// Memory Profiling: Analytics, Statistics, and Historical Data
//
// This module provides comprehensive statistical analysis and historical data management
// for the ToRSh memory profiling system. It handles trend analysis, performance baselines,
// anomaly detection, and report generation for long-term performance monitoring.

use std::collections::{HashMap, BTreeMap, VecDeque};
use std::time::{Instant, Duration, SystemTime, UNIX_EPOCH};
use scirs2_core::error::{CoreError, Result};
use serde::{Serialize, Deserialize};

/// Comprehensive statistics for memory profiling metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryStatistics {
    pub total_allocations: usize,
    pub total_deallocations: usize,
    pub peak_memory_usage: usize,
    pub average_memory_usage: f64,
    pub current_memory_usage: usize,
    pub memory_churn_rate: f64,
    pub allocation_rate: f64,
    pub deallocation_rate: f64,
    pub fragmentation_index: f64,
    pub efficiency_score: f64,
    pub cache_hit_ratio: f64,
    pub bandwidth_utilization: f64,
    pub pressure_incidents: usize,
    pub optimization_opportunities: usize,
}

/// Historical data point for trend analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HistoricalDataPoint {
    pub timestamp: SystemTime,
    pub statistics: MemoryStatistics,
    pub context: AnalyticsContext,
    pub custom_metrics: HashMap<String, f64>,
}

/// Context information for analytics data points
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AnalyticsContext {
    pub session_id: String,
    pub workload_type: String,
    pub model_architecture: Option<String>,
    pub batch_size: Option<usize>,
    pub device_info: DeviceInfo,
    pub environment: EnvironmentInfo,
}

/// Device information for analytics context
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DeviceInfo {
    pub device_type: String,
    pub total_memory: usize,
    pub compute_capability: Option<String>,
    pub driver_version: Option<String>,
}

/// Environment information for analytics context
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EnvironmentInfo {
    pub framework_version: String,
    pub rust_version: String,
    pub optimization_level: String,
    pub feature_flags: Vec<String>,
}

/// Performance baseline for comparison
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceBaseline {
    pub name: String,
    pub created_at: SystemTime,
    pub reference_statistics: MemoryStatistics,
    pub confidence_interval: ConfidenceInterval,
    pub sample_size: usize,
    pub measurement_duration: Duration,
    pub conditions: BaselineConditions,
}

/// Confidence interval for statistical measurements
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConfidenceInterval {
    pub confidence_level: f64,
    pub lower_bound: f64,
    pub upper_bound: f64,
    pub margin_of_error: f64,
}

/// Conditions under which baseline was established
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BaselineConditions {
    pub workload_description: String,
    pub hardware_configuration: String,
    pub software_environment: String,
    pub expected_variance: f64,
}

/// Trend analysis results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrendAnalysis {
    pub metric_name: String,
    pub trend_direction: TrendDirection,
    pub trend_strength: f64,
    pub slope: f64,
    pub correlation_coefficient: f64,
    pub statistical_significance: f64,
    pub prediction: TrendPrediction,
    pub analysis_period: Duration,
    pub data_points: usize,
}

/// Direction of observed trend
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum TrendDirection {
    Increasing,
    Decreasing,
    Stable,
    Volatile,
    Cyclical,
}

/// Prediction based on trend analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrendPrediction {
    pub projected_value: f64,
    pub confidence: f64,
    pub time_horizon: Duration,
    pub factors: Vec<String>,
}

/// Anomaly detection result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AnomalyDetection {
    pub anomaly_type: AnomalyType,
    pub severity: AnomalySeverity,
    pub detected_at: SystemTime,
    pub affected_metrics: Vec<String>,
    pub anomaly_score: f64,
    pub threshold_value: f64,
    pub actual_value: f64,
    pub description: String,
    pub recommended_actions: Vec<String>,
}

/// Type of detected anomaly
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum AnomalyType {
    SuddenSpike,
    SuddenDrop,
    GradualIncrease,
    GradualDecrease,
    Oscillation,
    Flatline,
    OutOfBounds,
    PatternBreak,
}

/// Severity level of anomaly
#[derive(Debug, Clone, PartialEq, PartialOrd, Serialize, Deserialize)]
pub enum AnomalySeverity {
    Critical,
    High,
    Medium,
    Low,
    Informational,
}

/// Comprehensive analytics report
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AnalyticsReport {
    pub report_id: String,
    pub generated_at: SystemTime,
    pub period: ReportPeriod,
    pub summary: ReportSummary,
    pub detailed_statistics: MemoryStatistics,
    pub trend_analyses: Vec<TrendAnalysis>,
    pub anomalies: Vec<AnomalyDetection>,
    pub performance_comparison: Option<PerformanceComparison>,
    pub recommendations: Vec<AnalyticsRecommendation>,
    pub visualizations: Vec<VisualizationData>,
}

/// Time period for report generation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ReportPeriod {
    pub start_time: SystemTime,
    pub end_time: SystemTime,
    pub duration: Duration,
    pub data_points: usize,
    pub sampling_rate: Duration,
}

