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from six import text_type 

"""Classes for storing and manipulating a phylogenetic tree. 

 

These trees can be either strictly binary, or have polytomies 

(multiple children to a parent node). 

 

Trees consist of Nodes (or branches) that connect two nodes. The Tree can 

be created only from a newick formatted string read either from file or from a 

string object. Other formats will be added as time permits. 

 

Tree can: 

- Deal with either rooted or unrooted tree's and can 

convert between these types. 

- Return a sub-tree given a list of tip-names 

- Identify an edge given two tip names. This method facilitates the 

statistical modelling by simplyifying the syntax for specifying 

sub-regions of a tree. 

- Assess whether two Tree instances represent the same topology. 

 

Definition of relevant terms or abbreviations: 

- edge: also known as a branch on a tree. 

- node: the point at which two edges meet 

- tip: a sequence or species 

- clade: all and only the nodes (including tips) that descend 

from a node 

- stem: the edge immediately preceeding a clade 

""" 

import sys 

from numpy import zeros, argsort 

from copy import deepcopy 

import re 

import codecs 

from .newick import parse_string as newick_parse_string 

#from cogent.util.transform import comb 

#from cogent.maths.stats.test import correlation 

from operator import or_ 

#from cogent.util.misc import InverseDict 

from random import shuffle 

 

from six import text_type 

 

__author__ = "Gavin Huttley, Peter Maxwell and Rob Knight" 

__copyright__ = "Copyright 2007-2011, The Cogent Project" 

__credits__ = ["Gavin Huttley", "Peter Maxwell", "Rob Knight", 

"Andrew Butterfield", "Catherine Lozupone", "Micah Hamady", 

"Jeremy Widmann", "Zongzhi Liu", "Daniel McDonald", 

"Justin Kuczynski"] 

__license__ = "GPL" 

__version__ = "1.5.1" 

__maintainer__ = "Gavin Huttley" 

__email__ = "gavin.huttley@anu.edu.au" 

__status__ = "Production" 

 

if sys.version_info[0] > 2: # pragma: no cover 

from functools import reduce 

 

def cmp(a, b): 

return (a < b) - (b < a) 

 

 

def comb(items, n=None): 

"""Yields each successive combination of n items. 

 

items: a slicable sequence. 

n: number of items in each combination 

This version from Raymond Hettinger, 2006/03/23 

""" 

if n is None: 

n = len(items) 

for i in range(len(items)): 

v = items[i:i+1] 

if n == 1: 

yield v 

else: 

rest = items[i+1:] 

for c in comb(rest, n-1): 

yield v + c 

#def distance_from_r_squared(m1, m2): 

# """Estimates distance as 1-r^2: no correl = max distance""" 

# return 1 - (correlation(m1.flat, m2.flat)[0])**2 

# 

#def distance_from_r(m1, m2): 

# """Estimates distance as (1-r)/2: neg correl = max distance""" 

# return (1-correlation(m1.flat, m2.flat)[0])/2 

 

class TreeError(Exception): 

pass 

 

class TreeNode(object): 

"""Store information about a tree node. Mutable. 

 

Parameters: 

Name: label for the node, assumed to be unique. 

Children: list of the node's children. 

Params: dict containing arbitrary parameters for the node. 

NameLoaded: ? 

""" 

_exclude_from_copy = dict.fromkeys(['_parent','Children']) 

 

def __init__(self, Name=None, Children=None, Parent=None, Params=None, \ 

NameLoaded=True, **kwargs): 

"""Returns new TreeNode object.""" 

self.Name = Name 

self.NameLoaded = NameLoaded 

if Params is None: 

Params = {} 

self.params = Params 

self.Children = [] 

if Children is not None: 

self.extend(Children) 

self._parent = Parent 

if (Parent is not None) and not (self in Parent.Children): 

Parent.append(self) 

 

# added taxa here, for convenience when using tree-class in lingpy JML 

self.taxa = self.getTipNames() 

 

### built-in methods and list interface support 

def __repr__(self): 

"""Returns reconstructable string representation of tree. 

 

WARNING: Does not currently set the class to the right type. 

""" 

return 'Tree("%s")' % self.getNewick() 

 

def __str__(self): 

"""Returns Newick-format string representation of tree.""" 

return self.getNewick() 

 

def compareName(self, other): 

"""Compares TreeNode by name""" 

if self is other: 

return 0 

try: 

return cmp(self.Name, other.Name) 

except AttributeError: 

return cmp(text_type(type(self)), text_type(type(other))) 

 

def compareByNames(self, other): 

"""Equality test for trees by name""" 

# if they are the same object then they must be the same tree... 

if self is other: 

return True 

self_names = self.getNodeNames() 

other_names = other.getNodeNames() 

self_names.sort() 

other_names.sort() 

return self_names == other_names 

 

def _to_self_child(self, i): 

"""Converts i to self's type, with self as its parent. 

 

Cleans up refs from i's original parent, but doesn't give self ref to i. 

""" 

c = self.__class__ 

if isinstance(i, c): 

if i._parent not in (None, self): 

i._parent.Children.remove(i) 

else: 

i = c(i) 

i._parent = self 

return i 

 

def append(self, i): 

"""Appends i to self.Children, in-place, cleaning up refs.""" 

self.Children.append(self._to_self_child(i)) 

 

def extend(self, items): 

"""Extends self.Children by items, in-place, cleaning up refs.""" 

self.Children.extend(map(self._to_self_child, items)) 

 

def insert(self, index, i): 

"""Inserts an item at specified position in self.Children.""" 

self.Children.insert(index, self._to_self_child(i)) 

 

def pop(self, index=-1): 

"""Returns and deletes child of self at index (default: -1)""" 

result = self.Children.pop(index) 

result._parent = None 

return result 

 

def remove(self, target): 

"""Removes node by name instead of identity. 

 

Returns True if node was present, False otherwise. 

""" 

if isinstance(target, TreeNode): 

target = target.Name 

for (i, curr_node) in enumerate(self.Children): 

if curr_node.Name == target: 

self.removeNode(curr_node) 

return True 

return False 

 

def __getitem__(self, i): 

"""Node delegates slicing to Children; faster to access them 

directly.""" 

return self.Children[i] 

 

def __setitem__(self, i, val): 

"""Node[i] = x sets the corresponding item in Children.""" 

curr = self.Children[i] 

if isinstance(i, slice): 

for c in curr: 

c._parent = None 

coerced_val = map(self._to_self_child, val) 

self.Children[i] = coerced_val[:] 

else: #assume we got a single index 

curr._parent = None 

coerced_val = self._to_self_child(val) 

self.Children[i] = coerced_val 

 

def __delitem__(self, i): 

"""del node[i] deletes index or slice from self.Children.""" 

curr = self.Children[i] 

if isinstance(i, slice): 

for c in curr: 

c._parent = None 

else: 

curr._parent = None 

del self.Children[i] 

 

def __iter__(self): 

"""Node iter iterates over the Children.""" 

return iter(self.Children) 

 

def __len__(self): 

"""Node len returns number of children.""" 

return len(self.Children) 

 

#support for copy module 

def copyRecursive(self, memo=None, _nil=[], constructor='ignored'): 

"""Returns copy of self's structure, including shallow copy of attrs. 

 

constructor is ignored; required to support old tree unit tests. 

""" 

result = self.__class__() 

efc = self._exclude_from_copy 

for k, v in self.__dict__.items(): 

if k not in efc: #avoid infinite recursion 

result.__dict__[k] = deepcopy(self.__dict__[k]) 

for c in self: 

result.append(c.copy()) 

return result 

 

def copy(self, memo=None, _nil=[], constructor='ignored'): 

"""Returns a copy of self using an iterative approach""" 

def __copy_node(n): 

result = n.__class__() 

efc = n._exclude_from_copy 

for k,v in n.__dict__.items(): 

if k not in efc: 

result.__dict__[k] = deepcopy(n.__dict__[k]) 

return result 

 

root = __copy_node(self) 

nodes_stack = [[root, self, len(self.Children)]] 

 

while nodes_stack: 

#check the top node, any children left unvisited? 

top = nodes_stack[-1] 

new_top_node, old_top_node, unvisited_children = top 

 

if unvisited_children: 

top[2] -= 1 

old_child = old_top_node.Children[-unvisited_children] 

new_child = __copy_node(old_child) 

new_top_node.append(new_child) 

nodes_stack.append([new_child, old_child, \ 

len(old_child.Children)]) 

else: #no unvisited children 

nodes_stack.pop() 

return root 

 

__deepcopy__ = deepcopy = copy 

 

def copyTopology(self, constructor=None): 

"""Copies only the topology and labels of a tree, not any extra data. 

 

Useful when you want another copy of the tree with the same structure 

and labels, but want to e.g. assign different branch lengths and 

environments. Does not use deepcopy from the copy module, so _much_ 

faster than the copy() method. 

""" 

if constructor is None: 

constructor = self.__class__ 

children = [c.copyTopology(constructor) for c in self.Children] 

return constructor(Name=self.Name[:], Children=children) 

 

#support for basic tree operations -- finding objects and moving in the tree 

def _get_parent(self): 

"""Accessor for parent. 

