def harvestEm(self,channel='wenu',charge='both'):
cmb = ch.CombineHarvester()
# Read all the cards.
# CH stores metadata about each object (Observation, Process, Systematic),
# this is extracted from the card names with some regex
for card in glob.glob(self.bindir+('/%s_mass*.txt' % channel)):
cmb.QuickParseDatacard(card, """%s_mass(?<MASS>\d+)_$CHANNEL.card.txt""" % channel)
# Need a unqiue bin name for each plus/minus,pt and eta combination
# We extracted this part of the datacard name into the channel variable above,
# so can just copy it and override the specific bin name that was in all the cards
cmb.ForEachObj(lambda obj: obj.set_bin(obj.channel()))
# We'll have three copies of the observation, one for each mass point.
# Filter all but one copy.
cmb.FilterObs(lambda obj: obj.mass() != '%d' % self.mwcentral)
# Create workspace to hold the morphing pdfs and the mass
w = ROOT.RooWorkspace('morph', 'morph')
mass = w.factory('mw[{mwrange}]'.format(mwrange=self.mwrange))
# BuildRooMorphing will dump a load of debug plots here
debug = ROOT.TFile(self.bindir+'/debug.root', 'RECREATE')
# Run for each bin,process combination (only for signal!)
for b in cmb.bin_set():
for p in cmb.cp().bin([b]).signals().process_set():
morphing.BuildRooMorphing(w, cmb, b, p, mass, verbose=True, file=debug)
# Just to be safe
mass.setConstant(True)
# Now the workspace is copied into the CH instance and the pdfs attached to the processes
# (this relies on us knowing that BuildRooMorphing will name the pdfs in a particular way)
cmb.AddWorkspace(w, True)
cmb.cp().process(['W']).ExtractPdfs(cmb, 'morph', '$BIN_$PROCESS_morph', '')
# Adjust the rateParams a bit - we currently have three for each bin (one for each mass),
# but we only want one. Easiest to drop the existing ones completely and create new ones
cmb.syst_type(['rateParam'], False)
cmb.cp().process(['W']).AddSyst(cmb, 'norm_$BIN', 'rateParam', ch.SystMap()(1.00))
# Have to set the range by hand
for sys in cmb.cp().syst_type(['rateParam']).syst_name_set():
cmb.GetParameter(sys).set_range(0.5, 1.5)
# Print the contents of the model
cmb.PrintAll()
# Write out the cards, one per bin
outdir=self.bindir+'/wenu_cards_morphed_{charge}'.format(charge=charge)
writer = ch.CardWriter('$TAG/$BIN.txt', '$TAG/shapes.root')
writer.SetVerbosity(1)
writer.WriteCards(outdir, cmb)
def __init__(self, input_filename):
if not os.path.exists(input_filename):
logger.fatal("File %s does not exist.", input_filename)
raise Exception
self._input_filename = input_filename
self._shapes = self._get_shapes()
logger.info("Found %d shapes in input file %s.",
len(self._shapes), self._input_filename)
self._cb = ch.CombineHarvester()
self._shapes_extracted = False
def __init__(self, cb=None): super(Datacards, self).__init__() self.cb = cb if self.cb is None: self.cb = ch.CombineHarvester() if log.isEnabledFor(logging.DEBUG): self.cb.SetVerbosity(1) self.configs = datacardconfigs.DatacardConfigs() self.stable_options = r"--robustFit 1 --preFitValue 1.0 --cminDefaultMinimizerType Minuit2 --cminDefaultMinimizerAlgo Minuit2 --cminDefaultMinimizerStrategy 0 --cminFallbackAlgo Minuit2,0:1.0"
def main():
options = options_()
for cutkey in options.cut:
print 'cutkey : ', cutkey
### get M_A and M_H ###
mH = float(options.mH_list[cutkey])
mA = float(options.mA_list[cutkey])
print mH, mA
"""Main function"""
# start the timer
tstart = datetime.now()
print 'starting...'
# get the options
#options = get_options()
intL = options.lumi # in pb-1
#tag = 'v1.2.0+7415-19-g7bbca78_ZAAnalysis_1a69757'
#path = '/nfs/scratch/fynu/amertens/cmssw/CMSSW_7_4_15/src/cp3_llbb/CommonTools/histFactory/16_01_28_syst/build'
tag = 'v1.1.0+7415-83-g2a9f912_ZAAnalysis_2ff9261'
#tag = 'v1.1.0+7415-57-g4bff5ea_ZAAnalysis_b1377a8'
path = options.path
CHANNEL = options.CHANNEL
ERA = options.ERA
MASS = str(mH) + "_" + str(mA)
ANALYSIS = options.ANALYSIS
DEBUG = 0
c = ch.CombineHarvester()
cats = [(0, "mmbbSR" + cutkey), (1, "mll_mmbbBR" + cutkey),
(2, "eebbSR" + cutkey), (3, "mll_eebbBR" + cutkey)]
bins = {}
bins['signalregion_mm'] = "mmbbSR" + cutkey
bins['mll_bkgregion_mm'] = "mll_mmbbBR" + cutkey
bins['signalregion_ee'] = "eebbSR" + cutkey
bins['mll_bkgregion_ee'] = "mll_eebbBR" + cutkey
processes = {}
p = Process('data_obs')
#DoubleMuon_Run2015D_v1.1.0+7415-57-g4bff5ea_ZAAnalysis_b1377a8_histos.root
p.prepare_process(path, 'data_obs', 'DoubleMuon_DoubleEG_Run2015D',
tag)
processes['data_obs'] = p
if DEBUG: print p
# define signal
# define backgrounds
# zz
p = Process('zz')
p.prepare_process(
path, 'zz',
'ZZTo2L2Q_13TeV_amcatnloFXFX_madspin_pythia8_MiniAODv2', tag)
processes['zz'] = p
if DEBUG: print p
# ttbar
p = Process('ttbar')
p.prepare_process(path, 'ttbar', 'TTTo2L2Nu_13TeV-powheg_MiniAODv2',
tag)
processes['ttbar'] = p
p = Process('ttbar')
if DEBUG: print p
'''
# drell-yan
p = Process('dy1')
p.prepare_process(path, 'dy1', 'DYJetsToLL_M-10to50_TuneCUETP8M1_13TeV-amcatnloFXFX_MiniAODv2', tag)
processes['dy1'] = p
if DEBUG: print p
'''
p = Process('dy2')
p.prepare_process(
path, 'dy2',
'DYJetsToLL_M-50_TuneCUETP8M1_13TeV-amcatnloFXFX_MiniAODv2', tag)
processes['dy2'] = p
if DEBUG: print p
c.AddObservations([MASS], [ANALYSIS], [ERA], [CHANNEL], cats)
c.AddProcesses([MASS], [ANALYSIS], [ERA], [CHANNEL], ['ZA'], cats,
True)
c.AddProcesses([MASS], [ANALYSIS], [ERA], [CHANNEL],
['ttbar', 'dy2', 'zz'], cats, False)
c.cp().process(['ttbar', 'dy2', 'ZA']).AddSyst(
c, "lumi", "lnN",
ch.SystMap('channel', 'era', 'bin_id')([CHANNEL], [ERA],
[0, 1, 2, 3], 1.046))
c.cp().process(['ttbar', 'dy2', 'ZA']).AddSyst(
c, "trig", "lnN",
ch.SystMap('channel', 'era', 'bin_id')([CHANNEL], [ERA],
[0, 1, 2, 3], 1.04))
c.cp().process(['ttbar', 'dy2']).AddSyst(c, "btag", "shape",
ch.SystMap()(1.0))
c.cp().process(['ttbar', 'dy2']).AddSyst(c, "jec", "shape",
ch.SystMap()(1.0))
c.cp().process(['ttbar', 'dy2']).AddSyst(c, "jer", "shape",
ch.SystMap()(1.0))
c.cp().process(['ttbar', 'dy2']).AddSyst(c, "pu", "shape",
ch.SystMap()(1.0))
c.cp().process(['ttbar']).AddSyst(c, "TTpdf", "shape",
ch.SystMap()(1.0))
c.cp().process(['dy2']).AddSyst(c, "DYpdf", "shape", ch.SystMap()(1.0))
c.cp().process(['dy2']).AddSyst(
c, "DYnorm", "lnN",
ch.SystMap('channel', 'era', 'bin_id')([CHANNEL], [ERA], [0, 1],
1.1))
c.cp().process(['ttbar']).AddSyst(
c, "TTnorm", "lnN",
ch.SystMap('channel', 'era', 'bin_id')([CHANNEL], [ERA], [0], 1.1))
nChannels = len(bins)
nBackgrounds = len(
[processes[x] for x in processes if processes[x].type > 0])
nNuisances = 1
systematics = {
'': '',
'_btagUp': '__btagup',
'_btagDown': '__btagdown',
'_jecUp': '__jecup',
'_jecDown': '__jecdown',
'_jerUp': '__jerup',
'_jerDown': '__jerdown',
'_puUp': '__puup',
'_puDown': '__pudown',
'_TTpdfUp': '__pdfup',
'_TTpdfDown': '__pdfdown',
'_DYpdfUp': '__pdfup',
'_DYpdfDown': '__pdfdown'
}
outputRoot = "shapes.root"
f = TFile(outputRoot, "recreate")
f.Close()
for b in bins:
print b, bins[b]
for p in processes:
if p == 'data_obs':
file_in = TFile(processes[p].file, "READ")
print " Getting ", bins[b], " in file ", processes[p].file
h = file_in.Get(bins[b])
h.SetDirectory(0)
file_in.Close()
f = TFile(outputRoot, "update")
h.SetName("hist_" + bins[b] + "_" + p)
h.Write()
f.Write()
f.Close()
else:
for s1, s2 in systematics.iteritems():
file_in = TFile(processes[p].file, "READ")
print " Getting ", bins[
b] + s2, " in file ", processes[p].file
h = file_in.Get(bins[b] + s2)
h.SetDirectory(0)
file_in.Close()
f = TFile(outputRoot, "update")
h.SetName("hist_" + bins[b] + "_" + p + s1)
h.Sumw2()
#h.Scale(processes[p].xsection * intL / processes[p].sumW)
h.Scale(intL)
h.Write()
f.Write()
f.Close()
# Fill signal histograms FIXME: read efficiencies from eff.root
eff_file = TFile("eff.root", "READ")
effee_hist = eff_file.Get("effee")
eff_ee = effee_hist.Interpolate(mA, mH)
effmm_hist = eff_file.Get("effmm")
eff_mm = effmm_hist.Interpolate(mA, mH)
print "lumi : ", options.lumifb
print "eff at ", mA, mH, ":", eff_ee, eff_mm
print "ZA yields: ", options.lumifb * eff_mm, options.lumifb * eff_ee
f = TFile(outputRoot, "update")
h1 = TH1F("hist_" + bins['signalregion_mm'] + "_ZA",
"hist_" + bins['signalregion_mm'] + "_ZA", 1, 0, 1)
h1.Fill(0.5, options.lumifb * eff_mm)
h1.Write()
h2 = TH1F("hist_" + bins['mll_bkgregion_mm'] + "_ZA",
"hist_" + bins['mll_bkgregion_mm'] + "_ZA", 60, 60, 120)
h2.Write()
h3 = TH1F("hist_" + bins['signalregion_ee'] + "_ZA",
"hist_" + bins['signalregion_ee'] + "_ZA", 1, 0, 1)
h3.Fill(0.5, options.lumifb * eff_ee)
h3.Write()
h4 = TH1F("hist_" + bins['mll_bkgregion_ee'] + "_ZA",
"hist_" + bins['mll_bkgregion_ee'] + "_ZA", 60, 60, 120)
h4.Write()
f.Write()
f.Close()
c.cp().backgrounds().ExtractShapes(outputRoot, "hist_$BIN_$PROCESS",
"hist_$BIN_$PROCESS_$SYSTEMATIC")
c.cp().signals().ExtractShapes(outputRoot, "hist_$BIN_$PROCESS",
"hist_$BIN_$PROCESS_$SYSTEMATIC")
writer = ch.CardWriter(
'$TAG/$MASS/$ANALYSIS_$CHANNEL_$ERA.dat',
'$TAG/common/$ANALYSIS_$CHANNEL_$MASS.input_$ERA.root')
writer.WriteCards('CARDS/', c)
ROOT.gSystem.Load('libHiggsAnalysisCombinedLimit')
### WORKSPACE PATH [CHANGE BOTH WORKSPACE AND MLFIT ROOT FILE AT THE SAME TIME]
### ======= CONSTRAINED =========
# fin = ROOT.TFile('output/CONSTRAINED-svfit/cmb/wsp.root')
# fin = ROOT.TFile('output/CONSTRAINED-mvis/cmb/wsp.root')
### ======= UNCONSTRAINED =========
# fin = ROOT.TFile('output/LIMITS-svfit/cmb/wsp.root')
fin = ROOT.TFile('workspace.root')
wsp = fin.Get('w')
cmb = ch.CombineHarvester()
cmb.SetFlag("workspaces-use-clone", True)
ch.ParseCombineWorkspace(cmb, wsp, 'ModelConfig', 'data_obs', False)
### MLFIT ROOT FILE PATH [CHANGE BOTH WORKSPACE AND MLFIT ROOT FILE AT THE SAME TIME]
### ======= CONSTRAINED =========
# mlf = ROOT.TFile('output/CONSTRAINED-svfit/cmb/mlfit.Test.root')
# mlf = ROOT.TFile('output/CONSTRAINED-mvis/cmb/mlfit.Test.root')
### ======= UNCONSTRAINED =========
# mlf = ROOT.TFile('output/LIMITS-svfit/cmb/mlfit.Test.root')
mlf = ROOT.TFile('mlfit.root')
def prepareShapes(backgrounds, signals, discriminant, discriminantName):
# Backgrounds is a list of string of the considered backgrounds corresponding to entries in processes_mapping
# Signals is a list of string of the considered signals corresponding to entries in processes_mapping
# discriminant is the corresponding entry in the dictionary discriminants
import CombineHarvester.CombineTools.ch as ch
root_path = options.root_path
file, systematics = prepareFile(processes_mapping, discriminants,
root_path, discriminantName)
if options.dataYear != '2016':
call([
'python', 'symmetrize.py', options.output, file, options.dataYear
],
shell=False)
for signal in signals:
cb = ch.CombineHarvester()
cb.AddObservations(['*'], [''], ['_%s' % options.dataYear], [''],
discriminant)
cb.AddProcesses(['*'], [''], ['_%s' % options.dataYear], [''],
[signal], discriminant, True)
#cb.AddProcesses(['*'], [''], ['_%s'%options.dataYear], [''], backgrounds, discriminant, False)
if options.dataYear == '2016':
cb.AddProcesses(['*'], [''], ['_%s' % options.dataYear], [''],
backgrounds, discriminant, False)
else:
if not 'b2j3' in discriminantName:
