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 dfs(self, num, target, pos, cur_str, cur_val, mul, ret):
        if pos >= len(num):
            if cur_val == target:
                ret.append(cur_str)
        else:
            for i in xrange(pos, len(num)):
                if i != pos and num[pos] == "0":
                    continue
                nxt_val = int(num[pos:i+1])

                if not cur_str:
                    self.dfs(num, target, i+1, "%d"%nxt_val, nxt_val, nxt_val, ret)
                else:
                    self.dfs(num, target, i+1, cur_str+"+%d"%nxt_val, cur_val+nxt_val, nxt_val, ret)
                    self.dfs(num, target, i+1, cur_str+"-%d"%nxt_val, cur_val-nxt_val, -nxt_val, ret)
                    self.dfs(num, target, i+1, cur_str+"*%d"%nxt_val, cur_val-mul+mul*nxt_val, mul*nxt_val, ret)