Converts an array of items with ids and parent ids to a nested tree in a performant `O(n)` way. Runs in browsers and node.
Curated collection of data structures for the JavaScript/TypeScript.
Crazy fast http radix based router
Server-side DOM implementation based on Mozilla's dom.js
Server-side DOM implementation based on Mozilla's dom.js
TypeScript definitions for d3-array
JavaScript charting framework
ECMAScript (ESTree) AST walker
An unopinionated OpenAPI framework for express
Attach comments to estree nodes
filter in array tree
walk paths fast and efficiently
Rope-based persistent sequence type
Walk any kind of tree structure depth- or breadth-first. Supports promises and advanced map-reduce operations with a very small API.
Combines a list of arrays, returning a single array with unique values, using strict equality for comparisons.
TypeScript definitions for range-parser
TypeScript definitions for d3-polygon
TypeScript definitions for d3-contour
Library for fetching Country, its States and Cities
Array manipulation, ordering, searching, summarizing, etc.
a CSS selector compiler/engine
Efficient Binary heap (priority queue, binary tree) data structure for JavaScript / TypeScript. Includes JavaScript methods, Python's heapq module methods, and Java's PriorityQueue methods.
remove items from a javascript array without generating memory garbage
Codecs for various data structures
This library performs diffs of CSV data, or any table-like source. Unlike a standard diff that compares line by line, and is sensitive to the ordering of records, CSV-Diff identifies common lines by key field(s), and then compares the contents of the fields in each line. Data may be supplied in the form of CSV files, or as an array of arrays. The diff process provides a fine level of control over what to diff, and can optionally ignore certain types of changes (e.g. changes in position). CSV-Diff is particularly well suited to data in parent-child format. Parent- child data does not lend itself well to standard text diffs, as small changes in the organisation of the tree at an upper level can lead to big movements in the position of descendant records. By instead matching records by key, CSV-Diff avoids this issue, while still being able to detect changes in sibling order. This gem implements the core diff algorithm, and handles the loading and diffing of CSV files (or Arrays of Arrays). It also supports converting data in XML format into tabular form, so that it can then be processed like any other CSV or table-like source. It returns a CSVDiff object containing the details of differences in object form. This is useful for projects that need diff capability, but want to handle the reporting or actioning of differences themselves. For a pre-built diff reporting capability, see the csv-diff-report gem, which provides a command-line tool for generating diff reports in HTML, Excel, or text formats.
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.