sealedlab

Discipline as code for computational experiments. Six small, dependency-free pieces (numpy optional), each of which exists because a specific failure happened in a real research repository and was either caught by it or would have been.

Install

pip install sealedlab
pip install "sealedlab[guard]"        # adds numpy for the built-in canary

or straight from the repository: pip install git+https://github.com/uosew/sealedlab.git. (The project was briefly named evidence-gate; that PyPI name belongs to an unrelated project.)

Sixty seconds

import sealedlab as eg

sha = eg.seal_file("PREREG.md")                       # record this before running anything
led = eg.Ledger("ledger.jsonl"); led.append("prereg_sealed", "hash recorded before any run", sha256=sha)

guard = eg.enforce(strict=True)                        # raises if a known-defect canary fires
envelope = {"prereg_sha256": sha, "runtime_guard": guard}

eg.seal_truth("panel/", truth={"case_01": {"law": ["u_xx"]}}, public={"case_01": {"n": 400}})
# ... run the solver blind on panel/cases ... write panel/predictions.json ...
truth, predictions, audit = eg.open_truth("panel/", "panel/predictions.json")   # hashes predictions first

def judge(d):                                          # your scoring rule, frozen in the preregistration
    return "REJECTED" if d["false_claims"] else "CONFIRMED" if d["claims"] >= 30 else "INCONCLUSIVE"
suite = (eg.MutationSuite(judge, baseline={"false_claims": 0, "claims": 40})
         .add("one false claim", lambda d: {**d, "false_claims": 1}, expect="REJECTED")
         .add("too few claims",  lambda d: {**d, "claims": 10},       expect="INCONCLUSIVE"))
verdict = suite.verdict(score(truth, predictions))    # refused unless every mutation bit

card = eg.ClaimCard("resolution bound, blind-4", verdict, "PREREG.md", sha, hypothesis="...",
                    evidence={"claims": 59, "violations": 0}, does_not_show=["terms outside the library"])
card.write("cards/", "blind4")

r = eg.Record("H1", claim_status="SUPPORTED", evidence_status="COMPLETE", implementation_status="READY")
r.activate()                                           # PolicyNotAuthorized: SUPPORTED is not VALIDATED

Where this comes from

Extracted from one research repository (weak-form PDE discovery, NAS self-evolution, a local knowledge engine) where, over three weeks, this discipline stopped seven false positives across unrelated domains: a strategy whose +0.040 effect inverted sign on an independent panel; a "2.86x speed-up" that was an archive asymmetry; a "7% gain" that measured a design-guaranteed property; a fitness function declared broken from a winners-only sample; graph expansion that lost to plain retrieval; two detector variants that recovered one true case each and admitted a decoy. It also found 14 false claims in 423 blind opportunities that a "zero false claims" characterization had hidden for a month — because the truth had been sealed and the scorer was written with mutation tests before it was run.

The built-in canary targets numpy's temporary-elision operand mutation on CPython 3.14 with numpy < 2.3 (numpy issues #28681, #30435, fixed in 2.3.0). It is kept as a regression canary: the point of the guard is the fail-closed policy, not that one bug.

Status

0.1.0, alpha. Used by its author; not yet used by anyone else — which is the measurement this package is missing. Pull requests that add a canary for a defect you have reproduced, or a failure story that one of these pieces would have caught, are the most useful kind.

License

MIT.