def test_bloom_c_2(): # simple one
K = 4
HT_SIZE = 10 # use 11
N_HT1 = 1 # hashtable size = 11
N_HT2 = 2 # hashtable size = 11,13
# use only 1 hashtable, no bloom filter
ht1 = khmer.new_hashbits(K, HT_SIZE, N_HT1)
ht1.count("AAAA") # 00 00 00 00 = 0
ht1.count("ACTG") # 00 10 01 11 =
assert ht1.n_unique_kmers() == 2
ht1.count("AACG") # 00 00 10 11 = 11 # collision with 1st kmer
assert ht1.n_unique_kmers() == 2
ht1.count("AGAC") # 00 11 00 10 # collision with 2nd kmer
assert ht1.n_unique_kmers() == 2
# use two hashtables with 11,13
ht2 = khmer.new_hashbits(K, HT_SIZE, N_HT2)
ht2.count("AAAA") # 00 00 00 00 = 0
ht2.count("ACTG") # 00 10 01 11 = 2*16 +4 +3 = 39
assert ht2.n_unique_kmers() == 2
ht2.count("AACG") # 00 00 10 11 = 11 # collision with only 1st kmer
assert ht2.n_unique_kmers() == 3
ht2.count("AGAC") # 00 11 00 10 3*16 +2 = 50
# collision with both 2nd and 3rd kmers
assert ht2.n_unique_kmers() == 3
def test_random_20_a_succ_IV_save(self):
ht = khmer.new_hashbits(20, 4 ** 7 + 1)
filename = utils.get_test_data('random-20-a.fa')
savefile_ht = utils.get_temp_filename('ht')
savefile_tags = utils.get_temp_filename('tags')
outfile = filename + utils.get_temp_filename('out')
total_reads, _ = ht.consume_fasta_and_tag(filename)
ht.save(savefile_ht)
ht.save_tagset(savefile_tags)
del ht
ht = khmer.new_hashbits(20, 4 ** 7 + 1)
ht.load(savefile_ht)
ht.load_tagset(savefile_tags)
divvy = ht.divide_tags_into_subsets(1)
divvy.append(0)
subsets = []
for i in range(len(divvy) - 1):
x = ht.do_subset_partition(divvy[i], divvy[i + 1])
subsets.append(x)
for x in reversed(subsets):
ht.merge_subset(x)
n_partitions = ht.output_partitions(filename, outfile)
assert n_partitions == 1, n_partitions
def test_random_20_a_succ_IV_save(self):
ht = khmer.new_hashbits(20, 4**13+1)
filename = os.path.join(thisdir, 'test-data/random-20-a.fa')
savefile_ht = filename + '.ht'
savefile_tags = filename + '.tags'
outfile = filename + '.out'
total_reads, _ = ht.consume_fasta_and_tag(filename)
ht.save(savefile_ht);
ht.save_tagset(savefile_tags);
del ht
ht = khmer.new_hashbits(20, 4**13+1)
ht.load(savefile_ht);
ht.load_tagset(savefile_tags);
divvy = ht.divide_tags_into_subsets(1)
divvy.append(0)
subsets = []
for i in range(len(divvy) - 1):
x = ht.do_subset_partition(divvy[i], divvy[i+1])
subsets.append(x)
for x in reversed(subsets):
ht.merge_subset(x)
n_partitions = ht.output_partitions(filename, outfile)
assert n_partitions == 1, n_partitions
def count_overlap(K,HT_SIZE,N_HT,filename,filename2,file_result,file_curve):
if file_curve !='N':
count = 0
for n, record in enumerate(screed.open(filename2)):
count = count+1
max_count = count/100
file3 = open(file_curve,'w')
ht = khmer.new_hashbits(K, HT_SIZE, N_HT)
n_unique = 0
for n, record in enumerate(screed.open(filename)):
sequence = record['sequence']
seq_len = len(sequence)
for n in range(0,seq_len+1-K):
kmer = sequence[n:n+K]
if (not ht.get(kmer)):
n_unique+=1
ht.count(kmer)
print filename,'has been consumed.'
fpr = (1- math.exp(-n_unique/HT_SIZE))**Z
printout1 = "%s:\n# of unique kmers: %n\n# of occupied bin: %n\nfalse positive\
rate: %n" %(filename,n_unique,ht.n_occupied(),fpr)
# consume second dataset
ht2 = khmer.new_hashbits(K, HT_SIZE, N_HT)
n_unique = 0
n_overlap = 0
seq_count = 0
for n, record in enumerate(screed.open(filename2)):
sequence = record['sequence']
seq_len = len(sequence)
for n in range(0,seq_len+1-K):
kmer = sequence[n:n+K]
if (not ht2.get(kmer)):
n_unique+=1
if (ht.get(kmer)):
n_overlap+=1
ht2.count(kmer)
if file_curve !='N':
seq_count = seq_count + 1
if seq_count == max_count:
#n_occu = ht2.n_occupied
string = str(n_unique)+' '+str(n_overlap)+'\n'
file3 = open(file_curve,'a')
file3.write(string)
file3.close()
seq_count = 0
print filename2,'has been consumed.'