/// Summary section of analytics report
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ReportSummary {
    pub key_findings: Vec<String>,
    pub performance_highlights: Vec<String>,
    pub areas_of_concern: Vec<String>,
    pub overall_health_score: f64,
    pub change_from_baseline: f64,
    pub notable_patterns: Vec<String>,
}

/// Performance comparison against baselines
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceComparison {
    pub baseline_name: String,
    pub current_vs_baseline: HashMap<String, ComparisonResult>,
    pub overall_improvement: f64,
    pub regression_areas: Vec<String>,
    pub improvement_areas: Vec<String>,
}

/// Result of metric comparison
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComparisonResult {
    pub current_value: f64,
    pub baseline_value: f64,
    pub percentage_change: f64,
    pub statistical_significance: f64,
    pub interpretation: String,
}

/// Analytics-based recommendation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AnalyticsRecommendation {
    pub priority: RecommendationPriority,
    pub category: RecommendationCategory,
    pub title: String,
    pub description: String,
    pub supporting_evidence: Vec<String>,
    pub expected_impact: ImpactEstimate,
    pub implementation_effort: EffortEstimate,
    pub timeline: Duration,
}

#[derive(Debug, Clone, PartialEq, PartialOrd, Serialize, Deserialize)]
pub enum RecommendationPriority {
    Immediate,
    High,
    Medium,
    Low,
    Planning,
}

#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum RecommendationCategory {
    PerformanceOptimization,
    ResourceManagement,
    InfrastructureUpgrade,
    ConfigurationTuning,
    MonitoringImprovement,
    CapacityPlanning,
}

/// Impact estimate for recommendations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ImpactEstimate {
    pub performance_improvement: f64,
    pub cost_reduction: f64,
    pub risk_mitigation: f64,
    pub confidence_level: f64,
}

/// Effort estimate for implementation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EffortEstimate {
    pub development_time: Duration,
    pub complexity_score: f64,
    pub required_expertise: Vec<String>,
    pub dependencies: Vec<String>,
}

/// Data structure for visualization support
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VisualizationData {
    pub chart_type: ChartType,
    pub title: String,
    pub x_axis: AxisDefinition,
    pub y_axis: AxisDefinition,
    pub data_series: Vec<DataSeries>,
    pub annotations: Vec<ChartAnnotation>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ChartType {
    TimeSeries,
    Histogram,
    ScatterPlot,
    Heatmap,
    BarChart,
    PieChart,
    BoxPlot,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AxisDefinition {
    pub label: String,
    pub unit: String,
    pub scale: ScaleType,
    pub range: Option<(f64, f64)>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ScaleType {
    Linear,
    Logarithmic,
    Categorical,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DataSeries {
    pub name: String,
    pub data_points: Vec<(f64, f64)>,
    pub style: SeriesStyle,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SeriesStyle {
    pub color: String,
    pub line_style: LineStyle,
    pub marker_style: MarkerStyle,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum LineStyle {
    Solid,
    Dashed,
    Dotted,
    None,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum MarkerStyle {
    Circle,
    Square,
    Triangle,
    Diamond,
    None,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ChartAnnotation {
    pub annotation_type: AnnotationType,
    pub position: (f64, f64),
    pub text: String,
    pub style: AnnotationStyle,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AnnotationType {
    Point,
    Line,
    Rectangle,
    Text,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AnnotationStyle {
    pub color: String,
    pub font_size: f64,
    pub transparency: f64,
}

/// Main memory analytics engine
pub struct MemoryAnalytics {
    historical_data: VecDeque<HistoricalDataPoint>,
    baselines: HashMap<String, PerformanceBaseline>,
    trend_analyses: HashMap<String, TrendAnalysis>,
    anomaly_detectors: HashMap<String, AnomalyDetector>,
    custom_metrics: HashMap<String, Vec<f64>>,
    configuration: AnalyticsConfiguration,
    current_statistics: Option<MemoryStatistics>,
}

/// Configuration for analytics engine
#[derive(Debug, Clone)]
pub struct AnalyticsConfiguration {
    pub max_history_size: usize,
    pub retention_period: Duration,
    pub sampling_interval: Duration,
    pub anomaly_sensitivity: f64,
    pub trend_analysis_window: Duration,
    pub statistical_confidence: f64,
    pub enable_prediction: bool,
    pub enable_auto_baseline: bool,
}

impl Default for AnalyticsConfiguration {
    fn default() -> Self {
        Self {
            max_history_size: 10000,
            retention_period: Duration::from_secs(30 * 24 * 60 * 60),
            sampling_interval: Duration::from_secs(60),
            anomaly_sensitivity: 0.95,
            trend_analysis_window: Duration::from_secs(7 * 24 * 60 * 60),
            statistical_confidence: 0.95,
            enable_prediction: true,
            enable_auto_baseline: true,
        }
    }
}

/// Anomaly detector for specific metrics
pub struct AnomalyDetector {
    metric_name: String,
    baseline_mean: f64,
    baseline_std: f64,
    sensitivity: f64,
    history: VecDeque<f64>,
    thresholds: AnomalyThresholds,
}

