def main(argv=None):
#%% Check argv
if argv == None:
argv = sys.argv
start = time.time()
ver = "1.4.8"
date = 20210127
author = "Y. Morishita"
print("\n{} ver{} {} {}".format(os.path.basename(argv[0]), ver, date,
author),
flush=True)
print("{} {}".format(os.path.basename(argv[0]), ' '.join(argv[1:])),
flush=True)
## For parallel processing
global n_para_gap, G, Aloop, unwpatch, imdates, incdir, ifgdir, length, width,\
coef_r2m, ifgdates, ref_unw, cycle, keep_incfile, resdir, restxtfile, \
cmap_vel, cmap_wrap, wavelength
#%% Set default
ifgdir = []
tsadir = []
inv_alg = 'LS'
try:
n_para = len(os.sched_getaffinity(0))
except:
n_para = multi.cpu_count()
n_para_inv = 1
memory_size = 4000
gamma = 0.0001
n_unw_r_thre = []
keep_incfile = False
cmap_vel = SCM.roma.reversed()
cmap_noise = 'viridis'
cmap_noise_r = 'viridis_r'
cmap_wrap = SCM.romaO
# q = multi.get_context('fork')
q = multi.get_context('spawn')
compress = 'gzip'
#%% Read options
try:
try:
opts, args = getopt.getopt(argv[1:], "hd:t:", [
"help", "mem_size=", "gamma=", "n_unw_r_thre=", "keep_incfile",
"inv_alg=", "n_para="
])
except getopt.error as msg:
raise Usage(msg)
for o, a in opts:
if o == '-h' or o == '--help':
print(__doc__)
return 0
elif o == '-d':
ifgdir = a
elif o == '-t':
tsadir = a
elif o == '--mem_size':
memory_size = float(a)
elif o == '--gamma':
gamma = float(a)
elif o == '--n_unw_r_thre':
n_unw_r_thre = float(a)
elif o == '--keep_incfile':
keep_incfile = True
elif o == '--inv_alg':
inv_alg = a
elif o == '--n_para':
n_para = int(a)
if not ifgdir:
raise Usage('No data directory given, -d is not optional!')
elif not os.path.isdir(ifgdir):
raise Usage('No {} dir exists!'.format(ifgdir))
elif not os.path.exists(os.path.join(ifgdir, 'slc.mli.par')):
raise Usage('No slc.mli.par file exists in {}!'.format(ifgdir))
except Usage as err:
print("\nERROR:", file=sys.stderr, end='')
print(" " + str(err.msg), file=sys.stderr)
print("\nFor help, use -h or --help.\n", file=sys.stderr)
return 2
#%% Directory settings
ifgdir = os.path.abspath(ifgdir)
if not tsadir:
tsadir = os.path.join(os.path.dirname(ifgdir),
'TS_' + os.path.basename(ifgdir))
if not os.path.isdir(tsadir):
print('\nNo {} exists!'.format(tsadir), file=sys.stderr)
return 1
tsadir = os.path.abspath(tsadir)
resultsdir = os.path.join(tsadir, 'results')
infodir = os.path.join(tsadir, 'info')
netdir = os.path.join(tsadir, 'network')
bad_ifg11file = os.path.join(infodir, '11bad_ifg.txt')
bad_ifg12file = os.path.join(infodir, '12bad_ifg.txt')
reffile = os.path.join(infodir, '12ref.txt')
if not os.path.exists(reffile): ## for old LiCSBAS12 < v1.1
reffile = os.path.join(infodir, 'ref.txt')
incdir = os.path.join(tsadir, '13increment')
if not os.path.exists(incdir): os.mkdir(incdir)
resdir = os.path.join(tsadir, '13resid')
if not os.path.exists(resdir): os.mkdir(resdir)
restxtfile = os.path.join(infodir, '13resid.txt')
cumh5file = os.path.join(tsadir, 'cum.h5')
#%% Check files
try:
if not os.path.exists(bad_ifg11file):
raise Usage('No 11bad_ifg.txt file exists in {}!'.format(infodir))
if not os.path.exists(bad_ifg12file):
raise Usage('No 12bad_ifg.txt file exists in {}!'.format(infodir))
if not os.path.exists(reffile):
raise Usage('No 12ref.txt file exists in {}!'.format(infodir))
except Usage as err:
print("\nERROR:", file=sys.stderr, end='')
print(" " + str(err.msg), file=sys.stderr)
print("\nFor help, use -h or --help.\n", file=sys.stderr)
return 2
#%% Set preliminaly reference
with open(reffile, "r") as f:
refarea = f.read().split()[0] #str, x1/x2/y1/y2
refx1, refx2, refy1, refy2 = [int(s) for s in re.split('[:/]', refarea)]
#%% Check RAM
mem_avail = (psutil.virtual_memory().available) / 2**20 #MB
if memory_size > mem_avail / 2:
print('\nNot enough memory available compared to mem_size ({} MB).'.
format(memory_size))
print('Reduce mem_size automatically to {} MB.'.format(
int(mem_avail / 2)))
memory_size = int(mem_avail / 2)
#%% Read data information
### Get size
mlipar = os.path.join(ifgdir, 'slc.mli.par')
width = int(io_lib.get_param_par(mlipar, 'range_samples'))
length = int(io_lib.get_param_par(mlipar, 'azimuth_lines'))
speed_of_light = 299792458 #m/s
radar_frequency = float(io_lib.get_param_par(mlipar,
'radar_frequency')) #Hz
wavelength = speed_of_light / radar_frequency #meter
coef_r2m = -wavelength / 4 / np.pi * 1000 #rad -> mm, positive is -LOS
### Calc pixel spacing depending on IFG or GEOC, used in later spatial filter
dempar = os.path.join(ifgdir, 'EQA.dem_par')
width_geo = int(io_lib.get_param_par(dempar, 'width'))
length_geo = int(io_lib.get_param_par(dempar, 'nlines'))
dlat = float(io_lib.get_param_par(dempar, 'post_lat')) #negative
dlon = float(io_lib.get_param_par(dempar, 'post_lon')) #positive
lat1 = float(io_lib.get_param_par(dempar, 'corner_lat'))
lon1 = float(io_lib.get_param_par(dempar, 'corner_lon'))
if width == width_geo and length == length_geo: ## Geocoded
print('\nIn geographical coordinates', flush=True)
centerlat = lat1 + dlat * (length / 2)
ra = float(io_lib.get_param_par(dempar, 'ellipsoid_ra'))
recip_f = float(
