def mst_calculation(ifg_paths_or_instance, params):
if isinstance(ifg_paths_or_instance, list):
ifgs = pre_prepare_ifgs(ifg_paths_or_instance, params)
mst_grid = mst.mst_parallel(ifgs, params)
else:
nan_conversion = params[cf.NAN_CONVERSION]
assert isinstance(ifg_paths_or_instance, matlab_mst.IfgListPyRate)
ifgs = ifg_paths_or_instance.ifgs
for i in ifgs:
if not i.mm_converted:
i.nodata_value = params[cf.NO_DATA_VALUE]
i.convert_to_mm()
ifg_instance_updated, epoch_list = \
matlab_mst.get_nml(ifg_paths_or_instance,
nodata_value=params[cf.NO_DATA_VALUE],
nan_conversion=nan_conversion)
mst_grid = matlab_mst.matlab_mst_bool(ifg_instance_updated)
# write mst output to a file
mst_mat_binary_file = join(params[cf.OUT_DIR], 'mst_mat')
np.save(file=mst_mat_binary_file, arr=mst_grid)
for i in ifgs:
i.close()
return mst_grid
def setUpClass(cls):
params = cf.get_config_params(common.TEST_CONF_ROIPAC)
cls.temp_out_dir = tempfile.mkdtemp()
sys.argv = ['run_prepifg.py', common.TEST_CONF_ROIPAC]
params[cf.OUT_DIR] = cls.temp_out_dir
run_prepifg.main(params)
params[cf.REF_EST_METHOD] = 2
xlks, ylks, crop = cf.transform_params(params)
base_ifg_paths = cf.original_ifg_paths(params[cf.IFG_FILE_LIST])
dest_paths = cf.get_dest_paths(base_ifg_paths, crop, params, xlks)
# start run_pyrate copy
ifgs = shared.pre_prepare_ifgs(dest_paths, params)
mst_grid = common.mst_calculation(dest_paths, params)
refx, refy = run_pyrate.ref_pixel_calc(dest_paths, params)
# Estimate and remove orbit errors
pyrate.orbital.remove_orbital_error(ifgs, params)
ifgs = common.prepare_ifgs_without_phase(dest_paths, params)
_, ifgs = rpe.estimate_ref_phase(ifgs, params, refx, refy)
maxvar = [vcm.cvd(i, params)[0] for i in ifgs]
vcmt = vcm.get_vcmt(ifgs, maxvar)
params[cf.TIME_SERIES_METHOD] = 1
params[cf.PARALLEL] = 0
# Calculate time series
cls.tsincr_0, cls.tscum_0, _ = common.calculate_time_series(
ifgs, params, vcmt, mst=mst_grid)
params[cf.PARALLEL] = 1
cls.tsincr_1, cls.tscum_1, cls.tsvel_1 = \
common.calculate_time_series(ifgs, params, vcmt, mst=mst_grid)
params[cf.PARALLEL] = 2
cls.tsincr_2, cls.tscum_2, cls.tsvel_2 = \
common.calculate_time_series(ifgs, params, vcmt, mst=mst_grid)
# load the matlab data
ts_dir = os.path.join(common.SML_TEST_DIR, 'matlab_time_series')
tsincr_path = os.path.join(ts_dir, 'ts_incr_interp0_method1.csv')
ts_incr = np.genfromtxt(tsincr_path)
# the matlab tsvel return is a bit pointless and not tested here
# tserror is not returned
# tserr_path = os.path.join(SML_TIME_SERIES_DIR,
# 'ts_error_interp0_method1.csv')
# ts_err = np.genfromtxt(tserr_path, delimiter=',')
tscum_path = os.path.join(ts_dir, 'ts_cum_interp0_method1.csv')
ts_cum = np.genfromtxt(tscum_path)
cls.ts_incr = np.reshape(ts_incr,
newshape=cls.tsincr_0.shape,
order='F')
cls.ts_cum = np.reshape(ts_cum, newshape=cls.tscum_0.shape, order='F')
def setUpClass(cls):
params = cf.get_config_params(common.TEST_CONF_ROIPAC)
cls.temp_out_dir = tempfile.mkdtemp()
sys.argv = ['run_prepifg.py', common.TEST_CONF_ROIPAC]
params[cf.OUT_DIR] = cls.temp_out_dir
run_prepifg.main(params)
params[cf.OUT_DIR] = cls.temp_out_dir
params[cf.REF_EST_METHOD] = 2
params[cf.PARALLEL] = False
xlks, ylks, crop = cf.transform_params(params)
base_ifg_paths = cf.original_ifg_paths(params[cf.IFG_FILE_LIST])
dest_paths = cf.get_dest_paths(base_ifg_paths, crop,
params, xlks)
# start run_pyrate copy
ifgs = shared.pre_prepare_ifgs(dest_paths, params)
mst_grid = tests.common.mst_calculation(dest_paths, params)
# Estimate reference pixel location
refx, refy = run_pyrate.ref_pixel_calc(dest_paths, params)
# Estimate and remove orbit errors
pyrate.orbital.remove_orbital_error(ifgs, params)
for i in ifgs:
i.close()
ifgs = shared.pre_prepare_ifgs(dest_paths, params)
cls.ref_phs, cls.ifgs = estimate_ref_phase(ifgs, params, refx, refy)
