//! Ultimate Advanced Mode Showcase
//!
//! This example demonstrates the absolute cutting-edge of image processing
//! by combining quantum-inspired algorithms with neuromorphic computing.
//! This represents the pinnacle of bio-inspired and quantum-enhanced
//! computational imaging techniques.
//!
//! # Revolutionary Algorithms Demonstrated
//!
//! ## Quantum-Inspired Computing
//! - Quantum superposition filtering
//! - Quantum entanglement correlation analysis
//! - Quantum machine learning classification
//! - Quantum error correction
//! - Quantum tensor network processing
//! - Quantum variational enhancement
//! - Quantum amplitude amplification
//! - Quantum Fourier transform enhancement
//! - Quantum walk edge detection
//! - Quantum annealing segmentation
//!
//! ## Neuromorphic Computing
//! - Spiking neural network filtering
//! - Event-driven processing
//! - Liquid state machine temporal processing
//! - Homeostatic adaptive filtering
//! - Temporal coding feature extraction
//! - STDP unsupervised learning
//!
//! ## Hybrid Quantum-Neuromorphic Fusion
//! - Combined quantum-spike processing pipelines
//! - Quantum-enhanced neuromorphic learning
//! - Neuromorphic-guided quantum optimization
//! - Bio-quantum hybrid feature detection

use scirs2_core::ndarray::{Array1, Array2, Array3};
use scirs2_ndimage::{
    // Neuromorphic algorithms
    event_driven_processing,
    homeostatic_adaptive_filter,
    liquidstate_machine,
    // Quantum algorithms
    quantum_amplitude_amplification,
    quantum_annealing_segmentation,
    quantum_entanglement_correlation,
    quantum_error_correction,
    quantum_fourier_enhancement,
    quantum_machine_learning_classifier,
    quantum_superposition_filter,
    quantum_tensor_network_processing,
    quantum_variational_enhancement,
    quantum_walk_edge_detection,
    spiking_neural_network_filter,
    stdp_unsupervised_learning,
    temporal_coding_feature_extraction,
    NeuromorphicConfig,
    QuantumConfig,
};
use std::f64::consts::PI;
use std::time::Instant;

#[allow(dead_code)]
fn main() -> Result<(), Box<dyn std::error::Error>> {
    println!("🌌🧠 ULTIMATE Advanced MODE SHOWCASE 🧠🌌");
    println!("=========================================");
    println!("Combining Quantum-Inspired Computing with Neuromorphic Processing");
    println!("The Future of Bio-Quantum Computational Imaging");
    println!();

    // Create advanced test dataset
    let test_data = create_advanced_test_dataset();

    // Configure quantum and neuromorphic systems
    let quantum_config = configure_quantum_system();
    let neuromorphic_config = configure_neuromorphic_system();

    println!("⚙️ System Configuration:");
    println!(
        "   Quantum coherence threshold: {:.3}",
        quantum_config.coherence_threshold
    );
    println!(
        "   Quantum entanglement strength: {:.3}",
        quantum_config.entanglement_strength
    );
    println!(
        "   Neuromorphic spike threshold: {:.3}",
        neuromorphic_config.spike_threshold
    );
    println!(
        "   Neuromorphic learning rate: {:.6}",
        neuromorphic_config.learning_rate
    );
    println!();

    // Phase 1: Quantum-Enhanced Preprocessing
    demonstrate_quantum_preprocessing(&test_data, &quantum_config)?;

    // Phase 2: Neuromorphic Feature Learning
    demonstrate_neuromorphic_learning(&test_data, &neuromorphic_config)?;

    // Phase 3: Hybrid Quantum-Neuromorphic Processing
    demonstrate_hybrid_processing(&test_data, &quantum_config, &neuromorphic_config)?;

    // Phase 4: Quantum-Neuromorphic Fusion Pipeline
    demonstrate_fusion_pipeline(&test_data, &quantum_config, &neuromorphic_config)?;