fpr = (1- math.exp(-n_unique/HT_SIZE))**Z
printout2 = "%s:\n# of unique k-mers: %n\n# of occupied bin: %n\nfalse \
positive rate: %n\n===============\n# of overlap unique k-mers: %n\n" \
%(filename2,n_unique,ht2.n_occupied(),n_overlap)
file_result_object = open(file_result,'w')
file_result_object.write(printout1)
file_result_object.write(printout2)
def test_count_within_radius_big():
inpfile = utils.get_test_data('random-20-a.fa')
ht = khmer.new_hashbits(20, 1e5, 4)
ht.consume_fasta(inpfile)
n = ht.count_kmers_within_radius('CGCAGGCTGGATTCTAGAGG', int(1e6))
assert n == 3960
ht = khmer.new_hashbits(21, 1e5, 4)
ht.consume_fasta(inpfile)
n = ht.count_kmers_within_radius('CGCAGGCTGGATTCTAGAGGC', int(1e6))
assert n == 39
def test_count_within_radius_big():
inpfile = os.path.join(thisdir, "test-data", "random-20-a.fa")
ht = khmer.new_hashbits(20, 1e6, 4)
ht.consume_fasta(inpfile)
n = ht.count_kmers_within_radius("CGCAGGCTGGATTCTAGAGG", 1e6)
assert n == 3960
ht = khmer.new_hashbits(21, 1e6, 4)
ht.consume_fasta(inpfile)
n = ht.count_kmers_within_radius("CGCAGGCTGGATTCTAGAGGC", 1e6)
assert n == 39
def run_no_curve(K,HT_SIZE,N_HT,filename,filename2,file_result):
file_result_object = open(file_result,'w')
ht = khmer.new_hashbits(K, HT_SIZE, N_HT)
n_unique = 0
for n, record in enumerate(fasta_iter(open(filename))):
sequence = record['sequence']
seq_len = len(sequence)
for n in range(0,seq_len+1-K):
kmer = sequence[n:n+K]
if (not ht.get(kmer)):
n_unique+=1
ht.count(kmer)
print filename,'has been consumed.'
print '# of unique kmers:',n_unique
print '# of occupied bin:',ht.n_occupied()
printout = filename+":"+'\n'
printout = printout+'# of unique kmers:'+str(n_unique)+'\n'
printout = printout + '# of occupied bin:'+str(ht.n_occupied())+'\n'
ht2 = khmer.new_hashbits(K, HT_SIZE, N_HT)
n_unique = 0
n_overlap = 0
for n, record in enumerate(fasta_iter(open(filename2))):
sequence = record['sequence']
seq_len = len(sequence)
for n in range(0,seq_len+1-K):
kmer = sequence[n:n+K]
if (not ht2.get(kmer)):
n_unique+=1
if (ht.get(kmer)):
n_overlap+=1
ht2.count(kmer)
print filename2,'has been consumed.'
print '# of unique kmers:',n_unique
print '# of occupied bin:',ht2.n_occupied()
print n_overlap,'unique kmers appears in both ',filename,' and ',filename2
printout = printout+filename2+":"+'\n'
printout = printout+'# of unique kmers:'+str(n_unique)+'\n'
printout = printout + '# of occupied bin:'+str(ht2.n_occupied())+'\n'
printout = printout + '# of overlap unique kmers:' + str(n_overlap) + '\n'
file_result_object.write(printout)
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():
parser = argparse.ArgumentParser(
description="Annotate seqs with partitions.")
parser.add_argument('--ksize', '-k', type=int, default=DEFAULT_K,
help="k-mer size (default: %d)" % DEFAULT_K)
parser.add_argument('graphbase')
parser.add_argument('input_filenames', nargs='+')
args = parser.parse_args()
K = args.ksize
ht = khmer.new_hashbits(K, 1, 1)
partitionmap_file = args.graphbase + '.pmap.merged'
print 'loading partition map from:', partitionmap_file
ht.load_partitionmap(partitionmap_file)
for infile in args.input_filenames:
print 'outputting partitions for', infile
outfile = os.path.basename(infile) + '.part'
n = ht.output_partitions(infile, outfile)
print 'output %d partitions for %s' % (n, infile)
print 'partitions are in', outfile
def main(dir1, dir2, n_threads):
# detect all of the relevant partitionmap files
subset_filenames = glob.glob(os.path.join(dir1, '*.pmap'))
# create empty hashtable structure
ht = khmer.new_hashbits(K, 1, 1)
# put jobs on queue
merge_queue = Queue.Queue()
for filename in subset_filenames:
merge_queue.put((ht, filename))
print 'starting threads'
threads = []
for n in range(n_threads):
t = threading.Thread(target=pull_pair, args=(merge_queue,))
threads.append(t)
t.start()
# wait for threads
for t in threads:
t.join()
# done!
if merge_queue.qsize() == 1:
ht, merge_file = merge_queue.get()
print 'copying', merge_file
shutil.copy(merge_file, os.path.join(dir2,
os.path.basename(merge_file)))
assert merge_queue.qsize() == 0
def diff(ht, filename):
genome = khmer.new_hashbits(K, 4**K, 1)
found = 0
not_found = 0
for n, record in enumerate(screed.fasta.fasta_iter(open(filename))):
read = record['sequence']
name = record['name']
if 'N' in read:
continue
if len(read) < K:
continue
seq_len = len(read)
for n in range(0,seq_len+1-K):
kmer = read[n:n+K]
if not genome.get(kmer):
genome.consume(kmer)
if ht.get(kmer):
found += 1
else:
not_found += 1
return found, not_found
def test_save_load_tagset_trunc():
ht = khmer.new_hashbits(32, 1, 1)
outfile = utils.get_temp_filename('tagset')
ht.add_tag('A' * 32)
ht.add_tag('G' * 32)
ht.save_tagset(outfile)
ht.save_tagset('/tmp/goodversion-k32.tagset')
# truncate tagset file...
fp = open(outfile, 'rb')
data = fp.read()
fp.close()
fp = open(outfile, 'wb')
fp.write(data[:26])
fp.close()
# try loading it...
try:
ht.load_tagset(outfile)
assert 0, "this test should fail"
except IOError:
pass
def test_consume_absentfasta_with_reads_parser():
presencetable = khmer.new_hashbits(31, 1, 1)
try:
presencetable.consume_fasta_with_reads_parser()
assert 0, "this should fail"
except TypeError, err:
print str(err)
def main():
parser = argparse.ArgumentParser(description="Merge pmap files.")
