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 for i, fr in enumerate(fit_results): 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) 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] value = get_value_title(k_value, tau_value) title = 'pull distribution mean \& sigma for {0}'.format(value) histogram_properties.title = title histogram_properties.y_limits = [-0.5, 2] histogram_properties.xerr = True compare_measurements(models={ '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 = [0.92, 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_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_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[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_fit_results(fit_results, initial_values, channel): global variable, output_folder title = electron_histogram_title if channel == 'electron' else muon_histogram_title histogram_properties = Histogram_properties() histogram_properties.title = title histogram_properties.x_axis_title = variable + ' [GeV]' histogram_properties.mc_error = 0.0 histogram_properties.legend_location = 'upper right' # we will need 4 histograms: TTJet, SingleTop, QCD, V+Jets for sample in ['TTJet', 'SingleTop', 'QCD', 'V+Jets']: histograms = {} # absolute eta measurement as baseline h_absolute_eta = None h_before = None histogram_properties.y_axis_title = 'Fitted number of events for ' + samples_latex[ sample] for fit_var_input in fit_results.keys(): latex_string = create_latex_string(fit_var_input) fit_data = fit_results[fit_var_input][sample] h = value_error_tuplelist_to_hist(fit_data, bin_edges[variable]) if fit_var_input == 'absolute_eta': h_absolute_eta = h elif fit_var_input == 'before': h_before = h else: histograms[latex_string] = h graphs = spread_x(histograms.values(), bin_edges[variable]) for key, graph in zip(histograms.keys(), graphs): histograms[key] = graph filename = sample.replace('+', '_') + '_fit_var_comparison_' + channel histogram_properties.name = filename histogram_properties.y_limits = 0, limit_range_y( h_absolute_eta)[1] * 1.3 histogram_properties.x_limits = bin_edges[variable][0], bin_edges[ variable][-1] h_initial_values = value_error_tuplelist_to_hist( initial_values[sample], bin_edges[variable]) h_initial_values.Scale(closure_tests['simple'][sample]) compare_measurements(models={ fit_variables_latex['absolute_eta']: h_absolute_eta, 'initial values': h_initial_values, 'before': h_before }, measurements=histograms, show_measurement_errors=True, histogram_properties=histogram_properties, save_folder=output_folder, save_as=['png', 'pdf'])
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[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_fit_results(fit_results, initial_values, channel): global variable, output_folder title = electron_histogram_title if channel == "electron" else muon_histogram_title histogram_properties = Histogram_properties() histogram_properties.title = title histogram_properties.x_axis_title = variable + " [GeV]" histogram_properties.mc_error = 0.0 histogram_properties.legend_location = "upper right" # we will need 4 histograms: TTJet, SingleTop, QCD, V+Jets for sample in ["TTJet", "SingleTop", "QCD", "V+Jets"]: histograms = {} # absolute eta measurement as baseline h_absolute_eta = None h_before = None histogram_properties.y_axis_title = "Fitted number of events for " + samples_latex[sample] for fit_var_input in fit_results.keys(): latex_string = create_latex_string(fit_var_input) fit_data = fit_results[fit_var_input][sample] h = value_error_tuplelist_to_hist(fit_data, bin_edges[variable]) if fit_var_input == "absolute_eta": h_absolute_eta = h elif fit_var_input == "before": h_before = h else: histograms[latex_string] = h graphs = spread_x(histograms.values(), bin_edges[variable]) for key, graph in zip(histograms.keys(), graphs): histograms[key] = graph filename = sample.replace("+", "_") + "_fit_var_comparison_" + channel histogram_properties.name = filename histogram_properties.y_limits = 0, limit_range_y(h_absolute_eta)[1] * 1.3 histogram_properties.x_limits = bin_edges[variable][0], bin_edges[variable][-1] h_initial_values = value_error_tuplelist_to_hist(initial_values[sample], bin_edges[variable]) h_initial_values.Scale(closure_tests["simple"][sample]) compare_measurements( models={ fit_variables_latex["absolute_eta"]: h_absolute_eta, "initial values": h_initial_values, "before": h_before, }, measurements=histograms, show_measurement_errors=True, histogram_properties=histogram_properties, save_folder=output_folder, save_as=["png", "pdf"], )
