def test_weights(Simulator, nl):
name = 'test_weights'
n1, n2 = 100, 50
def func(t):
return np.array([np.sin(4 * t), np.cos(12 * t)])
transform = np.array([[0.6, -0.4]])
m = nengo.Network(label=name, seed=3902)
with m:
m.config[nengo.Ensemble].neuron_type = nl()
u = nengo.Node(output=func)
a = nengo.Ensemble(n1, dimensions=2, radius=1.5)
b = nengo.Ensemble(n2, dimensions=1)
bp = nengo.Probe(b)
nengo.Connection(u, a)
nengo.Connection(a, b, transform=transform,
solver=LstsqL2(weights=True))
sim = Simulator(m)
sim.run(2.)
t = sim.trange()
x = func(t).T
y = np.dot(x, transform.T)
z = filtfilt(sim.data[bp], 10, axis=0)
assert allclose(t, y.flatten(), z.flatten(),
plotter=Plotter(Simulator, nl),
filename='test_connection.' + name + '.pdf',
atol=0.1, rtol=0, buf=100, delay=10)
def test_weights(Simulator, nl, plt, seed):
n1, n2 = 100, 50
def func(t):
return np.array([np.sin(4 * t), np.cos(12 * t)])
transform = np.array([[0.6, -0.4]])
m = nengo.Network(label='test_weights', seed=seed)
with m:
m.config[nengo.Ensemble].neuron_type = nl()
u = nengo.Node(output=func)
a = nengo.Ensemble(n1, dimensions=2, radius=1.5)
b = nengo.Ensemble(n2, dimensions=1)
bp = nengo.Probe(b)
nengo.Connection(u, a)
nengo.Connection(a, b, transform=transform,
solver=LstsqL2(weights=True))
sim = Simulator(m)
sim.run(1.)
t = sim.trange()
x = func(t).T
y = np.dot(x, transform.T)
z = nengo.synapses.filtfilt(sim.data[bp], 0.005, dt=sim.dt)
assert allclose(t, y, z, atol=0.1, buf=0.1, delay=0.01, plt=plt)
def test_weights(Simulator, nl):
name = 'test_weights'
n1, n2 = 100, 50
def func(t):
return np.array([np.sin(4 * t), np.cos(12 * t)])
transform = np.array([[0.6, -0.4]])
m = nengo.Model(name, seed=3902)
u = nengo.Node(output=func)
a = nengo.Ensemble(nl(n1), dimensions=2, radius=1.5)
b = nengo.Ensemble(nl(n2), dimensions=1)
bp = nengo.Probe(b)
nengo.Connection(u, a)
nengo.Connection(a, b, transform=transform,
weight_solver=nengo.decoders.lstsq_L2nz)
sim = Simulator(m)
sim.run(2.)
t = sim.trange()
x = func(t).T
y = np.dot(x, transform.T)
z = filtfilt(sim.data[bp], 10, axis=0)
assert allclose(t, y.flatten(), z.flatten(),
plotter=Plotter(Simulator, nl),
filename='test_connection.' + name + '.pdf',
atol=0.1, rtol=0, buf=100, delay=10)
def test_weights(Simulator, nl, plt, seed):
n1, n2 = 100, 50
def func(t):
return [np.sin(4 * t), np.cos(12 * t)]
transform = np.array([[0.6, -0.4]])
m = nengo.Network(label="test_weights", seed=seed)
with m:
m.config[nengo.Ensemble].neuron_type = nl()
u = nengo.Node(output=func)
a = nengo.Ensemble(n1, dimensions=2, radius=1.5)
b = nengo.Ensemble(n2, dimensions=1)
bp = nengo.Probe(b)
nengo.Connection(u, a)
nengo.Connection(a,
b,
transform=transform,
solver=LstsqL2(weights=True))
with Simulator(m) as sim:
sim.run(1.0)
