tip modelling and deconvolution

backend/nodes/resample.py

@@ -0,0 +1,52 @@
+from __future__ import annotations
+
+import numpy as np
+
+from backend.node_registry import register_node
+from backend.data_types import DataField
+
+
+@register_node(display_name="Resample")
+class Resample:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "field": ("DATA_FIELD",),
+                "width": ("INT", {"default": 256, "min": 2, "max": 16384, "step": 1}),
+                "height": ("INT", {"default": 256, "min": 2, "max": 16384, "step": 1}),
+                "interpolation": (["linear", "cubic", "nearest"], {"default": "linear"}),
+            }
+        }
+
+    OUTPUTS = (
+        ('DATA_FIELD', 'resampled'),
+    )
+    FUNCTION = "process"
+
+    DESCRIPTION = (
+        "Resample a DATA_FIELD to a new pixel resolution while preserving physical dimensions. "
+        "Physical size (xreal, yreal) is unchanged; pixel size dx/dy scales accordingly. "
+        "Equivalent to gwy_data_field_new_resampled with GWY_INTERPOLATION_LINEAR / "
+        "GWY_INTERPOLATION_CUBIC / GWY_INTERPOLATION_ROUND (scale.c)."
+    )
+
+    _ORDERS = {"nearest": 0, "linear": 1, "cubic": 3}
+
+    def process(self, field: DataField, width: int, height: int, interpolation: str) -> tuple:
+        from scipy.ndimage import zoom
+
+        if interpolation not in self._ORDERS:
+            raise ValueError(
+                f"Unknown interpolation {interpolation!r}. "
+                f"Choose from: {list(self._ORDERS)}"
+            )
+
+        old_yres, old_xres = field.data.shape
+        zy = height / old_yres
+        zx = width / old_xres
+
+        result = zoom(field.data, (zy, zx), order=self._ORDERS[interpolation])
+
+        # Physical dimensions stay the same; xres/yres are re-derived from data.shape.
+        return (field.replace(data=result, xreal=field.xreal, yreal=field.yreal),)