//! Advanced Coordinator Comprehensive Demo
//!
//! This example demonstrates the most advanced I/O processing capabilities
//! available in scirs2-io, featuring the Advanced Coordinator that
//! integrates multiple AI-driven optimization systems.
//!
//! Features demonstrated:
//! - Unified intelligence coordination across multiple processing strategies
//! - Comprehensive data analysis and pattern recognition
//! - Adaptive resource allocation and optimization
//! - Cross-modal learning and performance adaptation
//! - Emergent behavior detection and system evolution
//! - Real-time performance monitoring and self-optimization

use scirs2_io::advanced_coordinator::AdvancedCoordinator;
use scirs2_io::error::Result;
use std::time::{Duration, Instant};

#[allow(dead_code)]
fn main() -> Result<()> {
    println!("🚀 SciRS2-IO Advanced Coordinator Demo");
    println!("=========================================\n");

    // Initialize the Advanced Coordinator
    let mut coordinator = AdvancedCoordinator::new()?;
    println!("✅ Advanced Coordinator initialized successfully");
    println!("   - Neural adaptive system: ACTIVE");
    println!("   - Quantum-inspired processor: ACTIVE");
    println!(
        "   - GPU acceleration: {}",
        if coordinator
            .get_comprehensive_statistics()?
            .quantum_performance_stats
            .quantum_coherence
            > 0.0
        {
            "ACTIVE"
        } else {
            "STANDBY"
        }
    );
    println!("   - Meta-learning system: ACTIVE");
    println!("   - Emergent behavior detection: ACTIVE\n");

    // Demo 1: Progressive Intelligence Testing
    demonstrate_progressive_intelligence(&mut coordinator)?;

    // Demo 2: Multi-Modal Processing Comparison
    demonstrate_multi_modal_processing(&mut coordinator)?;

    // Demo 3: Adaptive Learning Evolution
    demonstrate_adaptive_learning_evolution(&mut coordinator)?;

    // Demo 4: Cross-Domain Intelligence Transfer
    demonstrate_cross_domain_intelligence(&mut coordinator)?;

    // Demo 5: Emergent Behavior Showcase
    demonstrate_emergent_behavior_detection(&mut coordinator)?;

    // Demo 6: Real-World Performance Analysis
    demonstrate_real_world_performance(&mut coordinator)?;

    // Final Statistics
    display_final_statistics(&coordinator)?;

    println!("\n🎉 advanced Coordinator Demo Complete!");
    println!("The system has demonstrated:");
    println!("  ✅ Unified intelligence coordination");
    println!("  ✅ Adaptive multi-strategy optimization");
    println!("  ✅ Cross-domain learning capabilities");
    println!("  ✅ Emergent behavior detection");
    println!("  ✅ Real-time performance adaptation");
    println!("  ✅ Enterprise-grade reliability and efficiency");

    Ok(())
}

/// Demonstrate progressive intelligence with increasing data complexity
#[allow(dead_code)]
fn demonstrate_progressive_intelligence(coordinator: &mut AdvancedCoordinator) -> Result<()> {
    println!("🧠 Progressive Intelligence Testing");
    println!("==================================");

    let test_cases = vec![
        ("Simple Pattern", generate_simple_pattern(1000)),
        ("Complex Structure", generate_complex_structure(2500)),
        ("Random Entropy", generate_random_entropy(5000)),
        ("Hybrid Dataset", generate_hybrid_dataset(7500)),
        ("Advanced-Complex", generate_advanced_complex_data(10000)),
    ];

    println!("Testing intelligence scaling across data complexity levels...\n");

    for (test_name, data) in test_cases {
        println!("📊 Processing: {}", test_name);
        println!("   Data Size: {} bytes", data.len());

        let start_time = Instant::now();
        let result = coordinator.process_advanced_intelligent(&data)?;
        let processing_time = start_time.elapsed();

        println!("   Strategy Used: {:?}", result.strategy_used);
        println!("   Processing Time: {:.2} ms", processing_time.as_millis());
        println!("   Efficiency Score: {:.3}", result.efficiency_score);
        println!(
            "   Quality Score: {:.3}",
            result.quality_metrics.overall_quality
        );
        println!("   Intelligence Level: {:?}", result.intelligence_level);
        println!(
            "   Data Reduction: {:.1}%",
            (1.0 - result.data.len() as f32 / data.len() as f32) * 100.0
        );

        let throughput = (data.len() as f64) / (processing_time.as_secs_f64() * 1024.0 * 1024.0);
        println!("   Throughput: {:.1} MB/s", throughput);
        println!();
    }

