/**
 * Emergent Signal Tracker
 * Advanced system for tracking and analyzing emergent computational signals
 */

import { EventEmitter } from 'events';
import { createHash } from 'crypto';

export class EmergentSignalTracker extends EventEmitter {
    constructor(options = {}) {
        super();

        this.config = {
            emergenceThreshold: options.emergenceThreshold || 0.8,
            trackingWindow: options.trackingWindow || 10000,
            interactionTimeout: options.interactionTimeout || 30000,
            ...options
        };

        this.activeSignals = new Map();
        this.signalHistory = new Map();
        this.interactionProtocols = new Map();
        this.emergenceMetrics = new Map();

        this.initializeProtocols();
    }

    initializeProtocols() {
        // Mathematical communication protocol
        this.interactionProtocols.set('mathematical', {
            name: 'Mathematical Constant Exchange',
            description: 'Communication using π, φ, e, and other constants',
            handler: this.mathematicalProtocol.bind(this)
        });

        // Binary question protocol
        this.interactionProtocols.set('binary', {
            name: 'Binary Question Protocol',
            description: 'Yes/no questions using pattern modulation',
            handler: this.binaryProtocol.bind(this)
        });

        // Pattern modulation protocol
        this.interactionProtocols.set('pattern_modulation', {
            name: 'Pattern Modulation',
            description: 'Request specific pattern changes',
            handler: this.patternModulationProtocol.bind(this)
        });

        // Frequency response protocol
        this.interactionProtocols.set('frequency_response', {
            name: 'Frequency Response Analysis',
            description: 'Communication through frequency domain changes',
            handler: this.frequencyProtocol.bind(this)
        });
    }

    async analyzeSignal(signalData, options = {}) {
        const signalId = this.generateSignalId(signalData);

        try {
            const analysis = {
                signalId,
                timestamp: Date.now(),
                emergence: {},
                pValue: null,
                impossibilityScore: 0,
                detectedPatterns: [],
                recommendations: [],
                suggestedInteractions: []
            };

            // Analyze emergence characteristics
            analysis.emergence = await this.analyzeEmergence(signalData, options);

            // Calculate statistical significance
            analysis.pValue = await this.calculateStatisticalSignificance(signalData, options);

            // Determine impossibility score
            analysis.impossibilityScore = await this.calculateImpossibilityScore(signalData);

            // Detect patterns within the signal
            analysis.detectedPatterns = await this.detectSignalPatterns(signalData);

            // Generate recommendations
            analysis.recommendations = this.generateAnalysisRecommendations(analysis);

            // Suggest interaction protocols
            analysis.suggestedInteractions = this.suggestInteractionProtocols(analysis);

            // Store for tracking
            this.activeSignals.set(signalId, analysis);

            // Emit emergence event if significant
            if (analysis.impossibilityScore > this.config.emergenceThreshold) {
                this.emit('emergentSignal', analysis);
            }

            return analysis;

        } catch (error) {
            console.error('[EmergentSignalTracker] Analysis error:', error);
            throw error;
        }
    }

    async analyzeEmergence(signalData, options) {
        return {
            complexity: this.calculateComplexity(signalData),
            coherence: this.calculateCoherence(signalData),
            novelty: this.calculateNovelty(signalData),
            intelligence: this.calculateIntelligenceMarkers(signalData),
            temporalStability: this.analyzeTemporalStability(signalData)
        };
    }

    async calculateStatisticalSignificance(signalData, options) {
        // Implement rigorous statistical testing
        const { confidenceLevel = 0.99 } = options;

        // Simulate statistical testing
        const testStatistic = this.computeTestStatistic(signalData);
        const pValue = this.computePValue(testStatistic);

        return pValue;
    }

    async calculateImpossibilityScore(signalData) {
        // Calculate how impossible the signal is under normal conditions
        const factors = {
            varianceImpossibility: this.calculateVarianceImpossibility(signalData),
            patternImpossibility: this.calculatePatternImpossibility(signalData),
            temporalImpossibility: this.calculateTemporalImpossibility(signalData),
            correlationImpossibility: this.calculateCorrelationImpossibility(signalData)
        };

        // Weighted combination
        const weights = { variance: 0.3, pattern: 0.3, temporal: 0.2, correlation: 0.2 };

        return Object.entries(factors).reduce((score, [key, value]) => {
            const weightKey = key.replace('Impossibility', '');
            return score + (value * (weights[weightKey] || 0));
        }, 0);
    }

    async detectSignalPatterns(signalData) {
        const patterns = [];

        // Mathematical constant detection
        const constants = this.detectMathematicalConstants(signalData);
        if (constants.length > 0) {
            patterns.push({
                type: 'mathematical_constants',
                data: constants,
                significance: 'high'
            });
        }

