def read_exclude_date(inps, date_list_all):
inps.excludeDate = ptime.yyyymmdd(inps.excludeDate)
if not inps.excludeDate:
inps.excludeDate = []
else:
log('input exclude dates: {}'.format(inps.excludeDate))
if inps.startDate:
log('input start date: ' + inps.startDate)
inps.excludeDate += [
i for i in date_list_all
if float(i) < float(ptime.yyyymmdd(inps.startDate))
]
inps.excludeDate = sorted(inps.excludeDate)
if inps.endDate:
log('input end date: ' + inps.endDate)
inps.excludeDate += [
i for i in date_list_all
if float(i) > float(ptime.yyyymmdd(inps.endDate))
]
inps.excludeDate = sorted(inps.excludeDate)
if inps.excludeDate:
log('exclude dates: ({})\n{}'.format(len(inps.excludeDate),
inps.excludeDate))
return inps.excludeDate
def read_template2inps(template_file, inps=None):
"""Read input template options into Namespace inps"""
if not inps:
inps = cmd_line_parse()
inpsDict = vars(inps)
print('read options from template file: '+os.path.basename(template_file))
template = readfile.read_template(inps.template_file)
template = ut.check_template_auto_value(template)
# Update inps if key existed in template file
prefix = 'pysar.network.'
keyList = [i for i in list(inpsDict.keys()) if prefix+i in template.keys()]
for key in keyList:
value = template[prefix+key]
if key in ['coherenceBased', 'keepMinSpanTree']:
inpsDict[key] = value
elif value:
if key in ['minCoherence', 'tempBaseMax', 'perpBaseMax']:
inpsDict[key] = float(value)
elif key in ['connNumMax']:
inpsDict[key] = int(value)
elif key in ['maskFile', 'referenceFile']:
inpsDict[key] = value
elif key == 'aoiYX':
tmp = [i.strip() for i in value.split(',')]
sub_y = sorted([int(i.strip()) for i in tmp[0].split(':')])
sub_x = sorted([int(i.strip()) for i in tmp[1].split(':')])
inps.aoi_pix_box = (sub_x[0], sub_y[0], sub_x[1], sub_y[1])
elif key == 'aoiLALO':
tmp = [i.strip() for i in value.split(',')]
sub_lat = sorted([float(i.strip()) for i in tmp[0].split(':')])
sub_lon = sorted([float(i.strip()) for i in tmp[1].split(':')])
inps.aoi_geo_box = (sub_lon[0], sub_lat[1], sub_lon[1], sub_lat[0])
# Check lookup file
if not inps.lookupFile:
print('Warning: no lookup table file found! Can not use '+key+' option without it.')
print('skip this option.')
inps.aoi_pix_box = None
elif key in ['startDate', 'endDate']:
inpsDict[key] = ptime.yyyymmdd(value)
elif key == 'excludeDate':
inpsDict[key] = ptime.yyyymmdd(value.replace(',', ' ').split())
elif key == 'excludeIfgIndex':
inpsDict[key] += value.replace(',', ' ').split()
inps.excludeIfgIndex = read_input_index_list(inps.excludeIfgIndex, stackFile=inps.file)
# Turn reset on if 1) no input options found to drop ifgram AND 2) there is template input
if all(not i for i in [inps.referenceFile, inps.tempBaseMax, inps.perpBaseMax, inps.connNumMax,
inps.excludeIfgIndex, inps.excludeDate, inps.coherenceBased,
inps.startDate, inps.endDate, inps.reset, inps.manual]):
print('No input option found to remove interferogram')
print('Keep all interferograms by enable --reset option')
inps.reset = True
return inps
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 select_master_date(date_list, pbase_list=[]):
"""Select super master date based on input temporal and/or perpendicular baseline info.
Return master date in YYYYMMDD format.
