def test_omit_gap_sequences(self):
expected = self.a2
self.assertEqual(self.a2.omit_gap_sequences(1.0), expected)
self.assertEqual(self.a2.omit_gap_sequences(0.20), expected)
expected = Alignment([self.r2])
self.assertEqual(self.a2.omit_gap_sequences(0.19), expected)
self.assertEqual(self.empty.omit_gap_sequences(0.0), self.empty)
self.assertEqual(self.empty.omit_gap_sequences(0.2), self.empty)
self.assertEqual(self.empty.omit_gap_sequences(1.0), self.empty)
# Test to ensure floating point precision bug isn't present. See the
# tests for Alignment.position_frequencies for more details.
aln = Alignment([DNA('.' * 33, id='abc'), DNA('-' * 33, id='def')])
self.assertEqual(aln.omit_gap_sequences(1 - np.finfo(float).eps),
Alignment([]))
def test_omit_gap_sequences(self):
expected = self.a2
self.assertEqual(self.a2.omit_gap_sequences(1.0), expected)
self.assertEqual(self.a2.omit_gap_sequences(0.20), expected)
expected = Alignment([self.r2])
self.assertEqual(self.a2.omit_gap_sequences(0.19), expected)
self.assertEqual(self.empty.omit_gap_sequences(0.0), self.empty)
self.assertEqual(self.empty.omit_gap_sequences(0.2), self.empty)
self.assertEqual(self.empty.omit_gap_sequences(1.0), self.empty)
# Test to ensure floating point precision bug isn't present. See the
# tests for Alignment.position_frequencies for more details.
aln = Alignment([DNA('.' * 33, id='abc'), DNA('-' * 33, id='def')])
self.assertEqual(aln.omit_gap_sequences(1 - np.finfo(float).eps),
Alignment([]))
class AlignmentTests(TestCase):
def setUp(self):
self.d1 = DNA('..ACC-GTTGG..', metadata={'id': "d1"})
self.d2 = DNA('TTACCGGT-GGCC', metadata={'id': "d2"})
self.d3 = DNA('.-ACC-GTTGC--', metadata={'id': "d3"})
self.r1 = RNA('UUAU-', metadata={'id': "r1"})
self.r2 = RNA('ACGUU', metadata={'id': "r2"})
self.seqs1 = [self.d1, self.d2, self.d3]
self.seqs2 = [self.r1, self.r2]
self.a1 = Alignment(self.seqs1)
self.a2 = Alignment(self.seqs2)
self.a3 = Alignment(self.seqs2, score=42.0,
start_end_positions=[(0, 3), (5, 9)])
self.a4 = Alignment(self.seqs2, score=-42.0,
start_end_positions=[(1, 4), (6, 10)])
# no sequences
self.empty = Alignment([])
# sequences, but no positions
self.no_positions = Alignment([RNA('', metadata={'id': 'a'}),
RNA('', metadata={'id': 'b'})])
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):
expected = [[0, 6. / 13, 4. / 13],
[6. / 13, 0, 7. / 13],
[4. / 13, 7. / 13, 0]]
expected = DistanceMatrix(expected, ['d1', 'd2', 'd3'])
actual = self.a1.distances()
self.assertEqual(actual, expected)
# alt distance function provided
def dumb_distance(s1, s2):
return 42.
expected = [[0, 42., 42.],
[42., 0, 42.],
[42., 42., 0]]
expected = DistanceMatrix(expected, ['d1', 'd2', 'd3'])
actual = self.a1.distances(dumb_distance)
self.assertEqual(actual, expected)
def test_score(self):
self.assertEqual(self.a3.score(), 42.0)
self.assertEqual(self.a4.score(), -42.0)
def test_start_end_positions(self):
self.assertEqual(self.a3.start_end_positions(), [(0, 3), (5, 9)])
self.assertEqual(self.a4.start_end_positions(), [(1, 4), (6, 10)])
def test_subalignment(self):
# keep seqs by ids
actual = self.a1.subalignment(seqs_to_keep=['d1', 'd3'])
expected = Alignment([self.d1, self.d3])
self.assertEqual(actual, expected)
# keep seqs by indices
actual = self.a1.subalignment(seqs_to_keep=[0, 2])
expected = Alignment([self.d1, self.d3])
self.assertEqual(actual, expected)
# keep seqs by ids (invert)
actual = self.a1.subalignment(seqs_to_keep=['d1', 'd3'],
invert_seqs_to_keep=True)
expected = Alignment([self.d2])
self.assertEqual(actual, expected)
# keep seqs by indices (invert)
actual = self.a1.subalignment(seqs_to_keep=[0, 2],
invert_seqs_to_keep=True)
expected = Alignment([self.d2])
self.assertEqual(actual, expected)
# keep positions
actual = self.a1.subalignment(positions_to_keep=[0, 2, 3])
d1 = DNA('.AC', metadata={'id': "d1"})
d2 = DNA('TAC', metadata={'id': "d2"})
d3 = DNA('.AC', metadata={'id': "d3"})
expected = Alignment([d1, d2, d3])
self.assertEqual(actual, expected)
# keep positions (invert)
actual = self.a1.subalignment(positions_to_keep=[0, 2, 3],
invert_positions_to_keep=True)
d1 = DNA('.C-GTTGG..', metadata={'id': "d1"})
d2 = DNA('TCGGT-GGCC', metadata={'id': "d2"})
d3 = DNA('-C-GTTGC--', metadata={'id': "d3"})
expected = Alignment([d1, d2, d3])
self.assertEqual(actual, expected)
# keep seqs and positions
actual = self.a1.subalignment(seqs_to_keep=[0, 2],
positions_to_keep=[0, 2, 3])
d1 = DNA('.AC', metadata={'id': "d1"})
d3 = DNA('.AC', metadata={'id': "d3"})
expected = Alignment([d1, d3])
self.assertEqual(actual, expected)
# keep seqs and positions (invert)
actual = self.a1.subalignment(seqs_to_keep=[0, 2],
positions_to_keep=[0, 2, 3],
invert_seqs_to_keep=True,
invert_positions_to_keep=True)
