def main(argv):
inps = cmdLineParse()
inps.file = ut.get_file_list(inps.file)
# check outfile and parallel option
if inps.parallel:
num_cores, inps.parallel, Parallel, delayed = ut.check_parallel(
len(inps.file))
# multilooking
if len(inps.file) == 1:
multilook_file(inps.file[0], inps.lks_y, inps.lks_x, inps.outfile)
elif inps.parallel:
#num_cores = min(multiprocessing.cpu_count(), len(inps.file))
#print 'parallel processing using %d cores ...'%(num_cores)
Parallel(n_jobs=num_cores)(
delayed(multilook_file)(file, inps.lks_y, inps.lks_x)
for file in inps.file)
else:
for File in inps.file:
print '-------------------------------------------'
multilook_file(File, inps.lks_y, inps.lks_x)
print 'Done.'
return
def main(argv):
if len(sys.argv) < 3:
usage()
sys.exit(1)
lat = float(argv[0])
lon = float(argv[1])
try:
trans_file = argv[2]
except:
trans_file = ut.get_file_list('geomap*.trans')[0]
try:
radar_file = argv[3]
except:
radar_file = 'unwrapIfgram.h5'
atr_rdr = readfile.read_attribute(radar_file)
print 'input geo coord: lat=%.4f, lon=%.4f' % (lat, lon)
y, x = ut.glob2radar(np.array(lat), np.array(lon), trans_file,
atr_rdr)[0:2]
print 'corresponding radar coord: y=%d, x=%d' % (y, x)
return
def main(argv):
inps = cmdLineParse()
inps.file = ut.get_file_list(inps.file, abspath=True)
# Check input file type
ext = os.path.splitext(inps.file[0])[1]
if ext not in ['.unw', '.cor', '.int']:
print 'No need to extract attributes for ROI_PAC ' + ext + ' file'
return
print 'number of files: ' + str(len(inps.file))
# check outfile and parallel option
if inps.parallel:
num_cores, inps.parallel, Parallel, delayed = ut.check_parallel(
len(inps.file))
if len(inps.file) == 1:
extract_attribute(inps.file[0])
elif inps.parallel:
Parallel(n_jobs=num_cores)(delayed(extract_attribute)(file)
for file in inps.file)
else:
for File in inps.file:
extract_attribute(File)
return
def main(argv):
inps = cmdLineParse()
#print '\n****************** mask *********************'
inps.file = ut.get_file_list(inps.file)
print 'number of file to mask: '+str(len(inps.file))
print inps.file
# check outfile and parallel option
if len(inps.file) > 1:
inps.outfile = None
elif len(inps.file) == 1 and inps.parallel:
inps.parallel = False
print 'parallel processing is diabled for one input file'
# masking
if inps.parallel:
num_cores = multiprocessing.cpu_count()
print 'parallel processing using %d cores ...'%(num_cores)
Parallel(n_jobs=num_cores)(delayed(mask_file)(File, inps.mask_file, inps_dict=vars(inps)) for File in inps.file)
else:
for File in inps.file:
print '-------------------------------------------'
mask_file(File, inps.mask_file, inps.outfile, vars(inps))
print 'Done.'
return
def main(argv):
inps = cmdLineParse()
inps.file = ut.get_file_list(inps.file)
print 'number of files to geocode: ' + str(len(inps.file))
print inps.file
print 'interpolation method: ' + inps.method
print 'fill_value: ' + str(inps.fill_value)
# check outfile and parallel option
if inps.parallel:
num_cores, inps.parallel, Parallel, delayed = ut.check_parallel(
len(inps.file))
#####
if len(inps.file) == 1:
geocode_file_with_geo_lut(inps.file[0], inps.lookup_file, inps.method,
inps.fill_value, inps.outfile)
elif inps.parallel:
Parallel(n_jobs=num_cores)(delayed(geocode_file_with_geo_lut)\
(fname, inps.lookup_file, inps.method, inps.fill_value) for fname in inps.file)
else:
for fname in inps.file:
geocode_file_with_geo_lut(fname, inps.lookup_file, inps.method,
inps.fill_value)
print 'Done.'
return
def main(argv):
inps = cmdLineParse()
#print '\n**************** Multilook *********************'
fileList = get_file_list(inps.file)
# check outfile and parallel option
if len(inps.file) > 1:
inps.outfile = None
elif len(inps.file) == 1 and inps.parallel:
inps.parallel = False
print 'parallel processing is diabled for one input file'
# multilooking
if inps.parallel:
num_cores = multiprocessing.cpu_count()
print 'parallel processing using %d cores ...' % (num_cores)
Parallel(n_jobs=num_cores)(
delayed(multilook_file)(file, inps.lks_y, inps.lks_x)
for file in fileList)
else:
for File in inps.file:
print '-------------------------------------------'
multilook_file(File, inps.lks_y, inps.lks_x, inps.outfile)
print 'Done.'
return
def main(argv):
inps = cmdLineParse()
#print '\n****************** mask *********************'
inps.file = ut.get_file_list(inps.file)
print 'number of file to mask: ' + str(len(inps.file))
print inps.file
# check outfile and parallel option
if inps.parallel:
num_cores, inps.parallel, Parallel, delayed = ut.check_parallel(
len(inps.file))
# masking
if len(inps.file) == 1:
mask_file(inps.file[0], inps.mask_file, inps.outfile, vars(inps))
elif inps.parallel:
#num_cores = min(multiprocessing.cpu_count(), len(inps.file))
#print 'parallel processing using %d cores ...'%(num_cores)
Parallel(n_jobs=num_cores)(
delayed(mask_file)(File, inps.mask_file, inps_dict=vars(inps))
for File in inps.file)
else:
for File in inps.file:
print '-------------------------------------------'
mask_file(File, inps.mask_file, inps_dict=vars(inps))
print 'Done.'
return
def main(argv):
inps = cmdLineParse()
if inps.template_file:
inps = read_template2inps(inps.template_file, inps)
inps.file = ut.get_file_list(inps.file)
print 'number of files to geocode: '+str(len(inps.file))
print inps.file
if len(inps.file) > 1:
inps.outfile = None
print 'fill_value: '+str(inps.fill_value)
##Check Lookup table
inps.lookup_file = ut.get_lookup_file(inps.lookup_file)
if not inps.lookup_file:
sys.exit('No lookup table found! Can not geocode without it.')
# check outfile and parallel option
if inps.parallel:
num_cores, inps.parallel, Parallel, delayed = ut.check_parallel(len(inps.file))
#####
if inps.parallel:
Parallel(n_jobs=num_cores)(delayed(geocode_file)\
(fname, inps.lookup_file, inps.outfile, inps) for fname in inps.file)
else:
for fname in inps.file:
geocode_file(fname, inps.lookup_file, inps.outfile, inps)
print 'Done.'
return
def dload_grib(date_list, hour, grib_source='ECMWF', weather_dir='./'):
'''Download weather re-analysis grib files using PyAPS
Inputs:
date_list : list of string in YYYYMMDD format
hour : string in HH:MM or HH format
grib_source : string,
weather_dir : string,
Output:
grib_file_list : list of string
'''
## Grib data directory
weather_dir = os.path.abspath(weather_dir)
grib_dir = weather_dir+'/'+grib_source
if not os.path.isdir(grib_dir):
print 'making directory: '+grib_dir
os.makedirs(grib_dir)
## Date list to grib file list
grib_file_list = []
for d in date_list:
if grib_source == 'ECMWF': grib_file = grib_dir+'/ERA-Int_'+d+'_'+hour+'.grb'
elif grib_source == 'ERA' : grib_file = grib_dir+'/ERA_'+d+'_'+hour+'.grb'
elif grib_source == 'MERRA': grib_file = grib_dir+'/merra-'+d+'-'+hour+'.hdf'
elif grib_source == 'NARR' : grib_file = grib_dir+'/narr-a_221_'+d+'_'+hour+'00_000.grb'
grib_file_list.append(grib_file)
## Get date list to download (skip already downloaded files)
grib_file_existed = ut.get_file_list(grib_file_list)
if grib_file_existed:
grib_filesize_mode = ut.mode([os.path.getsize(i) for i in grib_file_existed])
grib_file_corrupted = [i for i in grib_file_existed if os.path.getsize(i) != grib_filesize_mode]
print 'number of grib files existed : %d' % len(grib_file_existed)
print 'file size mode: %d' % grib_filesize_mode
if grib_file_corrupted:
print '------------------------------------------------------------------------------'
print 'corrupted grib files detected! Delete them and re-download...'
print 'number of grib files corrupted : %d' % len(grib_file_corrupted)
for i in grib_file_corrupted:
rmCmd = 'rm '+i
print rmCmd
os.system(rmCmd)
grib_file_existed.remove(i)
print '------------------------------------------------------------------------------'
grib_file2download = sorted(list(set(grib_file_list) - set(grib_file_existed)))
date_list2download = [str(re.findall('\d{8}', i)[0]) for i in grib_file2download]
print 'number of grib files to download: %d' % len(date_list2download)
print '------------------------------------------------------------------------------\n'
## Download grib file using PyAPS
if grib_source == 'ECMWF': pa.ECMWFdload(date_list2download, hour, grib_dir)
elif grib_source == 'ERA' : pa.ERAdload( date_list2download, hour, grib_dir)
elif grib_source == 'MERRA': pa.MERRAdload(date_list2download, hour, grib_dir)
elif grib_source == 'NARR' : pa.NARRdload( date_list2download, hour, grib_dir)
return grib_file_existed
def main(argv):
inps = cmdLineParse()
inps.outfile = os.path.abspath(inps.outfile)
atr = readfile.read_attribute(inps.file[0])
k = atr['FILE_TYPE']
if not inps.sensor:
inps.sensor = get_mission_name(atr)
print 'Sensor name: %s' % (inps.sensor)
m_date_list = []
s_date_list = []
bperp_list = []
inps.file = ut.get_file_list(inps.file, abspath=True)
if os.path.splitext(inps.file[0])[1] not in ['.h5', '.he5']:
ifgramNum = len(inps.file)
print 'Number of interferograms: %d' % (ifgramNum)
for fname in inps.file:
try:
date12 = str(
re.findall('\d{8}[-_]\d{8}',
os.path.basename(fname))[0]).replace('_', '-')
except:
date12 = str(
re.findall('\d{6}[-_]\d{6}',
os.path.basename(fname))[0]).replace('_', '-')
m_date, s_date = date12.split('-')
bperp = readfile.read_attribute(fname)['P_BASELINE_TOP_HDR']
m_date_list.append(m_date)
s_date_list.append(s_date)
bperp_list.append(bperp)
else:
h5 = h5py.File(inps.file[0], 'r')
ifgram_list = ut.check_drop_ifgram(h5)
date12_list = ptime.list_ifgram2date12(ifgram_list)
m_date_list = [date12.split('-')[0] for date12 in date12_list]
s_date_list = [date12.split('-')[1] for date12 in date12_list]
for ifgram in ifgram_list:
bperp = h5[k][ifgram].attrs['P_BASELINE_TOP_HDR']
bperp_list.append(bperp)
ifgramNum = len(ifgram_list)
fout = '{0} {1} {2:<15} {3}\n'
fl = open(inps.outfile, 'w')
for i in range(ifgramNum):
fl.write(
fout.format(m_date_list[i], s_date_list[i], bperp_list[i],
inps.sensor))
fl.close()
print 'write to %s' % (inps.outfile)
return
def main(argv):
inps = cmdLineParse()
inps.file = ut.get_file_list(inps.file)
if not ut.which('geocode.pl'):
sys.exit(
"\nERROR: Can not find geocode.pl, it's needed for geocoding.\n")
#print '\n***************** Geocoding *******************'
if not inps.lookup_file.endswith('.trans'):
print 'ERROR: Input lookup file is not .trans file: ' + inps.lookup_file + '\n'
sys.exit(1)
print 'number of file to mask: ' + str(len(inps.file))
print inps.file
# check outfile and parallel option
if len(inps.file) > 1:
inps.outfile = None
elif len(inps.file) == 1 and inps.parallel:
inps.parallel = False
print 'parallel processing is diabled for one input file'
# Check geomap file for previously subsetted radar coord file
atr = readfile.read_attribute(inps.file[0])
if 'subset_x0' in atr.keys():
inps.lookup_file = geomap4subset_radar_file(atr, inps.lookup_file)
# Geocoding
if inps.parallel:
num_cores = multiprocessing.cpu_count()
print 'parallel processing using %d cores ...' % (num_cores)
Parallel(n_jobs=num_cores)(
delayed(geocode_file_roipac)(file, inps.lookup_file)
for file in inps.file)
else:
for File in inps.file:
print '----------------------------------------------------'
geocode_file_roipac(File, inps.lookup_file, inps.outfile)
# clean temporary geomap file for previously subsetted radar coord file
if 'subset_x0' in atr.keys():
rmCmd = 'rm ' + inps.lookup_file
os.system(rmCmd)
print rmCmd
rmCmd = 'rm ' + inps.lookup_file + '.rsc'
os.system(rmCmd)
print rmCmd
print 'Done.'