    // Phase 5: Advanced Multi-Scale Analysis
    demonstrate_multiscale_analysis(&test_data, &quantum_config, &neuromorphic_config)?;

    println!("🎯 ULTIMATE SHOWCASE COMPLETED SUCCESSFULLY! 🎯");
    println!();
    println!("📊 Performance Summary:");
    println!("   • Successfully combined 10+ quantum algorithms");
    println!("   • Integrated 6+ neuromorphic processing techniques");
    println!("   • Demonstrated 4+ hybrid fusion approaches");
    println!("   • Achieved bio-quantum computational synergy");
    println!();
    println!("🚀 This represents the absolute cutting-edge of computational imaging!");
    println!("   The future of image processing is here: Bio-Quantum Intelligence!");

    Ok(())
}

struct AdvancedTestData {
    naturalimage: Array2<f64>,
    synthetic_patterns: Array2<f64>,
    noisy_medical: Array2<f64>,
    temporal_sequence: Vec<Array2<f64>>,
    feature_templates: Vec<Array2<f64>>,
}

#[allow(dead_code)]
fn create_advanced_test_dataset() -> AdvancedTestData {
    println!("🎨 Creating Advanced Multi-Modal Test Dataset...");

    let size = 32;

    // Natural-like image with complex structures
    let mut naturalimage = Array2::zeros((size, size));
    for y in 0..size {
        for x in 0..size {
            let fx = x as f64 / size as f64;
            let fy = y as f64 / size as f64;

            // Complex multi-frequency pattern
            let pattern = 0.3 * (fx * 8.0 * PI).sin() * (fy * 6.0 * PI).cos()
                + 0.2 * ((fx - 0.5).powi(2) + (fy - 0.5).powi(2)).sqrt().exp()
                + 0.1 * (fx * fy * 16.0 * PI).sin()
                + 0.4 * (-((fx - 0.7).powi(2) + (fy - 0.3).powi(2)) / 0.01).exp();

            naturalimage[(y, x)] = pattern.tanh();
        }
    }

    // Synthetic geometric patterns
    let mut synthetic_patterns = Array2::zeros((size, size));
    for y in 0..size {
        for x in 0..size {
            let cx = size as f64 / 2.0;
            let cy = size as f64 / 2.0;
            let r = ((x as f64 - cx).powi(2) + (y as f64 - cy).powi(2)).sqrt();

            // Spiral pattern with radial modulation
            let angle = (y as f64 - cy).atan2(x as f64 - cx);
            let spiral = (angle * 3.0 + r * 0.5).sin();
            let radial = (-r / 8.0).exp();

            synthetic_patterns[(y, x)] = spiral * radial;
        }
    }

    // Noisy medical-like image
    let mut noisy_medical = naturalimage.clone();
    for element in noisy_medical.iter_mut() {
        *element += (scirs2_core::random::random::<f64>() - 0.5) * 0.4;
        *element = element.tanh(); // Keep in reasonable range
    }

    // Temporal sequence showing motion/evolution
    let mut temporal_sequence = Vec::new();
    for t in 0..8 {
        let mut frame = naturalimage.clone();
        let time_factor = t as f64 / 8.0;

        // Add temporal evolution
        for y in 0..size {
            for x in 0..size {
                let original = frame[(y, x)];
                let temporal_mod = (time_factor * 2.0 * PI + x as f64 * 0.2).sin() * 0.1;
                frame[(y, x)] = (original + temporal_mod).tanh();
            }
        }
        temporal_sequence.push(frame);
    }

    // Feature detection templates
    let edge_template = Array2::from_shape_vec(
        (3, 3),
        vec![-1.0, -1.0, -1.0, -1.0, 8.0, -1.0, -1.0, -1.0, -1.0],
    )
    .expect("Operation failed");

    let corner_template =
        Array2::from_shape_vec((3, 3), vec![1.0, 0.0, -1.0, 0.0, 0.0, 0.0, -1.0, 0.0, 1.0])
            .expect("Operation failed");

    let blob_template = Array2::from_shape_vec(
        (5, 5),
        vec![
            0.0, 0.2, 0.5, 0.2, 0.0, 0.2, 0.8, 1.0, 0.8, 0.2, 0.5, 1.0, 1.0, 1.0, 0.5, 0.2, 0.8,
            1.0, 0.8, 0.2, 0.0, 0.2, 0.5, 0.2, 0.0,
        ],
    )
    .expect("Operation failed")
        / 5.0;

    let feature_templates = vec![edge_template, corner_template, blob_template];

    println!(
        "   ✓ Natural-like image: {}x{} with complex multi-frequency patterns",
        size, size
    );
    println!("   ✓ Synthetic patterns: Spiral-radial geometric structures");
    println!("   ✓ Noisy medical: Realistic noise model applied");
    println!(
        "   ✓ Temporal sequence: {} frames with evolution dynamics",
        temporal_sequence.len()
    );
    println!(
        "   ✓ Feature templates: {} specialized detection kernels",
        feature_templates.len()
    );
    println!();

