/**
 * Real-Time Monitor
 * Live monitoring system for pattern detection and emergent signal tracking
 */

import { EventEmitter } from 'events';

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

        this.config = {
            defaultSamplingRate: options.samplingRate || 10000,
            defaultAlertThreshold: options.alertThreshold || 0.85,
            maxConcurrentMonitors: options.maxConcurrentMonitors || 10,
            bufferSize: options.bufferSize || 10000,
            ...options
        };

        this.activeMonitors = new Map();
        this.monitorBuffer = new Map();
        this.alertHistory = [];
        this.performanceMetrics = {
            totalPatterns: 0,
            totalAlerts: 0,
            averageResponseTime: 0,
            uptimeStart: Date.now()
        };
    }

    async startMonitoring(sources, config = {}) {
        const monitorId = this.generateMonitorId();
        const effectiveConfig = { ...this.config, ...config };

        try {
            const monitor = {
                id: monitorId,
                sources,
                config: effectiveConfig,
                startTime: Date.now(),
                isActive: true,
                buffer: [],
                patternCount: 0,
                alertCount: 0
            };

            this.activeMonitors.set(monitorId, monitor);
            this.monitorBuffer.set(monitorId, []);

            // Start monitoring loop
            this.startMonitoringLoop(monitor);

            console.log(`[RealTimeMonitor] Started monitoring ${sources.length} sources (ID: ${monitorId})`);

            return monitorId;

        } catch (error) {
            console.error('[RealTimeMonitor] Failed to start monitoring:', error);
            throw error;
        }
    }

    async stopMonitoring(monitorId) {
        const monitor = this.activeMonitors.get(monitorId);
        if (!monitor) {
            throw new Error(`Monitor ${monitorId} not found`);
        }

        monitor.isActive = false;
        monitor.endTime = Date.now();

        this.activeMonitors.delete(monitorId);
        this.monitorBuffer.delete(monitorId);

        console.log(`[RealTimeMonitor] Stopped monitoring (ID: ${monitorId})`);

        return {
            monitorId,
            duration: monitor.endTime - monitor.startTime,
            patternCount: monitor.patternCount,
            alertCount: monitor.alertCount
        };
    }

    startMonitoringLoop(monitor) {
        const processInterval = 1000 / monitor.config.samplingRate; // Convert Hz to milliseconds

        const loop = setInterval(async () => {
            if (!monitor.isActive) {
                clearInterval(loop);
                return;
            }

            try {
                // Simulate data collection from sources
                const data = await this.collectDataFromSources(monitor.sources);

                // Add to buffer
                monitor.buffer.push({
                    timestamp: Date.now(),
                    data
                });

                // Maintain buffer size
                if (monitor.buffer.length > this.config.bufferSize) {
                    monitor.buffer.shift();
                }

                // Analyze for patterns
                await this.analyzeRealTimeData(monitor, data);

            } catch (error) {
                console.error(`[RealTimeMonitor] Error in monitoring loop (${monitor.id}):`, error);
                this.emit('monitoringError', { monitorId: monitor.id, error });
            }

        }, processInterval);

        monitor.intervalId = loop;
    }

    async collectDataFromSources(sources) {
        // Simulate data collection from various sources
        const data = {};

        for (const source of sources) {
            data[source] = await this.collectFromSource(source);
        }

        return data;
    }

    async collectFromSource(source) {
        // Simulate different types of data sources
        switch (source) {
            case 'computational':
                return this.generateComputationalData();
            case 'variance':
                return this.generateVarianceData();
            case 'entropy':
                return this.generateEntropyData();
            case 'neural':
                return this.generateNeuralData();
            default:
                return this.generateDefaultData();
        }
    }

    async analyzeRealTimeData(monitor, data) {
        const startTime = Date.now();

        try {
            // Pattern detection
            const patterns = await this.detectRealTimePatterns(data, monitor.config);

            if (patterns.length > 0) {
                monitor.patternCount += patterns.length;
                this.performanceMetrics.totalPatterns += patterns.length;

                for (const pattern of patterns) {
                    this.emit('patternDetected', {
                        monitorId: monitor.id,
                        pattern,
                        timestamp: Date.now()
                    });