t = sim.trange()
x = np.array(func(t)).T
y = np.dot(x, transform.T)
z = nengo.Lowpass(0.005).filtfilt(sim.data[bp], dt=sim.dt)
assert allclose(t, y, z, atol=0.1, buf=0.1, delay=0.01, plt=plt)
def test_function_points(Simulator, seed, rng, plt):
x = rng.uniform(-1, 1, size=(1000, 1))
y = -x
with nengo.Network(seed=seed) as model:
u = nengo.Node(nengo.processes.WhiteSignal(1.0, high=10, rms=0.4))
a = nengo.Ensemble(100, 1)
v = nengo.Node(size_in=1)
nengo.Connection(u, a, synapse=None)
nengo.Connection(a, v, eval_points=x, function=y)
up = nengo.Probe(u, synapse=nengo.Alpha(0.01))
vp = nengo.Probe(v, synapse=nengo.Alpha(0.01))
with Simulator(model, seed=seed) as sim:
sim.run(1.0)
assert allclose(sim.trange(),
-sim.data[up],
sim.data[vp],
buf=0.01,
delay=0.005,
atol=5e-2,
rtol=3e-2,
plt=plt)
def test_compare_solvers(Simulator, plt, seed):
pytest.importorskip('sklearn')
N = 70
decoder_solvers = [
Lstsq(),
LstsqNoise(),
LstsqL2(),
LstsqL2nz(),
LstsqL1(max_iter=5000)
]
weight_solvers = [
LstsqL1(weights=True, max_iter=5000),
LstsqDrop(weights=True)
]
tfinal = 4
def input_function(t):
return np.interp(t, [1, 3], [-1, 1], left=-1, right=1)
model = nengo.Network(seed=seed)
with model:
u = nengo.Node(output=input_function)
a = nengo.Ensemble(N, dimensions=1)
nengo.Connection(u, a)
ap = nengo.Probe(a)
probes = []
names = []
for solver in decoder_solvers + weight_solvers:
b = nengo.Ensemble(N, dimensions=1, seed=seed + 1)
nengo.Connection(a, b, solver=solver)
probes.append(nengo.Probe(b))
names.append(
"%s(%s)" %
(type(solver).__name__, 'w' if solver.weights else 'd'))
with Simulator(model) as sim:
sim.run(tfinal)
t = sim.trange()
# ref = sim.data[up]
ref = nengo.Lowpass(0.02).filtfilt(sim.data[ap], dt=sim.dt)
outputs = np.array([sim.data[probe][:, 0] for probe in probes]).T
outputs_f = nengo.Lowpass(0.02).filtfilt(outputs, dt=sim.dt)
close = allclose(t,
ref,
outputs_f,
atol=0.07,
rtol=0,
buf=0.1,
delay=0.007,
plt=plt,
labels=names,
individual_results=True)
for name, c in zip(names, close):
assert c, "Solver '%s' does not meet tolerances" % name
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, plt, seed):
dt = 1e-3
tau = 0.01
t, x, yhat = run_synapse(Simulator, seed, Lowpass(tau), dt=dt, n_neurons=100)
y = filt(x, tau, dt=dt)
assert allclose(t, y, yhat, delay=dt, plt=plt)
def test_lowpass(Simulator, plt, seed):
dt = 1e-3
tau = 0.03
t, x, yhat = run_synapse(Simulator, seed, Lowpass(tau), dt=dt)
y = Lowpass(tau).filt(x, dt=dt, y0=0)
assert allclose(t, y, yhat, delay=dt, plt=plt)
def test_lowpass(Simulator, plt, seed):
dt = 1e-3
tau = 0.03
t, x, yhat = run_synapse(Simulator, seed, Lowpass(tau), dt=dt)
y = Lowpass(tau).filt(x, dt=dt, y0=0)
assert allclose(t, y, yhat, delay=dt, plt=plt)