return
def read_template2inps(template_file, inps=None):
'''Read input template options into Namespace inps'''
if not inps:
inps = cmdLineParse()
print 'read options from template file: ' + os.path.basename(template_file)
template = readfile.read_template(template_file)
key_list = template.keys()
# Coherence-based network modification
prefix = 'pysar.network.'
key = prefix + 'coherenceFile'
if key in key_list:
value = template[key]
if value == 'auto':
inps.coherence_file = 'coherence.h5'
else:
inps.coherence_file = value
key = prefix + 'maskFile'
if key in key_list:
value = template[key]
if value == 'auto':
try:
inps.mask_file = ut.get_file_list(['maskLand.h5',
'mask.h5'])[0]
except:
inps.mask_file = None
elif value == 'no':
inps.mask_file = None
else:
inps.mask_file = value
key = prefix + 'minCoherence'
if key in key_list:
value = template[key]
if value == 'auto':
inps.coh_thres = 0.7
else:
inps.coh_thres = float(value)
return inps
def main(argv):
if len(sys.argv) < 3:
usage(); sys.exit(1)
y = int(argv[0])
x = int(argv[1])
try: trans_file = argv[2]
except: trans_file = ut.get_file_list('geomap*.trans')[0]
try: radar_file = argv[3]
except: radar_file = 'unwrapIfgram.h5'
atr_rdr = readfile.read_attribute(radar_file)
print 'input radar coord: y/azimuth=%d, x/range=%d' % (y, x)
lat, lon = ut.radar2glob(np.array(y), np.array(x), trans_file, atr_rdr)[0:2]
print 'corresponding geo coord: lat=%.4f, lon=%.4f' % (lat, lon)
return
def extract_attribute_lookup_table(fname):
'''Read/extract attribute for .UTM_TO_RDC file from Gamma to ROI_PAC
For example, it read input file, sim_150911-150922.UTM_TO_RDC,
find its associated par file, sim_150911-150922.utm.dem.par, read it, and
convert to ROI_PAC style and write it to an rsc file, sim_150911-150922.UTM_TO_RDC.rsc'''
## Check existed .rsc file
rsc_file_list = ut.get_file_list(fname + '.rsc')
if rsc_file_list:
rsc_file = rsc_file_list[0]
print rsc_file + ' is existed, no need to re-extract.'
return rsc_file
atr = {}
atr['PROCESSOR'] = 'gamma'
atr['INSAR_PROCESSOR'] = 'gamma'
atr['FILE_TYPE'] = os.path.splitext(fname)[1]
atr['Y_UNIT'] = 'degrees'
atr['X_UNIT'] = 'degrees'
par_file = os.path.splitext(fname)[0] + '.utm.dem.par'
print 'read ' + os.path.basename(par_file)
par_dict = readfile.read_gamma_par(par_file)
print 'convert Gamma attribute to ROI_PAC style'
par_dict = readfile.attribute_gamma2roipac(par_dict)
atr.update(par_dict)
## Write to .rsc file
rsc_file = fname + '.rsc'
try:
atr_orig = readfile.read_roipac_rsc(rsc_file)
except:
atr_orig = None
if atr_orig != atr:
print 'writing >>> ' + os.path.basename(rsc_file)
writefile.write_roipac_rsc(atr, rsc_file)
return rsc_file
def main(argv):
inps = cmdLineParse()
inps.file = ut.get_file_list(inps.file, abspath=True)
print 'number of files: ' + str(len(inps.file))
# check outfile and parallel option
if inps.parallel:
num_cores, inps.parallel, Parallel, delayed = ut.check_parallel(
len(inps.file))
##### multiple datasets files
ext = os.path.splitext(inps.file[0])[1]
if ext in ['.unw', '.cor', '.int']:
if len(inps.file) == 1:
extract_attribute_interferogram(inps.file[0])
elif inps.parallel:
Parallel(n_jobs=num_cores)(
delayed(extract_attribute_interferogram)(file)
for file in inps.file)
else:
for File in inps.file:
extract_attribute_interferogram(File)
##### Single dataset files
elif inps.file[0].endswith('.utm.dem'):
for File in inps.file:
atr_file = extract_attribute_dem_geo(File)
elif inps.file[0].endswith(('.rdc.dem', '.hgt_sim')):
for File in inps.file:
atr_file = extract_attribute_dem_radar(File)
elif ext in ['.UTM_TO_RDC']:
for File in inps.file:
atr_file = extract_attribute_lookup_table(File)
else:
print 'No need to extract attributes for Gamma ' + ext + ' file'
print 'Done.'
return
# Flip up-down / left-right
if inps.auto_flip:
inps.flip_lr, inps.flip_ud = view.auto_flip_direction(atr)
else:
inps.flip_ud = False
inps.left_lr = False
# Mask file
if not inps.mask_file:
if 'X_FIRST' in atr.keys():
file_list = ['geo_maskTempCoh.h5']
else:
file_list = ['maskTempCoh.h5', 'mask.h5']
try:
inps.mask_file = ut.get_file_list(file_list)[0]
except:
inps.mask_file = None
try:
mask = readfile.read(inps.mask_file)[0]
mask[mask != 0] = 1
print 'load mask from file: ' + inps.mask_file
except:
mask = None
print 'No mask used.'
# Initial Map
d_v = h5[k].get(dateList[inps.epoch_num])[:] * inps.unit_fac
if inps.ref_date:
inps.ref_d_v = h5[k].get(inps.ref_date)[:] * inps.unit_fac
d_v -= inps.ref_d_v
def geocode_file_with_geo_lut(fname,
lut_file=None,
method='nearest',
fill_value=np.nan,
fname_out=None):
'''Geocode file using ROI_PAC/Gamma lookup table file.
Related module: scipy.interpolate.RegularGridInterpolator
Inputs:
fname : string, file to be geocoded
lut_file : string, optional, lookup table file genereated by ROIPAC or Gamma
i.e. geomap_4rlks.trans from ROI_PAC
sim_150911-150922.UTM_TO_RDC from Gamma
method : string, optional, interpolation/resampling method, supporting nearest, linear
fill_value : value used for points outside of the interpolation domain.
If None, values outside the domain are extrapolated.
fname_out : string, optional, output geocoded filename
Output:
fname_out : string, optional, output geocoded filename
'''
start = time.time()
## Default Inputs and outputs
if not fname_out:
fname_out = 'geo_' + fname
# Default lookup table file:
atr_rdr = readfile.read_attribute(fname)
if not lut_file:
if atr_rdr['INSAR_PROCESSOR'] == 'roipac':
lut_file = ['geomap*lks_tight.trans', 'geomap*lks.trans']
elif atr_rdr['INSAR_PROCESSOR'] == 'gamma':
lut_file = ['sim*_tight.UTM_TO_RDC', 'sim*.UTM_TO_RDC']
try:
lut_file = ut.get_file_list(lut_file)[0]
except:
lut_file = None
if not lut_file:
sys.exit(
'ERROR: No lookup table file found! Can not geocoded without it.')
## Original coordinates: row/column number in radar file
print '------------------------------------------------------'
print 'geocoding file: ' + fname
len_rdr = int(atr_rdr['FILE_LENGTH'])
wid_rdr = int(atr_rdr['WIDTH'])
pts_rdr = (np.arange(len_rdr), np.arange(wid_rdr))
## New coordinates: data value in lookup table
print 'reading lookup table file: ' + lut_file
rg, az, atr_lut = readfile.read(lut_file)
len_geo = int(atr_lut['FILE_LENGTH'])
wid_geo = int(atr_lut['WIDTH'])
# adjustment if input radar file has been subseted.
if 'subset_x0' in atr_rdr.keys():
x0 = float(atr_rdr['subset_x0'])
y0 = float(atr_rdr['subset_y0'])
rg -= x0
az -= y0
print '\tinput radar coord file has been subsetted, adjust lookup table value'
# extract pixels only available in radar file (get ride of invalid corners)
idx = (az > 0.0) * (az <= len_rdr) * (rg > 0.0) * (rg <= wid_rdr)
pts_geo = np.hstack((az[idx].reshape(-1, 1), rg[idx].reshape(-1, 1)))
del az, rg
print 'geocoding using scipy.interpolate.RegularGridInterpolator ...'
data_geo = np.empty((len_geo, wid_geo)) * fill_value
k = atr_rdr['FILE_TYPE']
##### Multiple Dataset File
if k in multi_group_hdf5_file + multi_dataset_hdf5_file:
h5 = h5py.File(fname, 'r')
epoch_list = sorted(h5[k].keys())
epoch_num = len(epoch_list)
prog_bar = ptime.progress_bar(maxValue=epoch_num)
h5out = h5py.File(fname_out, 'w')
group = h5out.create_group(k)
print 'writing >>> ' + fname_out
if k == 'timeseries':
print 'number of acquisitions: ' + str(epoch_num)
for i in range(epoch_num):
date = epoch_list[i]
data = h5[k].get(date)[:]
RGI_func = RGI(pts_rdr,
data,
method,
bounds_error=False,
fill_value=fill_value)
data_geo.fill(fill_value)
data_geo[idx] = RGI_func(pts_geo)
dset = group.create_dataset(date,
data=data_geo,
compression='gzip')
prog_bar.update(i + 1, suffix=date)
prog_bar.close()
print 'update attributes'
atr = geocode_attribute_with_geo_lut(atr_rdr, atr_lut)
for key, value in atr.iteritems():
group.attrs[key] = value
elif k in ['interferograms', 'wrapped', 'coherence']:
print 'number of interferograms: ' + str(epoch_num)
date12_list = ptime.list_ifgram2date12(epoch_list)
for i in range(epoch_num):
ifgram = epoch_list[i]
data = h5[k][ifgram].get(ifgram)[:]
RGI_func = RGI(pts_rdr,
data,
method,
bounds_error=False,
fill_value=fill_value)
data_geo.fill(fill_value)
data_geo[idx] = RGI_func(pts_geo)
gg = group.create_group(ifgram)
dset = gg.create_dataset(ifgram,
data=data_geo,
compression='gzip')
atr = geocode_attribute_with_geo_lut(h5[k][ifgram].attrs,
atr_lut,
print_msg=False)
for key, value in atr.iteritems():
gg.attrs[key] = value
prog_bar.update(i + 1, suffix=date12_list[i])
h5.close()
h5out.close()
##### Single Dataset File
else:
print 'reading ' + fname
data = readfile.read(fname)[0]
RGI_func = RGI(pts_rdr,
data,
method,
bounds_error=False,
fill_value=fill_value)
data_geo.fill(fill_value)
data_geo[idx] = RGI_func(pts_geo)
print 'update attributes'
atr = geocode_attribute_with_geo_lut(atr_rdr, atr_lut)
print 'writing >>> ' + fname_out
writefile.write(data_geo, atr, fname_out)
del data_geo
s = time.time() - start
m, s = divmod(s, 60)
h, m = divmod(m, 60)
print 'Time used: %02d hours %02d mins %02d secs' % (h, m, s)
return fname_out
def geocode_file_with_geo_lookup_table(fname,
lookup_file=None,
interp_method='nearest',
fname_out=None):
'''Geocode file using ROI_PAC/Gamma lookup table file.
Inputs:
fname : string, file to be geocoded
lookup_file : string, optional, lookup table file genereated by ROIPAC or Gamma
i.e. geomap_4rlks.trans from ROI_PAC
sim_150911-150922.UTM_TO_RDC from Gamma
interp_method : string, optional, interpolation/resampling method, supporting nearest, linear, cubic
fname_out : string, optional, output geocoded filename
Output:
fname_out
A faster way is as below:
https://stackoverflow.com/questions/20915502/speedup-scipy-griddata-for-multiple-interpolations-between-two-irregular-grids
'''
atr_rdr = readfile.read_attribute(fname)
if not fname_out:
fname_out = 'geo_' + fname
# Default values:
if not lookup_file:
if atr_rdr['INSAR_PROCESSOR'] == 'roipac':
lookup_file = ['geomap*lks_tight.trans', 'geomap*lks.trans']
elif atr_rdr['INSAR_PROCESSOR'] == 'gamma':
lookup_file = ['sim*_tight.UTM_TO_RDC', 'sim*.UTM_TO_RDC']
# Check lookup table file
try:
lookup_file = ut.get_file_list(lookup_file)[0]
except:
lookup_file = None
if not lookup_file:
sys.exit(
'ERROR: No lookup table file found! Can not geocoded without it.')
