def __init__(self, label, signal, profileVars, R0_,
d_lumi, d_xs_dy, d_xs_st, tag, genPre, sigPre, dirIncrement, genDirPre, d_wbb,
quiet = False, hackZeroBins=False, templateID=None, defaults = {},
log=None, fixSM=False,altData=None, lumiFactor=1.0):
np.random.seed(1981)
for item in ['label','quiet','fixSM','profileVars'] : setattr(self,item,eval(item))
self.log = log if log else sys.stdout
if type(R0_) == tuple:
diffR0_ = R0_[1]
R0_ = R0_[0]
else: diffR0_ = None
prePre = dirIncrement in [0,4,5]
channels = dict([((lep,part),
inputs.channel_data(lep, part, tag, signal, sigPre,
"R%02d" % (R0_ + dirIncrement),
genDirPre, prePre=prePre, templateID=templateID,
hackZeroBins=hackZeroBins and 'QCD'==part))
for lep in ['el', 'mu']
for part in ['top', 'QCD']
])
channels['gen'] = inputs.channel_data('mu', 'top', tag,
'%s; %s'%(genNameX,genNameY),
sigPrefix = sigPre if dirIncrement in [0,4,5] else '',
dirPrefix=genDirPre, genDirPre=genDirPre,
getTT=True, prePre = prePre)
if diffR0_ :
for lepPart,chan in channels.items():
if type(lepPart) != tuple: continue
lep,part = lepPart
chan.subtract(inputs.channel_data(lep,part,tag,signal,sigPre,
"R%02d" % (diffR0_ + dirIncrement),
genDirPre, prePre = prePre ))
if d_wbb: [[h.SetBinContent(iX,3, (1+d_wbb)*h.GetBinContent(iX,3))
for h in chan.samples['wj'].datas
for iX in range(1,1+h.GetNbinsX())]
for name,chan in channels.items() if name!='gen']
print "###", label
print>>self.log, "###", label
self.model = model.topModel(channels, asymmetry=True)
self.model.w.arg('lumi_factor').setVal(lumiFactor)
for k,v in defaults.items(): self.model.w.arg(k).setVal(v)
for item in ['d_lumi', 'd_xs_dy', 'd_xs_st']: self.model.w.arg(item).setVal(eval(item))
self.fitArgs = [r.RooFit.Extended(True), r.RooFit.NumCPU(1),
r.RooFit.PrintLevel(-1)]
self.model.import_data(altData)
if fixSM: self.doSM()
else: self.doFit()
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)
import os,inputs,ROOT as r
os.system('mkdir -p cerba')
txtname = 'cerba/cerba_efficiencies.txt'
with open(txtname,'w') as txt:
print>>txt, '# sample efficiency-5j/4j efficiency3dcut'
for lep in ['el','mu'] :
chan = inputs.channel_data(lep, 'top', signal='jetMoments2Sum_triD')
chan4 = inputs.channel_data(lep, 'top', signal='fitTopTanhRapiditySum_triD')
print>>txt
print>>txt, '# ', lep
filename = "cerba/cerba_%s.root"%lep
f = r.TFile.Open(filename,'RECREATE')
for item in chan.samples:
eff5jet = chan.samples[item].datas[0].Integral()/chan4.samples[item].datas[0].Integral()
h = chan.samples[item].datas[0].ProjectionX(item,3,3)
eff3d = h.Integral() / chan.samples[item].datas[0].Integral()
if item!='data': h.Scale(1./h.Integral())
h.Write()
print>>txt, '\t'.join([str(i) for i in [item, eff5jet,eff3d]])
f.Close()
print "Wrote", filename
print "Wrote", txtname
import math
r.gROOT.SetBatch(1)
