def set_initial_map():
global d_v, h5, k, dateList, inps, data_lim
d_v = h5['timeseries'][inps.epoch_num][:] * inps.unit_fac
# Initial Map
print(str(dateList))
d_v = readfile.read(inps.timeseries_file, datasetName=dateList[inps.epoch_num])[0] * inps.unit_fac
#d_v = h5[k].get(dateList[inps.epoch_num])[:]*inps.unit_fac
if inps.ref_date:
inps.ref_d_v = readfile.read(inps.timeseries_file, datasetName=inps.ref_date)[0]*inps.unit_fac
d_v -= inps.ref_d_v
if mask is not None:
d_v = mask_matrix(d_v, mask)
if inps.ref_yx:
d_v -= d_v[inps.ref_yx[0], inps.ref_yx[1]]
data_lim = [np.nanmin(d_v), np.nanmax(d_v)]
if not inps.ylim_mat:
inps.ylim_mat = data_lim
print(('Initial data range: '+str(data_lim)))
print(('Display data range: '+str(inps.ylim_mat)))
print(('Initial data range: ' + str(data_lim)))
print(('Display data range: ' + str(inps.ylim)))
def mask_file(fname, mask_file, out_file, inps=None):
""" Mask input fname with mask_file
Inputs:
fname/mask_file - string,
inps_dict - dictionary including the following options:
subset_x/y - list of 2 ints, subset in x/y direction
threshold - float, threshold/minValue to generate mask
Output:
out_file - string
"""
if not inps:
inps = cmd_line_parse()
# read mask_file
mask = readfile.read(mask_file)[0]
mask = update_mask_with_inps(mask, inps)
# masking input file
dsNames = readfile.get_dataset_list(fname)
maxDigit = max([len(i) for i in dsNames])
dsDict = {}
for dsName in dsNames:
if dsName not in ['coherence']:
print('masking {d:<{w}} from {f} ...'.format(d=dsName, w=maxDigit, f=fname))
data = readfile.read(fname, datasetName=dsName, print_msg=False)[0]
data = mask_matrix(data, mask, fill_value=inps.fill_value)
dsDict[dsName] = data
# default output filename
if not out_file:
fbase, fext = os.path.splitext(fname)
out_file = '{}_msk{}'.format(fbase, fext)
writefile.write(dsDict, out_file=out_file, ref_file=fname)
return out_file
def read_isce_bperp_file(fname, out_shape, box=None):
'''Read ISCE coarse grid perpendicular baseline file, and project it to full size
Parameters: self : geometry object,
fname : str, bperp file name
outShape : tuple of 2int, shape of file in full resolution
box : tuple of 4 int, subset range in (x0, y0, x1, y1) with respect to full resolution
Returns: data : 2D array of float32
Example: fname = '$PROJECT_DIR/merged/baselines/20160418/bperp'
data = self.read_sice_bperp_file(fname, (3600,2200), box=(200,400,1000,1000))
'''
# read original data
data_c = readfile.read(fname)[0]
# resize to full resolution
data_min, data_max = np.nanmin(data_c), np.nanmax(data_c)
if data_max != data_min:
data_c = (data_c - data_min) / (data_max - data_min)
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
data = resize(data_c, out_shape, order=1, mode='edge')
if data_max != data_min:
data = data * (data_max - data_min) + data_min
# for debug
debug_mode=False
if debug_mode:
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(8,6));
im = ax1.imshow(readfile.read(fname)[0]); fig.colorbar(im, ax=ax1)
im = ax2.imshow(data); fig.colorbar(im, ax=ax2)
plt.show()
if box is not None:
data = data[box[1]:box[3], box[0]:box[2]]
return data
def prepare_regular_grid_interpolator(self):
"""Prepare aux data for RGI module"""
# source points in regular grid
src_length = int(self.src_metadata['LENGTH'])
src_width = int(self.src_metadata['WIDTH'])
self.src_pts = (np.arange(src_length), np.arange(src_width))
# destination points
dest_y = readfile.read(self.file, datasetName='azimuthCoord')[0]
dest_x = readfile.read(self.file, datasetName='rangeCoord')[0]
if 'SUBSET_XMIN' in self.src_metadata.keys():
print('input data file was cropped before.')
dest_y[dest_y != 0.] -= float(self.src_metadata['SUBSET_YMIN'])
dest_x[dest_x != 0.] -= float(self.src_metadata['SUBSET_XMIN'])
self.interp_mask = np.multiply(np.multiply(dest_y > 0, dest_y < src_length),
np.multiply(dest_x > 0, dest_x < src_width))
self.dest_pts = np.hstack((dest_y[self.interp_mask].reshape(-1, 1),
dest_x[self.interp_mask].reshape(-1, 1)))
# destimation data size
self.length = int(self.lut_metadata['LENGTH'])
self.width = int(self.lut_metadata['WIDTH'])
lat0 = float(self.lut_metadata['Y_FIRST'])
lon0 = float(self.lut_metadata['X_FIRST'])
lat_step = float(self.lut_metadata['Y_STEP'])
lon_step = float(self.lut_metadata['X_STEP'])
self.laloStep = (lat_step, lon_step)
self.SNWE = (lat0 + lat_step * (self.length - 1),
lat0,
lon0,
lon0 + lon_step * (self.width - 1))
def add_file(fnames, out_file=None):
"""Generate sum of all input files
Parameters: fnames : list of str, path/name of input files to be added
out_file : str, optional, path/name of output file
Returns: out_file : str, path/name of output file
Example: 'mask_all.h5' = add_file(['mask_1.h5','mask_2.h5','mask_3.h5'], 'mask_all.h5')
"""
# Default output file name
ext = os.path.splitext(fnames[0])[1]
if not out_file:
out_file = os.path.splitext(fnames[0])[0]
for i in range(1, len(fnames)):
out_file += '_plus_' + os.path.splitext(os.path.basename(fnames[i]))[0]
out_file += ext
atr = readfile.read_attribute(fnames[0])
dsNames = readfile.get_dataset_list(fnames[0])
dsDict = {}
for dsName in dsNames:
print('adding {} ...'.format(dsName))
data = readfile.read(fnames[0], datasetName=dsName)[0]
for i in range(1, len(fnames)):
d = readfile.read(fnames[i], datasetName=dsName)[0]
data = add_matrix(data, d)
dsDict[dsName] = data
writefile.write(dsDict, out_file=out_file, metadata=atr, ref_file=fnames[0])
return out_file
def correct_local_oscilator_drift(fname, rg_dist_file=None, out_file=None):
print('-'*50)
print('correct Local Oscilator Drift for Envisat using an empirical model (Marinkovic and Larsen, 2013)')
print('-'*50)
atr = readfile.read_attribute(fname)
# Check Sensor Type
platform = atr['PLATFORM']
print('platform: '+platform)
if not platform.lower() in ['env', 'envisat']:
print('No need to correct LOD for '+platform)
return
# output file name
if not out_file:
out_file = '{}_LODcor{}'.format(os.path.splitext(fname)[0], os.path.splitext(fname)[1])
# Get LOD ramp rate from empirical model
if not rg_dist_file:
print('calculate range distance from file metadata')
rg_dist = get_relative_range_distance(atr)
else:
print('read range distance from file: %s' % (rg_dist_file))
rg_dist = readfile.read(rg_dist_file, datasetName='slantRangeDistance', print_msg=False)[0]
rg_dist -= rg_dist[int(atr['REF_Y']), int(atr['REF_X'])]
ramp_rate = np.array(rg_dist * 3.87e-7, np.float32)
# Correct LOD Ramp for Input fname
range2phase = -4*np.pi / float(atr['WAVELENGTH'])
k = atr['FILE_TYPE']
if k == 'timeseries':
# read
obj = timeseries(fname)
obj.open()
data = obj.read()
# correct LOD
diff_year = np.array(obj.yearList)
diff_year -= diff_year[obj.refIndex]
for i in range(data.shape[0]):
data[i, :, :] -= ramp_rate * diff_year[i]
# write
obj_out = timeseries(out_file)
obj_out.write2hdf5(data, refFile=fname)
elif k in ['.unw']:
data, atr = readfile.read(fname)
dates = ptime.yyyymmdd2years(ptime.yyyymmdd(atr['DATE12'].split('-')))
dt = dates[1] - dates[0]
data -= ramp_rate * range2phase * dt
writefile.write(data, out_file=out_file, metadata=atr)
else:
print('No need to correct for LOD for %s file' % (k))
return out_file
def extract_geometry_metadata(geom_dir, metadata=dict()):
"""extract metadata from geometry files"""
def get_nonzero_row_number(data, buffer=2):
"""Find the first and last row number of rows without zero value
for multiple swaths data
"""
if np.all(data):
r0, r1 = 0 + buffer, -1 - buffer
else:
row_flag = np.sum(data != 0., axis=1) == data.shape[1]
row_idx = np.where(row_flag)[0]
r0, r1 = row_idx[0] + buffer, row_idx[-1] - buffer
return r0, r1
# grab existing files
geom_files = [os.path.join(os.path.abspath(geom_dir), '{}.rdr'.format(i))
for i in ['hgt','lat','lon','los']]
geom_files = [i for i in geom_files if os.path.isfile(i)]
print('extract metadata from geometry files: {}'.format(
[os.path.basename(i) for i in geom_files]))
# get A/RLOOKS
metadata = extract_multilook_number(geom_dir, metadata)
# update pixel_size for multilooked data
metadata['rangePixelSize'] *= metadata['RLOOKS']
metadata['azimuthPixelSize'] *= metadata['ALOOKS']
# get LAT/LON_REF1/2/3/4 and HEADING into metadata
for geom_file in geom_files:
if 'lat' in os.path.basename(geom_file):
data = readfile.read(geom_file)[0]
r0, r1 = get_nonzero_row_number(data)
metadata['LAT_REF1'] = str(data[r0, 0])
metadata['LAT_REF2'] = str(data[r0, -1])
metadata['LAT_REF3'] = str(data[r1, 0])
metadata['LAT_REF4'] = str(data[r1, -1])
if 'lon' in os.path.basename(geom_file):
data = readfile.read(geom_file)[0]
r0, r1 = get_nonzero_row_number(data)
metadata['LON_REF1'] = str(data[r0, 0])
metadata['LON_REF2'] = str(data[r0, -1])
metadata['LON_REF3'] = str(data[r1, 0])
metadata['LON_REF4'] = str(data[r1, -1])
if 'los' in os.path.basename(geom_file):
data = readfile.read(geom_file, datasetName='az')[0]
data[data == 0.] = np.nan
az_angle = np.nanmean(data)
# convert isce azimuth angle to roipac orbit heading angle
head_angle = -1 * (270 + az_angle)
head_angle -= np.round(head_angle / 360.) * 360.
metadata['HEADING'] = str(head_angle)
return metadata
def get_boxes4deforming_area(vel_file, mask_file, win_size=30, min_percentage=0.2, ramp_type='quadratic', display=False):
"""Get list of boxes to cover the deforming areas.
