def calc_feature_distribution(feature_idx_list):
    # get col of selected features
    feature_idx_list = [int(i) for i in feature_idx_list]
    selected_features = tfidf_vectors[:, feature_idx_list]

    # get comics (rows) where selected features are nonzero
    nonzero_comics, _ = selected_features.nonzero()
    nonzero_comics, comic_counts = np.unique(nonzero_comics, return_counts=True)
    nonzero_comics = nonzero_comics + 1

    single = []
    both = []
    for idx,comic_sn in enumerate(nonzero_comics):
        if comic_counts[idx] > 1:
            both.append(str(comic_sn))
        else:
            single.append(str(comic_sn))

    feature_dict = dict(single=single, both=both)
    feature_data = json.dumps([feature_dict])
    return feature_data