scirs2-core 0.4.3

//! Neuromorphic computing patterns for JIT compilation

use crate::advanced_jit_compilation::config::NeuromorphicConfig;
use crate::error::CoreResult;
use std::collections::HashMap;
use std::time::{Duration, Instant};

/// Neuromorphic computing patterns for JIT compilation
#[derive(Debug)]
pub struct NeuromorphicJitCompiler {
    /// Spiking neural network compiler
    snn_compiler: SpikingNeuralNetworkCompiler,
    /// Synaptic plasticity engine
    plasticity_engine: SynapticPlasticityEngine,
    /// Event-driven optimizer
    event_optimizer: EventDrivenOptimizer,
    /// Temporal dynamics compiler
    temporal_compiler: TemporalDynamicsCompiler,
    /// Neuromorphic configuration
    #[allow(dead_code)]
    config: NeuromorphicConfig,
}

/// Spiking neural network compilation engine
#[derive(Debug)]
pub struct SpikingNeuralNetworkCompiler {
    /// Neuron models
    #[allow(dead_code)]
    neuron_models: HashMap<String, NeuronModel>,
    /// Synapse models
    #[allow(dead_code)]
    synapse_models: HashMap<String, SynapseModel>,
    /// Network topology
    #[allow(dead_code)]
    network_topology: NetworkTopology,
    /// Spike pattern cache
    #[allow(dead_code)]
    spike_cache: SpikePatternCache,
}

/// Neuron model for neuromorphic compilation
#[derive(Debug, Clone)]
pub struct NeuronModel {
    /// Model name
    pub name: String,
    /// Model type
    pub model_type: NeuronType,
    /// Parameters
    pub parameters: HashMap<String, f64>,
    /// Update equation
    pub update_equation: String,
    /// Spike threshold
    pub spike_threshold: f64,
    /// Reset potential
    pub reset_potential: f64,
}

/// Types of neuron models
#[derive(Debug, Clone)]
pub enum NeuronType {
    LeakyIntegrateAndFire,
    IzhikevichModel,
    HodgkinHuxley,
    AdaptiveExponential,
    PoissonGenerator,
    Custom(String),
}

/// Synapse model for connections
#[derive(Debug, Clone)]
pub struct SynapseModel {
    /// Model name
    pub name: String,
    /// Synapse type
    pub synapse_type: SynapseType,
    /// Weight
    pub weight: f64,
    /// Delay
    pub delay: f64,
    /// Plasticity rule
    pub plasticity_rule: Option<PlasticityRule>,
}

/// Types of synaptic connections
#[derive(Debug, Clone)]
pub enum SynapseType {
    Excitatory,
    Inhibitory,
    Modulatory,
    Gap,
    Custom(String),
}

/// Plasticity rules for synaptic adaptation
#[derive(Debug, Clone)]
pub struct PlasticityRule {
    /// Rule type
    pub rule_type: PlasticityType,
    /// Learning rate
    pub learningrate: f64,
    /// Time constants
    pub time_constants: Vec<f64>,
    /// Weight bounds
    pub weight_bounds: (f64, f64),
}

/// Types of plasticity rules
#[derive(Debug, Clone)]
pub enum PlasticityType {
    STDP, // Spike-Timing Dependent Plasticity
    VoltagePlasticity,
    Homeostatic,
    Metaplasticity,
    Custom(String),
}

/// Network topology representation
#[derive(Debug, Clone)]
pub struct NetworkTopology {
    /// Layers in the network
    pub layers: Vec<Layer>,
    /// Connections between layers
    pub connections: Vec<Connection>,
    /// Population statistics
    pub population_stats: PopulationStatistics,
}

/// Layer of neurons
#[derive(Debug, Clone)]
pub struct Layer {
    /// Layer ID
    pub id: usize,
    /// Layer name
    pub name: String,
    /// Number of neurons
    pub size: usize,
    /// Neuron model
    pub neuron_model: String,
    /// Layer type
    pub layer_type: LayerType,
}

/// Types of neural layers
#[derive(Debug, Clone)]
pub enum LayerType {
    Input,
    Hidden,
    Output,
    Reservoir,
    Memory,
    Custom(String),
}

/// Connection between layers
#[derive(Debug, Clone)]
pub struct Connection {
    /// Source layer ID
    pub source_layer: usize,
    /// Target layer ID
    pub target_layer: usize,
    /// Connection pattern
    pub pattern: ConnectionPattern,
    /// Synapse model
    pub synapse_model: String,
}

