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
113 lines (112 loc) • 4.04 kB
JavaScript
;
var __importDefault = (this && this.__importDefault) || function (mod) {
return (mod && mod.__esModule) ? mod : { "default": mod };
};
Object.defineProperty(exports, "__esModule", { value: true });
var centroid_1 = __importDefault(require("@turf/centroid"));
var invariant_1 = require("@turf/invariant");
var meta_1 = require("@turf/meta");
/**
* 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
*/
function pNormDistance(feature1, feature2, p) {
if (p === void 0) { p = 2; }
var coordinate1 = invariant_1.getCoord(feature1);
var coordinate2 = invariant_1.getCoord(feature2);
var xDiff = coordinate1[0] - coordinate2[0];
var yDiff = coordinate1[1] - coordinate2[1];
if (p === 1) {
return Math.abs(xDiff) + Math.abs(yDiff);
}
return Math.pow(Math.pow(xDiff, p) + Math.pow(yDiff, p), 1 / p);
}
exports.pNormDistance = pNormDistance;
/**
*
*
* @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);
*/
function distanceWeight(fc, options) {
options = options || {};
var threshold = options.threshold || 10000;
var p = options.p || 2;
var binary = options.binary || false;
var alpha = options.alpha || -1;
var rowTransform = options.standardization || false;
var features = [];
meta_1.featureEach(fc, function (feature) {
features.push(centroid_1.default(feature));
});
// computing the distance between the features
var weights = [];
for (var i = 0; i < features.length; i++) {
weights[i] = [];
}
for (var i = 0; i < features.length; i++) {
for (var j = i; j < features.length; j++) {
if (i === j) {
weights[i][j] = 0;
}
var dis = pNormDistance(features[i], features[j], p);
weights[i][j] = dis;
weights[j][i] = dis;
}
}
// binary or distance decay
for (var i = 0; i < features.length; i++) {
for (var j = 0; j < features.length; j++) {
var dis = weights[i][j];
if (dis === 0) {
continue;
}
if (binary) {
if (dis <= threshold) {
weights[i][j] = 1.0;
}
else {
weights[i][j] = 0.0;
}
}
else {
if (dis <= threshold) {
weights[i][j] = Math.pow(dis, alpha);
}
else {
weights[i][j] = 0.0;
}
}
}
}
if (rowTransform) {
for (var i = 0; i < features.length; i++) {
var rowSum = weights[i].reduce(function (sum, currentVal) {
return sum + currentVal;
}, 0);
for (var j = 0; j < features.length; j++) {
weights[i][j] = weights[i][j] / rowSum;
}
}
}
return weights;
}
exports.default = distanceWeight;