def plot_bias(h_unfold_model, h_data_model, unfolded_data, variable, channel, come, method): hp = Histogram_properties() hp.name = '{channel}_bias_test_for_{variable}_at_{come}TeV'.format( channel=channel, variable=variable, come=come, ) v_latex = latex_labels.variables_latex[variable] unit = '' if variable in ['HT', 'ST', 'MET', 'WPT']: unit = ' [GeV]' hp.x_axis_title = v_latex + unit hp.y_axis_title = 'Events' hp.title = 'Closure tests for {variable}'.format(variable=v_latex) output_folder = 'plots/unfolding/bias_test/{0}/'.format(method) compare_measurements(models={ 'MC truth': h_data_model, 'unfold model': h_unfold_model }, measurements={'unfolded reco': unfolded_data}, show_measurement_errors=True, histogram_properties=hp, save_folder=output_folder, save_as=['png', 'pdf'])
def plot_bias_in_all_bins(biases, mean_bias, centre_of_mass, channel, variable, tau_value, output_folder, output_formats, bin_edges): h_bias = Hist(bin_edges, type='D') n_bins = h_bias.nbins() assert len(biases) == n_bins for i, bias in enumerate(biases): h_bias.SetBinContent(i+1, bias) histogram_properties = Histogram_properties() name_mpt = 'bias_{0}_{1}_{2}TeV' histogram_properties.name = name_mpt.format( variable, channel, centre_of_mass ) histogram_properties.y_axis_title = 'Bias' histogram_properties.x_axis_title = latex_labels.variables_latex[variable] title = 'pull distribution mean \& sigma for {0}'.format(tau_value) histogram_properties.title = title histogram_properties.y_limits = [0, 10] histogram_properties.xerr = True compare_measurements( models={ 'Mean bias':make_line_hist(bin_edges, mean_bias) }, measurements={ 'Bias': h_bias }, show_measurement_errors=True, histogram_properties=histogram_properties, save_folder=output_folder, save_as=output_formats)
def plot_bias_in_all_bins(biases, mean_bias, centre_of_mass, channel, variable, tau_value, output_folder, output_formats, bin_edges): h_bias = Hist(bin_edges, type='D') n_bins = h_bias.nbins() assert len(biases) == n_bins for i, bias in enumerate(biases): h_bias.SetBinContent(i + 1, bias) histogram_properties = Histogram_properties() name_mpt = 'bias_{0}_{1}_{2}TeV' histogram_properties.name = name_mpt.format(variable, channel, centre_of_mass) histogram_properties.y_axis_title = 'Bias' histogram_properties.x_axis_title = latex_labels.variables_latex[variable] title = 'pull distribution mean \& sigma for {0}'.format(tau_value) histogram_properties.title = title histogram_properties.y_limits = [0, 10] histogram_properties.xerr = True compare_measurements( models={'Mean bias': make_line_hist(bin_edges, mean_bias)}, measurements={'Bias': h_bias}, show_measurement_errors=True, histogram_properties=histogram_properties, save_folder=output_folder, save_as=output_formats)
def plot_closure(unfolded_and_truths, variable, channel, come, method): hp = Histogram_properties() hp.name = '{channel}_closure_test_for_{variable}_at_{come}TeV'.format( channel=channel, variable=variable, come=come, ) v_latex = latex_labels.variables_latex[variable] unit = '' if variable in ['HT', 'ST', 'MET', 'WPT', 'lepton_pt']: unit = ' [GeV]' hp.x_axis_title = v_latex + unit # plt.ylabel( r, CMS.y_axis_title ) hp.y_axis_title = r'$\frac{1}{\sigma} \frac{d\sigma}{d' + v_latex + '}$' + unit hp.title = 'Closure tests for {variable}'.format(variable=v_latex) output_folder = 'plots/unfolding/closure_test/{0}/'.format(method) models = OrderedDict() measurements = OrderedDict() for sample in unfolded_and_truths: models[sample + ' truth'] = unfolded_and_truths[sample]['truth'] measurements[sample + ' unfolded'] = unfolded_and_truths[sample]['unfolded'] compare_measurements( models = models, measurements = measurements, show_measurement_errors=True, histogram_properties=hp, save_folder=output_folder, save_as=['pdf'], match_models_to_measurements = True)
def plot_bias(h_unfold_model, h_data_model, unfolded_data, variable, channel, come, method): hp = Histogram_properties() hp.name = '{channel}_bias_test_for_{variable}_at_{come}TeV'.format( channel=channel, variable=variable, come=come, ) v_latex = latex_labels.variables_latex[variable] unit = '' if variable in ['HT', 'ST', 'MET', 'WPT']: unit = ' [GeV]' hp.x_axis_title = v_latex + unit hp.y_axis_title = 'Events' hp.title = 'Closure tests for {variable}'.format(variable=v_latex) output_folder = 'plots/unfolding/bias_test/{0}/'.format(method) compare_measurements(models={'MC truth': h_data_model, 'unfold model': h_unfold_model}, measurements={'unfolded reco': unfolded_data}, show_measurement_errors=True, histogram_properties=hp, save_folder=output_folder, save_as=['png', 'pdf'])
def plot_bias(unfolded_and_truths, variable, channel, come, method): hp = Histogram_properties() hp.name = 'Bias_{channel}_{variable}_at_{come}TeV'.format( channel=channel, variable=variable, come=come, ) v_latex = latex_labels.variables_latex[variable] unit = '' if variable in ['HT', 'ST', 'MET', 'WPT', 'lepton_pt']: unit = ' [GeV]' hp.x_axis_title = v_latex + unit # plt.ylabel( r, CMS.y_axis_title ) hp.y_axis_title = 'Unfolded / Truth' hp.y_limits = [0.92, 1.08] hp.title = 'Bias for {variable}'.format(variable=v_latex) output_folder = 'plots/unfolding/bias_test/' measurements = { 'Central' : unfolded_and_truths['Central']['bias'] } models = {} for sample in unfolded_and_truths: if sample == 'Central' : continue models[sample] = unfolded_and_truths[sample]['bias'] compare_measurements( models = models, measurements = measurements, show_measurement_errors=True, histogram_properties=hp, save_folder=output_folder, save_as=['pdf'], match_models_to_measurements = True)
def plot_results(results): ''' Takes results fo the form: {centre-of-mass-energy: { channel : { variable : { fit_variable : { test : { sample : []}, } } } } } ''' global options output_base = 'plots/fit_checks/chi2' for COMEnergy in results.keys(): tmp_result_1 = results[COMEnergy] for channel in tmp_result_1.keys(): tmp_result_2 = tmp_result_1[channel] for variable in tmp_result_2.keys(): tmp_result_3 = tmp_result_2[variable] for fit_variable in tmp_result_3.keys(): tmp_result_4 = tmp_result_3[fit_variable] # histograms should be {sample: {test : histogram}} histograms = {} for test, chi2 in tmp_result_4.iteritems(): for sample in chi2.keys(): if not histograms.has_key(sample): histograms[sample] = {} # reverse order of test and sample histograms[sample][test] = value_tuplelist_to_hist( chi2[sample], bin_edges_vis[variable]) for sample in histograms.keys(): hist_properties = Histogram_properties() hist_properties.name = sample.replace('+', '') + '_chi2' hist_properties.title = '$\\chi^2$ distribution for fit output (' + sample + ')' hist_properties.x_axis_title = '$' + latex_labels.variables_latex[ variable] + '$ [TeV]' hist_properties.y_axis_title = '$\chi^2 = \\left({N_{fit}} - N_{{exp}}\\right)^2$' hist_properties.set_log_y = True hist_properties.y_limits = (1e-20, 1e20) path = output_base + '/' + COMEnergy + 'TeV/' + channel + '/' + variable + '/' + fit_variable + '/' if options.test: path = output_base + '/test/' measurements = {} for test, histogram in histograms[sample].iteritems(): measurements[test.replace('_', ' ')] = histogram compare_measurements( {}, measurements, show_measurement_errors=False, histogram_properties=hist_properties, save_folder=path, save_as=['pdf'])
def draw_regularisation_histograms( h_truth, h_measured, h_response, h_fakes = None, h_data = None ): global method, variable, output_folder, output_formats, test k_max = h_measured.nbins() unfolding = Unfolding( h_truth, h_measured, h_response, h_fakes, method = method, k_value = k_max, error_treatment = 4, verbose = 1 ) RMSerror, MeanResiduals, RMSresiduals, Chi2 = unfolding.test_regularisation ( h_data, k_max ) histogram_properties = Histogram_properties() histogram_properties.name = 'chi2_%s_channel_%s' % ( channel, variable ) histogram_properties.title = '$\chi^2$ for $%s$ in %s channel, %s test' % ( variables_latex[variable], channel, test ) histogram_properties.x_axis_title = '$i$' histogram_properties.y_axis_title = '$\chi^2$' histogram_properties.set_log_y = True make_plot(Chi2, 'chi2', histogram_properties, output_folder, output_formats, draw_errorbar = True, draw_legend = False) histogram_properties = Histogram_properties() histogram_properties.name = 'RMS_error_%s_channel_%s' % ( channel, variable ) histogram_properties.title = 'Mean error for $%s$ in %s channel, %s test' % ( variables_latex[variable], channel, test ) histogram_properties.x_axis_title = '$i$' histogram_properties.y_axis_title = 'Mean error' make_plot(RMSerror, 'RMS', histogram_properties, output_folder, output_formats, draw_errorbar = True, draw_legend = False) histogram_properties = Histogram_properties() histogram_properties.name = 'RMS_residuals_%s_channel_%s' % ( channel, variable ) histogram_properties.title = 'RMS of residuals for $%s$ in %s channel, %s test' % ( variables_latex[variable], channel, test ) histogram_properties.x_axis_title = '$i$' histogram_properties.y_axis_title = 'RMS of residuals' if test == 'closure': histogram_properties.set_log_y = True make_plot(RMSresiduals, 'RMSresiduals', histogram_properties, output_folder, output_formats, draw_errorbar = True, draw_legend = False) histogram_properties = Histogram_properties() histogram_properties.name = 'mean_residuals_%s_channel_%s' % ( channel, variable ) histogram_properties.title = 'Mean of residuals for $%s$ in %s channel, %s test' % ( variables_latex[variable], channel, test ) histogram_properties.x_axis_title = '$i$' histogram_properties.y_axis_title = 'Mean of residuals' make_plot(MeanResiduals, 'MeanRes', histogram_properties, output_folder, output_formats, draw_errorbar = True, draw_legend = False)
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( central_mc, expected_result = None, measured_result = None, results = {}, variable = 'MET', channel = 'electron', bin_edges = [] ): global input_file, plot_location, ttbar_xsection, luminosity, centre_of_mass, method, test, log_plots channel_label = '' if channel == 'electron': channel_label = 'e+jets, $\geq$4 jets' elif channel == 'muon': channel_label = '$\mu$+jets, $\geq$4 jets' else: channel_label = '$e, \mu$ + jets combined, $\geq$4 jets' if test == 'data': title_template = 'CMS Preliminary, $\mathcal{L} = %.1f$ fb$^{-1}$ at $\sqrt{s}$ = %d TeV \n %s' title = title_template % ( luminosity / 1000., centre_of_mass, channel_label ) else: title_template = 'CMS Simulation at $\sqrt{s}$ = %d TeV \n %s' title = title_template % ( centre_of_mass, channel_label ) models = {latex_labels.measurements_latex['MADGRAPH'] : central_mc} if expected_result and test == 'data': models.update({'fitted data' : expected_result}) # scale central MC to lumi nEvents = input_file.EventFilter.EventCounter.GetBinContent( 1 ) # number of processed events lumiweight = ttbar_xsection * luminosity / nEvents central_mc.Scale( lumiweight ) elif expected_result: models.update({'expected' : expected_result}) if measured_result and test != 'data': models.update({'measured' : measured_result}) measurements = collections.OrderedDict() for key, value in results['k_value_results'].iteritems(): measurements['k = ' + str( key )] = value # get some spread in x graphs = spread_x( measurements.values(), bin_edges ) for key, graph in zip( measurements.keys(), graphs ): measurements[key] = graph histogram_properties = Histogram_properties() histogram_properties.name = channel + '_' + variable + '_' + method + '_' + test histogram_properties.title = title + ', ' + latex_labels.b_tag_bins_latex['2orMoreBtags'] histogram_properties.x_axis_title = '$' + latex_labels.variables_latex[variable] + '$' histogram_properties.y_axis_title = r'Events' # histogram_properties.y_limits = [0, 0.03] histogram_properties.x_limits = [bin_edges[0], bin_edges[-1]] if log_plots: histogram_properties.set_log_y = True histogram_properties.name += '_log' compare_measurements( models, measurements, show_measurement_errors = True, histogram_properties = histogram_properties, save_folder = plot_location, save_as = ['pdf'] )
