def get_interpolated_data(df: pd.DataFrame, e_min=np.nan, e_max=np.nan, e_step=np.nan):
nbr_point = int((e_max - e_min) / e_step + <NUM_LIT:1>)<EOL>x_axis = np.linspace(e_min, e_max, nbr_point).round(<NUM_LIT:6>)<EOL>y_axis_function = interp1d(x=df['<STR_LIT>'], y=df['<STR_LIT>'], kind='<STR_LIT>')<EOL>y_axis = y_axis_function(x_axis)<EOL>return {'<STR_LIT>': x_axis, '<STR_LIT>': y_axis}<EOL>
return the interpolated x and y axis for the given x range [e_min, e_max] with step defined

    :param df: input data frame
    :type df: pandas.DataFrame
    :param e_min: left energy range in eV of new interpolated data
    :type e_min: float
    :param e_max: right energy range in eV of new interpolated data
    :type e_max: float
    :param e_step: energy step in eV for interpolation
    :type e_step: float

    :return: x_axis and y_axis of interpolated data over specified range
    :rtype: dict