all these changes

frontend/node_modules/@turf/center-median/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/center-median/README.md

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+# @turf/center-median
+
+<!-- Generated by documentation.js. Update this documentation by updating the source code. -->
+
+## centerMedian
+
+Takes a [FeatureCollection][1] of points and calculates the median center,
+algorithimically. The median center is understood as the point that is
+requires the least total travel from all other points.
+
+Turfjs has four different functions for calculating the center of a set of
+data. Each is useful depending on circumstance.
+
+`@turf/center` finds the simple center of a dataset, by finding the
+midpoint between the extents of the data. That is, it divides in half the
+farthest east and farthest west point as well as the farthest north and
+farthest south.
+
+`@turf/center-of-mass` imagines that the dataset is a sheet of paper.
+The center of mass is where the sheet would balance on a fingertip.
+
+`@turf/center-mean` takes the averages of all the coordinates and
+produces a value that respects that. Unlike `@turf/center`, it is
+sensitive to clusters and outliers. It lands in the statistical middle of a
+dataset, not the geographical. It can also be weighted, meaning certain
+points are more important than others.
+
+`@turf/center-median` takes the mean center and tries to find, iteratively,
+a new point that requires the least amount of travel from all the points in
+the dataset. It is not as sensitive to outliers as `@turf/center`, but it is
+attracted to clustered data. It, too, can be weighted.
+
+**Bibliography**
+
+Harold W. Kuhn and Robert E. Kuenne, “An Efficient Algorithm for the
+Numerical Solution of the Generalized Weber Problem in Spatial
+Economics,” _Journal of Regional Science_ 4, no. 2 (1962): 21–33,
+doi:[https://doi.org/10.1111/j.1467-9787.1962.tb00902.x][2].
+
+James E. Burt, Gerald M. Barber, and David L. Rigby, _Elementary
+Statistics for Geographers_, 3rd ed., New York: The Guilford
+Press, 2009, 150–151.
+
+**Parameters**
+
+-   `features` **[FeatureCollection][3]&lt;any>** Any GeoJSON Feature Collection
+-   `options` **[Object][4]** Optional parameters (optional, default `{}`)
+    -   `options.weight` **[string][5]?** the property name used to weight the center
+    -   `options.tolerance` **[number][6]** the difference in distance between candidate medians at which point the algorighim stops iterating. (optional, default `0.001`)
+    -   `options.counter` **[number][6]** how many attempts to find the median, should the tolerance be insufficient. (optional, default `10`)
+
+**Examples**
+
+```javascript
+var points = turf.points([[0, 0], [1, 0], [0, 1], [5, 8]]);
+var medianCenter = turf.centerMedian(points);
+
+//addToMap
+var addToMap = [points, medianCenter]
+```
+
+Returns **[Feature][7]&lt;[Point][8]>** The median center of the collection
+
+[1]: https://tools.ietf.org/html/rfc7946#section-3.3
+
+[2]: https://doi.org/10.1111/j.1467-9787.1962.tb00902.x
+
+[3]: https://tools.ietf.org/html/rfc7946#section-3.3
+
+[4]: https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object
+
+[5]: https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/String
+
+[6]: https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Number
+
+[7]: https://tools.ietf.org/html/rfc7946#section-3.2
+
+[8]: https://tools.ietf.org/html/rfc7946#section-3.1.2
+
+<!-- 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/center-median
+```
+
+Or install the Turf module that includes it as a function:
+
+```sh
+$ npm install @turf/turf
+```

