def test_bad_filt():
sys = PadeDelay(0.1, order=4).X
with pytest.raises(ValueError):
sys.filt(np.ones((4, 4)))
with pytest.raises(ValueError):
sys.filt(np.ones((4, 1)), filtfilt=True)
with pytest.raises(ValueError):
sys.filt(np.ones((4,)), copy=False)
def test_readout(legendre, d, tol):
theta = 0.1
if legendre:
sys = LegendreDelay(theta, d)
readout = _legendre_readout
else:
sys = PadeDelay(theta, d)
readout = _pade_readout
# decoding at r=1 (t=-theta) is equivalent to decoding a delay of theta
C = readout(d, 1)
assert np.allclose(sys.C, C)
assert np.allclose(sys.D, 0)
freqs = np.linspace(0, 5, 100)
s = 2j * np.pi * freqs
# check that frequency response has small error at low-frequencies
# for a variety of different readouts
for r in np.linspace(0, 1, 100):
C = readout(d, r)
sys = LinearSystem((sys.A, sys.B, C, sys.D), analog=True)
error = np.abs(sys(s) - np.exp(-r*theta*s))
assert 0 < rms(error) < tol, r
def test_output_filter(Simulator, seed, rng):
dt = 0.001
T = 1.0
sys = PadeDelay(0.1, order=3, p=3)
assert sys.has_passthrough
synapse = 0.01
with Network(seed=seed) as model:
stim = nengo.Node(
output=nengo.processes.WhiteSignal(T, high=10, seed=seed))
subnet = LinearNetwork(sys,
1,
synapse=synapse,
output_synapse=synapse,
dt=dt,
neuron_type=nengo.neurons.Direct())
nengo.Connection(stim, subnet.input, synapse=None)
assert subnet.input_synapse is None
p_ideal = nengo.Probe(subnet.input, synapse=sys)
p_output = nengo.Probe(subnet.output, synapse=None)
with Simulator(model, dt=dt) as sim:
sim.run(T)
assert np.allclose(sim.data[p_output][:-1], sim.data[p_ideal][1:])
def test_doubleexp_discrete():
sys = PadeDelay(0.1, order=5)
tau1 = 0.05
tau2 = 0.02
dt = 0.002
syn = DoubleExp(tau1, tau2)
FH = ss2sim(sys, syn, dt=dt)
a1 = np.exp(-dt / tau1)
a2 = np.exp(-dt / tau2)
t1 = 1 / (1 - a1)
t2 = 1 / (1 - a2)
c = [a1 * a2 * t1 * t2, - (a1 + a2) * t1 * t2, t1 * t2]
sys = cont2discrete(sys, dt=dt)
A = sys.A
FHA = c[2] * np.dot(A, A) + c[1] * A + c[0] * np.eye(len(A))
B = sys.B
FHB = (c[2] * A + (c[1] + c[2]) * np.eye(len(A))).dot(B)
assert np.allclose(FH.A, FHA)
assert np.allclose(FH.B, FHB)
assert np.allclose(FH.C, sys.C)
assert np.allclose(FH.D, sys.D)
def delayed_synapse():
a = 0.1 # desired delay
b = 0.01 # synapse delay
tau = 0.01 # recurrent tau
hz = 15 # input frequency
t = 1.0 # simulation time
dt = 0.00001 # simulation timestep
order = 6 # order of pade approximation
tau_probe = 0.02
dexp_synapse = DoubleExp(tau, tau / 5)
sys_lambert = lambert_delay(a, b, tau, order - 1, order)
synapse = (cont2discrete(Lowpass(tau), dt=dt) *
DiscreteDelay(int(b / dt)))
n_neurons = 2000
neuron_type = PerfectLIF()
A, B, C, D = sys_lambert.observable.transform(5*np.eye(order)).ss
sys_normal = PadeDelay(a, order)
assert len(sys_normal) == order
with Network(seed=0) as model:
stim = Node(output=WhiteSignal(t, high=hz, y0=0))
x = EnsembleArray(n_neurons / order, len(A), neuron_type=neuron_type)
output = Node(size_in=1)
Connection(x.output, x.input, transform=A, synapse=synapse)
Connection(stim, x.input, transform=B, synapse=synapse)
Connection(x.output, output, transform=C, synapse=None)
Connection(stim, output, transform=D, synapse=None)
lowpass_delay = LinearNetwork(
sys_normal, n_neurons_per_ensemble=n_neurons / order,
synapse=tau, input_synapse=tau,
dt=None, neuron_type=neuron_type, radii=1.0)
Connection(stim, lowpass_delay.input, synapse=None)
dexp_delay = LinearNetwork(
sys_normal, n_neurons_per_ensemble=n_neurons / order,
synapse=dexp_synapse, input_synapse=dexp_synapse,
dt=None, neuron_type=neuron_type, radii=1.0)
Connection(stim, dexp_delay.input, synapse=None)
p_stim = Probe(stim, synapse=tau_probe)
p_output_delayed = Probe(output, synapse=tau_probe)
p_output_lowpass = Probe(lowpass_delay.output, synapse=tau_probe)
p_output_dexp = Probe(dexp_delay.output, synapse=tau_probe)
with Simulator(model, dt=dt, seed=0) as sim:
sim.run(t)
return (a, dt, sim.trange(), sim.data[p_stim],
sim.data[p_output_delayed], sim.data[p_output_lowpass],
sim.data[p_output_dexp])
def time_cells(order):