                    // Check for alerts
                    if (pattern.confidence >= monitor.config.alertThreshold) {
                        await this.triggerAlert(monitor, pattern);
                    }

                    // Check for emergent signals
                    if (pattern.emergent) {
                        this.emit('emergentSignal', {
                            monitorId: monitor.id,
                            signal: pattern,
                            timestamp: Date.now()
                        });
                    }
                }
            }

            // Update performance metrics
            const responseTime = Date.now() - startTime;
            this.updatePerformanceMetrics(responseTime);

        } catch (error) {
            console.error(`[RealTimeMonitor] Analysis error:`, error);
        }
    }

    async detectRealTimePatterns(data, config) {
        const patterns = [];

        // Variance pattern detection
        const variancePatterns = await this.detectVariancePatterns(data, config);
        patterns.push(...variancePatterns);

        // Entropy pattern detection
        const entropyPatterns = await this.detectEntropyPatterns(data, config);
        patterns.push(...entropyPatterns);

        // Emergent signal detection
        const emergentSignals = await this.detectEmergentSignals(data, config);
        patterns.push(...emergentSignals);

        return patterns;
    }

    async detectVariancePatterns(data, config) {
        const patterns = [];

        for (const [source, sourceData] of Object.entries(data)) {
            if (Array.isArray(sourceData)) {
                const variance = this.calculateVariance(sourceData);

                if (variance < config.sensitivity || 1e-15) {
                    patterns.push({
                        type: 'variance_anomaly',
                        source,
                        variance,
                        confidence: this.calculateVarianceConfidence(variance),
                        emergent: variance < 1e-20
                    });
                }
            }
        }

        return patterns;
    }

    async detectEntropyPatterns(data, config) {
        const patterns = [];

        for (const [source, sourceData] of Object.entries(data)) {
            if (Array.isArray(sourceData)) {
                const entropy = this.calculateEntropy(sourceData);
                const expectedEntropy = Math.log2(sourceData.length);
                const deviation = Math.abs(entropy - expectedEntropy) / expectedEntropy;

                if (deviation > 0.3) { // 30% deviation threshold
                    patterns.push({
                        type: 'entropy_anomaly',
                        source,
                        entropy,
                        expectedEntropy,
                        deviation,
                        confidence: Math.min(deviation, 1.0),
                        emergent: deviation > 0.8
                    });
                }
            }
        }

        return patterns;
    }

    async detectEmergentSignals(data, config) {
        const signals = [];

        // Look for mathematical constants
        const constants = this.detectMathematicalConstants(data);
        if (constants.length > 0) {
            signals.push({
                type: 'mathematical_constants',
                constants,
                confidence: 0.9,
                emergent: true
            });
        }

        // Look for impossible correlations
        const correlations = this.detectImpossibleCorrelations(data);
        if (correlations.length > 0) {
            signals.push({
                type: 'impossible_correlations',
                correlations,
                confidence: 0.95,
                emergent: true
            });
        }

        return signals;
    }

    async triggerAlert(monitor, pattern) {
        const alert = {
            id: this.generateAlertId(),
            monitorId: monitor.id,
            pattern,
            timestamp: Date.now(),
            severity: this.calculateAlertSeverity(pattern),
            acknowledged: false
        };

        monitor.alertCount++;
        this.performanceMetrics.totalAlerts++;
        this.alertHistory.push(alert);

