def drawFromDC():
config = BetterConfigParser()
config.read(opts.config)
print "opts.config:", opts.config
dataname = ''
if 'Zmm' in opts.bin: dataname = 'Zmm'
elif 'Zee' in opts.bin: dataname = 'Zee'
elif 'Wmunu' in opts.bin: dataname = 'Wmn'
elif 'Wenu' in opts.bin: dataname = 'Wen'
elif 'Znunu' in opts.bin: dataname = 'Znn'
elif 'Wtn' in opts.bin: dataname = 'Wtn'
print 'Variable printing'
print opts.var
if (opts.var == ''):
var = 'BDT'
if dataname == 'Zmm' or dataname == 'Zee': var = 'BDT_Zll'
elif dataname == 'Wmn' or dataname == 'Wen': var = 'BDT_Wln'
elif dataname == 'Znn':
if 'HighPt' in opts.bin: var = 'BDT_ZnnHighPt'
elif 'LowPt' in opts.bin: var = 'BDT_ZnnLowPt'
elif 'LowCSV' in opts.bin: var = 'BDT_ZnnLowCSV'
else: var = 'BDT_Znn'
if dataname == '' or var == 'BDT':
raise RuntimeError, "Did not recognise mode or var from %s" % opts.bin
else:
var = opts.var
region = 'BDT'
ws_var = config.get('plotDef:%s' % var, 'relPath')
ws_var = ROOT.RooRealVar(ws_var, ws_var, -1., 1.)
blind = eval(config.get('Plot:%s' % region, 'blind'))
Stack = StackMaker(config, var, region, True)
if 'LowPt' in opts.bin or 'ch1_Wenu' == opts.bin or 'ch2_Wmunu' == opts.bin:
print 'Niklas %s' % opts.bin
Stack.addFlag2 = 'Low p_{T}(V)'
elif 'MedPt' in opts.bin or 'ch1_Wenu2' == opts.bin or 'ch2_Wmunu2' == opts.bin:
Stack.addFlag2 = 'Intermediate p_{T}(V)'
elif 'HighPt' in opts.bin or 'ch1_Wenu3' == opts.bin or 'ch2_Wmunu3' == opts.bin:
Stack.addFlag2 = 'High p_{T}(V)'
else:
Stack.addFlag2 = ''
preFit = False
addName = 'PostFit_%s' % (opts.fit)
if not opts.mlfit:
addName = 'PreFit'
preFit = True
Stack.options['pdfName'] = '%s_%s_%s.pdf' % (var, opts.bin, addName)
log = eval(config.get('Plot:%s' % region, 'log'))
setup = config.get('Plot_general', 'setup').split(',')
if dataname == 'Zmm' or dataname == 'Zee':
try:
setup.remove('W1b')
setup.remove('W2b')
setup.remove('Wlight')
setup.remove('WH')
except:
print '@INFO: Wb / Wligh / WH not present in the datacard'
# if not dataname == 'Znn' and 'QCD' in setup:
# setup.remove('QCD')
Stack.setup = setup
Dict = eval(config.get('LimitGeneral', 'Dict'))
lumi = eval(config.get('Plot_general', 'lumi'))
options = copy(opts)
options.dataname = "data_obs"
options.mass = 0
options.format = "%8.3f +/- %6.3f"
options.channel = opts.bin
options.excludeSyst = []
options.norm = False
options.stat = False
options.bin = True # fake that is a binary output, so that we parse shape lines
options.out = "tmp.root"
options.fileName = args[0]
options.cexpr = False
options.fixpars = False
options.libs = []
options.verbose = 0
options.poisson = 0
options.nuisancesToExclude = []
options.noJMax = None
theBinning = ROOT.RooFit.Binning(Stack.nBins, Stack.xMin, Stack.xMax)
file = open(opts.dc, "r")
os.chdir(os.path.dirname(opts.dc))
DC = parseCard(file, options)
if not DC.hasShapes: DC.hasShapes = True
MB = ShapeBuilder(DC, options)
theShapes = {}
theSyst = {}
nuiVar = {}
if opts.mlfit:
nuiVar = readBestFit(opts.mlfit)
if not opts.bin in DC.bins:
raise RuntimeError, "Cannot open find %s in bins %s of %s" % (
opts.bin, DC.bins, opts.dc)
for b in DC.bins:
if options.channel != None and (options.channel != b): continue
exps = {}
expNui = {}
shapeNui = {}
reducedShapeNui = {}
for (p, e) in DC.exp[b].items(
): # so that we get only self.DC.processes contributing to this bin
exps[p] = [e, []]
expNui[p] = [e, []]
for (lsyst, nofloat, pdf, pdfargs, errline) in DC.systs:
if pdf in ('param', 'flatParam'): continue
# begin skip systematics
skipme = False
for xs in options.excludeSyst:
if re.search(xs, lsyst):
skipme = True
if skipme: continue
# end skip systematics
counter = 0
for p in DC.exp[b].keys(
): # so that we get only self.DC.processes contributing to this bin
if errline[b][p] == 0: continue
# if p == 'QCD' and not 'QCD' in setup: continue
if pdf == 'gmN':
exps[p][1].append(1 / sqrt(pdfargs[0] + 1))
elif pdf == 'gmM':
exps[p][1].append(errline[b][p])
elif type(errline[b][p]) == list:
kmax = max(errline[b][p][0], errline[b][p][1],
1.0 / errline[b][p][0], 1.0 / errline[b][p][1])
exps[p][1].append(kmax - 1.)
elif pdf == 'lnN':
lnNVar = max(errline[b][p], 1.0 / errline[b][p]) - 1.
if not nuiVar.has_key('%s_%s' % (opts.fit, lsyst)):
nui = 0.
else:
nui = nuiVar['%s_%s' % (opts.fit, lsyst)][0]
lnNVar = lnNVar * nuiVar['%s_%s' %
(opts.fit, lsyst)][1]
exps[p][1].append(lnNVar)
expNui[p][1].append(abs(1 - errline[b][p]) * nui)
elif ("shape" in pdf):
#print 'shape %s %s: %s'%(pdf,p,lsyst)
s0 = MB.getShape(b, p)
sUp = MB.getShape(b, p, lsyst + "Up")
sDown = MB.getShape(b, p, lsyst + "Down")
if (s0.InheritsFrom("RooDataHist")):
s0 = ROOT.RooAbsData.createHistogram(
s0, p, ws_var, theBinning)
s0.SetName(p)
sUp = ROOT.RooAbsData.createHistogram(
sUp, p + lsyst + 'Up', ws_var, theBinning)
sUp.SetName(p + lsyst + 'Up')
sDown = ROOT.RooAbsData.createHistogram(
sDown, p + lsyst + 'Down', ws_var, theBinning)
sDown.SetName(p + lsyst + 'Down')
theShapes[p] = s0.Clone()
theShapes[p + lsyst + 'Up'] = sUp.Clone()
theShapes[p + lsyst + 'Down'] = sDown.Clone()
if not nuiVar.has_key('%s_%s' % (opts.fit, lsyst)):
nui = 0.
reducedNui = 1.
else:
nui = nuiVar['%s_%s' % (opts.fit, lsyst)][0]
reducedNui = nuiVar['%s_%s' % (opts.fit, lsyst)][1]
shapeNui[p + lsyst] = nui
reducedShapeNui[lsyst] = reducedNui
if not 'CMS_vhbb_stat' in lsyst:
if counter == 0:
theSyst[lsyst] = s0.Clone()
theSyst[lsyst + 'Up'] = sUp.Clone()
theSyst[lsyst + 'Down'] = sDown.Clone()
else:
theSyst[lsyst].Add(s0)
theSyst[lsyst + 'Up'].Add(sUp.Clone())
theSyst[lsyst + 'Down'].Add(sDown.Clone())
counter += 1
procs = DC.exp[b].keys()
procs.sort()
print "Original procs:", procs
# if not 'QCD' in setup and 'QCD' in procs:
# procs.remove('QCD')
if not 'W2b' in setup and 'WjHF' in procs:
procs.remove('WjHF')
if not 'Wlight' in setup and 'WjLF' in procs:
procs.remove('WjLF')
fmt = ("%%-%ds " % max([len(p) for p in procs])) + " " + options.format
#Compute norm uncertainty and best fit
theNormUncert = {}
theBestFit = {}
for p in procs:
relunc = sqrt(sum([x * x for x in exps[p][1]]))
print fmt % (p, exps[p][0], exps[p][0] * relunc)
theNormUncert[p] = relunc
absBestFit = sum([x for x in expNui[p][1]])
theBestFit[p] = 1. + absBestFit
histos = []
typs = []
setup2 = copy(setup)
shapesUp = [[] for _ in range(0, len(setup2))]
shapesDown = [[] for _ in range(0, len(setup2))]
sigCount = 0
signalList = ['ZH', 'WH']
#signalList = ['VVb']
for p in procs:
b = opts.bin
for s in setup:
if not Dict[s] == p: continue
if s in signalList:
if sigCount == 0:
Overlay = copy(theShapes[Dict[s]])
else:
Overlay.Add(theShapes[Dict[s]])
sigCount += 1
else:
histos.append(theShapes[Dict[s]])
typs.append(s)
for (lsyst, nofloat, pdf, pdfargs, errline) in DC.systs:
if errline[b][p] == 0: continue
if ("shape" in pdf) and not 'CMS_vhbb_stat' in lsyst:
print 'syst %s' % lsyst
shapesUp[setup2.index(s)].append(theShapes[Dict[s] +
lsyst + 'Up'])
shapesDown[setup2.index(s)].append(
theShapes[Dict[s] + lsyst + 'Down'])
#-------------
#Compute absolute uncertainty from shapes
counter = 0
for (lsyst, nofloat, pdf, pdfargs, errline) in DC.systs:
sumErr = 0
for p in procs:
sumErr += errline[b][p]
if ("shape"
in pdf) and not 'CMS_vhbb_stat' in lsyst and not sumErr == 0:
theSystUp = theSyst[lsyst + 'Up'].Clone()
theSystUp.Add(theSyst[lsyst].Clone(), -1.)
