def test_logsmooth(self):
"""Test function for logsmooth()"""
f0 = 1./8
OSR = 64
order = 8
N = 8192
H = synthesizeNTF(order, OSR, 1, 1.5, f0)
# fB = int(np.ceil(N/(2. * OSR)))
quadrature = False
M = 1
f1, f2 = ds_f1f2(OSR, f0, quadrature)
Amp = undbv(-3)
f = .3
fin = np.round(((1 - f)/2*f1 + (f + 1)/2 * f2) * N)
t = np.arange(0, N).reshape((1, -1))
u = Amp * M * np.cos((2*np.pi/N)*fin*t)
v, _, _, _ = simulateDSM(u, H, M + 1)
window = ds_hann(N)
# NBW = 1.5/N
spec0 = fft(v * window) / (M*N/4)
### -1 is really important THIS IS NOT MATLAB!!
fl, pl = logsmooth(spec0, fin - 1)
fname = pkg_resources.resource_filename(__name__,
"test_data/test_logsmooth.mat")
data = scipy.io.loadmat(fname)
self.assertTrue(np.allclose(fl, data['fl'], atol=1e-8, rtol=1e-5))
self.assertTrue(np.allclose(pl, data['pl'], atol=1e-8, rtol=1e-5))
def test_logsmooth(self):
"""Test function for logsmooth()"""
f0 = 1. / 8
OSR = 64
order = 8
N = 8192
H = synthesizeNTF(order, OSR, 1, 1.5, f0)
# fB = int(np.ceil(N/(2. * OSR)))
quadrature = False
M = 1
f1, f2 = ds_f1f2(OSR, f0, quadrature)
Amp = undbv(-3)
f = .3
fin = np.round(((1 - f) / 2 * f1 + (f + 1) / 2 * f2) * N)
t = np.arange(0, N).reshape((1, -1))
u = Amp * M * np.cos((2 * np.pi / N) * fin * t)
v, _, _, _ = simulateDSM(u, H, M + 1)
window = ds_hann(N)
# NBW = 1.5/N
spec0 = fft(v * window) / (M * N / 4)
### -1 is really important THIS IS NOT MATLAB!!
fl, pl = logsmooth(spec0, fin - 1)
fname = pkg_resources.resource_filename(
__name__, "test_data/test_logsmooth.mat")
data = scipy.io.loadmat(fname)
self.assertTrue(np.allclose(fl, data['fl'], atol=1e-8, rtol=1e-5))
self.assertTrue(np.allclose(pl, data['pl'], atol=1e-8, rtol=1e-5))
def test_ds_hann(self):
"""Test function for ds_hann()"""
self.assertTrue(
np.allclose(self.res,
np.hanning(10) - ds.ds_hann(10),
atol=1e-8,
rtol=1e-5))
def test_plotSpectrum(self):
"""Test function for plotSpectrum()"""
f0 = 0
osr = 32
quadrature = False
Hinf = 1.5
order = 3
ntf = ds.synthesizeNTF(order, osr, 0, Hinf, f0)
f1, f2 = ds.ds_f1f2(osr, f0, quadrature)
delta = 2
Amp = ds.undbv(-3)
f = 0.3
N = 2**12
f1_bin = np.round(f1*N)
f2_bin = np.round(f2*N)
fin = np.round(((1 - f)/2*f1 + (f + 1)/2*f2) * N)
t = np.arange(0, N)
u = Amp*np.cos((2*np.pi/N)*fin*t)
v, xn, xmax, y = ds.simulateDSM(u, ntf, 2)
window = ds.ds_hann(N)
NBW = 1.5/N
spec0 = fft(v * window)/(N/4)
freq = np.linspace(0, 0.5, N/2 + 1)
# plotting
plt.subplot(211)
plt.plot(freq, ds.dbv(spec0[:N/2 + 1]), 'c', linewidth=1, label='$S$')
plt.hold(True)
spec_smoothed = ds.circ_smooth(np.abs(spec0)**2., 16)
plt.plot(freq, ds.dbp(spec_smoothed[:N/2 + 1]), 'b--', linewidth=2,
label='$\\mathrm{circ\\_smooth}(S)$')
