help="Use lookup table beam profile", default="") args = parser.parse_args() res_buffer = ResultBuffer() sim_types = ["gpu"] if args.enable_cpu: sim_types.append("cpu") for sim_type in sim_types: sim = RfSimulator(sim_type) device_name = "" if sim_type == "gpu": sim.set_parameter("gpu_device", "%d" % args.device_no) device_name = sim.get_parameter("cur_device_name") elif sim_type == "cpu": sim.set_parameter("num_cpu_cores", "%d" % args.num_cpu_cores) res_buffer.add_msg("=== SIMULATION RESULTS WITH %s %s ===" % (sim_type.upper(), device_name)) sim.set_parameter("verbose", "0") sim.set_parameter("sound_speed", "1540.0") sim.set_parameter("radial_decimation", "30") sim.set_parameter("use_arc_projection", args.arc_proj) res_buffer.add_msg("Arc projection: %s" % args.arc_proj) sim.set_parameter("phase_delay", args.phase_delay) res_buffer.add_msg("Complex phase delay: %s" % args.phase_delay) # configure the RF excitation ts = 1.0 / args.fs tc = 1.0 / args.fc
def do_simulation(args): if args.use_gpu: sim = RfSimulator("gpu") sim.set_parameter("gpu_device", "%d" % args.device_no) gpu_name = sim.get_parameter("cur_device_name") print "Using device %d: %s" % (args.device_no, gpu_name) else: sim = RfSimulator("cpu") sim.set_parameter("verbose", "0") with h5py.File(args.h5_file, "r") as f: scatterers_data = f["data"][()] sim.add_fixed_scatterers(scatterers_data) print "The number of scatterers is %d" % scatterers_data.shape[0] # configure simulation parameters sim.set_parameter("sound_speed", "1540.0") sim.set_parameter("radial_decimation", "10") sim.set_parameter("phase_delay", "on") sim.set_parameter("noise_amplitude", "%f" % args.noise_ampl) # configure the RF excitation fs = 80e6 ts = 1.0 / fs fc = 5.0e6 tc = 1.0 / fc t_vector = np.arange(-16 * tc, 16 * tc, ts) bw = 0.3 samples = np.array(gausspulse(t_vector, bw=bw, fc=fc), dtype="float32") center_index = int(len(t_vector) / 2) sim.set_excitation(samples, center_index, fs, fc) # define the scan sequence origins = np.zeros((args.num_lines, 3), dtype="float32") origins[:, 0] = np.linspace(args.x0, args.x1, args.num_lines) x_axis = np.array([1.0, 0.0, 0.0]) z_axis = np.array([0.0, 0.0, 1.0]) directions = np.array(np.tile(z_axis, (args.num_lines, 1)), dtype="float32") length = 0.06 lateral_dirs = np.array(np.tile(x_axis, (args.num_lines, 1)), dtype="float32") timestamps = np.zeros((args.num_lines, ), dtype="float32") sim.set_scan_sequence(origins, directions, length, lateral_dirs, timestamps) # configure the beam profile sim.set_analytical_beam_profile(1e-3, 1e-3) frame_sim_times = [] for frame_no in range(args.num_frames): start_time = time() iq_lines = sim.simulate_lines() frame_sim_times.append(time() - start_time) if args.save_simdata_file != "": with h5py.File(args.save_simdata_file, "w") as f: f["sim_data_real"] = np.array(np.real(iq_lines), dtype="float32") f["sim_data_imag"] = np.array(np.imag(iq_lines), dtype="float32") print "Simulation output written to %s" % args.save_simdata_file print "Simulation time: %f +- %f s (N=%d)" % ( np.mean(frame_sim_times), np.std(frame_sim_times), args.num_frames) if args.pdf_file != "" and not args.visualize: import matplotlib as mpl mpl.use("Agg") if args.pdf_file != "" or args.visualize: import matplotlib.pyplot as plt num_samples, num_lines = iq_lines.shape plt.figure(1, figsize=(18, 9)) plt.subplot(1, 2, 1) plt.plot(np.real(iq_lines[:, num_lines / 2])) plt.title("Middle RF line") plt.subplot(1, 2, 2) plt.imshow(np.real(abs(iq_lines)), aspect="auto", interpolation="nearest") if args.pdf_file != "": plt.savefig(args.pdf_file) print "Image written to disk." if args.visualize: plt.show()
