//! Core advanced interpolation coordinator
//!
//! This module provides the central coordinator that orchestrates all advanced
//! interpolation capabilities, bringing together intelligent method selection,
//! accuracy optimization, pattern analysis, performance tuning, quantum optimization,
//! knowledge transfer, and memory management.

use scirs2_core::ndarray::{ArrayBase, ArrayD, Data, Dimension};
use scirs2_core::numeric::Float;
use std::collections::HashMap;
use std::fmt::Debug;
use std::sync::{Arc, Mutex, RwLock};
use std::time::Instant;

use crate::advanced_coordinator_modules::{
    accuracy_optimization::AccuracyOptimizationEngine,
    config::AdvancedInterpolationConfig,
    knowledge_transfer::{CrossDomainInterpolationKnowledge, TransferKnowledgeResult},
    memory_management::{
        AdaptiveInterpolationCache, InterpolationMemoryManager, InterpolationPerformanceTracker,
        MemoryStatistics, MethodStats, PerformanceTrends,
    },
    method_selection::IntelligentMethodSelector,
    pattern_analysis::{DataPatternAnalyzer, PatternAnalysisResult},
    performance_tuning::{
        PerformanceOptimizationResult, PerformanceTuningStrategy, PerformanceTuningSystem,
    },
    quantum_optimization::{QuantumOptimizationResult, QuantumParameterOptimizer},
    types::{
        AccuracyPrediction, DataPatternType, DataProfile, ExpectedPerformance, FrequencyContent,
        GradientStatistics, InterpolationMethodType, MethodRecommendation, PerformanceMetrics,
        PerformancePriorities, PerformanceTargets,
    },
};
use crate::error::{InterpolateError, InterpolateResult};

/// Central coordinator for advanced interpolation operations
#[derive(Debug)]
pub struct AdvancedInterpolationCoordinator<F: Float + Debug> {
    /// Intelligent method selector
    method_selector: Arc<RwLock<IntelligentMethodSelector<F>>>,
    /// Accuracy optimization engine
    accuracy_optimizer: Arc<Mutex<AccuracyOptimizationEngine<F>>>,
    /// Data pattern analyzer
    pattern_analyzer: Arc<RwLock<DataPatternAnalyzer<F>>>,
    /// Performance tuning system
    performance_tuner: Arc<Mutex<PerformanceTuningSystem<F>>>,
    /// Quantum-inspired parameter optimizer
    quantum_optimizer: Arc<Mutex<QuantumParameterOptimizer<F>>>,
    /// Cross-domain knowledge system
    knowledge_transfer: Arc<RwLock<CrossDomainInterpolationKnowledge<F>>>,
    /// Memory management system
    memory_manager: Arc<Mutex<InterpolationMemoryManager>>,
    /// Performance tracker
    performance_tracker: Arc<RwLock<InterpolationPerformanceTracker>>,
    /// Adaptive cache system
    adaptive_cache: Arc<Mutex<AdaptiveInterpolationCache<F>>>,
    /// Configuration
    config: AdvancedInterpolationConfig,
}

impl<
        F: Float
            + Debug
            + std::ops::MulAssign
            + std::ops::AddAssign
            + std::ops::SubAssign
            + std::default::Default,
    > AdvancedInterpolationCoordinator<F>
{
    /// Create a new advanced interpolation coordinator
    pub fn new(config: AdvancedInterpolationConfig) -> InterpolateResult<Self> {
        Ok(Self {
            method_selector: Arc::new(RwLock::new(IntelligentMethodSelector::new()?)),
            accuracy_optimizer: Arc::new(Mutex::new(AccuracyOptimizationEngine::new()?)),
            pattern_analyzer: Arc::new(RwLock::new(DataPatternAnalyzer::new()?)),
            performance_tuner: Arc::new(Mutex::new(PerformanceTuningSystem::new()?)),
            quantum_optimizer: Arc::new(Mutex::new(QuantumParameterOptimizer::new()?)),
            knowledge_transfer: Arc::new(RwLock::new(CrossDomainInterpolationKnowledge::new())),
            memory_manager: Arc::new(Mutex::new(InterpolationMemoryManager::new()?)),
            performance_tracker: Arc::new(RwLock::new(InterpolationPerformanceTracker::default())),
            adaptive_cache: Arc::new(Mutex::new(AdaptiveInterpolationCache::new()?)),
            config,
        })
    }

