def compute_num_denom(type, bin, lep_cat, iLep, lepStr, base_cut, WP_tag,
WP_probe, FR_array, config, bin_NP):
#print 'Inside compute_num_denom'
this_base_cut = copy.deepcopy(base_cut)
this_num_denom = 0.0
if FR_array[bin][0] == 1 and lep_cat == 'ele_ele':
return 0.0
if FR_array[bin][0] == 0 and lep_cat == 'mu_mu':
return 0.0
## Apply cuts for this bin, probe lepton
#this_base_cut.append(lepStr+str(iLep+1)+'_lepMVA > '+str(WP_probe))
this_base_cut.append(lepStr + str(iLep + 1) + '_lepCut >= ' +
str(WP_probe))
this_base_cut.append(lepStr + str(iLep + 1) + '_isMuon == ' +
str(FR_array[bin][0]))
this_base_cut.append(lepStr + str(iLep + 1) + '_pt > ' +
str(FR_array[bin][1]))
this_base_cut.append(lepStr + str(iLep + 1) + '_pt < ' +
str(FR_array[bin][2]))
this_base_cut.append('abs(' + lepStr + str(iLep + 1) + '_eta) > ' +
str(FR_array[bin][3]))
this_base_cut.append('abs(' + lepStr + str(iLep + 1) + '_eta) < ' +
str(FR_array[bin][4]))
this_base_cut.append(lepStr + str(iLep + 1) + '_jetBTagCSV > ' +
str(FR_array[bin][5]))
this_base_cut.append(lepStr + str(iLep + 1) + '_jetBTagCSV < ' +
str(FR_array[bin][6]))
#data_cut = [lepStr+str(abs(iLep-1)+1)+'_lepMVA > '+str(WP_tag)]
#MC_cut = [lepStr+str(abs(iLep-1)+1)+'_lepMVA > '+str(WP_tag)]
#QF_cut = [lepStr+str(abs(iLep-1)+1)+'_lepMVA > '+str(WP_tag)]
#NP_cut = [lepStr+str(abs(iLep-1)+1)+'_lepMVA < '+str(WP_tag)]
data_cut = [lepStr + str(abs(iLep - 1) + 1) + '_lepCut >= ' + str(WP_tag)]
MC_cut = [lepStr + str(abs(iLep - 1) + 1) + '_lepCut >= ' + str(WP_tag)]
QF_cut = [lepStr + str(abs(iLep - 1) + 1) + '_lepCut >= ' + str(WP_tag)]
NP_cut = [lepStr + str(abs(iLep - 1) + 1) + '_lepCut < ' + str(WP_tag)]
## Apply cuts for this bin, tag lepton
if type == 'NP_tag':
tag_lepton_bin_cut = [
lepStr + str(abs(iLep - 1) + 1) + '_isMuon == ' +
str(FR_array[bin_NP][0])
]
tag_lepton_bin_cut[0] += (' && ' + lepStr + str(abs(iLep - 1) + 1) +
'_pt > ' + str(FR_array[bin_NP][1]))
tag_lepton_bin_cut[0] += (' && ' + lepStr + str(abs(iLep - 1) + 1) +
'_pt < ' + str(FR_array[bin_NP][2]))
tag_lepton_bin_cut[0] += (' && abs(' + lepStr +
str(abs(iLep - 1) + 1) + '_eta) > ' +
str(FR_array[bin_NP][3]))
tag_lepton_bin_cut[0] += (' && abs(' + lepStr +
str(abs(iLep - 1) + 1) + '_eta) < ' +
str(FR_array[bin_NP][4]))
tag_lepton_bin_cut[0] += (' && ' + lepStr + str(abs(iLep - 1) + 1) +
'_jetBTagCSV > ' + str(FR_array[bin_NP][5]))
tag_lepton_bin_cut[0] += (' && ' + lepStr + str(abs(iLep - 1) + 1) +
'_jetBTagCSV < ' + str(FR_array[bin_NP][6]))
for sample in config['samples']:
sample_dict = config['samples'][sample] if config['samples'][
sample] else {
'systematics': ['common'],
'weights': ['common']
}
## Get basic sample information
tree_sample = sample_dict.get('tree sample', sample)
additional_cuts = sample_dict.get('additional cuts', [])
cuts_to_remove = sample_dict.get('cuts to remove', [])
sample_info = plot_helper.SampleInformation(tree_sample)
## Only compute relevant samples
#print 'sample '+sample+', type '+type+', sample_type '+sample_info.sample_type
if type == 'data' and ((not sample_info.sample_type == 'data') or
('sideband' in sample)):
continue
if type == 'MC' and not sample_info.sample_type == 'MC':
continue
if type == 'QF' and not 'QF' in sample:
continue
if 'NP' in type and not 'NP' in sample:
continue
#print 'Proceeding with calculation'
## Get tree file and summaryTree
if lep_cat == 'mu_mu' or lep_cat == 'mu_ele' or lep_cat == 'ele_ele':
source_file_name = '%s/%s_%s_all.root' % (
config['input_trees_directory'], tree_sample,
config['SS label'])
elif lep_cat == '3l':
source_file_name = '%s/%s_%s_all.root' % (
config['input_trees_directory'], tree_sample,
config['3l label'])
source_file = ROOT.TFile(source_file_name)
tree = source_file.Get('summaryTree')
draw_string_maker = plot_helper.DrawStringMaker()
## Apply proper selection criteria
if sample in config['lepton categories'][lep_cat]['data samples']:
draw_string_maker.append_selection_requirements(
this_base_cut, data_cut)
elif sample_info.sample_type == 'MC':
draw_string_maker.append_selection_requirements(
this_base_cut, MC_cut)
elif 'QF_sideband' in sample and sample.replace(
'_QF_sideband',
'') in config['lepton categories'][lep_cat]['data samples']:
draw_string_maker.append_selection_requirements(
this_base_cut, QF_cut)
elif 'NP_sideband' in sample and sample.replace(
'_NP_sideband',
'') in config['lepton categories'][lep_cat]['data samples']:
draw_string_maker.append_selection_requirements(
this_base_cut, NP_cut)
else:
#print sample+' not included in '+lep_cat+' num_denom'
continue
if type == 'NP_tag':
draw_string_maker.append_selection_requirements(tag_lepton_bin_cut)
draw_string_maker.remove_selection_requirements(cuts_to_remove)
draw_string_maker.append_selection_requirements(additional_cuts)