def compare_unfolding_methods(measurement='normalised_xsection', add_before_unfolding=False, channel='combined'): file_template = '/hdfs/TopQuarkGroup/run2/dpsData/' file_template += 'data/normalisation/background_subtraction/13TeV/' file_template += '{variable}/VisiblePS/central/' file_template += '{measurement}_{channel}_RooUnfold{method}.txt' variables = ['MET', 'HT', 'ST', 'NJets', 'lepton_pt', 'abs_lepton_eta', 'WPT'] for variable in variables: svd = file_template.format( variable=variable, method='Svd', channel=channel, measurement=measurement) bayes = file_template.format( variable=variable, method='Bayes', channel=channel, measurement=measurement) data = read_data_from_JSON(svd) before_unfolding = data['TTJet_measured_withoutFakes'] svd_data = data['TTJet_unfolded'] bayes_data = read_data_from_JSON(bayes)['TTJet_unfolded'] h_svd = value_error_tuplelist_to_hist( svd_data, bin_edges_vis[variable]) h_bayes = value_error_tuplelist_to_hist( bayes_data, bin_edges_vis[variable]) h_before_unfolding = value_error_tuplelist_to_hist( before_unfolding, bin_edges_vis[variable]) properties = Histogram_properties() properties.name = '{0}_compare_unfolding_methods_{1}_{2}'.format( measurement, variable, channel) properties.title = 'Comparison of unfolding methods' properties.path = 'plots' properties.has_ratio = True properties.xerr = True properties.x_limits = ( bin_edges_vis[variable][0], bin_edges_vis[variable][-1]) properties.x_axis_title = variables_latex[variable] if 'xsection' in measurement: properties.y_axis_title = r'$\frac{1}{\sigma} \frac{d\sigma}{d' + \ variables_latex[variable] + '}$' else: properties.y_axis_title = r'$t\bar{t}$ normalisation' histograms = {'SVD': h_svd, 'Bayes': h_bayes} if add_before_unfolding: histograms['before unfolding'] = h_before_unfolding properties.name += '_ext' properties.has_ratio = False plot = Plot(histograms, properties) plot.draw_method = 'errorbar' compare_histograms(plot)
def compare_combine_before_after_unfolding(measurement='normalised_xsection', add_before_unfolding=False): file_template = 'data/normalisation/background_subtraction/13TeV/' file_template += '{variable}/VisiblePS/central/' file_template += '{measurement}_{channel}_RooUnfold{method}.txt' variables = ['MET', 'HT', 'ST', 'NJets', 'lepton_pt', 'abs_lepton_eta', 'WPT'] for variable in variables: combineBefore = file_template.format( variable=variable, method='Svd', channel='combinedBeforeUnfolding', measurement=measurement) combineAfter = file_template.format( variable=variable, method='Svd', channel='combined', measurement=measurement) data = read_data_from_JSON(combineBefore) before_unfolding = data['TTJet_measured'] combineBefore_data = data['TTJet_unfolded'] combineAfter_data = read_data_from_JSON(combineAfter)['TTJet_unfolded'] h_combineBefore = value_error_tuplelist_to_hist( combineBefore_data, bin_edges_vis[variable]) h_combineAfter = value_error_tuplelist_to_hist( combineAfter_data, bin_edges_vis[variable]) h_before_unfolding = value_error_tuplelist_to_hist( before_unfolding, bin_edges_vis[variable]) properties = Histogram_properties() properties.name = '{0}_compare_combine_before_after_unfolding_{1}'.format( measurement, variable) properties.title = 'Comparison of combining before/after unfolding' properties.path = 'plots' properties.has_ratio = True properties.xerr = True properties.x_limits = ( bin_edges_vis[variable][0], bin_edges_vis[variable][-1]) properties.x_axis_title = variables_latex[variable] if 'xsection' in measurement: properties.y_axis_title = r'$\frac{1}{\sigma} \frac{d\sigma}{d' + \ variables_latex[variable] + '}$' else: properties.y_axis_title = r'$t\bar{t}$ normalisation' histograms = {'Combine before unfolding': h_combineBefore, 'Combine after unfolding': h_combineAfter} if add_before_unfolding: histograms['before unfolding'] = h_before_unfolding properties.name += '_ext' properties.has_ratio = False plot = Plot(histograms, properties) plot.draw_method = 'errorbar' compare_histograms(plot)
def plot_results ( results ): ''' Takes results fo the form: {centre-of-mass-energy: { channel : { variable : { fit_variable : { test : { sample : []}, } } } } } ''' global options output_base = 'plots/fit_checks/chi2' for COMEnergy in results.keys(): tmp_result_1 = results[COMEnergy] for channel in tmp_result_1.keys(): tmp_result_2 = tmp_result_1[channel] for variable in tmp_result_2.keys(): tmp_result_3 = tmp_result_2[variable] for fit_variable in tmp_result_3.keys(): tmp_result_4 = tmp_result_3[fit_variable] # histograms should be {sample: {test : histogram}} histograms = {} for test, chi2 in tmp_result_4.iteritems(): for sample in chi2.keys(): if not histograms.has_key(sample): histograms[sample] = {} # reverse order of test and sample histograms[sample][test] = value_tuplelist_to_hist(chi2[sample], bin_edges_vis[variable]) for sample in histograms.keys(): hist_properties = Histogram_properties() hist_properties.name = sample.replace('+', '') + '_chi2' hist_properties.title = '$\\chi^2$ distribution for fit output (' + sample + ')' hist_properties.x_axis_title = '$' + latex_labels.variables_latex[variable] + '$ [TeV]' hist_properties.y_axis_title = '$\chi^2 = \\left({N_{fit}} - N_{{exp}}\\right)^2$' hist_properties.set_log_y = True hist_properties.y_limits = (1e-20, 1e20) path = output_base + '/' + COMEnergy + 'TeV/' + channel + '/' + variable + '/' + fit_variable + '/' if options.test: path = output_base + '/test/' measurements = {} for test, histogram in histograms[sample].iteritems(): measurements[test.replace('_',' ')] = histogram compare_measurements({}, measurements, show_measurement_errors = False, histogram_properties = hist_properties, save_folder = path, save_as = ['pdf'])
def plot_bias(unfolded_and_truths, variable, channel, come, method, prefix, plot_systematics=False): hp = Histogram_properties() hp.name = 'Bias_{prefix}_{channel}_{variable}_at_{come}TeV'.format( prefix=prefix, channel=channel, variable=variable, come=come, ) v_latex = latex_labels.variables_latex[variable] unit = '' if variable in ['HT', 'ST', 'MET', 'WPT', 'lepton_pt']: unit = ' [GeV]' hp.x_axis_title = v_latex + unit # plt.ylabel( r, CMS.y_axis_title ) hp.y_axis_title = 'Unfolded / Truth' hp.y_limits = [0.7, 1.5] hp.title = 'Bias for {variable}'.format(variable=v_latex) hp.legend_location = (0.98, 0.95) output_folder = 'plots/unfolding/bias_test/' measurements = {} # measurements = { 'Central' : unfolded_and_truths['Central']['bias'] } # for bin in range(0, unfolded_and_truths['Central']['bias'].GetNbinsX() + 1 ): # unfolded_and_truths['Central']['bias'].SetBinError(bin,0) models = OrderedDict() lineStyles = [] for sample in unfolded_and_truths: if sample == 'Central' or sample == 'Nominal': lineStyles.append('dashed') else: lineStyles.append('dotted') models[sample] = unfolded_and_truths[sample]['bias'] if plot_systematics: models['systematicsup'], models['systematicsdown'] = get_systematics(variable,channel,come,method) lineStyles.append('solid') lineStyles.append('solid') compare_measurements( models = models, measurements = measurements, show_measurement_errors=True, histogram_properties=hp, save_folder=output_folder, save_as=['pdf'], line_styles_for_models = lineStyles, match_models_to_measurements = True )
def compare_unfolding_uncertainties(): file_template = '/hdfs/TopQuarkGroup/run2/dpsData/' file_template += 'data/normalisation/background_subtraction/13TeV/' file_template += '{variable}/VisiblePS/central/' file_template += 'unfolded_normalisation_combined_RooUnfold{method}.txt' variables = ['MET', 'HT', 'ST', 'NJets', 'lepton_pt', 'abs_lepton_eta', 'WPT'] # variables = ['ST'] for variable in variables: svd = file_template.format( variable=variable, method='Svd') bayes = file_template.format( variable=variable, method='Bayes') data = read_data_from_JSON(svd) before_unfolding = data['TTJet_measured_withoutFakes'] svd_data = data['TTJet_unfolded'] bayes_data = read_data_from_JSON(bayes)['TTJet_unfolded'] before_unfolding = [e / v * 100 for v, e in before_unfolding] svd_data = [e / v * 100 for v, e in svd_data] bayes_data = [e / v * 100 for v, e in bayes_data] h_svd = value_tuplelist_to_hist( svd_data, bin_edges_vis[variable]) h_bayes = value_tuplelist_to_hist( bayes_data, bin_edges_vis[variable]) h_before_unfolding = value_tuplelist_to_hist( before_unfolding, bin_edges_vis[variable]) properties = Histogram_properties() properties.name = 'compare_unfolding_uncertainties_{0}'.format( variable) properties.title = 'Comparison of unfolding uncertainties' properties.path = 'plots' properties.has_ratio = False properties.xerr = True properties.x_limits = ( bin_edges_vis[variable][0], bin_edges_vis[variable][-1]) properties.x_axis_title = variables_latex[variable] properties.y_axis_title = 'relative uncertainty (\\%)' properties.legend_location = (0.98, 0.95) histograms = {'SVD': h_svd, 'Bayes': h_bayes, 'before unfolding': h_before_unfolding} plot = Plot(histograms, properties) plot.draw_method = 'errorbar' compare_histograms(plot)
def compare_combine_before_after_unfolding_uncertainties(): file_template = 'data/normalisation/background_subtraction/13TeV/' file_template += '{variable}/VisiblePS/central/' file_template += 'unfolded_normalisation_{channel}_RooUnfoldSvd.txt' variables = ['MET', 'HT', 'ST', 'NJets', 'lepton_pt', 'abs_lepton_eta', 'WPT'] # variables = ['ST'] for variable in variables: beforeUnfolding = file_template.format( variable=variable, channel='combinedBeforeUnfolding') afterUnfolding = file_template.format( variable=variable, channel='combined') data = read_data_from_JSON(beforeUnfolding) before_unfolding = data['TTJet_measured'] beforeUnfolding_data = data['TTJet_unfolded'] afterUnfolding_data = read_data_from_JSON(afterUnfolding)['TTJet_unfolded'] before_unfolding = [e / v * 100 for v, e in before_unfolding] beforeUnfolding_data = [e / v * 100 for v, e in beforeUnfolding_data] afterUnfolding_data = [e / v * 100 for v, e in afterUnfolding_data] h_beforeUnfolding = value_tuplelist_to_hist( beforeUnfolding_data, bin_edges_vis[variable]) h_afterUnfolding = value_tuplelist_to_hist( afterUnfolding_data, bin_edges_vis[variable]) h_before_unfolding = value_tuplelist_to_hist( before_unfolding, bin_edges_vis[variable]) properties = Histogram_properties() properties.name = 'compare_combine_before_after_unfolding_uncertainties_{0}'.format( variable) properties.title = 'Comparison of unfolding uncertainties' properties.path = 'plots' properties.has_ratio = False properties.xerr = True properties.x_limits = ( bin_edges_vis[variable][0], bin_edges_vis[variable][-1]) properties.x_axis_title = variables_latex[variable] properties.y_axis_title = 'relative uncertainty (\\%)' properties.legend_location = (0.98, 0.95) histograms = {'Combine before unfolding': h_beforeUnfolding, 'Combine after unfolding': h_afterUnfolding, # 'before unfolding': h_before_unfolding } plot = Plot(histograms, properties) plot.draw_method = 'errorbar' compare_histograms(plot)
def plot_fit_results(fit_results, centre_of_mass, channel, variable, k_value, tau_value, output_folder, output_formats, bin_edges): h_mean = Hist(bin_edges, type='D') h_sigma = Hist(bin_edges, type='D') n_bins = h_mean.nbins() assert len(fit_results) == n_bins mean_abs_pull = 0 for i, fr in enumerate(fit_results): mean_abs_pull += abs(fr.mean) h_mean.SetBinContent(i + 1, fr.mean) h_mean.SetBinError(i + 1, fr.meanError) h_sigma.SetBinContent(i + 1, fr.sigma) h_sigma.SetBinError(i + 1, fr.sigmaError) mean_abs_pull /= n_bins histogram_properties = Histogram_properties() name_mpt = 'pull_distribution_mean_and_sigma_{0}_{1}_{2}TeV' histogram_properties.name = name_mpt.format( variable, channel, centre_of_mass ) histogram_properties.y_axis_title = r'$\mu_{\text{pull}}$ ($\sigma_{\text{pull}}$)' histogram_properties.x_axis_title = latex_labels.variables_latex[variable] histogram_properties.legend_location = (0.98, 0.48) value = get_value_title(k_value, tau_value) title = 'pull distribution mean \& sigma for {0}'.format(tau_value) histogram_properties.title = title histogram_properties.y_limits = [-2, 2] histogram_properties.xerr = True compare_measurements( models={ # 'mean $|\mu|$':make_line_hist(bin_edges,mean_abs_pull), 'ideal $\mu$': make_line_hist(bin_edges, 0), 'ideal $\sigma$': make_line_hist(bin_edges, 1), }, measurements={ r'$\mu_{\text{pull}}$': h_mean, r'$\sigma_{\text{pull}}$': h_sigma }, show_measurement_errors=True, histogram_properties=histogram_properties, save_folder=output_folder, save_as=output_formats)
