quantrs2-device 0.1.3

//! Implementation methods for the enhanced monitoring system

use super::components::*;
use super::core::*;
use super::storage::*;
use super::types::*;
use async_trait::async_trait;
use chrono::{DateTime, Duration as ChronoDuration, Utc};
use serde::{Deserialize, Serialize};
use std::collections::{BTreeMap, HashMap, HashSet, VecDeque};
use std::sync::{Arc, Mutex, RwLock};
use std::time::Duration;
use uuid::Uuid;

use crate::performance_analytics_dashboard::NotificationDispatcher;
use crate::quantum_network::distributed_protocols::{NodeId, NodeInfo, PerformanceMetrics};
use crate::quantum_network::network_optimization::{
    FeatureVector, MLModel, NetworkOptimizationError, PredictionResult, Priority,
};

impl EnhancedQuantumNetworkMonitor {
    /// Create a new enhanced quantum network monitor
    pub fn new(config: EnhancedMonitoringConfig) -> Self {
        Self {
            metrics_collector: Arc::new(RealTimeMetricsCollector::new(&config.metrics_config)),
            analytics_engine: Arc::new(QuantumNetworkAnalyticsEngine::new(
                &config.analytics_config,
            )),
            anomaly_detector: Arc::new(QuantumAnomalyDetector::new(
                &config.anomaly_detection_config,
            )),
            predictive_analytics: Arc::new(QuantumNetworkPredictor::new(&config.predictive_config)),
            alert_system: Arc::new(QuantumNetworkAlertSystem::new(&config.alert_config)),
            historical_data_manager: Arc::new(QuantumHistoricalDataManager::new(
                &config.storage_config,
            )),
            optimization_recommender: Arc::new(QuantumOptimizationRecommender::new(&())),
            dashboard_system: Arc::new(QuantumNetworkDashboard::new(&())),
            config_manager: Arc::new(config),
        }
    }

    /// Start comprehensive monitoring
    pub async fn start_monitoring(&self) -> Result<()> {
        // Start metrics collection
        self.metrics_collector.start_collection().await?;

        // Start real-time analytics
        self.analytics_engine.start_analytics().await?;

        // Start anomaly detection
        self.anomaly_detector.start_detection().await?;

        // Start predictive analytics
        self.predictive_analytics.start_prediction().await?;

        // Start alert system
        self.alert_system.start_alerting().await?;

        // Initialize dashboard
        self.dashboard_system.initialize().await?;

        Ok(())
    }

    /// Stop monitoring
    pub async fn stop_monitoring(&self) -> Result<()> {
        // Stop all monitoring components
        self.metrics_collector.stop_collection().await?;
        self.analytics_engine.stop_analytics().await?;
        self.anomaly_detector.stop_detection().await?;
        self.predictive_analytics.stop_prediction().await?;
        self.alert_system.stop_alerting().await?;

        Ok(())
    }

    /// Get comprehensive monitoring status
    pub async fn get_monitoring_status(&self) -> Result<MonitoringStatus> {
        Ok(MonitoringStatus {
            overall_status: OverallStatus::Healthy,
            metrics_collection_status: self.metrics_collector.get_status().await?,
            analytics_status: self.analytics_engine.get_status().await?,
            anomaly_detection_status: self.anomaly_detector.get_status().await?,
            predictive_analytics_status: self.predictive_analytics.get_status().await?,
            alert_system_status: self.alert_system.get_status().await?,
            total_data_points_collected: self.get_total_data_points().await?,
            active_alerts: self.get_active_alerts_count().await?,
            system_health_score: self.calculate_system_health_score().await?,
        })
    }

    /// Get real-time metrics
    pub async fn get_real_time_metrics(
        &self,
        metric_types: &[MetricType],
    ) -> Result<Vec<MetricDataPoint>> {
        self.metrics_collector
            .get_real_time_metrics(metric_types)
            .await
    }

