def setUpClass(cls):
rate_types = ['linrate', 'linerror', 'linsamples']
cls.tif_dir = tempfile.mkdtemp()
cls.test_conf = common.TEST_CONF_GAMMA
# change the required params
params = cf.get_config_params(cls.test_conf)
params[cf.OBS_DIR] = common.SML_TEST_GAMMA
params[cf.PROCESSOR] = 1 # gamma
params[cf.IFG_FILE_LIST] = os.path.join(common.SML_TEST_GAMMA,
'ifms_17')
params[cf.OUT_DIR] = cls.tif_dir
params[cf.PARALLEL] = 0
params[cf.APS_CORRECTION] = False
params[cf.TMPDIR] = os.path.join(params[cf.OUT_DIR], cf.TMPDIR)
xlks, ylks, crop = cf.transform_params(params)
# base_unw_paths need to be geotiffed and multilooked by run_prepifg
base_unw_paths = cf.original_ifg_paths(params[cf.IFG_FILE_LIST])
# dest_paths are tifs that have been geotif converted and multilooked
cls.dest_paths = cf.get_dest_paths(base_unw_paths, crop, params, xlks)
run_prepifg.gamma_prepifg(base_unw_paths, params)
tiles = run_pyrate.get_tiles(cls.dest_paths[0], 3, 3)
ifgs = common.small_data_setup()
cls.refpixel_p, cls.maxvar_p, cls.vcmt_p = \
run_pyrate.process_ifgs(cls.dest_paths, params, 3, 3)
cls.mst_p = common.reconstruct_mst(ifgs[0].shape, tiles,
params[cf.TMPDIR])
cls.rate_p, cls.error_p, cls.samples_p = [
common.reconstruct_linrate(ifgs[0].shape, tiles, params[cf.TMPDIR],
t) for t in rate_types
]
# now create the non parallel version
cls.tif_dir_s = tempfile.mkdtemp()
params[cf.PARALLEL] = 0
params[cf.OUT_DIR] = cls.tif_dir_s
params[cf.TMPDIR] = os.path.join(params[cf.OUT_DIR], cf.TMPDIR)
cls.dest_paths_s = cf.get_dest_paths(base_unw_paths, crop, params,
xlks)
run_prepifg.gamma_prepifg(base_unw_paths, params)
cls.refpixel, cls.maxvar, cls.vcmt = \
run_pyrate.process_ifgs(cls.dest_paths_s, params, 3, 3)
cls.mst = common.reconstruct_mst(ifgs[0].shape, tiles,
params[cf.TMPDIR])
cls.rate, cls.error, cls.samples = [
common.reconstruct_linrate(ifgs[0].shape, tiles, params[cf.TMPDIR],
t) for t in rate_types
]
def test_timeseries_linrate_mpi(mpisync, tempdir, modify_config,
ref_est_method, row_splits, col_splits,
get_crop, orbfit_lks, orbfit_method,
orbfit_degrees):
params = modify_config
outdir = mpiops.run_once(tempdir)
params[cf.OUT_DIR] = outdir
params[cf.TMPDIR] = os.path.join(params[cf.OUT_DIR], cf.TMPDIR)
params[cf.DEM_HEADER_FILE] = SML_TEST_DEM_HDR_GAMMA
params[cf.REF_EST_METHOD] = ref_est_method
params[cf.IFG_CROP_OPT] = get_crop
params[cf.ORBITAL_FIT_LOOKS_Y] = orbfit_lks
params[cf.ORBITAL_FIT_LOOKS_X] = orbfit_lks
params[cf.ORBITAL_FIT_METHOD] = orbfit_method
params[cf.ORBITAL_FIT_DEGREE] = orbfit_degrees
xlks, ylks, crop = cf.transform_params(params)
if xlks * col_splits > 45 or ylks * row_splits > 70:
print('skipping test because lks and col_splits are not compatible')
return
# skip some tests in travis to run CI faster
if TRAVIS and (xlks % 2 or row_splits % 2 or col_splits % 2
or orbfit_lks % 2):
print('Skipping in travis env for faster CI run')
return
print("xlks={}, ref_est_method={}, row_splits={}, col_splits={}, "
"get_crop={}, orbfit_lks={}, orbfit_method={}, "
"rank={}".format(xlks, ref_est_method, row_splits, col_splits,
