def MakeSingleJetBkgEstimate(base_dir_jet, channels=[], outputDir=None):
if not isinstance(channels, list):
channels = [channels]
if outputDir is not None:
if not os.path.isdir(outputDir):
os.makedirs(outputDir)
regions = ['EB', 'EE']
# get the pt bins in the format expected
# in the pickle files
pt_bins_jetfile = [str(x) for x in _ptbins[:-1]]
pt_bins_jetfile.append('max')
# regex expression to match directories
# where jet fake results should be found
# if the Corr directories are used
# it will prioritize to the minimum values
jet_dirs_key = 'JetSinglePhotonFakeNomIso'
#jet_dirs_key = 'JetFakeTemplateFitPlotsCorr(\d+)-(\d+)-(\d+)AsymIso'
# get the directories mapped to the integer values matched
# if no values are matched the directory is matched ot (0,0,0)
jet_dir_key_map = get_mapped_directory(base_dir_jet, jet_dirs_key)
for channel in channels:
file_key = 'results__%s__(EB|EE)__pt_(\d+)-(\d+|max).pickle' % channel
file_key_syst = 'results__syst__%s__(EB|EE)__pt_(\d+)-(\d+|max).pickle' % channel
jet_files = get_dirs_and_files(base_dir_jet, jet_dirs_key, file_key)
jet_files_syst = get_dirs_and_files(base_dir_jet, jet_dirs_key,
file_key_syst)
jet_pred = get_jet_single_fake_results(jet_files, jet_files_syst,
regions, pt_bins_jetfile,
jet_dir_key_map, base_dir_jet)
print 'Predicted Jet fakes for channel %s' % channel
for r1 in regions:
for idx, ptmin in enumerate(pt_bins_jetfile[:-1]):
ptmax = pt_bins_jetfile[idx + 1]
bin = (r1, ptmin, ptmax)
print 'Region %s, pt %s-%s' % (r1, ptmin, ptmax)
print 'Predicted Stat r = %s, predicted f = %s' % (
jet_pred['stat']['r'][bin], jet_pred['stat']['f'][bin])
print 'Predicted Syst r = %s, predicted f = %s' % (
jet_pred['syst']['r'][bin], jet_pred['syst']['f'][bin])
print 'Predicted Stat+Syst r = %s, predicted f = %s' % (
jet_pred['stat+syst']['r'][bin],
jet_pred['stat+syst']['f'][bin])
file = open(outputDir + '/jet_fake_results__%s.pickle' % channel, 'w')
pickle.dump(jet_pred, file)
file.close()
def MakeSingleJetBkgEstimate( base_dir_jet, channels=[], outputDir=None ) :
if not isinstance(channels, list ) :
channels = [channels]
if outputDir is not None :
if not os.path.isdir( outputDir ) :
os.makedirs( outputDir )
regions = ['EB', 'EE']
# get the pt bins in the format expected
# in the pickle files
pt_bins_jetfile = [str(x) for x in _ptbins[:-1]]
pt_bins_jetfile.append( 'max')
# regex expression to match directories
# where jet fake results should be found
# if the Corr directories are used
# it will prioritize to the minimum values
jet_dirs_key = 'JetSinglePhotonFakeNomIso'
#jet_dirs_key = 'JetFakeTemplateFitPlotsCorr(\d+)-(\d+)-(\d+)AsymIso'
# get the directories mapped to the integer values matched
# if no values are matched the directory is matched ot (0,0,0)
jet_dir_key_map = get_mapped_directory( base_dir_jet, jet_dirs_key )
