[Jammy2211]

Summary

Fixes the five autocti_workspace scripts that the cti-upkeep 3a smoke triage found failing, and adds them to smoke_tests.txt (36 → 44 entries) so CI covers them. Workspace only: none of the failures needed a library change. Closes #45.

Re-run from a fresh clone in dependency order, the five failures turned out to be:

script	cause	fix
dataset_1d/advanced/temporal/database.py	each fit is a FactorGraphModel, so max_log_likelihood() returns one entry per dataset plus factor, with no .time	take the per-dataset entry [0], as fit.py does with result_list[0].instance
dataset_1d/advanced/temporal/plot.py	same as above, for median_pdf / errors_at_*_sigma	same [0]
plot/diagnostics/imaging_ci/well_filling.py	float() on the 1-element binned EPER array (NumPy 2 refuses it)	take [0]
imaging_ci/advanced/chaining/noise_scaling.py	(1) read poisson/simple/norm_*, but poisson_traps.py writes poisson/norm_*; (2) called set_noise_scaling_map_dict(), which PyAutoCTI removed when the maps became a dict; (3) never passed the imaging_ci_full_list it builds, so Result.noise_scaling_map_dict hit dataset_full=None; (4) under profile_release the visualization of the full dataset needs the maps too	path fixed; assign noise_scaling_map_dict directly on both the masked and full datasets; pass dataset_full to all three searches' analyses
plot/fpa/imaging_ci/database.py	did not reproduce: passes once its simulator and fit run first. The 11-05 PIXSCAY error was a triage ordering / stale-output artefact	no change; promoted with its simulator and fit

The "SQLAlchemy error" the triage saw on temporal/plot.py only happens on a second local run: under test mode the script deletes output/<name>.sqlite, but the database lives in output/test_mode/<name>.sqlite. CI always runs fresh, so it's filed separately as PyAutoMind/draft/bug/autocti_workspace/database_scripts_stale_sqlite_under_test_mode.md (6 scripts share the pattern).

Scripts Changed

• scripts/dataset_1d/advanced/temporal/database.py: per-dataset instance [0], with an explanation in the prose
• scripts/dataset_1d/advanced/temporal/plot.py: per-dataset instance [0] (median PDF and both error bounds)
• scripts/plot/diagnostics/imaging_ci/well_filling.py: scalar EPER via [0]
• scripts/imaging_ci/advanced/chaining/noise_scaling.py: dataset path, dataset_full, noise scaling maps on both datasets
• smoke_tests.txt: +8 entries, each after its producers (temporal/database.py and temporal/plot.py; poisson_traps.py and noise_scaling.py; the plot/fpa/imaging_ci simulator, fit and database; well_filling.py)
• AGENTS.md: smoke count 36 → 44
• notebooks/: the four matching notebooks regenerated with generate.py autocti

Upstream PR

None (workspace only).

Test Plan

• Each fixed script run from a fresh clone, after its producers
• Full smoke_tests.txt from a fresh clone, profile_smoke: 44/44 passed, 573 s
• Full smoke_tests.txt from a fresh clone, profile_release: 43/44 on the first run; the failure (noise_scaling.py, full-dataset noise scaling under visualization) was fixed in f00ade8 and re-run green under both profiles
• PR CI smoke (3.12, 3.13)

Timing note: the plot/fpa/imaging_ci chain is the costliest addition: about 230 s under profile_smoke (fit 108 s) and about 430 s under profile_release (fit 296 s). It's kept because the prompt's Witness requires all five scripts in smoke; it's a candidate for /ci_speedup if the release gate's wall time matters.

Heart: STALE at ship time, with one reason, acknowledged by the human: "release validation stale: source moved since rehearsal (PyAutoNerves)".

Tier: glance. Merge mode: auto-merge on green if the Witness passes.

🤖 Generated with Claude Code