seed = 0
n_neurons = 300
theta = 4.784
tau = 0.1
radius = 0.3
realizer = Balanced
# The following was patched from nengolib commit
# 7e204e0c305e34a4f63d0a6fbba7197862bbcf22, prior to
# aee92b8fc45749f07f663fe696745cf0a33bfa17, so that
# the generated PDF is consistent with the version that the
# overlay was added to.
def PadeDelay(c, q):
j = np.arange(1, q+1, dtype=np.float64)
u = (q + j - 1) * (q - j + 1) / (c * j)
A = np.zeros((q, q))
B = np.zeros((q, 1))
C = np.zeros((1, q))
D = np.zeros((1,))
A[0, :] = B[0, 0] = -u[0]
A[1:, :-1][np.diag_indices(q-1)] = u[1:]
C[0, :] = - j / float(q) * (-1) ** (q - j)
return LinearSystem((A, B, C, D), analog=True)
F = PadeDelay(theta, order)
synapse = Alpha(tau)
pulse_s = 0
pulse_w = 1.0
pulse_h = 1.5
T = 6.0
dt = 0.001
pulse = np.zeros(int(T/dt))
pulse[int(pulse_s/dt):int((pulse_s + pulse_w)/dt)] = pulse_h
with Network(seed=seed) as model:
u = Node(output=PresentInput(pulse, dt))
delay = LinearNetwork(
F, n_neurons_per_ensemble=n_neurons / len(F), synapse=synapse,
input_synapse=None, radii=radius, dt=dt, realizer=realizer())
Connection(u, delay.input, synapse=None)
p_x = Probe(delay.state.input, synapse=None)
p_a = Probe(delay.state.add_neuron_output(), synapse=None)
with Simulator(model, dt=dt) as sim:
sim.run(T)
return sim.trange(), sim.data[p_x], sim.data[p_a]
def test_invalid_sample():
process = WhiteSignal(1.0, high=10)
sys = PadeDelay(0.1, order=4)
dist = EvalPoints(sys, process)
with pytest.raises(ValidationError):
dist.sample(100, len(sys)) # needs to equal sys.size_out
dist = Encoders(sys, process)
with pytest.raises(ValidationError):
dist.sample(100, len(sys)) # needs to equal sys.size_out
def test_principle3_continuous():
sys = PadeDelay(0.1, order=5)
tau = 0.01
syn = Lowpass(tau)
FH = ss2sim(sys, syn, dt=None)
assert np.allclose(FH.A, tau * sys.A + np.eye(len(sys)))
assert np.allclose(FH.B, tau * sys.B)
assert np.allclose(FH.C, sys.C)
assert np.allclose(FH.D, sys.D)
def test_non_siso_filtering(rng):
sys = PadeDelay(0.1, order=4)
length = 1000
SIMO = sys.X
assert not SIMO.is_SISO
assert SIMO.size_in == 1
assert SIMO.size_out == len(sys)
x = SIMO.impulse(length)
for i, (sub1, sub2) in enumerate(zip(sys, SIMO)):
assert sub1 == sub2
y1 = sub1.impulse(length)
y2 = sub2.impulse(length)
_transclose(shift(y1), shift(y2), x[:, i])
B = np.asarray([[1, 2, 3], [0, 0, 0], [0, 0, 0], [0, 0, 0]]) * sys.B
u = rng.randn(length, 3)
Bu = u.dot([1, 2, 3])
assert Bu.shape == (length,)
MISO = LinearSystem((sys.A, B, sys.C, np.zeros((1, 3))), analog=True)
assert not MISO.is_SISO
assert MISO.size_in == 3
assert MISO.size_out == 1
y = cont2discrete(MISO, dt=0.001).filt(u)
assert y.shape == (length,)
assert np.allclose(shift(sys.filt(Bu)), y)
MIMO = MISO.X
assert not MIMO.is_SISO
assert MIMO.size_in == 3
assert MIMO.size_out == 4
y = MIMO.filt(u)
I = np.eye(len(sys))
for i, sub1 in enumerate(MIMO):
sub2 = LinearSystem((sys.A, B, I[i:i+1], np.zeros((1, 3))))
_transclose(sub1.filt(u), sub2.filt(u), y[:, i])
def discrete_example(seed, dt):
n_neurons = 1000
theta = 0.1
freq = 50
q = 27
radii = 1.0
sys = PadeDelay(theta, q)
T = 5000*(dt+0.001)
rms = 1.0
signal = WhiteSignal(T, high=freq, rms=rms, y0=0)
tau = 0.1
tau_probe = 0.02
reg = 0.1
# Determine radii using direct mode
with LinearNetwork(
