Giorgio Gilestro
ab3dc10587
- paper/pnas -> paper/manuscript (venue-neutral)
- configs/layer1 -> configs/inheritance, src/knowledge -> src/inheritance
(imported as `inheritance`), make layer1 -> make inheritance; layer2 alias dropped
- inheritance and trained-network bundles named after the manuscript figure
they feed (fig2_grounding_sweep, figS3_rebaselining, ...), or descriptively
where they feed none; configs keep their `experiment:` value so parquet
hashes are unchanged, only output.dir moves
- figure scripts, SI figure sources, notebooks, REPRODUCING.md, README and the
SI Methods/tables updated; make clean no longer deletes tracked manifests;
reproduce.sh hashes the s{seed}/ layouts too
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Y64o8FKP7rCuXzC48pxpMm
67 lines
3.1 KiB
Python
67 lines
3.1 KiB
Python
"""E8 figure — the vertical claim: n-parent recombination exceeds any parent (Fisher–Muller).
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The society headline. Decorrelated *parents* are specialists (expert on some loci, agnostic on the
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rest); an *offspring* recombined from all of them can be fitter than any parent — capability that
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*exceeds* every component, not just recovers a ceiling. Unlike biological sex there is no two-parent
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limit, so capability climbs toward the optimum as the parent pool grows and decorrelates.
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Two panels: (A) deployed capability (mode-genotype fitness) vs parent count at ρ=0 — sexual
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recombination reaches the optimum (a genotype no parent had) while the best single parent and the
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mean-mixture "model soup" plateau below; (B) the decorrelation control — sexual capability vs parent
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count for ρ ∈ {0, 0.5, 1}: decorrelated parents (ρ=0) climb to the optimum, identical parents (ρ=1)
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buy nothing. Reads only the committed bundle.
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Usage: python figures/plot_figS9_specialist_superparent.py [results/figS9_specialist_superparent]
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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, mean_ci, savefig, letter_axes # noqa: E402
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def main(results_dir: str = "results/figS9_specialist_superparent") -> None:
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df, cfg = load_bundle(results_dir)
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L = cfg["society"]["L"]
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rhos = sorted(df["rho"].unique())
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fig, axes = plt.subplots(1, 2, figsize=(13, 5))
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# Panel A: best-parent vs average vs sexual, at rho=0.
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ax = axes[0]
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d0 = df[df["rho"] == 0.0]
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for col, c, lab in [("best_parent", "#7f7f7f", "best single parent"),
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("average", "#1f77b4", "average (model soup)"),
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("sexual", "#d62728", "sexual recombination")]:
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k, m, ci = mean_ci(d0, "K_T", col)
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ax.errorbar(k, m, yerr=ci, fmt="-o", color=c, capsize=3, label=lab)
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ax.axhline(L, ls=":", color="green", lw=1, label=f"optimum ($L$={L})")
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ax.set(xlabel="number of parents $K_T$", ylabel="deployed capability (mode fitness)",
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title="Recombination exceeds any parent (ρ=0):\nsexual reaches the optimum; soup & best-parent plateau")
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ax.legend(frameon=False, fontsize=9)
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# Panel B: sexual capability vs K_T for each rho (decorrelation control).
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ax = axes[1]
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colors = plt.cm.viridis(np.linspace(0, 0.8, len(rhos)))
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for rho, c in zip(rhos, colors):
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sub = df[df["rho"] == rho]
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k, m, ci = mean_ci(sub, "K_T", "sexual")
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ax.errorbar(k, m, yerr=ci, fmt="-o", color=c, capsize=3, label=fr"ρ={rho:g}")
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ax.axhline(L, ls=":", color="green", lw=1, label=f"optimum ($L$={L})")
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ax.set(xlabel="number of parents $K_T$", ylabel="sexual-recombination capability",
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title="Decorrelation is the fuel:\nρ=0 climbs to the optimum; ρ=1 (clones) buy nothing")
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ax.legend(frameon=False, fontsize=9)
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fig.tight_layout()
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letter_axes(fig)
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savefig(fig, results_dir, "figS9_specialist_superparent")
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if __name__ == "__main__":
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main(*sys.argv[1:])
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