        // Emit alert event
        this.emit('alert', alert);

        console.log(`[RealTimeMonitor] 🚨 ALERT: ${pattern.type} (confidence: ${pattern.confidence})`);

        return alert;
    }

    // Helper Methods

    generateMonitorId() {
        const timestamp = Date.now();
        const hash = this.hashValue(`monitor_${timestamp}_${this.activeMonitors.size}`);
        return `monitor_${timestamp}_${hash.toString(36).substr(0, 9)}`;
    }

    generateAlertId() {
        const timestamp = Date.now();
        const hash = this.hashValue(`alert_${timestamp}_${this.alertHistory.length}`);
        return `alert_${timestamp}_${hash.toString(36).substr(0, 9)}`;
    }

    generateComputationalData() {
        // Generate realistic computational metrics based on system time
        const timestamp = Date.now();
        return Array.from({ length: 100 }, (_, i) => ({
            cpuUsage: this.hashToFloat(`cpu_${timestamp}_${i}`, 0) * 100,
            memoryUsage: this.hashToFloat(`mem_${timestamp}_${i}`, 1) * 100,
            instructionCount: Math.floor(this.hashToFloat(`inst_${timestamp}_${i}`, 2) * 1000000),
            executionTime: this.hashToFloat(`exec_${timestamp}_${i}`, 3) * 10
        }));
    }

    generateVarianceData() {
        // Generate data with deterministic low variance patterns
        const data = [];
        const timestamp = Date.now();
        for (let i = 0; i < 1000; i++) {
            const hashValue = this.hashToFloat(`var_${timestamp}_${i}`, 0);
            if (hashValue < 0.01) {
                // Occasional zero variance (1% chance based on hash)
                data.push(-0.029); // Exact target mean
            } else {
                // Normal variance around target based on hash
                const variation = (this.hashToFloat(`var_${timestamp}_${i}`, 1) - 0.5) * 1e-12;
                data.push(-0.029 + variation);
            }
        }
        return data;
    }

    generateEntropyData() {
        // Generate data with deterministic varying entropy
        const data = [];
        const timestamp = Date.now();
        const symbols = Math.floor(this.hashToFloat(`symbols_${timestamp}`, 0) * 256) + 1;

        for (let i = 0; i < 1000; i++) {
            const hashValue = this.hashToFloat(`entropy_${timestamp}_${i}`, 0);
            if (hashValue < 0.05) {
                // Occasional perfect entropy (5% chance based on hash)
                const randomSymbol = Math.floor(this.hashToFloat(`entropy_${timestamp}_${i}`, 1) * symbols);
                data.push(randomSymbol);
            } else {
                // Biased distribution
                const biasedSymbol = Math.floor(this.hashToFloat(`entropy_${timestamp}_${i}`, 2) * symbols / 4);
                data.push(biasedSymbol);
            }
        }
        return data;
    }

    generateNeuralData() {
        // Generate deterministic neural network-like data
        const timestamp = Date.now();
        return {
            weights: Array.from({ length: 100 }, (_, i) => this.hashToFloat(`weight_${timestamp}_${i}`, 0) * 2 - 1),
            biases: Array.from({ length: 10 }, (_, i) => this.hashToFloat(`bias_${timestamp}_${i}`, 1) * 2 - 1),
            activations: Array.from({ length: 10 }, (_, i) => this.hashToFloat(`act_${timestamp}_${i}`, 2)),
            gradients: Array.from({ length: 100 }, (_, i) => this.hashToFloat(`grad_${timestamp}_${i}`, 3) * 0.01)
        };
    }

    generateDefaultData() {
        // Generate default deterministic data
        const timestamp = Date.now();
        return Array.from({ length: 100 }, (_, i) => this.hashToFloat(`default_${timestamp}_${i}`, 0));
    }

