def test_scale(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
filename_left = self.empty()
filename_right = self.empty()
with utils.open_profile(self.profile(counts_left, 8)) as handle_left:
with utils.open_profile(self.profile(counts_right, 8)) as handle_right:
with utils.open_profile(filename_left, 'w') as out_left:
with utils.open_profile(filename_right, 'w') as out_right:
kmer.scale(handle_left, handle_right, out_left, out_right)
if sum(counts_left.values()) < sum(counts_right.values()):
scale_left = sum(counts_right.values()) / sum(counts_left.values())
scale_right = 1.0
else:
scale_left = 1.0
scale_right = sum(counts_left.values()) / sum(counts_right.values())
for s in counts_left:
counts_left[s] *= scale_left
for s in counts_right:
counts_right[s] *= scale_right
utils.test_profile_file(filename_left, counts_left, 8)
utils.test_profile_file(filename_right, counts_right, 8)
def test_scale(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
filename_left = self.empty()
filename_right = self.empty()
with utils.open_profile(self.profile(counts_left, 8)) as handle_left:
with utils.open_profile(self.profile(counts_right, 8)) as handle_right:
with utils.open_profile(filename_left, 'w') as out_left:
with utils.open_profile(filename_right, 'w') as out_right:
kmer.scale(handle_left, handle_right, out_left, out_right)
if sum(counts_left.values()) < sum(counts_right.values()):
scale_left = sum(counts_right.values()) / sum(counts_left.values())
scale_right = 1.0
else:
scale_left = 1.0
scale_right = sum(counts_left.values()) / sum(counts_right.values())
for s in counts_left:
counts_left[s] *= scale_left
for s in counts_right:
counts_right[s] *= scale_right
utils.test_profile_file(filename_left, counts_left, 8)
utils.test_profile_file(filename_right, counts_right, 8)
def namedEntitySimilarityFeatureExtractor(self, originalDocuments,
machineSummary, humanSummaries):
peer = utils.removeStopwords(
utils.getNamedEntities(self.nerTagger, machineSummary))
modelsOD = [
utils.removeStopwords(
utils.getNamedEntities(self.nerTagger, document))
for document in originalDocuments
]
modelsHS = [
utils.removeStopwords(
utils.getNamedEntities(self.nerTagger, document))
for document in humanSummaries
]
peerCount = utils.counts(peer)
modelsODCount = [utils.counts(model) for model in modelsOD]
modelsHSCount = [utils.counts(model) for model in modelsHS]
jsAvgOD = [
js.JS_Divergence(peerCount, model) for model in modelsODCount
]
jsAvgHS = [
js.JS_Divergence(peerCount, model) for model in modelsHSCount
]
resultOD = sum(jsAvgOD) / float(len(modelsODCount))
resultHS = sum(jsAvgHS) / float(len(modelsHSCount))
if resultOD > 1:
resultOD = 1
if resultHS > 1:
resultHS = 1
return [resultOD, resultHS]
def test_ProfileDistance_distance_k8(self):
counts_a = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_b = utils.counts(utils.SEQUENCES_RIGHT, 8)
profile_a = klib.Profile(utils.as_array(counts_a, 8))
profile_b = klib.Profile(utils.as_array(counts_b, 8))
k_dist = kdistlib.ProfileDistance()
np.testing.assert_almost_equal(k_dist.distance(profile_a, profile_b), 0.4626209322)
def test_profile_merge(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
profile_left = klib.Profile(utils.as_array(counts_left, 8))
profile_right = klib.Profile(utils.as_array(counts_right, 8))
profile_left.merge(profile_right)
utils.test_profile(profile_left, counts_left + counts_right, 8)
def test_profile_merge(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
profile_left = klib.Profile(utils.as_array(counts_left, 8))
profile_right = klib.Profile(utils.as_array(counts_right, 8))
profile_left.merge(profile_right)
utils.test_profile(profile_left, counts_left + counts_right, 8)
def test_distance_smooth(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