A pixel is identified as deforming if its velocity exceeds the MAD of the whole image.
Parameters: vel_file : str, path of velocity file
mask_file : str, path of mask file
win_size : int, length and width of the output box
min_percentage : float between 0 and 1, minimum percentage of deforming points in the box
ramp_type : str, type of phase ramps to be removed while evaluating the deformation
display : bool, plot the identification result or not
Returns: box_list : list of t-tuple of int, each indicating (col0, row0, col1, row1)
"""
print('-'*30)
print('get boxes on deforming areas')
mask = readfile.read(mask_file)[0]
vel, atr = readfile.read(vel_file)
print('removing a {} phase ramp from input velocity before the evaluation'.format(ramp_type))
vel = deramp(vel, mask, ramp_type=ramp_type, metadata=atr)[0] #remove ramp before the evaluation
# get deforming pixels
mad = ut.median_abs_deviation_threshold(vel[mask], center=0., cutoff=3) #deformation threshold
print('velocity threshold / median abs dev: {:.3f} cm/yr'.format(mad))
vel[mask == 0] = 0
mask_aoi = (vel >= mad) + (vel <= -1. * mad)
print('number of points: {}'.format(np.sum(mask_aoi)))
# get deforming boxes
box_list = []
min_num = min_percentage * (win_size ** 2)
length, width = vel.shape
num_row = np.ceil(length / win_size).astype(int)
num_col = np.ceil(width / win_size).astype(int)
for i in range(num_row):
r0 = i * win_size
r1 = min([length, r0 + win_size])
for j in range(num_col):
c0 = j * win_size
c1 = min([width, c0 + win_size])
box = (c0, r0, c1, r1)
if np.sum(mask_aoi[r0:r1, c0:c1]) >= min_num:
box_list.append(box)
print('number of boxes : {}'.format(len(box_list)))
if display:
fig, axs = plt.subplots(nrows=1, ncols=2, figsize=[12, 8], sharey=True)
vel[mask == 0] = np.nan
axs[0].imshow(vel, cmap='jet')
axs[1].imshow(mask_aoi, cmap='gray')
for box in box_list:
for ax in axs:
rect = Rectangle((box[0],box[1]), (box[2]-box[0]), (box[3]-box[1]), linewidth=2, edgecolor='r', fill=False)
ax.add_patch(rect)
plt.show()
return box_list
def multilook_file(infile, lks_y, lks_x, outfile=None):
lks_y = int(lks_y)
lks_x = int(lks_x)
# input file info
atr = readfile.read_attribute(infile)
k = atr['FILE_TYPE']
print('multilooking {} {} file: {}'.format(atr['PROCESSOR'], k, infile))
print('number of looks in y / azimuth direction: %d' % lks_y)
print('number of looks in x / range direction: %d' % lks_x)
# output file name
if not outfile:
if os.getcwd() == os.path.dirname(os.path.abspath(infile)):
ext = os.path.splitext(infile)[1]
outfile = os.path.splitext(infile)[0]+'_'+str(lks_y)+'alks_'+str(lks_x)+'rlks'+ext
else:
outfile = os.path.basename(infile)
#print('writing >>> '+outfile)
# read source data and multilooking
dsNames = readfile.get_dataset_list(infile)
maxDigit = max([len(i) for i in dsNames])
dsDict = dict()
for dsName in dsNames:
print('multilooking {d:<{w}} from {f} ...'.format(
d=dsName, w=maxDigit, f=os.path.basename(infile)))
data = readfile.read(infile, datasetName=dsName, print_msg=False)[0]
data = multilook_data(data, lks_y, lks_x)
dsDict[dsName] = data
atr = multilook_attribute(atr, lks_y, lks_x)
writefile.write(dsDict, out_file=outfile, metadata=atr, ref_file=infile)
return outfile
def plot_coherence_matrix4pixel(self, yx):
"""Plot coherence matrix for one pixel
Parameters: yx : list of 2 int
"""
# read coherence
box = (yx[1], yx[0], yx[1]+1, yx[0]+1)
coh = readfile.read(self.ifgram_file, datasetName='coherence', box=box)[0]
# prep metadata
plotDict = {}
plotDict['fig_title'] = 'Y = {}, X = {}'.format(yx[0], yx[1])
plotDict['colormap'] = self.colormap
plotDict['disp_legend'] = False
# plot
coh_mat = pp.plot_coherence_matrix(self.ax_mat,
date12List=self.date12_list,
cohList=coh.tolist(),
date12List_drop=self.ex_date12_list,
plot_dict=plotDict)[1]
self.fig.canvas.draw()
# status bar
def format_coord(x, y):
row, col = int(y+0.5), int(x+0.5)
date12 = sorted([self.date_list[row], self.date_list[col]])
date12 = ['{}-{}-{}'.format(i[0:4], i[4:6], i[6:8]) for i in date12]
return 'x={}, y={}, v={:.3f}'.format(date12[0], date12[1], coh_mat[row, col])
self.ax_mat.format_coord = format_coord
# info
vprint('-'*30)
vprint('pixel: yx = {}'.format(yx))
vprint('min/max coherence: {:.2f} / {:.2f}'.format(np.min(coh), np.max(coh)))
return
def read_lookup_table(self, print_msg=True):
if 'Y_FIRST' in self.lut_metadata.keys():
self.lut_y = readfile.read(self.lookup_file[0],
datasetName='azimuthCoord',
print_msg=print_msg)[0]
self.lut_x = readfile.read(self.lookup_file[1],
datasetName='rangeCoord',
print_msg=print_msg)[0]
else:
self.lut_y = readfile.read(self.lookup_file[0],
datasetName='latitude',
print_msg=print_msg)[0]
self.lut_x = readfile.read(self.lookup_file[1],
datasetName='longitude',
print_msg=print_msg)[0]
return self.lut_y, self.lut_x
def timeseries2ifgram(ts_file, ifgram_file, out_file='reconUnwrapIfgram.h5'):
# read time-series
atr = readfile.read_attribute(ts_file)
range2phase = -4.*np.pi / float(atr['WAVELENGTH'])
print('reading timeseries data from file {} ...'.format(ts_file))
ts_data = readfile.read(ts_file)[0] * range2phase
num_date, length, width = ts_data.shape
ts_data = ts_data.reshape(num_date, -1)
# reconstruct unwrapPhase
print('reconstructing the interferograms from timeseries')
stack_obj = ifgramStack(ifgram_file)
stack_obj.open(print_msg=False)
A1 = stack_obj.get_design_matrix4timeseries(stack_obj.get_date12_list(dropIfgram=False))[0]
num_ifgram = A1.shape[0]
A0 = -1.*np.ones((num_ifgram, 1))
A = np.hstack((A0, A1))
ifgram_est = np.dot(A, ts_data).reshape(num_ifgram, length, width)
ifgram_est = np.array(ifgram_est, dtype=ts_data.dtype)
del ts_data
# write to ifgram file
dsDict = {}
dsDict['unwrapPhase'] = ifgram_est
writefile.write(dsDict, out_file=out_file, ref_file=ifgram_file)
return ifgram_file
def file_operation(fname, operator, operand, out_file=None):
"""Mathmathic operation of file"""
# Basic Info
atr = readfile.read_attribute(fname)
k = atr['FILE_TYPE']
print('input is '+k+' file: '+fname)
print('operation: file %s %f' % (operator, operand))
# default output filename
if not out_file:
if operator in ['+', 'plus', 'add', 'addition']:
suffix = 'plus'
elif operator in ['-', 'minus', 'substract', 'substraction']:
suffix = 'minus'
elif operator in ['*', 'times', 'multiply', 'multiplication']:
suffix = 'multiply'
elif operator in ['/', 'obelus', 'divide', 'division']:
suffix = 'divide'
elif operator in ['^', 'pow', 'power']:
suffix = 'pow'
out_file = '{}_{}{}{}'.format(os.path.splitext(fname)[0], suffix,
str(operand), os.path.splitext(fname)[1])
atr = readfile.read_attribute(fname)
dsNames = readfile.get_dataset_list(fname)
dsDict = {}
for dsName in dsNames:
data = readfile.read(fname, datasetName=dsName)[0]
data = data_operation(data, operator, operand)
dsDict[dsName] = data
writefile.write(dsDict, out_file=out_file, metadata=atr, ref_file=fname)
return out_file
def read_aux_subset2inps(inps):
# Convert All Inputs into subset_y/x/lat/lon
# Input Priority: subset_y/x/lat/lon > reference > template > tight
if all(not i for i in [inps.subset_x,
inps.subset_y,
inps.subset_lat,
inps.subset_lon]):
# 1. Read subset info from Reference File
if inps.reference:
ref_atr = readfile.read_attribute(inps.reference)
pix_box, geo_box = get_coverage_box(ref_atr)
print('using subset info from '+inps.reference)
# 2. Read subset info from template options
elif inps.template_file:
pix_box, geo_box = read_subset_template2box(inps.template_file)
print('using subset info from '+inps.template_file)
# 3. Use subset from tight info
elif inps.tight:
inps.lookup_file = ut.get_lookup_file(inps.lookup_file)
if not inps.lookup_file:
raise Exception('No lookup file found! Can not use --tight option without it.')
atr_lut = readfile.read_attribute(inps.lookup_file)
coord = ut.coordinate(atr_lut)
if 'Y_FIRST' in atr_lut.keys():
rg_lut = readfile.read(inps.lookup_file, datasetName='range')[0]
rg_unique, rg_pos = np.unique(rg_lut, return_inverse=True)
idx_row, idx_col = np.where(rg_lut != rg_unique[np.bincount(rg_pos).argmax()])
pix_box = (np.min(idx_col) - 10, np.min(idx_row) - 10,
np.max(idx_col) + 10, np.max(idx_row) + 10)
geo_box = coord.box_pixel2geo(pix_box)
del rg_lut
else:
lat = readfile.read(inps.lookup_file, datasetName='latitude')[0]
lon = readfile.read(inps.lookup_file, datasetName='longitude')[0]
geo_box = (np.nanmin(lon), np.nanmax(lat),
np.nanmax(lon), np.nanmin(lat))
pix_box = None
del lat, lon
else:
raise Exception('No subset inputs found!')
