def METHOD_NAME():  # pragma: no cover
    """
    Generate expected data with infinite numerical precision using SymPy.
    :return: dataframe of expected values
    """
    if symbols is NotImplemented:
        LOGGER.critical("SymPy is required to generate expected data.")
        raise ImportError("could not import sympy")
    il, io, rs, rsh, nnsvt, vd = symbols('il, io, rs, rsh, nnsvt, vd')
    a = sy_exp(vd / nnsvt)
    b = 1.0 / rsh
    i = il - io * (a - 1.0) - vd * b
    v = vd - i * rs
    c = io * a / nnsvt
    grad_i = - c - b  # di/dvd
    grad_v = 1.0 - grad_i * rs  # dv/dvd
    # dp/dv = d(iv)/dv = v * di/dv + i
    grad = grad_i / grad_v  # di/dv
    p = i * v
    grad_p = v * grad + i  # dp/dv
    grad2i = -c / nnsvt
    grad2v = -grad2i * rs
    grad2p = (
        grad_v * grad + v * (grad2i/grad_v - grad_i*grad2v/grad_v**2) + grad_i
    )
    # generate exact values
    data = dict(zip((il, io, rs, rsh, nnsvt), ARGS))
    vdtest = np.linspace(0, estimate_voc(IL, I0, NNSVTH), IVCURVE_NPTS)
    expected = []
    for test in vdtest:
        data[vd] = test
        test_data = {
            'i': np.float64(i.evalf(subs=data)),
            'v': np.float64(v.evalf(subs=data)),
            'p': np.float64(p.evalf(subs=data)),
            'grad_i': np.float64(grad_i.evalf(subs=data)),
            'grad_v': np.float64(grad_v.evalf(subs=data)),
            'grad': np.float64(grad.evalf(subs=data)),
            'grad_p': np.float64(grad_p.evalf(subs=data)),
            'grad2p': np.float64(grad2p.evalf(subs=data))
        }
        LOGGER.debug(test_data)
        expected.append(test_data)
    return pd.DataFrame(expected, index=vdtest)