2569718930@qq.com
808 lines
30 KiB
Python
808 lines
30 KiB
Python
import os
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import requests
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import re
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import threading
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from typing import Optional, Dict, List
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from datetime import datetime, timedelta
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from loguru import logger
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from src.data_collection.open_meteo_cache import OpenMeteoCacheMixin
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from src.data_collection.settlement_sources import SettlementSourceMixin
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from src.data_collection.metar_sources import MetarSourceMixin
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from src.data_collection.mgm_sources import MgmSourceMixin
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from src.data_collection.nws_open_meteo_sources import NwsOpenMeteoSourceMixin
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from src.data_collection.wunderground_sources import WundergroundSourceMixin
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class WeatherDataCollector(OpenMeteoCacheMixin, SettlementSourceMixin, MetarSourceMixin, MgmSourceMixin, NwsOpenMeteoSourceMixin, WundergroundSourceMixin):
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"""
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Multi-source weather data collector
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Supports:
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- OpenWeatherMap (free, fast updates)
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- Weather Underground (Polymarket settlement source)
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- Visual Crossing (rich historical data)
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- NOAA Aviation Weather (METAR - airport observations)
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"""
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from src.data_collection.city_registry import CITY_REGISTRY
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CITY_TO_ICAO = {cid: info["icao"] for cid, info in CITY_REGISTRY.items()}
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# Alias
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CITY_TO_ICAO["nyc"] = "KLGA"
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# 城市周边 METAR 集群（用于在全球城市模拟类似安卡拉的多测站地图分布）
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CITY_METAR_CLUSTERS = {
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"buenos aires": ["SAEZ", "SABE", "SADP", "SADF", "SADL", "SADJ"],
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"istanbul": ["LTFM", "LTBA", "LTFJ"],
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"london": ["EGLL", "EGLC", "EGKK", "EGSS", "EGGW"],
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"new york": ["KLGA", "KJFK", "KEWR", "KTEB", "KHPN"],
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"los angeles": ["KLAX", "KBUR", "KLGB", "KSNA", "KVNY"],
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"san francisco": ["KSFO", "KOAK", "KSJC", "KHAF"],
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"aurora": ["KBKF", "KDEN", "KAPA", "KBJC"],
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"austin": ["KAUS", "KEDC", "KSAT"],
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"houston": ["KHOU", "KIAH", "KSGR", "KCXO"],
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"mexico city": ["MMMX", "MMSM", "MMTO"],
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"paris": ["LFPG", "LFPO", "LFPB"],
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"seoul": ["RKSI", "RKSS"],
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"hong kong": ["VHHH", "VMMC", "ZGSZ"],
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"taipei": ["RCSS", "RCTP"],
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"chengdu": ["ZUUU", "ZUTF"],
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"chongqing": ["ZUCK", "ZUPS"],
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"shenzhen": ["ZGSZ", "ZGGG"],
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"beijing": ["ZBAA", "ZBAD"],
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"wuhan": ["ZHHH", "ZHES"],
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"shanghai": ["ZSPD", "ZSSS", "ZSNB", "ZSHC"],
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"singapore": ["WSSS", "WSAP", "WMKK"],
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"tokyo": ["RJTT", "RJAA", "RJAH", "RJTJ"],
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"tel aviv": ["LLBG"],
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"milan": ["LIMC", "LIML", "LIME", "LIPO"],
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"toronto": ["CYYZ", "CYTZ", "CYKF"],
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"warsaw": ["EPWA", "EPMO", "EPLL"],
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"madrid": ["LEMD", "LETO", "LEGT"],
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"chicago": ["KORD", "KMDW", "KPWK", "KDPA"],
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"dallas": ["KDAL", "KDFW", "KADS", "KGKY"],
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"atlanta": ["KATL", "KPDK", "KFTY"],
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"miami": ["KMIA", "KOPF", "KTMB"],
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"seattle": ["KSEA", "KBFI", "KPAE"],
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"sao paulo": ["SBGR", "SBSP", "SBKP"],
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"munich": ["EDDM", "EDMO", "EDJA"],
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}
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US_CITIES = {
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"dallas",
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"nyc",
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"new york",
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"seattle",
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"miami",
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"atlanta",
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"chicago",
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"los angeles",
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"san francisco",
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"washington",
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"boston",
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"houston",
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"phoenix",
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"philadelphia",
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"new york's central park",
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"portland",
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"denver",
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"aurora",