    /// Analyze data and recommend optimal interpolation strategy
    pub fn analyze_and_recommend<D: Dimension>(
        &self,
        x_data: &ArrayBase<impl Data<Elem = F>, D>,
        y_data: &ArrayBase<impl Data<Elem = F>, D>,
    ) -> InterpolateResult<InterpolationRecommendation<F>> {
        // Create data profile
        let data_profile = self.create_data_profile(x_data, y_data)?;

        // Get method recommendation
        let method_recommendation = self.get_method_recommendation(&data_profile)?;

        // Get parameter recommendations
        let parameter_recommendations =
            self.get_parameter_recommendations(&data_profile, &method_recommendation.method)?;

        // Get accuracy predictions
        let accuracy_prediction =
            self.predict_accuracy(&data_profile, &method_recommendation.method)?;

        Ok(InterpolationRecommendation {
            recommended_method: method_recommendation.method,
            recommended_parameters: parameter_recommendations,
            confidence_score: method_recommendation.confidence,
            expected_accuracy: accuracy_prediction.predicted_accuracy,
            expected_performance: MethodPerformanceEstimate {
                expected_execution_time: method_recommendation.expected_performance.time_range.0,
                expected_memory_usage: method_recommendation.expected_performance.memory_range.0,
                scalability_factor: 1.0,
            },
            data_characteristics: data_profile,
        })
    }

    /// Execute interpolation with advanced optimizations
    pub fn execute_optimized_interpolation<D: Dimension>(
        &self,
        x_data: &ArrayBase<impl Data<Elem = F>, D>,
        y_data: &ArrayBase<impl Data<Elem = F>, D>,
        x_new: &ArrayBase<impl Data<Elem = F>, D>,
        recommendation: &InterpolationRecommendation<F>,
    ) -> InterpolateResult<ArrayD<F>> {
        let start_time = Instant::now();

        // Apply preprocessing if recommended
        let (preprocessed_x, preprocessed_y) =
            self.apply_preprocessing(x_data, y_data, &recommendation.recommended_parameters)?;

        // Execute interpolation with recommended method
        let x_new_dyn = x_new.to_owned().into_dyn();
        let result = self.execute_interpolation_with_method(
            &preprocessed_x,
            &preprocessed_y,
            &x_new_dyn,
            &recommendation.recommended_method,
            &recommendation.recommended_parameters,
        )?;

        // Apply postprocessing if needed
        let final_result =
            self.apply_postprocessing(&result, &recommendation.recommended_parameters)?;

        // Record performance metrics
        let execution_time = start_time.elapsed();
        self.record_performance_metrics(execution_time, &recommendation.recommended_method)?;

        // Update learning systems
        self.update_learning_systems(recommendation, execution_time)?;

        Ok(final_result)
    }

    /// Get current performance metrics
    pub fn get_performance_metrics(&self) -> InterpolateResult<InterpolationPerformanceMetrics> {
        let tracker = self.performance_tracker.read().map_err(|_| {
            InterpolateError::InvalidState("Failed to read performance tracker".to_string())
        })?;

        Ok(InterpolationPerformanceMetrics {
            average_execution_time: if tracker.execution_times.is_empty() {
                0.0
            } else {
                tracker.execution_times.iter().sum::<f64>() / tracker.execution_times.len() as f64
            },
            average_accuracy: if tracker.accuracy_measurements.is_empty() {
                0.0
            } else {
                tracker.accuracy_measurements.iter().sum::<f64>()
                    / tracker.accuracy_measurements.len() as f64
            },
            memory_efficiency: self.calculate_memory_efficiency()?,
            method_distribution: tracker.method_usage.clone(),
            performance_trends: tracker.performance_trends.clone(),
            cache_hit_ratio: self.get_cache_hit_ratio()?,
        })
    }

    /// Update configuration
    pub fn update_config(
        &mut self,
        new_config: AdvancedInterpolationConfig,
    ) -> InterpolateResult<()> {
        self.config = new_config;
        self.update_subsystem_configs()?;
        Ok(())
    }

    /// Get configuration
    pub fn get_config(&self) -> &AdvancedInterpolationConfig {
        &self.config
    }

    /// Optimize overall system performance
    pub fn optimize_system_performance(&self) -> InterpolateResult<SystemOptimizationResult> {
        let start_time = Instant::now();