for channel in channels :
file_key = 'results__%s__(EB|EE)__pt_(\d+)-(\d+|max).pickle' %channel
file_key_syst = 'results__syst__%s__(EB|EE)__pt_(\d+)-(\d+|max).pickle' %channel
jet_files = get_dirs_and_files( base_dir_jet, jet_dirs_key, file_key )
jet_files_syst = get_dirs_and_files( base_dir_jet, jet_dirs_key, file_key_syst )
jet_pred = get_jet_single_fake_results( jet_files , jet_files_syst , regions, pt_bins_jetfile, jet_dir_key_map, base_dir_jet )
print 'Predicted Jet fakes for channel %s' %channel
for r1 in regions :
for idx, ptmin in enumerate(pt_bins_jetfile[:-1] ) :
ptmax = pt_bins_jetfile[idx+1]
bin = (r1,ptmin,ptmax)
print 'Region %s, pt %s-%s' %( r1,ptmin,ptmax)
print 'Predicted Stat r = %s, predicted f = %s' %( jet_pred['stat']['r'][bin], jet_pred['stat']['f'][bin] )
print 'Predicted Syst r = %s, predicted f = %s' %( jet_pred['syst']['r'][bin], jet_pred['syst']['f'][bin] )
print 'Predicted Stat+Syst r = %s, predicted f = %s' %( jet_pred['stat+syst']['r'][bin], jet_pred['stat+syst']['f'][bin] )
file = open( outputDir + '/jet_fake_results__%s.pickle' %channel, 'w' )
pickle.dump( jet_pred, file )
file.close()
def MakeSingleEleBkgEstimate(base_dir_ele,
base_dir_jet,
file_bin_map,
file_bin_map_syst,
outputDir=None,
el_selection='el',
namePostfix=''):
el_acc = ['elw', 'elwzcr']
if el_selection not in el_acc:
print 'Input region not recognized, must be %s' % (','.join(el_acc))
return
samplesLLG.deactivate_all_samples()
samplesLLG.activate_sample('Data')
eta_bins = {
'EB': [0.0, 0.1, 0.5, 1.0, 1.44],
'EE': [1.57, 2.1, 2.2, 2.3, 2.4, 2.5]
}
regions = ['EB', 'EE']
results_nom = get_ele_single_fakefactors(base_dir_ele, file_bin_map,
regions, eta_bins, el_selection)
results_syst = get_ele_single_fakefactors(base_dir_ele, file_bin_map_syst,
regions, eta_bins, el_selection)
file_key_lead = 'results__%sinvpixlead__(EB|EE)__pt_(\d+)-(\d+|max).pickle' % el_selection
file_key_lead_syst = 'results__syst__%sinvpixlead__(EB|EE)__pt_(\d+)-(\d+|max).pickle' % el_selection
#jet_dirs_key = 'JetFakeTemplateFitPlotsCorr(\d+)-(\d+)-(\d+)AsymIso'
jet_dirs_key = 'JetSinglePhotonFakeNomIso'
jet_dir_key_map = get_mapped_directory(base_dir_jet, jet_dirs_key)
jet_files_lead = get_dirs_and_files(base_dir_jet, jet_dirs_key,
file_key_lead)
jet_files_lead_syst = get_dirs_and_files(base_dir_jet, jet_dirs_key,
file_key_lead_syst)
print 'jet_files_lead'
print jet_files_lead
pt_bins_jetfile = [str(x) for x in _ptbins[:-1]]
pt_bins_jetfile.append('max')
pred_lead = get_jet_single_fake_results(jet_files_lead,
jet_files_lead_syst, regions,
pt_bins_jetfile, jet_dir_key_map,
base_dir_jet)
# get fake factors and binning from file
rfile_coarse = ROOT.TFile.Open(
'%s/ElectronFakeFitsRatioCoarseEta/results.root' % base_dir_ele)
rhist_coarse = rfile_coarse.Get('ff')