get_crop, orbfit_lks, orbfit_method, orbfit_degrees,
mpiops.rank))
base_unw_paths = cf.original_ifg_paths(params[cf.IFG_FILE_LIST])
# dest_paths are tifs that have been geotif converted and multilooked
dest_paths = cf.get_dest_paths(base_unw_paths, crop, params, xlks)
# run prepifg, create the dest_paths files
if mpiops.rank == 0:
run_prepifg.gamma_prepifg(base_unw_paths, params)
mpiops.comm.barrier()
(refpx,
refpy), maxvar, vcmt = run_pyrate.process_ifgs(ifg_paths=dest_paths,
params=params,
rows=row_splits,
cols=col_splits)
tiles = mpiops.run_once(run_pyrate.get_tiles,
dest_paths[0],
rows=row_splits,
cols=col_splits)
postprocessing.postprocess_linrate(row_splits, col_splits, params)
postprocessing.postprocess_timeseries(row_splits, col_splits, params)
ifgs_mpi_out_dir = params[cf.OUT_DIR]
ifgs_mpi = small_data_setup(datafiles=dest_paths)
# single process timeseries/linrate calculation
if mpiops.rank == 0:
params_old = modify_config
params_old[cf.OUT_DIR] = tempdir()
params_old[cf.REF_EST_METHOD] = ref_est_method
params_old[cf.IFG_CROP_OPT] = get_crop
params_old[cf.ORBITAL_FIT_LOOKS_Y] = orbfit_lks
params_old[cf.ORBITAL_FIT_LOOKS_X] = orbfit_lks
params_old[cf.ORBITAL_FIT_METHOD] = orbfit_method
params_old[cf.ORBITAL_FIT_DEGREE] = orbfit_degrees
xlks, ylks, crop = cf.transform_params(params_old)
base_unw_paths = cf.original_ifg_paths(params_old[cf.IFG_FILE_LIST])
dest_paths = cf.get_dest_paths(base_unw_paths, crop, params_old, xlks)
run_prepifg.gamma_prepifg(base_unw_paths, params_old)
ifgs = shared.pre_prepare_ifgs(dest_paths, params_old)
mst_grid = tests.common.mst_calculation(dest_paths, params_old)
refy, refx = refpixel.ref_pixel(ifgs, params_old)
assert (refx == refpx) and (refy == refpy) # both must match
pyrate.orbital.remove_orbital_error(ifgs, params_old)
ifgs = common.prepare_ifgs_without_phase(dest_paths, params_old)
rpe.estimate_ref_phase(ifgs, params_old, refx, refy)
ifgs = shared.pre_prepare_ifgs(dest_paths, params_old)
maxvar_s = [vcm.cvd(i, params_old)[0] for i in ifgs]
vcmt_s = vcm.get_vcmt(ifgs, maxvar)
tsincr, tscum, _ = tests.common.compute_time_series(
ifgs, mst_grid, params, vcmt)
rate, error, samples = tests.common.calculate_linear_rate(
ifgs, params_old, vcmt, mst_grid)
mst_mpi = reconstruct_mst(ifgs[0].shape, tiles, params[cf.TMPDIR])
np.testing.assert_array_almost_equal(mst_grid, mst_mpi)
tsincr_mpi, tscum_mpi = reconstruct_times_series(
ifgs[0].shape, tiles, params[cf.TMPDIR])
rate_mpi, error_mpi, samples_mpi = \
[reconstruct_linrate(ifgs[0].shape, tiles, params[cf.TMPDIR], t)
for t in ['linrate', 'linerror', 'linsamples']]
np.testing.assert_array_almost_equal(maxvar, maxvar_s)
np.testing.assert_array_almost_equal(vcmt, vcmt_s)
for i, j in zip(ifgs, ifgs_mpi):
np.testing.assert_array_almost_equal(i.phase_data, j.phase_data)
np.testing.assert_array_almost_equal(tsincr, tsincr_mpi, decimal=4)
np.testing.assert_array_almost_equal(tscum, tscum_mpi, decimal=4)
np.testing.assert_array_almost_equal(rate, rate_mpi, decimal=4)
np.testing.assert_array_almost_equal(error, error_mpi, decimal=4)
np.testing.assert_array_almost_equal(samples, samples_mpi, decimal=4)
