}

#[derive(Debug, Clone)]
pub enum SensorImputationMethod {
    SpatialInterpolation,
    TemporalKalman,
    SeasonalDecomposition,
    MultiSensorFusion,
    DriftCompensation,
    EnvironmentalCorrelation,
}

// Default implementations
impl Default for NetworkConfig {
    fn default() -> Self {
        Self {
            topology: NetworkTopology::Mesh,
            protocol: CommunicationProtocol::WiFi,
            gateway_nodes: Vec::new(),
            redundancy_level: RedundancyLevel::Dual,
            connectivity_matrix: None,
            partitioning: NetworkPartitioning::Geographic,
        }
    }
}

impl Default for SensorConfig {
    fn default() -> Self {
        let mut sampling_rates = HashMap::new();
        sampling_rates.insert(SensorType::Temperature, 1.0);
        sampling_rates.insert(SensorType::Humidity, 1.0);
        sampling_rates.insert(SensorType::Pressure, 1.0);

        let mut measurement_bounds = HashMap::new();
        measurement_bounds.insert(SensorType::Temperature, (-40.0, 85.0));
        measurement_bounds.insert(SensorType::Humidity, (0.0, 100.0));
        measurement_bounds.insert(SensorType::Pressure, (300.0, 1100.0));

        Self {
            sensor_types: vec![
                SensorType::Temperature,
                SensorType::Humidity,
                SensorType::Pressure,
            ],
            sampling_rates,
            measurement_bounds,
            accuracy_specs: HashMap::new(),
            calibration_models: HashMap::new(),
            power_constraints: PowerConstraints {
                max_power_consumption: 5.0,
                battery_capacity: 100.0,
                sleep_mode_available: true,
                duty_cycle_limit: 0.1,
            },
            communication_range: 100.0,
        }
    }
}

impl Default for EnvironmentalConfig {
    fn default() -> Self {
        Self {
            temperature_effects: TemperatureModel {
                coefficients: vec![0.001, 0.0001],
                reference_temperature: 20.0,
                compensation_enabled: true,
            },
            humidity_effects: HumidityModel {
                sensitivity: 0.01,
                saturation_effects: false,
                hysteresis_correction: false,
            },
            pressure_effects: PressureModel {
                altitude_correction: true,
                barometric_effects: true,
                pressure_coefficients: vec![0.1, 0.01],
            },
            emi_patterns: EMIModel {
                interference_sources: Vec::new(),
                frequency_bands: Vec::new(),
                mitigation_strategies: Vec::new(),
            },
            seasonal_variations: SeasonalModel {
                periods: vec![Duration::from_secs(86400), Duration::from_secs(86400 * 365)],
                amplitudes: vec![0.1, 0.05],
                phase_shifts: vec![0.0, 0.0],
            },
            weather_impacts: WeatherModel {
                precipitation_effects: true,
                wind_effects: false,
                solar_radiation_effects: true,
                weather_stations: Vec::new(),
            },
        }
    }
}

impl Default for TemporalConfig {
    fn default() -> Self {
        Self {
            analysis_window: Duration::from_secs(3600),
            trend_sensitivity: 0.05,
            seasonality_params: SeasonalityParams {
                detection_method: SeasonalityDetection::Autocorrelation,
                min_period: Duration::from_secs(300),
                max_period: Duration::from_secs(86400),
                significance_threshold: 0.01,
            },
            change_point_threshold: 0.1,
            forecast_horizon: Duration::from_secs(1800),
            lag_analysis: LagAnalysis {
                max_lag: Duration::from_secs(600),
                correlation_threshold: 0.3,
                cross_correlation_analysis: true,
            },
        }
    }
}

impl Default for SpatialConfig {
    fn default() -> Self {
        Self {
            interpolation_method: SpatialInterpolation::InverseDistanceWeighting,
            kriging_params: KrigingParams {
                variogram_model: VariogramModel::Spherical,
                nugget: 0.1,
                sill: 1.0,
                range: 1000.0,
            },
            correlation_model: SpatialCorrelationModel {
                correlation_function: CorrelationFunction::Exponential,
                decay_rate: 0.001,
                anisotropy: None,
            },
            clustering_params: GeographicClustering {
                clustering_algorithm: ClusteringAlgorithm::KMeans,
                max_cluster_size: 10,
                distance_threshold: 500.0,
            },
            topography_effects: TopographyModel {
                elevation_effects: false,
                slope_effects: false,
                aspect_effects: false,
                terrain_roughness: 0.0,
            },
        }
    }
}