/// Connection patterns
#[derive(Debug, Clone)]
pub enum ConnectionPattern {
    FullyConnected,
    RandomSparse(f64),
    LocalConnectivity(usize),
    SmallWorld { prob: f64, k: usize },
    ScaleFree { gamma: f64 },
    Custom(String),
}

/// Population-level statistics
#[derive(Debug, Clone)]
pub struct PopulationStatistics {
    /// Total neurons
    pub total_neurons: usize,
    /// Total synapses
    pub total_synapses: usize,
    /// Average connectivity
    pub avg_connectivity: f64,
    /// Clustering coefficient
    pub clustering_coefficient: f64,
}

impl Default for PopulationStatistics {
    fn default() -> Self {
        Self {
            total_neurons: 0,
            total_synapses: 0,
            avg_connectivity: 0.0,
            clustering_coefficient: 0.0,
        }
    }
}

/// Cache for spike patterns
#[derive(Debug)]
pub struct SpikePatternCache {
    /// Cached patterns
    #[allow(dead_code)]
    patterns: HashMap<String, SpikePattern>,
    /// Pattern usage statistics
    #[allow(dead_code)]
    usage_stats: HashMap<String, PatternUsage>,
    /// Cache configuration
    #[allow(dead_code)]
    config: crate::advanced_jit_compilation::config::PatternCacheConfig,
}

/// Spike pattern representation
#[derive(Debug, Clone)]
pub struct SpikePattern {
    /// Pattern ID
    pub id: String,
    /// Spike times (milliseconds)
    pub spiketimes: Vec<f64>,
    /// Associated neurons
    pub neuron_ids: Vec<usize>,
    /// Pattern frequency
    pub frequency: f64,
    /// Pattern strength
    pub strength: f64,
}

/// Pattern usage statistics
#[derive(Debug, Clone)]
pub struct PatternUsage {
    /// Access count
    pub access_count: usize,
    /// Last access time
    pub last_access: Instant,
    /// Compilation time
    pub compilation_time: Duration,
    /// Optimization level
    pub optimization_level: u8,
}

/// Synaptic plasticity engine
#[derive(Debug)]
pub struct SynapticPlasticityEngine {
    /// Active plasticity rules
    #[allow(dead_code)]
    active_rules: HashMap<String, PlasticityRule>,
    /// Learning history
    #[allow(dead_code)]
    learning_history: Vec<LearningEvent>,
    /// Plasticity statistics
    #[allow(dead_code)]
    plasticity_stats: PlasticityStatistics,
}

/// Learning event record
#[derive(Debug, Clone)]
pub struct LearningEvent {
    /// Event timestamp
    pub timestamp: f64,
    /// Synapse ID
    pub synapse_id: usize,
    /// Weight change
    pub weight_delta: f64,
    /// Pre-synaptic spike time
    pub pre_spike_time: f64,
    /// Post-synaptic spike time
    pub post_spike_time: f64,
    /// Learning rule applied
    pub rule_applied: String,
}

/// Plasticity statistics
#[derive(Debug, Clone)]
pub struct PlasticityStatistics {
    /// Total learning events
    pub total_events: usize,
    /// Average weight change
    pub avg_weight_change: f64,
    /// Potentiation events
    pub potentiation_events: usize,
    /// Depression events
    pub depression_events: usize,
    /// Learning convergence rate
    pub convergence_rate: f64,
}

/// Event-driven optimizer for neuromorphic systems
#[derive(Debug)]
pub struct EventDrivenOptimizer {
    /// Event queue
    #[allow(dead_code)]
    event_queue: EventQueue,
    /// Optimization strategies
    #[allow(dead_code)]
    strategies: HashMap<String, crate::advanced_jit_compilation::optimizer::OptimizationStrategy>,
    /// Performance metrics
    #[allow(dead_code)]
    performance_metrics: EventPerformanceMetrics,
}

/// Event queue for spike-based processing
#[derive(Debug)]
pub struct EventQueue {
    /// Pending events
    #[allow(dead_code)]
    events: Vec<SpikeEvent>,
    /// Queue capacity
    #[allow(dead_code)]
    capacity: usize,
    /// Current time
    #[allow(dead_code)]
    current_time: f64,
}

/// Spike event representation
#[derive(Debug, Clone)]
pub struct SpikeEvent {
    /// Event time
    pub time: f64,
    /// Source neuron
    pub source_neuron: usize,
    /// Target neurons
    pub target_neurons: Vec<usize>,
    /// Event type
    pub event_type: EventType,
    /// Event strength
    pub strength: f64,
}