theSystUp.Multiply(theSystUp)
theSystDown = theSyst[lsyst + 'Down'].Clone()
theSystDown.Add(theSyst[lsyst].Clone(), -1.)
theSystDown.Multiply(theSystDown)
theSystUp.Scale(reducedShapeNui[lsyst])
theSystDown.Scale(reducedShapeNui[lsyst])
if counter == 0:
theAbsSystUp = theSystUp.Clone()
theAbsSystDown = theSystDown.Clone()
else:
theAbsSystUp.Add(theSystUp.Clone())
theAbsSystDown.Add(theSystDown.Clone())
counter += 1
#-------------
#Best fit for shapes
if not preFit:
histos, Overlay, typs = getBestFitShapes(procs, theShapes, shapeNui,
theBestFit, DC, setup, opts,
Dict)
counter = 0
errUp = []
total = []
errDown = []
nBins = histos[0].GetNbinsX()
print 'total bins %s' % nBins
Error = ROOT.TGraphAsymmErrors(histos[0])
theTotalMC = histos[0].Clone()
for h in range(1, len(histos)):
theTotalMC.Add(histos[h])
total = [[]] * nBins
errUp = [[]] * nBins
errDown = [[]] * nBins
for bin in range(1, nBins + 1):
binError = theTotalMC.GetBinError(bin)
if math.isnan(binError):
binError = 0.
total[bin - 1] = theTotalMC.GetBinContent(bin)
#Stat uncertainty of the MC outline
errUp[bin - 1] = [binError]
errDown[bin - 1] = [binError]
#Relative norm uncertainty of the individual MC
for h in range(0, len(histos)):
print "h:", h
print "bin:", bin
print "histos:", histos
print "theNormUncert:", theNormUncert
print "histos[h]:", histos[h]
errUp[bin - 1].append(histos[h].GetBinContent(bin) *
theNormUncert[histos[h].GetName()])
errDown[bin - 1].append(histos[h].GetBinContent(bin) *
theNormUncert[histos[h].GetName()])
#Shape uncertainty of the MC
for bin in range(1, nBins + 1):
#print sqrt(theSystUp.GetBinContent(bin))
errUp[bin - 1].append(sqrt(theAbsSystUp.GetBinContent(bin)))
errDown[bin - 1].append(sqrt(theAbsSystDown.GetBinContent(bin)))
#Add all in quadrature
totErrUp = [sqrt(sum([x**2 for x in bin])) for bin in errUp]
totErrDown = [sqrt(sum([x**2 for x in bin])) for bin in errDown]
#Make TGraph with errors
for bin in range(1, nBins + 1):
if not total[bin - 1] == 0:
point = histos[0].GetXaxis().GetBinCenter(bin)
Error.SetPoint(bin - 1, point, 1)
Error.SetPointEYlow(bin - 1, totErrDown[bin - 1] / total[bin - 1])
print 'down %s' % (totErrDown[bin - 1] / total[bin - 1])
Error.SetPointEYhigh(bin - 1, totErrUp[bin - 1] / total[bin - 1])
print 'up %s' % (totErrUp[bin - 1] / total[bin - 1])
#-----------------------
#Read data
data0 = MB.getShape(opts.bin, 'data_obs')
if (data0.InheritsFrom("RooDataHist")):
data0 = ROOT.RooAbsData.createHistogram(data0, 'data_obs', ws_var,
theBinning)
data0.SetName('data_obs')
datas = [data0]
datatyps = [None]
datanames = [dataname]
print "blind:", blind
print "'BDT' in var:", 'BDT' in var
if blind and 'BDT' in var:
print "I'm blinding..."
for bin in range(datas[0].GetNbinsX() / 2, datas[0].GetNbinsX() + 1):
datas[0].SetBinContent(bin, 0)
histos.append(copy(Overlay))
if 'ZH' in signalList and 'WH' in signalList:
typs.append('VH')
if 'ZH' in Stack.setup: Stack.setup.remove('ZH')
if 'WH' in Stack.setup: Stack.setup.remove('WH')
Stack.setup.insert(0, 'VH')
elif 'ZH' in signalList:
typs.append('ZH')
elif 'WH' in signalList:
typs.append('WH')
elif 'VVb' in signalList:
typs.append('VVb')
print Stack.setup
Stack.histos = histos
Stack.typs = typs
Stack.datas = datas
Stack.datatyps = datatyps
Stack.datanames = datanames
Stack.overlay = [Overlay]
Stack.AddErrors = Error
if dataname == 'Wtn':
lumi = 18300.
Stack.lumi = lumi
Stack.doPlot()
print 'i am done!\n'
def log_s_over_b_allRegions(fileList, Region):
print '-----> Running def log_s_over_b() for All Regions'
#--------------
# log s over b
#--------------
region_list = ['Zuu_high', 'Zee_high', 'Zuu_low', 'Zee_low']
histosL = {}
s_b_d_histos = {}
for file in fileList:
for region in region_list:
#print '\n\tRegion:', region
#print file
name = '%s' % file
histosL[region] = []
for th1 in get_th1(file, region):
if 'btag' in th1.GetName(): continue
if '_stats_' in th1.GetName(): continue
if '_j_' in th1.GetName(): continue
if 'eff' in th1.GetName(): continue
if 'LHE' in th1.GetName(): continue
if 'total' in th1.GetName(): continue
if 'total_signal' in th1.GetName(): continue
if 'total_background' in th1.GetName(): continue
#print '\n\t Retrieving TH1 from file: ', th1.GetName(), th1.Integral()
histosL[region].append(th1)
i = 0
j = 0
#print ' histosL:', histosL
for hist in histosL[region]:
if 'ZH' == hist.GetName() or 'ggZH' == hist.GetName():
#print '\n\t Setting ',hist, ' as signal...'
if j == 0:
hSignal = hist.Clone()
else:
hSignal.Add(hist)
j += 1
elif 'data_obs' in hist.GetName():
hData = hist.Clone()
else:
if i == 0:
hBkg = hist.Clone()
else:
hBkg.Add(hist)
i += 1
# temp hack
hData = hBkg + hSignal
s_b_d_histos[region] = {'b': hBkg, 's': hSignal, 'd': hData}
bmin = -4
bmax = 0
nbins = 10
log_s_over_b_b = ROOT.TH1F("log_s_over_b_b", "log_s_over_b_b", nbins, bmin,
bmax)
log_s_over_b_b.SetFillColor(4)
log_s_over_b_b.GetXaxis().SetTitle("log(S/B)")
log_s_over_b_b.GetYaxis().SetTitle("Events")
log_s_over_b_s = ROOT.TH1F("log_s_over_b_s", "log_s_over_b_s", nbins, bmin,
bmax)
log_s_over_b_s.SetFillColor(2)
log_s_over_b_d = ROOT.TH1F("log_s_over_b_d", "log_s_over_b_d", nbins, bmin,
bmax)
log_s_over_b = ROOT.THStack("log_s_over_b", "log_s_over_b")
stack_log_s_over_b = ROOT.THStack("stack_log_s_over_b",
"stack_log_s_over_b")
print '\ns_b_d_histos', s_b_d_histos
for key, s_b_d in s_b_d_histos.iteritems():
print '\n-----> Looping over Histogram: ', key, s_b_d
#for bin in range(0,s_b_d['b'].GetNbinsX()+1):
for bin in range(1, nbins + 1):
s = s_b_d['s'].GetBinContent(bin)
b = s_b_d['b'].GetBinContent(bin)
d = s_b_d['d'].GetBinContent(bin)
sErr = s_b_d['s'].GetBinError(bin)
bErr = s_b_d['b'].GetBinError(bin)
dErr = s_b_d['d'].GetBinError(bin)
logsb = -3.9
if b > 0. and s > 0.:
logsb = log10(s / b)
elif s > 0.:
logsb = -0.
print '\n\t Calculating log_s/b for bin', bin
print '\t\t sig:', s, ' bgk:', b
print '\t\t Log_s/b', logsb
print '\t\t Data Obs', d
newBin = log_s_over_b_b.FindBin(logsb)
log_s_over_b_b.SetBinContent(
newBin, b + log_s_over_b_b.GetBinContent(newBin))
log_s_over_b_s.SetBinContent(
newBin, s + log_s_over_b_s.GetBinContent(newBin))
log_s_over_b_d.SetBinContent(
newBin, d + log_s_over_b_d.GetBinContent(newBin))
log_s_over_b_b.SetBinError(
newBin,
sqrt(bErr * bErr + log_s_over_b_b.GetBinError(newBin) *
log_s_over_b_b.GetBinError(newBin)))
#log_s_over_b_s.SetBinError(newBin, sqrt(sErr*sErr+log_s_over_b_s.GetBinError(newBin)*log_s_over_b_s.GetBinError(newBin)))
log_s_over_b_d.SetBinError(
newBin,
sqrt(dErr * dErr + log_s_over_b_d.GetBinError(newBin) *
log_s_over_b_d.GetBinError(newBin)))
stack = StackMaker(config, 'logSB', 'plot1', False)
stack.setup = ['ZH', 'BKG']
stack.typs = ['ZH', 'BKG']
stack.lumi = 12900.
stack.histos = [log_s_over_b_s, log_s_over_b_b]
stack.datas = [log_s_over_b_d]
stack.datanames = 'data_obs'
stack.overlay = log_s_over_b_s
stack.filename = Region
stack.doPlot()
def plot(fileList):
signalRegion = True
#region = 'plot'
region = opts.region
var = 'HCSVmass'
stack = StackMaker(config, var, region, signalRegion)
histosL = []
overlayL = []
#7-9 for the higgs
#5-6 for the VV
binmin = 7
binmax = 9
max_sb = 0
max_ssb = 0
for file in fileList:
if max_sb < get_s_over_b(file, binmin, binmax):
max_sb = get_s_over_b(file, binmin, binmax)
if max_ssb < get_s_over_sb(file, binmin, binmax):
max_ssb = get_s_over_sb(file, binmin, binmax)
print max_ssb
print max_sb
for file in fileList:
print file
print get_s_over_b(file, binmin, binmax)
if eval(opts.rescale) == False:
max_sb = 1.
max_ssb = 1.
