tip modelling and deconvolution

backend/nodes/radial_profile.py

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+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="Radial Profile")
+class RadialProfile:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "field": ("DATA_FIELD",),
+                "cx": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "cy": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "n_bins": ("INT", {"default": 128, "min": 4, "max": 4096, "step": 1}),
+            }
+        }
+
+    OUTPUTS = (
+        ('LINE', 'profile'),
+    )
+    FUNCTION = "process"
+
+    DESCRIPTION = (
+        "Compute the azimuthally averaged radial profile from a centre point. "
+        "cx/cy give the centre as a fraction of the field width/height (0.5 = centre). "
+        "Output x-axis is radius in physical xy units. "
+        "Equivalent to gwy_data_field_angular_average used by Gwyddion's Radial Profile tool (rprofile.c)."
+    )
+
+    def process(self, field: DataField, cx: float, cy: float, n_bins: int) -> tuple:
+        yres, xres = field.data.shape
+
+        # Centre in physical coordinates (matches Gwyddion: xc = cx*xreal + xoff)
+        xc_phys = cx * field.xreal + field.xoff
+        yc_phys = cy * field.yreal + field.yoff
+
+        # Pixel-centre physical coordinates
+        xs = (np.arange(xres) + 0.5) * field.dx + field.xoff
+        ys = (np.arange(yres) + 0.5) * field.dy + field.yoff
+        gx, gy = np.meshgrid(xs, ys)
+
+        r = np.hypot(gx - xc_phys, gy - yc_phys).ravel()
+        values = field.data.ravel()
+
+        # Maximum radius — farthest pixel from centre
+        r_max = float(r.max())
+        if r_max == 0.0:
+            r_max = max(field.dx, field.dy)
+
+        # Bin by radius — matches Gwyddion's lineres-bin approach
+        bin_edges = np.linspace(0.0, r_max, n_bins + 1)
+        idx = np.clip(
+            np.floor(n_bins * r / r_max).astype(np.intp), 0, n_bins - 1
+        )
+
+        sums = np.zeros(n_bins)
+        counts = np.zeros(n_bins, dtype=np.intp)
+        np.add.at(sums, idx, values)
+        np.add.at(counts, idx, 1)
+
+        with np.errstate(invalid="ignore"):
+            profile = np.where(counts > 0, sums / counts, np.nan)
+
+        centers = 0.5 * (bin_edges[:-1] + bin_edges[1:])
+
+        return (LineData(
+            data=profile,
+            x_axis=centers,
+            x_unit=field.si_unit_xy,
+            y_unit=field.si_unit_z,
+        ),)