out = StringIO()
with utils.open_profile(self.profile(counts_left, 8, 'left')) as handle_left:
with utils.open_profile(self.profile(counts_right, 8, 'right')) as handle_right:
kmer.distance(handle_left, handle_right, out, do_smooth=True, precision=3)
assert out.getvalue() == 'left right 0.077\n'
def test_distance(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
out = StringIO()
with utils.open_profile(self.profile(counts_left, 8, 'left')) as handle_left:
with utils.open_profile(self.profile(counts_right, 8, 'right')) as handle_right:
kmer.distance(handle_left, handle_right, out)
assert out.getvalue() == 'left right %.10f\n' % 0.4626209323
def test_count_multi(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
filename = self.empty()
with open(self.fasta(utils.SEQUENCES_LEFT)) as handle_left:
with open(self.fasta(utils.SEQUENCES_RIGHT)) as handle_right:
with utils.open_profile(filename, 'w') as profile_handle:
kmer.count([handle_left, handle_right], profile_handle, 8, names=['a', 'b'])
utils.test_profile_file(filename, counts_left, 8, name='a')
utils.test_profile_file(filename, counts_right, 8, name='b')
def test_distance(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
out = StringIO()
with utils.open_profile(self.profile(counts_left, 8, 'left')) as handle_left:
with utils.open_profile(self.profile(counts_right, 8, 'right')) as handle_right:
kmer.distance(handle_left, handle_right, out)
assert out.getvalue() == 'left right %.10f\n' % 0.4626209323
def test_merge(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
filename = self.empty()
with utils.open_profile(self.profile(counts_left, 8)) as handle_left:
with utils.open_profile(self.profile(counts_right, 8)) as handle_right:
with utils.open_profile(filename, 'w') as profile_handle:
kmer.merge(handle_left, handle_right, profile_handle)
utils.test_profile_file(filename, counts_left + counts_right, 8)
def test_distance_smooth(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
out = StringIO()
with utils.open_profile(self.profile(counts_left, 8, 'left')) as handle_left:
with utils.open_profile(self.profile(counts_right, 8, 'right')) as handle_right:
kmer.distance(handle_left, handle_right, out, do_smooth=True, precision=3)
assert out.getvalue() == 'left right 0.077\n'
def test_ProfileDistance_distance_k8(self):
counts_a = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_b = utils.counts(utils.SEQUENCES_RIGHT, 8)
profile_a = klib.Profile(utils.as_array(counts_a, 8))
profile_b = klib.Profile(utils.as_array(counts_b, 8))
k_dist = kdistlib.ProfileDistance()
np.testing.assert_almost_equal(k_dist.distance(profile_a, profile_b),
0.4626209322)
def test_merge(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
filename = self.empty()
with utils.open_profile(self.profile(counts_left, 8)) as handle_left:
with utils.open_profile(self.profile(counts_right, 8)) as handle_right:
with utils.open_profile(filename, 'w') as profile_handle:
kmer.merge(handle_left, handle_right, profile_handle)
utils.test_profile_file(filename, counts_left + counts_right, 8)
def test_distance_pairwise_name(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
out = StringIO()
with utils.open_profile(self.profile(counts_left, 8, 'left')) as handle_left:
with utils.open_profile(self.profile(counts_right, 8, 'right')) as handle_right:
kmer.distance(handle_left, handle_right, out, precision=3,
custom_pairwise='numpy.multiply')
assert out.getvalue() == 'left right 0.084\n'
def test_distance_smooth_expr(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
out = StringIO()
with utils.open_profile(self.profile(counts_left, 8, 'left')) as handle_left:
with utils.open_profile(self.profile(counts_right, 8, 'right')) as handle_right:
