low pri features

tests/node_tests/deconvolution.py

@@ -0,0 +1,42 @@
+import numpy as np
+import pytest
+from tests.node_tests._shared import make_field
+
+
+def test_wiener_preserves_shape():
+    from backend.nodes.deconvolution import Deconvolution
+
+    node = Deconvolution()
+    field = make_field(shape=(32, 32))
+    result, = node.process(field, "wiener", 2.0, 0.01, 10)
+    assert result.data.shape == (32, 32)
+    assert np.isfinite(result.data).all()
+
+
+def test_richardson_lucy_preserves_shape():
+    from backend.nodes.deconvolution import Deconvolution
+
+    node = Deconvolution()
+    field = make_field(data=np.abs(np.random.default_rng(42).standard_normal((32, 32))) + 0.1)
+    result, = node.process(field, "richardson_lucy", 2.0, 0.01, 5)
+    assert result.data.shape == (32, 32)
+    assert np.isfinite(result.data).all()
+
+
+def test_wiener_flat_field():
+    from backend.nodes.deconvolution import Deconvolution
+
+    node = Deconvolution()
+    field = make_field(data=np.ones((32, 32)))
+    result, = node.process(field, "wiener", 2.0, 0.01, 10)
+    # A flat field convolved with anything is still flat; Wiener should preserve it
+    assert result.data.shape == (32, 32)
+
+
+def test_unknown_method():
+    from backend.nodes.deconvolution import Deconvolution
+
+    node = Deconvolution()
+    field = make_field(shape=(32, 32))
+    with pytest.raises(ValueError):
+        node.process(field, "unknown", 2.0, 0.01, 10)