Best way to assert for numpy.array equality?

I want to make some unit-tests for my app, and I need to compare two arrays. Since array.__eq__ returns a new array (so TestCase.assertEqual fails), what is the best way to assert for equality?

Currently I'm using

self.assertTrue((arr1 == arr2).all())

but I don't really like it

check out the assert functions in numpy.testing, e.g.

assert_array_equal

for floating point arrays equality test might fail and assert_almost_equal is more reliable.

update

A few versions ago numpy obtained assert_allclose which is now my favorite since it allows us to specify both absolute and relative error and doesn't require decimal rounding as the closeness criterion.

I think (arr1 == arr2).all() looks pretty nice. But you could use:

numpy.allclose(arr1, arr2)

but it's not quite the same.

An alternative, almost the same as your example is:

numpy.alltrue(arr1 == arr2)

Note that scipy.array is actually a reference numpy.array. That makes it easier to find the documentation.