dopamine/scripts/plot_one_episode.py

#!/usr/bin/env python3
"""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)