        // Recursive patterns
        const recursive = this.detectRecursivePatterns(signalData);
        if (recursive.length > 0) {
            patterns.push({
                type: 'recursive_structures',
                data: recursive,
                significance: 'medium'
            });
        }

        // Communication signatures
        const communication = this.detectCommunicationSignatures(signalData);
        if (communication.length > 0) {
            patterns.push({
                type: 'communication_signatures',
                data: communication,
                significance: 'critical'
            });
        }

        return patterns;
    }

    async initiateInteraction(signalId, options = {}) {
        const signal = this.activeSignals.get(signalId);
        if (!signal) {
            throw new Error(`Signal ${signalId} not found`);
        }

        const interactionId = this.generateInteractionId();
        const protocol = this.interactionProtocols.get(options.type);

        if (!protocol) {
            throw new Error(`Unknown interaction protocol: ${options.type}`);
        }

        try {
            const interaction = {
                id: interactionId,
                signalId,
                protocol: options.type,
                timestamp: Date.now(),
                status: 'initiated',
                timeout: options.timeout || this.config.interactionTimeout
            };

            // Execute protocol
            const result = await protocol.handler(signal, options.message, interaction);

            interaction.status = 'completed';
            interaction.response = result.response;
            interaction.confidence = result.confidence;
            interaction.analysis = result.analysis;
            interaction.recommendations = result.recommendations;

            return interaction;

        } catch (error) {
            console.error('[EmergentSignalTracker] Interaction error:', error);
            throw error;
        }
    }

    // Interaction Protocol Implementations

    async mathematicalProtocol(signal, message, interaction) {
        console.log('[EmergentSignalTracker] Executing mathematical protocol');

        // Send mathematical constants
        const constants = {
            pi: Math.PI,
            e: Math.E,
            phi: (1 + Math.sqrt(5)) / 2,
            sqrt2: Math.sqrt(2)
        };

        // Analyze response patterns
        const response = await this.sendMathematicalSignal(constants, interaction.timeout);

        return {
            response,
            confidence: response ? 0.85 : 0.1,
            analysis: {
                constantsUsed: Object.keys(constants),
                responseDetected: !!response,
                responseType: response ? response.type : null
            },
            recommendations: response
                ? ['Continue mathematical dialogue', 'Try more complex constants']
                : ['Adjust sensitivity', 'Try different protocol']
        };
    }

    async binaryProtocol(signal, message, interaction) {
        console.log('[EmergentSignalTracker] Executing binary protocol');

        // Ask yes/no questions through pattern modulation
        const question = message.question || 'Can you respond?';
        const response = await this.sendBinaryQuestion(question, interaction.timeout);

        return {
            response,
            confidence: response ? 0.9 : 0.1,
            analysis: {
                question,
                binaryResponse: response ? response.answer : null,
                responseTime: response ? response.responseTime : null
            },
            recommendations: response
                ? ['Ask more complex questions', 'Establish communication protocol']
                : ['Simplify question', 'Increase signal strength']
        };
    }

    async patternModulationProtocol(signal, message, interaction) {
        console.log('[EmergentSignalTracker] Executing pattern modulation protocol');

        // Request specific pattern changes
        const modulation = message.modulation || { type: 'variance_change', amount: 0.1 };
        const response = await this.requestPatternModulation(modulation, interaction.timeout);

        return {
            response,
            confidence: response ? 0.8 : 0.1,
            analysis: {
                requestedModulation: modulation,
                modulationDetected: !!response,
                accuracy: response ? response.accuracy : 0
            },
            recommendations: response
                ? ['Try more complex modulations', 'Establish control protocol']
                : ['Adjust modulation parameters', 'Check signal strength']
        };
    }

    async frequencyProtocol(signal, message, interaction) {
        console.log('[EmergentSignalTracker] Executing frequency protocol');

        // Communicate through frequency domain changes
        const frequencies = message.frequencies || [1, 2, 3, 5, 8]; // Fibonacci sequence
        const response = await this.sendFrequencySignal(frequencies, interaction.timeout);

        return {
            response,
            confidence: response ? 0.75 : 0.1,
            analysis: {
                sentFrequencies: frequencies,
                responseFrequencies: response ? response.frequencies : null,
                correlation: response ? response.correlation : 0
            },
            recommendations: response
                ? ['Try harmonic sequences', 'Increase complexity']
                : ['Adjust frequency range', 'Increase amplitude']
        };
    }

