def test_sum_descendants(self):
"""sum_descendants should sum total descendants w/ each state"""
id_, child = index_tree(self.t3)
a = zeros((11, 3)) + 99 # fill with junk
bindings = bind_to_array(child, a)
# load in leaf envs
a[0] = a[1] = a[2] = a[7] = [0, 1, 0]
a[3] = [1, 0, 0]
a[6] = [0, 0, 1]
sum_descendants(bindings)
self.assertEqual(
a,
array(
[
[0, 1, 0],
[0, 1, 0],
[0, 1, 0],
[1, 0, 0],
[0, 3, 0],
[1, 0, 0],
[0, 0, 1],
[0, 1, 0],
[1, 3, 0],
[0, 1, 1],
[1, 4, 1],
]
),
)
def test_weighted_unifrac_matrix(self):
"""weighted unifrac matrix should ret correct results for model tree"""
# should match web site calculations
envs = self.count_array
bound_indices = bind_to_array(self.nodes, envs)
sum_descendants(bound_indices)
bl = self.branch_lengths
tip_indices = [n._leaf_index for n in self.t.tips()]
result = weighted_unifrac_matrix(bl, envs, tip_indices)
exp = array([[0, 9.1, 4.5], [9.1, 0, 6.4], [4.5, 6.4, 0]])
assert (abs(result - exp)).max() < 0.001
# should work with branch length corrections
td = bl.copy()[:, newaxis]
tip_bindings = bind_to_parent_array(self.t, td)
tips = [n._leaf_index for n in self.t.tips()]
tip_distances(td, tip_bindings, tips)
result = weighted_unifrac_matrix(bl, envs, tip_indices, bl_correct=True, tip_distances=td)
exp = array(
[
[0, 9.1 / 11.5, 4.5 / (10.5 + 1.0 / 3)],
[9.1 / 11.5, 0, 6.4 / (11 + 1.0 / 3)],
[4.5 / (10.5 + 1.0 / 3), 6.4 / (11 + 1.0 / 3), 0],
]
)
assert (abs(result - exp)).max() < 0.001
def test_weighted_unifrac_matrix(self):
"""weighted unifrac matrix should return correct results for model tree"""
#should match web site calculations
envs = self.count_array
bound_indices = bind_to_array(self.nodes, envs)
sum_descendants(bound_indices)
bl = self.branch_lengths
tip_indices = [n._leaf_index for n in self.t.tips()]
result = weighted_unifrac_matrix(bl, envs, tip_indices)
exp = array([[0, 9.1, 4.5], [9.1, 0, \
6.4], [4.5, 6.4, 0]])
assert (abs(result - exp)).max() < 0.001
#should work with branch length corrections
td = bl.copy()[:, newaxis]
tip_bindings = bind_to_parent_array(self.t, td)
tips = [n._leaf_index for n in self.t.tips()]
tip_distances(td, tip_bindings, tips)
result = weighted_unifrac_matrix(bl,
envs,
tip_indices,
bl_correct=True,
tip_distances=td)
exp = array([[0, 9.1/11.5, 4.5/(10.5+1./3)], [9.1/11.5, 0, \
6.4/(11+1./3)], [4.5/(10.5+1./3), 6.4/(11+1./3), 0]])
assert (abs(result - exp)).max() < 0.001
def test_weighted_one_sample(self):
"""weighted one sample should match weighted matrix"""
#should match web site calculations
envs = self.count_array
bound_indices = bind_to_array(self.nodes, envs)
sum_descendants(bound_indices)
bl = self.branch_lengths
tip_indices = [n._leaf_index for n in self.t.tips()]
result = weighted_unifrac_matrix(bl, envs, tip_indices)
for i in range(len(result)):
one_sam_res = weighted_one_sample(i, bl, envs, tip_indices)
self.assertEqual(result[i], one_sam_res)
self.assertEqual(result[:,i], one_sam_res)
#should work with branch length corrections
td = bl.copy()[:,newaxis]
tip_bindings = bind_to_parent_array(self.t, td)
tips = [n._leaf_index for n in self.t.tips()]
tip_distances(td, tip_bindings, tips)
result = weighted_unifrac_matrix(bl, envs, tip_indices, bl_correct=True,
tip_distances=td)
for i in range(len(result)):
one_sam_res = weighted_one_sample(i, bl, envs, tip_indices,
bl_correct=True, tip_distances=td)
self.assertEqual(result[i], one_sam_res)
self.assertEqual(result[:,i], one_sam_res)
def test_weighted_one_sample(self):
