def process_fig2p14(n_diracs, seed, period, H_clean, freqs, idft_trunc,
samp_bw, center_freq, bw, n_cycles, bwr, samp_freq, snr_db,
max_ini, cadzow_iter, oversample_fact):
ck, tk = create_pulse_param(n_diracs, period=period, seed=seed)
y_samp, t_samp = sample_iq(ck, tk, period, samp_bw, center_freq, bw,
n_cycles, bwr)
# add noise
H_tot = add_noise(H_clean, snr_db, seed=seed)
""" Cadzow + TLS """
# estimate FS coefficients of sum of diracs
fs_coeff_hat = estimate_fourier_coeff(y_samp, t_samp, H=H_tot)
# denoise and recover parameters
fs_coeff_clean = cadzow_denoising(fs_coeff_hat,
n_diracs,
n_iter=cadzow_iter)
ann_filt = compute_ann_filt(fs_coeff_clean, n_diracs)
tk_hat = estimate_time_param(ann_filt, period)
ck_hat = estimate_amplitudes(fs_coeff_clean, freqs, tk_hat, period)
""" GenFRI, denoise and recover parameters SIMULATANEOUSLY """
G = idft_trunc * H_tot
fs_coeff_gen, min_error, c_opt, ini = gen_fri(G,
y_samp,
n_diracs,
stop_cri='max_iter',
max_ini=max_ini,
seed=seed)
tk_hat_gen = estimate_time_param(c_opt, period)
ck_hat_gen = estimate_amplitudes(fs_coeff_gen, freqs, tk_hat_gen, period)
"""
Evaluate
"""
_tk_err = compute_srr_db_points(tk, tk_hat)
_tk_err_gen = compute_srr_db_points(tk, tk_hat_gen)
y_rf, t_rf = sample_rf(ck, tk, period, samp_freq, center_freq, bw,
n_cycles, bwr)
y_rf_resynth, t_rf = sample_rf(ck_hat, tk_hat, period, samp_freq,
center_freq, bw, n_cycles, bwr)
_sig_err = compute_srr_db(y_rf, y_rf_resynth)
y_rf_resynth_gen, t_rf = sample_rf(ck_hat_gen, tk_hat_gen, period,
samp_freq, center_freq, bw, n_cycles,
bwr)
_sig_err_gen = compute_srr_db(y_rf, y_rf_resynth_gen)
return _tk_err, _sig_err, _tk_err_gen, _sig_err_gen
def process_noisy_samples_gen(n_diracs, seed, period, G, freqs, center_freq,
bw, n_cycles, bwr, samp_freq, snr_db, max_ini,
oversample_fact):
"""
Fig. 2.11-2.12
"""
stop_cri = 'max_iter' # 'mse' or 'max_iter'
ck, tk = create_pulse_param(n_diracs, period=period, seed=seed)
# oversample
samp_bw = (2 * oversample_fact * n_diracs + 1) / period
y_samp, t_samp = sample_iq(ck, tk, period, samp_bw, center_freq, bw,
n_cycles, bwr)
y_noisy = add_noise(y_samp, snr_db, seed=seed)
# denoising + recovery
fs_coeff_gen, min_error, c_opt, ini = gen_fri(G,
y_noisy,
n_diracs,
max_ini=max_ini,
stop_cri=stop_cri,
seed=seed)
tk_hat = estimate_time_param(c_opt, period)
ck_hat = estimate_amplitudes(fs_coeff_gen, freqs, tk_hat, period)
# compute errors
_tk_err = compute_srr_db_points(tk, tk_hat)
y_rf, t_rf = sample_rf(ck, tk, period, samp_freq, center_freq, bw,
n_cycles, bwr)
y_rf_resynth, t_rf = sample_rf(ck_hat, tk_hat, period, samp_freq,
center_freq, bw, n_cycles, bwr)
_sig_err = compute_srr_db(y_rf, y_rf_resynth)
return _tk_err, _sig_err
def process_noisy_samples(n_diracs, seed, period, H_tot, freqs, center_freq,
bw, n_cycles, bwr, samp_freq, snr_db, cadzow_iter,
oversample_fact):
"""
Fig. 2.8-2.9
"""
ck, tk = create_pulse_param(n_diracs, period=period, seed=seed)
# oversample
samp_bw = (2 * oversample_fact * n_diracs + 1) / period
y_samp, t_samp = sample_iq(ck, tk, period, samp_bw, center_freq, bw,
n_cycles, bwr)
y_noisy = add_noise(y_samp, snr_db, seed=seed)
# estimate fourier coefficients
fs_coeff_hat = estimate_fourier_coeff(y_noisy, t_samp, H=H_tot)
# denoising + recovery
fs_coeff_clean = cadzow_denoising(fs_coeff_hat,