kmer.distance(handle_left, handle_right, out, do_smooth=True,
precision=3, custom_summary='np.max(values)')
assert out.getvalue() == 'left right 0.474\n'
def test_distance_pairwise_expr(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
out = StringIO()
with utils.open_profile(self.profile(counts_left, 8, 'left')) as handle_left:
with utils.open_profile(self.profile(counts_right, 8, 'right')) as handle_right:
kmer.distance(handle_left, handle_right, out, precision=3,
custom_pairwise='abs(left - right) / (left + right + 1000)')
assert out.getvalue() == 'left right 0.001\n'
def test_distance_pairwise_name(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
out = StringIO()
with utils.open_profile(self.profile(counts_left, 8, 'left')) as handle_left:
with utils.open_profile(self.profile(counts_right, 8, 'right')) as handle_right:
kmer.distance(handle_left, handle_right, out, precision=3,
custom_pairwise='numpy.multiply')
assert out.getvalue() == 'left right 0.084\n'
def test_distance_smooth_expr(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
out = StringIO()
with utils.open_profile(self.profile(counts_left, 8, 'left')) as handle_left:
with utils.open_profile(self.profile(counts_right, 8, 'right')) as handle_right:
kmer.distance(handle_left, handle_right, out, do_smooth=True,
precision=3, custom_summary='np.max(values)')
assert out.getvalue() == 'left right 0.474\n'
def test_cat_prefixes(self):
counts_a = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_b = utils.counts(utils.SEQUENCES_RIGHT, 8)
filename = self.empty()
with utils.open_profile(self.profile(counts_a, 8, name='X')) as handle_a:
with utils.open_profile(self.profile(counts_b, 8, name='X')) as handle_b:
with utils.open_profile(filename, 'w') as profile_handle:
kmer.cat([handle_a, handle_b], profile_handle, prefixes=['a_', 'b_'])
utils.test_profile_file(filename, counts_a, 8, name='a_X')
utils.test_profile_file(filename, counts_b, 8, name='b_X')
def test_distance_pairwise_expr(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
out = StringIO()
with utils.open_profile(self.profile(counts_left, 8, 'left')) as handle_left:
with utils.open_profile(self.profile(counts_right, 8, 'right')) as handle_right:
kmer.distance(handle_left, handle_right, out, precision=3,
custom_pairwise='abs(left - right) / (left + right + 1000)')
assert out.getvalue() == 'left right 0.001\n'
def test_cat_prefixes(self):
counts_a = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_b = utils.counts(utils.SEQUENCES_RIGHT, 8)
filename = self.empty()
with utils.open_profile(self.profile(counts_a, 8, name='X')) as handle_a:
with utils.open_profile(self.profile(counts_b, 8, name='X')) as handle_b:
with utils.open_profile(filename, 'w') as profile_handle:
kmer.cat([handle_a, handle_b], profile_handle, prefixes=['a_', 'b_'])
utils.test_profile_file(filename, counts_a, 8, name='a_X')
utils.test_profile_file(filename, counts_b, 8, name='b_X')
def test_distance_matrix_two(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
profiles = [klib.Profile(utils.as_array(counts_left, 8), 'a'),
klib.Profile(utils.as_array(counts_right, 8), 'b')]
k_dist = kdistlib.ProfileDistance()
out = StringIO()
kdistlib.distance_matrix(profiles, out, 2, k_dist)
assert out.getvalue().strip().split('\n') == ['2', 'a', 'b', '0.46']
def test_ProfileDistance_distance_unmodified(self):
counts_a = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_b = utils.counts(utils.SEQUENCES_RIGHT, 8)
profile_a = klib.Profile(utils.as_array(counts_a, 8))
profile_b = klib.Profile(utils.as_array(counts_b, 8))
k_dist = kdistlib.ProfileDistance(do_balance=True)
k_dist.distance(profile_a, profile_b)
utils.test_profile(profile_a, counts_a, 8)
utils.test_profile(profile_b, counts_b, 8)