## Are the QF weights implemented properly?
weights = plot_helper.customize_list(
config['weights'], sample_dict.get('weights', ['common']))
## Apply MC weights
weights_cat = ['1.0']
if (lep_cat == 'mu_mu' or lep_cat == 'mu_ele'
or lep_cat == 'ele_ele') and sample_info.sample_type == 'MC':
weights_cat = config['weights SS']
elif lep_cat == '3l' and sample_info.sample_type == 'MC':
weights_cat = config['weights 3l']
if sample_info.sample_type == 'MC' and 'triggerSF' in weights:
matched_SF = draw_string_maker.get_matched_SF(lep_cat)
weights = [matched_SF if x == 'triggerSF' else x for x in weights]
#draw_string_maker.multiply_by_factors(weights, [systematic_weight_string])
draw_string_maker.multiply_by_factors(weights, weights_cat)
########################################
## ADD IN EFF and (1/EFF) WEIGHTS FOR MC
########################################
if sample_info.sample_type not in [
'MC', 'data'
] and 'sideband' not in sample_info.sample_type:
sys.exit('Invalid sample_type must be data, sideband, or MC' %
(sample_info.sample_type))
this_plot_integral = 0.0
plot = plot_helper.Plot(sample, 0, tree, 'distribution',
config['distribution'],
draw_string_maker.draw_string)
#if sample in config['lepton categories'][lep_cat]['data samples'] and plot.plot.Integral() == 0:
#print draw_string_maker.draw_string
#print draw_string_maker.draw_string
if sample_info.sample_type == 'MC':
plot.plot.Scale(sample_info.x_section * config['luminosity'] /
sample_info.num_generated)
this_plot_integral = plot.plot.Integral()
#this_plot_integral += plot.plot.Integral()*FR_hist_tight_mu.GetBinContent(ptBin+1, etaBin+1, csvBin+1)
#this_plot_integral += plot.plot.Integral()*FR_hist_tight_ele.GetBinContent(ptBin+1, etaBin+1, csvBin+1)
#print sample+' integral for '+lep_cat+', '+bin+', '+bin_NP+', lepton '+str(iLep+1)+', WP('+str(WP_probe)+','+str(WP_tag)+') = '+str(this_plot_integral)
this_num_denom += this_plot_integral
## End loop over samples
print 'num_denom for ' + type + ', ' + lep_cat + ', WP_probe ' + str(
WP_probe) + ', ' + bin + ', ' + bin_NP + ', lepton ' + str(
iLep + 1) + ' = ' + str(this_num_denom)
return this_num_denom
def make_histos(args, config, samples, lepton_categories, jet_tag_categories):
for sample, sample_dict in samples.items():
tree_sample = sample_dict.get('tree sample', sample)
additional_cuts = sample_dict.get('additional cuts', [])
cuts_to_remove = sample_dict.get('cuts to remove', [])
sample_info = plot_helper.SampleInformation(tree_sample)
for lepton_category in lepton_categories:
lepton_category_cut_strings = config['lepton categories'][lepton_category].get('cuts', {}).values()
if sample_info.sample_type == 'data' or 'sideband' in sample_info.sample_type:
if any([x==sample for x in config['lepton categories'][lepton_category].get('excluded samples', [])]):
config['weights'].append('0') #So we get empty histograms for hadding to get the inclusive category
#continue
if not plot_helper.is_matching_data_sample(config['lepton categories'][lepton_category]['data samples'], sample):
continue
for jet_tag_category, jet_tag_category_cut_strings in jet_tag_categories.items():
systematics_list = plot_helper.customize_systematics(config['systematics'], sample_dict.get('systematics', 'common'))
output_file_name = '%s/%s/%s_%s_%s_%s.root' % (config['output directory'], lepton_category, lepton_category, jet_tag_category, sample, config['output label'])
if args.limits:
output_file_name = '%s/%s/%s_%s_%s_%s.root' % (config['limits output directory'], lepton_category, lepton_category, jet_tag_category, sample, config['output label'])
if config['limits skip systematics']:
systematics_list = ['nominal']
elif config['skip systematics']:
systematics_list = ['nominal']
output_file = ROOT.TFile(output_file_name, 'RECREATE')
for systematic in systematics_list:
print 'Beginning next loop iteration. Sample: %10s Jet tag category: %-10s Lepton category: %-10s Systematic: %-10s' % (sample, jet_tag_category, lepton_category, systematic)
systematic_weight_string, systematic_label = plot_helper.get_systematic_info(systematic)
source_file_name = '%s/%s_%s_all.root' % (config['input_trees_directory'], tree_sample, config['label'])
if 'JES' in systematic or 'JER' in systematic:
source_file_name = '%s/%s_%s_%s_all.root' % (config['input_trees_directory'], tree_sample, config['label'], systematic)
if args.file:
source_file_name = args.file
source_file = ROOT.TFile(source_file_name)
tree = source_file.Get('summaryTree')
draw_string_maker = plot_helper.DrawStringMaker()
draw_string_maker.append_selection_requirements(config['common cuts'].values(),
lepton_category_cut_strings,
jet_tag_category_cut_strings,
