add nodes, fft acf 1d

backend/execution.py

@@ -224,14 +224,20 @@ class ExecutionEngine:
             return value
 
         if input_type == "INT":
-            numeric = float(value)
+            try:
+                numeric = float(value)
+            except (TypeError, ValueError):
+                return value
             if not isfinite(numeric):
                 raise ValueError(f"Expected a finite numeric value for INT input, got {value!r}")
             rounded = int(abs(numeric) + 0.5)
             return rounded if numeric >= 0 else -rounded
 
         if input_type == "FLOAT":
-            numeric = float(value)
+            try:
+                numeric = float(value)
+            except (TypeError, ValueError):
+                return value
             if not isfinite(numeric):
                 raise ValueError(f"Expected a finite numeric value for FLOAT input, got {value!r}")
             return numeric

backend/node_menu.py

@@ -14,6 +14,7 @@ from typing import Any
 MENU_LAYOUT: dict[str, list[str]] = {
     "Input": [
         "Image",
+        "IBWNote",
         "ImageDemo",
         "Folder",
         "Number",
@@ -55,7 +56,8 @@ MENU_LAYOUT: dict[str, list[str]] = {
         "InverseFFT2D",
         "FFTFilter1D",
         "FFTFilter2D",
-        "ACF",
+        "ACF2D",
+        "ACF1D",
         "PSDF",
     ],
     "Level & Correct": [
@@ -67,13 +69,15 @@ MENU_LAYOUT: dict[str, list[str]] = {
         "ScarRemoval",
     ],
     "Measure": [
+        "FFT1D",
         "AngleMeasure",
         "CrossSection",
         "Histogram",
         "Cursors",
         "Curvature",
         "FractalDimension",
-        "ACF",
+        "ACF2D",
+        "ACF1D",
         "PSDF",
         "Statistics",
         "Stats",

backend/nodes/__init__.py

@@ -2,6 +2,7 @@
 from backend.nodes import (
     # IO
     image,
+    ibw_note,
     image_demo,
     folder,
     coordinate,
@@ -51,7 +52,8 @@ from backend.nodes import (
     fractal_dimension,
     statistics_node,
     histogram,
-    acf,
+    acf_2d,
+    acf_1d,
     cursors,
     fft_2d,
     psdf,
@@ -60,4 +62,5 @@ from backend.nodes import (
     stats,
     watershed_segmentation,
     grain_analysis,
+    fft_1d,
 )

backend/nodes/acf_1d.py

@@ -0,0 +1,61 @@
+from __future__ import annotations
+
+import numpy as np
+
+from backend.node_registry import register_node
+from backend.data_types import LineData, MeasureTable
+from backend.nodes.spectral_common import acf_line_from_data
+
+
+def _first_positive_peak(acf: np.ndarray, lag_axis: np.ndarray) -> float | None:
+    """Return the lag of the first local maximum in the positive-lag half of the ACF."""
+    center = len(acf) // 2
+    pos_acf = acf[center + 1:]
+    pos_lags = lag_axis[center + 1:]
+    for i in range(1, len(pos_acf) - 1):
+        if pos_acf[i] >= pos_acf[i - 1] and pos_acf[i] > pos_acf[i + 1]:
+            return float(pos_lags[i])
+    return None
+
+
+@register_node(display_name="ACF 1D")
+class ACF1D:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "profile": ("LINE", {
+                    "label": "input",
+                    "accepted_types": ["LINE"],
+                }),
+                "level": (["mean", "none"], {"default": "mean"}),
+            }
+        }
+
+    OUTPUTS = (
+        ('LINE', 'acf'),
+        ('MEASURE_TABLE', 'measurement'),
+    )
+    FUNCTION = "process"
+
+    DESCRIPTION = (
+        "Compute the one-dimensional autocorrelation function of a line profile. "
+        "The output is symmetric about zero lag with the lag on the x-axis. "
+        "The measurement table reports the dominant period from the first positive peak."
+    )
+
+    def process(self, profile: LineData, level: str) -> tuple:
+        z = np.asarray(profile, dtype=np.float64)
+        if level == "mean":
+            z = z - z.mean()
+
+        acf_line = acf_line_from_data(profile, z)
+
+        x_unit = profile.x_unit if isinstance(profile, LineData) else ""
+        peak_lag = _first_positive_peak(acf_line.data, acf_line.x_axis)
+
+        rows = []
+        if peak_lag is not None:
+            rows.append({"quantity": "Peak period", "value": peak_lag, "unit": x_unit})
+
+        return (acf_line, MeasureTable(rows))

backend/nodes/acf_2d.py

@@ -5,8 +5,8 @@ from backend.data_types import DataField
 from backend.nodes.spectral_common import acf_field_from_data, preprocess_spectral_data
 