io_lib.get_param_par(dempar, 'ellipsoid_reciprocal_flattening'))
rb = ra * (1 - 1 / recip_f) ## polar radius
pixsp_a = 2 * np.pi * rb / 360 * abs(dlat)
pixsp_r = 2 * np.pi * ra / 360 * dlon * np.cos(np.deg2rad(centerlat))
else:
print('\nIn radar coordinates', flush=True)
pixsp_r_org = float(io_lib.get_param_par(mlipar,
'range_pixel_spacing'))
pixsp_a = float(io_lib.get_param_par(mlipar, 'azimuth_pixel_spacing'))
inc_agl = float(io_lib.get_param_par(mlipar, 'incidence_angle'))
pixsp_r = pixsp_r_org / np.sin(np.deg2rad(inc_agl))
### Set n_unw_r_thre and cycle depending on L- or C-band
if wavelength > 0.2: ## L-band
if not n_unw_r_thre: n_unw_r_thre = 0.5
cycle = 1.5 # 2pi/cycle for comparison png
elif wavelength <= 0.2: ## C-band
if not n_unw_r_thre: n_unw_r_thre = 1.0
cycle = 3 # 3*2pi/cycle for comparison png
#%% Read date and network information
### Get all ifgdates in ifgdir
ifgdates_all = tools_lib.get_ifgdates(ifgdir)
imdates_all = tools_lib.ifgdates2imdates(ifgdates_all)
n_im_all = len(imdates_all)
n_ifg_all = len(ifgdates_all)
### Read bad_ifg11 and 12
bad_ifg11 = io_lib.read_ifg_list(bad_ifg11file)
bad_ifg12 = io_lib.read_ifg_list(bad_ifg12file)
bad_ifg_all = list(set(bad_ifg11 + bad_ifg12))
bad_ifg_all.sort()
### Remove bad ifgs and images from list
ifgdates = list(set(ifgdates_all) - set(bad_ifg_all))
ifgdates.sort()
imdates = tools_lib.ifgdates2imdates(ifgdates)
n_ifg = len(ifgdates)
n_ifg_bad = len(set(bad_ifg11 + bad_ifg12))
n_im = len(imdates)
n_unw_thre = int(n_unw_r_thre * n_im)
### Make 13used_image.txt
imfile = os.path.join(infodir, '13used_image.txt')
with open(imfile, 'w') as f:
for i in imdates:
print('{}'.format(i), file=f)
### Calc dt in year
imdates_dt = ([
dt.datetime.strptime(imd, '%Y%m%d').toordinal() for imd in imdates
])
dt_cum = np.float32((np.array(imdates_dt) - imdates_dt[0]) / 365.25)
### Construct G and Aloop matrix for increment and n_gap
G = inv_lib.make_sb_matrix(ifgdates)
Aloop = loop_lib.make_loop_matrix(ifgdates)
#%% Plot network
## Read bperp data or dummy
bperp_file = os.path.join(ifgdir, 'baselines')
if os.path.exists(bperp_file):
bperp_all = io_lib.read_bperp_file(bperp_file, imdates_all)
bperp = io_lib.read_bperp_file(bperp_file, imdates)
else: #dummy
bperp_all = np.random.random(len(imdates_all)).tolist()
bperp = np.random.random(n_im).tolist()
pngfile = os.path.join(netdir, 'network13_all.png')
plot_lib.plot_network(ifgdates_all, bperp_all, [], pngfile)
pngfile = os.path.join(netdir, 'network13.png')
plot_lib.plot_network(ifgdates_all, bperp_all, bad_ifg_all, pngfile)
pngfile = os.path.join(netdir, 'network13_nobad.png')
plot_lib.plot_network(ifgdates_all,
bperp_all,
bad_ifg_all,
pngfile,
plot_bad=False)
#%% Get patch row number
if inv_alg == 'WLS':
n_store_data = n_ifg * 3 + n_im * 2 + n_im * 0.3 #
else:
n_store_data = n_ifg * 2 + n_im * 2 + n_im * 0.3 #not sure
n_patch, patchrow = tools_lib.get_patchrow(width, length, n_store_data,
memory_size)
#%% Display and output settings & parameters
print('')
print('Size of image (w,l) : {}, {}'.format(width, length))
print('# of all images : {}'.format(n_im_all))
print('# of images to be used : {}'.format(n_im))
print('# of all ifgs : {}'.format(n_ifg_all))
print('# of ifgs to be used : {}'.format(n_ifg))
print('# of removed ifgs : {}'.format(n_ifg_bad))
print('Threshold of used unw : {}'.format(n_unw_thre))
print('')
print('Reference area (X/Y) : {}:{}/{}:{}'.format(
refx1, refx2, refy1, refy2))
print('Allowed memory size : {} MB'.format(memory_size))
print('Number of patches : {}'.format(n_patch))
print('Inversion algorism : {}'.format(inv_alg))
print('Gamma value : {}'.format(gamma), flush=True)
with open(os.path.join(infodir, '13parameters.txt'), "w") as f:
print('range_samples: {}'.format(width), file=f)
print('azimuth_lines: {}'.format(length), file=f)
print('wavelength: {}'.format(wavelength), file=f)
print('n_im_all: {}'.format(n_im_all), file=f)
print('n_im: {}'.format(n_im), file=f)
print('n_ifg_all: {}'.format(n_ifg_all), file=f)
print('n_ifg: {}'.format(n_ifg), file=f)
print('n_ifg_bad: {}'.format(n_ifg_bad), file=f)
print('n_unw_thre: {}'.format(n_unw_thre), file=f)
print('ref_area: {}:{}/{}:{}'.format(refx1, refx2, refy1, refy2),
file=f)
print('memory_size: {} MB'.format(memory_size), file=f)
print('n_patch: {}'.format(n_patch), file=f)
print('inv_alg: {}'.format(inv_alg), file=f)
print('gamma: {}'.format(gamma), file=f)
print('pixel_spacing_r: {:.2f} m'.format(pixsp_r), file=f)
print('pixel_spacing_a: {:.2f} m'.format(pixsp_a), file=f)
#%% Ref phase for inversion
lengththis = refy2 - refy1
countf = width * refy1
countl = width * lengththis # Number to be read
ref_unw = []
for i, ifgd in enumerate(ifgdates):
unwfile = os.path.join(ifgdir, ifgd, ifgd + '.unw')
f = open(unwfile, 'rb')
f.seek(countf * 4, os.SEEK_SET) #Seek for >=2nd path, 4 means byte
### Read unw data (mm) at ref area
unw = np.fromfile(f, dtype=np.float32, count=countl).reshape(
(lengththis, width))[:, refx1:refx2] * coef_r2m
unw[unw == 0] = np.nan
if np.all(np.isnan(unw)):
print('All nan in ref area in {}.'.format(ifgd))
print('Rerun LiCSBAS12.')