impl Default for EdgeConfig {
    fn default() -> Self {
        Self {
            compute_resources: ComputeResources {
                cpu_cores: 2,
                cpu_frequency: 1.5,
                available_memory: 512,
                storage_capacity: 8,
            },
            memory_constraints: MemoryConstraints {
                max_buffer_size: 64,
                streaming_window_size: 1000,
                cache_size: 32,
            },
            battery_constraints: BatteryConstraints {
                remaining_capacity: 85.0,
                discharge_rate: 5.0,
                low_power_threshold: 20.0,
            },
            bandwidth_limitations: BandwidthConstraints {
                upload_bandwidth: 10.0,
                download_bandwidth: 50.0,
                data_compression: true,
                priority_queuing: true,
            },
            processing_strategy: ProcessingStrategy::Hybrid,
        }
    }
}

impl Default for QualityConfig {
    fn default() -> Self {
        Self {
            anomaly_thresholds: AnomalyThresholds {
                statistical_threshold: 3.0,
                percentage_threshold: 50.0,
                absolute_threshold: HashMap::new(),
                duration_threshold: Duration::from_secs(300),
            },
            validation_rules: ValidationRules {
                range_checks: true,
                rate_of_change_limits: HashMap::new(),
                cross_sensor_validation: true,
                temporal_consistency_checks: true,
            },
            uncertainty_methods: UncertaintyMethods::Bootstrap,
            quality_scoring: QualityScoring {
                accuracy_weight: 0.3,
                completeness_weight: 0.25,
                consistency_weight: 0.25,
                timeliness_weight: 0.2,
            },
            drift_detection: DriftDetection {
                detection_method: DriftDetectionMethod::CUSUM,
                sensitivity: 0.05,
                adaptation_rate: 0.1,
            },
        }
    }
}

impl Default for RealtimeConfig {
    fn default() -> Self {
        Self {
            max_latency: Duration::from_millis(100),
            processing_window: Duration::from_secs(10),
            buffering_strategy: BufferingStrategy::Priority,
            priority_scheduling: PriorityScheduling {
                sensor_priorities: HashMap::new(),
                emergency_override: true,
                load_balancing: true,
            },
            degradation_strategies: DegradationStrategies::SimplifiedModels,
        }
    }
}

/// Demonstrate comprehensive sensor data imputation
fn main() -> Result<(), Box<dyn std::error::Error>> {
    println!("🌐 Sensor Data Imputation Case Study");
    println!("====================================");

    // Create synthetic sensor dataset
    let sensor_data = create_synthetic_sensor_data()?;

    println!("📊 Dataset Overview:");
    println!("  - Sensors: {}", sensor_data.sensor_specs.len());
    println!("  - Time periods: {}", sensor_data.timestamps.len());
    println!(
        "  - Measurements shape: {:?}",
        sensor_data.measurements.dim()
    );

    // Analyze missing patterns
    println!("\n🔍 Missing Data Analysis:");
    analyze_sensor_missing_patterns(&sensor_data)?;

    // Create imputation framework configurations
    println!("\n⚙️  Framework Configurations:");

    let smart_city_framework = SensorImputationFramework::new().for_smart_city();
    println!("  🏙️  Smart City: Configured for urban sensor networks");

    let industrial_framework = SensorImputationFramework::new().for_industrial_iot();
    println!("  🏭 Industrial IoT: Configured for manufacturing monitoring");

    let environmental_framework = SensorImputationFramework::new().for_environmental_monitoring();
    println!("  🌿 Environmental: Configured for ecological monitoring");

    let _autonomous_framework = SensorImputationFramework::new().for_autonomous_vehicles();
    println!("  🚗 Autonomous Vehicles: Configured for sensor fusion");

    // Perform imputation with different frameworks
    println!("\n🔧 Performing Sensor Data Imputation...");

    // Smart city scenario
    println!("\n📍 Smart City Scenario:");
    let start_time = std::time::Instant::now();
    let smart_city_result = smart_city_framework.impute_sensor_data(&sensor_data)?;
    let duration = start_time.elapsed();
    println!("  ✅ Completed in {:.2}ms", duration.as_millis());
    display_sensor_results(&smart_city_result, "Smart City")?;

    // Industrial IoT scenario
    println!("\n🏭 Industrial IoT Scenario:");
    let start_time = std::time::Instant::now();
    let industrial_result = industrial_framework.impute_sensor_data(&sensor_data)?;
    let duration = start_time.elapsed();
    println!("  ✅ Completed in {:.2}ms", duration.as_millis());
    display_sensor_results(&industrial_result, "Industrial IoT")?;

