//! Data Structures for Advanced Integration
//!
//! This module provides data structures and result types for advanced
//! cross-module processing and integration.

use scirs2_core::ndarray::{Array1, Array2, Array3};
use std::time::Instant;

/// Advanced input data structure supporting multiple module types,
/// allowing for flexible cross-module coordination and processing.
#[derive(Debug, Default)]
pub struct AdvancedInputData {
    /// Image or vision processing data (height, width, channels)
    pub vision_data: Option<Array3<f64>>,
    /// Data for clustering algorithms
    pub clustering_data: Option<Array2<f64>>,
    /// Spatial processing data
    pub spatial_data: Option<Array2<f64>>,
    /// Neural network input data
    pub neural_data: Option<Array2<f64>>,
}

/// Result of cross-module Advanced processing with comprehensive metrics
///
/// Contains the fused results from multiple modules along with performance
/// metrics and efficiency measurements.
#[derive(Debug)]
pub struct CrossModuleAdvancedProcessingResult {
    /// Fused results from all participating modules
    pub fused_result: CrossModuleFusedResult,
    /// Detailed performance metrics for the processing session
    pub performance_metrics: AdvancedPerformanceMetrics,
    /// Synergy factor achieved between modules (1.0 = baseline)
    pub cross_module_synergy: f64,
    /// Resource utilization efficiency (0.0-1.0)
    pub resource_efficiency: f64,
    /// Meta-learning improvement factor over baseline
    pub meta_learning_improvement: f64,
    /// Total processing time in seconds
    pub processing_time: f64,
}

/// Fused output data from multiple processing modules
///
/// Contains the processed outputs from different modules along with
/// fusion confidence and methodology information.
#[derive(Debug)]
pub struct CrossModuleFusedResult {
    /// Processed vision/image data output
    pub vision_output: Option<Array3<f64>>,
    /// Clustering results (cluster assignments)
    pub clustering_output: Option<Array1<usize>>,
    /// Spatial processing results
    pub spatial_output: Option<Array2<f64>>,
    /// Neural network processing output
    pub neural_output: Option<Array2<f64>>,
    /// Confidence in the fusion process (0.0-1.0)
    pub fusion_confidence: f64,
    /// Description of the fusion methodology used
    pub fusion_method: String,
}

/// Comprehensive performance metrics for Advanced mode processing
///
/// Tracks performance across all modules and processing paradigms
/// including quantum, neuromorphic, and AI optimization metrics.
#[derive(Debug, Clone)]
pub struct AdvancedPerformanceMetrics {
    /// Overall system performance score (normalized 0.0-1.0)
    pub overall_performance: f64,
    /// Vision processing module performance score
    pub vision_performance: f64,
    /// Clustering module performance score
    pub clustering_performance: f64,
    /// Spatial processing module performance score
    pub spatial_performance: f64,
    /// Neural network module performance score
    pub neural_performance: f64,
    /// Quantum coherence measure for quantum-inspired algorithms
    pub quantum_coherence: f64,
    /// Neuromorphic adaptation efficiency measure
    pub neuromorphic_adaptation: f64,
    /// AI optimization performance gain factor
    pub ai_optimization_gain: f64,
}

/// Current status of Advanced mode across all modules
///
/// Provides a comprehensive view of the current state of Advanced
/// processing capabilities and their activation status.
#[derive(Debug)]
pub struct AdvancedModeStatus {
    /// Whether Advanced mode is currently active
    pub active: bool,
    /// List of modules currently using Advanced features
    pub active_modules: Vec<String>,
    /// Overall system performance under Advanced mode
    pub system_performance: f64,
    /// Estimated performance improvement over baseline
    pub performance_improvement: f64,
    /// Resource utilization under Advanced mode
    pub resource_utilization: f64,
    /// Time since Advanced mode was activated
    pub time_active: f64,
    /// Current quantum coherence levels
    pub quantum_coherence: f64,
    /// Current neuromorphic adaptation efficiency
    pub neuromorphic_efficiency: f64,
    /// AI optimization effectiveness
    pub ai_optimization_effectiveness: f64,
}

