def sarsim(cfg_file=None):
print(
'-------------------------------------------------------------------',
flush=True)
print(time.strftime("OCEANSAR SAR Simulator [%Y-%m-%d %H:%M:%S]",
time.localtime()),
flush=True)
# print('Copyright (c) Gerard Marull Paretas, Paco López-Dekker')
print(
'-------------------------------------------------------------------',
flush=True)
cfg_file = utils.get_parFile(parfile=cfg_file)
cfg = osrio.ConfigFile(cfg_file)
# Create output directory if it doesnt exist already
os.makedirs(cfg.sim.path, exist_ok=True)
src_path = os.path.dirname(os.path.abspath(__file__))
# RAW
if cfg.sim.raw_run:
print('Launching SAR RAW Generator...')
sar_raw(cfg.cfg_file_name,
os.path.join(cfg.sim.path, cfg.sim.raw_file),
os.path.join(cfg.sim.path, cfg.sim.ocean_file),
cfg.sim.ocean_reuse,
os.path.join(cfg.sim.path, cfg.sim.errors_file),
cfg.sim.errors_reuse,
plot_save=True)
# Processing
if cfg.sim.proc_run:
print('Launching SAR RAW Processor...')
sar_focus(cfg.cfg_file_name,
os.path.join(cfg.sim.path, cfg.sim.raw_file),
os.path.join(cfg.sim.path, cfg.sim.proc_file))
if cfg.sim.insar_run:
print('Launching InSAR L1b Processor...')
insar_process(cfg.cfg_file_name,
os.path.join(cfg.sim.path, cfg.sim.proc_file),
os.path.join(cfg.sim.path, cfg.sim.ocean_file),
os.path.join(cfg.sim.path, cfg.sim.insar_file))
if cfg.sim.ati_run:
print('Launching SAR ATI Processor...')
ati_process(cfg.cfg_file_name,
os.path.join(cfg.sim.path, cfg.sim.insar_file),
os.path.join(cfg.sim.path, cfg.sim.ocean_file),
os.path.join(cfg.sim.path, cfg.sim.ati_file))
if cfg.sim.L2_wavespectrum_run:
print('Launching L2 Wavespectrum Processor...')
print('----------------------------------', flush=True)
print(time.strftime("End of tasks [%Y-%m-%d %H:%M:%S]", time.localtime()),
flush=True)
print('----------------------------------', flush=True)
def skimsim(cfg_file=None):
print('-------------------------------------------------------------------', flush=True)
print(time.strftime("OCEANSAR SKIM Simulator [%Y-%m-%d %H:%M:%S]", time.localtime()), flush=True)
# print('Copyright (c) Gerard Marull Paretas, Paco López-Dekker')
print('-------------------------------------------------------------------', flush=True)
cfg_file = utils.get_parFile(parfile=cfg_file)
cfg = osrio.ConfigFile(cfg_file)
# Create output directory if it doesnt exist already
os.makedirs(cfg.sim.path, exist_ok=True)
src_path = os.path.dirname(os.path.abspath(__file__))
# RAW
if cfg.sim.raw_run:
print('Launching SAR RAW Generator...')
args = [cfg.sim.mpi_exec,
'-np', str(cfg.sim.mpi_num_proc),
sys.executable, src_path + os.sep + 'skim_raw.py',
'-c', cfg.cfg_file_name,
'-o', cfg.sim.path + os.sep + cfg.sim.raw_file,
'-oc', cfg.sim.path + os.sep + cfg.sim.ocean_file,
'-er', cfg.sim.path + os.sep + cfg.sim.errors_file]
if cfg.sim.ocean_reuse:
args.append('-ro')
if cfg.sim.errors_reuse:
args.append('-re')
returncode = subprocess.call(args)
if returncode != 0:
raise Exception('Something went wrong with SAR RAW Generator (return code %d)...' % returncode)
# Processing
if cfg.sim.proc_run:
print('Launching SAR RAW Processor...')
returncode = subprocess.call([sys.executable,
src_path + os.sep + 'skim_processor.py',
'-c', cfg.cfg_file_name,
'-r', cfg.sim.path + os.sep + cfg.sim.raw_file])
#,
# '-o', cfg.sim.path + os.sep + cfg.sim.pp_file])
if returncode != 0:
raise Exception('Something went wrong with SAR RAW Processor (return code %d)...' % returncode)
print('----------------------------------', flush=True)
print(time.strftime("End of tasks [%Y-%m-%d %H:%M:%S]", time.localtime()), flush=True)
print('----------------------------------', flush=True)
def batch_sarsim(template_file):
cfg_file = utils.get_parFile(parfile=template_file)
ref_cfg = osrio.ConfigFile(cfg_file)
step = 0
sim_path_ref = ref_cfg.sim.path + os.sep + 'sim_U%d_wdir%d_smag%d_sdir%d_inc%d_i%i'
n_all = (np.size(v_wind_U) * np.size(v_wind_dir) * np.size(v_current_mag) *
np.size(v_current_dir) * np.size(v_inc_angle) * n_rep)
for wind_U in v_wind_U:
for wind_dir in v_wind_dir:
for current_mag in v_current_mag:
for current_dir in v_current_dir:
for inc_angle in v_inc_angle:
for i_rep in np.arange(n_rep, dtype=np.int):
step = step + 1
print("")
print(
'CONFIGURATION AND LAUNCH OF SIMULATION %d of %d'
% (step, n_all))
### CONFIGURATION FILE ###
# Load template
cfg = ref_cfg
# Modify template, create directory & save
cfg.sim.path = sim_path_ref % (
wind_U, wind_dir, current_mag, current_dir,
inc_angle, i_rep)
cfg.ocean.wind_U = wind_U
cfg.ocean.wind_dir = wind_dir
cfg.ocean.current_mag = current_mag
cfg.ocean.current_dir = current_dir
cfg.sar.inc_angle = inc_angle
if not os.path.exists(cfg.sim.path):
os.makedirs(cfg.sim.path)
# Save configuration file into an alternate file
cfg.save(cfg.sim.path + os.sep + cfg_file_name)
### LAUNCH MACSAR ###
subprocess.call([
sys.executable, osr_script,
cfg.sim.path + os.sep + cfg_file_name
])
def ati_process(cfg_file, proc_output_file, ocean_file, output_file):
print('-------------------------------------------------------------------')
print(time.strftime("- OCEANSAR ATI Processor: [%Y-%m-%d %H:%M:%S]", time.localtime()))
print('-------------------------------------------------------------------')
print('Initializing...')
## CONFIGURATION FILE
cfg = tpio.ConfigFile(cfg_file)
# SAR
inc_angle = np.deg2rad(cfg.sar.inc_angle)
f0 = cfg.sar.f0
prf = cfg.sar.prf
num_ch = cfg.sar.num_ch
ant_L = cfg.sar.ant_L
alt = cfg.sar.alt
v_ground = cfg.sar.v_ground
rg_bw = cfg.sar.rg_bw
over_fs = cfg.sar.over_fs
pol = cfg.sar.pol
if pol == 'DP':
polt = ['hh', 'vv']
elif pol == 'hh':
polt = ['hh']
else:
polt = ['vv']
# ATI
rg_ml = cfg.ati.rg_ml
az_ml = cfg.ati.az_ml
ml_win = cfg.ati.ml_win
plot_save = cfg.ati.plot_save
plot_path = cfg.ati.plot_path
plot_format = cfg.ati.plot_format
plot_tex = cfg.ati.plot_tex
plot_surface = cfg.ati.plot_surface
plot_proc_ampl = cfg.ati.plot_proc_ampl
plot_coh = cfg.ati.plot_coh
plot_coh_all = cfg.ati.plot_coh_all
plot_ati_phase = cfg.ati.plot_ati_phase
plot_ati_phase_all = cfg.ati.plot_ati_phase_all
plot_vel_hist = cfg.ati.plot_vel_hist
plot_vel = cfg.ati.plot_vel
## CALCULATE PARAMETERS
if v_ground == 'auto': v_ground = geosar.orbit_to_vel(alt, ground=True)
k0 = 2.*np.pi*f0/const.c
rg_sampling = rg_bw*over_fs
# PROCESSED RAW DATA
proc_content = tpio.ProcFile(proc_output_file, 'r')
proc_data = proc_content.get('slc*')
proc_content.close()
# OCEAN SURFACE
surface = OceanSurface()
surface.load(ocean_file, compute=['D', 'V'])
surface.t = 0.
# OUTPUT FILE
output = open(output_file, 'w')
# OTHER INITIALIZATIONS
# Enable TeX
if plot_tex:
plt.rc('font', family='serif')
plt.rc('text', usetex=True)
# Create plots directory
plot_path = os.path.dirname(output_file) + os.sep + plot_path
if plot_save:
if not os.path.exists(plot_path):
os.makedirs(plot_path)
# SURFACE VELOCITIES
grg_grid_spacing = (const.c/2./rg_sampling/np.sin(inc_angle))
rg_res_fact = grg_grid_spacing / surface.dx
az_grid_spacing = (v_ground/prf)
az_res_fact = az_grid_spacing / surface.dy
res_fact = np.ceil(np.sqrt(rg_res_fact*az_res_fact))
# SURFACE RADIAL VELOCITY
v_radial_surf = surface.Vx*np.sin(inc_angle) - surface.Vz*np.cos(inc_angle)
v_radial_surf_ml = utils.smooth(utils.smooth(v_radial_surf, res_fact * rg_ml, axis=1), res_fact * az_ml, axis=0)
v_radial_surf_mean = np.mean(v_radial_surf)
v_radial_surf_std = np.std(v_radial_surf)
v_radial_surf_ml_std = np.std(v_radial_surf_ml)
# SURFACE HORIZONTAL VELOCITY
v_horizo_surf = surface.Vx
v_horizo_surf_ml = utils.smooth(utils.smooth(v_horizo_surf, res_fact * rg_ml, axis=1), res_fact * az_ml, axis=0)
v_horizo_surf_mean = np.mean(v_horizo_surf)
v_horizo_surf_std = np.std(v_horizo_surf)
v_horizo_surf_ml_std = np.std(v_horizo_surf_ml)
# Expected mean azimuth shift
sr0 = geosar.inc_to_sr(inc_angle, alt)
avg_az_shift = - v_radial_surf_mean / v_ground * sr0
std_az_shift = v_radial_surf_std / v_ground * sr0
##################
# ATI PROCESSING #
##################
print('Starting ATI processing...')
# Get dimensions & calculate region of interest
rg_span = surface.Lx
az_span = surface.Ly
rg_size = proc_data[0].shape[2]
az_size = proc_data[0].shape[1]
# Note: RG is projected, so plots are Ground Range
rg_min = 0
rg_max = np.int(rg_span/(const.c/2./rg_sampling/np.sin(inc_angle)))
az_min = np.int(az_size/2. + (-az_span/2. + avg_az_shift)/(v_ground/prf))
az_max = np.int(az_size/2. + (az_span/2. + avg_az_shift)/(v_ground/prf))
az_guard = np.int(std_az_shift / (v_ground / prf))
if (az_max - az_min) < (2 * az_guard - 10):
print('Not enough edge-effect free image')
return
# Adaptive coregistration
if cfg.sar.L_total:
ant_L = ant_L/np.float(num_ch)
dist_chan = ant_L/2
else:
if np.float(cfg.sar.Spacing) != 0:
dist_chan = np.float(cfg.sar.Spacing)/2
else:
dist_chan = ant_L/2
# dist_chan = ant_L/num_ch/2.
print('ATI Spacing: %f' % dist_chan)
inter_chan_shift_dist = dist_chan/(v_ground/prf)
# Subsample shift in azimuth
for chind in range(proc_data.shape[0]):
shift_dist = - chind * inter_chan_shift_dist
shift_arr = np.exp(-2j * np.pi * shift_dist *
np.roll(np.arange(az_size) - az_size/2,
int(-az_size / 2)) / az_size)
shift_arr = shift_arr.reshape((1, az_size, 1))
proc_data[chind] = np.fft.ifft(np.fft.fft(proc_data[chind], axis=1) *
shift_arr, axis=1)
# First dimension is number of channels, second is number of pols
ch_dim = proc_data.shape[0:2]
npol = ch_dim[1]
proc_data_rshp = [np.prod(ch_dim), proc_data.shape[2], proc_data.shape[3]]
# Compute extended covariance matrices...
proc_data = proc_data.reshape(proc_data_rshp)
# Intensities
i_all = []
for chind in range(proc_data.shape[0]):
this_i = utils.smooth(utils.smooth(np.abs(proc_data[chind])**2., rg_ml, axis=1, window=ml_win),
az_ml, axis=0, window=ml_win)
i_all.append(this_i[az_min:az_max, rg_min:rg_max])
i_all = np.array(i_all)
# .reshape((ch_dim) + (az_max - az_min, rg_max - rg_min))
interfs = []
cohs = []
tind = 0
coh_lut = np.zeros((proc_data.shape[0], proc_data.shape[0]), dtype=int)
for chind1 in range(proc_data.shape[0]):
for chind2 in range(chind1 + 1, proc_data.shape[0]):
coh_lut[chind1, chind2] = tind
tind = tind + 1
t_interf = utils.smooth(utils.smooth(proc_data[chind2] *
np.conj(proc_data[chind1]),
rg_ml, axis=1, window=ml_win),
az_ml, axis=0, window=ml_win)
interfs.append(t_interf[az_min:az_max, rg_min:rg_max])
cohs.append(t_interf[az_min:az_max, rg_min:rg_max] /
np.sqrt(i_all[chind1] * i_all[chind2]))
print('Generating plots and estimating values...')
# SURFACE HEIGHT
if plot_surface:
plt.figure()
plt.imshow(surface.Dz, cmap="ocean",
extent=[0, surface.Lx, 0, surface.Ly], origin='lower')
plt.title('Surface Height')
plt.xlabel('Ground range [m]')
plt.ylabel('Azimuth [m]')
cbar = plt.colorbar()
cbar.ax.set_xlabel('[m]')
if plot_save:
plt.savefig(plot_path + os.sep + 'plot_surface.' + plot_format,
bbox_inches='tight')
plt.close()
else:
plt.show()
# PROCESSED AMPLITUDE
if plot_proc_ampl:
for pind in range(npol):
save_path = (plot_path + os.sep + 'amp_dB_' + polt[pind]+
'.' + plot_format)
plt.figure()
plt.imshow(utils.db(i_all[pind]), aspect='equal',
origin='lower',
vmin=utils.db(np.max(i_all[pind]))-20,
extent=[0., rg_span, 0., az_span], interpolation='nearest',
cmap='viridis')
plt.xlabel('Ground range [m]')
plt.ylabel('Azimuth [m]')
plt.title("Amplitude")
plt.colorbar()
plt.savefig(save_path)
save_path = (plot_path + os.sep + 'amp_' + polt[pind]+
'.' + plot_format)
int_img = (i_all[pind])**0.5
vmin = np.mean(int_img) - 3 * np.std(int_img)
vmax = np.mean(int_img) + 3 * np.std(int_img)
plt.figure()
plt.imshow(int_img, aspect='equal',
origin='lower',
vmin=vmin, vmax=vmax,
extent=[0., rg_span, 0., az_span], interpolation='nearest',
cmap='viridis')
plt.xlabel('Ground range [m]')
plt.ylabel('Azimuth [m]')
plt.title("Amplitude")
plt.colorbar()
plt.savefig(save_path)
if plot_coh and ch_dim[0] > 1:
for pind in range(npol):
save_path = (plot_path + os.sep + 'ATI_coh_' +
polt[pind] + polt[pind] +
'.' + plot_format)
coh_ind = coh_lut[(pind, pind + npol)]
plt.figure()
plt.imshow(np.abs(cohs[coh_ind]), aspect='equal',
origin='lower',
vmin=0, vmax=1,
extent=[0., rg_span, 0., az_span],
cmap='bone')
plt.xlabel('Ground range [m]')
plt.ylabel('Azimuth [m]')
plt.title("ATI Coherence")
# plt.colorbar()
plt.savefig(save_path)
# ATI PHASE
tau_ati = dist_chan/v_ground
ati_phases = []
# Hack to avoid interferogram computation if there are no interferometric channels
if num_ch > 1:
npol_ = npol
else:
npol_ = 0
for pind in range(npol_):
save_path = (plot_path + os.sep + 'ATI_pha_' +
polt[pind] + polt[pind] +
'.' + plot_format)
coh_ind = coh_lut[(pind, pind + npol)]
ati_phase = uwphase(cohs[coh_ind])
ati_phases.append(ati_phase)
v_radial_est = -ati_phase / tau_ati / (k0 * 2.)
if plot_ati_phase:
phase_mean = np.mean(ati_phase)
phase_std = np.std(ati_phase)
vmin = np.max([-np.abs(phase_mean) - 4*phase_std,
-np.abs(ati_phase).max()])
vmax = np.min([np.abs(phase_mean) + 4*phase_std,
np.abs(ati_phase).max()])
plt.figure()
plt.imshow(ati_phase, aspect='equal',
origin='lower',
vmin=vmin, vmax=vmax,
extent=[0., rg_span, 0., az_span],
cmap='hsv')
plt.xlabel('Ground range [m]')
plt.ylabel('Azimuth [m]')
plt.title("ATI Phase")
plt.colorbar()
plt.savefig(save_path)
save_path = (plot_path + os.sep + 'ATI_rvel_' +
polt[pind] + polt[pind] +
'.' + plot_format)
vmin = -np.abs(v_radial_surf_mean) - 4. * v_radial_surf_std
vmax = np.abs(v_radial_surf_mean) + 4. * v_radial_surf_std
plt.figure()
plt.imshow(v_radial_est, aspect='equal',
origin='lower',
vmin=vmin, vmax=vmax,
extent=[0., rg_span, 0., az_span],
cmap='bwr')
plt.xlabel('Ground range [m]')
plt.ylabel('Azimuth [m]')
plt.title("Estimated Radial Velocity " + polt[pind])
plt.colorbar()
plt.savefig(save_path)
if npol_ == 4: # Bypass this for now
# Cross pol interferogram
coh_ind = coh_lut[(0, 1)]
save_path = (plot_path + os.sep + 'POL_coh_' +
polt[0] + polt[1] +
'.' + plot_format)
utils.image(np.abs(cohs[coh_ind]), max=1, min=0, aspect='equal',
cmap='gray', extent=[0., rg_span, 0., az_span],
xlabel='Ground range [m]', ylabel='Azimuth [m]',
title='XPOL Coherence',
usetex=plot_tex, save=plot_save, save_path=save_path)
save_path = (plot_path + os.sep + 'POL_pha_' +
polt[0] + polt[1] +
'.' + plot_format)
ati_phase = uwphase(cohs[coh_ind])
phase_mean = np.mean(ati_phase)
phase_std = np.std(ati_phase)
vmin = np.max([-np.abs(phase_mean) - 4*phase_std, -np.pi])
vmax = np.min([np.abs(phase_mean) + 4*phase_std, np.pi])
utils.image(ati_phase, aspect='equal',
min=vmin, max=vmax,
cmap=utils.bwr_cmap, extent=[0., rg_span, 0., az_span],
xlabel='Ground range [m]', ylabel='Azimuth [m]',
title='XPOL Phase', cbar_xlabel='[rad]',
usetex=plot_tex, save=plot_save, save_path=save_path)
if num_ch > 1:
ati_phases = np.array(ati_phases)
output.write('--------------------------------------------\n')
output.write('SURFACE RADIAL VELOCITY - NO SMOOTHING\n')
output.write('MEAN(SURF. V) = %.4f\n' % v_radial_surf_mean)
output.write('STD(SURF. V) = %.4f\n' % v_radial_surf_std)
output.write('--------------------------------------------\n\n')
output.write('--------------------------------------------\n')
output.write('SURFACE RADIAL VELOCITY - SMOOTHING (WIN. SIZE=%dx%d)\n' % (az_ml, rg_ml))
output.write('MEAN(SURF. V) = %.4f\n' % v_radial_surf_mean)
output.write('STD(SURF. V) = %.4f\n' % v_radial_surf_ml_std)
output.write('--------------------------------------------\n\n')
output.write('--------------------------------------------\n')
output.write('SURFACE HORIZONTAL VELOCITY - NO SMOOTHING\n')
output.write('MEAN(SURF. V) = %.4f\n' % v_horizo_surf_mean)
output.write('STD(SURF. V) = %.4f\n' % v_horizo_surf_std)
output.write('--------------------------------------------\n\n')
if plot_vel_hist:
# PLOT RADIAL VELOCITY
plt.figure()
plt.hist(v_radial_surf.flatten(), 200, normed=True, histtype='step')
#plt.hist(v_radial_surf_ml.flatten(), 500, normed=True, histtype='step')
plt.grid(True)
plt.xlim([-np.abs(v_radial_surf_mean) - 4.*v_radial_surf_std, np.abs(v_radial_surf_mean) + 4.* v_radial_surf_std])
plt.xlabel('Radial velocity [m/s]')
plt.ylabel('PDF')
plt.title('Surface velocity')
if plot_save:
plt.savefig(plot_path + os.sep + 'TRUE_radial_vel_hist.' + plot_format)
plt.close()
else:
plt.show()
plt.figure()
plt.hist(v_radial_surf_ml.flatten(), 200, normed=True, histtype='step')
#plt.hist(v_radial_surf_ml.flatten(), 500, normed=True, histtype='step')
plt.grid(True)
plt.xlim([-np.abs(v_radial_surf_mean) - 4.*v_radial_surf_std, np.abs(v_radial_surf_mean) + 4.* v_radial_surf_std])
plt.xlabel('Radial velocity [m/s]')
plt.ylabel('PDF')
plt.title('Surface velocity (low pass filtered)')
if plot_save:
plt.savefig(plot_path + os.sep + 'TRUE_radial_vel_ml_hist.' + plot_format)
plt.close()
else:
plt.show()
if plot_vel:
utils.image(v_radial_surf, aspect='equal', cmap=utils.bwr_cmap, extent=[0., rg_span, 0., az_span],
xlabel='Ground range [m]', ylabel='Azimuth [m]', title='Surface Radial Velocity', cbar_xlabel='[m/s]',
min=-np.abs(v_radial_surf_mean) - 4.*v_radial_surf_std, max=np.abs(v_radial_surf_mean) + 4.*v_radial_surf_std,
usetex=plot_tex, save=plot_save, save_path=plot_path + os.sep + 'TRUE_radial_vel.' + plot_format)
utils.image(v_radial_surf_ml, aspect='equal', cmap=utils.bwr_cmap, extent=[0., rg_span, 0., az_span],
xlabel='Ground range [m]', ylabel='Azimuth [m]', title='Surface Radial Velocity', cbar_xlabel='[m/s]',
min=-np.abs(v_radial_surf_mean) - 4.*v_radial_surf_std, max=np.abs(v_radial_surf_mean) + 4.*v_radial_surf_std,
usetex=plot_tex, save=plot_save, save_path=plot_path + os.sep + 'TRUE_radial_vel_ml.' + plot_format)
## ESTIMATED VELOCITIES
# Note: plot limits are taken from surface calculations to keep the same ranges
# ESTIMATE RADIAL VELOCITY
v_radial_ests = -ati_phases/tau_ati/(k0*2.)