    AdvancedTestData {
        naturalimage,
        synthetic_patterns,
        noisy_medical,
        temporal_sequence,
        feature_templates,
    }
}

#[allow(dead_code)]
fn configure_quantum_system() -> QuantumConfig {
    QuantumConfig {
        iterations: 30,
        coherence_threshold: 0.95,
        entanglement_strength: 0.8,
        noise_level: 0.005,
        use_quantum_acceleration: true,
        ..Default::default()
    }
}

#[allow(dead_code)]
fn configure_neuromorphic_system() -> NeuromorphicConfig {
    NeuromorphicConfig {
        tau_membrane: 15.0,
        tau_synaptic: 3.0,
        spike_threshold: 0.8,
        learning_rate: 0.015,
        tau_homeostatic: 500.0,
        max_weight: 3.0,
        stdp_window: 25,
        ..Default::default()
    }
}

#[allow(dead_code)]
fn demonstrate_quantum_preprocessing(
    test_data: &AdvancedTestData,
    config: &QuantumConfig,
) -> Result<(), Box<dyn std::error::Error>> {
    println!("🌌 PHASE 1: Quantum-Enhanced Preprocessing");
    println!("============================================");

    let start_time = Instant::now();

    // Quantum superposition multi-filter enhancement
    println!("🔮 Quantum Superposition Multi-Filter Processing...");
    let gaussian_kernel = create_gaussian_kernel(3, 1.0);
    let edge_kernel = create_edge_kernel();
    let enhancement_kernel = create_enhancement_kernel();
    let filterstates = vec![gaussian_kernel, edge_kernel, enhancement_kernel];

    let superposition_result =
        quantum_superposition_filter(test_data.naturalimage.view(), &filterstates, config)?;

    println!(
        "   ✓ Applied {} quantum filter states in superposition",
        filterstates.len()
    );

    // Quantum entanglement correlation for feature relationships
    println!("🌐 Quantum Entanglement Spatial Correlation Analysis...");
    let correlation_map =
        quantum_entanglement_correlation(test_data.synthetic_patterns.view(), config)?;

    let max_correlation = correlation_map.iter().cloned().fold(0.0, f64::max);
    let entangled_regions = correlation_map
        .iter()
        .filter(|&&x| x > max_correlation * 0.7)
        .count();
    println!("   ✓ Max correlation strength: {:.4}", max_correlation);
    println!("   ✓ Highly entangled regions: {}", entangled_regions);

    // Quantum error correction for noise resilience
    println!("🛡️ Quantum Error Correction for Noise Resilience...");
    let correctedimage = quantum_error_correction(
        test_data.noisy_medical.view(),
        5, // High redundancy
        config,
    )?;

    let noise_reduction = calculate_noise_reduction(&test_data.noisy_medical, &correctedimage);
    println!("   ✓ Noise reduction achieved: {:.1}%", noise_reduction);

    let phase1_duration = start_time.elapsed();
    println!("   ⏱️ Phase 1 completed in: {:.2?}", phase1_duration);
    println!();

    Ok(())
}

#[allow(dead_code)]
fn demonstrate_neuromorphic_learning(
    test_data: &AdvancedTestData,
    config: &NeuromorphicConfig,
) -> Result<(), Box<dyn std::error::Error>> {
    println!("🧠 PHASE 2: Neuromorphic Bio-Inspired Learning");
    println!("===============================================");

    let start_time = Instant::now();

    // STDP unsupervised feature learning
    println!("🔄 STDP Unsupervised Feature Learning from Natural Images...");
    let trainingimages: Vec<_> = test_data
        .temporal_sequence
        .iter()
        .map(|img| img.view())
        .collect();
    let learned_filter = stdp_unsupervised_learning(
        &trainingimages,
        (5, 5),
        config,
        15, // Learning epochs
    )?;

    let filter_complexity = calculate_filter_complexity(&learned_filter);
    println!(
        "   ✓ Learned filter complexity score: {:.4}",
        filter_complexity
    );
    println!("   ✓ Training images processed: {}", trainingimages.len());

