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PolyWeather/src/data_collection/weather_sources.py
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import requests
import re
import time
from typing import Optional, Dict, List
from datetime import datetime, timedelta
from loguru import logger
class WeatherDataCollector:
"""
Multi-source weather data collector
Supports:
- OpenWeatherMap (free, fast updates)
- Weather Underground (Polymarket settlement source)
- Visual Crossing (rich historical data)
- NOAA Aviation Weather (METAR - airport observations)
"""
# Polymarket 12 个天气市场对应的 ICAO 机场代码
# 这些是 Weather Underground 结算源使用的气象站
CITY_TO_ICAO = {
"seattle": "KSEA", # Seattle-Tacoma Airport
"london": "EGLC", # London City Airport
"dallas": "KDAL", # Dallas Love Field
"miami": "KMIA", # Miami International
"atlanta": "KATL", # Hartsfield-Jackson
"chicago": "KORD", # O'Hare International
"new york": "KLGA", # LaGuardia Airport
"nyc": "KLGA", # Alias
"seoul": "RKSI", # Incheon International
"ankara": "LTAC", # Esenboğa International
"toronto": "CYYZ", # Toronto Pearson
"wellington": "NZWN", # Wellington International
"buenos aires": "SAEZ", # Ezeiza International
}
def __init__(self, config: dict):
self.config = config
weather_cfg = config.get("weather", {})
self.wunderground_key = weather_cfg.get("wunderground_api_key")
self.meteoblue_key = weather_cfg.get("meteoblue_api_key")
self.timeout = 30 # 增加超时以支持高延迟 VPS
self.session = requests.Session()
# 设置代理
proxy = config.get("proxy")
if proxy:
if not proxy.startswith("http"):
proxy = f"http://{proxy}"
self.session.proxies = {"http": proxy, "https": proxy}
logger.info(f"正在使用天气数据代理: {proxy}")
logger.info("天气数据采集器初始化完成。")
def fetch_from_openweather(self, city: str, country: str = None) -> Optional[Dict]:
"""
Fetch current weather and forecast from OpenWeatherMap
Args:
city: City name
country: Country code (optional)
Returns:
dict: Weather data
"""
if not getattr(self, "openweather_key", None):
return None
query = f"{city},{country}" if country else city
try:
# Current weather
current_url = "https://api.openweathermap.org/data/2.5/weather"
current_response = self.session.get(
current_url,
params={"q": query, "appid": self.openweather_key, "units": "metric"},
timeout=self.timeout,
)
current_response.raise_for_status()
current_data = current_response.json()
# 5-day forecast
forecast_url = "https://api.openweathermap.org/data/2.5/forecast"
forecast_response = self.session.get(
forecast_url,
params={"q": query, "appid": self.openweather_key, "units": "metric"},
timeout=self.timeout,
)
forecast_response.raise_for_status()
forecast_data = forecast_response.json()
return {
"source": "openweathermap",
"timestamp": datetime.utcnow().isoformat(),
"current": {
"temp": current_data["main"]["temp"],
"feels_like": current_data["main"]["feels_like"],
"temp_min": current_data["main"]["temp_min"],
"temp_max": current_data["main"]["temp_max"],
"humidity": current_data["main"]["humidity"],
"pressure": current_data["main"]["pressure"],
"wind_speed": current_data["wind"]["speed"],
"clouds": current_data["clouds"]["all"],
"description": current_data["weather"][0]["description"],
},
"forecast": self._parse_openweather_forecast(forecast_data),
}
except requests.exceptions.RequestException as e:
logger.error(f"OpenWeatherMap request failed: {e}")
