def main():
info('count-overlap.py', ['counting'])
args = get_parser().parse_args()
report_on_config(args, hashtype='hashbits')
for infile in [args.ptfile, args.fafile]:
check_file_status(infile)
check_space([args.ptfile, args.fafile])
print 'loading k-mer presence table from', args.ptfile
ht1 = khmer.load_hashbits(args.ptfile)
kmer_size = ht1.ksize()
output = open(args.report_filename, 'w')
f_curve_obj = open(args.report_filename + '.curve', 'w')
ht2 = khmer.new_hashbits(kmer_size, args.min_tablesize, args.n_tables)
(n_unique, n_overlap, list_curve) = ht2.count_overlap(args.fafile, ht1)
printout1 = """\
dataset1(pt file): %s
dataset2: %s
# of unique k-mers in dataset2: %d
# of overlap unique k-mers: %d
""" % (args.ptfile, args.fafile, n_unique, n_overlap)
output.write(printout1)
for i in range(100):
to_print = str(list_curve[100 + i]) + ' ' + str(list_curve[i]) + '\n'
f_curve_obj.write(to_print)
def main():
info("filter-abund-single.py", ["counting"])
args = get_parser().parse_args()
check_file_status(args.datafile)
check_space([args.datafile])
if args.savetable:
check_space_for_hashtable(args.n_tables * args.min_tablesize)
report_on_config(args)
config = khmer.get_config()
config.set_reads_input_buffer_size(args.threads * 64 * 1024)
print "making k-mer counting table"
htable = khmer.new_counting_hash(args.ksize, args.min_tablesize, args.n_tables, args.threads)
# first, load reads into hash table
rparser = khmer.ReadParser(args.datafile, args.threads)
threads = []
print "consuming input, round 1 --", args.datafile
for _ in xrange(args.threads):
cur_thread = threading.Thread(target=htable.consume_fasta_with_reads_parser, args=(rparser,))
threads.append(cur_thread)
cur_thread.start()
for _ in threads:
_.join()
fp_rate = khmer.calc_expected_collisions(htable)
print "fp rate estimated to be %1.3f" % fp_rate
# now, trim.
# the filtering function.
def process_fn(record):
name = record["name"]
seq = record["sequence"]
if "N" in seq:
return None, None
trim_seq, trim_at = htable.trim_on_abundance(seq, args.cutoff)
if trim_at >= args.ksize:
return name, trim_seq
return None, None
# the filtering loop
print "filtering", args.datafile
outfile = os.path.basename(args.datafile) + ".abundfilt"
outfp = open(outfile, "w")
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(args.datafile), outfp)
print "output in", outfile
if args.savetable:
print "Saving k-mer counting table filename", args.savetable
print "...saving to", args.savetable
htable.save(args.savetable)
def main():
info('count-overlap.py', ['counting'])
args = get_parser().parse_args()
report_on_config(args, hashtype='hashbits')
for infile in [args.ptfile, args.fafile]:
check_file_status(infile)
check_space([args.ptfile, args.fafile])
print 'loading k-mer presence table from', args.ptfile
ht1 = khmer.load_hashbits(args.ptfile)
kmer_size = ht1.ksize()
output = open(args.report_filename, 'w')
f_curve_obj = open(args.report_filename + '.curve', 'w')
ht2 = khmer.new_hashbits(kmer_size, args.min_tablesize, args.n_tables)
(n_unique, n_overlap, list_curve) = ht2.count_overlap(args.fafile, ht1)
printout1 = """\
dataset1(pt file): %s
dataset2: %s
# of unique k-mers in dataset2: %d
# of overlap unique k-mers: %d
""" % (args.ptfile, args.fafile, n_unique, n_overlap)
output.write(printout1)
for i in range(100):
to_print = str(list_curve[100 + i]) + ' ' + str(list_curve[i]) + '\n'
f_curve_obj.write(to_print)
def main(args):
graph_type = 'nodegraph'
report_on_config(args, graphtype=graph_type)
base = args.output_filename
filenames = args.input_filenames
for fname in args.input_filenames:
check_input_files(fname, args.force)
graphsize = calculate_graphsize(args, graph_type)
space_needed = (args.n_tables * graphsize /
khmer._buckets_per_byte[graph_type])
check_space_for_graph(args.output_filename, space_needed, args.force)
print('Saving k-mer nodegraph to %s' % base, file=sys.stderr)
print('Loading kmers from sequences in %s' % repr(filenames),
file=sys.stderr)
if args.no_build_tagset:
print('We WILL NOT build the tagset.', file=sys.stderr)
else:
print('We WILL build the tagset (for partitioning/traversal).',
file=sys.stderr)
print('making nodegraph', file=sys.stderr)
nodegraph = khmer_args.create_nodegraph(args)
oxfuncs.build_graph(filenames, nodegraph, args.threads,
not args.no_build_tagset)
print('Total number of unique k-mers: {0}'.format(
nodegraph.n_unique_kmers()),
file=sys.stderr)
print('saving k-mer nodegraph in', base, file=sys.stderr)
nodegraph.save(base)
if not args.no_build_tagset:
print('saving tagset in', base + '.tagset', file=sys.stderr)
nodegraph.save_tagset(base + '.tagset')
info_fp = open(base + '.info', 'w')
info_fp.write('%d unique k-mers' % nodegraph.n_unique_kmers())
fp_rate = \
khmer.calc_expected_collisions(
nodegraph, args.force, max_false_pos=.15)
# 0.18 is ACTUAL MAX. Do not change.
print('false positive rate estimated to be %1.3f' % fp_rate,
file=sys.stderr)
print('\nfalse positive rate estimated to be %1.3f' % fp_rate,
file=info_fp)
print('wrote to ' + base + '.info and ' + base, file=sys.stderr)
if not args.no_build_tagset:
print('and ' + base + '.tagset', file=sys.stderr)
sys.exit(0)
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_fasta_with_reads_parser, 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:
print((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)
def main(args):
info('build-graph.py', ['graph', 'SeqAn'])
report_on_config(args, hashtype='nodegraph')
base = args.output_filename
filenames = args.input_filenames
for fname in args.input_filenames:
check_input_files(fname, args.force)
# if optimization args are given, do optimization
args = functions.do_sanity_checking(args, 0.01)
check_space(args.input_filenames, args.force)
check_space_for_hashtable(args, 'nodegraph', args.force)
print('Saving k-mer presence table to %s' % base, file=sys.stderr)
print('Loading kmers from sequences in %s' %
repr(filenames), file=sys.stderr)
if args.no_build_tagset:
print('We WILL NOT build the tagset.', file=sys.stderr)
else:
print('We WILL build the tagset (for partitioning/traversal).',
file=sys.stderr)
print('making nodegraph', file=sys.stderr)
htable = khmer_args.create_nodegraph(args)
functions.build_graph(filenames, htable, args.threads,
not args.no_build_tagset)
print('Total number of unique k-mers: {0}'.format(htable.n_unique_kmers()),
file=sys.stderr)
print('saving k-mer presence table in', base + '.pt', file=sys.stderr)
htable.save(base + '.pt')
if not args.no_build_tagset:
print('saving tagset in', base + '.tagset', file=sys.stderr)
htable.save_tagset(base + '.tagset')
info_fp = open(base + '.info', 'w')
info_fp.write('%d unique k-mers' % htable.n_unique_kmers())
fp_rate = \
khmer.calc_expected_collisions(htable, args.force, max_false_pos=.15)
# 0.18 is ACTUAL MAX. Do not change.
print('false positive rate estimated to be %1.3f' % fp_rate,
file=sys.stderr)
print('\nfalse positive rate estimated to be %1.3f' % fp_rate,
file=info_fp)
print('wrote to', base + '.info and', base + '.pt', file=sys.stderr)
if not args.no_build_tagset:
print('and ' + base + '.tagset', file=sys.stderr)
sys.exit(0)
def main(args):
graph_type = 'nodegraph'
report_on_config(args, graphtype=graph_type)
base = args.output_filename
filenames = args.input_filenames
for fname in args.input_filenames:
check_input_files(fname, args.force)
graphsize = calculate_graphsize(args, graph_type)
space_needed = (args.n_tables * graphsize /
khmer._buckets_per_byte[graph_type])
check_space_for_graph(args.output_filename, space_needed, args.force)
print('Saving k-mer nodegraph to %s' % base, file=sys.stderr)
print('Loading kmers from sequences in %s' %
repr(filenames), file=sys.stderr)
if args.no_build_tagset:
print('We WILL NOT build the tagset.', file=sys.stderr)
else:
print('We WILL build the tagset (for partitioning/traversal).',
file=sys.stderr)
print('making nodegraph', file=sys.stderr)
nodegraph = khmer_args.create_nodegraph(args)
oxfuncs.build_graph(filenames, nodegraph, args.threads,
not args.no_build_tagset)
print('Total number of unique k-mers: {0}'.format(
nodegraph.n_unique_kmers()), file=sys.stderr)
print('saving k-mer nodegraph in', base, file=sys.stderr)
nodegraph.save(base)
if not args.no_build_tagset:
print('saving tagset in', base + '.tagset', file=sys.stderr)
nodegraph.save_tagset(base + '.tagset')
info_fp = open(base + '.info', 'w')
info_fp.write('%d unique k-mers' % nodegraph.n_unique_kmers())
fp_rate = \
khmer.calc_expected_collisions(
nodegraph, args.force, max_false_pos=.15)
# 0.18 is ACTUAL MAX. Do not change.
print('false positive rate estimated to be %1.3f' % fp_rate,
file=sys.stderr)
print('\nfalse positive rate estimated to be %1.3f' % fp_rate,
file=info_fp)
print('wrote to ' + base + '.info and ' + base, file=sys.stderr)
if not args.no_build_tagset:
print('and ' + base + '.tagset', file=sys.stderr)
sys.exit(0)
def main():
info('optimal_args_hashbits.py', ['graph', 'SeqAn'])
args = get_parser().parse_args()
report_on_config(args, hashtype='hashbits')
filenames = args.input_filenames
base = filenames[0]
for _ in args.input_filenames:
check_input_files(_, False)
check_space(args.input_filenames, False)
print('Counting kmers from sequences in %s' % repr(filenames),
file=sys.stderr)
htable = khmer.new_hashbits(args.ksize, args.max_tablesize, args.n_tables)
target_method = htable.consume_fasta_with_reads_parser
for _, filename in enumerate(filenames):
rparser = khmer.ReadParser(filename)
threads = []
print('consuming input', filename, file=sys.stderr)
for num in xrange(args.threads):
cur_thread = threading.Thread(target=target_method,
args=(rparser, ))
threads.append(cur_thread)
cur_thread.start()
for thread in threads:
thread.join()
unique_kmers = htable.n_unique_kmers()
print('Total number of unique k-mers: {0}'.format(unique_kmers),
file=sys.stderr)
info_optimal = open(base + '.optimal_args', 'w')
fp_rate = khmer.calc_expected_collisions(htable)
print('fp rate estimated to be %1.3f' % fp_rate, file=sys.stderr)
if fp_rate > 0.15: # 0.18 is ACTUAL MAX. Do not change.
print("**", file=sys.stderr)
print(
"** ERROR: the graph structure is too small for this data set."
"Increase table size/# tables.",
file=sys.stderr)
print("**", file=sys.stderr)
if not False:
sys.exit(1)
to_print = output_gen(unique_kmers, fp_rate)
print(to_print, file=info_optimal)
print('optimal arguments were written to',
base + '.optimal_args',
file=sys.stderr)
def main():
info('optimal_args_nodegraph.py', ['graph', 'SeqAn'])
args = get_parser().parse_args()
report_on_config(args, graphtype='nodegraph')
filenames = args.input_filenames
base = filenames[0]
for _ in args.input_filenames:
check_input_files(_, False)
check_space(args.input_filenames, False)
print('Counting kmers from sequences in %s' % repr(filenames),
file=sys.stderr)
htable = khmer.new_nodegraph(args.ksize, args.max_tablesize, args.n_tables)
target_method = htable.consume_fasta_with_reads_parser
for _, filename in enumerate(filenames):
rparser = khmer.ReadParser(filename)
threads = []
print('consuming input', filename, file=sys.stderr)
for num in xrange(args.threads):
cur_thread = threading.Thread(
target=target_method, args=(rparser,))
threads.append(cur_thread)
cur_thread.start()
for thread in threads:
thread.join()
unique_kmers = htable.n_unique_kmers()
print('Total number of unique k-mers: {0}'.format(unique_kmers),
file=sys.stderr)
info_optimal = open(base + '.optimal_args', 'w')
fp_rate = khmer.calc_expected_collisions(htable)
print('fp rate estimated to be %1.3f' % fp_rate, file=sys.stderr)
if fp_rate > 0.15: # 0.18 is ACTUAL MAX. Do not change.
print("**", file=sys.stderr)
print("** ERROR: the graph structure is too small for this data set."
"Increase table size/# tables.", file=sys.stderr)
print("**", file=sys.stderr)
if not False:
sys.exit(1)
to_print = graphsize_args_report(unique_kmers, fp_rate)
print(to_print, file=info_optimal)
print('optimal arguments were written to', base + '.optimal_args',
file=sys.stderr)
def main(args):
info('build-graph.py', ['graph', 'SeqAn'])
report_on_config(args, hashtype='hashbits')
base = args.output_filename
filenames = args.input_filenames
for fname in args.input_filenames:
check_input_files(fname, args.force)
check_space(args.input_filenames, args.force)
check_space_for_hashtable(
(float(args.n_tables * args.min_tablesize) / 8.), args.force)
print >>sys.stderr, 'Saving k-mer presence table to %s' % base
print >>sys.stderr, 'Loading kmers from sequences in %s' % repr(filenames)
if args.no_build_tagset:
print >>sys.stderr, 'We WILL NOT build the tagset.'
else:
print >>sys.stderr, 'We WILL build the tagset', \
' (for partitioning/traversal).'
print >>sys.stderr, 'making k-mer presence table'
htable = khmer.new_hashbits(args.ksize, args.min_tablesize, args.n_tables)
functions.build_graph(filenames, htable, args.threads,
not args.no_build_tagset)
print >> sys.stderr, 'Total number of unique k-mers: {0}'.format(
htable.n_unique_kmers())
print >>sys.stderr, 'saving k-mer presence table in', base + '.pt'
htable.save(base + '.pt')
if not args.no_build_tagset:
print >>sys.stderr, 'saving tagset in', base + '.tagset'
htable.save_tagset(base + '.tagset')
info_fp = open(base + '.info', 'w')
info_fp.write('%d unique k-mers' % htable.n_unique_kmers())
fp_rate = \
khmer.calc_expected_collisions(htable, args.force, max_false_pos=.15)
# 0.18 is ACTUAL MAX. Do not change.
print >>sys.stderr, 'false positive rate estimated to be %1.3f' % fp_rate
print >>info_fp, '\nfalse positive rate estimated to be %1.3f' % fp_rate
print >> sys.stderr, 'wrote to', base + '.info and', base + '.pt'
if not args.no_build_tagset:
print >> sys.stderr, 'and ' + base + '.tagset'
sys.exit(0)
def test_report_on_config_bad_graphtype():
ksize = khmer_args.DEFAULT_K
n_tables = khmer_args.DEFAULT_N_TABLES
max_tablesize = khmer_args.DEFAULT_MAX_TABLESIZE
max_mem = 1e7
args = FakeArgparseObject(ksize, n_tables, max_tablesize, max_mem, 0)
try:
khmer_args.report_on_config(args, 'foograph')
assert 0, "the previous statement should raise an exception"
except ValueError as err:
assert "unknown graph type: foograph" in str(err), str(err)
def main(args):
info("build-graph.py", ["graph", "SeqAn"])
report_on_config(args, hashtype="hashbits")
base = args.output_filename
filenames = args.input_filenames
for fname in args.input_filenames:
check_input_files(fname, args.force)
check_space(args.input_filenames, args.force)
check_space_for_hashtable((float(args.n_tables * args.min_tablesize) / 8.0), args.force)
print("Saving k-mer presence table to %s" % base, file=sys.stderr)
print("Loading kmers from sequences in %s" % repr(filenames), file=sys.stderr)
if args.no_build_tagset:
print("We WILL NOT build the tagset.", file=sys.stderr)
else:
print("We WILL build the tagset (for partitioning/traversal).", file=sys.stderr)
print("making k-mer presence table", file=sys.stderr)
htable = khmer.new_hashbits(args.ksize, args.min_tablesize, args.n_tables)
functions.build_graph(filenames, htable, args.threads, not args.no_build_tagset)
print("Total number of unique k-mers: {0}".format(htable.n_unique_kmers()), file=sys.stderr)
print("saving k-mer presence table in", base + ".pt", file=sys.stderr)
htable.save(base + ".pt")
if not args.no_build_tagset:
print("saving tagset in", base + ".tagset", file=sys.stderr)
htable.save_tagset(base + ".tagset")
info_fp = open(base + ".info", "w")
info_fp.write("%d unique k-mers" % htable.n_unique_kmers())
fp_rate = khmer.calc_expected_collisions(htable, args.force, max_false_pos=0.15)
# 0.18 is ACTUAL MAX. Do not change.
print("false positive rate estimated to be %1.3f" % fp_rate, file=sys.stderr)
print("\nfalse positive rate estimated to be %1.3f" % fp_rate, file=info_fp)
print("wrote to", base + ".info and", base + ".pt", file=sys.stderr)
if not args.no_build_tagset:
print("and " + base + ".tagset", file=sys.stderr)
sys.exit(0)
def main(args):
info("build-graph.py", ["graph", "SeqAn"])
report_on_config(args, graphtype="nodegraph")
base = args.output_filename
filenames = args.input_filenames
for fname in args.input_filenames:
check_input_files(fname, args.force)
graphsize = calculate_graphsize(args, "nodegraph")
check_space_for_graph(args.output_filename, graphsize, args.force)
print("Saving k-mer nodegraph to %s" % base, file=sys.stderr)
print("Loading kmers from sequences in %s" % repr(filenames), file=sys.stderr)
if args.no_build_tagset:
print("We WILL NOT build the tagset.", file=sys.stderr)
else:
print("We WILL build the tagset (for partitioning/traversal).", file=sys.stderr)
print("making nodegraph", file=sys.stderr)
nodegraph = khmer_args.create_nodegraph(args)
oxfuncs.build_graph(filenames, nodegraph, args.threads, not args.no_build_tagset)
print("Total number of unique k-mers: {0}".format(nodegraph.n_unique_kmers()), file=sys.stderr)
print("saving k-mer nodegraph in", base, file=sys.stderr)
nodegraph.save(base)
if not args.no_build_tagset:
print("saving tagset in", base + ".tagset", file=sys.stderr)
nodegraph.save_tagset(base + ".tagset")
info_fp = open(base + ".info", "w")
info_fp.write("%d unique k-mers" % nodegraph.n_unique_kmers())
fp_rate = khmer.calc_expected_collisions(nodegraph, args.force, max_false_pos=0.15)
# 0.18 is ACTUAL MAX. Do not change.
print("false positive rate estimated to be %1.3f" % fp_rate, file=sys.stderr)
print("\nfalse positive rate estimated to be %1.3f" % fp_rate, file=info_fp)
print("wrote to " + base + ".info and " + base, file=sys.stderr)
if not args.no_build_tagset:
print("and " + base + ".tagset", file=sys.stderr)
sys.exit(0)
def main():
info('count-overlap.py', ['counting'])
args = get_parser().parse_args()
report_on_config(args, hashtype='hashbits')
for infile in [args.ptfile, args.fafile]:
check_input_files(infile, args.force)
check_space([args.ptfile, args.fafile], args.force)
print('loading k-mer presence table from', args.ptfile, file=sys.stderr)
ht1 = khmer.load_hashbits(args.ptfile)
kmer_size = ht1.ksize()
output = open(args.report_filename, 'w')
f_curve_obj = open(args.report_filename + '.curve', 'w')
if args.csv:
f_curve_obj_csv = csv.writer(f_curve_obj)
# write headers:
f_curve_obj_csv.writerow(['input_seq', 'overlap_kmer'])
ht2 = khmer.new_hashbits(kmer_size, args.min_tablesize, args.n_tables)
(n_unique, n_overlap, list_curve) = ht2.count_overlap(args.fafile, ht1)
printout1 = """\
dataset1(pt file): %s
dataset2: %s
# of unique k-mers in dataset2: %d
# of overlap unique k-mers: %d
""" % (args.ptfile, args.fafile, n_unique, n_overlap)
output.write(printout1)
for i in range(100):
if args.csv:
f_curve_obj_csv.writerow([list_curve[100 + i], list_curve[i]])
else:
print(list_curve[100 + i], list_curve[i], file=f_curve_obj)
print('wrote to: ' + args.report_filename, file=sys.stderr)
def main(): # pylint: disable=too-many-locals,too-many-statements
info("do-partition.py", ["graph"])
args = sanitize_help(get_parser()).parse_args()
report_on_config(args, graphtype="nodegraph")
for infile in args.input_filenames:
check_input_files(infile, args.force)
check_space(args.input_filenames, args.force)
print("Saving k-mer nodegraph to %s" % args.graphbase, file=sys.stderr)
print("Loading kmers from sequences in %s" % repr(args.input_filenames), file=sys.stderr)
print("--", file=sys.stderr)
print("SUBSET SIZE", args.subset_size, file=sys.stderr)
print("N THREADS", args.threads, file=sys.stderr)
print("--", file=sys.stderr)
# load-graph.py
print("making nodegraph", file=sys.stderr)
nodegraph = khmer_args.create_nodegraph(args)
for _, filename in enumerate(args.input_filenames):
print("consuming input", filename, file=sys.stderr)
nodegraph.consume_fasta_and_tag(filename)
# 0.18 is ACTUAL MAX. Do not change.
fp_rate = khmer.calc_expected_collisions(nodegraph, args.force, max_false_pos=0.15)
print("fp rate estimated to be %1.3f" % fp_rate, file=sys.stderr)
# partition-graph
# do we want to exhaustively traverse the graph?
stop_big_traversals = args.no_big_traverse
if stop_big_traversals:
print("** This script brakes for lumps: ", "stop_big_traversals is true.", file=sys.stderr)
else:
print("** Traverse all the things:", " stop_big_traversals is false.", file=sys.stderr)
