def has_no_fat_ends(neuron, multiple_of_mean=<NUM_LIT>, final_point_count=<NUM_LIT:5>):
bad_ids = []<EOL>for leaf in _nf.iter_sections(neuron.neurites, iterator_type=Tree.ileaf):<EOL><INDENT>mean_radius = np.mean(leaf.points[<NUM_LIT:1>:][-final_point_count:, COLS.R])<EOL>if mean_radius * multiple_of_mean <= leaf.points[-<NUM_LIT:1>, COLS.R]:<EOL><INDENT>bad_ids.append((leaf.id, leaf.points[-<NUM_LIT:1>:]))<EOL><DEDENT><DEDENT>return CheckResult(len(bad_ids) == <NUM_LIT:0>, bad_ids)<EOL>
Check if leaf points are too large

    Arguments:
        neuron(Neuron): The neuron object to test
        multiple_of_mean(float): how many times larger the final radius
        has to be compared to the mean of the final points
        final_point_count(int): how many points to include in the mean

    Returns:
        CheckResult with result list of ids of bad sections

    Note:
        A fat end is defined as a leaf segment whose last point is larger
        by a factor of `multiple_of_mean` than the mean of the points in
        `final_point_count`