for th1 in get_th1(file):
#th1.Sumw2()
if 's/b' in opts.fom:
th1.Scale(get_s_over_b(file, binmin, binmax) / max_sb)
if 's/s+b' in opts.fom:
th1.Scale(get_s_over_sb(file, binmin, binmax) / max_ssb)
if 'VV' in th1.GetName():
th1.SetName('VV')
if 'Zj1b' in th1.GetName():
th1.SetName('Zj2b')
if 'Wj1b' in th1.GetName():
th1.SetName('Wj2b')
# new stack for the overlay plot
if 'VH' in th1.GetName() or 'VV' in th1.GetName():
overlayL.append(th1)
histosL.append(th1)
print 'histoL'
print histosL
typs = []
typsL = []
datas = []
datasL = []
overlay_typs = []
#append the name just once
for histo in histosL:
typsL.append(histo.GetName())
if 'data' in histo.GetName():
datasL.append(histo)
if 'VH' in histo.GetName() or 'VV' in histo.GetName():
overlay_typs.append(histo.GetName())
#datasL.append(datas)
#typsL.append(typs)
print typsL
print 'Overlay list'
print overlayL
overlay_histo_dict = HistoMaker.orderandadd([{
overlay_typs[i]: overlayL[i]
} for i in range(len(overlayL))], ['VH', 'VV'])
overlayL2 = []
stack.histos = histosL
stack.typs = typsL
stack.datas = datasL
# stack.datatyps = Ldatatyps[v]
stack.datanames = 'data_obs'
for key in overlay_histo_dict:
overlayL2.append(overlay_histo_dict[key])
mjj_sub = eval(opts.sub)
if not mjj_sub:
stack.overlay = overlayL2
appendix = ''
if (eval(opts.rescale) == True):
appendix = '_rescaled_'
if 's/s+b' in opts.fom:
stack.options['pdfName'] = stack.options['pdfName'].replace(
'.pdf',
'_combined78tev_postFit_s_over_sb' + appendix + '.' + opts.format)
elif 's/b' in opts.fom:
stack.options['pdfName'] = stack.options['pdfName'].replace(
'.pdf',
'_combined78tev_postFit_s_over_b' + appendix + '.' + opts.format)
else:
stack.options['pdfName'] = stack.options['pdfName'].replace(
'.pdf', '_unweighted.' + opts.format)
# stack.options['pdfName'] = stack.options['pdfName'].replace('.pdf','_highPt_7tev.pdf')
# stack.options['pdfName'] = stack.options['pdfName'].replace('.pdf','_combined_postFit_s_over_b_Hpt_weight_1.pdf'
stack.lumi = 18940
if mjj_sub == False:
stack.doPlot()
elif mjj_sub == True:
stack.options['pdfName'] = stack.options['pdfName'].replace(
'.' + opts.format, '_subtracted.' + opts.format)
stack.doSubPlot(['VH', 'VV'])
print 'i am done!\n'
def log_s_over_b(fileList):
#--------------
# log s over b
#--------------
histosL = {}
s_b_d_histos = {}
for file in fileList:
print file
name = '%s' % file
histosL[name] = []
print 'file is', file
for th1 in get_th1(file):
#th1.Sumw2()
if 'VVLF' in th1.GetName():
th1.SetName('VV')
if 'Zj1b' in th1.GetName():
th1.SetName('Zj2b')
if 'Wj1b' in th1.GetName():
th1.SetName('Wj2b')
histosL[name].append(th1)
i = 0
for hist in histosL[name]:
if 'VH' in hist.GetName() and not 'VVHF' in hist.GetName():
#if 'VVHF' in hist.GetName():
hSignal = hist.Clone()
elif 'data_obs' in hist.GetName():
hData = hist.Clone()
else:
if i == 0:
hBkg = hist.Clone()
else:
hBkg.Add(hist)
i += 1
s_b_d_histos[name] = {'b': hBkg, 's': hSignal, 'd': hData}
bmin = -4
bmax = 0
nbins = 16
log_s_over_b_b = ROOT.TH1F("log_s_over_b_b", "log_s_over_b_b", nbins, bmin,
bmax)
log_s_over_b_b.SetFillColor(4)
log_s_over_b_b.GetXaxis().SetTitle("log(S/B)")
log_s_over_b_b.GetYaxis().SetTitle("Events")
log_s_over_b_s = ROOT.TH1F("log_s_over_b_s", "log_s_over_b_s", nbins, bmin,
bmax)
log_s_over_b_s.SetFillColor(2)
log_s_over_b_d = ROOT.TH1F("log_s_over_b_d", "log_s_over_b_d", nbins, bmin,
bmax)
log_s_over_b = ROOT.THStack("log_s_over_b", "log_s_over_b")
stack_log_s_over_b = ROOT.THStack("stack_log_s_over_b",
"stack_log_s_over_b")
for key, s_b_d in s_b_d_histos.iteritems():
for bin in range(0, s_b_d['b'].GetNbinsX() + 1):
s = s_b_d['s'].GetBinContent(bin)
b = s_b_d['b'].GetBinContent(bin)
d = s_b_d['d'].GetBinContent(bin)
sErr = s_b_d['s'].GetBinError(bin)
bErr = s_b_d['b'].GetBinError(bin)
dErr = s_b_d['d'].GetBinError(bin)
logsb = -3.9
if b > 0. and s > 0.:
logsb = log10(s / b)
elif s > 0.:
logsb = -0.
#print logsb
newBin = log_s_over_b_b.FindBin(logsb)
log_s_over_b_b.SetBinContent(
newBin, b + log_s_over_b_b.GetBinContent(newBin))
log_s_over_b_s.SetBinContent(
newBin, s + log_s_over_b_s.GetBinContent(newBin))
log_s_over_b_d.SetBinContent(
newBin, d + log_s_over_b_d.GetBinContent(newBin))
log_s_over_b_b.SetBinError(
newBin,
sqrt(bErr * bErr + log_s_over_b_b.GetBinError(newBin) *
log_s_over_b_b.GetBinError(newBin)))
log_s_over_b_s.SetBinError(
newBin,
sqrt(sErr * sErr + log_s_over_b_s.GetBinError(newBin) *
log_s_over_b_s.GetBinError(newBin)))
log_s_over_b_d.SetBinError(
newBin,
sqrt(dErr * dErr + log_s_over_b_d.GetBinError(newBin) *
log_s_over_b_d.GetBinError(newBin)))
stack = StackMaker(config, 'logSB', 'plot1', False)
stack.setup = ['VH', 'BKG']
stack.typs = ['VH', 'BKG']
stack.lumi = 18940.
stack.histos = [log_s_over_b_s, log_s_over_b_b]
stack.datas = [log_s_over_b_d]
stack.datanames = 'data_obs'
stack.overlay = log_s_over_b_s
stack.doPlot()
def drawFromDC():
config = BetterConfigParser()
config.read(opts.config)
region = opts.region
print "\nopts.config:",opts.config
print "opts:", opts
print "var:", opts.var
print "bin:", opts.bin
#Should Read this from the parser
datanames = config.get('dc:%s'%opts.bin,'data').split(' ')
print 'dataname is', datanames
region = opts.bin
var = opts.var
ws_var = config.get('plotDef:%s'%var,'relPath')
nbin = int(config.get('plotDef:%s'%var,'nBins'))
xmin = float(config.get('plotDef:%s'%var,'min'))
xmax = float(config.get('plotDef:%s'%var,'max'))
blind = eval(opts.blind)
postfit = eval(opts.postfit)
print 'config:', config
print 'var: ', var
print 'region: ', region
print 'blind: ', blind
print 'postfit: ', postfit
Group_dc = eval(config.get('Plot_general','Group_dc'))
Stack=StackMaker(config,var,region,True)
# check for pre or post fit options
preFit = False
addName = 'PostFit_%s' %(opts.fit)
if not opts.mlfit:
addName = 'PreFit'
preFit = True
print '\n-----> Fit Type(opts.fit) : ', opts.fit
print ' (opts.mlfit): ', opts.mlfit
print ' preFit : ', preFit
Stack.options['pdfName'] = '%s_%s_%s.pdf' %(var,opts.bin,addName)
#log = eval(config.get('Plot:%s'%region,'log'))
if 'Zee' in opts.bin or 'Zuu' in opts.bin:
#VH
setup = ['ggZHbb', 'qqZHbb','Zbb','Zb','Z_udscg','TT','VV2b','VVlight','ST']
#VV
#setup = ['VV2b','ggZHbb','qqZHbb','Z_udscg','Zb','Zbb','TT','VVlight','ST']
channel = 'ZllHbb'
if 'Zee' in opts.bin: lep_channel = 'Zee'
elif 'Zuu' in opts.bin: lep_channel = 'Zuu'
#region_dic = {'BDT':'SIG','CRZlight':'Zlf','CRZb':'Zhf','CRttbar':'TT'}
region_dic = {
'BDT':'BDT',
'CRZlight':'CRZlight',
'CRZb':'CRZb',
'CRttbar':'CRttbar',
'ZeeMass_lowpt':'ZeeMass_lowpt',
'ZeeMass_highpt':'ZeeMass_highpt',
'ZuuMass_lowpt':'ZuuMass_lowpt',
'ZuuMass_highpt':'ZuuMass_highpt',
'ZeeMassVV_lowpt':'ZeeMassVV_lowpt',
'ZeeMassVV_highpt':'ZeeMassVV_highpt',
'ZuuMassVV_lowpt':'ZuuMassVV_lowpt',
'ZuuMassVV_highpt':'ZuuMassVV_highpt',
'ZuuMass_Vptbin0':'ZuuMass_Vptbin0',
'ZuuMass_Vptbin1':'ZuuMass_Vptbin1',
'ZuuMass_Vptbin2':'ZuuMass_Vptbin2',
'ZeeMass_Vptbin0':'ZeeMass_Vptbin0',
'ZeeMass_Vptbin1':'ZeeMass_Vptbin1',
'ZeeMass_Vptbin2':'ZeeMass_Vptbin2',
}
print 'opts.bin is', opts.bin
region_name = [region_dic[key] for key in region_dic if (key in opts.bin)]
region_name = region_name[0]
print 'region_name is', region_name
pt_region_dic = {'lowpt':'lowpt','highpt':'highpt','bin0':'bin0','bin1':'bin1','bin2':'bin2'}
pt_region_name = [pt_region_dic[key] for key in pt_region_dic if (key in opts.bin)]
pt_region_name = pt_region_name[0]
else:
print '@ERROR: This is not a Zll region. Aborting'
sys.exit()
Stack.setup = setup
Dict = eval(config.get('LimitGeneral','Dict'))
lumi = eval(config.get('General','lumi'))
Stack.nBins = nbin
Stack.xMin = xmin
Stack.xMax = xmax
print '/n----> The Binning:'
print 'nBins:', Stack.nBins
print 'xMin:', Stack.xMin
print 'xMax:', Stack.xMax
theBinning = ROOT.RooFit.Binning(Stack.nBins,Stack.xMin,Stack.xMax)
#################
#We are now skiping a large part of the orginal code, as everything that remains to be done is to read the postfit plot from the mlfit.root
#How should the scale sys (lnN be handled) ?
