def get_fit_inputs(template, variable, channel):
inputs = {}
for var_bin in variable_bins_ROOT[variable]:
print var_bin
histogram = template % var_bin
histograms = get_histograms_from_files([histogram], histogram_files)
for sample in [channel, 'TTJet', 'V+Jets', 'SingleTop']:
n_bins = histograms[sample][histogram].GetNbinsX()
error = Double(0)
integral = histograms[sample][histogram].IntegralAndError(
1, n_bins, error)
if inputs.has_key(sample):
inputs[sample].append((integral, error))
else:
inputs[sample] = [(integral, error)]
inputs['QCD'] = []
for data, ttjet, vjets, singletop in zip(inputs[channel], inputs['TTJet'],
inputs['V+Jets'],
inputs['SingleTop']):
qcd = ufloat(data) - ufloat(ttjet) - ufloat(vjets) - ufloat(singletop)
inputs['QCD'].append((qcd.nominal_value, qcd.std_dev))
print inputs
return inputs
def compare_vjets_templates( variable = 'MET', met_type = 'patType1CorrectedPFMet',
title = 'Untitled', channel = 'electron' ):
''' Compares the V+jets templates in different bins
of the current variable'''
global fit_variable_properties, b_tag_bin, save_as
variable_bins = variable_bins_ROOT[variable]
histogram_template = get_histogram_template( variable )
for fit_variable in electron_fit_variables:
all_hists = {}
inclusive_hist = None
save_path = 'plots/%dTeV/fit_variables/%s/%s/' % ( measurement_config.centre_of_mass_energy, variable, fit_variable )
make_folder_if_not_exists( save_path + '/vjets/' )
max_bins = len( variable_bins )
for bin_range in variable_bins[0:max_bins]:
params = {'met_type': met_type, 'bin_range':bin_range, 'fit_variable':fit_variable, 'b_tag_bin':b_tag_bin, 'variable':variable}
fit_variable_distribution = histogram_template % params
# format: histograms['data'][qcd_fit_variable_distribution]
histograms = get_histograms_from_files( [fit_variable_distribution], histogram_files )
prepare_histograms( histograms, rebin = fit_variable_properties[fit_variable]['rebin'], scale_factor = measurement_config.luminosity_scale )
all_hists[bin_range] = histograms['V+Jets'][fit_variable_distribution]
# create the inclusive distributions
inclusive_hist = deepcopy( all_hists[variable_bins[0]] )
for bin_range in variable_bins[1:max_bins]:
inclusive_hist += all_hists[bin_range]
for bin_range in variable_bins[0:max_bins]:
if not all_hists[bin_range].Integral() == 0:
all_hists[bin_range].Scale( 1 / all_hists[bin_range].Integral() )
# normalise all histograms
inclusive_hist.Scale( 1 / inclusive_hist.Integral() )
# now compare inclusive to all bins
histogram_properties = Histogram_properties()
histogram_properties.x_axis_title = fit_variable_properties[fit_variable]['x-title']
histogram_properties.y_axis_title = fit_variable_properties[fit_variable]['y-title']
histogram_properties.y_axis_title = histogram_properties.y_axis_title.replace( 'Events', 'a.u.' )
histogram_properties.x_limits = [fit_variable_properties[fit_variable]['min'], fit_variable_properties[fit_variable]['max']]
histogram_properties.title = title
histogram_properties.additional_text = channel_latex[channel] + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.name = variable + '_' + fit_variable + '_' + b_tag_bin + '_VJets_template_comparison'
histogram_properties.y_max_scale = 1.5
measurements = {bin_range + ' GeV': histogram for bin_range, histogram in all_hists.iteritems()}
measurements = OrderedDict( sorted( measurements.items() ) )
fit_var = fit_variable.replace( 'electron_', '' )
fit_var = fit_var.replace( 'muon_', '' )
graphs = spread_x( measurements.values(), fit_variable_bin_edges[fit_var] )
for key, graph in zip( sorted( measurements.keys() ), graphs ):
measurements[key] = graph
compare_measurements( models = {'inclusive' : inclusive_hist},
measurements = measurements,
show_measurement_errors = True,
histogram_properties = histogram_properties,
save_folder = save_path + '/vjets/',
save_as = save_as )
def main():
global measurement_config, histogram_files
global electron_fit_variables, muon_fit_variables, fit_variable_properties
global b_tag_bin, category, histogram_files, variables
global b_tag_bin_ctl
title_template = '$%.1f$ fb$^{-1}$(%d TeV)'
e_title = title_template % ( measurement_config.new_luminosity / 1000., measurement_config.centre_of_mass_energy )
met_type = 'patType1CorrectedPFMet'
for variable in variables:
variable_bins = variable_bins_ROOT[variable]
histogram_template = get_histogram_template( variable )
for fit_variable in electron_fit_variables:
if '_bl' in fit_variable:
b_tag_bin_ctl = '1orMoreBtag'
else:
b_tag_bin_ctl = '0orMoreBtag'
save_path = 'plots/%dTeV/fit_variables/%s/%s/' % ( measurement_config.centre_of_mass_energy, variable, fit_variable )
make_folder_if_not_exists( save_path )
make_folder_if_not_exists( save_path + 'qcd/' )
make_folder_if_not_exists( save_path + 'vjets/' )
inclusive_histograms = {}
inclusive_fit_distribution = ''
inclusive_qcd_distribution = ''
for bin_range in variable_bins:
params = {'met_type': met_type, 'bin_range':bin_range, 'fit_variable':fit_variable, 'b_tag_bin':b_tag_bin, 'variable':variable}