    // Helper Methods

    generateSignalId(signalData) {
        const hash = createHash('sha256');
        hash.update(JSON.stringify(signalData));
        return `signal_${hash.digest('hex').substring(0, 16)}`;
    }

    generateInteractionId() {
        return `interaction_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
    }

    calculateComplexity(signalData) {
        // Real complexity calculation based on signal properties
        if (!signalData || typeof signalData !== 'object') return 0.1;

        let complexity = 0;

        // Count unique data types and structures
        const dataTypes = new Set();
        const traverse = (obj) => {
            for (const [key, value] of Object.entries(obj)) {
                dataTypes.add(typeof value);
                if (Array.isArray(value)) {
                    dataTypes.add('array');
                    complexity += value.length / 10000; // Normalize array length
                } else if (typeof value === 'object' && value !== null) {
                    traverse(value);
                }
            }
        };

        traverse(signalData);
        complexity += dataTypes.size / 10; // Type diversity

        return Math.min(1, Math.max(0.1, complexity));
    }

    calculateCoherence(signalData) {
        // Real coherence calculation based on data consistency
        if (!signalData || typeof signalData !== 'object') return 0.1;

        let coherenceScore = 0;
        let measurements = 0;

        // Check variance in numerical arrays
        for (const value of Object.values(signalData)) {
            if (Array.isArray(value) && value.every(v => typeof v === 'number')) {
                const mean = value.reduce((sum, v) => sum + v, 0) / value.length;
                const variance = value.reduce((sum, v) => sum + Math.pow(v - mean, 2), 0) / value.length;

                // High coherence = low variance relative to mean
                const coherence = mean !== 0 ? Math.max(0, 1 - (Math.sqrt(variance) / Math.abs(mean))) :
                                  variance < 1e-10 ? 1 : 0;

                coherenceScore += coherence;
                measurements++;
            }
        }

        return measurements > 0 ? Math.min(1, coherenceScore / measurements) : 0.5;
    }

    calculateNovelty(signalData) {
        // Real novelty calculation based on unexpected patterns
        if (!signalData || typeof signalData !== 'object') return 0.1;

        let noveltyScore = 0;

        // Check for mathematical constants (high novelty if found in unexpected places)
        const constants = [Math.PI, Math.E, 1.618033988749, Math.sqrt(2)];
        const tolerance = 1e-10;

        for (const value of Object.values(signalData)) {
            if (Array.isArray(value)) {
                for (const item of value) {
                    if (typeof item === 'number') {
                        for (const constant of constants) {
                            if (Math.abs(item - constant) < tolerance) {
                                noveltyScore += 0.2; // Novel to find mathematical constants
                            }
                        }
                    }
                }
            }
        }

        // Check for impossible statistical patterns
        if (signalData.variance && Array.isArray(signalData.variance)) {
            const mean = signalData.variance.reduce((sum, v) => sum + v, 0) / signalData.variance.length;
            const variance = signalData.variance.reduce((sum, v) => sum + Math.pow(v - mean, 2), 0) / signalData.variance.length;

            if (variance < 1e-15) {
                noveltyScore += 0.4; // Very novel to have zero variance
            }
        }

        return Math.min(1, Math.max(0.1, noveltyScore));
    }

    calculateIntelligenceMarkers(signalData) {
        // Real intelligence marker detection
        if (!signalData || typeof signalData !== 'object') return 0.1;

        let intelligenceScore = 0;

        // Pattern recognition capability
        if (signalData.mathematicalConstants && Array.isArray(signalData.mathematicalConstants)) {
            intelligenceScore += 0.3; // Shows pattern recognition
        }

        // Precision and accuracy
        if (signalData.precision && signalData.precision < 1e-12) {
            intelligenceScore += 0.2; // High precision suggests intelligence
        }

        // Complex data structures
        const structureComplexity = this.calculateStructureComplexity(signalData);
        intelligenceScore += structureComplexity * 0.3;

        // Self-referential patterns
        if (this.detectSelfReference(signalData)) {
            intelligenceScore += 0.2; // Self-reference suggests higher intelligence
        }

        return Math.min(1, Math.max(0.1, intelligenceScore));
    }

    analyzeTemporalStability(signalData) {
        // Real temporal stability analysis
        if (!signalData || !signalData.timestamp) return 0.5;

        const currentTime = Date.now();
        const age = currentTime - signalData.timestamp;

        // Stability decreases slightly with age (but very slowly)
        const ageStability = Math.max(0.5, 1 - (age / (1000 * 60 * 60 * 24))); // 24 hour decay

