77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
from __future__ import annotations
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import numpy as np
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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.execution_context import emit_table
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@register_node(display_name="Entropy")
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class Entropy:
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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": ("DATA_FIELD",),
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"mode": (["height values", "slope magnitude"], {"default": "height values"}),
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"n_bins": ("INT", {"default": 256, "min": 16, "max": 1024}),
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}
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}
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OUTPUTS = (
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('FLOAT', 'entropy'),
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('FLOAT', 'normalised_entropy'),
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)
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FUNCTION = "process"
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DESCRIPTION = (
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"Shannon entropy of the height or slope distribution. "
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"H = -\u03a3 p\u00b7ln(p)."
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)
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KEYWORDS = ("shannon", "information", "disorder")
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def process(self, field: DataField, mode: str, n_bins: int) -> tuple:
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n_bins = max(16, int(n_bins))
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data = np.asarray(field.data, dtype=np.float64)
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if mode == "slope magnitude":
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# Compute slope magnitude from Sobel-like finite differences.
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# Central differences along x and y (axis=1 and axis=0).
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# np.gradient uses central differences, same spirit as Sobel.
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dy, dx = np.gradient(data)
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values = np.hypot(dx, dy).ravel()
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else:
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values = data.ravel()
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# Remove non-finite values before binning.
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values = values[np.isfinite(values)]
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if values.size == 0:
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h = 0.0
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h_norm = 0.0
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else:
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counts, _ = np.histogram(values, bins=n_bins)
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total = counts.sum()
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if total == 0:
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h = 0.0
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h_norm = 0.0
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else:
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# Probability distribution; skip zero bins.
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p = counts[counts > 0].astype(np.float64) / float(total)
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h = float(-np.sum(p * np.log(p)))
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# Maximum possible entropy for n_bins equally occupied bins is ln(n_bins).
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h_max = float(np.log(n_bins))
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h_norm = h / h_max if h_max > 0.0 else 0.0
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table = RecordTable([
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{"quantity": "entropy", "value": h, "unit": "nat"},
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{"quantity": "normalised entropy", "value": h_norm, "unit": ""},
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{"quantity": "mode", "value": mode, "unit": ""},
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{"quantity": "n_bins", "value": n_bins, "unit": ""},
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])
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emit_table(table)
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return (h, h_norm)
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