How to use the pvlib.irradiance.extraradiation function in pvlib

The DataFrame contains the following new columns: poa_global,
            poa_diffuse, poa_direct

        References
        ----------
        .. [5] `pvlib globalinplane `_.
        .. [6] `pvlib atmosphere `_.

        See Also
        --------
        solarposition_hourly_mean, solarposition, angle_of_incidenc
        """
        # Determine the extraterrestrial radiation
        data['dni_extra'] = pvlib.irradiance.extraradiation(
            datetime_or_doy=data.index.dayofyear)

        # Determine the relative air mass
        data['airmass'] = pvlib.atmosphere.relativeairmass(data['zenith'])

        # Determine direct normal irradiation
        data['dni'] = (data['dirhi']) / np.sin(np.radians(90 - data['zenith']))

        # what for??
        data['dni'][data['zenith'] > 88] = data['dirhi']

        # Determine the sky diffuse irradiation in plane
        # with model of Perez (modell switch would be good)
        data['poa_sky_diffuse'] = pvlib.irradiance.perez(
            surface_tilt=self.powerplant.tilt,
            surface_azimuth=self.powerplant.azimuth,

"""
    Function used to calculate the luminance and the view factor terms from the
    Perez diffuse light transposition model, as implemented in the
    ``pvlib-python`` library.

    :param df_inputs: class:`pandas.DataFrame` with following columns:
        ['solar_zenith', 'solar_azimuth', 'array_tilt', 'array_azimuth', 'dhi',
        'dni']. Units are: ['deg', 'deg', 'deg', 'deg', 'W/m2', 'W/m2']
    :return: class:`pandas.DataFrame` with the following columns:
        ['solar_zenith', 'solar_azimuth', 'array_tilt', 'array_azimuth', 'dhi',
        'dni', 'vf_horizon', 'vf_circumsolar', 'vf_isotropic',
        'luminance_horizon', 'luminance_circumsolar', 'luminance_isotropic',
        'poa_isotropic', 'poa_circumsolar', 'poa_horizon', 'poa_total_diffuse']
    """

    dni_et = irradiance.extraradiation(df_inputs.index.dayofyear)
    am = atmosphere.relativeairmass(df_inputs.solar_zenith)

    # Need to treat the case when the sun is hitting the back surface of pvrow
    aoi_proj = aoi_projection(df_inputs.array_tilt, df_inputs.array_azimuth,
                              df_inputs.solar_zenith, df_inputs.solar_azimuth)
    sun_hitting_back_surface = ((aoi_proj < 0) &
                                (df_inputs.solar_zenith <= 90))
    df_inputs_back_surface = df_inputs.loc[sun_hitting_back_surface]
    # Reverse the surface normal to switch to back-surface circumsolar calc
    df_inputs_back_surface.loc[:, 'array_azimuth'] -= 180.
    df_inputs_back_surface.loc[:, 'array_azimuth'] = np.mod(
        df_inputs_back_surface.loc[:, 'array_azimuth'], 360.
    )
    df_inputs_back_surface.loc[:, 'array_tilt'] = (
        180. - df_inputs_back_surface.array_tilt)

# Compute insolation
    poa_insolation = pecos.pv.insolation(poa, tfilter=pm.tfilter)
    poa_insolation = poa_insolation.values[0]
    
    # Compute performance ratio
    PR = pecos.pv.performance_ratio(energy, poa_insolation, P_ref)
    
    # Compute performance index
    predicted_energy = pecos.pv.energy(modeled_dcpower, tfilter=pm.tfilter)
    predicted_energy = predicted_energy.values[0]
    PI = pecos.pv.performance_index(energy, predicted_energy)
    
    # Compute clearness index
    dni_insolation = pecos.pv.insolation(dni, tfilter=pm.tfilter)
    dni_insolation = dni_insolation.values[0]
    ea = pvlib.irradiance.extraradiation(index.dayofyear)
    ea = pd.Series(index=index, data=ea)
    ea_insolation = pecos.pv.insolation(ea, tfilter=pm.tfilter)
    ea_insolation = ea_insolation.values[0]
    Kt = pecos.pv.clearness_index(dni_insolation, ea_insolation)
    
    # Compute energy yield
    energy_yield = pecos.pv.energy_yield(energy, P_ref)
    
    # Collect metrics for reporting
    metrics = {'Performance Ratio': PR, 
               'Performance Index': PI,
               'Clearness Index': Kt,
               'Total Energy (kWh)': energy/3600/1000, # convert Ws to kWh
               'POA Insolation (kWh/m2)': poa_insolation/3600/1000, # convert Ws to kWh
               'Energy Yield (kWh/kWp)': energy_yield/3600} # convert s to h

df_inputs : `pandas.DataFrame`
        Dataframe with the following columns:
        ['solar_zenith', 'solar_azimuth', 'surface_tilt', 'surface_azimuth',
        'dhi', 'dni', 'vf_horizon', 'vf_circumsolar', 'vf_isotropic',
        'luminance_horizon', 'luminance_circumsolar', 'luminance_isotropic',
        'poa_isotropic', 'poa_circumsolar', 'poa_horizon', 'poa_total_diffuse']

    """
    # Create a dataframe to help filtering on all arrays
    df_inputs = pd.DataFrame(
        {'surface_tilt': surface_tilt, 'surface_azimuth': surface_azimuth,
         'solar_zenith': solar_zenith, 'solar_azimuth': solar_azimuth,
         'dni': dni, 'dhi': dhi},
        index=pd.DatetimeIndex(timestamps))

    dni_et = irradiance.extraradiation(df_inputs.index.dayofyear)
    am = atmosphere.relativeairmass(df_inputs.solar_zenith)

    # Need to treat the case when the sun is hitting the back surface of pvrow
    aoi_proj = aoi_projection(
        df_inputs.surface_tilt, df_inputs.surface_azimuth,
        df_inputs.solar_zenith, df_inputs.solar_azimuth)
    sun_hitting_back_surface = ((aoi_proj < 0) &
                                (df_inputs.solar_zenith <= 90))
    df_inputs_back_surface = df_inputs.loc[sun_hitting_back_surface].copy()
    # Reverse the surface normal to switch to back-surface circumsolar calc
    df_inputs_back_surface.loc[:, 'surface_azimuth'] = (
        df_inputs_back_surface.loc[:, 'surface_azimuth'] - 180.)
    df_inputs_back_surface.loc[:, 'surface_azimuth'] = np.mod(
        df_inputs_back_surface.loc[:, 'surface_azimuth'], 360.
    )
    df_inputs_back_surface.loc[:, 'surface_tilt'] = (

# parallelism not needed for this example
graph = Graph(parallel=False)

graph.add_node(
    unpack_df(pvlib.solarposition.get_solarposition),
    inputs=["index", "latitude", "longitude"],
    outputs=[
        "apparent_elevation",
        "apparent_zenith",
        "azimuth",
        "elevation",
        "equation_of_time",
        "zenith",
    ],
)
graph.add_node(pvlib.irradiance.extraradiation, inputs=["index"], outputs=["dni_extra"])
graph.add_node(
    pvlib.atmosphere.relativeairmass, inputs=["apparent_zenith"], outputs=["airmass"]
)
graph.add_node(
    pvlib.irradiance.haydavies,
    inputs=[
        "surface_tilt",
        "surface_azimuth",
        "DHI",
        "DNI",
        "dni_extra",
        "apparent_zenith",
        "azimuth",
    ],
    outputs=["poa_sky_diffuse"],
)