additional_cuts) #additional_cuts is empty by default
draw_string_maker.remove_selection_requirements(cuts_to_remove)
if not args.no_weights:
weights = plot_helper.customize_list(config['weights'], sample_dict.get('weights', ['common']))
if sample_info.sample_type == 'MC' and 'triggerSF' in weights:
matched_SF = draw_string_maker.get_matched_SF(lepton_category)
weights = [matched_SF if x=='triggerSF' else x for x in weights]
draw_string_maker.multiply_by_factors(weights, [systematic_weight_string])
if sample_info.sample_type not in ['MC', 'data'] and 'sideband' not in sample_info.sample_type:
sys.exit('Invalid sample_type must be data, sideband, or MC' % (sample_info.sample_type))
config = plot_helper.append_integral_histo(config)
distribution_items = config['distributions'].items()
if args.limits:
distribution_items = config['limits distributions'].items()
for distribution, parameters in distribution_items:
if sample not in parameters.get('samples', [sample]):
continue
draw_string_maker.remove_selection_requirements(parameters.get('cuts to remove', []))
draw_string_maker.append_selection_requirements(parameters.get('additional cuts', []))
plot_name = '%s%s' % (distribution, systematic_label)
plot = plot_helper.Plot(sample, output_file, tree, plot_name, parameters, draw_string_maker.draw_string)
if sample_info.sample_type == 'MC':
plot.plot.Scale(sample_info.x_section * config['luminosity'] / sample_info.num_generated)
output_file.Write()
if args.pdf:
plot.save_image('pdf')
if args.web:
plot.post_to_web(config, lepton_category)
source_file.Close() #end systematic
config_file = ROOT.TObjString(args.config_file_name)
output_file.cd()
config_file.Write('config_file')
output_file.Close() #end jet tag category
def make_histos(args, config, samples, lepton_categories, jet_tag_categories):
for sample, sample_dict in samples.items():
tree_sample = sample_dict.get('tree sample', sample)
additional_cuts = sample_dict.get('additional cuts', [])
cuts_to_remove = sample_dict.get('cuts to remove', [])
sample_info = plot_helper.SampleInformation(tree_sample)
for lepton_category in lepton_categories:
lepton_category_cut_strings = config['lepton categories'][
lepton_category].get('cuts', {}).values()
if sample_info.sample_type == 'data' or 'sideband' in sample_info.sample_type:
if any([
x == sample for x in config['lepton categories']
[lepton_category].get('excluded samples', [])
]):
config['weights'].append(
'0'
) #So we get empty histograms for hadding to get the inclusive category
#continue
if not plot_helper.is_matching_data_sample(
config['lepton categories'][lepton_category]
['data samples'], sample):
continue
for jet_tag_category, jet_tag_category_cut_strings in jet_tag_categories.items(
):
systematics_list = plot_helper.customize_systematics(
config['systematics'],
sample_dict.get('systematics', 'common'))
output_file_name = '%s/%s/%s_%s_%s_%s.root' % (
config['output directory'], lepton_category,
lepton_category, jet_tag_category, sample,
config['output label'])
if args.limits:
output_file_name = '%s/%s/%s_%s_%s_%s.root' % (
config['limits output directory'], lepton_category,
lepton_category, jet_tag_category, sample,
config['output label'])
if config['limits skip systematics']:
systematics_list = ['nominal']
elif config['skip systematics']:
systematics_list = ['nominal']
output_file = ROOT.TFile(output_file_name, 'RECREATE')
for systematic in systematics_list:
print 'Beginning next loop iteration. Sample: %10s Jet tag category: %-10s Lepton category: %-10s Systematic: %-10s' % (
sample, jet_tag_category, lepton_category, systematic)
systematic_weight_string, systematic_label = plot_helper.get_systematic_info(
systematic)
source_file_name = '%s/%s_%s_all.root' % (
config['input_trees_directory'], tree_sample,
config['label'])
if 'JES' in systematic or 'JER' in systematic:
source_file_name = '%s/%s_%s_%s_all.root' % (
config['input_trees_directory'], tree_sample,
config['label'], systematic)
if args.file:
source_file_name = args.file
source_file = ROOT.TFile(source_file_name)
tree = source_file.Get('summaryTree')
draw_string_maker = plot_helper.DrawStringMaker()
draw_string_maker.append_selection_requirements(
config['common cuts'].values(),
lepton_category_cut_strings,
jet_tag_category_cut_strings,
additional_cuts) #additional_cuts is empty by default
draw_string_maker.remove_selection_requirements(
cuts_to_remove)
if not args.no_weights:
weights = plot_helper.customize_list(
config['weights'],
sample_dict.get('weights', ['common']))
if sample_info.sample_type == 'MC' and 'triggerSF' in weights:
matched_SF = draw_string_maker.get_matched_SF(
lepton_category)