#
histos = []
typs = []
shapes = {}
shapesUp = [[] for _ in range(0,len(setup))]
shapesDown = [[] for _ in range(0,len(setup))]
#signalList = ['ggZHbb', 'qqZHbb']
#signalList = []
sigCount = 0
#Overlay ={}
Overlay = []
prefit_overlay = []
postfit_from_wc = False
if opts.mlfit.split('/')[-1] != 'mlfit.root':
postfit_from_wc = True
dirname = ''
####
#Open the mlfit.root and retrieve the mc
print 'opts.mlfit is', opts.mlfit
file = ROOT.TFile.Open(opts.mlfit)
#if file == None: raise RuntimeError, "Cannot open file %s" % theFile
#print '\n\n-----> Fit File: ',file
print '====================='
print 'POSTFIT IS', postfit
print '====================='
if postfit:
if not postfit_from_wc:
if not ROOT.gDirectory.cd('shapes_fit_s'):
print '@ERROR: didn\'t find the shapes_fit_s directory. Aborting'
sys.exit()
else:
pass
else:
if not ROOT.gDirectory.cd('shapes_prefit'):
print '@ERROR: didn\'t find the shapes_prefit directory. Aborting'
sys.exit()
folder_found = False
for dir in ROOT.gDirectory.GetListOfKeys():
dirinfo = dir.GetName().split('_')
print 'dir name is', dir.GetName().split('_')
##if not (dirinfo[0] == channel and dirinfo[2] == lep_channel and dirinfo[3] == region_name and dirinfo[4] == pt_region_name):
print 'dirinfo is', dirinfo
print 'lep_channel is', lep_channel
print 'region_name is', region_name
print 'pt_region_name is', pt_region_name
if not (dirinfo[0] == lep_channel and dirinfo[1] == region_name and dirinfo[2] == pt_region_name):
#for VV
if not (dirinfo[2] == region_name.split('_')[0] and dirinfo[3] == pt_region_name):
if not (len(dirinfo) > 3 and dirinfo[3] == region_name.split('_')[0] and dirinfo[4] == 'Vpt'+pt_region_name):
continue
if postfit_from_wc and 'prefit' in dirinfo:
continue
folder_found = True
dirname = dir.GetName()
#signal, use prefit
for s in setup:
if ('ZHbb' in s and postfit) and not postfit_from_wc:
print 'ERROR'
sys.exit()
ROOT.gDirectory.cd('/shapes_prefit')
ROOT.gDirectory.cd(dirname)
found = False
for subdir in ROOT.gDirectory.GetListOfKeys():
#print 'subdir name is', subdir.GetName()
if subdir.GetName() == Dict[s]:
found = True
hist = rebinHist(gDirectory.Get(subdir.GetName()).Clone(), nbin, xmin, xmax)
histos.append(hist)
typs.append(s)
#print 's is', s
#print 'signalList is', signalList
#if s in signalList:
# hist.SetTitle(s)
# Overlay.append(hist)
# print 'the Histogram title is', hist.GetTitle()
else:
#SF_ZJets = [0.95188, 0.94404, 1.0463]
#SF_TTbar = 1.0373
#;Vpt high
#SF_ZJets = [1.1235, 0.91368, 1.2435]
#SF_TTbar = 1.0601
#Start be getting the SF
print 'Gonna apply SF'
scale = 1
#if 'low' in opts.dc:
# if 'TT' in s: scale = 1.01
# if 'Z_udscg' in s: scale = 0.96
# if 'Zb' in s: scale = 0.99
# if 'Zbb' in s : scale = 1.04
#elif 'high' in opts.dc:
# if 'TT' in s: scale = 1.01
# if 'Z_udscg' in s: scale = 1.03
# if 'Zb' in s: scale = 0.96
# if 'Zbb' in s: scale = 1.23
#else:
# pass
# #ROOT.gDirectory.cd('/shapes_fit_s')
#if not postfit_from_wc:
# ROOT.gDirectory.cd('/shapes_prefit')
# print 'ERROR2'
# sys.exit()
#else:
# scale = 1
ROOT.gDirectory.cd(dirname)
found = False
for subdir in ROOT.gDirectory.GetListOfKeys():
print 'subdir name is', subdir.GetName()
#print 'Dict is ', Dict
if subdir.GetName() == Dict[s] or (postfit_from_wc and subdir.GetName() == s):
found = True
hist = rebinHist(gDirectory.Get(subdir.GetName()).Clone(), nbin, xmin, xmax, scale)
histos.append(hist)
typs.append(s)
print 's is', s
#print 'signalList is', signalList
#if s in signalList:
# hist.SetTitle(s)
# Overlay.append(hist)
# print 'the Histogram title is', hist.GetTitle()
#take prefit distr. for signal
if not found:
print '@ERROR: didn\'t find the postfit histogram. Aborting'
sys.exit()
if not postfit_from_wc:
ROOT.gDirectory.cd('/shapes_prefit/'+dirname)
print 'ERROR3'
sys.exit()
if not postfit_from_wc:
total = rebinHist(gDirectory.Get('total').Clone(), nbin, xmin, xmax)
total.SetTitle('prefit')
prefit_overlay.append(total)
break
if not folder_found:
print '@ERROR: Folder was not found.'
print 'lep_channel', lep_channel
print 'region_name', region_name
print 'pt_region_name', pt_region_name
sys.exit()
#retrieve the data
options = copy(opts)
options.dataname = "data_obs"
options.mass = 0
options.format = "%8.3f +/- %6.3f"
options.channel = opts.bin
options.excludeSyst = []
options.norm = False
options.stat = False
options.bin = True # fake that is a binary output, so that we parse shape lines
options.out = "tmp.root"
options.fileName = args[0]
options.cexpr = False
options.fixpars = False
options.libs = []
options.verbose = 0
options.poisson = 0
options.nuisancesToExclude = []
options.noJMax = None
#This needs to be done from the "dc" root file
print 'file is ',opts.dc
dc_file= open(opts.dc, "r")
os.chdir(os.path.dirname(opts.dc))
DC = parseCard(dc_file, options)
if not DC.hasShapes: DC.hasShapes = True
MB = ShapeBuilder(DC, options)
data0 = MB.getShape(opts.bin,'data_obs')
if (data0.InheritsFrom("RooDataHist")):
data0 = ROOT.RooAbsData.createHistogram(data0,'data_obs',ws_var,theBinning)
data0.SetName('data_obs')
datas=[data0]
if blind and 'BDT' in var:
for bin in range(datas[0].GetNbinsX()-3,datas[0].GetNbinsX()+1):
datas[0].SetBinContent(bin,0)
if blind and 'Mass' in var:
for bin in range(datas[0].GetNbinsX()-13,datas[0].GetNbinsX()-7):
datas[0].SetBinContent(bin,0)
datatyps = [None]
#print '\nshapes!!!', shapes
print '\nOVERLAY!!!', Overlay
#Add all the histos and overlay to the stackmaker such that they can be ploted
#print 'before Stack, histos are', histos
#sys.exit()
Stack.histos = histos
Stack.typs = typs
Stack.overlay = Overlay
Stack.prefit_overlay = prefit_overlay
Stack.datas = datas
Stack.datatyps = datatyps
Stack.datanames= datanames
Stack.AddErrors= True
Stack.lumi = lumi
if 'BDT' in var:
Stack.forceLog = True
Stack.doPlot()
print 'i am done!\n'
def drawFromDC():
config = BetterConfigParser()
config.read(opts.config)
region = opts.region
print "\nopts.config:",opts.config
print "opts:", opts
print "var:", opts.var
print "bin:", opts.bin
#dataname = 'Zll'
#dataname = 'SingleElectron_Run2016B_PromptReco'
dataname = ['SingleElectron_Run2016B_PromptReco','SingleElectron_Run2016C_PromptReco','SingleElectron_Run2016D_PromptReco','SingleElectron_Run2016F_PromptReco','SingleElectron_Run2016G_PromptReco']
#if 'Zuu' in opts.bin: dataname = 'Zuu'
#elif 'Zee' in opts.bin: dataname = 'Zee'
#elif 'Wmunu' in opts.bin: dataname = 'Wmn'
#elif 'Wenu' in opts.bin: dataname = 'Wen'
#elif 'Znunu' in opts.bin: dataname = 'Znn'
#elif 'Wtn' in opts.bin: dataname = 'Wtn'
#if (opts.var == ''):
# var = 'BDT'
# if dataname == 'Zmm' or dataname == 'Zee': var = 'BDT_Zll'
# elif dataname == 'Wmn' or dataname == 'Wen': var = 'BDT_Wln'
# elif dataname == 'Znn':
# if 'HighPt' in opts.bin: var = 'BDT_ZnnHighPt'
# if 'LowPt' in opts.bin: var = 'BDT_ZnnLowPt'
# if 'LowCSV' in opts.bin: var = 'BDT_ZnnLowCSV'
# if dataname == '' or var == 'BDT': raise RuntimeError, 'Did not recognise mode or var from '+opts.bin
#else:
# var = opts.var
if opts.var == 'BDT':
if 'LowPt' in opts.bin: var = 'gg_plus_ZH125_low_Zpt'
if 'MedPt' in opts.bin: var = 'gg_plus_ZH125_med_Zpt'
if 'HighPt' in opts.bin: var = 'gg_plus_ZH125_high_Zpt'
if 'VV' in opts.bin: var = 'VV_bdt'
#if 'BDT' in var:
# region = 'BDT'
#else:
region = opts.bin
var = opts.var
ws_var = config.get('plotDef:%s'%var,'relPath')
if 'gg_plus' in var:
ws_var = ROOT.RooRealVar(ws_var,ws_var,-1.,1.)