frontend/node_modules/@turf/center-median/dist/es/index.js

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+import centerMean from "@turf/center-mean";
+import distance from "@turf/distance";
+import centroid from "@turf/centroid";
+import { isNumber, point, isObject, featureCollection, } from "@turf/helpers";
+import { featureEach } from "@turf/meta";
+/**
+ * Takes a {@link FeatureCollection} of points and calculates the median center,
+ * algorithimically. The median center is understood as the point that is
+ * requires the least total travel from all other points.
+ *
+ * Turfjs has four different functions for calculating the center of a set of
+ * data. Each is useful depending on circumstance.
+ *
+ * `@turf/center` finds the simple center of a dataset, by finding the
+ * midpoint between the extents of the data. That is, it divides in half the
+ * farthest east and farthest west point as well as the farthest north and
+ * farthest south.
+ *
+ * `@turf/center-of-mass` imagines that the dataset is a sheet of paper.
+ * The center of mass is where the sheet would balance on a fingertip.
+ *
+ * `@turf/center-mean` takes the averages of all the coordinates and
+ * produces a value that respects that. Unlike `@turf/center`, it is
+ * sensitive to clusters and outliers. It lands in the statistical middle of a
+ * dataset, not the geographical. It can also be weighted, meaning certain
+ * points are more important than others.
+ *
+ * `@turf/center-median` takes the mean center and tries to find, iteratively,
+ * a new point that requires the least amount of travel from all the points in
+ * the dataset. It is not as sensitive to outliers as `@turf/center-mean`, but it is
+ * attracted to clustered data. It, too, can be weighted.
+ *
+ * **Bibliography**
+ *
+ * Harold W. Kuhn and Robert E. Kuenne, “An Efficient Algorithm for the
+ * Numerical Solution of the Generalized Weber Problem in Spatial
+ * Economics,” _Journal of Regional Science_ 4, no. 2 (1962): 21–33,
+ * doi:{@link https://doi.org/10.1111/j.1467-9787.1962.tb00902.x}.
+ *
+ * James E. Burt, Gerald M. Barber, and David L. Rigby, _Elementary
+ * Statistics for Geographers_, 3rd ed., New York: The Guilford
+ * Press, 2009, 150–151.
+ *
+ * @name centerMedian
+ * @param {FeatureCollection<any>} features Any GeoJSON Feature Collection
+ * @param {Object} [options={}] Optional parameters
+ * @param {string} [options.weight] the property name used to weight the center
+ * @param {number} [options.tolerance=0.001] the difference in distance between candidate medians at which point the algorighim stops iterating.
+ * @param {number} [options.counter=10] how many attempts to find the median, should the tolerance be insufficient.
+ * @returns {Feature<Point>} The median center of the collection
+ * @example
+ * var points = turf.points([[0, 0], [1, 0], [0, 1], [5, 8]]);
+ * var medianCenter = turf.centerMedian(points);
+ *
+ * //addToMap
+ * var addToMap = [points, medianCenter]
+ */
+function centerMedian(features, options) {
+    if (options === void 0) { options = {}; }
+    // Optional params
+    options = options || {};
+    if (!isObject(options))
+        throw new Error("options is invalid");
+    var counter = options.counter || 10;
+    if (!isNumber(counter))
+        throw new Error("counter must be a number");
+    var weightTerm = options.weight;
+    // Calculate mean center:
+    var meanCenter = centerMean(features, { weight: options.weight });
+    // Calculate center of every feature:
+    var centroids = featureCollection([]);
+    featureEach(features, function (feature) {
+        var _a;
+        centroids.features.push(centroid(feature, {
+            properties: { weight: (_a = feature.properties) === null || _a === void 0 ? void 0 : _a[weightTerm] },
+        }));
+    });
+    var properties = {
+        tolerance: options.tolerance,
+        medianCandidates: [],
+    };
+    return findMedian(meanCenter.geometry.coordinates, [0, 0], centroids, properties, counter);
+}
+/**
+ * Recursive function to find new candidate medians.
+ *
+ * @private
+ * @param {Position} candidateMedian current candidate median
+ * @param {Position} previousCandidate the previous candidate median
+ * @param {FeatureCollection<Point>} centroids the collection of centroids whose median we are determining
+ * @param {number} counter how many attempts to try before quitting.
+ * @returns {Feature<Point>} the median center of the dataset.
+ */
+function findMedian(candidateMedian, previousCandidate, centroids, properties, counter) {
+    var tolerance = properties.tolerance || 0.001;
+    var candidateXsum = 0;
+    var candidateYsum = 0;
+    var kSum = 0;
+    var centroidCount = 0;
+    featureEach(centroids, function (theCentroid) {
+        var _a;
+        var weightValue = (_a = theCentroid.properties) === null || _a === void 0 ? void 0 : _a.weight;
+        var weight = weightValue === undefined || weightValue === null ? 1 : weightValue;
+        weight = Number(weight);
+        if (!isNumber(weight))
+            throw new Error("weight value must be a number");
+        if (weight > 0) {
+            centroidCount += 1;
+            var distanceFromCandidate = weight * distance(theCentroid, candidateMedian);
+            if (distanceFromCandidate === 0)
+                distanceFromCandidate = 1;
+            var k = weight / distanceFromCandidate;
+            candidateXsum += theCentroid.geometry.coordinates[0] * k;
+            candidateYsum += theCentroid.geometry.coordinates[1] * k;
+            kSum += k;
+        }
+    });
+    if (centroidCount < 1)
+        throw new Error("no features to measure");
+    var candidateX = candidateXsum / kSum;
+    var candidateY = candidateYsum / kSum;
+    if (centroidCount === 1 ||
+        counter === 0 ||
+        (Math.abs(candidateX - previousCandidate[0]) < tolerance &&
+            Math.abs(candidateY - previousCandidate[1]) < tolerance)) {
+        return point([candidateX, candidateY], {
+            medianCandidates: properties.medianCandidates,
+        });
+    }
+    else {
+        properties.medianCandidates.push([candidateX, candidateY]);
+        return findMedian([candidateX, candidateY], candidateMedian, centroids, properties, counter - 1);
+    }
+}
+export default centerMedian;

