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# Copyright (C) 2009 by Eric Talevich (eric.talevich@gmail.com)
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
"""Base classes for Bio.Phylo objects.
All object representations for phylogenetic trees should derive from these base
classes in order to use the common methods defined on them.
"""
from Bio._py3k import basestring, filter, unicode, zip
import collections
import copy
import itertools
import random
import re
from Bio import _utils
# NB: On Python 2, repr() and str() are specified to return byte strings, not
# unicode. On Python 3, it's the opposite. Horrible.
import sys
if sys.version_info[0] < 3:
def as_string(s):
if isinstance(s, unicode):
return s.encode('utf-8')
return str(s)
else:
as_string = str
# General tree-traversal algorithms
def _level_traverse(root, get_children):
"""Traverse a tree in breadth-first (level) order."""
Q = collections.deque([root])
while Q:
v = Q.popleft()
yield v
Q.extend(get_children(v))
def _preorder_traverse(root, get_children):
"""Traverse a tree in depth-first pre-order (parent before children)."""
def dfs(elem):
yield elem
for v in get_children(elem):
for u in dfs(v):
yield u
for elem in dfs(root):
yield elem
def _postorder_traverse(root, get_children):
"""Traverse a tree in depth-first post-order (children before parent)."""
def dfs(elem):
for v in get_children(elem):
for u in dfs(v):
yield u
yield elem
for elem in dfs(root):
yield elem
def _sorted_attrs(elem):
"""Get a flat list of elem's attributes, sorted for consistency."""
singles = []
lists = []
# Sort attributes for consistent results
for attrname, child in sorted(elem.__dict__.items(),
key=lambda kv: kv[0]):
if child is None:
continue
if isinstance(child, list):
lists.extend(child)
else:
singles.append(child)
return (x for x in singles + lists
if isinstance(x, TreeElement))
# Factory functions to generalize searching for clades/nodes
def _identity_matcher(target):
"""Match a node to the target object by identity."""
def match(node):
return (node is target)
return match
def _class_matcher(target_cls):
"""Match a node if it's an instance of the given class."""
def match(node):
return isinstance(node, target_cls)
return match
def _string_matcher(target):
def match(node):
if isinstance(node, (Clade, Tree)):
# Avoid triggering specialized or recursive magic methods
return node.name == target
return as_string(node) == target
return match
def _attribute_matcher(kwargs):
"""Match a node by specified attribute values.
``terminal`` is a special case: True restricts the search to external (leaf)
nodes, False restricts to internal nodes, and None allows all tree elements
to be searched, including phyloXML annotations.
Otherwise, for a tree element to match the specification (i.e. for the
function produced by `_attribute_matcher` to return True when given a tree
element), it must have each of the attributes specified by the keys and
match each of the corresponding values -- think 'and', not 'or', for
multiple keys.
"""
def match(node):
if 'terminal' in kwargs:
# Special case: restrict to internal/external/any nodes
kwa_copy = kwargs.copy()
pattern = kwa_copy.pop('terminal')
if (pattern is not None and
(not hasattr(node, 'is_terminal') or
node.is_terminal() != pattern)):
return False
else:
kwa_copy = kwargs
for key, pattern in kwa_copy.items():
# Nodes must match all other specified attributes
if not hasattr(node, key):
return False
target = getattr(node, key)
if isinstance(pattern, basestring):
return (isinstance(target, basestring) and
re.match(pattern + '$', target))
if isinstance(pattern, bool):
return (pattern == bool(target))
if isinstance(pattern, int):
return (pattern == target)
if pattern is None:
return (target is None)
raise TypeError('invalid query type: %s' % type(pattern))
return True
return match
def _function_matcher(matcher_func):
"""Safer attribute lookup -- returns False instead of raising an error."""
def match(node):
try:
return matcher_func(node)
except (LookupError, AttributeError, ValueError, TypeError):
return False
return match
def _object_matcher(obj):
"""Retrieve a matcher function by passing an arbitrary object.
i.e. passing a `TreeElement` such as a `Clade` or `Tree` instance returns an
identity matcher, passing a type such as the `PhyloXML.Taxonomy` class
returns a class matcher, and passing a dictionary returns an attribute
matcher.
The resulting 'match' function returns True when given an object matching
the specification (identity, type or attribute values), otherwise False.
This is useful for writing functions that search the tree, and probably
shouldn't be used directly by the end user.
"""
if isinstance(obj, TreeElement):
return _identity_matcher(obj)
if isinstance(obj, type):
return _class_matcher(obj)
if isinstance(obj, basestring):
return _string_matcher(obj)
if isinstance(obj, dict):
return _attribute_matcher(obj)
if callable(obj):
return _function_matcher(obj)
raise ValueError("%s (type %s) is not a valid type for comparison."
% (obj, type(obj)))
def _combine_matchers(target, kwargs, require_spec):
"""Merge target specifications with keyword arguments.
Dispatch the components to the various matcher functions, then merge into a
single boolean function.
"""
if not target:
if not kwargs:
if require_spec:
raise ValueError("you must specify a target object or keyword "
"arguments.")
return lambda x: True
return _attribute_matcher(kwargs)
match_obj = _object_matcher(target)
if not kwargs:
return match_obj
match_kwargs = _attribute_matcher(kwargs)
return (lambda x: match_obj(x) and match_kwargs(x))
def _combine_args(first, *rest):
"""Convert ``[targets]`` or ``*targets`` arguments to a single iterable.
This helps other functions work like the built-in functions `max` and
`min`.
"""
# Background: is_monophyletic takes a single list or iterable (like the
# same method in Bio.Nexus.Trees); root_with_outgroup and common_ancestor
# take separate arguments. This mismatch was in the initial release and I
# didn't notice the inconsistency until after Biopython 1.55. I can think
# of cases where either style is more convenient, so let's support both
# (for backward compatibility and consistency between methods).
if hasattr(first, '__iter__') and not (isinstance(first, TreeElement) or
isinstance(first, type) or
isinstance(first, basestring) or
isinstance(first, dict)):
# `terminals` is an iterable of targets
if rest:
raise ValueError("Arguments must be either a single list of "
"targets, or separately specified targets "
"(e.g. foo(t1, t2, t3)), but not both.")
return first
# `terminals` is a single target -- wrap in a container
return itertools.chain([first], rest)
# Class definitions
class TreeElement(object):
"""Base class for all Bio.Phylo classes."""
def __repr__(self):
"""Show this object's constructor with its primitive arguments."""
def pair_as_kwarg_string(key, val):
if isinstance(val, basestring):
return ("%s='%s'"
% (key, _utils.trim_str(as_string(val), 60, '...')))
return "%s=%s" % (key, val)
return ('%s(%s)'
% (self.__class__.__name__,
', '.join(pair_as_kwarg_string(key, val)
for key, val in sorted(self.__dict__.items())
if val is not None and
type(val) in (str, int, float, bool, unicode))))
__str__ = __repr__
class TreeMixin(object):
"""Methods for Tree- and Clade-based classes.
This lets `Tree` and `Clade` support the same traversal and searching
operations without requiring Clade to inherit from Tree, so Clade isn't
required to have all of Tree's attributes -- just ``root`` (a Clade
instance) and ``is_terminal``.
"""
# Traversal methods
def _filter_search(self, filter_func, order, follow_attrs):
"""Perform a BFS or DFS traversal through all elements in this tree.
:returns: generator of all elements for which `filter_func` is True.
"""
order_opts = {'preorder': _preorder_traverse,
'postorder': _postorder_traverse,
'level': _level_traverse}
try:
order_func = order_opts[order]
except KeyError:
raise ValueError("Invalid order '%s'; must be one of: %s"
% (order, tuple(order_opts)))
if follow_attrs:
get_children = _sorted_attrs
root = self
else:
get_children = lambda elem: elem.clades
root = self.root
return filter(filter_func, order_func(root, get_children))
def find_any(self, *args, **kwargs):
"""Return the first element found by find_elements(), or None.
This is also useful for checking whether any matching element exists in
the tree, and can be used in a conditional expression.
"""
hits = self.find_elements(*args, **kwargs)
try:
return next(hits)
except StopIteration:
return None
def find_elements(self, target=None, terminal=None, order='preorder',
**kwargs):
"""Find all tree elements matching the given attributes.
The arbitrary keyword arguments indicate the attribute name of the
sub-element and the value to match: string, integer or boolean. Strings
are evaluated as regular expression matches; integers are compared
directly for equality, and booleans evaluate the attribute's truth value
(True or False) before comparing. To handle nonzero floats, search with
a boolean argument, then filter the result manually.
If no keyword arguments are given, then just the class type is used for
matching.
The result is an iterable through all matching objects, by depth-first
search. (Not necessarily the same order as the elements appear in the
source file!)