# Update subset_y/x/lat/lon
inps = subset_box2inps(inps, pix_box, geo_box)
return inps
def set_dem_file():
global ax_v, inps, img
if inps.dem_file:
dem = readfile.read(inps.dem_file, datasetName='height')[0]
ax_v = pp.plot_dem_yx(ax_v, dem)
img = ax_v.imshow(d_v, cmap=inps.colormap, clim=inps.ylim_mat, interpolation='nearest')
def run_load_data(self, step_name):
"""Load InSAR stacks into HDF5 files in ./inputs folder.
It 1) copy auxiliary files into work directory (for Unvi of Miami only)
2) load all interferograms stack files into mintpy/inputs directory.
3) check loading result
4) add custom metadata (optional, for HDF-EOS5 format only)
"""
# 1) copy aux files (optional)
self._copy_aux_file()
# 2) loading data
scp_args = '--template {}'.format(self.templateFile)
if self.customTemplateFile:
scp_args += ' {}'.format(self.customTemplateFile)
if self.projectName:
scp_args += ' --project {}'.format(self.projectName)
# run
print("load_data.py", scp_args)
mintpy.load_data.main(scp_args.split())
os.chdir(self.workDir)
# 3) check loading result
load_complete, stack_file, geom_file = ut.check_loaded_dataset(self.workDir, print_msg=True)[0:3]
# 3.1) output waterMask.h5
water_mask_file = 'waterMask.h5'
if 'waterMask' in readfile.get_dataset_list(geom_file):
print('generate {} from {} for conveniency'.format(water_mask_file, geom_file))
if ut.run_or_skip(out_file=water_mask_file, in_file=geom_file) == 'run':
water_mask, atr = readfile.read(geom_file, datasetName='waterMask')
atr['FILE_TYPE'] = 'waterMask'
writefile.write(water_mask, out_file=water_mask_file, metadata=atr)
# 4) add custom metadata (optional)
if self.customTemplateFile:
print('updating {}, {} metadata based on custom template file: {}'.format(
os.path.basename(stack_file),
os.path.basename(geom_file),
os.path.basename(self.customTemplateFile)))
# use ut.add_attribute() instead of add_attribute.py because of
# better control of special metadata, such as SUBSET_X/YMIN
ut.add_attribute(stack_file, self.customTemplate)
ut.add_attribute(geom_file, self.customTemplate)
# 5) if not load_complete, plot and raise exception
if not load_complete:
# plot result if error occured
self.plot_result(print_aux=False, plot=plot)
# go back to original directory
print('Go back to directory:', self.cwd)
os.chdir(self.cwd)
# raise error
msg = 'step {}: NOT all required dataset found, exit.'.format(step_name)
raise RuntimeError(msg)
return
def read_topographic_data(geom_file, metadata):
print('read DEM from file: '+geom_file)
dem = readfile.read(geom_file,
datasetName='height',
print_msg=False)[0]
print('considering the incidence angle of each pixel ...')
inc_angle = readfile.read(geom_file,
datasetName='incidenceAngle',
print_msg=False)[0]
dem *= 1.0/np.cos(inc_angle*np.pi/180.0)
ref_y = int(metadata['REF_Y'])
ref_x = int(metadata['REF_X'])
dem -= dem[ref_y, ref_x]
# Design matrix for elevation v.s. phase
# dem = dem.flatten()
return dem
def main(argv):
try:
dem_file = argv[1]
dem_error_file = argv[2]
except:
usage()
sys.exit(1)
print('Correcting the DEM')
dem, demrsc = readfile.read(dem_file)
dem_error = readfile.read(dem_error_file)
dem_out = dem + dem_error
writefile.write(dem_out, out_file='DEM_w_error.dem', metadata=demrsc)
date12_file = open('111111-222222_baseline.rsc', 'w')
date12_file.write('P_BASELINE_TOP_ODR'+' '+'000')
date12_file.close()
return
def read_file_data(epoch=None):
global atr, attributes, ref_dates_list
atr = readfile.read_attribute(h5_file.get())
file_type = atr['FILE_TYPE']
ref_dates_list = ["All"]
h5file = h5py.File(h5_file.get(), 'r')
if file_type in ['HDFEOS']:
ref_dates_list += h5file.attrs['DATE_TIMESERIES'].split()
else:
ref_dates_list = timeseries(h5_file.get()).get_date_list()
if epoch and epoch is not "All":
data, attributes = readfile.read(h5_file.get(), datasetName=ref_dates_list[epoch])
else:
data, attributes = readfile.read(h5_file.get(), datasetName=ref_dates_list[len(ref_dates_list) - 1])
return data
def get_incidence_angle(self, box=None):
if not self.extraMetadata or 'Y_FIRST' in self.extraMetadata.keys():
return None
if 'height' in self.dsNames:
dem = readfile.read(self.datasetDict['height'], datasetName='height')[0]
else:
dem = None
data = ut.incidence_angle(self.extraMetadata,
dem=dem,
dimension=2,
print_msg=False)
if box is not None:
data = data[box[1]:box[3], box[0]:box[2]]
return data
def get_los_geometry(self, insar_obj, print_msg=False):
lat, lon = self.get_stat_lat_lon(print_msg=print_msg)
# get LOS geometry
if isinstance(insar_obj, str):
# geometry file
atr = readfile.read_attribute(insar_obj)
coord = ut.coordinate(atr, lookup_file=insar_obj)
y, x = coord.geo2radar(lat, lon, print_msg=print_msg)[0:2]
box = (x, y, x+1, y+1)
inc_angle = readfile.read(insar_obj, datasetName='incidenceAngle', box=box, print_msg=print_msg)[0][0,0]
az_angle = readfile.read(insar_obj, datasetName='azimuthAngle', box=box, print_msg=print_msg)[0][0,0]
head_angle = ut.azimuth2heading_angle(az_angle)
elif isinstance(insar_obj, dict):
# use mean inc/head_angle from metadata
inc_angle = ut.incidence_angle(insar_obj, dimension=0, print_msg=print_msg)
head_angle = float(insar_obj['HEADING'])
# for old reading of los.rdr band2 data into headingAngle directly
if (head_angle + 180.) > 45.:
head_angle = ut.azimuth2heading_angle(head_angle)
else:
raise ValueError('input insar_obj is neight str nor dict: {}'.format(insar_obj))
return inc_angle, head_angle
def get_nonzero_phase_closure(ifgram_file, out_file=None, thres=0.1, unwDatasetName='unwrapPhase'):
"""Calculate/Read number of non-zero phase closure
Parameters: ifgram_file : string, path of ifgram stack file
out_file : string, path of num non-zero phase closure file
Returns: num_nonzero_closure : 2D np.array in size of (length, width)
"""
if not out_file:
out_file = 'numNonzeroPhaseClosure_{}.h5'.format(unwDatasetName)
if os.path.isfile(out_file) and readfile.read_attribute(out_file):
print('1. read number of nonzero phase closure from file: {}'.format(out_file))
num_nonzero_closure = readfile.read(out_file)[0]
else:
obj = ifgramStack(ifgram_file)
obj.open(print_msg=False)
length, width = obj.length, obj.width
ref_phase = obj.get_reference_phase(unwDatasetName=unwDatasetName, dropIfgram=False)
C = obj.get_design_matrix4triplet(obj.get_date12_list(dropIfgram=False))
# calculate phase closure line by line to save memory usage
num_nonzero_closure = np.zeros((length, width), np.float32)
print('1. calculating phase closure of all pixels from dataset - {} ...'.format(unwDatasetName))
line_step = 10
num_loop = int(np.ceil(length / line_step))
prog_bar = ptime.progressBar(maxValue=num_loop)
for i in range(num_loop):
# read phase
i0, i1 = i*line_step, min(length, (i+1)*line_step)
box = (0, i0, width, i1)
pha_data = ifginv.read_unwrap_phase(obj,
box,
ref_phase,
unwDatasetName=unwDatasetName,
dropIfgram=False,
print_msg=False)
# calculate phase closure
pha_closure = np.dot(C, pha_data)
pha_closure = np.abs(pha_closure - ut.wrap(pha_closure))
# get number of non-zero phase closure
num_nonzero = np.sum(pha_closure >= thres, axis=0)
num_nonzero_closure[i0:i1, :] = num_nonzero.reshape(i1-i0, width)
prog_bar.update(i+1, every=1, suffix='{}/{} lines'.format((i+1)*line_step, length))
prog_bar.close()
atr = dict(obj.metadata)
atr['FILE_TYPE'] = 'mask'
atr['UNIT'] = 1
writefile.write(num_nonzero_closure, out_file=out_file, metadata=atr)
return num_nonzero_closure
def configure(self):
inps = cmd_line_parse(self.iargs)
# read network info
inps = read_network_info(inps)
# copy inps to self object
for key, value in inps.__dict__.items():
setattr(self, key, value)
# auto figure size
if not self.fig_size:
ds_shape = readfile.read(self.img_file)[0].shape
fig_size = pp.auto_figure_size(ds_shape, disp_cbar=True, ratio=0.7)
self.fig_size = [fig_size[0]+fig_size[1], fig_size[1]]
vprint('create figure in size of {} inches'.format(self.fig_size))
return
def calculate_temporal_coherence_patch(ifgram_file, timeseries_file, box=None, ifg_num_file=None):
atr = readfile.read_attribute(timeseries_file)
if not box:
box = (0, 0, int(atr['WIDTH']), int(atr['LENGTH']))
# Read timeseries data
ts_obj = timeseries(timeseries_file)
ts_obj.open(print_msg=False)
print('reading timeseries data from file: {}'.format(timeseries_file))
ts_data = ts_obj.read(box=box, print_msg=False).reshape(ts_obj.numDate, -1)
ts_data = ts_data[1:, :]
ts_data *= -4*np.pi/float(atr['WAVELENGTH'])
# Read ifgram data
stack_obj = ifgramStack(ifgram_file)
stack_obj.open(print_msg=False)
A = stack_obj.get_design_matrix4timeseries(stack_obj.get_date12_list(dropIfgram=True))[0]
print('reading unwrapPhase data from file: {}'.format(ifgram_file))
ifgram_data = stack_obj.read(datasetName='unwrapPhase', box=box).reshape(A.shape[0], -1)
ref_value = stack_obj.get_reference_phase(dropIfgram=True).reshape((-1, 1))
ifgram_data -= np.tile(ref_value, (1, ifgram_data.shape[1]))
ifgram_diff = ifgram_data - np.dot(A, ts_data)
del ts_data
pixel_num = ifgram_data.shape[1]
temp_coh = np.zeros((pixel_num), np.float32)
# (fast) nasty solution, which used all phase value including invalid zero phase
if not ifg_num_file:
temp_coh = np.abs(np.sum(np.exp(1j*ifgram_diff), axis=0)) / ifgram_diff.shape[0]
# (slow) same solution as ifgram_inversion.py, considering:
# 1) invalid zero phase in ifgram
# 2) design matrix rank deficiency.
else:
print('considering different number of interferograms used in network inversion for each pixel')
ifg_num_map = readfile.read(ifg_num_file, box=box)[0].flatten()
prog_bar = ptime.progressBar(maxValue=pixel_num)
for i in range(pixel_num):
if ifg_num_map[i] > 0:
idx = ifgram_data[:, i] != 0.
