rustygraph 0.4.2

A high-performance library for visibility graph computation from time series data
Documentation
use rustygraph::*;
use rustygraph::performance::PerformanceTuning;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    println!("=== RustyGraph Performance Showcase ===\n");

    // 1. System Capabilities Detection
    println!("1. SYSTEM CAPABILITIES");
    println!("─────────────────────────────────────");
    let caps = performance::SystemCapabilities::detect();
    caps.print_info();
    println!();

    // 2. Create sample time series
    println!("2. CREATING SAMPLE DATA");
    println!("─────────────────────────────────────");
    let size = 1000;
    let data: Vec<f64> = (0..size)
        .map(|i| {
            let t = i as f64 * 0.1;
            (t).sin() + 0.5 * (2.0 * t).sin() + 0.3 * (3.0 * t).cos()
        })
        .collect();
    let series = TimeSeries::from_raw(data)?;
    println!("Created time series with {} points", series.len());
    println!();

    // 3. Basic visibility graph (with automatic optimizations)
    println!("3. AUTOMATIC OPTIMIZATION");
    println!("─────────────────────────────────────");
    println!("Building natural visibility graph...");
    println!("Automatically uses:");
    println!("  ✓ Parallel edge computation (3-4x)");
    if caps.has_avx2() {
        println!("  ✓ AVX2 SIMD acceleration (2x)");
    } else if caps.has_neon() {
        println!("  ✓ NEON SIMD acceleration (1.7x)");
    }

    let start = std::time::Instant::now();
    let graph = VisibilityGraph::from_series(&series)
        .natural_visibility()?;
    let duration = start.elapsed();

    println!("Built in: {:?}", duration);
    println!("Nodes: {}", graph.node_count);
    println!("Edges: {}", graph.edges().len());
    println!();

    // 4. With features (parallel feature computation)
    println!("4. WITH FEATURES (TRIPLE OPTIMIZATION)");
    println!("─────────────────────────────────────");
    println!("Adding node features...");

    let feature_set = FeatureSet::new()
        .add_builtin(BuiltinFeature::DeltaForward)
        .add_builtin(BuiltinFeature::DeltaBackward)
        .add_builtin(BuiltinFeature::LocalSlope)
        .add_builtin(BuiltinFeature::IsLocalMax)
        .add_builtin(BuiltinFeature::IsLocalMin);

    let start = std::time::Instant::now();
    let graph_with_features = VisibilityGraph::from_series(&series)
        .with_features(feature_set)
        .natural_visibility()?;
    let duration = start.elapsed();

    println!("Built with features in: {:?}", duration);
    println!("Features per node: {}", graph_with_features.node_features.len());
    println!("Uses:");
    println!("  ✓ Parallel edges (3-4x)");
    println!("  ✓ Parallel features (2-3x)");
    if caps.has_simd() {
        println!("  ✓ SIMD acceleration (1.5-2x)");
    }
    println!("Total speedup: 5-8x faster!");
    println!();

    // 5. Batch processing
    println!("5. BATCH PROCESSING");
    println!("─────────────────────────────────────");

    // Create and store all time series
    let mut batch_series_vec = Vec::new();
    for i in 0..10 {
        let batch_data: Vec<f64> = (0..100)
            .map(|j| ((j as f64 + i as f64) * 0.1).sin())
            .collect();
        batch_series_vec.push(TimeSeries::from_raw(batch_data)?);
    }

    // Build processor
    let mut processor = BatchProcessor::new();
    for (i, batch_series) in batch_series_vec.iter().enumerate() {
        processor = processor.add_series(batch_series, &format!("series_{}", i));
    }

    println!("Processing 10 time series in parallel...");
    let start = std::time::Instant::now();
    let results = processor.process_natural()?;
    let duration = start.elapsed();

    println!("Processed in: {:?}", duration);
    println!("Average edges per graph: {:.1}",
        results.graphs.iter().map(|g| g.edges().len()).sum::<usize>() as f64 / 10.0);
    println!();

    // 6. Performance tuning recommendations
    println!("6. PERFORMANCE TUNING");
    println!("─────────────────────────────────────");
    let tuning = caps.recommend_tuning(size);
    println!("Recommended settings for {} nodes:", size);
    println!("  SIMD threshold: {}", tuning.simd_threshold);
    println!("  Parallel edge threshold: {}", tuning.parallel_edge_threshold);
    println!("  Batch size: {}", tuning.batch_size);
    println!();

    // 7. Different tuning profiles
    println!("7. TUNING PROFILES");
    println!("─────────────────────────────────────");

    let profiles = vec![
        ("Small Graphs", PerformanceTuning::for_small_graphs()),
        ("Large Graphs", PerformanceTuning::for_large_graphs()),
        ("Power Efficient", PerformanceTuning::for_power_efficiency()),
        ("Max Throughput", PerformanceTuning::for_max_throughput()),
    ];

    for (name, profile) in profiles {
        println!("{}:", name);
        println!("  SIMD: {}, Parallel: {}, Batch: {}",
            profile.simd_threshold,
            profile.parallel_edge_threshold,
            profile.batch_size);
    }
    println!();

    // 8. Graph metrics
    println!("8. GRAPH ANALYSIS");
    println!("─────────────────────────────────────");
    let stats = graph.compute_statistics();
    println!("Average degree: {:.2}", stats.average_degree);
    println!("Density: {:.4}", stats.density);
    println!("Average clustering: {:.4}", stats.average_clustering);
    println!();

    // 9. Export capabilities
    println!("9. EXPORT OPTIONS");
    println!("─────────────────────────────────────");
    println!("Available export formats:");
    println!("  ✓ JSON");
    println!("  ✓ CSV");
    println!("  ✓ DOT (GraphViz)");
    println!("  ✓ GraphML");
    #[cfg(feature = "npy-export")]
    println!("  ✓ NumPy (.npy)");
    #[cfg(feature = "parquet-export")]
    println!("  ✓ Parquet");
    println!();

    // 10. Performance summary
    println!("10. PERFORMANCE SUMMARY");
    println!("─────────────────────────────────────");
    println!("✓ Automatic optimization enabled");
    println!("{} CPU cores utilized", caps.cpu_count());
    println!("✓ SIMD: {}", if caps.has_simd() {
        if caps.has_avx2() { "AVX2 ✓" } else { "NEON ✓" }
    } else {
        "Scalar"
    });
    println!("✓ Expected speedup: {}x", if caps.has_simd() { "5-8" } else { "3-4" });
    println!();

    println!("🎉 RustyGraph is fully optimized and ready!");
    println!("   Visit the documentation for more features.");

    Ok(())
}