Closes the "still open" reader-tool note from §XXXV.10: scripts/
extract_doe_region.py spools DOE-PERSIST-TRIAL's on-disk binary ring off
disk/artemis.img into a CSV -- direct file read by default, or via a real
loop device (--loop, losetup) per the literal request. Independent
Python reimplementation of doe_region.c's own ring-walk, verified
against a live 3-record write/read round trip.
analyse_multiuser_doe.R (house style, ggplot2/svglite) and
multiuser_doe_report.tex (same preamble convention as
bare_metal_doe_report.tex) auto-discover whatever data exists and
degrade gracefully to a placeholder when a campaign has 0 trials so far
-- the whole point being this can be rebuilt at any point during a
multi-day campaign, including mid-run. build_multiuser_doe_report.sh
chains extraction -> R -> svg_to_pdf.py -> pdflatex into one command.
Found and fixed a real bug while verifying: the R script's SCRIPT_DIR
detection silently wrote charts/tables/report output to the repo root
instead of the analysis directory when invoked from a different cwd --
switched to the robust commandArgs() "--file=" idiom. Verified live both
branches (0 trials and a real 3-record write), reverted synthetic test
data and incidental svg_to_pdf.py churn on ~85 unrelated committed
figures before committing. FABRIC-3.md §XXXV.11 has the full account.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BWpNjdwPtFLuVLaAq44L9K
every factor interaction, and a raw-data appendix
Expanded the campaign-mechanism validation report from a condensed
6-page summary into the full depth Captain Bob asked for: analyze all 9
cells as a conglomerate Latin square, then dive into each cell's own
data, then cover every within-ISA and cross-factor interaction
explicitly rather than averaging it away.
Report structure (127 pages, compiled clean, no undefined references):
- Front matter: context, methodology, the SWAP-MTX bug narrative
(console-interleaving fix + the Fisher-Yates correctness bug and its
fix, both already committed separately)
- Layer 1: aggregate 3x3 Latin square (heatmap, invariant-metrics table)
- Per-Cell Deep Dive (9 sections): cfg-level distribution, summary
table, and a rep-order execution-trajectory chart per cell -- the
trajectory charts are what actually visualize the order-dependence
finding rather than just stating it
- Per-ISA Deep Dive (3 sections): within-architecture seed comparison
(violin plots, Kruskal-Wallis, per-factor main effects)
- Factor Interactions (6 sections, every pairwise combination of the 4
L8 binary factors): both infer_dec_q and early_exit interaction plots
faceted by architecture, plus the three-way
factor x factor x architecture significance test
- Per-Factor Response (4 sections): linear response by architecture,
with an explicit note that a true quadratic term isn't identifiable
from this 2-level factorial design
- Appendix: full run_id-ordered raw data, all 4,320 rows across all 9
cells, as the primary-source backing for every statistic above
Generated programmatically (analyse_stadium_relaunch_fixed.R for the
aggregate layer, generate_stadium_deepdive.R for the per-cell/per-ISA/
interaction/appendix layers) rather than hand-authored, since content at
this scale needs to be data-driven to stay honest.
Also includes analyse_stadium_relaunch.R, the earlier script built
against the pre-fix (buggy-shuffle) dataset -- superseded but kept for
the record, matching how the underlying data commits were handled.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