#
# now, partition!
#
# divide the tags up into subsets
divvy = nodegraph.divide_tags_into_subsets(int(args.subset_size))
divvy = list(divvy)
n_subsets = len(divvy)
divvy.append(0)
# build a queue of tasks:
worker_q = queue.Queue()
# break up the subsets into a list of worker tasks
for _ in range(0, n_subsets):
start = divvy[_]
end = divvy[_ + 1]
worker_q.put((nodegraph, _, start, end))
print("enqueued %d subset tasks" % n_subsets, file=sys.stderr)
open("%s.info" % args.graphbase, "w").write("%d subsets total\n" % (n_subsets))
if n_subsets < args.threads:
args.threads = n_subsets
# start threads!
print("starting %d threads" % args.threads, file=sys.stderr)
print("---", file=sys.stderr)
threads = []
for _ in range(args.threads):
cur_thread = threading.Thread(target=worker, args=(worker_q, args.graphbase, stop_big_traversals))
threads.append(cur_thread)
cur_thread.start()
assert threading.active_count() == args.threads + 1
print("done starting threads", file=sys.stderr)
# wait for threads
for _ in threads:
_.join()
print("---", file=sys.stderr)
print("done making subsets! see %s.subset.*.pmap" % (args.graphbase,), file=sys.stderr)
# merge-partitions
pmap_files = glob.glob(args.graphbase + ".subset.*.pmap")
print("loading %d pmap files (first one: %s)" % (len(pmap_files), pmap_files[0]), file=sys.stderr)
nodegraph = khmer.Nodegraph(args.ksize, 1, 1)
for pmap_file in pmap_files:
print("merging", pmap_file, file=sys.stderr)
nodegraph.merge_subset_from_disk(pmap_file)
if args.remove_subsets:
print("removing pmap files", file=sys.stderr)
for pmap_file in pmap_files:
os.unlink(pmap_file)
# annotate-partitions
for infile in args.input_filenames:
print("outputting partitions for", infile, file=sys.stderr)
outfile = os.path.basename(infile) + ".part"
part_count = nodegraph.output_partitions(infile, outfile)
print("output %d partitions for %s" % (part_count, infile), file=sys.stderr)
print("partitions are in", outfile, file=sys.stderr)
def main():
info('load-graph.py', ['graph'])
args = get_parser().parse_args()
report_on_config(args, hashtype='hashbits')
base = args.output_filename
filenames = args.input_filenames
n_threads = int(args.n_threads)
for _ in args.input_filenames:
check_file_status(_)
check_space(args.input_filenames)
check_space_for_hashtable(float(args.n_tables * args.min_tablesize) / 8.)
print 'Saving k-mer presence table to %s' % base
print 'Loading kmers from sequences in %s' % repr(filenames)
if args.no_build_tagset:
print 'We WILL NOT build the tagset.'
else:
print 'We WILL build the tagset (for partitioning/traversal).'
print 'making k-mer presence table'
htable = khmer.new_hashbits(args.ksize, args.min_tablesize, args.n_tables)
if args.no_build_tagset:
target_method = htable.consume_fasta_with_reads_parser
else:
target_method = htable.consume_fasta_and_tag_with_reads_parser
config = khmer.get_config()
config.set_reads_input_buffer_size(n_threads * 64 * 1024)
for _, filename in enumerate(filenames):
rparser = khmer.ReadParser(filename, n_threads)
threads = []
print 'consuming input', filename
for _ in xrange(n_threads):
cur_thrd = threading.Thread(target=target_method, args=(rparser, ))
threads.append(cur_thrd)
cur_thrd.start()
for thread in threads:
thread.join()
if args.report_total_kmers:
print >> sys.stderr, 'Total number of k-mers: {0}'.format(
htable.n_occupied())
print 'saving k-mer presence table in', base + '.pt'
htable.save(base + '.pt')
if not args.no_build_tagset:
print 'saving tagset in', base + '.tagset'
htable.save_tagset(base + '.tagset')
info_fp = open(base + '.info', 'w')
info_fp.write('%d unique k-mers' % htable.n_unique_kmers())
fp_rate = khmer.calc_expected_collisions(htable)
print 'fp rate estimated to be %1.3f' % fp_rate
if args.write_fp_rate:
print >> info_fp, \
'\nfalse positive rate estimated to be %1.3f' % fp_rate
if fp_rate > 0.15: # 0.18 is ACTUAL MAX. Do not change.
print >> sys.stderr, "**"
print >> sys.stderr, ("** ERROR: the graph structure is too small for "
"this data set. Increase table size/# tables.")
print >> sys.stderr, "**"
sys.exit(1)
def main():
info('load-graph.py', ['graph'])
args = get_parser().parse_args()
report_on_config(args, hashtype='hashbits')
base = args.output_filename
filenames = args.input_filenames
n_threads = int(args.n_threads)
for _ in args.input_filenames:
check_file_status(_)
check_space(args.input_filenames)
check_space_for_hashtable(float(args.n_tables * args.min_tablesize) / 8.)
print 'Saving k-mer presence table to %s' % base
print 'Loading kmers from sequences in %s' % repr(filenames)
if args.no_build_tagset:
print 'We WILL NOT build the tagset.'
else:
print 'We WILL build the tagset (for partitioning/traversal).'
print 'making k-mer presence table'
htable = khmer.new_hashbits(args.ksize, args.min_tablesize, args.n_tables)
if args.no_build_tagset:
target_method = htable.consume_fasta_with_reads_parser
else:
target_method = htable.consume_fasta_and_tag_with_reads_parser
config = khmer.get_config()
config.set_reads_input_buffer_size(n_threads * 64 * 1024)
for _, filename in enumerate(filenames):
rparser = khmer.ReadParser(filename, n_threads)
threads = []
print 'consuming input', filename
for _ in xrange(n_threads):
cur_thrd = threading.Thread(target=target_method, args=(rparser, ))
threads.append(cur_thrd)
cur_thrd.start()
for thread in threads:
thread.join()
print 'saving k-mer presence table in', base + '.pt'
htable.save(base + '.pt')
if not args.no_build_tagset:
print 'saving tagset in', base + '.tagset'
htable.save_tagset(base + '.tagset')
info_fp = open(base + '.info', 'w')
info_fp.write('%d unique k-mers' % htable.n_unique_kmers())
fp_rate = khmer.calc_expected_collisions(htable)
print 'fp rate estimated to be %1.3f' % fp_rate
if fp_rate > 0.15: # 0.18 is ACTUAL MAX. Do not change.
print >> sys.stderr, "**"
print >> sys.stderr, ("** ERROR: the graph structure is too small for "
"this data set. Increase table size/# tables.")
print >> sys.stderr, "**"
sys.exit(1)
def main():
info('trim-low-abund.py', ['streaming'])
parser = get_parser()
args = parser.parse_args()
###
if len(set(args.input_filenames)) != len(args.input_filenames):
print >>sys.stderr, \
"Error: Cannot input the same filename multiple times."
sys.exit(1)
###
report_on_config(args)
check_valid_file_exists(args.input_filenames)
check_space(args.input_filenames, args.force)
if args.savetable:
check_space_for_hashtable(
args.n_tables * args.min_tablesize, args.force)
K = args.ksize
CUTOFF = args.cutoff
NORMALIZE_LIMIT = args.normalize_to
if args.loadtable:
print >>sys.stderr, 'loading k-mer counting table from', args.loadtable
ct = khmer.load_counting_hash(args.loadtable)
else:
print >>sys.stderr, 'making k-mer counting table'
ct = khmer.new_counting_hash(K, args.min_tablesize, args.n_tables)
tempdir = tempfile.mkdtemp('khmer', 'tmp', args.tempdir)
print >>sys.stderr, 'created temporary directory %s; ' \
'use -T to change location' % tempdir
# ### FIRST PASS ###
save_pass2_total = 0
n_bp = 0
n_reads = 0
written_bp = 0
written_reads = 0
trimmed_reads = 0
pass2list = []
for filename in args.input_filenames:
pass2filename = os.path.basename(filename) + '.pass2'
pass2filename = os.path.join(tempdir, pass2filename)
trimfilename = os.path.basename(filename) + '.abundtrim'
pass2list.append((filename, pass2filename, trimfilename))
screed_iter = screed.open(filename, parse_description=False)
pass2fp = open(pass2filename, 'w')
trimfp = open(trimfilename, 'w')
save_pass2 = 0
n = 0
paired_iter = broken_paired_reader(screed_iter, min_length=K,
force_single=args.ignore_pairs)
for n, is_pair, read1, read2 in paired_iter:
if n % 10000 == 0:
print >>sys.stderr, '...', n, filename, save_pass2, \
n_reads, n_bp, written_reads, written_bp
# we want to track paired reads here, to make sure that pairs
# are not split between first pass and second pass.
if is_pair:
n_reads += 2
n_bp += len(read1.sequence) + len(read2.sequence)
seq1 = read1.sequence.replace('N', 'A')
seq2 = read2.sequence.replace('N', 'A')
med1, _, _ = ct.get_median_count(seq1)
med2, _, _ = ct.get_median_count(seq2)
if med1 < NORMALIZE_LIMIT or med2 < NORMALIZE_LIMIT:
ct.consume(seq1)
ct.consume(seq2)
write_record_pair(read1, read2, pass2fp)
save_pass2 += 2
else:
_, trim_at1 = ct.trim_on_abundance(seq1, CUTOFF)
_, trim_at2 = ct.trim_on_abundance(seq2, CUTOFF)
if trim_at1 >= K:
read1 = trim_record(read1, trim_at1)
if trim_at2 >= K:
read2 = trim_record(read2, trim_at2)
if trim_at1 != len(seq1):
trimmed_reads += 1
if trim_at2 != len(seq2):
trimmed_reads += 1
write_record_pair(read1, read2, trimfp)
written_reads += 2
written_bp += trim_at1 + trim_at2
else:
n_reads += 1
n_bp += len(read1.sequence)
seq = read1.sequence.replace('N', 'A')
med, _, _ = ct.get_median_count(seq)
# has this portion of the graph saturated? if not,
# consume & save => pass2.
if med < NORMALIZE_LIMIT:
ct.consume(seq)
write_record(read1, pass2fp)
save_pass2 += 1
else: # trim!!
_, trim_at = ct.trim_on_abundance(seq, CUTOFF)
if trim_at >= K:
new_read = trim_record(read1, trim_at)
write_record(new_read, trimfp)
written_reads += 1
written_bp += trim_at
if trim_at != len(read1.sequence):
trimmed_reads += 1
pass2fp.close()
trimfp.close()
print '%s: kept aside %d of %d from first pass, in %s' % \
(filename, save_pass2, n, filename)
save_pass2_total += save_pass2
# ### SECOND PASS. ###
skipped_n = 0
skipped_bp = 0
for _, pass2filename, trimfilename in pass2list:
print 'second pass: looking at sequences kept aside in %s' % \
pass2filename
# note that for this second pass, we don't care about paired
# reads - they will be output in the same order they're read in,
# so pairs will stay together if not orphaned. This is in contrast
# to the first loop.
trimfp = open(trimfilename, 'a')
for n, read in enumerate(screed.open(pass2filename,
parse_description=False)):
if n % 10000 == 0:
print >>sys.stderr, '... x 2', n, pass2filename, \
written_reads, written_bp
seq = read.sequence.replace('N', 'A')
med, _, _ = ct.get_median_count(seq)
# do we retain low-abundance components unchanged?
if med < NORMALIZE_LIMIT and args.variable_coverage:
write_record(read, trimfp)
written_reads += 1
written_bp += len(read.sequence)
skipped_n += 1
skipped_bp += len(read.sequence)
# otherwise, examine/trim/truncate.
else: # med >= NORMALIZE LIMIT or not args.variable_coverage
_, trim_at = ct.trim_on_abundance(seq, CUTOFF)
if trim_at >= K:
new_read = trim_record(read, trim_at)
write_record(new_read, trimfp)
written_reads += 1
written_bp += trim_at
if trim_at != len(read.sequence):
trimmed_reads += 1
print >>sys.stderr, 'removing %s' % pass2filename
os.unlink(pass2filename)
print >>sys.stderr, 'removing temp directory & contents (%s)' % tempdir
shutil.rmtree(tempdir)
n_passes = 1.0 + (float(save_pass2_total) / n_reads)
percent_reads_trimmed = float(trimmed_reads + (n_reads - written_reads)) /\
n_reads * 100.0
print 'read %d reads, %d bp' % (n_reads, n_bp,)
print 'wrote %d reads, %d bp' % (written_reads, written_bp,)
print 'looked at %d reads twice (%.2f passes)' % (save_pass2_total,
n_passes)
print 'removed %d reads and trimmed %d reads (%.2f%%)' % \
(n_reads - written_reads, trimmed_reads, percent_reads_trimmed)
print 'trimmed or removed %.2f%% of bases (%d total)' % \
((1 - (written_bp / float(n_bp))) * 100.0, n_bp - written_bp)
if args.variable_coverage:
percent_reads_hicov = 100.0 * float(n_reads - skipped_n) / n_reads
print '%d reads were high coverage (%.2f%%);' % (n_reads - skipped_n,
percent_reads_hicov)
print 'skipped %d reads/%d bases because of low coverage' % \
(skipped_n, skipped_bp)
fp_rate = khmer.calc_expected_collisions(ct)
print >>sys.stderr, \
'fp rate estimated to be {fpr:1.3f}'.format(fpr=fp_rate)
if fp_rate > MAX_FALSE_POSITIVE_RATE:
print >> sys.stderr, "**"
print >> sys.stderr, ("** ERROR: the k-mer counting table is too small"
" for this data set. Increase tablesize/# "
"tables.")
print >> sys.stderr, "**"
print >> sys.stderr, "** Do not use these results!!"
sys.exit(1)
print 'output in *.abundtrim'
if args.savetable:
print >>sys.stderr, "Saving k-mer counting table to", args.savetable
ct.save(args.savetable)
def main():
info('sweep-reads-buffered.py', ['sweep'])
parser = get_parser()
args = parser.parse_args()
if args.min_tablesize < MIN_HSIZE:
args.min_tablesize = MIN_HSIZE
if args.ksize < MIN_KSIZE:
args.ksize = MIN_KSIZE
report_on_config(args, hashtype='hashbits')
K = args.ksize
HT_SIZE = args.min_tablesize
N_HT = args.n_tables
traversal_range = args.traversal_range
input_fastp = args.input_fastp
if not args.outdir:
outdir = os.path.dirname(input_fastp)
else:
outdir = args.outdir
max_buffers = args.max_buffers
output_pref = args.output_prefix
buf_size = args.buffer_size
max_reads = args.max_reads
check_input_files(args.input_fastp, args.force)
check_valid_file_exists(args.input_files)
all_input_files = [input_fastp]
all_input_files.extend(args.input_files)
# Check disk space availability
check_space(all_input_files, args.force)
# figure out input file type (FA/FQ) -- based on first file
ix = iter(screed.open(args.input_files[0]))
record = ix.next()
del ix
extension = 'fa'
if hasattr(record, 'quality'): # fastq!
extension = 'fq'
output_buffer = ReadBufferManager(
max_buffers, max_reads, buf_size, output_pref, outdir, extension)
# consume the partitioned fasta with which to label the graph
ht = khmer.LabelHash(K, HT_SIZE, N_HT)
try:
print >>sys.stderr, 'consuming input sequences...'
if args.label_by_pid:
print >>sys.stderr, '...labeling by partition id (pid)'
ht.consume_partitioned_fasta_and_tag_with_labels(input_fastp)
elif args.label_by_seq:
print >>sys.stderr, '...labeling by sequence'
for n, record in enumerate(screed.open(input_fastp)):
if n % 50000 == 0:
print >>sys.stderr, \
'...consumed {n} sequences...'.format(n=n)
ht.consume_sequence_and_tag_with_labels(record.sequence, n)
else:
print >>sys.stderr, \
'...labeling to create groups of size {s}'.format(
s=args.group_size)
label = -1
g = 0
try:
outfp = open('{pref}_base_{g}.{ext}'.format(pref=output_pref,
g=g,
ext=extension
), 'wb')
for n, record in enumerate(screed.open(input_fastp)):
if n % args.group_size == 0:
label += 1
if label > g:
g = label
outfp = open('{pref}_base_{g}.{ext}'.format(
pref=output_pref, g=g,
ext=extension), 'wb')
if n % 50000 == 0:
print >>sys.stderr, \
'...consumed {n} sequences...'.format(n=n)
ht.consume_sequence_and_tag_with_labels(record.sequence,
label)
write_record(record, outfp)
except IOError as e:
print >>sys.stderr, '!! ERROR !!', e
print >>sys.stderr, '...error splitting input. exiting...'
