remaining med value features

tests/node_tests/entropy.py

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+import numpy as np
+import pytest
+from tests.node_tests._shared import make_field
+
+
+def test_entropy_uniform_field_low():
+    """A field with a single unique value has zero entropy."""
+    from backend.nodes.entropy import Entropy
+
+    node = Entropy()
+    field = make_field(data=np.full((32, 32), 3.14))
+    h, h_norm = node.process(field, mode="height values", n_bins=256)
+    # All values fall in one bin → p=1 → H = 0
+    assert h == pytest.approx(0.0, abs=1e-10)
+    assert h_norm == pytest.approx(0.0, abs=1e-10)
+
+
+def test_entropy_random_field_positive():
+    """A random field should have positive entropy."""
+    from backend.nodes.entropy import Entropy
+
+    rng = np.random.default_rng(0)
+    field = make_field(data=rng.standard_normal((64, 64)))
+    node = Entropy()
+    h, h_norm = node.process(field, mode="height values", n_bins=256)
+    assert h > 0.0
+    assert 0.0 < h_norm <= 1.0
+
+
+def test_entropy_normalised_leq_one():
+    """Normalised entropy should never exceed 1."""
+    from backend.nodes.entropy import Entropy
+
+    rng = np.random.default_rng(2)
+    field = make_field(data=rng.uniform(0, 1, (64, 64)))
+    node = Entropy()
+    _, h_norm = node.process(field, mode="height values", n_bins=64)
+    assert h_norm <= 1.0 + 1e-12
+
+
+def test_entropy_slope_mode():
+    """Slope mode should work and return valid entropy values."""
+    from backend.nodes.entropy import Entropy
+
+    rng = np.random.default_rng(3)
+    field = make_field(data=rng.standard_normal((32, 32)))
+    node = Entropy()
+    h, h_norm = node.process(field, mode="slope magnitude", n_bins=128)
+    assert h > 0.0
+    assert 0.0 <= h_norm <= 1.0
+
+
+def test_entropy_more_uniform_is_higher():
+    """Uniformly distributed values have higher entropy than a spiked distribution."""
+    from backend.nodes.entropy import Entropy
+
+    rng = np.random.default_rng(4)
+    uniform = rng.uniform(0, 1, (64, 64))
+    spiked = np.zeros((64, 64))
+    spiked[0, 0] = 1.0
+
+    node = Entropy()
+    h_uniform, _ = node.process(make_field(data=uniform), mode="height values", n_bins=64)
+    h_spiked, _ = node.process(make_field(data=spiked), mode="height values", n_bins=64)
+    assert h_uniform > h_spiked
+
+
+def test_entropy_returns_floats():
+    from backend.nodes.entropy import Entropy
+
+    field = make_field()
+    node = Entropy()
+    h, h_norm = node.process(field, mode="height values", n_bins=256)
+    assert isinstance(h, float)
+    assert isinstance(h_norm, float)