"""
date8_list = ptime.yyyymmdd(date_list)
if not pbase_list:
# Choose date in the middle
m_date8 = date8_list[int(len(date8_list) / 2)]
else:
# Get temporal baseline 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 distance 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
disMat = np.sqrt(np.square(ttMat) + np.square(ppMat))
# Choose date minimize the total distance of temp/perp baseline
disMean = np.mean(disMat, 0)
m_idx = np.argmin(disMean)
m_date8 = date8_list[m_idx]
return m_date8
def read_exclude_date(ex_date_list, date_list_all):
"""Read exclude dates info
Parameters: ex_date_list : list of string, date in YYMMDD or YYYYMMDD format,
or text file with date in it
date_list_all : list of string, date in YYYYMMDD format
Returns: drop_date : 1D array of bool in size of (num_date,)
"""
# Read exclude date input
if ex_date_list:
tempList = []
for d in ex_date_list:
if os.path.isfile(d):
tempList += ptime.read_date_list(d)
else:
tempList.append(d)
ex_date_list = sorted(ptime.yyyymmdd(tempList))
print(('exclude the following dates for DEM error estimation:'
' ({})\n{}').format(len(ex_date_list), ex_date_list))
else:
ex_date_list = []
# convert to mark array
drop_date = np.array([i not in ex_date_list for i in date_list_all],
dtype=np.bool_)
return drop_date
def exclude_dates():
global inps, dateList
if inps.ex_date_list:
input_ex_date = list(inps.ex_date_list)
inps.ex_date_list = []
if input_ex_date:
for ex_date in input_ex_date:
if os.path.isfile(ex_date):
ex_date = ptime.read_date_list(ex_date)
else:
ex_date = [ptime.yyyymmdd(ex_date)]
inps.ex_date_list += list(
set(ex_date) - set(inps.ex_date_list))
# delete dates not existed in input file
inps.ex_date_list = sorted(
list(set(inps.ex_date_list).intersection(dateList)))
inps.ex_dates = ptime.date_list2vector(inps.ex_date_list)[0]
inps.ex_idx_list = sorted(
[dateList.index(i) for i in inps.ex_date_list])
print(('exclude date:' + str(inps.ex_date_list)))
def correct_local_oscilator_drift(fname, rg_dist_file=None, out_file=None):
print('-'*50)
print('correct Local Oscilator Drift for Envisat using an empirical model (Marinkovic and Larsen, 2013)')
print('-'*50)
atr = readfile.read_attribute(fname)
# Check Sensor Type
platform = atr['PLATFORM']
print('platform: '+platform)
if not platform.lower() in ['env', 'envisat']:
print('No need to correct LOD for '+platform)
return
# output file name
if not out_file:
out_file = '{}_LODcor{}'.format(os.path.splitext(fname)[0], os.path.splitext(fname)[1])
# Get LOD ramp rate from empirical model
if not rg_dist_file:
print('calculate range distance from file metadata')
rg_dist = get_relative_range_distance(atr)
else:
print('read range distance from file: %s' % (rg_dist_file))
rg_dist = readfile.read(rg_dist_file, datasetName='slantRangeDistance', print_msg=False)[0]
rg_dist -= rg_dist[int(atr['REF_Y']), int(atr['REF_X'])]
ramp_rate = np.array(rg_dist * 3.87e-7, np.float32)
# Correct LOD Ramp for Input fname
range2phase = -4*np.pi / float(atr['WAVELENGTH'])
k = atr['FILE_TYPE']
if k == 'timeseries':
# read
obj = timeseries(fname)
obj.open()
data = obj.read()
# correct LOD
diff_year = np.array(obj.yearList)
diff_year -= diff_year[obj.refIndex]
for i in range(data.shape[0]):
data[i, :, :] -= ramp_rate * diff_year[i]
# write
obj_out = timeseries(out_file)
obj_out.write2hdf5(data, refFile=fname)
elif k in ['.unw']:
data, atr = readfile.read(fname)
dates = ptime.yyyymmdd2years(ptime.yyyymmdd(atr['DATE12'].split('-')))
dt = dates[1] - dates[0]
data -= ramp_rate * range2phase * dt
writefile.write(data, out_file=out_file, metadata=atr)
else:
print('No need to correct for LOD for %s file' % (k))
return out_file
def read_exclude_date(inps, dateListAll):
# Merge ex_date/startDate/endDate into ex_date
yy_list_all = ptime.yyyymmdd2years(dateListAll)
exDateList = []
# 1. template_file
if inps.template_file:
print('read option from template file: ' + inps.template_file)
inps = read_template2inps(inps.template_file, inps)
# 2. ex_date
input_ex_date = list(inps.excludeDate)
if input_ex_date:
for ex_date in input_ex_date:
