def test_view_of_view_modification():
    adata = ad.AnnData(np.zeros((10, 10)))
    adata[0, :][:, 5:].X = np.ones(5)
    assert np.all(adata.X[0, 5:] == np.ones(5))
    adata[[1, 2], :][:, [1, 2]].X = np.ones((2, 2))
    assert np.all(adata.X[1:3, 1:3] == np.ones((2, 2)))

    adata.X = sparse.csr_matrix(adata.X)
    adata[0, :][:, 5:].X = np.ones(5) * 2
    assert np.all(asarray(adata.X)[0, 5:] == np.ones(5) * 2)
    adata[[1, 2], :][:, [1, 2]].X = np.ones((2, 2)) * 2
    assert np.all(asarray(adata.X)[1:3, 1:3] == np.ones((2, 2)) * 2)
def _ready(op, sess, msg):
  """Checks if the model is ready or not, as determined by op.

  Args:
    op: An op, either _ready_op or _ready_for_local_init_op, which defines the
      readiness of the model.
    sess: A `Session`.
    msg: A message to log to warning if not ready

  Returns:
    A tuple (is_ready, msg), where is_ready is True if ready and False
    otherwise, and msg is `None` if the model is ready, a `String` with the
    reason why it is not ready otherwise.
  """
  if op is None:
    return True, None
  else:
    try:
      ready_value = sess.run(op)
      # The model is considered ready if ready_op returns an empty 1-D tensor.
      # Also compare to `None` and dtype being int32 for backward
      # compatibility.
      if (ready_value is None or ready_value.dtype == np.int32 or
          ready_value.size == 0):
        return True, None
      else:
        # TODO(sherrym): If a custom ready_op returns other types of tensor,
        # or strings other than variable names, this message could be
        # confusing.
        non_initialized_varnames = ", ".join(
            [i.decode("utf-8") for i in ready_value])
        return False, "Variables not initialized: " + non_initialized_varnames
    except errors.FailedPreconditionError as e:
      if "uninitialized" not in str(e):
        logging.warning("%s : error [%s]", msg, str(e))
        raise e
      return False, str(e)