87 lines
3.0 KiB
Python
87 lines
3.0 KiB
Python
"""Grain cross-correlation — scatter plots of grain properties between two fields."""
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from __future__ import annotations
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import numpy as np
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from scipy.ndimage import label
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from backend.node_registry import register_node
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from backend.data_types import DataField, RecordTable
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from backend.nodes.helpers import mask_to_bool
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@register_node(display_name="Grain Cross")
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class GrainCross:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"field_a": ("DATA_FIELD",),
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"field_b": ("DATA_FIELD",),
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"mask": ("IMAGE",),
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"property_a": (["area", "mean_height", "max_height", "volume"],
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{"default": "mean_height"}),
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"property_b": (["area", "mean_height", "max_height", "volume"],
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{"default": "max_height"}),
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"min_size": ("INT", {"default": 10, "min": 1, "max": 100000, "step": 1}),
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}
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}
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OUTPUTS = (
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('RECORD_TABLE', 'correlation'),
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)
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FUNCTION = "process"
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DESCRIPTION = (
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"Correlate grain properties between two fields using a shared mask. "
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"Outputs a table of (property_a, property_b) pairs for each grain, "
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"plus Pearson correlation coefficient. "
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)
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KEYWORDS = ("pearson", "scatter", "correlate", "property")
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def process(self, field_a: DataField, field_b: DataField, mask: np.ndarray,
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property_a: str, property_b: str, min_size: int) -> tuple:
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data_a = np.asarray(field_a.data, dtype=np.float64)
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data_b = np.asarray(field_b.data, dtype=np.float64)
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grain = mask_to_bool(mask)
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labeled, n_grains = label(grain.astype(np.int32))
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pixel_area = field_a.dx * field_a.dy
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def _get_prop(data, gpx, prop):
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n_px = gpx.sum()
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if prop == "area":
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return n_px * pixel_area
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elif prop == "mean_height":
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return float(data[gpx].mean())
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elif prop == "max_height":
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return float(data[gpx].max())
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elif prop == "volume":
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base = float(data[~grain].mean()) if (~grain).any() else 0.0
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return float(np.sum(data[gpx] - base) * pixel_area)
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return 0.0
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vals_a, vals_b = [], []
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records = RecordTable()
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for gid in range(1, n_grains + 1):
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gpx = labeled == gid
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if gpx.sum() < min_size:
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continue
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va = _get_prop(data_a, gpx, property_a)
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vb = _get_prop(data_b, gpx, property_b)
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vals_a.append(va)
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vals_b.append(vb)
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records.append({
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"quantity": f"Grain {gid}",
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"value": f"{va:.4g} / {vb:.4g}",
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"unit": f"{property_a} / {property_b}",
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})
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# Pearson correlation
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if len(vals_a) >= 2:
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corr = float(np.corrcoef(vals_a, vals_b)[0, 1])
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records.append({"quantity": "Pearson r", "value": f"{corr:.4f}", "unit": ""})
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return (records,)
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