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, )