/// Thresholds for anomaly detection
#[derive(Debug, Clone)]
pub struct AnomalyThresholds {
    pub critical_upper: f64,
    pub critical_lower: f64,
    pub warning_upper: f64,
    pub warning_lower: f64,
    pub change_rate_threshold: f64,
}

impl Duration {
    fn from_days(days: u64) -> Self {
        Duration::from_secs(days * 24 * 3600)
    }
}

impl MemoryAnalytics {
    pub fn new() -> Self {
        Self::with_config(AnalyticsConfiguration::default())
    }

    pub fn with_config(config: AnalyticsConfiguration) -> Self {
        Self {
            historical_data: VecDeque::new(),
            baselines: HashMap::new(),
            trend_analyses: HashMap::new(),
            anomaly_detectors: HashMap::new(),
            custom_metrics: HashMap::new(),
            configuration: config,
            current_statistics: None,
        }
    }

    /// Record new memory statistics data point
    pub fn record_statistics(&mut self, statistics: MemoryStatistics, context: AnalyticsContext) -> Result<()> {
        let data_point = HistoricalDataPoint {
            timestamp: SystemTime::now(),
            statistics: statistics.clone(),
            context,
            custom_metrics: HashMap::new(),
        };

        self.historical_data.push_back(data_point);
        self.current_statistics = Some(statistics);

        // Maintain history size limit
        if self.historical_data.len() > self.configuration.max_history_size {
            self.historical_data.pop_front();
        }

        // Clean old data based on retention period
        self.clean_old_data();

        // Update anomaly detectors
        self.update_anomaly_detectors()?;

        // Auto-create baselines if enabled
        if self.configuration.enable_auto_baseline {
            self.update_auto_baselines()?;
        }

        Ok(())
    }

    /// Establish performance baseline
    pub fn create_baseline(
        &mut self,
        name: String,
        description: String,
        sample_duration: Duration,
    ) -> Result<()> {
        let recent_data = self.get_recent_data(sample_duration);
        if recent_data.is_empty() {
            return Err(CoreError::InvalidOperation("Insufficient data for baseline creation".to_string()));
        }

        let statistics = self.calculate_aggregate_statistics(&recent_data);
        let confidence_interval = self.calculate_confidence_interval(&recent_data, 0.95);

        let baseline = PerformanceBaseline {
            name: name.clone(),
            created_at: SystemTime::now(),
            reference_statistics: statistics,
            confidence_interval,
            sample_size: recent_data.len(),
            measurement_duration: sample_duration,
            conditions: BaselineConditions {
                workload_description: "Auto-generated baseline".to_string(),
                hardware_configuration: "Current configuration".to_string(),
                software_environment: "Current environment".to_string(),
                expected_variance: 0.1,
            },
        };

        self.baselines.insert(name, baseline);
        Ok(())
    }

    /// Perform comprehensive trend analysis
    pub fn analyze_trends(&mut self, analysis_period: Duration) -> Result<Vec<TrendAnalysis>> {
        let data = self.get_recent_data(analysis_period);
        if data.len() < 10 {
            return Ok(vec![]); // Insufficient data for trend analysis
        }

        let mut analyses = Vec::new();

        // Analyze key metrics
        let metrics = vec![
            ("peak_memory_usage", |s: &MemoryStatistics| s.peak_memory_usage as f64),
            ("average_memory_usage", |s: &MemoryStatistics| s.average_memory_usage),
            ("allocation_rate", |s: &MemoryStatistics| s.allocation_rate),
            ("fragmentation_index", |s: &MemoryStatistics| s.fragmentation_index),
            ("efficiency_score", |s: &MemoryStatistics| s.efficiency_score),
            ("cache_hit_ratio", |s: &MemoryStatistics| s.cache_hit_ratio),
        ];

        for (metric_name, extractor) in metrics {
            let values: Vec<f64> = data.iter().map(|d| extractor(&d.statistics)).collect();
            let trend = self.calculate_trend(&values, metric_name.to_string(), analysis_period)?;
            analyses.push(trend);
        }

        // Store trend analyses
        for analysis in &analyses {
            self.trend_analyses.insert(analysis.metric_name.clone(), analysis.clone());
        }

        Ok(analyses)
    }

    /// Detect anomalies in recent data
    pub fn detect_anomalies(&mut self) -> Result<Vec<AnomalyDetection>> {
        let mut anomalies = Vec::new();

        for (metric_name, detector) in &mut self.anomaly_detectors {
            if let Some(current_stats) = &self.current_statistics {
                let current_value = self.extract_metric_value(current_stats, metric_name);
                if let Some(anomaly) = detector.check_anomaly(current_value)? {
                    anomalies.push(anomaly);
                }
            }
        }

        // Sort by severity
        anomalies.sort_by(|a, b| b.severity.partial_cmp(&a.severity).unwrap_or(std::cmp::Ordering::Equal));