# ESTIMATE HORIZONTAL VELOCITY
v_horizo_ests = -ati_phases/tau_ati/(k0*2.)/np.sin(inc_angle)
#Trim edges
v_radial_ests = v_radial_ests[:, az_guard:-az_guard, 5:-5]
v_horizo_ests = v_horizo_ests[:, az_guard:-az_guard, 5:-5]
output.write('--------------------------------------------\n')
output.write('ESTIMATED RADIAL VELOCITY - NO SMOOTHING\n')
for pind in range(npol):
output.write("%s Polarization\n" % polt[pind])
output.write('MEAN(EST. V) = %.4f\n' % np.mean(v_radial_ests[pind]))
output.write('STD(EST. V) = %.4f\n' % np.std(v_radial_ests[pind]))
output.write('--------------------------------------------\n\n')
output.write('--------------------------------------------\n')
output.write('ESTIMATED RADIAL VELOCITY - SMOOTHING (WIN. SIZE=%dx%d)\n' % (az_ml, rg_ml))
for pind in range(npol):
output.write("%s Polarization\n" % polt[pind])
output.write('MEAN(EST. V) = %.4f\n' % np.mean(utils.smooth(utils.smooth(v_radial_ests[pind],
rg_ml, axis=1),
az_ml, axis=0)))
output.write('STD(EST. V) = %.4f\n' % np.std(utils.smooth(utils.smooth(v_radial_ests[pind],
rg_ml, axis=1),
az_ml, axis=0)))
output.write('--------------------------------------------\n\n')
output.write('--------------------------------------------\n')
output.write('ESTIMATED HORIZONTAL VELOCITY - NO SMOOTHING\n')
for pind in range(npol):
output.write("%s Polarization\n" % polt[pind])
output.write('MEAN(EST. V) = %.4f\n' % np.mean(v_horizo_ests[pind]))
output.write('STD(EST. V) = %.4f\n' % np.std(v_horizo_ests[pind]))
output.write('--------------------------------------------\n\n')
# Processed NRCS
NRCS_est_avg = 10*np.log10(np.mean(np.mean(i_all[:, az_guard:-az_guard, 5:-5], axis=-1), axis=-1))
output.write('--------------------------------------------\n')
for pind in range(npol):
output.write("%s Polarization\n" % polt[pind])
output.write('Estimated mean NRCS = %5.2f\n' % NRCS_est_avg[pind])
output.write('--------------------------------------------\n\n')
# Some bookkeeping information
output.write('--------------------------------------------\n')
output.write('GROUND RANGE GRID SPACING = %.4f\n' % grg_grid_spacing)
output.write('AZIMUTH GRID SPACING = %.4f\n' % az_grid_spacing)
output.write('--------------------------------------------\n\n')
output.close()
if plot_vel_hist and num_ch > 1:
# PLOT RADIAL VELOCITY
plt.figure()
plt.hist(v_radial_surf.flatten(), 200, normed=True, histtype='step',
label='True')
for pind in range(npol):
plt.hist(v_radial_ests[pind].flatten(), 200, normed=True,
histtype='step', label=polt[pind])
plt.grid(True)
plt.xlim([-np.abs(v_radial_surf_mean) - 4.*v_radial_surf_std,
np.abs(v_radial_surf_mean) + 4.*v_radial_surf_std])
plt.xlabel('Radial velocity [m/s]')
plt.ylabel('PDF')
plt.title('Estimated velocity')
plt.legend()
if plot_save:
plt.savefig(plot_path + os.sep + 'ATI_radial_vel_hist.' + plot_format)
plt.close()
else:
plt.show()
# Save some statistics to npz file
#
if num_ch > 1:
filenpz = os.path.join(os.path.dirname(output_file), 'ati_stats.npz')
# Mean coh
cohs = np.array(cohs)[:, az_guard:-az_guard, 5:-5]
np.savez(filenpz,
nrcs=NRCS_est_avg,
v_r_dop=np.mean(np.mean(v_radial_ests, axis=-1), axis=-1),
v_r_surf = v_radial_surf_mean,
v_r_surf_std = v_radial_surf_std,
coh_mean= np.mean(np.mean(cohs, axis=-1), axis=-1),
abscoh_mean=np.mean(np.mean(np.abs(cohs), axis=-1), axis=-1),
coh_lut=coh_lut,
pols=polt)
print('----------------------------------------')
print(time.strftime("ATI Processing finished [%Y-%m-%d %H:%M:%S]", time.localtime()))
print('----------------------------------------')
def delta_k_processing(raw_output_file, cfg_file):
# heading
# flush is true is for correctly showing
print(
'-------------------------------------------------------------------',
flush=True)
print('Delta-k processing begins...')
# parameters
cfg = tpio.ConfigFile(cfg_file)
pp_file = os.path.join(cfg.sim.path, cfg.sim.pp_file)
n_sar_a = cfg.processing.n_sar
Azi_img = cfg.processing.Azi_img
# radar
f0 = cfg.radar.f0
prf = cfg.radar.prf
# num_ch = cfg.radar.num_ch
alt = cfg.radar.alt
v_ground = cfg.radar.v_ground
wei_dop = 0
m_ind = 0
# CALCULATE PARAMETERS
l0 = const.c / f0
if v_ground == 'auto':
v_ground = geo.orbit_to_vel(alt, ground=True)
if Azi_img: # 2-D unfocusing
data = np.load(pp_file)
Doppler_av = np.mean(data['dop_pp_avg'])
dp_axis = np.linspace(-prf / 2, prf / 2, n_sar_a)
val_d = np.abs(dp_axis - Doppler_av)
m_d = np.where(val_d == min(val_d))
m_ind = np.int(m_d[0])
f_in = prf / n_sar_a
beamwide = l0 / cfg.radar.ant_L
Bw = 2 * v_ground / l0 * beamwide
Bw_eff = np.abs(Bw * np.sin(cfg.radar.azimuth))
wei_dop = Bw_eff / f_in
# Analyse different waves
sim_path_ref = cfg.sim.path + os.sep + 'wavelength%.1f'
for inn in range(np.size(cfg.processing.wave_scale)):
wave_scale = cfg.processing.wave_scale[inn]
path_p = sim_path_ref % (wave_scale)
if not (os.path.exists(path_p) | Azi_img):
os.makedirs(path_p)
else:
sim_path_ref = cfg.sim.path + os.sep + 'wavelength%.1f_unfocus'
path_p = sim_path_ref % (wave_scale)
if not os.path.exists(path_p):
os.makedirs(path_p)
# imaging signs
plot_pattern = False
plot_spectrum = False
# processing parameters and initial parameters
# radar parameters
Az_smaples = cfg.radar.n_pulses
PRF = cfg.radar.prf
fs = cfg.radar.Fs
R_samples = cfg.radar.n_rg
inc = cfg.radar.inc_angle
n_sar_r = cfg.processing.n_sar_r
R_n = round(cfg.ocean.Lx * cfg.ocean.dx * np.sin(inc * np.pi / 180) /
(const.c / 2 / fs))
rang_img = cfg.processing.rang_img
analysis_deltan = np.linspace(0, 500,
501) # for delta-k spectrum analysis
r_int_num = cfg.processing.r_int_num
az_int = cfg.processing.az_int
Delta_lag = cfg.processing.Delta_lag
num_az = cfg.processing.num_az
list = range(1, num_az)
analyse_num = range(2, Az_smaples - az_int - 1 - num_az)
Scene_scope = ((R_samples - 1) * const.c / fs / 2) / 2
k_w = 2 * np.pi / wave_scale
# initialization parameters
dk_higha = np.zeros((np.size(analyse_num), r_int_num), dtype=np.float)
# RCS_power = np.zeros_like(analysis_deltan)
# get raw data
raw_data = raw_data_extraction(raw_output_file)
raw_data = raw_data[0]
# showing pattern of the ocean waves
# intensity
raw_int = raw_data * np.conj(raw_data)
if plot_pattern:
plt.figure()
plt.imshow(np.abs(raw_int))
plt.xlabel("Range (pixel)")
plt.ylabel("Azimuth (pixel)")
plot_path = cfg.sim.path + os.sep + 'delta_k_spectrum_plots'
if not os.path.exists(plot_path):
os.makedirs(plot_path)
plt.savefig(os.path.join(plot_path, 'Pattern.png'))
plt.close()
intens_spectrum = np.mean(np.fft.fft(np.abs(raw_int), axis=1), axis=0)
if plot_spectrum:
plt.figure()
plt.plot(
10 *
np.log10(np.abs(intens_spectrum[1:np.int(R_samples / 2)])))
plt.xlabel("Delta_k [1/m]")
plt.ylabel("Power (dB)")
# Showing Delta-k spectrum for analysis
delta_k_spectrum(Scene_scope, r_int_num, inc, raw_data,
analysis_deltan, path_p)
# Calculating the required delta_f value
delta_f = cal_delta_f(inc, wave_scale)
delta_k = delta_f / const.c
ind_N = np.int(round(delta_k * (2 * Scene_scope) * 2))
# sar processing parameters transfor
s_r, r_int_num, ind_N, data_rshp, s_a, az_int, analyse_num, n_sar_a = Transform(
raw_data, R_samples, n_sar_r, r_int_num, Az_smaples, ind_N, az_int,
n_sar_a, analyse_num, rang_img, Azi_img)
# initialization parameters
dk_higha = np.zeros((np.size(analyse_num), r_int_num), dtype=np.float)
# = np.zeros_like(analysis_deltan)
Omiga_p = np.zeros((np.size(analyse_num), np.size(list) + 1),
dtype=np.float)
Omiga_p_z = np.zeros(np.size(analyse_num), dtype=np.float)
Phase_p = np.zeros((np.size(analyse_num), np.size(list) + 1),
dtype=np.float)
Phase_p_z = np.zeros(np.size(analyse_num), dtype=np.float)
# Delta-k processing
if rang_img & Azi_img: # 2-D unfocusing
for ind_x in range(s_r):
r_data = data_rshp[:, ind_x, :]
spck_f = np.fft.fftshift(np.fft.fft(r_data, axis=1),
axes=(1, ))
dk_high = spck_f[:, 0:r_int_num] * np.conj(
spck_f[:, ind_N:ind_N + r_int_num])
dk_higha = np.mean(dk_high, axis=1)
# azimuth sar azimuth processing
dimsin = dk_higha.shape
int_unfcs, data_a = unfocused_sar(dk_higha, n_sar_a)
data_ufcs = np.fft.fftshift(data_a, axes=(1, ))
for ind in range(np.size(analyse_num)):
(Omiga_p_z[ind], Phase_p_z[ind]) = Cal_pha_vel(
data_ufcs[analyse_num[ind]:analyse_num[ind] +
az_int, :], data_ufcs[0:az_int, :],
analyse_num[ind], PRF, k_w, n_sar_a, m_ind, wei_dop,
Azi_img)
Omiga_p_z[ind] = Omiga_p_z[ind] + Omiga_p_z[ind]
Phase_p_z[ind] = Phase_p_z[ind] + Phase_p_z[ind]
Omiga_p_z = Omiga_p_z / s_r
Phase_p_z = Phase_p_z / s_r
elif rang_img: # range unfocusing
for ind_x in range(s_r):
r_data = data_rshp[:, ind_x, :]
spck_f = np.fft.fftshift(np.fft.fft(r_data, axis=1),
axes=(1, ))
dk_high = spck_f[:, 0:r_int_num] * np.conj(
spck_f[:, ind_N:ind_N + r_int_num])
dk_higha = np.mean(dk_high, axis=1)
for ind in range(np.size(analyse_num)):
(Omiga_p_z[ind], Phase_p_z[ind]) = Cal_pha_vel(
dk_higha[analyse_num[ind]:analyse_num[ind] + az_int],
dk_higha[0:az_int], analyse_num[ind], PRF, k_w,
n_sar_a, m_ind, wei_dop, Azi_img)
Omiga_p_z[ind] = Omiga_p_z[ind] + Omiga_p_z[ind]
Phase_p_z[ind] = Phase_p_z[ind] + Phase_p_z[ind]
Omiga_p_z = Omiga_p_z / s_r
Phase_p_z = Phase_p_z / s_r
elif Azi_img: # azimuth unfocusing
spck_f = np.fft.fftshift(np.fft.fft(raw_data, axis=1), axes=(1, ))
dk_high = spck_f[:, 0:r_int_num] * np.conj(
spck_f[:, ind_N:ind_N + r_int_num])
dk_higha = np.mean(dk_high, axis=1)
# azimuth sar azimuth processing
int_unfcs, data_a = unfocused_sar(dk_higha, n_sar_a)
data_ufcs = np.fft.fftshift(data_a, axes=(1, ))
# Estimate phase
for ind in range(np.size(analyse_num)):
(Omiga_p_z[ind], Phase_p_z[ind]) = Cal_pha_vel(
data_ufcs[analyse_num[ind]:analyse_num[ind] + az_int, :],
data_ufcs[0:az_int, :], analyse_num[ind], PRF, k_w,
n_sar_a, m_ind, wei_dop, Azi_img)
# Considering delta-k processing with lags
elif Delta_lag:
spck_f = np.fft.fftshift(np.fft.fft(raw_data, axis=1), axes=(1, ))
for iii in list:
for ind in range(np.size(analyse_num)):
if iii == 0:
dk_inda = spck_f[iii:, 0:r_int_num] * np.conj(
spck_f[0:, ind_N:ind_N + r_int_num])
else:
dk_inda = spck_f[iii:, 0:r_int_num] * np.conj(
spck_f[0:-iii, ind_N:ind_N + r_int_num])
dk_higha = np.mean(dk_inda, axis=1)
(Omiga_p[ind, iii], Phase_p[ind, iii]) = Cal_pha_vel(
dk_higha[analyse_num[ind]:analyse_num[ind] + az_int],
dk_higha[0:az_int], analyse_num[ind], PRF, k_w,
n_sar_a, m_ind, wei_dop, Azi_img)
else: # none unfocusing
spck_f = np.fft.fftshift(np.fft.fft(raw_data, axis=1), axes=(1, ))
# Extracting the information of wave_scale
dk_high = spck_f[:, 0:r_int_num] * np.conj(
spck_f[:, ind_N:ind_N + r_int_num])
dk_higha = np.mean(dk_high, axis=1)
for ind in range(np.size(analyse_num)):
(Omiga_p_z[ind], Phase_p_z[ind]) = Cal_pha_vel(
dk_higha[analyse_num[ind]:analyse_num[ind] + az_int],
dk_higha[0:az_int], analyse_num[ind], PRF, k_w, n_sar_a,
m_ind, wei_dop, Azi_img)
# processing results
if Azi_img:
analyse_num = np.array(analyse_num) * n_sar_a
plt.figure()
plt.plot(analyse_num, Omiga_p_z)
plt.xlabel("Azimuth interval [Pixel]")
plt.ylabel("Angular velocity (rad/s)")
plot_path = path_p + os.sep + 'delta_k_spectrum_plots'
if not os.path.exists(path_p):
os.makedirs(path_p)
plt.savefig(os.path.join(plot_path, 'Angular_velocity.png'))
plt.close()
plt.figure()
plt.plot(analyse_num, Phase_p_z)
plt.xlabel("Azimuth interval [Pixel]")
plt.ylabel("Phase velocity (m/s)")
plt.savefig(os.path.join(plot_path, 'Phase_velocity.png'))
plt.close()
# save the result data
if Delta_lag: # lag case
Omiga_f = Omiga_p[:, 0]
Phase_f = Phase_p[:, 0]
else: # normal case
Omiga_f = Omiga_p_z
Phase_f = Phase_p_z
Omiga_s = Omiga_f
Phase_s = Phase_f
np.save(
os.path.join(plot_path, 'Angular_velocity.npy'),
[Omiga_s, np.mean(Omiga_s),
np.size(Omiga_s), analyse_num])
np.save(
os.path.join(plot_path, 'Phase_velocity.npy'),
[Phase_s, np.mean(Phase_s),
np.size(Phase_s), analyse_num])
def skim_process(cfg_file, raw_output_file):
###################
# INITIALIZATIONS #
###################
# CONFIGURATION FILE
cfg = tpio.ConfigFile(cfg_file)
info = utils.PrInfo(cfg.sim.verbosity, "processor")
# Say hello
info.msg(time.strftime("Starting: %Y-%m-%d %H:%M:%S", time.localtime()))
# PROCESSING
az_weighting = cfg.processing.az_weighting
doppler_bw = cfg.processing.doppler_bw
plot_format = cfg.processing.plot_format
plot_tex = cfg.processing.plot_tex
plot_save = cfg.processing.plot_save
plot_path = cfg.processing.plot_path
plot_raw = cfg.processing.plot_raw
plot_rcmc_dopp = cfg.processing.plot_rcmc_dopp
plot_rcmc_time = cfg.processing.plot_rcmc_time
plot_image_valid = cfg.processing.plot_image_valid
doppler_demod = cfg.processing.doppler_demod
pp_file = os.path.join(cfg.sim.path, cfg.sim.pp_file)
# radar
f0 = cfg.radar.f0
prf = cfg.radar.prf
# num_ch = cfg.radar.num_ch
alt = cfg.radar.alt
v_ground = cfg.radar.v_ground
# CALCULATE PARAMETERS
l0 = const.c / f0
if v_ground == 'auto':
v_ground = geo.orbit_to_vel(alt, ground=True)
rg_sampling = cfg.radar.Fs
# Range freqency axis
f_axis = np.linspace(0, rg_sampling, cfg.radar.n_rg)
wavenum_scale = f_axis * 4 * np.pi / const.c * np.sin(
np.radians(cfg.radar.inc_angle))
# RAW DATA
raw_file = tpio.RawFile(raw_output_file, 'r')
raw_data = raw_file.get('raw_data*')
info.msg("Raw data max: %f" % (np.max(np.abs(raw_data))))
dop_ref = raw_file.get('dop_ref')
sr0 = raw_file.get('sr0')
azimuth = raw_file.get('azimuth')
raw_file.close()
#n_az
# OTHER INITIALIZATIONS
# Create plots directory
plot_path = os.path.dirname(pp_file) + os.sep + plot_path
if plot_save:
if not os.path.exists(plot_path):
os.makedirs(plot_path)
########################
# PROCESSING MAIN LOOP #
########################
# Optimize matrix sizes
az_size_orig, rg_size_orig = raw_data[0].shape
optsize = utils.optimize_fftsize(raw_data[0].shape)
# optsize = [optsize[0], optsize[1]]
data = np.zeros(optsize, dtype=complex)
data[0:az_size_orig, 0:rg_size_orig] = raw_data[0]
# Doppler demodulation according to geometric Doppler
if doppler_demod:
info.msg("Doppler demodulation")
t_vec = (np.arange(optsize[0]) / prf).reshape((optsize[0], 1))
data[:, 0:rg_size_orig] = (data[:, 0:rg_size_orig] * np.exp(
(-2j * np.pi) * t_vec * dop_ref.reshape((1, rg_size_orig))))
