def get_giant_ifg_list(fnames):
m_date_list = []
s_date_list = []
pbase_list = []
ext = os.path.splitext(fnames[0])[1]
if ext == '.h5':
obj = ifgramStack(fnames[0])
obj.open()
m_date_list = obj.mDates[obj.dropIfgram].tolist()
s_date_list = obj.sDates[obj.dropIfgram].tolist()
pbase_list = obj.pbaseIfgram[obj.dropIfgram].tolist()
else:
ifgramNum = len(fnames)
print('Number of interferograms: %d' % (ifgramNum))
for fname in fnames:
atr = readfile.read_attribute(fname)
m_date, s_date = ptime.yymmdd(atr['DATE12'].split('-'))
pbase = (float(atr['P_BASELINE_TOP_HDR']) +
float(atr['P_BASELINE_BOTTOM_HDR'])) / 2.
m_date_list.append(m_date)
s_date_list.append(s_date)
pbase_list.append(pbase)
return m_date_list, s_date_list, pbase_list
def read_igram_pairs(igramFile):
"""Read pairs index from hdf5 file"""
# Read Igram file
h5file = h5py.File(igramFile, 'r')
k = list(h5file.keys())
if 'interferograms' in k:
k[0] = 'interferograms'
elif 'coherence' in k:
k[0] = 'coherence'
if k[0] not in ['interferograms', 'coherence', 'wrapped']:
print('Only interferograms / coherence / wrapped are supported.')
sys.exit(1)
dateList = ptime.ifgram_date_list(igramFile)
dateList6 = ptime.yymmdd(dateList)
pairs = []
igramList = list(h5file[k[0]].keys())
for igram in igramList:
date12 = h5file[k[0]][igram].attrs['DATE12'].split('-')
pairs.append([dateList6.index(date12[0]), dateList6.index(date12[1])])
h5file.close()
pairs = pair_sort(pairs)
return pairs
def read_baseline_info(baseline_file, reference_file):
"""Read date, bperp and/or DOP info
Parameters: baseline_file : str, path of bl_list.txt file
reference_file : str, path of ifgramStack.h5 file
Returns: date_list : list of str in YYMMDD format
tbase_list : list of int in days
pbase_list : list of float in meter
dop_list : None, list of 1D array in size of (3,)
"""
dop_list = None
if baseline_file:
date_list, pbase_list, dop_list = pnet.read_baseline_file(
baseline_file)[0:3]
date_list = ptime.yymmdd(date_list)
tbase_list = ptime.date_list2tbase(date_list)[0]
elif reference_file:
obj = ifgramStack(reference_file)
date12_list_all = obj.get_date12_list(dropIfgram=False)
date12_list_all = ptime.yymmdd_date12(date12_list_all)
m_dates = [i.split('-')[0] for i in date12_list_all]
s_dates = [i.split('-')[1] for i in date12_list_all]
date_list = sorted(list(set(m_dates + s_dates)))
tbase_list = ptime.date_list2tbase(date_list)[0]
pbase_list = obj.get_perp_baseline_timeseries(
dropIfgram=False).tolist()
return date_list, tbase_list, pbase_list, dop_list
def coherence_matrix(date12_list, coh_list, diagValue=np.nan):
"""Return coherence matrix based on input date12 list and its coherence
Inputs:
date12_list - list of string in YYMMDD-YYMMDD format
coh_list - list of float, average coherence for each interferograms
Output:
coh_matrix - 2D np.array with dimension length = date num
np.nan value for interferograms non-existed.
1.0 for diagonal elements
"""
# Get date list
date12_list = ptime.yymmdd_date12(date12_list)
m_dates = [date12.split('-')[0] for date12 in date12_list]
s_dates = [date12.split('-')[1] for date12 in date12_list]
date_list = sorted(ptime.yymmdd(list(set(m_dates + s_dates))))
date_num = len(date_list)
coh_mat = np.zeros([date_num, date_num])
coh_mat[:] = np.nan
for date12 in date12_list:
date1, date2 = date12.split('-')
idx1 = date_list.index(date1)
idx2 = date_list.index(date2)
coh = coh_list[date12_list.index(date12)]
coh_mat[idx1, idx2] = coh # symmetric
coh_mat[idx2, idx1] = coh
if diagValue is not np.nan:
for i in range(date_num): # diagonal value
coh_mat[i, i] = diagValue
return coh_mat
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 read_pairs_list(date12ListFile, dateList=[]):
"""Read Pairs List file like below:
070311-070426
070311-070611
...
