The Monte Carlo used to compare jurisdictions at a flat London-equivalent
spend, which silently overstated the cost-of-living for any move to a
cheaper region. Now every cross-jurisdiction simulation auto-scales
spending_gbp by the real Numbeo/Expatistan ratio between the user's
baseline city and the target city.
Architecture:
- fire_planner/col/baseline.py — 22 cities with headline Numbeo data
(source URLs + snapshot dates embedded) — fallback when scraper fails
- col/numbeo.py + col/expatistan.py — httpx async scrapers, regex-parsed,
polite 1.1s rate-limit, EUR/USD anchored
- col/cache.py — PG-backed cache (col_snapshot table, 1-year TTL)
- col/service.py — sync compute_col_ratio() for the simulator; async
lookup_city_cached() with source reconciliation for the refresh CronJob
- alembic 0005 — col_snapshot table, UNIQUE(city_slug, source_name)
Simulator wiring:
- SimulateRequest gains col_auto_adjust=True (default), col_baseline_city,
col_target_city. Defaults pick the jurisdiction's representative city.
- _resolve_col_adjustment scales spending_gbp before path-building.
- SimulateResult surfaces col_multiplier_applied + col_adjusted_spending_gbp.
CLIs:
- python -m fire_planner col-seed — loads BASELINES into col_snapshot
(post-migration seed step)
- python -m fire_planner col-refresh-stale --within-days 7 — used by the
weekly fire-planner-col-refresh CronJob
268 tests pass. Mypy strict + ruff clean.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Viktor Barzin
e72fd22a17