def plot(file, var, region):
signalRegion = False
stack = StackMaker(config, var, region, signalRegion)
histosL = []
overlayL = []
print file
datas = []
datasL = []
for th1 in get_th1(file):
print th1.GetBinLowEdge(0)
if 'VH' in th1.GetName():
overlayL.append(th1)
th1.SetLineWidth(1)
if 'data_obs' in th1.GetName():
datasL.append(th1)
else:
histosL.append(th1)
print 'histoL'
print histosL
typs = []
typsL = []
overlay_typs = []
#append the name just once
for histo in histosL:
typsL.append(histo.GetName())
print histo.GetName()
if 'VH' in histo.GetName():
overlay_typs.append(histo.GetName())
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.datanames = region
for key in overlay_histo_dict:
overlayL2.append(overlay_histo_dict[key])
appendix = ''
stack.options['pdfName'] = stack.options['pdfName'].replace(
'.pdf', '_unweighted.' + opts.format)
stack.lumi = 18940
stack.doPlot()
print 'i am done!\n'
def plot(file,var,region):
signalRegion = False
stack = StackMaker(config,var,region,signalRegion)
histosL = []
overlayL = []
print file
datas = []
datasL = []
for th1 in get_th1(file):
print th1.GetBinLowEdge(0)
if 'VH' in th1.GetName():
overlayL.append(th1)
th1.SetLineWidth(1)
if 'data_obs' in th1.GetName():
datasL.append(th1)
else:
histosL.append(th1)
print 'histoL'
print histosL
typs = []
typsL = []
overlay_typs=[]
#append the name just once
for histo in histosL:
typsL.append(histo.GetName())
print histo.GetName()
if 'VH' in histo.GetName():
overlay_typs.append(histo.GetName())
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.datanames=region
for key in overlay_histo_dict:
overlayL2.append(overlay_histo_dict[key])
appendix = ''
stack.options['pdfName'] = stack.options['pdfName'].replace('.pdf','_unweighted.'+opts.format)
stack.lumi = 18940
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()
l2.SetBorderSize(0)
l2.SetFillColor(0)
l2.SetFillStyle(4000)
l2.SetTextFont(62)
for channel,g in graphs.iteritems():
print channel
# l2.AddEntry(g,'ZH, Z#rightarrowl^{+}l^{-}',"pl")
l2.AddEntry(g,'ZH, Z#rightarrow#nu#nu',"pl")
# l2.AddEntry(g,channel,"pl")
#l2.AddEntry(g,"Stat.","l")
if(drawSys) : l2.AddEntry(g2,"Syst.","l")
l2.SetTextSize(0.035)
# l2.SetNColumns(3)
l2.Draw("same")
for channel,g in graphs.iteritems():
print channel
g.Draw("P same")
for label in labels:
StackMaker.myText("%.2f #pm %.2f" %(latex[label][1],latex[label][2]),conversion_x(xmin)-0.02,conversion_y(latex[label][3])-0.005,0.5)
if(drawSys) : g2.Draw("[] same")
StackMaker.myText("CMS Preliminary",conversion_x(xmin)+0.1,0.95,0.6)
StackMaker.myText("#sqrt{s} = 13TeV, L = 2.19 fb^{-1}",conversion_x(xmin)+0.1,0.92,0.6)
ROOT.gPad.SetLeftMargin(0.2)
ROOT.gPad.Update()
c.Print("histo.pdf")
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'
l2 = ROOT.TLegend(0.68, 0.80, 0.80, 0.85)
l2.SetLineWidth(2)
l2.SetBorderSize(0)
l2.SetFillColor(0)
l2.SetFillStyle(4000)
l2.SetTextFont(62)
for channel, g in graphs.iteritems():
print channel
l2.AddEntry(g, 'ZH, Z#rightarrowe^{+}e^{-}', "pl")
# l2.AddEntry(g,channel,"pl")
#l2.AddEntry(g,"Stat.","l")
if (drawSys): l2.AddEntry(g2, "Syst.", "l")
l2.SetTextSize(0.035)
# l2.SetNColumns(3)
l2.Draw("same")
for channel, g in graphs.iteritems():
print channel
g.Draw("P same")
for label in labels:
StackMaker.myText(
"%.2f #pm %.2f" % (latex[label][1], latex[label][2]),
conversion_x(xmin) - 0.02, conversion_y(latex[label][3]), 0.5)
if (drawSys): g2.Draw("[] same")
StackMaker.myText("CMS Preliminary", conversion_x(xmin) + 0.1, 0.95, 0.6)
StackMaker.myText("#sqrt{s} = 13TeV, L = 2.2 fb^{-1}",
conversion_x(xmin) + 0.1, 0.92, 0.6)
ROOT.gPad.SetLeftMargin(0.2)
ROOT.gPad.Update()
c.Print("histo.pdf")
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 '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'
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 'BDT' in var:
region = 'BDT'
else:
region = opts.bin
ws_var = config.get('plotDef:%s'%var,'relPath')
if region == 'BDT':
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'))
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 = 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))