return None
def _parse_openweather_forecast(self, data: dict) -> List[Dict]:
"""Parse OpenWeatherMap forecast data"""
forecasts = []
for item in data.get("list", []):
forecasts.append(
{
"datetime": item["dt_txt"],
"temp": item["main"]["temp"],
"temp_min": item["main"]["temp_min"],
"temp_max": item["main"]["temp_max"],
"humidity": item["main"]["humidity"],
"description": item["weather"][0]["description"],
}
)
return forecasts
def fetch_from_visualcrossing(
self, city: str, start_date: str = None, end_date: str = None
) -> Optional[Dict]:
"""
Fetch historical weather data from Visual Crossing
Args:
city: City name
start_date: Start date (YYYY-MM-DD)
end_date: End date (YYYY-MM-DD)
Returns:
dict: Historical weather data
"""
if not getattr(self, "visualcrossing_key", None):
return None
# Default to last 30 days if no dates provided
if not end_date:
end_date = datetime.now().strftime("%Y-%m-%d")
if not start_date:
start_date = (datetime.now() - timedelta(days=30)).strftime("%Y-%m-%d")
try:
url = f"https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/{city}/{start_date}/{end_date}"
response = self.session.get(
url,
params={
"unitGroup": "metric",
"key": self.visualcrossing_key,
"contentType": "json",
"include": "days",
},
timeout=self.timeout,
)
response.raise_for_status()
data = response.json()
return {
"source": "visualcrossing",
"timestamp": datetime.utcnow().isoformat(),
"location": data.get("resolvedAddress"),
"timezone": data.get("timezone"),
"days": [
{
"date": day["datetime"],
"temp_max": day.get("tempmax"),
"temp_min": day.get("tempmin"),
"temp_avg": day.get("temp"),
"humidity": day.get("humidity"),
"precip": day.get("precip"),
"conditions": day.get("conditions"),
}
for day in data.get("days", [])
],
}
except requests.exceptions.RequestException as e:
logger.error(f"Visual Crossing request failed: {e}")
return None
def get_icao_code(self, city: str) -> Optional[str]:
"""
根据城市名获取对应的 ICAO 机场代码
"""
normalized = city.lower().strip()
# 直接匹配
if normalized in self.CITY_TO_ICAO:
return self.CITY_TO_ICAO[normalized]
# 模糊匹配
for key, icao in self.CITY_TO_ICAO.items():
if key in normalized or normalized in key:
return icao
return None
def fetch_metar(self, city: str, use_fahrenheit: bool = False, utc_offset: int = 0) -> Optional[Dict]:
"""
从 NOAA Aviation Weather Center 获取 METAR 航空气象数据
这是 Polymarket 天气市场的结算数据源 (Weather Underground) 使用的相同气象站
Args:
city: 城市名称
use_fahrenheit: 是否转换为华氏度
Returns:
dict: METAR 数据,包含温度、露点、风速等
"""
icao = self.get_icao_code(city)
if not icao:
logger.warning(f"未找到城市 {city} 对应的 ICAO 代码")
return None
try:
# NOAA Aviation Weather API (免费,无需 Key)
url = "https://aviationweather.gov/api/data/metar"
params = {
"ids": icao,
"format": "json",
"hours": 24, # 抓取 24 小时数据以计算今日最高
"_t": int(time.time()),
}
response = self.session.get(
url,
params=params,
headers={"Cache-Control": "no-cache", "Pragma": "no-cache"},
timeout=self.timeout
)
response.raise_for_status()
data = response.json()
if not data:
return None
# 1. 取最新的观测作为当前状态
latest = data[0]
temp_c = latest.get("temp")
dewp_c = latest.get("dewp")
obs_time = latest.get("reportTime", "")
# 2. 精确计算“当地今天”的最高温
from datetime import timezone, timedelta
now_utc = datetime.now(timezone.utc)
local_now = now_utc + timedelta(seconds=utc_offset)
local_midnight = local_now.replace(hour=0, minute=0, second=0, microsecond=0)
