lib_era5_land_pixel.py

    return raster_stats


def extract_weather_dataframe(rain_path: str, ET0_path: str, shapefile: str, config_file: str, save_path: str) -> None:
    """
    Extract a weather dataframe for each variable (Rain, ET0) and merge them in one
    dataframe. This dataframe is saved as ``csv`` file.

    Arguments
    =========

    1. rain_path: ``str``
        path to rain Geotiff file
    2. ET0_path: ``str``
        path to ET0 Geotiff file
    3. shapefile: ``str``
        path to shapefile
    4. config_file: ``str``
        path to config file
    5. save_path: ``str``
        save path for weather dataframe

    Returns
    =======

    ``None``
    """
    
    # Generate arguments for multiprocessing
    args = [(rain_path, shapefile, config_file), (ET0_path, shapefile, config_file)]
    
    print('\nStarting weather data extraction on two cores..\n')
    
    # Extract weather values for both weather varialbes
    with Pool(2) as p:
        results = p.map(extract_rasterstats, args)
    
    # Collect results in a single dataframe
    weather_dataframe = pd.DataFrame(results[0], columns = ['date', 'id', 'Rain', 'LC'])
    weather_dataframe['ET0'] = pd.DataFrame(results[1], columns = ['date', 'id', 'ET0', 'LC'])['ET0']
    
    # Reorder columns
    weather_dataframe = weather_dataframe.reindex(columns = ['date', 'id', 'Rain', 'ET0', 'LC'])
    
    # Format datatypes
    weather_dataframe['Rain'] = np.round(weather_dataframe['Rain']).astype(int)
    weather_dataframe['ET0'] = np.round(weather_dataframe['ET0']).astype(int)
    
    # Change date type
    weather_dataframe['date'] = pd.to_datetime(weather_dataframe['date'])
    
    # Save dataframe to csv
    weather_dataframe.to_csv(save_path, index = False)
    
    return None