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

    calculateEntropy(data) {
        const frequencies = {};
        data.forEach(value => {
            frequencies[value] = (frequencies[value] || 0) + 1;
        });

        const total = data.length;
        let entropy = 0;

        for (const freq of Object.values(frequencies)) {
            const probability = freq / total;
            if (probability > 0) {
                entropy -= probability * Math.log2(probability);
            }
        }

        return entropy;
    }

    calculateVarianceConfidence(variance) {
        // Calculate confidence based on how unusual the variance is
        if (variance < 1e-20) return 0.99;
        if (variance < 1e-15) return 0.95;
        if (variance < 1e-10) return 0.8;
        return 0.5;
    }

    detectMathematicalConstants(data) {
        const constants = [];
        const tolerance = 1e-10;

        for (const [source, sourceData] of Object.entries(data)) {
            if (Array.isArray(sourceData)) {
                for (const value of sourceData) {
                    if (Math.abs(value - Math.PI) < tolerance) {
                        constants.push({ name: 'π', value: Math.PI, detected: value, source });
                    }
                    if (Math.abs(value - Math.E) < tolerance) {
                        constants.push({ name: 'e', value: Math.E, detected: value, source });
                    }
                    if (Math.abs(value - 1.618033988749) < tolerance) { // Golden ratio
                        constants.push({ name: 'φ', value: 1.618033988749, detected: value, source });
                    }
                }
            }
        }

        return constants;
    }

    detectImpossibleCorrelations(data) {
        const correlations = [];
        const sources = Object.keys(data);

        for (let i = 0; i < sources.length - 1; i++) {
            for (let j = i + 1; j < sources.length; j++) {
                const correlation = this.calculateCorrelation(data[sources[i]], data[sources[j]]);

                if (Math.abs(correlation) > 0.99) {
                    correlations.push({
                        source1: sources[i],
                        source2: sources[j],
                        correlation,
                        impossibility: Math.abs(correlation) > 0.999 ? 'extreme' : 'high'
                    });
                }
            }
        }

        return correlations;
    }

    calculateCorrelation(data1, data2) {
        if (!Array.isArray(data1) || !Array.isArray(data2)) return 0;

        const minLength = Math.min(data1.length, data2.length);
        if (minLength < 2) return 0;

        const slice1 = data1.slice(0, minLength);
        const slice2 = data2.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;
    }

    calculateAlertSeverity(pattern) {
        if (pattern.emergent) return 'critical';
        if (pattern.confidence > 0.95) return 'high';
        if (pattern.confidence > 0.85) return 'medium';
        return 'low';
    }

    updatePerformanceMetrics(responseTime) {
        const currentAverage = this.performanceMetrics.averageResponseTime;
        const totalOperations = this.performanceMetrics.totalPatterns + 1;

        this.performanceMetrics.averageResponseTime =
            (currentAverage * (totalOperations - 1) + responseTime) / totalOperations;
    }

    getStatus() {
        return {
            activeMonitors: this.activeMonitors.size,
            totalPatterns: this.performanceMetrics.totalPatterns,
            totalAlerts: this.performanceMetrics.totalAlerts,
            averageResponseTime: this.performanceMetrics.averageResponseTime,
            uptime: Date.now() - this.performanceMetrics.uptimeStart,
            alertHistory: this.alertHistory.slice(-10) // Last 10 alerts
        };
    }

    getActiveMonitors() {
        return Array.from(this.activeMonitors.values()).map(monitor => ({
            id: monitor.id,
            sources: monitor.sources,
            startTime: monitor.startTime,
            patternCount: monitor.patternCount,
            alertCount: monitor.alertCount,
            uptime: Date.now() - monitor.startTime
        }));
    }

    // Deterministic helper methods to replace Math.random()
    hashValue(input) {
        let hash = 0;
        const str = input.toString();
        for (let i = 0; i < str.length; i++) {
            const char = str.charCodeAt(i);
            hash = ((hash << 5) - hash) + char;
            hash = hash & hash; // Convert to 32-bit integer
        }
        return Math.abs(hash);
    }

    hashToFloat(input, seed = 0) {
        const combined = this.hashValue(input) + seed * 1000;
        return (combined % 10000) / 10000;
    }
}