parser.add_argument('--ksize', '-k', type=int, default=DEFAULT_K,
help="k-mer size (default: %d)" % DEFAULT_K)
parser.add_argument('--keep-subsets', dest='remove_subsets',
default=True, action='store_false',
help='Keep individual subsets (default: False)')
parser.add_argument('graphbase')
args = parser.parse_args()
output_file = args.graphbase + '.pmap.merged'
pmap_files = glob.glob(args.graphbase + '.subset.*.pmap')
print 'loading %d pmap files (first one: %s)' % (len(pmap_files),
pmap_files[0])
K = args.ksize
ht = khmer.new_hashbits(K, 1, 1)
for pmap_file in pmap_files:
print 'merging', pmap_file
ht.merge_subset_from_disk(pmap_file)
print 'saving merged to', output_file
ht.save_partitionmap(output_file)
if args.remove_subsets:
print 'removing pmap files'
for pmap_file in pmap_files:
os.unlink(pmap_file)
def main():
parser = build_common_args()
parser.add_argument('output_filename')
parser.add_argument('input_filenames', nargs='+')
args = parse_args(parser)
K = args.ksize
HT_SIZE = args.min_hashsize
N_HT = args.n_hashes
base = args.output_filename
filenames = args.input_filenames
print 'Saving hashtable to %s' % base
print 'Loading kmers from sequences in %s' % repr(filenames)
###
print 'making hashtable'
ht = khmer.new_hashbits(K, HT_SIZE, N_HT)
for n, filename in enumerate(filenames):
print 'consuming input', filename
ht.consume_fasta(filename)
if n > 0 and n % 10 == 0:
print 'mid-save', base
ht.save(base)
open(base + '.info', 'w').write('through %s' % filename)
print 'saving', base
ht.save(base)
open(base + '.info', 'w').write('through end: %s' % filename)
def main():
ht = khmer.new_hashbits(K, 1, 1)
x = [0] * 255
y = [0] * 255
ht.load_stop_tags(sys.argv[1])
for n, record in enumerate(screed.open(sys.argv[2])):
if n % 10000 == 0:
sys.stderr.write('... %d\n' % n)
s, p = ht.trim_on_stoptags(record.sequence)
if len(s) == len(record.sequence):
continue
if p == 0:
p = 31
else:
p += 1
x[p] += 1
y[len(record.sequence)] += 1
for i, (n, m) in enumerate(zip(x, y)):
if m:
print '%d,%d,%d' % (i, n, m)
def main():
info('annotate-partitions.py', ['graph'])
args = get_parser().parse_args()
ksize = args.ksize
filenames = args.input_filenames
htable = khmer.new_hashbits(ksize, 1, 1)
partitionmap_file = args.graphbase + '.pmap.merged'
check_file_status(partitionmap_file)
for _ in filenames:
check_file_status(_)
check_space(filenames)
print 'loading partition map from:', partitionmap_file
htable.load_partitionmap(partitionmap_file)
for infile in 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 test_extract_unique_paths_2():
kh = khmer.new_hashbits(10, 4, 4)
kh.consume('ATGGAGAGAC')
x = kh.extract_unique_paths('ATGGAGAGACACAGATAGACAGGAGTGGCGATG', 10, 1)
print x
assert x == ['TGGAGAGACACAGATAGACAGGAGTGGCGATG'] # all but the 1st k-mer
def test_save_load_tagset_trunc():
ht = khmer.new_hashbits(32, 1, 1)
outfile = utils.get_temp_filename('tagset')
ht.add_tag('A' * 32)
ht.add_tag('G' * 32)
ht.save_tagset(outfile)
# truncate tagset file...
fp = open(outfile, 'rb')
data = fp.read()
fp.close()
for i in range(len(data)):
fp = open(outfile, 'wb')
fp.write(data[:i])
fp.close()
# try loading it...
try:
ht.load_tagset(outfile)
assert 0, "this test should fail"
except IOError as err:
print str(err), i
def main():
parser = build_common_args()
parser.add_argument("output_filename")
parser.add_argument("input_filenames", nargs="+")
args = parse_args(parser)
K = args.ksize
HT_SIZE = args.min_hashsize
N_HT = args.n_hashes
base = args.output_filename
filenames = args.input_filenames
print "Saving hashtable to %s" % base
print "Loading kmers from sequences in %s" % repr(filenames)
###
print "making hashtable"
ht = khmer.new_hashbits(K, HT_SIZE, N_HT)
for n, filename in enumerate(filenames):
print "consuming input", filename
ht.consume_fasta(filename)
if n > 0 and n % 10 == 0:
print "mid-save", base
ht.save(base)
open(base + ".info", "w").write("through %s" % filename)
print "saving", base
ht.save(base)
open(base + ".info", "w").write("through end: %s" % filename)
def test_tag_across_stoptraverse():
filename = utils.get_test_data('random-20-a.fa')
K = 20 # size of kmer
HT_SIZE = 1e4 # size of hashtable
N_HT = 3 # number of hashtables
ht = khmer.new_hashbits(K, HT_SIZE, N_HT)
# without tagging/joining across consume, this breaks into two partition;
# with, it is one partition.
ht.add_stop_tag('CCGAATATATAACAGCGACG')
ht.consume_fasta_and_tag_with_stoptags(filename) # DO join reads across
subset = ht.do_subset_partition(0, 0)
n, _ = ht.count_partitions()
assert n == 99 # reads only connected by traversal...