def test_ProfileDistance_distance_unmodified(self):
counts_a = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_b = utils.counts(utils.SEQUENCES_RIGHT, 8)
profile_a = klib.Profile(utils.as_array(counts_a, 8))
profile_b = klib.Profile(utils.as_array(counts_b, 8))
k_dist = kdistlib.ProfileDistance(do_balance=True)
k_dist.distance(profile_a, profile_b)
utils.test_profile(profile_a, counts_a, 8)
utils.test_profile(profile_b, counts_b, 8)
def test_distance_matrix_smooth(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
out = StringIO()
with utils.open_profile(self.multi_profile(8,
[counts_left,
counts_right,
counts_left],
['a', 'b', 'c'])) as handle:
kmer.distance_matrix(handle, out, do_smooth=True, precision=3)
assert out.getvalue().strip().split('\n') == ['3', 'a', 'b', 'c', '0.077', '0.000 0.077']
def test_distance_matrix_smooth(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
out = StringIO()
with utils.open_profile(self.multi_profile(8,
[counts_left,
counts_right,
counts_left],
['a', 'b', 'c'])) as handle:
kmer.distance_matrix(handle, out, do_smooth=True, precision=3)
assert out.getvalue().strip().split('\n') == ['3', 'a', 'b', 'c', '0.077', '0.000 0.077']
def test_distance_matrix_pairwise_expr(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
out = StringIO()
with utils.open_profile(self.multi_profile(8,
[counts_left,
counts_right,
counts_left],
['a', 'b', 'c'])) as handle:
kmer.distance_matrix(handle, out, precision=3,
custom_pairwise='abs(left - right) / (left + right + 1000)')
assert out.getvalue().strip().split('\n') == ['3', 'a', 'b', 'c', '0.001', '0.000 0.001']
def test_count_multi_by_record(self):
counts_by_record_left = [utils.counts(record, 8) for record in utils.SEQUENCES_LEFT]
counts_by_record_right = [utils.counts(record, 8) for record in utils.SEQUENCES_RIGHT]
names_left = [str(i) for i, _ in enumerate(counts_by_record_left)]
names_right = [str(i) for i, _ in enumerate(counts_by_record_right)]
filename = self.empty()
with open(self.fasta(utils.SEQUENCES_LEFT, names=names_left)) as handle_left:
with open(self.fasta(utils.SEQUENCES_RIGHT, names=names_right)) as handle_right:
with utils.open_profile(filename, 'w') as profile_handle:
kmer.count([handle_left, handle_right], profile_handle, 8, names=['a', 'b'], by_record=True)
for name, counts in zip(names_left, counts_by_record_left):
utils.test_profile_file(filename, counts, 8, name='a_' + name)
for name, counts in zip(names_right, counts_by_record_right):
utils.test_profile_file(filename, counts, 8, name='b_' + name)
def test_merge_custom_name(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
filename = self.empty()
with utils.open_profile(self.profile(counts_left, 8)) as handle_left:
with utils.open_profile(self.profile(counts_right, 8)) as handle_right:
with utils.open_profile(filename, 'w') as profile_handle:
kmer.merge(handle_left, handle_right, profile_handle, custom_merger='numpy.multiply')
counts_mult = Counter(dict((s, counts_left[s] * counts_right[s])
for s in set(counts_left) & set(counts_right)))
utils.test_profile_file(filename, counts_mult, 8)
def test_distance_matrix_pairwise_name(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
out = StringIO()
with utils.open_profile(self.multi_profile(8,
[counts_left,
counts_right,
counts_left],
['a', 'b', 'c'])) as handle:
kmer.distance_matrix(handle, out, precision=3,
custom_pairwise='numpy.multiply')
assert out.getvalue().strip().split('\n') == ['3', 'a', 'b', 'c', '0.084', '1.206 0.084']
def test_distance_matrix_pairwise_name(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
out = StringIO()
with utils.open_profile(self.multi_profile(8,
[counts_left,
counts_right,
counts_left],
['a', 'b', 'c'])) as handle:
kmer.distance_matrix(handle, out, precision=3,
