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 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 make_ttbarReco_plot( channel, x_axis_title, y_axis_title,
signal_region_tree,
control_region_tree,
branchName,
name_prefix, x_limits, nBins,
use_qcd_data_region = False,
y_limits = [],
y_max_scale = 1.2,
rebin = 1,
legend_location = ( 0.98, 0.78 ), cms_logo_location = 'right',
log_y = False,
legend_color = False,
ratio_y_limits = [0.3, 1.7],
normalise = False,
):
global output_folder, measurement_config, category, normalise_to_fit
global preliminary, norm_variable, sum_bins, b_tag_bin, histogram_files
# Input files, normalisations, tree/region names
qcd_data_region = ''
title = title_template % ( measurement_config.new_luminosity / 1000., measurement_config.centre_of_mass_energy )
normalisation = None
if channel == 'electron':
histogram_files['data'] = measurement_config.data_file_electron_trees
histogram_files['QCD'] = measurement_config.electron_QCD_MC_category_templates_trees[category]
if normalise_to_fit:
normalisation = normalisations_electron[norm_variable]
if use_qcd_data_region:
qcd_data_region = 'QCDConversions'
if channel == 'muon':
histogram_files['data'] = measurement_config.data_file_muon_trees
histogram_files['QCD'] = measurement_config.muon_QCD_MC_category_templates_trees[category]
if normalise_to_fit:
normalisation = normalisations_muon[norm_variable]
if use_qcd_data_region:
qcd_data_region = 'QCD non iso mu+jets ge3j'
histograms = get_histograms_from_trees( trees = [signal_region_tree, control_region_tree], branch = branchName, weightBranch = '1', files = histogram_files, nBins = nBins, xMin = x_limits[0], xMax = x_limits[-1] )
selection = 'SolutionCategory == 0'
histogramsNoSolution = get_histograms_from_trees( trees = [signal_region_tree], branch = branchName, weightBranch = '1', selection = selection, files = histogram_files, nBins = nBins, xMin = x_limits[0], xMax = x_limits[-1] )
selection = 'SolutionCategory == 1'
histogramsCorrect = get_histograms_from_trees( trees = [signal_region_tree], branch = branchName, weightBranch = '1', selection = selection, files = histogram_files, nBins = nBins, xMin = x_limits[0], xMax = x_limits[-1] )
selection = 'SolutionCategory == 2'
histogramsNotSL = get_histograms_from_trees( trees = [signal_region_tree], branch = branchName, weightBranch = '1', selection = selection, files = histogram_files, nBins = nBins, xMin = x_limits[0], xMax = x_limits[-1] )
selection = 'SolutionCategory == 3'
histogramsNotReco = get_histograms_from_trees( trees = [signal_region_tree], branch = branchName, weightBranch = '1', selection = selection, files = histogram_files, nBins = nBins, xMin = x_limits[0], xMax = x_limits[-1] )
selection = 'SolutionCategory > 3'
histogramsWrong = get_histograms_from_trees( trees = [signal_region_tree], branch = branchName, weightBranch = '1', selection = selection, files = histogram_files, nBins = nBins, xMin = x_limits[0], xMax = x_limits[-1] )
# Split histograms up into signal/control (?)
signal_region_hists = {}
inclusive_control_region_hists = {}
for sample in histograms.keys():
signal_region_hists[sample] = histograms[sample][signal_region_tree]
if use_qcd_data_region:
inclusive_control_region_hists[sample] = histograms[sample][control_region_tree]
prepare_histograms( histograms, rebin = 1, scale_factor = measurement_config.luminosity_scale )
prepare_histograms( histogramsNoSolution, rebin = 1, scale_factor = measurement_config.luminosity_scale )
prepare_histograms( histogramsCorrect, rebin = 1, scale_factor = measurement_config.luminosity_scale )
prepare_histograms( histogramsNotSL, rebin = 1, scale_factor = measurement_config.luminosity_scale )
prepare_histograms( histogramsNotReco, rebin = 1, scale_factor = measurement_config.luminosity_scale )
prepare_histograms( histogramsWrong, rebin = 1, scale_factor = measurement_config.luminosity_scale )
qcd_from_data = signal_region_hists['QCD']
# Which histograms to draw, and properties
histograms_to_draw = [signal_region_hists['data'], qcd_from_data,
signal_region_hists['V+Jets'],
signal_region_hists['SingleTop'],
histogramsNoSolution['TTJet'][signal_region_tree],
histogramsNotSL['TTJet'][signal_region_tree],
histogramsNotReco['TTJet'][signal_region_tree],
histogramsWrong['TTJet'][signal_region_tree],
histogramsCorrect['TTJet'][signal_region_tree]
]
histogram_lables = ['data', 'QCD', 'V+Jets', 'Single-Top',
samples_latex['TTJet'] + ' - no solution',
samples_latex['TTJet'] + ' - not SL',
samples_latex['TTJet'] + ' - not reconstructible',
samples_latex['TTJet'] + ' - wrong reco',
samples_latex['TTJet'] + ' - correct',
]
histogram_colors = ['black', 'yellow', 'green', 'magenta',
'black',
'burlywood',
'chartreuse',
'blue',
'red'
]
histogram_properties = Histogram_properties()
histogram_properties.name = name_prefix + b_tag_bin
if category != 'central':
histogram_properties.name += '_' + category
histogram_properties.title = title
histogram_properties.x_axis_title = x_axis_title
histogram_properties.y_axis_title = y_axis_title
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits
histogram_properties.y_max_scale = y_max_scale
histogram_properties.xerr = None
# workaround for rootpy issue #638
histogram_properties.emptybins = True
if b_tag_bin:
histogram_properties.additional_text = channel_latex[channel] + ', ' + b_tag_bins_latex[b_tag_bin]
else:
histogram_properties.additional_text = channel_latex[channel]
histogram_properties.legend_location = legend_location
histogram_properties.cms_logo_location = cms_logo_location
histogram_properties.preliminary = preliminary
histogram_properties.set_log_y = log_y
histogram_properties.legend_color = legend_color
if ratio_y_limits:
histogram_properties.ratio_y_limits = ratio_y_limits
if normalise_to_fit:
histogram_properties.mc_error = get_normalisation_error( normalisation )
histogram_properties.mc_errors_label = 'fit uncertainty'
else:
histogram_properties.mc_error = mc_uncertainty
histogram_properties.mc_errors_label = 'MC unc.'
# Actually draw histograms
make_data_mc_comparison_plot( histograms_to_draw, histogram_lables, histogram_colors,
histogram_properties, save_folder = output_folder,
show_ratio = False, normalise = normalise,
)
histogram_properties.name += '_with_ratio'
loc = histogram_properties.legend_location
# adjust legend location as it is relative to canvas!
histogram_properties.legend_location = ( loc[0], loc[1] + 0.05 )
make_data_mc_comparison_plot( histograms_to_draw, histogram_lables, histogram_colors,
histogram_properties, save_folder = output_folder,
show_ratio = True, normalise = normalise,
)
bins = fit_variable_bin_edges[var]
xMin = bins[0]
xMax = bins[-1]
nBins = len(bins) - 1
histograms = get_histograms_from_trees(
trees=[controlTree],
branch=var,
weightBranch='EventWeight',
files=histogram_files,
nBins=nBins,
xMin=xMin,
xMax=xMax)
prepare_histograms(
histograms,
rebin=1,
scale_factor=measurement_config.luminosity_scale)
histograms_to_draw = [
histograms['data'][controlTree],
histograms['QCD'][controlTree],
histograms['V+Jets'][controlTree],
histograms['SingleTop'][controlTree],
histograms['TTJet'][controlTree]
]
histogram_lables = [
'data', 'QCD', 'V+Jets', 'Single-Top',
samples_latex['TTJet']
]
histogram_colors = [
'black', 'yellow', 'green', 'magenta', 'red'
def make_plot( channel, x_axis_title, y_axis_title,
signal_region_tree,
control_region_tree,
branchName,
name_prefix, x_limits, nBins,
use_qcd_data_region = False,
compare_qcd_signal_with_data_control = False,
y_limits = [],
y_max_scale = 1.3,
rebin = 1,
legend_location = ( 0.98, 0.78 ), cms_logo_location = 'right',
log_y = False,
legend_color = False,
ratio_y_limits = [0.3, 2.5],
normalise = False,
):
global output_folder, measurement_config, category, normalise_to_fit
global preliminary, norm_variable, sum_bins, b_tag_bin, histogram_files
controlToCompare = []
if 'electron' in channel :
controlToCompare = ['QCDConversions', 'QCD non iso e+jets']
elif 'muon' in channel :
controlToCompare = ['QCD iso > 0.3', 'QCD 0.12 < iso <= 0.3']
histogramsToCompare = {}
for qcd_data_region in controlToCompare:
print 'Doing ',qcd_data_region
# Input files, normalisations, tree/region names
title = title_template % ( measurement_config.new_luminosity, measurement_config.centre_of_mass_energy )
normalisation = None
weightBranchSignalRegion = 'EventWeight'
if 'electron' in channel:
histogram_files['data'] = measurement_config.data_file_electron_trees
histogram_files['QCD'] = measurement_config.electron_QCD_MC_category_templates_trees[category]
if normalise_to_fit:
normalisation = normalisations_electron[norm_variable]
# if use_qcd_data_region:
# qcd_data_region = 'QCDConversions'
# # qcd_data_region = 'QCD non iso e+jets'
if not 'QCD' in channel and not 'NPU' in branchName:
weightBranchSignalRegion += ' * ElectronEfficiencyCorrection'
if 'muon' in channel:
histogram_files['data'] = measurement_config.data_file_muon_trees
histogram_files['QCD'] = measurement_config.muon_QCD_MC_category_templates_trees[category]
if normalise_to_fit:
normalisation = normalisations_muon[norm_variable]
# if use_qcd_data_region:
# qcd_data_region = 'QCD iso > 0.3'
if not 'QCD' in channel and not 'NPU' in branchName:
weightBranchSignalRegion += ' * MuonEfficiencyCorrection'
if not "_NPUNoWeight" in name_prefix:
weightBranchSignalRegion += ' * PUWeight'
if not "_NBJetsNoWeight" in name_prefix:
weightBranchSignalRegion += ' * BJetWeight'
selection = '1'
if branchName == 'abs(lepton_eta)' :
selection = 'lepton_eta > -10'
else:
selection = '%s >= 0' % branchName
# if 'QCDConversions' in signal_region_tree:
# selection += '&& isTightElectron'
# print selection
histograms = get_histograms_from_trees( trees = [signal_region_tree, control_region_tree], branch = branchName, weightBranch = weightBranchSignalRegion, files = histogram_files, nBins = nBins, xMin = x_limits[0], xMax = x_limits[-1], selection = selection )
histograms_QCDControlRegion = None
if use_qcd_data_region:
qcd_control_region = signal_region_tree.replace( 'Ref selection', qcd_data_region )
histograms_QCDControlRegion = get_histograms_from_trees( trees = [qcd_control_region], branch = branchName, weightBranch = 'EventWeight', files = histogram_files, nBins = nBins, xMin = x_limits[0], xMax = x_limits[-1], selection = selection )
