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 test_snr_is_inf(self): """ Test that a paricular SNR is infinite. """ N = self.N hwfft = np.zeros((N / 2, )) hwfft[512] = 1.0 # specially crafted to have Inf snr snr = ds.calculateSNR(hwfft[:N / 2], 512) self.assertEqual(snr, np.Inf)
def test_snr_is_40(self): """ Test that a particular SNR is within roundings errors of 40 (dB?) """ N = self.N snr = ds.calculateSNR(self.hwfft[:N / 2], int(N * self.f1)) # Consider replacing with assertAlmostEqual self.assertTrue(np.allclose(snr, 40, atol=1e-8, rtol=1e-8))
def test_snr_is_inf(self): """ Test that a paricular SNR is infinite. """ N = self.N hwfft = np.zeros((N/2, )) hwfft[512] = 1.0 # specially crafted to have Inf snr snr = ds.calculateSNR(hwfft[:N/2], 512) self.assertEqual(snr, np.Inf)
def test_snr_is_40(self): """ Test that a particular SNR is within roundings errors of 40 (dB?) """ N = self.N snr = ds.calculateSNR(self.hwfft[:N/2], int(N*self.f1)) # Consider replacing with assertAlmostEqual self.assertTrue(np.allclose(snr, 40, atol=1e-8, rtol=1e-8))
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_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()