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