def test_k_word_frequencies(self):
expected1 = defaultdict(float)
expected1['A'] = 3 / 7.
expected1['C'] = 1 / 7.
expected1['G'] = 1 / 7.
expected1['T'] = 2 / 7.
expected2 = defaultdict(float)
expected2['G'] = 1 / 3.
expected2['T'] = 2 / 3.
self.assertEqual(self.s1.k_word_frequencies(k=1),
[expected1, expected2])
expected1 = defaultdict(float)
expected1['GAT'] = 1 / 2.
expected1['TAC'] = 1 / 2.
expected2 = defaultdict(float)
expected2['TTG'] = 1 / 1.
self.assertEqual(self.s1.k_word_frequencies(k=3, overlapping=False),
[expected1, expected2])
self.assertEqual(self.empty.k_word_frequencies(k=1), [])
# Test to ensure floating point precision bug isn't present. See the
# tests for BiologicalSequence.k_word_frequencies for more details.
sc = SequenceCollection([RNA('C' * 10, id='s1'),
RNA('G' * 10, id='s2')])
self.assertEqual(sc.k_word_frequencies(1),
[defaultdict(float, {'C': 1.0}),
defaultdict(float, {'G': 1.0})])
def test_k_word_frequencies(self):
expected1 = defaultdict(float)
expected1['A'] = 3 / 7.
expected1['C'] = 1 / 7.
expected1['G'] = 1 / 7.
expected1['T'] = 2 / 7.
expected2 = defaultdict(float)
expected2['G'] = 1 / 3.
expected2['T'] = 2 / 3.
self.assertEqual(self.s1.k_word_frequencies(k=1),
[expected1, expected2])
expected1 = defaultdict(float)
expected1['GAT'] = 1 / 2.
expected1['TAC'] = 1 / 2.
expected2 = defaultdict(float)
expected2['TTG'] = 1 / 1.
self.assertEqual(self.s1.k_word_frequencies(k=3, overlapping=False),
[expected1, expected2])
self.assertEqual(self.empty.k_word_frequencies(k=1), [])
# Test to ensure floating point precision bug isn't present. See the
# tests for BiologicalSequence.k_word_frequencies for more details.
sc = SequenceCollection(
[RNA('C' * 10, id='s1'),
RNA('G' * 10, id='s2')])
self.assertEqual(
sc.k_word_frequencies(1),
[defaultdict(float, {'C': 1.0}),
defaultdict(float, {'G': 1.0})])
def test_init(self):
"""Initialization functions as expected with varied input types
"""
SequenceCollection(self.seqs1)
SequenceCollection(self.seqs2)
SequenceCollection(self.seqs3)
SequenceCollection([])
def test_init_validate(self):
SequenceCollection(self.seqs1, validate=True)
SequenceCollection(self.seqs1, validate=True)
# can't validate self.seqs2 as a DNASequence
self.assertRaises(SequenceCollectionError,
SequenceCollection,
self.invalid_s1,
validate=True)
def test_init_validate(self):
"""initialization with validation functions as expected
"""
SequenceCollection(self.seqs1, validate=True)
SequenceCollection(self.seqs1, validate=True)
# can't validate self.seqs2 as a DNASequence
self.assertRaises(SequenceCollectionError, SequenceCollection,
self.invalid_s1, validate=True)
def extract_seq_ids(data, fmt='fasta', variant=None):
"""
Given FASTQ-format data (string), parse out only the
sequence IDs and return.
"""
fh = StringIO(data)
if fmt == 'fastq':
sc = SequenceCollection.read(fh, format=fmt, variant=variant)
else:
sc = SequenceCollection.read(fh, format=fmt)
return frozenset(entry.id for entry in sc)
def extract_seq_ids(data, fmt='fasta', variant=None):
"""
Given FASTQ-format data (string), parse out only the
sequence IDs and return.
"""
fh = StringIO(data)
if fmt == 'fastq':
sc = SequenceCollection.read(fh, format=fmt, variant=variant)
else:
sc = SequenceCollection.read(fh, format=fmt)
return frozenset(entry.id for entry in sc)
def test_degap(self):
expected = [(id_, seq.replace('.', '').replace('-', ''))
for id_, seq in self.seqs1_t]
expected = SequenceCollection.from_fasta_records(expected, DNASequence)
actual = self.a1.degap()
self.assertEqual(actual, expected)
expected = [(id_, seq.replace('.', '').replace('-', ''))
for id_, seq in self.seqs2_t]
expected = SequenceCollection.from_fasta_records(expected, RNASequence)
actual = self.a2.degap()
self.assertEqual(actual, expected)
def test_degap(self):
"""degap functions as expected
"""
expected = [(id_, seq.replace(".", "").replace("-", "")) for id_, seq in self.seqs1_t]
expected = SequenceCollection.from_fasta_records(expected, DNASequence)
actual = self.a1.degap()
self.assertEqual(actual, expected)
expected = [(id_, seq.replace(".", "").replace("-", "")) for id_, seq in self.seqs2_t]
expected = SequenceCollection.from_fasta_records(expected, RNASequence)
actual = self.a2.degap()
self.assertEqual(actual, expected)
def test_distances(self):
s1 = SequenceCollection([DNA("ACGT", "d1"), DNA("ACGG", "d2")])
expected = [[0, 0.25], [0.25, 0]]
expected = DistanceMatrix(expected, ['d1', 'd2'])
actual = s1.distances(hamming)
self.assertEqual(actual, expected)
# alt distance function provided
def dumb_distance(s1, s2):
return 42.
expected = [[0, 42.], [42., 0]]
expected = DistanceMatrix(expected, ['d1', 'd2'])
actual = s1.distances(dumb_distance)
self.assertEqual(actual, expected)
def test_degap(self):
expected = SequenceCollection([
DNA('ACCGTTGG', metadata={'id': "d1"}),
DNA('TTACCGGTGGCC', metadata={'id': "d2"}),
DNA('ACCGTTGC', metadata={'id': "d3"})
])
actual = self.a1.degap()
self.assertEqual(actual, expected)
expected = SequenceCollection([
RNA('UUAU', metadata={'id': "r1"}),
RNA('ACGUU', metadata={'id': "r2"})
])
actual = self.a2.degap()
self.assertEqual(actual, expected)
def test_distances(self):
s1 = SequenceCollection([DNA("ACGT", "d1"), DNA("ACGG", "d2")])
expected = [[0, 0.25],
[0.25, 0]]
expected = DistanceMatrix(expected, ['d1', 'd2'])
actual = s1.distances(hamming)
self.assertEqual(actual, expected)
# alt distance function provided
def dumb_distance(s1, s2):
return 42.
expected = [[0, 42.],
[42., 0]]
expected = DistanceMatrix(expected, ['d1', 'd2'])
actual = s1.distances(dumb_distance)
self.assertEqual(actual, expected)
def test_ne(self):
self.assertFalse(self.s1 != self.s1)
self.assertTrue(self.s1 != self.s2)
# SequenceCollections with different number of sequences are not equal
self.assertTrue(self.s1 != SequenceCollection([self.d1]))
class FakeSequenceCollection(SequenceCollection):
pass
# SequenceCollections of different types are not equal
self.assertTrue(self.s4 != FakeSequenceCollection([self.d1, self.d3]))
self.assertTrue(self.s4 != Alignment([self.d1, self.d3]))
# SequenceCollections with different sequences are not equal
self.assertTrue(self.s1 != SequenceCollection([self.d1, self.r1]))
def convert_phylip(infile, outfile, format):
seqs = SequenceCollection.read(
infile, format='phylip',
data_parser=phylip.relaxed_ids
)
seqs.write(outfile, format=format)
def test_init_fail_no_id(self):
seq = Sequence('ACGTACGT')
with six.assertRaisesRegex(
self, SequenceCollectionError,
"'id' must be included in the sequence "
"metadata"):
SequenceCollection([seq])
def test_fastq_to_sequence_collection(self):
for valid_files, kwargs, components in self.valid_configurations:
for valid in valid_files:
for observed_kwargs in kwargs:
_drop_kwargs(observed_kwargs, 'seq_num')
constructor = observed_kwargs.get('constructor', Sequence)
expected_kwargs = {}
expected_kwargs['lowercase'] = 'introns'
observed_kwargs['lowercase'] = 'introns'
expected = SequenceCollection([
constructor(c[2],
metadata={
'id': c[0],
'description': c[1]
},
positional_metadata={
'quality': np.array(c[3], np.uint8)
},
**expected_kwargs) for c in components
])
observed = _fastq_to_sequence_collection(
valid, **observed_kwargs)
self.assertEqual(observed, expected)
def test_valid_files(self):
for valid, kwargs, components in self.valid_files:
for kwarg in kwargs:
_drop_kwargs(kwarg, 'seq_num')
constructor = kwarg.get('constructor', Sequence)
expected = SequenceCollection([
constructor(c['sequence'],
metadata={
'id': c['id'],
'machine_name': c['machine_name'],
'run_number': c['run_number'],
'lane_number': c['lane_number'],
'tile_number': c['tile_number'],
'x': c['x'],
'y': c['y'],
'index': c['index'],
'read_number': c['read_number']
},
positional_metadata={
'quality':
np.array(c['quality'], dtype=np.uint8)
}) for c in components
])
observed = _qseq_to_sequence_collection(valid, **kwarg)
self.assertEqual(observed, expected)
def test_fastq_to_sequence_collection(self):
for valid_files, kwargs, components in self.valid_configurations:
for valid in valid_files:
for observed_kwargs in kwargs:
_drop_kwargs(observed_kwargs, 'seq_num')
constructor = observed_kwargs.get('constructor', Sequence)
# Can't use partials for this because the read
# function below can't operate on partials
expected_kwargs = {}
if hasattr(constructor, 'lowercase'):
expected_kwargs['lowercase'] = 'introns'
observed_kwargs['lowercase'] = 'introns'
expected = SequenceCollection(
[constructor(
c[2], metadata={'id': c[0], 'description': c[1]},
positional_metadata={'quality': np.array(c[3],
np.uint8)},
**expected_kwargs)
for c in components])
observed = _fastq_to_sequence_collection(valid,