return 1
ref_unw.append(np.nanmean(unw))
f.close()
#%% Open cum.h5 for output
if os.path.exists(cumh5file): os.remove(cumh5file)
cumh5 = h5.File(cumh5file, 'w')
cumh5.create_dataset('imdates', data=[np.int32(imd) for imd in imdates])
if not np.all(np.abs(np.array(bperp)) <= 1): # if not dummy
cumh5.create_dataset('bperp', data=bperp)
cum = cumh5.require_dataset('cum', (n_im, length, width),
dtype=np.float32,
compression=compress)
vel = cumh5.require_dataset('vel', (length, width),
dtype=np.float32,
compression=compress)
vconst = cumh5.require_dataset('vintercept', (length, width),
dtype=np.float32,
compression=compress)
gap = cumh5.require_dataset('gap', (n_im - 1, length, width),
dtype=np.int8,
compression=compress)
if width == width_geo and length == length_geo: ## if geocoded
cumh5.create_dataset('corner_lat', data=lat1)
cumh5.create_dataset('corner_lon', data=lon1)
cumh5.create_dataset('post_lat', data=dlat)
cumh5.create_dataset('post_lon', data=dlon)
#%% For each patch
for i_patch, rows in enumerate(patchrow):
print('\nProcess {0}/{1}th line ({2}/{3}th patch)...'.format(
rows[1], patchrow[-1][-1], i_patch + 1, n_patch),
flush=True)
start2 = time.time()
#%% Read data
### Allocate memory
lengththis = rows[1] - rows[0]
n_pt_all = lengththis * width
unwpatch = np.zeros((n_ifg, lengththis, width), dtype=np.float32)
if inv_alg == 'WLS':
cohpatch = np.zeros((n_ifg, lengththis, width), dtype=np.float32)
### For each ifg
print(" Reading {0} ifg's unw data...".format(n_ifg), flush=True)
countf = width * rows[0]
countl = width * lengththis
for i, ifgd in enumerate(ifgdates):
unwfile = os.path.join(ifgdir, ifgd, ifgd + '.unw')
f = open(unwfile, 'rb')
f.seek(countf * 4,
os.SEEK_SET) #Seek for >=2nd patch, 4 means byte
### Read unw data (mm) at patch area
unw = np.fromfile(f, dtype=np.float32, count=countl).reshape(
(lengththis, width)) * coef_r2m
unw[unw == 0] = np.nan # Fill 0 with nan
unw = unw - ref_unw[i]
unwpatch[i] = unw
f.close()
### Read coh file at patch area for WLS
if inv_alg == 'WLS':
cohfile = os.path.join(ifgdir, ifgd, ifgd + '.cc')
f = open(cohfile, 'rb')
if os.path.getsize(cohfile) == length * width: ## uint8 format
f.seek(countf, os.SEEK_SET) #Seek for >=2nd patch
cohpatch[i, :, :] = (np.fromfile(
f, dtype=np.uint8, count=countl).reshape(
(lengththis, width))).astype(np.float32) / 255
else: ## old float32 format
f.seek(countf * 4,
os.SEEK_SET) #Seek for >=2nd patch, 4 means byte
cohpatch[i, :, :] = np.fromfile(f,
dtype=np.float32,
count=countl).reshape(
(lengththis, width))
cohpatch[cohpatch == 0] = np.nan
unwpatch = unwpatch.reshape(
(n_ifg, n_pt_all)).transpose() #(n_pt_all, n_ifg)
### Calc variance from coherence for WLS
if inv_alg == 'WLS':
cohpatch = cohpatch.reshape(
(n_ifg, n_pt_all)).transpose() #(n_pt_all, n_ifg)
cohpatch[
cohpatch <
0.01] = 0.01 ## because negative value possible due to geocode
cohpatch[
cohpatch > 0.99] = 0.99 ## because >1 possible due to geocode
varpatch = (1 - cohpatch**2) / (2 * cohpatch**2)
del cohpatch
#%% Remove points with less valid data than n_unw_thre
ix_unnan_pt = np.where(
np.sum(~np.isnan(unwpatch), axis=1) > n_unw_thre)[0]
n_pt_unnan = len(ix_unnan_pt)
unwpatch = unwpatch[ix_unnan_pt, :] ## keep only unnan data
if inv_alg == 'WLS':
varpatch = varpatch[ix_unnan_pt, :] ## keep only unnan data
print(' {}/{} points removed due to not enough ifg data...'.format(
n_pt_all - n_pt_unnan, n_pt_all),
flush=True)
#%% Compute number of gaps, ifg_noloop, maxTlen point-by-point
if n_pt_unnan != 0:
ns_gap_patch = np.zeros((n_pt_all), dtype=np.float32) * np.nan
gap_patch = np.zeros((n_im - 1, n_pt_all), dtype=np.int8)
ns_ifg_noloop_patch = np.zeros(
(n_pt_all), dtype=np.float32) * np.nan
maxTlen_patch = np.zeros((n_pt_all), dtype=np.float32) * np.nan
### Determine n_para
n_pt_patch_min = 1000
if n_pt_patch_min * n_para > n_pt_unnan:
## Too much n_para
n_para_gap = int(np.floor(n_pt_unnan / n_pt_patch_min))
if n_para_gap == 0: n_para_gap = 1
else:
n_para_gap = n_para
print('\n Identifing gaps, and counting n_gap and n_ifg_noloop,')
print(' with {} parallel processing...'.format(n_para_gap),
flush=True)
### Devide unwpatch by n_para for parallel processing
p = q.Pool(n_para_gap)
# _result = np.array(p.map(count_gaps_wrapper, range(n_para_gap)), dtype=object)
_result = np.array(p.map(count_gaps_wrapper,
[(i, n_para_gap, G, Aloop, unwpatch)
for i in range(n_para_gap)]),
dtype=object)
p.close()
ns_gap_patch[ix_unnan_pt] = np.hstack(_result[:, 0]) #n_pt
gap_patch[:, ix_unnan_pt] = np.hstack(_result[:, 1]) #n_im-1, n_pt
ns_ifg_noloop_patch[ix_unnan_pt] = np.hstack(_result[:, 2])
### maxTlen
_maxTlen = np.zeros((n_pt_unnan), dtype=np.float32) #temporaly
_Tlen = np.zeros((n_pt_unnan), dtype=np.float32) #temporaly
for imx in range(n_im - 1):
_Tlen = _Tlen + (dt_cum[imx + 1] - dt_cum[imx]) ## Adding dt
_Tlen[gap_patch[imx,
ix_unnan_pt] == 1] = 0 ## reset to 0 if gap
_maxTlen[_maxTlen < _Tlen] = _Tlen[
_maxTlen < _Tlen] ## Set Tlen to maxTlen
maxTlen_patch[ix_unnan_pt] = _maxTlen
#%% Time series inversion
print('\n Small Baseline inversion by {}...\n'.format(inv_alg),
flush=True)
if inv_alg == 'WLS':
inc_tmp, vel_tmp, vconst_tmp = inv_lib.invert_nsbas_wls(
unwpatch, varpatch, G, dt_cum, gamma, n_para_inv)
else:
inc_tmp, vel_tmp, vconst_tmp = inv_lib.invert_nsbas(
unwpatch, G, dt_cum, gamma, n_para_inv)
### Set to valuables
inc_patch = np.zeros(
(n_im - 1, n_pt_all), dtype=np.float32) * np.nan
vel_patch = np.zeros((n_pt_all), dtype=np.float32) * np.nan
vconst_patch = np.zeros((n_pt_all), dtype=np.float32) * np.nan
inc_patch[:, ix_unnan_pt] = inc_tmp
vel_patch[ix_unnan_pt] = vel_tmp
