code: def hicup_truncating_chart (self):
          
  
          # Specify the order of the different possible categories
          keys = OrderedDict()
          keys['Not_Truncated_Reads'] = { 'color': '#2f7ed8', 'name': 'Not Truncated' }
          keys['Truncated_Read']      = { 'color': '#0d233a', 'name': 'Truncated' }
  
          # Construct a data structure for the plot - duplicate the samples for read 1 and read 2
          data = {}
          for s_name in self.hicup_data:
              data['{} Read 1'.format(s_name)] = {}
              data['{} Read 2'.format(s_name)] = {}
              data['{} Read 1'.format(s_name)]['Not_Truncated_Reads'] = self.hicup_data[s_name]['Not_Truncated_Reads_1']
              data['{} Read 2'.format(s_name)]['Not_Truncated_Reads'] = self.hicup_data[s_name]['Not_Truncated_Reads_2']
              data['{} Read 1'.format(s_name)]['Truncated_Read'] = self.hicup_data[s_name]['Truncated_Read_1']
              data['{} Read 2'.format(s_name)]['Truncated_Read'] = self.hicup_data[s_name]['Truncated_Read_2']
  
          # Config for the plot
          config = {
              'id': 'hicup_truncated_reads_plot',
              'title': 'HiCUP: Truncated Reads',
              'ylab': '# Reads',
              'cpswitch_counts_label': 'Number of Reads'
          }
  
          return bargraph.plot(data, keys, config)
docs: Generate the HiCUP Truncated reads plot