"""
# Read date12 list file
date12List = sorted(
list(np.loadtxt(date12ListFile, dtype=bytes).astype(str)))
# Get dateList from date12List
if not dateList:
dateList = []
for date12 in date12List:
dates = date12.split('-')
if not dates[0] in dateList:
dateList.append(dates[0])
if not dates[1] in dateList:
dateList.append(dates[1])
dateList.sort()
date6List = ptime.yymmdd(dateList)
# Get pair index
pairs_idx = []
for date12 in date12List:
dates = date12.split('-')
pair_idx = [date6List.index(dates[0]), date6List.index(dates[1])]
pairs_idx.append(pair_idx)
return pairs_idx
def select_master_interferogram(date12_list,
date_list,
pbase_list,
m_date=None):
"""Select reference interferogram based on input temp/perp baseline info
If master_date is specified, select its closest slave_date, which is newer than master_date;
otherwise, choose the closest pair among all pairs as master interferogram.
Example:
master_date12 = pnet.select_master_ifgram(date12_list, date_list, pbase_list)
'080211-080326' = pnet.select_master_ifgram(date12_list, date_list, pbase_list, m_date='080211')
"""
pbase_array = np.array(pbase_list, dtype='float64')
# Get temporal baseline
date8_list = ptime.yyyymmdd(date_list)
date6_list = ptime.yymmdd(date8_list)
tbase_array = np.array(ptime.date_list2tbase(date8_list)[0],
dtype='float64')
# Normalization (Pepe and Lanari, 2006, TGRS)
temp2perp_scale = (max(pbase_array) - min(pbase_array)) / (
max(tbase_array) - min(tbase_array))
tbase_array *= temp2perp_scale
# Calculate sqrt of temp/perp baseline for input pairs
idx1 = np.array(
[date6_list.index(date12.split('-')[0]) for date12 in date12_list])
idx2 = np.array(
[date6_list.index(date12.split('-')[1]) for date12 in date12_list])
base_distance = np.sqrt((tbase_array[idx2] - tbase_array[idx1])**2 +
(pbase_array[idx2] - pbase_array[idx1])**2)
# Get master interferogram index
if not m_date:
# Choose pair with shortest temp/perp baseline
m_date12_idx = np.argmin(base_distance)
else:
m_date = ptime.yymmdd(m_date)
# Choose pair contains m_date with shortest temp/perp baseline
m_date12_idx_array = np.array([
date12_list.index(date12) for date12 in date12_list
if m_date + '-' in date12
])
min_base_distance = np.min(base_distance[m_date12_idx_array])
m_date12_idx = np.where(base_distance == min_base_distance)[0][0]
m_date12 = date12_list[m_date12_idx]
return m_date12
def write_pairs_list(pairs, dateList, outName):
"""Write pairs list file."""
dateList6 = ptime.yymmdd(dateList)
fl = open(outName, 'w')
for idx in pairs:
date12 = dateList6[idx[0]] + '-' + dateList6[idx[1]] + '\n'
fl.write(date12)
fl.close()
return 1
def select_pairs_all(date_list):
"""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]
return date12_list
def read_baseline_file(baselineFile, exDateList=[]):
"""Read bl_list.txt without dates listed in exDateList
# Date Bperp dop0/PRF dop1/PRF dop2/PRF PRF slcDir
070106 0.0 0.03 0.0000000 0.00000000000 2155.2 /scratch/KyushuT422F650AlosA/SLC/070106/
070709 2631.9 0.07 0.0000000 0.00000000000 2155.2 /scratch/KyushuT422F650AlosA/SLC/070709/
070824 2787.3 0.07 0.0000000 0.00000000000 2155.2 /scratch/KyushuT422F650AlosA/SLC/070824/
...