if os.path.isfile(ex_date):
ex_date = ptime.read_date_list(ex_date)
else:
ex_date = [ptime.yyyymmdd(ex_date)]
exDateList += list(set(ex_date) - set(exDateList))
# delete dates not existed in input file
exDateList = list(set(exDateList).intersection(dateListAll))
print('exclude date:' + str(exDateList))
# 3. startDate
if inps.startDate:
print('start date: ' + inps.startDate)
yy_min = ptime.yyyymmdd2years(ptime.yyyymmdd(inps.startDate))
for i in range(len(dateListAll)):
date = dateListAll[i]
if yy_list_all[i] < yy_min and date not in exDateList:
print(' remove date: ' + date)
exDateList.append(date)
# 4. endDate
if inps.endDate:
print('end date: ' + inps.endDate)
yy_max = ptime.yyyymmdd2years(ptime.yyyymmdd(inps.endDate))
for i in range(len(dateListAll)):
date = dateListAll[i]
if yy_list_all[i] > yy_max and date not in exDateList:
print(' remove date: ' + date)
exDateList.append(date)
exDateList = list(set(exDateList))
return exDateList
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 read_template2inps(template_file, inps=None):
"""Read input template file into inps.ex_date"""
if not inps:
inps = cmd_line_parse()
template = readfile.read_template(template_file)
# Read template option
prefix = 'pysar.topographicResidual.'
key = prefix+'polyOrder'
if key in template.keys():
value = template[key]
if value == 'auto':
inps.poly_order = 2
else:
inps.poly_order = int(value)
key = prefix+'excludeDate'
if key in template.keys():
value = template[key]
if value not in ['auto', 'no']:
value = value.replace(',', ' ').split()
value = ptime.yyyymmdd(value)
inps.ex_date += value
key = prefix+'stepFuncDate'
if key in template.keys():
value = template[key]
if value not in ['auto', 'no']:
value = value.replace(',', ' ').split()
value = ptime.yyyymmdd(value)
inps.step_date += value
key = prefix+'phaseVelocity'
if key in template.keys():
value = template[key]
if value.lower() not in ['auto', 'no']:
inps.min_phase_velocity = True
return inps
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 read_template2inps(template_file, inps=None):
"""Read input template file into inps.excludeDate"""
if not inps:
inps = cmd_line_parse()
inpsDict = vars(inps)
print('read options from template file: ' +
os.path.basename(template_file))
template = readfile.read_template(inps.template_file)
template = ut.check_template_auto_value(template)
# Read template option
prefix = 'pysar.velocity.'
keyList = [
i for i in list(inpsDict.keys()) if prefix + i in template.keys()
]
for key in keyList:
value = template[prefix + key]
if value:
if key in ['startDate', 'endDate']:
inpsDict[key] = ptime.yyyymmdd(value)
elif key in ['excludeDate']:
inpsDict[key] = ptime.yyyymmdd(value.replace(',', ' ').split())
return inps
def read_exclude_date(inps, dateListAll):
# Merge ex_date/startDate/endDate into ex_date
yy_list_all = ptime.yyyymmdd2years(dateListAll)
exDateList = []
# 1. template_file
if inps.template_file:
print('read option from template file: ' + inps.template_file)
inps = read_template2inps(inps.template_file, inps)
# 2. ex_date
exDateList += ptime.read_date_list(list(inps.excludeDate),
date_list_all=dateListAll)
if exDateList:
print('exclude date:' + str(exDateList))
# 3. startDate
if inps.startDate:
print('start date: ' + inps.startDate)
yy_min = ptime.yyyymmdd2years(ptime.yyyymmdd(inps.startDate))
for i in range(len(dateListAll)):
date = dateListAll[i]
if yy_list_all[i] < yy_min and date not in exDateList:
print(' remove date: ' + date)
exDateList.append(date)
# 4. endDate
if inps.endDate:
print('end date: ' + inps.endDate)
yy_max = ptime.yyyymmdd2years(ptime.yyyymmdd(inps.endDate))
for i in range(len(dateListAll)):
date = dateListAll[i]
if yy_list_all[i] > yy_max and date not in exDateList:
print(' remove date: ' + date)
exDateList.append(date)
exDateList = list(set(exDateList))
return exDateList
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 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_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 read_ref_date(inps):
if not inps.refDate:
print('No reference date input, skip this step.')
