def test_PredictSNR():
order = 6
Ts = 80e-6
fp = 625.
OSR = 1. / Ts / 2. / fp
ds = deltaSigma.DeltaSigma(OSR, order)
ds.simSNR()
def test_checkScale():
order = 6
Ts = 80e-6
fp = 625.
OSR = 1. / Ts / 2. / fp
ds = deltaSigma.DeltaSigma(OSR, order)
print("ABCD before:\n%s" % ds.ABCD)
ds.checkScale()
print(ds.ABCD)
print(ds.umax)
print(ds.S)
def test_SimSpectrum():
size = 8092
Ts = 80e-6
fp = 500.
order = 8
OSR = 1. / Ts / 2. / fp
ds = deltaSigma.DeltaSigma(OSR, order)
amplitude = 1.
frequency = fp * 2. / 3.
phase = 0.
steeringVector = np.zeros(order)
signal = Sin(Ts, amplitude, frequency, phase, steeringVector)
t = np.arange(size) * Ts
plt.figure()
ds.simSpectrum(signal, t)
def test_Simulate():
size = 8092
Ts = 80e-6
fp = 625.
order = 2
OSR = 1. / Ts / 2. / fp
ds = deltaSigma.DeltaSigma(OSR, order)
amplitude = 1.
frequency = fp * 2. / 3.
phase = 0.
steeringVector = np.zeros(order)
signal = Sin(Ts, amplitude, frequency, phase, steeringVector)
t = np.arange(size) * Ts
v = ds.simulate(signal, t)
plt.plot(v)
plt.figure()
plt.plot(ds.states.transpose())
def test_MASH_SIM_SPECTRUM():
size = 8092
Ts = 80e-6
fp = 500.
order = 1
Number = 8
OSR = 1. / Ts / 2. / fp
ds = [deltaSigma.DeltaSigma(OSR, order) for x in range(Number)]
mash = deltaSigma.MASH(OSR, ds)
print("OSR = %s" % OSR)
amplitude = 1.
frequency = fp * 2. / 3.
phase = 0.
steeringVector = np.zeros(order)
signal = Sin(Ts, amplitude, frequency, phase, steeringVector)
t = np.arange(size) * Ts
plt.figure()
mash.simSpectrum(signal, t)
def test_MASH_RECONSTRUCTION():
size = 8092
Ts = 80e-6
fp = 500.
order = 1
Number = 4
OSR = 1. / Ts / 2. / fp
ds = [deltaSigma.DeltaSigma(OSR, order) for x in range(Number)]
mash = deltaSigma.MASH(OSR, ds)
amplitude = 1.
frequency = fp * 2. / 3.
phase = 0.
steeringVector = np.zeros(order)
signal = Sin(Ts, amplitude, frequency, phase, steeringVector)
t = np.arange(size) * Ts
u = signal.scalarFunction(t)
v = mash.simulate(signal, t)
u_hat = mash.reconstruction(v)
plt.figure()
plt.plot(t, u, label="u")
plt.plot(t, u_hat, label="u_hat")
# In[38]: size = n_samples fp = 1. / (OSR * 2) * 1. Ts = 1. # Ts = 1. / 2. / fp / OSR # input input = ADC.system.Sin(Ts, 0.01, fp, 0, steeringVector=np.zeros(order)) # input = ADC.system.BandlimitedNoise(1e-8, 2*2*OSR, 0) t = np.linspace(0., Ts * (size - 1), size) u = input.scalarFunction(t) # u = u - np.mean(u) ds = [deltaSigma.DeltaSigma(OSR, x, nlev=n_lev) for x in order_mash_block] mash = deltaSigma.MASH(OSR, ds) dsH = deltaSigma.DeltaSigma(OSR, systemOrder, nlev=n_lev * order) # ds = deltaSigma.DeltaSigma(OSR, order, nlev = n_lev) mash.simSpectrum() dsH.simSpectrum() bits = mash.simulate(input, t) bitsH = dsH.simulate(input, t) u_hat = mash.reconstruction(bits) u_hatH = bitsH # # Offset Adjustment