bashrs 6.66.0

Rust-to-Shell transpiler for deterministic bootstrap scripts
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use super::*;

#[test]
fn test_calculate_mean() {
    let values = vec![1.0, 2.0, 3.0, 4.0, 5.0];
    assert_eq!(calculate_mean(&values), 3.0);
}

#[test]
fn test_calculate_median_odd() {
    let values = vec![1.0, 2.0, 3.0, 4.0, 5.0];
    assert_eq!(calculate_median(&values), 3.0);
}

#[test]
fn test_calculate_median_even() {
    let values = vec![1.0, 2.0, 3.0, 4.0];
    assert_eq!(calculate_median(&values), 2.5);
}

#[test]
fn test_calculate_variance() {
    let values = vec![1.0, 2.0, 3.0, 4.0, 5.0];
    let mean = 3.0;
    assert_eq!(calculate_variance(&values, mean), 2.0);
}

#[test]
fn test_statistics_calculate() {
    let values = vec![1.0, 2.0, 3.0, 4.0, 5.0];
    let stats = Statistics::calculate(&values);
    assert_eq!(stats.mean_ms, 3.0);
    assert_eq!(stats.median_ms, 3.0);
    assert_eq!(stats.min_ms, 1.0);
    assert_eq!(stats.max_ms, 5.0);
}

#[test]
fn test_truncate_path() {
    let path = "/very/long/path/to/some/script.sh";
    // max_len=20 -> "..." (3) + last 17 chars = "...to/some/script.sh" (20 total)
    assert_eq!(truncate_path(path, 20), "...to/some/script.sh");
    assert_eq!(truncate_path("short.sh", 20), "short.sh");
}

#[test]
fn test_memory_statistics_calculate() {
    let memory_kb = vec![1024.0, 2048.0, 1536.0, 2048.0, 1024.0];
    let stats = MemoryStatistics::calculate(&memory_kb);
    assert_eq!(stats.mean_kb, 1536.0);
    assert_eq!(stats.median_kb, 1536.0);
    assert_eq!(stats.min_kb, 1024.0);
    assert_eq!(stats.max_kb, 2048.0);
    assert_eq!(stats.peak_kb, 2048.0);
}

#[test]
fn test_statistics_with_memory() {
    let time_results = vec![10.0, 20.0, 15.0];
    let memory_results = vec![1024.0, 2048.0, 1536.0];
    let stats = Statistics::calculate_with_memory(&time_results, Some(&memory_results));

    assert_eq!(stats.mean_ms, 15.0);
    assert!(stats.memory.is_some());

    let mem = stats.memory.unwrap();
    assert_eq!(mem.mean_kb, 1536.0);
    assert_eq!(mem.median_kb, 1536.0);
}

#[test]
fn test_statistics_without_memory() {
    let time_results = vec![10.0, 20.0, 15.0];
    let stats = Statistics::calculate(&time_results);

    assert_eq!(stats.mean_ms, 15.0);
    assert!(stats.memory.is_none());
}

// ============================================================================
// Issue #12 Phase 1: MAD-based Outlier Detection Tests (RED Phase)
// ============================================================================

#[test]
fn test_issue_012_mad_calculation() {
    // ARRANGE: Dataset with known MAD
    let values = vec![1.0, 2.0, 3.0, 4.0, 5.0];

    // ACT
    let mad = calculate_mad(&values);

    // ASSERT: MAD = median(|xi - median(x)|)
    // median = 3.0, deviations = [2, 1, 0, 1, 2], MAD = 1.0
    assert_eq!(mad, 1.0);
}

#[test]
fn test_issue_012_mad_with_outlier() {
    // ARRANGE: Dataset with outlier
    let values = vec![1.0, 2.0, 3.0, 4.0, 100.0]; // 100.0 is outlier

    // ACT
    let mad = calculate_mad(&values);

    // ASSERT: MAD should be robust to outlier
    // median = 3.0, deviations = [2, 1, 0, 1, 97], MAD = 1.0
    assert_eq!(mad, 1.0);
}

#[test]
fn test_issue_012_detect_outliers_none() {
    // ARRANGE: Normal distribution, no outliers
    let values = vec![10.0, 11.0, 12.0, 13.0, 14.0];

    // ACT: Detect outliers with 3.0 threshold (standard)
    let outliers = detect_outliers(&values, 3.0);

    // ASSERT: No outliers
    assert!(outliers.is_empty());
}

#[test]
fn test_issue_012_detect_outliers_single() {
    // ARRANGE: Dataset with one clear outlier
    let values = vec![10.0, 11.0, 12.0, 13.0, 100.0];

    // ACT
    let outliers = detect_outliers(&values, 3.0);

    // ASSERT: Index 4 (100.0) is outlier
    assert_eq!(outliers.len(), 1);
    assert_eq!(outliers[0], 4);
}

#[test]
fn test_issue_012_detect_outliers_multiple() {
    // ARRANGE: Dataset with multiple outliers
    let values = vec![10.0, 11.0, 12.0, 100.0, 200.0];

    // ACT
    let outliers = detect_outliers(&values, 3.0);

    // ASSERT: Indices 3 and 4 are outliers
    assert_eq!(outliers.len(), 2);
    assert!(outliers.contains(&3));
    assert!(outliers.contains(&4));
}

#[test]
fn test_issue_012_statistics_includes_mad() {
    // ARRANGE
    let values = vec![10.0, 11.0, 12.0, 13.0, 14.0];

    // ACT
    let stats = Statistics::calculate(&values);

    // ASSERT: Statistics should include MAD
    assert!(stats.mad_ms > 0.0);
}

#[test]
fn test_issue_012_statistics_includes_outliers() {
    // ARRANGE: Dataset with outlier
    let values = vec![10.0, 11.0, 12.0, 13.0, 100.0];

    // ACT
    let stats = Statistics::calculate(&values);

    // ASSERT: Outliers should be detected
    assert_eq!(stats.outlier_indices.len(), 1);
    assert_eq!(stats.outlier_indices[0], 4);
}

// ============================================================================
// Issue #12 Phase 1: Geometric & Harmonic Mean Tests (RED Phase)
// ============================================================================

#[test]
fn test_issue_012_geometric_mean() {
    // ARRANGE
    let values = vec![1.0, 2.0, 4.0, 8.0];

    // ACT
    let geo_mean = calculate_geometric_mean(&values);

    // ASSERT: (1 * 2 * 4 * 8)^(1/4) = 2.828...
    assert!((geo_mean - 2.828).abs() < 0.01);
}

#[test]
fn test_issue_012_harmonic_mean() {
    // ARRANGE
    let values = vec![1.0, 2.0, 4.0];

    // ACT
    let harm_mean = calculate_harmonic_mean(&values);

    // ASSERT: 3 / (1/1 + 1/2 + 1/4) = 3 / 1.75 = 1.714...
    assert!((harm_mean - 1.714).abs() < 0.01);
}

include!("bench_tests_extracted_issue.rs");