def test_not_set_subset_X(matrix_type, subset_func):
    adata = ad.AnnData(matrix_type(asarray(sparse.random(20, 20))))
    init_hash = joblib.hash(adata)
    orig_X_val = adata.X.copy()
    while True:
        subset_idx = slice_subset(adata.obs_names)
        if len(adata[subset_idx, :]) > 2:
            break
    subset = adata[subset_idx, :]

    subset = adata[:, subset_idx]

    internal_idx = _normalize_index(
        subset_func(np.arange(subset.X.shape[1])), subset.var_names
    )
    assert subset.is_view
    subset.X[:, internal_idx] = 1
    assert not subset.is_view
    assert not np.any(asarray(adata.X != orig_X_val))

    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