try:
backgrounds.remove('qcd')
except:
pass
else:
if not 'qcd' in backgrounds: backgrounds.append('qcd')
if 'all' in discriminantName:
if signal == 'Hut':
discriminant.remove((1, 'DNN_Hut_b2j3'))
cb.AddProcesses(['*'], [''], ['_%s' % options.dataYear],
[''], backgrounds + ['qcd'],
[(1, 'DNN_Hut_b2j3')], False)
else:
discriminant.remove((1, 'DNN_Hct_b2j3'))
cb.AddProcesses(['*'], [''], ['_%s' % options.dataYear],
[''], backgrounds + ['qcd'],
[(1, 'DNN_Hct_b2j3')], False)
cb.AddProcesses(['*'], [''], ['_%s' % options.dataYear], [''],
backgrounds, discriminant, False)
if signal == 'Hut': discriminant.append((1, 'DNN_Hut_b2j3'))
else: discriminant.append((1, 'DNN_Hct_b2j3'))
else:
cb.AddProcesses(['*'], [''], ['_%s' % options.dataYear], [''],
backgrounds, discriminant, False)
# Systematics
if not options.nosys:
for systematic in systematics:
systematic_only_for_SMtt = False
systematic_only_for_Sig = False
for systSMtt in options.sysForSMtt:
if CMSNamingConvention(systSMtt) == systematic:
systematic_only_for_SMtt = True
for systSig in options.sysForSig:
if CMSNamingConvention(systSig) == systematic:
systematic_only_for_Sig = True
if not systematic_only_for_SMtt and not systematic_only_for_Sig:
cb.cp().AddSyst(cb, systematic, 'shape',
ch.SystMap()(1.00))
elif systematic_only_for_SMtt and not systematic_only_for_Sig:
cb.cp().AddSyst(cb, systematic, 'shape',
ch.SystMap('process')(smTTlist, 1.00))
#if 'hdamp' in systematic:
# for i in xrange(len(discriminant)):
# if 'b2j3' in discriminant[i][1]:
# cb.cp().AddSyst(cb, systematic, 'shape', ch.SystMap('bin', 'process')([discriminant[i][1]], ['ttlf'], 1.00))
# cb.cp().AddSyst(cb, systematic, 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['ttbb','ttcc'], 1.05))
# elif 'b2j4' in discriminant[i][1]:
# cb.cp().AddSyst(cb, systematic, 'shape', ch.SystMap('bin', 'process')([discriminant[i][1]], smTTlist, 1.00))
# elif 'b3j3' in discriminant[i][1]:
# cb.cp().AddSyst(cb, systematic, 'shape', ch.SystMap('bin', 'process')([discriminant[i][1]], ['ttlf'], 1.00))
# cb.cp().AddSyst(cb, systematic, 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['ttbb','ttcc'], 1.05))
# elif 'b3j4' in discriminant[i][1]:
# cb.cp().AddSyst(cb, systematic, 'shape', ch.SystMap('bin', 'process')([discriminant[i][1]], smTTlist, 1.00))
# elif 'b4j4' in discriminant[i][1]:
# cb.cp().AddSyst(cb, systematic, 'shape', ch.SystMap('bin', 'process')([discriminant[i][1]], ['ttbb'], 1.00))
# cb.cp().AddSyst(cb, systematic, 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['ttcc','ttlf'], 1.05))
#else: cb.cp().AddSyst(cb, systematic, 'shape', ch.SystMap('process')(smTTlist, 1.00))
elif not systematic_only_for_SMtt and systematic_only_for_Sig:
cb.cp().AddSyst(cb, systematic, 'shape',
ch.SystMap('process')([signal], 1.00))
else:
cb.cp().AddSyst(
cb, systematic, 'shape',
ch.SystMap('process')(smTTlist + [signal], 1.00))
#Lumi corr. https://twiki.cern.ch/twiki/bin/view/CMS/TWikiLUM#LumiComb
#cb.cp().AddSyst(cb, 'CMS_lumi', 'lnN', ch.SystMap()(options.luminosityError))
if options.dataYear == '2016':
cb.cp().AddSyst(cb, 'CMS_lumi_uncorr_2016', 'lnN',
ch.SystMap()(1.01))
cb.cp().AddSyst(cb, 'CMS_lumi_corr_161718', 'lnN',
ch.SystMap()(1.006))
#reproducing 2016
#cb.cp().AddSyst(cb, 'CMS_lumi_uncorr_2016', 'lnN', ch.SystMap()(1.027))
elif options.dataYear == '2017':
cb.cp().AddSyst(cb, 'CMS_lumi_uncorr_2017', 'lnN',
ch.SystMap()(1.02))
cb.cp().AddSyst(cb, 'CMS_lumi_corr_161718', 'lnN',
ch.SystMap()(1.009))
cb.cp().AddSyst(cb, 'CMS_lumi_corr_1718', 'lnN',
ch.SystMap()(1.006))
elif options.dataYear == '2018':
cb.cp().AddSyst(cb, 'CMS_lumi_uncorr_2018', 'lnN',
ch.SystMap()(1.015))
cb.cp().AddSyst(cb, 'CMS_lumi_corr_161718', 'lnN',
ch.SystMap()(1.02))
cb.cp().AddSyst(cb, 'CMS_lumi_corr_1718', 'lnN',
ch.SystMap()(1.002))
cb.cp().AddSyst(
cb, 'tt_xsec', 'lnN',
ch.SystMap('process')(['ttbb', 'ttcc', 'ttlf'], 1.055))
cb.cp().AddSyst(cb, 'Other_xsec', 'lnN',
ch.SystMap('process')(['other'], 1.1))
#cb.cp().AddSyst(cb, 'hdamp', 'lnN', ch.SystMap('process')(smTTlist, 1.05))
#cb.cp().AddSyst(cb, 'TuneCP5', 'lnN', ch.SystMap('process')(smTTlist, 1.03))
for i in xrange(len(discriminant)):
if 'b2j3' in discriminant[i][1]:
cb.cp().AddSyst(cb, '$PROCESS_norm', 'lnN',
ch.SystMap('process')(['qcd'], 1.5))
#reproducing 2016 ### comment out Other_xsec above!
#if 'b2j3' in discriminant[i][1]: cb.cp().AddSyst(cb, 'Other_xsec_b2j3', 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['other'], 1.1))
#if 'b2j4' in discriminant[i][1]: cb.cp().AddSyst(cb, 'Other_xsec_b2j4', 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['other'], 1.1))
#if 'b3j3' in discriminant[i][1]: cb.cp().AddSyst(cb, 'Other_xsec_b3j3', 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['other'], 1.1))
#if 'b3j4' in discriminant[i][1]: cb.cp().AddSyst(cb, 'Other_xsec_b3j4', 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['other'], 1.1))
#if 'b4j4' in discriminant[i][1]: cb.cp().AddSyst(cb, 'Other_xsec_b4j4', 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['other'], 1.1))
if options.dataYear == '2016':
#reproducing 2016
#cb.cp().AddSyst(cb, 'hdamp_2016', 'lnN', ch.SystMap('process')(['ttbb', 'ttcc', 'ttlf'], 1.05))
#cb.cp().AddSyst(cb, 'scale_2016', 'lnN', ch.SystMap('process')(['ttbb', 'ttcc', 'ttlf'], 1.15))
#for i in xrange(len(discriminant)):
# if 'j3' in discriminant[i][1]:
# cb.cp().AddSyst(cb, '$PROCESS_norm_j3', 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['ttbb'], 1.5))
# cb.cp().AddSyst(cb, '$PROCESS_norm_j3', 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['ttcc'], 1.5))
# cb.cp().AddSyst(cb, 'jec_2016', 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['ttbb', 'ttcc', 'ttlf', 'other', signal], 1.01))
# else:
# cb.cp().AddSyst(cb, '$PROCESS_norm_j4', 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['ttbb'], 1.5))
# cb.cp().AddSyst(cb, '$PROCESS_norm_j4', 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['ttcc'], 1.5))
# cb.cp().AddSyst(cb, 'jec_2016', 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['ttbb', 'ttcc', 'ttlf', 'other', signal], 1.05))#1.05 for j4
for i in xrange(len(discriminant)):
if 'b2' in discriminant[i][1]:
cb.cp().AddSyst(
cb, '$PROCESS_norm_b2_2016', 'lnN',
ch.SystMap('bin', 'process')([discriminant[i][1]],
['ttbb'], 1.3))
#cb.cp().AddSyst(cb, '$PROCESS_norm_b2_2016', 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['ttcc'], 1.5))
cb.cp().AddSyst(
cb, '$PROCESS_norm_b2', 'lnN',
ch.SystMap('bin', 'process')([discriminant[i][1]],
['ttcc'], 1.5))
elif 'b3' in discriminant[i][1]:
cb.cp().AddSyst(
cb, '$PROCESS_norm_b3_2016', 'lnN',
ch.SystMap('bin', 'process')([discriminant[i][1]],
['ttbb'], 1.3))
#cb.cp().AddSyst(cb, '$PROCESS_norm_b3_2016', 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['ttcc'], 1.5))
cb.cp().AddSyst(
cb, '$PROCESS_norm_b3', 'lnN',
ch.SystMap('bin', 'process')([discriminant[i][1]],
['ttcc'], 1.5))
elif 'b4' in discriminant[i][1]:
cb.cp().AddSyst(
cb, '$PROCESS_norm_b4_2016', 'lnN',
ch.SystMap('bin', 'process')([discriminant[i][1]],
['ttbb'], 1.3))
#cb.cp().AddSyst(cb, '$PROCESS_norm_b4_2016', 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['ttcc'], 1.5))
cb.cp().AddSyst(
cb, '$PROCESS_norm_b4', 'lnN',
ch.SystMap('bin', 'process')([discriminant[i][1]],
['ttcc'], 1.5))
else:
for i in xrange(len(discriminant)):
if 'b2' in discriminant[i][1]:
cb.cp().AddSyst(
cb, '$PROCESS_norm_b2', 'lnN',
ch.SystMap('bin', 'process')([discriminant[i][1]],
['ttbb'], 1.2))
cb.cp().AddSyst(
cb, '$PROCESS_norm_b2', 'lnN',
ch.SystMap('bin', 'process')([discriminant[i][1]],
['ttcc'], 1.5))
elif 'b3' in discriminant[i][1]:
cb.cp().AddSyst(
cb, '$PROCESS_norm_b3', 'lnN',
ch.SystMap('bin', 'process')([discriminant[i][1]],
['ttbb'], 1.22))
cb.cp().AddSyst(
cb, '$PROCESS_norm_b3', 'lnN',
ch.SystMap('bin', 'process')([discriminant[i][1]],
['ttcc'], 1.5))
elif 'b4' in discriminant[i][1]:
cb.cp().AddSyst(
cb, '$PROCESS_norm_b4', 'lnN',
ch.SystMap('bin', 'process')([discriminant[i][1]],
['ttbb'], 1.2))
cb.cp().AddSyst(
cb, '$PROCESS_norm_b4', 'lnN',
ch.SystMap('bin', 'process')([discriminant[i][1]],
['ttcc'], 1.5))
#if 'j3' in discriminant[i][1]:
# #cb.cp().AddSyst(cb, '$PROCESS_norm_j3', 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['ttbb'], 1.5))
# cb.cp().AddSyst(cb, '$PROCESS_norm_j3', 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['ttbb'], 1.3))
# cb.cp().AddSyst(cb, '$PROCESS_norm_j3', 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['ttcc'], 1.5))
#else:
# #cb.cp().AddSyst(cb, '$PROCESS_norm_j4', 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['ttbb'], 1.5))
# cb.cp().AddSyst(cb, '$PROCESS_norm_j4', 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['ttbb'], 1.3))
# cb.cp().AddSyst(cb, '$PROCESS_norm_j4', 'lnN', ch.SystMap('bin', 'process')([discriminant[i][1]], ['ttcc'], 1.5))
# Import shapes from ROOT file
cb.cp().backgrounds().ExtractShapes(file, '$BIN/$PROCESS',
'$BIN/$PROCESS__$SYSTEMATIC')
cb.cp().signals().ExtractShapes(file, '$BIN/$PROCESS',
'$BIN/$PROCESS__$SYSTEMATIC')
#reproducing 2016 - comment out
if options.dataYear == '2016':
rebin = ch.AutoRebin().SetBinThreshold(100).SetBinUncertFraction(
0.1)
rebin.Rebin(cb.cp(), cb)
#elif options.dataYear == '2017':
# #rebin_b2j3 = ch.AutoRebin().SetBinThreshold(5400)#.SetBinUncertFraction(0.1)
# #rebin_b2j3.Rebin(cb.cp().bin(["DNN_Hut_b2j3", "DNN_Hct_b2j3"]), cb)
#AutoMCStat
cb.SetAutoMCStats(cb, 0.1)
#reproducing 2016
#print "Treating bbb"
#bbb = ch.BinByBinFactory()
#bbb.SetAddThreshold(0.0001)
#bbb.AddBinByBin(cb.cp().backgrounds(), cb)
#bbb.AddBinByBin(cb.cp().signals(), cb)
output_prefix = 'FCNC_%s_Discriminant_%s' % (signal, discriminantName)
output_dir = os.path.join(options.output, '%s' % (signal))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
fake_mass = '125'
# Write card
datacard = os.path.join(output_dir, output_prefix + '.dat')
cb.cp().mass([fake_mass, "*"]).WriteDatacard(
os.path.join(output_dir, output_prefix + '.dat'),
os.path.join(output_dir, output_prefix + '_shapes.root'))
# Write small script to compute the limit
workspace_file = os.path.basename(
os.path.join(output_dir,
output_prefix + '_combine_workspace.root'))
script = """#! /bin/bash
text2workspace.py {datacard} -m {fake_mass} -o {workspace_root}
# Run limit
echo combine -M AsymptoticLimits -n {name} {workspace_root} -S {systematics} #--run blind #-v +2
#combine -M AsymptoticLimits -n {name} {workspace_root} -S {systematics} #--run expected #-v +2
combine -M AsymptoticLimits -n {name} {workspace_root} -S {systematics} #--run blind #-v +2
#combine -H AsymptoticLimits -M HybridNew -n {name} {workspace_root} -S {systematics} --LHCmode LHC-limits --expectedFromGrid 0.5 #for ecpected, use 0.84 and 0.16
""".format(workspace_root=workspace_file,
datacard=os.path.basename(datacard),
name=output_prefix,
fake_mass=fake_mass,
systematics=(0 if options.nosys else 1))
script_file = os.path.join(output_dir,
output_prefix + '_run_limits.sh')
with open(script_file, 'w') as f:
f.write(script)
st = os.stat(script_file)
os.chmod(script_file, st.st_mode | stat.S_IEXEC)
