Viktor Barzin
e72fd22a17
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>
36 lines
1.2 KiB
Python
36 lines
1.2 KiB
Python
"""Cost-of-living module — feeds the simulator with real per-city spend ratios.
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The simulator's `spending_gbp` is denominated in the user's BASELINE city
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(typically London). When a scenario moves the user to a different TARGET
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city, this module returns the ratio `target_total / baseline_total` so
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the simulator can scale `spending_gbp` to local prices before running
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paths.
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Phase 1 (current): hand-curated baselines from Numbeo public pages, with
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source URLs and fetch dates embedded so future-us can refresh by hand.
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Phase 2 (planned): live scrapers for Numbeo + Expatistan, DB cache with
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30-day TTL, nightly refresh CronJob.
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"""
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from __future__ import annotations
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from fire_planner.col.models import CategoryBreakdown, CityCostIndex, ColSource
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from fire_planner.col.service import (
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JURISDICTION_REPRESENTATIVE_CITY,
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compute_col_ratio,
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lookup_city,
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lookup_city_cached,
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reconcile_sources,
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representative_city_for,
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)
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__all__ = [
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"CategoryBreakdown",
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"CityCostIndex",
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"ColSource",
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"JURISDICTION_REPRESENTATIVE_CITY",
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"compute_col_ratio",
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"lookup_city",
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"lookup_city_cached",
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"reconcile_sources",
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"representative_city_for",
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]
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