except IOError as e:
print >>sys.stderr, '!! ERROR: !!', e
print >>sys.stderr, '...error consuming \
{i}. exiting...'.format(i=input_fastp)
print >>sys.stderr, 'done consuming input sequence. \
added {t} tags and {l} \
labels...'.format(t=ht.n_tags(), l=ht.n_labels())
label_dict = defaultdict(int)
label_number_dist = []
n_orphaned = 0
n_labeled = 0
n_mlabeled = 0
total_t = time.clock()
start_t = time.clock()
for read_file in args.input_files:
print >>sys.stderr, '** sweeping {read_file} for labels...'.format(
read_file=read_file)
file_t = 0.0
try:
read_fp = screed.open(read_file)
except IOError as error:
print >>sys.stderr, '!! ERROR: !!', error
print >>sys.stderr, '*** Could not open {fn}, skipping...'.format(
fn=read_file)
else:
for _, record in enumerate(read_fp):
if _ % 50000 == 0:
end_t = time.clock()
batch_t = end_t - start_t
file_t += batch_t
print >>sys.stderr, '\tswept {n} reads [{nc} labeled, \
{no} orphaned] \
** {sec}s ({sect}s total)' \
.format(n=_, nc=n_labeled,
no=n_orphaned,
sec=batch_t, sect=file_t)
start_t = time.clock()
seq = record.sequence
name = record.name
try:
labels = ht.sweep_label_neighborhood(seq, traversal_range)
except ValueError as e:
pass
else:
if hasattr(record, 'quality'):
seq_str = fmt_fastq(name, seq, record.quality, labels)
else:
seq_str = fmt_fasta(name, seq, labels)
label_number_dist.append(len(labels))
if labels:
n_labeled += 1
if len(labels) > 1:
output_buffer.queue(seq_str, 'multi')
n_mlabeled += 1
label_dict['multi'] += 1
else:
output_buffer.queue(seq_str, labels[0])
label_dict[labels[0]] += 1
else:
n_orphaned += 1
output_buffer.queue(seq_str, 'orphaned')
label_dict['orphaned'] += 1
print >>sys.stderr, '** End of file {fn}...'.format(fn=read_file)
output_buffer.flush_all()
read_fp.close()
# gotta output anything left in the buffers at the end!
print >>sys.stderr, '** End of run...'
output_buffer.flush_all()
total_t = time.clock() - total_t
if output_buffer.num_write_errors > 0 or output_buffer.num_file_errors > 0:
print >>sys.stderr, '! WARNING: Sweep finished with errors !'
print >>sys.stderr, '** {writee} reads not written'.format(
writee=output_buffer.num_write_errors)
print >>sys.stderr, '** {filee} errors opening files'.format(
filee=output_buffer.num_file_errors)
print >>sys.stderr, 'swept {n_reads} for labels...'.format(
n_reads=n_labeled + n_orphaned)
print >>sys.stderr, '...with {nc} labeled and {no} orphaned'.format(
nc=n_labeled, no=n_orphaned)
print >>sys.stderr, '...and {nmc} multilabeled'.format(nmc=n_mlabeled)
print >>sys.stderr, '** outputting label number distribution...'
fn = os.path.join(outdir, '{pref}.dist.txt'.format(pref=output_pref))
with open(fn, 'wb') as outfp:
for nc in label_number_dist:
outfp.write('{nc}\n'.format(nc=nc))
fn = os.path.join(outdir, '{pref}.counts.csv'.format(pref=output_pref))
print >>sys.stderr, '** outputting label read counts...'
with open(fn, 'wb') as outfp:
for k in label_dict:
outfp.write('{l},{c}\n'.format(l=k, c=label_dict[k]))
def main(): # pylint: disable=too-many-branches,too-many-statements
info('saturate-by-median.py', ['diginorm'])
args = get_parser().parse_args()
report_on_config(args)
report_fp = args.report
report_frequency = args.report_frequency
check_valid_file_exists(args.input_filenames)
check_space(args.input_filenames)
if args.savetable:
check_space_for_hashtable(args.n_tables * args.min_tablesize)
# list to save error files along with throwing exceptions
if args.force:
corrupt_files = []
if args.loadtable:
print 'loading k-mer counting table from', args.loadtable
htable = khmer.load_counting_hash(args.loadtable)
else:
print 'making k-mer counting table'
htable = khmer.new_counting_hash(args.ksize, args.min_tablesize,
args.n_tables)
total = 0
discarded = 0
for index, input_filename in enumerate(args.input_filenames):
total_acc = 0
discarded_acc = 0
try:
total_acc, discarded_acc = normalize_by_median(
input_filename, htable, args, report_fp, report_frequency)
except IOError as err:
handle_error(err, input_filename)
if not args.force:
print >> sys.stderr, '** Exiting!'
sys.exit(1)
else:
print >> sys.stderr, '*** Skipping error file, moving on...'
corrupt_files.append(input_filename)
else:
if total_acc == 0 and discarded_acc == 0:
print 'SKIPPED empty file', input_filename
else:
total += total_acc
discarded += discarded_acc
print 'DONE with {inp}; kept {kept} of {total} or {perc:2}%'\
.format(inp=input_filename,
kept=total - discarded, total=total,
perc=int(100. - discarded / float(total) * 100.))
if args.savetable:
print 'Saving k-mer counting table through', input_filename
print '...saving to', args.savetable
htable.save(args.savetable)
fp_rate = khmer.calc_expected_collisions(htable)
print 'fp rate estimated to be {fpr:1.3f}'.format(fpr=fp_rate)
if args.force and len(corrupt_files) > 0:
print >> sys.stderr, "** WARNING: Finished with errors!"
print >> sys.stderr, "** IOErrors occurred in the following files:"
print >> sys.stderr, "\t", " ".join(corrupt_files)
if fp_rate > MAX_FALSE_POSITIVE_RATE:
print >> sys.stderr, "**"
print >> sys.stderr, ("** ERROR: the k-mer counting table is too small"
" for this data set. Increase tablesize/# "
"tables.")
print >> sys.stderr, "**"
print >> sys.stderr, "** Do not use these results!!"
sys.exit(1)
def main():
info('correct-reads.py', ['streaming'])
args = sanitize_help(get_parser()).parse_args()
###
if len(set(args.input_filenames)) != len(args.input_filenames):
print("Error: Cannot input the same filename multiple times.",
file=sys.stderr)
sys.exit(1)
###
report_on_config(args)
check_valid_file_exists(args.input_filenames)
check_space(args.input_filenames, args.force)
tablesize = calculate_graphsize(args, 'countgraph')
if args.savegraph:
check_space_for_graph(args.savegraph, tablesize, args.force)
K = args.ksize
CUTOFF = args.cutoff
NORMALIZE_LIMIT = args.normalize_to
if args.loadgraph:
print('loading k-mer countgraph from', args.loadgraph, file=sys.stderr)
ct = Countgraph.load(args.loadgraph)
else:
print('making k-mer countgraph', file=sys.stderr)
ct = create_countgraph(args, multiplier=8 / (9. + 0.3))
tempdir = tempfile.mkdtemp('khmer', 'tmp', args.tempdir)
print('created temporary directory %s; use -T to change location' %
tempdir,
file=sys.stderr)
aligner = khmer.ReadAligner(ct, args.cutoff, args.bits_theta)
# ### FIRST PASS ###
save_pass2_total = 0
n_bp = 0
n_reads = 0
written_bp = 0
written_reads = 0
corrected_reads = 0
pass2list = []
for filename in args.input_filenames:
pass2filename = os.path.basename(filename) + '.pass2'
pass2filename = os.path.join(tempdir, pass2filename)
if args.out is None:
corrfp = open(os.path.basename(filename) + '.corr', 'w')
else:
corrfp = args.out
pass2list.append((filename, pass2filename, corrfp))
screed_iter = screed.open(filename, parse_description=False)
pass2fp = open(pass2filename, 'w')
save_pass2 = 0
n = 0
paired_iter = broken_paired_reader(screed_iter,
min_length=K,
force_single=args.ignore_pairs)
for n, is_pair, read1, read2 in paired_iter:
if n % 10000 == 0:
print('...',
n,
filename,
save_pass2,
n_reads,
n_bp,
written_reads,
written_bp,
file=sys.stderr)
# we want to track paired reads here, to make sure that pairs
# are not split between first pass and second pass.
if is_pair:
n_reads += 2
n_bp += len(read1.sequence) + len(read2.sequence)
seq1 = read1.sequence.replace('N', 'A')
seq2 = read2.sequence.replace('N', 'A')
med1, _, _ = ct.get_median_count(seq1)
med2, _, _ = ct.get_median_count(seq2)
if med1 < NORMALIZE_LIMIT or med2 < NORMALIZE_LIMIT:
ct.consume(seq1)
ct.consume(seq2)
write_record_pair(read1, read2, pass2fp)
save_pass2 += 2
else:
is_aligned, new_seq1 = correct_sequence(aligner, seq1)
if is_aligned:
if new_seq1 != read1.sequence:
corrected_reads += 1
read1.sequence = new_seq1
if hasattr(read1, 'quality'):
fix_quality(read1)
is_aligned, new_seq2 = correct_sequence(aligner, seq2)
if is_aligned:
if new_seq2 != read2.sequence:
corrected_reads += 1
read2.sequence = new_seq2
if hasattr(read2, 'quality'):
fix_quality(read2)
write_record_pair(read1, read2, corrfp)
written_reads += 2
written_bp += len(read1)
written_bp += len(read2)
else:
n_reads += 1
n_bp += len(read1.sequence)
seq = read1.sequence.replace('N', 'A')
med, _, _ = ct.get_median_count(seq)
# has this portion of the graph saturated? if not,
# consume & save => pass2.
if med < NORMALIZE_LIMIT:
ct.consume(seq)
write_record(read1, pass2fp)
save_pass2 += 1
else: # trim!!
is_aligned, new_seq = correct_sequence(aligner, seq)
if is_aligned:
if new_seq != read1.sequence:
corrected_reads += 1
read1.sequence = new_seq
if hasattr(read1, 'quality'):
fix_quality(read1)
write_record(read1, corrfp)
written_reads += 1
written_bp += len(new_seq)
pass2fp.close()
print('%s: kept aside %d of %d from first pass, in %s' %
(filename, save_pass2, n, filename),
file=sys.stderr)
save_pass2_total += save_pass2
# ### SECOND PASS. ###
skipped_n = 0
skipped_bp = 0
for _, pass2filename, corrfp in pass2list:
print(('second pass: looking at sequences kept aside in %s') %
pass2filename,
file=sys.stderr)
# note that for this second pass, we don't care about paired
# reads - they will be output in the same order they're read in,
# so pairs will stay together if not orphaned. This is in contrast
# to the first loop.
for n, read in enumerate(
screed.open(pass2filename, parse_description=False)):
if n % 10000 == 0:
print('... x 2',
n,
pass2filename,
written_reads,
written_bp,
file=sys.stderr)
seq = read.sequence.replace('N', 'A')
med, _, _ = ct.get_median_count(seq)
# do we retain low-abundance components unchanged?
if med < NORMALIZE_LIMIT and args.variable_coverage:
write_record(read, corrfp)
written_reads += 1
written_bp += len(read.sequence)
skipped_n += 1
skipped_bp += len(read.sequence)
# otherwise, examine/correct.
else: # med >= NORMALIZE LIMIT or not args.variable_coverage
is_aligned, new_seq = correct_sequence(aligner, seq)
if is_aligned:
if new_seq != read.sequence:
corrected_reads += 1
read.sequence = new_seq
if hasattr(read, 'quality'):
fix_quality(read)
write_record(read, corrfp)
written_reads += 1
written_bp += len(new_seq)
print('removing %s' % pass2filename, file=sys.stderr)
os.unlink(pass2filename)
print('removing temp directory & contents (%s)' % tempdir, file=sys.stderr)
shutil.rmtree(tempdir)
n_passes = 1.0 + (float(save_pass2_total) / n_reads)
percent_reads_corrected = float(corrected_reads +
(n_reads - written_reads)) /\
n_reads * 100.0
print('read %d reads, %d bp' % (
n_reads,
n_bp,
), file=sys.stderr)
print('wrote %d reads, %d bp' % (
written_reads,
written_bp,
),
file=sys.stderr)
print('looked at %d reads twice (%.2f passes)' %
(save_pass2_total, n_passes),
file=sys.stderr)
print('removed %d reads and corrected %d reads (%.2f%%)' %
(n_reads - written_reads, corrected_reads, percent_reads_corrected),
file=sys.stderr)
print('removed %.2f%% of bases (%d total)' %
((1 - (written_bp / float(n_bp))) * 100.0, n_bp - written_bp),
file=sys.stderr)
if args.variable_coverage:
percent_reads_hicov = 100.0 * float(n_reads - skipped_n) / n_reads
print('%d reads were high coverage (%.2f%%);' %
(n_reads - skipped_n, percent_reads_hicov),
file=sys.stderr)
print(('skipped %d reads/%d bases because of low coverage') %
(skipped_n, skipped_bp),
file=sys.stderr)
fp_rate = \
khmer.calc_expected_collisions(ct, args.force, max_false_pos=.8)
# for max_false_pos see Zhang et al., http://arxiv.org/abs/1309.2975
print('fp rate estimated to be {fpr:1.3f}'.format(fpr=fp_rate),
file=sys.stderr)
print('output in *.corr', file=sys.stderr)
if args.savegraph:
print("Saving k-mer countgraph to", args.savegraph, file=sys.stderr)
ct.save(args.savegraph)
def main():
info('load-into-counting.py', ['counting', 'SeqAn'])
args = sanitize_help(get_parser()).parse_args()
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)
check_file_writable(base)
check_file_writable(base + ".info")
print('Saving k-mer countgraph to %s' % base, file=sys.stderr)
print('Loading kmers from sequences in %s' % repr(filenames),
file=sys.stderr)
# clobber the '.info' file now, as we always open in append mode below
if os.path.exists(base + '.info'):
os.remove(base + '.info')
print('making countgraph', file=sys.stderr)
countgraph = khmer_args.create_countgraph(args)
countgraph.set_use_bigcount(args.bigcount)
filename = None
total_num_reads = 0
for index, filename in enumerate(filenames):
rparser = khmer.ReadParser(filename)
threads = []
print('consuming input', filename, file=sys.stderr)
for _ in range(args.threads):
cur_thrd = \
threading.Thread(
target=countgraph.consume_fasta_with_reads_parser,
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)
print('mid-save', base, file=sys.stderr)
countgraph.save(base)
with open(base + '.info', 'a') as info_fh:
print('through', filename, file=info_fh)
total_num_reads += rparser.num_reads
n_kmers = countgraph.n_unique_kmers()
print('Total number of unique k-mers:', n_kmers, file=sys.stderr)
with open(base + '.info', 'a') as info_fp:
print('Total number of unique k-mers:', n_kmers, file=info_fp)
print('saving', base, file=sys.stderr)
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(base + '.info', '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
print("Writing summmary info to", mr_file, file=sys.stderr)
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")
print('fp rate estimated to be %1.3f' % fp_rate, file=sys.stderr)
print('DONE.', file=sys.stderr)
print('wrote to:', base + '.info', file=sys.stderr)
def main(): # pylint: disable=too-many-locals,too-many-branches
info('abundance-dist-single.py', ['counting', 'SeqAn'])
args = get_parser().parse_args()
report_on_config(args)
check_input_files(args.input_sequence_filename, args.force)
check_space([args.input_sequence_filename], args.force)
if args.savetable:
check_space_for_hashtable(args, 'countgraph', args.force)
if (not args.squash_output
and os.path.exists(args.output_histogram_filename)):
print('ERROR: %s exists; not squashing.' %
args.output_histogram_filename,
file=sys.stderr)
sys.exit(1)
else:
hist_fp = open(args.output_histogram_filename, 'w')
if args.csv:
hist_fp_csv = csv.writer(hist_fp)
# write headers:
hist_fp_csv.writerow(
['abundance', 'count', 'cumulative', 'cumulative_fraction'])
print('making countgraph', file=sys.stderr)
counting_hash = khmer_args.create_countgraph(args, multiplier=1.1)
counting_hash.set_use_bigcount(args.bigcount)
print('building k-mer tracking table', file=sys.stderr)
tracking = khmer_args.create_nodegraph(args, multiplier=1.1)
print('kmer_size:', counting_hash.ksize(), file=sys.stderr)
print('k-mer counting table sizes:',
counting_hash.hashsizes(),
file=sys.stderr)
print('outputting to', args.output_histogram_filename, file=sys.stderr)
# start loading
rparser = khmer.ReadParser(args.input_sequence_filename)
threads = []
print('consuming input, round 1 --',
args.input_sequence_filename,
file=sys.stderr)
for _ in range(args.threads):
thread = \
threading.Thread(
target=counting_hash.consume_fasta_with_reads_parser,
args=(rparser, )
)
threads.append(thread)
thread.start()
for thread in threads:
thread.join()
if args.report_total_kmers:
print('Total number of unique k-mers: {0}'.format(
counting_hash.n_unique_kmers()),
file=sys.stderr)
abundance_lists = []
def __do_abundance_dist__(read_parser):
abundances = counting_hash.abundance_distribution_with_reads_parser(
read_parser, tracking)
abundance_lists.append(abundances)
print('preparing hist from %s...' % args.input_sequence_filename,
file=sys.stderr)
rparser = khmer.ReadParser(args.input_sequence_filename)
threads = []
print('consuming input, round 2 --',
args.input_sequence_filename,
file=sys.stderr)
for _ in range(args.threads):
thread = \
threading.Thread(
target=__do_abundance_dist__,
args=(rparser, )
)
threads.append(thread)
thread.start()
for thread in threads:
thread.join()
assert len(abundance_lists) == args.threads, len(abundance_lists)
abundance = {}
for abundance_list in abundance_lists:
for i, count in enumerate(abundance_list):
abundance[i] = abundance.get(i, 0) + count
total = sum(abundance.values())
if 0 == total:
print(
"ERROR: abundance distribution is uniformly zero; "
"nothing to report.",
file=sys.stderr)
print("\tPlease verify that the input files are valid.",
file=sys.stderr)
sys.exit(1)
sofar = 0
for _, i in sorted(abundance.items()):
if i == 0 and not args.output_zero:
continue
sofar += i
frac = sofar / float(total)
if args.csv:
hist_fp_csv.writerow([_, i, sofar, round(frac, 3)])
else:
print(_, i, sofar, round(frac, 3), file=hist_fp)
if sofar == total:
break
if args.savetable:
print('Saving k-mer counting table ', args.savetable, file=sys.stderr)
print('...saving to', args.savetable, file=sys.stderr)
counting_hash.save(args.savetable)
print('wrote to: ' + args.output_histogram_filename, file=sys.stderr)
def main():
info('trim-low-abund.py', ['streaming'])
parser = sanitize_help(get_parser())
args = parser.parse_args()
###
if len(set(args.input_filenames)) != len(args.input_filenames):
print("Error: Cannot input the same filename multiple times.",
file=sys.stderr)
sys.exit(1)
###
report_on_config(args)
check_valid_file_exists(args.input_filenames)
check_space(args.input_filenames, args.force)
if args.savegraph:
graphsize = calculate_graphsize(args, 'countgraph')
check_space_for_graph(args.savegraph, graphsize, args.force)
if ('-' in args.input_filenames or '/dev/stdin' in args.input_filenames) \
and not args.output:
print("Accepting input from stdin; output filename must "
"be provided with -o.", file=sys.stderr)
sys.exit(1)
if args.loadgraph:
print('loading countgraph from', args.loadgraph, file=sys.stderr)
ct = khmer.load_countgraph(args.loadgraph)
else:
print('making countgraph', file=sys.stderr)
ct = khmer_args.create_countgraph(args)
K = ct.ksize()
CUTOFF = args.cutoff
NORMALIZE_LIMIT = args.normalize_to
tempdir = tempfile.mkdtemp('khmer', 'tmp', args.tempdir)
print('created temporary directory %s; '
'use -T to change location' % tempdir, file=sys.stderr)
# ### FIRST PASS ###
save_pass2_total = 0
n_bp = 0
n_reads = 0
written_bp = 0
written_reads = 0
trimmed_reads = 0
pass2list = []
for filename in args.input_filenames:
pass2filename = os.path.basename(filename) + '.pass2'
pass2filename = os.path.join(tempdir, pass2filename)
if args.output is None:
trimfp = get_file_writer(open(os.path.basename(filename) +
'.abundtrim', 'wb'),
args.gzip, args.bzip)
else:
trimfp = get_file_writer(args.output, args.gzip, args.bzip)
pass2list.append((filename, pass2filename, trimfp))
screed_iter = screed.open(filename)
pass2fp = open(pass2filename, 'w')
save_pass2 = 0
n = 0
paired_iter = broken_paired_reader(screed_iter, min_length=K,
force_single=args.ignore_pairs)
for n, is_pair, read1, read2 in paired_iter:
if n % 10000 == 0:
print('...', n, filename, save_pass2, n_reads, n_bp,
written_reads, written_bp, file=sys.stderr)
# we want to track paired reads here, to make sure that pairs
# are not split between first pass and second pass.
if is_pair:
n_reads += 2
n_bp += len(read1.sequence) + len(read2.sequence)
seq1 = read1.sequence.replace('N', 'A')
seq2 = read2.sequence.replace('N', 'A')
med1, _, _ = ct.get_median_count(seq1)
med2, _, _ = ct.get_median_count(seq2)
if med1 < NORMALIZE_LIMIT or med2 < NORMALIZE_LIMIT:
ct.consume(seq1)
ct.consume(seq2)
write_record_pair(read1, read2, pass2fp)
save_pass2 += 2
else:
_, trim_at1 = ct.trim_on_abundance(seq1, CUTOFF)
_, trim_at2 = ct.trim_on_abundance(seq2, CUTOFF)
if trim_at1 >= K:
read1 = trim_record(read1, trim_at1)
if trim_at2 >= K:
read2 = trim_record(read2, trim_at2)
if trim_at1 != len(seq1):
trimmed_reads += 1
if trim_at2 != len(seq2):
trimmed_reads += 1
write_record_pair(read1, read2, trimfp)
written_reads += 2
written_bp += trim_at1 + trim_at2
else:
n_reads += 1
n_bp += len(read1.sequence)
seq = read1.sequence.replace('N', 'A')
med, _, _ = ct.get_median_count(seq)
# has this portion of the graph saturated? if not,
# consume & save => pass2.
if med < NORMALIZE_LIMIT:
ct.consume(seq)
write_record(read1, pass2fp)
save_pass2 += 1
else: # trim!!