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"austin",
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"san diego",
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"detroit",
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"cleveland",
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"minneapolis",
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"st. louis",
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}
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TURKISH_PROVINCES = {
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"ankara": ("17128", "Ankara"),
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"istanbul": ("17060", "Istanbul"),
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}
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def __init__(self, config: dict):
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self.config = config
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weather_cfg = config.get("weather", {})
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self.wunderground_key = weather_cfg.get("wunderground_api_key")
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self.timeout = 30 # 增加超时以支持高延迟 VPS
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self.session = requests.Session()
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self.open_meteo_cache_ttl_sec = int(
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os.getenv("OPEN_METEO_CACHE_TTL_SEC", "900")
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)
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self.open_meteo_ensemble_cache_ttl_sec = int(
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os.getenv("OPEN_METEO_ENSEMBLE_CACHE_TTL_SEC", "900")
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)
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self.open_meteo_multi_model_cache_ttl_sec = int(
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os.getenv("OPEN_METEO_MULTI_MODEL_CACHE_TTL_SEC", "900")
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)
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self.multi_model_cache_version = str(
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os.getenv("OPEN_METEO_MULTI_MODEL_CACHE_VERSION", "v2")
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).strip() or "v2"
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self._open_meteo_cache: Dict[str, Dict] = {}
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self._ensemble_cache: Dict[str, Dict] = {}
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self._multi_model_cache: Dict[str, Dict] = {}
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self._open_meteo_cache_lock = threading.Lock()
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self._ensemble_cache_lock = threading.Lock()
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self._multi_model_cache_lock = threading.Lock()
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# Open-Meteo 共享 429 冷却计时器：触发限流后所有 OM 端点暂停请求
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self._open_meteo_rate_limit_until: float = 0.0
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self._open_meteo_rl_cooldown: int = int(
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os.getenv("OPEN_METEO_RATE_LIMIT_COOLDOWN_SEC", "900") # 默认 15 分钟
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)
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self._open_meteo_rl_lock = threading.Lock()
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# Open-Meteo burst control: avoid hammering API with many cities at once.
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self._open_meteo_min_interval_sec: float = float(
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os.getenv("OPEN_METEO_MIN_CALL_INTERVAL_SEC", "3")
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)
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self._open_meteo_last_call_ts: float = 0.0
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self._open_meteo_call_lock = threading.Lock()
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self.metar_cache_ttl_sec = int(
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os.getenv("METAR_CACHE_TTL_SEC", "600") # 默认 10 分钟
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)
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self._metar_cache: Dict[str, Dict] = {}
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self._metar_cache_lock = threading.Lock()
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self.taf_cache_ttl_sec = int(
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os.getenv("TAF_CACHE_TTL_SEC", "900")
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)
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self._taf_cache: Dict[str, Dict] = {}
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self._taf_cache_lock = threading.Lock()
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self.settlement_cache_ttl_sec = int(
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os.getenv("SETTLEMENT_SOURCE_CACHE_TTL_SEC", "120")
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)
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self._settlement_cache: Dict[str, Dict] = {}
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self._settlement_cache_lock = threading.Lock()
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self.cwa_open_data_auth = (
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os.getenv("CWA_OPEN_DATA_AUTH")
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or os.getenv("CWA_OPEN_DATA_API_KEY")
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or "rdec-key-123-45678-011121314"
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).strip()
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# 磁盘持久化缓存：重启后即可加载上次的预报数据，避免冷启动请求爆发
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self._disk_cache_path = os.getenv(
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"OPEN_METEO_DISK_CACHE_PATH", "/app/data/open_meteo_cache.json"
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)
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self._disk_cache_max_age_sec = int(
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os.getenv("OPEN_METEO_DISK_CACHE_MAX_AGE_SEC", "86400")
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)
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self._disk_cache_lock = threading.Lock()
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self._disk_cache_last_mtime: float = 0.0
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self._load_open_meteo_disk_cache()
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logger.info(
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f"Open-Meteo 磁盘缓存路径: {self._disk_cache_path} (max_age={self._disk_cache_max_age_sec}s)"
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)
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# 设置代理
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proxy = config.get("proxy")
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if proxy:
