def test_similarity_transform():
sys = Alpha(0.1)
TA, TB, TC, TD = sys.transform(np.eye(2), np.eye(2)).ss
A, B, C, D = sys2ss(sys)
assert np.allclose(A, TA)
assert np.allclose(B, TB)
assert np.allclose(C, TC)
assert np.allclose(D, TD)
T = [[1, 1], [-0.5, 0]]
rsys = sys.transform(T)
assert ss_equal(rsys, sys.transform(T, inv(T)))
TA, TB, TC, TD = rsys.ss
assert not np.allclose(A, TA)
assert not np.allclose(B, TB)
assert not np.allclose(C, TC)
assert np.allclose(D, TD)
assert sys_equal(sys, (TA, TB, TC, TD))
length = 1000
dt = 0.001
x_old = np.asarray(
[sub.impulse(length=length, dt=dt) for sub in sys])
x_new = np.asarray(
[sub.impulse(length=length, dt=dt) for sub in rsys])
# dot(T, x_new(t)) = x_old(t)
assert np.allclose(np.dot(T, x_new), x_old)
def test_identity(radii):
sys = Alpha(0.1)
identity = Identity()
assert repr(identity) == "Identity()"
I = np.eye(len(sys))
realize_result = identity(sys, radii)
assert realize_result.sys is sys
assert np.allclose(realize_result.T, I * radii)
assert np.allclose(realize_result.Tinv, inv(I * radii))
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))
def test_impulse_dt():
length = 1000
sys = Alpha(0.1)
# the dt should not alter the magnitude of the response
assert np.allclose(
max(sys.impulse(length, dt=0.001)),
max(sys.impulse(length, dt=0.0005)),
atol=1e-4)
def test_mapping(Simulator, plt, seed):
sys = Alpha(0.1)
syn = Lowpass(0.01)
gsyn = 2*syn # scaled lowpass
isyn = 2/s # scaled integrator
dt = 0.001
ss = ss2sim(sys, syn, None) # normal lowpass, continuous
dss = ss2sim(sys, syn, dt) # normal lowpass, discrete
gss = ss2sim(sys, gsyn, None) # scaled lowpass, continuous
gdss = ss2sim(sys, gsyn, dt) # scaled lowpass, discrete
iss = ss2sim(sys, isyn, None) # scaled integrator, continuous
idss = ss2sim(sys, isyn, dt) # scaled integrator, discrete
assert ss.analog and gss.analog and iss.analog
assert not (dss.analog or gdss.analog or idss.analog)
with Network(seed=seed) as model:
stim = nengo.Node(output=lambda t: np.sin(20*np.pi*t))
probes = []
for mapped, synapse in ((ss, syn), (dss, syn), (gss, gsyn),
(gdss, gsyn), (iss, isyn), (idss, isyn)):
A, B, C, D = mapped.ss
x = nengo.Node(size_in=2)
y = nengo.Node(size_in=1)
nengo.Connection(stim, x, transform=B, synapse=synapse)
nengo.Connection(x, x, transform=A, synapse=synapse)
nengo.Connection(x, y, transform=C, synapse=None)
nengo.Connection(stim, y, transform=D, synapse=None)
probes.append(nengo.Probe(y))
p_stim = nengo.Probe(stim)
pss, pdss, pgss, pgdss, piss, pidss = probes
with Simulator(model, dt=dt) as sim:
sim.run(1.0)
expected = shift(sys.filt(sim.data[p_stim], dt))
plt.plot(sim.trange(), sim.data[pss], label="Continuous", alpha=0.5)
plt.plot(sim.trange(), sim.data[pdss], label="Discrete", alpha=0.5)
plt.plot(sim.trange(), sim.data[pgss], label="Gain Cont.", alpha=0.5)
plt.plot(sim.trange(), sim.data[pgdss], label="Gain Disc.", alpha=0.5)
plt.plot(sim.trange(), sim.data[piss], label="Integ Cont.", alpha=0.5)
plt.plot(sim.trange(), sim.data[pidss], label="Integ Disc.", alpha=0.5)
plt.plot(sim.trange(), expected, label="Expected", linestyle='--')
plt.legend()
assert np.allclose(sim.data[pss], expected, atol=0.01)
assert np.allclose(sim.data[pdss], expected)
assert np.allclose(sim.data[pgss], expected, atol=0.01)