sys, n_neurons_per_ensemble=1, input_synapse=tau, synapse=tau,
dt=dt, neuron_type=Direct(),
realizer=Balanced()) as model:
Connection(Node(output=signal), model.input, synapse=None)
p_x = Probe(model.state.input, synapse=None)
with Simulator(model, dt=dt, seed=seed+1) as sim:
sim.run(T)
radii *= np.max(abs(sim.data[p_x]), axis=0)
logging.info("Radii: %s", radii)
with Network(seed=seed) as model:
u = Node(output=signal)
kwargs = dict(
n_neurons_per_ensemble=n_neurons / len(sys),
input_synapse=tau, synapse=tau, radii=radii,
solver=LstsqL2(reg=reg), realizer=Balanced())
delay_disc = LinearNetwork(sys, dt=dt, **kwargs)
delay_cont = LinearNetwork(sys, dt=None, **kwargs)
Connection(u, delay_disc.input, synapse=None)
Connection(u, delay_cont.input, synapse=None)
p_u = Probe(u, synapse=tau_probe)
p_y_disc = Probe(delay_disc.output, synapse=tau_probe)
p_y_cont = Probe(delay_cont.output, synapse=tau_probe)
with Simulator(model, dt=dt, seed=seed) as sim:
sim.run(T)
return (theta, dt, sim.trange(), sim.data[p_u],
sim.data[p_y_disc], sim.data[p_y_cont])
def test_principle3_discrete():
sys = PadeDelay(0.1, order=5)
tau = 0.01
dt = 0.002
syn = Lowpass(tau)
FH = ss2sim(sys, syn, dt=dt)
a = np.exp(-dt / tau)
sys = cont2discrete(sys, dt=dt)
assert np.allclose(FH.A, (sys.A - a * np.eye(len(sys))) / (1 - a))
assert np.allclose(FH.B, sys.B / (1 - a))
assert np.allclose(FH.C, sys.C)
assert np.allclose(FH.D, sys.D)
# We can also do the discretization ourselves and then pass in dt=None
assert ss_equal(
ss2sim(sys, cont2discrete(syn, dt=dt), dt=None), FH)
def test_radii(Simulator, seed, plt):
sys = canonical(PadeDelay(0.2, order=3))
dt = 0.001
T = 0.5
plt.figure()
# Precompute the exact bounds for an impulse stimulus
radii = []
for sub in sys:
response = sub.impulse(int(T / dt), dt=dt)
amplitude = np.max(abs(response))
assert amplitude >= 1e-4 # otherwise numerical issues
radii.append(amplitude)
plt.plot(response / amplitude, linestyle='--')
with Network(seed=seed) as model:
# Impulse stimulus
stim = nengo.Node(output=lambda t: 1 / dt if t <= dt else 0)
# Set explicit radii for controllable realization
subnet = LinearNetwork(sys,
n_neurons_per_ensemble=1,
synapse=0.2,
input_synapse=0.2,
dt=dt,
radii=radii,
realizer=Identity(),
neuron_type=nengo.neurons.Direct())
nengo.Connection(stim, subnet.input, synapse=None)
p = nengo.Probe(subnet.state.output, synapse=None)
with Simulator(model, dt=dt) as sim:
sim.run(T)
plt.plot(sim.data[p], lw=5, alpha=0.5)
assert np.allclose(np.max(abs(sim.data[p]), axis=0), 1, atol=1e-4)
Q = observe_gram(balsys)
diag = np.diag_indices(len(P))
offdiag = np.ones_like(P, dtype=bool)
offdiag[diag] = False
offdiag = np.where(offdiag)
assert np.allclose(P[diag], S)
assert np.allclose(P[offdiag], 0)
assert np.allclose(Q[diag], S)
assert np.allclose(Q[offdiag], 0)
@pytest.mark.parametrize(
"sys",
[PadeDelay(0.1, 4), PadeDelay(0.2, 5, 5),
Alpha(0.2)])
def test_hankel(sys):
assert np.allclose(hsvd(sys), balanced_transformation(sys)[2])
def test_l1_norm_known():
# Check that Lowpass has a norm of exactly 1
l1, rtol = l1_norm(Lowpass(0.1))
assert np.allclose(l1, 1)
assert np.allclose(rtol, 0)
# Check that passthrough is handled properly
assert np.allclose(l1_norm(Lowpass(0.1) + 5)[0], 6)
assert np.allclose(l1_norm(Lowpass(0.1) - 5)[0], 6)
