adding more nodes

backend/nodes/multi_profile.py

@@ -0,0 +1,72 @@
+"""Multiple profiles — extract and compare profiles from multiple images."""
+
+from __future__ import annotations
+
+import numpy as np
+
+from backend.node_registry import register_node
+from backend.data_types import DataField, LineData
+
+
+@register_node(display_name="Multiple Profiles")
+class MultipleProfiles:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "field_a": ("DATA_FIELD",),
+                "field_b": ("DATA_FIELD",),
+                "row": ("INT", {"default": -1, "min": -1, "max": 10000, "step": 1}),
+                "direction": (["horizontal", "vertical"], {"default": "horizontal"}),
+                "mode": (["overlay", "mean", "difference"], {"default": "overlay"}),
+            }
+        }
+
+    OUTPUTS = (
+        ('LINE_DATA', 'profile'),
+    )
+    FUNCTION = "process"
+
+    DESCRIPTION = (
+        "Extract and compare line profiles from two fields. "
+        "Row=-1 uses the center row/column. Modes: overlay returns field_a's "
+        "profile, mean averages both, difference subtracts b from a. "
+    )
+
+    def process(self, field_a: DataField, field_b: DataField,
+                row: int, direction: str, mode: str) -> tuple:
+        a = np.asarray(field_a.data, dtype=np.float64)
+        b = np.asarray(field_b.data, dtype=np.float64)
+
+        if direction == "horizontal":
+            if row < 0:
+                row = a.shape[0] // 2
+            row = min(row, a.shape[0] - 1, b.shape[0] - 1)
+            pa = a[row, :]
+            pb = b[row, :min(a.shape[1], b.shape[1])]
+            pa = pa[:len(pb)]
+            dx = field_a.dx
+            x_unit = field_a.si_unit_xy
+        else:
+            if row < 0:
+                row = a.shape[1] // 2
+            row = min(row, a.shape[1] - 1, b.shape[1] - 1)
+            pa = a[:, row]
+            pb = b[:min(a.shape[0], b.shape[0]), row]
+            pa = pa[:len(pb)]
+            dx = field_a.dy
+            x_unit = field_a.si_unit_xy
+
+        x_axis = np.arange(len(pa)) * dx
+
+        if mode == "overlay":
+            result = pa
+        elif mode == "mean":
+            result = 0.5 * (pa + pb)
+        elif mode == "difference":
+            result = pa - pb
+        else:
+            result = pa
+
+        return (LineData(data=result, x_axis=x_axis, x_unit=x_unit,
+                         y_unit=field_a.si_unit_z),)