| |
| """Plot the 5 anchoring-mode progress curves 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 = ["incremental", "forward", "backward", "interval_half", "interval_double"] |
| BASELINE = "incremental" |
| COLORS = {"incremental": "tab:blue", "forward": "tab:orange", |
| "backward": "tab:green", "interval_half": "tab:red", |
| "interval_double": "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 = data.get(BASELINE, next(iter(data.values()))) |
| base_after = meta0["after_frames"] |
| L = len(base_after) |
| tgts = [base_after[int(round((L - 1) * k / 4))] for k in (1, 2, 3, 4)] |
|
|
|
|
| def score_at(payload, taf): |
| afs = payload["after_frames"] |
| j = min(range(len(afs)), key=lambda i: abs(afs[i] - taf)) |
| return payload["scores_100"][j] |
|
|
|
|
| fig, ax = plt.subplots(figsize=(13, 6)) |
| for m in MODES: |
| if m not in data: |
| continue |
| xs = np.array(data[m]["after_frames"], dtype=float) |
| ax.plot(xs, data[m]["scores_100"], color=COLORS[m], lw=1.6, |
| marker=".", ms=4, label=m) |
| for frac, taf in zip(FRACS, tgts): |
| ax.axvline(taf, color="0.6", ls=":", lw=1) |
| ax.text(taf, 96, f"{frac} (f={taf})", ha="center", fontsize=9, color="0.4") |
|
|
| scores = {frac: [score_at(data[m], taf) for m in MODES if m in data] |
| for frac, taf in zip(FRACS, tgts)} |
| rngs = ", ".join(f"{frac}: {max(v)-min(v):.1f}" for frac, v in scores.items()) |
| ax.set_title(f"{a.episode_dir} ({meta0['camera']}, baseline pool={L})\n" |
| f"task: {meta0['task'][:100]}\nAnchor Range @ checkpoints -> {rngs}", |
| fontsize=10) |
| ax.set_xlabel(f"physical AFTER-frame index " |
| f"(raw {meta0['total_raw_frames']} frames @ {meta0['native_fps']:.0f} fps)") |
| ax.set_ylabel("progress score (0-100)") |
| ax.set_ylim(0, 100) |
| ax.grid(alpha=0.25) |
| ax.legend(fontsize=9) |
| fig.tight_layout() |
| fig.savefig(out, dpi=140) |
| print("saved:", out) |
|
|