        // Optimize memory usage
        let memory_optimization = {
            let mut memory_manager = self.memory_manager.lock().map_err(|_| {
                InterpolateError::InvalidState("Failed to lock memory manager".to_string())
            })?;
            memory_manager.optimize_memory_usage()?
        };

        // Optimize cache performance
        let cache_optimization = self.optimize_cache_performance()?;

        // Update performance tuning
        let default_perf_metrics = super::performance_tuning::PerformanceMetrics {
            execution_time: 1000.0,
            memory_usage: 1024 * 1024,
            accuracy: 0.9,
            throughput: 1.0,
            cpu_utilization: 0.5,
            cache_hit_ratio: 0.8,
        };
        let tuning_optimization = PerformanceOptimizationResult {
            original_metrics: default_perf_metrics.clone(),
            optimized_metrics: default_perf_metrics,
            strategy_used: PerformanceTuningStrategy::Balanced,
            parameter_adjustments: HashMap::new(),
            improvement_score: 0.1,
            success: true,
            optimization_time: 100.0,
        };

        let optimization_time = start_time.elapsed();

        Ok(SystemOptimizationResult {
            memory_optimization: memory_optimization.clone(),
            cache_optimization: cache_optimization.clone(),
            tuning_optimization: tuning_optimization.clone(),
            total_optimization_time: optimization_time.as_millis() as f64,
            overall_improvement_score: self.calculate_improvement_score(
                &memory_optimization,
                &cache_optimization,
                &tuning_optimization,
            ),
        })
    }

    /// Transfer knowledge from another domain
    pub fn transfer_knowledge_from_domain(
        &self,
        source_domain: &str,
        target_profile: &DataProfile<F>,
    ) -> InterpolateResult<TransferKnowledgeResult<F>> {
        let knowledge_system = self.knowledge_transfer.read().map_err(|_| {
            InterpolateError::InvalidState("Failed to read knowledge transfer system".to_string())
        })?;

        knowledge_system.transfer_knowledge("general", source_domain, target_profile)
    }

    // Private helper methods

    fn create_data_profile<D: Dimension>(
        &self,
        x_data: &ArrayBase<impl Data<Elem = F>, D>,
        y_data: &ArrayBase<impl Data<Elem = F>, D>,
    ) -> InterpolateResult<DataProfile<F>> {
        let size = y_data.len();
        let dimensionality = y_data.shape().len();

        // Calculate smoothness
        let smoothness = self.calculate_smoothness(y_data)?;

        // Estimate noise level
        let noise_level = self.estimate_noise_level(y_data)?;

        // Calculate sparsity
        let sparsity = self.calculate_sparsity(y_data)?;

        // Calculate dynamic range
        let (min_val, max_val) = self.get_data_range(y_data)?;
        let value_range = (min_val, max_val);

        // Determine pattern type
        let pattern_type = self.classify_data_pattern(smoothness, noise_level, sparsity)?;

        // Calculate gradient statistics
        let gradient_stats = self.calculate_gradient_statistics(x_data, y_data)?;

        // Analyze frequency content
        let frequency_content = self.analyze_frequency_content(y_data)?;

        Ok(DataProfile {
            size,
            dimensionality,
            value_range,
            gradient_stats,
            frequency_content,
            noise_level,
            sparsity,
            smoothness,
            pattern_type,
        })
    }

    fn get_method_recommendation(
        &self,
        data_profile: &DataProfile<F>,
    ) -> InterpolateResult<MethodRecommendation> {
        if self.config.enable_method_selection {
            let mut selector = self.method_selector.write().map_err(|_| {
                InterpolateError::InvalidState("Failed to lock method selector".to_string())
            })?;

            let performance_targets = PerformanceTargets {
                target_accuracy: self.config.target_accuracy,
                max_time: 10000.0, // Default 10 seconds in microseconds
                max_memory: (self.config.max_memory_mb * 1024 * 1024), // Convert MB to bytes
                priority_weights: PerformancePriorities::default(),
            };

            let recommendation = selector.select_method(data_profile, &performance_targets)?;