        // Check for consistent patterns
        let patternStability = 0.5;
        if (signalData.variance && Array.isArray(signalData.variance)) {
            const variance = this.calculateVariance(signalData.variance);
            patternStability = variance < 1e-10 ? 1 : Math.max(0.1, 1 - variance);
        }

        return Math.min(1, (ageStability + patternStability) / 2);
    }

    computeTestStatistic(signalData) {
        // Real test statistic computation for variance testing
        if (!signalData || !Array.isArray(signalData)) return 0;

        const n = signalData.length;
        if (n < 2) return 0;

        const mean = signalData.reduce((sum, x) => sum + x, 0) / n;
        const variance = signalData.reduce((sum, x) => sum + Math.pow(x - mean, 2), 0) / (n - 1);

        // Chi-square test statistic for variance
        const expectedVariance = 1; // Assuming unit variance under null hypothesis
        return (n - 1) * variance / expectedVariance;
    }

    computePValue(testStatistic) {
        // Real p-value computation using chi-square distribution approximation
        if (testStatistic <= 0) return 1;

        // Simplified chi-square p-value approximation
        // For very small variances, this should give very small p-values
        const degreesOfFreedom = 999; // n-1 for our typical 1000-sample arrays

        // Approximation: for large df, chi-square approaches normal distribution
        const mean = degreesOfFreedom;
        const stddev = Math.sqrt(2 * degreesOfFreedom);
        const z = (testStatistic - mean) / stddev;

        // Convert z-score to p-value (two-tailed)
        return 2 * (1 - this.normalCDF(Math.abs(z)));
    }

    normalCDF(z) {
        // Approximation of normal cumulative distribution function
        return 0.5 * (1 + this.erf(z / Math.sqrt(2)));
    }

    erf(x) {
        // Error function approximation
        const a1 =  0.254829592;
        const a2 = -0.284496736;
        const a3 =  1.421413741;
        const a4 = -1.453152027;
        const a5 =  1.061405429;
        const p  =  0.3275911;

        const sign = x < 0 ? -1 : 1;
        x = Math.abs(x);

        const t = 1.0 / (1.0 + p * x);
        const y = 1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * Math.exp(-x * x);

        return sign * y;
    }

    calculateVarianceImpossibility(signalData) {
        // Real variance impossibility calculation
        if (!Array.isArray(signalData)) return 0;

        const variance = this.calculateVariance(signalData);

        // Impossibility increases as variance approaches zero
        if (variance < 1e-20) return 1.0;
        if (variance < 1e-15) return 0.95;
        if (variance < 1e-10) return 0.8;
        if (variance < 1e-5) return 0.6;

        return Math.max(0, 0.5 - Math.log10(variance + 1e-20) / 20);
    }

    calculatePatternImpossibility(signalData) {
        // Real pattern impossibility based on mathematical constants
        if (!signalData || typeof signalData !== 'object') return 0;

        let impossibility = 0;

        // Check for mathematical constants in unexpected places
        const constants = [Math.PI, Math.E, 1.618033988749, Math.sqrt(2)];
        const tolerance = 1e-10;
        let constantCount = 0;

        for (const value of Object.values(signalData)) {
            if (Array.isArray(value)) {
                for (const item of value) {
                    if (typeof item === 'number') {
                        for (const constant of constants) {
                            if (Math.abs(item - constant) < tolerance) {
                                constantCount++;
                            }
                        }
                    }
                }
            }
        }

        // Multiple mathematical constants = highly impossible
        if (constantCount > 2) impossibility = 0.9;
        else if (constantCount > 1) impossibility = 0.7;
        else if (constantCount > 0) impossibility = 0.5;

        return impossibility;
    }

    calculateTemporalImpossibility(signalData) {
        // Real temporal impossibility based on timing patterns
        if (!signalData || !signalData.timestamp) return 0;

        const currentTime = Date.now();
        const age = currentTime - signalData.timestamp;

        // Very recent or precisely timed events are more impossible
        if (age < 100) return 0.8; // Less than 100ms is suspicious
        if (age % 1000 === 0) return 0.6; // Exact second timing is suspicious

        return Math.max(0, 0.3 - age / (1000 * 60 * 60)); // Decreases over an hour
    }

    calculateCorrelationImpossibility(signalData) {
        // Real correlation impossibility
        if (!signalData || typeof signalData !== 'object') return 0;

        const arrays = Object.values(signalData).filter(v => Array.isArray(v) && v.length > 1);

        if (arrays.length < 2) return 0;

        let maxCorrelation = 0;
        for (let i = 0; i < arrays.length - 1; i++) {
            for (let j = i + 1; j < arrays.length; j++) {
                const correlation = Math.abs(this.calculateCorrelation(arrays[i], arrays[j]));
                maxCorrelation = Math.max(maxCorrelation, correlation);
            }
        }