# Pulse pair
info.msg("Range over-sampling")
data_ovs = range_oversample(data)
info.msg("Pulse-pair processing")
dop_pp_avg, dop_pha_avg, coh = pulse_pair(
data_ovs[0:az_size_orig, 0:2 * rg_size_orig], prf)
krv, mean_int_profile, int_spe, phase_spec = rar_spectra(
data_ovs[0:az_size_orig, 0:2 * rg_size_orig],
2 * rg_sampling,
rgsmth=8)
kxv = krv * np.sin(np.radians(cfg.radar.inc_angle))
range_spectrum(data[0:az_size_orig, 0:rg_size_orig], rg_sampling,
plot_path)
info.msg("Mean DCA (pulse-pair average): %f Hz" % (np.mean(dop_pp_avg)))
info.msg("Mean DCA (pulse-pair phase average): %f Hz" %
(np.mean(dop_pha_avg)))
info.msg("Mean coherence: %f " % (np.mean(coh)))
# Unfocused SAR
info.msg("Unfocused SAR")
int_unfcs, data_ufcs = unfocused_sar(
data_ovs[0:az_size_orig, 0:2 * rg_size_orig], cfg.processing.n_sar)
krv2, sar_int_spec = sar_spectra(int_unfcs, 2 * rg_sampling, rgsmth=8)
# Some delta-k on focused sar data
info.msg("Unfocused SAR delta-k spectrum")
dkr, dk_avg, dk_signal = sar_delta_k(data_ufcs, 2 * rg_sampling, dksmoth=8)
dk_pulse_pairs, dk_omega = sar_delta_k_omega(dk_signal,
cfg.processing.n_sar / prf,
dksmoth=16)
# For verification, comment out
# dkr_sl, dk_avg_sl, dk_signal_sl = sar_delta_k_slow(data_ufcs, 2 * rg_sampling, dksmoth=8)
# dk_pulse_pairs_sl, dk_omega_sl = sar_delta_k_omega(dk_signal_sl, cfg.processing.n_sar/prf, dksmoth=32)
info.msg(
time.strftime("Processing done [%Y-%m-%d %H:%M:%S]", time.localtime()))
if plot_raw:
plt.figure()
plt.imshow(np.real(raw_data[0]),
vmin=-np.max(np.abs(raw_data[0])),
vmax=np.max(np.abs(raw_data[0])),
origin='lower',
aspect=np.float(raw_data[0].shape[1]) /
np.float(raw_data[0].shape[0]),
cmap='viridis')
#plt.title()
plt.xlabel("Range [samples]")
plt.ylabel("Azimuth [samples")
plt.savefig(plot_path + os.sep + 'plot_raw_real.%s' % plot_format,
dpi=150)
plt.close()
plt.figure()
plt.imshow(np.abs(raw_data[0]),
vmin=0,
vmax=np.max(np.abs(raw_data[0])),
origin='lower',
aspect=np.float(raw_data[0].shape[1]) /
np.float(raw_data[0].shape[0]),
cmap='viridis')
#plt.title()
plt.xlabel("Range [samples]")
plt.ylabel("Azimuth [samples")
plt.savefig(plot_path + os.sep + 'plot_raw_abs.%s' % plot_format,
dpi=150)
plt.close()
plt.figure()
plt.imshow(np.fft.fftshift(int_unfcs, axes=(0, )),
origin='lower',
aspect='auto',
cmap='viridis')
# plt.title()
plt.xlabel("Range [samples]")
plt.ylabel("Doppler [samples")
plt.savefig(plot_path + os.sep + 'ufcs_int.%s' % plot_format, dpi=150)
plt.close()
plt.figure()
plt.plot(mean_int_profile)
plt.xlabel("Range samples [Pixels]")
plt.ylabel("Intensity")
plt.savefig(plot_path + os.sep + 'mean_int.%s' % plot_format)
plt.close()
plt.figure()
plt.plot(np.fft.fftshift(kxv), np.fft.fftshift(int_spe))
plt.ylim((int_spe[20:np.int(cfg.radar.n_rg) - 20].min() / 2,
int_spe[20:np.int(cfg.radar.n_rg) - 20].max() * 1.5))
plt.xlim((0, kxv.max()))
plt.xlabel("$k_x$ [rad/m]")
plt.ylabel("$S_I$")
plt.savefig(plot_path + os.sep + 'int_spec.%s' % plot_format)
plt.close()
plt.figure()
plt.plot(np.fft.fftshift(kxv), np.fft.fftshift(sar_int_spec))
plt.ylim((sar_int_spec[20:np.int(cfg.radar.n_rg) - 20].min() / 2,
sar_int_spec[20:np.int(cfg.radar.n_rg) - 20].max() * 1.5))
plt.xlim((0, kxv.max()))
plt.xlabel("$k_x$ [rad/m]")
plt.ylabel("$S_I$")
plt.savefig(plot_path + os.sep + 'sar_int_spec.%s' % plot_format)
plt.close()
plt.figure()
plt.plot(np.fft.fftshift(kxv), np.fft.fftshift(phase_spec))
plt.ylim((phase_spec[20:np.int(cfg.radar.n_rg) - 20].min() / 2,
phase_spec[20:np.int(cfg.radar.n_rg) - 20].max() * 1.5))
plt.xlabel("$k_x$ [rad/m]")
plt.xlim((0, kxv.max()))
plt.ylabel("$S_{Doppler}$")
plt.savefig(plot_path + os.sep + 'pp_phase_spec.%s' % plot_format)
plt.figure()
dkx = dkr * np.sin(np.radians(cfg.radar.inc_angle))
plt.plot(dkx, dk_avg)
plt.ylim((dk_avg[20:dkx.size - 20].min() / 2,
dk_avg[20:dkx.size - 20].max() * 1.5))
plt.xlim((0.1, dkx.max()))
plt.xlabel("$\Delta k_x$ [rad/m]")
plt.ylabel("$S_I$")
plt.savefig(plot_path + os.sep + 'sar_delta_k_spec.%s' % plot_format)
plt.close()
# plt.figure()
# dkx_sl = dkr_sl * np.sin(np.radians(cfg.radar.inc_angle))
# plt.plot(dkx_sl, dk_avg_sl)
# plt.ylim((dk_avg_sl[20:dkx.size-20].min()/2,
# dk_avg_sl[20:dkx.size-20].max()*1.5))
# plt.xlim((0, dkx.max()))
# plt.xlabel("$\Delta k_x$ [rad/m]")
# plt.ylabel("$S_I$")
# plt.savefig(plot_path + os.sep + 'sar_delta_k_spec_slow.%s' % plot_format)
# plt.close()
plt.figure()
# plt.plot(dkx, dk_omega[0], label="lag=1")
plt.plot(dkx, dk_omega[1], label="lag=2")
# plt.plot(dkx, dk_omega_sl[0] + 1, label="slow-lag=1")
# plt.plot(dkx, dk_omega_sl[1] + 1, label="slow-lag=2")
plt.plot(dkx, dk_omega[3], label="lag=4")
# plt.plot(dkx, dk_omega[-1], label="lag=max")
plt.ylim((-20, 20))
#plt.ylim((dk_avg[20:dkx.size - 20].min() / 2,
# dk_avg[20:dkx.size - 20].max() * 1.5))
plt.xlim((0.05, 0.8))
plt.xlabel("$\Delta k_x$ [rad/m]")
plt.ylabel("$\omega$ [rad/s]")
plt.legend(loc=0)
plt.savefig(plot_path + os.sep + 'sar_delta_k_omega.%s' % plot_format)
plt.close()
info.msg("Saving output to %s" % pp_file)
np.savez(pp_file,
dop_pp_avg=dop_pp_avg,
dop_pha_avg=dop_pha_avg,
coh=coh,
ufcs_intensity=int_unfcs,
mean_int_profile=mean_int_profile,
int_spec=int_spe,
sar_int_spec=sar_int_spec,
ppphase_spec=phase_spec,
kx=kxv,
dk_spec=dk_avg,
dk_omega=dk_omega,
dkx=dkx)
info.msg(time.strftime("All done [%Y-%m-%d %H:%M:%S]", time.localtime()))
def __init__(self,
cfg_file=None,
ntimes=2,
t_step=10e-3,
pol='DP',
winddir=0,
U10=None,
po_model=None):
""" This function generates a (short) time series of surface realizations.
:param scf_file: the full path to the configuration with all OCEANSAR parameters
:param ntimes: number of time samples generated.
:param t_step: spacing between time samples. This can be interpreted as the Pulse Repetition Interval
:param wind:dir: to force wind direction
:param U10: to force wind force
:param po_model: one of None, spa (stationary phase approximation, or facet approach)
:returns: a tuple with the configuration object, the surfaces, the radial velocities for each grid point,
and the complex scattering coefficients
"""
cfg_file = utils.get_parFile(parfile=cfg_file)
cfg = ocs_io.ConfigFile(cfg_file)
self.cfg = cfg
self.use_hmtf = cfg.srg.use_hmtf
self.scat_spec_enable = cfg.srg.scat_spec_enable
if po_model is None:
self.scat_spec_mode = cfg.srg.scat_spec_mode
else:
self.scat_spec_mode = po_model
self.scat_bragg_enable = cfg.srg.scat_bragg_enable
self.scat_bragg_model = cfg.srg.scat_bragg_model
self.scat_bragg_d = cfg.srg.scat_bragg_d
self.scat_bragg_spec = cfg.srg.scat_bragg_spec
self.scat_bragg_spread = cfg.srg.scat_bragg_spread
# SAR
try:
radcfg = cfg.sar
except AttributeError:
radcfg = cfg.radar
self.inc_angle = np.deg2rad(radcfg.inc_angle)
self.alt = radcfg.alt
self.f0 = radcfg.f0
self.prf = radcfg.prf
if pol is None:
self.pol = radcfg.pol
else:
self.pol = pol
l0 = const.c / self.f0
k0 = 2. * np.pi * self.f0 / const.c
if self.pol == 'DP':
self.do_hh = True
self.do_vv = True
elif self.pol == 'hh':
self.do_hh = True
self.do_vv = False
else:
self.do_hh = False
self.do_vv = True
# OCEAN / OTHERS
ocean_dt = cfg.ocean.dt
self.surface = OceanSurface()
compute = ['D', 'Diff', 'Diff2', 'V']
print("Initializating surface")
if winddir is not None:
self.wind_dir = np.radians(winddir)
else:
self.wind_dir = np.deg2rad(cfg.ocean.wind_dir)
if U10 is None:
self.wind_u = cfg.ocean.wind_U
else:
self.wind_u = U10
if self.use_hmtf:
compute.append('hMTF')
self.surface.init(cfg.ocean.Lx,
cfg.ocean.Ly,
cfg.ocean.dx,
cfg.ocean.dy,
cfg.ocean.cutoff_wl,
cfg.ocean.spec_model,
cfg.ocean.spread_model,
self.wind_dir,
cfg.ocean.wind_fetch,
self.wind_u,
cfg.ocean.current_mag,
np.deg2rad(cfg.ocean.current_dir),
cfg.ocean.swell_enable,
cfg.ocean.swell_ampl,
np.deg2rad(cfg.ocean.swell_dir),
cfg.ocean.swell_wl,
compute,
cfg.ocean.opt_res,
cfg.ocean.fft_max_prime,
choppy_enable=cfg.ocean.choppy_enable)
# Get a surface realization calculated
print("Computing surface realizations")
self.surface.t = 0
self.ntimes = ntimes
self.diffx = np.zeros((ntimes, self.surface.Ny, self.surface.Nx))
self.diffy = np.zeros((ntimes, self.surface.Ny, self.surface.Nx))
self.diffxx = np.zeros((ntimes, self.surface.Ny, self.surface.Nx))
self.diffxy = np.zeros((ntimes, self.surface.Ny, self.surface.Nx))
self.diffyy = np.zeros((ntimes, self.surface.Ny, self.surface.Nx))
self.dx = np.zeros((ntimes, self.surface.Ny, self.surface.Nx))
self.dy = np.zeros((ntimes, self.surface.Ny, self.surface.Nx))
self.dz = np.zeros((ntimes, self.surface.Ny, self.surface.Nx))
self.diffx[0, :, :] = self.surface.Diffx
self.diffy[0, :, :] = self.surface.Diffy
self.diffxx[0, :, :] = self.surface.Diffxx
self.diffyy[0, :, :] = self.surface.Diffyy
self.diffxy[0, :, :] = self.surface.Diffxy
self.dx[0, :, :] = self.surface.Dx
self.dy[0, :, :] = self.surface.Dy
self.dz[0, :, :] = self.surface.Dz
if self.use_hmtf:
self.h_mtf = np.zeros((ntimes, self.surface.Ny, self.surface.Nx))
self.h_mtf[0, :, :] = self.surface.hMTF
self.t_step = t_step
for az_step in range(1, ntimes):
t_now = az_step * t_step
self.surface.t = t_now
self.diffx[az_step, :, :] = self.surface.Diffx
self.diffy[az_step, :, :] = self.surface.Diffy
self.diffxx[az_step, :, :] = self.surface.Diffxx
self.diffyy[az_step, :, :] = self.surface.Diffyy
self.diffxy[az_step, :, :] = self.surface.Diffxy
self.dx[az_step, :, :] = self.surface.Dx
self.dy[az_step, :, :] = self.surface.Dy
self.dz[az_step, :, :] = self.surface.Dz
if self.use_hmtf:
self.h_mtf[az_step, :, :] = self.surface.hMTF
self.inc_is_set = False
def fastraw(cfg_file, output_file, ocean_file, reuse_ocean_file, errors_file, reuse_errors_file,
plot_save=True):
###################
# INITIALIZATIONS #
###################
## MPI SETUP
comm = MPI.COMM_WORLD
size, rank = comm.Get_size(), comm.Get_rank()
## WELCOME
if rank == 0:
print('-------------------------------------------------------------------')
print(time.strftime("- OCEANSAR FAST RAW SAR GENERATOR: %Y-%m-%d %H:%M:%S", time.localtime()))
print('-------------------------------------------------------------------')
## CONFIGURATION FILE
# Note: variables are 'copied' to reduce code verbosity
cfg = tpio.ConfigFile(cfg_file)
# RAW
wh_tol = cfg.srg.wh_tol
nesz = cfg.srg.nesz
use_hmtf = cfg.srg.use_hmtf
scat_spec_enable = cfg.srg.scat_spec_enable
scat_spec_mode = cfg.srg.scat_spec_mode
scat_bragg_enable = cfg.srg.scat_bragg_enable
scat_bragg_model = cfg.srg.scat_bragg_model
scat_bragg_d = cfg.srg.scat_bragg_d
scat_bragg_spec = cfg.srg.scat_bragg_spec
scat_bragg_spread = cfg.srg.scat_bragg_spread
# SAR
inc_angle = np.deg2rad(cfg.sar.inc_angle)
f0 = cfg.sar.f0
pol = cfg.sar.pol
squint_r = np.degrees(cfg.sar.squint)
if pol == 'DP':
do_hh = True
do_vv = True
elif pol == 'hh':
do_hh = True
do_vv = False
else:
do_hh = False
do_vv = True
prf = cfg.sar.prf
num_ch = int(cfg.sar.num_ch)
ant_l = cfg.sar.ant_L
alt = cfg.sar.alt
v_ground = cfg.sar.v_ground
rg_bw = cfg.sar.rg_bw
over_fs = cfg.sar.over_fs
sigma_n_tx = cfg.sar.sigma_n_tx
phase_n_tx = np.deg2rad(cfg.sar.phase_n_tx)
sigma_beta_tx = cfg.sar.sigma_beta_tx
phase_beta_tx = np.deg2rad(cfg.sar.phase_beta_tx)
sigma_n_rx = cfg.sar.sigma_n_rx
phase_n_rx = np.deg2rad(cfg.sar.phase_n_rx)
sigma_beta_rx = cfg.sar.sigma_beta_rx
phase_beta_rx = np.deg2rad(cfg.sar.phase_beta_rx)
# OCEAN / OTHERS
ocean_dt = cfg.ocean.dt
add_point_target = False
use_numba = True
n_sinc_samples = 8
sinc_ovs = 20
chan_sinc_vec = raw.calc_sinc_vec(n_sinc_samples, sinc_ovs, Fs=over_fs)
# OCEAN SURFACE
print('Initializing ocean surface...')
surface = OceanSurface()
# Setup compute values
compute = ['D', 'Diff', 'Diff2']
if use_hmtf:
compute.append('hMTF')
# Try to reuse initialized surface
if reuse_ocean_file:
try:
surface.load(ocean_file, compute)
except RuntimeError:
pass
if (not reuse_ocean_file) or (not surface.initialized):
if hasattr(cfg.ocean, 'use_buoy_data'):
if cfg.ocean.use_buoy_data:
bdataf = cfg.ocean.buoy_data_file
date = datetime.datetime(np.int(cfg.ocean.year),
np.int(cfg.ocean.month),
np.int(cfg.ocean.day),
np.int(cfg.ocean.hour),
np.int(cfg.ocean.minute), 0)
date, bdata = tpio.load_buoydata(bdataf, date)
buoy_spec = tpio.BuoySpectra(bdata, heading=cfg.sar.heading, depth=cfg.ocean.depth)
dirspectrum_func = buoy_spec.Sk2
# Since the wind direction is included in the buoy data
wind_dir = 0
else:
dirspectrum_func = None
wind_dir = np.deg2rad(cfg.ocean.wind_dir)
else:
dirspectrum_func = None
wind_dir = np.deg2rad(cfg.ocean.wind_dir)
surface.init(cfg.ocean.Lx, cfg.ocean.Ly, cfg.ocean.dx,
cfg.ocean.dy, cfg.ocean.cutoff_wl,
cfg.ocean.spec_model, cfg.ocean.spread_model,
wind_dir,
cfg.ocean.wind_fetch, cfg.ocean.wind_U,
cfg.ocean.current_mag,
np.deg2rad(cfg.ocean.current_dir),
cfg.ocean.dir_swell_dir,
cfg.ocean.freq_r, cfg.ocean.sigf,
cfg.ocean.sigs, cfg.ocean.Hs,
cfg.ocean.swell_dir_enable,
cfg.ocean.swell_enable, cfg.ocean.swell_ampl,
np.deg2rad(cfg.ocean.swell_dir),
cfg.ocean.swell_wl,
compute, cfg.ocean.opt_res,
cfg.ocean.fft_max_prime,
choppy_enable=cfg.ocean.choppy_enable,
depth=cfg.ocean.depth,
dirspectrum_func=dirspectrum_func)
surface.save(ocean_file)
# Now we plot the directional spectrum
# self.wave_dirspec[good_k] = dirspectrum_func(self.kx[good_k], self.ky[good_k])
plt.figure()
plt.imshow(np.fft.fftshift(surface.wave_dirspec),
extent=[surface.kx.min(), surface.kx.max(),
surface.ky.min(), surface.ky.max()],
origin='lower',
cmap='inferno_r')
plt.grid(True)
pltax = plt.gca()
pltax.set_xlim((-0.1, 0.1))
pltax.set_ylim((-0.1, 0.1))
narr_length = 0.08 # np.min([surface_full.kx.max(), surface_full.ky.max()])
pltax.arrow(0, 0,
-narr_length * np.sin(np.radians(cfg.sar.heading)),
narr_length * np.cos(np.radians(cfg.sar.heading)),
fc="k", ec="k")
plt.xlabel('$k_x$ [rad/m]')
plt.ylabel('$k_y$ [rad/m]')
plt.colorbar()
#plt.show()
# Create plots directory
plot_path = os.path.dirname(output_file) + os.sep + 'raw_plots'
if plot_save:
if not os.path.exists(plot_path):
os.makedirs(plot_path)
plt.savefig(os.path.join(plot_path, 'input_dirspectrum.png'))
plt.close()
# CALCULATE PARAMETERS
if rank == 0:
print('Initializing simulation parameters...')