FH = ss2sim(sys, syn, dt=dt)
a = np.exp(-dt / tau)
sys = cont2discrete(sys, dt=dt)
assert np.allclose(FH.A, (sys.A - a * np.eye(len(sys))) / (1 - a))
assert np.allclose(FH.B, sys.B / (1 - a))
assert np.allclose(FH.C, sys.C)
assert np.allclose(FH.D, sys.D)
# We can also do the discretization ourselves and then pass in dt=None
assert ss_equal(
ss2sim(sys, cont2discrete(syn, dt=dt), dt=None), FH)
@pytest.mark.parametrize("sys", [PadeDelay(0.1, order=5), Lowpass(0.1)])
def test_doubleexp_continuous(sys):
tau1 = 0.05
tau2 = 0.02
syn = DoubleExp(tau1, tau2)
FH = ss2sim(sys, syn, dt=None)
A = sys.A
FHA = tau1 * tau2 * np.dot(A, A) + (tau1 + tau2) * A + np.eye(len(A))
B = sys.B
FHB = (tau1 * tau2 * A + (tau1 + tau2) * np.eye(len(A))).dot(B)
assert np.allclose(FH.A, FHA)
assert np.allclose(FH.B, FHB)
assert np.allclose(FH.C, sys.C)
assert np.allclose(FH.D, sys.D)
def go(name, tau, factory, recurrent_solver=Default,
pre_fixture=None, post_fixture=None):
set_style()
theta = 0.1
order = 3
freq = 3
power = 1.0 # chosen to keep radii within [-1, 1]
# print("PadeDelay(%s, %s) => %f%% error @ %sHz" % (
# theta, order, 100*abs(pade_delay_error(theta*freq, order=order)), freq))
pd = PadeDelay(theta=theta, order=order)
# Heuristic for normalizing state so that each dimension is ~[-1, +1]
rz = Balanced()(pd, radii=1./(np.arange(len(pd))+1))
sys = rz.realization
# Compute matrix to transform from state (x) -> sampled window (u)
t_samples = 100
C = np.asarray([readout(len(pd), r)
for r in np.linspace(0, 1, t_samples)]).dot(rz.T)
assert C.shape == (t_samples, len(sys))
n_neurons = 128 # per dimension
map_hw = ss2sim(sys, synapse=Lowpass(tau), dt=None) # analog mapping
assert np.allclose(map_hw.A, tau*sys.A + np.eye(len(sys)))
assert np.allclose(map_hw.B, tau*sys.B)
with nengo.Network() as model:
if pre_fixture is not None:
pre_fixture(model)
u = nengo.Node(output=0, label='u')
p_u = nengo.Probe(u, synapse=None)
# This is needed because a single node can't connect to multiple
# different ensembles. We need a separate node for each ensemble.
Bu = [nengo.Node(output=lambda _, u, b_i=map_hw.B[i].squeeze(): b_i*u,
size_in=1, label='Bu[%d]' % i)
for i in range(len(sys))]
X = []
for i in range(len(sys)):
ens = nengo.Ensemble(
n_neurons=n_neurons, dimensions=1, label='X[%d]' % i)
X.append(ens)
P = []
for i in range(len(sys)):
nengo.Connection(u, Bu[i], synapse=None)
nengo.Connection(Bu[i], X[i], synapse=tau)
for j in range(len(sys)):
nengo.Connection(X[j], X[i], synapse=tau,
function=lambda x_j, a_ij=map_hw.A[i, j]: a_ij*x_j,
solver=recurrent_solver)
P.append(nengo.Probe(X[i], synapse=None))
def do_trial(name, seed, length=2000, dt=0.001, tau_probe=0.02,
sanity=False, **kwargs):
# Note: depends on the globals, (factory, C, model, u, p_u, P, sys)
process = nengo.processes.WhiteSignal(
period=length*dt, rms=power, high=freq, y0=0, seed=seed)
test_u = process.run_steps(length, dt=dt)
x_ideal = sys.X.filt(test_u, dt=dt)
if sanity:
analyze("ideal-%s" % name,
t=process.ntrange(length, dt=dt),
u=test_u,
x_hat=x_ideal,
x_ideal=x_ideal,
C=C,
theta=theta,
dump_file=False,
do_plot=False)
u.output = process
with factory(network=model, dt=dt) as sim:
sim.run(length*dt)
if post_fixture:
post_fixture(sim)
assert np.allclose(test_u, np.asarray(sim.data[p_u]))