ds.plotSpectrum(spec0, fin, 'r', linewidth=2,
label='$\\mathrm{plotSpectrum}(S)$')
Snn = np.abs(ds.evalTF(ntf, np.exp(2j*np.pi*freq)))**2 * 2/12*(delta)**2
plt.plot(freq, ds.dbp(Snn*NBW), 'm', linewidth=1.5,
label='$\mathrm{from\\ NTF}$')
plt.text(0.5, -3, 'NBW = %.1e ' % NBW, horizontalalignment='right',
verticalalignment='top')
ds.figureMagic((0, 0.5), None, None, (-140, 0), 20, None)
plt.ylabel('Spectrum [dB]')
ax = plt.gca()
ax.set_title('Smoothing and plotting for LOG and LIN axes')
plt.legend(loc=4)
plt.subplot(212)
plt.plot(freq, ds.dbv(spec0[:N/2 + 1]), 'c', linewidth=1, label='$S$')
plt.hold(True)
ds.plotSpectrum(spec0, fin, '--r', linewidth=2,
label='$\\mathrm{plotSpectrum}(S)$')
plt.plot(freq, ds.dbp(spec_smoothed[:N/2 + 1]), 'b', linewidth=2,
label='$\\mathrm{circ\\_smooth}(S)$')
plt.plot(freq, ds.dbp(Snn*NBW), 'm', linewidth=1.5,
label='$\mathrm{from\\ NTF}$')
plt.text(0.5, -3, 'NBW = %.1e ' % NBW, horizontalalignment='right',
verticalalignment='top')
ds.figureMagic((0, 0.5), None, None, (-140, 0), 20, None)
ax = plt.gca()
ax.set_xscale('linear')
plt.ylabel('Spectrum [dB]')
plt.xlabel('Normalized frequency ($f_s \\rightarrow 1$)')
plt.legend(loc=4)
def testMultipleQ2(self):
"""Test function for DS simulation with nq>1 2/2"""
# filtering and simulation
filtM1 = [0., 0., 0., 2., -1.]
filtM2 = [1., -2., 1.]
ntf_eq = zpk_multiply(self.ntfs[1, 1], self.ntfs[1, 1])
M = self.nlev[0] - 1
osr = 64
f0 = 0.
f1, f2 = ds.ds_f1f2(OSR=64, f0=0., complex_flag=False)
delta = 2
Amp = ds.undbv(-3) # Test tone amplitude, relative to full-scale.
f = 0.3 # will be adjusted to a bin
N = 2**12
f1_bin = int(np.round(f1 * N))
f2_bin = int(np.round(f2 * N))
fin = np.round(((1 - f) / 2 * f1 + (f + 1) / 2 * f2) * N)
# input sine
t = np.arange(0, N).reshape((1, -1))
u = Amp * M * np.cos((2 * np.pi / N) * fin * t)
vx, _, xmax, y = ds.simulateDSM(u, self.ABCD, nlev=self.nlev)
# separate output #1 and output #2
v1 = vx[0, :]
v2 = vx[1, :]
# filter and combine
vf = lfilter(filtM1, [1.], v1) + lfilter(filtM2, [1.], v2)
# compute the spectra
window = ds.ds_hann(N)
NBW = 1.5 / N
spec0 = np.fft.fft(vf * window) / (M * N / 2) / ds.undbv(-6)
spec1 = np.fft.fft(v1 * window) / (M * N / 2) / ds.undbv(-6)
spec2 = np.fft.fft(v1 * window) / (M * N / 2) / ds.undbv(-6)
freq = np.linspace(0, 0.5, N // 2 + 1)
# smooth, calculate the theorethical response and the SNR for VF
spec0_smoothed = ds.circ_smooth(np.abs(spec0)**2., 16)
Snn0 = np.abs(ds.evalTF(ntf_eq, np.exp(
2j * np.pi * freq)))**2 * 2 / 12 * (delta / M)**2
snr0 = ds.calculateSNR(spec0[f1_bin:f2_bin + 1], fin - f1_bin)