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if not proxy.startswith("http"):
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proxy = f"http://{proxy}"
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self.session.proxies = {"http": proxy, "https": proxy}
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logger.info(f"正在使用天气数据代理: {proxy}")
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logger.info("天气数据采集器初始化完成。")
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def fetch_from_openweather(self, city: str, country: str = None) -> Optional[Dict]:
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"""
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Fetch current weather and forecast from OpenWeatherMap
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Args:
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city: City name
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country: Country code (optional)
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Returns:
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dict: Weather data
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"""
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if not getattr(self, "openweather_key", None):
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return None
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query = f"{city},{country}" if country else city
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try:
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# Current weather
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current_url = "https://api.openweathermap.org/data/2.5/weather"
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current_response = self.session.get(
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current_url,
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params={"q": query, "appid": self.openweather_key, "units": "metric"},
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timeout=self.timeout,
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)
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current_response.raise_for_status()
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current_data = current_response.json()
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# 5-day forecast
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forecast_url = "https://api.openweathermap.org/data/2.5/forecast"
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forecast_response = self.session.get(
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forecast_url,
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params={"q": query, "appid": self.openweather_key, "units": "metric"},
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timeout=self.timeout,
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)
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forecast_response.raise_for_status()
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forecast_data = forecast_response.json()
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return {
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"source": "openweathermap",
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"timestamp": datetime.utcnow().isoformat(),
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"current": {
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"temp": current_data["main"]["temp"],
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"feels_like": current_data["main"]["feels_like"],
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"temp_min": current_data["main"]["temp_min"],
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"temp_max": current_data["main"]["temp_max"],
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"humidity": current_data["main"]["humidity"],
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"pressure": current_data["main"]["pressure"],
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"wind_speed": current_data["wind"]["speed"],
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"clouds": current_data["clouds"]["all"],
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"description": current_data["weather"][0]["description"],
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},
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"forecast": self._parse_openweather_forecast(forecast_data),
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}
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except requests.exceptions.RequestException as e:
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logger.error(f"OpenWeatherMap request failed: {e}")
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return None
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def _parse_openweather_forecast(self, data: dict) -> List[Dict]:
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"""Parse OpenWeatherMap forecast data"""
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forecasts = []
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for item in data.get("list", []):
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forecasts.append(
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{
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"datetime": item["dt_txt"],
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"temp": item["main"]["temp"],
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"temp_min": item["main"]["temp_min"],
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"temp_max": item["main"]["temp_max"],
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"humidity": item["main"]["humidity"],
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"description": item["weather"][0]["description"],
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}
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)
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return forecasts
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def fetch_from_visualcrossing(
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self, city: str, start_date: str = None, end_date: str = None
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) -> Optional[Dict]:
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"""
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Fetch historical weather data from Visual Crossing
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Args:
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city: City name
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start_date: Start date (YYYY-MM-DD)
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end_date: End date (YYYY-MM-DD)
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Returns:
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dict: Historical weather data
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"""
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if not getattr(self, "visualcrossing_key", None):
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return None
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# Default to last 30 days if no dates provided