utc_midnight = local_midnight - timedelta(seconds=utc_offset)
max_so_far_c = -999
for obs in data:
obs_report_time = obs.get("reportTime", "")
try:
clean_time = obs_report_time.replace(" ", "T")
if not clean_time.endswith("Z"): clean_time += "Z"
report_dt = datetime.fromisoformat(clean_time.replace("Z", "+00:00"))
if report_dt >= utc_midnight:
t = obs.get("temp")
if t is not None and t > max_so_far_c:
max_so_far_c = t
except:
continue
# 转换为单位
if use_fahrenheit:
temp = temp_c * 9 / 5 + 32 if temp_c is not None else None
max_so_far = max_so_far_c * 9 / 5 + 32 if max_so_far_c > -900 else None
dewp = dewp_c * 9 / 5 + 32 if dewp_c is not None else None
unit = "fahrenheit"
else:
temp = temp_c
max_so_far = max_so_far_c if max_so_far_c > -900 else None
dewp = dewp_c
unit = "celsius"
result = {
"source": "metar",
"icao": icao,
"station_name": latest.get("name", icao),
"timestamp": datetime.utcnow().isoformat(),
"observation_time": obs_time,
"current": {
"temp": round(temp, 1) if temp is not None else None,
"max_temp_so_far": round(max_so_far, 1) if max_so_far is not None else None,
"dewpoint": round(dewp, 1) if dewp is not None else None,
"humidity": latest.get("rh"),
"wind_speed_kt": latest.get("wspd"),
"wind_dir": latest.get("wdir"),
"visibility_mi": latest.get("visib"),
"wx_desc": latest.get("wxString"),
"altimeter": latest.get("altim"),
"clouds": latest.get("clouds", []),
},
"unit": unit,
}
logger.info(
f"✈️ METAR {icao}: {temp:.1f}°{'F' if use_fahrenheit else 'C'} "
f"(obs: {obs_time})"
)
return result
except requests.exceptions.RequestException as e:
logger.error(f"METAR 请求失败 ({icao}): {e}")
return None
except (KeyError, IndexError, TypeError) as e:
logger.error(f"METAR 数据解析失败 ({icao}): {e}")
return None
def fetch_from_mgm(self, istno: str) -> Optional[Dict]:
"""
从土耳其气象局 (MGM) 获取实时数据和预测 (由用户提供其内部 API)
"""
base_url = "https://servis.mgm.gov.tr/web"
# 必须带 Origin,否则会被反爬拦截
headers = {
"Origin": "https://www.mgm.gov.tr",
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36"
}
results = {}
try:
# 1. 实时数据 (添加时间戳防止 CDN 缓存)
import time
obs_resp = self.session.get(
f"{base_url}/sondurumlar?istno={istno}&_={int(time.time()*1000)}",
headers=headers,
timeout=self.timeout
)
if obs_resp.status_code == 200:
data = obs_resp.json()
if data:
latest = data[0] if isinstance(data, list) else data
# MGM 数据字段映射
# ruzgarHiz 实测为 km/h,转为 m/s 需要除以 3.6
ruz_hiz_kmh = latest.get("ruzgarHiz", 0)
results["current"] = {
"temp": latest.get("sicaklik"),
"feels_like": latest.get("hissedilenSicaklik") or latest.get("sicaklik"),
"humidity": latest.get("nem"),
"wind_speed_ms": round(ruz_hiz_kmh / 3.6, 1) if ruz_hiz_kmh is not None else None,
"wind_speed_kt": round(ruz_hiz_kmh / 1.852, 1) if ruz_hiz_kmh is not None else None,
"wind_dir": latest.get("ruzgarYon"),
"rain_24h": latest.get("toplamYagis"),
"pressure": latest.get("aktuelBasinc"),
"cloud_cover": latest.get("kapalilik"), # 0-8 八分位云量
"mgm_max_temp": latest.get("maxSicaklik"), # MGM 官方实测最高温
"time": latest.get("veriZamani"), # 观测时间
"station_name": latest.get("istasyonAd") or latest.get("adi") or latest.get("merkezAd") or "Ankara Esenboğa"
}
# 2. 每日预报
daily_resp = self.session.get(f"{base_url}/tahminler/gunluk?istno={istno}", headers=headers, timeout=self.timeout)
if daily_resp.status_code == 200:
forecasts = daily_resp.json()