d2 = DNA('TCGGT-GGCC', metadata={'id': "d2"})
expected = Alignment([d2])
self.assertEqual(actual, expected)
def test_subalignment_filter_out_everything(self):
exp = Alignment([])
# no sequences
obs = self.a1.subalignment(seqs_to_keep=None, invert_seqs_to_keep=True)
self.assertEqual(obs, exp)
# no positions
obs = self.a1.subalignment(positions_to_keep=None,
invert_positions_to_keep=True)
self.assertEqual(obs, exp)
def test_init_not_equal_lengths(self):
invalid_seqs = [self.d1, self.d2, self.d3,
DNA('.-ACC-GTGC--', metadata={'id': "i2"})]
self.assertRaises(AlignmentError, Alignment,
invalid_seqs)
def test_init_equal_lengths(self):
seqs = [self.d1, self.d2, self.d3]
Alignment(seqs)
def test_iter_positions(self):
actual = list(self.a2.iter_positions())
expected = [
[RNA('U', metadata={'id': 'r1'}), RNA('A', metadata={'id': 'r2'})],
[RNA('U', metadata={'id': 'r1'}), RNA('C', metadata={'id': 'r2'})],
[RNA('A', metadata={'id': 'r1'}), RNA('G', metadata={'id': 'r2'})],
[RNA('U', metadata={'id': 'r1'}), RNA('U', metadata={'id': 'r2'})],
[RNA('-', metadata={'id': 'r1'}), RNA('U', metadata={'id': 'r2'})]
]
self.assertEqual(actual, expected)
actual = list(self.a2.iter_positions(constructor=str))
expected = [list('UA'),
list('UC'),
list('AG'),
list('UU'),
list('-U')]
self.assertEqual(actual, expected)
def test_majority_consensus(self):
# empty cases
self.assertEqual(
self.empty.majority_consensus(), Sequence(''))
self.assertEqual(
self.no_positions.majority_consensus(), RNA(''))
# alignment where all sequences are the same
aln = Alignment([DNA('AG', metadata={'id': 'a'}),
DNA('AG', metadata={'id': 'b'})])
self.assertEqual(aln.majority_consensus(), DNA('AG'))
# no ties
d1 = DNA('TTT', metadata={'id': "d1"})
d2 = DNA('TT-', metadata={'id': "d2"})
d3 = DNA('TC-', metadata={'id': "d3"})
a1 = Alignment([d1, d2, d3])
self.assertEqual(a1.majority_consensus(), DNA('TT-'))
# ties
d1 = DNA('T', metadata={'id': "d1"})
d2 = DNA('A', metadata={'id': "d2"})
a1 = Alignment([d1, d2])
self.assertTrue(a1.majority_consensus() in
[DNA('T'), DNA('A')])
def test_omit_gap_positions(self):
expected = self.a2
self.assertEqual(self.a2.omit_gap_positions(1.0), expected)
self.assertEqual(self.a2.omit_gap_positions(0.51), expected)
r1 = RNA('UUAU', metadata={'id': "r1"})
r2 = RNA('ACGU', metadata={'id': "r2"})
expected = Alignment([r1, r2])
self.assertEqual(self.a2.omit_gap_positions(0.49), expected)
r1 = RNA('UUAU', metadata={'id': "r1"})
r2 = RNA('ACGU', metadata={'id': "r2"})
expected = Alignment([r1, r2])
self.assertEqual(self.a2.omit_gap_positions(0.0), expected)
self.assertEqual(self.empty.omit_gap_positions(0.0), self.empty)
self.assertEqual(self.empty.omit_gap_positions(0.49), self.empty)
self.assertEqual(self.empty.omit_gap_positions(1.0), self.empty)
# Test to ensure floating point precision bug isn't present. See the
# tests for Alignment.position_frequencies for more details.
seqs = []
for i in range(33):
seqs.append(DNA('-.', metadata={'id': str(i)}))
aln = Alignment(seqs)
self.assertEqual(aln.omit_gap_positions(1 - np.finfo(float).eps),
Alignment([DNA('', metadata={'id': str(i)})
for i in range(33)]))
def test_omit_gap_sequences(self):
expected = self.a2
self.assertEqual(self.a2.omit_gap_sequences(1.0), expected)
self.assertEqual(self.a2.omit_gap_sequences(0.20), expected)
expected = Alignment([self.r2])
self.assertEqual(self.a2.omit_gap_sequences(0.19), expected)
self.assertEqual(self.empty.omit_gap_sequences(0.0), self.empty)
self.assertEqual(self.empty.omit_gap_sequences(0.2), self.empty)
self.assertEqual(self.empty.omit_gap_sequences(1.0), self.empty)
# Test to ensure floating point precision bug isn't present. See the
# tests for Alignment.position_frequencies for more details.
aln = Alignment([DNA('.' * 33, metadata={'id': 'abc'}),
DNA('-' * 33, metadata={'id': 'def'})])
self.assertEqual(aln.omit_gap_sequences(1 - np.finfo(float).eps),
Alignment([]))
def test_position_counters(self):
self.assertEqual(self.empty.position_counters(), [])
self.assertEqual(self.no_positions.position_counters(), [])
expected = [Counter({'U': 1, 'A': 1}),
Counter({'U': 1, 'C': 1}),
Counter({'A': 1, 'G': 1}),
Counter({'U': 2}),
Counter({'-': 1, 'U': 1})]
self.assertEqual(self.a2.position_counters(), expected)
def test_position_frequencies(self):
self.assertEqual(self.empty.position_frequencies(), [])
self.assertEqual(self.no_positions.position_frequencies(), [])
expected = [defaultdict(float, {'U': 0.5, 'A': 0.5}),
defaultdict(float, {'U': 0.5, 'C': 0.5}),
defaultdict(float, {'A': 0.5, 'G': 0.5}),
defaultdict(float, {'U': 1.0}),
defaultdict(float, {'-': 0.5, 'U': 0.5})]
self.assertEqual(self.a2.position_frequencies(), expected)
def test_position_frequencies_floating_point_precision(self):
