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 test_question_reviewers_not_needed_if_num_suggestions_eqs_max(self):
        stats = suggestion_services.get_community_contribution_stats()
        stats.question_suggestion_count = 2
        stats.question_reviewer_count = 2
        config_services.set_property(
            'committer_id', 'max_number_of_suggestions_per_reviewer', 1)

        reviewers_are_needed = stats.are_question_reviewers_needed()

        self.assertFalse(reviewers_are_needed)