code cleanup

irescue/count.py

@@ -82,45 +82,6 @@ def parse_maps(maps_file, feature_index):
                 eqcl = [EquivalenceClass(i, umi, feat, count)]
         yield it, eqcl
 
-# def get_substr_slices(umi_length, idx_size):
-#     '''
-#     Create slices to split a UMI into approximately equal size substrings
-#     Returns a list of tuples that can be passed to slice function
-#     '''
-#     cs, r = divmod(umi_length, idx_size)
-#     sub_sizes = [cs + 1] * r + [cs] * (idx_size - r)
-#     offset = 0
-#     slices = []
-#     for s in sub_sizes:
-#         slices.append((offset, offset + s))
-#         offset += s
-#     return slices
-
-# def build_substr_idx(equivalence_classes, length, threshold):
-#     '''
-#     Group equivalence classes into subgroups having a common substring
-#     '''
-#     slices = get_substr_slices(length, threshold+1)
-#     substr_idx = {k: defaultdict(set) for k in slices}
-#     for idx in slices:
-#         for ec in equivalence_classes:
-#             sub = ec.umi[slice(*idx)]
-#             substr_idx[idx][sub].add(ec)
-#     return substr_idx
-
-# def gen_ec_pairs(equivalence_classes, substr_idx):
-#     '''
-#     Yields equivalence classes pairs from build_substr_idx()
-#     '''
-#     for i, ec in enumerate(equivalence_classes, start=1):
-#         neighbours = set()
-#         for idx, substr_map in substr_idx.items():
-#             sub = ec.umi[slice(*idx)]
-#             neighbours = neighbours.union(substr_map[sub])
-#         neighbours.difference_update(equivalence_classes[:i])
-#         for nbr in neighbours:
-#             yield ec, nbr
-
 def compute_cell_counts(equivalence_classes, features_index, dumpEC):
     """
     Calculate TE counts of a single cell, given a list of equivalence classes.