# smooth, calculate the theorethical response and the SNR for V1
spec1_smoothed = ds.circ_smooth(np.abs(spec1)**2., 16)
Snn1 = np.abs(ds.evalTF(self.ntfs[0, 0], np.exp(
2j * np.pi * freq)))**2 * 2 / 12 * (delta / M)**2
snr1 = ds.calculateSNR(spec1[f1_bin:f2_bin + 1], fin - f1_bin)
assert snr0 > 40
assert snr1 > 40
assert snr0 - snr1 > 40
def setUp(self):
fname = pkg_resources.resource_filename(
__name__, "test_data/test_bplogsmooth.mat")
self.data = scipy.io.loadmat(fname)
f0 = 1./8
OSR = 64
order = 8
N = 8192
H = ds.synthesizeNTF(order, OSR, 1, 1.5, f0)
fB = int(np.ceil(N/(2. * OSR)))
ftest = int(mround(f0*N + 1./3*fB))
u = 0.5*np.sin(2*np.pi*ftest/N*np.arange(N))
v, xn, xmax, y = ds.simulateDSM(u, H)
spec = np.fft.fft(v*ds.ds_hann(N))/(N/4)
X = spec[:N//2 + 1]
self.f, self.p = ds.bplogsmooth(X, ftest, f0)
def test_bilogplot(self):
"""Test function for bilogplot()"""
f0 = 1./8
OSR = 64
order = 8
N = 8192
H = ds.synthesizeNTF(order, OSR, 1, 1.5, f0)
fB = int(np.ceil(N/(2. * OSR)))
ftest = int(np.round(f0*N + 1./3 * fB))
u = 0.5*np.sin(2*np.pi*ftest/N*np.arange(N))
v, xn, xmax, y = ds.simulateDSM(u, H)
spec = np.fft.fft(v*ds.ds_hann(N))/(N/4)
X = spec[:N/2 + 1]
plt.figure()
# graphical function: we check it doesn't fail
ds.bilogplot(X, f0*N, ftest, (.03, .3, .3), (-140, 0, 10, 20))
def test_bilogplot(self):
"""Test function for bilogplot()"""
f0 = 1. / 8
OSR = 64
order = 8
N = 8192
H = ds.synthesizeNTF(order, OSR, 1, 1.5, f0)
fB = int(np.ceil(N / (2. * OSR)))
ftest = int(np.round(f0 * N + 1. / 3 * fB))
u = 0.5 * np.sin(2 * np.pi * ftest / N * np.arange(N))
v, xn, xmax, y = ds.simulateDSM(u, H)
spec = np.fft.fft(v * ds.ds_hann(N)) / (N / 4)
X = spec[:N / 2 + 1]
plt.figure()
# graphical function: we check it doesn't fail
ds.bilogplot(X, f0 * N, ftest, (.03, .3, .3), (-140, 0, 10, 20))
def setUp(self):
fname = pkg_resources.resource_filename(
__name__, "test_data/test_bplogsmooth.mat")
self.data = scipy.io.loadmat(fname)
f0 = 1./8
OSR = 64
order = 8
N = 8192
H = ds.synthesizeNTF(order, OSR, 1, 1.5, f0)
fB = int(np.ceil(N/(2. * OSR)))
ftest = int(mround(f0*N + 1./3*fB))
u = 0.5*np.sin(2*np.pi*ftest/N*np.arange(N))
v, xn, xmax, y = ds.simulateDSM(u, H)
spec = np.fft.fft(v*ds.ds_hann(N))/(N/4)
X = spec[:N/2 + 1]
self.f, self.p = ds.bplogsmooth(X, ftest, f0)
def testMultipleQ2(self):
"""Test function for DS simulation with nq>1 2/2"""
# filtering and simulation
filtM1 = [0., 0., 0., 2., -1.]
filtM2 = [1., -2., 1.]
ntf_eq = zpk_multiply(self.ntfs[1, 1], self.ntfs[1, 1])
M = self.nlev[0] - 1
osr = 64
f0 = 0.
f1, f2 = ds.ds_f1f2(OSR=64, f0=0., complex_flag=False)
delta = 2
Amp = ds.undbv(-3) # Test tone amplitude, relative to full-scale.
