add nodes, fft acf 1d

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,
+        )