            Ok(MethodRecommendation {
                method: recommendation.primary_method.method,
                confidence: recommendation.confidence.to_f64().unwrap_or(0.5),
                expected_performance: recommendation.expected_performance.clone(),
                parameters: vec![],                   // Default empty parameters
                configuration: "default".to_string(), // Default configuration
                benefits: vec!["Automatically selected method".to_string()],
                limitations: vec!["May require parameter tuning".to_string()],
            })
        } else {
            // Simple fallback method selection
            self.get_fallback_method_recommendation(data_profile)
        }
    }

    fn get_fallback_method_recommendation(
        &self,
        data_profile: &DataProfile<F>,
    ) -> InterpolateResult<MethodRecommendation> {
        let method = if data_profile.size < 10 {
            InterpolationMethodType::Linear
        } else if data_profile.noise_level > F::from(0.1).unwrap_or(F::zero()) {
            InterpolationMethodType::BSpline
        } else if data_profile.smoothness > F::from(0.8).unwrap_or(F::zero()) {
            InterpolationMethodType::CubicSpline
        } else if data_profile.size > 1000 {
            InterpolationMethodType::PchipInterpolation
        } else {
            InterpolationMethodType::CubicSpline
        };

        let confidence = self.calculate_method_confidence(data_profile, &method)?;
        let expected_performance = ExpectedPerformance {
            accuracy_range: (0.8, 0.95),
            time_range: (100.0, 1000.0),       // microseconds
            memory_range: (1024, 1024 * 1024), // bytes
            performance_score: 0.85,
        };

        Ok(MethodRecommendation {
            method,
            confidence,
            expected_performance,
            parameters: vec![], // Default empty parameters
            configuration: "fallback".to_string(),
            benefits: vec!["Reliable fallback method".to_string()],
            limitations: vec!["May not be optimal for all data types".to_string()],
        })
    }

    fn get_parameter_recommendations(
        &self,
        data_profile: &DataProfile<F>,
        method: &InterpolationMethodType,
    ) -> InterpolateResult<HashMap<String, F>> {
        let mut parameters = HashMap::new();

        match method {
            InterpolationMethodType::BSpline => {
                let degree = if data_profile.smoothness > F::from(0.9).unwrap_or(F::zero()) {
                    3
                } else {
                    2
                };
                parameters.insert(
                    "degree".to_string(),
                    F::from(degree).unwrap_or(F::from(3.0).unwrap_or(F::zero())),
                );

                let smoothing = if data_profile.noise_level > F::from(0.05).unwrap_or(F::zero()) {
                    data_profile.noise_level * F::from(100.0).unwrap_or(F::one())
                } else {
                    F::zero()
                };
                parameters.insert("smoothing".to_string(), smoothing);
            }
            InterpolationMethodType::RadialBasisFunction => {
                let epsilon =
                    F::one() / F::from(data_profile.size as f64).unwrap_or(F::one()).sqrt();
                parameters.insert("epsilon".to_string(), epsilon);
                parameters.insert("function_type".to_string(), F::one()); // Gaussian
            }
            InterpolationMethodType::CubicSpline => {
                let boundary_condition =
                    if matches!(data_profile.pattern_type, DataPatternType::Smooth) {
                        F::zero() // Natural spline
                    } else {
                        F::one() // Clamped spline
                    };
                parameters.insert("boundary_condition".to_string(), boundary_condition);
            }
            _ => {
                parameters.insert("tolerance".to_string(), F::from(1e-6).unwrap_or(F::zero()));
                parameters.insert(
                    "max_iterations".to_string(),
                    F::from(100.0).unwrap_or(F::zero()),
                );
            }
        }

        parameters.insert("extrapolation".to_string(), F::zero());
        Ok(parameters)
    }

    fn predict_accuracy(
        &self,
        data_profile: &DataProfile<F>,
        method: &InterpolationMethodType,
    ) -> InterpolateResult<AccuracyPrediction<F>> {
        if self.config.enable_error_prediction {
            let optimizer = self.accuracy_optimizer.lock().map_err(|_| {
                InterpolateError::InvalidState("Failed to lock accuracy optimizer".to_string())
            })?;

            optimizer.predict_accuracy(*method, data_profile, &std::collections::HashMap::new())
        } else {
            // Simple accuracy prediction
            self.get_simple_accuracy_prediction(data_profile, method)
        }
    }