def test_decoders(Simulator, plt, seed):
dt = 1e-3
tau = 0.01
t, x, yhat = run_synapse(
Simulator, seed, Lowpass(tau), dt=dt, n_neurons=100)
y = filt(x, tau, dt=dt)
assert allclose(t, y, yhat, delay=dt, plt=plt)
def test_alpha(Simulator, plt, seed):
dt = 1e-3
tau = 0.03
num, den = [1], [tau**2, 2 * tau, 1]
t, x, yhat = run_synapse(Simulator, seed, Alpha(tau), dt=dt)
y = LinearFilter(num, den).filt(x, dt=dt, y0=0)
assert allclose(t, y, yhat, delay=dt, atol=5e-6, plt=plt)
def test_alpha(Simulator, plt, seed):
dt = 1e-3
tau = 0.03
num, den = [1], [tau**2, 2*tau, 1]
t, x, yhat = run_synapse(Simulator, seed, Alpha(tau), dt=dt)
y = LinearFilter(num, den).filt(x, dt=dt, y0=0)
assert allclose(t, y, yhat, delay=dt, atol=5e-6, 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_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_solvers(Simulator, nl_nodirect):
N = 100
decoder_solvers = [
Lstsq(), LstsqNoise(),
LstsqL2(), LstsqL2nz(),
LstsqL1()
]
weight_solvers = [LstsqL1(weights=True), LstsqDrop(weights=True)]
dt = 1e-3
tfinal = 4
def input_function(t):
return np.interp(t, [1, 3], [-1, 1], left=-1, right=1)
model = nengo.Network('test_solvers', seed=290)
with model:
u = nengo.Node(output=input_function)
a = nengo.Ensemble(nl_nodirect(N), dimensions=1)
nengo.Connection(u, a)
ap = nengo.Probe(a)
probes = []
names = []
for solver in decoder_solvers + weight_solvers:
b = nengo.Ensemble(nl_nodirect(N), dimensions=1, seed=99)
nengo.Connection(a, b, solver=solver)
probes.append(nengo.Probe(b))
names.append(
"%s(%s)" %
(solver.__class__.__name__, 'w' if solver.weights else 'd'))
sim = Simulator(model, dt=dt)
sim.run(tfinal)
t = sim.trange()
# ref = sim.data[up]
ref = filtfilt(sim.data[ap], 20)
outputs = np.array([sim.data[probe] for probe in probes])
outputs_f = filtfilt(outputs, 20, axis=1)
close = allclose(t,
ref,
outputs_f,
plotter=Plotter(Simulator, nl_nodirect),
filename='test_decoders.test_solvers.pdf',
labels=names,
atol=0.05,
rtol=0,
buf=100,
delay=7)
for name, c in zip(names, close):
assert c, "Solver '%s' does not meet tolerances" % name
def test_scalar(Simulator, nl, plt, seed):
"""A network that represents sin(t)."""
N = 40
f = lambda t: np.sin(6.3 * t)
m = nengo.Network(label='test_scalar', seed=seed)
with m:
m.config[nengo.Ensemble].neuron_type = nl()
input = nengo.Node(output=f)
A = nengo.Ensemble(N, 1, label='A')
nengo.Connection(input, A)
in_p = nengo.Probe(input, 'output')
A_p = nengo.Probe(A, 'decoded_output', synapse=0.02)
sim = Simulator(m)
sim.run(1.0)
t = sim.trange()
target = f(t)
assert allclose(t, target, sim.data[in_p], rtol=1e-3, atol=1e-5)
assert allclose(t, target, sim.data[A_p], atol=0.1, delay=0.03, plt=plt)
def test_scalar(Simulator, nl, plt, seed):
"""A network that represents sin(t)."""
