Advanced Customization¶
This guide explores the advanced customization capabilities of Hypergraph-DB visualization, enabling you to create unique and professional hypergraph visualizations.
Advanced Styling System¶
π¨ Dynamic Style Engine¶
Data-Driven Style Mapping¶
class StyleMapper {
constructor(visualization) {
this.viz = visualization;
this.mappings = new Map();
}
// Create style mapping rules
createMapping(property, attribute, scale) {
const mapping = {
property, // Visual property (color, size, opacity)
attribute, // Data attribute (degree, weight, type)
scale // Scale function
};
this.mappings.set(property, mapping);
return this;
}
// Apply style mappings
applyMappings(elements) {
elements.forEach(element => {
this.mappings.forEach((mapping, property) => {
const value = element.data[mapping.attribute];
const mappedValue = mapping.scale(value);
element.style[property] = mappedValue;
});
});
}
}
// Usage example
const mapper = new StyleMapper(visualization);
// Map node size based on degree
mapper.createMapping('size', 'degree',
d3.scaleLinear().domain([1, 20]).range([5, 30])
);
// Map node color based on type
mapper.createMapping('color', 'type',
d3.scaleOrdinal()
.domain(['researcher', 'institution', 'paper'])
.range(['#e74c3c', '#3498db', '#2ecc71'])
);
// Map edge opacity based on weight
mapper.createMapping('opacity', 'weight',
d3.scaleLinear().domain([0, 1]).range([0.3, 1.0])
);
π Advanced Animation System¶
Complex Transition Animations¶
class AnimationEngine {
constructor() {
this.animations = new Map();
this.timeline = gsap.timeline();
}
// Create node entrance animation
animateNodeEntry(nodes) {
return gsap.fromTo(nodes.nodes(),
{
scale: 0,
opacity: 0,
rotation: 180
},
{
scale: 1,
opacity: 1,
rotation: 0,
duration: 0.8,
ease: "back.out(1.7)",
stagger: 0.1
}
);
}
// Create edge drawing animation
animateEdgeDrawing(edges) {
// Set initial path length to 0
edges.each(function() {
const pathLength = this.getTotalLength();
d3.select(this)
.attr('stroke-dasharray', pathLength)
.attr('stroke-dashoffset', pathLength);
});
// Animate path drawing
return gsap.to(edges.nodes(), {
strokeDashoffset: 0,
duration: 1.5,
ease: "power2.inOut",
stagger: 0.05
});
}
}
3D Visualization¶
π WebGL Integration¶
Three.js Basic Setup¶
class Hypergraph3D {
constructor(container) {
this.container = container;
this.scene = new THREE.Scene();
this.camera = new THREE.PerspectiveCamera(75,
window.innerWidth / window.innerHeight, 0.1, 1000);
this.renderer = new THREE.WebGLRenderer({ antialias: true });
this.controls = new THREE.OrbitControls(this.camera, this.renderer.domElement);
this.init();
}
init() {
// Setup renderer
this.renderer.setSize(window.innerWidth, window.innerHeight);
this.renderer.setClearColor(0xffffff);
this.container.appendChild(this.renderer.domElement);
// Setup camera position
this.camera.position.set(0, 0, 100);
// Add lighting
const ambientLight = new THREE.AmbientLight(0x404040, 0.6);
this.scene.add(ambientLight);
const directionalLight = new THREE.DirectionalLight(0xffffff, 0.8);
directionalLight.position.set(50, 50, 50);
this.scene.add(directionalLight);
this.animate();
}
createNode(data) {
const geometry = new THREE.SphereGeometry(data.size || 5, 32, 32);
const material = new THREE.MeshLambertMaterial({
color: data.color || 0x3498db
});
const mesh = new THREE.Mesh(geometry, material);
mesh.position.set(data.x || 0, data.y || 0, data.z || 0);
mesh.userData = data;
this.scene.add(mesh);
return mesh;
}
}
Custom Layout Algorithms¶
π§ Machine Learning-Driven Layouts¶
t-SNE Layout¶
class TSNELayout {
constructor(nodes, options = {}) {
this.nodes = nodes;
this.perplexity = options.perplexity || 30;
this.learningRate = options.learningRate || 200;
this.maxIterations = options.maxIterations || 1000;
this.similarities = this.computeSimilarities();
this.positions = this.initializePositions();
}
computeSimilarities() {
const n = this.nodes.length;
const similarities = new Array(n).fill(null).map(() => new Array(n).fill(0));
// Compute node feature similarity
for (let i = 0; i < n; i++) {
for (let j = 0; j < n; j++) {
if (i !== j) {
similarities[i][j] = this.featureSimilarity(
this.nodes[i], this.nodes[j]
);
}
}
}
return similarities;
}
featureSimilarity(nodeA, nodeB) {
// Calculate similarity based on node attributes
const features = ['degree', 'betweenness', 'clustering'];
let similarity = 0;
features.forEach(feature => {
const diff = Math.abs((nodeA[feature] || 0) - (nodeB[feature] || 0));
similarity += Math.exp(-diff);
});
return similarity / features.length;
}
run() {
for (let iter = 0; iter < this.maxIterations; iter++) {
this.updatePositions();
if (iter % 100 === 0) {
console.log(`t-SNE iteration ${iter}`);
}
}
return this.positions;
}
}
Performance Optimization¶
β‘ Virtualization and LOD¶
Viewport Culling¶