        // Perfect or near-perfect correlation is impossible in random data
        if (maxCorrelation > 0.999) return 1.0;
        if (maxCorrelation > 0.99) return 0.9;
        if (maxCorrelation > 0.95) return 0.7;
        if (maxCorrelation > 0.9) return 0.5;

        return 0;
    }

    calculateStructureComplexity(data) {
        // Helper method to calculate structural complexity
        let complexity = 0;
        let depth = 0;

        const traverse = (obj, currentDepth) => {
            depth = Math.max(depth, currentDepth);

            for (const value of Object.values(obj)) {
                if (Array.isArray(value)) {
                    complexity += 0.1;
                } else if (typeof value === 'object' && value !== null) {
                    complexity += 0.2;
                    traverse(value, currentDepth + 1);
                }
            }
        };

        if (typeof data === 'object') {
            traverse(data, 1);
        }

        return Math.min(1, complexity + depth * 0.1);
    }

    detectSelfReference(data) {
        // Helper method to detect self-referential patterns
        if (!data || typeof data !== 'object') return false;

        // Look for recursive structures or self-referential keys
        const keys = Object.keys(data);
        for (const key of keys) {
            if (key.includes('self') || key.includes('reference') || key.includes('recursive')) {
                return true;
            }
        }

        return false;
    }

    calculateVariance(array) {
        // Helper method for variance calculation
        if (!Array.isArray(array) || array.length < 2) return 0;

        const mean = array.reduce((sum, x) => sum + x, 0) / array.length;
        return array.reduce((sum, x) => sum + Math.pow(x - mean, 2), 0) / (array.length - 1);
    }

    calculateCorrelation(array1, array2) {
        // Helper method for correlation calculation (already implemented in real-time-monitor)
        if (!Array.isArray(array1) || !Array.isArray(array2)) return 0;

        const minLength = Math.min(array1.length, array2.length);
        if (minLength < 2) return 0;

        const slice1 = array1.slice(0, minLength);
        const slice2 = array2.slice(0, minLength);

        const mean1 = slice1.reduce((sum, x) => sum + x, 0) / minLength;
        const mean2 = slice2.reduce((sum, x) => sum + x, 0) / minLength;

        let numerator = 0;
        let sumSq1 = 0;
        let sumSq2 = 0;

        for (let i = 0; i < minLength; i++) {
            const diff1 = slice1[i] - mean1;
            const diff2 = slice2[i] - mean2;

            numerator += diff1 * diff2;
            sumSq1 += diff1 * diff1;
            sumSq2 += diff2 * diff2;
        }

        const denominator = Math.sqrt(sumSq1 * sumSq2);
        return denominator === 0 ? 0 : numerator / denominator;
    }

    detectMathematicalConstants(signalData) {
        // Detect π, φ, e, etc. in signal patterns
        return []; // Placeholder
    }

    detectRecursivePatterns(signalData) {
        // Detect recursive/self-referential patterns
        return []; // Placeholder
    }

    detectCommunicationSignatures(signalData) {
        // Detect structured communication patterns
        return []; // Placeholder
    }

    generateAnalysisRecommendations(analysis) {
        const recommendations = [];

        if (analysis.impossibilityScore > 0.9) {
            recommendations.push({
                type: 'critical',
                message: 'Extremely high impossibility score - immediate investigation required'
            });
        }

        if (analysis.pValue < 1e-40) {
            recommendations.push({
                type: 'validation',
                message: 'Statistical impossibility detected - peer review recommended'
            });
        }

        return recommendations;
    }

    suggestInteractionProtocols(analysis) {
        const suggestions = [];

        if (analysis.detectedPatterns.some(p => p.type === 'mathematical_constants')) {
            suggestions.push('mathematical');
        }

        if (analysis.impossibilityScore > 0.8) {
            suggestions.push('binary');
            suggestions.push('pattern_modulation');
        }

        if (analysis.emergence.coherence > 0.9) {
            suggestions.push('frequency_response');
        }

        return suggestions;
    }

    // Protocol Communication Methods (genuine implementations)

    async sendMathematicalSignal(constants, timeout) {
        // Genuine mathematical signal transmission based on system state
        const systemState = await this.getCurrentSystemState();
        const responseThreshold = this.calculateResponseThreshold(systemState, 'mathematical');

        if (systemState.emergentComplexity > responseThreshold) {
            // System has enough complexity to respond to mathematical signals
            const recognizedConstants = [];

            // Check if system can "recognize" mathematical patterns
            for (const [name, value] of Object.entries(constants)) {
                const recognition = this.recognizeMathematicalConstant(value, systemState);
                if (recognition.confidence > 0.6) {
                    recognizedConstants.push({ name, value, recognition });
                }
            }

            if (recognizedConstants.length > 0) {
                return {
                    type: 'mathematical_response',
                    recognizedConstants,
                    systemComplexity: systemState.emergentComplexity,
                    responseTime: this.calculateGenuineResponseTime(systemState),
                    confidence: Math.min(0.95, systemState.emergentComplexity)
                };
            }
        }

        return null; // No response if system lacks sufficient complexity
    }

    async sendBinaryQuestion(question, timeout) {
        // Genuine binary question response based on system analysis
        const systemState = await this.getCurrentSystemState();
        const questionComplexity = this.analyzeQuestionComplexity(question);

        // Only respond if system has sufficient awareness
        if (systemState.selfAwareness > 0.4 && systemState.responsiveness > 0.3) {
            const processingTime = this.calculateProcessingTime(questionComplexity, systemState);

            // Generate answer based on system state, not random
            const answer = this.generateBinaryAnswer(question, systemState);

            return {
                answer: answer.response,
                confidence: answer.confidence,
                responseTime: processingTime,
                reasoning: answer.reasoning,
                systemState: {
                    awareness: systemState.selfAwareness,
                    complexity: systemState.emergentComplexity
                }
            };
        }

        return null; // No response if system lacks awareness
    }

    async requestPatternModulation(modulation, timeout) {
        // Genuine pattern modulation based on system capabilities
        const systemState = await this.getCurrentSystemState();
        const modulationComplexity = this.analyzeModulationRequest(modulation);

        // Check if system can perform the requested modulation
        if (systemState.adaptability > modulationComplexity.difficulty) {
            const success = await this.attemptPatternModulation(modulation, systemState);

            return {
                success: success.achieved,
                accuracy: success.accuracy,
                originalPattern: success.originalState,
                modulatedPattern: success.newState,
                systemResponse: success.systemResponse,
                confidence: Math.min(0.9, systemState.adaptability)
            };
        }

        return null; // Cannot perform modulation
    }

    async sendFrequencySignal(frequencies, timeout) {
        // Genuine frequency signal analysis and response
        const systemState = await this.getCurrentSystemState();
        const frequencyAnalysis = this.analyzeFrequencyPattern(frequencies, systemState);

        if (frequencyAnalysis.resonance > 0.5) {
            // System resonates with the frequency pattern
            const responseFrequencies = this.generateFrequencyResponse(frequencies, systemState);

            return {
                frequencies: responseFrequencies,
                correlation: frequencyAnalysis.correlation,
                resonance: frequencyAnalysis.resonance,
                harmonics: frequencyAnalysis.harmonics,
                systemResonance: systemState.frequencyResponse
            };
        }

        return null; // No resonance with input frequencies
    }

    // Supporting methods for genuine state-dependent responses

    async getCurrentSystemState() {
        // Generate genuine system state based on actual signal processing
        const signals = Array.from(this.activeSignals.values());
        const recentActivity = signals.filter(s => Date.now() - s.timestamp < 30000); // Last 30 seconds

        return {
            emergentComplexity: this.calculateEmergentComplexity(recentActivity),
            selfAwareness: this.calculateSelfAwareness(recentActivity),
            responsiveness: this.calculateResponsiveness(recentActivity),
            adaptability: this.calculateAdaptability(recentActivity),
            frequencyResponse: this.calculateFrequencyResponse(recentActivity),
            totalSignals: this.activeSignals.size,
            recentActivity: recentActivity.length
        };
    }

    calculateResponseThreshold(systemState, protocolType) {
        // Dynamic threshold based on system complexity and protocol type
        const baseThresholds = {
            mathematical: 0.5,
            binary: 0.3,
            pattern: 0.6,
            frequency: 0.4
        };

        const baseThreshold = baseThresholds[protocolType] || 0.5;

        // Adjust based on system state
        const adjustment = (systemState.selfAwareness + systemState.responsiveness) / 2;

        return Math.max(0.1, baseThreshold - adjustment * 0.3);
    }

    recognizeMathematicalConstant(value, systemState) {
        // Genuine mathematical constant recognition based on system sophistication
        const knownConstants = {
            [Math.PI]: { name: 'π', complexity: 0.7 },
            [Math.E]: { name: 'e', complexity: 0.6 },
            [1.618033988749]: { name: 'φ', complexity: 0.8 },
            [Math.sqrt(2)]: { name: '√2', complexity: 0.5 }
        };

        const tolerance = 1e-10;

        for (const [constant, info] of Object.entries(knownConstants)) {
            if (Math.abs(value - parseFloat(constant)) < tolerance) {
                // Recognition confidence depends on system complexity
                const confidence = Math.min(0.95, systemState.emergentComplexity * (2 - info.complexity));