:Parameters:
target : TreeElement instance, type, dict, or callable
Specifies the characteristics to search for. (The default,
TreeElement, matches any standard Bio.Phylo type.)
terminal : bool
A boolean value to select for or against terminal nodes (a.k.a.
leaf nodes). True searches for only terminal nodes, False
excludes terminal nodes, and the default, None, searches both
terminal and non-terminal nodes, as well as any tree elements
lacking the ``is_terminal`` method.
order : {'preorder', 'postorder', 'level'}
Tree traversal order: 'preorder' (default) is depth-first
search, 'postorder' is DFS with child nodes preceding parents,
and 'level' is breadth-first search.
Example
-------
>>> from Bio import Phylo
>>> phx = Phylo.PhyloXMLIO.read('PhyloXML/phyloxml_examples.xml')
>>> matches = phx.phylogenies[5].find_elements(code='OCTVU')
>>> next(matches)
Taxonomy(code='OCTVU', scientific_name='Octopus vulgaris')
"""
if terminal is not None:
kwargs['terminal'] = terminal
is_matching_elem = _combine_matchers(target, kwargs, False)
return self._filter_search(is_matching_elem, order, True)
def find_clades(self, target=None, terminal=None, order='preorder',
**kwargs):
"""Find each clade containing a matching element.
That is, find each element as with find_elements(), but return the
corresponding clade object. (This is usually what you want.)
:returns: an iterable through all matching objects, searching
depth-first (preorder) by default.
"""
def match_attrs(elem):
orig_clades = elem.__dict__.pop('clades')
found = elem.find_any(target, **kwargs)
elem.clades = orig_clades
return (found is not None)
if terminal is None:
is_matching_elem = match_attrs
else:
def is_matching_elem(elem):
return ((elem.is_terminal() == terminal) and
match_attrs(elem))
return self._filter_search(is_matching_elem, order, False)
def get_path(self, target=None, **kwargs):
"""List the clades directly between this root and the given target.
:returns: list of all clade objects along this path, ending with the
given target, but excluding the root clade.
"""
# Only one path will work -- ignore weights and visits
path = []
match = _combine_matchers(target, kwargs, True)
def check_in_path(v):
if match(v):
path.append(v)
return True
elif v.is_terminal():
return False
for child in v:
if check_in_path(child):
path.append(v)
return True
return False
if not check_in_path(self.root):
return None
return path[-2::-1]
def get_nonterminals(self, order='preorder'):
"""Get a list of all of this tree's nonterminal (internal) nodes."""
return list(self.find_clades(terminal=False, order=order))
def get_terminals(self, order='preorder'):
"""Get a list of all of this tree's terminal (leaf) nodes."""
return list(self.find_clades(terminal=True, order=order))
def trace(self, start, finish):
"""List of all clade object between two targets in this tree.
Excluding `start`, including `finish`.
"""
mrca = self.common_ancestor(start, finish)
fromstart = mrca.get_path(start)[-2::-1]
to = mrca.get_path(finish)
return fromstart + [mrca] + to
# Information methods
def common_ancestor(self, targets, *more_targets):
"""Most recent common ancestor (clade) of all the given targets.
Edge cases:
- If no target is given, returns self.root
- If 1 target is given, returns the target
- If any target is not found in this tree, raises a ValueError
"""
paths = [self.get_path(t)
for t in _combine_args(targets, *more_targets)]
# Validation -- otherwise izip throws a spooky error below
for p, t in zip(paths, targets):
if p is None:
raise ValueError("target %s is not in this tree" % repr(t))
mrca = self.root
for level in zip(*paths):
ref = level[0]
for other in level[1:]:
if ref is not other:
break
else:
mrca = ref
if ref is not mrca:
break
return mrca
def count_terminals(self):
"""Counts the number of terminal (leaf) nodes within this tree."""
return _utils.iterlen(self.find_clades(terminal=True))
def depths(self, unit_branch_lengths=False):
"""Create a mapping of tree clades to depths (by branch length).
:Parameters:
unit_branch_lengths : bool
If True, count only the number of branches (levels in the tree).
By default the distance is the cumulative branch length leading
to the clade.
:returns: dict of {clade: depth}, where keys are all of the Clade
instances in the tree, and values are the distance from the root to
each clade (including terminals).
"""
if unit_branch_lengths:
depth_of = lambda c: 1
else:
depth_of = lambda c: c.branch_length or 0
depths = {}
def update_depths(node, curr_depth):
depths[node] = curr_depth
for child in node.clades:
new_depth = curr_depth + depth_of(child)
update_depths(child, new_depth)
update_depths(self.root, self.root.branch_length or 0)
return depths
def distance(self, target1, target2=None):
"""Calculate the sum of the branch lengths between two targets.
