clean up node naming

backend/nodes/mask_threshold.py

@@ -0,0 +1,86 @@
+from __future__ import annotations
+import numpy as np
+from backend.node_registry import register_node
+from backend.execution_context import emit_preview, emit_overlay
+from backend.data_types import DataField, encode_preview, RecordTable
+from backend.nodes.helpers import _mask_overlay
+
+
+@register_node(display_name="Threshold Mask")
+class ThresholdMask:
+    _CUSTOM_PREVIEW = True
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "field": ("DATA_FIELD",),
+                "method": (["absolute", "relative", "otsu"],),
+                "threshold": ("FLOAT", {"default": 0.0, "min": -1e9, "max": 1e9, "step": 0.001, "socket_only": True}),
+                "direction": (["above", "below"],),
+            }
+        }
+
+    OUTPUTS = (
+        ('IMAGE', 'mask'),
+        ('RECORD_TABLE', 'threshold'),
+    )
+    FUNCTION = "process"
+
+    DESCRIPTION = (
+        "Create a binary mask by thresholding data. "
+        "Otsu automatically finds the optimal threshold. "
+        "Equivalent to Gwyddion's threshold and otsu_threshold modules."
+    )
+
+    _broadcast_fn = None
+    _current_node_id: str = ""
+
+    def process(self, field: DataField, method: str, threshold: float, direction: str) -> tuple:
+        data = field.data
+
+        raw_counts, bin_edges = np.histogram(data.ravel(), bins=256)
+        bin_centers = 0.5 * (bin_edges[:-1] + bin_edges[1:])
+        counts = raw_counts.astype(np.float64)
+        xmin = float(bin_centers[0]) if len(bin_centers) else 0.0
+        xmax = float(bin_centers[-1]) if len(bin_centers) else 1.0
+
+        if method == "otsu":
+            from skimage.filters import threshold_otsu
+            t = threshold_otsu(data)
+        elif method == "absolute":
+            t = float(threshold)
+        elif method == "relative":
+            dmin, dmax = data.min(), data.max()
+            t = dmin + float(threshold) * (dmax - dmin)
+        else:
+            raise ValueError(f"Unknown threshold method: {method}")
+
+        span = xmax - xmin if xmax != xmin else 1.0
+        threshold_frac = float(np.clip((t - xmin) / span, 0.0, 1.0))
+
+        emit_overlay({
+            "kind": "threshold_histogram",
+            "section_title": "Histogram",
+            "line": counts.tolist(),
+            "x_axis": bin_centers.tolist(),
+            "x_unit": field.si_unit_z,
+            "threshold_frac": threshold_frac,
+            "x_min": xmin,
+            "x_max": xmax,
+            "method": method,
+            "locked": method == "otsu",
+        })
+
+        if direction == "above":
+            mask = (data >= t).astype(np.uint8) * 255
+        else:
+            mask = (data < t).astype(np.uint8) * 255
+
+        emit_preview(encode_preview(_mask_overlay(field, mask)))
+
+        table = RecordTable([
+            {"quantity": "threshold", "value": threshold, "unit": field.si_unit_xy},
+        ])
+        
+        return (mask, table)