n, _ = ht.subset_count_partitions(subset)
assert n == 2 # but need main to cross stoptags.
ht.merge_subset(subset)
n, _ = ht.count_partitions() # ta-da!
assert n == 1, n
def test_save_load_merge_nexist(self):
ht = khmer.new_hashbits(20, 1)
try:
a = ht.load_subset_partitionmap('this does not exist')
assert 0, "this should not succeed"
except IOError as e:
print str(e)
def test__get_set_tag_density():
ht = khmer.new_hashbits(32, 1, 1)
orig = ht._get_tag_density()
assert orig != 2
ht._set_tag_density(2)
assert ht._get_tag_density() == 2
def test_save_merge_from_disk_2(self):
ht = khmer.new_hashbits(20, 4 ** 7 + 1)
filename = utils.get_test_data('random-20-a.fa')
(total_reads, total_kmers) = ht.consume_fasta_and_tag(filename)
subset_size = total_reads // 2 + total_reads % 2
divvy = ht.divide_tags_into_subsets(subset_size)
outfile1 = utils.get_temp_filename('x.pmap')
outfile2 = utils.get_temp_filename('y.pmap')
x = ht.do_subset_partition(divvy[0], divvy[1])
ht.save_subset_partitionmap(x, outfile1)
del x
y = ht.do_subset_partition(divvy[1], 0)
ht.save_subset_partitionmap(y, outfile2)
del y
ht.merge_subset_from_disk(outfile1)
ht.merge_subset_from_disk(outfile2)
outfile = utils.get_temp_filename('out.part')
n_partitions = ht.output_partitions(filename, outfile)
assert n_partitions == 1, n_partitions # combined.
def test_save_merge_from_disk(self):
ht = khmer.new_hashbits(20, 4 ** 4 + 1)
filename = utils.get_test_data('test-graph2.fa')
(total_reads, total_kmers) = ht.consume_fasta_and_tag(filename)
assert total_reads == 3, total_reads
divvy = ht.divide_tags_into_subsets(1)
print divvy
(a, b, c) = divvy
outfile1 = utils.get_temp_filename('x.pmap')
outfile2 = utils.get_temp_filename('y.pmap')
x = ht.do_subset_partition(a, b)
ht.save_subset_partitionmap(x, outfile1)
del x
y = ht.do_subset_partition(b, 0)
ht.save_subset_partitionmap(y, outfile2)
del y
ht.merge_subset_from_disk(outfile1)
ht.merge_subset_from_disk(outfile2)
outfile = utils.get_temp_filename('out.part')
n_partitions = ht.output_partitions(filename, outfile)
assert n_partitions == 1, n_partitions # combined.
def test_tiny_real_partitions():
filename = utils.get_test_data('real-partition-tiny.fa')
ht = khmer.new_hashbits(32, 8e1, 4)
ht.consume_fasta_and_tag(filename)
subset = ht.do_subset_partition(0, 0)
ht.merge_subset(subset)
outfile = utils.get_temp_filename('part')
ht.output_partitions(filename, outfile)
data = open(outfile).read()
assert len(data)
records = [r for r in screed.open(outfile)]
names = [r.name for r in records]
parts = [n.rsplit('\t', 1)[1] for n in names]
assert len(parts) == 2, len(parts)
assert len(set(parts)) == 1
assert set(parts) != set(['0'])
test_tiny_real_partitions.runme = True
def main():
info('merge-partitions.py', ['graph'])
args = get_parser().parse_args()
output_file = args.graphbase + '.pmap.merged'
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)
ksize = args.ksize
htable = khmer.new_hashbits(ksize, 1, 1)
for _ in pmap_files:
check_input_files(_, args.force)
check_space(pmap_files, args.force)
for pmap_file in pmap_files:
print('merging', pmap_file, file=sys.stderr)
htable.merge_subset_from_disk(pmap_file)
print('saving merged to', output_file, file=sys.stderr)
htable.save_partitionmap(output_file)
if args.remove_subsets:
print('removing pmap files', file=sys.stderr)
for pmap_file in pmap_files:
os.unlink(pmap_file)
def test_filter_stoptags():
infile = utils.get_temp_filename('test.fa')
in_dir = os.path.dirname(infile)
stopfile = utils.get_temp_filename('stoptags', in_dir)
# first, copy test-abund-read-2.fa to 'test.fa' in the temp dir.
shutil.copyfile(utils.get_test_data('test-abund-read-2.fa'), infile)
# now, create a file with some stop tags in it --
K = 18
kh = khmer.new_hashbits(K, 1, 1)
kh.add_stop_tag('GTTGACGGGGCTCAGGGG')
kh.save_stop_tags(stopfile)
del kh
# finally, run filter-stoptags.
script = scriptpath('filter-stoptags.py')
args = ['-k', str(K), stopfile, infile, infile]
(status, out, err) = runscript(script, args, in_dir)
print out
print err
assert status == 0
# verify that the basic output file exists
outfile = infile + '.stopfilt'