        Ok(anomalies)
    }

    /// Generate comprehensive analytics report
    pub fn generate_report(&mut self, period: Duration) -> Result<AnalyticsReport> {
        let start_time = SystemTime::now() - period;
        let end_time = SystemTime::now();
        let data = self.get_recent_data(period);

        if data.is_empty() {
            return Err(CoreError::InvalidOperation("No data available for report generation".to_string()));
        }

        let detailed_statistics = self.calculate_aggregate_statistics(&data);
        let trend_analyses = self.analyze_trends(period)?;
        let anomalies = self.detect_anomalies()?;

        let summary = self.generate_summary(&detailed_statistics, &trend_analyses, &anomalies);
        let performance_comparison = self.compare_with_baseline(&detailed_statistics);
        let recommendations = self.generate_recommendations(&detailed_statistics, &trend_analyses, &anomalies)?;
        let visualizations = self.prepare_visualizations(&data, &trend_analyses);

        let report = AnalyticsReport {
            report_id: format!("report_{}", SystemTime::now().duration_since(UNIX_EPOCH).expect("time should be after UNIX_EPOCH").as_secs()),
            generated_at: SystemTime::now(),
            period: ReportPeriod {
                start_time,
                end_time,
                duration: period,
                data_points: data.len(),
                sampling_rate: self.configuration.sampling_interval,
            },
            summary,
            detailed_statistics,
            trend_analyses,
            anomalies,
            performance_comparison,
            recommendations,
            visualizations,
        };

        Ok(report)
    }

    /// Get recent historical data within specified duration
    fn get_recent_data(&self, duration: Duration) -> Vec<&HistoricalDataPoint> {
        let cutoff = SystemTime::now() - duration;
        self.historical_data
            .iter()
            .filter(|data| data.timestamp >= cutoff)
            .collect()
    }

    /// Calculate aggregate statistics from data points
    fn calculate_aggregate_statistics(&self, data: &[&HistoricalDataPoint]) -> MemoryStatistics {
        if data.is_empty() {
            return MemoryStatistics {
                total_allocations: 0,
                total_deallocations: 0,
                peak_memory_usage: 0,
                average_memory_usage: 0.0,
                current_memory_usage: 0,
                memory_churn_rate: 0.0,
                allocation_rate: 0.0,
                deallocation_rate: 0.0,
                fragmentation_index: 0.0,
                efficiency_score: 0.0,
                cache_hit_ratio: 0.0,
                bandwidth_utilization: 0.0,
                pressure_incidents: 0,
                optimization_opportunities: 0,
            };
        }

        let count = data.len() as f64;

        MemoryStatistics {
            total_allocations: data.iter().map(|d| d.statistics.total_allocations).sum(),
            total_deallocations: data.iter().map(|d| d.statistics.total_deallocations).sum(),
            peak_memory_usage: data.iter().map(|d| d.statistics.peak_memory_usage).max().unwrap_or(0),
            average_memory_usage: data.iter().map(|d| d.statistics.average_memory_usage).sum::<f64>() / count,
            current_memory_usage: data.last().map(|d| d.statistics.current_memory_usage).unwrap_or(0),
            memory_churn_rate: data.iter().map(|d| d.statistics.memory_churn_rate).sum::<f64>() / count,
            allocation_rate: data.iter().map(|d| d.statistics.allocation_rate).sum::<f64>() / count,
            deallocation_rate: data.iter().map(|d| d.statistics.deallocation_rate).sum::<f64>() / count,
            fragmentation_index: data.iter().map(|d| d.statistics.fragmentation_index).sum::<f64>() / count,
            efficiency_score: data.iter().map(|d| d.statistics.efficiency_score).sum::<f64>() / count,
            cache_hit_ratio: data.iter().map(|d| d.statistics.cache_hit_ratio).sum::<f64>() / count,
            bandwidth_utilization: data.iter().map(|d| d.statistics.bandwidth_utilization).sum::<f64>() / count,
            pressure_incidents: data.iter().map(|d| d.statistics.pressure_incidents).sum(),
            optimization_opportunities: data.iter().map(|d| d.statistics.optimization_opportunities).sum(),
        }
    }

    /// Calculate confidence interval for metric values
    fn calculate_confidence_interval(&self, data: &[&HistoricalDataPoint], confidence_level: f64) -> ConfidenceInterval {
        let values: Vec<f64> = data.iter().map(|d| d.statistics.efficiency_score).collect();

        if values.is_empty() {
            return ConfidenceInterval {
                confidence_level,
                lower_bound: 0.0,
                upper_bound: 0.0,
                margin_of_error: 0.0,
            };
        }

        let mean = values.iter().sum::<f64>() / values.len() as f64;
        let variance = values.iter().map(|&x| (x - mean).powi(2)).sum::<f64>() / values.len() as f64;
        let std_dev = variance.sqrt();
        let std_error = std_dev / (values.len() as f64).sqrt();

        // Use t-distribution critical value (approximated)
        let t_critical = 1.96; // For 95% confidence, large samples
        let margin_of_error = t_critical * std_error;

        ConfidenceInterval {
            confidence_level,
            lower_bound: mean - margin_of_error,
            upper_bound: mean + margin_of_error,
            margin_of_error,
        }
    }

    /// Calculate trend for a series of values
    fn calculate_trend(&self, values: &[f64], metric_name: String, period: Duration) -> Result<TrendAnalysis> {
        if values.len() < 3 {
            return Err(CoreError::InvalidOperation("Insufficient data for trend analysis".to_string()));
        }