return inps.timeseries_file
elif os.path.isfile(inps.refDate):
print('read reference date from file: ' + inps.refDate)
inps.refDate = ptime.read_date_txt(inps.refDate)[0]
inps.refDate = ptime.yyyymmdd(inps.refDate)
print('input reference date: {}'.format(inps.refDate))
# check input reference date
date_list = timeseries(inps.timeseries_file[0]).get_date_list()
if inps.refDate not in date_list:
msg = 'input reference date: {} is not found.'.format(inps.refDate)
msg += '\nAll available dates:\n{}'.format(date_list)
raise Exception(msg)
return inps.refDate
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_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 ref_date_file(inFile, refDate, outFile=None):
"""Change input file reference date to a different one."""
if not outFile:
outFile = os.path.splitext(inFile)[0] + '_refDate.h5'
refDate = ptime.yyyymmdd(refDate)
print('input reference date: ' + refDate)
# Input file info
obj = timeseries(inFile)
obj.open()
atr = dict(obj.metadata)
if atr['FILE_TYPE'] != 'timeseries':
print('ERROR: input file is {}, only timeseries is supported.'.format(
atr['FILE_TYPE']))
return None
if refDate not in obj.dateList:
print(
'ERROR: Input reference date was not found!\nAll dates available: {}'
.format(obj.dateList))
return None
if refDate == atr['REF_DATE']:
print('Same reference date chosen as existing reference date.')
print('Copy {} to {}'.format(inFile, outFile))
shutil.copy2(inFile, outFile)
return outFile
# Referencing in time
data = obj.read()
data -= np.tile(
data[obj.dateList.index(refDate), :, :].reshape(
1, data.shape[1], data.shape[2]), (data.shape[0], 1, 1))
atr['REF_DATE'] = refDate
if not outFile:
outFile = '{}_refDate.h5'.format(os.path.splitext(inFile)[0])
outObj = timeseries(outFile)
outObj.write2hdf5(data, refFile=inFile, metadata=atr)
return outFile
def read_exclude_date(input_ex_date, dateListAll):
ex_date_list = []
ex_dates = []
ex_flag = np.ones((len(dateListAll)), np.bool_)
if input_ex_date:
input_ex_date = list(input_ex_date)
if input_ex_date:
for ex_date in input_ex_date:
if os.path.isfile(ex_date):
ex_date = ptime.read_date_list(ex_date)
else:
ex_date = [ptime.yyyymmdd(ex_date)]
ex_date_list += list(set(ex_date) - set(ex_date_list))
# delete dates not existed in input file
ex_date_list = sorted(
list(set(ex_date_list).intersection(dateListAll)))
ex_dates = ptime.date_list2vector(ex_date_list)[0]
for i in ex_date_list:
ex_flag[dateListAll.index(i)] = False
print('exclude date:' + str(ex_date_list))
return ex_date_list, ex_dates, ex_flag
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 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 read_template2inps(templateFile, inps=None):
"""Read network options from template file into Namespace variable inps"""
if not inps:
inps = cmd_line_parse()
inpsDict = vars(inps)
# Read template file
template = readfile.read_template(templateFile)
template = ut.check_template_auto_value(
template, auto_file='../defaults/selectNetwork.cfg')
if not template:
log('Empty template: ' + templateFile)
return None
prefix = 'selectNetwork.'