# Split histograms up into signal/control (?)
signal_region_hists = {}
control_region_hists = {}
for sample in histograms.keys():
signal_region_hists[sample] = histograms[sample][signal_region_tree]
if compare_qcd_signal_with_data_control:
if sample is 'data':
signal_region_hists[sample] = histograms[sample][control_region_tree]
elif sample is 'QCD' :
signal_region_hists[sample] = histograms[sample][signal_region_tree]
else:
del signal_region_hists[sample]
if use_qcd_data_region:
control_region_hists[sample] = histograms_QCDControlRegion[sample][qcd_control_region]
# Prepare histograms
if normalise_to_fit:
# only scale signal region to fit (results are invalid for control region)
prepare_histograms( signal_region_hists, rebin = rebin,
scale_factor = measurement_config.luminosity_scale,
normalisation = normalisation )
elif normalise_to_data:
totalMC = 0
for sample in signal_region_hists:
if sample is 'data' : continue
totalMC += signal_region_hists[sample].Integral()
newScale = signal_region_hists['data'].Integral() / totalMC
prepare_histograms( signal_region_hists, rebin = rebin,
scale_factor = newScale,
)
else:
print measurement_config.luminosity_scale
prepare_histograms( signal_region_hists, rebin = rebin,
scale_factor = measurement_config.luminosity_scale )
prepare_histograms( control_region_hists, rebin = rebin,
scale_factor = measurement_config.luminosity_scale )
# Use qcd from data control region or not
qcd_from_data = None
if use_qcd_data_region:
qcd_from_data = clean_control_region( control_region_hists,
subtract = ['TTJet', 'V+Jets', 'SingleTop'] )
# Normalise control region correctly
nBins = signal_region_hists['QCD'].GetNbinsX()
n, error = signal_region_hists['QCD'].integral(0,nBins+1,error=True)
n_qcd_predicted_mc_signal = ufloat( n, error)
n, error = control_region_hists['QCD'].integral(0,nBins+1,error=True)
n_qcd_predicted_mc_control = ufloat( n, error)
n, error = qcd_from_data.integral(0,nBins+1,error=True)
n_qcd_control_region = ufloat( n, error)
if not n_qcd_control_region == 0:
dataDrivenQCDScale = n_qcd_predicted_mc_signal / n_qcd_predicted_mc_control
print 'Overall scale : ',dataDrivenQCDScale
qcd_from_data.Scale( dataDrivenQCDScale.nominal_value )
signalToControlScale = n_qcd_predicted_mc_signal / n_qcd_control_region
dataToMCscale = n_qcd_control_region / n_qcd_predicted_mc_control
print "Signal to control :",signalToControlScale
print "QCD scale : ",dataToMCscale
else:
qcd_from_data = signal_region_hists['QCD']
# Which histograms to draw, and properties
histograms_to_draw = []
histogram_lables = []
histogram_colors = []
if compare_qcd_signal_with_data_control :
histograms_to_draw = [signal_region_hists['data'], qcd_from_data ]
histogram_lables = ['data', 'QCD']
histogram_colors = ['black', 'yellow']
else :
histograms_to_draw = [signal_region_hists['data'], qcd_from_data,
signal_region_hists['V+Jets'],
signal_region_hists['SingleTop'],
signal_region_hists['TTJet']]
histogram_lables = ['data', 'QCD', 'V+Jets', 'Single-Top', samples_latex['TTJet']]
histogram_colors = [colours['data'], colours['QCD'], colours['V+Jets'], colours['Single-Top'], colours['TTJet'] ]
print list(qcd_from_data.y())
histogramsToCompare[qcd_data_region] = qcd_from_data
print histogramsToCompare
histogram_properties = Histogram_properties()
histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + branchName
histogram_properties.title = title
histogram_properties.x_axis_title = x_axis_title
histogram_properties.y_axis_title = y_axis_title
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = ( 0.98, 0.78 )
histogram_properties.ratio_y_limits = ratio_y_limits
if 'electron' in channel:
make_control_region_comparison(histogramsToCompare['QCDConversions'], histogramsToCompare['QCD non iso e+jets'],
name_region_1='Conversions', name_region_2='Non Iso',
histogram_properties=histogram_properties, save_folder=output_folder)
elif 'muon' in channel:
make_control_region_comparison(histogramsToCompare['QCD iso > 0.3'], histogramsToCompare['QCD 0.12 < iso <= 0.3'],
name_region_1='QCD iso > 0.3', name_region_2='QCD 0.12 < iso <= 0.3',
histogram_properties=histogram_properties, save_folder=output_folder)
# '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)'
histogram_properties.x_limits = [0, 800]
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 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)
bins = bin_edges[var]
xMin = bins[0]
xMax = bins[-1]
nBins = 40
histograms = get_histograms_from_trees( trees = [signalTree, controlTree], branch = var, weightBranch = 'EventWeight', files = histogram_files, nBins = nBins, xMin = xMin, xMax = xMax )
histogram_dataDerivedQCD = get_data_derived_qcd( { h : histograms[h][controlTree] for h in ['data','TTJet','SingleTop','V+Jets','QCD']}, histograms['QCD'][signalTree])
for sample in histograms:
signalNorm = histograms[sample][signalTree].integral( overflow = True )
controlNorm = histograms[sample][controlTree].integral( overflow = True )
if signalNorm < 1. : signalNorm = 1.
if controlNorm < 0.1 : contorlNorm = 1.
