add masking to stats

tests/node_tests/statistics.py

@@ -26,3 +26,46 @@ def test_statistics():
     assert const_stats["RMS"] == 0.0
     assert const_stats["skewness"] == 0.0
     assert const_stats["kurtosis"] == 0.0
+
+
+def test_statistics_with_mask():
+    """A mask restricts the stats to pixels where mask != 0."""
+    from backend.nodes.statistics import Statistics
+    node = Statistics()
+
+    data = np.array([[1, 2], [3, 4]], dtype=np.float64)
+    field = make_field(data=data)
+    # Mask selects only pixels >= 3 (bottom row).
+    mask = np.array([[0, 0], [255, 255]], dtype=np.uint8)
+
+    table, = node.process(field, mask=mask)
+    stats = {row["quantity"]: row["value"] for row in table}
+    assert stats["min"] == 3.0
+    assert stats["max"] == 4.0
+    assert stats["mean"] == 3.5
+
+
+def test_statistics_mask_shape_mismatch():
+    from backend.nodes.statistics import Statistics
+    node = Statistics()
+
+    field = make_field(data=np.zeros((4, 4)))
+    bad_mask = np.zeros((3, 3), dtype=np.uint8)
+    try:
+        node.process(field, mask=bad_mask)
+        raise AssertionError("expected shape mismatch to raise")
+    except ValueError:
+        pass
+
+
+def test_statistics_empty_mask():
+    from backend.nodes.statistics import Statistics
+    node = Statistics()
+
+    field = make_field(data=np.ones((4, 4)))
+    empty_mask = np.zeros((4, 4), dtype=np.uint8)
+    try:
+        node.process(field, mask=empty_mask)
+        raise AssertionError("expected empty mask to raise")
+    except ValueError:
+        pass