scaled_f = {}
for r1 in regions:
for idx, ptmin in enumerate(pt_bins_jetfile[:-1]):
ptmax = pt_bins_jetfile[idx + 1]
ff_lead = -1
bin_eb = rhist_coarse.GetYaxis().FindBin(1.0)
bin_ee = rhist_coarse.GetYaxis().FindBin(2.0)
bin_pt = rhist_coarse.GetXaxis().FindBin(int(ptmin))
if r1 == 'EB':
ff_lead = rhist_coarse.GetBinContent(bin_pt, bin_eb)
print 'ptmin = %s, ptmax = %s, histptmin = %d, histptmax = %d' % (
ptmin, ptmax,
rhist_coarse.GetXaxis().GetBinLowEdge(bin_pt),
rhist_coarse.GetXaxis().GetBinUpEdge(bin_pt))
print 'reg = EB, histetamin = %f, histetamax = %f' % (
rhist_coarse.GetYaxis().GetBinLowEdge(bin_eb),
rhist_coarse.GetYaxis().GetBinUpEdge(bin_eb))
if r1 == 'EE':
ff_lead = rhist_coarse.GetBinContent(bin_pt, bin_ee)
print 'reg = EE, histetamin = %f, histetamax = %f' % (
rhist_coarse.GetYaxis().GetBinLowEdge(bin_ee),
rhist_coarse.GetYaxis().GetBinUpEdge(bin_ee))
scaled_f[(r1, ptmin,
ptmax)] = pred_lead['stat+syst']['f'][(r1, ptmin,
ptmax)] * ff_lead
for r1 in regions:
for idx, ptmin in enumerate(pt_bins_jetfile[:-1]):
ptmax = pt_bins_jetfile[idx + 1]
print '%s, pt %s-%s' % (r1, ptmin, ptmax)
print 'Pred f = %s, scaled f = %s' % (pred_lead['stat+syst']['f'][
(r1, ptmin, ptmax)], scaled_f[(r1, ptmin, ptmax)])
results_subtracted = {}
results_subtracted['stat+syst'] = {}
results_subtracted['stat+syst']['result'] = {}
for (r1, ptmin,
ptmax), val in results_nom['stat+syst']['result'].iteritems():
print 'Reg = %s, pt %s-%s, N Pix CR* ff = %s, N Jet Pix CR * ff = %s, pred = %s' % (
r1, ptmin, ptmax, val, scaled_f[(r1, ptmin, ptmax)],
val - scaled_f[(r1, ptmin, ptmax)])
results_subtracted['stat+syst']['result'][(
r1, ptmin, ptmax)] = val - scaled_f[(r1, ptmin, ptmax)]
if outputDir is not None:
if not os.path.isdir(outputDir):
os.makedirs(outputDir)
file_raw = open(
outputDir +
'/electron_fake_results%s__noJetFakeSubtraction.pickle' %
namePostfix, 'w')
pickle.dump(results_nom, file_raw)
file_raw.close()
file_sub = open(
outputDir + '/electron_fake_results%s.pickle' % namePostfix, 'w')
pickle.dump(results_subtracted, file_sub)
file_sub.close()
def MakeSingleJetBkgEstimate( base_dir_jet, channel='', pt_bins=[],eta_bins={}, outputDir=None ) :
if outputDir is not None :
if not os.path.isdir( outputDir ) :
os.makedirs( outputDir )
regions = ['EB', 'EE']
# get the pt bins in the format expected
# in the pickle files
pt_bins_jetfile = [str(x) for x in pt_bins[:-1]]
pt_bins_jetfile.append( 'max')
# regex expression to match directories
# where jet fake results should be found
# if the Corr directories are used
# it will prioritize to the minimum values
jet_dirs_key = 'JetSinglePhotonFakeNomIso'
#jet_dirs_key = 'JetFakeTemplateFitPlotsCorr(\d+)-(\d+)-(\d+)AsymIso'
