def test_ntf_butt_bp8_cvxpy_cvxopt(self):
try:
import cvxpy # analysis:ignore
except:
raise SkipTest("Modeler 'cvxpy' not installed")
ntf1 = ntf_fir_weighting(self.order, self.hz, modeler='cvxpy',
show_progress=False)
np.testing.assert_allclose(np.sort(ntf1[0]), self.z_e, rtol=1e-7)
def test_ntf_butt_bp8_cvxpy_cvxopt(self):
try:
import cvxpy # analysis:ignore
except:
raise SkipTest("Modeler 'cvxpy' not installed")
ntf1 = ntf_fir_weighting(self.order,
self.hz,
modeler='cvxpy',
show_progress=False)
np.testing.assert_allclose(np.sort(ntf1[0]), self.z_e, rtol=1e-7)
def test_ntf_butt_bp8_vs_legacy(self):
# Compute q0 in two ways
ir = impulse_response(self.hz, db=80)
ntf1 = ntf_fir_weighting(self.order, self.hz, show_progress=False)
q0 = q0_from_filter_ir(self.order, ir)
ntf2 = ntf_fir_from_q0(q0, show_progress=False)
mf1 = quantization_noise_gain(ntf1, self.hz)
mf2 = quantization_noise_gain(ntf2, self.hz)
np.testing.assert_allclose(mf2, mf1, rtol=1e-7)
mf3 = quantization_noise_gain_by_conv(ntf1, self.hz)
np.testing.assert_allclose(mf3, mf1, rtol=5e-6)
def test_ntf_butt_bp8_vs_legacy(self):
# Compute q0 in two ways
ir = impulse_response(self.hz, db=80)
ntf1 = ntf_fir_weighting(self.order, self.hz, show_progress=False)
q0 = q0_from_filter_ir(self.order, ir)
ntf2 = ntf_fir_from_q0(q0, show_progress=False)
mf1 = quantization_noise_gain(ntf1, self.hz)
mf2 = quantization_noise_gain(ntf2, self.hz)
np.testing.assert_allclose(mf2, mf1, rtol=1e-7)
mf3 = quantization_noise_gain_by_conv(ntf1, self.hz)
np.testing.assert_allclose(mf3, mf1, rtol=5e-6)
def bench_ntf_fir_weighting_cvxpy_cvxopt(self):
try:
import cvxpy # analysis:ignore
except:
raise SkipTest("Modeler 'cvxpy' not installed")
print("Benchmarking NTF FIR synthesis with 'cvxpy' modeler + `cvxopt`")
tic = time.clock()
ntf = ntf_fir_weighting(self.order, self.hz, self.H_inf,
modeler='cvxpy', show_progress=False)
timing = time.clock()-tic
mf = quantization_noise_gain(ntf, self.hz)
vv = np.abs(evalTF(ntf, np.exp(2j*np.pi*self.ff)))
pe = np.max(vv)-self.H_inf
print("Timing: {:6.2f}".format(timing))
print("Accuracy on constraint: {:e}".format(pe))
print("Accuracy on goal: {:e}".format(mf))
def bench_ntf_fir_weighting_cvxpy_cvxopt(self):
try:
import cvxpy # analysis:ignore
except:
raise SkipTest("Modeler 'cvxpy' not installed")
print("Benchmarking NTF FIR synthesis with 'cvxpy' modeler + `cvxopt`")
tic = time.clock()
ntf = ntf_fir_weighting(self.order, self.hz, self.H_inf,
modeler='cvxpy', show_progress=False)
timing = time.clock()-tic
mf = quantization_noise_gain(ntf, self.hz)
vv = np.abs(evalTF(ntf, np.exp(2j*np.pi*self.ff)))
pe = np.max(vv)-self.H_inf
print("Timing: {:6.2f}".format(timing))
print("Accuracy on constraint: {:e}".format(pe))
print("Accuracy on goal: {:e}".format(mf))
return 1. if f < B/fphi else 1e-12
# Introduce a weighting component that magnifies weight for
# frequencies < B/10
def weight2(f):
return 50.-49.*f/(B/fphi/10.) if f < B/fphi/10. else 1.