# Test that a position with no variation yields a frequency of exactly
# 1.0. Note that it is important to use self.assertEqual here instead
# of self.assertAlmostEqual because we want to test for exactly 1.0. A
# previous implementation of Alignment.position_frequencies added
# (1 / sequence_count) for each occurrence of a character in a position
# to compute the frequencies (see
# https://github.com/biocore/scikit-bio/issues/801). In certain cases,
# this yielded a frequency slightly less than 1.0 due to roundoff
# error. The test case here uses an alignment of 10 sequences with no
# variation at a position. This test case exposes the roundoff error
# present in the previous implementation because 1/10 added 10 times
# yields a number slightly less than 1.0. This occurs because 1/10
# cannot be represented exactly as a floating point number.
seqs = []
for i in range(10):
seqs.append(DNA('A', metadata={'id': str(i)}))
aln = Alignment(seqs)
self.assertEqual(aln.position_frequencies(),
[defaultdict(float, {'A': 1.0})])
def test_position_entropies(self):
# tested by calculating values as described in this post:
# http://stackoverflow.com/a/15476958/3424666
expected = [0.69314, 0.69314, 0.69314, 0.0, np.nan]
np.testing.assert_almost_equal(self.a2.position_entropies(),
expected, 5)
expected = [1.0, 1.0, 1.0, 0.0, np.nan]
np.testing.assert_almost_equal(self.a2.position_entropies(base=2),
expected, 5)
np.testing.assert_almost_equal(self.empty.position_entropies(base=2),
[])
def test_kmer_frequencies(self):
expected = [defaultdict(float, {'U': 3 / 5, 'A': 1 / 5, '-': 1 / 5}),
defaultdict(float, {'A': 1 / 5, 'C': 1 / 5, 'G': 1 / 5,
'U': 2 / 5})]
actual = self.a2.kmer_frequencies(k=1, relative=True)
for a, e in zip(actual, expected):
self.assertEqual(sorted(a), sorted(e), 5)
np.testing.assert_almost_equal(sorted(a.values()),
sorted(e.values()), 5)
def test_sequence_length(self):
self.assertEqual(self.a1.sequence_length(), 13)
self.assertEqual(self.a2.sequence_length(), 5)
self.assertEqual(self.empty.sequence_length(), 0)
def test_validate_lengths(self):
self.assertTrue(self.a1._validate_lengths())
self.assertTrue(self.a2._validate_lengths())
self.assertTrue(self.empty._validate_lengths())
self.assertTrue(Alignment([
DNA('TTT', metadata={'id': "d1"})])._validate_lengths())
class AlignmentTests(TestCase):
def setUp(self):
self.d1 = DNASequence('..ACC-GTTGG..', id="d1")
self.d2 = DNASequence('TTACCGGT-GGCC', id="d2")
self.d3 = DNASequence('.-ACC-GTTGC--', id="d3")
self.r1 = RNASequence('UUAU-', id="r1")
self.r2 = RNASequence('ACGUU', id="r2")
self.seqs1 = [self.d1, self.d2, self.d3]
self.seqs2 = [self.r1, self.r2]
self.seqs1_t = [('d1', '..ACC-GTTGG..'), ('d2', 'TTACCGGT-GGCC'),
('d3', '.-ACC-GTTGC--')]
self.seqs2_t = [('r1', 'UUAU-'), ('r2', 'ACGUU')]
self.a1 = Alignment(self.seqs1)
self.a2 = Alignment(self.seqs2)
self.a3 = Alignment(self.seqs2, score=42.0,
start_end_positions=[(0, 3), (5, 9)])
self.a4 = Alignment(self.seqs2, score=-42.0,
start_end_positions=[(1, 4), (6, 10)])
self.empty = Alignment([])
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):
"""distances functions as expected
"""
expected = [[0, 6. / 13, 4. / 13],
[6. / 13, 0, 7. / 13],
[4. / 13, 7. / 13, 0]]
expected = DistanceMatrix(expected, ['d1', 'd2', 'd3'])
actual = self.a1.distances()
self.assertEqual(actual, expected)
# alt distance function provided
def dumb_distance(s1, s2):
return 42.
expected = [[0, 42., 42.],
[42., 0, 42.],
[42., 42., 0]]
expected = DistanceMatrix(expected, ['d1', 'd2', 'd3'])
actual = self.a1.distances(dumb_distance)
self.assertEqual(actual, expected)
def test_score(self):
self.assertEqual(self.a3.score(), 42.0)
self.assertEqual(self.a4.score(), -42.0)
def test_start_end_positions(self):
self.assertEqual(self.a3.start_end_positions(), [(0, 3), (5, 9)])
self.assertEqual(self.a4.start_end_positions(), [(1, 4), (6, 10)])
def test_subalignment(self):
"""subalignment functions as expected
"""
# keep seqs by ids
actual = self.a1.subalignment(seqs_to_keep=['d1', 'd3'])
expected = Alignment([self.d1, self.d3])
self.assertEqual(actual, expected)
# keep seqs by indices
actual = self.a1.subalignment(seqs_to_keep=[0, 2])
expected = Alignment([self.d1, self.d3])
self.assertEqual(actual, expected)
# keep seqs by ids (invert)
actual = self.a1.subalignment(seqs_to_keep=['d1', 'd3'],
invert_seqs_to_keep=True)
expected = Alignment([self.d2])
self.assertEqual(actual, expected)
# keep seqs by indices (invert)
actual = self.a1.subalignment(seqs_to_keep=[0, 2],
invert_seqs_to_keep=True)
expected = Alignment([self.d2])
self.assertEqual(actual, expected)
# keep positions
actual = self.a1.subalignment(positions_to_keep=[0, 2, 3])
d1 = DNASequence('.AC', id="d1")
d2 = DNASequence('TAC', id="d2")
d3 = DNASequence('.AC', id="d3")
expected = Alignment([d1, d2, d3])
self.assertEqual(actual, expected)
# keep positions (invert)
actual = self.a1.subalignment(positions_to_keep=[0, 2, 3],
invert_positions_to_keep=True)
d1 = DNASequence('.C-GTTGG..', id="d1")
d2 = DNASequence('TCGGT-GGCC', id="d2")
d3 = DNASequence('-C-GTTGC--', id="d3")
expected = Alignment([d1, d2, d3])
self.assertEqual(actual, expected)
# keep seqs and positions
actual = self.a1.subalignment(seqs_to_keep=[0, 2],
positions_to_keep=[0, 2, 3])
d1 = DNASequence('.AC', id="d1")
d3 = DNASequence('.AC', id="d3")
expected = Alignment([d1, d3])
self.assertEqual(actual, expected)
# keep seqs and positions (invert)
actual = self.a1.subalignment(seqs_to_keep=[0, 2],
positions_to_keep=[0, 2, 3],
invert_seqs_to_keep=True,
invert_positions_to_keep=True)
d2 = DNASequence('TCGGT-GGCC', id="d2")
expected = Alignment([d2])
self.assertEqual(actual, expected)
def test_subalignment_filter_out_everything(self):
exp = Alignment([])
# no sequences
obs = self.a1.subalignment(seqs_to_keep=None, invert_seqs_to_keep=True)
self.assertEqual(obs, exp)
# no positions
obs = self.a1.subalignment(positions_to_keep=None,
invert_positions_to_keep=True)
self.assertEqual(obs, exp)
def test_init_validate(self):
"""initialization with validation functions as expected
"""
Alignment(self.seqs1, validate=True)
# invalid DNA character
invalid_seqs1 = [self.d1, self.d2, self.d3,
DNASequence('.-ACC-GTXGC--', id="i1")]
self.assertRaises(SequenceCollectionError, Alignment,
invalid_seqs1, validate=True)
# invalid lengths (they're not all equal)
invalid_seqs2 = [self.d1, self.d2, self.d3,
DNASequence('.-ACC-GTGC--', id="i2")]
self.assertRaises(SequenceCollectionError, Alignment,
invalid_seqs2, validate=True)
def test_is_valid(self):
"""is_valid functions as expected
"""
self.assertTrue(self.a1.is_valid())
self.assertTrue(self.a2.is_valid())
self.assertTrue(self.empty.is_valid())
# invalid because of length mismatch
d1 = DNASequence('..ACC-GTTGG..', id="d1")
d2 = DNASequence('TTACCGGT-GGC', id="d2")
self.assertFalse(Alignment([d1, d2]).is_valid())
# invalid because of invalid charaters
d1 = DNASequence('..ACC-GTXGG..', id="d1")
d2 = DNASequence('TTACCGGT-GGCC', id="d2")
self.assertFalse(Alignment([d1, d2]).is_valid())
def test_iter_positions(self):
"""iter_positions functions as expected