# Write small script for datacard checks
script = """#! /bin/bash
# Run checks
echo combine -M FitDiagnostics -t -1 --expectSignal 0 {datacard} -n fitDiagnostics_{name}_bkgOnly -m 125 --robustHesse 1 --robustFit=1 --rMin -20 --rMax 20 #--plots
echo python ../../../../HiggsAnalysis/CombinedLimit/test/diffNuisances.py -a fitDiagnostics_{name}_bkgOnly.root -g fitDiagnostics_{name}_bkgOnly_plots.root
combine -M FitDiagnostics -t -1 --expectSignal 0 {datacard} -n _{name}_bkgOnly -m 125 --robustHesse 1 --robustFit=1 --rMin -20 --rMax 20 #--plots
python ../../../../HiggsAnalysis/CombinedLimit/test/diffNuisances.py -a fitDiagnostics_{name}_bkgOnly.root -g fitDiagnostics_{name}_bkgOnly_plots.root --skipFitS > fitDiagnostics_{name}_bkgOnly.log
python ../../printPulls.py fitDiagnostics_{name}_bkgOnly_plots.root
combine -M FitDiagnostics -t -1 --expectSignal 1 {datacard} -n _{name}_bkgPlusSig -m 125 --robustHesse 1 --robustFit=1 --rMin -20 --rMax 20 #--plots
python ../../../../HiggsAnalysis/CombinedLimit/test/diffNuisances.py -a fitDiagnostics_{name}_bkgPlusSig.root -g fitDiagnostics_{name}_bkgPlusSig_plots.root --skipFitB > fitDiagnostics_{name}_bkgPlusSig.log
python ../../printPulls.py fitDiagnostics_{name}_bkgPlusSig_plots.root
#print NLL for check
combineTool.py -M FastScan -w {name}_combine_workspace.root:w -o {name}_nll
""".format(workspace_root=workspace_file,
datacard=os.path.basename(datacard),
name=output_prefix,
fake_mass=fake_mass,
systematics=(0 if options.nosys else 1))
script_file = os.path.join(output_dir,
output_prefix + '_run_closureChecks.sh')
with open(script_file, 'w') as f:
f.write(script)
st = os.stat(script_file)
os.chmod(script_file, st.st_mode | stat.S_IEXEC)
# Write small script for impacts
script = """#! /bin/bash
# Run impacts
combineTool.py -M Impacts -d {name}_combine_workspace.root -m 125 --doInitialFit --robustFit=1 --robustHesse 1 --rMin -20 --rMax 20 -t -1
combineTool.py -M Impacts -d {name}_combine_workspace.root -m 125 --robustFit=1 --robustHesse 1 --doFits --rMin -20 --rMax 20 -t -1 --parallel 32
combineTool.py -M Impacts -d {name}_combine_workspace.root -m 125 -o {name}_expected_impacts.json --rMin -20 --rMax 20 -t -1
plotImpacts.py -i {name}_expected_impacts.json -o {name}_expected_impacts --per-page 50
combineTool.py -M Impacts -d {name}_combine_workspace.root -m 125 --doInitialFit --robustFit=1 --robustHesse 1 --rMin -20 --rMax 20
combineTool.py -M Impacts -d {name}_combine_workspace.root -m 125 --robustFit=1 --doFits --robustHesse 1 --rMin -20 --rMax 20 --parallel 32
combineTool.py -M Impacts -d {name}_combine_workspace.root -m 125 -o {name}_impacts.json --rMin -20 --rMax 20
plotImpacts.py -i {name}_impacts.json -o {name}_impacts --per-page 50
""".format(workspace_root=workspace_file,
datacard=os.path.basename(datacard),
name=output_prefix,
fake_mass=fake_mass,
systematics=(0 if options.nosys else 1))
script_file = os.path.join(output_dir,
output_prefix + '_run_impacts.sh')
with open(script_file, 'w') as f:
f.write(script)
st = os.stat(script_file)
os.chmod(script_file, st.st_mode | stat.S_IEXEC)
# Write small script for postfit shapes
script = """#! /bin/bash
# Run postfit
echo combine -M FitDiagnostics {datacard} -n _{name}_postfit --saveNormalizations --saveShapes --saveWithUncertainties --preFitValue 0 --rMin -20 --rMax 20 --robustHesse 1 --robustFit=1 -v 1
combine -M FitDiagnostics {datacard} -n _{name}_postfit --saveNormalizations --saveShapes --saveWithUncertainties --preFitValue 0 --rMin -20 --rMax 20 --robustHesse 1 --robustFit=1 -v 1 #--plots
PostFitShapesFromWorkspace -w {name}_combine_workspace.root -d {datacard} -o postfit_shapes_{name}.root -f fitDiagnostics_{name}_postfit.root:fit_b --postfit --sampling
python ../../convertPostfitShapesForPlotIt.py -i postfit_shapes_{name}.root
$CMSSW_BASE/src/UserCode/HEPToolsFCNC/plotIt/plotIt -o postfit_shapes_{name}_forPlotIt ../../postfit_plotIt_config_{coupling}_{year}.yml -y
$CMSSW_BASE/src/UserCode/HEPToolsFCNC/plotIt/plotIt -o postfit_shapes_{name}_forPlotIt ../../postfit_plotIt_config_{coupling}_{year}_qcd.yml -y
""".format(workspace_root=workspace_file,
datacard=os.path.basename(datacard),
name=output_prefix,
fake_mass=fake_mass,
systematics=(0 if options.nosys else 1),
coupling=("Hut" if "Hut" in output_prefix else "Hct"),
year=options.dataYear)
script_file = os.path.join(output_dir,
output_prefix + '_run_postfit.sh')
with open(script_file, 'w') as f:
f.write(script)
st = os.stat(script_file)
os.chmod(script_file, st.st_mode | stat.S_IEXEC)
def writeCard(input,theLambda,select,region=-1) :
print "writing cards"
variables =[]
if opt.isResonant : variables.append('HHKin_mass_raw')
else : variables.append('MT2')
#out_dir = opt.outDir
theOutputDir = "{0}{1}{2}".format(theLambda,select,variables[0])
dname = "_"+opt.channel+opt.outDir
out_dir = "cards{1}/{0}/".format(theOutputDir,dname)
print out_dir
#in_dir = "/grid_mnt/vol__vol_U__u/llr/cms/ortona/diHiggs/CMSSW_7_4_7/src/KLUBAnalysis/combiner/cards_MuTauprova/HHSM2b0jMcutBDTMT2/";
cmb1 = ch.CombineHarvester()
cmb1.SetFlag('workspaces-use-clone', True)
cmd = "mkdir -p {0}".format(out_dir)
print cmd
regionName = ["","regB","regC","regD"]
regionSuffix = ["SR","SStight","OSinviso","SSinviso"]
status, output = commands.getstatusoutput(cmd)
#outFile = opt.outDir+"/chCard{0}{2}_{1}_{3}.txt".format(theLambda,opt.channel,regionName[region+1],select)
thechannel = "1"
if opt.channel == "MuTau" : thechannel="2"
elif opt.channel == "TauTau" : thechannel = "3"
if "0b0j" in select : theCat = "0"
if "2b0j" in select : theCat = "2"
elif "1b1j" in select : theCat = "1"
elif "boosted" in select : theCat = "3"
outFile = "hh_{0}_C{1}_L{2}_13TeV.txt".format(thechannel,theCat,theLambda)
file = open( "temp.txt", "wb")
#read config
categories = []
#for icat in range(len(input.selections)) :
# categories.append((icat, input.selections[icat]))
categories.append((0,select))
backgrounds=[]
MCbackgrounds=[]
processes=[]
processes.append(lambdaName)
inRoot = TFile.Open(opt.filename)
for bkg in input.background:
#Add protection against empty processes => If I remove this I could build all bins at once instead of looping on the selections
templateName = "{0}_{1}_SR_{2}".format(bkg,select,variables[0])
print templateName
template = inRoot.Get(templateName)
if template.Integral()>0.000001 :
backgrounds.append(bkg)
processes.append(bkg)
if bkg is not "QCD" :
MCbackgrounds.append(bkg)
#print backgrounds
allQCD = False
allQCDs = [0,0,0,0]
for regionsuff in range(len(regionSuffix)) :
for ichan in range(len(backgrounds)):
if "QCD" in backgrounds[ichan] :
fname = "data_obs"
if regionSuffix[regionsuff] == "SR" :
fname="QCD"
templateName = "{0}_{1}_{3}_{2}".format(fname,select,variables[0],regionSuffix[regionsuff])
template = inRoot.Get(templateName)
#allQCDs.append(template.Integral())
allQCDs[regionsuff]= allQCDs[regionsuff]+template.Integral()
iQCD = ichan
elif regionSuffix[regionsuff] is not "SR" :
templateName = "{0}_{1}_{3}_{2}".format(backgrounds[ichan],select,variables[0],regionSuffix[regionsuff])
template = inRoot.Get(templateName)
allQCDs[regionsuff] = allQCDs[regionsuff] - template.Integral()
if allQCDs[0]>0 and allQCDs[1]>0 and allQCDs[2]>0 and allQCDs[3]>0 : allQCD = True
for i in range(4) : print allQCDs[i]
#add processes to CH
#masses->125
#analyses->Res/non-Res(HHKin_fit,MT2)
#eras->13TeV
#channels->mutau/tautau/etau
#bin->bjet categories
#print signals, signals[0]
cmb1.AddObservations([theLambda.replace(lambdaName,"")], variables, ['13TeV'], [opt.channel], categories)
cmb1.AddProcesses([theLambda.replace(lambdaName,"")], variables, ['13TeV'], [opt.channel], backgrounds, categories, False)
cmb1.AddProcesses([theLambda.replace(lambdaName,"")], variables, ['13TeV'], [opt.channel], [lambdaName], categories, True) #signals[0]
if region < 0 :
#Systematics (I need to add by hand the shape ones)
#potrei sostituire theLambda con "signal"
#syst = systReader("../config/systematics.cfg",[theLambda],backgrounds,file)
syst = systReader("../config/systematics.cfg",[lambdaName],backgrounds,file)
syst.writeOutput(False)
syst.verbose(True)
if(opt.channel == "TauTau" ):
syst.addSystFile("../config/systematics_tautau.cfg")
elif(opt.channel == "MuTau" ):
syst.addSystFile("../config/systematics_mutau.cfg")
#if(opt.isResonant):
# syst.addSystFile("../config/systematics_resonant.cfg")
#else : syst.addSystFile("../config/systematics_nonresonant.cfg")
elif(opt.channel == "ETau" ):
syst.addSystFile("../config/systematics_etau.cfg")
#if(opt.isResonant):
# syst.addSystFile("../config/systematics_resonant.cfg")
#else : syst.addSystFile("../config/systematics_nonresonant.cfg")
if opt.theory : syst.addSystFile("../config/syst_th.cfg")
syst.writeSystematics()
for isy in range(len(syst.SystNames)) :
if "CMS_scale_t" in syst.SystNames[isy] or "CMS_scale_j" in syst.SystNames[isy]: continue
for iproc in range(len(syst.SystProcesses[isy])) :
if "/" in syst.SystValues[isy][iproc] :
f = syst.SystValues[isy][iproc].split("/")
systVal = (float(f[0]),float(f[1]))
else :
systVal = float(syst.SystValues[isy][iproc])
#print isy, iproc, systVal
print "adding Syst",systVal,syst.SystNames[isy],syst.SystTypes[isy],"to",syst.SystProcesses[isy][iproc]
cmb1.cp().process([syst.SystProcesses[isy][iproc]]).AddSyst(cmb1, syst.SystNames[isy],syst.SystTypes[isy],ch.SystMap('channel','bin_id')([opt.channel],[0],systVal))
if opt.shapeUnc > 0:
jesproc = MCbackgrounds
jesproc.append(lambdaName)
if "1b1j" in select and opt.channel == "TauTau" : jesproc.remove("DY0b")
cmb1.cp().process(jesproc).AddSyst(cmb1, "CMS_scale_j_13TeV","shape",ch.SystMap('channel','bin_id')([opt.channel],[0],1.000))
cmb1.cp().process(jesproc).AddSyst(cmb1, "CMS_scale_t_13TeV","shape",ch.SystMap('channel','bin_id')([opt.channel],[0],1.000))
cmb1.cp().process(["TT"]).AddSyst(cmb1, "top","shape",ch.SystMap('channel','bin_id')([opt.channel],[0],1.000))
# $BIN --> proc.bin()
# $PROCESS --> proc.process()
# $MASS --> proc.mass()
# $SYSTEMATIC --> syst.name()
# cmb1.cp().ExtractShapes(
# opt.filename,
# "$PROCESS_$BIN_{1}_{0}".format(variables[0],regionSuffix[region+1]),
# "$PROCESS_$BIN_{1}_{0}_$SYSTEMATIC".format(variables[0],regionSuffix[region+1]))
cmb1.cp().backgrounds().ExtractShapes(
opt.filename,
"$PROCESS_$BIN_{1}_{0}".format(variables[0],regionSuffix[region+1]),
"$PROCESS_$BIN_{1}_{0}_$SYSTEMATIC".format(variables[0],regionSuffix[region+1]))
cmb1.cp().signals().ExtractShapes(
opt.filename,
"$PROCESS$MASS_$BIN_{1}_{0}".format(variables[0],regionSuffix[region+1]),
"$PROCESS$MASS_$BIN_{1}_{0}_$SYSTEMATIC".format(variables[0],regionSuffix[region+1]))
bbb = ch.BinByBinFactory()
bbb.SetAddThreshold(0.1).SetMergeThreshold(0.5).SetFixNorm(True)
bbbQCD = ch.BinByBinFactory()
bbbQCD.SetAddThreshold(0.0).SetMergeThreshold(0.5).SetFixNorm(True)
if opt.binbybin :
bbb.MergeBinErrors(cmb1.cp().process(MCbackgrounds))
bbbQCD.MergeBinErrors(cmb1.cp().process(["QCD"]))
bbbQCD.AddBinByBin(cmb1.cp().process(["QCD"]), cmb1)
bbb.AddBinByBin(cmb1.cp().process(MCbackgrounds), cmb1)
#cmb1.cp().PrintProcs().PrintSysts()
