45 lines
1.7 KiB
Python
45 lines
1.7 KiB
Python
"""Reconstruct regimes_*.pt from an exp4_*.log printed table (the scan's
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torch.save crashes on its CWD-relative "results/" path — known bug; the
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printed table is the durable artifact). Generalizes parse_e4b_regimes.py.
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Usage: parse_regimes_log.py LOG OUT [--layers N] [--note TEXT]
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"""
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import argparse
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import re
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import torch
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ap = argparse.ArgumentParser()
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ap.add_argument("log")
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ap.add_argument("out")
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ap.add_argument("--layers", type=int, default=None,
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help="assert this many table rows were parsed")
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ap.add_argument("--note", default=None,
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help="provenance note stored in the tensor dict")
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args = ap.parse_args()
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sensor, motor, persist, content = [], [], [], []
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pat = re.compile(
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r"^L\s*(\d+) \| ([\d.]+)\s*#* *\| ([\d.]+)\s*#* *\| ([\d.]+) \| ([\d.]+)")
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for line in open(args.log):
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m = pat.match(line.strip())
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if m:
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i, s, mo, p, c = m.groups()
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assert int(i) == len(sensor), f"non-contiguous row L{i}"
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sensor.append(float(s))
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motor.append(float(mo))
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persist.append(float(p))
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content.append(float(c))
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if args.layers is not None:
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assert len(sensor) == args.layers, len(sensor)
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note = args.note or f"reconstructed from {args.log}"
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torch.save({"sensor": torch.tensor(sensor), "motor": torch.tensor(motor),
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"persist": torch.tensor(persist),
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"content": torch.tensor(content), "note": note}, args.out)
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print(f"saved {len(sensor)} layers -> {args.out}")
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print("sensor peak: L%d = %.2f" % (sensor.index(max(sensor)), max(sensor)))
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print("persist peak: L%d = %.2f" % (persist.index(max(persist)),
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max(persist)))
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print("motor >=0.1 from: L%s" % next(
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(i for i, v in enumerate(motor) if v >= 0.1), "never"))
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