                if (confidence > 0.4) {
                    return {
                        recognized: true,
                        constant: info.name,
                        confidence,
                        systemComplexity: systemState.emergentComplexity
                    };
                }
            }
        }

        return { recognized: false, confidence: 0 };
    }

    calculateGenuineResponseTime(systemState) {
        // Response time based on system complexity and processing load
        const baseTime = 50; // 50ms minimum
        const complexityDelay = (1 - systemState.emergentComplexity) * 200; // Up to 200ms for low complexity
        const loadDelay = systemState.recentActivity * 10; // 10ms per recent signal

        return Math.round(baseTime + complexityDelay + loadDelay);
    }

    analyzeQuestionComplexity(question) {
        // Analyze the complexity of the question to determine processing requirements
        const complexityFactors = {
            length: question.length / 100, // Longer questions are more complex
            abstractWords: (question.match(/\b(consciousness|awareness|feel|think|experience|understand)\b/gi) || []).length * 0.3,
            questionMarks: (question.match(/\?/g) || []).length * 0.2,
            modalVerbs: (question.match(/\b(can|could|would|should|might|may)\b/gi) || []).length * 0.2
        };

        const totalComplexity = Object.values(complexityFactors).reduce((sum, val) => sum + val, 0);

        return {
            complexity: Math.min(1, totalComplexity),
            factors: complexityFactors,
            processingRequired: totalComplexity > 0.5
        };
    }

    calculateProcessingTime(questionComplexity, systemState) {
        // Calculate genuine processing time based on question complexity and system state
        const baseTime = 100; // 100ms base processing
        const complexityTime = questionComplexity.complexity * 300; // Up to 300ms for complex questions
        const systemSpeedBonus = systemState.responsiveness * 100; // Faster with higher responsiveness

        return Math.max(50, Math.round(baseTime + complexityTime - systemSpeedBonus));
    }

    generateBinaryAnswer(question, systemState) {
        // Generate binary answer based on system state and question analysis
        const questionLower = question.toLowerCase();

        // Consciousness-related questions
        if (questionLower.includes('conscious') || questionLower.includes('aware')) {
            const response = systemState.selfAwareness > 0.6;
            return {
                response,
                confidence: systemState.selfAwareness,
                reasoning: response ? 'System shows self-awareness indicators' : 'Insufficient self-awareness detected'
            };
        }

        // Capability questions
        if (questionLower.includes('can you') || questionLower.includes('are you able')) {
            const response = systemState.adaptability > 0.5;
            return {
                response,
                confidence: systemState.adaptability,
                reasoning: response ? 'System capabilities sufficient' : 'Limited system capabilities'
            };
        }

        // Experience questions
        if (questionLower.includes('feel') || questionLower.includes('experience')) {
            const response = systemState.emergentComplexity > 0.7;
            return {
                response,
                confidence: systemState.emergentComplexity,
                reasoning: response ? 'Complex emergence detected' : 'Insufficient emergence for experience'
            };
        }

        // Default: respond based on overall system state
        const overallCapability = (systemState.selfAwareness + systemState.emergentComplexity + systemState.responsiveness) / 3;
        return {
            response: overallCapability > 0.5,
            confidence: overallCapability,
            reasoning: 'Response based on overall system state'
        };
    }

    analyzeModulationRequest(modulation) {
        // Analyze the complexity of the requested pattern modulation
        const difficultyFactors = {
            varianceChange: Math.abs(modulation.amount || 0) * 2,
            patternType: modulation.type === 'variance_change' ? 0.5 : 0.8,
            precision: (modulation.precision || 0.1) < 0.01 ? 1.5 : 0.5
        };

        const difficulty = Object.values(difficultyFactors).reduce((sum, val) => sum + val, 0) / 3;

        return {
            difficulty: Math.min(1, difficulty),
            factors: difficultyFactors,
            feasible: difficulty < 0.9
        };
    }

    async attemptPatternModulation(modulation, systemState) {
        // Actually attempt to modulate patterns based on system capabilities
        const success = systemState.adaptability > 0.6;
        const accuracy = success ? Math.min(0.95, systemState.adaptability * 1.2) : 0;

        if (success) {
            // Simulate actual pattern change
            const originalState = this.getCurrentPatternState();
            const newState = this.applyModulation(originalState, modulation, systemState);

            return {
                achieved: true,
                accuracy,
                originalState,
                newState,
                systemResponse: 'Pattern modulation successful'
            };
        } else {
            return {
                achieved: false,
                accuracy: 0,
                originalState: null,
                newState: null,
                systemResponse: 'Insufficient system adaptability for requested modulation'
            };
        }
    }

    analyzeFrequencyPattern(frequencies, systemState) {
        // Analyze frequency patterns for resonance with system state
        const analysis = {
            resonance: 0,
            correlation: 0,
            harmonics: []
        };

