2569718930@qq.com
599 lines
22 KiB
Python
599 lines
22 KiB
Python
import requests
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import re
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import time
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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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class WeatherDataCollector:
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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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# Polymarket 12 个天气市场对应的 ICAO 机场代码
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# 这些是 Weather Underground 结算源使用的气象站
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CITY_TO_ICAO = {
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"seattle": "KSEA", # Seattle-Tacoma Airport
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"london": "EGLC", # London City Airport
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"dallas": "KDAL", # Dallas Love Field
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"miami": "KMIA", # Miami International
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"atlanta": "KATL", # Hartsfield-Jackson
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"chicago": "KORD", # O'Hare International
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"new york": "KLGA", # LaGuardia Airport
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"nyc": "KLGA", # Alias
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"seoul": "RKSI", # Incheon International
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"ankara": "LTAC", # Esenboğa International
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"toronto": "CYYZ", # Toronto Pearson
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"wellington": "NZWN", # Wellington International
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"buenos aires": "SAEZ", # Ezeiza International
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}
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def __init__(self, config: dict):
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self.config = config
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self.wunderground_key = config.get("wunderground_api_key")
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self.timeout = 10
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self.session = requests.Session()
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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 get_icao_code(self, city: str) -> Optional[str]:
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"""
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根据城市名获取对应的 ICAO 机场代码
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"""
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normalized = city.lower().strip()
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# 直接匹配
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if normalized in self.CITY_TO_ICAO:
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return self.CITY_TO_ICAO[normalized]
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# 模糊匹配
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for key, icao in self.CITY_TO_ICAO.items():
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if key in normalized or normalized in key:
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return icao
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return None
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def fetch_metar(self, city: str, use_fahrenheit: bool = False) -> Optional[Dict]:
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"""
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从 NOAA Aviation Weather Center 获取 METAR 航空气象数据
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这是 Polymarket 天气市场的结算数据源 (Weather Underground) 使用的相同气象站
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Args:
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city: 城市名称
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use_fahrenheit: 是否转换为华氏度
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Returns:
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dict: METAR 数据，包含温度、露点、风速等
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"""
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icao = self.get_icao_code(city)
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if not icao:
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logger.warning(f"未找到城市 {city} 对应的 ICAO 代码")
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return None
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try:
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# NOAA Aviation Weather API (免费，无需 Key)
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url = "https://aviationweather.gov/api/data/metar"
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params = {
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"ids": icao,
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"format": "json",
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"hours": 3, # 获取最近3小时的观测
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"_t": int(time.time()), # 禁用缓存
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}
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response = self.session.get(
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url,
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params=params,
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headers={"Cache-Control": "no-cache", "Pragma": "no-cache"},
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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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if not data:
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logger.warning(f"METAR 数据为空: {icao}")
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return None
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# 取最新的观测记录
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latest = data[0]
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# 提取温度 (METAR 原始单位是摄氏度)
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temp_c = latest.get("temp")
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dewp_c = latest.get("dewp")
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# 转换为华氏度（如果需要）
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if use_fahrenheit and temp_c is not None:
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temp = temp_c * 9 / 5 + 32
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dewp = dewp_c * 9 / 5 + 32 if dewp_c is not None else None
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unit = "fahrenheit"
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else:
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temp = temp_c
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dewp = dewp_c
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unit = "celsius"
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# 解析观测时间
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obs_time = latest.get("reportTime", "")
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result = {
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"source": "metar",
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"icao": icao,
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"station_name": latest.get("name", icao),
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"timestamp": datetime.utcnow().isoformat(),
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"observation_time": obs_time,
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"raw_metar": latest.get("rawOb", ""),
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"current": {
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"temp": round(temp, 1) if temp is not None else None,
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"dewpoint": round(dewp, 1) if dewp is not None else None,
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"humidity": latest.get("rh"), # 相对湿度
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"wind_speed_kt": latest.get("wspd"), # 风速 (knots)
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"wind_dir": latest.get("wdir"), # 风向 (度)
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"visibility_miles": latest.get("visib"), # 能见度 (英里)
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"altimeter": latest.get("altim"), # 气压
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"flight_category": latest.get("fltcat"), # VFR/IFR 等
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"clouds": latest.get("clouds", []),
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},
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"unit": unit,
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}
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logger.info(
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f"✈️ METAR {icao}: {temp:.1f}°{'F' if use_fahrenheit else 'C'} "
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f"(obs: {obs_time})"
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)
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return result
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except requests.exceptions.RequestException as e:
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logger.error(f"METAR 请求失败 ({icao}): {e}")
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return None