N = 40
f = lambda t: np.sin(2 * np.pi * t)
m = nengo.Network(label='test_scalar', seed=seed)
with m:
m.config[nengo.Ensemble].neuron_type = nl()
input = nengo.Node(output=f)
A = nengo.Ensemble(N, 1, label='A')
nengo.Connection(input, A)
in_p = nengo.Probe(input, 'output')
A_p = nengo.Probe(A, 'decoded_output', synapse=0.02)
sim = Simulator(m)
sim.run(1.0)
t = sim.trange()
target = f(t)
assert allclose(t, target, sim.data[in_p], rtol=1e-3, atol=1e-5)
assert allclose(t, target, sim.data[A_p], atol=0.1, delay=0.03, 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_linearfilter(Simulator, plt, seed):
dt = 1e-3
# The following num, den are for a 4th order analog Butterworth filter,
# generated with `scipy.signal.butter(4, 1. / 0.03, analog=True)`
num = np.array([1234567.90123457])
den = np.array([1.0, 87.104197658425107, 3793.5706248589954,
96782.441842694592, 1234567.9012345686])
t, x, yhat = run_synapse(Simulator, seed, LinearFilter(num, den), dt=dt)
y = filt(x, LinearFilter(num, den), dt=dt)
assert allclose(t, y, yhat, delay=dt, plt=plt)
def test_vector(Simulator, nl, plt, seed):
"""A network that represents sin(t), cos(t), cos(t)**2."""
N = 100
f = lambda t: [np.sin(6.3*t), np.cos(6.3*t), np.cos(6.3*t)**2]
m = nengo.Network(label='test_vector', seed=seed)
with m:
m.config[nengo.Ensemble].neuron_type = nl()
input = nengo.Node(output=f)
A = nengo.Ensemble(N * 3, 3, radius=1.5)
nengo.Connection(input, A)
in_p = nengo.Probe(input)
A_p = nengo.Probe(A, synapse=0.03)
sim = Simulator(m)
sim.run(1.0)
t = sim.trange()
target = np.vstack(f(t)).T
assert allclose(t, target, sim.data[in_p], rtol=1e-3, atol=1e-5)
assert allclose(t, target, sim.data[A_p],
plt=plt, atol=0.1, delay=0.03, buf=0.1)
def test_vector(Simulator, nl, plt, seed):
"""A network that represents sin(t), cos(t), cos(t)**2."""
N = 100
f = lambda t: [np.sin(6.3*t), np.cos(6.3*t), np.cos(6.3*t)**2]
m = nengo.Network(label='test_vector', seed=seed)
with m:
m.config[nengo.Ensemble].neuron_type = nl()
input = nengo.Node(output=f)
A = nengo.Ensemble(N * 3, 3, radius=1.5)
nengo.Connection(input, A)
in_p = nengo.Probe(input)
A_p = nengo.Probe(A, synapse=0.03)
sim = Simulator(m)
sim.run(1.0)
t = sim.trange()
target = np.vstack(f(t)).T
assert allclose(t, target, sim.data[in_p], rtol=1e-3, atol=1e-5)
assert allclose(t, target, sim.data[A_p],
plt=plt, atol=0.1, delay=0.03, buf=0.1)
def test_linearfilter(Simulator, plt, seed):
dt = 1e-3
# The following num, den are for a 4th order analog Butterworth filter,
# generated with `scipy.signal.butter(4, 0.2, analog=False)`
num = np.array([0.00482434, 0.01929737, 0.02894606, 0.01929737, 0.00482434])
den = np.array([1.0, -2.36951301, 2.31398841, -1.05466541, 0.18737949])
synapse = LinearFilter(num, den, analog=False)
t, x, yhat = run_synapse(Simulator, seed, synapse, dt=dt)
y = filt(x, synapse, dt=dt)
assert allclose(t, y, yhat, delay=dt, plt=plt)
def test_linearfilter(Simulator, plt, seed):
dt = 1e-3
# The following num, den are for a 4th order analog Butterworth filter,
# generated with `scipy.signal.butter(4, 0.2, analog=False)`
num = np.array(