_, trim_at = ct.trim_on_abundance(seq, CUTOFF)
if trim_at >= K:
new_read = trim_record(read1, trim_at)
write_record(new_read, trimfp)
written_reads += 1
written_bp += trim_at
if trim_at != len(read1.sequence):
trimmed_reads += 1
pass2fp.close()
print('%s: kept aside %d of %d from first pass, in %s' %
(filename, save_pass2, n, filename),
file=sys.stderr)
save_pass2_total += save_pass2
# ### SECOND PASS. ###
skipped_n = 0
skipped_bp = 0
for _, pass2filename, trimfp in pass2list:
print('second pass: looking at sequences kept aside in %s' %
pass2filename,
file=sys.stderr)
# note that for this second pass, we don't care about paired
# reads - they will be output in the same order they're read in,
# so pairs will stay together if not orphaned. This is in contrast
# to the first loop.
for n, read in enumerate(screed.open(pass2filename)):
if n % 10000 == 0:
print('... x 2', n, pass2filename,
written_reads, written_bp, file=sys.stderr)
seq = read.sequence.replace('N', 'A')
med, _, _ = ct.get_median_count(seq)
# do we retain low-abundance components unchanged?
if med < NORMALIZE_LIMIT and args.variable_coverage:
write_record(read, trimfp)
written_reads += 1
written_bp += len(read.sequence)
skipped_n += 1
skipped_bp += len(read.sequence)
# otherwise, examine/trim/truncate.
else: # med >= NORMALIZE LIMIT or not args.variable_coverage
_, trim_at = ct.trim_on_abundance(seq, CUTOFF)
if trim_at >= K:
new_read = trim_record(read, trim_at)
write_record(new_read, trimfp)
written_reads += 1
written_bp += trim_at
if trim_at != len(read.sequence):
trimmed_reads += 1
print('removing %s' % pass2filename, file=sys.stderr)
os.unlink(pass2filename)
print('removing temp directory & contents (%s)' % tempdir, file=sys.stderr)
shutil.rmtree(tempdir)
n_passes = 1.0 + (float(save_pass2_total) / n_reads)
percent_reads_trimmed = float(trimmed_reads + (n_reads - written_reads)) /\
n_reads * 100.0
print('read %d reads, %d bp' % (n_reads, n_bp,), file=sys.stderr)
print('wrote %d reads, %d bp' % (written_reads, written_bp,),
file=sys.stderr)
print('looked at %d reads twice (%.2f passes)' % (save_pass2_total,
n_passes),
file=sys.stderr)
print('removed %d reads and trimmed %d reads (%.2f%%)' %
(n_reads - written_reads, trimmed_reads, percent_reads_trimmed),
file=sys.stderr)
print('trimmed or removed %.2f%% of bases (%d total)' %
((1 - (written_bp / float(n_bp))) * 100.0, n_bp - written_bp),
file=sys.stderr)
if args.variable_coverage:
percent_reads_hicov = 100.0 * float(n_reads - skipped_n) / n_reads
print('%d reads were high coverage (%.2f%%);' % (n_reads - skipped_n,
percent_reads_hicov),
file=sys.stderr)
print('skipped %d reads/%d bases because of low coverage' %
(skipped_n, skipped_bp),
file=sys.stderr)
fp_rate = \
khmer.calc_expected_collisions(ct, args.force, max_false_pos=.8)
# for max_false_pos see Zhang et al., http://arxiv.org/abs/1309.2975
print('fp rate estimated to be {fpr:1.3f}'.format(fpr=fp_rate),
file=sys.stderr)
print('output in *.abundtrim', file=sys.stderr)
if args.savegraph:
print("Saving k-mer countgraph to",
args.savegraph, file=sys.stderr)
ct.save(args.savegraph)
def main():
info('filter-abund-single.py', ['counting', 'SeqAn'])
args = get_parser().parse_args()
check_input_files(args.datafile, args.force)
check_space([args.datafile], args.force)
if args.savetable:
check_space_for_hashtable(
args.n_tables * args.min_tablesize, args.force)
report_on_config(args)
print >>sys.stderr, 'making k-mer counting table'
htable = khmer.new_counting_hash(args.ksize, args.min_tablesize,
args.n_tables)
# first, load reads into hash table
rparser = khmer.ReadParser(args.datafile)
threads = []
print >>sys.stderr, 'consuming input, round 1 --', args.datafile
for _ in xrange(args.threads):
cur_thread = \
threading.Thread(
target=htable.consume_fasta_with_reads_parser,
args=(rparser, )
)
threads.append(cur_thread)
cur_thread.start()
for _ in threads:
_.join()
if args.report_total_kmers:
print >> sys.stderr, 'Total number of unique k-mers: {0}'.format(
htable.n_unique_kmers())
fp_rate = khmer.calc_expected_collisions(htable, args.force)
print >>sys.stderr, 'fp rate estimated to be %1.3f' % fp_rate
# now, trim.
# the filtering function.
def process_fn(record):
name = record['name']
seq = record['sequence']
if 'N' in seq:
return None, None
trim_seq, trim_at = htable.trim_on_abundance(seq, args.cutoff)
if trim_at >= args.ksize:
return name, trim_seq
return None, None
# the filtering loop
print >>sys.stderr, 'filtering', args.datafile
outfile = os.path.basename(args.datafile) + '.abundfilt'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(args.datafile), outfp)
print >>sys.stderr, 'output in', outfile
if args.savetable:
print >>sys.stderr, 'Saving k-mer counting table filename', \
args.savetable
print >>sys.stderr, '...saving to', args.savetable
htable.save(args.savetable)
print >>sys.stderr, 'wrote to: ', outfile
def main(): # pylint: disable=too-many-locals,too-many-branches
info('abundance-dist-single.py', ['counting', 'SeqAn'])
args = get_parser().parse_args()
report_on_config(args)
check_input_files(args.input_sequence_filename, args.force)
check_space([args.input_sequence_filename], args.force)
if args.savetable:
check_space_for_hashtable(args.n_tables * args.min_tablesize,
args.force)
if (not args.squash_output and
os.path.exists(args.output_histogram_filename)):
print('ERROR: %s exists; not squashing.' %
args.output_histogram_filename, file=sys.stderr)
sys.exit(1)
else:
hist_fp = open(args.output_histogram_filename, 'w')
if args.csv:
hist_fp_csv = csv.writer(hist_fp)
# write headers:
hist_fp_csv.writerow(['abundance', 'count', 'cumulative',
'cumulative_fraction'])
print('making k-mer counting table', file=sys.stderr)
counting_hash = khmer.new_counting_hash(args.ksize, args.min_tablesize,
args.n_tables)
counting_hash.set_use_bigcount(args.bigcount)
print('building k-mer tracking table', file=sys.stderr)
tracking = khmer.new_hashbits(counting_hash.ksize(), args.min_tablesize,
args.n_tables)
print('kmer_size:', counting_hash.ksize(), file=sys.stderr)
print('k-mer counting table sizes:',
counting_hash.hashsizes(), file=sys.stderr)
print('outputting to', args.output_histogram_filename, file=sys.stderr)
# start loading
rparser = khmer.ReadParser(args.input_sequence_filename)
threads = []
print('consuming input, round 1 --',
args.input_sequence_filename, file=sys.stderr)
for _ in range(args.threads):
thread = \
threading.Thread(
target=counting_hash.consume_fasta_with_reads_parser,
args=(rparser, )
)
threads.append(thread)
thread.start()
for thread in threads:
thread.join()
if args.report_total_kmers:
print('Total number of unique k-mers: {0}'.format(
counting_hash.n_unique_kmers()), file=sys.stderr)
abundance_lists = []
def __do_abundance_dist__(read_parser):
abundances = counting_hash.abundance_distribution_with_reads_parser(
read_parser, tracking)
abundance_lists.append(abundances)
print('preparing hist from %s...' %
args.input_sequence_filename, file=sys.stderr)
rparser = khmer.ReadParser(args.input_sequence_filename)
threads = []
print('consuming input, round 2 --',
args.input_sequence_filename, file=sys.stderr)
for _ in range(args.threads):
thread = \
threading.Thread(
target=__do_abundance_dist__,
args=(rparser, )
)
threads.append(thread)
thread.start()
for thread in threads:
thread.join()
assert len(abundance_lists) == args.threads, len(abundance_lists)
abundance = {}
for abundance_list in abundance_lists:
for i, count in enumerate(abundance_list):
abundance[i] = abundance.get(i, 0) + count
total = sum(abundance.values())
if 0 == total:
print("ERROR: abundance distribution is uniformly zero; "
"nothing to report.", file=sys.stderr)
print(
"\tPlease verify that the input files are valid.", file=sys.stderr)
sys.exit(1)
sofar = 0
for _, i in sorted(abundance.items()):
if i == 0 and not args.output_zero:
continue
sofar += i
frac = sofar / float(total)
if args.csv:
hist_fp_csv.writerow([_, i, sofar, round(frac, 3)])
else:
print(_, i, sofar, round(frac, 3), file=hist_fp)
if sofar == total:
break
if args.savetable:
print('Saving k-mer counting table ', args.savetable, file=sys.stderr)
print('...saving to', args.savetable, file=sys.stderr)
counting_hash.save(args.savetable)
print('wrote to: ' + args.output_histogram_filename, file=sys.stderr)
def main():
info('load-into-counting.py', ['counting'])
args = get_parser().parse_args()
report_on_config(args)
base = args.output_countingtable_filename
filenames = args.input_sequence_filename
for name in args.input_sequence_filename:
check_file_status(name)
check_space(args.input_sequence_filename)
check_space_for_hashtable(args.n_tables * args.min_tablesize)
print 'Saving k-mer counting table to %s' % base
print 'Loading kmers from sequences in %s' % repr(filenames)
print 'making k-mer counting table'
htable = khmer.new_counting_hash(args.ksize, args.min_tablesize,
args.n_tables, args.n_threads)
htable.set_use_bigcount(args.bigcount)
config = khmer.get_config()
config.set_reads_input_buffer_size(args.n_threads * 64 * 1024)
for index, filename in enumerate(filenames):
rparser = khmer.ReadParser(filename, args.n_threads)
threads = []
print 'consuming input', filename
for _ in xrange(args.n_threads):
cur_thrd = \
threading.Thread(
target=htable.consume_fasta_with_reads_parser,
args=(rparser, )
)
threads.append(cur_thrd)
cur_thrd.start()
for _ in threads:
_.join()
if index > 0 and index % 10 == 0:
check_space_for_hashtable(args.n_tables * args.min_tablesize)
print 'mid-save', base
htable.save(base)
open(base + '.info', 'w').write('through %s' % filename)
print 'saving', base
htable.save(base)
info_fp = open(base + '.info', 'w')
info_fp.write('through end: %s\n' % filename)
# Change 0.2 only if you really grok it. HINT: You don't.
fp_rate = khmer.calc_expected_collisions(htable)
print 'fp rate estimated to be %1.3f' % fp_rate
print >> info_fp, 'fp rate estimated to be %1.3f' % fp_rate
if fp_rate > 0.20:
print >> sys.stderr, "**"
print >> sys.stderr, ("** ERROR: the k-mer counting table is too small"
" this data set. Increase tablesize/# tables.")
print >> sys.stderr, "**"
sys.exit(1)
print 'DONE.'
def main():
parser = sanitize_help(get_parser())
args = parser.parse_args()
if not args.quiet:
info('trim-low-abund.py', ['streaming'])
configure_logging(args.quiet)
###
if len(set(args.input_filenames)) != len(args.input_filenames):
log_error("Error: Cannot input the same filename multiple times.")
sys.exit(1)
if args.trim_at_coverage != DEFAULT_TRIM_AT_COVERAGE and \
not args.variable_coverage:
log_error("Error: --trim-at-coverage/-Z given, but "
"--variable-coverage/-V not specified.")
sys.exit(1)
if args.diginorm_coverage != DEFAULT_DIGINORM_COVERAGE and \
not args.diginorm:
log_error("Error: --diginorm-coverage given, but "
"--diginorm not specified.")
sys.exit(1)
if args.diginorm and args.single_pass:
log_error("Error: --diginorm and --single-pass are incompatible!\n"
"You probably want to use normalize-by-median.py instead.")
sys.exit(1)
###
report_on_config(args)
check_valid_file_exists(args.input_filenames)
check_space(args.input_filenames, args.force)
if args.savegraph:
graphsize = calculate_graphsize(args, 'countgraph')
check_space_for_graph(args.savegraph, graphsize, args.force)
if ('-' in args.input_filenames or '/dev/stdin' in args.input_filenames) \
and not args.output:
log_error("Accepting input from stdin; output filename must "
"be provided with -o.")
sys.exit(1)
if args.loadgraph:
log_info('loading countgraph from {graph}', graph=args.loadgraph)
ct = khmer.load_countgraph(args.loadgraph)
else:
log_info('making countgraph')
ct = khmer_args.create_countgraph(args)
K = ct.ksize()
tempdir = tempfile.mkdtemp('khmer', 'tmp', args.tempdir)
log_info(
'created temporary directory {temp};\n'
'use -T to change location',
temp=tempdir)
trimmer = Trimmer(ct, not args.variable_coverage, args.cutoff,
args.trim_at_coverage)
if args.diginorm:
trimmer.set_diginorm(args.diginorm_coverage)
# ### FIRST PASS ###
save_pass2_total = 0
written_bp = 0
written_reads = 0
# only create the file writer once if outfp is specified; otherwise,
# create it for each file.
if args.output:
trimfp = get_file_writer(args.output, args.gzip, args.bzip)
pass2list = []
for filename in args.input_filenames:
# figure out temporary filename for 2nd pass
pass2filename = os.path.basename(filename) + '.pass2'
pass2filename = os.path.join(tempdir, pass2filename)
pass2fp = open(pass2filename, 'w')
# construct output filenames
if args.output is None:
# note: this will be saved in trimfp.
outfp = open(os.path.basename(filename) + '.abundtrim', 'wb')
# get file handle w/gzip, bzip
trimfp = get_file_writer(outfp, args.gzip, args.bzip)
# record all this info
pass2list.append((filename, pass2filename, trimfp))
# input file stuff: get a broken_paired reader.
screed_iter = screed.open(filename)
paired_iter = broken_paired_reader(screed_iter,
min_length=K,
force_single=args.ignore_pairs)
# main loop through the file.
n_start = trimmer.n_reads
save_start = trimmer.n_saved
watermark = REPORT_EVERY_N_READS
for read in trimmer.pass1(paired_iter, pass2fp):
if (trimmer.n_reads - n_start) > watermark:
log_info(
"... {filename} {n_saved} {n_reads} {n_bp} "
"{w_reads} {w_bp}",
filename=filename,
n_saved=trimmer.n_saved,
n_reads=trimmer.n_reads,
n_bp=trimmer.n_bp,
w_reads=written_reads,
w_bp=written_bp)
watermark += REPORT_EVERY_N_READS
# write out the trimmed/etc sequences that AREN'T going to be
# revisited in a 2nd pass.
write_record(read, trimfp)
written_bp += len(read)
written_reads += 1
pass2fp.close()
log_info("{filename}: kept aside {kept} of {total} from first pass",
filename=filename,
kept=trimmer.n_saved - save_start,
total=trimmer.n_reads - n_start)
# first pass goes across all the data, so record relevant stats...
n_reads = trimmer.n_reads
n_bp = trimmer.n_bp
n_skipped = trimmer.n_skipped
bp_skipped = trimmer.bp_skipped
save_pass2_total = trimmer.n_saved
# ### SECOND PASS. ###
# nothing should have been skipped yet!
assert trimmer.n_skipped == 0
assert trimmer.bp_skipped == 0
if args.single_pass:
pass2list = []
# go back through all the files again.
for _, pass2filename, trimfp in pass2list:
log_info('second pass: looking at sequences kept aside in {pass2}',
pass2=pass2filename)
# note that for this second pass, we don't care about paired
# reads - they will be output in the same order they're read in,
# so pairs will stay together if not orphaned. This is in contrast
# to the first loop. Hence, force_single=True below.
screed_iter = screed.open(pass2filename, parse_description=False)
paired_iter = broken_paired_reader(screed_iter,
min_length=K,
force_single=True)
watermark = REPORT_EVERY_N_READS
for read in trimmer.pass2(paired_iter):
if (trimmer.n_reads - n_start) > watermark:
log_info('... x 2 {a} {b} {c} {d} {e} {f} {g}',
a=trimmer.n_reads - n_start,
b=pass2filename,
c=trimmer.n_saved,
d=trimmer.n_reads,
e=trimmer.n_bp,
f=written_reads,
g=written_bp)
watermark += REPORT_EVERY_N_READS
write_record(read, trimfp)
written_reads += 1
written_bp += len(read)
log_info('removing {pass2}', pass2=pass2filename)
os.unlink(pass2filename)
# if we created our own trimfps, close 'em.
if not args.output:
trimfp.close()
log_info('removing temp directory & contents ({temp})', temp=tempdir)
shutil.rmtree(tempdir)
trimmed_reads = trimmer.trimmed_reads
n_passes = 1.0 + (float(save_pass2_total) / n_reads)
percent_reads_trimmed = float(trimmed_reads + (n_reads - written_reads)) /\
n_reads * 100.0
log_info('read {read} reads, {bp} bp', read=n_reads, bp=n_bp)
log_info('wrote {wr} reads, {wbp} bp', wr=written_reads, wbp=written_bp)
log_info('looked at {st} reads twice ({np:.2f} passes)',
st=save_pass2_total,
np=n_passes)
log_info('removed {r} reads and trimmed {t} reads ({p:.2f}%)',
r=n_reads - written_reads,
t=trimmed_reads,
p=percent_reads_trimmed)
log_info('trimmed or removed {p:.2f}%% of bases ({bp} total)',
p=(1 - (written_bp / float(n_bp))) * 100.0,
bp=n_bp - written_bp)
if args.variable_coverage:
percent_reads_hicov = 100.0 * float(n_reads - n_skipped) / n_reads
log_info('{n} reads were high coverage ({p:.2f}%);',
n=n_reads - n_skipped,
p=percent_reads_hicov)
log_info('skipped {r} reads/{bp} bases because of low coverage',
r=n_skipped,
bp=bp_skipped)
fp_rate = \
khmer.calc_expected_collisions(ct, args.force, max_false_pos=.8)
# for max_false_pos see Zhang et al., http://arxiv.org/abs/1309.2975
log_info('fp rate estimated to be {fpr:1.3f}', fpr=fp_rate)
log_info('output in *.abundtrim')
if args.savegraph:
log_info("Saving k-mer countgraph to {graph}", graph=args.savegraph)
ct.save(args.savegraph)
def main(): # pylint: disable=too-many-locals,too-many-statements
info('do-partition.py', ['graph'])
args = get_parser().parse_args()
report_on_config(args, hashtype='hashbits')
for infile in args.input_filenames:
check_file_status(infile)
check_space(args.input_filenames)
print 'Saving k-mer presence table to %s' % args.graphbase
print 'Loading kmers from sequences in %s' % repr(args.input_filenames)
print '--'
print 'SUBSET SIZE', args.subset_size
print 'N THREADS', args.n_threads
print '--'
# load-graph
print 'making k-mer presence table'
htable = khmer.new_hashbits(args.ksize, args.min_tablesize, args.n_tables)
for _, filename in enumerate(args.input_filenames):
print 'consuming input', filename
htable.consume_fasta_and_tag(filename)
fp_rate = khmer.calc_expected_collisions(htable)
print 'fp rate estimated to be %1.3f' % fp_rate
if fp_rate > 0.15: # 0.18 is ACTUAL MAX. Do not change.
print >> sys.stderr, "**"
print >> sys.stderr, ("** ERROR: the graph structure is too small for"
" this data set. Increase k-mer presence table "
"size/num of tables.")
print >> sys.stderr, "**"
sys.exit(1)
# partition-graph
# do we want to exhaustively traverse the graph?
stop_big_traversals = args.no_big_traverse
if stop_big_traversals:
print '** This script brakes for lumps: stop_big_traversals is true.'
else:
print '** Traverse all the things: stop_big_traversals is false.'
