def createWorkspace(options):
"""
Reads out the histograms from the pickle file and converts them
to a RooDataHist
Prepare PDFs
Save all to a RooWorkspace
"""
# Read file
cachefile = open(options.input, 'r')
masshistos = pickle.load(cachefile)
cachefile.close()
print '>>> Read mass histos from (%s)' % options.input
## Scale all the mass histograms in one go:
cachefile = open(".xsecweights.pck", 'r')
xsecweights = pickle.load(cachefile)
cachefile.close()
print '>>> Read xsec weights from cache (.xsecweights.pck)'
from extractNtrkWeights import extractNTrkWeights
ntkWeights = extractNTrkWeights()
for key, hist in masshistos.iteritems():
## Xsection scaling
hist.Scale(LUMI * xsecweights[CHANMASSTOPROCNAME[(key[1], key[2])]])
## Ntrack bin reweighting to data
try:
hist.Scale(ntkWeights['inclusive'][key[4]])
except IndexError:
pass
bkgmasshistos = None
try:
cachefile = open(options.inputBkg, 'r')
bkgmasshistos = pickle.load(cachefile)
## Note that these are already scaled to xs*lumi
## and weighted to data ntrack multiplicities
cachefile.close()
print '>>> Read background shapes from (%s)' % options.inputBkg
except IOError:
print '>>> No valid background shapes file found'
# Extract the configurations from the diffhistos dictionary
config = readConfig(masshistos)
chselList, massList, trkMultList, combList, procList = config
print 'Selected channels available :', chselList
print 'Mass points available: ', massList
print 'Track multiplicities available: ', trkMultList
print 'Combinations available: ', combList
print 'Processes available: ', procList
# Initiate a workspace where the observable is the SVLMass
# and the variable to fit is mtop
ws = ROOT.RooWorkspace('w')
SVLmass = ws.factory('SVLMass[100,0,300]')
mtop = ws.factory('mtop[172.5,100,200]')
sigStrength = ws.factory('mu[1.0,0.0,5.0]')
# Import binned PDFs from histograms read from file
for chsel in chselList:
for trk in trkMultList:
#backgrounds
try:
# These are already properly scaled by xsec
hbkg = bkgmasshistos[(chsel, trk)]
name = 'SVLMass_unm_%s_bg_%d' % (chsel, trk)
getattr(ws,
'import')(ROOT.RooDataHist(name, name,
ROOT.RooArgList(SVLmass),
hbkg))
ws.factory('%s_bgexp_%d[%f]' % (chsel, trk, hbkg.Integral()))
except Exception, e:
raise e
#signal
for mass in massList:
# ttbar
for comb in ['cor', 'wro']:
htt = masshistos[(chsel, 'tt', mass, comb, trk)]
getattr(ws, 'import')(ROOT.RooDataHist(
htt.GetName(), htt.GetTitle(),
ROOT.RooArgList(SVLmass), htt))
# Only correct combinations for single top
ht = None
for stProc in ['t', 'tbar', 'tW', 'tbarW']:
try: # get all available single top templates
h = masshistos[(chsel, stProc, mass, 'cor', trk)]
if ht is None:
ht = h.Clone("SVLMass_%s_%s_%d_t_%d" %
('cor', chsel, 10 * mass, trk))
else:
ht.Add(h)
except KeyError:
pass
if ht:
getattr(ws, 'import')(ROOT.RooDataHist(
ht.GetName(), ht.GetTitle(), ROOT.RooArgList(SVLmass),
ht))
# Unmatched for tt and wrong+unmatched for single top are merged
htt_unm, ht_wrounm = None, None
for mass in massList:
htt = masshistos[(chsel, 'tt', mass, 'unm', trk)]
if htt_unm is None:
htt_unm = htt.Clone("SVLMass_unm_%s_tt_%d" % (chsel, trk))
