low pri features

tests/node_tests/distribution_coercion.py

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+import numpy as np
+from tests.node_tests._shared import make_field
+
+
+def test_output_shape():
+    from backend.nodes.distribution_coercion import DistributionCoercion
+
+    node = DistributionCoercion()
+    field = make_field(shape=(48, 64))
+
+    for dist in ("uniform", "gaussian", "levels"):
+        (result,) = node.process(field, distribution=dist, n_levels=4, processing="field")
+        assert result.data.shape == field.data.shape
+
+
+def test_uniform_distribution():
+    from backend.nodes.distribution_coercion import DistributionCoercion
+
+    node = DistributionCoercion()
+    rng = np.random.default_rng(7)
+    data = rng.exponential(scale=2.0, size=(64, 64))
+    field = make_field(data=data)
+
+    (result,) = node.process(field, distribution="uniform", n_levels=4, processing="field")
+
+    assert np.isclose(result.data.min(), data.min())
+    assert np.isclose(result.data.max(), data.max())
+
+    # Histogram should be roughly uniform — check that no bin has more than
+    # 2x the expected count.
+    counts, _ = np.histogram(result.data.ravel(), bins=10)
+    expected = result.data.size / 10
+    assert all(c < 2.0 * expected for c in counts)
+
+
+def test_levels_count():
+    from backend.nodes.distribution_coercion import DistributionCoercion
+
+    node = DistributionCoercion()
+    field = make_field(shape=(64, 64))
+
+    for n in (2, 5, 10):
+        (result,) = node.process(field, distribution="levels", n_levels=n, processing="field")
+        assert len(np.unique(result.data)) == n
+
+
+def test_row_mode():
+    from backend.nodes.distribution_coercion import DistributionCoercion
+
+    node = DistributionCoercion()
+    field = make_field(shape=(32, 48))
+
+    (result,) = node.process(field, distribution="uniform", n_levels=4, processing="rows")
+    assert result.data.shape == field.data.shape
+
+    # Each row should span the row's own min/max
+    for i in range(field.data.shape[0]):
+        row_in = field.data[i]
+        row_out = result.data[i]
+        assert np.isclose(row_out.min(), row_in.min())
+        assert np.isclose(row_out.max(), row_in.max())