def simulate_network(ts_sim,
date12_list,
decor_day,
coh_resid,
L=75,
num_sample=int(1e4),
baseline_file='bl_list.txt',
sensor_name='Sen',
inc_angle=33.4):
"""Simulate coherence --> decorrelation noise --> ifgram phase and estimated coherence"""
coh_sim = pnet.simulate_coherence(date12_list,
baseline_file='bl_list.txt',
sensor_name=sensor_name,
inc_angle=inc_angle,
decor_time=decor_day,
coh_resid=coh_resid)
decor_noise = simulate_decorrelation_noises(date12_list,
coh_sim,
L=int(L),
size=num_sample)
m_dates = [i.split('_')[0] for i in date12_list]
s_dates = [i.split('_')[1] for i in date12_list]
date_list = sorted(list(set(m_dates + s_dates)))
ifgram_sim = timeseries2ifgram(ts_sim,
date_list,
date12_list,
display=False)
ifgram_est = decor_noise + np.tile(ifgram_sim.reshape(-1, 1),
(1, num_sample))
coh_est = estimate_coherence(ifgram_est, L=L, win_size=25)
return ifgram_est, coh_est, ifgram_sim, coh_sim
def simulate_network(ts_sim, date12_list, decor_day, coh_resid, L=75, num_repeat=int(1e4),
baseline_file='bl_list.txt', sensor_name='Sen', inc_angle=33.4):
"""Simulate the InSAR stack for one pixel, including:
simulated coherence --> decorrelation noise
simulated ifgram phase with / without decorrelation noise
estimated coherence"""
# simulated (true) phase
m_dates = [i.split('_')[0] for i in date12_list]
s_dates = [i.split('_')[1] for i in date12_list]
date_list = sorted(list(set(m_dates + s_dates)))
ifgram_sim = timeseries2ifgram(ts_sim, date_list, date12_list, display=False)
# simulated (true) coherence
coh_sim = pnet.simulate_coherence(date12_list,
baseline_file='bl_list.txt',
sensor_name=sensor_name,
inc_angle=inc_angle,
decor_time=decor_day,
coh_resid=coh_resid)
# simulated (estimated) phase
decor_noise = coherence2decorrelation_phase(coh_sim, L=int(L), num_repeat=num_repeat)
ifgram_est = decor_noise + np.tile(ifgram_sim.reshape(-1,1), (1, num_repeat))
# estimated coherence
coh_est = estimate_coherence(ifgram_est, L=L, win_size=25)
return ifgram_est, coh_est, ifgram_sim, coh_sim
def simulate_network(ts_sim, date12_list, decor_day, coh_resid, L=75, num_sample=int(1e4),
baseline_file='bl_list.txt', sensor_name='Sen', inc_angle=33.4):
"""Simulate coherence --> decorrelation noise --> ifgram phase and estimated coherence"""
coh_sim = pnet.simulate_coherence(date12_list,
baseline_file='bl_list.txt',
sensor_name=sensor_name,
inc_angle=inc_angle,
decor_time=decor_day,
coh_resid=coh_resid)
decor_noise = simulate_decorrelation_noises(date12_list, coh_sim, L=int(L), size=num_sample)
m_dates = [i.split('_')[0] for i in date12_list]
s_dates = [i.split('_')[1] for i in date12_list]
date_list = sorted(list(set(m_dates + s_dates)))
ifgram_sim = timeseries2ifgram(ts_sim, date_list, date12_list, display=False)
ifgram_est = decor_noise + np.tile(ifgram_sim.reshape(-1,1), (1, num_sample))
coh_est = estimate_coherence(ifgram_est, L=L, win_size=25)
return ifgram_est, coh_est, ifgram_sim, coh_sim
def write_ifgram_list(inps):
# Output directory/filename
inps.outfile = os.path.abspath(inps.outfile)
inps.out_dir = os.path.dirname(inps.outfile)
if not os.path.isdir(inps.out_dir):
os.makedirs(inps.out_dir)
# Calculate ifgram's temp/perp baseline
ifgram_num = len(inps.date12_list)
ifgram_pbase_list = []
ifgram_tbase_list = []
for i in range(ifgram_num):
m_date, s_date = inps.date12_list[i].split('-')
m_idx = inps.date_list.index(m_date)
s_idx = inps.date_list.index(s_date)
pbase = inps.pbase_list[s_idx] - inps.pbase_list[m_idx]
tbase = inps.tbase_list[s_idx] - inps.tbase_list[m_idx]
ifgram_pbase_list.append(pbase)
ifgram_tbase_list.append(tbase)
# calculate ifgram's predicted coherence
try:
inps.cohList = pnet.simulate_coherence(
inps.date12_list, inps.baseline_file,
sensor_name=inps.sensor).flatten().tolist()
except:
inps.cohList = None
# Write txt file
f = open(inps.outfile, 'w')
f.write('#Interferograms configuration generated by select_network.py\n')
f.write('# Date12 Btemp(days) Bperp(m) sim_coherence\n')
for i in range(ifgram_num):
line = '{} {:6.0f} {:6.1f}'.format(inps.date12_list[i],
ifgram_tbase_list[i],
ifgram_pbase_list[i])
if inps.cohList:
line += ' {:1.4f}'.format(inps.cohList[i])
f.write(line + '\n')
f.close()
log('write network/pairs info into file: {}'.format(inps.outfile))
return inps.outfile