/// Types of neuromorphic events
#[derive(Debug, Clone)]
pub enum EventType {
    Spike,
    WeightUpdate,
    ThresholdAdjustment,
    StateReset,
    Custom(String),
}

/// Performance metrics for event processing
#[derive(Debug, Clone)]
pub struct EventPerformanceMetrics {
    /// Events processed per second
    pub events_per_second: f64,
    /// Average event latency
    pub avg_latency: Duration,
    /// Queue utilization
    pub queue_utilization: f64,
    /// Optimization efficiency
    pub optimization_efficiency: f64,
}

/// Temporal dynamics compiler
#[derive(Debug)]
pub struct TemporalDynamicsCompiler {
    /// Time series patterns
    #[allow(dead_code)]
    temporal_patterns: HashMap<String, TemporalPattern>,
    /// Dynamics models
    #[allow(dead_code)]
    dynamics_models: HashMap<String, DynamicsModel>,
    /// Temporal statistics
    #[allow(dead_code)]
    temporal_stats: TemporalStatistics,
}

/// Temporal pattern representation
#[derive(Debug, Clone)]
pub struct TemporalPattern {
    /// Pattern ID
    pub id: String,
    /// Time series data
    pub time_series: Vec<(f64, f64)>, // (time, value)
    /// Pattern period
    pub period: Option<f64>,
    /// Pattern complexity
    pub complexity: f64,
    /// Fourier components
    pub fourier_components: Vec<FourierComponent>,
}

/// Fourier component of temporal pattern
#[derive(Debug, Clone)]
pub struct FourierComponent {
    /// Frequency
    pub frequency: f64,
    /// Amplitude
    pub amplitude: f64,
    /// Phase
    pub phase: f64,
}

/// Dynamics model for temporal evolution
#[derive(Debug, Clone)]
pub struct DynamicsModel {
    /// Model name
    pub name: String,
    /// Model type
    pub model_type: DynamicsType,
    /// State variables
    pub state_variables: Vec<String>,
    /// Differential equations
    pub equations: Vec<String>,
    /// Model parameters
    pub parameters: HashMap<String, f64>,
}

/// Types of dynamics models
#[derive(Debug, Clone)]
pub enum DynamicsType {
    LinearDynamics,
    NonlinearDynamics,
    ChaoticDynamics,
    StochasticDynamics,
    HybridDynamics,
    Custom(String),
}

/// Temporal statistics
#[derive(Debug, Clone)]
pub struct TemporalStatistics {
    /// Total patterns analyzed
    pub total_patterns: usize,
    /// Average pattern length
    pub avg_pattern_length: f64,
    /// Dominant frequencies
    pub dominant_frequencies: Vec<f64>,
    /// Temporal complexity measure
    pub temporal_complexity: f64,
    /// Prediction accuracy
    pub prediction_accuracy: f64,
}

/// Placeholder for neural network structure
#[derive(Debug, Clone)]
pub struct NeuralNetwork {
    /// Network layers
    pub layers: Vec<String>,
    /// Network connections
    pub connections: Vec<(usize, usize)>,
}

/// Compiled SNN representation
#[derive(Debug, Clone)]
pub struct CompiledSNN {
    pub spike_processingcode: String,
    pub plasticitycode: String,
    pub compilation_time: Instant,
    pub network_stats: PopulationStatistics,
    pub optimization_level: u8,
}

/// Spike optimization result
#[derive(Debug, Clone)]
pub struct SpikeOptimizationResult {
    pub optimizations: Vec<PatternOptimization>,
    pub total_patterns: usize,
    pub avg_speedup: f64,
    pub compilation_time: Duration,
}

/// Pattern optimization details
#[derive(Debug, Clone)]
pub struct PatternOptimization {
    pub pattern_id: String,
    pub originalcode: String,
    pub optimizedcode: String,
    pub performance_gain: f64,
    pub memory_reduction: f64,
}

/// Spike characteristics analysis
#[derive(Debug, Clone)]
pub struct SpikeCharacteristics {
    pub inter_spike_intervals: Vec<f64>,
    pub burst_patterns: Vec<BurstPattern>,
    pub frequency_spectrum: FrequencySpectrum,
    pub temporal_correlation: f64,
    pub complexity_measure: f64,
}

/// Burst pattern detection
#[derive(Debug, Clone)]
pub struct BurstPattern {
    pub start_time: f64,
    pub end_time: f64,
    pub spike_count: usize,
    pub avg_frequency: f64,
}