if forecasts and isinstance(forecasts, list):
today = forecasts[0]
results["today_high"] = today.get("enYuksekGun1")
results["today_low"] = today.get("enDusukGun1")
return results if "current" in results else None
except Exception as e:
logger.error(f"MGM API 请求失败 ({istno}): {e}")
return None
def fetch_nws(self, lat: float, lon: float) -> Optional[Dict]:
"""
从 NWS (美国国家气象局) 获取高精度预报
仅适用于美国城市,全球 VPS 均可访问
"""
try:
# 1. 获取网格点
points_url = f"https://api.weather.gov/points/{lat},{lon}"
headers = {"User-Agent": "PolyWeather/1.0 (weather-bot)"}
points_resp = self.session.get(points_url, headers=headers, timeout=self.timeout)
points_resp.raise_for_status()
points_data = points_resp.json()
forecast_url = points_data.get("properties", {}).get("forecast")
if not forecast_url:
return None
# 2. 获取预报
forecast_resp = self.session.get(forecast_url, headers=headers, timeout=self.timeout)
forecast_resp.raise_for_status()
forecast_data = forecast_resp.json()
periods = forecast_data.get("properties", {}).get("periods", [])
if not periods:
return None
# 3. 提取今日最高温(找 isDaytime=True 的第一个)
today_high = None
for p in periods:
if p.get("isDaytime") and "High" in p.get("name", ""):
today_high = p.get("temperature")
break
# 如果没有明确的 High,取第一个 daytime 的温度
if today_high is None:
for p in periods:
if p.get("isDaytime"):
today_high = p.get("temperature")
break
return {
"source": "nws",
"today_high": today_high,
"unit": "fahrenheit",
}
except Exception as e:
logger.warning(f"NWS 请求失败: {e}")
return None
def fetch_from_open_meteo(
self,
lat: float,
lon: float,
forecast_days: int = 14,
use_fahrenheit: bool = False,
) -> Optional[Dict]:
"""
Fetch weather from Open-Meteo with forecast data
Args:
lat: Latitude
lon: Longitude
forecast_days: Number of forecast days to fetch (default 14 to cover all market dates)
use_fahrenheit: Whether to return temperatures in Fahrenheit (for US markets)
"""
try:
url = "https://api.open-meteo.com/v1/forecast"
params = {
"latitude": lat,
"longitude": lon,
"current_weather": "true",
"hourly": "temperature_2m",
"daily": "temperature_2m_max,apparent_temperature_max,sunrise,sunset",
"timezone": "auto",
"forecast_days": forecast_days,
"_t": int(time.time()), # 禁用缓存,强制刷新
}
# 显式指定单位,防止 API 默认行为漂移
if use_fahrenheit:
params["temperature_unit"] = "fahrenheit"
else:
params["temperature_unit"] = "celsius"
response = self.session.get(
url,
params=params,
headers={"Cache-Control": "no-cache", "Pragma": "no-cache"},
timeout=self.timeout,
)
response.raise_for_status()
data = response.json()
current = data.get("current_weather", {})
utc_offset = data.get("utc_offset_seconds", 0)
timezone_name = data.get("timezone", "UTC")
# 处理多模型数据 (如果请求了 models 参数,返回结构会变化)
daily_data = data.get("daily", {})
if "temperature_2m_max_ecmwf_ifs04" in daily_data:
ecmwf_max = daily_data.get("temperature_2m_max_ecmwf_ifs04", [])
hrrr_max = daily_data.get("temperature_2m_max_ncep_hrrr_conus", [])
# 记录今日模型分歧
daily_data["model_split"] = {
"ecmwf": ecmwf_max[0] if ecmwf_max else None,
"hrrr": hrrr_max[0] if hrrr_max else None
}
# 智能合并:HRRR 仅覆盖 48 小时,远期用 ECMWF 补全
merged_max = []
for i in range(len(ecmwf_max)):
hrrr_val = hrrr_max[i] if i < len(hrrr_max) else None
ecmwf_val = ecmwf_max[i] if i < len(ecmwf_max) else None
# 优先 HRRR,其次 ECMWF,都没有就跳过
if hrrr_val is not None:
merged_max.append(hrrr_val)
elif ecmwf_val is not None:
merged_max.append(ecmwf_val)
