code: def frequencies_plot(self, xmin=0, xmax=200):
          
  
          helptext = '''
              A possible way to assess the complexity of a library even in
              absence of a reference sequence is to look at the kmer profile of the reads.
              The idea is to count all the kmers (_i.e._, sequence of length `k`) that occur
              in the reads. In this way it is possible to know how many kmers occur
              `1,2,.., N` times and represent this as a plot.
              This plot tell us for each x, how many k-mers (y-axis) are present in the
              dataset in exactly x-copies.
  
              In an ideal world (no errors in sequencing, no bias, no  repeated regions)
              this plot should be as close as  possible to a gaussian distribution.
              In reality we will always see a peak for `x=1` (_i.e._, the errors)
              and another peak close to the expected coverage. If the genome is highly
              heterozygous a second peak at half of the coverage can be expected.'''
  
          pconfig = {
              'id': 'Jellyfish_kmer_plot',
              'title': 'Jellyfish: K-mer plot',
              'ylab': 'Counts',
              'xlab': 'k-mer frequency',
              'xDecimals': False,
              'xmin': xmin,
              'xmax': xmax
          }
  
          self.add_section(
              anchor = 'jellyfish_kmer_plot',
              description = 'The K-mer plot lets you estimate library complexity and coverage from k-mer content.',
              helptext = helptext,
              plot = linegraph.plot(self.jellyfish_data, pconfig)
          )
docs: Generate the qualities plot