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Studying binary trees for your exam/assignment/interview? Try BinaryTree

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[开发(python) 所属分类 开发(python) | 发布者 店小二05 | 时间 2016 | 作者 红领巾 ] 0人收藏点击收藏
BinaryTree: python Library for Learning Binary Trees
Studying binary trees for your exam/assignment/interview? Try BinaryTree
Introduction

Are you studying binary trees for your next exam, assignment or technical interview?

BinaryTreeis a minimal Python library which provides you with a simple API to generate, visualize and inspect binary trees so you can skip the tedious work of mocking up test trees, and dive right into practising your algorithms! Heaps and BSTs (binary search trees) are also supported.

Installation

To install a stable version from PyPi :

~$ pip install binarytree

To install the latest version directly fromGitHub:

~$ git clone https://github.com/joowani/binarytree.git ~$ python binarytree/setup.py install

You may need to use sudo depending on your environment setup.

Getting Started

BinaryTreeuses the following class to represent a tree node:

class Node(object): def __init__(self, value): self.value = value self.left = None self.right = None

Generate and pretty-print binary trees:

from binarytree import tree, bst, heap, pprint # Generate a random binary tree my_tree = tree(height=5, balanced=False) # Generate a random binary search tree my_bst = bst(height=5) # Generate random max heap my_heap = heap(height=1, max=True) # Pretty print the trees in stdout pprint(my_tree) pprint(my_bst) pprint(my_heap)

List representations are also supported:

from heapq import heapify from binarytree import tree, convert, pprint my_list = [7, 3, 2, 6, 9, 4, 1, 5, 8] # Convert the list into a tree structure my_tree = convert(my_list) # Convert the list into a heap structure heapify(my_list) my_tree = convert(my_list) # Convert the tree back to a list my_list = convert(my_tree) # Pretty-printing also works on lists pprint(my_list)

Inspect a tree to quickly see its various properties:

from binarytree import tree, inspect my_tree = tree(height=10) result = inspect(my_tree) print(result['height']) print(result['is_bst']) print(result['is_height_balanced']) print(result['is_max_heap']) print(result['is_min_heap']) print(result['is_weight_balanced']) print(result['leaf_count']) print(result['max_leaf_depth']) print(result['max_value']) print(result['min_leaf_depth']) print(result['min_value']) print(result['node_count'])

Import the Node class directly to build your own trees:

from binarytree import Node, pprint root = Node(1) root.left = Node(2) root.right = Node(3) root.left.left = Node(4) root.left.right = Node(5) pprint(root)

If the default Node class does not meet your requirements, you can define and use your own custom node specification:

from binarytree import setup, tree, pprint # Define your own null/sentinel value (default: None) null = -1 # Define own node class (default: binarytree.Node) class MyNode(object): def __init__(self, data, left, right): self.data = data self.l_child = left self.r_child = right # Call setup in the beginning to apply the custom specification setup( node_init_func=lambda v: MyNode(v, null, null), node_class=MyNode, null_value=null, value_attr='data', left_attr='l_child', right_attr='r_child' ) my_custom_tree = tree() pprint(my_custom_tree)

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