/// Quantum processing metrics
///
/// Tracks quantum-inspired algorithm performance and coherence measures.
#[derive(Debug, Clone)]
pub struct QuantumProcessingMetrics {
    /// Quantum coherence measure (0.0-1.0)
    pub coherence: f64,
    /// Entanglement strength in fusion operations
    pub entanglement_strength: f64,
    /// Quantum advantage factor over classical methods
    pub quantum_advantage: f64,
    /// Decoherence rate
    pub decoherence_rate: f64,
    /// Number of quantum operations performed
    pub quantum_operations: usize,
}

/// Neuromorphic processing metrics
///
/// Tracks neuromorphic adaptation and learning performance.
#[derive(Debug, Clone)]
pub struct NeuromorphicProcessingMetrics {
    /// Adaptation efficiency (0.0-1.0)
    pub adaptation_efficiency: f64,
    /// Learning rate convergence
    pub learning_convergence: f64,
    /// Plasticity measure
    pub plasticity: f64,
    /// Spike timing precision
    pub spike_timing_precision: f64,
    /// Energy efficiency compared to traditional neural networks
    pub energy_efficiency: f64,
}

/// Fusion quality indicators
///
/// Measures the quality and effectiveness of multi-paradigm fusion.
#[derive(Debug, Clone)]
pub struct FusionQualityIndicators {
    /// Overall fusion quality score (0.0-1.0)
    pub overall_quality: f64,
    /// Coherence between different processing paradigms
    pub paradigm_coherence: f64,
    /// Information preservation during fusion
    pub information_preservation: f64,
    /// Fusion stability over time
    pub temporal_stability: f64,
    /// Complementarity measure between approaches
    pub complementarity: f64,
}

/// Emergent behavior detection
///
/// Tracks and analyzes emergent behaviors in the Advanced system.
#[derive(Debug, Clone)]
pub struct EmergentBehaviorDetection {
    /// Number of emergent patterns detected
    pub patterns_detected: usize,
    /// Novelty score of detected behaviors
    pub novelty_score: f64,
    /// Complexity increase measure
    pub complexity_increase: f64,
    /// Behavioral stability
    pub stability: f64,
    /// Potential for system evolution
    pub evolution_potential: f64,
}

/// Advanced Advanced Processing Result
///
/// Comprehensive result structure for individual Advanced processing operations.
#[derive(Debug)]
pub struct AdvancedAdvancedProcessingResult {
    /// Success status of the operation
    pub success: bool,
    /// Detailed processing metrics
    pub metrics: AdvancedPerformanceMetrics,
    /// Quantum processing specifics
    pub quantum_metrics: QuantumProcessingMetrics,
    /// Neuromorphic processing specifics
    pub neuromorphic_metrics: NeuromorphicProcessingMetrics,
    /// Fusion quality assessment
    pub fusion_quality: FusionQualityIndicators,
    /// Emergent behavior analysis
    pub emergent_behavior: EmergentBehaviorDetection,
    /// Processing time breakdown
    pub timing_breakdown: ProcessingTimingBreakdown,
    /// Resource usage statistics
    pub resource_usage: ResourceUsageStatistics,
    /// Quality assurance metrics
    pub quality_assurance: QualityAssuranceMetrics,
}

/// Processing timing breakdown
#[derive(Debug, Clone)]
pub struct ProcessingTimingBreakdown {
    /// Total processing time
    pub total_time: f64,
    /// Quantum processing time
    pub quantum_time: f64,
    /// Neuromorphic processing time
    pub neuromorphic_time: f64,
    /// Classical processing time
    pub classical_time: f64,
    /// Fusion operation time
    pub fusion_time: f64,
    /// Overhead time
    pub overhead_time: f64,
}

/// Resource usage statistics
#[derive(Debug, Clone)]
pub struct ResourceUsageStatistics {
    /// CPU utilization percentage
    pub cpu_utilization: f64,
    /// Memory usage in MB
    pub memory_usage: f64,
    /// GPU utilization percentage
    pub gpu_utilization: f64,
    /// Energy consumption in joules
    pub energy_consumption: f64,
    /// Network bandwidth used
    pub network_bandwidth: f64,
}

/// Quality assurance metrics
#[derive(Debug, Clone)]
pub struct QualityAssuranceMetrics {
    /// Output quality score
    pub output_quality: f64,
    /// Consistency measure
    pub consistency: f64,
    /// Reliability score
    pub reliability: f64,
    /// Error rate
    pub error_rate: f64,
    /// Confidence interval
    pub confidence_interval: (f64, f64),
}