**observed_kwargs)
self.assertEqual(observed, expected)
def main():
args = handle_program_options()
if osp.isfile(args.out_dir):
print("--out_dir (-o) option must be a valid directory and not a file",
file=sys.stderr)
sys.exit(1)
# will fail gracefully if dir exists
skbu.create_dir(args.out_dir)
metagenomes = []
if args.metagenome_id is not None:
metagenomes.append(args.metagenome_id)
elif args.metagenome_file is not None:
metagenomes.extend(parse_metagenome_file(args.metagenome_file))
if args.verbose:
msg = 'Processing requested for {} metagenome(s) found in: {}'
print(msg.format(len(metagenomes), args.metagenome_file))
# MG-RAST stage.file ids for downloading
derep_passed = '150.1'
screen_passed = '299.1'
for mg_id in metagenomes:
if args.verbose:
print('Processing metagenome: {}'.format(mg_id))
print('\tDownloading: Dereplication Passed...', end='')
sys.stdout.flush()
derepp_rsp = mgapi.mgrast_request('download', mg_id,
{'file': derep_passed},
auth_key=args.auth_key)
derepp_sc = SequenceCollection.read(StringIO(derepp_rsp.text),
format='fastq',
variant='illumina1.8')
if args.verbose:
print('{} sequences'.format(len(derepp_sc)))
print('\tDownloading: Screen Passed...', end='')
sys.stdout.flush()
screenp_rsp = mgapi.mgrast_request('download', mg_id,
{'file': screen_passed},
auth_key=args.auth_key)
screenp_ids = extract_seq_ids(screenp_rsp.text, fmt='fastq',
variant='illumina1.8')
if args.verbose:
print('{} sequences'.format(len(screenp_ids)))
# filter dereplication passed with IDs from screen passed
failed_screen = filter_seqs(derepp_sc, screenp_ids)
if args.verbose:
nsp = len(screenp_ids)
print('\tRemoved {} sequences from Dereplication Passed'.format(nsp))
print('\tleaving {} sequences'.format(len(failed_screen)))
out_fp = osp.join(args.out_dir, mg_id + '_screen_failed.fastq')
failed_screen.write(out_fp, format='fastq', variant='illumina1.8')
if args.verbose:
print('Sequence data written to: ' + out_fp)
def test_make_mini_otu_files(self):
os.system("mkdir tmp")
self.extension_seqs = SequenceCollection.read(self.extension_seqs)
result = _make_mini_otu_files(self.key_node,
self.extension_genus_dic_few,
self.extension_seqs)
os.system("rm -r tmp")
self.assertEqual(result, """>P1\nTTAAAAAA\n""")
def test_degap(self):
expected = SequenceCollection([
RNA('GAUUACA', metadata={'id': "r1"}),
RNA('UUG', metadata={'id': "r2"}),
RNA('UUGCC', metadata={'id': "r3"})
])
actual = self.s2.degap()
self.assertEqual(actual, expected)
def test_make_mini_otu_files(self):
os.system("mkdir tmp")
self.extension_seqs = SequenceCollection.read(self.extension_seqs)
result = _make_mini_otu_files(self.key_node,
self.extension_genus_dic_few,
self.extension_seqs)
os.system("rm -r tmp")
self.assertEqual(result, """>P1\nTTAAAAAA\n""")
def setUp(self):
self.d1 = DNA('GATTACA', metadata={'id': "d1"})
self.d2 = DNA('TTG', metadata={'id': "d2"})
self.d3 = DNA('GTATACA', metadata={'id': "d3"})
self.r1 = RNA('GAUUACA', metadata={'id': "r1"})
self.r2 = RNA('UUG', metadata={'id': "r2"})
self.r3 = RNA('U-----UGCC--', metadata={'id': "r3"})
self.seqs1 = [self.d1, self.d2]
self.seqs2 = [self.r1, self.r2, self.r3]
self.seqs3 = self.seqs1 + self.seqs2
self.seqs4 = [self.d1, self.d3]
self.s1 = SequenceCollection(self.seqs1)
self.s2 = SequenceCollection(self.seqs2)
self.s3 = SequenceCollection(self.seqs3)
self.s4 = SequenceCollection(self.seqs4)
self.empty = SequenceCollection([])
def test_ne(self):
"""inequality operator functions as expected
"""
self.assertFalse(self.s1 != self.s1)
self.assertTrue(self.s1 != self.s2)
# SequenceCollections with different number of sequences are not equal
self.assertTrue(self.s1 != SequenceCollection([self.d1]))
class FakeSequenceCollection(SequenceCollection):
pass
# SequenceCollections of different types are not equal
self.assertTrue(self.s1 != FakeSequenceCollection([self.d1, self.d2]))
self.assertTrue(self.s1 != Alignment([self.d1, self.d2]))
# SequenceCollections with different sequences are not equal
self.assertTrue(self.s1 !=
SequenceCollection([self.d1, self.r1]))
def test_generator_to_fasta_sequence_lowercase_exception(self):
seq = Sequence('ACgt', metadata={'id': ''})
fh = io.StringIO()
with six.assertRaisesRegex(self, AttributeError,
"lowercase specified but class Sequence "
"does not support lowercase "
"functionality"):
_generator_to_fasta(SequenceCollection([seq]), fh,
lowercase='introns')
fh.close()
def test_distances(self):
s1 = SequenceCollection([DNA("ACGT", metadata={'id': "d1"}),
DNA("ACGG", metadata={'id': "d2"})])
expected = [[0, 0.25],
[0.25, 0]]
expected = DistanceMatrix(expected, ['d1', 'd2'])
def h(s1, s2):
return hamming(s1.values, s2.values)
actual = s1.distances(h)
self.assertEqual(actual, expected)
# alt distance function provided
def dumb_distance(s1, s2):
return 42.
expected = [[0, 42.],
[42., 0]]
expected = DistanceMatrix(expected, ['d1', 'd2'])
actual = s1.distances(dumb_distance)
self.assertEqual(actual, expected)
def test_eq(self):
self.assertTrue(self.s1 == self.s1)
self.assertFalse(self.s1 == self.s2)
# different objects can be equal
self.assertTrue(self.s1 == SequenceCollection([self.d1, self.d2]))
self.assertTrue(SequenceCollection([self.d1, self.d2]) == self.s1)
# SequenceCollections with different number of sequences are not equal
self.assertFalse(self.s1 == SequenceCollection([self.d1]))
class FakeSequenceCollection(SequenceCollection):
pass
# SequenceCollections of different types are not equal
self.assertFalse(self.s4 == FakeSequenceCollection([self.d1, self.d3]))
self.assertFalse(self.s4 == Alignment([self.d1, self.d3]))
# SequenceCollections with different sequences are not equal
self.assertFalse(self.s1 == SequenceCollection([self.d1, self.r1]))
def setUp(self):
"""Initialize values to be used in tests
"""
self.d1 = DNASequence("GATTACA", id="d1")
self.d2 = DNASequence("TTG", id="d2")
self.d1_lower = DNASequence("gattaca", id="d1")
self.d2_lower = DNASequence("ttg", id="d2")
self.r1 = RNASequence("GAUUACA", id="r1")
self.r2 = RNASequence("UUG", id="r2")
self.r3 = RNASequence("U-----UGCC--", id="r3")
self.i1 = DNASequence("GATXACA", id="i1")
self.seqs1 = [self.d1, self.d2]
self.seqs1_lower = [self.d1_lower, self.d2_lower]
self.seqs2 = [self.r1, self.r2, self.r3]
self.seqs3 = self.seqs1 + self.seqs2
self.seqs1_t = [("d1", "GATTACA"), ("d2", "TTG")]
self.seqs2_t = [("r1", "GAUUACA"), ("r2", "UUG"), ("r3", "U-----UGCC--")]
self.seqs3_t = self.seqs1_t + self.seqs2_t
self.s1 = SequenceCollection(self.seqs1)
self.s1_lower = SequenceCollection(self.seqs1_lower)
self.s2 = SequenceCollection(self.seqs2)
self.s3 = SequenceCollection(self.seqs3)
self.empty = SequenceCollection([])
self.invalid_s1 = SequenceCollection([self.i1])
def setUp(self):
self.d1 = DNASequence('GATTACA', id="d1")
self.d2 = DNASequence('TTG', id="d2")
self.d3 = DNASequence('GTATACA', id="d3")
self.d1_lower = DNASequence('gattaca', id="d1")
self.d2_lower = DNASequence('ttg', id="d2")
self.d3_lower = DNASequence('gtataca', id="d3")
self.r1 = RNASequence('GAUUACA', id="r1")
self.r2 = RNASequence('UUG', id="r2")
self.r3 = RNASequence('U-----UGCC--', id="r3")
self.i1 = DNASequence('GATXACA', id="i1")
self.seqs1 = [self.d1, self.d2]
self.seqs1_lower = [self.d1_lower, self.d2_lower]
self.seqs2 = [self.r1, self.r2, self.r3]
self.seqs3 = self.seqs1 + self.seqs2
self.seqs4 = [self.d1, self.d3]
self.seqs1_t = [('d1', 'GATTACA'), ('d2', 'TTG')]
self.seqs2_t = [('r1', 'GAUUACA'), ('r2', 'UUG'),
('r3', 'U-----UGCC--')]
self.seqs3_t = self.seqs1_t + self.seqs2_t
self.s1 = SequenceCollection(self.seqs1)
self.s1_lower = SequenceCollection(self.seqs1_lower)
self.s2 = SequenceCollection(self.seqs2)
self.s3 = SequenceCollection(self.seqs3)
self.s4 = SequenceCollection(self.seqs4)
self.empty = SequenceCollection([])
self.invalid_s1 = SequenceCollection([self.i1])
def setUp(self):
self.d1 = DNA('GATTACA', metadata={'id': "d1"})
self.d2 = DNA('TTG', metadata={'id': "d2"})
self.d3 = DNA('GTATACA', metadata={'id': "d3"})
self.r1 = RNA('GAUUACA', metadata={'id': "r1"})
self.r2 = RNA('UUG', metadata={'id': "r2"})
self.r3 = RNA('U-----UGCC--', metadata={'id': "r3"})
self.seqs1 = [self.d1, self.d2]
self.seqs2 = [self.r1, self.r2, self.r3]
self.seqs3 = self.seqs1 + self.seqs2
self.seqs4 = [self.d1, self.d3]
self.s1 = SequenceCollection(self.seqs1)
self.s2 = SequenceCollection(self.seqs2)
self.s3 = SequenceCollection(self.seqs3)
self.s4 = SequenceCollection(self.seqs4)
self.empty = SequenceCollection([])
def test_kmer_frequencies(self):
expected1 = Counter({'GAT': 1, 'TAC': 1})
expected2 = Counter({'TTG': 1})
self.assertEqual(
self.s1.kmer_frequencies(k=3, overlap=False, relative=False),
[expected1, expected2])
expected1 = defaultdict(float)
expected1['A'] = 3 / 7.