assert np.allclose(sim.data[pgdss], expected)
assert np.allclose(sim.data[piss], expected, atol=0.01)
assert np.allclose(sim.data[pidss], expected)
def test_modred(rng):
dt = 0.001
isys = Lowpass(0.05)
noise = 0.5 * Lowpass(0.01) + 0.5 * Alpha(0.005)
p = 0.999
sys = p * isys + (1 - p) * noise
T, Tinv, S = balanced_transformation(sys)
balsys = sys.transform(T, Tinv)
# Keeping just the best state should remove the 3 noise dimensions
# Discarding the lowest state should do at least as well
for keep_states in (S.argmax(),
list(set(range(len(sys))) - set((S.argmin(), )))):
delsys = modred(balsys, keep_states)
assert delsys.order_den == np.asarray(keep_states).size
u = rng.normal(size=2000)
expected = sys.filt(u, dt)
actual = delsys.filt(u, dt)
assert rmse(expected, actual) < 1e-4
step = np.zeros(2000)
step[50:] = 1.0
dcsys = modred(balsys, keep_states, method='dc')
assert np.allclose(dcsys.dcgain, balsys.dcgain)
# use of shift related to nengo issue #938
assert not sys.has_passthrough
assert dcsys.has_passthrough
expected = shift(sys.filt(step, dt))
actual = dcsys.filt(step, dt)
assert rmse(expected, actual) < 1e-4
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)
assert isinstance(str(SIMO), str)
assert ss_equal(canonical(SIMO), SIMO)
for sub1, sub2 in zip(sys, SIMO):
assert ss_equal(sub1, sub2)
MISO = LinearSystem((A, [[1, 1]], C, [[0, 1]]))
assert not MISO.is_SISO
assert MISO.size_in == 2
assert MISO.size_out == 1
assert MISO.shape == (1, 2)
assert MISO.has_passthrough
assert ss_equal(_eval(MISO), MISO)
assert isinstance(str(MISO), str)
MIMO = LinearSystem((A, [[1, 1]], np.eye(len(A)), np.zeros((2, 2))))
assert not MIMO.is_SISO
assert MIMO.size_in == MIMO.size_out == 2
assert MIMO.shape == (2, 2)
assert not MIMO.has_passthrough
assert ss_equal(_eval(MIMO), MIMO)
assert isinstance(str(MIMO), str)
for sub1, sub2 in zip(MISO, MIMO):
assert ss_equal(sub1, sub2)
def test_balreal():
isys = Lowpass(0.05)
noise = 0.5 * Lowpass(0.01) + 0.5 * Alpha(0.005)
p = 0.8
sys = p * isys + (1 - p) * noise
T, Tinv, S = balanced_transformation(sys)
assert np.allclose(inv(T), Tinv)
assert np.allclose(S, hsvd(sys))
balsys = sys.transform(T, Tinv)
assert balsys == sys
assert np.all(S >= 0)
assert np.all(S[0] > 0.3)
assert np.all(S[1:] < 0.05)
assert np.allclose(sorted(S, reverse=True), S)
P = control_gram(balsys)
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)
def test_balred(rng):
dt = 0.001
sys = Alpha(0.01) + Lowpass(0.001)
u = rng.normal(size=2000)
expected = sys.filt(u, dt)
def check(order, within, tol, method='del'):
redsys = balred(sys, order, method=method)
assert redsys.order_den <= order
actual = redsys.filt(u, dt)
assert abs(rmse(expected, actual) - within) < tol
with warns(UserWarning):
check(4, 0, 1e-13)
with warns(UserWarning):
check(3, 0, 1e-13)
check(2, 0.03, 0.01)
check(1, 0.3, 0.1)
def test_grams():
sys = 0.6 * Alpha(0.01) + 0.4 * Lowpass(0.05)
A, B, C, D = sys2ss(sys)
P = control_gram(sys)
assert np.allclose(np.dot(A, P) + np.dot(P, A.T), -np.dot(B, B.T))
assert matrix_rank(P) == len(P) # controllable
Q = observe_gram(sys)
assert np.allclose(np.dot(A.T, Q) + np.dot(Q, A), -np.dot(C.T, C))
assert matrix_rank(Q) == len(Q) # observable
def test_func_realize(radii):
sys = Alpha(0.1)
T = np.asarray([[1., 2.], [0, -1.]])