r.gROOT.ProcessLine(".L tdrstyle.C")
r.setTDRStyle()
r.tdrStyle.SetErrorX(r.TStyle().GetErrorX())
r.tdrStyle.SetPadTopMargin(0.065)
r.TGaxis.SetMaxDigits(3)
r.tdrStyle.SetEndErrorSize(6)
#r.tdrStyle.SetPadRightMargin(0.06)
def unqueue(h):
return utils.unQueuedBins(h,5,[-1,1],[-1,1])
threeD = True
check = False
channels = dict([(lep, channel_data(lep, 'top', signal='fitTopQueuedBin5_TridiscriminantWTopQCD', threeD=True)) for lep in ['el', 'mu']])
linetypes = [1, 2]
comps = ['ttgg','ttag','ttqg','ttqq']
colors = [r.kBlack, r.kGreen, r.kBlue, r.kRed]
if check:
channels_def = dict([(lep, channel_data(lep, 'top', signal='fitTopQueuedBin5_TridiscriminantWTopQCD', threeD=False)) for lep in ['el', 'mu']])
for lep,ch in channels.items():
for comp in comps:
c = ch.samples[comp].datas
d = channels_def[lep].samples[comp].datas
for a,b in zip(c,d):
b.Add(a,-1)
print ' '.join(str(b.GetBinContent(i)) for i in range(2+b.GetNbinsX()))
r.gROOT.ProcessLine(".L tdrstyle.C")
r.setTDRStyle()
r.tdrStyle.SetErrorX(r.TStyle().GetErrorX())
r.tdrStyle.SetPadTopMargin(0.065)
r.TGaxis.SetMaxDigits(3)
#r.tdrStyle.SetPadRightMargin(0.06)
from inputs import channel_data
d_xs_wj = 0.758705 #0.75986
d_xs_tt = 0.165759 #0.166398
factor_qcd = {'el':3.1996, #3.20075,
'mu':1.25478 #1.25533
}
channels = dict([(('_'.join([lep,par])),
channel_data(lep, par, signal='fitTopQueuedBin5_TridiscriminantWTopQCD'))
for lep in ['el', 'mu'] for par in ['top','QCD']])
for n,c in channels.items():
lep,par = n.split('_')
c.samples.update(channel_data(lep, par, signal='fitTopQueuedBin5_TridiscriminantWTopQCD', getTT=True).samples)
for item in ['gg','qq','qg','ag'] : del c.samples['tt'+item]
c.samples['tt'].eff /= 4
c.samples['tt'].xs *= (1+d_xs_tt)
c.samples['wj'].xs *= (1+d_xs_wj)
print c
tfile = {'mu_QCD':r.TFile.Open('data/control_QCD_mu_ph_sn_jn_20.root'),
'mu_top':r.TFile.Open('data/control_top_mu_ph_sn_jn_20.root'),
'el_QCD':r.TFile.Open('data/control_QCD_el_ph_sn_jn_20.root'),
def unqueue(h):
return lib.unQueuedBins(h,5,[-1,1],[-1,1])
threeD = False
comps = ['ttag','ttqg','ttqq']
colors = [r.kBlack, r.kGreen, r.kBlue, r.kRed]
projections = {}
book = autoBook("stuff")
for extra in [True,False,'only']:
for template in [None]+range(1000):
print template
#sys.stdout.flush()
channels = dict([(lep, channel_data(lep, 'top', signal='fitTopQueuedBin5_TridiscriminantWTopQCD', threeD=threeD, extra=extra, templateID=template,getTT=True)) for lep in ['el', 'mu']])
for lep,ch in channels.items():
for comp,color in zip(comps,colors):
name = '_'.join(['extra' if extra=='only' else 'total' if extra else 'orig' ,lep, comp])
if template==None: name += '_actual'
tot = unqueue(ch.samples[comp].datas[0].ProjectionX())
v = (100*lib.asymmetry(tot.ProjectionX())[0],
100*lib.asymmetry(tot.ProjectionY())[0])
book.fill( v, name, (100,100), (-3,-3), (3,3))