expected1['C'] = 1 / 7.
expected1['G'] = 1 / 7.
expected1['T'] = 2 / 7.
expected2 = defaultdict(float)
expected2['G'] = 1 / 3.
expected2['T'] = 2 / 3.
self.assertEqual(self.s1.kmer_frequencies(k=1, relative=True),
[expected1, expected2])
expected1 = defaultdict(float)
expected1['GAT'] = 1 / 2.
expected1['TAC'] = 1 / 2.
expected2 = defaultdict(float)
expected2['TTG'] = 1 / 1.
self.assertEqual(
self.s1.kmer_frequencies(k=3, overlap=False, relative=True),
[expected1, expected2])
self.assertEqual(self.empty.kmer_frequencies(k=1, relative=True), [])
# Test to ensure floating point precision bug isn't present. See the
# tests for Sequence.kmer_frequencies for more details.
sc = SequenceCollection([
RNA('C' * 10, metadata={'id': 's1'}),
RNA('G' * 10, metadata={'id': 's2'})
])
self.assertEqual(
sc.kmer_frequencies(1, relative=True),
[defaultdict(float, {'C': 1.0}),
defaultdict(float, {'G': 1.0})])
def test_distances(self):
s1 = SequenceCollection([
DNA("ACGT", metadata={'id': "d1"}),
DNA("ACGG", metadata={'id': "d2"})
])
expected = [[0, 0.25], [0.25, 0]]
expected = DistanceMatrix(expected, ['d1', 'd2'])
def h(s1, s2):
return hamming(s1.values, s2.values)
actual = s1.distances(h)
self.assertEqual(actual, expected)
# alt distance function provided
def dumb_distance(s1, s2):
return 42.
expected = [[0, 42.], [42., 0]]
expected = DistanceMatrix(expected, ['d1', 'd2'])
actual = s1.distances(dumb_distance)
self.assertEqual(actual, expected)
def filter_seqs(seqs, remove_ids):
"""
Given a collections of sequences and a set of IDs to remove,
return all sequences that do not match those IDs.
:type seqs: skbio.SequenceCollection
:param seqs: The sequences to be filtered
:type remove_ids: set
:param remove_ids: A set of sequence IDs to remove from the sequence data
:rtype: SequenceCollection
"""
return SequenceCollection(
[seq for seq in seqs if seq.id not in remove_ids])
def test_valid_files(self):
for valid, kwargs, components in self.valid_files:
for kwarg in kwargs:
_drop_kwargs(kwarg, 'seq_num')
constructor = kwarg.get('constructor', BiologicalSequence)
expected = SequenceCollection([constructor(c[1], id=c[0],
quality=c[2]) for c in
components])
observed = _qseq_to_sequence_collection(valid, **kwarg)
# TODO remove when #656 is resolved
self.assertEqual(observed, expected)
for o, e in zip(observed, expected):
self.assertTrue(o.equals(e))
def test_kmer_frequencies(self):
expected1 = Counter({'GAT': 1, 'TAC': 1})
expected2 = Counter({'TTG': 1})
self.assertEqual(
self.s1.kmer_frequencies(k=3, overlap=False, relative=False),
[expected1, expected2])
expected1 = defaultdict(float)
expected1['A'] = 3 / 7.
expected1['C'] = 1 / 7.
expected1['G'] = 1 / 7.
expected1['T'] = 2 / 7.
expected2 = defaultdict(float)
expected2['G'] = 1 / 3.
expected2['T'] = 2 / 3.
self.assertEqual(self.s1.kmer_frequencies(k=1, relative=True),
[expected1, expected2])
expected1 = defaultdict(float)
expected1['GAT'] = 1 / 2.
expected1['TAC'] = 1 / 2.
expected2 = defaultdict(float)
expected2['TTG'] = 1 / 1.
self.assertEqual(
self.s1.kmer_frequencies(k=3, overlap=False, relative=True),
[expected1, expected2])
self.assertEqual(self.empty.kmer_frequencies(k=1, relative=True), [])
# Test to ensure floating point precision bug isn't present. See the
# tests for Sequence.kmer_frequencies for more details.
sc = SequenceCollection([RNA('C' * 10, metadata={'id': 's1'}),
RNA('G' * 10, metadata={'id': 's2'})])
self.assertEqual(sc.kmer_frequencies(1, relative=True),
[defaultdict(float, {'C': 1.0}),
defaultdict(float, {'G': 1.0})])
def test_update_ids_default_behavior(self):
# 3 seqs
exp_sc = SequenceCollection([
RNA('GAUUACA', id="1"),
RNA('UUG', id="2"),
RNA('U-----UGCC--', id="3")
])
exp_id_map = {'1': 'r1', '2': 'r2', '3': 'r3'}
obs_sc, obs_id_map = self.s2.update_ids()
self._assert_sequence_collections_equal(obs_sc, exp_sc)
self.assertEqual(obs_id_map, exp_id_map)
# empty
obs_sc, obs_id_map = self.empty.update_ids()
self._assert_sequence_collections_equal(obs_sc, self.empty)
self.assertEqual(obs_id_map, {})
def test_update_ids_prefix(self):
# 3 seqs
exp_sc = SequenceCollection([
RNA('GAUUACA', id="abc1"),
RNA('UUG', id="abc2"),
RNA('U-----UGCC--', id="abc3")
])
exp_id_map = {'abc1': 'r1', 'abc2': 'r2', 'abc3': 'r3'}
obs_sc, obs_id_map = self.s2.update_ids(prefix='abc')
self._assert_sequence_collections_equal(obs_sc, exp_sc)
self.assertEqual(obs_id_map, exp_id_map)
# empty
obs_sc, obs_id_map = self.empty.update_ids(prefix='abc')
self._assert_sequence_collections_equal(obs_sc, self.empty)
self.assertEqual(obs_id_map, {})
def test_update_ids_ids_parameter(self):
# 3 seqs
exp_sc = SequenceCollection([
RNA('GAUUACA', id="abc"),
RNA('UUG', id="def"),
RNA('U-----UGCC--', id="ghi")
])
exp_id_map = {'abc': 'r1', 'def': 'r2', 'ghi': 'r3'}
obs_sc, obs_id_map = self.s2.update_ids(ids=('abc', 'def', 'ghi'))
self._assert_sequence_collections_equal(obs_sc, exp_sc)
self.assertEqual(obs_id_map, exp_id_map)
# empty
obs_sc, obs_id_map = self.empty.update_ids(ids=[])
self._assert_sequence_collections_equal(obs_sc, self.empty)
self.assertEqual(obs_id_map, {})
def test_sequence_collection_to_fastq_kwargs_passed(self):
for components, kwargs_expected_fp in self.valid_files:
for kwargs, expected_fp in kwargs_expected_fp:
obj = SequenceCollection([
NucleotideSequence(c[2],
id=c[0],
description=c[1],
quality=c[3]) for c in components
])
fh = StringIO()
_sequence_collection_to_fastq(obj, fh, **kwargs)
observed = fh.getvalue()
fh.close()
with open(expected_fp, 'U') as f:
expected = f.read()
self.assertEqual(observed, expected)
def test_update_ids_fn_parameter(self):
def append_42(ids):
return [id_ + '-42' for id_ in ids]
# 3 seqs
exp_sc = SequenceCollection([
RNA('GAUUACA', id="r1-42"),
RNA('UUG', id="r2-42"),
RNA('U-----UGCC--', id="r3-42")
])
exp_id_map = {'r1-42': 'r1', 'r2-42': 'r2', 'r3-42': 'r3'}
obs_sc, obs_id_map = self.s2.update_ids(fn=append_42)
self._assert_sequence_collections_equal(obs_sc, exp_sc)
self.assertEqual(obs_id_map, exp_id_map)
# empty
obs_sc, obs_id_map = self.empty.update_ids(fn=append_42)
self._assert_sequence_collections_equal(obs_sc, self.empty)
self.assertEqual(obs_id_map, {})
def test_sequence_collection_to_fastq_kwargs_passed(self):
for components, kwargs_expected_fp in self.valid_files:
for kwargs, expected_fp in kwargs_expected_fp:
obj = SequenceCollection([
DNA(c[2], metadata={'id': c[0], 'description': c[1]},
positional_metadata={'quality': c[3]},
lowercase='introns')
for c in components])
fh = StringIO()
kwargs['lowercase'] = 'introns'
_sequence_collection_to_fastq(obj, fh, **kwargs)
observed = fh.getvalue()
fh.close()
with open(expected_fp, 'U') as f:
expected = f.read()
self.assertEqual(observed, expected)
def generateReference(assay_list):
from skbio import DNA
from skbio import SequenceCollection
reference = []
for assay in assay_list:
name = assay.name
if assay.AND:
for operand in assay.AND:
if isinstance(operand, Target):
name = name + "_%s" % operand.gene_name if operand.gene_name else name
for amplicon in operand.amplicons:
name = name + "_%s" % amplicon.variant_name if amplicon.variant_name else name
seq = DNA(amplicon.sequence, id=name)
reference.append(seq)
else:
for amplicon in assay.target.amplicons:
name = assay.name + "_%s" % amplicon.variant_name if amplicon.variant_name else name
seq = DNA(amplicon.sequence, {'id': name})
reference.append(seq)
return SequenceCollection(reference)
log_choices = ["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]
parser.add_argument(
'--log-level', '-l', default="INFO", choices=log_choices,
help="Set logging level. Default is info."
