def timeseries2ifgram(ts_sim,
date_list,
date12_list,
wvl=0.055,
display=False):
range2phase = -4.0 * np.pi / wvl
num_ifgram = len(date12_list)
ifgram_sim = np.zeros((num_ifgram, 1), np.float32)
for i in range(num_ifgram):
m_date, s_date = date12_list[i].split('_')
m_idx = date_list.index(m_date)
s_idx = date_list.index(s_date)
ifgram_sim[i] = ts_sim[s_idx] - ts_sim[m_idx]
ifgram_sim *= range2phase
if display:
ifgram_sim_mat = pnet.coherence_matrix(date12_list, ifgram_sim)
plt.figure()
plt.imshow(ifgram_sim_mat, cmap='jet')
plt.xlabel('Image number')
plt.ylabel('Image number')
cbar = plt.colorbar()
cbar.set_label('Phase (radian)')
plt.title('Interferometric Phase')
plt.show()
return ifgram_sim
def simulate_decorrelation_noises(date12_list, cohs, L=20, size:int=1, display=False, scale=1.0):
'''Simuate decorrelation phase noise for input interferometric pairs
Inputs:
date12_list - list of string in YYMMDD-YYMMDD format, indicating pairs configuration
cohs - 2D np.array in size of (ifgram_num,1)
L - int, multilook number
Output:
decorNoises - 2D np.array in size of (ifgram_num, 1)
Example:
from pysar.utils import network as pnet
date12_list_all = pnet.get_date12_list('ifgram_list_all.txt')
cohs = pnet.simulate_coherence(date12_list_all, decor_time=1000, coh_resid=0.2, display=True, inc_angle=22.8)
decorNoises = simulate_decorrelation_noises(date12_list_all, cohs, L=20, display=True)
'''
ifgram_num = len(cohs)
decorNoises = np.zeros((ifgram_num, size), np.float32)
for i in range(ifgram_num):
decorNoises[i, :] = sample_decorrelation_phase(int(L), cohs[i], size=size, scale=scale)
if display:
decorNoisesMat = pnet.coherence_matrix(date12_list, decorNoises)
plt.figure()
#plt.imshow(decorNoisesMat, vmin=-np.pi, vmax=np.pi, cmap='jet')
plt.imshow(decorNoisesMat, cmap='jet')
plt.xlabel('Image number')
plt.ylabel('Image number')
cbar = plt.colorbar()
cbar.set_label('Decorrelation Phase Noise (radian)')
plt.title('Decorrelation Noise')
plt.show()
return decorNoises
def plot_coherence_matrix(ax, date12List, cohList, date12List_drop=[], plot_dict={}):
"""Plot Coherence Matrix of input network
if date12List_drop is not empty, plot KEPT pairs in the upper triangle and
ALL pairs in the lower triangle.
"""
# Figure Setting
if not 'fontsize' in plot_dict.keys(): plot_dict['fontsize'] = 12
if not 'linewidth' in plot_dict.keys(): plot_dict['linewidth'] = 2
if not 'markercolor' in plot_dict.keys(): plot_dict['markercolor'] = 'orange'
if not 'markersize' in plot_dict.keys(): plot_dict['markersize'] = 16
if not 'disp_title' in plot_dict.keys(): plot_dict['disp_title'] = True
if not 'cbar_label' in plot_dict.keys(): plot_dict['cbar_label'] = 'Coherence'
date12List = ptime.yyyymmdd_date12(date12List)
coh_mat = pnet.coherence_matrix(date12List, cohList)
if date12List_drop:
# Date Convert
m_dates = [i.split('_')[0] for i in date12List]
s_dates = [i.split('_')[1] for i in date12List]
dateList = sorted(list(set(m_dates + s_dates)))
# Set dropped pairs' value to nan, in upper triangle only.
for date12 in date12List_drop:
idx1, idx2 = [dateList.index(i) for i in date12.split('_')]
coh_mat[idx1, idx2] = np.nan
# Show diagonal value as black, to be distinguished from un-selected interferograms
diag_mat = np.diag(np.ones(coh_mat.shape[0]))
diag_mat[diag_mat == 0.] = np.nan
im = ax.imshow(diag_mat, cmap='gray_r', vmin=0.0, vmax=1.0, interpolation='nearest')
im = ax.imshow(coh_mat, cmap='jet', vmin=0.0, vmax=1.0, interpolation='nearest')
date_num = coh_mat.shape[0]
if date_num < 30:
tick_list = list(range(0, date_num, 5))
else:
tick_list = list(range(0, date_num, 10))
ax.get_xaxis().set_ticks(tick_list)
ax.get_yaxis().set_ticks(tick_list)
ax.set_xlabel('Image Number', fontsize=plot_dict['fontsize'])
ax.set_ylabel('Image Number', fontsize=plot_dict['fontsize'])
if plot_dict['disp_title']:
ax.set_title('Coherence Matrix')
# Colorbar
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "3%", pad="3%")
cbar = plt.colorbar(im, cax=cax)
cbar.set_label(plot_dict['cbar_label'], fontsize=plot_dict['fontsize'])
# Legend
if date12List_drop:
ax.plot([], [], label='Upper: used ifgrams')
ax.plot([], [], label='Lower: all ifgrams')
ax.legend(handlelength=0)
return ax
def plot_coherence_history(ax, date12List, cohList, plot_dict={}):
"""Plot min/max Coherence of all interferograms for each date"""
# Figure Setting
if not 'fontsize' in plot_dict.keys(): plot_dict['fontsize'] = 12
if not 'linewidth' in plot_dict.keys(): plot_dict['linewidth'] = 2
if not 'markercolor' in plot_dict.keys(): plot_dict['markercolor'] = 'orange'
if not 'markersize' in plot_dict.keys(): plot_dict['markersize'] = 16
if not 'disp_title' in plot_dict.keys(): plot_dict['disp_title'] = True
if not 'every_year' in plot_dict.keys(): plot_dict['every_year'] = 1
# Get date list
date12List = ptime.yyyymmdd_date12(date12List)
m_dates = [date12.split('_')[0] for date12 in date12List]
s_dates = [date12.split('_')[1] for date12 in date12List]
dateList = sorted(ptime.yyyymmdd(list(set(m_dates + s_dates))))
dates, datevector = ptime.date_list2vector(dateList)
bar_width = ut.most_common(np.diff(dates).tolist())*3/4
x_list = [i-bar_width/2 for i in dates]
coh_mat = pnet.coherence_matrix(date12List, cohList)
ax.bar(x_list, np.nanmax(coh_mat, axis=0), bar_width.days, label='Max Coherence')
ax.bar(x_list, np.nanmin(coh_mat, axis=0), bar_width.days, label='Min Coherence')
if plot_dict['disp_title']:
ax.set_title('Coherence History of All Related Interferograms')
ax = auto_adjust_xaxis_date(ax, datevector, plot_dict['fontsize'],
every_year=plot_dict['every_year'])[0]
ax.set_ylim([0.0, 1.0])
ax.set_xlabel('Time [years]', fontsize=plot_dict['fontsize'])
ax.set_ylabel('Coherence', fontsize=plot_dict['fontsize'])
ax.legend(loc='lower right')
return ax
