def plot_box(boxlim, qp_values, box_init, uncs, coverage, density, ticklabel_formatter='{} ({})', boxlim_formatter='{: .2g}', table_formatter='{:.3g}'):
    'Helper function for parallel coordinate style visualization\n    of a box\n\n    Parameters\n    ----------\n    boxlim : DataFrame\n    qp_values : dict\n    box_init : DataFrame\n    uncs : list\n    coverage : float\n    density : float\n    ticklabel_formatter : str\n    boxlim_formatter : str\n    table_formatter : str\n\n    Returns\n    -------\n    a Figure instance\n\n\n    '
    norm_box_lim = _normalize(boxlim, box_init, uncs)
    (fig, ax) = _setup_figure(uncs)
    for (j, u) in enumerate(uncs):
        xj = ((len(uncs) - j) - 1)
        plot_unc(box_init, xj, j, 0, norm_box_lim, boxlim, u, ax)
        dtype = box_init[u].dtype
        props = {'facecolor': 'white', 'edgecolor': 'white', 'alpha': 0.25}
        y = xj
        if (dtype == object):
            elements = sorted(list(box_init[u][0]))
            max_value = (len(elements) - 1)
            values = boxlim.loc[(0, u)]
            x = [elements.index(entry) for entry in values]
            x = [(entry / max_value) for entry in x]
            for (xi, label) in zip(x, values):
                ax.text(xi, (y - 0.2), label, ha='center', va='center', bbox=props, color='blue', fontweight='normal')
        else:
            props = {'facecolor': 'white', 'edgecolor': 'white', 'alpha': 0.25}
            x = norm_box_lim[(j, 0)]
            if (not np.allclose(x, 0)):
                label = boxlim_formatter.format(boxlim.loc[(0, u)])
                ax.text(x, (y - 0.2), label, ha='center', va='center', bbox=props, color='blue', fontweight='normal')
            x = norm_box_lim[j][1]
            if (not np.allclose(x, 1)):
                label = boxlim_formatter.format(boxlim.loc[(1, u)])
                ax.text(x, (y - 0.2), label, ha='center', va='center', bbox=props, color='blue', fontweight='normal')
            x = 0
            label = boxlim_formatter.format(box_init.loc[(0, u)])
            ax.text((x - 0.01), y, label, ha='right', va='center', bbox=props, color='black', fontweight='normal')
            x = 1
            label = boxlim_formatter.format(box_init.loc[(1, u)])
            ax.text((x + 0.01), y, label, ha='left', va='center', bbox=props, color='black', fontweight='normal')
        qp_formatted = {}
        for (key, values) in qp_values.items():
            values = [vi for vi in values if (vi != (- 1))]
            if (len(values) == 1):
                value = '{:.2g}'.format(values[0])
            else:
                value = '{:.2g}, {:.2g}'.format(*values)
            qp_formatted[key] = value
        labels = [ticklabel_formatter.format(u, qp_formatted[u]) for u in uncs]
        labels = labels[::(- 1)]
        ax.set_yticklabels(labels)
        ax.set_xticklabels([])
    coverage = table_formatter.format(coverage)
    density = table_formatter.format(density)
    ax.table(cellText=[[coverage], [density]], colWidths=([0.1] * 2), rowLabels=['coverage', 'density'], colLabels=None, loc='right', bbox=[1.2, 0.9, 0.1, 0.1])
    plt.subplots_adjust(left=0.1, right=0.75)
    return fig
Helper function for parallel coordinate style visualization
of a box

Parameters
----------
boxlim : DataFrame
qp_values : dict
box_init : DataFrame
uncs : list
coverage : float
density : float
ticklabel_formatter : str
boxlim_formatter : str
table_formatter : str

Returns
-------
a Figure instance
def plot_box(boxlim, qp_values, box_init, uncs, coverage, density, ticklabel_formatter='{} ({})', boxlim_formatter='{: .2g}', table_formatter='{:.3g}'):
    'Helper function for parallel coordinate style visualization\n    of a box\n\n    Parameters\n    ----------\n    boxlim : DataFrame\n    qp_values : dict\n    box_init : DataFrame\n    uncs : list\n    coverage : float\n    density : float\n    ticklabel_formatter : str\n    boxlim_formatter : str\n    table_formatter : str\n\n    Returns\n    -------\n    a Figure instance\n\n\n    '
    norm_box_lim = _normalize(boxlim, box_init, uncs)
    (fig, ax) = _setup_figure(uncs)
    for (j, u) in enumerate(uncs):
        xj = ((len(uncs) - j) - 1)
        plot_unc(box_init, xj, j, 0, norm_box_lim, boxlim, u, ax)
        dtype = box_init[u].dtype
        props = {'facecolor': 'white', 'edgecolor': 'white', 'alpha': 0.25}
        y = xj
        if (dtype == object):
            elements = sorted(list(box_init[u][0]))
            max_value = (len(elements) - 1)
            values = boxlim.loc[(0, u)]
            x = [elements.index(entry) for entry in values]
            x = [(entry / max_value) for entry in x]
            for (xi, label) in zip(x, values):
                ax.text(xi, (y - 0.2), label, ha='center', va='center', bbox=props, color='blue', fontweight='normal')
        else:
            props = {'facecolor': 'white', 'edgecolor': 'white', 'alpha': 0.25}
            x = norm_box_lim[(j, 0)]
            if (not np.allclose(x, 0)):
                label = boxlim_formatter.format(boxlim.loc[(0, u)])
                ax.text(x, (y - 0.2), label, ha='center', va='center', bbox=props, color='blue', fontweight='normal')
            x = norm_box_lim[j][1]
            if (not np.allclose(x, 1)):
                label = boxlim_formatter.format(boxlim.loc[(1, u)])
                ax.text(x, (y - 0.2), label, ha='center', va='center', bbox=props, color='blue', fontweight='normal')
            x = 0
            label = boxlim_formatter.format(box_init.loc[(0, u)])
            ax.text((x - 0.01), y, label, ha='right', va='center', bbox=props, color='black', fontweight='normal')
            x = 1
            label = boxlim_formatter.format(box_init.loc[(1, u)])
            ax.text((x + 0.01), y, label, ha='left', va='center', bbox=props, color='black', fontweight='normal')
        qp_formatted = {}
        for (key, values) in qp_values.items():
            values = [vi for vi in values if (vi != (- 1))]
            if (len(values) == 1):
                value = '{:.2g}'.format(values[0])
            else:
                value = '{:.2g}, {:.2g}'.format(*values)
            qp_formatted[key] = value
        labels = [ticklabel_formatter.format(u, qp_formatted[u]) for u in uncs]
        labels = labels[::(- 1)]
        ax.set_yticklabels(labels)
        ax.set_xticklabels([])
    coverage = table_formatter.format(coverage)
    density = table_formatter.format(density)
    ax.table(cellText=[[coverage], [density]], colWidths=([0.1] * 2), rowLabels=['coverage', 'density'], colLabels=None, loc='right', bbox=[1.2, 0.9, 0.1, 0.1])
    plt.subplots_adjust(left=0.1, right=0.75)
    return fig