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if not end_date:
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end_date = datetime.now().strftime("%Y-%m-%d")
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if not start_date:
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start_date = (datetime.now() - timedelta(days=30)).strftime("%Y-%m-%d")
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try:
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url = f"https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/{city}/{start_date}/{end_date}"
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response = self.session.get(
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url,
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params={
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"unitGroup": "metric",
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"key": self.visualcrossing_key,
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"contentType": "json",
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"include": "days",
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},
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timeout=self.timeout,
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)
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response.raise_for_status()
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data = response.json()
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return {
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"source": "visualcrossing",
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"timestamp": datetime.utcnow().isoformat(),
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"location": data.get("resolvedAddress"),
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"timezone": data.get("timezone"),
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"days": [
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{
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"date": day["datetime"],
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"temp_max": day.get("tempmax"),
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"temp_min": day.get("tempmin"),
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"temp_avg": day.get("temp"),
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"humidity": day.get("humidity"),
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"precip": day.get("precip"),
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"conditions": day.get("conditions"),
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}
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for day in data.get("days", [])
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],
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}
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except requests.exceptions.RequestException as e:
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logger.error(f"Visual Crossing request failed: {e}")
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return None
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def extract_date_from_title(self, title: str) -> Optional[str]:
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"""
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从标题中提取日期并标准化为 YYYY-MM-DD
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支持: "February 6", "2月6日", "2-6" 等
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"""
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# 1. 尝试英文月份
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months = {
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"January": "01",
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"February": "02",
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"March": "03",
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"April": "04",
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"May": "05",
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"June": "06",
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"July": "07",
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"August": "08",
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"September": "09",
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"October": "10",
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"November": "11",
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"December": "12",
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}
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for month_name, month_val in months.items():
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if month_name in title:
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match = re.search(f"{month_name}\\s+(\\d+)", title)
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if match:
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day = int(match.group(1))
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year = datetime.now().year
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return f"{year}-{month_val}-{day:02d}"
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# 2. 尝试中文格式 "2月7日" 或 "02月07日"
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zh_match = re.search(r"(\d{1,2})月(\d{1,2})日", title)
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if zh_match:
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month = int(zh_match.group(1))
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day = int(zh_match.group(2))
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year = datetime.now().year
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return f"{year}-{month:02d}-{day:02d}"
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# 3. 尝试 ISO 格式 YYYY-MM-DD
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iso_match = re.search(r"(\d{4})-(\d{2})-(\d{2})", title)
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if iso_match:
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return iso_match.group(0)
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return None
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def get_coordinates(self, city: str) -> Optional[Dict[str, float]]:
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"""
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使用 Open-Meteo Geocoding API 获取城市坐标 (免费, 无需 Key)
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"""
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from src.data_collection.city_registry import CITY_REGISTRY
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normalized_city = city.lower().strip()
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# 1. Check registry first (Source of Truth)
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if normalized_city in CITY_REGISTRY:
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info = CITY_REGISTRY[normalized_city]
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return {"lat": info["lat"], "lon": info["lon"]}
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# 2. Hardcoded specific cases or aliases
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static_aliases = {