else:
ws_var = ROOT.RooRealVar(ws_var,ws_var, 0, 1)
#blind = eval(config.get('Plot:%s'%region,'blind'))
#blind = True
#blind = False
blind = eval(opts.blind)
print 'config:', config
print 'var: ', var
print 'region: ', region
Stack=StackMaker(config,var,region,True)
if 'LowPt' in opts.bin or 'ch1_Wenu' == opts.bin or 'ch2_Wmunu' == opts.bin:
Stack.addFlag2 = 'Low p_{T}(V)'
elif 'MedPt' in opts.bin or 'ch1_Wenu2' == opts.bin or 'ch2_Wmunu2' == opts.bin:
Stack.addFlag2 = 'Intermediate p_{T}(V)'
elif 'HighPt' in opts.bin or 'ch1_Wenu3' == opts.bin or 'ch2_Wmunu3' == opts.bin:
Stack.addFlag2 = 'High p_{T}(V)'
# check for pre or post fit options
preFit = False
addName = 'PostFit_%s' %(opts.fit)
if not opts.mlfit:
addName = 'PreFit'
preFit = True
print '\n-----> Fit Type(opts.fit) : ', opts.fit
print ' (opts.mlfit): ', opts.mlfit
print ' preFit : ', preFit
Stack.options['pdfName'] = '%s_%s_%s.pdf' %(var,opts.bin,addName)
log = eval(config.get('Plot:%s'%region,'log'))
setup = ['Z_udscg','Zb','Zbb','TT','VVlight','ST','VV2b','qqZHbb','ggZHbb']
if dataname == 'Zmm' or dataname == 'Zee':
try:
setup.remove('W1b')
setup.remove('W2b')
setup.remove('Wlight')
setup.remove('WH')
except: print '@INFO: Wb / Wligh / WH not present in the datacard'
if not dataname == 'Znn' and 'QCD' in setup:
setup.remove('QCD')
Stack.setup = setup
Dict = eval(config.get('LimitGeneral','Dict'))
#Dict = eval(config.get('Plot_general','Group'))
lumi = eval(config.get('Plot_general','lumi'))
options = copy(opts)
options.dataname = "data_obs"
options.mass = 0
options.format = "%8.3f +/- %6.3f"
options.channel = opts.bin
options.excludeSyst = []
options.norm = False
options.stat = False
options.bin = True # fake that is a binary output, so that we parse shape lines
options.out = "tmp.root"
options.fileName = args[0]
options.cexpr = False
options.fixpars = False
options.libs = []
options.verbose = 0
options.poisson = 0
options.nuisancesToExclude = []
options.noJMax = None
theBinning = ROOT.RooFit.Binning(Stack.nBins,Stack.xMin,Stack.xMax)
file = open(opts.dc, "r")
os.chdir(os.path.dirname(opts.dc))
print '\nDC Path:', os.path.dirname(opts.dc)
DC = parseCard(file, options)
if not DC.hasShapes: DC.hasShapes = True
MB = ShapeBuilder(DC, options)
theShapes = {}
theSyst = {}
nuiVar = {}
print '\n\n ------> Mlfit File: ', opts.mlfit
if opts.mlfit:
nuiVar = readBestFit(opts.mlfit)
if not opts.bin in DC.bins: raise RuntimeError, "Cannot open find %s in bins %s of %s" % (opts.bin,DC.bins,opts.dc)
print '\n-----> Looping over bins in datacard...'
for b in DC.bins:
print ' bin: ', b
if options.channel != None and (options.channel != b): continue
exps = {}
expNui = {}
shapeNui = {}
reducedShapeNui = {}
for (p,e) in DC.exp[b].items(): # so that we get only self.DC.processes contributing to this bin
exps[p] = [ e, [] ]
expNui[p] = [ e, [] ]
print '\n-----> Datacard systematics: ', DC.systs
for (lsyst,nofloat,pdf,pdfargs,errline) in DC.systs:
print '\n-----> Looping over systematics in datacard: ', (lsyst,nofloat,pdf,pdfargs,errline)
if pdf in ('param', 'flatParam'): continue
# begin skip systematics
skipme = False
for xs in options.excludeSyst:
if re.search(xs, lsyst):
skipme = True
if skipme:
print '\n-----> skipping systematics...'
continue
# end skip systematics
counter = 0
print '\n\t-----> Looping over keys in datacard: ', DC.exp[b].keys()
for p in DC.exp[b].keys(): # so that we get only self.DC.processes contributing to this bin
print '\n\t-----> Looping over process in this bin: ', p
if errline[b][p] == 0: continue
if p == 'QCD' and not 'QCD' in setup: continue
if pdf == 'gmN':
exps[p][1].append(1/sqrt(pdfargs[0]+1));
elif pdf == 'gmM':
exps[p][1].append(errline[b][p]);
elif type(errline[b][p]) == list:
kmax = max(errline[b][p][0], errline[b][p][1], 1.0/errline[b][p][0], 1.0/errline[b][p][1]);
exps[p][1].append(kmax-1.);
elif pdf == 'lnN':
lnNVar = max(errline[b][p], 1.0/errline[b][p])-1.
if not nuiVar.has_key('%s_%s'%(opts.fit,lsyst)):
nui = 0.
else:
nui= nuiVar['%s_%s'%(opts.fit,lsyst)][0]
lnNVar = lnNVar*nuiVar['%s_%s'%(opts.fit,lsyst)][1]
exps[p][1].append(lnNVar)
expNui[p][1].append(abs(1-errline[b][p])*nui);
elif 'shape' in pdf:
print '\n\t-----> Filling the Shapes for this process...'
#print 'shape %s %s: %s'%(pdf,p,lsyst)
s0 = MB.getShape(b,p)
sUp = MB.getShape(b,p,lsyst+"Up")
sDown = MB.getShape(b,p,lsyst+"Down")
if (s0.InheritsFrom("RooDataHist")):
s0 = ROOT.RooAbsData.createHistogram(s0,p,ws_var,theBinning)
s0.SetName(p)
sUp = ROOT.RooAbsData.createHistogram(sUp,p+lsyst+'Up',ws_var,theBinning)
sUp.SetName(p+lsyst+'Up')
sDown = ROOT.RooAbsData.createHistogram(sDown,p+lsyst+'Down',ws_var,theBinning)
sDown.SetName(p+lsyst+'Down')
theShapes[p] = s0.Clone()
theShapes[p+lsyst+'Up'] = sUp.Clone()
theShapes[p+lsyst+'Down'] = sDown.Clone()
if not nuiVar.has_key('%s_%s'%(opts.fit,lsyst)):
nui = 0.
reducedNui = 1.
else:
nui= nuiVar['%s_%s'%(opts.fit,lsyst)][0]
reducedNui= nuiVar['%s_%s'%(opts.fit,lsyst)][1]
shapeNui[p+lsyst] = nui
reducedShapeNui[lsyst] = reducedNui
if not 'CMS_vhbb_stat' in lsyst:
if counter == 0:
theSyst[lsyst] = s0.Clone()
theSyst[lsyst+'Up'] = sUp.Clone()
theSyst[lsyst+'Down'] = sDown.Clone()
else:
theSyst[lsyst].Add(s0)
theSyst[lsyst+'Up'].Add(sUp.Clone())
theSyst[lsyst+'Down'].Add(sDown.Clone())
counter += 1
procs = DC.exp[b].keys(); procs.sort()
if not 'QCD' in setup and 'QCD' in procs:
procs.remove('QCD')
if not 'W2b' in setup and 'WjHF' in procs:
procs.remove('WjHF')
if not 'Wlight' in setup and 'WjLF' in procs:
procs.remove('WjLF')
fmt = ("%%-%ds " % max([len(p) for p in procs]))+" "+options.format;
#Compute norm uncertainty and best fit
theNormUncert = {}
theBestFit = {}
print '\n-----> Computing norm uncertaint and best fit...'
for p in procs:
relunc = sqrt(sum([x*x for x in exps[p][1]]))
print fmt % (p, exps[p][0], exps[p][0]*relunc)
theNormUncert[p] = relunc
absBestFit = sum([x for x in expNui[p][1]])
theBestFit[p] = 1.+absBestFit
histos = []
typs = []
setup2=copy(setup)
shapesUp = [[] for _ in range(0,len(setup2))]
shapesDown = [[] for _ in range(0,len(setup2))]
sigCount = 0
#signalList = ['Zbb','WH']
#signalList = ['Zbb']
signalList = ['ZH']
#signalList = ['ggZHbb','qqZHbb']
Overlay ={}
# for shape analysis?
for p in procs:
b = opts.bin
print 'process: ', p
print 'setup:',setup
print 'Dict:', Dict
#print 'theShapes:', theShapes
for s in setup:
print '-----> Fillings the shapes for: ', s
#print Dict[s], p
if Dict[s] != p:
print 'not equal', p
print 'not equal', Dict[s]
continue
if s in signalList:
if sigCount ==0:
Overlay=copy(theShapes[Dict[s]])
else:
Overlay.Add(theShapes[Dict[s]])
sigCount += 1
else:
histos.append(theShapes[Dict[s]])
typs.append(s)
for (lsyst,nofloat,pdf,pdfargs,errline) in DC.systs:
if errline[b][p] == 0: continue
if ("shape" in pdf) and not 'CMS_vhbb_stat' in lsyst:
print 'syst %s'%lsyst
shapesUp[setup2.index(s)].append(theShapes[Dict[s]+lsyst+'Up'])
shapesDown[setup2.index(s)].append(theShapes[Dict[s]+lsyst+'Down'])
#-------------
#Compute absolute uncertainty from shapes
counter = 0
for (lsyst,nofloat,pdf,pdfargs,errline) in DC.systs:
sumErr = 0
for p in procs:
sumErr += errline[b][p]
print '---> PDF:',pdf, lsyst
if ("shape" in pdf) and not 'CMS_vhbb_stat' in lsyst and not sumErr == 0:
theSystUp = theSyst[lsyst+'Up'].Clone()
theSystUp.Add(theSyst[lsyst].Clone(),-1.)