vconst_patch[ix_unnan_pt] = vconst_tmp
### Calculate residuals
res_patch = np.zeros((n_ifg, n_pt_all), dtype=np.float32) * np.nan
res_patch[:, ix_unnan_pt] = unwpatch.T - np.dot(G, inc_tmp)
res_sumsq = np.nansum(res_patch**2, axis=0)
res_n = np.float32((~np.isnan(res_patch)).sum(axis=0))
res_n[res_n == 0] = np.nan # To avoid 0 division
res_rms_patch = np.sqrt(res_sumsq / res_n)
### Cumulative displacememt
cum_patch = np.zeros((n_im, n_pt_all), dtype=np.float32) * np.nan
cum_patch[1:, :] = np.cumsum(inc_patch, axis=0)
## Fill 1st image with 0 at unnan points from 2nd images
bool_unnan_pt = ~np.isnan(cum_patch[1, :])
cum_patch[0, bool_unnan_pt] = 0
## Drop (fill with nan) interpolated cum by 2 continuous gaps
for i in range(n_im - 2): ## from 1->n_im-1
gap2 = gap_patch[i, :] + gap_patch[i + 1, :]
bool_gap2 = (gap2 == 2
) ## true if 2 continuous gaps for each point
cum_patch[i + 1, :][bool_gap2] = np.nan
## Last (n_im th) image. 1 gap means interpolated
cum_patch[-1, :][gap_patch[-1, :] == 1] = np.nan
#%% Fill by np.nan if n_pt_unnan == 0
else:
cum_patch = np.zeros((n_im, n_pt_all), dtype=np.float32) * np.nan
vel_patch = np.zeros((n_pt_all), dtype=np.float32) * np.nan
vconst_patch = np.zeros((n_pt_all), dtype=np.float32) * np.nan
gap_patch = np.zeros((n_im - 1, n_pt_all), dtype=np.int8)
inc_patch = np.zeros(
(n_im - 1, n_pt_all), dtype=np.float32) * np.nan
res_patch = np.zeros((n_ifg, n_pt_all), dtype=np.float32) * np.nan
res_rms_patch = np.zeros((n_pt_all), dtype=np.float32) * np.nan
ns_gap_patch = np.zeros((n_pt_all), dtype=np.float32) * np.nan
ns_ifg_noloop_patch = np.zeros(
(n_pt_all), dtype=np.float32) * np.nan
maxTlen_patch = np.zeros((n_pt_all), dtype=np.float32) * np.nan
#%% Output data and image
### cum.h5 file
cum[:, rows[0]:rows[1], :] = cum_patch.reshape(
(n_im, lengththis, width))
vel[rows[0]:rows[1], :] = vel_patch.reshape((lengththis, width))
vconst[rows[0]:rows[1], :] = vconst_patch.reshape((lengththis, width))
gap[:, rows[0]:rows[1], :] = gap_patch.reshape(
(n_im - 1, lengththis, width))
### Others
openmode = 'w' if rows[0] == 0 else 'a' #w only 1st patch
## For each imd. cum and inc
for imx, imd in enumerate(imdates):
## Incremental displacement
if imd == imdates[-1]: continue #skip last
incfile = os.path.join(incdir,
'{0}_{1}.inc'.format(imd, imdates[imx + 1]))
with open(incfile, openmode) as f:
inc_patch[imx, :].tofile(f)
## For each ifgd. resid
for i, ifgd in enumerate(ifgdates):
resfile = os.path.join(resdir, '{0}.res'.format(ifgd))
with open(resfile, openmode) as f:
res_patch[i, :].tofile(f)
## velocity and noise indecies in results dir
names = [
'vel', 'vintercept', 'resid_rms', 'n_gap', 'n_ifg_noloop',
'maxTlen'
]
data = [
vel_patch, vconst_patch, res_rms_patch, ns_gap_patch,
ns_ifg_noloop_patch, maxTlen_patch
]
for i in range(len(names)):
file = os.path.join(resultsdir, names[i])
with open(file, openmode) as f:
data[i].tofile(f)
#%% Finish patch
elapsed_time2 = int(time.time() - start2)
hour2 = int(elapsed_time2 / 3600)
minite2 = int(np.mod((elapsed_time2 / 60), 60))
sec2 = int(np.mod(elapsed_time2, 60))
print(" Elapsed time for {0}th patch: {1:02}h {2:02}m {3:02}s".format(
i_patch + 1, hour2, minite2, sec2),
flush=True)
#%% Find stable ref point
print('\nFind stable reference point...', flush=True)
### Compute RMS of time series with reference to all points
sumsq_cum_wrt_med = np.zeros((length, width), dtype=np.float32)
for i in range(n_im):
sumsq_cum_wrt_med = sumsq_cum_wrt_med + (cum[i, :, :] -
np.nanmedian(cum[i, :, :]))**2
rms_cum_wrt_med = np.sqrt(sumsq_cum_wrt_med / n_im)
### Mask by minimum n_gap
n_gap = io_lib.read_img(os.path.join(resultsdir, 'n_gap'), length, width)
min_n_gap = np.nanmin(n_gap)
mask_n_gap = np.float32(n_gap == min_n_gap)
mask_n_gap[mask_n_gap == 0] = np.nan
rms_cum_wrt_med = rms_cum_wrt_med * mask_n_gap
### Find stable reference
min_rms = np.nanmin(rms_cum_wrt_med)
refy1s, refx1s = np.where(rms_cum_wrt_med == min_rms)
refy1s, refx1s = refy1s[0], refx1s[0] ## Only first index
refy2s, refx2s = refy1s + 1, refx1s + 1
print('Selected ref: {}:{}/{}:{}'.format(refx1s, refx2s, refy1s, refy2s),
flush=True)
### Rerferencing cumulative displacement and vel to new stable ref
for i in range(n_im):
cum[i, :, :] = cum[i, :, :] - cum[i, refy1s, refx1s]
vel = vel - vel[refy1s, refx1s]
vconst = vconst - vconst[refy1s, refx1s]
### Save image
rms_cum_wrt_med_file = os.path.join(infodir, '13rms_cum_wrt_med')
with open(rms_cum_wrt_med_file, 'w') as f:
rms_cum_wrt_med.tofile(f)
pngfile = os.path.join(infodir, '13rms_cum_wrt_med.png')
plot_lib.make_im_png(rms_cum_wrt_med, pngfile, cmap_noise_r,
'RMS of cum wrt median (mm)',
np.nanpercentile(rms_cum_wrt_med, 1),
np.nanpercentile(rms_cum_wrt_med, 99))
### Save ref
cumh5.create_dataset('refarea',
data='{}:{}/{}:{}'.format(refx1s, refx2s, refy1s,
refy2s))
refsfile = os.path.join(infodir, '13ref.txt')
with open(refsfile, 'w') as f:
print('{}:{}/{}:{}'.format(refx1s, refx2s, refy1s, refy2s), file=f)
if width == width_geo and length == length_geo: ## Geocoded
### Make ref_stable.kml
reflat = lat1 + dlat * refy1s
reflon = lon1 + dlon * refx1s
io_lib.make_point_kml(reflat, reflon,
os.path.join(infodir, '13ref.kml'))
#%% Close h5 file
cumh5.close()
#%% Output png images
### Incremental displacement
_n_para = n_im - 1 if n_para > n_im - 1 else n_para
print(
'\nOutput increment png images with {} parallel processing...'.format(
_n_para),
flush=True)
p = q.Pool(_n_para)
# p.map(inc_png_wrapper, range(n_im-1))
p.map(inc_png_wrapper,
[(i, imdates, incdir, ifgdir, length, width, coef_r2m, ifgdates,
ref_unw, cycle, cmap_wrap, keep_incfile) for i in range(n_im - 1)])
p.close()