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"new york's central park": "new york",
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"nyc": "new york"
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}
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if normalized_city in static_aliases:
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root_city = static_aliases[normalized_city]
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info = CITY_REGISTRY[root_city]
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return {"lat": info["lat"], "lon": info["lon"]}
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for key in CITY_REGISTRY:
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if key in normalized_city:
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logger.debug(f"地理编码命中模糊映射: {city} -> {key}")
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info = CITY_REGISTRY[key]
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return {"lat": info["lat"], "lon": info["lon"]}
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try:
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url = "https://geocoding-api.open-meteo.com/v1/search"
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response = self.session.get(
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url,
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params={"name": city, "count": 1, "language": "en", "format": "json"},
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timeout=15, # 增加超时时间到 15s
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)
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response.raise_for_status()
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results = response.json().get("results", [])
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if results:
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res = results[0]
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return {
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"lat": res.get("latitude"),
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"lon": res.get("longitude"),
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"name": res.get("name"),
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"country": res.get("country"),
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}
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except Exception as e:
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logger.error(f"地理编码失败 ({city}): {e}")
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return None
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def extract_city_from_question(self, question: str) -> Optional[str]:
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"""
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从 Polymarket 问题描述或 Slug 中提取城市名称
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"""
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q = question.lower()
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# 1. 优先尝试已知城市列表 (硬编码匹配)
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known_cities = {
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"london": "London",
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"伦敦": "London",
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"new york": "New York",
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"new york's central park": "New York",
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"nyc": "New York",
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"纽约": "New York",
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"seattle": "Seattle",
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"西雅图": "Seattle",
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"chicago": "Chicago",
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"芝加哥": "Chicago",
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"dallas": "Dallas",
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"达拉斯": "Dallas",
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"miami": "Miami",
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"迈阿密": "Miami",
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"atlanta": "Atlanta",
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"亚特兰大": "Atlanta",
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"istanbul": "Istanbul",
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"ist": "Istanbul",
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"ltfm": "Istanbul",
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"伊斯坦布尔": "Istanbul",
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"seoul": "Seoul",
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"首尔": "Seoul",
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"hong kong": "Hong Kong",
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"hong kong international airport": "Hong Kong",
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"香港": "Hong Kong",
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"shek kong": "Shek Kong",
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"vhsk": "Shek Kong",
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"石岗": "Shek Kong",
|
|
"石崗": "Shek Kong",
|
|
"lau fau shan": "Lau Fau Shan",
|
|
"lfs": "Lau Fau Shan",
|
|
"流浮山": "Lau Fau Shan",
|
|
"taipei": "Taipei",
|
|
"台北": "Taipei",
|
|
"臺北": "Taipei",
|
|
"chengdu": "Chengdu",
|
|
"成都": "Chengdu",
|
|
"chongqing": "Chongqing",
|
|
"重庆": "Chongqing",
|
|
"shenzhen": "Shenzhen",
|
|
"深圳": "Shenzhen",
|
|
"beijing": "Beijing",
|
|
"北京": "Beijing",
|
|
"wuhan": "Wuhan",
|
|
"武汉": "Wuhan",
|
|
"shanghai": "Shanghai",
|
|
"上海": "Shanghai",
|
|
"singapore": "Singapore",
|
|
"新加坡": "Singapore",
|
|
"tokyo": "Tokyo",
|
|
"东京": "Tokyo",
|
|
"東京": "Tokyo",
|
|
"milan": "Milan",
|
|
"米兰": "Milan",
|
|
"米蘭": "Milan",
|
|
"madrid": "Madrid",
|
|
"马德里": "Madrid",
|
|
"馬德里": "Madrid",
|
|
"tel aviv": "Tel Aviv",
|
|
"特拉维夫": "Tel Aviv",
|
|
"toronto": "Toronto",
|
|
"多伦多": "Toronto",
|
|
"ankara": "Ankara",
|
|
"安卡拉": "Ankara",
|
|
"wellington": "Wellington",
|
|
"惠灵顿": "Wellington",
|
|
"buenos aires": "Buenos Aires",
|
|
"布宜诺斯艾利斯": "Buenos Aires",
|
|
"warsaw": "Warsaw",
|
|
"华沙": "Warsaw",
|
|
"華沙": "Warsaw",
|
|
}
|
|
|
|
for key, val in known_cities.items():
|
|
if key in q:
|
|
return val
|
|
|
|
# 2. 从英文模板中提取
|
|
triggers = [
|
|
"temperature in ",
|
|
"temp in ",
|
|
"weather in ",
|
|
"highest-temperature-in-",
|
|
"temperature-in-",
|
|
]
|
|
for trigger in triggers:
|
|
if trigger in q:
|
|
part = q.split(trigger)[1]
|
|
delimiters = [
|
|
" on ",
|
|
" at ",
|
|
" above ",
|
|
" below ",
|
|
" be ",
|
|
" is ",
|
|
" will ",
|
|
" has ",
|
|
" reached ",
|
|
"?",
|
|
" (",
|
|
", ",
|
|
"-",
|
|
]
|
|
city = part
|
|
for d in delimiters:
|
|
if d in city:
|
|
city = city.split(d)[0]
|
|
return city.strip().title()
|
|
|
|
return None
|
|
|
|
def _evict_city_caches(
|
|
self,
|
|
city: str,
|
|
lat: Optional[float],
|
|
lon: Optional[float],
|
|
use_fahrenheit: bool,
|
|
) -> None:
|
|
"""Drop in-memory caches for one city before a force-refresh query."""