)
return parser
if __name__ == '__main__':
parser = get_argument_parser()
args = parser.parse_args()
level = getattr(logging, args.log_level.upper(), logging.INFO)
logging.basicConfig(level=level)
sequences = SequenceCollection.read(args.infile, format=args.format)
if args.parallel == 0 and len(sequences) > 16:
pool_size = multiprocessing.cpu_count()
else:
pool_size = 1
dmatrix = create_distance_matrix(sequences, d2.distance, pool_size,
statistic=d2.d2_neighbourhood_dna)
print(dmatrix)
phylo_tree = nj(dmatrix)
print(phylo_tree.ascii_art())
phylo_tree.write(args.outfile, format=args.target)
class SequenceCollectionTests(TestCase):
def setUp(self):
self.d1 = DNA('GATTACA', metadata={'id': "d1"})
self.d2 = DNA('TTG', metadata={'id': "d2"})
self.d3 = DNA('GTATACA', metadata={'id': "d3"})
self.r1 = RNA('GAUUACA', metadata={'id': "r1"})
self.r2 = RNA('UUG', metadata={'id': "r2"})
self.r3 = RNA('U-----UGCC--', metadata={'id': "r3"})
self.seqs1 = [self.d1, self.d2]
self.seqs2 = [self.r1, self.r2, self.r3]
self.seqs3 = self.seqs1 + self.seqs2
self.seqs4 = [self.d1, self.d3]
self.s1 = SequenceCollection(self.seqs1)
self.s2 = SequenceCollection(self.seqs2)
self.s3 = SequenceCollection(self.seqs3)
self.s4 = SequenceCollection(self.seqs4)
self.empty = SequenceCollection([])
def test_init(self):
SequenceCollection(self.seqs1)
SequenceCollection(self.seqs2)
SequenceCollection(self.seqs3)
SequenceCollection([])
def test_init_fail(self):
# sequences with overlapping ids
s1 = [self.d1, self.d1]
self.assertRaises(SequenceCollectionError, SequenceCollection, s1)
def test_init_fail_no_id(self):
seq = Sequence('ACGTACGT')
with six.assertRaisesRegex(self, SequenceCollectionError,
"'id' must be included in the sequence "
"metadata"):
SequenceCollection([seq])
def test_contains(self):
self.assertTrue('d1' in self.s1)
self.assertTrue('r2' in self.s2)
self.assertFalse('r2' in self.s1)
def test_eq(self):
self.assertTrue(self.s1 == self.s1)
self.assertFalse(self.s1 == self.s2)
# different objects can be equal
self.assertTrue(self.s1 == SequenceCollection([self.d1, self.d2]))
self.assertTrue(SequenceCollection([self.d1, self.d2]) == self.s1)
# SequenceCollections with different number of sequences are not equal
self.assertFalse(self.s1 == SequenceCollection([self.d1]))
class FakeSequenceCollection(SequenceCollection):
pass
# SequenceCollections of different types are not equal
self.assertFalse(self.s4 == FakeSequenceCollection([self.d1, self.d3]))
self.assertFalse(self.s4 == Alignment([self.d1, self.d3]))
# SequenceCollections with different sequences are not equal
self.assertFalse(self.s1 == SequenceCollection([self.d1, self.r1]))
def test_getitem(self):
self.assertEqual(self.s1[0], self.d1)
self.assertEqual(self.s1[1], self.d2)
self.assertEqual(self.s2[0], self.r1)
self.assertEqual(self.s2[1], self.r2)
self.assertRaises(IndexError, self.empty.__getitem__, 0)
self.assertRaises(KeyError, self.empty.__getitem__, '0')
def test_iter(self):
s1_iter = iter(self.s1)
count = 0
for actual, expected in zip(s1_iter, self.seqs1):
count += 1
self.assertEqual(actual, expected)
self.assertEqual(count, len(self.seqs1))
self.assertRaises(StopIteration, lambda: next(s1_iter))
def test_len(self):
self.assertEqual(len(self.s1), 2)
self.assertEqual(len(self.s2), 3)
self.assertEqual(len(self.s3), 5)
self.assertEqual(len(self.empty), 0)
def test_ne(self):
self.assertFalse(self.s1 != self.s1)
self.assertTrue(self.s1 != self.s2)
# SequenceCollections with different number of sequences are not equal
self.assertTrue(self.s1 != SequenceCollection([self.d1]))
class FakeSequenceCollection(SequenceCollection):
pass
# SequenceCollections of different types are not equal
self.assertTrue(self.s4 != FakeSequenceCollection([self.d1, self.d3]))
self.assertTrue(self.s4 != Alignment([self.d1, self.d3]))
# SequenceCollections with different sequences are not equal
self.assertTrue(self.s1 !=
SequenceCollection([self.d1, self.r1]))
def test_repr(self):
self.assertEqual(repr(self.s1),
"<SequenceCollection: n=2; "
"mean +/- std length=5.00 +/- 2.00>")
self.assertEqual(repr(self.s2),
"<SequenceCollection: n=3; "
"mean +/- std length=7.33 +/- 3.68>")
self.assertEqual(repr(self.s3),
"<SequenceCollection: n=5; "
"mean +/- std length=6.40 +/- 3.32>")
self.assertEqual(repr(self.empty),
"<SequenceCollection: n=0; "
"mean +/- std length=0.00 +/- 0.00>")
def test_reversed(self):
s1_iter = reversed(self.s1)
count = 0
for actual, expected in zip(s1_iter, self.seqs1[::-1]):
count += 1
self.assertEqual(actual, expected)
self.assertEqual(count, len(self.seqs1))
self.assertRaises(StopIteration, lambda: next(s1_iter))
def test_kmer_frequencies(self):
expected1 = Counter({'GAT': 1, 'TAC': 1})
expected2 = Counter({'TTG': 1})
self.assertEqual(
self.s1.kmer_frequencies(k=3, overlap=False, relative=False),
[expected1, expected2])
expected1 = defaultdict(float)
expected1['A'] = 3 / 7.
expected1['C'] = 1 / 7.
expected1['G'] = 1 / 7.
expected1['T'] = 2 / 7.
expected2 = defaultdict(float)
expected2['G'] = 1 / 3.
expected2['T'] = 2 / 3.
self.assertEqual(self.s1.kmer_frequencies(k=1, relative=True),
[expected1, expected2])
expected1 = defaultdict(float)
expected1['GAT'] = 1 / 2.
expected1['TAC'] = 1 / 2.
expected2 = defaultdict(float)
expected2['TTG'] = 1 / 1.
self.assertEqual(
self.s1.kmer_frequencies(k=3, overlap=False, relative=True),
[expected1, expected2])
self.assertEqual(self.empty.kmer_frequencies(k=1, relative=True), [])
# Test to ensure floating point precision bug isn't present. See the
# tests for Sequence.kmer_frequencies for more details.
sc = SequenceCollection([RNA('C' * 10, metadata={'id': 's1'}),
RNA('G' * 10, metadata={'id': 's2'})])
self.assertEqual(sc.kmer_frequencies(1, relative=True),
[defaultdict(float, {'C': 1.0}),
defaultdict(float, {'G': 1.0})])
def test_str(self):
exp1 = ">d1\nGATTACA\n>d2\nTTG\n"
self.assertEqual(str(self.s1), exp1)
exp2 = ">r1\nGAUUACA\n>r2\nUUG\n>r3\nU-----UGCC--\n"
self.assertEqual(str(self.s2), exp2)
exp4 = ""
self.assertEqual(str(self.empty), exp4)
def test_distances(self):
s1 = SequenceCollection([DNA("ACGT", metadata={'id': "d1"}),
DNA("ACGG", metadata={'id': "d2"})])
expected = [[0, 0.25],
[0.25, 0]]
expected = DistanceMatrix(expected, ['d1', 'd2'])
def h(s1, s2):
return hamming(s1.values, s2.values)
actual = s1.distances(h)
self.assertEqual(actual, expected)
# alt distance function provided
def dumb_distance(s1, s2):
return 42.