        // Calculate linear regression
        let n = values.len() as f64;
        let x_values: Vec<f64> = (0..values.len()).map(|i| i as f64).collect();

        let x_mean = x_values.iter().sum::<f64>() / n;
        let y_mean = values.iter().sum::<f64>() / n;

        let mut numerator = 0.0;
        let mut denominator = 0.0;

        for i in 0..values.len() {
            let x_diff = x_values[i] - x_mean;
            let y_diff = values[i] - y_mean;
            numerator += x_diff * y_diff;
            denominator += x_diff * x_diff;
        }

        let slope = if denominator != 0.0 { numerator / denominator } else { 0.0 };

        // Calculate correlation coefficient
        let correlation = if denominator > 0.0 {
            let y_variance = values.iter().map(|&y| (y - y_mean).powi(2)).sum::<f64>();
            numerator / (denominator * y_variance).sqrt()
        } else {
            0.0
        };

        // Determine trend direction
        let trend_direction = if slope.abs() < 0.001 {
            TrendDirection::Stable
        } else if slope > 0.0 {
            TrendDirection::Increasing
        } else {
            TrendDirection::Decreasing
        };

        // Calculate trend strength
        let trend_strength = correlation.abs();

        // Generate prediction
        let prediction = TrendPrediction {
            projected_value: y_mean + slope * n,
            confidence: trend_strength,
            time_horizon: Duration::from_secs(3600), // 1 hour ahead
            factors: vec!["Linear trend extrapolation".to_string()],
        };

        Ok(TrendAnalysis {
            metric_name,
            trend_direction,
            trend_strength,
            slope,
            correlation_coefficient: correlation,
            statistical_significance: trend_strength, // Simplified
            prediction,
            analysis_period: period,
            data_points: values.len(),
        })
    }

    /// Clean old data based on retention period
    fn clean_old_data(&mut self) {
        let cutoff = SystemTime::now() - self.configuration.retention_period;
        while let Some(front) = self.historical_data.front() {
            if front.timestamp < cutoff {
                self.historical_data.pop_front();
            } else {
                break;
            }
        }
    }

    /// Update anomaly detectors with new data
    fn update_anomaly_detectors(&mut self) -> Result<()> {
        if let Some(stats) = &self.current_statistics {
            // Initialize detectors for key metrics if they don't exist
            let key_metrics = vec![
                "peak_memory_usage",
                "allocation_rate",
                "fragmentation_index",
                "efficiency_score",
                "cache_hit_ratio",
            ];

            for metric in key_metrics {
                if !self.anomaly_detectors.contains_key(metric) {
                    let detector = AnomalyDetector::new(
                        metric.to_string(),
                        self.configuration.anomaly_sensitivity,
                    );
                    self.anomaly_detectors.insert(metric.to_string(), detector);
                }

                if let Some(detector) = self.anomaly_detectors.get_mut(metric) {
                    let value = self.extract_metric_value(stats, metric);
                    detector.update(value);
                }
            }
        }

        Ok(())
    }

    /// Extract specific metric value from statistics
    fn extract_metric_value(&self, stats: &MemoryStatistics, metric_name: &str) -> f64 {
        match metric_name {
            "peak_memory_usage" => stats.peak_memory_usage as f64,
            "allocation_rate" => stats.allocation_rate,
            "fragmentation_index" => stats.fragmentation_index,
            "efficiency_score" => stats.efficiency_score,
            "cache_hit_ratio" => stats.cache_hit_ratio,
            "bandwidth_utilization" => stats.bandwidth_utilization,
            _ => 0.0,
        }
    }

    /// Update automatic baselines
    fn update_auto_baselines(&mut self) -> Result<()> {
        // Create weekly baseline if sufficient data exists
        if self.historical_data.len() > 1000 {
            let week_duration = Duration::from_secs(7 * 24 * 60 * 60);
            if !self.baselines.contains_key("auto_weekly") {
                self.create_baseline("auto_weekly".to_string(), "Automatic weekly baseline".to_string(), week_duration)?;
            }
        }

        Ok(())
    }

    /// Generate report summary
    fn generate_summary(
        &self,
        statistics: &MemoryStatistics,
        trends: &[TrendAnalysis],
        anomalies: &[AnomalyDetection],
    ) -> ReportSummary {
        let mut key_findings = Vec::new();
        let mut performance_highlights = Vec::new();
        let mut areas_of_concern = Vec::new();

        // Analyze overall health
        let health_score = (statistics.efficiency_score + statistics.cache_hit_ratio) / 2.0;

        if health_score > 0.8 {
            performance_highlights.push("Excellent overall memory performance".to_string());
        } else if health_score > 0.6 {
            key_findings.push("Good memory performance with room for optimization".to_string());
        } else {
            areas_of_concern.push("Memory performance below optimal levels".to_string());
        }

        // Analyze trends
        for trend in trends {
            if trend.trend_direction == TrendDirection::Increasing && trend.metric_name == "fragmentation_index" {
                areas_of_concern.push("Increasing memory fragmentation detected".to_string());
            } else if trend.trend_direction == TrendDirection::Increasing && trend.metric_name == "efficiency_score" {
                performance_highlights.push("Memory efficiency showing positive trend".to_string());
            }
        }

