def test_set_var(adata, subset_func):
    init_hash = joblib.hash(adata)

    subset = adata[:, subset_func(adata.var_names)]

    new_var = pd.DataFrame(
        dict(a=np.ones(subset.n_vars), b=np.ones(subset.n_vars)),
        index=subset.var_names,
    )

    assert subset.is_view
    subset.var = new_var
    assert not subset.is_view
    assert np.all(subset.var == new_var)

    assert joblib.hash(adata) == init_hash
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)