 

If using an algorithm that accesses Parent a lot, it will be much 

faster to access self._parent directly, but don't do it if mutating 

self._parent! (or, if you must, remember to clean up the refs). 

""" 

return self._parent 

 

def _set_parent(self, Parent): 

"""Mutator for parent: cleans up refs in old parent.""" 

if self._parent is not None: 

self._parent.removeNode(self) 

self._parent = Parent 

if (Parent is not None) and (not self in Parent.Children): 

Parent.Children.append(self) 

 

Parent = property(_get_parent, _set_parent) 

 

def indexInParent(self): 

"""Returns index of self in parent.""" 

return self._parent.Children.index(self) 

 

def isTip(self): 

"""Returns True if the current node is a tip, i.e. has no children.""" 

return not self.Children 

 

def isRoot(self): 

"""Returns True if the current is a root, i.e. has no parent.""" 

return self._parent is None 

 

def traverse(self, self_before=True, self_after=False, include_self=True): 

"""Returns iterator over descendants. Iterative: safe for large trees. 

 

Notes 

----- 

self_before includes each node before its descendants if True. 

self_after includes each node after its descendants if True. 

include_self includes the initial node if True. 

 

self_before and self_after are independent. If neither is True, only 

terminal nodes will be returned. 

 

Note that if self is terminal, it will only be included once even if 

self_before and self_after are both True. 

 

This is a depth-first traversal. Since the trees are not binary, 

preorder and postorder traversals are possible, but inorder traversals 

would depend on the data in the tree and are not handled here. 

""" 

if self_before: 

if self_after: 

return self.pre_and_postorder(include_self=include_self) 

else: 

return self.preorder(include_self=include_self) 

else: 

if self_after: 

return self.postorder(include_self=include_self) 

else: 

return self.tips(include_self=include_self) 

 

def levelorder(self, include_self=True): 

"""Performs levelorder iteration over tree""" 

queue = [self] 

while queue: 

curr = queue.pop(0) 

if include_self or (curr is not self): 

yield curr 

if curr.Children: 

queue.extend(curr.Children) 

 

def preorder(self, include_self=True): 

"""Performs preorder iteration over tree.""" 

stack = [self] 

while stack: 

curr = stack.pop() 

if include_self or (curr is not self): 

yield curr 

if curr.Children: 

stack.extend(curr.Children[::-1]) #20% faster than reversed  

def postorder(self, include_self=True): 

"""Performs postorder iteration over tree. 

 

This is somewhat inelegant compared to saving the node and its index 

on the stack, but is 30% faster in the average case and 3x faster in 

the worst case (for a comb tree). 

 

Zongzhi Liu's slower but more compact version is:: 

 

def postorder_zongzhi(self): 

stack = [[self, 0]] 

while stack: 

curr, child_idx = stack[-1] 

if child_idx < len(curr.Children): 

stack[-1][1] += 1 

stack.append([curr.Children[child_idx], 0]) 

else: 

yield stack.pop()[0] 

""" 

child_index_stack = [0] 

curr = self 

curr_children = self.Children 

curr_children_len = len(curr_children) 

while 1: 

curr_index = child_index_stack[-1] 

#if there are children left, process them 

if curr_index < curr_children_len: 

curr_child = curr_children[curr_index] 

#if the current child has children, go there 

if curr_child.Children: 

child_index_stack.append(0) 

curr = curr_child 

curr_children = curr.Children 

curr_children_len = len(curr_children) 

curr_index = 0 

#otherwise, yield that child 

else: 

yield curr_child 

child_index_stack[-1] += 1 

#if there are no children left, return self, and move to 

#self's parent 

else: 

if include_self or (curr is not self): 

yield curr 

if curr is self: 

break 

# add this line to prevent error if parent is none @LinguList 

if curr.Parent is None: 

break 

curr = curr.Parent 

curr_children = curr.Children 

curr_children_len = len(curr_children) 

child_index_stack.pop() 

child_index_stack[-1] += 1 

 

def pre_and_postorder(self, include_self=True): 

"""Performs iteration over tree, visiting node before and after.""" 

#handle simple case first 

if not self.Children: 

if include_self: 

yield self 

raise StopIteration 

child_index_stack = [0] 

curr = self 

curr_children = self.Children 

while 1: 

curr_index = child_index_stack[-1] 

if not curr_index: 

if include_self or (curr is not self): 

yield curr 

#if there are children left, process them 

if curr_index < len(curr_children): 

curr_child = curr_children[curr_index] 

#if the current child has children, go there 

if curr_child.Children: 

child_index_stack.append(0) 

curr = curr_child 

curr_children = curr.Children 

curr_index = 0 

#otherwise, yield that child 

else: 

yield curr_child 

child_index_stack[-1] += 1 

#if there are no children left, return self, and move to 

#self's parent 

else: 

if include_self or (curr is not self): 

yield curr 

if curr is self: 

break 

curr = curr.Parent 

curr_children = curr.Children 

child_index_stack.pop() 

child_index_stack[-1] += 1 

 

def traverse_recursive(self, self_before=True, self_after=False, \ 

include_self=True): 

"""Returns iterator over descendants. IMPORTANT: read notes below. 

 

Notes 

----- 

traverse_recursive is slower than traverse, and can lead to stack 

errors. However, you _must_ use traverse_recursive if you plan to 

modify the tree topology as you walk over it (e.g. in post-order), 

because the iterative methods use their own stack that is not updated 

if you alter the tree. 

 

self_before includes each node before its descendants if True. 

self_after includes each node after its descendants if True. 

include_self includes the initial node if True. 

 

self_before and self_after are independent. If neither is True, only 

terminal nodes will be returned. 

 

Note that if self is terminal, it will only be included once even if 

self_before and self_after are both True. 

 

This is a depth-first traversal. Since the trees are not binary, 

preorder and postorder traversals are possible, but inorder traversals 

would depend on the data in the tree and are not handled here. 

""" 

if self.Children: 

if self_before and include_self: 

yield self 

for child in self.Children: 

for i in child.traverse_recursive(self_before, self_after): 

yield i 

if self_after and include_self: 

yield self 

elif include_self: 

yield self 

 

def ancestors(self): 

"""Returns all ancestors back to the root. Dynamically calculated.""" 

result = [] 

curr = self._parent 

while curr is not None: 

result.append(curr) 

curr = curr._parent 

return result 

 

def root(self): 

"""Returns root of the tree self is in. Dynamically calculated.""" 

curr = self 

while curr._parent is not None: 

curr = curr._parent 

return curr 

 

def isroot(self): 

"""Returns True if root of a tree, i.e. no parent.""" 

return self._parent is None 

 

def siblings(self): 

"""Returns all nodes that are children of the same parent as self. 

 

Notes 

----- 

Excludes self from the list. Dynamically calculated. 

 

""" 

if self._parent is None: 

return [] 

result = self._parent.Children[:] 

result.remove(self) 

return result 

 

def iterTips(self, include_self=False): 

"""Iterates over tips descended from self, [] if self is a tip.""" 

#bail out in easy case 

if not self.Children: 

if include_self: 

yield self 

raise StopIteration 

#use stack-based method: robust to large trees 

stack = [self] 

while stack: 

curr = stack.pop() 

if curr.Children: 

stack.extend(curr.Children[::-1]) #20% faster than reversed 

else: 

yield curr 

 

def tips(self, include_self=False): 

"""Returns tips descended from self, [] if self is a tip.""" 

return list(self.iterTips(include_self=include_self)) 

 

def iterNontips(self, include_self=False): 

"""Iterates over nontips descended from self, [] if none. 

 

include_self, if True (default is False), will return the current 

node as part of the list of nontips if it is a nontip.""" 

for n in self.traverse(True, False, include_self): 

if n.Children: 

yield n 

 

def nontips(self, include_self=False): 

"""Returns nontips descended from self.""" 

return list(self.iterNontips(include_self=include_self)) 

 

def istip(self): 

"""Returns True if is tip, i.e. no children.""" 

return not self.Children 

 

def tipChildren(self): 

"""Returns direct children of self that are tips.""" 

return [i for i in self.Children if not i.Children] 

 

def nonTipChildren(self): 

"""Returns direct children in self that have descendants.""" 

return [i for i in self.Children if i.Children] 

 

def childGroups(self): 

"""Returns list containing lists of children sharing a state. 

 

In other words, returns runs of tip and nontip children. 

""" 

#bail out in trivial cases of 0 or 1 item 

if not self.Children: 

return [] 

if len(self.Children) == 1: 

return [self.Children[0]] 

#otherwise, have to do it properly... 

result = [] 

curr = [] 

state = None 

for i in self.Children: 

curr_state = bool(i.Children) 

if curr_state == state: 

curr.append(i) 

else: 

if curr: 

result.append(curr) 

curr = [] 

curr.append(i) 

state = curr_state 

#handle last group 

result.append(curr) 

return result 

 

def lastCommonAncestor(self, other): 

"""Finds last common ancestor of self and other, or None. 

 

Always tests by identity. 

