File size: 2,711 Bytes
45e45cb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | #!/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)
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