### Residual for each ifg. png and txt.
with open(restxtfile, "w") as f:
print('# RMS of residual (mm)', file=f)
_n_para = n_ifg if n_para > n_ifg else n_para
print('\nOutput residual png images with {} parallel processing...'.format(
_n_para),
flush=True)
p = q.Pool(_n_para)
# p.map(resid_png_wrapper, range(n_ifg))
p.map(resid_png_wrapper, [(i, ifgdates, resdir, length, width, restxtfile,
cmap_vel, wavelength, keep_incfile)
for i in range(n_ifg)])
p.close()
### Velocity and noise indices
cmins = [None, None, None, None, None, None]
cmaxs = [None, None, None, None, None, None]
cmaps = [
cmap_vel, cmap_vel, cmap_noise_r, cmap_noise_r, cmap_noise_r,
cmap_noise
]
titles = [
'Velocity (mm/yr)', 'Intercept of velocity (mm)',
'RMS of residual (mm)', 'Number of gaps in SB network',
'Number of ifgs with no loops',
'Max length of connected SB network (yr)'
]
print('\nOutput noise png images...', flush=True)
for i in range(len(names)):
file = os.path.join(resultsdir, names[i])
data = io_lib.read_img(file, length, width)
pngfile = file + '.png'
## Get color range if None
if cmins[i] is None:
cmins[i] = np.nanpercentile(data, 1)
if cmaxs[i] is None:
cmaxs[i] = np.nanpercentile(data, 99)
if cmins[i] == cmaxs[i]: cmins[i] = cmaxs[i] - 1
plot_lib.make_im_png(data, pngfile, cmaps[i], titles[i], cmins[i],
cmaxs[i])
#%% Finish
elapsed_time = time.time() - start
hour = int(elapsed_time / 3600)
minite = int(np.mod((elapsed_time / 60), 60))
sec = int(np.mod(elapsed_time, 60))
print("\nElapsed time: {0:02}h {1:02}m {2:02}s".format(hour, minite, sec))
print('\n{} Successfully finished!!\n'.format(os.path.basename(argv[0])))
print('Output directory: {}\n'.format(os.path.relpath(tsadir)))
def main(argv=None):
#%% Check argv
if argv == None:
argv = sys.argv
start = time.time()
print("{} {}".format(os.path.basename(argv[0]), ' '.join(argv[1:])),
flush=True)
#%% Set default
ifgdir = []
tsadir = []
refflag = 'auto' ## not supported
inv_alg = 'LS'
n_core = 1
memory_size = 4000
gamma = 0.0001
n_unw_r_thre = []
keep_incfile = False
#%% Read options
try:
try:
opts, args = getopt.getopt(argv[1:], "hd:t:r:", [
"help", "mem_size=", "gamma=", "n_unw_r_thre=", "keep_incfile",
"inv_alg=", "n_core="
])
except getopt.error as msg:
raise Usage(msg)
for o, a in opts:
if o == '-h' or o == '--help':
print(__doc__)
return 0
elif o == '-d':
ifgdir = a
elif o == '-t':
tsadir = a
elif o == '-r':
refflag = a
elif o == '--mem_size':
memory_size = float(a)
elif o == '--gamma':
gamma = float(a)
elif o == '--n_unw_r_thre':
n_unw_r_thre = float(a)
elif o == '--keep_incfile':
keep_incfile = True
elif o == '--inv_alg':
inv_alg = a
elif o == '--n_core':
n_core = int(a)
if not ifgdir:
raise Usage('No data directory given, -d is not optional!')
elif not os.path.isdir(ifgdir):
raise Usage('No {} dir exists!'.format(ifgdir))
elif not os.path.exists(os.path.join(ifgdir, 'slc.mli.par')):
raise Usage('No slc.mli.par file exists in {}!'.format(ifgdir))
except Usage as err:
print("\nERROR:", file=sys.stderr, end='')
print(" " + str(err.msg), file=sys.stderr)
print("\nFor help, use -h or --help.\n", file=sys.stderr)
return 2
#%% Directory settings
ifgdir = os.path.abspath(ifgdir)
if not tsadir:
tsadir = os.path.join(os.path.dirname(ifgdir),
'TS_' + os.path.basename(ifgdir))
if not os.path.isdir(tsadir):
print('\nNo {} exists!'.format(tsadir), file=sys.stderr)
return 1
tsadir = os.path.abspath(tsadir)
resultsdir = os.path.join(tsadir, 'results')
infodir = os.path.join(tsadir, 'info')
netdir = os.path.join(tsadir, 'network')
bad_ifg11file = os.path.join(infodir, '11bad_ifg.txt')
bad_ifg12file = os.path.join(infodir, '12bad_ifg.txt')
reffile = os.path.join(infodir, 'ref.txt')
incdir = os.path.join(tsadir, '13increment')
if not os.path.exists(incdir): os.mkdir(incdir)
resdir = os.path.join(tsadir, '13resid')
if not os.path.exists(resdir): os.mkdir(resdir)
restxtfile = os.path.join(infodir, '13resid.txt')
cumh5file = os.path.join(tsadir, 'cum.h5')
#%% Check files
try:
if not os.path.exists(bad_ifg11file):
raise Usage('No 11bad_ifg.txt file exists in {}!'.format(tsadir))
if not os.path.exists(bad_ifg12file):
raise Usage('No 12bad_ifg.txt file exists in {}!'.format(tsadir))
if not os.path.exists(reffile):
raise Usage('No ref.txt file exists in {}!'.format(tsadir))
except Usage as err:
print("\nERROR:", file=sys.stderr, end='')
print(" " + str(err.msg), file=sys.stderr)
print("\nFor help, use -h or --help.\n", file=sys.stderr)
return 2
#%% Check and set reference
if refflag == 'auto': # Automatic setting based on ref.txt
with open(reffile, "r") as f:
refarea = f.read().split()[0] #str, x1/x2/y1/y2
refx1, refx2, refy1, refy2 = [
int(s) for s in re.split('[:/]', refarea)
]
#%% Read data information
### Get size
mlipar = os.path.join(ifgdir, 'slc.mli.par')
width = int(io_lib.get_param_par(mlipar, 'range_samples'))
length = int(io_lib.get_param_par(mlipar, 'azimuth_lines'))
speed_of_light = 299792458 #m/s
radar_frequency = float(io_lib.get_param_par(mlipar,
'radar_frequency')) #Hz
wavelength = speed_of_light / radar_frequency #meter
coef_r2m = -wavelength / 4 / np.pi * 1000 #rad -> mm, positive is -LOS
### Calc pixel spacing depending on IFG or GEOC, used in later spatial filter
dempar = os.path.join(ifgdir, 'EQA.dem_par')
width_geo = int(io_lib.get_param_par(dempar, 'width'))
length_geo = int(io_lib.get_param_par(dempar, 'nlines'))
dlat = float(io_lib.get_param_par(dempar, 'post_lat')) #negative
dlon = float(io_lib.get_param_par(dempar, 'post_lon')) #positive
lat1 = float(io_lib.get_param_par(dempar, 'corner_lat'))
