def select_pairs_sequential(date_list,
num_connection=2,
date12_format='YYMMDD-YYMMDD'):
"""Select Pairs in a Sequential way:
For each acquisition, find its num_connection nearest acquisitions in the past time.
Inputs:
date_list : list of date in YYMMDD/YYYYMMDD format
Reference:
Fattahi, H., and F. Amelung (2013), DEM Error Correction in InSAR Time Series, IEEE TGRS, 51(7), 4249-4259.
"""
date8_list = sorted(ptime.yyyymmdd(date_list))
date6_list = ptime.yymmdd(date8_list)
date_idx_list = list(range(len(date6_list)))
# Get pairs index list
date12_idx_list = []
for date_idx in date_idx_list:
for i in range(num_connection):
if date_idx - i - 1 >= 0:
date12_idx_list.append([date_idx - i - 1, date_idx])
date12_idx_list = [sorted(idx) for idx in sorted(date12_idx_list)]
# Convert index into date12
date12_list = [
date6_list[idx[0]] + '-' + date6_list[idx[1]]
for idx in date12_idx_list
]
if date12_format == 'YYYYMMDD_YYYYMMDD':
date12_list = ptime.yyyymmdd_date12(date12_list)
return date12_list
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 select_pairs_all(date_list, date12_format='YYMMDD-YYMMDD'):
"""Select All Possible Pairs/Interferograms
Input : date_list - list of date in YYMMDD/YYYYMMDD format
Output: date12_list - list date12 in YYMMDD-YYMMDD format
Reference:
Berardino, P., G. Fornaro, R. Lanari, and E. Sansosti (2002), A new algorithm for surface deformation monitoring
based on small baseline differential SAR interferograms, IEEE TGRS, 40(11), 2375-2383.
"""
date8_list = sorted(ptime.yyyymmdd(date_list))
date6_list = ptime.yymmdd(date8_list)
date12_list = list(itertools.combinations(date6_list, 2))
date12_list = [date12[0] + '-' + date12[1] for date12 in date12_list]
if date12_format == 'YYYYMMDD_YYYYMMDD':
date12_list = ptime.yyyymmdd_date12(date12_list)
return date12_list
def select_pairs_mst(date_list, pbase_list, date12_format='YYMMDD-YYMMDD'):
"""Select Pairs using Minimum Spanning Tree technique
Connection Cost is calculated using the baseline distance in perp and scaled temporal baseline (Pepe and Lanari,
2006, TGRS) plane.
Inputs:
date_list : list of date in YYMMDD/YYYYMMDD format
pbase_list : list of float, perpendicular spatial baseline
References:
Pepe, A., and R. Lanari (2006), On the extension of the minimum cost flow algorithm for phase unwrapping
of multitemporal differential SAR interferograms, IEEE TGRS, 44(9), 2374-2383.
Perissin D., Wang T. (2012), Repeat-pass SAR interferometry with partially coherent targets. IEEE TGRS. 271-280
"""
# Get temporal baseline in days
date6_list = ptime.yymmdd(date_list)
date8_list = ptime.yyyymmdd(date_list)
tbase_list = ptime.date_list2tbase(date8_list)[0]
# Normalization (Pepe and Lanari, 2006, TGRS)
temp2perp_scale = (max(pbase_list) - min(pbase_list)) / (max(tbase_list) -
min(tbase_list))
tbase_list = [tbase * temp2perp_scale for tbase in tbase_list]
# Get weight matrix
ttMat1, ttMat2 = np.meshgrid(np.array(tbase_list), np.array(tbase_list))
ppMat1, ppMat2 = np.meshgrid(np.array(pbase_list), np.array(pbase_list))
ttMat = np.abs(ttMat1 - ttMat2) # temporal distance matrix
ppMat = np.abs(ppMat1 - ppMat2) # spatial distance matrix
# 2D distance matrix in temp/perp domain
weightMat = np.sqrt(np.square(ttMat) + np.square(ppMat))
weightMat = sparse.csr_matrix(weightMat) # compress sparse row matrix
# MST path based on weight matrix
mstMat = sparse.csgraph.minimum_spanning_tree(weightMat)
# Convert MST index matrix into date12 list
[s_idx_list, m_idx_list] = [
date_idx_array.tolist() for date_idx_array in sparse.find(mstMat)[0:2]
]
date12_list = []
for i in range(len(m_idx_list)):
idx = sorted([m_idx_list[i], s_idx_list[i]])
date12 = date6_list[idx[0]] + '-' + date6_list[idx[1]]
date12_list.append(date12)
if date12_format == 'YYYYMMDD_YYYYMMDD':
date12_list = ptime.yyyymmdd_date12(date12_list)
return date12_list
def select_pairs_star(date_list,
m_date=None,
pbase_list=[],
date12_format='YYMMDD-YYMMDD'):
"""Select Star-like network/interferograms/pairs, it's a single master network, similar to PS approach.
