pitch = float(np.squeeze(np.array(f['pitch'])))
n_elements = int(np.squeeze(np.array(f['n_elements'])))
period = time_vec[-1] + samp_time
probe_geometry = pitch * np.arange(n_elements)
probe_geometry -= np.mean(probe_geometry)
# extract locations of strong reflectors and corresponding echo locations across channels
x_true = true_positions[0, :][-n_points:]
z_true = true_positions[2, :][-n_points:]
print("%d strong reflectors" % n_points)
print("%d total reflectors" % true_positions.shape[1])
""" Crop and beamform """
# prepare data
min_time = distance2time(min_depth, speed_sound)
max_time = distance2time(max_depth, speed_sound)
first_sample = int(np.floor(min_time / samp_time))
last_sample = int(np.ceil(max_time / samp_time))
t0 = samp_time * first_sample
rf_data_trunc = rf_data[:, first_sample:last_sample]
time_vec_trunc = time_vec[first_sample:last_sample] - t0
duration = time_vec_trunc[-1] - time_vec_trunc[0] + samp_time
n_samples = rf_data_trunc.shape[1]
# check that beamformed image is still alright
scal_fact = 1e2
bf_data = das_beamform(rf_data_trunc,
samp_freq,
pitch,
probe_geometry,
"No data available. Loading NDE data and recovering for each channel..."
)
print()
if run_sweep:
# signed 16 bit integer [-128,127]
ndt_rawdata = np.genfromtxt(os.path.join(os.path.dirname(__file__), '..',
'data', 'ndt_rawdata.csv'),
delimiter=',')
n_samples = len(ndt_rawdata)
time_vec = np.arange(n_samples) / samp_freq
depth = time_vec[-1] / 2 * speed_sound
n_elem_tx = ndt_rawdata.shape[1]
""" Select portion of recording """
min_samp = int(distance2time(min_depth, speed_sound) * samp_freq)
t0 = time_vec[min_samp]
max_samp = int(distance2time(max_depth, speed_sound) * samp_freq)
tN = time_vec[max_samp]
t_samp = time_vec[min_samp:max_samp] - t0
n_samples = len(t_samp)
""" From FRI parameters, determine necessary sampling """
period = t_samp[-1] - t_samp[0] + 1 / samp_freq
fs_ind_center = int(np.ceil(center_freq * period))
period = fs_ind_center / center_freq # adjust period
K = n_points
M = K * oversample_fact
n_samples_fri = 2 * M + 1 # also number of FS coefficients
# subsampling
center_freq = clk_verasonics / 12
samp_freq = clk_verasonics / 3
bw = 2 / 3
bwr = -6
n_cycles = 2.5
speed_sound = 1540
seed = 0
# user parameters
n_diracs = 15
depth = 5e-2 # in meters
viz = True
"""
Create FRI parameters
"""
period = distance2time(depth, speed_sound)
ck, tk = create_pulse_param(n_diracs, period=period, seed=seed)
"""
Critical sampling
"""
M = n_diracs
n_samples = 2 * M + 1
samp_bw = n_samples / period
Ts = 1 / samp_bw
y_samp, t_samp = sample_iq(ck, tk, period, samp_bw, center_freq, bw, n_cycles,
bwr)
"""
Estimate FS coefficients of sum of diracs
"""
freqs_fft = np.fft.fftfreq(n_samples, Ts)
sub_idx = np.linspace(0, n_elem_tx - 1, n_elem_rx).astype(np.int)
array_pos = probe_geometry[sub_idx]
rx_echoes = rx_echoes[sub_idx] + t0
else:
"""
Subsample array
"""
sub_idx = np.linspace(0, n_elem_tx - 1, n_elem_rx).astype(np.int)
array_pos = probe_geometry[sub_idx]
"""
Estimate TOFs with pulse stream recovery
"""
# determine region to sample
min_samp = int(distance2time(min_depth, speed_sound) * samp_freq)
t0 = time_vec[min_samp]
max_samp = int(distance2time(max_depth, speed_sound) * samp_freq)
tN = time_vec[max_samp]
t_samp = time_vec[min_samp:max_samp] - t0
n_samples = len(t_samp)
# FRI parameters
period = t_samp[-1] - t_samp[0]
fs_ind_center = int(np.ceil(center_freq * period))
period = fs_ind_center / center_freq
K = n_points
M = K * oversample_fact
n_samples_fri = 2 * M + 1 # also number of FS coefficients
# subsampling