Add registered SynthID phase probing

tests/test_synthid_phase_carrier.py

@@ -118,3 +118,30 @@ def test_scoring_can_canonicalize_geometry(tmp_path: Path) -> None:
 
     assert score.path == str(mismatch)
     assert score.peak_count == 4
+
+
+def test_translation_search_recovers_shifted_carrier(tmp_path: Path) -> None:
+    positives: list[Path] = []
+    for index in range(4):
+        path = tmp_path / f"positive-{index}.png"
+        _write_image(path, phase=0.4, seed=index)
+        positives.append(path)
+    heldout = tmp_path / "heldout.png"
+    shifted = tmp_path / "shifted.png"
+    _write_image(heldout, phase=0.4, seed=10)
+    with Image.open(heldout) as source:
+        pixels = np.asarray(source).copy()
+    Image.fromarray(np.roll(pixels, shift=(1, 1), axis=(0, 1)), mode="RGB").save(shifted)
+    model = carrier.discover_model(positives, peak_count=8, min_radius=1.0)
+
+    fixed = carrier.score_image(shifted, model)
+    unregistered = carrier.score_translations(shifted, model, max_shift=0)
+    registered = carrier.score_translations(shifted, model, max_shift=2)
+
+    assert unregistered.score == pytest.approx(fixed.score)
+    assert unregistered.active_weight_fraction == pytest.approx(fixed.active_weight_fraction)
+    assert registered.score > fixed.score
+    assert abs(registered.row_shift) <= 2
+    assert abs(registered.column_shift) <= 2
+    with pytest.raises(ValueError, match="between 0 and 32"):
+        carrier.score_translations(shifted, model, max_shift=33)

tests/test_synthid_v3_codebook_probe.py

@@ -30,6 +30,29 @@ def _write_codebook(path: Path, *, height: int, width: int, phase: float) -> Non
     np.savez(path, **payload)
 
 
+def _write_dense_codebook(path: Path, *, height: int, width: int, phase: float) -> None:
+    half_width = width // 2 + 1
+    magnitudes = np.zeros((height, half_width, 3), dtype=np.float16)
+    phases = np.zeros_like(magnitudes)
+    coherence = np.zeros_like(magnitudes, dtype=np.uint8)
+    rows = np.asarray([7, 11, 13, 17])
+    columns = np.asarray([5, 9, 12, 15])
+    for channel in range(3):
+        magnitudes[rows, columns, channel] = np.log2(1.0 + np.asarray([1000.0, 10.0, 10.0, 10.0]))
+        phases[rows, columns, channel] = phase
+        coherence[rows, columns, channel] = 255
+    np.savez(
+        path,
+        format_version=np.asarray(2),
+        **{
+            f"{height}x{width}/sparse": np.asarray(0),
+            f"{height}x{width}/mag": magnitudes,
+            f"{height}x{width}/phase": phases,
+            f"{height}x{width}/cons": coherence,
+        },
+    )
+
+
 def _write_carrier(path: Path, *, height: int, width: int, phase: float) -> None:
     yy, xx = np.mgrid[:height, :width]
     carrier = 80.0 + 20.0 * np.cos(2.0 * np.pi * (7.0 * yy / height + 5.0 * xx / width) + phase)
@@ -51,6 +74,40 @@ def test_numeric_codebook_scores_matching_phase(tmp_path: Path) -> None:
     assert score.phase_score > 0.0
 
 
+def test_dense_numeric_codebook_scores_matching_phase(tmp_path: Path) -> None:
+    height = width = 64
+    codebook = tmp_path / "dense-codebook.npz"
+    image = tmp_path / "image.png"
+    _write_dense_codebook(codebook, height=height, width=width, phase=0.4)
+    _write_carrier(image, height=height, width=width, phase=0.4)
+
+    model = probe.load_v3_model(codebook, height=height, width=width, peak_count=4, min_radius=1.0)
+    score = probe.score_image(image, model)
+
+    assert score.peak_count == 4
+    assert score.phase_score > 0.0
+
+
+def test_translation_search_recovers_shifted_carrier(tmp_path: Path) -> None:
+    height = width = 64
+    codebook = tmp_path / "codebook.npz"
+    image = tmp_path / "image.png"
+    shifted = tmp_path / "shifted.png"
+    _write_codebook(codebook, height=height, width=width, phase=0.4)
+    _write_carrier(image, height=height, width=width, phase=0.4)
+    with Image.open(image) as source:
+        pixels = np.asarray(source).copy()
+    Image.fromarray(np.roll(pixels, shift=(1, 1), axis=(0, 1)), mode="RGB").save(shifted)
+    model = probe.load_v3_model(codebook, height=height, width=width, peak_count=4, min_radius=1.0)
+
+    fixed = probe.score_image(shifted, model)
+    registered = probe.score_translations(shifted, model, max_shift=2)
+
+    assert registered.phase_score > fixed.phase_score
+    assert abs(registered.row_shift) <= 2
+    assert abs(registered.column_shift) <= 2
+
+
 def test_rejects_wrong_format(tmp_path: Path) -> None:
     artifact = tmp_path / "bad.npz"
     np.savez(artifact, format_version=np.asarray(1))