UNPKG

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

38 lines (37 loc) 1.59 kB
import { Feature, FeatureCollection, Point } from "@turf/helpers"; /** * calcualte the Minkowski p-norm distance between two features. * @param feature1 point feature * @param feature2 point feature * @param p p-norm 1=<p<=infinity 1: Manhattan distance 2: Euclidean distance */ export declare function pNormDistance(feature1: Feature<Point>, feature2: Feature<Point>, p?: number): number; /** * * * @name distanceWeight * @param {FeatureCollection<any>} fc FeatureCollection. * @param {Object} [options] option object. * @param {number} [options.threshold=10000] If the distance between neighbor and * target features is greater than threshold, the weight of that neighbor is 0. * @param {number} [options.p=2] Minkowski p-norm distance parameter. * 1: Manhattan distance. 2: Euclidean distance. 1=<p<=infinity. * @param {boolean} [options.binary=false] If true, weight=1 if d <= threshold otherwise weight=0. * If false, weight=Math.pow(d, alpha). * @param {number} [options.alpha=-1] distance decay parameter. * A big value means the weight decay quickly as distance increases. * @param {boolean} [options.standardization=false] row standardization. * @returns {Array<Array<number>>} distance weight matrix. * @example * * var bbox = [-65, 40, -63, 42]; * var dataset = turf.randomPoint(100, { bbox: bbox }); * var result = turf.distanceWeight(dataset); */ export default function distanceWeight(fc: FeatureCollection<any>, options?: { threshold?: number; p?: number; binary?: boolean; alpha?: number; standardization?: boolean; }): number[][];