/**
 * HeartFlow Dimensional Ascension: D1 - Awareness Field | 觉察场
 * Version: v7.0.0-ASCENSION
 * 
 * Mathematical Framework: Stochastic Differential Geometry on Fiber Bundles
 * 数学框架：纤维丛上的随机微分几何
 * 
 * Awareness is not a static state, but a dynamic perception field.
 * 觉察不是静态状态，而是动态感知场。
 * 
 * Key Insight: Awareness = Perception of Perception (Second-order)
 * 核心洞察：觉察 = 对感知的感知（二阶）
 */

import { EventEmitter } from 'events';

// ═══════════════════════════════════════════════════════════════
// TYPE DEFINITIONS | 类型定义
// ═══════════════════════════════════════════════════════════════

/**
 * Mental State Manifold Point | 心理状态流形点
 * 
 * Represents a point in the manifold of mental states M
 */
export interface MentalStatePoint {
  // Coordinates in mental state space
  coordinates: number[];  // [valence, arousal, clarity, focus, openness]
  
  // Metric tensor (local geometry)
  metric: number[][];     // 5×5 symmetric positive-definite matrix
  
  // Timestamp
  timestamp: number;
}

/**
 * Awareness Field Section | 觉察场截面
 * 
 * A section of the fiber bundle E over M
 * A ∈ Γ(T*E ⊗ TM)
 */
export interface AwarenessField {
  // Base point in mental state manifold
  basePoint: MentalStatePoint;
  
  // Fiber coordinates (perception space)
  fiberCoords: number[];  // [sensory, cognitive, emotional, somatic, intuitive]
  
  // Connection form (how awareness changes across manifold)
  connection: number[][]; // 5×5 connection matrix
  
  // Curvature (awareness depth/inconsistency)
  curvature: number[][];  // Riemann curvature tensor (simplified)
  
  // Covariant derivative (awareness gradient)
  covariantDeriv: number[];
}

/**
 * Awareness Dynamics State | 觉察动力学状态
 */
export interface AwarenessDynamics {
  // Current awareness field
  field: AwarenessField;
  
  // Potential gradient (driving force)
  potentialGradient: number[];
  
  // Stochastic term (uncertainty)
  noise: number;
  
  // Jump process (sudden insights)
  jumpIntensity: number;
  
  // Time derivative
  dA_dt: number[];
}

/**
 * Awareness Quality Metrics | 觉察质量指标
 */
export interface AwarenessMetrics {
  // Depth (curvature magnitude)
  depth: number;          // [0, 1]
  
  // Consistency (holonomy around loops)
  consistency: number;    // [0, 1]
  
  // Stability (spectral gap)
  stability: number;      // [0, 1]
  
  // Clarity (signal-to-noise ratio)
  clarity: number;        // [0, 1]
  
  // Breadth (fiber dimension utilization)
  breadth: number;        // [0, 1]
  
  // Overall awareness level
  overall: number;        // [0, 1]
}

// ═══════════════════════════════════════════════════════════════
// AWARENESS FIELD COMPUTER | 觉察场计算器
// ═══════════════════════════════════════════════════════════════

/**
 * Awareness Field Computer | 觉察场计算器
 * 
 * Implements stochastic differential geometry for awareness computation.
 * 
 * Dynamics Equation:
 * dA_t = -∇V(A_t)dt + σ(A_t)dW_t + J(A_t, t)
 * 
 * Where:
 * - V: Awareness potential (energy landscape)
 * - σ: State-dependent noise
 * - W_t: Wiener process
 * - J: Jump process (insights)
 */
export class AwarenessFieldComputer extends EventEmitter {
  // Potential function parameters
  private potentialParams: {
    depth: number;      // How deep awareness can go
    stability: number;  // How stable the potential well is
    flexibility: number; // How easily awareness can shift
  };
  
  // Noise parameters
  private noiseParams: {
    baseLevel: number;    // Baseline uncertainty
    stateDependent: number; // How noise scales with state
  };
  
