def test_time_series_unit(self):
"""
Checks that the code works the same as the calculated example
"""
imaster = asarray([1, 1, 2, 2, 3, 3, 4, 5])
islave = asarray([2, 4, 3, 4, 5, 6, 6, 6])
timeseries = asarray([0.0, 0.1, 0.6, 0.8, 1.1, 1.3])
phase = asarray([0.5, 4, 2.5, 3.5, 2.5, 3.5, 2.5, 1])
nan_fraction = asarray([0.5, 0.4, 0.2, 0.3, 0.1, 0.3, 0.2, 0.1])
now = date.today()
dates = [now + timedelta(days=(t * 365.25)) for t in timeseries]
dates.sort()
master = [dates[m_num - 1] for m_num in imaster]
slave = [dates[s_num - 1] for s_num in islave]
self.ifgs = [
SinglePixelIfg(m, s, p, n)
for m, s, p, n in zip(master, slave, phase, nan_fraction)
]
tsincr, tscum, tsvel = time_series(self.ifgs,
params=self.params,
vcmt=self.vcmt,
mst=None)
expected = asarray([[[0.50, 3.0, 4.0, 5.5, 6.5]]])
assert_array_almost_equal(tscum, expected, decimal=2)
def _timeseries_calc(ifg_paths, params, vcmt, tiles, preread_ifgs):
"""
MPI wrapper for time series calculation.
"""
if params[cf.TIME_SERIES_CAL] == 0:
log.info('Time Series Calculation not required')
return
if params[cf.TIME_SERIES_METHOD] == 1:
log.info('Calculating time series using Laplacian Smoothing method')
elif params[cf.TIME_SERIES_METHOD] == 2:
log.info('Calculating time series using SVD method')
output_dir = params[cf.TMPDIR]
process_tiles = mpiops.array_split(tiles)
for t in process_tiles:
log.debug('Calculating time series for tile {}'.format(t.index))
ifg_parts = [shared.IfgPart(p, t, preread_ifgs) for p in ifg_paths]
mst_tile = np.load(
os.path.join(output_dir, 'mst_mat_{}.npy'.format(t.index)))
res = timeseries.time_series(ifg_parts, params, vcmt, mst_tile)
tsincr, tscum, _ = res
np.save(file=os.path.join(output_dir, 'tsincr_{}.npy'.format(t.index)),
arr=tsincr)
np.save(file=os.path.join(output_dir, 'tscuml_{}.npy'.format(t.index)),
arr=tscum)
mpiops.comm.barrier()
def _calc_svd_time_series(ifg_paths, params, preread_ifgs, tiles):
"""
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(
os.path.join(params[cf.TMPDIR], 'mst_mat_{}.npy'.format(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 = mpiops.run_once(_assemble_tsincr, ifg_paths, params,
preread_ifgs, tiles, nvels)
log.debug('Finished calculating time series for spatio-temporal filter')
return tsincr_g
def _calc_svd_time_series(ifg_paths: List[str], params: dict,
preread_ifgs: dict, tiles: List[Tile]) -> np.ndarray:
"""
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 incremental time series via SVD method for APS '
'correction')
# copy params temporarily
new_params = deepcopy(params)
new_params[C.TIME_SERIES_METHOD] = 2 # use SVD method
process_tiles = mpiops.array_split(tiles)
nvels = None
for t in process_tiles:
log.debug(f'Calculating time series for tile {t.index} during APS '
f'correction')
ifgp = [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(ifgp, new_params, vcmt=None, mst=mst_tile)[0]
np.save(file=os.path.join(params[C.TMPDIR],
f'tsincr_aps_{t.index}.npy'),
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 calculate_time_series(ifgs, params, vcmt, mst):
res = timeseries.time_series(ifgs, params, vcmt, mst)
for r in res:
if len(r.shape) != 3:
raise timeseries.TimeSeriesError
tsincr, tscum, tsvel = res
return tsincr, tscum, tsvel