else:
# 两个都没有,用占位符 (理论上不应该发生)
merged_max.append(ecmwf_val) # None
daily_data["temperature_2m_max"] = merged_max
# 映射逐小时数据
hourly_data = data.get("hourly", {})
if "temperature_2m_ncep_hrrr_conus" in hourly_data:
hourly_data["temperature_2m"] = hourly_data["temperature_2m_ncep_hrrr_conus"]
# 计算精确的当地时间
now_utc = datetime.utcnow()
local_now = now_utc + timedelta(seconds=utc_offset)
local_time_str = local_now.strftime("%Y-%m-%d %H:%M")
return {
"source": "open-meteo",
"timestamp": now_utc.isoformat(),
"timezone": timezone_name,
"utc_offset": utc_offset,
"current": {
"temp": current.get("temperature"),
"local_time": local_time_str,
},
"hourly": hourly_data,
"daily": daily_data,
"unit": "fahrenheit" if use_fahrenheit else "celsius",
}
except Exception as e:
logger.error(f"Open-Meteo forecast failed: {e}")
return None
def fetch_from_meteoblue(
self,
lat: float,
lon: float,
timezone_name: str = "UTC",
use_fahrenheit: bool = False,
) -> Optional[Dict]:
"""
通过 Meteoblue 官方 API 获取高精度预测数据
"""
if not self.meteoblue_key:
logger.warning("Meteoblue API Key 未配置,跳过抓取。")
return None
try:
# 1. 调用官方 API (使用 basic-day 包,它是多模型 ML 融合结果)
# 格式: https://my.meteoblue.com/packages/basic-day?apikey=KEY&lat=LAT&lon=LON&format=json
url = "https://my.meteoblue.com/packages/basic-day"
params = {
"apikey": self.meteoblue_key,
"lat": lat,
"lon": lon,
"format": "json",
"as_daylight": "true"
}
response = self.session.get(
url,
params=params,
timeout=self.timeout
)
response.raise_for_status()
data = response.json()
day_data = data.get("data_day", {})
max_temps = day_data.get("temperature_max", [])
if not max_temps:
logger.warning(f"Meteoblue API 返回数据中找不到最高温 (坐标: {lat},{lon})")
return None
# 2. 转换单位
def c_to_f(c):
return round((c * 9/5) + 32, 1)
result = {
"source": "meteoblue",
"today_high": None,
"daily_highs": [],
"unit": "fahrenheit" if use_fahrenheit else "celsius",
"url": f"https://www.meteoblue.com/en/weather/week/{lat}N{lon}E" # 仅供参考
}
# 提取今日最高
mb_today_c = max_temps[0]
result["today_high"] = c_to_f(mb_today_c) if use_fahrenheit else mb_today_c
# 提取接下来几天的最高温
if use_fahrenheit:
result["daily_highs"] = [c_to_f(t) for t in max_temps]
else:
result["daily_highs"] = max_temps
logger.info(f"✅ Meteoblue API 获取成功 ({lat},{lon}): 今天 {result['today_high']}{result['unit']}")
return result
except Exception as e:
logger.error(f"Meteoblue API fetch failed: {e}")
return None
def extract_date_from_title(self, title: str) -> Optional[str]:
"""
从标题中提取日期并标准化为 YYYY-MM-DD
支持: "February 6", "2月6日", "2-6" 等
"""
# 1. 尝试英文月份
months = {
"January": "01", "February": "02", "March": "03", "April": "04",
"May": "05", "June": "06", "July": "07", "August": "08",
"September": "09", "October": "10", "November": "11", "December": "12",
}
for month_name, month_val in months.items():
if month_name in title:
match = re.search(f"{month_name}\\s+(\\d+)", title)
if match:
day = int(match.group(1))
year = datetime.now().year
return f"{year}-{month_val}-{day:02d}"
# 2. 尝试中文格式 "2月7日" 或 "02月07日"
zh_match = re.search(r"(\d{1,2})月(\d{1,2})日", title)
if zh_match:
month = int(zh_match.group(1))
day = int(zh_match.group(2))
year = datetime.now().year
return f"{year}-{month:02d}-{day:02d}"
# 3. 尝试 ISO 格式 YYYY-MM-DD
iso_match = re.search(r"(\d{4})-(\d{2})-(\d{2})", title)
if iso_match:
return iso_match.group(0)
return None
