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 data():
    metric = request.args.get('metric', None)
    try:
        raw_series = REDIS_CONN.get(metric)
        if not raw_series:
            resp = json.dumps({'results': 'Error: No metric by that name'})
            return resp, 404
        else:
            unpacker = Unpacker(use_list = False)
            unpacker.feed(raw_series)
            timeseries = [item[:2] for item in unpacker]
            resp = json.dumps({'results': timeseries})
            return resp, 200
    except Exception as e:
        error = "Error: " + e
        resp = json.dumps({'results': error})
        return resp, 500