code: def dist_abs(self, src, tar, gap_cost=1, sim_func=sim_ident):
          
          d_mat = np_zeros((len(src) + 1, len(tar) + 1), dtype=np_float32)
  
          for i in range(len(src) + 1):
              d_mat[i, 0] = 0
          for j in range(len(tar) + 1):
              d_mat[0, j] = 0
          for i in range(1, len(src) + 1):
              for j in range(1, len(tar) + 1):
                  match = d_mat[i - 1, j - 1] + sim_func(src[i - 1], tar[j - 1])
                  delete = d_mat[i - 1, j] - gap_cost
                  insert = d_mat[i, j - 1] - gap_cost
                  d_mat[i, j] = max(0, match, delete, insert)
          return d_mat[d_mat.shape[0] - 1, d_mat.shape[1] - 1]
docs: Return the Smith-Waterman score of two strings.
  
          Parameters
          ----------
          src : str
              Source string for comparison
          tar : str
              Target string for comparison
          gap_cost : float
              The cost of an alignment gap (1 by default)
          sim_func : function
              A function that returns the similarity of two characters (identity
              similarity by default)
  
          Returns
          -------
          float
              Smith-Waterman score
  
          Examples
          --------
          >>> cmp = SmithWaterman()
          >>> cmp.dist_abs('cat', 'hat')
          2.0
          >>> cmp.dist_abs('Niall', 'Neil')
          1.0
          >>> cmp.dist_abs('aluminum', 'Catalan')
          0.0
          >>> cmp.dist_abs('ATCG', 'TAGC')
          1.0