for Tinv in (None, inv(T)):
realize_result = _realize(sys, radii, T, Tinv)
assert realize_result.sys is sys
assert np.allclose(inv(realize_result.T), realize_result.Tinv)
rsys = realize_result.realization
assert ss_equal(rsys, sys.transform(realize_result.T))
# Check that the state vector are related by T
length = 1000
dt = 0.001
x_old = np.asarray([sub.impulse(length, dt) for sub in sys])
x_new = np.asarray([sub.impulse(length, dt) for sub in rsys])
r = np.atleast_2d(np.asarray(radii).T).T
assert np.allclose(np.dot(T, x_new * r), x_old)
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)
# Check that Alpha scaled by a has a norm of approximately abs(a)
for a in (-2, 3):
for desired_rtol in (1e-1, 1e-2, 1e-4, 1e-8):
l1, rtol = l1_norm(a * Alpha(0.1), rtol=desired_rtol)
assert np.allclose(l1, abs(a), rtol=rtol)
assert rtol <= desired_rtol
def test_state_norm(plt):
# Choose a filter, timestep, and number of simulation timesteps
sys = Alpha(0.1)
dt = 0.000001
length = 2000000
assert np.allclose(dt * length, 2.0)
# Check that the power of each state equals the H2-norm of each state
# The analog case is the same after scaling since dt is approx 0.
response = sys.X.impulse(length, dt)
actual = norm(response, axis=0) * dt
assert np.allclose(actual, state_norm(cont2discrete(sys, dt)))
assert np.allclose(actual, state_norm(sys) * np.sqrt(dt))
step = int(0.002 / dt)
plt.figure()
plt.plot(response[::step, 0], label="$x_0$")
plt.plot(response[::step, 1], label="$x_1$")
plt.plot(np.dot(response[::step], sys.C.T), label="$y$")
plt.legend()
def test_sys_conversions():
sys = Alpha(0.1)
tf = sys2tf(sys)
ss = sys2ss(sys)
zpk = sys2zpk(sys)
assert sys_equal(sys2ss(tf), ss)
assert sys_equal(sys2ss(ss), ss) # unchanged
assert sys_equal(sys2tf(tf), tf) # unchanged
assert sys_equal(sys2tf(ss), tf)
assert sys_equal(sys2zpk(zpk), zpk) # sanity check
assert sys_equal(sys2zpk(tf), zpk) # sanity check
assert sys_equal(sys2zpk(ss), zpk)
assert sys_equal(sys2tf(zpk), tf)
assert sys_equal(sys2ss(zpk), ss)
# should also work with nengo's synapse types
assert sys_equal(sys2zpk(nengo.Alpha(0.1)), zpk)
assert sys_equal(sys2tf(nengo.Alpha(0.1)), tf)
assert sys_equal(sys2ss(nengo.Alpha(0.1)), ss)
# system can also be just a scalar
assert sys_equal(sys2tf(2.0), (1, 0.5))
assert np.allclose(sys2ss(5)[3], 5)
assert sys_equal(sys2zpk(5), 5)
with pytest.raises(ValueError):
sys2ss(np.zeros(5))
with pytest.raises(ValueError):
sys2zpk(np.zeros(5))
with pytest.raises(ValueError):
sys2tf(np.zeros(5))
with pytest.raises(ValueError):
# _ss2tf(...): passthrough must be single element
sys2tf(([], [], [], [1, 2]))
sub2 = LinearSystem((sys.A, B, I[i:i+1], np.zeros((1, 3))))
_transclose(sub1.filt(u), sub2.filt(u), y[:, i])
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)
@pytest.mark.parametrize("sys", [
Lowpass(0.01), Alpha(0.2), LinearSystem(([1, 1], [0.01, 1]))])
def test_simulation(sys, Simulator, plt, seed):
assert isinstance(sys, LinearSystem)
old_sys = nengo.LinearFilter(sys.num, sys.den)
assert sys == old_sys
with Network() as model:
stim = nengo.Node(output=nengo.processes.WhiteSignal(
1.0, high=10, seed=seed))
out_new = nengo.Node(size_in=2)
out_old = nengo.Node(size_in=2)
nengo.Connection(stim, out_new, transform=[[1], [-1]], synapse=sys)
nengo.Connection(stim, out_old, transform=[[1], [-1]], synapse=old_sys)