fit_variable_distribution = histogram_template % params
qcd_fit_variable_distribution = fit_variable_distribution.replace( 'Ref selection', 'QCDConversions' )
qcd_fit_variable_distribution = qcd_fit_variable_distribution.replace( b_tag_bin, b_tag_bin_ctl )
histograms = get_histograms_from_files( [fit_variable_distribution, qcd_fit_variable_distribution], histogram_files )
plot_fit_variable( histograms, fit_variable, variable, bin_range, fit_variable_distribution, qcd_fit_variable_distribution, e_title, save_path )
# sum histograms for inclusive plots
for sample, hist in histograms.iteritems():
inclusive_fit_distribution = fit_variable_distribution.replace( bin_range, "inclusive" )
inclusive_qcd_distribution = qcd_fit_variable_distribution.replace( bin_range, "inclusive" )
if not inclusive_histograms.has_key( sample ):
inclusive_histograms[sample] = {}
inclusive_histograms[sample][inclusive_fit_distribution] = hist[fit_variable_distribution].clone()
inclusive_histograms[sample][inclusive_qcd_distribution] = hist[qcd_fit_variable_distribution].clone()
else:
inclusive_histograms[sample][inclusive_fit_distribution] += hist[fit_variable_distribution]
inclusive_histograms[sample][inclusive_qcd_distribution] += hist[qcd_fit_variable_distribution]
plot_fit_variable( inclusive_histograms, fit_variable, variable,
'inclusive', inclusive_fit_distribution,
inclusive_qcd_distribution, e_title, save_path )
compare_qcd_control_regions( variable, met_type, e_title )
compare_vjets_btag_regions( variable, met_type, e_title )
compare_vjets_templates( variable, met_type, e_title )
def compare_vjets_btag_regions( variable = 'MET', met_type = 'patType1CorrectedPFMet',
title = 'Untitled', channel = 'electron' ):
''' Compares the V+Jets template in different b-tag bins'''
global fit_variable_properties, b_tag_bin, save_as, b_tag_bin_ctl
b_tag_bin_ctl = '0orMoreBtag'
variable_bins = variable_bins_ROOT[variable]
histogram_template = get_histogram_template( variable )
for fit_variable in electron_fit_variables:
if '_bl' in fit_variable:
b_tag_bin_ctl = '1orMoreBtag'
else:
b_tag_bin_ctl = '0orMoreBtag'
save_path = 'plots/%dTeV/fit_variables/%s/%s/' % ( measurement_config.centre_of_mass_energy, variable, fit_variable )
make_folder_if_not_exists( save_path + '/vjets/' )
histogram_properties = Histogram_properties()
histogram_properties.x_axis_title = fit_variable_properties[fit_variable]['x-title']
histogram_properties.y_axis_title = fit_variable_properties[fit_variable]['y-title']
histogram_properties.y_axis_title = histogram_properties.y_axis_title.replace( 'Events', 'a.u.' )
histogram_properties.x_limits = [fit_variable_properties[fit_variable]['min'], fit_variable_properties[fit_variable]['max']]
histogram_properties.title = title
histogram_properties.additional_text = channel_latex[channel] + ', ' + b_tag_bins_latex[b_tag_bin_ctl]
histogram_properties.y_max_scale = 1.5
for bin_range in variable_bins:
params = {'met_type': met_type, 'bin_range':bin_range, 'fit_variable':fit_variable, 'b_tag_bin':b_tag_bin, 'variable':variable}
fit_variable_distribution = histogram_template % params
fit_variable_distribution_ctl = fit_variable_distribution.replace( b_tag_bin, b_tag_bin_ctl )
# format: histograms['data'][qcd_fit_variable_distribution]
histograms = get_histograms_from_files( [fit_variable_distribution, fit_variable_distribution_ctl], {'V+Jets' : histogram_files['V+Jets']} )
prepare_histograms( histograms, rebin = fit_variable_properties[fit_variable]['rebin'], scale_factor = measurement_config.luminosity_scale )
histogram_properties.name = variable + '_' + bin_range + '_' + fit_variable + '_' + b_tag_bin_ctl + '_VJets_template_comparison'
histograms['V+Jets'][fit_variable_distribution].Scale( 1 / histograms['V+Jets'][fit_variable_distribution].Integral() )
histograms['V+Jets'][fit_variable_distribution_ctl].Scale( 1 / histograms['V+Jets'][fit_variable_distribution_ctl].Integral() )
compare_measurements( models = {'no b-tag' : histograms['V+Jets'][fit_variable_distribution_ctl]},
measurements = {'$>=$ 2 b-tags': histograms['V+Jets'][fit_variable_distribution]},
show_measurement_errors = True,
histogram_properties = histogram_properties,
save_folder = save_path + '/vjets/',
save_as = save_as )
def get_fit_inputs(template, variable, channel):
inputs = {}
for var_bin in variable_bins_ROOT[variable]:
print var_bin
histogram = template % var_bin
histograms = get_histograms_from_files([histogram], histogram_files)
for sample in [channel, 'TTJet', 'V+Jets', 'SingleTop']:
n_bins = histograms[sample][histogram].GetNbinsX()
error = Double(0)
integral = histograms[sample][histogram].IntegralAndError(1, n_bins, error)
if inputs.has_key(sample):
inputs[sample].append((integral, error))
else:
inputs[sample] = [(integral, error)]
inputs['QCD'] = []
for data,ttjet, vjets, singletop in zip(inputs[channel], inputs['TTJet'], inputs['V+Jets'], inputs['SingleTop']):
qcd = ufloat(data) - ufloat(ttjet) - ufloat(vjets) - ufloat(singletop)