expected = [[0, 42.],
[42., 0]]
expected = DistanceMatrix(expected, ['d1', 'd2'])
actual = s1.distances(dumb_distance)
self.assertEqual(actual, expected)
def test_distribution_stats(self):
actual1 = self.s1.distribution_stats()
self.assertEqual(actual1[0], 2)
self.assertAlmostEqual(actual1[1], 5.0, 3)
self.assertAlmostEqual(actual1[2], 2.0, 3)
actual2 = self.s2.distribution_stats()
self.assertEqual(actual2[0], 3)
self.assertAlmostEqual(actual2[1], 7.333, 3)
self.assertAlmostEqual(actual2[2], 3.682, 3)
actual3 = self.s3.distribution_stats()
self.assertEqual(actual3[0], 5)
self.assertAlmostEqual(actual3[1], 6.400, 3)
self.assertAlmostEqual(actual3[2], 3.323, 3)
actual4 = self.empty.distribution_stats()
self.assertEqual(actual4[0], 0)
self.assertEqual(actual4[1], 0.0)
self.assertEqual(actual4[2], 0.0)
def test_degap(self):
expected = SequenceCollection([
RNA('GAUUACA', metadata={'id': "r1"}),
RNA('UUG', metadata={'id': "r2"}),
RNA('UUGCC', metadata={'id': "r3"})])
actual = self.s2.degap()
self.assertEqual(actual, expected)
def test_get_seq(self):
self.assertEqual(self.s1.get_seq('d1'), self.d1)
self.assertEqual(self.s1.get_seq('d2'), self.d2)
def test_ids(self):
self.assertEqual(self.s1.ids(), ['d1', 'd2'])
self.assertEqual(self.s2.ids(), ['r1', 'r2', 'r3'])
self.assertEqual(self.s3.ids(),
['d1', 'd2', 'r1', 'r2', 'r3'])
self.assertEqual(self.empty.ids(), [])
def test_update_ids_default_behavior(self):
# 3 seqs
exp_sc = SequenceCollection([
RNA('GAUUACA', metadata={'id': "1"}),
RNA('UUG', metadata={'id': "2"}),
RNA('U-----UGCC--', metadata={'id': "3"})
])
exp_id_map = {'1': 'r1', '2': 'r2', '3': 'r3'}
obs_sc, obs_id_map = self.s2.update_ids()
self.assertEqual(obs_sc, exp_sc)
self.assertEqual(obs_id_map, exp_id_map)
# empty
obs_sc, obs_id_map = self.empty.update_ids()
self.assertEqual(obs_sc, self.empty)
self.assertEqual(obs_id_map, {})
def test_update_ids_prefix(self):
# 3 seqs
exp_sc = SequenceCollection([
RNA('GAUUACA', metadata={'id': "abc1"}),
RNA('UUG', metadata={'id': "abc2"}),
RNA('U-----UGCC--', metadata={'id': "abc3"})
])
exp_id_map = {'abc1': 'r1', 'abc2': 'r2', 'abc3': 'r3'}
obs_sc, obs_id_map = self.s2.update_ids(prefix='abc')
self.assertEqual(obs_sc, exp_sc)
self.assertEqual(obs_id_map, exp_id_map)
# empty
obs_sc, obs_id_map = self.empty.update_ids(prefix='abc')
self.assertEqual(obs_sc, self.empty)
self.assertEqual(obs_id_map, {})
def test_update_ids_func_parameter(self):
def append_42(ids):
return [id_ + '-42' for id_ in ids]
# 3 seqs
exp_sc = SequenceCollection([
RNA('GAUUACA', metadata={'id': "r1-42"}),
RNA('UUG', metadata={'id': "r2-42"}),
RNA('U-----UGCC--', metadata={'id': "r3-42"})
])
exp_id_map = {'r1-42': 'r1', 'r2-42': 'r2', 'r3-42': 'r3'}
obs_sc, obs_id_map = self.s2.update_ids(func=append_42)
self.assertEqual(obs_sc, exp_sc)
self.assertEqual(obs_id_map, exp_id_map)
# empty
obs_sc, obs_id_map = self.empty.update_ids(func=append_42)
self.assertEqual(obs_sc, self.empty)
self.assertEqual(obs_id_map, {})
def test_update_ids_ids_parameter(self):
# 3 seqs
exp_sc = SequenceCollection([
RNA('GAUUACA', metadata={'id': "abc"}),
RNA('UUG', metadata={'id': "def"}),
RNA('U-----UGCC--', metadata={'id': "ghi"})
])
exp_id_map = {'abc': 'r1', 'def': 'r2', 'ghi': 'r3'}
obs_sc, obs_id_map = self.s2.update_ids(ids=('abc', 'def', 'ghi'))
self.assertEqual(obs_sc, exp_sc)
self.assertEqual(obs_id_map, exp_id_map)
# empty
obs_sc, obs_id_map = self.empty.update_ids(ids=[])
self.assertEqual(obs_sc, self.empty)
self.assertEqual(obs_id_map, {})
def test_update_ids_sequence_attributes_propagated(self):
# 1 seq
exp_sc = Alignment([
DNA('ACGT', metadata={'id': "abc", 'description': 'desc'},
positional_metadata={'quality': range(4)})
])
exp_id_map = {'abc': 'seq1'}
obj = Alignment([
DNA('ACGT', metadata={'id': "seq1", 'description': 'desc'},
positional_metadata={'quality': range(4)})
])
obs_sc, obs_id_map = obj.update_ids(ids=('abc',))
self.assertEqual(obs_sc, exp_sc)
self.assertEqual(obs_id_map, exp_id_map)
# 2 seqs
exp_sc = Alignment([
DNA('ACGT', metadata={'id': "abc", 'description': 'desc1'},
positional_metadata={'quality': range(4)}),
DNA('TGCA', metadata={'id': "def", 'description': 'desc2'},
positional_metadata={'quality': range(4)[::-1]})
])
exp_id_map = {'abc': 'seq1', 'def': 'seq2'}
obj = Alignment([
DNA('ACGT', metadata={'id': "seq1", 'description': 'desc1'},
positional_metadata={'quality': (0, 1, 2, 3)}),
DNA('TGCA', metadata={'id': "seq2", 'description': 'desc2'},
positional_metadata={'quality': (3, 2, 1, 0)})
])
obs_sc, obs_id_map = obj.update_ids(ids=('abc', 'def'))
self.assertEqual(obs_sc, exp_sc)
self.assertEqual(obs_id_map, exp_id_map)
def test_update_ids_invalid_parameter_combos(self):
with six.assertRaisesRegex(self, SequenceCollectionError,
'ids and func'):
self.s1.update_ids(func=lambda e: e, ids=['foo', 'bar'])
with six.assertRaisesRegex(self, SequenceCollectionError, 'prefix'):
self.s1.update_ids(ids=['foo', 'bar'], prefix='abc')
with six.assertRaisesRegex(self, SequenceCollectionError, 'prefix'):
self.s1.update_ids(func=lambda e: e, prefix='abc')
def test_update_ids_invalid_ids(self):
# incorrect number of new ids
with six.assertRaisesRegex(self, SequenceCollectionError, '3 != 2'):
self.s1.update_ids(ids=['foo', 'bar', 'baz'])
with six.assertRaisesRegex(self, SequenceCollectionError, '4 != 2'):
self.s1.update_ids(func=lambda e: ['foo', 'bar', 'baz', 'abc'])
# duplicates
with six.assertRaisesRegex(self, SequenceCollectionError, 'foo'):
self.s2.update_ids(ids=['foo', 'bar', 'foo'])
with six.assertRaisesRegex(self, SequenceCollectionError, 'bar'):
self.s2.update_ids(func=lambda e: ['foo', 'bar', 'bar'])
def test_is_empty(self):
self.assertFalse(self.s1.is_empty())
self.assertFalse(self.s2.is_empty())
self.assertFalse(self.s3.is_empty())
self.assertTrue(self.empty.is_empty())
def test_iteritems(self):
self.assertEqual(list(self.s1.iteritems()),
[(s.metadata['id'], s) for s in self.s1])
def test_sequence_count(self):
self.assertEqual(self.s1.sequence_count(), 2)
self.assertEqual(self.s2.sequence_count(), 3)
self.assertEqual(self.s3.sequence_count(), 5)
self.assertEqual(self.empty.sequence_count(), 0)
def test_sequence_lengths(self):
self.assertEqual(self.s1.sequence_lengths(), [7, 3])
self.assertEqual(self.s2.sequence_lengths(), [7, 3, 12])
self.assertEqual(self.s3.sequence_lengths(), [7, 3, 7, 3, 12])
self.assertEqual(self.empty.sequence_lengths(), [])
from qiime_default_reference import get_template_alignment, get_reference_sequences from skbio import SequenceCollection gapped_sequences = [(s.id, str(s)) for s in SequenceCollection.read(get_template_alignment())][:500] sequences = [(s.id, str(s)) for s in SequenceCollection.read(get_reference_sequences())][:500] motif_1 = "GGTGCAAGCCGGTGGAAACA"