# create two simulator instances - one for spline-only and one fixed-only sim_fixed = RfSimulator("gpu") sim_spline = RfSimulator("gpu") sim_fixed.set_parameter("verbose", "0") sim_spline.set_parameter("verbose", "0") sim_fixed.set_print_debug(False) sim_spline.set_print_debug(False) sim_fixed.set_parameter("sound_speed", "%f" % c0) sim_spline.set_parameter("sound_speed", "%f" % c0) sim_fixed.set_parameter("phase_delay", "on") sim_spline.set_parameter("phase_delay", "on") sim_fixed.set_parameter("radial_decimation", "5") sim_spline.set_parameter("radial_decimation", "5") num_gpus = int(sim_fixed.get_parameter("num_cuda_devices")) print "System has %d CUDA devices" % num_gpus sim_fixed.set_parameter("gpu_device", "%d" % args.gpu_device_no) sim_spline.set_parameter("gpu_device", "%d" % args.gpu_device_no) print "Fixed simulator uses %s" % sim_fixed.get_parameter( "cur_device_name") print "Spline simulator uses %s" % sim_spline.get_parameter( "cur_device_name") # define excitation signal t_vector = np.arange(-16 / args.fc, 16 / args.fc, 1.0 / args.fs) samples = np.array(gausspulse(t_vector, bw=args.bw, fc=args.fc), dtype="float32") center_index = int(len(t_vector) / 2) demod_freq = args.fc sim_fixed.set_excitation(samples, center_index, args.fs, demod_freq)
# create two simulator instances - one for spline-only and one fixed-only sim_fixed = RfSimulator("gpu") sim_spline = RfSimulator("gpu") sim_fixed.set_parameter("verbose", "0") sim_spline.set_parameter("verbose", "0") sim_fixed.set_print_debug(False) sim_spline.set_print_debug(False) sim_fixed.set_parameter("sound_speed", "%f" % c0) sim_spline.set_parameter("sound_speed", "%f" % c0) sim_fixed.set_parameter("phase_delay", "on") sim_spline.set_parameter("phase_delay", "on") sim_fixed.set_parameter("radial_decimation", "5") sim_spline.set_parameter("radial_decimation", "5") num_gpus = int(sim_fixed.get_parameter("num_cuda_devices")) print("System has %d CUDA devices" % num_gpus) sim_fixed.set_parameter("gpu_device", "%d" % args.gpu_device_no) sim_spline.set_parameter("gpu_device", "%d" % args.gpu_device_no) print("Fixed simulator uses %s" % sim_fixed.get_parameter("cur_device_name")) print("Spline simulator uses %s" % sim_spline.get_parameter("cur_device_name")) # define excitation signal t_vector = np.arange(-16 / args.fc, 16 / args.fc, 1.0 / args.fs) samples = np.array(gausspulse(t_vector, bw=args.bw, fc=args.fc), dtype="float32") center_index = int(len(t_vector) / 2) demod_freq = args.fc sim_fixed.set_excitation(samples, center_index, args.fs, demod_freq)
if cur_time >= args.end_time: cur_time = args.start_time timestamps = np.array(timestamps, dtype="float32") # precompute fixed-scatterer datasets fixed_scatterers = create_fixed_datasets(args, control_points, amplitudes, spline_degree, knot_vector, timestamps) # create two simulator instances - one for spline-only and one fixed-only sim_fixed = RfSimulator("gpu") sim_spline = RfSimulator("gpu") sim_fixed.set_parameter("verbose", "0"); sim_spline.set_parameter("verbose", "0") sim_fixed.set_print_debug(False); sim_spline.set_print_debug(False) sim_fixed.set_parameter("sound_speed", "%f" % c0); sim_spline.set_parameter("sound_speed", "%f" % c0) sim_fixed.set_parameter("phase_delay", "on"); sim_spline.set_parameter("phase_delay", "on") sim_fixed.set_parameter("radial_decimation", "5"); sim_spline.set_parameter("radial_decimation", "5") num_gpus = int(sim_fixed.get_parameter("num_cuda_devices")) print "System has %d CUDA devices" % num_gpus