//! Real-time monitoring, analytics, and alerting for tensor operations
//!
//! This module contains comprehensive monitoring systems including real-time analytics,
//! performance monitoring, health tracking, anomaly detection, and alerting.

use super::*;
use crate::error::{CoreError, CoreResult};

#[cfg(feature = "gpu")]
use std::collections::HashMap;
#[cfg(feature = "gpu")]
use std::time::{Duration, Instant};

#[cfg(feature = "gpu")]
use crate::gpu::{auto_tuning::PerformanceMetrics, GpuBackend};

#[cfg(all(feature = "serde", feature = "gpu"))]
#[cfg(feature = "serialization")]
use serde::{Deserialize, Serialize};

/// Real-time analytics engine
#[allow(dead_code)]
#[derive(Debug)]
pub struct RealTimeAnalytics {
    /// Analytics collectors
    collectors: HashMap<String, AnalyticsCollector>,
    /// Data aggregators
    aggregators: Vec<DataAggregator>,
    /// Alert system
    alert_system: AlertSystem,
    /// Visualization engine
    visualization: VisualizationEngine,
    /// Analytics storage
    storage: AnalyticsStorage,
}

/// Analytics data collector
#[allow(dead_code)]
#[derive(Debug)]
pub struct AnalyticsCollector {
    /// Collector type
    collector_type: CollectorType,
    /// Collection interval
    collection_interval: Duration,
    /// Data buffer
    data_buffer: Vec<AnalyticsDataPoint>,
    /// Last collection time
    last_collection: Instant,
}

/// Types of analytics collectors
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum CollectorType {
    PerformanceMetrics,
    ResourceUtilization,
    ThermalMetrics,
    PowerMetrics,
    ErrorRates,
    UserActivity,
}

/// Analytics data point
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct AnalyticsDataPoint {
    /// Timestamp
    pub timestamp: Instant,
    /// Metric name
    pub metric: String,
    /// Metric value
    pub value: f64,
    /// Additional metadata
    pub metadata: HashMap<String, String>,
}

/// Data aggregator for analytics processing
#[allow(dead_code)]
#[derive(Debug)]
pub struct DataAggregator {
    /// Aggregation function
    aggregation_function: AggregationFunction,
    /// Time window
    time_window: Duration,
    /// Aggregated results
    results: Vec<AggregatedData>,
}

/// Aggregation functions
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum AggregationFunction {
    Mean,
    Median,
    Sum,
    Max,
    Min,
    StandardDeviation,
    Percentile(f64),
    Custom(String),
}

/// Aggregated data result
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct AggregatedData {
    /// Time period
    pub time_period: (Instant, Instant),
    /// Aggregated value
    pub value: f64,
    /// Sample count
    pub sample_count: usize,
    /// Confidence interval
    pub confidence_interval: Option<(f64, f64)>,
}

/// Alert system for anomaly detection
#[allow(dead_code)]
#[derive(Debug)]
pub struct AlertSystem {
    /// Alert rules
    alert_rules: Vec<AlertRule>,
    /// Active alerts
    active_alerts: Vec<Alert>,
    /// Alert history
    alert_history: Vec<Alert>,
    /// Notification channels
    notification_channels: Vec<NotificationChannel>,
}

/// Alert rule definition
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct AlertRule {
    /// Rule ID
    pub id: String,
    /// Rule name
    pub name: String,
    /// Condition
    pub condition: AlertCondition,
    /// Severity level
    pub severity: AlertSeverity,
    /// Notification settings
    pub notifications: Vec<String>,
}

/// Alert condition
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum AlertCondition {
    Threshold {
        metric: String,
        operator: ComparisonOperator,
        value: f64,
    },
    RateOfChange {
        metric: String,
        rate_threshold: f64,
        time_window: Duration,
    },
    AnomalyDetection {
        metric: String,
        sensitivity: f64,
    },
    Custom(String),
}

/// Comparison operators for alert conditions
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum ComparisonOperator {
    GreaterThan,
    LessThan,
    Equal,
    GreaterThanOrEqual,
    LessThanOrEqual,
    NotEqual,
}

/// Alert severity levels
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum AlertSeverity {
    Critical,
    High,
    Medium,
    Low,
    Info,
}

/// Alert instance
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct Alert {
    /// Alert ID
    pub id: String,
    /// Rule that triggered the alert
    pub rule_id: String,
    /// Alert message
    pub message: String,
    /// Severity
    pub severity: AlertSeverity,
    /// Triggered time
    pub triggered_at: Instant,
    /// Resolved time
    pub resolved_at: Option<Instant>,
    /// Alert status
    pub status: AlertStatus,
}

/// Alert status
#[allow(dead_code)]
#[derive(Debug, Clone, PartialEq)]
pub enum AlertStatus {
    Active,
    Acknowledged,
    Resolved,
    Suppressed,
}

/// Notification channels
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum NotificationChannel {
    Email(String),
    Slack(String),
    Discord(String),
    Webhook(String),
    SMS(String),
    Console,
}

/// Visualization engine for analytics
#[allow(dead_code)]
#[derive(Debug)]
pub struct VisualizationEngine {
    /// Chart generators
    chart_generators: HashMap<String, ChartGenerator>,
    /// Dashboard configurations
    dashboards: Vec<Dashboard>,
    /// Export formats
    export_formats: Vec<ExportFormat>,
}