class SequenceCollectionTests(TestCase):
"""Tests of the SequenceCollection class """
def setUp(self):
"""Initialize values to be used in tests
"""
self.d1 = DNASequence('GATTACA', id="d1")
self.d2 = DNASequence('TTG', id="d2")
self.d1_lower = DNASequence('gattaca', id="d1")
self.d2_lower = DNASequence('ttg', id="d2")
self.r1 = RNASequence('GAUUACA', id="r1")
self.r2 = RNASequence('UUG', id="r2")
self.r3 = RNASequence('U-----UGCC--', id="r3")
self.i1 = DNASequence('GATXACA', id="i1")
self.seqs1 = [self.d1, self.d2]
self.seqs1_lower = [self.d1_lower, self.d2_lower]
self.seqs2 = [self.r1, self.r2, self.r3]
self.seqs3 = self.seqs1 + self.seqs2
self.seqs1_t = [('d1', 'GATTACA'), ('d2', 'TTG')]
self.seqs2_t = [('r1', 'GAUUACA'), ('r2', 'UUG'),
('r3', 'U-----UGCC--')]
self.seqs3_t = self.seqs1_t + self.seqs2_t
self.s1 = SequenceCollection(self.seqs1)
self.s1_lower = SequenceCollection(self.seqs1_lower)
self.s2 = SequenceCollection(self.seqs2)
self.s3 = SequenceCollection(self.seqs3)
self.empty = SequenceCollection([])
self.invalid_s1 = SequenceCollection([self.i1])
def test_init(self):
"""Initialization functions as expected with varied input types
"""
SequenceCollection(self.seqs1)
SequenceCollection(self.seqs2)
SequenceCollection(self.seqs3)
SequenceCollection([])
def test_init_fail(self):
"""initialization with sequences with overlapping ids fails
"""
s1 = [self.d1, self.d1]
self.assertRaises(SequenceCollectionError, SequenceCollection, s1)
def test_init_validate(self):
"""initialization with validation functions as expected
"""
SequenceCollection(self.seqs1, validate=True)
SequenceCollection(self.seqs1, validate=True)
# can't validate self.seqs2 as a DNASequence
self.assertRaises(SequenceCollectionError, SequenceCollection,
self.invalid_s1, validate=True)
def test_from_fasta_records(self):
"""Initialization from list of tuples functions as expected
"""
SequenceCollection.from_fasta_records(self.seqs1_t, DNASequence)
SequenceCollection.from_fasta_records(self.seqs2_t, RNASequence)
SequenceCollection.from_fasta_records(self.seqs3_t, NucleotideSequence)
def test_contains(self):
"""in operator functions as expected
"""
self.assertTrue('d1' in self.s1)
self.assertTrue('r2' in self.s2)
self.assertFalse('r2' in self.s1)
def test_eq(self):
"""equality operator functions as expected
"""
self.assertTrue(self.s1 == self.s1)
self.assertFalse(self.s1 == self.s2)
# different objects can be equal
self.assertTrue(self.s1 == SequenceCollection([self.d1, self.d2]))
self.assertTrue(SequenceCollection([self.d1, self.d2]) == self.s1)
# SequenceCollections with different number of sequences are not equal
self.assertFalse(self.s1 == SequenceCollection([self.d1]))
class FakeSequenceCollection(SequenceCollection):
pass
# SequenceCollections of different types are not equal
self.assertFalse(self.s1 == FakeSequenceCollection([self.d1, self.d2]))
self.assertFalse(self.s1 == Alignment([self.d1, self.d2]))
# SequenceCollections with different sequences are not equal
self.assertFalse(self.s1 == SequenceCollection([self.d1, self.r1]))
def test_getitem(self):
"""getitem functions as expected
"""
self.assertEqual(self.s1[0], self.d1)
self.assertEqual(self.s1[1], self.d2)
self.assertEqual(self.s2[0], self.r1)
self.assertEqual(self.s2[1], self.r2)
self.assertRaises(IndexError, self.empty.__getitem__, 0)
self.assertRaises(KeyError, self.empty.__getitem__, '0')
def test_iter(self):
"""iter functions as expected
"""
s1_iter = iter(self.s1)
count = 0
for actual, expected in zip(s1_iter, self.seqs1):
count += 1
self.assertEqual(actual, expected)
self.assertEqual(count, len(self.seqs1))
self.assertRaises(StopIteration, lambda: next(s1_iter))
def test_len(self):
"""len functions as expected
"""
self.assertEqual(len(self.s1), 2)
self.assertEqual(len(self.s2), 3)
self.assertEqual(len(self.s3), 5)
self.assertEqual(len(self.empty), 0)
def test_ne(self):
"""inequality operator functions as expected
"""
self.assertFalse(self.s1 != self.s1)
self.assertTrue(self.s1 != self.s2)
# SequenceCollections with different number of sequences are not equal
self.assertTrue(self.s1 != SequenceCollection([self.d1]))
class FakeSequenceCollection(SequenceCollection):
pass
# SequenceCollections of different types are not equal
self.assertTrue(self.s1 != FakeSequenceCollection([self.d1, self.d2]))
self.assertTrue(self.s1 != Alignment([self.d1, self.d2]))
# SequenceCollections with different sequences are not equal
self.assertTrue(self.s1 !=
SequenceCollection([self.d1, self.r1]))
def test_repr(self):
"""repr functions as expected
"""
self.assertEqual(repr(self.s1),
"<SequenceCollection: n=2; "
"mean +/- std length=5.00 +/- 2.00>")
self.assertEqual(repr(self.s2),
"<SequenceCollection: n=3; "
"mean +/- std length=7.33 +/- 3.68>")
self.assertEqual(repr(self.s3),
"<SequenceCollection: n=5; "
"mean +/- std length=6.40 +/- 3.32>")
self.assertEqual(repr(self.empty),
"<SequenceCollection: n=0; "
"mean +/- std length=0.00 +/- 0.00>")
def test_reversed(self):
"""reversed functions as expected
"""
s1_iter = reversed(self.s1)
count = 0
for actual, expected in zip(s1_iter, self.seqs1[::-1]):
count += 1
self.assertEqual(actual, expected)
self.assertEqual(count, len(self.seqs1))
self.assertRaises(StopIteration, lambda: next(s1_iter))
def test_k_word_frequencies(self):
"""k_word_frequencies functions as expected
"""
expected1 = defaultdict(int)
expected1['A'] = 3 / 7.
expected1['C'] = 1 / 7.
expected1['G'] = 1 / 7.
expected1['T'] = 2 / 7.
expected2 = defaultdict(int)
expected2['G'] = 1 / 3.
expected2['T'] = 2 / 3.
self.assertEqual(self.s1.k_word_frequencies(k=1),
[expected1, expected2])
expected1 = defaultdict(int)
expected1['GAT'] = 1 / 2.
expected1['TAC'] = 1 / 2.
expected2 = defaultdict(int)
expected2['TTG'] = 1 / 1.
self.assertEqual(self.s1.k_word_frequencies(k=3, overlapping=False),
[expected1, expected2])
self.assertEqual(self.empty.k_word_frequencies(k=1), [])
def test_str(self):
"""str functions as expected
"""
exp1 = ">d1\nGATTACA\n>d2\nTTG\n"
self.assertEqual(str(self.s1), exp1)
exp2 = ">r1\nGAUUACA\n>r2\nUUG\n>r3\nU-----UGCC--\n"
self.assertEqual(str(self.s2), exp2)
exp4 = ""
self.assertEqual(str(self.empty), exp4)
def test_distances(self):
"""distances functions as expected
"""
s1 = SequenceCollection([DNA("ACGT", "d1"), DNA("ACGG", "d2")])
expected = [[0, 0.25],
[0.25, 0]]
expected = DistanceMatrix(expected, ['d1', 'd2'])
actual = s1.distances(hamming)
self.assertEqual(actual, expected)
# alt distance function provided
def dumb_distance(s1, s2):
return 42.