"""
actual = list(self.a2.iter_positions())
expected = [[RNASequence(j) for j in i] for i in
['UA', 'UC', 'AG', 'UU', '-U']]
self.seqs2_t = [('r1', 'UUAU-'), ('r2', 'ACGUU')]
self.assertEqual(actual, expected)
actual = list(self.a2.iter_positions(constructor=str))
expected = [list('UA'),
list('UC'),
list('AG'),
list('UU'),
list('-U')]
self.seqs2_t = [('r1', 'UUAU-'), ('r2', 'ACGUU')]
self.assertEqual(actual, expected)
def test_majority_consensus(self):
"""majority_consensus functions as expected
"""
d1 = DNASequence('TTT', id="d1")
d2 = DNASequence('TT-', id="d2")
d3 = DNASequence('TC-', id="d3")
a1 = Alignment([d1, d2, d3])
self.assertEqual(a1.majority_consensus(), DNASequence('TT-'))
d1 = DNASequence('T', id="d1")
d2 = DNASequence('A', id="d2")
a1 = Alignment([d1, d2])
self.assertTrue(a1.majority_consensus() in
[DNASequence('T'), DNASequence('A')])
self.assertEqual(self.empty.majority_consensus(), '')
def test_omit_gap_positions(self):
"""omitting gap positions functions as expected
"""
expected = self.a2
self.assertEqual(self.a2.omit_gap_positions(1.0), expected)
self.assertEqual(self.a2.omit_gap_positions(0.51), expected)
r1 = RNASequence('UUAU', id="r1")
r2 = RNASequence('ACGU', id="r2")
expected = Alignment([r1, r2])
self.assertEqual(self.a2.omit_gap_positions(0.49), expected)
r1 = RNASequence('UUAU', id="r1")
r2 = RNASequence('ACGU', id="r2")
expected = Alignment([r1, r2])
self.assertEqual(self.a2.omit_gap_positions(0.0), expected)
self.assertEqual(self.empty.omit_gap_positions(0.0), self.empty)
self.assertEqual(self.empty.omit_gap_positions(0.49), self.empty)
self.assertEqual(self.empty.omit_gap_positions(1.0), self.empty)
def test_omit_gap_sequences(self):
"""omitting gap sequences functions as expected
"""
expected = self.a2
self.assertEqual(self.a2.omit_gap_sequences(1.0), expected)
self.assertEqual(self.a2.omit_gap_sequences(0.20), expected)
expected = Alignment([self.r2])
self.assertEqual(self.a2.omit_gap_sequences(0.19), expected)
self.assertEqual(self.empty.omit_gap_sequences(0.0), self.empty)
self.assertEqual(self.empty.omit_gap_sequences(0.2), self.empty)
self.assertEqual(self.empty.omit_gap_sequences(1.0), self.empty)
def test_position_counters(self):
"""position_counters functions as expected
"""
expected = [Counter({'U': 1, 'A': 1}),
Counter({'U': 1, 'C': 1}),
Counter({'A': 1, 'G': 1}),
Counter({'U': 2}),
Counter({'-': 1, 'U': 1})]
self.assertEqual(self.a2.position_counters(), expected)
self.assertEqual(self.empty.position_counters(), [])
def test_position_frequencies(self):
"""computing position frequencies functions as expected
"""
expected = [defaultdict(int, {'U': 0.5, 'A': 0.5}),
defaultdict(int, {'U': 0.5, 'C': 0.5}),
defaultdict(int, {'A': 0.5, 'G': 0.5}),
defaultdict(int, {'U': 1.0}),
defaultdict(int, {'-': 0.5, 'U': 0.5})]
self.assertEqual(self.a2.position_frequencies(), expected)
self.assertEqual(self.empty.position_frequencies(), [])
def test_position_entropies(self):
"""computing positional uncertainties functions as expected
tested by calculating values as described in this post:
http://stackoverflow.com/a/15476958/3424666
"""
expected = [0.69314, 0.69314, 0.69314, 0.0, np.nan]
np.testing.assert_almost_equal(self.a2.position_entropies(),
expected, 5)
expected = [1.0, 1.0, 1.0, 0.0, np.nan]
np.testing.assert_almost_equal(self.a2.position_entropies(base=2),
expected, 5)
np.testing.assert_almost_equal(self.empty.position_entropies(base=2),
[])
def test_k_word_frequencies(self):
"""k_word_frequencies functions as expected
"""
expected = [defaultdict(int, {'U': 3 / 5, 'A': 1 / 5, '-': 1 / 5}),
defaultdict(int, {'A': 1 / 5, 'C': 1 / 5, 'G': 1 / 5,
'U': 2 / 5})]
actual = self.a2.k_word_frequencies(k=1)
for a, e in zip(actual, expected):
self.assertEqual(sorted(a), sorted(e), 5)
np.testing.assert_almost_equal(sorted(a.values()),
sorted(e.values()), 5)
def test_sequence_length(self):
"""sequence_length functions as expected
"""
self.assertEqual(self.a1.sequence_length(), 13)
self.assertEqual(self.a2.sequence_length(), 5)
self.assertEqual(self.empty.sequence_length(), 0)
def test_to_phylip(self):
"""to_phylip functions as expected
"""
d1 = DNASequence('..ACC-GTTGG..', id="d1")
d2 = DNASequence('TTACCGGT-GGCC', id="d2")
d3 = DNASequence('.-ACC-GTTGC--', id="d3")
a = Alignment([d1, d2, d3])
phylip_str, id_map = a.to_phylip(map_labels=False)
self.assertEqual(id_map, {'d1': 'd1',
'd3': 'd3',
'd2': 'd2'})
expected = "\n".join(["3 13",
"d1 ..ACC-GTTGG..",
"d2 TTACCGGT-GGCC",
"d3 .-ACC-GTTGC--"])
self.assertEqual(phylip_str, expected)
def test_to_phylip_map_labels(self):
"""to_phylip functions as expected with label mapping
"""
d1 = DNASequence('..ACC-GTTGG..', id="d1")
d2 = DNASequence('TTACCGGT-GGCC', id="d2")
d3 = DNASequence('.-ACC-GTTGC--', id="d3")
a = Alignment([d1, d2, d3])
phylip_str, id_map = a.to_phylip(map_labels=True, label_prefix="s")
self.assertEqual(id_map, {'s1': 'd1',
's3': 'd3',
's2': 'd2'})
expected = "\n".join(["3 13",
"s1 ..ACC-GTTGG..",
"s2 TTACCGGT-GGCC",
"s3 .-ACC-GTTGC--"])
self.assertEqual(phylip_str, expected)
def test_to_phylip_unequal_sequence_lengths(self):
d1 = DNASequence('A-CT', id="d1")
d2 = DNASequence('TTA', id="d2")
d3 = DNASequence('.-AC', id="d3")
a = Alignment([d1, d2, d3])
with self.assertRaises(SequenceCollectionError):
a.to_phylip()
def test_to_phylip_no_sequences(self):
with self.assertRaises(SequenceCollectionError):
Alignment([]).to_phylip()
def test_to_phylip_no_positions(self):
d1 = DNASequence('', id="d1")
d2 = DNASequence('', id="d2")
a = Alignment([d1, d2])
with self.assertRaises(SequenceCollectionError):
a.to_phylip()
def test_validate_lengths(self):
"""
"""
self.assertTrue(self.a1._validate_lengths())
self.assertTrue(self.a2._validate_lengths())
self.assertTrue(self.empty._validate_lengths())
self.assertTrue(Alignment([
DNASequence('TTT', id="d1")])._validate_lengths())
self.assertFalse(Alignment([
DNASequence('TTT', id="d1"),
DNASequence('TT', id="d2")])._validate_lengths())
class AlignmentTests(TestCase):
def setUp(self):
self.d1 = DNASequence('..ACC-GTTGG..', id="d1")
self.d2 = DNASequence('TTACCGGT-GGCC', id="d2")
self.d3 = DNASequence('.-ACC-GTTGC--', id="d3")
self.r1 = RNASequence('UUAU-', id="r1")
self.r2 = RNASequence('ACGUU', id="r2")
self.seqs1 = [self.d1, self.d2, self.d3]
self.seqs2 = [self.r1, self.r2]
self.seqs1_t = [('d1', '..ACC-GTTGG..'), ('d2', 'TTACCGGT-GGCC'),
('d3', '.-ACC-GTTGC--')]
self.seqs2_t = [('r1', 'UUAU-'), ('r2', 'ACGUU')]
self.a1 = Alignment(self.seqs1)
self.a2 = Alignment(self.seqs2)
self.a3 = Alignment(self.seqs2, score=42.0,
start_end_positions=[(0, 3), (5, 9)])
self.a4 = Alignment(self.seqs2, score=-42.0,
start_end_positions=[(1, 4), (6, 10)])
self.empty = Alignment([])
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):
"""distances functions as expected
"""
expected = [[0, 6. / 13, 4. / 13],
[6. / 13, 0, 7. / 13],
[4. / 13, 7. / 13, 0]]
expected = DistanceMatrix(expected, ['d1', 'd2', 'd3'])
actual = self.a1.distances()
self.assertEqual(actual, expected)
# alt distance function provided
def dumb_distance(s1, s2):
return 42.