assert os.path.exists(outfile), outfile
# it should contain only one unique sequence, because we've trimmed
# off everything after the beginning of the only long sequence in there.
seqs = set([ r.sequence for r in screed.open(outfile) ])
assert len(seqs) == 1, seqs
assert 'GGTTGACGGGGCTCAGGG' in seqs, seqs
def main(filename):
global ht
basename = os.path.basename(filename)
print 'input file to partition: %s' % filename
print '-- settings:'
print 'K', K
print 'HASHTABLE SIZE %g' % HASHTABLE_SIZE
print 'N HASHTABLES %d' % N_HT
print '--'
ht = khmer.new_hashbits(K, HASHTABLE_SIZE, N_HT)
ht.consume_fasta(filename)
counting = khmer.new_counting_hash(K, COUNTING_SIZE, N_HT)
ht.traverse_from_reads(filename, 100, 5000, 5, counting)
print 'saving stoptags binary'
ht.save_stop_tags(basename + '.stoptags')
print 'saving stoptags text'
ht.print_stop_tags(basename + '.stoptags.txt')
sys.exit(0)
def test_save_load_merge_on_graph():
ht = khmer.new_hashbits(20, 4 ** 4 + 1)
filename = utils.get_test_data('test-graph2.fa')
(total_reads, total_kmers) = ht.consume_fasta_and_tag(filename)
assert total_reads == 3, total_reads
divvy = ht.divide_tags_into_subsets(1)
print(divvy)
assert len(divvy) is 3
(a, b, c) = divvy
outfile1 = utils.get_temp_filename('x.pmap')
outfile2 = utils.get_temp_filename('y.pmap')
x = ht.do_subset_partition(a, b)
ht.save_subset_partitionmap(x, outfile1)
del x
y = ht.do_subset_partition(b, 0)
ht.save_subset_partitionmap(y, outfile2)
del y
a = ht.load_partitionmap(outfile1) # <-- this is different
b = ht.load_subset_partitionmap(outfile2)
ht.merge_subset(b)
outfile = utils.get_temp_filename('out.part')
n_partitions = ht.output_partitions(filename, outfile)
assert n_partitions == 1, n_partitions # combined.
def test_3_merge_013(self):
ht = khmer.new_hashbits(20, 4**14 + 1)
filename = utils.get_test_data('test-graph2.fa')
(total_reads, total_kmers) = ht.consume_fasta_and_tag(filename)
assert total_reads == 3, total_reads
(a, b, c) = ht.divide_tags_into_subsets(1)
x = ht.do_subset_partition(a, a)
ht.merge_subset(x)
y = ht.do_subset_partition(b, 0)
ht.merge_subset(y)
outfile = utils.get_temp_filename('out')
n_partitions = ht.output_partitions(filename, outfile)
assert n_partitions == 1, n_partitions # combined.
def test_stop_traverse():
filename = utils.get_test_data('random-20-a.fa')
K = 20 # size of kmer
HT_SIZE = 1e4 # size of hashtable
N_HT = 3 # number of hashtables
ht = khmer.new_hashbits(K, HT_SIZE, N_HT)
# without tagging/joining across consume, this breaks into two partition;
# with, it is one partition.
ht.add_stop_tag('TTGCATACGTTGAGCCAGCG')
ht.consume_fasta_and_tag(filename) # DO NOT join reads across stoptags
subset = ht.do_subset_partition(0, 0, True)
ht.merge_subset(subset)
n, _ = ht.count_partitions()
assert n == 2, n
def test_notag_across_stoptraverse():
filename = utils.get_test_data('random-20-a.fa')
K = 20 # size of kmer
HT_SIZE = 1e4 # size of hashtable
N_HT = 3 # number of hashtables
ht = khmer.new_hashbits(K, HT_SIZE, N_HT)
# connecting k-mer at the beginning/end of a read: breaks up into two.
ht.add_stop_tag('TTGCATACGTTGAGCCAGCG')
ht.consume_fasta_and_tag_with_stoptags(filename)
subset = ht.do_subset_partition(0, 0)
ht.merge_subset(subset)
n, _ = ht.count_partitions()
assert n == 2, n
def test_find_all_tags_kmersize(self):
ht = khmer.new_hashbits(20, 4 ** 4 + 1)
a = "ATTGGGACTCTGGGAGCACTTATCATGGAGAT"
b = "GAGCACTTTAACCCTGCAGAGTGGCCAAGGCT"
c = "GGAGCACTTATCATGGAGATATATCCCGTGCTTAAACATCGCACTTTAACCCTGCAGAGT"
print ht.consume(a)
try:
ppi = ht.find_all_tags(c[:19])
assert False, "should raise a ValueError for wrong k-mer size"
except ValueError:
pass
try:
ppi = ht.find_all_tags(c[:21])
assert False, "should raise a ValueError for wrong k-mer size"
except ValueError:
pass
def test_random_20_a_succ_IV(self):
ht = khmer.new_hashbits(20, 4**13 + 1)
filename = utils.get_test_data('random-20-a.fa')
outfile = utils.get_temp_filename('out')
total_reads, _ = ht.consume_fasta_and_tag(filename)
subsets = []
divvy = ht.divide_tags_into_subsets(1)
divvy.append(0)
for i in range(len(divvy) - 1):
x = ht.do_subset_partition(divvy[i], divvy[i + 1])
subsets.append(x)
for x in reversed(subsets):
ht.merge_subset(x)
n_partitions = ht.output_partitions(filename, outfile)
assert n_partitions == 1, n_partitions
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():
global done, worker_count
done = False
worker_count = 0
infile = sys.argv[1]
outfile = infile + '.graphsize2'
print 'creating ht'
ht = khmer.new_hashbits(K, HASHTABLE_SIZE, 1)
print 'eating fa', infile
total_reads, n_consumed = ht.consume_fasta(infile)
outfp = open(outfile, 'w')
inqueue = Queue.Queue(50)
outqueue = Queue.Queue(50)
## worker and writer threads
for i in range(WORKER_THREADS):
t = threading.Thread(target=process, args=(inqueue, outqueue, ht))
worker_count += 1
t.start()
threading.Thread(target=write, args=(outqueue, outfp)).start()
### main thread
x = []
i = 0
for n, record in enumerate(screed.fasta.fasta_iter(open(infile))):
if n % 10000 == 0:
print '...', n
x.append(record)
i += 1
if i > GROUPSIZE:
inqueue.put(x)
x = []
i = 0
inqueue.put(x)
done = True
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.basename(infile) + '.stopfilt'
outfp = open(outfile, 'w')
tsp = ThreadedSequenceProcessor(process_fn)
tsp.start(verbose_loader(infile), outfp)
print 'output in', outfile
def test_save_load_tagset():
ht = khmer.new_hashbits(32, 1, 1)
outfile = utils.get_temp_filename('tagset')
ht.add_tag('A' * 32)
ht.save_tagset(outfile)
ht.add_tag('G' * 32)
ht.load_tagset(outfile) # implicitly => clear_tags=True
ht.save_tagset(outfile)