""" 

my_lineage = set([id(node) for node in [self] + self.ancestors()]) 

curr = other 

while curr is not None: 

if id(curr) in my_lineage: 

return curr 

curr = curr._parent 

return None 

 

def lowestCommonAncestor(self, tipnames): 

"""Lowest common ancestor for a list of tipnames 

 

This should be around O(H sqrt(n)), where H is height and n is the 

number of tips passed in. 

""" 

if len(tipnames) == 1: 

return self.getNodeMatchingName(tipnames[0]) 

 

tipnames = set(tipnames) 

tips = [tip for tip in self.tips() if tip.Name in tipnames] 

 

if len(tips) == 0: 

return None 

 

for t in tips: 

prev = t 

curr = t.Parent 

 

while curr and not hasattr(curr,'black'): 

setattr(curr,'black',[prev]) 

prev = curr 

curr = curr.Parent 

 

# increase black count, multiple children lead to here 

if curr: 

curr.black.append(prev) 

 

curr = self 

while len(curr.black) == 1: 

 

# curr = curr.black[0] 

# changed above statement in order to check for missing attribute 

# in lower node JML 

currX = curr.black[0] 

if not hasattr(currX,'black'): return curr 

else: curr = currX 

 

# clear all black attributes from the tree, added by JML 

for n,t in self.getNodesDict().items(): 

try: 

delattr(t,'black') 

except: 

pass 

 

return curr 

 

lca = lastCommonAncestor #for convenience 

 

#support for more advanced tree operations 

def get_LCA(self,*nodes): 

""" 

Find lowest common ancestor of a given number of nodes. 

 

Notes 

----- 

This function is supposed to yield the same output as 

lowestCommonAncestor does. It was added in order to overcome certain 

problems in the original function, resulting from attributes added to a 

PhyloNode-object that make the use at time unsecure. Furthermore, it 

works with an arbitrary list of nodes (including tips and internal 

nodes). 

""" 

# XXX function added by JML 

 

# check for nodes that are not in the list of the nodes of self 

if not set(nodes).issubset(set(self.getNodeNames())): 

raise ValueError( 

"[i] There are nodes that do not occur on the tree." 

) 

 

# make a dictionary that stores which nodes have been visited 

visited = set() 

 

# pick one node at random (first one) 

queue = [nodes[0]] 

 

while len(visited) < len(nodes):# sum(visited) < len(visited): 

 

# get nodes from queue 

n = queue.pop(0) 

node = self.getNodeMatchingName(n) 

 

# get all tips 

tips = node.getNodeNames() 

 

for t in tips: 

if t in nodes: 

visited.add(t) 

 

# check for visited 

if len(visited) == len(nodes): 

return node 

 

p = node.Parent.Name 

 

# append parent to queue 

queue += [p] 

 

return self.getNodeMatchingName(n) 

 

def separation(self, other): 

"""Returns number of edges separating self and other.""" 

#detect trivial case 

if self is other: 

return 0 

#otherwise, check the list of ancestors 

my_ancestors = dict.fromkeys(map(id, [self] + self.ancestors())) 

count = 0 

while other is not None: 

if id(other) in my_ancestors: 

#need to figure out how many steps there were back from self 

curr = self 

while not(curr is None or curr is other): 

count += 1 

curr = curr._parent 

return count 

else: 

count += 1 

other = other._parent 

return None 

 

def descendantArray(self, tip_list=None): 

"""Returns numpy array with nodes in rows and descendants in columns. 

 

A value of 1 indicates that the decendant is a descendant of that node/ 

A value of 0 indicates that it is not 

 

Also returns a list of nodes in the same order as they are listed 

in the array. 

 

tip_list is a list of the names of the tips that will be considered, 

in the order they will appear as columns in the final array. Internal 

nodes will appear as rows in preorder traversal order. 

""" 

 

#get a list of internal nodes 

node_list = [node for node in self.traverse() if node.Children] 

node_list = sorted(node_list, key=lambda x: str(x)) 

 

#get a list of tip names if one is not supplied 

if not tip_list: 

tip_list = [n.Name for n in self.tips()] 

tip_list.sort() 

#make a blank array of the right dimensions to alter 

result = zeros([len(node_list), len(tip_list)]) 

#put 1 in the column for each child of each node 

for (i, node) in enumerate(node_list): 

children = [n.Name for n in node.tips()] 

for (j, dec) in enumerate(tip_list): 

if dec in children: 

result[i,j] = 1 

return result, node_list 

 

def _default_tree_constructor(self): 

return TreeBuilder(constructor=self.__class__).edgeFromEdge 

 

def nameUnnamedNodes(self): 

"""sets the Data property of unnamed nodes to an arbitrary value 

 

Internal nodes are often unnamed and so this function assigns a 

value for referencing.""" 

#make a list of the names that are already in the tree 

names_in_use = [] 

for node in self.traverse(): 

if node.Name: 

names_in_use.append(node.Name) 

#assign unique names to the Data property of nodes where Data = None 

name_index = 1 

for node in self.traverse(): 

if not node.Name: 

new_name = 'node' + str(name_index) 

#choose a new name if name is already in tree 

while new_name in names_in_use: 

name_index += 1 

new_name = 'node' + str(name_index) 

node.Name = new_name 

names_in_use.append(new_name) 

name_index += 1 

 

def makeTreeArray(self, dec_list=None): 

"""Makes an array with nodes in rows and descendants in columns. 

 

A value of 1 indicates that the decendant is a descendant of that node/ 

A value of 0 indicates that it is not 

 

also returns a list of nodes in the same order as they are listed 

in the array""" 

#get a list of internal nodes 

node_list = [node for node in self.traverse() if node.Children] 

node_list = sorted(node_list, key=lambda x: str(x)) 

 

#get a list of tips() Name if one is not supplied 

if not dec_list: 

dec_list = [dec.Name for dec in self.tips()] 

dec_list.sort() 

#make a blank array of the right dimensions to alter 

result = zeros((len(node_list), len(dec_list))) 

#put 1 in the column for each child of each node 

for i, node in enumerate(node_list): 

children = [dec.Name for dec in node.tips()] 

for j, dec in enumerate(dec_list): 

if dec in children: 

result[i,j] = 1 

return result, node_list 

 

def prune(self): 

"""Reconstructs correct topology after nodes have been removed. 

 

Internal nodes with only one child will be removed and new connections 

will be made to reflect change. 

""" 

#traverse tree to decide nodes to be removed. 

nodes_to_remove = [] 

for node in self.traverse(): 

if (node.Parent is not None) and (len(node.Children)==1): 

nodes_to_remove.append(node) 

for node in nodes_to_remove: 

#save current parent 

curr_parent=node.Parent 

#save child 

child=node.Children[0] 

#remove current node by setting parent to None 

node.Parent=None 

#Connect child to current node's parent 

child.Parent=curr_parent 

 

def sameShape(self, other): 

"""Ignores lengths and order, so trees should be sorted first""" 

if len(self.Children) != len(other.Children): 

return False 

if self.Children: 

for (self_child, other_child) in zip(self.Children, other.Children): 

if not self_child.sameShape(other_child): 

return False 

return True 

else: 

return self.Name == other.Name 

 

def getNewickRecursive(self, with_distances=False, semicolon=True, \ 

escape_name=True): 

"""Return the newick string for this edge. 

 

Arguments: 

- with_distances: whether branch lengths are included. 

- semicolon: end tree string with a semicolon 

- escape_name: if any of these characters []'"(),:;_ exist in a 

nodes name, wrap the name in single quotes 

""" 

newick = [] 

 

subtrees = [child.getNewick(with_distances, semicolon=False) 

for child in self.Children] 

if subtrees: 

newick.append("(%s)" % ",".join(subtrees)) 

 

if self.NameLoaded: 

if self.Name is None: 

name = '' 

else: 

name = str(self.Name) 

if escape_name and not (name.startswith("'") and \ 

name.endswith("'")): 

if re.search("""[]['"(),:;_]""", name): 

name = "'%s'" % name.replace("'","''") 

else: 

name = name.replace(' ','_') 

newick.append(name) 

 

if isinstance(self, PhyloNode): 

if with_distances and self.Length is not None: 

newick.append(":%s" % self.Length) 

 

if semicolon: 

newick.append(";") 

 

return ''.join(newick) 

 

def getNewick(self, with_distances=False, semicolon=True, escape_name=True): 

"""Return the newick string for this tree. 

 

Arguments: 

- with_distances: whether branch lengths are included. 