# Use discrete principle 3, offline, to get x_hat
# from the unfiltered spikes representing x.
# This is analagous to probing the PSC, pre-encoding.
syn_probe = Lowpass(tau_probe)
map_out = ss2sim(sys, synapse=syn_probe, dt=dt)
x_raw = np.asarray([sim.data[p] for p in P]).squeeze()
f = map_out.A.dot(x_raw) + map_out.B.dot(test_u.T)
x_hat = syn_probe.filt(f, axis=1, dt=dt).T
return analyze(
name=name, t=sim.trange(), u=test_u,
x_hat=x_hat, x_ideal=x_ideal, C=C,
theta=theta, **kwargs)
data = defaultdict(list)
for trial in range(25):
for seed in range(1, 11):
data['Trial'].append(trial)
data['Test Case (#)'].append(seed)
data['NRMSE'].append(
do_trial(name="scratch-%s-DN-%d-%d" % (name, trial, seed),
seed=seed, dump_file=False))
df = DataFrame(data)
df.to_pickle(datapath("%s-delay-network.pkl" % name))
return bs.bootstrap(np.asarray(df['NRMSE']),
stat_func=bs_stats.mean, alpha=1-0.95) # 95% CI
def figure_delay_full(targets):
q = 12
freq_times_theta = 1.0
theta = 0.2 # affects resolution of plot alongside dt
T = 10 * theta
dt = 0.005
seed = 5 # chosen to look "interesting"
u = WhiteSignal(T, high=freq_times_theta / theta, seed=seed, y0=0,
rms=0.4).run(T, dt=dt)
t = np.arange(0, T, dt)
i = q - 1 - np.arange(q, dtype=np.float64)
assert np.allclose(delay_readout(q, theta, theta),
(-1)**(q - 1 - i) * (i + 1) / q)
num_thetas = 200
num_freqs = 200
props = np.linspace(0, 1.0, num_thetas)
freqs = np.linspace(0, 10. / theta, num_freqs)
s = 2.j * np.pi * freqs
cmap = sns.diverging_palette(h_neg=34,
h_pos=215,
s=99,
l=66,
sep=1,
center="dark",
n=num_thetas)
with sns.axes_style('ticks'):
with sns.plotting_context('paper', font_scale=2.8):
pylab.figure(figsize=(18, 5))
gs = gridspec.GridSpec(1, 2, width_ratios=[1, 1.618])
gs.update(wspace=0.3)
ax1 = plt.subplot(gs[0])
ax2 = plt.subplot(gs[1])
ax1.set_title(r"Temporal Coding Accuracy").set_y(1.05)
ax2.set_title(r"Decoding at $\theta$ = Frequency$^{-1}$").set_y(
1.05)
for i, thetap in enumerate(props * theta):
A, B, _, D = PadeDelay(theta, order=q).ss
C = delay_readout(q, thetap, theta)[::-1]
tf = LinearSystem((A, B, C, D))
ax1.plot(freqs * theta,
abs(np.exp(-s * thetap) - tf(s)),
c=cmap[i],
alpha=0.7)
ax2.plot(t / theta, tf.filt(u, dt=dt), c=cmap[i], alpha=0.5)
s = 0.4
pts = np.asarray([[freq_times_theta, 0],
[(1 - s) * freq_times_theta, -s / 2],
[(1 + s) * freq_times_theta, -s / 2]])
ax1.add_patch(Polygon(pts, closed=True, color='black'))
ax1.set_xlabel(r"Frequency $\times \, \theta$ [Hz $\times$ s]",
labelpad=20)
ax1.set_ylabel(r"Absolute Error", labelpad=20)
ax2.set_xlabel(r"Time $\times \, \theta^{-1}$ [s / s]",
labelpad=20)
ax2.set_ylabel(r"Output")
lc = LineCollection(len(cmap) * [[(0, 0)]], lw=10, colors=cmap)
ax2.legend([lc], [r"$\theta'$"],
handlelength=2,
handler_map={type(lc): HandlerDashedLines()},
bbox_to_anchor=(1.02, 1),
loc=2,
borderaxespad=0.)
sns.despine(offset=15)
savefig(targets[0])
def delay_example():
seed = 2
n_neurons = 1000
theta = 1.0
sys = PadeDelay(theta, 6)
T = 20.0
dt = 0.001
freq = 1
rms = 0.4
tau = 0.1
#tau_probe = 0.02
radii = np.ones(len(sys)) # initial guess
desired_radius = 0.8 # aiming to get this as largest x
num_iter = 5 # number of times to simulate and retry new radius
# could also do this simply by the direct method in discrete_example
# but this is just to demonstrate that you can do something iterative
# within the same network
for _ in range(num_iter):
with Network(seed=seed) as model:
signal = WhiteSignal(T, high=freq, rms=rms, y0=0)
u = Node(output=signal)
delay = LinearNetwork(
sys, n_neurons_per_ensemble=n_neurons / len(sys), synapse=tau,
input_synapse=tau, radii=radii, realizer=Balanced(), dt=None)
Connection(u, delay.input, synapse=None)
# Since delay.state.input is the PSC x, when we can transform
# that with C to get y (note D=0) without applying any filters
assert np.allclose(delay.D, 0)
output = Node(size_in=1)
Connection(delay.state.input, output, transform=delay.C,
synapse=None)
# Alternative: create an output tau*dy + y such that when
# filtered we get back y! Note: dy = C(Ax + Bu), since D=0.
#Connection(delay.state.output, output,
# transform=tau*delay.C.dot(delay.A), synapse=tau)
#Connection(u, output,
# transform=tau*delay.C.dot(delay.B), synapse=tau)
#Connection(delay.output, output, synapse=tau)
p_u = Probe(u, synapse=None)
p_x = Probe(delay.state.input, synapse=None)
p_a = Probe(delay.state.add_neuron_output(), synapse=None)
p_y = Probe(output, synapse=None)
with Simulator(model, dt=dt, seed=seed) as sim:
sim.run(T)
# place the worst case at x=desired_radius and re-run
worst_x = np.max(np.abs(sim.data[p_x]), axis=0)
radii *= (worst_x / desired_radius)
logging.info("Radii: %s\nWorst x: %s", radii, worst_x)
return (theta, dt, sim.trange(), sim.data[p_u], sim.data[p_x],
sim.data[p_a], sim.data[p_y])
def figure_pca(targets):
orders = [3, 6, 9, 12, 15, 27]
theta = 10.
