"""Generate the paper's headline figures from cached results (no GPU).
    python figures.py        # writes figures/*.pdf + *.png from results_A/
"""
from __future__ import annotations

import json
from pathlib import Path

import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt

import metrics
from config import MODELS
from run_pipeline import (_para_rotation, _iid_split_rotation, _mean_ood_rotation, _mean_ood_drop)

OUT = Path("figures"); OUT.mkdir(exist_ok=True)
PREDS = ["raptor_stability", "pac", "augmentation_robustness", "whitened_cosine_id",
         "fragility", "xie_feature_dispersion", "sip_eigengap"]
LBL = {"raptor_stability": "RAPTOR\n(dispersion)", "pac": "PAC (ours)",
       "augmentation_robustness": "aug-robust", "whitened_cosine_id": "whitened-cos",
       "fragility": "Fragility", "xie_feature_dispersion": "Xie-disp", "sip_eigengap": "SIP"}


def load_rows(results_dir, predfile="predictors.jsonl"):
    R = Path(results_dir)
    evals = [json.loads(l) for l in (R / "eval.jsonl").read_text().splitlines() if l.strip()]
    preds = {(p["model"], p["dataset"], p["seed"]): p["predictors"]
             for p in (json.loads(l) for l in (R / predfile).read_text().splitlines() if l.strip())}
    rows = []
    for e in evals:
        if e.get("probe") != "logreg":
            continue
        k = (e["model"], e["dataset"], e["seed"])
        if k not in preds:
            continue
        para, iids = _para_rotation(e), _iid_split_rotation(e)
        excess = (para - iids) if (para == para and iids == iids) else np.nan
        rows.append({"model": e["model"], "naive": _mean_ood_rotation(e), "placebo": iids,
                     "excess": excess, "drop": _mean_ood_drop(e), **preds[k]})
    return rows


def rho(rows, pred, target):
    s = np.array([r.get(pred, np.nan) for r in rows], float)
    y = np.array([r[target] for r in rows], float)
    ok = ~(np.isnan(s) | np.isnan(y))
    return metrics.spearman(s[ok], -y[ok])[0] if ok.sum() >= 8 else np.nan


def fig_circularity(rows):
    targets = [("naive", "naive rotation"), ("placebo", "IID-resample\n(placebo)"),
               ("excess", "EXCESS\n(shift-specific)")]
    x = np.arange(len(PREDS)); w = 0.26
    fig, ax = plt.subplots(figsize=(9, 4.2))
    colors = ["#4C72B0", "#999999", "#C44E52"]
    for i, (t, name) in enumerate(targets):
        vals = [rho(rows, p, t) for p in PREDS]
        ax.bar(x + (i - 1) * w, vals, w, label=name, color=colors[i])
    ax.axhline(0, color="k", lw=0.8)
    ax.set_xticks(x); ax.set_xticklabels([LBL[p] for p in PREDS], fontsize=8)
    ax.set_ylabel("Spearman ρ (predictor, −target)")
    ax.set_title("Predicting probe direction rotation: naive ρ is circular (≈ placebo);\n"
                 "on EXCESS dispersion turns anti-predictive while augmentation is least-bad")
    ax.legend(fontsize=8, loc="lower left"); ax.set_ylim(-0.45, 1.0)
    fig.tight_layout(); fig.savefig(OUT / "fig1_circularity.pdf"); fig.savefig(OUT / "fig1_circularity.png", dpi=150)
    plt.close(fig)


def fig_sizeladder(rows):
    models = sorted({r["model"] for r in rows}, key=lambda m: MODELS[m].params_m if m in MODELS else 0)
    xs = [MODELS[m].params_m for m in models if m in MODELS]
    fig, axes = plt.subplots(1, 2, figsize=(10, 4))
    for ax, tgt, title in [(axes[0], "naive", "naive rotation"), (axes[1], "excess", "EXCESS rotation")]:
        for p, c in [("raptor_stability", "#4C72B0"), ("augmentation_robustness", "#C44E52"), ("pac", "#55A868")]:
            ys = [rho([r for r in rows if r["model"] == m], p, tgt) for m in models if m in MODELS]
            ax.plot(xs, ys, "o-", label=LBL[p].replace("\n", " "), color=c)
        ax.axhline(0, color="k", lw=0.6); ax.set_xscale("log")
        ax.set_xlabel("params (M, log)"); ax.set_ylabel("Spearman ρ"); ax.set_title(title)
        ax.legend(fontsize=8)
    fig.suptitle("Size-ladder: predictability structure is scale-invariant (no inverse-scaling)")
    fig.tight_layout(); fig.savefig(OUT / "fig2_sizeladder.pdf"); fig.savefig(OUT / "fig2_sizeladder.png", dpi=150)
    plt.close(fig)


def fig_mechanism(rows):
    """Why augmentation > dispersion on EXCESS: dispersion (IID bootstrap) is unrelated/anti to
    the shift-specific component; augmentation (a label-preserving perturbation) tracks it."""
    fig, axes = plt.subplots(1, 2, figsize=(9, 4), sharey=True)
    for ax, p, c in [(axes[0], "raptor_stability", "#4C72B0"),
                     (axes[1], "augmentation_robustness", "#C44E52")]:
        s = np.array([r.get(p, np.nan) for r in rows], float)
        y = np.array([r["excess"] for r in rows], float)
        ok = ~(np.isnan(s) | np.isnan(y))
        s, y = s[ok], y[ok]
        ax.scatter(s, y, s=8, alpha=0.3, color=c)
        if len(s) > 2:
            b, a = np.polyfit(s, y, 1)
            xs = np.linspace(s.min(), s.max(), 20)
            ax.plot(xs, a + b * xs, color="k", lw=1.5)
        r = metrics.spearman(s, -y)[0]
        ax.set_title(f"{LBL[p].replace(chr(10),' ')}\nρ(score,−excess)={r:+.2f}")
        ax.set_xlabel(f"{p} (a-priori stability)"); ax.axhline(0, color="grey", lw=0.6)
    axes[0].set_ylabel("EXCESS rotation (shift-specific)")
    fig.suptitle("Mechanism: dispersion is blind to shift-specific fragility; augmentation is not")
    fig.tight_layout(); fig.savefig(OUT / "fig3_mechanism.pdf"); fig.savefig(OUT / "fig3_mechanism.png", dpi=150)
    plt.close(fig)


if __name__ == "__main__":
    rows = load_rows("results_A/results")
    print(f"loaded {len(rows)} configs")
    fig_circularity(rows)
    fig_sizeladder(rows)
    fig_mechanism(rows)
    print("wrote figures/fig1_circularity, fig2_sizeladder, fig3_mechanism (.pdf/.png)")