low pri features

tests/node_tests/super_resolution.py

@@ -0,0 +1,51 @@
+import numpy as np
+from tests.node_tests._shared import make_field
+
+
+def test_output_shape_single():
+    """Single input with upscale=2 gives 2x output size."""
+    from backend.nodes.super_resolution import SuperResolution
+
+    field = make_field(shape=(32, 32))
+    node = SuperResolution()
+    result, = node.process(field, upscale=2)
+    assert result.data.shape == (64, 64)
+
+
+def test_output_shape_multi():
+    """Multiple inputs still give 2x output size."""
+    from backend.nodes.super_resolution import SuperResolution
+
+    rng = np.random.default_rng(0)
+    f1 = make_field(data=rng.standard_normal((32, 32)))
+    f2 = make_field(data=rng.standard_normal((32, 32)))
+    f3 = make_field(data=rng.standard_normal((32, 32)))
+    node = SuperResolution()
+    result, = node.process(f1, upscale=2, field2=f2, field3=f3)
+    assert result.data.shape == (64, 64)
+
+
+def test_finite_values():
+    """Output values must all be finite."""
+    from backend.nodes.super_resolution import SuperResolution
+
+    rng = np.random.default_rng(1)
+    f1 = make_field(data=rng.standard_normal((32, 32)))
+    f2 = make_field(data=rng.standard_normal((32, 32)))
+    node = SuperResolution()
+    result, = node.process(f1, upscale=2, field2=f2)
+    assert np.all(np.isfinite(result.data))
+
+
+def test_upscale_factor():
+    """Output dimensions should equal input dimensions times upscale factor."""
+    from backend.nodes.super_resolution import SuperResolution
+
+    field = make_field(shape=(32, 32))
+    node = SuperResolution()
+    for factor in (2, 3, 4):
+        result, = node.process(field, upscale=factor)
+        expected = (32 * factor, 32 * factor)
+        assert result.data.shape == expected, (
+            f"upscale={factor}: expected {expected}, got {result.data.shape}"
+        )