#!/usr/bin/env python3
"""Plot the 5 demo-mode scores (4 checkpoints each) for a single episode.

Usage:
  python plot_one_episode.py chunk-000_episode_000039
  python plot_one_episode.py chunk-000_episode_000039 --out /tmp/x.png
"""
import argparse
import json
from pathlib import Path

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

MODES = ["demo5_uniform", "demo3_sparse", "demo9_dense", "demo5_jitterA", "demo5_jitterB"]
COLORS = {"demo5_uniform": "tab:blue", "demo3_sparse": "tab:orange",
          "demo9_dense": "tab:green", "demo5_jitterA": "tab:red",
          "demo5_jitterB": "tab:purple"}
FRACS = ["1/4", "2/4", "3/4", "end"]

p = argparse.ArgumentParser()
p.add_argument("episode_dir", help="e.g. chunk-000_episode_000039")
p.add_argument("--results-root",
               default=str(Path(__file__).resolve().parent.parent / "results_full"))
p.add_argument("--out", default=None)
a = p.parse_args()

ep_dir = Path(a.results_root) / "episode_results" / a.episode_dir
out = Path(a.out) if a.out else ep_dir / "curves.png"

data = {}
for m in MODES:
    f = ep_dir / f"{m}.json"
    if f.exists():
        data[m] = json.loads(f.read_text())
if not data:
    raise SystemExit(f"no mode json found in {ep_dir}")

meta0 = next(iter(data.values()))
n = meta0["pool_n"]
xt = np.arange(len(FRACS))

fig, ax = plt.subplots(figsize=(11, 6))
for m in MODES:
    if m not in data:
        continue
    ys = data[m]["scores_100"]
    xs = [i for i, y in enumerate(ys) if y is not None]
    yy = [y for y in ys if y is not None]
    dl = data[m].get("demo_labels", [])
    ax.plot(xs, yy, "-o", color=COLORS[m], lw=1.7, ms=7,
            label=f"{m}  (demo {data[m].get('n_demo','?')}: {dl})")

# Demo Range per checkpoint
rngs = []
for ci, frac in enumerate(FRACS):
    vals = [data[m]["scores_100"][ci] for m in MODES
            if m in data and data[m]["scores_100"][ci] is not None]
    rngs.append(f"{frac}: {max(vals)-min(vals):.1f}" if len(vals) >= 2 else f"{frac}: n/a")

ax.set_xticks(xt)
ax.set_xticklabels([f"{f}\n(f{ti})" for f, ti in zip(FRACS, meta0["target_frame_indices"])])
ax.set_xlabel("checkpoint (target frame position in pool)")
ax.set_ylabel("progress score (0-100)")
ax.set_ylim(0, 100)
ax.grid(alpha=0.25)
ax.legend(fontsize=8)
ax.set_title(f"{a.episode_dir}  ({meta0['camera']}, pool={n})\n"
             f"task: {meta0['task'][:100]}\nDemo Range @ checkpoints -> {', '.join(rngs)}",
             fontsize=10)
fig.tight_layout()
fig.savefig(out, dpi=140)
print("saved:", out)