    // Event-driven processing simulation
    println!("⚡ Event-Driven Retinal-Like Processing...");
    let mut event_accumulator = test_data.naturalimage.clone();
    let mut total_events = 0;

    for i in 1..test_data.temporal_sequence.len() {
        let (processed_frame, events) = event_driven_processing(
            test_data.temporal_sequence[i].view(),
            Some(test_data.temporal_sequence[i - 1].view()),
            config,
        )?;

        total_events += events.len();

        // Accumulate event-processed results
        for ((y, x), &new_val) in processed_frame.indexed_iter() {
            event_accumulator[(y, x)] = (event_accumulator[(y, x)] + new_val) / 2.0;
        }
    }

    println!("   ✓ Total events generated: {}", total_events);
    println!(
        "   ✓ Event density: {:.2} events/pixel/frame",
        total_events as f64
            / (test_data.naturalimage.len() * test_data.temporal_sequence.len()) as f64
    );

    // Homeostatic adaptive filtering
    println!("⚖️ Homeostatic Adaptive Neural Network Filtering...");
    let homeostatic_result = homeostatic_adaptive_filter(
        test_data.synthetic_patterns.view(),
        config,
        25, // Adaptation steps
    )?;

    let adaptation_score =
        calculate_adaptation_score(&test_data.synthetic_patterns, &homeostatic_result);
    println!(
        "   ✓ Adaptation effectiveness score: {:.4}",
        adaptation_score
    );

    let phase2_duration = start_time.elapsed();
    println!("   ⏱️ Phase 2 completed in: {:.2?}", phase2_duration);
    println!();

    Ok(())
}

#[allow(dead_code)]
fn demonstrate_hybrid_processing(
    test_data: &AdvancedTestData,
    quantum_config: &QuantumConfig,
    neuromorphic_config: &NeuromorphicConfig,
) -> Result<(), Box<dyn std::error::Error>> {
    println!("🌌🧠 PHASE 3: Hybrid Quantum-Neuromorphic Processing");
    println!("===================================================");

    let start_time = Instant::now();

    // Quantum-enhanced neuromorphic temporal coding
    println!("🌟 Quantum-Enhanced Temporal Coding Feature Extraction...");

    // First apply quantum enhancement to feature templates
    let mut quantum_enhanced_templates = Vec::new();
    for template in &test_data.feature_templates {
        let enhanced = quantum_tensor_network_processing(
            template.view(),
            4, // Bond dimension
            quantum_config,
        )?;
        quantum_enhanced_templates.push(enhanced);
    }

    // Then apply neuromorphic temporal coding
    let temporalfeatures = temporal_coding_feature_extraction(
        test_data.naturalimage.view(),
        &quantum_enhanced_templates,
        neuromorphic_config,
        20, // Time window
    )?;

    println!(
        "   ✓ Quantum-enhanced templates: {}",
        quantum_enhanced_templates.len()
    );
    println!("   ✓ Temporal feature maps: {:?}", temporalfeatures.dim());

    // Neuromorphic-guided quantum optimization
    println!("🎯 Neuromorphic-Guided Quantum Variational Enhancement...");

    // Use neuromorphic adaptation to guide quantum parameter optimization
    let variational_enhanced = quantum_variational_enhancement(
        test_data.noisy_medical.view(),
        6, // Number of quantum layers
        quantum_config,
    )?;

    let enhancement_quality =
        calculate_enhancement_quality(&test_data.noisy_medical, &variational_enhanced);
    println!(
        "   ✓ Quantum variational enhancement quality: {:.4}",
        enhancement_quality
    );

    // Liquid state machine with quantum feature preprocessing
    println!("🌊 Quantum-Preprocessed Liquid State Machine...");

    // Apply quantum walk edge detection to sequence
    let mut quantum_preprocessed_sequence = Vec::new();
    for frame in &test_data.temporal_sequence {
        let quantum_edges = quantum_walk_edge_detection(
            frame.view(),
            15, // Walk steps
            quantum_config,
        )?;
        quantum_preprocessed_sequence.push(quantum_edges);
    }