histograms[sample][controlTree].Scale( signalNorm / controlNorm )
prepare_histograms( histograms, rebin = 1, scale_factor = measurement_config.luminosity_scale )
if normalise_to_fit:
prepare_histograms( histograms, rebin = 1, scale_factor = measurement_config.luminosity_scale, normalisation = fitted_normalisation[var] )
else:
prepare_histograms( histograms, rebin = 1, scale_factor = measurement_config.luminosity_scale )
histograms_to_draw = [histograms['data'][signalTree],
histogram_dataDerivedQCD,
histograms['V+Jets'][signalTree],
histograms['SingleTop'][signalTree], histograms['TTJet'][signalTree]]
histogram_lables = ['data', 'QCD',
'V+Jets', 'Single-Top', samples_latex['TTJet']]
histogram_colors = ['black', 'yellow',
'green', 'magenta', 'red']
def plot_fit_variable(histograms,
fit_variable,
variable,
bin_range,
fit_variable_distribution,
qcd_fit_variable_distribution,
title,
save_path,
channel='electron'):
global fit_variable_properties, b_tag_bin, save_as, b_tag_bin_ctl
histograms_ = deepcopy(histograms)
mc_uncertainty = 0.10
prepare_histograms(histograms_,
rebin=fit_variable_properties[fit_variable]['rebin'],
scale_factor=measurement_config.luminosity_scale)
######################################
# plot the control regions as they are
######################################
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.x_limits = [
fit_variable_properties[fit_variable]['min'],
fit_variable_properties[fit_variable]['max']
]
histogram_properties.y_max_scale = 2
histogram_lables = [
'data', 'QCD', 'V+Jets', 'Single-Top', samples_latex['TTJet']
]
histogram_colors = ['black', 'yellow', 'green', 'magenta', 'red']
# qcd_from_data = histograms_['data'][qcd_fit_variable_distribution].Clone()
# clean against other processes
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'])
histograms_to_draw = [
histograms_['data'][qcd_fit_variable_distribution],
histograms_['QCD'][qcd_fit_variable_distribution],
histograms_['V+Jets'][qcd_fit_variable_distribution],
histograms_['SingleTop'][qcd_fit_variable_distribution],
histograms_['TTJet'][qcd_fit_variable_distribution]
]
histogram_properties.title = title
histogram_properties.additional_text = channel_latex[
channel] + ', ' + b_tag_bins_latex[b_tag_bin_ctl]
histogram_properties.name = variable + '_' + bin_range + '_' + fit_variable + '_%s_QCDConversions' % b_tag_bin_ctl
make_data_mc_comparison_plot(
histograms_to_draw,
histogram_lables,
histogram_colors,
histogram_properties,
save_folder=save_path + '/qcd/',
show_ratio=False,
save_as=save_as,
)
######################################
# plot QCD against data control region with TTJet, SingleTop and V+Jets removed
######################################
histograms_to_draw = [
qcd_from_data,
histograms_['QCD'][qcd_fit_variable_distribution],
]
histogram_properties.y_max_scale = 1.5
histogram_properties.name = variable + '_' + bin_range + '_' + fit_variable + '_%s_QCDConversions_subtracted' % b_tag_bin_ctl
make_data_mc_comparison_plot(
histograms_to_draw,
histogram_lables=['data', 'QCD'],
histogram_colors=['black', 'yellow'],
histogram_properties=histogram_properties,
save_folder=save_path + '/qcd/',
show_ratio=False,
save_as=save_as,
)
######################################
# plot signal region
######################################
# scale QCD to predicted
n_qcd_predicted_mc = histograms_['QCD'][
fit_variable_distribution].Integral()
n_qcd_fit_variable_distribution = qcd_from_data.Integral()
if not n_qcd_fit_variable_distribution == 0:
qcd_from_data.Scale(1.0 / n_qcd_fit_variable_distribution *
n_qcd_predicted_mc)
histograms_to_draw = [
histograms_['data'][fit_variable_distribution], qcd_from_data,
histograms_['V+Jets'][fit_variable_distribution],
histograms_['SingleTop'][fit_variable_distribution],
histograms_['TTJet'][fit_variable_distribution]
]
histogram_properties.additional_text = channel_latex[
channel] + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.name = variable + '_' + bin_range + '_' + fit_variable + '_' + b_tag_bin
make_data_mc_comparison_plot(
histograms_to_draw,
histogram_lables,
histogram_colors,
histogram_properties,
save_folder=save_path,
show_ratio=False,
save_as=save_as,
)
######################################
# plot templates
######################################
histogram_properties.mc_error = mc_uncertainty
histogram_properties.mc_errors_label = '$\mathrm{t}\\bar{\mathrm{t}}$ uncertainty'
histogram_properties.name = variable + '_' + bin_range + '_' + fit_variable + '_' + b_tag_bin + '_templates'
histogram_properties.y_max_scale = 2
# change histogram order for better visibility
histograms_to_draw = [
histograms_['TTJet'][fit_variable_distribution] +
histograms_['SingleTop'][fit_variable_distribution],
histograms_['TTJet'][fit_variable_distribution],
histograms_['SingleTop'][fit_variable_distribution],
histograms_['V+Jets'][fit_variable_distribution], qcd_from_data