Usage:
m_date : master date, choose it based on the following cretiria:
1) near the center in temporal and spatial baseline
2) prefer winter season than summer season for less temporal decorrelation
Reference:
Ferretti, A., C. Prati, and F. Rocca (2001), Permanent scatterers in SAR interferometry, IEEE TGRS, 39(1), 8-20.
"""
date8_list = sorted(ptime.yyyymmdd(date_list))
date6_list = ptime.yymmdd(date8_list)
# Select master date if not existed
if not m_date:
m_date = select_master_date(date8_list, pbase_list)
print(('auto select master date: ' + m_date))
# Check input master date
m_date8 = ptime.yyyymmdd(m_date)
if m_date8 not in date8_list:
print('Input master date is not existed in date list!')
print(('Input master date: ' + str(m_date8)))
print(('Input date list: ' + str(date8_list)))
m_date8 = None
# Generate star/ps network
m_idx = date8_list.index(m_date8)
date12_idx_list = [
sorted([m_idx, s_idx]) for s_idx in range(len(date8_list))
if s_idx is not m_idx
]
date12_list = [
date6_list[idx[0]] + '-' + date6_list[idx[1]]
for idx in date12_idx_list
]
if date12_format == 'YYYYMMDD_YYYYMMDD':
date12_list = ptime.yyyymmdd_date12(date12_list)
return date12_list
def select_pairs_delaunay(date_list,
pbase_list,
norm=True,
date12_format='YYMMDD-YYMMDD'):
"""Select Pairs using Delaunay Triangulation based on temporal/perpendicular baselines
Inputs:
date_list : list of date in YYMMDD/YYYYMMDD format
pbase_list : list of float, perpendicular spatial baseline
norm : normalize temporal baseline to perpendicular baseline
Key points
1. Define a ratio between perpendicular and temporal baseline axis units (Pepe and Lanari, 2006, TGRS).
2. Pairs with too large perpendicular / temporal baseline or Doppler centroid difference should be removed
after this, using a threshold, to avoid strong decorrelations (Zebker and Villasenor, 1992, TGRS).
Reference:
Pepe, A., and R. Lanari (2006), On the extension of the minimum cost flow algorithm for phase unwrapping
of multitemporal differential SAR interferograms, IEEE TGRS, 44(9), 2374-2383.
Zebker, H. A., and J. Villasenor (1992), Decorrelation in interferometric radar echoes, IEEE TGRS, 30(5), 950-959.
"""
# Get temporal baseline in days
date6_list = ptime.yymmdd(date_list)
date8_list = ptime.yyyymmdd(date_list)
tbase_list = ptime.date_list2tbase(date8_list)[0]
# Normalization (Pepe and Lanari, 2006, TGRS)
if norm:
temp2perp_scale = (max(pbase_list) - min(pbase_list)) / (
max(tbase_list) - min(tbase_list))
tbase_list = [tbase * temp2perp_scale for tbase in tbase_list]
# Generate Delaunay Triangulation
date12_idx_list = Triangulation(tbase_list, pbase_list).edges.tolist()
date12_idx_list = [sorted(idx) for idx in sorted(date12_idx_list)]
# Convert index into date12
date12_list = [
date6_list[idx[0]] + '-' + date6_list[idx[1]]
for idx in date12_idx_list
]
if date12_format == 'YYYYMMDD_YYYYMMDD':
date12_list = ptime.yyyymmdd_date12(date12_list)
return date12_list
def select_pairs_hierarchical(date_list,
pbase_list,
temp_perp_list,
date12_format='YYMMDD-YYMMDD'):
"""Select Pairs in a hierarchical way using list of temporal and perpendicular baseline thresholds
For each temporal/perpendicular combination, select all possible pairs; and then merge all combination results
together for the final output (Zhao, 2015).