- semicolon: end tree string with a semicolon 

- escape_name: if any of these characters []'"(),:;_ exist in a 

nodes name, wrap the name in single quotes 

 

NOTE: This method returns the Newick representation of this node 

and its descendents. This method is a modification of an implementation 

by Zongzhi Liu 

""" 

result = ['('] 

nodes_stack = [[self, len(self.Children)]] 

node_count = 1 

 

while nodes_stack: 

node_count += 1 

#check the top node, any children left unvisited? 

top = nodes_stack[-1] 

top_node, num_unvisited_children = top 

if num_unvisited_children: #has any child unvisited 

top[1] -= 1 #decrease the #of children unvisited 

next_child = top_node.Children[-num_unvisited_children] # - for order 

#pre-visit 

if next_child.Children: 

result.append('(') 

nodes_stack.append([next_child, len(next_child.Children)]) 

else: #no unvisited children 

nodes_stack.pop() 

#post-visit 

if top_node.Children: 

result[-1] = ')' 

 

if top_node.NameLoaded: 

if top_node.Name is None: 

name = '' 

else: 

name = text_type(top_node.Name) 

if escape_name and not (name.startswith("'") and \ 

name.endswith("'")): 

if re.search("""[]['"(),:;_]""", name): 

name = "'%s'" % name.replace("'", "''") 

else: 

name = name.replace(' ','_') 

result.append(name) 

 

if isinstance(self, PhyloNode): 

if with_distances and top_node.Length is not None: 

#result.append(":%s" % top_node.Length) 

result[-1] = "%s:%s" % (result[-1], top_node.Length) 

 

result.append(',') 

 

len_result = len(result) 

if len_result == 2: # single node no name 

if semicolon: 

return ";" 

else: 

return '' 

elif len_result == 3: # single node with name 

if semicolon: 

return "%s;" % result[1] 

else: 

return result[1] 

else: 

if semicolon: 

result[-1] = ';' 

else: 

result.pop(-1) 

return ''.join(result) 

 

def removeNode(self, target): 

"""Removes node by identity instead of value. 

 

Returns True if node was present, False otherwise. 

""" 

to_delete = None 

for i, curr_node in enumerate(self.Children): 

if curr_node is target: 

to_delete = i 

break 

if to_delete is None: 

return False 

else: 

del self[to_delete] 

return True 

 

def getEdgeNames(self, tip1name, tip2name, 

getclade, getstem, outgroup_name=None): 

"""Return the list of stem and/or sub tree (clade) edge name(s). 

This is done by finding the common intersection, and then getting 

the list of names. If the clade traverses the root, then use the 

outgroup_name argument to ensure valid specification. 

 

Arguments: 

- tip1/2name: edge 1/2 names 

- getstem: whether the name of the clade stem edge is returned. 

- getclade: whether the names of the edges within the clade are 

returned 

- outgroup_name: if provided the calculation is done on a version of 

the tree re-rooted relative to the provided tip. 

 

Usage: 

The returned list can be used to specify subtrees for special 

parameterisation. For instance, say you want to allow the primates 

to have a different value of a particular parameter. In this case, 

provide the results of this method to the parameter controller 

method `setParamRule()` along with the parameter name etc.. 

""" 

# If outgroup specified put it at the top of the tree so that clades are 

# defined by their distance from it. This makes a temporary tree with 

# a named edge at it's root, but it's only used here then discarded. 

if outgroup_name is not None: 

outgroup = self.getNodeMatchingName(outgroup_name) 

if outgroup.Children: 

raise TreeError('Outgroup (%s) must be a tip' % outgroup_name) 

self = outgroup.unrootedDeepcopy() 

 

join_edge = self.getConnectingNode(tip1name, tip2name) 

 

edge_names = [] 

 

if getstem: 

if join_edge.isroot(): 

raise TreeError('LCA(%s,%s) is the root and so has no stem' % 

(tip1name, tip2name)) 

else: 

edge_names.append(join_edge.Name) 

 

if getclade: 

#get the list of names contained by join_edge 

for child in join_edge.Children: 

branchnames = child.getNodeNames(includeself = 1) 

edge_names.extend(branchnames) 

 

return edge_names 

 

def _getNeighboursExcept(self, parent=None): 

# For walking the tree as if it was unrooted. 

return [c for c in (tuple(self.Children) + (self.Parent,)) 

if c is not None and c is not parent] 

 

def _getDistances(self, endpoints=None): 

"""Iteratively calcluates all of the root-to-tip and tip-to-tip 

distances, resulting in a tuple of: 

- A list of (name, path length) pairs. 

- A dictionary of (tip1,tip2):distance pairs 

""" 

## linearize the tips in postorder. 

# .__start, .__stop compose the slice in tip_order. 

if endpoints is None: 

tip_order = list(self.tips()) 

else: 

tip_order = [] 

for i,name in enumerate(endpoints): 

node = self.getNodeMatchingName(name) 

tip_order.append(node) 

for i, node in enumerate(tip_order): 

node.__start, node.__stop = i, i+1 

 

num_tips = len(tip_order) 

result = {} 

tipdistances = zeros((num_tips), float) #distances from tip to curr node 

 

def update_result(): 

# set tip_tip distance between tips of different child 

for child1, child2 in comb(node.Children, 2): 

for tip1 in range(child1.__start, child1.__stop): 

for tip2 in range(child2.__start, child2.__stop): 

name1 = tip_order[tip1].Name 

name2 = tip_order[tip2].Name 

result[(name1,name2)] = \ 

tipdistances[tip1] + tipdistances[tip2] 

result[(name2,name1)] = \ 

tipdistances[tip1] + tipdistances[tip2] 

 

for node in self.traverse(self_before=False, self_after=True): 

if not node.Children: 

continue 

## subtree with solved child wedges 

starts, stops = [], [] #to calc ._start and ._stop for curr node 

for child in node.Children: 

if hasattr(child, 'Length') and child.Length is not None: 

child_len = child.Length 

else: 

child_len = 1 # default length 

tipdistances[child.__start : child.__stop] += child_len 

starts.append(child.__start); stops.append(child.__stop) 

node.__start, node.__stop = min(starts), max(stops) 

## update result if nessessary 

if len(node.Children) > 1: #not single child 

update_result() 

 

from_root = [] 

for i,n in enumerate(tip_order): 

from_root.append((n.Name, tipdistances[i])) 

return from_root, result 

 

def getDistances(self, endpoints=None): 

"""The distance matrix as a dictionary. 

 

Usage: 

Grabs the branch lengths (evolutionary distances) as 

a complete matrix (i.e. a,b and b,a). 

""" 

 

(root_dists, endpoint_dists) = self._getDistances(endpoints) 

return endpoint_dists 

 

def setMaxTipTipDistance(self): 

"""Propagate tip distance information up the tree 

 

This method was originally implemented by Julia Goodrich with the intent 

of being able to determine max tip to tip distances between nodes on  

large trees efficiently. The code has been modified to track the  

specific tips the distance is between 

""" 

for n in self.postorder(): 

if n.isTip(): 

n.MaxDistTips = [[0.0, n.Name], [0.0, n.Name]] 

else: 

if len(n.Children) == 1: 

tip_a, tip_b = n.Children[0].MaxDistTips 

tip_a[0] += n.Children[0].Length or 0.0 

tip_b[0] += n.Children[0].Length or 0.0 

else: 

tip_info = [(max(c.MaxDistTips), c) for c in n.Children] 

dists = [i[0][0] for i in tip_info] 

best_idx = argsort(dists)[-2:] 

tip_a, child_a = tip_info[best_idx[0]] 

tip_b, child_b = tip_info[best_idx[1]] 

tip_a[0] += child_a.Length or 0.0 

tip_b[0] += child_b.Length or 0.0 

n.MaxDistTips = [tip_a, tip_b] 

 

def getMaxTipTipDistance(self): 

"""Returns the max tip tip distance between any pair of tips 

 

Returns (dist, tip_names, internal_node) 

""" 

if not hasattr(self, 'MaxDistTips'): 

self.setMaxTipTipDistance() 

 

longest = 0.0 

names = [None,None] 

best_node = None 

for n in self.nontips(include_self=True): 

tip_a, tip_b = n.MaxDistTips 

dist = (tip_a[0] + tip_b[0]) 

 

if dist > longest: 

longest = dist 

best_node = n 

names = [tip_a[1], tip_b[1]] 

return longest, names, best_node 

 

def maxTipTipDistance(self): 

"""returns the max distance between any pair of tips 

 

Also returns the tip names that it is between as a tuple""" 

distmtx, tip_order = self.tipToTipDistances() 

idx_max = divmod(distmtx.argmax(),distmtx.shape[1]) 

max_pair = (tip_order[idx_max[0]].Name, tip_order[idx_max[1]].Name) 

return distmtx[idx_max], max_pair 

 

def _getSubTree(self, included_names, constructor=None, keep_root=False): 

"""An equivalent node with possibly fewer children, or None""" 

 

# Renumber autonamed edges 

if constructor is None: 

constructor = self._default_tree_constructor() 

 

if self.Name in included_names: 

return self.deepcopy(constructor=constructor) 

else: 

# don't need to pass keep_root to children, though 

# internal nodes will be elminated this way 

children = [child._getSubTree(included_names, constructor) 

for child in self.Children] 

children = [child for child in children if child is not None] 

if len(children) == 0: 

result = None 

elif len(children) == 1 and not keep_root: 

# Merge parameter dictionaries by adding lengths and making 

# weighted averages of other parameters. This should probably 

# be moved out of here into a ParameterSet class (Model?) or 

# tree subclass. 

params = {} 

child = children[0] 

if self.Length is not None and child.Length is not None: 

shared_params = [n for (n,v) in self.params.items() 

if v is not None 

and child.params.get(n) is not None 

and n is not "length"] 

length = self.Length + child.Length 

if length: 

params = dict([(n, 

(self.params[n]*self.Length + 

child.params[n]*child.Length) / length) 

for n in shared_params]) 

params['length'] = length 

result = child 

result.params = params 

else: 

result = constructor(self, tuple(children)) 

return result 

 

def getSubTree(self, name_list, ignore_missing=False, keep_root=False): 

"""A new instance of a sub tree that contains all the otus that are 

listed in name_list. 