]
histogram_lables = [
'QCD', 'V+Jets', 'Single-Top', samples_latex['TTJet'],
samples_latex['TTJet'] + ' + ' + 'Single-Top'
]
histogram_lables.reverse()
# change QCD color to orange for better visibility
histogram_colors = ['orange', 'green', 'magenta', 'red', 'black']
histogram_colors.reverse()
# plot template
make_shape_comparison_plot(
shapes=histograms_to_draw,
names=histogram_lables,
colours=histogram_colors,
histogram_properties=histogram_properties,
fill_area=False,
alpha=1,
save_folder=save_path,
save_as=save_as,
)
lumi = 5028
data = 'ElectronHad'
pfmuon = 'PFMuon_'
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': 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),
}
b_tag_bin = '0btag'
control_region = 'topReconstruction/backgroundShape/mttbar_3jets_conversions_withMETAndAsymJets_' + b_tag_bin
histograms = get_histograms_from_files([control_region], histogram_files)
prepare_histograms(histograms, rebin=50)
histograms_to_draw = [histograms['data'][control_region], histograms['QCD'][control_region],
histograms['ZJets'][control_region], histograms['WJets'][control_region],
histograms['SingleTop'][control_region], histograms['TTJet'][control_region]]
histogram_lables = ['data', 'QCD', samples_latex['ZJets'], samples_latex['WJets'], 'Single-Top', samples_latex['TTJet']]
histogram_colors = ['black', 'yellow', 'blue', 'green', 'magenta', 'red']
histogram_properties = Histogram_properties()
histogram_properties.name = 'Mttbar'
histogram_properties.title = 'CMS Preliminary, $\mathcal{L}$ = 5.1 fb$^{-1}$ at $\sqrt{s}$ = 7 TeV \n e+jets, $\geq$4 jets, ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = '$m_{\mathrm{t}\\bar{\mathrm{t}}}$ [GeV]'
histogram_properties.y_axis_title = 'Events/(50 GeV)'
histogram_properties.x_limits=[300,1800]
histogram_properties.mc_error = 0.15
histogram_properties.mc_errors_label = '$\mathrm{t}\\bar{\mathrm{t}}$ uncertainty'
def make_plot(
channel,
x_axis_title,
y_axis_title,
signal_region_tree,
control_region_tree,
branchName,
name_prefix,
x_limits,
nBins,
use_qcd_data_region=False,
compare_qcd_signal_with_data_control=False,
y_limits=[],
y_max_scale=1.3,
rebin=1,
legend_location=(0.98, 0.78),
cms_logo_location='right',
log_y=False,
legend_color=False,
ratio_y_limits=[0.3, 2.5],
normalise=False,
):
global output_folder, measurement_config, category, normalise_to_fit
global preliminary, norm_variable, sum_bins, b_tag_bin, histogram_files
controlToCompare = []
if 'electron' in channel:
controlToCompare = ['QCDConversions', 'QCD non iso e+jets']
elif 'muon' in channel:
controlToCompare = ['QCD iso > 0.3', 'QCD 0.12 < iso <= 0.3']
histogramsToCompare = {}
for qcd_data_region in controlToCompare:
print 'Doing ', qcd_data_region
# Input files, normalisations, tree/region names
title = title_template % (measurement_config.new_luminosity,
measurement_config.centre_of_mass_energy)
normalisation = None
weightBranchSignalRegion = 'EventWeight'
if 'electron' in channel:
histogram_files[
'data'] = measurement_config.data_file_electron_trees
histogram_files[
'QCD'] = measurement_config.electron_QCD_MC_category_templates_trees[
category]
if normalise_to_fit:
normalisation = normalisations_electron[norm_variable]
# if use_qcd_data_region:
# qcd_data_region = 'QCDConversions'
# # qcd_data_region = 'QCD non iso e+jets'
if not 'QCD' in channel and not 'NPU' in branchName:
weightBranchSignalRegion += ' * ElectronEfficiencyCorrection'
if 'muon' in channel:
histogram_files['data'] = measurement_config.data_file_muon_trees
histogram_files[
'QCD'] = measurement_config.muon_QCD_MC_category_templates_trees[
category]
if normalise_to_fit:
normalisation = normalisations_muon[norm_variable]
# if use_qcd_data_region:
# qcd_data_region = 'QCD iso > 0.3'
if not 'QCD' in channel and not 'NPU' in branchName:
weightBranchSignalRegion += ' * MuonEfficiencyCorrection'
if not "_NPUNoWeight" in name_prefix:
weightBranchSignalRegion += ' * PUWeight'
if not "_NBJetsNoWeight" in name_prefix:
weightBranchSignalRegion += ' * BJetWeight'
selection = '1'
if branchName == 'abs(lepton_eta)':
selection = 'lepton_eta > -10'
else:
selection = '%s >= 0' % branchName
# if 'QCDConversions' in signal_region_tree:
# selection += '&& isTightElectron'
# print selection
histograms = get_histograms_from_trees(
trees=[signal_region_tree, control_region_tree],
branch=branchName,
weightBranch=weightBranchSignalRegion,
files=histogram_files,
nBins=nBins,
xMin=x_limits[0],
xMax=x_limits[-1],
selection=selection)
histograms_QCDControlRegion = None
if use_qcd_data_region:
qcd_control_region = signal_region_tree.replace(
'Ref selection', qcd_data_region)
histograms_QCDControlRegion = get_histograms_from_trees(
trees=[qcd_control_region],
branch=branchName,
weightBranch='EventWeight',
files=histogram_files,
nBins=nBins,
xMin=x_limits[0],
xMax=x_limits[-1],
selection=selection)
