The goal of this module is to find the best schematic layout for a set of `SchematicChip`s and `SchematicGroups` containing `SchematicPins` connected to each other with `SchematicTraces`
Client for Apache Kafka v0.9.x, v0.10.x and v0.11.x
Move a file, directory, or symlink - Even works across devices
visx voronoi
Fluid server lambda driver components
An automatic layout system for schematics that uses the **PMARS** pattern:
visx delaunay
An Azure Storage Blob solution to store checkpoints when using Event Hubs.
D3 plugin which computes a Weighted Voronoi tesselation
Two-dimensional recursive spatial subdivision.
eslint rules published by the CDK team. Contains CDK rules and others.
Master Boot Record (MBR)
GUID Partition Table (GPT)
Scrypted plugin: Paradox Security System via MQTT (PAI/PAI-MQTT style).
Consistent dependency versions in large JavaScript Monorepos
Ensures that certain Kafka topics exist
A helper to optimistically set Symbol.toStringTag, when possible.
Set a function's length property
One-dimensional recursive spatial subdivision.
Set a function's name property
Three-dimensional recursive spatial subdivision.
Ultra Mega Enumerator is a lightweight library designed to enumerate various combinatorial objects.
Rust Binary for linux x64
Robustly set the [[Prototype]] of an object
This gem allows you to set the Partitioned cookie directive.
Set Partition
Generate integer, set and multiset partitions.
Cap2 is a Ruby library for managing the POSIX 1003.1e capabilities available in Linux kernels. These capabilities are a partitioning of the all powerful root privilege into a set of distinct privileges. See capabilites(7) for more information.
With this gem you can generate a set of any number of days for a month or for a year, is useful for example when you have to divide a month in a fixed number of days, to create table partitions based on a date.
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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