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Simple, minimal, but powerful tools to handle any kind of hierarchical (tree) structures

Project description

Generic tree utilities for Python

Trees are one of the most ubiquitous data structures. It is amazing how often we as programmers tend to reimplement the same algorithms for different tree formats and stuctures.

This module defines generic tree-traverse and tree-reduce algorithms that can be used with any tree-like object such as filesystem paths, lists, nested dictionaries an expression tree or even specialized tree classes! The only thing that must be provided is a function to get child nodes from a parent node.

Also, trees are usually represented in some fields (such as bioinformatics) in the newick format, which is nontrivial to parse, so this module includes a function to do this.

Usage and examples

Install from PyPi:

pip install treet

Import the basic functions, traverse, reduce and parse_newick:

import treet

Use with any kind of structured tree!

Any kind of structured data is supported, in this case, nested dictionaries:

tree = {
    'label':'A', 'children':[
        {'label':'B', 'children':[]},
        {'label':'C', 'children': [
            {'label':'D', 'children':[]}, 
            {'label':'E', 'children':[]}
        ]}
    ]
}

def children(node):
    return node['children']

[node['label'] 
    for node in treet.traverse(tree, children, mode='inorder')]

# Output --> ['B, 'A', 'D', 'C', 'E']

def as_list(node, children):
    if not children:
        return node['label']
    else:
        return children

treet.reduce(tree, children, reduce_fn=as_list)

# Output --> ['B, ['D', 'E']]

Even with user-defined classes!

Dump a tree in a specialized class format to a string in the newick format.

class Tree:
    def __init__(self, label, children=None):
        self.label = label
        self.children = children if children else []

    def is_leaf(self):
        return len(self.children) == 0

tree = Tree('A', [
        Tree('B'),
        Tree('C',[Tree('D'),Tree('E')])
    ]
)

def get_children(node):
    return node.children

def node_to_newick(node, children):
    if node.is_leaf():
        return node.label
    else:
        return f"({','.join(children)})"


treet.reduce(tree, get_children, node_to_newick)

# Output --> '(B,(D,E))'

Parse a newick-formatted tree structure

Assemble the Newick string to a custom data format:

def parse_node_data(data_string):
    '''
    Example: 
      'data1=xx,data2=yy' 
        -> {'data1':'xx', 'data2': 'yy'}
    '''
    items = data_string.split(',')
    key_value_pairs = (item.split('=') for item in items)
    return dict(key_value_pairs)

def parse_branch_length(length_str):
    return float(length_str) if length_str else 0.0

def tree_builder(label, children, branch_length, node_data):
    return {
        'label': label,
        'length': branch_length,
        'data': node_data,
        'children': children}

newick = "(A:0.2[dat=23,other=45], B:12.4[dat=122,other=xyz])root[x=y];"

treet.parse_newick(
    newick,
    aggregator=tree_builder,
    feature_parser=parse_node_data,
    distance_parser=parse_branch_length
)

# Output ->
{'label': 'root', 'length':0.0, 'data': {'x':'y'},
 'children': [
    {'label': 'A', 'length':0.2, 'data':{'dat':'23','other':'45'}, 
     'children': []},
    {'label': 'B', 'length':12.4, 'data':{'dat':'122','other':'xyz'},
     'children': []}, 
]}

Compose to perform complex algorithms

Get the subtree induced by a subset of the leaves:

tree = (('A',('B',('C','D'))),'E')

def is_leaf(node): 
    return isinstance(node, str)

def get_children(node):
    return node if not is_leaf(node) else []

def induced_subtree(leafs):
    def induced_subtree_generator(node, children):
        if children:
            return tuple(ch for ch in children if not ch is None)
        else:
            return node if node in leafs else None
    return induced_subtree_generator

leafs = ['B', 'D', 'E']
induced = treet.reduce(tree, get_children, induced_subtree(leafs))
print(induced)

# Output --> ((('B',('D',)),),'E')


def merge_unary_nodes(node, children):
    if is_leaf(node):
        return node

    new_children = [
        ch[0] if (len(ch) == 1) else ch
        for ch in children
    ]
    return tuple(new_children)

treet.reduce(induced, get_children, merge_unary_nodes)

# Output --> (('B','D'),'E')

Use even with filesystem paths!

Traverse the /usr directory in breadth-first order:

from pathlib import Path

def enter_folder(path):
    path = Path(path)
    return list(path.iterdir()) if path.is_dir() else []

for item in treet.traverse('/usr', enter_folder, mode='breadth_first'):
    print(item)

# Output -->
# /
# /proc
# /usr
# ...
# /usr/share
# /usr/bin
# /usr/sbin
# ...
# /usr/bin/jinfo
# /usr/bin/m2400w
# ...

Meta

Author: Ad115 - Githuba.garcia230395@gmail.com

Distributed under the MIT license. See LICENSE more information.

Contributing

To run tests: pytest treet/* --hypothesis-show-statistics --verbose

To run static type check: mypy treet/*.py

To run coverage analysis: coverage run --source=. -m pytest treet/* --hypothesis-show-statistics --verbose

  1. Check for open issues or open a fresh issue to start a discussion around a feature idea or a bug.
  2. Fork the repository on GitHub to start making your changes to a feature branch, derived from the master branch.
  3. Write a test which shows that the bug was fixed or that the feature works as expected.
  4. Send a pull request and bug the maintainer until it gets merged and published.

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