lon1 = float(io_lib.get_param_par(dempar, 'corner_lon'))
if width == width_geo and length == length_geo: ## Geocoded
print('In geographical coordinates', flush=True)
centerlat = lat1 + dlat * (length / 2)
ra = float(io_lib.get_param_par(dempar, 'ellipsoid_ra'))
recip_f = float(
io_lib.get_param_par(dempar, 'ellipsoid_reciprocal_flattening'))
rb = ra * (1 - 1 / recip_f) ## polar radius
pixsp_a = 2 * np.pi * rb / 360 * abs(dlat)
pixsp_r = 2 * np.pi * ra / 360 * dlon * np.cos(np.deg2rad(centerlat))
### Make ref.kml
reflat = lat1 + dlat * refy1
reflon = lon1 + dlon * refx1
make_point_kml(reflat, reflon, os.path.join(infodir, 'ref.kml'))
else:
print('In radar coordinates', flush=True)
pixsp_r_org = float(io_lib.get_param_par(mlipar,
'range_pixel_spacing'))
pixsp_a = float(io_lib.get_param_par(mlipar, 'azimuth_pixel_spacing'))
inc_agl = float(io_lib.get_param_par(mlipar, 'incidence_angle'))
pixsp_r = pixsp_r_org / np.sin(np.deg2rad(inc_agl))
### Set n_unw_r_thre and cycle depending on L- or C-band
if wavelength > 0.2: ## L-band
if not n_unw_r_thre: n_unw_r_thre = 0.5
cycle = 1.5 # 2pi/cycle for comparison png
elif wavelength <= 0.2: ## C-band
if not n_unw_r_thre: n_unw_r_thre = 1.0
cycle = 3 # 3*2pi/cycle for comparison png
#%% Read date and network information
### Get all ifgdates in ifgdir
ifgdates_all = tools_lib.get_ifgdates(ifgdir)
imdates_all = tools_lib.ifgdates2imdates(ifgdates_all)
n_im_all = len(imdates_all)
n_ifg_all = len(ifgdates_all)
### Read bad_ifg11 and 12
bad_ifg11 = io_lib.read_ifg_list(bad_ifg11file)
bad_ifg12 = io_lib.read_ifg_list(bad_ifg12file)
bad_ifg_all = list(set(bad_ifg11 + bad_ifg12))
bad_ifg_all.sort()
### Remove bad ifgs and images from list
ifgdates = list(set(ifgdates_all) - set(bad_ifg_all))
ifgdates.sort()
imdates = tools_lib.ifgdates2imdates(ifgdates)
n_ifg = len(ifgdates)
n_ifg_bad = len(set(bad_ifg11 + bad_ifg12))
n_im = len(imdates)
n_unw_thre = int(n_unw_r_thre * n_im)
### Make 13used_image.txt
imfile = os.path.join(infodir, '13used_image.txt')
with open(imfile, 'w') as f:
for i in imdates:
print('{}'.format(i), file=f)
### Calc dt in year
imdates_dt = ([
dt.datetime.strptime(imd, '%Y%m%d').toordinal() for imd in imdates
])
dt_cum = np.float32((np.array(imdates_dt) - imdates_dt[0]) / 365.25)
### Construct G and Aloop matrix for increment and n_gap
G = inv_lib.make_sb_matrix(ifgdates)
Aloop = loop_lib.make_loop_matrix(ifgdates)
n_loop = Aloop.shape[0] # (n_loop,n_ifg)
#%% Plot network
## Read bperp data or dummy
bperp_file = os.path.join(ifgdir, 'baselines')
if os.path.exists(bperp_file):
bperp_all = io_lib.read_bperp_file(bperp_file, imdates_all)
bperp = io_lib.read_bperp_file(bperp_file, imdates)
else: #dummy
bperp_all = np.random.random(len(imdates_all)).tolist()
bperp = np.random.random(n_im).tolist()
pngfile = os.path.join(netdir, 'network13_all.png')
plot_lib.plot_network(ifgdates_all, bperp_all, [], pngfile, pdf=True)
pngfile = os.path.join(netdir, 'network13.png')
plot_lib.plot_network(ifgdates_all,
bperp_all,
bad_ifg_all,
pngfile,
pdf=True)
pngfile = os.path.join(netdir, 'network13_nobad.png')
plot_lib.plot_network(ifgdates_all,
bperp_all,
bad_ifg_all,
pngfile,
plot_bad=False,
pdf=True)
#%% Get patch row number
if inv_alg == 'WLS':
n_store_data = n_ifg * 3 + n_im * 2 + n_im * 0.3 #
else:
n_store_data = n_ifg * 2 + n_im * 2 + n_im * 0.3 #not sure
n_patch, patchrow = tools_lib.get_patchrow(width, length, n_store_data,
memory_size)
#%% Display and output settings & paramters
print('')
print('Size of image (w,l) : {}, {}'.format(width, length))
print('# of all images : {}'.format(n_im_all))
print('# of images to be used : {}'.format(n_im))
print('# of all ifgs : {}'.format(n_ifg_all))
print('# of ifgs to be used : {}'.format(n_ifg))
print('# of removed ifgs : {}'.format(n_ifg_bad))
print('Threshold of used unw : {}'.format(n_unw_thre))
print('')
print('Reference area (X/Y) : {}:{}/{}:{}'.format(
refx1, refx2, refy1, refy2))
print('Allowed memory size : {} MB'.format(memory_size))
print('Number of patches : {}'.format(n_patch))
print('Inversion algorism : {}'.format(inv_alg))
print('Gamma value : {}'.format(gamma), flush=True)
with open(os.path.join(infodir, 'parameters.txt'), "w") as f:
print('range_samples: {}'.format(width), file=f)
print('azimuth_lines: {}'.format(length), file=f)
print('wavelength: {}'.format(wavelength), file=f)
print('n_im_all: {}'.format(n_im_all), file=f)
print('n_im: {}'.format(n_im), file=f)
print('n_ifg_all: {}'.format(n_ifg_all), file=f)
print('n_ifg: {}'.format(n_ifg), file=f)
print('n_ifg_bad: {}'.format(n_ifg_bad), file=f)
print('n_unw_thre: {}'.format(n_unw_thre), file=f)
print('ref_area: {}:{}/{}:{}'.format(refx1, refx2, refy1, refy2),
file=f)
print('memory_size: {} MB'.format(memory_size), file=f)
print('n_patch: {}'.format(n_patch), file=f)
print('inv_alg: {}'.format(inv_alg), file=f)
print('gamma: {}'.format(gamma), file=f)
print('pixel_spacing_r: {:.2f} m'.format(pixsp_r), file=f)
print('pixel_spacing_a: {:.2f} m'.format(pixsp_a), file=f)
#%% Ref phase for inversion
lengththis = refy2 - refy1
countf = width * refy1
countl = width * lengththis # Number to be read
ref_unw = []
for i, ifgd in enumerate(ifgdates):
unwfile = os.path.join(ifgdir, ifgd, ifgd + '.unw')
f = open(unwfile, 'rb')
f.seek(countf * 4, os.SEEK_SET) #Seek for >=2nd path, 4 means byte
### Read unw data (mm) at ref area
unw = np.fromfile(f, dtype=np.float32, count=countl).reshape(
(lengththis, width))[:, refx1:refx2] * coef_r2m
unw[unw == 0] = np.nan
if np.all(np.isnan(unw)):
print('All nan in ref area in {}.'.format(ifgd))
print('Rerun LiCSBAS12.')
