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('filter-abund.py', ['counting'])
args = sanitize_help(get_parser()).parse_args()
check_input_files(args.input_graph, args.force)
infiles = args.input_filename
if ('-' in infiles or '/dev/stdin' in infiles) and not \
args.single_output_file:
print("Accepting input from stdin; output filename must "
"be provided with -o.", file=sys.stderr)
sys.exit(1)
for filename in infiles:
check_input_files(filename, args.force)
check_space(infiles, args.force)
print('loading countgraph:', args.input_graph,
file=sys.stderr)
countgraph = khmer.load_countgraph(args.input_graph)
ksize = countgraph.ksize()
print("K:", ksize, file=sys.stderr)
# 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, _, _ = countgraph.get_median_count(seqN)
if med < args.normalize_to:
return name, seq
_, trim_at = countgraph.trim_on_abundance(seqN, args.cutoff)
if trim_at >= 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
if args.single_output_file:
outfile = args.single_output_file.name
outfp = get_file_writer(args.single_output_file, args.gzip, args.bzip)
# the filtering loop
for infile in infiles:
print('filtering', infile, file=sys.stderr)
if not args.single_output_file:
outfile = os.path.basename(infile) + '.abundfilt'
outfp = open(outfile, 'wb')
outfp = get_file_writer(outfp, args.gzip, args.bzip)
tsp = ThreadedSequenceProcessor(process_fn, n_workers=args.threads)
tsp.start(verbose_loader(infile), outfp)
print('output in', outfile, file=sys.stderr)
def main():
parser = build_counting_multifile_args()
parser.add_argument('--cutoff',
'-C',
dest='coverage',
default=DEFAULT_COVERAGE,
type=int,
help="Diginorm coverage.")
parser.add_argument('--max-error-region',
'-M',
dest='max_error_region',
default=DEFAULT_MAX_ERROR_REGION,
type=int,
help="Max length of error region allowed")
args = parser.parse_args()
counting_ht = args.input_table
infiles = args.input_filenames
print 'file with ht: %s' % counting_ht
print 'loading hashtable'
ht = khmer.load_counting_hash(counting_ht)
K = ht.ksize()
C = args.coverage
max_error_region = args.max_error_region
print "K:", K
print "C:", C
print "max error region:", max_error_region
# the filtering function.
def process_fn(record):
# read_aligner is probably not threadsafe?
aligner = khmer.new_readaligner(ht, 1, C, max_error_region)
name = record['name']
seq = record['sequence']
seq = seq.replace('N', 'A')
grXreAlign, reXgrAlign = aligner.align(seq)
if len(reXgrAlign) > 0:
graph_seq = grXreAlign.replace('-', '')
seq = graph_seq
return name, seq
# the filtering loop
for infile in infiles:
print 'filtering', infile
outfile = os.path.basename(infile) + '.corr'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(infile), outfp)
print 'output in', outfile
def main():
parser = build_counting_multifile_args()
parser.add_argument(
"--cutoff", "-C", dest="coverage", default=DEFAULT_COVERAGE, type=int, help="Diginorm coverage."
)
parser.add_argument(
"--max-error-region",
"-M",
dest="max_error_region",
default=DEFAULT_MAX_ERROR_REGION,
type=int,
help="Max length of error region allowed",
)
args = parser.parse_args()
counting_ht = args.input_table
infiles = args.input_filenames
print "file with ht: %s" % counting_ht
print "loading hashtable"
ht = khmer.load_counting_hash(counting_ht)
K = ht.ksize()
C = args.coverage
max_error_region = args.max_error_region
print "K:", K
print "C:", C
print "max error region:", max_error_region
# the filtering function.
def process_fn(record):
# read_aligner is probably not threadsafe?
aligner = khmer.new_readaligner(ht, 1, C, max_error_region)
name = record["name"]
seq = record["sequence"]
seq = seq.replace("N", "A")
grXreAlign, reXgrAlign = aligner.align(seq)
if len(reXgrAlign) > 0:
graph_seq = grXreAlign.replace("-", "")
seq = graph_seq
return name, seq
# the filtering loop
for infile in infiles:
print "filtering", infile
outfile = os.path.basename(infile) + ".corr"
outfp = open(outfile, "w")
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(infile), outfp)
print "output in", outfile
def main():
parser = build_counting_multifile_args()
parser.add_argument(
"--cutoff", "-C", dest="cutoff", default=DEFAULT_CUTOFF, type=int, help="Trim at k-mers below this abundance."
