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_unifrac_one_sample(self):
"""unifrac_one_sample should match unifrac_matrix"""
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)
for i in range(len(result)):
one_sam_res = unifrac_one_sample(i, bl, m)
self.assertEqual(result[i], one_sam_res)
self.assertEqual(result[:, i], one_sam_res)
# should work ok on asymmetric metrics
result = unifrac_matrix(bl, m, metric=unnormalized_G, is_symmetric=False)
for i in range(len(result)):
one_sam_res = unifrac_one_sample(i, bl, m, metric=unnormalized_G)
self.assertEqual(result[i], one_sam_res)
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_unifrac_one_sample(self):
"""unifrac_one_sample should match unifrac_matrix"""
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)
for i in range(len(result)):
one_sam_res = unifrac_one_sample(i, bl, m)
self.assertEqual(result[i], one_sam_res)
self.assertEqual(result[:, i], one_sam_res)
#should work ok on asymmetric metrics
result = unifrac_matrix(bl,
m,
metric=unnormalized_G,
is_symmetric=False)
for i in range(len(result)):
one_sam_res = unifrac_one_sample(i, bl, m, metric=unnormalized_G)
self.assertEqual(result[i], one_sam_res)