#!/Users/runner/bioconda/conda-bld/khmer_1597109715336/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_place/bin/python
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# Contact: khmer-project@idyll.org
# pylint: disable=missing-docstring,invalid-name
"""
Sequence trimming by abundance w/o countgraph.

Trim sequences at k-mers of the given abundance for the given file,
without loading a prebuilt countgraph.  Output sequences will be
placed in 'infile.abundfilt'.

% python scripts/filter-abund-single.py <data>

Use '-h' for parameter help.
"""
import os
import sys
import threading
import textwrap
import khmer

from khmer import ReadParser
from khmer.utils import broken_paired_reader, write_record
from khmer import khmer_args
from khmer.khmer_args import (build_counting_args, report_on_config,
                              add_threading_args, calculate_graphsize,
                              sanitize_help, check_argument_range)
from khmer.kfile import (check_input_files, check_space,
                         check_space_for_graph,
                         add_output_compression_type,
                         get_file_writer)
from khmer.khmer_logger import (configure_logging, log_info, log_error,
                                log_warn)
from khmer.trimming import (trim_record)

DEFAULT_NORMALIZE_LIMIT = 20
DEFAULT_CUTOFF = 2


def get_parser():
    epilog = """\
    Trimmed sequences will be placed in
    ``${input_sequence_filename}.abundfilt``.

    This script is constant memory.

    To trim reads based on k-mer abundance across multiple files, use
    :program:`load-into-counting.py` and :program:`filter-abund.py`.

    Example::

        filter-abund-single.py -k 20 -x 5e7 -C 2 data/100k-filtered.fa
    """
    parser = build_counting_args(
        descr="Trims sequences at a minimum k-mer abundance "
        "(in memory version).", epilog=textwrap.dedent(epilog),
        citations=['counting', 'SeqAn'])
    add_threading_args(parser)

    parser.add_argument('-C', '--cutoff', default=DEFAULT_CUTOFF,
                        type=check_argument_range(0, 256, "cutoff"),
                        help="Trim at k-mers below this abundance.")
    parser.add_argument('-V', '--variable-coverage', action='store_true',
                        dest='variable_coverage', default=False,
                        help='Only trim low-abundance k-mers from sequences '
                        'that have high coverage.')
    parser.add_argument('-Z', '--normalize-to', type=int, dest='normalize_to',
                        help='Base the variable-coverage cutoff on this median'
                        ' k-mer abundance.',
                        default=DEFAULT_NORMALIZE_LIMIT)
    parser.add_argument('--savegraph', metavar="filename", default='',
                        help="If present, the name of the file to save the "
                        "k-mer countgraph to")
    parser.add_argument('-o', '--outfile', metavar='optional_output_filename',
                        default=None, help='Override default output filename '
                        'and output trimmed sequences into a file with the '
                        'given filename.')
    parser.add_argument('datafile', metavar='input_sequence_filename',
                        help="FAST[AQ] sequence file to trim")
    parser.add_argument('-f', '--force', default=False, action='store_true',
                        help='Overwrite output file if it exists')
    parser.add_argument('-q', '--quiet', dest='quiet', default=False,
                        action='store_true')
    add_output_compression_type(parser)
    return parser


def main():
    args = sanitize_help(get_parser()).parse_args()

    configure_logging(args.quiet)
    check_input_files(args.datafile, args.force)
    check_space([args.datafile], args.force)

    if args.savegraph:
        tablesize = calculate_graphsize(args, 'countgraph')
        check_space_for_graph(args.savegraph, tablesize, args.force)

    report_on_config(args)

    log_info('making countgraph')
    graph = khmer_args.create_countgraph(args)

    # first, load reads into graph
    rparser = khmer.ReadParser(args.datafile)
    threads = []
    log_info('consuming input, round 1 -- {datafile}', datafile=args.datafile)
    for _ in range(args.threads):
        cur_thread = \
            threading.Thread(
                target=graph.consume_seqfile,
                args=(rparser, )
            )
        threads.append(cur_thread)
        cur_thread.start()

    for _ in threads:
        _.join()

    log_info('Total number of unique k-mers: {nk}', nk=graph.n_unique_kmers())

    fp_rate = khmer.calc_expected_collisions(graph, args.force)
    log_info('fp rate estimated to be {fpr:1.3f}', fpr=fp_rate)

    # the filtering loop
    log_info('filtering {datafile}', datafile=args.datafile)
    if args.outfile is None:
        outfile = os.path.basename(args.datafile) + '.abundfilt'
    else:
        outfile = args.outfile
    outfp = open(outfile, 'wb')
    outfp = get_file_writer(outfp, args.gzip, args.bzip)

    paired_iter = broken_paired_reader(ReadParser(args.datafile),
                                       min_length=graph.ksize(),
                                       force_single=True)

    for n, is_pair, read1, read2 in paired_iter:
        assert not is_pair
        assert read2 is None

        trimmed_record, _ = trim_record(graph, read1, args.cutoff,
                                        args.variable_coverage,
                                        args.normalize_to)
        if trimmed_record:
            write_record(trimmed_record, outfp)

    log_info('output in {outfile}', outfile=outfile)

    if args.savegraph:
        log_info('Saving k-mer countgraph filename {graph}',
                 graph=args.savegraph)
        graph.save(args.savegraph)


if __name__ == '__main__':
    main()