 

ignore_missing: if False, getSubTree will raise a ValueError if  

name_list contains names that aren't nodes in the tree 

 

keep_root: if False, the root of the subtree will be the last common 

ancestor of all nodes kept in the subtree. Root to tip distance is 

then (possibly) different from the original tree 

If True, the root to tip distance remains constant, but root may only 

have one child node. 

""" 

edge_names = set(self.getNodeNames(includeself=1, tipsonly=False)) 

if not ignore_missing: 

# this may take a long time 

for name in name_list: 

if name not in edge_names: 

raise ValueError("edge %s not found in tree" % name) 

 

new_tree = self._getSubTree(name_list, keep_root=keep_root) 

if new_tree is None: 

raise TreeError("no tree created in make sub tree") 

elif new_tree.istip(): 

raise TreeError("only a tip was returned from selecting sub tree") 

else: 

new_tree.Name = "root" 

# keep unrooted 

if len(self.Children) > 2: 

new_tree = new_tree.unrooted() 

return new_tree 

 

def _edgecount(self, parent, cache): 

""""The number of edges beyond 'parent' in the direction of 'self', 

unrooted""" 

neighbours = self._getNeighboursExcept(parent) 

key = (id(parent), id(self)) 

if key not in cache: 

cache[key] = 1 + sum([child._edgecount(self, cache) 

for child in neighbours]) 

return cache[key] 

 

def _imbalance(self, parent, cache): 

"""The edge count from here, (except via 'parent'), divided into that 

from the heaviest neighbour, and that from the rest of them. 'cache' 

should be a dictionary that can be shared by calls to self.edgecount, 

it stores the edgecount for each node (from self) without having to 

put it on the tree itself.""" 

max_weight = 0 

total_weight = 0 

for child in self._getNeighboursExcept(parent): 

weight = child._edgecount(self, cache) 

total_weight += weight 

if weight > max_weight: 

max_weight = weight 

biggest_branch = child 

return (max_weight, total_weight-max_weight, biggest_branch) 

 

def _sorted(self, sort_order): 

"""Score all the edges, sort them, and return minimum score and a 

sorted tree. 

""" 

# Only need to duplicate whole tree because of .Parent pointers 

 

constructor = self._default_tree_constructor() 

 

if not self.Children: 

tree = self.deepcopy(constructor) 

score = sort_order.index(self.Name) 

else: 

scored_subtrees = [child._sorted(sort_order) 

for child in self.Children] 

scored_subtrees.sort() 

children = tuple([child.deepcopy(constructor) 

for (score, child) in scored_subtrees]) 

tree = constructor(self, children) 

 

non_null_scores = [score 

for (score, child) in scored_subtrees if score is not None] 

score = (non_null_scores or [None])[0] 

return (score, tree) 

 

def sorted(self, sort_order=[]): 

"""An equivalent tree sorted into a standard order. If this is not 

specified then alphabetical order is used. At each node starting from 

root, the algorithm will try to put the descendant which contains the 

lowest scoring tip on the left. 

""" 

tip_names = self.getTipNames() 

tip_names.sort() 

full_sort_order = sort_order + tip_names 

(score, tree) = self._sorted(full_sort_order) 

return tree 

 

def _asciiArt(self, char1='-', show_internal=True, compact=False, 

labels=False): 

""" 

Notes 

----- 

Added a labels-keyword to this function. This is useful for debugging, 

since it allows the user to replace all nodes with a specific label, 

and print it to the tree, accordingly. 

""" 

# XXX Added labels-keywords: JML 

LEN = 10 

PAD = ' ' * LEN 

PA = ' ' * (LEN-1) 

if not labels: 

namestr = self.Name or '' # prevents name of NoneType 

else: 

namestr = labels[self.Name] 

if self.Children: 

mids = [] 

result = [] 

for c in self.Children: 

if c is self.Children[0]: 

char2 = '/' 

elif c is self.Children[-1]: 

char2 = '\\' 

else: 

char2 = '-' 

(clines, mid) = c._asciiArt(char2, show_internal, compact, 

labels) 

mids.append(mid+len(result)) 

result.extend(clines) 

if not compact: 

result.append('') 

if not compact: 

result.pop() 

(lo, hi, end) = (mids[0], mids[-1], len(result)) 

prefixes = [PAD] * (lo+1) + [PA+'|'] * (hi-lo-1) + [PAD] * (end-hi) 

mid = (lo + hi) // 2 

prefixes[mid] = char1 + '-'*(LEN-2) + prefixes[mid][-1] 

result = [p+l for (p,l) in zip(prefixes, result)] 

if show_internal: 

stem = result[mid] 

result[mid] = stem[0] + namestr + stem[len(namestr)+1:] 

return (result, mid) 

else: 

return ([char1 + '-' + namestr], 0) 

 

def asciiArt(self, show_internal=True, compact=False, labels=False): 

"""Returns a string containing an ascii drawing of the tree. 

 

Parameters 

---------- 

show_internal: bool 

includes internal edge names. 

compact: bool 

use exactly one line per tip. 

labels: {bool, list} 

specify specific labels for all nodes in the tree. 

 

Notes 

----- 

The labels-keyword was added to the function by JML. 

""" 

# XXX added labels-keywords JML 

(lines, mid) = self._asciiArt( 

show_internal=show_internal, compact=compact, labels=labels) 

return '\n'.join(lines) 

 

#def _getXmlLines(self, indent=0, parent_params=None): 

# """Return the xml strings for this edge. 

# """ 

# params = {} 

# if parent_params is not None: 

# params.update(parent_params) 

# pad = ' ' * indent 

# xml = ["%s<clade>" % pad] 

# if self.NameLoaded: 

# xml.append("%s <name>%s</name>" % (pad, self.Name)) 

# for (n,v) in self.params.items(): 

# if v == params.get(n, None): 

# continue 

# xml.append("%s <param><name>%s</name><value>%s</value></param>" 

# % (pad, n, v)) 

# params[n] = v 

# for child in self.Children: 

# xml.extend(child._getXmlLines(indent + 1, params)) 

# xml.append(pad + "</clade>") 

# return xml 

# 

#def getXML(self): 

# """Return XML formatted tree string.""" 

# header = ['<?xml version="1.0"?>'] # <!DOCTYPE ... 

# return '\n'.join(header + self._getXmlLines()) 

 

def writeToFile(self, filename, with_distances=True, format=None): 

"""Save the tree to filename 

 

Arguments: 

- filename: self-evident 

- with_distances: whether branch lengths are included in string. 

- format: default is newick, xml is alternate. Argument overrides 

the filename suffix. All attributes are saved in the xml format. 

""" 

if format: 

xml = format.lower() == 'xml' 

else: 

xml = filename.lower().endswith('xml') 

 

if xml: 

data = self.getXML() 

else: 

data = self.getNewick(with_distances=with_distances) 

outf = codecs.open(filename, "w",'utf-8') 

outf.writelines(data) 

outf.close() 

 

def getNodeNames(self, includeself=True, tipsonly=False): 

"""Return a list of edges from this edge - may or may not include self. 

This node (or first connection) will be the first, and then they will 

be listed in the natural traverse order. 

""" 

if tipsonly: 

nodes = self.traverse(self_before=False, self_after=False) 

else: 

nodes = list(self.traverse()) 

if not includeself: 

nodes = nodes[:-1] 

return [node.Name for node in nodes] 

 

def getTipNames(self, includeself=False): 

"""return the list of the names of all tips contained by this edge 

""" 

return self.getNodeNames(includeself, tipsonly=True) 

 

def getEdgeVector(self): 

"""Collect the list of edges in postfix order""" 

return [node for node in self.traverse(False, True)] 

 

def _getNodeMatchingName(self, name): 

""" 

find the edge with the name, or return None 

""" 

for node in self.traverse(self_before=True, self_after=False): 

if node.Name == name: 

return node 

return None 

 

def getNodeMatchingName(self, name): 

node = self._getNodeMatchingName(name) 

if node is None: 

raise TreeError("No node named '%s' in %s" % 

(name, self.getTipNames())) 

return node 

 

def getConnectingNode(self, name1, name2): 

"""Finds the last common ancestor of the two named edges.""" 

edge1 = self.getNodeMatchingName(name1) 

edge2 = self.getNodeMatchingName(name2) 

lca = edge1.lastCommonAncestor(edge2) 

if lca is None: 

raise TreeError("No LCA found for %s and %s" % (name1, name2)) 

return lca 

 

def getConnectingEdges(self, name1, name2): 

"""returns a list of edges connecting two nodes 

 

includes self and other in the list""" 

edge1 = self.getNodeMatchingName(name1) 

edge2 = self.getNodeMatchingName(name2) 

LCA = self.getConnectingNode(name1, name2) 

node_path = [edge1] 

node_path.extend(edge1.ancestors()) 

#remove nodes deeper than the LCA 

LCA_ind = node_path.index(LCA) 

node_path = node_path[:LCA_ind+1] 

#remove LCA and deeper nodes from anc list of other 

anc2 = edge2.ancestors() 

LCA_ind = anc2.index(LCA) 

anc2 = anc2[:LCA_ind] 

anc2.reverse() 

node_path.extend(anc2) 

node_path.append(edge2) 

return node_path 

 

def getParamValue(self, param, edge): 

"""returns the parameter value for named edge""" 

return self.getNodeMatchingName(edge).params[param] 

 

def setParamValue(self, param, edge, value): 

"""set's the value for param at named edge""" 

self.getNodeMatchingName(edge).params[param] = value 

 

def reassignNames(self, mapping, nodes=None): 

"""Reassigns node names based on a mapping dict 

 

mapping : dict, old_name -> new_name 

nodes : specific nodes for renaming (such as just tips, etc...) 

