def test_distance_matrix_one(self):
counts = utils.counts(utils.SEQUENCES, 8)
profiles = [klib.Profile(utils.as_array(counts, 8), 'a')]
k_dist = kdistlib.ProfileDistance()
out = StringIO()
kdistlib.distance_matrix(profiles, out, 2, k_dist)
assert out.getvalue().strip().split('\n') == ['1', 'a']
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_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_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_collapse(self):
a = np.random.random_integers(0, 20, 100)
start = 30
length = 40
step = length / 4
expected = [
sum(a[start + x[0]:start + x[1]])
for x in [(x * step, (x + 1) * step) for x in range(4)]
]
k_dist = kdistlib.ProfileDistance()
np.testing.assert_array_equal(k_dist._collapse(a, start, length),
expected)
def test_ProfileDistance_distance(self):
counts_a = Counter([
'AC', 'AG', 'AT', 'CA', 'CC', 'CG', 'CT', 'GA', 'GC', 'GG', 'GT',
'TA', 'TG', 'TT'
])
counts_b = Counter([
'AC', 'AT', 'CA', 'CC', 'CG', 'CT', 'GA', 'GC', 'GG', 'GT', 'TA',
'TC', 'TG', 'TT'
])
profile_a = klib.Profile(utils.as_array(counts_a, 2))
profile_b = klib.Profile(utils.as_array(counts_b, 2))
k_dist = kdistlib.ProfileDistance()
assert k_dist.distance(profile_a, profile_b) == 0.0625
def test_ProfileDistance_dynamic_smooth(self):
# If we use function=min and threshold=0, we should get the following
# transformation:
#
# | before | after
# ----------+------------------+-----------------
# | 0111111111111011 | 3000111111113000
# profile A | ACGTACGTACGTACGT | ACGTACGTACGTACGT
# | AAAACCCCGGGGTTTT | AAAACCCCGGGGTTTT
# ----------+------------------+-----------------
# | 0101111111111111 | 2000111111114000
# profile B | ACGTACGTACGTACGT | ACGTACGTACGTACGT
# | AAAACCCCGGGGTTTT | AAAACCCCGGGGTTTT
counts_a = Counter([
'AC', 'AG', 'AT', 'CA', 'CC', 'CG', 'CT', 'GA', 'GC', 'GG', 'GT',
'TA', 'TG', 'TT'
])
counts_b = Counter([
'AC', 'AT', 'CA', 'CC', 'CG', 'CT', 'GA', 'GC', 'GG', 'GT', 'TA',
'TC', 'TG', 'TT'
])
profile_a = klib.Profile(utils.as_array(counts_a, 2))
profile_b = klib.Profile(utils.as_array(counts_b, 2))
k_dist = kdistlib.ProfileDistance()
k_dist.dynamic_smooth(profile_a, profile_b)
counts_a = Counter([
'AA', 'AA', 'AA', 'CA', 'CC', 'CG', 'CT', 'GA', 'GC', 'GG', 'GT',
'TA', 'TA', 'TA'
])
counts_b = Counter([
'AA', 'AA', 'CA', 'CC', 'CG', 'CT', 'GA', 'GC', 'GG', 'GT', 'TA',
'TA', 'TA', 'TA'
])
np.testing.assert_array_equal(profile_a.counts,
utils.as_array(counts_a, 2))
np.testing.assert_array_equal(profile_b.counts,
utils.as_array(counts_b, 2))