#
# now, partition!
#
# divide the tags up into subsets
divvy = htable.divide_tags_into_subsets(int(args.subset_size))
n_subsets = len(divvy)
divvy.append(0)
# build a queue of tasks:
worker_q = Queue.Queue()
# break up the subsets into a list of worker tasks
for _ in range(0, n_subsets):
start = divvy[_]
end = divvy[_ + 1]
worker_q.put((htable, _, start, end))
print 'enqueued %d subset tasks' % n_subsets
open('%s.info' % args.graphbase, 'w').write('%d subsets total\n'
% (n_subsets))
if n_subsets < args.n_threads:
args.n_threads = n_subsets
# start threads!
print 'starting %d threads' % args.n_threads
print '---'
threads = []
for _ in range(args.n_threads):
cur_thread = threading.Thread(target=worker,
args=(worker_q, args.graphbase,
stop_big_traversals))
threads.append(cur_thread)
cur_thread.start()
print 'done starting threads'
# wait for threads
for _ in threads:
_.join()
print '---'
print 'done making subsets! see %s.subset.*.pmap' % (args.graphbase,)
# merge-partitions
pmap_files = glob.glob(args.graphbase + '.subset.*.pmap')
print 'loading %d pmap files (first one: %s)' % (len(pmap_files),
pmap_files[0])
htable = khmer.new_hashbits(args.ksize, 1, 1)
for pmap_file in pmap_files:
print 'merging', pmap_file
htable.merge_subset_from_disk(pmap_file)
if args.remove_subsets:
print 'removing pmap files'
for pmap_file in pmap_files:
os.unlink(pmap_file)
# annotate-partitions
for infile in args.input_filenames:
print 'outputting partitions for', infile
outfile = os.path.basename(infile) + '.part'
part_count = htable.output_partitions(infile, outfile)
print 'output %d partitions for %s' % (part_count, infile)
print 'partitions are in', outfile
def main():
#info('sweep-files.py', ['sweep'])
parser = get_parser()
args = parser.parse_args()
if args.max_tablesize < MIN_HSIZE:
args.max_tablesize = MIN_HSIZE
if args.ksize < MIN_KSIZE:
args.ksize = MIN_KSIZE
report_on_config(args, hashtype='nodegraph')
K = args.ksize
HT_SIZE = args.max_tablesize
N_HT = args.n_tables
traversal_range = args.traversal_range
outputs = {}
# Consume the database files and assign each a unique label in the
# de Bruin graph; open a file and output queue for each file as well.
ht = khmer.LabelHash(K, HT_SIZE, N_HT)
try:
print('consuming and labeling input sequences...', file=sys.stderr)
for i, dbfile in enumerate(args.db):
name = args.output_prefix + os.path.basename(dbfile)
outfp = open(os.path.join(args.outdir, name) + '.sweep', 'wb')
outq = IODeque(args.max_queue_size, outfp)
outputs[i] = outq
for n, record in enumerate(screed.open(dbfile)):
if n % 50000 == 0:
print('...consumed {n} sequences...'.format(n=n),
file=sys.stderr)
ht.consume_sequence_and_tag_with_labels(record.sequence, i)
except (IOError, OSError) as e:
print('!! ERROR: !!', e, file=sys.stderr)
print('...error setting up outputs. exiting...', file=sys.stderr)
print('done consuming input sequence. \
added {t} tags and {l} labels...' \
.format(t=ht.n_tags(), l=ht.n_labels()), file=sys.stderr)
n_orphaned = 0
n_labeled = 0
n_mlabeled = 0
# Iterate through all the reads and check for the labels with which they
# intersect. Queue to the corresponding label when found.
for read_file in args.query:
print('** sweeping {read_file} for labels...'.format(
read_file=read_file),
file=sys.stderr)
try:
read_fp = screed.open(read_file)
except IOError as error:
print('!! ERROR: !!', error, file=sys.stderr)
print('*** Could not open {fn}, skipping...'.format(fn=read_file),
file=sys.stderr)
else:
for n, record in enumerate(read_fp):
if n % 50000 == 0 and n > 0:
print('\tswept {n} reads [{nc} labeled, {no} orphaned]' \
.format(n=n, nc=n_labeled,
no=n_orphaned), file=sys.stderr)
seq = record.sequence
try:
labels = ht.sweep_label_neighborhood(seq, traversal_range)
except ValueError as e:
# sweep_label_neighborhood throws a ValueError when
# len(seq) < K. just catch it and move on.
pass
else:
if labels:
n_labeled += 1
if len(labels) > 1:
n_mlabeled += 1
for label in labels:
outputs[label].append(record)
else:
n_orphaned += 1
print('** End of file {fn}...'.format(fn=read_file),
file=sys.stderr)
read_fp.close()
# gotta output anything left in the buffers at the end!
print('** End of run...', file=sys.stderr)
for q in list(outputs.values()):
q.clear()
print('swept {n_reads}...'.format(n_reads=n_labeled + n_orphaned),
file=sys.stderr)
print('...with {nc} labeled and {no} orphaned'.format(nc=n_labeled,
no=n_orphaned),
file=sys.stderr)
print('...and {nmc} multilabeled'.format(nmc=n_mlabeled), file=sys.stderr)
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_fasta_with_reads_parser,
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:
print((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)
def main():
info('filter-abund-single.py', ['counting', 'SeqAn'])
args = get_parser().parse_args()
check_input_files(args.datafile, args.force)
check_space([args.datafile], args.force)
if args.savetable:
check_space_for_hashtable(args, 'countgraph', args.force)
report_on_config(args)
print('making countgraph', file=sys.stderr)
htable = khmer_args.create_countgraph(args)
# first, load reads into hash table
rparser = khmer.ReadParser(args.datafile)
threads = []
print('consuming input, round 1 --', args.datafile, file=sys.stderr)
for _ in range(args.threads):
cur_thread = \
threading.Thread(
target=htable.consume_fasta_with_reads_parser,
args=(rparser, )
)
threads.append(cur_thread)
cur_thread.start()
for _ in threads:
_.join()
if args.report_total_kmers:
print('Total number of unique k-mers: {0}'.format(
htable.n_unique_kmers()), file=sys.stderr)
fp_rate = khmer.calc_expected_collisions(htable, args.force)
print('fp rate estimated to be %1.3f' % fp_rate, file=sys.stderr)
# now, trim.
# the filtering function.
def process_fn(record):
name = record.name
seq = record.sequence
seqN = seq.replace('N', 'A')
_, trim_at = htable.trim_on_abundance(seqN, args.cutoff)
if trim_at >= args.ksize:
# be sure to not to change the 'N's in the trimmed sequence -
# so, return 'seq' and not 'seqN'.
return name, seq[:trim_at]
return None, None
# the filtering loop
print('filtering', args.datafile, file=sys.stderr)
outfile = os.path.basename(args.datafile) + '.abundfilt'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(args.datafile), outfp)
print('output in', outfile, file=sys.stderr)
if args.savetable:
print('Saving k-mer counting table filename',
args.savetable, file=sys.stderr)
print('...saving to', args.savetable, file=sys.stderr)
htable.save(args.savetable)
print('wrote to: ', outfile, file=sys.stderr)
def main():
info("sweep-reads-buffered.py", ["sweep"])
parser = sanitize_epilog(get_parser())
args = parser.parse_args()
if args.max_tablesize < MAX_HSIZE:
args.max_tablesize = MAX_HSIZE
if args.ksize < MIN_KSIZE:
args.ksize = MIN_KSIZE
report_on_config(args, graphtype="nodegraph")
K = args.ksize
HT_SIZE = args.max_tablesize
N_HT = args.n_tables
traversal_range = args.traversal_range
input_fastp = args.input_fastp
if not args.outdir:
outdir = os.path.dirname(input_fastp)
else:
outdir = args.outdir
max_buffers = args.max_buffers
output_pref = args.output_prefix
buf_size = args.buffer_size
max_reads = args.max_reads
check_input_files(args.input_fastp, args.force)
check_valid_file_exists(args.input_files)
all_input_files = [input_fastp]
all_input_files.extend(args.input_files)
# Check disk space availability
check_space(all_input_files, args.force)
# figure out input file type (FA/FQ) -- based on first file
ix = iter(screed.open(args.input_files[0]))
record = next(ix)
del ix
extension = "fa"
if hasattr(record, "quality"): # fastq!
extension = "fq"
output_buffer = ReadBufferManager(max_buffers, max_reads, buf_size, output_pref, outdir, extension)
# consume the partitioned fasta with which to label the graph
ht = khmer.GraphLabels(K, HT_SIZE, N_HT)
try:
print("consuming input sequences...", file=sys.stderr)
if args.label_by_pid:
print("...labeling by partition id (pid)", file=sys.stderr)
ht.consume_partitioned_fasta_and_tag_with_labels(input_fastp)
elif args.label_by_seq:
print("...labeling by sequence", file=sys.stderr)
for n, record in enumerate(screed.open(input_fastp)):
if n % 50000 == 0:
print("...consumed {n} sequences...".format(n=n), file=sys.stderr)
ht.consume_sequence_and_tag_with_labels(record.sequence, n)
else:
print("...labeling to create groups of size {s}".format(s=args.group_size), file=sys.stderr)
label = -1
g = 0
try:
outfp = open("{pref}_base_{g}.{ext}".format(pref=output_pref, g=g, ext=extension), "wb")
for n, record in enumerate(screed.open(input_fastp)):
if n % args.group_size == 0:
label += 1
if label > g:
g = label
outfp = open("{pref}_base_{g}.{ext}".format(pref=output_pref, g=g, ext=extension), "wb")
if n % 50000 == 0:
print("...consumed {n} sequences...".format(n=n), file=sys.stderr)
ht.consume_sequence_and_tag_with_labels(record.sequence, label)
write_record(record, outfp)
except (IOError, OSError) as e:
print("!! ERROR !!", e, file=sys.stderr)
print("...error splitting input. exiting...", file=sys.stderr)
except (IOError, OSError) as e:
print("!! ERROR: !!", e, file=sys.stderr)
print(
"...error consuming \
{i}. exiting...".format(
i=input_fastp
),
file=sys.stderr,
)
print(
"done consuming input sequence. \
added {t} tags and {l} \
labels...".format(
t=ht.graph.n_tags(), l=ht.n_labels()
)
)
label_dict = defaultdict(int)
label_number_dist = []
n_orphaned = 0
n_labeled = 0
n_mlabeled = 0
total_t = time.clock()
start_t = time.clock()
for read_file in args.input_files:
print("** sweeping {read_file} for labels...".format(read_file=read_file), file=sys.stderr)
file_t = 0.0
try:
read_fp = screed.open(read_file)
except (IOError, OSError) as error:
print("!! ERROR: !!", error, file=sys.stderr)
print("*** Could not open {fn}, skipping...".format(fn=read_file), file=sys.stderr)
else:
for _, record in enumerate(read_fp):
if _ % 50000 == 0:
end_t = time.clock()
batch_t = end_t - start_t
file_t += batch_t
print(
"\tswept {n} reads [{nc} labeled, \
{no} orphaned] \
** {sec}s ({sect}s total)".format(
n=_, nc=n_labeled, no=n_orphaned, sec=batch_t, sect=file_t
),
file=sys.stderr,
)
start_t = time.clock()
seq = record.sequence
name = record.name
try:
labels = ht.sweep_label_neighborhood(seq, traversal_range)
except ValueError as e:
pass
else:
if hasattr(record, "quality"):
seq_str = fmt_fastq(name, seq, record.quality, labels)
else:
seq_str = fmt_fasta(name, seq, labels)
label_number_dist.append(len(labels))
if labels:
n_labeled += 1
if len(labels) > 1:
output_buffer.queue(seq_str, "multi")
n_mlabeled += 1
label_dict["multi"] += 1
else:
output_buffer.queue(seq_str, labels[0])
label_dict[labels[0]] += 1
else:
n_orphaned += 1
output_buffer.queue(seq_str, "orphaned")
label_dict["orphaned"] += 1
print("** End of file {fn}...".format(fn=read_file), file=sys.stderr)
output_buffer.flush_all()
read_fp.close()
# gotta output anything left in the buffers at the end!
print("** End of run...", file=sys.stderr)
output_buffer.flush_all()
total_t = time.clock() - total_t
if output_buffer.num_write_errors > 0 or output_buffer.num_file_errors > 0:
print("! WARNING: Sweep finished with errors !", file=sys.stderr)
print("** {writee} reads not written".format(writee=output_buffer.num_write_errors), file=sys.stderr)
print("** {filee} errors opening files".format(filee=output_buffer.num_file_errors), file=sys.stderr)
print("swept {n_reads} for labels...".format(n_reads=n_labeled + n_orphaned), file=sys.stderr)
print("...with {nc} labeled and {no} orphaned".format(nc=n_labeled, no=n_orphaned), file=sys.stderr)
print("...and {nmc} multilabeled".format(nmc=n_mlabeled), file=sys.stderr)
print("** outputting label number distribution...", file=sys.stderr)
fn = os.path.join(outdir, "{pref}.dist.txt".format(pref=output_pref))
with open(fn, "w", encoding="utf-8") as outfp:
for nc in label_number_dist:
outfp.write("{nc}\n".format(nc=nc))
fn = os.path.join(outdir, "{pref}.counts.csv".format(pref=output_pref))
print("** outputting label read counts...", file=sys.stderr)
with open(fn, "w", encoding="utf-8") as outfp:
for k in label_dict:
outfp.write("{l},{c}\n".format(l=k, c=label_dict[k]))
def main():
info('correct-reads.py', ['streaming'])
parser = get_parser()
args = parser.parse_args()
###
if len(set(args.input_filenames)) != len(args.input_filenames):
print >>sys.stderr, \
"Error: Cannot input the same filename multiple times."
sys.exit(1)
###
report_on_config(args)
check_valid_file_exists(args.input_filenames)
check_space(args.input_filenames, args.force)
if args.savegraph:
check_space_for_graph(
args.n_tables * args.min_tablesize, args.force)
K = args.ksize
CUTOFF = args.cutoff
NORMALIZE_LIMIT = args.normalize_to
if args.loadgraph:
print >>sys.stderr, 'loading k-mer countgraph from', args.loadgraph
ct = khmer.load_countgraph(args.loadgraph)
else:
print >>sys.stderr, 'making k-mer countgraph'
ct = khmer.new_countgraph(K, args.min_tablesize, args.n_tables)
tempdir = tempfile.mkdtemp('khmer', 'tmp', args.tempdir)
print >>sys.stderr, 'created temporary directory %s; ' \
'use -T to change location' % tempdir
aligner = khmer.ReadAligner(ct, args.cutoff, args.bits_theta)
# ### FIRST PASS ###
save_pass2_total = 0
n_bp = 0
n_reads = 0
written_bp = 0
written_reads = 0
corrected_reads = 0
pass2list = []
for filename in args.input_filenames:
pass2filename = os.path.basename(filename) + '.pass2'
pass2filename = os.path.join(tempdir, pass2filename)
if args.out is None:
corrfp = open(os.path.basename(filename) + '.corr', 'w')
else:
corrfp = args.out
pass2list.append((filename, pass2filename, corrfp))
screed_iter = screed.open(filename, parse_description=False)
pass2fp = open(pass2filename, 'w')
save_pass2 = 0
n = 0
paired_iter = broken_paired_reader(screed_iter, min_length=K,
force_single=args.ignore_pairs)
for n, is_pair, read1, read2 in paired_iter:
if n % 10000 == 0:
print >>sys.stderr, '...', n, filename, save_pass2, \
n_reads, n_bp, written_reads, written_bp
# we want to track paired reads here, to make sure that pairs
# are not split between first pass and second pass.
if is_pair:
n_reads += 2
n_bp += len(read1.sequence) + len(read2.sequence)
seq1 = read1.sequence.replace('N', 'A')
seq2 = read2.sequence.replace('N', 'A')
med1, _, _ = ct.get_median_count(seq1)
med2, _, _ = ct.get_median_count(seq2)
if med1 < NORMALIZE_LIMIT or med2 < NORMALIZE_LIMIT:
ct.consume(seq1)
ct.consume(seq2)
write_record_pair(read1, read2, pass2fp)
save_pass2 += 2
else:
is_aligned, new_seq1 = correct_sequence(aligner, seq1)
if is_aligned:
if new_seq1 != read1.sequence:
corrected_reads += 1
read1.sequence = new_seq1
if hasattr(read1, 'quality'):
fix_quality(read1)
is_aligned, new_seq2 = correct_sequence(aligner, seq2)
if is_aligned:
if new_seq2 != read2.sequence:
corrected_reads += 1
read2.sequence = new_seq2
if hasattr(read2, 'quality'):
fix_quality(read2)
write_record_pair(read1, read2, corrfp)
written_reads += 2
written_bp += len(read1)
written_bp += len(read2)
else:
n_reads += 1
n_bp += len(read1.sequence)
seq = read1.sequence.replace('N', 'A')
med, _, _ = ct.get_median_count(seq)
# has this portion of the graph saturated? if not,
# consume & save => pass2.
if med < NORMALIZE_LIMIT:
ct.consume(seq)
write_record(read1, pass2fp)
save_pass2 += 1
else: # trim!!
