def __init__(self, year, inData):
print self.INPUT_PATH + '/fakeRate' + inData + year + '.root'
self.num = getObjFromFile(
self.INPUT_PATH + '/fakeRate' + inData + year + '.root',
'fakeRate_num')
self.denom = getObjFromFile(
self.INPUT_PATH + '/fakeRate' + inData + year + '.root',
'fakeRate_denom')
def getReweightingFunction(data="PU_2016_36000_XSecCentral", useMC=None):
# Data
histoData = getObjFromFile(dataDir + data + '.root', 'pileup')
histoData.Scale(1. / histoData.Integral())
# MC
if not useMC:
mcProfile = ROOT.TH1D('mc', 'mc', 100, 0, 100)
sys.stdout = open(os.devnull, 'w')
from SimGeneral.MixingModule.mix_2016_25ns_Moriond17MC_PoissonOOTPU_cfi import mix
sys.stdout = sys.__stdout__
for i, value in enumerate(mix.input.nbPileupEvents.probValue):
mcProfile.SetBinContent(i + 1, value) # pylint: disable=E1101
else:
mcProfile = useMC
mcProfile.Scale(1. / mcProfile.Integral())
# Create reweighting histo
reweightingHisto = histoData.Clone('reweightingHisto')
reweightingHisto.Divide(mcProfile)
# Define reweightingFunc
def reweightingFunc(nTrueInt):
return reweightingHisto.GetBinContent(
reweightingHisto.FindBin(nTrueInt))
return reweightingFunc
def __init__(self, inData, year):
self.num = getObjFromFile(
self.INPUT_PATH + '/fakeRate' + inData + year + '.root',
'fakeRate_num')
self.denom = getObjFromFile(
self.INPUT_PATH + '/fakeRate' + inData + year + '.root',
'fakeRate_denom')
self.FR = self.num.Clone()
self.FR.Divide(self.denom)
self.maxpt = self.FR.GetXaxis().GetBinUpEdge(
self.FR.GetXaxis().GetLast())
self.maxeta = self.FR.GetYaxis().GetBinUpEdge(
self.FR.GetYaxis().GetLast())
self.lastptbincenter = self.FR.GetXaxis().GetBinCenter(
self.FR.GetXaxis().GetLast())
self.lastetabincenter = self.FR.GetYaxis().GetBinCenter(
self.FR.GetYaxis().GetLast())
def getHist(self, subjob, name, subPath = None):
if subPath is None: subPath = self.path
if subPath.endswith('.root'):
hCounter = getObjFromFile(subPath, 'blackJackAndHookers/'+name)
return hCounter
else:
hCounter = None
for f in self.arrangeFilesInSubjobs()[subjob]:
if hCounter is None: hCounter = self.getHist(subjob,name, f)
else: hCounter.Add(self.getHist(subjob, name, f))
return hCounter
def getHist(self, name, subPath=None):
if subPath is None: subPath = self.path
if subPath.endswith('.root'):
hCounter = getObjFromFile(subPath, 'blackJackAndHookers/' + name)
return hCounter
else:
hCounter = None
listOfFiles = glob.glob(self.path + '/*.root')
print self.path + '/*.root'
for f in listOfFiles:
if hCounter is None: hCounter = self.getHist(name, f)
else: hCounter.Add(self.getHist(name, f))
return hCounter
def __init__(self, name, *args):
self.name = name
self.efficiency_num = []
self.efficiency_denom = None
self.input_path = None
self.bins = None