"""weighted one sample should match weighted matrix"""
#should match web site calculations
envs = self.count_array
bound_indices = bind_to_array(self.nodes, envs)
sum_descendants(bound_indices)
bl = self.branch_lengths
tip_indices = [n._leaf_index for n in self.t.tips()]
result = weighted_unifrac_matrix(bl, envs, tip_indices)
for i in range(len(result)):
one_sam_res = weighted_one_sample(i, bl, envs, tip_indices)
self.assertEqual(result[i], one_sam_res)
self.assertEqual(result[:, i], one_sam_res)
#should work with branch length corrections
td = bl.copy()[:, newaxis]
tip_bindings = bind_to_parent_array(self.t, td)
tips = [n._leaf_index for n in self.t.tips()]
tip_distances(td, tip_bindings, tips)
result = weighted_unifrac_matrix(bl,
envs,
tip_indices,
bl_correct=True,
tip_distances=td)
for i in range(len(result)):
one_sam_res = weighted_one_sample(i,
bl,
envs,
tip_indices,
bl_correct=True,
tip_distances=td)
self.assertEqual(result[i], one_sam_res)
self.assertEqual(result[:, i], one_sam_res)
def test_weighted_unifrac_vector(self):
"""weighted_unifrac_vector should ret correct results for model tree"""
envs = self.count_array
bound_indices = bind_to_array(self.nodes, envs)
sum_descendants(bound_indices)
bl = self.branch_lengths
tip_indices = [n._leaf_index for n in self.t.tips()]
result = weighted_unifrac_vector(bl, envs, tip_indices)
self.assertFloatEqual(
result[0],
sum(
[
abs(1.0 / 2 - 2.0 / 8) * 1,
abs(1.0 / 2 - 1.0 / 8) * 2,
abs(0 - 1.0 / 8) * 3,
abs(0 - 3.0 / 8) * 1,
abs(0 - 1.0 / 8) * 1,
abs(0 - 4.0 / 8) * 2,
abs(2.0 / 2 - 3.0 / 8) * 4,
abs(0.0 - 5.0 / 8) * 3.0,
]
),
)
self.assertFloatEqual(
result[1],
sum(
[
abs(0 - 0.6) * 1,
abs(0.2 - 0.2) * 2,
abs(0.2 - 0) * 3,
abs(0.6 - 0) * 1,
abs(0 - 0.2) * 1,
abs(0.6 - 0.2) * 2,
abs(0.2 - 0.8) * 4,
abs(0.8 - 0.2) * 3,
]
),
)
self.assertFloatEqual(
result[2],
sum(
[
abs(2.0 / 3 - 1.0 / 7) * 1,
abs(0 - 2.0 / 7) * 2,
abs(0 - 1.0 / 7) * 3,
abs(0 - 3.0 / 7) * 1,
abs(1.0 / 3 - 0) * 1,
abs(1.0 / 3 - 3.0 / 7) * 2,
abs(2.0 / 3 - 3.0 / 7) * 4,
abs(1.0 / 3 - 4.0 / 7) * 3,
]
),
)
def test_sum_descendants(self):
"""sum_descendants should sum total descendants w/ each state"""
id_, child = index_tree(self.t3)
a = zeros((11, 3)) + 99 #fill with junk
bindings = bind_to_array(child, a)
#load in leaf envs
a[0] = a[1] = a[2] = a[7] = [0, 1, 0]
a[3] = [1, 0, 0]
a[6] = [0, 0, 1]
sum_descendants(bindings)
self.assertEqual(a, \
array([[0,1,0],[0,1,0],[0,1,0],[1,0,0],[0,3,0],[1,0,0],\
[0,0,1],[0,1,0],[1,3,0],[0,1,1],[1,4,1]])
)
def test_weighted_unifrac_vector(self):
"""weighted_unifrac_vector should return correct results for model tree"""
envs = self.count_array
bound_indices = bind_to_array(self.nodes, envs)
sum_descendants(bound_indices)
bl = self.branch_lengths
tip_indices = [n._leaf_index for n in self.t.tips()]
result = weighted_unifrac_vector(bl, envs, tip_indices)
self.assertFloatEqual(
result[0],
sum([
abs(1. / 2 - 2. / 8) * 1,
abs(1. / 2 - 1. / 8) * 2,
abs(0 - 1. / 8) * 3,
abs(0 - 3. / 8) * 1,
abs(0 - 1. / 8) * 1,
abs(0 - 4. / 8) * 2,
abs(2. / 2 - 3. / 8) * 4,
abs(0. - 5. / 8) * 3.
]))
self.assertFloatEqual(
result[1],
sum([
abs(0 - .6) * 1,
abs(.2 - .2) * 2,
abs(.2 - 0) * 3,
abs(.6 - 0) * 1,
abs(0 - .2) * 1,
abs(.6 - .2) * 2,
abs(.2 - .8) * 4,
abs(.8 - .2) * 3
]))
self.assertFloatEqual(
result[2],
sum([
abs(2. / 3 - 1. / 7) * 1,
abs(0 - 2. / 7) * 2,
abs(0 - 1. / 7) * 3,
abs(0 - 3. / 7) * 1,
abs(1. / 3 - 0) * 1,
abs(1. / 3 - 3. / 7) * 2,
abs(2. / 3 - 3. / 7) * 4,
abs(1. / 3 - 4. / 7) * 3
]))