n_diracs,
n_iter=cadzow_iter)
ann_filt = compute_ann_filt(fs_coeff_clean, n_diracs)
tk_hat = estimate_time_param(ann_filt, period)
ck_hat = estimate_amplitudes(fs_coeff_clean, freqs, tk_hat, period)
# compute errors
_tk_err = compute_srr_db_points(tk, tk_hat)
y_rf, t_rf = sample_rf(ck, tk, period, samp_freq, center_freq, bw,
n_cycles, bwr)
y_rf_resynth, t_rf = sample_rf(ck_hat, tk_hat, period, samp_freq,
center_freq, bw, n_cycles, bwr)
_sig_err = compute_srr_db(y_rf, y_rf_resynth)
return _tk_err, _sig_err
H_tot = total_freq_response(freqs, center_freq, bw, n_cycles, bwr)
fs_coeff_hat = Y / H_tot
"""
FRI recovery
"""
ann_filt = compute_ann_filt(fs_coeff_hat, n_diracs)
tk_hat = estimate_time_param(ann_filt, period)
ck_hat = estimate_amplitudes(fs_coeff_hat, freqs, tk_hat, period)
"""
Evaluate
"""
evaluate_recovered_param(ck, tk, ck_hat, tk_hat)
"""
Plot measured data, alongside typical RF data
"""
y_rf, t_rf = sample_rf(ck, tk, period, samp_freq, center_freq, bw, n_cycles,
bwr)
if viz:
"""rf data + pulse locations"""
time_scal = 1e5
plt.figure()
plt.plot(time_scal * t_rf, y_rf, label="RF data", alpha=0.65)
baseline = plt.stem(time_scal * tk,
ck,
'g',
markerfmt='go',
label="Parameters")[2]
plt.setp(baseline, color='g')
baseline.set_xdata([0, time_scal * period])
plt.xlim([0, time_scal * period])
plt.grid()
# TGC
y_samp_sub = y_samp_sub * atten
# recovery
fs_coeff_hat = estimate_fourier_coeff(y_samp_sub, t_samp_sub, H=H_tot)
fs_coeff_hat_clean = cadzow_denoising(fs_coeff_hat,
K,
n_iter=cadzow_iter)
ann_filt = compute_ann_filt(fs_coeff_hat_clean, K)
tk_hat = estimate_time_param(ann_filt, period)
ck_hat = estimate_amplitudes(fs_coeff_hat_clean, fs_ind / period,
tk_hat, period)
# evaluate
y_rf = sample_rf(ck_hat, tk_hat, period, samp_freq, center_freq,
bw_pulse, n_cycles)[0]
srr = compute_srr_db(y_samp, y_rf)
print("SRR : %f dB" % srr)
rx_echoes[chan_idx] = tk_hat
amplitudes[chan_idx] = ck_hat
tot_time = time.time() - start_time
print("TOTAL TIME : %f min" % (tot_time / 60))
""" save data """
time_stamp = datetime.datetime.now().strftime("%m_%d_%Hh%M")
results_dir = os.path.join(os.path.dirname(__file__),
"nde_standard_%s" % (time_stamp))
os.makedirs(results_dir)
np.savez(os.path.join(results_dir, "results"),
rx_echoes=rx_echoes,
def process_fig2p10(n_diracs,
period,
snr_db,
center_freq,
bw,
n_cycles,
bwr,
samp_freq,
cadzow_iter,
oversample_fact,
viz=False,
seed=0):
"""
Fig. 2.8-2.9
"""
# create FRI parameters
ck, tk = create_pulse_param(n_diracs, period=period, seed=seed)
# set oversampling
M = oversample_fact * n_diracs
n_samples = 2 * M + 1
samp_bw = n_samples / period
Ts = 1 / samp_bw
# oversample
y_samp, t_samp = sample_iq(ck, tk, period, samp_bw, center_freq, bw,
n_cycles, bwr)
y_noisy = add_noise(y_samp, snr_db, seed=seed)
# estimate fourier coefficients
freqs_fft = np.fft.fftfreq(n_samples, Ts)
increasing_order = np.argsort(freqs_fft)
freqs_fft = freqs_fft[increasing_order]
freqs = freqs_fft + center_freq
H_tot = total_freq_response(freqs, center_freq, bw, n_cycles, bwr)
fs_coeff_hat = estimate_fourier_coeff(y_noisy, t_samp, H=H_tot)
# denoising + recovery
fs_coeff_clean = cadzow_denoising(fs_coeff_hat,
n_diracs,
n_iter=cadzow_iter)
ann_filt = compute_ann_filt(fs_coeff_clean, n_diracs)