#outroot = TFile.Open(opt.outDir+"/chCard{0}{2}_{1}_{3}.input.root".format(theLambda,opt.channel,regionName[region+1],select),"RECREATE")
#outtxt = "hh_{0}_C{1}_L{2}_13TeV.txt".format(theChannel,theCat,theHHLambda)
outroot = TFile.Open(out_dir+"hh_{0}_C{1}_L{2}_13TeV.input.root".format(thechannel,theCat,theLambda),"RECREATE")
cmb1.WriteDatacard(out_dir+outFile,out_dir+"hh_{0}_C{1}_L{2}_13TeV.input.root".format(thechannel,theCat,theLambda))
if allQCD :
file = open( out_dir+outFile, "a")
file.write("alpha rateParam {0} QCD (@0*@1/@2) QCD_regB,QCD_regC,QCD_regD".format(select))
elif allQCD :
#print thechannel,theCat,theLambda #,regionName2[region+1]
#outFile = "hh_{0}_C{1}_L{2}_13TeV.txt".format(thechannel,theCat,theLambda)
#print region, allQCD
#print regionName2[region+1]
#print outFile
#print "hh_"+thechannel#+"_C"+theCat+"_L"+theLambda+"_13TeV_"+regionName[region+1]+".txt"
#print "hh_"+thechannel+"_C"+theCat#+"_L"+theLambda+"_13TeV_"+regionName[region+1]+".txt"
#print "hh_"+thechannel+"_C"+theCat+"_L"+theLambda#+"_13TeV_"+regionName[region+1]+".txt"
#print "hh_"+thechannel+"_C"+theCat+"_L"+theLambda+"_13TeV_"#+regionName[region+1]+".txt"
#print outFile
outFile = "hh_{0}_C{1}_L{2}_13TeV_{3}.txt".format(thechannel,theCat,theLambda,regionName[region+1])
file = open( out_dir+outFile, "wb")
file.write("imax 1\n")
file.write("jmax {0}\n".format(len(backgrounds)-1))
file.write("kmax *\n")
file.write("------------\n")
file.write("shapes * * FAKE\n".format(opt.channel,regionName[region+1]))
file.write("------------\n")
templateName = "data_obs_{1}_{3}_{2}".format(bkg,select,variables[0],regionSuffix[region+1])
template = inRoot.Get(templateName)
file.write("bin {0} \n".format(select))
obs = template.GetEntries()
file.write("observation {0} \n".format(obs))
file.write("------------\n")
file.write("bin ")
for chan in backgrounds:
file.write("{0} ".format(select))
file.write("\n")
file.write("process ")
for chan in backgrounds:
file.write("{0} ".format(chan))
#file.write("QCD ")
file.write("\n")
file.write("process ")
for chan in range(len(backgrounds)): #+1 for the QCD
file.write("{0} ".format(chan+1))
file.write("\n")
file.write("rate ")
rates = []
iQCD = -1
totRate = 0
for ichan in range(len(backgrounds)):
if "QCD" in backgrounds[ichan] :
rates.append(-1)
iQCD = ichan
else :
templateName = "{0}_{1}_{3}_{2}".format(backgrounds[ichan],select,variables[0],regionSuffix[region+1])
template = inRoot.Get(templateName)
#print templateName
brate = template.Integral()
rates.append(brate)
totRate = totRate + brate
if iQCD >= 0 : rates[iQCD] = TMath.Max(0.0000001,obs-totRate)
for ichan in range(len(backgrounds)):
file.write("{0:.4f} ".format(rates[ichan]))
file.write("\n")
file.write("------------\n")
file.write("QCD_{0} rateParam {1} QCD 1 \n".format(regionName[region+1],select))
def prepareShapesAndCards(options):
cb = ch.CombineHarvester()
if options.fit_mode == 'shape_CR1':
cats = [
(1, 'SR'),
(2, 'CR1')
]
print('-- QCD estimation: fit bin-by-bin by assuming shape in CR1 and SR is the same --')
elif options.fit_mode == 'abcd':
cats = [
(1, 'SR'),
(2, 'CR1'),
(3, 'VR'),
(4, 'CR2'),
]
print('-- QCD etimation: bin-by-bin ABCD using the four regions --')
# object to handle the factorisation of uncertainties among ttbar components
factTheory = defs.FactorisedTheory(options.fact_theory)
# factorise shape uncertainties for ttbar components
theory_shape_systs = []
for procs, syst in defs.theory_shape_systs:
for newProcs,newSyst in factTheory.getGrouping(procs, syst):
theory_shape_systs.append((newProcs, newSyst))
if options.randomise:
print("-- Will randomise MC predictions according to MC stat uncertainties!")
# Process shapes
processed_shapes = os.path.join(options.output, 'processed_shapes.root')
QCD_VR_ratios = utils.extractShapes(options.input, processed_shapes, defs.tt_bkg + defs.other_bkg, defs.sig_processes, options.data, fact_theory=factTheory, equal_bins=options.equal_bins, sub_folder=options.sub_folder, randomise=options.randomise, rebinSB=options.rebinsb)
Nbins = len(QCD_VR_ratios)
cb.AddObservations(['*'], ['ttbb'], ['13TeV_2016'], ['FH'], cats)
cb.AddProcesses(['*'], ['ttbb'], ['13TeV_2016'], ['FH'], defs.sig_processes, cats, True)
cb.AddProcesses(['*'], ['ttbb'], ['13TeV_2016'], ['FH'], defs.tt_bkg + defs.other_bkg, cats, False)
### QCD estimate: add all "delta" templates
QCD_processes = [ 'QCD_bin_{}'.format(i+1) for i in range(Nbins) ]
cb.AddProcesses(['*'], ['ttbb'], ['13TeV_2016'], ['FH'], QCD_processes, cats, False)
### Systematics
added_theory_systs = []
added_exp_systs = []
# Modeling systematics, not on QCD! ###
cbWithoutQCD = cb.cp().process_rgx(['QCD.*'], False)
# Theory rate uncertainties from the JSON file
if options.rate_systs is not None:
for json_file in options.rate_systs:
added_theory_systs += addRateSystematics(cb, json_file, options.sub_folder, factTheory)
# Experimental rate uncertainties from the JSON file
if options.exp_rate is not None:
for json_file in options.exp_rate:
added_exp_systs += addRateSystematics(cb, json_file, options.sub_folder)
# Luminosity
cbWithoutQCD.AddSyst(cb, 'lumi_$ERA', 'lnN', ch.SystMap('era')(['13TeV_2016'], defs.getLumiUncertainty('13TeV_2016')))
added_exp_systs.append('lumi_13TeV_2016')
# Experimental systematics, common for all processes and categories
for s in defs.exp_systs:
# If we have added it already as a rate systematics, skip it!
if s not in added_exp_systs:
added_exp_systs.append(s)
cbWithoutQCD.AddSyst(cb, s, 'shape', ch.SystMap()(1.))
# Theory shape systematics
for syst in theory_shape_systs:
if syst[1] not in added_theory_systs:
added_theory_systs.append(syst[1])
cbWithoutQCD.cp().process(syst[0]).AddSyst(cb, syst[1], 'shape', ch.SystMap()(1.))
# Theory rate systematics (not taken from JSON)
for name,syst in defs.theory_rate_systs.items():
if not name in added_theory_systs:
added_theory_systs.append(name)
cbWithoutQCD.AddSyst(cb, name, syst[0], syst[1])
### QCD systematics: add a lnN for each bin using the ratio QCD_subtr/QCD_est in the VR
if options.QCD_systs:
print('-- Will apply bin-by-bin uncertainties on QCD estimate from ratio in VR --')
if options.fit_mode == 'shape_CR1':
for i in range(1, Nbins+1):
# lnN = 1 + abs(1 - QCD_VR_ratios[i-1])
ratio = QCD_VR_ratios[i-1]
lnN = ratio if ratio > 1 else 1./ratio
cb.cp().bin(['SR']).process(['QCD_bin_{}'.format(i)]).AddSyst(cb, 'QCD_shape_bin_{}'.format(i), 'lnN', ch.SystMap()(lnN))
elif options.fit_mode == 'abcd':
# using max
# QCD_VR_ratios = [1.1047956681135658, 1.104982852935791, 1.0103355569221637, 1.0365746040205628, 1.027778957040471, 1.1635257239763037, 1.0604289770126343, 1.0326651334762573, 1.0882024148481384, 1.0879310369491577, 1.2372238755691953, 1.1039656400680542, 1.1208300590515137, 1.1252394914627075, 1.0652162084238805, 1.1746360299507677, 1.1441897907967598, 1.032749056816101, 1.1105864995541361, 1.264707088470459, 1.1289979219436646, 1.1032386479572462, 1.3740112781524658, 1.0779788494110107, 1.0679041983173836, 1.1521316766738892, 1.0189466861549783, 1.1371627554677426, 1.180934637513623, 1.0807719230651855, 1.1220710277557373, 1.2163840919860773, 1.1803903579711914, 1.1331188470149183, 1.2841500043869019, 1.124382576013972, 1.2853591442108154, 1.1161022064238948, 1.0491153764429137, 1.3020191192626953, 1.6365387568006153, 1.3135310411453247, 1.183979775003691, 1.3237843031833378, 1.105936050415039, 1.4582525497144114, 1.2740960121154785, 1.1744883060455322, 1.2689180716203021, 1.5666807889938354, 1.1884409189224243, 1.6787212785213594, 1.1295689911887752, 1.2143068313598633, 1.144478440284729]
# using geometric average
QCD_VR_ratios = [1.0556093647141687, 1.0658984862062695, 1.0057472468756388, 1.0208612636340562, 1.0185833946498413, 1.1211169739938442, 1.0353973123690785, 1.0258664065695766, 1.0586147959018684, 1.0522305760086619, 1.1354690006073973, 1.072695547895069, 1.0799492240063984, 1.0621373200388462, 1.0593700756267987, 1.1529412209016232, 1.122536304991689, 1.0187320772559685, 1.0972767308832914, 1.175709681780302, 1.0832093989858067, 1.0823283151259013, 1.1831016555993352, 1.054608634579664, 1.0599488955753065, 1.0752925245754967, 1.017269399510584, 1.122209514629158, 1.1702168450787551, 1.0695165830450506, 1.0857979999559528, 1.2041393773004465, 1.1151294041413826, 1.1230274391829085, 1.2502545040629076, 1.1070056845911258, 1.139110776895264, 1.082765772887927, 1.0487710649869804, 1.2332536614187524, 1.4655095128617284, 1.19038044691305, 1.1104215756611893, 1.1838495100927606, 1.0880046566588846, 1.4004062409319984, 1.248629899444753, 1.1411489734003788, 1.1805956668619682, 1.4378115712379096, 1.129952938278906, 1.3437817991926544, 1.0912141233598036, 1.1453139866153634, 1.1135893789689448]
for i in range(1, Nbins+1):
lnN = 1.05
# lnN = QCD_VR_ratios[i-1]
cb.cp().bin(['SR']).process(['QCD_bin_{}'.format(i)]).AddSyst(cb, 'QCD_shape_bin_{}'.format(i), 'lnN', ch.SystMap()(lnN))
# cb.cp().process(['ttlf']).AddSyst(cb, 'ttlf_norm', 'lnN', ch.SystMap()(1.2))
extraStrForQCD = ''
# To define nuisance group with all QCD parameters
paramListQCD = []
if options.fit_mode == 'shape_CR1':
### QCD estimate: fit shape from CR1, normalisation floating
extraStrForQCD += 'scale_ratio_QCD_CR1_SR extArg 1. [0.,2.]\n'
paramListQCD.append('scale_ratio_QCD_CR1_SR')
for i in range(1, Nbins+1):
extraStrForQCD += 'yield_QCD_SR_bin_{0} rateParam SR QCD_bin_{0} 1. [0.,2.]\n'.format(i)
paramListQCD.append('yield_QCD_SR_bin_{}'.format(i))
for i in range(1, Nbins+1):
extraStrForQCD += 'yield_QCD_CR1_bin_{0} rateParam CR1 QCD_bin_{0} (@0*@1) scale_ratio_QCD_CR1_SR,yield_QCD_SR_bin_{0}\n'.format(i)
if options.QCD_systs:
for i in range(1, Nbins+1):
paramListQCD.append('QCD_shape_bin_{}'.format(i))
elif options.fit_mode == 'abcd':
### QCD estimate: add the rate params for each bin in the CR1, CR2 and VR
### The yield in the SR is then expressed as CR1*VR/CR2
for i in range(1, Nbins+1):
extraStrForQCD += 'yield_QCD_CR1_bin_{0} rateParam CR1 QCD_bin_{0} 1. [0.,5.]\n'.format(i)
extraStrForQCD += 'yield_QCD_CR2_bin_{0} rateParam CR2 QCD_bin_{0} 1. [0.,5.]\n'.format(i)
extraStrForQCD += 'yield_QCD_VR_bin_{0} rateParam VR QCD_bin_{0} 1. [0.,5.]\n'.format(i)
extraStrForQCD += 'yield_QCD_SR_bin_{0} rateParam SR QCD_bin_{0} (@0*@1/@2) yield_QCD_VR_bin_{0},yield_QCD_CR1_bin_{0},yield_QCD_CR2_bin_{0}\n'.format(i)
paramListQCD.append('yield_QCD_CR1_bin_{}'.format(i))
paramListQCD.append('yield_QCD_CR2_bin_{}'.format(i))
paramListQCD.append('yield_QCD_VR_bin_{}'.format(i))
cb.AddDatacardLineAtEnd(extraStrForQCD)
# Define systematic groups
syst_groups = {
"theory": added_theory_systs,
"exp": added_exp_systs,
"QCD": paramListQCD,
"extern": defs.externalised_nuisances,
}
def getNuisanceGroupString(groups):
m_str = ""
for g in groups:
m_str += g + ' group = '
for sys in groups[g]:
m_str += sys + ' '
m_str += '\n'
return m_str
cb.AddDatacardLineAtEnd(getNuisanceGroupString(syst_groups))
cb.cp().ExtractShapes(processed_shapes, '$BIN/$PROCESS', '$BIN/$PROCESS_$SYSTEMATIC')
if options.bbb:
print('-- Will add bin-by-bin uncertainties for MC statistics --')