# SR/GR/INC Matrixes
sr0 = geosar.inc_to_sr(inc_angle, alt)
gr0 = geosar.inc_to_gr(inc_angle, alt)
gr = surface.x + gr0
sr, inc, _ = geosar.gr_to_geo(gr, alt)
sr -= np.min(sr)
inc = inc.reshape(1, inc.size)
sr = sr.reshape(1, sr.size)
gr = gr.reshape(1, gr.size)
sin_inc = np.sin(inc)
cos_inc = np.cos(inc)
# lambda, K, resolution, time, etc.
l0 = const.c/f0
k0 = 2.*np.pi*f0/const.c
sr_res = const.c/(2.*rg_bw)
if cfg.sar.L_total:
ant_l = ant_l/np.float(num_ch)
d_chan = ant_l
else:
if np.float(cfg.sar.Spacing) != 0:
d_chan = np.float(cfg.sar.Spacing)
else:
d_chan = ant_l
if v_ground == 'auto':
v_ground = geosar.orbit_to_vel(alt, ground=True)
t_step = 1./prf
t_span = (1.5*(sr0*l0/ant_l) + surface.Ly)/v_ground
az_steps = np.int(np.floor(t_span/t_step))
# Number of RG samples
max_sr = np.max(sr) + wh_tol + (np.max(surface.y) + (t_span/2.)*v_ground)**2./(2.*sr0)
min_sr = np.min(sr) - wh_tol
rg_samp_orig = np.int(np.ceil(((max_sr - min_sr)/sr_res)*over_fs))
rg_samp = np.int(utils.optimize_fftsize(rg_samp_orig))
# Other initializations
if do_hh:
proc_raw_hh = np.zeros([num_ch, az_steps, rg_samp], dtype=np.complex)
if do_vv:
proc_raw_vv = np.zeros([num_ch, az_steps, rg_samp], dtype=np.complex)
t_last_rcs_bragg = -1.
last_progress = -1
nrcs_avg_vv = np.zeros(az_steps, dtype=np.float)
nrcs_avg_hh = np.zeros(az_steps, dtype=np.float)
## RCS MODELS
# Specular
if scat_spec_enable:
if scat_spec_mode == 'kodis':
rcs_spec = rcs.RCSKodis(inc, k0, surface.dx, surface.dy)
elif scat_spec_mode == 'fa' or scat_spec_mode == 'spa':
spec_ph0 = np.random.uniform(0., 2.*np.pi,
size=[surface.Ny, surface.Nx])
rcs_spec = rcs.RCSKA(scat_spec_mode, k0, surface.x, surface.y,
surface.dx, surface.dy)
else:
raise NotImplementedError('RCS mode %s for specular scattering not implemented' % scat_spec_mode)
# Bragg
if scat_bragg_enable:
phase_bragg = np.zeros([2, surface.Ny, surface.Nx])
bragg_scats = np.zeros([2, surface.Ny, surface.Nx], dtype=np.complex)
# dop_phase_p = np.random.uniform(0., 2.*np.pi, size=[surface.Ny, surface.Nx])
# dop_phase_m = np.random.uniform(0., 2.*np.pi, size=[surface.Ny, surface.Nx])
tau_c = closure.grid_coherence(cfg.ocean.wind_U,surface.dx, f0)
rndscat_p = closure.randomscat_ts(tau_c, (surface.Ny, surface.Nx), prf)
rndscat_m = closure.randomscat_ts(tau_c, (surface.Ny, surface.Nx), prf)
# NOTE: This ignores slope, may be changed
k_b = 2.*k0*sin_inc
c_b = sin_inc*np.sqrt(const.g/k_b + 0.072e-3*k_b)
if scat_bragg_model == 'romeiser97':
current_dir = np.deg2rad(cfg.ocean.current_dir)
current_vec = (cfg.ocean.current_mag *
np.array([np.cos(current_dir),
np.sin(current_dir)]))
U_dir = np.deg2rad(cfg.ocean.wind_dir)
U_vec = (cfg.ocean.wind_U *
np.array([np.cos(U_dir), np.sin(U_dir)]))
U_eff_vec = U_vec - current_vec
rcs_bragg = rcs.RCSRomeiser97(k0, inc, pol,
surface.dx, surface.dy,
linalg.norm(U_eff_vec),
np.arctan2(U_eff_vec[1],
U_eff_vec[0]),
surface.wind_fetch,
scat_bragg_spec, scat_bragg_spread,
scat_bragg_d)
else:
raise NotImplementedError('RCS model %s for Bragg scattering not implemented' % scat_bragg_model)
surface_area = surface.dx * surface.dy * surface.Nx * surface.Ny
###################
# SIMULATION LOOP #
###################
if rank == 0:
print('Computing profiles...')
for az_step in np.arange(az_steps, dtype=np.int):
# AZIMUTH & SURFACE UPDATE
t_now = az_step * t_step
az_now = (t_now - t_span/2.)*v_ground
# az = np.repeat((surface.y - az_now)[:, np.newaxis], surface.Nx, axis=1)
az = (surface.y - az_now).reshape((surface.Ny, 1))
surface.t = t_now
# COMPUTE RCS FOR EACH MODEL
# Note: SAR processing is range independent as slant range is fixed
sin_az = az / sr0
az_proj_angle = np.arcsin(az / gr0)
# Note: Projected displacements are added to slant range
sr_surface = (sr - cos_inc*surface.Dz + az/2*sin_az
+ surface.Dx*sin_inc + surface.Dy*sin_az)
if do_hh:
scene_hh = np.zeros([int(surface.Ny), int(surface.Nx)], dtype=np.complex)
if do_vv:
scene_vv = np.zeros([int(surface.Ny), int(surface.Nx)], dtype=np.complex)
# Point target
if add_point_target and rank == 0:
sr_pt = (sr[0, surface.Nx/2] + az[surface.Ny/2, 0]/2 *
sin_az[surface.Ny/2, surface.Nx/2])
pt_scat = (100. * np.exp(-1j * 2. * k0 * sr_pt))
if do_hh:
scene_hh[surface.Ny/2, surface.Nx/2] = pt_scat
if do_vv:
scene_vv[surface.Ny/2, surface.Nx/2] = pt_scat
sr_surface[surface.Ny/2, surface.Nx/2] = sr_pt
# Specular
if scat_spec_enable:
if scat_spec_mode == 'kodis':
Esn_sp = np.sqrt(4.*np.pi)*rcs_spec.field(az_proj_angle, sr_surface,
surface.Diffx, surface.Diffy,
surface.Diffxx, surface.Diffyy, surface.Diffxy)
if do_hh:
scene_hh += Esn_sp
if do_vv:
scene_vv += Esn_sp
else:
# FIXME
if do_hh:
pol_tmp = 'hh'
Esn_sp = (np.exp(-1j*(2.*k0*sr_surface)) * (4.*np.pi)**1.5 *
rcs_spec.field(1, 1, pol_tmp[0], pol_tmp[1],
inc, inc,
az_proj_angle, az_proj_angle + np.pi,
surface.Dz,
surface.Diffx, surface.Diffy,
surface.Diffxx,
surface.Diffyy,
surface.Diffxy))
scene_hh += Esn_sp
if do_vv:
pol_tmp = 'vv'
Esn_sp = (np.exp(-1j*(2.*k0*sr_surface)) * (4.*np.pi)**1.5 *
rcs_spec.field(1, 1, pol_tmp[0], pol_tmp[1],
inc, inc,
az_proj_angle, az_proj_angle + np.pi,
surface.Dz,
surface.Diffx, surface.Diffy,
surface.Diffxx,
surface.Diffyy,
surface.Diffxy))
scene_vv += Esn_sp
nrcs_avg_hh[az_step] += (np.sum(np.abs(Esn_sp)**2) / surface_area)
nrcs_avg_vv[az_step] += nrcs_avg_hh[az_step]
# Bragg
if scat_bragg_enable:
if (t_now - t_last_rcs_bragg) > ocean_dt:
if scat_bragg_model == 'romeiser97':
if pol == 'DP':
rcs_bragg_hh, rcs_bragg_vv = rcs_bragg.rcs(az_proj_angle,
surface.Diffx,
surface.Diffy)
elif pol=='hh':
rcs_bragg_hh = rcs_bragg.rcs(az_proj_angle,
surface.Diffx,
surface.Diffy)
else:
rcs_bragg_vv = rcs_bragg.rcs(az_proj_angle,
surface.Diffx,
surface.Diffy)
if use_hmtf:
# Fix Bad MTF points
surface.hMTF[np.where(surface.hMTF < -1)] = -1
if do_hh:
rcs_bragg_hh[0] *= (1 + surface.hMTF)
rcs_bragg_hh[1] *= (1 + surface.hMTF)
if do_vv:
rcs_bragg_vv[0] *= (1 + surface.hMTF)
rcs_bragg_vv[1] *= (1 + surface.hMTF)
t_last_rcs_bragg = t_now
if do_hh:
scat_bragg_hh = np.sqrt(rcs_bragg_hh)
nrcs_bragg_hh_instant_avg = np.sum(rcs_bragg_hh) / surface_area
nrcs_avg_hh[az_step] += nrcs_bragg_hh_instant_avg
if do_vv:
scat_bragg_vv = np.sqrt(rcs_bragg_vv)
nrcs_bragg_vv_instant_avg = np.sum(rcs_bragg_vv) / surface_area
nrcs_avg_vv[az_step] += nrcs_bragg_vv_instant_avg
# Doppler phases (Note: Bragg radial velocity taken constant!)
surf_phase = - (2 * k0) * sr_surface
cap_phase = (2 * k0) * t_step * c_b * (az_step + 1)
phase_bragg[0] = surf_phase - cap_phase # + dop_phase_p
phase_bragg[1] = surf_phase + cap_phase # + dop_phase_m
bragg_scats[0] = rndscat_m.scats(t_now)
bragg_scats[1] = rndscat_p.scats(t_now)
if do_hh:
scene_hh += ne.evaluate('sum(scat_bragg_hh * exp(1j*phase_bragg) * bragg_scats, axis=0)')
if do_vv:
scene_vv += ne.evaluate('sum(scat_bragg_vv * exp(1j*phase_bragg) * bragg_scats, axis=0)')
# ANTENNA PATTERN
# FIXME: this assume co-located Tx and Tx, so it will not work for true bistatic configurations
if cfg.sar.L_total:
beam_pattern = sinc_1tx_nrx(sin_az, ant_l * num_ch, f0, num_ch, field=True)
else:
beam_pattern = sinc_1tx_nrx(sin_az, ant_l, f0, 1, field=True)
# GENERATE CHANEL PROFILES
for ch in np.arange(num_ch, dtype=np.int):
if do_hh:
scene_bp = scene_hh * beam_pattern
# Add channel phase & compute profile
scene_bp *= np.exp(-1j*k0*d_chan*ch*sin_az)
if use_numba:
raw.chan_profile_numba(sr_surface.flatten(),
scene_bp.flatten(),
sr_res / over_fs,
min_sr,
chan_sinc_vec,
n_sinc_samples, sinc_ovs,
proc_raw_hh[ch][az_step])
else:
raw.chan_profile_weave(sr_surface.flatten(),
scene_bp.flatten(),
sr_res / over_fs,
min_sr,
chan_sinc_vec,
n_sinc_samples, sinc_ovs,
proc_raw_hh[ch][az_step])
if do_vv:
scene_bp = scene_vv * beam_pattern
# Add channel phase & compute profile
scene_bp *= np.exp(-1j*k0*d_chan*ch*sin_az)
if use_numba:
raw.chan_profile_numba(sr_surface.flatten(),
scene_bp.flatten(),
sr_res / over_fs,
min_sr,
chan_sinc_vec,
n_sinc_samples, sinc_ovs,
proc_raw_vv[ch][az_step])
else:
raw.chan_profile_weave(sr_surface.flatten(),
scene_bp.flatten(),
sr_res / over_fs,
min_sr,
chan_sinc_vec,
n_sinc_samples, sinc_ovs,
proc_raw_vv[ch][az_step])
# SHOW PROGRESS (%)
current_progress = np.int((100 * az_step) / az_steps)
if current_progress != last_progress:
last_progress = current_progress
print('SP,%d,%d,%d' % (rank, size, current_progress))
# PROCESS REDUCED RAW DATA & SAVE (ROOT)
if rank == 0:
print('Processing and saving results...')
# Filter and decimate
#range_filter = np.ones_like(total_raw)
#range_filter[:, :, rg_samp/(2*2*cfg.sar.over_fs):-rg_samp/(2*2*cfg.sar.over_fs)] = 0
#total_raw = np.fft.ifft(range_filter*np.fft.fft(total_raw))
if do_hh:
proc_raw_hh = proc_raw_hh[:, :, :rg_samp_orig]
if do_vv:
proc_raw_vv = proc_raw_vv[:, :, :rg_samp_orig]
# Calibration factor (projected antenna pattern integrated in azimuth)
az_axis = np.arange(-t_span/2.*v_ground, t_span/2.*v_ground, sr0*const.c/(np.pi*f0*ant_l*10.))
if cfg.sar.L_total:
pattern = sinc_1tx_nrx(az_axis/sr0, ant_l * num_ch, f0,
num_ch, field=True)
else:
pattern = sinc_1tx_nrx(az_axis/sr0, ant_l, f0, 1,
field=True)
cal_factor = (1. / np.sqrt(np.trapz(np.abs(pattern)**2., az_axis) *
sr_res/np.sin(inc_angle)))
if do_hh:
noise = (utils.db2lin(nesz, amplitude=True) / np.sqrt(2.) *
(np.random.normal(size=proc_raw_hh.shape) +
1j*np.random.normal(size=proc_raw_hh.shape)))
total_raw_hh = proc_raw_hh * cal_factor + noise
if do_vv:
noise = (utils.db2lin(nesz, amplitude=True) / np.sqrt(2.) *
(np.random.normal(size=proc_raw_vv.shape) +
1j*np.random.normal(size=proc_raw_vv.shape)))
total_raw_vv = proc_raw_vv * cal_factor + noise
# Add slow-time error
# if use_errors:
# if do_hh:
# total_raw_hh *= errors.beta_noise
# if do_vv:
# total_raw_vv *= errors.beta_noise
# Save RAW data (and other properties, used by 3rd party software)
if do_hh and do_vv:
rshp = (1,) + proc_raw_hh.shape
proc_raw = np.concatenate((proc_raw_hh.reshape(rshp),
proc_raw_vv.reshape(rshp)))
rshp = (1,) + nrcs_avg_hh.shape
NRCS_avg = np.concatenate((nrcs_avg_hh.reshape(rshp),
nrcs_avg_vv.reshape(rshp)))
elif do_hh:
rshp = (1,) + proc_raw_hh.shape
proc_raw = proc_raw_hh.reshape(rshp)
rshp = (1,) + nrcs_avg_hh.shape
NRCS_avg = nrcs_avg_hh.reshape(rshp)
else:
rshp = (1,) + proc_raw_vv.shape
proc_raw = proc_raw_vv.reshape(rshp)
rshp = (1,) + nrcs_avg_vv.shape
NRCS_avg = nrcs_avg_vv.reshape(rshp)
raw_file = tpio.RawFile(output_file, 'w', proc_raw.shape)
raw_file.set('inc_angle', np.rad2deg(inc_angle))
raw_file.set('f0', f0)
raw_file.set('num_ch', num_ch)
raw_file.set('ant_l', ant_l)
raw_file.set('prf', prf)
raw_file.set('v_ground', v_ground)
raw_file.set('orbit_alt', alt)
raw_file.set('sr0', sr0)
raw_file.set('rg_sampling', rg_bw*over_fs)
raw_file.set('rg_bw', rg_bw)
raw_file.set('raw_data*', proc_raw)
raw_file.set('NRCS_avg', NRCS_avg)
raw_file.close()
print(time.strftime("Finished [%Y-%m-%d %H:%M:%S]", time.localtime()))
def skimraw(cfg_file,
output_file,
ocean_file,
reuse_ocean_file,
errors_file,
reuse_errors_file,
plot_save=True):
###################
# INITIALIZATIONS #
###################
# MPI SETUP
comm = MPI.COMM_WORLD
size, rank = comm.Get_size(), comm.Get_rank()
# WELCOME
if rank == 0:
print(
'-------------------------------------------------------------------'
)
print(
time.strftime("- OCEANSAR SKIM RAW GENERATOR: %Y-%m-%d %H:%M:%S",
time.localtime()))
# print('- Copyright (c) Gerard Marull Paretas, Paco Lopez Dekker')
print(
'-------------------------------------------------------------------'
)
# CONFIGURATION FILE
# Note: variables are 'copied' to reduce code verbosity
cfg = tpio.ConfigFile(cfg_file)
info = utils.PrInfo(cfg.sim.verbosity, "SKIM raw")
# RAW
wh_tol = cfg.srg.wh_tol
nesz = cfg.srg.nesz
use_hmtf = cfg.srg.use_hmtf
scat_spec_enable = cfg.srg.scat_spec_enable
scat_spec_mode = cfg.srg.scat_spec_mode
scat_bragg_enable = cfg.srg.scat_bragg_enable
scat_bragg_model = cfg.srg.scat_bragg_model
scat_bragg_d = cfg.srg.scat_bragg_d
scat_bragg_spec = cfg.srg.scat_bragg_spec
scat_bragg_spread = cfg.srg.scat_bragg_spread
# SAR
inc_angle = np.deg2rad(cfg.radar.inc_angle)
f0 = cfg.radar.f0
pol = cfg.radar.pol
squint_r = np.radians(90 - cfg.radar.azimuth)
if pol == 'DP':
do_hh = True
do_vv = True
elif pol == 'hh':
do_hh = True
do_vv = False
else:
do_hh = False
do_vv = True
prf = cfg.radar.prf
num_ch = int(cfg.radar.num_ch)
ant_L = cfg.radar.ant_L
alt = cfg.radar.alt
v_ground = cfg.radar.v_ground
rg_bw = cfg.radar.rg_bw
over_fs = cfg.radar.Fs / cfg.radar.rg_bw
sigma_n_tx = cfg.radar.sigma_n_tx
phase_n_tx = np.deg2rad(cfg.radar.phase_n_tx)
sigma_beta_tx = cfg.radar.sigma_beta_tx
phase_beta_tx = np.deg2rad(cfg.radar.phase_beta_tx)
sigma_n_rx = cfg.radar.sigma_n_rx
phase_n_rx = np.deg2rad(cfg.radar.phase_n_rx)
sigma_beta_rx = cfg.radar.sigma_beta_rx
phase_beta_rx = np.deg2rad(cfg.radar.phase_beta_rx)
# OCEAN / OTHERS
ocean_dt = cfg.ocean.dt
if hasattr(cfg.sim, "cal_targets"):
if cfg.sim.cal_targets is False:
add_point_target = False # This for debugging
point_target_floats = True # Not really needed, but makes coding easier later
else:
print("Adding cal targets")
add_point_target = True
if cfg.sim.cal_targets.lower() == 'floating':
point_target_floats = True
else:
point_target_floats = False
else:
add_point_target = False # This for debugging
point_target_floats = True
n_sinc_samples = 10
sinc_ovs = 20
chan_sinc_vec = raw.calc_sinc_vec(n_sinc_samples, sinc_ovs, Fs=over_fs)
# Set win direction with respect to beam
# I hope the following line is correct, maybe sign is wrong
wind_dir = cfg.radar.azimuth - cfg.ocean.wind_dir
# OCEAN SURFACE
if rank == 0:
print('Initializing ocean surface...')
surface_full = OceanSurface()
# Setup compute values
compute = ['D', 'Diff', 'Diff2']
if use_hmtf:
compute.append('hMTF')
# Try to reuse initialized surface
if reuse_ocean_file:
try:
surface_full.load(ocean_file, compute)
except RuntimeError:
pass
if (not reuse_ocean_file) or (not surface_full.initialized):
if hasattr(cfg.ocean, 'use_buoy_data'):
if cfg.ocean.use_buoy_data:
bdataf = cfg.ocean.buoy_data_file
date = datetime.datetime(np.int(cfg.ocean.year),
np.int(cfg.ocean.month),
np.int(cfg.ocean.day),
np.int(cfg.ocean.hour),
np.int(cfg.ocean.minute), 0)
date, bdata = tpio.load_buoydata(bdataf, date)
# FIX-ME: direction needs to consider also azimuth of beam
buoy_spec = tpio.BuoySpectra(bdata,
heading=cfg.radar.heading,
depth=cfg.ocean.depth)
dirspectrum_func = buoy_spec.Sk2
# Since the wind direction is included in the buoy data
wind_dir = 0
else:
dirspectrum_func = None
if cfg.ocean.swell_dir_enable:
dir_swell_spec = s_spec.ardhuin_swell_spec
else:
dir_swell_spec = None
wind_dir = np.deg2rad(wind_dir)
else:
if cfg.ocean.swell_dir_enable:
dir_swell_spec = s_spec.ardhuin_swell_spec
else:
dir_swell_spec = None
dirspectrum_func = None
wind_dir = np.deg2rad(wind_dir)
surface_full.init(
cfg.ocean.Lx,
cfg.ocean.Ly,
cfg.ocean.dx,
cfg.ocean.dy,
cfg.ocean.cutoff_wl,
cfg.ocean.spec_model,
cfg.ocean.spread_model,
wind_dir,
cfg.ocean.wind_fetch,
cfg.ocean.wind_U,
cfg.ocean.current_mag,
np.deg2rad(cfg.radar.azimuth - cfg.ocean.current_dir),
cfg.radar.azimuth - cfg.ocean.dir_swell_dir,
cfg.ocean.freq_r,
cfg.ocean.sigf,
cfg.ocean.sigs,
cfg.ocean.Hs,
cfg.ocean.swell_dir_enable,
cfg.ocean.swell_enable,
cfg.ocean.swell_ampl,
np.deg2rad(cfg.radar.azimuth - cfg.ocean.swell_dir),
cfg.ocean.swell_wl,
compute,
cfg.ocean.opt_res,
cfg.ocean.fft_max_prime,
choppy_enable=cfg.ocean.choppy_enable,
depth=cfg.ocean.depth,
dirspectrum_func=dirspectrum_func,
dir_swell_spec=dir_swell_spec)
surface_full.save(ocean_file)
# Now we plot the directional spectrum
# self.wave_dirspec[good_k] = dirspectrum_func(self.kx[good_k], self.ky[good_k])
plt.figure()
plt.imshow(np.fft.fftshift(surface_full.wave_dirspec),
extent=[
surface_full.kx.min(),
surface_full.kx.max(),
surface_full.ky.min(),
surface_full.ky.max()
],
origin='lower',
cmap='inferno_r')
plt.grid(True)
pltax = plt.gca()
pltax.set_xlim((-1, 1))
pltax.set_ylim((-1, 1))
Narr_length = 0.08 # np.min([surface_full.kx.max(), surface_full.ky.max()])
pltax.arrow(0,
0,
-Narr_length * np.sin(np.radians(cfg.radar.heading)),
Narr_length * np.cos(np.radians(cfg.radar.heading)),
fc="k",
ec="k")
plt.xlabel('$k_x$ [rad/m]')
plt.ylabel('$k_y$ [rad/m]')
plt.colorbar()
#plt.show()
# Create plots directory
plot_path = os.path.dirname(output_file) + os.sep + 'raw_plots'
if plot_save:
if not os.path.exists(plot_path):
os.makedirs(plot_path)
plt.savefig(os.path.join(plot_path, 'input_dirspectrum.png'))
plt.close()
if cfg.ocean.swell_dir_enable:
plt.figure()
plt.imshow(np.fft.fftshift(np.abs(surface_full.swell_dirspec)),
extent=[
surface_full.kx.min(),
surface_full.kx.max(),
surface_full.ky.min(),
surface_full.ky.max()
],
origin='lower',
cmap='inferno_r')
plt.grid(True)
pltax = plt.gca()
pltax.set_xlim((-0.1, 0.1))
pltax.set_ylim((-0.1, 0.1))
Narr_length = 0.08 # np.min([surface_full.kx.max(), surface_full.ky.max()])
pltax.arrow(
0,
0,
-Narr_length * np.sin(np.radians(cfg.radar.heading)),
Narr_length * np.cos(np.radians(cfg.radar.heading)),
fc="k",
ec="k")
plt.xlabel('$k_x$ [rad/m]')
plt.ylabel('$k_y$ [rad/m]')
plt.colorbar()
#plt.show()
# Create plots directory
plot_path = os.path.dirname(output_file) + os.sep + 'raw_plots'
if plot_save:
if not os.path.exists(plot_path):
os.makedirs(plot_path)
plt.savefig(
os.path.join(plot_path, 'input_dirspectrum_combined.png'))
plt.close()
else:
surface_full = None
# Initialize surface balancer
surface = OceanSurfaceBalancer(surface_full, ocean_dt)
# CALCULATE PARAMETERS
if rank == 0:
print('Initializing simulation parameters...')