        // Analyze anomalies
        let critical_anomalies = anomalies.iter().filter(|a| a.severity == AnomalySeverity::Critical).count();
        if critical_anomalies > 0 {
            areas_of_concern.push(format!("{} critical anomalies require immediate attention", critical_anomalies));
        }

        ReportSummary {
            key_findings,
            performance_highlights,
            areas_of_concern,
            overall_health_score: health_score,
            change_from_baseline: 0.0, // Would calculate from actual baseline
            notable_patterns: vec![], // Would be populated based on pattern analysis
        }
    }

    /// Compare current performance with baseline
    fn compare_with_baseline(&self, statistics: &MemoryStatistics) -> Option<PerformanceComparison> {
        if let Some(baseline) = self.baselines.get("auto_weekly") {
            let mut comparisons = HashMap::new();

            let metrics = vec![
                ("efficiency_score", statistics.efficiency_score, baseline.reference_statistics.efficiency_score),
                ("cache_hit_ratio", statistics.cache_hit_ratio, baseline.reference_statistics.cache_hit_ratio),
                ("fragmentation_index", statistics.fragmentation_index, baseline.reference_statistics.fragmentation_index),
            ];

            for (metric_name, current, baseline_val) in metrics {
                let percentage_change = ((current - baseline_val) / baseline_val) * 100.0;
                comparisons.insert(metric_name.to_string(), ComparisonResult {
                    current_value: current,
                    baseline_value: baseline_val,
                    percentage_change,
                    statistical_significance: 0.95, // Would calculate properly
                    interpretation: if percentage_change > 5.0 {
                        "Significant improvement".to_string()
                    } else if percentage_change < -5.0 {
                        "Significant regression".to_string()
                    } else {
                        "No significant change".to_string()
                    },
                });
            }

            Some(PerformanceComparison {
                baseline_name: "auto_weekly".to_string(),
                current_vs_baseline: comparisons,
                overall_improvement: 0.0, // Would calculate
                regression_areas: vec![],
                improvement_areas: vec![],
            })
        } else {
            None
        }
    }

    /// Generate analytics recommendations
    fn generate_recommendations(
        &self,
        _statistics: &MemoryStatistics,
        trends: &[TrendAnalysis],
        anomalies: &[AnomalyDetection],
    ) -> Result<Vec<AnalyticsRecommendation>> {
        let mut recommendations = Vec::new();

        // Recommendations based on trends
        for trend in trends {
            if trend.trend_direction == TrendDirection::Increasing && trend.metric_name == "fragmentation_index" {
                recommendations.push(AnalyticsRecommendation {
                    priority: RecommendationPriority::High,
                    category: RecommendationCategory::PerformanceOptimization,
                    title: "Address increasing memory fragmentation".to_string(),
                    description: "Memory fragmentation is trending upward, which may lead to performance degradation".to_string(),
                    supporting_evidence: vec![format!("Fragmentation index increased by {:.2}% over analysis period", trend.slope * 100.0)],
                    expected_impact: ImpactEstimate {
                        performance_improvement: 15.0,
                        cost_reduction: 5.0,
                        risk_mitigation: 20.0,
                        confidence_level: trend.trend_strength,
                    },
                    implementation_effort: EffortEstimate {
                        development_time: Duration::from_secs(3 * 24 * 60 * 60),
                        complexity_score: 6.0,
                        required_expertise: vec!["Memory management".to_string()],
                        dependencies: vec![],
                    },
                    timeline: Duration::from_secs(7 * 24 * 60 * 60),
                });
            }
        }

        // Recommendations based on anomalies
        for anomaly in anomalies {
            if anomaly.severity >= AnomalySeverity::High {
                recommendations.push(AnalyticsRecommendation {
                    priority: RecommendationPriority::Immediate,
                    category: RecommendationCategory::ResourceManagement,
                    title: format!("Investigate {} anomaly", anomaly.anomaly_type as u8),
                    description: anomaly.description.clone(),
                    supporting_evidence: anomaly.recommended_actions.clone(),
                    expected_impact: ImpactEstimate {
                        performance_improvement: 25.0,
                        cost_reduction: 0.0,
                        risk_mitigation: 40.0,
                        confidence_level: anomaly.anomaly_score,
                    },
                    implementation_effort: EffortEstimate {
                        development_time: Duration::from_secs(4 * 60 * 60),
                        complexity_score: 3.0,
                        required_expertise: vec!["System monitoring".to_string()],
                        dependencies: vec![],
                    },
                    timeline: Duration::from_secs(24 * 60 * 60),
                });
            }
        }

        Ok(recommendations)
    }

    /// Prepare visualization data for charts
    fn prepare_visualizations(&self, data: &[&HistoricalDataPoint], trends: &[TrendAnalysis]) -> Vec<VisualizationData> {
        let mut visualizations = Vec::new();