weights = [
matched_SF if x == 'triggerSF' else x
for x in weights
]
draw_string_maker.multiply_by_factors(
weights, [systematic_weight_string])
if sample_info.sample_type not in [
'MC', 'data'
] and 'sideband' not in sample_info.sample_type:
sys.exit(
'Invalid sample_type must be data, sideband, or MC'
% (sample_info.sample_type))
config = plot_helper.append_integral_histo(config)
distribution_items = config['distributions'].items()
if args.limits:
distribution_items = config[
'limits distributions'].items()
for distribution, parameters in distribution_items:
if sample not in parameters.get('samples', [sample]):
continue
draw_string_maker.remove_selection_requirements(
parameters.get('cuts to remove', []))
draw_string_maker.append_selection_requirements(
parameters.get('additional cuts', []))
plot_name = '%s%s' % (distribution, systematic_label)
plot = plot_helper.Plot(sample, output_file, tree,
plot_name, parameters,
draw_string_maker.draw_string)
if sample_info.sample_type == 'MC':
plot.plot.Scale(sample_info.x_section *
config['luminosity'] /
sample_info.num_generated)
output_file.Write()
if args.pdf:
plot.save_image('pdf')
if args.web:
plot.post_to_web(config, lepton_category)
source_file.Close() #end systematic
config_file = ROOT.TObjString(args.config_file_name)
output_file.cd()
config_file.Write('config_file')
output_file.Close() #end jet tag category
def compute_num_denom(bin, lep_cat, iLep, lepStr, base_cut, WP_tag, WP_probe, FR_array, FR_hist_tight_mu, FR_hist_tight_ele, config):
#print 'Inside compute_num_denom'
this_base_cut = copy.deepcopy(base_cut)
this_num_denom = 0.0
if FR_array[bin][0] == 1 and lep_cat == 'ele_ele':
return 0.0
if FR_array[bin][0] == 0 and lep_cat == 'mu_mu':
return 0.0
#this_base_cut.append(lepStr+str(iLep+1)+'_lepMVA > '+str(WP_probe))
this_base_cut.append(lepStr+str(iLep+1)+'_lepCut >= '+str(WP_probe))
this_base_cut.append(lepStr+str(iLep+1)+'_isMuon == '+str(FR_array[bin][0]))
this_base_cut.append(lepStr+str(iLep+1)+'_pt > '+str(FR_array[bin][1]))
this_base_cut.append(lepStr+str(iLep+1)+'_pt < '+str(FR_array[bin][2]))
this_base_cut.append('abs('+lepStr+str(iLep+1)+'_eta) > '+str(FR_array[bin][3]))
this_base_cut.append('abs('+lepStr+str(iLep+1)+'_eta) < '+str(FR_array[bin][4]))
this_base_cut.append(lepStr+str(iLep+1)+'_jetBTagCSV > '+str(FR_array[bin][5]))
this_base_cut.append(lepStr+str(iLep+1)+'_jetBTagCSV < '+str(FR_array[bin][6]))
#data_cut = [lepStr+str(abs(iLep-1)+1)+'_lepMVA > '+str(WP_tag)]
#MC_cut = [lepStr+str(abs(iLep-1)+1)+'_lepMVA > '+str(WP_tag)]
#QF_cut = [lepStr+str(abs(iLep-1)+1)+'_lepMVA > '+str(WP_tag)]
#NP_cut = [lepStr+str(abs(iLep-1)+1)+'_lepMVA < '+str(WP_tag)]
data_cut = [lepStr+str(abs(iLep-1)+1)+'_lepCut >= '+str(WP_tag)]
MC_cut = [lepStr+str(abs(iLep-1)+1)+'_lepCut >= '+str(WP_tag)]
QF_cut = [lepStr+str(abs(iLep-1)+1)+'_lepCut >= '+str(WP_tag)]
NP_cut = [lepStr+str(abs(iLep-1)+1)+'_lepCut < '+str(WP_tag)]
these_integrals = {}
pool = multiprocessing.Pool(processes=10)
work = []
for sample in config['samples']:
sample_dict = config['samples'][sample] if config['samples'][sample] else {'systematics':['common'], 'weights':['common']}
tree_sample = sample_dict.get('tree sample', sample)
additional_cuts = sample_dict.get('additional cuts', [])
cuts_to_remove = sample_dict.get('cuts to remove', [])
sample_info = plot_helper.SampleInformation(tree_sample)
if lep_cat == 'mu_mu' or lep_cat == 'mu_ele' or lep_cat == 'ele_ele':
source_file_name = '%s/%s_%s_all.root' % (config['input_trees_directory'], tree_sample, config['SS label'])
elif lep_cat == '3l':
source_file_name = '%s/%s_%s_all.root' % (config['input_trees_directory'], tree_sample, config['3l label'])
source_file = ROOT.TFile(source_file_name)
tree = source_file.Get('summaryTree')
draw_string_maker = plot_helper.DrawStringMaker()
if sample in config['lepton categories'][lep_cat]['data samples']:
draw_string_maker.append_selection_requirements(this_base_cut, data_cut)
elif sample_info.sample_type == 'MC':
draw_string_maker.append_selection_requirements(this_base_cut, MC_cut)
elif 'QF_sideband' in sample and sample.replace('_QF_sideband','') in config['lepton categories'][lep_cat]['data samples']:
draw_string_maker.append_selection_requirements(this_base_cut, QF_cut)
elif 'NP_sideband' in sample and sample.replace('_NP_sideband','') in config['lepton categories'][lep_cat]['data samples']:
draw_string_maker.append_selection_requirements(this_base_cut, NP_cut)
else:
#print sample+' not included in '+lep_cat+' num_denom'
continue
draw_string_maker.remove_selection_requirements(cuts_to_remove)
draw_string_maker.append_selection_requirements(additional_cuts)