def plot_bias(unfolded_and_truths, variable, channel, come, method): hp = Histogram_properties() hp.name = 'Bias_{channel}_{variable}_at_{come}TeV'.format( channel=channel, variable=variable, come=come, ) v_latex = latex_labels.variables_latex[variable] unit = '' if variable in ['HT', 'ST', 'MET', 'WPT', 'lepton_pt']: unit = ' [GeV]' hp.x_axis_title = v_latex + unit # plt.ylabel( r, CMS.y_axis_title ) hp.y_axis_title = 'Unfolded / Truth' hp.y_limits = [0.85, 1.15] hp.title = 'Bias for {variable}'.format(variable=v_latex) output_folder = 'plots/unfolding/bias_test/' measurements = { 'Central' : unfolded_and_truths['Central']['bias'] } for bin in range(0, unfolded_and_truths['Central']['bias'].GetNbinsX() + 1 ): unfolded_and_truths['Central']['bias'].SetBinError(bin,0) # central_truth = unfolded_and_truths['Central']['truth'] # for label, hists in unfolded_and_truths.iteritems(): # truth = hists['truth'] # print label # for i,j in zip( list(truth.y()), list(central_truth.y())) : # print abs(1-i/j)*100 models = {} for sample in unfolded_and_truths: if sample == 'Central' : continue models[sample] = unfolded_and_truths[sample]['bias'] compare_measurements( models = models, measurements = measurements, show_measurement_errors=True, histogram_properties=hp, save_folder=output_folder, save_as=['pdf'], match_models_to_measurements = True)
def plot_fit_results(fit_results, centre_of_mass, channel, variable, k_value, tau_value, output_folder, output_formats, bin_edges): h_mean = Hist(bin_edges, type='D') h_sigma = Hist(bin_edges, type='D') n_bins = h_mean.nbins() assert len(fit_results) == n_bins mean_abs_pull = 0 for i, fr in enumerate(fit_results): mean_abs_pull += abs(fr.mean) h_mean.SetBinContent(i + 1, fr.mean) h_mean.SetBinError(i + 1, fr.meanError) h_sigma.SetBinContent(i + 1, fr.sigma) h_sigma.SetBinError(i + 1, fr.sigmaError) mean_abs_pull /= n_bins histogram_properties = Histogram_properties() name_mpt = 'pull_distribution_mean_and_sigma_{0}_{1}_{2}TeV' histogram_properties.name = name_mpt.format(variable, channel, centre_of_mass) histogram_properties.y_axis_title = r'$\mu_{\text{pull}}$ ($\sigma_{\text{pull}}$)' histogram_properties.x_axis_title = latex_labels.variables_latex[variable] histogram_properties.legend_location = (0.98, 0.48) value = get_value_title(k_value, tau_value) title = 'pull distribution mean \& sigma for {0}'.format(tau_value) histogram_properties.title = title histogram_properties.y_limits = [-2, 2] histogram_properties.xerr = True compare_measurements( models={ # 'mean $|\mu|$':make_line_hist(bin_edges,mean_abs_pull), 'ideal $\mu$': make_line_hist(bin_edges, 0), 'ideal $\sigma$': make_line_hist(bin_edges, 1), }, measurements={ r'$\mu_{\text{pull}}$': h_mean, r'$\sigma_{\text{pull}}$': h_sigma }, show_measurement_errors=True, histogram_properties=histogram_properties, save_folder=output_folder, save_as=output_formats)
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 plot_fit_results( histograms, category, channel ): global variable, b_tag_bin, output_folder, phase_space from dps.utils.plotting import Histogram_properties, make_data_mc_comparison_plot fit_variables = histograms.keys() variableBins = None if phase_space == 'VisiblePS': variableBins = variable_bins_visiblePS_ROOT elif phase_space == 'FullPS': variableBins = variable_bins_ROOT for variable_bin in variableBins[variable]: path = output_folder + str( measurement_config.centre_of_mass_energy ) + 'TeV/' + variable + '/' + category + '/fit_results/' make_folder_if_not_exists( path ) for fit_variable in fit_variables: plotname = channel + '_' + fit_variable + '_bin_' + variable_bin # check if template plots exist already for output_format in output_formats: if os.path.isfile( plotname + '.' + output_format ): continue # plot with matplotlib h_data = histograms[fit_variable][variable_bin]['data'] h_signal = histograms[fit_variable][variable_bin]['signal'] h_background = histograms[fit_variable][variable_bin]['background'] histogram_properties = Histogram_properties() histogram_properties.name = plotname histogram_properties.x_axis_title = fit_variables_latex[fit_variable] histogram_properties.y_axis_title = 'Events/(%s)' % get_unit_string(fit_variable) label, _ = get_cms_labels( channel ) histogram_properties.title = label histogram_properties.x_limits = measurement_config.fit_boundaries[fit_variable] make_data_mc_comparison_plot( [h_data, h_background, h_signal], ['data', 'background', 'signal'], ['black', 'green', 'red'], histogram_properties, save_folder = path, save_as = output_formats )
def plot_closure(unfolded_and_truths, variable, channel, come, method, quantity): hp = Histogram_properties() hp.name = '{quantity}_{channel}_closure_test_for_{variable}_at_{come}TeV'.format( quantity=quantity, channel=channel, variable=variable, come=come, ) v_latex = latex_labels.variables_latex[variable] unit = '' if variable in ['HT', 'ST', 'MET', 'WPT', 'lepton_pt']: unit = ' [GeV]' hp.x_axis_title = v_latex + unit if quantity == 'number_of_unfolded_events': hp.y_axis_title = 'Number of unfolded events' elif quantity == 'normalised_xsection': hp.y_axis_title = 'Normalised Cross Section' hp.title = 'Closure tests for {variable}'.format(variable=v_latex) output_folder = 'plots/unfolding/closure_test/' models = OrderedDict() measurements = OrderedDict() for sample in unfolded_and_truths: models[sample + ' truth'] = unfolded_and_truths[sample]['truth'] measurements[sample + ' unfolded'] = unfolded_and_truths[sample]['unfolded'] compare_measurements( models = models, measurements = measurements, show_measurement_errors=True, histogram_properties=hp, save_folder=output_folder, save_as=['pdf'], match_models_to_measurements = True )
def compare_QCD_control_regions_to_MC(): config = XSectionConfig(13) ctrl_e1 = 'TTbar_plus_X_analysis/EPlusJets/QCDConversions/FitVariables' ctrl_e2 = 'TTbar_plus_X_analysis/EPlusJets/QCD non iso e+jets/FitVariables' mc_e = 'TTbar_plus_X_analysis/EPlusJets/Ref selection/FitVariables' data_file_e = config.data_file_electron_trees ttbar_file = config.ttbar_category_templates_trees['central'] vjets_file = config.VJets_category_templates_trees['central'] singleTop_file = config.SingleTop_category_templates_trees['central'] qcd_file_e = config.electron_QCD_MC_tree_file ctrl_mu1 = 'TTbar_plus_X_analysis/MuPlusJets/QCD iso > 0.3/FitVariables' ctrl_mu2 = 'TTbar_plus_X_analysis/MuPlusJets/QCD 0.12 < iso <= 0.3/FitVariables' mc_mu = 'TTbar_plus_X_analysis/MuPlusJets/Ref selection/FitVariables' data_file_mu = config.data_file_muon_trees qcd_file_mu = config.muon_QCD_MC_tree_file weight_branches_electron = [ "EventWeight", "PUWeight", "BJetWeight", "ElectronEfficiencyCorrection" ] weight_branches_mu = [ "EventWeight", "PUWeight", "BJetWeight", "MuonEfficiencyCorrection" ] variables = ['MET', 'HT', 'ST', 'NJets', 'lepton_pt', 'abs_lepton_eta', 'WPT'] # variables = ['abs_lepton_eta'] for variable in variables: branch = variable selection = '{0} >= 0'.format(branch) if variable == 'abs_lepton_eta': branch = 'abs(lepton_eta)' selection = 'lepton_eta >= -3' for channel in ['electron', 'muon']: data_file = data_file_e qcd_file = qcd_file_e ctrl1 = ctrl_e1 ctrl2 = ctrl_e2 mc = mc_e weight_branches = weight_branches_electron if channel == 'muon': data_file = data_file_mu qcd_file = qcd_file_mu ctrl1 = ctrl_mu1 ctrl2 = ctrl_mu2 mc = mc_mu weight_branches = weight_branches_mu inputs = { 'branch': branch, 'weight_branches': weight_branches, 'tree': ctrl1, 'bin_edges': bin_edges_vis[variable], 'selection': selection, } hs_ctrl1 = { 'data': get_histogram_from_tree(input_file=data_file, **inputs), 'TTJet': get_histogram_from_tree(input_file=ttbar_file, **inputs), 'VJets': get_histogram_from_tree(input_file=vjets_file, **inputs), 'SingleTop': get_histogram_from_tree(input_file=singleTop_file, **inputs), 'QCD': get_histogram_from_tree(input_file=qcd_file, **inputs), } inputs['tree'] = ctrl2 hs_ctrl2 = { 'data': get_histogram_from_tree(input_file=data_file, **inputs), 'TTJet': get_histogram_from_tree(input_file=ttbar_file, **inputs), 'VJets': get_histogram_from_tree(input_file=vjets_file, **inputs), 'SingleTop': get_histogram_from_tree(input_file=singleTop_file, **inputs), 'QCD': get_histogram_from_tree(input_file=qcd_file, **inputs), } inputs['tree'] = mc h_qcd = get_histogram_from_tree(input_file=qcd_file, **inputs) h_ctrl1 = clean_control_region( hs_ctrl1, data_label='data', subtract=['TTJet', 'VJets', 'SingleTop'], fix_to_zero=True) h_ctrl2 = clean_control_region( hs_ctrl2, data_label='data', subtract=['TTJet', 'VJets', 'SingleTop'], fix_to_zero=True) n_qcd_ctrl1 = hs_ctrl1['QCD'].integral() n_qcd_ctrl2 = hs_ctrl2['QCD'].integral() n_data1 = h_ctrl1.integral() n_data2 = h_ctrl2.integral() n_qcd_sg = h_qcd.integral() ratio_ctrl1 = n_data1 / n_qcd_ctrl1 ratio_ctrl2 = n_data2 / n_qcd_ctrl2 qcd_estimate_ctrl1 = n_qcd_sg * ratio_ctrl1 qcd_estimate_ctrl2 = n_qcd_sg * ratio_ctrl2 h_ctrl1.Scale(qcd_estimate_ctrl1 / n_data1) h_ctrl2.Scale(qcd_estimate_ctrl2 / n_data2) properties = Histogram_properties() properties.name = 'compare_qcd_control_regions_to_mc_{0}_{1}_channel'.format( variable, channel) properties.title = 'Comparison of QCD control regions ({0} channel)'.format( channel) properties.path = 'plots' properties.has_ratio = False properties.xerr = True properties.x_limits = ( bin_edges_vis[variable][0], bin_edges_vis[variable][-1]) properties.x_axis_title = variables_latex[variable] properties.y_axis_title = 'number of QCD events' histograms = {'control region 1': h_ctrl1, 'control region 2': h_ctrl2, 'MC prediction': h_qcd} diff = absolute(h_ctrl1 - h_ctrl2) lower = h_ctrl1 - diff upper = h_ctrl1 + diff err_e = ErrorBand('uncertainty', lower, upper) plot_e = Plot(histograms, properties) plot_e.draw_method = 'errorbar' plot_e.add_error_band(err_e) compare_histograms(plot_e)
elif template == 'data': histograms[template].Scale( initial_values_[template][whichBin][0]) pass histogramsToDraw = [ histograms['data'], histograms['QCD'], histograms['V+Jets'], histograms['SingleTop'], histograms['TTJet'] ] histogram_lables = ['data', 'QCD', 'V+Jets', 'Single-Top', 'TTJet'] histogram_colors = ['black', 'yellow', 'green', 'magenta', 'red'] histogram_properties = Histogram_properties() histogram_properties.name = 'Closure_'+'simple'+'_'+fitVariable+'_'+variable+'_'+str(whichBin) histogram_properties.x_axis_title = fit_variable_properties[fitVariable]['x-title'] histogram_properties.y_axis_title = fit_variable_properties[fitVariable]['y-title'] make_data_mc_comparison_plot( histogramsToDraw, histogram_lables, histogram_colors, histogram_properties, save_folder = 'data/closure_test/'+test+'/absolute_eta_M3_angle_bl/8TeV/', show_ratio = False, save_as = ['pdf'], ) pass pass pass pass
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 do_shape_check(channel, control_region_1, control_region_2, variable, normalisation, title, x_title, y_title, x_limits, y_limits, name_region_1='conversions', name_region_2='non-isolated electrons', name_region_3='fit results', rebin=1): global b_tag_bin # QCD shape comparison if channel == 'electron': histograms = get_histograms_from_files( [control_region_1, control_region_2], histogram_files) region_1 = histograms[channel][control_region_1].Clone( ) - histograms['TTJet'][control_region_1].Clone( ) - histograms['V+Jets'][control_region_1].Clone( ) - histograms['SingleTop'][control_region_1].Clone() region_2 = histograms[channel][control_region_2].Clone( ) - histograms['TTJet'][control_region_2].Clone( ) - histograms['V+Jets'][control_region_2].Clone( ) - histograms['SingleTop'][control_region_2].Clone() region_1.Rebin(rebin) region_2.Rebin(rebin) histogram_properties = Histogram_properties() histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_' + b_tag_bin histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin] histogram_properties.x_axis_title = x_title histogram_properties.y_axis_title = 'arbitrary units/(0.1)' histogram_properties.x_limits = x_limits histogram_properties.y_limits = y_limits[0] histogram_properties.mc_error = 0.0 histogram_properties.legend_location = 'upper right' make_control_region_comparison( region_1, region_2, name_region_1=name_region_1, name_region_2=name_region_2, histogram_properties=histogram_properties, save_folder=output_folder) # QCD shape comparison to fit results histograms = get_histograms_from_files([control_region_1], histogram_files) region_1_tmp = histograms[channel][control_region_1].Clone( ) - histograms['TTJet'][control_region_1].Clone( ) - histograms['V+Jets'][control_region_1].Clone( ) - histograms['SingleTop'][control_region_1].Clone() region_1 = rebin_asymmetric(region_1_tmp, bin_edges_vis[variable]) fit_results_QCD = normalisation[variable]['QCD'] region_2 = value_error_tuplelist_to_hist(fit_results_QCD, bin_edges_vis[variable]) histogram_properties = Histogram_properties() histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_fits_with_conversions_' + b_tag_bin histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin] histogram_properties.x_axis_title = x_title histogram_properties.y_axis_title = 'arbitrary units/(0.1)' histogram_properties.x_limits = x_limits histogram_properties.y_limits = y_limits[1] histogram_properties.mc_error = 0.0 histogram_properties.legend_location = 'upper right' make_control_region_comparison( region_1, region_2, name_region_1=name_region_1, name_region_2=name_region_3, histogram_properties=histogram_properties, save_folder=output_folder) histograms = get_histograms_from_files([control_region_2], histogram_files) region_1_tmp = histograms[channel][control_region_2].Clone( ) - histograms['TTJet'][control_region_2].Clone( ) - histograms['V+Jets'][control_region_2].Clone( ) - histograms['SingleTop'][control_region_2].Clone() region_1 = rebin_asymmetric(region_1_tmp, bin_edges_vis[variable]) fit_results_QCD = normalisation[variable]['QCD'] region_2 = value_error_tuplelist_to_hist(fit_results_QCD, bin_edges_vis[variable]) histogram_properties = Histogram_properties() histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_fits_with_noniso_' + b_tag_bin histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin] histogram_properties.x_axis_title = x_title histogram_properties.y_axis_title = 'arbitrary units/(0.1)' histogram_properties.x_limits = x_limits histogram_properties.y_limits = y_limits[1] histogram_properties.mc_error = 0.0 histogram_properties.legend_location = 'upper right' make_control_region_comparison(region_1, region_2, name_region_1=name_region_2, name_region_2=name_region_3, histogram_properties=histogram_properties, save_folder=output_folder)
def debug_last_bin(): ''' For debugging why the last bin in the problematic variables deviates a lot in _one_ of the channels only. ''' file_template = '/hdfs/TopQuarkGroup/run2/dpsData/' file_template += 'data/normalisation/background_subtraction/13TeV/' file_template += '{variable}/VisiblePS/central/' file_template += 'normalised_xsection_{channel}_RooUnfoldSvd{suffix}.txt' problematic_variables = ['HT', 'MET', 'NJets', 'lepton_pt'] for variable in problematic_variables: results = {} Result = namedtuple( 'Result', ['before_unfolding', 'after_unfolding', 'model']) for channel in ['electron', 'muon', 'combined']: input_file_data = file_template.format( variable=variable, channel=channel, suffix='_with_errors', ) input_file_model = file_template.format( variable=variable, channel=channel, suffix='', ) data = read_data_from_JSON(input_file_data) data_model = read_data_from_JSON(input_file_model) before_unfolding = data['TTJet_measured_withoutFakes'] after_unfolding = data['TTJet_unfolded'] model = data_model['powhegPythia8'] # only use the last bin h_before_unfolding = value_errors_tuplelist_to_graph( [before_unfolding[-1]], bin_edges_vis[variable][-2:]) h_after_unfolding = value_errors_tuplelist_to_graph( [after_unfolding[-1]], bin_edges_vis[variable][-2:]) h_model = value_error_tuplelist_to_hist( [model[-1]], bin_edges_vis[variable][-2:]) r = Result(before_unfolding, after_unfolding, model) h = Result(h_before_unfolding, h_after_unfolding, h_model) results[channel] = (r, h) models = {'POWHEG+PYTHIA': results['combined'][1].model} h_unfolded = [results[channel][1].after_unfolding for channel in [ 'electron', 'muon', 'combined']] tmp_hists = spread_x(h_unfolded, bin_edges_vis[variable][-2:]) measurements = {} for channel, hist in zip(['electron', 'muon', 'combined'], tmp_hists): value = results[channel][0].after_unfolding[-1][0] error = results[channel][0].after_unfolding[-1][1] label = '{c_label} ({value:1.2g} $\pm$ {error:1.2g})'.format( c_label=channel, value=value, error=error, ) measurements[label] = hist properties = Histogram_properties() properties.name = 'normalised_xsection_compare_channels_{0}_{1}_last_bin'.format( variable, channel) properties.title = 'Comparison of channels' properties.path = 'plots' properties.has_ratio = True properties.xerr = False properties.x_limits = ( bin_edges_vis[variable][-2], bin_edges_vis[variable][-1]) properties.x_axis_title = variables_latex[variable] properties.y_axis_title = r'$\frac{1}{\sigma} \frac{d\sigma}{d' + \ variables_latex[variable] + '}$' properties.legend_location = (0.95, 0.40) if variable == 'NJets': properties.legend_location = (0.97, 0.80) properties.formats = ['png'] compare_measurements(models=models, measurements=measurements, show_measurement_errors=True, histogram_properties=properties, save_folder='plots/', save_as=properties.formats)
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' make_data_mc_comparison_plot(histograms_to_draw, histogram_lables, histogram_colors, histogram_properties)
def plotHistograms( histogram_files, var_to_plot, output_folder): ''' ''' global measurement_config weightBranchSignalRegion = 'EventWeight * PUWeight * BJetWeight' weightBranchControlRegion = 'EventWeight' # Names of QCD regions to use qcd_data_region = '' qcd_data_region_electron = 'QCD non iso e+jets' qcd_data_region_muon = 'QCD non iso mu+jets 1p5to3' sr_e_tree = 'TTbar_plus_X_analysis/EPlusJets/Ref selection/AnalysisVariables' sr_mu_tree = 'TTbar_plus_X_analysis/MuPlusJets/Ref selection/AnalysisVariables' cr_e_tree = 'TTbar_plus_X_analysis/EPlusJets/{}/AnalysisVariables'.format(qcd_data_region_electron) cr_mu_tree = 'TTbar_plus_X_analysis/MuPlusJets/{}/AnalysisVariables'.format(qcd_data_region_muon) print "Trees : " print "\t {}".format(sr_e_tree) print "\t {}".format(sr_mu_tree) print "\t {}".format(cr_e_tree) print "\t {}".format(cr_mu_tree) histogram_files_electron = dict(histogram_files) histogram_files_electron['data'] = measurement_config.data_file_electron histogram_files_electron['QCD'] = measurement_config.electron_QCD_MC_trees histogram_files_muon = dict(histogram_files) histogram_files_muon['data'] = measurement_config.data_file_muon histogram_files_muon['QCD'] = measurement_config.muon_QCD_MC_trees signal_region_hists = {} control_region_hists = {} for var in var_to_plot: selectionSignalRegion = '{} >= 0'.format(var) # Print all the weights applied to this plot print "Variable : {}".format(var) print "Weight applied : {}".format(weightBranchSignalRegion) print "Selection applied : {}".format(selectionSignalRegion) histograms_electron = get_histograms_from_trees( trees = [sr_e_tree], branch = var, weightBranch = weightBranchSignalRegion + ' * ElectronEfficiencyCorrection', files = histogram_files_electron, nBins = 20, xMin = control_plots_bins[var][0], xMax = control_plots_bins[var][-1], selection = selectionSignalRegion ) histograms_muon = get_histograms_from_trees( trees = [sr_mu_tree], branch = var, weightBranch = weightBranchSignalRegion + ' * MuonEfficiencyCorrection', files = histogram_files_muon, nBins = 20, xMin = control_plots_bins[var][0], xMax = control_plots_bins[var][-1], selection = selectionSignalRegion ) histograms_electron_QCDControlRegion = get_histograms_from_trees( trees = [cr_e_tree], branch = var, weightBranch = weightBranchControlRegion, files = histogram_files_electron, nBins = 20, xMin = control_plots_bins[var][0], xMax = control_plots_bins[var][-1], selection = selectionSignalRegion ) histograms_muon_QCDControlRegion = get_histograms_from_trees( trees = [cr_mu_tree], branch = var, weightBranch = weightBranchControlRegion, files = histogram_files_muon, nBins = 20, xMin = control_plots_bins[var][0], xMax = control_plots_bins[var][-1], selection = selectionSignalRegion ) # Combine the electron and muon histograms for sample in histograms_electron: h_electron = histograms_electron[sample][sr_e_tree] h_muon = histograms_muon[sample][sr_mu_tree] h_qcd_electron = histograms_electron_QCDControlRegion[sample][cr_e_tree] h_qcd_muon = histograms_muon_QCDControlRegion[sample][cr_mu_tree] signal_region_hists[sample] = h_electron + h_muon control_region_hists[sample] = h_qcd_electron + h_qcd_muon # NORMALISE TO LUMI prepare_histograms( signal_region_hists, scale_factor = measurement_config.luminosity_scale ) prepare_histograms( control_region_hists, scale_factor = measurement_config.luminosity_scale ) # BACKGROUND SUBTRACTION FOR QCD qcd_from_data = None qcd_from_data = clean_control_region( control_region_hists, subtract = ['TTJet', 'V+Jets', 'SingleTop'] ) # DATA DRIVEN QCD 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) dataDrivenQCDScale = n_qcd_predicted_mc_signal / n_qcd_predicted_mc_control qcd_from_data.Scale( dataDrivenQCDScale.nominal_value ) signal_region_hists['QCD'] = qcd_from_data # PLOTTING histograms_to_draw = [] histogram_lables = [] histogram_colors = [] histograms_to_draw = [ # signal_region_hists['data'], # qcd_from_data, # signal_region_hists['V+Jets'], signal_region_hists['SingleTop'], signal_region_hists['ST_s'], signal_region_hists['ST_t'], signal_region_hists['ST_tW'], signal_region_hists['STbar_t'], signal_region_hists['STbar_tW'], # signal_region_hists['TTJet'], ] histogram_lables = [ 'data', # 'QCD', # 'V+Jets', # 'Single-Top', 'ST-s', 'ST-t', 'ST-tW', 'STbar-t', 'STbar-tW', # samples_latex['TTJet'], ] histogram_colors = [ colours['data'], # colours['QCD'], # colours['V+Jets'], # colours['Single-Top'], colours['ST_s'], colours['ST_t'], colours['ST_tW'], colours['STbar_t'], colours['STbar_tW'], # colours['TTJet'], ] # Find maximum y of samples maxData = max( list(signal_region_hists['SingleTop'].y()) ) y_limits = [0, maxData * 1.5] log_y = False if log_y: y_limits = [0.1, maxData * 100 ] # Lumi title of plots title_template = '%.1f fb$^{-1}$ (%d TeV)' title = title_template % ( measurement_config.new_luminosity/1000., measurement_config.centre_of_mass_energy ) x_axis_title = '$%s$ [GeV]' % variables_latex[var] y_axis_title = 'Events/(%i GeV)' % binWidth(control_plots_bins[var]) # More histogram settings to look semi decent histogram_properties = Histogram_properties() histogram_properties.name = var + '_with_ratio' histogram_properties.title = title histogram_properties.x_axis_title = x_axis_title histogram_properties.y_axis_title = y_axis_title histogram_properties.x_limits = control_plots_bins[var] histogram_properties.y_limits = y_limits histogram_properties.y_max_scale = 1.4 histogram_properties.xerr = None histogram_properties.emptybins = True histogram_properties.additional_text = channel_latex['combined'] histogram_properties.legend_location = ( 0.9, 0.73 ) histogram_properties.cms_logo_location = 'left' histogram_properties.preliminary = True histogram_properties.set_log_y = log_y histogram_properties.legend_color = False histogram_properties.ratio_y_limits = [0.1,1.9] if log_y: histogram_properties.name += '_logy' loc = histogram_properties.legend_location 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, ) histogram_properties.name = var + '_ST_TTJet_Shape' if log_y: histogram_properties.name += '_logy' histogram_properties.y_axis_title = 'Normalised Distribution' histogram_properties.y_limits = [0,0.5] make_shape_comparison_plot( shapes = [ signal_region_hists['TTJet'], signal_region_hists['ST_t'], signal_region_hists['ST_tW'], signal_region_hists['ST_s'], signal_region_hists['STbar_t'], signal_region_hists['STbar_tW'], ], names = [ samples_latex['TTJet'], 'Single-Top t channel', 'Single-Top tW channel', 'Single-Top s channel', 'Single-AntiTop t channel', 'Single-AntiTop tW channel', ], colours = [ colours['TTJet'], colours['ST_t'], colours['ST_tW'], colours['ST_s'], colours['STbar_t'], colours['STbar_tW'], ], histogram_properties = histogram_properties, save_folder = output_folder, fill_area = False, add_error_bars = False, save_as = ['pdf'], make_ratio = True, alpha = 1, ) print_output(signal_region_hists, output_folder, var, 'combined') return
