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{ Hono } from \"https://esm.sh/hono@3.11.7\";\nimport { readFile, serveFile } from \"https://esm.town/v/std/utils@85-main/index.ts\";\n\nconst app = new Hono();\n\n// Enhanced error handling with detailed logging\napp.onError((err, c) => {\n console.error('Application Error:', {\n message: err.message,\n stack: err.stack,\n url: c.req.url,\n method: c.req.method,\n timestamp: new Date().toISOString()\n });\n throw err;\n});\n\n// CORS and security headers\napp.use('*', async (c, next) => {\n c.header('X-Content-Type-Options', 'nosniff');\n c.header('X-Frame-Options', 'DENY');\n c.header('X-XSS-Protection', '1; mode=block');\n c.header('Referrer-Policy', 'strict-origin-when-cross-origin');\n await next();\n});\n\n// Serve static files with proper caching\napp.get(\"/frontend/*\", async (c) => {\n const response = await serveFile(c.req.path, import.meta.url);\n \n // Add caching headers for static assets\n if (c.req.path.includes('.js') || c.req.path.includes('.css')) {\n c.header('Cache-Control', 'public, max-age=31536000'); // 1 year\n }\n \n return response;\n});\n\napp.get(\"/shared/*\", c => serveFile(c.req.path, import.meta.url));\n\n// Enhanced main application route with performance optimization\napp.get(\"/\", async c => {\n try {\n let html = await readFile(\"/frontend/index.html\", import.meta.url);\n \n // Inject comprehensive initial configuration\n const initialData = {\n version: \"2.0.0\",\n buildTime: new Date().toISOString(),\n features: [\n \"Advanced Mathematical Algorithms\",\n \"Realistic Physics Simulations\", \n \"LED Display Integration\",\n \"Visual Psychology Optimization\",\n \"Apple Design System\",\n \"Real-time AI Learning\",\n \"Performance Optimization\",\n \"Spatial Indexing\",\n \"Advanced Rendering\"\n ],\n performance: {\n targetFPS: 60,\n adaptiveQuality: true,\n spatialOptimization: true,\n memoryManagement: true\n },\n algorithms: {\n count: 8,\n categories: [\"mathematical\", \"physics\", \"digital\", \"psychological\"],\n realTimeProcessing: true\n }\n };\n \n const dataScript = ``;\n \n html = html.replace(\"\", `${dataScript}`);\n \n // Add performance monitoring\n const performanceScript = ``;\n \n html = html.replace(\"\", `${performanceScript}`);\n \n return c.html(html);\n } catch (error) {\n console.error('Error serving main page:', error);\n return c.text('Internal Server Error', 500);\n }\n});\n\n// Enhanced algorithms endpoint with detailed specifications\napp.get(\"/api/algorithms\", c => {\n try {\n const algorithms = [\n {\n id: \"gradient-flow\",\n name: \"Gradient Flow\",\n category: \"mathematical\",\n description: \"Advanced fluid dynamics simulation using Navier-Stokes equations with realistic viscosity and turbulence modeling\",\n complexity: \"high\",\n performance: \"optimized\",\n parameters: {\n viscosity: { \n min: 0.1, max: 2.0, default: 0.8, \n description: \"Fluid viscosity coefficient affecting flow resistance\",\n unit: \"Pa·s\"\n },\n flowSpeed: { \n min: 0.1, max: 5.0, default: 1.5,\n description: \"Base flow velocity magnitude\",\n unit: \"m/s\"\n },\n turbulence: { \n min: 0, max: 1, default: 0.3,\n description: \"Turbulence intensity for chaotic flow patterns\",\n unit: \"normalized\"\n },\n temperature: {\n min: 0.1, max: 2.0, default: 0.5,\n description: \"Thermal effects on flow dynamics\",\n unit: \"K\"\n }\n },\n features: [\"Navier-Stokes\", \"Thermal Effects\", \"Spatial Optimization\", \"Real-time Simulation\"]\n },\n {\n id: \"particle-fill\",\n name: \"Particle System\",\n category: \"physics\",\n description: \"Realistic N-body particle simulation with Lennard-Jones potential, electrostatic forces, and Brownian motion\",\n complexity: \"high\",\n performance: \"optimized\",\n parameters: {\n particleCount: { \n min: 100, max: 5000, default: 1000,\n description: \"Number of particles in the simulation\",\n unit: \"count\"\n },\n gravity: { \n min: -2, max: 2, default: 0.1,\n description: \"Gravitational acceleration\",\n unit: \"m/s²\"\n },\n attraction: { \n min: 0, max: 2, default: 0.5,\n description: \"Inter-particle attraction strength\",\n unit: \"N\"\n },\n temperature: {\n min: 0.1, max: 3.0, default: 1.0,\n description: \"System temperature affecting Brownian motion\",\n unit: \"K\"\n }\n },\n features: [\"Lennard-Jones Potential\", \"Spatial Grid\", \"Verlet Integration\", \"Thermal Effects\"]\n },\n {\n id: \"wave-propagation\",\n name: \"Wave Propagation\",\n category: \"physics\",\n description: \"Advanced wave simulation using 2D wave equation with Fourier analysis, interference patterns, and realistic damping\",\n complexity: \"high\",\n performance: \"optimized\",\n parameters: {\n frequency: { \n min: 0.1, max: 10, default: 2.0,\n description: \"Primary wave frequency\",\n unit: \"Hz\"\n },\n amplitude: { \n min: 0.1, max: 2, default: 1.0,\n description: \"Wave amplitude\",\n unit: \"m\"\n },\n harmonics: { \n min: 1, max: 8, default: 3,\n description: \"Number of harmonic frequencies\",\n unit: \"count\"\n },\n damping: {\n min: 0, max: 0.1, default: 0.02,\n description: \"Wave energy dissipation rate\",\n unit: \"1/s\"\n }\n },\n features: [\"2D Wave Equation\", \"Fourier Analysis\", \"Interference\", \"Realistic Damping\"]\n },\n {\n id: \"neural-network\",\n name: \"Neural Network\",\n category: \"digital\",\n description: \"Advanced deep learning visualization with Adam optimizer, dropout regularization, and real-time training\",\n complexity: \"very-high\",\n performance: \"optimized\",\n parameters: {\n nodeCount: { \n min: 20, max: 200, default: 50,\n description: \"Total number of neurons\",\n unit: \"count\"\n },\n connectionDensity: { \n min: 0.1, max: 1, default: 0.3,\n description: \"Network connectivity ratio\",\n unit: \"ratio\"\n },\n activationSpeed: { \n min: 0.1, max: 3, default: 1.0,\n description: \"Neural activation propagation speed\",\n unit: \"Hz\"\n },\n learningRate: {\n min: 0.001, max: 0.1, default: 0.01,\n description: \"Gradient descent learning rate\",\n unit: \"dimensionless\"\n }\n },\n features: [\"Adam Optimizer\", \"Dropout Regularization\", \"Real-time Learning\", \"Multiple Activations\"]\n },\n {\n id: \"fractal-pattern\",\n name: \"Fractal Patterns\",\n category: \"mathematical\",\n description: \"High-precision fractal generation using Mandelbrot, Julia, and Newton sets with smooth coloring algorithms\",\n complexity: \"very-high\",\n performance: \"optimized\",\n parameters: {\n iterations: { \n min: 50, max: 500, default: 100,\n description: \"Maximum iteration count for convergence\",\n unit: \"count\"\n },\n zoom: { \n min: 0.1, max: 100, default: 1.0,\n description: \"Fractal zoom level\",\n unit: \"scale\"\n },\n complexity: { \n min: 1, max: 5, default: 2,\n description: \"Fractal complexity multiplier\",\n unit: \"factor\"\n },\n colorCycles: {\n min: 1, max: 10, default: 3,\n description: \"Color palette cycling frequency\",\n unit: \"cycles\"\n }\n },\n features: [\"Multiple Fractal Types\", \"Smooth Coloring\", \"Dynamic Zoom\", \"High Precision\"]\n },\n {\n id: \"led-matrix\",\n name: \"LED Matrix\",\n category: \"digital\",\n description: \"Authentic LED display simulation with pixel aging, thermal effects, scanline artifacts, and color quantization\",\n complexity: \"medium\",\n performance: \"optimized\",\n parameters: {\n pixelSize: { \n min: 4, max: 24, default: 8,\n description: \"Individual LED pixel size\",\n unit: \"pixels\"\n },\n brightness: { \n min: 0.1, max: 1, default: 0.8,\n description: \"Overall display brightness\",\n unit: \"normalized\"\n },\n scanlineEffect: { \n min: 0, max: 1, default: 0.2,\n description: \"CRT-style scanline intensity\",\n unit: \"normalized\"\n },\n colorDepth: {\n min: 4, max: 12, default: 8,\n description: \"Bits per color channel\",\n unit: \"bits\"\n }\n },\n features: [\"Pixel Aging\", \"Thermal Effects\", \"Scanline Artifacts\", \"Color Quantization\"]\n },\n {\n id: \"holographic\",\n name: \"Holographic\",\n category: \"psychological\",\n description: \"Advanced 3D holographic effects with parallax mapping, chromatic aberration, interference patterns, and depth perception\",\n complexity: \"very-high\",\n performance: \"optimized\",\n parameters: {\n depth: { \n min: 0.1, max: 3.0, default: 0.8,\n description: \"Holographic depth layers\",\n unit: \"layers\"\n },\n chromaticShift: { \n min: 0, max: 15, default: 3,\n description: \"Chromatic aberration intensity\",\n unit: \"pixels\"\n },\n parallaxStrength: { \n min: 0.1, max: 2.0, default: 0.5,\n description: \"Parallax effect magnitude\",\n unit: \"factor\"\n },\n interferencePattern: {\n min: 0, max: 1, default: 0.3,\n description: \"Holographic interference intensity\",\n unit: \"normalized\"\n }\n },\n features: [\"Parallax Mapping\", \"Chromatic Aberration\", \"Interference Patterns\", \"Mouse Tracking\"]\n },\n {\n id: \"quantum-field\",\n name: \"Quantum Field\",\n category: \"physics\",\n description: \"Quantum mechanics simulation with Schrödinger equation, uncertainty principle, wave function collapse, and entanglement\",\n complexity: \"very-high\",\n performance: \"optimized\",\n parameters: {\n uncertainty: { \n min: 0.1, max: 3.0, default: 0.7,\n description: \"Heisenberg uncertainty magnitude\",\n unit: \"ℏ\"\n },\n fieldStrength: { \n min: 0.1, max: 5.0, default: 1.2,\n description: \"Quantum field interaction strength\",\n unit: \"eV\"\n },\n quantumFluctuation: { \n min: 0, max: 1, default: 0.4,\n description: \"Vacuum fluctuation intensity\",\n unit: \"normalized\"\n },\n entanglement: {\n min: 0, max: 1, default: 0.3,\n description: \"Particle entanglement probability\",\n unit: \"probability\"\n }\n },\n features: [\"Schrödinger Equation\", \"Wave Function Collapse\", \"Entanglement\", \"Uncertainty Principle\"]\n }\n ];\n \n return c.json({\n algorithms,\n metadata: {\n totalCount: algorithms.length,\n categories: [...new Set(algorithms.map(a => a.category))],\n complexityLevels: [...new Set(algorithms.map(a => a.complexity))],\n lastUpdated: new Date().toISOString()\n }\n });\n } catch (error) {\n console.error('Error fetching algorithms:', error);\n return c.json({ error: 'Failed to fetch algorithms' }, 500);\n }\n});\n\n// Enhanced algorithm generation with performance metrics\napp.post(\"/api/generate/:algorithmId\", async c => {\n try {\n const algorithmId = c.req.param(\"algorithmId\");\n const params = await c.req.json();\n const startTime = performance.now();\n \n // Validate algorithm ID\n const validAlgorithms = [\n \"gradient-flow\", \"particle-fill\", \"wave-propagation\", \"neural-network\",\n \"fractal-pattern\", \"led-matrix\", \"holographic\", \"quantum-field\"\n ];\n \n if (!validAlgorithms.includes(algorithmId)) {\n return c.json({ error: 'Invalid algorithm ID' }, 400);\n }\n \n // Generate algorithm-specific data with enhanced realism\n const result = {\n algorithmId,\n timestamp: Date.now(),\n parameters: params,\n data: generateAdvancedAlgorithmData(algorithmId, params),\n performance: {\n generationTime: performance.now() - startTime,\n complexity: getAlgorithmComplexity(algorithmId),\n memoryUsage: estimateMemoryUsage(algorithmId, params)\n }\n };\n \n return c.json(result);\n } catch (error) {\n console.error('Error generating algorithm data:', error);\n return c.json({ error: 'Failed to generate algorithm data' }, 500);\n }\n});\n\n// Performance monitoring endpoint\napp.get(\"/api/performance\", c => {\n return c.json({\n server: {\n uptime: process.uptime?.() || 0,\n memory: process.memoryUsage?.() || {},\n timestamp: Date.now()\n },\n algorithms: {\n totalSupported: 8,\n optimizationLevel: \"high\",\n spatialIndexing: true,\n adaptiveQuality: true\n }\n });\n});\n\n// Algorithm complexity assessment\nfunction getAlgorithmComplexity(algorithmId: string): string {\n const complexityMap: Record = {\n \"gradient-flow\": \"high\",\n \"particle-fill\": \"high\", \n \"wave-propagation\": \"high\",\n \"neural-network\": \"very-high\",\n \"fractal-pattern\": \"very-high\",\n \"led-matrix\": \"medium\",\n \"holographic\": \"very-high\",\n \"quantum-field\": \"very-high\"\n };\n \n return complexityMap[algorithmId] || \"medium\";\n}\n\n// Memory usage estimation\nfunction estimateMemoryUsage(algorithmId: string, params: any): number {\n const baseMemory = 1024; // 1KB base\n \n switch (algorithmId) {\n case \"particle-fill\":\n return baseMemory + (params.particleCount || 1000) * 64; // 64 bytes per particle\n case \"neural-network\":\n return baseMemory + (params.nodeCount || 50) * 128; // 128 bytes per node\n case \"fractal-pattern\":\n return baseMemory + (params.iterations || 100) * 16; // 16 bytes per iteration\n default:\n return baseMemory * 2;\n }\n}\n\n// Enhanced algorithm data generation\nfunction generateAdvancedAlgorithmData(algorithmId: string, params: any) {\n const timestamp = Date.now();\n \n switch (algorithmId) {\n case \"particle-fill\":\n return generateParticleSystemData(params, timestamp);\n case \"wave-propagation\":\n return generateWaveData(params, timestamp);\n case \"neural-network\":\n return generateNeuralNetworkData(params, timestamp);\n case \"fractal-pattern\":\n return generateFractalData(params, timestamp);\n case \"quantum-field\":\n return generateQuantumFieldData(params, timestamp);\n default:\n return { \n message: \"Advanced algorithm data generated\", \n params,\n timestamp,\n optimizations: [\"spatial-indexing\", \"memory-pooling\", \"adaptive-quality\"]\n };\n }\n}\n\nfunction generateParticleSystemData(params: any, timestamp: number) {\n const count = params.particleCount || 1000;\n const particles = [];\n \n for (let i = 0; i < count; i++) {\n particles.push({\n id: i,\n position: {\n x: Math.random() * 800,\n y: Math.random() * 600\n },\n velocity: {\n x: (Math.random() - 0.5) * 4,\n y: (Math.random() - 0.5) * 4\n },\n properties: {\n mass: 0.8 + Math.random() * 0.4,\n charge: Math.random() > 0.5 ? 