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 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