 
-@register_node(display_name="ACF")
+@register_node(display_name="ACF 2D")
-class ACF:
+class ACF2D:
     @classmethod
     def INPUT_TYPES(cls):
         return {

backend/nodes/cursors.py

@@ -106,8 +106,10 @@ class Cursors:
             "b_locked": locked,
         })
 
+        length = float(np.hypot(xb - xa, yb - ya))
         table = MeasureTable([
-            {"quantity": "dx",  "value": xb - xa, "unit": x_unit},
+            {"quantity": "Length", "value": length,  "unit": x_unit},
+            {"quantity": "dx",     "value": xb - xa, "unit": x_unit},
             {"quantity": "dy",  "value": yb - ya, "unit": y_unit},
             {"quantity": "A x", "value": xa, "unit": x_unit},
             {"quantity": "A y", "value": ya, "unit": y_unit},

backend/nodes/fft_1d.py

@@ -0,0 +1,70 @@
+from __future__ import annotations
+
+import numpy as np
+
+from backend.node_registry import register_node
+from backend.data_types import LineData, MeasureTable
+
+
+@register_node(display_name="FFT 1D")
+class FFT1D:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "profile": ("LINE", {
+                    "label": "input",
+                    "accepted_types": ["LINE"],
+                }),
+            }
+        }
+    
+    OUTPUTS = (
+        ("LINE", "frequency_plot"),
+        ('MEASURE_TABLE', 'measurement'),
+    )
+
+    FUNCTION = "process"
+
+    DESCRIPTION = (
+        "Returns the FFT spectrum of the line, and identifies peaks."
+    )
+
+    _broadcast_overlay_fn = None
+    _current_node_id: str = ""
+
+    def process(
+            self, profile,
+    ) -> tuple:
+        line_data = np.asarray(profile, dtype=np.float64)
+        n = len(line_data)
+
+        if isinstance(profile, LineData) and profile.x_axis is not None and len(profile.x_axis) > 1:
+            d = float(profile.x_axis[1] - profile.x_axis[0])
+            spatial_unit = profile.x_unit or "m"
+        else:
+            d = 1.0
+            spatial_unit = "m"
+
+        spectrum = np.abs(np.fft.rfft(line_data))
+        freq_axis = np.fft.rfftfreq(n, d)
+
+        # Exclude DC component, convert to period, sort short→long
+        spectrum = spectrum[1:][::-1]
+        period_axis = (1.0 / freq_axis[1:])[::-1]
+
+        peak_period = float(period_axis[np.argmax(spectrum)])
+
+        table = MeasureTable([
+            {"quantity": "Peak period", "value": peak_period, "unit": spatial_unit},
+        ])
+
+        return (
+            LineData(
+                data=spectrum,
+                x_axis=period_axis,
+                x_unit=spatial_unit,
+                y_unit=profile.y_unit if isinstance(profile, LineData) else "",
+            ),
+            table,
+        )

backend/nodes/histogram.py

@@ -89,8 +89,8 @@ class Histogram:
         })
 
         table = MeasureTable([
-            {"quantity": "delta X",    "value": xb - xa, "unit": field.si_unit_z},
             {"quantity": "delta Y",    "value": yb - ya, "unit": count_unit},
+            {"quantity": "delta X",    "value": xb - xa, "unit": field.si_unit_z},
             {"quantity": "A position", "value": xa, "unit": field.si_unit_z},
             {"quantity": "A count",    "value": ya, "unit": count_unit},
             {"quantity": "B position", "value": xb, "unit": field.si_unit_z},

