add masking to stats

backend/nodes/statistics.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 import numpy as np
 from backend.node_registry import register_node
 from backend.data_types import DataField, RecordTable
+from backend.nodes.helpers import mask_to_bool
 
 
 @register_node(display_name="Statistics")
@@ -11,7 +12,10 @@ class Statistics:
         return {
             "required": {
                 "field": ("DATA_FIELD",),
-            }
+            },
+            "optional": {
+                "mask": ("IMAGE",),
+            },
         }
 
     OUTPUTS = (
@@ -21,13 +25,24 @@ class Statistics:
 
     DESCRIPTION = (
         "Compute basic surface statistics: min, max, mean, RMS roughness, median, "
-        "and skewness."
+        "and skewness. When a mask is provided, only pixels inside the mask are "
+        "included."
     )
 
     KEYWORDS = ("mean", "rms", "min", "max", "skewness", "kurtosis", "median", "roughness")
 
-    def process(self, field: DataField) -> tuple:
+    def process(self, field: DataField, mask: np.ndarray | None = None) -> tuple:
         d = field.data
+        if mask is not None:
+            selector = mask_to_bool(mask)
+            if selector.shape != d.shape:
+                raise ValueError(
+                    f"Mask shape {selector.shape} does not match field shape {d.shape}"
+                )
+            d = d[selector]
+            if d.size == 0:
+                raise ValueError("Mask selects no pixels")
+
         mean = float(d.mean())
         rms = float(np.sqrt(np.mean((d - mean) ** 2)))
         skewness = float(np.mean(((d - mean) / rms) ** 3)) if rms > 0 else 0.0

docs/nodes/Statistics.md

@@ -7,6 +7,7 @@ Compute basic surface statistics: min, max, mean, RMS roughness, median, and ske
 | Name | Type | Required | Description |
 |------|------|----------|-------------|
 | field | DATA_FIELD | Yes | Input field to analyze |
+| mask | IMAGE | No | Optional binary mask — only pixels inside the mask contribute to the statistics |
 
 ## Outputs
 
@@ -20,4 +21,4 @@ None.
 
 ## Notes
 
-- None.
+- When a mask is provided, it must match the field's pixel resolution. Only pixels where the mask is non-zero are included in the statistics.

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