# Overview

Atlassian Sourcetree is a free Git and Mercurial client for Windows.

Atlassian Sourcetree is a free Git and Mercurial client for Mac.

## Binary Tree Package

## Abstract

This package provides Binary- RedBlack- and AVL-Trees written in Python and Cython.

This Classes are much slower than the built-in dict class, but all iterators/generators yielding data in sorted key order.

### Source of Algorithms

AVL- and RBTree algorithms taken from Julienne Walker: http://eternallyconfuzzled.com/jsw_home.aspx

### Trees written in Python (only standard library)

BinaryTree-- unbalanced binary treeAVLTree-- balanced AVL-TreeRBTree-- balanced Red-Black-Tree

### Trees written with C-Functions and Cython as wrapper

FastBinaryTree-- unbalanced binary treeFastAVLTree-- balanced AVL-TreeFastRBTree-- balanced Red-Black-Tree

All trees provides the same API, the pickle protocol is supported.

FastXTrees has C-structs as tree-node structure and C-implementation for low level operations: insert, remove, get_value, max_item, min_item.

#### Constructor

- Tree() -> new empty tree;
- Tree(mapping) -> new tree initialized from a mapping (requires only an items() method)
- Tree(seq) -> new tree initialized from seq [(k1, v1), (k2, v2), ... (kn, vn)]

#### Methods

- __contains__(k) -> True if T has a key k, else False, O(log(n))
- __delitem__(y) <==> del T[y], del[s:e], O(log(n))
- __getitem__(y) <==> T[y], T[s:e], O(log(n))
- __iter__() <==> iter(T)
- __len__() <==> len(T), O(1)
- __max__() <==> max(T), get max item (k,v) of T, O(log(n))
- __min__() <==> min(T), get min item (k,v) of T, O(log(n))
- __and__(other) <==> T & other, intersection
- __or__(other) <==> T | other, union
- __sub__(other) <==> T - other, difference
- __xor__(other) <==> T ^ other, symmetric_difference
- __repr__() <==> repr(T)
- __setitem__(k, v) <==> T[k] = v, O(log(n))
- clear() -> None, remove all items from T, O(n)
- copy() -> a shallow copy of T, O(n*log(n))
- discard(k) -> None, remove k from T, if k is present, O(log(n))
- get(k[,d]) -> T[k] if k in T, else d, O(log(n))
- is_empty() -> True if len(T) == 0, O(1)
- items([reverse]) -> generator for (k, v) items of T, O(n)
- keys([reverse]) -> generator for keys of T, O(n)
- values([reverse]) -> generator for values of T, O(n)
- pop(k[,d]) -> v, remove specified key and return the corresponding value, O(log(n))
- popitem() -> (k, v), remove and return some (key, value) pair as a 2-tuple, O(log(n))
- setdefault(k[,d]) -> T.get(k, d), also set T[k]=d if k not in T, O(log(n))
- update(E) -> None. Update T from dict/iterable E, O(E*log(n))
- foreach(f, [order]) -> visit all nodes of tree (0 = 'inorder', -1 = 'preorder' or +1 = 'postorder') and call f(k, v) for each node, O(n)

#### slicing by keys

- itemslice(s, e) -> generator for (k, v) items of T for s <= key < e, O(n)
- keyslice(s, e) -> generator for keys of T for s <= key < e, O(n)
- valueslice(s, e) -> generator for values of T for s <= key < e, O(n)
- T[s:e] -> TreeSlice object, with keys in range s <= key < e, O(n)
- del T[s:e] -> remove items by key slicing, for s <= key < e, O(n)
start/end parameter:

- if 's' is None or T[:e] TreeSlice/iterator starts with value of min_key();
- if 'e' is None or T[s:] TreeSlice/iterator ends with value of max_key();
- T[:] is a TreeSlice which represents the whole tree;
TreeSlice is a tree wrapper with range check, and contains no references to objects, deleting objects in the associated tree also deletes the object in the TreeSlice.