[0.00482434, 0.01929737, 0.02894606, 0.01929737, 0.00482434])
den = np.array([1., -2.36951301, 2.31398841, -1.05466541, 0.18737949])
synapse = LinearFilter(num, den, analog=False)
t, x, yhat = run_synapse(Simulator, seed, synapse, dt=dt)
y = synapse.filt(x, dt=dt, y0=0)
assert allclose(t, y, yhat, delay=dt, plt=plt)
def test_linearfilter(Simulator, plt, seed):
dt = 1e-3
# The following num, den are for a 4th order analog Butterworth filter,
# generated with `scipy.signal.butter(4, 1. / 0.03, analog=True)`
num = np.array([1234567.90123457])
den = np.array([
1.0, 87.104197658425107, 3793.5706248589954, 96782.441842694592,
1234567.9012345686
])
t, x, yhat = run_synapse(Simulator, seed, LinearFilter(num, den), dt=dt)
y = filt(x, LinearFilter(num, den), dt=dt)
assert allclose(t, y, yhat, delay=dt, plt=plt)
def test_alpha(Simulator, plt):
dt = 1e-3
tau = 0.03
b, a = [0.00054336, 0.00053142], [1, -1.9344322, 0.93550699]
# ^^^ these coefficients found for tau=0.03 and dt=1e-3
# scipy.signal.cont2discrete(([1], [tau**2, 2*tau, 1]), dt)
# b = [0.00054336283526056767, 0.00053142123234546667]
# a = [1, -1.9344322009640118, 0.93550698503161778]
# ^^^ these coefficients found by the exact algorithm used in Builder
t, x, yhat = run_synapse(Simulator, nengo.synapses.Alpha(tau), dt=dt)
y = lti(x, (b, a))
assert allclose(t, y, yhat, delay=dt, atol=5e-6, plt=plt)
def test_general(Simulator, plt):
import scipy.signal
dt = 1e-3
order = 4
tau = 0.03
num, den = scipy.signal.butter(order, 1. / tau, analog=True)
num, den = num.real, den.real
numi, deni, dt = scipy.signal.cont2discrete((num, den), dt)
t, x, yhat = run_synapse(
Simulator, nengo.synapses.LinearFilter(num, den), dt=dt)
y = lti(x, (numi, deni))
assert allclose(t, y, yhat, plt=plt)
def test_triangle(Simulator, plt, seed):
dt = 1e-3
tau = 0.03
t, x, ysim = run_synapse(Simulator, seed, Triangle(tau), dt=dt)
yfilt = Triangle(tau).filt(x, dt=dt, y0=0)
# compare with convolved filter
n_taps = int(round(tau / dt)) + 1
num = np.arange(n_taps, 0, -1, dtype=float)
num /= num.sum()
y = np.convolve(x.ravel(), num)[:len(t)]
y.shape = (-1, 1)
assert np.allclose(y, yfilt, rtol=0)
assert allclose(t, y, ysim, delay=dt, rtol=0, plt=plt)
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_triangle(Simulator, plt, seed):
dt = 1e-3
tau = 0.03
t, x, ysim = run_synapse(Simulator, seed, Triangle(tau), dt=dt)
yfilt = Triangle(tau).filt(x, dt=dt, y0=0)
# compare with convolved filter
n_taps = int(round(tau / dt)) + 1
num = np.arange(n_taps, 0, -1, dtype=float)
num /= num.sum()
y = np.convolve(x.ravel(), num)[:len(t)]
y.shape = (-1, 1)
assert np.allclose(y, yfilt, rtol=0)
assert allclose(t, y, ysim, delay=dt, rtol=0, plt=plt)
def test_solvers(Simulator, nl_nodirect):
N = 100
decoder_solvers = [lstsq, lstsq_noise, lstsq_L2, lstsq_L2nz, lstsq_L1]
weight_solvers = [lstsq_L1, lstsq_drop]
dt = 1e-3
tfinal = 4
def input_function(t):
return np.interp(t, [1, 3], [-1, 1], left=-1, right=1)
model = nengo.Network('test_solvers', seed=290)
with model:
u = nengo.Node(output=input_function)