Inputs:
date_list : list of date in YYMMDD/YYYYMMDD format
pbase_list : list of float, perpendicular spatial baseline
temp_perp_list : list of list of 2 floats, for list of temporal/perp baseline, e.g.
[[32.0, 800.0], [48.0, 600.0], [64.0, 200.0]]
Examples:
pairs = select_pairs_hierarchical(date_list, pbase_list, [[32.0, 800.0], [48.0, 600.0], [64.0, 200.0]])
Reference:
Zhao, W., (2015), Small deformation detected from InSAR time-series and their applications in geophysics, Doctoral
dissertation, Univ. of Miami, Section 6.3.
"""
# Get all date12
date12_list_all = select_pairs_all(date_list)
# Loop of Threshold
print('List of temporal and perpendicular spatial baseline thresholds:')
print(temp_perp_list)
date12_list = []
for temp_perp in temp_perp_list:
tbase_max = temp_perp[0]
pbase_max = temp_perp[1]
date12_list_tmp = threshold_temporal_baseline(date12_list_all,
tbase_max,
keep_seasonal=False)
date12_list_tmp = threshold_perp_baseline(date12_list_tmp, date_list,
pbase_list, pbase_max)
date12_list += date12_list_tmp
date12_list = sorted(list(set(date12_list)))
if date12_format == 'YYYYMMDD_YYYYMMDD':
date12_list = ptime.yyyymmdd_date12(date12_list)
return date12_list
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
def simulate_coherence(date12_list,
baseline_file='bl_list.txt',
sensor_name='Env',
inc_angle=22.8,
decor_time=200.0,
coh_resid=0.2,
display=False):
"""Simulate coherence for a given set of interferograms
Inputs:
date12_list - list of string in YYMMDD-YYMMDD format, indicating pairs configuration
baseline_file - string, path of baseline list text file
sensor_name - string, SAR sensor name
inc_angle - float, incidence angle
decor_time - float / 2D np.array in size of (1, pixel_num)
decorrelation rate in days, time for coherence to drop to 1/e of its initial value
coh_resid - float / 2D np.array in size of (1, pixel_num)
long-term coherence, minimum attainable coherence value
display - bool, display result as matrix or not
Output:
cohs - 2D np.array in size of (ifgram_num, pixel_num)
Example:
date12_list = pnet.get_date12_list('ifgram_list.txt')
cohs = simulate_coherences(date12_list, 'bl_list.txt', sensor_name='Tsx')
References:
Zebker, H. A., & Villasenor, J. (1992). Decorrelation in interferometric radar echoes.
IEEE-TGRS, 30(5), 950-959.
Hanssen, R. F. (2001). Radar interferometry: data interpretation and error analysis
(Vol. 2). Dordrecht, Netherlands: Kluwer Academic Pub.
Morishita, Y., & Hanssen, R. F. (2015). Temporal decorrelation in L-, C-, and X-band satellite
radar interferometry for pasture on drained peat soils. IEEE-TGRS, 53(2), 1096-1104.
Parizzi, A., Cong, X., & Eineder, M. (2009). First Results from Multifrequency Interferometry.
A comparison of different decorrelation time constants at L, C, and X Band. ESA Scientific
Publications(SP-677), 1-5.
"""
date_list, pbase_list, dop_list = read_baseline_file(baseline_file)[0:3]
tbase_list = ptime.date_list2tbase(date_list)[0]
# Thermal decorrelation (Zebker and Villasenor, 1992, Eq.4)
SNR = sensor.signal2noise_ratio(sensor_name)
coh_thermal = 1. / (1. + 1. / SNR)
pbase_c = critical_perp_baseline(sensor_name, inc_angle)
bandwidth_az = sensor.azimuth_bandwidth(sensor_name)
date12_list = ptime.yyyymmdd_date12(date12_list)
ifgram_num = len(date12_list)
if isinstance(decor_time, (int, float)):
pixel_num = 1
decor_time = float(decor_time)
else:
pixel_num = decor_time.shape[1]
if decor_time == 0.:
decor_time = 0.01
cohs = np.zeros((ifgram_num, pixel_num), np.float32)
for i in range(ifgram_num):
if display:
sys.stdout.write('\rinterferogram = %4d/%4d' % (i, ifgram_num))
sys.stdout.flush()
m_date, s_date = date12_list[i].split('_')
m_idx = date_list.index(m_date)
s_idx = date_list.index(s_date)
pbase = pbase_list[s_idx] - pbase_list[m_idx]
tbase = tbase_list[s_idx] - tbase_list[m_idx]
# Geometric decorrelation (Hanssen, 2001, Eq. 4.4.12)
coh_geom = (pbase_c - abs(pbase)) / pbase_c
if coh_geom < 0.:
coh_geom = 0.