tfile = r.TFile.Open("jiggled_asymmetries.root","RECREATE")
for key,hist in book.items():
hist.Write()
tfile.Close()
r.gROOT.SetBatch(1)
r.gROOT.ProcessLine(".L lib/tdrstyle.C")
r.setTDRStyle()
r.tdrStyle.SetErrorX(r.TStyle().GetErrorX())
r.tdrStyle.SetPadTopMargin(0.065)
r.TGaxis.SetMaxDigits(3)
r.tdrStyle.SetEndErrorSize(6)
#r.tdrStyle.SetPadRightMargin(0.06)
def unqueue(h):
return lib.unQueuedBins(h,5,[-1,1],[-1,1])
threeD = True
check = False
extra = True
channels = dict([(lep, channel_data(lep, 'top', signal='fitTopQueuedBin5_TridiscriminantWTopQCD', threeD=threeD, extra=extra)) for lep in ['el', 'mu']])
linetypes = [1, 2]
comps = ['ttgg','ttag','ttqg','ttqq']
colors = [r.kBlack, r.kGreen, r.kBlue, r.kRed]
if check:
channels_def = dict([(lep, channel_data(lep, 'top', signal='fitTopQueuedBin5_TridiscriminantWTopQCD', threeD=threeD, extra=extra)) for lep in ['el', 'mu']])
for lep,ch in channels.items():
for comp in comps:
c = ch.samples[comp].datas
d = channels_def[lep].samples[comp].datas
for a,b in zip(c,d):
b.Add(a,-1)
print ' '.join(str(b.GetBinContent(i)) for i in range(2+b.GetNbinsX()))
import os, inputs, ROOT as r
os.system('mkdir -p cerba')
txtname = 'cerba/cerba_efficiencies.txt'
with open(txtname, 'w') as txt:
print >> txt, '# sample efficiency-5j/4j efficiency3dcut'
for lep in ['el', 'mu']:
chan = inputs.channel_data(lep, 'top', signal='jetMoments2Sum_triD')
chan4 = inputs.channel_data(lep,
'top',
signal='fitTopTanhRapiditySum_triD')
print >> txt
print >> txt, '# ', lep
filename = "cerba/cerba_%s.root" % lep
f = r.TFile.Open(filename, 'RECREATE')
for item in chan.samples:
eff5jet = chan.samples[item].datas[0].Integral(
) / chan4.samples[item].datas[0].Integral()
h = chan.samples[item].datas[0].ProjectionX(item, 3, 3)
eff3d = h.Integral() / chan.samples[item].datas[0].Integral()
if item != 'data': h.Scale(1. / h.Integral())
h.Write()
print >> txt, '\t'.join([str(i) for i in [item, eff5jet, eff3d]])
f.Close()
print "Wrote", filename
print "Wrote", txtname
import ROOT as r
r.gROOT.SetBatch(1)
r.gStyle.SetOptStat(0)
dists = ['fitTopPtOverSumPt_triD','fitTopTanhRapiditySum_triD','fitTopQueuedBin7TridiscriminantWTopQCD']
processes = ['ttgg','ttqg','ttqq','ttag']
colors = [r.kBlack,r.kRed,r.kBlue,r.kViolet]
fileName = 'graphics/fractions.pdf'
can = r.TCanvas()
can.Divide(2,2)
can.Print(fileName+'[')
for dist in dists :
xname = dist.split('_')[0].replace('fitTopQueuedBin7TridiscriminantWTopQCD','unrolling of X_T by X_L [reco]').replace('fitTopTanhRapiditySum','tanh|t#bar{t}.y| [reco]').replace('fitTopTanhAvgRapidity','tanh(|t.y+#bar{t}.y|/2) [reco]').replace('fitTopPtOverSumPt','t#bar{t}.pt / (t.pt + #bar{t}.pt) [reco]')
channels = dict((lepton,inputs.channel_data(lepton,'top',signal=dist,getTT=True)) for lepton in ['el','mu'])
for i,(lep,c) in enumerate(channels.items()) :
