def test_same_result(self):
a = random_walk(1, 10000, 508842746).reshape(10000)
nlags = 100
msd_slow = hdt.msd(a, nlags=nlags, fft=False)
msd_fast = hdt.msd(a, nlags=nlags, fft=True)
assert_almost_equal(msd_slow, msd_fast)
b = random_walk(3, 10000)
msd_slow = hdt.msd(b, nlags=nlags, fft=False)
msd_fast = hdt.msd(b, nlags=nlags, fft=True)
assert_almost_equal(msd_slow, msd_fast)
def test_msd_arange_fft(self):
a = np.arange(90)
msd = hdt.msd(a)
assert (len(a) - 1) == len(msd)
# for a few values the result is off by about 0.33. I don't know where
# this comes from but this solution is only a good aproxximation for
# large values of N anyway
assert_almost_equal((a**2)[:-1], msd, decimal=0)
def test_ValueError(self):
a = np.ones((3, 3, 3))
with pytest.raises(ValueError):
hdt.msd(a)
def test_array_like(self):
a = list(np.arange(9))
msd = hdt.msd(a, fft=False)
assert 8 == len(msd)
assert_almost_equal(np.power(a, 2)[:-1], msd)
def test_msd_constant_fft(self):
a = np.ones(9) * 42
msd = hdt.msd(a)
assert_almost_equal(np.zeros(len(a) - 1), msd)
def test_msd_arange_slow(self):
a = np.arange(9)
msd = hdt.msd(a, fft=False)
assert 8 == len(msd)
assert_almost_equal((a**2)[:-1], msd)