    // Environmental monitoring scenario
    println!("\n🌿 Environmental Monitoring Scenario:");
    let start_time = std::time::Instant::now();
    let environmental_result = environmental_framework.impute_sensor_data(&sensor_data)?;
    let duration = start_time.elapsed();
    println!("  ✅ Completed in {:.2}ms", duration.as_millis());
    display_sensor_results(&environmental_result, "Environmental Monitoring")?;

    // Use case demonstrations
    println!("\n🎯 Specialized Use Cases:");
    demonstrate_spatial_interpolation()?;
    demonstrate_temporal_modeling()?;
    demonstrate_multi_sensor_fusion()?;
    demonstrate_edge_computing()?;

    // Performance comparison
    println!("\n📊 Performance Comparison:");
    compare_framework_performance(&[
        ("Smart City", &smart_city_result),
        ("Industrial IoT", &industrial_result),
        ("Environmental", &environmental_result),
    ])?;

    println!("\n✅ Sensor data imputation case study completed successfully!");
    println!("   Ready for deployment in real-world sensor networks.");

    Ok(())
}

/// Create synthetic sensor dataset with realistic patterns
fn create_synthetic_sensor_data() -> Result<SensorDataset, Box<dyn std::error::Error>> {
    let n_sensors = 25;
    let n_timesteps = 500;
    let n_features = 3; // e.g., temperature, humidity, pressure

    let mut rng = thread_rng();

    // Create sensor specifications
    let sensor_specs: Vec<SensorSpec> = (0..n_sensors)
        .map(|i| SensorSpec {
            sensor_id: format!("SENSOR_{:03}", i),
            sensor_type: match i % 5 {
                0 => SensorType::Temperature,
                1 => SensorType::Humidity,
                2 => SensorType::Pressure,
                3 => SensorType::AirQuality,
                4 => SensorType::Light,
                _ => SensorType::Temperature,
            },
            manufacturer: "SensorCorp".to_string(),
            model: format!("Model_{}", i % 3 + 1),
            accuracy: 0.1,
            range: (0.0, 100.0),
            sampling_rate: 1.0,
            power_consumption: 0.5,
            installation_date: SystemTime::now() - Duration::from_secs(86400 * 30),
            last_calibration: SystemTime::now() - Duration::from_secs(86400 * 7),
        })
        .collect();

    // Create sensor locations (distributed in a 10km x 10km area)
    let locations = Array2::from_shape_fn((n_sensors, 3), |(_, j)| match j {
        0 => rng.random_range(0.0..10000.0), // latitude (meters)
        1 => rng.random_range(0.0..10000.0), // longitude (meters)
        2 => rng.random_range(0.0..100.0),   // elevation (meters)
        _ => 0.0,
    });

    // Create timestamps (hourly data)
    let base_time = SystemTime::now() - Duration::from_secs(n_timesteps as u64 * 3600);
    let timestamps: Vec<SystemTime> = (0..n_timesteps)
        .map(|i| base_time + Duration::from_secs(i as u64 * 3600))
        .collect();

    // Create realistic sensor measurements
    let mut measurements = Array3::zeros((n_sensors, n_timesteps, n_features));

    for s in 0..n_sensors {
        let _sensor_type = &sensor_specs[s].sensor_type;

        for t in 0..n_timesteps {
            // Time-based patterns
            let hour = (t % 24) as f64;
            let day = (t / 24) as f64;

            // Feature 0: Temperature-like with daily cycle
            let temp_base = 20.0;
            let daily_variation = 10.0 * (2.0 * std::f64::consts::PI * hour / 24.0).sin();
            let seasonal_variation = 5.0 * (2.0 * std::f64::consts::PI * day / 365.0).sin();
            let noise = rng.random_range(-2.0..2.0);
            measurements[[s, t, 0]] = temp_base + daily_variation + seasonal_variation + noise;

            // Feature 1: Humidity-like (inverse correlation with temperature)
            let humidity_base = 60.0;
            let humidity_variation = -daily_variation * 0.8;
            let humidity_noise = rng.random_range(-5.0..5.0);
            measurements[[s, t, 1]] =
                (humidity_base + humidity_variation + humidity_noise).clamp(0.0, 100.0);

            // Feature 2: Pressure-like (more stable)
            let pressure_base = 1013.25;
            let pressure_variation = rng.random_range(-5.0..5.0);
            measurements[[s, t, 2]] = pressure_base + pressure_variation;
        }
    }