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 = ['ZH','WH']
# 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
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]
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]
print '\nDATA HIST:', data0
print 'Data name:', dataname
if blind and 'BDT' in var:
for bin in range(10,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)
histos.append(copy(Overlay))
if 'ZH' in signalList and 'WH' in signalList:
typs.append('ZH')
if 'ZH' in Stack.setup: Stack.setup.remove('ZH')
if 'WH' in Stack.setup: Stack.setup.remove('WH')
Stack.setup.insert(0,'ZH')
elif 'ZH' in signalList:
typs.append('ZH')
elif 'WH' in signalList:
typs.append('WH')
elif 'VVb' in signalList:
typs.append('VVb')
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 region == 'BDT':
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 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] = []
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
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_low_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_low_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.)
print 'config:', config
print 'var: ', var
#if 'Wen' in region:
# region = 'WenHighPt'
#if 'Wmn' in region:
# region = 'WmnHighPt'
if 'Znn' in region:
region = 'Znn_13TeV_Signal'
print 'region: ', region
blind = eval(config.get('Plot:%s' % region, 'blind'))
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'
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)
]
region_name = 'SIG'
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'
if 'med' in opts.bin or 'Med' in opts.bin: pt_region_name = 'med'
# ['ZllHbb', 'ch1', 'Zmm', 'SIG', 'low']
if 'Zmm' in lep_channel and 'low' in pt_region_name: zll_index = 'ch1'
elif 'Zmm' in lep_channel and 'high' in pt_region_name:
zll_index = 'ch3'
elif 'Zee' in lep_channel and 'high' in pt_region_name:
zll_index = 'ch4'
elif 'Zee' in lep_channel and 'low' in pt_region_name:
zll_index = 'ch2'
else:
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 = opts.bin
elif 'Zee' in lep_channel and 'high' in pt_region_name and 'minCMVA' not in opts.var:
stat_name = opts.bin
elif 'Zmm' in lep_channel and 'low' in pt_region_name and 'minCMVA' not in opts.var:
stat_name = opts.bin
elif 'Zmm' in lep_channel and 'high' in pt_region_name and 'minCMVA' not in opts.var:
stat_name = opts.bin
else:
stat_name = opts.bin
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 'Wen' in opts.bin:
lep_channel = 'Wen'
if 'tt' in region: region_name = 'ttWen'
elif 'whf' in region:
if 'High' in opts.bin:
region_name = 'whfWenHigh'
else:
region_name = 'whfWenLow'
elif 'wlf' in region:
region_name = 'wlfWen'
else:
region_name = opts.bin
if 'Wmn' in opts.bin:
lep_channel = 'Wmn'
if 'tt' in region: region_name = 'ttWmn'
elif 'whf' in region:
if 'High' in opts.bin:
region_name = 'whfWmnHigh'
else:
region_name = 'whfWmnLow'
elif 'wlf' in region:
region_name = 'wlfWmn'
else:
region_name = opts.bin
pt_region_name = 'none'
nBins = 40
xMin = -1
xMax = 1
stat_name = 'BDT_' + opts.bin + '_'
if 'Znn' in opts.bin:
setup = [
'ZH', 'ggZH', 'WH', 'DY2b', 'DY1b', 'DYlight', 'Wj2b', 'Wj1b',
'Wj0b', 'TT', 'VVHF', 'VVLF', 'ST'
]
signalList = ['ZH']
channel = 'Znn'
lep_channel = 'Znn'
if 'nAddJet1' in opts.bin:
pt_region_name = 'nAddJet1'
elif 'nAddJet0' in opts.bin:
pt_region_name = 'nAddJet0'
else:
pt_region_name = 'High'
region_name = 'SR'
nBins = 25
xMin = -1
xMax = 1
if 'QCD' in opts.dc:
region_name = 'QCD'
stat_name = 'Znn_13TeV_QCD'
elif 'TT' in opts.dc:
region_name = 'TT'
stat_name = 'Znn_13TeV_TT'
elif 'Zbb' in opts.dc:
region_name = 'Zbb'
stat_name = 'Znn_13TeV_Zbb'
elif 'Zlight' in opts.dc:
region_name = 'Zlight'
stat_name = 'Znn_13TeV_Zlight'
else:
stat_name = opts.bin
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
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] == region_name and dirinfo[1] == 'postfit'):
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] == lep_channel and dirinfo[2] == pt_region_name and dirinfo[3] == 'postfit'):