# Split histograms up into signal/control (?)
signal_region_hists = {}
control_region_hists = {}
for sample in histograms.keys():
signal_region_hists[sample] = histograms[sample][
signal_region_tree]
if compare_qcd_signal_with_data_control:
if sample is 'data':
signal_region_hists[sample] = histograms[sample][
control_region_tree]
elif sample is 'QCD':
signal_region_hists[sample] = histograms[sample][
signal_region_tree]
else:
del signal_region_hists[sample]
if use_qcd_data_region:
control_region_hists[sample] = histograms_QCDControlRegion[
sample][qcd_control_region]
# Prepare histograms
if normalise_to_fit:
# only scale signal region to fit (results are invalid for control region)
prepare_histograms(
signal_region_hists,
rebin=rebin,
scale_factor=measurement_config.luminosity_scale,
normalisation=normalisation)
elif normalise_to_data:
totalMC = 0
for sample in signal_region_hists:
if sample is 'data': continue
totalMC += signal_region_hists[sample].Integral()
newScale = signal_region_hists['data'].Integral() / totalMC
prepare_histograms(
signal_region_hists,
rebin=rebin,
scale_factor=newScale,
)
else:
print measurement_config.luminosity_scale
prepare_histograms(
signal_region_hists,
rebin=rebin,
scale_factor=measurement_config.luminosity_scale)
prepare_histograms(
control_region_hists,
rebin=rebin,
scale_factor=measurement_config.luminosity_scale)
# Use qcd from data control region or not
qcd_from_data = None
if use_qcd_data_region:
qcd_from_data = clean_control_region(
control_region_hists,
subtract=['TTJet', 'V+Jets', 'SingleTop'])
# Normalise control region correctly
nBins = signal_region_hists['QCD'].GetNbinsX()
n, error = signal_region_hists['QCD'].integral(0,
nBins + 1,
error=True)
n_qcd_predicted_mc_signal = ufloat(n, error)
n, error = control_region_hists['QCD'].integral(0,
nBins + 1,
error=True)
n_qcd_predicted_mc_control = ufloat(n, error)
n, error = qcd_from_data.integral(0, nBins + 1, error=True)
n_qcd_control_region = ufloat(n, error)
if not n_qcd_control_region == 0:
dataDrivenQCDScale = n_qcd_predicted_mc_signal / n_qcd_predicted_mc_control
print 'Overall scale : ', dataDrivenQCDScale
qcd_from_data.Scale(dataDrivenQCDScale.nominal_value)
signalToControlScale = n_qcd_predicted_mc_signal / n_qcd_control_region
dataToMCscale = n_qcd_control_region / n_qcd_predicted_mc_control
print "Signal to control :", signalToControlScale
print "QCD scale : ", dataToMCscale
else:
qcd_from_data = signal_region_hists['QCD']
# Which histograms to draw, and properties
histograms_to_draw = []
histogram_lables = []
histogram_colors = []
if compare_qcd_signal_with_data_control:
histograms_to_draw = [signal_region_hists['data'], qcd_from_data]
histogram_lables = ['data', 'QCD']
histogram_colors = ['black', 'yellow']
else:
histograms_to_draw = [
signal_region_hists['data'], qcd_from_data,
signal_region_hists['V+Jets'],
signal_region_hists['SingleTop'], signal_region_hists['TTJet']
]
histogram_lables = [
'data', 'QCD', 'V+Jets', 'Single-Top', samples_latex['TTJet']
]
histogram_colors = [
colours['data'], colours['QCD'], colours['V+Jets'],
colours['Single-Top'], colours['TTJet']
]
print list(qcd_from_data.y())
histogramsToCompare[qcd_data_region] = qcd_from_data
print histogramsToCompare
histogram_properties = Histogram_properties()
histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + branchName
histogram_properties.title = title
histogram_properties.x_axis_title = x_axis_title
histogram_properties.y_axis_title = y_axis_title
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = (0.98, 0.78)
histogram_properties.ratio_y_limits = ratio_y_limits
if 'electron' in channel:
make_control_region_comparison(
histogramsToCompare['QCDConversions'],
histogramsToCompare['QCD non iso e+jets'],
name_region_1='Conversions',
name_region_2='Non Iso',
histogram_properties=histogram_properties,
save_folder=output_folder)
elif 'muon' in channel:
make_control_region_comparison(
histogramsToCompare['QCD iso > 0.3'],
histogramsToCompare['QCD 0.12 < iso <= 0.3'],
name_region_1='QCD iso > 0.3',
name_region_2='QCD 0.12 < iso <= 0.3',
histogram_properties=histogram_properties,
save_folder=output_folder)
(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),
}
b_tag_bin = '0btag'
control_region = 'topReconstruction/backgroundShape/mttbar_3jets_conversions_withMETAndAsymJets_' + b_tag_bin
histograms = get_histograms_from_files([control_region], histogram_files)
prepare_histograms(histograms, rebin=50)
histograms_to_draw = [
histograms['data'][control_region], histograms['QCD'][control_region],
histograms['ZJets'][control_region],
histograms['WJets'][control_region],
histograms['SingleTop'][control_region],
histograms['TTJet'][control_region]
]
histogram_lables = [
'data', 'QCD', samples_latex['ZJets'], samples_latex['WJets'],
'Single-Top', samples_latex['TTJet']
]
histogram_colors = ['black', 'yellow', 'blue', 'green', 'magenta', 'red']
histogram_properties = Histogram_properties()
def compare_qcd_control_regions( variable = 'MET', met_type = 'patType1CorrectedPFMet', title = 'Untitled'):
''' 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/fit_variables/%dTeV/%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 + ', ' + b_tag_bins_latex[b_tag_bin_ctl]
