tralda

tralda

A Python library for tree algorithms and data structures.

Installation

The package requires Python 3.7 or higher.

Easy installation with pip

The tralda package is available on PyPI:

pip install tralda

For details about how to install Python packages see here.

Installation with the setup file

Alternatively, you can download or clone the repo, go to the root folder of package and install it using the command:

python setup.py install

Dependencies

The package has several dependencies (which are installed automatically when using pip):

• NetworkX
• Numpy

Usage and description

Tree data structure

The class Tree implements the tree data structure which is essential for most of the modules in the package and can be imported from the subpackage tralda.datastructures. It provides methods for tree traversals and manipulation, output in Newick format, as well as the linear-time computation of last common ancestors by Bender & Farach-Colton (class LCA which is initialized with an instance of type Tree). Tree instances can be serialized in pickle or json format.

Overview of the functions of the class TreeNode: (Click to expand)

Function	Description
attributes()	generator for the node attributes
add_child(child_node)	add child_node as a child
add_child_right_of(child_node, right_of)	add child_node as a child to the right of right_of
remove_child(child_node)	remove the child child_node
detach()	remove the node from its parent's children
is_leaf()	check if the node is a leaf
child_subsequence(left_node, right_node)	list of children between left_node and right_node

Overview of the functions of the class Tree: (Click to expand)

Function	Description
leaves()	generator for the leaf nodes
preorder()	generator for preorder (=top-down) traversal
postorder()	generator for postorder (=bottom-up) traversal
inner_vertices()	generator for inner nodes
edges()	generator for the edges of the tree
euler_generator()	generator for an Euler tour
leaf_dict()	compute the list of leaf nodes in the subtree of each node, and return them as a dict
contract(edges)	contract all edges in the collection edges
get_triples()	return a list of all triples that are displayed by the tree
delete_and_reconnect(node)	delete node and reconnect its children to its parent
copy()	construct a copy of the tree (node attributes are only copied as references, but mutable data types should be avoided as node attribute values)
to_newick()	return a str representation of the tree in Newick format
random_tree(N, binary=False)	return a random tree with N leaves that is optionally forced to be binary; new children are stepwise attached to randomly selected nodes until N are reached
to_nx()	return a NetworkX DiGraph version of the tree (with the ids of the TreeNode instances as nodes) and its root (also represented by the id)
parse_nx(G, root)	convert a tree encoded as a NetworkX DiGraph (together with the root) back into a Tree
serialize(filename, mode=None)	serialize a tree in JSON or pickle format specified by mode; default is None, in which case the mode is inferred from the filename ending
load(filename, mode=None)	load a tree from file in JSON or pickle format specified by mode; default is None, in which case the mode is inferred from the filename ending
is_binary()	check if the tree is binary
is_phylogenetic()	check if the tree is phylogenetic (all inner nodes have at least one child)
equal_topology(other)	check whether this tree and other have the same topology based on the leaves' label attributes
is_refinement	check whether this tree refines other based on the leaves' label attributes

Overview of the functions of the class LCA: (Click to expand)

Function	Description
get(a, b)	get the last common ancestor of nodes a and b
displays_triple(a, b, c)	check whether the triple ab
are_comparable(u, v)	check whether u and v are comparable in terms of the ancestor relation
ancestor_or_equal(u, v)	check whether u is equal to or an ancestor of v
ancestor_not_equal(u, v)	check whether u is a strict ancestor of v
descendant_or_equal(u, v)	check whether u is equal to or a descendant of v
descendant_not_equal(u, v)	check whether u is a strict descendant of v
consistent_triples(triples)	list with the subset of triples that are displayed by the tree
consistent_triple_generator	generator for the items in triples that are displayed

Example usage: (Click to expand)

from tralda.datastructures import Tree, LCA

# construct a random tree with 20 leaves
tree = Tree.random_tree(20)

# serialization, reload via Tree.load('path/to/file.json')
tree.serialize('path/to/file.json')

# linear-time processing of the tree for constant-time
# last common ancestor queries
lca_T = LCA(tree)

# l.c.a. queries via 'TreeNode' instances or labels (if the nodes
# in the tree have the label attribute set)
print( lca_T(4, 7) )

# triple queries (e.g. is 3 5 | 2 displayed?)
print( lca_T.displays_triple(3, 5, 2) )

Supertree computation

The subpackage tralda.supertree implements a number of algorithms for the computation of supertrees:

• BUILD (Aho et al. 1981), class Build or function BUILD_supertree
• BuildST (Deng & Fernández-Baca 2016), class BuildST or function build_st
• Loose_Cons_Tree (Jansson et al. 2016), class LooseConsensusTree or function loose_consensus_tree
• LinCR (Schaller et al. 2021), class LinCR or function linear_common_refinement

The latter two algorithms compute the loose consensus tree and the common refinement, resp., for a sequence of trees on the same set of leaves in linear time.

Cographs and cotrees

The subpackage tralda.cograph contains an efficient algorithm for cograph recognition and heuristics for cograph editing:

• function to_cotree recognizes cographs and returns a Tree representation in the positive case (Corneil et al. 1985)
• function edit_to_cograph edits an arbitrary graph to a cograph (algorithm from Crespelle 2019) and returns a Tree representation

Other data structures

The following auxiliary data structures can be imported from the subpackage tralda.datastructures:

• linked list: class LinkedList
• doubly-linked list: class DoublyLinkedList
• HDT dynamic graph data structure (Holm, de Lichtenberg & Thorup in 2001): class HDTGraph
• AVL trees: classes TreeSet and TreeDict implement data structures for sorted sets and dictionaries, respectively

Citation and references

If you use tralda in your project or code from it, please consider citing:

• Schaller, D., Hellmuth, M., Stadler, P.F. (2021) A Simple Linear-Time Algorithm for the Common Refinement of Rooted Phylogenetic Trees on a Common Leaf Set.

Additional references to algorithms that were implemented are given in the source code.

Please report any bugs and questions in the Issues section. Also, feel free to make suggestions for improvement and/or new functionalities.