# get the directories mapped to the integer values matched
# if no values are matched the directory is matched ot (0,0,0)
jet_dir_key_map = get_mapped_directory( base_dir_jet, jet_dirs_key )
file_key_eta = 'results__%s__(\d\.\d\d-\d\.\d\d)__pt_(\d+)-(\d+|max).pickle' %channel
file_key_eta_syst = 'results__syst__%s__(\d\.\d\d-\d\.\d\d)__pt_(\d+)-(\d+|max).pickle' %channel
file_key = 'results__%s__(EB|EE)__pt_(\d+)-(\d+|max).pickle' %channel
file_key_syst = 'results__syst__%s__(EB|EE)__pt_(\d+)-(\d+|max).pickle' %channel
#jet_files_eta = get_dirs_and_files( base_dir_jet, jet_dirs_key, file_key_eta )
#jet_files_eta_syst = get_dirs_and_files( base_dir_jet, jet_dirs_key, file_key_eta_syst )
jet_files = get_dirs_and_files( base_dir_jet, jet_dirs_key, file_key )
jet_files_syst = get_dirs_and_files( base_dir_jet, jet_dirs_key, file_key_syst )
#update_jet_files_dict( jet_files, jet_files_eta )
#update_jet_files_dict( jet_files_syst, jet_files_eta_syst )
jet_pred = get_jet_single_fake_results( jet_files , jet_files_syst , regions, pt_bins_jetfile, jet_dir_key_map, base_dir_jet, eta_bins=eta_bins )
print 'Predicted Jet fakes for channel %s' %channel
for r1 in regions :
for idx, ptmin in enumerate(pt_bins_jetfile[:-1] ) :
ptmax = pt_bins_jetfile[idx+1]
if eta_bins :
for etamin, etamax in eta_bins[r1] :
bin = ('%.2f-%.2f'%(etamin,etamax), ptmin, ptmax)
print 'Region %s, pt %s-%s' %( bin[0],ptmin,ptmax)
print 'Predicted Stat r = %s, predicted f = %s' %( jet_pred['stat']['r'][bin], jet_pred['stat']['f'][bin] )
print 'Predicted Syst r = %s, predicted f = %s' %( jet_pred['syst']['r'][bin], jet_pred['syst']['f'][bin] )
print 'Predicted Stat+Syst r = %s, predicted f = %s' %( jet_pred['stat+syst']['r'][bin], jet_pred['stat+syst']['f'][bin] )
else :
bin = (r1,ptmin,ptmax)
print 'Region %s, pt %s-%s' %( r1,ptmin,ptmax)
print 'Predicted Stat r = %s, predicted f = %s' %( jet_pred['stat']['r'][bin], jet_pred['stat']['f'][bin] )
print 'Predicted Syst r = %s, predicted f = %s' %( jet_pred['syst']['r'][bin], jet_pred['syst']['f'][bin] )
print 'Predicted Stat+Syst r = %s, predicted f = %s' %( jet_pred['stat+syst']['r'][bin], jet_pred['stat+syst']['f'][bin] )
return jet_pred
def MakeSingleEleBkgEstimate(base_dir_ele, base_dir_jet, file_bin_map, file_bin_map_syst, eta_bins, elffdir, el_selection='elw', outputDir=None) :
el_acc = ['elw', 'elwzcr', 'elwzcrloose', 'elwsr', 'elwsrlowmt']
if el_selection not in el_acc :
print 'Input region not recognized, must be %s' %(','.join(el_acc) )
return
samplesLLG.deactivate_all_samples()
samplesLLG.activate_sample('Data')
regions = ['EB','EE']
results_nom = get_ele_single_fakefactors( base_dir_ele, file_bin_map, regions, eta_bins, el_selection )
# prevent killing from drawing too many plots
#results_syst = get_ele_single_fakefactors( base_dir_ele, file_bin_map_syst, regions, eta_bins, el_selection )