# Get the final weighting function as the product of the two
def w2(f):
return w1(f)*weight2(f)
print("... computing optimal NTF...")
ntf1 = ntf_fir_weighting(order, w1, quad_opts={'points': [B/fphi]})
ntf2 = ntf_fir_weighting(order, w2, quad_opts={'points': [B/fphi, B/fphi/10]})
# Prepare frequency axis for plotting
fmin = 10**np.ceil(np.log10(B/OSR/100))
fmax = fphi/2
ff = np.logspace(np.log10(fmin), np.log10(fmax), 1000)
resp_w1 = np.asarray(map(w1, ff/fphi))
resp_ntf1 = np.abs(evalTF(ntf1, np.exp(1j*2*np.pi*ff/fphi)))
resp_w2 = np.asarray(map(w2, ff/fphi))
resp_ntf2 = np.abs(evalTF(ntf2, np.exp(1j*2*np.pi*ff/fphi)))
ffa = np.logspace(np.log10(0.5E-5), np.log10(0.5), 1024)
vv_a2 = dbv(np.abs(evalTF(ntf2, np.exp(1j*2*np.pi*ffa))))
vv = np.fromiter(list(map(w1, ff)), np.double)
plt.figure()
plt.plot(ff, vv)
plt.xlim(1e-5, 1. / 2)
plt.ylim(0, 1.1)
plt.xscale('log', basex=10)
plt.gca().set_xticks([1E-5, 1. / 2])
plt.gca().set_xticklabels(['$10^{-5}$', r'$\frac{1}{2}$'])
plt.xlabel('$f$', x=1.)
plt.ylabel('$w(f)$', y=0.9)
plt.grid(True, 'both')
plt.suptitle('Weighting function')
plt.tight_layout(rect=[0, 0, 1, 0.98])
print("... computing optimal NTF")
ntf_opti = ntf_fir_weighting(order, w1, H_inf=H_inf)
ntf_opti_mag = lambda f: np.abs(evalTF(ntf_opti, np.exp(-2j * np.pi * f)))
print("... computing delsig NTF")
ntf_delsig = synthesizeNTF(delsig_order, OSR, 3, H_inf, 0)
ntf_delsig_mag = lambda f: np.abs(evalTF(ntf_delsig, np.exp(-2j * np.pi * f)))
print("... plotting optimal NTF amplitude response")
vv_mag = dbv(ntf_opti_mag(ff))
vv_mag_delsig = dbv(ntf_delsig_mag(ff))
plt.figure()
plt.plot(ff, vv_mag, label='Proposed')
plt.plot(ff,
vv_mag_delsig,
'r-o',
linewidth=0.5,
vv = np.fromiter(map(w1, ff), np.double)
plt.figure()
plt.plot(ff, vv)
plt.xlim(1e-5, 1./2)
plt.ylim(0, 1.1)
plt.xscale('log', basex=10)
plt.gca().set_xticks([1E-5, 1./2])
plt.gca().set_xticklabels(['$10^{-5}$', r'$\frac{1}{2}$'])
plt.xlabel('$f$', x=1.)
plt.ylabel('$w(f)$', y=0.9)
plt.grid(True, 'both')
plt.suptitle('Weighting function')
plt.tight_layout(rect=[0, 0, 1, 0.98])
print("... computing optimal NTF")
ntf_opti = ntf_fir_weighting(order, w1, H_inf=H_inf)
ntf_opti_mag = lambda f: np.abs(evalTF(ntf_opti, np.exp(-2j*np.pi*f)))
print("... computing delsig NTF")
ntf_delsig = synthesizeNTF(delsig_order, OSR, 3, H_inf, 0)
ntf_delsig_mag = lambda f: np.abs(evalTF(ntf_delsig, np.exp(-2j*np.pi*f)))
print("... plotting optimal NTF amplitude response")
vv_mag = dbv(ntf_opti_mag(ff))
vv_mag_delsig = dbv(ntf_delsig_mag(ff))
plt.figure()
plt.plot(ff, vv_mag, label='Proposed')
plt.plot(ff, vv_mag_delsig, 'r-o', linewidth=0.5, markevery=24, markersize=3,
label='Reference')
plt.xlim(1e-5, 1./2)
# plt.ylim(0,1.1)
plt.xlim(1e-5, 1. / 2)
plt.ylim(0, 1.1)
plt.xscale('log', basex=10)
plt.gca().set_xticks([1E-5, 1. / 2])
plt.gca().set_xticklabels(['$10^{-5}$', r'$\frac{1}{2}$'])
plt.xlabel('$f$', x=1.)
