convex-hull-wp

0.0.5 • Public • Published

convex-hull-wp

A small and fast module for generating convex hulls from a set of points based on the algorithm by Wijeweera & Pinidiyaarachchi.

Install

npm install convex-hull-wp

// Or for CLI usage
npm install convex-hull-wp -g

Documentation

Takes an array of [x, y] coordinates and returns the same

    const convexHull = require('convex-hull-wp')

    const coords = [[42, 23], [46, 15], [51, 27], [34, 22], [54, 22]]
    convexHull(coords)
    // => [ [ 34, 22 ], [ 51, 27 ], [ 54, 22 ], [ 46, 15 ], [ 34, 22 ] ]

CLI Documentation

Takes an input geojson file, and writes an output Feature Polygon

    convex-hull-wp --input /Data/in.geojson --output /Data/out.geojson
    // => Convex Hull Done

Options

--input OR -i Required The filepath of an geojson file. Eg --input some_input.geojson --output OR -o The filepath to write the output to. Eg --output hull.geojson --stdout Write the output to stdout rather than to file. If true the output file will not be written. --quiet or -q Hides any non-error messages.

Benchmarks

This library performs very well compared to equivalent js libraries.

// 10 points
// Convex Hull - WP x 1,631,956 ops/sec ±1.09% (93 runs sampled)
// monotone-convex-hull-2d x 631,516 ops/sec ±1.12% (87 runs sampled)
// convexhullJs x 712,072 ops/sec ±0.90% (91 runs sampled)
// convexHull x 569,445 ops/sec ±0.52% (94 runs sampled)
// - Fastest is Convex Hull - WP

// 1000 points
// Convex Hull - WP x 28,722 ops/sec ±0.93% (89 runs sampled)
// monotone-convex-hull-2d x 4,580 ops/sec ±0.61% (91 runs sampled)
// convexhullJs x 5,521 ops/sec ±0.88% (91 runs sampled)
// convexHull x 4,635 ops/sec ±0.87% (93 runs sampled)
// - Fastest is Convex Hull - WP

Further Reading

An Efficient Convex Hull Algorithm for a Planer Set of Points - by Wijeweera & Pinidiyaarachchi Interestingly this algorithm could be sped up by applying parallel processing to each quadant of the hull.

Readme

Keywords

none

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Install

npm i convex-hull-wp

Weekly Downloads

8

Version

0.0.5

License

MIT

Unpacked Size

35.4 kB

Total Files

10

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Collaborators

  • rowanwins