low pri features

tests/node_tests/drift_correction.py

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+import numpy as np
+import pytest
+from tests.node_tests._shared import make_field
+
+
+def test_drift_correction_flat():
+    from backend.nodes.drift_correction import DriftCorrection
+
+    node = DriftCorrection()
+    field = make_field(data=np.zeros((32, 32)))
+    result, = node.process(field, "previous_row", "horizontal")
+    assert result.data.shape == (32, 32)
+    assert np.allclose(result.data, 0.0, atol=1e-10)
+
+
+def test_drift_correction_preserves_shape():
+    from backend.nodes.drift_correction import DriftCorrection
+
+    node = DriftCorrection()
+    field = make_field(shape=(48, 64))
+    for ref in ("previous_row", "mean_row"):
+        for direction in ("horizontal", "vertical"):
+            result, = node.process(field, ref, direction)
+            assert result.data.shape == (48, 64)
+
+
+def test_drift_correction_reduces_drift():
+    """A field with artificial row-by-row drift should have less variance after correction."""
+    from backend.nodes.drift_correction import DriftCorrection
+
+    node = DriftCorrection()
+    rng = np.random.default_rng(42)
+    base = rng.standard_normal((32, 64))
+    # Add artificial drift: shift each row by cumulative offset
+    drifted = base.copy()
+    for i in range(1, 32):
+        drifted[i] = np.roll(base[i], i)
+
+    field = make_field(data=drifted)
+    result, = node.process(field, "previous_row", "horizontal")
+    # The corrected field should have lower inter-row variance
+    row_means_before = np.var(np.diff(drifted, axis=0))
+    row_means_after = np.var(np.diff(result.data, axis=0))
+    assert row_means_after <= row_means_before
+
+
+def test_drift_correction_mean_row_reference():
+    from backend.nodes.drift_correction import DriftCorrection
+
+    node = DriftCorrection()
+    field = make_field(shape=(32, 32))
+    result, = node.process(field, "mean_row", "horizontal")
+    assert result.data.shape == (32, 32)