with h5py.File(args.h5_file, "r") as f:
# load the fixed dataset if exists
if "data" in f:
scatterers_data = f["data"].value
print("Configuring %d fixed scatterers" % scatterers_data.shape[0])
sim_cpu.add_fixed_scatterers(scatterers_data)
sim_gpu.add_fixed_scatterers(scatterers_data)
# load the spline dataset if exists
if "spline_degree" in f:
amplitudes = f["amplitudes"].value
control_points = f["control_points"].value
knot_vector = f["knot_vector"].value
spline_degree = f["spline_degree"].value
print("Configuring %d spline scatterers" % control_points.shape[0])
sim_cpu.add_spline_scatterers(spline_degree, knot_vector, control_points, amplitudes)
sim_gpu.add_spline_scatterers(spline_degree, knot_vector, control_points, amplitudes)
# configure simulation parameters
sim_cpu.set_parameter("verbose", "0"); sim_gpu.set_parameter("verbose", "0")
sim_cpu.set_parameter("sound_speed", "1540.0"); sim_gpu.set_parameter("sound_speed", "1540.0")
sim_cpu.set_parameter("radial_decimation", "10"); sim_gpu.set_parameter("radial_decimation", "10")
sim_cpu.set_parameter("phase_delay", "on"); sim_gpu.set_parameter("phase_delay", "on")
# configure the RF excitation
fs = 50e6
ts = 1.0/fs
fc = 2.5e6
tc = 1.0/fc
t_vector = np.arange(-16*tc, 16*tc, ts)
bw = 0.2
lateral_dirs = np.empty((args.num_beams_total, 3), dtype="float32")
y_axis = np.array([0.0, 1.0, 0.0])
lateral_dir = np.cross(y_axis, direction)
for beam_no in range(args.num_beams_total):
origins[beam_no, :] = origin
directions[beam_no, :] = direction
lateral_dirs[beam_no, :] = lateral_dir
# set the beam profile
sim_fixed.set_analytical_beam_profile(args.sigma_lateral,
args.sigma_elevational)
sim_spline.set_analytical_beam_profile(args.sigma_lateral,
args.sigma_elevational)
# configure spline simulator
sim_spline.add_spline_scatterers(int(spline_degree), knot_vector,
control_points, amplitudes)
sim_spline.set_scan_sequence(origins, directions, args.line_length,
lateral_dirs, timestamps)
# fixed
iq_lines_fixed, sim_time_fixed = run_fixed_simulation(
sim_fixed, origin, direction, lateral_dir, args.line_length,
timestamps, fixed_scatterers)
if args.only_save_png:
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
plt.figure(1)
make_mmode_image(iq_lines_fixed)
plt.title("M-Mode produced with the fixed algorithm : %f sec" %
origins = np.empty((args.num_beams_total, 3), dtype="float32")
directions = np.empty((args.num_beams_total, 3), dtype="float32")
lateral_dirs = np.empty((args.num_beams_total, 3), dtype="float32")
y_axis = np.array([0.0, 1.0, 0.0])
lateral_dir = np.cross(y_axis, direction)
for beam_no in range(args.num_beams_total):
origins[beam_no, :] = origin
directions[beam_no, :] = direction
lateral_dirs[beam_no, :] = lateral_dir
# set the beam profile
sim_fixed.set_analytical_beam_profile(args.sigma_lateral, args.sigma_elevational)
sim_spline.set_analytical_beam_profile(args.sigma_lateral, args.sigma_elevational)
# configure spline simulator
sim_spline.add_spline_scatterers(spline_degree, knot_vector, control_points, amplitudes)
sim_spline.set_scan_sequence(origins, directions, args.line_length, lateral_dirs, timestamps)
# fixed
iq_lines_fixed, sim_time_fixed = run_fixed_simulation(sim_fixed, origin, direction, lateral_dir,
args.line_length, timestamps, fixed_scatterers)
if args.only_save_png:
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
plt.figure(1)
make_mmode_image(iq_lines_fixed)
plt.title("M-Mode produced with the fixed algorithm : %f sec" % sim_time_fixed)
if args.only_save_png:
plt.savefig("mmode_fixed_alg.png")
