def _stacking_for_tile(tile, params):
"""
Wrapper for stacking calculation on a single tile
"""
preread_ifgs = params[C.PREREAD_IFGS]
vcmt = params[C.VCMT]
ifg_paths = [
ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]
]
output_dir = params[C.TMPDIR]
log.debug(f"Stacking of tile {tile.index}")
ifg_parts = [
shared.IfgPart(p, tile, preread_ifgs, params) for p in ifg_paths
]
mst_tile = np.load(Configuration.mst_path(params, tile.index))
rate, error, samples = stack_rate_array(ifg_parts, params, vcmt, mst_tile)
np.save(file=os.path.join(output_dir,
'stack_rate_{}.npy'.format(tile.index)),
arr=rate)
np.save(file=os.path.join(output_dir,
'stack_error_{}.npy'.format(tile.index)),
arr=error)
np.save(file=os.path.join(output_dir,
'stack_samples_{}.npy'.format(tile.index)),
arr=samples)
def _calc_svd_time_series(ifg_paths, params, preread_ifgs,
tiles: List[shared.Tile]):
"""
Helper function to obtain time series for spatio-temporal filter
using SVD method
"""
# Is there other existing functions that can perform this same job?
log.info('Calculating time series via SVD method for ' 'APS correction')
# copy params temporarily
new_params = deepcopy(params)
new_params[cf.TIME_SERIES_METHOD] = 2 # use SVD method
process_tiles = mpiops.array_split(tiles)
nvels = None
for t in process_tiles:
log.debug(
'Calculating time series for tile {} during APS correction'.format(
t.index))
ifg_parts = [
shared.IfgPart(p, t, preread_ifgs, params) for p in ifg_paths
]
mst_tile = np.load(Configuration.mst_path(params, t.index))
tsincr = time_series(ifg_parts, new_params, vcmt=None, mst=mst_tile)[0]
np.save(file=os.path.join(params[cf.TMPDIR],
'tsincr_aps_{}.npy'.format(t.index)),
arr=tsincr)
nvels = tsincr.shape[2]
nvels = mpiops.comm.bcast(nvels, root=0)
mpiops.comm.barrier()
# need to assemble tsincr from all processes
tsincr_g = _assemble_tsincr(ifg_paths, params, preread_ifgs, tiles, nvels)
log.debug('Finished calculating time series for spatio-temporal filter')
return tsincr_g
def __run_once(self):
tiles = self.params[cf.TILES]
mst_files = [Configuration.mst_path(self.params, t.index) for t in tiles]
correct._copy_mlooked(self.params)
correct._create_ifg_dict(self.params)
save_numpy_phase(self.ifg_paths, self.params)
mst.mst_calc_wrapper(self.params)
assert all(m.exists() for m in mst_files)
return [os.stat(o).st_mtime for o in mst_files]
def _save_mst_tile(tile: Tile, params: dict) -> None:
"""
Convenient inner loop for mst tile saving
"""
preread_ifgs = params[cf.PREREAD_IFGS]
dest_tifs = [ifg_path.tmp_sampled_path for ifg_path in params[cf.INTERFEROGRAM_FILES]]
mst_file_process_n = Configuration.mst_path(params, index=tile.index)
if mst_file_process_n.exists():
return
mst_tile = mst_multiprocessing(tile, dest_tifs, preread_ifgs, params)
# locally save the mst_mat
np.save(file=mst_file_process_n, arr=mst_tile)
def __calc_time_series_for_tile(tile, params):
"""
Wrapper for time series calculation on a single tile
"""
preread_ifgs = params[cf.PREREAD_IFGS]
vcmt = params[cf.VCMT]
ifg_paths = [
ifg_path.tmp_sampled_path
for ifg_path in params[cf.INTERFEROGRAM_FILES]
]
output_dir = params[cf.TMPDIR]
log.debug(f"Calculating time series for tile {tile.index}")
ifg_parts = [
shared.IfgPart(p, tile, preread_ifgs, params) for p in ifg_paths
]
mst_tile = np.load(Configuration.mst_path(params, tile.index))
tsincr, tscuml, _ = time_series(ifg_parts, params, vcmt, mst_tile)
np.save(file=os.path.join(output_dir, 'tscuml_{}.npy'.format(tile.index)),
arr=tscuml)
# optional save of tsincr npy tiles
if params["savetsincr"] == 1:
np.save(file=os.path.join(output_dir,
'tsincr_{}.npy'.format(tile.index)),
arr=tsincr)
tscuml = np.insert(tscuml, 0, 0,
axis=2) # add zero epoch to tscuml 3D array
log.info('Calculating linear regression of cumulative time series')
linrate, intercept, r_squared, std_err, samples = linear_rate_array(
tscuml, ifg_parts, params)
np.save(file=os.path.join(output_dir,
'linear_rate_{}.npy'.format(tile.index)),
arr=linrate)
np.save(file=os.path.join(output_dir,
'linear_intercept_{}.npy'.format(tile.index)),
arr=intercept)
np.save(file=os.path.join(output_dir,
'linear_rsquared_{}.npy'.format(tile.index)),
arr=r_squared)
np.save(file=os.path.join(output_dir,
'linear_error_{}.npy'.format(tile.index)),
arr=std_err)
np.save(file=os.path.join(output_dir,
'linear_samples_{}.npy'.format(tile.index)),
arr=samples)