y_ttalt
filePattern = 'data/stats_top_mu_%s.root'
tag = 'ph_sn_jn_20'
tfile = r.TFile.Open(filePattern%tag)
h = lib.get(tfile,'genTopTanhDeltaAbsY_genTopDPtDPhi/'+ sampleList[0])
Ac_y_ttalt = lib.asymmetry(h.ProjectionX())[0]
Ac_phi_ttalt = lib.asymmetry(h.ProjectionY())[0]
tfile.Close()
model = self.central.model
model.import_alt_model(alt_channels, pars['nobg'])
wGen = model.w
# bring xs_ttalt to the most consistant value possible
wGen.arg('d_xs_ttalt').setVal((wGen.arg('expect_mu_tt').getVal() + wGen.arg('expect_el_tt').getVal()) /
(wGen.arg('expect_mu_ttalt').getVal() + wGen.arg('expect_el_ttalt').getVal()) - 1)
if not (-0.5 < wGen.arg('d_xs_ttalt').getVal() < 1.5):
print 'ttalt xs invalid! Adjust calibration_specs!'
exit()
# not clear how to do the same for factor_*_qcd (equivalent bg representations)
truth = {'Ac': Ac_y_ttalt if genNames['XL'][3:] in pars['signal'] else Ac_phi_ttalt}
altItems = ['expect_%s_ttalt'%s for s in ['el','mu']]
for item in (set(fit.modelItems()+altItems)-set()): truth[item] = wGen.arg(item).getVal()
truth.update({'Ac_raw_el_model':model.Ac_raw('el','alt%smodel'%pars['nobg']),
'Ac_raw_mu_model':model.Ac_raw('mu','alt%smodel'%pars['nobg'])})
mcstudy = r.RooMCStudy(wGen.pdf('alt%smodel'%pars['nobg']),
wGen.argSet(','.join(model.observables+['channel'])),
r.RooFit.Binned(True),
r.RooFit.Extended(True)
)
mcstudy.generate(N,0,True)
for i in range(*calSlice):
alt = mcstudy.genData(i)
pars['label'] = '%s_cal%s%03d'%(label,which,i)
pars['quiet'] = True
with open(self.outNameBase + pars['label'] + '.log', 'w') as log:
pars['log']=log
f = fit(altData=alt, **pars)
f.ttreeWrite(self.outNameBase + pars['label'] + '.root', truth)
if self.doVis: f.model.visualize(self.outNameBase + pars['label'] + '.pdf', nobg=pars['nobg'])
def __init__(self, label, signal, R0_,
doVis=False, evalSystematics=[],
ensembles=None, ensSlice=(None,None),
calibrations=None, calSlice=(None,None),
outDir='output/', templateID=None, only="", nobg="",
rebin=False, no3D=False, twoStage=False, alttt=None, sepchan=False):
os.system('mkdir -p %s' % outDir)
self.doVis = doVis
self.outNameBase = (outDir +
'_'.join(label.split(',')) + only +
('_%s'%nobg if nobg else '') +
('_rebin' if rebin else '') +
('_no3D' if no3D else '') +
('_twoStage' if twoStage else '') +
('_t%03d'%templateID if templateID!=None else '') +
('_alttt-%s'%alttt if alttt else '') +
('_sepchan' if sepchan else ''))
with open(self.outNameBase + 'SM.log', 'w') as log:
pars = systematics.central()
pars.update({'fixSM':True,'log':log,'only':only, 'rebin':rebin, 'twoStage':twoStage, 'alttt':alttt, 'sepchan':sepchan})
pars['label'] += 'SM'
self.SM = fit(signal=signal, R0_=R0_, **pars)
self.SM.model.print_n(log)
with open(self.outNameBase + '.log', 'w') as log:
pars = systematics.central()
pars.update({'log':log, 'only':only, 'rebin':rebin, 'twoStage':twoStage, 'alttt':alttt, 'sepchan':sepchan})
if templateID!=None: pars['label'] = 'T%03d'%templateID
self.central = fit(signal=signal, R0_=R0_, templateID=templateID, **pars)
self.central.model.print_n(log)
self.central.ttreeWrite(self.outNameBase+'.root')
defaults = dict([(v,self.central.model.w.arg(v).getVal()) for v in
['alpha', 'd_xs_tt', 'd_xs_wj', 'factor_elqcd', 'factor_muqcd']])
if doVis: self.central.model.visualize(printName=self.outNameBase+'.pdf', twoStage=twoStage)
if ensembles:
pars = systematics.central()
pars.update({'signal':signal, 'R0_':R0_, 'log':log, 'only':only, 'nobg':nobg, 'rebin':rebin,'no3D':no3D, 'twoStage':twoStage, 'alttt':alttt, 'sepchan':sepchan})
for ensPars in ensemble_specs():
if ensPars['label'] not in ensembles: continue
ensPars.update({'ensSlice':ensSlice})
self.ensembles(pars, **ensPars)
if calibrations:
pars = systematics.central()
pars.update({'signal':signal, 'R0_':R0_, 'log':log, 'only':only, 'nobg':nobg, 'rebin':rebin, 'no3D':no3D, 'twoStage':twoStage, 'alttt':alttt, 'sepchan':sepchan})
for calPars in calibration_specs():
if calPars['which'] not in calibrations: continue
calPars.update({'calSlice':calSlice})
self.calibrations(pars, **calPars)
for sys in systematics.systematics():
if sys['label'] not in evalSystematics: continue
pars = systematics.central()
pars.update(sys)
pars.update({'only':only,'rebin':rebin, 'twoStage':twoStage, 'sepchan':sepchan})
fname = self.outNameBase +'_sys_'+ sys['label'] + '.log'
with open(fname, 'w') as log:
f = fit(signal=signal, R0_=R0_,
quiet=False, defaults=defaults, log=log, **pars)
f.ttreeWrite(fname.replace('.log','.root'))