'temporary pruning of identical rows and columns')
unique_col, representing = ht.prune_identical_alleles(binary,
report_groups=True)
representing_df = pd.DataFrame([[a1, a2]
for a1, a_l in representing.items()
for a2 in a_l],
columns=['representative', 'represented'])
temp_pruned = ht.prune_identical_reads(unique_col)
if VERBOSE:
print("\n", ht.now(), 'Size of mtx with unique rows and columns:',
temp_pruned.shape)
print(ht.now(), 'determining minimal set of non-overshadowed alleles')
minimal_alleles = ht.prune_overshadowed_alleles(temp_pruned)
if VERBOSE:
print("\n", ht.now(),
'Keeping only the minimal number of required alleles',
minimal_alleles.shape)
binary = binary[minimal_alleles]
if VERBOSE:
print("\n", ht.now(), 'Creating compact model...')
if is_paired and unpaired_weight > 0:
if use_discordant:
compact_mtx, compact_occ = ht.get_compact_model(
binary_p[minimal_alleles],
alleles_to_keep = filter(is_frequent, binary.columns)
binary = binary[alleles_to_keep]
if args.verbose:
print "\n", ht.now(), 'temporary pruning of identical rows and columns'
unique_col, representing = ht.prune_identical_alleles(binary, report_groups=True)
representing_df = pd.DataFrame([[a1, a2] for a1, a_l in representing.iteritems() for a2 in a_l],
columns=['representative', 'represented'])
temp_pruned = ht.prune_identical_reads(unique_col)
if args.verbose:
print "\n", ht.now(), 'Size of mtx with unique rows and columns:', temp_pruned.shape
print ht.now(), 'determining minimal set of non-overshadowed alleles'
minimal_alleles = ht.prune_overshadowed_alleles(temp_pruned)
if args.verbose:
print "\n", ht.now(), 'Keeping only the minimal number of required alleles', minimal_alleles.shape
binary = binary[minimal_alleles]
if args.verbose:
print "\n", ht.now(), 'Creating compact model...'
compact_mtx, compact_occ = ht.get_compact_model(binary)
allele_ids = binary.columns
groups_4digit = defaultdict(list)
for allele in allele_ids:
type_4digit = get_4digit(allele)
