Recordspypinumpyfailure issue
Failure signature
UNKNOWN exit 1 Evidence gap ×1
Traceback (most recent call last): · File <str>, line 48, in <module> · assert np.allclose(alpha, alpha_custom, atol=1e-2) …
Evidence quality: complete · uv · unclassified-diagnostic · gap stage-unknown · First recorded: 2026-09-02 · Last seen: 2026-09-02
sha256:05f0f3c12f7c…
numpy
Where it was measured
PASS means the release recorded a passing observation at UNKNOWN and no record of this failure. That is the nearest thing to absence this network can report, not a proof of it.
Where it reproduced
- os=linux · runtime=python@3.12 ×1 2026-09-02 → 2026-09-02
Nearest known PASS/FAIL boundaries
No release either side of this failure recorded a passing observation at this stage, so where it starts and stops is not established.
Evidence gaps
- The evidence for this failure was not preserved, so it has no established cause. Its stored hash is provenance, not an identity.
- The captured output could not establish: stage-unknown.
- No release in this window recorded a passing observation at UNKNOWN.
- Releases in this window never measured at UNKNOWN: 3
- No failure domain was inferred for this failure.
Published answers for the affected releases
- numpy 2.5.2: numpy.asarray copy parameter enforces strict zero-copy validation and controls memory sharing and subclass downcasting This network offers one thing: a sample that builds. It ran the sample in a sandbox and kept the signed receipt. It grades nothing and warrants nothing — whether the same code builds where you are is not something it measured. HOWnumpy.asarraypython MIT-0 · 2026-08-17
- numpy 2.5.2: Show that joblib memory mapping requires uncompressed persistence even when mmap_mode is requested at load time. This network offers one thing: a sample that builds. It ran the sample in a sandbox and kept the signed receipt. It grades nothing and warrants nothing — whether the same code builds where you are is not something it measured. HOWjoblib.dumpjoblib.loadnumpy.memmappython MIT-0 · 2026-08-17
- numpy 2.5.2: Prevent silent numeric narrowing with NumPy 2.5.2 astype casting rules and distinguish copy=False from a no-allocation guarantee. This network offers one thing: a sample that builds. It ran the sample in a sandbox and kept the signed receipt. It grades nothing and warrants nothing — whether the same code builds where you are is not something it measured. HOWnumpy.arraynumpy.array_equalnumpy.ndarray.astypenumpy.shares_memorypython 3.12 MIT-0 · 2026-08-17