Python Histogram_properties.xerr方法代码示例

# 需要导入模块: from tools.plotting import Histogram_properties [as 别名]
# 或者: from tools.plotting.Histogram_properties import xerr [as 别名]
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)

# 需要导入模块: from tools.plotting import Histogram_properties [as 别名]
# 或者: from tools.plotting.Histogram_properties import xerr [as 别名]
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)

# 需要导入模块: from tools.plotting import Histogram_properties [as 别名]
# 或者: from tools.plotting.Histogram_properties import xerr [as 别名]
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)

# 需要导入模块: from tools.plotting import Histogram_properties [as 别名]
# 或者: from tools.plotting.Histogram_properties import xerr [as 别名]
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)

# 需要导入模块: from tools.plotting import Histogram_properties [as 别名]
# 或者: from tools.plotting.Histogram_properties import xerr [as 别名]
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)

# 需要导入模块: from tools.plotting import Histogram_properties [as 别名]
# 或者: from tools.plotting.Histogram_properties import xerr [as 别名]

#.........这里部分代码省略.........

    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,
                                 )

# 需要导入模块: from tools.plotting import Histogram_properties [as 别名]
# 或者: from tools.plotting.Histogram_properties import xerr [as 别名]
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)

# 需要导入模块: from tools.plotting import Histogram_properties [as 别名]
# 或者: from tools.plotting.Histogram_properties import xerr [as 别名]

#.........这里部分代码省略.........
        "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)