from __future__ import division
from deltasigma import *
import warnings
warnings.filterwarnings('ignore')
np.set_printoptions(suppress=True, precision=3)
order = 5
osr = 32
nlev = 2
f0 = 0.
Hinf = 1.5
form = 'CRFB'
ntf = synthesizeNTF(order, osr, 2, Hinf, f0) # Optimized zero placement
print "Synthesized a %d-order NTF, with roots:\n" % order
print " Zeros:\t\t\t Poles:"
for z, p in zip(ntf[0], ntf[1]):
print "(%f, %fj)\t(%f, %fj)" % (np.real(z), np.imag(z), np.real(p), np.imag(p))
print ""
Synthesized a 5-order NTF, with roots: Zeros: Poles: (0.996045, -0.088847j) (0.806557, -0.119823j) (0.996045, 0.088847j) (0.806557, 0.119823j) (0.998603, -0.052839j) (0.898071, -0.219819j) (0.998603, 0.052839j) (0.898071, 0.219819j) (1.000000, 0.000000j) (0.777767, 0.000000j)
plotPZ(ntf, showlist=True)
a, g, b, c = realizeNTF(ntf, form)
b = np.hstack(( # Use a single feed-in for the input
np.atleast_2d(b[0, 0]),
np.zeros((1, max(b.shape)-1))
))
ABCD = stuffABCD(a, g, b, c, form)
print "ABCD Matrix:"
print ABCD
ABCD Matrix: [[ 1. 0. 0. 0. 0. 0.001 -0.001] [ 1. 1. -0.003 0. 0. 0. -0.008] [ 1. 1. 0.997 0. 0. 0. -0.063] [ 0. 0. 1. 1. -0.008 0. -0.244] [ 0. 0. 1. 1. 0.992 0. -0.802] [ 0. 0. 0. 0. 1. 0. 0. ]]
DocumentNTF(ABCD, osr, f0)
f = gcf()
f.set_size_inches((15, 6))
figure(figsize=(15,8))
PlotExampleSpectrum(ntf, M=1, osr=osr, f0=f0)
snr, amp = simulateSNR(ntf, osr, None, f0, nlev)
figure(figsize=(15,8))
if nlev == 2:
snr_pred, amp_pred, k0, k1, se = predictSNR(ntf, osr)
plot(amp_pred, snr_pred, '-', label='predicted')
hold(True)
plot(amp, snr,'o-.g', label='simulated')
xlabel('Input Level (dBFS)')
ylabel('SQNR (dB)')
peak_snr, peak_amp = peakSNR(snr, amp)
msg = 'peak SQNR = %4.1fdB \n@ amp = %4.1fdB ' % (peak_snr, peak_amp)
text(peak_amp-10,peak_snr,msg, horizontalalignment='right', verticalalignment='center');
msg = 'OSR = %d ' % osr
text(-2, 5, msg, horizontalalignment='right');
hold(False)
figureMagic([-100, 0], 10, None, [0, 100], 10, None, [12, 6], 'Time-Domain Simulations')
legend(loc=2);
# Dynamic range scaling
print 'Doing dynamic range scaling... ',
ABCD0 = ABCD.copy()
ABCD, umax, S = scaleABCD(ABCD0, nlev, f0)
print 'Done.'
print "Maximum input magnitude: %.3f" % umax
Doing dynamic range scaling... Done. Maximum input magnitude: 0.583
print 'Verifying dynamic range scaling... ',
u = np.linspace(0, 0.95*umax, 30)
N = 1e4
N0 = 50
test_tone = np.cos(2*np.pi*f0*np.arange(N))
test_tone[:N0] = test_tone[:N0]*(0.5 - 0.5*np.cos(2*np.pi/N0*np.arange(N0)))
maxima = np.zeros((order, u.shape[0]))
for i in np.arange(u.shape[0]):
ui = u[i]
v, xn, xmax, y = simulateDSM(ui*test_tone, ABCD, nlev)
maxima[:, i] = xmax[:, 0]
if (xmax > 1e2).any():
print 'Warning, umax from scaleABCD was too high.'
umax = ui
u = u[:i]
maxima = maxima[:, :i]
break
print 'Done.'
print "Maximum DC input level: %.3f" % umax
Verifying dynamic range scaling... Done. Maximum DC input level: 0.583
colors = get_cmap('jet')(np.linspace(0, 1.0, order))
hold(True)
for i in range(order):
plot(u,maxima[i,:], 'o-', color=colors[i], label='State %d' % i)
grid(True)
#text(umax/2, 0.05, 'DC input', horizontalalignment='center', verticalalignment='center')
figureMagic([0, umax], None, None, [0, 1] , 0.1, 2, [12, 6], 'State Maxima')
xlabel('DC input')
ylabel('Maxima')
legend(loc='best');
a, g, b, c = mapABCD(ABCD, form)
adc = {
'order':order,
'osr':osr,
'nlev':nlev,
'f0':f0,
'ntf':ntf,
'ABCD':ABCD,
'umax':umax,
'peak_snr':peak_snr,
'form':form,
'coefficients':{
'a':a,
'g':g,
'b':b,
'c':c
}
}
print "ADC coefficients:"
print " %s\n %s" % ('a', adc['coefficients']['a'])
print " %s\n %s" % ('g', adc['coefficients']['g'])
print " %s\n %s" % ('b', adc['coefficients']['b'])
print " %s\n %s" % ('c', adc['coefficients']['c'])
ADC coefficients: a [ 0.1 0.17 0.213 0.363 0.365] g [ 0.015 0.018] b [ 0.1 0. 0. 0. 0. 0. ] c [ 0.137 0.191 0.384 0.44 1.53 ]