def METHOD_NAME(self):
    prop_scores = [[1., 0.5, None, 0., 0.5], [None, 0.7, None, 0.3]]
    scoring_weights = [[1., 0., 1., 1., 0.1], [1., 0.5, 0.5, 0.5]]
    expected = [[2., 1.05, 2.4, 2.05, 3/5, 2/4], [1., 0.5, 1.8, 2., 3/4, 0.]]
    for i, scores in enumerate(prop_scores):
        logging.debug('iteration i=%s', i)
        total_score = ontomatch.instancematching.get_total_score_for_row(scores)
        self.assertEqual(total_score, expected[i][0])
        total_score = ontomatch.instancematching.get_total_score_for_row(scores, scoring_weights[i])
        self.assertEqual(total_score, expected[i][1])
        total_score = ontomatch.instancematching.get_total_score_for_row(scores, missing_score=0.4)
        self.assertEqual(total_score, expected[i][2])
        total_score = ontomatch.instancematching.get_total_score_for_row(scores, scoring_weights[i], missing_score=1.)
        self.assertEqual(total_score, expected[i][3])
        total_score = ontomatch.instancematching.get_total_score_for_row(scores, missing_score=1., aggregation_mode='mean', average_min_prop_count=1)
        self.assertEqual(total_score, expected[i][4])
        total_score = ontomatch.instancematching.get_total_score_for_row(scores, aggregation_mode='mean', average_min_prop_count=3)
        self.assertEqual(total_score, expected[i][5])