theSystUp.Multiply(theSystUp)
theSystDown = theSyst[lsyst+'Down'].Clone()
theSystDown.Add(theSyst[lsyst].Clone(),-1.)
theSystDown.Multiply(theSystDown)
theSystUp.Scale(reducedShapeNui[lsyst])
theSystDown.Scale(reducedShapeNui[lsyst])
if counter == 0:
theAbsSystUp = theSystUp.Clone()
theAbsSystDown = theSystDown.Clone()
else:
theAbsSystUp.Add(theSystUp.Clone())
theAbsSystDown.Add(theSystDown.Clone())
counter +=1
#-------------
#Best fit for shapes
if not preFit:
# Set the preFit as an overlay
print '\n Making prefit overlay...'
print procs
i = 0
for hist in theShapes:
if hist not in procs: continue
print 'Process:', hist
print 'Shape:', theShapes[hist]
print 'i:', i
if i == 0:
prefit_overlay=copy(theShapes[hist])
#prefit_overlay=theShapes[hist]
print 'First Integral:', theShapes[hist].Integral()
i+=1
else:
#prefit_overlay.Add(theShapes[hist], 1.0)
prefit_overlay.Add(theShapes[hist])
print 'Integral:', theShapes[hist].Integral()
print 'prefit_overlay:', prefit_overlay
print 'Integral:', prefit_overlay.Integral()
print '\n-----> Getting best fit shapes(for postFit)...'
histos, Overlay, typs = getBestFitShapes(procs,theShapes,shapeNui,theBestFit,DC,setup,opts,Dict)
counter = 0
errUp=[]
total=[]
errDown=[]
nBins = histos[0].GetNbinsX()
#print histos
# temp hack to get histo names right
#names = ['ggZH','DY2B', 'DY1B', 'DYlight', 'TT', 'VV']
#for name,i in enumerate(histos):
# i.SetName(names[name])
#Overlay.SetName('ZH')
# end hack
print '\n total bins %s'%nBins
print '\n histos: ',histos
print '\n theNormUncert: ',theNormUncert
print '\n Overlay: ', Overlay
Error = ROOT.TGraphAsymmErrors(histos[0])
theTotalMC = histos[0].Clone()
for h in range(1,len(histos)):
theTotalMC.Add(histos[h])
total = [[]]*nBins
errUp = [[]]*nBins
errDown = [[]]*nBins
print '\n\n\t\t -----> The Histos: ', histos
for bin in range(1,nBins+1):
binError = theTotalMC.GetBinError(bin)
if math.isnan(binError):
binError = 0.
total[bin-1]=theTotalMC.GetBinContent(bin)
#Stat uncertainty of the MC outline
errUp[bin-1] = [binError]
errDown[bin-1] = [binError]
# Temp hack to fix theNormUncert naming
temp_theNormUncert = {}
for i,hist in enumerate(histos):
for x in theNormUncert:
#print '\nx: ', x
if x in histos[i].GetName():
temp_theNormUncert[histos[i].GetName()] = theNormUncert[x]
#print temp_theNormUncert
#Relative norm uncertainty of the individual MC
for h in range(0,len(histos)):
#errUp[bin-1].append(histos[h].GetBinContent(bin)*theNormUncert[histos[h].GetName()])
#errDown[bin-1].append(histos[h].GetBinContent(bin)*theNormUncert[histos[h].GetName()])
errUp[bin-1].append(histos[h].GetBinContent(bin)*temp_theNormUncert[histos[h].GetName()])
errDown[bin-1].append(histos[h].GetBinContent(bin)*temp_theNormUncert[histos[h].GetName()])
#Shape uncertainty of the MC
for bin in range(1,nBins+1):
#print sqrt(theSystUp.GetBinContent(bin))
errUp[bin-1].append(sqrt(theAbsSystUp.GetBinContent(bin)))
errDown[bin-1].append(sqrt(theAbsSystDown.GetBinContent(bin)))
#Add all in quadrature
totErrUp=[sqrt(sum([x**2 for x in bin])) for bin in errUp]
totErrDown=[sqrt(sum([x**2 for x in bin])) for bin in errDown]
#Make TGraph with errors
for bin in range(1,nBins+1):
if not total[bin-1] == 0:
point=histos[0].GetXaxis().GetBinCenter(bin)
Error.SetPoint(bin-1,point,1)
Error.SetPointEYlow(bin-1,totErrDown[bin-1]/total[bin-1])
#print 'down %s'%(totErrDown[bin-1]/total[bin-1])
Error.SetPointEYhigh(bin-1,totErrUp[bin-1]/total[bin-1])
#print 'up %s'%(totErrUp[bin-1]/total[bin-1])
#-----------------------
#Read data
data0 = MB.getShape(opts.bin,'data_obs')
if (data0.InheritsFrom("RooDataHist")):
data0 = ROOT.RooAbsData.createHistogram(data0,'data_obs',ws_var,theBinning)
data0.SetName('data_obs')
datas=[data0]
datatyps = [None]
#datanames=[dataname]
datanames= dataname
print '\nDATA HIST:', data0
print 'Data name:', dataname
if blind:
for bin in range(12,datas[0].GetNbinsX()+1):
datas[0].SetBinContent(bin,0)
#for bin in range(0,datas[0].GetNbinsX()+1):
# print 'Data in bin x:', datas[0].GetBinContent(bin)
if 'VV' in opts.bin:
signalList = ['VVb',' VVlight']
print 'Signal List:', signalList
histos.append(copy(Overlay))
if 'Zbb' in signalList and 'WH' in signalList:
typs.append('Zbb')
if 'Zbb' in Stack.setup: Stack.setup.remove('Zbb')
if 'WH' in Stack.setup: Stack.setup.remove('WH')
Stack.setup.insert(0,'Zbb')
elif 'Zbb' in signalList:
typs.append('Zbb')
elif 'WH' in signalList:
typs.append('WH')
elif 'ZH' in signalList:
typs.append('ZH')
if 'VVb' in signalList:
typs.append('VVb')
#typs.append('VVlight')
print '\n-----> Stack.setup(double check)...'
print 'Histos:', histos
print 'typs:', typs
Stack.histos = histos
Stack.typs = typs
Stack.datas = datas
Stack.datatyps = datatyps
Stack.datanames= datanames
#Stack.prefit_overlay = [prefit_overlay]
if '13TeV' in region:
Stack.overlay = [Overlay]
print '\n\n\t\t Overlay: ',Stack.overlay
Stack.AddErrors=Error
if dataname == 'Wtn':
lumi = 18300.
Stack.lumi = lumi
Stack.doPlot()
print 'i am done!\n'
def drawFromDC():
config = BetterConfigParser()
config.read(opts.config)
region = opts.region
print "\nopts.config:", opts.config
print "opts:", opts
print "var:", opts.var
print "bin:", opts.bin
dataname = 'Zll'
if 'Zuu' in opts.bin: dataname = 'Zuu'
elif 'Zee' in opts.bin: dataname = 'Zee'
elif 'Wmn' in opts.bin: dataname = 'Wmn'
elif 'Wen' in opts.bin: dataname = 'Wen'
elif 'Znn' in opts.bin: dataname = 'Znn'
elif 'Wtn' in opts.bin: dataname = 'Wtn'
if (opts.var == ''):
var = 'BDT'
if dataname == 'Zmm' or dataname == 'Zee': var = 'BDT_Zll'
elif dataname == 'Wmn' or dataname == 'Wen': var = 'BDT_Wln'
elif dataname == 'Znn': var = 'BDT_Znn'
#if 'HighPt' in opts.bin: var = 'BDT_ZnnHighPt'
#if 'LowPt' in opts.bin: var = 'BDT_ZnnLowPt'
#if 'LowCSV' in opts.bin: var = 'BDT_ZnnLowCSV'
if dataname == '' or var == 'BDT':
raise RuntimeError, 'Did not recognise mode or var from ' + opts.bin
else:
var = opts.var
if opts.var == 'BDT':
if 'LowPt' in opts.bin: var = 'gg_plus_ZH125_low_Zpt'
elif 'MedPt' in opts.bin: var = 'gg_plus_ZH125_med_Zpt'
elif 'HighPt' in opts.bin: var = 'gg_plus_ZH125_high_Zpt'
elif 'VV' in opts.bin: var = 'VV_bdt'
else: var = 'gg_plus_ZH125_high_Zpt'
if opts.var == 'CRBDT':
if 'LowPt' in opts.bin: var = 'gg_plus_ZH125_low_Zpt_CR'
elif 'MedPt' in opts.bin: var = 'gg_plus_ZH125_med_Zpt_CR'
elif 'HighPt' in opts.bin: var = 'gg_plus_ZH125_high_Zpt_CR'
else: var = 'gg_plus_ZH125_high_Zpt_CR'
#if 'BDT' in var:
# region = 'BDT'
#else:
region = opts.bin
ws_var = config.get('plotDef:%s' % var, 'relPath')
print 'ws_var:', ws_var
if opts.var == 'BDT':
ws_var = ROOT.RooRealVar(ws_var, ws_var, -1, 1.)
else:
ws_var = ROOT.RooRealVar(ws_var, ws_var, -1, 1.)
