create a tree from an array of objects or a flat array from tree, and others operations on tree
Binary Merkle Tree operations on data
Tree operations for Kung Fig.
treejson CLI for agent-friendly JSON tree operations
Utilities for tree operations
A high-performance native B-tree database for Node.js. Made by Evelocore. With B-tree Operations.
An awesome local database management system with nodejs. Made by Evelocore. With B-tree Operations.
This lib provides some useful functionalities for tree operations. `walk`, `buildTree` and `TreeAgent`
Powerful functions for various tree operations.
Binary Merkle Tree operations on data
A simple script for tree operations, like query and update.
A lightweight JavaScript utility library with array, object and tree operations
Backs out file tree changes
walk paths fast and efficiently
A sorted list of key-value pairs in a fast, typed in-memory B+ tree with a powerful API.
Tree operations
Azure AI Projects client library.
NIS CLI for agent-friendly JSON tree operations
Bindings for the Watchman file watching service
Async and sync crawler for json object
Basic libary for Shyft-specific merkle tree operations.
Merge tree
mdast utility to remove empty paragraphs from a tree
Headless core for visual-json — the visual JSON editor. Schema-aware, embeddable, extensible.
Construct Generalized Suffix tree using Ukkonen's algorithm
Calculates the minimum set of operations that transform one tree into another.
Organizes ActiveRecord models into a tree/hierarchy using a materialized path implementation based around PostgreSQL's ltree datatype. ltree's operators ensure that queries are fast and easily understood.
VebTree is a production-quality Van Emde Boas tree implementation providing O(log log U) time complexity for insert, delete, search, successor, and predecessor operations on integer sets. The core algorithm is implemented in C++17 for maximum performance with an idiomatic Ruby API. Perfect for applications requiring fast integer set operations, range queries, and successor/predecessor lookups within a bounded universe.
A priority queue which implements a lazy binomial heap. It supports the change priority operation, being suitable for algorithms like Dijkstra's shortest path and Prim's minimum spanning tree. It can be instantiated as a min-priority queue as well as a max-priority queue.
Pure-Ruby implemention of Red-Black tree, a self-balancing binary search tree with O(log n) search, insert and delete operations.
Green Garden is the definitive Ruby gem to handle common Trees operations by using fancy code argued on Tree Traversal theorem.
HashTree::Set and HashTree::Map provides simple tree operations. It is still very much work-in-progress
Human Query Language for full text search engines. Provides a lenient parser and associated tools for a self-contained and search-engine agnostic query language suitable for use by end users. Lenient in that is will produce a parse tree for any input, given a default operator and by generally ignoring any unparsable syntax. Suitable for use by end users in that it supports potentially several operator variants and a query language not unlike some major web search and other commercial search engines.
map.rb is a string/symbol indifferent ordered hash that works in all rubies. out of the over 200 ruby gems i have written, this is the one i use every day, in all my projects. some may be accustomed to using ActiveSupport::HashWithIndiffentAccess and, although there are some similarities, map.rb is more complete, works without requiring a mountain of code, and has been in production usage for over 15 years. it has no dependencies, and suports a myriad of other, 'tree-ish' operators that will allow you to slice and dice data like a giraffee with a giant weed whacker.
== FEATURES/PROBLEMS: Chirp is a DSL for manipulating file systems. In the same way that rake is aimed at making system buildig easy, chirp is designed to make doing things to entire file systems easy. Chirp is still very much in development and you should be very careful with it: since chirp does operate on whole directory trees, it is capable of doing a lot of damage. == SYNOPSIS: FIX (code sample of usage)
In computer science, a disjoint-set data structure, also called a union–find data structure or merge–find set, is a data structure that keeps track of a set of elements partitioned into a number of disjoint (non-overlapping) subsets. It provides near-constant-time operations (bounded by the inverse Ackermann function) to add new sets, to merge existing sets, and to determine whether elements are in the same set. In addition to many other uses (see the Applications section), disjoint-sets play a key role in Kruskal's algorithm for finding the minimum spanning tree of a graph. A disjoint-set forest consists of a number of elements each of which stores an id, a parent pointer, and, in efficient algorithms, a value called the "rank". The parent pointers of elements are arranged to form one or more trees, each representing a set. If an element's parent pointer points to no other element, then the element is the root of a tree and is the representative member of its set. A set may consist of only a single element. However, if the element has a parent, the element is part of whatever set is identified by following the chain of parents upwards until a representative element (one without a parent) is reached at the root of the tree. Forests can be represented compactly in memory as arrays in which parents are indicated by their array index. Disjoint-set data structures model the partitioning of a set, for example to keep track of the connected components of an undirected graph. This model can then be used to determine whether two vertices belong to the same component, or whether adding an edge between them would result in a cycle. The Union–Find algorithm is used in high-performance implementations of unification. This data structure is used by the Boost Graph Library to implement its Incremental Connected Components functionality. It is also a key component in implementing Kruskal's algorithm to find the minimum spanning tree of a graph. Note that the implementation as disjoint-set forests doesn't allow the deletion of edges, even without path compression or the rank heuristic. Sharir and Agarwal report connections between the worst-case behavior of disjoint-sets and the length of Davenport–Schinzel sequences, a combinatorial structure from computational geometry.
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