How to use the pvlib.pvsystem.sapm function in pvlib

celltemp = pvlib.pvsystem.sapm_celltemp(poa, wind, temperature)

    # Compute absolute airmass
    airmass_relative  = pvlib.atmosphere.get_relative_airmass(solarposition['zenith'])
    airmass_absolute = pvlib.atmosphere.get_absolute_airmass(airmass_relative)
    
    # Compute aoi
    aoi = pvlib.irradiance.aoi(location['Latitude'], 180, solarposition['zenith'], 
                               solarposition['azimuth'])
    
    # Compute effective irradiance
    Ee = pvlib.pvsystem.sapm_effective_irradiance(poa, poa_diffuse, airmass_absolute, 
                                                  aoi, sapm_parameters)
    
    # Run SAPM
    sapm_model = pvlib.pvsystem.sapm(Ee, celltemp['temp_cell'], sapm_parameters)
    
    # Compute the relative error between observed and predicted DC Power.  
    # Add the composite signal and run a range test
    modeled_dcpower = sapm_model['p_mp']*sapm_parameters['Ns']*sapm_parameters['Np']
    dc_power_relative_error = np.abs(dcpower - modeled_dcpower)/dcpower
    pm.add_dataframe(dc_power_relative_error.to_frame('DC Power Relative Error'))
    pm.check_range([0,0.1], 'DC Power Relative Error') 
    
    # Compute normalized efficiency, add the composite signal, and run a range test
    P_ref = sapm_parameters['Vmpo']*sapm_parameters['Impo']* \
                sapm_parameters['Ns']*sapm_parameters['Np'] # DC Power rating
    NE = pecos.pv.normalized_efficiency(dcpower, poa, P_ref)
    pm.add_dataframe(NE.to_frame('Normalized Efficiency'))
    pm.check_range([0.8, 1.2], 'Normalized Efficiency') 
    
    # Compute energy

data = pd.concat([data, pvlib.pvsystem.sapm_celltemp(
            poa_global=data['poa_global'],
            wind_speed=data['v_wind'],
            temp_air=data['temp_air_celsius'],
            model='Open_rack_cell_polymerback')], axis=1, join='inner')

        # Retrieve the module data object
        module_data = self.fetch_module_data(**kwargs)

        data['effective_irradiance'] = pvlib.pvsystem.sapm_effective_irradiance(
            poa_direct=data['poa_direct'], poa_diffuse=data['poa_diffuse'],
            airmass_absolute=data['airmass'], aoi=data['aoi'],
            module=module_data)

        # Apply the Sandia PV Array Performance Model (SAPM) to get a
        data = pd.concat([data, pvlib.pvsystem.sapm(
            effective_irradiance=data['effective_irradiance'],
            temp_cell=data['temp_cell'],
            module=module_data)], axis=1, join='inner')

        # Set NaN values to zero
        data['p_mp'][
            pd.isnull(data['p_mp'])] = 0
        return data

def pvmodule_detail(request, pvmodule_id):
    pvmod = get_object_or_404(PVModule, pk=pvmodule_id)
    fieldnames = PVModule._meta.get_fields()
    pvmod_dict = {k.name: getattr(pvmod, k.name) for k in fieldnames}
    for k in ['IXO', 'IXXO', 'C4', 'C5', 'C6', 'C7']:
        if pvmod_dict[k] is None:
            pvmod_dict[k] = 0.
    celltemps = np.linspace(0, 100, 5)
    effirrad, celltemp = np.meshgrid(np.linspace(0.1, 1, 10), celltemps)
    results = sapm(effirrad, celltemp, pvmod_dict)
    eff = results['p_mp'] / effirrad / pvmod.Area * 100 / 1000
    fig = figure(
        x_axis_label='effective irradiance, Ee [suns]',
        y_axis_label='efficiency [%]',
        title=pvmod.Name,
        plot_width=800, plot_height=600, sizing_mode='scale_width'
    )
    r = fig.multi_line(
        effirrad.tolist(), eff.tolist(), color=cmap, line_width=4)
    legend = Legend(items=[
        LegendItem(label='{:d} [C]'.format(int(ct)), renderers=[r], index=n)
        for n, ct in enumerate(celltemps)])
    fig.add_layout(legend)
    plot_script, plot_div = components(fig)
    return render(
        request, 'pvmodule_detail.html', {