custom_pairwise='numpy.multiply')
assert out.getvalue().strip().split('\n') == ['3', 'a', 'b', 'c', '0.084', '1.206 0.084']
def test_merge_custom_name(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
filename = self.empty()
with utils.open_profile(self.profile(counts_left, 8)) as handle_left:
with utils.open_profile(self.profile(counts_right, 8)) as handle_right:
with utils.open_profile(filename, 'w') as profile_handle:
kmer.merge(handle_left, handle_right, profile_handle, custom_merger='numpy.multiply')
counts_mult = Counter(dict((s, counts_left[s] * counts_right[s])
for s in set(counts_left) & set(counts_right)))
utils.test_profile_file(filename, counts_mult, 8)
def test_distance_matrix_two(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
profiles = [
klib.Profile(utils.as_array(counts_left, 8), 'a'),
klib.Profile(utils.as_array(counts_right, 8), 'b')
]
k_dist = kdistlib.ProfileDistance()
out = StringIO()
kdistlib.distance_matrix(profiles, out, 2, k_dist)
assert out.getvalue().strip().split('\n') == ['2', 'a', 'b', '0.46']
def test_distance_matrix_pairwise_expr(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
out = StringIO()
with utils.open_profile(self.multi_profile(8,
[counts_left,
counts_right,
counts_left],
['a', 'b', 'c'])) as handle:
kmer.distance_matrix(handle, out, precision=3,
custom_pairwise='abs(left - right) / (left + right + 1000)')
assert out.getvalue().strip().split('\n') == ['3', 'a', 'b', 'c', '0.001', '0.000 0.001']
def test_merge_custom_expr(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
filename = self.empty()
with utils.open_profile(self.profile(counts_left, 8)) as handle_left:
with utils.open_profile(self.profile(counts_right, 8)) as handle_right:
with utils.open_profile(filename, 'w') as profile_handle:
kmer.merge(handle_left, handle_right, profile_handle, custom_merger='(left + right) * np.logical_xor(left, right)')
counts_xor = counts_left + counts_right
for s in set(counts_left) & set(counts_right):
del counts_xor[s]
utils.test_profile_file(filename, counts_xor, 8)
def test_merge_custom_expr(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
filename = self.empty()
with utils.open_profile(self.profile(counts_left, 8)) as handle_left:
with utils.open_profile(self.profile(counts_right, 8)) as handle_right:
with utils.open_profile(filename, 'w') as profile_handle:
kmer.merge(handle_left, handle_right, profile_handle, custom_merger='(left + right) * np.logical_xor(left, right)')
counts_xor = counts_left + counts_right
for s in set(counts_left) & set(counts_right):
del counts_xor[s]
utils.test_profile_file(filename, counts_xor, 8)
def test_count(self):
counts = utils.counts(utils.SEQUENCES, 8)
filename = self.empty()
with open(self.fasta(utils.SEQUENCES)) as fasta_handle:
with utils.open_profile(filename, 'w') as profile_handle:
kmer.count([fasta_handle], profile_handle, 8)
utils.test_profile_file(filename, counts, 8)
def _test_profile_split(self, sequences, length):
counts = utils.counts(sequences, length)
profile = klib.Profile(utils.as_array(counts, length))
left, right = profile.split()
assert len(left) == len(right)
assert sum(left) + sum(right) == sum(counts.values()) * 2
indices_left = {}
indices_right = {}
indices_palindrome = {}
for s, c in counts.items():
r = utils.reverse_complement(s)
if s < r:
indices_left[utils.count_index(s)] = c * 2
elif s > r:
indices_right[utils.count_index(r)] = counts[s] * 2
else:
indices_palindrome[utils.count_index(s)] = c
assert ([c for c in left if c > 0] == [
c for i, c in sorted(
list(indices_left.items()) + list(indices_palindrome.items()))
])
assert ([c for c in right if c > 0] == [
c for i, c in sorted(
list(indices_right.items()) + list(indices_palindrome.items()))
])
def test_main_info(self, capsys):
