def computeBkgYieldsFromABCDData(self):
'''
Estimate background yields from data using the ABCD method
A = b_mass < 6.27 && hnl_charge == 0 (SR)
B = b_mass < 6.27 && hnl_charge != 0
C = b_mass > 6.27 && hnl_charge == 0
D = b_mass > 6.27 && hnl_charge != 0
N_A = N_B * N_C/N_D
'''
f_data = ROOT.TFile.Open('root://t3dcachedb.psi.ch:1094/'+self.data_file.filename, 'READ')
#quantity = Quantity(name_flat='hnl_mass', nbins=1, bin_min=0, bin_max=1000)
quantity = Quantity(name_flat='hnl_mass', nbins=1, bin_min=2.83268, bin_max=3.13932)
bin_selection = self.selection
#hist_data_B = PlottingTools.createHisto(self, f_data, 'signal_tree', quantity, branchname='flat', selection=bin_selection+' && b_mass<6.27 && hnl_charge!=0')
#hist_data_C = PlottingTools.createHisto(self, f_data, 'signal_tree', quantity, branchname='flat', selection=bin_selection+' && b_mass>6.27 && hnl_charge==0')
#hist_data_D = PlottingTools.createHisto(self, f_data, 'signal_tree', quantity, branchname='flat', selection=bin_selection+' && b_mass>6.27 && hnl_charge!=0')
hist_data_B = PlottingTools.createHisto(self, f_data, 'signal_tree', quantity, branchname='flat', selection=bin_selection+' && hnl_cos2d>0.993 && sv_prob<0.05')
hist_data_C = PlottingTools.createHisto(self, f_data, 'signal_tree', quantity, branchname='flat', selection=bin_selection+' && hnl_cos2d<0.993 && sv_prob>0.05')
hist_data_D = PlottingTools.createHisto(self, f_data, 'signal_tree', quantity, branchname='flat', selection=bin_selection+' && hnl_cos2d<0.993 && sv_prob<0.05')
#hist_data.Sumw2()
n_obs_data_B = hist_data_B.GetBinContent(1)
n_obs_data_C = hist_data_C.GetBinContent(1)
n_obs_data_D = hist_data_D.GetBinContent(1)
n_err_data_B = math.sqrt(hist_data_B.GetBinContent(1))
n_err_data_C = math.sqrt(hist_data_C.GetBinContent(1))
n_err_data_D = math.sqrt(hist_data_D.GetBinContent(1))
n_obs_data_A = n_obs_data_B * (n_obs_data_C / n_obs_data_D)
n_err_data_A = n_obs_data_A * (n_err_data_B / n_obs_data_B + n_err_data_C / n_obs_data_C + n_err_data_D / n_obs_data_D)
return int(n_obs_data_A), int(n_err_data_A)
def getSignalEfficiency(self):
'''
eff(bin) = N_flat(bin) / N_gen
N_gen = N_reco / filter_efficiency
'''
# get number of generated events
f = ROOT.TFile.Open('root://t3dcachedb.psi.ch:1094/'+self.signal_file.filename, 'READ')
n_reco = PlottingTools.getNminiAODEvts(self, f)
n_gen = n_reco / self.signal_file.filter_efficiency
# get number of selected reco events
quantity = Quantity(name_flat='hnl_mass', nbins=1, bin_min=0, bin_max=1000)
hist_flat_bin = PlottingTools.createHisto(self, f, 'signal_tree', quantity, branchname='flat', selection='ismatched==1' if self.selection=='' else 'ismatched==1 && '+self.selection)
n_selected_bin = hist_flat_bin.GetBinContent(1)
#print 'n_selected_bin',n_selected_bin
efficiency = n_selected_bin / n_gen
#print 'efficiency',efficiency
return efficiency
def computeApproxWeightQCDMCtoData(self, quantity, selection):
'''
weight = lumi_data / lumi_mc = N_data * sigma_mc / (N_mc * sigma_data) estimated as N_data / N_mc
'''
f_data = ROOT.TFile.Open('root://t3dcachedb.psi.ch:1094/'+self.data_file.filename, 'READ')
hist_data = PlottingTools.createHisto(self, f_data, 'signal_tree', quantity, branchname='flat', selection=selection)
hist_data.Sumw2()
n_obs_data = hist_data.GetBinContent(1)
n_err_data = math.sqrt(n_obs_data) #hist_data.GetBinError(1)
hist_mc_tot = PlottingTools.createWeightedHistoQCDMC(self, self.qcd_files, self.white_list, quantity=quantity, selection=selection)
n_obs_mc = hist_mc_tot.GetBinContent(1)
n_err_mc = math.sqrt(n_obs_mc) #hist_mc_tot.GetBinError(1)
weight = n_obs_data / n_obs_mc
if n_obs_data != 0 and n_obs_mc != 0:
err = weight* (n_err_data / n_obs_data + n_err_mc / n_obs_mc)
else:
err = 0
return weight, err