def test_negative_scalar_index(adata, index: int, obs: bool):
    pos_index = index + (adata.n_obs if obs else adata.n_vars)

    if obs:
        adata_pos_subset = adata[pos_index]
        adata_neg_subset = adata[index]
    else:
        adata_pos_subset = adata[:, pos_index]
        adata_neg_subset = adata[:, index]

    np.testing.assert_array_equal(
        adata_pos_subset.obs_names, adata_neg_subset.obs_names
    )
    np.testing.assert_array_equal(
        adata_pos_subset.var_names, adata_neg_subset.var_names
    )
def __init__(self, cost_withGradients):
        super(CostModel, self).__init__()

        self.cost_type = cost_withGradients

        # --- Set-up evaluation cost
        if self.cost_type is None:
            self.cost_withGradients = constant_cost_withGradients
            self.cost_type = 'Constant cost'

        elif self.cost_type == 'evaluation_time':
            self.cost_model = GPModel()
            self.cost_withGradients = self._cost_gp_withGradients
            self.num_updates = 0
        else:
            self.cost_withGradients = cost_withGradients
            self.cost_type = 'User defined cost'