inputs['QCD'].append((qcd.nominal_value, qcd.std_dev))
print inputs
return inputs
path_to_files + 'WJetsToLNu_%spb_PFElectron_%sPF2PATJets_PFMET.root' %
(str(lumi), pfmuon),
'ZJets':
path_to_files + 'DYJetsToLL_%spb_PFElectron_%sPF2PATJets_PFMET.root' %
(str(lumi), pfmuon),
'QCD':
path_to_files + 'QCD_%spb_PFElectron_%sPF2PATJets_PFMET.root' %
(str(lumi), pfmuon),
'SingleTop':
path_to_files + 'SingleTop_%spb_PFElectron_%sPF2PATJets_PFMET.root' %
(str(lumi), pfmuon),
}
control_region = 'QCDStudy/PFIsolation_controlRegion_0btag'
histograms = get_histograms_from_files([control_region], histogram_files)
prepare_histograms(histograms, rebin=rebin)
nonQCDMC = histograms['TTJet'][control_region] + histograms['WJets'][
control_region] + histograms['ZJets'][control_region] + histograms[
'SingleTop'][control_region]
make_control_region_data_mc_comparision(histograms,
control_region,
'PFIsolation_0btag',
x_label='relative isolation',
x_min=0,
x_max=1.6,
y_label='Events/0.1')
make_control_region_comparison(histograms['data'][control_region],
histograms['QCD'][control_region],
histogram_files = {
'TTJet': path_to_files + 'TTJet_%spb_PFElectron_%sPF2PATJets_PFMET.root' % (str(lumi), pfmuon),
'data' : path_to_files + '%s_%spb_PFElectron_%sPF2PATJets_PFMET.root' % (data, str(lumi), pfmuon),
'WJets': path_to_files + 'WJetsToLNu_%spb_PFElectron_%sPF2PATJets_PFMET.root' % (str(lumi), pfmuon),
'ZJets': path_to_files + 'DYJetsToLL_%spb_PFElectron_%sPF2PATJets_PFMET.root' % (str(lumi), pfmuon),
'QCD': '/storage/TopQuarkGroup/results/histogramfiles/AN-11-265_V2/QCD_%spb_PFElectron_%sPF2PATJets_PFMET.root' % (str(1959.75), ''),
'SingleTop': path_to_files + 'SingleTop_%spb_PFElectron_%sPF2PATJets_PFMET.root' % (str(lumi), pfmuon),
}
control_region_1 = 'topReconstruction/backgroundShape/mttbar_3jets_conversions_withMETAndAsymJets_0btag'
control_region_2 = 'topReconstruction/backgroundShape/mttbar_3jets_antiIsolated_withMETAndAsymJets_0btag'
control_region_3 = 'topReconstruction/backgroundShape/mttbar_conversions_withMETAndAsymJets_0btag'
control_region_4 = 'topReconstruction/backgroundShape/mttbar_antiIsolated_withMETAndAsymJets_0btag'
histograms_to_read = [control_region_1, control_region_2, control_region_3, control_region_4]
histograms = get_histograms_from_files(histograms_to_read, histogram_files)
prepare_histograms(histograms, rebin=rebin)
for _,histogram in histograms['QCD'].iteritems():
histogram.Scale(5028./1959.75)
make_control_region_data_mc_comparision(histograms, control_region_1, 'mttbar_3jets_conversions_withMETAndAsymJets_0btag')
make_control_region_data_mc_comparision(histograms, control_region_2, 'mttbar_3jets_antiIsolated_withMETAndAsymJets_0btag')
make_control_region_data_mc_comparision(histograms, control_region_3, 'mttbar_conversions_withMETAndAsymJets_0btag')
make_control_region_data_mc_comparision(histograms, control_region_4, 'mttbar_antiIsolated_withMETAndAsymJets_0btag')
make_control_region_comparison(histograms['data'][control_region_1],
histograms['data'][control_region_2],
'conversions',
'non-isolated electrons',
'mttbar_3jets_withMETAndAsymJets_0btag')
make_control_region_comparison(histograms['data'][control_region_3],
# 'MET':get_fitted_normalisation('MET', 'muon'),
# 'HT':get_fitted_normalisation('HT', 'muon'),
# 'ST':get_fitted_normalisation('ST', 'muon'),
# 'MT':get_fitted_normalisation('MT', 'muon'),
# 'WPT':get_fitted_normalisation('WPT', 'muon')
# }
title_template = 'CMS Preliminary, $\mathcal{L} = %.1f$ fb$^{-1}$ at $\sqrt{s}$ = %d TeV \n %s'
e_title = title_template % (measurement_config.new_luminosity/ 1000.,
measurement_config.centre_of_mass_energy,
'e+jets, $\geq$4 jets')
#bjet_invariant_mass
#bjet invariant mass
b_tag_bin = '4orMoreBtags'
control_region = 'TTbar_plus_X_analysis/EPlusJets/Ref selection/bjet_invariant_mass_' + b_tag_bin
histograms = get_histograms_from_files([control_region], histogram_files)
prepare_histograms(histograms, rebin=20, scale_factor = measurement_config.luminosity_scale)
qcd_predicted_mc = histograms['QCD'][control_region]
histograms_to_draw = [histograms['data'][control_region], qcd_predicted_mc,
histograms['V+Jets'][control_region],
histograms['SingleTop'][control_region], histograms['TTJet'][control_region]]
histogram_lables = ['data', 'QCD', 'V+Jets', 'Single-Top', samples_latex['TTJet']]
histogram_colors = ['black', 'yellow', 'green', 'magenta', 'red']
histogram_properties = Histogram_properties()
histogram_properties.name = 'EPlusJets_BJets_invmass_' + b_tag_bin
histogram_properties.title = e_title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = '$M_{\mathrm{b}\\bar{\mathrm{b}}}$'
histogram_properties.y_axis_title = 'Normalised events/(20 GeV)'
def do_shape_check(channel,
control_region_1,
control_region_2,
variable,
normalisation,
title,
x_title,
y_title,
x_limits,
y_limits,
name_region_1='conversions',
name_region_2='non-isolated electrons',
name_region_3='fit results',
rebin=1):
global b_tag_bin
# QCD shape comparison
if channel == 'electron':