# Check obsolete option prefix
for i in ['selectPairs.', 'select.network.']:
if any(i in key for key in template.keys()):
msg = 'obsolete option prefix detected: {}\n'.format(i)
msg += 'Use {} instead'.format(prefix)
raise Exception(msg)
if all(prefix not in key for key in template.keys()):
msg = 'no valid input option deteced in template file!\n'
msg += 'Check the template below for supported options:\n'
msg += TEMPLATE
raise Exception(msg)
# convert template into inpsDict
keyList = [
i for i in list(inpsDict.keys()) if prefix + i in template.keys()
]
for key in keyList:
value = template[prefix + key]
# bool
if key in ['keepSeasonal']:
inpsDict[key] = value
elif value:
# str
if key in ['method', 'referenceFile', 'tempPerpList']:
inpsDict[key] = value
# date in YYYYMMDD
elif key in ['masterDate', 'startDate', 'endDate']:
inpsDict[key] = ptime.yyyymmdd(value)
# list of dates in YYYYMMDD
elif key in ['excludeDate']:
inps.excludeDate = ptime.yyyymmdd(
[i.strip() for i in value.split(',')])
# float
elif key in [
'perpBaseMax', 'tempBaseMax', 'tempBaseMin',
'dopOverlapMin'
]:
inpsDict[key] = float(value)
# int
elif key in ['connNum']:
inpsDict[key] = int(value)
# read tempPerpList from str
if isinstance(inps.tempPerpList, str):
inps.tempPerpList = [[float(j) for j in i.split(',')]
for i in inps.tempPerpList.split(';')]
# Initial network using input methods
inps.method = inps.method.lower().replace('-', '_')
if inps.method in ['star', 'ps']:
inps.method = 'star'
elif inps.method.startswith('seq'):
inps.method = 'sequential'
elif inps.method.startswith('hierar'):
inps.method = 'hierarchical'
elif inps.method in ['mst', 'min_spanning_tree', 'minimum_spanning_tree']:
inps.method = 'mst'
# for coherence prediction
key = 'PLATFORM'
if key in template.keys() and not inps.sensor:
inps.sensor = template[key]
key = 'COH_COLOR_JUMP'
if key in template.keys():
inps.coh_thres = float(template[key])
# project name and sensor
project_name = os.path.splitext(os.path.basename(inps.template_file))[0]
log('project name: ' + project_name)
if not inps.sensor:
inps.sensor = sensor.project_name2sensor(project_name)[0]
# Output directory/filename
if not inps.outfile:
if autoPath and 'SCRATCHDIR' in os.environ:
inps.out_dir = os.getenv(
'SCRATCHDIR') + '/' + project_name + '/PROCESS'
else:
try:
inps.out_dir = os.path.dirname(
os.path.abspath(inps.referenceFile))
except:
inps.out_dir = os.path.dirname(
os.path.abspath(inps.baseline_file))
inps.outfile = inps.out_dir + '/ifgram_list.txt'
# Auto path of bl_list.txt file (for Miami user)
if not inps.baseline_file and autoPath and 'SCRATCHDIR' in os.environ:
bl_file = os.path.join(os.getenv('SCRATCHDIR'),
'{}/SLC/bl_list.txt'.format(project_name))
if os.path.isfile(bl_file):
inps.baseline_file = bl_file
if not inps.referenceFile and not inps.baseline_file:
raise Exception(
'No baseline file or reference file found! At least one is required.'
)
return inps
def read_inps_dict2geometry_dict_object(inpsDict):
# eliminate dsName by processor
if inpsDict['processor'] in ['isce', 'doris']:
datasetName2templateKey.pop('azimuthCoord')
datasetName2templateKey.pop('rangeCoord')
elif inpsDict['processor'] in ['roipac', 'gamma']:
datasetName2templateKey.pop('latitude')
datasetName2templateKey.pop('longitude')
else:
print('Un-recognized InSAR processor: {}'.format(
inpsDict['processor']))
# inpsDict --> dsPathDict
print('-' * 50)
print('searching geometry files info')
print('input data files:')
