def test_set_obsm(adata):
    init_hash = joblib.hash(adata)

    dim0_size = np.random.randint(2, adata.shape[0] - 1)
    dim1_size = np.random.randint(1, 99)
    orig_obsm_val = adata.obsm["o"].copy()
    subset_idx = np.random.choice(adata.obs_names, dim0_size, replace=False)

    subset = adata[subset_idx, :]
    assert subset.is_view
    subset.obsm = dict(o=np.ones((dim0_size, dim1_size)))
    assert not subset.is_view
    assert np.all(orig_obsm_val == adata.obsm["o"])  # Checking for mutation
    assert np.all(subset.obsm["o"] == np.ones((dim0_size, dim1_size)))

    subset = adata[subset_idx, :]
    subset_hash = joblib.hash(subset)
    with pytest.raises(ValueError):
        subset.obsm = dict(o=np.ones((dim0_size + 1, dim1_size)))
    with pytest.raises(ValueError):
        subset.varm = dict(o=np.ones((dim0_size - 1, dim1_size)))
    assert subset_hash == joblib.hash(subset)

    # Only modification have been made to a view
    assert init_hash == joblib.hash(adata)
def aggregate(cls, dataset, dimensions, function, **kwargs):
        data = dataset.data
        cols = [d.name for d in dataset.kdims if d in dimensions]
        vdims = dataset.dimensions('value', label='name')
        reindexed = data[cols+vdims]
        agg = function.__name__
        if len(dimensions):
            agg_map = {'amin': 'min', 'amax': 'max'}
            agg = agg_map.get(agg, agg)
            grouped = reindexed.groupby(cols, sort=False)
            if not hasattr(grouped, agg):
                raise ValueError('%s aggregation is not supported on cudf DataFrame.' % agg)
            df = getattr(grouped, agg)().reset_index()
        else:
            agg_map = {'amin': 'min', 'amax': 'max', 'size': 'count'}
            agg = agg_map.get(agg, agg)
            if not hasattr(reindexed, agg):
                raise ValueError('%s aggregation is not supported on cudf DataFrame.' % agg)
            agg = getattr(reindexed, agg)()
            data = dict(((col, [v]) for col, v in zip(agg.index.values_host, agg.to_array())))
            df = util.pd.DataFrame(data, columns=list(agg.index.values_host))

        dropped = []
        for vd in vdims:
            if vd not in df.columns:
                dropped.append(vd)
        return df, dropped