def test_passthrough_filter(Simulator):
m = nengo.Network(label="test_passthrough", seed=0)
with m:
omega = 2 * np.pi * 5
u = nengo.Node(output=lambda t: np.sin(omega * t))
passthrough = nengo.Node(size_in=1)
v = nengo.Node(output=lambda t, x: x, size_in=1)
synapse = 0.3
nengo.Connection(u, passthrough, synapse=None)
nengo.Connection(passthrough, v, synapse=synapse)
up = nengo.Probe(u)
vp = nengo.Probe(v)
dt = 0.001
sim = Simulator(m, dt=dt)
sim.run(1.0)
t = sim.trange()
x = sim.data[up]
y = filt(x, synapse / dt)
z = sim.data[vp]
with Plotter(Simulator) as plt:
plt.plot(t, x)
plt.plot(t, y)
plt.plot(t, z)
plt.savefig("test_node.test_passthrough_filter.pdf")
plt.close()
# TODO: we have a two-step delay here since the passthrough node is not
# actually optimized out. We should look into doing this.
assert np.allclose(y[:-2], z[2:])
def test_passthrough_filter(Simulator):
m = nengo.Network(label="test_passthrough", seed=0)
with m:
omega = 2 * np.pi * 5
u = nengo.Node(output=lambda t: np.sin(omega * t))
passthrough = nengo.Node(size_in=1)
v = nengo.Node(output=lambda t, x: x, size_in=1)
synapse = 0.3
nengo.Connection(u, passthrough, synapse=None)
nengo.Connection(passthrough, v, synapse=synapse)
up = nengo.Probe(u)
vp = nengo.Probe(v)
dt = 0.001
sim = Simulator(m, dt=dt)
sim.run(1.0)
t = sim.trange()
x = sim.data[up]
y = filt(x, synapse / dt)
z = sim.data[vp]
with Plotter(Simulator) as plt:
plt.plot(t, x)
plt.plot(t, y)
plt.plot(t, z)
plt.savefig("test_node.test_passthrough_filter.pdf")
plt.close()
assert np.allclose(y[:-1], z[1:])
def test_passthrough_filter(Simulator):
m = nengo.Network(label="test_passthrough", seed=0)
with m:
omega = 2 * np.pi * 5
u = nengo.Node(output=lambda t: np.sin(omega * t))
passthrough = nengo.Node(size_in=1)
v = nengo.Node(output=lambda t, x: x, size_in=1)
synapse = 0.3
nengo.Connection(u, passthrough, synapse=None)
nengo.Connection(passthrough, v, synapse=synapse)
up = nengo.Probe(u)
vp = nengo.Probe(v)
dt = 0.001
sim = Simulator(m, dt=dt)
sim.run(1.0)
t = sim.trange()
x = sim.data[up]
y = filt(x, synapse / dt)
z = sim.data[vp]
with Plotter(Simulator) as plt:
plt.plot(t, x)
plt.plot(t, y)
plt.plot(t, z)
plt.savefig("test_node.test_passthrough_filter.pdf")
plt.close()
# TODO: we have a two-step delay here since the passthrough node is not
# actually optimized out. We should look into doing this.
assert np.allclose(y[:-2], z[2:])
def test_lowpass(Simulator, plt):
dt = 1e-3
tau = 0.03
t, x, yhat = run_synapse(Simulator, nengo.synapses.Lowpass(tau), dt=dt)
y = filt(x, tau / dt)
assert allclose(t, y, yhat, delay=dt, plt=plt)
def test_decoders(Simulator, nl, plt):
dt = 1e-3
tau = 0.01
t, x, yhat = run_synapse(
Simulator, nengo.synapses.Lowpass(tau), dt=dt, n_neurons=100)
y = filt(x, tau / dt)
assert allclose(t, y, yhat, delay=dt, plt=plt)
def test_lowpass(Simulator):
dt = 1e-3
tau = 0.03
t, x, yhat = run_synapse(Simulator, nengo.synapses.Lowpass(tau), dt=dt)
y = filt(x, tau / dt)
assert allclose(t, y.flatten(), yhat.flatten(), delay=1,
plotter=Plotter(Simulator),
filename='test_synapse.test_lowpass.pdf')
def test_filtfilt():
dt = 1e-3
tend = 3.
t = dt * np.arange(tend / dt)
nt = len(t)
tau = 0.03 / dt
u = np.random.normal(size=nt)
x = filt(u, tau)
x = filt(x[::-1], tau, x0=x[-1])[::-1]
y = filtfilt(u, tau)
with Plotter(nengo.Simulator) as plt:
plt.plot(t, x)
plt.plot(t, y, '--')
plt.savefig('utils.test_filtering.test_filtfilt.pdf')
plt.close()
assert np.allclose(x, y)
def test_decoders(Simulator, nl):
dt = 1e-3
tau = 0.01
t, x, yhat = run_synapse(
Simulator, nengo.synapses.Lowpass(tau), dt=dt, n_neurons=100)
y = filt(x, tau / dt)
assert allclose(t, y.flatten(), yhat.flatten(), delay=1,
plotter=Plotter(Simulator, nl),
filename='test_synapse.test_decoders.pdf')
def test_lowpass(Simulator):
dt = 1e-3
tau = 0.03
t, x, yhat = run_synapse(Simulator, nengo.synapses.Lowpass(tau), dt=dt)
y = filt(x, tau / dt)
assert allclose(t,
y.flatten(),
yhat.flatten(),
delay=1,
plotter=Plotter(Simulator),
filename='test_synapse.test_lowpass.pdf')
def test_lti_lowpass():
dt = 1e-3
tend = 3.
t = dt * np.arange(tend / dt)
nt = len(t)
tau = 1e-2
d = -np.expm1(-dt / tau)
a = [d]
b = [1, d - 1]
u = np.random.normal(size=(nt, 10))
x = filt(u, tau / dt)
y = lti(u, (a, b))
assert np.allclose(x, y)
def test_decoders(Simulator, nl):
dt = 1e-3
tau = 0.01
t, x, yhat = run_synapse(Simulator,
nengo.synapses.Lowpass(tau),
dt=dt,
n_neurons=100)
y = filt(x, tau / dt)
assert allclose(t,
y.flatten(),
yhat.flatten(),
delay=1,
plotter=Plotter(Simulator, nl),
filename='test_synapse.test_decoders.pdf')
def test_filt():
dt = 1e-3
tend = 3.
t = dt * np.arange(tend / dt)
nt = len(t)
tau = 0.1 / dt
u = np.random.normal(size=nt)
tk = np.arange(0, 30 * tau)
k = 1. / tau * np.exp(-tk / tau)
x = np.convolve(u, k, mode='full')[:nt]
y = filt(u, tau)
with Plotter(nengo.Simulator) as plt:
plt.plot(t, x)
plt.plot(t, y, '--')
plt.savefig('utils.test_filtering.test_filt.pdf')
plt.close()
assert np.allclose(x, y, atol=1e-3, rtol=1e-2)
