def main(iargs=None):
inps = cmd_line_parse(iargs)
# Calculate look angle
atr = readfile.read_attribute(inps.file)
dem = None
if inps.dem_file:
dem = readfile.read(inps.dem_file, datasetName='height')[0]
angle = ut.incidence_angle(atr, dem=dem, dimension=2)
# Geo coord
if 'Y_FIRST' in atr.keys():
print(
'Input file is geocoded, only center incident angle is calculated: '
)
print(angle)
length = int(atr['LENGTH'])
width = int(atr['WIDTH'])
angle_mat = np.zeros((length, width), np.float32)
angle_mat[:] = angle
angle = angle_mat
atr['FILE_TYPE'] = 'mask'
atr['UNIT'] = 'degree'
if 'REF_DATE' in atr.keys():
atr.pop('REF_DATE')
if not inps.outfile:
inps.outfile = 'incidenceAngle.h5'
writefile.write(angle, out_file=inps.outfile, metadata=atr)
return inps.outfile
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_estimation(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 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 main(iargs=None):
inps = cmd_line_parse(iargs)
if not inps.outfile:
inps.outfile = os.path.splitext(inps.file)[0]+'.h5'
if inps.data_type:
if inps.data_type in ['float', 'float32', 'np.float32']:
inps.data_type = np.float32
elif inps.data_type in ['float64', 'np.float64']:
inps.data_type = np.float64
elif inps.data_type in ['int', 'int16', 'np.int16']:
inps.data_type = np.int16
elif inps.data_type in ['bool', 'np.bool_']:
inps.data_type = np.bool_
elif inps.data_type in ['complex', 'np.complex64']:
inps.data_type = np.complex64
elif inps.data_type in ['complex128', 'np.complex128']:
inps.data_type = np.complex128
else:
raise ValueError('un-recognized input data type: {}'.format(inps.data_type))
atr = readfile.read_attribute(inps.file)
dsNames = readfile.get_dataset_list(inps.file)
dsDict = {}
for dsName in dsNames:
data = readfile.read(inps.file, datasetName=dsName)[0]
if inps.data_type:
data = np.array(data, inps.data_type)
dsDict[dsName] = data
writefile.write(dsDict, out_file=inps.outfile, metadata=atr)
return inps.outfile
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 main(iargs=None):
inps = cmd_line_parse(iargs)
# read timeseries data
obj = timeseries(inps.timeseries_file)
obj.open()
ts_data = obj.read()
inps.date_list = list(obj.dateList)
# read topographic data (DEM)
dem = read_topographic_data(inps.geom_file, obj.metadata)
# estimate phase/elevation ratio parameters
X = estimate_phase_elevation_ratio(dem, ts_data, inps)
# correct trop delay in timeseries
trop_data = estimate_tropospheric_delay(dem, X, obj.metadata)
mask = ts_data == 0.
ts_data -= trop_data
ts_data[mask] = 0.
# write time-series file
metadata = dict(obj.metadata)
metadata['pysar.troposphericDelay.polyOrder'] = str(inps.poly_order)
if not inps.outfile:
inps.outfile = '{}_tropHgt.h5'.format(
os.path.splitext(inps.timeseries_file)[0])
writefile.write(ts_data,
out_file=inps.outfile,
metadata=metadata,
ref_file=inps.timeseries_file)
return inps.outfile
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 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 mask_file(fname, mask_file, out_file=None, 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()
if not out_file:
out_file = '{}_masked{}'.format(
os.path.splitext(fname)[0],
os.path.splitext(fname)[1])
# 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
writefile.write(dsDict, out_file=out_file, ref_file=fname)
def run_unwrap_error_closure(inps, dsNameIn='unwrapPhase', dsNameOut='unwrapPhase_phaseClosure', fast_mode=False):
"""Run unwrapping error correction in network of interferograms using phase closure.
