def plot_num_triplet_with_nonzero_integer_ambiguity(fname, display=False, font_size=12, fig_size=[9,3]):
"""Plot the histogram for the number of triplets with non-zero integer ambiguity
Fig. 3d-e in Yunjun et al. (2019, CAGEO).
"""
# read data
data, atr = readfile.read(fname)
vmax = int(np.nanmax(data))
# plot
fig, axs = plt.subplots(nrows=1, ncols=2, figsize=fig_size)
# subplot 1 - map
ax = axs[0]
im = ax.imshow(data, cmap='RdBu_r', interpolation='nearest')
# reference point
if all(key in atr.keys() for key in ['REF_Y','REF_X']):
ax.plot(int(atr['REF_X']), int(atr['REF_Y']), 's', color='white', ms=3)
# format
pp.auto_flip_direction(atr, ax=ax, print_msg=False)
fig.colorbar(im, ax=ax)
ax.set_title(r'$T_{int}$', fontsize=font_size)
# subplot 2 - histogram
ax = axs[1]
ax.hist(data[~np.isnan(data)].flatten(), range=(0, vmax), log=True, bins=vmax)
# axis format
ax.set_xlabel(r'# of triplets w non-zero int ambiguity $T_{int}$', fontsize=font_size)
ax.set_ylabel('# of pixels', fontsize=font_size)
ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax.yaxis.set_major_locator(ticker.LogLocator(base=10.0, numticks=15))
ax.yaxis.set_minor_locator(ticker.LogLocator(base=10.0, numticks=15,
subs=(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9)))
ax.yaxis.set_minor_formatter(ticker.NullFormatter())
for ax in axs:
ax.tick_params(which='both', direction='in', labelsize=font_size,
bottom=True, top=True, left=True, right=True)
fig.tight_layout()
# output
out_fig = '{}.png'.format(os.path.splitext(fname)[0])
print('plot and save figure to file', out_fig)
fig.savefig(out_fig, bbox_inches='tight', transparent=True, dpi=300)
if display:
plt.show()
else:
plt.close(fig)
return
def flip_map():
global inps, atr
if inps.auto_flip:
inps.flip_lr, inps.flip_ud = pp.auto_flip_direction(atr)
else:
inps.flip_ud = False
inps.flip_lr = False
def flip_map():
global inps, atr
if inps.auto_flip:
inps.flip_lr, inps.flip_ud = pp.auto_flip_direction(atr)
else:
inps.flip_ud = False
inps.flip_lr = False
def get_common_region_int_ambiguity(ifgram_file,
cc_mask_file,
water_mask_file=None,
num_sample=100,
dsNameIn='unwrapPhase'):
"""Solve the phase unwrapping integer ambiguity for the common regions among all interferograms
Parameters: ifgram_file : str, path of interferogram stack file
cc_mask_file : str, path of common connected components file
water_mask_file : str, path of water mask file
num_sample : int, number of pixel sampled for each region
dsNameIn : str, dataset name of the unwrap phase to be corrected
Returns: common_regions : list of skimage.measure._regionprops._RegionProperties object
modified by adding two more variables:
sample_coords : 2D np.ndarray in size of (num_sample, 2) in int64 format
int_ambiguity : 1D np.ndarray in size of (num_ifgram,) in int format
"""
print('-' * 50)
print(
'calculating the integer ambiguity for the common regions defined in',
cc_mask_file)
# stack info
stack_obj = ifgramStack(ifgram_file)
stack_obj.open()
date12_list = stack_obj.get_date12_list(dropIfgram=True)
num_ifgram = len(date12_list)
C = matrix(
ifgramStack.get_design_matrix4triplet(date12_list).astype(float))
ref_phase = stack_obj.get_reference_phase(unwDatasetName=dsNameIn,
dropIfgram=True).reshape(
num_ifgram, -1)
# prepare common label
print('read common mask from', cc_mask_file)
cc_mask = readfile.read(cc_mask_file)[0]
if water_mask_file is not None and os.path.isfile(water_mask_file):
water_mask = readfile.read(water_mask_file)[0]
print('refine common mask based on water mask file', water_mask_file)
cc_mask[water_mask == 0] = 0
label_img, num_label = connectComponent.get_large_label(cc_mask,
min_area=2.5e3,
print_msg=True)
common_regions = measure.regionprops(label_img)
print('number of common regions:', num_label)
# add sample_coords / int_ambiguity
print('number of samples per region:', num_sample)
print('solving the phase-unwrapping integer ambiguity for {}'.format(
dsNameIn))
print(
'\tbased on the closure phase of interferograms triplets (Yunjun et al., 2019)'
)
print(
'\tusing the L1-norm regularzed least squares approximation (LASSO) ...'