# Doppler centroid decorrelation (Hanssen, 2001, Eq. 4.4.13)
if not dop_list:
coh_dc = 1.
else:
coh_dc = calculate_doppler_overlap(dop_list[m_idx],
dop_list[s_idx], bandwidth_az)
if coh_dc < 0.:
coh_dc = 0.
# Option 1: Temporal decorrelation - exponential delay model (Parizzi et al., 2009; Morishita and Hanssen, 2015)
coh_temp = np.multiply(
(coh_thermal - coh_resid), np.exp(
-1 * abs(tbase) / decor_time)) + coh_resid
coh = coh_geom * coh_dc * coh_temp
cohs[i, :] = coh
#epsilon = 1e-3
#cohs[cohs < epsilon] = epsilon
if display:
print('')
if display:
print(('critical perp baseline: %.f m' % pbase_c))
cohs_mat = coherence_matrix(date12_list, cohs)
plt.figure()
plt.imshow(cohs_mat, vmin=0.0, vmax=1.0, cmap='jet')
plt.xlabel('Image number')
plt.ylabel('Image number')
cbar = plt.colorbar()
cbar.set_label('Coherence')
plt.title('Coherence matrix')
plt.show()
return cohs
def get_date12_to_drop(inps):
"""Get date12 list to dropped
Return [] if no ifgram to drop, thus KEEP ALL ifgrams;
None if nothing to change, exit without doing anything.
"""
obj = ifgramStack(inps.file)
obj.open()
date12ListAll = obj.date12List
dateList = obj.dateList
print('number of interferograms: {}'.format(len(date12ListAll)))
# Get date12_to_drop
date12_to_drop = []
# reference file
if inps.referenceFile:
date12_to_keep = ifgramStack(inps.referenceFile).get_date12_list(dropIfgram=True)
print('--------------------------------------------------')
print('use reference pairs info from file: {}'.format(inps.referenceFile))
print('number of interferograms in reference: {}'.format(len(date12_to_keep)))
tempList = sorted(list(set(date12ListAll) - set(date12_to_keep)))
date12_to_drop += tempList
print('date12 not in reference file: ({})\n{}'.format(len(tempList), tempList))
# coherence file
if inps.coherenceBased:
print('--------------------------------------------------')
print('use coherence-based network modification')
coord = ut.coordinate(obj.metadata, lookup_file=inps.lookupFile)
if inps.aoi_geo_box and inps.lookupFile:
print('input AOI in (lon0, lat1, lon1, lat0): {}'.format(inps.aoi_geo_box))
inps.aoi_pix_box = coord.bbox_geo2radar(inps.aoi_geo_box)
if inps.aoi_pix_box:
inps.aoi_pix_box = coord.check_box_within_data_coverage(inps.aoi_pix_box)
print('input AOI in (x0,y0,x1,y1): {}'.format(inps.aoi_pix_box))
# Calculate spatial average coherence
cohList = ut.spatial_average(inps.file,
datasetName='coherence',
maskFile=inps.maskFile,
box=inps.aoi_pix_box,
saveList=True)[0]
coh_date12_list = list(np.array(date12ListAll)[np.array(cohList) >= inps.minCoherence])
# MST network
if inps.keepMinSpanTree:
print('Get minimum spanning tree (MST) of interferograms with inverse of coherence.')