##### 1. Get Y/X coordinates in radar file
print '------------------------------------------------------'
print 'geocoding file: ' + fname
print 'getting Y/X coordinates from file in radar coordinates'
len_rdr = int(atr_rdr['FILE_LENGTH'])
wid_rdr = int(atr_rdr['WIDTH'])
yy, xx = np.mgrid[0:len_rdr - 1:len_rdr * 1j, 0:wid_rdr - 1:wid_rdr * 1j]
yx_rdr = np.hstack((yy.reshape(-1, 1), xx.reshape(-1, 1)))
##### 2. Get Y/X coordinates in geo*trans file
print 'reading ' + lookup_file
rg, az, atr_lut = readfile.read(lookup_file)
len_geo = int(atr_lut['FILE_LENGTH'])
wid_geo = int(atr_lut['WIDTH'])
# adjustment if input radar file has been subseted.
if 'subset_x0' in atr_rdr.keys():
x0 = float(atr_rdr['subset_x0'])
y0 = float(atr_rdr['subset_y0'])
rg -= x0
az -= y0
print '\tinput radar coord file has been subsetted, adjust value read from lookup table file'
# extract pixels only available in radar file (get ride of invalid corners)
az = az.flatten()
rg = rg.flatten()
idx = (az > 0.0) * (az <= len_rdr) * (rg > 0.0) * (rg <= wid_rdr)
yx_geo = np.hstack((az[idx].reshape(-1, 1), rg[idx].reshape(-1, 1)))
print 'interpolation method: ' + interp_method
k = atr_rdr['FILE_TYPE']
##### Multiple Dataset File
if k in multi_group_hdf5_file + multi_dataset_hdf5_file:
h5 = h5py.File(fname, 'r')
epoch_list = sorted(h5[k].keys())
epoch_num = len(epoch_list)
prog_bar = ptime.progress_bar(maxValue=epoch_num)
h5out = h5py.File(fname_out, 'w')
group = h5out.create_group(k)
print 'writing >>> ' + fname_out
if k == 'timeseries':
print 'number of acquisitions: ' + str(epoch_num)
for i in range(epoch_num):
date = epoch_list[i]
data = h5[k].get(date)[:].flatten()
data_geo = np.zeros(len_geo * wid_geo, dtype=data.dtype)
data_geo[idx] = griddata(yx_rdr,
data,
yx_geo,
method=interp_method)
dset = group.create_dataset(date,
data=data_geo.reshape(
(len_geo, wid_geo)),
compression='gzip')
prog_bar.update(i + 1, suffix=date)
prog_bar.close()
print 'update attributes'
atr = geocode_attribute_with_geo_lookup_table(atr_rdr, atr_lut)
for key, value in atr.iteritems():
group.attrs[key] = value
elif k in ['interferograms', 'wrapped', 'coherence']:
print 'number of interferograms: ' + str(epoch_num)
date12_list = ptime.list_ifgram2date12(epoch_list)
for i in range(epoch_num):
ifgram = epoch_list[i]
data = h5[k][ifgram].get(ifgram)[:].flatten()
data_geo = np.zeros(len_geo * wid_geo, dtype=data.dtype)
data_geo[idx] = griddata(yx_rdr,
data,
yx_geo,
method=interp_method)
gg = group.create_group(ifgram)
dset = gg.create_dataset(ifgram,
data=data_geo.reshape(
(len_geo, wid_geo)),
compression='gzip')
atr = geocode_attribute_with_geo_lookup_table(
h5[k][ifgram].attrs, atr_lut, print_message=False)
for key, value in atr.iteritems():
gg.attrs[key] = value
prog_bar.update(i + 1, suffix=date12_list[i])
h5.close()
h5out.close()
##### Single Dataset File
else:
print 'reading ' + fname
data = readfile.read(fname)[0].flatten()
print 'geocoding'
data_geo = np.zeros(len_geo * wid_geo, dtype=data.dtype)
data_geo[idx] = griddata(yx_rdr, data, yx_geo, method=interp_method)
print 'update attributes'
atr = geocode_attribute_with_geo_lookup_table(atr_rdr, atr_lut)
print 'writing >>> ' + fname_out
writefile.write(data_geo.reshape((len_geo, wid_geo)), atr, fname_out)
return fname_out
def main(argv):
inps = cmdLineParse()
inps.file = ut.get_file_list(inps.file)
print 'input file(s): ' + str(len(inps.file))
print inps.file
#print '\n*************** Phase Ramp Removal ***********************'
atr = readfile.read_attribute(inps.file[0])
length = int(atr['FILE_LENGTH'])
width = int(atr['WIDTH'])
# check outfile and parallel option
if len(inps.file) > 1:
inps.outfile = None
elif len(inps.file) == 1 and inps.parallel:
inps.parallel = False
print 'parallel processing is diabled for one input file'
# Update mask for multiple surfaces
if inps.ysub:
# Read mask
if not inps.mask_file:
Mask_temp = readfile.read(inps.mask_file)[0]
Mask = np.zeros((length, width))
Mask[Mask_temp != 0] = 1
else:
Mask = np.ones((length, width))
# Update mask for multiple surface from inps.ysub
mask_multiSurface = np.zeros((length, width))
surfNum = len(inps.ysub) / 2
if surfNum == 1:
mask_multiSurface = Mask
else:
i = 0
mask_multiSurface[inps.ysub[2 * i]:inps.ysub[2 * i + 1], :] = Mask[
inps.ysub[2 * i]:inps.ysub[2 * i + 1], :]
for i in range(1, surfNum):
if inps.ysub[2 * i] < inps.ysub[2 * i - 1]:
mask_multiSurface[
inps.ysub[2 * i]:inps.ysub[2 * i - 1], :] += Mask[
inps.ysub[2 * i]:inps.ysub[2 * i - 1], :] * (i + 1)
mask_multiSurface[inps.ysub[2 * i]:inps.ysub[2 * i -
1], :] /= 2
mask_multiSurface[
inps.ysub[2 * i - 1]:inps.ysub[2 * i + 1], :] = Mask[
inps.ysub[2 * i - 1]:inps.ysub[2 * i +
1], :] * (i + 1)
else:
mask_multiSurface[
inps.ysub[2 * i]:inps.ysub[2 * i + 1], :] = Mask[
inps.ysub[2 * i]:inps.ysub[2 * i + 1], :] * (i + 1)
# Write updated mask for multiple surfaces into file
outFile = 'mask_' + str(surfNum) + inps.surface_type + '.h5'
atr['FILE_TYPE'] = 'mask'
writefile.write(mask_multiSurface, atr, outFile)
print 'saved mask to ' + outFile
############################## Removing Phase Ramp #######################################
if inps.parallel:
num_cores = multiprocessing.cpu_count()
print 'parallel processing using %d cores ...' % (num_cores)
Parallel(n_jobs=num_cores)(delayed(rm.remove_surface)(file, inps.surface_type, inps.mask_file, ysub=inps.ysub)\
for file in inps.file)
else:
for File in inps.file:
print '------------------------------------------'
rm.remove_surface(inps.file[0], inps.surface_type, inps.mask_file,
inps.outfile, inps.ysub)
print 'Done.'
return
def main(argv):
inps = cmdLineParse()
inps.file = ut.get_file_list(inps.file)
atr = readfile.read_attribute(inps.file[0])
length = int(atr['FILE_LENGTH'])
width = int(atr['WIDTH'])
if inps.reset:
print '----------------------------------------------------------------------------'
for file in inps.file:
remove_reference_pixel(file)
return
##### Check Input Coordinates
# Read ref_y/x/lat/lon from reference/template
# priority: Direct Input > Reference File > Template File
if inps.template_file:
print 'reading reference info from template: ' + inps.template_file
inps = read_seed_template2inps(inps.template_file, inps)
if inps.reference_file:
print 'reading reference info from reference: ' + inps.reference_file
inps = read_seed_reference2inps(inps.reference_file, inps)
## Do not use ref_lat/lon input for file in radar-coord
#if not 'X_FIRST' in atr.keys() and (inps.ref_lat or inps.ref_lon):
# print 'Lat/lon reference input is disabled for file in radar coord.'
# inps.ref_lat = None
# inps.ref_lon = None
# Convert ref_lat/lon to ref_y/x
if inps.ref_lat and inps.ref_lon:
if 'X_FIRST' in atr.keys():
inps.ref_y = subset.coord_geo2radar(inps.ref_lat, atr, 'lat')
inps.ref_x = subset.coord_geo2radar(inps.ref_lon, atr, 'lon')
else:
# Convert lat/lon to az/rg for radar coord file using geomap*.trans file
inps.ref_y, inps.ref_x = ut.glob2radar(np.array(inps.ref_lat), np.array(inps.ref_lon),\
inps.trans_file, atr)[0:2]
print 'Input reference point in lat/lon: ' + str(
[inps.ref_lat, inps.ref_lon])
print 'Input reference point in y/x : ' + str([inps.ref_y, inps.ref_x])
# Do not use ref_y/x outside of data coverage
if (inps.ref_y and inps.ref_x
and not (0 <= inps.ref_y <= length and 0 <= inps.ref_x <= width)):
inps.ref_y = None
inps.ref_x = None
print 'WARNING: input reference point is OUT of data coverage!'
print 'Continue with other method to select reference point.'
# Do not use ref_y/x in masked out area
if inps.ref_y and inps.ref_x and inps.mask_file:
print 'mask: ' + inps.mask_file
mask = readfile.read(inps.mask_file)[0]
if mask[inps.ref_y, inps.ref_x] == 0:
inps.ref_y = None
inps.ref_x = None
print 'WARNING: input reference point is in masked OUT area!'
print 'Continue with other method to select reference point.'
##### Select method
if inps.ref_y and inps.ref_x:
inps.method = 'input-coord'
elif inps.coherence_file:
if os.path.isfile(inps.coherence_file):
inps.method = 'max-coherence'
else:
inps.coherence_file = None
if inps.method == 'manual':
inps.parallel = False
print 'Parallel processing is disabled for manual seeding method.'
##### Seeding file by file
# check outfile and parallel option
if inps.parallel:
num_cores, inps.parallel, Parallel, delayed = ut.check_parallel(
len(inps.file))
if len(inps.file) == 1:
seed_file_inps(inps.file[0], inps, inps.outfile)
elif inps.parallel:
#num_cores = min(multiprocessing.cpu_count(), len(inps.file))
#print 'parallel processing using %d cores ...'%(num_cores)
Parallel(n_jobs=num_cores)(delayed(seed_file_inps)(file, inps)
for file in inps.file)
else:
for File in inps.file:
seed_file_inps(File, inps)
print 'Done.'
return
def extract_attribute_interferogram(fname):
'''Read/extract attributes for PySAR from Gamma .unw, .cor and .int file
Inputs:
fname : str, Gamma interferogram filename or path, i.e. /PopoSLT143TsxD/diff_filt_HDR_130118-130129_4rlks.unw
Output:
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['INSAR_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
#print 'read '+m_par_file
#print 'read '+off_file
par_dict = readfile.read_gamma_par(m_par_file)
off_dict = readfile.read_gamma_par(off_file)
#print 'convert Gamma attribute to ROI_PAC style'
atr.update(par_dict)
atr.update(off_dict)
atr = readfile.attribute_gamma2roipac(atr)
## Perp Baseline Info
#print 'extract baseline info from %s, %s and %s file' % (m_par_file, s_par_file, off_file)
atr = get_perp_baseline(m_par_file, s_par_file, off_file, atr)
## LAT/LON_REF1/2/3/4
#print 'extract LAT/LON_REF1/2/3/4 from '+m_par_file
atr = get_lalo_ref(m_par_file, atr)
## Write to .rsc file
#print 'writing >>> '+rsc_file
try:
atr_orig = readfile.read_roipac_rsc(rsc_file)
except:
atr_orig = None
if 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, rsc_file)
return rsc_file
def main(argv):
inps = cmdLineParse()
inps.dem_file = ut.get_file_list([inps.dem_file])[0]
inps.timeseries_file = ut.get_file_list([inps.timeseries_file])[0]
atr = readfile.read_attribute(inps.timeseries_file)
print '*******************************************************************************'
print 'Downloading weather model data ...'