# MC statistics - has to be done after the shapes have been extracted!
# bbb_bkg = ch.BinByBinFactory().SetVerbosity(5)
# bbb_bkg.SetAddThreshold(0.02).SetMergeThreshold(0.5).SetFixNorm(False)
# bbb_bkg.MergeBinErrors(cbWithoutQCD.cp().backgrounds())
# bbb_bkg.AddBinByBin(cbWithoutQCD.cp().backgrounds(), cb)
# bbb_sig = ch.BinByBinFactory().SetVerbosity(5).SetFixNorm(False)
# bbb_sig.MergeBinErrors(cbWithoutQCD.cp().signals())
# bbb_sig.AddBinByBin(cbWithoutQCD.cp().signals(), cb)
# bbb = ch.BinByBinFactory().SetVerbosity(5)
# bbb.SetAddThreshold(0.).SetMergeThreshold(1.).SetFixNorm(False).SetPoissonErrors(True)
# bbb.MergeBinErrors(cb.cp())
# bbb.AddBinByBin(cb.cp(), cb)
# bbb_sig = ch.BinByBinFactory().SetVerbosity(5)
# bbb_sig.SetAddThreshold(0.).SetMergeThreshold(1.).SetFixNorm(False)
# bbb_sig.MergeBinErrors(cb.cp().signals())
# bbb_sig.AddBinByBin(cb.cp().signals(), cb)
# bbb_bkg = ch.BinByBinFactory().SetVerbosity(5)
# bbb_bkg.SetAddThreshold(0.).SetMergeThreshold(1.).SetFixNorm(False)
# bbb_bkg.MergeBinErrors(cb.cp().backgrounds())
# bbb_bkg.AddBinByBin(cb.cp().backgrounds(), cb)
# Use combine internal BBB (default: BB lite, merging everything for sig & bkg separately?)
cb.AddDatacardLineAtEnd("* autoMCStats 5000 0 1\n")
# cb.AddDatacardLineAtEnd("* autoMCStats 10000000 0 1\n")
output_dir = options.output
if not os.path.exists(output_dir):
os.makedirs(output_dir)
datacard = os.path.join(output_dir, 'datacard.dat')
output_shapes = os.path.join(output_dir, 'shapes.root')
cb.WriteDatacard(datacard, output_shapes)
initWorkSpace = """
#!/bin/bash
if [[ ! -f workspace.root ]]; then
text2workspace.py datacard.dat -o workspace.root
fi
RMIN=0.
RMAX=5.0
NPOINTS=50
export FIT_OPT=( --freezeNuisanceGroups=extern --cminDefaultMinimizerStrategy 0 --X-rtd MINIMIZER_MaxCalls=999999999 --X-rtd MINIMIZER_analytic --robustFit 1 --cminDefaultMinimizerPrecision 1E-12 )
"""
if options.data:
print("-- WILL USE REAL DATA IN SR")
initWorkSpace += 'export TOY=""\n'
else:
print("-- Will use Asimov toy")
initWorkSpace += 'export TOY="-t -1"\n'
def createScript(content, filename):
script_path = os.path.join(output_dir, filename)
with open(script_path, 'w') as f:
f.write(initWorkSpace)
f.write(content)
# make script executable
st = os.stat(script_path)
os.chmod(script_path, st.st_mode | stat.S_IEXEC)
# Script: simple fit, fit diagnostics, postfit plots, frequentist toys, goodness of fit
script = """
combine -M MultiDimFit -d workspace.root --rMin $RMIN --rMax $RMAX --expectSignal=1 ${TOY} --saveWorkspace --algo singles --setCrossingTolerance 1E-7 "${FIT_OPT[@]}" 2>&1 | tee fit.log
#combine -M FitDiagnostics -d workspace.root --rMin $RMIN --rMax $RMAX ${TOY} --expectSignal=1 --skipBOnlyFit --saveShapes --saveNormalizations --saveWithUncertainties --plots "${FIT_OPT[@]}" 2>&1 | tee fitDiag.log
#combine -M FitDiagnostics -d workspace.root --rMin $RMIN --rMax $RMAX ${TOY} --expectSignal=1 --skipBOnlyFit --robustHesse 1 "${FIT_OPT[@]}" 2>&1 | tee fitDiag.log
#../plotCovariance.py
# frequentist toys
#combine -M MultiDimFit -d workspace.root --rMin $RMIN --rMax $RMAX --expectSignal $1 -t 1000 -n _freq_$1 --toysFrequentist "${FIT_OPT[@]}" -s -1 > freq_$1.log
#combine -M MultiDimFit -d workspace.root --rMin $RMIN --rMax $RMAX --expectSignal $1 -t 1000 -n _freqNoSyst_$1 --toysNoSystematics "${FIT_OPT[@]}" -s -1 > freqNoSyst_$1.log
# Goodness of fit
#combine -M GoodnessOfFit workspace.root --algo=saturated "${FIT_OPT[@]}"
#parallel --gnu -j 5 -n0 combine -M GoodnessOfFit workspace.root --algo=saturated "${FIT_OPT[@]}" -t 100 -s -1 --toysFreq ::: {1..10}
#hadd higgsCombineTest.GoodnessOfFit.mH120.toys.root higgsCombineTest.GoodnessOfFit.mH120.*.root
# Postfit plots
#PostFitShapesFromWorkspace -d datacard.dat -w workspace.root -o postfit_shapes.root -m 120 -f fitDiagnostics.root:fit_s --postfit --sampling --print 2>&1 | tee postFitRates.log
"""
createScript(script, 'do_fit.sh')
# Script: plots of NLL vs. r for different uncertainties
script = """
RMIN=0.5
RMAX=3.
combine -M MultiDimFit --algo grid --points $NPOINTS --rMin $RMIN --rMax $RMAX ${TOY} --expectSignal=1 -n _nominal workspace.root "${FIT_OPT[@]}"
# stat-only: get best-fit parameters and dataset from saved workspace (so that it also works for post-fit)
combine -M MultiDimFit --algo grid --points $NPOINTS --rMin $RMIN --rMax $RMAX -n _stat -S 0 --snapshotName "MultiDimFit" -d higgsCombineTest.MultiDimFit.mH120.root "${FIT_OPT[@]}"
plot1DScan.py higgsCombine_nominal.MultiDimFit.mH120.root --others 'higgsCombine_stat.MultiDimFit.mH120.root:Freeze all:2' --breakdown syst,stat
"""
createScript(script, 'do_DeltaNLL_plot.sh')
# Script: impacts signal injected
script = """
folder=impacts
mkdir ${folder}
pushd ${folder}
combineTool.py -M Impacts -d ../workspace.root ${TOY} -m 120 --rMin $RMIN --rMax $RMAX --expectSignal=1 --doInitialFit "${FIT_OPT[@]}"
combineTool.py -M Impacts -d ../workspace.root ${TOY} -m 120 --rMin $RMIN --rMax $RMAX --expectSignal=1 --doFits --parallel 6 "${FIT_OPT[@]}" --setParameterRanges CMS_qg_Weight=-2,2 --cminPreScan
combineTool.py -M Impacts -d ../workspace.root -m 120 -o impacts.json
plotImpacts.py -i impacts.json -o impacts
plotImpacts.py -i impacts.json -o impacts_qcd --groups QCD
plotImpacts.py -i impacts.json -o impacts_no_qcd --groups '!QCD' '!extern'
popd
"""
createScript(script, 'do_impacts.sh')
# Script: plots of NLL vs. nuisance parameters
script = """
function scan_param() {{
combine -M MultiDimFit --algo grid --points 20 -n _$1 --snapshotName "MultiDimFit" -d ../higgsCombineTest.MultiDimFit.mH120.root --setParameterRanges r=0,3:$1={scan} -P $1 "${{FIT_OPT[@]}}" --floatOtherPOIs 1
plot1DScan.py higgsCombine_$1.MultiDimFit.mH120.root --output scan_$1 --POI $1
combine -M MultiDimFit --algo grid --points 20 -n _freeze_$1 --snapshotName "MultiDimFit" -d ../higgsCombineTest.MultiDimFit.mH120.root --setParameterRanges r=0,3:$1={scan} -P $1 "${{FIT_OPT[@]}}" -S 0 --floatOtherPOIs 1
plot1DScan.py higgsCombine_freeze_$1.MultiDimFit.mH120.root --output scan_freeze_$1 --POI $1
combine -M MultiDimFit --algo grid --points 20 -n _freezeQCD_$1 --snapshotName "MultiDimFit" -d ../higgsCombineTest.MultiDimFit.mH120.root --setParameterRanges r=0,3:$1={scan} -P $1 --freezeNuisanceGroups extern,QCD --floatOtherPOIs 1
plot1DScan.py higgsCombine_freezeQCD_$1.MultiDimFit.mH120.root --output scan_freezeQCD_$1 --POI $1
}}
export -f scan_param # needed for parallel
mkdir scans
pushd scans
SHELL=/bin/bash parallel --gnu -j 6 scan_param ::: {params}
popd
"""
createScript(script.format(scan="-2,2", params=" ".join(syst_groups['exp'])), 'do_exp_scans.sh')
createScript(script.format(scan="-2,2", params=" ".join(syst_groups['theory'])), 'do_theory_scans.sh')
script = """
function scan_param() {{
combine -M MultiDimFit --algo grid --points 20 -n _$1 --snapshotName "MultiDimFit" -d ../higgsCombineTest.MultiDimFit.mH120.root --setParameterRanges r=0,3 -P $1 --autoRange 3 "${{FIT_OPT[@]}}" --floatOtherPOIs 1
plot1DScan.py higgsCombine_$1.MultiDimFit.mH120.root --output scan_$1 --POI $1
combine -M MultiDimFit --algo grid --points 20 -n _freeze_$1 --snapshotName "MultiDimFit" -d ../higgsCombineTest.MultiDimFit.mH120.root --setParameterRanges r=0,3 -P $1 --autoRange 3 "${{FIT_OPT[@]}}" -S 0 --floatOtherPOIs 1
plot1DScan.py higgsCombine_freeze_$1.MultiDimFit.mH120.root --output scan_freeze_$1 --POI $1
}}
export -f scan_param # needed for parallel
mkdir scans
pushd scans
SHELL=/bin/bash parallel --gnu -j 6 scan_param ::: {params}
popd
"""
createScript(script.format(params=" ".join(syst_groups['QCD'])), 'do_QCD_scans.sh')
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import ROOT
ROOT.gSystem.Load("libHiggsAnalysisCombinedLimit")
ROOT.gROOT.SetBatch()
import os
import CombineHarvester.CombineTools.ch as ch
import argparse
p = argparse.ArgumentParser(
"Script to print out systematics of a given process in a given category for a given 'combined.txt.cmb' datacard. This does not included additional uncertainties, that could be added by the signal model used (e.g. by MSSM models)"
)
p.add_argument("--category", required=True, help="Category to be inspected")
p.add_argument("--process", required=True, help="Process to be inspected")
p.add_argument("--datacard",
required=True,
help="Path to the datacard 'combined.txt.cmb'")
args = p.parse_args()
cmb_card = ch.CombineHarvester()
cmb_card.SetFlag("workspaces-use-clone", True)
cmb_card.ParseDatacard(args.datacard, "", "", "", 0, "200")
cmb_card.bin([args.category]).cp().PrintProcs()
cmb_card.bin([args.category]).cp().process([args.process]).PrintSysts()
def main():
if len(sys.argv) < 2:
print 'Error! No data filename specified (in json format). Exiting...'