)
for i in range(num_label):
common_reg = common_regions[i]
# sample_coords
idx = sorted(
np.random.choice(common_reg.area, num_sample, replace=False))
common_reg.sample_coords = common_reg.coords[idx, :].astype(int)
# solve for int_ambiguity
U = np.zeros((num_ifgram, num_sample))
if common_reg.label == label_img[stack_obj.refY, stack_obj.refX]:
print('{}/{} skip calculation for the reference region'.format(
i + 1, num_label))
else:
prog_bar = ptime.progressBar(maxValue=num_sample,
prefix='{}/{}'.format(
i + 1, num_label))
for j in range(num_sample):
# read unwrap phase
y, x = common_reg.sample_coords[j, :]
unw = ifginv.read_unwrap_phase(stack_obj,
box=(x, y, x + 1, y + 1),
ref_phase=ref_phase,
unwDatasetName=dsNameIn,
dropIfgram=True,
print_msg=False).reshape(
num_ifgram, -1)
# calculate closure_int
closure_pha = np.dot(C, unw)
closure_int = matrix(
np.round(
(closure_pha - ut.wrap(closure_pha)) / (2. * np.pi)))
# solve for U
U[:, j] = np.round(
l1regls(-C, closure_int, alpha=1e-2,
show_progress=0)).flatten()
prog_bar.update(j + 1, every=5)
prog_bar.close()
# add int_ambiguity
common_reg.int_ambiguity = np.median(U, axis=1)
common_reg.date12_list = date12_list
#sort regions by size to facilitate the region matching later
common_regions.sort(key=lambda x: x.area, reverse=True)
# plot sample result
fig_size = pp.auto_figure_size(label_img.shape, disp_cbar=False)
fig, ax = plt.subplots(figsize=fig_size)
ax.imshow(label_img, cmap='jet')
for common_reg in common_regions:
ax.plot(common_reg.sample_coords[:, 1],
common_reg.sample_coords[:, 0],
'k.',
ms=2)
pp.auto_flip_direction(stack_obj.metadata, ax, print_msg=False)
out_img = 'common_region_sample.png'
fig.savefig(out_img, bbox_inches='tight', transparent=True, dpi=300)
print('saved common regions and sample pixels to file', out_img)
return common_regions
def update_inps_with_file_metadata(inps, metadata):
# Subset
# Convert subset input into bounding box in radar / geo coordinate
# geo_box = None if atr is not geocoded.
coord = ut.coordinate(metadata)
inps.pix_box, inps.geo_box = subset.subset_input_dict2box(
vars(inps), metadata)
inps.pix_box = coord.check_box_within_data_coverage(inps.pix_box)
inps.geo_box = coord.box_pixel2geo(inps.pix_box)
# Out message
data_box = (0, 0, inps.width, inps.length)
vprint('data coverage in y/x: ' + str(data_box))
vprint('subset coverage in y/x: ' + str(inps.pix_box))
vprint('data coverage in lat/lon: ' + str(coord.box_pixel2geo(data_box)))
vprint('subset coverage in lat/lon: ' + str(inps.geo_box))
vprint(
'------------------------------------------------------------------------'
)
# DEM contour display
if max(inps.pix_box[3] - inps.pix_box[1],
inps.pix_box[2] - inps.pix_box[0]) > 2e3:
inps.disp_dem_contour = False
if inps.dem_file:
vprint(
'area exceed 2000 pixels, turn off default DEM contour display'
)
# Multilook, if too many subplots in one figure for less memory and faster speed
if inps.multilook_num > 1:
inps.multilook = True
# Colormap
inps.colormap = pp.check_colormap_input(metadata,
inps.colormap,
datasetName=inps.dset[0],
cmap_lut=inps.cmap_lut,
print_msg=inps.print_msg)
# Reference Point
# Convert ref_lalo if existed, to ref_yx, and use ref_yx for the following
# ref_yx is referenced to input data coverage, not subseted area for display
if inps.ref_lalo and inps.geo_box:
inps.ref_yx = [
coord.lalo2yx(inps.ref_lalo[0], coord_type='lat'),
coord.lalo2yx(inps.ref_lalo[1], coord_type='lon')
]
vprint('input reference point in lat/lon: {}'.format(inps.ref_lalo))
vprint('input reference point in y /x : {}'.format(inps.ref_yx))
# ref_lalo
if inps.ref_yx and inps.geo_box:
inps.ref_lalo = [
coord.yx2lalo(inps.ref_yx[0], coord_type='y'),
coord.yx2lalo(inps.ref_yx[1], coord_type='x')
]
elif 'REF_LAT' in metadata.keys():
inps.ref_lalo = [
float(metadata['REF_LAT']),
float(metadata['REF_LON'])
]
else:
inps.ref_lalo = None
# Points of interest
inps = pp.read_point2inps(inps, coord)
# Unit and Wrap
inps.disp_unit, inps.wrap = pp.check_disp_unit_and_wrap(
metadata,
disp_unit=inps.disp_unit,
wrap=inps.wrap,
wrap_range=inps.wrap_range,
print_msg=inps.print_msg)
# Min / Max - Display
if not inps.vlim:
if (any(i in inps.key.lower() for i in ['coherence', '.cor'])
or (inps.key == 'ifgramStack'
