adding more nodes

backend/nodes/shade.py

@@ -0,0 +1,66 @@
+"""Shaded presentation — render surface with directional lighting."""
+
+from __future__ import annotations
+
+import numpy as np
+from scipy.ndimage import sobel
+
+from backend.node_registry import register_node
+from backend.data_types import DataField
+
+
+@register_node(display_name="Shade")
+class Shade:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "field": ("DATA_FIELD",),
+                "azimuth": ("FLOAT", {"default": 315.0, "min": 0.0, "max": 360.0, "step": 1.0}),
+                "elevation": ("FLOAT", {"default": 45.0, "min": 5.0, "max": 85.0, "step": 1.0}),
+                "blend": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.05}),
+            }
+        }
+
+    OUTPUTS = (
+        ('DATA_FIELD', 'shaded'),
+    )
+    FUNCTION = "process"
+
+    DESCRIPTION = (
+        "Render a surface with directional hillshade lighting. "
+        "Azimuth controls the light direction (0=north, 90=east). "
+        "Elevation controls the light angle above the horizon. "
+        "Blend mixes original data (0) with shaded relief (1). "
+    )
+
+    def process(self, field: DataField, azimuth: float, elevation: float,
+                blend: float) -> tuple:
+        data = np.asarray(field.data, dtype=np.float64)
+
+        gx = sobel(data, axis=1) / (8.0 * field.dx)
+        gy = sobel(data, axis=0) / (8.0 * field.dy)
+
+        az_rad = np.radians(azimuth)
+        el_rad = np.radians(elevation)
+
+        # Lambertian shading
+        lx = np.cos(el_rad) * np.sin(az_rad)
+        ly = np.cos(el_rad) * np.cos(az_rad)
+        lz = np.sin(el_rad)
+
+        normal_z = 1.0 / np.sqrt(gx**2 + gy**2 + 1.0)
+        normal_x = -gx * normal_z
+        normal_y = -gy * normal_z
+
+        shade = np.clip(normal_x * lx + normal_y * ly + normal_z * lz, 0.0, 1.0)
+
+        # Normalize original data to [0, 1]
+        dmin, dmax = data.min(), data.max()
+        if dmax > dmin:
+            norm_data = (data - dmin) / (dmax - dmin)
+        else:
+            norm_data = np.ones_like(data) * 0.5
+
+        result = (1.0 - blend) * norm_data + blend * shade
+        return (field.replace(data=result, si_unit_z=""),)