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)
# loop for calc
print('\n' + '-' * 50)
print(
'calculating solid Earth tides using solid.for (D. Milbert, 2018) ...')
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,
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
atr = readfile.read_attribute(geom_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
print(os.path.abspath(__file__))
# prepare inputs
dt_obj = dt.datetime(2020, 12, 25, 14, 7, 44)
atr = {
'LENGTH': 400,
'WIDTH': 500,
'X_FIRST': -118.2,
'Y_FIRST': 33.8,
'X_STEP': 0.000833333,
'Y_STEP': -0.000833333,
}
# calculate
(tide_e, tide_n,
tide_u) = pysolid.calc_solid_earth_tides_grid(dt_obj, atr, verbose=True)
# plot
out_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'pic'))
os.makedirs(out_dir, exist_ok=True)
out_fig = os.path.join(out_dir, 'grid.png')
pysolid.plot_solid_earth_tides_grid(tide_e,
tide_n,
tide_u,
dt_obj,
out_fig=out_fig,
display=False)
# open the plotted figures
if sys.platform in ['linux']: