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.LabelHash(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.LabelHash(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_count_within_radius_big():
inpfile = utils.get_test_data('random-20-a.fa')
ht = khmer.LabelHash(20, 1e6, 4)
ht.consume_fasta(inpfile)
n = ht.count_kmers_within_radius('CGCAGGCTGGATTCTAGAGG', int(1e6))
assert n == 3960
ht = khmer.LabelHash(21, 1e6, 4)
ht.consume_fasta(inpfile)
n = ht.count_kmers_within_radius('CGCAGGCTGGATTCTAGAGGC', int(1e6))
assert n == 39
def test_tag_across_stoptraverse():
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
ht = khmer.LabelHash(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__get_set_tag_density():
ht = khmer.LabelHash(32, 1, 1)
orig = ht._get_tag_density()
assert orig != 2
ht._set_tag_density(2)
assert ht._get_tag_density() == 2
def test_extract_unique_paths_2():
kh = khmer.LabelHash(10, 1e5, 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_find_radius_for_volume():
inpfile = utils.get_test_data('all-A.fa')
ht = khmer.LabelHash(4, 1e6, 2)
ht.consume_fasta(inpfile)
assert ht.find_radius_for_volume('AAAA', 0, 100) == 0
assert ht.find_radius_for_volume('AAAA', 1, 100) == 0
assert ht.find_radius_for_volume('AAAA', 2, 100) == 100
def test_find_stoptags():
ht = khmer.LabelHash(5, 1, 1)
ht.add_stop_tag("AAAAA")
assert ht.identify_stoptags_by_position("AAAAA") == [0]
assert ht.identify_stoptags_by_position("AAAAAA") == [0, 1]
assert ht.identify_stoptags_by_position("TTTTT") == [0]
assert ht.identify_stoptags_by_position("TTTTTT") == [0, 1]
def test_count_kmer_degree():
inpfile = utils.get_test_data('all-A.fa')
ht = khmer.LabelHash(4, 1e6, 2)
ht.consume_fasta(inpfile)
assert ht.kmer_degree('AAAA') == 2
assert ht.kmer_degree('AAAT') == 1
assert ht.kmer_degree('AATA') == 0
assert ht.kmer_degree('TAAA') == 1
def test_extract_unique_paths_0():
kh = khmer.LabelHash(10, 1e5, 4)
x = kh.extract_unique_paths('ATGGAGAGACACAGATAGACAGGAGTGGCGATG', 10, 1)
assert x == ['ATGGAGAGACACAGATAGACAGGAGTGGCGATG']
kh.consume('ATGGAGAGACACAGATAGACAGGAGTGGCGATG')
x = kh.extract_unique_paths('ATGGAGAGACACAGATAGACAGGAGTGGCGATG', 10, 1)
assert not x
def test_count_within_radius_simple():
inpfile = utils.get_test_data('all-A.fa')
ht = khmer.LabelHash(4, 1e6, 2)
print ht.consume_fasta(inpfile)
n = ht.count_kmers_within_radius('AAAA', 1)
assert n == 1
n = ht.count_kmers_within_radius('AAAA', 10)
assert n == 1
def test_extract_unique_paths_4():
kh = khmer.LabelHash(10, 1e5, 4)
kh.consume('ATGGAGAGAC')
kh.consume('AGTGGCGATG')
kh.consume('ATAGACAGGA')
x = kh.extract_unique_paths('ATGGAGAGACACAGATAGACAGGAGTGGCGATG', 10, 1)
print x
assert x == ['TGGAGAGACACAGATAGACAGG', 'TAGACAGGAGTGGCGAT']
def test_filter_if_present():
ht = khmer.LabelHash(32, 1e6, 2)
maskfile = utils.get_test_data('filter-test-A.fa')
inputfile = utils.get_test_data('filter-test-B.fa')
outfile = utils.get_temp_filename('filter')
ht.consume_fasta(maskfile)
ht.filter_if_present(inputfile, outfile)
records = list(fasta_iter(open(outfile)))
assert len(records) == 1
assert records[0]['name'] == '3'
def test_load_partitioned():
inpfile = utils.get_test_data('combine_parts_1.fa')
ht = khmer.LabelHash(32, 1, 1)
ht.consume_partitioned_fasta(inpfile)
assert ht.count_partitions() == (2, 0)
s1 = "CATGCAGAAGTTCCGCAACCATACCGTTCAGT"