    /// Get historical metrics
    pub async fn get_historical_metrics(
        &self,
        metric_type: MetricType,
        start_time: DateTime<Utc>,
        end_time: DateTime<Utc>,
    ) -> Result<Vec<MetricDataPoint>> {
        self.historical_data_manager
            .get_historical_data(metric_type, start_time, end_time)
            .await
    }

    /// Get anomaly detection results
    pub async fn get_anomaly_results(&self, time_window: Duration) -> Result<Vec<AnomalyResult>> {
        self.anomaly_detector
            .get_recent_anomalies(time_window)
            .await
    }

    /// Get predictions
    pub async fn get_predictions(
        &self,
        metric_type: MetricType,
        prediction_horizon: Duration,
    ) -> Result<PredictionResult> {
        self.predictive_analytics
            .get_prediction(metric_type, prediction_horizon)
            .await
    }

    /// Get optimization recommendations
    pub async fn get_optimization_recommendations(
        &self,
    ) -> Result<Vec<OptimizationRecommendation>> {
        self.optimization_recommender.get_recommendations().await
    }

    // Helper methods
    async fn get_total_data_points(&self) -> Result<u64> {
        Ok(self
            .metrics_collector
            .get_collection_statistics()
            .await?
            .total_data_points)
    }

    async fn get_active_alerts_count(&self) -> Result<u32> {
        Ok(self.alert_system.get_active_alerts().await?.len() as u32)
    }

    async fn calculate_system_health_score(&self) -> Result<f64> {
        // Calculate a comprehensive health score based on multiple factors
        let metrics_health = self.metrics_collector.get_health_score().await?;
        let analytics_health = self.analytics_engine.get_health_score().await?;
        let anomaly_health = self.anomaly_detector.get_health_score().await?;
        let prediction_health = self.predictive_analytics.get_health_score().await?;
        let alert_health = self.alert_system.get_health_score().await?;

        // Weighted average of component health scores
        let overall_health = alert_health.mul_add(
            0.1,
            prediction_health.mul_add(
                0.15,
                anomaly_health.mul_add(0.2, metrics_health.mul_add(0.3, analytics_health * 0.25)),
            ),
        );

        Ok(overall_health)
    }
}

/// Monitoring status information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MonitoringStatus {
    pub overall_status: OverallStatus,
    pub metrics_collection_status: ComponentStatus,
    pub analytics_status: ComponentStatus,
    pub anomaly_detection_status: ComponentStatus,
    pub predictive_analytics_status: ComponentStatus,
    pub alert_system_status: ComponentStatus,
    pub total_data_points_collected: u64,
    pub active_alerts: u32,
    pub system_health_score: f64,
}

/// Overall monitoring system status
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OverallStatus {
    Healthy,
    Warning,
    Critical,
    Offline,
}

/// Component status information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComponentStatus {
    pub status: ComponentState,
    pub last_update: DateTime<Utc>,
    pub performance_metrics: ComponentPerformanceMetrics,
    pub error_count: u32,
}

/// Component state
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ComponentState {
    Running,
    Starting,
    Stopping,
    Stopped,
    Error,
}

/// Component performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComponentPerformanceMetrics {
    pub throughput: f64,
    pub latency: Duration,
    pub error_rate: f64,
    pub resource_utilization: f64,
}

/// Anomaly detection result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AnomalyResult {
    pub anomaly_id: Uuid,
    pub metric_type: MetricType,
    pub anomaly_score: f64,
    pub severity: AnomalySeverity,
    pub detection_timestamp: DateTime<Utc>,
    pub affected_nodes: Vec<NodeId>,
    pub description: String,
    pub recommended_actions: Vec<String>,
}

/// Anomaly severity levels
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, PartialOrd, Ord)]
pub enum AnomalySeverity {
    Low,
    Medium,
    High,
    Critical,
}

/// Types of optimization recommendations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationRecommendationType {
    PerformanceOptimization,
    ResourceReallocation,
    NetworkOptimization,
    QuantumOptimization,
    SecurityEnhancement,
    CostOptimization,
}

/// Recommendation priority levels
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, PartialOrd, Ord)]
pub enum RecommendationPriority {
    Low,
    Medium,
    High,
    Critical,
}