sim_fixed.set_parameter("gpu_device", "%d" % args.gpu_device_no) sim_spline.set_parameter("gpu_device", "%d" % args.gpu_device_no) print "Fixed simulator uses %s" % sim_fixed.get_parameter("cur_device_name") print "Spline simulator uses %s" % sim_spline.get_parameter("cur_device_name") # define excitation signal t_vector = np.arange(-16/args.fc, 16/args.fc, 1.0/args.fs) samples = np.array(gausspulse(t_vector, bw=args.bw, fc=args.fc), dtype="float32") center_index = int(len(t_vector)/2) demod_freq = args.fc sim_fixed.set_excitation(samples, center_index, args.fs, demod_freq) sim_spline.set_excitation(samples, center_index, args.fs, demod_freq) # create big scan sequence with all M-mode beams (for the spline algorithm)
def do_simulation(args): if args.use_gpu: sim = RfSimulator("gpu") sim.set_parameter("gpu_device", "%d"%args.device_no) gpu_name = sim.get_parameter("cur_device_name") print "Using device %d: %s" % (args.device_no, gpu_name) else: sim = RfSimulator("cpu") sim.set_parameter("verbose", "0") with h5py.File(args.h5_file, "r") as f: scatterers_data = f["data"][()] sim.add_fixed_scatterers(scatterers_data) print "The number of scatterers is %d" % scatterers_data.shape[0] # configure simulation parameters sim.set_parameter("sound_speed", "1540.0") sim.set_parameter("radial_decimation", "10") sim.set_parameter("phase_delay", "on") sim.set_parameter("noise_amplitude", "%f" % args.noise_ampl) # configure the RF excitation fs = 80e6 ts = 1.0/fs fc = 5.0e6 tc = 1.0/fc t_vector = np.arange(-16*tc, 16*tc, ts) bw = 0.3 samples = np.array(gausspulse(t_vector, bw=bw, fc=fc), dtype="float32") center_index = int(len(t_vector)/2) sim.set_excitation(samples, center_index, fs, fc) # configure the beam profile sim.set_analytical_beam_profile(1e-3, 1e-3) for i, y in enumerate(np.linspace(-0.005, 0.005, 100)): print(f"Simulating frame {i}") # define the scan sequence origins = np.zeros((args.num_lines, 3), dtype="float32") origins[:,1] = y origins[:,0] = np.linspace(args.x0, args.x1, args.num_lines) x_axis = np.array([1.0, 0.0, 0.0]) z_axis = np.array([0.0, 0.0, 1.0]) directions = np.array(np.tile(z_axis, (args.num_lines, 1)), dtype="float32") length = 0.06 lateral_dirs = np.array(np.tile(x_axis, (args.num_lines, 1)), dtype="float32") timestamps = np.zeros((args.num_lines,), dtype="float32") sim.set_scan_sequence(origins, directions, length, lateral_dirs, timestamps) iq_lines = sim.simulate_lines() bmode = np.array(abs(iq_lines), dtype="float32") gain = 1 dyn_range = 40 normalize_factor = np.max(bmode.flatten()) bmode = 20*np.log10(gain*bmode/normalize_factor) bmode = 255.0*(bmode+dyn_range)/dyn_range # clamp to [0, 255] bmode[bmode < 0] = 0.0 bmode[bmode > 255.0] = 255.0 fig = plt.figure(frameon=False) fig.set_size_inches(2*bmode.shape[1], bmode.shape[0]) ax = plt.Axes(fig, [0., 0., 1., 1.]) ax.set_axis_off() fig.add_axes(ax) ax.imshow(np.real(abs(iq_lines)), aspect="auto", cmap=plt.get_cmap("gray")) plt.savefig(f"sweep_{i:02d}.png", dpi=1) plt.close(fig)
parser.add_argument("--enable_cpu", help="Also time CPU impl (slow)", action="store_true") parser.add_argument("--lut_file", help="Use lookup table beam profile", default="") args = parser.parse_args() res_buffer = ResultBuffer() sim_types = ["gpu"] if args.enable_cpu: sim_types.append("cpu") for sim_type in sim_types: sim = RfSimulator(sim_type) device_name = "" if sim_type == "gpu": sim.set_parameter("gpu_device", "%d" % args.device_no) device_name = sim.get_parameter("cur_device_name") elif sim_type == "cpu": sim.set_parameter("num_cpu_cores", "%d" % args.num_cpu_cores) res_buffer.add_msg("=== SIMULATION RESULTS WITH %s %s ===" % (sim_type.upper(), device_name)) sim.set_parameter("verbose", "0") sim.set_parameter("sound_speed", "1540.0") sim.set_parameter("radial_decimation", "30") sim.set_parameter("use_arc_projection", args.arc_proj) res_buffer.add_msg("Arc projection: %s" % args.arc_proj) sim.set_parameter("phase_delay", args.phase_delay) res_buffer.add_msg("Complex phase delay: %s" % args.phase_delay) # configure the RF excitation ts = 1.0/args.fs tc = 1.0/args.fc t_vector = np.arange(-16*tc, 16*tc, ts)
