add line correction and scar removal nodes

tests/test_nodes.py

@@ -571,6 +571,130 @@ def test_fix_zero():
     print("  PASS\n")
 
 
+def test_line_correction():
+    print("=== Test: LineCorrection ===")
+    from backend.node_registry import get_node_info
+    from backend.nodes.line_correction import LineCorrection
+
+    node = LineCorrection()
+    assert get_node_info("LineCorrection")["category"] == "Flatten"
+
+    rows = 96
+    cols = 128
+    y = np.linspace(0.0, 1.0, rows, dtype=np.float64)
+    x = np.linspace(-1.0, 1.0, cols, dtype=np.float64)
+    signal = (
+        0.15 * np.sin(8.0 * np.pi * x)[None, :]
+        + 0.05 * np.cos(4.0 * np.pi * y)[:, None]
+    )
+    row_offsets = 1.5 * np.sin(3.0 * np.pi * y) + 0.25 * np.cos(7.0 * np.pi * y)
+    field = make_field(
+        data=signal + row_offsets[:, None],
+        xreal=2.5e-6,
+        yreal=1.5e-6,
+    )
+
+    corrected, background, shifts = node.process(
+        field,
+        method="median",
+        direction="horizontal",
+        masking="ignore",
+        trim_fraction=0.05,
+        polynomial_degree=1,
+    )
+    expected_shifts = row_offsets - row_offsets.mean()
+    assert corrected.data.shape == field.data.shape
+    assert background.data.shape == field.data.shape
+    assert np.allclose(corrected.data + background.data, field.data)
+    assert isinstance(shifts, LineData)
+    assert shifts.x_unit == field.si_unit_xy
+    assert shifts.y_unit == field.si_unit_z
+    assert np.isclose(shifts.x_axis[0], 0.0)
+    assert np.isclose(shifts.x_axis[-1], field.yreal)
+    assert np.corrcoef(shifts.data, expected_shifts)[0, 1] > 0.999
+    assert corrected.data.mean(axis=1).std() < field.data.mean(axis=1).std() * 0.03
+
+    poly_background = (
+        row_offsets[:, None]
+        + (0.35 * y - 0.15)[:, None] * x[None, :]
+        + (0.10 + 0.05 * y)[:, None] * (x[None, :] ** 2)
+    )
+    poly_signal = 0.08 * np.sin(10.0 * np.pi * x)[None, :] * (1.0 + 0.15 * np.cos(2.0 * np.pi * y)[:, None])
+    poly_field = make_field(data=poly_signal + poly_background)
+
+    leveled, poly_bg, poly_shifts = node.process(
+        poly_field,
+        method="polynomial",
+        direction="horizontal",
+        masking="ignore",
+        trim_fraction=0.05,
+        polynomial_degree=2,
+    )
+    assert np.allclose(leveled.data + poly_bg.data, poly_field.data)
+    assert np.corrcoef(leveled.data.ravel(), poly_signal.ravel())[0, 1] > 0.995
+    assert len(poly_shifts) == rows
+
+    print("  PASS\n")
+
+
+def test_scar_removal():
+    print("=== Test: ScarRemoval ===")
+    from backend.node_registry import get_node_info
+    from backend.nodes.scar_removal import ScarRemoval
+
+    node = ScarRemoval()
+    assert get_node_info("ScarRemoval")["category"] == "Filter"
+
+    rows = 96
+    cols = 128
+    yy, xx = np.mgrid[0:rows, 0:cols]
+    base = (
+        0.005 * xx
+        + 0.01 * yy
+        + 0.12 * np.sin(2.0 * np.pi * xx / cols)
+        + 0.07 * np.cos(2.0 * np.pi * yy / rows)
+    )
+    scarred = base.copy()
+    scarred[24, 20:92] += 1.8
+    scarred[25, 20:92] += 1.6
+    scarred[60, 12:116] -= 1.7
+
+    field = make_field(data=scarred)
+    corrected, scar_mask = node.process(
+        field,
+        scar_type="both",
+        threshold_high=0.6,
+        threshold_low=0.2,
+        min_length=12,
+        max_width=4,
+    )
+
+    mask_bool = scar_mask > 127
+    assert scar_mask.dtype == np.uint8
+    assert scar_mask.shape == field.data.shape
+    assert np.count_nonzero(mask_bool) > 0
+    assert np.count_nonzero(mask_bool[24:26, 20:92]) > 0
+    assert np.count_nonzero(mask_bool[60:61, 12:116]) > 0
+    assert np.allclose(corrected.data[~mask_bool], field.data[~mask_bool])
+
+    before_rmse = np.sqrt(np.mean((field.data[mask_bool] - base[mask_bool]) ** 2))
+    after_rmse = np.sqrt(np.mean((corrected.data[mask_bool] - base[mask_bool]) ** 2))
+    assert after_rmse < before_rmse * 0.35
+
+    clean_corrected, clean_mask = node.process(
+        make_field(data=base),
+        scar_type="both",
+        threshold_high=0.6,
+        threshold_low=0.2,
+        min_length=12,
+        max_width=4,
+    )
+    assert np.count_nonzero(clean_mask) == 0
+    assert np.allclose(clean_corrected.data, base)
+
+    print("  PASS\n")
+
+
 # =========================================================================
 # Analysis (non-FFT)
 # =========================================================================
@@ -2522,6 +2646,8 @@ if __name__ == "__main__":
     test_plane_level()
     test_poly_level()
     test_fix_zero()
+    test_line_correction()
+    test_scar_removal()
 
     # Analysis
     test_statistics()