adding more nodes

backend/nodes/outlier_mask.py

@@ -0,0 +1,54 @@
+"""Outlier masking — mark statistical outlier pixels."""
+
+from __future__ import annotations
+
+import numpy as np
+
+from backend.node_registry import register_node
+from backend.data_types import DataField
+from backend.nodes.helpers import bool_to_mask
+
+
+@register_node(display_name="Outlier Mask")
+class OutlierMask:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "field": ("DATA_FIELD",),
+                "sigma_threshold": ("FLOAT", {"default": 3.0, "min": 1.0, "max": 10.0, "step": 0.1}),
+                "mode": (["both", "high", "low"], {"default": "both"}),
+            }
+        }
+
+    OUTPUTS = (
+        ('IMAGE', 'mask'),
+    )
+    FUNCTION = "process"
+
+    DESCRIPTION = (
+        "Create a mask marking pixels that deviate more than N standard "
+        "deviations from the mean. Mode selects whether to flag high outliers, "
+        "low outliers, or both. Quick way to identify noise spikes and defects. "
+    )
+
+    def process(self, field: DataField, sigma_threshold: float, mode: str) -> tuple:
+        data = np.asarray(field.data, dtype=np.float64)
+        mean = data.mean()
+        std = data.std()
+
+        if std < 1e-30:
+            return (bool_to_mask(np.zeros(data.shape, dtype=bool)),)
+
+        z = (data - mean) / std
+
+        if mode == "both":
+            outliers = np.abs(z) > sigma_threshold
+        elif mode == "high":
+            outliers = z > sigma_threshold
+        elif mode == "low":
+            outliers = z < -sigma_threshold
+        else:
+            raise ValueError(f"Unknown mode: {mode!r}")
+
+        return (bool_to_mask(outliers),)