def METHOD_NAME():
    reference = flex.double()
    for j in range(3 * 100):
        reference.append(random.random())
    query = flex.double()
    for j in range(3 * 10):
        query.append(random.random())
    ann = ann_adaptor(data = reference, dim = 3, k = 1)
    ann.query(query)
    # workout code - see how far separated on average they are - which should
    # in principle decrease as the number of positions in the reference set
    # increases
    offsets = []
    for j in range(10):
        q = matrix.col([query[3 * j + k] for k in range(3)])
        r = matrix.col([reference[3 * ann.nn[j] + k] for k in range(3)])
        offsets.append((q - r).length())
    return meansd(offsets)