""" 

if nodes is None: 

nodes = self.traverse() 

 

for n in nodes: 

if n.Name in mapping: 

n.Name = mapping[n.Name] 

 

def getNodesDict(self): 

"""Returns a dict keyed by node name, value is node 

 

Will raise TreeError if non-unique names are encountered 

""" 

res = {} 

 

for n in self.traverse(): 

if n.Name in res: 

raise TreeError("getNodesDict requires unique node names") 

else: 

res[n.Name] = n 

 

return res 

 

def subset(self): 

"""Returns set of names that descend from specified node""" 

return frozenset([i.Name for i in self.tips()]) 

 

def subsets(self): 

"""Returns all sets of names that come from specified node and its kids""" 

sets = [] 

for i in self.traverse(self_before=False, self_after=True, \ 

include_self=False): 

if not i.Children: 

i.__leaf_set = frozenset([i.Name]) 

else: 

leaf_set = reduce(or_, [c.__leaf_set for c in i.Children]) 

if len(leaf_set) > 1: 

sets.append(leaf_set) 

i.__leaf_set = leaf_set 

return frozenset(sets) 

 

def compareBySubsets(self, other, exclude_absent_taxa=False): 

"""Returns fraction of overlapping subsets where self and other differ. 

 

Other is expected to be a tree object compatible with PhyloNode. 

 

Notes 

----- 

Names present in only one of the two trees will count as  

mismatches: if you don't want this behavior, strip out the non-matching 

tips first. 

""" 

self_sets, other_sets = self.subsets(), other.subsets() 

if exclude_absent_taxa: 

in_both = self.subset() & other.subset() 

self_sets = [i & in_both for i in self_sets] 

self_sets = frozenset([i for i in self_sets if len(i) > 1]) 

other_sets = [i & in_both for i in other_sets] 

other_sets = frozenset([i for i in other_sets if len(i) > 1]) 

total_subsets = len(self_sets) + len(other_sets) 

intersection_length = len(self_sets & other_sets) 

if not total_subsets: #no common subsets after filtering, so max dist 

return 1 

return 1 - 2*intersection_length/float(total_subsets) 

 

#def tipToTipDistances(self, default_length=1): 

# """Returns distance matrix between all pairs of tips, and a tip order. 

#  

# Warning: .__start and .__stop added to self and its descendants. 

 

# tip_order contains the actual node objects, not their names (may be 

# confusing in some cases). 

# """ 

# ## linearize the tips in postorder. 

# # .__start, .__stop compose the slice in tip_order. 

# tip_order = list(self.tips()) 

# for i, tip in enumerate(tip_order): 

# tip.__start, tip.__stop = i, i+1 

 

# num_tips = len(tip_order) 

# result = zeros((num_tips, num_tips), float) #tip by tip matrix 

# tipdistances = zeros((num_tips), float) #distances from tip to curr node 

 

# def update_result():  

# # set tip_tip distance between tips of different child 

# for child1, child2 in comb(node.Children, 2): 

# for tip1 in range(child1.__start, child1.__stop): 

# for tip2 in range(child2.__start, child2.__stop): 

# result[tip1,tip2] = \ 

# tipdistances[tip1] + tipdistances[tip2] 

 

# for node in self.traverse(self_before=False, self_after=True): 

# if not node.Children: 

# continue 

# ## subtree with solved child wedges 

# starts, stops = [], [] #to calc ._start and ._stop for curr node 

# for child in node.Children: 

# if hasattr(child, 'Length') and child.Length is not None: 

# child_len = child.Length 

# else: 

# child_len = default_length 

# tipdistances[child.__start : child.__stop] += child_len 

# starts.append(child.__start); stops.append(child.__stop) 

# node.__start, node.__stop = min(starts), max(stops) 

# ## update result if nessessary 

# if len(node.Children) > 1: #not single child 

# update_result() 

# return result+result.T, tip_order  

 

class PhyloNode(TreeNode): 

 

def __init__(self, *args, **kwargs): 

length = kwargs.get('Length', None) 

params = kwargs.get('Params', {}) 

if 'length' not in params: 

params['length'] = length 

kwargs['Params'] = params 

super(PhyloNode, self).__init__(*args, **kwargs) 

 

def _set_length(self, value): 

if not hasattr(self, "params"): 

self.params = {} 

self.params["length"] = value 

 

def _get_length(self): 

return self.params.get("length", None) 

 

Length = property(_get_length, _set_length) 

 

def getNewick(self, with_distances=False, semicolon=True, escape_name=True): 

return TreeNode.getNewick(self, with_distances, semicolon, escape_name) 

 

def __str__(self): 

"""Returns string version of self, with names and distances.""" 

return self.getNewick(with_distances=True) 

 

 

def distance(self, other): 

"""Returns branch length between self and other.""" 

#never any length between self and other 

if self is other: 

return 0 

#otherwise, find self's ancestors and find the first ancestor of 

#other that is in the list 

self_anc = self.ancestors() 

self_anc_dict = dict([(id(n),n) for n in self_anc]) 

self_anc_dict[id(self)] = self 

 

count = 0 

while other is not None: 

if id(other) in self_anc_dict: 

#found the first shared ancestor -- need to sum other branch 

curr = self 

while curr is not other: 

if curr.Length: 

count += curr.Length 

curr = curr._parent 

return count 

else: 

if other.Length: 

count += other.Length 

other = other._parent 

return None 

 

def totalDescendingBranchLength(self): 

"""Returns total descending branch length from self""" 

return sum([n.Length for n in self.traverse(include_self=False) \ 

if n.Length is not None]) 

 

#def tipsWithinDistance(self, distance): 

# """Returns tips within specified distance from self 

 

# Branch lengths of None will be interpreted as 0 

# """ 

# def get_distance(d1, d2): 

# if d2 is None: 

# return d1 

# else: 

# return d1 + d2 

 

# to_process = [(self, 0.0)] 

# tips_to_save = [] 

 

# curr_node, curr_dist = to_process[0] 

 

# seen = set([id(self)]) 

# while to_process: 

# curr_node, curr_dist = to_process.pop(0) 

#  

# # have we've found a tip within distance? 

# if curr_node.isTip() and curr_node != self: 

# tips_to_save.append(curr_node) 

# continue 

#  

# # add the parent node if it is within distance 

# parent_dist = get_distance(curr_dist, curr_node.Length) 

# if curr_node.Parent is not None and parent_dist <= distance and \ 

# id(curr_node.Parent) not in seen: 

# to_process.append((curr_node.Parent, parent_dist)) 

# seen.add(id(curr_node.Parent)) 

 

# # add children if we haven't seen them and if they are in distance 

# for child in curr_node.Children: 

# if id(child) in seen: 

# continue 

# seen.add(id(child)) 

 

# child_dist = get_distance(curr_dist, child.Length) 

# if child_dist <= distance: 

# to_process.append((child, child_dist)) 

 

# return tips_to_save 

 

def prune(self): 

"""Reconstructs correct tree after nodes have been removed. 

 

Internal nodes with only one child will be removed and new connections 

and Branch lengths will be made to reflect change.  

