def test_brownnoise(Simulator, seed, plt):
d = 5000
t = 0.5
std = 1.5
process = BrownNoise(dist=Gaussian(0, std))
with nengo.Network() as model:
u = nengo.Node(process, size_out=d)
up = nengo.Probe(u)
with Simulator(model, seed=seed) as sim:
sim.run(t)
samples = sim.data[up]
trange = sim.trange()
expected_std = std * np.sqrt(trange)
atol = 3.5 * expected_std / np.sqrt(d)
plt.subplot(2, 1, 1)
plt.title("Five Brown noise signals")
plt.plot(trange, samples[:, :5])
plt.subplot(2, 1, 2)
plt.ylabel("Standard deviation")
plt.plot(trange, np.abs(np.std(samples, axis=1)), label="Actual")
plt.plot(trange, expected_std, label="Expected")
plt.legend(loc='best')
assert np.all(np.abs(np.mean(samples, axis=1)) < atol)
assert np.all(np.abs(np.std(samples, axis=1) - expected_std) < atol)
def test_processes():
assert (
repr(WhiteSignal(0.2, 10, rms=0.3))
== "WhiteSignal(period=0.2, high=10, rms=0.3)"
)
check_init_args(WhiteNoise, ["dist", "scale"])
check_repr(WhiteNoise(scale=False))
assert repr(WhiteNoise()) == "WhiteNoise()"
assert repr(WhiteNoise(scale=False)) == "WhiteNoise(scale=False)"
check_init_args(FilteredNoise, ["synapse", "dist", "scale"])
check_repr(FilteredNoise(scale=False))
assert repr(FilteredNoise()) == "FilteredNoise()"
assert repr(FilteredNoise(scale=False)) == "FilteredNoise(scale=False)"
check_init_args(BrownNoise, ["dist"])
check_repr(BrownNoise())
assert repr(BrownNoise()) == "BrownNoise()"
check_init_args(PresentInput, ["inputs", "presentation_time"])
check_repr(PresentInput(inputs=np.array([1.2, 3.4]), presentation_time=5))
assert (
repr(PresentInput((1.2, 3.4), 5))
== "PresentInput(inputs=array([1.2, 3.4]), presentation_time=5)"
)
check_init_args(WhiteSignal, ["period", "high", "rms", "y0"])
check_repr(WhiteSignal(period=1.2, high=3.4, rms=5.6, y0=7.8))
assert repr(WhiteSignal(1, 2)) == "WhiteSignal(period=1, high=2)"
assert (
repr(WhiteSignal(1.2, 3.4, 5.6, 7.8))
== "WhiteSignal(period=1.2, high=3.4, rms=5.6, y0=7.8)"
)
check_init_args(Piecewise, ["data", "interpolation"])
check_repr(Piecewise(data={1: 0.1, 2: 0.2, 3: 0.3}))
assert (
repr(Piecewise({1: 0.1, 2: 0.2, 3: 0.3}))
== "Piecewise(data={1: array([0.1]), 2: array([0.2]), 3: array([0.3])})"
)
def test_brownnoise(rng, plt):
d = 5000
t = 0.5
dt = 0.001
std = 1.5
process = BrownNoise(dist=Gaussian(0, std))
samples = process.run(t, d=d, dt=dt, rng=rng)
trange = process.trange(t, dt=dt)
expected_std = std * np.sqrt(trange)
atol = 3.5 * expected_std / np.sqrt(d)
plt.subplot(2, 1, 1)
plt.title("Five Brown noise signals")
plt.plot(trange, samples[:, :5])
plt.subplot(2, 1, 2)
plt.ylabel("Standard deviation")
plt.plot(trange, np.abs(np.std(samples, axis=1)), label="Actual")
plt.plot(trange, expected_std, label="Expected")
plt.legend(loc='best')
assert np.all(np.abs(np.mean(samples, axis=1)) < atol)
assert np.all(np.abs(np.std(samples, axis=1) - expected_std) < atol)
def test_brownnoise(rng, plt):
d = 5000
t = 0.5
dt = 0.001
std = 1.5
process = BrownNoise(dist=Gaussian(0, std))
samples = process.run(t, d=d, dt=dt, rng=rng)
trange = process.trange(t, dt=dt)
expected_std = std * np.sqrt(trange)
atol = 3.5 * expected_std / np.sqrt(d)
plt.subplot(2, 1, 1)
plt.title("Five Brown noise signals")
plt.plot(trange, samples[:, :5])
plt.subplot(2, 1, 2)
plt.ylabel("Standard deviation")
plt.plot(trange, np.abs(np.std(samples, axis=1)), label="Actual")
plt.plot(trange, expected_std, label="Expected")
plt.legend(loc='best')
assert np.all(np.abs(np.mean(samples, axis=1)) < atol)
assert np.all(np.abs(np.std(samples, axis=1) - expected_std) < atol)
def test_brownnoise(rng, plt):
d = 5000
t = 500
dt = 0.001
samples = nengo.processes.sample(t, BrownNoise(), dt=dt, d=d, rng=rng)
trange = np.arange(1, t + 1) * dt
expected_std = np.sqrt((np.arange(t) + 1) * dt)
atol = 3.5 * expected_std / np.sqrt(d)
plt.subplot(2, 1, 1)
plt.title("Five Brown noise signals")
plt.plot(trange, samples[:, :5])
plt.subplot(2, 1, 2)
plt.ylabel("Standard deviation")
plt.plot(trange, np.abs(np.std(samples, axis=1)), label="Actual")
plt.plot(trange, expected_std, label="Expected")
plt.legend(loc='best')
assert np.all(np.abs(np.mean(samples, axis=1)) < atol)
assert np.all(np.abs(np.std(samples, axis=1) - expected_std) < atol)