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except (KeyError, IndexError, TypeError) as e:
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logger.error(f"METAR 数据解析失败 ({icao}): {e}")
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return None
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def fetch_from_open_meteo(
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self,
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lat: float,
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lon: float,
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forecast_days: int = 14,
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use_fahrenheit: bool = False,
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) -> Optional[Dict]:
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"""
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Fetch weather from Open-Meteo with forecast data
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Args:
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lat: Latitude
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lon: Longitude
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forecast_days: Number of forecast days to fetch (default 14 to cover all market dates)
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use_fahrenheit: Whether to return temperatures in Fahrenheit (for US markets)
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"""
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try:
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url = "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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"current_weather": "true",
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"hourly": "temperature_2m",
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"daily": "temperature_2m_max,apparent_temperature_max",
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"timezone": "auto",
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"forecast_days": forecast_days,
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"_t": int(time.time()), # 禁用缓存，强制刷新
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}
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# 对于美国市场，使用华氏度
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if use_fahrenheit:
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params["temperature_unit"] = "fahrenheit"
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response = self.session.get(
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url,
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params=params,
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headers={"Cache-Control": "no-cache", "Pragma": "no-cache"},
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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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current = data.get("current_weather", {})
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utc_offset = data.get("utc_offset_seconds", 0)
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timezone_name = data.get("timezone", "UTC")
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# 计算精确的当地时间而不是气象站 bucket 时间
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now_utc = datetime.utcnow()
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local_now = now_utc + timedelta(seconds=utc_offset)
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local_time_str = local_now.strftime("%Y-%m-%d %H:%M")
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return {
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"source": "open-meteo",
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"timestamp": now_utc.isoformat(),
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"timezone": timezone_name,
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"utc_offset": utc_offset,
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"current": {
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"temp": current.get("temperature"),
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"local_time": local_time_str,
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},
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"hourly": data.get("hourly", {}),
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"daily": data.get("daily", {}),
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"unit": "fahrenheit" if use_fahrenheit else "celsius",
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}
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except Exception as e:
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logger.error(f"Open-Meteo forecast 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", "February": "02", "March": "03", "April": "04",
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"May": "05", "June": "06", "July": "07", "August": "08",
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"September": "09", "October": "10", "November": "11", "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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# 预设常用城市坐标，避免网络波动导致启动失败
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static_coords = {
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"london": {"lat": 51.5074, "lon": -0.1278},
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"new york": {"lat": 40.7128, "lon": -74.0060},
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"new york's central park": {"lat": 40.7812, "lon": -73.9665},
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"nyc": {"lat": 40.7128, "lon": -74.0060},
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"seattle": {"lat": 47.6062, "lon": -122.3321},
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"chicago": {"lat": 41.8781, "lon": -87.6298},
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"dallas": {"lat": 32.7767, "lon": -96.7970},
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"miami": {"lat": 25.7617, "lon": -80.1918},
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"atlanta": {"lat": 33.7490, "lon": -84.3880},
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"seoul": {"lat": 37.5665, "lon": 126.9780},
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"toronto": {"lat": 43.6532, "lon": -79.3832},
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"ankara": {"lat": 39.9334, "lon": 32.8597},
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"wellington": {"lat": -41.2865, "lon": 174.7762},
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"buenos aires": {"lat": -34.6037, "lon": -58.3816},
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}
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normalized_city = city.lower().strip()
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if normalized_city in static_coords:
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return static_coords[normalized_city]
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# 模糊匹配映射 (针对包含城市名的情况)
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for key in static_coords:
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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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return static_coords[key]
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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", "伦敦": "London",
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"new york": "New York", "new york's central park": "New York", "nyc": "New York", "纽约": "New York",
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"seattle": "Seattle", "西雅图": "Seattle",
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"chicago": "Chicago", "芝加哥": "Chicago",
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"dallas": "Dallas", "达拉斯": "Dallas",
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"miami": "Miami", "迈阿密": "Miami",
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"atlanta": "Atlanta", "亚特兰大": "Atlanta",
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"seoul": "Seoul", "首尔": "Seoul",
|
|
"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"
|
|
}
|
|
|
|
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 fetch_all_sources(
|
|
self, city: str, lat: float = None, lon: float = None, country: str = None
|
|
) -> Dict:
|
|
"""
|
|
Fetch weather data from all available sources
|
|
"""
|
|
results = {}
|
|
|
|
# 判断是否为美国市场（使用华氏度）
|
|
us_cities = {
|
|
"dallas",
|
|
"nyc",
|
|
"new york",
|
|
"seattle",
|
|
"miami",
|
|
"atlanta",
|
|
"chicago",
|
|
"los angeles",
|
|
"san francisco",
|
|
"washington",
|
|
"boston",
|
|
"houston",
|
|
"phoenix",
|
|
"philadelphia",
|
|
"new york's central park",
|
|
"portland",
|
|
"denver",
|
|
"austin",
|
|
"san diego",
|
|
"detroit",
|
|
"cleveland",
|
|
"minneapolis",
|
|
"st. louis",
|
|
}
|
|
city_lower = city.lower().strip()
|
|
# 检查城市名是否在美国城市列表中（支持完全匹配或包含关系）
|
|
use_fahrenheit = city_lower in us_cities or any(
|
|
us_city in city_lower for us_city in us_cities
|
|
)
|
|
|
|
if use_fahrenheit:
|
|
logger.info(f"🌡️ {city} 使用华氏度 (°F)")
|
|
else:
|
|
logger.info(f"🌡️ {city} 使用摄氏度 (°C)")
|
|
|
|
# METAR (Airport Weather - Same source as Weather Underground settlement)
|
|
metar_data = self.fetch_metar(city, use_fahrenheit=use_fahrenheit)
|
|
if metar_data:
|
|
results["metar"] = metar_data
|
|
|
|
# Open-Meteo (Primary Free Source - No Key)
|
|
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
|
|
|
|
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,
|
|
}
|