feat: introduce WeatherDataCollector for multi-source weather data retrieval from OpenWeatherMap, Visual Crossing, and METAR.
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AmandaloveYang
@@ -369,14 +369,33 @@ class WeatherDataCollector:
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"station_name": latest.get("istasyonAd") or latest.get("adi") or latest.get("merkezAd") or "Ankara Esenboğa"
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}
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# 2. 每日预报
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daily_resp = self.session.get(f"{base_url}/tahminler/gunluk?istno={istno}", headers=headers, timeout=self.timeout)
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if daily_resp.status_code == 200:
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forecasts = daily_resp.json()
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if forecasts and isinstance(forecasts, list):
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today = forecasts[0]
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results["today_high"] = today.get("enYuksekGun1")
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results["today_low"] = today.get("enDusukGun1")
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# 2. 每日预报（尝试两个可能的 API 路径）
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forecast_urls = [
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f"{base_url}/tahminler/gunluk?istno={istno}",
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f"https://servis.mgm.gov.tr/api/tahminler/gunluk?istno={istno}",
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]
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for forecast_url in forecast_urls:
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try:
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daily_resp = self.session.get(forecast_url, headers=headers, timeout=self.timeout)
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if daily_resp.status_code == 200:
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forecasts = daily_resp.json()
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if forecasts and isinstance(forecasts, list):
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today = forecasts[0]
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high_val = today.get("enYuksekGun1")
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low_val = today.get("enDusukGun1")
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if high_val is not None:
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results["today_high"] = high_val
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results["today_low"] = low_val
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logger.info(f"📋 MGM 每日预报: 最高 {high_val}°C, 最低 {low_val}°C (from {forecast_url})")
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break
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else:
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# 记录所有可用字段，方便调试
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available_keys = [k for k in today.keys() if "yuksek" in k.lower() or "sicaklik" in k.lower() or "gun" in k.lower()]
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logger.warning(f"MGM 每日预报: enYuksekGun1 为空，可用字段: {available_keys}")
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else:
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logger.debug(f"MGM forecast URL {forecast_url} returned {daily_resp.status_code}")
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except Exception as e:
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logger.debug(f"MGM forecast URL {forecast_url} failed: {e}")
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return results if "current" in results else None
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except Exception as e:
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@@ -618,6 +637,86 @@ class WeatherDataCollector:
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logger.warning(f"Ensemble API 请求失败: {e}")
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return None
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def fetch_multi_model(
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self,
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lat: float,
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lon: float,
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use_fahrenheit: bool = False,
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) -> Optional[Dict]:
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"""
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从 Open-Meteo 获取多个独立 NWP 模型的预报
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用于真正的多模型共识评分
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模型列表:
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- ECMWF IFS (欧洲中期天气预报中心)
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- GFS (美国 NOAA)
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- ICON (德国气象局 DWD)
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- GEM (加拿大气象局)
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- JMA (日本气象厅)
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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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models = "ecmwf_ifs025,gfs_seamless,icon_seamless,gem_seamless,jma_seamless"
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params = {
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"latitude": lat,
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"longitude": lon,
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"daily": "temperature_2m_max",
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"models": models,
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"timezone": "auto",
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"forecast_days": 1,
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"_t": int(time.time()),
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}
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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"},
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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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# Open-Meteo 多模型返回格式:
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# "daily": {
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# "temperature_2m_max_ecmwf_ifs025": [12.3],
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# "temperature_2m_max_gfs_seamless": [11.8],
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# ...
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# }
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daily = data.get("daily", {})
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model_labels = {
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"ecmwf_ifs025": "ECMWF",
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"gfs_seamless": "GFS",
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"icon_seamless": "ICON",
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"gem_seamless": "GEM",
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"jma_seamless": "JMA",
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}
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forecasts = {}
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for model_key, label in model_labels.items():
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key = f"temperature_2m_max_{model_key}"
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values = daily.get(key, [])
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if values and values[0] is not None:
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forecasts[label] = round(values[0], 1)
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if not forecasts:
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logger.warning("Multi-model: 无有效模型数据")
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return None
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labels_str = ", ".join([f"{k}={v}" for k, v in forecasts.items()])
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logger.info(f"🔬 Multi-model ({len(forecasts)}个): {labels_str}")
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return {
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"source": "multi_model",
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"forecasts": forecasts, # {"ECMWF": 12.3, "GFS": 11.8, ...}
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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.warning(f"Multi-model API 请求失败: {e}")
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return None
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def fetch_from_meteoblue(
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self,
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lat: float,
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@@ -725,22 +824,23 @@ class WeatherDataCollector:
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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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# 坐标使用 METAR 机场位置（Polymarket 以机场数据结算）
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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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"london": {"lat": 51.5053, "lon": 0.0553}, # EGLC London City
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"paris": {"lat": 49.0097, "lon": 2.5478}, # LFPG Charles de Gaulle
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"new york": {"lat": 40.7750, "lon": -73.8750}, # KLGA LaGuardia
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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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"nyc": {"lat": 40.7750, "lon": -73.8750}, # KLGA LaGuardia
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"seattle": {"lat": 47.4499, "lon": -122.3118}, # KSEA Sea-Tac
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"chicago": {"lat": 41.9769, "lon": -87.9081}, # KORD O'Hare
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"dallas": {"lat": 32.8459, "lon": -96.8509}, # KDAL Love Field
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"miami": {"lat": 25.7933, "lon": -80.2906}, # KMIA International
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"atlanta": {"lat": 33.6367, "lon": -84.4281}, # KATL Hartsfield-Jackson
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"seoul": {"lat": 37.4691, "lon": 126.4510}, # RKSI Incheon
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"toronto": {"lat": 43.6759, "lon": -79.6294}, # CYYZ Pearson
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"ankara": {"lat": 40.1281, "lon": 32.9950}, # LTAC Esenboğa
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"wellington": {"lat": -41.3272, "lon": 174.8053}, # NZWN Wellington
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"buenos aires": {"lat": -34.8222, "lon": -58.5358}, # SAEZ Ezeiza
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}
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normalized_city = city.lower().strip()
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@@ -895,6 +995,11 @@ class WeatherDataCollector:
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ens_data = self.fetch_ensemble(lat, lon, use_fahrenheit=use_fahrenheit)
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if ens_data:
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results["ensemble"] = ens_data
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# 多模型预报 (所有城市通用，用于共识评分)
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mm_data = self.fetch_multi_model(lat, lon, use_fahrenheit=use_fahrenheit)
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if mm_data:
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results["multi_model"] = mm_data
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else:
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# Open-Meteo 失败时，仍然尝试获取 METAR 和 NWS
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metar_data = self.fetch_metar(city, use_fahrenheit=use_fahrenheit)
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