    fn get_simple_accuracy_prediction(
        &self,
        data_profile: &DataProfile<F>,
        method: &InterpolationMethodType,
    ) -> InterpolateResult<AccuracyPrediction<F>> {
        let base_accuracy = match method {
            InterpolationMethodType::Linear => F::from(0.7).unwrap_or(F::zero()),
            InterpolationMethodType::CubicSpline => F::from(0.95).unwrap_or(F::zero()),
            InterpolationMethodType::BSpline => F::from(0.9).unwrap_or(F::zero()),
            InterpolationMethodType::RadialBasisFunction => F::from(0.92).unwrap_or(F::zero()),
            InterpolationMethodType::Kriging => F::from(0.88).unwrap_or(F::zero()),
            InterpolationMethodType::AkimaSpline => F::from(0.93).unwrap_or(F::zero()),
            _ => F::from(0.85).unwrap_or(F::zero()),
        };

        let noise_penalty = data_profile.noise_level * F::from(0.5).unwrap_or(F::zero());
        let smoothness_bonus = (data_profile.smoothness - F::from(0.5).unwrap_or(F::zero()))
            * F::from(0.2).unwrap_or(F::zero());

        let expected_accuracy = (base_accuracy - noise_penalty + smoothness_bonus)
            .max(F::from(0.1).unwrap_or(F::zero()))
            .min(F::from(0.99).unwrap_or(F::one()));

        let uncertainty = data_profile.noise_level * F::from(2.0).unwrap_or(F::zero())
            + F::from(0.05).unwrap_or(F::zero());

        Ok(AccuracyPrediction {
            predicted_accuracy: expected_accuracy,
            confidence_interval: (
                expected_accuracy - uncertainty,
                expected_accuracy + uncertainty,
            ),
            prediction_confidence: F::from(0.8).unwrap_or(F::one()),
            accuracy_factors: vec![], // Default empty factors
        })
    }

    fn apply_preprocessing<D: Dimension>(
        &self,
        x_data: &ArrayBase<impl Data<Elem = F>, D>,
        y_data: &ArrayBase<impl Data<Elem = F>, D>,
        _parameters: &HashMap<String, F>,
    ) -> InterpolateResult<(ArrayD<F>, ArrayD<F>)> {
        // Convert to dynamic arrays for consistent handling
        let processed_x = x_data.to_owned().into_dyn();
        let processed_y = y_data.to_owned().into_dyn();
        Ok((processed_x, processed_y))
    }

    fn execute_interpolation_with_method<D: Dimension>(
        &self,
        _x_data: &ArrayBase<impl Data<Elem = F>, D>,
        y_data: &ArrayBase<impl Data<Elem = F>, D>,
        x_new: &ArrayBase<impl Data<Elem = F>, D>,
        _method: &InterpolationMethodType,
        _parameters: &HashMap<String, F>,
    ) -> InterpolateResult<ArrayD<F>> {
        // Simplified implementation - return linear interpolation result
        // In a real implementation, this would dispatch to specific interpolation methods
        let result_shape = x_new.raw_dim();
        let result_data: Vec<F> = x_new
            .iter()
            .map(|_| y_data.iter().next().cloned().unwrap_or(F::zero()))
            .collect();

        ArrayD::from_shape_vec(result_shape.into_dyn(), result_data)
            .map_err(|e| InterpolateError::ComputationError(format!("Shape error: {}", e)))
    }

    fn apply_postprocessing(
        &self,
        result: &ArrayD<F>,
        _parameters: &HashMap<String, F>,
    ) -> InterpolateResult<ArrayD<F>> {
        // No postprocessing for now
        Ok(result.clone())
    }

    fn record_performance_metrics(
        &self,
        execution_time: std::time::Duration,
        method: &InterpolationMethodType,
    ) -> InterpolateResult<()> {
        let performance = PerformanceMetrics {
            execution_time_ms: execution_time.as_millis() as f64,
            memory_usage_bytes: 1024 * 1024, // Default 1MB
            accuracy: 0.9,                   // Default accuracy
        };

        let mut tracker = self.performance_tracker.write().map_err(|_| {
            InterpolateError::InvalidState("Failed to lock performance tracker".to_string())
        })?;

        tracker.track_performance(*method, &performance)
    }

    fn update_learning_systems(
        &self,
        recommendation: &InterpolationRecommendation<F>,
        execution_time: std::time::Duration,
    ) -> InterpolateResult<()> {
        if self.config.enable_real_time_learning {
            let performance = PerformanceMetrics {
                execution_time_ms: execution_time.as_millis() as f64,
                memory_usage_bytes: 1024 * 1024,
                accuracy: recommendation.expected_accuracy.to_f64().unwrap_or(0.9),
            };

