def rar_spectra(data, fs, rgsmth=4):
nrg = data.shape[1]
pp = data[1:] * np.conj(data[0:-1])
# Intensity spectrum
data_int = utils.smooth(np.abs(data) ** 2, 10, axis=0)
mean_intensity_profile = np.mean(data_int, axis=0)
mask = mean_intensity_profile > (mean_intensity_profile.mean() / 10)
mask = mask.reshape((1, mask.size))
data_int_m = mask * data_int
mean_int = np.sum(data_int_m) / np.sum(mask) / data.shape[0]
mean_intensity_profile2 = mean_intensity_profile - mean_int * mask.flatten()
han = np.hanning(data.shape[1]).reshape((1, data.shape[1]))
data_ac = data_int_m - mask * mean_int
intens_spectrum = utils.smooth(np.mean(np.abs(np.fft.fft(han * data_ac, axis=1)) ** 2, axis=0), rgsmth)/nrg
intens_spectrum_noug = utils.smooth(np.abs(np.fft.fft(han * np.mean(data_ac, axis=0), axis=1)) ** 2, rgsmth)/nrg
phase_spectrum = np.mean(np.abs(np.fft.fft(mask * np.angle(utils.smooth(pp, 10, axis=0)), axis=1)) ** 2, axis=0)
phase_spectrum = utils.smooth(phase_spectrum, rgsmth)/nrg
kr = 2 * np.pi * np.fft.fftfreq(intens_spectrum.size, const.c / 2 / fs)
return kr, mean_intensity_profile2, intens_spectrum, phase_spectrum, intens_spectrum_noug.flatten()
def sar_delta_k_omega(dk_sig, inter_aperture_time, dksmoth=4):
n_block = dk_sig.shape[0]
dk_pps = np.zeros((n_block-1, dk_sig.shape[2]), dtype=np.complex)
dk_omega = np.zeros_like(dk_pps, dtype=np.float)
for ind_lag in range(1, n_block):
dk_pp = dk_sig[ind_lag:, :, :] * np.conj(dk_sig[0:n_block - ind_lag, :, :])
dk_pps[ind_lag-1] = np.mean(np.mean(dk_pp, axis=0), axis=0)
# dk_pps[ind_lag - 1] = np.mean(dk_pp, axis=0)[0]
# Angular frequencies
dk_omega[ind_lag-1] = np.angle(utils.smooth(dk_pps[ind_lag-1], dksmoth)) / (inter_aperture_time * ind_lag)
#d k_omega = utils.smooth(dk_omega, dksmoth, axis=1)
return dk_pps, dk_omega
def rar_delta_k_omega(rdk_sig, prt, lags=[1, 2, 4, 8, 16, 32, 64], dksmoth=4):
n_pulses = rdk_sig.shape[0]
lags = np.array(lags)
dk_pps = np.zeros((lags.size, rdk_sig.shape[1]), dtype=np.complex)
dk_omega = np.zeros_like(dk_pps, dtype=np.float)
for ind_lag in range(lags.size):
dk_pp = rdk_sig[lags[ind_lag]:, :] * np.conj(rdk_sig[0:n_pulses - lags[ind_lag], :])
dk_pps[ind_lag-1] = np.mean(dk_pp, axis=0)
# dk_pps[ind_lag - 1] = np.mean(dk_pp, axis=0)[0]
# Angular frequencies
dk_omega[ind_lag-1] = np.angle(utils.smooth(dk_pps[ind_lag-1], dksmoth)) / (prt * lags[ind_lag])
return dk_pps, dk_omega
def sar_spectra(sardata, fs, rgsmth=4):
# azimuth int profile
nrg = sardata.shape[1]
az_prof = np.mean(sardata, axis=1)
mask = sardata > (az_prof.reshape((az_prof.size, 1)) / 10)
az_prof = np.sum(sardata, axis=1) / np.sum(mask, axis=1)
# Remove average
han = np.hanning(sardata.shape[1]).reshape((1, sardata.shape[1]))
sardata_ac = mask * (sardata - az_prof.reshape((az_prof.size, 1)))
sarint_spec = np.mean(np.abs(np.fft.fft(sardata_ac * han, axis=1))**2,
axis=0) / nrg
sarint_spec = utils.smooth(sarint_spec, rgsmth)
kr = 2 * np.pi * np.fft.fftfreq(sarint_spec.size, const.c / 2 / fs)
return kr, sarint_spec
def view_surface(radsurf, rel, ml=2):
"""
:param rel: realization of radar surface
:param ml:
:return:
"""
w_h = radsurf.dz[0]
v_r = rel["v_r"][0]
cfg = radsurf.cfg
v_r_mean = np.mean(v_r)
v_r_std = np.std(v_r)
plt.figure()
plt.imshow(w_h,
aspect='equal',
cmap=mpl.cm.winter,
extent=[0., cfg.ocean.Lx, 0, cfg.ocean.Ly],
origin='lower',
vmin=-(np.abs(w_h).max()),
vmax=(np.abs(w_h).max()))
plt.xlabel('Ground range [m]')
plt.ylabel('Azimuth [m]')
plt.title('Surface Height')
plt.colorbar()
sigma = (utils.smooth(np.abs(rel["scene_hh"][0])**2, ml) / cfg.ocean.dx /
cfg.ocean.dy)
s_mean = np.mean(sigma)
dmin = np.max([0, s_mean - 1.5 * np.std(sigma)])
dmin = utils.db(s_mean - 2 * np.std(sigma))
#dmin = 0
dmax = utils.db(s_mean + 2 * np.std(sigma))
dmin = dmax - 15
dmax = utils.db(sigma.max())
# utils.image(utils.db(sigma), aspect='equal', cmap=utils.sea_cmap,
# extent=[0., cfg.ocean.Lx, 0, cfg.ocean.Ly],
# xlabel='Ground range [m]', ylabel='Azimuth [m]',
# title='Backscattering', cbar_xlabel='dB',
# min=dmin, max=dmax)
plt.figure()
plt.imshow(utils.db(sigma),
aspect='equal',
cmap=mpl.cm.viridis,
extent=[0., cfg.ocean.Lx, 0, cfg.ocean.Ly],
origin='lower',
vmin=dmin,
vmax=dmax)
plt.xlabel('Ground range [m]')
plt.ylabel('Azimuth [m]')
plt.title('Radar Scattering')
plt.colorbar()
def sar_delta_k_slow(sar_data, fs, dksmoth=4):
# Number of range samples
nrg = sar_data.shape[2]
ndk = int(nrg/2)
# FFT in range and re-order
sar_data_f = np.fft.fftshift(np.fft.fft(sar_data, axis=2), axes=(2,))
dk_sig = np.zeros((sar_data.shape[0], sar_data.shape[1], ndk), dtype=np.complex)
for ind in range(0, ndk):
dk_ind = sar_data_f[:, :, ind:] * np.conj(sar_data_f[:, :, 0:nrg - ind])
dk_sig[:, :, ind] = np.mean(dk_ind, axis=2)
dk_avg = utils.smooth(np.mean(np.abs(np.mean(dk_sig, axis=0))**2, axis=0), dksmoth)
# dkr = np.fft.fftshift(2 * np.pi * np.fft.fftfreq(ndk, const.c / 2 / fs))
dkr = fs / nrg * 2 / const.c * 2 * np.pi * np.arange(ndk)
return dkr, dk_avg, dk_sig
def view_surface(S, ml=2):
w_h = S[1]
v_r = S[2]
cfg = S[0]
v_r_mean = np.mean(v_r)
v_r_std = np.std(v_r)
plt.figure()
plt.imshow(w_h,
aspect='equal',
cmap=mpl.cm.winter,
extent=[0., cfg.ocean.Lx, 0, cfg.ocean.Ly],
origin='lower',
vmin=-(np.abs(w_h).max()),
vmax=(np.abs(w_h).max()))
plt.xlabel('Ground range [m]')
plt.ylabel('Azimuth [m]')