expected = [[0, 42., 42.],
[42., 0, 42.],
[42., 42., 0]]
expected = DistanceMatrix(expected, ['d1', 'd2', 'd3'])
actual = self.a1.distances(dumb_distance)
self.assertEqual(actual, expected)
def test_score(self):
self.assertEqual(self.a3.score(), 42.0)
self.assertEqual(self.a4.score(), -42.0)
def test_start_end_positions(self):
self.assertEqual(self.a3.start_end_positions(), [(0, 3), (5, 9)])
self.assertEqual(self.a4.start_end_positions(), [(1, 4), (6, 10)])
def test_subalignment(self):
"""subalignment functions as expected
"""
# keep seqs by ids
actual = self.a1.subalignment(seqs_to_keep=['d1', 'd3'])
expected = Alignment([self.d1, self.d3])
self.assertEqual(actual, expected)
# keep seqs by indices
actual = self.a1.subalignment(seqs_to_keep=[0, 2])
expected = Alignment([self.d1, self.d3])
self.assertEqual(actual, expected)
# keep seqs by ids (invert)
actual = self.a1.subalignment(seqs_to_keep=['d1', 'd3'],
invert_seqs_to_keep=True)
expected = Alignment([self.d2])
self.assertEqual(actual, expected)
# keep seqs by indices (invert)
actual = self.a1.subalignment(seqs_to_keep=[0, 2],
invert_seqs_to_keep=True)
expected = Alignment([self.d2])
self.assertEqual(actual, expected)
# keep positions
actual = self.a1.subalignment(positions_to_keep=[0, 2, 3])
d1 = DNASequence('.AC', id="d1")
d2 = DNASequence('TAC', id="d2")
d3 = DNASequence('.AC', id="d3")
expected = Alignment([d1, d2, d3])
self.assertEqual(actual, expected)
# keep positions (invert)
actual = self.a1.subalignment(positions_to_keep=[0, 2, 3],
invert_positions_to_keep=True)
d1 = DNASequence('.C-GTTGG..', id="d1")
d2 = DNASequence('TCGGT-GGCC', id="d2")
d3 = DNASequence('-C-GTTGC--', id="d3")
expected = Alignment([d1, d2, d3])
self.assertEqual(actual, expected)
# keep seqs and positions
actual = self.a1.subalignment(seqs_to_keep=[0, 2],
positions_to_keep=[0, 2, 3])
d1 = DNASequence('.AC', id="d1")
d3 = DNASequence('.AC', id="d3")
expected = Alignment([d1, d3])
self.assertEqual(actual, expected)
# keep seqs and positions (invert)
actual = self.a1.subalignment(seqs_to_keep=[0, 2],
positions_to_keep=[0, 2, 3],
invert_seqs_to_keep=True,
invert_positions_to_keep=True)
d2 = DNASequence('TCGGT-GGCC', id="d2")
expected = Alignment([d2])
self.assertEqual(actual, expected)
def test_subalignment_filter_out_everything(self):
exp = Alignment([])
# no sequences
obs = self.a1.subalignment(seqs_to_keep=None, invert_seqs_to_keep=True)
self.assertEqual(obs, exp)
# no positions
obs = self.a1.subalignment(positions_to_keep=None,
invert_positions_to_keep=True)
self.assertEqual(obs, exp)
def test_init_validate(self):
"""initialization with validation functions as expected
"""
Alignment(self.seqs1, validate=True)
# invalid DNA character
invalid_seqs1 = [self.d1, self.d2, self.d3,
DNASequence('.-ACC-GTXGC--', id="i1")]
self.assertRaises(SequenceCollectionError, Alignment,
invalid_seqs1, validate=True)
# invalid lengths (they're not all equal)
invalid_seqs2 = [self.d1, self.d2, self.d3,
DNASequence('.-ACC-GTGC--', id="i2")]
self.assertRaises(SequenceCollectionError, Alignment,
invalid_seqs2, validate=True)
def test_is_valid(self):
"""is_valid functions as expected
"""
self.assertTrue(self.a1.is_valid())
self.assertTrue(self.a2.is_valid())
self.assertTrue(self.empty.is_valid())
# invalid because of length mismatch
d1 = DNASequence('..ACC-GTTGG..', id="d1")
d2 = DNASequence('TTACCGGT-GGC', id="d2")
self.assertFalse(Alignment([d1, d2]).is_valid())
# invalid because of invalid charaters
d1 = DNASequence('..ACC-GTXGG..', id="d1")
d2 = DNASequence('TTACCGGT-GGCC', id="d2")
self.assertFalse(Alignment([d1, d2]).is_valid())
def test_iter_positions(self):
"""iter_positions functions as expected
"""
actual = list(self.a2.iter_positions())
expected = [[RNASequence(j) for j in i] for i in
['UA', 'UC', 'AG', 'UU', '-U']]
self.seqs2_t = [('r1', 'UUAU-'), ('r2', 'ACGUU')]
self.assertEqual(actual, expected)
actual = list(self.a2.iter_positions(constructor=str))
expected = [list('UA'),
list('UC'),
list('AG'),
list('UU'),
list('-U')]
self.seqs2_t = [('r1', 'UUAU-'), ('r2', 'ACGUU')]
self.assertEqual(actual, expected)
def test_majority_consensus(self):
d1 = DNASequence('TTT', id="d1")
d2 = DNASequence('TT-', id="d2")
d3 = DNASequence('TC-', id="d3")
a1 = Alignment([d1, d2, d3])
self.assertTrue(a1.majority_consensus().equals(DNASequence('TT-')))
d1 = DNASequence('T', id="d1")
d2 = DNASequence('A', id="d2")
a1 = Alignment([d1, d2])
self.assertTrue(a1.majority_consensus() in
[DNASequence('T'), DNASequence('A')])
self.assertEqual(self.empty.majority_consensus(), '')
def test_majority_consensus_constructor(self):
d1 = DNASequence('TTT', id="d1")
d2 = DNASequence('TT-', id="d2")
d3 = DNASequence('TC-', id="d3")
a1 = Alignment([d1, d2, d3])
obs = npt.assert_warns(UserWarning, a1.majority_consensus,
constructor=str)
self.assertEqual(obs, 'TT-')
def test_omit_gap_positions(self):
"""omitting gap positions functions as expected
"""
expected = self.a2
self.assertEqual(self.a2.omit_gap_positions(1.0), expected)
self.assertEqual(self.a2.omit_gap_positions(0.51), expected)
r1 = RNASequence('UUAU', id="r1")
r2 = RNASequence('ACGU', id="r2")
expected = Alignment([r1, r2])
self.assertEqual(self.a2.omit_gap_positions(0.49), expected)
r1 = RNASequence('UUAU', id="r1")
r2 = RNASequence('ACGU', id="r2")
expected = Alignment([r1, r2])
self.assertEqual(self.a2.omit_gap_positions(0.0), expected)
self.assertEqual(self.empty.omit_gap_positions(0.0), self.empty)
self.assertEqual(self.empty.omit_gap_positions(0.49), self.empty)
self.assertEqual(self.empty.omit_gap_positions(1.0), self.empty)