maxDigit = max([len(i) for i in list(datasetName2templateKey.keys())])
dsPathDict = {}
for dsName in [
i for i in geometryDatasetNames
if i in datasetName2templateKey.keys()
]:
key = datasetName2templateKey[dsName]
if key in inpsDict.keys():
files = sorted(glob.glob(str(inpsDict[key])))
if len(files) > 0:
if dsName == 'bperp':
bperpDict = {}
for file in files:
date = ptime.yyyymmdd(
os.path.basename(os.path.dirname(file)))
bperpDict[date] = file
dsPathDict[dsName] = bperpDict
print('{:<{width}}: {path}'.format(dsName,
width=maxDigit,
path=inpsDict[key]))
print('number of bperp files: {}'.format(
len(list(bperpDict.keys()))))
else:
dsPathDict[dsName] = files[0]
print('{:<{width}}: {path}'.format(dsName,
width=maxDigit,
path=files[0]))
# Check required dataset
dsName0 = geometryDatasetNames[0]
if dsName0 not in dsPathDict.keys():
print('WARNING: No reqired {} data files found!'.format(dsName0))
# metadata
ifgramRadarMetadata = None
ifgramKey = datasetName2templateKey['unwrapPhase']
if ifgramKey in inpsDict.keys():
ifgramFiles = glob.glob(str(inpsDict[ifgramKey]))
if len(ifgramFiles) > 0:
atr = readfile.read_attribute(ifgramFiles[0])
if 'Y_FIRST' not in atr.keys():
ifgramRadarMetadata = atr.copy()
# dsPathDict --> dsGeoPathDict + dsRadarPathDict
dsNameList = list(dsPathDict.keys())
dsGeoPathDict = {}
dsRadarPathDict = {}
for dsName in dsNameList:
if dsName == 'bperp':
atr = readfile.read_attribute(
next(iter(dsPathDict[dsName].values())))
else:
atr = readfile.read_attribute(dsPathDict[dsName])
if 'Y_FIRST' in atr.keys():
dsGeoPathDict[dsName] = dsPathDict[dsName]
else:
dsRadarPathDict[dsName] = dsPathDict[dsName]
geomRadarObj = None
geomGeoObj = None
if len(dsRadarPathDict) > 0:
geomRadarObj = geometryDict(processor=inpsDict['processor'],
datasetDict=dsRadarPathDict,
extraMetadata=ifgramRadarMetadata)
if len(dsGeoPathDict) > 0:
geomGeoObj = geometryDict(processor=inpsDict['processor'],
datasetDict=dsGeoPathDict,
extraMetadata=None)
return geomRadarObj, geomGeoObj
def read_inps_dict2ifgram_stack_dict_object(inpsDict):
"""Read input arguments into dict of ifgramStackDict object"""
# inpsDict --> dsPathDict
print('-' * 50)
print('searching interferometric pairs info')
print('input data files:')
maxDigit = max([len(i) for i in list(datasetName2templateKey.keys())])
dsPathDict = {}
dsNumDict = {}
for dsName in [
i for i in ifgramDatasetNames
if i in datasetName2templateKey.keys()
]:
key = datasetName2templateKey[dsName]
if key in inpsDict.keys():
files = sorted(glob.glob(str(inpsDict[key])))
if len(files) > 0:
dsPathDict[dsName] = files
dsNumDict[dsName] = len(files)
print('{:<{width}}: {path}'.format(dsName,
width=maxDigit,
path=inpsDict[key]))
# Check required dataset
dsName0 = 'unwrapPhase'
if dsName0 not in dsPathDict.keys():
print('WARNING: No reqired {} data files found!'.format(dsName0))
return None
# Check number of files for all dataset types
dsNumList = list(dsNumDict.values())
if any(i != dsNumList[0] for i in dsNumList):
print(
'WARNING: Not all types of dataset have the same number of files:')
for key, value in dsNumDict.items():
print('number of {:<{width}}: {num}'.format(key,
width=maxDigit,
num=value))
print('number of files per type: {}'.format(dsNumList[0]))
# Check data dimension for all files
# dsPathDict --> pairsDict --> stackObj
dsNameList = list(dsPathDict.keys())
pairsDict = {}
for dsPath in dsPathDict[dsName0]:
dates = ptime.yyyymmdd(
readfile.read_attribute(dsPath)['DATE12'].split('-'))