def make_correlation_plot_from_file( channel, variable, fit_variables, CoM, title, x_title, y_title, x_limits, y_limits, rebin = 1, save_folder = 'plots/fitchecks/', save_as = ['pdf', 'png'] ): # global b_tag_bin parameters = ["TTJet", "SingleTop", "V+Jets", "QCD"] parameters_latex = [] for template in parameters: parameters_latex.append(samples_latex[template]) input_file = open( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), "r" ) # cycle through the lines in the file for line_number, line in enumerate( input_file ): # for now, only make plots for the fits for the central measurement if "central" in line: # matrix we want begins 11 lines below the line with the measurement ("central") line_number = line_number + 11 break input_file.close() #Note: For some reason, the fit outputs the correlation matrix with the templates in the following order: #parameter1: QCD #parameter2: SingleTop #parameter3: TTJet #parameter4: V+Jets for variable_bin in variable_bins_ROOT[variable]: weights = {} if channel == 'electron': #formula to calculate the number of lines below "central" to access in each loop number_of_lines_down = (variable_bins_ROOT[variable].index( variable_bin ) * 12) #Get QCD correlations matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down ) weights["QCD_QCD"] = matrix_line.split()[2] weights["QCD_SingleTop"] = matrix_line.split()[3] weights["QCD_TTJet"] = matrix_line.split()[4] weights["QCD_V+Jets"] = matrix_line.split()[5] #Get SingleTop correlations matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 1 ) weights["SingleTop_QCD"] = matrix_line.split()[2] weights["SingleTop_SingleTop"] = matrix_line.split()[3] weights["SingleTop_TTJet"] = matrix_line.split()[4] weights["SingleTop_V+Jets"] = matrix_line.split()[5] #Get TTJet correlations matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 2 ) weights["TTJet_QCD"] = matrix_line.split()[2] weights["TTJet_SingleTop"] = matrix_line.split()[3] weights["TTJet_TTJet"] = matrix_line.split()[4] weights["TTJet_V+Jets"] = matrix_line.split()[5] #Get V+Jets correlations matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 3 ) weights["V+Jets_QCD"] = matrix_line.split()[2] weights["V+Jets_SingleTop"] = matrix_line.split()[3] weights["V+Jets_TTJet"] = matrix_line.split()[4] weights["V+Jets_V+Jets"] = matrix_line.split()[5] if channel == 'muon': #formula to calculate the number of lines below "central" to access in each bin loop number_of_lines_down = ( len( variable_bins_ROOT [variable] ) * 12 ) + ( variable_bins_ROOT[variable].index( variable_bin ) * 12 ) #Get QCD correlations matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down ) weights["QCD_QCD"] = matrix_line.split()[2] weights["QCD_SingleTop"] = matrix_line.split()[3] weights["QCD_TTJet"] = matrix_line.split()[4] weights["QCD_V+Jets"] = matrix_line.split()[5] #Get SingleTop correlations matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 1 ) weights["SingleTop_QCD"] = matrix_line.split()[2] weights["SingleTop_SingleTop"] = matrix_line.split()[3] weights["SingleTop_TTJet"] = matrix_line.split()[4] weights["SingleTop_V+Jets"] = matrix_line.split()[5] #Get TTJet correlations matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 2 ) weights["TTJet_QCD"] = matrix_line.split()[2] weights["TTJet_SingleTop"] = matrix_line.split()[3] weights["TTJet_TTJet"] = matrix_line.split()[4] weights["TTJet_V+Jets"] = matrix_line.split()[5] #Get V+Jets correlations matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 3 ) weights["V+Jets_QCD"] = matrix_line.split()[2] weights["V+Jets_SingleTop"] = matrix_line.split()[3] weights["V+Jets_TTJet"] = matrix_line.split()[4] weights["V+Jets_V+Jets"] = matrix_line.split()[5] #Create histogram histogram_properties = Histogram_properties() histogram_properties.title = title histogram_properties.name = 'Correlations_' + channel + '_' + variable + '_' + variable_bin histogram_properties.y_axis_title = y_title histogram_properties.x_axis_title = x_title histogram_properties.y_limits = y_limits histogram_properties.x_limits = x_limits histogram_properties.mc_error = 0.0 histogram_properties.legend_location = 'upper right' #initialise 2D histogram a = Hist2D( 4, 0, 4, 4, 0, 4 ) #fill histogram for i in range( len( parameters ) ): for j in range( len( parameters ) ): a.fill( float( i ), float( j ), float( weights["%s_%s" % ( parameters[i], parameters[j] )] ) ) # create figure plt.figure( figsize = CMS.figsize, dpi = CMS.dpi, facecolor = CMS.facecolor ) # make subplot(?) fig, ax = plt.subplots( nrows = 1, ncols = 1 ) rplt.hist2d( a ) plt.subplots_adjust( right = 0.8 ) #Set labels and formats for titles and axes plt.ylabel( histogram_properties.y_axis_title ) plt.xlabel( histogram_properties.x_axis_title ) plt.title( histogram_properties.title ) x_limits = histogram_properties.x_limits y_limits = histogram_properties.y_limits ax.set_xticklabels( parameters_latex ) ax.set_yticklabels( parameters_latex ) ax.set_xticks( [0.5, 1.5, 2.5, 3.5] ) ax.set_yticks( [0.5, 1.5, 2.5, 3.5] ) plt.setp( ax.get_xticklabels(), visible = True ) plt.setp( ax.get_yticklabels(), visible = True ) #create and draw colour bar to the right of the main plot im = rplt.imshow( a, axes = ax, vmin = -1.0, vmax = 1.0 ) #set location and dimensions (left, lower, width, height) cbar_ax = fig.add_axes( [0.85, 0.10, 0.05, 0.8] ) fig.colorbar( im, cax = cbar_ax ) for xpoint in range( len( parameters ) ): for ypoint in range( len( parameters ) ): correlation_value = weights["%s_%s" % ( parameters[xpoint], parameters[ypoint] )] ax.annotate( correlation_value, xy = ( xpoint + 0.5, ypoint + 0.5 ), ha = 'center', va = 'center', bbox = dict( fc = 'white', ec = 'none' ) ) for save in save_as: plt.savefig( save_folder + histogram_properties.name + '.' + save ) plt.close(fig) plt.close('all')
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)
def plot(regularisation_settings, results, use_current_k_values = False): variable = regularisation_settings.variable channel = regularisation_settings.channel com = regularisation_settings.centre_of_mass_energy output_folder = regularisation_settings.output_folder output_format = regularisation_settings.output_format measurement_config = XSectionConfig(com) name = 'reg_param_from_global_correlation_%s_channel_%s' % (channel, variable) hp = Histogram_properties() hp.name = name hp.x_axis_title = r'log($\tau$)' hp.y_axis_title = r'$\bar{\rho}(\tau)$' hp.title = r'global correlation for $%s$, %s channel, $\sqrt{s} = %d$ TeV' hp.title = hp.title % (variables_latex[variable], channel, com) k_results, tau_results = results optimal_tau, minimal_rho, tau_values, rho_values = tau_results optimal_k, optimal_k_rho, k_values, k_tau_values, k_rho_values = k_results plt.figure( figsize = ( 16, 16 ), dpi = 200, facecolor = 'white' ) plt.plot( tau_values, rho_values ) plt.plot( k_tau_values, k_rho_values, 'ro' ) plt.title(hp.title, CMS.title) plt.xlabel( hp.x_axis_title, CMS.x_axis_title ) plt.ylabel( hp.y_axis_title, CMS.y_axis_title ) plt.tick_params( **CMS.axis_label_major ) plt.tick_params( **CMS.axis_label_minor ) ax = plt.axes() current_k, closest_tau, _, _ = get_k_tau_set(measurement_config, channel, variable, results) current_k_rho = k_rho_values[k_values.index(current_k)] # first best k tau_index = k_values.index(optimal_k) closest_tau_best_k = k_tau_values[tau_index] ax.annotate( r"$\tau = %.2g$" % optimal_tau, xy = ( optimal_tau, minimal_rho ), xycoords = 'data', xytext = ( optimal_tau*0.9, minimal_rho*1.15 ), textcoords = 'data', bbox=dict(boxstyle="round4", fc="w"), arrowprops = dict( arrowstyle = "fancy,head_length=0.4,head_width=0.4,tail_width=0.4", connectionstyle = "arc3" ), size = 40, ) ax.annotate( r"$\tau(k_b = %d) = %.2g$" % ( optimal_k, closest_tau_best_k ), xy = ( closest_tau_best_k, optimal_k_rho ), xycoords = 'data', xytext = ( closest_tau_best_k * 10, optimal_k_rho ), textcoords = 'data', bbox=dict(boxstyle="round4", fc="w"), arrowprops = dict( arrowstyle = "<-", connectionstyle = "arc3", lw = 3 ), size = 40, ) # then current k if use_current_k_values: ax.annotate( r"$\tau(k_c = %d) = %.2g$" % (current_k, closest_tau), xy = ( closest_tau, current_k_rho ), xycoords = 'data', xytext = ( closest_tau, current_k_rho*0.9 ), textcoords = 'data', bbox=dict(boxstyle="round4", fc="w"), arrowprops = dict( arrowstyle = "<-", connectionstyle = "arc3", lw = 3 ), size = 40, ) plt.xscale('log') make_folder_if_not_exists(output_folder) for f in output_format: plt.savefig( output_folder + '/' + hp.name + '.' + f )
def main(): config = XSectionConfig(13) file_for_powhegPythia = File(config.unfolding_central_firstHalf, 'read') file_for_ptReweight_up = File(config.unfolding_ptreweight_up_firstHalf, 'read') file_for_ptReweight_down = File(config.unfolding_ptreweight_down_firstHalf, 'read') file_for_amcatnlo_pythia8 = File(config.unfolding_amcatnlo_pythia8, 'read') file_for_powhegHerwig = File(config.unfolding_powheg_herwig, 'read') file_for_etaReweight_up = File(config.unfolding_etareweight_up, 'read') file_for_etaReweight_down = File(config.unfolding_etareweight_down, 'read') file_for_data_template = 'data/normalisation/background_subtraction/13TeV/{variable}/VisiblePS/central/normalisation_{channel}.txt' for channel in config.analysis_types.keys(): if channel is 'combined': continue for variable in config.variables: print variable # for variable in ['HT']: # Get the central powheg pythia distributions _, _, response_central, fakes_central = get_unfold_histogram_tuple( inputfile=file_for_powhegPythia, variable=variable, channel=channel, centre_of_mass=13, load_fakes=True, visiblePS=True) measured_central = asrootpy(response_central.ProjectionX('px', 1)) truth_central = asrootpy(response_central.ProjectionY()) # Get the reweighted powheg pythia distributions _, _, response_pt_reweighted_up, _ = get_unfold_histogram_tuple( inputfile=file_for_ptReweight_up, variable=variable, channel=channel, centre_of_mass=13, load_fakes=False, visiblePS=True) measured_pt_reweighted_up = asrootpy( response_pt_reweighted_up.ProjectionX('px', 1)) truth_pt_reweighted_up = asrootpy( response_pt_reweighted_up.ProjectionY()) _, _, response_pt_reweighted_down, _ = get_unfold_histogram_tuple( inputfile=file_for_ptReweight_down, variable=variable, channel=channel, centre_of_mass=13, load_fakes=False, visiblePS=True) measured_pt_reweighted_down = asrootpy( response_pt_reweighted_down.ProjectionX('px', 1)) truth_pt_reweighted_down = asrootpy( response_pt_reweighted_down.ProjectionY()) # _, _, response_eta_reweighted_up, _ = get_unfold_histogram_tuple( # inputfile=file_for_etaReweight_up, # variable=variable, # channel=channel, # centre_of_mass=13, # load_fakes=False, # visiblePS=True # ) # measured_eta_reweighted_up = asrootpy(response_eta_reweighted_up.ProjectionX('px',1)) # truth_eta_reweighted_up = asrootpy(response_eta_reweighted_up.ProjectionY()) # _, _, response_eta_reweighted_down, _ = get_unfold_histogram_tuple( # inputfile=file_for_etaReweight_down, # variable=variable, # channel=channel, # centre_of_mass=13, # load_fakes=False, # visiblePS=True # ) # measured_eta_reweighted_down = asrootpy(response_eta_reweighted_down.ProjectionX('px',1)) # truth_eta_reweighted_down = asrootpy(response_eta_reweighted_down.ProjectionY()) # Get the distributions for other MC models _, _, response_amcatnlo_pythia8, _ = get_unfold_histogram_tuple( inputfile=file_for_amcatnlo_pythia8, variable=variable, channel=channel, centre_of_mass=13, load_fakes=False, visiblePS=True) measured_amcatnlo_pythia8 = asrootpy( response_amcatnlo_pythia8.ProjectionX('px', 1)) truth_amcatnlo_pythia8 = asrootpy( response_amcatnlo_pythia8.ProjectionY()) _, _, response_powhegHerwig, _ = get_unfold_histogram_tuple( inputfile=file_for_powhegHerwig, variable=variable, channel=channel, centre_of_mass=13, load_fakes=False, visiblePS=True) measured_powhegHerwig = asrootpy( response_powhegHerwig.ProjectionX('px', 1)) truth_powhegHerwig = asrootpy(response_powhegHerwig.ProjectionY()) # Get the data input (data after background subtraction, and fake removal) file_for_data = file_for_data_template.format(variable=variable, channel=channel) data = read_tuple_from_file(file_for_data)['TTJet'] data = value_error_tuplelist_to_hist(data, reco_bin_edges_vis[variable]) data = removeFakes(measured_central, fakes_central, data) # Plot all three hp = Histogram_properties() hp.name = 'Reweighting_check_{channel}_{variable}_at_{com}TeV'.format( channel=channel, variable=variable, com='13', ) v_latex = latex_labels.variables_latex[variable] unit = '' if variable in ['HT', 'ST', 'MET', 'WPT', 'lepton_pt']: unit = ' [GeV]' hp.x_axis_title = v_latex + unit hp.x_limits = [ reco_bin_edges_vis[variable][0], reco_bin_edges_vis[variable][-1] ] hp.ratio_y_limits = [0.1, 1.9] hp.ratio_y_title = 'Reweighted / Central' hp.y_axis_title = 'Number of events' hp.title = 'Reweighting check for {variable}'.format( variable=v_latex) measured_central.Rebin(2) measured_pt_reweighted_up.Rebin(2) measured_pt_reweighted_down.Rebin(2) # measured_eta_reweighted_up.Rebin(2) # measured_eta_reweighted_down.Rebin(2) measured_amcatnlo_pythia8.Rebin(2) measured_powhegHerwig.Rebin(2) data.Rebin(2) measured_central.Scale(1 / measured_central.Integral()) measured_pt_reweighted_up.Scale( 1 / measured_pt_reweighted_up.Integral()) measured_pt_reweighted_down.Scale( 1 / measured_pt_reweighted_down.Integral()) measured_amcatnlo_pythia8.Scale( 1 / measured_amcatnlo_pythia8.Integral()) measured_powhegHerwig.Scale(1 / measured_powhegHerwig.Integral()) # measured_eta_reweighted_up.Scale( 1 / measured_eta_reweighted_up.Integral() ) # measured_eta_reweighted_down.Scale( 1/ measured_eta_reweighted_down.Integral() ) data.Scale(1 / data.Integral()) print list(measured_central.y()) print list(measured_amcatnlo_pythia8.y()) print list(measured_powhegHerwig.y()) print list(data.y()) compare_measurements( # models = {'Central' : measured_central, 'PtReweighted Up' : measured_pt_reweighted_up, 'PtReweighted Down' : measured_pt_reweighted_down, 'EtaReweighted Up' : measured_eta_reweighted_up, 'EtaReweighted Down' : measured_eta_reweighted_down}, models=OrderedDict([ ('Central', measured_central), ('PtReweighted Up', measured_pt_reweighted_up), ('PtReweighted Down', measured_pt_reweighted_down), ('amc@nlo', measured_amcatnlo_pythia8), ('powhegHerwig', measured_powhegHerwig) ]), measurements={'Data': data}, show_measurement_errors=True, histogram_properties=hp, save_folder='plots/unfolding/reweighting_check', save_as=['pdf'], line_styles_for_models=[ 'solid', 'solid', 'solid', 'dashed', 'dashed' ], show_ratio_for_pairs=OrderedDict([ ('PtUpVsCentral', [measured_pt_reweighted_up, measured_central]), ('PtDownVsCentral', [measured_pt_reweighted_down, measured_central]), ('amcatnloVsCentral', [measured_amcatnlo_pythia8, measured_central]), ('powhegHerwigVsCentral', [measured_powhegHerwig, measured_central]), ('DataVsCentral', [data, measured_central]) ]), )
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)
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' ] histogram_properties = Histogram_properties() histogram_properties.name = 'QCD_nonIso_%s_%s' % (channel, var) if control_region == 'QCDConversions': histogram_properties.name = 'QCD_Conversions_%s_%s' % ( channel, var) if category != 'central': histogram_properties.name += '_' + category if channel == 'EPlusJets': histogram_properties.title = e_title elif channel == 'MuPlusJets': histogram_properties.title = mu_title eventsPerBin = (xMax - xMin) / nBins if fit_variables_units_latex[var] != '': histogram_properties.x_axis_title = '%s [%s]' % ( fit_variables_latex[var], fit_variables_units_latex[var])
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] histogram_properties.mc_error = 0.15 make_data_mc_comparison_plot(histograms_to_draw, histogram_lables, histogram_colors, histogram_properties, save_folder = output_folder, show_ratio = False) histogram_properties.name += '_with_ratio' make_data_mc_comparison_plot(histograms_to_draw, histogram_lables, histogram_colors, histogram_properties, save_folder = output_folder, show_ratio = True) #bjet invariant mass b_tag_bin = '3btags' control_region = 'TTbar_plus_X_analysis/EPlusJets/Ref selection/bjet_invariant_mass_' + b_tag_bin
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, )
def main(): config = XSectionConfig(13) file_for_powhegPythia = File(config.unfolding_central_firstHalf, 'read') file_for_ptReweight_up = File(config.unfolding_ptreweight_up_firstHalf, 'read') file_for_ptReweight_down = File(config.unfolding_ptreweight_down_firstHalf, 'read') file_for_amcatnlo_pythia8 = File(config.unfolding_amcatnlo_pythia8, 'read') file_for_powhegHerwig = File(config.unfolding_powheg_herwig, 'read') file_for_etaReweight_up = File(config.unfolding_etareweight_up, 'read') file_for_etaReweight_down = File(config.unfolding_etareweight_down, 'read') file_for_data_template = 'data/normalisation/background_subtraction/13TeV/{variable}/VisiblePS/central/normalisation_{channel}.txt' for channel in config.analysis_types.keys(): if channel is 'combined':continue for variable in config.variables: print variable # for variable in ['HT']: # Get the central powheg pythia distributions _, _, response_central, fakes_central = get_unfold_histogram_tuple( inputfile=file_for_powhegPythia, variable=variable, channel=channel, centre_of_mass=13, load_fakes=True, visiblePS=True ) measured_central = asrootpy(response_central.ProjectionX('px',1)) truth_central = asrootpy(response_central.ProjectionY()) # Get the reweighted powheg pythia distributions _, _, response_pt_reweighted_up, _ = get_unfold_histogram_tuple( inputfile=file_for_ptReweight_up, variable=variable, channel=channel, centre_of_mass=13, load_fakes=False, visiblePS=True ) measured_pt_reweighted_up = asrootpy(response_pt_reweighted_up.ProjectionX('px',1)) truth_pt_reweighted_up = asrootpy(response_pt_reweighted_up.ProjectionY()) _, _, response_pt_reweighted_down, _ = get_unfold_histogram_tuple( inputfile=file_for_ptReweight_down, variable=variable, channel=channel, centre_of_mass=13, load_fakes=False, visiblePS=True ) measured_pt_reweighted_down = asrootpy(response_pt_reweighted_down.ProjectionX('px',1)) truth_pt_reweighted_down = asrootpy(response_pt_reweighted_down.ProjectionY()) # _, _, response_eta_reweighted_up, _ = get_unfold_histogram_tuple( # inputfile=file_for_etaReweight_up, # variable=variable, # channel=channel, # centre_of_mass=13, # load_fakes=False, # visiblePS=True # ) # measured_eta_reweighted_up = asrootpy(response_eta_reweighted_up.ProjectionX('px',1)) # truth_eta_reweighted_up = asrootpy(response_eta_reweighted_up.ProjectionY()) # _, _, response_eta_reweighted_down, _ = get_unfold_histogram_tuple( # inputfile=file_for_etaReweight_down, # variable=variable, # channel=channel, # centre_of_mass=13, # load_fakes=False, # visiblePS=True # ) # measured_eta_reweighted_down = asrootpy(response_eta_reweighted_down.ProjectionX('px',1)) # truth_eta_reweighted_down = asrootpy(response_eta_reweighted_down.ProjectionY()) # Get the distributions for other MC models _, _, response_amcatnlo_pythia8, _ = get_unfold_histogram_tuple( inputfile=file_for_amcatnlo_pythia8, variable=variable, channel=channel, centre_of_mass=13, load_fakes=False, visiblePS=True ) measured_amcatnlo_pythia8 = asrootpy(response_amcatnlo_pythia8.ProjectionX('px',1)) truth_amcatnlo_pythia8 = asrootpy(response_amcatnlo_pythia8.ProjectionY()) _, _, response_powhegHerwig, _ = get_unfold_histogram_tuple( inputfile=file_for_powhegHerwig, variable=variable, channel=channel, centre_of_mass=13, load_fakes=False, visiblePS=True ) measured_powhegHerwig = asrootpy(response_powhegHerwig.ProjectionX('px',1)) truth_powhegHerwig = asrootpy(response_powhegHerwig.ProjectionY()) # Get the data input (data after background subtraction, and fake removal) file_for_data = file_for_data_template.format( variable = variable, channel = channel ) data = read_tuple_from_file(file_for_data)['TTJet'] data = value_error_tuplelist_to_hist( data, reco_bin_edges_vis[variable] ) data = removeFakes( measured_central, fakes_central, data ) # Plot all three hp = Histogram_properties() hp.name = 'Reweighting_check_{channel}_{variable}_at_{com}TeV'.format( channel=channel, variable=variable, com='13', ) v_latex = latex_labels.variables_latex[variable] unit = '' if variable in ['HT', 'ST', 'MET', 'WPT', 'lepton_pt']: unit = ' [GeV]' hp.x_axis_title = v_latex + unit hp.x_limits = [ reco_bin_edges_vis[variable][0], reco_bin_edges_vis[variable][-1]] hp.ratio_y_limits = [0.1,1.9] hp.ratio_y_title = 'Reweighted / Central' hp.y_axis_title = 'Number of events' hp.title = 'Reweighting check for {variable}'.format(variable=v_latex) measured_central.Rebin(2) measured_pt_reweighted_up.Rebin(2) measured_pt_reweighted_down.Rebin(2) # measured_eta_reweighted_up.Rebin(2) # measured_eta_reweighted_down.Rebin(2) measured_amcatnlo_pythia8.Rebin(2) measured_powhegHerwig.Rebin(2) data.Rebin(2) measured_central.Scale( 1 / measured_central.Integral() ) measured_pt_reweighted_up.Scale( 1 / measured_pt_reweighted_up.Integral() ) measured_pt_reweighted_down.Scale( 1 / measured_pt_reweighted_down.Integral() ) measured_amcatnlo_pythia8.Scale( 1 / measured_amcatnlo_pythia8.Integral() ) measured_powhegHerwig.Scale( 1 / measured_powhegHerwig.Integral() ) # measured_eta_reweighted_up.Scale( 1 / measured_eta_reweighted_up.Integral() ) # measured_eta_reweighted_down.Scale( 1/ measured_eta_reweighted_down.Integral() ) data.Scale( 1 / data.Integral() ) print list(measured_central.y()) print list(measured_amcatnlo_pythia8.y()) print list(measured_powhegHerwig.y()) print list(data.y()) compare_measurements( # models = {'Central' : measured_central, 'PtReweighted Up' : measured_pt_reweighted_up, 'PtReweighted Down' : measured_pt_reweighted_down, 'EtaReweighted Up' : measured_eta_reweighted_up, 'EtaReweighted Down' : measured_eta_reweighted_down}, models = OrderedDict([('Central' , measured_central), ('PtReweighted Up' , measured_pt_reweighted_up), ('PtReweighted Down' , measured_pt_reweighted_down), ('amc@nlo' , measured_amcatnlo_pythia8), ('powhegHerwig' , measured_powhegHerwig) ] ), measurements = {'Data' : data}, show_measurement_errors=True, histogram_properties=hp, save_folder='plots/unfolding/reweighting_check', save_as=['pdf'], line_styles_for_models = ['solid','solid','solid','dashed','dashed'], show_ratio_for_pairs = OrderedDict( [ ('PtUpVsCentral' , [ measured_pt_reweighted_up, measured_central ] ), ('PtDownVsCentral' , [ measured_pt_reweighted_down, measured_central ] ), ('amcatnloVsCentral' , [ measured_amcatnlo_pythia8, measured_central ] ), ('powhegHerwigVsCentral' , [ measured_powhegHerwig, measured_central ] ), ('DataVsCentral' , [data, measured_central] ) ]), )
h2.fillstyle = 'solid' h2.fillcolor = 'red' h2.linecolor = 'red' h2.linewidth = 0 h2.legendstyle = 'F' stack = HistStack() stack.Add(h1) stack.Add(h2) # plot with matplotlib plot_with_plotting_script = True if plot_with_plotting_script: properties = Histogram_properties() properties.name = 'matplotlib_hist' properties.x_axis_title = 'Mass' properties.y_axis_title = 'Events' make_data_mc_comparison_plot( [h3, h1, h2], ['data', 'background', 'signal'], ['black', 'green', 'red'], properties ) properties.name += '_with_ratio' make_data_mc_comparison_plot( [h3, h1, h2], ['data', 'background', 'signal'], ['black', 'green', 'red'], properties, show_ratio = True ) properties.name = 'matplotlib_hist_comparison' properties.y_limits = [0, 0.4] make_control_region_comparison( h1, h2, 'background', 'signal', properties ) else: fig = plt.figure(figsize=(14, 10), dpi=300)#, facecolor='white') axes = plt.axes() axes.xaxis.set_minor_locator(AutoMinorLocator())
# We want to store this variable in a histogram # 80 bins, from 0 to 400 (GeV) h_gen_met = Hist(80, 0, 400) # since we are planning to run over many events, let's cache the fill function fill = h_gen_met.Fill # ready to read all events n_processed_events = 0 stop_at = 10**5 # this is enough for this example for event in chain: gen_met = event.__getattr__("unfolding.genMET") fill(gen_met) n_processed_events += 1 if (n_processed_events % 50000 == 0): print 'Processed', n_processed_events, 'events.' if n_processed_events >= stop_at: break print 'Processed', n_processed_events, 'events.' # lets draw this histogram # define the style histogram_properties = Histogram_properties() histogram_properties.name = 'read_ntuples_gen_met' # it will be saved as that histogram_properties.title = 'My awesome MET plot' histogram_properties.x_axis_title = 'MET [GeV]' histogram_properties.y_axis_title = 'Events / 5 GeV' make_plot(h_gen_met, r'$t\bar{t}$', histogram_properties, save_folder='examples/plots/', save_as=['png'])
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, )