else:
htt_unm.Add(htt)
for comb in ['unm', 'wro']:
for stProc in ['t', 'tbar', 'tW', 'tbarW']:
try: # get all available single top templates
ht = masshistos[(chsel, stProc, mass, comb, trk)]
if ht_wrounm is None:
ht_wrounm = ht.Clone("SVLMass_wrounm_%s_t_%d" %
(chsel, trk))
else:
ht_wrounm.Add(ht)
except KeyError:
pass
if htt_unm:
getattr(ws,
'import')(ROOT.RooDataHist(htt_unm.GetName(),
htt_unm.GetTitle(),
ROOT.RooArgList(SVLmass),
htt_unm))
if ht_wrounm:
getattr(ws,
'import')(ROOT.RooDataHist(ht_wrounm.GetName(),
ht_wrounm.GetTitle(),
ROOT.RooArgList(SVLmass),
ht_wrounm))
def createWorkspace(options):
"""
Reads out the histograms from the pickle file and converts them
to a RooDataHist
Prepare PDFs
Save all to a RooWorkspace
"""
# Read file
cachefile = open(options.input,'r')
masshistos = pickle.load(cachefile)
cachefile.close()
print '>>> Read mass histos from (%s)' % options.input
## Scale all the mass histograms in one go:
cachefile = open(".xsecweights.pck", 'r')
xsecweights = pickle.load(cachefile)
cachefile.close()
print '>>> Read xsec weights from cache (.xsecweights.pck)'
from extractNtrkWeights import extractNTrkWeights
ntkWeights = extractNTrkWeights()
for key, hist in masshistos.iteritems():
## Xsection scaling
hist.Scale(LUMI*xsecweights[CHANMASSTOPROCNAME[(key[1], key[2])]])
## Ntrack bin reweighting to data
try: hist.Scale(ntkWeights['inclusive'][key[4]])
except IndexError: pass
bkgmasshistos=None
try:
cachefile = open(options.inputBkg,'r')
bkgmasshistos = pickle.load(cachefile)
## Note that these are already scaled to xs*lumi
## and weighted to data ntrack multiplicities
cachefile.close()
print '>>> Read background shapes from (%s)' % options.inputBkg
except IOError:
print '>>> No valid background shapes file found'
# Extract the configurations from the diffhistos dictionary
config = readConfig(masshistos)
chselList, massList, trkMultList, combList, procList = config
print 'Selected channels available :', chselList
print 'Mass points available: ', massList
print 'Track multiplicities available: ', trkMultList
print 'Combinations available: ', combList
print 'Processes available: ' , procList
# Initiate a workspace where the observable is the SVLMass
# and the variable to fit is mtop
ws = ROOT.RooWorkspace('w')
SVLmass = ws.factory('SVLMass[100,0,300]')
mtop = ws.factory('mtop[172.5,100,200]')
sigStrength = ws.factory('mu[1.0,0.0,5.0]')
# Import binned PDFs from histograms read from file
for chsel in chselList:
for trk in trkMultList:
#backgrounds
try:
# These are already properly scaled by xsec
hbkg = bkgmasshistos[(chsel,trk)]
name = 'SVLMass_unm_%s_bg_%d'%(chsel,trk)
getattr(ws,'import')(ROOT.RooDataHist(name,name, ROOT.RooArgList(SVLmass), hbkg))
ws.factory('%s_bgexp_%d[%f]'%(chsel,trk,hbkg.Integral()))
except Exception, e:
raise e
#signal
for mass in massList:
# ttbar
for comb in ['cor','wro']:
htt = masshistos[(chsel,'tt',mass,comb,trk)]
getattr(ws,'import')(ROOT.RooDataHist(htt.GetName(), htt.GetTitle(), ROOT.RooArgList(SVLmass), htt))
# Only correct combinations for single top
ht = None