# For the `capsys` fixture, see:
# http://pytest.org/latest/capture.html
counts = utils.counts(utils.SEQUENCES, 8)
filename = self.profile(counts, 8, 'a')
kmer.main(['info', filename])
out, err = capsys.readouterr()
expected = 'File format version: 1.0.0\n'
expected += 'Produced by: kMer unit tests\n\n'
expected += 'Profile: a\n'
expected += '- k-mer length: 8 (%d k-mers)\n' % (4**8)
expected += '- Zero counts: %i\n' % (4**8 - len(counts))
expected += '- Non-zero counts: %i\n' % len(counts)
expected += '- Sum of counts: %i\n' % sum(counts.values())
expected += '- Mean of counts: %.3f\n' % np.mean([0] *
(4**8 - len(counts)) +
list(counts.values()))
expected += '- Median of counts: %.3f\n' % np.median(
[0] * (4**8 - len(counts)) + list(counts.values()))
expected += '- Standard deviation of counts: %.3f\n' % np.std(
[0] * (4**8 - len(counts)) + list(counts.values()))
assert out == expected
def test_positive(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
filename_left = self.empty()
filename_right = self.empty()
with utils.open_profile(self.profile(counts_left, 8)) as handle_left:
with utils.open_profile(self.profile(counts_right, 8)) as handle_right:
with utils.open_profile(filename_left, 'w') as out_left:
with utils.open_profile(filename_right, 'w') as out_right:
kmer.positive(handle_left, handle_right, out_left, out_right)
utils.test_profile_file(filename_left, Counter(s for s in counts_left.elements()
if s in counts_right), 8)
utils.test_profile_file(filename_right, Counter(s for s in counts_right.elements()
if s in counts_left), 8)
def test_profile_from_file(self):
counts = utils.counts(utils.SEQUENCES, 4)
with utils.open_profile(self.profile(counts, 4),
'r') as profile_handle:
profile = klib.Profile.from_file(profile_handle)
utils.test_profile(profile, counts, 4)
def test_convert(self):
counts = utils.counts(utils.SEQUENCES, 8)
filename = self.empty()
with open(self.profile_old_format(counts, 8)) as handle:
with utils.open_profile(filename, 'w') as profile_handle:
kmer.convert([handle], profile_handle)
utils.test_profile_file(filename, counts, 8)
def test_positive(self):
counts_left = utils.counts(utils.SEQUENCES_LEFT, 8)
counts_right = utils.counts(utils.SEQUENCES_RIGHT, 8)
filename_left = self.empty()
filename_right = self.empty()
with utils.open_profile(self.profile(counts_left, 8)) as handle_left:
with utils.open_profile(self.profile(counts_right, 8)) as handle_right:
with utils.open_profile(filename_left, 'w') as out_left:
with utils.open_profile(filename_right, 'w') as out_right:
kmer.positive(handle_left, handle_right, out_left, out_right)
utils.test_profile_file(filename_left, Counter(s for s in counts_left.elements()
if s in counts_right), 8)
utils.test_profile_file(filename_right, Counter(s for s in counts_right.elements()
if s in counts_left), 8)
def test_count(self):
counts = utils.counts(utils.SEQUENCES, 8)
filename = self.empty()
with open(self.fasta(utils.SEQUENCES)) as fasta_handle:
with utils.open_profile(filename, 'w') as profile_handle:
kmer.count([fasta_handle], profile_handle, 8)
utils.test_profile_file(filename, counts, 8)
def test_profile_reverse_complement(self):
counts = utils.counts(utils.SEQUENCES, 8)
profile = klib.Profile(utils.as_array(counts, 8))
for i in range(profile.length):
assert (profile.binary_to_dna(profile.reverse_complement(i)) ==
utils.reverse_complement(profile.binary_to_dna(i)))
def _test_profile_split(self, sequences, length):
counts = utils.counts(sequences, length)
profile = klib.Profile(utils.as_array(counts, length))
left, right = profile.split()
assert len(left) == len(right)
assert sum(left) + sum(right) == sum(counts.values()) * 2
indices_left = {}
indices_right = {}
indices_palindrome = {}
for s, c in counts.items():
