def test_modify_view_component(matrix_type, mapping_name):
    adata = ad.AnnData(
        np.zeros((10, 10)),
        **{mapping_name: dict(m=matrix_type(asarray(sparse.random(10, 10))))},
    )
    init_hash = joblib.hash(adata)

    subset = adata[:5, :][:, :5]
    assert subset.is_view
    m = getattr(subset, mapping_name)["m"]
    m[0, 0] = 100
    assert not subset.is_view
    assert getattr(subset, mapping_name)["m"][0, 0] == 100

    assert init_hash == joblib.hash(adata)
def __calculate_initial_covariances(self, initial_clusters):
        covariances = []
        for initial_cluster in initial_clusters:
            if len(initial_cluster) > 1:
                cluster_sample = [self.__sample[index_point] for index_point in initial_cluster]
                covariances.append(numpy.cov(cluster_sample, rowvar=False))
            else:
                dimension = len(self.__sample[0])
                covariances.append(numpy.zeros((dimension, dimension)) + random.random() / 10.0)