low pri features

tests/node_tests/psf_estimation.py

@@ -0,0 +1,56 @@
+import numpy as np
+from scipy.ndimage import gaussian_filter
+from tests.node_tests._shared import make_field
+
+
+def _make_test_pair(shape=(64, 64), sigma=2.0):
+    """Return (measured, ideal) where measured = gaussian_filter(ideal)."""
+    ideal = make_field(shape=shape)
+    measured_data = gaussian_filter(ideal.data, sigma=sigma)
+    measured = make_field(data=measured_data)
+    return measured, ideal
+
+
+def test_output_shape():
+    from backend.nodes.psf_estimation import PSFEstimation
+
+    node = PSFEstimation()
+    measured, ideal = _make_test_pair()
+    psf_field, _ = node.process(measured, ideal, "wiener", 0.01, 32)
+    assert psf_field.data.shape == (32, 32)
+
+
+def test_psf_normalized():
+    from backend.nodes.psf_estimation import PSFEstimation
+
+    node = PSFEstimation()
+    measured, ideal = _make_test_pair()
+    for method in ("wiener", "least_squares", "gaussian_fit"):
+        psf_field, _ = node.process(measured, ideal, method, 0.01, 32)
+        assert abs(psf_field.data.sum() - 1.0) < 1e-6, (
+            f"{method}: PSF sum = {psf_field.data.sum()}"
+        )
+
+
+def test_gaussian_fit_parameters():
+    from backend.nodes.psf_estimation import PSFEstimation
+
+    node = PSFEstimation()
+    measured, ideal = _make_test_pair()
+    _, parameters = node.process(measured, ideal, "gaussian_fit", 0.01, 32)
+    names = {row["quantity"] for row in parameters}
+    assert "sigma_x" in names
+    assert "sigma_y" in names
+    assert "amplitude" in names
+
+
+def test_all_methods_finite():
+    from backend.nodes.psf_estimation import PSFEstimation
+
+    node = PSFEstimation()
+    measured, ideal = _make_test_pair()
+    for method in ("wiener", "least_squares", "gaussian_fit"):
+        psf_field, _ = node.process(measured, ideal, method, 0.01, 32)
+        assert np.isfinite(psf_field.data).all(), (
+            f"{method}: PSF contains non-finite values"
+        )