for stProc in ['t','tbar','tW','tbarW']:
try: # get all available single top templates
h = masshistos[(chsel,stProc,mass,'cor',trk)]
if ht is None:
ht = h.Clone("SVLMass_%s_%s_%d_t_%d"%('cor',chsel,10*mass,trk))
else:
ht.Add(h)
except KeyError: pass
if ht:
getattr(ws,'import')(ROOT.RooDataHist(ht.GetName(), ht.GetTitle(), ROOT.RooArgList(SVLmass), ht))
# Unmatched for tt and wrong+unmatched for single top are merged
htt_unm, ht_wrounm = None, None
for mass in massList:
htt = masshistos[(chsel,'tt',mass,'unm',trk)]
if htt_unm is None : htt_unm=htt.Clone("SVLMass_unm_%s_tt_%d"%(chsel,trk))
else : htt_unm.Add(htt)
for comb in ['unm','wro']:
for stProc in ['t','tbar','tW','tbarW']:
try: # get all available single top templates
ht = masshistos[(chsel,stProc,mass,comb,trk)]
if ht_wrounm is None : ht_wrounm=ht.Clone("SVLMass_wrounm_%s_t_%d"%(chsel,trk))
else : ht_wrounm.Add(ht)
except KeyError: pass
if htt_unm:
getattr(ws,'import')(ROOT.RooDataHist(htt_unm.GetName(), htt_unm.GetTitle(),
ROOT.RooArgList(SVLmass), htt_unm))
if ht_wrounm:
getattr(ws,'import')(ROOT.RooDataHist(ht_wrounm.GetName(), ht_wrounm.GetTitle(),
ROOT.RooArgList(SVLmass), ht_wrounm))
def main(args, opt):
os.system('mkdir -p %s'%opt.outDir)
mcfiles = {} # procname -> filename
datafiles = {} # procname -> filename
try:
for fname in os.listdir(args[0]):
if not osp.splitext(fname)[1] == '.root': continue
isdata,procname,splitno = resolveFilename(fname)
if isdata:
if not procname in datafiles:
datafiles[procname] = []
datafiles[procname].append(osp.join(args[0],fname))
else:
if 'QCD' in procname: continue ## exclude QCD
if procname == 'TTJets_MSDecays_172v5': continue ## have those already
if 'SingleT' in procname: continue ## have those already
if not procname in mcfiles:
mcfiles[procname] = []
mcfiles[procname].append(osp.join(args[0],fname))
except IndexError:
print "Please provide a valid input directory"
exit(-1)
## Produce (or read) the histogram data
bghistos = makeBackgroundHistos(mcfiles, opt)
cachefile = open(".xsecweights.pck", 'r')
xsecweights = pickle.load(cachefile)
cachefile.close()
print '>>> Read xsec weights from cache (.xsecweights.pck)'
cachefile = open(".svldyscalefactors.pck", 'r')
dySFs = pickle.load(cachefile)
cachefile.close()
print '>>> Read DY scale factors from cache (.svldyscalefactors.pck)'
cachefile = open(".svlqcdtemplates.pck", 'r')
qcdTemplates = pickle.load(cachefile)
cachefile.close()
print '>>> Read QCD templates from cache (.svlqcdtemplates.pck)'
## Read SV Track multiplicity weights:
from extractNtrkWeights import extractNTrkWeights
ntkWeights = extractNTrkWeights()
## Now add them up with proper scales
mcprocesses = [k for k in mcfiles.keys() if not 'Data8TeV' in k]
bghistos_added = sumBGHistos(processes=mcprocesses,
bghistos=bghistos,
xsecweights=xsecweights,
ntkWeights=ntkWeights,
dySFs=dySFs,
qcdTemplates=qcdTemplates,
opt=opt)
bghistos_added_dyup = sumBGHistos(processes=mcprocesses,
bghistos=bghistos,
xsecweights=xsecweights,
ntkWeights=ntkWeights,
dySFs=dySFs,
qcdTemplates=qcdTemplates,
opt=opt,
dyScale=1.3)
bghistos_added_dydn = sumBGHistos(processes=mcprocesses,