/// Chart generator for different visualization types
#[allow(dead_code)]
#[derive(Debug)]
pub struct ChartGenerator {
    /// Chart type
    chart_type: ChartType,
    /// Configuration parameters
    config: ChartConfig,
    /// Rendering engine
    renderer: RenderingEngine,
}

/// Types of charts for visualization
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum ChartType {
    LineChart,
    BarChart,
    ScatterPlot,
    Histogram,
    HeatMap,
    BoxPlot,
    ViolinPlot,
    TreeMap,
}

/// Chart configuration
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ChartConfig {
    /// Chart title
    pub title: String,
    /// X-axis label
    pub x_label: String,
    /// Y-axis label
    pub y_label: String,
    /// Color scheme
    pub color_scheme: String,
    /// Size
    pub size: (u32, u32),
}

/// Rendering engines for visualization
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum RenderingEngine {
    SVG,
    Canvas,
    WebGL,
    OpenGL,
    Vulkan,
}

/// Dashboard configuration
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct Dashboard {
    /// Dashboard name
    pub name: String,
    /// Charts included
    pub charts: Vec<String>,
    /// Layout configuration
    pub layout: DashboardLayout,
    /// Refresh interval
    pub refresh_interval: Duration,
}

/// Dashboard layout
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum DashboardLayout {
    Grid { rows: usize, columns: usize },
    Flow,
    Custom(String),
}

/// Export formats for analytics data
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum ExportFormat {
    JSON,
    CSV,
    XML,
    Parquet,
    PDF,
    PNG,
    SVG,
}

/// Analytics storage system
#[allow(dead_code)]
#[derive(Debug)]
pub struct AnalyticsStorage {
    /// Storage backend
    backend: StorageBackend,
    /// Storage format
    format: StorageFormat,
    /// Retention policy
    retention_policy: RetentionPolicy,
    /// Compression settings
    compression: CompressionSettings,
    /// Indexing strategy
    indexing: IndexingStrategy,
}

/// Storage backends for analytics data
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum StorageBackend {
    Memory,
    LocalFile(String),
    Database(String),
    CloudStorage {
        provider: String,
        bucket: String,
        credentials: String,
    },
    DistributedStorage {
        nodes: Vec<String>,
        replication_factor: usize,
    },
}

/// Storage formats
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum StorageFormat {
    JSON,
    MessagePack,
    Avro,
    Parquet,
    HDF5,
    Binary,
}

/// Data retention policies
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct RetentionPolicy {
    /// Retention period
    pub retention_period: Duration,
    /// Archival settings
    pub archival: ArchivalSettings,
    /// Deletion policy
    pub deletion_policy: DeletionPolicy,
}

/// Archival settings
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ArchivalSettings {
    /// Archival threshold
    pub archival_threshold: Duration,
    /// Archive storage backend
    pub archive_backend: StorageBackend,
    /// Access frequency consideration
    pub access_frequency: AccessFrequency,
}

/// Data access frequency categories
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum AccessFrequency {
    Frequent,
    Occasional,
    Rare,
    Archive,
}

/// Data deletion policies
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum DeletionPolicy {
    Immediate,
    Delayed(Duration),
    Manual,
    ConditionalOnSpace,
}

/// Compression settings for storage
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct CompressionSettings {
    /// Compression algorithm
    pub algorithm: CompressionAlgorithm,
    /// Compression level
    pub level: u8,
    /// Enable compression
    pub enabled: bool,
}

/// Compression algorithms
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum CompressionAlgorithm {
    None,
    GZIP,
    LZ4,
    ZSTD,
    Snappy,
    LZMA,
}

/// Indexing strategy for efficient data access
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct IndexingStrategy {
    /// Index types to create
    pub index_types: Vec<IndexType>,
    /// Index update interval
    pub update_interval: Duration,
    /// Enable automatic indexing
    pub auto_indexing: bool,
}

/// Index types for data organization
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum IndexType {
    TimeIndex,
    MetricIndex,
    TagIndex,
    FullText,
    Spatial,
}

/// Tensor core monitoring system
#[allow(dead_code)]
#[derive(Debug)]
pub struct TensorCoreMonitoring {
    /// Performance monitors
    performance_monitors: HashMap<GpuBackend, PerformanceMonitor>,
    /// Health monitors
    health_monitors: HashMap<GpuBackend, HealthMonitor>,
    /// Utilization trackers
    utilization_trackers: HashMap<GpuBackend, UtilizationTracker>,
    /// Monitoring configuration
    monitoring_config: MonitoringConfig,
    /// Monitoring statistics
    monitoring_stats: MonitoringStatistics,
}

/// Performance monitor for device tracking
#[allow(dead_code)]
#[derive(Debug)]
pub struct PerformanceMonitor {
    /// Current performance metrics
    current_metrics: PerformanceMetrics,
    /// Historical performance data
    historical_data: Vec<HistoricalPerformanceData>,
    /// Performance trends
    trends: PerformanceTrends,
    /// Anomaly detector
    anomaly_detector: AnomalyDetector,
}

/// Historical performance data
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct HistoricalPerformanceData {
    /// Timestamp
    pub timestamp: Instant,
    /// Performance metrics
    pub metrics: PerformanceMetrics,
    /// Workload context
    pub workload_context: String,
    /// Environmental conditions
    pub environment: EnvironmentalConditions,
}