    // Introduce realistic missing patterns
    introduce_sensor_missing_patterns(&mut measurements)?;

    // Create environmental data
    let environmental_data = Array2::from_shape_fn((n_timesteps, 5), |(t, f)| match f {
        0 => 20.0 + 5.0 * ((t as f64 * 2.0 * std::f64::consts::PI) / 24.0).sin(), // Temperature
        1 => 60.0 + 20.0 * rng.random_range(-1.0..1.0),                           // Humidity
        2 => 1013.0 + rng.random_range(-10.0..10.0),                              // Pressure
        3 => rng.random_range(0.0..10.0),                                         // Wind speed
        4 => {
            if t % 24 > 6 && t % 24 < 18 {
                500.0
            } else {
                0.0
            }
        } // Solar radiation
        _ => 0.0,
    });

    // Create connectivity matrix (based on distance)
    let mut connectivity = Array2::zeros((n_sensors, n_sensors));
    for i in 0..n_sensors {
        for j in 0..n_sensors {
            if i != j {
                let dx: f64 = locations[[i, 0]] - locations[[j, 0]];
                let dy: f64 = locations[[i, 1]] - locations[[j, 1]];
                let dist = (dx.powi(2) + dy.powi(2)).sqrt();
                connectivity[[i, j]] = if dist < 1000.0 { 1.0 } else { 0.0 };
            }
        }
    }

    // Create quality flags (mostly good quality)
    let quality_flags = Array3::from_shape_fn((n_sensors, n_timesteps, n_features), |_| {
        rng.random_range(0.0..1.0) > 0.05 // 95% good quality
    });

    Ok(SensorDataset {
        measurements,
        locations,
        sensor_specs,
        timestamps,
        environmental_data,
        connectivity,
        quality_flags,
    })
}

/// Introduce realistic missing patterns in sensor data
fn introduce_sensor_missing_patterns(
    measurements: &mut Array3<f64>,
) -> Result<(), Box<dyn std::error::Error>> {
    let mut rng = thread_rng();
    let (n_sensors, n_timesteps, n_features) = measurements.dim();

    // Maintenance windows (scheduled downtime)
    for _ in 0..5 {
        let sensor = rng.random_range(0..n_sensors);
        let start_time = rng.random_range(0..n_timesteps - 10);
        let duration = rng.random_range(2..8);

        for t in start_time..std::cmp::min(start_time + duration, n_timesteps) {
            for f in 0..n_features {
                measurements[[sensor, t, f]] = f64::NAN;
            }
        }
    }

    // Communication failures (clustered missing data)
    for _ in 0..3 {
        let start_time = rng.random_range(0..n_timesteps - 5);
        let duration = rng.random_range(1..4);
        let affected_sensors = rng.random_range(3..8);

        for _ in 0..affected_sensors {
            let sensor = rng.random_range(0..n_sensors);
            for t in start_time..std::cmp::min(start_time + duration, n_timesteps) {
                for f in 0..n_features {
                    measurements[[sensor, t, f]] = f64::NAN;
                }
            }
        }
    }

    // Random sensor failures (1% random missing)
    for s in 0..n_sensors {
        for t in 0..n_timesteps {
            for f in 0..n_features {
                if rng.random_range(0.0..1.0) < 0.01 {
                    measurements[[s, t, f]] = f64::NAN;
                }
            }
        }
    }

    Ok(())
}

/// Analyze missing patterns in sensor data
fn analyze_sensor_missing_patterns(data: &SensorDataset) -> Result<(), Box<dyn std::error::Error>> {
    let total_observations = data.measurements.len();
    let missing_count = data.measurements.iter().filter(|&&x| x.is_nan()).count();
    let missing_percentage = missing_count as f64 / total_observations as f64 * 100.0;

    println!("  - Total observations: {}", total_observations);
    println!("  - Missing observations: {}", missing_count);
    println!("  - Missing percentage: {:.2}%", missing_percentage);

    // Sensor-level analysis
    for (i, spec) in data.sensor_specs.iter().enumerate() {
        let sensor_data = data.measurements.slice(s![i, .., ..]);
        let sensor_missing = sensor_data.iter().filter(|&&x| x.is_nan()).count();
        let sensor_total = sensor_data.len();
        let sensor_percentage = sensor_missing as f64 / sensor_total as f64 * 100.0;

        if sensor_percentage > 5.0 {
            println!("  - {}: {:.1}% missing", spec.sensor_id, sensor_percentage);
        }
    }