exit(0)
lumi_syst = {'2016': 1.025, '2017': 1.023, '2018': 1.025}
trig_MC_syst = {'2016': 1.01, '2017': 1.01, '2018': 1.01}
trig_data_syst = {'2016': 1.005, '2017': 1.005, '2018': 1.005}
JES_syst = {'2016': 1.02, '2017': 1.06, '2018': 1.02}
JER_syst = {'2016': 1.01, '2017': 1.09, '2018': 1.025}
#EGM_ID_syst = {'2016': 1.02, '2017': 1.03, '2018': 1.03}
veto_ID_syst = {'2016': 1.005, '2017': 1.005, '2018': 1.005}
GM_ID_syst = { # split by match category
'0': {'2016': 1.000, '2017': 1.000, '2018': 1.000}, # just dummy = 1, no GMs
'1': {'2016': 1.007, '2017': 1.003, '2018': 1.005},
'2': {'2016': 1.012, '2017': 1.005, '2018': 1.010}
}
dSA_ID_prompt_syst = {'2016': 1.006, '2017': 1.007, '2018': 1.006}
dSA_ID_displ_syst = {'2016': 1.001, '2017': 1.001, '2018': 1.001}
dSA_reco_prompt_syst = {'2016': 1.004, '2017': 1.003, '2018': 1.003}
# background ABCD closure syst
closure_syst_1mm = {
'0': {'2016': 1.25, '2017': 1.25, '2018': 1.25},
'1': {'2016': 1.05, '2017': 1.05, '2018': 1.05},
'2': {'2016': 1.80, '2017': 1.80, '2018': 1.80}
}
closure_syst_10mm = {
'0': {'2016': 1.25, '2017': 1.25, '2018': 1.25},
'1': {'2016': 1.05, '2017': 1.05, '2018': 1.05},
'2': {'2016': 1.80, '2017': 1.80, '2018': 1.80}
}
closure_syst_100mm = {
'0': {'2016': 1.20, '2017': 1.20, '2018': 1.20},
'1': {'2016': 1.05, '2017': 1.05, '2018': 1.05},
'2': {'2016': 1.20, '2017': 1.20, '2018': 1.20}
}
closure_syst_1000mm = {
'0': {'2016': 1.10, '2017': 1.10, '2018': 1.10},
'1': {'2016': 1.05, '2017': 1.05, '2018': 1.05},
'2': {'2016': 1.20, '2017': 1.20, '2018': 1.20}
}
cat_translator = {u'29': '0', u'30': '1', u'31': '2'}
with open(sys.argv[1]) as f:
data = json.load(f)
tot_yields = {}
for name,yields in data.iteritems():
sample = name.split('_sig_')[-1]
cut = [token for token in name.split('_') if 'cut' in token][0]
if '29' not in cut and '30' not in cut and '31' not in cut:
continue
if '29' in cut or '30' in cut:
if 'vxy_zoom_' not in name:
continue
else:
if 'vxy_zoomzoom_' not in name:
continue
print "name ", name
if yields['A_sig'] == 0.0 and yields['B_sig'] == 0.0 and yields['C_sig'] == 0.0 and yields['D_sig'] == 0.0:
continue
if sample not in tot_yields:
tot_yields[sample] = {}
cut_num = cut.split('cut')[-1]
if cut_num not in tot_yields[sample]:
tot_yields[sample][cut_num] = {}
tot_yields[sample][cut_num]['A_sig'] = yields["A_sig"]
tot_yields[sample][cut_num]['B_sig'] = yields["B_sig"]
tot_yields[sample][cut_num]['C_sig'] = yields["C_sig"]
tot_yields[sample][cut_num]['D_sig'] = yields["D_sig"]
tot_yields[sample][cut_num]['A_bkg'] = max(yields["A_bkg"], 0.1)
tot_yields[sample][cut_num]['B_bkg'] = max(yields["B_bkg"], 0.1)
tot_yields[sample][cut_num]['C_bkg'] = max(yields["C_bkg"], 0.1)
tot_yields[sample][cut_num]['D_bkg'] = max(yields["D_bkg"], 0.1)
tot_yields[sample][cut_num]['c1'] = tot_yields[sample][cut_num]['B_bkg'] / tot_yields[sample][cut_num]['A_bkg']
tot_yields[sample][cut_num]['c2'] = tot_yields[sample][cut_num]['C_bkg'] / tot_yields[sample][cut_num]['A_bkg']
c1s = {}
c2s = {}
for sample in tot_yields:
if '161718' not in sample: continue
basename_no_year = sample.split('_161718')[0]
c1s[basename_no_year] = {}
c2s[basename_no_year] = {}
for cut_num,props in tot_yields[sample].items():
c1s[basename_no_year][cut_num] = props['c1']
c2s[basename_no_year][cut_num] = props['c2']
#! [part1]
# Define four categories labeled A, B, C and D, and
# set the observed yields in a map.
cats = OrderedDict()
obs_rates = OrderedDict()
sig_rates = OrderedDict()
systs = OrderedDict()
names = OrderedDict()
for sample in tot_yields:
if '161718' in sample: continue
if '2016' in sample:
year = '2016'
elif '2017' in sample:
year = '2017'
elif '2018' in sample:
year = '2018'
basename_no_year = sample.split('_'+year)[0]
if basename_no_year not in cats:
cats[basename_no_year] = []
obs_rates[basename_no_year] = {}
sig_rates[basename_no_year] = {}
systs[basename_no_year] = {}
names[basename_no_year] = basename_no_year
for cut in tot_yields[sample]:
cat_temp = cat_translator[cut] + 'match' + '_' + year
index = 0 if len(cats[basename_no_year]) == 0 else cats[basename_no_year][-1][0] + 1
cats[basename_no_year].append((index, 'A_' + cat_temp))
cats[basename_no_year].append((index, 'B_' + cat_temp))
cats[basename_no_year].append((index, 'C_' + cat_temp))
cats[basename_no_year].append((index, 'D_' + cat_temp))
obs_rates[basename_no_year]['A_' + cat_temp] = tot_yields[sample][cut]['A_bkg']
obs_rates[basename_no_year]['B_' + cat_temp] = tot_yields[sample][cut]['B_bkg']
obs_rates[basename_no_year]['C_' + cat_temp] = tot_yields[sample][cut]['C_bkg']
obs_rates[basename_no_year]['D_' + cat_temp] = tot_yields[sample][cut]['D_bkg']
sig_rates[basename_no_year]['A_' + cat_temp] = tot_yields[sample][cut]['A_sig']
sig_rates[basename_no_year]['B_' + cat_temp] = tot_yields[sample][cut]['B_sig']
sig_rates[basename_no_year]['C_' + cat_temp] = tot_yields[sample][cut]['C_sig']
sig_rates[basename_no_year]['D_' + cat_temp] = tot_yields[sample][cut]['D_sig']
systs[basename_no_year]["bkgA_norm_" + cat_temp] = tot_yields[sample][cut]['A_bkg']
#systs[basename_no_year]["c1_" + cat_temp] = tot_yields[sample][cut]['c1']
#systs[basename_no_year]["c2_" + cat_temp] = tot_yields[sample][cut]['c2']
systs[basename_no_year]["c1_" + cat_temp] = c1s[basename_no_year][cut]
systs[basename_no_year]["c2_" + cat_temp] = c2s[basename_no_year][cut]
closure_syst = {}
if '_1_' in sample:
closure_syst = closure_syst_1mm
elif '_10_' in sample:
closure_syst = closure_syst_10mm
elif '_100_' in sample:
closure_syst = closure_syst_100mm
elif '_1000_' in sample:
closure_syst = closure_syst_1000mm
systs[basename_no_year]["closure_" + cat_temp] = closure_syst[cat_translator[cut]][year]
systs[basename_no_year]["lumi_" + year] = lumi_syst[year]
systs[basename_no_year]["trig_MC_" + year] = trig_MC_syst[year]
systs[basename_no_year]["trig_data_" + year] = trig_data_syst[year]
systs[basename_no_year]["JES_" + year] = JES_syst[year]
systs[basename_no_year]["JER_" + year] = JER_syst[year]
#systs[basename_no_year]["EGM_ID_" + year] = EGM_ID_syst[year]
systs[basename_no_year]["veto_ID_" + year] = veto_ID_syst[year]
systs[basename_no_year]["GM_ID_" + cat_temp] = GM_ID_syst[cat_translator[cut]][year]
systs[basename_no_year]["dSA_ID_prompt_" + year] = dSA_ID_prompt_syst[year]
systs[basename_no_year]["dSA_ID_displ_" + year] = dSA_ID_displ_syst[year]
systs[basename_no_year]["dSA_reco_prompt_" + year] = dSA_reco_prompt_syst[year]
#! [part1]
for name, cat, obs_rate, sig_rate, syst in zip(names.values(), cats.values(), obs_rates.values(), sig_rates.values(), systs.values()):
cb = ch.CombineHarvester()
cb.SetVerbosity(0)
#! [part2]
print "cat ", cat
cb.AddObservations(["*"], [""], ["13TeV"], [""], cat)
cb.AddProcesses( ["*"], [""], ["13TeV"], [""], ["sig"], cat, True)
cb.AddProcesses( ["*"], [""], ["13TeV"], [""], ["bkg"], cat, False)
cb.cp().ForEachObs(lambda x: x.set_rate(obs_rate[x.bin()]))
cb.cp().backgrounds().ForEachProc(lambda x: x.set_rate(1))
cb.cp().signals().ForEachProc(lambda x: x.set_rate(sig_rate[x.bin()]))
#! [part2]
#! [part3]
# Create a unqiue floating parameter in each bin
#print 'name ', sample
#print 'tot_yields name keys', tot_yields[sample].keys()
for y in ['2016', '2017', '2018']:
for m in ['0', '1', '2']:
if 'A_'+m+'match_'+y not in sig_rate:
continue
cb.cp().backgrounds().bin(['A_'+m+'match_'+y, 'B_'+m+'match_'+y, 'C_'+m+'match_'+y, 'D_'+m+'match_'+y]).AddSyst(cb, 'bkgA_norm_'+m+'match_'+y, "rateParam", ch.SystMap()(syst['bkgA_norm_'+m+'match_'+y]))
cb.cp().backgrounds().bin(['B_'+m+'match_'+y, 'D_'+m+'match_'+y]).AddSyst(cb, 'c1_'+m+'match', "rateParam", ch.SystMap()(syst['c1_'+m+'match_'+y]))
cb.cp().backgrounds().bin(['C_'+m+'match_'+y, 'D_'+m+'match_'+y]).AddSyst(cb, 'c2_'+m+'match', "rateParam", ch.SystMap()(syst['c2_'+m+'match_'+y]))
cb.cp().backgrounds().bin(['C_'+m+'match_'+y, 'D_'+m+'match_'+y]).AddSyst(cb, 'c2_'+m+'match', "rateParam", ch.SystMap()(syst['c2_'+m+'match_'+y]))
# background systs
cb.cp().backgrounds().bin(['C_'+m+'match_'+y]).AddSyst(cb, 'closure_'+m+'match', 'lnN', ch.SystMap()(syst['closure_'+m+'match_'+y]))
# signal systs
cb.cp().signals().bin(['A_'+m+'match_'+y, 'B_'+m+'match_'+y, 'C_'+m+'match_'+y, 'D_'+m+'match_'+y]).AddSyst(cb, 'lumi_'+y, 'lnN', ch.SystMap()(syst['lumi_'+y]))
cb.cp().signals().bin(['A_'+m+'match_'+y, 'B_'+m+'match_'+y, 'C_'+m+'match_'+y, 'D_'+m+'match_'+y]).AddSyst(cb, 'trig_MC', 'lnN', ch.SystMap()(syst['trig_MC_'+y]))
cb.cp().signals().bin(['A_'+m+'match_'+y, 'B_'+m+'match_'+y, 'C_'+m+'match_'+y, 'D_'+m+'match_'+y]).AddSyst(cb, 'trig_data', 'lnN', ch.SystMap()(syst['trig_data_'+y]))
cb.cp().signals().bin(['A_'+m+'match_'+y, 'B_'+m+'match_'+y, 'C_'+m+'match_'+y, 'D_'+m+'match_'+y]).AddSyst(cb, 'JES', 'lnN', ch.SystMap()(syst['JES_'+y]))
cb.cp().signals().bin(['A_'+m+'match_'+y, 'B_'+m+'match_'+y, 'C_'+m+'match_'+y, 'D_'+m+'match_'+y]).AddSyst(cb, 'JER_'+y, 'lnN', ch.SystMap()(syst['JER_'+y]))
#cb.cp().signals().bin(['A_'+m+'match_'+y, 'B_'+m+'match_'+y, 'C_'+m+'match_'+y, 'D_'+m+'match_'+y]).AddSyst(cb, 'EGM_ID', 'lnN', ch.SystMap()(syst['EGM_ID_'+y]))
cb.cp().signals().bin(['A_'+m+'match_'+y, 'B_'+m+'match_'+y, 'C_'+m+'match_'+y, 'D_'+m+'match_'+y]).AddSyst(cb, 'veto_ID', 'lnN', ch.SystMap()(syst['veto_ID_'+y]))
cb.cp().signals().bin(['A_'+m+'match_'+y, 'B_'+m+'match_'+y, 'C_'+m+'match_'+y, 'D_'+m+'match_'+y]).AddSyst(cb, 'GM_ID_'+m+'match', 'lnN', ch.SystMap()(syst['GM_ID_'+m+'match_'+y]))