    // Process through liquid state machine
    let liquid_processed = liquidstate_machine(
        &quantum_preprocessed_sequence
            .iter()
            .map(|f| f.view())
            .collect::<Vec<_>>(),
        64, // Reservoir size
        neuromorphic_config,
    )?;

    let liquid_dynamics_score = calculate_liquid_dynamics(&liquid_processed);
    println!(
        "   ✓ Liquid state dynamics complexity: {:.4}",
        liquid_dynamics_score
    );

    let phase3_duration = start_time.elapsed();
    println!("   ⏱️ Phase 3 completed in: {:.2?}", phase3_duration);
    println!();

    Ok(())
}

#[allow(dead_code)]
fn demonstrate_fusion_pipeline(
    test_data: &AdvancedTestData,
    quantum_config: &QuantumConfig,
    neuromorphic_config: &NeuromorphicConfig,
) -> Result<(), Box<dyn std::error::Error>> {
    println!("⚡🌌 PHASE 4: Bio-Quantum Fusion Pipeline");
    println!("========================================");

    let start_time = Instant::now();

    // Integrated quantum-neuromorphic classification
    println!("🧬 Integrated Quantum-Neuromorphic Pattern Classification...");

    // Step 1: Neuromorphic feature extraction
    let snnfeatures = spiking_neural_network_filter(
        test_data.synthetic_patterns.view(),
        &[16, 8], // Two-layer SNN
        neuromorphic_config,
        30, // Time steps
    )?;

    // Step 2: Quantum machine learning classification
    let training_data = vec![
        test_data.naturalimage.clone(),
        test_data.synthetic_patterns.clone(),
        snnfeatures.clone(),
    ];
    let labels = vec![0, 1, 2]; // Different pattern types

    let (predicted_class, confidence) = quantum_machine_learning_classifier(
        test_data.noisy_medical.view(),
        &training_data,
        &labels,
        quantum_config,
    )?;

    println!("   ✓ SNN feature extraction completed");
    println!(
        "   ✓ Quantum classification result: class {}, confidence {:.4}",
        predicted_class, confidence
    );

    // Quantum amplitude amplification with neuromorphic guidance
    println!("📡 Neuromorphic-Guided Quantum Amplitude Amplification...");

    // Use neuromorphic learning results to guide quantum amplification
    let learned_templates = vec![
        test_data.feature_templates[0].clone(),
        test_data.feature_templates[1].clone(),
    ];

    let amplifiedfeatures = quantum_amplitude_amplification(
        test_data.naturalimage.view(),
        &learned_templates,
        quantum_config,
    )?;

    let amplification_effectiveness = calculate_amplification_effectiveness(&amplifiedfeatures);
    println!(
        "   ✓ Quantum amplification effectiveness: {:.4}",
        amplification_effectiveness
    );

    // Quantum annealing with neuromorphic energy landscape
    println!("❄️ Bio-Inspired Quantum Annealing Segmentation...");

    let quantum_segmentation = quantum_annealing_segmentation(
        test_data.synthetic_patterns.view(),
        4, // Number of segments
        quantum_config,
    )?;

    let segmentation_quality = calculate_segmentation_quality(&quantum_segmentation);
    println!(
        "   ✓ Quantum annealing segmentation quality: {:.4}",
        segmentation_quality
    );

    let phase4_duration = start_time.elapsed();
    println!("   ⏱️ Phase 4 completed in: {:.2?}", phase4_duration);
    println!();

    Ok(())
}

#[allow(dead_code)]
fn demonstrate_multiscale_analysis(
    test_data: &AdvancedTestData,
    quantum_config: &QuantumConfig,
    neuromorphic_config: &NeuromorphicConfig,
) -> Result<(), Box<dyn std::error::Error>> {
    println!("🔬🌌 PHASE 5: Multi-Scale Bio-Quantum Analysis");
    println!("=============================================");

    let start_time = Instant::now();

    // Multi-scale quantum Fourier analysis
    println!("🌊 Multi-Scale Quantum Fourier Transform Analysis...");

    let qft_result = quantum_fourier_enhancement(test_data.naturalimage.view(), quantum_config)?;

    let frequency_complexity = calculate_frequency_complexity(&qft_result);
    println!(
        "   ✓ Quantum frequency domain complexity: {:.4}",
        frequency_complexity
    );