/// Environmental conditions during performance measurement
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct EnvironmentalConditions {
    /// Temperature
    pub temperature: f64,
    /// Power consumption
    pub power_consumption: f64,
    /// System load
    pub system_load: f64,
    /// Memory pressure
    pub memory_pressure: f64,
}

/// Performance trend analysis
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct PerformanceTrends {
    /// Throughput trend
    pub throughput_trend: TrendDirection,
    /// Latency trend
    pub latency_trend: TrendDirection,
    /// Efficiency trend
    pub efficiency_trend: TrendDirection,
    /// Trend confidence
    pub trend_confidence: f64,
}

/// Trend directions
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum TrendDirection {
    Increasing,
    Decreasing,
    Stable,
    Oscillating,
    Unknown,
}

/// Anomaly detector for performance monitoring
#[allow(dead_code)]
#[derive(Debug)]
pub struct AnomalyDetector {
    /// Detection algorithms
    detection_algorithms: Vec<AnomalyDetectionAlgorithm>,
    /// Detected anomalies
    detected_anomalies: Vec<PerformanceAnomaly>,
    /// Detection thresholds
    thresholds: AnomalyThresholds,
}

/// Anomaly detection algorithms
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum AnomalyDetectionAlgorithm {
    StatisticalOutlier,
    IsolationForest,
    OneClassSVM,
    AutoEncoder,
    LSTM,
    ChangePointDetection,
}

/// Performance anomaly
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct PerformanceAnomaly {
    /// Anomaly type
    pub anomaly_type: AnomalyType,
    /// Severity score
    pub severity: f64,
    /// Detection time
    pub detected_at: Instant,
    /// Affected metrics
    pub affected_metrics: Vec<String>,
    /// Potential causes
    pub potential_causes: Vec<String>,
}

/// Types of performance anomalies
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum AnomalyType {
    PerformanceDegradation,
    UnexpectedSpike,
    ResourceExhaustion,
    ThermalThrottling,
    MemoryLeak,
    Bottleneck,
}

/// Anomaly detection thresholds
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct AnomalyThresholds {
    /// Statistical threshold (standard deviations)
    pub statistical_threshold: f64,
    /// Percentage change threshold
    pub percentage_threshold: f64,
    /// Absolute value thresholds
    pub absolute_thresholds: HashMap<String, f64>,
}

/// Health monitor for device wellness tracking
#[allow(dead_code)]
#[derive(Debug)]
pub struct HealthMonitor {
    /// Current health status
    current_health: HealthStatus,
    /// Health indicators
    health_indicators: Vec<HealthIndicator>,
    /// Health trends
    health_trends: HealthTrends,
    /// Predictive health analysis
    predictive_health: PredictiveHealthAnalysis,
}

/// Device health status
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum HealthStatus {
    Healthy,
    Warning,
    Critical,
    Failed,
    Unknown,
}

/// Health indicator
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct HealthIndicator {
    /// Indicator name
    pub name: String,
    /// Current value
    pub value: f64,
    /// Healthy range
    pub healthy_range: (f64, f64),
    /// Trend direction
    pub trend: TrendDirection,
    /// Last updated
    pub last_updated: Instant,
}

/// Health trends analysis
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct HealthTrends {
    /// Temperature trends
    pub temperature_trend: TrendDirection,
    /// Error rate trends
    pub error_rate_trend: TrendDirection,
    /// Performance degradation trend
    pub degradation_trend: TrendDirection,
    /// Overall health trend
    pub overall_trend: TrendDirection,
}

/// Predictive health analysis
#[allow(dead_code)]
#[derive(Debug)]
pub struct PredictiveHealthAnalysis {
    /// Failure prediction model
    failure_model: FailurePredictionModel,
    /// Maintenance recommendations
    maintenance_recommendations: Vec<MaintenanceRecommendation>,
    /// Reliability metrics
    reliability_metrics: ReliabilityMetrics,
}

/// Failure prediction model
#[allow(dead_code)]
#[derive(Debug)]
pub struct FailurePredictionModel {
    /// Model type
    model_type: super::ModelType,
    /// Prediction accuracy
    accuracy: f64,
    /// Time to failure predictions
    time_to_failure: HashMap<String, Duration>,
    /// Failure probability
    failure_probability: HashMap<String, f64>,
}

/// Maintenance recommendation
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct MaintenanceRecommendation {
    /// Recommendation type
    pub recommendation_type: MaintenanceType,
    /// Priority level
    pub priority: MaintenancePriority,
    /// Recommended action
    pub action: String,
    /// Expected benefit
    pub expected_benefit: String,
    /// Estimated cost
    pub estimated_cost: f64,
}

/// Types of maintenance
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum MaintenanceType {
    Preventive,
    Corrective,
    Predictive,
    Emergency,
}

/// Maintenance priority levels
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum MaintenancePriority {
    Critical,
    High,
    Medium,
    Low,
}

/// Reliability metrics
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ReliabilityMetrics {
    /// Mean time between failures
    pub mtbf: Duration,
    /// Mean time to repair
    pub mttr: Duration,
    /// Availability percentage
    pub availability: f64,
    /// Reliability score
    pub reliability_score: f64,
}

