##################
# Simulated data #
##################
sampling_period = 30
frequency = pd.Timedelta(sampling_period, unit='s')
start_time = '01/01/2018 00:00:00'
N = 7 * 1440 * 2 # *sampling_period
n_array = np.geomspace(5, 1000, num=40, endpoint=True, dtype=int)
q_array = [-5, -3, -1, 0, 1, 3, 5]
# Stochastic generators
bn = BrownianNoise(t=1)
fbm = FractionalBrownianMotion(hurst=0.9, t=1)
fgn = FractionalGaussianNoise(hurst=0.6, t=1)
np.random.seed(0)
# Brownian noise: h(q) = 1+H with H=0.5
bn_sample = generate_series(bn.sample(N - 1),
start=start_time,
sampling_period=sampling_period)
# Fractional Brownian motion: h(q) = 1+H with H = 0.9 (in this example)
fbm_sample = generate_series(fbm.sample(N - 1),
start=start_time,
sampling_period=sampling_period)
# Fractional Gaussian noise: h(q) = H with H = 0.6 (in this example)
fgn_sample = generate_series(fgn.sample(N),
def test_fractional_gaussian_noise_str_repr(hurst, t):
instance = FractionalGaussianNoise(hurst, t)
assert isinstance(repr(instance), str)
assert isinstance(str(instance), str)
def test_fractional_gaussian_noise_sample(hurst, t, algorithm, n, zero):
instance = FractionalGaussianNoise(hurst, t)
s = instance.sample(n, algorithm)
assert len(s) == n
def test_fractional_gaussian_noise_fallback(t, mocker):
instance = FractionalGaussianNoise(0.99, t) # high hurst
mocker.patch('stochastic.noise.fractional_gaussian_noise.logging')
s = instance.sample(
8, 'daviesharte') # low n, with daviesharte, fallback to hosking
assert fractional_gaussian_noise.logging.warning.called
def test_fractional_gaussian_noise_algorithm(t, n, algorithm_fixture):
instance = FractionalGaussianNoise(0.7, t)
with pytest.raises(ValueError):
s = instance.sample(n, algorithm_fixture)
def test_fractional_gaussian_noise_hurst(hurst_fixture, t):
with pytest.raises((ValueError, TypeError)):
instance = FractionalGaussianNoise(hurst_fixture, t)