def get_coordinates(self, city: str) -> Optional[Dict[str, float]]:
"""
使用 Open-Meteo Geocoding API 获取城市坐标 (免费, 无需 Key)
"""
# 预设常用城市坐标,避免网络波动导致启动失败
static_coords = {
"london": {"lat": 51.5074, "lon": -0.1278},
"new york": {"lat": 40.7128, "lon": -74.0060},
"new york's central park": {"lat": 40.7812, "lon": -73.9665},
"nyc": {"lat": 40.7128, "lon": -74.0060},
"seattle": {"lat": 47.6062, "lon": -122.3321},
"chicago": {"lat": 41.8781, "lon": -87.6298},
"dallas": {"lat": 32.7767, "lon": -96.7970},
"miami": {"lat": 25.7617, "lon": -80.1918},
"atlanta": {"lat": 33.7490, "lon": -84.3880},
"seoul": {"lat": 37.5665, "lon": 126.9780},
"toronto": {"lat": 43.6532, "lon": -79.3832},
"ankara": {"lat": 39.9334, "lon": 32.8597},
"wellington": {"lat": -41.2865, "lon": 174.7762},
"buenos aires": {"lat": -34.6037, "lon": -58.3816},
}
normalized_city = city.lower().strip()
if normalized_city in static_coords:
return static_coords[normalized_city]
# 模糊匹配映射 (针对包含城市名的情况)
for key in static_coords:
if key in normalized_city:
logger.debug(f"地理编码命中模糊映射: {city} -> {key}")
return static_coords[key]
try:
url = "https://geocoding-api.open-meteo.com/v1/search"
response = self.session.get(
url,
params={"name": city, "count": 1, "language": "en", "format": "json"},
timeout=15, # 增加超时时间到 15s
)
response.raise_for_status()
results = response.json().get("results", [])
if results:
res = results[0]
return {
"lat": res.get("latitude"),
"lon": res.get("longitude"),
"name": res.get("name"),
"country": res.get("country"),
}
except Exception as e:
logger.error(f"地理编码失败 ({city}): {e}")
return None
def extract_city_from_question(self, question: str) -> Optional[str]:
"""
从 Polymarket 问题描述或 Slug 中提取城市名称
"""
q = question.lower()
# 1. 优先尝试已知城市列表 (硬编码匹配)
known_cities = {
"london": "London", "伦敦": "London",
"new york": "New York", "new york's central park": "New York", "nyc": "New York", "纽约": "New York",
"seattle": "Seattle", "西雅图": "Seattle",
"chicago": "Chicago", "芝加哥": "Chicago",
"dallas": "Dallas", "达拉斯": "Dallas",
"miami": "Miami", "迈阿密": "Miami",
"atlanta": "Atlanta", "亚特兰大": "Atlanta",
"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()
# 严格判断是否为美国市场(必须完全匹配列表或缩写)
use_fahrenheit = city_lower 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)")
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)
metar_data = self.fetch_metar(city, use_fahrenheit=use_fahrenheit, utc_offset=utc_offset)
if metar_data:
results["metar"] = metar_data
# 对安卡拉,额外获取 MGM 官方数据
if city_lower == "ankara":
mgm_data = self.fetch_from_mgm("17128")
if mgm_data:
results["mgm"] = mgm_data
# 对伦敦,获取 Meteoblue 预测 (公认最准)
if city_lower == "london":
mb_data = self.fetch_from_meteoblue(
lat, lon,
timezone_name=open_meteo.get("timezone", "UTC"),
use_fahrenheit=use_fahrenheit
)
if mb_data:
results["meteoblue"] = mb_data
# 对美国城市,额外获取 NWS 高精预报
if use_fahrenheit:
nws_data = self.fetch_nws(lat, lon)
if nws_data:
results["nws"] = nws_data
else:
# Open-Meteo 失败时,仍然尝试获取 METAR 和 NWS
metar_data = self.fetch_metar(city, use_fahrenheit=use_fahrenheit)
if metar_data:
results["metar"] = metar_data
if use_fahrenheit:
nws_data = self.fetch_nws(lat, lon)
if nws_data:
results["nws"] = nws_data
else:
# 降级方案(无经纬度)
metar_data = self.fetch_metar(city, use_fahrenheit=use_fahrenheit)
if metar_data:
results["metar"] = metar_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,
}