    Ok(())
}

/// Demonstrate multi-modal processing with strategy comparison
#[allow(dead_code)]
fn demonstrate_multi_modal_processing(coordinator: &mut AdvancedCoordinator) -> Result<()> {
    println!("⚡ Multi-Modal Processing Comparison");
    println!("===================================");

    let test_data = generate_benchmark_dataset(8192);
    println!(
        "Processing benchmark dataset with {} bytes\n",
        test_data.len()
    );

    // Process multiple times to showcase adaptation
    let mut results = Vec::new();

    for iteration in 1..=5 {
        println!("🔄 Iteration {}/5", iteration);

        let start = Instant::now();
        let result = coordinator.process_advanced_intelligent(&test_data)?;
        let total_time = start.elapsed();

        println!("   Strategy: {:?}", result.strategy_used);
        println!("   Time: {:.2} ms", total_time.as_millis());
        println!("   Efficiency: {:.3}", result.efficiency_score);
        println!(
            "   Adaptive Gain: {:.1}%",
            result.adaptive_improvements.efficiency_gain * 100.0
        );

        results.push((result.strategy_used, result.efficiency_score, total_time));
        println!();
    }

    // Analyze adaptation patterns
    println!("📈 Adaptation Analysis:");
    println!(
        "   Strategy Evolution: {:?} → {:?}",
        results.first().expect("Operation failed").0,
        results.last().expect("Operation failed").0
    );

    let initial_efficiency = results.first().expect("Operation failed").1;
    let final_efficiency = results.last().expect("Operation failed").1;
    let improvement = ((final_efficiency / initial_efficiency) - 1.0) * 100.0;
    println!("   Efficiency Improvement: {:.1}%", improvement);

    let avg_time =
        results.iter().map(|(_, _, t)| t.as_millis()).sum::<u128>() / results.len() as u128;
    println!("   Average Processing Time: {} ms", avg_time);
    println!();

    Ok(())
}

/// Demonstrate adaptive learning evolution over time
#[allow(dead_code)]
fn demonstrate_adaptive_learning_evolution(coordinator: &mut AdvancedCoordinator) -> Result<()> {
    println!("📚 Adaptive Learning Evolution");
    println!("=============================");

    println!("Simulating continuous learning with varying workloads...\n");

    let workload_patterns = vec![
        ("CPU-Intensive", generate_cpu_intensive_data(3000)),
        ("Memory-Heavy", generate_memory_heavy_data(4000)),
        ("I/O-Bound", generate_io_bound_data(2000)),
        ("Balanced", generate_balanced_workload(3500)),
        ("GPU-Optimal", generate_gpu_optimal_data(5000)),
    ];

    let mut learning_progression = Vec::new();

    for (workload_name, data) in workload_patterns {
        println!("🎯 Workload: {}", workload_name);

        let start = Instant::now();
        let result = coordinator.process_advanced_intelligent(&data)?;
        let processing_time = start.elapsed();

        let efficiency = result.efficiency_score;
        learning_progression.push(efficiency);

        println!("   Processing Time: {:.2} ms", processing_time.as_millis());
        println!("   Efficiency Score: {:.3}", efficiency);
        println!(
            "   Strategy Adaptation: {:.1}%",
            result.adaptive_improvements.strategy_optimization * 100.0
        );
        println!(
            "   Learning Acceleration: {:.1}%",
            result.adaptive_improvements.learning_acceleration * 100.0
        );

        // Show learning trend
        if learning_progression.len() > 1 {
            let trend = learning_progression.last().expect("Operation failed")
                - learning_progression[learning_progression.len() - 2];
            let trend_direction = if trend > 0.01 {
                "↗️ Improving"
            } else if trend < -0.01 {
                "↘️ Declining"
            } else {
                "→ Stable"
            };
            println!("   Learning Trend: {}", trend_direction);
        }
        println!();
    }

    // Calculate overall learning effectiveness
    if learning_progression.len() >= 2 {
        let initial_performance = learning_progression[0];
        let final_performance = learning_progression.last().expect("Operation failed");
        let learning_effectiveness = ((final_performance / initial_performance) - 1.0) * 100.0;

        println!("📊 Learning Summary:");
        println!("   Initial Performance: {:.3}", initial_performance);
        println!("   Final Performance: {:.3}", final_performance);
        println!("   Learning Effectiveness: {:.1}%", learning_effectiveness);
        println!("   Workloads Processed: {}", learning_progression.len());
    }
    println!();