msg = ('Drop ifgrams with '
'1) average coherence < {} AND '
'2) not in MST network: '.format(inps.minCoherence))
mst_date12_list = pnet.threshold_coherence_based_mst(date12ListAll, cohList)
mst_date12_list = ptime.yyyymmdd_date12(mst_date12_list)
else:
msg = 'Drop ifgrams with average coherence < {}: '.format(inps.minCoherence)
mst_date12_list = []
tempList = sorted(list(set(date12ListAll) - set(coh_date12_list + mst_date12_list)))
date12_to_drop += tempList
msg += '({})'.format(len(tempList))
if len(tempList) <= 200:
msg += '\n{}'.format(tempList)
print(msg)
# temp baseline threshold
if inps.tempBaseMax:
tempIndex = np.abs(obj.tbaseIfgram) > inps.tempBaseMax
tempList = list(np.array(date12ListAll)[tempIndex])
date12_to_drop += tempList
print('--------------------------------------------------')
print('Drop ifgrams with temporal baseline > {} days: ({})\n{}'.format(
inps.tempBaseMax, len(tempList), tempList))
# perp baseline threshold
if inps.perpBaseMax:
tempIndex = np.abs(obj.pbaseIfgram) > inps.perpBaseMax
tempList = list(np.array(date12ListAll)[tempIndex])
date12_to_drop += tempList
print('--------------------------------------------------')
print('Drop ifgrams with perp baseline > {} meters: ({})\n{}'.format(
inps.perpBaseMax, len(tempList), tempList))
# connection number threshold
if inps.connNumMax:
seq_date12_list = pnet.select_pairs_sequential(dateList, inps.connNumMax)
seq_date12_list = ptime.yyyymmdd_date12(seq_date12_list)
tempList = [i for i in date12ListAll if i not in seq_date12_list]
date12_to_drop += tempList
print('--------------------------------------------------')
msg = 'Drop ifgrams with temporal baseline beyond {} neighbors: ({})'.format(
inps.connNumMax, len(tempList))
if len(tempList) <= 200:
msg += '\n{}'.format(tempList)
print(msg)
# excludeIfgIndex
if inps.excludeIfgIndex:
tempList = [date12ListAll[i] for i in inps.excludeIfgIndex]
date12_to_drop += tempList
print('--------------------------------------------------')
print('Drop ifgrams with the following index number: {}'.format(len(tempList)))
for i in range(len(tempList)):
print('{} : {}'.format(i, tempList[i]))
#len(tempList), zip(inps.excludeIfgIndex, tempList)))
# excludeDate
if inps.excludeDate:
tempList = [i for i in date12ListAll if any(j in inps.excludeDate for j in i.split('_'))]
date12_to_drop += tempList
print('-'*50+'\nDrop ifgrams including the following dates: ({})\n{}'.format(
len(tempList), inps.excludeDate))
print('-'*30+'\n{}'.format(tempList))
# startDate
if inps.startDate:
minDate = int(inps.startDate)
tempList = [i for i in date12ListAll if any(int(j) < minDate for j in i.split('_'))]
date12_to_drop += tempList
print('--------------------------------------------------')
print('Drop ifgrams with date earlier than: {} ({})\n{}'.format(
inps.startDate, len(tempList), tempList))
# endDate
if inps.endDate:
maxDate = int(inps.endDate)
tempList = [i for i in date12ListAll if any(int(j) > maxDate for j in i.split('_'))]
date12_to_drop += tempList
print('--------------------------------------------------')
print('Drop ifgrams with date later than: {} ({})\n{}'.format(
inps.endDate, len(tempList), tempList))
# Manually drop pairs
if inps.manual:
tempList = manual_select_pairs_to_remove(inps.file)
if tempList is None:
return None
tempList = [i for i in tempList if i in date12ListAll]
print('date12 selected to remove: ({})\n{}'.format(len(tempList), tempList))
date12_to_drop += tempList
# drop duplicate date12 and sort in order
date12_to_drop = sorted(list(set(date12_to_drop)))
date12_to_keep = sorted(list(set(date12ListAll) - set(date12_to_drop)))
print('--------------------------------------------------')
print('number of interferograms to remove: {}'.format(len(date12_to_drop)))
print('number of interferograms to keep : {}'.format(len(date12_to_keep)))
date_to_keep = [d for date12 in date12_to_keep for d in date12.split('_')]
date_to_keep = sorted(list(set(date_to_keep)))
date_to_drop = sorted(list(set(dateList) - set(date_to_keep)))
if len(date_to_drop) > 0:
print('number of acquisitions to remove: {}\n{}'.format(len(date_to_drop), date_to_drop))
date12ListKept = obj.get_date12_list(dropIfgram=True)
date12ListDropped = sorted(list(set(date12ListAll) - set(date12ListKept)))
if date12_to_drop == date12ListDropped:
print('Calculated date12 to drop is the same as exsiting marked input file, skip updating file.')
date12_to_drop = None
elif date12_to_drop == date12ListAll:
raise Exception('Zero interferogram left! Please adjust your setting and try again.')