r = utils.reverse_complement(s)
if s < r:
indices_left[utils.count_index(s)] = c * 2
elif s > r:
indices_right[utils.count_index(r)] = counts[s] * 2
else:
indices_palindrome[utils.count_index(s)] = c
assert ([c for c in left if c > 0] ==
[c for i, c in sorted(list(indices_left.items()) +
list(indices_palindrome.items()))])
assert ([c for c in right if c > 0] ==
[c for i, c in sorted(list(indices_right.items()) +
list(indices_palindrome.items()))])
def test_convert(self):
counts = utils.counts(utils.SEQUENCES, 8)
filename = self.empty()
with open(self.profile_old_format(counts, 8)) as handle:
with utils.open_profile(filename, 'w') as profile_handle:
kmer.convert([handle], profile_handle)
utils.test_profile_file(filename, counts, 8)
def test_profile_reverse_complement_palindrome(self):
counts = utils.counts(['ACCTAGGT'], 8)
profile = klib.Profile(utils.as_array(counts, 8))
for i in range(profile.length):
assert (profile.binary_to_dna(profile.reverse_complement(i)) ==
utils.reverse_complement(profile.binary_to_dna(i)))
def test_get_balance(self):
counts = utils.counts(utils.SEQUENCES, 8)
out = StringIO()
with utils.open_profile(self.profile(counts, 8)) as input_handle:
kmer.get_balance(input_handle, out, precision=3)
assert out.getvalue() == '1 0.669\n'
def test_profile_print_counts(self, capsys):
counts = utils.counts(utils.SEQUENCES, 4)
profile = klib.Profile(utils.as_array(counts, 4))
profile.print_counts()
out, err = capsys.readouterr()
assert out == ''.join('%s %d\n' % (''.join(s), counts[''.join(s)])
for s in itertools.product('ACGT', repeat=4))
def test_profile_balance(self):
counts = utils.counts(utils.SEQUENCES, 8)
profile = klib.Profile(utils.as_array(counts, 8))
profile.balance()
counts.update(dict((utils.reverse_complement(s), c)
for s, c in counts.items()))
utils.test_profile(profile, counts, 8)
def test_profile_balance_palindrome(self):
counts = utils.counts(['AATT'], 4)
profile = klib.Profile(utils.as_array(counts, 4))
profile.balance()
counts.update(dict((utils.reverse_complement(s), c)
for s, c in counts.items()))
utils.test_profile(profile, counts, 4)
def test_profile_reverse_complement_palindrome(self):
counts = utils.counts(['ACCTAGGT'], 8)
profile = klib.Profile(utils.as_array(counts, 8))
for i in range(profile.length):
assert (profile.binary_to_dna(
profile.reverse_complement(i)) == utils.reverse_complement(
profile.binary_to_dna(i)))
def test_profile_reverse_complement(self):
counts = utils.counts(utils.SEQUENCES, 8)
profile = klib.Profile(utils.as_array(counts, 8))
for i in range(profile.length):
assert (profile.binary_to_dna(
profile.reverse_complement(i)) == utils.reverse_complement(
profile.binary_to_dna(i)))
def test_get_balance(self):
counts = utils.counts(utils.SEQUENCES, 8)
out = StringIO()
with utils.open_profile(self.profile(counts, 8)) as input_handle:
kmer.get_balance(input_handle, out, precision=3)
assert out.getvalue() == '1 0.669\n'
def test_profile_print_counts(self, capsys):
counts = utils.counts(utils.SEQUENCES, 4)
profile = klib.Profile(utils.as_array(counts, 4))
profile.print_counts()
out, err = capsys.readouterr()
assert out == ''.join('%s %d\n' % (''.join(s), counts[''.join(s)])
for s in itertools.product('ACGT', repeat=4))
def _test_from_fasta_by_record(self, sequences, k, prefix=None):
counts_by_record = [utils.counts(sequence, k) for sequence in sequences]
names = [str(i) for i, _ in enumerate(counts_by_record)]
with open(self.fasta(sequences, names=names)) as fasta_handle:
profiles = klib.Profile.from_fasta_by_record(fasta_handle, k, prefix=prefix)
for name, counts, profile in zip(names, counts_by_record, profiles):
prefixed_name = prefix + '_' + name if prefix else name
utils.test_profile(profile, counts, k, name=prefixed_name)