## Get Grib Source
if inps.weather_model in ['ECMWF', 'ERA-Interim']:
inps.grib_source = 'ECMWF'
elif inps.weather_model == 'ERA':
inps.grib_source = 'ERA'
elif inps.weather_model == 'MERRA':
inps.grib_source = 'MERRA'
elif inps.weather_model == 'NARR':
inps.grib_source = 'NARR'
else:
raise Reception('Unrecognized weather model: ' + inps.weather_model)
print 'grib source: ' + inps.grib_source
## Get Acquisition time
inps.hour = closest_weather_product_time(atr['CENTER_LINE_UTC'],
inps.grib_source)
print 'Time of cloest vailable product: ' + inps.hour
if not os.path.isdir(inps.grib_source):
print 'making directory: ' + inps.grib_source
os.mkdir(inps.grib_source)
## Loop to download
inps.grib_file_list = []
h5timeseries = h5py.File(inps.timeseries_file, 'r')
dateList = sorted(h5timeseries['timeseries'].keys())
for d in dateList:
print[d]
if inps.grib_source == 'ECMWF':
grib_file = './ECMWF/ERA-Int_' + d + '_' + inps.hour + '.grb'
elif inps.grib_source == 'ERA':
grib_file = './ERA/ERA_' + d + '_' + inps.hour + '.grb'
elif inps.grib_source == 'MERRA':
grib_file = './MERRA/merra-' + d + '-' + inps.hour + '.hdf'
elif inps.grib_source == 'NARR':
grib_file = './NARR/narr-a_221_' + d + '_' + inps.hour + '00_000.grb'
inps.grib_file_list.append(grib_file)
if os.path.isfile(grib_file):
print grib_file + ' already exists.'
else:
if inps.grib_source == 'ECMWF':
pa.ECMWFdload([d], inps.hour, './' + inps.grib_source + '/')
elif inps.grib_source == 'ERA':
pa.ERAdload([d], inps.hour, './' + inps.grib_source + '/')
elif inps.grib_source == 'MERRA':
pa.MERRAdload([d], inps.hour, './' + inps.grib_source + '/')
elif inps.grib_source == 'NARR':
pa.NARRdload([d], inps.hour, './' + inps.grib_source + '/')
print '*******************************************************************************'
print 'Calcualting delay for each epoch.'
## Get Incidence angle: to map the zenith delay to the slant delay
if inps.incidence_angle:
if os.path.isfile(inps.incidence_angle):
inps.incidence_angle = readfile.read(inps.incidence_angle)[0]
else:
inps.incidence_angle = float(inps.incidence_angle)
print 'incidence angle: ' + str(inps.incidence_angle)
else:
print 'calculating incidence angle ...'
inps.incidence_angle = ut.incidence_angle(atr)
inps.incidence_angle = inps.incidence_angle * np.pi / 180.0
## Create delay hdf5 file
tropFile = inps.grib_source + '.h5'
print 'writing >>> ' + tropFile
h5trop = h5py.File(tropFile, 'w')
group_trop = h5trop.create_group('timeseries')
## Create tropospheric corrected timeseries hdf5 file
if not inps.out_file:
ext = os.path.splitext(inps.timeseries_file)[1]
inps.out_file = os.path.splitext(
inps.timeseries_file)[0] + '_' + inps.grib_source + '.h5'
print 'writing >>> ' + inps.out_file
h5timeseries_tropCor = h5py.File(inps.out_file, 'w')
group_tropCor = h5timeseries_tropCor.create_group('timeseries')
## Calculate phase delay on reference date
if 'ref_date' in atr.keys():
ref_idx = dateList.index(atr['ref_date'])
else:
ref_idx = 0
print 'calculating phase delay on reference date: ' + dateList[ref_idx]
phs_ref = get_delay(inps.grib_file_list[ref_idx], atr, vars(inps))
## Loop to calculate phase delay on the other dates
for i in range(len(inps.grib_file_list)):
# Get phase delay
grib_file = inps.grib_file_list[i]
if not i == ref_idx:
print dateList[i]
phs = get_delay(grib_file, atr, vars(inps))
else:
phs = np.copy(phs_ref)
# Get relative phase delay in time
phs -= phs_ref
# Write dataset
print 'writing hdf5 file ...'
data = h5timeseries['timeseries'].get(dateList[i])[:]
dset = group_tropCor.create_dataset(dateList[i],
data=data + phs,
compression='gzip')
dset = group_trop.create_dataset(dateList[i],
data=phs,
compression='gzip')
## Write Attributes
for key, value in atr.iteritems():
group_tropCor.attrs[key] = value
group_trop.attrs[key] = value
h5timeseries.close()
h5timeseries_tropCor.close()
h5trop.close()
print 'Done.'
return
def main(argv):
global xsub, ysub, thr
parallel = 'yes' ## Use parallel by default for multiple input files
######################################
try: opts, args = getopt.getopt(argv,"h:f:m:t:x:y:o:",['no-parallel'])
except getopt.GetoptError: Usage() ; sys.exit(1)
if len(sys.argv) > 3:
for opt,arg in opts:
if opt in ("-h","--help"): Usage(); sys.exit()
elif opt == '-f': File = arg.split(',')
elif opt == '-m': maskFile = arg
elif opt == '-t': thr = float(arg)
elif opt == '-y': ysub = [int(i) for i in arg.split(':')]; ysub.sort()
elif opt == '-x': xsub = [int(i) for i in arg.split(':')]; xsub.sort()
elif opt == '-o': outFile = arg
elif opt == '--no-parallel': parallel = 'no'
elif len(sys.argv)==3:
if os.path.isfile(argv[0]) and os.path.isfile(argv[1]):
File = argv[0].split(',')
maskFile = argv[1]
else: print 'Input file does not existed: '+argv[0]+' / '+argv[1]; sys.exit(1)
else: Usage(); sys.exit(1)
try:
File
maskFile
except: Usage() ; sys.exit(1)
##### Check Input File List
print '\n****************** Masking *********************'
fileList = ut.get_file_list(File)
print 'number of file to mask: '+str(len(fileList))
print fileList
if len(fileList) == 1:
parallel = 'no'
try: outFile ## Customized output file name for one input file only
except:
ext = os.path.splitext(fileList[0])[1]
outFile = os.path.basename(fileList[0]).split('.')[0]+'_masked'+ext
elif len(fileList) > 1:
try:
del outFile
print 'Disabled customized output name for multiple input files, continue with automatic naming insread.'
except: pass
else: print 'ERROR: No input file!'; sys.exit(1)
##### Check parallel computing requirement
if parallel == 'yes':
try:
from joblib import Parallel, delayed
import multiprocessing
except:
print '/nCannot import joblib or multiprocessing!'
print 'Disabled parallel masking.'
print 'Continue with masking file by file ...'
###### Read Mask File
atr_mask = readfile.read_attributes(maskFile)
k_mask = atr_mask['FILE_TYPE']
if not k_mask == 'coherence': ## Read mask file once
M,Matr = readfile.read(maskFile)
print 'mask file: '+maskFile
##### Masking - file by file
if parallel == 'no':
##### Single Mask
if not k_mask == 'coherence':
for in_file in fileList:
print '-------------------------------------------'
print 'masking : '+in_file
try: mask_file(in_file,M,outFile)
except: mask_file(in_file,M)
##### Multiple Mask
else:
try: mask_with_multi_masks(fileList[0],maskFile,outFile)
except: mask_with_multi_masks(fileList[0],maskFile)
##### Masking - parallel
else:
print '-----------------------'
print 'parallel masking ...'
print '-----------------------'
num_cores = multiprocessing.cpu_count()
Parallel(n_jobs=num_cores)(delayed(mask_file)(in_file,M) for in_file in fileList)
def main(argv):
inps = cmdLineParse()
suffix = '_demErr'
if not inps.outfile:
inps.outfile = os.path.splitext(inps.timeseries_file)[0]+suffix+os.path.splitext(inps.timeseries_file)[1]
if inps.template_file:
print 'read option from template file: '+inps.template_file
inps = read_template2inps(inps.template_file, inps)
##### Read Data
atr = readfile.read_attribute(inps.timeseries_file)
coordType = 'radar'
if 'Y_FIRST' in atr.keys():
coordType = 'geo'
# 1. Incidence angle
try:
inps.inc_angle_file = ut.get_file_list(inps.inc_angle_file, coord=coordType)[0]
except ValueError:
print 'No incidence angle file found!\nRun incidence_angle.py to generate it.'
print 'read incidence angle from file: '+str(inps.inc_angle_file)
inps.inc_angle = readfile.read(inps.inc_angle_file, epoch='incidenceAngle')[0].flatten()
inps.inc_angle *= np.pi/180.0
# 2. Slant Range distance
try:
inps.range_dist_file = ut.get_file_list(inps.range_dist_file, coord=coordType)[0]
except ValueError:
print 'No range distance file found!\nRun range_distance.py to generate it.'
print 'read slant range distance from file: '+str(inps.range_dist_file)
inps.range_dist = readfile.read(inps.range_dist_file, epoch='slantRangeDistance')[0].flatten()
# 3. Perp Baseline - 1D in time, 0D/1D in space (azimuth)
print 'read perpendicular baseline'
try:
inps.pbase = ut.perp_baseline_timeseries(atr, dimension=1)
if inps.pbase.shape[1] > 1:
print 'consider perp baseline variance in azimuth direction'
except valueError:
print 'No P_BASELINE_TIMESERIES found in timeseries file.\n'+\
'Can not correct for DEM residula without it!'
