"""Per-seed readouts for the LLM speciation tier (Fig. 5C-D), the source of its SI Table S2 row.

Seed 1 ran locally; seeds 2-3 via hpc/llm_speciation_seeds.pbs into ``results/llm_speciation/s{seed}/``.
Two pre-registered falsifiers, checked seed by seed:

- the conflict cliff: at full conflict (x = 1.0) the merged model's best-convention accuracy on the
  shared prompts falls below BOTH parents' own-convention accuracy;
- the duration null: over the epoch sweep the merged model's mean private-family accuracy does not
  fall below its value at the shortest training while the parents hold their own families.

Usage:  python figures/stats_llm_speciation_seeds.py [results/llm_speciation]
"""

from __future__ import annotations

import sys
from pathlib import Path

import numpy as np
import pandas as pd

sys.path.insert(0, str(Path(__file__).parent))
from _figlib import load_seed_bundles  # noqa: E402


def pick(df: pd.DataFrame, mode: str, model: str, metric: str) -> pd.DataFrame:
    """Per-seed accuracy against x for one (mode, model, metric)."""
    sub = df[(df["mode"] == mode) & (df["model"] == model) & (df["metric"] == metric)]
    return sub.pivot_table(index="x", columns="seed", values="accuracy", aggfunc="mean")


def summary(piv: pd.DataFrame) -> pd.DataFrame:
    n = piv.shape[1]
    out = piv.copy()
    out.columns = [f"s{c}" for c in out.columns]
    out["mean"] = piv.mean(axis=1)
    out["ci95"] = 1.96 * piv.std(axis=1, ddof=1) / np.sqrt(n) if n > 1 else np.nan
    return out.round(3)


def main(root: str = "results/llm_speciation") -> None:
    df, _ = load_seed_bundles(root)
    seeds = sorted(df["seed"].unique())
    print(f"seeds: {seeds}\n")
    print("## Conflict sweep: merged model, best convention on the shared prompts")
    merge = pick(df, "conflict", "merge_soup", "coherence")
    print(summary(merge).to_string(), "\n")
    pa = pick(df, "conflict", "parent_a", "ambig_asc")
    pb = pick(df, "conflict", "parent_b", "ambig_desc")
    x1 = merge.index.max()
    print(f"## Conflict cliff at x = {x1}: merge below both parents?  (per seed)")
    for s in seeds:
        m, a, b = merge.loc[x1, s], pa.loc[x1, s], pb.loc[x1, s]
        print(f"  seed {s}: merge {m:.3f}  parent A {a:.3f}  parent B {b:.3f}  -> {'cliff' if m < min(a, b) else 'NO cliff'}")
    print()
    print("## Duration sweep: merged model, mean private-family accuracy")
    dur = pick(df, "duration", "merge_soup", "mean_private")
    print(summary(dur).to_string(), "\n")
    print("## Duration null: merged accuracy at the longest vs shortest training (per seed)")
    lo, hi = dur.index.min(), dur.index.max()
    for s in seeds:
        d = dur.loc[hi, s] - dur.loc[lo, s]
        print(f"  seed {s}: {dur.loc[lo, s]:.3f} -> {dur.loc[hi, s]:.3f}  (Δ {d:+.3f})  -> "
              f"{'no isolation' if d >= -0.05 else 'DEGRADES'}")
    for fam, model in (("strings", "parent_a"), ("arith", "parent_b")):
        p = pick(df, "duration", model, fam)
        print(f"  {model} own-task range over epochs, seed means: {p.mean(axis=1).min():.3f}–{p.mean(axis=1).max():.3f}")


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