bghistos=bghistos,
xsecweights=xsecweights,
ntkWeights=ntkWeights,
dySFs=dySFs,
qcdTemplates=qcdTemplates,
opt=opt,
dyScale=0.7)
bghistos_added_qcdup = sumBGHistos(processes=mcprocesses,
bghistos=bghistos,
xsecweights=xsecweights,
ntkWeights=ntkWeights,
dySFs=dySFs,
qcdTemplates=qcdTemplates,
opt=opt,
qcdScale=1.1)
bghistos_added_qcddn = sumBGHistos(processes=mcprocesses,
bghistos=bghistos,
xsecweights=xsecweights,
ntkWeights=ntkWeights,
dySFs=dySFs,
qcdTemplates=qcdTemplates,
opt=opt,
qcdScale=0.9)
## Produce data histograms
datahistos = makeDataHistos(datafiles, opt)
datahistos_added = sumDataHistos(datafiles.keys(), datahistos)
# Rebin also data, if required:
if opt.rebin>0:
for hist in datahistos_added.values():
hist.Rebin(opt.rebin)
## Save the background only shapes separately as templates for the fit
cachefile = open(".svlbgtemplates.pck", 'w')
pickle.dump(bghistos_added, cachefile, pickle.HIGHEST_PROTOCOL)
print '>>> Dumped bg templates to cache (.svlbgtemplates.pck)'
cachefile.close()
## Read syst histos:
cachefile = open(".svlsysthistos.pck", 'r')
systhistos = pickle.load(cachefile)
print '>>> Read systematics histograms from cache (.svlsysthistos.pck)'
cachefile.close()
## Read mass scan histos:
cachefile = open(".svlmasshistos.pck", 'r')
masshistos = pickle.load(cachefile)
print '>>> Read mass scan histograms from cache (.svlmasshistos.pck)'
# (tag, chan, mass, comb) -> histo
# (tag, chan, mass, comb, ntk) -> histo
cachefile.close()
ofi = ROOT.TFile.Open(osp.join(opt.outDir,'pe_inputs.root'),'RECREATE')
ofi.cd()
#####################################################
## Central mass point and syst samples
for syst in ([s for s,_,_,_ in ALLSYSTS] +
['dyup','dydown','qcdup','qcddown','ntkmult']):
odir = ofi.mkdir(syst + '_172v5')
odir.cd()
for tag,_,_ in SELECTIONS:
for ntk,_ in NTRKBINS:
hname = "SVLMass_%s_%s_%s" % (tag,syst+'_172v5',ntk)
if not syst in ['dyup','dydown','qcdup','qcddown','ntkmult',
'tchscaleup','tchscaledown',
'twchscaleup','twchscaledown']:
hfinal = systhistos[(tag,syst,'tot',ntk)].Clone(hname)
else:
hfinal = systhistos[(tag,'nominal','tot',ntk)].Clone(hname)
try:
## Systs from separate samples
if syst in ['tchscaleup','tchscaledown',
'twchscaleup','twchscaledown']:
scale = LUMI*xsecweights[CHANMASSTOPROCNAME[('tt', 172.5)]]
else:
scale = LUMI*xsecweights[SYSTTOPROCNAME[syst][0]]
except KeyError:
## Systs from event weights
scale = LUMI*xsecweights[CHANMASSTOPROCNAME[('tt', 172.5)]]
hfinal.Scale(scale)
## Renormalize some variations with event weights
if syst in SYSTSTOBERENORMALIZED:
normintegral = systhistos[(tag,'nominal','tot',ntk)].Integral()
normintegral *= LUMI*xsecweights[CHANMASSTOPROCNAME[('tt', 172.5)]]
normintegral /= hfinal.Integral()
hfinal.Scale(normintegral)
## Add single top
stProcs=['t', 'tbar', 'tW', 'tbarW']
stSystProcs=[]
if 'tchscale' in syst:
stProcs=['tW', 'tbarW']
stSystProcs=['t', 'tbar']
if 'twchscale' in syst:
stProcs=['t', 'tbar']
stSystProcs=['tW', 'tbarW']
for st in stProcs:
hsinglet = masshistos[(tag, st, 172.5,'tot',ntk)].Clone('%s_%s'%(hname,st))