def test_omit_gap_sequences(self):
"""omitting gap sequences functions as expected
"""
expected = self.a2
self.assertEqual(self.a2.omit_gap_sequences(1.0), expected)
self.assertEqual(self.a2.omit_gap_sequences(0.20), expected)
expected = Alignment([self.r2])
self.assertEqual(self.a2.omit_gap_sequences(0.19), expected)
self.assertEqual(self.empty.omit_gap_sequences(0.0), self.empty)
self.assertEqual(self.empty.omit_gap_sequences(0.2), self.empty)
self.assertEqual(self.empty.omit_gap_sequences(1.0), self.empty)
def test_position_counters(self):
"""position_counters functions as expected
"""
expected = [Counter({'U': 1, 'A': 1}),
Counter({'U': 1, 'C': 1}),
Counter({'A': 1, 'G': 1}),
Counter({'U': 2}),
Counter({'-': 1, 'U': 1})]
self.assertEqual(self.a2.position_counters(), expected)
self.assertEqual(self.empty.position_counters(), [])
def test_position_frequencies(self):
"""computing position frequencies functions as expected
"""
expected = [defaultdict(int, {'U': 0.5, 'A': 0.5}),
defaultdict(int, {'U': 0.5, 'C': 0.5}),
defaultdict(int, {'A': 0.5, 'G': 0.5}),
defaultdict(int, {'U': 1.0}),
defaultdict(int, {'-': 0.5, 'U': 0.5})]
self.assertEqual(self.a2.position_frequencies(), expected)
self.assertEqual(self.empty.position_frequencies(), [])
def test_position_entropies(self):
"""computing positional uncertainties functions as expected
tested by calculating values as described in this post:
http://stackoverflow.com/a/15476958/3424666
"""
expected = [0.69314, 0.69314, 0.69314, 0.0, np.nan]
np.testing.assert_almost_equal(self.a2.position_entropies(),
expected, 5)
expected = [1.0, 1.0, 1.0, 0.0, np.nan]
np.testing.assert_almost_equal(self.a2.position_entropies(base=2),
expected, 5)
np.testing.assert_almost_equal(self.empty.position_entropies(base=2),
[])
def test_k_word_frequencies(self):
"""k_word_frequencies functions as expected
"""
expected = [defaultdict(int, {'U': 3 / 5, 'A': 1 / 5, '-': 1 / 5}),
defaultdict(int, {'A': 1 / 5, 'C': 1 / 5, 'G': 1 / 5,
'U': 2 / 5})]
actual = self.a2.k_word_frequencies(k=1)
for a, e in zip(actual, expected):
self.assertEqual(sorted(a), sorted(e), 5)
np.testing.assert_almost_equal(sorted(a.values()),
sorted(e.values()), 5)
def test_sequence_length(self):
"""sequence_length functions as expected
"""
self.assertEqual(self.a1.sequence_length(), 13)
self.assertEqual(self.a2.sequence_length(), 5)
self.assertEqual(self.empty.sequence_length(), 0)
def test_to_phylip(self):
d1 = DNASequence('..ACC-GTTGG..', id="d1")
d2 = DNASequence('TTACCGGT-GGCC', id="d2")
d3 = DNASequence('.-ACC-GTTGC--', id="d3")
a = Alignment([d1, d2, d3])
phylip_str, id_map = npt.assert_warns(UserWarning, a.to_phylip,
map_labels=False)
self.assertEqual(id_map, {'d1': 'd1',
'd3': 'd3',
'd2': 'd2'})
expected = "\n".join(["3 13",
"d1 ..ACC-GTTGG..",
"d2 TTACCGGT-GGCC",
"d3 .-ACC-GTTGC--"])
self.assertEqual(phylip_str, expected)
def test_to_phylip_map_labels(self):
d1 = DNASequence('..ACC-GTTGG..', id="d1")
d2 = DNASequence('TTACCGGT-GGCC', id="d2")
d3 = DNASequence('.-ACC-GTTGC--', id="d3")
a = Alignment([d1, d2, d3])
phylip_str, id_map = npt.assert_warns(UserWarning, a.to_phylip,
map_labels=True,
label_prefix="s")
self.assertEqual(id_map, {'s1': 'd1',
's3': 'd3',
's2': 'd2'})
expected = "\n".join(["3 13",
"s1 ..ACC-GTTGG..",
"s2 TTACCGGT-GGCC",
"s3 .-ACC-GTTGC--"])
self.assertEqual(phylip_str, expected)
def test_to_phylip_unequal_sequence_lengths(self):
d1 = DNASequence('A-CT', id="d1")
d2 = DNASequence('TTA', id="d2")
d3 = DNASequence('.-AC', id="d3")
a = Alignment([d1, d2, d3])
with self.assertRaises(SequenceCollectionError):
npt.assert_warns(UserWarning, a.to_phylip)
def test_to_phylip_no_sequences(self):
with self.assertRaises(SequenceCollectionError):
npt.assert_warns(UserWarning, Alignment([]).to_phylip)
def test_to_phylip_no_positions(self):
d1 = DNASequence('', id="d1")
d2 = DNASequence('', id="d2")
a = Alignment([d1, d2])
with self.assertRaises(SequenceCollectionError):
npt.assert_warns(UserWarning, a.to_phylip)
def test_validate_lengths(self):
self.assertTrue(self.a1._validate_lengths())
self.assertTrue(self.a2._validate_lengths())
self.assertTrue(self.empty._validate_lengths())
self.assertTrue(Alignment([
DNASequence('TTT', id="d1")])._validate_lengths())
self.assertFalse(Alignment([
DNASequence('TTT', id="d1"),
DNASequence('TT', id="d2")])._validate_lengths())
class AlignmentTests(TestCase):
def setUp(self):
self.d1 = DNASequence('..ACC-GTTGG..', id="d1")
self.d2 = DNASequence('TTACCGGT-GGCC', id="d2")
self.d3 = DNASequence('.-ACC-GTTGC--', id="d3")
self.r1 = RNASequence('UUAU-', id="r1")
self.r2 = RNASequence('ACGUU', id="r2")
self.seqs1 = [self.d1, self.d2, self.d3]
self.seqs2 = [self.r1, self.r2]
self.seqs1_t = [('d1', '..ACC-GTTGG..'), ('d2', 'TTACCGGT-GGCC'),
('d3', '.-ACC-GTTGC--')]
self.seqs2_t = [('r1', 'UUAU-'), ('r2', 'ACGUU')]
self.a1 = Alignment(self.seqs1)
self.a2 = Alignment(self.seqs2)
self.a3 = Alignment(self.seqs2,
score=42.0,
start_end_positions=[(0, 3), (5, 9)])
self.a4 = Alignment(self.seqs2,
score=-42.0,
start_end_positions=[(1, 4), (6, 10)])
# no sequences
self.empty = Alignment([])
# sequences, but no positions
self.no_positions = Alignment([RNA('', id='a'), RNA('', id='b')])
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_distances(self):
expected = [[0, 6. / 13, 4. / 13], [6. / 13, 0, 7. / 13],
[4. / 13, 7. / 13, 0]]
expected = DistanceMatrix(expected, ['d1', 'd2', 'd3'])
actual = self.a1.distances()
self.assertEqual(actual, expected)
# alt distance function provided
def dumb_distance(s1, s2):
return 42.
