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| import openmeteo_requests | |
| import requests_cache | |
| import pandas as pd | |
| from retry_requests import retry | |
| def download_historical_weather_data( | |
| latitude: float, | |
| longitude: float, | |
| start_year: int, | |
| end_year: int, | |
| ) -> pd.DataFrame: | |
| # Setup the Open-Meteo API client with cache and retry on error | |
| cache_session = requests_cache.CachedSession('.cache', expire_after = -1) | |
| retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2) | |
| openmeteo = openmeteo_requests.Client(session = retry_session) | |
| # Make sure all required weather variables are listed here | |
| # The order of variables in hourly or daily is important to assign them correctly below | |
| url = "https://archive-api.open-meteo.com/v1/archive" | |
| params = { | |
| "latitude": latitude, | |
| "longitude": longitude, | |
| "start_date": f"{start_year}-01-01", | |
| "end_date": f"{end_year}-12-31", | |
| "daily": ["temperature_2m_max", "temperature_2m_min", "temperature_2m_mean", | |
| "precipitation_sum", "precipitation_hours", | |
| "shortwave_radiation_sum", | |
| "et0_fao_evapotranspiration"], | |
| "timezone": "GMT" | |
| } | |
| responses = openmeteo.weather_api(url, params=params) | |
| # Process first location. Add a for-loop for multiple locations or weather models | |
| response = responses[0] | |
| print(f"Coordinates {response.Latitude()}°N {response.Longitude()}°E") | |
| print(f"Elevation {response.Elevation()} m asl") | |
| print(f"Timezone {response.Timezone()} {response.TimezoneAbbreviation()}") | |
| print(f"Timezone difference to GMT+0 {response.UtcOffsetSeconds()} s") | |
| # Process daily data. The order of variables needs to be the same as requested. | |
| daily = response.Daily() | |
| daily_temperature_2m_max = daily.Variables(0).ValuesAsNumpy() | |
| daily_temperature_2m_min = daily.Variables(1).ValuesAsNumpy() | |
| daily_temperature_2m_mean = daily.Variables(2).ValuesAsNumpy() | |
| daily_precipitation_sum = daily.Variables(3).ValuesAsNumpy() | |
| daily_precipitation_hours = daily.Variables(4).ValuesAsNumpy() | |
| daily_shortwave_radiation_sum = daily.Variables(5).ValuesAsNumpy() | |
| daily_et0_fao_evapotranspiration = daily.Variables(6).ValuesAsNumpy() | |
| daily_data = { | |
| "date": pd.date_range( | |
| start=pd.to_datetime(daily.Time(), unit="s", utc=True), | |
| end=pd.to_datetime(daily.TimeEnd(), unit="s", utc=True), | |
| freq=pd.Timedelta(seconds=daily.Interval()), | |
| inclusive="left" | |
| ), | |
| "temperature_2m_max": daily_temperature_2m_max, | |
| "temperature_2m_min": daily_temperature_2m_min, | |
| "temperature_2m_mean": daily_temperature_2m_mean, | |
| "precipitation_sum": daily_precipitation_sum, | |
| "precipitation_hours": daily_precipitation_hours, | |
| "shortwave_radiation_sum": daily_shortwave_radiation_sum, | |
| "et0_fao_evapotranspiration": daily_et0_fao_evapotranspiration | |
| } | |
| daily_dataframe = pd.DataFrame(data = daily_data) | |
| return daily_dataframe | |