StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics https://doi.org/10.1109/TVCG.2020.3030352
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StackGenVis/frontend/node_modules/supercluster/index.js

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import KDBush from 'kdbush';
const defaultOptions = {
minZoom: 0, // min zoom to generate clusters on
maxZoom: 16, // max zoom level to cluster the points on
radius: 40, // cluster radius in pixels
extent: 512, // tile extent (radius is calculated relative to it)
nodeSize: 64, // size of the KD-tree leaf node, affects performance
log: false, // whether to log timing info
// a reduce function for calculating custom cluster properties
reduce: null, // (accumulated, props) => { accumulated.sum += props.sum; }
// properties to use for individual points when running the reducer
map: props => props // props => ({sum: props.my_value})
};
export default class Supercluster {
constructor(options) {
this.options = extend(Object.create(defaultOptions), options);
this.trees = new Array(this.options.maxZoom + 1);
}
load(points) {
const {log, minZoom, maxZoom, nodeSize} = this.options;
if (log) console.time('total time');
const timerId = `prepare ${ points.length } points`;
if (log) console.time(timerId);
this.points = points;
// generate a cluster object for each point and index input points into a KD-tree
let clusters = [];
for (let i = 0; i < points.length; i++) {
if (!points[i].geometry) continue;
clusters.push(createPointCluster(points[i], i));
}
this.trees[maxZoom + 1] = new KDBush(clusters, getX, getY, nodeSize, Float32Array);
if (log) console.timeEnd(timerId);
// cluster points on max zoom, then cluster the results on previous zoom, etc.;
// results in a cluster hierarchy across zoom levels
for (let z = maxZoom; z >= minZoom; z--) {
const now = +Date.now();
// create a new set of clusters for the zoom and index them with a KD-tree
clusters = this._cluster(clusters, z);
this.trees[z] = new KDBush(clusters, getX, getY, nodeSize, Float32Array);
if (log) console.log('z%d: %d clusters in %dms', z, clusters.length, +Date.now() - now);
}
if (log) console.timeEnd('total time');
return this;
}
getClusters(bbox, zoom) {
let minLng = ((bbox[0] + 180) % 360 + 360) % 360 - 180;
const minLat = Math.max(-90, Math.min(90, bbox[1]));
let maxLng = bbox[2] === 180 ? 180 : ((bbox[2] + 180) % 360 + 360) % 360 - 180;
const maxLat = Math.max(-90, Math.min(90, bbox[3]));
if (bbox[2] - bbox[0] >= 360) {
minLng = -180;
maxLng = 180;
} else if (minLng > maxLng) {
const easternHem = this.getClusters([minLng, minLat, 180, maxLat], zoom);
const westernHem = this.getClusters([-180, minLat, maxLng, maxLat], zoom);
return easternHem.concat(westernHem);
}
const tree = this.trees[this._limitZoom(zoom)];
const ids = tree.range(lngX(minLng), latY(maxLat), lngX(maxLng), latY(minLat));
const clusters = [];
for (const id of ids) {
const c = tree.points[id];
clusters.push(c.numPoints ? getClusterJSON(c) : this.points[c.index]);
}
return clusters;
}
getChildren(clusterId) {
const originId = clusterId >> 5;
const originZoom = clusterId % 32;
const errorMsg = 'No cluster with the specified id.';
const index = this.trees[originZoom];
if (!index) throw new Error(errorMsg);
const origin = index.points[originId];
if (!origin) throw new Error(errorMsg);
const r = this.options.radius / (this.options.extent * Math.pow(2, originZoom - 1));
const ids = index.within(origin.x, origin.y, r);
const children = [];
for (const id of ids) {
const c = index.points[id];
if (c.parentId === clusterId) {
children.push(c.numPoints ? getClusterJSON(c) : this.points[c.index]);
}
}
if (children.length === 0) throw new Error(errorMsg);
return children;
}
getLeaves(clusterId, limit, offset) {
limit = limit || 10;
offset = offset || 0;
const leaves = [];
this._appendLeaves(leaves, clusterId, limit, offset, 0);
return leaves;
}
getTile(z, x, y) {
const tree = this.trees[this._limitZoom(z)];
const z2 = Math.pow(2, z);
const {extent, radius} = this.options;
const p = radius / extent;
const top = (y - p) / z2;
const bottom = (y + 1 + p) / z2;
const tile = {
features: []
};
this._addTileFeatures(
tree.range((x - p) / z2, top, (x + 1 + p) / z2, bottom),
tree.points, x, y, z2, tile);
if (x === 0) {
this._addTileFeatures(
tree.range(1 - p / z2, top, 1, bottom),
tree.points, z2, y, z2, tile);
}
if (x === z2 - 1) {
this._addTileFeatures(
tree.range(0, top, p / z2, bottom),
tree.points, -1, y, z2, tile);
}
return tile.features.length ? tile : null;
}
getClusterExpansionZoom(clusterId) {
let clusterZoom = (clusterId % 32) - 1;
while (clusterZoom <= this.options.maxZoom) {
const children = this.getChildren(clusterId);
clusterZoom++;
if (children.length !== 1) break;