dt = 0.01
T = theta
length = int(T / dt)
t = np.linspace(0, T - dt, length)
t_norm = np.linspace(0, 1, len(t))
cmap = sns.diverging_palette(h_neg=34,
h_pos=215,
s=99,
l=66,
sep=1,
center="dark",
as_cmap=True)
class MidpointNormalize(colors.Normalize):
"""Stolen from http://matplotlib.org/users/colormapnorms.html"""
def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
self.midpoint = midpoint
colors.Normalize.__init__(self, vmin, vmax, clip)
def __call__(self, value, clip=None):
# I'm ignoring masked values and all kinds of edge cases to make a
# simple example...
x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
return np.ma.masked_array(np.interp(value, x, y))
with sns.axes_style('white'):
with sns.plotting_context('paper', font_scale=2.8):
pylab.figure(figsize=(22, 7))
gs = gridspec.GridSpec(2, len(orders), height_ratios=[1.3, 1])
for k, order in enumerate(orders):
F = PadeDelay(theta, order)
A = F.A
dA, dB, _, _ = cont2discrete(F, dt=dt).ss
dx = np.empty((length, len(F)))
x = np.empty((length, len(F)))
x[0, :] = dB.squeeze() # the x0 from delta input
for j in range(length - 1):
dx[j, :] = A.dot(x[j, :])
x[j + 1, :] = dA.dot(x[j, :])
dx[-1, :] = A.dot(x[-1, :])
# Compute PCA of trajectory for top half
pca = PCA(x, standardize=False)
p = pca.Y[:, :3]
logging.info("%d Accounted Variance: %s", order,
np.sum(pca.fracs[:3]) / np.sum(pca.fracs))
# Compute curve for bottom half (and color map center)
dist = np.cumsum(np.linalg.norm(dx, axis=1))
dist = dist / np.max(dist)
infl = np.where((np.diff(np.diff(dist)) >= 0)
& (t_norm[:-2] >= 0))[0][-1]
cnorm = MidpointNormalize(midpoint=t_norm[infl])
ax = plt.subplot(gs[k], projection='3d')
ax.set_title(r"$q = %d$" % order).set_y(1.1)
# Draw in reverse order so the start is on top
ax.scatter(p[::-1, 0],
p[::-1, 1],
p[::-1, 2],
lw=5,
c=t_norm[::-1],
cmap=cmap,
norm=cnorm,
alpha=0.5)
ax.set_xticks([])
ax.set_yticks([])
ax.set_zticks([])
if k == 0:
ax.annotate('PCA',
xy=(-50, 150),
ha='left',
va='top',
size=22,
rotation=90,
bbox=None,
xycoords='axes points')
ax.view_init(elev=25, azim=150)
ax = plt.subplot(gs[len(orders) + k])
ax.scatter(t, dist, lw=5, c=t_norm, cmap=cmap, norm=cnorm)
ax.vlines(t[infl], 0, 1, linestyle='--', lw=3, alpha=0.7)
if k == 0:
ax.set_yticks([0, 1])
ax.set_ylabel("Length of Curve")
else:
ax.set_yticks([])
ax.set_xticks([0, theta / 2, theta])
ax.set_xticklabels(
[r"$0$", r"$\frac{\theta}{2}$", r"$\theta$"])
ax.xaxis.set_tick_params(pad=10)
ax.set_xlabel("Time [s]", labelpad=20)
sns.despine(offset=10, ax=ax)
savefig(targets[0])
([[-20, 1], [-100, 0]], [[0], [100]], [[1, 0]], [[0]]))
def test_is_stable():
sys = Lowpass(0.1)
assert sys.is_stable
assert not (~s).is_stable # integrator
assert LinearSystem(1).is_stable
assert (~(z * (z - 0.5))).is_stable
assert not (z / (z - 1)).is_stable # discrete integrator
@pytest.mark.parametrize("sys", [PadeDelay(0.1, 4), PadeDelay(0.2, 5, 5)])
def test_decompose_states(sys):
assert np.dot(sys.C, list(sys)) + sys.D == sys
def test_non_siso_manipulation():
sys = Alpha(0.1)
A, B, C, D = sys.ss
SIMO = LinearSystem((A, B, np.eye(len(A)), [[0], [0]]))
assert not SIMO.is_SISO
assert SIMO.size_in == 1
assert SIMO.size_out == 2
assert SIMO.shape == (2, 1)
assert not SIMO.has_passthrough
assert ss_equal(_eval(SIMO), SIMO)
rsys = realize_result.realization
assert ss_equal(rsys, sys.transform(realize_result.T))
# Check that it's still the same system, even though different matrices
assert sys_equal(sys, rsys)
if radii == 1:
assert ss_equal(sys, rsys)
else:
assert not np.allclose(sys.B, rsys.B)
assert not np.allclose(sys.C, rsys.C)
# Check that the state vectors have scaled power
assert np.allclose(state_norm(sys) / radii, state_norm(rsys))
@pytest.mark.parametrize("sys", [PadeDelay(0.1, 4), PadeDelay(0.05, 5, 5)])
def test_balreal_normalization(sys):
radii = np.arange(len(sys)) + 1
balanced = Balanced()
assert repr(balanced) == "Balanced()"
realizer_result = balanced(sys, radii)
T, Tinv, _ = balanced_transformation(sys)
assert np.allclose(realizer_result.T / radii[None, :], T)
assert np.allclose(realizer_result.Tinv * radii[:, None], Tinv)