histograms = get_histograms_from_files(
[control_region_1, control_region_2], histogram_files)
region_1 = histograms[channel][control_region_1].Clone(
) - histograms['TTJet'][control_region_1].Clone(
) - histograms['V+Jets'][control_region_1].Clone(
) - histograms['SingleTop'][control_region_1].Clone()
region_2 = histograms[channel][control_region_2].Clone(
) - histograms['TTJet'][control_region_2].Clone(
) - histograms['V+Jets'][control_region_2].Clone(
) - histograms['SingleTop'][control_region_2].Clone()
region_1.Rebin(rebin)
region_2.Rebin(rebin)
histogram_properties = Histogram_properties()
histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_' + b_tag_bin
histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = x_title
histogram_properties.y_axis_title = 'arbitrary units/(0.1)'
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits[0]
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
make_control_region_comparison(
region_1,
region_2,
name_region_1=name_region_1,
name_region_2=name_region_2,
histogram_properties=histogram_properties,
save_folder=output_folder)
# QCD shape comparison to fit results
histograms = get_histograms_from_files([control_region_1],
histogram_files)
region_1_tmp = histograms[channel][control_region_1].Clone(
) - histograms['TTJet'][control_region_1].Clone(
) - histograms['V+Jets'][control_region_1].Clone(
) - histograms['SingleTop'][control_region_1].Clone()
region_1 = rebin_asymmetric(region_1_tmp, bin_edges_vis[variable])
fit_results_QCD = normalisation[variable]['QCD']
region_2 = value_error_tuplelist_to_hist(fit_results_QCD,
bin_edges_vis[variable])
histogram_properties = Histogram_properties()
histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_fits_with_conversions_' + b_tag_bin
histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = x_title
histogram_properties.y_axis_title = 'arbitrary units/(0.1)'
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits[1]
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
make_control_region_comparison(
region_1,
region_2,
name_region_1=name_region_1,
name_region_2=name_region_3,
histogram_properties=histogram_properties,
save_folder=output_folder)
histograms = get_histograms_from_files([control_region_2], histogram_files)
region_1_tmp = histograms[channel][control_region_2].Clone(
) - histograms['TTJet'][control_region_2].Clone(
) - histograms['V+Jets'][control_region_2].Clone(
) - histograms['SingleTop'][control_region_2].Clone()
region_1 = rebin_asymmetric(region_1_tmp, bin_edges_vis[variable])
fit_results_QCD = normalisation[variable]['QCD']
region_2 = value_error_tuplelist_to_hist(fit_results_QCD,
bin_edges_vis[variable])
histogram_properties = Histogram_properties()
histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_fits_with_noniso_' + b_tag_bin
histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = x_title
histogram_properties.y_axis_title = 'arbitrary units/(0.1)'
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits[1]
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
make_control_region_comparison(region_1,
region_2,
name_region_1=name_region_2,
name_region_2=name_region_3,
histogram_properties=histogram_properties,
save_folder=output_folder)
def main():
global measurement_config, histogram_files
global electron_fit_variables, muon_fit_variables, fit_variable_properties
global b_tag_bin, category, histogram_files, variables
global b_tag_bin_ctl
title_template = '$%.1f$ fb$^{-1}$(%d TeV)'
e_title = title_template % (measurement_config.new_luminosity / 1000.,
measurement_config.centre_of_mass_energy)
met_type = 'patType1CorrectedPFMet'
for variable in variables:
variable_bins = variable_bins_ROOT[variable]
histogram_template = get_histogram_template(variable)
for fit_variable in electron_fit_variables:
if '_bl' in fit_variable:
b_tag_bin_ctl = '1orMoreBtag'
else:
b_tag_bin_ctl = '0orMoreBtag'
save_path = 'plots/%dTeV/fit_variables/%s/%s/' % (
measurement_config.centre_of_mass_energy, variable,
fit_variable)
make_folder_if_not_exists(save_path)
make_folder_if_not_exists(save_path + 'qcd/')
make_folder_if_not_exists(save_path + 'vjets/')
inclusive_histograms = {}
inclusive_fit_distribution = ''
inclusive_qcd_distribution = ''
for bin_range in variable_bins:
params = {
'met_type': met_type,
'bin_range': bin_range,
'fit_variable': fit_variable,
'b_tag_bin': b_tag_bin,
'variable': variable
}
fit_variable_distribution = histogram_template % params
qcd_fit_variable_distribution = fit_variable_distribution.replace(
'Ref selection', 'QCDConversions')
qcd_fit_variable_distribution = qcd_fit_variable_distribution.replace(
b_tag_bin, b_tag_bin_ctl)
histograms = get_histograms_from_files(
[fit_variable_distribution, qcd_fit_variable_distribution],
histogram_files)
plot_fit_variable(histograms, fit_variable, variable,
bin_range, fit_variable_distribution,
qcd_fit_variable_distribution, e_title,
save_path)
# sum histograms for inclusive plots
for sample, hist in histograms.iteritems():