hsinglet.Scale(LUMI*xsecweights[CHANMASSTOPROCNAME[(st, 172.5)]])
hfinal.Add(hsinglet)
for st in stSystProcs:
hsinglet = systhistos[(tag, syst, 'tot', ntk)].Clone('%s_%s'%(hname,st))
hsinglet.Scale(LUMI*xsecweights[CHANMASSTOPROCNAME[(st, 172.5)]])
hfinal.Add(hsinglet)
## Add the backgrounds
if not syst in ['dyup','dydown','qcdup','qcddown']:
hfinal.Add(bghistos_added[(tag,ntk)])
else: ## From the scaled bghistos if necessary
bghistos_added_scaled = {
'dyup' : bghistos_added_dyup,
'dydown' : bghistos_added_dydn,
'qcdup' : bghistos_added_qcdup,
'qcddown' : bghistos_added_qcddn,
}[syst]
hfinal.Add(bghistos_added_scaled[(tag,ntk)])
## Rebin if requested
if opt.rebin>0:
hfinal.Rebin(opt.rebin)
## Scale by SV track multiplicity weights:
if not syst == 'ntkmult':
hfinal.Scale(ntkWeights['inclusive'][ntk])
## Write out to file
hfinal.Write(hname, ROOT.TObject.kOverwrite)
#####################################################
## Non-central mass points
ROOT.gSystem.Load('libUserCodeTopMassSecVtx.so')
from ROOT import th1fmorph
# extract mass points from dictionary
mass_points = sorted(list(set([key[2] for key in masshistos.keys()])))
mass_points = mass_points[1:-1] # remove outermost points
debughistos = []
for mass in mass_points:
if mass == 172.5: continue
mname = 'nominal_%s' % str(mass).replace('.','v')
odir = ofi.mkdir(mname)
odir.cd()
for tag,_,_ in SELECTIONS:
for ntk,_ in NTRKBINS:
hname = "SVLMass_%s_%s_%s" % (tag,mname,ntk)
hfinal = masshistos[(tag,'tt',mass,'tot',ntk)].Clone(hname)
hfinal.Scale(LUMI*xsecweights[CHANMASSTOPROCNAME[('tt', mass)]])
## Add single top (t-channel, for which we have the samples)
for st in ['t', 'tbar']:
hsinglet = masshistos[(tag, st, mass,'tot',ntk)].Clone('%s_%s'%(hname,st))
hsinglet.Scale(LUMI*xsecweights[CHANMASSTOPROCNAME[(st, mass)]])
hfinal.Add(hsinglet)
## Add single top (tW-channel, for which we don't have samples)
## Morph between the two extreme mass points to get
## the non existing ones
for st in ['tW', 'tbarW']:
if mass not in [166.5, 178.5]:
hsingletW = th1fmorph('%s_%s_morph'%(hname,st),
'%s_%s_morphed'%(hname,st),
masshistos[(tag, 'tW', 166.5,'tot',ntk)],
masshistos[(tag, 'tW', 178.5,'tot',ntk)],
166.5, 178.5, mass,
masshistos[(tag, 'tW', 166.5,'tot',ntk)].Integral())
hsingletW.Scale(LUMI*xsecweights[CHANMASSTOPROCNAME[(st, 166.5)]]
* TWXSECS[mass]/TWXSECS[166.5])
hsingletW.SetDirectory(0)
else:
hsingletW = masshistos[(tag, st, mass,'tot',ntk)].Clone('%s_%s'%(hname,st))
hsingletW.Scale(LUMI*xsecweights[CHANMASSTOPROCNAME[(st, mass)]])
hfinal.Add(hsingletW)
## Add the combined backgrounds
hfinal.Add(bghistos_added[(tag,ntk)])
## Rebin if requested
if opt.rebin>0:
hfinal.Rebin(opt.rebin)
## Scale by SV track multiplicity weights:
hfinal.Scale(ntkWeights['inclusive'][ntk])
## Write out to file
hfinal.Write(hname, ROOT.TObject.kOverwrite)
## Write also data histos
ofi.cd()
odir = ofi.mkdir('data')
odir.cd()
for tag,_,_ in SELECTIONS:
for ntk,_ in NTRKBINS:
hname = "SVLMass_%s_data_%s" % (tag,ntk)
datahistos_added[(tag,ntk)].Write(hname, ROOT.TObject.kOverwrite)
print ('>>> Wrote pseudo experiment inputs to file (%s)' %