is_aligned, new_seq = correct_sequence(aligner, seq)
if is_aligned:
if new_seq != read1.sequence:
corrected_reads += 1
read1.sequence = new_seq
if hasattr(read1, 'quality'):
fix_quality(read1)
write_record(read1, corrfp)
written_reads += 1
written_bp += len(new_seq)
pass2fp.close()
print >>sys.stderr, '%s: kept aside %d of %d from first pass, in %s' \
% (filename, save_pass2, n, filename)
save_pass2_total += save_pass2
# ### SECOND PASS. ###
skipped_n = 0
skipped_bp = 0
for _, pass2filename, corrfp in pass2list:
print >>sys.stderr, ('second pass: looking at sequences kept aside '
'in %s') % pass2filename
# note that for this second pass, we don't care about paired
# reads - they will be output in the same order they're read in,
# so pairs will stay together if not orphaned. This is in contrast
# to the first loop.
for n, read in enumerate(screed.open(pass2filename,
parse_description=False)):
if n % 10000 == 0:
print >>sys.stderr, '... x 2', n, pass2filename, \
written_reads, written_bp
seq = read.sequence.replace('N', 'A')
med, _, _ = ct.get_median_count(seq)
# do we retain low-abundance components unchanged?
if med < NORMALIZE_LIMIT and args.variable_coverage:
write_record(read, corrfp)
written_reads += 1
written_bp += len(read.sequence)
skipped_n += 1
skipped_bp += len(read.sequence)
# otherwise, examine/correct.
else: # med >= NORMALIZE LIMIT or not args.variable_coverage
is_aligned, new_seq = correct_sequence(aligner, seq)
if is_aligned:
if new_seq != read.sequence:
corrected_reads += 1
read.sequence = new_seq
if hasattr(read, 'quality'):
fix_quality(read)
write_record(read, corrfp)
written_reads += 1
written_bp += len(new_seq)
print >>sys.stderr, 'removing %s' % pass2filename
os.unlink(pass2filename)
print >>sys.stderr, 'removing temp directory & contents (%s)' % tempdir
shutil.rmtree(tempdir)
n_passes = 1.0 + (float(save_pass2_total) / n_reads)
percent_reads_corrected = float(corrected_reads +
(n_reads - written_reads)) /\
n_reads * 100.0
print >>sys.stderr, 'read %d reads, %d bp' % (n_reads, n_bp,)
print >>sys.stderr, 'wrote %d reads, %d bp' % (written_reads, written_bp,)
print >>sys.stderr, 'looked at %d reads twice (%.2f passes)' % \
(save_pass2_total, n_passes)
print >>sys.stderr, 'removed %d reads and corrected %d reads (%.2f%%)' % \
(n_reads - written_reads, corrected_reads, percent_reads_corrected)
print >>sys.stderr, 'removed %.2f%% of bases (%d total)' % \
((1 - (written_bp / float(n_bp))) * 100.0, n_bp - written_bp)
if args.variable_coverage:
percent_reads_hicov = 100.0 * float(n_reads - skipped_n) / n_reads
print >>sys.stderr, '%d reads were high coverage (%.2f%%);' % \
(n_reads - skipped_n, percent_reads_hicov)
print >>sys.stderr, ('skipped %d reads/%d bases because of low'
'coverage') % (skipped_n, skipped_bp)
fp_rate = \
khmer.calc_expected_collisions(ct, args.force, max_false_pos=.8)
# for max_false_pos see Zhang et al., http://arxiv.org/abs/1309.2975
print >>sys.stderr, \
'fp rate estimated to be {fpr:1.3f}'.format(fpr=fp_rate)
print >>sys.stderr, 'output in *.corr'
if args.savegraph:
print >>sys.stderr, "Saving k-mer countgraph to", args.savegraph
ct.save(args.savegraph)
def main():
info('collect-reads.py', ['counting'])
args = get_parser().parse_args()
report_on_config(args)
base = args.output_countingtable_filename
filenames = args.input_sequence_filename
for name in args.input_sequence_filename:
check_input_files(name, False)
check_space(args.input_sequence_filename, False)
check_space_for_hashtable(args.n_tables * args.min_tablesize, False)
print 'Saving k-mer counting table to %s' % base
print 'Loading sequences from %s' % repr(filenames)
if args.output:
print 'Outputting sequences to', args.output
print 'making k-mer counting table'
htable = khmer.new_counting_hash(args.ksize, args.min_tablesize)
htable.set_use_bigcount(args.bigcount)
total_coverage = 0.
n = 0
for index, filename in enumerate(filenames):
for record in screed.open(filename):
seq = record.sequence.upper()
if 'N' in seq:
seq = seq.replace('N', 'A')
try:
med, _, _ = htable.get_median_count(seq)
except ValueError:
continue
total_coverage += med
n += 1
if total_coverage / float(n) > args.coverage:
print 'reached target average coverage:', \
total_coverage / float(n)
break
htable.consume(seq)
if args.output:
args.output.write(output_single(record))
if n % 100000 == 0:
print '...', index, filename, n, total_coverage / float(n)
if total_coverage / float(n) > args.coverage:
break
print 'Collected %d reads' % (n, )
if args.report_total_kmers:
print >> sys.stderr, 'Total number of k-mers: {0}'.format(
htable.n_occupied())
print 'saving', base
htable.save(base)
info_fp = open(base + '.info', 'w')
info_fp.write('through end: %s\n' % filenames[-1])
# Change 0.2 only if you really grok it. HINT: You don't.
fp_rate = khmer.calc_expected_collisions(htable,
args.force,
max_false_pos=.2)
print 'fp rate estimated to be %1.3f' % fp_rate
print >> info_fp, 'fp rate estimated to be %1.3f' % fp_rate
print 'DONE.'
def main():
info('load-into-counting.py', ['counting'])
args = get_parser().parse_args()
report_on_config(args)
base = args.output_countingtable_filename
filenames = args.input_sequence_filename
for name in args.input_sequence_filename:
check_file_status(name)
check_space(args.input_sequence_filename)
check_space_for_hashtable(args.n_tables * args.min_tablesize)
print 'Saving k-mer counting table to %s' % base
print 'Loading kmers from sequences in %s' % repr(filenames)
print 'making k-mer counting table'
htable = khmer.new_counting_hash(args.ksize, args.min_tablesize,
args.n_tables, args.n_threads)
htable.set_use_bigcount(args.bigcount)
config = khmer.get_config()
config.set_reads_input_buffer_size(args.n_threads * 64 * 1024)
for index, filename in enumerate(filenames):
rparser = khmer.ReadParser(filename, args.n_threads)
threads = []
print 'consuming input', filename
for _ in xrange(args.n_threads):
cur_thrd = \
threading.Thread(
target=htable.consume_fasta_with_reads_parser,
args=(rparser, )
)
threads.append(cur_thrd)
cur_thrd.start()
for _ in threads:
_.join()
if index > 0 and index % 10 == 0:
check_space_for_hashtable(args.n_tables * args.min_tablesize)
print 'mid-save', base
htable.save(base)
open(base + '.info', 'w').write('through %s' % filename)
print 'saving', base
htable.save(base)
info_fp = open(base + '.info', 'w')
info_fp.write('through end: %s\n' % filename)
# Change 0.2 only if you really grok it. HINT: You don't.
fp_rate = khmer.calc_expected_collisions(htable)
print 'fp rate estimated to be %1.3f' % fp_rate
print >> info_fp, 'fp rate estimated to be %1.3f' % fp_rate
if fp_rate > 0.20:
print >> sys.stderr, "**"
print >> sys.stderr, ("** ERROR: the k-mer counting table is too small"
" this data set. Increase tablesize/# tables.")
print >> sys.stderr, "**"
sys.exit(1)
print 'DONE.'
def main(): # pylint: disable=too-many-locals,too-many-statements
args = sanitize_help(get_parser()).parse_args()
report_on_config(args, graphtype='nodegraph')
for infile in args.input_filenames:
check_input_files(infile, args.force)
check_space(args.input_filenames, args.force)
print('Saving k-mer nodegraph to %s' % args.graphbase, file=sys.stderr)
print('Loading kmers from sequences in %s' % repr(args.input_filenames),
file=sys.stderr)
print('--', file=sys.stderr)
print('SUBSET SIZE', args.subset_size, file=sys.stderr)
print('N THREADS', args.threads, file=sys.stderr)
print('--', file=sys.stderr)
# load-graph.py
print('making nodegraph', file=sys.stderr)
nodegraph = khmer_args.create_nodegraph(args)
for _, filename in enumerate(args.input_filenames):
print('consuming input', filename, file=sys.stderr)
nodegraph.consume_seqfile_and_tag(filename)
# 0.18 is ACTUAL MAX. Do not change.
fp_rate = \
khmer.calc_expected_collisions(
nodegraph, args.force, max_false_pos=.15)
print('fp rate estimated to be %1.3f' % fp_rate, file=sys.stderr)
# partition-graph
# do we want to exhaustively traverse the graph?
stop_big_traversals = args.no_big_traverse
if stop_big_traversals:
print('** This script brakes for lumps: ',
'stop_big_traversals is true.',
file=sys.stderr)
else:
print('** Traverse all the things:',
' stop_big_traversals is false.',
file=sys.stderr)
#
# now, partition!
#
# divide the tags up into subsets
divvy = nodegraph.divide_tags_into_subsets(int(args.subset_size))
divvy = list(divvy)
n_subsets = len(divvy)
divvy.append(0)
# build a queue of tasks:
worker_q = queue.Queue()
# break up the subsets into a list of worker tasks
for _ in range(0, n_subsets):
start = divvy[_]
end = divvy[_ + 1]
worker_q.put((nodegraph, _, start, end))
print('enqueued %d subset tasks' % n_subsets, file=sys.stderr)
open('%s.info' % args.graphbase,
'w').write('%d subsets total\n' % (n_subsets))
if n_subsets < args.threads:
args.threads = n_subsets
# start threads!
print('starting %d threads' % args.threads, file=sys.stderr)
print('---', file=sys.stderr)
threads = []
for _ in range(args.threads):
cur_thread = threading.Thread(target=worker,
args=(worker_q, args.graphbase,
stop_big_traversals))
threads.append(cur_thread)
cur_thread.start()
print('done starting threads', file=sys.stderr)
# wait for threads
for _ in threads:
_.join()
print('---', file=sys.stderr)
print('done making subsets! see %s.subset.*.pmap' % (args.graphbase, ),
file=sys.stderr)
# merge-partitions
pmap_files = glob.glob(args.graphbase + '.subset.*.pmap')
print('loading %d pmap files (first one: %s)' %
(len(pmap_files), pmap_files[0]),
file=sys.stderr)
nodegraph = khmer.Nodegraph(args.ksize, 1, 1)
for pmap_file in pmap_files:
print('merging', pmap_file, file=sys.stderr)
nodegraph.merge_subset_from_disk(pmap_file)
if not args.keep_subsets:
print('removing pmap files', file=sys.stderr)
for pmap_file in pmap_files:
os.unlink(pmap_file)
# annotate-partitions
for infile in args.input_filenames:
print('outputting partitions for', infile, file=sys.stderr)
outfile = os.path.basename(infile) + '.part'
part_count = nodegraph.output_partitions(infile, outfile)
print('output %d partitions for %s' % (part_count, infile),
file=sys.stderr)
print('partitions are in', outfile, file=sys.stderr)
def main(): # pylint: disable=too-many-branches,too-many-statements
info('saturate-by-median.py', ['diginorm'])
parser = sanitize_help(get_parser())
args = parser.parse_args()
report_on_config(args)
report_fp = args.report
report_frequency = args.report_frequency
check_valid_file_exists(args.input_filenames)
check_space(args.input_filenames, False)
if args.savegraph:
check_space_for_graph(args, 'countgraph', False)
# list to save error files along with throwing exceptions
if args.force:
corrupt_files = []
if args.loadgraph:
print('loading k-mer countgraph from', args.loadgraph)
htable = khmer.load_countgraph(args.loadgraph)
else:
print('making countgraph')
htable = create_countgraph(args)
total = 0
discarded = 0
for index, input_filename in enumerate(args.input_filenames):
total_acc = 0
discarded_acc = 0
try:
total_acc, discarded_acc = normalize_by_median(input_filename,
htable, args,
report_fp,
report_frequency)
except IOError as err:
handle_error(err, input_filename)
if not args.force:
print("NOTE: This can be overridden using the --force"
" argument", file=sys.stderr)
print('** Exiting!', file=sys.stderr)
sys.exit(1)
else:
print('*** Skipping error file, moving on...', file=sys.stderr)
corrupt_files.append(input_filename)
else:
if total_acc == 0 and discarded_acc == 0:
print('SKIPPED empty file', input_filename)
else:
total += total_acc
discarded += discarded_acc
print('DONE with {inp}; kept {kept} of {total} or {perc:2}%'\
.format(inp=input_filename,
kept=total - discarded, total=total,
perc=int(100. - discarded / float(total) * 100.)))
if args.savegraph:
print('Saving k-mer countgraph through', input_filename)
print('...saving to', args.savegraph)
htable.save(args.savegraph)
# re: threshold, see Zhang et al.,
# http://arxiv.org/abs/1309.2975
fp_rate = khmer.calc_expected_collisions(htable, args.force, max_false_pos=.8)
print('fp rate estimated to be {fpr:1.3f}'.format(fpr=fp_rate))
if args.force and len(corrupt_files) > 0:
print("** WARNING: Finished with errors!", file=sys.stderr)
print("** I/O Errors occurred in the following files:", file=sys.stderr)
print("\t", " ".join(corrupt_files), file=sys.stderr)
def main(): # pylint: disable=too-many-branches,too-many-statements
parser = sanitize_help(get_parser())
args = parser.parse_args()
configure_logging(args.quiet)
report_on_config(args)
report_fp = args.report
force_single = args.force_single
# check for similar filenames
# if we're using a single output file only check for identical filenames
# otherwise, check for identical BASE names as well.
filenames = []
basenames = []
for pathfilename in args.input_filenames:
filenames.append(pathfilename)
if args.single_output_file:
continue # nothing more to worry about
basename = os.path.basename(pathfilename)
if basename in basenames:
log_error('ERROR: Duplicate filename--Cannot handle this!')
log_error('** Exiting!')
sys.exit(1)
basenames.append(basename)
# check that files exist and there is sufficient output disk space.
check_valid_file_exists(args.input_filenames)
check_space(args.input_filenames, args.force)
if args.savegraph is not None:
graphsize = calculate_graphsize(args, 'countgraph')
check_space_for_graph(args.savegraph, graphsize, args.force)
# load or create counting table.
if args.loadgraph:
log_info('loading k-mer countgraph from {graph}', graph=args.loadgraph)
countgraph = khmer.load_countgraph(args.loadgraph)
else:
log_info('making countgraph')
countgraph = khmer_args.create_countgraph(args)
# create an object to handle diginorm of all files
norm = Normalizer(args.cutoff, countgraph)
with_diagnostics = WithDiagnostics(norm, report_fp, args.report_frequency)
# make a list of all filenames and if they're paired or not;
# if we don't know if they're paired, default to allowing but not
# forcing pairing.
files = []
for element in filenames:
files.append([element, args.paired])
if args.unpaired_reads:
files.append([args.unpaired_reads, False])
corrupt_files = []
outfp = None
output_name = None
if args.single_output_file:
outfp = get_file_writer(args.single_output_file, args.gzip, args.bzip)
else:
if '-' in filenames or '/dev/stdin' in filenames:
print(
"Accepting input from stdin; output filename must "
"be provided with '-o'.",
file=sys.stderr)
sys.exit(1)
#
# main loop: iterate over all files given, do diginorm.
#
for filename, require_paired in files:
if not args.single_output_file:
output_name = os.path.basename(filename) + '.keep'
outfp = open(output_name, 'wb')
outfp = get_file_writer(outfp, args.gzip, args.bzip)
# failsafe context manager in case an input file breaks
with catch_io_errors(filename, outfp, args.single_output_file,
args.force, corrupt_files):
screed_iter = clean_input_reads(screed.open(filename))
reader = broken_paired_reader(screed_iter,
min_length=args.ksize,
force_single=force_single,
require_paired=require_paired)
# actually do diginorm
for record in with_diagnostics(reader, filename):
if record is not None:
write_record(record, outfp)
log_info('output in {name}', name=describe_file_handle(outfp))
if not args.single_output_file:
outfp.close()
# finished - print out some diagnostics.
log_info('Total number of unique k-mers: {umers}',
umers=countgraph.n_unique_kmers())
if args.savegraph is not None:
log_info('...saving to {name}', name=args.savegraph)
countgraph.save(args.savegraph)
fp_rate = \
khmer.calc_expected_collisions(countgraph, False, max_false_pos=.8)
# for max_false_pos see Zhang et al., http://arxiv.org/abs/1309.2975
log_info('fp rate estimated to be {fpr:1.3f}', fpr=fp_rate)
if args.force and len(corrupt_files) > 0:
log_error("** WARNING: Finished with errors!")
log_error("** I/O Errors occurred in the following files:")
log_error("\t" + " ".join(corrupt_files))
def main(): # pylint: disable=too-many-locals,too-many-branches
args = sanitize_help(get_parser()).parse_args()
graph_type = 'smallcountgraph' if args.small_count else 'countgraph'
configure_logging(args.quiet)
report_on_config(args, graph_type)
check_input_files(args.input_sequence_filename, args.force)
if args.savegraph is not None:
graphsize = calculate_graphsize(args, graph_type)
check_space_for_graph(args.savegraph, graphsize, args.force)
if (not args.squash_output
and os.path.exists(args.output_histogram_filename)):
log_error('ERROR: {output} exists; not squashing.',
output=args.output_histogram_filename)
sys.exit(1)
else:
hist_fp = open(args.output_histogram_filename, 'w')
hist_fp_csv = csv.writer(hist_fp)
# write headers:
hist_fp_csv.writerow(
['abundance', 'count', 'cumulative', 'cumulative_fraction'])
log_info('making countgraph')
# In case the user specified a maximum memory usage, use 8/(9+eps) of that
# for the countgraph and 1/(9+eps) for the tracking nodegraph
# `eps` is used to account for the memory used by the python interpreter
countgraph = khmer_args.create_countgraph(args, multiplier=8 / (9. + 0.3))
countgraph.set_use_bigcount(args.bigcount)
log_info('building k-mer tracking graph')
tracking = khmer_args.create_matching_nodegraph(countgraph)
log_info('kmer_size: {ksize}', ksize=countgraph.ksize())
log_info('k-mer countgraph sizes: {sizes}', sizes=countgraph.hashsizes())
log_info('outputting to {output}', output=args.output_histogram_filename)
# start loading
rparser = khmer.ReadParser(args.input_sequence_filename)
threads = []
log_info('consuming input, round 1 -- {input}',
input=args.input_sequence_filename)
for _ in range(args.threads):
thread = \
threading.Thread(
target=countgraph.consume_seqfile_with_reads_parser,
args=(rparser, )
)
threads.append(thread)
thread.start()
for thread in threads:
thread.join()
log_info('Total number of unique k-mers: {nk}',
nk=countgraph.n_unique_kmers())
abundance_lists = []
def __do_abundance_dist__(read_parser):
abundances = countgraph.abundance_distribution_with_reads_parser(
read_parser, tracking)
abundance_lists.append(abundances)
log_info('preparing hist from {seqfile}...',
seqfile=args.input_sequence_filename)
rparser = khmer.ReadParser(args.input_sequence_filename)
threads = []
log_info('consuming input, round 2 -- {filename}',
filename=args.input_sequence_filename)
for _ in range(args.threads):
thread = \
threading.Thread(
target=__do_abundance_dist__,
args=(rparser, )
)
threads.append(thread)
thread.start()
for thread in threads:
thread.join()
assert len(abundance_lists) == args.threads, len(abundance_lists)
abundance = {}
for abundance_list in abundance_lists:
for i, count in enumerate(abundance_list):
abundance[i] = abundance.get(i, 0) + count
total = sum(abundance.values())
if 0 == total:
log_error("ERROR: abundance distribution is uniformly zero; "
"nothing to report.")
log_error("\tPlease verify that the input files are valid.")
sys.exit(1)
sofar = 0
for _, i in sorted(abundance.items()):
if i == 0 and not args.output_zero:
continue
sofar += i
frac = sofar / float(total)
hist_fp_csv.writerow([_, i, sofar, round(frac, 3)])
if sofar == total:
break
if args.savegraph is not None:
log_info('Saving k-mer countgraph to {savegraph}',
savegraph=args.savegraph)
countgraph.save(args.savegraph)
log_info('wrote to: {output}', output=args.output_histogram_filename)
def main():
info('sweep-reads-buffered.py', ['sweep'])
parser = get_parser()
args = parser.parse_args()
if args.min_tablesize < MIN_HSIZE:
args.min_tablesize = MIN_HSIZE
if args.ksize < MIN_KSIZE:
args.ksize = MIN_KSIZE
report_on_config(args, hashtype='hashbits')
K = args.ksize
HT_SIZE = args.min_tablesize
N_HT = args.n_tables
traversal_range = args.traversal_range
input_fastp = args.input_fastp
if not args.outdir:
outdir = os.path.dirname(input_fastp)
else:
outdir = args.outdir
max_buffers = args.max_buffers
output_pref = args.output_prefix
buf_size = args.buffer_size
max_reads = args.max_reads
check_file_status(args.input_fastp)
check_valid_file_exists(args.input_files)
all_input_files = [input_fastp]
all_input_files.extend(args.input_files)
# Check disk space availability
check_space(all_input_files)
# figure out input file type (FA/FQ) -- based on first file
ix = iter(screed.open(args.input_files[0]))
record = ix.next()
del ix
extension = 'fa'
if hasattr(record, 'accuracy'): # fastq!
