Source code for tseda.dataloader.coffee_prices_data_loader

"""Data loader for the ICO coffee prices dataset."""

from .local_dataloader import LocalDataLoader
import pandas as pd



class CoffeePricesDataLoader(LocalDataLoader):
    """Load and expose the coffee prices CSV as a named ``signal`` series."""



    def __init__(self, file_path: str = "data/coffee_prices.csv"):
        """Configure the loader with the default coffee prices CSV path.

        Args:
            file_path: Path to the coffee prices CSV file.
        """
        super().__init__(file_path)




    def load_coffee_prices(self) -> pd.DataFrame:
        """Load coffee prices and normalize expected column names.

        Returns:
            DataFrame with columns ``date`` and ``signal``. Returns an empty
            DataFrame if source data cannot be loaded.
        """
        data = self.load_data()
        data.columns = ["date", "signal"]
        data.date = pd.to_datetime(data.date)


        if not data.empty:
            # Additional processing specific to coffee prices can be added here
            return data
        else:
            print("No data loaded.")
            return pd.DataFrame()




    def get_series(self) -> pd.Series:
        """Extract the signal series from the normalized coffee dataset.

        Returns:
            ``signal`` series indexed by ``date``. Returns an empty float series
            when no data is available.
        """
        data = self.load_coffee_prices()
        data.index = data.date
        if not data.empty:
            return data["signal"]
        else:
            print("No data available to extract series.")
            return pd.Series(dtype=float)