inclusive_fit_distribution = fit_variable_distribution.replace(
bin_range, "inclusive")
inclusive_qcd_distribution = qcd_fit_variable_distribution.replace(
bin_range, "inclusive")
if not inclusive_histograms.has_key(sample):
inclusive_histograms[sample] = {}
inclusive_histograms[sample][
inclusive_fit_distribution] = hist[
fit_variable_distribution].clone()
inclusive_histograms[sample][
inclusive_qcd_distribution] = hist[
qcd_fit_variable_distribution].clone()
else:
inclusive_histograms[sample][
inclusive_fit_distribution] += hist[
fit_variable_distribution]
inclusive_histograms[sample][
inclusive_qcd_distribution] += hist[
qcd_fit_variable_distribution]
plot_fit_variable(inclusive_histograms, fit_variable, variable,
'inclusive', inclusive_fit_distribution,
inclusive_qcd_distribution, e_title, save_path)
compare_qcd_control_regions(variable, met_type, e_title)
compare_vjets_btag_regions(variable, met_type, e_title)
compare_vjets_templates(variable, met_type, e_title)
'/storage/TopQuarkGroup/results/histogramfiles/AN-11-265_V2/QCD_%spb_PFElectron_%sPF2PATJets_PFMET.root'
% (str(1959.75), ''),
'SingleTop':
path_to_files + 'SingleTop_%spb_PFElectron_%sPF2PATJets_PFMET.root' %
(str(lumi), pfmuon),
}
control_region_1 = 'topReconstruction/backgroundShape/mttbar_3jets_conversions_withMETAndAsymJets_0btag'
control_region_2 = 'topReconstruction/backgroundShape/mttbar_3jets_antiIsolated_withMETAndAsymJets_0btag'
control_region_3 = 'topReconstruction/backgroundShape/mttbar_conversions_withMETAndAsymJets_0btag'
control_region_4 = 'topReconstruction/backgroundShape/mttbar_antiIsolated_withMETAndAsymJets_0btag'
histograms_to_read = [
control_region_1, control_region_2, control_region_3, control_region_4
]
histograms = get_histograms_from_files(histograms_to_read, histogram_files)
prepare_histograms(histograms, rebin=rebin)
for _, histogram in histograms['QCD'].iteritems():
histogram.Scale(5028. / 1959.75)
make_control_region_data_mc_comparision(
histograms, control_region_1,
'mttbar_3jets_conversions_withMETAndAsymJets_0btag')
make_control_region_data_mc_comparision(
histograms, control_region_2,
'mttbar_3jets_antiIsolated_withMETAndAsymJets_0btag')
make_control_region_data_mc_comparision(
histograms, control_region_3,
'mttbar_conversions_withMETAndAsymJets_0btag')
make_control_region_data_mc_comparision(
histograms, control_region_4,
def compare_vjets_btag_regions(variable='MET',
met_type='patType1CorrectedPFMet',
title='Untitled',
channel='electron'):
''' Compares the V+Jets template in different b-tag bins'''
global fit_variable_properties, b_tag_bin, save_as, b_tag_bin_ctl
b_tag_bin_ctl = '0orMoreBtag'
variable_bins = variable_bins_ROOT[variable]
histogram_template = get_histogram_template(variable)
for fit_variable in electron_fit_variables:
if '_bl' in fit_variable:
b_tag_bin_ctl = '1orMoreBtag'
else:
b_tag_bin_ctl = '0orMoreBtag'
save_path = 'plots/%dTeV/fit_variables/%s/%s/' % (
measurement_config.centre_of_mass_energy, variable, fit_variable)
make_folder_if_not_exists(save_path + '/vjets/')
histogram_properties = Histogram_properties()
histogram_properties.x_axis_title = fit_variable_properties[
fit_variable]['x-title']
histogram_properties.y_axis_title = fit_variable_properties[
fit_variable]['y-title']
histogram_properties.y_axis_title = histogram_properties.y_axis_title.replace(
'Events', 'a.u.')
histogram_properties.x_limits = [
fit_variable_properties[fit_variable]['min'],
fit_variable_properties[fit_variable]['max']
]
histogram_properties.title = title
histogram_properties.additional_text = channel_latex[
channel] + ', ' + b_tag_bins_latex[b_tag_bin_ctl]
histogram_properties.y_max_scale = 1.5
for bin_range in variable_bins:
params = {
'met_type': met_type,
'bin_range': bin_range,
'fit_variable': fit_variable,
'b_tag_bin': b_tag_bin,
'variable': variable
}
fit_variable_distribution = histogram_template % params
fit_variable_distribution_ctl = fit_variable_distribution.replace(
b_tag_bin, b_tag_bin_ctl)
# format: histograms['data'][qcd_fit_variable_distribution]
histograms = get_histograms_from_files(
[fit_variable_distribution, fit_variable_distribution_ctl],
{'V+Jets': histogram_files['V+Jets']})
prepare_histograms(
histograms,
rebin=fit_variable_properties[fit_variable]['rebin'],
scale_factor=measurement_config.luminosity_scale)
histogram_properties.name = variable + '_' + bin_range + '_' + fit_variable + '_' + b_tag_bin_ctl + '_VJets_template_comparison'
histograms['V+Jets'][fit_variable_distribution].Scale(
1 / histograms['V+Jets'][fit_variable_distribution].Integral())
histograms['V+Jets'][fit_variable_distribution_ctl].Scale(
1 /
histograms['V+Jets'][fit_variable_distribution_ctl].Integral())
compare_measurements(
models={
'no b-tag':
histograms['V+Jets'][fit_variable_distribution_ctl]
},
measurements={
'$>=$ 2 b-tags':
histograms['V+Jets'][fit_variable_distribution]
},
show_measurement_errors=True,
histogram_properties=histogram_properties,
save_folder=save_path + '/vjets/',