assert ht.get(s1)
s2 = "CAAATGTACATGCACTTAAAATCATCCAGCCG"
assert ht.get(s2)
s3 = "CATGCAGAAGTTCCGCAACCATACCGTTCAGTTCCTGGTGGCTA"[-32:]
assert ht.get(s3)
def test_n_occupied_1():
filename = utils.get_test_data('random-20-a.fa')
K = 20 # size of kmer
HT_SIZE = 100000 # size of hashtable
N_HT = 1 # number of hashtables
# test modified c++ n_occupied code
ht1 = khmer.LabelHash(K, HT_SIZE, N_HT)
for n, record in enumerate(fasta_iter(open(filename))):
ht1.consume(record['sequence'])
# this number calculated independently
assert ht1.n_occupied() == 3877
def test_simple_median():
hi = khmer.LabelHash(6, 1e6, 2)
(median, average, stddev) = hi.get_median_count("AAAAAA")
print median, average, stddev
assert median == 0
assert average == 0.0
assert stddev == 0.0
hi.consume("AAAAAA")
(median, average, stddev) = hi.get_median_count("AAAAAA")
print median, average, stddev
assert median == 1
assert average == 1.0
assert stddev == 0.0
def test_bloom_c_1():
# test c++ 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
ht3 = khmer.LabelHash(K, HT_SIZE, N_HT)
for n, record in enumerate(fasta_iter(open(filename))):
ht3.consume(record['sequence'])
assert ht3.n_occupied() == 3882
assert ht3.n_unique_kmers() == 3960
def test_circumference():
ht = khmer.LabelHash(4, 1e6, 2)
ht.count('ATGC')
ht.count('GATG')
ht.count('ATGG')
x = ht.count_kmers_on_radius('GATG', 1, 200)
assert x == 2
ht.count('ATGA')
x = ht.count_kmers_on_radius('GATG', 1, 200)
assert x == 3, x
ht.count('TGAT')
x = ht.count_kmers_on_radius('GATG', 1, 200)
assert x == 4, x
def test_n_occupied_2(): # simple one
K = 4
HT_SIZE = 10 # use 11
N_HT = 1
ht1 = khmer.LabelHash(K, HT_SIZE, N_HT)
ht1.count('AAAA') # 00 00 00 00 = 0
assert ht1.n_occupied() == 1
ht1.count('ACTG') # 00 10 01 11 =
assert ht1.n_occupied() == 2
ht1.count('AACG') # 00 00 10 11 = 11 # collision 1
assert ht1.n_occupied() == 2
ht1.count('AGAC') # 00 11 00 10 # collision 2
assert ht1.n_occupied() == 2
def test_notag_across_stoptraverse():
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
ht = khmer.LabelHash(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_stop_traverse():
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
ht = khmer.LabelHash(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_save_load_tagset():
ht = khmer.LabelHash(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_save_load_tagset_noclear():
ht = khmer.LabelHash(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 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.LabelHash(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.LabelHash(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_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.LabelHash(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 test_combine_pe():
inpfile = utils.get_test_data('combine_parts_1.fa')
ht = khmer.LabelHash(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 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 test_find_stoptags2():
ht = khmer.LabelHash(4, 1, 1)
ht.add_stop_tag("ATGC")
x = ht.identify_stoptags_by_position("ATGCATGCGCAT")
assert x == [0, 2, 4, 8], x
def test_get_ksize():
kh = khmer.LabelHash(22, 1, 1)
assert kh.ksize() == 22
def test_get_hashsizes():
kh = khmer.LabelHash(22, 100, 4)
assert kh.hashsizes() == [101, 103, 107, 109], kh.hashsizes()
