adding more nodes

2026-04-03 23:11:52 -07:00
parent 5d4c6dfcea
commit 7747c1c7bc
146 changed files with 4950 additions and 145 deletions
--- a/backend/nodes/grain_distributions.py
+++ b/backend/nodes/grain_distributions.py
@@ -0,0 +1,99 @@
+"""Grain property distributions — compute histograms of grain properties."""
+
+from __future__ import annotations
+
+import numpy as np
+from scipy.ndimage import label
+
+from backend.node_registry import register_node
+from backend.data_types import DataField, LineData
+from backend.nodes.helpers import mask_to_bool
+
+
+@register_node(display_name="Grain Distributions")
+class GrainDistributions:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "field": ("DATA_FIELD",),
+                "mask": ("IMAGE",),
+                "property": (["area", "equiv_diameter", "mean_height", "max_height",
+                              "volume", "boundary_length"], {"default": "area"}),
+                "n_bins": ("INT", {"default": 30, "min": 5, "max": 200, "step": 1}),
+                "min_size": ("INT", {"default": 10, "min": 1, "max": 100000, "step": 1}),
+            }
+        }
+
+    OUTPUTS = (
+        ('LINE_DATA', 'distribution'),
+    )
+    FUNCTION = "process"
+
+    DESCRIPTION = (
+        "Compute a histogram of a grain property from a labeled mask. "
+        "Supported properties: area, equivalent diameter, mean height, "
+        "max height, volume, and boundary length. "
+    )
+
+    def process(self, field: DataField, mask: np.ndarray, property: str,
+                n_bins: int, min_size: int) -> tuple:
+        data = np.asarray(field.data, dtype=np.float64)
+        grain_mask = mask_to_bool(mask)
+        labeled, n_grains = label(grain_mask.astype(np.int32))
+
+        pixel_area = field.dx * field.dy
+        xy_unit = field.si_unit_xy or "m"
+        z_unit = field.si_unit_z or "m"
+
+        values = []
+        for gid in range(1, n_grains + 1):
+            gpx = labeled == gid
+            n_px = int(gpx.sum())
+            if n_px < min_size:
+                continue
+
+            if property == "area":
+                values.append(n_px * pixel_area)
+            elif property == "equiv_diameter":
+                area = n_px * pixel_area
+                values.append(2.0 * np.sqrt(area / np.pi))
+            elif property == "mean_height":
+                values.append(float(data[gpx].mean()))
+            elif property == "max_height":
+                values.append(float(data[gpx].max()))
+            elif property == "volume":
+                base = float(data[~grain_mask].mean()) if (~grain_mask).any() else 0.0
+                values.append(float(np.sum(data[gpx] - base) * pixel_area))
+            elif property == "boundary_length":
+                # Count boundary pixels (pixels with at least one non-grain neighbour)
+                padded = np.pad(gpx, 1, mode='constant', constant_values=False)
+                boundary = gpx & ~(
+                    padded[:-2, 1:-1] & padded[2:, 1:-1] &
+                    padded[1:-1, :-2] & padded[1:-1, 2:]
+                )
+                values.append(int(boundary.sum()) * max(field.dx, field.dy))
+
+        if len(values) == 0:
+            values = [0.0]
+
+        # Unit labels
+        unit_map = {
+            "area": f"{xy_unit}²",
+            "equiv_diameter": xy_unit,
+            "mean_height": z_unit,
+            "max_height": z_unit,
+            "volume": f"{xy_unit}²·{z_unit}",
+            "boundary_length": xy_unit,
+        }
+
+        arr = np.array(values)
+        counts, edges = np.histogram(arr, bins=n_bins)
+        centers = 0.5 * (edges[:-1] + edges[1:])
+
+        return (LineData(
+            data=counts.astype(np.float64),
+            x_axis=centers,
+            x_unit=unit_map.get(property, ""),
+            y_unit="count",
+        ),)