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272 lines
9.4 KiB
Python
272 lines
9.4 KiB
Python
"""Unit tests for the web-features enrichment of the estimate response.
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Covers the ADDITIVE + OPTIONAL fields surfaced for the new web views (map /
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comp distribution / price↔exposure / ppm² trend):
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- estimator._listing_to_analog / _deal_to_analog — now carry lat/lon
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- estimator._fetch_price_trend — monthly ppm² series shape (mock db)
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- estimator._compute_last_scraped_at — absolute freshness timestamp
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No real DB / network: the trend test injects a fake Session whose execute()
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returns canned mapping rows.
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NOTE: importing app.services.estimator pulls app.core.config.Settings, which
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requires DATABASE_URL — set BEFORE importing app modules (same pattern as the
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sibling estimator unit tests).
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"""
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import os
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os.environ.setdefault("DATABASE_URL", "postgresql+psycopg://test:test@localhost:5432/test")
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from datetime import UTC, datetime
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import pytest
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from app.schemas.trade_in import AnalogLot, PriceTrendPoint
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from app.services import estimator
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# --------------------------------------------------------------------------- #
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# Per-comp coords on analogs / deals (map + exposure views)
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# --------------------------------------------------------------------------- #
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def test_listing_to_analog_carries_lat_lon() -> None:
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row = {
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"address": "ул. Тестовая, 1",
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"area_m2": 50.0,
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"rooms": 2,
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"floor": 3,
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"total_floors": 9,
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"price_rub": 10_000_000,
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"price_per_m2": 200_000,
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"listing_date": None,
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"days_on_market": 14,
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"photo_urls": None,
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"source": "cian",
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"source_url": "https://example.test/1",
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"distance_m": 120.0,
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"lat": 56.8389,
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"lon": 60.6057,
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}
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lot = estimator._listing_to_analog(row)
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assert isinstance(lot, AnalogLot)
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assert lot.lat == pytest.approx(56.8389)
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assert lot.lon == pytest.approx(60.6057)
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# exposure + comp-distribution keys still present
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assert lot.days_on_market == 14
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assert lot.price_per_m2 == 200_000
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assert lot.area_m2 == 50.0
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assert lot.rooms == 2
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def test_deal_to_analog_carries_lat_lon() -> None:
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row = {
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"address": "ул. Тестовая, 2",
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"area_m2": 60.0,
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"rooms": 2,
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"floor": 5,
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"total_floors": 10,
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"price_rub": 11_000_000,
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"price_per_m2": 183_333,
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"deal_date": None,
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"days_on_market": None,
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"cadastral_number": "66:41:0204016:10",
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"source": "rosreestr",
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"distance_m": 80.0,
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"lat": 56.84,
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"lon": 60.61,
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}
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lot = estimator._deal_to_analog(row)
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assert lot.lat == pytest.approx(56.84)
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assert lot.lon == pytest.approx(60.61)
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assert lot.tier == "T0_per_house" # kadastr with участок → per-house tier
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# --------------------------------------------------------------------------- #
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# date_precision honesty flag (#1995) — rosreestr deals are quarter-granular,
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# real listings are day-granular. See _date_precision_for_source docstring +
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# data/sql/01_schema_rosreestr_deals.sql for the underlying data-source reason.
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# --------------------------------------------------------------------------- #
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def test_deal_to_analog_marks_rosreestr_source_as_quarter_precision() -> None:
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"""#1995: deals.source='rosreestr' → date_precision='quarter' — deal_date is
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period_start_date (первый день квартала), НЕ реальная дата регистрации.
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Это честная маркировка данных, а не баг (live-аудит prod: 9 кварталов,
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ровно 1 distinct deal_date на квартал)."""
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row = {
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"address": "ул. Тестовая, 2",
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"area_m2": 60.0,
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"rooms": 2,
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"floor": 5,
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"total_floors": 10,
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"price_rub": 11_000_000,
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"price_per_m2": 183_333,
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"deal_date": None,
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"days_on_market": None,
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"cadastral_number": "66:41:0204016:10",
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"source": "rosreestr",
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"distance_m": 80.0,
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"lat": 56.84,
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"lon": 60.61,
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}
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lot = estimator._deal_to_analog(row)
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assert lot.date_precision == "quarter"
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def test_listing_to_analog_marks_real_source_as_day_precision() -> None:
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"""Listings (avito/cian/yandex/domklik) carry a real scrape/parse date →
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date_precision='day'."""
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row = {
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"address": "ул. Тестовая, 1",
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"area_m2": 50.0,
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"rooms": 2,
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"floor": 3,
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"total_floors": 9,
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"price_rub": 10_000_000,
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"price_per_m2": 200_000,
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"listing_date": None,
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"days_on_market": 14,
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"photo_urls": None,
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"source": "cian",
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"source_url": "https://example.test/1",
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"distance_m": 120.0,
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"lat": 56.8389,
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"lon": 60.6057,
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}
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lot = estimator._listing_to_analog(row)
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assert lot.date_precision == "day"
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def test_date_precision_none_when_source_unknown() -> None:
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"""source отсутствует (устаревшая/неполная строка) → date_precision=None —
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не заявляем precision, которую не можем подтвердить."""
