def test_nj_dm1(self):
self.assertEqual(nj(self.dm1, result_constructor=str),
self.expected1_str)
# what is the correct way to compare TreeNode objects for equality?
actual_TreeNode = nj(self.dm1)
self.assertEqual(actual_TreeNode.compare_tip_distances(
self.expected1_TreeNode), 0.0)
def test_nj_dm1(self):
self.assertEqual(nj(self.dm1, result_constructor=str),
self.expected1_str)
# what is the correct way to compare TreeNode objects for equality?
actual_TreeNode = nj(self.dm1)
self.assertEqual(actual_TreeNode.compare_tip_distances(
self.expected1_TreeNode), 0.0)
def test_nj_zero_branch_length(self):
# no nodes have negative branch length when we disallow negative
# branch length. self is excluded as branch length is None
tree = nj(self.dm4)
for n in tree.postorder(include_self=False):
self.assertTrue(n.length >= 0)
# only tips associated with the large distance in the input
# have positive branch lengths when we allow negative branch
# length
tree = nj(self.dm4, False)
self.assertTrue(tree.find('a').length > 0)
self.assertTrue(tree.find('b').length < 0)
self.assertTrue(tree.find('c').length < 0)
self.assertTrue(tree.find('d').length < 0)
self.assertTrue(tree.find('e').length > 0)
def test_nj_zero_branch_length(self):
# no nodes have negative branch length when we disallow negative
# branch length. self is excluded as branch length is None
tree = nj(self.dm4)
for n in tree.postorder(include_self=False):
self.assertTrue(n.length >= 0)
# only tips associated with the large distance in the input
# have positive branch lengths when we allow negative branch
# length
tree = nj(self.dm4, False)
self.assertTrue(tree.find('a').length > 0)
self.assertTrue(tree.find('b').length < 0)
self.assertTrue(tree.find('c').length < 0)
self.assertTrue(tree.find('d').length < 0)
self.assertTrue(tree.find('e').length > 0)
def test_nj_trivial(self):
data = [[0, 3, 2],
[3, 0, 3],
[2, 3, 0]]
dm = DistanceMatrix(data, list('abc'))
expected_str = "(b:2.000000, a:1.000000, c:1.000000);"
self.assertEqual(nj(dm, result_constructor=str), expected_str)
def test_nj_trivial(self):
data = [[0, 3, 2],
[3, 0, 3],
[2, 3, 0]]
dm = DistanceMatrix(data, list('abc'))
expected_str = "(b:2.000000, a:1.000000, c:1.000000);"
self.assertEqual(nj(dm, result_constructor=str), expected_str)
def get_nj_tree_newick(pairwise_differences, samples):
pairwise_differences = copy.deepcopy(pairwise_differences)
np.fill_diagonal(pairwise_differences, 0)
distance_matrix = skbio.DistanceMatrix(pairwise_differences, ids=samples)
# This computes a neighbour-joining tree and outputs a newick string
nj_newick = skbio.nj(distance_matrix, result_constructor=str)
return nj_newick
def create_tree_of_trees(trees: dict, out, metric='score', outgroup=''):
output_dir, output_filename = parse_path(out)
names = sorted(trees.keys())
n = len(names)
m = np.zeros((n, n))
for i, ref in enumerate(names[:-1]):
targets = [trees[names[j]] for j in range(i + 1, n)]
dists = get_dist_trees(trees[ref], targets, metric)
m[i + 1:n, i] = dists
m += m.T
dist_m = DistanceMatrix(m, names)
tree = ete3.Tree(nj(dist_m, result_constructor=str))
if outgroup is not None:
outgroup_tree(tree)
if out:
tree.write(outfile=out)
return tree
def test_nj_dm3(self):
actual_TreeNode = nj(self.dm3)
self.assertAlmostEqual(actual_TreeNode.compare_tip_distances(
self.expected3_TreeNode), 0.0)
def test_nj_dm3(self):
actual_TreeNode = nj(self.dm3)
self.assertAlmostEqual(
actual_TreeNode.compare_tip_distances(self.expected3_TreeNode),
0.0)