frontend/node_modules/@turf/center-median/dist/es/package.json

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+{"type":"module"}

frontend/node_modules/@turf/center-median/dist/js/index.d.ts

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+import { FeatureCollection, Feature, Point, Position } from "@turf/helpers";
+/**
+ * Takes a {@link FeatureCollection} of points and calculates the median center,
+ * algorithimically. The median center is understood as the point that is
+ * requires the least total travel from all other points.
+ *
+ * Turfjs has four different functions for calculating the center of a set of
+ * data. Each is useful depending on circumstance.
+ *
+ * `@turf/center` finds the simple center of a dataset, by finding the
+ * midpoint between the extents of the data. That is, it divides in half the
+ * farthest east and farthest west point as well as the farthest north and
+ * farthest south.
+ *
+ * `@turf/center-of-mass` imagines that the dataset is a sheet of paper.
+ * The center of mass is where the sheet would balance on a fingertip.
+ *
+ * `@turf/center-mean` takes the averages of all the coordinates and
+ * produces a value that respects that. Unlike `@turf/center`, it is
+ * sensitive to clusters and outliers. It lands in the statistical middle of a
+ * dataset, not the geographical. It can also be weighted, meaning certain
+ * points are more important than others.
+ *
+ * `@turf/center-median` takes the mean center and tries to find, iteratively,
+ * a new point that requires the least amount of travel from all the points in
+ * the dataset. It is not as sensitive to outliers as `@turf/center-mean`, but it is
+ * attracted to clustered data. It, too, can be weighted.
+ *
+ * **Bibliography**
+ *
+ * Harold W. Kuhn and Robert E. Kuenne, “An Efficient Algorithm for the
+ * Numerical Solution of the Generalized Weber Problem in Spatial
+ * Economics,” _Journal of Regional Science_ 4, no. 2 (1962): 21–33,
+ * doi:{@link https://doi.org/10.1111/j.1467-9787.1962.tb00902.x}.
+ *
+ * James E. Burt, Gerald M. Barber, and David L. Rigby, _Elementary
+ * Statistics for Geographers_, 3rd ed., New York: The Guilford
+ * Press, 2009, 150–151.
+ *
+ * @name centerMedian
+ * @param {FeatureCollection<any>} features Any GeoJSON Feature Collection
+ * @param {Object} [options={}] Optional parameters
+ * @param {string} [options.weight] the property name used to weight the center
+ * @param {number} [options.tolerance=0.001] the difference in distance between candidate medians at which point the algorighim stops iterating.
+ * @param {number} [options.counter=10] how many attempts to find the median, should the tolerance be insufficient.
+ * @returns {Feature<Point>} The median center of the collection
+ * @example
+ * var points = turf.points([[0, 0], [1, 0], [0, 1], [5, 8]]);
+ * var medianCenter = turf.centerMedian(points);
+ *
+ * //addToMap
+ * var addToMap = [points, medianCenter]
+ */
+declare function centerMedian(features: FeatureCollection<any>, options?: {
+    weight?: string;
+    tolerance?: number;
+    counter?: number;
+}): Feature<Point, {
+    medianCandidates: Array<Position>;
+    [key: string]: any;
+}>;
+export default centerMedian;