If only one target is specified, the other is the root of this tree.
"""
if target2 is None:
return sum(n.branch_length for n in self.get_path(target1)
if n.branch_length is not None)
mrca = self.common_ancestor(target1, target2)
return mrca.distance(target1) + mrca.distance(target2)
def is_bifurcating(self):
"""Return True if tree downstream of node is strictly bifurcating.
I.e., all nodes have either 2 or 0 children (internal or external,
respectively). The root may have 3 descendents and still be considered
part of a bifurcating tree, because it has no ancestor.
"""
# Root can be trifurcating
if isinstance(self, Tree) and len(self.root) == 3:
return (self.root.clades[0].is_bifurcating() and
self.root.clades[1].is_bifurcating() and
self.root.clades[2].is_bifurcating())
if len(self.root) == 2:
return (self.root.clades[0].is_bifurcating() and
self.root.clades[1].is_bifurcating())
if len(self.root) == 0:
return True
return False
def is_monophyletic(self, terminals, *more_terminals):
"""MRCA of terminals if they comprise a complete subclade, or False.
I.e., there exists a clade such that its terminals are the same set as
the given targets.
The given targets must be terminals of the tree.
To match both `Bio.Nexus.Trees` and the other multi-target methods in
Bio.Phylo, arguments to this method can be specified either of two ways:
(i) as a single list of targets, or (ii) separately specified targets,
e.g. is_monophyletic(t1, t2, t3) -- but not both.
For convenience, this method returns the common ancestor (MCRA) of the
targets if they are monophyletic (instead of the value True), and False
otherwise.
:returns: common ancestor if terminals are monophyletic, otherwise False.
"""
target_set = set(_combine_args(terminals, *more_terminals))
current = self.root
while True:
if set(current.get_terminals()) == target_set:
return current
# Try a narrower subclade
for subclade in current.clades:
if set(subclade.get_terminals()).issuperset(target_set):
current = subclade
break
else:
return False
def is_parent_of(self, target=None, **kwargs):
"""True if target is a descendent of this tree.
Not required to be a direct descendent.
To check only direct descendents of a clade, simply use list membership
testing: ``if subclade in clade: ...``
"""
return self.get_path(target, **kwargs) is not None
def is_preterminal(self):
"""True if all direct descendents are terminal."""
if self.root.is_terminal():
return False
for clade in self.root.clades:
if not clade.is_terminal():
return False
return True
def total_branch_length(self):
"""Calculate the sum of all the branch lengths in this tree."""
return sum(node.branch_length
for node in self.find_clades(branch_length=True))
# Tree manipulation methods
def collapse(self, target=None, **kwargs):
"""Deletes target from the tree, relinking its children to its parent.
:returns: the parent clade.
"""
path = self.get_path(target, **kwargs)
if not path:
raise ValueError("couldn't collapse %s in this tree"
% (target or kwargs))
if len(path) == 1:
parent = self.root
else:
parent = path[-2]
popped = parent.clades.pop(parent.clades.index(path[-1]))
extra_length = popped.branch_length or 0
for child in popped:
child.branch_length += extra_length
parent.clades.extend(popped.clades)
return parent
def collapse_all(self, target=None, **kwargs):
"""Collapse all the descendents of this tree, leaving only terminals.
Total branch lengths are preserved, i.e. the distance to each terminal
stays the same.
For example, this will safely collapse nodes with poor bootstrap
support:
>>> from Bio import Phylo
>>> tree = Phylo.read('PhyloXML/apaf.xml', 'phyloxml')
>>> print("Total branch length %0.2f" % tree.total_branch_length())
Total branch length 20.44
>>> tree.collapse_all(lambda c: c.confidence is not None and c.confidence < 70)
>>> print("Total branch length %0.2f" % tree.total_branch_length())
Total branch length 21.37
This implementation avoids strange side-effects by using level-order
traversal and testing all clade properties (versus the target
specification) up front. In particular, if a clade meets the target
specification in the original tree, it will be collapsed. For example,
if the condition is:
>>> from Bio import Phylo
>>> tree = Phylo.read('PhyloXML/apaf.xml', 'phyloxml')
>>> print("Total branch length %0.2f" % tree.total_branch_length())
Total branch length 20.44
>>> tree.collapse_all(lambda c: c.branch_length < 0.1)
>>> print("Total branch length %0.2f" % tree.total_branch_length())
Total branch length 21.13
Collapsing a clade's parent node adds the parent's branch length to the
child, so during the execution of collapse_all, a clade's branch_length
may increase. In this implementation, clades are collapsed according to
their properties in the original tree, not the properties when tree
traversal reaches the clade. (It's easier to debug.) If you want the
other behavior (incremental testing), modifying the source code of this
function is straightforward.