|
|
if lat is not None and lon is not None:
|
|
base = f"{round(float(lat), 4)}:{round(float(lon), 4)}"
|
|
unit = "f" if use_fahrenheit else "c"
|
|
open_meteo_key = f"{base}:14:{unit}"
|
|
ensemble_key = f"{base}:{unit}"
|
|
cache_city = str(city or "").strip().lower()
|
|
multi_model_key = (
|
|
f"{base}:{cache_city}:{unit}:{self.multi_model_cache_version}"
|
|
)
|
|
|
|
with self._open_meteo_cache_lock:
|
|
self._open_meteo_cache.pop(open_meteo_key, None)
|
|
with self._ensemble_cache_lock:
|
|
self._ensemble_cache.pop(ensemble_key, None)
|
|
with self._multi_model_cache_lock:
|
|
self._multi_model_cache.pop(multi_model_key, None)
|
|
|
|
icao = self.get_icao_code(city)
|
|
if icao:
|
|
prefix = f"{icao}:"
|
|
with self._metar_cache_lock:
|
|
for key in list(self._metar_cache.keys()):
|
|
if key.startswith(prefix):
|
|
self._metar_cache.pop(key, None)
|
|
normalized = str(city or "").strip().lower()
|
|
with self._settlement_cache_lock:
|
|
city_meta = self.CITY_REGISTRY.get(normalized) or {}
|
|
settlement_source = str(city_meta.get("settlement_source") or "").strip().lower()
|
|
if settlement_source == "hko":
|
|
station_code = (
|
|
str(city_meta.get("settlement_station_code") or "").strip()
|
|
or str(city_meta.get("icao") or "").strip()
|
|
or normalized
|
|
)
|
|
self._settlement_cache.pop(f"hko:{station_code.lower()}", None)
|
|
elif settlement_source == "noaa":
|
|
station_code = (
|
|
str(city_meta.get("settlement_station_code") or "").strip()
|
|
or str(city_meta.get("icao") or "").strip()
|
|
or normalized
|
|
)
|
|
self._settlement_cache.pop(f"noaa:{station_code.lower()}", None)
|
|
|
|
def _uses_fahrenheit(self, city_lower: str) -> bool:
|
|
return city_lower in self.US_CITIES
|
|
|
|
def _supports_aviationweather(self, city_lower: str) -> bool:
|
|
city_meta = self.CITY_REGISTRY.get(str(city_lower or "").strip().lower(), {}) or {}
|
|
return not bool(city_meta.get("disable_aviationweather"))
|
|
|
|
def _log_temperature_unit(self, city: str, use_fahrenheit: bool) -> None:
|
|
unit = "华氏度 (°F)" if use_fahrenheit else "摄氏度 (°C)"
|
|
logger.info(f"🌡️ {city} 使用{unit}")
|
|
|
|
def _attach_settlement_sources(self, results: Dict, city_lower: str) -> None:
|
|
settlement_current = self.fetch_settlement_current(city_lower)
|
|
if settlement_current:
|
|
results["settlement_current"] = settlement_current
|
|
|
|
if city_lower in ["hong_kong", "hong kong", "香港", "hk"]:
|
|
hko_forecast = self.fetch_hko_forecast()
|
|
if hko_forecast:
|
|
results["hko_forecast"] = hko_forecast
|
|
|
|
def _attach_turkish_mgm_data(self, results: Dict, city_lower: str) -> None:
|
|
if city_lower not in self.TURKISH_PROVINCES:
|
|
return
|
|
istno, province = self.TURKISH_PROVINCES[city_lower]
|
|
mgm_data = self.fetch_from_mgm(istno)
|
|
if not mgm_data:
|
|
return
|
|
results["mgm"] = mgm_data
|
|
nearby = self.fetch_mgm_nearby_stations(province, root_ist_no=istno)
