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


def get_tmy(latitude: float, longitude: float) -> pd.DataFrame:
    tmy = pvlib.iotools.get_pvgis_tmy(
        latitude,
        longitude,
        outputformat="json",
        usehorizon=True,
        userhorizon=None,
        startyear=2005,
        endyear=2020,
        map_variables=True,
        url="https://re.jrc.ec.europa.eu/api/v5_2/",
        timeout=60,
    )[0]

    daily_tmy = tmy.groupby(tmy.index.date).agg(
        {"temp_air": ["min", "max", "mean"], "wind_speed": "mean", "ghi": "sum"}
    )
    daily_tmy.columns = daily_tmy.columns.droplevel(0)
    daily_tmy.columns = ["temp_min", "temp_max", "temp_mean", "wind", "irradiance"]

    # On récupère les données de précipitations d'Open-Meteo car elles ne sont pas inclues dans la tmy
    # Setup the Open-Meteo API client with cache and retry on error
    # TODO : utiliser package météo
    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)

    url = "https://archive-api.open-meteo.com/v1/archive"
    params = {
        "latitude": latitude,
        "longitude": longitude,
        "start_date": "2005-01-01",
        "end_date": "2022-01-01",
        "daily": "precipitation_sum",
    }
    responses = openmeteo.weather_api(url, params=params)

    response = responses[0]
    daily = response.Daily()
    precipitation_sum = daily.Variables(0).ValuesAsNumpy()

    daily_rain = {
        "date": pd.date_range(
            start=pd.to_datetime(daily.Time(), unit="s"),
            end=pd.to_datetime(daily.TimeEnd(), unit="s"),
            freq=pd.Timedelta(seconds=daily.Interval()),
            inclusive="left",
        )
    }
    daily_rain["rain"] = precipitation_sum

    daily_rain = pd.DataFrame(data=daily_rain)
    daily_rain.date = pd.to_datetime(daily_rain.date)
    daily_rain = daily_rain.set_index("date")

    # on sélectionne les mois de daily_rain qui sont les mois dans la tmy de pvlib
    df = daily_tmy.join(daily_rain)  # c'est là qu'on garde que les mois d'intérêt
    df.index = df.index.map(lambda t: t.replace(year=1990))

    return df


def get_open_meteo(latitude: float, longitude: float) -> 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": "2019-01-01",
        "end_date": "2023-12-31",
        "daily": ["temperature_2m_max", "temperature_2m_min", "temperature_2m_mean", "precipitation_sum"]
    }
    responses = openmeteo.weather_api(url, params=params)
    response = responses[0]

    # 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_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"
    )}
    daily_data["temperature_2m_max"] = daily_temperature_2m_max
    daily_data["temperature_2m_min"] = daily_temperature_2m_min
    daily_data["temperature_2m_mean"] = daily_temperature_2m_mean
    daily_data["precipitation_sum"] = daily_precipitation_sum

    daily_dataframe = pd.DataFrame(data = daily_data)
    daily_dataframe.date = pd.to_datetime(daily_dataframe.date)
    #daily_dataframe = daily_dataframe.set_index("date")
    daily_dataframe['year'] = daily_dataframe['date'].dt.year
    daily_dataframe['month'] = daily_dataframe['date'].dt.month

    return daily_dataframe

def climatic_indicators(daily_dataframe):

    nbre_gel_tardif = daily_dataframe.loc[(daily_dataframe['month']>=3)&(daily_dataframe['month']<=5)&(daily_dataframe['temperature_2m_min']<=0)].groupby('year').count()['date'].mean()
    nbre_temp_extreme = daily_dataframe.loc[(daily_dataframe['temperature_2m_max']>=35)].groupby('year').count()['date'].mean()
    prec_month = daily_dataframe[(daily_dataframe['month']>=6)&(daily_dataframe['month']<=9)].groupby(['year','month'])['precipitation_sum'].sum().reset_index()
    prec_moyenne = prec_month['precipitation_sum'].mean()

    return nbre_gel_tardif,nbre_temp_extreme, prec_moyenne