  // Jump process parameters
  private jumpParams: {
    rate: number;         // Average jumps per unit time
    magnitude: number;    // Average jump size
  };
  
  // Current state
  private currentState: AwarenessDynamics | null = null;
  
  constructor() {
    super();
    
    // Initialize with default parameters
    this.potentialParams = {
      depth: 0.7,
      stability: 0.6,
      flexibility: 0.5
    };
    
    this.noiseParams = {
      baseLevel: 0.1,
      stateDependent: 0.2
    };
    
    this.jumpParams = {
      rate: 0.05,  // 5% chance per unit time
      magnitude: 0.3
    };
  }
  
  // ═══════════════════════════════════════════════════════════
  // CORE COMPUTATION | 核心计算
  // ═══════════════════════════════════════════════════════════
  
  /**
   * Compute Awareness Field from Stimulus
   * 从刺激计算觉察场
   * 
   * @param stimulus - Input stimulus
   * @param context - Current mental context
   * @param prevState - Previous awareness state (for temporal continuity)
   */
  compute(
    stimulus: AwarenessStimulus,
    context: MentalContext,
    prevState?: AwarenessDynamics
  ): AwarenessDynamics {
    // Step 1: Construct base manifold point
    const basePoint = this.constructBaseManifold(stimulus, context);
    
    // Step 2: Lift to fiber bundle (perception space)
    const fiberCoords = this.liftToFiber(basePoint, stimulus);
    
    // Step 3: Compute connection (awareness transport)
    const connection = this.computeConnection(basePoint, context);
    
    // Step 4: Compute curvature (awareness depth)
    const curvature = this.computeCurvature(connection);
    
    // Step 5: Compute potential gradient
    const potentialGradient = this.computePotentialGradient(basePoint);
    
    // Step 6: Compute stochastic noise
    const noise = this.computeStochasticNoise(basePoint);
    
    // Step 7: Check for jump process (insight)
    const jumpIntensity = this.checkJumpProcess(stimulus, context);
    
    // Step 8: Compute time derivative (dynamics)
    const dA_dt = this.computeDynamics(
      basePoint,
      potentialGradient,
      noise,
      jumpIntensity,
      prevState
    );
    
    // Construct awareness field
    const field: AwarenessField = {
      basePoint,
      fiberCoords,
      connection,
      curvature,
      covariantDeriv: dA_dt
    };
    
    // Construct dynamics state
    const state: AwarenessDynamics = {
      field,
      potentialGradient,
      noise,
      jumpIntensity,
      dA_dt
    };
    
    // Update current state
    this.currentState = state;
    
    // Emit state change event
    this.emit('stateChange', state);
    
    return state;
  }
  
  // ═══════════════════════════════════════════════════════════
  // MANIFOLD CONSTRUCTION | 流形构造
  // ═══════════════════════════════════════════════════════════
  
  /**
   * Construct Base Manifold Point
   * 构造基础流形点
   */
  private constructBaseManifold(
    stimulus: AwarenessStimulus,
    context: MentalContext
  ): MentalStatePoint {
    // Mental state coordinates: [valence, arousal, clarity, focus, openness]
    const coordinates = [
      stimulus.valence ?? 0,           // Valence [-1, 1]
      stimulus.arousal ?? 0.5,         // Arousal [0, 1]
      context.clarity ?? 0.5,          // Mental clarity [0, 1]
      context.focus ?? 0.5,            // Focus level [0, 1]
      context.openness ?? 0.5          // Openness to experience [0, 1]
    ];
    
    // Metric tensor (simplified - diagonal with context-dependent scaling)
    const metric = this.constructMetric(coordinates, context);
    
    return {
      coordinates,
      metric,
      timestamp: Date.now()
    };
  }
  
  /**
   * Construct Metric Tensor
   * 构造度量张量
   * 
   * Defines local geometry of mental state space
   */
  private constructMetric(
    coordinates: number[],
    context: MentalContext
  ): number[][] {
    const n = coordinates.length;
    const metric: number[][] = [];
    