if not (dirinfo[0] == lep_channel and dirinfo[2] == region_name
and dirinfo[3] == 'postfit'):
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] == lep_channel and dirinfo[1] == region_name and
dirinfo[2] == pt_region_name and dirinfo[3] == 'postfit'):
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()]
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)
histos.append(hist)
typs.append(s)
if s in signalList:
hist.SetTitle(s)
Overlay.append(hist)
#print 'the Histogram title is', hist.GetTitle()
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)
#else:
for bin in range(0, datas[0].GetNbinsX() + 1):
print 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
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'
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 doPlot():
vars = (config.get(section, 'vars')).split(',')
data = config.get(section, 'Datas')
mc = eval(config.get('Plot_general', 'samples'))
print ' with Vars:', vars
print ' Using MC samples: ', mc
print ' and Data samples: ', data
# Is set in plots. Can be the mass of ZH/ggZH.
SignalRegion = False
if config.has_option(section, 'Signal'):
mc.append(config.get(section, 'Signal'))
SignalRegion = True
#print '\n\n\t\t=====SignalRegion:', SignalRegion
datasamples = info.get_samples(data)
mcsamples = info.get_samples(mc)
GroupDict = eval(config.get('Plot_general', 'Group'))
#GETALL AT ONCE
options = []
Stacks = []
for i in range(len(vars)):
Stacks.append(StackMaker(config, vars[i], region, SignalRegion))
options.append(Stacks[i].options)
# Init the HistoMaker class
Plotter = HistoMaker(mcsamples + datasamples, path, config, options,
GroupDict)
#print '\nProducing Plot of %s\n'%vars[v]
Lhistos = [[] for _ in range(0, len(vars))]
Ltyps = [[] for _ in range(0, len(vars))]
Ldatas = [[] for _ in range(0, len(vars))]
Ldatatyps = [[] for _ in range(0, len(vars))]
Ldatanames = [[] for _ in range(0, len(vars))]
#Find out Lumi:
lumicounter = 0.
lumi = 0.
for job in datasamples:
lumi += float(job.lumi)
lumicounter += 1.
if lumicounter > 0:
lumi = lumi / lumicounter
print '\n\t\t Luminosity Auto Calculation: ', lumi
# temp hack
#lumi = 3000.
Plotter.lumi = lumi
mass = Stacks[0].mass
# Define the counters for statistics counting
mass_count = Counter()
bdt_count = Counter()
for job in mcsamples:
# hDictList returns list of TH1Fs of the specified vars to plot
if addBlindingCut:
hDictList = Plotter.get_histos_from_tree(
job,
config.get('Cuts', region) + ' & ' + addBlindingCut)
else:
hDictList = Plotter.get_histos_from_tree(job)
#print '\n\n\t\tJob name: ', job.name
if job.name == 'ZH125':
#or job.name == 'ggZH125':
Overlaylist = deepcopy(
[hDictList[v].values()[0] for v in range(0, len(vars))])
for v in range(0, len(vars)):
Lhistos[v].append(hDictList[v].values()[0])
Ltyps[v].append(hDictList[v].keys()[0])
hist = hDictList[v].values()[0]
# end variable loop
# end MC sample loop
# Loop over data
for job in datasamples:
# temp hack to turn off data
#break
#hTemp, typ = Plotter.get_histos_from_tree(job)
if addBlindingCut:
dDictList = Plotter.get_histos_from_tree(
job,
config.get('Cuts', region) + ' & ' + addBlindingCut)
else:
dDictList = Plotter.get_histos_from_tree(job)
for v in range(0, len(vars)):
Ldatas[v].append(dDictList[v].values()[0])
Ldatatyps[v].append(dDictList[v].keys()[0])
Ldatanames[v].append(job.name)
# produce the final plots and save
for v in range(0, len(vars)):
histos = Lhistos[v]
typs = Ltyps[v]
Stacks[v].histos = Lhistos[v]
Stacks[v].typs = Ltyps[v]
Stacks[v].datas = Ldatas[v]
Stacks[v].datatyps = Ldatatyps[v]
Stacks[v].datanames = Ldatanames[v]
if SignalRegion:
Stacks[v].overlay = Overlaylist[v]
Stacks[v].lumi = lumi
Stacks[v].doPlot()
Stacks[v].overlay = []
Stacks[v].histos = Lhistos[v]
Stacks[v].typs = Ltyps[v]
Stacks[v].datas = Ldatas[v]
Stacks[v].datatyps = Ldatatyps[v]
Stacks[v].datanames = Ldatanames[v]
Stacks[v].normalize = True
Stacks[v].options['pdfName'] = Stacks[v].options['pdfName'].replace(
'.pdf', '_norm.pdf')
#Stacks[v].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 log_s_over_b(fileList):
print '-----> Running def log_s_over_b()...'