# up = nengo.Probe(u)
a = nengo.Ensemble(nl_nodirect(N), dimensions=1)
ap = nengo.Probe(a)
nengo.Connection(u, a)
probes = []
names = []
for solver, arg in ([(s, 'decoder_solver') for s in decoder_solvers] +
[(s, 'weight_solver') for s in weight_solvers]):
b = nengo.Ensemble(nl_nodirect(N), dimensions=1, seed=99)
nengo.Connection(a, b, **{arg: solver})
probes.append(nengo.Probe(b))
names.append(solver.__name__ + (" (%s)" % arg[0]))
sim = nengo.Simulator(model, dt=dt)
sim.run(tfinal)
t = sim.trange()
# ref = sim.data[up]
ref = filtfilt(sim.data[ap], 20)
outputs = np.array([sim.data[probe] for probe in probes])
outputs_f = filtfilt(outputs, 20, axis=1)
close = allclose(t, ref, outputs_f,
plotter=Plotter(Simulator, nl_nodirect),
filename='test_decoders.test_solvers.pdf',
labels=names,
atol=0.05, rtol=0, buf=100, delay=7)
for name, c in zip(names, close):
assert c, "Solver '%s' does not meet tolerances" % name
def test_compare_solvers(Simulator, nl_nodirect):
N = 70
decoder_solvers = [
Lstsq(), LstsqNoise(), LstsqL2(), LstsqL2nz(), LstsqL1()]
weight_solvers = [LstsqL1(weights=True), LstsqDrop(weights=True)]
dt = 1e-3
tfinal = 4
def input_function(t):
return np.interp(t, [1, 3], [-1, 1], left=-1, right=1)
model = nengo.Network('test_solvers', seed=290)
with model:
model.config[nengo.Ensemble].neuron_type = nl_nodirect()
u = nengo.Node(output=input_function)
a = nengo.Ensemble(N, dimensions=1)
nengo.Connection(u, a)
ap = nengo.Probe(a)
probes = []
names = []
for solver in decoder_solvers + weight_solvers:
b = nengo.Ensemble(N, dimensions=1, seed=99)
nengo.Connection(a, b, solver=solver)
probes.append(nengo.Probe(b))
names.append("%s(%s)" % (
solver.__class__.__name__, 'w' if solver.weights else 'd'))
sim = Simulator(model, dt=dt)
sim.run(tfinal)
t = sim.trange()
# ref = sim.data[up]
ref = filtfilt(sim.data[ap], 20)
outputs = np.array([sim.data[probe][:, 0] for probe in probes])
outputs_f = filtfilt(outputs, 20, axis=1)
close = allclose(t, ref, outputs_f,
plotter=Plotter(Simulator, nl_nodirect),
filename='test_solvers.test_solvers.pdf',
labels=names,
atol=0.05, rtol=0, buf=100, delay=7)
for name, c in zip(names, close):
assert c, "Solver '%s' does not meet tolerances" % name
def test_compare_solvers(Simulator, plt, seed):
pytest.importorskip('sklearn')
N = 70
decoder_solvers = [
Lstsq(), LstsqNoise(), LstsqL2(), LstsqL2nz(), LstsqL1()]
weight_solvers = [LstsqL1(weights=True), LstsqDrop(weights=True)]
tfinal = 4
def input_function(t):
return np.interp(t, [1, 3], [-1, 1], left=-1, right=1)
model = nengo.Network(seed=seed)
with model:
u = nengo.Node(output=input_function)
a = nengo.Ensemble(N, dimensions=1)
nengo.Connection(u, a)
ap = nengo.Probe(a)
probes = []
names = []
for solver in decoder_solvers + weight_solvers:
b = nengo.Ensemble(N, dimensions=1, seed=seed + 1)
nengo.Connection(a, b, solver=solver)
probes.append(nengo.Probe(b))
names.append("%s(%s)" % (
solver.__class__.__name__, 'w' if solver.weights else 'd'))
sim = Simulator(model)
sim.run(tfinal)
t = sim.trange()
# ref = sim.data[up]
ref = nengo.synapses.filtfilt(sim.data[ap], 0.02, dt=sim.dt)
