コード例 #1
0
    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"),
                  int(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"],
コード例 #2
0
    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)
    threads = get_num_threads(config.getint("ilp", "threads"))
    if VERBOSE:
        print("\nstarting ilp solver with %s threads..." % threads)
        print("\n", ht.now(), 'Initializing OptiType model...')

    op = OptiType(sparse_dict,
                  compact_occ,
                  groups_4digit,
                  table,
                  args.beta,
                  2,
                  config.get("ilp", "solver"),
                  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"]]
コード例 #3
0
        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)
        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)]