#--------------
# log s over b
#--------------
histosL={}
s_b_d_histos = {}
for file in fileList:
print file
name = '%s' %file
histosL[name] = []
for th1 in get_th1(file):
#print '\n\t Retrieving TH1 from file: ', th1
#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
j = 0
for hist in histosL[name]:
if 'ZH' in hist.GetName() or 'ggZH' in 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:
#print '\n\t Setting ',hist, ' as background...'
hBkg.Add(hist)
i += 1
# temp hack
hData = hSignal
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")
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: ',s_b_d
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 '\n\t Calculating log_s/b for bin', bin
print '\t\t sig:',s,' bgk:',b
print '\t\t Log_s/b',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 = ['ZH','BKG']
stack.typs = ['ZH','BKG']
stack.lumi = 10000.
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 doPlot():
vars = (config.get(section, 'vars')).split(',')
data = config.get(section,'Datas')
mc = eval(config.get('Plot_general','samples'))
SignalRegion = False
if config.has_option(section,'Signal'):
mc.append(config.get(section,'Signal'))
SignalRegion = True
datasamples = info.get_samples(data)
mcsamples = info.get_samples(mc)
GroupDict = eval(config.get('Plot_general','Group'))
#GETALL AT ONCE
options = []
Stacks = []
for i in range(len(vars)):
Stacks.append(StackMaker(config,vars[i],region,SignalRegion))
options.append(Stacks[i].options)
print options
Plotter=HistoMaker(mcsamples+datasamples,path,config,options,GroupDict)
#print '\nProducing Plot of %s\n'%vars[v]
Lhistos = [[] for _ in range(0,len(vars))]
Ltyps = [[] for _ in range(0,len(vars))]
Ldatas = [[] for _ in range(0,len(vars))]
Ldatatyps = [[] for _ in range(0,len(vars))]
Ldatanames = [[] for _ in range(0,len(vars))]
#Find out Lumi:
lumicounter=0.
lumi=0.
for job in datasamples:
lumi+=float(job.lumi)
lumicounter+=1.
if lumicounter > 0:
lumi=lumi/lumicounter
Plotter.lumi=lumi
mass = Stacks[0].mass
for job in mcsamples:
#hTempList, typList = Plotter.get_histos_from_tree(job)
if addBlindingCut:
hDictList = Plotter.get_histos_from_tree(job,config.get('Cuts',region)+' & ' + addBlindingCut)
else:
hDictList = Plotter.get_histos_from_tree(job)
if job.name == mass:
print job.name
Overlaylist= deepcopy([hDictList[v].values()[0] for v in range(0,len(vars))])
for v in range(0,len(vars)):
Lhistos[v].append(hDictList[v].values()[0])
Ltyps[v].append(hDictList[v].keys()[0])
for job in datasamples:
#hTemp, typ = Plotter.get_histos_from_tree(job)
if addBlindingCut:
dDictList = Plotter.get_histos_from_tree(job,config.get('Cuts',region)+' & ' + addBlindingCut)
else:
dDictList = Plotter.get_histos_from_tree(job)
for v in range(0,len(vars)):
Ldatas[v].append(dDictList[v].values()[0])
Ldatatyps[v].append(dDictList[v].keys()[0])
Ldatanames[v].append(job.name)
for v in range(0,len(vars)):
histos = Lhistos[v]
typs = Ltyps[v]
Stacks[v].histos = Lhistos[v]
Stacks[v].typs = Ltyps[v]
Stacks[v].datas = Ldatas[v]
Stacks[v].datatyps = Ldatatyps[v]
Stacks[v].datanames= Ldatanames[v]
#if SignalRegion:
# Stacks[v].overlay = Overlaylist[v]
Stacks[v].lumi = lumi
Stacks[v].doPlot()
Stacks[v].histos = Lhistos[v]
Stacks[v].typs = Ltyps[v]
Stacks[v].datas = Ldatas[v]
Stacks[v].datatyps = Ldatatyps[v]
Stacks[v].datanames= Ldatanames[v]
Stacks[v].normalize = True
Stacks[v].options['pdfName'] = Stacks[v].options['pdfName'].replace('.pdf','_norm.pdf')
Stacks[v].doPlot()
print 'i am done!\n'
def plot(fileList):
print '-----> Running def plot()...'