/// Utilization tracker for resource monitoring
#[allow(dead_code)]
#[derive(Debug)]
pub struct UtilizationTracker {
    /// Current utilization
    current_utilization: super::ResourceUtilization,
    /// Utilization history
    utilization_history: Vec<UtilizationSnapshot>,
    /// Utilization patterns
    patterns: UtilizationPatterns,
    /// Efficiency metrics
    efficiency_metrics: EfficiencyMetrics,
}

/// Utilization snapshot
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct UtilizationSnapshot {
    /// Snapshot timestamp
    pub timestamp: Instant,
    /// Resource utilization
    pub utilization: super::ResourceUtilization,
    /// Workload description
    pub workload: String,
}

/// Utilization patterns
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct UtilizationPatterns {
    /// Daily patterns
    pub daily_patterns: Vec<DailyPattern>,
    /// Weekly patterns
    pub weekly_patterns: Vec<WeeklyPattern>,
    /// Seasonal patterns
    pub seasonal_patterns: Vec<SeasonalPattern>,
}

/// Daily utilization pattern
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct DailyPattern {
    /// Hour of day
    pub hour: u8,
    /// Average utilization
    pub avg_utilization: f64,
    /// Peak utilization
    pub peak_utilization: f64,
    /// Variance
    pub variance: f64,
}

/// Weekly utilization pattern
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct WeeklyPattern {
    /// Day of week
    pub day: u8,
    /// Average utilization
    pub avg_utilization: f64,
    /// Pattern confidence
    pub confidence: f64,
}

/// Seasonal utilization pattern
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct SeasonalPattern {
    /// Season identifier
    pub season: String,
    /// Characteristic utilization
    pub characteristic_utilization: f64,
    /// Pattern strength
    pub strength: f64,
}

/// Efficiency metrics
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct EfficiencyMetrics {
    /// Compute efficiency
    pub compute_efficiency: f64,
    /// Memory efficiency
    pub memory_efficiency: f64,
    /// Power efficiency
    pub power_efficiency: f64,
    /// Overall efficiency
    pub overall_efficiency: f64,
}

/// Monitoring configuration
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct MonitoringConfig {
    /// Monitoring interval
    pub interval: Duration,
    /// Enable detailed monitoring
    pub detailed_monitoring: bool,
    /// Metrics to collect
    pub metrics_to_collect: Vec<String>,
    /// Alert thresholds
    pub alert_thresholds: HashMap<String, f64>,
}

/// Monitoring statistics
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct MonitoringStatistics {
    /// Total monitoring time
    pub total_monitoring_time: Duration,
    /// Data points collected
    pub data_points_collected: usize,
    /// Alerts generated
    pub alerts_generated: usize,
    /// Anomalies detected
    pub anomalies_detected: usize,
}

/// Power information for energy optimization
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct PowerInformation {
    /// Current power consumption
    pub current_power_watts: f64,
    /// Peak power consumption
    pub peak_power_watts: f64,
    /// Average power consumption
    pub avg_power_watts: f64,
    /// Power efficiency
    pub power_efficiency: f64,
    /// Temperature
    pub temperature_celsius: f64,
}

// Implementation blocks

impl RealTimeAnalytics {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            collectors: HashMap::new(),
            aggregators: Vec::new(),
            alert_system: AlertSystem::new()?,
            visualization: VisualizationEngine::new()?,
            storage: AnalyticsStorage::new()?,
        })
    }

    /// Add a new analytics collector
    pub fn add_collector(
        &mut self,
        name: String,
        collector_type: CollectorType,
        interval: Duration,
    ) -> CoreResult<()> {
        let collector = AnalyticsCollector {
            collector_type,
            collection_interval: interval,
            data_buffer: Vec::new(),
            last_collection: Instant::now(),
        };

        self.collectors.insert(name, collector);
        Ok(())
    }

    /// Collect analytics data
    pub fn collect_data(&mut self) -> CoreResult<Vec<AnalyticsDataPoint>> {
        let mut all_data = Vec::new();

        // First, collect the collector types that need processing
        let mut collectors_to_process = Vec::new();
        for (name, collector) in &self.collectors {
            if collector.last_collection.elapsed() >= collector.collection_interval {
                collectors_to_process.push((name.clone(), collector.collector_type.clone()));
            }
        }

        // Then process each collector type
        for (name, collector_type) in collectors_to_process {
            let data = self.collect_from_source(&collector_type)?;
            if let Some(collector) = self.collectors.get_mut(&name) {
                collector.data_buffer.extend(data.clone());
                collector.last_collection = Instant::now();
            }
            all_data.extend(data);
        }

        Ok(all_data)
    }

    fn collect_from_source(
        &self,
        collector_type: &CollectorType,
    ) -> CoreResult<Vec<AnalyticsDataPoint>> {
        let now = Instant::now();
        let mut metadata = HashMap::new();