|
|
if nearby:
|
|
results["mgm_nearby"] = nearby
|
|
|
|
def _attach_global_nearby_cluster(
|
|
self, results: Dict, city_lower: str, use_fahrenheit: bool
|
|
) -> None:
|
|
if city_lower not in self.CITY_METAR_CLUSTERS or "mgm_nearby" in results:
|
|
return
|
|
cluster_icaos = self.CITY_METAR_CLUSTERS[city_lower]
|
|
cluster_data = self.fetch_metar_nearby_cluster(
|
|
cluster_icaos, use_fahrenheit=use_fahrenheit
|
|
)
|
|
if cluster_data:
|
|
results["mgm_nearby"] = cluster_data
|
|
results["nearby_source"] = "metar_cluster"
|
|
|
|
def _attach_warsaw_official_nearby(
|
|
self, results: Dict, use_fahrenheit: bool
|
|
) -> None:
|
|
if "mgm_nearby" in results:
|
|
return
|
|
|
|
official_rows = []
|
|
epwa_rows = self.fetch_metar_nearby_cluster(["EPWA"], use_fahrenheit=use_fahrenheit)
|
|
if epwa_rows:
|
|
epwa = dict(epwa_rows[0])
|
|
epwa["name"] = "Warszawa-Okęcie (EPWA)"
|
|
official_rows.append(epwa)
|
|
|
|
imgw_row = self.fetch_imgw_synoptic_station_current(
|
|
"Warszawa",
|
|
display_name="Warszawa (IMGW synoptic)",
|
|
use_fahrenheit=use_fahrenheit,
|
|
)
|
|
if imgw_row:
|
|
official_rows.append(imgw_row)
|
|
|
|
if official_rows:
|
|
results["mgm_nearby"] = official_rows
|
|
results["nearby_source"] = "official_cluster"
|
|
|
|
def _attach_nws_and_models(
|
|
self,
|
|
results: Dict,
|
|
city: str,
|
|
lat: float,
|
|
lon: float,
|
|
use_fahrenheit: bool,
|
|
) -> None:
|
|
if use_fahrenheit:
|
|
nws_data = self.fetch_nws(lat, lon)
|
|
if nws_data:
|
|
results["nws"] = nws_data
|
|
|
|
ensemble_data = self.fetch_ensemble(lat, lon, use_fahrenheit=use_fahrenheit)
|
|
if ensemble_data:
|
|
results["ensemble"] = ensemble_data
|
|
|
|
multi_model_data = self.fetch_multi_model(
|
|
lat, lon, city=city, use_fahrenheit=use_fahrenheit
|
|
)
|
|
if multi_model_data:
|
|
results["multi_model"] = multi_model_data
|
|
|
|
def fetch_all_sources(
|
|
self,
|
|
city: str,
|
|
lat: float = None,
|
|
lon: float = None,
|
|
country: str = None,
|
|
force_refresh: bool = False,
|
|
) -> Dict:
|
|
"""
|
|
Fetch weather data from all available sources
|
|
"""
|
|
results = {}
|
|
city_lower = city.lower().strip()
|
|
use_fahrenheit = self._uses_fahrenheit(city_lower)
|
|
supports_aviationweather = self._supports_aviationweather(city_lower)
|
|
|
|
if force_refresh:
|
|
self._evict_city_caches(
|
|
city=city,
|
|
lat=lat,
|
|
lon=lon,
|
|
use_fahrenheit=use_fahrenheit,
|
|
)
|
|
self._log_temperature_unit(city, use_fahrenheit)
|
|
self._attach_settlement_sources(results, city_lower)
|
|
|
|
if lat and lon:
|
|
open_meteo = self.fetch_from_open_meteo(
|
|
lat, lon, use_fahrenheit=use_fahrenheit
|
|
)
|
|
if open_meteo:
|
|
results["open-meteo"] = open_meteo
|
|
# 获取时区偏移以过滤 METAR
|
|
utc_offset = open_meteo.get("utc_offset", 0)
|
|
if supports_aviationweather:
|
|
metar_data = self.fetch_metar(
|
|
city, use_fahrenheit=use_fahrenheit, utc_offset=utc_offset