/// Frequency spectrum analysis
#[derive(Debug, Clone)]
pub struct FrequencySpectrum {
    pub mean_frequency: f64,
    pub peak_frequency: f64,
    pub spectral_entropy: f64,
    pub dominant_frequencies: Vec<f64>,
}

/// Spike performance prediction
#[derive(Debug, Clone)]
pub struct SpikePerformancePrediction {
    pub speedup_factor: f64,
    pub memory_reduction: f64,
    pub energy_efficiency: f64,
    pub latency_reduction: f64,
}

impl NeuromorphicJitCompiler {
    /// Create a new neuromorphic JIT compiler
    pub fn new(config: NeuromorphicConfig) -> CoreResult<Self> {
        let snn_compiler = SpikingNeuralNetworkCompiler::new(&config)?;
        let plasticity_engine = SynapticPlasticityEngine::new(&config)?;
        let event_optimizer = EventDrivenOptimizer::new(&config)?;
        let temporal_compiler = TemporalDynamicsCompiler::new(&config)?;

        Ok(Self {
            snn_compiler,
            plasticity_engine,
            event_optimizer,
            temporal_compiler,
            config,
        })
    }

    /// Compile spiking neural network to optimized code
    pub fn compile_snn(
        &self,
        _network: &NeuralNetwork,
        _time_step: f64,
    ) -> CoreResult<CompiledSNN> {
        // Generate optimized spike processing code
        let topology = NetworkTopology {
            layers: Vec::new(),
            connections: Vec::new(),
            population_stats: PopulationStatistics::default(),
        };
        let spikecode = self.snn_compiler.generate_spikecode(&topology)?;

        // Optimize temporal dynamics
        let temporalcode = self.temporal_compiler.compile_dynamics(&spikecode)?;

        // Apply event-driven optimizations
        let optimizedcode = self
            .event_optimizer
            .optimize_event_processing(&temporalcode)?;

        // Generate plasticity updates
        let plasticitycode = self.plasticity_engine.generate_plasticitycode(&topology)?;

        Ok(CompiledSNN {
            spike_processingcode: optimizedcode,
            plasticitycode,
            compilation_time: Instant::now(),
            network_stats: PopulationStatistics::default(),
            optimization_level: 3,
        })
    }

    /// Optimize for spike-based computation patterns
    pub fn optimize_spike_patterns(
        &mut self,
        patterns: &[SpikePattern],
    ) -> CoreResult<SpikeOptimizationResult> {
        let mut optimization_results = Vec::new();

        for pattern in patterns {
            // Analyze pattern characteristics
            let characteristics = self.analyze_spike_characteristics(pattern)?;

            // Generate optimized code for pattern
            let optimizedcode = self.generate_optimized_spikecode(pattern, &characteristics)?;

            // Performance prediction
            let predicted_performance = self.predict_spike_performance(&optimizedcode)?;

            optimization_results.push(PatternOptimization {
                pattern_id: pattern.id.clone(),
                originalcode: "spike_patterncode".to_string(), // Simplified
                optimizedcode,
                performance_gain: predicted_performance.speedup_factor,
                memory_reduction: predicted_performance.memory_reduction,
            });
        }

        let avg_speedup = optimization_results
            .iter()
            .map(|opt| opt.performance_gain)
            .sum::<f64>()
            / patterns.len() as f64;

        Ok(SpikeOptimizationResult {
            optimizations: optimization_results,
            total_patterns: patterns.len(),
            avg_speedup,
            compilation_time: Duration::from_millis(100), // Simplified
        })
    }

    /// Analyze spike pattern characteristics
    fn analyze_spike_characteristics(
        &self,
        pattern: &SpikePattern,
    ) -> CoreResult<SpikeCharacteristics> {
        Ok(SpikeCharacteristics {
            inter_spike_intervals: self.calculate_isi(&pattern.spiketimes)?,
            burst_patterns: self.detect_bursts(&pattern.spiketimes)?,
            frequency_spectrum: self.analyze_frequency_spectrum(&pattern.spiketimes)?,
            temporal_correlation: self.calculate_temporal_correlation(&pattern.spiketimes)?,
            complexity_measure: self.calculate_complexity(&pattern.spiketimes)?,
        })
    }

    /// Calculate inter-spike intervals
    fn calculate_isi(&self, spiketimes: &[f64]) -> CoreResult<Vec<f64>> {
        if spiketimes.len() < 2 {
            return Ok(Vec::new());
        }

        let mut intervals = Vec::new();
        for i in 1_usize..spiketimes.len() {
            let prev_idx = i.saturating_sub(1);
            intervals.push(spiketimes[i] - spiketimes[prev_idx]);
        }

