node-red-contrib-tak-registration
Version:
A Node-RED node to register to TAK and to help wrap files as datapackages to send to TAK
63 lines (62 loc) • 2.83 kB
JavaScript
;
var __importDefault = (this && this.__importDefault) || function (mod) {
return (mod && mod.__esModule) ? mod : { "default": mod };
};
Object.defineProperty(exports, "__esModule", { value: true });
var clone_1 = __importDefault(require("@turf/clone"));
var meta_1 = require("@turf/meta");
var skmeans_1 = __importDefault(require("skmeans"));
/**
* Takes a set of {@link Point|points} and partition them into clusters using the k-mean .
* It uses the [k-means algorithm](https://en.wikipedia.org/wiki/K-means_clustering)
*
* @name clustersKmeans
* @param {FeatureCollection<Point>} points to be clustered
* @param {Object} [options={}] Optional parameters
* @param {number} [options.numberOfClusters=Math.sqrt(numberOfPoints/2)] numberOfClusters that will be generated
* @param {boolean} [options.mutate=false] allows GeoJSON input to be mutated (significant performance increase if true)
* @returns {FeatureCollection<Point>} Clustered Points with an additional two properties associated to each Feature:
* - {number} cluster - the associated clusterId
* - {[number, number]} centroid - Centroid of the cluster [Longitude, Latitude]
* @example
* // create random points with random z-values in their properties
* var points = turf.randomPoint(100, {bbox: [0, 30, 20, 50]});
* var options = {numberOfClusters: 7};
* var clustered = turf.clustersKmeans(points, options);
*
* //addToMap
* var addToMap = [clustered];
*/
function clustersKmeans(points, options) {
if (options === void 0) { options = {}; }
// Default Params
var count = points.features.length;
options.numberOfClusters =
options.numberOfClusters || Math.round(Math.sqrt(count / 2));
// numberOfClusters can't be greater than the number of points
// fallbacks to count
if (options.numberOfClusters > count)
options.numberOfClusters = count;
// Clone points to prevent any mutations (enabled by default)
if (options.mutate !== true)
points = clone_1.default(points);
// collect points coordinates
var data = meta_1.coordAll(points);
// create seed to avoid skmeans to drift
var initialCentroids = data.slice(0, options.numberOfClusters);
// create skmeans clusters
var skmeansResult = skmeans_1.default(data, options.numberOfClusters, initialCentroids);
// store centroids {clusterId: [number, number]}
var centroids = {};
skmeansResult.centroids.forEach(function (coord, idx) {
centroids[idx] = coord;
});
// add associated cluster number
meta_1.featureEach(points, function (point, index) {
var clusterId = skmeansResult.idxs[index];
point.properties.cluster = clusterId;
point.properties.centroid = centroids[clusterId];
});
return points;
}
exports.default = clustersKmeans;