frontend/node_modules/@turf/center-median/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 center_mean_1 = __importDefault(require("@turf/center-mean"));
+var distance_1 = __importDefault(require("@turf/distance"));
+var centroid_1 = __importDefault(require("@turf/centroid"));
+var helpers_1 = require("@turf/helpers");
+var meta_1 = require("@turf/meta");
+/**
+ * Takes a {@link FeatureCollection} of points and calculates the median center,
+ * algorithimically. The median center is understood as the point that is
+ * requires the least total travel from all other points.
+ *
+ * Turfjs has four different functions for calculating the center of a set of
+ * data. Each is useful depending on circumstance.
+ *
+ * `@turf/center` finds the simple center of a dataset, by finding the
+ * midpoint between the extents of the data. That is, it divides in half the
+ * farthest east and farthest west point as well as the farthest north and
+ * farthest south.
+ *
+ * `@turf/center-of-mass` imagines that the dataset is a sheet of paper.
+ * The center of mass is where the sheet would balance on a fingertip.
+ *
+ * `@turf/center-mean` takes the averages of all the coordinates and
+ * produces a value that respects that. Unlike `@turf/center`, it is
+ * sensitive to clusters and outliers. It lands in the statistical middle of a
+ * dataset, not the geographical. It can also be weighted, meaning certain
+ * points are more important than others.
+ *
+ * `@turf/center-median` takes the mean center and tries to find, iteratively,
+ * a new point that requires the least amount of travel from all the points in
+ * the dataset. It is not as sensitive to outliers as `@turf/center-mean`, but it is
+ * attracted to clustered data. It, too, can be weighted.
+ *
+ * **Bibliography**
+ *
+ * Harold W. Kuhn and Robert E. Kuenne, “An Efficient Algorithm for the
+ * Numerical Solution of the Generalized Weber Problem in Spatial
+ * Economics,” _Journal of Regional Science_ 4, no. 2 (1962): 21–33,
+ * doi:{@link https://doi.org/10.1111/j.1467-9787.1962.tb00902.x}.
+ *
+ * James E. Burt, Gerald M. Barber, and David L. Rigby, _Elementary
+ * Statistics for Geographers_, 3rd ed., New York: The Guilford
+ * Press, 2009, 150–151.
+ *
+ * @name centerMedian
+ * @param {FeatureCollection<any>} features Any GeoJSON Feature Collection
+ * @param {Object} [options={}] Optional parameters
+ * @param {string} [options.weight] the property name used to weight the center
+ * @param {number} [options.tolerance=0.001] the difference in distance between candidate medians at which point the algorighim stops iterating.
+ * @param {number} [options.counter=10] how many attempts to find the median, should the tolerance be insufficient.
+ * @returns {Feature<Point>} The median center of the collection
+ * @example
+ * var points = turf.points([[0, 0], [1, 0], [0, 1], [5, 8]]);
+ * var medianCenter = turf.centerMedian(points);
+ *
+ * //addToMap
+ * var addToMap = [points, medianCenter]
+ */
+function centerMedian(features, options) {
+    if (options === void 0) { options = {}; }
+    // Optional params
+    options = options || {};
+    if (!helpers_1.isObject(options))
+        throw new Error("options is invalid");
+    var counter = options.counter || 10;
+    if (!helpers_1.isNumber(counter))
+        throw new Error("counter must be a number");
+    var weightTerm = options.weight;
+    // Calculate mean center:
+    var meanCenter = center_mean_1.default(features, { weight: options.weight });
+    // Calculate center of every feature:
+    var centroids = helpers_1.featureCollection([]);
+    meta_1.featureEach(features, function (feature) {
+        var _a;
+        centroids.features.push(centroid_1.default(feature, {
