def test_bool_descendants(self):
"""bool_descendants should be true if any descendant true"""
# self.t3 = DndParser('(((a,b,c),(d)),(e,f))', UniFracTreeNode)
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]
bool_descendants(bindings)
self.assertEqual(
a,
array(
[
[0, 1, 0],
[0, 1, 0],
[0, 1, 0],
[1, 0, 0],
[0, 1, 0],
[1, 0, 0],
[0, 0, 1],
[0, 1, 0],
[1, 1, 0],
[0, 1, 1],
[1, 1, 1],
]
),
)
def test_unifrac_matrix(self):
"""unifrac_matrix should return correct results for model tree"""
m = array([[1,0,1],[1,1,0],[0,1,0],[0,0,1],[0,1,0],[0,1,1],[1,1,1],\
[0,1,1],[1,1,1]])
bl = self.branch_lengths
result = unifrac_matrix(bl, m)
self.assertEqual(result, array([[0, 10/16.,8/13.],[10/16.,0,8/17.],\
[8/13.,8/17.,0]]))
#should work if we tell it the measure is asymmetric
result = unifrac_matrix(bl, m, is_symmetric=False)
self.assertEqual(result, array([[0, 10/16.,8/13.],[10/16.,0,8/17.],\
[8/13.,8/17.,0]]))
#should work if the measure really is asymmetric
result = unifrac_matrix(bl,
m,
metric=unnormalized_G,
is_symmetric=False)
self.assertEqual(result, array([[0, 1/17.,2/17.],[9/17.,0,6/17.],\
[6/17.,2/17.,0]]))
#should also match web site calculations
envs = self.count_array
bound_indices = bind_to_array(self.nodes, envs)
bool_descendants(bound_indices)
result = unifrac_matrix(bl, envs)
exp = array([[0, 0.6250, 0.6154], [0.6250, 0, \
0.4706], [0.6154, 0.4707, 0]])
assert (abs(result - exp)).max() < 0.001
def test_env_unique_fraction(self):
"""should report unique fraction of bl in each env """
# testing old unique fraction
cur_count_array = self.count_array.copy()
bound_indices = bind_to_array(self.nodes, cur_count_array)
total_bl = sum(self.branch_lengths)
bool_descendants(bound_indices)
env_bl_sums, env_bl_ufracs = env_unique_fraction(
self.branch_lengths, cur_count_array)
# env A has 0 unique bl, B has 4, C has 1
self.assertEqual(env_bl_sums, [0, 4, 1])
self.assertEqual(env_bl_ufracs, [0, 4 / 17.0, 1 / 17.0])
cur_count_array = self.old_count_array.copy()
bound_indices = bind_to_array(self.old_nodes, cur_count_array)
total_bl = sum(self.old_branch_lengths)
bool_descendants(bound_indices)
env_bl_sums, env_bl_ufracs = env_unique_fraction(
self.old_branch_lengths, cur_count_array)
# env A has 0 unique bl, B has 4, C has 1
self.assertEqual(env_bl_sums, env_bl_sums)
self.assertEqual(env_bl_sums, [1.29, 0.33999999999999997, 0.63])
self.assertEqual(env_bl_ufracs,
[1.29 / 2.9, 0.33999999999999997 / 2.9, 0.63 / 2.9])
def test_delete_empty_parents(self):
"""delete_empty_parents should remove empty parents from bound indices"""
id_to_node, node_first_last = index_tree(self.t)
bound_indices = bind_to_array(node_first_last, self.count_array[:,0:1])
bool_descendants(bound_indices)
self.assertEqual(len(bound_indices), 4)
deleted = delete_empty_parents(bound_indices)
self.assertEqual(len(deleted), 2)
for d in deleted:
self.assertEqual(d[0][0], 1)
def test_delete_empty_parents(self):
"""delete_empty_parents should remove empty parents from bound indices"""
id_to_node, node_first_last = index_tree(self.t)
bound_indices = bind_to_array(node_first_last, self.count_array[:,
0:1])
bool_descendants(bound_indices)
self.assertEqual(len(bound_indices), 4)
deleted = delete_empty_parents(bound_indices)
self.assertEqual(len(deleted), 2)
for d in deleted:
self.assertEqual(d[0][0], 1)
def test_bool_descendants(self):
"""bool_descendants should be true if any descendant true"""
#self.t3 = DndParser('(((a,b,c),(d)),(e,f))', UniFracTreeNode)
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]
bool_descendants(bindings)
self.assertEqual(a, \
array([[0,1,0],[0,1,0],[0,1,0],[1,0,0],[0,1,0],[1,0,0],\
[0,0,1],[0,1,0],[1,1,0],[0,1,1],[1,1,1]])
)
def test_unifrac_matrix(self):
"""unifrac_matrix should return correct results for model tree"""
m = array([[1, 0, 1], [1, 1, 0], [0, 1, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1], [1, 1, 1], [0, 1, 1], [1, 1, 1]])
bl = self.branch_lengths
result = unifrac_matrix(bl, m)
self.assertEqual(result, array([[0, 10 / 16.0, 8 / 13.0], [10 / 16.0, 0, 8 / 17.0], [8 / 13.0, 8 / 17.0, 0]]))
# should work if we tell it the measure is asymmetric
result = unifrac_matrix(bl, m, is_symmetric=False)
self.assertEqual(result, array([[0, 10 / 16.0, 8 / 13.0], [10 / 16.0, 0, 8 / 17.0], [8 / 13.0, 8 / 17.0, 0]]))
# should work if the measure really is asymmetric
result = unifrac_matrix(bl, m, metric=unnormalized_G, is_symmetric=False)
self.assertEqual(result, array([[0, 1 / 17.0, 2 / 17.0], [9 / 17.0, 0, 6 / 17.0], [6 / 17.0, 2 / 17.0, 0]]))
# should also match web site calculations
envs = self.count_array
bound_indices = bind_to_array(self.nodes, envs)
bool_descendants(bound_indices)
result = unifrac_matrix(bl, envs)
exp = array([[0, 0.6250, 0.6154], [0.6250, 0, 0.4706], [0.6154, 0.4707, 0]])
assert (abs(result - exp)).max() < 0.001
def test_env_unique_fraction(self):
"""should report unique fraction of bl in each env """
# testing old unique fraction
cur_count_array = self.count_array.copy()
bound_indices = bind_to_array(self.nodes, cur_count_array)
total_bl = sum(self.branch_lengths)
bool_descendants(bound_indices)
env_bl_sums, env_bl_ufracs = env_unique_fraction(self.branch_lengths, cur_count_array)
# env A has 0 unique bl, B has 4, C has 1
self.assertEqual(env_bl_sums, [0,4,1])
self.assertEqual(env_bl_ufracs, [0,4/17.0,1/17.0])
cur_count_array = self.old_count_array.copy()
bound_indices = bind_to_array(self.old_nodes, cur_count_array)
total_bl = sum(self.old_branch_lengths)
bool_descendants(bound_indices)
env_bl_sums, env_bl_ufracs = env_unique_fraction(self.old_branch_lengths, cur_count_array)
# env A has 0 unique bl, B has 4, C has 1
self.assertEqual(env_bl_sums, env_bl_sums)
self.assertEqual(env_bl_sums, [1.29, 0.33999999999999997, 0.63])
self.assertEqual(env_bl_ufracs, [1.29/2.9,0.33999999999999997/2.9, 0.63/2.9])