expected = [[0, 42.],
[42., 0]]
expected = DistanceMatrix(expected, ['d1', 'd2'])
actual = s1.distances(dumb_distance)
self.assertEqual(actual, expected)
def test_distribution_stats(self):
"""distribution_stats functions as expected
"""
actual1 = self.s1.distribution_stats()
self.assertEqual(actual1[0], 2)
self.assertAlmostEqual(actual1[1], 5.0, 3)
self.assertAlmostEqual(actual1[2], 2.0, 3)
actual2 = self.s2.distribution_stats()
self.assertEqual(actual2[0], 3)
self.assertAlmostEqual(actual2[1], 7.333, 3)
self.assertAlmostEqual(actual2[2], 3.682, 3)
actual3 = self.s3.distribution_stats()
self.assertEqual(actual3[0], 5)
self.assertAlmostEqual(actual3[1], 6.400, 3)
self.assertAlmostEqual(actual3[2], 3.323, 3)
actual4 = self.empty.distribution_stats()
self.assertEqual(actual4[0], 0)
self.assertEqual(actual4[1], 0.0)
self.assertEqual(actual4[2], 0.0)
def test_degap(self):
"""degap functions as expected
"""
expected = [(id_, seq.replace('.', '').replace('-', ''))
for id_, seq in self.seqs2_t]
expected = SequenceCollection.from_fasta_records(expected, RNASequence)
actual = self.s2.degap()
self.assertEqual(actual, expected)
def test_get_seq(self):
"""getseq functions asexpected
"""
self.assertEqual(self.s1.get_seq('d1'), self.d1)
self.assertEqual(self.s1.get_seq('d2'), self.d2)
def test_ids(self):
"""ids functions as expected
"""
self.assertEqual(self.s1.ids(), ['d1', 'd2'])
self.assertEqual(self.s2.ids(), ['r1', 'r2', 'r3'])
self.assertEqual(self.s3.ids(),
['d1', 'd2', 'r1', 'r2', 'r3'])
self.assertEqual(self.empty.ids(), [])
def test_int_map(self):
"""int_map functions as expected
"""
expected1 = {"1": self.d1, "2": self.d2}
expected2 = {"1": "d1", "2": "d2"}
self.assertEqual(self.s1.int_map(), (expected1, expected2))
expected1 = {"h-1": self.d1, "h-2": self.d2}
expected2 = {"h-1": "d1", "h-2": "d2"}
self.assertEqual(self.s1.int_map(prefix='h-'), (expected1, expected2))
def test_is_empty(self):
"""is_empty functions as expected
"""
self.assertFalse(self.s1.is_empty())
self.assertFalse(self.s2.is_empty())
self.assertFalse(self.s3.is_empty())
self.assertTrue(self.empty.is_empty())
def test_is_valid(self):
"""is_valid functions as expected
"""
self.assertTrue(self.s1.is_valid())
self.assertTrue(self.s2.is_valid())
self.assertTrue(self.s3.is_valid())
self.assertTrue(self.empty.is_valid())
self.assertFalse(self.invalid_s1.is_valid())
def test_iteritems(self):
"""iteritems functions as expected
"""
self.assertEqual(list(self.s1.iteritems()),
[(s.id, s) for s in self.s1])
def test_lower(self):
"""lower functions as expected
"""
self.assertEqual(self.s1.lower(), self.s1_lower)
def test_sequence_count(self):
"""num_seqs functions as expected
"""
self.assertEqual(self.s1.sequence_count(), 2)
self.assertEqual(self.s2.sequence_count(), 3)
self.assertEqual(self.s3.sequence_count(), 5)
self.assertEqual(self.empty.sequence_count(), 0)
def test_sequence_lengths(self):
"""sequence_lengths functions as expected
"""
self.assertEqual(self.s1.sequence_lengths(), [7, 3])
self.assertEqual(self.s2.sequence_lengths(), [7, 3, 12])
self.assertEqual(self.s3.sequence_lengths(), [7, 3, 7, 3, 12])
self.assertEqual(self.empty.sequence_lengths(), [])
def test_to_fasta(self):
"""to_fasta functions as expected
"""
exp1 = ">d1\nGATTACA\n>d2\nTTG\n"
self.assertEqual(self.s1.to_fasta(), exp1)
exp2 = ">r1\nGAUUACA\n>r2\nUUG\n>r3\nU-----UGCC--\n"
self.assertEqual(self.s2.to_fasta(), exp2)
def test_toFasta(self):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
exp = ">d1\nGATTACA\n>d2\nTTG\n"
self.assertEqual(self.s1.toFasta(), exp)
def test_upper(self):
"""upper functions as expected
"""
self.assertEqual(self.s1_lower.upper(), self.s1)
def test_from_fasta_records(self):
"""Initialization from list of tuples functions as expected
"""
SequenceCollection.from_fasta_records(self.seqs1_t, DNASequence)
SequenceCollection.from_fasta_records(self.seqs2_t, RNASequence)
SequenceCollection.from_fasta_records(self.seqs3_t, NucleotideSequence)
class SequenceCollectionTests(TestCase):
def setUp(self):
self.d1 = DNASequence('GATTACA', id="d1")
self.d2 = DNASequence('TTG', id="d2")
self.d3 = DNASequence('GTATACA', id="d3")
self.d1_lower = DNASequence('gattaca', id="d1")
self.d2_lower = DNASequence('ttg', id="d2")
self.d3_lower = DNASequence('gtataca', id="d3")
self.r1 = RNASequence('GAUUACA', id="r1")
self.r2 = RNASequence('UUG', id="r2")
self.r3 = RNASequence('U-----UGCC--', id="r3")
self.i1 = DNASequence('GATXACA', id="i1")
self.seqs1 = [self.d1, self.d2]
self.seqs1_lower = [self.d1_lower, self.d2_lower]
self.seqs2 = [self.r1, self.r2, self.r3]
self.seqs3 = self.seqs1 + self.seqs2
self.seqs4 = [self.d1, self.d3]
self.seqs1_t = [('d1', 'GATTACA'), ('d2', 'TTG')]
self.seqs2_t = [('r1', 'GAUUACA'), ('r2', 'UUG'),
('r3', 'U-----UGCC--')]
self.seqs3_t = self.seqs1_t + self.seqs2_t
self.s1 = SequenceCollection(self.seqs1)
self.s1_lower = SequenceCollection(self.seqs1_lower)
self.s2 = SequenceCollection(self.seqs2)
self.s3 = SequenceCollection(self.seqs3)
self.s4 = SequenceCollection(self.seqs4)
self.empty = SequenceCollection([])
self.invalid_s1 = SequenceCollection([self.i1])
def test_init(self):
SequenceCollection(self.seqs1)
SequenceCollection(self.seqs2)
SequenceCollection(self.seqs3)
SequenceCollection([])
def test_init_fail(self):
# sequences with overlapping ids
s1 = [self.d1, self.d1]
self.assertRaises(SequenceCollectionError, SequenceCollection, s1)
def test_init_validate(self):
SequenceCollection(self.seqs1, validate=True)
SequenceCollection(self.seqs1, validate=True)
# can't validate self.seqs2 as a DNASequence
self.assertRaises(SequenceCollectionError, SequenceCollection,
self.invalid_s1, validate=True)
def test_contains(self):
self.assertTrue('d1' in self.s1)
self.assertTrue('r2' in self.s2)
self.assertFalse('r2' in self.s1)
def test_eq(self):
self.assertTrue(self.s1 == self.s1)
self.assertFalse(self.s1 == self.s2)
# different objects can be equal
self.assertTrue(self.s1 == SequenceCollection([self.d1, self.d2]))
self.assertTrue(SequenceCollection([self.d1, self.d2]) == self.s1)
# SequenceCollections with different number of sequences are not equal
self.assertFalse(self.s1 == SequenceCollection([self.d1]))
class FakeSequenceCollection(SequenceCollection):
pass
# SequenceCollections of different types are not equal
self.assertFalse(self.s4 == FakeSequenceCollection([self.d1, self.d3]))
self.assertFalse(self.s4 == Alignment([self.d1, self.d3]))
# SequenceCollections with different sequences are not equal
self.assertFalse(self.s1 == SequenceCollection([self.d1, self.r1]))
def test_getitem(self):
self.assertEqual(self.s1[0], self.d1)
self.assertEqual(self.s1[1], self.d2)
self.assertEqual(self.s2[0], self.r1)
self.assertEqual(self.s2[1], self.r2)
self.assertRaises(IndexError, self.empty.__getitem__, 0)
self.assertRaises(KeyError, self.empty.__getitem__, '0')
def test_iter(self):
s1_iter = iter(self.s1)
count = 0
for actual, expected in zip(s1_iter, self.seqs1):
count += 1
self.assertEqual(actual, expected)
self.assertEqual(count, len(self.seqs1))
self.assertRaises(StopIteration, lambda: next(s1_iter))
def test_len(self):
self.assertEqual(len(self.s1), 2)
self.assertEqual(len(self.s2), 3)
self.assertEqual(len(self.s3), 5)
self.assertEqual(len(self.empty), 0)
def test_ne(self):
self.assertFalse(self.s1 != self.s1)
self.assertTrue(self.s1 != self.s2)
# SequenceCollections with different number of sequences are not equal
self.assertTrue(self.s1 != SequenceCollection([self.d1]))
class FakeSequenceCollection(SequenceCollection):
pass
# SequenceCollections of different types are not equal
self.assertTrue(self.s4 != FakeSequenceCollection([self.d1, self.d3]))
self.assertTrue(self.s4 != Alignment([self.d1, self.d3]))
# SequenceCollections with different sequences are not equal
self.assertTrue(self.s1 !=
SequenceCollection([self.d1, self.r1]))
def test_repr(self):
self.assertEqual(repr(self.s1),
"<SequenceCollection: n=2; "
"mean +/- std length=5.00 +/- 2.00>")
self.assertEqual(repr(self.s2),
"<SequenceCollection: n=3; "
"mean +/- std length=7.33 +/- 3.68>")
self.assertEqual(repr(self.s3),
"<SequenceCollection: n=5; "
"mean +/- std length=6.40 +/- 3.32>")
self.assertEqual(repr(self.empty),
"<SequenceCollection: n=0; "
"mean +/- std length=0.00 +/- 0.00>")
def test_reversed(self):
s1_iter = reversed(self.s1)
count = 0
for actual, expected in zip(s1_iter, self.seqs1[::-1]):
count += 1
self.assertEqual(actual, expected)
self.assertEqual(count, len(self.seqs1))
self.assertRaises(StopIteration, lambda: next(s1_iter))
def test_k_word_frequencies(self):
expected1 = defaultdict(float)
expected1['A'] = 3 / 7.
expected1['C'] = 1 / 7.
expected1['G'] = 1 / 7.
expected1['T'] = 2 / 7.
expected2 = defaultdict(float)
expected2['G'] = 1 / 3.
expected2['T'] = 2 / 3.
self.assertEqual(self.s1.k_word_frequencies(k=1),
[expected1, expected2])
expected1 = defaultdict(float)
expected1['GAT'] = 1 / 2.
expected1['TAC'] = 1 / 2.
expected2 = defaultdict(float)
expected2['TTG'] = 1 / 1.
self.assertEqual(self.s1.k_word_frequencies(k=3, overlapping=False),
[expected1, expected2])
self.assertEqual(self.empty.k_word_frequencies(k=1), [])
# Test to ensure floating point precision bug isn't present. See the
# tests for BiologicalSequence.k_word_frequencies for more details.
sc = SequenceCollection([RNA('C' * 10, id='s1'),
RNA('G' * 10, id='s2')])
self.assertEqual(sc.k_word_frequencies(1),
[defaultdict(float, {'C': 1.0}),
defaultdict(float, {'G': 1.0})])
def test_str(self):
exp1 = ">d1\nGATTACA\n>d2\nTTG\n"
self.assertEqual(str(self.s1), exp1)
exp2 = ">r1\nGAUUACA\n>r2\nUUG\n>r3\nU-----UGCC--\n"
self.assertEqual(str(self.s2), exp2)
exp4 = ""
self.assertEqual(str(self.empty), exp4)
def test_distances(self):
s1 = SequenceCollection([DNA("ACGT", "d1"), DNA("ACGG", "d2")])
expected = [[0, 0.25],
[0.25, 0]]
expected = DistanceMatrix(expected, ['d1', 'd2'])
actual = s1.distances(hamming)
self.assertEqual(actual, expected)
# alt distance function provided
def dumb_distance(s1, s2):
return 42.