and inps.dset[0].split('-')[0] in ['coherence'])
or inps.dset[0] == 'cmask'):
inps.vlim = [0.0, 1.0]
elif inps.key in ['.int'] or inps.wrap:
inps.vlim = inps.wrap_range
# Transparency - Alpha
if not inps.transparency:
# Auto adjust transparency value when showing shaded relief DEM
if inps.dem_file and inps.disp_dem_shade:
inps.transparency = 0.8
else:
inps.transparency = 1.0
# Flip Left-Right / Up-Down
if inps.auto_flip:
inps.flip_lr, inps.flip_ud = pp.auto_flip_direction(
metadata, print_msg=inps.print_msg)
# Figure Title
if not inps.fig_title:
inps.fig_title = pp.auto_figure_title(metadata['FILE_PATH'],
datasetNames=inps.dset,
inps_dict=vars(inps))
vprint('figure title: ' + inps.fig_title)
# Figure output file name
if not inps.outfile:
inps.outfile = ['{}{}'.format(inps.fig_title, inps.fig_ext)]
inps = update_figure_setting(inps)
return inps
def read_init_info(inps):
# Time Series Info
atr = readfile.read_attribute(inps.file[0])
inps.key = atr['FILE_TYPE']
if inps.key == 'timeseries':
obj = timeseries(inps.file[0])
elif inps.key == 'giantTimeseries':
obj = giantTimeseries(inps.file[0])
elif inps.key == 'HDFEOS':
obj = HDFEOS(inps.file[0])
else:
raise ValueError('input file is {}, not timeseries.'.format(inps.key))
obj.open(print_msg=inps.print_msg)
inps.seconds = atr.get('CENTER_LINE_UTC', 0)
if not inps.file_label:
inps.file_label = []
for fname in inps.file:
fbase = os.path.splitext(os.path.basename(fname))[0]
fbase = fbase.replace('timeseries', '')
inps.file_label.append(fbase)
# default mask file
if not inps.mask_file and 'msk' not in inps.file[0]:
dir_name = os.path.dirname(inps.file[0])
if 'Y_FIRST' in atr.keys():
inps.mask_file = os.path.join(dir_name, 'geo_maskTempCoh.h5')
else:
inps.mask_file = os.path.join(dir_name, 'maskTempCoh.h5')
if not os.path.isfile(inps.mask_file):
inps.mask_file = None
## date info
inps.date_list = obj.dateList
inps.num_date = len(inps.date_list)
if inps.start_date:
inps.date_list = [i for i in inps.date_list if int(i) >= int(inps.start_date)]
if inps.end_date:
inps.date_list = [i for i in inps.date_list if int(i) <= int(inps.end_date)]
inps.num_date = len(inps.date_list)
inps.dates, inps.yearList = ptime.date_list2vector(inps.date_list)
(inps.ex_date_list,
inps.ex_dates,
inps.ex_flag) = read_exclude_date(inps.ex_date_list, inps.date_list)
# reference date/index
if not inps.ref_date:
inps.ref_date = atr.get('REF_DATE', None)
if inps.ref_date:
inps.ref_idx = inps.date_list.index(inps.ref_date)
else:
inps.ref_idx = None
# date/index of interest for initial display
if not inps.idx:
if (not inps.ref_idx) or (inps.ref_idx < inps.num_date / 2.):
inps.idx = inps.num_date - 2
else:
inps.idx = 2
# Display Unit
(inps.disp_unit,
inps.unit_fac) = pp.scale_data2disp_unit(metadata=atr, disp_unit=inps.disp_unit)[1:3]
# Map info - coordinate unit
inps.coord_unit = atr.get('Y_UNIT', 'degrees').lower()
# Read Error List
inps.ts_plot_func = plot_ts_scatter
inps.error_ts = None
inps.ex_error_ts = None
if inps.error_file:
# assign plot function
inps.ts_plot_func = plot_ts_errorbar
# read error file
error_fc = np.loadtxt(inps.error_file, dtype=bytes).astype(str)
inps.error_ts = error_fc[:, 1].astype(np.float)*inps.unit_fac
# update error file with exlcude date
if inps.ex_date_list:
e_ts = inps.error_ts[:]
inps.ex_error_ts = e_ts[inps.ex_flag == 0]
inps.error_ts = e_ts[inps.ex_flag == 1]
# Zero displacement for 1st acquisition
if inps.zero_first:
inps.zero_idx = min(0, np.min(np.where(inps.ex_flag)[0]))
# default lookup table file and coordinate object
if not inps.lookup_file:
inps.lookup_file = ut.get_lookup_file('./inputs/geometryRadar.h5')
inps.coord = ut.coordinate(atr, inps.lookup_file)
## size and lalo info
inps.pix_box, inps.geo_box = subset.subset_input_dict2box(vars(inps), atr)
inps.pix_box = inps.coord.check_box_within_data_coverage(inps.pix_box)
inps.geo_box = inps.coord.box_pixel2geo(inps.pix_box)
data_box = (0, 0, int(atr['WIDTH']), int(atr['LENGTH']))
vprint('data coverage in y/x: '+str(data_box))
vprint('subset coverage in y/x: '+str(inps.pix_box))
vprint('data coverage in lat/lon: '+str(inps.coord.box_pixel2geo(data_box)))
vprint('subset coverage in lat/lon: '+str(inps.geo_box))
vprint('------------------------------------------------------------------------')
