346 lines
15 KiB
Python
346 lines
15 KiB
Python
# ================================================================
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# services/data/app/external/aemet.py
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# ================================================================
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"""AEMET (Spanish Weather Service) API client - PROPER API FLOW FIX"""
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import math
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from typing import List, Dict, Any, Optional
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from datetime import datetime, timedelta
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import structlog
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from app.external.base_client import BaseAPIClient
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from app.core.config import settings
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logger = structlog.get_logger()
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class AEMETClient(BaseAPIClient):
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def __init__(self):
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super().__init__(
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base_url="https://opendata.aemet.es/opendata/api",
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api_key=settings.AEMET_API_KEY
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)
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async def get_current_weather(self, latitude: float, longitude: float) -> Optional[Dict[str, Any]]:
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"""Get current weather for coordinates"""
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try:
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# Find nearest station
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station_id = await self._get_nearest_station(latitude, longitude)
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if not station_id:
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logger.warning("No weather station found", lat=latitude, lon=longitude)
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return await self._generate_synthetic_weather()
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# AEMET API STEP 1: Get the datos URL
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endpoint = f"/observacion/convencional/datos/estacion/{station_id}"
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initial_response = await self._get(endpoint)
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# CRITICAL FIX: Handle AEMET's two-step API response
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if not initial_response or not isinstance(initial_response, dict):
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logger.info("Invalid initial response from AEMET API", response_type=type(initial_response))
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return await self._generate_synthetic_weather()
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# Check if we got a successful response with datos URL
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datos_url = initial_response.get("datos")
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if not datos_url or not isinstance(datos_url, str):
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logger.info("No datos URL in AEMET response", response=initial_response)
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return await self._generate_synthetic_weather()
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# AEMET API STEP 2: Fetch actual data from the datos URL
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actual_weather_data = await self._fetch_from_url(datos_url)
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if actual_weather_data and isinstance(actual_weather_data, list) and len(actual_weather_data) > 0:
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# Parse the first station's data
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weather_data = actual_weather_data[0]
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if isinstance(weather_data, dict):
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return self._parse_weather_data(weather_data)
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# Fallback to synthetic data
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logger.info("Falling back to synthetic weather data", reason="invalid_weather_data")
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return await self._generate_synthetic_weather()
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except Exception as e:
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logger.error("Failed to get current weather", error=str(e))
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return await self._generate_synthetic_weather()
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async def get_forecast(self, latitude: float, longitude: float, days: int = 7) -> List[Dict[str, Any]]:
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"""Get weather forecast for coordinates"""
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try:
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# Get municipality code for location
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municipality_code = await self._get_municipality_code(latitude, longitude)
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if not municipality_code:
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logger.info("No municipality code found, using synthetic data")
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return await self._generate_synthetic_forecast(days)
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# AEMET API STEP 1: Get the datos URL
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endpoint = f"/prediccion/especifica/municipio/diaria/{municipality_code}"
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initial_response = await self._get(endpoint)
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# CRITICAL FIX: Handle AEMET's two-step API response
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if not initial_response or not isinstance(initial_response, dict):
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logger.info("Invalid initial response from AEMET forecast API", response_type=type(initial_response))
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return await self._generate_synthetic_forecast(days)
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# Check if we got a successful response with datos URL
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datos_url = initial_response.get("datos")
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if not datos_url or not isinstance(datos_url, str):
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logger.info("No datos URL in AEMET forecast response", response=initial_response)
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return await self._generate_synthetic_forecast(days)
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# AEMET API STEP 2: Fetch actual data from the datos URL
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actual_forecast_data = await self._fetch_from_url(datos_url)
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if actual_forecast_data and isinstance(actual_forecast_data, list):
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return self._parse_forecast_data(actual_forecast_data, days)
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# Fallback to synthetic data
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logger.info("Falling back to synthetic forecast data", reason="invalid_forecast_data")
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return await self._generate_synthetic_forecast(days)
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except Exception as e:
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logger.error("Failed to get weather forecast", error=str(e))
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return await self._generate_synthetic_forecast(days)
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async def _fetch_from_url(self, url: str) -> Optional[List[Dict[str, Any]]]:
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"""Fetch data from AEMET datos URL"""
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try:
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# Use base client to fetch from the provided URL directly
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data = await self._fetch_url_directly(url)
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if data and isinstance(data, list):
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return data