save_as=save_as)
def compare_qcd_control_regions(variable='MET',
met_type='patType1CorrectedPFMet',
title='Untitled',
channel='electron'):
''' Compares the templates from the control regions in different bins
of the current variable'''
global fit_variable_properties, b_tag_bin, save_as, b_tag_bin_ctl
variable_bins = variable_bins_ROOT[variable]
histogram_template = get_histogram_template(variable)
for fit_variable in electron_fit_variables:
all_hists = {}
inclusive_hist = None
if '_bl' in fit_variable:
b_tag_bin_ctl = '1orMoreBtag'
else:
b_tag_bin_ctl = '0orMoreBtag'
save_path = 'plots/%dTeV/fit_variables/%s/%s/' % (
measurement_config.centre_of_mass_energy, variable, fit_variable)
make_folder_if_not_exists(save_path + '/qcd/')
max_bins = 3
for bin_range in variable_bins[0:max_bins]:
params = {
'met_type': met_type,
'bin_range': bin_range,
'fit_variable': fit_variable,
'b_tag_bin': b_tag_bin,
'variable': variable
}
fit_variable_distribution = histogram_template % params
qcd_fit_variable_distribution = fit_variable_distribution.replace(
'Ref selection', 'QCDConversions')
qcd_fit_variable_distribution = qcd_fit_variable_distribution.replace(
b_tag_bin, b_tag_bin_ctl)
# format: histograms['data'][qcd_fit_variable_distribution]
histograms = get_histograms_from_files(
[qcd_fit_variable_distribution], histogram_files)
prepare_histograms(
histograms,
rebin=fit_variable_properties[fit_variable]['rebin'],
scale_factor=measurement_config.luminosity_scale)
histograms_for_cleaning = {
'data': histograms['data'][qcd_fit_variable_distribution],
'V+Jets': histograms['V+Jets'][qcd_fit_variable_distribution],
'SingleTop':
histograms['SingleTop'][qcd_fit_variable_distribution],
'TTJet': histograms['TTJet'][qcd_fit_variable_distribution]
}
qcd_from_data = clean_control_region(
histograms_for_cleaning,
subtract=['TTJet', 'V+Jets', 'SingleTop'])
# clean
all_hists[bin_range] = qcd_from_data
# create the inclusive distributions
inclusive_hist = deepcopy(all_hists[variable_bins[0]])
for bin_range in variable_bins[1:max_bins]:
inclusive_hist += all_hists[bin_range]
for bin_range in variable_bins[0:max_bins]:
if not all_hists[bin_range].Integral() == 0:
all_hists[bin_range].Scale(1 / all_hists[bin_range].Integral())
# normalise all histograms
inclusive_hist.Scale(1 / inclusive_hist.Integral())
# now compare inclusive to all bins
histogram_properties = Histogram_properties()
histogram_properties.x_axis_title = fit_variable_properties[
fit_variable]['x-title']
histogram_properties.y_axis_title = fit_variable_properties[
fit_variable]['y-title']
histogram_properties.y_axis_title = histogram_properties.y_axis_title.replace(
'Events', 'a.u.')
histogram_properties.x_limits = [
fit_variable_properties[fit_variable]['min'],
fit_variable_properties[fit_variable]['max']
]
# histogram_properties.y_limits = [0, 0.5]
histogram_properties.title = title
histogram_properties.additional_text = channel_latex[
channel] + ', ' + b_tag_bins_latex[b_tag_bin_ctl]
histogram_properties.name = variable + '_' + fit_variable + '_' + b_tag_bin_ctl + '_QCD_template_comparison'
histogram_properties.y_max_scale = 1.5
measurements = {
bin_range + ' GeV': histogram
for bin_range, histogram in all_hists.iteritems()
}
measurements = OrderedDict(sorted(measurements.items()))
compare_measurements(models={'inclusive': inclusive_hist},
measurements=measurements,
show_measurement_errors=True,
histogram_properties=histogram_properties,
save_folder=save_path + '/qcd/',
save_as=save_as)
path_to_files +
'DYJetsToLL_%spb_PFElectron_%sPF2PATJets_PFMET%s.root' %
(str(lumi), pfmuon, suffix),
'QCD':
path_to_files + 'QCD_%spb_PFElectron_%sPF2PATJets_PFMET%s.root' %
(str(lumi), pfmuon, suffix),
'SingleTop':
path_to_files + 'SingleTop_%spb_PFElectron_%sPF2PATJets_PFMET%s.root' %
(str(lumi), pfmuon, suffix)
}
electron_selection = 'EventCount/TTbarEplusJetsRefSelection'
muon_selection = 'EventCount/TTbarMuPlusJetsRefSelection'
cuts = cuts_electrons
histograms = get_histograms_from_files([electron_selection],
histogram_files)
print '=' * 50
printCutFlow(histograms, electron_selection, luminosity_scale)
data = 'SingleMu'
histogram_files[
'data'] = path_to_files + '%s_%spb_PFElectron_%sPF2PATJets_PFMET.root' % (
data, str(lumi), pfmuon)
histogram_files[
'QCD'] = path_to_files + 'QCD_Muon_%spb_PFElectron_%sPF2PATJets_PFMET%s.root' % (
str(lumi), pfmuon, suffix)
histograms = get_histograms_from_files([muon_selection], histogram_files)
cuts = cuts_muons
print '=' * 50
printCutFlow(histograms, muon_selection, luminosity_scale)
def compare_qcd_control_regions( variable = 'MET', met_type = 'patType1CorrectedPFMet', title = 'Untitled', channel = 'electron' ):
''' Compares the templates from the control regions in different bins
of the current variable'''
global fit_variable_properties, b_tag_bin, save_as, b_tag_bin_ctl
variable_bins = variable_bins_ROOT[variable]
histogram_template = get_histogram_template( variable )
for fit_variable in electron_fit_variables:
all_hists = {}
inclusive_hist = None