# calculate multilook_num
# ONLY IF:
# inps.multilook is True (no --nomultilook input) AND
# inps.multilook_num ==1 (no --multilook-num input)
# Note: inps.multilook is used for this check ONLY
# Note: multilooking is only applied to the 3D data cubes and their related operations:
# e.g. spatial indexing, referencing, etc. All the other variables are in the original grid
# so that users get the same result as the non-multilooked version.
if inps.multilook and inps.multilook_num == 1:
inps.multilook_num = pp.auto_multilook_num(inps.pix_box, inps.num_date,
max_memory=inps.maxMemory,
print_msg=inps.print_msg)
## reference pixel
if not inps.ref_lalo and 'REF_LAT' in atr.keys():
inps.ref_lalo = (float(atr['REF_LAT']), float(atr['REF_LON']))
if inps.ref_lalo:
# set longitude to [-180, 180)
if inps.coord_unit.lower().startswith('deg') and inps.ref_lalo[1] >= 180.:
inps.ref_lalo[1] -= 360.
# ref_lalo --> ref_yx if not set in cmd
if not inps.ref_yx:
inps.ref_yx = inps.coord.geo2radar(inps.ref_lalo[0], inps.ref_lalo[1], print_msg=False)[0:2]
# use REF_Y/X if ref_yx not set in cmd
if not inps.ref_yx and 'REF_Y' in atr.keys():
inps.ref_yx = (int(atr['REF_Y']), int(atr['REF_X']))
# ref_yx --> ref_lalo if in geo-coord
# for plotting purpose only
if inps.ref_yx and 'Y_FIRST' in atr.keys():
inps.ref_lalo = inps.coord.radar2geo(inps.ref_yx[0], inps.ref_yx[1], print_msg=False)[0:2]
# do not plot native reference point if it's out of the coverage due to subset
if (inps.ref_yx and 'Y_FIRST' in atr.keys()
and inps.ref_yx == (int(atr.get('REF_Y',-999)), int(atr.get('REF_X',-999)))
and not ( inps.pix_box[0] <= inps.ref_yx[1] < inps.pix_box[2]
and inps.pix_box[1] <= inps.ref_yx[0] < inps.pix_box[3])):
inps.disp_ref_pixel = False
print('the native REF_Y/X is out of subset box, thus do not display')
## initial pixel coord
if inps.lalo:
inps.yx = inps.coord.geo2radar(inps.lalo[0], inps.lalo[1], print_msg=False)[0:2]
try:
inps.lalo = inps.coord.radar2geo(inps.yx[0], inps.yx[1], print_msg=False)[0:2]
except:
inps.lalo = None
## figure settings
# Flip up-down / left-right
if inps.auto_flip:
inps.flip_lr, inps.flip_ud = pp.auto_flip_direction(atr, print_msg=inps.print_msg)
# Transparency - Alpha
if not inps.transparency:
# Auto adjust transparency value when showing shaded relief DEM
if inps.dem_file and inps.disp_dem_shade:
inps.transparency = 0.7
else:
inps.transparency = 1.0
## display unit ans wrap
# if wrap_step == 2*np.pi (default value), set disp_unit_img = radian;
# otherwise set disp_unit_img = disp_unit
inps.disp_unit_img = inps.disp_unit
if inps.wrap:
inps.range2phase = -4. * np.pi / float(atr['WAVELENGTH'])
if 'cm' == inps.disp_unit.split('/')[0]: inps.range2phase /= 100.
elif 'mm' == inps.disp_unit.split('/')[0]: inps.range2phase /= 1000.
elif 'm' == inps.disp_unit.split('/')[0]: inps.range2phase /= 1.
else:
raise ValueError('un-recognized display unit: {}'.format(inps.disp_unit))
if (inps.wrap_range[1] - inps.wrap_range[0]) == 2*np.pi:
inps.disp_unit_img = 'radian'
inps.vlim = inps.wrap_range
inps.cbar_label = 'Displacement [{}]'.format(inps.disp_unit_img)
## fit a suite of time func to the time series
inps.model, inps.num_param = ts2vel.read_inps2model(inps, date_list=inps.date_list)
# dense TS for plotting
inps.date_list_fit = ptime.get_date_range(inps.date_list[0], inps.date_list[-1])
inps.dates_fit = ptime.date_list2vector(inps.date_list_fit)[0]
inps.G_fit = time_func.get_design_matrix4time_func(
date_list=inps.date_list_fit,
model=inps.model,
seconds=inps.seconds)
return inps, atr
def read_init_info(inps):
# Time Series Info
ts_file0 = inps.file[0]
atr = readfile.read_attribute(ts_file0)
inps.key = atr['FILE_TYPE']
if inps.key == 'timeseries':
obj = timeseries(ts_file0)
elif inps.key == 'giantTimeseries':
obj = giantTimeseries(ts_file0)
elif inps.key == 'HDFEOS':
obj = HDFEOS(ts_file0)
else:
raise ValueError('input file is {}, not timeseries.'.format(inps.key))
obj.open(print_msg=inps.print_msg)
if not inps.file_label:
inps.file_label = [str(i) for i in list(range(len(inps.file)))]
