def setUpClass(cls):
params = Configuration(common.TEST_CONF_ROIPAC).__dict__
cls.temp_out_dir = tempfile.mkdtemp()
sys.argv = ['prepifg.py', common.TEST_CONF_ROIPAC]
params[cf.OUT_DIR] = cls.temp_out_dir
conv2tif.main(params)
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],
params[cf.OBS_DIR])
dest_paths = cf.get_dest_paths(base_ifg_paths, crop, params, xlks)
# start run_pyrate copy
ifgs = common.pre_prepare_ifgs(dest_paths, params)
mst_grid = common.mst_calculation(dest_paths, params)
refx, refy = process._ref_pixel_calc(dest_paths, params)
# Estimate and remove orbit errors
pyrate.core.orbital.remove_orbital_error(ifgs, params)
ifgs = common.prepare_ifgs_without_phase(dest_paths, params)
for ifg in ifgs:
ifg.close()
_, ifgs = process._ref_phase_estimation(dest_paths, params, refx, refy)
ifgs[0].open()
r_dist = covariance.RDist(ifgs[0])()
ifgs[0].close()
maxvar = [covariance.cvd(i, params, r_dist)[0] for i in dest_paths]
for ifg in ifgs:
ifg.open()
vcmt = covariance.get_vcmt(ifgs, maxvar)
for ifg in ifgs:
ifg.close()
ifg.open()
ifg.nodata_value = 0.0
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)
# load the legacy data
ts_dir = os.path.join(common.SML_TEST_DIR, 'time_series')
tsincr_path = os.path.join(ts_dir, 'ts_incr_interp0_method1.csv')
ts_incr = np.genfromtxt(tsincr_path)
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.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 = common.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)
r_dist = covariance.RDist(ifgs[0])()
maxvar = [covariance.cvd(i, params, r_dist)[0] for i in ifgs]
vcmt = covariance.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 = Configuration(common.TEST_CONF_ROIPAC).__dict__
cls.temp_out_dir = tempfile.mkdtemp()
params[cf.OUT_DIR] = cls.temp_out_dir
params[cf.PARALLEL] = 0
conv2tif.main(params)
prepifg.main(params)
params[cf.REF_EST_METHOD] = 2
base_ifg_paths = [
c.unwrapped_path for c in params[cf.INTERFEROGRAM_FILES]
]
headers = [roipac.roipac_header(i, params) for i in base_ifg_paths]
dest_paths = [
Path(cls.temp_out_dir).joinpath(Path(
c.sampled_path).name).as_posix()
for c in params[cf.INTERFEROGRAM_FILES][:-2]
]
# start run_pyrate copy
ifgs = common.pre_prepare_ifgs(dest_paths, params)
mst_grid = common.mst_calculation(dest_paths, params)
refx, refy = process._ref_pixel_calc(dest_paths, params)
# Estimate and remove orbit errors
pyrate.core.orbital.remove_orbital_error(ifgs, params, headers)
ifgs = common.prepare_ifgs_without_phase(dest_paths, params)
for ifg in ifgs:
ifg.close()
_, ifgs = process._ref_phase_estimation(dest_paths, params, refx, refy)
ifgs[0].open()
r_dist = covariance.RDist(ifgs[0])()
ifgs[0].close()
# Calculate interferogram noise
maxvar = [covariance.cvd(i, params, r_dist)[0] for i in dest_paths]
for ifg in ifgs:
ifg.open()
vcmt = covariance.get_vcmt(ifgs, maxvar)
for ifg in ifgs:
ifg.close()
ifg.open()
ifg.nodata_value = 0.0
params[cf.TIME_SERIES_METHOD] = 2
params[cf.PARALLEL] = 1
# Calculate time series
cls.tsincr, cls.tscum, _ = common.calculate_time_series(ifgs,
params,
vcmt,
mst=mst_grid)
params[cf.PARALLEL] = 0
# Calculate time series serailly by the pixel
cls.tsincr_0, cls.tscum_0, _ = common.calculate_time_series(
ifgs, params, vcmt, mst=mst_grid)
# copy legacy data
SML_TIME_SERIES_DIR = os.path.join(common.SML_TEST_DIR, 'time_series')
tsincr_path = os.path.join(SML_TIME_SERIES_DIR,
'ts_incr_interp0_method2.csv')
ts_incr = np.genfromtxt(tsincr_path)
tscum_path = os.path.join(SML_TIME_SERIES_DIR,
'ts_cum_interp0_method2.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 setup_class(cls):
params = Configuration(common.TEST_CONF_ROIPAC).__dict__
params[cf.TEMP_MLOOKED_DIR] = os.path.join(params[cf.OUT_DIR],
cf.TEMP_MLOOKED_DIR)
conv2tif.main(params)
prepifg.main(params)
params[cf.REF_EST_METHOD] = 2
xlks, _, crop = cf.transform_params(params)
dest_paths, headers = common.repair_params_for_correct_tests(
params[cf.OUT_DIR], params)
correct._copy_mlooked(params)
copied_dest_paths = [
os.path.join(params[cf.TEMP_MLOOKED_DIR], os.path.basename(d))
for d in dest_paths
]
del dest_paths
# start run_pyrate copy
ifgs = common.pre_prepare_ifgs(copied_dest_paths, params)
mst_grid = common.mst_calculation(copied_dest_paths, params)
refx, refy = pyrate.core.refpixel.ref_pixel_calc_wrapper(params)
params[cf.REFX] = refx
params[cf.REFY] = refy
params[cf.ORBFIT_OFFSET] = True
# Estimate and remove orbit errors
pyrate.core.orbital.remove_orbital_error(ifgs, params)
ifgs = common.prepare_ifgs_without_phase(copied_dest_paths, params)
for ifg in ifgs:
ifg.close()
correct._update_params_with_tiles(params)
_, ifgs = pyrate.core.ref_phs_est.ref_phase_est_wrapper(params)
ifgs[0].open()
r_dist = covariance.RDist(ifgs[0])()
ifgs[0].close()
# Calculate interferogram noise
maxvar = [
covariance.cvd(i, params, r_dist)[0] for i in copied_dest_paths
]
for ifg in ifgs:
ifg.open()
vcmt = covariance.get_vcmt(ifgs, maxvar)
for ifg in ifgs:
ifg.close()
ifg.open()
ifg.nodata_value = 0.0
params[cf.TIME_SERIES_METHOD] = 2
params[cf.PARALLEL] = 1
# Calculate time series
cls.tsincr, cls.tscum, _ = common.calculate_time_series(ifgs,
params,
vcmt,
mst=mst_grid)
params[cf.PARALLEL] = 0
# Calculate time series serailly by the pixel
cls.tsincr_0, cls.tscum_0, _ = common.calculate_time_series(
ifgs, params, vcmt, mst=mst_grid)
# copy legacy data
SML_TIME_SERIES_DIR = os.path.join(common.SML_TEST_DIR, 'time_series')
tsincr_path = os.path.join(SML_TIME_SERIES_DIR,
'ts_incr_interp0_method2.csv')
ts_incr = np.genfromtxt(tsincr_path)
tscum_path = os.path.join(SML_TIME_SERIES_DIR,
'ts_cum_interp0_method2.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')
cls.params = params