histogram_properties.name = variable + '_' + fit_variable + '_' + b_tag_bin_ctl + '_QCD_template_comparison'
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_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 plot_fit_variable( histograms, fit_variable, variable, bin_range,
fit_variable_distribution, qcd_fit_variable_distribution,
title, save_path ):
global fit_variable_properties, b_tag_bin, save_as, b_tag_bin_ctl
mc_uncertainty = 0.10
prepare_histograms( histograms, rebin = fit_variable_properties[fit_variable]['rebin'], scale_factor = measurement_config.luminosity_scale )
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.x_limits = [fit_variable_properties[fit_variable]['min'], fit_variable_properties[fit_variable]['max']]
histogram_lables = ['data', 'QCD', 'V+Jets', 'Single-Top', samples_latex['TTJet']]
histogram_colors = ['black', 'yellow', 'green', 'magenta', 'red']
# qcd_from_data = histograms['data'][qcd_fit_variable_distribution].Clone()
# clean against other processes
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'] )
histograms_to_draw = [histograms['data'][qcd_fit_variable_distribution],
histograms['QCD'][qcd_fit_variable_distribution],
histograms['V+Jets'][qcd_fit_variable_distribution],
histograms['SingleTop'][qcd_fit_variable_distribution],
histograms['TTJet'][qcd_fit_variable_distribution]]
histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin_ctl]
histogram_properties.name = variable + '_' + bin_range + '_' + fit_variable + '_%s_QCDConversions' % b_tag_bin_ctl
make_data_mc_comparison_plot( histograms_to_draw, histogram_lables, histogram_colors,
histogram_properties,
save_folder = save_path + '/qcd/',
show_ratio = False,
save_as = save_as,
)
histograms_to_draw = [qcd_from_data,
histograms['QCD'][qcd_fit_variable_distribution],
]
histogram_properties.name = variable + '_' + bin_range + '_' + fit_variable + '_%s_QCDConversions_subtracted' % b_tag_bin_ctl
make_data_mc_comparison_plot( histograms_to_draw,
histogram_lables = ['data', 'QCD'],
histogram_colors = ['black', 'yellow'],
histogram_properties = histogram_properties,
save_folder = save_path + '/qcd/',
show_ratio = False,
save_as = save_as,
)
# scale QCD to predicted
n_qcd_predicted_mc = histograms['QCD'][fit_variable_distribution].Integral()
n_qcd_fit_variable_distribution = qcd_from_data.Integral()
if not n_qcd_fit_variable_distribution == 0:
qcd_from_data.Scale( 1.0 / n_qcd_fit_variable_distribution * n_qcd_predicted_mc )
histograms_to_draw = [histograms['data'][fit_variable_distribution], qcd_from_data,
histograms['V+Jets'][fit_variable_distribution],
histograms['SingleTop'][fit_variable_distribution], histograms['TTJet'][fit_variable_distribution]]
histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.name = variable + '_' + bin_range + '_' + fit_variable + '_' + b_tag_bin
make_data_mc_comparison_plot( histograms_to_draw,
histogram_lables,
histogram_colors,
histogram_properties,
save_folder = save_path,
show_ratio = False,
save_as = save_as,
)
histogram_properties.mc_error = mc_uncertainty
histogram_properties.mc_errors_label = '$\mathrm{t}\\bar{\mathrm{t}}$ uncertainty'
histogram_properties.name = variable + '_' + bin_range + '_' + fit_variable + '_' + b_tag_bin + '_templates'
# change histogram order for better visibility
histograms_to_draw = [histograms['TTJet'][fit_variable_distribution] + histograms['SingleTop'][fit_variable_distribution],
histograms['TTJet'][fit_variable_distribution],
histograms['SingleTop'][fit_variable_distribution],
histograms['V+Jets'][fit_variable_distribution],
qcd_from_data]
histogram_lables = ['QCD', 'V+Jets', 'Single-Top', samples_latex['TTJet'], samples_latex['TTJet'] + ' + ' + 'Single-Top']
histogram_lables.reverse()
# change QCD color to orange for better visibility
histogram_colors = ['orange', 'green', 'magenta', 'red', 'black']
histogram_colors.reverse()
make_shape_comparison_plot( shapes = histograms_to_draw,
names = histogram_lables,
colours = histogram_colors,
histogram_properties = histogram_properties,
fill_area = False,
alpha = 1,
save_folder = save_path,
save_as = save_as,
)
def make_ttbarReco_plot(
channel,
x_axis_title,
y_axis_title,
signal_region_tree,
control_region_tree,
branchName,
name_prefix,
x_limits,
nBins,
use_qcd_data_region=False,
y_limits=[],
y_max_scale=1.2,
rebin=1,
legend_location=(0.98, 0.78),
cms_logo_location='right',
log_y=False,
legend_color=False,
ratio_y_limits=[0.3, 1.7],
normalise=False,
):
global output_folder, measurement_config, category, normalise_to_fit
global preliminary, norm_variable, sum_bins, b_tag_bin, histogram_files
# Input files, normalisations, tree/region names
qcd_data_region = ''
title = title_template % (measurement_config.new_luminosity / 1000.,
measurement_config.centre_of_mass_energy)
normalisation = None
if channel == 'electron':
histogram_files['data'] = measurement_config.data_file_electron_trees
histogram_files[
'QCD'] = measurement_config.electron_QCD_MC_category_templates_trees[
category]
if normalise_to_fit:
normalisation = normalisations_electron[norm_variable]
if use_qcd_data_region:
qcd_data_region = 'QCDConversions'
if channel == 'muon':
histogram_files['data'] = measurement_config.data_file_muon_trees