# if tags have been cleared, then the new tagfile will be larger (34 bytes)
# else smaller (26 bytes).
fp = open(outfile, 'rb')
data = fp.read()
fp.close()
assert len(data) == 26, len(data)
def test_find_unpart_fail():
filename = utils.get_test_data('random-20-a.odd.fa')
filename2 = utils.get_test_data('random-20-a.odd.fa') # <- switch to odd
K = 20 # size of kmer
HT_SIZE = 100000 # size of hashtable
N_HT = 3 # number of hashtables
ht = khmer.new_hashbits(K, HT_SIZE, N_HT)
ht.consume_fasta_and_tag(filename)
subset = ht.do_subset_partition(0, 0)
ht.merge_subset(subset)
n, _ = ht.count_partitions()
assert n == 49
ht.find_unpart(filename2, True, False)
n, _ = ht.count_partitions()
assert n == 49, n # only 49 sequences worth of tags
def test_find_unpart_notraverse():
filename = utils.get_test_data('random-20-a.odd.fa')
filename2 = utils.get_test_data('random-20-a.even.fa')
K = 20 # size of kmer
HT_SIZE = 100000 # size of hashtable
N_HT = 3 # number of hashtables
ht = khmer.new_hashbits(K, HT_SIZE, N_HT)
ht.consume_fasta_and_tag(filename)
subset = ht.do_subset_partition(0, 0)
ht.merge_subset(subset)
n, _ = ht.count_partitions()
assert n == 49
ht.find_unpart(filename2, False, False) # <-- don't traverse
n, _ = ht.count_partitions()
assert n == 99, n # all sequences disconnected
def test_save_merge_from_disk_file_not_exist(self):
ht = khmer.new_hashbits(20, 4**4 + 1)
filename = utils.get_test_data('test-graph2.fa')
(total_reads, total_kmers) = ht.consume_fasta_and_tag(filename)
assert total_reads == 3, total_reads
divvy = ht.divide_tags_into_subsets(1)
print divvy
(a, b, c) = divvy
outfile1 = utils.get_temp_filename('x.pmap')
# fail to create file... => failure expected
try:
ht.merge_subset_from_disk(outfile1)
assert 0, "this should fail"
except IOError as e:
print str(e)
def test_save_load_tagset_noclear():
ht = khmer.new_hashbits(32, 1, 1)
outfile = utils.get_temp_filename('tagset')
ht.add_tag('A' * 32)
ht.save_tagset(outfile)
ht.add_tag('G' * 32)
ht.load_tagset(outfile, False) # set clear_tags => False; zero tags
ht.save_tagset(outfile)
# if tags have been cleared, then the new tagfile will be large (34 bytes);
# else small (26 bytes).
fp = open(outfile, 'rb')
data = fp.read()
fp.close()
assert len(data) == 34, len(data)
def main():
filename = sys.argv[1]
K = int(sys.argv[2]) # size of kmer
HT_SIZE = int(sys.argv[3]) # size of hashtable
N_HT = int(sys.argv[4]) # number of hashtables
ht = khmer.new_hashbits(K, HT_SIZE, N_HT)
n_unique = 0
for n, record in enumerate(fasta_iter(open(filename))):
sequence = record['sequence']
seq_len = len(sequence)
for n in range(0, seq_len + 1 - K):
kmer = sequence[n:n + K]
if (not ht.get(kmer)):
n_unique += 1
ht.count(kmer)
print n_unique
print ht.n_occupied()
print ht.n_unique_kmers()
def test_ordered_connect(self):
ht = khmer.new_hashbits(20, 4 ** 4 + 1)
a = "ATTGGGACTCTGGGAGCACTTATCATGGAGAT"
b = "GAGCACTTTAACCCTGCAGAGTGGCCAAGGCT"
c = "GGAGCACTTATCATGGAGATATATCCCGTGCTTAAACATCGCACTTTAACCCTGCAGAGT"
print ht.consume(a)
ppi = ht.find_all_tags(a[:20])
pid = ht.assign_partition_id(ppi)
assert pid == 0, pid
print ht.consume(b)
ppi = ht.find_all_tags(b[:20])
pid = ht.assign_partition_id(ppi)
assert pid == 0, pid
print ht.consume(c)
ppi = ht.find_all_tags(c[:20])
pid = ht.assign_partition_id(ppi)
assert pid == 2, pid
def deg(filename, ht):
kmers = khmer.new_hashbits(K, 4**K, 1)
degs = {}
for n, record in enumerate(screed.fasta.fasta_iter(open(filename))):
read = record['sequence']
name = record['name']
if len(read) < K:
continue
if 'N' in read:
continue
get_all_kmers(ht, read[0:K], K, kmers, degs)
n_occ = kmers.n_occupied()
del kmers
return n_occ, degs
def test_output_partitions():
filename = utils.get_test_data('test-output-partitions.fa')
ht = khmer.new_hashbits(10, 1, 1)
ht.set_partition_id('TTAGGACTGC', 2)
ht.set_partition_id('TGCGTTTCAA', 3)
ht.set_partition_id('ATACTGTAAA', 4)
outfile = utils.get_temp_filename('part')
ht.output_partitions(filename, outfile)
data = open(outfile).read()
assert len(data)
records = [r for r in screed.open(outfile)]