plt.title('Surface Height')
plt.colorbar()
sigma = (utils.smooth(np.abs(S[-1][0])**2, ml) / cfg.ocean.dx /
cfg.ocean.dy)
s_mean = np.mean(sigma)
dmin = np.max([0, s_mean - 1.5 * np.std(sigma)])
dmin = utils.db(s_mean - 2 * np.std(sigma))
#dmin = 0
dmax = utils.db(s_mean + 2 * np.std(sigma))
dmin = dmax - 15
dmax = utils.db(sigma.max())
# utils.image(utils.db(sigma), aspect='equal', cmap=utils.sea_cmap,
# extent=[0., cfg.ocean.Lx, 0, cfg.ocean.Ly],
# xlabel='Ground range [m]', ylabel='Azimuth [m]',
# title='Backscattering', cbar_xlabel='dB',
# min=dmin, max=dmax)
plt.figure()
plt.imshow(utils.db(sigma),
aspect='equal',
cmap=mpl.cm.viridis,
extent=[0., cfg.ocean.Lx, 0, cfg.ocean.Ly],
origin='lower',
vmin=dmin,
vmax=dmax)
plt.xlabel('Ground range [m]')
plt.ylabel('Azimuth [m]')
plt.title('Radar Scattering')
plt.colorbar()
def sar_delta_k(csardata, fs, dksmoth=4):
# Number of range samples
nrg = csardata.shape[2]
sardata = np.abs(csardata)**2
az_prof = np.mean(np.mean(sardata, axis=2), axis=0)
burst_avg = np.mean(sardata, axis=0)
mask = burst_avg > (az_prof.reshape((az_prof.size, 1)) / 10)
mask = mask.reshape((1,) + mask.shape)
# Remove average
han = np.hanning(sardata.shape[2]).reshape((1, 1, sardata.shape[2]))
sardata_ac = mask * (sardata - az_prof.reshape((1, az_prof.size, 1)))
dk_sig = np.fft.fft(sardata_ac * han, axis=2) / nrg
# Keep only positive frequencies, since input signal was real
ndk = int(nrg/2)
dk_sig = dk_sig[:, :, 0:int(ndk)]
dk_avg = utils.smooth(np.mean(np.abs(np.mean(dk_sig, axis=0))**2, axis=0), dksmoth)
# After smoothing we would down-sample, but not needed for simulator
dkr = fs / nrg * 2 / const.c * 2 * np.pi * np.arange(ndk)
return dkr, dk_avg, dk_sig
def rar_delta_k(data, fs, dksmoth=4):
# Number of range samples
nrg = data.shape[1]
idata = np.abs(data)**2
az_prof = np.mean(idata, axis=1)
burst_avg = np.mean(idata, axis=0)
mask = burst_avg > burst_avg.max()/10
mask = mask.reshape((1, mask.size))
# Remove average
han = np.hanning(idata.shape[1]).reshape((1, idata.shape[1]))
idata_ac = mask * (idata - az_prof.reshape((az_prof.size, 1)))
rdk_sig = np.fft.fft(idata_ac * han, axis=1) / nrg
# Keep only positive frequencies, since input signal was real
ndk = int(nrg/2)
rdk_sig = rdk_sig[:, 0:int(ndk)]
rdk_avg = utils.smooth(np.abs(np.mean(rdk_sig, axis=0))**2, dksmoth)
# After smoothing we would down-sample, but not needed for simulator
dkr = fs / nrg * 2 / const.c * 2 * np.pi * np.arange(ndk)
return dkr, rdk_avg, rdk_sig
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 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('----------------------------------------')
