def calc_temporal_coherence(ifgram, G, X):
"""Calculate the temporal coherence from the network inversion results
Parameters: ifgram - 2D np.array in size of (num_ifgram, num_pixel), phase or offset
G - 2D np.array in size of (num_ifgram, num_date-1), design matrix A or B
X - 2D np.array in size of (num_date-1, num_pixel), solution
Returns: temp_coh - 1D np.array in size of (num_pixel), temporal coherence
"""
num_ifgram, num_pixel = ifgram.shape
temp_coh = np.zeros(num_pixel, dtype=np.float32)
# chunk_size as the number of pixels
chunk_size = int(ut.round_to_1(2e5 / num_ifgram))
if num_pixel > chunk_size:
num_chunk = int(np.ceil(num_pixel / chunk_size))
num_chunk_step = int(ut.round_to_1(num_chunk / 5))
print(('calcualting temporal coherence in chunks of {} pixels'
': {} chunks in total ...').format(chunk_size, num_chunk))
for i in range(num_chunk):
c0 = i * chunk_size
c1 = min((i + 1) * chunk_size, num_pixel)
# calc residual
ifgram_diff = ifgram[:, c0:c1] - np.dot(G, X[:, c0:c1])
# calc temporal coherence
temp_coh[c0:c1] = np.abs(np.sum(np.exp(1j * ifgram_diff),
axis=0)) / num_ifgram
# print out message
if (i + 1) % num_chunk_step == 0:
print('chunk {} / {}'.format(i + 1, num_chunk))
else:
# calc residual
ifgram_diff = ifgram - np.dot(G, X)
# calc temporal coherence
temp_coh = np.abs(np.sum(np.exp(1j * ifgram_diff),
axis=0)) / num_ifgram
return temp_coh
def split2boxes(ts_file,
geom_file=None,
memory_size=4,
num_step=0,
print_msg=True):
"""Split into chunks in rows to reduce memory usage
Parameters: ts_file - str, path of time-series h5 file
memory_size - float, max memory to use in GB
print_msg - bool
Returns: box_list - list of tuple of 4 int
num_box - int, number of boxes
"""
ts_obj = timeseries(ts_file)
ts_obj.open(print_msg=False)
length, width = ts_obj.length, ts_obj.width
# 1st dimension size: ts (obs / cor / res / step) + dem_err/inc_angle/rg_dist (+pbase)
num_epoch = ts_obj.numDate * 3 + num_step + 3
if geom_file:
geom_obj = geometry(geom_file)
geom_obj.open(print_msg=False)
if 'bperp' in geom_obj.datasetNames:
num_epoch += ts_obj.numDate
# split in lines based on the input memory limit
y_step = (memory_size * (1e3**3)) / (num_epoch * width * 4)
# calibrate based on experience
y_step = int(ut.round_to_1(y_step * 0.7))
num_box = int((length - 1) / y_step) + 1
if print_msg and num_box > 1:
print('maximum memory size: %.1E GB' % memory_size)
print('split %d lines into %d patches for processing' %
(length, num_box))
print(' with each patch up to %d lines' % y_step)
# y_step / num_box --> box_list
box_list = []
for i in range(num_box):
y0 = i * y_step
y1 = min([length, y0 + y_step])
box = (0, y0, width, y1)
box_list.append(box)
return box_list, num_box
def split2boxes(ifgram_file, memory_size=4, print_msg=True):
"""Split into chunks in rows to reduce memory usage
Parameters: dataset_shape - tuple of 3 int
memory_size - float, max memory to use in GB
print_msg - bool
Returns: box_list - list of tuple of 4 int
num_box - int, number of boxes
"""
ifg_obj = ifgramStack(ifgram_file)
ifg_obj.open(print_msg=False)
# 1st dimension size: defo obs (phase / offset) + weight + time-series
num_epoch = ifg_obj.numIfgram * 2 + ifg_obj.numDate + 5
length, width = ifg_obj.length, ifg_obj.width
# split in lines based on the input memory limit
y_step = (memory_size * (1e3**3)) / (num_epoch * width * 4)
# calibrate based on experience
y_step = int(ut.round_to_1(y_step * 0.6))
num_box = int((length - 1) / y_step) + 1
if print_msg and num_box > 1:
print('maximum memory size: %.1E GB' % memory_size)
print('split %d lines into %d patches for processing' %
(length, num_box))
print(' with each patch up to %d lines' % y_step)
# y_step / num_box --> box_list
box_list = []
for i in range(num_box):
y0 = i * y_step
y1 = min([length, y0 + y_step])
box = (0, y0, width, y1)
box_list.append(box)
return box_list, num_box
def calc_solid_earth_tides_timeseries(ts_file,
geom_file,
set_file,
date_wise_acq_time=False,
update_mode=True,
verbose=False):
"""Calculate the time-series of solid Earth tides (SET) in LOS direction.
