"""E11 figure — the dynamic Lamarckian society: the vertical claim (C3).

A finite population of agents evolves on a rugged NK landscape (reality). The **full** society —
grounding + directed recombination (sex) + quality-diversity selection — climbs to the global optimum
while maintaining diversity longest. A 4-arm ablation shows every operator is load-bearing, each
breaking distinctly: **no_grounding** collapses to a fit-looking but actually-poor consensus
(self-consumption); **no_sex** plateaus (can't recombine to escape local optima); **no_diversity**
(greedy) collapses diversity fastest and stalls at a worse local optimum.

Three panels over generations: (A) best real capability — the vertical climb, full highest, no_grounding
crashing below the rest; (B) population diversity — full explores longest, no_grounding collapses
almost immediately; (C) the self-consumption signature — conformity minus true fitness (how far the
population's mutual agreement exceeds its real capability), largest for no_grounding. Reads only the
committed bundle.

Usage:  python figures/plot_E11.py [results/E11]
"""

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  # noqa: E402

_ARMS = [("full", "#2ca02c", "full society"),
         ("no_sex", "#ff7f0e", "no sex (no recombination)"),
         ("no_diversity", "#9467bd", "no diversity (greedy)"),
         ("no_grounding", "#d62728", "no grounding (self-consumption)")]


def main(results_dir: str = "results/E11") -> None:
    df, _ = load_bundle(results_dir)
    arms = [a for a in _ARMS if a[0] in set(df["arm"].unique())]
    g_opt = df["global_opt"].mean()

    fig, axes = plt.subplots(1, 3, figsize=(16, 4.8))

    def traj(ax, col, title, ylabel, hline=None):
        for name, c, lab in arms:
            sub = df[df["arm"] == name]
            g, m, ci = mean_ci(sub, "generation", col)
            ax.plot(g, m, "-", color=c, lw=1.9, label=lab)
            ax.fill_between(g, m - ci, m + ci, color=c, alpha=0.15)
        if hline is not None:
            ax.axhline(hline[0], ls=":", color="gray", lw=1, label=hline[1])
        ax.set(xlabel="generation", ylabel=ylabel, title=title)
        ax.legend(frameon=False, fontsize=8)

    traj(axes[0], "best_fitness", "The vertical climb: general capability\n"
         "(full climbs highest; no-grounding collapses)", "best real fitness",
         hline=(g_opt, "global optimum"))
    traj(axes[1], "diversity", "Specialties maintained: diversity during search\n"
         "(full explores longest; ablations collapse fast)", "population diversity")
    traj(axes[2], "conformity_true_gap", "Self-consumption signature:\n"
         "agreement minus real capability", "conformity − true fitness")

    fig.suptitle("E11 — the dynamic Lamarckian society: grounding + directed sex + diversity climb to "
                 "the optimum; remove any one and it breaks (the vertical claim, C3)", y=1.02, fontsize=12)
    fig.tight_layout()
    savefig(fig, results_dir, "E11")


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