import openmeteo_requests
import requests_cache
import pandas as pd
from retry_requests import retry


# Setup the Open-Meteo API client with cache and retry on error
cache_session = requests_cache.CachedSession('.cache', expire_after = 3600)
retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2)
openmeteo = openmeteo_requests.Client(session = retry_session)


def get_info_meteo(latitude, longitude):
    """Function that creates a Json file containing the weather data of the location
    ARGS: 
        latitude (Float) : latitude coordinate
        longitude (Float): longitude coordinate
    
    """
    url = "https://api.open-meteo.com/v1/forecast"
    params = {
        "latitude": latitude, 
        "longitude": longitude, #"cloud_cover_low", "cloud_cover_mid","cloud_cover_high"
        "hourly": {"relative_humidity_2m", "soil_temperature_0cm", "soil_moisture_0_to_1cm", "cloud_cover"},
        "daily": {"temperature_2m_max","precipitation_sum", "wind_speed_10m_max", "sunshine_duration", "rain_sum"},
        "past_days" : 5,
        "forecast_days" : 7
        
    } 
    responses = openmeteo.weather_api(url, params=params)
    response = responses[0]

    #Hourly dataframe

    hourly = response.Hourly()
    hourly_relative_humidity_2m = hourly.Variables(0).ValuesAsNumpy()
    hourly_soil_temperature_0cm = hourly.Variables(1).ValuesAsNumpy()
    hourly_soil_moisture_0_to_1cm = hourly.Variables(2).ValuesAsNumpy()
    hourly_cloud_cover = hourly.Variables(3).ValuesAsNumpy()

    hourly_data = {"date": pd.date_range(
        start = pd.to_datetime(hourly.Time(), unit = "s", utc = True),
        end = pd.to_datetime(hourly.TimeEnd(), unit = "s", utc = True),
        freq = pd.Timedelta(seconds = hourly.Interval()),
        inclusive = "left"
    )}

    hourly_data["relative_humidity_2m_%"] = hourly_relative_humidity_2m
    hourly_data["soil_temperature_0cm_C"] = hourly_soil_temperature_0cm
    hourly_data["soil_moisture_0_to_1cm_m³"] = hourly_soil_moisture_0_to_1cm
    hourly_data["cloud_cover_%"] = hourly_cloud_cover

    hourly_dataframe = pd.DataFrame(data = hourly_data)


    hourly_dataframe = pd.DataFrame(data = hourly_data)

    #Average hourly data per day
    hourly_dataframe['day_date'] = hourly_dataframe['date'].dt.strftime('%Y-%m-%d')

    tmp1 = hourly_dataframe.groupby('day_date')[['relative_humidity_2m_%', 'soil_temperature_0cm_C', 'soil_moisture_0_to_1cm_m³']].mean()

    avg_hourly_dataframe = tmp1.rename(columns={'relative_humidity_2m_%': 'avg_hourly_relative_humidity_2m_%', 'soil_temperature_0cm_C': 'avg_hourly_soil_temperature_0cm_C', 'soil_moisture_0_to_1cm_m³': 'avg_hourly_soil_moisture_0_to_1cm_m³'})

    #med cloud couverture
    tmp2 = hourly_dataframe.groupby('day_date')[['cloud_cover_%']].median()

    med_cloud_cover = tmp2.rename(columns={'cloud_cover_%': 'med_cloud_cover_%'})

    #Daily dataframe 
    daily = response.Daily()
    daily_temperature_2m_max = daily.Variables(0).ValuesAsNumpy()
    daily_precipitation_sum = daily.Variables(1).ValuesAsNumpy()
    daily_wind_speed_10m_max = daily.Variables(2).ValuesAsNumpy()
    daily_sunshine_duration = daily.Variables(3).ValuesAsNumpy()
    daily_rain_sum = daily.Variables(4).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='D',  
        inclusive="left"
    )}

    daily_data["daily_temperature_2m_max_C"] = daily_temperature_2m_max
    daily_data["daily_precipitation_sum_mm"] = daily_precipitation_sum
    daily_data["daily_wind_speed_10m_max_km/h"] = daily_wind_speed_10m_max
    daily_data["daily_sunshine_duration_sec"] = daily_sunshine_duration
    daily_data["daily_rain_sum_mm"] = daily_rain_sum

    daily_dataframe = pd.DataFrame(data=daily_data)

    daily_dataframe['day_date'] = daily_dataframe['date'].dt.strftime('%Y-%m-%d')

    total_df = pd.merge(daily_dataframe, avg_hourly_dataframe, on="day_date", how="left")
    total_df = pd.merge(total_df, med_cloud_cover, on="day_date", how="left")

    total_df['date'] = total_df['day_date']
    total_df = total_df.drop('day_date', axis=1)
    
    return total_df
    #total_df.to_json("weather_data.json", orient='columns')