#####################################
# A dictionary of data files for a given pair.
# One pair may have several types of dataset.
# example ifgramPathDict = {'unwrapPhase': /pathToFile/filt.unw, 'iono':/PathToFile/iono.bil}
# All path of data file must contain the master and slave date, either in file name or folder name.
ifgramPathDict = {}
for i in range(len(dsNameList)):
dsName = dsNameList[i]
dsPath1 = dsPathDict[dsName][0]
if all(d[2:8] in dsPath1 for d in dates):
ifgramPathDict[dsName] = dsPath1
else:
dsPath2 = [
i for i in dsPathDict[dsName]
if all(d[2:8] in i for d in dates)
]
if len(dsPath2) > 0:
ifgramPathDict[dsName] = dsPath2[0]
else:
print('WARNING: {} file missing for pair {}'.format(
dsName, dates))
ifgramObj = ifgramDict(dates=tuple(dates), datasetDict=ifgramPathDict)
pairsDict[tuple(dates)] = ifgramObj
if len(pairsDict) > 0:
stackObj = ifgramStackDict(pairsDict=pairsDict)
else:
stackObj = None
return stackObj
def check_inputs(inps):
parser = create_parser()
# output directories/files
atr = dict()
pysar_dir = None
if inps.timeseries_file:
atr = readfile.read_attribute(inps.timeseries_file)
pysar_dir = os.path.dirname(inps.timeseries_file)
if not inps.outfile:
fbase = os.path.splitext(inps.timeseries_file)[0]
inps.outfile = '{}_{}.h5'.format(fbase, inps.trop_model)
elif inps.geom_file:
atr = readfile.read_attribute(inps.geom_file)
pysar_dir = os.path.join(os.path.dirname(inps.geom_file), '..')
else:
pysar_dir = os.path.abspath(os.getcwd())
# trop_file
inps.trop_file = os.path.join(pysar_dir,
'INPUTS/{}.h5'.format(inps.trop_model))
print('output tropospheric delay file: {}'.format(inps.trop_file))
# hour
if not inps.hour:
if 'CENTER_LINE_UTC' in atr.keys():
inps.hour = ptime.closest_weather_product_time(
atr['CENTER_LINE_UTC'], inps.trop_model)
else:
parser.print_usage()
raise Exception('no input for hour')
print('time of cloest available product: {}:00 UTC'.format(inps.hour))
# date list
if inps.timeseries_file:
print('read date list from timeseries file: {}'.format(
inps.timeseries_file))
ts_obj = timeseries(inps.timeseries_file)
ts_obj.open(print_msg=False)
inps.date_list = ts_obj.dateList
elif len(inps.date_list) == 1:
if os.path.isfile(inps.date_list[0]):
print('read date list from text file: {}'.format(
inps.date_list[0]))
inps.date_list = ptime.yyyymmdd(
np.loadtxt(inps.date_list[0], dtype=bytes,
usecols=(0, )).astype(str).tolist())
else:
parser.print_usage()
raise Exception('ERROR: input date list < 2')
# Grib data directory
inps.grib_dir = os.path.join(inps.weather_dir, inps.trop_model)
if not os.path.isdir(inps.grib_dir):
os.makedirs(inps.grib_dir)
print('making directory: ' + inps.grib_dir)
# Date list to grib file list
inps.grib_file_list = date_list2grib_file(inps.date_list, inps.hour,
inps.trop_model, inps.grib_dir)
if 'REF_Y' in atr.keys():
inps.ref_yx = [int(atr['REF_Y']), int(atr['REF_X'])]
print('reference pixel: {}'.format(inps.ref_yx))
# Coordinate system: geocoded or not
inps.geocoded = False
if 'Y_FIRST' in atr.keys():
inps.geocoded = True
print('geocoded: {}'.format(inps.geocoded))
# Prepare DEM, inc_angle, lat/lon file for PyAPS to read
if inps.geom_file:
geom_atr = readfile.read_attribute(inps.geom_file)
print('converting DEM/incAngle for PyAPS to read')
# DEM
data = readfile.read(inps.geom_file,
datasetName='height',
print_msg=False)[0]
inps.dem_file = 'pyapsDem.hgt'
writefile.write(data, inps.dem_file, metadata=geom_atr)
# inc_angle
inps.inc_angle = readfile.read(inps.geom_file,
datasetName='incidenceAngle',
print_msg=False)[0]
inps.inc_angle_file = 'pyapsIncAngle.flt'
writefile.write(inps.inc_angle, inps.inc_angle_file, metadata=geom_atr)
# latitude
try:
data = readfile.read(inps.geom_file,
datasetName='latitude',
print_msg=False)[0]
print('converting lat for PyAPS to read')
inps.lat_file = 'pyapsLat.flt'
writefile.write(data, inps.lat_file, metadata=geom_atr)
except:
inps.lat_file = None
# longitude
try:
data = readfile.read(inps.geom_file,
datasetName='longitude',
print_msg=False)[0]
print('converting lon for PyAPS to read')
inps.lon_file = 'pyapsLon.flt'
writefile.write(data, inps.lon_file, metadata=geom_atr)
except:
inps.lon_file = None
return inps, atr