        // Memory usage over time
        let memory_data: Vec<(f64, f64)> = data.iter().enumerate()
            .map(|(i, d)| (i as f64, d.statistics.current_memory_usage as f64))
            .collect();

        visualizations.push(VisualizationData {
            chart_type: ChartType::TimeSeries,
            title: "Memory Usage Over Time".to_string(),
            x_axis: AxisDefinition {
                label: "Time".to_string(),
                unit: "hours".to_string(),
                scale: ScaleType::Linear,
                range: None,
            },
            y_axis: AxisDefinition {
                label: "Memory Usage".to_string(),
                unit: "bytes".to_string(),
                scale: ScaleType::Linear,
                range: None,
            },
            data_series: vec![DataSeries {
                name: "Memory Usage".to_string(),
                data_points: memory_data,
                style: SeriesStyle {
                    color: "#1f77b4".to_string(),
                    line_style: LineStyle::Solid,
                    marker_style: MarkerStyle::Circle,
                },
            }],
            annotations: vec![],
        });

        // Add trend visualizations
        for trend in trends {
            if trend.trend_strength > 0.5 {
                visualizations.push(VisualizationData {
                    chart_type: ChartType::TimeSeries,
                    title: format!("Trend: {}", trend.metric_name),
                    x_axis: AxisDefinition {
                        label: "Time".to_string(),
                        unit: "period".to_string(),
                        scale: ScaleType::Linear,
                        range: None,
                    },
                    y_axis: AxisDefinition {
                        label: trend.metric_name.clone(),
                        unit: "value".to_string(),
                        scale: ScaleType::Linear,
                        range: None,
                    },
                    data_series: vec![], // Would populate with actual trend data
                    annotations: vec![ChartAnnotation {
                        annotation_type: AnnotationType::Text,
                        position: (0.8, 0.9),
                        text: format!("Slope: {:.4}", trend.slope),
                        style: AnnotationStyle {
                            color: "#ff7f0e".to_string(),
                            font_size: 12.0,
                            transparency: 0.8,
                        },
                    }],
                });
            }
        }

        visualizations
    }
}

impl AnomalyDetector {
    pub fn new(metric_name: String, sensitivity: f64) -> Self {
        Self {
            metric_name,
            baseline_mean: 0.0,
            baseline_std: 1.0,
            sensitivity,
            history: VecDeque::new(),
            thresholds: AnomalyThresholds {
                critical_upper: 3.0,
                critical_lower: -3.0,
                warning_upper: 2.0,
                warning_lower: -2.0,
                change_rate_threshold: 0.5,
            },
        }
    }

    pub fn update(&mut self, value: f64) {
        self.history.push_back(value);
        if self.history.len() > 1000 {
            self.history.pop_front();
        }

        // Update baseline statistics
        if self.history.len() > 10 {
            let values: Vec<f64> = self.history.iter().cloned().collect();
            self.baseline_mean = values.iter().sum::<f64>() / values.len() as f64;
            self.baseline_std = (values.iter()
                .map(|x| (x - self.baseline_mean).powi(2))
                .sum::<f64>() / values.len() as f64).sqrt();
        }
    }

    pub fn check_anomaly(&self, value: f64) -> Result<Option<AnomalyDetection>> {
        if self.baseline_std == 0.0 {
            return Ok(None); // Not enough data
        }

        let z_score = (value - self.baseline_mean) / self.baseline_std;

        let (anomaly_type, severity) = if z_score > self.thresholds.critical_upper {
            (AnomalyType::SuddenSpike, AnomalySeverity::Critical)
        } else if z_score < self.thresholds.critical_lower {
            (AnomalyType::SuddenDrop, AnomalySeverity::Critical)
        } else if z_score > self.thresholds.warning_upper {
            (AnomalyType::SuddenSpike, AnomalySeverity::High)
        } else if z_score < self.thresholds.warning_lower {
            (AnomalyType::SuddenDrop, AnomalySeverity::High)
        } else {
            return Ok(None); // No anomaly
        };

        Ok(Some(AnomalyDetection {
            anomaly_type,
            severity,
            detected_at: SystemTime::now(),
            affected_metrics: vec![self.metric_name.clone()],
            anomaly_score: z_score.abs(),
            threshold_value: if z_score > 0.0 { self.thresholds.warning_upper } else { self.thresholds.warning_lower },
            actual_value: value,
            description: format!("Anomalous {} value detected: {:.2} (z-score: {:.2})", self.metric_name, value, z_score),
            recommended_actions: vec![
                "Investigate recent changes in workload or configuration".to_string(),
                "Check for resource contention or system issues".to_string(),
                "Consider adjusting monitoring thresholds if this becomes normal".to_string(),
            ],
        }))
    }
}

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

#[cfg(test)]
mod tests {
    use super::*;

    fn create_test_statistics(efficiency: f64, cache_hit: f64) -> MemoryStatistics {
        MemoryStatistics {
            total_allocations: 1000,
            total_deallocations: 900,
            peak_memory_usage: 1024 * 1024,
            average_memory_usage: 512.0 * 1024.0,
            current_memory_usage: 800 * 1024,
            memory_churn_rate: 0.1,
            allocation_rate: 10.0,
            deallocation_rate: 9.0,
            fragmentation_index: 0.2,
            efficiency_score: efficiency,
            cache_hit_ratio: cache_hit,
            bandwidth_utilization: 0.7,
            pressure_incidents: 0,
            optimization_opportunities: 2,
        }
    }

    fn create_test_context() -> AnalyticsContext {
        AnalyticsContext {
            session_id: "test_session".to_string(),
            workload_type: "training".to_string(),
            model_architecture: Some("resnet50".to_string()),
            batch_size: Some(32),
            device_info: DeviceInfo {
                device_type: "GPU".to_string(),
                total_memory: 8 * 1024 * 1024 * 1024,
                compute_capability: Some("7.5".to_string()),
                driver_version: Some("11.2".to_string()),
            },
            environment: EnvironmentInfo {
                framework_version: "0.1.0".to_string(),
                rust_version: "1.70.0".to_string(),
                optimization_level: "release".to_string(),
                feature_flags: vec!["cuda".to_string()],
            },
        }
    }