# SR/GR/INC Matrixes
sr0 = geosar.inc_to_sr(inc_angle, alt)
gr0 = geosar.inc_to_gr(inc_angle, alt)
gr = surface.x + gr0
sr, inc, _ = geosar.gr_to_geo(gr, alt)
print(sr.dtype)
look = geosar.inc_to_look(inc, alt)
min_sr = np.min(sr)
# sr -= np.min(sr)
#inc = np.repeat(inc[np.newaxis, :], surface.Ny, axis=0)
#sr = np.repeat(sr[np.newaxis, :], surface.Ny, axis=0)
#gr = np.repeat(gr[np.newaxis, :], surface.Ny, axis=0)
#Let's try to safe some memory and some operations
inc = inc.reshape(1, inc.size)
look = look.reshape(1, inc.size)
sr = sr.reshape(1, sr.size)
gr = gr.reshape(1, gr.size)
sin_inc = np.sin(inc)
cos_inc = np.cos(inc)
# lambda, K, resolution, time, etc.
l0 = const.c / f0
k0 = 2. * np.pi * f0 / const.c
sr_res = const.c / (2. * rg_bw)
sr_smp = const.c / (2 * cfg.radar.Fs)
if cfg.radar.L_total:
ant_L = ant_L / np.float(num_ch)
if v_ground == 'auto':
v_ground = geosar.orbit_to_vel(alt, ground=True)
v_orb = geosar.orbit_to_vel(alt, ground=False)
else:
v_orb = v_ground
t_step = 1. / prf
az_steps = int(cfg.radar.n_pulses)
t_span = az_steps / prf
rg_samp = np.int(utils.optimize_fftsize(cfg.radar.n_rg))
#min_sr = np.mean(sr) - rg_samp / 2 * sr_smp
print(sr_smp)
max_sr = np.mean(sr) + rg_samp / 2 * sr_smp
sr_prof = (np.arange(rg_samp) - rg_samp / 2) * sr_smp + np.mean(sr)
gr_prof, inc_prof, look_prof, b_prof = geosar.sr_to_geo(sr_prof, alt)
look_prof = look_prof.reshape((1, look_prof.size))
sr_prof = sr_prof.reshape((1, look_prof.size))
dop_ref = 2 * v_orb * np.sin(look_prof) * np.sin(squint_r) / l0
print("skim_raw: Doppler Centroid is %f Hz" % (np.mean(dop_ref)))
if cfg.srg.two_scale_Doppler:
# We will compute less surface realizations
n_pulses_b = utils.optimize_fftsize(int(
cfg.srg.surface_coh_time * prf)) / 2
print("skim_raw: down-sampling rate =%i" % (n_pulses_b))
# n_pulses_b = 4
az_steps_ = int(np.ceil(az_steps / n_pulses_b))
t_step = t_step * n_pulses_b
# Maximum length in azimuth that we can consider to have the same geometric Doppler
dy_integ = cfg.srg.phase_err_tol / (2 * k0 * v_ground / min_sr *
cfg.srg.surface_coh_time)
surface_dy = surface.y[1] - surface.y[0]
ny_integ = (dy_integ / surface.dy)
print("skim_raw: ny_integ=%f" % (ny_integ))
if ny_integ < 1:
ny_integ = 1
else:
ny_integ = int(2**np.floor(np.log2(ny_integ)))
info.msg("skim_raw: size of intermediate radar data: %f MB" %
(8 * ny_integ * az_steps_ * rg_samp * 1e-6),
importance=1)
info.msg("skim_raw: ny_integ=%i" % (ny_integ), importance=1)
if do_hh:
proc_raw_hh = np.zeros(
[az_steps_, int(surface.Ny / ny_integ), rg_samp],
dtype=np.complex)
proc_raw_hh_step = np.zeros([surface.Ny, rg_samp],
dtype=np.complex)
if do_vv:
proc_raw_vv = np.zeros(
[az_steps_, int(surface.Ny / ny_integ), rg_samp],
dtype=np.complex)
proc_raw_vv_step = np.zeros([surface.Ny, rg_samp],
dtype=np.complex)
# Doppler centroid
# sin(a+da) = sin(a) + cos(a)*da - 1/2*sin(a)*da**2
az = surface.y.reshape((surface.Ny, 1))
# FIX-ME: this is a coarse approximation
da = az / gr_prof
sin_az = np.sin(
squint_r) + np.cos(squint_r) * da - 0.5 * np.sin(squint_r) * da**2
dop0 = 2 * v_orb * np.sin(look_prof) * sin_az / l0
# az / 2 * sin_sr _az
# az_now = (t_now - t_span / 2.) * v_ground * np.cos(squint_r)
# az = np.repeat((surface.y - az_now)[:, np.newaxis], surface.Nx, axis=1)
# az = (surface.y - az_now).reshape((surface.Ny, 1))
# print("Max az: %f" % (np.max(az)))
#dop0 = np.mean(np.reshape(dop0, (surface.Ny/ny_integ, ny_integ, rg_samp)), axis=1)
s_int = np.int(surface.Ny / ny_integ)
dop0 = np.mean(np.reshape(dop0, (s_int, np.int(ny_integ), rg_samp)),
axis=1)
else:
az_steps_ = az_steps
if do_hh:
proc_raw_hh = np.zeros([az_steps, rg_samp], dtype=np.complex)
if do_vv:
proc_raw_vv = np.zeros([az_steps, rg_samp], dtype=np.complex)
t_last_rcs_bragg = -1.
last_progress = -1
NRCS_avg_vv = np.zeros(az_steps, dtype=np.float)
NRCS_avg_hh = np.zeros(az_steps, dtype=np.float)
## RCS MODELS
# Specular
if scat_spec_enable:
if scat_spec_mode == 'kodis':
rcs_spec = rcs.RCSKodis(inc, k0, surface.dx, surface.dy)
elif scat_spec_mode == 'fa' or scat_spec_mode == 'spa':
spec_ph0 = np.random.uniform(0.,
2. * np.pi,
size=[surface.Ny, surface.Nx])
rcs_spec = rcs.RCSKA(scat_spec_mode, k0, surface.x, surface.y,
surface.dx, surface.dy)
else:
raise NotImplementedError(
'RCS mode %s for specular scattering not implemented' %
scat_spec_mode)
# Bragg
if scat_bragg_enable:
phase_bragg = np.zeros([2, surface.Ny, surface.Nx])
bragg_scats = np.zeros([2, surface.Ny, surface.Nx], dtype=np.complex)
# dop_phase_p = np.random.uniform(0., 2.*np.pi, size=[surface.Ny, surface.Nx])
# dop_phase_m = np.random.uniform(0., 2.*np.pi, size=[surface.Ny, surface.Nx])
tau_c = closure.grid_coherence(cfg.ocean.wind_U, surface.dx, f0)
rndscat_p = closure.randomscat_ts(tau_c, (surface.Ny, surface.Nx), prf)
rndscat_m = closure.randomscat_ts(tau_c, (surface.Ny, surface.Nx), prf)
# NOTE: This ignores slope, may be changed
k_b = 2. * k0 * sin_inc
c_b = sin_inc * np.sqrt(const.g / k_b + 0.072e-3 * k_b)
if scat_bragg_model == 'romeiser97':
current_dir = np.deg2rad(cfg.ocean.current_dir)
current_vec = (cfg.ocean.current_mag * np.array(
[np.cos(current_dir), np.sin(current_dir)]))
U_dir = np.deg2rad(cfg.ocean.wind_dir)
U_vec = (cfg.ocean.wind_U *
np.array([np.cos(U_dir), np.sin(U_dir)]))
U_eff_vec = U_vec - current_vec
rcs_bragg = rcs.RCSRomeiser97(
k0, inc, pol, surface.dx, surface.dy, linalg.norm(U_eff_vec),
np.arctan2(U_eff_vec[1], U_eff_vec[0]), surface.wind_fetch,
scat_bragg_spec, scat_bragg_spread, scat_bragg_d)
else:
raise NotImplementedError(
'RCS model %s for Bragg scattering not implemented' %
scat_bragg_model)
surface_area = surface.dx * surface.dy * surface.Nx * surface.Ny
###################
# SIMULATION LOOP #
###################
if rank == 0:
print('Computing profiles...')
for az_step in np.arange(az_steps_, dtype=np.int):
# AZIMUTH & SURFACE UPDATE
t_now = az_step * t_step
az_now = (t_now - t_span / 2.) * v_ground * np.cos(squint_r)
# az = np.repeat((surface.y - az_now)[:, np.newaxis], surface.Nx, axis=1)
az = (surface.y - az_now).reshape((surface.Ny, 1))
surface.t = t_now
if az_step == 0:
# Check wave-height
info.msg(
"Standard deviation of wave-height (peak-to-peak; i.e. x2): %f"
% (2 * np.std(surface.Dz)))
#if az_step == 0:
# print("Max Dx: %f" % (np.max(surface.Dx)))
# print("Max Dy: %f" % (np.max(surface.Dy)))
# print("Max Dz: %f" % (np.max(surface.Dz)))
# print("Max Diffx: %f" % (np.max(surface.Diffx)))
# print("Max Diffy: %f" % (np.max(surface.Diffy)))
# print("Max Diffxx: %f" % (np.max(surface.Diffxx)))
# print("Max Diffyy: %f" % (np.max(surface.Diffyy)))
# print("Max Diffxy: %f" % (np.max(surface.Diffxy)))
# COMPUTE RCS FOR EACH MODEL
# Note: SAR processing is range independent as slant range is fixed
sin_az = az / sr
az_proj_angle = np.arcsin(az / gr0)
# Note: Projected displacements are added to slant range
if point_target_floats is False: # This can only happen if point targets are enabled
surface.Dx[int(surface.Ny / 2), int(surface.Nx / 2)] = 0
surface.Dy[int(surface.Ny / 2), int(surface.Nx / 2)] = 0
surface.Dz[int(surface.Ny / 2), int(surface.Nx / 2)] = 0
if cfg.srg.two_scale_Doppler:
# slant-range for phase
sr_surface = (sr - cos_inc * surface.Dz + surface.Dx * sin_inc +
surface.Dy * sin_az)
if cfg.srg.rcm:
# add non common rcm
sr_surface4rcm = sr_surface + az / 2 * sin_az
else:
sr_surface4rcm = sr_surface
else:
# FIXME: check if global shift is included, in case we care about slow simulations
# slant-range for phase and Doppler
sr_surface = (sr - cos_inc * surface.Dz + az / 2 * sin_az +
surface.Dx * sin_inc + surface.Dy * sin_az)
sr_surface4rcm = sr_surface
if do_hh:
scene_hh = np.zeros(
[int(surface.Ny), int(surface.Nx)], dtype=np.complex)
if do_vv:
scene_vv = np.zeros(
[int(surface.Ny), int(surface.Nx)], dtype=np.complex)
# Specular
if scat_spec_enable:
if scat_spec_mode == 'kodis':
Esn_sp = np.sqrt(4. * np.pi) * rcs_spec.field(
az_proj_angle, sr_surface, surface.Diffx, surface.Diffy,
surface.Diffxx, surface.Diffyy, surface.Diffxy)
if do_hh:
scene_hh += Esn_sp
if do_vv:
scene_vv += Esn_sp
else:
# FIXME
if do_hh:
pol_tmp = 'hh'
Esn_sp = (
np.exp(-1j * (2. * k0 * sr_surface)) *
(4. * np.pi)**1.5 * rcs_spec.field(
1, 1, pol_tmp[0], pol_tmp[1], inc, inc,
az_proj_angle, az_proj_angle + np.pi, surface.Dz,
surface.Diffx, surface.Diffy, surface.Diffxx,
surface.Diffyy, surface.Diffxy))
scene_hh += Esn_sp
if do_vv:
pol_tmp = 'vv'
Esn_sp = (
np.exp(-1j * (2. * k0 * sr_surface)) *
(4. * np.pi)**1.5 * rcs_spec.field(
1, 1, pol_tmp[0], pol_tmp[1], inc, inc,
az_proj_angle, az_proj_angle + np.pi, surface.Dz,
surface.Diffx, surface.Diffy, surface.Diffxx,
surface.Diffyy, surface.Diffxy))
scene_vv += Esn_sp
NRCS_avg_hh[az_step] += (np.sum(np.abs(Esn_sp)**2) / surface_area)
NRCS_avg_vv[az_step] += NRCS_avg_hh[az_step]
# Bragg
if scat_bragg_enable:
if (t_now - t_last_rcs_bragg) > ocean_dt:
if scat_bragg_model == 'romeiser97':
if pol == 'DP':
RCS_bragg_hh, RCS_bragg_vv = rcs_bragg.rcs(
az_proj_angle, surface.Diffx, surface.Diffy)
elif pol == 'hh':
RCS_bragg_hh = rcs_bragg.rcs(az_proj_angle,
surface.Diffx,
surface.Diffy)
else:
RCS_bragg_vv = rcs_bragg.rcs(az_proj_angle,
surface.Diffx,
surface.Diffy)
if use_hmtf:
# Fix Bad MTF points
surface.hMTF[np.where(surface.hMTF < -1)] = -1
if do_hh:
RCS_bragg_hh[0] *= (1 + surface.hMTF)
RCS_bragg_hh[1] *= (1 + surface.hMTF)
if do_vv:
RCS_bragg_vv[0] *= (1 + surface.hMTF)
RCS_bragg_vv[1] *= (1 + surface.hMTF)
t_last_rcs_bragg = t_now
if do_hh:
scat_bragg_hh = np.sqrt(RCS_bragg_hh)
NRCS_bragg_hh_instant_avg = np.sum(RCS_bragg_hh) / surface_area
NRCS_avg_hh[az_step] += NRCS_bragg_hh_instant_avg
if do_vv:
scat_bragg_vv = np.sqrt(RCS_bragg_vv)
NRCS_bragg_vv_instant_avg = np.sum(RCS_bragg_vv) / surface_area
NRCS_avg_vv[az_step] += NRCS_bragg_vv_instant_avg
# Doppler phases (Note: Bragg radial velocity taken constant!)
surf_phase = -(2 * k0) * sr_surface
cap_phase = (2 * k0) * t_step * c_b * (az_step + 1)
phase_bragg[0] = surf_phase - cap_phase # + dop_phase_p
phase_bragg[1] = surf_phase + cap_phase # + dop_phase_m
bragg_scats[0] = rndscat_m.scats(t_now)
bragg_scats[1] = rndscat_p.scats(t_now)
if do_hh:
scene_hh += ne.evaluate(
'sum(scat_bragg_hh * exp(1j*phase_bragg) * bragg_scats, axis=0)'
)
if do_vv:
scene_vv += ne.evaluate(
'sum(scat_bragg_vv * exp(1j*phase_bragg) * bragg_scats, axis=0)'
)
if add_point_target:
# Now we replace scattering at center by fixed value
pt_y = int(surface.Ny / 2)
pt_x = int(surface.Nx / 2)
if do_hh:
scene_hh[pt_y, pt_x] = 1000 * np.exp(
-1j * 2 * k0 * sr_surface[pt_y, pt_x])
if do_vv:
scene_vv[pt_y, pt_x] = 1000 * np.exp(
-1j * 2 * k0 * sr_surface[pt_y, pt_x])
## ANTENNA PATTERN
## FIXME: this assume co-located Tx and Tx, so it will not work for true bistatic configurations
if cfg.radar.L_total:
beam_pattern = sinc_1tx_nrx(sin_az,
ant_L * num_ch,
f0,
num_ch,
field=True)
else:
beam_pattern = sinc_1tx_nrx(sin_az, ant_L, f0, 1, field=True)
# GENERATE CHANEL PROFILES
if cfg.srg.two_scale_Doppler:
sr_surface_ = sr_surface4rcm
if do_hh:
proc_raw_hh_step[:, :] = 0
proc_raw_hh_ = proc_raw_hh_step
scene_bp_hh = scene_hh * beam_pattern
if do_vv:
proc_raw_vv_step[:, :] = 0
proc_raw_vv_ = proc_raw_vv_step
scene_bp_vv = scene_vv * beam_pattern
else:
sr_surface_ = sr_surface4rcm.flatten()
if do_hh:
proc_raw_hh_ = proc_raw_hh[az_step]
scene_bp_hh = (scene_hh * beam_pattern).flatten()
if do_vv:
proc_raw_vv_ = proc_raw_vv[az_step]
scene_bp_vv = (scene_vv * beam_pattern).flatten()
if do_hh:
raw.chan_profile_numba(sr_surface_,
scene_bp_hh,
sr_smp,
sr_prof.min(),
chan_sinc_vec,
n_sinc_samples,
sinc_ovs,
proc_raw_hh_,
rg_only=cfg.srg.two_scale_Doppler)
if do_vv:
raw.chan_profile_numba(sr_surface_,
scene_bp_vv,
sr_smp,
sr_prof.min(),
chan_sinc_vec,
n_sinc_samples,
sinc_ovs,
proc_raw_vv_,
rg_only=cfg.srg.two_scale_Doppler)
if cfg.srg.two_scale_Doppler:
#Integrate in azimuth
s_int = np.int(surface.Ny / ny_integ)
if do_hh:
proc_raw_hh[az_step] = np.sum(np.reshape(
proc_raw_hh_, (s_int, ny_integ, rg_samp)),
axis=1)
info.msg("Max abs(HH): %f" %
np.max(np.abs(proc_raw_hh[az_step])),
importance=1)
if do_vv:
#print(proc_raw_vv.shape)
proc_raw_vv[az_step] = np.sum(np.reshape(
proc_raw_vv_, (s_int, ny_integ, rg_samp)),
axis=1)
info.msg("Max abs(VV): %f" %
np.max(np.abs(proc_raw_vv[az_step])),
importance=1)
# SHOW PROGRESS (%)
current_progress = np.int((100 * az_step) / az_steps_)
if current_progress != last_progress:
last_progress = current_progress
info.msg('SP,%d,%d,%d%%' % (rank, size, current_progress),
importance=1)
if cfg.srg.two_scale_Doppler:
# No we have to up-sample and add Doppler
info.msg("skim_raw: Dopplerizing and upsampling")
print(dop0.max())
print(n_pulses_b)
print(prf)
if do_hh:
proc_raw_hh = upsample_and_dopplerize(proc_raw_hh, dop0,
n_pulses_b, prf)
if do_vv:
proc_raw_vv = upsample_and_dopplerize(proc_raw_vv, dop0,
n_pulses_b, prf)
# MERGE RESULTS
if do_hh:
total_raw_hh = np.empty_like(proc_raw_hh) if rank == 0 else None
comm.Reduce(proc_raw_hh, total_raw_hh, op=MPI.SUM, root=0)
if do_vv:
total_raw_vv = np.empty_like(proc_raw_vv) if rank == 0 else None
comm.Reduce(proc_raw_vv, total_raw_vv, op=MPI.SUM, root=0)
## PROCESS REDUCED RAW DATA & SAVE (ROOT)
if rank == 0:
info.msg('calibrating and saving results...')
