clean up node naming

backend/execution.py

@@ -585,13 +585,16 @@ class ExecutionEngine:
             plt.close(fig)
             fallback_image = f"data:image/png;base64,{base64.b64encode(buf.getvalue()).decode()}"
 
-            return {
+            result_dict = {
                 "kind": "line_plot",
                 "line": y.tolist(),
                 "x_axis": x.tolist(),
                 "interactive": False,
                 "fallback_image": fallback_image,
             }
+            if y_meta is not None and y_meta.x_unit:
+                result_dict["x_unit"] = y_meta.x_unit
+            return result_dict
         except Exception:
             return None
 

backend/nodes/__init__.py

@@ -1,63 +1,60 @@
 # Import all node modules to trigger @register_node decorators.
 from backend.nodes import (
     # IO
+    colormap,
+    crop_resize,
+    fft_2d_invert,
+    filter_fft_1d,
+    filter_fft_2d,
+    filter_gaussian,
+    filter_median,
+    flip,
+    font,
     image,
-    ibw_note,
     image_demo,
     folder,
     coordinate,
     coordinate_pair,
+    level_facet,
+    level_plane,
+    level_poly,
+    mask_draw,
+    mask_threshold,
+    note,
     number,
     range_slider,
+    rotate,
     save,
-    save_image,
-    # Filters
-    gaussian_filter,
-    median_filter,
     edge_detect,
-    fft_filter_1d,
-    fft_filter_2d,
-    # Modify
     colormap_adjust,
-    crop_resize_field,
-    rotate_field,
-    flip_field,
-    # Level
-    plane_level_field,
-    facet_level_field,
-    poly_level_field,
     fix_zero,
     line_correction,
     # Mask
-    draw_mask,
-    threshold_mask,
     mask_morphology,
     mask_invert,
     mask_operations,
     grain_distance_transform,
+    save_layers,
     # Correction
     scar_removal,
     # Display
-    color_map,
-    font_node,
     annotations,
     angle_measure,
     markup,
     preview_image,
+    statistics,
     view_3d,
     print_table,
     value_display,
     # Analysis
     curvature,
     fractal_dimension,
-    statistics_node,
     histogram,
     acf_2d,
     acf_1d,
     cursors,
     fft_2d,
     psdf,
-    inverse_fft_2d,
     cross_section,
     stats,
     watershed_segmentation,

backend/nodes/cursors.py

@@ -98,6 +98,7 @@ class Cursors:
             "section_title": "Cursors",
             "line": y.tolist(),
             "x_axis": x.tolist(),
+            "x_unit": x_unit,
             "x1": x1,
             "x2": x2,
             "y1": float(y1),

backend/nodes/fft_1d.py

@@ -21,7 +21,7 @@ class FFT1D:
     
     OUTPUTS = (
         ("LINE", "frequency_plot"),
-        ('RECORD_TABLE', 'measurement'),
+        ('RECORD_TABLE', 'max'),
     )
 
     FUNCTION = "process"

backend/nodes/fft_2d.py

@@ -9,7 +9,7 @@ from backend.nodes.spectral_common import (
 )
 
 
-@register_node(display_name="2D FFT")
+@register_node(display_name="FFT 2D")
 class FFT2D:
     @classmethod
     def INPUT_TYPES(cls):

backend/nodes/filter_fft_1d.py

@@ -5,7 +5,7 @@ from backend.data_types import LineData
 from backend.nodes.helpers import _cached_1d_transfer
 
 
-@register_node(display_name="1D FFT Filter")
+@register_node(display_name="FFT Filter 1D")
 class FFTFilter1D:
     """Bandpass / lowpass / highpass / notch filtering of 1-D line profiles.
 

backend/nodes/filter_fft_2d.py

@@ -5,7 +5,7 @@ from backend.data_types import DataField
 from backend.nodes.helpers import _cached_2d_transfer
 
 
-@register_node(display_name="2D FFT Filter")
+@register_node(display_name="FFT Filter 2D")
 class FFTFilter2D:
     """Frequency-domain filtering of 2-D data fields (images).
 

backend/nodes/histogram.py

@@ -80,6 +80,7 @@ class Histogram:
             "section_title": "Histogram",
             "line": counts.tolist(),
             "x_axis": bin_centers.astype(np.float64).tolist(),
+            "x_unit": field.si_unit_z,
             "x1": float(np.clip(x1, 0.0, 1.0)),
             "x2": float(np.clip(x2, 0.0, 1.0)),
             "y1": float(y1),

backend/nodes/mask_threshold.py

@@ -1,8 +1,8 @@
 from __future__ import annotations
 import numpy as np
 from backend.node_registry import register_node
-from backend.execution_context import emit_preview
-from backend.data_types import DataField, encode_preview
+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
 
 
@@ -15,14 +15,15 @@ class ThresholdMask:
         return {
             "required": {
                 "field": ("DATA_FIELD",),
-                "method": (["otsu", "absolute", "relative"],),
-                "threshold": ("FLOAT", {"default": 0.0, "min": -1e9, "max": 1e9, "step": 0.001}),
+                "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"
 
@@ -38,6 +39,12 @@ class ThresholdMask:
     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)
@@ -49,12 +56,31 @@ class ThresholdMask:
         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
 
-        overlay = _mask_overlay(field, mask)
-        emit_preview(encode_preview(overlay))
+        emit_preview(encode_preview(_mask_overlay(field, mask)))
 
-        return (mask,)
+        table = RecordTable([
+            {"quantity": "threshold", "value": threshold, "unit": field.si_unit_xy},
+        ])
+        
+        return (mask, table)