code: def best_score(self, seqs, scan_rc=True, normalize=False):
          
          self.set_threshold(threshold=0.0)
          if normalize and len(self.meanstd) == 0:
              self.set_meanstd()
              means = np.array([self.meanstd[m][0] for m in self.motif_ids])
              stds = np.array([self.meanstd[m][1] for m in self.motif_ids])
  
          for matches in self.scan(seqs, 1, scan_rc):
              scores = np.array([sorted(m, key=lambda x: x[0])[0][0] for m in matches if len(m) > 0])
              if normalize:
                  scores = (scores - means) / stds
              yield scores
docs: give the score of the best match of each motif in each sequence
          returns an iterator of lists containing floats