def save_linrate(ifgs_dict, params, tiles, out_type):
"""
Save linear rate outputs.
:param ifgs_dict: xxxx
:param params: xxxx
:param tiles: xxxx
:param out_type: xxxx
:return xxxx
"""
log.info('Starting PyRate postprocessing {}'.format(out_type))
gt, md, wkt = ifgs_dict['gt'], ifgs_dict['md'], ifgs_dict['wkt']
epochlist = ifgs_dict['epochlist']
ifgs = [v for v in ifgs_dict.values() if isinstance(v, PrereadIfg)]
dest = os.path.join(params[cf.OUT_DIR], out_type + ".tif")
md[ifc.EPOCH_DATE] = epochlist.dates
if out_type == 'linrate':
md[ifc.DATA_TYPE] = ifc.LINRATE
elif out_type == 'linerror':
md[ifc.DATA_TYPE] = ifc.LINERROR
else:
md[ifc.DATA_TYPE] = ifc.LINSAMP
rate = np.zeros(shape=ifgs[0].shape, dtype=np.float32)
for t in tiles:
rate_file = os.path.join(params[cf.TMPDIR],
out_type + '_{}.npy'.format(t.index))
rate_tile = np.load(file=rate_file)
rate[t.top_left_y:t.bottom_right_y,
t.top_left_x:t.bottom_right_x] = rate_tile
shared.write_output_geotiff(md, gt, wkt, rate, dest, np.nan)
npy_rate_file = os.path.join(params[cf.OUT_DIR], out_type + '.npy')
np.save(file=npy_rate_file, arr=rate)
log.info('Finished PyRate postprocessing {}'.format(out_type))
def write_timeseries_geotiff(ifgs, params, tsincr, pr_type):
# setup metadata for writing into result files
gt, md, wkt = get_geotiff_header_info(ifgs[0].data_path)
epochlist = algorithm.get_epochs(ifgs)[0]
for i in range(tsincr.shape[2]):
md[ifc.EPOCH_DATE] = epochlist.dates[i + 1]
md['SEQUENCE_POSITION'] = i + 1 # sequence position
data = tsincr[:, :, i]
dest = join(params[cf.OUT_DIR],
pr_type + "_" + str(epochlist.dates[i + 1]) + ".tif")
md[ifc.DATA_TYPE] = pr_type
write_output_geotiff(md, gt, wkt, data, dest, np.nan)
def write_linrate_tifs(ifgs, params, res):
# log.info('Writing linrate results')
rate, error, samples = res
gt, md, wkt = get_geotiff_header_info(ifgs[0].data_path)
epochlist = algorithm.get_epochs(ifgs)[0]
dest = join(params[cf.OUT_DIR], "linrate.tif")
md[ifc.EPOCH_DATE] = epochlist.dates
md[ifc.DATA_TYPE] = ifc.LINRATE
write_output_geotiff(md, gt, wkt, rate, dest, np.nan)
dest = join(params[cf.OUT_DIR], "linerror.tif")
md[ifc.DATA_TYPE] = ifc.LINERROR
write_output_geotiff(md, gt, wkt, error, dest, np.nan)
dest = join(params[cf.OUT_DIR], "linsamples.tif")
md[ifc.DATA_TYPE] = ifc.LINSAMP
write_output_geotiff(md, gt, wkt, samples, dest, np.nan)
write_linrate_numpy_files(error, rate, samples, params)
def postprocess_timeseries(rows, cols, params):
"""
Postprocess time series output.
:param rows: xxxx
:param cols: xxxx
:param params: xxxx
:return xxxx
"""
# pylint: disable=too-many-locals
xlks, _, crop = cf.transform_params(params)
base_unw_paths = cf.original_ifg_paths(params[cf.IFG_FILE_LIST])
dest_tifs = cf.get_dest_paths(base_unw_paths, crop, params, xlks)
output_dir = params[cf.TMPDIR]
# load previously saved prepread_ifgs dict
preread_ifgs_file = join(output_dir, 'preread_ifgs.pk')
ifgs = cp.load(open(preread_ifgs_file, 'rb'))
# metadata and projections
gt, md, wkt = ifgs['gt'], ifgs['md'], ifgs['wkt']
epochlist = ifgs['epochlist']
ifgs = [v for v in ifgs.values() if isinstance(v, PrereadIfg)]
tiles = run_pyrate.get_tiles(dest_tifs[0], rows, cols)
# load the first tsincr file to determine the number of time series tifs
tsincr_file = os.path.join(output_dir, 'tsincr_0.npy')
tsincr = np.load(file=tsincr_file)
# pylint: disable=no-member
no_ts_tifs = tsincr.shape[2]
# we create 2 x no_ts_tifs as we are splitting tsincr and tscuml
# to all processes.
process_tifs = mpiops.array_split(range(2 * no_ts_tifs))
# depending on nvelpar, this will not fit in memory
# e.g. nvelpar=100, nrows=10000, ncols=10000, 32bit floats need 40GB memory
# 32 * 100 * 10000 * 10000 / 8 bytes = 4e10 bytes = 40 GB
# the double for loop helps us overcome the memory limit
log.info('process {} will write {} ts (incr/cuml) tifs of '
'total {}'.format(mpiops.rank, len(process_tifs), no_ts_tifs * 2))
for i in process_tifs:
tscum_g = np.empty(shape=ifgs[0].shape, dtype=np.float32)
if i < no_ts_tifs:
for n, t in enumerate(tiles):
tscum_file = os.path.join(output_dir,
'tscuml_{}.npy'.format(n))
tscum = np.load(file=tscum_file)
md[ifc.EPOCH_DATE] = epochlist.dates[i + 1]
# sequence position; first time slice is #0
md['SEQUENCE_POSITION'] = i + 1
tscum_g[t.top_left_y:t.bottom_right_y,
t.top_left_x:t.bottom_right_x] = tscum[:, :, i]
dest = os.path.join(
params[cf.OUT_DIR],
'tscuml' + "_" + str(epochlist.dates[i + 1]) + ".tif")
md[ifc.DATA_TYPE] = ifc.CUML
shared.write_output_geotiff(md, gt, wkt, tscum_g, dest, np.nan)
else:
tsincr_g = np.empty(shape=ifgs[0].shape, dtype=np.float32)
i %= no_ts_tifs
for n, t in enumerate(tiles):
tsincr_file = os.path.join(output_dir,
'tsincr_{}.npy'.format(n))
tsincr = np.load(file=tsincr_file)
md[ifc.EPOCH_DATE] = epochlist.dates[i + 1]
# sequence position; first time slice is #0
md['SEQUENCE_POSITION'] = i + 1
tsincr_g[t.top_left_y:t.bottom_right_y,
t.top_left_x:t.bottom_right_x] = tsincr[:, :, i]
dest = os.path.join(
params[cf.OUT_DIR],
'tsincr' + "_" + str(epochlist.dates[i + 1]) + ".tif")
md[ifc.DATA_TYPE] = ifc.INCR
shared.write_output_geotiff(md, gt, wkt, tsincr_g, dest,
np.nan)
log.info('process {} finished writing {} ts (incr/cuml) tifs of '
'total {}'.format(mpiops.rank, len(process_tifs), no_ts_tifs * 2))