signalRegion = True
region = 'signal_Zuu_low_Zpt'
var = 'HCSV_mass'
# for subtracted bkg mass plot
#mjj_sub = True
opts.fom = 's/s+b'
print '-----> Making Stacks...'
stack = StackMaker(config, var, region, signalRegion)
histosL = []
overlayL = []
#7-9 for the higgs
#5-6 for the VV
binmin = 7
binmax = 9
#binmin=5
#binmax=6
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: ', max_ssb
print '-----> max_sb : ', max_sb
for file in fileList:
#if eval(opts.rescale) == False:
# max_sb = 1.
# max_ssb = 1.
for th1 in get_th1_allRegions(file):
print 'Scaling factor for ', th1, ':', get_s_over_sb(
file, binmin, binmax)
if 's/b' in opts.fom:
th1.Scale(get_s_over_b(th1, binmin, binmax) / max_sb)
if 's/s+b' in opts.fom:
print '--->Scaling by s/s+b...'
#th1.Scale(get_s_over_sb(th1,binmin,binmax)/max_ssb)
th1.Scale(get_s_over_sb(file, binmin, binmax))
if 'Zj1b' in th1.GetName():
th1.SetName('DY1b')
if 'Zj2b' in th1.GetName():
th1.SetName('DY2b')
if 'Zj0b' in th1.GetName():
th1.SetName('DYlight')
if 's_Top' in th1.GetName():
th1.SetName('ST')
if 'Wj1b' in th1.GetName():
th1.SetName('Wj1b')
#if mjj_sub:
# if 'VVHF' in th1.GetName():
# th1.SetName('VV')
# if 'VH' in th1.GetName():
# th1.SetName('VH')
# new stack for the overlay plot
if 'VH' in th1.GetName() or 'VVHF' in th1.GetName(
) or 'ZH' 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(
) or 'ZH' in th1.GetName():
overlay_typs.append(histo.GetName())
if 'TT' in histo.GetName(): datasL.append(histo) # temp hack
#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:
print '\n\t----> Making Nominal Mass plot...'
stack.doPlot()
elif mjj_sub == True:
print '\n\t----> Making Subtracted Mass plot...'
stack.options['pdfName'] = stack.options['pdfName'].replace(
'.' + opts.format, '_subtracted.' + opts.format)
#stack.doSubPlot(['VH','VV'])
stack.doSubPlot(['VVHF', 'ZH', 'ggZH'])
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 plot(fileList):
signalRegion = True
region = 'plot'
var = 'Hmass'
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 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 doPlot():
#print "Read Ploting Region information"
#print "===============================\n"
vars = (config.get(section, 'vars')).split(
',') #get the variables to be ploted in each region
#print "The variables are", vars, "\n"
data = eval(config.get(
section, 'Datas')) # read the data corresponding to each CR (section)
mc = eval(config.get('Plot_general',
'samples')) # read the list of mc samples
total_lumi = eval(config.get('Plot_general', 'lumi'))
#print 'total lumi is', total_lumi
print "The list of mc samples is", mc
#print "Check if is Signal Region"
#print "=========================\n"
SignalRegion = False
if config.has_option(section, 'Signal'):
mc.append(config.get(section, 'Signal'))
SignalRegion = True
#print "After addind the signal, the mc is", mc
#print "Getting information on data and mc samples"
#print "==========================================\n"
#print "Getting data sample"
datasamples = info.get_samples(data)
#print "datasamples is\n", datasamples
#print "Getting mc sample"
mcsamples = info.get_samples(mc)
#print "mc sample is\n"
#for sample in mcsamples:
#print "sample name", sample.name
GroupDict = eval(
config.get('Plot_general', 'Group')
) #Contained in plots. Listed in general, under Group [Samples] group. This is a dictionnary that descriebes what samples should share the same color under the stack plot.