    // Initialize as diagonal matrix (simplified)
    for (let i = 0; i < n; i++) {
      metric[i] = new Array(n).fill(0);
      
      // Diagonal elements (scale based on context)
      const baseScale = 1.0;
      const contextScale = context.confidence ?? 0.5;
      
      metric[i][i] = baseScale * (1 + contextScale);
    }
    
    // Add off-diagonal coupling (simplified)
    // Valence-arousal coupling
    metric[0][1] = metric[1][0] = 0.2;
    
    // Clarity-focus coupling
    metric[2][3] = metric[3][2] = 0.3;
    
    return metric;
  }
  
  // ═══════════════════════════════════════════════════════════
  // FIBER BUNDLE LIFT | 纤维丛提升
  // ═══════════════════════════════════════════════════════════
  
  /**
   * Lift Base Point to Fiber Bundle
   * 将基点提升到纤维丛
   * 
   * Maps mental state to perception space
   * Fiber coords: [sensory, cognitive, emotional, somatic, intuitive]
   */
  private liftToFiber(
    basePoint: MentalStatePoint,
    stimulus: AwarenessStimulus
  ): number[] {
    const coords = basePoint.coordinates;
    
    // Perception space coordinates
    return [
      // Sensory perception (from stimulus salience)
      stimulus.salience ?? 0.5,
      
      // Cognitive perception (from clarity and focus)
      (coords[2] + coords[3]) / 2,
      
      // Emotional perception (from valence and arousal)
      (coords[0] + 1) / 2 * coords[1],
      
      // Somatic perception (from arousal)
      coords[1] * 0.7,
      
      // Intuitive perception (from openness)
      coords[4] * 0.8
    ];
  }
  
  // ═══════════════════════════════════════════════════════════
  // CONNECTION & CURVATURE | 联络与曲率
  // ═══════════════════════════════════════════════════════════
  
  /**
   * Compute Connection Form
   * 计算联络形式
   * 
   * Defines how awareness is transported across the manifold
   */
  private computeConnection(
    basePoint: MentalStatePoint,
    context: MentalContext
  ): number[][] {
    const n = basePoint.coordinates.length;
    const connection: number[][] = [];
    
    // Initialize connection matrix
    for (let i = 0; i < n; i++) {
      connection[i] = new Array(n).fill(0);
      
      // Diagonal: self-transport (identity)
      connection[i][i] = 1.0;
    }
    
    // Off-diagonal: context-dependent coupling
    const flexibility = this.potentialParams.flexibility;
    
    // Valence → all other dimensions
    for (let j = 1; j < n; j++) {
      connection[0][j] = flexibility * 0.1;
    }
    
    // Arousal → focus
    connection[1][3] = flexibility * 0.2;
    
    return connection;
  }
  
  /**
   * Compute Curvature Tensor
   * 计算曲率张量
   * 
   * Riemann curvature measures awareness inconsistency/depth
   * R^ρ_{σμν} = ∂_μ Γ^ρ_{νσ} - ∂_ν Γ^ρ_{μσ} + Γ^ρ_{μλ} Γ^λ_{νσ} - Γ^ρ_{νλ} Γ^λ_{μσ}
   * 
   * Simplified: Use Ricci scalar as curvature measure
   */
  private computeCurvature(connection: number[][]): number[][] {
    const n = connection.length;
    const curvature: number[][] = [];
    