- TreeSlice[k] -> get value for key k, raises KeyError if k not exists in range s:e

- TreeSlice[s1:e1] -> TreeSlice object, with keys in range s1 <= key < e1

- new lower bound is max(s, s1)
- new upper bound is min(e, e1)
TreeSlice methods:

- items() -> generator for (k, v) items of T, O(n)
- keys() -> generator for keys of T, O(n)
- values() -> generator for values of T, O(n)
- __iter__ <==> keys()
- __repr__ <==> repr(T)
- __contains__(key)-> True if TreeSlice has a key k, else False, O(log(n))

#### prev/succ operations

- prev_item(key) -> get (k, v) pair, where k is predecessor to key, O(log(n))
- prev_key(key) -> k, get the predecessor of key, O(log(n))
- succ_item(key) -> get (k,v) pair as a 2-tuple, where k is successor to key, O(log(n))
- succ_key(key) -> k, get the successor of key, O(log(n))

#### Heap methods

- max_item() -> get largest (key, value) pair of T, O(log(n))
- max_key() -> get largest key of T, O(log(n))
- min_item() -> get smallest (key, value) pair of T, O(log(n))
- min_key() -> get smallest key of T, O(log(n))
- pop_min() -> (k, v), remove item with minimum key, O(log(n))
- pop_max() -> (k, v), remove item with maximum key, O(log(n))
- nlargest(i[,pop]) -> get list of i largest items (k, v), O(i*log(n))
- nsmallest(i[,pop]) -> get list of i smallest items (k, v), O(i*log(n))

#### Set methods (using frozenset)

- intersection(t1, t2, ...) -> Tree with keys
commonto all trees- union(t1, t2, ...) -> Tree with keys from
eithertrees- difference(t1, t2, ...) -> Tree with keys in T but not any of t1, t2, ...
- symmetric_difference(t1) -> Tree with keys in either T and t1 but not both
- issubset(S) -> True if every element in T is in S
- issuperset(S) -> True if every element in S is in T
- isdisjoint(S) -> True if T has a null intersection with S

#### Classmethods

- fromkeys(S[,v]) -> New tree with keys from S and values equal to v.

## Performance

Profiling with timeit(): 5000 unique random int keys, time in seconds

unbalanced Trees | CPython 2.7.2 | FastBinaryTree | ipy 2.7.0 | pypy 1.7.0 |
---|---|---|---|---|

build time 100x | 7,55 | 0,60 | 2,51 | 0,29 |

build & delete time 100x | 13,34 | 1,48 | 4,45 | 0,47 |

search 100x all keys | 2,86 | 0,96 | 0,27 | 0,06 |

AVLTrees | CPython 2.7.2 | FastAVLTree | ipy 2.7.0 | pypy 1.7.0 |
---|---|---|---|---|

build time 100x | 22,66 | 0,65 | 10,45 | 1,29 |

build & delete time 100x | 36,71 | 1,47 | 20,89 | 3,02 |

search 100x all keys | 2,34 | 0,85 | 0,89 | 0,14 |

RBTrees | CPython 2.7.2 | FastRBTree | ipy 2.7.0 | pypy 1.7.0 |
---|---|---|---|---|

build time 100x | 14,78 | 0,65 | 4,43 | 0,49 |

build & delete time 100x | 39,34 | 1,63 | 12,43 | 1,32 |

search 100x all keys | 2,32 | 0,86 | 0,86 | 0,13 |

## News

Version 1.0.0

- bug fixes
- status: 5 - Production/Stable
- removed useless TreeIterator() class and T.treeiter() method.
- patch from Max Motovilov to use Visual Studio 2008 for building C-extensions

Version 0.4.0

- API change!!!
- full Python 3 support, also for Cython implementations
- removed user defined compare() function - keys have to be comparable!
- removed T.has_key(), use 'key in T'
- keys(), items(), values() generating 'views'
- removed iterkeys(), itervalues(), iteritems() methods
- replaced index slicing by key slicing
- removed index() and item_at()
- repr() produces a correct representation
- installs on systems without cython (tested with pypy)
- new license: GNU Library or Lesser General Public License (LGPL)

## Installation

from source:

python setup.py install

## Documentation

this README.txt

bintrees can be found on bitbucket.org at: