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
tsadir = []
filtwidth_km = 2
filtwidth_yr = []
deg_ramp = []
maskflag = True
cumname = 'cum.h5'
#%% Read options
try:
try:
opts, args = getopt.getopt(argv[1:], "ht:s:y:r:",
["help", "nomask"])
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 == '-t':
tsadir = a
elif o == '-s':
filtwidth_km = float(a)
elif o == '-y':
filtwidth_yr = float(a)
elif o == '-r':
deg_ramp = a
elif o == '--nomask':
maskflag = False
if not tsadir:
raise Usage('No tsa directory given, -t is not optional!')
elif not os.path.isdir(tsadir):
raise Usage('No {} dir exists!'.format(tsadir))
elif not os.path.exists(os.path.join(tsadir, cumname)):
raise Usage('No {} exists in {}!'.format(cumname, 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
#%% Directory and file setting
tsadir = os.path.abspath(tsadir)
cumfile = os.path.join(tsadir, cumname)
resultsdir = os.path.join(tsadir, 'results')
parmfile = os.path.join(tsadir, 'info', 'parameters.txt')
pixsp_r = float(io_lib.get_param_par(parmfile, 'pixel_spacing_r'))
pixsp_a = float(io_lib.get_param_par(parmfile, 'pixel_spacing_a'))
x_stddev = filtwidth_km * 1000 / pixsp_r
y_stddev = filtwidth_km * 1000 / pixsp_a
wavelength = float(io_lib.get_param_par(parmfile, 'wavelength')) #meter
coef_r2m = -wavelength / 4 / np.pi * 1000 #rad -> mm, positive is -LOS
if wavelength > 0.2: ## L-band
cycle = 1.5 # 2pi/cycle for comparison png
elif wavelength <= 0.2: ## C-band
cycle = 3 # 3*2pi/cycle for comparison png
filtdir = os.path.join(tsadir, '16filt')
if not os.path.exists(filtdir): os.mkdir(filtdir)
cumffile = os.path.join(tsadir, 'cum_filt.h5')
vconstfile = os.path.join(resultsdir, 'vintercept.filt')
velfile = os.path.join(resultsdir, 'vel.filt')
cumh5 = h5.File(cumfile, 'r')
if os.path.exists(cumffile): os.remove(cumffile)
cumfh5 = h5.File(cumffile, 'w')
#%% Dates
imdates = cumh5['imdates'][()].astype(str).tolist()
cum_org = cumh5['cum']
n_im, length, width = cum_org.shape
### 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)
### Save dates and refarea into cumf
cumfh5.create_dataset('imdates', data=cumh5['imdates'])
cumfh5.create_dataset('refarea', data=cumh5['refarea'])
cumfh5.create_dataset('gap', data=cumh5['gap'])
try: ## if dummy, no bperp field
cumfh5.create_dataset('bperp', data=cumh5['bperp'])
except:
print('No bperp field found in {}. Skip.'.format(cumname))
try:
cumfh5.create_dataset('corner_lat', data=cumh5['corner_lat'])
cumfh5.create_dataset('corner_lon', data=cumh5['corner_lon'])
cumfh5.create_dataset('post_lat', data=cumh5['post_lat'])
cumfh5.create_dataset('post_lon', data=cumh5['post_lon'])
except: ## not geocoded
print('No latlon field found in {}. Skip.'.format(cumname))
### temporal filter width
if not filtwidth_yr and filtwidth_yr != 0:
filtwidth_yr = dt_cum[-1] / (n_im - 1) * 3 ## avg interval*3
#%% Display settings
print('')
print('Size of image (w,l) : {0}, {1}'.format(width, length))
print('Number of images : {}'.format(n_im))
print('Width of filter in space : {} km ({:.1f}x{:.1f} pixel)'.format(
filtwidth_km, x_stddev, y_stddev))
print('Width of filter in time : {:.3f} yr ({} days)'.format(
filtwidth_yr, int(filtwidth_yr * 365.25)))
print('Deramp flag : {}'.format(deg_ramp), flush=True)
#%% Load Mask (1: unmask, 0: mask, nan: no cum data)
if maskflag:
maskfile = os.path.join(resultsdir, 'mask')
mask = io_lib.read_img(maskfile, length, width)
mask[mask == 0] = np.nan ## 0->nan
else:
mask = np.ones((length, width), dtype=np.float32)
mask[np.isnan(cum_org[0, :, :])] = np.nan
#%% First, deramp if indicated
cum = np.zeros((cum_org.shape), dtype=np.float32) * np.nan
if not deg_ramp:
cum = cum_org[()]
## Remove past *_deramp.png if exist
for png in glob.glob(os.path.join(filtdir, '*_deramp.png')):
os.remove(png)
else:
print('\nDeramp ifgs with the degree of {}...'.format(deg_ramp),
flush=True)
ramp1 = []
for i in range(n_im):
if np.mod(i, 10) == 0:
print(" {0:3}/{1:3}th image...".format(i, n_im), flush=True)
ramp, _ = tools_lib.fit2d(cum_org[i, :, :], deg=deg_ramp)
cum[i, :, :] = cum_org[i, :, :] - ramp
## Output comparison image of deramp
data3 = [
np.angle(np.exp(1j * (data / coef_r2m / cycle)) * cycle) for
data in [cum_org[i, :, :] * mask, ramp, cum[i, :, :] * mask]
]
title3 = [
'Before deramp ({}pi/cycle)'.format(cycle * 2),
'ramp phase (deg:{}, {}pi/cycle)'.format(deg_ramp, cycle * 2),
'After deramp ({}pi/cycle)'.format(cycle * 2)
]
pngfile = os.path.join(filtdir, imdates[i] + '_deramp.png')
plot_lib.make_3im_png(data3,
pngfile,
'insar',
title3,
vmin=-np.pi,
vmax=np.pi,
cbar=False)
## Output comparison image of deramp for increment
if i != 0:
data3 = [
np.angle(np.exp(1j * (data / coef_r2m / cycle)) * cycle)
for data in [(cum_org[i, :, :] - cum_org[i - 1, :, :]) *
mask, ramp -
ramp1, (cum[i, :, :] - cum[i - 1, :, :]) *
mask]
]
title3 = [
'Before deramp ({}pi/cycle)'.format(cycle * 2),
'ramp phase (deg:{}, {}pi/cycle)'.format(
deg_ramp, cycle * 2),
'After deramp ({}pi/cycle)'.format(cycle * 2)
]
pngfile = os.path.join(
filtdir, '{}_{}_deramp.png'.format(imdates[i - 1],
imdates[i]))
plot_lib.make_3im_png(data3,
pngfile,
'insar',
title3,
vmin=-np.pi,
vmax=np.pi,
cbar=False)
ramp1 = ramp.copy() ## backup last ramp
#%% Filter each image
cum_filt = cumfh5.require_dataset('cum', (n_im, length, width),
dtype=np.float32)
cum_hptlps1 = []
print('\nHP filter in time, LP filter in space...', flush=True)
for i in range(n_im):
if np.mod(i, 10) == 0:
print(" {0:3}/{1:3}th image...".format(i, n_im), flush=True)
#%% Second, HP in time
if filtwidth_yr == 0.0:
cum_hpt = cum[i, :, :] ## No temporal filter
else:
time_diff_sq = (dt_cum[i] - dt_cum)**2
## Limit reading data within filtwidth_yr**8
ixs = time_diff_sq < filtwidth_yr * 8
weight_factor = np.tile(
np.exp(-time_diff_sq[ixs] / 2 / filtwidth_yr**2)[:, np.newaxis,
np.newaxis],
(1, length, width)) #len(ixs), length, width
## Take into account nan in cum
weight_factor = weight_factor * (~np.isnan(cum[ixs, :, :]))
## Normalize weight
with warnings.catch_warnings(
): ## To silence warning by zero division
warnings.simplefilter('ignore', RuntimeWarning)
weight_factor = weight_factor / np.sum(weight_factor, axis=0)
cum_lpt = np.nansum(cum[ixs, :, :] * weight_factor, axis=0)
cum_hpt = cum[i, :, :] - cum_lpt
#%% Third, LP in space and subtract from original
if filtwidth_km == 0.0:
cum_filt[i, :, :] = cum[i, :, :] ## No spatial
else:
with warnings.catch_warnings(): ## To silence warning
if i == 0:
cum_hpt = cum_hpt + sys.float_info.epsilon ##To distinguish from 0 of filtered nodata
# warnings.simplefilter('ignore', FutureWarning)
warnings.simplefilter('ignore', RuntimeWarning)
kernel = Gaussian2DKernel(x_stddev, y_stddev)
cum_hptlps = convolve_fft(
cum_hpt * mask, kernel, fill_value=np.nan,
allow_huge=True) ## fill edge 0 for interpolation
cum_hptlps[cum_hptlps == 0] = np.nan ## fill 0 with nan
cum_filt[i, :, :] = cum[i, :, :] - cum_hptlps
#%% Output comparison image
data3 = [
np.angle(np.exp(1j * (data / coef_r2m / cycle)) * cycle) for data
in [cum[i, :, :] * mask, cum_hptlps, cum_filt[i, :, :] * mask]
]
title3 = [
'Before filter ({}pi/cycle)'.format(cycle * 2),
'Filter phase ({}pi/cycle)'.format(cycle * 2),
'After filter ({}pi/cycle)'.format(cycle * 2)
]
pngfile = os.path.join(filtdir, imdates[i] + '_filt.png')
plot_lib.make_3im_png(data3,
pngfile,
'insar',
title3,
vmin=-np.pi,
vmax=np.pi,
cbar=False)
### Output comparison image for increment
if i != 0:
data3 = [
np.angle(np.exp(1j * (data / coef_r2m / cycle)) * cycle)
for data in [(cum[i, :, :] - cum[i - 1, :, :]) *
mask, cum_hptlps - cum_hptlps1,
(cum_filt[i, :, :] - cum_filt[i - 1, :, :]) *
mask]
]
title3 = [
'Before filter ({}pi/cycle)'.format(cycle * 2),
'Filter phase ({}pi/cycle)'.format(cycle * 2),
'After filter ({}pi/cycle)'.format(cycle * 2)
]
pngfile = os.path.join(filtdir, imdates[i] + '_filt.png')
pngfile = os.path.join(
filtdir, '{}_{}_filt.png'.format(imdates[i - 1], imdates[i]))
plot_lib.make_3im_png(data3,
pngfile,
'insar',
title3,
vmin=-np.pi,
vmax=np.pi,
cbar=False)
cum_hptlps1 = cum_hptlps.copy() ## backup last filt phase
#%% Calc filtered velocity
print('\nCalculate velocity of filtered time series...', flush=True)
G = np.stack((np.ones_like(dt_cum), dt_cum), axis=1)
vconst = np.zeros((length, width), dtype=np.float32) * np.nan
vel = np.zeros((length, width), dtype=np.float32) * np.nan
bool_unnan = ~np.isnan(cum_filt[0, :, :]).reshape(length,
width) ## not all nan
cum_pt = cum_filt[()].reshape(n_im, length *
width)[:, bool_unnan.ravel()] #n_im x n_pt
n_pt_unnan = bool_unnan.sum()
vconst_tmp = np.zeros((n_pt_unnan), dtype=np.float32) * np.nan
vel_tmp = np.zeros((n_pt_unnan), dtype=np.float32) * np.nan
bool_nonan_pt = np.all(~np.isnan(cum_pt), axis=0)
### First, calc vel point without nan
print(' First, solving {0:6}/{1:6}th points with full cum...'.format(
bool_nonan_pt.sum(), n_pt_unnan),
flush=True)
vconst_tmp[bool_nonan_pt], vel_tmp[bool_nonan_pt] = np.linalg.lstsq(
G, cum_pt[:, bool_nonan_pt], rcond=None)[0]
### Next, calc vel point with nan
print(' Next, solving {0:6}/{1:6}th points with nan in cum...'.format(
(~bool_nonan_pt).sum(), n_pt_unnan),
flush=True)
mask_cum = ~np.isnan(cum_pt[:, ~bool_nonan_pt])
vconst_tmp[~bool_nonan_pt], vel_tmp[
~bool_nonan_pt] = inv_lib.censored_lstsq_slow(
G, cum_pt[:, ~bool_nonan_pt], mask_cum)
vconst[bool_unnan], vel[bool_unnan] = vconst_tmp, vel_tmp
vconst.tofile(vconstfile)
vel.tofile(velfile)
if maskflag:
vel_mskd = vel * mask
vconst_mskd = vconst * mask
vconst_mskd.tofile(vconstfile + '.mskd')
vel_mskd.tofile(velfile + '.mskd')
cumfh5.create_dataset('vel', data=vel.reshape(length, width))
cumfh5.create_dataset('vintercept', data=vconst.reshape(length, width))
#%% Add info and close
cumfh5.create_dataset('filtwidth_yr', data=filtwidth_yr)
cumfh5.create_dataset('filtwidth_km', data=filtwidth_km)
cumfh5.create_dataset('deramp_flag', data=deg_ramp)
cumh5.close()
cumfh5.close()
#%% Output image
cmap = 'jet'
pngfile = os.path.join(resultsdir, 'vel.filt.png')
title = 'Filtered velocity (mm/yr)'
vmin = np.nanpercentile(vel, 1)
vmax = np.nanpercentile(vel, 99)
plot_lib.make_im_png(vel, pngfile, cmap, title, vmin, vmax)
## vintercept
pngfile = os.path.join(resultsdir, 'vintercept.filt.png')
title = 'Intercept of filtered velocity (mm)'
vmin = np.nanpercentile(vconst, 1)
vmax = np.nanpercentile(vconst, 99)
plot_lib.make_im_png(vconst, pngfile, cmap, title, vmin, vmax)
if maskflag:
pngfile = os.path.join(resultsdir, 'vel.filt.mskd.png')
title = 'Masked filtered velocity (mm/yr)'
vmin = np.nanpercentile(vel_mskd, 1)
vmax = np.nanpercentile(vel_mskd, 99)
plot_lib.make_im_png(vel_mskd, pngfile, cmap, title, vmin, vmax)
## vintercept
pngfile = os.path.join(resultsdir, 'vintercept.filt.mskd.png')
title = 'Masked intercept of filtered velocity (mm)'
vmin = np.nanpercentile(vconst_mskd, 1)
vmax = np.nanpercentile(vconst_mskd, 99)
plot_lib.make_im_png(vconst_mskd, pngfile, cmap, title, vmin, vmax)
#%% 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: {}\n'.format(os.path.relpath(cumffile)), flush=True)
print('To plot the time-series:')
print('LiCSBAS_plot_ts.py -i {} &\n'.format(os.path.relpath(cumffile)))
def main(argv=None):
#%% Check argv
if argv == None:
argv = sys.argv
start = time.time()
ver = 1.3
date = 20200909
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 cum, mask, deg_ramp, hgt_linearflag, hgt, hgt_min, hgt_max,\
filtcumdir, filtincdir, imdates, cycle, coef_r2m, models, \
filtwidth_yr, filtwidth_km, dt_cum, x_stddev, y_stddev
## global cum_org from hdf5 contaminate in paralell warpper? So pass them by arg.
#%% Set default
tsadir = []
filtwidth_km = 2
filtwidth_yr = []
deg_ramp = []
hgt_linearflag = False
hgt_min = 200 ## meter
hgt_max = 10000 ## meter
maskflag = True
n_para = len(os.sched_getaffinity(0))
cumname = 'cum.h5'
cmap_vel = SCM.roma.reversed()
cmap_noise_r = 'viridis_r'
#%% Read options
try:
try:
opts, args = getopt.getopt(argv[1:], "ht:s:y:r:", [
"help", "hgt_linear", "hgt_min=", "hgt_max=", "nomask",
"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 == '-t':
tsadir = a
elif o == '-s':
filtwidth_km = float(a)
elif o == '-y':
filtwidth_yr = float(a)
elif o == '-r':
deg_ramp = a
elif o == '--hgt_linear':
hgt_linearflag = True
elif o == '--hgt_min':
hgt_min = int(a)
elif o == '--hgt_max':
hgt_max = int(a)
elif o == '--nomask':
maskflag = False
elif o == '--n_para':
n_para = int(a)
if not tsadir:
raise Usage('No tsa directory given, -t is not optional!')
elif not os.path.isdir(tsadir):
raise Usage('No {} dir exists!'.format(tsadir))
elif not os.path.exists(os.path.join(tsadir, cumname)):
raise Usage('No {} exists in {}!'.format(cumname, 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
#%% Directory and file setting
tsadir = os.path.abspath(tsadir)
cumfile = os.path.join(tsadir, cumname)
resultsdir = os.path.join(tsadir, 'results')
infodir = os.path.join(tsadir, 'info')
inparmfile = os.path.join(infodir, '13parameters.txt')
if not os.path.exists(inparmfile): ## for old LiCSBAS13 <v1.2
inparmfile = os.path.join(infodir, 'parameters.txt')
outparmfile = os.path.join(infodir, '16parameters.txt')
pixsp_r = float(io_lib.get_param_par(inparmfile, 'pixel_spacing_r'))
pixsp_a = float(io_lib.get_param_par(inparmfile, 'pixel_spacing_a'))
x_stddev = filtwidth_km * 1000 / pixsp_r
y_stddev = filtwidth_km * 1000 / pixsp_a
wavelength = float(io_lib.get_param_par(inparmfile, 'wavelength')) #meter
coef_r2m = -wavelength / 4 / np.pi * 1000 #rad -> mm, positive is -LOS
if wavelength > 0.2: ## L-band
cycle = 1.5 # 2pi/cycle for comparison png
elif wavelength <= 0.2: ## C-band
cycle = 3 # 3*2pi/cycle for comparison png
filtincdir = os.path.join(tsadir, '16filt_increment')
if os.path.exists(filtincdir): shutil.rmtree(filtincdir)
os.mkdir(filtincdir)
filtcumdir = os.path.join(tsadir, '16filt_cum')
if os.path.exists(filtcumdir): shutil.rmtree(filtcumdir)
os.mkdir(filtcumdir)
cumffile = os.path.join(tsadir, 'cum_filt.h5')
vconstfile = os.path.join(resultsdir, 'vintercept.filt')
velfile = os.path.join(resultsdir, 'vel.filt')
cumh5 = h5.File(cumfile, 'r')
if os.path.exists(cumffile): os.remove(cumffile)
cumfh5 = h5.File(cumffile, 'w')
#%% Dates
imdates = cumh5['imdates'][()].astype(str).tolist()
cum_org = cumh5['cum']
n_im, length, width = cum_org.shape
if n_para > n_im:
n_para = n_im
### 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)
### Save dates and other info into cumf
cumfh5.create_dataset('imdates', data=cumh5['imdates'])
cumfh5.create_dataset('gap', data=cumh5['gap'])
if 'bperp' in list(cumh5.keys()): ## if dummy, no bperp field
cumfh5.create_dataset('bperp', data=cumh5['bperp'])
else:
print('No bperp field found in {}. Skip.'.format(cumname))
if 'corner_lat' in list(cumh5.keys()):
lat1 = float(cumh5['corner_lat'][()])
lon1 = float(cumh5['corner_lon'][()])
dlat = float(cumh5['post_lat'][()])
dlon = float(cumh5['post_lon'][()])
cumfh5.create_dataset('corner_lat', data=cumh5['corner_lat'])
cumfh5.create_dataset('corner_lon', data=cumh5['corner_lon'])
cumfh5.create_dataset('post_lat', data=cumh5['post_lat'])
cumfh5.create_dataset('post_lon', data=cumh5['post_lon'])
else: ## not geocoded
print('No latlon field found in {}. Skip.'.format(cumname))
### temporal filter width
if not filtwidth_yr and filtwidth_yr != 0:
filtwidth_yr = dt_cum[-1] / (n_im - 1) * 3 ## avg interval*3
### hgt_linear
if hgt_linearflag:
hgtfile = os.path.join(resultsdir, 'hgt')
if not os.path.exists(hgtfile):
print('\nERROR: No hgt file exist in results dir!',
file=sys.stderr)
print('--hgt_linear option cannot be used.', file=sys.stderr)
return 2
hgt = io_lib.read_img(hgtfile, length, width)
hgt[np.isnan(hgt)] = 0
else:
hgt = []
#%% Display settings
print('')
print('Size of image (w,l) : {0}, {1}'.format(width, length))
print('Number of images : {}'.format(n_im))
print('Width of filter in space : {} km ({:.1f}x{:.1f} pixel)'.format(
filtwidth_km, x_stddev, y_stddev))
print('Width of filter in time : {:.3f} yr ({} days)'.format(
filtwidth_yr, int(filtwidth_yr * 365.25)))
print('Deramp flag : {}'.format(deg_ramp), flush=True)
print('hgt-linear flag : {}'.format(hgt_linearflag), flush=True)
if hgt_linearflag:
print('Minimum hgt : {} m'.format(hgt_min), flush=True)
print('Maximum hgt : {} m'.format(hgt_max), flush=True)
with open(outparmfile, "w") as f:
print('filtwidth_km: {}'.format(filtwidth_km), file=f)
print('filtwidth_xpixels: {:.1f}'.format(x_stddev), file=f)
print('filtwidth_ypixels: {:.1f}'.format(y_stddev), file=f)
print('filtwidth_yr: {:.3f}'.format(filtwidth_yr), file=f)
print('filtwidth_day: {}'.format(int(filtwidth_yr * 365.25)), file=f)
print('deg_ramp: {}'.format(deg_ramp), file=f)
print('hgt_linear: {}'.format(hgt_linearflag * 1), file=f)
print('hgt_min: {}'.format(hgt_min), file=f)
print('hgt_max: {}'.format(hgt_max), file=f)
#%% Load Mask (1: unmask, 0: mask, nan: no cum data)
if maskflag:
maskfile = os.path.join(resultsdir, 'mask')
mask = io_lib.read_img(maskfile, length, width)
mask[mask == 0] = np.nan ## 0->nan
else:
mask = np.ones((length, width), dtype=np.float32)
mask[np.isnan(cum_org[0, :, :])] = np.nan
#%% First, deramp and hgt-linear if indicated
cum = np.zeros((cum_org.shape), dtype=np.float32) * np.nan
if not deg_ramp and not hgt_linearflag:
cum = cum_org[()]
else:
if not deg_ramp:
print('\nEstimate hgt-linear component,', flush=True)
elif not hgt_linearflag:
print('\nDeramp ifgs with the degree of {},'.format(deg_ramp),
flush=True)
else:
print('\nDeramp ifgs with the degree of {} and hgt-linear,'.format(
deg_ramp),
flush=True)
print('with {} parallel processing...'.format(n_para), flush=True)
args = [(i, cum_org[i, :, :]) for i in range(n_im)]
### Parallel processing
p = multi.Pool(n_para)
_result = np.array(p.map(deramp_wrapper, args), dtype=object)
p.close()
del args
models = _result[:, 1]
for i in range(n_im):
cum[i, :, :] = _result[i, 0]
del _result
### Only for output increment png files
print(
'\nCreate png for increment with {} parallel processing...'.format(
n_para),
flush=True)
args = [(i, cum_org[i, :, :], cum_org[i - 1, :, :])
for i in range(1, n_im)]
p = multi.Pool(n_para)
p.map(deramp_wrapper2, args)
p.close()
del args
#%% Filter each image
cum_filt = cumfh5.require_dataset('cum', (n_im, length, width),
dtype=np.float32)
print('\nHP filter in time, LP filter in space,', flush=True)
print('with {} parallel processing...'.format(n_para), flush=True)
### Parallel processing
p = multi.Pool(n_para)
cum_filt[:, :, :] = np.array(p.map(filter_wrapper, range(n_im)),
dtype=np.float32)
p.close()
### Only for output increment png files
print('\nCreate png for increment with {} parallel processing...'.format(
n_para),
flush=True)
args = [(i, cum_filt[i, :, :] - cum_filt[i - 1, :, :])
for i in range(1, n_im)]
p = multi.Pool(n_para)
p.map(filter_wrapper2, args)
p.close()
del args
#%% 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_filt[i, :, :] - np.nanmedian(cum_filt[i, :, :]))**2
rms_cum_wrt_med = np.sqrt(sumsq_cum_wrt_med / n_im) * mask
### 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 to new stable ref
for i in range(n_im):
cum_filt[i, :, :] = cum_filt[i, :, :] - cum[i, refy1s, refx1s]
### Save image
rms_cum_wrt_med_file = os.path.join(infodir, '16rms_cum_wrt_med')
with open(rms_cum_wrt_med_file, 'w') as f:
rms_cum_wrt_med.tofile(f)
pngfile = os.path.join(infodir, '16rms_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
cumfh5.create_dataset('refarea',
data='{}:{}/{}:{}'.format(refx1s, refx2s, refy1s,
refy2s))
refsfile = os.path.join(infodir, '16ref.txt')
with open(refsfile, 'w') as f:
print('{}:{}/{}:{}'.format(refx1s, refx2s, refy1s, refy2s), file=f)
if 'corner_lat' in list(cumh5.keys()): ## Geocoded
### Make ref_stable.kml
reflat = lat1 + dlat * refy1s
reflon = lon1 + dlon * refx1s
io_lib.make_point_kml(reflat, reflon,
os.path.join(infodir, '16ref.kml'))
#%% Calc filtered velocity
print('\nCalculate velocity of filtered time series...', flush=True)
G = np.stack((np.ones_like(dt_cum), dt_cum), axis=1)
vconst = np.zeros((length, width), dtype=np.float32) * np.nan
vel = np.zeros((length, width), dtype=np.float32) * np.nan
bool_unnan = ~np.isnan(cum_filt[0, :, :]).reshape(length,
width) ## not all nan
cum_pt = cum_filt[()].reshape(n_im, length *
width)[:, bool_unnan.ravel()] #n_im x n_pt
n_pt_unnan = bool_unnan.sum()
vconst_tmp = np.zeros((n_pt_unnan), dtype=np.float32) * np.nan
vel_tmp = np.zeros((n_pt_unnan), dtype=np.float32) * np.nan
bool_nonan_pt = np.all(~np.isnan(cum_pt), axis=0)
### First, calc vel point without nan
print(' First, solving {0:6}/{1:6}th points with full cum...'.format(
bool_nonan_pt.sum(), n_pt_unnan),
flush=True)
vconst_tmp[bool_nonan_pt], vel_tmp[bool_nonan_pt] = np.linalg.lstsq(
G, cum_pt[:, bool_nonan_pt], rcond=None)[0]
### Next, calc vel point with nan
print(' Next, solving {0:6}/{1:6}th points with nan in cum...'.format(
(~bool_nonan_pt).sum(), n_pt_unnan),
flush=True)
mask_cum = ~np.isnan(cum_pt[:, ~bool_nonan_pt])
vconst_tmp[~bool_nonan_pt], vel_tmp[
~bool_nonan_pt] = inv_lib.censored_lstsq_slow(
G, cum_pt[:, ~bool_nonan_pt], mask_cum)
vconst[bool_unnan], vel[bool_unnan] = vconst_tmp, vel_tmp
vconst.tofile(vconstfile)
vel.tofile(velfile)
if maskflag:
vel_mskd = vel * mask
vconst_mskd = vconst * mask
vconst_mskd.tofile(vconstfile + '.mskd')
vel_mskd.tofile(velfile + '.mskd')
cumfh5.create_dataset('vel', data=vel.reshape(length, width))
cumfh5.create_dataset('vintercept', data=vconst.reshape(length, width))
#%% Add info and close
cumfh5.create_dataset('filtwidth_yr', data=filtwidth_yr)
cumfh5.create_dataset('filtwidth_km', data=filtwidth_km)
cumfh5.create_dataset('deramp_flag', data=deg_ramp)
cumfh5.create_dataset('hgt_linear_flag', data=hgt_linearflag * 1)
cumh5.close()
cumfh5.close()
#%% Output image
pngfile = os.path.join(resultsdir, 'vel.filt.png')
title = 'Filtered velocity (mm/yr)'
vmin = np.nanpercentile(vel, 1)
vmax = np.nanpercentile(vel, 99)
plot_lib.make_im_png(vel, pngfile, cmap_vel, title, vmin, vmax)
## vintercept
pngfile = os.path.join(resultsdir, 'vintercept.filt.png')
title = 'Intercept of filtered velocity (mm)'
vmin = np.nanpercentile(vconst, 1)
vmax = np.nanpercentile(vconst, 99)
plot_lib.make_im_png(vconst, pngfile, cmap_vel, title, vmin, vmax)
if maskflag:
pngfile = os.path.join(resultsdir, 'vel.filt.mskd.png')
title = 'Masked filtered velocity (mm/yr)'
vmin = np.nanpercentile(vel_mskd, 1)
vmax = np.nanpercentile(vel_mskd, 99)
plot_lib.make_im_png(vel_mskd, pngfile, cmap_vel, title, vmin, vmax)
## vintercept
pngfile = os.path.join(resultsdir, 'vintercept.filt.mskd.png')
title = 'Masked intercept of filtered velocity (mm)'
vmin = np.nanpercentile(vconst_mskd, 1)
vmax = np.nanpercentile(vconst_mskd, 99)
plot_lib.make_im_png(vconst_mskd, pngfile, cmap_vel, title, vmin, vmax)
#%% 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: {}\n'.format(os.path.relpath(cumffile)), flush=True)
print('To plot the time-series:')
print('LiCSBAS_plot_ts.py -i {} &\n'.format(os.path.relpath(cumffile)))
def main(argv=None):
#%% Check argv
if argv == None:
argv = sys.argv
start = time.time()
ver = 1.2
date = 20200228
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)
#%% Set default
tsadir = []
filtwidth_km = 2
filtwidth_yr = []
deg_ramp = []
hgt_linearflag = False
hgt_min = 200 ## meter
hgt_max = 10000 ## meter
maskflag = True
cumname = 'cum.h5'
cmap_vel = SCM.roma.reversed()
cmap_noise_r = 'viridis_r'
#%% Read options
try:
try:
opts, args = getopt.getopt(
argv[1:], "ht:s:y:r:",
["help", "hgt_linear", "hgt_min=", "hgt_max=", "nomask"])
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 == '-t':
tsadir = a
elif o == '-s':
filtwidth_km = float(a)
elif o == '-y':
filtwidth_yr = float(a)
elif o == '-r':
deg_ramp = a
elif o == '--hgt_linear':
hgt_linearflag = True
elif o == '--hgt_min':
hgt_min = int(a)
elif o == '--hgt_max':
hgt_max = int(a)
elif o == '--nomask':
maskflag = False
if not tsadir:
raise Usage('No tsa directory given, -t is not optional!')
elif not os.path.isdir(tsadir):
raise Usage('No {} dir exists!'.format(tsadir))
elif not os.path.exists(os.path.join(tsadir, cumname)):
raise Usage('No {} exists in {}!'.format(cumname, 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
#%% Directory and file setting
tsadir = os.path.abspath(tsadir)
cumfile = os.path.join(tsadir, cumname)
resultsdir = os.path.join(tsadir, 'results')
infodir = os.path.join(tsadir, 'info')
inparmfile = os.path.join(infodir, '13parameters.txt')
if not os.path.exists(inparmfile): ## for old LiCSBAS13 <v1.2
inparmfile = os.path.join(infodir, 'parameters.txt')
outparmfile = os.path.join(infodir, '16parameters.txt')
pixsp_r = float(io_lib.get_param_par(inparmfile, 'pixel_spacing_r'))
pixsp_a = float(io_lib.get_param_par(inparmfile, 'pixel_spacing_a'))
x_stddev = filtwidth_km * 1000 / pixsp_r
y_stddev = filtwidth_km * 1000 / pixsp_a
wavelength = float(io_lib.get_param_par(inparmfile, 'wavelength')) #meter
coef_r2m = -wavelength / 4 / np.pi * 1000 #rad -> mm, positive is -LOS
if wavelength > 0.2: ## L-band
cycle = 1.5 # 2pi/cycle for comparison png
elif wavelength <= 0.2: ## C-band
cycle = 3 # 3*2pi/cycle for comparison png
filtincdir = os.path.join(tsadir, '16filt_increment')
if os.path.exists(filtincdir): shutil.rmtree(filtincdir)
os.mkdir(filtincdir)
filtcumdir = os.path.join(tsadir, '16filt_cum')
if os.path.exists(filtcumdir): shutil.rmtree(filtcumdir)
os.mkdir(filtcumdir)
cumffile = os.path.join(tsadir, 'cum_filt.h5')
vconstfile = os.path.join(resultsdir, 'vintercept.filt')
velfile = os.path.join(resultsdir, 'vel.filt')
cumh5 = h5.File(cumfile, 'r')
if os.path.exists(cumffile): os.remove(cumffile)
cumfh5 = h5.File(cumffile, 'w')
#%% Dates
imdates = cumh5['imdates'][()].astype(str).tolist()
cum_org = cumh5['cum']
n_im, length, width = cum_org.shape
### 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)
### Save dates and other info into cumf
cumfh5.create_dataset('imdates', data=cumh5['imdates'])
cumfh5.create_dataset('gap', data=cumh5['gap'])
if 'bperp' in list(cumh5.keys()): ## if dummy, no bperp field
cumfh5.create_dataset('bperp', data=cumh5['bperp'])
else:
print('No bperp field found in {}. Skip.'.format(cumname))
if 'corner_lat' in list(cumh5.keys()):
lat1 = float(cumh5['corner_lat'][()])
lon1 = float(cumh5['corner_lon'][()])
dlat = float(cumh5['post_lat'][()])
dlon = float(cumh5['post_lon'][()])
cumfh5.create_dataset('corner_lat', data=cumh5['corner_lat'])
cumfh5.create_dataset('corner_lon', data=cumh5['corner_lon'])
cumfh5.create_dataset('post_lat', data=cumh5['post_lat'])
cumfh5.create_dataset('post_lon', data=cumh5['post_lon'])
else: ## not geocoded
print('No latlon field found in {}. Skip.'.format(cumname))
### temporal filter width
if not filtwidth_yr and filtwidth_yr != 0:
filtwidth_yr = dt_cum[-1] / (n_im - 1) * 3 ## avg interval*3
### hgt_linear
if hgt_linearflag:
hgtfile = os.path.join(resultsdir, 'hgt')
if not os.path.exists(hgtfile):
print('\nERROR: No hgt file exist in results dir!',
file=sys.stderr)
print('--hgt_linear option cannot be used.', file=sys.stderr)
return 2
hgt = io_lib.read_img(hgtfile, length, width)
hgt[np.isnan(hgt)] = 0
else:
hgt = []
#%% Display settings
print('')
print('Size of image (w,l) : {0}, {1}'.format(width, length))
print('Number of images : {}'.format(n_im))
print('Width of filter in space : {} km ({:.1f}x{:.1f} pixel)'.format(
filtwidth_km, x_stddev, y_stddev))
print('Width of filter in time : {:.3f} yr ({} days)'.format(
filtwidth_yr, int(filtwidth_yr * 365.25)))
print('Deramp flag : {}'.format(deg_ramp), flush=True)
print('hgt-linear flag : {}'.format(hgt_linearflag), flush=True)
if hgt_linearflag:
print('Minimum hgt : {} m'.format(hgt_min), flush=True)
print('Maximum hgt : {} m'.format(hgt_max), flush=True)
with open(outparmfile, "w") as f:
print('filtwidth_km: {}'.format(filtwidth_km), file=f)
print('filtwidth_xpixels: {:.1f}'.format(x_stddev), file=f)
print('filtwidth_ypixels: {:.1f}'.format(y_stddev), file=f)
print('filtwidth_yr: {:.3f}'.format(filtwidth_yr), file=f)
print('filtwidth_day: {}'.format(int(filtwidth_yr * 365.25)), file=f)
print('deg_ramp: {}'.format(deg_ramp), file=f)
print('hgt_linear: {}'.format(hgt_linearflag * 1), file=f)
print('hgt_min: {}'.format(hgt_min), file=f)
print('hgt_max: {}'.format(hgt_max), file=f)
#%% Load Mask (1: unmask, 0: mask, nan: no cum data)
if maskflag:
maskfile = os.path.join(resultsdir, 'mask')
mask = io_lib.read_img(maskfile, length, width)
mask[mask == 0] = np.nan ## 0->nan
else:
mask = np.ones((length, width), dtype=np.float32)
mask[np.isnan(cum_org[0, :, :])] = np.nan
#%% First, deramp and hgt-linear if indicated
cum = np.zeros((cum_org.shape), dtype=np.float32) * np.nan
if not deg_ramp and not hgt_linearflag:
cum = cum_org[()]
else:
if not deg_ramp:
print('\nEstimate hgt-linear component...', flush=True)
elif not hgt_linearflag:
print('\nDeramp ifgs with the degree of {}...'.format(deg_ramp),
flush=True)
else:
print(
'\nDeramp ifgs with the degree of {} and hgt-linear...'.format(
deg_ramp),
flush=True)
ramp1 = [] ## backup last ramp to plot increment
fit_hgt1 = [] ## backup last fit_hgt to plot increment
model1 = []
deramp_title3 = [
'Before deramp ({}pi/cycle)'.format(cycle * 2),
'ramp phase (deg:{})'.format(deg_ramp),
'After deramp ({}pi/cycle)'.format(cycle * 2)
]
for i in range(n_im):
if np.mod(i, 10) == 0:
print(" {0:3}/{1:3}th image...".format(i, n_im), flush=True)
fit, model = tools_lib.fit2dh(cum_org[i, :, :] * mask, deg_ramp,
hgt, hgt_min,
hgt_max) ## fit is not masked
cum[i, :, :] = cum_org[i, :, :] - fit
if hgt_linearflag:
fit_hgt = hgt * model[
-1] * mask ## extract only hgt-linear component
cum_bf = cum[
i, :, :] + fit_hgt ## After deramp before hgt-linear
## Output comparison image of hgt_linear
std_before = np.nanstd(cum_bf)
std_after = np.nanstd(cum[i, :, :] * mask)
data3 = [
np.angle(np.exp(1j * (data / coef_r2m / cycle)) * cycle)
for data in [cum_bf, fit_hgt, cum[i, :, :] * mask]
]
title3 = [
'Before hgt-linear (STD: {:.1f}mm)'.format(std_before),
'hgt-linear phase ({:.1f}mm/km)'.format(model[-1] * 1000),
'After hgt-linear (STD: {:.1f}mm)'.format(std_after)
]
pngfile = os.path.join(filtcumdir,
imdates[i] + '_hgt_linear.png')
plot_lib.make_3im_png(data3,
pngfile,
'insar',
title3,
vmin=-np.pi,
vmax=np.pi,
cbar=False)
pngfile = os.path.join(filtcumdir,
imdates[i] + '_hgt_corr.png')
title = '{} ({:.1f}mm/km, based on {}<=hgt<={})'.format(
imdates[i], model[-1] * 1000, hgt_min, hgt_max)
plot_lib.plot_hgt_corr(cum_bf, fit_hgt, hgt, title, pngfile)
## Output comparison image of hgt_linear for increment
if i != 0: ## first image has no increment
inc = (cum[i, :, :] - cum[i - 1, :, :]) * mask
std_before = np.nanstd(inc + fit_hgt - fit_hgt1)
std_after = np.nanstd(inc)
data3 = [
np.angle(
np.exp(1j * (data / coef_r2m / cycle)) * cycle)
for data in
[inc + fit_hgt - fit_hgt1, fit_hgt - fit_hgt1, inc]
]
title3 = [
'Before hgt-linear (STD: {:.1f}mm)'.format(std_before),
'hgt-linear phase ({:.1f}mm/km)'.format(
(model[-1] - model1) * 1000),
'After hgt-linear (STD: {:.1f}mm)'.format(std_after)
]
pngfile = os.path.join(
filtincdir,
'{}_{}_hgt_linear.png'.format(imdates[i - 1],
imdates[i]))
plot_lib.make_3im_png(data3,
pngfile,
'insar',
title3,
vmin=-np.pi,
vmax=np.pi,
cbar=False)
pngfile = os.path.join(
filtincdir,
'{}_{}_hgt_corr.png'.format(imdates[i - 1],
imdates[i]))
title = '{}_{} ({:.1f}mm/km, based on {}<=hgt<={})'.format(
imdates[i - 1], imdates[i],
(model[-1] - model1) * 1000, hgt_min, hgt_max)
plot_lib.plot_hgt_corr(inc + fit_hgt - fit_hgt1,
fit_hgt - fit_hgt1, hgt, title,
pngfile)
fit_hgt1 = fit_hgt.copy() ## backup last fit_hgt
model1 = model[-1]
else:
fit_hgt = 0 ## for plot deframp
if deg_ramp:
ramp = (fit - fit_hgt) * mask
## Output comparison image of deramp
data3 = [
np.angle(np.exp(1j * (data / coef_r2m / cycle)) * cycle)
for data in [
cum_org[i, :, :] *
mask, ramp, cum_org[i, :, :] * mask - ramp
]
]
pngfile = os.path.join(filtcumdir, imdates[i] + '_deramp.png')
plot_lib.make_3im_png(data3,
pngfile,
'insar',
deramp_title3,
vmin=-np.pi,
vmax=np.pi,
cbar=False)
## Output comparison image of deramp for increment
if i != 0: ## first image has no increment
inc_org = (cum_org[i, :, :] - cum_org[i - 1, :, :]) * mask
data3 = [
np.angle(
np.exp(1j * (data / coef_r2m / cycle)) * cycle)
for data in
[inc_org, ramp - ramp1, inc_org - (ramp - ramp1)]
]
pngfile = os.path.join(
filtincdir,
'{}_{}_deramp.png'.format(imdates[i - 1], imdates[i]))
plot_lib.make_3im_png(data3,
pngfile,
'insar',
deramp_title3,
vmin=-np.pi,
vmax=np.pi,
cbar=False)
ramp1 = ramp.copy() ## backup last ramp
#%% Filter each image
cum_filt = cumfh5.require_dataset('cum', (n_im, length, width),
dtype=np.float32)
cum_hptlps1 = []
print('\nHP filter in time, LP filter in space...', flush=True)
for i in range(n_im):
if np.mod(i, 10) == 0:
print(" {0:3}/{1:3}th image...".format(i, n_im), flush=True)
#%% Second, HP in time
if filtwidth_yr == 0.0:
cum_hpt = cum[i, :, :] ## No temporal filter
else:
time_diff_sq = (dt_cum[i] - dt_cum)**2
## Limit reading data within filtwidth_yr**8
ixs = time_diff_sq < filtwidth_yr * 8
weight_factor = np.tile(
np.exp(-time_diff_sq[ixs] / 2 / filtwidth_yr**2)[:, np.newaxis,
np.newaxis],
(1, length, width)) #len(ixs), length, width
## Take into account nan in cum
weight_factor = weight_factor * (~np.isnan(cum[ixs, :, :]))
## Normalize weight
with warnings.catch_warnings(
): ## To silence warning by zero division
warnings.simplefilter('ignore', RuntimeWarning)
weight_factor = weight_factor / np.sum(weight_factor, axis=0)
cum_lpt = np.nansum(cum[ixs, :, :] * weight_factor, axis=0)
cum_hpt = cum[i, :, :] - cum_lpt
#%% Third, LP in space and subtract from original
if filtwidth_km == 0.0:
cum_filt[i, :, :] = cum[i, :, :] ## No spatial
else:
with warnings.catch_warnings(): ## To silence warning
if i == 0:
cum_hpt = cum_hpt + sys.float_info.epsilon ##To distinguish from 0 of filtered nodata
# warnings.simplefilter('ignore', FutureWarning)
warnings.simplefilter('ignore', RuntimeWarning)
kernel = Gaussian2DKernel(x_stddev, y_stddev)
cum_hptlps = convolve_fft(
cum_hpt * mask, kernel, fill_value=np.nan,
allow_huge=True) ## fill edge 0 for interpolation
cum_hptlps[cum_hptlps == 0] = np.nan ## fill 0 with nan
cum_filt[i, :, :] = cum[i, :, :] - cum_hptlps
#%% Output comparison image
data3 = [
np.angle(np.exp(1j * (data / coef_r2m / cycle)) * cycle) for data