self.workingPoints = None
if len(
args
) == 2: #if (bins, workingpoints) is given, instantiate new histograms
self.bins = args[0]
self.workingPoints = args[1]
for i, WP in enumerate(self.workingPoints):
self.efficiency_num.append(
ROOT.TH1D(name + '_' + WP + '_num',
name + '_' + WP + '_num',
len(self.bins) - 1, self.bins))
self.efficiency_num[i].Sumw2()
self.efficiency_denom = ROOT.TH1D(name + '_denom', name + '_denom',
len(self.bins) - 1, self.bins)
self.efficiency_denom.Sumw2()
elif len(args) == 1 and isinstance(args[0], str):
self.input_path = args[0]
self.workingPoints = []
f = ROOT.TFile(self.input_path)
names = [key.GetName() for key in f.GetListOfKeys()]
f.Close()
for n in names:
if '_denom' in n:
self.efficiency_denom = getObjFromFile(self.input_path, n)
elif '_num' in n:
self.efficiency_num.append(
getObjFromFile(self.input_path, n))
self.workingPoints.append(n.split('_')[-2])
self.bins = getLowEdges(self.efficiency_denom)
def __init__(self, name, *args):
self.name = name
self.eff_numerator = None
self.eff_denominator = None
self.misid_numerator = None
self.misid_denominator = None
self.WP = None
self.path = None
if args and isinstance(args[0], list):
self.WP = args[0]
self.eff_numerator = TH1D(name + '_eff_numerator',
name + '_eff_numerator', len(self.WP), 0,
len(self.WP))
self.misid_numerator = TH1D(name + '_misid_numerator',
name + '_misid_numerator',
len(self.WP), 0, len(self.WP))
self.eff_denominator = TH1D(name + '_eff_denominator',
name + '_eff_denominator', 1, 0, 1)
self.misid_denominator = TH1D(name + '_misid_denominator',
name + '_misid_denominator', 1, 0, 1)
self.eff_numerator.Sumw2()
self.misid_numerator.Sumw2()
self.eff_denominator.Sumw2()
self.misid_denominator.Sumw2()
elif args and isinstance(args[0], str):
self.path = args[0]
self.eff_numerator = getObjFromFile(self.path,
name + '_eff_numerator')
self.eff_denominator = getObjFromFile(self.path,
name + '_eff_denominator')
self.misid_numerator = getObjFromFile(self.path,
name + '_misid_numerator')
self.misid_denominator = getObjFromFile(
self.path, name + '_misid_denominator')
else:
pass
'ptTau': 'Taus',
'etaTau': 'Taus',
'Mll': 'Events',
'met': 'Events'
}
log_map = {'ptTau': False, 'etaTau': True, 'Mll': True, 'met': True}
for var in varNames:
predicted = []
observed = None
store_data_observed = None
legendNames = []
for sample in sampleNames:
f = glob.glob(input_dir + '/' + sample + '/*' + var + '.root')
if sample == 'Data_' + args.year:
observed = getObjFromFile(f[0], 'inData_' + var)
store_data_observed = getObjFromFile(f[0],
'inData_' + var + '_sideBand')
else:
tmp_pred = getObjFromFile(f[0], 'inData_' + var)
tmp_pred_corr = getObjFromFile(f[0], 'inData_' + var + '_sideBand')
tmp_pred.Add(tmp_pred_corr, -1.)
