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_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_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)