)
parser.add_argument("-V", "--variable-coverage", action="store_true", dest="variable_coverage", default=False)
parser.add_argument(
"--normalize-to",
"-Z",
type=int,
dest="normalize_to",
help="base variable-coverage cutoff on this median k-mer abundance",
default=DEFAULT_NORMALIZE_LIMIT,
)
args = parser.parse_args()
counting_ht = args.input_table
infiles = args.input_filenames
print "file with ht: %s" % counting_ht
print "loading hashtable"
ht = khmer.load_counting_hash(counting_ht)
K = ht.ksize()
print "K:", K
### the filtering function.
def process_fn(record):
name = record["name"]
seq = record["sequence"]
if "N" in seq:
return None, None
if args.variable_coverage: # only trim when sequence has high enough C
med, _, _ = ht.get_median_count(seq)
if med < args.normalize_to:
return name, seq
trim_seq, trim_at = ht.trim_on_abundance(seq, args.cutoff)
if trim_at >= K:
return name, trim_seq
return None, None
### the filtering loop
for infile in infiles:
print "filtering", infile
outfile = os.path.basename(infile) + ".abundfilt"
outfp = open(outfile, "w")
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(infile), outfp)
print "output in", outfile
def main():
parser = build_counting_multifile_args()
parser.add_argument('--cutoff', '-C', dest='coverage',
default=DEFAULT_COVERAGE, type=int,
help="Diginorm coverage.")
parser.add_argument('--max-error-region', '-M', dest='max_error_region',
default=DEFAULT_MAX_ERROR_REGION, type=int,
help="Max length of error region allowed")
args = parser.parse_args()
counting_ht = args.input_table
infiles = args.input_filenames
print 'file with ht: %s' % counting_ht
print 'loading hashtable'
ht = khmer.load_counting_hash(counting_ht)
K = ht.ksize()
C = args.coverage
max_error_region = args.max_error_region
print "K:", K
print "C:", C
print "max error region:", max_error_region
# the filtering function.
def process_fn(record):
# read_aligner is probably not threadsafe?
aligner = khmer.new_readaligner(ht, 1, C, max_error_region)
name = record['name']
seq = record['sequence']
seq = seq.replace('N', 'A')
grXreAlign, reXgrAlign = aligner.align(seq)
if len(reXgrAlign) > 0:
graph_seq = grXreAlign.replace('-', '')
seq = graph_seq
return name, seq
# the filtering loop
for infile in infiles:
print 'filtering', infile
outfile = os.path.basename(infile) + '.corr'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(infile), outfp)
print 'output in', outfile
def main():
info('filter-abund.py', ['counting'])
args = get_parser().parse_args()
check_input_files(args.input_table, args.force)
infiles = args.input_filename
for filename in infiles:
check_input_files(filename, args.force)
check_space(infiles, args.force)
print('loading counting table:', args.input_table,
file=sys.stderr)
htable = khmer.load_counting_hash(args.input_table)
ksize = htable.ksize()
print("K:", ksize, file=sys.stderr)
# 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, _, _ = htable.get_median_count(seqN)
if med < args.normalize_to:
return name, seq
_, trim_at = htable.trim_on_abundance(seqN, args.cutoff)
if trim_at >= 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
for infile in infiles:
print('filtering', infile, file=sys.stderr)
if args.single_output_filename != '':
outfile = args.single_output_filename
outfp = open(outfile, 'a')
else:
outfile = os.path.basename(infile) + '.abundfilt'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn, n_workers=args.threads)
tsp.start(verbose_loader(infile), outfp)
print('output in', outfile, file=sys.stderr)
def main():
info('filter-abund.py', ['counting'])
args = get_parser().parse_args()
counting_ht = args.input_table
infiles = args.input_filename
for _ in infiles:
check_file_status(_)
check_space(infiles)
print 'loading hashtable'
htable = khmer.load_counting_hash(counting_ht)
ksize = htable.ksize()