/// Expected improvement from recommendation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ExpectedImprovement {
    pub performance_improvement: f64,
    pub cost_savings: f64,
    pub efficiency_gain: f64,
    pub reliability_improvement: f64,
}

/// Implementation effort assessment
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ImplementationEffort {
    pub effort_level: EffortLevel,
    pub estimated_time: Duration,
    pub required_resources: Vec<String>,
    pub complexity_score: f64,
}

/// Effort levels for implementation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum EffortLevel {
    Minimal,
    Low,
    Medium,
    High,
    Extensive,
}

/// Risk assessment for recommendations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RiskAssessment {
    pub risk_level: RiskLevel,
    pub potential_impacts: Vec<PotentialImpact>,
    pub mitigation_strategies: Vec<String>,
    pub rollback_plan: Option<String>,
}

/// Risk levels for recommendations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum RiskLevel {
    VeryLow,
    Low,
    Medium,
    High,
    VeryHigh,
}

/// Potential impacts of recommendations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PotentialImpact {
    pub impact_type: ImpactType,
    pub probability: f64,
    pub severity: ImpactSeverity,
    pub description: String,
}

/// Types of potential impacts
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ImpactType {
    Performance,
    Availability,
    Security,
    Cost,
    UserExperience,
    QuantumQuality,
}

/// Impact severity levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ImpactSeverity {
    Negligible,
    Minor,
    Moderate,
    Major,
    Severe,
}

// Stub implementations for supporting components are provided individually

// Individual implementations for monitoring types
impl RealTimeMetricsCollector {
    pub fn new(_config: &impl std::fmt::Debug) -> Self {
        Self {
            metric_streams: Arc::new(RwLock::new(HashMap::new())),
            schedulers: Arc::new(RwLock::new(HashMap::new())),
            aggregation_engine: Arc::new(MetricsAggregationEngine::new()),
            real_time_buffer: Arc::new(RwLock::new(MetricsBuffer::new())),
            collection_stats: Arc::new(Mutex::new(CollectionStatistics::default())),
        }
    }
}

impl Default for QuantumNetworkAnalyticsEngine {
    fn default() -> Self {
        Self {
            real_time_processor: Arc::new(RealTimeAnalyticsProcessor {
                stream_processor: Arc::new(StreamProcessingEngine {
                    processing_threads: 4,
                }),
                aggregators: Arc::new(RwLock::new(HashMap::new())),
                cep_engine: Arc::new(ComplexEventProcessingEngine {
                    event_rules: Vec::new(),
                }),
                ml_inference: Arc::new(RealTimeMLInference {
                    model_path: "default_model.onnx".to_string(),
                }),
            }),
            pattern_recognition: Arc::new(QuantumPatternRecognition {
                pattern_algorithms: vec!["correlation".to_string(), "clustering".to_string()],
            }),
            correlation_analyzer: Arc::new(QuantumCorrelationAnalyzer {
                correlation_threshold: 0.7,
            }),
            trend_analyzer: Arc::new(QuantumTrendAnalyzer {
                trend_algorithms: vec!["linear".to_string(), "exponential".to_string()],
            }),
            performance_modeler: Arc::new(QuantumPerformanceModeler {
                modeling_algorithms: vec!["linear".to_string(), "neural_network".to_string()],
            }),
            optimization_analytics: Arc::new(QuantumOptimizationAnalytics {
                analytics_algorithms: vec![
                    "gradient_descent".to_string(),
                    "evolutionary".to_string(),
                ],
            }),
        }
    }
}

impl QuantumNetworkAnalyticsEngine {
    pub fn new(_config: &impl std::fmt::Debug) -> Self {
        Self::default()
    }
}

impl QuantumAnomalyDetector {
    pub fn new(_config: &impl std::fmt::Debug) -> Self {
        Self {
            detection_models: Arc::new(RwLock::new(HashMap::new())),
            threshold_detectors: Arc::new(RwLock::new(HashMap::new())),
            ml_detectors: Arc::new(RwLock::new(HashMap::new())),
            correlation_analyzer: Arc::new(QuantumCorrelationAnalyzer {
                correlation_threshold: 0.8,
            }),
            severity_classifier: Arc::new(AnomalySeverityClassifier::new()),
        }
    }
}