temp_diff = ifgram_diff[idx, i]
temp_coh[i] = np.abs(np.sum(np.exp(1j*temp_diff), axis=0)) / temp_diff.shape[0]
prog_bar.update(i+1, every=1000, suffix='{}/{}'.format(i+1, pixel_num))
prog_bar.close()
temp_coh = np.reshape(temp_coh, (box[3]-box[1], box[2]-box[0]))
return temp_coh
def generate_temporal_coherence_mask(self):
"""Generate reliable pixel mask from temporal coherence"""
geom_file = ut.check_loaded_dataset(self.workDir, print_msg=False)[2]
tcoh_file = 'temporalCoherence.h5'
mask_file = 'maskTempCoh.h5'
tcoh_min = self.template['mintpy.networkInversion.minTempCoh']
scp_args = '{} -m {} -o {} --shadow {}'.format(tcoh_file, tcoh_min, mask_file, geom_file)
print('generate_mask.py', scp_args)
# update mode: run only if:
# 1) output file exists and newer than input file, AND
# 2) all config keys are the same
config_keys = ['mintpy.networkInversion.minTempCoh']
print('update mode: ON')
flag = 'skip'
if ut.run_or_skip(out_file=mask_file, in_file=tcoh_file, print_msg=False) == 'run':
flag = 'run'
else:
print('1) output file: {} already exists and newer than input file: {}'.format(mask_file, tcoh_file))
atr = readfile.read_attribute(mask_file)
if any(str(self.template[i]) != atr.get(i, 'False') for i in config_keys):
flag = 'run'
print('2) NOT all key configration parameters are the same: {}'.format(config_keys))
else:
print('2) all key configuration parameters are the same: {}'.format(config_keys))
print('run or skip: {}'.format(flag))
if flag == 'run':
mintpy.generate_mask.main(scp_args.split())
# update configKeys
atr = {}
for key in config_keys:
atr[key] = self.template[key]
ut.add_attribute(mask_file, atr)
# check number of pixels selected in mask file for following analysis
num_pixel = np.sum(readfile.read(mask_file)[0] != 0.)
print('number of reliable pixels: {}'.format(num_pixel))
min_num_pixel = float(self.template['mintpy.networkInversion.minNumPixel'])
if num_pixel < min_num_pixel:
msg = "Not enough reliable pixels (minimum of {}). ".format(int(min_num_pixel))
msg += "Try the following:\n"
msg += "1) Check the reference pixel and make sure it's not in areas with unwrapping errors\n"
msg += "2) Check the network and make sure it's fully connected without subsets"
raise RuntimeError(msg)
return
def main(iargs=None):
inps = cmd_line_parse(iargs)
# Read data
data, atr = readfile.read(inps.file, datasetName=inps.dset)
# 2. Write GMT .grd file
if not inps.outfile:
outbase = pp.auto_figure_title(inps.file,
datasetNames=inps.dset,
inps_dict=vars(inps))
inps.outfile = '{}.grd'.format(outbase)
inps.outfile = write_grd_file(data, atr, inps.outfile)
print('Done.')
return inps.outfile
def run_network_modification(self, step_name):
"""Modify network of interferograms before the network inversion."""
# check the existence of ifgramStack.h5
stack_file, geom_file = ut.check_loaded_dataset(self.workDir,
print_msg=False)[1:3]
coh_txt = '{}_coherence_spatialAvg.txt'.format(
os.path.splitext(os.path.basename(stack_file))[0])
try:
net_fig = [
i for i in ['Network.pdf', 'pic/Network.pdf']
if os.path.isfile(i)
][0]
except:
net_fig = None
# 1) output waterMask.h5 to simplify the detection/use of waterMask
water_mask_file = 'waterMask.h5'
if 'waterMask' in readfile.get_dataset_list(geom_file):
print('generate {} from {} for conveniency'.format(
water_mask_file, geom_file))
if ut.run_or_skip(out_file=water_mask_file,
in_file=geom_file) == 'run':
water_mask, atr = readfile.read(geom_file,
datasetName='waterMask')
atr['FILE_TYPE'] = 'waterMask'
writefile.write(water_mask,
out_file=water_mask_file,
metadata=atr)
# 2) modify network
scp_args = '{} -t {}'.format(stack_file, self.templateFile)
print('modify_network.py', scp_args)
mintpy.modify_network.main(scp_args.split())
# 3) plot network
scp_args = '{} -t {} --nodisplay'.format(stack_file, self.templateFile)
print('\nplot_network.py', scp_args)
if ut.run_or_skip(out_file=net_fig,
in_file=[stack_file, coh_txt, self.templateFile],
check_readable=False) == 'run':
mintpy.plot_network.main(scp_args.split())
# 4) aux files: maskConnComp and avgSpatialCoh
self.generate_ifgram_aux_file()
return
def main(iargs=None):
inps = cmd_line_parse(iargs)
atr = readfile.read_attribute(inps.file[0])
box = read_inps2box(inps, atr)
num_file = len(inps.file)
# initiate output
dsDict = {}
if num_file == 1:
dsDict[inps.dset_name] = np.zeros((box[3]-box[1], box[2]-box[0]), dtype=inps.data_type)
else:
dsDict[inps.dset_name] = np.zeros((num_file, box[3]-box[1], box[2]-box[0]), dtype=inps.data_type)
# add "date" dataset for timeseries
if inps.dset_name and inps.dset_name == 'timeseries':
date_list = [os.path.basename(os.path.dirname(x)) for x in inps.file]
date_str_format = ptime.get_date_str_format(date_list[0])
dsDict['date'] = np.array(date_list, dtype=np.string_)
# metadata
if inps.metadata:
print('add/update the following metadata:')
for meta_str in inps.metadata:
key, value = meta_str.split('=')
atr[key] = value
print(f'{key} : {value}')
# read
for i, fname in enumerate(inps.file):
print(f'reading file {i+1} / {num_file}: {fname}')
ds_names = readfile.get_dataset_list(fname)
ds_name = inps.dset_name if inps.dset_name in ds_names else None
data = readfile.read(fname, datasetName=ds_name, box=box)[0]
if num_file == 1:
dsDict[inps.dset_name][:] = data
else:
dsDict[inps.dset_name][i, :, :] = data
# write
atr['LENGTH'] = box[3] - box[1]
atr['WIDTH'] = box[2] - box[0]
writefile.write(dsDict, out_file=inps.outfile, metadata=atr)
return inps.outfile
def get_lookup_file(filePattern=None, abspath=False, print_msg=True):
"""Find lookup table file with/without input file pattern"""
# Search Existing Files
if not filePattern:
filePattern = [
'geometryRadar.h5', 'geometryGeo_tight.h5', 'geometryGeo.h5',
'geomap*lks_tight.trans', 'geomap*lks.trans',
'sim*_tight.UTM_TO_RDC', 'sim*.UTM_TO_RDC'
]
filePattern = [os.path.join('inputs', i)
for i in filePattern] + filePattern
existFiles = []
try:
existFiles = get_file_list(filePattern)
except:
if print_msg:
print('ERROR: No geometry / lookup table file found!')
print('It should be like:')
print(filePattern)
return None
# Check Files Info
outFile = None
for fname in existFiles:
atr = readfile.read_attribute(fname)
for dsName in ['rangeCoord', 'longitude']:
try:
dset = readfile.read(fname,
datasetName=dsName,
print_msg=False)[0]
outFile = fname
break
except:
pass
if not outFile:
if print_msg:
print('No lookup table info range/lat found in files.')
return None
# Path Format
if abspath:
outFile = os.path.abspath(outFile)
return outFile
def dem_jpeg(dem_file):
"""generate dem.jepg file based on Yunjun's code"""
out_file = dem_file + '.jpeg'
rsc_file = out_file + '.rsc'
shutil.copy2(dem_file + '.rsc', rsc_file)
# read data
dem = readfile.read(dem_file)[0]
print('dem.shape:', dem.shape)
# figure size
ds_shape = tuple(reversed(dem.shape))
fig_dpi = 300
fig_size = [i / fig_dpi for i in ds_shape]
print('fig_size:', fig_size)
# color range
disp_min = np.nanmin(dem) - 4000
disp_max = np.nanmax(dem) + 2000
# prepare shaded relief
ls = LightSource(azdeg=315, altdeg=45)
dem_shade = ls.shade(dem,
vert_exag=0.3,
cmap=plt.get_cmap('gray'),
vmin=disp_min,
vmax=disp_max)
dem_shade[np.isnan(dem_shade[:, :, 0])] = np.nan
print('dem_shade.shape:', dem_shade.shape)
# plot
fig, ax = plt.subplots(figsize=fig_size)
ax.imshow(dem_shade, interpolation='spline16', origin='upper')
# get rid of whitespace on the side
ax.axis('off')
ax.get_xaxis().set_ticks([])
ax.get_yaxis().set_ticks([])
fig.subplots_adjust(left=0, right=1, bottom=0, top=1)
# output
print('save figure to file {}'.format(out_file))
plt.savefig(out_file, transparent=True, dpi=300, pad_inches=0.0)
#resize to desired size(FA 8/19, unclear why size is wrong)
im = Image.open(out_file)
im_out = im.resize(dem.shape, Image.NEAREST)
im_out.save(out_file)
plt.show()
def write_json(inps,point_disp,point_angle):
"""write points_disp.json"""
atr = readfile.read(inps.file[0])[1]
lat0 = float(atr['Y_FIRST'])
lon0 = float(atr['X_FIRST'])
lat_step = float(atr['Y_STEP'])
lon_step = float(atr['X_STEP'])
lat_ref = float(atr['REF_LAT'])
lon_ref = float(atr['REF_LON'])
# write points_disp.json
displacement = {"Ref_lat":lat_ref, "Ref_lon":lon_ref, "Y_FIRST": lat0, "X_FIRST": lon0, "Y_STEP": lat_step, "X_STEP": lon_step, "lalo_value":point_disp.tolist()}
open('points_disp' + '.json', "w").write(json.dumps(displacement))
# write points_angle.json
angles = {"Y_FIRST": lat0, "X_FIRST": lon0, "Y_STEP": lat_step, "X_STEP": lon_step, "lalo_value": point_angle.tolist()}
open('points_angle' + '.json', "w").write(json.dumps(angles))
return
def ref_date_file(ts_file, ref_date, outfile=None):
"""Change input file reference date to a different one.