Parameters: inps : Namespace of input arguments including the following:
ifgram_file : string, path of ifgram stack file
maskFile : string, path of mask file mark the pixels to be corrected
dsNameIn : string, dataset name to read in
dsnameOut : string, dataset name to write out
fast_mode : bool, enable fast processing mode or not
Returns: inps.ifgram_file : string, path of corrected ifgram stack file
"""
print('-'*50)
print('Unwrapping Error Coorrection based on Phase Closure Consistency (Fattahi, 2015) ...')
if fast_mode:
print('fast mode: ON, the following asuumption is ignored for fast processing')
print('\tzero phase jump constraint on ifgrams without unwrap error')
else:
print(('this step can be very slow'
'you could terminate this and try --fast option for quick correction process.'))
print('-'*50)
stack_obj = ifgramStack(inps.ifgram_file)
stack_obj.open()
ref_phase = stack_obj.get_reference_phase(unwDatasetName=dsNameIn, dropIfgram=False)
num_nonzero_closure = np.zeros((stack_obj.length, stack_obj.width), dtype=np.int16)
# split ifgram_file into blocks to save memory
num_tri = stack_obj.get_design_matrix4ifgram_triangle(dropIfgram=True).shape[0]
length, width = stack_obj.get_size()[1:3]
box_list = ifginv.split_into_boxes(dataset_shape=(num_tri, length, width), chunk_size=200e6)
num_box = len(box_list)
for i in range(num_box):
box = box_list[i]
if num_box > 1:
print('\n------- Processing Patch {} out of {} --------------'.format(i+1, num_box))
# estimate/correct ifgram
unw_cor, num_closure = run_unwrap_error_patch(inps.ifgram_file,
box=box,
mask_file=inps.maskFile,
ref_phase=ref_phase,
fast_mode=fast_mode,
dsNameIn=dsNameIn)
num_nonzero_closure[box[1]:box[3], box[0]:box[2]] = num_closure
# write ifgram
write_hdf5_file_patch(inps.ifgram_file,
data=unw_cor,
box=box,
dsName=dsNameOut)
# write number of nonzero phase closure into file
num_file = 'numNonzeroPhaseClosure.h5'
atr = dict(stack_obj.metadata)
atr['FILE_TYPE'] = 'mask'
atr['UNIT'] = '1'
writefile.write(num_nonzero_closure, out_file=num_file, metadata=atr)
print('writing >>> {}'.format(num_file))
return inps.ifgram_file
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()
if not inps.nprocs:
inps.nprocs = multiprocessing.cpu_count()
# resample input files one by one
for infile in inps.file:
print('-' * 50+'\nresampling file: {}'.format(infile))
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)))
data = readfile.read(infile,
datasetName=dsName,
print_msg=False)[0]
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 main(iargs=None):
inps = cmd_line_parse(iargs)
data, atr, out_file = read_data(inps)
print('writing >>> {}'.format(out_file))
writefile.write(data, out_file=out_file, metadata=atr)
return inps.outfile
def main(iargs=None):
inps = cmd_line_parse(iargs)
data, atr, out_file = read_data(inps)
atr = clean_metadata4roipac(atr)
writefile.write(data, out_file=out_file, metadata=atr)
return inps.outfile
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 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 PYSAR/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)
pysar.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 filter_file(fname, filter_type, filter_par=None, fname_out=None):
"""Filter 2D matrix with selected filter
Inputs:
fname : string, name/path of file to be filtered
filter_type : string, filter type
filter_par : string, optional, parameter for low/high pass filter
for low/highpass_avg, it's kernel size in int
for low/highpass_gaussain, it's sigma in float
Output:
fname_out : string, optional, output file name/path
"""
# Info
filter_type = filter_type.lower()
atr = readfile.read_attribute(fname)
k = atr['FILE_TYPE']
msg = 'filtering {} file: {} using {} filter'.format(k, fname, filter_type)
if filter_type.endswith('avg'):
if not filter_par:
filter_par = 5
msg += ' with kernel size of {}'.format(filter_par)
elif filter_type.endswith('gaussian'):
if not filter_par:
filter_par = 3.0
msg += ' with sigma of {:.1f}'.format(filter_par)
print(msg)
# output filename
if not fname_out:
fname_out = '{}_{}{}'.format(
os.path.splitext(fname)[0], filter_type,
os.path.splitext(fname)[1])
# filtering file
dsNames = readfile.get_dataset_list(fname)
maxDigit = max([len(i) for i in dsNames])
dsDict = dict()
for dsName in dsNames:
msg = 'filtering {d:<{w}} from {f} '.format(d=dsName,
w=maxDigit,
f=os.path.basename(fname))
data = readfile.read(fname, datasetName=dsName, print_msg=False)[0]
if len(data.shape) == 3:
num_loop = data.shape[0]
for i in range(num_loop):
data[i, :, :] = filter_data(data[i, :, :], filter_type,
filter_par)
sys.stdout.write('\r{} {}/{} ...'.format(msg, i + 1, num_loop))
sys.stdout.flush()
print('')
else:
data = filter_data(data, filter_type, filter_par)
dsDict[dsName] = data
writefile.write(dsDict, out_file=fname_out, metadata=atr, ref_file=fname)
return fname_out
def water_mask2conn_comp_mask(water_mask_file,
ref_yx,
out_file='maskConnComp.h5',
min_num_pixel=5e4,
display=False):
"""Generate connected component mask file from water mask file
Parameters: water_mask_file : str, path of water mask file
ref_yx : tuple of 2 int, row/col number of reference point
out_file : str, filename of output connected components mask
min_num_pixel : float, min number of pixels to be identified as conn comp
display : bool, display generated conn comp mask
Returns: out_file : str, filename of output connected components mask
"""
print('-' * 50)
print('generate connected component mask from water mask file: ',
water_mask_file)
water_mask, atr = readfile.read(water_mask_file)
mask_cc = np.zeros(water_mask.shape, dtype=np.int16)
# first conn comp - reference conn comp
num_cc = 1
label_mask = ndimage.label(water_mask)[0]
mask_cc += label_mask == label_mask[ref_yx[0], ref_yx[1]]
# all the other conn comps
water_mask ^= mask_cc == mask_cc[ref_yx[0], ref_yx[1]]
mask_ccs = ut.get_all_conn_components(water_mask,
min_num_pixel=min_num_pixel)
if mask_ccs:
for mask_cci in mask_ccs:
num_cc += 1
mask_cc += mask_cci * num_cc
# write file
atr['FILE_TYPE'] = 'mask'
atr['REF_Y'] = str(ref_yx[0])
atr['REF_X'] = str(ref_yx[1])
writefile.write(mask_cc, out_file=out_file, metadata=atr)
# plot
out_img = '{}.png'.format(os.path.splitext(out_file)[0])
fig, ax = plt.subplots(figsize=[6, 8])
im = ax.imshow(mask_cc)
# colorbar
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "3%", pad="3%")
cbar = plt.colorbar(im, cax=cax, ticks=np.arange(num_cc + 1))
ax = pp.auto_flip_direction(atr, ax=ax, print_msg=False)
fig.savefig(out_img, bbox_inches='tight', transparent=True, dpi=300)
print('save figure to {}'.format(out_img))
if display:
plt.show()
return out_file
def main(iargs=None):
inps = cmd_line_parse(iargs)
if not inps.outfile:
inps.outfile = os.path.splitext(inps.file)[0] + '.h5'
atr = readfile.read_attribute(inps.file)
dsNames = readfile.get_dataset_list(inps.file)
dsDict = {}
for dsName in dsNames:
data = readfile.read(inps.file, datasetName=dsName)[0]
dsDict[dsName] = data
writefile.write(dsDict, out_file=inps.outfile, metadata=atr)
return inps.outfile
def nonzero_mask(File, out_file='maskConnComp.h5', datasetName=None):
"""Generate mask file for non-zero value of input multi-group hdf5 file"""
atr = readfile.read_attribute(File)
k = atr['FILE_TYPE']
if k == 'ifgramStack':
mask = ifgramStack(File).nonzero_mask(datasetName=datasetName)
else:
print('Only ifgramStack file is supported for now, input is '+k)
return None
atr['FILE_TYPE'] = 'mask'
writefile.write(mask, out_file=out_file, metadata=atr)
return out_file
def main(iargs=None):
inps = cmd_line_parse(iargs)
temp_coh = calculate_temporal_coherence(ifgram_file=inps.ifgram_file,
timeseries_file=inps.timeseries_file,
ifg_num_file=inps.ifg_num_file)