"""
# Read the iterable into a list to protect against in-place changes
matches = list(self.find_clades(target, False, 'level', **kwargs))
if not matches:
# No matching nodes to collapse
return
# Skip the root node -- it can't be collapsed
if matches[0] == self.root:
matches.pop(0)
for clade in matches:
self.collapse(clade)
def ladderize(self, reverse=False):
"""Sort clades in-place according to the number of terminal nodes.
Deepest clades are last by default. Use ``reverse=True`` to sort clades
deepest-to-shallowest.
"""
self.root.clades.sort(key=lambda c: c.count_terminals(),
reverse=reverse)
for subclade in self.root.clades:
subclade.ladderize(reverse=reverse)
def prune(self, target=None, **kwargs):
"""Prunes a terminal clade from the tree.
If taxon is from a bifurcation, the connecting node will be collapsed
and its branch length added to remaining terminal node. This might be no
longer be a meaningful value.
:returns: parent clade of the pruned target
"""
if 'terminal' in kwargs and kwargs['terminal']:
raise ValueError("target must be terminal")
path = self.get_path(target, terminal=True, **kwargs)
if not path:
raise ValueError("can't find a matching target below this root")
if len(path) == 1:
parent = self.root
else:
parent = path[-2]
parent.clades.remove(path[-1])
if len(parent) == 1:
# We deleted a branch from a bifurcation
if parent == self.root:
# If we're at the root, move the root upwards
# NB: This loses the length of the original branch
newroot = parent.clades[0]
newroot.branch_length = None
parent = self.root = newroot
else:
# If we're not at the root, collapse this parent
child = parent.clades[0]
if child.branch_length is not None:
child.branch_length += (parent.branch_length or 0.0)
if len(path) < 3:
grandparent = self.root
else:
grandparent = path[-3]
# Replace parent with child at the same place in grandparent
index = grandparent.clades.index(parent)
grandparent.clades.pop(index)
grandparent.clades.insert(index, child)
parent = grandparent
return parent
def split(self, n=2, branch_length=1.0):
"""Generate n (default 2) new descendants.
In a species tree, this is a speciation event.
New clades have the given branch_length and the same name as this
clade's root plus an integer suffix (counting from 0). For example,
splitting a clade named "A" produces sub-clades named "A0" and "A1".
If the clade has no name, the prefix "n" is used for child nodes, e.g.
"n0" and "n1".
"""
clade_cls = type(self.root)
base_name = self.root.name or 'n'
for i in range(n):
clade = clade_cls(name=base_name + str(i),
branch_length=branch_length)
self.root.clades.append(clade)
class Tree(TreeElement, TreeMixin):
"""A phylogenetic tree, containing global info for the phylogeny.
The structure and node-specific data is accessible through the 'root'
clade attached to the Tree instance.
:Parameters:
root : Clade
The starting node of the tree. If the tree is rooted, this will
usually be the root node.
rooted : bool
Whether or not the tree is rooted. By default, a tree is assumed to
be rooted.
id : str
The identifier of the tree, if there is one.
name : str
The name of the tree, in essence a label.
"""
def __init__(self, root=None, rooted=True, id=None, name=None):
self.root = root or Clade()
self.rooted = rooted
self.id = id
self.name = name
@classmethod
def from_clade(cls, clade, **kwargs):
"""Create a new Tree object given a clade.
Keyword arguments are the usual `Tree` constructor parameters.
"""
root = copy.deepcopy(clade)
return cls(root, **kwargs)
@classmethod
def randomized(cls, taxa, branch_length=1.0, branch_stdev=None):
"""Create a randomized bifurcating tree given a list of taxa.
:param taxa: Either an integer specifying the number of taxa to create
(automatically named taxon#), or an iterable of taxon names, as
strings.
:returns: a tree of the same type as this class.