Parameters: ts_file : str, timeseries file to be changed
ref_date : str, date in YYYYMMDD format
outfile : if str, save to a different file
if None, modify the data value in the existing input file
"""
print('-' * 50)
print('change reference date for file: {}'.format(ts_file))
atr = readfile.read_attribute(ts_file)
if ref_date == atr['REF_DATE']:
print('same reference date chosen as existing reference date.')
if not outfile:
print('Nothing to be done.')
return ts_file
else:
print('Copy {} to {}'.format(ts_file, outfile))
shutil.copy2(ts_file, outfile)
return outfile
else:
obj = timeseries(ts_file)
obj.open(print_msg=False)
ref_idx = obj.dateList.index(ref_date)
print('reading data ...')
ts_data = readfile.read(ts_file)[0]
ts_data -= np.tile(
ts_data[ref_idx, :, :].reshape(1, obj.length, obj.width),
(obj.numDate, 1, 1))
if not outfile:
print('open {} with r+ mode'.format(ts_file))
with h5py.File(ts_file, 'r+') as f:
print(
"update /timeseries dataset and 'REF_DATE' attribute value"
)
f['timeseries'][:] = ts_data
f.attrs['REF_DATE'] = ref_date
print('close {}'.format(ts_file))
else:
atr['REF_DATE'] = ref_date
writefile.write(ts_data, outfile, metadata=atr, ref_file=ts_file)
return outfile
def plot(self):
# read 3D time-series
self.ts_data, self.mask = read_timeseries_data(self)[0:2]
# Figure 1 - Cumulative Displacement Map
self.fig_img = plt.figure(self.figname_img, figsize=self.figsize_img)
# Figure 1 - Axes 1 - Displacement Map
self.ax_img = self.fig_img.add_axes([0.125, 0.25, 0.75, 0.65])
img_data = np.array(self.ts_data[0][self.init_idx, :, :])
if self.velocity:
img_data, attr00 = readfile.read(self.velocity,
datasetName='velocity')
img_data = img_data * 100
img_data[self.mask == 0] = np.nan
self.plot_init_image(img_data)
# Figure 1 - Axes 2 - Time Slider
self.ax_tslider = self.fig_img.add_axes([0.2, 0.1, 0.6, 0.07])
self.plot_init_time_slider(init_idx=self.init_idx,
ref_idx=self.ref_idx)
self.tslider.on_changed(self.update_time_slider)
# Figure 2 - Time Series Displacement - Point
self.fig_pts, self.ax_pts = plt.subplots(num=self.figname_pts,
figsize=self.figsize_pts)
if self.yx:
d_ts = self.plot_point_timeseries(self.yx)
# Output
if self.save_fig:
save_ts_plot(self.yx, self.fig_img, self.fig_pts, d_ts, self)
# Final linking of the canvas to the plots.
self.cid = self.fig_img.canvas.mpl_connect('button_press_event',
self.update_plot_timeseries)
if self.disp_fig:
vprint('showing ...')
plt.show()
return
def select_max_coherence_yx(coh_file, mask=None, min_coh=0.85):
"""Select pixel with coherence > min_coh in random"""
print('random select pixel with coherence > {}'.format(min_coh))
print('\tbased on coherence file: '+coh_file)
coh, coh_atr = readfile.read(coh_file)
if mask is not None:
coh[mask == 0] = 0.0
coh_mask = coh >= min_coh
if np.all(coh_mask == 0.):
msg = ('No pixel with average spatial coherence > {} '
'are found for automatic reference point selection!').format(min_coh)
msg += '\nTry the following:'
msg += '\n 1) manually specify the reference point using mintpy.reference.yx/lalo option.'
msg += '\n 2) change mintpy.reference.minCoherence to a lower value.'
raise RuntimeError(msg)
y, x = random_select_reference_yx(coh_mask, print_msg=False)
#y, x = np.unravel_index(np.argmax(coh), coh.shape)
print('y/x: {}'.format((y, x)))
return y, x
def select_max_coherence_yx(coh_file, mask=None, min_coh=0.85):
"""Select pixel with coherence > min_coh in random"""
print('random select pixel with coherence > {}'.format(min_coh))
print('\tbased on coherence file: '+coh_file)
coh, coh_atr = readfile.read(coh_file)
if not mask is None:
coh[mask == 0] = 0.0
coh_mask = coh >= min_coh
if np.all(coh_mask == 0.):
msg = ('No pixel with average spatial coherence > {} '
'are found for automatic reference point selection!').format(min_coh)
msg += '\nTry the following:'
msg += '\n 1) manually specify the reference point using mintpy.reference.yx/lalo option.'
msg += '\n 2) change mintpy.reference.minCoherence to a lower value.'
raise RuntimeError(msg)
y, x = random_select_reference_yx(coh_mask, print_msg=False)
#y, x = np.unravel_index(np.argmax(coh), coh.shape)
print('y/x: {}'.format((y, x)))
return y, x
def estimate_linear_velocity(inps):
# read time-series data
print('reading data from file {} ...'.format(inps.timeseries_file))
ts_data, atr = readfile.read(inps.timeseries_file)
ts_data = ts_data[inps.dropDate, :, :].reshape(inps.numDate, -1)
if atr['UNIT'] == 'mm':
ts_data *= 1. / 1000.
length, width = int(atr['LENGTH']), int(atr['WIDTH'])
# The following is equivalent
# X = scipy.linalg.lstsq(A, ts_data, cond=1e-15)[0]
# It is not used because it can not handle NaN value in ts_data
A = timeseries.get_design_matrix4average_velocity(inps.dateList)
X = np.dot(np.linalg.pinv(A), ts_data)
vel = np.array(X[0, :].reshape(length, width), dtype=dataType)
# velocity STD (Eq. (10), Fattahi and Amelung, 2015)
ts_diff = ts_data - np.dot(A, X)
t_diff = A[:, 0] - np.mean(A[:, 0])
vel_std = np.sqrt(
np.sum(ts_diff**2, axis=0) / np.sum(t_diff**2) / (inps.numDate - 2))
vel_std = np.array(vel_std.reshape(length, width), dtype=dataType)
# prepare attributes
atr['FILE_TYPE'] = 'velocity'
atr['UNIT'] = 'm/year'
atr['START_DATE'] = inps.dateList[0]
atr['END_DATE'] = inps.dateList[-1]
atr['DATE12'] = '{}_{}'.format(inps.dateList[0], inps.dateList[-1])
# config parameter
print('add/update the following configuration metadata:\n{}'.format(
configKeys))
for key in configKeys:
atr[key_prefix + key] = str(vars(inps)[key])
# write to HDF5 file
dsDict = dict()
dsDict['velocity'] = vel
dsDict['velocityStd'] = vel_std
writefile.write(dsDict, out_file=inps.outfile, metadata=atr)
return inps.outfile
def hdf_to_geotif(inps):
"""transfer"""
fname = inps.input_HDFEOS[0] # for asc
output_path = inps.outdir[0]
output_tif = output_path + inps.output[0]
print('output tiff file name : %s' % output_tif)
atr = readfile.read_attribute(fname)
data = readfile.read(fname)[0]
if not inps.unit == 'None':
print('using the orgianl unit of input')
disp = data
elif inps.unit[0] == 'm':
# the unit of velocity is m/year
disp = data * 100 # change unit to cm/year
elif inps.unit[0] == 'radian':
# the unit of data is radian
wavelength = float(atr['WAVELENGTH'])
disp = (((-1) * (data * wavelength)) / (4 * np.pi)) * 100 # change unit to cm
xmin = float(atr['X_FIRST']) # corresponding to X_FIRST
ymax = float(atr['Y_FIRST']) # corresponding to Y_FIRST
nrows, ncols = np.shape(disp)
xres = float(atr['X_STEP']) # corresponding to X_STEP
yres = (-1) * float(atr['Y_STEP']) # corresponding to X_STEP
# xmin, ymin, xmax, ymax = [np.nanmin(lon), np.nanmin(lat), np.nanmax(lon), np.nanmax(lat)]
# nrows, ncols = np.shape(disp)
# xres = (xmax - xmin) / float(ncols)
# yres = (ymax - ymin) / float(nrows)
geotransform = [xmin, xres, 0, ymax, 0, -yres]
raster = gdal.GetDriverByName('GTiff').Create(output_tif, ncols, nrows, 1, gdal.GDT_Float32)
raster.SetGeoTransform(geotransform)
srs = osr.SpatialReference()
srs.ImportFromEPSG(4326) # WGS 84 - WGS84 - World Geodetic System 1984, used in GPS
raster.SetProjection(srs.ExportToWkt())
raster.GetRasterBand(1).WriteArray(disp)
raster.FlushCache()
print('finish conversion!')
def set_mask():
global mask, inps, atr
if not inps.mask_file:
if os.path.basename(inps.timeseries_file).startswith('geo_'):
file_list = ['geo_maskTempCoh.h5']
else:
file_list = ['maskTempCoh.h5', 'maskConnComp.h5']
try:
inps.mask_file = ut.get_file_list(file_list)[0]
except:
inps.mask_file = None
try:
mask = readfile.read(inps.mask_file, datasetName='mask')[0]
mask[mask!=0] = 1
print(('load mask from file: '+inps.mask_file))
except:
mask = None
print('No mask used.')
def set_mask():
global mask, inps, atr
if not inps.mask_file:
if os.path.basename(inps.timeseries_file).startswith('geo_'):
file_list = ['geo_maskTempCoh.h5']
else:
file_list = ['maskTempCoh.h5', 'maskConnComp.h5']
try:
inps.mask_file = ut.get_file_list(file_list)[0]
except:
inps.mask_file = None
try:
mask = readfile.read(inps.mask_file, datasetName='mask')[0]
mask[mask != 0] = 1
print(('load mask from file: ' + inps.mask_file))
except:
mask = None
print('No mask used.')
def get_lookup_file(filePattern=None, abspath=False, print_msg=True):
"""Find lookup table file with/without input file pattern"""
# Search Existing Files
if not filePattern:
filePattern = ['geometryRadar.h5',
'geometryGeo_tight.h5', 'geometryGeo.h5',
'geomap*lks_tight.trans', 'geomap*lks.trans',
'sim*_tight.UTM_TO_RDC', 'sim*.UTM_TO_RDC']
filePattern = [os.path.join('inputs', i) for i in filePattern] + filePattern
existFiles = []
try:
existFiles = get_file_list(filePattern)
except:
if print_msg:
print('ERROR: No geometry / lookup table file found!')