/// Performance metrics structure
///
/// Simplified performance tracking for individual operations.
#[derive(Debug, Clone)]
pub struct PerformanceMetrics {
    /// Processing accuracy
    pub accuracy: f64,
    /// Processing speed (operations per second)
    pub speed: f64,
    /// Resource efficiency
    pub efficiency: f64,
    /// Quality score
    pub quality: f64,
    /// Latency in milliseconds
    pub latency: f64,
}

/// Uncertainty quantification for Advanced results
///
/// Provides uncertainty measures and confidence intervals for Advanced processing results.
#[derive(Debug, Clone)]
pub struct UncertaintyQuantification {
    /// Overall confidence level (0.0-1.0)
    pub confidence_level: f64,
    /// Uncertainty in results
    pub result_uncertainty: f64,
    /// Model uncertainty
    pub model_uncertainty: f64,
    /// Data uncertainty
    pub data_uncertainty: f64,
    /// Confidence intervals for key metrics
    pub confidence_intervals: std::collections::HashMap<String, (f64, f64)>,
}

impl AdvancedInputData {
    /// Create new advanced input data with vision data
    pub fn with_vision_data(vision_data: Array3<f64>) -> Self {
        Self {
            vision_data: Some(vision_data),
            clustering_data: None,
            spatial_data: None,
            neural_data: None,
        }
    }

    /// Create new advanced input data with clustering data
    pub fn with_clustering_data(clustering_data: Array2<f64>) -> Self {
        Self {
            vision_data: None,
            clustering_data: Some(clustering_data),
            spatial_data: None,
            neural_data: None,
        }
    }

    /// Check if any data is available
    pub fn has_data(&self) -> bool {
        self.vision_data.is_some()
            || self.clustering_data.is_some()
            || self.spatial_data.is_some()
            || self.neural_data.is_some()
    }

    /// Get the number of available data sources
    pub fn data_source_count(&self) -> usize {
        let mut count = 0;
        if self.vision_data.is_some() {
            count += 1;
        }
        if self.clustering_data.is_some() {
            count += 1;
        }
        if self.spatial_data.is_some() {
            count += 1;
        }
        if self.neural_data.is_some() {
            count += 1;
        }
        count
    }
}

impl Default for CrossModuleFusedResult {
    fn default() -> Self {
        Self {
            vision_output: None,
            clustering_output: None,
            spatial_output: None,
            neural_output: None,
            fusion_confidence: 0.0,
            fusion_method: "none".to_string(),
        }
    }
}

impl Default for AdvancedPerformanceMetrics {
    fn default() -> Self {
        Self {
            overall_performance: 0.0,
            vision_performance: 0.0,
            clustering_performance: 0.0,
            spatial_performance: 0.0,
            neural_performance: 0.0,
            quantum_coherence: 0.0,
            neuromorphic_adaptation: 0.0,
            ai_optimization_gain: 0.0,
        }
    }
}

impl Default for QuantumProcessingMetrics {
    fn default() -> Self {
        Self {
            coherence: 0.0,
            entanglement_strength: 0.0,
            quantum_advantage: 1.0,
            decoherence_rate: 0.0,
            quantum_operations: 0,
        }
    }
}

impl Default for NeuromorphicProcessingMetrics {
    fn default() -> Self {
        Self {
            adaptation_efficiency: 0.0,
            learning_convergence: 0.0,
            plasticity: 0.0,
            spike_timing_precision: 0.0,
            energy_efficiency: 1.0,
        }
    }
}

impl Default for ProcessingTimingBreakdown {
    fn default() -> Self {
        Self {
            total_time: 0.0,
            quantum_time: 0.0,
            neuromorphic_time: 0.0,
            classical_time: 0.0,
            fusion_time: 0.0,
            overhead_time: 0.0,
        }
    }
}

impl Default for ResourceUsageStatistics {
    fn default() -> Self {
        Self {
            cpu_utilization: 0.0,
            memory_usage: 0.0,
            gpu_utilization: 0.0,
            energy_consumption: 0.0,
            network_bandwidth: 0.0,
        }
    }
}