Examples:
date8List, perpBaseList, dopList, prfList, slcDirList = read_baseline_file(baselineFile)
date8List, perpBaseList, dopList, prfList, slcDirList = read_baseline_file(baselineFile,['080520','100726'])
date8List, perpBaseList = read_baseline_file(baselineFile)[0:2]
"""
exDateList = ptime.yymmdd(exDateList)
if not exDateList:
exDateList = []
# Read baseline file into lines
fb = open(baselineFile)
lines = []
for line in fb:
l = str.replace(line, '\n', '').strip()
lines.append(l)
fb.close()
# Read each line and put the values into arrays
date6List = []
perpBaseList = []
dopplerList = []
slcDirList = []
for line in lines:
c = line.split() # splits on white space
date = c[0]
if not date in exDateList:
date6List.append(date)
perpBaseList.append(float(c[1]))
try:
dop = np.array([float(c[2]), float(c[3]), float(c[4])])
prf = float(c[5])
dop *= prf
dopplerList.append(dop)
except:
pass
try:
slcDirList.append(c[6])
except:
pass
date8List = ptime.yyyymmdd(date6List)
return date8List, perpBaseList, dopplerList, slcDirList
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 date12_list2index(date12_list, date_list=[]):
"""Convert list of date12 string into list of index"""
# Get dateList from date12List
if not date_list:
m_dates = [date12.split('-')[0] for date12 in date12_list]
s_dates = [date12.split('-')[1] for date12 in date12_list]
date_list = list(set(m_dates + s_dates))
date6_list = ptime.yymmdd(sorted(ptime.yyyymmdd(date_list)))
# Get pair index
pairs_idx = []
for date12 in date12_list:
dates = date12.split('-')
pair_idx = [date6_list.index(dates[0]), date6_list.index(dates[1])]
pairs_idx.append(pair_idx)
return pairs_idx
def threshold_perp_baseline(date12_list,
date_list,
pbase_list,
pbase_max,
pbase_min=0.0):
"""Remove pairs/interoferogram out of [pbase_min, pbase_max]
Inputs:
date12_list : list of string for date12 in YYMMDD-YYMMDD format
date_list : list of string for date in YYMMDD/YYYYMMDD format, optional
pbase_list : list of float for perpendicular spatial baseline
pbase_max : float, maximum perpendicular baseline
pbase_min : float, minimum perpendicular baseline
Output:
date12_list_out : list of string for date12 in YYMMDD-YYMMDD format
Example:
date12_list = threshold_perp_baseline(date12_list, date_list, pbase_list, 500)
"""
if not date12_list:
return []
# Get date6_list
if not date_list:
m_dates = [date12.split('-')[0] for date12 in date12_list]
s_dates = [date12.split('-')[1] for date12 in date12_list]
date_list = sorted(ptime.yyyymmdd(list(set(m_dates + s_dates))))
if not len(date_list) == len(pbase_list):
print(
'ERROR: number of existing dates is not equal to number of perp baseline!'
)
print('date list is needed for threshold filtering!')
print('skip filtering.')
return date12_list
date6_list = ptime.yymmdd(date_list)
# Threshold
date12_list_out = []
for date12 in date12_list:
date1, date2 = date12.split('-')
idx1 = date6_list.index(date1)
idx2 = date6_list.index(date2)
pbase = abs(pbase_list[idx1] - pbase_list[idx2])
if pbase_min <= pbase <= pbase_max:
date12_list_out.append(date12)
return date12_list_out
def threshold_doppler_overlap(date12_list,
date_list,
dop_list,
bandwidth_az,
dop_overlap_min=0.15):
"""Remove pairs/interoferogram with doppler overlap larger than critical value
Inputs:
date12_list : list of string, for date12 in YYMMDD-YYMMDD format
date_list : list of string, for date in YYMMDD/YYYYMMDD format, optional
dop_list : list of list of 3 float, for centroid Doppler frequency
bandwidth_az : float, bandwidth in azimuth direction
dop_overlap_min : float, minimum overlap of azimuth Doppler frequency
Outputs:
date12_list : list of string, for date12 in YYMMDD-YYMMDD format
"""
if not date12_list:
return []
# Get date6_list
if not date_list:
m_dates = [date12.split('-')[0] for date12 in date12_list]
s_dates = [date12.split('-')[1] for date12 in date12_list]
date_list = sorted(ptime.yyyymmdd(list(set(m_dates + s_dates))))
if not len(date_list) == len(pbase_list):
print(
'ERROR: number of existing dates is not equal to number of perp baseline!'