        Ok(intervals)
    }

    /// Detect burst patterns in spike trains
    fn detect_bursts(&self, spiketimes: &[f64]) -> CoreResult<Vec<BurstPattern>> {
        let mut bursts = Vec::new();
        let isi_threshold = 10.0; // milliseconds

        let mut burst_start = None;
        let mut current_burst_spikes = Vec::new();

        for &spike_time in spiketimes {
            if let Some(last_spike) = current_burst_spikes.last() {
                if spike_time - last_spike <= isi_threshold {
                    current_burst_spikes.push(spike_time);
                } else {
                    // End current burst if it has enough spikes
                    if current_burst_spikes.len() >= 3 {
                        bursts.push(BurstPattern {
                            start_time: burst_start.expect("Operation failed"),
                            end_time: *current_burst_spikes.last().expect("Operation failed"),
                            spike_count: current_burst_spikes.len(),
                            avg_frequency: current_burst_spikes.len() as f64
                                / (current_burst_spikes.last().expect("Operation failed")
                                    - burst_start.expect("Operation failed")),
                        });
                    }
                    // Start new potential burst
                    burst_start = Some(spike_time);
                    current_burst_spikes = vec![spike_time];
                }
            } else {
                burst_start = Some(spike_time);
                current_burst_spikes = vec![spike_time];
            }
        }

        Ok(bursts)
    }

    /// Analyze frequency spectrum of spike train
    fn analyze_frequency_spectrum(&self, spiketimes: &[f64]) -> CoreResult<FrequencySpectrum> {
        // Simplified frequency analysis
        let total_time = spiketimes.last().unwrap_or(&0.0) - spiketimes.first().unwrap_or(&0.0);
        let mean_frequency = if total_time > 0.0 {
            spiketimes.len() as f64 / total_time
        } else {
            0.0
        };

        Ok(FrequencySpectrum {
            mean_frequency,
            peak_frequency: mean_frequency * 1.2, // Simplified
            spectral_entropy: 0.8,                // Placeholder
            dominant_frequencies: vec![mean_frequency],
        })
    }

    /// Calculate temporal correlation
    fn calculate_temporal_correlation(&self, spiketimes: &[f64]) -> CoreResult<f64> {
        // Simplified autocorrelation calculation
        if spiketimes.len() < 2 {
            return Ok(0.0);
        }

        let intervals = self.calculate_isi(spiketimes)?;
        let mean_isi = intervals.iter().sum::<f64>() / intervals.len() as f64;
        let variance = intervals
            .iter()
            .map(|&isi| (isi - mean_isi).powi(2))
            .sum::<f64>()
            / intervals.len() as f64;

        // Coefficient of variation as a measure of regularity
        let cv = if mean_isi > 0.0 {
            variance.sqrt() / mean_isi
        } else {
            0.0
        };

        // Return inverse of CV as correlation measure
        Ok(1.0 / (1.0 + cv))
    }

    /// Calculate spike pattern complexity
    fn calculate_complexity(&self, spiketimes: &[f64]) -> CoreResult<f64> {
        if spiketimes.len() < 2 {
            return Ok(0.0);
        }

        let intervals = self.calculate_isi(spiketimes)?;

        // Use Shannon entropy of ISI distribution as complexity measure
        let mut isi_histogram = HashMap::new();
        let bin_size = 1.0; // 1ms bins

        for &isi in &intervals {
            let bin = (isi / bin_size).floor() as i32;
            *isi_histogram.entry(bin).or_insert(0) += 1;
        }

        let total_intervals = intervals.len() as f64;
        let mut entropy = 0.0;

        for &count in isi_histogram.values() {
            let probability = count as f64 / total_intervals;
            if probability > 0.0 {
                entropy -= probability * probability.log2();
            }
        }