expected = [[0, 42., 42.], [42., 0, 42.], [42., 42., 0]]
expected = DistanceMatrix(expected, ['d1', 'd2', 'd3'])
actual = self.a1.distances(dumb_distance)
self.assertEqual(actual, expected)
def test_score(self):
self.assertEqual(self.a3.score(), 42.0)
self.assertEqual(self.a4.score(), -42.0)
def test_start_end_positions(self):
self.assertEqual(self.a3.start_end_positions(), [(0, 3), (5, 9)])
self.assertEqual(self.a4.start_end_positions(), [(1, 4), (6, 10)])
def test_subalignment(self):
# keep seqs by ids
actual = self.a1.subalignment(seqs_to_keep=['d1', 'd3'])
expected = Alignment([self.d1, self.d3])
self.assertEqual(actual, expected)
# keep seqs by indices
actual = self.a1.subalignment(seqs_to_keep=[0, 2])
expected = Alignment([self.d1, self.d3])
self.assertEqual(actual, expected)
# keep seqs by ids (invert)
actual = self.a1.subalignment(seqs_to_keep=['d1', 'd3'],
invert_seqs_to_keep=True)
expected = Alignment([self.d2])
self.assertEqual(actual, expected)
# keep seqs by indices (invert)
actual = self.a1.subalignment(seqs_to_keep=[0, 2],
invert_seqs_to_keep=True)
expected = Alignment([self.d2])
self.assertEqual(actual, expected)
# keep positions
actual = self.a1.subalignment(positions_to_keep=[0, 2, 3])
d1 = DNASequence('.AC', id="d1")
d2 = DNASequence('TAC', id="d2")
d3 = DNASequence('.AC', id="d3")
expected = Alignment([d1, d2, d3])
self.assertEqual(actual, expected)
# keep positions (invert)
actual = self.a1.subalignment(positions_to_keep=[0, 2, 3],
invert_positions_to_keep=True)
d1 = DNASequence('.C-GTTGG..', id="d1")
d2 = DNASequence('TCGGT-GGCC', id="d2")
d3 = DNASequence('-C-GTTGC--', id="d3")
expected = Alignment([d1, d2, d3])
self.assertEqual(actual, expected)
# keep seqs and positions
actual = self.a1.subalignment(seqs_to_keep=[0, 2],
positions_to_keep=[0, 2, 3])
d1 = DNASequence('.AC', id="d1")
d3 = DNASequence('.AC', id="d3")
expected = Alignment([d1, d3])
self.assertEqual(actual, expected)
# keep seqs and positions (invert)
actual = self.a1.subalignment(seqs_to_keep=[0, 2],
positions_to_keep=[0, 2, 3],
invert_seqs_to_keep=True,
invert_positions_to_keep=True)
d2 = DNASequence('TCGGT-GGCC', id="d2")
expected = Alignment([d2])
self.assertEqual(actual, expected)
def test_subalignment_filter_out_everything(self):
exp = Alignment([])
# no sequences
obs = self.a1.subalignment(seqs_to_keep=None, invert_seqs_to_keep=True)
self.assertEqual(obs, exp)
# no positions
obs = self.a1.subalignment(positions_to_keep=None,
invert_positions_to_keep=True)
self.assertEqual(obs, exp)
def test_init_not_equal_lengths(self):
invalid_seqs = [
self.d1, self.d2, self.d3,
DNASequence('.-ACC-GTGC--', id="i2")
]
self.assertRaises(AlignmentError, Alignment, invalid_seqs)
def test_init_equal_lengths(self):
seqs = [self.d1, self.d2, self.d3]
Alignment(seqs)
def test_init_validate(self):
Alignment(self.seqs1, validate=True)
# invalid DNA character
invalid_seqs1 = [
self.d1, self.d2, self.d3,
DNASequence('.-ACC-GTXGC--', id="i1")
]
self.assertRaises(SequenceCollectionError,
Alignment,
invalid_seqs1,
validate=True)
def test_iter_positions(self):
actual = list(self.a2.iter_positions())
expected = [[RNASequence(j) for j in i]
for i in ['UA', 'UC', 'AG', 'UU', '-U']]
self.seqs2_t = [('r1', 'UUAU-'), ('r2', 'ACGUU')]
self.assertEqual(actual, expected)
actual = list(self.a2.iter_positions(constructor=str))
expected = [list('UA'), list('UC'), list('AG'), list('UU'), list('-U')]
self.seqs2_t = [('r1', 'UUAU-'), ('r2', 'ACGUU')]
self.assertEqual(actual, expected)
def test_majority_consensus(self):
d1 = DNASequence('TTT', id="d1")
d2 = DNASequence('TT-', id="d2")
d3 = DNASequence('TC-', id="d3")
a1 = Alignment([d1, d2, d3])
self.assertTrue(a1.majority_consensus().equals(DNASequence('TT-')))
d1 = DNASequence('T', id="d1")
d2 = DNASequence('A', id="d2")
a1 = Alignment([d1, d2])
self.assertTrue(a1.majority_consensus() in
[DNASequence('T'), DNASequence('A')])
self.assertEqual(self.empty.majority_consensus(), '')
def test_majority_consensus_constructor(self):
d1 = DNASequence('TTT', id="d1")
d2 = DNASequence('TT-', id="d2")
d3 = DNASequence('TC-', id="d3")
a1 = Alignment([d1, d2, d3])
obs = npt.assert_warns(DeprecationWarning,
a1.majority_consensus,
constructor=str)
self.assertEqual(obs, 'TT-')
def test_omit_gap_positions(self):
expected = self.a2
self.assertEqual(self.a2.omit_gap_positions(1.0), expected)
self.assertEqual(self.a2.omit_gap_positions(0.51), expected)
r1 = RNASequence('UUAU', id="r1")
r2 = RNASequence('ACGU', id="r2")
expected = Alignment([r1, r2])
self.assertEqual(self.a2.omit_gap_positions(0.49), expected)
r1 = RNASequence('UUAU', id="r1")
r2 = RNASequence('ACGU', id="r2")
expected = Alignment([r1, r2])
self.assertEqual(self.a2.omit_gap_positions(0.0), expected)
self.assertEqual(self.empty.omit_gap_positions(0.0), self.empty)
self.assertEqual(self.empty.omit_gap_positions(0.49), self.empty)
self.assertEqual(self.empty.omit_gap_positions(1.0), self.empty)