## Are the QF weights implemented properly?
weights = plot_helper.customize_list(config['weights'], sample_dict.get('weights', ['common']))
weights_cat = ['1.0']
if (lep_cat == 'mu_mu' or lep_cat == 'mu_ele' or lep_cat == 'ele_ele') and sample_info.sample_type == 'MC':
weights_cat = config['weights SS']
elif lep_cat == '3l' and sample_info.sample_type == 'MC':
weights_cat = config['weights 3l']
if sample_info.sample_type == 'MC' and 'triggerSF' in weights:
matched_SF = draw_string_maker.get_matched_SF(lep_cat)
weights = [matched_SF if x=='triggerSF' else x for x in weights]
#draw_string_maker.multiply_by_factors(weights, [systematic_weight_string])
draw_string_maker.multiply_by_factors(weights, weights_cat)
if sample_info.sample_type not in ['MC', 'data'] and 'sideband' not in sample_info.sample_type:
sys.exit('Invalid sample_type must be data, sideband, or MC' % (sample_info.sample_type))
#this_plot_integral = 0.0
if 'NP_sideband' in sample:
#print sample
for isMuon in range(2):
if isMuon == 1:
for ptBin in range(FR_hist_tight_mu.GetNbinsX()):
for etaBin in range(FR_hist_tight_mu.GetNbinsY()):
for csvBin in range(FR_hist_tight_mu.GetNbinsZ()):
tag_lepton_bin_cut = [lepStr+str(abs(iLep-1)+1)+'_isMuon == '+str(isMuon)]
tag_lepton_bin_cut[0] += (' && '+lepStr+str(abs(iLep-1)+1)+'_pt > '+str(FR_hist_tight_mu.GetXaxis().GetBinLowEdge(ptBin+1)))
tag_lepton_bin_cut[0] += (' && '+lepStr+str(abs(iLep-1)+1)+'_pt < '+str(FR_hist_tight_mu.GetXaxis().GetBinLowEdge(ptBin+1)+FR_hist_tight_mu.GetXaxis().GetBinWidth(ptBin+1)))
tag_lepton_bin_cut[0] += (' && abs('+lepStr+str(abs(iLep-1)+1)+'_eta) > '+str(FR_hist_tight_mu.GetYaxis().GetBinLowEdge(etaBin+1)))
tag_lepton_bin_cut[0] += (' && abs('+lepStr+str(abs(iLep-1)+1)+'_eta) < '+str(FR_hist_tight_mu.GetYaxis().GetBinLowEdge(etaBin+1)+FR_hist_tight_mu.GetYaxis().GetBinWidth(etaBin+1)))
tag_lepton_bin_cut[0] += (' && '+lepStr+str(abs(iLep-1)+1)+'_jetBTagCSV > '+str(FR_hist_tight_mu.GetZaxis().GetBinLowEdge(csvBin+1)))
tag_lepton_bin_cut[0] += (' && '+lepStr+str(abs(iLep-1)+1)+'_jetBTagCSV < '+str(FR_hist_tight_mu.GetZaxis().GetBinLowEdge(csvBin+1)+FR_hist_tight_mu.GetZaxis().GetBinWidth(csvBin+1)))
#print tag_lepton_bin_cut
#print FR_hist_tight_mu.GetBinContent(ptBin+1, etaBin+1)
draw_string_maker.append_selection_requirements(tag_lepton_bin_cut)
these_integrals['%s_%d_%d_%d_%d' % (sample, isMuon, ptBin, etaBin, csvBin)] = -99.0
# output_file = ROOT.TFile('temp_'+str(FR_hist_tight_mu.GetNbinsX())+'_'+str(FR_hist_tight_mu.GetNbinsY())+'_'+str(FR_hist_tight_mu.GetNbinsZ())+'.root', 'RECREATE')
# plot = plot_helper.Plot(sample, output_file, tree, 'distribution', config['distribution'], draw_string_maker.draw_string)
# #print draw_string_maker.draw_string
# #print plot.plot.Integral()
# this_plot_integral += plot.plot.Integral()*FR_hist_tight_mu.GetBinContent(ptBin+1, etaBin+1, csvBin+1)
thread_index = FR_hist_tight_mu.GetNbinsX()*100 + FR_hist_tight_mu.GetNbinsY()*10 + FR_hist_tight_mu.GetNbinsZ()
scale = FR_hist_tight_mu.GetBinContent(ptBin+1, etaBin+1, csvBin+1)
#these_integrals['%s_%d_%d_%d_%d' % (sample, isMuon, ptBin, etaBin, csvBin)] = get_one_integral(thread_index, sample, tree, config['distribution'], draw_string_maker.draw_string, scale)
work.append( ('%s_%d_%d_%d_%d' % (sample, isMuon, ptBin, etaBin, csvBin), pool.apply_async(get_one_integral, [thread_index, sample, config['distribution'], draw_string_maker.draw_string, scale, source_file_name]) ) )
draw_string_maker.remove_selection_requirements(tag_lepton_bin_cut)
# output_file.Close()
else:
for ptBin in range(FR_hist_tight_ele.GetNbinsX()):
for etaBin in range(FR_hist_tight_ele.GetNbinsY()):
for csvBin in range(FR_hist_tight_ele.GetNbinsZ()):
