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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:

def snlinverter(v_dc, p_dc, inverter):
    r'''
    Converts DC power and voltage to AC power using Sandia's
    Grid-Connected PV Inverter model.

    Determines the AC power output of an inverter given the DC voltage,
    DC power, and appropriate Sandia Grid-Connected Photovoltaic
    Inverter Model parameters. The output, ac_power, is clipped at the
    maximum power output, and gives a negative power during low-input
    power conditions, but does NOT account for maximum power point
    tracking voltage windows nor maximum current or voltage limits on
    the inverter.

    Parameters
    ----------
    v_dc : numeric
        DC voltages, in volts, which are provided as input to the
        inverter. Vdc must be >= 0.

    p_dc : numeric
        A scalar or DataFrame of DC powers, in watts, which are provided
        as input to the inverter. Pdc must be >= 0.

    inverter : dict-like
        A dict-like object defining the inverter to be used, giving the
        inverter performance parameters according to the Sandia
        Grid-Connected Photovoltaic Inverter Model (SAND 2007-5036) [1].
        A set of inverter performance parameters are provided with
        pvlib, or may be generated from a System Advisor Model (SAM) [2]
        library using retrievesam. See Notes for required keys.

    Returns
    -------
    ac_power : numeric
        Modeled AC power output given the input DC voltage, Vdc, and
        input DC power, Pdc. When ac_power would be greater than Pac0,
        it is set to Pac0 to represent inverter "clipping". When
        ac_power would be less than Ps0 (startup power required), then
        ac_power is set to -1*abs(Pnt) to represent nightly power
        losses. ac_power is not adjusted for maximum power point
        tracking (MPPT) voltage windows or maximum current limits of the
        inverter.

    Notes
    -----

    Required inverter keys are:

    ======   ============================================================
    Column   Description
    ======   ============================================================
    Pac0     AC-power output from inverter based on input power
             and voltage (W)
    Pdc0     DC-power input to inverter, typically assumed to be equal
             to the PV array maximum power (W)
    Vdc0     DC-voltage level at which the AC-power rating is achieved
             at the reference operating condition (V)
    Ps0      DC-power required to start the inversion process, or
             self-consumption by inverter, strongly influences inverter
             efficiency at low power levels (W)
    C0       Parameter defining the curvature (parabolic) of the
             relationship between ac-power and dc-power at the reference
             operating condition, default value of zero gives a
             linear relationship (1/W)
    C1       Empirical coefficient allowing Pdco to vary linearly
             with dc-voltage input, default value is zero (1/V)
    C2       Empirical coefficient allowing Pso to vary linearly with
             dc-voltage input, default value is zero (1/V)
    C3       Empirical coefficient allowing Co to vary linearly with
             dc-voltage input, default value is zero (1/V)
    Pnt      AC-power consumed by inverter at night (night tare) to
             maintain circuitry required to sense PV array voltage (W)
    ======   ============================================================

    References
    ----------
    [1] SAND2007-5036, "Performance Model for Grid-Connected
    Photovoltaic Inverters by D. King, S. Gonzalez, G. Galbraith, W.
    Boyson

    [2] System Advisor Model web page. https://sam.nrel.gov.

    See also
    --------
    sapm
    singlediode
    '''

    Paco = inverter['Paco']
    Pdco = inverter['Pdco']
    Vdco = inverter['Vdco']
    Pso = inverter['Pso']
    C0 = inverter['C0']
    C1 = inverter['C1']
    C2 = inverter['C2']
    C3 = inverter['C3']
    Pnt = inverter['Pnt']

    A = Pdco * (1 + C1*(v_dc - Vdco))
    B = Pso * (1 + C2*(v_dc - Vdco))
    C = C0 * (1 + C3*(v_dc - Vdco))

    ac_power = (Paco/(A-B) - C*(A-B)) * (p_dc-B) + C*((p_dc-B)**2)
    ac_power = np.minimum(Paco, ac_power)
    ac_power = np.where(p_dc < Pso, -1.0 * abs(Pnt), ac_power)

    if isinstance(p_dc, pd.Series):
        ac_power = pd.Series(ac_power, index=p_dc.index)

    return ac_power