adding more nodes

backend/nodes/trimmed_mean.py

@@ -0,0 +1,54 @@
+"""Trimmed mean filter — mean filter excluding extreme percentiles."""
+
+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="Trimmed Mean")
+class TrimmedMean:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "field": ("DATA_FIELD",),
+                "radius": ("INT", {"default": 3, "min": 1, "max": 50, "step": 1}),
+                "trim_fraction": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 0.45, "step": 0.01}),
+            }
+        }
+
+    OUTPUTS = (
+        ('DATA_FIELD', 'filtered'),
+    )
+    FUNCTION = "process"
+
+    DESCRIPTION = (
+        "Apply a local mean filter that excludes the lowest and highest "
+        "fraction of values in each window. More robust than Gaussian for "
+        "data with outlier spikes. trim_fraction=0 is a plain mean; "
+        "trim_fraction=0.5 approaches the median. "
+    )
+
+    def process(self, field: DataField, radius: int, trim_fraction: float) -> tuple:
+        data = np.asarray(field.data, dtype=np.float64)
+        yres, xres = data.shape
+        result = np.zeros_like(data)
+
+        padded = np.pad(data, radius, mode='edge')
+
+        for iy in range(yres):
+            for ix in range(xres):
+                window = padded[iy:iy + 2 * radius + 1, ix:ix + 2 * radius + 1].ravel()
+                n = len(window)
+                k = int(n * trim_fraction)
+                if k > 0:
+                    sorted_w = np.sort(window)
+                    trimmed = sorted_w[k:n - k]
+                else:
+                    trimmed = window
+                result[iy, ix] = trimmed.mean()
+
+        return (field.replace(data=result),)