clusterId = children[0].properties.cluster_id;
}
return clusterZoom;
}
_appendLeaves(result, clusterId, limit, offset, skipped) {
const children = this.getChildren(clusterId);
for (const child of children) {
const props = child.properties;
if (props && props.cluster) {
if (skipped + props.point_count <= offset) {
// skip the whole cluster
skipped += props.point_count;
} else {
// enter the cluster
skipped = this._appendLeaves(result, props.cluster_id, limit, offset, skipped);
// exit the cluster
}
} else if (skipped < offset) {
// skip a single point
skipped++;
} else {
// add a single point
result.push(child);
}
if (result.length === limit) break;
}
return skipped;
}
_addTileFeatures(ids, points, x, y, z2, tile) {
for (const i of ids) {
const c = points[i];
const f = {
type: 1,
geometry: [[
Math.round(this.options.extent * (c.x * z2 - x)),
Math.round(this.options.extent * (c.y * z2 - y))
]],
tags: c.numPoints ? getClusterProperties(c) : this.points[c.index].properties
};
const id = c.numPoints ? c.id : this.points[c.index].id;
if (id !== undefined) {
f.id = id;
}
tile.features.push(f);
}
}
_limitZoom(z) {
return Math.max(this.options.minZoom, Math.min(z, this.options.maxZoom + 1));
}
_cluster(points, zoom) {
const clusters = [];
const {radius, extent, reduce} = this.options;
const r = radius / (extent * Math.pow(2, zoom));
// loop through each point
for (let i = 0; i < points.length; i++) {
const p = points[i];
// if we've already visited the point at this zoom level, skip it
if (p.zoom <= zoom) continue;
p.zoom = zoom;
// find all nearby points
const tree = this.trees[zoom + 1];
const neighborIds = tree.within(p.x, p.y, r);
let numPoints = p.numPoints || 1;
let wx = p.x * numPoints;
let wy = p.y * numPoints;
let clusterProperties = reduce && numPoints > 1 ? this._map(p, true) : null;
// encode both zoom and point index on which the cluster originated
const id = (i << 5) + (zoom + 1);
for (const neighborId of neighborIds) {
const b = tree.points[neighborId];
// filter out neighbors that are already processed
if (b.zoom <= zoom) continue;
b.zoom = zoom; // save the zoom (so it doesn't get processed twice)
const numPoints2 = b.numPoints || 1;
wx += b.x * numPoints2; // accumulate coordinates for calculating weighted center
wy += b.y * numPoints2;
numPoints += numPoints2;
b.parentId = id;
if (reduce) {
if (!clusterProperties) clusterProperties = this._map(p, true);
reduce(clusterProperties, this._map(b));
}
}
if (numPoints === 1) {
clusters.push(p);
} else {
p.parentId = id;
clusters.push(createCluster(wx / numPoints, wy / numPoints, id, numPoints, clusterProperties));
}
}
return clusters;
}
_map(point, clone) {
if (point.numPoints) {
return clone ? extend({}, point.properties) : point.properties;
}
const original = this.points[point.index].properties;
const result = this.options.map(original);
return clone && result === original ? extend({}, result) : result;
}
}
function createCluster(x, y, id, numPoints, properties) {
return {
x, // weighted cluster center
y,
zoom: Infinity, // the last zoom the cluster was processed at
id, // encodes index of the first child of the cluster and its zoom level
parentId: -1, // parent cluster id
numPoints,
properties
};
}
function createPointCluster(p, id) {
const [x, y] = p.geometry.coordinates;
return {
x: lngX(x), // projected point coordinates
y: latY(y),
zoom: Infinity, // the last zoom the point was processed at
index: id, // index of the source feature in the original input array,
parentId: -1 // parent cluster id
};
}
function getClusterJSON(cluster) {
return {
type: 'Feature',
id: cluster.id,
properties: getClusterProperties(cluster),
geometry: {
type: 'Point',
coordinates: [xLng(cluster.x), yLat(cluster.y)]
}
};
}
function getClusterProperties(cluster) {
const count = cluster.numPoints;
const abbrev =
count >= 10000 ? `${Math.round(count / 1000) }k` :
count >= 1000 ? `${Math.round(count / 100) / 10 }k` : count;
return extend(extend({}, cluster.properties), {
cluster: true,
cluster_id: cluster.id,
point_count: count,
point_count_abbreviated: abbrev
});
}
// longitude/latitude to spherical mercator in [0..1] range
function lngX(lng) {
return lng / 360 + 0.5;
}
function latY(lat) {
const sin = Math.sin(lat * Math.PI / 180);
const y = (0.5 - 0.25 * Math.log((1 + sin) / (1 - sin)) / Math.PI);
return y < 0 ? 0 : y > 1 ? 1 : y;
}
// spherical mercator to longitude/latitude
function xLng(x) {
return (x - 0.5) * 360;
}
function yLat(y) {
const y2 = (180 - y * 360) * Math.PI / 180;
return 360 * Math.atan(Math.exp(y2)) / Math.PI - 90;
}
function extend(dest, src) {
for (const id in src) dest[id] = src[id];
return dest;
}
function getX(p) {
return p.x;
}
function getY(p) {
return p.y;
}