osp.join(opt.outDir,'pe_inputs.root'))
ofi.Write()
ofi.Close()
return 0
def main(args, opt):
os.system('mkdir -p %s' % opt.outDir)
mcfiles = {} # procname -> filename
datafiles = {} # procname -> filename
try:
for fname in os.listdir(args[0]):
if not osp.splitext(fname)[1] == '.root': continue
isdata, procname, splitno = resolveFilename(fname)
if isdata:
if not procname in datafiles:
datafiles[procname] = []
datafiles[procname].append(osp.join(args[0], fname))
else:
if 'QCD' in procname: continue ## exclude QCD
if procname == 'TTJets_MSDecays_172v5':
continue ## have those already
if 'SingleT' in procname: continue ## have those already
if not procname in mcfiles:
mcfiles[procname] = []
mcfiles[procname].append(osp.join(args[0], fname))
except IndexError:
print "Please provide a valid input directory"
exit(-1)
## Produce (or read) the histogram data
bghistos = makeBackgroundHistos(mcfiles, opt)
cachefile = open(".xsecweights.pck", 'r')
xsecweights = pickle.load(cachefile)
cachefile.close()
print '>>> Read xsec weights from cache (.xsecweights.pck)'
cachefile = open(".svldyscalefactors.pck", 'r')
dySFs = pickle.load(cachefile)
cachefile.close()
print '>>> Read DY scale factors from cache (.svldyscalefactors.pck)'
cachefile = open(".svlqcdtemplates.pck", 'r')
qcdTemplates = pickle.load(cachefile)
cachefile.close()
print '>>> Read QCD templates from cache (.svlqcdtemplates.pck)'
## Read SV Track multiplicity weights:
from extractNtrkWeights import extractNTrkWeights
ntkWeights = extractNTrkWeights()
## Now add them up with proper scales
mcprocesses = [k for k in mcfiles.keys() if not 'Data8TeV' in k]
bghistos_added = sumBGHistos(processes=mcprocesses,
bghistos=bghistos,
xsecweights=xsecweights,
ntkWeights=ntkWeights,
dySFs=dySFs,
qcdTemplates=qcdTemplates,
opt=opt)
bghistos_added_dyup = sumBGHistos(processes=mcprocesses,
bghistos=bghistos,
xsecweights=xsecweights,
ntkWeights=ntkWeights,
dySFs=dySFs,
qcdTemplates=qcdTemplates,
opt=opt,
dyScale=1.3)
bghistos_added_dydn = sumBGHistos(processes=mcprocesses,
bghistos=bghistos,
xsecweights=xsecweights,
ntkWeights=ntkWeights,
dySFs=dySFs,
qcdTemplates=qcdTemplates,
opt=opt,
dyScale=0.7)
bghistos_added_qcdup = sumBGHistos(processes=mcprocesses,
bghistos=bghistos,
xsecweights=xsecweights,
ntkWeights=ntkWeights,
dySFs=dySFs,
qcdTemplates=qcdTemplates,
opt=opt,
qcdScale=1.1)
bghistos_added_qcddn = sumBGHistos(processes=mcprocesses,
bghistos=bghistos,
xsecweights=xsecweights,
ntkWeights=ntkWeights,
dySFs=dySFs,
qcdTemplates=qcdTemplates,
opt=opt,
qcdScale=0.9)
## Produce data histograms
datahistos = makeDataHistos(datafiles, opt)
datahistos_added = sumDataHistos(datafiles.keys(), datahistos)
# Rebin also data, if required:
if opt.rebin > 0:
for hist in datahistos_added.values():
hist.Rebin(opt.rebin)
## Save the background only shapes separately as templates for the fit
cachefile = open(".svlbgtemplates.pck", 'w')
pickle.dump(bghistos_added, cachefile, pickle.HIGHEST_PROTOCOL)
print '>>> Dumped bg templates to cache (.svlbgtemplates.pck)'
cachefile.close()
## Read syst histos:
cachefile = open(".svlsysthistos.pck", 'r')