# default mask file
if not inps.mask_file and 'masked' not in ts_file0:
dir_name = os.path.dirname(ts_file0)
if 'Y_FIRST' in atr.keys():
inps.mask_file = os.path.join(dir_name, 'geo_maskTempCoh.h5')
else:
inps.mask_file = os.path.join(dir_name, 'maskTempCoh.h5')
if not os.path.isfile(inps.mask_file):
inps.mask_file = None
# date info
inps.date_list = obj.dateList
inps.num_date = len(inps.date_list)
if inps.start_date:
inps.date_list = [
i for i in inps.date_list if int(i) >= int(inps.start_date)
]
if inps.end_date:
inps.date_list = [
i for i in inps.date_list if int(i) <= int(inps.end_date)
]
inps.num_date = len(inps.date_list)
inps.dates, inps.yearList = ptime.date_list2vector(inps.date_list)
(inps.ex_date_list, inps.ex_dates,
inps.ex_flag) = read_exclude_date(inps.ex_date_list, inps.date_list)
# initial display index
#if atr['REF_DATE'] in inps.date_list:
# inps.ref_idx = inps.date_list.index(atr['REF_DATE'])
#else:
# inps.ref_idx = 0
if inps.ref_date:
inps.ref_idx = inps.date_list.index(inps.ref_date)
else:
inps.ref_idx = 3
if not inps.idx:
if inps.ref_idx < inps.num_date / 2.:
inps.idx = inps.num_date - 3
else:
inps.idx = 3
# Display Unit
(inps.disp_unit,
inps.unit_fac) = pp.scale_data2disp_unit(metadata=atr,
disp_unit=inps.disp_unit)[1:3]
# Map info - coordinate unit
inps.coord_unit = atr.get('Y_UNIT', 'degrees').lower()
# Read Error List
inps.ts_plot_func = plot_ts_scatter
inps.error_ts = None
inps.ex_error_ts = None
if inps.error_file:
# assign plot function
inps.ts_plot_func = plot_ts_errorbar
# read error file
error_fc = np.loadtxt(inps.error_file, dtype=bytes).astype(str)
inps.error_ts = error_fc[:, 1].astype(np.float) * inps.unit_fac
# update error file with exlcude date
if inps.ex_date_list:
e_ts = inps.error_ts[:]
inps.ex_error_ts = e_ts[inps.ex_flag == 0]
inps.error_ts = e_ts[inps.ex_flag == 1]
# Zero displacement for 1st acquisition
if inps.zero_first:
inps.zero_idx = min(0, np.min(np.where(inps.ex_flag)[0]))
# default lookup table file
if not inps.lookup_file:
inps.lookup_file = ut.get_lookup_file('./inputs/geometryRadar.h5')
inps.coord = ut.coordinate(atr, inps.lookup_file)
# size and lalo info
inps.pix_box, inps.geo_box = subset.subset_input_dict2box(vars(inps), atr)
inps.pix_box = inps.coord.check_box_within_data_coverage(inps.pix_box)
inps.geo_box = inps.coord.box_pixel2geo(inps.pix_box)
data_box = (0, 0, int(atr['WIDTH']), int(atr['LENGTH']))
vprint('data coverage in y/x: ' + str(data_box))
vprint('subset coverage in y/x: ' + str(inps.pix_box))
vprint('data coverage in lat/lon: ' +
str(inps.coord.box_pixel2geo(data_box)))
vprint('subset coverage in lat/lon: ' + str(inps.geo_box))
vprint(
'------------------------------------------------------------------------'
)
# reference pixel
if not inps.ref_lalo and 'REF_LAT' in atr.keys():
inps.ref_lalo = (float(atr['REF_LAT']), float(atr['REF_LON']))
if inps.ref_lalo:
if inps.ref_lalo[1] > 180.:
inps.ref_lalo[1] -= 360.
inps.ref_yx = inps.coord.geo2radar(inps.ref_lalo[0],
inps.ref_lalo[1],
print_msg=False)[0:2]
if not inps.ref_yx and 'REF_Y' in atr.keys():
inps.ref_yx = [int(atr['REF_Y']), int(atr['REF_X'])]
# Initial Pixel Coord
if inps.lalo:
inps.yx = inps.coord.geo2radar(inps.lalo[0],
inps.lalo[1],
print_msg=False)[0:2]
try:
inps.lalo = inps.coord.radar2geo(inps.yx[0],
inps.yx[1],
print_msg=False)[0:2]
except:
inps.lalo = None
# Flip up-down / left-right
if inps.auto_flip:
inps.flip_lr, inps.flip_ud = pp.auto_flip_direction(
atr, print_msg=inps.print_msg)
# Transparency - Alpha
if not inps.transparency:
# Auto adjust transparency value when showing shaded relief DEM
if inps.dem_file and inps.disp_dem_shade:
inps.transparency = 0.7
else:
inps.transparency = 1.0
# display unit ans wrap
# if wrap_step == 2*np.pi (default value), set disp_unit_img = radian;
# otherwise set disp_unit_img = disp_unit
inps.disp_unit_img = inps.disp_unit
if inps.wrap:
inps.range2phase = -4. * np.pi / float(atr['WAVELENGTH'])
if 'cm' == inps.disp_unit.split('/')[0]: inps.range2phase /= 100.
elif 'mm' == inps.disp_unit.split('/')[0]: inps.range2phase /= 1000.