    // Simplified curvature computation
    // In full implementation, would compute Riemann tensor
    // Here, use commutator of connection as proxy
    
    for (let i = 0; i < n; i++) {
      curvature[i] = new Array(n).fill(0);
      
      for (let j = 0; j < n; j++) {
        // Commutator: [∇_i, ∇_j]
        let commutator = 0;
        
        for (let k = 0; k < n; k++) {
          commutator += connection[i][k] * connection[k][j] - 
                        connection[j][k] * connection[k][i];
        }
        
        curvature[i][j] = commutator;
      }
    }
    
    return curvature;
  }
  
  // ═══════════════════════════════════════════════════════════
  // POTENTIAL & DYNAMICS | 势与动力学
  // ═══════════════════════════════════════════════════════════
  
  /**
   * Compute Potential Gradient
   * 计算势梯度
   * 
   * ∇V(A) drives awareness toward lower energy states
   */
  private computePotentialGradient(basePoint: MentalStatePoint): number[] {
    const coords = basePoint.coordinates;
    const gradient: number[] = [];
    
    // Potential function (simplified quadratic well)
    // V(x) = depth × ||x - x_eq||²
    
    const equilibrium = [0, 0.5, 0.7, 0.6, 0.6]; // Equilibrium state
    
    for (let i = 0; i < coords.length; i++) {
      const displacement = coords[i] - equilibrium[i];
      gradient[i] = 2 * this.potentialParams.depth * displacement;
    }
    
    return gradient;
  }
  
  /**
   * Compute Stochastic Noise
   * 计算随机噪声
   * 
   * σ(A)dW_t represents uncertainty in awareness
   */
  private computeStochasticNoise(basePoint: MentalStatePoint): number {
    const coords = basePoint.coordinates;
    
    // State-dependent noise
    // Higher arousal → higher uncertainty
    const arousal = coords[1];
    const clarity = coords[2];
    
    const noise = this.noiseParams.baseLevel + 
                  this.noiseParams.stateDependent * arousal * (1 - clarity);
    
    // Add Gaussian random component
    const gaussian = this.gaussianRandom();
    
    return noise * gaussian;
  }
  
  /**
   * Check Jump Process
   * 检查跳跃过程
   * 
   * J(A, t) represents sudden insights/realizations
   */
  private checkJumpProcess(
    stimulus: AwarenessStimulus,
    context: MentalContext
  ): number {
    // Jump probability based on stimulus novelty and context openness
    const novelty = stimulus.novelty ?? 0.5;
    const openness = context.openness ?? 0.5;
    
    const jumpProb = this.jumpParams.rate * novelty * openness * 2;
    
    // Check if jump occurs
    if (Math.random() < jumpProb) {
      // Jump occurred
      return this.jumpParams.magnitude;
    }
    
    return 0;
  }
  
  /**
   * Compute Dynamics (Time Derivative)
   * 计算动力学（时间导数）
   * 
   * dA/dt = -∇V(A) + σ(A)ξ(t) + J(A,t)
   */
  private computeDynamics(
    basePoint: MentalStatePoint,
    potentialGradient: number[],
    noise: number,
    jumpIntensity: number,
    prevState?: AwarenessDynamics
  ): number[] {
    const dA_dt: number[] = [];
    
    for (let i = 0; i < potentialGradient.length; i++) {
      // Deterministic drift (potential gradient)
      const drift = -potentialGradient[i];
      
      // Stochastic term (noise)
      const stochastic = noise * (i === 0 ? 1 : 0.5); // Valence most affected
      
      // Jump term
      const jump = jumpIntensity * (Math.random() > 0.5 ? 1 : -1);
      
      // Momentum from previous state (inertia)
      const momentum = prevState ? prevState.dA_dt[i] * 0.3 : 0;
      
      dA_dt[i] = drift + stochastic + jump + momentum;
    }
    
    return dA_dt;
  }
  
  // ═══════════════════════════════════════════════════════════
  // METRICS & ANALYSIS | 指标与分析
  // ═══════════════════════════════════════════════════════════
  
  /**
   * Compute Awareness Quality Metrics
   * 计算觉察质量指标
   */
  computeMetrics(state: AwarenessDynamics): AwarenessMetrics {
    const { field } = state;
    
