#!/opt/conda/conda-bld/khmer_1737587986878/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placeho/bin/python
# This file is part of khmer, https://github.com/dib-lab/khmer/, and is
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# Contact: khmer-project@idyll.org
# pylint: disable=missing-docstring,invalid-name,no-member
"""
Build a counting Bloom filter from the given sequences, save in <countgraph>.

% load-into-counting.py <countgraph> <data1> [ <data2> <...> ]

Use '-h' for parameter help.
"""

import json
import os
import sys
import threading
import textwrap
import khmer
from khmer import khmer_args
from khmer.khmer_args import (build_counting_args, report_on_config, info,
                              add_threading_args, calculate_graphsize,
                              sanitize_help)
from khmer.kfile import check_file_writable
from khmer.kfile import check_input_files
from khmer.kfile import check_space_for_graph
from khmer.khmer_logger import (configure_logging, log_info, log_error,
                                log_warn)


def get_parser():
    epilog = """\
    Note: with :option:`-b`/:option:`--no-bigcount` the output will be the
    exact size of the k-mer countgraph and this script will use a constant
    amount of memory. In exchange k-mer counts will stop at 255. The memory
    usage of this script with :option:`-b` will be about 1.15x the product of
    the :option:`-x` and :option:`-N` numbers.

    Example::

        load-into-counting.py -k 20 -x 5e7 out data/100k-filtered.fa

    Multiple threads can be used to accelerate the process, if you have extra
    cores to spare.

    Example::

        load-into-counting.py -k 20 -x 5e7 -T 4 out data/100k-filtered.fa
    """

    parser = build_counting_args("Build a k-mer countgraph from the given"
                                 " sequences.", epilog=textwrap.dedent(epilog),
                                 citations=['counting', 'SeqAn'])
    add_threading_args(parser)
    parser.add_argument('output_countgraph_filename', help="The name of the"
                        " file to write the k-mer countgraph to.")
    parser.add_argument('input_sequence_filename', nargs='+',
                        help="The names of one or more FAST[AQ] input "
                        "sequence files.")
    parser.add_argument('-b', '--no-bigcount', dest='bigcount', default=True,
                        action='store_false', help="The default behaviour is "
                        "to count past 255 using bigcount. This flag turns "
                        "bigcount off, limiting counts to 255.")
    parser.add_argument('-s', '--summary-info', type=str, default=None,
                        metavar="FORMAT", choices=[str('json'), str('tsv')],
                        help="What format should the machine readable run "
                        "summary be in? (`json` or `tsv`, disabled by"
                        " default)")
    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')
    return parser


def main():

    args = sanitize_help(get_parser()).parse_args()

    configure_logging(args.quiet)
    report_on_config(args)

    base = args.output_countgraph_filename
    filenames = args.input_sequence_filename

    for name in args.input_sequence_filename:
        check_input_files(name, args.force)

    tablesize = calculate_graphsize(args, 'countgraph')
    check_space_for_graph(args.output_countgraph_filename, tablesize,
                          args.force)

    info_filename = base + ".info"
    check_file_writable(base)
    check_file_writable(info_filename)

    log_info('Saving k-mer countgraph to {base}', base=base)
    log_info('Loading kmers from sequences in {filenames}',
             filenames=repr(filenames))

    # clobber the '.info' file now, as we always open in append mode below
    with open(info_filename, 'w') as info_fp:
        print('khmer version:', khmer.__version__, file=info_fp)

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

    filename = None

    total_num_reads = 0

    for index, filename in enumerate(filenames):

        rparser = khmer.ReadParser(filename)
        threads = []
        log_info('consuming input {input}', input=filename)
        for _ in range(args.threads):
            cur_thrd = \
                threading.Thread(
                    target=countgraph.consume_seqfile,
                    args=(rparser, )
                )
            threads.append(cur_thrd)
            cur_thrd.start()

        for thread in threads:
            thread.join()

        if index > 0 and index % 10 == 0:
            tablesize = calculate_graphsize(args, 'countgraph')
            check_space_for_graph(base, tablesize, args.force)
            log_info('mid-save {base}', base=base)

            countgraph.save(base)
        with open(info_filename, 'a') as info_fh:
            print('through', filename, file=info_fh)
        total_num_reads += rparser.num_reads

    n_kmers = countgraph.n_unique_kmers()
    log_info('Total number of unique k-mers: {nk}', nk=n_kmers)
    with open(info_filename, 'a') as info_fp:
        print('Total number of unique k-mers:', n_kmers, file=info_fp)

    log_info('saving {base}', base=base)
    countgraph.save(base)

    # Change max_false_pos=0.2 only if you really grok it. HINT: You don't
    fp_rate = \
        khmer.calc_expected_collisions(
            countgraph, args.force, max_false_pos=.2)

    with open(info_filename, 'a') as info_fp:
        print('fp rate estimated to be %1.3f\n' % fp_rate, file=info_fp)

    if args.summary_info:
        mr_fmt = args.summary_info.lower()
        mr_file = base + '.info.' + mr_fmt
        log_info("Writing summmary info to {mr_file}", mr_file=mr_file)
        with open(mr_file, 'w') as mr_fh:
            if mr_fmt == 'json':
                mr_data = {
                    "ht_name": os.path.basename(base),
                    "fpr": fp_rate,
                    "num_kmers": n_kmers,
                    "files": filenames,
                    "mrinfo_version": "0.2.0",
                    "num_reads": total_num_reads,
                }
                json.dump(mr_data, mr_fh)
                mr_fh.write('\n')
            elif mr_fmt == 'tsv':
                mr_fh.write("ht_name\tfpr\tnum_kmers\tnum_reads\tfiles\n")
                vals = [
                    os.path.basename(base),
                    "{:1.3f}".format(fp_rate),
                    str(n_kmers),
                    str(total_num_reads),
                    ";".join(filenames),
                ]
                mr_fh.write("\t".join(vals) + "\n")

    log_info('fp rate estimated to be {fpr:1.3f}', fpr=fp_rate)

    log_info('DONE.')
    log_info('wrote to: {filename}', filename=info_filename)


if __name__ == '__main__':
    main()

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