Find all sequences of elements in an array
Fast and easy parser of statements in source code in any language ✂️
ByteStream is a library making possible to manipulates single bytes and bits on pure JavaScript
Rope-based persistent sequence type
Manage ordered sequences of FluentBundles
Returns an array containing the initial elements which both input arrays have in common
A parser for ANSI escape sequences
Contains parsers and serializers for ASN.1 (currently BER only)
JavaScript parser, mangler/compressor and beautifier toolkit for ES6+
## Installation
Apply a sequence of filter functions to an object
Create parsers using parser combinators with helpful error messages
Run a series of dependent gulp tasks in order
A small JS+SVG library for drawing railroad syntax diagrams.
mdast utility to find and replace text in a tree
Universal Module for Secure Random Generator in JavaScript
Find a file or directory by walking up parent directories
An Implementation of ES Observables
Drop-in replacement for run-sequence for gulp 4
Nucleotide sequence manipulation and analysis library
Parse and generate RS-delimited JSON sequences according to draft-ietf-json-text-sequence
TypeScript definitions for rx-lite-backpressure
An xterm.js addon providing an interface for ConEmu's progress sequence. See https://conemu.github.io/en/AnsiEscapeCodes.html#ConEmu_specific_OSC for sequence details.
Very generic autoincrement plugin for mongoose
ORF Finder is a library that with a sequence of nucletotides it finds the all the possible ORFs in the sequence. It will look for a sequence that starts with a start codon and ends with a stop codon. It will default to the beggining of the sequence if it cannot find an ORF long enought with the start codons. It will also use the end of the sequence if no stop codons are present in the sequence reading frame.
Find the optimal alignment of two sequences of Ruby Objects.
A gem for finding the largest integer value from the Fibonacci Sequence smaller than the given integer
this is for ModCloth
Ruby implementation of the Sequitur algorithm. This algorithm automatically finds repetitions and hierarchical structures in a given sequence of input tokens. It encodes the input into a context-free grammar. The Sequitur algorithm can be used to a) compress a sequence of items, b) discover patterns in an sequence, c) generate grammar rules that can represent a given input.
The Keypad module provides methods to determine all sequences of letters associated to a given number according to standard phone keypad. The "inverse" function, i.e., find the digits corresponding to a certain word, is also provided.
The tool validates the input predicted genes and provides useful information (length validation, gene merge validation, sequence duplication checking, ORF finding) based on the similarities to genes in public databases.
the gem allows you to find the n largest numbers for a given input text sequence
A Ruby Gem used for calculating a consensus (most frequent) DNA sequence from an array of ALIGNED sequences. Useful for bioinformatics pipelines to create a reference sequence when using another sequence aligning utility. Also, helpful in finding consensus regions for primer design or viral genotyping.
Find rational approximation to given real number. Based on the theory of continued fractions if x = a1 + 1/(a2 + 1/(a3 + 1/(a4 + ...))) then best approximation is found by truncating this series (with some adjustments in the last term). Note the fraction can be recovered as the first column of the matrix ( a1 1 ) ( a2 1 ) ( a3 1 ) ... ( 1 0 ) ( 1 0 ) ( 1 0 ) Instead of keeping the sequence of continued fraction terms, we just keep the last partial product of these matrices.
Yatte (Yet Another Terminal Text Editor) is a minimal, experimental terminal-based text editor written in Ruby. Built with raw ANSI escape sequences and io/console, it features syntax highlighting, fuzzy file finding, project-wide search, multi-tab editing, undo/redo, git gutter indicators, and crash recovery.
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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