systhistos = pickle.load(cachefile)
print '>>> Read systematics histograms from cache (.svlsysthistos.pck)'
cachefile.close()
## Read mass scan histos:
cachefile = open(".svlmasshistos.pck", 'r')
masshistos = pickle.load(cachefile)
print '>>> Read mass scan histograms from cache (.svlmasshistos.pck)'
# (tag, chan, mass, comb) -> histo
# (tag, chan, mass, comb, ntk) -> histo
cachefile.close()
## Signal only (tt+t+tW) shapes
signalonly = {}
ofi = ROOT.TFile.Open(osp.join(opt.outDir, 'pe_inputs.root'), 'RECREATE')
ofi.cd()
#####################################################
## Central mass point and syst samples
to_be_processed = ([s for s, _, _, _ in ALLSYSTS] +
['dyup', 'dydown', 'qcdup', 'qcddown', 'ntkmult'])
if opt.skip_systs: to_be_processed = ['nominal']
for syst in to_be_processed:
odir = ofi.mkdir(syst + '_172v5')
odir.cd()
for tag, _, _ in SELECTIONS:
for ntk, _ in NTRKBINS:
hname = "SVLMass_%s_%s_%s" % (tag, syst + '_172v5', ntk)
if not syst in [
'dyup', 'dydown', 'qcdup', 'qcddown', 'ntkmult',
'tchscaleup', 'tchscaledown', 'twchscaleup',
'twchscaledown'
]:
hfinal = systhistos[(tag, syst, 'tot', ntk)].Clone(hname)
else:
hfinal = systhistos[(tag, 'nominal', 'tot',
ntk)].Clone(hname)
try:
## Systs from separate samples
if syst in [
'tchscaleup', 'tchscaledown', 'twchscaleup',
'twchscaledown'
]:
scale = LUMI * xsecweights[CHANMASSTOPROCNAME[('tt',
172.5)]]
else:
scale = LUMI * xsecweights[SYSTTOPROCNAME[syst][0]]
except KeyError:
## Systs from event weights
scale = LUMI * xsecweights[CHANMASSTOPROCNAME[('tt',
172.5)]]
hfinal.Scale(scale)
## Renormalize some variations with event weights
if syst in SYSTSTOBERENORMALIZED:
normintegral = systhistos[(tag, 'nominal', 'tot',
ntk)].Integral()
normintegral *= LUMI * xsecweights[CHANMASSTOPROCNAME[
('tt', 172.5)]]
normintegral /= hfinal.Integral()
hfinal.Scale(normintegral)
## Add single top
stProcs = ['t', 'tbar', 'tW', 'tbarW']
stSystProcs = []
if 'tchscale' in syst:
stProcs = ['tW', 'tbarW']
stSystProcs = ['t', 'tbar']
if 'twchscale' in syst:
stProcs = ['t', 'tbar']
stSystProcs = ['tW', 'tbarW']
for st in stProcs:
hsinglet = masshistos[(tag, st, 172.5, 'tot',
ntk)].Clone('%s_%s' % (hname, st))
hsinglet.Scale(
LUMI * xsecweights[CHANMASSTOPROCNAME[(st, 172.5)]])
hfinal.Add(hsinglet)
for st in stSystProcs:
hsinglet = systhistos[(tag, syst, 'tot',
ntk)].Clone('%s_%s' % (hname, st))
hsinglet.Scale(
LUMI * xsecweights[CHANMASSTOPROCNAME[(st, 172.5)]])
hfinal.Add(hsinglet)
## Save signal only shapes
if syst == 'nominal':
signalonly[(tag, 172.5,
ntk)] = hfinal.Clone('%s_sigonly' % hname)
signalonly[(tag, 172.5,
ntk)].Scale(ntkWeights['inclusive'][ntk])
if opt.rebin > 0:
signalonly[(tag, 172.5, ntk)].Rebin(opt.rebin)
## Add the backgrounds
if not syst in ['dyup', 'dydown', 'qcdup', 'qcddown']:
hfinal.Add(bghistos_added[(tag, ntk)])
else: ## From the scaled bghistos if necessary
bghistos_added_scaled = {
'dyup': bghistos_added_dyup,
'dydown': bghistos_added_dydn,
'qcdup': bghistos_added_qcdup,
'qcddown': bghistos_added_qcddn,
}[syst]
hfinal.Add(bghistos_added_scaled[(tag, ntk)])
## Rebin if requested
if opt.rebin > 0:
hfinal.Rebin(opt.rebin)
## Scale by SV track multiplicity weights:
if not syst == 'ntkmult':
hfinal.Scale(ntkWeights['inclusive'][ntk])
## Write out to file
hfinal.Write(hname, ROOT.TObject.kOverwrite)
#####################################################
## Non-central mass points
ROOT.gSystem.Load('libUserCodeTopMassSecVtx.so')
from ROOT import th1fmorph
# extract mass points from dictionary
mass_points = sorted(list(set([key[2] for key in masshistos.keys()])))
mass_points = mass_points[1:-1] # remove outermost points
debughistos = []
for mass in mass_points:
if mass == 172.5: continue
mname = 'nominal_%s' % str(mass).replace('.', 'v')
odir = ofi.mkdir(mname)
odir.cd()
for tag, _, _ in SELECTIONS:
for ntk, _ in NTRKBINS:
hname = "SVLMass_%s_%s_%s" % (tag, mname, ntk)
hfinal = masshistos[(tag, 'tt', mass, 'tot', ntk)].Clone(hname)
hfinal.Scale(LUMI *
xsecweights[CHANMASSTOPROCNAME[('tt', mass)]])
## Add single top (t-channel, for which we have the samples)
for st in ['t', 'tbar']:
hsinglet = masshistos[(tag, st, mass, 'tot',
ntk)].Clone('%s_%s' % (hname, st))
hsinglet.Scale(LUMI *
xsecweights[CHANMASSTOPROCNAME[(st, mass)]])
hfinal.Add(hsinglet)
## Add single top (tW-channel, for which we don't have samples)
## Morph between the two extreme mass points to get
## the non existing ones
for st in ['tW', 'tbarW']:
if mass not in [166.5, 178.5]:
hsingletW = th1fmorph(
'%s_%s_morph' % (hname, st),
'%s_%s_morphed' % (hname, st),
masshistos[(tag, 'tW', 166.5, 'tot', ntk)],
masshistos[(tag, 'tW', 178.5, 'tot', ntk)], 166.5,
178.5, mass, masshistos[(tag, 'tW', 166.5, 'tot',
ntk)].Integral())
hsingletW.Scale(
LUMI *
xsecweights[CHANMASSTOPROCNAME[(st, 166.5)]] *
TWXSECS[mass] / TWXSECS[166.5])
hsingletW.SetDirectory(0)
else:
hsingletW = masshistos[(tag, st, mass, 'tot',
ntk)].Clone('%s_%s' %
(hname, st))
hsingletW.Scale(
LUMI * xsecweights[CHANMASSTOPROCNAME[(st, mass)]])
hfinal.Add(hsingletW)
## Save signal only shapes
signalonly[(tag, mass,
ntk)] = hfinal.Clone('%s_sigonly' % hname)
signalonly[(tag, mass,
ntk)].Scale(ntkWeights['inclusive'][ntk])
if opt.rebin > 0:
signalonly[(tag, mass, ntk)].Rebin(opt.rebin)
## Add the combined backgrounds
hfinal.Add(bghistos_added[(tag, ntk)])
## Rebin if requested
if opt.rebin > 0:
hfinal.Rebin(opt.rebin)
## Scale by SV track multiplicity weights:
hfinal.Scale(ntkWeights['inclusive'][ntk])
## Write out to file
hfinal.Write(hname, ROOT.TObject.kOverwrite)
## Save the signal only shapes (tt+t+tW) as input for the combined plot
cachefile = open(".svlsignalshapes.pck", 'w')
pickle.dump(signalonly, cachefile, pickle.HIGHEST_PROTOCOL)
print '>>> Dumped signal only shapes to cache (.svlsignalshapes.pck)'
cachefile.close()
## Write also data histos
ofi.cd()
odir = ofi.mkdir('data')
odir.cd()
for tag, _, _ in SELECTIONS:
for ntk, _ in NTRKBINS:
hname = "SVLMass_%s_data_%s" % (tag, ntk)
datahistos_added[(tag, ntk)].Write(hname, ROOT.TObject.kOverwrite)
print('>>> Wrote pseudo experiment inputs to file (%s)' %
osp.join(opt.outDir, 'pe_inputs.root'))
ofi.Write()
ofi.Close()
return 0
