def get_array(radiance_data, output_rows, output_columns):
    """Extracts radiance and cloud mask data into a
    correctly-shaped array for a study area.

    Default values meant for specific use with the
    Penn State campus data.

    Parameters
    ----------
    radiance_data : pandas dataframe
        Dataframe containing the radiance data
        (radiance values or cloud mask values).

    output_rows : int, optional
        Number of rows in the output array (used
        in the reshaping). Defaults to 18.

    output_columns : int, optional
        Number of columns in the output array (used
        in the reshaping). Defaults to 40.

    Returns
    -------
    radiance_array : numpy array
        Numpy array containg the formatted radiance data
        (radiance values or cloud mask values).

    Example
    ------
        >>> # Define path to radiance data
        >>> psu_radiance_path = os.path.join(
        ...     radiance_directory,
        ...     "PSU_Radiance_RavelOrderF.csv")
        >>> # Read radiance values to dataframe
        >>> psu_radiance_df = pd.read_csv(psu_radiance_path)
        >>> # Create array from dataframe
        >>> psu_radiance_arr = get_array(psu_radiance_df, 18, 40)
        >>> # Display type
        numpy.ma.core.MaskedArray
        >>> Display array shape
        >>> psu_radiance_arr.shape
        (18, 40)
    """
    # Convert dataframe to numpy array, reshape array, and transpose array
    # Rows and columns must be flipped in .reshape due to how the data
    #  is read into the dataframe
    radiance_array = radiance_data.to_numpy().reshape((output_columns, output_rows)).transpose()

    # Return correctly-shaped array
    return radiance_array