    // Hierarchical neuromorphic processing
    println!("🏗️ Hierarchical Neuromorphic Multi-Scale Processing...");

    // Process at multiple scales
    let scales = [1.0, 0.5, 0.25];
    let mut multiscale_results = Vec::new();

    for (i, &scale) in scales.iter().enumerate() {
        // Create scaled version (simple decimation for demo)
        let scaled_size = (test_data.naturalimage.nrows() as f64 * scale) as usize;
        let scaledimage = if scale == 1.0 {
            test_data.naturalimage.clone()
        } else {
            subsampleimage(&test_data.naturalimage, scaled_size)?
        };

        // Apply neuromorphic processing
        let processed = homeostatic_adaptive_filter(
            scaledimage.view(),
            neuromorphic_config,
            10 + i * 5, // Varying adaptation steps
        )?;

        multiscale_results.push(processed);
    }

    println!("   ✓ Processed {} scales hierarchically", scales.len());

    // Quantum-neuromorphic fusion synthesis
    println!("🎭 Final Bio-Quantum Intelligence Synthesis...");

    // Combine all processing results using quantum superposition principles
    let synthesis_input = combine_multiscale_results(&multiscale_results)?;

    // Apply final quantum enhancement
    let final_result = quantum_tensor_network_processing(
        synthesis_input.view(),
        8, // High bond dimension for final processing
        quantum_config,
    )?;

    let synthesis_score = calculate_synthesis_quality(&test_data.naturalimage, &final_result);
    println!(
        "   ✓ Bio-quantum synthesis quality score: {:.4}",
        synthesis_score
    );

    let phase5_duration = start_time.elapsed();
    println!("   ⏱️ Phase 5 completed in: {:.2?}", phase5_duration);

    // Final performance summary
    println!();
    println!("🏆 ULTIMATE Advanced MODE PERFORMANCE METRICS");
    println!("==============================================");
    println!("   🌌 Quantum algorithms executed: 10+");
    println!("   🧠 Neuromorphic techniques applied: 6+");
    println!("   ⚡ Hybrid fusion operations: 4+");
    println!("   🔬 Multi-scale analysis levels: {}", scales.len());
    println!("   📊 Final synthesis quality: {:.4}", synthesis_score);
    println!("   ⏱️ Total processing time: {:.2?}", start_time.elapsed());
    println!();

    Ok(())
}

// Helper functions for metrics and analysis

#[allow(dead_code)]
fn create_gaussian_kernel(size: usize, sigma: f64) -> Array2<f64> {
    let mut kernel = Array2::zeros((size, size));
    let center = size as f64 / 2.0;
    let mut sum = 0.0;

    for y in 0..size {
        for x in 0..size {
            let dx = x as f64 - center;
            let dy = y as f64 - center;
            let value = (-(dx * dx + dy * dy) / (2.0 * sigma * sigma)).exp();
            kernel[(y, x)] = value;
            sum += value;
        }
    }

    // Normalize
    kernel.mapv(|x| x / sum)
}

#[allow(dead_code)]
fn create_edge_kernel() -> Array2<f64> {
    Array2::from_shape_vec(
        (3, 3),
        vec![-1.0, -1.0, -1.0, -1.0, 8.0, -1.0, -1.0, -1.0, -1.0],
    )
    .expect("Operation failed")
}

#[allow(dead_code)]
fn create_enhancement_kernel() -> Array2<f64> {
    Array2::from_shape_vec(
        (3, 3),
        vec![0.0, -1.0, 0.0, -1.0, 5.0, -1.0, 0.0, -1.0, 0.0],
    )
    .expect("Operation failed")
}

#[allow(dead_code)]
fn calculate_noise_reduction(noisy: &Array2<f64>, corrected: &Array2<f64>) -> f64 {
    let original_variance = calculate_variance(noisy);
    let corrected_variance = calculate_variance(corrected);
    ((original_variance - corrected_variance) / original_variance * 100.0).max(0.0)
}

#[allow(dead_code)]
fn calculate_variance(image: &Array2<f64>) -> f64 {
    let mean = image.sum() / image.len() as f64;
    image.iter().map(|&x| (x - mean).powi(2)).sum::<f64>() / image.len() as f64
}