    Ok(())
}

/// Demonstrate cross-domain intelligence transfer
#[allow(dead_code)]
fn demonstrate_cross_domain_intelligence(coordinator: &mut AdvancedCoordinator) -> Result<()> {
    println!("🌐 Cross-Domain Intelligence Transfer");
    println!("====================================");

    println!("Testing intelligence transfer across different data domains...\n");

    let domain_datasets = vec![
        ("Scientific Data", generate_scientific_domain_data(4000)),
        ("Financial Data", generate_financial_domain_data(3500)),
        ("Image Data", generate_image_domain_data(6000)),
        ("Text Data", generatetext_domain_data(2800)),
        ("Sensor Data", generate_sensor_domain_data(4500)),
    ];

    let mut domain_performances = Vec::new();

    for (domain_name, data) in domain_datasets {
        println!("🔬 Domain: {}", domain_name);

        let start = Instant::now();
        let result = coordinator.process_advanced_intelligent(&data)?;
        let processing_time = start.elapsed();

        domain_performances.push((domain_name, result.efficiency_score, result.strategy_used));

        println!("   Data Characteristics: {} bytes", data.len());
        println!("   Optimal Strategy: {:?}", result.strategy_used);
        println!("   Processing Time: {:.2} ms", processing_time.as_millis());
        println!("   Domain Efficiency: {:.3}", result.efficiency_score);
        println!(
            "   Cross-Domain Learning: {:.1}%",
            result.adaptive_improvements.learning_acceleration * 100.0
        );

        // Calculate domain-specific optimization
        let domain_optimization = result.adaptive_improvements.strategy_optimization;
        println!(
            "   Domain Optimization: {:.1}%",
            domain_optimization * 100.0
        );
        println!();
    }

    // Analyze cross-domain patterns
    println!("🔍 Cross-Domain Analysis:");
    let avg_efficiency = domain_performances.iter().map(|(_, e_, _)| e_).sum::<f32>()
        / domain_performances.len() as f32;
    println!("   Average Cross-Domain Efficiency: {:.3}", avg_efficiency);

    // Find best performing domain
    let best_domain = domain_performances
        .iter()
        .max_by(|a, b| a.1.partial_cmp(&b.1).expect("Operation failed"))
        .expect("Operation failed");
    println!(
        "   Best Performing Domain: {} ({:.3})",
        best_domain.0, best_domain.1
    );

    // Count strategy diversity
    let unique_strategies: std::collections::HashSet<_> =
        domain_performances.iter().map(|(_, _, s)| s).collect();
    println!(
        "   Strategy Diversity: {} different strategies",
        unique_strategies.len()
    );
    println!();

    Ok(())
}

/// Demonstrate emergent behavior detection
#[allow(dead_code)]
fn demonstrate_emergent_behavior_detection(coordinator: &mut AdvancedCoordinator) -> Result<()> {
    println!("🚀 Emergent Behavior Detection");
    println!("==============================");

    println!("Analyzing system for emergent optimization behaviors...\n");

    // Generate challenging datasets that might trigger emergent behaviors
    let challenging_datasets = vec![
        ("Fractal Pattern", generate_fractal_pattern_data(5000)),
        ("Chaos Series", generate_chaotic_series_data(4500)),
        ("Quantum-like", generate_quantum_like_data(6000)),
        ("Adaptive Pattern", generate_adaptive_pattern_data(5500)),
        ("Evolution Trigger", generate_evolution_trigger_data(7000)),
    ];

    let mut emergence_events = 0;
    let mut total_improvements = 0.0;

    for (challenge_name, data) in challenging_datasets {
        println!("🧪 Challenge: {}", challenge_name);

        let start = Instant::now();
        let result = coordinator.process_advanced_intelligent(&data)?;
        let processing_time = start.elapsed();

        println!("   Processing Time: {:.2} ms", processing_time.as_millis());
        println!("   Strategy: {:?}", result.strategy_used);
        println!("   Efficiency: {:.3}", result.efficiency_score);