file_key_lead_eta = 'results__%sinvpixlead__(\d\.\d\d-\d\.\d\d)__pt_(\d+)-(\d+|max).pickle' %el_selection
file_key_lead_syst_eta = 'results__syst__%sinvpixlead__(\d\.\d\d-\d\.\d\d)__pt_(\d+)-(\d+|max).pickle' %el_selection
file_key_lead = 'results__%sinvpixlead__(EB|EE)__pt_(\d+)-(\d+|max).pickle' %el_selection
file_key_lead_syst = 'results__syst__%sinvpixlead__(EB|EE)__pt_(\d+)-(\d+|max).pickle' %el_selection
#jet_dirs_key = 'JetFakeTemplateFitPlotsCorr(\d+)-(\d+)-(\d+)AsymIso'
jet_dirs_key = 'JetSinglePhotonFakeNomIso'
jet_dir_key_map = get_mapped_directory( base_dir_jet, jet_dirs_key )
jet_files_lead_eta = get_dirs_and_files( base_dir_jet, jet_dirs_key, file_key_lead_eta )
jet_files_lead_syst_eta = get_dirs_and_files( base_dir_jet, jet_dirs_key, file_key_lead_syst_eta )
jet_files_lead = get_dirs_and_files( base_dir_jet, jet_dirs_key, file_key_lead )
jet_files_lead_syst = get_dirs_and_files( base_dir_jet, jet_dirs_key, file_key_lead_syst )
update_jet_files_dict( jet_files_lead, jet_files_lead_eta )
update_jet_files_dict( jet_files_lead_syst, jet_files_lead_syst_eta )
print 'jet_files_lead'
print jet_files_lead
pt_bins_jetfile = [str(x) for x in _ptbins[:-1]]
pt_bins_jetfile.append( 'max')
pred_lead = get_jet_single_fake_results( jet_files_lead, jet_files_lead_syst, regions, pt_bins_jetfile, jet_dir_key_map, base_dir_jet, eta_bins=eta_bins)
# get fake factors and binning from file
ff_man_coarse = FakeFactorManager( '%s/%s/results.pickle' %(base_dir_ele, elffdir), ['fake_ratio'] )
#ff_man_coarse = FakeFactorManager( '%s/ElectronFakeFitsRatio/results.pickle' %base_dir_ele, ['fake_ratio'] )
#ff_man_coarse = FakeFactorManager( '%s/ElectronFakeFitsRatioTAndPMCTemplate/results.pickle' %base_dir_ele, ['fake_ratio'] )
#ff_man_coarse = FakeFactorManager( '%s/ElectronFakeManualWithOlap2/results.pickle' %base_dir_ele, ['fake_ratio'] )
# determine number of eta bins used
# if only one eta bin is requested
# per region, some different behavior occurrs
eta_lens = []
for reg in regions :
eta_lens.append( len(eta_bins[reg] ) )
eta_lens = list(set(eta_lens))
single_eta_bin = False
if len(eta_lens) == 1 and eta_lens[0]==1 :
single_eta_bin = True
scaled_f = {}
for r1 in regions :
for idx, ptmin in enumerate(pt_bins_jetfile[:-1]) :
ptmax = pt_bins_jetfile[idx+1]
if r1 == 'EB' :
ff_lead = ff_man_coarse.get_pt_eta_ff( ptmin, ptmax, 0.0, 1.44 )
if r1 == 'EE' :
ff_lead = ff_man_coarse.get_pt_eta_ff( ptmin, ptmax, 1.57, 2.5 )
scaled_f[ (r1,ptmin,ptmax) ] = pred_lead['stat+syst']['f'][( r1,ptmin,ptmax)]*ff_lead
if not single_eta_bin :
for etamin, etamax in eta_bins[r1] :
ff_lead = ff_man_coarse.get_pt_eta_ff( ptmin, ptmax, etamin, etamax)
etabin = '%.2f-%.2f' %( etamin, etamax)
scaled_f [(etabin, ptmin, ptmax) ] =pred_lead['stat+syst']['f'][( etabin,ptmin,ptmax)]*ff_lead
for r1 in regions :
