414 lines
15 KiB
Python
414 lines
15 KiB
Python
import json
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import logging
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import math
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from typing import Annotated, Any
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import httpx
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from fastapi import APIRouter, Depends, HTTPException
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from sqlalchemy import text
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from sqlalchemy.orm import Session
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from app.core.db import get_db
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from app.schemas.parcel import ParcelDetail, ParcelSearchRequest, ParcelSearchResponse
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logger = logging.getLogger(__name__)
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router = APIRouter()
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# Базовые уровни шума по типу источника (дБ на 10м) — источник: WHO Environmental Noise Guidelines
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NOISE_L_BASE: dict[str, float] = {
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"highway:motorway": 75.0,
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"highway:trunk": 75.0,
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"highway:primary": 70.0,
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"highway:secondary": 65.0,
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"highway:tertiary": 60.0,
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"highway:residential": 55.0,
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"railway": 72.0,
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"industrial": 65.0,
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"aerodrome": 70.0,
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}
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def _wind_label(deg: float) -> str:
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"""Перевести угол направления ветра (0-360) в 8-позиционную розу на русском."""
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rose = ["Север", "С-В", "Восток", "Ю-В", "Юг", "Ю-З", "Запад", "С-З"]
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idx = round(deg / 45) % 8
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return rose[idx]
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def _fetch_air_quality_sync(lat: float, lon: float) -> dict | None:
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"""Синхронный запрос к Open-Meteo Air Quality API.
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Возвращает данные текущего часа (первый элемент hourly). None если API
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недоступен или вернул неожиданный формат.
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"""
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try:
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with httpx.Client(timeout=5) as c:
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r = c.get(
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"https://air-quality-api.open-meteo.com/v1/air-quality",
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params={
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"latitude": lat,
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"longitude": lon,
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"hourly": "pm2_5,pm10,nitrogen_dioxide",
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"forecast_days": 1,
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},
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)
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r.raise_for_status()
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data = r.json()
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hourly = data.get("hourly", {})
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if not hourly.get("time"):
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return None
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return {
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"pm2_5": hourly["pm2_5"][0] if hourly.get("pm2_5") else None,
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"pm10": hourly["pm10"][0] if hourly.get("pm10") else None,
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"no2": hourly["nitrogen_dioxide"][0] if hourly.get("nitrogen_dioxide") else None,
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"ts": hourly["time"][0],
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"source": "open-meteo",
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}
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except Exception as e:
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logger.warning("air quality fetch failed: %s", e)
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return None
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def _fetch_wind_sync(lat: float, lon: float) -> dict | None:
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"""Синхронный запрос к Open-Meteo Forecast API для данных о ветре.
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Возвращает доминирующее направление ветра (circular mean по 7 дням) и
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максимальную скорость. None если API недоступен.
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"""
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try:
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with httpx.Client(timeout=5) as c:
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r = c.get(
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"https://api.open-meteo.com/v1/forecast",
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params={
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"latitude": lat,
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"longitude": lon,
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"daily": "winddirection_10m_dominant,windspeed_10m_max",
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"forecast_days": 7,
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},
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)
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r.raise_for_status()
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daily = r.json().get("daily", {})
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dirs = daily.get("winddirection_10m_dominant") or []
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speeds = daily.get("windspeed_10m_max") or []
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if not dirs:
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return None
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# Circular mean направления ветра (vector sum) — избегает jump 359→1
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x = sum(math.cos(math.radians(d)) for d in dirs)
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y = sum(math.sin(math.radians(d)) for d in dirs)
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dominant = (math.degrees(math.atan2(y, x)) + 360) % 360
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return {
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"dominant_direction_deg": round(dominant),
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"dominant_direction_label": _wind_label(dominant),
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"max_speed_m_s": round(max(speeds), 1) if speeds else None,
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"forecast_days": len(dirs),
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"source": "open-meteo",
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}
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except Exception:
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return None
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# Веса POI-категорий для scoring (Максим: трамвай = минус)
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_POI_WEIGHTS: dict[str, float] = {
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"school": 1.5,
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"kindergarten": 1.5,
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"pharmacy": 0.8,
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"hospital": 0.6,
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"shop_mall": 1.2,
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"shop_supermarket": 1.0,
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"shop_small": 0.5,
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"park": 1.8,
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"bus_stop": 0.3,
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"metro_stop": 1.5,
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"tram_stop": -0.5, # негативный вес — шум / вибрация
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}
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@router.post("/search", response_model=ParcelSearchResponse)
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async def search_parcels(payload: ParcelSearchRequest) -> ParcelSearchResponse:
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"""Search parcels by filters + scoring.
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TODO Stage 2b: PostGIS query + scorer service.