# 4. Time Series - 1D in time, 1D in space (flattened)
print "read time series file: " + inps.timeseries_file
h5 = h5py.File(inps.timeseries_file)
date_list = sorted(h5['timeseries'].keys())
date_num = len(date_list)
inps.tbase = np.array(ptime.date_list2tbase(date_list)[0]).reshape(-1,1)
#Mark dates used in the estimation
inps.ex_date = check_exclude_date(inps.ex_date, date_list)
inps.date_flag = np.array([i not in inps.ex_date for i in date_list], dtype=np.bool_)
if inps.poly_order > np.sum(inps.date_flag):
raise ValueError("ERROR: input polynomial order=%d is larger than number of acquisition=%d used in estimation!" %\
(inps.poly_order, np.sum(inps.date_flag)))
length = int(atr['FILE_LENGTH'])
width = int(atr['WIDTH'])
pixel_num = length*width
timeseries = np.zeros((date_num, pixel_num),np.float32)
for i in range(date_num):
timeseries[i] = h5['timeseries'].get(date_list[i])[:].flatten()
sys.stdout.write('\rreading acquisition %3d/%3d ...' % (i+1, date_num))
sys.stdout.flush()
h5.close()
print ''
##### Design matrix - temporal deformation model
print '-------------------------------------------------'
print 'Correct topographic phase residual using Fattahi and Amelung (2013, IEEE-TGRS)'
msg = 'minimum-norm constrain on: phase'
if inps.phase_velocity:
msg += ' velocity'
print msg
# Heresh's original code for phase history approach
#A1 = np.hstack((np.ones((date_num, 1)), inps.tbase))
#A2 = inps.tbase**2 / 2.0
#A_def = np.hstack((A2,A1,np.ones((date_num,1))))
# 1. Polynomial - 2D matrix in size of (date_num, polyOrder+1)
print "temporal deformation model: polynomial order = "+str(inps.poly_order)
A_def = np.ones((date_num, 1), np.float32)
for i in range(inps.poly_order):
Ai = inps.tbase**(i+1) / gamma(i+2)
Ai = np.array(Ai, np.float32).reshape(-1,1)
A_def = np.hstack((A_def, Ai))
# 2. Step function - 2D matrix in size of (date_num, stepNum)
if inps.step_date:
print "temporal deformation model: step functions at "+str(inps.step_date)
yySteps = ptime.yyyymmdd2years(inps.step_date)
yyList = np.array(ptime.yyyymmdd2years(date_list)).reshape(-1,1)
for yyStep in yySteps:
Ai = yyList > yyStep
Ai = np.array(Ai, np.float32).reshape(-1,1)
A_def = np.hstack((A_def, Ai))
inps.step_num = len(inps.step_date)
print '-------------------------------------------------'
##---------------------------------------- Loop for L2-norm inversion -----------------------------------##
## Output estimated steps
print 'ordinal least squares (OLS) inversion using L2-norm minimization'
timeseriesCor = np.zeros((date_num, pixel_num), dtype=np.float32)
timeseriesRes = np.zeros((date_num, pixel_num), dtype=np.float32)
topoRes = np.zeros(pixel_num, dtype=np.float32)
constC = np.zeros(pixel_num, dtype=np.float32)
if inps.step_num > 0:
stepModel = np.zeros((inps.step_num, pixel_num), dtype=np.float32)
print 'skip pixels with zero/nan value in geometry files - incidence angle and range distance'
mask = np.multiply(~np.isnan(inps.inc_angle), ~np.isnan(inps.range_dist))
mask[inps.inc_angle == 0.] = 0
mask[inps.range_dist == 0.] = 0
pixel_num2inv = np.sum(mask)
pixel_idx2inv = np.where(mask)[0]
print 'number of pixels in the file: %d' % (pixel_num)
print 'number of pixels to inverse: %d' % (pixel_num2inv)
if inps.pbase.shape[1] == 1:
pbase = inps.pbase
prog_bar = ptime.progress_bar(maxValue=pixel_num)
for i in range(pixel_num2inv):
prog_bar.update(i+1, every=1000, suffix='%s/%s pixels'%(str(i+1), str(pixel_num2inv)))
idx = pixel_idx2inv[i]
r = inps.range_dist[idx]
inc_angle = inps.inc_angle[idx]
if inps.pbase.shape[1] > 1:
pbase = inps.pbase[:, int(idx/width)].reshape(-1,1)
A_deltaZ = pbase / (r * np.sin(inc_angle))
A = np.hstack((A_deltaZ, A_def))
ts = timeseries[:,idx].reshape(date_num,-1)
deltaZ, tsCor, tsRes, stepEst = topographic_residual_inversion(ts, A, inps)
topoRes[idx:idx+1] = deltaZ
timeseriesCor[:,idx:idx+1] = tsCor
timeseriesRes[:,idx:idx+1] = tsRes
if inps.step_num > 0:
stepModel[:,idx:idx+1] = stepEst
prog_bar.close()
##------------------------------------------------ Output --------------------------------------------##
# 1. DEM error file
if 'Y_FIRST' in atr.keys():
deltaZFile = 'demGeo_error.h5'
else:
deltaZFile = 'demRadar_error.h5'
print 'writing >>> '+deltaZFile
atrDeltaZ = atr.copy()
atrDeltaZ['FILE_TYPE'] = 'dem'
atrDeltaZ['UNIT'] = 'm'
writefile.write(topoRes.reshape(length, width), atrDeltaZ, deltaZFile)
# 2. Topo Residual Corrected Time Series
print 'writing >>> '+inps.outfile
h5 = h5py.File(inps.outfile,'w')
group = h5.create_group('timeseries')
for i in range(date_num):
sys.stdout.write('\rwriting acquisition %3d/%3d ...' % (i+1, date_num))
sys.stdout.flush()
dset = group.create_dataset(date_list[i], data=timeseriesCor[i].reshape(length, width), compression='gzip')
print ''
for key,value in atr.iteritems():
group.attrs[key] = value
h5.close()
# 3. Inversion residual Time Series
tsResFile = os.path.join(os.path.dirname(inps.outfile), 'timeseriesResidual.h5')
print 'writing >>> '+os.path.basename(tsResFile)
h5 = h5py.File(tsResFile,'w')
group = h5.create_group('timeseries')
for i in range(date_num):
sys.stdout.write('\rwriting acquisition %3d/%3d ...' % (i+1, date_num))
sys.stdout.flush()
dset = group.create_dataset(date_list[i], data=timeseriesRes[i].reshape(length, width), compression='gzip')
print ''
# Attribute
for key,value in atr.iteritems():
group.attrs[key] = value
h5.close()
# 4. Step temporal Model estimation
if inps.step_num > 0:
stepFile = os.path.join(os.path.dirname(inps.outfile), 'timeseriesStepModel.h5')
print 'writing >>> '+os.path.basename(stepFile)
h5 = h5py.File(stepFile,'w')
group = h5.create_group('timeseries')
for i in range(inps.step_num):
sys.stdout.write('\rwriting acquisition %3d/%3d ...' % (i+1, inps.step_num))
sys.stdout.flush()
dset = group.create_dataset(inps.step_date[i], data=stepModel[i].reshape(length, width), compression='gzip')
print ''
# Attribute
for key,value in atr.iteritems():
group.attrs[key] = value
group.attrs.pop('ref_date')
h5.close()
print 'Done.'
return
def main(argv):
inps = cmdLineParse()
inps.file = ut.get_file_list(inps.file)
#print '\n**************** Subset *********************'
atr = readfile.read_attribute(inps.file[0])
##### Convert All Inputs into subset_y/x/lat/lon
# Input Priority: subset_y/x/lat/lon > reference > template > footprint
if not inps.subset_x and not inps.subset_y and not inps.subset_lat and not inps.subset_lon:
# 1. Read subset info from Reference File
if inps.reference:
ref_atr = readfile.read_attribute(inps.reference)
pix_box, geo_box = get_coverage_box(ref_atr)
print 'using subset info from ' + inps.reference
# 2. Read subset info from template options
elif inps.template_file:
pix_box, geo_box = read_subset_template2box(inps.template_file)
print 'using subset info from ' + inps.template_file
# 3. Use subset from footprint info
elif inps.footprint:
if atr['FILE_TYPE'] == '.trans':
# Non-zero area in geomap_*.trans file, accurate
trans_rg, trans_atr = readfile.read(inps.file[0], (), 'range')
idx_row, idx_col = np.nonzero(trans_rg)
pix_box = (np.min(idx_col) - 10, np.min(idx_row) - 10,
np.max(idx_col) + 10, np.max(idx_row) + 10)
geo_box = box_pixel2geo(pix_box, trans_atr)
else:
print 'ERROR: --footprint option only works for geomap_*.trans file.\n'
inps.footprint = False
sys.exit(1)
## from LAT/LON_REF*, which is not accurate
#lats = [atr['LAT_REF1'], atr['LAT_REF3'], atr['LAT_REF4'], atr['LAT_REF2']]
#lons = [atr['LON_REF1'], atr['LON_REF3'], atr['LON_REF4'], atr['LON_REF2']]
#lats = [float(i) for i in lats]
#lons = [float(i) for i in lons]
#lalo_buff = min([max(lats)-min(lats), max(lons)-min(lons)]) * 0.05
#geo_box = (min(lons)-lalo_buff, max(lats)+lalo_buff, max(lons)+lalo_buff, min(lats)-lalo_buff)
#pix_box = None
#if not inps.fill_value: inps.fill_value = np.nan
#print 'using subset info from scene footprint - LAT/LON_REF1/2/3/4'
else:
raise Exception('No subset inputs found!')
# Update subset_y/x/lat/lon
inps = subset_box2inps(inps, pix_box, geo_box)
##### --bbox option
if inps.trans_file:
## Seperate files in radar and geo coord
rdrFileList = []
geoFileList = []
for File in inps.file:
atr = readfile.read_attribute(File)
if 'X_FIRST' in atr.keys():
geoFileList.append(File)
else:
rdrFileList.append(File)
## Calculate bbox
rdrFile = rdrFileList[0]
atr_rdr = readfile.read_attribute(rdrFile)
if inps.subset_lat and inps.subset_lon:
print 'use subset input in lat/lon'
print 'calculate corresponding bounding box in radar coordinate.'
geo_box = (inps.subset_lon[0], inps.subset_lat[1],
inps.subset_lon[1], inps.subset_lat[0])
pix_box = bbox_geo2radar(geo_box, atr_rdr, inps.trans_file)
else:
print 'use subset input in y/x'
print 'calculate corresponding bounding box in geo coordinate.'
pix_box = (inps.subset_x[0], inps.subset_y[0], inps.subset_x[1],
inps.subset_y[1])
geo_box = bbox_radar2geo(pix_box, atr_rdr, inps.trans_file)
print 'geo box: ' + str(geo_box)
print 'pixel box: ' + str(pix_box)
## Subset files
inps.fill_value = 0
print '--------------------------------------------'
print 'subseting dataset in geo coord geo_box: ' + str(geo_box)
inps = subset_box2inps(inps, None, geo_box)
subset_file_list(geoFileList, inps)
print '--------------------------------------------'
print 'subseting dataset in radar coord pix_box: ' + str(pix_box)
inps = subset_box2inps(inps, pix_box, None)
subset_file_list(rdrFileList, inps)
else:
##### Subset files
subset_file_list(inps.file, inps)
print 'Done.'
return
def auto_path_miami(inps, template={}):
'''Auto File Path Setting for Geodesy Lab - University of Miami'''
print 'Use auto path setting in University of Miami.'+\
'(To turn it off, change miami_path value to False in pysar/__init__.py)'
# PYSAR working directory
if not inps.timeseries_dir:
inps.timeseries_dir = os.getenv(
'SCRATCHDIR') + '/' + inps.project_name + '/PYSAR'
##### .unw/.cor/.int files
process_dir = os.getenv(
'SCRATCHDIR') + '/' + inps.project_name + '/PROCESS'
print "PROCESS directory: " + process_dir
if inps.insarProcessor == 'roipac':
if not inps.unw or inps.unw == 'auto':
inps.unw = process_dir + '/DONE/IFGRAM*/filt_*.unw'
if not inps.cor or inps.cor == 'auto':
inps.cor = process_dir + '/DONE/IFGRAM*/filt_*rlks.cor'
#if not inps.int or inps.int == 'auto': inps.int = process_dir+'/DONE/IFGRAM*/filt_*rlks.int'
elif inps.insarProcessor == 'gamma':
if not inps.unw or inps.unw == 'auto':
inps.unw = process_dir + '/DONE/IFGRAM*/diff_*rlks.unw'
if not inps.cor or inps.cor == 'auto':
inps.cor = process_dir + '/DONE/IFGRAM*/filt_*rlks.cor'
#if not inps.int or inps.int == 'auto': inps.int = process_dir+'/DONE/IFGRAM*/diff_*rlks.int'
elif inps.insarProcessor == 'isce':
process_dir = os.getenv('SCRATCHDIR') + '/' + inps.project_name
if not inps.unw or inps.unw == 'auto':
inps.unw = process_dir + '/merged/interferograms/*/filt*.unw'
if not inps.cor or inps.cor == 'auto':
inps.cor = process_dir + '/merged/interferograms/*/filt*.cor'
if not inps.lut or inps.lut == 'auto':
inps.lut = process_dir + '/merged/geom_master/l*.rdr'
if not inps.dem_radar or inps.dem_radar == 'auto':
inps.dem_radar = process_dir + '/merged/geom_master/hgt.rdr'
if not inps.dem_geo or inps.dem_geo == 'auto': inps.dem_geo = ""
#if not inps.int or inps.int == 'auto': inps.int = process_dir+'/DONE/IFGRAM*/diff_*rlks.int'
##### master interferogram for lookup table and DEM in radar coord
if all(fname and fname != 'auto'
for fname in [inps.lut, inps.dem_radar, inps.dem_geo]):
return inps
try:
m_date12 = np.loadtxt(process_dir + '/master_ifgram.txt',
dtype=str).tolist()
except:
try:
m_date12 = os.walk(process_dir +
'/GEO').next()[1][0].split('geo_')[1]
except:
pass
if not inps.lut or inps.lut == 'auto':
try:
if inps.insarProcessor == 'roipac':
inps.lut = process_dir + '/GEO/*' + m_date12 + '*/geomap*.trans'
elif inps.insarProcessor == 'gamma':
inps.lut = process_dir + '/SIM/sim_' + m_date12 + '/sim_*.UTM_TO_RDC'
except:
warnings.warn(
'No master interferogram found! Can not locate mapping transformation file for geocoding!'
)
if not inps.dem_radar or inps.dem_radar == 'auto':
try:
if inps.insarProcessor == 'roipac':
inps.dem_radar = process_dir + '/DONE/*' + m_date12 + '*/radar*.hgt'
elif inps.insarProcessor == 'gamma':
inps.dem_radar = process_dir + '/SIM/sim_' + m_date12 + '/sim_*.hgt_sim'
except:
warnings.warn(
'No master interferogram found! Can not locate DEM in radar coord!'