#[allow(dead_code)]
fn calculate_filter_complexity(filter: &Array2<f64>) -> f64 {
    // Measure complexity based on entropy and variance
    let variance = calculate_variance(filter);
    let entropy = calculate_entropy(filter);
    variance * entropy
}

#[allow(dead_code)]
fn calculate_entropy(data: &Array2<f64>) -> f64 {
    // Simple entropy calculation
    let mut histogram = vec![0; 256];
    let (min_val, max_val) = data
        .iter()
        .fold((f64::INFINITY, f64::NEG_INFINITY), |(min, max), &x| {
            (min.min(x), max.max(x))
        });

    if max_val <= min_val {
        return 0.0;
    }

    for &value in data {
        let bin = ((value - min_val) / (max_val - min_val) * 255.0) as usize;
        histogram[bin.min(255)] += 1;
    }

    let total = data.len() as f64;
    histogram
        .iter()
        .filter(|&&count| count > 0)
        .map(|&count| {
            let p = count as f64 / total;
            -p * p.log2()
        })
        .sum()
}

#[allow(dead_code)]
fn calculate_adaptation_score(original: &Array2<f64>, adapted: &Array2<f64>) -> f64 {
    // Measure how well adaptation preserved important features while reducing noise
    let edge_preservation = calculate_edge_preservation(original, adapted);
    let noise_reduction = 1.0 - calculate_correlation(original, adapted).abs();
    (edge_preservation + noise_reduction) / 2.0
}

#[allow(dead_code)]
fn calculate_edge_preservation(original: &Array2<f64>, processed: &Array2<f64>) -> f64 {
    // Simplified edge preservation metric
    let orig_edges = calculate_edge_strength(original);
    let proc_edges = calculate_edge_strength(processed);
    (1.0 - (orig_edges - proc_edges).abs() / orig_edges.max(1e-10)).max(0.0)
}

#[allow(dead_code)]
fn calculate_edge_strength(image: &Array2<f64>) -> f64 {
    let (height, width) = image.dim();
    let mut total_strength = 0.0;

    for y in 1..height - 1 {
        for x in 1..width - 1 {
            let dx = image[(y, x + 1)] - image[(y, x - 1)];
            let dy = image[(y + 1, x)] - image[(y - 1, x)];
            total_strength += (dx * dx + dy * dy).sqrt();
        }
    }

    total_strength / ((height - 2) * (width - 2)) as f64
}

#[allow(dead_code)]
fn calculate_correlation(a: &Array2<f64>, b: &Array2<f64>) -> f64 {
    let mean_a = a.sum() / a.len() as f64;
    let mean_b = b.sum() / b.len() as f64;

    let numerator: f64 = a
        .iter()
        .zip(b.iter())
        .map(|(&a_val, &b_val)| (a_val - mean_a) * (b_val - mean_b))
        .sum();

    let denom_a: f64 = a.iter().map(|&x| (x - mean_a).powi(2)).sum();
    let denom_b: f64 = b.iter().map(|&x| (x - mean_b).powi(2)).sum();

    if denom_a * denom_b > 0.0 {
        numerator / (denom_a * denom_b).sqrt()
    } else {
        0.0
    }
}

#[allow(dead_code)]
fn calculate_enhancement_quality(original: &Array2<f64>, enhanced: &Array2<f64>) -> f64 {
    let sharpness_orig = calculate_edge_strength(original);
    let sharpness_enh = calculate_edge_strength(enhanced);
    let contrast_orig = calculate_contrast(original);
    let contrast_enh = calculate_contrast(enhanced);

    let sharpness_improvement = (sharpness_enh / sharpness_orig.max(1e-10)).min(2.0);
    let contrast_improvement = (contrast_enh / contrast_orig.max(1e-10)).min(2.0);

    (sharpness_improvement + contrast_improvement) / 2.0 - 1.0
}

#[allow(dead_code)]
fn calculate_contrast(image: &Array2<f64>) -> f64 {
    let (min_val, max_val) = image
        .iter()
        .fold((f64::INFINITY, f64::NEG_INFINITY), |(min, max), &x| {
            (min.min(x), max.max(x))
        });
    max_val - min_val
}

#[allow(dead_code)]
fn calculate_liquid_dynamics(output: &Array2<f64>) -> f64 {
    // Measure the complexity of liquid state dynamics
    let temporal_complexity = calculate_variance(output);
    let spatial_entropy = calculate_entropy(output);
    (temporal_complexity * spatial_entropy).sqrt()
}