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"""
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return ParcelSearchResponse(items=[], total=0)
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@router.get("/{parcel_id}", response_model=ParcelDetail)
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async def get_parcel(parcel_id: str) -> ParcelDetail:
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"""TODO Stage 2b: fetch parcel by id from DB."""
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raise HTTPException(status_code=501, detail="Not implemented yet")
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@router.post("/{cad_num}/analyze")
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def analyze_parcel(
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cad_num: str,
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db: Annotated[Session, Depends(get_db)],
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) -> dict[str, Any]:
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"""Анализ участка: близость к социалке + district context + конкуренты.
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Порядок поиска геометрии: cad_quarters_geom → cad_buildings.
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"""
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# 1) Получить геометрию участка — GeoJSON строка через ST_AsGeoJSON
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row = (
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db.execute(
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text("""
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SELECT ST_AsGeoJSON(g.geom) AS geom_geojson,
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g.geom AS geom_wkb,
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'cad_quarter' AS source
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FROM cad_quarters_geom g
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WHERE g.cad_number = :c
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UNION ALL
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SELECT ST_AsGeoJSON(b.geom) AS geom_geojson,
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b.geom AS geom_wkb,
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'cad_building' AS source
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FROM cad_buildings b
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WHERE b.cad_num = :c
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UNION ALL
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SELECT ST_AsGeoJSON(p.geom) AS geom_geojson,
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p.geom AS geom_wkb,
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'cad_parcel' AS source
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FROM cad_parcels_geom p
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WHERE p.cad_num = :c
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LIMIT 1
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"""),
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{"c": cad_num},
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)
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.mappings()
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.first()
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)
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if not row:
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raise HTTPException(
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status_code=404,
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detail=f"Геометрия для {cad_num} не найдена. Загрузи через NSPD geo.",
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)
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geom_geojson: str = row["geom_geojson"]
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source: str = row["source"]
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# Используем ST_AsText для передачи геометрии в последующие запросы.
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# Все PostGIS-запросы принимают текстовый WKT через ST_GeomFromText.
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geom_row = (
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db.execute(
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text("""
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SELECT ST_AsText(g.geom) AS wkt
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FROM (
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SELECT g.geom FROM cad_quarters_geom g WHERE g.cad_number = :c
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UNION ALL
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SELECT b.geom FROM cad_buildings b WHERE b.cad_num = :c
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UNION ALL
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SELECT p.geom FROM cad_parcels_geom p WHERE p.cad_num = :c
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) g
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LIMIT 1
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"""),
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{"c": cad_num},
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)
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.mappings()
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.first()
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)
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geom_wkt: str = geom_row["wkt"] # type: ignore[index]
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# 2) District context — ближайший район ЕКБ
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district_row = (
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db.execute(
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text("""
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SELECT district_name,
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median_price_per_m2,
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ST_Distance(
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d.geom::geography,
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ST_Centroid(ST_GeomFromText(:wkt, 4326))::geography
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) AS dist_to_center
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FROM ekb_districts d
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WHERE ST_DWithin(
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d.geom::geography,
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ST_Centroid(ST_GeomFromText(:wkt, 4326))::geography,
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5000
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)
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ORDER BY dist_to_center ASC
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LIMIT 1
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"""),
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{"wkt": geom_wkt},
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)
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.mappings()
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.first()
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)
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# 3) POI в радиусе 1 км — список с distance_m
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poi_rows = (
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db.execute(
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text("""
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SELECT category,
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name,
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lat,
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lon,
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ST_Distance(
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p.geom::geography,
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ST_Centroid(ST_GeomFromText(:wkt, 4326))::geography
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) AS distance_m,
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last_osm_edit_date
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FROM osm_poi_ekb p
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WHERE ST_DWithin(
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p.geom::geography,
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ST_Centroid(ST_GeomFromText(:wkt, 4326))::geography,
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1000
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)
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ORDER BY distance_m ASC
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"""),
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{"wkt": geom_wkt},
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)
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.mappings()
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.all()
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)
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# 4) Scoring: weighted sum с distance decay
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score = 0.0
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by_category: dict[str, list[dict[str, Any]]] = {}
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for p in poi_rows:
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cat: str = p["category"]
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w = _POI_WEIGHTS.get(cat, 0.0)
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# distance decay: 1.0 на 0м, 0.5 на ~500м, ~0 на 1000м
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decay = max(0.0, 1.0 - float(p["distance_m"]) / 1000.0)
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score += w * decay
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by_category.setdefault(cat, []).append(
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{
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"name": p["name"],
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"distance_m": round(float(p["distance_m"])),
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"lat": float(p["lat"]) if p["lat"] is not None else None,
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"lon": float(p["lon"]) if p["lon"] is not None else None,
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"last_edit": (
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p["last_osm_edit_date"].isoformat() if p["last_osm_edit_date"] else None
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),
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}
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)
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# 5) Конкуренты в радиусе 3 км из DOM.РФ.