def do_simulation(args): if args.use_gpu: sim = RfSimulator("gpu") sim.set_parameter("gpu_device", "%d"%args.device_no) gpu_name = sim.get_parameter("cur_device_name") print("Using device %d: %s" % (args.device_no, gpu_name)) else: sim = RfSimulator("cpu") sim.set_parameter("verbose", "0") with h5py.File(args.h5_file, "r") as f: scatterers_data = f["data"].value # TODO: inspect type sim.add_fixed_scatterers(scatterers_data) print("The number of scatterers is %d" % scatterers_data.shape[0]) # configure simulation parameters sim.set_parameter("sound_speed", "1540.0") sim.set_parameter("radial_decimation", "10") sim.set_parameter("phase_delay", "on") sim.set_parameter("noise_amplitude", "%f" % args.noise_ampl) # configure the RF excitation fs = 80e6 ts = 1.0/fs fc = 5.0e6 tc = 1.0/fc t_vector = np.arange(-16*tc, 16*tc, ts) bw = 0.3 samples = np.array(gausspulse(t_vector, bw=bw, fc=fc), dtype="float32") center_index = int(len(t_vector)/2) sim.set_excitation(samples, center_index, fs, fc) # define the scan sequence origins = np.zeros((args.num_lines, 3), dtype="float32") origins[:,0] = np.linspace(args.x0, args.x1, args.num_lines) x_axis = np.array([1.0, 0.0, 0.0]) z_axis = np.array([0.0, 0.0, 1.0]) directions = np.array(np.tile(z_axis, (args.num_lines, 1)), dtype="float32") length = 0.06 lateral_dirs = np.array(np.tile(x_axis, (args.num_lines, 1)), dtype="float32") timestamps = np.zeros((args.num_lines,), dtype="float32") sim.set_scan_sequence(origins, directions, length, lateral_dirs, timestamps) # configure the beam profile sim.set_analytical_beam_profile(1e-3, 1e-3) frame_sim_times = [] for frame_no in range(args.num_frames): start_time = time() iq_lines = sim.simulate_lines() frame_sim_times.append(time()-start_time) if args.save_simdata_file != "": with h5py.File(args.save_simdata_file, "w") as f: f["sim_data_real"] = np.array(np.real(iq_lines), dtype="float32") f["sim_data_imag"] = np.array(np.imag(iq_lines), dtype="float32") print("Simulation output written to %s" % args.save_simdata_file) print("Simulation time: %f +- %f s (N=%d)" % (np.mean(frame_sim_times), np.std(frame_sim_times), args.num_frames)) if args.pdf_file != "" and not args.visualize: import matplotlib as mpl mpl.use("Agg") if args.pdf_file != "" or args.visualize: num_samples, num_lines = iq_lines.shape center_data = abs (iq_lines[:, num_lines//2].real) data = abs (iq_lines) # data = 20 * np.log10(1e-2 + data / data.mean ()) import matplotlib.pyplot as plt print ('iq_lines.shape = (num_samples: {}, num_lines: {})'.format (num_samples, num_lines)) fig = plt.figure(1, figsize=(24, 12)) # fig = plt.figure(1, figsize=(9, 6)) ax = plt.subplot(1,2,1) plt.plot(center_data, color=(153/255,102/255,204/255)) plt.xlabel ('Depth', fontsize=14, labelpad=15) plt.ylabel ('Amplitude', fontsize=14, labelpad=15) plt.yticks ([]) plt.grid () plt.title ('Middle RF-Line', fontsize=16, pad=15) for side in ['top', 'right', 'left']: ax.spines[side].set_visible (False) ax = plt.subplot(1,2,2) image = plt.imshow (data, cmap='gray', aspect=2, interpolation="nearest") # cbar = fig.colorbar (image) # cbar.set_label (' (dB)', fontsize=12) plt.xlabel ('Width', fontsize=14, labelpad=15) plt.ylabel ('Depth', fontsize=14, labelpad=15) from os import path name = path.basename (args.h5_file).split ('.')