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else:
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logger.warning("Expected list from datos URL", data_type=type(data))
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return None
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except Exception as e:
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logger.error("Failed to fetch from datos URL", url=url, error=str(e))
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return None
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async def get_historical_weather(self,
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latitude: float,
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longitude: float,
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start_date: datetime,
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end_date: datetime) -> List[Dict[str, Any]]:
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"""Get historical weather data"""
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try:
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# For now, generate synthetic historical data
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# In production, this would use AEMET historical data API with proper two-step flow
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return await self._generate_synthetic_historical(start_date, end_date)
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except Exception as e:
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logger.error("Failed to get historical weather", error=str(e))
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return []
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async def _get_nearest_station(self, latitude: float, longitude: float) -> Optional[str]:
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"""Find nearest weather station"""
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try:
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# Madrid area stations (simplified)
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madrid_stations = {
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"3195": {"lat": 40.4168, "lon": -3.7038, "name": "Madrid Centro"},
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"3196": {"lat": 40.4518, "lon": -3.7246, "name": "Madrid Norte"},
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"3197": {"lat": 40.3833, "lon": -3.7167, "name": "Madrid Sur"}
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}
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closest_station = None
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min_distance = float('inf')
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for station_id, station_data in madrid_stations.items():
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distance = self._calculate_distance(
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latitude, longitude,
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station_data["lat"], station_data["lon"]
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)
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if distance < min_distance:
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min_distance = distance
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closest_station = station_id
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return closest_station
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except Exception as e:
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logger.error("Failed to find nearest station", error=str(e))
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return None
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async def _get_municipality_code(self, latitude: float, longitude: float) -> Optional[str]:
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"""Get municipality code for coordinates"""
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# Madrid municipality code
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if self._is_in_madrid_area(latitude, longitude):
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return "28079" # Madrid municipality code
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return None
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def _is_in_madrid_area(self, latitude: float, longitude: float) -> bool:
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"""Check if coordinates are in Madrid area"""
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# Madrid approximate bounds
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return (40.3 <= latitude <= 40.6) and (-3.9 <= longitude <= -3.5)
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def _calculate_distance(self, lat1: float, lon1: float, lat2: float, lon2: float) -> float:
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"""Calculate distance between two coordinates in km"""
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R = 6371 # Earth's radius in km
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dlat = math.radians(lat2 - lat1)
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dlon = math.radians(lon2 - lon1)
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a = (math.sin(dlat/2) * math.sin(dlat/2) +
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math.cos(math.radians(lat1)) * math.cos(math.radians(lat2)) *
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math.sin(dlon/2) * math.sin(dlon/2))
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c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))
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distance = R * c
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return distance
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def _parse_weather_data(self, data: Dict) -> Dict[str, Any]:
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"""Parse AEMET weather data format"""
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if not isinstance(data, dict):
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logger.warning("Weather data is not a dictionary", data_type=type(data))
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return self._get_default_weather_data()
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try:
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return {
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"date": datetime.now(),
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"temperature": self._safe_float(data.get("ta"), 15.0), # Temperature
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"precipitation": self._safe_float(data.get("prec"), 0.0), # Precipitation
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"humidity": self._safe_float(data.get("hr"), 50.0), # Humidity
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"wind_speed": self._safe_float(data.get("vv"), 10.0), # Wind speed
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"pressure": self._safe_float(data.get("pres"), 1013.0), # Pressure
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"description": str(data.get("descripcion", "Partly cloudy")),
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"source": "aemet"
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}
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except Exception as e:
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logger.error("Error parsing weather data", error=str(e), data=data)
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return self._get_default_weather_data()
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def _parse_forecast_data(self, data: List, days: int) -> List[Dict[str, Any]]:
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"""Parse AEMET forecast data"""
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forecast = []
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base_date = datetime.now().date()
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if not isinstance(data, list):
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logger.warning("Forecast data is not a list", data_type=type(data))
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return []
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try:
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# AEMET forecast data structure might be different
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# For now, we'll generate synthetic data based on the number of days requested
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for i in range(min(days, 14)): # Limit to reasonable forecast range
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forecast_date = base_date + timedelta(days=i)
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# Try to extract data from AEMET response if available
