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>
64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
"""Pydantic models for per-city cost-of-living data.
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Every category figure is monthly GBP for a single person — the
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denomination the simulator expects when scaling `spending_gbp`. The
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source object retains the original currency, FX rate, and snapshot
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date so we can re-validate or update a stale baseline.
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"""
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from __future__ import annotations
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from datetime import date
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from decimal import Decimal
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from typing import Literal
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from pydantic import BaseModel, ConfigDict, Field
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SourceName = Literal["numbeo", "expatistan", "baseline", "manual"]
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class ColSource(BaseModel):
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"""Provenance for a CityCostIndex entry — where did the numbers come
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from and when. The simulator surfaces this in the SimulateResult so
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the user can audit which baseline was applied."""
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model_config = ConfigDict(frozen=True)
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name: SourceName
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url: str | None = None
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snapshot_date: date
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raw_currency: str = "GBP"
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gbp_per_unit: Decimal = Decimal("1")
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class CategoryBreakdown(BaseModel):
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"""Per-category monthly costs in GBP for a single person."""
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model_config = ConfigDict(frozen=True)
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rent_1bed_center: Decimal
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rent_1bed_outside: Decimal | None = None
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groceries: Decimal
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restaurants: Decimal
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transport: Decimal
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utilities: Decimal
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leisure: Decimal
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class CityCostIndex(BaseModel):
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"""One city's headline cost-of-living snapshot."""
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model_config = ConfigDict(frozen=True)
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city: str
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city_slug: str = Field(min_length=1)
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country: str
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total_single_no_rent_gbp: Decimal
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total_single_with_rent_gbp: Decimal
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breakdown: CategoryBreakdown
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source: ColSource
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@property
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def total_monthly_gbp(self) -> Decimal:
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"""The number the simulator uses for ratios — `with rent` is the
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right anchor because moving location changes rent too."""
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return self.total_single_with_rent_gbp
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