)
print('date list is needed for threshold filtering!')
print('skip filtering.')
return date12_list
date6_list = ptime.yymmdd(date_list)
# Threshold
date12_list_out = []
for date12 in date12_list:
date1, date2 = date12.split('-')
idx1 = date6_list.index(date1)
idx2 = date6_list.index(date2)
dop_overlap = calculate_doppler_overlap(dop_list[idx1], dop_list[idx2],
bandwidth_az)
if dop_overlap >= dop_overlap_min:
date12_list_out.append(date12)
return date12_list_out
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 coherence_matrix(date12_list,
coh_list,
diag_value=np.nan,
fill_triangle='both',
date_list=None):
"""Return coherence matrix based on input date12 list and its coherence
Inputs:
date12_list - list of string in YYMMDD-YYMMDD format
coh_list - list of float, average coherence for each interferograms
diag_value - number, value to be filled in the diagonal
fill_triangle - str, 'both', 'upper', 'lower'
Output:
coh_matrix - 2D np.array with dimension length = date num
np.nan value for interferograms non-existed.
1.0 for diagonal elements
"""
# Get date list
date12_list = ptime.yymmdd_date12(date12_list)
if not date_list:
m_dates = [date12.split('-')[0] for date12 in date12_list]
s_dates = [date12.split('-')[1] for date12 in date12_list]
date_list = sorted(list(set(m_dates + s_dates)))
date_list = ptime.yymmdd(date_list)
date_num = len(date_list)
coh_mat = np.zeros([date_num, date_num])
coh_mat[:] = np.nan
for date12 in date12_list:
date1, date2 = date12.split('-')
idx1 = date_list.index(date1)
idx2 = date_list.index(date2)
coh = coh_list[date12_list.index(date12)]
if fill_triangle in ['upper', 'both']:
coh_mat[idx1, idx2] = coh # symmetric
if fill_triangle in ['lower', 'both']:
coh_mat[idx2, idx1] = coh
if diag_value is not np.nan:
for i in range(date_num): # diagonal value
coh_mat[i, i] = diag_value
return coh_mat
def threshold_coherence_based_mst(date12_list, coh_list):
"""Return a minimum spanning tree of network based on the coherence inverse.
Inputs:
date12_list - list of string in YYMMDD-YYMMDD format
coh_list - list of float, average coherence for each interferogram
Output:
mst_date12_list - list of string in YYMMDD-YYMMDD format, for MST network of interferograms
"""
# coh_list --> coh_mat --> weight_mat
coh_mat = coherence_matrix(date12_list, coh_list)
mask = ~np.isnan(coh_mat)
wei_mat = np.zeros(coh_mat.shape)
wei_mat[:] = np.inf
wei_mat[mask] = 1 / coh_mat[mask]
# MST path based on weight matrix
wei_mat_csr = sparse.csr_matrix(wei_mat)
mst_mat_csr = sparse.csgraph.minimum_spanning_tree(wei_mat_csr)
# Get date6_list
date12_list = ptime.yymmdd_date12(date12_list)
m_dates = [date12.split('-')[0] for date12 in date12_list]
s_dates = [date12.split('-')[1] for date12 in date12_list]
date6_list = ptime.yymmdd(
sorted(ptime.yyyymmdd(list(set(m_dates + s_dates)))))
# Convert MST index matrix into date12 list
[s_idx_list, m_idx_list] = [
date_idx_array.tolist()
for date_idx_array in sparse.find(mst_mat_csr)[0:2]
]
mst_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]]
mst_date12_list.append(date12)
return mst_date12_list
def threshold_temporal_baseline(date12_list,
btemp_max,
keep_seasonal=True,
btemp_min=0.0):
"""Remove pairs/interferograms out of min/max/seasonal temporal baseline limits
Inputs:
date12_list : list of string for date12 in YYMMDD-YYMMDD format