# end run_pyrate copy
for i in ifgs:
i.close()
def setUpClass(cls):
params = cf.get_config_params(TEST_CONF_ROIPAC)
cls.temp_out_dir = tempfile.mkdtemp()
sys.argv = ['run_prepifg.py', TEST_CONF_ROIPAC]
params[cf.OUT_DIR] = cls.temp_out_dir
params[cf.REF_EST_METHOD] = 2
run_prepifg.main(params)
xlks, ylks, crop = cf.transform_params(params)
base_ifg_paths = cf.original_ifg_paths(params[cf.IFG_FILE_LIST])
dest_paths = cf.get_dest_paths(base_ifg_paths, crop, params, xlks)
ifgs = shared.pre_prepare_ifgs(dest_paths, params)
refx, refy = run_pyrate.ref_pixel_calc(dest_paths, params)
pyrate.orbital.remove_orbital_error(ifgs, params)
ifgs = prepare_ifgs_without_phase(dest_paths, params)
_, ifgs = rpe.estimate_ref_phase(ifgs, params, refx, refy)
# Calculate interferogram noise
cls.maxvar = [cvd(i, params)[0] for i in ifgs]
cls.vcmt = get_vcmt(ifgs, cls.maxvar)
def setUpClass(cls):
params = config.get_config_params(common.TEST_CONF_ROIPAC)
cls.tmp_dir = tempfile.mkdtemp()
shared.copytree(common.SML_TEST_TIF, cls.tmp_dir)
tifs = glob.glob(os.path.join(cls.tmp_dir, "*.tif"))
for t in tifs:
os.chmod(t, 0o644)
small_ifgs = common.small_data_setup(datafiles=tifs)
ifg_paths = [i.data_path for i in small_ifgs]
cls.ifg_ret = shared.pre_prepare_ifgs(ifg_paths, params=params)
for i in cls.ifg_ret:
i.close()
nan_conversion = params[cf.NAN_CONVERSION]
# prepare a second set
cls.tmp_dir2 = tempfile.mkdtemp()
shared.copytree(common.SML_TEST_TIF, cls.tmp_dir2)
tifs = glob.glob(os.path.join(cls.tmp_dir2, "*.tif"))
for t in tifs:
os.chmod(t, 0o644)
small_ifgs = common.small_data_setup(datafiles=tifs)
ifg_paths = [i.data_path for i in small_ifgs]
cls.ifgs = [shared.Ifg(p) for p in ifg_paths]
for i in cls.ifgs:
if not i.is_open:
i.open(readonly=False)
if nan_conversion: # nan conversion happens here in networkx mst
i.nodata_value = params[cf.NO_DATA_VALUE]
i.convert_to_nans()
if not i.mm_converted:
i.convert_to_mm()
i.close()
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):
params = cf.get_config_params(TEST_CONF_ROIPAC)
cls.temp_out_dir = tempfile.mkdtemp()
sys.argv = ['run_prepifg.py', TEST_CONF_ROIPAC]
params[cf.OUT_DIR] = cls.temp_out_dir
params[cf.TMPDIR] = os.path.join(params[cf.OUT_DIR], cf.TMPDIR)
shared.mkdir_p(params[cf.TMPDIR])
run_prepifg.main(params)
params[cf.REF_EST_METHOD] = 2
xlks, _, crop = cf.transform_params(params)
base_ifg_paths = cf.original_ifg_paths(params[cf.IFG_FILE_LIST])
dest_paths = cf.get_dest_paths(base_ifg_paths, crop, params, xlks)
# start run_pyrate copy
ifgs = shared.pre_prepare_ifgs(dest_paths, params)
mst_grid = tests.common.mst_calculation(dest_paths, params)
refx, refy = run_pyrate.ref_pixel_calc(dest_paths, params)
# Estimate and remove orbit errors
pyrate.orbital.remove_orbital_error(ifgs, params)
ifgs = prepare_ifgs_without_phase(dest_paths, params)
_, ifgs = rpe.estimate_ref_phase(ifgs, params, refx, refy)
maxvar = [vcm_module.cvd(i, params)[0] for i in ifgs]
vcmt = vcm_module.get_vcmt(ifgs, maxvar)
# Calculate linear rate map
params[cf.PARALLEL] = 1
cls.rate, cls.error, cls.samples = tests.common.calculate_linear_rate(
ifgs, params, vcmt, mst_mat=mst_grid)
params[cf.PARALLEL] = 2
cls.rate_2, cls.error_2, cls.samples_2 = \
tests.common.calculate_linear_rate(ifgs, params, vcmt,
mst_mat=mst_grid)
params[cf.PARALLEL] = 0
# Calculate linear rate map
cls.rate_s, cls.error_s, cls.samples_s = \
tests.common.calculate_linear_rate(ifgs, params, vcmt,
mst_mat=mst_grid)
matlab_linrate_dir = os.path.join(SML_TEST_DIR, 'matlab_linrate')
cls.rate_matlab = np.genfromtxt(os.path.join(matlab_linrate_dir,
'stackmap.csv'),
delimiter=',')
cls.error_matlab = np.genfromtxt(os.path.join(matlab_linrate_dir,
'errormap.csv'),
delimiter=',')
cls.samples_matlab = np.genfromtxt(os.path.join(
matlab_linrate_dir, 'coh_sta.csv'),
delimiter=',')