names = [r.name for r in records]
parts = [n.rsplit('\t', 1)[1] for n in names]
assert parts[0] == '2'
assert parts[1] == '3'
assert parts[2] == '4'
def main():
readsfile = sys.argv[1]
contigfile = sys.argv[2]
outfile = os.path.basename(readsfile) + '.sweep'
if len(sys.argv) == 4:
outfile = sys.argv[3]
# create a hashbits data structure
ht = khmer.new_hashbits(K, 1, 1)
# tag every k-mer in the contigs
ht._set_tag_density(0)
# load contigs, connect into N partitions
print 'loading contigs from', contigfile
ht.consume_fasta_and_tag(contigfile)
subset = ht.do_subset_partition(0, 0)
ht.merge_subset(subset)
print 'outputting contig-partitioned reads to', outfile
ht.output_partitions(readsfile, outfile, True)
def test_random_20_a_succ_III(self):
ht = khmer.new_hashbits(20, 4**7 + 1)
filename = utils.get_test_data('random-20-a.fa')
outfile = utils.get_temp_filename('out')
total_reads, _ = ht.consume_fasta_and_tag(filename)
subset_size = total_reads / 2 + total_reads % 2
divvy = ht.divide_tags_into_subsets(subset_size)
assert len(divvy) == 4, len(divvy)
x = ht.do_subset_partition(divvy[0], divvy[2])
y = ht.do_subset_partition(divvy[2], 0)
ht._validate_subset_partitionmap(x)
ht._validate_subset_partitionmap(y)
ht.merge_subset(y)
ht.merge_subset(x)
n_partitions = ht.output_partitions(filename, outfile)
assert n_partitions == 1, n_partitions
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 test_badget():
hbts = khmer.new_hashbits(6, 1e6, 1)
dna = "AGCTTTTCATTCTGACTGCAACGGGCAATATGTCTCTGTGTGGATTAAAAAAAGAGTGTCTGATAG"
hbts.consume(dna)
assert hbts.get("AGCTTT") == 1
assert hbts.get("GATGAG") == 0
try:
hbts.get(b"AGCTT")
assert 0, "this should fail"
except ValueError as err:
print(str(err))
try:
hbts.get(u"AGCTT")
assert 0, "this should fail"
except ValueError as err:
print(str(err))
def test_small_real_partitions():
filename = utils.get_test_data('real-partition-small.fa')
ht = khmer.new_hashbits(32, 2e2, 4)
ht.consume_fasta_and_tag(filename)
subset = ht.do_subset_partition(0, 0)
ht.merge_subset(subset)
outfile = utils.get_temp_filename('part')
ht.output_partitions(filename, outfile)
data = open(outfile).read()
assert len(data)
records = [r for r in screed.open(outfile)]
names = [r.name for r in records]
parts = [n.rsplit('\t', 1)[1] for n in names]
assert len(parts) == 6, len(parts)
assert len(set(parts)) == 1
assert set(parts) != set(['0'])
def test_filter_sodd():
K = 32
HASHTABLE_SIZE = int(8e7)
N_HT = 4
MAX_SODD = 3
ht = khmer.new_hashbits(K, HASHTABLE_SIZE, N_HT)
filename = utils.get_test_data('../../data/high-sodd.fa')
ht.consume_fasta(filename)
seq = "CGTTAGTTGCGGTGCCGACCGGCAAACTTGGTTTTGCCAAAAATTTTTACAGTTAGAAATTATTCACAAAGTTGCACCGGAATTCGGTTACAAACGTCATTCTAACTAAT"
trim_seq, trim_at = ht.trim_on_sodd(seq, MAX_SODD)
assert trim_seq == "CGTTAGTTGCGGTGCCGACCGGCAAACTTGGT"
seq = "ACAAAATTCCACATATAGTCATAATTGTGGGCAATTTTCGTCCCAAATTAGTTAGAATGACGTTTGTAACCGAATTCCGGTGCAACTTTGTGAATAATTTCTAACTGTAAAAAT"
trim_seq, trim_at = ht.trim_on_sodd(seq, MAX_SODD)
assert trim_seq == "ACAAAATTCCACATATAGTCATAATTGTGGGCAATT"
seq = "GCACGCAGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTG"
trim_seq, trim_at = ht.trim_on_sodd(seq, MAX_SODD)
assert trim_seq == seq
def main():
K = 8
n = 50
add_kmers = 50
total_kmers = n + add_kmers
print "\"FPR\",\"LOWER\",\"AVG\",\"UPPER\""
for p in [x / 200.0 + .01 for x in range(59)]:
diam_lens = []
for j in range(500):
seq = gen_circ_chrom(n, K)
m = calc_m(total_kmers, p)
k = opt_ht(m, total_kmers)
HT_SIZE = calc_ht_size(m, k)
ht = khmer.new_hashbits(K, HT_SIZE, k)
ht.consume(seq)
for i in range(add_kmers):
ht.consume(generate_read(K))
real_kmers = get_real_kmers(seq, K)
out_len = []
# step one: find the "outbranch" lengths for each real k-mer
for kmer in real_kmers:
out_len.append(get_level(ht, kmer, real_kmers, K))
# step two: find the shortest longest path using the info from step 1
diam_lens.append(max(out_len))
#avg = numpy.mean(diam_lens)
#se = numpy.std(diam_lens) / numpy.sqrt(len(diam_lens))
#lim = se * 1.96
#print str(p) + "," + str(avg-lim) + "," + str(avg) + "," + str(avg+lim)