# write file
atr = readfile.read_attribute(inps.timeseries_file)
atr['FILE_TYPE'] = 'temporalCoherence'
atr['UNIT'] = '1'
writefile.write(temp_coh, out_file=inps.outfile, metadata=atr)
return inps.outfile
def create_threshold_mask(inps):
if inps.dset:
print('read %s %s' % (inps.file, inps.dset))
else:
print('read %s' % (inps.file))
data, atr = readfile.read(inps.file, datasetName=inps.dset)
if len(data.shape) > 2:
print('ERROR: Only 2D dataset is supported for threshold method, input is 3D')
sys.exit(1)
length = int(atr['LENGTH'])
width = int(atr['WIDTH'])
print('create initial mask with the same size as the input file and all = 1')
mask = np.ones((length, width), dtype=np.float32)
if inps.nonzero:
print('all pixels with zero value = 0')
mask[data == 0] = 0
# min threshold
if inps.vmin is not None:
mask[data < inps.vmin] = 0
print('all pixels with value < %s = 0' % str(inps.vmin))
# max threshold
if inps.vmax is not None:
mask[data > inps.vmax] = 0
print('all pixels with value > %s = 0' % str(inps.vmax))
# nan value
mask[np.isnan(data)] = 0
print('all pixels with nan value = 0')
# subset in Y
if inps.subset_y is not None:
y0, y1 = sorted(inps.subset_y)
mask[0:y0, :] = 0
mask[y1:length, :] = 0
print('all pixels with y OUT of [%d, %d] = 0' % (y0, y1))
# subset in x
if inps.subset_x is not None:
x0, x1 = sorted(inps.subset_x)
mask[:, 0:x0] = 0
mask[:, x1:width] = 0
print('all pixels with x OUT of [%d, %d] = 0' % (x0, x1))
# Write mask file
atr['FILE_TYPE'] = 'mask'
writefile.write(mask, out_file=inps.outfile, metadata=atr)
return inps.outfile
def run_resample(inps):
"""resample 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)
res_obj.get_geometry_definition()
inps.nprocs = multiprocessing.cpu_count()
# resample input files one by one
for infile in inps.file:
print('-' * 50+'\nresampling file: {}'.format(infile))
outfile = auto_output_filename(infile, inps)
if inps.updateMode and not ut.update_file(outfile, [infile, inps.lookupFile]):
print('update mode is ON, skip geocoding.')
return outfile
# 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('resampling {d:<{w}} from {f} using {n} processor cores ...'.format(
d=dsName, w=maxDigit, f=os.path.basename(infile), n=inps.nprocs))
data = readfile.read(infile, datasetName=dsName, print_msg=False)[0]
res_data = resample_data(data, inps, res_obj)
dsResDict[dsName] = res_data
# update metadata
atr = readfile.read_attribute(infile, datasetName=inps.dset)
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('\ntime used: {:02.0f} mins {:02.1f} secs\nDone.'.format(m, s))
return outfile
def prepare_roipac_dem(demFile, geocoded=False):
print('convert input DEM to ROIPAC format')
dem, atr = readfile.read(demFile, datasetName='height')
if geocoded:
ext = '.dem'
else:
ext = '.hgt'
demFileOut = '{}4pyaps{}'.format(os.path.splitext(demFile)[0], ext)
demFileOut = writefile.write(dem, out_file=demFileOut, metadata=atr)
return demFileOut
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 main(argv):
try:
timeseries_file = argv[0]
except:
usage()
sys.exit(1)
try:
out_file = argv[1]
except:
out_file = 'sum_' + timeseries_file
# Read Timeseries
obj = timeseries(timeseries_file)
obj.open()
D = obj.read().reshape(obj.numDate, -1)
# Calculate Sum
sumD = np.zeros(D.shape)
for i in range(obj.numDate):
sumD[i, :] = np.sum(np.abs(D - D[i, :]), axis=0) / obj.numDate
sys.stdout.write('\rcalculating epochs sum {}/{} ...'.format(
i + 1, obj.numDate))
sys.stdout.flush()
print('')
del D
# Normalize to 0 and 1
# with high atmosphere equal to 0 and no atmosphere equal to 1
sumD -= np.max(sumD, 0)
sumD *= -1
sumD /= np.max(sumD, 0)
sumD[np.isnan(sumD)] = 1
# Write sum epochs file
sumD = np.reshape(sumD, (obj.numDate, obj.length, obj.width))
atr = dict(obj.metadata)
atr['UNIT'] = '1'
writefile.write(sumD,
out_file=out_file,
metadata=atr,
ref_file=timeseries_file)
print('Done.')