titlename = {'el':'Electrons+Jets','mu':'Muons+Jets'}[lep]
can.cd(i+1)
denom = c.samples['tt'].datasX[0]
denom.SetMinimum(0)
denom.SetTitle('%s;%s;%s'%(titlename,xname,'selected t#bar{t} events / bin / 19.5 / fb'))
denom.GetYaxis().SetTitleOffset(1.3)
denom.Draw('hist')
can.cd(i+3)
hists = [c.samples[p].datasX[0] for p in processes]
for h in hists :
h.Divide(denom)
h.SetMinimum(0)
h.SetMaximum(1.0)
h.SetTitle(';%s;%s'%(xname,'production fraction'))
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'])
r.tdrStyle.SetPadTopMargin(0.055)
r.TGaxis.SetMaxDigits(3)
r.tdrStyle.SetEndErrorSize(4)
#r.tdrStyle.SetPadRightMargin(0.06)
rebin = True
import systematics
pars = systematics.measurement_pars()
pars.update(systematics.central())
print pars
channels = dict([(lep, channel_data(lep, 'top', tag=pars['tag'],
signal=pars['signal'],
sigPrefix=pars['sigPre'],
dirPrefix="R%02d" % (pars['R0_'] + pars['dirIncrement']),
genDirPre=pars['genDirPre'],
rebin = rebin
)) for lep in ['el','mu']])
linetypes = [1, 2]
print channels['el']
print channels['mu']
comps = ['tt']
projections = {}
for lt,(lep,ch) in zip(linetypes,channels.items()):
can = r.TCanvas()
can.Divide(2, 2)
can.Print(fileName + '[')
for dist in dists:
xname = dist.split('_')[0].replace(
'fitTopQueuedBin7TridiscriminantWTopQCD',
'unrolling of X_T by X_L [reco]').replace(
'fitTopTanhRapiditySum', 'tanh|t#bar{t}.y| [reco]').replace(
'fitTopTanhAvgRapidity',
'tanh(|t.y+#bar{t}.y|/2) [reco]').replace(
'fitTopPtOverSumPt',
't#bar{t}.pt / (t.pt + #bar{t}.pt) [reco]')
channels = dict(
(lepton, inputs.channel_data(lepton, 'top', signal=dist, getTT=True))
for lepton in ['el', 'mu'])
for i, (lep, c) in enumerate(channels.items()):
titlename = {'el': 'Electrons+Jets', 'mu': 'Muons+Jets'}[lep]
can.cd(i + 1)
denom = c.samples['tt'].datasX[0]
denom.SetMinimum(0)
denom.SetTitle(
'%s;%s;%s' %
(titlename, xname, 'selected t#bar{t} events / bin / 19.5 / fb'))
denom.GetYaxis().SetTitleOffset(1.3)
denom.Draw('hist')
can.cd(i + 3)
hists = [c.samples[p].datasX[0] for p in processes]
for h in hists:
h.Divide(denom)
def __init__(self,
label,
signal,
profileVars,
R0_,
d_lumi,
d_xs_dy,
d_xs_st,
tag,
genPre,
sigPre,
dirIncrement,
genDirPre,
d_wbb,
quiet=False,
hackZeroBins=False,
templateID=None,
defaults={},
log=None,
fixSM=False,
altData=None,
lumiFactor=1.0):
np.random.seed(1981)
for item in ['label', 'quiet', 'fixSM', 'profileVars']:
setattr(self, item, eval(item))
self.log = log if log else sys.stdout
if type(R0_) == tuple:
diffR0_ = R0_[1]
R0_ = R0_[0]
else:
diffR0_ = None
prePre = dirIncrement in [0, 4, 5]
extra = True
channels = dict([((lep, part),
inputs.channel_data(lep,
part,
tag,
signal,
sigPre,
"R%02d" % (R0_ + dirIncrement),
genDirPre,
prePre=prePre,
templateID=templateID,
extra=extra,
d_wbb=d_wbb,
hackZeroBins=hackZeroBins