assert np.allclose(inv(T), Tinv)
assert sys_equal(sys, realizer_result.realization)
def figure_principle3(targets):
theta = 0.1
tau = 0.1 * theta
lmbda = tau
orders = range(6, 28)
freqs = np.linspace(0.1 / theta, 16 / theta, 1000)
s = 2.j * np.pi * freqs
y = np.exp(-theta * s)
Hinvs = (tau * s + 1) * np.exp(lmbda * s)
cmap_lamb = sns.color_palette("GnBu_d", len(orders))[::-1]
cmap_ignore = sns.color_palette("OrRd_d", len(orders))[::-1]
data = np.empty((2, len(DISCRETE_DTS), DISCRETE_SEEDS))
for seed in range(DISCRETE_SEEDS):
for i, dt in enumerate(DISCRETE_DTS):
npfile = np.load(DISCRETE_SIM % (seed, i))
assert np.allclose(npfile['dt'], dt)
delay = npfile['delay']
# t = npfile['t']
stim = npfile['stim']
disc = npfile['disc']
cont = npfile['cont']
target = ideal_delay(stim, delay, dt)
e_disc = nrmse(disc, target=target)
e_cont = nrmse(cont, target=target)
data[0, i, seed] = e_disc
data[1, i, seed] = e_cont
i = np.where(DISCRETE_DTS == 0.001)[0][0]
assert np.allclose(DISCRETE_DTS[i], 0.001)
e_disc = np.mean(data, axis=2)[0, i]
e_cont = np.mean(data, axis=2)[1, i]
improvement = (e_cont - e_disc) / e_cont * 100
logging.info("Paper constant: Improvement at 1 ms: %s (%s -> %s)",
improvement, e_cont, e_disc)
with sns.axes_style('ticks'):
with sns.plotting_context('paper', font_scale=2.8):
f, (ax1, ax2) = pylab.subplots(1, 2, figsize=(18, 5))
ax1.set_title("Discrete Lowpass Improvement").set_y(1.05)
for i, condition, cpal, marker in ((1, 'Principle 3',
sns.color_palette("OrRd_d"),
'X'),
(0, 'Extension',
sns.color_palette("GnBu_d"),
'o')):
sns.tsplot(data[i].T,
1000 * DISCRETE_DTS,
condition=condition,
color=cpal,
marker=marker,
markersize=15,
lw=3,
ci=95,
alpha=0.7,
ax=ax1)
ax1.vlines([1.0],
np.min(data[0]),
2.0,
linestyle='--',
color='black',
lw=4,
alpha=0.7,
zorder=0)
ax1.set_xlabel("Discrete Time-step [ms]", labelpad=20)
ax1.set_ylabel("Absolute Error", labelpad=20)
ax1.set_xlim(0, 1000 * DISCRETE_DTS[-1] + 0.1)
ax2.set_title("Delayed Lowpass Improvement").set_y(1.05)
for i, q in enumerate(orders):
sys = PadeDelay(theta, order=q)
mapped = ss2sim(sys, Lowpass(tau), dt=None)
lambert = lambert_delay(theta, lmbda, tau, q - 1, q)
y_lamb = lambert(Hinvs)
y_ignore = mapped(Hinvs)
ax2.semilogy(freqs * theta,
abs(y - y_ignore),
lw=2,
alpha=0.8,
zorder=len(orders) - i,
c=cmap_ignore[i])
ax2.semilogy(freqs * theta,
abs(y - y_lamb),
lw=2,
alpha=0.8,
zorder=len(orders) - i,
c=cmap_lamb[i])
lc_ignore = LineCollection(len(orders) * [[(0, 0)]],
lw=10,
colors=cmap_ignore)
lc_lamb = LineCollection(len(orders) * [[(0, 0)]],
lw=10,
colors=cmap_lamb)
ax2.legend([lc_ignore, lc_lamb], ['Principle 3', 'Extension'],
handlelength=2,
handler_map={LineCollection: HandlerDashedLines()})
ax2.set_xlabel(r"Frequency $\times \, \theta$ [Hz $\times$ s]",
labelpad=20)
sns.despine(offset=15)
savefig(targets[0])
def compute_error(phi_times_freq, depth, order, p=None):
pf = np.asarray(phi_times_freq)
# switch to a delay of 1 for simplicity
# this works due to the substitution of variables: theta*s <-> 1*s'
sys = PadeDelay(1., order, p=p)
return sys.evaluate(pf / depth)**depth - np.exp(-2j * np.pi * pf)
def figure_lambert(targets):
npfile = np.load(LAMBERT_SIM)
delay = npfile['delay']
dt = npfile['dt']
t = npfile['t']
stim = npfile['stim']
delayed = npfile['delayed']
lowpass = npfile['lowpass']
dexp = npfile['dexp']
target = ideal_delay(stim, delay, dt)
e_delayed = nrmse(delayed, target=target)
e_lowpass = nrmse(lowpass, target=target)
e_dexp = nrmse(dexp, target=target)
improvement = (e_lowpass - e_delayed) / e_lowpass * 100
logging.info("Paper constant: Lambert improvement: %s", improvement)
logging.info("Paper constant: Delayed NRMSE: %s", e_delayed)
logging.info("Paper constant: Lowpass NRMSE: %s", e_lowpass)
logging.info("Paper constant: Double Exp NRMSE: %s", e_dexp)
sample_rate = 100
t = t[::sample_rate]
stim = stim[::sample_rate]
delayed = delayed[::sample_rate]
lowpass = lowpass[::sample_rate]
dexp = dexp[::sample_rate]
target = target[::sample_rate]
tau_over_theta = 0.1
lambda_over_tau = 1.0
max_freq_times_theta = 4.0
theta = 0.1 # <-- the graph is still the same, no matter theta!
