fix naming

tests/node_tests/scar_removal.py

@@ -0,0 +1,48 @@
+import numpy as np
+from backend.node_registry import get_node_info
+from tests.node_tests._shared import make_field
+
+
+def test_scar_removal():
+    from backend.nodes.scar_removal import ScarRemoval
+
+    node = ScarRemoval()
+    info = get_node_info("ScarRemoval")
+    assert info["category"] == "Filter"
+    assert {entry["category"] for entry in info["menu_categories"]} == {"Filter", "Level & Correct"}
+
+    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)