print "K:", ksize
# the filtering function.
def process_fn(record):
name = record['name']
seq = record['sequence']
if 'N' in seq:
return None, None
if args.variable_coverage: # only trim when sequence has high enough C
med, _, _ = htable.get_median_count(seq)
if med < args.normalize_to:
return name, seq
trim_seq, trim_at = htable.trim_on_abundance(seq, args.cutoff)
if trim_at >= ksize:
return name, trim_seq
return None, None
# the filtering loop
for infile in infiles:
print 'filtering', infile
if args.single_output_filename != '':
outfile = args.single_output_filename
outfp = open(outfile, 'a')
else:
outfile = os.path.basename(infile) + '.abundfilt'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn, n_workers=args.threads)
tsp.start(verbose_loader(infile), outfp)
print 'output in', outfile
def main():
info('filter-abund.py', ['counting'])
args = get_parser().parse_args()
counting_ht = args.input_table
infiles = args.input_filename
for _ in infiles:
check_file_status(_, args.force)
check_space(infiles, args.force)
print >>sys.stderr, 'loading hashtable'
htable = khmer.load_counting_hash(counting_ht)
ksize = htable.ksize()
print >>sys.stderr, "K:", ksize
# the filtering function.
def process_fn(record):
name = record['name']
seq = record['sequence']
if 'N' in seq:
return None, None
if args.variable_coverage: # only trim when sequence has high enough C
med, _, _ = htable.get_median_count(seq)
if med < args.normalize_to:
return name, seq
trim_seq, trim_at = htable.trim_on_abundance(seq, args.cutoff)
if trim_at >= ksize:
return name, trim_seq
return None, None
# the filtering loop
for infile in infiles:
print >>sys.stderr, 'filtering', infile
if args.single_output_filename != '':
outfile = args.single_output_filename
outfp = open(outfile, 'a')
else:
outfile = os.path.basename(infile) + '.abundfilt'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn, n_workers=args.threads)
tsp.start(verbose_loader(infile), outfp)
print >>sys.stderr, 'output in', outfile
def main():
parser = build_counting_multifile_args()
parser.add_argument('--cutoff',
'-C',
dest='cutoff',
default=DEFAULT_CUTOFF,
type=int,
help="Trim at k-mers below this abundance.")
parser.add_argument('-o', '--outputpath', dest='outputpath', default='.')
args = parser.parse_args()
counting_ht = args.input_table
infiles = args.input_filenames
outpath = args.outputpath
print 'file with ht: %s' % counting_ht
print 'loading hashtable'
ht = khmer.load_counting_hash(counting_ht)
K = ht.ksize()
print "K:", K
### the filtering function.
def process_fn(record):
name = record['name']
seq = record['sequence']
if 'N' in seq:
return None, None
trim_seq, trim_at = ht.trim_on_abundance(seq, args.cutoff)
if trim_at >= K:
return name, trim_seq
return None, None
### the filtering loop
for infile in infiles:
print 'filtering', infile
outfile = outpath + '/' + os.path.basename(infile) + '.abundfilt'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(infile), outfp)
print 'output in', outfile
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-k',
default=DEFAULT_K,
type=int,
help='k-mer size',
dest='ksize')
parser.add_argument('stoptags_file')
parser.add_argument('input_filenames', nargs='+')
args = parser.parse_args()
K = args.ksize
stoptags = args.stoptags_file
infiles = args.input_filenames
print 'loading stop tags, with K', K
ht = khmer.new_hashbits(K, 1, 1)
ht.load_stop_tags(stoptags)
def process_fn(record):
name = record['name']
seq = record['sequence']
if 'N' in seq:
return None, None
trim_seq, trim_at = ht.trim_on_stoptags(seq)
if trim_at >= K:
return name, trim_seq
return None, None
### the filtering loop
for infile in infiles:
print 'filtering', infile
outfile = os.path.dirname(infile) + '/' + os.path.basename(
infile) + '.stopfilt'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(infile), outfp)
print 'output in', outfile
def main():
parser = build_counting_multifile_args()
parser.add_argument('--cutoff', '-C', dest='cutoff',
default=DEFAULT_CUTOFF, type=int,
help="Trim at k-mers below this abundance.")
