docs: def plot(self, bins=250, **kwargs):
          """An example plot function. You have to subclass this method."""
          # Data #
          counts = [sum(map(len, b.contigs)) for b in self.parent.bins]
          # Linear bins in logarithmic space #
          if 'log' in kwargs.get('x_scale', ''):
              start, stop = numpy.log10(1), numpy.log10(max(counts))
              bins = list(numpy.logspace(start=start, stop=stop, num=bins))
              bins.insert(0, 0)
          # Plot #
          fig = pyplot.figure()
          pyplot.hist(counts, bins=bins, color='gray')
          axes = pyplot.gca()
          # Information #
          title = 'Distribution of the total nucleotide count in the bins'
          axes.set_title(title)
          axes.set_xlabel('Number of nucleotides in a bin')
          axes.set_ylabel('Number of bins with that many nucleotides in them')
          # Save it #
          self.save_plot(fig, axes, **kwargs)
          pyplot.close(fig)
          # For convenience #
          return self