testing float array for near equality without storing a reference array

When writing tests for numerical code, a typical thing I have to do is to assert that an output float array is what I expect. Since I do not want to store the entire reference array, I usually just compare the sum of the array entries against a reference number. To be sure, I take the sum of some powers, e.g.,

import foobar
import numpy

def test_array():
    x = foobar.generate_values()
    assert isinstance(x, numpy.ndarray)
    assert x.dtype == numpy.float64
    ref_sum1 = 1.4311
    assert numpy.abs(numpy.sum(x) - ref_sum1) < 1.0e-13
    ref_sum2 = 8.2343
    assert numpy.abs(numpy.sum(x ** 2) - ref_sum2) < 1.0e-13
    ref_sum3 = -0.7534
    assert numpy.abs(numpy.sum(x ** 3) - ref_sum3) < 1.0e-13

But wait! This doesn't catch permutations of x, and certainly there are other caveats too.

Is there a more systematic way of testing for (near) equality in float arrays?