tag_lepton_bin_cut = [lepStr+str(abs(iLep-1)+1)+'_isMuon == '+str(isMuon)]
tag_lepton_bin_cut[0] += (' && '+lepStr+str(abs(iLep-1)+1)+'_pt > '+str(FR_hist_tight_ele.GetXaxis().GetBinLowEdge(ptBin+1)))
tag_lepton_bin_cut[0] += (' && '+lepStr+str(abs(iLep-1)+1)+'_pt < '+str(FR_hist_tight_ele.GetXaxis().GetBinLowEdge(ptBin+1)+FR_hist_tight_ele.GetXaxis().GetBinWidth(ptBin+1)))
tag_lepton_bin_cut[0] += (' && abs('+lepStr+str(abs(iLep-1)+1)+'_eta) > '+str(FR_hist_tight_ele.GetYaxis().GetBinLowEdge(etaBin+1)))
tag_lepton_bin_cut[0] += (' && abs('+lepStr+str(abs(iLep-1)+1)+'_eta) < '+str(FR_hist_tight_ele.GetYaxis().GetBinLowEdge(etaBin+1)+FR_hist_tight_ele.GetYaxis().GetBinWidth(etaBin+1)))
tag_lepton_bin_cut[0] += (' && '+lepStr+str(abs(iLep-1)+1)+'_jetBTagCSV > '+str(FR_hist_tight_ele.GetZaxis().GetBinLowEdge(csvBin+1)))
tag_lepton_bin_cut[0] += (' && '+lepStr+str(abs(iLep-1)+1)+'_jetBTagCSV < '+str(FR_hist_tight_ele.GetZaxis().GetBinLowEdge(csvBin+1)+FR_hist_tight_ele.GetZaxis().GetBinWidth(csvBin+1)))
#print tag_lepton_bin_cut
#print FR_hist_tight_ele.GetBinContent(ptBin+1, etaBin+1)
draw_string_maker.append_selection_requirements(tag_lepton_bin_cut)
these_integrals['%s_%d_%d_%d_%d' % (sample, isMuon, ptBin, etaBin, csvBin)] = -99.0
# output_file = ROOT.TFile('temp_'+str(FR_hist_tight_mu.GetNbinsX())+'_'+str(FR_hist_tight_mu.GetNbinsY())+'_'+str(FR_hist_tight_mu.GetNbinsZ())+'.root', 'RECREATE')
# plot = plot_helper.Plot(sample, output_file, tree, 'distribution', config['distribution'], draw_string_maker.draw_string)
# #print draw_string_maker.draw_string
# #print plot.plot.Integral()
# this_plot_integral += plot.plot.Integral()*FR_hist_tight_ele.GetBinContent(ptBin+1, etaBin+1, csvBin+1)
thread_index = FR_hist_tight_ele.GetNbinsX()*100 + FR_hist_tight_ele.GetNbinsY()*10 + FR_hist_tight_ele.GetNbinsZ()
scale = FR_hist_tight_ele.GetBinContent(ptBin+1, etaBin+1, csvBin+1)
#these_integrals['%s_%d_%d_%d_%d' % (sample, isMuon, ptBin, etaBin, csvBin)] = get_one_integral(thread_index, sample, tree, config['distribution'], draw_string_maker.draw_string, scale)
work.append( ('%s_%d_%d_%d_%d' % (sample, isMuon, ptBin, etaBin, csvBin), pool.apply_async(get_one_integral, [thread_index, sample, config['distribution'], draw_string_maker.draw_string, scale, source_file_name]) ) )
draw_string_maker.remove_selection_requirements(tag_lepton_bin_cut)
# output_file.Close()
else:
these_integrals[sample] = -99.0
# output_file = ROOT.TFile('temp_'+str(FR_hist_tight_mu.GetNbinsX())+'_'+str(FR_hist_tight_mu.GetNbinsY())+'_'+str(FR_hist_tight_mu.GetNbinsZ())+'.root', 'RECREATE')
# plot = plot_helper.Plot(sample, output_file, tree, 'distribution', config['distribution'], draw_string_maker.draw_string)
# #if sample in config['lepton categories'][lep_cat]['data samples'] and plot.plot.Integral() == 0:
# #print draw_string_maker.draw_string
# #print draw_string_maker.draw_string
# if sample_info.sample_type == 'MC':
# plot.plot.Scale(sample_info.x_section * config['luminosity'] / sample_info.num_generated)
# this_plot_integral = plot.plot.Integral()
thread_index = FR_hist_tight_mu.GetNbinsX()*100 + FR_hist_tight_mu.GetNbinsY()*10 + FR_hist_tight_mu.GetNbinsZ()
scale = 1.0
if sample_info.sample_type == 'MC':
scale = sample_info.x_section * config['luminosity'] / sample_info.num_generated
#these_integrals[sample] = get_one_integral(thread_index, sample, tree, config['distribution'], draw_string_maker.draw_string, scale)
work.append( (sample, pool.apply_async(get_one_integral, [thread_index, sample, config['distribution'], draw_string_maker.draw_string, scale, source_file_name]) ) )
#for (id, res) in work:
#these_integrals[id] = res.get()
#print 'id %s in work yields %f' % (id, these_integrals[id])
