Parameters
		----------
		corpus

		Returns
		-------
		float, pd.Series
		float: point on x-axis at even characteristicness
		pd.Series: term -> value between 0 and 1, sorted by score in a descending manner
		Background scores from corpus

def get_scores(self, corpus):
		'''
		Parameters
		----------
		corpus

		Returns
		-------
		float, pd.Series
		float: point on x-axis at even characteristicness
		pd.Series: term -> value between 0 and 1, sorted by score in a descending manner
		Background scores from corpus
		'''
		term_ranks = self.term_ranker(corpus).get_ranks()

		freq_df = pd.DataFrame({
			'corpus': term_ranks.sum(axis=1),
			'standard': self.background_frequencies.get_background_frequency_df()['background']}
		).dropna()
		corpus_rank = rankdata(freq_df.corpus, 'dense')
		standard_rank = rankdata(freq_df.standard, 'dense')
		scores = corpus_rank/corpus_rank.max() - standard_rank/standard_rank.max()


		#scores = RankDifference().get_scores(bg['corpus'], bg['bg']).sort_values()
		# import pdb; pdb.set_trace()
		if self.rerank_ranks:
			rank_scores, zero_marker = self._rerank_scores(scores)
			freq_df['score'] = pd.Series(rank_scores, index=freq_df.index)
		else:
			if scores.min() < 0 and scores.max() > 0:
				zero_marker = -scores.min() / (scores.max() - scores.min())
			elif scores.min() > 0:
				zero_marker = 0
			else:
				zero_marker = 1
			freq_df['score'] = scale(scores)
		return zero_marker, freq_df.sort_values(by='score', ascending=False)['score']