Set numeric interval limits as input to plotting function in Matplotlib

The idea is to parameterize the range element (i.e. 0 and 5 in your sample code) into interval_limits. To do so I have assumed that the parameter interval_limits will be a list of 2 values in the following form: [min_value, max_value] or concretely given your sample, interval_limits should be a list of 0, 5 like the following:

interval_limits = [0, 5]

Based on the assumption, I have modified your code a little bit. Pay attention to the new block where I assign the first element of interval_limits into a new variable min_limit and the 2nd element of interval_limits into another new variable max_limit and then I have modified the label string using the '%.2f format (feel free to change into whatever format you want)

Here's the code:

def plotHistDistances(pat_name, lesion_id, rootdir, distanceMap, num_voxels, title, ablation_date, interval_limits):
    ##########################################
    # NEW COODE SECTION
    ##########################################
    # Check if interval_limits contains all the limits
    if len(interval_limits) != 2:
        raise ValueError("2 limits are expected, got {} instead.".format(len(interval_limits)))
    # Assign the limits
    min_limit = interval_limits[0]
    max_limit = interval_limits[1]
    ##########################################
    # END OF NEW CODE SECTION
    ##########################################

    # PLOT THE HISTOGRAM FOR THE MAUERER EUCLIDIAN DISTANCES
    lesion_id_str = str(lesion_id)
    lesion_id = lesion_id_str.split('.')[0]
    figName_hist = 'Pat_' + str(pat_name) + '_Lesion' + str(lesion_id) + '_AblationDate_' + ablation_date + '_histogram'
    min_val = int(np.floor(min(distanceMap)))
    max_val = int(np.ceil(max(distanceMap)))
    fig, ax = plt.subplots(figsize=(18, 16))

    col_height, bins, patches = ax.hist(distanceMap, ec='darkgrey', bins=range(min_val - 1, max_val + 1))

    voxels_nonablated = []
    voxels_insuffablated = []
    voxels_ablated = []

    for b, p, col_val in zip(bins, patches, col_height):
        if b < min_limit:
            voxels_nonablated.append(col_val)
        elif min_limit <= b <= max_limit:
            voxels_insuffablated.append(col_val)
        elif b > max_limit:
            voxels_ablated.append(col_val)
    # %%
    '''calculate the total percentage of surface for ablated, non-ablated, insufficiently ablated'''

    voxels_nonablated = np.asarray(voxels_nonablated)
    voxels_insuffablated = np.asarray(voxels_insuffablated)
    voxels_ablated = np.asarray(voxels_ablated)

    sum_perc_nonablated = ((voxels_nonablated / num_voxels) * 100).sum()
    sum_perc_insuffablated = ((voxels_insuffablated / num_voxels) * 100).sum()
    sum_perc_ablated = ((voxels_ablated / num_voxels) * 100).sum()
    # %%
    '''iterate through the bins to change the colors of the patches bases on the range [mm]'''
    for b, p, col_val in zip(bins, patches, col_height):
        if b < min_limit:
            plt.setp(p, label='Ablation Surface Margin ' + r'$x < %.2f$' % min_limit + 'mm :' + " %.2f" % sum_perc_nonablated + '%')
        elif min_limit <= b <= max_limit:
            plt.setp(p, 'facecolor', 'orange',
                     label='Ablation Surface Margin ' + r'$%.2f \leq  x \leq %.2f$' % (min_limit, max_limit) + 'mm: ' + "%.2f" % sum_perc_insuffablated + '%')
        elif b > max_limit:
            plt.setp(p, 'facecolor', 'darkgreen',
                     label='Ablation Surface Margin ' + r'$x > %.2f$' % max_limit + 'mm: ' + " %.2f" % sum_perc_ablated + '%')
    # %%
    '''edit the axes limits and labels'''
    plt.xlabel('Euclidean Distances [mm]', fontsize=30, color='black')
    plt.tick_params(labelsize=28, color='black')
    ax.tick_params(colors='black', labelsize=28)
    plt.grid(True)
    # TODO: set equal axis limits
    ax.set_xlim([-15, 15])

    # edit the y-ticks: change to percentage of surface
    yticks, locs = plt.yticks()
    percent = (yticks / num_voxels) * 100
    percentage_surface_rounded = np.round(percent)
    yticks_percent = [str(x) + '%' for x in percentage_surface_rounded]
    new_yticks = (percentage_surface_rounded * yticks) / percent
    new_yticks[0] = 0
    plt.yticks(new_yticks, yticks_percent)
    #    plt.yticks(yticks,yticks_percent)
    plt.ylabel('Percentage of tumor surface voxels', fontsize=30, color='black')

    handles, labels = plt.gca().get_legend_handles_labels()
    by_label = OrderedDict(zip(labels, handles))
    plt.legend(by_label.values(), by_label.keys(), fontsize=30, loc='best')

    plt.title(title + '. Patient ' + str(pat_name) + '. Lesion ' + str(lesion_id), fontsize=30)

Disclaimer: I have not tested this code since I do not have the full set of parameter to reproduce the result, but that should work. If it doesn't feel free to provide me the set of parameters you use and I will see how I can rectify the issue