# Filter and decimate
#range_filter = np.ones_like(total_raw)
#range_filter[:, :, rg_samp/(2*2*cfg.radar.over_fs):-rg_samp/(2*2*cfg.radar.over_fs)] = 0
#total_raw = np.fft.ifft(range_filter*np.fft.fft(total_raw))
if do_hh:
total_raw_hh = total_raw_hh[:, :cfg.radar.n_rg]
if do_vv:
total_raw_vv = total_raw_vv[:, :cfg.radar.n_rg]
# Calibration factor (projected antenna pattern integrated in azimuth)
az_axis = np.arange(-t_span / 2. * v_ground, t_span / 2. * v_ground,
sr0 * const.c / (np.pi * f0 * ant_L * 10.))
if cfg.radar.L_total:
pattern = sinc_1tx_nrx(az_axis / sr0,
ant_L * num_ch,
f0,
num_ch,
field=True)
else:
pattern = sinc_1tx_nrx(az_axis / sr0, ant_L, f0, 1, field=True)
cal_factor = (1. / np.sqrt(
np.trapz(np.abs(pattern)**2., az_axis) * sr_res /
np.sin(inc_angle)))
if do_hh:
noise = (utils.db2lin(nesz, amplitude=True) / np.sqrt(2.) *
(np.random.normal(size=total_raw_hh.shape) +
1j * np.random.normal(size=total_raw_hh.shape)))
total_raw_hh = total_raw_hh * cal_factor + noise
if do_vv:
noise = (utils.db2lin(nesz, amplitude=True) / np.sqrt(2.) *
(np.random.normal(size=total_raw_vv.shape) +
1j * np.random.normal(size=total_raw_vv.shape)))
total_raw_vv = total_raw_vv * cal_factor + noise
# Add slow-time error
# if use_errors:
# if do_hh:
# total_raw_hh *= errors.beta_noise
# if do_vv:
# total_raw_vv *= errors.beta_noise
# Save RAW data
if do_hh and do_vv:
rshp = (1, ) + total_raw_hh.shape
total_raw = np.concatenate(
(total_raw_hh.reshape(rshp), total_raw_vv.reshape(rshp)))
rshp = (1, ) + NRCS_avg_hh.shape
NRCS_avg = np.concatenate(
(NRCS_avg_hh.reshape(rshp), NRCS_avg_vv.reshape(rshp)))
elif do_hh:
rshp = (1, ) + total_raw_hh.shape
total_raw = total_raw_hh.reshape(rshp)
rshp = (1, ) + NRCS_avg_hh.shape
NRCS_avg = NRCS_avg_hh.reshape(rshp)
else:
rshp = (1, ) + total_raw_vv.shape
total_raw = total_raw_vv.reshape(rshp)
rshp = (1, ) + NRCS_avg_vv.shape
NRCS_avg = NRCS_avg_vv.reshape(rshp)
raw_file = tpio.SkimRawFile(output_file, 'w', total_raw.shape)
raw_file.set('inc_angle', np.rad2deg(inc_angle))
raw_file.set('f0', f0)
# raw_file.set('num_ch', num_ch)
raw_file.set('ant_L', ant_L)
raw_file.set('prf', prf)
raw_file.set('v_ground', v_ground)
raw_file.set('orbit_alt', alt)
raw_file.set('sr0', sr0)
raw_file.set('rg_sampling', rg_bw * over_fs)
raw_file.set('rg_bw', rg_bw)
raw_file.set('raw_data*', total_raw)
raw_file.set('NRCS_avg', NRCS_avg)
raw_file.set('azimuth', cfg.radar.azimuth)
raw_file.set('dop_ref', dop_ref)
raw_file.close()
print(time.strftime("Finished [%Y-%m-%d %H:%M:%S]", time.localtime()))
def delta_k_processing(raw_data, cfg_file):
#parameters
cfg = tpio.ConfigFile(cfg_file)
Az_smaples = cfg.radar.n_pulses
# Az_smaples = 1028
PRF = cfg.radar.prf
fs = cfg.radar.Fs
R_samples = cfg.radar.n_rg
#Sp_x = cfg.ocean.dx
inc = cfg.radar.inc_angle
#processing parameters
analysis_deltan = np.linspace(0,300,301) #for delta-k spectrum analysis
RCS_power = np.zeros_like(analysis_deltan)
wave_scale = 25 #44.1#44.1#29.3#26.4#100##25.3#26.21#26.5##25.34#26.31##10#
r_int_num = 700
r_int_num_fd = 400
az_int = 300
num_az = 1
list = range(1,num_az)
analyse_num = range(2,Az_smaples - az_int -1 - num_az)
Omiga_p = np.zeros((np.size(analyse_num),np.size(list)+1), dtype=np.float)
Omiga_p_z = np.zeros(np.size(analyse_num), dtype=np.float)
fil_Omiga_p = np.zeros(np.size(analyse_num), dtype=np.float)
fd = np.zeros(np.size(analyse_num), dtype=np.float)
pha1 = np.zeros(np.size(analyse_num), dtype=np.float)
Scene_scope = ((R_samples-1) * const.c / fs / 2) / 2
dk_higha = np.zeros((np.size(analyse_num),r_int_num), dtype='complex128')
#pulse_pulse processing
#PP = False
#intensity
raw_int = np.abs(raw_data) # * np.conj(raw_data)
if plot_pattern:
plt.figure()
#plt.imshow(np.log10(np.abs(RCS)))
xs = 2 * const.c / fs * np.linspace(0,R_samples-1,R_samples)
ys = np.linspace(0,Az_smaples-1,Az_smaples) / PRF
plt.imshow(np.abs(raw_int))
#plt.xlim(xs)
#plt.ylim(ys)
#plt.imshow(ys,xs,np.abs(raw_int))
#plt.grid(False)
plt.xlabel("Range [m]")
plt.ylabel("Slow time (s)")
intens_spectrum = np.mean(np.fft.fft(np.abs(raw_data)*2, axis=1), axis=0)
aoff = 2
intens_xspectrum = np.mean(np.fft.fft(raw_data[0:-aoff] * np.conj(raw_data[aoff:]), axis=1), axis=0)
intens_xispectrum = np.mean(np.fft.fft(raw_int[0:-aoff] * np.conj(raw_int[aoff:]), axis=1), axis=0)
if plot_spectrum:
plt.figure()
#plt.plot(np.linspace(1,np.int(Sp_x/2),np.int(Sp_x/2)-1) / 4 / Scene_scope, 10 * np.log10(np.abs(intens_spectrum[1:np.int(Sp_x/2)]) / np.abs(intens_spectrum[1:np.int(Sp_x/2)]).max()))
plt.plot(10 * np.log10(np.abs(intens_spectrum[1:np.int(R_samples/2)])))
plt.plot(10 * np.log10(np.abs(intens_xspectrum[1:np.int(R_samples / 2)])), '--')
plt.plot(10 * np.log10(np.abs(intens_xispectrum[1:np.int(R_samples / 2)])), ':')
plt.xlabel("Delta_k [1/m]")
plt.ylabel("Power (dB)")
spck_f = np.fft.fftshift(np.fft.fft(raw_data, axis=1), axes=(1,))
#Calculating the required delta_f value
delta_f = cal_delta_f(const.c, inc, wave_scale)
delta_k = delta_f / const.c
ind_N = np.int(round(delta_k * (2 * Scene_scope) * 2))
#Extracting the information of wave_scale
dk_high = spck_f[aoff:,0:r_int_num] * np.conj(spck_f[0:-aoff,ind_N:ind_N+r_int_num])
analysis_deltaf = analysis_deltan / 4 / Scene_scope * const.c
for ind in range(np.size(analysis_deltan)):
dk_ind = spck_f[:,0:r_int_num] * np.conj(spck_f[:,ind:ind+r_int_num])
RCS_power[ind] = np.mean(np.abs(np.mean(dk_ind,axis=0)))
#RCS_power[ind] = np.abs(np.mean(dk_ind))
if np.size(list)>0:
for iii in list:
for ind in range(np.size(analyse_num)):
#dk_ind = spck_f[:,0:-1-ind] * np.conj(spck_f[:,ind:-1]) * amp_delta_k
dk_inda = spck_f[iii:,0:r_int_num] * np.conj(spck_f[0:-iii,ind_N:ind_N+r_int_num])
#RCS_power[ind_N] = RCS_power[ind_N] + np.mean(np.abs(np.mean(dk_ind1,axis=0)))
#dk_higha[ind,:] = np.mean(dk_ind1, axis=0)
#estimate the wave velocity
dk_higha = np.mean(dk_inda, axis=1)
#dk_higha = np.mean(dk_high, axis=1)
#for ind in range(np.size(analyse_num)):
#pha = -np.angle(np.mean(dk_higha[analyse_num[ind]:] * np.conj(dk_higha[0:-analyse_num[ind]])))
pha = -np.angle(np.mean(dk_higha[analyse_num[ind]:analyse_num[ind]+az_int] * np.conj(dk_higha[0:az_int])))
#pha1[ind] = pha
Omiga_p[ind,iii] = pha / analyse_num[ind] * PRF
#dk_ind = spck_f[:,0:-1-ind] * np.conj(spck_f[:,ind:-1]) * amp_delta_k
print(iii / (np.size(list)+1))
dk_higha = np.mean(dk_high, axis=1)
for ind in range(np.size(analyse_num)):
pha = -np.angle(np.mean(dk_higha[analyse_num[ind]:analyse_num[ind]+az_int] * np.conj(dk_higha[0:az_int])))
Omiga_p_z[ind] = pha / analyse_num[ind] * PRF
Omiga_p[:,0] = Omiga_p_z
fil_Omiga_p = np.mean(Omiga_p,axis=1)
else:
dk_higha = np.mean(dk_high, axis=1)
for ind in range(np.size(analyse_num)):
print(ind)
pha = -np.angle(np.mean(dk_higha[analyse_num[ind]:analyse_num[ind]+az_int] * np.conj(dk_higha[0:az_int])))
Omiga_p_z[ind] = pha / analyse_num[ind] * PRF
fil_Omiga_p = Omiga_p_z
#print(v_p)
#Doppler frequency estimation with different time intervals
#for ind in range(np.size(analyse_num)):
# dk_ind_t = spck_f[0,0:r_int_num_fd] * np.conj(spck_f[analyse_num[ind],ind_N:ind_N+r_int_num_fd])
# fd[ind] = np.angle(np.mean(dk_ind_t)) / (analyse_num[ind] / PRF) / 2 / np.pi - Omiga_p[ind] / 2 / np.pi
#print(fd[inn,0])
#Maxim = np.max(RCS_power[1:np.size(analysis_deltan)])
xx = analysis_deltaf[1:np.size(analysis_deltan)] / 1e6
xxn = const.c / 2 / analysis_deltaf[1:np.size(analysis_deltan)] / np.sin(12 * np.pi /180)
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.plot(xx, 10*(np.log10(RCS_power[1:np.size(analysis_deltan)])));
#ax2 = ax1.twiny() # this is the important function
#ax2.plot(xx,xxn)
#ax2.invert_xaxis()
#plt.plot(xx, 10*(np.log10(RCS_power[1:np.size(analysis_deltan)])))
ax1.set_xlabel("Delta_f [MHz]")
#ax2.set_xlabel("Wave_length [m]")
ax1.set_ylabel("Power (dB)")
plt.figure()
plt.plot(analyse_num, fil_Omiga_p)
plt.xlabel("Azimuth interval [Pixel]")
plt.ylabel("Angular velocity (rad/s)")
#Remove polyfit
#poly = np.polyfit(analyse_num,fil_Omiga_p,deg=7)
#z = np.polyval(poly, analyse_num)
#plt.plot(analyse_num, z)
#print(z)
if np.size(list)>0:
return Omiga_p
else:
return Omiga_p_z
def surface_rel(cfg_file=None, inc_deg=None, ntimes=2, t_step=10e-3):
""" This function generates a (short) time series of surface realizations.
:param scf_file: the full path to the configuration with all OCEANSAR parameters
:param inc_deg: the incident angle, in degree
:param ntimes: number of time samples generated.
:param t_step: spacing between time samples. This can be interpreted as the Pulse Repetition Interval
:returns: a tuple with the configuration object, the surfaces, the radial velocities for each grid point,
and the complex scattering coefficients
"""
cfg_file = utils.get_parFile(parfile=cfg_file)
cfg = ocs_io.ConfigFile(cfg_file)
use_hmtf = cfg.srg.use_hmtf
scat_spec_enable = cfg.srg.scat_spec_enable
scat_spec_mode = cfg.srg.scat_spec_mode
scat_bragg_enable = cfg.srg.scat_bragg_enable
scat_bragg_model = cfg.srg.scat_bragg_model
scat_bragg_d = cfg.srg.scat_bragg_d
scat_bragg_spec = cfg.srg.scat_bragg_spec
scat_bragg_spread = cfg.srg.scat_bragg_spread
# SAR
inc_angle = np.deg2rad(cfg.sar.inc_angle)
alt = cfg.sar.alt
f0 = cfg.sar.f0
prf = cfg.sar.prf
pol = cfg.sar.pol
l0 = const.c / f0
k0 = 2. * np.pi * f0 / const.c
if pol == 'DP':
do_hh = True
do_vv = True
elif pol == 'hh':
do_hh = True
do_vv = False
else:
do_hh = False
do_vv = True
# OCEAN / OTHERS
ocean_dt = cfg.ocean.dt
surface = OceanSurface()
compute = ['D', 'Diff', 'Diff2', 'V', 'A']
if use_hmtf:
compute.append('hMTF')
surface.init(cfg.ocean.Lx,
cfg.ocean.Ly,
cfg.ocean.dx,
cfg.ocean.dy,
cfg.ocean.cutoff_wl,
cfg.ocean.spec_model,
cfg.ocean.spread_model,
np.deg2rad(cfg.ocean.wind_dir),
cfg.ocean.wind_fetch,
cfg.ocean.wind_U,
cfg.ocean.current_mag,
np.deg2rad(cfg.ocean.current_dir),
0,
0,
0,
0,
0,
False,
cfg.ocean.swell_enable,
cfg.ocean.swell_ampl,
np.deg2rad(cfg.ocean.swell_dir),
cfg.ocean.swell_wl,
compute,
cfg.ocean.opt_res,
cfg.ocean.fft_max_prime,
choppy_enable=cfg.ocean.choppy_enable)
# Get a surface realization calculated
surface.t = 0
return surface
def sar_focus(cfg_file, raw_output_file, output_file):
###################
# INITIALIZATIONS #
###################
print(
'-------------------------------------------------------------------')
print(
time.strftime("- OCEANSAR SAR Processor: %Y-%m-%d %H:%M:%S",
time.localtime()))
print(
'-------------------------------------------------------------------')
# CONFIGURATION FILE
cfg = tpio.ConfigFile(cfg_file)
# PROCESSING
az_weighting = cfg.processing.az_weighting
doppler_bw = cfg.processing.doppler_bw
plot_format = cfg.processing.plot_format
plot_tex = cfg.processing.plot_tex
plot_save = cfg.processing.plot_save
plot_path = cfg.processing.plot_path
plot_raw = cfg.processing.plot_raw
plot_rcmc_dopp = cfg.processing.plot_rcmc_dopp
plot_rcmc_time = cfg.processing.plot_rcmc_time
plot_image_valid = cfg.processing.plot_image_valid
# SAR
f0 = cfg.sar.f0
prf = cfg.sar.prf
num_ch = cfg.sar.num_ch
alt = cfg.sar.alt
v_ground = cfg.sar.v_ground
rg_bw = cfg.sar.rg_bw
over_fs = cfg.sar.over_fs
# CALCULATE PARAMETERS
l0 = const.c / f0
if v_ground == 'auto':
v_ground = geo.orbit_to_vel(alt, ground=True)
rg_sampling = rg_bw * over_fs
# RAW DATA
raw_file = tpio.RawFile(raw_output_file, 'r')
raw_data = raw_file.get('raw_data*')
sr0 = raw_file.get('sr0')
raw_file.close()
# OTHER INITIALIZATIONS
# Create plots directory
plot_path = os.path.dirname(output_file) + os.sep + plot_path
if plot_save:
if not os.path.exists(plot_path):
os.makedirs(plot_path)
slc = []
########################
# PROCESSING MAIN LOOP #
########################
for ch in np.arange(num_ch):
if plot_raw:
utils.image(np.real(raw_data[0, ch]),
min=-np.max(np.abs(raw_data[0, ch])),
max=np.max(np.abs(raw_data[0, ch])),
cmap='gray',
aspect=np.float(raw_data[0, ch].shape[1]) /
np.float(raw_data[0, ch].shape[0]),
title='Raw Data',
xlabel='Range samples',
ylabel='Azimuth samples',
usetex=plot_tex,
save=plot_save,
save_path=plot_path + os.sep + 'plot_raw_real_%d.%s' %
(ch, plot_format),
dpi=150)
utils.image(np.imag(raw_data[0, ch]),
min=-np.max(np.abs(raw_data[0, ch])),
max=np.max(np.abs(raw_data[0, ch])),
cmap='gray',
aspect=np.float(raw_data[0, ch].shape[1]) /
np.float(raw_data[0, ch].shape[0]),
title='Raw Data',
xlabel='Range samples',
ylabel='Azimuth samples',
usetex=plot_tex,
save=plot_save,
save_path=plot_path + os.sep + 'plot_raw_imag_%d.%s' %
(ch, plot_format),
dpi=150)
utils.image(np.abs(raw_data[0, ch]),
min=0,
max=np.max(np.abs(raw_data[0, ch])),
cmap='gray',
aspect=np.float(raw_data[0, ch].shape[1]) /
np.float(raw_data[0, ch].shape[0]),
title='Raw Data',
xlabel='Range samples',
ylabel='Azimuth samples',
usetex=plot_tex,
save=plot_save,
save_path=plot_path + os.sep + 'plot_raw_amp_%d.%s' %
(ch, plot_format),
dpi=150)
utils.image(np.angle(raw_data[0, ch]),
min=-np.pi,
max=np.pi,
cmap='gray',
aspect=np.float(raw_data[0, ch].shape[1]) /
np.float(raw_data[0, ch].shape[0]),
title='Raw Data',
xlabel='Range samples',
ylabel='Azimuth samples',
usetex=plot_tex,
save=plot_save,
save_path=plot_path + os.sep + 'plot_raw_phase_%d.%s' %
(ch, plot_format),
dpi=150)
# Optimize matrix sizes
az_size_orig, rg_size_orig = raw_data[0, ch].shape
optsize = utils.optimize_fftsize(raw_data[0, ch].shape)
optsize = [raw_data.shape[0], optsize[0], optsize[1]]
data = np.zeros(optsize, dtype=complex)
data[:, :raw_data[0, ch].shape[0], :raw_data[
0, ch].shape[1]] = raw_data[:, ch, :, :]
az_size, rg_size = data.shape[1:]
# RCMC Correction
print('Applying RCMC correction... [Channel %d/%d]' % (ch + 1, num_ch))
#fr = np.linspace(-rg_sampling/2., rg_sampling/2., rg_size)
fr = (np.arange(rg_size) - rg_size / 2) * rg_sampling / rg_size
fr = np.roll(fr, int(-rg_size / 2))
fa = (np.arange(az_size) - az_size / 2) * prf / az_size
fa = np.roll(fa, int(-az_size / 2))
## Compensation of ANTENNA PATTERN
## FIXME this will not work for a long separation betwen Tx and Rx!!!
ant_L = cfg.sar.ant_L
# fa = 2 * v_orb / l0 * sin_az
sin_az = fa * l0 / (2 * v_ground)
if cfg.sar.L_total:
beam_pattern = sinc_1tx_nrx(sin_az,
ant_L * num_ch,
f0,
num_ch,
field=True)
else:
beam_pattern = sinc_1tx_nrx(sin_az, ant_L, f0, 1, field=True)
#fa[az_size/2:] = fa[az_size/2:] - prf
rcmc_fa = sr0 / np.sqrt(1 - (fa * (l0 / 2.) / v_ground)**2.) - sr0
data = np.fft.fft2(data)
# for i in np.arange(az_size):
# data[i,:] *= np.exp(1j*2*np.pi*2*rcmc_fa[i]/const.c*fr)
data = (data * np.exp(4j * np.pi * rcmc_fa.reshape(
(1, az_size, 1)) / const.c * fr.reshape((1, 1, rg_size))))
data = np.fft.ifft(data, axis=2)
if plot_rcmc_dopp:
utils.image(np.abs(data[0]),
min=0.,
max=3. * np.mean(np.abs(data)),
cmap='gray',
aspect=np.float(rg_size) / np.float(az_size),
title='RCMC Data (Range Dopler Domain)',
usetex=plot_tex,
save=plot_save,
save_path=plot_path + os.sep + 'plot_rcmc_dopp_%d.%s' %
(ch, plot_format))
if plot_rcmc_time:
rcmc_time = np.fft.ifft(data[0],
axis=0)[:az_size_orig, :rg_size_orig]
rcmc_time_max = np.max(np.abs(rcmc_time))
utils.image(np.real(rcmc_time),
min=-rcmc_time_max,
max=rcmc_time_max,
cmap='gray',
aspect=np.float(rg_size) / np.float(az_size),
title='RCMC Data (Time Domain)',
usetex=plot_tex,
save=plot_save,
save_path=plot_path + os.sep +
'plot_rcmc_time_real_%d.%s' % (ch, plot_format))
utils.image(np.imag(rcmc_time),
min=-rcmc_time_max,
max=rcmc_time_max,
cmap='gray',
aspect=np.float(rg_size) / np.float(az_size),
title='RCMC Data (Time Domain)',
usetex=plot_tex,
save=plot_save,
save_path=plot_path + os.sep +
'plot_rcmc_time_imag_%d.%s' % (ch, plot_format))
# Azimuth compression
print('Applying azimuth compression... [Channel %d/%d]' %
(ch + 1, num_ch))
n_samp = 2 * (np.int(doppler_bw / (fa[1] - fa[0])) / 2)
weighting = (az_weighting - (1. - az_weighting) *
np.cos(2 * np.pi * np.linspace(0, 1., int(n_samp))))
# Compensate amplitude loss
L_win = np.sum(np.abs(weighting)**2) / weighting.size
weighting /= np.sqrt(L_win)
if fa.size > n_samp:
zeros = np.zeros(az_size)
zeros[0:int(n_samp)] = weighting
# zeros[:n_samp/2] = weighting[:n_samp/2]
# zeros[-n_samp/2:] = weighting[-n_samp/2:]
weighting = np.roll(zeros, int(-n_samp / 2))
weighting = np.where(
np.abs(beam_pattern) > 0, weighting / beam_pattern, 0)
ph_ac = 4. * np.pi / l0 * sr0 * \
(np.sqrt(1. - (fa * l0 / 2. / v_ground)**2.) - 1.)
