test(mri): add deterministic synthetic NIfTI fixture (6 subjects × 2 sites)

tests/fixtures/build_mri_fixture.py

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+"""Generate deterministic synthetic MRI fixtures for the Day-3 pipeline tests.
+
+Six 8×8×8 NIfTI volumes split across two simulated sites. Each volume is a
+spherical "brain" with isotropic Gaussian noise plus a per-site additive bias
+that ComBat is expected to remove. The fixture is committed alongside this
+script so test runs are reproducible without re-running.
+
+Channels:
+  - Site A: subject_0, subject_1, subject_2 (bias = +0.0 a.u.)
+  - Site B: subject_3, subject_4, subject_5 (bias = +5.0 a.u.)
+
+NOTE: byte-determinism of the .nii.gz output is coupled to nibabel==5.2.1
+(pinned in requirements.txt) and a fixed nibabel.Nifti1Image header. If the
+nibabel pin is upgraded, re-run this script and commit the rebuilt artifacts
+alongside the dependency bump.
+"""
+from __future__ import annotations
+
+import csv
+from pathlib import Path
+
+import nibabel as nib
+import numpy as np
+
+
+SITE_A_BIAS = 0.0
+SITE_B_BIAS = 5.0
+VOLUME_SHAPE = (8, 8, 8)
+SUBJECTS = (
+    ("subject_0", "A"),
+    ("subject_1", "A"),
+    ("subject_2", "A"),
+    ("subject_3", "B"),
+    ("subject_4", "B"),
+    ("subject_5", "B"),
+)
+
+
+def _spherical_brain(rng: np.random.Generator, bias: float) -> np.ndarray:
+    """Build an 8×8×8 volume: spherical brain (radius 3) + noise + site bias."""
+    d, h, w = VOLUME_SHAPE
+    z, y, x = np.indices((d, h, w))
+    cz, cy, cx = (d - 1) / 2.0, (h - 1) / 2.0, (w - 1) / 2.0
+    radius2 = (z - cz) ** 2 + (y - cy) ** 2 + (x - cx) ** 2
+    brain_mask = radius2 <= 3.0**2
+    # Brain intensity ~10 a.u., background ~0.1 a.u. (so default threshold splits cleanly).
+    volume = np.where(brain_mask, 10.0, 0.1).astype(np.float64)
+    volume += rng.standard_normal(VOLUME_SHAPE) * 0.5
+    volume[brain_mask] += bias
+    return volume
+
+
+def build(out_dir: Path | None = None) -> Path:
+    out = out_dir if out_dir is not None else Path(__file__).parent / "mri_sample"
+    out.mkdir(parents=True, exist_ok=True)
+
+    rng = np.random.default_rng(seed=42)
+    affine = np.eye(4)
+
+    sites_rows: list[tuple[str, str]] = []
+    for subject_id, site in SUBJECTS:
+        bias = SITE_A_BIAS if site == "A" else SITE_B_BIAS
+        volume = _spherical_brain(rng, bias=bias)
+        img = nib.Nifti1Image(volume, affine=affine)
+        nib.save(img, out / f"{subject_id}.nii.gz")
+        sites_rows.append((subject_id, site))
+
+    with (out / "sites.csv").open("w", newline="") as fh:
+        writer = csv.writer(fh)
+        writer.writerow(["subject_id", "site"])
+        writer.writerows(sites_rows)
+    return out
+
+
+if __name__ == "__main__":
+    p = build()
+    print(f"Wrote MRI fixture to {p}")

tests/fixtures/mri_sample/sites.csv

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+subject_id,site
+subject_0,A
+subject_1,A
+subject_2,A
+subject_3,B
+subject_4,B
+subject_5,B