elif 'm' == inps.disp_unit.split('/')[0]: inps.range2phase /= 1.
else:
raise ValueError('un-recognized display unit: {}'.format(
inps.disp_unit))
if (inps.wrap_range[1] - inps.wrap_range[0]) == 2 * np.pi:
inps.disp_unit_img = 'radian'
inps.vlim = inps.wrap_range
inps.cbar_label = 'Displacement [{}]'.format(inps.disp_unit_img)
return inps, atr
def detect_unwrap_error(ifgram_file, mask_file, mask_cc_file='maskConnComp.h5', unwDatasetName='unwrapPhase',
cutoff=1., min_num_pixel=1e4):
"""Detect unwrapping error based on phase closure and extract coherent conn comps
based on its histogram distribution
Check:
https://en.wikipedia.org/wiki/Otsu%27s_method
from skimage.filters import threshold_otsu
Parameters: ifgram_file : string, path of ifgram stack file
mask_file : string, path of mask file, e.g. waterMask.h5, maskConnComp.h5
mask_cc_file: string, path of mask file for coherent conn comps
cutoff : float, cutoff value for the mean number of nonzero phase closure
to be selected as coherent conn comps candidate
min_num_pixel : float, min number of pixels left after morphology operation
to be determined as coherent conn comps
Returns: mask_cc_file : string, path of mask file for coherent conn comps
"""
print('-'*50)
print('detect unwraping error based on phase closure')
obj = ifgramStack(ifgram_file)
obj.open(print_msg=False)
C = obj.get_design_matrix4triplet(obj.get_date12_list(dropIfgram=False))
num_nonzero_closure = get_nonzero_phase_closure(ifgram_file, unwDatasetName=unwDatasetName)
# get histogram of num_nonzero_phase_closure
mask = readfile.read(mask_file)[0]
mask *= num_nonzero_closure != 0.
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=[12, 4])
num4disp = np.array(num_nonzero_closure, dtype=np.float32)
num4disp[mask == 0] = np.nan
im = ax[0].imshow(num4disp)
ax[0].set_xlabel('Range [pix.]')
ax[0].set_ylabel('Azimuth [pix.]')
ax[0] = pp.auto_flip_direction(obj.metadata, ax=ax[0], print_msg=False)
cbar = fig.colorbar(im, ax=ax[0])
cbar.set_label('number of non-zero phase closure')
print('2. extract coherent conn comps with unwrap error based on histogram distribution')
max_nonzero_closure = int(np.max(num_nonzero_closure[mask]))
bin_value, bin_edge = ax[1].hist(num_nonzero_closure[mask].flatten(),
range=(0, max_nonzero_closure),
log=True,
bins=max_nonzero_closure)[0:2]
ax[1].set_xlabel('number of non-zero phase closure')
ax[1].set_ylabel('number of pixels')
if 'Closure' not in unwDatasetName:
print('eliminate pixels with number of nonzero phase closure < 5% of total phase closure number')
print('\twhich can be corrected using phase closure alone.')
bin_value[:int(C.shape[0]*0.05)] = 0.
bin_value_thres = ut.median_abs_deviation_threshold(bin_value, cutoff=cutoff)
print('median abs deviation cutoff value: {}'.format(cutoff))
plt.plot([0, max_nonzero_closure], [bin_value_thres, bin_value_thres])
out_img = 'numUnwErr_stat.png'
fig.savefig(out_img, bbox_inches='tight', transparent=True, dpi=300)
print('save unwrap error detection result to {}'.format(out_img))
# histogram --> candidates of coherence conn comps --> mask_cc
# find pixel clusters sharing similar number of non-zero phase closure
print('searching connected components with more than {} pixels'.format(min_num_pixel))
bin_label, n_bins = ndimage.label(bin_value > bin_value_thres)
mask_cc = np.zeros(num_nonzero_closure.shape, dtype=np.int16)
# first conn comp - reference conn comp with zero non-zero phase closure
num_cc = 1
mask_cc1 = num_nonzero_closure == 0.