return 1
ref_unw.append(np.nanmean(unw))
f.close()
#%% Open cum.h5 for output
if os.path.exists(cumh5file): os.remove(cumh5file)
cumh5 = h5.File(cumh5file, 'w')
cumh5.create_dataset('imdates', data=[np.int32(imd) for imd in imdates])
cumh5.create_dataset('refarea', data=refarea)
if not np.all(np.abs(np.array(bperp)) <= 1): # if not dummy
cumh5.create_dataset('bperp', data=bperp)
cum = cumh5.require_dataset('cum', (n_im, length, width), dtype=np.float32)
vel = cumh5.require_dataset('vel', (length, width), dtype=np.float32)
vconst = cumh5.require_dataset('vintercept', (length, width),
dtype=np.float32)
gap = cumh5.require_dataset('gap', (n_im - 1, length, width),
dtype=np.int8)
if width == width_geo and length == length_geo: ## if geocoded
cumh5.create_dataset('corner_lat', data=lat1)
cumh5.create_dataset('corner_lon', data=lon1)
cumh5.create_dataset('post_lat', data=dlat)
cumh5.create_dataset('post_lon', data=dlon)
#%% For each patch
i_patch = 1
for rows in patchrow:
print('\nProcess {0}/{1}th line ({2}/{3}th patch)...'.format(
rows[1], patchrow[-1][-1], i_patch, n_patch),
flush=True)
start2 = time.time()
#%% Read data
### Allocate memory
lengththis = rows[1] - rows[0]
n_pt_all = lengththis * width
unwpatch = np.zeros((n_ifg, lengththis, width), dtype=np.float32)
if inv_alg == 'WLS':
cohpatch = np.zeros((n_ifg, lengththis, width), dtype=np.float32)
### For each ifg
print(" Reading {0} ifg's unw data...".format(n_ifg), flush=True)
countf = width * rows[0]
countl = width * lengththis
for i, ifgd in enumerate(ifgdates):
unwfile = os.path.join(ifgdir, ifgd, ifgd + '.unw')
f = open(unwfile, 'rb')
f.seek(countf * 4, os.SEEK_SET) #Seek for >=2nd path, 4 means byte
### Read unw data (mm) at patch area
unw = np.fromfile(f, dtype=np.float32, count=countl).reshape(
(lengththis, width)) * coef_r2m
unw[unw == 0] = np.nan # Fill 0 with nan
unw = unw - ref_unw[i]
unwpatch[i] = unw
f.close()
### Read coh file at patch area for WLS
if inv_alg == 'WLS':
cohfile = os.path.join(ifgdir, ifgd, ifgd + '.cc')
f = open(cohfile, 'rb')
f.seek(countf * 4,
os.SEEK_SET) #Seek for >=2nd path, 4 means byte
cohpatch[i, :, :] = np.fromfile(f,
dtype=np.float32,
count=countl).reshape(
(lengththis, width))
unwpatch = unwpatch.reshape(
(n_ifg, n_pt_all)).transpose() #(n_pt_all, n_ifg)
### Calc variance from coherence for WLS
if inv_alg == 'WLS':
cohpatch = cohpatch.reshape(
(n_ifg, n_pt_all)).transpose() #(n_pt_all, n_ifg)
cohpatch[
cohpatch <
0.01] = 0.01 ## because negative value possible due to geocode
cohpatch[
cohpatch > 0.99] = 0.99 ## because >1 possible due to geocode
varpatch = (1 - cohpatch**2) / (2 * cohpatch**2)
del cohpatch
#%% Remove points with less valid data than n_unw_thre
ix_unnan_pt = np.where(
np.sum(~np.isnan(unwpatch), axis=1) > n_unw_thre)[0]
n_pt_unnan = len(ix_unnan_pt)
unwpatch = unwpatch[ix_unnan_pt, :] ## keep only unnan data
if inv_alg == 'WLS':
varpatch = varpatch[ix_unnan_pt, :] ## keep only unnan data
print(' {}/{} points removed due to not enough ifg data...'.format(
n_pt_all - n_pt_unnan, n_pt_all),
flush=True)
#%% Compute number of gaps, ifg_noloop, maxTlen point-by-point
ns_gap_patch = np.zeros((n_pt_all), dtype=np.float32) * np.nan
gap_patch = np.zeros((n_im - 1, n_pt_all), dtype=np.int8)
ns_ifg_noloop_patch = np.zeros((n_pt_all), dtype=np.float32) * np.nan
maxTlen_patch = np.zeros((n_pt_all), dtype=np.float32) * np.nan
### n_gap
print('\n Identifing gaps and counting n_gap...', flush=True)
# ns_unw_unnan4inc = (np.matmul(np.int8(G[:, :, None]), (~np.isnan(unwpatch.T))[:, None, :])).sum(axis=0, dtype=np.int16) #n_ifg, n_im-1, n_pt -> n_im-1, n_pt
ns_unw_unnan4inc = np.array([
(G[:, i] * (~np.isnan(unwpatch))).sum(axis=1, dtype=np.int16)
for i in range(n_im - 1)
]) #n_ifg*(n_pt,n_ifg) -> (n_im-1,n_pt)
ns_gap_patch[ix_unnan_pt] = (ns_unw_unnan4inc == 0).sum(axis=0) #n_pt
gap_patch[:, ix_unnan_pt] = ns_unw_unnan4inc == 0
del ns_unw_unnan4inc
### n_ifg_noloop
print(' Counting n_ifg_noloop...', flush=True)
# n_ifg*(n_pt,n_ifg)->(n_loop,n_pt)
# Number of ifgs for each loop at eath point.
# 3 means complete loop, 1 or 2 means broken loop.
ns_ifg4loop = np.array([
(np.abs(Aloop[i, :]) * (~np.isnan(unwpatch))).sum(axis=1)
for i in range(n_loop)
])