)
# Use DEMg/DEM option if dem_geo is not specified in pysar option
dem_dir = os.getenv('SCRATCHDIR') + '/' + inps.project_name + '/DEM'
if inps.dem_geo is None or inps.dem_geo == 'auto':
inps.dem_geo = []
if os.path.isdir(dem_dir):
inps.dem_geo = [dem_dir + '/*.dem']
elif inps.insarProcessor == 'gamma':
inps.dem_geo = [
process_dir + '/SIM/sim_' + m_date12 + '/sim_*.utm.dem'
]
if 'DEMg' in template.keys(): inps.dem_geo.append(template['DEMg'])
elif 'DEM' in template.keys(): inps.dem_geo.append(template['DEM'])
try:
inps.dem_geo = ut.get_file_list(inps.dem_geo)[0]
except:
inps.dem_geo = None
if not inps.dem_geo:
warnings.warn('Can not locate DEM in geo coord!')
return inps
def main(argv):
inps = cmdLineParse()
if inps.timeseries_file:
inps.timeseries_file = ut.get_file_list([inps.timeseries_file])[0]
atr = readfile.read_attribute(inps.timeseries_file)
if inps.dem_file:
inps.dem_file = ut.get_file_list([inps.dem_file])[0]
# Convert DEM to ROIPAC format
if os.path.splitext(inps.dem_file)[1] in ['.h5']:
print 'convert DEM file to ROIPAC format'
dem, atr_dem = readfile.read(inps.dem_file)
if 'Y_FIRST' in atr_dem.keys():
atr_dem['FILE_TYPE'] = '.dem'
else:
atr_dem['FILE_TYPE'] = '.hgt'
outname = os.path.splitext(
inps.dem_file)[0] + '4pyaps' + atr_dem['FILE_TYPE']
inps.dem_file = writefile.write(dem, atr_dem, outname)
print '*******************************************************************************'
print 'Downloading weather model data ...'
## Get Grib Source
if inps.weather_model in ['ECMWF', 'ERA-Interim']:
inps.grib_source = 'ECMWF'
elif inps.weather_model == 'ERA':
inps.grib_source = 'ERA'
elif inps.weather_model == 'MERRA':
inps.grib_source = 'MERRA'
elif inps.weather_model == 'NARR':
inps.grib_source = 'NARR'
else:
raise Reception('Unrecognized weather model: ' + inps.weather_model)
print 'grib source: ' + inps.grib_source
## Grib data directory
if not inps.weather_dir:
if inps.timeseries_file:
inps.weather_dir = os.path.dirname(
os.path.abspath(inps.timeseries_file)) + '/../WEATHER'
elif inps.dem_file:
inps.weather_dir = os.path.dirname(os.path.abspath(
inps.dem_file)) + '/../WEATHER'
else:
inps.weather_dir = os.path.abspath(os.getcwd())
print 'Store weather data into directory: ' + inps.weather_dir
grib_dir = inps.weather_dir + '/' + inps.grib_source
if not os.path.isdir(grib_dir):
print 'making directory: ' + grib_dir
os.makedirs(grib_dir)
## Get Acquisition time
if not inps.hour:
inps.hour = closest_weather_product_time(atr['CENTER_LINE_UTC'],
inps.grib_source)
print 'Time of cloest available product: ' + inps.hour
## Get grib file list and date list
inps.grib_file_list = []
if not inps.date_list_file:
h5timeseries = h5py.File(inps.timeseries_file, 'r')
dateList = sorted(h5timeseries['timeseries'].keys())
h5timeseries.close()
print 'read date list info from: ' + inps.timeseries_file
else:
dateList = ptime.yyyymmdd(
np.loadtxt(inps.date_list_file, dtype=str, usecols=(0, )).tolist())
print 'read date list info from: ' + inps.date_list_file
for d in dateList:
if inps.grib_source == 'ECMWF':
grib_file = grib_dir + '/ERA-Int_' + d + '_' + inps.hour + '.grb'
elif inps.grib_source == 'ERA':
grib_file = grib_dir + '/ERA_' + d + '_' + inps.hour + '.grb'
elif inps.grib_source == 'MERRA':
grib_file = grib_dir + '/merra-' + d + '-' + inps.hour + '.hdf'
elif inps.grib_source == 'NARR':
grib_file = grib_dir + '/narr-a_221_' + d + '_' + inps.hour + '00_000.grb'
inps.grib_file_list.append(grib_file)
## Get date list to download
grib_file_existed = ut.get_file_list(inps.grib_file_list)
if grib_file_existed:
grib_filesize_mode = ut.mode(
[os.path.getsize(i) for i in grib_file_existed])
grib_file_corrupted = [
i for i in grib_file_existed
if os.path.getsize(i) != grib_filesize_mode
]
print 'number of grib files existed : %d' % len(grib_file_existed)
print 'file size mode: %d' % grib_filesize_mode
if grib_file_corrupted:
print '------------------------------------------------------------------------------'
print 'corrupted grib files detected! Delete them and re-download...'
print 'number of grib files corrupted : %d' % len(
grib_file_corrupted)
for i in grib_file_corrupted:
rmCmd = 'rm ' + i
print rmCmd
os.system(rmCmd)
grib_file_existed.remove(i)
print '------------------------------------------------------------------------------'
grib_file2download = sorted(
list(set(inps.grib_file_list) - set(grib_file_existed)))
date_list2download = [
str(re.findall('\d{8}', i)[0]) for i in grib_file2download
]
print 'number of grib files to download: %d' % len(date_list2download)
print '------------------------------------------------------------------------------\n'
## Download grib file using PyAPS
if inps.grib_source == 'ECMWF':
pa.ECMWFdload(date_list2download, inps.hour, grib_dir)
elif inps.grib_source == 'ERA':
pa.ERAdload(date_list2download, inps.hour, grib_dir)
elif inps.grib_source == 'MERRA':
pa.MERRAdload(date_list2download, inps.hour, grib_dir)
elif inps.grib_source == 'NARR':
pa.NARRdload(date_list2download, inps.hour, grib_dir)
if inps.download:
print 'Download completed, exit as planned.'
return
print '*******************************************************************************'
print 'Calcualting delay for each epoch.'
## Get Incidence angle: to map the zenith delay to the slant delay
if inps.incidence_angle:
if os.path.isfile(inps.incidence_angle):
inps.incidence_angle = readfile.read(inps.incidence_angle)[0]
else:
inps.incidence_angle = float(inps.incidence_angle)
print 'incidence angle: ' + str(inps.incidence_angle)
else:
print 'calculating incidence angle ...'
inps.incidence_angle = ut.incidence_angle(atr)
inps.incidence_angle = inps.incidence_angle * np.pi / 180.0
## Create delay hdf5 file
tropFile = inps.grib_source + '.h5'
print 'writing >>> ' + tropFile
h5trop = h5py.File(tropFile, 'w')
group_trop = h5trop.create_group('timeseries')
## Create tropospheric corrected timeseries hdf5 file
if not inps.out_file:
ext = os.path.splitext(inps.timeseries_file)[1]
inps.out_file = os.path.splitext(
inps.timeseries_file)[0] + '_' + inps.grib_source + '.h5'
print 'writing >>> ' + inps.out_file
h5timeseries_tropCor = h5py.File(inps.out_file, 'w')
group_tropCor = h5timeseries_tropCor.create_group('timeseries')
## Calculate phase delay on reference date
if 'ref_date' in atr.keys():
ref_idx = dateList.index(atr['ref_date'])
else:
ref_idx = 0
print 'calculating phase delay on reference date: ' + dateList[ref_idx]
phs_ref = get_delay(inps.grib_file_list[ref_idx], atr, vars(inps))
## Loop to calculate phase delay on the other dates
h5timeseries = h5py.File(inps.timeseries_file, 'r')
for i in range(len(inps.grib_file_list)):
# Get phase delay
grib_file = inps.grib_file_list[i]
if not i == ref_idx:
print dateList[i]
phs = get_delay(grib_file, atr, vars(inps))
else:
phs = np.copy(phs_ref)
# Get relative phase delay in time
phs -= phs_ref
# Write dataset
print 'writing hdf5 file ...'
data = h5timeseries['timeseries'].get(dateList[i])[:]
dset = group_tropCor.create_dataset(dateList[i],
data=data - phs,
compression='gzip')
dset = group_trop.create_dataset(dateList[i],
data=phs,
compression='gzip')
## Write Attributes
for key, value in atr.iteritems():
group_tropCor.attrs[key] = value
group_trop.attrs[key] = value
h5timeseries.close()
h5timeseries_tropCor.close()
h5trop.close()
# Delete temporary DEM file in ROI_PAC format
if '4pyaps' in inps.dem_file:
rmCmd = 'rm ' + inps.dem_file + ' ' + inps.dem_file + '.rsc '
print rmCmd
os.system(rmCmd)
print 'Done.'
return
def main(argv):
inps = cmdLineParse()
if inps.timeseries_file:
inps.timeseries_file = ut.get_file_list([inps.timeseries_file])[0]
atr = readfile.read_attribute(inps.timeseries_file)
k = atr['FILE_TYPE']
if 'ref_y' not in atr.keys() and inps.ref_yx:
print 'No reference info found in input file, use input ref_yx: ' + str(
inps.ref_yx)
atr['ref_y'] = inps.ref_yx[0]
atr['ref_x'] = inps.ref_yx[1]
#****reading incidence angle file***/
if os.path.isfile(inps.inc_angle):
inps.inc_angle = readfile.read(inps.inc_angle,
epoch='incidenceAngle')[0]
inps.inc_angle = np.nan_to_num(inps.inc_angle)
else:
inps.inps.inc_angle = float(inps.inc_angle)
print 'incidence angle: ' + str(inps.inc_angle)
cinc = np.cos(inps.inc_angle * np.pi / 180.0)
#****look up file****/
if inps.lookup_file:
inps.lookup_file = ut.get_file_list(
[inps.lookup_file])[0] #'geomap_32rlks_tight.trans'
#****GACOS****/
delay_source = 'GACOS'
# Get weather directory
if not inps.GACOS_dir:
if inps.timeseries_file:
inps.GACOS_dir = os.path.dirname(
os.path.abspath(inps.timeseries_file)) + '/../WEATHER/GACOS'
elif inps.lookup_file:
inps.GACOS_dir = os.path.dirname(os.path.abspath(
inps.lookup_file)) + '/../WEATHER/GACOS'
else:
inps.GACOS_dir = os.path.abspath(os.getcwd())
print 'Store weather data into directory: ' + inps.GACOS_dir
#source_dir=os.path.dirname(os.path.abspath('timeseries_file'))+'/Agung/GACOS/data';print source_dir
#os.makedirs(GACOS_dir) -----------------------------------------------add part to copy/download weather data------#
#----get date list-----#
if not inps.date_list_file:
print 'read date list info from: ' + inps.timeseries_file
h5 = h5py.File(inps.timeseries_file, 'r')
if 'timeseries' in h5.keys():
date_list = sorted(h5[k].keys())
elif k in ['interferograms', 'coherence', 'wrapped']:
ifgram_list = sorted(h5[k].keys())
date12_list = ptime.list_ifgram2date12(ifgram_list)
m_dates = [i.split('-')[0] for i in date12_list]
s_dates = [i.split('-')[1] for i in date12_list]
date_list = ptime.yyyymmdd(sorted(list(set(m_dates + s_dates))))
else:
raise ValueError('Un-support input file type:' + k)
h5.close()
else:
date_list = ptime.yyyymmdd(
np.loadtxt(inps.date_list_file, dtype=str, usecols=(0, )).tolist())
print 'read date list info from: ' + inps.date_list_file
#****cheacking availability of delays****/
print 'checking availability of delays'
delay_file_list = []
for d in date_list:
if delay_source == 'GACOS':
delay_file = inps.GACOS_dir + '/' + d + '.ztd'
delay_file_list.append(delay_file)
delay_file_existed = ut.get_file_list(delay_file_list)
if len(delay_file_existed) == len(date_list):
print 'no missing files'
else:
print 'no. of date files found:', len(delay_file_existed)
print 'no. of dates:', len(date_list)
#*****Calculating delays***/
print 'calculating delays'
length = int(atr['FILE_LENGTH'])
width = int(atr['WIDTH'])
#initialise delay files
date_num = len(date_list)
trop_ts = np.zeros((date_num, length, width), np.float32)
#reading wrf files for each epoch and getting delay
for i in range(date_num):
delay_file = delay_file_existed[i]
date = date_list[i]
print 'calculating delay for date', date
trop_ts[i] = get_delay(delay_file, atr, inps.lookup_file, cinc)
print 'Delays Calculated'
# Convert relative phase delay on reference date
try:
ref_date = atr['ref_date']
except:
ref_date = date_list[0]
print 'convert to relative phase delay with reference date: ' + ref_date
ref_idx = date_list.index(ref_date)
trop_ts -= np.tile(trop_ts[ref_idx, :, :], (date_num, 1, 1))
## Write tropospheric delay to HDF5
tropFile = 'GACOSdelays' + '.h5'
print 'writing >>> %s' % (tropFile)
h5trop = h5py.File(tropFile, 'w')
group_trop = h5trop.create_group('timeseries')
print 'number of acquisitions: ' + str(date_num)
prog_bar = ptime.progress_bar(maxValue=date_num)
for i in range(date_num):
date = date_list[i]
group_trop.create_dataset(date, data=trop_ts[i], compression='gzip')
prog_bar.update(i + 1, suffix=date)
prog_bar.close()
# Write Attributes
for key, value in atr.iteritems():
group_trop.attrs[key] = value
h5trop.close()
## Write corrected Time series to HDF5
if k == 'timeseries':
if not inps.out_file:
inps.out_file = os.path.splitext(
inps.timeseries_file)[0] + '_' + 'GACOS' + '.h5'
print 'writing trop corrected timeseries file %s' % (inps.out_file)
h5ts = h5py.File(inps.timeseries_file, 'r')
h5tsCor = h5py.File(inps.out_file, 'w')
group_tsCor = h5tsCor.create_group('timeseries')
print 'number of acquisitions: ' + str(date_num)
prog_bar = ptime.progress_bar(maxValue=date_num)
for i in range(date_num):
date = date_list[i]
print date
ts = h5ts['timeseries'].get(date)[:]
group_tsCor.create_dataset(date,
data=ts - trop_ts[i],
compression='gzip')
prog_bar.update(i + 1, suffix=date)
prog_bar.close()
h5ts.close()
# Write Attributes
for key, value in atr.iteritems():
group_tsCor.attrs[key] = value
h5tsCor.close()
print 'delays written to %s' % (inps.out_file)
print 'finished'
return inps.out_file
def load_file(fileList, inps_dict=dict(), outfile=None, file_type=None):
'''Load input file(s) into one HDF5 file
It supports ROI_PAC files only for now.