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assert estimator._date_precision_for_source(None) is None
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def test_analog_lat_lon_optional_none_when_missing() -> None:
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# Tier S address-only Avito lots can lack geom → lat/lon absent → None (graceful).
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row = {
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"address": "ул. Без Координат, 3",
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"area_m2": 40.0,
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"rooms": 1,
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"floor": None,
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"total_floors": None,
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"price_rub": 8_000_000,
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"price_per_m2": 200_000,
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"listing_date": None,
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"days_on_market": None,
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"photo_urls": None,
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"source": "avito",
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"source_url": None,
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"distance_m": None,
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}
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lot = estimator._listing_to_analog(row)
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assert lot.lat is None
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assert lot.lon is None
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# --------------------------------------------------------------------------- #
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# _compute_last_scraped_at
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# --------------------------------------------------------------------------- #
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def test_last_scraped_at_returns_max_timestamp() -> None:
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older = datetime(2026, 5, 1, 10, 0, tzinfo=UTC)
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newer = datetime(2026, 5, 28, 9, 30, tzinfo=UTC)
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lots = [{"scraped_at": older}, {"scraped_at": newer}]
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assert estimator._compute_last_scraped_at(lots) == newer
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def test_last_scraped_at_empty_returns_none() -> None:
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assert estimator._compute_last_scraped_at([]) is None
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# --------------------------------------------------------------------------- #
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# _fetch_price_trend (mock db)
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# --------------------------------------------------------------------------- #
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class _FakeResult:
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def __init__(self, rows: list[dict]) -> None:
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self._rows = rows
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def mappings(self) -> "_FakeResult":
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return self
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def all(self) -> list[dict]:
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return self._rows
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class _FakeSession:
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"""Minimal Session stub: returns canned rows per execute() call in order."""
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def __init__(self, *results: list[dict]) -> None:
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self._results = list(results)
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self.calls = 0
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def execute(self, *_args, **_kwargs) -> _FakeResult:
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rows = self._results[self.calls] if self.calls < len(self._results) else []
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self.calls += 1
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return _FakeResult(rows)
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def rollback(self) -> None: # pragma: no cover — not exercised on happy path
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pass
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def test_fetch_price_trend_none_when_no_house_id() -> None:
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db = _FakeSession()
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assert estimator._fetch_price_trend(db, target_house_id=None) is None
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assert db.calls == 0 # short-circuits before any query
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def test_fetch_price_trend_prefers_houses_price_dynamics() -> None:
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hpd_rows = [
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{"month": "2025-01", "ppm2": 200_000},
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{"month": "2025-02", "ppm2": 205_000},
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{"month": "2025-03", "ppm2": 210_000},
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]
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db = _FakeSession(hpd_rows) # first query hits → no fallback needed
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trend = estimator._fetch_price_trend(db, target_house_id=123)
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assert trend is not None
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assert db.calls == 1 # did NOT touch the fallback source
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assert trend == [
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{"month": "2025-01", "ppm2": 200_000},
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{"month": "2025-02", "ppm2": 205_000},
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{"month": "2025-03", "ppm2": 210_000},
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]
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# Shape contract: each point validates as PriceTrendPoint(month:str, ppm2:int).
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points = [PriceTrendPoint(**p) for p in trend]
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assert all(isinstance(p.month, str) and isinstance(p.ppm2, int) for p in points)
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def test_fetch_price_trend_falls_back_to_placement_history() -> None:
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fallback_rows = [
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{"month": "2024-06", "ppm2": 190_000},
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{"month": "2024-07", "ppm2": 195_000},
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{"month": "2024-08", "ppm2": 198_000},
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]
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# First query (houses_price_dynamics) empty → second (placement_history) hits.
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db = _FakeSession([], fallback_rows)
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trend = estimator._fetch_price_trend(db, target_house_id=235340)
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assert trend == fallback_rows
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assert db.calls == 2
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def test_fetch_price_trend_none_when_below_min_points() -> None:
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# Both sources return < min_points (3) → None (graceful, frontend hides chart).
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db = _FakeSession([{"month": "2025-01", "ppm2": 200_000}], [])
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assert estimator._fetch_price_trend(db, target_house_id=999) is None
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def test_price_trend_point_shape() -> None:
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p = PriceTrendPoint(month="2026-05", ppm2=251_429)
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assert p.month == "2026-05"
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assert p.ppm2 == 251_429
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dumped = p.model_dump()
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assert set(dumped) == {"month", "ppm2"}
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if __name__ == "__main__": # pragma: no cover
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raise SystemExit(pytest.main([__file__, "-q"]))
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