parser.add_argument('-o', '--outputpath', dest='outputpath', default='.')
args = parser.parse_args()
counting_ht = args.input_table
infiles = args.input_filenames
outpath = args.outputpath
print 'file with ht: %s' % counting_ht
print 'loading hashtable'
ht = khmer.load_counting_hash(counting_ht)
K = ht.ksize()
print "K:", K
### the filtering function.
def process_fn(record):
name = record['name']
seq = record['sequence']
if 'N' in seq:
return None, None
trim_seq, trim_at = ht.trim_on_abundance(seq, args.cutoff)
if trim_at >= K:
return name, trim_seq
return None, None
### the filtering loop
for infile in infiles:
print 'filtering', infile
outfile = outpath + '/' + os.path.basename(infile) + '.abundfilt'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(infile), outfp)
print 'output in', outfile
def main():
parser = build_counting_multifile_args()
parser.add_argument('--cutoff',
'-C',
dest='cutoff',
default=DEFAULT_CUTOFF,
type=int,
help="Trim at reads above this median abundance.")
args = parser.parse_args()
counting_ht = args.input_table
infiles = args.input_filenames
print 'file with ht: %s' % counting_ht
print 'loading hashtable'
ht = khmer.load_counting_hash(counting_ht)
K = ht.ksize()
print "K:", K
### the filtering function.
def process_fn(record):
name = record['name']
seq = record['sequence']
med, _, _ = ht.get_median_count(seq)
if med >= args.cutoff:
return name, seq
return None, None
### the filtering loop
for infile in infiles:
print 'filtering', infile
outfile = os.path.basename(infile) + '.himed'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(infile), outfp)
print 'output in', outfile
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-k', default=DEFAULT_K, type=int, help='k-mer size',
dest='ksize')
parser.add_argument('stoptags_file')
parser.add_argument('input_filenames', nargs='+')
args = parser.parse_args()
K = args.ksize
stoptags = args.stoptags_file
infiles = args.input_filenames
print 'loading stop tags, with K', K
ht = khmer.new_hashbits(K, 1, 1)
ht.load_stop_tags(stoptags)
def process_fn(record):
name = record['name']
seq = record['sequence']
if 'N' in seq:
return None, None
trim_seq, trim_at = ht.trim_on_stoptags(seq)
if trim_at >= K:
return name, trim_seq
return None, None
### the filtering loop
for infile in infiles:
print 'filtering', infile
outfile = os.path.dirname(infile) + '/' + os.path.basename(infile) + '.stopfilt'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(infile), outfp)
print 'output in', outfile
def main():
parser = build_counting_args()
parser.add_argument('--coverage',
'-C',
dest='coverage',
default=DEFAULT_COVERAGE,
type=int)
args = parser.parse_args()
counting_ht = args.input_table
infiles = args.input_filenames
print('file with ht: %s' % counting_ht)
print('loading hashtable')
ht = khmer.load_countgraph(counting_ht)
K = ht.ksize()
print("K:", K)
# the filtering function.
def process_fn(record):
name = record['name']
seq = record['sequence']
med, avg, dev = ht.get_median_count(seq)
if random.randint(1, med) > args.coverage:
return None, None
return name, seq
# the filtering loop
for infile in infiles:
print('filtering', infile)
outfile = os.path.basename(infile) + '.medfilt'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(infile), outfp)
print('output in', outfile)
def main():
parser = build_counting_multifile_args()
parser.add_argument('--cutoff', '-C', dest='cutoff',
default=DEFAULT_CUTOFF, type=int,
help="Trim at reads above this median abundance.")
args = parser.parse_args()
counting_ht = args.input_table
infiles = args.input_filenames
print 'file with ht: %s' % counting_ht
print 'loading hashtable'
ht = khmer.load_counting_hash(counting_ht)
K = ht.ksize()
