def consensus(trees, cutoff=0.5, callback=None):
"""
Generate a consensus tree by counting splits and using the splits with
frequencies above the cutoff to resolve a star tree.
:param trees: iterable containing Phylo.BaseTree objects
:param cutoff: float, bootstrap threshold (default 0.5)
:param callback: function, optional callback
:return: Phylo.BaseTree
"""
ntrees = 1
tree = next(trees)
# store terminal labels and branch lengths
tip_index = {}
for i, tip in enumerate(tree.get_terminals()):
tip_index.update({tip.name: i})
ntips = len(tip_index)
if callback:
callback("Recording splits and branch lengths", level='DEBUG')
splits = {}
terminals = dict([(tn, 0) for tn in tip_index.keys()])
while True:
# record terminal branch lengths
for tip in tree.get_terminals():
terminals[tip.name] += tip.branch_length
# record splits in tree
tree = label_nodes(tree, tip_index) # aggregates tip indices down tree
for node in tree.get_nonterminals():
key = ','.join(map(str, node.tip_index))
if key not in splits:
splits.update({key: {'sum': 0., 'count': 0}})
if node.branch_length is not None:
# None interpreted as zero length (e.g., root branch)
splits[key]['sum'] += node.branch_length
splits[key]['count'] += 1
try:
tree = next(trees)
if callback:
callback(".. {} completed ".format(ntrees), level="DEBUG")
ntrees += 1
except StopIteration:
if callback:
callback("... done", level='DEBUG')
break
# filter splits by frequency (support) threshold
intermed = [(k.count(',') + 1, k, v) for k, v in splits.items()
if v['count'] / ntrees >= cutoff]
intermed.sort() # sort by level (tips to root)
del splits # free some memory
# construct consensus tree
if callback:
callback("Building consensus tree", level='DEBUG')
orphans = dict([(tip_index[tname],
Clade(name=tname, branch_length=totlen / ntrees))
for tname, totlen in terminals.items()])
for _, key, val in intermed:
# average branch lengths across relevant trees
bl = val['sum'] / val['count']
support = val['count'] / ntrees
node = Clade(branch_length=bl, confidence=support)
for child in map(int, key.split(',')):
branch = orphans.pop(child, None)
if branch:
node.clades.append(branch)
# use a single tip name to label ancestral node
newkey = tip_index[node.get_terminals()[0].name]
orphans.update({newkey: node})
return orphans.popitem()[1]
def consensus(trees, cutoff=0.5, callback=None):
"""
Generate a consensus tree by counting splits and using the splits with
frequencies above the cutoff to resolve a star tree.
:param trees: iterable containing Phylo.BaseTree objects
:param cutoff: float, bootstrap threshold (default 0.5)
:return: Phylo.BaseTree
"""
if type(trees) is not list:
# resolve generator object
trees = list(trees)
count = len(trees)
# store terminal labels and branch lengths
tip_index = {}
for i, tip in enumerate(trees[0].get_terminals()):
tip_index.update({tip.name: i})
if callback:
callback("Recording splits and branch lengths")
splits = {}
terminals = dict([(tn, []) for tn in tip_index.keys()])
for phy in trees:
# record terminal branch lengths
for tip in phy.get_terminals():
terminals[tip.name].append(tip.branch_length)
# record splits in tree
phy = label_nodes(phy, tip_index)
for node in phy.get_nonterminals():
key = tuple(node.tip_index)
if key not in splits:
splits.update({key: []})
splits[key].append(node.branch_length)
# filter splits by frequency threshold
intermed = [(len(k), k, v) for k, v in splits.items()
if len(v) / count >= cutoff]
intermed.sort()
# construct consensus tree
if callback:
callback("Building consensus tree")
orphans = dict([(tip_index[tname],
Clade(name=tname, branch_length=sum(tdata) / len(tdata)))
for tname, tdata in terminals.items()])
for _, key, val in intermed:
# average branch lengths across relevant trees
if all([v is None for v in splits[key]]):
bl = None
else:
bl = sum(splits[key]) / len(splits[key])
support = len(val) / count
node = Clade(branch_length=bl, confidence=support)
for child in key:
branch = orphans.pop(child, None)
if branch:
node.clades.append(branch)
# use a single tip name to label ancestral node
newkey = tip_index[node.get_terminals()[0].name]
orphans.update({newkey: node})
return orphans.popitem()[1]