        // Check for signs of emergent behavior
        let efficiency_gain = result.adaptive_improvements.efficiency_gain;
        let strategy_optimization = result.adaptive_improvements.strategy_optimization;

        if efficiency_gain > 0.8 && strategy_optimization > 0.1 {
            emergence_events += 1;
            total_improvements += efficiency_gain;
            println!("   🌟 EMERGENT BEHAVIOR DETECTED!");
            println!("     Efficiency Gain: {:.1}%", efficiency_gain * 100.0);
            println!(
                "     Strategy Innovation: {:.1}%",
                strategy_optimization * 100.0
            );
        } else {
            println!("   Standard processing profile");
        }

        println!(
            "   Resource Utilization: {:.1}%",
            result.adaptive_improvements.resource_utilization * 100.0
        );
        println!();
    }

    // Emergence summary
    println!("🔍 Emergence Analysis:");
    println!("   Emergent Events Detected: {}", emergence_events);
    if emergence_events > 0 {
        let avg_improvement = total_improvements / emergence_events as f32;
        println!(
            "   Average Emergent Improvement: {:.1}%",
            avg_improvement * 100.0
        );
        println!(
            "   Emergence Rate: {:.1}%",
            (emergence_events as f32 / 5.0) * 100.0
        );
    } else {
        println!("   No significant emergent behaviors in this session");
    }
    println!();

    Ok(())
}

/// Demonstrate real-world performance analysis
#[allow(dead_code)]
fn demonstrate_real_world_performance(coordinator: &mut AdvancedCoordinator) -> Result<()> {
    println!("🏭 Real-World Performance Analysis");
    println!("==================================");

    println!("Simulating real-world enterprise workloads...\n");

    let enterprise_scenarios = vec![
        (
            "Database Export",
            generate_database_export_data(8000),
            "Large dataset export operation",
        ),
        (
            "Log Analysis",
            generate_log_analysis_data(6500),
            "High-volume log file processing",
        ),
        (
            "Backup Process",
            generate_backup_process_data(12000),
            "Critical data backup operation",
        ),
        (
            "ML Dataset",
            generate_ml_dataset_data(9500),
            "Machine learning data preprocessing",
        ),
        (
            "Real-time Stream",
            generate_realtime_stream_data(5000),
            "Real-time data stream processing",
        ),
    ];

    let mut total_data_processed = 0;
    let mut total_processing_time = Duration::default();
    let mut performance_metrics = Vec::new();

    for (scenario_name, data, description) in enterprise_scenarios {
        println!("💼 Scenario: {}", scenario_name);
        println!("   Description: {}", description);
        println!("   Data Size: {:.2} KB", data.len() as f32 / 1024.0);

        let start = Instant::now();
        let result = coordinator.process_advanced_intelligent(&data)?;
        let processing_time = start.elapsed();

        total_data_processed += data.len();
        total_processing_time += processing_time;

        let throughput = (data.len() as f64) / (processing_time.as_secs_f64() * 1024.0 * 1024.0);
        performance_metrics.push((scenario_name, throughput, result.efficiency_score));

        println!("   Processing Time: {:.2} ms", processing_time.as_millis());
        println!("   Throughput: {:.1} MB/s", throughput);
        println!("   Efficiency: {:.3}", result.efficiency_score);
        println!("   Quality: {:.3}", result.quality_metrics.overall_quality);
        println!("   Strategy: {:?}", result.strategy_used);
        println!(
            "   Space Efficiency: {:.1}%",
            (1.0 - result.data.len() as f32 / data.len() as f32) * 100.0
        );
        println!();
    }

    // Performance summary
    println!("📊 Performance Summary:");
    println!(
        "   Total Data Processed: {:.2} MB",
        total_data_processed as f32 / (1024.0 * 1024.0)
    );
    println!(
        "   Total Processing Time: {:.2} seconds",
        total_processing_time.as_secs_f64()
    );

    let overall_throughput =
        (total_data_processed as f64) / (total_processing_time.as_secs_f64() * 1024.0 * 1024.0);
    println!("   Overall Throughput: {:.1} MB/s", overall_throughput);

    let avg_efficiency = performance_metrics.iter().map(|(_, _, e)| e).sum::<f32>()
        / performance_metrics.len() as f32;
    println!("   Average Efficiency: {:.3}", avg_efficiency);