bool_loop = (ns_ifg4loop == 3
) #(n_loop,n_pt) identify complete loop only
# n_loop*(n_loop,n_pt)*n_pt->(n_ifg,n_pt)
# Number of loops for each ifg at eath point.
ns_loop4ifg = np.array([((np.abs(Aloop[:, i]) * bool_loop.T).T *
(~np.isnan(unwpatch[:, i]))).sum(axis=0)
for i in range(n_ifg)]) #
ns_ifg_noloop_tmp = (ns_loop4ifg == 0).sum(axis=0) #n_pt
ns_nan_ifg = np.isnan(unwpatch).sum(axis=1) #n_pt, nan ifg count
ns_ifg_noloop_patch[ix_unnan_pt] = ns_ifg_noloop_tmp - ns_nan_ifg
del bool_loop, ns_ifg4loop, ns_loop4ifg
### maxTlen
_maxTlen = np.zeros((n_pt_unnan), dtype=np.float32) #temporaly
_Tlen = np.zeros((n_pt_unnan), dtype=np.float32) #temporaly
for imx in range(n_im - 1):
_Tlen = _Tlen + (dt_cum[imx + 1] - dt_cum[imx]) ## Adding dt
_Tlen[gap_patch[imx, ix_unnan_pt] == 1] = 0 ## reset to 0 if gap
_maxTlen[_maxTlen < _Tlen] = _Tlen[_maxTlen <
_Tlen] ## Set Tlen to maxTlen
maxTlen_patch[ix_unnan_pt] = _maxTlen
#%% Time series inversion
print('\n Small Baseline inversion by {}...\n'.format(inv_alg),
flush=True)
if inv_alg == 'WLS':
inc_tmp, vel_tmp, vconst_tmp = inv_lib.invert_nsbas_wls(
unwpatch, varpatch, G, dt_cum, gamma, n_core)
else:
inc_tmp, vel_tmp, vconst_tmp = inv_lib.invert_nsbas(
unwpatch, G, dt_cum, gamma, n_core)
### Set to valuables
inc_patch = np.zeros((n_im - 1, n_pt_all), dtype=np.float32) * np.nan
vel_patch = np.zeros((n_pt_all), dtype=np.float32) * np.nan
vconst_patch = np.zeros((n_pt_all), dtype=np.float32) * np.nan
inc_patch[:, ix_unnan_pt] = inc_tmp
vel_patch[ix_unnan_pt] = vel_tmp
vconst_patch[ix_unnan_pt] = vconst_tmp
### Calculate residuals
res_patch = np.zeros((n_ifg, n_pt_all), dtype=np.float32) * np.nan
res_patch[:, ix_unnan_pt] = unwpatch.T - np.dot(G, inc_tmp)
res_sumsq = np.nansum(res_patch**2, axis=0)
res_n = np.float32((~np.isnan(res_patch)).sum(axis=0))
res_n[res_n == 0] = np.nan # To avoid 0 division
res_rms_patch = np.sqrt(res_sumsq / res_n)
### Cumulative displacememt
cum_patch = np.zeros((n_im, n_pt_all), dtype=np.float32) * np.nan
cum_patch[1:, :] = np.cumsum(inc_patch, axis=0)
## Fill 1st image with 0 at unnan points from 2nd images
bool_unnan_pt = ~np.isnan(cum_patch[1, :])
cum_patch[0, bool_unnan_pt] = 0
## Drop (fill with nan) interpolated cum by 2 continuous gaps
for i in range(n_im - 2): ## from 1->n_im-1
gap2 = gap_patch[i, :] + gap_patch[i + 1, :]
bool_gap2 = (gap2 == 2
) ## true if 2 continuous gaps for each point
cum_patch[i + 1, :][bool_gap2] = np.nan
## Last (n_im th) image. 1 gap means interpolated
cum_patch[-1, :][gap_patch[-1, :] == 1] = np.nan
#%% Output data and image
### cum.h5 file
cum[:, rows[0]:rows[1], :] = cum_patch.reshape(
(n_im, lengththis, width))
vel[rows[0]:rows[1], :] = vel_patch.reshape((lengththis, width))
vconst[rows[0]:rows[1], :] = vconst_patch.reshape((lengththis, width))
gap[:, rows[0]:rows[1], :] = gap_patch.reshape(
(n_im - 1, lengththis, width))
### Others
openmode = 'w' if rows[0] == 0 else 'a' #w only 1st patch
## For each imd. cum and inc
for imx, imd in enumerate(imdates):
## Incremental displacement
if imd == imdates[-1]: continue #skip last
incfile = os.path.join(incdir,
'{0}_{1}.inc'.format(imd, imdates[imx + 1]))
with open(incfile, openmode) as f:
inc_patch[imx, :].tofile(f)
## For each ifgd. resid
for i, ifgd in enumerate(ifgdates):
resfile = os.path.join(resdir, '{0}.res'.format(ifgd))
with open(resfile, openmode) as f:
res_patch[i, :].tofile(f)
## velocity and noise indecies in results dir
names = [
'vel', 'vintercept', 'resid_rms', 'n_gap', 'n_ifg_noloop',
'maxTlen'
]
data = [
vel_patch, vconst_patch, res_rms_patch, ns_gap_patch,
ns_ifg_noloop_patch, maxTlen_patch
]
for i in range(len(names)):
file = os.path.join(resultsdir, names[i])
with open(file, openmode) as f:
data[i].tofile(f)
#%% Finish patch
elapsed_time2 = int(time.time() - start2)
hour2 = int(elapsed_time2 / 3600)
minite2 = int(np.mod((elapsed_time2 / 60), 60))
sec2 = int(np.mod(elapsed_time2, 60))
print(" Elapsed time for {0}th patch: {1:02}h {2:02}m {3:02}s".format(
i_patch, hour2, minite2, sec2),
flush=True)
i_patch += 1 #Next patch count
#%% Close h5 file
cumh5.close()
#%% Output png images
print('\nOutput png images...', flush=True)
### Incremental displacement
for imx, imd in enumerate(imdates):
if imd == imdates[-1]: continue #skip last for increment
## Comparison of increment and daisy chain pair
ifgd = '{}_{}'.format(imd, imdates[imx + 1])
incfile = os.path.join(incdir, '{}.inc'.format(ifgd))
unwfile = os.path.join(ifgdir, ifgd, '{}.unw'.format(ifgd))
pngfile = os.path.join(incdir, '{}.inc_comp.png'.format(ifgd))
inc = io_lib.read_img(incfile, length, width)
try:
unw = io_lib.read_img(unwfile, length, width) * coef_r2m
ix_ifg = ifgdates.index(ifgd)
unw = unw - ref_unw[ix_ifg]
except:
unw = np.zeros((length, width), dtype=np.float32) * np.nan
### Output png for comparison
data3 = [
np.angle(np.exp(1j * (data / coef_r2m / cycle)) * cycle)
for data in [unw, inc, inc - unw]
]
title3 = ['Daisy-chain IFG', 'Inverted', 'Difference']
pngfile = os.path.join(incdir, '{}.increment.png'.format(ifgd))
plot_lib.make_3im_png(data3,
pngfile,
'insar',
title3,
vmin=-np.pi,
vmax=np.pi,
cbar=False)
if not keep_incfile:
os.remove(incfile)
### Residual for each ifg. png and txt.
cmap = 'jet'
with open(restxtfile, "w") as f:
print('# RMS of residual (mm)', file=f)
for ifgd in ifgdates:
infile = os.path.join(resdir, '{}.res'.format(ifgd))
resid = io_lib.read_img(infile, length, width)
resid_rms = np.sqrt(np.nanmean(resid**2))
with open(restxtfile, "a") as f:
print('{} {:5.2f}'.format(ifgd, resid_rms), file=f)
pngfile = infile + '.png'
title = 'Residual (mm) of {} (RMS:{:.2f}mm)'.format(ifgd, resid_rms)
plot_lib.make_im_png(resid, pngfile, cmap, title,
-wavelength / 2 * 1000, wavelength / 2 * 1000)
if not keep_incfile:
os.remove(infile)
### Velocity and noise indices
#names = ['vel', 'vconst', 'resid_rms', 'n_gap', 'n_ifg_noloop', 'maxTlen']
cmins = [None, None, None, None, None, None]
cmaxs = [None, None, None, None, None, None]
cmaps = ['jet', 'jet', 'viridis_r', 'viridis_r', 'viridis_r', 'viridis']
titles = [
'Velocity (mm/yr)', 'Intercept of velocity (mm)',
'RMS of residual (mm)', 'Number of gaps in SB network',
'Number of ifgs with no loops',
'Max length of connected SB network (yr)'
]
for i in range(len(names)):
file = os.path.join(resultsdir, names[i])
data = io_lib.read_img(file, length, width)
pngfile = file + '.png'
## Get color range if None
if cmins[i] is None:
cmins[i] = np.nanpercentile(data, 1)
if cmaxs[i] is None:
cmaxs[i] = np.nanpercentile(data, 99)
if cmins[i] == cmaxs[i]: cmins[i] = cmaxs[i] - 1
plot_lib.make_im_png(data, pngfile, cmaps[i], titles[i], cmins[i],
cmaxs[i])
#%% Finish
elapsed_time = time.time() - start
hour = int(elapsed_time / 3600)
minite = int(np.mod((elapsed_time / 60), 60))
sec = int(np.mod(elapsed_time, 60))
print("\nElapsed time: {0:02}h {1:02}m {2:02}s".format(hour, minite, sec))
print('\n{} Successfully finished!!\n'.format(os.path.basename(argv[0])))
print('Output directory: {}\n'.format(os.path.relpath(tsadir)))