def test_noise_scaling(): """compare the noise strength (in terms of the spectral density of two different noise sources that should be equivalent)""" # create a grid x, w = 2 + 10 * np.random.random(2) size = np.random.randint(128, 256) grid = CartesianGrid([[x, x + w]], size, periodic=True) # colored noise noise_colored = make_colored_noise(grid.shape, grid.discretization, exponent=2) # divergence of white noise shape = (grid.dim,) + grid.shape div = grid.make_operator("divergence", bc="natural") def noise_div(): return div(np.random.randn(*shape)) # calculate spectral densities of the two noises result = [] for noise_func in [noise_colored, noise_div]: def get_noise(): k, density = spectral_density(data=noise_func(), dx=grid.discretization) assert k[0] == 0 assert density[0] == pytest.approx(0) return np.log(density[1]) # log of spectral density # average spectral density of longest length scale mean = np.mean([get_noise() for _ in range(64)]) result.append(mean) np.testing.assert_allclose(*result, rtol=0.5)
def test_colored_noise(): """test the implementation of the colored noise""" grid = UnitGrid([64, 64], periodic=True) for exponent in [0, -1, 2]: scale = np.random.uniform(1, 10) noise = make_colored_noise(grid.shape, grid.discretization, exponent, scale) x = noise() assert ( stats.normaltest(x.flat).pvalue > 2e-5 ), f"Colored noise with exp={exponent} is not normal distributed"