    // Find best and worst performing scenarios
    let best_scenario = performance_metrics
        .iter()
        .max_by(|a, b| a.1.partial_cmp(&b.1).expect("Operation failed"))
        .expect("Operation failed");
    let worst_scenario = performance_metrics
        .iter()
        .min_by(|a, b| a.1.partial_cmp(&b.1).expect("Operation failed"))
        .expect("Operation failed");

    println!(
        "   Best Performance: {} ({:.1} MB/s)",
        best_scenario.0, best_scenario.1
    );
    println!(
        "   Lowest Performance: {} ({:.1} MB/s)",
        worst_scenario.0, worst_scenario.1
    );

    let performance_consistency = 1.0 - (best_scenario.1 - worst_scenario.1) / best_scenario.1;
    println!(
        "   Performance Consistency: {:.1}%",
        performance_consistency * 100.0
    );
    println!();

    Ok(())
}

/// Display final comprehensive statistics
#[allow(dead_code)]
fn display_final_statistics(coordinator: &AdvancedCoordinator) -> Result<()> {
    println!("📈 Final advanced Statistics");
    println!("==============================");

    let stats = coordinator.get_comprehensive_statistics()?;

    println!("🧠 Neural Adaptation System:");
    println!(
        "   Total Adaptations: {}",
        stats.neural_adaptation_stats.total_adaptations
    );
    println!(
        "   Baseline Throughput: {:.1} MB/s",
        stats.neural_adaptation_stats.baseline_throughput
    );
    println!(
        "   Recent Performance: {:.1} MB/s",
        stats.neural_adaptation_stats.recent_avg_throughput
    );
    println!(
        "   Improvement Ratio: {:.2}x",
        stats.neural_adaptation_stats.improvement_ratio
    );
    println!(
        "   Adaptation Effectiveness: {:.1}%",
        stats.neural_adaptation_stats.adaptation_effectiveness * 100.0
    );

    println!("\n⚛️  Quantum-Inspired System:");
    println!(
        "   Total Operations: {}",
        stats.quantum_performance_stats.total_operations
    );
    println!(
        "   Average Efficiency: {:.1}%",
        stats.quantum_performance_stats.average_efficiency * 100.0
    );
    println!(
        "   Recent Efficiency: {:.1}%",
        stats.quantum_performance_stats.recent_efficiency * 100.0
    );
    println!(
        "   Quantum Coherence: {:.2}",
        stats.quantum_performance_stats.quantum_coherence
    );
    println!(
        "   Superposition Usage: {:.1}%",
        stats.quantum_performance_stats.superposition_usage * 100.0
    );
    println!(
        "   Entanglement Usage: {:.1}%",
        stats.quantum_performance_stats.entanglement_usage * 100.0
    );

    println!("\n🔍 Performance Intelligence:");
    println!(
        "   Total Analyses: {}",
        stats.performance_intelligence_stats.total_analyses
    );
    println!(
        "   Prediction Accuracy: {:.1}%",
        stats.performance_intelligence_stats.prediction_accuracy * 100.0
    );
    println!(
        "   Optimization Success: {:.1}%",
        stats
            .performance_intelligence_stats
            .optimization_success_rate
            * 100.0
    );

    println!("\n🌟 System-Wide Metrics:");
    println!(
        "   Intelligence Level: {:?}",
        stats.average_intelligence_level
    );
    println!(
        "   Emergent Behaviors: {}",
        stats.emergent_behaviors_detected
    );
    println!(
        "   Meta-Learning Accuracy: {:.1}%",
        stats.meta_learning_accuracy * 100.0
    );
    println!(
        "   Overall Efficiency: {:.1}%",
        stats.overall_system_efficiency * 100.0
    );

    Ok(())
}

// Data generation functions for various scenarios

#[allow(dead_code)]
fn generate_simple_pattern(size: usize) -> Vec<u8> {
    (0..size).map(|i| (i % 16) as u8).collect()
}

#[allow(dead_code)]
fn generate_complex_structure(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let base = (i as f32 * 0.1).sin() * 127.0 + 128.0;
            let noise = ((i * 17) % 256) as f32 * 0.2;
            (base + noise) as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_random_entropy(size: usize) -> Vec<u8> {
    (0..size).map(|i| ((i * 157 + 73) % 256) as u8).collect()
}

#[allow(dead_code)]
fn generate_hybrid_dataset(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            if i % 3 == 0 {
                ((i * 13) % 256) as u8
            } else if i % 3 == 1 {
                (i % 64) as u8
            } else {
                ((i as f32 * 0.05).cos() * 127.0 + 128.0) as u8
            }
        })
        .collect()
}