[0].replace ('_', ' ').title () plt.title ('Simulated "{}"'.format (name), fontsize=16, pad=15) plt.grid () plt.tick_params (axis='both', which='both', bottom=True, top=False, labelbottom=True, left=True, right=False, labelleft=True) # cbar.ax.tick_params (axis='y', which='both', bottom=False, top=False, # labelbottom=False, left=False, right=False, labelright=True) # for side in ['top', 'right', 'bottom', 'left']: # cbar.ax.spines[side].set_visible (False) for side in ['top', 'right', 'bottom', 'left']: ax.spines[side].set_visible (False) # plt.xticks (tuple (np.arange (0, num_lines, 50)) + (num_lines,)) for side in ['bottom', 'left']: ax.spines[side].set_position(('outward', 1)) if args.pdf_file != "": plt.savefig(args.pdf_file) print("Image written to disk.") if args.visualize: plt.show()
def do_simulation(args): if args.use_gpu: sim = RfSimulator("gpu") sim.set_parameter("gpu_device", "%d"%args.device_no) gpu_name = sim.get_parameter("cur_device_name") print "Using device %d: %s" % (args.device_no, gpu_name) else: sim = RfSimulator("cpu") sim.set_parameter("verbose", "0") with h5py.File(args.h5_file, "r") as f: scatterers_data = f["data"].value sim.add_fixed_scatterers(scatterers_data) print "The number of scatterers is %d" % scatterers_data.shape[0] # configure simulation parameters sim.set_parameter("sound_speed", "1540.0") sim.set_parameter("radial_decimation", "10") sim.set_parameter("phase_delay", "on") sim.set_parameter("noise_amplitude", "%f" % args.noise_ampl) # configure the RF excitation fs = 80e6 ts = 1.0/fs fc = 5.0e6 tc = 1.0/fc t_vector = np.arange(-16*tc, 16*tc, ts) bw = 0.3 samples = np.array(gausspulse(t_vector, bw=bw, fc=fc), dtype="float32") center_index = int(len(t_vector)/2) sim.set_excitation(samples, center_index, fs, fc) # define the scan sequence origins = np.zeros((args.num_lines, 3), dtype="float32") origins[:,0] = np.linspace(args.x0, args.x1, args.num_lines) x_axis = np.array([1.0, 0.0, 0.0]) z_axis = np.array([0.0, 0.0, 1.0]) directions = np.array(np.tile(z_axis, (args.num_lines, 1)), dtype="float32") length = 0.06 lateral_dirs = np.array(np.tile(x_axis, (args.num_lines, 1)), dtype="float32") timestamps = np.zeros((args.num_lines,), dtype="float32") sim.set_scan_sequence(origins, directions, length, lateral_dirs, timestamps) # configure the beam profile sim.set_analytical_beam_profile(1e-3, 1e-3) frame_sim_times = [] for frame_no in range(args.num_frames): start_time = time() iq_lines = sim.simulate_lines() frame_sim_times.append(time()-start_time) if args.save_simdata_file != "": with h5py.File(args.save_simdata_file, "w") as f: f["sim_data_real"] = np.array(np.real(iq_lines), dtype="float32") f["sim_data_imag"] = np.array(np.imag(iq_lines), dtype="float32") print "Simulation output written to %s" % args.save_simdata_file print "Simulation time: %f +- %f s (N=%d)" % (np.mean(frame_sim_times), np.std(frame_sim_times), args.num_frames) if args.pdf_file != "" and not args.visualize: import matplotlib as mpl mpl.use("Agg") if args.pdf_file != "" or args.visualize: import matplotlib.pyplot as plt num_samples, num_lines = iq_lines.shape plt.figure(1, figsize=(18,9)) plt.subplot(1,2,1) plt.plot(np.real(iq_lines[:, num_lines/2])) plt.title("Middle RF line") plt.subplot(1,2,2) plt.imshow(np.real(abs(iq_lines)), aspect="auto", interpolation="nearest") if args.pdf_file != "": plt.savefig(args.pdf_file) print "Image written to disk." if args.visualize: plt.show()