def test_mito_matrix(self):
D = g_mito_matrix
n = len(D)
observed_Q = get_Q_matrix(D)
expected_Q = g_mito_matrix_q
# assert that the diagonal elements of the observed Q matrix are exactly zero
for i, row in enumerate(observed_Q):
self.assertEqual(row[i], 0)
# assert that the observed Q matrix is approximately equal to the expected Q matrix
abs_tol = .001
for i in range(n):
for j in range(n):
abs_delta = abs(observed_Q[i][j] - expected_Q[i][j])
self.failUnless(abs_delta < abs_tol)
# use neighbor joining to reconstruct the tree
observed_tree = make_tree(D, g_mito_states)
# load the expected tree
expected_tree = NewickIO.parse(g_mito_tree_string, FelTree.NewickTree)
# for the observed and expected trees calculate the induced partitions and corresponding branch lengths
observed_partitions_and_lengths = TreeComparison.get_partitions_and_branch_lengths(
observed_tree)
expected_partitions_and_lengths = TreeComparison.get_partitions_and_branch_lengths(
expected_tree)
# the number of partitions should be the same
self.assertEqual(len(observed_partitions_and_lengths),
len(expected_partitions_and_lengths))
# the partitions should be the same
observed_partitions = set(
[part for part, length in observed_partitions_and_lengths])
expected_partitions = set(
[part for part, length in expected_partitions_and_lengths])
observed_only = observed_partitions - expected_partitions
expected_only = expected_partitions - observed_partitions
lines = [
'observed partitions include: ' + str(observed_only),
'expected partitions include: ' + str(expected_only)
]
self.assertEqual(observed_partitions, expected_partitions,
'\n'.join(lines))
# corresponding partitions should have the same lengths
observed_part_to_length = dict(observed_partitions_and_lengths)
expected_part_to_length = dict(expected_partitions_and_lengths)
lines = []
for part in observed_partitions:
observed_length = observed_part_to_length[part]
expected_length = expected_part_to_length[part]
abs_tol = .00001
abs_delta = abs(observed_length - expected_length)
if abs_delta > abs_tol:
lines.append('partition:' + str(part))
lines.append('observed branch length:' + str(observed_length))
lines.append('expected branch length:' + str(expected_length))
error_message = '\n'.join(lines)
self.failIf(error_message, error_message)
def test_mito_matrix(self):
D = g_mito_matrix
n = len(D)
observed_Q = get_Q_matrix(D)
expected_Q = g_mito_matrix_q
# assert that the diagonal elements of the observed Q matrix are exactly zero
for i, row in enumerate(observed_Q):
self.assertEqual(row[i], 0)
# assert that the observed Q matrix is approximately equal to the expected Q matrix
abs_tol = .001
for i in range(n):
for j in range(n):
abs_delta = abs(observed_Q[i][j] - expected_Q[i][j])
self.failUnless(abs_delta < abs_tol)
# use neighbor joining to reconstruct the tree
observed_tree = make_tree(D, g_mito_states)
# load the expected tree
expected_tree = NewickIO.parse(g_mito_tree_string, FelTree.NewickTree)
# for the observed and expected trees calculate the induced partitions and corresponding branch lengths
observed_partitions_and_lengths = TreeComparison.get_partitions_and_branch_lengths(observed_tree)
expected_partitions_and_lengths = TreeComparison.get_partitions_and_branch_lengths(expected_tree)
# the number of partitions should be the same
self.assertEqual(len(observed_partitions_and_lengths), len(expected_partitions_and_lengths))
# the partitions should be the same
observed_partitions = set([part for part, length in observed_partitions_and_lengths])
expected_partitions = set([part for part, length in expected_partitions_and_lengths])
observed_only = observed_partitions - expected_partitions
expected_only = expected_partitions - observed_partitions
lines = [
'observed partitions include: ' + str(observed_only),
'expected partitions include: ' + str(expected_only)
]
self.assertEqual(observed_partitions, expected_partitions, '\n'.join(lines))
# corresponding partitions should have the same lengths
observed_part_to_length = dict(observed_partitions_and_lengths)
expected_part_to_length = dict(expected_partitions_and_lengths)
lines = []
for part in observed_partitions:
observed_length = observed_part_to_length[part]
expected_length = expected_part_to_length[part]
abs_tol = .00001
abs_delta = abs(observed_length - expected_length)
if abs_delta > abs_tol:
lines.append('partition:' + str(part))
lines.append('observed branch length:' + str(observed_length))
lines.append('expected branch length:' + str(expected_length))
error_message = '\n'.join(lines)
self.failIf(error_message, error_message)