#GETALL AT ONCE
options = []
Stacks = []
#print "Start Loop over the list of variables(to fill the StackMaker )" print "==============================================================\n"
for i in range(
len(vars)
): # loop over the list of variables to be ploted in each reagion
#print "The variable is ", vars[i], "\n"
Stacks.append(
StackMaker(config, vars[i], region, SignalRegion)
) # defined in myutils DoubleStackMaker. The StackMaker merge together all the informations necessary to perform the plot (plot region, variables, samples and signal region ). "options" contains the variables information, including the cuts.
options.append(Stacks[i].options)
#print "Finished Loop over the list of variables(to fill the StackMaker )"
#print "================================================================\n"
#print 'loop options',options
# print 'options',options
#Prepare cached files in the temporary (tmpSamples) folder.
Plotter = HistoMaker(mcsamples + datasamples, path, config, options,
GroupDict, filelist, opts.mergeplot)
if len(filelist) > 0 or opts.mergeplot:
print('ONLY CACHING PERFORMED, EXITING')
sys.exit(1)
#print '\nProducing Plot of %s\n'%vars[v]
Lhistos = [[] for _ in range(0, len(vars))]
Ltyps = [[] for _ in range(0, len(vars))]
Ldatas = [[] for _ in range(0, len(vars))]
Ldatatyps = [[] for _ in range(0, len(vars))]
Ldatanames = [[] for _ in range(0, len(vars))]
Ljobnames = [[] for _ in range(0, len(vars))]
#print "Summing up the Luminosity"
#print "=========================\n"
#! Sums up the luminosity of the data:
lumicounter = 0.
lumi = 0.
if datasamples == []:
lumi = total_lumi
else:
print "Will run over datasamples to sum up the lumi"
for job in datasamples:
print "Datasample is", job
lumi += float(job.lumi)
lumicounter += 1.
if lumicounter > 0:
lumi = lumi / lumicounter
print "The lumi is", lumi, "\n"
Plotter.lumi = lumi
mass = Stacks[0].mass
#print 'mcsamples',mcsamples
inputs = []
for job in mcsamples:
# print 'job.name'
# cutOverWrite = None
# if addBlindingCut:
# cutOverWrite = config.get('Cuts',region)+' & ' + addBlindingCut
inputs.append((Plotter, "get_histos_from_tree", (job, True)))
#print 'inputs are', inputs
# if('pisa' in config.get('Configuration','whereToLaunch')):
multiprocess = int(config.get('Configuration', 'nprocesses'))
# multiprocess=0
outputs = []
if multiprocess > 1:
from multiprocessing import Pool
from myutils import GlobalFunction
p = Pool(multiprocess)
#print 'launching get_histos_from_tree with ',multiprocess,' processes'
outputs = p.map(GlobalFunction, inputs)
else:
#print 'launching get_histos_from_tree with ',multiprocess,' processes'
for input_ in inputs:
outputs.append(getattr(
input_[0],
input_[1])(*input_[2])) #ie. Plotter.get_histos_from_tree(job)
#print 'get_histos_from_tree DONE'
Overlaylist = []
for i, job in enumerate(mcsamples):
#print 'job.name',job.name,"mass==",mass
#hTempList, typList = Plotter.get_histos_from_tree(job)
hDictList = outputs[i]
if job.name in mass:
#print 'job.name == mass'
histoList = []
for v in range(0, len(vars)):
histoCopy = deepcopy(hDictList[v].values()[0])
histoCopy.SetTitle(job.name)
histoList.append(histoCopy)
Overlaylist.append(histoList)
# Overlaylist.append(deepcopy([hDictList[v].values()[0] for v in range(0,len(vars))]))
# >>>>>>> silviodonato/master
for v in range(0, len(vars)):
Lhistos[v].append(hDictList[v].values()[0])