def split_ifgram_file(ifgram_file, chunk_size=100e6):
stack_obj = ifgramStack(ifgram_file)
stack_obj.open(print_msg=False)
metadata = dict(stack_obj.metadata)
# get reference phase
ref_phase = get_ifgram_reference_phase(ifgram_file)
# get list of boxes
box_list = split_into_boxes(ifgram_file,
chunk_size=chunk_size,
print_msg=True)
num_box = len(box_list)
# read/write each patch file
outfile_list = []
for i in range(num_box):
box = box_list[i]
outfile = '{}_{:03d}{}'.format(
os.path.splitext(ifgram_file)[0], i + 1,
os.path.splitext(ifgram_file)[1])
# datasets
print('-' * 50)
print('reading all datasets in {} from file: {} ...'.format(
box, ifgram_file))
dsNames = readfile.get_dataset_list(ifgram_file)
dsDict = {}
dsDict['refPhase'] = ref_phase
for dsName in dsNames:
data = stack_obj.read(datasetName=dsName, box=box, print_msg=False)
dsDict[dsName] = data
# metadata
metadata['LENGTH'] = box[3] - box[1]
metadata['WIDTH'] = box[2] - box[0]
writefile.write(dsDict,
out_file=outfile,
metadata=metadata,
ref_file=ifgram_file)
outfile_list.append(outfile)
return outfile_list
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 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 = design_matrix(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, 2016)
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 main(argv):
try:
File = argv[0]
atr = readfile.read_attribute(File)
except:
usage()
sys.exit(1)
try:
outFile = argv[1]
except:
outFile = 'rangeDistance.h5'
# Calculate look angle
range_dis = ut.range_distance(atr, dimension=2)
# Geo coord
if 'Y_FIRST' in atr.keys():
print(
'Input file is geocoded, only center range distance is calculated: '
)
print(range_dis)
length = int(atr['LENGTH'])
width = int(atr['WIDTH'])
range_dis_mat = np.zeros((length, width), np.float32)
range_dis_mat[:] = range_dis
range_dis = range_dis_mat
print('writing >>> ' + outFile)
atr['FILE_TYPE'] = 'mask'
atr['UNIT'] = 'm'
try:
atr.pop('REF_DATE')
except:
pass
writefile.write(range_dis, out_file=outFile, metadata=atr)
return outFile
def write2hdf5_file(ifgram_file,
metadata,
ts,
temp_coh,
ts_std=None,
num_inv_ifg=None,
suffix='',
inps=None):
stack_obj = ifgramStack(ifgram_file)
stack_obj.open(print_msg=False)
date_list = stack_obj.get_date_list(dropIfgram=True)
# File 1 - timeseries.h5
ts_file = '{}{}.h5'.format(suffix, os.path.splitext(inps.outfile[0])[0])
metadata['REF_DATE'] = date_list[0]
metadata['FILE_TYPE'] = 'timeseries'
metadata['UNIT'] = 'm'
print('-' * 50)
print('converting phase to range')
phase2range = -1 * float(stack_obj.metadata['WAVELENGTH']) / (4. * np.pi)
ts *= phase2range
print('calculating perpendicular baseline timeseries')
pbase = stack_obj.get_perp_baseline_timeseries(dropIfgram=True)
ts_obj = timeseries(ts_file)
ts_obj.write2hdf5(data=ts, dates=date_list, bperp=pbase, metadata=metadata)
# File 2 - temporalCoherence.h5
out_file = '{}{}.h5'.format(suffix, os.path.splitext(inps.outfile[1])[0])
metadata['FILE_TYPE'] = 'temporalCoherence'
metadata['UNIT'] = '1'
print('-' * 50)
writefile.write(temp_coh, out_file=out_file, metadata=metadata)
# File 3 - timeseriesDecorStd.h5
if not np.all(ts_std == 0.):
out_file = 'timeseriesDecorStd{}.h5'.format(suffix)
metadata['FILE_TYPE'] = 'timeseries'
metadata['UNIT'] = 'm'
ts_std *= abs(phase2range)
print('-' * 50)
writefile.write(ts_std,
out_file=out_file,
metadata=metadata,
ref_file=ts_file)
# File 4 - numInvIfgram.h5
out_file = 'numInvIfgram{}.h5'.format(suffix)
metadata['FILE_TYPE'] = 'mask'
metadata['UNIT'] = '1'
print('-' * 50)
writefile.write(num_inv_ifg, out_file=out_file, metadata=metadata)
return