adding more nodes

backend/nodes/pixel_binning.py

@@ -0,0 +1,58 @@
+"""Pixel binning — downsample by averaging NxN pixel blocks."""
+
+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="Pixel Binning")
+class PixelBinning:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "field": ("DATA_FIELD",),
+                "bin_size": ("INT", {"default": 2, "min": 2, "max": 32, "step": 1}),
+                "method": (["mean", "sum", "median"], {"default": "mean"}),
+            }
+        }
+
+    OUTPUTS = (
+        ('DATA_FIELD', 'binned'),
+    )
+    FUNCTION = "process"
+
+    DESCRIPTION = (
+        "Downsample by grouping NxN pixel blocks and computing their mean, "
+        "sum, or median. Faster and more controlled than interpolation-based "
+        "resampling. Pixels that don't fill a complete block are trimmed. "
+    )
+
+    def process(self, field: DataField, bin_size: int, method: str) -> tuple:
+        data = np.asarray(field.data, dtype=np.float64)
+        yres, xres = data.shape
+
+        # Trim to multiple of bin_size
+        ny = (yres // bin_size) * bin_size
+        nx = (xres // bin_size) * bin_size
+        trimmed = data[:ny, :nx]
+
+        # Reshape into blocks
+        blocks = trimmed.reshape(ny // bin_size, bin_size, nx // bin_size, bin_size)
+
+        if method == "mean":
+            result = blocks.mean(axis=(1, 3))
+        elif method == "sum":
+            result = blocks.sum(axis=(1, 3))
+        elif method == "median":
+            result = np.median(blocks, axis=(1, 3))
+        else:
+            raise ValueError(f"Unknown method: {method!r}")
+
+        # Update physical dimensions
+        new_xreal = field.dx * nx
+        new_yreal = field.dy * ny
+        return (field.replace(data=result, xreal=new_xreal, yreal=new_yreal),)