threads,
verbosity=VERBOSE)
result = op.solve(args.enumerate)
if VERBOSE:
print("\n", ht.now(), 'Result dataframe has been constructed...')
result_4digit = result.applymap(get_types)
for iii in ["A1", "A2", "B1", "B2", "C1", "C2"]:
if not iii in result_4digit:
result_4digit[iii] = None
r = result_4digit[["A1", "A2", "B1", "B2", "C1", "C2", "nof_reads", "obj"]]
# write CSV to out. And generate plots
r.to_csv(out_csv,
sep="\t",
columns=["A1", "A2", "B1", "B2", "C1", "C2", "nof_reads", "obj"],
header=["A1", "A2", "B1", "B2", "C1", "C2", "Reads", "Objective"])
hlatype = result.iloc[0][["A1", "A2", "B1", "B2", "C1",
"C2"]].drop_duplicates().dropna()
features_used = [('intron', 1), ('exon', 2), ('intron', 2), ('exon', 3), ('intron', 3)] \
if not args.rna else [('exon',2),('exon',3)]
plot_variables = [
pos, read_details, pos2, read_details2,
(binary_p, binary_un, binary_mis)
] if is_paired else [pos, read_details]
coverage_mat = ht.calculate_coverage(plot_variables, features, hlatype,
features_used)
ht.plot_coverage(out_plot, coverage_mat, table, features, features_used)
groups_4digit = defaultdict(list)
for allele in allele_ids:
type_4digit = get_4digit(allele)
groups_4digit[type_4digit].append(allele)
sparse_dict = ht.mtx_to_sparse_dict(compact_mtx)
if args.verbose:
print "\n", ht.now(), 'Initializing OptiType model...'
op = OptiType(sparse_dict, compact_occ, groups_4digit, table, args.beta, 2,
config.get("OPTIMIZATION", "SOLVER"), config.get("OPTIMIZATION", "THREADS"), verbosity=verbosity)
result = op.solve(args.enumerate)
if args.verbose:
print "\n", ht.now(), 'Result dataframe has been constructed...'
result_4digit = result.applymap(get_types)
r = result_4digit[["A1", "A2", "B1", "B2", "C1", "C2", "nof_reads", "obj"]]
#write CSV to out. and generate Plots.
r.to_csv(out_csv, sep="\t",
cols=["A1", "A2", "B1", "B2", "C1", "C2", "nof_reads", "obj"],
header=["A1", "A2", "B1", "B2", "C1", "C2", "Reads", "Objective"])
hlatype = result.irow(0)[["A1", "A2", "B1", "B2", "C1", "C2"]].drop_duplicates()
features_used = [('intron', 1), ('exon', 2), ('intron', 2), ('exon', 3), ('intron', 3)] \
if not args.rna else [('exon',2),('exon',3)]
plot_variables = [pos, etc, desc, pos2, etc2, desc2, binary] if is_paired else [pos, etc, desc]
coverage_mat = ht.calculate_coverage(plot_variables, features, hlatype, features_used)
ht.plot_coverage(out_plot, coverage_mat, table, features, features_used)