def METHOD_NAME(self):
    self.coords = np.array(
        [
            12.83,
            2.56,
            2.18,
            12.09,
            2.87,
            2.74,
            00.25,
            3.32,
            1.68,
            3.36,
            3.00,
            1.81,
            3.51,
            2.51,
            2.60,
            4.27,
            3.22,
            1.56,
        ]
    )
    self.atype = [0, 1, 1, 0, 1, 1]
    self.box = np.array([19.0, 0.0, 0.0, 0.0, 13.0, 0.0, 0.0, 0.0, 13.0])
    self.expected_e = np.array(
        [
            -9.255934839310273787e01,
            -1.863253376736990106e02,
            -1.857237299341402945e02,
            -9.279308539717486326e01,
            -1.863708105823244239e02,
            -1.863635196514972563e02,
        ]
    )
    self.expected_f = np.array(
        [
            -2.161037360255332107e00,
            9.052994347015581589e-01,
            1.635379623977007979e00,
            2.161037360255332107e00,
            -9.052994347015581589e-01,
            -1.635379623977007979e00,
            -1.167128117249453811e-02,
            1.371975700096064992e-03,
            -1.575265180249604477e-03,
            6.226508593971802341e-01,
            -1.816734122009256991e-01,
            3.561766019664774907e-01,
            -1.406075393906316626e-02,
            3.789140061530929526e-01,
            -6.018777878642909140e-01,
            -5.969188242856223736e-01,
            -1.986125696522633155e-01,
            2.472764510780630642e-01,
        ]
    )
    self.expected_v = np.array(
        [
            -7.042445481792056761e-01,
            2.950213647777754078e-01,
            5.329418202437231633e-01,
            2.950213647777752968e-01,
            -1.235900311906896754e-01,
            -2.232594111831812944e-01,
            5.329418202437232743e-01,
            -2.232594111831813499e-01,
            -4.033073234276823849e-01,
            -8.949230984097404917e-01,
            3.749002169013777030e-01,
            6.772391014992630298e-01,
            3.749002169013777586e-01,
            -1.570527935667933583e-01,
            -2.837082722496912512e-01,
            6.772391014992631408e-01,
            -2.837082722496912512e-01,
            -5.125052659994422388e-01,
            4.858210330291591605e-02,
            -6.902596153269104431e-03,
            6.682612642430500391e-03,
            -5.612247004554610057e-03,
            9.767795567660207592e-04,
            -9.773758942738038254e-04,
            5.638322117219018645e-03,
            -9.483806049779926932e-04,
            8.493873281881353637e-04,
            -2.941738570564985666e-01,
            -4.482529909499673171e-02,
            4.091569840186781021e-02,
            -4.509020615859140463e-02,
            -1.013919988807244071e-01,
            1.551440772665269030e-01,
            4.181857726606644232e-02,
            1.547200233064863484e-01,
            -2.398213304685777592e-01,
            -3.218625798524068354e-02,
            -1.012438450438508421e-02,
            1.271639330380921855e-02,
            3.072814938490859779e-03,
            -9.556241797915024372e-02,
            1.512251983492413077e-01,
            -8.277872384009607454e-03,
            1.505412040827929787e-01,
            -2.386150620881526407e-01,
            -2.312295470054945568e-01,
            -6.631490213524345034e-02,
            7.932427266386249398e-02,
            -8.053754366323923053e-02,
            -3.294595881137418747e-02,
            4.342495071150231922e-02,
            1.004599500126941436e-01,
            4.450400364869536163e-02,
            -5.951077548033092968e-02,
        ]
    )