plt.ylabel('$w(f)$', y=0.9)
plt.grid(True, 'both')
plt.suptitle('Weighting function')
plt.tight_layout(rect=[0, 0, 1, 0.98])
print("... computing optimal NTF")
ntf_opti = ntf_fir_weighting(order,
w1,
H_inf=H_inf,
cvxopt_opts={
'reltol': 1E-11,
'abstol': 1E-9,
'feastol': 1E-9
})
ntf_opti_mag = lambda f: np.abs(evalTF(ntf_opti, np.exp(-2j * np.pi * f)))
print("... plotting optimal NTF amplitude response")
vv_mag = dbv(ntf_opti_mag(ff))
plt.figure()
plt.plot(ff, vv_mag)
plt.xlim(1e-5, 1. / 2)
# plt.ylim(0, 1.1)
plt.xscale('log', basex=10)
plt.gca().set_xticks([1E-5, 1. / 2])
plt.gca().set_xticklabels(['$10^{-5}$', r'$\frac{1}{2}$'])
plt.xlabel('$f$', x=1.)
plt.figure()
plt.plot(ff, vv)
plt.xlim(1e-5, 1./2)
plt.ylim(0, 1.1)
plt.xscale('log', basex=10)
plt.gca().set_xticks([1E-5, 1./2])
plt.gca().set_xticklabels(['$10^{-5}$', r'$\frac{1}{2}$'])
plt.xlabel('$f$', x=1.)
plt.ylabel('$w(f)$', y=0.9)
plt.grid(True, 'both')
plt.suptitle('Weighting function')
plt.tight_layout(rect=[0, 0, 1, 0.98])
print("... computing optimal NTF")
ntf_opti = ntf_fir_weighting(order, w1, H_inf=H_inf,
cvxopt_opts={'reltol': 1E-11,
'abstol': 1E-9,
'feastol': 1E-9})
ntf_opti_mag = lambda f: np.abs(evalTF(ntf_opti, np.exp(-2j*np.pi*f)))
print("... plotting optimal NTF amplitude response")
vv_mag = dbv(ntf_opti_mag(ff))
plt.figure()
plt.plot(ff, vv_mag)
plt.xlim(1e-5, 1./2)
# plt.ylim(0, 1.1)
plt.xscale('log', basex=10)
plt.gca().set_xticks([1E-5, 1./2])
plt.gca().set_xticklabels(['$10^{-5}$', r'$\frac{1}{2}$'])
plt.xlabel('$f$', x=1.)
plt.ylabel((r'$\left|\mathit{NTF}\,\left(\mathrm{e}^{\mathrm{i} 2\pi f}' +
return 1. if f < B / fphi else 1e-12
# Introduce a weighting component that magnifies weight for
# frequencies < B/10
def weight2(f):
return 50. - 49. * f / (B / fphi / 10.) if f < B / fphi / 10. else 1.
# Get the final weighting function as the product of the two
def w2(f):
return w1(f) * weight2(f)
print("... computing optimal NTF...")
ntf1 = ntf_fir_weighting(order, w1, quad_opts={'points': [B / fphi]})
ntf2 = ntf_fir_weighting(order,
w2,
quad_opts={'points': [B / fphi, B / fphi / 10]})
# Prepare frequency axis for plotting
fmin = 10**np.ceil(np.log10(B / OSR / 100))
fmax = fphi / 2
ff = np.logspace(np.log10(fmin), np.log10(fmax), 1000)
resp_w1 = np.fromiter(list(map(w1, ff / fphi)), np.double)
resp_ntf1 = np.abs(evalTF(ntf1, np.exp(1j * 2 * np.pi * ff / fphi)))
resp_w2 = np.fromiter(list(map(w2, ff / fphi)), np.double)
resp_ntf2 = np.abs(evalTF(ntf2, np.exp(1j * 2 * np.pi * ff / fphi)))
ffa = np.logspace(np.log10(0.5E-5), np.log10(0.5), 1024)
