Convert the np.ndarray `values` into a complete balanced tree.

        Assumes `values` is sorted ascending. Returns a list `t` of the same length in which t[i] >
        t[2i+1] and t[i] < t[2i+2] for all i.

def _treeify(values):
        """Convert the np.ndarray `values` into a complete balanced tree.

        Assumes `values` is sorted ascending. Returns a list `t` of the same length in which t[i] >
        t[2i+1] and t[i] < t[2i+2] for all i."""
        if len(values) == 1:  # this case causes problems later
            return values
        tree = np.empty_like(values)
        # Tree indices work as follows:
        # 0 is the root
        # 2n+1 is the left child of n
        # 2n+2 is the right child of n
        # So we now rearrange `values` into that format...

        # The first step is to remove the bottom row of leaves, which might not be exactly full
        last_full_row = int(np.log2(len(values) + 1) - 1)
        len_ragged_row = len(values) - (2 ** (last_full_row + 1) - 1)
        if len_ragged_row > 0:
            bottom_row_ix = np.s_[: 2 * len_ragged_row : 2]
            tree[-len_ragged_row:] = values[bottom_row_ix]
            values = np.delete(values, bottom_row_ix)

        # Now `values` is length 2**n - 1, so can be packed efficiently into a tree
        # Last row of nodes is indices 0, 2, ..., 2**n - 2
        # Second-last row is indices 1, 5, ..., 2**n - 3
        # nth-last row is indices (2**n - 1)::(2**(n+1))
        values_start = 0
        values_space = 2
        values_len = 2 ** last_full_row
        while values_start < len(values):
            tree[values_len - 1 : 2 * values_len - 1] = values[values_start::values_space]
            values_start += int(values_space / 2)
            values_space *= 2
            values_len = int(values_len / 2)
        return tree