print "K:", K
### the filtering function.
def process_fn(record):
name = record['name']
seq = record['sequence']
med, _, _ = ht.get_median_count(seq)
if med >= args.cutoff:
return name, seq
return None, None
### the filtering loop
for infile in infiles:
print 'filtering', infile
outfile = os.path.basename(infile) + '.himed'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(infile), outfp)
print 'output in', outfile
def main():
info("filter-stoptags.py", ["graph"])
args = get_parser().parse_args()
stoptags = args.stoptags_file
infiles = args.input_filenames
for _ in infiles:
check_file_status(_)
check_space(infiles)
print "loading stop tags, with K", args.ksize
htable = khmer.new_hashbits(args.ksize, 1, 1)
htable.load_stop_tags(stoptags)
def process_fn(record):
name = record["name"]
seq = record["sequence"]
if "N" in seq:
return None, None
trim_seq, trim_at = htable.trim_on_stoptags(seq)
if trim_at >= args.ksize:
return name, trim_seq
return None, None
# the filtering loop
for infile in infiles:
print "filtering", infile
outfile = os.path.basename(infile) + ".stopfilt"
outfp = open(outfile, "w")
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(infile), outfp)
print "output in", outfile
def main():
info('filter-stoptags.py', ['graph'])
args = get_parser().parse_args()
stoptags = args.stoptags_file
infiles = args.input_filenames
for _ in infiles:
check_input_files(_, args.force)
check_space(infiles, args.force)
print >>sys.stderr, 'loading stop tags, with K', args.ksize
htable = khmer.new_hashbits(args.ksize, 1, 1)
htable.load_stop_tags(stoptags)
def process_fn(record):
name = record['name']
seq = record['sequence']
if 'N' in seq:
return None, None
trim_seq, trim_at = htable.trim_on_stoptags(seq)
if trim_at >= args.ksize:
return name, trim_seq
return None, None
# the filtering loop
for infile in infiles:
print >>sys.stderr, 'filtering', infile
outfile = os.path.basename(infile) + '.stopfilt'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(infile), outfp)
print >>sys.stderr, 'output in', outfile
def main():
parser = build_counting_args()
parser.add_argument('--coverage', '-C', dest='coverage',
default=DEFAULT_COVERAGE, type=int)
args = parser.parse_args()
counting_ht = args.input_table
infiles = args.input_filenames
print 'file with ht: %s' % counting_ht
print 'loading hashtable'
ht = khmer.load_counting_hash(counting_ht)
K = ht.ksize()
print "K:", K
# the filtering function.
def process_fn(record):
name = record['name']
seq = record['sequence']
med, avg, dev = ht.get_median_count(seq)
if random.randint(1, med) > args.coverage:
return None, None
return name, seq
# the filtering loop
for infile in infiles:
print 'filtering', infile
outfile = os.path.basename(infile) + '.medfilt'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(infile), outfp)
print 'output in', outfile
def main():
args = sanitize_help(get_parser()).parse_args()
stoptags = args.stoptags_file
infiles = args.input_filenames
for _ in infiles:
check_input_files(_, args.force)
check_space(infiles, args.force)
print('loading stop tags, with K', args.ksize, file=sys.stderr)
nodegraph = Nodegraph(args.ksize, 1, 1)
nodegraph.load_stop_tags(stoptags)
def process_fn(record):
name = record.name
seq = record.sequence
if 'N' in seq:
return None, None
trim_seq, trim_at = nodegraph.trim_on_stoptags(seq)
if trim_at >= args.ksize:
return name, trim_seq
return None, None
# the filtering loop
for infile in infiles:
print('filtering', infile, file=sys.stderr)
outfile = os.path.basename(infile) + '.stopfilt'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(infile), outfp)
print('output in', outfile, file=sys.stderr)
def main():
args = sanitize_help(get_parser()).parse_args()
stoptags = args.stoptags_file
infiles = args.input_filenames
for _ in infiles:
check_input_files(_, args.force)
check_space(infiles, args.force)
print('loading stop tags, with K', args.ksize, file=sys.stderr)
nodegraph = Nodegraph(args.ksize, 1, 1)
nodegraph.load_stop_tags(stoptags)
def process_fn(record):
name = record.name
seq = record.sequence
if 'N' in seq:
return None, None
trim_seq, trim_at = nodegraph.trim_on_stoptags(seq)
if trim_at >= args.ksize:
return name, trim_seq
return None, None
# the filtering loop
for infile in infiles:
print('filtering', infile, file=sys.stderr)
outfile = os.path.basename(infile) + '.stopfilt'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(infile), outfp)
print('output in', outfile, file=sys.stderr)
def main():
parser = build_counting_multifile_args()
parser.add_argument("--coverage", "-C", dest="coverage", default=DEFAULT_COVERAGE, type=int)
args = parser.parse_args()
counting_ht = args.input_table
infiles = args.input_filenames
print "file with ht: %s" % counting_ht
print "loading hashtable"
ht = khmer.load_counting_hash(counting_ht)
K = ht.ksize()