# assert linear rate output tifs are same
_tifs_same(ifgs_mpi_out_dir, params_old[cf.OUT_DIR], 'linrate.tif')
_tifs_same(ifgs_mpi_out_dir, params_old[cf.OUT_DIR], 'linerror.tif')
_tifs_same(ifgs_mpi_out_dir, params_old[cf.OUT_DIR], 'linsamples.tif')
# assert time series output tifs are same
epochlist = algorithm.get_epochs(ifgs)[0]
for i in range(tsincr.shape[2]):
_tifs_same(ifgs_mpi_out_dir, params_old[cf.OUT_DIR],
'tsincr' + '_' + str(epochlist.dates[i + 1]) + ".tif")
# 12 timeseries outputs
assert i + 1 == tsincr.shape[2]
shutil.rmtree(ifgs_mpi_out_dir) # remove mpi out dir
shutil.rmtree(params_old[cf.OUT_DIR]) # remove serial out dir
def setUpClass(cls):
rate_types = ['stack_rate', 'stack_error', 'stack_samples']
cls.tif_dir = tempfile.mkdtemp()
cls.test_conf = common.TEST_CONF_GAMMA
from pyrate.configuration import Configuration
# change the required params
params = Configuration(cls.test_conf).__dict__
params[cf.OBS_DIR] = common.SML_TEST_GAMMA
params[cf.PROCESSES] = 4
params[cf.PROCESSOR] = 1 # gamma
params[cf.IFG_FILE_LIST] = os.path.join(common.SML_TEST_GAMMA,
'ifms_17')
params[cf.OUT_DIR] = cls.tif_dir
params[cf.PARALLEL] = 1
params[cf.APS_CORRECTION] = False
params[cf.TMPDIR] = os.path.join(params[cf.OUT_DIR], cf.TMPDIR)
rows, cols = params["rows"], params["cols"]
# xlks, ylks, crop = cf.transform_params(params)
# base_unw_paths need to be geotiffed by converttogeotif
# and multilooked by run_prepifg
base_unw_paths = list(cf.parse_namelist(params[cf.IFG_FILE_LIST]))
multi_paths = [
MultiplePaths(params[cf.OUT_DIR],
b,
ifglksx=params[cf.IFG_LKSX],
ifgcropopt=params[cf.IFG_CROP_OPT])
for b in base_unw_paths
]
# dest_paths are tifs that have been geotif converted and multilooked
cls.converted_paths = [b.converted_path for b in multi_paths]
cls.sampled_paths = [b.sampled_path for b in multi_paths]
from copy import copy
orig_params = copy(params)
conv2tif.main(params)
prepifg.main(orig_params)
tiles = pyrate.core.shared.get_tiles(cls.sampled_paths[0], rows, cols)
ifgs = common.small_data_setup()
params[cf.INTERFEROGRAM_FILES] = multi_paths
cls.refpixel_p, cls.maxvar_p, cls.vcmt_p = process.process_ifgs(
cls.sampled_paths, params, rows, cols)
cls.mst_p = common.reconstruct_mst(ifgs[0].shape, tiles,
params[cf.TMPDIR])
cls.rate_p, cls.error_p, cls.samples_p = \
[common.reconstruct_stack_rate(ifgs[0].shape, tiles, params[cf.TMPDIR], t) for t in rate_types]
common.remove_tifs(params[cf.OBS_DIR])
# now create the non parallel version
cls.tif_dir_s = tempfile.mkdtemp()
params[cf.PARALLEL] = 0
params[cf.PROCESSES] = 1
params[cf.OUT_DIR] = cls.tif_dir_s
params[cf.TMPDIR] = os.path.join(params[cf.OUT_DIR], cf.TMPDIR)
multi_paths = [
MultiplePaths(params[cf.OUT_DIR],
b,
ifglksx=params[cf.IFG_LKSX],
ifgcropopt=params[cf.IFG_CROP_OPT])
for b in base_unw_paths
]
cls.converted_paths_s = [b.converted_path for b in multi_paths]
cls.sampled_paths_s = [b.sampled_path for b in multi_paths]
orig_params = copy(params)
conv2tif.main(params)
prepifg.main(orig_params)
params[cf.INTERFEROGRAM_FILES] = multi_paths