|
|
)
|
|
if metar_data:
|
|
results["metar"] = metar_data
|
|
if supports_aviationweather and city_lower != "hong kong":
|
|
taf_data = self.fetch_taf(city, utc_offset=utc_offset)
|
|
if taf_data:
|
|
results["taf"] = taf_data
|
|
|
|
self._attach_turkish_mgm_data(results, city_lower)
|
|
if city_lower == "warsaw":
|
|
self._attach_warsaw_official_nearby(results, use_fahrenheit)
|
|
self._attach_global_nearby_cluster(
|
|
results, city_lower, use_fahrenheit
|
|
)
|
|
self._attach_nws_and_models(
|
|
results, city, lat, lon, use_fahrenheit
|
|
)
|
|
else:
|
|
fallback_utc_offset = int(
|
|
self.CITY_REGISTRY.get(city_lower, {}).get("tz_offset", 0)
|
|
)
|
|
if supports_aviationweather:
|
|
metar_data = self.fetch_metar(
|
|
city,
|
|
use_fahrenheit=use_fahrenheit,
|
|
utc_offset=fallback_utc_offset,
|
|
)
|
|
if metar_data:
|
|
results["metar"] = metar_data
|
|
if supports_aviationweather and city_lower != "hong kong":
|
|
taf_data = self.fetch_taf(city, utc_offset=fallback_utc_offset)
|
|
if taf_data:
|
|
results["taf"] = taf_data
|
|
|
|
self._attach_turkish_mgm_data(results, city_lower)
|
|
if city_lower == "warsaw":
|
|
self._attach_warsaw_official_nearby(results, use_fahrenheit)
|
|
self._attach_global_nearby_cluster(
|
|
results, city_lower, use_fahrenheit
|
|
)
|
|
self._attach_nws_and_models(
|
|
results, city, lat, lon, use_fahrenheit
|
|
)
|
|
else:
|
|
if supports_aviationweather:
|
|
metar_data = self.fetch_metar(city, use_fahrenheit=use_fahrenheit)
|
|
if metar_data:
|
|
results["metar"] = metar_data
|
|
if supports_aviationweather and city_lower != "hong kong":
|
|
taf_data = self.fetch_taf(city)
|
|
if taf_data:
|
|
results["taf"] = taf_data
|
|
|
|
return results
|
|
|
|
def check_consensus(self, forecasts: Dict) -> Dict:
|
|
"""
|
|
Check consensus across multiple weather sources
|
|
|
|
Args:
|
|
forecasts: Dict of forecasts from different sources
|
|
|
|
Returns:
|
|
dict: Consensus analysis
|
|
"""
|
|
predictions = []
|
|
for source, data in forecasts.items():
|
|
if data and "current" in data:
|
|
predictions.append({"source": source, "temp": data["current"]["temp"]})
|
|
|
|
if len(predictions) == 0:
|
|
return {"consensus": False, "reason": "No weather data available"}
|
|
|
|
temps = [p["temp"] for p in predictions]
|
|
avg_temp = sum(temps) / len(temps)
|
|
|
|
# If only one source, consensus is implicitly true
|
|
if len(predictions) == 1:
|
|
return {
|
|
"consensus": True,
|
|
"average_temp": avg_temp,
|
|
"max_difference": 0.0,
|
|
"predictions": predictions,
|
|
"note": "Single source only",
|
|
}
|
|
|
|
max_diff = max(abs(t - avg_temp) for t in temps)
|
|
# Consensus if all predictions within 2.5°C
|
|
is_consensus = max_diff <= 2.5
|
|
|
|
return {
|
|
"consensus": is_consensus,
|
|
"average_temp": avg_temp,
|
|
"max_difference": max_diff,
|
|
"predictions": predictions,
|
|
}
|