Query and handle JSON and JavaScript objects.
High-performance JSON Pointer implementation
Some utilities for JSON pointers described by RFC 6901
json pointer - failsafe data retrieval from js and json objects
JSON parser and stringifier with JSON Pointer support
An RFC-6901 JSON Pointer implementation
Implementation of JSON Pointer (RFC 6901).
RCF 6901 implementation of JSON Pointer
Generate outline for laser pointer tool
Simple JSON Addressing.
Persistent ordered mapping from strings
Evaluate JSON Pointer expressions against ApiDOM.
Utility functions to deal with references in objects
TypeScript definitions for jsonpointer
Provides low-level interfaces and helper methods for authentication in Azure SDK
Match human-quality input to potential matches by edit distance.
Simple key-value storage with support for multiple backends
TypeScript definitions for jsonpath-plus
Kendo UI Angular Gauges
Lowercase the keys of an object
Copy a descriptor from object A to object B
A Quick description of the component
Map object keys and values into a new object
This gem creates a thin shell to encapsulate primitive literal types such as integers, floats and symbols. There are a family of wrappers which mimic the behavior of what they contain. Primitive types have several drawbacks: no constructor to call, can't create instance variables, and can't create singleton methods. There is some utility in wrapping a primitive type. You can simulate a call by reference for example. You can also simulate mutability, and pointers. Some wrappers are dedicated to holding a single type while others may hold a family of types such as the `Number` wrapper. What is interesting to note is Number objects do not derive from `Numeric`, but instead derive from `Value` (the wrapper base class); but at the same time, `Number` objects mimic the methods of `Fixnum`, `Complex`, `Float`, etc. Many of the wrappers can be used in an expression without having to call an access method. There are also new types: `Bool` which wraps `true,false` and `Property` which wraps `Hash` types. The `Property` object auto-methodizes the key names of the Hash. Also `Fraction` supports mixed fractions.
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.