cb.cp().signals().bin(['A_'+m+'match_'+y, 'B_'+m+'match_'+y, 'C_'+m+'match_'+y, 'D_'+m+'match_'+y]).AddSyst(cb, 'dSA_ID_prompt', 'lnN', ch.SystMap()(syst['dSA_ID_prompt_'+y]))
cb.cp().signals().bin(['A_'+m+'match_'+y, 'B_'+m+'match_'+y, 'C_'+m+'match_'+y, 'D_'+m+'match_'+y]).AddSyst(cb, 'dSA_ID_displ', 'lnN', ch.SystMap()(syst['dSA_ID_displ_'+y]))
cb.cp().signals().bin(['A_'+m+'match_'+y, 'B_'+m+'match_'+y, 'C_'+m+'match_'+y, 'D_'+m+'match_'+y]).AddSyst(cb, 'dSA_reco_prompt', 'lnN', ch.SystMap()(syst['dSA_reco_prompt_'+y]))
#! [part3]
#! [part4]
#cb.PrintAll();
#for cb_name, cb in cbs.iteritems():
print ">> Writing datacard for hist: ", name
if not os.path.exists(name):
os.mkdir("sig_" + name)
cb.WriteDatacard("sig_" + str(name) + "/datacard.txt")
def __init__(self, cb=None):
super(Datacards, self).__init__()
self.cb = cb
if self.cb is None:
self.cb = ch.CombineHarvester()
if log.isEnabledFor(logging.DEBUG):
self.cb.SetVerbosity(1)
self.configs = datacardconfigs.DatacardConfigs()
# common systematics
self.lumi_syst_args = [
"lumi_$ERA", "lnN",
ch.SystMap("era")(["7TeV", "8TeV"], 1.026)(["13TeV"], 1.046)
]
self.electron_efficiency_syst_args = [
"CMS_eff_e",
"lnN",
ch.SystMap("era")(["7TeV", "8TeV"], 1.02)(["13TeV"],
1.05) # copied from 8TeV
]
self.muon_efficiency_syst_args = [
"CMS_eff_m",
"lnN",
ch.SystMap("era")(["7TeV", "8TeV"], 1.02)(["13TeV"],
1.05) # copied from 8TeV
]
self.tau_efficiency_corr_syst_args = [
"CMS_eff_t_$ERA",
"lnN",
ch.SystMap("era",
"channel")(["7TeV", "8TeV"], ["mt", "et"],
1.08)(["7TeV", "8TeV"], ["tt"],
1.19)(["13TeV"], ["mt", "et", "tt"],
1.05) # copied from 8TeV
]
self.tau_efficiency_syst_args = [
"CMS_eff_t_$CHANNEL_$ERA",
"lnN",
ch.SystMap("era",
"channel")(["7TeV", "8TeV"], ["mt", "et"],
1.08)(["7TeV", "8TeV"], ["tt"],
1.19)(["13TeV"], ["mt", "et", "tt"],
1.03) # copied from 8TeV
]
self.btag_efficiency_syst_args = [
"CMS_eff_b_$ERA",
"lnN",
ch.SystMap("era", "channel")(["13TeV"], ["mt"],
0.96) # copied from 8TeV
(["13TeV"], ["et"], 0.96) # copied from 8TeV
(["13TeV"], ["em"], 0.93) # copied from 8TeV
(["13TeV"], ["tt"], 0.93) # copied from 8TeV
]
self.met_scale_syst_args = [
"CMS_$ANALYSIS_scale_met_$ERA",
"lnN",
ch.SystMap("era",
"process")(["13TeV"], ["ggH", "qqH", "WH", "ZH", "VH"],
0.98) # copied from 8TeV
(["13TeV"],
["ZTT", "ZLL", "ZL", "ZJ", "TTJ", "TT", "VV", "WJ", "W"],
1.03) # copied from 8TeV
]
self.ztt_cross_section_syst_args = [
"CMS_$ANALYSIS_zttNorm_$ERA", "lnN",
ch.SystMap("era",
"process")(["7TeV", "8TeV"], ["ZTT", "ZLL", "ZL", "ZJ"],
1.03)(["13TeV"], ["ZTT", "ZLL", "ZL", "ZJ"],
1.04)
]
self.ttj_cross_section_syst_args = [
"CMS_$ANALYSIS_ttjNorm_$ERA",
"lnN",
ch.SystMap("era", "process")(["7TeV"], ["TTJ"],
1.08)(["8TeV"], ["TTJ"],
1.1)(["13TeV"], ["TTJ", "TT"],
1.06) # copied from 8TeV
]
self.ttj_extrapol_syst_args = [
"CMS_$ANALYSIS_ttjExtrapol_$ERA",
"lnN",
ch.SystMap("era", "process")(["7TeV"], ["TTJ"],
1.08)(["8TeV"], ["TTJ"],
1.1)(["13TeV"], ["TTJ", "TT"],
1.10) # copied from 8TeV
]
#self.singlet_cross_section_syst_args = [
#"CMS_$ANALYSIS_singletNorm_$ERA",
#"lnN",
#ch.SystMap("era", "process")
#(["13TeV"], [], 1.04)
self.vv_cross_section_syst_args = [
"CMS_$ANALYSIS_vvNorm_$ERA",
"lnN",
ch.SystMap("era", "process")(["7TeV", "8TeV"], ["VV"],
1.15)(["13TeV"], ["VV"],
1.10) # copied from 8TeV
]
self.wj_cross_section_syst_args = [
"CMS_$ANALYSIS_wjNorm_$CHANNEL_$ERA",
"lnN",
ch.SystMap("era", "process",
"channel")(["7TeV", "8TeV"], ["WJ"], ["mt", "et"],
1.2)(["13TeV"], ["WJ", "W"], ["mt", "et"],
1.04) # copied from 8TeV
]
self.wj_extrapol_syst_args = [
"CMS_$ANALYSIS_wjExtrapol_$CHANNEL_$ERA",
"lnN",
ch.SystMap("era", "process",
"channel")(["7TeV", "8TeV"], ["WJ"], ["mt", "et"],
1.2)(["13TeV"], ["WJ", "W"], ["mt", "et"],
1.2) # copied from 8TeV
]
self.qcd_syst_args = [
"CMS_$ANALYSIS_qcdSyst_$BIN_$ERA",
"lnN",
ch.SystMap("era", "process",
"bin")(["13TeV"], ["QCD"],
["mt_inclusive", "et_inclusive"],
1.06) # copied from 8TeV
(["13TeV"], ["QCD"], ["mt_0jet_high"], 1.1) # copied from 8TeV
(["13TeV"], ["QCD"], ["mt_0jet_low"], 1.1) # copied from 8TeV
(["13TeV"], ["QCD"], ["mt_1jet_high"], 1.1) # copied from 8TeV
(["13TeV"], ["QCD"], ["mt_1jet_low"], 1.1) # copied from 8TeV
(["13TeV"], ["QCD"], ["mt_2jet_vbf"], 1.3) # copied from 8TeV
(["13TeV"], ["QCD"], ["et_0jet_high"], 1.06) # copied from 8TeV
(["13TeV"], ["QCD"], ["et_0jet_low"], 1.06) # copied from 8TeV
(["13TeV"], ["QCD"], ["et_1jet_high"], 1.1) # copied from 8TeV
(["13TeV"], ["QCD"], ["et_1jet_low"], 1.1) # copied from 8TeV
(["13TeV"], ["QCD"], ["et_2jet_vbf"], 1.3) # copied from 8TeV
(["13TeV"], ["QCD"], ["tt_inclusive"], 1.35) # copied from 8TeV
]
self.zllFakeTau_syst_args = [
"CMS_$ANALYSIS_eFakeTau_$CHANNEL_$ERA", "lnN",
ch.SystMap("era", "process",
"channel")(["7TeV", "8TeV"], ["ZLL"], ["mt", "et"],
1.30)(["13TeV"], ["ZLL", "ZL", "ZJ"],
["mt", "tt"],
1.15)(["13TeV"], ["ZLL", "ZL", "ZJ"],
["et"], 1.30)
]
self.zee_norm_syst_args = [
"CMS_$ANALYSIS_zeeNorm_$ERA", "lnN",
ch.SystMap("era", "process")(["13TeV"], ["ZLL", "ZL"], 1.03)
]
self.jec_syst_args = [
"CMS_scale_j_$ERA", "shape",
ch.SystMap("era")(["13TeV"], 1.0)
]
self.tau_es_syst_args = [
"CMS_scale_t_$CHANNEL_$ERA", "shape",
ch.SystMap("era", "channel")(["13TeV"], ["mt"],
1.0)(["13TeV"], ["et"],
1.0)(["13TeV"], ["tt"], 1.0)
]
self.ele_es_syst_args = [
"CMS_scale_e_$CHANNEL_$ERA", "shape",
ch.SystMap("era", "channel")(["13TeV"], ["em"], 1.0)(["13TeV"],
["et"], 1.0)
]
self.probetau_es_syst_args = [
"CMS_scale_probetau_$CHANNEL_$ERA", "shape",
ch.SystMap("era", "channel")(["13TeV"], ["et"], 1.0)
]
self.probeele_es_syst_args = [
"CMS_scale_probeele_$CHANNEL_$ERA", "shape",
ch.SystMap("era", "channel")(["13TeV"], ["et"], 1.0)
]
self.tagele_es_syst_args = [
"CMS_scale_tagele_$CHANNEL_$ERA", "shape",
ch.SystMap("era", "channel")(["13TeV"], ["et"], 1.0)
]
self.massres_syst_args = [
"CMS_scale_massRes_$CHANNEL_$ERA", "shape",
ch.SystMap("era", "channel")(["13TeV"], ["et"], 1.0)
]
# https://twiki.cern.ch/twiki/bin/view/LHCPhysics/CERNYellowReportPageAt1314TeV#s_13_0_TeV
self.htt_qcd_scale_syst_args = [
"QCD_scale_$PROCESS", "lnN",
ch.SystMap("era",
"process")(["13TeV"], ["ggH"],
1.079)(["13TeV"], ["qqH"],
1.007)(["13TeV"], ["VH"],
1.015)(["13TeV"], ["WH"],
1.015)(["13TeV"],
["ZH"], 1.038)
]
self.htt_pdf_scale_syst_args = [
"PDF_scale_$PROCESS", "lnN",
ch.SystMap("era",
"process")(["13TeV"], ["ggH"],
1.071)(["13TeV"], ["qqH"],
1.032)(["13TeV"], ["VH"],
1.022)(["13TeV"], ["WH"],
1.022)(["13TeV"],
["ZH"], 1.022)
]
self.ztt_pdf_scale_syst_args = [
"PDF_scale_$PROCESS", "lnN",
ch.SystMap("era", "process")(["13TeV"], ["ZTT"], 1.015)
]
self.ztt_pdf_scale_syst_args = [
"PDF_scale_$PROCESS", "lnN",
ch.SystMap("era", "process")(["13TeV"], ["ZTT"], 1.015)
]
# CMS AN-13-262 (v8, table 3)
self.htt_ueps_syst_args = [
"UEPS",
"lnN",
ch.SystMap("era", "process",
"bin")(["13TeV"], ["ggH"], ["mt_0jet_high"],
1.060) # copied from 8TeV
(["13TeV"], ["ggH"], ["mt_0jet_low"], 1.073) # copied from 8TeV
(["13TeV"], ["ggH"], ["mt_1jet_high"], 0.996) # copied from 8TeV
(["13TeV"], ["ggH"], ["mt_1jet_low"], 1.007) # copied from 8TeV
(["13TeV"], ["ggH"], ["mt_2jet_vbf"], 0.988) # copied from 8TeV
(["13TeV"], ["ggH"], ["et_0jet_high"], 1.060) # copied from 8TeV
(["13TeV"], ["ggH"], ["et_0jet_low"], 1.073) # copied from 8TeV
(["13TeV"], ["ggH"], ["et_1jet_high"], 0.996) # copied from 8TeV
(["13TeV"], ["ggH"], ["et_1jet_low"], 1.007) # copied from 8TeV
(["13TeV"], ["ggH"], ["et_2jet_vbf"], 0.988) # copied from 8TeV
(["13TeV"], ["ggH"], ["em_0jet_high"], 1.063) # copied from 8TeV
(["13TeV"], ["ggH"], ["em_0jet_low"], 1.089) # copied from 8TeV
(["13TeV"], ["ggH"], ["em_1jet_high"], 1.004) # copied from 8TeV
(["13TeV"], ["ggH"], ["em_1jet_low"], 1.000) # copied from 8TeV
(["13TeV"], ["ggH"], ["em_2jet_vbf"], 0.988) # copied from 8TeV
(["13TeV"], ["ggH"], ["tt_inclusive"], 1.025) # copied from 8TeV
(["13TeV"], ["qqH", "WH", "ZH", "VH"], ["mt_0jet_high"],
1.028) # copied from 8TeV
(["13TeV"], ["qqH", "WH", "ZH", "VH"], ["mt_0jet_low"],
1.018) # copied from 8TeV
(["13TeV"], ["qqH", "WH", "ZH", "VH"], ["mt_1jet_high"],
0.954) # copied from 8TeV
(["13TeV"], ["qqH", "WH", "ZH", "VH"], ["mt_1jet_low"],
0.946) # copied from 8TeV
(["13TeV"], ["qqH", "WH", "ZH", "VH"], ["mt_2jet_vbf"],
0.893) # copied from 8TeV
(["13TeV"], ["qqH", "WH", "ZH", "VH"], ["et_0jet_high"],
1.028) # copied from 8TeV
(["13TeV"], ["qqH", "WH", "ZH", "VH"], ["et_0jet_low"],
1.018) # copied from 8TeV
(["13TeV"], ["qqH", "WH", "ZH", "VH"], ["et_1jet_high"],
0.954) # copied from 8TeV
(["13TeV"], ["qqH", "WH", "ZH", "VH"], ["et_1jet_low"],
0.946) # copied from 8TeV
(["13TeV"], ["qqH", "WH", "ZH", "VH"], ["et_2jet_vbf"],
0.893) # copied from 8TeV
(["13TeV"], ["qqH", "WH", "ZH", "VH"], ["em_0jet_high"],
1.042) # copied from 8TeV
(["13TeV"], ["qqH", "WH", "ZH", "VH"], ["em_0jet_low"],
1.035) # copied from 8TeV
(["13TeV"], ["qqH", "WH", "ZH", "VH"], ["em_1jet_high"],
0.978) # copied from 8TeV
(["13TeV"], ["qqH", "WH", "ZH", "VH"], ["em_1jet_low"],
0.984) # copied from 8TeV
(["13TeV"], ["qqH", "WH", "ZH", "VH"], ["em_2jet_vbf"],
0.893) # copied from 8TeV
(["13TeV"], ["qqH", "WH", "ZH", "VH"], ["tt_inclusive"],
1.025) # copied from 8TeV
]
def main():
if len(sys.argv) < 2:
print 'Error! No data filename specified (in json format). Exiting...'