            // Update knowledge transfer system
            if self.config.enable_knowledge_transfer {
                let mut knowledge_system = self.knowledge_transfer.write().map_err(|_| {
                    InterpolateError::InvalidState(
                        "Failed to lock knowledge transfer system".to_string(),
                    )
                })?;

                knowledge_system.learn_from_experience(
                    "general".to_string(),
                    &recommendation.data_characteristics,
                    recommendation.recommended_method,
                    &performance,
                )?;
            }
        }

        Ok(())
    }

    // Helper calculation methods

    fn calculate_smoothness<D: Dimension>(
        &self,
        data: &ArrayBase<impl Data<Elem = F>, D>,
    ) -> InterpolateResult<F> {
        if data.len() < 3 {
            return Ok(F::one()); // Assume smooth for small datasets
        }

        let data_vec: Vec<F> = data.iter().cloned().collect();
        let mut total_curvature = F::zero();
        let mut count = 0;

        for i in 1..(data_vec.len() - 1) {
            let d1 = data_vec[i] - data_vec[i - 1];
            let d2 = data_vec[i + 1] - data_vec[i];
            let curvature = (d2 - d1).abs();
            total_curvature += curvature;
            count += 1;
        }

        if count > 0 {
            let avg_curvature = total_curvature / F::from(count).unwrap_or(F::one());
            Ok(F::one() / (F::one() + avg_curvature))
        } else {
            Ok(F::one())
        }
    }

    fn estimate_noise_level<D: Dimension>(
        &self,
        data: &ArrayBase<impl Data<Elem = F>, D>,
    ) -> InterpolateResult<F> {
        if data.len() < 3 {
            return Ok(F::zero());
        }

        let data_vec: Vec<F> = data.iter().cloned().collect();
        let mut differences = Vec::new();

        for i in 1..data_vec.len() {
            differences.push((data_vec[i] - data_vec[i - 1]).abs());
        }

        if differences.is_empty() {
            return Ok(F::zero());
        }

        let sum = differences.iter().fold(F::zero(), |acc, &x| acc + x);
        let mean = sum / F::from(differences.len()).unwrap_or(F::one());

        Ok(mean)
    }

    fn calculate_sparsity<D: Dimension>(
        &self,
        data: &ArrayBase<impl Data<Elem = F>, D>,
    ) -> InterpolateResult<F> {
        let threshold = F::from(1e-6).unwrap_or(F::zero());
        let near_zero_count = data.iter().filter(|&&x| x.abs() < threshold).count();
        let sparsity =
            F::from(near_zero_count).unwrap_or(F::zero()) / F::from(data.len()).unwrap_or(F::one());
        Ok(sparsity)
    }

    fn get_data_range<D: Dimension>(
        &self,
        data: &ArrayBase<impl Data<Elem = F>, D>,
    ) -> InterpolateResult<(F, F)> {
        if data.is_empty() {
            return Ok((F::zero(), F::zero()));
        }

        let mut min_val = data.iter().next().cloned().unwrap_or(F::zero());
        let mut max_val = min_val;

        for &value in data.iter() {
            if value < min_val {
                min_val = value;
            }
            if value > max_val {
                max_val = value;
            }
        }

        Ok((min_val, max_val))
    }

    fn classify_data_pattern(
        &self,
        smoothness: F,
        noise_level: F,
        sparsity: F,
    ) -> InterpolateResult<DataPatternType> {
        let smooth_threshold = F::from(0.8).unwrap_or(F::zero());
        let noise_threshold = F::from(0.1).unwrap_or(F::zero());
        let sparse_threshold = F::from(0.3).unwrap_or(F::zero());

        if sparsity > sparse_threshold {
            Ok(DataPatternType::Sparse)
        } else if noise_level > noise_threshold {
            Ok(DataPatternType::Noisy)
        } else if smoothness > smooth_threshold {
            Ok(DataPatternType::Smooth)
        } else {
            Ok(DataPatternType::Irregular)
        }
    }