def plot_fit_results( fit_results, initial_values, channel ): global variable, output_folder title = electron_histogram_title if channel == 'electron' else muon_histogram_title histogram_properties = Histogram_properties() histogram_properties.title = title histogram_properties.x_axis_title = variable + ' [GeV]' histogram_properties.mc_error = 0.0 histogram_properties.legend_location = 'upper right' # we will need 4 histograms: TTJet, SingleTop, QCD, V+Jets for sample in ['TTJet', 'SingleTop', 'QCD', 'V+Jets']: histograms = {} # absolute eta measurement as baseline h_absolute_eta = None h_before = None histogram_properties.y_axis_title = 'Fitted number of events for ' + samples_latex[sample] for fit_var_input in fit_results.keys(): latex_string = create_latex_string( fit_var_input ) fit_data = fit_results[fit_var_input][sample] h = value_error_tuplelist_to_hist( fit_data, bin_edges[variable] ) if fit_var_input == 'absolute_eta': h_absolute_eta = h elif fit_var_input == 'before': h_before = h else: histograms[latex_string] = h graphs = spread_x( histograms.values(), bin_edges[variable] ) for key, graph in zip( histograms.keys(), graphs ): histograms[key] = graph filename = sample.replace( '+', '_' ) + '_fit_var_comparison_' + channel histogram_properties.name = filename histogram_properties.y_limits = 0, limit_range_y( h_absolute_eta )[1] * 1.3 histogram_properties.x_limits = bin_edges[variable][0], bin_edges[variable][-1] h_initial_values = value_error_tuplelist_to_hist( initial_values[sample], bin_edges[variable] ) h_initial_values.Scale(closure_tests['simple'][sample]) compare_measurements( models = {fit_variables_latex['absolute_eta']:h_absolute_eta, 'initial values' : h_initial_values, 'before': h_before}, measurements = histograms, show_measurement_errors = True, histogram_properties = histogram_properties, save_folder = output_folder, save_as = ['png', 'pdf'] )
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(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_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 for i, fr in enumerate(fit_results): 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) 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] value = get_value_title(k_value, tau_value) title = 'pull distribution mean \& sigma for {0}'.format(value) histogram_properties.title = title histogram_properties.y_limits = [-0.5, 2] histogram_properties.xerr = True compare_measurements( models={ '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 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[variable]) fit_results_QCD = normalisation[variable]['QCD'] region_2 = value_error_tuplelist_to_hist(fit_results_QCD, bin_edges[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[variable]) fit_results_QCD = normalisation[variable]['QCD'] region_2 = value_error_tuplelist_to_hist(fit_results_QCD, bin_edges[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 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 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')
histogram_colors = ['black', 'yellow', 'green', 'magenta', 'red'] histogram_properties = Histogram_properties() histogram_properties.name = '%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 histogram_properties.x_axis_title = '%s [GeV]' % ( variables_latex[var] ) histogram_properties.y_axis_title = 'Events/(%.2g GeV)' % (eventsPerBin) histogram_properties.y_limits = [0, histograms['data'][signalTree].GetMaximum() * 1.3 ] # histogram_properties.set_log_y = True 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 ) if normalise_to_fit : continue # Fit variables (inclusive) for var in ['M3', 'angle_bl', 'M_bl', 'absolute_eta']: print '--->',var signal_region = 'Ref selection' signalTree = 'TTbar_plus_X_analysis/%s/%s/FitVariables' % ( channel, signal_region ) bins = fit_variable_bin_edges[var]
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()) axes.yaxis.set_minor_locator(AutoMinorLocator()) # axes.yaxis.set_major_locator(MultipleLocator(20)) axes.tick_params(which='major', labelsize=15, length=8) axes.tick_params(which='minor', length=4) rplt.errorbar(h3, xerr=False, emptybins=False, axes=axes, zorder=4) rplt.hist(stack, stacked=True, axes=axes, zorder=1) plt.xlabel('Mass', position=(1., 0.), ha='right') plt.ylabel('Events', position=(0., 1.), va='bottom', ha='right') plt.legend(numpoints=1)
# 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()) axes.yaxis.set_minor_locator(AutoMinorLocator()) # axes.yaxis.set_major_locator(MultipleLocator(20)) axes.tick_params(which='major', labelsize=15, length=8) axes.tick_params(which='minor', length=4) rplt.errorbar(h3, xerr=False, emptybins=False, axes=axes, zorder = 4) rplt.hist(stack, stacked=True, axes=axes, zorder = 1) plt.xlabel('Mass', position=(1., 0.), ha='right') plt.ylabel('Events', position=(0., 1.), va='bottom', ha='right') plt.legend(numpoints=1)
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 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[variable]) fit_results_QCD = normalisation[variable]["QCD"] region_2 = value_error_tuplelist_to_hist(fit_results_QCD, bin_edges[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[variable]) fit_results_QCD = normalisation[variable]["QCD"] region_2 = value_error_tuplelist_to_hist(fit_results_QCD, bin_edges[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 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[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[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 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, )