"""E10 figure — directed sex beats biological sex (the distinctly-AI superpower).

On rugged (epistatic) landscapes, blind "biological" sex — random mates, no offspring selection —
suffers outbreeding depression: offspring are worse than the parents. But an AI can do what biology
cannot: choose complementary mates, evaluate *many* recombinant offspring, and keep only the fittest,
over several rounds, with no two-parent limit. This **directed sex** avoids the catastrophe and
matches or exceeds the best parent even when skills are entangled.

Two panels: (A) deployed capability vs landscape ruggedness — best single parent, random (blind) sex,
directed sex, and the global optimum; (B) each strategy's edge over the best parent, making the
random-sex collapse and the directed-sex rescue explicit. Reads only the committed bundle.

Usage:  python figures/plot_figS11_directed_recombination.py [results/figS11_directed_recombination]
"""

from __future__ import annotations

import sys
from pathlib import Path

import matplotlib.pyplot as plt

sys.path.insert(0, str(Path(__file__).parent))
from _figlib import load_bundle, mean_ci, savefig, letter_axes  # noqa: E402


def main(results_dir: str = "results/figS11_directed_recombination") -> None:
    df, _ = load_bundle(results_dir)

    fig, axes = plt.subplots(1, 2, figsize=(13, 5))

    # Panel A: the three strategies + global optimum vs ruggedness.
    ax = axes[0]
    for col, c, lab in [("global_opt", "green", "global optimum"),
                        ("directed_sex", "#d62728", "directed sex (AI: choose + select)"),
                        ("best_parent", "#7f7f7f", "best single parent"),
                        ("random_sex", "#1f77b4", "random sex (blind, biology)")]:
        k, m, ci = mean_ci(df, "K", col)
        ls = ":" if col == "global_opt" else "-o"
        ax.plot(k, m, ls, color=c, label=lab) if col == "global_opt" else \
            ax.errorbar(k, m, yerr=ci, fmt=ls, color=c, capsize=3, label=lab)
    ax.set(xlabel="landscape ruggedness $K$ (epistasis)", ylabel="deployed capability (fitness)",
           title="Random sex craters with ruggedness;\ndirected sex tracks/exceeds the best parent")
    ax.legend(frameon=False, fontsize=8)

    # Panel B: edge over best parent (random collapse vs directed rescue).
    ax = axes[1]
    bp = df.groupby("K")["best_parent"].mean()
    for col, c, lab in [("directed_sex", "#d62728", "directed sex"),
                        ("random_sex", "#1f77b4", "random sex")]:
        s = df.groupby("K")[col].mean() - bp
        ax.plot(s.index, s.values, "-o", color=c, label=lab)
    ax.axhline(0, ls=":", color="gray", lw=1, label="best parent")
    ax.set(xlabel="landscape ruggedness $K$", ylabel="capability − best parent",
           title="Directed sex stays ≥ parents; blind sex\nfalls far below (outbreeding depression)")
    ax.legend(frameon=False, fontsize=9)

    fig.tight_layout()
    letter_axes(fig)
    savefig(fig, results_dir, "figS11_directed_recombination")


if __name__ == "__main__":
    main(*sys.argv[1:])