if '_bl' in fit_variable:
b_tag_bin_ctl = '1orMoreBtag'
else:
b_tag_bin_ctl = '0orMoreBtag'
save_path = 'plots/%dTeV/fit_variables/%s/%s/' % ( measurement_config.centre_of_mass_energy, variable, fit_variable )
make_folder_if_not_exists( save_path + '/qcd/' )
max_bins = 3
for bin_range in variable_bins[0:max_bins]:
params = {'met_type': met_type, 'bin_range':bin_range, 'fit_variable':fit_variable, 'b_tag_bin':b_tag_bin, 'variable':variable}
fit_variable_distribution = histogram_template % params
qcd_fit_variable_distribution = fit_variable_distribution.replace( 'Ref selection', 'QCDConversions' )
qcd_fit_variable_distribution = qcd_fit_variable_distribution.replace( b_tag_bin, b_tag_bin_ctl )
# format: histograms['data'][qcd_fit_variable_distribution]
histograms = get_histograms_from_files( [qcd_fit_variable_distribution], histogram_files )
prepare_histograms( histograms, rebin = fit_variable_properties[fit_variable]['rebin'], scale_factor = measurement_config.luminosity_scale )
histograms_for_cleaning = {'data':histograms['data'][qcd_fit_variable_distribution],
'V+Jets':histograms['V+Jets'][qcd_fit_variable_distribution],
'SingleTop':histograms['SingleTop'][qcd_fit_variable_distribution],
'TTJet':histograms['TTJet'][qcd_fit_variable_distribution]}
qcd_from_data = clean_control_region( histograms_for_cleaning, subtract = ['TTJet', 'V+Jets', 'SingleTop'] )
# clean
all_hists[bin_range] = qcd_from_data
# create the inclusive distributions
inclusive_hist = deepcopy( all_hists[variable_bins[0]] )
for bin_range in variable_bins[1:max_bins]:
inclusive_hist += all_hists[bin_range]
for bin_range in variable_bins[0:max_bins]:
if not all_hists[bin_range].Integral() == 0:
all_hists[bin_range].Scale( 1 / all_hists[bin_range].Integral() )
# normalise all histograms
inclusive_hist.Scale( 1 / inclusive_hist.Integral() )
# now compare inclusive to all bins
histogram_properties = Histogram_properties()
histogram_properties.x_axis_title = fit_variable_properties[fit_variable]['x-title']
histogram_properties.y_axis_title = fit_variable_properties[fit_variable]['y-title']
histogram_properties.y_axis_title = histogram_properties.y_axis_title.replace( 'Events', 'a.u.' )
histogram_properties.x_limits = [fit_variable_properties[fit_variable]['min'], fit_variable_properties[fit_variable]['max']]
# histogram_properties.y_limits = [0, 0.5]
histogram_properties.title = title
histogram_properties.additional_text = channel_latex[channel] + ', ' + b_tag_bins_latex[b_tag_bin_ctl]
histogram_properties.name = variable + '_' + fit_variable + '_' + b_tag_bin_ctl + '_QCD_template_comparison'
histogram_properties.y_max_scale = 1.5
measurements = {bin_range + ' GeV': histogram for bin_range, histogram in all_hists.iteritems()}
measurements = OrderedDict( sorted( measurements.items() ) )
compare_measurements( models = {'inclusive' : inclusive_hist},
measurements = measurements,
show_measurement_errors = True,
histogram_properties = histogram_properties,
save_folder = save_path + '/qcd/',
save_as = save_as )
def compare_vjets_templates(variable='MET',
met_type='patType1CorrectedPFMet',
title='Untitled',
channel='electron'):
''' Compares the V+jets templates in different bins
of the current variable'''
global fit_variable_properties, b_tag_bin, save_as
variable_bins = variable_bins_ROOT[variable]
histogram_template = get_histogram_template(variable)
for fit_variable in electron_fit_variables:
all_hists = {}
inclusive_hist = None
save_path = 'plots/%dTeV/fit_variables/%s/%s/' % (
measurement_config.centre_of_mass_energy, variable, fit_variable)
make_folder_if_not_exists(save_path + '/vjets/')
max_bins = len(variable_bins)
for bin_range in variable_bins[0:max_bins]:
params = {
'met_type': met_type,
'bin_range': bin_range,
'fit_variable': fit_variable,
'b_tag_bin': b_tag_bin,
'variable': variable
}
fit_variable_distribution = histogram_template % params
# format: histograms['data'][qcd_fit_variable_distribution]
histograms = get_histograms_from_files([fit_variable_distribution],
histogram_files)
prepare_histograms(
histograms,
rebin=fit_variable_properties[fit_variable]['rebin'],
scale_factor=measurement_config.luminosity_scale)
all_hists[bin_range] = histograms['V+Jets'][
fit_variable_distribution]
# create the inclusive distributions
inclusive_hist = deepcopy(all_hists[variable_bins[0]])
for bin_range in variable_bins[1:max_bins]:
inclusive_hist += all_hists[bin_range]
for bin_range in variable_bins[0:max_bins]:
if not all_hists[bin_range].Integral() == 0:
all_hists[bin_range].Scale(1 / all_hists[bin_range].Integral())
# normalise all histograms
inclusive_hist.Scale(1 / inclusive_hist.Integral())
# now compare inclusive to all bins
histogram_properties = Histogram_properties()
histogram_properties.x_axis_title = fit_variable_properties[
fit_variable]['x-title']
histogram_properties.y_axis_title = fit_variable_properties[
fit_variable]['y-title']
histogram_properties.y_axis_title = histogram_properties.y_axis_title.replace(
'Events', 'a.u.')
