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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 read_lamination_parameters(thickness, laminaprop, rho, xiA1, xiA2, xiA3, xiA4, xiB1, xiB2, xiB3, xiB4, xiD1, xiD2, xiD3, xiD4, xiE1, xiE2, xiE3, xiE4):
 r"""Calculates a laminate based on the lamination parameters. The lamination parameters: `\xi_{A1} \cdots \xi_{A4}`, `\xi_{B1} \cdots \xi_{B4}`, `\xi_{C1} \cdots \xi_{C4}`, `\xi_{D1} \cdots \xi_{D4}`, `\xi_{E1} \cdots \xi_{E4}` are used to calculate the laminate constitutive matrix. Parameters thickness : float The total thickness of the laminate laminaprop : tuple The laminaprop tuple used to define the laminate material. rho : float Material density. xiA1 to xiD4 : float The 16 lamination parameters used to define the laminate. Returns ------- lam : Laminate laminate with the ABD and ABDE matrices already calculated """

    lam = Laminate()
    lam.h = thickness
    lam.matobj = read_laminaprop(laminaprop, rho)
    lam.xiA = np.array([1, xiA1, xiA2, xiA3, xiA4], dtype=np.float64)
    lam.xiB = np.array([0, xiB1, xiB2, xiB3, xiB4], dtype=np.float64)
    lam.xiD = np.array([1, xiD1, xiD2, xiD3, xiD4], dtype=np.float64)
    lam.xiE = np.array([1, xiE1, xiE2, xiE3, xiE4], dtype=np.float64)

    lam.calc_ABDE_from_lamination_parameters()
    return lam