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# NB: domrf_kn_objects имеет ~3 snapshot per obj_id → DISTINCT ON по
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# latest snapshot, иначе дубликаты ЖК в выдаче.
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competitor_rows = (
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db.execute(
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text("""
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WITH latest_obj AS (
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SELECT DISTINCT ON (obj_id) *
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FROM domrf_kn_objects
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WHERE latitude IS NOT NULL
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ORDER BY obj_id, snapshot_date DESC NULLS LAST
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)
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SELECT obj_id,
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comm_name,
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dev_name,
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obj_class,
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flat_count,
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district_name,
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ST_Distance(
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ST_SetSRID(ST_MakePoint(o.longitude, o.latitude), 4326)::geography,
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ST_Centroid(ST_GeomFromText(:wkt, 4326))::geography
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) AS distance_m
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FROM latest_obj o
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WHERE ST_DWithin(
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ST_SetSRID(ST_MakePoint(o.longitude, o.latitude), 4326)::geography,
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ST_Centroid(ST_GeomFromText(:wkt, 4326))::geography,
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3000
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)
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ORDER BY o.flat_count DESC NULLS LAST
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LIMIT 20
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"""),
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{"wkt": geom_wkt},
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)
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.mappings()
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.all()
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)
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# 6) Centroid координаты для внешних API (air quality / wind)
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centroid_row = (
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db.execute(
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text("""
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SELECT ST_X(ST_Centroid(ST_GeomFromText(:wkt, 4326))) AS lon,
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ST_Y(ST_Centroid(ST_GeomFromText(:wkt, 4326))) AS lat
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"""),
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{"wkt": geom_wkt},
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)
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.mappings()
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.first()
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)
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centroid_lat: float = float(centroid_row["lat"]) if centroid_row else 56.838
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centroid_lon: float = float(centroid_row["lon"]) if centroid_row else 60.605
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# 7) Noise score — шумовые источники в радиусе 2 км
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noise_rows = (
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db.execute(
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text("""
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SELECT source_type, road_class, name,
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ST_Distance(
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n.geom::geography,
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ST_Centroid(ST_GeomFromText(:wkt, 4326))::geography
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) AS distance_m
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FROM osm_noise_sources_ekb n
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WHERE ST_DWithin(
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n.geom::geography,
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ST_Centroid(ST_GeomFromText(:wkt, 4326))::geography,
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2000
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)
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ORDER BY distance_m ASC
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LIMIT 30
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"""),
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{"wkt": geom_wkt},
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)
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.mappings()
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.all()
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)
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noise_db_max = 0.0
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nearby_noise_sources: list[dict[str, Any]] = []
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for nr in noise_rows:
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src = nr["source_type"]
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key = f"{src}:{nr['road_class']}" if src == "highway" and nr["road_class"] else src
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base_db = NOISE_L_BASE.get(key, 50.0)
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d = max(float(nr["distance_m"]), 10.0)
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noise_db = base_db - 20.0 * math.log10(d / 10.0)
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noise_db_max = max(noise_db_max, noise_db)
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if noise_db >= 50.0: # WHO порог дискомфорта
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nearby_noise_sources.append(
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{
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"source_type": src,
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"road_class": nr["road_class"],
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"name": nr["name"],
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"distance_m": round(d),
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"estimated_db": round(noise_db, 1),
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}
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)
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# noise_score: 0..1, чем тише тем лучше. 45 dB=1.0 (тихо), 75 dB=0.0 (шумно).
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noise_score = max(0.0, min(1.0, (75.0 - noise_db_max) / 30.0))
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if noise_db_max < 50.0:
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noise_level = "тихо"
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elif noise_db_max < 65.0:
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noise_level = "умеренный"
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else:
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noise_level = "шумно"
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# 8) Air quality — Open-Meteo (best-effort, null при недоступности)
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air_q = _fetch_air_quality_sync(centroid_lat, centroid_lon)
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# 9) Wind — Open-Meteo (best-effort, null при недоступности)
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wind_data = _fetch_wind_sync(centroid_lat, centroid_lon)
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return {
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"cad_num": cad_num,
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"source": source,
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"geom_geojson": json.loads(geom_geojson) if geom_geojson else None,
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"district": dict(district_row) if district_row else None,
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"score": round(score, 2),
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"score_breakdown": by_category,
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"poi_count": len(poi_rows),
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"competitors": [dict(c) for c in competitor_rows],
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"noise": {
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"score": round(noise_score, 2),
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"estimated_db": round(noise_db_max, 1),
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"level": noise_level,
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"sources": nearby_noise_sources[:10],
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},
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"air_quality": air_q,
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"wind": wind_data,
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}
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