class ViewportCulling {
constructor(visualization) {
this.viz = visualization;
this.viewportBounds = null;
this.margin = 100; // Viewport margin
}
updateViewportBounds() {
const transform = this.viz.getTransform();
const { width, height } = this.viz.getSize();
this.viewportBounds = {
left: -transform.x / transform.k - this.margin,
top: -transform.y / transform.k - this.margin,
right: (-transform.x + width) / transform.k + this.margin,
bottom: (-transform.y + height) / transform.k + this.margin
};
}
isInViewport(element) {
if (!this.viewportBounds) return true;
const { left, top, right, bottom } = this.viewportBounds;
return element.x >= left && element.x <= right &&
element.y >= top && element.y <= bottom;
}
filterVisibleElements(elements) {
this.updateViewportBounds();
return elements.filter(element => this.isInViewport(element));
}
}
Level of Detail Control¶
class LevelOfDetail {
constructor() {
this.thresholds = [
{ scale: 0.1, level: 'minimal' },
{ scale: 0.5, level: 'low' },
{ scale: 1.0, level: 'medium' },
{ scale: 2.0, level: 'high' },
{ scale: 5.0, level: 'maximum' }
];
}
getLODLevel(scale) {
for (let i = this.thresholds.length - 1; i >= 0; i--) {
if (scale >= this.thresholds[i].scale) {
return this.thresholds[i].level;
}
}
return 'minimal';
}
applyLOD(elements, scale) {
const level = this.getLODLevel(scale);
elements.forEach(element => {
switch (level) {
case 'minimal':
this.applyMinimalLOD(element);
break;
case 'low':
this.applyLowLOD(element);
break;
case 'medium':
this.applyMediumLOD(element);
break;
case 'high':
this.applyHighLOD(element);
break;
case 'maximum':
this.applyMaximumLOD(element);
break;
}
});
}
}
π Web Workers for Parallel Computing¶
Layout Computation Worker¶
// layout-worker.js
class LayoutWorker {
constructor() {
self.onmessage = this.handleMessage.bind(this);
}
handleMessage(event) {
const { type, data } = event.data;
switch (type) {
case 'forceLayout':
this.computeForceLayout(data);
break;
case 'clustering':
this.computeClustering(data);
break;
}
}
computeForceLayout(data) {
const { nodes, edges, iterations } = data;
for (let i = 0; i < iterations; i++) {
// Force-directed calculation
this.applyForces(nodes, edges);
// Report progress periodically
if (i % 10 === 0) {
self.postMessage({
type: 'progress',
iteration: i,
total: iterations
});
}
}
// Return results
self.postMessage({
type: 'layoutComplete',
positions: nodes.map(n => ({ id: n.id, x: n.x, y: n.y }))
});
}
}
new LayoutWorker();
Interactive Data Exploration¶
π Multi-dimensional Filtering¶
Dynamic Filter Component¶
class MultiDimensionalFilter {
constructor(data, container) {
this.data = data;
this.container = container;
this.filters = new Map();
this.callbacks = [];
this.createFilterUI();
}
createFilterUI() {
const filterPanel = d3.select(this.container)
.append('div')
.attr('class', 'filter-panel');
// Analyze data attributes
const attributes = this.analyzeAttributes();
attributes.forEach(attr => {
this.createAttributeFilter(filterPanel, attr);
});
}
analyzeAttributes() {
const attributes = [];
const sample = this.data[0];
Object.keys(sample).forEach(key => {
const values = this.data.map(d => d[key]);
const uniqueValues = [...new Set(values)];
const attribute = {
name: key,
type: this.detectType(values),
values: uniqueValues,
min: Math.min(...values.filter(v => typeof v === 'number')),
max: Math.max(...values.filter(v => typeof v === 'number'))
};
attributes.push(attribute);
});
return attributes;
}
}
π Real-time Data Binding¶
Data Stream Visualization¶
class RealTimeHypergraph {
constructor(container) {
this.container = container;
this.data = { nodes: [], edges: [] };
this.updateQueue = [];
this.isAnimating = false;
this.visualization = new HypergraphVisualization(container);
this.startUpdateLoop();
}
// Add data update to queue
queueUpdate(update) {
this.updateQueue.push({
...update,
timestamp: Date.now()
});
}
// Start update loop
startUpdateLoop() {
const processUpdates = () => {
if (this.updateQueue.length > 0 && !this.isAnimating) {
this.processNextUpdate();
}
requestAnimationFrame(processUpdates);
};
processUpdates();
}
// WebSocket connection for real-time data
connectWebSocket(url) {
this.ws = new WebSocket(url);
this.ws.onmessage = (event) => {
const update = JSON.parse(event.data);
this.queueUpdate(update);
};
this.ws.onopen = () => {
console.log('WebSocket connected');
};
this.ws.onclose = () => {
console.log('WebSocket disconnected');
// Attempt reconnection
setTimeout(() => this.connectWebSocket(url), 5000);
};
}
}
Next Steps¶
Now that you've mastered advanced customization techniques for Hypergraph-DB, you can:
- π Apply these techniques to real projects
- π Check the API Reference
- π‘ Explore Practical Examples
- π€ Join Community Discussions
Create amazing hypergraph visualization experiences!