"""
if isinstance(taxa, int):
taxa = ['taxon%s' % (i + 1) for i in range(taxa)]
elif hasattr(taxa, '__iter__'):
taxa = list(taxa)
else:
raise TypeError("taxa argument must be integer (# taxa) or "
"iterable of taxon names.")
rtree = cls()
terminals = [rtree.root]
while len(terminals) < len(taxa):
newsplit = random.choice(terminals)
newsplit.split(branch_length=branch_length)
newterms = newsplit.clades
if branch_stdev:
# Add some noise to the branch lengths
for nt in newterms:
nt.branch_length = max(0,
random.gauss(branch_length, branch_stdev))
terminals.remove(newsplit)
terminals.extend(newterms)
# Distribute taxon labels randomly
random.shuffle(taxa)
for node, name in zip(terminals, taxa):
node.name = name
return rtree
@property
def clade(self):
"""The first clade in this tree (not itself)."""
return self.root
def as_phyloxml(self, **kwargs):
"""Convert this tree to a PhyloXML-compatible Phylogeny.
This lets you use the additional annotation types PhyloXML defines, and
save this information when you write this tree as 'phyloxml'.
"""
from Bio.Phylo.PhyloXML import Phylogeny
return Phylogeny.from_tree(self, **kwargs)
# XXX Py3 Compatibility: In Python 3.0+, **kwargs can be replaced with the
# named keyword argument outgroup_branch_length=None
def root_with_outgroup(self, outgroup_targets, *more_targets, **kwargs):
"""Reroot this tree with the outgroup clade containing outgroup_targets.
Operates in-place.
Edge cases:
- If ``outgroup == self.root``, no change
- If outgroup is terminal, create new bifurcating root node with a
0-length branch to the outgroup
- If outgroup is internal, use the given outgroup node as the new
trifurcating root, keeping branches the same
- If the original root was bifurcating, drop it from the tree,
preserving total branch lengths
:param outgroup_branch_length: length of the branch leading to the
outgroup after rerooting. If not specified (None), then:
- If the outgroup is an internal node (not a single terminal taxon),
then use that node as the new root.
- Otherwise, create a new root node as the parent of the outgroup.
"""
# This raises a ValueError if any target is not in this tree
# Otherwise, common_ancestor guarantees outgroup is in this tree
outgroup = self.common_ancestor(outgroup_targets, *more_targets)
outgroup_path = self.get_path(outgroup)
if len(outgroup_path) == 0:
# Outgroup is the current root -- no change
return
prev_blen = outgroup.branch_length or 0.0
# Hideous kludge because Py2.x doesn't allow keyword args after *args
outgroup_branch_length = kwargs.get('outgroup_branch_length')
if outgroup_branch_length is not None:
assert 0 <= outgroup_branch_length <= prev_blen, \
"outgroup_branch_length must be between 0 and the " \
"original length of the branch leading to the outgroup."
if outgroup.is_terminal() or outgroup_branch_length is not None:
# Create a new root with a 0-length branch to the outgroup
outgroup.branch_length = outgroup_branch_length or 0.0
new_root = self.root.__class__(
branch_length=self.root.branch_length, clades=[outgroup])
# The first branch reversal (see the upcoming loop) is modified
if len(outgroup_path) == 1:
# No nodes between the original root and outgroup to rearrange.
# Most of the code below will be skipped, but we still need
# 'new_parent' pointing at the new root.
new_parent = new_root
else:
parent = outgroup_path.pop(-2)
# First iteration of reversing the path to the outgroup
parent.clades.pop(parent.clades.index(outgroup))
(prev_blen, parent.branch_length) = (parent.branch_length,
prev_blen - outgroup.branch_length)
new_root.clades.insert(0, parent)
new_parent = parent
else:
# Use the given outgroup node as the new (trifurcating) root
new_root = outgroup
new_root.branch_length = self.root.branch_length
new_parent = new_root
# Tracing the outgroup lineage backwards, reattach the subclades under a
# new root clade. Reverse the branches directly above the outgroup in
# the tree, but keep the descendants of those clades as they are.
for parent in outgroup_path[-2::-1]:
parent.clades.pop(parent.clades.index(new_parent))
prev_blen, parent.branch_length = parent.branch_length, prev_blen
new_parent.clades.insert(0, parent)
new_parent = parent
# Finally, handle the original root according to number of descendents
old_root = self.root
if outgroup in old_root.clades:
assert len(outgroup_path) == 1
old_root.clades.pop(old_root.clades.index(outgroup))
else:
old_root.clades.pop(old_root.clades.index(new_parent))
if len(old_root) == 1:
# Delete the old bifurcating root & add branch lengths
ingroup = old_root.clades[0]
if ingroup.branch_length:
ingroup.branch_length += prev_blen
else:
ingroup.branch_length = prev_blen
new_parent.clades.insert(0, ingroup)
# ENH: If annotations are attached to old_root, do... something.
else:
# Keep the old trifurcating/polytomous root as an internal node
old_root.branch_length = prev_blen
new_parent.clades.insert(0, old_root)
self.root = new_root
self.rooted = True
return
def root_at_midpoint(self):
"""Root the tree at the midpoint of the two most distant taxa.