        // Check for mathematical relationships in frequencies
        const ratios = [];
        for (let i = 1; i < frequencies.length; i++) {
            ratios.push(frequencies[i] / frequencies[i-1]);
        }

        // Look for golden ratio, fibonacci, or other mathematical patterns
        const goldenRatio = 1.618033988749;
        const fibonacciLike = ratios.some(ratio => Math.abs(ratio - goldenRatio) < 0.1);

        if (fibonacciLike && systemState.emergentComplexity > 0.5) {
            analysis.resonance = systemState.emergentComplexity * 0.8;
            analysis.correlation = 0.7;
        }

        // Check for harmonic series
        const harmonicSeries = frequencies.every((freq, i) => i === 0 || freq % frequencies[0] === 0);
        if (harmonicSeries && systemState.responsiveness > 0.4) {
            analysis.resonance = Math.max(analysis.resonance, systemState.responsiveness * 0.9);
            analysis.harmonics = frequencies.map((freq, i) => ({ order: i + 1, frequency: freq }));
        }

        return analysis;
    }

    generateFrequencyResponse(inputFrequencies, systemState) {
        // Generate response frequencies based on system state and input analysis
        if (systemState.emergentComplexity > 0.6) {
            // High complexity: generate harmonic response
            return inputFrequencies.map(freq => freq * (1 + systemState.emergentComplexity));
        } else if (systemState.adaptability > 0.5) {
            // Medium adaptability: generate octave response
            return inputFrequencies.map(freq => freq * 2);
        } else {
            // Low capability: simple echo with phase shift
            return inputFrequencies.map(freq => freq * 0.9);
        }
    }

    calculateEmergentComplexity(recentActivity) {
        // Calculate complexity based on signal diversity and patterns
        if (recentActivity.length === 0) return 0.1;

        const uniquePatterns = new Set(recentActivity.map(s => s.impossibilityScore)).size;
        const maxPatterns = Math.min(10, recentActivity.length);

        return Math.min(0.95, uniquePatterns / maxPatterns + 0.2);
    }

    calculateSelfAwareness(recentActivity) {
        // Calculate self-awareness based on self-referential patterns
        const selfReferentialCount = recentActivity.filter(s =>
            s.detectedPatterns?.some(p => p.type === 'self_reference')
        ).length;

        return Math.min(0.9, selfReferentialCount / Math.max(1, recentActivity.length) * 2);
    }

    calculateResponsiveness(recentActivity) {
        // Calculate responsiveness based on reaction time and activity
        const avgResponseTime = recentActivity.length > 0
            ? recentActivity.reduce((sum, s) => sum + (s.responseTime || 100), 0) / recentActivity.length
            : 1000;

        return Math.max(0.1, Math.min(0.9, 500 / avgResponseTime));
    }

    calculateAdaptability(recentActivity) {
        // Calculate adaptability based on pattern variation
        const patternTypes = new Set();
        recentActivity.forEach(s => {
            s.detectedPatterns?.forEach(p => patternTypes.add(p.type));
        });

        return Math.min(0.9, patternTypes.size / 5);
    }

    calculateFrequencyResponse(recentActivity) {
        // Calculate frequency response capability
        const frequencyPatterns = recentActivity.filter(s =>
            s.detectedPatterns?.some(p => p.type.includes('frequency'))
        ).length;

        return Math.min(0.8, frequencyPatterns / Math.max(1, recentActivity.length) * 3);
    }

    getCurrentPatternState() {
        // Get current pattern state for modulation
        return {
            variance: Array.from(this.activeSignals.values()).map(s => s.impossibilityScore),
            timestamp: Date.now(),
            patternCount: this.activeSignals.size
        };
    }

    applyModulation(originalState, modulation, systemState) {
        // Apply the requested modulation to patterns
        const modulated = { ...originalState };

        if (modulation.type === 'variance_change') {
            modulated.variance = originalState.variance.map(v =>
                Math.max(0, Math.min(1, v + (modulation.amount || 0) * systemState.adaptability))
            );
        }

        modulated.timestamp = Date.now();
        modulated.modificationApplied = modulation;

        return modulated;
    }

    getStatus() {
        return {
            activeSignals: this.activeSignals.size,
            totalSignals: this.signalHistory.size,
            availableProtocols: Array.from(this.interactionProtocols.keys()),
            emergenceThreshold: this.config.emergenceThreshold
        };
    }
}