mask_cc1s = ut.get_all_conn_components(mask_cc1, min_num_pixel=min_num_pixel)
for mask_cc1 in mask_cc1s:
mask_cc += mask_cc1
# other conn comps - target conn comps to be corrected for unwrap error
for i in range(n_bins):
idx = np.where(bin_label == i+1)[0]
mask_cci0 = np.multiply(num_nonzero_closure >= bin_edge[idx[0]],
num_nonzero_closure < bin_edge[idx[-1]+1])
mask_ccis = ut.get_all_conn_components(mask_cci0, min_num_pixel=min_num_pixel)
if mask_ccis:
for mask_cci in mask_ccis:
num_cc += 1
mask_cc += mask_cci * num_cc
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=[8, 4])
im = ax[0].imshow(mask_cci0)
im = ax[1].imshow(mask_cci)
fig.savefig('mask_cc{}.png'.format(num_cc),
bbox_inches='tight', transparent=True, dpi=300)
# save to hdf5 file
num_bridge = num_cc - 1
atr = dict(obj.metadata)
atr['FILE_TYPE'] = 'mask'
atr['UNIT'] = 1
writefile.write(mask_cc, out_file=mask_cc_file, metadata=atr)
# plot and save figure to img file
out_img = '{}.png'.format(os.path.splitext(mask_cc_file)[0])
fig, ax = plt.subplots(figsize=[6, 8])
im = ax.imshow(mask_cc)
ax = pp.auto_flip_direction(atr, ax=ax, print_msg=False)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "3%", pad="3%")
cbar = plt.colorbar(im, cax=cax, ticks=np.arange(num_bridge+2))
fig.savefig(out_img, bbox_inches='tight', transparent=True, dpi=300)
print('save to {}'.format(out_img))
return mask_cc_file
def get_common_region_int_ambiguity(ifgram_file, cc_mask_file, water_mask_file=None, num_sample=100,
dsNameIn='unwrapPhase'):
"""Solve the phase unwrapping integer ambiguity for the common regions among all interferograms
Parameters: ifgram_file : str, path of interferogram stack file
cc_mask_file : str, path of common connected components file
water_mask_file : str, path of water mask file
num_sample : int, number of pixel sampled for each region
dsNameIn : str, dataset name of the unwrap phase to be corrected
Returns: common_regions : list of skimage.measure._regionprops._RegionProperties object
modified by adding two more variables:
sample_coords : 2D np.ndarray in size of (num_sample, 2) in int64 format
int_ambiguity : 1D np.ndarray in size of (num_ifgram,) in int format
"""
print('-'*50)
print('calculating the integer ambiguity for the common regions defined in', cc_mask_file)
# stack info
stack_obj = ifgramStack(ifgram_file)
stack_obj.open()
date12_list = stack_obj.get_date12_list(dropIfgram=True)
num_ifgram = len(date12_list)
C = matrix(ifgramStack.get_design_matrix4triplet(date12_list).astype(float))
ref_phase = stack_obj.get_reference_phase(unwDatasetName=dsNameIn, dropIfgram=True).reshape(num_ifgram, -1)
# prepare common label
print('read common mask from', cc_mask_file)
cc_mask = readfile.read(cc_mask_file)[0]
if water_mask_file is not None and os.path.isfile(water_mask_file):
water_mask = readfile.read(water_mask_file)[0]
print('refine common mask based on water mask file', water_mask_file)
cc_mask[water_mask == 0] = 0
label_img, num_label = connectComponent.get_large_label(cc_mask, min_area=2.5e3, print_msg=True)
common_regions = measure.regionprops(label_img)
print('number of common regions:', num_label)
# add sample_coords / int_ambiguity
print('number of samples per region:', num_sample)
print('solving the phase-unwrapping integer ambiguity for {}'.format(dsNameIn))
print('\tbased on the closure phase of interferograms triplets (Yunjun et al., 2019)')
print('\tusing the L1-norm regularzed least squares approximation (LASSO) ...')
for i in range(num_label):
common_reg = common_regions[i]
# sample_coords
idx = sorted(np.random.choice(common_reg.area, num_sample, replace=False))
common_reg.sample_coords = common_reg.coords[idx, :].astype(int)
# solve for int_ambiguity
U = np.zeros((num_ifgram, num_sample))
if common_reg.label == label_img[stack_obj.refY, stack_obj.refX]:
print('{}/{} skip calculation for the reference region'.format(i+1, num_label))
else:
prog_bar = ptime.progressBar(maxValue=num_sample, prefix='{}/{}'.format(i+1, num_label))
for j in range(num_sample):
# read unwrap phase
y, x = common_reg.sample_coords[j, :]
unw = ifginv.read_unwrap_phase(stack_obj,
box=(x, y, x+1, y+1),
ref_phase=ref_phase,
unwDatasetName=dsNameIn,
dropIfgram=True,
print_msg=False).reshape(num_ifgram, -1)
# calculate closure_int
closure_pha = np.dot(C, unw)
closure_int = matrix(np.round((closure_pha - ut.wrap(closure_pha)) / (2.*np.pi)))
# solve for U
U[:,j] = np.round(l1regls(-C, closure_int, alpha=1e-2, show_progress=0)).flatten()
prog_bar.update(j+1, every=5)
prog_bar.close()
# add int_ambiguity
common_reg.int_ambiguity = np.median(U, axis=1)
common_reg.date12_list = date12_list
#sort regions by size to facilitate the region matching later
common_regions.sort(key=lambda x: x.area, reverse=True)
# plot sample result
fig_size = pp.auto_figure_size(label_img.shape, disp_cbar=False)
fig, ax = plt.subplots(figsize=fig_size)
ax.imshow(label_img, cmap='jet')
for common_reg in common_regions:
ax.plot(common_reg.sample_coords[:,1],
common_reg.sample_coords[:,0], 'k.', ms=2)
pp.auto_flip_direction(stack_obj.metadata, ax, print_msg=False)
out_img = 'common_region_sample.png'
fig.savefig(out_img, bbox_inches='tight', transparent=True, dpi=300)
print('saved common regions and sample pixels to file', out_img)
return common_regions
def detect_unwrap_error(ifgram_file,
mask_file,
mask_cc_file='maskConnComp.h5',
unwDatasetName='unwrapPhase',
cutoff=1.,
min_num_pixel=1e4):
"""Detect unwrapping error based on phase closure and extract coherent conn comps
based on its histogram distribution
Check:
https://en.wikipedia.org/wiki/Otsu%27s_method
from skimage.filters import threshold_otsu
Parameters: ifgram_file : string, path of ifgram stack file
mask_file : string, path of mask file, e.g. waterMask.h5, maskConnComp.h5
mask_cc_file: string, path of mask file for coherent conn comps
cutoff : float, cutoff value for the mean number of nonzero phase closure
to be selected as coherent conn comps candidate
min_num_pixel : float, min number of pixels left after morphology operation
to be determined as coherent conn comps
Returns: mask_cc_file : string, path of mask file for coherent conn comps
"""
print('-' * 50)
print('detect unwraping error based on phase closure')
obj = ifgramStack(ifgram_file)
obj.open(print_msg=False)
C = obj.get_design_matrix4triplet(obj.get_date12_list(dropIfgram=False))
num_nonzero_closure = get_nonzero_phase_closure(
ifgram_file, unwDatasetName=unwDatasetName)
# get histogram of num_nonzero_phase_closure
mask = readfile.read(mask_file)[0]
mask *= num_nonzero_closure != 0.