# for i in np.arange(rg_size):
# data[:,i] *= np.exp(1j*ph_ac)*weighting
data = data * (np.exp(1j * ph_ac) * weighting).reshape((1, az_size, 1))
data = np.fft.ifft(data, axis=1)
print('Finishing... [Channel %d/%d]' % (ch + 1, num_ch))
# Reduce to initial dimension
data = data[:, :int(az_size_orig), :int(rg_size_orig)]
# Removal of non valid samples
n_val_az_2 = np.floor(doppler_bw / 2. /
(2. * v_ground**2. / l0 / sr0) * prf / 2.) * 2.
# data = raw_data[ch, n_val_az_2:(az_size_orig - n_val_az_2 - 1), :]
data = data[:, int(n_val_az_2):int(az_size_orig - n_val_az_2 - 1), :]
if plot_image_valid:
plt.figure()
plt.imshow(np.abs(data[0]),
origin='lower',
vmin=0,
vmax=np.max(np.abs(data)),
aspect=np.float(rg_size_orig) / np.float(az_size_orig),
cmap='gray')
plt.xlabel("Range")
plt.ylabel("Azimuth")
plt.savefig(
os.path.join(plot_path,
('plot_image_valid_%d.%s' % (ch, plot_format))))
slc.append(data)
# Save processed data
slc = np.array(slc, dtype=np.complex)
print("Shape of SLC: " + str(slc.shape), flush=True)
proc_file = tpio.ProcFile(output_file, 'w', slc.shape)
proc_file.set('slc*', slc)
proc_file.close()
print('-----------------------------------------')
print(
time.strftime("Processing finished [%Y-%m-%d %H:%M:%S]",
time.localtime()))
print('-----------------------------------------')
def l2_wavespectrum(cfg_file, proc_output_file, ocean_file, output_file):
print(
'-------------------------------------------------------------------')
print(
time.strftime("- OCEANSAR L2 Wavespectra: [%Y-%m-%d %H:%M:%S]",
time.localtime()))
print(
'-------------------------------------------------------------------')
print('Initializing...')
## CONFIGURATION FILE
cfg = tpio.ConfigFile(cfg_file)
# SAR
inc_angle = np.deg2rad(cfg.sar.inc_angle)
f0 = cfg.sar.f0
prf = cfg.sar.prf
num_ch = cfg.sar.num_ch
ant_L = cfg.sar.ant_L
alt = cfg.sar.alt
v_ground = cfg.sar.v_ground
rg_bw = cfg.sar.rg_bw
over_fs = cfg.sar.over_fs
pol = cfg.sar.pol
if pol == 'DP':
polt = ['hh', 'vv']
elif pol == 'hh':
polt = ['hh']
else:
polt = ['vv']
# L2 wavespectrum
rg_ml = cfg.L2_wavespectrum.rg_ml
az_ml = cfg.L2_wavespectrum.az_ml
krg_ml = cfg.L2_wavespectrum.krg_ml
kaz_ml = cfg.L2_wavespectrum.kaz_ml
ml_win = cfg.L2_wavespectrum.ml_win
plot_save = cfg.L2_wavespectrum.plot_save
plot_path = cfg.L2_wavespectrum.plot_path
plot_format = cfg.L2_wavespectrum.plot_format
plot_tex = cfg.L2_wavespectrum.plot_tex
plot_surface = cfg.L2_wavespectrum.plot_surface
plot_proc_ampl = cfg.L2_wavespectrum.plot_proc_ampl
plot_spectrum = cfg.L2_wavespectrum.plot_spectrum
n_sublook = cfg.L2_wavespectrum.n_sublook
sublook_weighting = cfg.L2_wavespectrum.sublook_az_weighting
## CALCULATE PARAMETERS
if v_ground == 'auto': v_ground = geosar.orbit_to_vel(alt, ground=True)
k0 = 2. * np.pi * f0 / const.c
rg_sampling = rg_bw * over_fs
# PROCESSED RAW DATA
proc_content = tpio.ProcFile(proc_output_file, 'r')
proc_data = proc_content.get('slc*')
proc_content.close()
# OCEAN SURFACE
surface = OceanSurface()
surface.load(ocean_file, compute=['D', 'V'])
surface.t = 0.
# OUTPUT FILE
output = open(output_file, 'w')
# OTHER INITIALIZATIONS
# Enable TeX
if plot_tex:
plt.rc('font', family='serif')
plt.rc('text', usetex=True)
# Create plots directory
plot_path = os.path.dirname(output_file) + os.sep + plot_path
if plot_save:
if not os.path.exists(plot_path):
os.makedirs(plot_path)
# SURFACE VELOCITIES
grg_grid_spacing = (const.c / 2. / rg_sampling / np.sin(inc_angle))
rg_res_fact = grg_grid_spacing / surface.dx
az_grid_spacing = (v_ground / prf)
az_res_fact = az_grid_spacing / surface.dy
res_fact = np.ceil(np.sqrt(rg_res_fact * az_res_fact))
# SURFACE RADIAL VELOCITY
v_radial_surf = surface.Vx * np.sin(inc_angle) - surface.Vz * np.cos(
inc_angle)
v_radial_surf_ml = utils.smooth(utils.smooth(v_radial_surf,
res_fact * rg_ml,
axis=1),
res_fact * az_ml,
axis=0)
v_radial_surf_mean = np.mean(v_radial_surf)
v_radial_surf_std = np.std(v_radial_surf)
v_radial_surf_ml_std = np.std(v_radial_surf_ml)
# Expected mean azimuth shift
sr0 = geosar.inc_to_sr(inc_angle, alt)
avg_az_shift = -v_radial_surf_mean / v_ground * sr0
std_az_shift = v_radial_surf_std / v_ground * sr0
print('Starting Wavespectrum processing...')
# Get dimensions & calculate region of interest
rg_span = surface.Lx
az_span = surface.Ly
rg_size = proc_data[0].shape[2]
az_size = proc_data[0].shape[1]
# Note: RG is projected, so plots are Ground Range
rg_min = 0
rg_max = np.int(rg_span / (const.c / 2. / rg_sampling / np.sin(inc_angle)))
az_min = np.int(az_size / 2. + (-az_span / 2. + avg_az_shift) /
(v_ground / prf))
az_max = np.int(az_size / 2. + (az_span / 2. + avg_az_shift) /
(v_ground / prf))
az_guard = np.int(std_az_shift / (v_ground / prf))
az_min = az_min + az_guard
az_max = az_max - az_guard
if (az_max - az_min) < (2 * az_guard - 10):
print('Not enough edge-effect free image')
return
# Adaptive coregistration
if cfg.sar.L_total:
ant_L = ant_L / np.float(num_ch)
dist_chan = ant_L / 2
else:
if np.float(cfg.sar.Spacing) != 0:
dist_chan = np.float(cfg.sar.Spacing) / 2
else:
dist_chan = ant_L / 2
# dist_chan = ant_L/num_ch/2.
print('ATI Spacing: %f' % dist_chan)
inter_chan_shift_dist = dist_chan / (v_ground / prf)
# Subsample shift in azimuth
for chind in range(proc_data.shape[0]):
shift_dist = -chind * inter_chan_shift_dist
shift_arr = np.exp(
-2j * np.pi * shift_dist *
np.roll(np.arange(az_size) - az_size / 2, int(-az_size / 2)) /
az_size)
shift_arr = shift_arr.reshape((1, az_size, 1))
proc_data[chind] = np.fft.ifft(np.fft.fft(proc_data[chind], axis=1) *
shift_arr,
axis=1)
# First dimension is number of channels, second is number of pols
ch_dim = proc_data.shape[0:2]
npol = ch_dim[1]
proc_data_rshp = [np.prod(ch_dim), proc_data.shape[2], proc_data.shape[3]]
# Compute extended covariance matrices...
proc_data = proc_data.reshape(proc_data_rshp)
# Intensities
i_all = []
for chind in range(proc_data.shape[0]):
this_i = utils.smooth(utils.smooth(np.abs(proc_data[chind])**2.,
rg_ml,
axis=1,
window=ml_win),
az_ml,
axis=0,
window=ml_win)
i_all.append(this_i[az_min:az_max, rg_min:rg_max])
i_all = np.array(i_all)
## Wave spectra computation
## Processed Doppler bandwidth
proc_bw = cfg.processing.doppler_bw
PRF = cfg.sar.prf
fa = np.fft.fftfreq(proc_data_rshp[1], 1 / PRF)
# Filters
sublook_filt = []
sublook_bw = proc_bw / n_sublook
for i_sbl in range(n_sublook):
fa_min = -1 * proc_bw / 2 + i_sbl * sublook_bw
fa_max = fa_min + sublook_bw
fa_c = (fa_max + fa_min) / 2
win = np.where(
np.logical_and(fa > fa_min, fa < fa_max),
(sublook_weighting -
(1 - sublook_weighting) * np.cos(2 * np.pi *
(fa - fa_min) / sublook_bw)), 0)
sublook_filt.append(win)
# Apply sublooks
az_downsmp = int(np.floor(az_ml / 2))
rg_downsmp = int(np.floor(rg_ml / 2))
sublooks = []
sublooks_f = []
for i_sbl in range(n_sublook):
# Go to frequency domain
sublook_data = np.fft.ifft(np.fft.fft(proc_data, axis=1) *
sublook_filt[i_sbl].reshape(
(1, proc_data_rshp[1], 1)),
axis=1)
# Get intensities
sublook_data = np.abs(sublook_data)**2
# Multilook
for chind in range(proc_data.shape[0]):
sublook_data[chind] = utils.smooth(utils.smooth(
sublook_data[chind], rg_ml, axis=1),
az_ml,
axis=0)
# Keep only valid part and down sample
sublook_data = sublook_data[:, az_min:az_max:az_downsmp,
rg_min:rg_max:rg_downsmp]
sublooks.append(sublook_data)
sublooks_f.append(
np.fft.fft(np.fft.fft(sublook_data - np.mean(sublook_data),
axis=1),
axis=2))
kaz = 2 * np.pi * np.fft.fftfreq(sublook_data.shape[1],
az_downsmp * az_grid_spacing)
kgrg = 2 * np.pi * np.fft.fftfreq(sublook_data.shape[2],
rg_downsmp * grg_grid_spacing)
xspecs = []
tind = 0
xspec_lut = np.zeros((len(sublooks), len(sublooks)), dtype=int)
for ind1 in range(len(sublooks)):
for ind2 in range(ind1 + 1, len(sublooks)):
xspec_lut[ind1, ind2] = tind
tind = tind + 1
xspec = sublooks_f[ind1] * np.conj(sublooks_f[ind2])
xspecs.append(xspec)
with open(output_file, 'wb') as output:
pickle.dump(xspecs, output, pickle.HIGHEST_PROTOCOL)
pickle.dump([kaz, kgrg], output, pickle.HIGHEST_PROTOCOL)
# PROCESSED AMPLITUDE
if plot_proc_ampl:
for pind in range(npol):
save_path = (plot_path + os.sep + 'amp_dB_' + polt[pind] + '.' +
plot_format)
plt.figure()
plt.imshow(utils.db(i_all[pind]),
aspect='equal',
origin='lower',
vmin=utils.db(np.max(i_all[pind])) - 20,
extent=[0., rg_span, 0., az_span],
interpolation='nearest',
cmap='viridis')
plt.xlabel('Ground range [m]')
plt.ylabel('Azimuth [m]')
plt.title("Amplitude")
plt.colorbar()
plt.savefig(save_path)
save_path = (plot_path + os.sep + 'amp_' + polt[pind] + '.' +
plot_format)
int_img = (i_all[pind])**0.5
vmin = np.mean(int_img) - 3 * np.std(int_img)
vmax = np.mean(int_img) + 3 * np.std(int_img)
plt.figure()
plt.imshow(int_img,
aspect='equal',
origin='lower',
vmin=vmin,
vmax=vmax,
extent=[0., rg_span, 0., az_span],
interpolation='nearest',
cmap='viridis')
plt.xlabel('Ground range [m]')
plt.ylabel('Azimuth [m]')
plt.title("Amplitude")
plt.colorbar()
plt.savefig(save_path)
## FIXME: I am plotting the cross spectrum for the first polarization and the first channel only, which is not
## very nice. To be fixed, in particular por multiple polarizations
for ind1 in range(len(sublooks)):
for ind2 in range(ind1 + 1, len(sublooks)):
save_path_abs = os.path.join(plot_path,
('xspec_abs_%i%i.%s' %
(ind1 + 1, ind2 + 1, plot_format)))
save_path_pha = os.path.join(plot_path,
('xspec_pha_%i%i.%s' %
(ind1 + 1, ind2 + 1, plot_format)))
save_path_im = os.path.join(plot_path,
('xspec_im_%i%i.%s' %
(ind1 + 1, ind2 + 1, plot_format)))
ml_xspec = utils.smooth(utils.smooth(np.fft.fftshift(
xspecs[xspec_lut[ind1, ind2]][0]),
krg_ml,
axis=1),
kaz_ml,
axis=0)
plt.figure()
plt.imshow(np.abs(ml_xspec),
origin='lower',
cmap='inferno_r',
extent=[kgrg.min(),
kgrg.max(),
kaz.min(),
kaz.max()],
interpolation='nearest')
plt.grid(True)
pltax = plt.gca()
pltax.set_xlim((-0.1, 0.1))
pltax.set_ylim((-0.1, 0.1))
northarr_length = 0.075 # np.min([surface_full.kx.max(), surface_full.ky.max()])
pltax.arrow(0,
0,
-northarr_length * np.sin(np.radians(cfg.sar.heading)),
northarr_length * np.cos(np.radians(cfg.sar.heading)),
fc="k",
ec="k")
plt.xlabel('$k_x$ [rad/m]')
plt.ylabel('$k_y$ [rad/m]')
plt.colorbar()
plt.savefig(save_path_abs)
plt.close()
plt.figure()
ml_xspec_pha = np.angle(ml_xspec)
ml_xspec_im = np.imag(ml_xspec)
immax = np.abs(ml_xspec_im).max()
whimmax = np.abs(ml_xspec_im).flatten().argmax()
phmax = np.abs(ml_xspec_pha.flatten()[whimmax])
plt.imshow(ml_xspec_pha,
origin='lower',
cmap='bwr',
extent=[kgrg.min(),
kgrg.max(),
kaz.min(),
kaz.max()],
interpolation='nearest',
vmin=-2 * phmax,
vmax=2 * phmax)
plt.grid(True)
pltax = plt.gca()
pltax.set_xlim((-0.1, 0.1))
pltax.set_ylim((-0.1, 0.1))
northarr_length = 0.075 # np.min([surface_full.kx.max(), surface_full.ky.max()])
pltax.arrow(0,
0,
-northarr_length * np.sin(np.radians(cfg.sar.heading)),
northarr_length * np.cos(np.radians(cfg.sar.heading)),
fc="k",
ec="k")
plt.xlabel('$k_x$ [rad/m]')
plt.ylabel('$k_y$ [rad/m]')
plt.colorbar()
plt.savefig(save_path_pha)
plt.close()
plt.figure()
plt.imshow(ml_xspec_im,
origin='lower',
cmap='bwr',
extent=[kgrg.min(),
kgrg.max(),
kaz.min(),
kaz.max()],
interpolation='nearest',
vmin=-2 * immax,
vmax=2 * immax)
plt.grid(True)
pltax = plt.gca()
pltax.set_xlim((-0.1, 0.1))
pltax.set_ylim((-0.1, 0.1))
northarr_length = 0.075 # np.min([surface_full.kx.max(), surface_full.ky.max()])
pltax.arrow(0,
0,
-northarr_length * np.sin(np.radians(cfg.sar.heading)),
northarr_length * np.cos(np.radians(cfg.sar.heading)),
fc="k",
ec="k")
plt.xlabel('$k_x$ [rad/m]')
plt.ylabel('$k_y$ [rad/m]')
plt.colorbar()
plt.savefig(save_path_im)
plt.close()
def ati_process(cfg_file, insar_output_file, ocean_file, output_file):
print(
'-------------------------------------------------------------------')
print(
time.strftime("- OCEANSAR ATI Processor: [%Y-%m-%d %H:%M:%S]",
time.localtime()))
print(
'-------------------------------------------------------------------')
print('Initializing...')
## CONFIGURATION FILE
cfg = tpio.ConfigFile(cfg_file)
# SAR
pol = cfg.sar.pol
if pol == 'DP':
polt = ['hh', 'vv']
elif pol == 'hh':
polt = ['hh']
else:
polt = ['vv']
# ATI
ml_win = cfg.ati.ml_win
plot_save = cfg.ati.plot_save
plot_path = cfg.ati.plot_path
plot_format = cfg.ati.plot_format
plot_tex = cfg.ati.plot_tex
plot_surface = cfg.ati.plot_surface
plot_proc_ampl = cfg.ati.plot_proc_ampl
plot_coh = cfg.ati.plot_coh
plot_coh_all = cfg.ati.plot_coh_all
plot_ati_phase = cfg.ati.plot_ati_phase
plot_ati_phase_all = cfg.ati.plot_ati_phase_all
plot_vel_hist = cfg.ati.plot_vel_hist
plot_vel = cfg.ati.plot_vel
# PROCESSED InSAR L1b DATA
insar_data = tpio.L1bFile(insar_output_file, 'r')
i_all = insar_data.get('ml_intensity')
cohs = insar_data.get('ml_coherence') * np.exp(
1j * insar_data.get('ml_phase'))
coh_lut = insar_data.get('coh_lut')
sr0 = insar_data.get('sr0')
inc_angle = insar_data.get('inc_angle')
b_ati = insar_data.get('b_ati')
b_xti = insar_data.get('b_xti')
f0 = insar_data.get('f0')
az_sampling = insar_data.get('az_sampling')
num_ch = insar_data.get('num_ch')
rg_sampling = insar_data.get('rg_sampling')
v_ground = insar_data.get('v_ground')
alt = insar_data.get('orbit_alt')
inc_angle = np.deg2rad(insar_data.get('inc_angle'))
rg_ml = insar_data.get('rg_ml')
az_ml = insar_data.get('az_ml')
insar_data.close()
# CALCULATE PARAMETERS
k0 = 2. * np.pi * f0 / const.c
# OCEAN SURFACE
surface = OceanSurface()
surface.load(ocean_file, compute=['D', 'V'])
surface.t = 0.
# OUTPUT FILE
output = open(output_file, 'w')
# OTHER INITIALIZATIONS
# Enable TeX
if plot_tex:
plt.rc('font', family='serif')
plt.rc('text', usetex=True)
# Create plots directory
plot_path = os.path.dirname(output_file) + os.sep + plot_path
if plot_save:
if not os.path.exists(plot_path):
os.makedirs(plot_path)
# SURFACE VELOCITIES
grg_grid_spacing = (const.c / 2. / rg_sampling / np.sin(inc_angle))
rg_res_fact = grg_grid_spacing / surface.dx
az_grid_spacing = (v_ground / az_sampling)
az_res_fact = az_grid_spacing / surface.dy
res_fact = np.ceil(np.sqrt(rg_res_fact * az_res_fact))
# SURFACE RADIAL VELOCITY
v_radial_surf = surface.Vx * np.sin(inc_angle) - surface.Vz * np.cos(
inc_angle)
v_radial_surf_ml = utils.smooth(utils.smooth(v_radial_surf,
res_fact * rg_ml,
axis=1),
res_fact * az_ml,
axis=0)
v_radial_surf_mean = np.mean(v_radial_surf)
v_radial_surf_std = np.std(v_radial_surf)
v_radial_surf_ml_std = np.std(v_radial_surf_ml)
# SURFACE HORIZONTAL VELOCITY
v_horizo_surf = surface.Vx
v_horizo_surf_ml = utils.smooth(utils.smooth(v_horizo_surf,
res_fact * rg_ml,
axis=1),
res_fact * az_ml,
axis=0)
v_horizo_surf_mean = np.mean(v_horizo_surf)
v_horizo_surf_std = np.std(v_horizo_surf)
v_horizo_surf_ml_std = np.std(v_horizo_surf_ml)
# Expected mean azimuth shift
sr0 = geosar.inc_to_sr(inc_angle, alt)
avg_az_shift = -v_radial_surf_mean / v_ground * sr0
std_az_shift = v_radial_surf_std / v_ground * sr0
az_guard = np.int(std_az_shift / (v_ground / az_sampling))
##################
# ATI PROCESSING #
##################
print('Starting ATI processing...')