and 'QCD' == part))
for lep in ['el', 'mu'] for part in ['top', 'QCD']])
channels['gen'] = inputs.channel_data(
'mu',
'top',
tag,
'%s; %s' % (genNameX, genNameY),
sigPrefix=sigPre if dirIncrement in [0, 4, 5] else '',
extra=extra,
dirPrefix=genDirPre,
genDirPre=genDirPre,
getTT=True,
prePre=prePre)
if diffR0_:
for lepPart, chan in channels.items():
if type(lepPart) != tuple: continue
lep, part = lepPart
chan.subtract(
inputs.channel_data(lep,
part,
tag,
signal,
sigPre,
"R%02d" % (diffR0_ + dirIncrement),
genDirPre,
prePre=prePre))
print "###", label
print >> self.log, "###", label
self.model = model.topModel(channels, asymmetry=True)
self.model.w.arg('lumi_factor').setVal(lumiFactor)
for k, v in defaults.items():
self.model.w.arg(k).setVal(v)
for item in ['d_lumi', 'd_xs_dy', 'd_xs_st']:
self.model.w.arg(item).setVal(eval(item))
self.fitArgs = [
r.RooFit.Extended(True),
r.RooFit.NumCPU(1),
r.RooFit.PrintLevel(-1)
]
self.model.import_data(altData)
if fixSM: self.doSM()
else: self.doFit()
comps = ['ttag', 'ttqg', 'ttqq']
colors = [r.kBlack, r.kGreen, r.kBlue, r.kRed]
projections = {}
book = autoBook("stuff")
for extra in [True, False, 'only']:
for template in [None] + range(1000):
print template
#sys.stdout.flush()
channels = dict([
(lep,
channel_data(lep,
'top',
signal='fitTopQueuedBin5_TridiscriminantWTopQCD',
threeD=threeD,
extra=extra,
templateID=template,
getTT=True)) for lep in ['el', 'mu']
])
for lep, ch in channels.items():
for comp, color in zip(comps, colors):
name = '_'.join([
'extra' if extra == 'only' else
'total' if extra else 'orig', lep, comp
])
if template == None: name += '_actual'
tot = unqueue(ch.samples[comp].datas[0].ProjectionX())
v = (100 * lib.asymmetry(tot.ProjectionX())[0],
100 * lib.asymmetry(tot.ProjectionY())[0])
book.fill(v, name, (100, 100), (-3, -3), (3, 3))
def __init__(self, label, signal, R0_,
d_lumi, d_xs_dy, d_xs_st, tag, genPre, sigPre, dirIncrement, genDirPre, d_wbb,
quiet = False, templateID=None, defaults = {},
log=None, fixSM=False, altData=None, lumiFactor=1.0,
only="", nobg="", rebin=False, no3D=False, twoStage=False, fixedValues={}, alttt=None, sepchan=False, twossigma={}, Rst=None,
partsuffix='', symmQCD=False):
np.random.seed(1981)
if alttt and label[:7]=='central': label = alttt + label
for item in ['label','quiet','fixSM','only','nobg'] : setattr(self,item,eval(item))
self.log = log if log else sys.stdout
parNames = ['label','signal','R0_','d_lumi','d_xs_dy','d_xs_st','tag','genPre','sigPre',
'dirIncrement','genDirPre','d_wbb','quiet','templateID','defaults','log','fixSM',
'altData','lumiFactor','only','nobg','rebin','no3D','twoStage', 'alttt','sepchan','twossigma','Rst', 'partsuffix','symmQCD']
self.pars = dict([(p,eval(p)) for p in parNames])
print>>log, sorted(self.pars.items())
print>>log, ''
if sepchan:
assert not only