return date12_to_drop
def plot_network(ax, date12List, dateList, pbaseList, plot_dict={}, date12List_drop=[], print_msg=True):
"""Plot Temporal-Perp baseline Network
Inputs
ax : matplotlib axes object
date12List : list of string for date12 in YYYYMMDD_YYYYMMDD format
dateList : list of string, for date in YYYYMMDD format
pbaseList : list of float, perp baseline, len=number of acquisition
plot_dict : dictionary with the following items:
fontsize
linewidth
markercolor
markersize
cohList : list of float, coherence value of each interferogram, len = number of ifgrams
disp_min/max : float, min/max range of the color display based on cohList
colormap : string, colormap name
coh_thres : float, coherence of where to cut the colormap for display
disp_title : bool, show figure title or not, default: True
disp_drop: bool, show dropped interferograms or not, default: True
Output
ax : matplotlib axes object
"""
# 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
# For colorful display of coherence
if not 'cohList' in plot_dict.keys(): plot_dict['cohList'] = None
if not 'cbar_label' in plot_dict.keys(): plot_dict['cbar_label'] = 'Average Spatial Coherence'
if not 'disp_min' in plot_dict.keys(): plot_dict['disp_min'] = 0.2
if not 'disp_max' in plot_dict.keys(): plot_dict['disp_max'] = 1.0
if not 'colormap' in plot_dict.keys(): plot_dict['colormap'] = 'RdBu'
if not 'disp_title' in plot_dict.keys(): plot_dict['disp_title'] = True
if not 'coh_thres' in plot_dict.keys(): plot_dict['coh_thres'] = None
if not 'disp_drop' in plot_dict.keys(): plot_dict['disp_drop'] = True
if not 'every_year' in plot_dict.keys(): plot_dict['every_year'] = 1
cohList = plot_dict['cohList']
disp_min = plot_dict['disp_min']
disp_max = plot_dict['disp_max']
coh_thres = plot_dict['coh_thres']
transparency = 0.7
# Date Convert
dateList = ptime.yyyymmdd(sorted(dateList))
dates, datevector = ptime.date_list2vector(dateList)
tbaseList = ptime.date_list2tbase(dateList)[0]
## maxBperp and maxBtemp
date12List = ptime.yyyymmdd_date12(date12List)
ifgram_num = len(date12List)
pbase12 = np.zeros(ifgram_num)
tbase12 = np.zeros(ifgram_num)
for i in range(ifgram_num):
m_date, s_date = date12List[i].split('_')
m_idx = dateList.index(m_date)
s_idx = dateList.index(s_date)
pbase12[i] = pbaseList[s_idx] - pbaseList[m_idx]
tbase12[i] = tbaseList[s_idx] - tbaseList[m_idx]
if print_msg:
print('max perpendicular baseline: {:.2f} m'.format(np.max(np.abs(pbase12))))
print('max temporal baseline: {} days'.format(np.max(tbase12)))
## Keep/Drop - date12
date12List_keep = sorted(list(set(date12List) - set(date12List_drop)))
idx_date12_keep = [date12List.index(i) for i in date12List_keep]
idx_date12_drop = [date12List.index(i) for i in date12List_drop]
if not date12List_drop:
plot_dict['disp_drop'] = False
## Keep/Drop - date
m_dates = [i.split('_')[0] for i in date12List_keep]
s_dates = [i.split('_')[1] for i in date12List_keep]
dateList_keep = ptime.yyyymmdd(sorted(list(set(m_dates + s_dates))))
dateList_drop = sorted(list(set(dateList) - set(dateList_keep)))
idx_date_keep = [dateList.index(i) for i in dateList_keep]
idx_date_drop = [dateList.index(i) for i in dateList_drop]
# Ploting
# ax=fig.add_subplot(111)
# Colorbar when conherence is colored
if cohList is not None:
data_min = min(cohList)
data_max = max(cohList)
# Normalize
normalization = False
if normalization:
cohList = [(coh-data_min) / (data_min-data_min) for coh in cohList]
disp_min = data_min
disp_max = data_max
if print_msg:
print('showing coherence')
print(('colormap: '+plot_dict['colormap']))
print(('display range: '+str([disp_min, disp_max])))
print(('data range: '+str([data_min, data_max])))