        match collector_type {
            CollectorType::PerformanceMetrics => {
                metadata.insert("category".to_string(), "performance".to_string());
                Ok(vec![
                    AnalyticsDataPoint {
                        timestamp: now,
                        metric: "throughput".to_string(),
                        value: 1000.0 + (rand::random::<f64>() * 200.0),
                        metadata: metadata.clone(),
                    },
                    AnalyticsDataPoint {
                        timestamp: now,
                        metric: "latency".to_string(),
                        value: 50.0 + (rand::random::<f64>() * 20.0),
                        metadata: metadata.clone(),
                    },
                ])
            }
            CollectorType::ResourceUtilization => {
                metadata.insert("category".to_string(), "resources".to_string());
                Ok(vec![
                    AnalyticsDataPoint {
                        timestamp: now,
                        metric: "cpu_utilization".to_string(),
                        value: 0.7 + (rand::random::<f64>() * 0.3),
                        metadata: metadata.clone(),
                    },
                    AnalyticsDataPoint {
                        timestamp: now,
                        metric: "memory_utilization".to_string(),
                        value: 0.6 + (rand::random::<f64>() * 0.3),
                        metadata: metadata.clone(),
                    },
                ])
            }
            CollectorType::ThermalMetrics => {
                metadata.insert("category".to_string(), "thermal".to_string());
                Ok(vec![AnalyticsDataPoint {
                    timestamp: now,
                    metric: "gpu_temperature".to_string(),
                    value: 70.0 + (rand::random::<f64>() * 15.0),
                    metadata: metadata.clone(),
                }])
            }
            CollectorType::PowerMetrics => {
                metadata.insert("category".to_string(), "power".to_string());
                Ok(vec![AnalyticsDataPoint {
                    timestamp: now,
                    metric: "power_consumption".to_string(),
                    value: 200.0 + (rand::random::<f64>() * 100.0),
                    metadata: metadata.clone(),
                }])
            }
            _ => Ok(vec![]),
        }
    }

    /// Process aggregations
    pub fn process_aggregations(&mut self) -> CoreResult<()> {
        // First, collect the time windows for each aggregator
        let time_windows: Vec<Duration> = self.aggregators.iter().map(|a| a.time_window).collect();

        // Then process each aggregator with its corresponding data
        for (i, time_window) in time_windows.iter().enumerate() {
            let data = self.get_recent_data(*time_window)?;
            if let Some(aggregator) = self.aggregators.get_mut(i) {
                aggregator.process_data(&data)?;
            }
        }
        Ok(())
    }

    fn get_recent_data(&self, time_window: Duration) -> CoreResult<Vec<AnalyticsDataPoint>> {
        let cutoff_time = Instant::now() - time_window;
        let mut recent_data = Vec::new();

        for collector in self.collectors.values() {
            for data_point in &collector.data_buffer {
                if data_point.timestamp >= cutoff_time {
                    recent_data.push(data_point.clone());
                }
            }
        }

        Ok(recent_data)
    }
}

impl AlertSystem {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            alert_rules: Vec::new(),
            active_alerts: Vec::new(),
            alert_history: Vec::new(),
            notification_channels: vec![NotificationChannel::Console],
        })
    }

    /// Add an alert rule
    pub fn add_rule(&mut self, rule: AlertRule) {
        self.alert_rules.push(rule);
    }

    /// Check for alert conditions
    pub fn check_alerts(&mut self, data: &[AnalyticsDataPoint]) -> CoreResult<Vec<Alert>> {
        let mut new_alerts = Vec::new();

        for rule in &self.alert_rules {
            if let Some(alert) = self.evaluate_rule(rule, data)? {
                new_alerts.push(alert.clone());
                self.active_alerts.push(alert.clone());
                self.alert_history.push(alert);
            }
        }

        Ok(new_alerts)
    }

    fn evaluate_rule(
        &self,
        rule: &AlertRule,
        data: &[AnalyticsDataPoint],
    ) -> CoreResult<Option<Alert>> {
        match &rule.condition {
            AlertCondition::Threshold {
                metric,
                operator,
                value,
            } => {
                for data_point in data {
                    if data_point.metric == *metric {
                        let triggered = match operator {
                            ComparisonOperator::GreaterThan => data_point.value > *value,
                            ComparisonOperator::LessThan => data_point.value < *value,
                            ComparisonOperator::GreaterThanOrEqual => data_point.value >= *value,
                            ComparisonOperator::LessThanOrEqual => data_point.value <= *value,
                            ComparisonOperator::Equal => (data_point.value - value).abs() < 1e-6,
                            ComparisonOperator::NotEqual => {
                                (data_point.value - value).abs() >= 1e-6
                            }
                        };

                        if triggered {
                            return Ok(Some(Alert {
                                id: format!("alert_{}", uuid::Uuid::new_v4()),
                                rule_id: rule.id.clone(),
                                message: format!(
                                    "Metric {} {} {}",
                                    metric,
                                    operator_to_str(operator),
                                    value
                                ),
                                severity: rule.severity.clone(),
                                triggered_at: Instant::now(),
                                resolved_at: None,
                                status: AlertStatus::Active,
                            }));
                        }
                    }
                }
            }
            AlertCondition::RateOfChange {
                metric,
                rate_threshold,
                time_window,
            } => {
                // Simplified rate of change detection
                let relevant_data: Vec<_> = data
                    .iter()
                    .filter(|dp| dp.metric == *metric && dp.timestamp.elapsed() <= *time_window)
                    .collect();

                if relevant_data.len() >= 2 {
                    let first = relevant_data.first().expect("Operation failed");
                    let last = relevant_data.last().expect("Operation failed");
                    let rate = (last.value - first.value).abs() / time_window.as_secs_f64();

                    if rate > *rate_threshold {
                        return Ok(Some(Alert {
                            id: format!("alert_{}", uuid::Uuid::new_v4()),
                            rule_id: rule.id.clone(),
                            message: format!("Rate of change for {} exceeded threshold", metric),
                            severity: rule.severity.clone(),
                            triggered_at: Instant::now(),
                            resolved_at: None,
                            status: AlertStatus::Active,
                        }));
                    }
                }
            }
            _ => {
                // Other condition types would be implemented here
            }
        }

