low pri features

tests/node_tests/dwt_anisotropy.py

@@ -0,0 +1,64 @@
+import numpy as np
+import pytest
+from tests.node_tests._shared import make_field
+
+
+def test_output_shape():
+    """Anisotropy map shape must match the input field."""
+    from backend.nodes.dwt_anisotropy import DWTAnisotropy
+
+    node = DWTAnisotropy()
+    field = make_field(shape=(64, 64))
+    aniso_field, stats = node.process(field, n_levels=4, ratio_threshold=0.2)
+    assert aniso_field.data.shape == (64, 64)
+
+
+def test_isotropic_surface():
+    """A random isotropic surface should have X/Y energy ratios near 1.0."""
+    from backend.nodes.dwt_anisotropy import DWTAnisotropy
+
+    rng = np.random.default_rng(42)
+    # Use a larger field so deeper levels still have enough coefficients
+    data = rng.standard_normal((128, 128))
+    field = make_field(data=data)
+    node = DWTAnisotropy()
+    aniso_field, stats = node.process(field, n_levels=3, ratio_threshold=0.2)
+
+    for row in stats:
+        assert 0.5 < row["ratio"] < 2.0, (
+            f"Level {row['level']} ratio {row['ratio']:.3f} too far from 1.0 for isotropic surface"
+        )
+
+
+def test_statistics_table():
+    """Statistics output is a list of dicts with the expected keys."""
+    from backend.nodes.dwt_anisotropy import DWTAnisotropy
+
+    node = DWTAnisotropy()
+    field = make_field(shape=(64, 64))
+    aniso_field, stats = node.process(field, n_levels=3, ratio_threshold=0.2)
+
+    assert isinstance(stats, list)
+    assert len(stats) == 3
+    expected_keys = {"level", "x_energy", "y_energy", "ratio", "anisotropic"}
+    for row in stats:
+        assert isinstance(row, dict)
+        assert set(row.keys()) == expected_keys
+
+
+def test_anisotropic_detection():
+    """Horizontal stripes should produce a ratio clearly different from 1.0."""
+    from backend.nodes.dwt_anisotropy import DWTAnisotropy
+
+    # Create horizontal stripes: constant along columns, varying along rows
+    data = np.tile(np.sin(np.linspace(0, 10 * np.pi, 64)), (64, 1))
+    field = make_field(data=data)
+    node = DWTAnisotropy()
+    aniso_field, stats = node.process(field, n_levels=4, ratio_threshold=0.2)
+
+    # At least one level should show a ratio far from 1.0
+    has_anisotropic = any(abs(row["ratio"] - 1.0) > 0.2 for row in stats)
+    assert has_anisotropic, (
+        f"Expected anisotropic detection for horizontal stripes, ratios: "
+        f"{[row['ratio'] for row in stats]}"
+    )