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=[12, 4])
num4disp = np.array(num_nonzero_closure, dtype=np.float32)
num4disp[mask == 0] = np.nan
im = ax[0].imshow(num4disp)
ax[0].set_xlabel('Range [pix.]')
ax[0].set_ylabel('Azimuth [pix.]')
ax[0] = pp.auto_flip_direction(obj.metadata, ax=ax[0], print_msg=False)
cbar = fig.colorbar(im, ax=ax[0])
cbar.set_label('number of non-zero phase closure')
print(
'2. extract coherent conn comps with unwrap error based on histogram distribution'
)
max_nonzero_closure = int(np.max(num_nonzero_closure[mask]))
bin_value, bin_edge = ax[1].hist(num_nonzero_closure[mask].flatten(),
range=(0, max_nonzero_closure),
log=True,
bins=max_nonzero_closure)[0:2]
ax[1].set_xlabel('number of non-zero phase closure')
ax[1].set_ylabel('number of pixels')
if 'Closure' not in unwDatasetName:
print(
'eliminate pixels with number of nonzero phase closure < 5% of total phase closure number'
)
print('\twhich can be corrected using phase closure alone.')
bin_value[:int(C.shape[0] * 0.05)] = 0.
bin_value_thres = ut.median_abs_deviation_threshold(bin_value,
cutoff=cutoff)
print('median abs deviation cutoff value: {}'.format(cutoff))
plt.plot([0, max_nonzero_closure], [bin_value_thres, bin_value_thres])
out_img = 'numUnwErr_stat.png'
fig.savefig(out_img, bbox_inches='tight', transparent=True, dpi=300)
print('save unwrap error detection result to {}'.format(out_img))
# histogram --> candidates of coherence conn comps --> mask_cc
# find pixel clusters sharing similar number of non-zero phase closure
print('searching connected components with more than {} pixels'.format(
min_num_pixel))
bin_label, n_bins = ndimage.label(bin_value > bin_value_thres)
mask_cc = np.zeros(num_nonzero_closure.shape, dtype=np.int16)
# first conn comp - reference conn comp with zero non-zero phase closure
num_cc = 1
mask_cc1 = num_nonzero_closure == 0.
mask_cc1s = ut.get_all_conn_components(mask_cc1,
min_num_pixel=min_num_pixel)
for mask_cc1 in mask_cc1s:
mask_cc += mask_cc1
# other conn comps - target conn comps to be corrected for unwrap error
for i in range(n_bins):
idx = np.where(bin_label == i + 1)[0]
mask_cci0 = np.multiply(num_nonzero_closure >= bin_edge[idx[0]],
num_nonzero_closure < bin_edge[idx[-1] + 1])
mask_ccis = ut.get_all_conn_components(mask_cci0,
min_num_pixel=min_num_pixel)
if mask_ccis:
for mask_cci in mask_ccis:
num_cc += 1
mask_cc += mask_cci * num_cc
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=[8, 4])
im = ax[0].imshow(mask_cci0)
im = ax[1].imshow(mask_cci)
fig.savefig('mask_cc{}.png'.format(num_cc),
bbox_inches='tight',
transparent=True,
dpi=300)
# save to hdf5 file
num_bridge = num_cc - 1
atr = dict(obj.metadata)
atr['FILE_TYPE'] = 'mask'
atr['UNIT'] = 1
writefile.write(mask_cc, out_file=mask_cc_file, metadata=atr)
# plot and save figure to img file
out_img = '{}.png'.format(os.path.splitext(mask_cc_file)[0])
fig, ax = plt.subplots(figsize=[6, 8])
im = ax.imshow(mask_cc)
ax = pp.auto_flip_direction(atr, ax=ax, print_msg=False)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "3%", pad="3%")
cbar = plt.colorbar(im, cax=cax, ticks=np.arange(num_bridge + 2))
fig.savefig(out_img, bbox_inches='tight', transparent=True, dpi=300)
print('save to {}'.format(out_img))
return mask_cc_file
