def test_view_mixin_copies_data(adata, array_type: type, attr):
    N = 100
    adata = ad.AnnData(
        obs=pd.DataFrame(index=np.arange(N)), var=pd.DataFrame(index=np.arange(N))
    )

    X = array_type(sparse.eye(N, N).multiply(np.arange(1, N + 1)))
    if attr == "X":
        adata.X = X
    else:
        getattr(adata, attr)["arr"] = X

    view = adata[:50]

    if attr == "X":
        arr_view = view.X
    else:
        arr_view = getattr(view, attr)["arr"]

    arr_view_copy = arr_view.copy()

    if sparse.issparse(X):
        assert not np.shares_memory(arr_view.indices, arr_view_copy.indices)
        assert not np.shares_memory(arr_view.indptr, arr_view_copy.indptr)
        assert not np.shares_memory(arr_view.data, arr_view_copy.data)

        arr_view_copy.data[0] = -5
        assert not np.array_equal(arr_view_copy.data, arr_view.data)
    else:
        assert not np.shares_memory(arr_view, arr_view_copy)

        arr_view_copy[0, 0] = -5
        assert not np.array_equal(arr_view_copy, arr_view)
def test_deviation():
    n1, n2 = symbols('n1, n2')
    r1 = Ray3D(Point3D(-1, -1, 1), Point3D(0, 0, 0))
    n = Matrix([0, 0, 1])
    i = Matrix([-1, -1, -1])
    normal_ray = Ray3D(Point3D(0, 0, 0), Point3D(0, 0, 1))
    P = Plane(Point3D(0, 0, 0), normal_vector=[0, 0, 1])
    assert deviation(r1, 1, 1, normal=n) == 0
    assert deviation(r1, 1, 1, plane=P) == 0
    assert deviation(r1, 1, 1.1, plane=P).evalf(3) + 0.119 < 1e-3
    assert deviation(i, 1, 1.1, normal=normal_ray).evalf(3) + 0.119 < 1e-3
    assert deviation(r1, 1.33, 1, plane=P) is None  # TIR
    assert deviation(r1, 1, 1, normal=[0, 0, 1]) == 0
    assert deviation([-1, -1, -1], 1, 1, normal=[0, 0, 1]) == 0
    assert ae(deviation(0.5, 1, 2), -0.25793, 5)
    assert ae(deviation(0.5, 2, 1), 0.78293, 5)