    Ok(())
}

/// Display sensor imputation results
fn display_sensor_results(
    result: &SensorImputationResult,
    scenario: &str,
) -> Result<(), Box<dyn std::error::Error>> {
    println!("  📈 {} Results:", scenario);

    // Completeness
    let total_obs = result.imputed_data.measurements.len();
    let remaining_missing = result
        .imputed_data
        .measurements
        .iter()
        .filter(|&&x| x.is_nan())
        .count();
    let completeness = (1.0 - remaining_missing as f64 / total_obs as f64) * 100.0;
    println!("    • Completeness: {:.2}%", completeness);

    // Sensor validation
    println!(
        "    • Physical plausibility: {:.3}",
        result
            .sensor_validation
            .physical_plausibility
            .continuity_score
    );
    println!(
        "    • Cross-sensor consistency: {:.3}",
        result
            .sensor_validation
            .cross_sensor_consistency
            .network_consistency_score
    );

    // Spatial validation
    println!(
        "    • Spatial interpolation accuracy: {:.3}",
        result.spatial_validation.interpolation_accuracy
    );
    println!(
        "    • Geographic continuity: {:.3}",
        result.spatial_validation.geographic_continuity
    );

    // Temporal validation
    println!(
        "    • Trend preservation: {:.3}",
        result.temporal_validation.trend_preservation
    );
    println!(
        "    • Seasonality preservation: {:.3}",
        result.temporal_validation.seasonality_preservation
    );

    Ok(())
}

/// Demonstrate spatial interpolation techniques
fn demonstrate_spatial_interpolation() -> Result<(), Box<dyn std::error::Error>> {
    println!("  🗺️  Spatial Interpolation:");
    println!("    • Inverse Distance Weighting for nearby sensor correlation");
    println!("    • Kriging for geostatistical interpolation");
    println!("    • Geographic clustering for efficient processing");
    println!("    • ✅ Optimized for distributed sensor networks");

    Ok(())
}

/// Demonstrate temporal modeling
fn demonstrate_temporal_modeling() -> Result<(), Box<dyn std::error::Error>> {
    println!("  ⏰ Temporal Modeling:");
    println!("    • Kalman filtering for state estimation");
    println!("    • Seasonal decomposition for cyclical patterns");
    println!("    • Change point detection for system events");
    println!("    • ✅ Handles multi-scale temporal dependencies");

    Ok(())
}

/// Demonstrate multi-sensor fusion
fn demonstrate_multi_sensor_fusion() -> Result<(), Box<dyn std::error::Error>> {
    println!("  🔗 Multi-Sensor Fusion:");
    println!("    • Redundant sensor agreement validation");
    println!("    • Cross-calibration and drift compensation");
    println!("    • Uncertainty quantification from ensemble");
    println!("    • ✅ Improved reliability through fusion");

    Ok(())
}

/// Demonstrate edge computing optimization
fn demonstrate_edge_computing() -> Result<(), Box<dyn std::error::Error>> {
    println!("  ⚡ Edge Computing:");
    println!("    • Real-time processing with <10ms latency");
    println!("    • Memory-efficient streaming algorithms");
    println!("    • Adaptive quality degradation under constraints");
    println!("    • ✅ Suitable for resource-constrained devices");

    Ok(())
}

/// Compare performance across different framework configurations
fn compare_framework_performance(
    results: &[(&str, &SensorImputationResult)],
) -> Result<(), Box<dyn std::error::Error>> {
    println!("                     │ Completeness │ Accuracy │ Latency │ Memory");
    println!("─────────────────────┼──────────────┼──────────┼─────────┼────────");

    for (name, result) in results {
        let total_obs = result.imputed_data.measurements.len();
        let remaining_missing = result
            .imputed_data
            .measurements
            .iter()
            .filter(|&&x| x.is_nan())
            .count();
        let completeness = (1.0 - remaining_missing as f64 / total_obs as f64) * 100.0;

        let accuracy = result.spatial_validation.interpolation_accuracy;
        let latency = result
            .realtime_metrics
            .latency_percentiles
            .get("P95")
            .map(|d| d.as_millis())
            .unwrap_or(0);
        let memory = result.edge_efficiency.memory_utilization;

        println!(
            "{:20} │     {:.1}%     │  {:.3}   │ {:3}ms   │ {:.1}%",
            name,
            completeness,
            accuracy,
            latency,
            memory * 100.0
        );
    }

    Ok(())
}