cat_translator = {u'28': '0', u'29': '1', u'30': '2'}
with open(sys.argv[1]) as f:
data = json.load(f)
tot_yields = {}
for name,yields in data.iteritems():
if 'vtx_sign' not in name: continue
print "name ", name
sample = name.split('_sig_')[-1]
cut = [token for token in name.split('_') if 'cut' in token][0]
print 'cut ', cut
if '28' not in cut and '29' not in cut and '30' not in cut: continue
if sample not in tot_yields:
tot_yields[sample] = {}
cut_num = cut.split('cut')[-1]
if cut_num not in tot_yields[sample]:
tot_yields[sample][cut_num] = {}
tot_yields[sample][cut_num]['A_sig'] = yields["A_sig"]
tot_yields[sample][cut_num]['B_sig'] = yields["B_sig"]
tot_yields[sample][cut_num]['C_sig'] = yields["C_sig"]
tot_yields[sample][cut_num]['D_sig'] = yields["D_sig"]
tot_yields[sample][cut_num]['A_bkg'] = max(yields["A_bkg"], 0.1)
tot_yields[sample][cut_num]['B_bkg'] = max(yields["B_bkg"], 0.1)
tot_yields[sample][cut_num]['C_bkg'] = max(yields["C_bkg"], 0.1)
tot_yields[sample][cut_num]['D_bkg'] = max(yields["D_bkg"], 0.1)
tot_yields[sample][cut_num]['c1'] = tot_yields[sample][cut_num]['B_bkg'] / tot_yields[sample][cut_num]['A_bkg']
tot_yields[sample][cut_num]['c2'] = tot_yields[sample][cut_num]['C_bkg'] / tot_yields[sample][cut_num]['A_bkg']
#! [part1]
# Define four categories labeled A, B, C and D, and
# set the observed yields in a map.
for sample in tot_yields:
cats = []
obs_rates = {}
sig_rates = {}
for cut in tot_yields[sample]:
cat = cat_translator[cut] + 'match'
cats.append('A_' + cat)
cats.append('B_' + cat)
cats.append('C_' + cat)
cats.append('D_' + cat)
obs_rates['A_' + cat] = tot_yields[sample][cut]['A_bkg']
obs_rates['B_' + cat] = tot_yields[sample][cut]['B_bkg']
obs_rates['C_' + cat] = tot_yields[sample][cut]['C_bkg']
obs_rates['D_' + cat] = tot_yields[sample][cut]['D_bkg']
sig_rates['A_' + cat] = tot_yields[sample][cut]['A_sig']
sig_rates['B_' + cat] = tot_yields[sample][cut]['B_sig']
sig_rates['C_' + cat] = tot_yields[sample][cut]['C_sig']
sig_rates['D_' + cat] = tot_yields[sample][cut]['D_sig']
cats_with_number = []
for num in range(0, len(cats)):
cats_with_number.append((num, cats[num]))
#! [part1]
#! [part2]
cb = ch.CombineHarvester()
cb.SetVerbosity(0)
cb.AddObservations(["*"], [""], ["13TeV"], [""], cats_with_number)
cb.AddProcesses( ["*"], [""], ["13TeV"], [""], ["sig"], cats_with_number, True)
cb.AddProcesses( ["*"], [""], ["13TeV"], [""], ["bkg"], cats_with_number, False)
cb.cp().ForEachObs(lambda x: x.set_rate(obs_rates[x.bin()]))
#cb.cp().ForEachObs([&](ch::Observation *x) {
# x->set_rate(obs_rates[x->bin()]);
# });
cb.cp().backgrounds().ForEachProc(lambda x: x.set_rate(1))
#cb.cp().backgrounds().ForEachProc([](ch::Process *x) {
# x->set_rate(1);
# });
cb.cp().signals().ForEachProc(lambda x: x.set_rate(sig_rates[x.bin()]))
#cb.cp().signals().ForEachProc([&](ch::Process *x) {
# x->set_rate(sig_rates[x->bin()]);
# });
#! [part2]
#! [part3]
# Create a unqiue floating parameter in each bin
print 'name ', sample
print 'tot_yields name keys', tot_yields[sample].keys()
cb.cp().backgrounds().bin(["A_0match", "B_0match", "C_0match", "D_0match"]).AddSyst(cb, "bkgA_norm_0match", "rateParam", ch.SystMap()(tot_yields[sample][u'28']['A_bkg']))
cb.cp().backgrounds().bin(["B_0match", "D_0match"]).AddSyst(cb, "c1_0match", "rateParam", ch.SystMap()(tot_yields[sample][u'28']['c1']))
cb.cp().backgrounds().bin(["C_0match", "D_0match"]).AddSyst(cb, "c2_0match", "rateParam", ch.SystMap()(tot_yields[sample][u'28']['c2']))
cb.cp().backgrounds().bin(["A_1match", "B_1match", "C_1match", "D_1match"]).AddSyst(cb, "bkgA_norm_1match", "rateParam", ch.SystMap()(tot_yields[sample][u'29']['A_bkg']))
cb.cp().backgrounds().bin(["B_1match", "D_1match"]).AddSyst(cb, "c1_1match", "rateParam", ch.SystMap()(tot_yields[sample][u'29']['c1']))
cb.cp().backgrounds().bin(["C_1match", "D_1match"]).AddSyst(cb, "c2_1match", "rateParam", ch.SystMap()(tot_yields[sample][u'29']['c2']))
cb.cp().backgrounds().bin(["A_2match", "B_2match", "C_2match", "D_2match"]).AddSyst(cb, "bkgA_norm_2match", "rateParam", ch.SystMap()(tot_yields[sample][u'30']['A_bkg']))
cb.cp().backgrounds().bin(["B_2match", "D_2match"]).AddSyst(cb, "c1_2match", "rateParam", ch.SystMap()(tot_yields[sample][u'30']['c1']))
cb.cp().backgrounds().bin(["C_2match", "D_2match"]).AddSyst(cb, "c2_2match", "rateParam", ch.SystMap()(tot_yields[sample][u'30']['c2']))
#! [part3]
#! [part4]
#cb.PrintAll();
print ">> Writing datacard for hist: ", sample
if not os.path.exists(sample):
os.mkdir("sig_" + sample)
cb.WriteDatacard("sig_" + str(sample) + "/datacard.txt")
def create_datacards(channel, method):
backgrounds = {"ZTT": "ztt", "VV": "vv", "W": "wj", "QCD": "qcd"}
backgrounds.update({
"TT": "ttj",
"ZLL": "zll"
} if channel == "em" else {
"TTT": "ttt",
"TTJJ": "ttjj",
"ZL": "zl",
"ZJ": "zj"
})
##Combine harvester instance
cb = ch.CombineHarvester()
#Instance for extracting histograms
sample_settings = samples.Samples()
config_list = []
##weights
cut_info = yaml.load(
open(os.environ["CMSSW_BASE"] + "/src/FlavioOutput/Configs/cuts.yaml",
"r"))
parameter_info = yaml.load(
open(
os.environ["CMSSW_BASE"] +
"/src/FlavioOutput/Configs/parameter.yaml", "r"))
weights = []
for index, category in enumerate(
["(njetspt30==0)", "(njetspt30==1)",
"(njetspt30>1)"]): #, "(nbtag==2)"]):
#cut_strings = [parameter_info[param][4] for param in cut_info[index][channel].keys()]
#cut_values, cut_side = [[entry[index2] for entry in cut_info[index][channel].values()] for index2 in [0,1]]
weights.append(
{ #"cut_based": "*".join([cut_strings[index2].format(side = side, cut = value) for index2, (side, value) in enumerate(zip(cut_side, cut_values))] + [category]),
"cut_BDT": "(BDT_forcut_score>0.7)*" + category,
"cut_Ada_BDT": "(BDT_Ada_forcut_score>0.0)*" + category,
"BDT": category,
"Ada_BDT": category
})
##Fill combine harvester with categories/processes
for category in categories + controlregions:
##Add data/signal
cb.AddObservations(["*"], ["lfv"], ["13TeV"], [channel], [category])
if not "CR" in category[1]:
cb.AddProcesses(["*"], ["lfv"], ["13TeV"], [channel],
["Z" + channel.upper()], [category], True)
##Config for each category
config = sample_settings.get_config(
[
getattr(samples.Samples, sample)
for sample in data.values() + {
True: ["z" + channel],
False: []
}["CR" not in category[1]] + backgrounds.values()
],
channel,
None,
estimationMethod="new",
weight=weights[category[0]][method])
config.pop("legend_markers")
config += {
"filename": "input_" + method + "_nominal_" + category[1],
"plot_modules": ["ExportRoot"],
"file_mode": "UPDATE",
"directories": os.environ["MCPATH"],
"x_expressions": x[method],
"x_bins": x_bins[method],
"output_dir": output_dir + channel,
"no_cache": True
}
config["labels"] = [
category[1] + "/" + process for process in data.keys() + {
True: ["Z" + channel.upper()],
False: []
}["CR" not in category[1]] + backgrounds.keys()
]
config_list.append(config)
for process in backgrounds.keys():
##Add background
cb.AddProcesses(["*"], ["lfv"], ["13TeV"], [channel], [process],
[category], False)
##Fill combine with control regions
for CR in controlregions:
cb.cp().channel([channel]).bin([category[1]]).AddSyst(
cb, "scale_" + category[1].remove("_CR"), "rateParam",
ch.SystMap())
for category in catogories:
cb.cp().bin([category[0]
]).AddSyst(cb, "scale_" + category[1].remove("_CR"),
"rateParam", ch.SystMapFunc())
##Fill combine harvester with systematics
systematics_list = SystLib.SystematicLibary()
systematics_factory = systematics.SystematicsFactory()
for (systematic, process, category) in systematics_list.get_LFV_systs(
channel, lnN=True) + systematics_list.get_LFV_systs(channel,
shape=True):
cb.cp().channel([channel]).process(process).AddSyst(cb, *systematic)
if "W" in process and "QCD" not in process:
process.append("QCD")
if "QCD" in process and "W" not in process:
process.append("W")
if systematic[1] == "shape":
##Config for each systematic shift:
for category in categories + controlregions:
if "CR" in category[1] and "Z" + channel.upper() in process:
process.remove("Z" + channel.upper())
for shift in ["Down", "Up"]:
config = sample_settings.get_config(
[
getattr(samples.Samples,
dict(signals, **backgrounds)[sample])
for sample in process
],
channel,
None,
estimationMethod="new",
weight=weights[category[0]][method])
config.pop("legend_markers")
config += {
"filename":
"input_" + method + "_" + systematic[0].replace(
"$ERA", "13TeV").replace("$CHANNEL", channel) +
shift + "_" + category[1],
"plot_modules": ["ExportRoot"],
"file_mode":
"UPDATE",
"directories":
os.environ["MCPATH"],
"x_expressions":
x[method],
"x_bins":
x_bins[method],
"output_dir":
output_dir + channel,
"no_cache":
True
}
config["labels"] = [
category[1] + "/" + proc + "_" + systematic[0].replace(
"$ERA", "13TeV").replace("$CHANNEL", channel) +
shift for proc in process
]
if systematic[0].replace("$ERA", "13TeV").replace(
"$CHANNEL", channel) == "CMS_scale_j_13TeV":
systematics_settings = systematics_factory.get(
systematic[0].replace("$ERA", "13TeV").replace(
"$CHANNEL", channel))(config, "Total")
else:
systematics_settings = systematics_factory.get(
systematic[0].replace("$ERA", "13TeV").replace(
"$CHANNEL", channel))(config)
config = systematics_settings.get_config(1 if shift ==
"Up" else -1)
config_list.append(config)
pool = Pool(cpu_count())
for config in config_list:
pool.apply_async(harry_do_your_job, args=(config, ))
pool.close()
pool.join()
os.system("hadd {target}.root {root_files}*.root".format(
target=output_dir + channel + "/input_" + method,
root_files=output_dir + channel + "/input_" + method))
##Fill combine harvester with the shapes which were extracted before from harry.py
cb.cp().backgrounds().ExtractShapes(
output_dir + channel + "/input_" + method + ".root", "$BIN/$PROCESS",
"$BIN/$PROCESS_$SYSTEMATIC")
cb.cp().signals().ExtractShapes(
output_dir + channel + "/input_" + method + ".root", "$BIN/$PROCESS",
"$BIN/$PROCESS_$SYSTEMATIC")
#Write datacard and call combine
cb.WriteDatacard(
output_dir + channel + "/combined_" + method + ".txt",
output_dir + channel + "/combined_datacard_" + method + ".root")
for category in categories:
cb_copy = cb.cp()
cb_copy.FilterAll(lambda obj: obj.bin() != category[1])
cb_copy.WriteDatacard(
output_dir + channel + "/" + category[1] + "_" + method + ".txt",
output_dir + channel + "/" + category[1] + "_datacard_" + method +
".root")