p_new = nengo.Probe(out_new)
p_old = nengo.Probe(out_old)
def test_given_dt():
process = WhiteSignal(1.0, high=10)
assert EvalPoints(Alpha(0.1), process, dt=0.1).dt == 0.1
def test_invalid_process():
with pytest.raises(ValidationError):
assert EvalPoints(Alpha(0.1), process=1)
def test_alpha_whitesignal(Simulator, seed, rng, plt):
# Pick test LinearSystem
sys = Alpha(0.1)
state = sys.X
assert state.shape == (2, 1)
# Pick test process
n_steps = 1000
dt = 0.01
process = WhiteSignal(5.0, high=10, default_dt=dt, seed=seed)
# Sample evaluation points
dist = EvalPoints(state, process, n_steps=n_steps)
assert dist.n_steps == n_steps
assert dist.dt == dt # taken from process
assert isinstance(repr(dist), str)
n_eval_points = 500
eval_points = dist.sample(n_eval_points, 2, rng=rng)
assert eval_points.shape == (n_eval_points, 2)
# Sample encoders
encoders = Encoders(state, process).sample(n_eval_points, 2, rng=rng)
assert encoders.shape == (n_eval_points, 2)
plt.figure()
# plt.scatter(*encoders.T, label="Encoders")
plt.scatter(*eval_points.T, s=2, marker='*', label="Eval Points")
# Check that most evaluation points fall within radii
x_m, zero, x_p = sorted(np.unique(encoders[:, 0]))
assert np.allclose(-x_m, x_p)
assert np.allclose(zero, 0)
sl = (1 / x_m < eval_points[:, 0]) & (eval_points[:, 0] < 1 / x_p)
assert np.count_nonzero(sl) / float(n_eval_points) >= 0.99
y_m, zero, y_p = sorted(np.unique(encoders[:, 1]))
assert np.allclose(zero, 0)
assert np.allclose(-y_m, y_p)
sl = (1 / y_m < eval_points[:, 1]) & (eval_points[:, 1] < 1 / y_p)
assert np.count_nonzero(sl) / float(n_eval_points) >= 0.99
# Simulate same process / system in nengo network
with Network() as model:
output = nengo.Node(output=process)
probes = [nengo.Probe(output, synapse=sub) for sub in sys]
with Simulator(model, dt=dt) as sim:
sim.run(n_steps * dt)
plt.scatter(sim.data[probes[0]],
sim.data[probes[1]],
s=1,
alpha=0.5,
label="Simulated")
plt.legend()
# Check that each eval_point is a subset / sample from the ideal
ideal = np.asarray(
[sim.data[probes[0]].squeeze(), sim.data[probes[1]].squeeze()]).T
assert ideal.shape == (n_steps, 2)
for pt in eval_points:
dists = np.linalg.norm(ideal - pt[None, :], axis=1)
assert dists.shape == (n_steps, )
assert np.allclose(np.min(dists), 0)
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)
# Check that Alpha scaled by a has a norm of approximately abs(a)
def test_sys_multiplication():
# Check that alpha is just two lowpass multiplied together
assert Lowpass(0.1) * Lowpass(0.1) == Alpha(0.1)
sim.run(T)
# lower bound includes both approximation error and the gap between
# random {-1, 1} flip-flop inputs and the true worst-case input
worst_x = np.max(abs(sim.data[p]), axis=0)
assert (lower <= worst_x + eps).all()
assert (worst_x <= 1 + eps).all()
def test_l1_repr():
assert (repr(L1Norm(rtol=.1,
max_length=10)) == "L1Norm(rtol=0.1, max_length=10)")
@pytest.mark.parametrize("sys,lower", [(Lowpass(0.005), 1.0),
(Alpha(0.01), 0.3),
(Bandpass(50, 5), 0.05),
(Highpass(0.01, 4), 0.1),
(PadeDelay(0.1, 2, 2), 0.3)])
def test_hankel_normalization(Simulator, rng, sys, lower):
_test_normalization(Simulator,
sys,
rng,
Hankel(),
l1_lower=0.5,
lower=lower)
@pytest.mark.parametrize("radius", [0.5, 5, 10])
@pytest.mark.parametrize("sys", [Lowpass(0.005)])
def test_normalization_radius(Simulator, rng, sys, radius):