for idx, ptmin in enumerate(pt_bins_jetfile[:-1]) :
ptmax = pt_bins_jetfile[idx+1]
print '%s, pt %s-%s' %( r1, ptmin, ptmax)
print 'Pred f = %s, scaled f = %s' %( pred_lead['stat+syst']['f'][(r1,ptmin,ptmax)], scaled_f[(r1,ptmin,ptmax)] )
if not single_eta_bin :
#if eta_bins :
for etamin, etamax in eta_bins[r1] :
etabin = '%.2f-%.2f' %( etamin, etamax)
print '%s, pt %s-%s' %( etabin, ptmin, ptmax)
print 'Pred f = %s, scaled f = %s' %( pred_lead['stat+syst']['f'][(etabin,ptmin,ptmax)], scaled_f[(etabin,ptmin,ptmax)] )
results_subtracted = {}
results_subtracted['stat+syst'] = {}
results_subtracted['stat+syst']['result'] = {}
for reg_bin, val in results_nom['stat+syst']['result'].iteritems() :
r1 = reg_bin[0]
ptmin = reg_bin[1]
ptmax = reg_bin[2]
if reg_bin not in scaled_f :
continue
print 'Reg = %s, pt %s-%s, N Pix CR* ff = %s, N Jet Pix CR * ff = %s, pred = %s' %( r1, ptmin, ptmax, val, scaled_f[reg_bin], val- scaled_f[reg_bin] )
results_subtracted['stat+syst']['result'][reg_bin] = val-scaled_f[ reg_bin ]
if not single_eta_bin :
for r1 in regions :
for etamin, etamax in eta_bins[r1] :
etabin = '%.2f-%.2f' %( etamin, etamax)
print 'Reg = %s, pt %s-%s, N Pix CR* ff = %s, N Jet Pix CR * ff = %s, pred = %s' %( etabin, ptmin, ptmax, val, scaled_f[(etabin, ptmin, ptmax)], val- scaled_f[(etabin, ptmin, ptmax)] )
results_subtracted['stat+syst']['result'][(etabin, ptmin, ptmax)] = val-scaled_f[ (etabin,ptmin,ptmax) ]
if outputDir is not None :
if not os.path.isdir( outputDir ) :
os.makedirs( outputDir )
file_raw = open( outputDir + '/electron_fake_results%s__noJetFakeSubtraction.pickle' %el_selection, 'w' )
pickle.dump( results_nom, file_raw )
file_raw.close()
file_sub = open( outputDir + '/electron_fake_results%s.pickle' %el_selection, 'w' )
pickle.dump( results_subtracted, file_sub )
file_sub.close()
def MakeSingleEleBkgEstimate(base_dir_ele, base_dir_jet, file_bin_map, file_bin_map_syst, outputDir=None, el_selection='el', namePostfix='') :
el_acc = ['elw', 'elwzcr']
if el_selection not in el_acc :
print 'Input region not recognized, must be %s' %(','.join(el_acc) )
return
samplesLLG.deactivate_all_samples()
samplesLLG.activate_sample('Data')
eta_bins = {'EB': [0.0, 0.1, 0.5, 1.0, 1.44 ], 'EE' : [1.57, 2.1, 2.2, 2.3, 2.4, 2.5] }
regions = ['EB','EE']
results_nom = get_ele_single_fakefactors( base_dir_ele, file_bin_map, regions, eta_bins, el_selection )
results_syst = get_ele_single_fakefactors( base_dir_ele, file_bin_map_syst, regions, eta_bins, el_selection )
file_key_lead = 'results__%sinvpixlead__(EB|EE)__pt_(\d+)-(\d+|max).pickle' %el_selection
file_key_lead_syst = 'results__syst__%sinvpixlead__(EB|EE)__pt_(\d+)-(\d+|max).pickle' %el_selection
#jet_dirs_key = 'JetFakeTemplateFitPlotsCorr(\d+)-(\d+)-(\d+)AsymIso'
jet_dirs_key = 'JetSinglePhotonFakeNomIso'