predicted.append(tmp_pred)
legendNames.append(sample)
predicted.append(store_data_observed)
legendNames.append('Nonprompt prediction')
plt.plotDataVSMC(observed,
predicted,
xtitle_map[var],
legendNames,
xtitle_map = {'ptTau': 'P_{T}(#tau) [GeV]', 'etaTau': '#eta(#tau)', 'Mll': 'M(ll) [GeV]', 'met':'E_{T}^{miss} [GeV]'}
ytitle_map = {'ptTau': 'Taus', 'etaTau': 'Taus', 'Mll': 'Events', 'met':'Events'}
log_map = {'ptTau': False, 'etaTau': False, 'Mll': False, 'met':False}
categ_map = {'Categ_C': 'C: OSSF + #tau', 'Categ_D': 'D: OSOF + #tau', 'Categ_E': 'E: SS + #tau' , 'Categ_F':'F: e/#mu + #tau#tau', 'Check': 'FR measurement region'}
makeDirIfNeeded(args.output_dir+'/'+args.sample)
file_paths = glob.glob(args.input_dir + '/' + args.sample + '/*.root')
year = 2016
if '2017' in args.input_dir:
year = 2017
elif '2018' in args.input_dir:
year = 2018
for f in file_paths:
print f
h_name = f.rsplit('/', 1)[1]
h_name = h_name.split('.')[0]
print h_name
split_names = h_name.rsplit('_', 1)
categ_name = split_names[0]
var_name = split_names[1]
extraText = []
if categ_name in categ_map.keys():
extraText.append(plt.extraTextFormat(categ_map[categ_name], 0.5, 0.9))
dist_observed = getObjFromFile(f, h_name)
dist_predicted = getObjFromFile(f, h_name+'_sideBand')
plt.plotClosure(CleanNegativeBins(dist_observed), CleanNegativeBins(dist_predicted), xtitle_map[var_name], ytitle_map[var_name], args.sample, extraText, args.output_dir + '/' +args.sample+'/'+h_name, year, yLog=log_map[var_name])
background_hists_comb = []
signal_hists_comb = []
ntau = ['singletau', 'ditau']
#scales = (1./280., 1./281., 1./237)
scales = (1., 1., 1.)
for n in ntau:
tmp_mu_hists_bkgr = []
tmp_mu_hists_sig = []
for mu_cut_index in range(NUMBER_OF_MU_WP):
tmp_hists = []
for bkgr in background_names:
tmp_hists.append(
getObjFromFile(
basefolderInput + '/Combined/' + bkgr + '_' + n + '_' +
str(mu_cut_index) + '.root',
'combined_hist' + bkgr + n + str(mu_cut_index)))
tmp_mu_hists_bkgr.append(tmp_hists)
tmp_hists = []
for sig, scale in zip(signal_names, scales):
hist = getObjFromFile(
basefolderInput + '/Combined/' + sig + '_' + n + '_' +
str(mu_cut_index) + '.root',
'combined_hist' + sig + n + str(mu_cut_index))
hist.Scale(scale)
tmp_hists.append(hist)
tmp_mu_hists_sig.append(tmp_hists)
background_hists_comb.append(tmp_mu_hists_bkgr)
signal_hists_comb.append(tmp_mu_hists_sig)
for n in ntau:
def load_fakerate(self, name, path):
self.misid_numerator = getObjFromFile(path, name + '_misid_numerator')
self.misid_denominator = getObjFromFile(path,
name + '_misid_denominator')
def load_efficiency(self, name, path):
self.eff_numerator = getObjFromFile(path, name + '_eff_numerator')
self.eff_denominator = getObjFromFile(path, name + '_eff_denominator')
'/user/lwezenbe/private/PhD/Code/TauStudy/Significance/Data/' +
args.inputFile + '.conf')
sample = Sample.getSampleFromList(sampleList, args.sampleName)
#Is this sample signal or background
isSignal = False
keywords = ['SMS', 'TChi', 'Slep', 'Snu']
for key in keywords:
if key in sample.name: isSignal = True
#Initialize chain
Chain = sample.initTree()
#If SUSY, load in the hCounterSUSY
if isSignal:
hCounterSUSY = getObjFromFile(sample.path, 'hCounterSUSY')
#Initialize event selector
import eventSelector
evSel = eventSelector.eventSelector(Chain, int(args.ele_cut_index),
int(args.mu_cut_index))
#Initialize histograms to save
#These consist of the total number of events that are left after performing the event selection
#Each bin represents a different Old MVA Iso working point
Single_Tau_Output = ROOT.TH1D('singletau', 'singletau',
len(evSel.IsoWorkingPoints), 0,
len(evSel.IsoWorkingPoints))
Di_Tau_Output = ROOT.TH1D('ditau', 'ditau', len(evSel.IsoWorkingPoints), 0,
len(evSel.IsoWorkingPoints))
basefolder = '/storage_mnt/storage/user/lwezenbe/private/PhD/Results/TauStudy/Significance/Histos'