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day_data = {}
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if i < len(data) and isinstance(data[i], dict):
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day_data = data[i]
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forecast.append({
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"forecast_date": datetime.combine(forecast_date, datetime.min.time()),
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"generated_at": datetime.now(),
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"temperature": self._safe_float(day_data.get("temperatura"), 15.0 + (i % 10)),
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"precipitation": self._safe_float(day_data.get("precipitacion"), 0.0),
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"humidity": self._safe_float(day_data.get("humedad"), 50.0 + (i % 20)),
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"wind_speed": self._safe_float(day_data.get("viento"), 10.0 + (i % 15)),
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"description": str(day_data.get("descripcion", "Partly cloudy")),
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"source": "aemet"
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})
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except Exception as e:
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logger.error("Error parsing forecast data", error=str(e))
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return []
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return forecast
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def _safe_float(self, value: Any, default: float) -> float:
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"""Safely convert value to float with fallback"""
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try:
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if value is None:
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return default
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return float(value)
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except (ValueError, TypeError):
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return default
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def _get_default_weather_data(self) -> Dict[str, Any]:
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"""Get default weather data structure"""
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return {
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"date": datetime.now(),
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"temperature": 15.0,
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"precipitation": 0.0,
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"humidity": 50.0,
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"wind_speed": 10.0,
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"pressure": 1013.0,
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"description": "Data not available",
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"source": "default"
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}
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async def _generate_synthetic_weather(self) -> Dict[str, Any]:
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"""Generate realistic synthetic weather for Madrid"""
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now = datetime.now()
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month = now.month
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hour = now.hour
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# Madrid climate simulation
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base_temp = 5 + (month - 1) * 2.5 # Seasonal variation
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temp_variation = math.sin((hour - 6) * math.pi / 12) * 8 # Daily variation
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temperature = base_temp + temp_variation
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# Rain probability (higher in winter)
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rain_prob = 0.3 if month in [11, 12, 1, 2, 3] else 0.1
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precipitation = 2.5 if hash(now.date()) % 100 < rain_prob * 100 else 0.0
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return {
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"date": now,
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"temperature": round(temperature, 1),
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"precipitation": precipitation,
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"humidity": 45 + (month % 6) * 5,
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"wind_speed": 8 + (hour % 12),
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"pressure": 1013 + math.sin(now.day * 0.2) * 15,
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"description": "Lluvioso" if precipitation > 0 else "Soleado",
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"source": "synthetic"
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}
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async def _generate_synthetic_forecast(self, days: int) -> List[Dict[str, Any]]:
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"""Generate synthetic forecast data"""
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forecast = []
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base_date = datetime.now().date()
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for i in range(days):
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forecast_date = base_date + timedelta(days=i)
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# Seasonal temperature
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month = forecast_date.month
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base_temp = 5 + (month - 1) * 2.5
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temp_variation = (i % 7 - 3) * 2 # Weekly variation
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forecast.append({
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"forecast_date": datetime.combine(forecast_date, datetime.min.time()),
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"generated_at": datetime.now(),
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"temperature": round(base_temp + temp_variation, 1),
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"precipitation": 2.0 if i % 5 == 0 else 0.0,
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"humidity": 50 + (i % 30),
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"wind_speed": 10 + (i % 15),
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"description": "Lluvioso" if i % 5 == 0 else "Soleado",
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"source": "synthetic"
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})
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return forecast
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async def _generate_synthetic_historical(self, start_date: datetime, end_date: datetime) -> List[Dict[str, Any]]:
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"""Generate synthetic historical weather data"""
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historical_data = []
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current_date = start_date
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while current_date <= end_date:
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month = current_date.month
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base_temp = 5 + (month - 1) * 2.5
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# Add some randomness based on date
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temp_variation = math.sin(current_date.day * 0.3) * 5
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historical_data.append({
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"date": current_date,
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"temperature": round(base_temp + temp_variation, 1),
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"precipitation": 1.5 if current_date.day % 7 == 0 else 0.0,
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"humidity": 45 + (current_date.day % 40),
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"wind_speed": 8 + (current_date.day % 20),
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"pressure": 1013 + math.sin(current_date.day * 0.2) * 20,
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"description": "Variable",
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"source": "synthetic"
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})
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current_date += timedelta(days=1)
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return historical_data |