Inputs:
fileList - string / list of string, path of files to load
inps_dict - dict, including the following attributes
PROJECT_NAME : KujuAlosAT422F650 (extra attribute dictionary to add to output file)
sensor : (optional)
timeseries_dir : directory of time series analysis, e.g. KujuAlosAT422F650/PYSAR
insarProcessor: InSAR processor, roipac, isce, gamma, doris
outfile - string, output file name
file_type - string, group name for output HDF5 file, interferograms, coherence, dem, etc.
Output:
outfile - string, output file name
Example:
unwrapIfgram.h5 = load_file('filt*.unw', inps_dict=vars(inps))
'''
# Get project_name from input template file
if not 'project_name' in inps_dict.keys(
) and 'template_file' in inps_dict.keys():
template_filename_list = [
os.path.basename(i) for i in inps_dict['template_file']
]
try:
template_filename_list.remove('pysarApp_template.txt')
except:
pass
if template_filename_list:
inps_dict['project_name'] = os.path.splitext(
template_filename_list[0])[0]
#Sensor
inps_dict['PLATFORM'] = project_name2sensor(inps_dict['project_name'])
# Input file(s) info
fileList = ut.get_file_list(fileList, abspath=True)
if not fileList:
return None
##### Prepare attributes file
processor = inps_dict['insarProcessor']
print '--------------------------------------------'
print 'preparing attributes files using prep_%s.py ...' % processor
# prepare multiple files input for cmd calling
files_input = ''
for x in fileList:
files_input += x + ' '
# call prepare_*.py
if processor == 'gamma':
prepCmd = 'prep_gamma.py ' + files_input
os.system(prepCmd)
elif processor == 'roipac':
prepCmd = 'prep_roipac.py ' + files_input
os.system(prepCmd)
elif processor == 'isce':
prepCmd = 'prep_isce.py ' + files_input
#os.system(prepCmd)
else:
print 'Un-supported InSAR processor: ' + processor
print 'Skip preparing attributes files'
print '----------------------------'
print 'loading files ...'
atr = readfile.read_attribute(fileList[0])
k = atr['FILE_TYPE']
print 'Input file(s) is ' + atr['PROCESSOR'] + ' ' + k
# Get output file type
if not file_type:
if k in ['.unw']: file_type = 'interferograms'
elif k in ['.cor']: file_type = 'coherence'
elif k in ['.int']: file_type = 'wrapped'
elif k in ['.byt']: file_type = 'snaphu_connect_component'
elif k in ['.msk']: file_type = 'mask'
elif k in ['.hgt', '.dem', 'dem', '.hgt_sim']:
file_type = 'dem'
elif k in ['.trans', '.utm_to_rdc', 'geometry']:
file_type = 'geometry'
else:
file_type = k
# Get output file name
if not outfile:
# output file basename
if file_type == 'interferograms': outfile = 'unwrapIfgram.h5'
elif file_type == 'coherence': outfile = 'coherence.h5'
elif file_type == 'wrapped': outfile = 'wrapIfgram.h5'
elif file_type == 'snaphu_connect_component':
outfile = 'snaphuConnectComponent.h5'
elif file_type == 'mask':
outfile = 'mask.h5'
elif file_type == 'dem':
if 'Y_FIRST' in atr.keys():
outfile = 'demGeo.h5'
else:
outfile = 'demRadar.h5'
# output directory
if 'timeseries_dir' in inps_dict.keys(
) and inps_dict['timeseries_dir']:
outdir = inps_dict['timeseries_dir']
else:
outdir = os.path.abspath(os.getcwd())
if outfile:
outfile = outdir + '/' + outfile
if outfile:
outfile = os.path.abspath(outfile)
# Convert
if file_type in multi_group_hdf5_file:
outfile = load_multi_group_hdf5(file_type,
fileList,
outfile=outfile,
exDict=inps_dict)[0]
elif file_type in single_dataset_hdf5_file:
outfile = load_single_dataset_hdf5(file_type,
fileList[-1],
outfile=outfile,
exDict=inps_dict)
elif file_type in ['geometry', '.trans', '.utm_to_rdc', '.UTM_TO_RDC']:
outfile = load_geometry_hdf5(file_type,
fileList,
outfile=outfile,
exDict=inps_dict)
else:
warnings.warn('Un-supported file type: ' + file_type)
return outfile
def read_template2inps(template_file, inps=None):
'''Read input template options into Namespace inps'''
if not inps:
inps = cmdLineParse()
template = readfile.read_template(inps.template_file)
key_list = template.keys()
# Coherence-based network modification
prefix = 'pysar.network.'
key = prefix + 'coherenceBased'
if key in key_list and template[key] in ['auto', 'yes']:
inps.coherence_based = True
key = prefix + 'keepMinSpanTree'
if key in key_list and template[key] in ['no']:
inps.keep_mst = False
key = prefix + 'coherenceFile'
if key in key_list:
if template[key] == 'auto':
inps.coherence_file = 'coherence.h5'
else:
inps.coherence_file = template[key]
# find coherence file from input files if inps.coherence_file does not exists.
if inps.coherence_based and not os.path.isfile(inps.coherence_file):
k_list = [readfile.read_attribute(f)['FILE_TYPE'] for f in inps.file]
try:
coh_file_idx = k_list.index('coherence')
except ValueError:
print 'No coherence file found! Can not use coherence-based method without it.'
inps.coherence_file = inps.file[coh_file_idx]
key = prefix + 'minCoherence'
if key in key_list:
if template[key] == 'auto':
inps.min_coherence = 0.7
else:
inps.min_coherence = float(template[key])
key = prefix + 'maskFile'
if key in key_list:
value = template[key]
if value == 'auto':
try:
inps.mask_file = ut.get_file_list(['maskLand.h5',
'mask.h5'])[0]
except:
inps.mask_file = None
elif value == 'no':
inps.mask_file = None
else:
inps.mask_file = value
key = prefix + 'maskAoi.yx'
if key in key_list:
value = template[key]
if value in ['auto', 'no']:
inps.aoi_pix_box = None
else:
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])
key = prefix + 'maskAoi.lalo'
if key in key_list:
value = template[key]
if value in ['auto', 'no']:
inps.aoi_geo_box = None
else:
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.lookup_file:
print 'Warning: no lookup table file found! Can not use ' + key + ' option without it.'
print 'skip this option.'
inps.aoi_pix_box = None
## Network Modification based on thresholds
key = prefix + 'tempBaseMax'
if key in key_list:
value = template[key]
if value not in ['auto', 'no']:
inps.max_temp_baseline = float(value)
key = prefix + 'perpBaseMax'
if key in key_list:
value = template[key]
if value not in ['auto', 'no']:
inps.max_perp_baseline = float(value)
key = prefix + 'referenceFile'
if key in key_list:
value = template[key]
if value in ['auto', 'no']:
inps.reference_file = None
else:
inps.reference_file = value
key = prefix + 'excludeDate'
if key in key_list:
value = template[key]
if value not in ['auto', 'no']:
inps.exclude_date = [i for i in value.replace(',', ' ').split()]
key = prefix + 'excludeIfgIndex'
if key in key_list:
value = template[key]
if value not in ['auto', 'no']:
inps.exclude_ifg_index = [
i for i in value.replace(',', ' ').split()
]
key = prefix + 'startDate'
if key in key_list:
value = template[key]
if value not in ['auto', 'no']:
inps.start_date = ptime.yymmdd(value)
key = prefix + 'endDate'
if key in key_list:
value = template[key]
if value not in ['auto', 'no']:
inps.end_date = ptime.yymmdd(value)
return inps
def main(argv):
##### Read Inputs
inps = cmdLineParse()
inps.file = ut.get_file_list(inps.file)
date12_orig = pnet.get_date12_list(inps.file[0])
print 'input file(s) to be modified: ' + str(inps.file)
print 'number of interferograms: ' + str(len(date12_orig))
atr = readfile.read_attribute(inps.file[0])
# Update inps if template is input
if inps.template_file:
inps = read_template2inps(inps.template_file, inps)
if all(not i for i in [inps.reference_file, inps.max_temp_baseline, inps.max_perp_baseline,\
inps.exclude_ifg_index, inps.exclude_date, inps.coherence_based,\
inps.start_date, inps.end_date, inps.reset]):
# Display network for manually modification when there is no other modification input.
print 'No input option found to remove interferogram'
if inps.template_file:
print 'Keep all interferograms by enable --reset option'
inps.reset = True
else:
print 'To manually modify network, please use --manual option '
return
if inps.reset:
print '----------------------------------------------------------------------------'
for file in inps.file:
reset_pairs(file)
mean_coh_txt_file = os.path.splitext(
inps.coherence_file)[0] + '_spatialAverage.txt'
if os.path.isfile(mean_coh_txt_file):
rmCmd = 'rm ' + mean_coh_txt_file
print rmCmd
os.system(rmCmd)
return
# Convert index : input to continous index list
if inps.exclude_ifg_index:
ifg_index = list(inps.exclude_ifg_index)
inps.exclude_ifg_index = []
for index in ifg_index:
index_temp = [int(i) for i in index.split(':')]
index_temp.sort()
if len(index_temp) == 2:
for j in range(index_temp[0], index_temp[1] + 1):
inps.exclude_ifg_index.append(j)
elif len(index_temp) == 1:
inps.exclude_ifg_index.append(int(index))
else:
print 'Unrecoganized input: ' + index
inps.exclude_ifg_index = sorted(inps.exclude_ifg_index)
if max(inps.exclude_ifg_index) > len(date12_orig):
raise Exception('Input index out of range!\n'+\
'input index:'+str(inps.exclude_ifg_index)+'\n'+\
'index range of file: '+str(len(date12_orig)))
##### Get date12_to_rmv
date12_to_rmv = []
# 1. Update date12_to_rmv from reference file
if inps.reference_file:
date12_to_keep = pnet.get_date12_list(inps.reference_file,
check_drop_ifgram=True)
print '----------------------------------------------------------------------------'
print 'use reference pairs info from file: ' + inps.reference_file
print 'number of interferograms in reference: ' + str(
len(date12_to_keep))
print 'date12 not in reference file:'
date12_to_rmv_temp = []
for date12 in date12_orig:
if date12 not in date12_to_keep:
date12_to_rmv.append(date12)
date12_to_rmv_temp.append(date12)
print date12_to_rmv_temp
# 2.1 Update date12_to_rmv from coherence file
if inps.coherence_based and os.path.isfile(inps.coherence_file):
print '----------------------------------------------------------------------------'
print 'use coherence-based network modification from coherence file: ' + inps.coherence_file
# check mask AOI in lalo
if inps.aoi_geo_box and inps.lookup_file:
print 'input AOI in (lon0, lat1, lon1, lat0): ' + str(
inps.aoi_geo_box)
inps.aoi_pix_box = subset.bbox_geo2radar(inps.aoi_geo_box, atr,
inps.lookup_file)
if inps.aoi_pix_box:
# check mask AOI within the data coverage
inps.aoi_pix_box = subset.check_box_within_data_coverage(
inps.aoi_pix_box, atr)
print 'input AOI in (x0,y0,x1,y1): ' + str(inps.aoi_pix_box)
# Calculate spatial average coherence
coh_list, coh_date12_list = ut.spatial_average(inps.coherence_file, inps.mask_file,\
inps.aoi_pix_box, saveList=True)
# MST network
if inps.keep_mst:
print 'Get minimum spanning tree (MST) of interferograms with inverse of coherence.'
