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 ldlq(bch):
    """creates the surface plots of a BCH reader object
    with a ld-lq identification"""
    beta = bch.ldq['beta']
    i1 = bch.ldq['i1']
    torque = bch.ldq['torque']
    ld = np.array(bch.ldq['ld'])
    lq = np.array(bch.ldq['lq'])
    psid = bch.ldq['psid']
    psiq = bch.ldq['psiq']

    rows = 3
    fig = plt.figure(figsize=(10, 4*rows))
    fig.suptitle('Ld-Lq Identification {}'.format(bch.filename), fontsize=16)
    fig.add_subplot(rows, 2, 1, projection='3d')
    i1beta_torque(i1, beta, torque)

    fig.add_subplot(rows, 2, 2, projection='3d')
    i1beta_psid(i1, beta, psid)

    fig.add_subplot(rows, 2, 3, projection='3d')
    i1beta_psiq(i1, beta, psiq)

    fig.add_subplot(rows, 2, 4, projection='3d')
    try:
        i1beta_psim(i1, beta, bch.ldq['psim'])
    except:
        i1beta_up(i1, beta, bch.ldq['up'])

    fig.add_subplot(rows, 2, 5, projection='3d')
    i1beta_ld(i1, beta, ld)

    fig.add_subplot(rows, 2, 6, projection='3d')
    i1beta_lq(i1, beta, lq)