tcjs/tests/calculator_tests.rs

use ceramic_radioactivity::{
    calculate_sample, AcceptanceLimits, CalibrationParams, Conclusion, NuclideMeasurements,
    SampleInput,
};

fn default_input() -> SampleInput {
    SampleInput {
        ra: NuclideMeasurements {
            measured_values: vec![100.0, 102.0, 98.0, 101.0, 99.0, 100.0],
            calibration: CalibrationParams {
                factor: 0.916,
                expanded_uncertainty_percent: 6.3,
                coverage_factor: 2.0,
            },
        },
        th: NuclideMeasurements {
            measured_values: vec![110.0, 111.0, 109.0, 110.0, 112.0, 108.0],
            calibration: CalibrationParams {
                factor: 0.884,
                expanded_uncertainty_percent: 6.9,
                coverage_factor: 2.0,
            },
        },
        k: NuclideMeasurements {
            measured_values: vec![560.0, 565.0, 555.0, 562.0, 558.0, 561.0],
            calibration: CalibrationParams {
                factor: 0.961,
                expanded_uncertainty_percent: 6.7,
                coverage_factor: 2.0,
            },
        },
        limits: AcceptanceLimits {
            ira_limit: 1.0,
            ir_limit: 1.0,
        },
    }
}

#[test]
fn calculates_indices_and_ok_conclusion_for_six_measurements() {
    let result = calculate_sample(default_input()).expect("valid sample should calculate");

    assert_close(result.ra.mean_measured, 100.0, 1e-9);
    assert_close(result.ra.mean_calibrated, 91.6, 1e-9);
    assert_close(result.th.mean_calibrated, 97.24, 1e-9);
    assert_close(result.k.mean_calibrated, 538.320_166_666_666_6, 1e-9);
    assert_close(result.ira.value, 0.458, 1e-9);
    assert_close(result.ir.value, 0.749_739_035_821_535_9, 1e-9);
    assert_eq!(result.conclusion, Conclusion::Ok);
}

#[test]
fn asks_for_more_measurements_when_uncertainty_is_high_and_n_is_below_six() {
    let mut input = default_input();
    input.ra.measured_values = vec![10.0, 200.0, 400.0];
    input.th.measured_values = vec![10.0, 200.0, 400.0];
    input.k.measured_values = vec![10.0, 200.0, 400.0];

    let result = calculate_sample(input).expect("valid sample should calculate");

    assert_eq!(result.measurement_count, 3);
    assert_eq!(result.conclusion, Conclusion::IncreaseMeasurementsToSix);
}

#[test]
fn asks_for_recalibration_when_uncertainty_is_high_after_six_measurements() {
    let mut input = default_input();
    input.ra.measured_values = vec![10.0, 200.0, 400.0, 10.0, 200.0, 400.0];
    input.th.measured_values = vec![10.0, 200.0, 400.0, 10.0, 200.0, 400.0];
    input.k.measured_values = vec![10.0, 200.0, 400.0, 10.0, 200.0, 400.0];

    let result = calculate_sample(input).expect("valid sample should calculate");

    assert_eq!(result.measurement_count, 6);
    assert_eq!(result.conclusion, Conclusion::RecalibrateInstrument);
}

#[test]
fn rejects_mismatched_measurement_counts() {
    let mut input = default_input();
    input.k.measured_values.pop();

    let err = calculate_sample(input).expect_err("mismatched counts should fail");

    assert!(err.to_string().contains("same measurement count"));
}

#[test]
fn monte_carlo_matches_analytical_mean_and_uncertainty() {
    let result = calculate_sample(default_input()).expect("valid sample should calculate");

    assert_eq!(result.mcm.iterations, 10_000);

    // MCM 仿真均值/标准偏差应与 GUM 解析结果一致（仿真随机误差在 ~1% 量级）。
    assert_close(result.mcm.ira.mean, result.ira.value, 5e-4);
    assert_close(result.mcm.ir.mean, result.ir.value, 5e-4);
    assert_close(
        result.mcm.ira.std_dev,
        result.ira.standard_uncertainty,
        result.ira.standard_uncertainty * 0.05,
    );
    assert_close(
        result.mcm.ir.std_dev,
        result.ir.standard_uncertainty,
        result.ir.standard_uncertainty * 0.05,
    );

    // 95% 包含区间应包住均值。
    assert!(result.mcm.ira.p2_5 < result.mcm.ira.mean);
    assert!(result.mcm.ira.mean < result.mcm.ira.p97_5);

    // 默认样本的 IRa≈0.46、Ir≈0.75 都远低于标准值 1.0，合格概率应为 1。
    assert_close(result.mcm.ira.pass_probability, 1.0, 1e-12);
    assert_close(result.mcm.ir.pass_probability, 1.0, 1e-12);
    assert_close(result.mcm.overall_pass_probability, 1.0, 1e-12);
    assert_close(result.mcm.overall_fail_probability, 0.0, 1e-12);
}

#[test]
fn monte_carlo_is_deterministic_for_same_input() {
    let first = calculate_sample(default_input()).expect("valid sample should calculate");
    let second = calculate_sample(default_input()).expect("valid sample should calculate");

    assert_eq!(first.mcm, second.mcm);
}

#[test]
fn monte_carlo_gives_about_half_pass_probability_when_limit_equals_mean() {
    let mut input = default_input();
    let analytical = calculate_sample(input.clone()).expect("valid sample should calculate");

    // 将 IRa 标准值设为其均值，合格概率应接近 0.5。
    input.limits.ira_limit = analytical.ira.value;
    let result = calculate_sample(input).expect("valid sample should calculate");

    assert_close(result.mcm.ira.pass_probability, 0.5, 0.03);
    assert_close(
        result.mcm.ira.fail_probability,
        1.0 - result.mcm.ira.pass_probability,
        1e-12,
    );
}

fn assert_close(actual: f64, expected: f64, tolerance: f64) {
    assert!(
        (actual - expected).abs() <= tolerance,
        "actual {actual} expected {expected} tolerance {tolerance}"
    );
}