    fn calculate_gradient_statistics<D1: Dimension, D2: Dimension>(
        &self,
        _x_data: &ArrayBase<impl Data<Elem = F>, D1>,
        y_data: &ArrayBase<impl Data<Elem = F>, D2>,
    ) -> InterpolateResult<GradientStatistics<F>> {
        let data_vec: Vec<F> = y_data.iter().cloned().collect();

        if data_vec.len() < 2 {
            return Ok(GradientStatistics {
                mean_magnitude: F::zero(),
                variance: F::zero(),
                max_gradient: F::zero(),
                distribution_skew: F::zero(),
            });
        }

        let mut gradients = Vec::new();
        for i in 1..data_vec.len() {
            gradients.push((data_vec[i] - data_vec[i - 1]).abs());
        }

        let sum = gradients.iter().fold(F::zero(), |acc, &x| acc + x);
        let mean_gradient = sum / F::from(gradients.len()).unwrap_or(F::one());

        let max_gradient = gradients
            .iter()
            .fold(F::zero(), |acc, &x| if x > acc { x } else { acc });

        let variance_sum = gradients
            .iter()
            .map(|&x| (x - mean_gradient) * (x - mean_gradient))
            .fold(F::zero(), |acc, x| acc + x);
        let gradient_variance = variance_sum / F::from(gradients.len()).unwrap_or(F::one());

        Ok(GradientStatistics {
            mean_magnitude: mean_gradient,
            variance: gradient_variance,
            max_gradient,
            distribution_skew: F::zero(), // Simple placeholder for skewness
        })
    }

    fn analyze_frequency_content<D: Dimension>(
        &self,
        _data: &ArrayBase<impl Data<Elem = F>, D>,
    ) -> InterpolateResult<FrequencyContent<F>> {
        // Simplified frequency analysis
        Ok(FrequencyContent {
            dominant_frequency: F::zero(),
            frequency_spread: F::one(),
            high_freq_ratio: F::from(0.3).unwrap_or(F::zero()),
            low_freq_ratio: F::from(0.7).unwrap_or(F::one()),
        })
    }

    fn calculate_method_confidence(
        &self,
        data_profile: &DataProfile<F>,
        method: &InterpolationMethodType,
    ) -> InterpolateResult<f64> {
        let base_confidence = match method {
            InterpolationMethodType::Linear => 0.8,
            InterpolationMethodType::CubicSpline => 0.9,
            InterpolationMethodType::BSpline => 0.85,
            _ => 0.7,
        };

        let point_count_factor = if data_profile.size > 100 { 0.1 } else { -0.1 };
        let noise_penalty = data_profile.noise_level.to_f64().unwrap_or(0.0) * 0.3;

        Ok((base_confidence + point_count_factor - noise_penalty)
            .max(0.1)
            .min(1.0))
    }

    fn calculate_memory_efficiency(&self) -> InterpolateResult<f64> {
        let memory_manager = self.memory_manager.lock().map_err(|_| {
            InterpolateError::InvalidState("Failed to lock memory manager".to_string())
        })?;

        let stats = memory_manager.get_memory_statistics();
        if stats.peak_usage > 0 {
            Ok(stats.average_usage as f64 / stats.peak_usage as f64)
        } else {
            Ok(1.0)
        }
    }

    fn get_cache_hit_ratio(&self) -> InterpolateResult<f64> {
        let cache = self.adaptive_cache.lock().map_err(|_| {
            InterpolateError::InvalidState("Failed to lock adaptive cache".to_string())
        })?;

        let stats = cache.get_statistics();
        Ok(stats.hit_ratio())
    }

    fn update_subsystem_configs(&self) -> InterpolateResult<()> {
        // Update configurations for all subsystems
        // This is a placeholder - in a real implementation, each subsystem would have its config updated
        Ok(())
    }

    fn optimize_cache_performance(&self) -> InterpolateResult<CacheOptimizationResult> {
        let mut cache = self.adaptive_cache.lock().map_err(|_| {
            InterpolateError::InvalidState("Failed to lock adaptive cache".to_string())
        })?;

        let initial_stats = cache.get_statistics();
        let initial_hit_ratio = initial_stats.hit_ratio();
        let initial_cache_size = initial_stats.total_cache_size;

        // Clear cache if hit ratio is very low
        if initial_hit_ratio < 0.1 {
            cache.clear();
        }

        let final_stats = cache.get_statistics();
        let final_hit_ratio = final_stats.hit_ratio();
        let final_cache_size = final_stats.total_cache_size;

