File size: 4,376 Bytes
4bcbe8c | 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 81 82 83 84 85 86 87 88 89 90 | #!/usr/bin/env python3
"""Measure motion after the actual PDO target stops; read original native CSV."""
import argparse
import hashlib
import json
from pathlib import Path
import numpy as np
import pandas as pd
def main():
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("csv", type=Path)
parser.add_argument("--output", required=True, type=Path)
args = parser.parse_args()
cols = ["elapsed_s", "cycle", "axis", "position_rad", "target_position_rad",
"enabled", "fault", "error_code", "active_mode", "operational"]
df = pd.read_csv(args.csv, comment="#", usecols=cols)
if df.duplicated(["cycle", "axis"]).any():
raise ValueError("duplicate cycle/axis")
arrays = {c: df.pivot(index="cycle", columns="axis", values=c).to_numpy()
for c in cols if c not in ("cycle", "axis")}
if arrays["position_rad"].shape[1] != 6:
raise ValueError("expected six axes")
t = arrays["elapsed_s"][:, 0]
q, u = arrays["position_rad"], arrays["target_position_rad"]
valid = (np.isfinite(q).all(axis=1) & np.isfinite(u).all(axis=1)
& (arrays["enabled"] == 1).all(axis=1)
& (arrays["fault"] == 0).all(axis=1)
& (arrays["error_code"] == 0).all(axis=1)
& (arrays["active_mode"] == 8).all(axis=1)
& (arrays["operational"] == 1).all(axis=1))
constant = np.r_[False, (np.abs(np.diff(u, axis=0)) < 1e-10).all(axis=1)]
constant &= valid & np.r_[False, valid[:-1]]
constant &= np.r_[False, (np.diff(t) > 0) & (np.diff(t) < .01)]
edges = np.diff(np.r_[False, constant, False].astype(int))
records, segments = [], []
for start, end in zip(np.flatnonzero(edges == 1), np.flatnonzero(edges == -1)):
if t[end-1] - t[start] < 3:
continue
local_t = t[start:end] - t[start]
actual = np.degrees(q[start:end])
record = {"start_s": float(t[start]), "duration_s": float(local_t[-1])}
for name, lo, hi in [("first_3s", 0, 3), ("after_0p5_to_3s", .5, 3),
("after_3s", 3, float("inf"))]:
sample = actual[(local_t >= lo) & (local_t < hi)]
record[name + "_ptp_deg"] = np.ptp(sample, axis=0).tolist() if len(sample) else None
record["final_error_deg"] = np.degrees(u[end-1] - q[end-1]).tolist()
# Retrospective settling: last departure from a ±0.02° final-position band.
# It is a measurement, not a causal online completion rule.
record["last_departure_0p02deg_s"] = [
float(local_t[np.flatnonzero(np.abs(actual[:, j]-actual[-1, j]) > .02)[-1]])
if np.any(np.abs(actual[:, j]-actual[-1, j]) > .02) else 0.0
for j in range(6)]
records.append(record)
segments.append((start, end))
if not records:
raise ValueError("no healthy constant-target holds >=3 s")
sha = hashlib.sha256()
with args.csv.open("rb") as source:
for block in iter(lambda: source.read(1024*1024), b""):
sha.update(block)
report = {"source": str(args.csv), "sha256": sha.hexdigest(), "hold_count": len(records),
"max_after_0p5_to_3s_ptp_deg": np.max(
[r["after_0p5_to_3s_ptp_deg"] for r in records], axis=0).tolist(),
"holds": records}
args.output.mkdir(parents=True, exist_ok=True)
(args.output / "report.json").write_text(json.dumps(report, indent=2) + "\n")
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
worst = int(np.argmax([max(r["after_0p5_to_3s_ptp_deg"]) for r in records]))
start, end = segments[worst]
fig, axes = plt.subplots(3, 2, figsize=(11, 8), sharex=True)
for j, ax in enumerate(axes.flat):
ax.plot(t[start:end]-t[start], np.degrees(q[start:end, j]-u[start, j]), label="actual - target")
ax.axhline(0, color="black", lw=.7, label="constant PDO target")
ax.set(title=f"J{j+1}", ylabel="error (deg)", xlabel="time after target stops (s)")
ax.grid(alpha=.3)
axes[0, 0].legend()
fig.suptitle(f"Native PDO hold at {t[start]:.3f} s; target constant on all six axes")
fig.tight_layout()
fig.savefig(args.output / "worst_hold.png", dpi=150)
print(json.dumps({k: v for k, v in report.items() if k != "holds"}, indent=2))
if __name__ == "__main__":
main()
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