def timeseries_calc(ifg_paths, params, vcmt, tiles, preread_ifgs):
"""
Time series calculation.
:param ifg_paths: List of interferogram paths
:param params: Parameters dictionary corresponding to config file
:param vcmt: vcmt array
:param tiles: List of all tiles used during MPI processes
:param preread_ifgs: Dictionary containing interferogram characteristics for efficient computing
:return xxxx
"""
process_tiles = mpiops.array_split(tiles)
log.info('Calculating time series')
output_dir = params[cf.TMPDIR]
for t in process_tiles:
log.info('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 linrate_calc(ifg_paths, params, vcmt, tiles, preread_ifgs):
"""
MPI capable linrate calculation.
:param ifg_paths: List of interferogram paths
:param params: Parameters dictionary corresponding to config file
:param vcmt: vcmt array
:param tiles: List of all tiles used during MPI processes
:param preread_ifgs: Dictionary containing interferogram characteristics for efficient computing
:return xxxx
"""
process_tiles = mpiops.array_split(tiles)
log.info('Calculating linear rate')
output_dir = params[cf.TMPDIR]
for t in process_tiles:
log.info('calculating lin rate of tile {}'.format(t.index))
ifg_parts = [shared.IfgPart(p, t, preread_ifgs) for p in ifg_paths]
mst_grid_n = np.load(
os.path.join(output_dir, 'mst_mat_{}.npy'.format(t.index)))
rate, error, samples = linrate.linear_rate(ifg_parts, params, vcmt,
mst_grid_n)
# declare file names
np.save(file=os.path.join(output_dir,
'linrate_{}.npy'.format(t.index)),
arr=rate)
np.save(file=os.path.join(output_dir,
'linerror_{}.npy'.format(t.index)),
arr=error)
np.save(file=os.path.join(output_dir,
'linsamples_{}.npy'.format(t.index)),
arr=samples)
mpiops.comm.barrier()
def _linrate_calc(ifg_paths, params, vcmt, tiles, preread_ifgs):
"""
MPI wrapper for linrate calculation
"""
process_tiles = mpiops.array_split(tiles)
log.info('Calculating linear rate map')
output_dir = params[cf.TMPDIR]
for t in process_tiles:
log.info('Calculating linear rate of tile {}'.format(t.index))
ifg_parts = [shared.IfgPart(p, t, preread_ifgs) for p in ifg_paths]
mst_grid_n = np.load(os.path.join(output_dir,
'mst_mat_{}.npy'.format(t.index)))
rate, error, samples = linrate.linear_rate(ifg_parts, params,
vcmt, mst_grid_n)
# declare file names
np.save(file=os.path.join(output_dir,
'linrate_{}.npy'.format(t.index)),
arr=rate)
np.save(file=os.path.join(output_dir,
'linerror_{}.npy'.format(t.index)),
arr=error)
np.save(file=os.path.join(output_dir,
'linsamples_{}.npy'.format(t.index)),
arr=samples)
mpiops.comm.barrier()
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.info('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 '
'spatio-temporal filter')
# copy params temporarily
new_params = deepcopy(params)
new_params[cf.TIME_SERIES_METHOD] = 2 # use SVD method
process_tiles = mpiops.array_split(tiles)
output_dir = params[cf.TMPDIR]
nvels = None
for t in process_tiles:
log.info('Calculating time series for tile {} during aps '
'correction'.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)))
tsincr = time_series(ifg_parts, new_params, vcmt=None, mst=mst_tile)[0]
np.save(file=os.path.join(output_dir,
'tsincr_aps_{}.npy'.format(t.index)),
arr=tsincr)
nvels = tsincr.shape[2]
nvels = mpiops.comm.bcast(nvels, root=0)
# need to assemble tsincr from all processes
tsincr_g = mpiops.run_once(_assemble_tsincr, ifg_paths, params,
preread_ifgs, tiles, nvels)
log.info('Finished calculating time series for spatio-temporal filter')
return tsincr_g