Giorgio Gilestro
84124de143
Clarity pass over the main text (36-item audit), Discussion rewrite and cut, acknowledgements, Souly et al. as ref 62, lettered SI panels, model section moved under Results; plus the untracked curriculum/society/compose/smol configs, runners, figures, stats and tests that the SI already cites. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Y64o8FKP7rCuXzC48pxpMm
65 lines
2.9 KiB
Python
65 lines
2.9 KiB
Python
"""E9 figure — landscape robustness: when recombination helps, and the outbreeding-depression risk.
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The credibility test for the sexual metaphor. E8 used an additive landscape where recombination
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trivially helps; here parents are local optima ("trained models") of a Kauffman NK landscape whose
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ruggedness (epistasis) is tunable. Blindly recombining entangled models breaks co-adapted allele
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blocks and offspring fall *below* the parents — outbreeding depression — worse the more rugged the
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landscape and the higher the recombination rate. With selection (best offspring), a nonzero optimal
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recombination rate re-emerges. Design rule: merge freely when skills are complementary; merge
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sparingly (and always select) when they are entangled.
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Two panels: (A) the risk — mean offspring fitness minus best-parent vs recombination rate, one curve
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per ruggedness K (all ≤0, steeper as K grows); (B) with offspring selection — best-of-brood fitness
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vs rate per K, showing an intermediate optimum on rugged landscapes. Reads only the bundle.
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Usage: python figures/plot_E9.py [results/E9]
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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import matplotlib.pyplot as plt
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import numpy as np
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sys.path.insert(0, str(Path(__file__).parent))
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from _figlib import load_bundle, savefig, letter_axes # noqa: E402
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def main(results_dir: str = "results/E9") -> None:
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df, _ = load_bundle(results_dir)
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Ks = sorted(df["K"].unique())
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rates = sorted(df["rate"].unique())
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colors = plt.cm.viridis(np.linspace(0, 0.85, len(Ks)))
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bp = df.groupby("K")["best_parent"].mean()
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fig, axes = plt.subplots(1, 2, figsize=(13, 5))
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# Panel A: the risk — mean offspring minus best parent vs rate, per K.
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ax = axes[0]
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for K, c in zip(Ks, colors):
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s = df[df["K"] == K].groupby("rate")["mean_offspring"].mean() - bp[K]
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ax.plot(s.index, s.values, "-o", color=c, ms=4, label=f"K={K}")
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ax.axhline(0, ls=":", color="gray", lw=1)
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ax.set(xlabel="recombination rate", ylabel="mean offspring − best parent",
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title="The risk: outbreeding depression\n(worse with ruggedness K and recombination rate)")
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ax.legend(frameon=False, fontsize=8, title="ruggedness")
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# Panel B: with selection — best offspring vs rate, per K (intermediate optimum on rugged).
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ax = axes[1]
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for K, c in zip(Ks, colors):
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s = df[df["K"] == K].groupby("rate")["best_offspring"].mean()
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ax.plot(s.index, s.values, "-o", color=c, ms=4, label=f"K={K}")
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ax.axhline(bp[K], ls=":", color=c, lw=0.8, alpha=0.6)
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ax.set(xlabel="recombination rate", ylabel="best-of-brood fitness (with selection)",
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title="With offspring selection, an optimal\nrecombination rate re-emerges (dotted = parents)")
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ax.legend(frameon=False, fontsize=8, title="ruggedness")
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fig.tight_layout()
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letter_axes(fig)
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savefig(fig, results_dir, "E9")
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if __name__ == "__main__":
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main(*sys.argv[1:])
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