extension = 'fq'
output_buffer = ReadBufferManager(max_buffers, max_reads, buf_size,
output_pref, outdir, extension)
# consume the partitioned fasta with which to label the graph
ht = khmer.LabelHash(K, HT_SIZE, N_HT)
try:
print >> sys.stderr, 'consuming input sequences...'
if args.label_by_pid:
print >> sys.stderr, '...labeling by partition id (pid)'
ht.consume_partitioned_fasta_and_tag_with_labels(input_fastp)
elif args.label_by_seq:
print >> sys.stderr, '...labeling by sequence'
for n, record in enumerate(screed.open(input_fastp)):
if n % 50000 == 0:
print >>sys.stderr, \
'...consumed {n} sequences...'.format(n=n)
ht.consume_sequence_and_tag_with_labels(record.sequence, n)
else:
print >>sys.stderr, \
'...labeling to create groups of size {s}'.format(
s=args.group_size)
label = -1
g = 0
try:
outfp = open(
'{pref}_base_{g}.{ext}'.format(pref=output_pref,
g=g,
ext=extension), 'wb')
for n, record in enumerate(screed.open(input_fastp)):
if n % args.group_size == 0:
label += 1
if label > g:
g = label
outfp = open(
'{pref}_base_{g}.{ext}'.format(
pref=output_pref, g=g, ext=extension),
'wb')
if n % 50000 == 0:
print >>sys.stderr, \
'...consumed {n} sequences...'.format(n=n)
ht.consume_sequence_and_tag_with_labels(
record.sequence, label)
if hasattr(record, 'accuracy'):
outfp.write('@{name}\n{seq}+{accuracy}\n'.format(
name=record.name,
seq=record.sequence,
accuracy=record.accuracy))
else:
outfp.write('>{name}\n{seq}\n'.format(
name=record.name, seq=record.sequence))
except IOError as e:
print >> sys.stderr, '!! ERROR !!', e
print >> sys.stderr, '...error splitting input. exiting...'
except IOError as e:
print >> sys.stderr, '!! ERROR: !!', e
print >> sys.stderr, '...error consuming \
{i}. exiting...'.format(i=input_fastp)
print >> sys.stderr, 'done consuming input sequence. \
added {t} tags and {l} \
labels...'.format(t=ht.n_tags(), l=ht.n_labels())
label_dict = defaultdict(int)
label_number_dist = []
n_orphaned = 0
n_labeled = 0
n_mlabeled = 0
total_t = time.clock()
start_t = time.clock()
for read_file in args.input_files:
print >> sys.stderr, '** sweeping {read_file} for labels...'.format(
read_file=read_file)
file_t = 0.0
try:
read_fp = screed.open(read_file)
except IOError as error:
print >> sys.stderr, '!! ERROR: !!', error
print >> sys.stderr, '*** Could not open {fn}, skipping...'.format(
fn=read_file)
else:
for _, record in enumerate(read_fp):
if _ % 50000 == 0:
end_t = time.clock()
batch_t = end_t - start_t
file_t += batch_t
print >>sys.stderr, '\tswept {n} reads [{nc} labeled, \
{no} orphaned] \
** {sec}s ({sect}s total)' \
.format(n=_, nc=n_labeled,
no=n_orphaned,
sec=batch_t, sect=file_t)
start_t = time.clock()
seq = record.sequence
name = record.name
try:
labels = ht.sweep_label_neighborhood(seq, traversal_range)
except ValueError as e:
pass
else:
if hasattr(record, 'accuracy'):
seq_str = fmt_fastq(name, seq, record.accuracy, labels)
else:
seq_str = fmt_fasta(name, seq, labels)
label_number_dist.append(len(labels))
if labels:
n_labeled += 1
if len(labels) > 1:
output_buffer.queue(seq_str, 'multi')
n_mlabeled += 1
label_dict['multi'] += 1
else:
output_buffer.queue(seq_str, labels[0])
label_dict[labels[0]] += 1
else:
n_orphaned += 1
output_buffer.queue(seq_str, 'orphaned')
label_dict['orphaned'] += 1
print >> sys.stderr, '** End of file {fn}...'.format(fn=read_file)
output_buffer.flush_all()
read_fp.close()
# gotta output anything left in the buffers at the end!
print >> sys.stderr, '** End of run...'
output_buffer.flush_all()
total_t = time.clock() - total_t
if output_buffer.num_write_errors > 0 or output_buffer.num_file_errors > 0:
print >> sys.stderr, '! WARNING: Sweep finished with errors !'
print >> sys.stderr, '** {writee} reads not written'.format(
writee=output_buffer.num_write_errors)
print >> sys.stderr, '** {filee} errors opening files'.format(
filee=output_buffer.num_file_errors)
print >> sys.stderr, 'swept {n_reads} for labels...'.format(
n_reads=n_labeled + n_orphaned)
print >> sys.stderr, '...with {nc} labeled and {no} orphaned'.format(
nc=n_labeled, no=n_orphaned)
print >> sys.stderr, '...and {nmc} multilabeled'.format(nmc=n_mlabeled)
print >> sys.stderr, '** outputting label number distribution...'
fn = os.path.join(outdir, '{pref}.dist.txt'.format(pref=output_pref))
with open(fn, 'wb') as outfp:
for nc in label_number_dist:
outfp.write('{nc}\n'.format(nc=nc))
fn = os.path.join(outdir, '{pref}.counts.csv'.format(pref=output_pref))
print >> sys.stderr, '** outputting label read counts...'
with open(fn, 'wb') as outfp:
for k in label_dict:
outfp.write('{l},{c}\n'.format(l=k, c=label_dict[k]))
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_with_reads_parser,
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)
def main():
args = sanitize_help(get_parser()).parse_args()
if not args.quiet:
info('filter-abund-single.py', ['counting', 'SeqAn'])
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_fasta_with_reads_parser,
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)
# now, trim.
# the filtering function.
def process_fn(record):
name = record.name
seq = record.sequence
seqN = seq.replace('N', 'A')
if args.variable_coverage: # only trim when sequence has high enough C
med, _, _ = graph.get_median_count(seqN)
if med < args.normalize_to:
return name, seq
_, trim_at = graph.trim_on_abundance(seqN, args.cutoff)
if trim_at >= args.ksize:
# be sure to not to change the 'N's in the trimmed sequence -
# so, return 'seq' and not 'seqN'.
return name, seq[:trim_at]
return None, None
# 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)
tsp = ThreadedSequenceProcessor(process_fn, verbose=not args.quiet)
tsp.start(verbose_loader(args.datafile), 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)
def main(): # pylint: disable=too-many-branches,too-many-statements
parser = sanitize_help(get_parser())
args = parser.parse_args()
configure_logging(args.quiet)
report_on_config(args)
report_fp = args.report
force_single = args.force_single
# check for similar filenames
# if we're using a single output file only check for identical filenames
# otherwise, check for identical BASE names as well.
filenames = []
basenames = []
for pathfilename in args.input_filenames:
filenames.append(pathfilename)
if args.single_output_file:
continue # nothing more to worry about
basename = os.path.basename(pathfilename)
if basename in basenames:
log_error('ERROR: Duplicate filename--Cannot handle this!')
log_error('** Exiting!')
sys.exit(1)
basenames.append(basename)
# check that files exist and there is sufficient output disk space.
check_valid_file_exists(args.input_filenames)
check_space(args.input_filenames, args.force)
if args.savegraph is not None:
graphsize = calculate_graphsize(args, 'countgraph')
check_space_for_graph(args.savegraph, graphsize, args.force)
# load or create counting table.
if args.loadgraph:
log_info('loading k-mer countgraph from {graph}',
graph=args.loadgraph)
countgraph = Countgraph.load(args.loadgraph)
else:
log_info('making countgraph')
countgraph = khmer_args.create_countgraph(args)
# create an object to handle diginorm of all files
norm = Normalizer(args.cutoff, countgraph)
with_diagnostics = WithDiagnostics(norm, report_fp, args.report_frequency)
# make a list of all filenames and if they're paired or not;
# if we don't know if they're paired, default to allowing but not
# forcing pairing.
files = []
for element in filenames:
files.append([element, args.paired])
if args.unpaired_reads:
files.append([args.unpaired_reads, False])
corrupt_files = []
outfp = None
output_name = None
if args.single_output_file:
outfp = get_file_writer(args.single_output_file, args.gzip, args.bzip)
else:
if '-' in filenames or '/dev/stdin' in filenames:
print("Accepting input from stdin; output filename must "
"be provided with '-o'.", file=sys.stderr)
sys.exit(1)
#
# main loop: iterate over all files given, do diginorm.
#
for filename, require_paired in files:
if not args.single_output_file:
output_name = os.path.basename(filename) + '.keep'
outfp = open(output_name, 'wb')
outfp = get_file_writer(outfp, args.gzip, args.bzip)
# failsafe context manager in case an input file breaks
with catch_io_errors(filename, outfp, args.single_output_file,
args.force, corrupt_files):
screed_iter = clean_input_reads(screed.open(filename))
reader = broken_paired_reader(screed_iter, min_length=args.ksize,
force_single=force_single,
require_paired=require_paired)
# actually do diginorm
for record in with_diagnostics(reader, filename):
if record is not None:
write_record(record, outfp)
log_info('output in {name}', name=describe_file_handle(outfp))
if not args.single_output_file:
outfp.close()
# finished - print out some diagnostics.
log_info('Total number of unique k-mers: {umers}',
umers=countgraph.n_unique_kmers())
if args.savegraph is not None:
log_info('...saving to {name}', name=args.savegraph)
countgraph.save(args.savegraph)
fp_rate = \
khmer.calc_expected_collisions(countgraph, False, max_false_pos=.8)
# for max_false_pos see Zhang et al., http://arxiv.org/abs/1309.2975
log_info('fp rate estimated to be {fpr:1.3f}', fpr=fp_rate)
if args.force and len(corrupt_files) > 0:
log_error("** WARNING: Finished with errors!")
log_error("** I/O Errors occurred in the following files:")
log_error("\t" + " ".join(corrupt_files))
def main(): # pylint: disable=too-many-branches,too-many-statements
info('normalize-by-median.py', ['diginorm'])
args = get_parser().parse_args()
report_on_config(args)
report_fp = args.report
# check for similar filenames
filenames = []
for pathfilename in args.input_filenames:
filename = pathfilename.split('/')[-1]
if (filename in filenames):
print >>sys.stderr, "WARNING: At least two input files are named \
%s . (The script normalize-by-median.py can not handle this, only one .keep \
file for one of the input files will be generated.)" % filename
else:
filenames.append(filename)
# check for others
check_valid_file_exists(args.input_filenames)
check_space(args.input_filenames, args.force)
if args.savetable:
check_space_for_hashtable(
args.n_tables * args.min_tablesize, args.force)
# list to save error files along with throwing exceptions
corrupt_files = []
if args.loadtable:
print 'loading k-mer counting table from', args.loadtable
htable = khmer.load_counting_hash(args.loadtable)
else:
print >> sys.stderr, 'making k-mer counting table'
htable = khmer.new_counting_hash(args.ksize, args.min_tablesize,
args.n_tables)
input_filename = None
for index, input_filename in enumerate(args.input_filenames):
total_acc, discarded_acc, corrupt_files = \
normalize_by_median_and_check(
input_filename, htable, args.single_output_file,
args.fail_save, args.paired, args.cutoff, args.force,
corrupt_files, report_fp)
if (args.dump_frequency > 0 and
index > 0 and index % args.dump_frequency == 0):
print 'Backup: Saving k-mer counting file through', input_filename
if args.savetable:
hashname = args.savetable
print '...saving to', hashname
else:
hashname = 'backup.ct'
print 'Nothing given for savetable, saving to', hashname
htable.save(hashname)
if args.paired and args.unpaired_reads:
args.paired = False
output_name = args.unpaired_reads
if not args.single_output_file:
output_name = os.path.basename(args.unpaired_reads) + '.keep'
outfp = open(output_name, 'w')
total_acc, discarded_acc, corrupt_files = \
normalize_by_median_and_check(
args.unpaired_reads, htable, args.single_output_file,
args.fail_save, args.paired, args.cutoff, args.force,
corrupt_files, report_fp)
if args.report_total_kmers:
print >> sys.stderr, 'Total number of unique k-mers: {0}'.format(
htable.n_unique_kmers())
if args.savetable:
print 'Saving k-mer counting table through', input_filename
print '...saving to', args.savetable
htable.save(args.savetable)
fp_rate = \
khmer.calc_expected_collisions(htable, args.force, max_false_pos=.8)
# for max_false_pos see Zhang et al., http://arxiv.org/abs/1309.2975
print >> sys.stderr, \
'fp rate estimated to be {fpr:1.3f}'.format(fpr=fp_rate)
if args.force and len(corrupt_files) > 0:
print >> sys.stderr, "** WARNING: Finished with errors!"
print >> sys.stderr, "** IOErrors occurred in the following files:"
print >> sys.stderr, "\t", " ".join(corrupt_files)
def main():
info('trim-low-abund.py', ['streaming'])
parser = get_parser()
args = parser.parse_args()
###
if len(set(args.input_filenames)) != len(args.input_filenames):
print >>sys.stderr, \
"Error: Cannot input the same filename multiple times."
sys.exit(1)
###
report_on_config(args)
check_valid_file_exists(args.input_filenames)
check_space(args.input_filenames, args.force)
if args.savetable:
check_space_for_hashtable(args.n_tables * args.min_tablesize,
args.force)
K = args.ksize
CUTOFF = args.cutoff
NORMALIZE_LIMIT = args.normalize_to
if args.loadtable:
print >> sys.stderr, 'loading k-mer counting table from', args.loadtable
ct = khmer.load_counting_hash(args.loadtable)
else:
print >> sys.stderr, 'making k-mer counting table'
ct = khmer.new_counting_hash(K, args.min_tablesize, args.n_tables)
tempdir = tempfile.mkdtemp('khmer', 'tmp', args.tempdir)
print >>sys.stderr, 'created temporary directory %s; ' \
'use -T to change location' % tempdir
# ### FIRST PASS ###
save_pass2_total = 0
n_bp = 0
n_reads = 0
written_bp = 0
written_reads = 0
trimmed_reads = 0
pass2list = []
for filename in args.input_filenames:
pass2filename = os.path.basename(filename) + '.pass2'
pass2filename = os.path.join(tempdir, pass2filename)
if args.out is None:
trimfp = open(os.path.basename(filename) + '.abundtrim', 'w')
else:
trimfp = args.out
pass2list.append((filename, pass2filename, trimfp))
screed_iter = screed.open(filename, parse_description=False)
pass2fp = open(pass2filename, 'w')
save_pass2 = 0
n = 0
paired_iter = broken_paired_reader(screed_iter,
min_length=K,
force_single=args.ignore_pairs)
for n, is_pair, read1, read2 in paired_iter:
if n % 10000 == 0:
print >>sys.stderr, '...', n, filename, save_pass2, \
n_reads, n_bp, written_reads, written_bp
# we want to track paired reads here, to make sure that pairs
# are not split between first pass and second pass.
if is_pair:
n_reads += 2
n_bp += len(read1.sequence) + len(read2.sequence)
seq1 = read1.sequence.replace('N', 'A')
seq2 = read2.sequence.replace('N', 'A')
med1, _, _ = ct.get_median_count(seq1)
med2, _, _ = ct.get_median_count(seq2)
if med1 < NORMALIZE_LIMIT or med2 < NORMALIZE_LIMIT:
ct.consume(seq1)
ct.consume(seq2)
write_record_pair(read1, read2, pass2fp)
save_pass2 += 2
else:
_, trim_at1 = ct.trim_on_abundance(seq1, CUTOFF)
_, trim_at2 = ct.trim_on_abundance(seq2, CUTOFF)
if trim_at1 >= K:
read1 = trim_record(read1, trim_at1)
if trim_at2 >= K:
read2 = trim_record(read2, trim_at2)
if trim_at1 != len(seq1):
trimmed_reads += 1
if trim_at2 != len(seq2):
trimmed_reads += 1
write_record_pair(read1, read2, trimfp)
written_reads += 2
written_bp += trim_at1 + trim_at2
else:
n_reads += 1
n_bp += len(read1.sequence)
seq = read1.sequence.replace('N', 'A')
med, _, _ = ct.get_median_count(seq)
# has this portion of the graph saturated? if not,
# consume & save => pass2.
if med < NORMALIZE_LIMIT:
ct.consume(seq)
write_record(read1, pass2fp)
save_pass2 += 1
else: # trim!!
_, trim_at = ct.trim_on_abundance(seq, CUTOFF)
if trim_at >= K:
new_read = trim_record(read1, trim_at)
write_record(new_read, trimfp)
written_reads += 1
written_bp += trim_at
if trim_at != len(read1.sequence):
trimmed_reads += 1
pass2fp.close()
print >>sys.stderr, '%s: kept aside %d of %d from first pass, in %s' \
% (filename, save_pass2, n, filename)
save_pass2_total += save_pass2
# ### SECOND PASS. ###
skipped_n = 0
skipped_bp = 0
for _, pass2filename, trimfp in pass2list:
print >> sys.stderr, ('second pass: looking at sequences kept aside '
'in %s') % pass2filename
# note that for this second pass, we don't care about paired
# reads - they will be output in the same order they're read in,
# so pairs will stay together if not orphaned. This is in contrast
# to the first loop.
for n, read in enumerate(
screed.open(pass2filename, parse_description=False)):
if n % 10000 == 0:
print >>sys.stderr, '... x 2', n, pass2filename, \
written_reads, written_bp
seq = read.sequence.replace('N', 'A')
med, _, _ = ct.get_median_count(seq)
# do we retain low-abundance components unchanged?
if med < NORMALIZE_LIMIT and args.variable_coverage:
write_record(read, trimfp)
written_reads += 1
written_bp += len(read.sequence)
skipped_n += 1
skipped_bp += len(read.sequence)
# otherwise, examine/trim/truncate.
else: # med >= NORMALIZE LIMIT or not args.variable_coverage
_, trim_at = ct.trim_on_abundance(seq, CUTOFF)
if trim_at >= K:
new_read = trim_record(read, trim_at)
write_record(new_read, trimfp)
written_reads += 1
written_bp += trim_at
if trim_at != len(read.sequence):
trimmed_reads += 1
print >> sys.stderr, 'removing %s' % pass2filename
os.unlink(pass2filename)
print >> sys.stderr, 'removing temp directory & contents (%s)' % tempdir
shutil.rmtree(tempdir)
n_passes = 1.0 + (float(save_pass2_total) / n_reads)
percent_reads_trimmed = float(trimmed_reads + (n_reads - written_reads)) /\
n_reads * 100.0
print >> sys.stderr, 'read %d reads, %d bp' % (
n_reads,
n_bp,
)
print >> sys.stderr, 'wrote %d reads, %d bp' % (
written_reads,
written_bp,
)
print >>sys.stderr, 'looked at %d reads twice (%.2f passes)' % \
(save_pass2_total, n_passes)
print >>sys.stderr, 'removed %d reads and trimmed %d reads (%.2f%%)' % \
(n_reads - written_reads, trimmed_reads, percent_reads_trimmed)
print >>sys.stderr, 'trimmed or removed %.2f%% of bases (%d total)' % \
((1 - (written_bp / float(n_bp))) * 100.0, n_bp - written_bp)
if args.variable_coverage:
percent_reads_hicov = 100.0 * float(n_reads - skipped_n) / n_reads
print >>sys.stderr, '%d reads were high coverage (%.2f%%);' % \
(n_reads - skipped_n, percent_reads_hicov)
print >> sys.stderr, ('skipped %d reads/%d bases because of low'
'coverage') % (skipped_n, skipped_bp)
fp_rate = \
khmer.calc_expected_collisions(ct, args.force, max_false_pos=.8)
# for max_false_pos see Zhang et al., http://arxiv.org/abs/1309.2975
print >>sys.stderr, \
'fp rate estimated to be {fpr:1.3f}'.format(fpr=fp_rate)
print >> sys.stderr, 'output in *.abundtrim'
if args.savetable:
print >> sys.stderr, "Saving k-mer counting table to", args.savetable
ct.save(args.savetable)
def main(): # pylint: disable=too-many-branches,too-many-statements
start_time = time.time()
parser = sanitize_help(get_parser())
args = parser.parse_args()
configure_logging(args.quiet)
report_on_config(args)
report_fp = args.report
force_single = args.force_single
# check for similar filenames
# if we're using a single output file only check for identical filenames
# otherwise, check for identical BASE names as well.
filenames = []
basenames = []
for pathfilename in args.input_filenames:
filenames.append(pathfilename)
if args.single_output_file:
continue # nothing more to worry about
basename = os.path.basename(pathfilename)
if basename in basenames:
log_error('ERROR: Duplicate filename--Cannot handle this!')