""" 

#traverse tree to decide nodes to be removed. 

nodes_to_remove = [] 

for node in self.traverse(): 

if (node.Parent is not None) and (len(node.Children)==1): 

nodes_to_remove.append(node) 

for node in nodes_to_remove: 

#save current parent 

curr_parent=node.Parent 

#save child 

child=node.Children[0] 

#remove current node by setting parent to None 

node.Parent=None 

#Connect child to current node's parent 

child.Parent=curr_parent 

#Add the Length of the removed node to the Length of the Child 

if child.Length is None or node.Length is None: 

child.Length = child.Length or node.Length 

else: 

child.Length = child.Length + node.Length 

 

def unrootedDeepcopy(self, constructor=None, parent=None): 

# walks the tree unrooted-style, ie: treating self.Parent as just 

# another child 'parent' is where we got here from, ie: the neighbour 

# that we don't need to explore. 

if constructor is None: 

constructor = self._default_tree_constructor() 

 

neighbours = self._getNeighboursExcept(parent) 

children = [] 

for child in neighbours: 

children.append(child.unrootedDeepcopy(constructor, parent=self)) 

 

# we might be walking UP the tree, so: 

if parent is None: 

# base edge 

edge = None 

elif parent.Parent is self: 

# self's parent is becoming self's child, and edge params are stored 

# by the child 

edge = parent 

else: 

assert parent is self.Parent 

edge = self 

 

result = constructor(edge, tuple(children)) 

if parent is None: 

result.Name = "root" 

return result 

 

def bifurcating(self, constructor=None): 

# With every node having 2 or fewer children. 

if constructor is None: 

constructor = self._default_tree_constructor() 

children = [child.bifurcating(constructor) for child in self.Children] 

while len(children) > 2: 

extra = constructor(None, tuple(children[-2:])) 

children[-2:] = [extra] 

result = constructor(self, tuple(children)) 

return result 

 

def balanced(self): 

"""Tree 'rooted' here with no neighbour having > 50% of the edges. 

 

Notes 

----- 

Using a balanced tree can substantially improve performance of 

the likelihood calculations. Note that the resulting tree has a 

different orientation with the effect that specifying clades or 

stems for model parameterisation should be done using the 

'outgroup_name' argument. 

""" 

# this should work OK on ordinary 3-way trees, not so sure about 

# other cases. Given 3 neighbours, if one has > 50% of edges it 

# can only improve things to divide it up, worst case: 

# (51),25,24 -> (50,1),49. 

# If no neighbour has >50% we can't improve on where we are, eg: 

# (49),25,26 -> (20,19),51 

last_edge = None 

edge = self 

known_weight = 0 

cache = {} 

while 1: 

(max_weight, remaining_weight, next_edge) = edge._imbalance( 

last_edge, cache) 

known_weight += remaining_weight 

if max_weight <= known_weight+2: 

break 

last_edge = edge 

edge = next_edge 

known_weight += 1 

return edge.unrootedDeepcopy() 

 

def sameTopology(self, other): 

"""Tests whether two trees have the same topology.""" 

tip_names = self.getTipNames() 

root_at = tip_names[0] 

me = self.rootedWithTip(root_at).sorted(tip_names) 

them = other.rootedWithTip(root_at).sorted(tip_names) 

return self is other or me.sameShape(them) 

 

def unrooted(self): 

"""A tree with at least 3 children at the root. 

""" 

constructor = self._default_tree_constructor() 

need_to_expand = len(self.Children) < 3 

new_children = [] 

for oldnode in self.Children: 

if oldnode.Children and need_to_expand: 

for sib in oldnode.Children: 

sib = sib.deepcopy(constructor) 

if sib.Length is not None and oldnode.Length is not None: 

sib.Length += oldnode.Length 

new_children.append(sib) 

need_to_expand = False 

else: 

new_children.append(oldnode.deepcopy(constructor)) 

return constructor(self, new_children) 

 

def rootedAt(self, edge_name): 

"""Return a new tree rooted at the provided node. 

 

Usage: 

This can be useful for drawing unrooted trees with an orientation 

that reflects knowledge of the true root location. 

""" 

newroot = self.getNodeMatchingName(edge_name) 

if not newroot.Children: 

raise TreeError("Can't use a tip (%s) as the root" % 

repr(edge_name)) 

return newroot.unrootedDeepcopy() 

 

def rootedWithTip(self, outgroup_name): 

"""A new tree with the named tip as one of the root's children""" 

tip = self.getNodeMatchingName(outgroup_name) 

return tip.Parent.unrootedDeepcopy() 

 

def rootAtMidpoint(self): 

""" return a new tree rooted at midpoint of the two tips farthest apart 

 

this fn doesn't preserve the internal node naming or structure, 

but does keep tip to tip distances correct. uses unrootedDeepcopy() 

""" 

# max_dist, tip_names = tree.maxTipTipDistance() 

# this is slow 

 

 

max_dist, tip_names = self.maxTipTipDistance() 

half_max_dist = max_dist/2.0 

if max_dist == 0.0: # only pathological cases with no lengths 

return self.unrootedDeepcopy() 

# print tip_names 

tip1 = self.getNodeMatchingName(tip_names[0]) 

tip2 = self.getNodeMatchingName(tip_names[1]) 

lca = self.getConnectingNode(tip_names[0],tip_names[1]) # last comm ancestor 

if tip1.distance(lca) > half_max_dist: 

climb_node = tip1 

else: 

climb_node = tip2 

 

dist_climbed = 0.0 

while dist_climbed + climb_node.Length < half_max_dist: 

dist_climbed += climb_node.Length 

climb_node = climb_node.Parent 

 

# now midpt is either at on the branch to climb_node's parent 

# or midpt is at climb_node's parent 

# print dist_climbed, half_max_dist, 'dists cl hamax' 

if dist_climbed + climb_node.Length == half_max_dist: 

# climb to midpoint spot 

climb_node = climb_node.Parent 

if climb_node.isTip(): 

raise RuntimeError('error trying to root tree at tip') 

else: 

# print climb_node.Name, 'clmb node' 

return climb_node.unrootedDeepcopy() 

 

else: 

# make a new node on climb_node's branch to its parent 

old_br_len = climb_node.Length 

new_root = type(self)() 

new_root.Parent = climb_node.Parent 

climb_node.Parent = new_root 

climb_node.Length = half_max_dist - dist_climbed 

new_root.Length = old_br_len - climb_node.Length 

return new_root.unrootedDeepcopy() 

 

 

def _find_midpoint_nodes(self, max_dist, tip_pair): 

"""returns the nodes surrounding the maxTipTipDistance midpoint  

 

WAS used for midpoint rooting. ORPHANED NOW 

max_dist: The maximum distance between any 2 tips 

tip_pair: Names of the two tips associated with max_dist 

""" 

half_max_dist = max_dist/2.0 

#get a list of the nodes that separate the tip pair 

node_path = self.getConnectingEdges(tip_pair[0], tip_pair[1]) 

tip1 = self.getNodeMatchingName(tip_pair[0]) 

for index, node in enumerate(node_path): 

dist = tip1.distance(node) 

if dist > half_max_dist: 

return node, node_path[index-1] 

 

def setTipDistances(self): 

"""Sets distance from each node to the most distant tip.""" 

for node in self.traverse(self_before=False, self_after=True): 

if node.Children: 

node.TipDistance = max([c.Length + c.TipDistance for \ 

c in node.Children]) 

else: 

node.TipDistance = 0 

 

def scaleBranchLengths(self, max_length=100, ultrametric=False): 

"""Scales BranchLengths in place to integers for ascii output. 

 

Warning: tree might not be exactly the length you specify. 

 

Set ultrametric=True if you want all the root-tip distances to end 

up precisely the same. 

""" 

self.setTipDistances() 

orig_max = max([n.TipDistance for n in self.traverse()]) 

if not ultrametric: #easy case -- just scale and round 

for node in self.traverse(): 

curr = node.Length 

if curr is not None: 

node.ScaledBranchLength = \ 

max(1, int(round(1.0*curr/orig_max*max_length))) 

else: #hard case -- need to make sure they all line up at the end 

for node in self.traverse(self_before=False, self_after=True): 

if not node.Children: #easy case: ignore tips 

node.DistanceUsed = 0 

continue 

#if we get here, we know the node has children 

#figure out what distance we want to set for this node 

ideal_distance=int(round(node.TipDistance/orig_max*max_length)) 

min_distance = max([c.DistanceUsed for c in node.Children]) + 1 

distance = max(min_distance, ideal_distance) 

for c in node.Children: 

c.ScaledBranchLength = distance - c.DistanceUsed 

node.DistanceUsed = distance 

#reset the BranchLengths 

for node in self.traverse(self_before=True, self_after=False): 

if node.Length is not None: 

node.Length = node.ScaledBranchLength 

if hasattr(node, 'ScaledBranchLength'): 

del node.ScaledBranchLength 

if hasattr(node, 'DistanceUsed'): 

del node.DistanceUsed 

if hasattr(node, 'TipDistance'): 

del node.TipDistance 

 

def _getDistances(self, endpoints=None): 

"""Iteratively calcluates all of the root-to-tip and tip-to-tip 

distances, resulting in a tuple of: 

- A list of (name, path length) pairs. 