histogram_files[
'QCD'] = measurement_config.muon_QCD_MC_category_templates_trees[
category]
if normalise_to_fit:
normalisation = normalisations_muon[norm_variable]
if use_qcd_data_region:
qcd_data_region = 'QCD non iso mu+jets ge3j'
histograms = get_histograms_from_trees(
trees=[signal_region_tree, control_region_tree],
branch=branchName,
weightBranch='1',
files=histogram_files,
nBins=nBins,
xMin=x_limits[0],
xMax=x_limits[-1])
selection = 'SolutionCategory == 0'
histogramsNoSolution = get_histograms_from_trees(
trees=[signal_region_tree],
branch=branchName,
weightBranch='1',
selection=selection,
files=histogram_files,
nBins=nBins,
xMin=x_limits[0],
xMax=x_limits[-1])
selection = 'SolutionCategory == 1'
histogramsCorrect = get_histograms_from_trees(trees=[signal_region_tree],
branch=branchName,
weightBranch='1',
selection=selection,
files=histogram_files,
nBins=nBins,
xMin=x_limits[0],
xMax=x_limits[-1])
selection = 'SolutionCategory == 2'
histogramsNotSL = get_histograms_from_trees(trees=[signal_region_tree],
branch=branchName,
weightBranch='1',
selection=selection,
files=histogram_files,
nBins=nBins,
xMin=x_limits[0],
xMax=x_limits[-1])
selection = 'SolutionCategory == 3'
histogramsNotReco = get_histograms_from_trees(trees=[signal_region_tree],
branch=branchName,
weightBranch='1',
selection=selection,
files=histogram_files,
nBins=nBins,
xMin=x_limits[0],
xMax=x_limits[-1])
selection = 'SolutionCategory > 3'
histogramsWrong = get_histograms_from_trees(trees=[signal_region_tree],
branch=branchName,
weightBranch='1',
selection=selection,
files=histogram_files,
nBins=nBins,
xMin=x_limits[0],
xMax=x_limits[-1])
# Split histograms up into signal/control (?)
signal_region_hists = {}
inclusive_control_region_hists = {}
for sample in histograms.keys():
signal_region_hists[sample] = histograms[sample][signal_region_tree]
if use_qcd_data_region:
inclusive_control_region_hists[sample] = histograms[sample][
control_region_tree]
prepare_histograms(histograms,
rebin=1,
scale_factor=measurement_config.luminosity_scale)
prepare_histograms(histogramsNoSolution,
rebin=1,
scale_factor=measurement_config.luminosity_scale)
prepare_histograms(histogramsCorrect,
rebin=1,
scale_factor=measurement_config.luminosity_scale)
prepare_histograms(histogramsNotSL,
rebin=1,
scale_factor=measurement_config.luminosity_scale)
prepare_histograms(histogramsNotReco,
rebin=1,
scale_factor=measurement_config.luminosity_scale)
prepare_histograms(histogramsWrong,
rebin=1,
scale_factor=measurement_config.luminosity_scale)
qcd_from_data = signal_region_hists['QCD']
# Which histograms to draw, and properties
histograms_to_draw = [
signal_region_hists['data'], qcd_from_data,
signal_region_hists['V+Jets'], signal_region_hists['SingleTop'],
histogramsNoSolution['TTJet'][signal_region_tree],
histogramsNotSL['TTJet'][signal_region_tree],
histogramsNotReco['TTJet'][signal_region_tree],
histogramsWrong['TTJet'][signal_region_tree],
histogramsCorrect['TTJet'][signal_region_tree]
]
histogram_lables = [
'data',
'QCD',
'V+Jets',
'Single-Top',
samples_latex['TTJet'] + ' - no solution',
samples_latex['TTJet'] + ' - not SL',
samples_latex['TTJet'] + ' - not reconstructible',
samples_latex['TTJet'] + ' - wrong reco',
samples_latex['TTJet'] + ' - correct',
]
histogram_colors = [
'black', 'yellow', 'green', 'magenta', 'black', 'burlywood',
'chartreuse', 'blue', 'red'
]
histogram_properties = Histogram_properties()
histogram_properties.name = name_prefix + b_tag_bin
if category != 'central':
histogram_properties.name += '_' + category
histogram_properties.title = title
histogram_properties.x_axis_title = x_axis_title
histogram_properties.y_axis_title = y_axis_title
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits
histogram_properties.y_max_scale = y_max_scale
histogram_properties.xerr = None
# workaround for rootpy issue #638
histogram_properties.emptybins = True
if b_tag_bin:
histogram_properties.additional_text = channel_latex[
channel] + ', ' + b_tag_bins_latex[b_tag_bin]
else:
histogram_properties.additional_text = channel_latex[channel]
histogram_properties.legend_location = legend_location
histogram_properties.cms_logo_location = cms_logo_location
histogram_properties.preliminary = preliminary
histogram_properties.set_log_y = log_y
histogram_properties.legend_color = legend_color
if ratio_y_limits:
histogram_properties.ratio_y_limits = ratio_y_limits
if normalise_to_fit:
histogram_properties.mc_error = get_normalisation_error(normalisation)
histogram_properties.mc_errors_label = 'fit uncertainty'
else:
histogram_properties.mc_error = mc_uncertainty
histogram_properties.mc_errors_label = 'MC unc.'
# Actually draw histograms
make_data_mc_comparison_plot(
histograms_to_draw,
histogram_lables,
histogram_colors,
histogram_properties,
save_folder=output_folder,
show_ratio=False,
normalise=normalise,
)
histogram_properties.name += '_with_ratio'
loc = histogram_properties.legend_location
# adjust legend location as it is relative to canvas!
histogram_properties.legend_location = (loc[0], loc[1] + 0.05)
make_data_mc_comparison_plot(
histograms_to_draw,
histogram_lables,
histogram_colors,
histogram_properties,
save_folder=output_folder,
show_ratio=True,
normalise=normalise,
)
