"""E8 figure — the vertical claim: n-parent recombination exceeds any parent (Fisher–Muller).

The society headline. Decorrelated *parents* are specialists (expert on some loci, agnostic on the
rest); an *offspring* recombined from all of them can be fitter than any parent — capability that
*exceeds* every component, not just recovers a ceiling. Unlike biological sex there is no two-parent
limit, so capability climbs toward the optimum as the parent pool grows and decorrelates.

Two panels: (A) deployed capability (mode-genotype fitness) vs parent count at ρ=0 — sexual
recombination reaches the optimum (a genotype no parent had) while the best single parent and the
mean-mixture "model soup" plateau below; (B) the decorrelation control — sexual capability vs parent
count for ρ ∈ {0, 0.5, 1}: decorrelated parents (ρ=0) climb to the optimum, identical parents (ρ=1)
buy nothing. Reads only the committed bundle.

Usage:  python figures/plot_figS9_specialist_superparent.py [results/figS9_specialist_superparent]
"""

from __future__ import annotations

import sys
from pathlib import Path

import matplotlib.pyplot as plt
import numpy as np

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/figS9_specialist_superparent") -> None:
    df, cfg = load_bundle(results_dir)
    L = cfg["society"]["L"]
    rhos = sorted(df["rho"].unique())

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

    # Panel A: best-parent vs average vs sexual, at rho=0.
    ax = axes[0]
    d0 = df[df["rho"] == 0.0]
    for col, c, lab in [("best_parent", "#7f7f7f", "best single parent"),
                        ("average", "#1f77b4", "average (model soup)"),
                        ("sexual", "#d62728", "sexual recombination")]:
        k, m, ci = mean_ci(d0, "K_T", col)
        ax.errorbar(k, m, yerr=ci, fmt="-o", color=c, capsize=3, label=lab)
    ax.axhline(L, ls=":", color="green", lw=1, label=f"optimum ($L$={L})")
    ax.set(xlabel="number of parents $K_T$", ylabel="deployed capability (mode fitness)",
           title="Recombination exceeds any parent (ρ=0):\nsexual reaches the optimum; soup & best-parent plateau")
    ax.legend(frameon=False, fontsize=9)

    # Panel B: sexual capability vs K_T for each rho (decorrelation control).
    ax = axes[1]
    colors = plt.cm.viridis(np.linspace(0, 0.8, len(rhos)))
    for rho, c in zip(rhos, colors):
        sub = df[df["rho"] == rho]
        k, m, ci = mean_ci(sub, "K_T", "sexual")
        ax.errorbar(k, m, yerr=ci, fmt="-o", color=c, capsize=3, label=fr"ρ={rho:g}")
    ax.axhline(L, ls=":", color="green", lw=1, label=f"optimum ($L$={L})")
    ax.set(xlabel="number of parents $K_T$", ylabel="sexual-recombination capability",
           title="Decorrelation is the fuel:\nρ=0 climbs to the optimum; ρ=1 (clones) buy nothing")
    ax.legend(frameon=False, fontsize=9)

    fig.tight_layout()
    letter_axes(fig)
    savefig(fig, results_dir, "figS9_specialist_superparent")


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