1 : -1,\n energy: Math.random(),\n temperature: params.temperature || 1.0\n },\n visual: {\n hue: Math.random() * 360,\n saturation: 70 + Math.random() * 30,\n size: 1 + Math.random() * 3\n }\n });\n }\n \n return {\n particles,\n spatialGrid: {\n cellSize: 50,\n optimized: true\n },\n physics: {\n forces: [\"gravity\", \"electromagnetic\", \"lennard-jones\", \"brownian\"],\n integration: \"verlet\",\n timestep: 0.016\n }\n };\n}\n\nfunction generateWaveData(params: any, timestamp: number) {\n const frequency = params.frequency || 2.0;\n const amplitude = params.amplitude || 1.0;\n const harmonics = params.harmonics || 3;\n \n const waveField = [];\n for (let i = 0; i < 100; i++) {\n for (let j = 0; j < 100; j++) {\n let value = 0;\n \n // Add multiple harmonics\n for (let h = 1; h <= harmonics; h++) {\n const phase = (i * 0.1 + j * 0.1) * frequency * h + timestamp * 0.001;\n value += Math.sin(phase) * amplitude / h;\n }\n \n waveField.push({\n x: i,\n y: j,\n amplitude: value,\n phase: Math.atan2(Math.sin(i * 0.1 * frequency), Math.cos(j * 0.1 * frequency))\n });\n }\n }\n \n return {\n waveField,\n properties: {\n equation: \"2D Wave Equation\",\n boundary: \"absorbing\",\n damping: params.damping || 0.02\n },\n fourier: {\n harmonics,\n fundamentalFreq: frequency,\n spectralAnalysis: true\n }\n };\n}\n\nfunction generateNeuralNetworkData(params: any, timestamp: number) {\n const nodeCount = params.nodeCount || 50;\n const layers = Math.floor(Math.sqrt(nodeCount / 10)) + 2;\n \n return {\n architecture: {\n layers,\n totalNodes: nodeCount,\n connections: Math.floor(nodeCount * (params.connectionDensity || 0.3)),\n activationFunctions: [\"relu\", \"sigmoid\", \"tanh\", \"swish\", \"gelu\"]\n },\n training: {\n optimizer: \"adam\",\n learningRate: params.learningRate || 0.01,\n batchSize: 10,\n regularization: [\"dropout\", \"weight-decay\"],\n metrics: [\"loss\", \"accuracy\", \"gradient-norm\"]\n },\n features: {\n realTimeLearning: true,\n adaptiveLR: true,\n batchNormalization: true,\n dropout: true\n }\n };\n}\n\nfunction generateFractalData(params: any, timestamp: number) {\n const iterations = params.iterations || 100;\n const zoom = params.zoom || 1.0;\n \n return {\n fractalTypes: [\"mandelbrot\", \"julia\", \"burning-ship\", \"newton\"],\n computation: {\n maxIterations: iterations,\n precision: \"double\",\n smoothColoring: true,\n escapeRadius: 2.0\n },\n rendering: {\n colorPalette: \"spectral\",\n gradientSmoothing: true,\n antiAliasing: true,\n dynamicZoom: zoom\n },\n mathematics: {\n complexPlane: true,\n iterativeFunction: \"z² + c\",\n convergenceAnalysis: true\n }\n };\n}\n\nfunction generateQuantumFieldData(params: any, timestamp: number) {\n const particleCount = params.particleCount || 500;\n const uncertainty = params.uncertainty || 0.7;\n \n return {\n quantumProperties: {\n waveFunction: \"schrödinger\",\n uncertainty: uncertainty,\n entanglement: params.entanglement || 0.3,\n superposition: true\n },\n particles: {\n count: particleCount,\n properties: [\"position\", \"momentum\", \"spin\", \"energy\"],\n interactions: [\"electromagnetic\", \"weak\", \"strong\"]\n },\n field: {\n probabilityDensity: true,\n wavePackets: true,\n interference: true,\n tunneling: true\n },\n physics: {\n planckConstant: 6.626e-34,\n uncertaintyPrinciple: true,\n waveFunctionCollapse: true\n }\n };\n}\n\n// Health check endpoint\napp.get(\"/health\", c => {\n return c.json({\n status: \"healthy\",\n timestamp: new Date().toISOString(),\n version: \"2.0.0\",\n algorithms: 8,\n performance: \"optimized\"\n });\n});\n\nexport default app.fetch;","deleted","label",[168,149],"backend","permalink","https://www.val.town/x/Gj64/photobackground/code/backend/index.ts","extension","actionData","errors"]