all these changes

frontend/node_modules/@turf/distance-weight/LICENSE

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+The MIT License (MIT)
+
+Copyright (c) 2017 TurfJS
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+the Software, and to permit persons to whom the Software is furnished to do so,
+subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

frontend/node_modules/@turf/distance-weight/README.md

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+# @turf/distance-weight
+
+<!-- Generated by documentation.js. Update this documentation by updating the source code. -->
+
+## pNormDistance
+
+calcualte the Minkowski p-norm distance between two features.
+
+**Parameters**
+
+-   `feature1`  point feature
+-   `feature2`  point feature
+-   `p`  p-norm 1=&lt;p&lt;=infinity 1: Manhattan distance 2: Euclidean distance
+
+## distanceWeight
+
+**Parameters**
+
+-   `fc` **[FeatureCollection](https://tools.ietf.org/html/rfc7946#section-3.3)&lt;any>** FeatureCollection.
+-   `options` **[Object](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object)?** option object.
+    -   `options.threshold` **[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)** If the distance between neighbor and
+        target features is greater than threshold, the weight of that neighbor is 0. (optional, default `10000`)
+    -   `options.p` **[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)** Minkowski p-norm distance parameter.
+        1: Manhattan distance. 2: Euclidean distance. 1=&lt;p&lt;=infinity. (optional, default `2`)
+    -   `options.binary` **[boolean](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Boolean)** If true, weight=1 if d &lt;= threshold otherwise weight=0.
+         If false, weight=Math.pow(d, alpha). (optional, default `false`)
+    -   `options.alpha` **[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)** distance decay parameter.
+        A big value means the weight decay quickly as distance increases. (optional, default `-1`)
+    -   `options.standardization` **[boolean](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Boolean)** row standardization. (optional, default `false`)
+
+**Examples**
+
+```javascript
+var bbox = [-65, 40, -63, 42];
+var dataset = turf.randomPoint(100, { bbox: bbox });
+var result = turf.distanceWeight(dataset);
+```
+
+Returns **[Array](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Array)&lt;[Array](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Array)&lt;[number](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number)>>** distance weight matrix.
+
+<!-- This file is automatically generated. Please don't edit it directly:
+if you find an error, edit the source file (likely index.js), and re-run
+./scripts/generate-readmes in the turf project. -->
+
+---
+
+This module is part of the [Turfjs project](http://turfjs.org/), an open source
+module collection dedicated to geographic algorithms. It is maintained in the
+[Turfjs/turf](https://github.com/Turfjs/turf) repository, where you can create
+PRs and issues.
+
+### Installation
+
+Install this module individually:
+
+```sh
+$ npm install @turf/distance-weight
+```
+
+Or install the Turf module that includes it as a function:
+
+```sh
+$ npm install @turf/turf
+```

frontend/node_modules/@turf/distance-weight/dist/es/index.js

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+import centroid from "@turf/centroid";
+import { getCoord } from "@turf/invariant";
+import { featureEach } from "@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
+ */
+export function pNormDistance(feature1, feature2, p) {
+    if (p === void 0) { p = 2; }
+    var coordinate1 = getCoord(feature1);
+    var coordinate2 = 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);
+}
+/**
+ *
+ *
+ * @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, 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 = [];
+    featureEach(fc, function (feature) {
+        features.push(centroid(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;
+}

frontend/node_modules/@turf/distance-weight/dist/es/package.json

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+{"type":"module"}

frontend/node_modules/@turf/distance-weight/dist/js/index.d.ts

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+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[][];

frontend/node_modules/@turf/distance-weight/dist/js/index.js

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+"use strict";
+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;

frontend/node_modules/@turf/distance-weight/package.json

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+{
+  "name": "@turf/distance-weight",
+  "version": "6.5.0",
+  "description": "turf distance-weight module",
+  "author": "Turf Authors",
+  "contributors": [
+    "Haoming Zhuang <@zhuang-hao-ming>"
+  ],
+  "license": "MIT",
+  "bugs": {
+    "url": "https://github.com/Turfjs/turf/issues"
+  },
+  "homepage": "https://github.com/Turfjs/turf",
+  "repository": {
+    "type": "git",
+    "url": "git://github.com/Turfjs/turf.git"
+  },
+  "funding": "https://opencollective.com/turf",
+  "publishConfig": {
+    "access": "public"
+  },
+  "keywords": [
+    "turf",
+    "distance-weight"
+  ],
+  "main": "dist/js/index.js",
+  "module": "dist/es/index.js",
+  "exports": {
+    "./package.json": "./package.json",
+    ".": {
+      "import": "./dist/es/index.js",
+      "require": "./dist/js/index.js"
+    }
+  },
+  "types": "dist/js/index.d.ts",
+  "sideEffects": false,
+  "files": [
+    "dist"
+  ],
+  "scripts": {
+    "bench": "ts-node bench.js",
+    "build": "npm-run-all build:*",
+    "build:es": "tsc --outDir dist/es --module esnext --declaration false && echo '{\"type\":\"module\"}' > dist/es/package.json",
+    "build:js": "tsc",
+    "docs": "node ../../scripts/generate-readmes",
+    "test": "npm-run-all test:*",
+    "test:tape": "ts-node -r esm test.js"
+  },
+  "devDependencies": {
+    "@types/tape": "*",
+    "benchmark": "*",
+    "load-json-file": "*",
+    "npm-run-all": "*",
+    "tape": "*",
+    "ts-node": "*",
+    "tslint": "*",
+    "typescript": "*",
+    "write-json-file": "*"
+  },
+  "dependencies": {
+    "@turf/centroid": "^6.5.0",
+    "@turf/helpers": "^6.5.0",
+    "@turf/invariant": "^6.5.0",
+    "@turf/meta": "^6.5.0"
+  },
+  "gitHead": "5375941072b90d489389db22b43bfe809d5e451e"
+}