def test_alpha(Simulator, 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-5)
def test_alpha(Simulator, 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-5)
def test_linear_analog(Simulator, seed):
dt = 1e-3
# The following num, den are for a 4th order analog Butterworth filter,
# generated with `scipy.signal.butter(4, 200, analog=True)`
num = np.array([1.60000000e+09])
den = np.array([1.00000000e+00, 5.22625186e+02, 1.36568542e+05,
2.09050074e+07, 1.60000000e+09])
synapse = LinearFilter(num, den, analog=True)
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, atol=5e-4)
def test_linear_analog(Simulator, seed):
dt = 1e-3
# The following num, den are for a 4th order analog Butterworth filter,
# generated with `scipy.signal.butter(4, 200, analog=True)`
num = np.array([1.60000000e+09])
den = np.array([
1.00000000e+00, 5.22625186e+02, 1.36568542e+05, 2.09050074e+07,
1.60000000e+09
])
synapse = LinearFilter(num, den, analog=True)
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, atol=5e-4)
def test_zero_matrices(Simulator, zero, seed):
dt = 1e-3
A = np.diag(np.ones(2) * dt)
B = np.zeros((2, 1))
B[0] = 1
C = np.ones((1, 2))
D = np.ones((1, ))
if zero == "C":
C[...] = 0
elif zero == "D":
D[...] = 0
num, den = ss2tf(A, B, C, D)
num = num.flatten()
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 * 2 if zero == "D" else dt, atol=5e-5)
def test_zero_matrices(Simulator, zero, seed):
dt = 1e-3
A = np.diag(np.ones(2) * dt)
B = np.zeros((2, 1))
B[0] = 1
C = np.ones((1, 2))
D = np.ones((1,))
if zero == "C":
C[...] = 0
elif zero == "D":
D[...] = 0
num, den = ss2tf(A, B, C, D)
num = num.flatten()
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 * 2 if zero == "D" else dt, atol=5e-5)