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 _linrate_calc(ifg_paths, params, vcmt, tiles, preread_ifgs):
"""
MPI wrapper for linrate calculation
"""
process_tiles = mpiops.array_split(tiles)
log.info('Calculating rate map from stacking')
output_dir = params[cf.TMPDIR]
for t in process_tiles:
log.debug('Stacking 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 _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 _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: 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 __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)
def _process_dem_error_per_tile(tile: Tile, params: dict) -> None:
"""
Convenience function for processing DEM error in tiles
:param tile: pyrate.core.shared.Tile Class object.
:param params: Dictionary of PyRate configuration parameters.
"""
ifg_paths = [
ifg_path.tmp_sampled_path for ifg_path in params[C.INTERFEROGRAM_FILES]
]
ifg0_path = ifg_paths[0]
ifg0 = Ifg(ifg0_path)
ifg0.open(readonly=True)
# read lon and lat values of multi-looked ifg (first ifg only)
lon, lat = geometry.get_lonlat_coords(ifg0)
# read azimuth and range coords and DEM from tif files generated in prepifg
geom_files = Configuration.geometry_files(params)
rdc_az_file = geom_files['rdc_azimuth']
geom_az = Geometry(rdc_az_file)
rdc_rg_file = geom_files['rdc_range']
geom_rg = Geometry(rdc_rg_file)
dem_file = params[C.DEM_FILE_PATH].sampled_path
dem = DEM(dem_file)
preread_ifgs = params[C.PREREAD_IFGS]
threshold = params[C.DE_PTHR]
ifg_parts = [
shared.IfgPart(p, tile, preread_ifgs, params) for p in ifg_paths
]
lon_parts = lon(tile)
lat_parts = lat(tile)
az_parts = geom_az(tile)
rg_parts = geom_rg(tile)
dem_parts = dem(tile)
log.debug(
f"Calculating per-pixel baseline for tile {tile.index} during DEM error correction"
)
bperp, look_angle, range_dist = _calculate_bperp_wrapper(
ifg_paths, az_parts, rg_parts, lat_parts, lon_parts, dem_parts)
log.debug(
f"Calculating DEM error for tile {tile.index} during DEM error correction"
)
# mst_tile = np.load(Configuration.mst_path(params, tile.index))
# calculate the DEM error estimate and the correction values for each IFG
# current implementation uses the look angle and range distance matrix of the primary SLC in the last IFG
# todo: check the impact of using the same information from another SLC
dem_error, dem_error_correction, _ = calc_dem_errors(
ifg_parts, bperp, look_angle, range_dist, threshold)
# dem_error contains the estimated DEM error for each pixel (i.e. the topographic change relative to the DEM)
# size [row, col]
# dem_error_correction contains the correction value for each interferogram
# size [num_ifg, row, col]
# save tiled data in tmpdir
np.save(file=os.path.join(params[C.TMPDIR],
'dem_error_{}.npy'.format(tile.index)),
arr=dem_error)
# swap the axes of 3D array to fit the style used in function assemble_tiles
tmp_array = np.moveaxis(dem_error_correction, 0, -1)
# new dimension is [row, col, num_ifg]
# save tiled data into tmpdir
np.save(file=os.path.join(
params[C.TMPDIR], 'dem_error_correction_{}.npy'.format(tile.index)),
arr=tmp_array)
# Calculate and save the average perpendicular baseline for the tile
bperp_avg = np.nanmean(bperp, axis=(1, 2), dtype=np.float64)
np.save(file=os.path.join(params[C.TMPDIR],
'bperp_avg_{}.npy'.format(tile.index)),
arr=bperp_avg)