- A dictionary of (tip1,tip2):distance pairs 

""" 

## linearize the tips in postorder. 

# .__start, .__stop compose the slice in tip_order. 

if endpoints is None: 

tip_order = list(self.tips()) 

else: 

tip_order = [] 

for i,name in enumerate(endpoints): 

node = self.getNodeMatchingName(name) 

tip_order.append(node) 

for i, node in enumerate(tip_order): 

node.__start, node.__stop = i, i+1 

 

num_tips = len(tip_order) 

result = {} 

tipdistances = zeros((num_tips), float) #distances from tip to curr node 

 

def update_result(): 

# set tip_tip distance between tips of different child 

for child1, child2 in comb(node.Children, 2): 

for tip1 in range(child1.__start, child1.__stop): 

for tip2 in range(child2.__start, child2.__stop): 

name1 = tip_order[tip1].Name 

name2 = tip_order[tip2].Name 

result[(name1,name2)] = \ 

tipdistances[tip1] + tipdistances[tip2] 

result[(name2,name1)] = \ 

tipdistances[tip1] + tipdistances[tip2] 

 

for node in self.traverse(self_before=False, self_after=True): 

if not node.Children: 

continue 

## subtree with solved child wedges 

starts, stops = [], [] #to calc ._start and ._stop for curr node 

for child in node.Children: 

if hasattr(child, 'Length') and child.Length is not None: 

child_len = child.Length 

else: 

child_len = 1 # default length 

tipdistances[child.__start : child.__stop] += child_len 

starts.append(child.__start); stops.append(child.__stop) 

node.__start, node.__stop = min(starts), max(stops) 

## update result if nessessary 

if len(node.Children) > 1: #not single child 

update_result() 

 

from_root = [] 

for i,n in enumerate(tip_order): 

from_root.append((n.Name, tipdistances[i])) 

return from_root, result 

 

def getDistances(self, endpoints=None): 

"""The distance matrix as a dictionary. 

 

Usage: 

Grabs the branch lengths (evolutionary distances) as 

a complete matrix (i.e. a,b and b,a).""" 

 

(root_dists, endpoint_dists) = self._getDistances(endpoints) 

return endpoint_dists 

 

def tipToTipDistances(self, endpoints=None, default_length=1): 

"""Returns distance matrix between all pairs of tips, and a tip order. 

 

Warning: .__start and .__stop added to self and its descendants. 

 

tip_order contains the actual node objects, not their names (may be 

confusing in some cases). 

""" 

all_tips = self.tips() 

if endpoints is None: 

tip_order = list(all_tips) 

else: 

if isinstance(endpoints[0], PhyloNode): 

tip_order = endpoints 

else: 

tip_order = [self.getNodeMatchingName(n) for n in endpoints] 

 

## linearize all tips in postorder 

# .__start, .__stop compose the slice in tip_order. 

for i, node in enumerate(all_tips): 

node.__start, node.__stop = i, i+1 

 

# the result map provides index in the result matrix 

result_map = dict([(n.__start,i) for i,n in enumerate(tip_order)]) 

num_all_tips = len(all_tips) # total number of tips 

num_tips = len(tip_order) # total number of tips in result 

result = zeros((num_tips, num_tips), float) # tip by tip matrix 

tipdistances = zeros((num_all_tips), float) # dist from tip to curr node 

 

def update_result(): 

# set tip_tip distance between tips of different child 

for child1, child2 in comb(node.Children, 2): 

for tip1 in range(child1.__start, child1.__stop): 

if tip1 not in result_map: 

continue 

res_tip1 = result_map[tip1] 

for tip2 in range(child2.__start, child2.__stop): 

if tip2 not in result_map: 

continue 

result[res_tip1,result_map[tip2]] = \ 

tipdistances[tip1] + tipdistances[tip2] 

 

for node in self.traverse(self_before=False, self_after=True): 

if not node.Children: 

continue 

## subtree with solved child wedges 

starts, stops = [], [] #to calc ._start and ._stop for curr node 

for child in node.Children: 

if hasattr(child, 'Length') and child.Length is not None: 

child_len = child.Length 

else: 

child_len = default_length 

tipdistances[child.__start : child.__stop] += child_len 

starts.append(child.__start); stops.append(child.__stop) 

node.__start, node.__stop = min(starts), max(stops) 

## update result if nessessary 

if len(node.Children) > 1: #not single child 

update_result() 

return result+result.T, tip_order 

 

def compareByPartitions(self, other, debug=False): 

 

# get all tree nodes 

nodesA = self.getNodesDict() 

nodesB = other.getNodesDict() 

 

# define function to return all partitions as sets of taxa 

def get_partitions(nodes,root): 

 

partitions = [] 

for node,tree in nodes.items(): 

 

tmp_taxa = tuple(sorted(tree.taxa)) 

tmp_other = tuple(sorted([t for t in root.taxa if t not in 

tmp_taxa])) 

partitions += [tuple(sorted([tmp_taxa,tmp_other]))] 

 

return set(partitions) 

 

partA = get_partitions(nodesA,self) 

partB = get_partitions(nodesB,other) 

 

if debug: 

print(sorted(partA)) 

print(sorted(partB)) 

print(partA.symmetric_difference(partB)) 

 

return len(partA.symmetric_difference(partB)) 

 

 

 

#def compareByTipDistances(self, other, sample=None, dist_f=distance_from_r,\ 

# shuffle_f=shuffle): 

# """Compares self to other using tip-to-tip distance matrices. 

 

# Value returned is dist_f(m1, m2) for the two matrices. Default is 

# to use the Pearson correlation coefficient, with +1 giving a distance 

# of 0 and -1 giving a distance of +1 (the madimum possible value). 

# Depending on the application, you might instead want to use 

# distance_from_r_squared, which counts correlations of both +1 and -1 

# as identical (0 distance). 

#  

# Note: automatically strips out the names that don't match (this is 

# necessary for this method because the distance between non-matching  

# names and matching names is undefined in the tree where they don't  

# match, and because we need to reorder the names in the two trees to  

# match up the distance matrices). 

# """ 

# self_names = dict([(i.Name, i) for i in self.tips()]) 

# other_names = dict([(i.Name, i) for i in other.tips()]) 

# common_names = frozenset(self_names.keys()) & \ 

# frozenset(other_names.keys()) 

# common_names = list(common_names) 

 

# if not common_names: 

# raise ValueError, "No names in common between the two trees.""" 

# if len(common_names) <= 2: 

# return 1 #the two trees must match by definition in this case 

 

# if sample is not None: 

# shuffle_f(common_names) 

# common_names = common_names[:sample] 

#  

# self_nodes = [self_names[k] for k in common_names] 

# other_nodes = [other_names[k] for k in common_names] 

 

# self_matrix = self.tipToTipDistances(endpoints=self_nodes)[0] 

# other_matrix = other.tipToTipDistances(endpoints=other_nodes)[0] 

 

# return dist_f(self_matrix, other_matrix) 

 

class TreeBuilder(object): 

# Some tree code which isn't needed once the tree is finished. 

# Mostly exists to give edges unique names 

# Children must be created before their parents. 

 

def __init__(self, mutable=False, constructor=PhyloNode): 

self._used_names = {'edge':-1} 

self._known_edges = {} 

self.TreeNodeClass = constructor 

 

def _unique_name(self, name): 

# Unnamed edges become edge.0, edge.1 edge.2 ... 

# Other duplicates go mouse mouse.2 mouse.3 ... 

if not name: 

name = 'edge' 

if name in self._used_names: 

self._used_names[name] += 1 

name += '.' + str(self._used_names[name]) 

name = self._unique_name(name) # in case of names like 'edge.1.1' 

else: 

self._used_names[name] = 1 

return name 

 

def _params_for_edge(self, edge): 

# default is just to keep it 

return edge.params 

 

def edgeFromEdge(self, edge, children, params=None): 

"""Callback for tree-to-tree transforms like getSubTree""" 

if edge is None: 

assert not params 

return self.createEdge(children, "root", {}, False) 

else: 

if params is None: 

params = self._params_for_edge(edge) 

return self.createEdge( 

children, edge.Name, params, nameLoaded=edge.NameLoaded) 

 

def createEdge(self, children, name, params, nameLoaded=True): 

"""Callback for newick parser""" 

if children is None: 

children = [] 

node = self.TreeNodeClass( 

Children = list(children), 

Name = self._unique_name(name), 

NameLoaded = nameLoaded and (name is not None), 

Params = params, 

) 

self._known_edges[id(node)] = node 

return node 

 

 

def LoadTree(filename=None, treestring=None, tip_names=None, underscore_unmunge=False): 

 

"""Constructor for tree. 

 

Arguments, use only one of: 

- filename: a file containing a newick or xml formatted tree. 

- treestring: a newick or xml formatted tree string. 

- tip_names: a list of tip names. 

 

Notes 

----- 

Underscore_unmunging is turned off by default, although it is part 

of the Newick format. Set underscore_unmunge to True to replace underscores 

with spaces in all names read. 

""" 

if filename: 

assert not (treestring or tip_names) 

# slight modification for easy import of treestrings instead of 

# file-reading by JML 

if filename.endswith(';'): 

treestring = filename 

else: 

treestring = codecs.open(filename,'r','utf-8').read() 

if treestring: 

assert not tip_names 

tree_builder = TreeBuilder().createEdge 

#FIXME: More general strategy for underscore_unmunge 

tree = newick_parse_string(treestring, tree_builder, underscore_unmunge=underscore_unmunge) 

if not tree.NameLoaded: 

tree.Name = 'root' 

elif tip_names: 

tree_builder = TreeBuilder().createEdge 

tips = [tree_builder([], tip_name, {}) for tip_name in tip_names] 

tree = tree_builder(tips, 'root', {}) 

else: 

raise TreeError('filename or treestring not specified') 

return tree