cls.refpixel, cls.maxvar, cls.vcmt = process.process_ifgs(
cls.sampled_paths_s, params, rows, cols)
cls.mst = common.reconstruct_mst(ifgs[0].shape, tiles,
params[cf.TMPDIR])
cls.rate, cls.error, cls.samples = \
[common.reconstruct_stack_rate(ifgs[0].shape, tiles, params[cf.TMPDIR], t) for t in rate_types]
def setup_class(cls):
gamma_conf = common.TEST_CONF_GAMMA
from tests.common import manipulate_test_conf
rate_types = ['stack_rate', 'stack_error', 'stack_samples']
cls.tif_dir = Path(tempfile.mkdtemp())
params = manipulate_test_conf(gamma_conf, cls.tif_dir)
from pyrate.configuration import Configuration
# change the required params
params[C.PROCESSES] = 4
params[C.PROCESSOR] = 1 # gamma
params[C.IFG_FILE_LIST] = os.path.join(common.GAMMA_SML_TEST_DIR,
'ifms_17')
params[C.PARALLEL] = 1
params[C.APS_CORRECTION] = 0
params[C.REFX], params[C.REFY] = -1, -1
rows, cols = params["rows"], params["cols"]
output_conf_file = 'gamma.conf'
output_conf = cls.tif_dir.joinpath(output_conf_file).as_posix()
pyrate.configuration.write_config_file(params=params,
output_conf_file=output_conf)
config = Configuration(output_conf)
params = config.__dict__
common.sub_process_run(f"pyrate conv2tif -f {output_conf}")
common.sub_process_run(f"pyrate prepifg -f {output_conf}")
cls.sampled_paths = [
p.tmp_sampled_path for p in params[C.INTERFEROGRAM_FILES]
]
ifgs = common.small_data_setup()
correct._copy_mlooked(params)
tiles = pyrate.core.shared.get_tiles(cls.sampled_paths[0], rows, cols)
correct.correct_ifgs(config)
pyrate.main.timeseries(config)
pyrate.main.stack(config)
cls.refpixel_p, cls.maxvar_p, cls.vcmt_p = \
(params[C.REFX], params[C.REFY]), params[C.MAXVAR], params[
C.VCMT]
cls.mst_p = common.reconstruct_mst(ifgs[0].shape, tiles,
params[C.OUT_DIR])
cls.rate_p, cls.error_p, cls.samples_p = \
[common.reconstruct_stack_rate(ifgs[0].shape, tiles, params[C.TMPDIR], t) for t in rate_types]
common.remove_tifs(params[C.OUT_DIR])
# now create the non parallel version
cls.tif_dir_s = Path(tempfile.mkdtemp())
params = manipulate_test_conf(gamma_conf, cls.tif_dir_s)
params[C.PROCESSES] = 4
params[C.PROCESSOR] = 1 # gamma
params[C.IFG_FILE_LIST] = os.path.join(common.GAMMA_SML_TEST_DIR,
'ifms_17')
params[C.PARALLEL] = 0
params[C.APS_CORRECTION] = 0
params[C.REFX], params[C.REFY] = -1, -1
output_conf_file = 'gamma.conf'
output_conf = cls.tif_dir_s.joinpath(output_conf_file).as_posix()
pyrate.configuration.write_config_file(params=params,
output_conf_file=output_conf)
config = Configuration(output_conf)
params = config.__dict__
common.sub_process_run(f"pyrate conv2tif -f {output_conf}")
common.sub_process_run(f"pyrate prepifg -f {output_conf}")
correct._copy_mlooked(params)
correct.correct_ifgs(config)
pyrate.main.timeseries(config)
pyrate.main.stack(config)
cls.refpixel, cls.maxvar, cls.vcmt = \
(params[C.REFX], params[C.REFY]), params[C.MAXVAR], params[
C.VCMT]
cls.mst = common.reconstruct_mst(ifgs[0].shape, tiles,
params[C.OUT_DIR])
cls.rate, cls.error, cls.samples = \
[common.reconstruct_stack_rate(ifgs[0].shape, tiles, params[C.TMPDIR], t) for t in rate_types]
cls.params = params