Ltyps[v].append(hDictList[v].keys()[0])
Ljobnames[v].append(job.name)
#print "len(vars)=",len(vars)
#print "Ltyps is", Ltyps
##invert Overlaylist[variable][job] -> Overlaylist[job][variable]
#print "len(Overlaylist) before: ",len(Overlaylist)
#print "Overlaylist",Overlaylist
# newOverlaylist = [[None]*len(Overlaylist)]*len(vars)
# for i,OverlaySameSample in enumerate(Overlaylist):
# for j,Overlay in enumerate(OverlaySameSample):
# newOverlaylist[j][i] = Overlay
# Overlaylist = newOverlaylist
Overlaylist = [list(a) for a in zip(*Overlaylist)]
#print "len(Overlaylist) after: ",len(Overlaylist)
#print "Overlaylist",Overlaylist
##merge overlays in groups
for i in range(len(Overlaylist)):
newhistos = {}
#print "len(Overlaylist[i]):",Overlaylist[i]
for histo in Overlaylist[i]:
#print "histo.GetName()",histo.GetName(),
#print "histo.GetTitle()",histo.GetTitle(),
group = GroupDict[histo.GetTitle()]
if not group in newhistos.keys():
histo.SetTitle(group)
newhistos[group] = histo
else:
#print "Before newhistos[group].Integral()",newhistos[group].Integral(),
newhistos[group].Add(histo)
#print "After newhistos[group].Integral()",newhistos[group].Integral()
Overlaylist[i] = newhistos.values()
# ### ORIGINAL ###
# print 'mcsamples',mcsamples
# for job in mcsamples:
# print 'job.name',job.name
# #hTempList, typList = Plotter.get_histos_from_tree(job)
# if addBlindingCut:
# hDictList = Plotter.get_histos_from_tree(job,config.get('Cuts',region)+' & ' + addBlindingCut)
# else:
# print 'going to get_histos_from_tree'
# hDictList = Plotter.get_histos_from_tree(job)
# if job.name == mass:
# print 'job.name', job.name
# Overlaylist= deepcopy([hDictList[v].values()[0] for v in range(0,len(vars))])
# for v in range(0,len(vars)):
# Lhistos[v].append(hDictList[v].values()[0])
# Ltyps[v].append(hDictList[v].keys()[0])
# Ljobnames[v].append(job.name)
#print "DATA samples\n"
#! Get the data histograms
for job in datasamples:
if addBlindingCut:
dDictList = Plotter.get_histos_from_tree(
job,
config.get('Cuts', region) + ' & ' + addBlindingCut)
else:
dDictList = Plotter.get_histos_from_tree(job)
#! add the variables list for each job (Samples)
for v in range(0, len(vars)):
Ldatas[v].append(dDictList[v].values()[0])
Ldatatyps[v].append(dDictList[v].keys()[0])
Ldatanames[v].append(job.name)
for v in range(0, len(vars)):
#print "Ltyps[v]:",Ltyps[v]
#print "Lhistos[v]:",Lhistos[v]
#print "Ldatas[v]:",Ldatas[v]
#print "Ldatatyps[v]:",Ldatatyps[v]
#print "Ldatanames[v]:",Ldatanames[v]
#print "lumi:",lumi
histos = Lhistos[v]
typs = Ltyps[v]
Stacks[v].histos = Lhistos[v]
Stacks[v].typs = Ltyps[v]
Stacks[v].datas = Ldatas[v]
Stacks[v].datatyps = Ldatatyps[v]
Stacks[v].datanames = Ldatanames[v]
if SignalRegion:
Stacks[v].overlay = Overlaylist[v] ## from
Stacks[v].lumi = lumi
Stacks[v].jobnames = Ljobnames[v]
Stacks[v].normalize = eval(config.get(section, 'Normalize'))
Stacks[v].doPlot()
##FIXME##
# Stacks[v].histos = Lhistos[v]
# Stacks[v].typs = Ltyps[v]
# Stacks[v].datas = Ldatas[v]
# Stacks[v].datatyps = Ldatatyps[v]
# Stacks[v].datanames= Ldatanames[v]
# Stacks[v].normalize = True
# Stacks[v].options['pdfName'] = Stacks[v].options['pdfName'].replace('.pdf','_norm.pdf')
# Stacks[v].doPlot()
print 'i am done!\n'
print 'DOPLOT END'
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 '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, 0, 1.)