in [cum[i, :, :] * mask, cum_hptlps, cum_filt[i, :, :] * mask]
]
title3 = [
'Before filter ({}pi/cycle)'.format(cycle * 2),
'Filter phase ({}pi/cycle)'.format(cycle * 2),
'After filter ({}pi/cycle)'.format(cycle * 2)
]
pngfile = os.path.join(filtcumdir, imdates[i] + '_filt.png')
plot_lib.make_3im_png(data3,
pngfile,
'insar',
title3,
vmin=-np.pi,
vmax=np.pi,
cbar=False)
### Output comparison image for increment
if i != 0:
data3 = [
np.angle(np.exp(1j * (data / coef_r2m / cycle)) * cycle)
for data in [(cum[i, :, :] - cum[i - 1, :, :]) *
mask, cum_hptlps - cum_hptlps1,
(cum_filt[i, :, :] - cum_filt[i - 1, :, :]) *
mask]
]
title3 = [
'Before filter ({}pi/cycle)'.format(cycle * 2),
'Filter phase ({}pi/cycle)'.format(cycle * 2),
'After filter ({}pi/cycle)'.format(cycle * 2)
]
pngfile = os.path.join(
filtincdir, '{}_{}_filt.png'.format(imdates[i - 1],
imdates[i]))
plot_lib.make_3im_png(data3,
pngfile,
'insar',
title3,
vmin=-np.pi,
vmax=np.pi,
cbar=False)
cum_hptlps1 = cum_hptlps.copy() ## backup last filt phase
#%% 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_filt[i, :, :] - np.nanmedian(cum_filt[i, :, :]))**2
rms_cum_wrt_med = np.sqrt(sumsq_cum_wrt_med / n_im) * mask
### 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 to new stable ref
for i in range(n_im):
cum_filt[i, :, :] = cum_filt[i, :, :] - cum[i, refy1s, refx1s]
### Save image
rms_cum_wrt_med_file = os.path.join(infodir, '16rms_cum_wrt_med')
with open(rms_cum_wrt_med_file, 'w') as f:
rms_cum_wrt_med.tofile(f)
pngfile = os.path.join(infodir, '16rms_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
cumfh5.create_dataset('refarea',
data='{}:{}/{}:{}'.format(refx1s, refx2s, refy1s,
refy2s))
refsfile = os.path.join(infodir, '16ref.txt')
with open(refsfile, 'w') as f:
print('{}:{}/{}:{}'.format(refx1s, refx2s, refy1s, refy2s), file=f)
if 'corner_lat' in list(cumh5.keys()): ## Geocoded
### Make ref_stable.kml
reflat = lat1 + dlat * refy1s
reflon = lon1 + dlon * refx1s
io_lib.make_point_kml(reflat, reflon,
os.path.join(infodir, '16ref.kml'))
#%% Calc filtered velocity
print('\nCalculate velocity of filtered time series...', flush=True)
G = np.stack((np.ones_like(dt_cum), dt_cum), axis=1)
vconst = np.zeros((length, width), dtype=np.float32) * np.nan
vel = np.zeros((length, width), dtype=np.float32) * np.nan
bool_unnan = ~np.isnan(cum_filt[0, :, :]).reshape(length,
width) ## not all nan
cum_pt = cum_filt[()].reshape(n_im, length *
width)[:, bool_unnan.ravel()] #n_im x n_pt
n_pt_unnan = bool_unnan.sum()
vconst_tmp = np.zeros((n_pt_unnan), dtype=np.float32) * np.nan
vel_tmp = np.zeros((n_pt_unnan), dtype=np.float32) * np.nan
bool_nonan_pt = np.all(~np.isnan(cum_pt), axis=0)
### First, calc vel point without nan
print(' First, solving {0:6}/{1:6}th points with full cum...'.format(
bool_nonan_pt.sum(), n_pt_unnan),
flush=True)
vconst_tmp[bool_nonan_pt], vel_tmp[bool_nonan_pt] = np.linalg.lstsq(
G, cum_pt[:, bool_nonan_pt], rcond=None)[0]
### Next, calc vel point with nan
print(' Next, solving {0:6}/{1:6}th points with nan in cum...'.format(
(~bool_nonan_pt).sum(), n_pt_unnan),
flush=True)
mask_cum = ~np.isnan(cum_pt[:, ~bool_nonan_pt])
vconst_tmp[~bool_nonan_pt], vel_tmp[
~bool_nonan_pt] = inv_lib.censored_lstsq_slow(
G, cum_pt[:, ~bool_nonan_pt], mask_cum)
vconst[bool_unnan], vel[bool_unnan] = vconst_tmp, vel_tmp
vconst.tofile(vconstfile)
vel.tofile(velfile)
if maskflag:
vel_mskd = vel * mask
vconst_mskd = vconst * mask
vconst_mskd.tofile(vconstfile + '.mskd')
vel_mskd.tofile(velfile + '.mskd')
cumfh5.create_dataset('vel', data=vel.reshape(length, width))
cumfh5.create_dataset('vintercept', data=vconst.reshape(length, width))
#%% Add info and close
cumfh5.create_dataset('filtwidth_yr', data=filtwidth_yr)
cumfh5.create_dataset('filtwidth_km', data=filtwidth_km)
cumfh5.create_dataset('deramp_flag', data=deg_ramp)
cumfh5.create_dataset('hgt_linear_flag', data=hgt_linearflag * 1)
cumh5.close()
cumfh5.close()
#%% Output image
pngfile = os.path.join(resultsdir, 'vel.filt.png')
title = 'Filtered velocity (mm/yr)'
vmin = np.nanpercentile(vel, 1)
vmax = np.nanpercentile(vel, 99)
plot_lib.make_im_png(vel, pngfile, cmap_vel, title, vmin, vmax)
## vintercept
pngfile = os.path.join(resultsdir, 'vintercept.filt.png')
title = 'Intercept of filtered velocity (mm)'
vmin = np.nanpercentile(vconst, 1)
vmax = np.nanpercentile(vconst, 99)
plot_lib.make_im_png(vconst, pngfile, cmap_vel, title, vmin, vmax)
if maskflag:
pngfile = os.path.join(resultsdir, 'vel.filt.mskd.png')
title = 'Masked filtered velocity (mm/yr)'
vmin = np.nanpercentile(vel_mskd, 1)
vmax = np.nanpercentile(vel_mskd, 99)
plot_lib.make_im_png(vel_mskd, pngfile, cmap_vel, title, vmin, vmax)
## vintercept
pngfile = os.path.join(resultsdir, 'vintercept.filt.mskd.png')
title = 'Masked intercept of filtered velocity (mm)'
vmin = np.nanpercentile(vconst_mskd, 1)
vmax = np.nanpercentile(vconst_mskd, 99)
plot_lib.make_im_png(vconst_mskd, pngfile, cmap_vel, title, vmin, vmax)
#%% 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: {}\n'.format(os.path.relpath(cumffile)), flush=True)
print('To plot the time-series:')
print('LiCSBAS_plot_ts.py -i {} &\n'.format(os.path.relpath(cumffile)))