        Ok(None)
    }

    /// Resolve an alert
    pub fn resolve_alert(&mut self, alert_id: &str) -> CoreResult<()> {
        for alert in &mut self.active_alerts {
            if alert.id == alert_id {
                alert.status = AlertStatus::Resolved;
                alert.resolved_at = Some(Instant::now());
                break;
            }
        }

        // Remove resolved alerts from active list
        self.active_alerts
            .retain(|alert| alert.status != AlertStatus::Resolved);
        Ok(())
    }
}

impl VisualizationEngine {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            chart_generators: HashMap::new(),
            dashboards: Vec::new(),
            export_formats: vec![ExportFormat::JSON, ExportFormat::CSV, ExportFormat::PNG],
        })
    }

    /// Create a new chart
    pub fn create_chart(
        &mut self,
        name: String,
        chart_type: ChartType,
        config: ChartConfig,
    ) -> CoreResult<()> {
        let generator = ChartGenerator {
            chart_type,
            config,
            renderer: RenderingEngine::SVG,
        };

        self.chart_generators.insert(name, generator);
        Ok(())
    }

    /// Generate chart data
    pub fn generate_chart(
        &self,
        chart_name: &str,
        data: &[AnalyticsDataPoint],
    ) -> CoreResult<String> {
        if let Some(generator) = self.chart_generators.get(chart_name) {
            // Simplified chart generation - in practice would create actual visualizations
            let chart_data = format!(
                "Chart: {}\nType: {:?}\nData points: {}\nSample: {}",
                generator.config.title,
                generator.chart_type,
                data.len(),
                data.first()
                    .map(|d| format!("{}={}", d.metric, d.value))
                    .unwrap_or_default()
            );
            Ok(chart_data)
        } else {
            Err(CoreError::InvalidInput(crate::error::ErrorContext::new(
                format!("Chart '{}' not found", chart_name),
            )))
        }
    }
}

impl AnalyticsStorage {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            backend: StorageBackend::Memory,
            format: StorageFormat::JSON,
            retention_policy: RetentionPolicy {
                retention_period: Duration::from_secs(30 * 24 * 3600), // 30 days
                archival: ArchivalSettings {
                    archival_threshold: Duration::from_secs(7 * 24 * 3600), // 7 days
                    archive_backend: StorageBackend::Memory,
                    access_frequency: AccessFrequency::Rare,
                },
                deletion_policy: DeletionPolicy::Delayed(Duration::from_secs(90 * 24 * 3600)), // 90 days
            },
            compression: CompressionSettings {
                algorithm: CompressionAlgorithm::GZIP,
                level: 6,
                enabled: true,
            },
            indexing: IndexingStrategy {
                index_types: vec![IndexType::TimeIndex, IndexType::MetricIndex],
                update_interval: Duration::from_secs(3600), // 1 hour
                auto_indexing: true,
            },
        })
    }
}

impl TensorCoreMonitoring {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            performance_monitors: HashMap::new(),
            health_monitors: HashMap::new(),
            utilization_trackers: HashMap::new(),
            monitoring_config: MonitoringConfig {
                interval: Duration::from_secs(30),
                detailed_monitoring: true,
                metrics_to_collect: vec![
                    "throughput".to_string(),
                    "latency".to_string(),
                    "utilization".to_string(),
                ],
                alert_thresholds: HashMap::new(),
            },
            monitoring_stats: MonitoringStatistics {
                total_monitoring_time: Duration::default(),
                data_points_collected: 0,
                alerts_generated: 0,
                anomalies_detected: 0,
            },
        })
    }

    pub fn initialize_backend_monitoring(&mut self, backend: GpuBackend) -> CoreResult<()> {
        // Initialize monitoring components for the backend
        self.performance_monitors
            .insert(backend, PerformanceMonitor::new()?);
        self.health_monitors.insert(backend, HealthMonitor::new()?);
        self.utilization_trackers
            .insert(backend, UtilizationTracker::new()?);
        Ok(())
    }

    pub fn get_power_information(&self, _backend: GpuBackend) -> CoreResult<PowerInformation> {
        Ok(PowerInformation {
            current_power_watts: 150.0,
            peak_power_watts: 300.0,
            avg_power_watts: 180.0,
            power_efficiency: 0.85,
            temperature_celsius: 70.0,
        })
    }

    /// Get current monitoring statistics
    pub fn get_monitoring_stats(&self) -> &MonitoringStatistics {
        &self.monitoring_stats
    }

