Metadata-Version: 1.1
Name: leftrb
Version: 0.1
Summary: Leftrb/LLRB is a Left-Leaning Red-Black (LLRB) implementation
of 2–3 balanced binary search trees in Python.

The LLRB method of implementing 2-3 trees is a recent improvement over the traditional
implementation — it maintains an additional invariant that all red links must lean left
except during inserts and deletes. Because of this, they can be implemented by adding
just a few lines of code to standard BST algorithms.

This is a port of the code in the article “Left-leaning Red-Black Trees”
by Robert Sedgewick of Princeton University at
http://www.cs.princeton.edu/~rs/talks/LLRB/LLRB.pdf
Home-page: https://github.com/peterhil/leftrb
Author: Peter Hillerström
Author-email: peter.hillerstrom@gmail.com
License: MIT License
Description: # Leftrb/LLRB
        
        LLRB is a Left-Leaning Red-Black (LLRB) implementation of 2–3 balanced binary
        search trees in Python. This is a straightforward port of the code presented
        by Robert Sedgewick in his paper.
        
        ## Overview
        
        A balanced binary search tree (BBST) maintains elements in sorted order under dynamic 
        updates (inserts and deletes) and can support various order-specific queries.
        
        Red-black trees are the de facto standard BBST algorithms, and are the underlying
        data structure for symbol-table implementations within C++, Java, Python, BSD Unix,
        Linux and many other modern systems.
        
        All red–black trees are based on implementing 2-3 or 2-3-4 trees within a binary tree,
        using  red links to bind together internal nodes into 3-nodes or 4-nodes. Search, insert
        and delete operations are O(log n) and space requirements are O(n).
        
        However, many traditional implementations have lots of repetitive code on the symmetric
        branches of rotation and deletion operations. So they are not easy to reason about and 
        augment with other properties, which is what BBST's are often used for: They are used
        to implement other common data structures like Priority Queues and Interval Trees.
        
        The LLRB method of implementing 2-3 trees is a recent improvement over the traditional
        implementation — it maintains an additional invariant that all red links must lean left
        except during inserts and deletes. Because of this, they can be implemented by adding
        just a few lines of code to standard BST algorithms.
        
        The LLRB tree is based on combining three ideas:
        
        - Use a recursive implementation.
        - Require that all 3-nodes lean left.
        - Perform rotations on the way up the tree (after the recursive calls).
        
        The LLRB approach was discovered relatively recently (in 2008) by Robert Sedgewick
        of Princeton University. For original code and more information read the paper *"Left-leaning Red-Black Trees"* at [http://www.cs.princeton.edu/~rs/talks/LLRB/LLRB.pdf](http://www.cs.princeton.edu/~rs/talks/LLRB/LLRB.pdf)
        
        ## Installation
        
        From Python package index:  
        
            pip install leftrb
        
        or from Github source:  
        
            git clone https://github.com/peterhil/leftb.git
            cd leftrb 
            python setyp.py install
        
        ## About
        
        Leftrb/LLRB was written by [Peter Hillerström](http://composed.nu/peterhil/).  
        
        Follow me on Twitter [@peterhil](http://www.twitter.com/peterhil)!
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Other Environment
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Other/Nonlisted Topic
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Utilities
