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 test_impl(nsyms):
            max_num_days = 100
            all_res = 0.0
            for i in sdc.prange(nsyms):
                s_open = 20 * np.ones(max_num_days)
                s_low = 28 * np.ones(max_num_days)
                s_close = 19 * np.ones(max_num_days)
                df = pd.DataFrame({'Open': s_open, 'Low': s_low, 'Close': s_close})
                df['Stdev'] = df['Close'].rolling(window=90).std()
                df['Moving Average'] = df['Close'].rolling(window=20).mean()
                df['Criteria1'] = (df['Open'] - df['Low'].shift(1)) < -df['Stdev']
                df['Criteria2'] = df['Open'] > df['Moving Average']
                df['BUY'] = df['Criteria1'] & df['Criteria2']
                df['Pct Change'] = (df['Close'] - df['Open']) / df['Open']
                df['Rets'] = df['Pct Change'][df['BUY']]
                all_res += df['Rets'].mean()
            return all_res