[michalmaj]

Summary

Implements the last integration piece of the NumPy/Pandas plan: a mandatory Final Analytics Epilogue after Level 7 (not an eighth level) that closes the "is the game balanced?" question Level 1 opened. Student uses NumPy a third time on a deep-dive battle log, and Pandas a third time on a multi-run tournament history — both generated by really running Level 7 Project 03's BattleService.

Levels 1-7 and the Data Recall Checkpoint are untouched (git diff --stat against the merge base is empty for all of them).

Key findings from verifying the real L7 contract

• rpg.domain/rpg.services/rpg.repositories in level_7.../projects/03_concurrent_tournament_runner/ are fully complete and importable (only the API router layer has student TODOs) — used directly, no reimplementation.
• BattleService.simulate() calls the stdlib random module's global functions (random.randint(...)), not an injected RNG — confirmed empirically that random.seed(n) before a sequence of calls makes it exactly reproducible with zero changes to frozen code.
• Structural finding: given the frozen hero/monster stat table, every one of the 9 class×monster matchups is deterministically 100% or 0% (margins are too wide for randint(1,6) noise to ever flip an outcome — confirmed via both margin analysis and 150-trial-per-matchup empirical testing with zero flips). This makes per-class/per-monster win rates seed-invariant. The variant fixture therefore uses a genuinely different (but equally real, still fully simulated) tournament run-allocation — not a different seed alone — to produce different win_rate_by_class values and a different most_balanced_class answer. Full details and honest reporting of which conclusions are structurally invariant (hardest_monster, rank_monsters, unfavorable_matchups) vs. which genuinely change are in the task's final report.

What's added

• final_analytics_epilogue/{README.md, task.py, check.py, data/*.{json,csv}, scripts/generate_fixtures.py}
• 8 functions: 3 NumPy (load_battle_arrays, deep_dive_stats, battle_matrix_summary), 5 Pandas (load_tournament_history, win_rate_by_class, rank_monsters, unfavorable_matchups, balance_conclusions), 1 synthesis (build_final_report)
• Dual-fixture-pair behavioral checker (ground truth via stdlib json/csv/statistics/math only) plus technique gates: AST-based for NumPy reductions/boolean-mask-work/2D-axis-construction (percentile/quantile via runtime spy, following the Data Recall Checkpoint precedent) and for pandas groupby across the three grouping functions.
• generate_fixtures.py is an author-only provenance tool; verified byte-for-byte reproducible across two independent clean regenerations.
• Course-flow integration: root README, COURSE_MAP.md, tools/course_status.py (new mandatory checkpoint tracked via the existing print_checkpoint() helper — course no longer reports "complete" until the epilogue is also done), tools/author_check.py (new check_epilogue() + doc-coverage registration).

Test plan

• Scaffold → FAIL
• Reference solution → PASS (both fixture pairs)
• Alternative-valid Variant B (np.asarray/np.quantile/np.count_nonzero/np.stack/.query/named .agg/different helper names) → PASS
• Fake A (hardcoded everything) → PASS primary, FAIL variant
• Fake B (ceremonial NumPy arrays, stats via plain Python) → FAIL (all gaps reported)
• Fake C (only percentile real NumPy) → FAIL
• Fake D (real 2D matrix, manual row loop instead of axis) → FAIL
• Fake E (DataFrame real, groupby replaced by .unique() loop) → FAIL technique gate specifically (isolated from tie-break order)
• Fake F (only half the rows analyzed) → FAIL on values
• Fake G (hardcoded conclusions) → PASS primary, FAIL variant
• Fake H (build_final_report ignores input, returns primary's answer) → PASS primary, FAIL variant
• tools/author_check.py → 750 checks passed, 0 errors
• tools/course_status.py → epilogue tracked, "course complete" gated correctly (verified via temporary .progress simulation, cleaned up after)
• Data Recall Checkpoint checker still runs correctly (smoke test)
• Levels 1-7 and Data Recall Checkpoint untouched (git diff --stat against merge base empty)
• Fixture generator: two independent clean regenerations produce byte-for-byte identical output across all 4 files
• Scaffold restored byte-for-byte after every test run; no leftover .progress/cache/temp generator output in the working tree

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