print>>log, 'fitting el'
pars_el = dict(self.pars); pars_el.update({'sepchan':False, 'only':'_el'})
self.fit_el = fit(**pars_el)
print>>log, ''
print>>log, 'fitting mu'
pars_mu = dict(self.pars); pars_mu.update({'sepchan':False, 'only':'_mu'})
self.fit_mu = fit(**pars_mu)
print>>log, ''
if type(R0_) == tuple:
diffR0_ = R0_[1]
R0_ = R0_[0]
else: diffR0_ = None
prePre = dirIncrement in [0,4,5]
channels = dict([((lep,part),
inputs.channel_data(lep, part+partsuffix, tag, signal, sigPre,
"R%02d" % (R0_ + dirIncrement),
genDirPre, prePre=prePre, templateID=templateID,
d_wbb = d_wbb, rebin=rebin, no3D=no3D, only3D=(twoStage and fixSM), Rst=Rst))
for lep in ['el', 'mu']
for part in ['top', 'QCD']
])
if symmQCD:
for lep in ['el','mu']:
for samp in channels[(lep,'QCD')].samples.values():
samp.datas = (samp.datas[1],samp.datas[1],samp.datas[2])
samp.datasX = (samp.datasX[1],samp.datasX[1],samp.datasX[2])
samp.datasY = (samp.datasY[1],samp.datasY[1],samp.datasY[2])
channels['gen'] = inputs.channel_data('mu', 'top'+partsuffix, tag,
'%s; %s'%(genNameX,genNameY),
sigPrefix = sigPre if dirIncrement in [0,4,5] else '',
dirPrefix=genDirPre, genDirPre=genDirPre,
prePre = prePre, sampleList=['tt'], rename=False)
if alttt:
print 'Using %s templates rather than POWHEG' % alttt
for lep in ['el', 'mu']:
for part in ['top','QCD']:
chan = inputs.channel_data(lep, part+partsuffix, tag, signal, sigPre, "R%02d" %(R0_ + dirIncrement),
genDirPre, prePre=prePre, rebin=rebin, no3D=no3D,
only3D=(twoStage and fixSM), sampleList=["calib_%s.pu.sf"%alttt if 'q' not in alttt else 'ttj_%s.pu.sf'%alttt])
chan = inputs.channel_data('mu','top'+partsuffix, tag,
'%s_%s'%(genNameX,genNameY),
sigPrefix = '',
dirPrefix=genDirPre, genDirPre=genDirPre,
prePre = prePre, sampleList=["calib_%s.pu.sf"%alttt if 'q' not in alttt else 'ttj_%s.pu.sf'%alttt], fullDir = '')
channels['gen'].samples['tt'] = chan.samples['ttalt']
if diffR0_ :
assert not alttt
for lepPart,chan in channels.items():
if type(lepPart) != tuple: continue
lep,part = lepPart
chan.subtract(inputs.channel_data(lep,part+partsuffix,tag,signal,sigPre,
"R%02d" % (diffR0_ + dirIncrement),
genDirPre, prePre = prePre, rebin=rebin ))
print "###", label
print>>self.log, "###", label
self.model = model.topModel(channels)
self.model.w.arg('lumi_factor').setVal(lumiFactor)
for k,v in defaults.items(): self.model.w.arg(k).setVal(v)
for item in ['d_lumi', 'd_xs_dy', 'd_xs_st']: self.model.w.arg(item).setVal(eval(item))
self.fitArgs = [r.RooFit.Extended(True), r.RooFit.NumCPU(1),
r.RooFit.PrintLevel(-1)]
self.model.import_data(altData)
for k,v in fixedValues.items():
self.model.w.arg(k).setVal(v)
self.model.w.arg(k).setConstant()
if fixSM: self.doSM()
elif twoStage: self.doTwoStage()
else: self.doFit()
self.model.print_n(self.log)
self.model.print_n(self.log,qcd=True)
