Fix traffic data
This commit is contained in:
Urtzi Alfaro
160
services/data/app/external/aemet.py
vendored
160
services/data/app/external/aemet.py
vendored
@@ -1,7 +1,7 @@
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# ================================================================
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# services/data/app/external/aemet.py
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# services/data/app/external/aemet.py - FIXED VERSION
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# ================================================================
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"""AEMET (Spanish Weather Service) API client - PROPER API FLOW FIX"""
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"""AEMET (Spanish Weather Service) API client - FIXED FORECAST PARSING"""
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import math
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from typing import List, Dict, Any, Optional
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@@ -209,7 +209,7 @@ class AEMETClient(BaseAPIClient):
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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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"""Parse AEMET forecast data - FIXED VERSION"""
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forecast = []
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base_date = datetime.now().date()
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@@ -218,31 +218,121 @@ class AEMETClient(BaseAPIClient):
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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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# AEMET forecast structure is complex - parse what we can and fill gaps with synthetic data
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logger.debug("Processing AEMET forecast data", data_length=len(data))
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# If we have actual AEMET data, try to parse it
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if len(data) > 0 and isinstance(data[0], dict):
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aemet_data = data[0]
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logger.debug("AEMET forecast keys", keys=list(aemet_data.keys()) if isinstance(aemet_data, dict) else "not_dict")
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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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# Try to extract daily forecasts from AEMET structure
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dias = aemet_data.get('prediccion', {}).get('dia', []) if isinstance(aemet_data, dict) else []
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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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if isinstance(dias, list) and len(dias) > 0:
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# Parse AEMET daily forecast format
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for i, dia in enumerate(dias[:days]):
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if not isinstance(dia, dict):
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continue
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forecast_date = base_date + timedelta(days=i)
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# Extract temperature data (AEMET has complex temp structure)
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temp_data = dia.get('temperatura', {})
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if isinstance(temp_data, dict):
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temp_max = self._extract_temp_value(temp_data.get('maxima'))
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temp_min = self._extract_temp_value(temp_data.get('minima'))
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avg_temp = (temp_max + temp_min) / 2 if temp_max and temp_min else 15.0
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else:
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avg_temp = 15.0
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# Extract precipitation probability
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precip_data = dia.get('probPrecipitacion', [])
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precip_prob = 0.0
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if isinstance(precip_data, list) and len(precip_data) > 0:
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for precip_item in precip_data:
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if isinstance(precip_item, dict) and 'value' in precip_item:
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precip_prob = max(precip_prob, self._safe_float(precip_item.get('value'), 0.0))
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# Extract wind data
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viento_data = dia.get('viento', [])
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wind_speed = 10.0
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if isinstance(viento_data, list) and len(viento_data) > 0:
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for viento_item in viento_data:
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if isinstance(viento_item, dict) and 'velocidad' in viento_item:
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speed_values = viento_item.get('velocidad', [])
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if isinstance(speed_values, list) and len(speed_values) > 0:
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wind_speed = self._safe_float(speed_values[0], 10.0)
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break
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# Generate description based on precipitation probability
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if precip_prob > 70:
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description = "Lluvioso"
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elif precip_prob > 30:
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description = "Parcialmente nublado"
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else:
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description = "Soleado"
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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(avg_temp, 1),
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"precipitation": precip_prob / 10, # Convert percentage to mm estimate
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"humidity": 50.0 + (i % 20), # Estimate
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"wind_speed": round(wind_speed, 1),
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"description": description,
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"source": "aemet"
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})
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logger.debug("Parsed forecast day", day=i, temp=avg_temp, precip=precip_prob)
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# If we successfully parsed some days, fill remaining with synthetic
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remaining_days = days - len(forecast)
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if remaining_days > 0:
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synthetic_forecast = self._generate_synthetic_forecast_sync(remaining_days, len(forecast))
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forecast.extend(synthetic_forecast)
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# If no valid AEMET data was parsed, use synthetic
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if len(forecast) == 0:
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logger.info("No valid AEMET forecast data found, using synthetic")
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forecast = self._generate_synthetic_forecast_sync(days, 0)
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return forecast
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except Exception as e:
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logger.error("Error parsing AEMET forecast data", error=str(e))
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# Fallback to synthetic forecast
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forecast = self._generate_synthetic_forecast_sync(days, 0)
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# Ensure we always return the requested number of days
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if len(forecast) < days:
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remaining = days - len(forecast)
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synthetic_remaining = self._generate_synthetic_forecast_sync(remaining, len(forecast))
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forecast.extend(synthetic_remaining)
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return forecast[:days] # Ensure we don't exceed requested days
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def _extract_temp_value(self, temp_data) -> Optional[float]:
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"""Extract temperature value from AEMET complex temperature structure"""
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if temp_data is None:
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return None
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if isinstance(temp_data, (int, float)):
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return float(temp_data)
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if isinstance(temp_data, str):
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try:
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return float(temp_data)
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except ValueError:
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return None
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if isinstance(temp_data, dict) and 'valor' in temp_data:
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return self._safe_float(temp_data['valor'], None)
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if isinstance(temp_data, list) and len(temp_data) > 0:
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first_item = temp_data[0]
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if isinstance(first_item, dict) and 'valor' in first_item:
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return self._safe_float(first_item['valor'], None)
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return None
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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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@@ -292,32 +382,36 @@ class AEMETClient(BaseAPIClient):
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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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def _generate_synthetic_forecast_sync(self, days: int, start_offset: int = 0) -> List[Dict[str, Any]]:
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"""Generate synthetic forecast data synchronously"""
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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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forecast_date = base_date + timedelta(days=start_offset + 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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temp_variation = ((start_offset + 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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"precipitation": 2.0 if (start_offset + i) % 5 == 0 else 0.0,
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"humidity": 50 + ((start_offset + i) % 30),
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"wind_speed": 10 + ((start_offset + i) % 15),
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"description": "Lluvioso" if (start_offset + 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_forecast(self, days: int) -> List[Dict[str, Any]]:
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"""Generate synthetic forecast data (async version for compatibility)"""
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return self._generate_synthetic_forecast_sync(days, 0)
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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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