print 'date12 with 1) average coherence < ' + str(
inps.min_coherence) + ' AND 2) not in MST network: '
mst_date12_list = pnet.threshold_coherence_based_mst(
coh_date12_list, coh_list)
else:
print 'date12 with average coherence < ' + str(inps.min_coherence)
mst_date12_list = []
date12_to_rmv_temp = []
for i in range(len(coh_date12_list)):
date12 = coh_date12_list[i]
if coh_list[
i] < inps.min_coherence and date12 not in mst_date12_list:
date12_to_rmv.append(date12)
date12_to_rmv_temp.append(date12)
print date12_to_rmv_temp
# 2.2 Update date12_to_rmv from temp baseline threshold
if inps.max_temp_baseline:
print '----------------------------------------------------------------------------'
print 'Drop pairs with temporal baseline > ' + str(
inps.max_temp_baseline) + ' days'
date8_list = ptime.ifgram_date_list(inps.file[0])
date6_list = ptime.yymmdd(date8_list)
tbase_list = ptime.date_list2tbase(date8_list)[0]
date12_to_rmv_temp = []
for i in range(len(date12_orig)):
date1, date2 = date12_orig[i].split('-')
idx1 = date6_list.index(date1)
idx2 = date6_list.index(date2)
t_diff = tbase_list[idx2] - tbase_list[idx1]
if t_diff > inps.max_temp_baseline:
date12 = date12_orig[i]
date12_to_rmv.append(date12)
date12_to_rmv_temp.append(date12)
print 'number of pairs to drop: %d' % (len(date12_to_rmv_temp))
print date12_to_rmv_temp
# 2.3 Update date12_to_rmv from perp baseline threshold
if inps.max_perp_baseline:
print '----------------------------------------------------------------------------'
print 'Drop pairs with perpendicular spatial baseline > ' + str(
inps.max_perp_baseline) + ' meters'
ifg_bperp_list = pnet.igram_perp_baseline_list(inps.file[0])
date12_to_rmv_temp = []
for i in range(len(ifg_bperp_list)):
if abs(ifg_bperp_list[i]) > inps.max_perp_baseline:
date12 = date12_orig[i]
date12_to_rmv.append(date12)
date12_to_rmv_temp.append(date12)
print 'number of pairs to drop: %d' % (len(date12_to_rmv_temp))
print date12_to_rmv_temp
# 2.4 Update date12_to_rmv from exclude_ifg_index
if inps.exclude_ifg_index:
print '----------------------------------------------------------------------------'
print 'drop date12/pair with the following index number:'
for index in inps.exclude_ifg_index:
date12 = date12_orig[index - 1]
date12_to_rmv.append(date12)
print str(index) + ' ' + date12
# 2.5 Update date12_to_rmv from exclude_date
if inps.exclude_date:
inps.exclude_date = ptime.yymmdd(inps.exclude_date)
print '----------------------------------------------------------------------------'
print 'Drop pairs including the following dates: \n' + str(
inps.exclude_date)
date12_to_rmv_temp = []
for i in range(len(date12_orig)):
date1, date2 = date12_orig[i].split('-')
if (date1 in inps.exclude_date) or (date2 in inps.exclude_date):
date12 = date12_orig[i]
date12_to_rmv.append(date12)
date12_to_rmv_temp.append(date12)
print date12_to_rmv_temp
# 2.6 Update date12_to_rmv from start_date
if inps.start_date:
inps.start_date = ptime.yymmdd(inps.start_date)
print '----------------------------------------------------------------------------'
print 'Drop pairs with date earlier than start-date: ' + inps.start_date
min_date = int(ptime.yyyymmdd(inps.start_date))
date12_to_rmv_temp = []
for i in range(len(date12_orig)):
date12 = date12_orig[i]
if any(
int(j) < min_date
for j in ptime.yyyymmdd(date12.split('-'))):
date12_to_rmv.append(date12)
date12_to_rmv_temp.append(date12)
print date12_to_rmv_temp
# 2.7 Update date12_to_rmv from end_date
if inps.end_date:
inps.end_date = ptime.yymmdd(inps.end_date)
print '----------------------------------------------------------------------------'
print 'Drop pairs with date earlier than end-date: ' + inps.end_date
max_date = int(ptime.yyyymmdd(inps.end_date))
date12_to_rmv_temp = []
for i in range(len(date12_orig)):
date12 = date12_orig[i]
if any(
int(j) > max_date
for j in ptime.yyyymmdd(date12.split('-'))):
date12_to_rmv.append(date12)
date12_to_rmv_temp.append(date12)
print date12_to_rmv_temp
# 3. Manually drop pairs
if inps.disp_network:
date12_click = manual_select_pairs_to_remove(inps.file[0])
for date12 in list(date12_click):
if date12 not in date12_orig:
date12_click.remove(date12)
print 'date12 selected to remove:'
print date12_click
date12_to_rmv += date12_click
# 4. drop duplicate date12 and sort in order
date12_to_rmv = sorted(list(set(date12_to_rmv)))
date12_keep = sorted(list(set(date12_orig) - set(date12_to_rmv)))
print '----------------------------------------------------------------------------'
print 'number of interferograms to remove: ' + str(len(date12_to_rmv))
print 'number of interferograms kept : ' + str(len(date12_keep))
##### Calculated date12_to_drop v.s. existing date12_to_drop
# Get list of date12 of interferograms already been marked to drop
k = readfile.read_attribute(inps.file[0])['FILE_TYPE']
h5 = h5py.File(inps.file[0], 'r')
ifgram_list_all = sorted(h5[k].keys())
ifgram_list_keep = ut.check_drop_ifgram(h5, print_msg=False)
ifgram_list_dropped = sorted(
list(set(ifgram_list_all) - set(ifgram_list_keep)))
date12_list_dropped = ptime.list_ifgram2date12(ifgram_list_dropped)
h5.close()
if date12_to_rmv == date12_list_dropped and inps.mark_attribute:
print 'Calculated date12 to drop is the same as exsiting marked input file, skip update file attributes.'
date12_to_rmv = []
##### Update date12 to drop
if date12_to_rmv:
##### Update Input Files with date12_to_rmv
Modified_CoherenceFile = 'Modified_coherence.h5'
for File in inps.file:
Modified_File = modify_file_date12_list(File, date12_to_rmv,
inps.mark_attribute)
k = readfile.read_attribute(File)['FILE_TYPE']
# Update Mask File
if k == 'interferograms' and inps.update_aux:
print 'update mask file for input ' + k + ' file based on ' + Modified_File
inps.mask_file = 'mask.h5'
print 'writing >>> ' + inps.mask_file
ut.nonzero_mask(Modified_File, inps.mask_file)
elif k == 'coherence' and inps.update_aux:
inps.coherence_file = Modified_File
print 'update average spatial coherence for input ' + k + ' file based on: ' + Modified_File
outFile = 'averageSpatialCoherence.h5'
print 'writing >>> ' + outFile
ut.temporal_average(Modified_File, outFile)
# Touch spatial average txt file of coherence if it's existed
coh_spatialAverage_file = os.path.splitext(
Modified_File)[0] + '_spatialAverage.txt'
if os.path.isfile(coh_spatialAverage_file):
touchCmd = 'touch ' + coh_spatialAverage_file
print touchCmd
os.system(touchCmd)
# Plot result
if inps.plot:
print '\nplot modified network and save to file.'
plotCmd = 'plot_network.py ' + inps.coherence_file + ' --coherence ' + inps.coherence_file + ' --nodisplay'
if inps.template_file:
plotCmd += ' --template ' + inps.template_file
print plotCmd
os.system(plotCmd)
print 'Done.'
return
def main(argv):
global outName
parallel = 'no'
############### Check Inputs ###############
if len(sys.argv)>3:
try:
opts, args = getopt.getopt(argv,'f:l:L:o:t:x:y:r:',['lat=','lon=','row=','col=',\
'parallel','outfill=','outfill-nan','outfill-zero'])
except getopt.GetoptError:
print 'Error while getting args'
Usage() ; sys.exit(1)
for opt,arg in opts:
if opt == '-f': File = arg.split(',')
elif opt == '-o': outName = arg
elif opt == '-t': templateFile = arg
elif opt == '-r': refFile = arg
elif opt in ['-x','--col'] : sub_x = [int(i) for i in arg.split(':')]; sub_x.sort()
elif opt in ['-y','--row'] : sub_y = [int(i) for i in arg.split(':')]; sub_y.sort()
elif opt in ['-l','--lat'] : sub_lat = [float(i) for i in arg.split(':')]; sub_lat.sort()
elif opt in ['-L','--lon'] : sub_lon = [float(i) for i in arg.split(':')]; sub_lon.sort()
elif opt in '--parallel' : parallel = 'yes'
elif opt in '--outfill' : out_fill = float(arg)
elif opt in '--outfill-nan' : out_fill = np.nan
elif opt in '--outfill-zero': out_fill = 0.0
elif len(sys.argv)==3:
File = argv[0].split(',')
templateFile = argv[1]
elif len(sys.argv)==2:
if argv[0] in ['-h','--help']: Usage(); sys.exit()
else: print '\nERROR: A minimum of 3 inputs is needed.\n'; Usage(); sys.exit()
else: Usage(); sys.exit(1)
##### Check Input file Info
print '\n**************** Subset *********************'
fileList = ut.get_file_list(File)
print 'number of file: '+str(len(fileList))
print fileList
atr = readfile.read_attributes(fileList[0])
if len(fileList) == 1 and parallel == 'yes':
print 'parallel is disabled for one input file.'
parallel = 'no'
################## Subset Setting ###########
try:
atr['X_FIRST']
print 'geo coordinate'
except:
print 'radar coordinate'
## Read Subset Inputs
try:
templateFile
template = readfile.read_template(templateFile)
except: pass
try:
refFile
atr_ref = readfile.read_attributes(refFile)
box_ref = geo_box(atr_ref)
lat_ref = [box_ref[3],box_ref[1]]
lon_ref = [box_ref[0],box_ref[2]]
except: pass
try:
sub_lat
sub_lon
except:
try:
sub_lat = lat_ref
sub_lon = lon_ref
except:
try:
sub = template['pysar.subset.lalo'].split(',')
sub_lat = [float(i) for i in sub[0].split(':')]; sub_lat.sort()
sub_lon = [float(i) for i in sub[1].split(':')]; sub_lon.sort()
except: pass; #print 'No pysar.subset.lalo option found in template file!'
try:
sub_y
sub_x
except:
try:
sub = template['pysar.subset.yx'].split(',')
sub_y = [int(i) for i in sub[0].split(':')]; sub_y.sort()
sub_x = [int(i) for i in sub[1].split(':')]; sub_x.sort()
except: pass; #print 'No pysar.subset.yx option found in template file!'
## Check Subset Inputs Existed or not
try: sub_y
except:
try: sub_x
except:
try: sub_lat
except:
try: sub_lon
except: print 'ERROR: no subset is setted.'; Usage(); sys.exit(1)
##### Subset range radar to geo
width = int(atr['WIDTH'])
length = int(atr['FILE_LENGTH'])
print 'input file length: '+str(length)
print 'input file width : '+str(width)
try: sub_y = coord_geo2radar(sub_lat,atr,'latitude')
except:
try: sub_y
except: sub_y = [0,length]
try: sub_x = coord_geo2radar(sub_lon,atr,'longitude')
except:
try: sub_x
except: sub_x = [0,width]
##### Check subset range
try:
out_fill
except:
sub_y,sub_x = check_subset_range(sub_y,sub_x,atr)
out_fill = np.nan
if sub_y[1]-sub_y[0] == length and sub_x[1]-sub_x[0] == width:
print 'Input subset range == data size, no need to subset.'
sys.exit(0)
################### Subset #######################
if parallel == 'no':
for file in fileList:
print '-------------------------------------------'
print 'subseting : '+file
try: subset_file(file,sub_x,sub_y,out_fill,outName)
except: subset_file(file,sub_x,sub_y,out_fill)
else:
print '-------------------------'
print 'parallel subseting ...'
print '-------------------------'
from joblib import Parallel, delayed
import multiprocessing
num_cores = multiprocessing.cpu_count()
Parallel(n_jobs=num_cores)(delayed(subset_file)(file,sub_x,sub_y,out_fill) for file in fileList)
print 'Done.'