blind = eval(config.get('Plot:%s' % region, 'blind'))
#blind = True
print 'config:', config
print 'var: ', var
print 'region: ', region
Stack = StackMaker(config, var, region, True)
if 'low' in opts.bin or 'ch1_Wenu' == opts.bin or 'ch2_Wmunu' == opts.bin:
Stack.addFlag2 = 'Low p_{T}(V)'
elif 'MedPt' in opts.bin or 'ch1_Wenu2' == opts.bin or 'ch2_Wmunu2' == opts.bin:
Stack.addFlag2 = 'Intermediate p_{T}(V)'
elif 'high' in opts.bin or 'ch1_Wenu3' == opts.bin or 'ch2_Wmunu3' == opts.bin:
Stack.addFlag2 = 'High p_{T}(V)'
# check for pre or post fit options
preFit = False
addName = 'PostFit_%s' % (opts.fit)
if not opts.mlfit:
addName = 'PreFit'
preFit = True
print '\n-----> Fit Type(opts.fit) : ', opts.fit
print ' (opts.mlfit): ', opts.mlfit
print ' preFit : ', preFit
print ' opts.bin : ', opts.bin
Stack.options['pdfName'] = '%s_%s_%s.pdf' % (var, opts.bin, addName)
log = eval(config.get('Plot:%s' % region, 'log'))
if 'Zll' in opts.bin or 'Zee' in opts.bin or 'Zuu' in opts.bin or 'Zmm' in opts.bin:
#setup = config.get('Plot_general','setup').split(',')
setup = [
'ZH', 'ggZH', 'DY2b', 'DY1b', 'DYlight', 'TT', 'VVHF', 'VVLF', 'ST'
]
signalList = ['ZH']
#channel = 'ZllHbb'
# For my own fits
channel = 'ZllHbb'
if 'Zee' in opts.bin: lep_channel = 'Zee'
elif 'Zuu' in opts.bin: lep_channel = 'Zmm'
region_dic = {
'BDT': 'SIG',
' Zlf': 'Zlf',
'Zhf': 'Zhf',
'TT': 'TT',
'13TeV': 'SIG'
}
region_name = [
region_dic[key] for key in region_dic if (key in opts.bin)
]
if 'minCMVA' not in opts.var:
region_name = region_name[0]
else:
if 'Zlf' in opts.bin: region_name = 'Zlf'
if 'Zhf' in opts.bin: region_name = 'Zhf'
if 'ttbar' in opts.bin: region_name = 'TT'
if 'low' in opts.bin or 'Low' in opts.bin: pt_region_name = 'low'
if 'high' in opts.bin or 'High' in opts.bin: pt_region_name = 'high'
# ['ZllHbb', 'ch1', 'Zmm', 'SIG', 'low']
if 'Zmm' in lep_channel and 'low' in pt_region_name: zll_index = 'ch1'
if 'Zmm' in lep_channel and 'high' in pt_region_name: zll_index = 'ch3'
if 'Zee' in lep_channel and 'high' in pt_region_name: zll_index = 'ch4'
if 'Zee' in lep_channel and 'low' in pt_region_name: zll_index = 'ch2'
# binning
nBins = Stack.nBins
xMin = Stack.xMin
xMax = Stack.xMax
# for stat hists
if 'Zee' in lep_channel and 'low' in pt_region_name and 'minCMVA' not in opts.var:
stat_name = 'ZeeLowPt_13TeV'
if 'Zee' in lep_channel and 'high' in pt_region_name and 'minCMVA' not in opts.var:
stat_name = 'ZeeHighPt_13TeV'
if 'Zmm' in lep_channel and 'low' in pt_region_name and 'minCMVA' not in opts.var:
stat_name = 'ZuuLowPt_13TeV'
if 'Zmm' in lep_channel and 'high' in pt_region_name and 'minCMVA' not in opts.var:
stat_name = 'ZuuHighPt_13TeV'
if 'minCMVA' in opts.var:
if 'low' in pt_region_name:
if 'Zmm' in lep_channel:
if 'Zlf' in region_name: stat_name = 'Zlf_low_Zuu'
if 'Zhf' in region_name: stat_name = 'Zhf_low_Zuu'
if 'TT' in region_name: stat_name = 'ttbar_low_Zuu'
else:
if 'Zlf' in region_name: stat_name = 'Zlf_low_Zee'
if 'Zhf' in region_name: stat_name = 'Zhf_low_Zee'
if 'TT' in region_name: stat_name = 'ttbar_low_Zee'
if 'high' in pt_region_name:
if 'Zmm' in lep_channel:
if 'Zlf' in region_name: stat_name = 'Zlf_high_Zuu'
if 'Zhf' in region_name: stat_name = 'Zhf_high_Zuu'
if 'TT' in region_name: stat_name = 'ttbar_high_Zuu'
else:
if 'Zlf' in region_name: stat_name = 'Zlf_high_Zee'
if 'Zhf' in region_name: stat_name = 'Zhf_high_Zee'
if 'TT' in region_name: stat_name = 'ttbar_high_Zee'
if 'Wmn' in opts.bin or 'Wen' in opts.bin:
setup = [
'WH', 'ZH', 'Wj2b', 'Wj1b', 'Wj0b', 'DY2b', 'DY1b', 'DYlight',
'TT', 'VVHF', 'VVLF', 'ST'
]
signalList = ['ZH', 'WH']
channel = 'WlnHbb'
if 'Wmn' in opts.bin: lep_channel = 'Wmn'
if 'Wen' in opts.bin: lep_channel = 'Wen'
pt_region_name = 'none'
if opts.var == 'BDT':
region_name = 'SR'
#region_name = lep_channel+'HighPt'
# binning
nBins = 23
xMin = -1
xMax = 1
#nBins = 10
#xMin = 0.3
#xMax = 1
#if 'Zbb' in opts.dc:
# print '--> Zbb Cards!!!'
# nBins = 11
# xMin = 0.2
# xMax = 1
stat_name = 'BDT_' + lep_channel + 'HighPt_'
else:
if 'tt' in region: region_name = 'TTCR'
if 'whf' in region:
if 'High' in opts.bin:
region_name = 'WHFCRhigh'
else:
region_name = 'WHFCRlow'
if 'wlf' in region: region_name = 'WLFCR'
# if 'tt' in region: region_name = 'tt'+lep_channel
# if 'whf' in region:
# if 'High' in opts.bin:
# region_name = 'whf'+lep_channel+'High'
# else: region_name = 'whf'+lep_channel+'Low'
# if 'wlf' in region: region_name = 'wlf'+lep_channel
stat_name = 'BDT_' + opts.bin + '_'
# binning
nBins = 15
xMin = -1
xMax = 1
if 'Znn' in opts.bin:
setup = [
'ZH', 'ggZH', 'WH', 'DY2b', 'DY1b', 'DYlight', 'Wj2b', 'Wj1b',
'Wj0b', 'TT', 'VVHF', 'VVLF', 'ST'
]
signalList = ['ZH', 'WH']
channel = 'ZnnHbb'
lep_channel = 'ZnnHbb'
pt_region_name = 'HighPt'
if opts.var == 'BDT':
region_name = 'Signal'
# binning
nBins = 35
xMin = -0.8
xMax = 1
stat_name = 'Znn_13TeV_Signal'
else:
if 'QCD' in opts.dc:
region_name = 'QCD'
stat_name = 'Znn_13TeV_QCD'
if 'TT' in opts.dc:
region_name = 'TT'
stat_name = 'Znn_13TeV_TT'
if 'Zbb' in opts.dc and 'TT' not in opts.dc:
region_name = 'Zbb'
stat_name = 'Znn_13TeV_Zbb'
if 'Zlight' in opts.dc:
region_name = 'Zlight'
stat_name = 'Znn_13TeV_Zlight'
# binning
nBins = 40
xMin = -1
xMax = 1
print '############'
print 'Channel is', channel
print 'lepton channel is', lep_channel
print 'region_name is', region_name
print 'pt region_name is', pt_region_name
print '/n----> The Binning:'
print 'nBins:', nBins
print 'xMin:', xMin
print 'xMax:', xMax
if dataname == 'Zmm' or dataname == 'Zee':
try:
setup.remove('W1b')
setup.remove('W2b')
setup.remove('Wlight')
setup.remove('WH')
except:
print '@INFO: Wb / Wligh / WH not present in the datacard'
if not dataname == 'Znn' and 'QCD' in setup:
setup.remove('QCD')
Stack.setup = setup
Dict = eval(config.get('LimitGeneral', 'Dict'))
lumi = eval(config.get('Plot_general', 'lumi'))
options = copy(opts)
options.dataname = "data_obs"
options.mass = 0
options.format = "%8.3f +/- %6.3f"
options.channel = opts.bin
options.excludeSyst = []
options.norm = False
options.stat = False
options.bin = True # fake that is a binary output, so that we parse shape lines
options.out = "tmp.root"
options.fileName = args[0]
options.filename = region
options.cexpr = False
options.fixpars = False
options.libs = []
options.verbose = 0
options.poisson = 0
options.nuisancesToExclude = []
options.noJMax = None
theBinning = ROOT.RooFit.Binning(nBins, xMin, xMax)
# for prefit erros
prefit_error_histos = []
postfit_error_histos = []
histos = []
typs = []
shapes = {}
shapesUp = [[] for _ in range(0, len(setup))]
shapesDown = [[] for _ in range(0, len(setup))]
sigCount = 0
Overlay = []
prefit_overlay = []
dirname = ''
####
#Open the mlfit.root and retrieve the mc
file = ROOT.TFile.Open(opts.mlfit)
if file == None: raise RuntimeError, "Cannot open file %s" % opts.mlfit
print '\n\n-----> Fit File: ', file
fit_dir = 'shapes_fit_s'
#if not ROOT.gDirectory.cd('shapes_fit_s'):
if not ROOT.gDirectory.cd(fit_dir):
print '@ERROR: didn\'t find the shapes_fit_s directory. Aborting'
sys.exit()
for dir in ROOT.gDirectory.GetListOfKeys():
dirinfo = dir.GetName().split('_')
print 'dirinfo:', dirinfo
if 'Znn' in dirinfo[0] and 'Znn' not in opts.bin: continue
if 'ZllHbb' in dirinfo[0] and ('Zmm' not in lep_channel
and 'Zee' not in lep_channel):
continue
if 'W' in dirinfo[0] and 'W' not in opts.bin: continue
if 'W' in opts.bin:
print 'channel, lepton channel, region_name:', channel, lep_channel, region_name
#if not (dirinfo[0] == channel and dirinfo[1] == lep_channel and dirinfo[2] == region_name):
if not (dirinfo[0] == channel and dirinfo[1] == lep_channel
and dirinfo[2] == region_name):
continue
else:
print '!!! Match !!!'