tk_hat = estimate_time_param(ann_filt, period)
ck_hat = estimate_amplitudes(fs_coeff_clean, freqs, tk_hat, period)
# compute errors
tk_err = compute_srr_db_points(tk, tk_hat)
y_rf, t_rf = sample_rf(ck, tk, period, samp_freq, center_freq, bw,
n_cycles, bwr)
y_rf_resynth, t_rf = sample_rf(ck_hat, tk_hat, period, samp_freq,
center_freq, bw, n_cycles, bwr)
sig_err = compute_srr_db(y_rf, y_rf_resynth)
print()
print("%d Diracs, %.02fx oversampling:" % (n_diracs, oversample_fact))
print("Locations SRR : %.02f dB" % tk_err)
print("Resynthesized error : %.02fdB" % sig_err)
"""visualize"""
if viz:
import matplotlib.pyplot as plt
time_scal = 1e5
plt.figure()
baseline = plt.stem(time_scal * tk,
ck,
'g',
markerfmt='go',
label="True")[2]
plt.setp(baseline, color='g')
baseline.set_xdata([0, time_scal * period])
baseline = plt.stem(time_scal * tk_hat,
ck_hat,
'r',
markerfmt='r^',
label="Estimate")[2]
plt.setp(baseline, color='r')
baseline.set_xdata(([0, time_scal * period]))
plt.xlabel("Time [%s seconds]" % str(1 / time_scal))
plt.xlim([0, time_scal * period])
plt.legend(loc='lower right')
plt.grid()
plt.tight_layout()
ax = plt.gca()
ax.axes.yaxis.set_ticklabels([])
# resynthesized signal
plt.figure()
plt.plot(time_scal * t_rf, y_rf, label="True", alpha=0.65)
plt.plot(time_scal * t_rf, y_rf_resynth, label="Estimate", alpha=0.65)
plt.xlim([0, time_scal * period])
plt.grid()
plt.xlabel("Time [%s seconds]" % str(1 / time_scal))
plt.tight_layout()
plt.legend(loc='lower right')
ax = plt.gca()
ax.axes.yaxis.set_ticklabels([])
n_samples = rf_data_trunc.shape[1]
""" FRI compression and resynthesis """
channel_idx = n_elements // 2
ck_hat, tk_hat, period = recover_parameters(rf_data_trunc[channel_idx, :],
time_vec_trunc,
K,
oversample_freq,
center_freq,
bandwidth,
num_cycles,
cadzow_iter=cadzow_iter)
print("Locations SRR [dB] : %f " %
compute_srr_db_points(true_tofs[channel_idx, :], tk_hat + t0))
# resynthesize
y_resynth = sample_rf(ck_hat, tk_hat, duration, samp_freq, center_freq,
bandwidth, num_cycles)[0]
print("Resynthesized SRR [dB] : %f " % compute_srr_db(
rf_data_trunc[channel_idx, :] / max(rf_data_trunc[channel_idx, :]),
y_resynth / max(y_resynth)))
plt.figure()
plt.plot(time_vec_trunc + t0,
rf_data_trunc[channel_idx, :] / max(rf_data_trunc[channel_idx, :]),
alpha=ALPHA,
label="Original")
plt.plot(time_vec_trunc + t0,
y_resynth / max(y_resynth),
alpha=ALPHA,
label="Resynthesized")
plt.xlim([min_time, max_time])
plt.grid()
FRI recovery
"""
ann_filt = compute_ann_filt(fs_coeff_hat, n_diracs)
tk_hat = estimate_time_param(ann_filt, period)
ck_hat = estimate_amplitudes(fs_coeff_hat, freqs, tk_hat, period)
"""
Evaluate
"""
evaluate_recovered_param(ck, tk, ck_hat, tk_hat)
tk_err = compute_srr_db_points(tk, tk_hat)
print("Locations SRR : %f dB" % tk_err)
"""
Visualize recovery
"""
y_rf, t_rf = sample_rf(ck, tk, period, samp_freq, center_freq, bw, n_cycles,
bwr)
y_rf_resynth, t_rf = sample_rf(ck_hat, tk_hat, period, samp_freq, center_freq,
bw, n_cycles, bwr)
err_sig = compute_srr_db(y_rf, y_rf_resynth)
print("Resynthesized error : %f dB" % err_sig)
if viz:
time_scal = 1e5
"""rf data + pulse locations"""
plt.figure()
plt.plot(time_scal * t_rf, y_rf, label="RF data", alpha=0.65)
baseline = plt.stem(time_scal * tk,
ck,
'g',
markerfmt='go',