f = 0.3 # will be adjusted to a bin
N = 2**12
f1_bin = np.round(f1*N)
f2_bin = np.round(f2*N)
fin = np.round(((1 - f)/2*f1 + (f + 1)/2*f2) * N)
# input sine
t = np.arange(0, N).reshape((1, -1))
u = Amp*M*np.cos((2*np.pi/N)*fin*t)
vx, _, xmax, y = ds.simulateDSM(u, self.ABCD, nlev=self.nlev)
# separate output #1 and output #2
v1 = vx[0, :]
v2 = vx[1, :]
# filter and combine
vf = lfilter(filtM1, [1.], v1) + lfilter(filtM2, [1.], v2)
# compute the spectra
window = ds.ds_hann(N)
NBW = 1.5/N
spec0 = np.fft.fft(vf*window)/(M*N/2)/ds.undbv(-6)
spec1 = np.fft.fft(v1*window)/(M*N/2)/ds.undbv(-6)
spec2 = np.fft.fft(v1*window)/(M*N/2)/ds.undbv(-6)
freq = np.linspace(0, 0.5, N/2 + 1)
# smooth, calculate the theorethical response and the SNR for VF
spec0_smoothed = ds.circ_smooth(np.abs(spec0)**2., 16)
Snn0 = np.abs(ds.evalTF(ntf_eq, np.exp(2j*np.pi*freq)))**2 * 2/12*(delta/M)**2
snr0 = ds.calculateSNR(spec0[f1_bin:f2_bin + 1], fin - f1_bin)
# smooth, calculate the theorethical response and the SNR for V1
spec1_smoothed = ds.circ_smooth(np.abs(spec1)**2., 16)
Snn1 = np.abs(ds.evalTF(self.ntfs[0, 0], np.exp(2j*np.pi*freq)))**2 * 2/12*(delta/M)**2
snr1 = ds.calculateSNR(spec1[f1_bin:f2_bin + 1], fin - f1_bin)
assert snr0 > 40
assert snr1 > 40
assert snr0-snr1 > 40
def test_plotSpectrum(self):
"""Test function for plotSpectrum()"""
f0 = 0
osr = 32
quadrature = False
Hinf = 1.5
order = 3
ntf = ds.synthesizeNTF(order, osr, 0, Hinf, f0)
f1, f2 = ds.ds_f1f2(osr, f0, quadrature)
delta = 2
Amp = ds.undbv(-3)
f = 0.3
N = 2**12
f1_bin = np.round(f1 * N)
f2_bin = np.round(f2 * N)
fin = np.round(((1 - f) / 2 * f1 + (f + 1) / 2 * f2) * N)
t = np.arange(0, N)
u = Amp * np.cos((2 * np.pi / N) * fin * t)
v, xn, xmax, y = ds.simulateDSM(u, ntf, 2)
window = ds.ds_hann(N)
NBW = 1.5 / N
spec0 = fft(v * window) / (N / 4)
freq = np.linspace(0, 0.5, N // 2 + 1)
# plotting
plt.subplot(211)
plt.plot(freq,
ds.dbv(spec0[:N // 2 + 1]),
'c',
linewidth=1,
label='$S$')
#plt.hold(True)
spec_smoothed = ds.circ_smooth(np.abs(spec0)**2., 16)
plt.plot(freq,
ds.dbp(spec_smoothed[:N // 2 + 1]),
'b--',
linewidth=2,
label='$\\mathrm{circ\\_smooth}(S)$')
ds.plotSpectrum(spec0,
fin,
'r',
linewidth=2,
label='$\\mathrm{plotSpectrum}(S)$')
Snn = np.abs(ds.evalTF(ntf, np.exp(
2j * np.pi * freq)))**2 * 2 / 12 * (delta)**2
plt.plot(freq,
ds.dbp(Snn * NBW),
'm',
linewidth=1.5,
label='$\\mathrm{from\\ NTF}$')
plt.text(0.5,
-3,
'NBW = %.1e ' % NBW,
horizontalalignment='right',
verticalalignment='top')
ds.figureMagic((0, 0.5), None, None, (-140, 0), 20, None)
plt.ylabel('Spectrum [dB]')
ax = plt.gca()
ax.set_title('Smoothing and plotting for LOG and LIN axes')
plt.legend(loc=4)
plt.subplot(212)
plt.plot(freq,
ds.dbv(spec0[:N // 2 + 1]),
'c',
linewidth=1,
label='$S$')
#plt.hold(True)
ds.plotSpectrum(spec0,
fin,
'--r',
linewidth=2,
label='$\\mathrm{plotSpectrum}(S)$')
plt.plot(freq,
ds.dbp(spec_smoothed[:N // 2 + 1]),
'b',
linewidth=2,
label='$\\mathrm{circ\\_smooth}(S)$')
plt.plot(freq,
ds.dbp(Snn * NBW),
'm',
linewidth=1.5,
label='$\\mathrm{from\\ NTF}$')
