def test_deepcopy_subset(adata, spmat: type):
    adata.obsp["arr"] = np.zeros((adata.n_obs, adata.n_obs))
    adata.obsp["spmat"] = spmat((adata.n_obs, adata.n_obs))

    adata = deepcopy(adata[:10].copy())

    assert isinstance(adata.obsp["arr"], np.ndarray)
    assert not isinstance(adata.obsp["arr"], ArrayView)
    np.testing.assert_array_equal(adata.obsp["arr"].shape, (10, 10))

    assert isinstance(adata.obsp["spmat"], spmat)
    assert not isinstance(
        adata.obsp["spmat"],
        SparseCSRView if spmat is sparse.csr_matrix else SparseCSCView,
    )
    np.testing.assert_array_equal(adata.obsp["spmat"].shape, (10, 10))
def update_cost_model(self, x, cost_x):
        """
        Updates the GP used to handle the cost.

        param x: input of the GP for the cost model.
        param x_cost: values of the time cost at the input locations.
        """

        if self.cost_type == 'evaluation_time':
            cost_evals = np.log(np.atleast_2d(np.asarray(cost_x)).T)

            if self.num_updates == 0:
                X_all = x
                costs_all = cost_evals
            else:
                X_all = np.vstack((self.cost_model.model.X,x))
                costs_all = np.vstack((self.cost_model.model.Y,cost_evals))

            self.num_updates += 1
            self.cost_model.updateModel(X_all, costs_all, None, None)