# output_file.Close()
#print sample+' integral for '+lep_cat+', '+bin+', lepton '+str(iLep+1)+' = '+str(this_plot_integral)
# if sample in config['lepton categories'][lep_cat]['data samples']:
# this_num_denom += this_plot_integral
# else:
# this_num_denom -= this_plot_integral
for (id, res) in work:
these_integrals[id] = res.get()
#pool.join()
#pool.close()
for sample in these_integrals:
#print '%s integral is %f' % (sample, these_integrals[sample])
if sample in config['lepton categories'][lep_cat]['data samples']:
this_num_denom += these_integrals[sample]
else:
this_num_denom -= these_integrals[sample]
print 'num_denom for '+lep_cat+', WP_probe '+str(WP_probe)+', '+bin+', lepton '+str(iLep+1)+' = '+str(this_num_denom)
return this_num_denom
def compute_num_denom(type, bin, lep_cat, iLep, lepStr, base_cut, WP_tag, WP_probe, FR_array, config, bin_NP):
#print 'Inside compute_num_denom'
this_base_cut = copy.deepcopy(base_cut)
this_num_denom = 0.0
if FR_array[bin][0] == 1 and lep_cat == 'ele_ele':
return 0.0
if FR_array[bin][0] == 0 and lep_cat == 'mu_mu':
return 0.0
## Apply cuts for this bin, probe lepton
#this_base_cut.append(lepStr+str(iLep+1)+'_lepMVA > '+str(WP_probe))
this_base_cut.append(lepStr+str(iLep+1)+'_lepCut >= '+str(WP_probe))
this_base_cut.append(lepStr+str(iLep+1)+'_isMuon == '+str(FR_array[bin][0]))
this_base_cut.append(lepStr+str(iLep+1)+'_pt > '+str(FR_array[bin][1]))
this_base_cut.append(lepStr+str(iLep+1)+'_pt < '+str(FR_array[bin][2]))
this_base_cut.append('abs('+lepStr+str(iLep+1)+'_eta) > '+str(FR_array[bin][3]))
this_base_cut.append('abs('+lepStr+str(iLep+1)+'_eta) < '+str(FR_array[bin][4]))
this_base_cut.append(lepStr+str(iLep+1)+'_jetBTagCSV > '+str(FR_array[bin][5]))
this_base_cut.append(lepStr+str(iLep+1)+'_jetBTagCSV < '+str(FR_array[bin][6]))
#data_cut = [lepStr+str(abs(iLep-1)+1)+'_lepMVA > '+str(WP_tag)]
#MC_cut = [lepStr+str(abs(iLep-1)+1)+'_lepMVA > '+str(WP_tag)]
#QF_cut = [lepStr+str(abs(iLep-1)+1)+'_lepMVA > '+str(WP_tag)]
#NP_cut = [lepStr+str(abs(iLep-1)+1)+'_lepMVA < '+str(WP_tag)]
data_cut = [lepStr+str(abs(iLep-1)+1)+'_lepCut >= '+str(WP_tag)]
MC_cut = [lepStr+str(abs(iLep-1)+1)+'_lepCut >= '+str(WP_tag)]
QF_cut = [lepStr+str(abs(iLep-1)+1)+'_lepCut >= '+str(WP_tag)]
NP_cut = [lepStr+str(abs(iLep-1)+1)+'_lepCut < '+str(WP_tag)]
## Apply cuts for this bin, tag lepton
if type == 'NP_tag':
tag_lepton_bin_cut = [lepStr+str(abs(iLep-1)+1)+'_isMuon == '+str(FR_array[bin_NP][0])]
tag_lepton_bin_cut[0] += (' && '+lepStr+str(abs(iLep-1)+1)+'_pt > '+str(FR_array[bin_NP][1]))
tag_lepton_bin_cut[0] += (' && '+lepStr+str(abs(iLep-1)+1)+'_pt < '+str(FR_array[bin_NP][2]))
tag_lepton_bin_cut[0] += (' && abs('+lepStr+str(abs(iLep-1)+1)+'_eta) > '+str(FR_array[bin_NP][3]))
tag_lepton_bin_cut[0] += (' && abs('+lepStr+str(abs(iLep-1)+1)+'_eta) < '+str(FR_array[bin_NP][4]))
tag_lepton_bin_cut[0] += (' && '+lepStr+str(abs(iLep-1)+1)+'_jetBTagCSV > '+str(FR_array[bin_NP][5]))
tag_lepton_bin_cut[0] += (' && '+lepStr+str(abs(iLep-1)+1)+'_jetBTagCSV < '+str(FR_array[bin_NP][6]))
for sample in config['samples']:
sample_dict = config['samples'][sample] if config['samples'][sample] else {'systematics':['common'], 'weights':['common']}
## Get basic sample information
tree_sample = sample_dict.get('tree sample', sample)
additional_cuts = sample_dict.get('additional cuts', [])
cuts_to_remove = sample_dict.get('cuts to remove', [])
sample_info = plot_helper.SampleInformation(tree_sample)
## Only compute relevant samples
#print 'sample '+sample+', type '+type+', sample_type '+sample_info.sample_type
if type == 'data' and ((not sample_info.sample_type == 'data') or ('sideband' in sample)):
continue
if type == 'MC' and not sample_info.sample_type == 'MC':