jet_dir_key_map = get_mapped_directory( base_dir_jet, jet_dirs_key )
jet_files_lead = get_dirs_and_files( base_dir_jet, jet_dirs_key, file_key_lead )
jet_files_lead_syst = get_dirs_and_files( base_dir_jet, jet_dirs_key, file_key_lead_syst )
print 'jet_files_lead'
print jet_files_lead
pt_bins_jetfile = [str(x) for x in _ptbins[:-1]]
pt_bins_jetfile.append( 'max')
pred_lead = get_jet_single_fake_results( jet_files_lead, jet_files_lead_syst, regions, pt_bins_jetfile, jet_dir_key_map, base_dir_jet )
# get fake factors and binning from file
rfile_coarse = ROOT.TFile.Open( '%s/ElectronFakeFitsRatioCoarseEta/results.root' %base_dir_ele )
rhist_coarse = rfile_coarse.Get( 'ff')
scaled_f = {}
for r1 in regions :
for idx, ptmin in enumerate(pt_bins_jetfile[:-1]) :
ptmax = pt_bins_jetfile[idx+1]
ff_lead = -1
bin_eb = rhist_coarse.GetYaxis().FindBin( 1.0 )
bin_ee = rhist_coarse.GetYaxis().FindBin( 2.0 )
bin_pt = rhist_coarse.GetXaxis().FindBin( int(ptmin ) )
if r1 == 'EB' :
ff_lead = rhist_coarse.GetBinContent( bin_pt, bin_eb )
print 'ptmin = %s, ptmax = %s, histptmin = %d, histptmax = %d' %( ptmin, ptmax, rhist_coarse.GetXaxis().GetBinLowEdge( bin_pt ), rhist_coarse.GetXaxis().GetBinUpEdge( bin_pt ) )
print 'reg = EB, histetamin = %f, histetamax = %f' %( rhist_coarse.GetYaxis().GetBinLowEdge( bin_eb ), rhist_coarse.GetYaxis().GetBinUpEdge( bin_eb ) )
if r1 == 'EE' :
ff_lead = rhist_coarse.GetBinContent( bin_pt, bin_ee )
print 'reg = EE, histetamin = %f, histetamax = %f' %( rhist_coarse.GetYaxis().GetBinLowEdge( bin_ee ), rhist_coarse.GetYaxis().GetBinUpEdge( bin_ee ) )
scaled_f[ (r1,ptmin,ptmax) ] = pred_lead['stat+syst']['f'][( r1,ptmin,ptmax)]*ff_lead
for r1 in regions :
for idx, ptmin in enumerate(pt_bins_jetfile[:-1]) :
ptmax = pt_bins_jetfile[idx+1]
print '%s, pt %s-%s' %( r1, ptmin, ptmax)
print 'Pred f = %s, scaled f = %s' %( pred_lead['stat+syst']['f'][(r1,ptmin,ptmax)], scaled_f[(r1,ptmin,ptmax)] )
results_subtracted = {}
results_subtracted['stat+syst'] = {}
results_subtracted['stat+syst']['result'] = {}
for (r1, ptmin, ptmax), val in results_nom['stat+syst']['result'].iteritems() :
print 'Reg = %s, pt %s-%s, N Pix CR* ff = %s, N Jet Pix CR * ff = %s, pred = %s' %( r1, ptmin, ptmax, val, scaled_f[(r1, ptmin, ptmax)], val- scaled_f[(r1, ptmin, ptmax)] )
results_subtracted['stat+syst']['result'][(r1, ptmin, ptmax)] = val-scaled_f[ (r1,ptmin,ptmax) ]
if outputDir is not None :
if not os.path.isdir( outputDir ) :
os.makedirs( outputDir )
file_raw = open( outputDir + '/electron_fake_results%s__noJetFakeSubtraction.pickle' %namePostfix, 'w' )
pickle.dump( results_nom, file_raw )
file_raw.close()
file_sub = open( outputDir + '/electron_fake_results%s.pickle' %namePostfix, 'w' )
pickle.dump( results_subtracted, file_sub )
file_sub.close()