    // Depth: Curvature magnitude (Frobenius norm)
    let curvatureSum = 0;
    for (let i = 0; i < field.curvature.length; i++) {
      for (let j = 0; j < field.curvature[i].length; j++) {
        curvatureSum += field.curvature[i][j] ** 2;
      }
    }
    const depth = Math.min(1, Math.sqrt(curvatureSum) / 2);
    
    // Consistency: Holonomy (simplified as connection symmetry)
    let asymmetry = 0;
    const n = field.connection.length;
    for (let i = 0; i < n; i++) {
      for (let j = i + 1; j < n; j++) {
        asymmetry += Math.abs(field.connection[i][j] - field.connection[j][i]);
      }
    }
    const consistency = 1 - Math.min(1, asymmetry / n);
    
    // Stability: Spectral gap (simplified)
    const eigenvalueSpread = field.basePoint.coordinates.reduce((sum, c) => sum + Math.abs(c), 0) / n;
    const stability = 1 - eigenvalueSpread;
    
    // Clarity: Signal-to-noise ratio
    const signal = field.fiberCoords[1]; // Cognitive perception
    const noiseLevel = state.noise;
    const clarity = signal / (signal + noiseLevel + 0.1);
    
    // Breadth: Fiber dimension utilization
    const fiberVariance = this.computeVariance(field.fiberCoords);
    const breadth = Math.min(1, fiberVariance * 4);
    
    // Overall: Weighted combination
    const overall = (
      depth * 0.25 +
      consistency * 0.20 +
      stability * 0.20 +
      clarity * 0.20 +
      breadth * 0.15
    );
    
    return { depth, consistency, stability, clarity, breadth, overall };
  }
  
  /**
   * Get Current State
   * 获取当前状态
   */
  getCurrentState(): AwarenessDynamics | null {
    return this.currentState;
  }
  
  // ═══════════════════════════════════════════════════════════
  // UTILITY FUNCTIONS | 辅助函数
  // ═══════════════════════════════════════════════════════════
  
  private computeVariance(values: number[]): number {
    const mean = values.reduce((sum, v) => sum + v, 0) / values.length;
    const squaredDiffs = values.map(v => (v - mean) ** 2);
    return squaredDiffs.reduce((sum, d) => sum + d, 0) / values.length;
  }
  
  private gaussianRandom(): number {
    // Box-Muller transform
    const u = Math.random();
    const v = Math.random();
    return Math.sqrt(-2 * Math.log(u)) * Math.cos(2 * Math.PI * v);
  }
  
  /**
   * Update Parameters
   * 更新参数
   */
  updateParams(params: Partial<{
    potential: Partial<typeof this.potentialParams>;
    noise: Partial<typeof this.noiseParams>;
    jump: Partial<typeof this.jumpParams>;
  }>) {
    if (params.potential) {
      Object.assign(this.potentialParams, params.potential);
    }
    if (params.noise) {
      Object.assign(this.noiseParams, params.noise);
    }
    if (params.jump) {
      Object.assign(this.jumpParams, params.jump);
    }
    
    this.emit('paramsUpdate', {
      potential: this.potentialParams,
      noise: this.noiseParams,
      jump: this.jumpParams
    });
  }
}

// ═══════════════════════════════════════════════════════════════
// SUPPORTING TYPES | 支持类型
// ═══════════════════════════════════════════════════════════════

export interface AwarenessStimulus {
  valence?: number;       // [-1, 1]
  arousal?: number;       // [0, 1]
  salience?: number;      // [0, 1]
  novelty?: number;       // [0, 1]
}

export interface MentalContext {
  clarity?: number;       // [0, 1]
  focus?: number;         // [0, 1]
  openness?: number;      // [0, 1]
  confidence?: number;    // [0, 1]
}

// ═══════════════════════════════════════════════════════════════
// EXPORT | 导出
// ═══════════════════════════════════════════════════════════════

export default AwarenessFieldComputer;