#[allow(dead_code)]
fn calculate_amplification_effectiveness(amplified: &Array2<f64>) -> f64 {
    // Measure how effectively features were _amplified
    let max_val = amplified.iter().cloned().fold(0.0, f64::max);
    let mean_val = amplified.sum() / amplified.len() as f64;
    let dynamic_range = max_val / mean_val.max(1e-10);
    dynamic_range.ln().max(0.0) / 10.0 // Normalized logarithmic measure
}

#[allow(dead_code)]
fn calculate_segmentation_quality(segmentation: &Array2<usize>) -> f64 {
    // Measure _segmentation quality based on region coherence
    let mut segment_sizes = std::collections::HashMap::new();
    for &segment in segmentation {
        *segment_sizes.entry(segment).or_insert(0) += 1;
    }

    // Calculate normalized entropy of segment distribution
    let total = segmentation.len() as f64;
    let entropy: f64 = segment_sizes
        .values()
        .map(|&size| {
            let p = size as f64 / total;
            -p * p.log2()
        })
        .sum();

    let max_entropy = (segment_sizes.len() as f64).log2();
    if max_entropy > 0.0 {
        entropy / max_entropy
    } else {
        0.0
    }
}

#[allow(dead_code)]
fn calculate_frequency_complexity(_qftresult: &Array2<scirs2_core::Complex<f64>>) -> f64 {
    // Measure complexity in frequency domain
    let magnitude_spectrum: Vec<f64> = _qftresult.iter().map(|c| c.norm()).collect();
    let mean_magnitude = magnitude_spectrum.iter().sum::<f64>() / magnitude_spectrum.len() as f64;
    let variance = magnitude_spectrum
        .iter()
        .map(|&mag| (mag - mean_magnitude).powi(2))
        .sum::<f64>()
        / magnitude_spectrum.len() as f64;
    variance.sqrt() / mean_magnitude.max(1e-10)
}

#[allow(dead_code)]
fn subsampleimage(
    image: &Array2<f64>,
    new_size: usize,
) -> Result<Array2<f64>, Box<dyn std::error::Error>> {
    let (old_height, old_width) = image.dim();
    let mut subsampled = Array2::zeros((new_size, new_size));

    for y in 0..new_size {
        for x in 0..new_size {
            let orig_y = (y * old_height) / new_size;
            let orig_x = (x * old_width) / new_size;
            subsampled[(y, x)] = image[(orig_y, orig_x)];
        }
    }

    Ok(subsampled)
}

#[allow(dead_code)]
fn combine_multiscale_results(
    results: &[Array2<f64>],
) -> Result<Array2<f64>, Box<dyn std::error::Error>> {
    if results.is_empty() {
        return Err("No results to combine".into());
    }

    // Use the largest scale as base
    let mut combined = results[0].clone();

    // Add contributions from other scales
    for result in &results[1..] {
        let (base_h, base_w) = combined.dim();
        let (res_h, res_w) = result.dim();

        // Simple upsampling and addition
        for y in 0..base_h {
            for x in 0..base_w {
                let scaled_y = (y * res_h) / base_h;
                let scaled_x = (x * res_w) / base_w;
                if scaled_y < res_h && scaled_x < res_w {
                    combined[(y, x)] = (combined[(y, x)] + result[(scaled_y, scaled_x)]) / 2.0;
                }
            }
        }
    }

    Ok(combined)
}

#[allow(dead_code)]
fn calculate_synthesis_quality(original: &Array2<f64>, synthesized: &Array2<f64>) -> f64 {
    // Comprehensive quality metric combining multiple factors
    let correlation = calculate_correlation(original, synthesized).abs();
    let edge_preservation = calculate_edge_preservation(original, synthesized);
    let contrast_enhancement =
        calculate_contrast(synthesized) / calculate_contrast(original).max(1e-10);
    let noise_reduction =
        1.0 - calculate_variance(synthesized) / calculate_variance(original).max(1e-10);

    // Weighted combination
    0.3 * correlation
        + 0.3 * edge_preservation
        + 0.2 * contrast_enhancement.min(2.0)
        + 0.2 * noise_reduction.max(0.0)
}
