def test_commonname_promotion(self):
"""correctly promote names if possible"""
consensus_tree = TreeNode.read(StringIO(u"(((s1,s2)g1,(s3,s4)g2,(s5,s6)g3)f1)o1;"))
rank_lookup = {'s': 6, 'g': 5, 'f': 4, 'o': 3, 'c': 2, 'p': 1, 'k': 0}
for n in consensus_tree.traverse(include_self=True):
n.Rank = rank_lookup[n.name[0]]
data = StringIO(u"((((1)s1,(2)s2),((3)s3,(4)s5)))o1;")
lookup = dict([(n.name, n)
for n in consensus_tree.traverse(include_self=True)])
exp = "((((1)s1,(2)s2)g1,((3)'g2; s3',(4)'g3; s5')))'o1; f1';"
t = TreeNode.read(data)
t.Rank = 3
t.children[0].Rank = None
t.children[0].children[0].Rank = None
t.children[0].children[1].Rank = None
t.children[0].children[0].children[0].Rank = 6
t.children[0].children[0].children[1].Rank = 6
t.children[0].children[1].children[0].Rank = 6
t.children[0].children[1].children[1].Rank = 6
backfill_names_gap(t, lookup)
commonname_promotion(t)
fp = StringIO()
t.write(fp)
self.assertEqual(fp.getvalue().strip(), exp)
def test_commonname_promotion(self):
"""correctly promote names if possible"""
consensus_tree = DndParser("(((s1,s2)g1,(s3,s4)g2,(s5,s6)g3)f1)o1;")
rank_lookup = {'s':6,'g':5,'f':4,'o':3,'c':2,'p':1,'k':0}
for n in consensus_tree.traverse(include_self=True):
n.Rank = rank_lookup[n.Name[0]]
input = "((((1)s1,(2)s2),((3)s3,(4)s5)))o1;"
lookup = dict([(n.Name, n) for n in consensus_tree.traverse(include_self=True)])
exp = "((((1)s1,(2)s2)g1,((3)'g2; s3',(4)'g3; s5')))'o1; f1';"
t = DndParser(input)
t.Rank = 3
t.Children[0].Rank = None
t.Children[0].Children[0].Rank = None
t.Children[0].Children[1].Rank = None
t.Children[0].Children[0].Children[0].Rank = 6
t.Children[0].Children[0].Children[1].Rank = 6
t.Children[0].Children[1].Children[0].Rank = 6
t.Children[0].Children[1].Children[1].Rank = 6
backfill_names_gap(t, lookup)
commonname_promotion(t)
self.assertEqual(t.getNewick(with_distances=False), exp)
def test_commonname_promotion(self):
"""correctly promote names if possible"""
consensus_tree = TreeNode.read(StringIO(u"(((s1,s2)g1,(s3,s4)g2,(s5,s6)g3)f1)o1;"))
rank_lookup = {'s': 6, 'g': 5, 'f': 4, 'o': 3, 'c': 2, 'p': 1, 'k': 0}
for n in consensus_tree.traverse(include_self=True):
n.Rank = rank_lookup[n.name[0]]
data = StringIO(u"((((1)s1,(2)s2),((3)s3,(4)s5)))o1;")
lookup = dict([(n.name, n)
for n in consensus_tree.traverse(include_self=True)])
exp = "((((1)s1,(2)s2)g1,((3)'g2; s3',(4)'g3; s5')))'o1; f1';"
t = TreeNode.read(data)
t.Rank = 3
t.children[0].Rank = None
t.children[0].children[0].Rank = None
t.children[0].children[1].Rank = None
t.children[0].children[0].children[0].Rank = 6
t.children[0].children[0].children[1].Rank = 6
t.children[0].children[1].children[0].Rank = 6
t.children[0].children[1].children[1].Rank = 6
backfill_names_gap(t, lookup)
commonname_promotion(t)
self.assertEqual(str(t).rstrip(), exp)
def test_commonname_promotion(self):
"""correctly promote names if possible"""
consensus_tree = TreeNode.from_newick(
"(((s1,s2)g1,(s3,s4)g2,(s5,s6)g3)f1)o1;")
rank_lookup = {'s': 6, 'g': 5, 'f': 4, 'o': 3, 'c': 2, 'p': 1, 'k': 0}
for n in consensus_tree.traverse(include_self=True):
n.Rank = rank_lookup[n.name[0]]
input = "((((1)s1,(2)s2),((3)s3,(4)s5)))o1;"
lookup = dict([(n.name, n)
for n in consensus_tree.traverse(include_self=True)])
exp = "((((1)s1,(2)s2)g1,((3)'g2; s3',(4)'g3; s5')))'o1; f1';"
t = TreeNode.from_newick(input)
t.Rank = 3
t.children[0].Rank = None
t.children[0].children[0].Rank = None
t.children[0].children[1].Rank = None
t.children[0].children[0].children[0].Rank = 6
t.children[0].children[0].children[1].Rank = 6
t.children[0].children[1].children[0].Rank = 6
t.children[0].children[1].children[1].Rank = 6
backfill_names_gap(t, lookup)
commonname_promotion(t)
self.assertEqual(t.to_newick(with_distances=False), exp)
def generate_constrings(tree, tipname_map, verbose=False):
"""Assigns taxonomy to unidentified sequences in tree.
Returns all sequence IDs on tree."""
counts = nlevel.collect_names_at_ranks_counts(tree)
min_count = 2
nlevel.decorate_ntips(tree)
nlevel.decorate_name_relative_freqs(tree, counts, min_count)
nlevel.set_ranksafe(tree)
nlevel.pick_names(tree)
nlevel.name_node_score_fold(tree)
if verbose:
print "Tree score: ", nlevel.score_tree(tree)
nlevel.set_preliminary_name_and_rank(tree)
contree, contree_lookup = nlevel.make_consensus_tree(tipname_map.values())
nlevel.backfill_names_gap(tree, contree_lookup)
nlevel.commonname_promotion(tree)
nlevel.make_names_unique(tree, append_suffix=False)
constrings = nlevel.pull_consensus_strings(tree)
return constrings