# Test to ensure floating point precision bug isn't present. See the
# tests for Alignment.position_frequencies for more details.
seqs = []
for i in range(33):
seqs.append(DNA('-.', id=str(i)))
aln = Alignment(seqs)
self.assertEqual(aln.omit_gap_positions(1 - np.finfo(float).eps),
Alignment([DNA('', id=str(i)) for i in range(33)]))
def test_omit_gap_sequences(self):
expected = self.a2
self.assertEqual(self.a2.omit_gap_sequences(1.0), expected)
self.assertEqual(self.a2.omit_gap_sequences(0.20), expected)
expected = Alignment([self.r2])
self.assertEqual(self.a2.omit_gap_sequences(0.19), expected)
self.assertEqual(self.empty.omit_gap_sequences(0.0), self.empty)
self.assertEqual(self.empty.omit_gap_sequences(0.2), self.empty)
self.assertEqual(self.empty.omit_gap_sequences(1.0), self.empty)
# Test to ensure floating point precision bug isn't present. See the
# tests for Alignment.position_frequencies for more details.
aln = Alignment([DNA('.' * 33, id='abc'), DNA('-' * 33, id='def')])
self.assertEqual(aln.omit_gap_sequences(1 - np.finfo(float).eps),
Alignment([]))
def test_position_counters(self):
self.assertEqual(self.empty.position_counters(), [])
self.assertEqual(self.no_positions.position_counters(), [])
expected = [
Counter({
'U': 1,
'A': 1
}),
Counter({
'U': 1,
'C': 1
}),
Counter({
'A': 1,
'G': 1
}),
Counter({'U': 2}),
Counter({
'-': 1,
'U': 1
})
]
self.assertEqual(self.a2.position_counters(), expected)
def test_position_frequencies(self):
self.assertEqual(self.empty.position_frequencies(), [])
self.assertEqual(self.no_positions.position_frequencies(), [])
expected = [
defaultdict(float, {
'U': 0.5,
'A': 0.5
}),
defaultdict(float, {
'U': 0.5,
'C': 0.5
}),
defaultdict(float, {
'A': 0.5,
'G': 0.5
}),
defaultdict(float, {'U': 1.0}),
defaultdict(float, {
'-': 0.5,
'U': 0.5
})
]
self.assertEqual(self.a2.position_frequencies(), expected)
def test_position_frequencies_floating_point_precision(self):
# Test that a position with no variation yields a frequency of exactly
# 1.0. Note that it is important to use self.assertEqual here instead
# of self.assertAlmostEqual because we want to test for exactly 1.0. A
# previous implementation of Alignment.position_frequencies added
# (1 / sequence_count) for each occurrence of a character in a position
# to compute the frequencies (see
# https://github.com/biocore/scikit-bio/issues/801). In certain cases,
# this yielded a frequency slightly less than 1.0 due to roundoff
# error. The test case here uses an alignment of 10 sequences with no
# variation at a position. This test case exposes the roundoff error
# present in the previous implementation because 1/10 added 10 times
# yields a number slightly less than 1.0. This occurs because 1/10
# cannot be represented exactly as a floating point number.
seqs = []
for i in range(10):
seqs.append(DNA('A', id=str(i)))
aln = Alignment(seqs)
self.assertEqual(aln.position_frequencies(),
[defaultdict(float, {'A': 1.0})])
def test_position_entropies(self):
# tested by calculating values as described in this post:
# http://stackoverflow.com/a/15476958/3424666
expected = [0.69314, 0.69314, 0.69314, 0.0, np.nan]
np.testing.assert_almost_equal(self.a2.position_entropies(), expected,
5)
expected = [1.0, 1.0, 1.0, 0.0, np.nan]
np.testing.assert_almost_equal(self.a2.position_entropies(base=2),
expected, 5)
np.testing.assert_almost_equal(self.empty.position_entropies(base=2),
[])
def test_k_word_frequencies(self):
expected = [
defaultdict(float, {
'U': 3 / 5,
'A': 1 / 5,
'-': 1 / 5
}),
defaultdict(float, {
'A': 1 / 5,
'C': 1 / 5,
'G': 1 / 5,
'U': 2 / 5
})
]
actual = self.a2.k_word_frequencies(k=1)
for a, e in zip(actual, expected):
self.assertEqual(sorted(a), sorted(e), 5)
np.testing.assert_almost_equal(sorted(a.values()),
sorted(e.values()), 5)
def test_sequence_length(self):
self.assertEqual(self.a1.sequence_length(), 13)
self.assertEqual(self.a2.sequence_length(), 5)
self.assertEqual(self.empty.sequence_length(), 0)
def test_to_phylip(self):
d1 = DNASequence('..ACC-GTTGG..', id="d1")
d2 = DNASequence('TTACCGGT-GGCC', id="d2")
d3 = DNASequence('.-ACC-GTTGC--', id="d3")
a = Alignment([d1, d2, d3])
phylip_str, id_map = npt.assert_warns(DeprecationWarning,
a.to_phylip,
map_labels=False)
self.assertEqual(id_map, {'d1': 'd1', 'd3': 'd3', 'd2': 'd2'})
expected = "\n".join([
"3 13", "d1 ..ACC-GTTGG..", "d2 TTACCGGT-GGCC", "d3 .-ACC-GTTGC--"
])
self.assertEqual(phylip_str, expected)
def test_to_phylip_map_labels(self):
d1 = DNASequence('..ACC-GTTGG..', id="d1")
d2 = DNASequence('TTACCGGT-GGCC', id="d2")
d3 = DNASequence('.-ACC-GTTGC--', id="d3")
a = Alignment([d1, d2, d3])
phylip_str, id_map = npt.assert_warns(DeprecationWarning,
a.to_phylip,
map_labels=True,
label_prefix="s")
self.assertEqual(id_map, {'s1': 'd1', 's3': 'd3', 's2': 'd2'})
expected = "\n".join([
"3 13", "s1 ..ACC-GTTGG..", "s2 TTACCGGT-GGCC", "s3 .-ACC-GTTGC--"
])
self.assertEqual(phylip_str, expected)
def test_to_phylip_no_sequences(self):
with self.assertRaises(SequenceCollectionError):
npt.assert_warns(DeprecationWarning, Alignment([]).to_phylip)
def test_to_phylip_no_positions(self):
d1 = DNASequence('', id="d1")
d2 = DNASequence('', id="d2")
a = Alignment([d1, d2])
with self.assertRaises(SequenceCollectionError):
npt.assert_warns(DeprecationWarning, a.to_phylip)
def test_validate_lengths(self):
self.assertTrue(self.a1._validate_lengths())
self.assertTrue(self.a2._validate_lengths())
self.assertTrue(self.empty._validate_lengths())
self.assertTrue(
Alignment([DNASequence('TTT', id="d1")])._validate_lengths())