tau = tau_over_theta * theta
lmbda = lambda_over_tau * tau
max_freq = max_freq_times_theta / theta
tau2 = tau / 5 # TODO: parameters copied from delayed_synapse()
q = 6
assert np.allclose(tau, 0.01)
assert np.allclose(lmbda, 0.01)
freqs = np.linspace(0, max_freq, 200)
s = 2.j * np.pi * freqs
axis = freqs * theta # scale-invariant axis
lw = 3
alpha = 0.8
cmap = sns.color_palette(None, 4)
F_lamb = lambert_delay(theta, lmbda, tau, q - 1, q)
F_low = ss2sim(PadeDelay(theta, order=q), Lowpass(tau), dt=None)
F_alpha = ss2sim(PadeDelay(theta, order=q), DoubleExp(tau, tau2), dt=None)
y_low = F_low(tau * s + 1)
y_lamb = F_lamb((tau * s + 1) * np.exp(lmbda * s))
y_alpha = F_alpha((tau * s + 1) * (tau2 * s + 1))
y = np.exp(-theta * s)
# Compute where lmbda*s + lmbda/tau is within the principal branch
tx = lmbda * 2 * np.pi * freqs
st = (lmbda / tau > -(tx / np.tan(tx))) & (tx < np.pi) | (tx == 0)
p = lmbda * s[st] + lmbda / tau
assert np.allclose(lambertw(p * np.exp(p)), p)
with sns.axes_style('ticks'):
with sns.plotting_context('paper', font_scale=2.8):
pylab.figure(figsize=(18, 5))
gs = gridspec.GridSpec(1, 2, width_ratios=[1, 1.618])
gs.update(wspace=0.3)
ax1 = plt.subplot(gs[1])
ax2 = plt.subplot(gs[0])
ax1.set_title(r"$0.1\,$s Delay of $15\,$Hz White Noise").set_y(
1.05)
ax1.plot(t, target, lw=4, c=cmap[0], zorder=4, linestyle='--')
ax1.plot(t, lowpass, alpha=alpha, c=cmap[1], zorder=2)
ax1.plot(t, dexp, alpha=alpha, c=cmap[3], zorder=2)
ax1.plot(t, delayed, alpha=alpha, c=cmap[2], zorder=3)
ax1.set_ylim(-0.5, 0.5)
ax1.set_xlabel("Time [s]", labelpad=20)
ax1.set_ylabel("Output")
ax2.set_title("Delay Accuracy").set_y(1.05)
ax2.plot(axis,
np.zeros_like(freqs),
lw=lw,
c=cmap[0],
zorder=4,
linestyle='--',
label=r"Ideal")
ax2.plot(axis,
abs(y - y_low),
lw=lw,
alpha=alpha,
c=cmap[1],
zorder=3,
label=r"Lowpass")
ax2.plot(axis,
abs(y - y_alpha),
lw=lw,
alpha=alpha,
c=cmap[3],
zorder=3,
label=r"Double-exponential")
ax2.plot(axis,
abs(y - y_lamb),
lw=lw,
alpha=alpha,
c=cmap[2],
zorder=3,
label=r"Delayed Lowpass")
s = 0.8
pts = np.asarray([[1.5, 0], [1.5 - s, -s], [1.5 + s, -s]])
ax2.add_patch(Polygon(pts, closed=True, color='black'))
ax2.set_xlabel(r"Frequency $\times \, \theta$ [Hz $\times$ s]",
labelpad=20)
ax2.set_ylabel(r"Absolute Error", labelpad=20)
ax2.legend(loc='upper left',
frameon=True).get_frame().set_alpha(0.8)
sns.despine(offset=15)
savefig(targets[0])