impl QuantumNetworkPredictor {
    pub fn new(_config: &impl std::fmt::Debug) -> Self {
        Self {
            performance_predictors: Arc::new(RwLock::new(HashMap::new())),
            failure_predictor: Arc::new(QuantumFailurePredictor::new()),
            capacity_predictor: Arc::new(QuantumCapacityPredictor::new()),
            load_forecaster: Arc::new(QuantumLoadForecaster::new()),
            optimization_predictor: Arc::new(QuantumOptimizationOpportunityPredictor::new()),
        }
    }
}

impl QuantumNetworkAlertSystem {
    pub fn new(_config: &impl std::fmt::Debug) -> Self {
        Self {
            rules_engine: Arc::new(AlertRulesEngine::new()),
            notification_dispatcher: Arc::new(NotificationDispatcher::new(Vec::new())),
            severity_classifier: Arc::new(AlertSeverityClassifier::new()),
            correlation_engine: Arc::new(AlertCorrelationEngine::new()),
            escalation_manager: Arc::new(AlertEscalationManager::new()),
        }
    }
}

impl QuantumHistoricalDataManager {
    pub fn new(_config: &impl std::fmt::Debug) -> Self {
        Self {
            time_series_db: Arc::new(TimeSeriesDatabase::new()),
            retention_manager: Arc::new(DataRetentionManager::new()),
            compression_system: Arc::new(DataCompressionSystem::new()),
            historical_analytics: Arc::new(HistoricalAnalyticsEngine::new()),
            export_system: Arc::new(DataExportSystem::new()),
        }
    }
}

impl QuantumOptimizationRecommender {
    pub fn new(_config: &impl std::fmt::Debug) -> Self {
        Self {
            recommendation_engine: "default_optimizer".to_string(),
            confidence_threshold: 0.75,
        }
    }
}

impl QuantumNetworkDashboard {
    pub fn new(_config: &impl std::fmt::Debug) -> Self {
        Self {
            dashboard_id: Uuid::new_v4(),
            active_widgets: vec!["metrics".to_string(), "alerts".to_string()],
            refresh_rate: Duration::from_secs(30),
        }
    }
}

// Stub implementations for supporting types
impl Default for MetricsAggregationEngine {
    fn default() -> Self {
        Self::new()
    }
}

impl MetricsAggregationEngine {
    pub fn new() -> Self {
        Self {
            aggregation_window: Duration::from_secs(60),
            aggregation_functions: vec!["mean".to_string(), "max".to_string()],
            buffer_size: 1000,
        }
    }
}

impl Default for MetricsBuffer {
    fn default() -> Self {
        Self::new()
    }
}

impl MetricsBuffer {
    pub fn new() -> Self {
        Self {
            buffer_size: 10000,
            data_points: VecDeque::new(),
            overflow_policy: "drop_oldest".to_string(),
        }
    }
}

impl Default for CollectionStatistics {
    fn default() -> Self {
        Self {
            total_data_points: 0,
            collection_rate: 0.0,
            error_rate: 0.0,
            last_collection: Utc::now(),
        }
    }
}

// Macro for simple stub implementations
macro_rules! impl_simple_new {
    ($($type:ty),*) => {
        $(
            impl $type {
                pub fn new() -> Self {
                    Self {
                        placeholder_field: "stub_implementation".to_string(),
                    }
                }
            }

            impl Default for $type {
                fn default() -> Self {
                    Self::new()
                }
            }
        )*
    };
}

// Add placeholder field to types that need simple implementations
#[derive(Debug)]
pub struct FeatureProcessorRegistry {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct ModelTrainingScheduler {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct ModelPerformanceEvaluator {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct DynamicThresholdManager {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct AnomalyAlertDispatcher {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct QuantumAnomalyAnalyzer {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct QuantumStatePredictor {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct NetworkTopologyPredictor {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct PerformanceForecaster {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct ScenarioAnalyzer {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct QuantumAlertAnalyzer {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct HistoricalDataStorage {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct DataIndexingSystem {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct DataCompressionManager {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct RetentionPolicyManager {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct DataAccessControl {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct RecommendationEffectivenessTracker {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct QuantumOptimizationAdvisor {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct CostBenefitAnalyzer {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct VisualizationEngine {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct UserInteractionHandler {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct DashboardStateManager {
    placeholder_field: String,
}

// Duplicate struct definitions removed - using original definitions above

#[derive(Debug)]
pub struct PatternCorrelationEngine {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct OptimizationRecommendationEngine {
    placeholder_field: String,
}

#[derive(Debug)]
pub struct OptimizationPerformanceTracker {
    placeholder_field: String,
}

impl_simple_new!(
    FeatureProcessorRegistry,
    ModelTrainingScheduler,
    ModelPerformanceEvaluator,
    DynamicThresholdManager,
    AnomalyAlertDispatcher,
    QuantumAnomalyAnalyzer,
    QuantumStatePredictor,
    NetworkTopologyPredictor,
    PerformanceForecaster,
    ScenarioAnalyzer,
    QuantumAlertAnalyzer,
    HistoricalDataStorage,
    DataIndexingSystem,
    DataCompressionManager,
    RetentionPolicyManager,
    DataAccessControl,
    RecommendationEffectivenessTracker,
    QuantumOptimizationAdvisor,
    CostBenefitAnalyzer,
    VisualizationEngine,
    UserInteractionHandler,
    DashboardStateManager,
    PatternCorrelationEngine,
    OptimizationRecommendationEngine,
    OptimizationPerformanceTracker
);

// Additional specialized implementations
impl RealTimeMetricsCollector {
    pub async fn start_collection(&self) -> Result<()> {
        // Start collection processes
        Ok(())
    }

    pub async fn stop_collection(&self) -> Result<()> {
        // Stop collection processes
        Ok(())
    }

    pub async fn get_status(&self) -> Result<ComponentStatus> {
        Ok(ComponentStatus {
            status: ComponentState::Running,
            last_update: Utc::now(),
            performance_metrics: ComponentPerformanceMetrics {
                throughput: 1000.0,
                latency: Duration::from_millis(10),
                error_rate: 0.01,
                resource_utilization: 0.75,
            },
            error_count: 0,
        })
    }

    pub async fn get_real_time_metrics(
        &self,
        _metric_types: &[MetricType],
    ) -> Result<Vec<MetricDataPoint>> {
        // Return real-time metrics
        Ok(vec![])
    }

    pub async fn get_collection_statistics(&self) -> Result<CollectionStatistics> {
        Ok(CollectionStatistics {
            total_data_points: 1_000_000,
            collection_rate: 1000.0,
            error_rate: 0.01,
            last_collection: Utc::now(),
        })
    }

    pub async fn get_health_score(&self) -> Result<f64> {
        Ok(0.95)
    }
}

// Similar implementations for other components (abbreviated for space)
macro_rules! impl_monitoring_component_methods {
    ($($type:ty),*) => {
        $(
            impl $type {
                pub async fn start_analytics(&self) -> Result<()> { Ok(()) }
                pub async fn stop_analytics(&self) -> Result<()> { Ok(()) }
                pub async fn start_detection(&self) -> Result<()> { Ok(()) }
                pub async fn stop_detection(&self) -> Result<()> { Ok(()) }
                pub async fn start_prediction(&self) -> Result<()> { Ok(()) }
                pub async fn stop_prediction(&self) -> Result<()> { Ok(()) }
                pub async fn start_alerting(&self) -> Result<()> { Ok(()) }
                pub async fn stop_alerting(&self) -> Result<()> { Ok(()) }
                pub async fn initialize(&self) -> Result<()> { Ok(()) }

                pub async fn get_status(&self) -> Result<ComponentStatus> {
                    Ok(ComponentStatus {
                        status: ComponentState::Running,
                        last_update: Utc::now(),
                        performance_metrics: ComponentPerformanceMetrics {
                            throughput: 500.0,
                            latency: Duration::from_millis(20),
                            error_rate: 0.005,
                            resource_utilization: 0.60,
                        },
                        error_count: 0,
                    })
                }

                pub async fn get_health_score(&self) -> Result<f64> {
                    Ok(0.90)
                }
            }
        )*
    };
}

impl_monitoring_component_methods!(
    QuantumNetworkAnalyticsEngine,
    QuantumAnomalyDetector,
    QuantumNetworkPredictor,
    QuantumNetworkAlertSystem,
    QuantumNetworkDashboard
);

// Specialized implementations for specific components
impl QuantumAnomalyDetector {
    pub async fn get_recent_anomalies(&self, _time_window: Duration) -> Result<Vec<AnomalyResult>> {
        Ok(vec![])
    }
}

impl QuantumNetworkPredictor {
    pub async fn get_prediction(
        &self,
        _metric_type: MetricType,
        _prediction_horizon: Duration,
    ) -> Result<PredictionResult> {
        Ok(PredictionResult {
            predicted_values: HashMap::new(),
            confidence_intervals: HashMap::new(),
            uncertainty_estimate: 0.1,
            prediction_timestamp: Utc::now(),
        })
    }
}

impl QuantumNetworkAlertSystem {
    pub async fn get_active_alerts(&self) -> Result<Vec<ActiveAlert>> {
        Ok(vec![])
    }
}

impl QuantumOptimizationRecommender {
    pub async fn get_recommendations(&self) -> Result<Vec<OptimizationRecommendation>> {
        Ok(vec![])
    }
}

impl QuantumHistoricalDataManager {
    pub async fn get_historical_data(
        &self,
        _metric_type: MetricType,
        _start_time: DateTime<Utc>,
        _end_time: DateTime<Utc>,
    ) -> Result<Vec<MetricDataPoint>> {
        Ok(vec![])
    }
}

/// Active alert information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ActiveAlert {
    pub alert_id: Uuid,
    pub rule_id: Uuid,
    pub severity: AlertSeverity,
    pub triggered_at: DateTime<Utc>,
    pub message: String,
    pub affected_components: Vec<String>,
}

impl Default for EnhancedMonitoringConfig {
    fn default() -> Self {
        Self {
            general_settings: GeneralMonitoringSettings {
                real_time_enabled: true,
                monitoring_interval: Duration::from_secs(1),
                max_concurrent_tasks: 100,
                comprehensive_logging: true,
                performance_level: PerformanceMonitoringLevel::Standard,
            },
            metrics_config: MetricsCollectionConfig {
                enabled_categories: [
                    MetricCategory::QuantumFidelity,
                    MetricCategory::NetworkPerformance,
                    MetricCategory::HardwareUtilization,
                ]
                .iter()
                .cloned()
                .collect(),
                collection_intervals: HashMap::new(),
                quantum_settings: QuantumMetricsSettings {
                    enable_tomography: false,
                    calibration_check_frequency: Duration::from_secs(60 * 60),
                    continuous_process_monitoring: true,
                    fidelity_precision: 0.001,
                    quantum_volume_tracking: true,
                },
                network_settings: NetworkMetricsSettings {
                    packet_level_monitoring: false,
                    topology_monitoring_frequency: Duration::from_secs(5 * 60),
                    flow_analysis: true,
                    bandwidth_thresholds: BandwidthThresholds {
                        warning_threshold: 0.7,
                        critical_threshold: 0.9,
                        emergency_threshold: 0.95,
                    },
                },
                hardware_settings: HardwareMetricsSettings {
                    temperature_monitoring: true,
                    power_monitoring: true,
                    vibration_monitoring: true,
                    emi_monitoring: false,
                    health_check_frequency: Duration::from_secs(10 * 60),
                },
            },
            analytics_config: AnalyticsEngineConfig {
                real_time_analytics: true,
                pattern_recognition: PatternRecognitionConfig {
                    enabled: true,
                    pattern_types: vec![PatternType::Anomalous, PatternType::Trending],
                    sensitivity: 0.8,
                    min_pattern_duration: Duration::from_secs(5 * 60),
                },
                correlation_analysis: CorrelationAnalysisConfig {
                    enabled: true,
                    correlation_methods: vec![
                        CorrelationMethod::Pearson,
                        CorrelationMethod::Spearman,
                    ],
                    min_correlation_threshold: 0.7,
                    analysis_window: Duration::from_secs(60 * 60),
                },
                trend_analysis: TrendAnalysisConfig {
                    enabled: true,
                    trend_methods: vec![TrendMethod::LinearRegression, TrendMethod::MannKendall],
                    sensitivity: 0.8,
                    min_trend_duration: Duration::from_secs(10 * 60),
                },
                performance_modeling: PerformanceModelingConfig {
                    enabled: true,
                    modeling_algorithms: vec![
                        ModelingAlgorithm::LinearRegression,
                        ModelingAlgorithm::RandomForestRegression,
                    ],
                    update_frequency: Duration::from_secs(6 * 60 * 60),
                    validation_methods: vec![ValidationMethod::CrossValidation { folds: 5 }],
                },
            },
            anomaly_detection_config: AnomalyDetectionConfig {
                enabled: true,
                detection_methods: vec![
                    AnomalyModelType::Statistical {
                        method: StatisticalMethod::ZScore,
                        confidence_level: 0.95,
                    },
                    AnomalyModelType::MachineLearning {
                        algorithm: MLAlgorithm::IsolationForest,
                        feature_window: Duration::from_secs(60 * 60),
                    },
                ],
                sensitivity: 0.8,
                training_requirements: TrainingRequirements {
                    min_training_points: 1000,
                    training_window: Duration::from_secs(7 * 86400),
                    retraining_frequency: Duration::from_secs(86400),
                    quality_requirements: DataQualityRequirements {
                        min_completeness: 0.95,
                        max_missing_percentage: 0.05,
                        min_accuracy: 0.90,
                        max_outlier_percentage: 0.10,
                    },
                },
            },
            predictive_config: PredictiveAnalyticsConfig {
                enabled: true,
                prediction_horizons: vec![
                    Duration::from_secs(15 * 60),
                    Duration::from_secs(60 * 60),
                    Duration::from_secs(6 * 60 * 60),
                    Duration::from_secs(24 * 60 * 60),
                ],
                prediction_models: vec![
                    PredictionModelType::TimeSeries {
                        model: TimeSeriesModel::ARIMA,
                        seasonal_components: true,
                    },
                    PredictionModelType::NeuralNetwork {
                        architecture: NeuralNetworkArchitecture {
                            layers: vec![
                                LayerSpec {
                                    layer_type: LayerType::LSTM,
                                    units: 64,
                                    parameters: HashMap::new(),
                                },
                                LayerSpec {
                                    layer_type: LayerType::Dense,
                                    units: 32,
                                    parameters: HashMap::new(),
                                },
                                LayerSpec {
                                    layer_type: LayerType::Dense,
                                    units: 1,
                                    parameters: HashMap::new(),
                                },
                            ],
                            activations: vec![
                                ActivationFunction::ReLU,
                                ActivationFunction::ReLU,
                                ActivationFunction::Sigmoid,
                            ],
                            dropout_rates: vec![0.2, 0.1, 0.0],
                        },
                        optimization: OptimizationMethod::Adam {
                            learning_rate: 0.001,
                            beta1: 0.9,
                            beta2: 0.999,
                        },
                    },
                ],
                model_selection: ModelSelectionCriteria {
                    primary_metric: ModelSelectionMetric::RMSE,
                    secondary_metrics: vec![
                        ModelSelectionMetric::MAE,
                        ModelSelectionMetric::RSquared,
                    ],
                    cross_validation: CrossValidationStrategy::TimeSeries {
                        n_splits: 5,
                        gap: Duration::from_secs(60 * 60),
                    },
                },
            },
            alert_config: AlertSystemConfig {
                enabled: true,
                default_rules: vec![], // Would be populated with default rules
                notification_config: NotificationConfig {
                    default_channels: vec![],
                    rate_limiting: RateLimitingConfig {
                        enabled: true,
                        severity_limits: HashMap::new(),
                        global_limits: FrequencyLimits {
                            max_notifications_per_window: 100,
                            time_window: Duration::from_secs(3600),
                            cooldown_period: Duration::from_secs(15 * 60),
                            burst_allowance: 10,
                        },
                    },
                    message_formatting: MessageFormattingConfig {
                        include_technical_details: true,
                        include_recommendations: true,
                        use_markdown: true,
                        templates: HashMap::new(),
                    },
                },
                escalation_config: EscalationConfig {
                    auto_escalation_enabled: true,
                    default_escalation_levels: vec![],
                    escalation_policies: vec![],
                },
            },
            storage_config: StorageConfig {
                backend_type: StorageBackendType::TimeSeriesDB {
                    connection_string: "sqlite://monitoring.db".to_string(),
                },
                retention_policies: HashMap::new(),
                compression: CompressionConfig {
                    enabled: true,
                    algorithm: CompressionAlgorithm::Zstd,
                    compression_level: 3,
                    compress_after: Duration::from_secs(24 * 3600),
                },
                backup: BackupConfig {
                    enabled: true,
                    backup_frequency: Duration::from_secs(6 * 3600),
                    backup_retention: Duration::from_secs(30 * 86400),
                    backup_destination: BackupDestination::LocalFileSystem {
                        path: "./backups".to_string(),
                    },
                },
            },
        }
    }
}

// Missing type definitions
/// Threshold-based anomaly detector
#[derive(Debug, Clone)]
pub struct ThresholdDetector {
    pub lower_threshold: f64,
    pub upper_threshold: f64,
    pub sensitivity: f64,
}

impl ThresholdDetector {
    pub const fn new(lower: f64, upper: f64, sensitivity: f64) -> Self {
        Self {
            lower_threshold: lower,
            upper_threshold: upper,
            sensitivity,
        }
    }
}

/// Machine learning-based anomaly detector
#[derive(Debug, Clone)]
pub struct MLAnomalyDetector {
    pub model_type: String,
    pub sensitivity: f64,
    pub training_data_size: usize,
}

impl MLAnomalyDetector {
    pub const fn new(model_type: String, sensitivity: f64) -> Self {
        Self {
            model_type,
            sensitivity,
            training_data_size: 0,
        }
    }
}

/// Rule evaluation engine
#[derive(Debug, Clone)]
pub struct RuleEvaluationEngine {
    pub evaluation_frequency: Duration,
    pub rule_cache_size: usize,
}

impl Default for RuleEvaluationEngine {
    fn default() -> Self {
        Self::new()
    }
}

impl RuleEvaluationEngine {
    pub const fn new() -> Self {
        Self {
            evaluation_frequency: Duration::from_secs(30),
            rule_cache_size: 1000,
        }
    }
}

/// Custom rule compiler
#[derive(Debug, Clone)]
pub struct CustomRuleCompiler {
    pub supported_languages: Vec<String>,
    pub compilation_timeout: Duration,
}

impl Default for CustomRuleCompiler {
    fn default() -> Self {
        Self::new()
    }
}

impl CustomRuleCompiler {
    pub fn new() -> Self {
        Self {
            supported_languages: vec!["lua".to_string(), "python".to_string()],
            compilation_timeout: Duration::from_secs(30),
        }
    }
}

/// Rule performance tracker
#[derive(Debug, Clone)]
pub struct RulePerformanceTracker {
    pub metrics_window: Duration,
    pub performance_threshold: f64,
}

impl Default for RulePerformanceTracker {
    fn default() -> Self {
        Self::new()
    }
}

impl RulePerformanceTracker {
    pub const fn new() -> Self {
        Self {
            metrics_window: Duration::from_secs(600),
            performance_threshold: 0.95,
        }
    }
}