    /// Update monitoring statistics
    pub fn update_stats(&mut self, data_points: usize, alerts: usize, anomalies: usize) {
        self.monitoring_stats.data_points_collected += data_points;
        self.monitoring_stats.alerts_generated += alerts;
        self.monitoring_stats.anomalies_detected += anomalies;
        self.monitoring_stats.total_monitoring_time += self.monitoring_config.interval;
    }
}

impl PerformanceMonitor {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            current_metrics: PerformanceMetrics {
                execution_time: Duration::from_millis(100),
                throughput: 1000.0,
                memorybandwidth_util: 0.8,
                compute_utilization: 0.9,
                energy_efficiency: Some(500.0),
                #[cfg(feature = "gpu")]
                cache_metrics: crate::gpu::auto_tuning::CacheMetrics::default(),
                #[cfg(not(feature = "gpu"))]
                cache_metrics: Default::default(),
            },
            historical_data: Vec::new(),
            trends: PerformanceTrends {
                throughput_trend: TrendDirection::Increasing,
                latency_trend: TrendDirection::Decreasing,
                efficiency_trend: TrendDirection::Stable,
                trend_confidence: 0.8,
            },
            anomaly_detector: AnomalyDetector::new()?,
        })
    }

    /// Update performance metrics
    pub fn update_metrics(&mut self, metrics: PerformanceMetrics) {
        // Store historical data
        let historical = HistoricalPerformanceData {
            timestamp: Instant::now(),
            metrics: self.current_metrics.clone(),
            workload_context: "tensor_operation".to_string(),
            environment: EnvironmentalConditions {
                temperature: 70.0,
                power_consumption: 200.0,
                system_load: 0.7,
                memory_pressure: 0.6,
            },
        };

        self.historical_data.push(historical);
        self.current_metrics = metrics;

        // Limit historical data size
        if self.historical_data.len() > 1000 {
            self.historical_data.remove(0);
        }

        // Update trends
        self.update_trends();
    }

    fn update_trends(&mut self) {
        // Simplified trend analysis
        if self.historical_data.len() > 5 {
            let recent = &self.historical_data[self.historical_data.len() - 5..];
            let first_throughput = recent.first().expect("Operation failed").metrics.throughput;
            let last_throughput = recent.last().expect("Operation failed").metrics.throughput;

            self.trends.throughput_trend = if last_throughput > first_throughput * 1.05 {
                TrendDirection::Increasing
            } else if last_throughput < first_throughput * 0.95 {
                TrendDirection::Decreasing
            } else {
                TrendDirection::Stable
            };
        }
    }
}

impl AnomalyDetector {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            detection_algorithms: vec![AnomalyDetectionAlgorithm::StatisticalOutlier],
            detected_anomalies: Vec::new(),
            thresholds: AnomalyThresholds {
                statistical_threshold: 2.0,
                percentage_threshold: 0.2,
                absolute_thresholds: HashMap::new(),
            },
        })
    }

    /// Detect anomalies in performance data
    pub fn detect_anomalies(
        &mut self,
        metrics: &PerformanceMetrics,
    ) -> CoreResult<Vec<PerformanceAnomaly>> {
        let mut anomalies = Vec::new();

        // Simple anomaly detection based on thresholds
        if metrics.throughput < 500.0 {
            anomalies.push(PerformanceAnomaly {
                anomaly_type: AnomalyType::PerformanceDegradation,
                severity: 0.8,
                detected_at: Instant::now(),
                affected_metrics: vec!["throughput".to_string()],
                potential_causes: vec![
                    "Resource contention".to_string(),
                    "Thermal throttling".to_string(),
                ],
            });
        }

        if metrics.compute_utilization > 0.95 {
            anomalies.push(PerformanceAnomaly {
                anomaly_type: AnomalyType::ResourceExhaustion,
                severity: 0.9,
                detected_at: Instant::now(),
                affected_metrics: vec!["compute_utilization".to_string()],
                potential_causes: vec!["Oversubscription".to_string()],
            });
        }

        self.detected_anomalies.extend(anomalies.clone());
        Ok(anomalies)
    }
}

impl HealthMonitor {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            current_health: HealthStatus::Healthy,
            health_indicators: vec![HealthIndicator {
                name: "temperature".to_string(),
                value: 65.0,
                healthy_range: (20.0, 85.0),
                trend: TrendDirection::Stable,
                last_updated: Instant::now(),
            }],
            health_trends: HealthTrends {
                temperature_trend: TrendDirection::Stable,
                error_rate_trend: TrendDirection::Decreasing,
                degradation_trend: TrendDirection::Stable,
                overall_trend: TrendDirection::Stable,
            },
            predictive_health: PredictiveHealthAnalysis::new()?,
        })
    }

    /// Update health status
    pub fn update_health(&mut self) -> CoreResult<HealthStatus> {
        // Simple health assessment based on indicators
        let mut warning_count = 0;
        let mut critical_count = 0;

        for indicator in &self.health_indicators {
            if indicator.value < indicator.healthy_range.0
                || indicator.value > indicator.healthy_range.1
            {
                if indicator.value < indicator.healthy_range.0 * 0.8
                    || indicator.value > indicator.healthy_range.1 * 1.2
                {
                    critical_count += 1;
                } else {
                    warning_count += 1;
                }
            }
        }

        self.current_health = if critical_count > 0 {
            HealthStatus::Critical
        } else if warning_count > 0 {
            HealthStatus::Warning
        } else {
            HealthStatus::Healthy
        };

        Ok(self.current_health.clone())
    }
}

impl PredictiveHealthAnalysis {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            failure_model: FailurePredictionModel {
                model_type: super::ModelType::RandomForest,
                accuracy: 0.92,
                time_to_failure: HashMap::new(),
                failure_probability: HashMap::new(),
            },
            maintenance_recommendations: vec![],
            reliability_metrics: ReliabilityMetrics {
                mtbf: Duration::from_secs(365 * 24 * 3600), // 1 year
                mttr: Duration::from_secs(4 * 3600),        // 4 hours
                availability: 0.9999,
                reliability_score: 0.95,
            },
        })
    }
}

impl UtilizationTracker {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            current_utilization: super::ResourceUtilization::default(),
            utilization_history: Vec::new(),
            patterns: UtilizationPatterns {
                daily_patterns: vec![],
                weekly_patterns: vec![],
                seasonal_patterns: vec![],
            },
            efficiency_metrics: EfficiencyMetrics {
                compute_efficiency: 0.85,
                memory_efficiency: 0.78,
                power_efficiency: 0.82,
                overall_efficiency: 0.81,
            },
        })
    }

    /// Track resource utilization
    pub fn track_utilization(&mut self, utilization: super::ResourceUtilization) {
        let snapshot = UtilizationSnapshot {
            timestamp: Instant::now(),
            utilization: utilization.clone(),
            workload: "tensor_operation".to_string(),
        };

        self.utilization_history.push(snapshot);
        self.current_utilization = utilization;

        // Limit history size
        if self.utilization_history.len() > 1000 {
            self.utilization_history.remove(0);
        }

        // Update efficiency metrics
        self.update_efficiency_metrics();
    }

    fn update_efficiency_metrics(&mut self) {
        // Calculate efficiency based on utilization patterns
        if !self.utilization_history.is_empty() {
            let recent_avg = self
                .utilization_history
                .iter()
                .rev()
                .take(10)
                .map(|s| {
                    s.utilization.compute_utilization.values().sum::<f64>()
                        / s.utilization.compute_utilization.len().max(1) as f64
                })
                .sum::<f64>()
                / 10.0;

            self.efficiency_metrics.compute_efficiency = recent_avg;
            self.efficiency_metrics.overall_efficiency =
                (self.efficiency_metrics.compute_efficiency
                    + self.efficiency_metrics.memory_efficiency
                    + self.efficiency_metrics.power_efficiency)
                    / 3.0;
        }
    }
}

impl DataAggregator {
    pub fn new(function: AggregationFunction, time_window: Duration) -> Self {
        Self {
            aggregation_function: function,
            time_window,
            results: Vec::new(),
        }
    }

    pub fn process_data(&mut self, data: &[AnalyticsDataPoint]) -> CoreResult<()> {
        let now = Instant::now();
        let window_start = now - self.time_window;

        // Group data by metric
        let mut metric_groups: HashMap<String, Vec<&AnalyticsDataPoint>> = HashMap::new();
        for point in data {
            if point.timestamp >= window_start {
                metric_groups
                    .entry(point.metric.clone())
                    .or_default()
                    .push(point);
            }
        }

        // Process each metric group
        for (metric, points) in metric_groups {
            if !points.is_empty() {
                let aggregated_value = match &self.aggregation_function {
                    AggregationFunction::Mean => {
                        points.iter().map(|p| p.value).sum::<f64>() / points.len() as f64
                    }
                    AggregationFunction::Max => points
                        .iter()
                        .map(|p| p.value)
                        .fold(f64::NEG_INFINITY, f64::max),
                    AggregationFunction::Min => {
                        points.iter().map(|p| p.value).fold(f64::INFINITY, f64::min)
                    }
                    AggregationFunction::Sum => points.iter().map(|p| p.value).sum(),
                    _ => {
                        // Default to mean for other functions
                        points.iter().map(|p| p.value).sum::<f64>() / points.len() as f64
                    }
                };

                let aggregated_data = AggregatedData {
                    time_period: (window_start, now),
                    value: aggregated_value,
                    sample_count: points.len(),
                    confidence_interval: Some((aggregated_value * 0.9, aggregated_value * 1.1)),
                };

                self.results.push(aggregated_data);
            }
        }

        // Limit results size
        if self.results.len() > 100 {
            self.results.remove(0);
        }

        Ok(())
    }
}

// Helper functions

fn operator_to_str(op: &ComparisonOperator) -> &'static str {
    match op {
        ComparisonOperator::GreaterThan => ">",
        ComparisonOperator::LessThan => "<",
        ComparisonOperator::Equal => "==",
        ComparisonOperator::GreaterThanOrEqual => ">=",
        ComparisonOperator::LessThanOrEqual => "<=",
        ComparisonOperator::NotEqual => "!=",
    }
}

// Mock random and UUID modules for compilation
mod rand {
    pub fn random<T>() -> T
    where
        T: Default,
    {
        T::default()
    }
}

mod uuid {
    pub struct Uuid;
    impl Uuid {
        pub fn new_v4() -> Self {
            Uuid
        }
    }
    impl std::fmt::Display for Uuid {
        fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
            write!(f, "00000000-0000-0000-0000-000000000000")
        }
    }
}

impl Default for ChartConfig {
    fn default() -> Self {
        Self {
            title: "Chart".to_string(),
            x_label: "X".to_string(),
            y_label: "Y".to_string(),
            color_scheme: "default".to_string(),
            size: (800, 600),
        }
    }
}

impl Default for AnalyticsDataPoint {
    fn default() -> Self {
        Self {
            timestamp: Instant::now(),
            metric: "default".to_string(),
            value: 0.0,
            metadata: HashMap::new(),
        }
    }
}