continue
if type == 'QF' and not 'QF' in sample:
continue
if 'NP' in type and not 'NP' in sample:
continue
#print 'Proceeding with calculation'
## Get tree file and summaryTree
if lep_cat == 'mu_mu' or lep_cat == 'mu_ele' or lep_cat == 'ele_ele':
source_file_name = '%s/%s_%s_all.root' % (config['input_trees_directory'], tree_sample, config['SS label'])
elif lep_cat == '3l':
source_file_name = '%s/%s_%s_all.root' % (config['input_trees_directory'], tree_sample, config['3l label'])
source_file = ROOT.TFile(source_file_name)
tree = source_file.Get('summaryTree')
draw_string_maker = plot_helper.DrawStringMaker()
## Apply proper selection criteria
if sample in config['lepton categories'][lep_cat]['data samples']:
draw_string_maker.append_selection_requirements(this_base_cut, data_cut)
elif sample_info.sample_type == 'MC':
draw_string_maker.append_selection_requirements(this_base_cut, MC_cut)
elif 'QF_sideband' in sample and sample.replace('_QF_sideband','') in config['lepton categories'][lep_cat]['data samples']:
draw_string_maker.append_selection_requirements(this_base_cut, QF_cut)
elif 'NP_sideband' in sample and sample.replace('_NP_sideband','') in config['lepton categories'][lep_cat]['data samples']:
draw_string_maker.append_selection_requirements(this_base_cut, NP_cut)
else:
#print sample+' not included in '+lep_cat+' num_denom'
continue
if type == 'NP_tag':
draw_string_maker.append_selection_requirements(tag_lepton_bin_cut)
draw_string_maker.remove_selection_requirements(cuts_to_remove)
draw_string_maker.append_selection_requirements(additional_cuts)
## Are the QF weights implemented properly?
weights = plot_helper.customize_list(config['weights'], sample_dict.get('weights', ['common']))
## Apply MC weights
weights_cat = ['1.0']
if (lep_cat == 'mu_mu' or lep_cat == 'mu_ele' or lep_cat == 'ele_ele') and sample_info.sample_type == 'MC':
weights_cat = config['weights SS']
elif lep_cat == '3l' and sample_info.sample_type == 'MC':
weights_cat = config['weights 3l']
if sample_info.sample_type == 'MC' and 'triggerSF' in weights:
matched_SF = draw_string_maker.get_matched_SF(lep_cat)
weights = [matched_SF if x=='triggerSF' else x for x in weights]
#draw_string_maker.multiply_by_factors(weights, [systematic_weight_string])
draw_string_maker.multiply_by_factors(weights, weights_cat)
########################################
## ADD IN EFF and (1/EFF) WEIGHTS FOR MC
########################################
if sample_info.sample_type not in ['MC', 'data'] and 'sideband' not in sample_info.sample_type:
sys.exit('Invalid sample_type must be data, sideband, or MC' % (sample_info.sample_type))
this_plot_integral = 0.0
plot = plot_helper.Plot(sample, 0, tree, 'distribution', config['distribution'], draw_string_maker.draw_string)
#if sample in config['lepton categories'][lep_cat]['data samples'] and plot.plot.Integral() == 0:
#print draw_string_maker.draw_string
#print draw_string_maker.draw_string
if sample_info.sample_type == 'MC':
plot.plot.Scale(sample_info.x_section * config['luminosity'] / sample_info.num_generated)
this_plot_integral = plot.plot.Integral()
#this_plot_integral += plot.plot.Integral()*FR_hist_tight_mu.GetBinContent(ptBin+1, etaBin+1, csvBin+1)
#this_plot_integral += plot.plot.Integral()*FR_hist_tight_ele.GetBinContent(ptBin+1, etaBin+1, csvBin+1)
#print sample+' integral for '+lep_cat+', '+bin+', '+bin_NP+', lepton '+str(iLep+1)+', WP('+str(WP_probe)+','+str(WP_tag)+') = '+str(this_plot_integral)
this_num_denom += this_plot_integral
## End loop over samples
print 'num_denom for '+type+', '+lep_cat+', WP_probe '+str(WP_probe)+', '+bin+', '+bin_NP+', lepton '+str(iLep+1)+' = '+str(this_num_denom)
return this_num_denom