Parameters: ts_file - str, path of the time-series HDF5 file
geom_file - str, path of the geometry HDF5 file
set_file - str, output SET time-sereis file
date_wise_acq_time - bool, use the exact date-wise acquisition time
Returns: set_file - str, output SET time-sereis file
"""
if update_mode and os.path.isfile(set_file):
print('update mode: ON')
print('skip re-calculating and use existing file: {}'.format(set_file))
return set_file
# prepare LOS geometry: geocoding if in radar-coordinates
inc_angle, head_angle, atr_geo = prepare_los_geometry(geom_file)
# get LOS unit vector
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
unit_vec = [
np.sin(inc_angle) * np.cos(head_angle) * -1,
np.sin(inc_angle) * np.sin(head_angle),
np.cos(inc_angle),
]
# prepare datetime
dt_objs = get_datetime_list(ts_file, date_wise_acq_time=date_wise_acq_time)
# initiate data matrix
num_date = len(dt_objs)
length = int(atr_geo['LENGTH'])
width = int(atr_geo['WIDTH'])
ts_tide = np.zeros((num_date, length, width), dtype=np.float32)
# default step size in meter: ~30 pixels
step_size = ut.round_to_1(abs(float(atr_geo['Y_STEP'])) * 108e3 * 30)
# loop for calc
print('\n' + '-' * 50)
print(
'calculating solid Earth tides using PySolid (Milbert, 2018; Yunjun et al., 2022) ...'
)
prog_bar = ptime.progressBar(maxValue=num_date, print_msg=not verbose)
for i, dt_obj in enumerate(dt_objs):
# calculate tide in ENU direction
(tide_e, tide_n,
tide_u) = pysolid.calc_solid_earth_tides_grid(dt_obj,
atr_geo,
step_size=step_size,
display=False,
verbose=verbose)
# convert ENU to LOS direction
# sign convention: positive for motion towards satellite
ts_tide[i, :, :] = (tide_e * unit_vec[0] + tide_n * unit_vec[1] +
tide_u * unit_vec[2])
prog_bar.update(i + 1,
suffix='{} ({}/{})'.format(dt_obj.isoformat(), i + 1,
num_date))
prog_bar.close()
# radar-coding if input in radar-coordinates
# use ts_file to avoid potential missing CENTER_LINE_UTC attributes in geom_file from alosStack
atr = readfile.read_attribute(ts_file)
if 'Y_FIRST' not in atr.keys():
print('radar-coding the LOS tides time-series ...')
res_obj = resample(lut_file=geom_file)
res_obj.open()
res_obj.src_meta = atr_geo
res_obj.prepare()
# resample data
box = res_obj.src_box_list[0]
ts_tide = res_obj.run_resample(src_data=ts_tide[:, box[1]:box[3],
box[0]:box[2]])
## output
# attribute
atr['FILE_TYPE'] = 'timeseries'
atr['UNIT'] = 'm'
for key in ['REF_Y', 'REF_X', 'REF_DATE']:
if key in atr.keys():
atr.pop(key)
# write
ds_dict = {}
ds_dict['timeseries'] = ts_tide
ds_dict['sensingMid'] = np.array(
[i.strftime('%Y%m%dT%H%M%S') for i in dt_objs], dtype=np.string_)
writefile.write(ds_dict, out_file=set_file, metadata=atr, ref_file=ts_file)
return set_file