        Ok(entropy)
    }

    /// Generate optimized code for spike pattern
    fn generate_optimized_spikecode(
        &self,
        pattern: &SpikePattern,
        characteristics: &SpikeCharacteristics,
    ) -> CoreResult<String> {
        // Generate specialized code based on pattern characteristics
        let mut code = String::new();

        code.push_str("// Optimized spike processing code\n");
        code.push_str(&format!("// Pattern ID: {id}\n", id = pattern.id));
        code.push_str(&format!(
            "// Mean frequency: {:.2} Hz\n",
            characteristics.frequency_spectrum.mean_frequency
        ));

        if characteristics.burst_patterns.is_empty() {
            // Regular spiking pattern
            code.push_str("inline void process_regular_spikes() {\n");
            code.push_str("    // Optimized for regular spike patterns\n");
            code.push_str("    // Use fixed-interval processing\n");
            code.push_str("}\n");
        } else {
            // Burst spiking pattern
            code.push_str("inline void process_burst_spikes() {\n");
            code.push_str("    // Optimized for burst patterns\n");
            code.push_str("    // Use adaptive time windows\n");
            code.push_str("}\n");
        }

        Ok(code)
    }

    /// Predict performance for optimized spike code
    fn predict_spike_performance(&self, code: &str) -> CoreResult<SpikePerformancePrediction> {
        // Simplified performance prediction
        let code_complexity = code.len() as f64;
        let baseline_performance = 1.0;

        // Estimate speedup based on code patterns
        let speedup_factor = if code.contains("regular_spikes") {
            2.5 // Regular patterns are easier to optimize
        } else if code.contains("burst_spikes") {
            1.8 // Burst patterns have moderate optimization potential
        } else {
            1.2 // General case
        };

        Ok(SpikePerformancePrediction {
            speedup_factor,
            memory_reduction: 0.15, // 15% memory reduction
            energy_efficiency: speedup_factor * 0.8,
            latency_reduction: speedup_factor * 0.9,
        })
    }
}

// Placeholder implementations for compiler components
impl SpikingNeuralNetworkCompiler {
    fn new(config: &NeuromorphicConfig) -> CoreResult<Self> {
        Ok(Self {
            neuron_models: HashMap::new(),
            synapse_models: HashMap::new(),
            network_topology: NetworkTopology {
                layers: Vec::new(),
                connections: Vec::new(),
                population_stats: PopulationStatistics {
                    total_neurons: 0,
                    total_synapses: 0,
                    avg_connectivity: 0.0,
                    clustering_coefficient: 0.0,
                },
            },
            spike_cache: SpikePatternCache {
                patterns: HashMap::new(),
                usage_stats: HashMap::new(),
                config: crate::advanced_jit_compilation::config::PatternCacheConfig {
                    max_patterns: 1000,
                    pattern_ttl: Duration::from_secs(3600),
                    enable_lru: true,
                },
            },
        })
    }

    fn generate_spikecode(&self, network: &NetworkTopology) -> CoreResult<String> {
        Ok("// Generated spike processing code\n".to_string())
    }
}

impl SynapticPlasticityEngine {
    fn new(config: &NeuromorphicConfig) -> CoreResult<Self> {
        Ok(Self {
            active_rules: HashMap::new(),
            learning_history: Vec::new(),
            plasticity_stats: PlasticityStatistics {
                total_events: 0,
                avg_weight_change: 0.0,
                potentiation_events: 0,
                depression_events: 0,
                convergence_rate: 0.0,
            },
        })
    }

    fn generate_plasticitycode(&self, network: &NetworkTopology) -> CoreResult<String> {
        Ok("// Generated plasticity code\n".to_string())
    }
}

impl EventDrivenOptimizer {
    fn new(config: &NeuromorphicConfig) -> CoreResult<Self> {
        Ok(Self {
            event_queue: EventQueue {
                events: Vec::new(),
                capacity: 10000,
                current_time: 0.0,
            },
            strategies: HashMap::new(),
            performance_metrics: EventPerformanceMetrics {
                events_per_second: 0.0,
                avg_latency: Duration::from_micros(0),
                queue_utilization: 0.0,
                optimization_efficiency: 0.0,
            },
        })
    }

    fn optimize_event_processing(&self, code: &str) -> CoreResult<String> {
        Ok(format!("// Event-optimized code\n{code}"))
    }
}

impl TemporalDynamicsCompiler {
    fn new(config: &NeuromorphicConfig) -> CoreResult<Self> {
        Ok(Self {
            temporal_patterns: HashMap::new(),
            dynamics_models: HashMap::new(),
            temporal_stats: TemporalStatistics {
                total_patterns: 0,
                avg_pattern_length: 0.0,
                dominant_frequencies: Vec::new(),
                temporal_complexity: 0.0,
                prediction_accuracy: 0.0,
            },
        })
    }

    fn compile_dynamics(&self, code: &str) -> CoreResult<String> {
        Ok(format!("// Temporal dynamics optimized code\n{code}"))
    }
}