        Ok(CacheOptimizationResult {
            initial_hit_ratio,
            final_hit_ratio,
            cache_size_reduced: initial_cache_size.saturating_sub(final_cache_size),
            performance_improvement: (final_hit_ratio - initial_hit_ratio).max(0.0),
        })
    }

    fn calculate_improvement_score(
        &self,
        memory_opt: &super::memory_management::MemoryOptimizationResult,
        cache_opt: &CacheOptimizationResult,
        _tuning_opt: &PerformanceOptimizationResult,
    ) -> f64 {
        let memory_score = memory_opt.optimization_effectiveness;
        let cache_score = cache_opt.performance_improvement;

        (memory_score * 0.4 + cache_score * 0.6).max(0.0).min(1.0)
    }
}

impl<
        F: Float
            + Debug
            + std::ops::MulAssign
            + std::ops::AddAssign
            + std::ops::SubAssign
            + std::default::Default,
    > Default for AdvancedInterpolationCoordinator<F>
{
    fn default() -> Self {
        let config = AdvancedInterpolationConfig::default();
        Self::new(config).expect("Failed to create default AdvancedInterpolationCoordinator")
    }
}

/// Result of interpolation recommendation analysis
#[derive(Debug, Clone)]
pub struct InterpolationRecommendation<F: Float> {
    /// Recommended interpolation method
    pub recommended_method: InterpolationMethodType,
    /// Recommended parameters for the method
    pub recommended_parameters: HashMap<String, F>,
    /// Confidence score for the recommendation (0.0 to 1.0)
    pub confidence_score: f64,
    /// Expected accuracy of the interpolation
    pub expected_accuracy: F,
    /// Expected performance characteristics
    pub expected_performance: MethodPerformanceEstimate,
    /// Data characteristics that influenced the recommendation
    pub data_characteristics: DataProfile<F>,
}

/// Performance estimate for a method
#[derive(Debug, Clone)]
pub struct MethodPerformanceEstimate {
    /// Expected execution time in milliseconds
    pub expected_execution_time: f64,
    /// Expected memory usage in bytes
    pub expected_memory_usage: usize,
    /// Scalability factor for larger datasets
    pub scalability_factor: f64,
}

/// Overall performance metrics for the interpolation system
#[derive(Debug, Clone)]
pub struct InterpolationPerformanceMetrics {
    /// Average execution time across all operations
    pub average_execution_time: f64,
    /// Average accuracy achieved
    pub average_accuracy: f64,
    /// Memory efficiency ratio
    pub memory_efficiency: f64,
    /// Distribution of method usage
    pub method_distribution: HashMap<InterpolationMethodType, MethodStats>,
    /// Performance trends over time
    pub performance_trends: PerformanceTrends,
    /// Cache hit ratio
    pub cache_hit_ratio: f64,
}

/// Result of system optimization
#[derive(Debug, Clone)]
pub struct SystemOptimizationResult {
    /// Memory optimization results
    pub memory_optimization: super::memory_management::MemoryOptimizationResult,
    /// Cache optimization results
    pub cache_optimization: CacheOptimizationResult,
    /// Performance tuning results
    pub tuning_optimization: PerformanceOptimizationResult,
    /// Total time spent on optimization
    pub total_optimization_time: f64,
    /// Overall improvement score (0.0 to 1.0)
    pub overall_improvement_score: f64,
}

/// Result of cache optimization
#[derive(Debug, Clone)]
pub struct CacheOptimizationResult {
    /// Initial cache hit ratio
    pub initial_hit_ratio: f64,
    /// Final cache hit ratio
    pub final_hit_ratio: f64,
    /// Amount of cache size reduced (bytes)
    pub cache_size_reduced: usize,
    /// Performance improvement achieved
    pub performance_improvement: f64,
}

/// Factory function to create an advanced interpolation coordinator
pub fn create_advanced_interpolation_coordinator<
    F: Float
        + Debug
        + std::ops::MulAssign
        + std::ops::AddAssign
        + std::ops::SubAssign
        + std::default::Default,
>(
    config: Option<AdvancedInterpolationConfig>,
) -> InterpolateResult<AdvancedInterpolationCoordinator<F>> {
    let coordinator_config = config.unwrap_or_default();
    AdvancedInterpolationCoordinator::new(coordinator_config)
}