print('It should be like:')
print(filePattern)
return None
# Check Files Info
outFile = None
for fname in existFiles:
atr = readfile.read_attribute(fname)
for dsName in ['rangeCoord', 'longitude']:
try:
dset = readfile.read(fname, datasetName=dsName, print_msg=False)[0]
outFile = fname
break
except:
pass
if not outFile:
if print_msg:
print('No lookup table info range/lat found in files.')
return None
# Path Format
if abspath:
outFile = os.path.abspath(outFile)
return outFile
def generate_polygon_cbox(inps):
"""generate covarage box
lat0,lon0- starting latitude/longitude (first row/column)
lat1,lon1- ending latitude/longitude (last row/column)
"""
file_name = inps.file[0]
atr = readfile.read(file_name)[1]
length, width = int(atr['LENGTH']), int(atr['WIDTH'])
if 'Y_FIRST' in atr.keys():
# geo coordinates
lat0 = float(atr['Y_FIRST'])
lon0 = float(atr['X_FIRST'])
lat_step = float(atr['Y_STEP'])
lon_step = float(atr['X_STEP'])
lat1 = lat0 + (length - 1) * lat_step
lon1 = lon0 + (width - 1) * lon_step
else:
# radar coordinates
lats = [float(atr['LAT_REF{}'.format(i)]) for i in [1, 2, 3, 4]]
lons = [float(atr['LON_REF{}'.format(i)]) for i in [1, 2, 3, 4]]
lat0 = np.mean(lats[0:2])
lat1 = np.mean(lats[2:4])
lon0 = np.mean(lons[0:3:2])
lon1 = np.mean(lons[1:4:2])
# lon/lat of four points: upperright; lowerright; lowerleft; upperleft
lon_ur = lon1
lat_ur = lat0
lon_lr = lon1
lat_lr = lat1
lon_ll = lon0
lat_ll = lat1
lon_ul = lon0
lat_ul = lat0
return lon_ur, lat_ur, lon_lr, lat_lr, lon_ll, lat_ll, lon_ul, lat_ul
def multilook_file(infile, lks_y, lks_x, outfile=None):
lks_y = int(lks_y)
lks_x = int(lks_x)
# input file info
atr = readfile.read_attribute(infile)
k = atr['FILE_TYPE']
print('multilooking {} {} file: {}'.format(atr['PROCESSOR'], k, infile))
print('number of looks in y / azimuth direction: %d' % lks_y)
print('number of looks in x / range direction: %d' % lks_x)
# output file name
if not outfile:
if os.getcwd() == os.path.dirname(os.path.abspath(infile)):
ext = os.path.splitext(infile)[1]
outfile = os.path.splitext(infile)[0] + '_' + str(
lks_y) + 'alks_' + str(lks_x) + 'rlks' + ext
else:
outfile = os.path.basename(infile)
#print('writing >>> '+outfile)
# read source data and multilooking
dsNames = readfile.get_dataset_list(infile)
maxDigit = max([len(i) for i in dsNames])
dsDict = dict()
for dsName in dsNames:
print('multilooking {d:<{w}} from {f} ...'.format(
d=dsName, w=maxDigit, f=os.path.basename(infile)))
data = readfile.read(infile, datasetName=dsName, print_msg=False)[0]
# keep timeseries data as 3D matrix when there is only one acquisition
# because readfile.read() will squeeze it to 2D
if atr['FILE_TYPE'] == 'timeseries' and len(data.shape) == 2:
data = np.reshape(data, (1, data.shape[0], data.shape[1]))
data = multilook_data(data, lks_y, lks_x)
dsDict[dsName] = data
atr = multilook_attribute(atr, lks_y, lks_x)
writefile.write(dsDict, out_file=outfile, metadata=atr, ref_file=infile)
return outfile
def bootstrap(timeseriesFile, bootCount):
ts_data, atr = readfile.read(timeseriesFile)
tsData = readfile.timeseries(timeseriesFile)
if atr['UNIT'] == 'mm':
ts_data *= 1. / 1000.
length, width = int(atr['LENGTH']), int(atr['WIDTH'])
dateList = tsData.get_date_list()
sampleNo = len(dateList)
vel = np.zeros((bootCount, (length * width)))
prog_bar = ptime.progressBar(maxValue=bootCount, prefix='Calculating ')
for i in range(bootCount):
bootSamples = list(
np.sort(resample(dateList, replace=True, n_samples=sampleNo)))
# dropList = [x for x in dateList if x not in bootSamples]
prog_bar.update(i + 1, suffix='Running boot number: ' + str(i + 1))
bootList = []
for k in bootSamples:
bootList.append(dateList.index(k))
numDate = len(bootList)
ts_data_sub = ts_data[bootList, :, :].reshape(numDate, -1)
A = tsData.get_design_matrix4average_velocity(bootSamples)
X = np.dot(np.linalg.pinv(A), ts_data_sub)
vel[i] = np.array(X[0, :], dtype='float32')
prog_bar.close()
print('Finished resampling and velocity calculation')
velMean = vel.mean(axis=0).reshape(length, width)
velStd = vel.std(axis=0).reshape(length, width)
print('Calculated mean and standard deviation of bootstrap estimations')
atr['FILE_TYPE'] = 'velocity'
atr['UNIT'] = 'm/year'
atr['START_DATE'] = bootSamples[0]
atr['END_DATE'] = bootSamples[-1]
atr['DATE12'] = '{}_{}'.format(bootSamples[0], bootSamples[-1])
return velMean, velStd, atr
def read_HDFEOS(inps):
"""read displacement from HDFEOS"""
print('read displacement, incidence and azimuth information')
# read metadata
HDFEOS_file = inps.input_HDFEOS[0]
atr = readfile.read_attribute(HDFEOS_file)
if inps.date == None:
date1 = atr['START_DATE']
date2 = atr['END_DATE']
else:
# date1 and date2
if '_' in "".join(inps.date):
date1, date2 = ptime.yyyymmdd("".join(inps.date).split('_'))
else:
date1 = atr['START_DATE']
date2 = ptime.yyyymmdd("".join(inps.date))
# read angle infomation
azimuth = readfile.read(HDFEOS_file, datasetName='/HDFEOS/GRIDS/timeseries/geometry/azimuthAngle')[0]
incidence = readfile.read(HDFEOS_file, datasetName='/HDFEOS/GRIDS/timeseries/geometry/incidenceAngle')[0]
if inps.velocity:
vel_file = 'velocity.h5'
iargs = [HDFEOS_file, '--start-date', date1, '--end-date', date2, '-o', vel_file, '--update']
print('\ntimeseries2velocity.py', ' '.join(iargs))
mintpy.timeseries2velocity.main(iargs)
data = readfile.read(vel_file, datasetName='velocity')[0]
os.remove(vel_file)
else:
# read / prepare data
slice_list = readfile.get_slice_list(HDFEOS_file)
# read/prepare data
dname = 'displacement'
slice_name1 = view.search_dataset_input(slice_list, '{}-{}'.format(dname, date1))[0][0]
slice_name2 = view.search_dataset_input(slice_list, '{}-{}'.format(dname, date2))[0][1]
data = readfile.read("".join(inps.input_HDFEOS), datasetName=slice_name2)[0]
data -= readfile.read("".join(inps.input_HDFEOS), datasetName=slice_name1)[0]
print("mask file")
maskfile = readfile.read(HDFEOS_file, datasetName='/HDFEOS/GRIDS/timeseries/quality/mask')[0]
data[maskfile == 0] = np.nan
azimuth[maskfile == 0] = np.nan
incidence[maskfile == 0] = np.nan
return date1, date2, data, atr, incidence, azimuth
def rewrite_slave(inps, offset):
s_atr = readfile.read_attribute("".join(inps.slave))
s_data = readfile.read("".join(inps.slave))[0]
s_data_offset = s_data + offset
#if inps.output == None:
out_file = '{}{}{}'.format(
os.path.split("".join(inps.slave))[1].split('.')[0], '_offset.',
os.path.split("".join(inps.slave))[1].split('.')[1])
#else:
# out_file = '{}{}{}'.format("".join(inps.output),'.',os.path.split("".join(inps.slave))[1].split('.')[1])
if inps.outdir != None:
out_dir = inps.outdir[0]
out_dir_file = out_dir + out_file
if inps.rewrite_slave:
print('Writing slave data %s' % "".join(inps.slave))
writefile.write(s_data_offset, out_file=out_dir_file, metadata=s_atr)
return s_data_offset
def ref_date_file(ts_file, ref_date, outfile=None):
"""Change input file reference date to a different one.
Parameters: ts_file : str, timeseries file to be changed
ref_date : str, date in YYYYMMDD format
outfile : if str, save to a different file
if None, modify the data value in the existing input file
"""
print('-'*50)
print('change reference date for file: {}'.format(ts_file))
atr = readfile.read_attribute(ts_file)
if ref_date == atr['REF_DATE']:
print('same reference date chosen as existing reference date.')
if not outfile:
print('Nothing to be done.')
return ts_file
else:
print('Copy {} to {}'.format(ts_file, outfile))
shutil.copy2(ts_file, outfile)
return outfile
else:
obj = timeseries(ts_file)
obj.open(print_msg=False)
ref_idx = obj.dateList.index(ref_date)
print('reading data ...')
ts_data = readfile.read(ts_file)[0]
ts_data -= np.tile(ts_data[ref_idx, :, :].reshape(1, obj.length, obj.width), (obj.numDate, 1, 1))
if not outfile:
print('open {} with r+ mode'.format(ts_file))
with h5py.File(ts_file, 'r+') as f:
print("update /timeseries dataset and 'REF_DATE' attribute value")
f['timeseries'][:] = ts_data
f.attrs['REF_DATE'] = ref_date
print('close {}'.format(ts_file))
else:
atr['REF_DATE'] = ref_date
writefile.write(ts_data, outfile, metadata=atr, ref_file=ts_file)
return outfile
def plot(self):
# Read data for transection
self.data_list = []
self.atr_list = []
for fname in self.file:
data, atr = readfile.read(fname, datasetName=self.dset)
data = pp.scale_data2disp_unit(data, metadata=atr, disp_unit=self.disp_unit)[0]
self.data_list.append(data)
self.atr_list.append(atr)
# Figure
self.fig, (self.ax_img, self.ax_txn) = plt.subplots(1, 2, num=self.figname, figsize=self.fig_size)
# Axes 1 - map with view.prep/plot_slice()
self.ax_img = view.plot_slice(self.ax_img, self.data_img, self.atr, self)[0]
# Axes 2 - transection
self.ax_txn.yaxis.tick_right()
self.ax_txn.yaxis.set_label_position("right")
# plot initial input transect
if self.start_yx and self.end_yx:
self.draw_line(self.start_yx, self.end_yx)
self.draw_transection(self.start_yx, self.end_yx, self.start_lalo, self.end_lalo)
self.fig.subplots_adjust(left=0.05, wspace=0.25)
# save
if self.save_fig:
outfile = '{}.pdf'.format(self.outfile_base)
self.fig.savefig(outfile, bbox_inches='tight', transparent=True, dpi=self.fig_dpi)
vprint('saved transect to', outfile)
self.cid = self.fig.canvas.mpl_connect('button_release_event', self.select_point)
if self.disp_fig:
vprint('showing ...')