backend/nodes/ibw_note.py

@@ -0,0 +1,78 @@
+from __future__ import annotations
+
+import re
+
+from backend.node_registry import register_node
+from backend.data_types import MeasureTable
+from backend.nodes.helpers import _resolve_path, _import_ibw_loader
+
+
+def _parse_ibw_note(note_bytes: bytes) -> list[dict]:
+    try:
+        text = note_bytes.decode("utf-8", errors="replace")
+    except Exception:
+        return []
+
+    rows = []
+    for line in text.splitlines():
+        line = line.strip()
+        if not line:
+            continue
+        match = re.match(r'^([^:=]+)[=:](.+)$', line)
+        if not match:
+            continue
+        key = match.group(1).strip()
+        raw_val = match.group(2).strip()
+        if not key:
+            continue
+        try:
+            value = float(raw_val)
+        except (ValueError, TypeError):
+            continue
+        rows.append({"quantity": key, "value": value, "unit": ""})
+
+    return rows
+
+
+@register_node(display_name="IBW Note")
+class IBWNote:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "filename": ("FILE_PICKER", {"default": "", "hide_when_input_connected": "path"}),
+            },
+            "optional": {
+                "path": ("FILE_PATH", {"label": "path"}),
+            },
+        }
+
+    OUTPUTS = (
+        ('MEASURE_TABLE', 'note'),
+    )
+    FUNCTION = "load"
+
+    DESCRIPTION = (
+        "Read the Note metadata from an .ibw file and display numeric entries "
+        "as a measurement table. Non-numeric note entries are skipped."
+    )
+
+    def load(self, filename: str = "", path: str | None = None) -> tuple:
+        selected = str(path).strip() if path is not None else str(filename).strip()
+        if not selected:
+            raise ValueError("No file selected.")
+        path_obj = _resolve_path(selected)
+        if not path_obj.exists():
+            raise FileNotFoundError(f"File not found: {path_obj}")
+        if path_obj.suffix.lower() != ".ibw":
+            raise ValueError(f"Expected an .ibw file, got: {path_obj.suffix}")
+
+        load_ibw = _import_ibw_loader()
+        wave = load_ibw(str(path_obj))
+        note_bytes = wave["wave"].get("note", b"") or b""
+
+        rows = _parse_ibw_note(note_bytes)
+        if not rows:
+            raise ValueError("No numeric metadata found in the .ibw note.")
+
+        return (MeasureTable(rows),)

backend/nodes/spectral_common.py

@@ -127,6 +127,35 @@ def psdf_field_from_data(field: DataField, data: np.ndarray) -> DataField:
     )
 
 
+def acf_line_from_data(profile, data: np.ndarray, *, nrange: int = 0):
+    from scipy.signal import fftconvolve
+    from backend.data_types import LineData
+
+    z = np.asarray(data, dtype=np.float64).ravel()
+    n = len(z)
+    nrange = int(nrange) if nrange else max(1, n // 2)
+    nrange = max(1, min(nrange, n))
+
+    corr_full = fftconvolve(z, z[::-1], mode="full")
+    center = n - 1
+    corr = corr_full[center - (nrange - 1):center + nrange]
+
+    counts = np.array([n - abs(lag) for lag in range(-(nrange - 1), nrange)], dtype=np.float64)
+    acf = corr / counts
+
+    x_unit = profile.x_unit if hasattr(profile, "x_unit") else ""
+    y_unit = _square_unit(profile.y_unit) if hasattr(profile, "y_unit") and profile.y_unit else ""
+
+    if hasattr(profile, "x_axis") and profile.x_axis is not None and len(profile.x_axis) > 1:
+        d = float(profile.x_axis[1] - profile.x_axis[0])
+    else:
+        d = 1.0
+
+    lag_axis = np.arange(-(nrange - 1), nrange, dtype=np.float64) * d
+
+    return LineData(data=acf, x_axis=lag_axis, x_unit=x_unit, y_unit=y_unit)
+
+
 def acf_field_from_data(field: DataField, data: np.ndarray, *, xrange: int = 0, yrange: int = 0) -> DataField:
     from scipy.signal import fftconvolve
 

frontend/src/App.jsx

@@ -905,6 +905,7 @@ function Flow() {
   const [isCanvasRightZooming, setIsCanvasRightZooming] = useState(false);
 
   const flowContainerRef = useRef(null);
+  const panTimerRef = useRef(null);
   const nodeDefsRef = useRef({});
   const nextIdRef = useRef(1);
   const autoRunTimer = useRef(null);
@@ -2753,6 +2754,16 @@ function Flow() {
     }
   }, []);
 
+  const onFlowContainerWheel = useCallback(() => {
+    const container = flowContainerRef.current;
+    if (!container) return;
+    container.classList.add('is-panning');
+    clearTimeout(panTimerRef.current);
+    panTimerRef.current = setTimeout(() => {
+      container.classList.remove('is-panning');
+    }, 150);
+  }, []);
+
   useEffect(() => {
     const handlePointerMove = (event) => {
       const zoomState = canvasRightZoomRef.current;
@@ -2868,6 +2879,7 @@ function Flow() {
           className={`flow-container${isCanvasRightZooming ? ' canvas-right-zooming' : ''}`}
           onDrop={onDropFile}
           onDragOver={onDragOver}
+          onWheel={onFlowContainerWheel}
           onPointerDownCapture={onFlowContainerPointerDown}
           onContextMenuCapture={onFlowContainerContextMenuCapture}
         >

frontend/src/styles.css

@@ -1164,6 +1164,15 @@ html, body, #root {
   z-index: 2;
 }
 
+.is-panning .cs-overlay,
+.is-panning .angle-overlay,
+.is-panning .lineplot-overlay,
+.is-panning .crop-overlay,
+.is-panning .mask-paint-overlay,
+.is-panning .markup-overlay {
+  pointer-events: none;
+}
+
 .markup-overlay {
   position: relative;
   overflow: hidden;