import pandas as pd
from prophet import Prophet
from prophet.diagnostics import cross_validation, performance_metrics

class GeoMagModel:
    """
    A class for geomagnetic data forecasting using Prophet.
    """

    def __init__(self, changepoint_prior_scale=0.1, weekly_seasonality=True):
        """
        Initialize the GeoMagModel with Prophet configuration.
        
        Args:
            changepoint_prior_scale (float): Flexibility of changepoint detection.
            weekly_seasonality (bool): Whether to include weekly seasonality.
        """
        self.changepoint_prior_scale = changepoint_prior_scale
        self.weekly_seasonality = weekly_seasonality
        self.model = None

    @staticmethod
    def prepare_for_prophet(df):
        """
        Prepare the DataFrame for Prophet by renaming columns as required.
        
        Args:
            df (pd.DataFrame): The cleaned DataFrame with 'timestamp' and 'Dst' columns.
        
        Returns:
            pd.DataFrame: A DataFrame with 'ds' (timestamp) and 'y' (Dst) columns for Prophet.
        """
        return df.rename(columns={'timestamp': 'ds', 'Dst': 'y'})

    def train(self, df):
        """
        Train the Prophet model on the given DataFrame.
        
        Args:
            df (pd.DataFrame): The DataFrame prepared for Prophet.
        """
        df = self.prepare_for_prophet(df)
        self.model = Prophet(interval_width=0.70, changepoint_prior_scale=self.changepoint_prior_scale)

        if self.weekly_seasonality:
            self.model.add_seasonality(name='weekly', period=7, fourier_order=5, prior_scale=10)

        self.model.fit(df)

    def forecast(self, periods=1, freq='h'):
        """
        Make a future forecast with the trained model.
        
        Args:
            periods (int): The number of future periods to forecast. Defaults to 1.
            freq (str): The frequency of the forecast ('h' for hours). Defaults to 'h'.
        
        Returns:
            pd.DataFrame: The forecast DataFrame with 'ds', 'yhat', 'yhat_lower', and 'yhat_upper' columns.
        """
        if self.model is None:
            raise ValueError("Model has not been trained. Call train() before forecast().")
        
        future_dates = self.model.make_future_dataframe(periods=periods, freq=freq)
        forecast = self.model.predict(future_dates)
        return forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]

    def cross_validate(self, initial='36 hours', period='12 hours', horizon='1 hours'):
        """
        Perform cross-validation on the trained Prophet model.
        
        Args:
            initial (str): Initial training period for cross-validation.
            period (str): Frequency of making predictions.
            horizon (str): Forecast horizon for each prediction.
        
        Returns:
            pd.DataFrame: Cross-validation results including metrics.
        """
        if self.model is None:
            raise ValueError("Model has not been trained. Call train() before cross_validate().")
        
        return cross_validation(self.model, initial=initial, period=period, horizon=horizon)