histogram_properties.x_limits = [
fit_variable_properties[fit_variable]['min'],
fit_variable_properties[fit_variable]['max']
]
histogram_properties.title = title
histogram_properties.additional_text = channel_latex[
channel] + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.name = variable + '_' + fit_variable + '_' + b_tag_bin + '_VJets_template_comparison'
histogram_properties.y_max_scale = 1.5
measurements = {
bin_range + ' GeV': histogram
for bin_range, histogram in all_hists.iteritems()
}
measurements = OrderedDict(sorted(measurements.items()))
fit_var = fit_variable.replace('electron_', '')
fit_var = fit_var.replace('muon_', '')
graphs = spread_x(measurements.values(),
fit_variable_bin_edges[fit_var])
for key, graph in zip(sorted(measurements.keys()), graphs):
measurements[key] = graph
compare_measurements(models={'inclusive': inclusive_hist},
measurements=measurements,
show_measurement_errors=True,
histogram_properties=histogram_properties,
save_folder=save_path + '/vjets/',
save_as=save_as)
def do_shape_check(channel, control_region_1, control_region_2, variable, normalisation, title, x_title, y_title, x_limits, y_limits,
name_region_1='conversions' , name_region_2='non-isolated electrons', name_region_3='fit results', rebin=1):
global b_tag_bin
# QCD shape comparison
if channel == 'electron':
histograms = get_histograms_from_files([control_region_1, control_region_2], histogram_files)
region_1 = histograms[channel][control_region_1].Clone() - histograms['TTJet'][control_region_1].Clone() - histograms['V+Jets'][control_region_1].Clone() - histograms['SingleTop'][control_region_1].Clone()
region_2 = histograms[channel][control_region_2].Clone() - histograms['TTJet'][control_region_2].Clone() - histograms['V+Jets'][control_region_2].Clone() - histograms['SingleTop'][control_region_2].Clone()
region_1.Rebin(rebin)
region_2.Rebin(rebin)
histogram_properties = Histogram_properties()
histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_' + b_tag_bin
histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = x_title
histogram_properties.y_axis_title = 'arbitrary units/(0.1)'
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits[0]
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
make_control_region_comparison(region_1, region_2,
name_region_1=name_region_1, name_region_2=name_region_2,
histogram_properties=histogram_properties, save_folder=output_folder)
# QCD shape comparison to fit results
histograms = get_histograms_from_files([control_region_1], histogram_files)
region_1_tmp = histograms[channel][control_region_1].Clone() - histograms['TTJet'][control_region_1].Clone() - histograms['V+Jets'][control_region_1].Clone() - histograms['SingleTop'][control_region_1].Clone()
region_1 = rebin_asymmetric(region_1_tmp, bin_edges_vis[variable])
fit_results_QCD = normalisation[variable]['QCD']
region_2 = value_error_tuplelist_to_hist(fit_results_QCD, bin_edges_vis[variable])
histogram_properties = Histogram_properties()
histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_fits_with_conversions_' + b_tag_bin
histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = x_title
histogram_properties.y_axis_title = 'arbitrary units/(0.1)'
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits[1]
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
make_control_region_comparison(region_1, region_2,
name_region_1=name_region_1, name_region_2=name_region_3,
histogram_properties=histogram_properties, save_folder=output_folder)
histograms = get_histograms_from_files([control_region_2], histogram_files)
region_1_tmp = histograms[channel][control_region_2].Clone() - histograms['TTJet'][control_region_2].Clone() - histograms['V+Jets'][control_region_2].Clone() - histograms['SingleTop'][control_region_2].Clone()
region_1 = rebin_asymmetric(region_1_tmp, bin_edges_vis[variable])
fit_results_QCD = normalisation[variable]['QCD']
region_2 = value_error_tuplelist_to_hist(fit_results_QCD, bin_edges_vis[variable])
histogram_properties = Histogram_properties()
histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_fits_with_noniso_' + b_tag_bin
histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = x_title
histogram_properties.y_axis_title = 'arbitrary units/(0.1)'
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits[1]
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
make_control_region_comparison(region_1, region_2,
name_region_1=name_region_2, name_region_2=name_region_3,
histogram_properties=histogram_properties, save_folder=output_folder)
data = 'SingleElectron'
pfmuon = 'PFMuon_'
histogram_files = {
'data' : path_to_files + '%s_%spb_PFElectron_%sPF2PATJets_PFMET.root' % (data, str(lumi), pfmuon),
'TTJet': path_to_files + 'TTJet_%spb_PFElectron_%sPF2PATJets_PFMET%s.root' % (str(lumi), pfmuon, suffix),
'WJets': path_to_files + 'WJets_%spb_PFElectron_%sPF2PATJets_PFMET%s.root' % (str(lumi), pfmuon, suffix),
'ZJets': path_to_files + 'DYJetsToLL_%spb_PFElectron_%sPF2PATJets_PFMET%s.root' % (str(lumi), pfmuon, suffix),
'QCD': path_to_files + 'QCD_%spb_PFElectron_%sPF2PATJets_PFMET%s.root' % (str(lumi), pfmuon, suffix),
'SingleTop': path_to_files + 'SingleTop_%spb_PFElectron_%sPF2PATJets_PFMET%s.root' % (str(lumi), pfmuon, suffix)
}
electron_selection = 'EventCount/TTbarEplusJetsRefSelection'
muon_selection = 'EventCount/TTbarMuPlusJetsRefSelection'
cuts = cuts_electrons
histograms = get_histograms_from_files([electron_selection], histogram_files)
print '='*50
printCutFlow(histograms, electron_selection, luminosity_scale)
data = 'SingleMu'
histogram_files['data'] = path_to_files + '%s_%spb_PFElectron_%sPF2PATJets_PFMET.root' % (data, str(lumi), pfmuon)
histogram_files['QCD'] = path_to_files + 'QCD_Muon_%spb_PFElectron_%sPF2PATJets_PFMET%s.root' % (str(lumi), pfmuon, suffix)
histograms = get_histograms_from_files([muon_selection], histogram_files)
cuts = cuts_muons
print '='*50
printCutFlow(histograms, muon_selection, luminosity_scale)