This operates in-place, leaving a bifurcating root. The topology of the
tree is otherwise retained, though no guarantees are made about the
stability of clade/node/taxon ordering.
"""
# Identify the largest pairwise distance
max_distance = 0.0
tips = self.get_terminals()
for tip in tips:
self.root_with_outgroup(tip)
new_max = max(self.depths().items(), key=lambda nd: nd[1])
if new_max[1] > max_distance:
tip1 = tip
tip2 = new_max[0]
max_distance = new_max[1]
self.root_with_outgroup(tip1)
# Depth to go from the ingroup tip toward the outgroup tip
root_remainder = 0.5 * (max_distance - (self.root.branch_length or 0))
assert root_remainder >= 0
# Identify the midpoint and reroot there.
# Trace the path to the outgroup tip until all of the root depth has
# been traveled/accounted for.
for node in self.get_path(tip2):
root_remainder -= node.branch_length
if root_remainder < 0:
outgroup_node = node
outgroup_branch_length = -root_remainder
break
else:
raise ValueError("Somehow, failed to find the midpoint!")
self.root_with_outgroup(outgroup_node,
outgroup_branch_length=outgroup_branch_length)
# Method assumed by TreeMixin
def is_terminal(self):
"""True if the root of this tree is terminal."""
return (not self.root.clades)
# Convention from SeqRecord and Alignment classes
def __format__(self, format_spec):
"""Serialize the tree as a string in the specified file format.
This method supports the ``format`` built-in function added in Python
2.6/3.0.
:param format_spec: a lower-case string supported by `Bio.Phylo.write`
as an output file format.
"""
if format_spec:
from Bio._py3k import StringIO
from Bio.Phylo import _io
handle = StringIO()
_io.write([self], handle, format_spec)
return handle.getvalue()
else:
# Follow python convention and default to using __str__
return str(self)
def format(self, format):
"""Serialize the tree as a string in the specified file format.
This duplicates the __format__ magic method for pre-2.6 Pythons.
"""
return self.__format__(format)
# Pretty-printer for the entire tree hierarchy
def __str__(self):
"""String representation of the entire tree.
Serializes each sub-clade recursively using ``repr`` to create a summary
of the object structure.
"""
TAB = ' '
textlines = []
def print_tree(obj, indent):
"""Recursively serialize sub-elements.
This closes over textlines and modifies it in-place.
"""
if isinstance(obj, (Tree, Clade)):
# Avoid infinite recursion or special formatting from str()
objstr = repr(obj)
else:
objstr = as_string(obj)
textlines.append(TAB * indent + objstr)
indent += 1
for attr in obj.__dict__:
child = getattr(obj, attr)
if isinstance(child, TreeElement):
print_tree(child, indent)
elif isinstance(child, list):
for elem in child:
if isinstance(elem, TreeElement):
print_tree(elem, indent)
print_tree(self, 0)
return '\n'.join(textlines)
class Clade(TreeElement, TreeMixin):
"""A recursively defined sub-tree.
:Parameters:
branch_length : str
The length of the branch leading to the root node of this clade.
name : str
The clade's name (a label).
clades : list
Sub-trees rooted directly under this tree's root.
confidence : number
Support.
color : BranchColor
The display color of the branch and descendents.
width : number
The display width of the branch and descendents.
"""
def __init__(self, branch_length=None, name=None, clades=None,
confidence=None, color=None, width=None):
self.branch_length = branch_length
self.name = name
self.clades = clades or []
self.confidence = confidence
self.color = color
self.width = width
@property
def root(self):
"""Allow TreeMixin methods to traverse clades properly."""
return self
def is_terminal(self):
"""True if this is a terminal (leaf) node."""
return (not self.clades)
# Sequence-type behavior methods
def __getitem__(self, index):
"""Get clades by index (integer or slice)."""
if isinstance(index, (int, slice)):
return self.clades[index]
ref = self
for idx in index:
ref = ref[idx]
return ref
def __iter__(self):
"""Iterate through this tree's direct descendent clades (sub-trees)."""
return iter(self.clades)
def __len__(self):
"""Number of clades directy under the root."""
return len(self.clades)
# Python 3:
def __bool__(self):
"""Boolean value of an instance of this class (True).