plt.show()
return
def manual_select_start_end_point(File, dset=None):
"""Manual Select Start/End Point in display figure."""
print('reading '+File+' ...')
data, atr = readfile.read(File, datasetName=dset)
print('displaying '+File+' ...')
fig = plt.figure()
ax = fig.add_subplot(111)
ax.imshow(data)
lines = []
def draw_line():
xy = plt.ginput(2)
x = [p[0] for p in xy]
y = [p[1] for p in xy]
line = plt.plot(x,y)
ax.figure.canvas.draw()
return line
line = draw_line()
import pdb; pdb.set_trace()
#xc = []
#yc = []
#print('1) Click on start and end point of the desired profile')
#print('2) Close the figure to continue the profile plotting')
#
#def onclick(event):
# if event.button == 1:
# xcc, ycc = int(event.xdata), int(event.ydata)
# xc.append(xcc)
# yc.append(ycc)
# print('({}, {})'.format(xcc, ycc))
# ax.plot(xcc, ycc, 'ro')
#cid = fig.canvas.mpl_connect('button_release_event', onclick)
plt.show()
#start_yx = [yc[0], xc[0]]
#end_yx = [yc[1], xc[1]]
#return start_yx, end_yx
return [0,0], [10,10]
def subset_dataset(fname,
dsName,
pix_box,
pix_box4data,
pix_box4subset,
fill_value=np.nan):
# read data
print('reading {d} in {b} from {f} ...'.format(d=dsName,
b=pix_box4data,
f=os.path.basename(fname)))
data, atr = readfile.read(fname,
datasetName=dsName,
box=pix_box4data,
print_msg=False)
ds_shape = data.shape
ds_ndim = len(ds_shape)
# keep timeseries data as 3D matrix when there is only one acquisition
# because readfile.read() will squeeze it to 2D
if atr['FILE_TYPE'] == 'timeseries' and ds_ndim == 2:
data = np.reshape(data, (1, ds_shape[0], ds_shape[1]))
# subset 2D data
if ds_ndim == 2:
data_out = np.ones((pix_box[3] - pix_box[1], pix_box[2] - pix_box[0]),
data.dtype) * fill_value
data_out[pix_box4subset[1]:pix_box4subset[3],
pix_box4subset[0]:pix_box4subset[2]] = data
# subset 3D data
elif ds_ndim == 3:
data_out = np.ones(
(ds_shape[0], pix_box[3] - pix_box[1], pix_box[2] - pix_box[0]),
data.dtype) * fill_value
data_out[:, pix_box4subset[1]:pix_box4subset[3],
pix_box4subset[0]:pix_box4subset[2]] = data
return data_out
def main(iargs=None):
inps = cmd_line_parse(iargs)
plt.switch_backend('Agg') # Backend setting
# Read data
data, atr = readfile.read(inps.file, datasetName=inps.dset)
# mask
mask = pp.read_mask(inps.file, mask_file=inps.mask_file, datasetName=inps.dset, print_msg=True)[0]
if mask is not None:
data = np.ma.masked_where(mask == 0., data)
# Data Operation - Display Unit & Rewrapping
(data,
inps.disp_unit,
inps.disp_scale,
inps.wrap) = pp.scale_data4disp_unit_and_rewrap(data,
metadata=atr,
disp_unit=inps.disp_unit,
wrap=inps.wrap,
wrap_range=inps.wrap_range)
if inps.wrap:
inps.vlim = inps.wrap_range
# Output filename
inps.fig_title = pp.auto_figure_title(inps.file,
datasetNames=inps.dset,
inps_dict=vars(inps))
if not inps.outfile:
inps.outfile = '{}.kmz'.format(inps.fig_title)
inps.outfile = os.path.abspath(inps.outfile)
# 2. Generate Google Earth KMZ
write_kmz_file(data,
metadata=atr,
out_file=inps.outfile,
inps=inps)
return inps.outfile
def plot_transect_location(ax, inps):
print('plot profile line in the 1st input file')
data0, atr0 = readfile.read(inps.file[0], datasetName=inps.dset)
ax.imshow(data0)
coord = ut.coordinate(atr0)
if inps.start_lalo and inps.end_lalo:
[y0, y1] = coord.lalo2yx([inps.start_lalo[0], inps.end_lalo[0]],
coord_type='lat')
[x0, x1] = coord.lalo2yx([inps.start_lalo[1], inps.end_lalo[1]],
coord_type='lon')
inps.start_yx = [y0, x0]
inps.end_yx = [y1, x1]
ax.plot([inps.start_yx[1], inps.end_yx[1]],
[inps.start_yx[0], inps.end_yx[0]], 'ro-')
ax.set_xlim(0, np.shape(data0)[1])
ax.set_ylim(np.shape(data0)[0], 0)
ax.set_title('Transect Line in ' + inps.file[0])
# Status bar
def format_coord(x, y):
col = int(x)
row = int(y)
if 0 <= col < data0.shape[1] and 0 <= row < data0.shape[0]:
z = data0[row, col]
if 'X_FIRST' in atr0.keys():
lat = coord.yx2lalo(row, coord_type='row')
lon = coord.yx2lalo(col, coord_type='col')
return 'lon=%.4f, lat=%.4f, x=%.0f, y=%.0f, value=%.4f' % (
lon, lat, x, y, z)
else:
return 'x=%.0f, y=%.0f, value=%.4f' % (x, y, z)
else:
return 'x=%.0f, y=%.0f' % (x, y)
ax.format_coord = format_coord
return
def get_incidence_angle(self, box=None):
"""Generate 2D slant range distance if missing from input template file"""
if not self.extraMetadata:
return None
if 'Y_FIRST' in self.extraMetadata.keys():
# for dataset in geo-coordinates, use contant value from INCIDENCE_ANGLE.
key = 'INCIDENCE_ANGLE'
print('geocoded input, use contant value from metadata {}'.format(
key))
if key in self.extraMetadata.keys():
length = int(self.extraMetadata['LENGTH'])
width = int(self.extraMetadata['WIDTH'])
inc_angle = float(self.extraMetadata[key])
data = np.ones((length, width), dtype=np.float32) * inc_angle
else:
return None
else:
# read DEM if available for more previse calculation
if 'height' in self.dsNames:
dem = readfile.read(self.datasetDict['height'],
datasetName='height')[0]
else:
dem = None
# for dataset in radar-coordinates, calculate 2D pixel-wise value from geometry
data = ut.incidence_angle(self.extraMetadata,
dem=dem,
dimension=2,
print_msg=False)
# subset
if box is not None:
data = data[box[1]:box[3], box[0]:box[2]]
return data
def ll2xy(inps):
"""transfer lat/lon to local coordination"""
inps.metadata = readfile.read_attribute(inps.file[0])
# read geometry
inps.lat, inps.lon = ut.get_lat_lon(inps.metadata)
#inps.inc_angle = readfile.read(inps.geometry[0], datasetName='incidenceAngle')[0]
#inps.head_angle = np.ones(inps.inc_angle.shape, dtype=np.float32) * float(inps.metadata['HEADING'])
#inps.height = readfile.read(inps.geometry[0], datasetName='height')[0]
# read mask file
inps.mask = readfile.read(inps.file[0])[0]
# mask data
#inps.lat[inps.mask==0] = np.nan
#inps.lon[inps.mask==0] = np.nan
#inps.inc_angle[inps.mask==0] = np.nan
#inps.head_angle[inps.mask==0] = np.nan
#inps.height[inps.mask==0] = np.nan
# conver latlon to xy
# origin point
origin_lat = (inps.lat[0, 0] + inps.lat[-1, 0]) / 2
origin_lon = (inps.lon[0, 0] + inps.lon[0, -1]) / 2
lat = np.transpose(inps.lat.reshape(-1, 1))
lon = np.transpose(inps.lon.reshape(-1, 1))
llh = np.vstack((lon, lat))
origin = np.array([origin_lon, origin_lat], dtype=float)
XY = np.transpose(
mut.llh2xy(llh, origin)
) * 1000 # unit of X/Y is meter and is a [N,2] matrix with [N,0] is X; [N,1] is Y
X = XY[:, 0]
Y = XY[:, 1]
return X, Y, origin
def timeseries2ifgram(ts_file, ifgram_file, out_file='reconUnwrapIfgram.h5'):
# read time-series
atr = readfile.read_attribute(ts_file)
range2phase = -4. * np.pi / float(atr['WAVELENGTH'])
print('reading timeseries data from file {} ...'.format(ts_file))
ts_data = readfile.read(ts_file)[0] * range2phase
num_date, length, width = ts_data.shape
ts_data = ts_data.reshape(num_date, -1)
# reconstruct unwrapPhase
print('reconstructing the interferograms from timeseries')
stack_obj = ifgramStack(ifgram_file)
stack_obj.open(print_msg=False)
date12_list = stack_obj.get_date12_list(dropIfgram=False)
A = stack_obj.get_design_matrix4timeseries(date12_list, refDate='no')[0]
ifgram_est = np.dot(A, ts_data).reshape(A.shape[0], length, width)
ifgram_est = np.array(ifgram_est, dtype=ts_data.dtype)
del ts_data
# write to ifgram file
dsDict = {}
dsDict['unwrapPhase'] = ifgram_est
writefile.write(dsDict, out_file=out_file, ref_file=ifgram_file)
return ifgram_file
def generate_polygon_sfoot(inps):
"""generate polygon for track using gmt formate"""
file_name = inps.file[0]
atr = readfile.read(file_name)[1]
# extract lon/lat of four points
polygon = atr['scene_footprint']
lonlats = re.findall(r'([\d+\.]+)', polygon)
# lon/lat of four points: upperright; lowerright; lowerleft; upperleft
lon_ur = float(lonlats[0])
lat_ur = float(lonlats[1])
lon_lr = float(lonlats[2])
lat_lr = float(lonlats[3])
lon_ll = float(lonlats[4])
lat_ll = float(lonlats[5])
lon_ul = float(lonlats[6])
lat_ul = float(lonlats[7])
return lon_ur, lat_ur, lon_lr, lat_lr, lon_ll, lat_ll, lon_ul, lat_ul
def prep_geometry_iono(geom_file, box=None, iono_height=450e3, print_msg=True):
"""Prepare geometry info of LOS vector at thin-shell ionosphere.