plt.text(0.5,
-3,
'NBW = %.1e ' % NBW,
horizontalalignment='right',
verticalalignment='top')
ds.figureMagic((0, 0.5), None, None, (-140, 0), 20, None)
ax = plt.gca()
ax.set_xscale('linear')
plt.ylabel('Spectrum [dB]')
plt.xlabel('Normalized frequency ($f_s \\rightarrow 1$)')
plt.legend(loc=4)
def test_ds_hann(self):
"""Test function for ds_hann()"""
self.assertTrue(np.allclose(self.res, np.hanning(10) - ds.ds_hann(10), atol=1e-8, rtol=1e-5))
def test_sim_noiseshaper(self):
fmt = Q(8, 18)
input = fmt.Signal()
dut = Noiseshaper(input, order=8, n_lev=64)
sim = Simulator(dut)
sim.add_clock(1 / 100e6)
input_hist = []
output_hist = []
integrators_hist = [[] for _ in dut.stages]
n = 8192
f_nyquist = int(np.ceil(n / (2. * dut.osr)))
f_test = np.floor(2. / 3. * f_nyquist)
u = dut.n_lev * 0.5 * np.sin(2 * np.pi * f_test / n * np.arange(n))
def testbench():
for x in u:
yield input.eq(x)
input_hist.append(fmt.to_float((yield input.value)))
output_hist.append(
fmt.to_float((yield dut.quantized_value.value)))
for i, integrator in enumerate(dut.stages):
integrators_hist[i].append(
fmt.to_float((yield integrator.value)))
yield
sim.add_sync_process(testbench)
sim.run()
from matplotlib import pyplot as plt
plt.plot(np.arange(n), output_hist, linewidth=1, label="output")
plt.plot(np.arange(n), input_hist, label="input")
plt.legend()
plt.show()
for i, integrator_hist in reversed(list(enumerate(integrators_hist))):
plt.plot(np.arange(n),
integrator_hist,
linewidth=1,
label="integrator {}".format(i))
plt.legend()
plt.show()
import deltasigma as ds
f = np.linspace(0, 0.5, int(n / 2. + 1))
v, xn, xmax, y = ds.simulateDSM(u,
dut.h,
nlev=len(dut.quantization_values))
spec = np.fft.fft(v * ds.ds_hann(n)) / (n / 4)
plt.plot(f, ds.dbv(spec[:int(n / 2. + 1)]), 'b', label='Simulation DS')
spec = np.fft.fft(output_hist * ds.ds_hann(n)) / (n / 4)
plt.plot(f,
ds.dbv(spec[:int(n / 2. + 1)]),
'g',
label='Simulation HW',
alpha=0.7)
ds.figureMagic([0, 0.5], 0.05, None, [-160, 0], 20, None, (16, 6),
'Output Spectrum')
plt.xlabel('Normalized Frequency')
plt.ylabel('dBFS')
snr = ds.calculateSNR(spec[2:f_nyquist + 1], f_test - 2)
plt.text(0.05,
-10,
'SNR = %4.1fdB @ OSR = %d' % (snr, dut.osr),
verticalalignment='center')
NBW = 1.5 / n
Sqq = 4 * ds.evalTF(dut.h, np.exp(2j * np.pi * f))**2 / 3.
plt.plot(f, ds.dbp(Sqq * NBW), 'm', linewidth=2, label='Expected PSD')
plt.text(0.49,
-90,
'NBW = %4.1E x $f_s$' % NBW,
horizontalalignment='right')
plt.legend(loc=4)
plt.show()
pwm_out = py_pwm.modulate(np.array(output_hist) + 32,
n_bits=6,
oversampling_ratio=1)
n = n * 64
f = np.linspace(0, 0.5, int(n / 2. + 1))
spec = np.fft.fft(pwm_out * ds.ds_hann(n)) / (n / 4)
plt.plot(f, ds.dbv(spec[:int(n / 2. + 1)]), 'b', label='PWM')
ds.figureMagic([0, 0.5], 0.05, None, [-160, 0], 20, None, (16, 6),
'Output Spectrum')
plt.xlabel('Normalized Frequency')
plt.ylabel('dBFS')
snr = ds.calculateSNR(spec[2:f_nyquist + 1], f_test - 2)
plt.text(0.05,
-10,
'SNR = %4.1fdB @ OSR = %d' % (snr, dut.osr),
verticalalignment='center')
plt.legend(loc=4)
plt.show()