exg 0.0.6

EXG (EEG/ECG/EMG) preprocessing — native Rust DSP + FIF reader, numerical parity with MNE-Python
Documentation 
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mod common;
use common::{load_vectors, max_abs_diff};
use exg::normalize::{baseline_correct_inplace, zscore_global_inplace};
use ndarray::{Array2, Array3};

#[test]
fn zscore_matches_numpy() {
    let vecs = load_vectors("zscore_global");
    let x_arr = vecs.get("input").unwrap();
    let y_ref = vecs.get("output").unwrap();
    let mean_ref = vecs.get("mean").unwrap()[[0]];
    let std_ref = vecs.get("std").unwrap()[[0]];

    let shape = x_arr.shape();
    let mut data =
        Array2::from_shape_vec((shape[0], shape[1]), x_arr.iter().cloned().collect()).unwrap();

    let (mean, std) = zscore_global_inplace(&mut data);

    approx::assert_abs_diff_eq!(mean, mean_ref, epsilon = 1e-4_f32);
    approx::assert_abs_diff_eq!(std, std_ref, epsilon = 1e-4_f32);

    let got_dyn = data.into_dyn();
    let max_err = max_abs_diff(&got_dyn, y_ref);
    assert!(max_err < 1e-5, "max abs error {max_err:.2e} >= 1e-5");
}

#[test]
fn zscore_postconditions() {
    let vecs = load_vectors("zscore_global");
    let x_arr = vecs.get("input").unwrap();
    let shape = x_arr.shape();
    let mut data =
        Array2::from_shape_vec((shape[0], shape[1]), x_arr.iter().cloned().collect()).unwrap();
    zscore_global_inplace(&mut data);

    let n = data.len() as f32;
    let mean: f32 = data.iter().sum::<f32>() / n;
    let std: f32 = (data.iter().map(|&v| (v - mean).powi(2)).sum::<f32>() / n).sqrt();

    assert!(mean.abs() < 1e-4, "post-zscore mean = {mean:.2e}");
    approx::assert_abs_diff_eq!(std, 1.0, epsilon = 1e-3_f32);
}

#[test]
fn baseline_correction_matches_mne() {
    let vecs = load_vectors("baseline_correction");
    let x_arr = vecs.get("input").expect("missing 'input'");
    let y_ref = vecs.get("output").expect("missing 'output'");

    let s = x_arr.shape();
    let (n_e, n_c, n_t) = (s[0], s[1], s[2]);
    let mut epochs =
        Array3::from_shape_vec((n_e, n_c, n_t), x_arr.iter().cloned().collect()).unwrap();

    baseline_correct_inplace(&mut epochs);

    let got_dyn = epochs.into_dyn();
    let max_err = max_abs_diff(&got_dyn, y_ref);
    assert!(max_err < 1e-5, "max abs error {max_err:.2e} >= 1e-5");
}

#[test]
fn baseline_per_channel_mean_is_zero() {
    let vecs = load_vectors("baseline_correction");
    let x_arr = vecs.get("input").unwrap();
    let s = x_arr.shape();
    let mut epochs =
        Array3::from_shape_vec((s[0], s[1], s[2]), x_arr.iter().cloned().collect()).unwrap();
    baseline_correct_inplace(&mut epochs);

    for e in 0..s[0] {
        for c in 0..s[1] {
            let m = epochs.slice(ndarray::s![e, c, ..]).mean().unwrap();
            assert!(
                m.abs() < 1e-4,
                "epoch={e} ch={c} mean={m:.2e} after baseline correction"
            );
        }
    }
}