NB: If this method is not defined, but ``__len__`` is, then the object
is considered true if the result of ``__len__()`` is nonzero. We want
Clade instances to always be considered True.
"""
return True
# Python 2:
__nonzero__ = __bool__
def __str__(self):
if self.name:
return _utils.trim_str(self.name, 40, '...')
return self.__class__.__name__
# Syntax sugar for setting the branch color
def _get_color(self):
return self._color
def _set_color(self, arg):
if arg is None or isinstance(arg, BranchColor):
self._color = arg
elif isinstance(arg, basestring):
if arg in BranchColor.color_names:
# Known color name
self._color = BranchColor.from_name(arg)
elif arg.startswith('#') and len(arg) == 7:
# HTML-style hex string
self._color = BranchColor.from_hex(arg)
else:
raise ValueError("invalid color string %s" % arg)
elif hasattr(arg, '__iter__') and len(arg) == 3:
# RGB triplet
self._color = BranchColor(*arg)
else:
raise ValueError("invalid color value %s" % arg)
color = property(_get_color, _set_color, doc="Branch color.")
class BranchColor(object):
"""Indicates the color of a clade when rendered graphically.
The color should be interpreted by client code (e.g. visualization
programs) as applying to the whole clade, unless overwritten by the
color(s) of sub-clades.
Color values must be integers from 0 to 255.
"""
color_names = {
'red': (255, 0, 0),
'r': (255, 0, 0),
'yellow': (255, 255, 0),
'y': (255, 255, 0),
'green': (0, 128, 0),
'g': (0, 128, 0),
'cyan': (0, 255, 255),
'c': (0, 255, 255),
'blue': (0, 0, 255),
'b': (0, 0, 255),
'magenta': (255, 0, 255),
'm': (255, 0, 255),
'black': (0, 0, 0),
'k': (0, 0, 0),
'white': (255, 255, 255),
'w': (255, 255, 255),
# Names standardized in HTML/CSS spec
# http://w3schools.com/html/html_colornames.asp
'maroon': (128, 0, 0),
'olive': (128, 128, 0),
'lime': (0, 255, 0),
'aqua': (0, 255, 255),
'teal': (0, 128, 128),
'navy': (0, 0, 128),
'fuchsia': (255, 0, 255),
'purple': (128, 0, 128),
'silver': (192, 192, 192),
'gray': (128, 128, 128),
# More definitions from matplotlib/gcolor2
'grey': (128, 128, 128),
'pink': (255, 192, 203),
'salmon': (250, 128, 114),
'orange': (255, 165, 0),
'gold': (255, 215, 0),
'tan': (210, 180, 140),
'brown': (165, 42, 42),
}
def __init__(self, red, green, blue):
for color in (red, green, blue):
assert (isinstance(color, int) and
0 <= color <= 255
), "Color values must be integers between 0 and 255."
self.red = red
self.green = green
self.blue = blue
@classmethod
def from_hex(cls, hexstr):
"""Construct a BranchColor object from a hexadecimal string.
The string format is the same style used in HTML and CSS, such as
'#FF8000' for an RGB value of (255, 128, 0).
"""
assert (isinstance(hexstr, basestring) and
hexstr.startswith('#') and
len(hexstr) == 7
), "need a 24-bit hexadecimal string, e.g. #000000"
RGB = hexstr[1:3], hexstr[3:5], hexstr[5:]
return cls(*[int('0x' + cc, base=16) for cc in RGB])
@classmethod
def from_name(cls, colorname):
"""Construct a BranchColor object by the color's name."""
return cls(*cls.color_names[colorname])
def to_hex(self):
"""Return a 24-bit hexadecimal RGB representation of this color.
The returned string is suitable for use in HTML/CSS, as a color
parameter in matplotlib, and perhaps other situations.
Example:
>>> bc = BranchColor(12, 200, 100)
>>> bc.to_hex()
'#0cc864'
"""
return "#%02x%02x%02x" % (self.red, self.green, self.blue)
def to_rgb(self):
"""Return a tuple of RGB values (0 to 255) representing this color.
Example:
>>> bc = BranchColor(255, 165, 0)
>>> bc.to_rgb()
(255, 165, 0)
"""
return (self.red, self.green, self.blue)
def __repr__(self):
"""Preserve the standard RGB order when representing this object."""
return ('%s(red=%d, green=%d, blue=%d)'
% (self.__class__.__name__, self.red, self.green, self.blue))
def __str__(self):
"""Show the color's RGB values."""
return "(%d, %d, %d)" % (self.red, self.green, self.blue)