Implementation of the UPWARD spec as a NodeJS server
The front-end library for modern boys and girls
Add security headers to UPWARD
Embedded Node.js database upward compatible with MongoDB
UPWARD specification, guide, and test suite.
upward-broad-rich
upward-exact
Embedded document store upward compatible with MongoDB
Recursively searches upward to find files/directories in parent paths.
an upward marquee react components
Number Counter is package for count number upward and downward.
A webpack resolver plugin to upward resolve file.
an upward marquee react components
are-upward
upward-teeth
upward module
👉 https://hyper.fun/c/material-icon-arrow-upward-outlined/1.3.0
Implementation of the UPWARD spec as a NodeJS server
``` $ yarn add @cristianodmtsb/upward-gp ```
upward-lucky
A webpack resolver plugin to upward resolve file.
UPWARD specification, guide, and test suite.
👉 https://hyper.fun/c/material-icon-arrow-upward-twotone/1.3.0
(sirojakmarp14-book-upward) - This function is used to convert multiple words into an interesting sentence containing the word sirojakmarp14-book-upward.
Small, fast `.env` parser and loader for Rust
Attribute macros for dotenvor
A fast bump allocator that supports allocation scopes / checkpoints. Aka an arena for values of arbitrary types.
Find files or directories upward in the directory tree.
Analyze a Rust crate's module graph and propose a provably-acyclic split into workspace crates, with per-cut refactor suggestions and a CI architecture-fitness guard.
Search upwards through the directories for a specific directory
Selective test runner for Rust workspaces. Runs only the tests affected by a code change, by driving rust-analyzer over LSP.
Zephyr build system companion.
command line tool for sql-fun
A package for converting to and from A1 spreadsheet notation
A package for converting to and from A1 spreadsheet notation
Visualizing p-adic numbers
Easy upwards directory traversal
ActiveRecord SQL Server Adapter. SQL Server 2012 and upward.
A simple utility for recursively searching upward for a file or directory.
This is a fork of ActiveRecord SQL Server Adapter for JRuby. SQL Server 2012 and upward.
ActiveRecord Cubrid Adapter. Cubrid 9 and upward. Based on cubrid gem.
ActiveRecord SQL Server Adapter. SQL Server 2012 and upward.
ActiveRecord SQL Server Adapter. SQL Server 2012 and upward.
mincore provides Ruby bindings for Linux cache manipulation, including cache inspection and deletion for a specific file. IMPORTANT : versions <= 0.0.9.2 have a buggy File.mincore(), 0.0.9.3 and upwards work.
Hiking Guide is a CLI Gem that allows users to browse trails in the US mid Atlantic region, taking descriptions from Hiking Upward.
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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