# Get dimensions & calculate region of interest
rg_span = surface.Lx
az_span = surface.Ly
# First dimension is number of channels, second is number of pols
ch_dim = i_all.shape[0:2]
npol = ch_dim[1]
print('Generating plots and estimating values...')
# SURFACE HEIGHT
if plot_surface:
plt.figure()
plt.imshow(surface.Dz,
cmap="ocean",
extent=[0, surface.Lx, 0, surface.Ly],
origin='lower')
plt.title('Surface Height')
plt.xlabel('Ground range [m]')
plt.ylabel('Azimuth [m]')
cbar = plt.colorbar()
cbar.ax.set_xlabel('[m]')
if plot_save:
plt.savefig(plot_path + os.sep + 'plot_surface.' + plot_format,
bbox_inches='tight')
plt.close()
else:
plt.show()
# PROCESSED AMPLITUDE
if plot_proc_ampl:
for pind in range(npol):
save_path = (plot_path + os.sep + 'amp_dB_' + polt[pind] + '.' +
plot_format)
plt.figure()
plt.imshow(utils.db(i_all[0, pind]),
aspect='equal',
origin='lower',
vmin=utils.db(np.max(i_all[pind])) - 20,
extent=[0., rg_span, 0., az_span],
interpolation='nearest',
cmap='viridis')
plt.xlabel('Ground range [m]')
plt.ylabel('Azimuth [m]')
plt.title("Amplitude")
plt.colorbar()
plt.savefig(save_path)
plt.close()
save_path = (plot_path + os.sep + 'amp_' + polt[pind] + '.' +
plot_format)
int_img = (i_all[0, pind])**0.5
vmin = np.mean(int_img) - 3 * np.std(int_img)
vmax = np.mean(int_img) + 3 * np.std(int_img)
plt.figure()
plt.imshow(int_img,
aspect='equal',
origin='lower',
vmin=vmin,
vmax=vmax,
extent=[0., rg_span, 0., az_span],
interpolation='nearest',
cmap='viridis')
plt.xlabel('Ground range [m]')
plt.ylabel('Azimuth [m]')
plt.title("Amplitude")
plt.colorbar()
plt.savefig(save_path)
plt.close()
if plot_coh and ch_dim[0] > 1:
for pind in range(npol):
save_path = (plot_path + os.sep + 'ATI_coh_' + polt[pind] +
polt[pind] + '.' + plot_format)
coh_ind = coh_lut[0, pind, 1, pind]
plt.figure()
plt.imshow(np.abs(cohs[coh_ind]),
aspect='equal',
origin='lower',
vmin=0,
vmax=1,
extent=[0., rg_span, 0., az_span],
cmap='bone')
plt.xlabel('Ground range [m]')
plt.ylabel('Azimuth [m]')
plt.title("ATI Coherence")
# plt.colorbar()
plt.savefig(save_path)
# ATI PHASE
tau_ati = b_ati / v_ground
ati_phases = []
# Hack to avoid interferogram computation if there are no interferometric channels
if num_ch > 1:
npol_ = npol
else:
npol_ = 0
for pind in range(npol_):
save_path = (plot_path + os.sep + 'ATI_pha_' + polt[pind] +
polt[pind] + '.' + plot_format)
coh_ind = coh_lut[(0, pind, 1, pind)]
ati_phase = uwphase(cohs[coh_ind])
ati_phases.append(ati_phase)
v_radial_est = -ati_phase / tau_ati[1] / (k0 * 2.)
if plot_ati_phase:
phase_mean = np.mean(ati_phase)
phase_std = np.std(ati_phase)
vmin = np.max([
-np.abs(phase_mean) - 4 * phase_std, -np.abs(ati_phase).max()
])
vmax = np.min(
[np.abs(phase_mean) + 4 * phase_std,
np.abs(ati_phase).max()])
plt.figure()
plt.imshow(ati_phase,
aspect='equal',
origin='lower',
vmin=vmin,
vmax=vmax,
extent=[0., rg_span, 0., az_span],
cmap='hsv')
plt.xlabel('Ground range [m]')
plt.ylabel('Azimuth [m]')
plt.title("ATI Phase")
plt.colorbar()
plt.savefig(save_path)
save_path = (plot_path + os.sep + 'ATI_rvel_' + polt[pind] +
polt[pind] + '.' + plot_format)
vmin = -np.abs(v_radial_surf_mean) - 4. * v_radial_surf_std
vmax = np.abs(v_radial_surf_mean) + 4. * v_radial_surf_std
plt.figure()
plt.imshow(v_radial_est,
aspect='equal',
origin='lower',
vmin=vmin,
vmax=vmax,
extent=[0., rg_span, 0., az_span],
cmap='bwr')
plt.xlabel('Ground range [m]')
plt.ylabel('Azimuth [m]')
plt.title("Estimated Radial Velocity " + polt[pind])
plt.colorbar()
plt.savefig(save_path)
if npol_ == 4: # Bypass this for now
# Cross pol interferogram
coh_ind = coh_lut[(0, 1)]
save_path = (plot_path + os.sep + 'POL_coh_' + polt[0] + polt[1] +
'.' + plot_format)
utils.image(np.abs(cohs[coh_ind]),
max=1,
min=0,
aspect='equal',
cmap='gray',
extent=[0., rg_span, 0., az_span],
xlabel='Ground range [m]',
ylabel='Azimuth [m]',
title='XPOL Coherence',
usetex=plot_tex,
save=plot_save,
save_path=save_path)
save_path = (plot_path + os.sep + 'POL_pha_' + polt[0] + polt[1] +
'.' + plot_format)
ati_phase = uwphase(cohs[coh_ind])
phase_mean = np.mean(ati_phase)
phase_std = np.std(ati_phase)
vmin = np.max([-np.abs(phase_mean) - 4 * phase_std, -np.pi])
vmax = np.min([np.abs(phase_mean) + 4 * phase_std, np.pi])
utils.image(ati_phase,
aspect='equal',
min=vmin,
max=vmax,
cmap=utils.bwr_cmap,
extent=[0., rg_span, 0., az_span],
xlabel='Ground range [m]',
ylabel='Azimuth [m]',
title='XPOL Phase',
cbar_xlabel='[rad]',
usetex=plot_tex,
save=plot_save,
save_path=save_path)
if num_ch > 1:
ati_phases = np.array(ati_phases)
output.write('--------------------------------------------\n')
output.write('SURFACE RADIAL VELOCITY - NO SMOOTHING\n')
output.write('MEAN(SURF. V) = %.4f\n' % v_radial_surf_mean)
output.write('STD(SURF. V) = %.4f\n' % v_radial_surf_std)
output.write('--------------------------------------------\n\n')
output.write('--------------------------------------------\n')
output.write(
'SURFACE RADIAL VELOCITY - SMOOTHING (WIN. SIZE=%dx%d)\n' %
(az_ml, rg_ml))
output.write('MEAN(SURF. V) = %.4f\n' % v_radial_surf_mean)
output.write('STD(SURF. V) = %.4f\n' % v_radial_surf_ml_std)
output.write('--------------------------------------------\n\n')
output.write('--------------------------------------------\n')
output.write('SURFACE HORIZONTAL VELOCITY - NO SMOOTHING\n')
output.write('MEAN(SURF. V) = %.4f\n' % v_horizo_surf_mean)
output.write('STD(SURF. V) = %.4f\n' % v_horizo_surf_std)
output.write('--------------------------------------------\n\n')
if plot_vel_hist:
# PLOT RADIAL VELOCITY
plt.figure()
plt.hist(v_radial_surf.flatten(),
200,
density=True,
histtype='step')
#plt.hist(v_radial_surf_ml.flatten(), 500, density=True, histtype='step')
plt.grid(True)
plt.xlim([
-np.abs(v_radial_surf_mean) - 4. * v_radial_surf_std,
np.abs(v_radial_surf_mean) + 4. * v_radial_surf_std
])
plt.xlabel('Radial velocity [m/s]')
plt.ylabel('PDF')
plt.title('Surface velocity')
if plot_save:
plt.savefig(plot_path + os.sep + 'TRUE_radial_vel_hist.' +
plot_format)
plt.close()
else:
plt.show()
plt.figure()
plt.hist(v_radial_surf_ml.flatten(),
200,
density=True,
histtype='step')
#plt.hist(v_radial_surf_ml.flatten(), 500, density=True, histtype='step')
plt.grid(True)
plt.xlim([
-np.abs(v_radial_surf_mean) - 4. * v_radial_surf_std,
np.abs(v_radial_surf_mean) + 4. * v_radial_surf_std
])
plt.xlabel('Radial velocity [m/s]')
plt.ylabel('PDF')
plt.title('Surface velocity (low pass filtered)')
if plot_save:
plt.savefig(plot_path + os.sep + 'TRUE_radial_vel_ml_hist.' +
plot_format)
plt.close()
else:
plt.show()
if plot_vel:
utils.image(
v_radial_surf,
aspect='equal',
cmap=utils.bwr_cmap,
extent=[0., rg_span, 0., az_span],
xlabel='Ground range [m]',
ylabel='Azimuth [m]',
title='Surface Radial Velocity',
cbar_xlabel='[m/s]',
min=-np.abs(v_radial_surf_mean) - 4. * v_radial_surf_std,
max=np.abs(v_radial_surf_mean) + 4. * v_radial_surf_std,
usetex=plot_tex,
save=plot_save,
save_path=plot_path + os.sep + 'TRUE_radial_vel.' +
plot_format)
utils.image(
v_radial_surf_ml,
aspect='equal',
cmap=utils.bwr_cmap,
extent=[0., rg_span, 0., az_span],
xlabel='Ground range [m]',
ylabel='Azimuth [m]',
title='Surface Radial Velocity',
cbar_xlabel='[m/s]',
min=-np.abs(v_radial_surf_mean) - 4. * v_radial_surf_std,
max=np.abs(v_radial_surf_mean) + 4. * v_radial_surf_std,
usetex=plot_tex,
save=plot_save,
save_path=plot_path + os.sep + 'TRUE_radial_vel_ml.' +
plot_format)
## ESTIMATED VELOCITIES
# Note: plot limits are taken from surface calculations to keep the same ranges
# ESTIMATE RADIAL VELOCITY
v_radial_ests = -ati_phases / tau_ati[1] / (k0 * 2.)
# ESTIMATE HORIZONTAL VELOCITY
v_horizo_ests = -ati_phases / tau_ati[1] / (k0 *
2.) / np.sin(inc_angle)
#Trim edges
v_radial_ests = v_radial_ests[:, az_guard:-az_guard, 5:-5]
v_horizo_ests = v_horizo_ests[:, az_guard:-az_guard, 5:-5]
output.write('--------------------------------------------\n')
output.write('ESTIMATED RADIAL VELOCITY - NO SMOOTHING\n')
for pind in range(npol):
output.write("%s Polarization\n" % polt[pind])
output.write('MEAN(EST. V) = %.4f\n' %
np.mean(v_radial_ests[pind]))
output.write('STD(EST. V) = %.4f\n' % np.std(v_radial_ests[pind]))
output.write('--------------------------------------------\n\n')
output.write('--------------------------------------------\n')
output.write(
'ESTIMATED RADIAL VELOCITY - SMOOTHING (WIN. SIZE=%dx%d)\n' %
(az_ml, rg_ml))
for pind in range(npol):
output.write("%s Polarization\n" % polt[pind])
output.write('MEAN(EST. V) = %.4f\n' % np.mean(
utils.smooth(utils.smooth(v_radial_ests[pind], rg_ml, axis=1),
az_ml,
axis=0)))
output.write('STD(EST. V) = %.4f\n' % np.std(
utils.smooth(utils.smooth(v_radial_ests[pind], rg_ml, axis=1),
az_ml,
axis=0)))
output.write('--------------------------------------------\n\n')
output.write('--------------------------------------------\n')
output.write('ESTIMATED HORIZONTAL VELOCITY - NO SMOOTHING\n')
for pind in range(npol):
output.write("%s Polarization\n" % polt[pind])
output.write('MEAN(EST. V) = %.4f\n' %
np.mean(v_horizo_ests[pind]))
output.write('STD(EST. V) = %.4f\n' % np.std(v_horizo_ests[pind]))
output.write('--------------------------------------------\n\n')
# Processed NRCS
NRCS_est_avg = 10 * np.log10(
np.mean(np.mean(i_all[:, :, az_guard:-az_guard, 5:-5], axis=-1),
axis=-1))
output.write('--------------------------------------------\n')
for pind in range(npol):
output.write("%s Polarization\n" % polt[pind])
output.write('Estimated mean NRCS = %5.2f\n' % NRCS_est_avg[0, pind])
output.write('--------------------------------------------\n\n')
# Some bookkeeping information
output.write('--------------------------------------------\n')
output.write('GROUND RANGE GRID SPACING = %.4f\n' % grg_grid_spacing)
output.write('AZIMUTH GRID SPACING = %.4f\n' % az_grid_spacing)
output.write('--------------------------------------------\n\n')
output.close()
if plot_vel_hist and num_ch > 1:
# PLOT RADIAL VELOCITY
plt.figure()
plt.hist(v_radial_surf.flatten(),
200,
density=True,
histtype='step',
label='True')
for pind in range(npol):
plt.hist(v_radial_ests[pind].flatten(),
200,
density=True,
histtype='step',
label=polt[pind])
plt.grid(True)
plt.xlim([
-np.abs(v_radial_surf_mean) - 4. * v_radial_surf_std,
np.abs(v_radial_surf_mean) + 4. * v_radial_surf_std
])
plt.xlabel('Radial velocity [m/s]')
plt.ylabel('PDF')
plt.title('Estimated velocity')
plt.legend()
if plot_save:
plt.savefig(plot_path + os.sep + 'ATI_radial_vel_hist.' +
plot_format)
plt.close()
else:
plt.show()
# Save some statistics to npz file
#
if num_ch > 1:
filenpz = os.path.join(os.path.dirname(output_file), 'ati_stats.npz')
# Mean coh
cohs = np.array(cohs)[:, az_guard:-az_guard, 5:-5]
np.savez(filenpz,
nrcs=NRCS_est_avg,
v_r_dop=np.mean(np.mean(v_radial_ests, axis=-1), axis=-1),
v_r_surf=v_radial_surf_mean,
v_r_surf_std=v_radial_surf_std,
coh_mean=np.mean(np.mean(cohs, axis=-1), axis=-1),
abscoh_mean=np.mean(np.mean(np.abs(cohs), axis=-1), axis=-1),
coh_lut=coh_lut,
pols=polt)
print('----------------------------------------')
print(
time.strftime("ATI Processing finished [%Y-%m-%d %H:%M:%S]",
time.localtime()))
print('----------------------------------------')
def postprocess_batch_sim(template_file,
plots=True,
fontsize=14,
pltsymb=['o', 'D', 's', '^', 'p']):
cfg_file = utils.get_parFile(parfile=template_file)
ref_cfg = osrio.ConfigFile(cfg_file)
step = 0
npol = (2 if ref_cfg.sar.pol == 'DP' else 1)
nch = int(ref_cfg.sar.num_ch)
nim = nch * npol
sim_path_ref = ref_cfg.sim.path + os.sep + 'sim_U%d_wdir%d_smag%d_sdir%d_inc%d_i%i'
n_all = (np.size(v_wind_U) * np.size(v_wind_dir) * np.size(v_current_mag) *
np.size(v_current_dir) * np.size(v_inc_angle) * n_rep)
mean_cohs = np.zeros(
(np.size(v_wind_U), np.size(v_wind_dir), np.size(v_current_mag),
np.size(v_current_dir), np.size(v_inc_angle), n_rep,
int((nim) * (nim - 1) / 2)),
dtype=np.complex)
mean_abscohs = np.zeros(
(np.size(v_wind_U), np.size(v_wind_dir), np.size(v_current_mag),
np.size(v_current_dir), np.size(v_inc_angle), n_rep,
int((nim) * (nim - 1) / 2)),
dtype=np.float)
nrcs = np.zeros(
(np.size(v_wind_U), np.size(v_wind_dir), np.size(v_current_mag),
np.size(v_current_dir), np.size(v_inc_angle), n_rep, nch * npol),
dtype=np.float)
v_r_dop = np.zeros(
(np.size(v_wind_U), np.size(v_wind_dir), np.size(v_current_mag),
np.size(v_current_dir), np.size(v_inc_angle), n_rep, npol),
dtype=np.float)
for ind_w_U in range(np.size(v_wind_U)):
for ind_w_dir in range(np.size(v_wind_dir)):
for ind_c_mag in range(np.size(v_current_mag)):
for ind_c_dir in range(np.size(v_current_dir)):
for ind_inc in range(np.size(v_inc_angle)):
for i_rep in range(n_rep):
# Read data
path = sim_path_ref % (v_wind_U[ind_w_U],
v_wind_dir[ind_w_dir],
v_current_mag[ind_c_mag],
v_current_dir[ind_c_dir],
v_inc_angle[ind_inc], i_rep)
try:
data_filename = os.path.join(
path, 'ati_stats.npz')
npzfile = np.load(data_filename)
mean_cohs[ind_w_U, ind_w_dir, ind_c_mag,
ind_c_dir, ind_inc,
i_rep] = npzfile['coh_mean']
mean_abscohs[ind_w_U, ind_w_dir, ind_c_mag,
ind_c_dir, ind_inc,
i_rep] = npzfile['abscoh_mean']
nrcs[ind_w_U, ind_w_dir, ind_c_mag, ind_c_dir,
ind_inc, i_rep] = npzfile['nrcs']
v_r_dop[ind_w_U, ind_w_dir, ind_c_mag,
ind_c_dir, ind_inc,
i_rep] = npzfile['v_r_dop']
coh_lut = npzfile['coh_lut']
except OSError:
print("Issues with %s" % data_filename)
mean_cohs[ind_w_U, ind_w_dir, ind_c_mag,
ind_c_dir, ind_inc, i_rep] = np.NaN
mean_abscohs[ind_w_U, ind_w_dir, ind_c_mag,
ind_c_dir, ind_inc,
i_rep] = np.NaN
nrcs[ind_w_U, ind_w_dir, ind_c_mag, ind_c_dir,
ind_inc, i_rep] = np.NaN
v_r_dop[ind_w_U, ind_w_dir, ind_c_mag,
ind_c_dir, ind_inc, i_rep] = np.NaN
if plots:
font = {'family': "Arial", 'weight': 'normal', 'size': fontsize}
mpl.rc('font', **font)
for ind_inc in range(np.size(v_inc_angle)):
plt.figure()
for ind in range(np.size(v_wind_U)):
plt.plot(v_wind_dir,
np.abs(mean_cohs[ind, :, 0, 0, ind_inc, 0, 4]),
pltsymb[int(np.mod(ind, len(pltsymb)))],
label=("U = %2.1f" % (v_wind_U[ind])))
plt.xlabel("Wind direction w.r.t. radar LOS [deg]")
plt.ylabel("$\gamma$")
plt.title(r"Coherence at $\theta_i=%i$" %
int(v_inc_angle[ind_inc]))
plt.legend(loc=0)
plt.grid(True)
plt.tight_layout()
plt.figure()
for ind in range(np.size(v_wind_U)):
plt.plot(v_wind_dir,
v_r_dop[ind, :, 0, 0, 1, 0, 1],
pltsymb[int(np.mod(ind, len(pltsymb)))],
label=("U = %2.1f" % (v_wind_U[ind])))
plt.xlabel("Wind direction w.r.t. radar LOS [deg]")
plt.ylabel("$v_{Dop} [m/s]$")
plt.legend(loc=0)
plt.grid(True)
plt.tight_layout()
plt.figure()
for ind in range(np.size(v_wind_U)):
plt.plot(v_wind_dir,
nrcs[ind, :, 0, 0, 1, 0, 1],
pltsymb[int(np.mod(ind, len(pltsymb)))],
label=("U = %2.1f" % (v_wind_U[ind])))
plt.xlabel("Wind direction w.r.t. radar LOS [deg]")
plt.ylabel("$NRCS [dB]$")
plt.legend(loc=0)
plt.grid(True)
plt.tight_layout()
return mean_cohs, mean_abscohs, v_r_dop, nrcs, coh_lut