Equation (11-12) in Yunjun et al. (2022, TGRS)
Parameters: geom_file - str, path to the geometry file in HDF5/MintPy format
box - tuple of 4 int, box of interest in (x0, y0, x1, y1)
iono_height - float, height of the assume effective thin-shell ionosphere in m
Returns: iono_inc_angle - 2D np.ndarray / float, incidence angle in degree
iono_lat/lon - float, latitude/longitude of LOS vector at the thin-shell in deg
iono_height - float, height of the assume effective thin-shell ionosphere in m
"""
def get_center_lat_lon(geom_file, box=None):
"""Get the lat/lon of the scene center"""
meta = readfile.read_attribute(geom_file)
if box is None:
box = (0, 0, int(meta['WIDTH']), int(meta['LENGTH']))
col_c = int((box[0] + box[2]) / 2)
row_c = int((box[1] + box[3]) / 2)
if 'Y_FIRST' in meta.keys():
lat0 = float(meta['Y_FIRST'])
lon0 = float(meta['X_FIRST'])
lat_step = float(meta['Y_STEP'])
lon_step = float(meta['X_STEP'])
lat_c = lat0 + lat_step * row_c
lon_c = lon0 + lon_step * col_c
else:
box_c = (col_c, row_c, col_c + 1, row_c + 1)
lat_c = float(
readfile.read(geom_file, datasetName='latitude', box=box_c)[0])
lon_c = float(
readfile.read(geom_file, datasetName='longitude',
box=box_c)[0])
return lat_c, lon_c
# inc_angle on the ground
inc_angle = readfile.read(geom_file, datasetName='incidenceAngle',
box=box)[0]
inc_angle = np.squeeze(inc_angle)
inc_angle[inc_angle == 0] = np.nan
inc_angle_center = np.nanmean(inc_angle)
if print_msg:
print('incidence angle on the ground min/max: {:.1f}/{:.1f} deg'.
format(np.nanmin(inc_angle), np.nanmax(inc_angle)))
# inc_angle on the thin-shell ionosphere - equation (11)
iono_inc_angle = incidence_angle_ground2iono(inc_angle,
iono_height=iono_height)
if print_msg:
print('incidence angle on the ionosphere min/max: {:.1f}/{:.1f} deg'.
format(np.nanmin(iono_inc_angle), np.nanmax(iono_inc_angle)))
# center lat/lon on the ground & thin-shell ionosphere - equation (12)
lat, lon = get_center_lat_lon(geom_file, box=box)
az_angle = readfile.read(geom_file, datasetName='azimuthAngle', box=box)[0]
az_angle[az_angle == 0] = np.nan
az_angle_center = np.nanmean(az_angle)
iono_lat, iono_lon = lalo_ground2iono(lat,
lon,
inc_angle=inc_angle_center,
az_angle=az_angle_center)
if print_msg:
print('center lat/lon on the ground : {:.4f}/{:.4f} deg'.format(
lat, lon))
print('center lat/lon on the ionosphere: {:.4f}/{:.4f} deg'.format(
iono_lat, iono_lon))
return iono_inc_angle, iono_lat, iono_lon, iono_height
def run_geocode(inps):
"""geocode all input files"""
start_time = time.time()
# Prepare geometry for geocoding
res_obj = resample(lookupFile=inps.lookupFile,
dataFile=inps.file[0],
SNWE=inps.SNWE,
laloStep=inps.laloStep,
processor=inps.processor)
res_obj.open()
# resample input files one by one
for infile in inps.file:
print('-' * 50+'\nresampling file: {}'.format(infile))
ext = os.path.splitext(infile)[1]
atr = readfile.read_attribute(infile, datasetName=inps.dset)
outfile = auto_output_filename(infile, inps)
if inps.updateMode and ut.run_or_skip(outfile, in_file=[infile, inps.lookupFile]) == 'skip':
print('update mode is ON, skip geocoding.')
continue
# read source data and resample
dsNames = readfile.get_dataset_list(infile, datasetName=inps.dset)
maxDigit = max([len(i) for i in dsNames])
dsResDict = dict()
for dsName in dsNames:
print('reading {d:<{w}} from {f} ...'.format(d=dsName,
w=maxDigit,
f=os.path.basename(infile)))
if ext in ['.h5','.he5']:
data = readfile.read(infile, datasetName=dsName, print_msg=False)[0]
else:
data, atr = readfile.read(infile, datasetName=dsName, print_msg=False)
# keep timeseries data as 3D matrix when there is only one acquisition
# because readfile.read() will squeeze it to 2D
if atr['FILE_TYPE'] == 'timeseries' and len(data.shape) == 2:
data = np.reshape(data, (1, data.shape[0], data.shape[1]))
res_data = res_obj.run_resample(src_data=data,
interp_method=inps.interpMethod,
fill_value=inps.fillValue,
nprocs=inps.nprocs,
print_msg=True)
dsResDict[dsName] = res_data
# update metadata
if inps.radar2geo:
atr = metadata_radar2geo(atr, res_obj)
else:
atr = metadata_geo2radar(atr, res_obj)
#if len(dsNames) == 1 and dsName not in ['timeseries']:
# atr['FILE_TYPE'] = dsNames[0]
# infile = None
writefile.write(dsResDict, out_file=outfile, metadata=atr, ref_file=infile)
m, s = divmod(time.time()-start_time, 60)
print('time used: {:02.0f} mins {:02.1f} secs.\n'.format(m, s))
return outfile
def read_timeseries_yx(y,
x,
ts_file,
ref_y=None,
ref_x=None,
zero_first=True,
win_size=1,
unit='m',
method='mean',
print_msg=True):
""" Read time-series of one pixel with input y/x
Parameters: y/x - int, row/column number of interest
ts_file - string, filename of time-series HDF5 file
ref_y/x - int, row/column number of reference pixel
zero_first - bool, shift the time-series so that it starts from zero
win_size - int, windows size centered at point of interest
unit - str, output displacement unit
method - str, method to calculate the output displacement and its dispersity
Returns: dates - 1D np.ndarray of datetime.datetime objects, i.e. datetime.datetime(2010, 10, 20, 0, 0)
dis - 1D np.ndarray of float32, displacement
dis_std - 1D np.ndarray of float32, displacement dispersity
"""
# read date
obj = timeseries(ts_file)
obj.open(print_msg=False)
dates = ptime.date_list2vector(obj.dateList)[0]
dates = np.array(dates)
# read displacement
if print_msg:
print('input y / x: {} / {}'.format(y, x))
box = (x, y, x + 1, y + 1)
dis = readfile.read(ts_file, box=box)[0]
dis_std = None
if win_size != 1:
buf = int(win_size / 2)
box_win = (x - buf, y - buf, x + buf + 1, y + buf + 1)
dis_win = readfile.read(ts_file,
box=box_win)[0].reshape(obj.numDate, -1)
if method == 'mean':
dis = np.nanmean(dis_win, axis=1)
dis_std = np.nanstd(dis_win, axis=1)
elif method == 'median':
dis = np.nanmedian(dis_win, axis=1)
dis_std = median_abs_deviation(dis_win)
else:
raise ValueError('un-recognized method: {}'.format(method))
# reference pixel
if ref_y is not None:
ref_box = (ref_x, ref_y, ref_x + 1, ref_y + 1)
dis -= readfile.read(ts_file, box=ref_box)[0]
#start at zero
if zero_first:
dis -= dis[0]
# custom output unit
if unit == 'm':
pass
elif unit == 'cm':
dis *= 100.
dis_std *= 100.
elif unit == 'mm':
dis *= 1000.
dis_std *= 1000.
else:
raise ValueError('un-supported output unit: {}'.format(unit))
return dates, dis, dis_std
def run_unwrap_error_phase_closure(ifgram_file,
common_regions,
water_mask_file=None,
ccName='connectComponent',
dsNameIn='unwrapPhase',
dsNameOut='unwrapPhase_phaseClosure'):
print('-' * 50)
print('correct unwrapping error in {} with phase closure ...'.format(
ifgram_file))
stack_obj = ifgramStack(ifgram_file)
stack_obj.open()
length, width = stack_obj.length, stack_obj.width
ref_y, ref_x = stack_obj.refY, stack_obj.refX
date12_list = stack_obj.get_date12_list(dropIfgram=False)
num_ifgram = len(date12_list)
shape_out = (num_ifgram, length, width)
# read water mask
if water_mask_file and os.path.isfile(water_mask_file):
print('read water mask from file:', water_mask_file)
water_mask = readfile.read(water_mask_file)[0]
else:
water_mask = None
# prepare output data writing
print('open {} with r+ mode'.format(ifgram_file))
f = h5py.File(ifgram_file, 'r+')
print('input dataset:', dsNameIn)
print('output dataset:', dsNameOut)
if dsNameOut in f.keys():
ds = f[dsNameOut]
print('access /{d} of np.float32 in size of {s}'.format(d=dsNameOut,
s=shape_out))
else:
ds = f.create_dataset(dsNameOut,
shape_out,
maxshape=(None, None, None),
chunks=True,
compression=None)
print('create /{d} of np.float32 in size of {s}'.format(d=dsNameOut,
s=shape_out))
# correct unwrap error ifgram by ifgram
prog_bar = ptime.progressBar(maxValue=num_ifgram)
for i in range(num_ifgram):
date12 = date12_list[i]
# read unwrap phase to be updated
unw_cor = np.squeeze(f[dsNameIn][i, :, :]).astype(np.float32)
unw_cor -= unw_cor[ref_y, ref_x]
# update kept interferograms only
if stack_obj.dropIfgram[i]:
# get local region info from connectComponent
cc = np.squeeze(f[ccName][i, :, :])
if water_mask is not None:
cc[water_mask == 0] = 0
cc_obj = connectComponent(conncomp=cc, metadata=stack_obj.metadata)
cc_obj.label()
local_regions = measure.regionprops(cc_obj.labelImg)
# matching regions and correct unwrap error
idx_common = common_regions[0].date12_list.index(date12)
for local_reg in local_regions:
local_mask = cc_obj.labelImg == local_reg.label
U = 0
for common_reg in common_regions:
y = common_reg.sample_coords[:, 0]
x = common_reg.sample_coords[:, 1]
if all(local_mask[y, x]):
U = common_reg.int_ambiguity[idx_common]
break
unw_cor[local_mask] += 2. * np.pi * U
# write to hdf5 file
ds[i, :, :] = unw_cor
prog_bar.update(i + 1, suffix=date12)
prog_bar.close()
ds.attrs['MODIFICATION_TIME'] = str(time.time())
f.close()
print('close {} file.'.format(ifgram_file))
return ifgram_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