code: def to_stormo_scoring_matrix( self, background=None ):
          
          alphabet_size = len( self.alphabet )
          if background is None:
              background = ones( alphabet_size, float32 ) / alphabet_size
          # Row totals as a one column array
          totals = numpy.sum( self.values, 1 )[:,newaxis]
          values = log2( self.values + background ) \
                 - log2( totals + 1 ) - log2( background )
          return ScoringMatrix.create_from_other( self, values.astype( float32 ) )
docs: Create a scoring matrix from this count matrix using the method from:
  
          Hertz, G.Z. and G.D. Stormo (1999). Identifying DNA and protein patterns with statistically 
          significant alignments of multiple sequences. Bioinformatics 15(7): 563-577.