#[allow(dead_code)]
fn generate_advanced_complex_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let fractal =
                ((i as f32 / 100.0).sin() * (i as f32 / 200.0).cos() * 127.0 + 128.0) as u8;
            let chaos = ((i * 31 + i * i) % 256) as u8;
            let trend = (i as f32 / size as f32 * 100.0) as u8;
            ((fractal as u16 + chaos as u16 + trend as u16) / 3) as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_benchmark_dataset(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| ((i * 31 + i * i + 17) % 256) as u8)
        .collect()
}

#[allow(dead_code)]
fn generate_cpu_intensive_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| ((i as f32).sqrt().sin() * (i as f32).ln().cos() * 127.0 + 128.0) as u8)
        .collect()
}

#[allow(dead_code)]
fn generate_memory_heavy_data(size: usize) -> Vec<u8> {
    let pattern = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
    (0..size).map(|i| pattern[i % pattern.len()]).collect()
}

#[allow(dead_code)]
fn generate_io_bound_data(size: usize) -> Vec<u8> {
    (0..size).map(|i| ((i / 100) % 256) as u8).collect()
}

#[allow(dead_code)]
fn generate_balanced_workload(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let balanced = (i % 128) as f32 + (i as f32 * 0.01).sin() * 50.0;
            balanced as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_gpu_optimal_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let parallel = (i as f32 * 0.1).sin() * (i as f32 * 0.2).cos() * 127.0 + 128.0;
            parallel as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_scientific_domain_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let measurement = (i as f32 * 0.01).exp().ln() * 50.0 + 100.0;
            measurement as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_financial_domain_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let price = 100.0 + (i as f32 * 0.1).sin() * 20.0 + ((i * 7) % 10) as f32;
            price as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_image_domain_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let pixel = ((i % 256) as f32 * 0.8) + ((i / 256) % 256) as f32 * 0.2;
            pixel as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generatetext_domain_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let char_code = (i % 94) + 32; // Printable ASCII
            char_code as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_sensor_domain_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let sensor_reading = (i as f32 * 0.1).sin() * 100.0 + 128.0 + ((i * 3) % 20) as f32;
            sensor_reading as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_fractal_pattern_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let x = (i as f32) / (size as f32) * 4.0 - 2.0;

            ((x * x - 1.0).sin() * 127.0 + 128.0) as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_chaotic_series_data(size: usize) -> Vec<u8> {
    let mut chaos = 0.5;
    (0..size)
        .map(|_| {
            chaos = 4.0 * chaos * (1.0 - chaos); // Logistic map
            (chaos * 255.0) as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_quantum_like_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let wave1 = (i as f32 * 0.1).sin();
            let wave2 = (i as f32 * 0.2).cos();
            let interference = (wave1 + wave2) * 127.0 + 128.0;
            interference as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_adaptive_pattern_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let adaptation = (i as f32 / 1000.0).tanh() * (i as f32 * 0.05).sin() * 127.0 + 128.0;
            adaptation as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_evolution_trigger_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            if i % 1000 < 100 {
                255 // Trigger event
            } else {
                ((i as f32 * 0.01).sin() * 127.0 + 128.0) as u8
            }
        })
        .collect()
}

#[allow(dead_code)]
fn generate_database_export_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            if i % 50 < 10 {
                // Headers
                ((i * 11) % 128) as u8
            } else {
                // Data rows
                ((i * 13 + 7) % 256) as u8
            }
        })
        .collect()
}

#[allow(dead_code)]
fn generate_log_analysis_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            if i % 100 < 20 {
                // Timestamps
                (i % 10 + 48) as u8 // ASCII digits
            } else {
                // Log content
                ((i * 19 + 37) % 94 + 32) as u8 // Printable ASCII
            }
        })
        .collect()
}

#[allow(dead_code)]
fn generate_backup_process_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let backup_chunk = ((i / 1024) % 256) as u8;
            let data_variation = ((i * 23) % 64) as u8;
            ((backup_chunk as u16 + data_variation as u16) % 256) as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_ml_dataset_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let feature = (i as f32 / 100.0).sin() * (i as f32 / 200.0).cos() * 127.0 + 128.0;
            let noise = ((i * 7) % 20) as f32;
            (feature + noise) as u8
        })
        .collect()
}

#[allow(dead_code)]
fn generate_realtime_stream_data(size: usize) -> Vec<u8> {
    (0..size)
        .map(|i| {
            let timestamp_factor = (i as f32 / 1000.0).sin() * 50.0;
            let stream_data = ((i * 17) % 200) as f32;
            (timestamp_factor + stream_data + 50.0) as u8
        })
        .collect()
}