btemp_max : float, maximum temporal baseline
btemp_min : float, minimum temporal baseline
keep_seasonal : keep interferograms with seasonal temporal baseline
Output:
date12_list_out : list of string for date12 in YYMMDD-YYMMDD format
Example:
date12_list = threshold_temporal_baseline(date12_list, 200)
date12_list = threshold_temporal_baseline(date12_list, 200, False)
"""
if not date12_list:
return []
# Get date list and tbase list
m_dates = [date12.split('-')[0] for date12 in date12_list]
s_dates = [date12.split('-')[1] for date12 in date12_list]
date8_list = sorted(ptime.yyyymmdd(list(set(m_dates + s_dates))))
date6_list = ptime.yymmdd(date8_list)
tbase_list = ptime.date_list2tbase(date8_list)[0]
# Threshold
date12_list_out = []
for date12 in date12_list:
date1, date2 = date12.split('-')
idx1 = date6_list.index(date1)
idx2 = date6_list.index(date2)
tbase = int(abs(tbase_list[idx1] - tbase_list[idx2]))
if btemp_min <= tbase <= btemp_max:
date12_list_out.append(date12)
elif keep_seasonal and tbase / 30 in [11, 12]:
date12_list_out.append(date12)
return date12_list_out
def extract_metadata4interferogram(fname):
"""Read/extract attributes for PySAR from Gamma .unw, .cor and .int file
Parameters: fname : str, Gamma interferogram filename or path,
i.e. /PopoSLT143TsxD/diff_filt_HDR_130118-130129_4rlks.unw
Returns: atr : dict, Attributes dictionary
"""
file_dir = os.path.dirname(fname)
file_basename = os.path.basename(fname)
rsc_file = fname+'.rsc'
# if os.path.isfile(rsc_file):
# return rsc_file
atr = {}
atr['PROCESSOR'] = 'gamma'
atr['FILE_TYPE'] = os.path.splitext(fname)[1]
# Get info: date12, num of loooks
try:
date12 = str(re.findall('\d{8}[-_]\d{8}', file_basename)[0])
except:
date12 = str(re.findall('\d{6}[-_]\d{6}', file_basename)[0])
m_date, s_date = date12.replace('-', '_').split('_')
atr['DATE12'] = ptime.yymmdd(m_date)+'-'+ptime.yymmdd(s_date)
lks = os.path.splitext(file_basename.split(date12)[1])[0]
# Read .off and .par file
off_file = file_dir+'/*'+date12+lks+'.off'
m_par_file = [file_dir+'/*'+m_date+lks+i for i in ['.amp.par', '.ramp.par']]
s_par_file = [file_dir+'/*'+s_date+lks+i for i in ['.amp.par', '.ramp.par']]
try:
off_file = ut.get_file_list(off_file)[0]
except:
print('\nERROR: Can not find .off file, it supposed to be like: '+off_file)
try:
m_par_file = ut.get_file_list(m_par_file)[0]
except:
print('\nERROR: Can not find master date .par file, it supposed to be like: '+m_par_file)
try:
s_par_file = ut.get_file_list(s_par_file)[0]
except:
print('\nERROR: Can not find slave date .par file, it supposed to be like: '+s_par_file)
par_dict = readfile.read_gamma_par(m_par_file)
off_dict = readfile.read_gamma_par(off_file)
atr.update(par_dict)
atr.update(off_dict)
# Perp Baseline Info
atr = get_perp_baseline(m_par_file, s_par_file, off_file, atr)
# LAT/LON_REF1/2/3/4
atr = get_lalo_ref(m_par_file, atr)
# Write to .rsc file
try:
atr_orig = readfile.read_roipac_rsc(rsc_file)
except:
atr_orig = dict()
if not set(atr.items()).issubset(set(atr_orig.items())):
atr_out = {**atr_orig, **atr}
print('merge %s, %s and %s into %s' % (os.path.basename(m_par_file),
os.path.basename(s_par_file),
os.path.basename(off_file),
os.path.basename(rsc_file)))
writefile.write_roipac_rsc(atr_out, out_file=rsc_file)
return rsc_file