print "K:", K
### the filtering function.
def process_fn(record):
name = record["name"]
seq = record["sequence"]
med, avg, dev = ht.get_median_count(seq)
if random.randint(1, med) > args.coverage:
return None, None
return name, seq
### the filtering loop
for infile in infiles:
print "filtering", infile
outfile = os.path.basename(infile) + ".medfilt"
outfp = open(outfile, "w")
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(infile), outfp)
print "output in", outfile
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():
parser = build_construct_args(
"Filter k-mers at the given abundance (inmem version).")
add_threading_args(parser)
parser.add_argument('--cutoff',
'-C',
dest='cutoff',
default=DEFAULT_CUTOFF,
type=int,
help="Trim at k-mers below this abundance.")
parser.add_argument('--savehash', dest='savehash', default='')
parser.add_argument('datafile')
args = parser.parse_args()
report_on_config(args)
K = args.ksize
HT_SIZE = args.min_hashsize
N_HT = args.n_hashes
n_threads = int(args.n_threads)
config = khmer.get_config()
bufsz = config.get_reads_input_buffer_size()
config.set_reads_input_buffer_size(n_threads * 64 * 1024)
print 'making hashtable'
ht = khmer.new_counting_hash(K, HT_SIZE, N_HT, n_threads)
filename = args.datafile
# first, load reads into hash table
rparser = khmer.ReadParser(filename, n_threads)
threads = []
print 'consuming input, round 1 --', filename
for tnum in xrange(n_threads):
t = \
threading.Thread(
target=ht.consume_fasta_with_reads_parser,
args=(rparser, )
)
threads.append(t)
t.start()
for t in threads:
t.join()
fp_rate = khmer.calc_expected_collisions(ht)
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 = ht.trim_on_abundance(seq, args.cutoff)
if trim_at >= K:
return name, trim_seq
return None, None
# the filtering loop
print 'filtering', filename
outfile = os.path.basename(filename) + '.abundfilt'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(filename), outfp)
print 'output in', outfile
if args.savehash:
print 'Saving hashfile', args.savehash
print '...saving to', args.savehash
ht.save(args.savehash)
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():
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')
_, 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():
info('filter-abund-single.py', ['counting', 'SeqAn'])
args = sanitize_help(get_parser()).parse_args()
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)
print('making countgraph', file=sys.stderr)
graph = khmer_args.create_countgraph(args)
# first, load reads into graph
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=graph.consume_fasta_with_reads_parser,
args=(rparser, )
)
threads.append(cur_thread)
cur_thread.start()
for _ in threads:
_.join()
print('Total number of unique k-mers: {0}'.format(graph.n_unique_kmers()),
file=sys.stderr)
fp_rate = khmer.calc_expected_collisions(graph, 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 = 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
print('filtering', args.datafile, file=sys.stderr)
outfile = os.path.basename(args.datafile) + '.abundfilt'
outfile = open(outfile, 'wb')
outfp = get_file_writer(outfile, args.gzip, args.bzip)
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(args.datafile), outfp)
print('output in', outfile.name, file=sys.stderr)
if args.savegraph:
print('Saving k-mer countgraph filename',
args.savegraph,
file=sys.stderr)
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():
parser = build_counting_multifile_args()
parser.add_argument('--cutoff',
'-C',
dest='cutoff',
default=DEFAULT_CUTOFF,
type=int,
help="Trim at k-mers below this abundance.")
