def test_set_varm(adata):
    init_hash = joblib.hash(adata)

    dim0_size = np.random.randint(2, adata.shape[1] - 1)
    dim1_size = np.random.randint(1, 99)
    orig_varm_val = adata.varm["o"].copy()
    subset_idx = np.random.choice(adata.var_names, dim0_size, replace=False)

    subset = adata[:, subset_idx]
    assert subset.is_view
    subset.varm = dict(o=np.ones((dim0_size, dim1_size)))
    assert not subset.is_view
    assert np.all(orig_varm_val == adata.varm["o"])  # Checking for mutation
    assert np.all(subset.varm["o"] == np.ones((dim0_size, dim1_size)))

    subset = adata[:, subset_idx]
    subset_hash = joblib.hash(subset)
    with pytest.raises(ValueError):
        subset.varm = dict(o=np.ones((dim0_size + 1, dim1_size)))
    with pytest.raises(ValueError):
        subset.varm = dict(o=np.ones((dim0_size - 1, dim1_size)))
    # subset should not be changed by failed setting
    assert subset_hash == joblib.hash(subset)
    assert init_hash == joblib.hash(adata)
def test_make_gym_concat(name, num_envs, state_shape, reward_scale):
    seed = 0
    frame_op = 'concat'  # used for image, or for concat vector
    frame_op_len = 4
    venv = make_gym_venv(name, num_envs, seed, frame_op=frame_op, frame_op_len=frame_op_len, reward_scale=reward_scale)
    venv.reset()
    for i in range(5):
        state, reward, done, info = venv.step([venv.action_space.sample()] * num_envs)

    assert isinstance(state, np.ndarray)
    stack_shape = (num_envs, frame_op_len * state_shape[0],) + state_shape[1:]
    assert state.shape == stack_shape
    assert isinstance(reward, np.ndarray)
    assert reward.shape == (num_envs,)
    assert isinstance(done, np.ndarray)
    assert done.shape == (num_envs,)
    assert len(info) == num_envs
    venv.close()