expected = [[0, 42.],
[42., 0]]
expected = DistanceMatrix(expected, ['d1', 'd2'])
actual = s1.distances(dumb_distance)
self.assertEqual(actual, expected)
def test_distribution_stats(self):
actual1 = self.s1.distribution_stats()
self.assertEqual(actual1[0], 2)
self.assertAlmostEqual(actual1[1], 5.0, 3)
self.assertAlmostEqual(actual1[2], 2.0, 3)
actual2 = self.s2.distribution_stats()
self.assertEqual(actual2[0], 3)
self.assertAlmostEqual(actual2[1], 7.333, 3)
self.assertAlmostEqual(actual2[2], 3.682, 3)
actual3 = self.s3.distribution_stats()
self.assertEqual(actual3[0], 5)
self.assertAlmostEqual(actual3[1], 6.400, 3)
self.assertAlmostEqual(actual3[2], 3.323, 3)
actual4 = self.empty.distribution_stats()
self.assertEqual(actual4[0], 0)
self.assertEqual(actual4[1], 0.0)
self.assertEqual(actual4[2], 0.0)
def test_degap(self):
expected = SequenceCollection([
RNASequence('GAUUACA', id="r1"),
RNASequence('UUG', id="r2"),
RNASequence('UUGCC', id="r3")])
actual = self.s2.degap()
self.assertEqual(actual, expected)
def test_get_seq(self):
self.assertEqual(self.s1.get_seq('d1'), self.d1)
self.assertEqual(self.s1.get_seq('d2'), self.d2)
def test_ids(self):
self.assertEqual(self.s1.ids(), ['d1', 'd2'])
self.assertEqual(self.s2.ids(), ['r1', 'r2', 'r3'])
self.assertEqual(self.s3.ids(),
['d1', 'd2', 'r1', 'r2', 'r3'])
self.assertEqual(self.empty.ids(), [])
def _assert_sequence_collections_equal(self, observed, expected):
"""Compare SequenceCollections strictly."""
# TODO remove this custom equality testing code when SequenceCollection
# has an equals method (part of #656). We need this method to include
# IDs in the comparison (not part of SequenceCollection.__eq__).
self.assertEqual(observed, expected)
for obs_seq, exp_seq in zip(observed, expected):
self.assertTrue(obs_seq.equals(exp_seq))
def test_update_ids_default_behavior(self):
# 3 seqs
exp_sc = SequenceCollection([
RNA('GAUUACA', id="1"),
RNA('UUG', id="2"),
RNA('U-----UGCC--', id="3")
])
exp_id_map = {'1': 'r1', '2': 'r2', '3': 'r3'}
obs_sc, obs_id_map = self.s2.update_ids()
self._assert_sequence_collections_equal(obs_sc, exp_sc)
self.assertEqual(obs_id_map, exp_id_map)
# empty
obs_sc, obs_id_map = self.empty.update_ids()
self._assert_sequence_collections_equal(obs_sc, self.empty)
self.assertEqual(obs_id_map, {})
def test_update_ids_prefix(self):
# 3 seqs
exp_sc = SequenceCollection([
RNA('GAUUACA', id="abc1"),
RNA('UUG', id="abc2"),
RNA('U-----UGCC--', id="abc3")
])
exp_id_map = {'abc1': 'r1', 'abc2': 'r2', 'abc3': 'r3'}
obs_sc, obs_id_map = self.s2.update_ids(prefix='abc')
self._assert_sequence_collections_equal(obs_sc, exp_sc)
self.assertEqual(obs_id_map, exp_id_map)
# empty
obs_sc, obs_id_map = self.empty.update_ids(prefix='abc')
self._assert_sequence_collections_equal(obs_sc, self.empty)
self.assertEqual(obs_id_map, {})
def test_update_ids_fn_parameter(self):
def append_42(ids):
return [id_ + '-42' for id_ in ids]
# 3 seqs
exp_sc = SequenceCollection([
RNA('GAUUACA', id="r1-42"),
RNA('UUG', id="r2-42"),
RNA('U-----UGCC--', id="r3-42")
])
exp_id_map = {'r1-42': 'r1', 'r2-42': 'r2', 'r3-42': 'r3'}
obs_sc, obs_id_map = self.s2.update_ids(fn=append_42)
self._assert_sequence_collections_equal(obs_sc, exp_sc)
self.assertEqual(obs_id_map, exp_id_map)
# empty
obs_sc, obs_id_map = self.empty.update_ids(fn=append_42)
self._assert_sequence_collections_equal(obs_sc, self.empty)
self.assertEqual(obs_id_map, {})
def test_update_ids_ids_parameter(self):
# 3 seqs
exp_sc = SequenceCollection([
RNA('GAUUACA', id="abc"),
RNA('UUG', id="def"),
RNA('U-----UGCC--', id="ghi")
])
exp_id_map = {'abc': 'r1', 'def': 'r2', 'ghi': 'r3'}
obs_sc, obs_id_map = self.s2.update_ids(ids=('abc', 'def', 'ghi'))
self._assert_sequence_collections_equal(obs_sc, exp_sc)
self.assertEqual(obs_id_map, exp_id_map)
# empty
obs_sc, obs_id_map = self.empty.update_ids(ids=[])
self._assert_sequence_collections_equal(obs_sc, self.empty)
self.assertEqual(obs_id_map, {})
def test_update_ids_sequence_attributes_propagated(self):
# 1 seq
exp_sc = Alignment([
DNA('ACGT', id="abc", description='desc', quality=range(4))
])
exp_id_map = {'abc': 'seq1'}
obj = Alignment([
DNA('ACGT', id="seq1", description='desc', quality=range(4))
])
obs_sc, obs_id_map = obj.update_ids(ids=('abc',))
self._assert_sequence_collections_equal(obs_sc, exp_sc)
self.assertEqual(obs_id_map, exp_id_map)
# 2 seqs
exp_sc = Alignment([
DNA('ACGT', id="abc", description='desc1', quality=range(4)),
DNA('TGCA', id="def", description='desc2', quality=range(4)[::-1])
])
exp_id_map = {'abc': 'seq1', 'def': 'seq2'}
obj = Alignment([
DNA('ACGT', id="seq1", description='desc1', quality=(0, 1, 2, 3)),
DNA('TGCA', id="seq2", description='desc2', quality=(3, 2, 1, 0))
])
obs_sc, obs_id_map = obj.update_ids(ids=('abc', 'def'))
self._assert_sequence_collections_equal(obs_sc, exp_sc)
self.assertEqual(obs_id_map, exp_id_map)
def test_update_ids_invalid_parameter_combos(self):
with self.assertRaisesRegexp(SequenceCollectionError, 'ids and fn'):
self.s1.update_ids(fn=lambda e: e, ids=['foo', 'bar'])
with self.assertRaisesRegexp(SequenceCollectionError, 'prefix'):
self.s1.update_ids(ids=['foo', 'bar'], prefix='abc')
with self.assertRaisesRegexp(SequenceCollectionError, 'prefix'):
self.s1.update_ids(fn=lambda e: e, prefix='abc')
def test_update_ids_invalid_ids(self):
# incorrect number of new ids
with self.assertRaisesRegexp(SequenceCollectionError, '3 != 2'):
self.s1.update_ids(ids=['foo', 'bar', 'baz'])
with self.assertRaisesRegexp(SequenceCollectionError, '4 != 2'):
self.s1.update_ids(fn=lambda e: ['foo', 'bar', 'baz', 'abc'])
# duplicates
with self.assertRaisesRegexp(SequenceCollectionError, 'foo'):
self.s2.update_ids(ids=['foo', 'bar', 'foo'])
with self.assertRaisesRegexp(SequenceCollectionError, 'bar'):
self.s2.update_ids(fn=lambda e: ['foo', 'bar', 'bar'])
def test_is_empty(self):
self.assertFalse(self.s1.is_empty())
self.assertFalse(self.s2.is_empty())
self.assertFalse(self.s3.is_empty())
self.assertTrue(self.empty.is_empty())
def test_is_valid(self):
self.assertTrue(self.s1.is_valid())
self.assertTrue(self.s2.is_valid())
self.assertTrue(self.s3.is_valid())
self.assertTrue(self.empty.is_valid())
self.assertFalse(self.invalid_s1.is_valid())
def test_iteritems(self):
self.assertEqual(list(self.s1.iteritems()),
[(s.id, s) for s in self.s1])
def test_lower(self):
self.assertEqual(self.s1.lower(), self.s1_lower)
def test_sequence_count(self):
self.assertEqual(self.s1.sequence_count(), 2)
self.assertEqual(self.s2.sequence_count(), 3)
self.assertEqual(self.s3.sequence_count(), 5)
self.assertEqual(self.empty.sequence_count(), 0)
def test_sequence_lengths(self):
self.assertEqual(self.s1.sequence_lengths(), [7, 3])
self.assertEqual(self.s2.sequence_lengths(), [7, 3, 12])
self.assertEqual(self.s3.sequence_lengths(), [7, 3, 7, 3, 12])
self.assertEqual(self.empty.sequence_lengths(), [])
def test_upper(self):
self.assertEqual(self.s1_lower.upper(), self.s1)