log_error('** Exiting!')
sys.exit(1)
basenames.append(basename)
# check that files exist and there is sufficient output disk space.
check_valid_file_exists(args.input_filenames)
check_space(args.input_filenames, args.force)
if args.savegraph is not None:
graphsize = calculate_graphsize(args, 'countgraph')
check_space_for_graph(args.savegraph, graphsize, args.force)
# load or create counting table.
if args.loadgraph:
log_info('loading k-mer countgraph from {graph}', graph=args.loadgraph)
countgraph1 = Countgraph.load(args.loadgraph)
# load second counting table.
if args.loadgraph2:
log_info('loading k-mer countgraph from {graph}',
graph=args.loadgraph2)
countgraph2 = Countgraph.load(args.loadgraph2)
# make a list of all filenames and if they're paired or not;
# if we don't know if they're paired, default to allowing but not
# forcing pairing.
files = []
for element in filenames:
files.append([element, args.paired])
if args.unpaired_reads:
files.append([args.unpaired_reads, False])
#
# main loop: iterate over all files given, do diginorm.
#
for filename, require_paired in files:
if not args.single_output_file:
output_name = os.path.basename(filename) + '.keep'
outfp = open(output_name, 'wb')
outfp = get_file_writer(outfp, args.gzip, args.bzip)
screed_iter = clean_input_reads(screed.open(filename))
reader = broken_paired_reader(screed_iter,
min_length=args.ksize,
force_single=force_single,
require_paired=require_paired)
# actually do diginorm
for _, is_paired, read0, read1 in reader:
for record in snarf(is_paired, read0, read1, countgraph1,
countgraph2):
if record is not None:
write_record(record, outfp)
print("--- %s seconds ---" % (time.time() - start_time))
def main(): # pylint: disable=too-many-locals,too-many-branches
info('abundance-dist-single.py', ['counting'])
args = get_parser().parse_args()
report_on_config(args)
check_file_status(args.input_sequence_filename)
check_space([args.input_sequence_filename])
if args.savetable:
check_space_for_hashtable(args.n_tables * args.min_tablesize)
if (not args.squash_output
and os.path.exists(args.output_histogram_filename)):
print >> sys.stderr, 'ERROR: %s exists; not squashing.' % \
args.output_histogram_filename
sys.exit(1)
else:
hist_fp = open(args.output_histogram_filename, 'w')
print 'making k-mer counting table'
counting_hash = khmer.new_counting_hash(args.ksize, args.min_tablesize,
args.n_tables, args.threads)
counting_hash.set_use_bigcount(args.bigcount)
print 'building k-mer tracking table'
tracking = khmer.new_hashbits(counting_hash.ksize(), args.min_tablesize,
args.n_tables)
print 'kmer_size:', counting_hash.ksize()
print 'k-mer counting table sizes:', counting_hash.hashsizes()
print 'outputting to', args.output_histogram_filename
khmer.get_config().set_reads_input_buffer_size(args.threads * 64 * 1024)
# start loading
rparser = khmer.ReadParser(args.input_sequence_filename, args.threads)
threads = []
print 'consuming input, round 1 --', args.input_sequence_filename
for _ in xrange(args.threads):
thread = \
threading.Thread(
target=counting_hash.consume_fasta_with_reads_parser,
args=(rparser, )
)
threads.append(thread)
thread.start()
for thread in threads:
thread.join()
if args.report_total_kmers:
print >> sys.stderr, 'Total number of k-mers: {0}'.format(
counting_hash.n_occupied())
abundance_lists = []
def __do_abundance_dist__(read_parser):
abundances = counting_hash.abundance_distribution_with_reads_parser(
read_parser, tracking)
abundance_lists.append(abundances)
print 'preparing hist from %s...' % args.input_sequence_filename
rparser = khmer.ReadParser(args.input_sequence_filename, args.threads)
threads = []
print 'consuming input, round 2 --', args.input_sequence_filename
for _ in xrange(args.threads):
thread = \
threading.Thread(
target=__do_abundance_dist__,
args=(rparser, )
)
threads.append(thread)
thread.start()
for thread in threads:
thread.join()
assert len(abundance_lists) == args.threads, len(abundance_lists)
abundance = {}
for abundance_list in abundance_lists:
for i, count in enumerate(abundance_list):
abundance[i] = abundance.get(i, 0) + count
total = sum(abundance.values())
if 0 == total:
print >> sys.stderr, \
"ERROR: abundance distribution is uniformly zero; " \
"nothing to report."
print >> sys.stderr, "\tPlease verify that the input files are valid."
sys.exit(1)
sofar = 0
for _, i in sorted(abundance.items()):
if i == 0 and not args.output_zero:
continue
sofar += i
frac = sofar / float(total)
print >> hist_fp, _, i, sofar, round(frac, 3)
if sofar == total:
break
if args.savetable:
print 'Saving k-mer counting table ', args.savetable
print '...saving to', args.savetable
counting_hash.save(args.savetable)
def main(): # pylint: disable=too-many-branches,too-many-statements
info('normalize-by-median.py', ['diginorm'])
args = get_parser().parse_args()
report_on_config(args)
report_fp = args.report
check_valid_file_exists(args.input_filenames)
check_space(args.input_filenames, args.force)
if args.savetable:
check_space_for_hashtable(
args.n_tables * args.min_tablesize, args.force)
# list to save error files along with throwing exceptions
if args.force:
corrupt_files = []
if args.loadtable:
print 'loading k-mer counting table from', args.loadtable
htable = khmer.load_counting_hash(args.loadtable)
else:
print 'making k-mer counting table'
htable = khmer.new_counting_hash(args.ksize, args.min_tablesize,
args.n_tables)
total = 0
discarded = 0
input_filename = None
for index, input_filename in enumerate(args.input_filenames):
if args.single_output_filename != '':
output_name = args.single_output_filename
outfp = open(args.single_output_filename, 'a')
else:
output_name = os.path.basename(input_filename) + '.keep'
outfp = open(output_name, 'w')
total_acc = 0
discarded_acc = 0
try:
total_acc, discarded_acc = normalize_by_median(input_filename,
outfp, htable, args,
report_fp)
except IOError as err:
handle_error(err, output_name, input_filename, args.fail_save,
htable)
if not args.force:
print >> sys.stderr, '** Exiting!'
sys.exit(1)
else:
print >> sys.stderr, '*** Skipping error file, moving on...'
corrupt_files.append(input_filename)
else:
if total_acc == 0 and discarded_acc == 0:
print 'SKIPPED empty file', input_filename
else:
total += total_acc
discarded += discarded_acc
print 'DONE with {inp}; kept {kept} of {total} or {perc:2}%'\
.format(inp=input_filename, kept=total - discarded,
total=total, perc=int(100. - discarded /
float(total) * 100.))
print 'output in', output_name
if (args.dump_frequency > 0 and
index > 0 and index % args.dump_frequency == 0):
print 'Backup: Saving k-mer counting file through', input_filename
if args.savetable:
hashname = args.savetable
print '...saving to', hashname
else:
hashname = 'backup.ct'
print 'Nothing given for savetable, saving to', hashname
htable.save(hashname)
if args.report_total_kmers:
print >> sys.stderr, 'Total number of unique k-mers: {0}'.format(
htable.n_unique_kmers())
if args.savetable:
print 'Saving k-mer counting table through', input_filename
print '...saving to', args.savetable
htable.save(args.savetable)
fp_rate = khmer.calc_expected_collisions(htable)
print 'fp rate estimated to be {fpr:1.3f}'.format(fpr=fp_rate)
if args.force and len(corrupt_files) > 0:
print >> sys.stderr, "** WARNING: Finished with errors!"
print >> sys.stderr, "** IOErrors occurred in the following files:"
print >> sys.stderr, "\t", " ".join(corrupt_files)
if fp_rate > MAX_FALSE_POSITIVE_RATE:
print >> sys.stderr, "**"
print >> sys.stderr, ("** ERROR: the k-mer counting table is too small"
" for this data set. Increase tablesize/# "
"tables.")
print >> sys.stderr, "**"
print >> sys.stderr, "** Do not use these results!!"
if not args.force:
sys.exit(1)
def main():
info('load-into-counting.py', ['counting', 'SeqAn'])
args = get_parser().parse_args()
report_on_config(args)
base = args.output_countingtable_filename
filenames = args.input_sequence_filename
for name in args.input_sequence_filename:
check_input_files(name, args.force)
check_space(args.input_sequence_filename, args.force)
check_space_for_hashtable(args.n_tables * args.min_tablesize, args.force)
check_file_writable(base)
check_file_writable(base + ".info")
print >>sys.stderr, 'Saving k-mer counting table to %s' % base
print >>sys.stderr, 'Loading kmers from sequences in %s' % repr(filenames)
# clobber the '.info' file now, as we always open in append mode below
if os.path.exists(base + '.info'):
os.remove(base + '.info')
print >>sys.stderr, 'making k-mer counting table'
htable = khmer.new_counting_hash(args.ksize, args.min_tablesize,
args.n_tables)
htable.set_use_bigcount(args.bigcount)
filename = None
for index, filename in enumerate(filenames):
rparser = khmer.ReadParser(filename)
threads = []
print >>sys.stderr, 'consuming input', filename
for _ in xrange(args.threads):
cur_thrd = \
threading.Thread(
target=htable.consume_fasta_with_reads_parser,
args=(rparser, )
)
threads.append(cur_thrd)
cur_thrd.start()
for thread in threads:
thread.join()
if index > 0 and index % 10 == 0:
check_space_for_hashtable(args.n_tables * args.min_tablesize,
args.force)
print >>sys.stderr, 'mid-save', base
htable.save(base)
with open(base + '.info', 'a') as info_fh:
print >> info_fh, 'through', filename
n_kmers = htable.n_unique_kmers()
if args.report_total_kmers:
print >> sys.stderr, 'Total number of unique k-mers:', n_kmers
with open(base + '.info', 'a') as info_fp:
print >>info_fp, 'Total number of unique k-mers:', n_kmers
print >>sys.stderr, 'saving', base
htable.save(base)
# Change max_false_pos=0.2 only if you really grok it. HINT: You don't
fp_rate = \
khmer.calc_expected_collisions(htable, args.force, max_false_pos=.2)
with open(base + '.info', 'a') as info_fp:
print >> info_fp, 'fp rate estimated to be %1.3f\n' % fp_rate
if args.summary_info:
mr_fmt = args.summary_info.lower()
mr_file = base + '.info.' + mr_fmt
print >> sys.stderr, "Writing summmary info to", 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": "0.1.0",
}
json.dump(mr_data, mr_fh)
mr_fh.write('\n')
elif mr_fmt == 'tsv':
mr_fh.write("ht_name\tfpr\tnum_kmers\tfiles\n")
mr_fh.write("{b:s}\t{fpr:1.3f}\t{k:d}\t{fls:s}\n".format(
b=os.path.basename(base), fpr=fp_rate, k=n_kmers,
fls=";".join(filenames)))
print >> sys.stderr, 'fp rate estimated to be %1.3f' % fp_rate
print >>sys.stderr, 'DONE.'
print >>sys.stderr, 'wrote to:', base + '.info'
def main(): # pylint: disable=too-many-locals,too-many-statements
info('do-partition.py', ['graph'])
args = get_parser().parse_args()
report_on_config(args, hashtype='hashbits')
for infile in args.input_filenames:
check_file_status(infile)
check_space(args.input_filenames)
print 'Saving k-mer presence table to %s' % args.graphbase
print 'Loading kmers from sequences in %s' % repr(args.input_filenames)
print '--'
print 'SUBSET SIZE', args.subset_size
print 'N THREADS', args.n_threads
print '--'
# load-graph
print 'making k-mer presence table'
htable = khmer.new_hashbits(args.ksize, args.min_tablesize, args.n_tables)
for _, filename in enumerate(args.input_filenames):
print 'consuming input', filename
htable.consume_fasta_and_tag(filename)
fp_rate = khmer.calc_expected_collisions(htable)
print 'fp rate estimated to be %1.3f' % fp_rate
if fp_rate > 0.15: # 0.18 is ACTUAL MAX. Do not change.
print >> sys.stderr, "**"
print >> sys.stderr, ("** ERROR: the graph structure is too small for"
" this data set. Increase k-mer presence table "
"size/num of tables.")
print >> sys.stderr, "**"
sys.exit(1)
# partition-graph
# do we want to exhaustively traverse the graph?
stop_big_traversals = args.no_big_traverse
if stop_big_traversals:
print '** This script brakes for lumps: stop_big_traversals is true.'
else:
print '** Traverse all the things: stop_big_traversals is false.'
#
# now, partition!
#
# divide the tags up into subsets
divvy = htable.divide_tags_into_subsets(int(args.subset_size))
n_subsets = len(divvy)
divvy.append(0)
# build a queue of tasks:
worker_q = Queue.Queue()
# break up the subsets into a list of worker tasks
for _ in range(0, n_subsets):
start = divvy[_]
end = divvy[_ + 1]
worker_q.put((htable, _, start, end))
print 'enqueued %d subset tasks' % n_subsets
open('%s.info' % args.graphbase,
'w').write('%d subsets total\n' % (n_subsets))
if n_subsets < args.n_threads:
args.n_threads = n_subsets
# start threads!
print 'starting %d threads' % args.n_threads
print '---'
threads = []
for _ in range(args.n_threads):
cur_thread = threading.Thread(target=worker,
args=(worker_q, args.graphbase,
stop_big_traversals))
threads.append(cur_thread)
cur_thread.start()
print 'done starting threads'
# wait for threads
for _ in threads:
_.join()
print '---'
print 'done making subsets! see %s.subset.*.pmap' % (args.graphbase, )
# merge-partitions
pmap_files = glob.glob(args.graphbase + '.subset.*.pmap')
print 'loading %d pmap files (first one: %s)' % (len(pmap_files),
pmap_files[0])
htable = khmer.new_hashbits(args.ksize, 1, 1)
for pmap_file in pmap_files:
print 'merging', pmap_file
htable.merge_subset_from_disk(pmap_file)
if args.remove_subsets:
print 'removing pmap files'
for pmap_file in pmap_files:
os.unlink(pmap_file)
# annotate-partitions
for infile in args.input_filenames:
print 'outputting partitions for', infile
outfile = os.path.basename(infile) + '.part'
part_count = htable.output_partitions(infile, outfile)
print 'output %d partitions for %s' % (part_count, infile)
print 'partitions are in', outfile
def main():
args = sanitize_help(get_parser()).parse_args()
if not args.quiet:
info('load-into-counting.py', ['counting', 'SeqAn'])
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)
countgraph.set_use_bigcount(args.bigcount)
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_fasta_with_reads_parser,
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)
def main():
info('filter-abund-single.py', ['counting', 'SeqAn'])
args = get_parser().parse_args()
check_input_files(args.datafile, args.force)
check_space([args.datafile], args.force)
if args.savetable:
check_space_for_hashtable(args.n_tables * args.min_tablesize,
args.force)
report_on_config(args)
print >> sys.stderr, 'making k-mer counting table'
htable = khmer.new_counting_hash(args.ksize, args.min_tablesize,
args.n_tables)
# first, load reads into hash table
rparser = khmer.ReadParser(args.datafile)
threads = []
print >> sys.stderr, 'consuming input, round 1 --', args.datafile
for _ in xrange(args.threads):
cur_thread = \
threading.Thread(
target=htable.consume_fasta_with_reads_parser,
args=(rparser, )
)
threads.append(cur_thread)
cur_thread.start()
for _ in threads:
_.join()
if args.report_total_kmers:
print >> sys.stderr, 'Total number of unique k-mers: {0}'.format(
htable.n_unique_kmers())
fp_rate = khmer.calc_expected_collisions(htable, args.force)
print >> sys.stderr, 'fp rate estimated to be %1.3f' % fp_rate
# now, trim.
# the filtering function.
def process_fn(record):
name = record.name
seq = record.sequence
seqN = seq.replace('N', 'A')
_, trim_at = htable.trim_on_abundance(seqN, args.cutoff)
if trim_at >= args.ksize:
# be sure to not to change the 'N's in the trimmed sequence -
# so, return 'seq' and not 'seqN'.
return name, seq[:trim_at]
return None, None
# the filtering loop
print >> sys.stderr, 'filtering', args.datafile
outfile = os.path.basename(args.datafile) + '.abundfilt'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(args.datafile), outfp)
print >> sys.stderr, 'output in', outfile
if args.savetable:
print >>sys.stderr, 'Saving k-mer counting table filename', \
args.savetable
print >> sys.stderr, '...saving to', args.savetable
htable.save(args.savetable)
print >> sys.stderr, 'wrote to: ', outfile
def main(): # pylint: disable=too-many-branches,too-many-statements
info('saturate-by-median.py', ['diginorm'])
args = get_parser().parse_args()
report_on_config(args)
report_fp = args.report
report_frequency = args.report_frequency
check_valid_file_exists(args.input_filenames)
check_space(args.input_filenames, False)
if args.savetable:
check_space_for_hashtable(args.n_tables * args.min_tablesize, False)
# list to save error files along with throwing exceptions
if args.force:
corrupt_files = []
if args.loadtable:
print 'loading k-mer counting table from', args.loadtable
htable = khmer.load_counting_hash(args.loadtable)
else:
print 'making k-mer counting table'
htable = khmer.new_counting_hash(args.ksize, args.min_tablesize,
args.n_tables)
total = 0
discarded = 0
for index, input_filename in enumerate(args.input_filenames):
total_acc = 0
discarded_acc = 0
try:
total_acc, discarded_acc = normalize_by_median(input_filename,
htable, args,
report_fp,
report_frequency)
except IOError as err:
handle_error(err, input_filename)
if not args.force:
print >> sys.stderr, '** Exiting!'
sys.exit(1)
else:
print >> sys.stderr, '*** Skipping error file, moving on...'
corrupt_files.append(input_filename)
else:
if total_acc == 0 and discarded_acc == 0:
print 'SKIPPED empty file', input_filename
else:
total += total_acc
discarded += discarded_acc
print 'DONE with {inp}; kept {kept} of {total} or {perc:2}%'\
.format(inp=input_filename,
kept=total - discarded, total=total,
perc=int(100. - discarded / float(total) * 100.))
if args.savetable:
print 'Saving k-mer counting table through', input_filename
print '...saving to', args.savetable
htable.save(args.savetable)
# re: threshold, see Zhang et al.,
# http://arxiv.org/abs/1309.2975
fp_rate = khmer.calc_expected_collisions(htable, args.force, max_false_pos=.8)
print 'fp rate estimated to be {fpr:1.3f}'.format(fpr=fp_rate)
if args.force and len(corrupt_files) > 0:
print >> sys.stderr, "** WARNING: Finished with errors!"
print >> sys.stderr, "** IOErrors occurred in the following files:"
print >> sys.stderr, "\t", " ".join(corrupt_files)