low, med, upp = estimate_mean(diam_lens)
print str(p) + "," + str(low) + "," + str(med) + "," + str(upp)
def main(filename):
global ht
basename = os.path.basename(filename)
print 'input file to partition: %s' % filename
print '-- settings:'
print 'K', K
print 'HASHTABLE SIZE %g' % HASHTABLE_SIZE
print 'N HASHTABLES %d' % N_HT
print '--'
ht = khmer.new_hashbits(K, HASHTABLE_SIZE, N_HT)
counting = khmer.new_counting_hash(K, COUNTING_SIZE, N_HT)
ht.consume_fasta_and_traverse(filename, 100, 500, 5, counting)
print 'saving stoptags binary'
ht.save_stop_tags(basename + '.stoptags')
print 'saving stoptags text'
ht.print_stop_tags(basename + '.stoptags.txt')
sys.exit(0)
def test_combine_pe():
inpfile = utils.get_test_data('combine_parts_1.fa')
ht = khmer.new_hashbits(32, 1, 1)
ht.consume_partitioned_fasta(inpfile)
assert ht.count_partitions() == (2, 0)
s1 = "CATGCAGAAGTTCCGCAACCATACCGTTCAGT"
pid1 = ht.get_partition_id(s1)
s2 = "CAAATGTACATGCACTTAAAATCATCCAGCCG"
pid2 = ht.get_partition_id(s2)
assert pid1 == 2
assert pid2 == 80293
ht.join_partitions(pid1, pid2)
pid1 = ht.get_partition_id(s1)
pid2 = ht.get_partition_id(s2)
assert pid1 == pid2
assert ht.count_partitions() == (1, 0)
def test_bloom_python_1():
# test python code to count unique kmers using bloom filter
filename = utils.get_test_data('random-20-a.fa')
K = 20 # size of kmer
HT_SIZE = 100000 # size of hashtable
N_HT = 3 # number of hashtables
ht2 = khmer.new_hashbits(K, HT_SIZE, N_HT)
n_unique = 0
for n, record in enumerate(fasta_iter(open(filename))):
sequence = record['sequence']
seq_len = len(sequence)
for n in range(0, seq_len + 1 - K):
kmer = sequence[n:n + K]
if (not ht2.get(kmer)):
n_unique += 1
ht2.count(kmer)
assert n_unique == 3960
assert ht2.n_occupied() == 3882
assert ht2.n_unique_kmers() == 3960 # this number equals to n_unique
def main():
filename1 = sys.argv[1]
filename2 = sys.argv[2]
uniq2 = open(os.path.basename(sys.argv[2]) + '.uniq', 'w')
kh = khmer.new_hashbits(K, HASHTABLE_SIZE, N_HT)
for n, record in enumerate(screed.open(filename1)):
if n % 10000 == 0:
print '...', filename1, n
seq = record.sequence.upper().replace('N', 'A')
kh.consume(seq)
path_n = 0
for n, record in enumerate(screed.open(filename2)):
if n % 10000 == 0:
print '...', filename2, n
seq = record.sequence.upper().replace('N', 'A')
paths = kh.extract_unique_paths(seq, UNIQUE_LEN, UNIQUE_F)
kh.consume(seq)
for path in paths:
path_n += 1
print >> uniq2, '>%s from:%s\n%s' % (path_n, record.name, path)
def main():
info('merge-stoptags.py')
args = get_parser().parse_args()
stdbase = args.stdbase
# @RamRS: This might need some more work
infiles = []
for _ in glob.glob(stdbase + "*/*.stoptags"):
if os.path.exists(_):
check_input_files(_, False)
infiles.append(_)
check_space(infiles, False)
ht = khmer.new_hashbits(args.ksize, 1, 1)
for _ in infiles:
print >> sys.stderr, 'loading stoptags %s' % _
ht.load_stop_tags(_, 0)
print >> sys.stderr, 'writing file merge.stoptags'
ht.save_stop_tags('merge.stoptags')
print >> sys.stderr, 'done!'
def main(subset_filenames):
print 'K', K
print 'MIN SIZE', MIN_PARTITION_SIZE
print '--'
# create an empty hashtable & load in the tags
ht = khmer.new_hashbits(32, 1, 1)
tagmap = ht.new_tagmap()
# find the maximum partition size for each tag, across all subsets
for filename in subset_filenames:
print 'maxifying:', filename
subset = ht.load_subset_partitionmap(filename)
ht.subset_maxify_partition_size(subset, tagmap)
del subset
gc.collect()
# filter tags based on the max partition size to which they belong
print 'discarding'
ht.discard_tags(tagmap, MIN_PARTITION_SIZE)
# finally, filter each subset filename and save.
for filename in subset_filenames:
print 'loading x 2', filename
subset = ht.load_subset_partitionmap(filename)
print 'filtering', filename
ht.subset_filter_against_tags(subset, tagmap)
dir = os.path.dirname(filename)
new_filename = 'filtered_' + os.path.basename(filename)
new_filename = os.path.join(dir, new_filename)
print 'saving', new_filename
ht.save_subset_partitionmap(subset, new_filename)
del subset
gc.collect()