if 'Znn' in opts.bin:
print 'channel, lepton channel, region_name, pt_region_name:', channel, lep_channel, region_name, pt_region_name
if not (dirinfo[0] == channel and dirinfo[1] == lep_channel
and dirinfo[2] == region_name):
continue
else:
print '!!! Match !!!'
if 'Zuu' in opts.bin or 'Zee' in opts.bin:
print 'channel, zll index, lepton channel, region_name, pt_region_name:', channel, zll_index, lep_channel, region_name, pt_region_name
if not (dirinfo[0] == channel and dirinfo[1] == zll_index
and dirinfo[2] == lep_channel and dirinfo[3] == region_name
and dirinfo[4] == pt_region_name):
continue
else:
print '!!! Match !!!'
print 'Directory:', dir.GetName()
dirname = dir.GetName()
# Pull out the MC stat uncertainties first
#hists_WenHighPt40.root
#vhbb_WenHighPt40_13TeV.txt
if 'W' in opts.bin:
stat_filename = opts.dc.replace('.txt', '.root')
stat_filename = stat_filename.replace('vhbb', 'hists')
stat_filename = stat_filename.replace('_13TeV', '')
else:
stat_filename = opts.dc.replace('.txt', '.root')
stat_filename = stat_filename.replace('DC_', '')
print '\n Opening card for MC stat hists:', stat_filename
print 'Dir name:', stat_name
stat_file = ROOT.TFile.Open(stat_filename)
if 'W' not in opts.bin: ROOT.gDirectory.cd(stat_name)
stat_hists = {}
for s in setup:
for dir in ROOT.gDirectory.GetListOfKeys():
#print 'dir:', dir.GetName()
if 'W' in opts.bin:
wlvname = dir.GetName().replace(stat_name, '')
if wlvname == Dict[s]:
stat_hists[wlvname] = ROOT.gDirectory.Get(
dir.GetName()).Clone()
if dir.GetName() == Dict[s]:
stat_hists[dir.GetName()] = ROOT.gDirectory.Get(
dir.GetName()).Clone()
#stat_file.Close()
print '\nStat_hists:', stat_hists
file = ROOT.TFile.Open(opts.mlfit)
#ROOT.gDirectory.cd('shapes_fit_s')
ROOT.gDirectory.cd(fit_dir)
ROOT.gDirectory.cd(dirname)
subdir_list = [x for x in ROOT.gDirectory.GetListOfKeys()]
i = 0
for s in setup:
print '\ns:', s
found = False
#for subdir in ROOT.gDirectory.GetListOfKeys():
for subdir in subdir_list:
print 'subdir name is', subdir.GetName()
print 'Dict Key is', Dict[s]
if subdir.GetName() == Dict[s]:
found = True
# Set Histos postFit shapes and preFit errors
hist = rebinHist(
ROOT.gDirectory.Get(subdir.GetName()).Clone(), nBins,
xMin, xMax, dirname, subdir, prefit_error_histos,
postfit_error_histos, stat_hists, opts.bin)
histos.append(hist)
typs.append(s)
if s in signalList:
hist.SetTitle(s)
if i == 0:
Overlay.append(hist)
i += 1
else:
Overlay[0].Add(hist)
break
if not found:
print '@ERROR: didn\'t find the postfit histogram. Aborting'
hist = ROOT.TH1F(Dict[s], Dict[s], nBins, xMin, xMax)
histos.append(hist)
typs.append(s)
#sys.exit()
#ROOT.gDirectory.cd('/shapes_prefit/'+dirname)
#total = rebinHist(ROOT.gDirectory.Get('total').Clone(), Stack.nBins, Stack.xMin, Stack.xMax)
#total.SetTitle('prefit')
#prefit_overlay.append(total)
break
# Get the total pre/post fit error
#print '\n Calculating final pre/post fit errors'
#print prefit_error_histos
for i, iErrorHist in enumerate(prefit_error_histos):
#print i,iErrorHist
if i == 0:
temp_prefit_error = iErrorHist.Clone()
else:
temp_prefit_error.Add(iErrorHist)
for i, iErrorHist in enumerate(postfit_error_histos):
#print i,iErrorHist
if i == 0:
temp_postfit_error = iErrorHist.Clone()
else:
temp_postfit_error.Add(iErrorHist)
final_prefit_error = ROOT.TGraphAsymmErrors(temp_prefit_error)
final_postfit_error = ROOT.TGraphAsymmErrors(temp_postfit_error)
total = [[]] * nBins
errUp = [[]] * nBins
errDown = [[]] * nBins
total_post = [[]] * nBins
errUp_post = [[]] * nBins
errDown_post = [[]] * nBins
# rebin the final errors
for bin in range(1, nBins + 1):
binError = temp_prefit_error.GetBinError(bin)
total[bin - 1] = temp_prefit_error.GetBinContent(bin)
errUp[bin - 1] = [binError]
errDown[bin - 1] = [binError]
binError_post = temp_postfit_error.GetBinError(bin)
total_post[bin - 1] = temp_postfit_error.GetBinContent(bin)
errUp_post[bin - 1] = [binError_post]
errDown_post[bin - 1] = [binError_post]
#Add all in quadrature
totErrUp = [sqrt(sum([x**2 for x in bin])) for bin in errUp]
totErrDown = [sqrt(sum([x**2 for x in bin])) for bin in errDown]
totErrUp_post = [sqrt(sum([x**2 for x in bin])) for bin in errUp_post]
totErrDown_post = [sqrt(sum([x**2 for x in bin])) for bin in errDown_post]
for bin in range(1, nBins + 1):
if not total[bin - 1] == 0:
point = histos[0].GetXaxis().GetBinCenter(bin)
final_prefit_error.SetPoint(bin - 1, point, 1)
final_prefit_error.SetPointEYlow(
bin - 1, totErrDown[bin - 1] / total[bin - 1])
final_prefit_error.SetPointEYhigh(
bin - 1, totErrUp[bin - 1] / total[bin - 1])
if not total_post[bin - 1] == 0:
point = histos[0].GetXaxis().GetBinCenter(bin)
final_postfit_error.SetPoint(bin - 1, point, 1)
final_postfit_error.SetPointEYlow(
bin - 1, totErrDown_post[bin - 1] / total_post[bin - 1])
final_postfit_error.SetPointEYhigh(
bin - 1, totErrUp_post[bin - 1] / total_post[bin - 1])
# =================================================
##### Read data
print '\n#### Datafile is ', opts.dc
dc_file = open(opts.dc, "r")
os.chdir(os.path.dirname(opts.dc))
DC = parseCard(dc_file, options)
if not DC.hasShapes: DC.hasShapes = True
MB = ShapeBuilder(DC, options)
data0 = MB.getShape(opts.bin, 'data_obs')
print data0
if (data0.InheritsFrom("RooDataHist")):
data0 = ROOT.RooAbsData.createHistogram(data0, 'data_obs', ws_var,
theBinning)
data0.SetName('data_obs')
datas = [data0]
datatyps = [None]
datanames = [dataname]
print '\nDATA HIST:', data0
print 'Data name:', dataname
# if opts.var == 'BDT':
# print '!!!! Blinding !!!!'
# if 'Zee' in dataname or 'Zuu' in dataname:
# for bin in range(4,datas[0].GetNbinsX()+1):
# datas[0].SetBinContent(bin,0)
# if 'Znn' in dataname:
# for bin in range(20,datas[0].GetNbinsX()+1):
# datas[0].SetBinContent(bin,0)
# if 'W' in dataname:
# for bin in range(4,datas[0].GetNbinsX()+1):
# #print datas[0].GetBinContent(bin,0)
# datas[0].SetBinContent(bin,0)
for bin in range(1, datas[0].GetNbinsX()):
print 'Data in Bin', bin, ':', datas[0].GetBinContent(bin, 0)
# =======================================================
#if 'VV' in opts.bin:
if isVV:
signalList = ['VVHF', ' VVHF']
print 'Signal List:', signalList
#histos.append(copy(Overlay))
if 'ZH' in signalList and 'WH' in signalList:
#typs.append('WH')
#if 'ZH' in Stack.setup: Stack.setup.remove('ZH')
#if 'WH' in Stack.setup: Stack.setup.remove('WH')
#Stack.setup.insert(0,'WH')
#print 'Stack.setup:', Stack.setup
typs.append('WH')
typs.append('ZH')
#elif 'ZH' in signalList:
#Stack.setup.remove('WH')
#typs.append('ggZH')
# typs.append('ZH')
if 'VVb' in signalList or 'VVHF' in signalList:
typs.append('WH')
typs.append('ZH')
typs.append('VVHF')
if 'VVHF' in Stack.setup:
Stack.setup.remove('VVHF')
Stack.setup.insert(0, 'VVHF')
if 'ZH' in Stack.setup:
Stack.setup.remove('ZH')
Stack.setup.insert(-1, 'ZH')
if 'WH' in Stack.setup:
Stack.setup.remove('WH')
Stack.setup.insert(-1, 'WH')
if 'ggZH' in Stack.setup:
Stack.setup.remove('ggZH')
Stack.setup.insert(-1, 'ggZH')
Stack.nBins = nBins
Stack.xMin = xMin
Stack.xMax = xMax
print '\n-----> Stack.setup(double check)...', Stack
print 'Post Histos:', histos
print 'Datas:', datas
print 'typs:', typs
print 'setup:', Stack.setup
Stack.histos = histos
Stack.typs = typs
Stack.datas = datas
Stack.datatyps = datatyps
Stack.datanames = datanames
Stack.filename = region
#if opts.var is not 'BDT':
#Stack.prefit_overlay = [prefit_overlay]
#if '13TeV' in region:
Stack.overlay = Overlay
print '\n\n\t\t Overlay: ', Stack.overlay
# Add custom postFit errors
#Stack.AddErrors = final_prefit_error
Stack.AddErrors_Postfit = final_postfit_error
if dataname == 'Wtn':
lumi = 18300.
Stack.lumi = lumi
Stack.doPlot()
print 'i am done!\n'
