abstracttree 0.0.2

Abstract base classes for tree data structures

This Python package contains a few abstract base classes for tree data structures. Trees are very common data structure that represents a hierarchy of common nodes. This package defines abstract base classes for these data structure in order to make code reusable.

Abstract base classes

from abstracttree import to_mermaid

to_mermaid(AbstractTree)

graph TD;
AbstractTree[AbstractTree];
UpTree[UpTree];
Tree[Tree];
MutableTree[MutableTree];
DownTree[DownTree];
Tree[Tree];
MutableTree[MutableTree];
MutableDownTree[MutableDownTree];
MutableTree[MutableTree];
AbstractTree-->UpTree;
UpTree-->Tree;
Tree-->MutableTree;
AbstractTree-->DownTree;
DownTree-->Tree;
DownTree-->MutableDownTree;
MutableDownTree-->MutableTree;

Downtrees are trees that have links to their direct children. Uptrees are trees that link to their parent. A Tree has links in both directions.

ABC	Inherits from	Abstract Methods	Mixin Methods
AbstractTree			nid, eqv()
UpTree	AbstractTree	parent	root, is_root, ancestors, path
DownTree	AbstractTree	children	nodes, descendants, leaves, levels, is_leaf, transform()
Tree	UpTree, DownTree		siblings
MutableDownTree	DownTree	add_child(), remove_child()	add_children()
MutableTree	Tree, MutableDownTree		detach()

In your own code, you can inherit from these trees. For example, if your tree only has links to children:

import abstracttree
from abstracttree import print_tree

class MyTree(abstracttree.DownTree):
    def __init__(self, value, children=()):
        self.value = value
        self._children = children
    
    def __str__(self):
        return "MyTree " + str(self.value)

    @property
    def children(self):
        return self._children

tree = MyTree(1, children=[MyTree(2), MyTree(3)])
print_tree(tree)

# This generates the following output:
# MyTree 1
# ├─ MyTree 2
# └─ MyTree 3

Adapter

In practice, not all existing tree data structures implement one of these abstract classes. As a bridge, you can use astree to convert these trees to a Tree instance. However, whenever possible, it's recommended to inherit from Tree directly for minimal overhead.

Examples:

# Trees from built-ins and standard library
astree(int)
astree(ast.parse("1 + 1 == 2"))
astree(pathlib.Path("abstracttree"))

# Anything that has parent and children attributes (anytree / bigtree / littletree)
astree(anytree.Node())

# Nested list
astree([[1, 2, 3], [4, 5, 6]])

# Tree from json-data
data = {"name": "a",
        "children": [
            {"name": "b", "children": []},
            {"name": "c", "children": []}
]}
astree(data, children=operator.itemgetter["children"])

# pyqt.QtWidget
astree(widget, children=lambda w: w.children(), parent = lambda w: w.parent())

# Tree from treelib
astree(tree.root, children=lambda nid: tree.children(nid), parent=lambda nid: tree.parent(nid))

# itertree
astree(tree, children=iter, parent=lambda t: t.parent)

# Infinite binary tree
inf_binary = astree(0, children=lambda n: (2*n + 1, 2*n + 2))

Utility functions

Pretty printing

tree = astree(seq, children=lambda x: [x[:-2], x[1:]] if x else [])
print_tree(tree)
print(to_string(tree))

# ['a', 'b', 'c', 'd']
# ├─ ['a', 'b']
# │  └─ ['b']
# └─ ['b', 'c', 'd']
#    ├─ ['b']
#    └─ ['c', 'd']
#       └─ ['d']

Plotting with matplotlib

import matplotlib.pyplot as plt

expr = ast.parse("y = x*x + 1")
plot_tree(expr)
plt.show()

Conversion to graphviz or mermaid

to_dot(tree)
to_mermaid(tree)

to_image(Path('.'), "filetree.png", how="dot")
to_image(AbstractTree, "class_hierarchy.svg", how="mermaid")
to_pillow(tree).show()

A few concrete tree implementations

• anytree
• treelib
• bigtree
• itertree
• dendropy
• ete
• littletree - also by me