def do_shape_check(channel, control_region_1, control_region_2, variable, normalisation, title, x_title, y_title, x_limits, y_limits, name_region_1='conversions' , name_region_2='non-isolated electrons', name_region_3='fit results', rebin=1): global b_tag_bin # QCD shape comparison if channel == 'electron': histograms = get_histograms_from_files([control_region_1, control_region_2], histogram_files) region_1 = histograms[channel][control_region_1].Clone() - histograms['TTJet'][control_region_1].Clone() - histograms['V+Jets'][control_region_1].Clone() - histograms['SingleTop'][control_region_1].Clone() region_2 = histograms[channel][control_region_2].Clone() - histograms['TTJet'][control_region_2].Clone() - histograms['V+Jets'][control_region_2].Clone() - histograms['SingleTop'][control_region_2].Clone() region_1.Rebin(rebin) region_2.Rebin(rebin) histogram_properties = Histogram_properties() histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_' + b_tag_bin histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin] histogram_properties.x_axis_title = x_title histogram_properties.y_axis_title = 'arbitrary units/(0.1)' histogram_properties.x_limits = x_limits histogram_properties.y_limits = y_limits[0] histogram_properties.mc_error = 0.0 histogram_properties.legend_location = 'upper right' make_control_region_comparison(region_1, region_2, name_region_1=name_region_1, name_region_2=name_region_2, histogram_properties=histogram_properties, save_folder=output_folder) # QCD shape comparison to fit results histograms = get_histograms_from_files([control_region_1], histogram_files) region_1_tmp = histograms[channel][control_region_1].Clone() - histograms['TTJet'][control_region_1].Clone() - histograms['V+Jets'][control_region_1].Clone() - histograms['SingleTop'][control_region_1].Clone() region_1 = rebin_asymmetric(region_1_tmp, bin_edges_vis[variable]) fit_results_QCD = normalisation[variable]['QCD'] region_2 = value_error_tuplelist_to_hist(fit_results_QCD, bin_edges_vis[variable]) histogram_properties = Histogram_properties() histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_fits_with_conversions_' + b_tag_bin histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin] histogram_properties.x_axis_title = x_title histogram_properties.y_axis_title = 'arbitrary units/(0.1)' histogram_properties.x_limits = x_limits histogram_properties.y_limits = y_limits[1] histogram_properties.mc_error = 0.0 histogram_properties.legend_location = 'upper right' make_control_region_comparison(region_1, region_2, name_region_1=name_region_1, name_region_2=name_region_3, histogram_properties=histogram_properties, save_folder=output_folder) histograms = get_histograms_from_files([control_region_2], histogram_files) region_1_tmp = histograms[channel][control_region_2].Clone() - histograms['TTJet'][control_region_2].Clone() - histograms['V+Jets'][control_region_2].Clone() - histograms['SingleTop'][control_region_2].Clone() region_1 = rebin_asymmetric(region_1_tmp, bin_edges_vis[variable]) fit_results_QCD = normalisation[variable]['QCD'] region_2 = value_error_tuplelist_to_hist(fit_results_QCD, bin_edges_vis[variable]) histogram_properties = Histogram_properties() histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_fits_with_noniso_' + b_tag_bin histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin] histogram_properties.x_axis_title = x_title histogram_properties.y_axis_title = 'arbitrary units/(0.1)' histogram_properties.x_limits = x_limits histogram_properties.y_limits = y_limits[1] histogram_properties.mc_error = 0.0 histogram_properties.legend_location = 'upper right' make_control_region_comparison(region_1, region_2, name_region_1=name_region_2, name_region_2=name_region_3, histogram_properties=histogram_properties, save_folder=output_folder)
# We want to store this variable in a histogram # 80 bins, from 0 to 400 (GeV) h_gen_met = Hist(80, 0, 400) # since we are planning to run over many events, let's cache the fill function fill = h_gen_met.Fill # ready to read all events n_processed_events = 0 stop_at = 10**5 # this is enough for this example for event in chain: gen_met = event.__getattr__("unfolding.genMET") fill(gen_met) n_processed_events += 1 if (n_processed_events % 50000 == 0): print 'Processed', n_processed_events, 'events.' if n_processed_events >= stop_at: break print 'Processed', n_processed_events, 'events.' # lets draw this histogram # define the style histogram_properties = Histogram_properties() histogram_properties.name = 'read_ntuples_gen_met' # it will be saved as that histogram_properties.title = 'My awesome MET plot' histogram_properties.x_axis_title = 'MET [GeV]' histogram_properties.y_axis_title = 'Events / 5 GeV' make_plot(h_gen_met, r'$t\bar{t}$', histogram_properties, save_folder = 'examples/plots/', save_as = ['png'])
def main(): config = XSectionConfig(13) file_for_powhegPythia = File(config.unfolding_central, "read") file_for_ptReweight_up = File(config.unfolding_ptreweight_up, "read") file_for_ptReweight_down = File(config.unfolding_ptreweight_down, "read") file_for_etaReweight_up = File(config.unfolding_etareweight_up, "read") file_for_etaReweight_down = File(config.unfolding_etareweight_down, "read") file_for_data_template = "data/normalisation/background_subtraction/13TeV/{variable}/VisiblePS/central/normalisation_combined_patType1CorrectedPFMet.txt" for channel in ["combined"]: for variable in config.variables: print variable # for variable in ['HT']: # Get the central powheg pythia distributions _, _, response_central, fakes_central = get_unfold_histogram_tuple( inputfile=file_for_powhegPythia, variable=variable, channel=channel, centre_of_mass=13, load_fakes=True, visiblePS=True, ) measured_central = asrootpy(response_central.ProjectionX("px", 1)) truth_central = asrootpy(response_central.ProjectionY()) # Get the reweighted powheg pythia distributions _, _, response_pt_reweighted_up, _ = get_unfold_histogram_tuple( inputfile=file_for_ptReweight_up, variable=variable, channel=channel, centre_of_mass=13, load_fakes=False, visiblePS=True, ) measured_pt_reweighted_up = asrootpy(response_pt_reweighted_up.ProjectionX("px", 1)) truth_pt_reweighted_up = asrootpy(response_pt_reweighted_up.ProjectionY()) _, _, response_pt_reweighted_down, _ = get_unfold_histogram_tuple( inputfile=file_for_ptReweight_down, variable=variable, channel=channel, centre_of_mass=13, load_fakes=False, visiblePS=True, ) measured_pt_reweighted_down = asrootpy(response_pt_reweighted_down.ProjectionX("px", 1)) truth_pt_reweighted_down = asrootpy(response_pt_reweighted_down.ProjectionY()) _, _, response_eta_reweighted_up, _ = get_unfold_histogram_tuple( inputfile=file_for_etaReweight_up, variable=variable, channel=channel, centre_of_mass=13, load_fakes=False, visiblePS=True, ) measured_eta_reweighted_up = asrootpy(response_eta_reweighted_up.ProjectionX("px", 1)) truth_eta_reweighted_up = asrootpy(response_eta_reweighted_up.ProjectionY()) _, _, response_eta_reweighted_down, _ = get_unfold_histogram_tuple( inputfile=file_for_etaReweight_down, variable=variable, channel=channel, centre_of_mass=13, load_fakes=False, visiblePS=True, ) measured_eta_reweighted_down = asrootpy(response_eta_reweighted_down.ProjectionX("px", 1)) truth_eta_reweighted_down = asrootpy(response_eta_reweighted_down.ProjectionY()) # Get the data input (data after background subtraction, and fake removal) file_for_data = file_for_data_template.format(variable=variable) data = read_data_from_JSON(file_for_data)["TTJet"] data = value_error_tuplelist_to_hist(data, reco_bin_edges_vis[variable]) data = removeFakes(measured_central, fakes_central, data) # Plot all three hp = Histogram_properties() hp.name = "Reweighting_check_{channel}_{variable}_at_{com}TeV".format( channel=channel, variable=variable, com="13" ) v_latex = latex_labels.variables_latex[variable] unit = "" if variable in ["HT", "ST", "MET", "WPT", "lepton_pt"]: unit = " [GeV]" hp.x_axis_title = v_latex + unit hp.y_axis_title = "Number of events" hp.title = "Reweighting check for {variable}".format(variable=v_latex) measured_central.Rebin(2) measured_pt_reweighted_up.Rebin(2) measured_pt_reweighted_down.Rebin(2) measured_eta_reweighted_up.Rebin(2) measured_eta_reweighted_down.Rebin(2) data.Rebin(2) measured_central.Scale(1 / measured_central.Integral()) measured_pt_reweighted_up.Scale(1 / measured_pt_reweighted_up.Integral()) measured_pt_reweighted_down.Scale(1 / measured_pt_reweighted_down.Integral()) measured_eta_reweighted_up.Scale(1 / measured_eta_reweighted_up.Integral()) measured_eta_reweighted_down.Scale(1 / measured_eta_reweighted_down.Integral()) data.Scale(1 / data.Integral()) compare_measurements( models={ "Central": measured_central, "PtReweighted Up": measured_pt_reweighted_up, "PtReweighted Down": measured_pt_reweighted_down, "EtaReweighted Up": measured_eta_reweighted_up, "EtaReweighted Down": measured_eta_reweighted_down, }, measurements={"Data": data}, show_measurement_errors=True, histogram_properties=hp, save_folder="plots/unfolding/reweighting_check", save_as=["pdf"], )
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 plotHistograms(histogram_files, var_to_plot, output_folder): ''' ''' global measurement_config weightBranchSignalRegion = 'EventWeight * PUWeight * BJetWeight' weightBranchControlRegion = 'EventWeight' # Names of QCD regions to use qcd_data_region = '' qcd_data_region_electron = 'QCD non iso e+jets' qcd_data_region_muon = 'QCD non iso mu+jets 1p5to3' sr_e_tree = 'TTbar_plus_X_analysis/EPlusJets/Ref selection/AnalysisVariables' sr_mu_tree = 'TTbar_plus_X_analysis/MuPlusJets/Ref selection/AnalysisVariables' cr_e_tree = 'TTbar_plus_X_analysis/EPlusJets/{}/AnalysisVariables'.format( qcd_data_region_electron) cr_mu_tree = 'TTbar_plus_X_analysis/MuPlusJets/{}/AnalysisVariables'.format( qcd_data_region_muon) print "Trees : " print "\t {}".format(sr_e_tree) print "\t {}".format(sr_mu_tree) print "\t {}".format(cr_e_tree) print "\t {}".format(cr_mu_tree) histogram_files_electron = dict(histogram_files) histogram_files_electron['data'] = measurement_config.data_file_electron histogram_files_electron['QCD'] = measurement_config.electron_QCD_MC_trees histogram_files_muon = dict(histogram_files) histogram_files_muon['data'] = measurement_config.data_file_muon histogram_files_muon['QCD'] = measurement_config.muon_QCD_MC_trees signal_region_hists = {} control_region_hists = {} for var in var_to_plot: selectionSignalRegion = '{} >= 0'.format(var) # Print all the weights applied to this plot print "Variable : {}".format(var) print "Weight applied : {}".format(weightBranchSignalRegion) print "Selection applied : {}".format(selectionSignalRegion) histograms_electron = get_histograms_from_trees( trees=[sr_e_tree], branch=var, weightBranch=weightBranchSignalRegion + ' * ElectronEfficiencyCorrection', files=histogram_files_electron, nBins=20, xMin=control_plots_bins[var][0], xMax=control_plots_bins[var][-1], selection=selectionSignalRegion) histograms_muon = get_histograms_from_trees( trees=[sr_mu_tree], branch=var, weightBranch=weightBranchSignalRegion + ' * MuonEfficiencyCorrection', files=histogram_files_muon, nBins=20, xMin=control_plots_bins[var][0], xMax=control_plots_bins[var][-1], selection=selectionSignalRegion) histograms_electron_QCDControlRegion = get_histograms_from_trees( trees=[cr_e_tree], branch=var, weightBranch=weightBranchControlRegion, files=histogram_files_electron, nBins=20, xMin=control_plots_bins[var][0], xMax=control_plots_bins[var][-1], selection=selectionSignalRegion) histograms_muon_QCDControlRegion = get_histograms_from_trees( trees=[cr_mu_tree], branch=var, weightBranch=weightBranchControlRegion, files=histogram_files_muon, nBins=20, xMin=control_plots_bins[var][0], xMax=control_plots_bins[var][-1], selection=selectionSignalRegion) # Combine the electron and muon histograms for sample in histograms_electron: h_electron = histograms_electron[sample][sr_e_tree] h_muon = histograms_muon[sample][sr_mu_tree] h_qcd_electron = histograms_electron_QCDControlRegion[sample][ cr_e_tree] h_qcd_muon = histograms_muon_QCDControlRegion[sample][cr_mu_tree] signal_region_hists[sample] = h_electron + h_muon control_region_hists[sample] = h_qcd_electron + h_qcd_muon # NORMALISE TO LUMI prepare_histograms(signal_region_hists, scale_factor=measurement_config.luminosity_scale) prepare_histograms(control_region_hists, scale_factor=measurement_config.luminosity_scale) # BACKGROUND SUBTRACTION FOR QCD qcd_from_data = None qcd_from_data = clean_control_region( control_region_hists, subtract=['TTJet', 'V+Jets', 'SingleTop']) # DATA DRIVEN QCD 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) dataDrivenQCDScale = n_qcd_predicted_mc_signal / n_qcd_predicted_mc_control qcd_from_data.Scale(dataDrivenQCDScale.nominal_value) signal_region_hists['QCD'] = qcd_from_data # PLOTTING histograms_to_draw = [] histogram_lables = [] histogram_colors = [] histograms_to_draw = [ # signal_region_hists['data'], # qcd_from_data, # signal_region_hists['V+Jets'], signal_region_hists['SingleTop'], signal_region_hists['ST_s'], signal_region_hists['ST_t'], signal_region_hists['ST_tW'], signal_region_hists['STbar_t'], signal_region_hists['STbar_tW'], # signal_region_hists['TTJet'], ] histogram_lables = [ 'data', # 'QCD', # 'V+Jets', # 'Single-Top', 'ST-s', 'ST-t', 'ST-tW', 'STbar-t', 'STbar-tW', # samples_latex['TTJet'], ] histogram_colors = [ colours['data'], # colours['QCD'], # colours['V+Jets'], # colours['Single-Top'], colours['ST_s'], colours['ST_t'], colours['ST_tW'], colours['STbar_t'], colours['STbar_tW'], # colours['TTJet'], ] # Find maximum y of samples maxData = max(list(signal_region_hists['SingleTop'].y())) y_limits = [0, maxData * 1.5] log_y = False if log_y: y_limits = [0.1, maxData * 100] # Lumi title of plots title_template = '%.1f fb$^{-1}$ (%d TeV)' title = title_template % (measurement_config.new_luminosity / 1000., measurement_config.centre_of_mass_energy) x_axis_title = '$%s$ [GeV]' % variables_latex[var] y_axis_title = 'Events/(%i GeV)' % binWidth(control_plots_bins[var]) # More histogram settings to look semi decent histogram_properties = Histogram_properties() histogram_properties.name = var + '_with_ratio' histogram_properties.title = title histogram_properties.x_axis_title = x_axis_title histogram_properties.y_axis_title = y_axis_title histogram_properties.x_limits = control_plots_bins[var] histogram_properties.y_limits = y_limits histogram_properties.y_max_scale = 1.4 histogram_properties.xerr = None histogram_properties.emptybins = True histogram_properties.additional_text = channel_latex['combined'] histogram_properties.legend_location = (0.9, 0.73) histogram_properties.cms_logo_location = 'left' histogram_properties.preliminary = True histogram_properties.set_log_y = log_y histogram_properties.legend_color = False histogram_properties.ratio_y_limits = [0.1, 1.9] if log_y: histogram_properties.name += '_logy' loc = histogram_properties.legend_location 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, ) histogram_properties.name = var + '_ST_TTJet_Shape' if log_y: histogram_properties.name += '_logy' histogram_properties.y_axis_title = 'Normalised Distribution' histogram_properties.y_limits = [0, 0.5] make_shape_comparison_plot( shapes=[ signal_region_hists['TTJet'], signal_region_hists['ST_t'], signal_region_hists['ST_tW'], signal_region_hists['ST_s'], signal_region_hists['STbar_t'], signal_region_hists['STbar_tW'], ], names=[ samples_latex['TTJet'], 'Single-Top t channel', 'Single-Top tW channel', 'Single-Top s channel', 'Single-AntiTop t channel', 'Single-AntiTop tW channel', ], colours=[ colours['TTJet'], colours['ST_t'], colours['ST_tW'], colours['ST_s'], colours['STbar_t'], colours['STbar_tW'], ], histogram_properties=histogram_properties, save_folder=output_folder, fill_area=False, add_error_bars=False, save_as=['pdf'], make_ratio=True, alpha=1, ) print_output(signal_region_hists, output_folder, var, 'combined') return
def drawHistograms( dictionaryOfHistograms, uncertaintyBand, config, channel, variable ) : histograms_to_draw = [ dictionaryOfHistograms['Data'], dictionaryOfHistograms['QCD'], dictionaryOfHistograms['V+Jets'], dictionaryOfHistograms['SingleTop'], dictionaryOfHistograms['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'], ] # Find maximum y of samples maxData = max( list(histograms_to_draw[0].y()) ) y_limits = [0, maxData * 1.4] # More histogram settings to look semi decent histogram_properties = Histogram_properties() histogram_properties.name = '{channel}_{variable}'.format(channel = channel, variable=variable) histogram_properties.title = '$%.1f$ fb$^{-1}$ (%d TeV)' % ( config.new_luminosity/1000., config.centre_of_mass_energy ) histogram_properties.x_axis_title = variables_latex[variable] histogram_properties.y_axis_title = 'Events' if variable in ['HT', 'ST', 'MET', 'WPT', 'lepton_pt']: histogram_properties.y_axis_title = 'Events / {binWidth} GeV'.format( binWidth=binWidth ) histogram_properties.x_axis_title = '{variable} (GeV)'.format( variable = variables_latex[variable] ) histogram_properties.x_limits = [ reco_bin_edges[0], reco_bin_edges[-1] ] histogram_properties.y_limits = y_limits histogram_properties.y_max_scale = 1.3 histogram_properties.xerr = None # workaround for rootpy issue #638 histogram_properties.emptybins = True histogram_properties.additional_text = channel_latex[channel.lower()] histogram_properties.legend_location = ( 0.9, 0.73 ) histogram_properties.cms_logo_location = 'left' histogram_properties.preliminary = True # histogram_properties.preliminary = False histogram_properties.set_log_y = False histogram_properties.legend_color = False histogram_properties.ratio_y_limits = [0.5, 1.5] # Draw histogram with ratio plot 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 = 'plots/control_plots_with_systematic/', show_ratio = True, normalise = False, systematics_for_ratio = uncertaintyBand, systematics_for_plot = uncertaintyBand, ) histogram_properties.set_log_y = True histogram_properties.y_limits = [0.1, y_limits[-1]*100 ] histogram_properties.legend_location = ( 0.9, 0.9 ) histogram_properties.name += '_logY' make_data_mc_comparison_plot( histograms_to_draw, histogram_lables, histogram_colors, histogram_properties, save_folder = 'plots/control_plots_with_systematic/logY/', show_ratio = True, normalise = False, systematics_for_ratio = uncertaintyBand, systematics_for_plot = uncertaintyBand, )
def make_correlation_plot_from_file(channel, variable, fit_variables, CoM, title, x_title, y_title, x_limits, y_limits, rebin=1, save_folder='plots/fitchecks/', save_as=['pdf', 'png']): # global b_tag_bin parameters = ["TTJet", "SingleTop", "V+Jets", "QCD"] parameters_latex = [] for template in parameters: parameters_latex.append(samples_latex[template]) input_file = open( "logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables), "r") # cycle through the lines in the file for line_number, line in enumerate(input_file): # for now, only make plots for the fits for the central measurement if "central" in line: # matrix we want begins 11 lines below the line with the measurement ("central") line_number = line_number + 11 break input_file.close() #Note: For some reason, the fit outputs the correlation matrix with the templates in the following order: #parameter1: QCD #parameter2: SingleTop #parameter3: TTJet #parameter4: V+Jets for variable_bin in variable_bins_ROOT[variable]: weights = {} if channel == 'electron': #formula to calculate the number of lines below "central" to access in each loop number_of_lines_down = ( variable_bins_ROOT[variable].index(variable_bin) * 12) #Get QCD correlations matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables), line_number + number_of_lines_down) weights["QCD_QCD"] = matrix_line.split()[2] weights["QCD_SingleTop"] = matrix_line.split()[3] weights["QCD_TTJet"] = matrix_line.split()[4] weights["QCD_V+Jets"] = matrix_line.split()[5] #Get SingleTop correlations matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables), line_number + number_of_lines_down + 1) weights["SingleTop_QCD"] = matrix_line.split()[2] weights["SingleTop_SingleTop"] = matrix_line.split()[3] weights["SingleTop_TTJet"] = matrix_line.split()[4] weights["SingleTop_V+Jets"] = matrix_line.split()[5] #Get TTJet correlations matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables), line_number + number_of_lines_down + 2) weights["TTJet_QCD"] = matrix_line.split()[2] weights["TTJet_SingleTop"] = matrix_line.split()[3] weights["TTJet_TTJet"] = matrix_line.split()[4] weights["TTJet_V+Jets"] = matrix_line.split()[5] #Get V+Jets correlations matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables), line_number + number_of_lines_down + 3) weights["V+Jets_QCD"] = matrix_line.split()[2] weights["V+Jets_SingleTop"] = matrix_line.split()[3] weights["V+Jets_TTJet"] = matrix_line.split()[4] weights["V+Jets_V+Jets"] = matrix_line.split()[5] if channel == 'muon': #formula to calculate the number of lines below "central" to access in each bin loop number_of_lines_down = (len(variable_bins_ROOT[variable]) * 12) + ( variable_bins_ROOT[variable].index(variable_bin) * 12) #Get QCD correlations matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables), line_number + number_of_lines_down) weights["QCD_QCD"] = matrix_line.split()[2] weights["QCD_SingleTop"] = matrix_line.split()[3] weights["QCD_TTJet"] = matrix_line.split()[4] weights["QCD_V+Jets"] = matrix_line.split()[5] #Get SingleTop correlations matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables), line_number + number_of_lines_down + 1) weights["SingleTop_QCD"] = matrix_line.split()[2] weights["SingleTop_SingleTop"] = matrix_line.split()[3] weights["SingleTop_TTJet"] = matrix_line.split()[4] weights["SingleTop_V+Jets"] = matrix_line.split()[5] #Get TTJet correlations matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables), line_number + number_of_lines_down + 2) weights["TTJet_QCD"] = matrix_line.split()[2] weights["TTJet_SingleTop"] = matrix_line.split()[3] weights["TTJet_TTJet"] = matrix_line.split()[4] weights["TTJet_V+Jets"] = matrix_line.split()[5] #Get V+Jets correlations matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables), line_number + number_of_lines_down + 3) weights["V+Jets_QCD"] = matrix_line.split()[2] weights["V+Jets_SingleTop"] = matrix_line.split()[3] weights["V+Jets_TTJet"] = matrix_line.split()[4] weights["V+Jets_V+Jets"] = matrix_line.split()[5] #Create histogram histogram_properties = Histogram_properties() histogram_properties.title = title histogram_properties.name = 'Correlations_' + channel + '_' + variable + '_' + variable_bin histogram_properties.y_axis_title = y_title histogram_properties.x_axis_title = x_title histogram_properties.y_limits = y_limits histogram_properties.x_limits = x_limits histogram_properties.mc_error = 0.0 histogram_properties.legend_location = 'upper right' #initialise 2D histogram a = Hist2D(4, 0, 4, 4, 0, 4) #fill histogram for i in range(len(parameters)): for j in range(len(parameters)): a.fill( float(i), float(j), float(weights["%s_%s" % (parameters[i], parameters[j])])) # create figure plt.figure(figsize=CMS.figsize, dpi=CMS.dpi, facecolor=CMS.facecolor) # make subplot(?) fig, ax = plt.subplots(nrows=1, ncols=1) rplt.hist2d(a) plt.subplots_adjust(right=0.8) #Set labels and formats for titles and axes plt.ylabel(histogram_properties.y_axis_title) plt.xlabel(histogram_properties.x_axis_title) plt.title(histogram_properties.title) x_limits = histogram_properties.x_limits y_limits = histogram_properties.y_limits ax.set_xticklabels(parameters_latex) ax.set_yticklabels(parameters_latex) ax.set_xticks([0.5, 1.5, 2.5, 3.5]) ax.set_yticks([0.5, 1.5, 2.5, 3.5]) plt.setp(ax.get_xticklabels(), visible=True) plt.setp(ax.get_yticklabels(), visible=True) #create and draw colour bar to the right of the main plot im = rplt.imshow(a, axes=ax, vmin=-1.0, vmax=1.0) #set location and dimensions (left, lower, width, height) cbar_ax = fig.add_axes([0.85, 0.10, 0.05, 0.8]) fig.colorbar(im, cax=cbar_ax) for xpoint in range(len(parameters)): for ypoint in range(len(parameters)): correlation_value = weights["%s_%s" % (parameters[xpoint], parameters[ypoint])] ax.annotate(correlation_value, xy=(xpoint + 0.5, ypoint + 0.5), ha='center', va='center', bbox=dict(fc='white', ec='none')) for save in save_as: plt.savefig(save_folder + histogram_properties.name + '.' + save) plt.close(fig) plt.close('all')
h2.fillstyle = "solid" h2.fillcolor = "red" h2.linecolor = "red" h2.linewidth = 0 h2.legendstyle = "F" stack = HistStack() stack.Add(h1) stack.Add(h2) # plot with matplotlib plot_with_plotting_script = True if plot_with_plotting_script: properties = Histogram_properties() properties.name = "matplotlib_hist" properties.x_axis_title = "Mass" properties.y_axis_title = "Events" make_data_mc_comparison_plot([h3, h1, h2], ["data", "background", "signal"], ["black", "green", "red"], properties) properties.name += "_with_ratio" make_data_mc_comparison_plot( [h3, h1, h2], ["data", "background", "signal"], ["black", "green", "red"], properties, show_ratio=True ) properties.name = "matplotlib_hist_comparison" properties.y_limits = [0, 0.4] make_control_region_comparison(h1, h2, "background", "signal", properties) else: fig = plt.figure(figsize=(14, 10), dpi=300) # , facecolor='white')