splitColormap = True
if splitColormap:
# Use lower/upper part of colormap to emphasis dropped interferograms
if not coh_thres:
# Find proper cut percentage so that all keep pairs are blue and drop pairs are red
cohList_keep = [cohList[i] for i in idx_date12_keep]
cohList_drop = [cohList[i] for i in idx_date12_drop]
if cohList_drop:
coh_thres = max(cohList_drop)
else:
coh_thres = min(cohList_keep)
if coh_thres < disp_min:
disp_min = 0.0
if print_msg:
print('data range exceed orginal display range, set new display range to: [0.0, %f]' % (disp_max))
c1_num = np.ceil(200.0 * (coh_thres - disp_min) / (disp_max - disp_min)).astype('int')
coh_thres = c1_num / 200.0 * (disp_max-disp_min) + disp_min
cmap = plt.get_cmap(plot_dict['colormap'])
colors1 = cmap(np.linspace(0.0, 0.3, c1_num))
colors2 = cmap(np.linspace(0.6, 1.0, 200 - c1_num))
cmap = LinearSegmentedColormap.from_list('truncate_RdBu', np.vstack((colors1, colors2)))
if print_msg:
print(('color jump at '+str(coh_thres)))
else:
cmap = plt.get_cmap(plot_dict['colormap'])
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "3%", pad="3%")
norm = mpl.colors.Normalize(vmin=disp_min, vmax=disp_max)
cbar = mpl.colorbar.ColorbarBase(cax, cmap=cmap, norm=norm)
cbar.set_label(plot_dict['cbar_label'], fontsize=plot_dict['fontsize'])
# plot low coherent ifgram first and high coherence ifgram later
cohList_keep = [cohList[date12List.index(i)] for i in date12List_keep]
date12List_keep = [x for _, x in sorted(zip(cohList_keep, date12List_keep))]
# Dot - SAR Acquisition
if idx_date_keep:
x_list = [dates[i] for i in idx_date_keep]
y_list = [pbaseList[i] for i in idx_date_keep]
ax.plot(x_list, y_list, 'ko', alpha=0.7,
ms=plot_dict['markersize'], mfc=plot_dict['markercolor'])
if idx_date_drop:
x_list = [dates[i] for i in idx_date_drop]
y_list = [pbaseList[i] for i in idx_date_drop]
ax.plot(x_list, y_list, 'ko', alpha=0.7,
ms=plot_dict['markersize'], mfc='gray')
## Line - Pair/Interferogram
# interferograms dropped
if plot_dict['disp_drop']:
for date12 in date12List_drop:
date1, date2 = date12.split('_')
idx1 = dateList.index(date1)
idx2 = dateList.index(date2)
x = np.array([dates[idx1], dates[idx2]])
y = np.array([pbaseList[idx1], pbaseList[idx2]])
if cohList is not None:
coh = cohList[date12List.index(date12)]
coh_idx = (coh - disp_min) / (disp_max - disp_min)
ax.plot(x, y, '--', lw=plot_dict['linewidth'],
alpha=transparency, c=cmap(coh_idx))
else:
ax.plot(x, y, '--', lw=plot_dict['linewidth'],
alpha=transparency, c='k')
# interferograms kept
for date12 in date12List_keep:
date1, date2 = date12.split('_')
idx1 = dateList.index(date1)
idx2 = dateList.index(date2)
x = np.array([dates[idx1], dates[idx2]])
y = np.array([pbaseList[idx1], pbaseList[idx2]])
if cohList is not None:
coh = cohList[date12List.index(date12)]
coh_idx = (coh - disp_min) / (disp_max - disp_min)
ax.plot(x, y, '-', lw=plot_dict['linewidth'],
alpha=transparency, c=cmap(coh_idx))
else:
ax.plot(x, y, '-', lw=plot_dict['linewidth'],
alpha=transparency, c='k')
if plot_dict['disp_title']:
ax.set_title('Interferogram Network', fontsize=plot_dict['fontsize'])
# axis format
ax = auto_adjust_xaxis_date(ax, datevector, plot_dict['fontsize'],
every_year=plot_dict['every_year'])[0]
ax = auto_adjust_yaxis(ax, pbaseList, plot_dict['fontsize'])
ax.set_xlabel('Time [years]', fontsize=plot_dict['fontsize'])
ax.set_ylabel('Perp Baseline [m]', fontsize=plot_dict['fontsize'])
# Legend
if plot_dict['disp_drop']:
solid_line = mlines.Line2D([], [], color='k', ls='solid', label='Interferograms')
dash_line = mlines.Line2D([], [], color='k', ls='dashed', label='Interferograms dropped')
ax.legend(handles=[solid_line, dash_line])
return ax