outputs = np.array([sim.data[probe][:, 0] for probe in probes]).T
outputs_f = nengo.synapses.filtfilt(outputs, 0.02, dt=sim.dt)
close = allclose(t, ref, outputs_f,
atol=0.07, rtol=0, buf=0.1, delay=0.007,
plt=plt, labels=names, individual_results=True)
for name, c in zip(names, close):
assert c, "Solver '%s' does not meet tolerances" % name
def test_function_points(Simulator, seed, rng, plt):
x = rng.uniform(-1, 1, size=(1000, 1))
y = -x
with nengo.Network(seed=seed) as model:
u = nengo.Node(nengo.processes.WhiteSignal(1.0, high=10, rms=0.4))
a = nengo.Ensemble(100, 1)
v = nengo.Node(size_in=1)
nengo.Connection(u, a, synapse=None)
nengo.Connection(a, v, eval_points=x, function=y)
up = nengo.Probe(u, synapse=nengo.Alpha(0.01))
vp = nengo.Probe(v, synapse=nengo.Alpha(0.01))
with Simulator(model, seed=seed) as sim:
sim.run(1.0)
assert allclose(sim.trange(), -sim.data[up], sim.data[vp],
buf=0.01, delay=0.005, atol=5e-2, rtol=3e-2, plt=plt)
def test_alpha(Simulator):
dt = 1e-3
tau = 0.03
b, a = [0.00054336, 0.00053142], [1, -1.9344322, 0.93550699]
# ^^^ these coefficients found for tau=0.03 and dt=1e-3
# scipy.signal.cont2discrete(([1], [tau**2, 2*tau, 1]), dt)
# b = [0.00054336283526056767, 0.00053142123234546667]
# a = [1, -1.9344322009640118, 0.93550698503161778]
# ^^^ these coefficients found by the exact algorithm used in Builder
t, x, yhat = run_synapse(Simulator, nengo.synapses.Alpha(tau), dt=dt)
y = lti(x, (b, a))
assert allclose(t,
y.flatten(),
yhat.flatten(),
delay=1,
atol=5e-6,
plotter=Plotter(Simulator),
filename='test_synapse.test_alpha.pdf')
def test_general(Simulator):
import scipy.signal
dt = 1e-3
order = 4
tau = 0.03
num, den = scipy.signal.butter(order, 1. / tau, analog=True)
num, den = num.real, den.real
numi, deni, dt = scipy.signal.cont2discrete((num, den), dt)
t, x, yhat = run_synapse(Simulator,
nengo.synapses.LinearFilter(num, den),
dt=dt)
y = lti(x, (numi, deni))
assert allclose(t,
y.flatten(),
yhat.flatten(),
plotter=Plotter(Simulator),
filename='test_synapse.test_general.pdf')
def test_weights(Simulator, nl):
name = 'test_weights'
n1, n2 = 100, 50
def func(t):
return np.array([np.sin(4 * t), np.cos(12 * t)])
transform = np.array([[0.6, -0.4]])
m = nengo.Network(label=name, seed=3902)
with m:
m.config[nengo.Ensemble].neuron_type = nl()
u = nengo.Node(output=func)
a = nengo.Ensemble(n1, dimensions=2, radius=1.5)
b = nengo.Ensemble(n2, dimensions=1)
bp = nengo.Probe(b)
nengo.Connection(u, a)
nengo.Connection(a,
b,
transform=transform,
solver=LstsqL2(weights=True))
sim = Simulator(m)
sim.run(2.)
t = sim.trange()
x = func(t).T
y = np.dot(x, transform.T)
z = filtfilt(sim.data[bp], 10, axis=0)
assert allclose(t,
y.flatten(),
z.flatten(),
plotter=Plotter(Simulator, nl),
filename='test_connection.' + name + '.pdf',
atol=0.1,
rtol=0,
buf=100,
delay=10)
def allclose_tol(*args, **kwargs):
"""Use looser tolerance"""
kwargs.setdefault('atol', 2e-7)
return allclose(*args, **kwargs)
def allclose_tol(*args, **kwargs):
"""Use looser tolerance"""
kwargs.setdefault('atol', 1e-7)
return allclose(*args, **kwargs)