+            properties: { weight: (_a = feature.properties) === null || _a === void 0 ? void 0 : _a[weightTerm] },
+        }));
+    });
+    var properties = {
+        tolerance: options.tolerance,
+        medianCandidates: [],
+    };
+    return findMedian(meanCenter.geometry.coordinates, [0, 0], centroids, properties, counter);
+}
+/**
+ * Recursive function to find new candidate medians.
+ *
+ * @private
+ * @param {Position} candidateMedian current candidate median
+ * @param {Position} previousCandidate the previous candidate median
+ * @param {FeatureCollection<Point>} centroids the collection of centroids whose median we are determining
+ * @param {number} counter how many attempts to try before quitting.
+ * @returns {Feature<Point>} the median center of the dataset.
+ */
+function findMedian(candidateMedian, previousCandidate, centroids, properties, counter) {
+    var tolerance = properties.tolerance || 0.001;
+    var candidateXsum = 0;
+    var candidateYsum = 0;
+    var kSum = 0;
+    var centroidCount = 0;
+    meta_1.featureEach(centroids, function (theCentroid) {
+        var _a;
+        var weightValue = (_a = theCentroid.properties) === null || _a === void 0 ? void 0 : _a.weight;
+        var weight = weightValue === undefined || weightValue === null ? 1 : weightValue;
+        weight = Number(weight);
+        if (!helpers_1.isNumber(weight))
+            throw new Error("weight value must be a number");
+        if (weight > 0) {
+            centroidCount += 1;
+            var distanceFromCandidate = weight * distance_1.default(theCentroid, candidateMedian);
+            if (distanceFromCandidate === 0)
+                distanceFromCandidate = 1;
+            var k = weight / distanceFromCandidate;
+            candidateXsum += theCentroid.geometry.coordinates[0] * k;
+            candidateYsum += theCentroid.geometry.coordinates[1] * k;
+            kSum += k;
+        }
+    });
+    if (centroidCount < 1)
+        throw new Error("no features to measure");
+    var candidateX = candidateXsum / kSum;
+    var candidateY = candidateYsum / kSum;
+    if (centroidCount === 1 ||
+        counter === 0 ||
+        (Math.abs(candidateX - previousCandidate[0]) < tolerance &&
+            Math.abs(candidateY - previousCandidate[1]) < tolerance)) {
+        return helpers_1.point([candidateX, candidateY], {
+            medianCandidates: properties.medianCandidates,
+        });
+    }
+    else {
+        properties.medianCandidates.push([candidateX, candidateY]);
+        return findMedian([candidateX, candidateY], candidateMedian, centroids, properties, counter - 1);
+    }
+}
+exports.default = centerMedian;

frontend/node_modules/@turf/center-median/package.json

@@ -0,0 +1,72 @@
+{
+  "name": "@turf/center-median",
+  "version": "6.5.0",
+  "description": "turf center-median module",
+  "author": "Turf Authors",
+  "contributors": [
+    "Moacir P. de Sá Pereira <@muziejus>"
+  ],
+  "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",
+    "center-median"
+  ],
+  "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": {
+    "@turf/center": "^6.5.0",
+    "@turf/center-of-mass": "^6.5.0",
+    "@turf/random": "^6.5.0",
+    "@turf/truncate": "^6.5.0",
+    "@types/tape": "*",
+    "benchmark": "*",
+    "load-json-file": "*",
+    "npm-run-all": "*",
+    "tape": "*",
+    "ts-node": "*",
+    "tslint": "*",
+    "typescript": "*",
+    "write-json-file": "*"
+  },
+  "dependencies": {
+    "@turf/center-mean": "^6.5.0",
+    "@turf/centroid": "^6.5.0",
+    "@turf/distance": "^6.5.0",
+    "@turf/helpers": "^6.5.0",
+    "@turf/meta": "^6.5.0"
+  },
+  "gitHead": "5375941072b90d489389db22b43bfe809d5e451e"
+}