    #[test]
    fn test_analytics_creation() {
        let analytics = MemoryAnalytics::new();
        assert_eq!(analytics.historical_data.len(), 0);
        assert_eq!(analytics.baselines.len(), 0);
    }

    #[test]
    fn test_statistics_recording() {
        let mut analytics = MemoryAnalytics::new();
        let stats = create_test_statistics(0.8, 0.9);
        let context = create_test_context();

        let result = analytics.record_statistics(stats, context);
        assert!(result.is_ok());
        assert_eq!(analytics.historical_data.len(), 1);
    }

    #[test]
    fn test_baseline_creation() {
        let mut analytics = MemoryAnalytics::new();

        // Add some data first
        for i in 0..20 {
            let stats = create_test_statistics(0.8 + i as f64 * 0.01, 0.9);
            let context = create_test_context();
            analytics.record_statistics(stats, context).unwrap();
        }

        let result = analytics.create_baseline(
            "test_baseline".to_string(),
            "Test baseline".to_string(),
            Duration::from_secs(300),
        );

        assert!(result.is_ok());
        assert!(analytics.baselines.contains_key("test_baseline"));
    }

    #[test]
    fn test_trend_analysis() {
        let mut analytics = MemoryAnalytics::new();

        // Create trending data (increasing efficiency)
        for i in 0..50 {
            let stats = create_test_statistics(0.5 + i as f64 * 0.01, 0.9);
            let context = create_test_context();
            analytics.record_statistics(stats, context).unwrap();
        }

        let trends = analytics.analyze_trends(Duration::from_secs(1000)).unwrap();
        assert!(!trends.is_empty());

        // Find efficiency trend
        let efficiency_trend = trends.iter().find(|t| t.metric_name == "efficiency_score");
        assert!(efficiency_trend.is_some());

        let trend = efficiency_trend.unwrap();
        assert_eq!(trend.trend_direction, TrendDirection::Increasing);
        assert!(trend.slope > 0.0);
    }

    #[test]
    fn test_anomaly_detection() {
        let mut analytics = MemoryAnalytics::new();

        // Add normal data
        for _ in 0..100 {
            let stats = create_test_statistics(0.8, 0.9);
            let context = create_test_context();
            analytics.record_statistics(stats, context).unwrap();
        }

        // Add anomalous data point
        let anomalous_stats = create_test_statistics(0.1, 0.1); // Very low efficiency
        analytics.record_statistics(anomalous_stats, create_test_context()).unwrap();

        let anomalies = analytics.detect_anomalies().unwrap();
        assert!(!anomalies.is_empty());
    }

    #[test]
    fn test_report_generation() {
        let mut analytics = MemoryAnalytics::new();

        // Add sufficient data for report
        for i in 0..100 {
            let stats = create_test_statistics(0.8, 0.9);
            let context = create_test_context();
            analytics.record_statistics(stats, context).unwrap();
        }

        let report = analytics.generate_report(Duration::from_secs(300));
        assert!(report.is_ok());

        let report = report.unwrap();
        assert!(!report.report_id.is_empty());
        assert!(report.detailed_statistics.efficiency_score > 0.0);
    }

    #[test]
    fn test_anomaly_detector() {
        let mut detector = AnomalyDetector::new("test_metric".to_string(), 0.95);

        // Train with normal data
        for i in 0..100 {
            detector.update(100.0 + i as f64 * 0.1);
        }

        // Test with anomalous value
        let anomaly = detector.check_anomaly(200.0).unwrap();
        assert!(anomaly.is_some());

        let anomaly = anomaly.unwrap();
        assert_eq!(anomaly.anomaly_type, AnomalyType::SuddenSpike);
    }

    #[test]
    fn test_confidence_interval_calculation() {
        let analytics = MemoryAnalytics::new();

        // Create test data
        let mut data_points = Vec::new();
        for i in 0..50 {
            let stats = create_test_statistics(0.8, 0.9);
            let context = create_test_context();
            data_points.push(HistoricalDataPoint {
                timestamp: SystemTime::now(),
                statistics: stats,
                context,
                custom_metrics: HashMap::new(),
            });
        }

        let data_refs: Vec<&HistoricalDataPoint> = data_points.iter().collect();
        let ci = analytics.calculate_confidence_interval(&data_refs, 0.95);

        assert_eq!(ci.confidence_level, 0.95);
        assert!(ci.lower_bound <= ci.upper_bound);
        assert!(ci.margin_of_error >= 0.0);
    }
}