blind = eval(config.get('Plot:%s' % region, 'blind'))
#blind = True
# If Zvv change region to relevant CR
if 'Znn' in region:
if 'QCD' in opts.dc:
region = 'Zvv_QCD'
if 'TT' in opts.dc:
region = 'Zvv_TT'
if 'Zbb' in opts.dc:
region = 'Zvv_Zbb'
if 'Zlight' in opts.dc:
region = 'Zvv_Zlight'
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 'minCMVA' in opts.var 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 = ''
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'
pt_region_dic = {
'lowpt': 'low',
'highpt': 'high',
'LowPt': 'low',
'HighPt': 'high'
}
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]
if 'low' in opts.bin: pt_region_name = 'low'
if 'high' in opts.bin: pt_region_name = 'high'
print 'region_name is', region_name
print 'pt region_name is', pt_region_name
if 'Wmn' in opts.bin or 'Wen' in opts.bin:
setup = [
'WH', 'ZH', 'DY2b', 'DY1b', 'DYlight', 'TT', 'VVHF', 'VVLF', 'ST',
'Wj0b', 'Wj1b', 'Wj2b'
]
signalList = ['ZH', 'WH']
if 'Znn' in opts.bin:
setup = [
'ZH', 'ggZH', 'DY2b', 'DY1b', 'DYlight', 'TT', 'VVHF', 'ST', 'WH',
'Wj0b', 'Wj1b', 'Wj2b'
]
signalList = ['ZH']
lep_channel = 'Znn'
pt_region_name = 'HighPt'
if opts.var == 'BDT':
region_name = ''
region_type = 'SR'
print 'region_name is', region_name
print 'region_type is', region_type
print 'pt region_name is', pt_region_name
print 'Lepton channel:', lep_channel
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(Stack.nBins, Stack.xMin, Stack.xMax)
if 'Wmn' in opts.bin or 'Wen' in opts.bin or 'Znn' in opts.bin: # SET: WLV MINCSV BINNING
if 'CSV' in opts.var:
Stack.nBins = 15
print '\n\t Changing Wlv/Zvv CSV bins to ', 15
theBinning = ROOT.RooFit.Binning(15, Stack.xMin, Stack.xMax)
print '/n----> The Binning:'
print 'nBins:', Stack.nBins
print 'xMin:', Stack.xMin
print 'xMax:', Stack.xMax
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
if not ROOT.gDirectory.cd('shapes_fit_s'):
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 opts.bin and 'BDT' in opts.var:
print 'lepton channel, pt_region_name, region_type:', dirinfo[
0], dirinfo[1], dirinfo[2]
if not (dirinfo[0] == lep_channel and dirinfo[1] == pt_region_name
and dirinfo[2] == region_type):
continue
elif len(dirinfo) == 5:
print 'channel, lepton channel, region_name, pt_region_name:', dirinfo[
0], dirinfo[1], dirinfo[2], dirinfo[3], dirinfo[4]
if not (dirinfo[0] == channel and dirinfo[2] == lep_channel
and dirinfo[3] == region_name
and dirinfo[4] == pt_region_name):
continue
elif len(dirinfo) == 4:
print 'channel, lepton channel, region_name, pt_region_name:', dirinfo[
0], dirinfo[1], dirinfo[2], dirinfo[3]
if not (dirinfo[1] == lep_channel and dirinfo[2] == region_name
and dirinfo[3] == pt_region_name):
continue
print 'Directory:', dir.GetName()
dirname = dir.GetName()
ROOT.gDirectory.cd(dirname)
for s in setup:
found = False
for subdir in ROOT.gDirectory.GetListOfKeys():
print 'subdir name is', subdir.GetName()
if subdir.GetName() == Dict[s]:
found = True
# Set Histos postFit shapes and preFit errors
hist = rebinHist(
ROOT.gDirectory.Get(subdir.GetName()).Clone(),
Stack.nBins, Stack.xMin, Stack.xMax)
histos.append(hist)
#error_histos.append(PostFit_Errors)
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()
if not found:
print '@ERROR: didn\'t find the postfit histogram. Aborting'
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
# =================================================
##### Read data
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]
datatyps = [None]
datanames = [dataname]
print '\nDATA HIST:', data0
print 'Data name:', dataname
if opts.var == 'BDT':
for bin in range(11, datas[0].GetNbinsX() + 1):
datas[0].SetBinContent(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')
print '\n-----> Stack.setup(double check)...', Stack
print 'Post Histos:', histos
print 'Datas:', datas
print 'typs:', typs
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 = theErrorGraph
if dataname == 'Wtn':
lumi = 18300.
Stack.lumi = lumi
Stack.doPlot()
print 'i am done!\n'
