How to use the meshplex.read function in meshplex

def test_jacobian(filename, control_values):
    filename = this_dir / "meshes" / filename
    mu = 1.0e-2

    mesh = meshplex.read(filename)
    m2 = meshio.read(filename)

    psi = m2.point_data["psi"][:, 0] + 1j * m2.point_data["psi"][:, 1]

    V = -1.0
    g = 1.0
    keo = pyfvm.get_fvm_matrix(mesh, edge_kernels=[Energy(mu)])

    def jacobian(psi):
        def _apply_jacobian(phi):
            cv = mesh.control_volumes
            y = keo * phi / cv + alpha * phi + gPsi0Squared * phi.conj()
            return y

        alpha = V + g * 2.0 * (psi.real ** 2 + psi.imag ** 2)
        gPsi0Squared = g * psi ** 2

def test_keo(filename, control_values):
    mu = 1.0e-2

    # read the mesh
    mesh = meshplex.read(this_dir / "meshes" / filename)

    keo = pyfvm.get_fvm_matrix(mesh, edge_kernels=[Energy(mu)])

    tol = 1.0e-13

    # Check that the matrix is Hermitian.
    KK = keo - keo.H
    assert abs(KK.sum()) < tol

    # Check the matrix sum.
    assert abs(control_values[0] - keo.sum()) < tol

    # Check the 1-norm of the matrix |Re(K)| + |Im(K)|.
    # This equals the 1-norm of the matrix defined by the block
    # structure
    #   Re(K) -Im(K)

def test_f(filename, control_values):
    mesh = meshplex.read(this_dir / "meshes" / filename)

    mu = 1.0e-2
    V = -1.0
    g = 1.0

    keo = pyfvm.get_fvm_matrix(mesh, edge_kernels=[Energy(mu)])

    # compute the Ginzburg-Landau residual
    m2 = meshio.read(this_dir / "meshes" / filename)
    psi = m2.point_data["psi"][:, 0] + 1j * m2.point_data["psi"][:, 1]
    cv = mesh.control_volumes
    # One divides by the control volumes here. No idea why this has been done in pynosh.
    # Perhaps to make sure that even the small control volumes have a significant
    # contribution to the residual?
    r = keo * psi / cv + psi * (V + g * abs(psi) ** 2)