"""Calibrate ISR knobs for degree-unit SO-101 joint data (defaults assume metres)."""
import sys, json
from pathlib import Path
import numpy as np

sys.path.insert(0, "/workspace/teleop_std_poc")
from isr_resample import _acceleration_magnitudes, isr_resample, _gripper_forced

T = Path("/workspace/std_mm_teleop/trajs")
files = sorted(T.glob("ep*.npz"), key=lambda p: int(p.stem[2:]))

step_all, acc_all, grip_d, spans = [], [], [], []
for f in files:
    d = np.load(f)
    pos, ts, g = d["positions"], d["timestamps"], d["gripper"]
    step_all.append(np.linalg.norm(np.diff(pos, axis=0), axis=1))
    acc_all.append(_acceleration_magnitudes(pos, ts))
    grip_d.append(np.abs(np.diff(g)))
    spans.append(g.max() - g.min())
step = np.concatenate(step_all); acc = np.concatenate(acc_all); gd = np.concatenate(grip_d)
print(f"per-frame joint step (deg): mean {step.mean():.3f} median {np.median(step):.3f} p90 {np.percentile(step,90):.3f}")
print(f"accel magnitude (deg/s^2): mean {acc.mean():.1f} median {np.median(acc):.1f}")
print(f"gripper |delta| per frame: median {np.median(gd):.3f} p90 {np.percentile(gd,90):.3f} max {gd.max():.2f}")
print(f"gripper span per episode : mean {np.mean(spans):.1f}")

# lambda_acc so the acceleration term carries a comparable weight to distance, as in the paper
# (paper: metres + lambda_acc 0.01 -> acc term is a minority but non-trivial share of the info)
for la in [0.0, 0.001, 0.002, 0.005, 0.01]:
    share = la * acc.mean() / (step.mean() + la * acc.mean())
    print(f"  lambda_acc={la:<6} -> acc share of per-frame info {100*share:.0f}%")

# gripper threshold: pick the knee between "real open/close" and per-frame jitter
for th in [0.5, 1.0, 2.0, 3.0]:
    frac = float((gd > th).mean())
    print(f"  grip_threshold={th:<4} -> {100*frac:.1f}% of frame pairs flagged as gripper events")

# d_target sweep on a 12-episode probe (keep ratio + gripper-forced share)
probe = files[:12]
la, gt = 0.002, 2.0
print("\nd_target sweep (12-episode probe, lambda_acc=%.3f, grip_th=%.1f):" % (la, gt))
for dt in [2, 4, 6, 8, 12, 16, 24]:
    ratios, forced_share = [], []
    for f in probe:
        d = np.load(f)
        pos, ts, g = d["positions"], d["timestamps"], d["gripper"]
        keep = isr_resample(pos, ts, g, dt, 1.0, la, gt)
        forced = _gripper_forced(g, len(pos), threshold=gt)
        ratios.append(len(keep) / len(pos))
        forced_share.append(len(forced) / max(len(keep), 1))
    print(f"  d_target={dt:<4} kept {100*np.mean(ratios):5.1f}% (min {100*min(ratios):.0f} max {100*max(ratios):.0f}) "
          f"| gripper-forced share of kept {100*np.mean(forced_share):.0f}%")