def plot_unc(box_init, xi, i, j, norm_box_lim, box_lim, u, ax, color=sns.color_palette()[0]):
    '\n\n    Parameters:\n    ----------\n    xi : int\n         the row at which to plot\n    i : int\n        the index of the uncertainty being plotted\n    j : int\n        the index of the box being plotted\n    u : string\n        the uncertainty being plotted:\n    ax : axes instance\n         the ax on which to plot\n\n    '
    dtype = box_init[u].dtype
    y = (xi - (j * 0.1))
    if (dtype == object):
        elements = sorted(list(box_init[u][0]))
        max_value = (len(elements) - 1)
        box_lim = box_lim[u][0]
        x = [elements.index(entry) for entry in box_lim]
        x = [(entry / max_value) for entry in x]
        y = ([y] * len(x))
        ax.scatter(x, y, edgecolor=color, facecolor=color)
    else:
        ax.plot(norm_box_lim[i], (y, y), c=color)
Parameters:
----------
xi : int
     the row at which to plot
i : int
    the index of the uncertainty being plotted
j : int
    the index of the box being plotted
u : string
    the uncertainty being plotted:
ax : axes instance
     the ax on which to plot
def plot_unc(box_init, xi, i, j, norm_box_lim, box_lim, u, ax, color=sns.color_palette()[0]):
    '\n\n    Parameters:\n    ----------\n    xi : int\n         the row at which to plot\n    i : int\n        the index of the uncertainty being plotted\n    j : int\n        the index of the box being plotted\n    u : string\n        the uncertainty being plotted:\n    ax : axes instance\n         the ax on which to plot\n\n    '
    dtype = box_init[u].dtype
    y = (xi - (j * 0.1))
    if (dtype == object):
        elements = sorted(list(box_init[u][0]))
        max_value = (len(elements) - 1)
        box_lim = box_lim[u][0]
        x = [elements.index(entry) for entry in box_lim]
        x = [(entry / max_value) for entry in x]
        y = ([y] * len(x))
        ax.scatter(x, y, edgecolor=color, facecolor=color)
    else:
        ax.plot(norm_box_lim[i], (y, y), c=color)