parser.add_argument('-V',
'--variable-coverage',
action='store_true',
dest='variable_coverage',
default=False)
parser.add_argument('--normalize-to',
'-Z',
type=int,
dest='normalize_to',
help='base variable-coverage cutoff on this median'
' k-mer abundance',
default=DEFAULT_NORMALIZE_LIMIT)
args = parser.parse_args()
counting_ht = args.input_table
infiles = args.input_filenames
print 'file with ht: %s' % counting_ht
print 'loading hashtable'
ht = khmer.load_counting_hash(counting_ht)
K = ht.ksize()
print "K:", K
# the filtering function.
def process_fn(record):
name = record['name']
seq = record['sequence']
if 'N' in seq:
return None, None
if args.variable_coverage: # only trim when sequence has high enough C
med, _, _ = ht.get_median_count(seq)
if med < args.normalize_to:
return name, seq
trim_seq, trim_at = ht.trim_on_abundance(seq, args.cutoff)
if trim_at >= K:
return name, trim_seq
return None, None
# the filtering loop
for infile in infiles:
print 'filtering', infile
outfile = os.path.basename(infile) + '.abundfilt'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(infile), outfp)
print 'output in', outfile
def main():
args = sanitize_help(get_parser()).parse_args()
if not args.quiet:
info('filter-abund.py', ['counting'])
configure_logging(args.quiet)
infiles = args.input_filename
if ('-' in infiles or '/dev/stdin' in infiles) and not \
args.single_output_file:
log_error("Accepting input from stdin; output filename must "
"be provided with -o.")
sys.exit(1)
for filename in infiles:
check_input_files(filename, args.force)
check_space(infiles, args.force)
log_info('loading countgraph: {graph}', graph=args.input_graph)
countgraph = khmer.load_countgraph(args.input_graph)
ksize = countgraph.ksize()
log_info("K: {ksize}", ksize=ksize)
# 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, _, _ = countgraph.get_median_count(seqN)
if med < args.normalize_to:
return name, seq
_, trim_at = countgraph.trim_on_abundance(seqN, args.cutoff)
if trim_at >= 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
if args.single_output_file:
outfile = args.single_output_file.name
outfp = get_file_writer(args.single_output_file, args.gzip, args.bzip)
# the filtering loop
for infile in infiles:
log_info('filtering {infile}', infile=infile)
if not args.single_output_file:
outfile = os.path.basename(infile) + '.abundfilt'
outfp = open(outfile, 'wb')
outfp = get_file_writer(outfp, args.gzip, args.bzip)
tsp = ThreadedSequenceProcessor(process_fn, n_workers=args.threads,
verbose=not args.quiet)
tsp.start(verbose_loader(infile), outfp)
log_info('output in {outfile}', outfile=outfile)
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():
parser = build_construct_args(
"Filter k-mers at the given abundance (inmem version).")
add_threading_args(parser)
parser.add_argument('--cutoff', '-C', dest='cutoff',
default=DEFAULT_CUTOFF, type=int,
help="Trim at k-mers below this abundance.")
parser.add_argument('--savehash', dest='savehash', default='')
parser.add_argument('datafile')
args = parser.parse_args()
report_on_config(args)
K = args.ksize
HT_SIZE = args.min_hashsize
N_HT = args.n_hashes
n_threads = int(args.n_threads)
config = khmer.get_config()
bufsz = config.get_reads_input_buffer_size()
config.set_reads_input_buffer_size(n_threads * 64 * 1024)
print 'making hashtable'
ht = khmer.new_counting_hash(K, HT_SIZE, N_HT, n_threads)
filename = args.datafile
### first, load reads into hash table
rparser = khmer.ReadParser(filename, n_threads)
threads = []
print 'consuming input, round 1 --', filename
for tnum in xrange(n_threads):
t = \
threading.Thread(
target=ht.consume_fasta_with_reads_parser,
args=(rparser, )
)
threads.append(t)
t.start()
for t in threads:
t.join()
fp_rate = khmer.calc_expected_collisions(ht)
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 = ht.trim_on_abundance(seq, args.cutoff)
if trim_at >= K:
return name, trim_seq
return None, None
### the filtering loop
print 'filtering', filename
outfile = os.path.basename(filename) + '.abundfilt'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(filename), outfp)
print 'output in', outfile
if args.savehash:
print 'Saving hashfile', args.savehash
print '...saving to', args.savehash
ht.save(args.savehash)
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()
if args.report_total_kmers:
print >> sys.stderr, 'Total number of k-mers: {0}'.format(
htable.n_occupied())
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)